paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688 values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
59bf922b-bed0-426f-9525-b1308939656d | cot-misr-marrying-convolution-and-transformer | 2303.06548 | null | https://arxiv.org/abs/2303.06548v1 | https://arxiv.org/pdf/2303.06548v1.pdf | CoT-MISR:Marrying Convolution and Transformer for Multi-Image Super-Resolution | As a method of image restoration, image super-resolution has been extensively studied at first. How to transform a low-resolution image to restore its high-resolution image information is a problem that researchers have been exploring. In the early physical transformation methods, the high-resolution pictures generated by these methods always have a serious problem of missing information, and the edges and details can not be well recovered. With the development of hardware technology and mathematics, people begin to use in-depth learning methods for image super-resolution tasks, from direct in-depth learning models, residual channel attention networks, bi-directional suppression networks, to tr networks with transformer network modules, which have gradually achieved good results. In the research of multi-graph super-resolution, thanks to the establishment of multi-graph super-resolution dataset, we have experienced the evolution from convolution model to transformer model, and the quality of super-resolution has been continuously improved. However, we find that neither pure convolution nor pure tr network can make good use of low-resolution image information. Based on this, we propose a new end-to-end CoT-MISR network. CoT-MISR network makes up for local and global information by using the advantages of convolution and tr. The validation of dataset under equal parameters shows that our CoT-MISR network has reached the optimal score index. | ['Chun Liu', 'Qing Song', 'Yang Nie', 'Mingming Xiu'] | 2023-03-12 | null | null | null | null | ['image-super-resolution'] | ['computer-vision'] | [ 3.05522203e-01 -2.48795733e-01 1.51735500e-01 -6.55120611e-02
-5.96910655e-01 2.24266306e-01 3.48740816e-01 -7.79183507e-01
-7.79175833e-02 8.24099720e-01 4.54514086e-01 3.04815441e-01
-1.91183880e-01 -1.04597640e+00 -4.87218887e-01 -7.47175753e-01
2.78125912e-01 -1.13816932e-01 4.89646316e-01 -7.00175762e-01
1.88855797e-01 3.96400690e-01 -1.40862679e+00 5.83130121e-01
1.01464939e+00 7.16481328e-01 5.14531493e-01 3.97518337e-01
-2.72239923e-01 8.87632012e-01 -2.68829256e-01 -1.34927928e-01
1.92000732e-01 -6.36598945e-01 -7.31739879e-01 -1.90843776e-01
3.11206967e-01 -4.96412665e-01 -7.84178913e-01 1.43769252e+00
7.01011062e-01 -8.06144774e-02 1.72872409e-01 -5.17347157e-01
-1.33362353e+00 6.55718327e-01 -9.04394269e-01 5.56304514e-01
2.93239594e-01 -1.59372345e-01 4.88192499e-01 -1.01178050e+00
6.82500958e-01 1.45779455e+00 7.50958323e-01 5.71524560e-01
-1.18541372e+00 -7.26132572e-01 -1.56762704e-01 4.33990419e-01
-1.40430963e+00 -3.04430842e-01 8.40808511e-01 5.63222803e-02
7.30265439e-01 8.14232677e-02 4.42348003e-01 1.03318453e+00
4.63673383e-01 1.47804141e-01 1.39070594e+00 -3.50373656e-01
-3.30324560e-01 8.63747224e-02 -1.33733943e-01 5.18528342e-01
1.79012895e-01 3.76246214e-01 -5.64019501e-01 5.11429071e-01
1.56747043e+00 2.22113252e-01 -5.53178072e-01 1.67601764e-01
-1.01122916e+00 3.82566482e-01 7.27415621e-01 7.96021700e-01
-2.78056443e-01 -2.00831443e-01 1.86669789e-02 3.43113393e-01
5.82252264e-01 3.74839067e-01 -5.05680889e-02 2.69052625e-01
-8.76712203e-01 -9.72705483e-02 8.82925689e-02 6.81696653e-01
6.38254225e-01 4.51253533e-01 2.21107993e-02 9.85940099e-01
-2.60232836e-01 2.72421539e-01 7.92085111e-01 -9.46994424e-01
1.85192704e-01 4.92254227e-01 4.27624173e-02 -1.09976947e+00
-3.38178635e-01 -5.97219050e-01 -1.63569474e+00 5.38750291e-01
-1.20391967e-02 6.86631799e-02 -9.81824696e-01 1.55369258e+00
-8.48911107e-02 2.26218924e-01 2.19874933e-01 1.28059328e+00
1.12971783e+00 8.82637680e-01 -2.82647878e-01 -5.53446293e-01
1.17042685e+00 -8.02296698e-01 -1.09231532e+00 1.26423677e-02
-2.00050563e-01 -7.25988507e-01 8.78270090e-01 4.58640307e-01
-1.34279370e+00 -1.06997001e+00 -1.27005899e+00 -4.20393616e-01
-2.18328536e-01 -1.85132205e-01 4.61033612e-01 2.00673804e-01
-1.33553839e+00 1.05635130e+00 -3.65269303e-01 -1.66432038e-01
5.07549524e-01 1.57102838e-01 -5.80600619e-01 -4.48849797e-01
-1.45299733e+00 1.00851178e+00 3.57786477e-01 1.86336517e-01
-6.15503311e-01 -6.03077173e-01 -3.40100259e-01 1.31953895e-01
2.30827898e-01 -6.71427131e-01 6.12361491e-01 -1.05250609e+00
-1.49894786e+00 5.33499479e-01 -9.10107940e-02 -2.10056633e-01
2.49856740e-01 -1.39703900e-01 -1.03907990e+00 2.66936004e-01
-8.17729905e-02 9.08419117e-02 1.05272985e+00 -1.28516364e+00
-7.11872816e-01 -3.88095796e-01 -1.37012973e-01 2.58600086e-01
-1.67113557e-01 2.59873390e-01 -4.53041755e-02 -5.31069219e-01
4.91624653e-01 -2.09441215e-01 -2.39774346e-01 -3.66201699e-01
5.68138286e-02 2.46918365e-01 9.37658966e-01 -9.82867002e-01
1.14099431e+00 -2.17874265e+00 3.58258247e-01 -3.47626805e-01
5.75354576e-01 4.80032057e-01 -1.01422668e-01 4.92204800e-02
-4.99046624e-01 3.30051959e-01 -6.10676296e-02 6.75181150e-02
-6.99101985e-01 -5.86244278e-02 -3.49807143e-01 1.86748669e-01
2.70578228e-02 7.92239249e-01 -7.51954079e-01 -5.89383543e-01
3.13020021e-01 1.04866982e+00 -1.99588224e-01 1.69843182e-01
2.58040607e-01 6.08356833e-01 -4.36065435e-01 3.72717232e-01
1.09546411e+00 -5.25334239e-01 -1.10407330e-01 -7.71452069e-01
-5.05581260e-01 -3.09119076e-01 -1.06694973e+00 1.67594457e+00
-3.81153524e-01 4.93218005e-01 1.86542615e-01 -8.47196460e-01
1.02845418e+00 3.31702709e-01 5.28951585e-01 -1.48707509e+00
-1.17299423e-01 1.34416565e-01 -2.53932178e-01 -5.56310534e-01
6.67964518e-01 -3.15930784e-01 4.43185627e-01 -5.90141723e-03
-1.25156924e-01 2.33236089e-01 -1.79094493e-01 1.16580866e-01
8.99274886e-01 2.50479877e-01 7.31297359e-02 -2.48482451e-02
5.78724265e-01 -2.01463297e-01 5.69891274e-01 4.70470548e-01
2.40375280e-01 1.16655374e+00 -3.56729552e-02 -5.27682662e-01
-1.43313873e+00 -9.92861927e-01 -1.64416730e-01 6.53181791e-01
4.60935771e-01 -1.67345498e-02 -7.03599989e-01 1.67130977e-01
-5.97077727e-01 2.89324015e-01 -3.77129912e-01 -2.17041969e-01
-8.75153899e-01 -9.62084889e-01 9.74655598e-02 2.25905806e-01
1.27583492e+00 -1.10815251e+00 -2.43487850e-01 3.10920060e-01
-2.95047820e-01 -1.26433337e+00 -3.39339942e-01 -1.55943811e-01
-1.04730892e+00 -7.66260862e-01 -9.34870720e-01 -8.76089811e-01
4.78524446e-01 7.92791605e-01 9.80358541e-01 8.48736465e-02
-4.48071420e-01 -1.96162552e-01 -3.34734410e-01 2.89480031e-01
-2.56914735e-01 -1.04788683e-01 -7.53300115e-02 1.72031611e-01
-1.87889352e-01 -1.11372995e+00 -5.94942153e-01 1.76893607e-01
-8.81137967e-01 5.07678807e-01 9.10417795e-01 8.67698312e-01
7.19402075e-01 5.53016007e-01 6.35187566e-01 -7.70275295e-01
6.00025654e-01 -1.56750664e-01 -4.81708229e-01 1.73021719e-01
-7.69963503e-01 1.31666690e-01 8.77183974e-01 -3.38978767e-01
-1.59956515e+00 -4.12854493e-01 -9.36460048e-02 -6.32846713e-01
-1.01154158e-02 2.94381410e-01 -3.62023324e-01 -3.15767199e-01
6.71006560e-01 5.14520586e-01 -5.52857388e-03 -7.51495004e-01
3.60733241e-01 5.65238357e-01 8.89909148e-01 2.24004164e-02
9.49463010e-01 5.15718579e-01 6.70803338e-02 -8.53431106e-01
-8.70590389e-01 1.94340181e-02 -6.08258009e-01 -7.97018036e-02
1.11103082e+00 -1.04025841e+00 -3.99055183e-01 6.43435180e-01
-1.06261647e+00 -3.05000376e-02 -1.98850438e-01 4.02694941e-01
-1.58419639e-01 5.27450621e-01 -8.88060749e-01 -4.26725090e-01
-3.76981348e-01 -1.01600993e+00 5.89167774e-01 7.43626773e-01
6.15070403e-01 -7.17676520e-01 -2.51370370e-01 2.55614519e-01
1.04914939e+00 2.19153106e-01 9.02941227e-01 2.13625550e-01
-1.09256649e+00 2.37468645e-01 -8.71010005e-01 4.20390368e-01
3.22727352e-01 -4.59566474e-01 -7.88205743e-01 -3.11965108e-01
3.97183537e-01 1.00462697e-01 9.78935242e-01 3.94285560e-01
1.16804910e+00 -2.45501906e-01 -1.97714642e-01 1.14658034e+00
1.89087737e+00 2.32722729e-01 1.34168327e+00 3.31412345e-01
8.76063287e-01 1.10935032e-01 3.74887794e-01 -5.10151908e-02
1.82903856e-01 6.82739317e-01 4.56850111e-01 -5.33718348e-01
-7.24154115e-01 -2.72482306e-01 2.51470178e-01 8.19793463e-01
-6.58527434e-01 9.24721733e-02 -3.13048482e-01 3.26938063e-01
-1.65824628e+00 -1.43571138e+00 -2.49015749e-01 2.12894297e+00
8.51893485e-01 5.41821942e-02 -2.33485371e-01 -1.04624495e-01
9.94814932e-01 3.06854904e-01 -4.89204049e-01 -1.08778477e-01
-5.66691101e-01 1.70912907e-01 5.07041156e-01 5.87663889e-01
-8.18920553e-01 9.58164215e-01 6.34094715e+00 1.03849316e+00
-1.30076051e+00 2.25742042e-01 6.45738542e-01 1.78988069e-01
-3.05210173e-01 -8.31881166e-02 -7.20154047e-01 4.36217546e-01
7.27879047e-01 -1.67782262e-01 9.52584088e-01 5.38900614e-01
3.00424427e-01 -5.92971919e-03 -5.17690778e-01 1.40665472e+00
8.45606402e-02 -1.46537960e+00 2.58568704e-01 -5.23228012e-02
8.11437368e-01 -1.08302884e-01 1.83319226e-01 1.08010761e-01
1.15546308e-01 -1.44898498e+00 1.90623105e-01 9.14645255e-01
1.25288713e+00 -7.93852270e-01 6.85479105e-01 2.49655977e-01
-1.16016114e+00 -1.12851672e-01 -8.00852239e-01 -4.61891443e-02
2.05446064e-01 7.06289411e-01 -1.40910521e-01 9.79395390e-01
9.37328279e-01 8.65181804e-01 -4.20458645e-01 8.38297725e-01
-7.95109645e-02 2.33735964e-01 2.17378497e-01 5.78987479e-01
-2.53304213e-01 -5.25093138e-01 6.18474782e-01 8.92672896e-01
5.31168163e-01 6.43715501e-01 -1.54453740e-01 1.20035672e+00
-7.96452572e-04 -2.15093404e-01 -4.28172350e-01 2.00850129e-01
1.27803952e-01 1.44136941e+00 -5.73476732e-01 -2.66686350e-01
-4.96450186e-01 1.18460238e+00 1.21045627e-01 5.51232100e-01
-7.37130940e-01 -3.71851712e-01 4.01361585e-01 4.50079530e-01
1.85545117e-01 -1.49252266e-01 -3.31176758e-01 -1.20866466e+00
-5.42855747e-02 -9.06942070e-01 8.46554432e-03 -1.28650653e+00
-1.06817985e+00 9.81075168e-01 -2.29926616e-01 -1.19514275e+00
7.13663027e-02 -3.30245465e-01 -4.03449386e-01 1.26334417e+00
-1.77695811e+00 -1.10472834e+00 -6.77649617e-01 7.58426309e-01
4.63680089e-01 -2.61067033e-01 5.81186235e-01 6.39333904e-01
-3.98388475e-01 2.72010386e-01 1.86591938e-01 1.04612254e-01
6.76324069e-01 -8.61101806e-01 1.21963441e-01 1.10436916e+00
-2.20304862e-01 3.96978915e-01 6.75985396e-01 -6.24294281e-01
-1.30790377e+00 -9.53488529e-01 4.38816994e-01 1.30489217e-02
2.31644437e-01 4.97637950e-02 -1.45922577e+00 3.98220271e-01
2.34297201e-01 7.44221359e-02 -2.45953292e-01 -1.94269925e-01
-2.78674811e-01 -5.09513557e-01 -1.29589689e+00 4.80319232e-01
1.29099059e+00 -5.49672425e-01 -5.34151077e-01 6.39334247e-02
1.13381898e+00 -4.68745351e-01 -8.72324347e-01 4.94674444e-01
2.48800755e-01 -1.34811378e+00 1.28693581e+00 -2.75049210e-01
7.65053093e-01 -4.74415869e-01 -1.95507961e-03 -1.27228558e+00
-1.02842021e+00 -4.63949800e-01 2.88086832e-01 1.26332891e+00
7.06498846e-02 -6.49551451e-01 3.91172200e-01 1.31457537e-01
-1.95646644e-01 -3.95870268e-01 -8.85748625e-01 -3.72378469e-01
-1.04201168e-01 2.93571770e-01 6.24036849e-01 9.34697807e-01
-3.13628942e-01 5.45108557e-01 -8.37168634e-01 3.66550207e-01
9.17296588e-01 2.22827703e-01 2.74673671e-01 -1.12781119e+00
-1.60074815e-01 -4.47490543e-01 -1.71281502e-01 -7.83204854e-01
-2.67341375e-01 -7.14313924e-01 -3.33062023e-01 -1.87646103e+00
5.82913339e-01 -8.58529210e-02 -3.38873178e-01 1.36444256e-01
-1.34085268e-01 4.20481682e-01 -4.85287979e-02 3.88348520e-01
-3.65363210e-01 4.62113589e-01 1.83229077e+00 8.25049430e-02
-1.67047322e-01 -2.93314099e-01 -8.97958636e-01 6.13560855e-01
6.90029144e-01 -3.78119498e-02 -2.61016607e-01 -4.95625645e-01
2.42367193e-01 5.47006369e-01 5.06295681e-01 -1.13057876e+00
3.07058752e-01 -1.09841950e-01 7.69017041e-01 -3.90654802e-01
3.88338208e-01 -7.85320938e-01 4.17655289e-01 7.15286732e-02
-1.50055990e-01 -2.52217078e-03 1.68308499e-03 2.87052959e-01
-4.37065363e-01 9.79659185e-02 1.28865623e+00 -5.89205921e-01
-9.82886672e-01 4.48688984e-01 5.32963164e-02 -5.73549233e-02
4.91198182e-01 -4.91306067e-01 -4.88635272e-01 -3.11262637e-01
-7.88221002e-01 -1.80252910e-01 5.05008221e-01 3.85932684e-01
1.07155514e+00 -1.30949605e+00 -9.28643167e-01 2.33399525e-01
-5.46422184e-01 -8.24793875e-02 9.19551611e-01 4.99943435e-01
-4.24171925e-01 1.01767488e-01 -7.18175113e-01 -3.14512879e-01
-1.15308440e+00 8.75554919e-01 6.41172051e-01 -3.58666658e-01
-1.29425097e+00 4.26617771e-01 3.27464789e-01 1.99221998e-01
-2.48404592e-01 -3.09530441e-02 -5.06675482e-01 -2.72536904e-01
9.74448860e-01 4.16302353e-01 -2.70872712e-01 -5.77475011e-01
1.08887248e-01 9.84710813e-01 -1.81675956e-01 5.33911660e-02
1.76562750e+00 -4.81264383e-01 -4.52446729e-01 1.35630995e-01
9.56174076e-01 -3.52364480e-02 -1.12745392e+00 -3.67662191e-01
-4.51386571e-01 -6.35206044e-01 4.11763579e-01 -9.00262892e-01
-1.49492335e+00 9.10387754e-01 9.77723718e-01 3.30809504e-01
1.49988818e+00 -3.13787907e-01 8.10248971e-01 -2.71910161e-01
6.70931518e-01 -9.62129056e-01 2.81248868e-01 3.94302607e-01
1.09295416e+00 -1.17260754e+00 5.28915599e-02 -4.72954720e-01
-3.66917521e-01 1.14127278e+00 6.77252114e-01 -2.70585626e-01
4.64040577e-01 4.11065787e-01 -1.70115829e-01 -4.90679853e-02
-4.47132140e-01 -1.83152899e-01 6.37158705e-03 7.31740773e-01
3.23342949e-01 -2.22853541e-01 -1.75283462e-01 5.57785332e-01
-8.78656358e-02 4.01410967e-01 8.54674637e-01 5.76857403e-02
-7.58752108e-01 -8.82281125e-01 -5.35565972e-01 9.48884860e-02
-6.04250014e-01 -2.39099190e-01 1.79250166e-01 7.18411863e-01
1.69861406e-01 7.19108701e-01 -2.03164116e-01 -4.82661426e-01
3.69668186e-01 -3.85825634e-01 5.87209404e-01 -1.90131351e-01
-3.63301635e-02 -1.84739325e-02 -3.13520968e-01 -6.94424927e-01
-4.90111619e-01 -8.70078355e-02 -1.30741870e+00 -4.27532375e-01
-4.00911421e-02 2.22363491e-02 3.32110703e-01 6.23759091e-01
3.72632980e-01 1.05255640e+00 5.56938350e-01 -9.70485687e-01
-3.01376432e-01 -1.01162457e+00 -8.32451582e-01 2.31464699e-01
4.24413681e-01 -2.84979373e-01 -3.30313951e-01 -1.08006015e-01] | [11.008358001708984, -2.07560396194458] |
ff8404c8-f3a2-4378-b5c1-adc0f6fdd31e | explaining-hate-speech-classification-with | 2306.00021 | null | https://arxiv.org/abs/2306.00021v1 | https://arxiv.org/pdf/2306.00021v1.pdf | Explaining Hate Speech Classification with Model Agnostic Methods | There have been remarkable breakthroughs in Machine Learning and Artificial Intelligence, notably in the areas of Natural Language Processing and Deep Learning. Additionally, hate speech detection in dialogues has been gaining popularity among Natural Language Processing researchers with the increased use of social media. However, as evidenced by the recent trends, the need for the dimensions of explainability and interpretability in AI models has been deeply realised. Taking note of the factors above, the research goal of this paper is to bridge the gap between hate speech prediction and the explanations generated by the system to support its decision. This has been achieved by first predicting the classification of a text and then providing a posthoc, model agnostic and surrogate interpretability approach for explainability and to prevent model bias. The bidirectional transformer model BERT has been used for prediction because of its state of the art efficiency over other Machine Learning models. The model agnostic algorithm LIME generates explanations for the output of a trained classifier and predicts the features that influence the model decision. The predictions generated from the model were evaluated manually, and after thorough evaluation, we observed that the model performs efficiently in predicting and explaining its prediction. Lastly, we suggest further directions for the expansion of the provided research work. | ['Ute Schmid', 'Durgesh Nandini'] | 2023-05-30 | null | null | null | null | ['hate-speech-detection'] | ['natural-language-processing'] | [ 2.77964234e-01 7.34786570e-01 -1.53614789e-01 -3.21483672e-01
-4.23984975e-02 -4.00346339e-01 9.93524611e-01 3.46412271e-01
-3.98090705e-02 3.73724878e-01 5.40372670e-01 -4.85316336e-01
-1.62401736e-01 -3.05402249e-01 -1.04797073e-01 -4.19263035e-01
3.39960828e-02 3.54242474e-01 -2.56094307e-01 -4.34199423e-02
5.57482958e-01 3.52369726e-01 -1.55411255e+00 5.76128900e-01
1.04297709e+00 7.15439975e-01 -2.43336484e-01 8.08669627e-01
-6.95098657e-03 1.37324870e+00 -7.38787293e-01 -6.91516399e-01
-2.04570517e-01 -6.54872119e-01 -1.05334437e+00 1.95965409e-01
6.28048740e-03 -2.31423467e-01 -1.27523586e-01 6.81995332e-01
2.38490254e-01 -1.16416365e-01 7.26125479e-01 -1.49537718e+00
-9.18613315e-01 7.45644808e-01 -1.67776838e-01 1.68193877e-01
2.67407566e-01 2.25222304e-01 1.09960175e+00 -8.34890127e-01
4.21789706e-01 1.30864060e+00 3.83206576e-01 7.55406201e-01
-1.15368223e+00 -6.49120152e-01 -5.27660698e-02 4.18566257e-01
-9.13120806e-01 -3.06100577e-01 7.53935337e-01 -7.53392637e-01
1.00401568e+00 3.39201897e-01 6.55017316e-01 1.34085071e+00
4.15501714e-01 9.30821538e-01 1.08624542e+00 -6.54024899e-01
2.88979530e-01 7.99102485e-01 2.88424015e-01 6.88153446e-01
-1.00947812e-01 1.85045853e-01 -5.76516330e-01 -1.80219173e-01
1.09276034e-01 -4.49523777e-02 -1.69259965e-01 8.34112465e-02
-8.73491406e-01 1.35692906e+00 2.59624928e-01 5.58525681e-01
-2.92657286e-01 -3.77697408e-01 4.56318647e-01 3.31557333e-01
8.90270650e-01 1.12992764e+00 -2.52141565e-01 -2.69480586e-01
-1.00876999e+00 2.08473235e-01 9.24216092e-01 4.54708010e-01
4.22702760e-01 -1.75291998e-03 -1.82203948e-01 5.48747957e-01
3.81943315e-01 1.48170650e-01 6.80754483e-01 -7.99816072e-01
1.83360323e-01 1.00537384e+00 -1.69349328e-01 -1.26626587e+00
-4.01739627e-01 -4.36274171e-01 -7.05657721e-01 1.02926798e-01
4.09365654e-01 -1.69328481e-01 -5.93836248e-01 1.27610636e+00
1.19932659e-01 -1.32866293e-01 8.94343033e-02 8.48677456e-01
5.21961153e-01 7.76313961e-01 2.68373191e-01 -2.25620434e-01
1.20144534e+00 -9.96165395e-01 -1.02075613e+00 -4.07006025e-01
9.86394107e-01 -7.51150012e-01 1.13235390e+00 5.65768838e-01
-5.99840939e-01 -3.97443801e-01 -9.66167271e-01 -1.36618882e-01
-4.62011874e-01 7.85005540e-02 6.96287990e-01 6.33034885e-01
-7.27859139e-01 5.25782049e-01 -3.96889389e-01 -5.29505432e-01
3.51675987e-01 2.32949466e-01 -1.86013117e-01 2.70006090e-01
-1.27105308e+00 1.33001888e+00 1.83283806e-01 2.43873850e-01
-5.44540942e-01 -3.95093381e-01 -6.71745658e-01 1.81256026e-01
1.31951243e-01 -3.00054938e-01 1.16620398e+00 -1.32443345e+00
-1.26249862e+00 8.17064762e-01 -1.82033181e-01 -8.32833350e-01
4.52145368e-01 -3.06915581e-01 -3.47789675e-01 -1.09463133e-01
-2.45011598e-02 5.19025028e-01 8.93679678e-01 -1.12073791e+00
-4.97958601e-01 -3.38111579e-01 -1.82616082e-03 -1.15431286e-01
-6.32899225e-01 1.84613407e-01 4.58733559e-01 -4.82080281e-01
-2.31747270e-01 -9.62249756e-01 6.05937093e-02 -3.37464809e-01
-6.48425877e-01 -5.05812705e-01 1.12176907e+00 -7.73340523e-01
1.52123189e+00 -2.09125757e+00 1.30500853e-01 1.31716326e-01
4.98536319e-01 5.94878793e-01 2.29769647e-01 6.14122987e-01
-1.47408649e-01 4.73524272e-01 -9.75699052e-02 -3.91804814e-01
3.31499763e-02 1.49893448e-01 -6.64969087e-01 2.82893628e-01
5.22852540e-01 5.57125509e-01 -8.08468819e-01 -3.02494317e-01
3.01286399e-01 5.13937294e-01 -6.04373276e-01 4.84167814e-01
-1.56616613e-01 3.59429836e-01 -2.64140427e-01 2.67997950e-01
1.55167490e-01 -1.92407832e-01 -7.54107237e-02 1.77543476e-01
-2.79695362e-01 4.66411650e-01 -4.80128169e-01 8.00574660e-01
-2.54816413e-01 1.23234677e+00 -3.37713391e-01 -7.50081003e-01
1.09207344e+00 6.19720101e-01 -7.81052187e-02 -4.00651127e-01
2.38796428e-01 1.46679431e-01 4.33633655e-01 -8.38523388e-01
4.76479530e-01 -2.39354253e-01 2.38826796e-01 5.95011830e-01
-2.19394013e-01 -7.34633952e-02 -7.99876302e-02 2.91342854e-01
9.22950923e-01 -1.39949098e-01 5.32357156e-01 -1.25166908e-01
6.46015823e-01 3.30324024e-01 4.47382070e-02 7.83905625e-01
-4.98934686e-01 3.26800019e-01 7.30847061e-01 -7.41965830e-01
-1.02670658e+00 -3.67842555e-01 1.62469968e-02 1.11051559e+00
-3.23102951e-01 -3.87187779e-01 -9.64132786e-01 -7.78549969e-01
-2.19175711e-01 1.27982056e+00 -9.61396635e-01 -4.41569567e-01
-2.55015612e-01 -3.73608410e-01 5.27355790e-01 1.63715556e-01
2.69554466e-01 -1.32937062e+00 -6.29867852e-01 -2.46253982e-02
-1.29780442e-01 -8.58233988e-01 4.52819467e-02 3.15999031e-01
-7.16740370e-01 -1.06005049e+00 1.62255522e-02 -4.22244072e-01
6.07469916e-01 2.22733781e-01 7.55876005e-01 6.10223711e-01
-7.71933794e-02 1.84599295e-01 -6.36880100e-01 -7.74899721e-01
-9.06482399e-01 2.55629748e-01 7.06509724e-02 1.35360315e-01
7.21310019e-01 -2.02580258e-01 -3.43593895e-01 1.00408122e-01
-1.07374609e+00 3.94361675e-01 4.24629331e-01 8.27898502e-01
-2.13633299e-01 3.54952179e-02 4.71712470e-01 -1.18143201e+00
1.01021874e+00 -7.97194302e-01 6.49399981e-02 2.25465912e-02
-9.21614289e-01 1.39919028e-01 9.35744047e-01 -3.26166004e-01
-1.04740798e+00 -1.17979750e-01 9.10950452e-03 -2.19359994e-01
-5.04821181e-01 5.87276816e-01 2.21664980e-01 3.81877959e-01
7.67652750e-01 -1.37708010e-02 3.14559072e-01 -2.12682903e-01
2.53690690e-01 1.05756283e+00 7.89407268e-02 -6.75228611e-02
8.57063591e-01 1.18954264e-01 -3.80169332e-01 -1.10673010e+00
-1.16234827e+00 -1.45880938e-01 -6.41160965e-01 -4.59875822e-01
9.18840051e-01 -4.13571864e-01 -5.58761299e-01 1.13793038e-01
-1.53648019e+00 -9.42913592e-02 5.90116605e-02 4.07664269e-01
-2.12070450e-01 1.24672145e-01 -4.30946559e-01 -1.33388674e+00
-3.09272438e-01 -1.18997502e+00 5.87969303e-01 1.56764358e-01
-1.10129142e+00 -1.25101650e+00 -6.60414472e-02 8.37571144e-01
3.16169590e-01 1.24910682e-01 1.35989499e+00 -1.49365056e+00
-7.91151002e-02 -4.05337989e-01 -8.24437588e-02 3.56036782e-01
5.00617921e-02 3.13237906e-01 -1.33233440e+00 4.36000265e-02
2.86552578e-01 -3.88745040e-01 3.01754236e-01 -1.83280371e-02
1.00496149e+00 -7.88040280e-01 -1.10286452e-01 -1.74672641e-02
8.21959317e-01 1.49054900e-01 3.64281714e-01 5.59348404e-01
5.10376215e-01 1.16899288e+00 4.74058270e-01 4.30550873e-01
2.64510304e-01 4.23647910e-01 5.59643447e-01 -1.24555171e-01
1.89440951e-01 -3.96365851e-01 4.05490398e-01 6.86922491e-01
1.85314849e-01 -2.12463722e-01 -1.08280694e+00 3.10805529e-01
-1.87346697e+00 -1.05603945e+00 -5.02745986e-01 1.84106064e+00
3.90850186e-01 9.90679264e-02 6.53865114e-02 4.55752522e-01
5.81967771e-01 1.64812863e-01 -3.36122692e-01 -1.11294782e+00
9.70967263e-02 -3.32369208e-01 -9.10616443e-02 6.61788344e-01
-8.83714020e-01 8.84976923e-01 6.26253700e+00 4.22057062e-01
-1.14587438e+00 -1.87972292e-01 9.55980241e-01 1.75962687e-01
-2.92187095e-01 -7.84196630e-02 -4.05637622e-01 4.41379637e-01
1.12111008e+00 -2.76898146e-01 4.53049809e-01 9.65317547e-01
8.21351588e-01 -1.00610621e-01 -1.29546165e+00 3.58034670e-01
3.27631712e-01 -1.01509345e+00 2.00679123e-01 2.23620892e-01
3.38538289e-01 -3.90668124e-01 2.04780295e-01 3.34072977e-01
-2.63870489e-02 -1.19452453e+00 7.64735281e-01 2.34769225e-01
-1.29838869e-01 -6.84734464e-01 1.00861287e+00 7.83384562e-01
-2.62986809e-01 -4.00503069e-01 -1.60142377e-01 -8.25366795e-01
-3.95616591e-01 3.31675142e-01 -1.50190794e+00 -1.63891643e-01
3.56725127e-01 4.32355881e-01 -6.65560484e-01 6.11304760e-01
-5.87632775e-01 1.10991800e+00 2.42463306e-01 -4.40888435e-01
4.02584702e-01 6.71527460e-02 4.68997031e-01 1.25294256e+00
-9.59660336e-02 1.84765551e-02 -1.24651738e-01 1.03543127e+00
2.91966170e-01 1.53518856e-01 -8.46944749e-01 -4.48255718e-01
4.52886224e-01 1.19540966e+00 -5.23097813e-01 -2.15149015e-01
-3.06332141e-01 8.26609492e-01 3.64276409e-01 1.17338628e-01
-8.48687768e-01 -1.27727851e-01 4.34727132e-01 2.49565795e-01
-1.70118049e-01 6.09020740e-02 -7.28480160e-01 -8.18031907e-01
-2.88168043e-01 -1.05848908e+00 2.39548966e-01 -8.63036096e-01
-1.22513425e+00 7.32479453e-01 -2.47370884e-01 -7.93389916e-01
-6.20092750e-01 -6.92263782e-01 -7.43440509e-01 7.74563253e-01
-1.17299449e+00 -1.05638885e+00 -6.73421845e-02 1.30204722e-01
8.65073562e-01 -2.21829295e-01 9.73253369e-01 -2.66375780e-01
-7.27765739e-01 3.74772161e-01 -1.88424796e-01 1.95131987e-01
5.02060890e-01 -1.19859171e+00 1.34606078e-01 7.46707976e-01
1.93165019e-02 7.16216087e-01 1.29680848e+00 -5.37298679e-01
-1.07011378e+00 -7.68522561e-01 1.59145117e+00 -6.74898088e-01
9.90437448e-01 -3.52059871e-01 -1.00782323e+00 4.80535805e-01
5.63986182e-01 -5.60279965e-01 1.11716485e+00 1.81178287e-01
-3.02785456e-01 3.34352195e-01 -1.02821934e+00 6.26766920e-01
3.83941710e-01 -6.10647142e-01 -8.07144582e-01 3.26515049e-01
5.35764217e-01 -5.46746003e-03 -5.33529162e-01 -1.12808630e-01
5.59393108e-01 -1.19657099e+00 2.46902287e-01 -9.05238152e-01
1.05121195e+00 3.78062353e-02 2.31755435e-01 -1.41917241e+00
-4.14410055e-01 -5.97310007e-01 -1.95102930e-01 1.27477562e+00
7.65780210e-01 -3.59613419e-01 7.69149482e-01 1.15483832e+00
4.30313572e-02 -8.14078391e-01 -5.27269006e-01 -1.00270525e-01
4.54599562e-04 -4.47736174e-01 2.00698763e-01 1.07644665e+00
4.24711168e-01 7.56543100e-01 -7.26226151e-01 9.76865813e-02
2.17523113e-01 -1.49328217e-01 8.63470554e-01 -1.36808252e+00
-1.01481296e-01 -3.31646502e-01 -3.49893421e-01 -6.57094419e-01
3.93815488e-01 -6.83579206e-01 -7.24863708e-02 -1.30717456e+00
3.83714139e-01 1.45282388e-01 2.12068662e-01 5.37671924e-01
-2.32189998e-01 9.69356447e-02 4.36704993e-01 3.75428855e-01
-4.20782655e-01 3.89776170e-01 1.07621562e+00 -9.21456292e-02
-1.82258293e-01 6.02914318e-02 -9.20844436e-01 8.45786214e-01
8.82258356e-01 -5.82170486e-01 -4.64677691e-01 -2.65908986e-01
1.51739046e-01 -1.06401511e-01 4.03362513e-01 -7.66145349e-01
3.76110785e-02 -3.08375154e-02 3.50460112e-01 -1.42150506e-01
1.65746644e-01 -9.61986363e-01 -2.88736850e-01 6.19417012e-01
-9.86451328e-01 9.15553123e-02 2.84314826e-02 3.62924695e-01
-2.65040368e-01 -2.89655298e-01 5.92083454e-01 1.19333947e-02
-3.50247830e-01 -1.79916352e-01 -9.65358436e-01 -4.29022238e-02
8.79962027e-01 -4.34710205e-01 -4.31899101e-01 -7.11848199e-01
-7.06311226e-01 1.79784447e-02 1.85756862e-01 7.64167607e-01
5.21463752e-01 -8.61873865e-01 -5.67280293e-01 2.10865870e-01
7.96702132e-02 -6.23540938e-01 -1.26902342e-01 7.64743149e-01
-1.37593448e-01 7.25071847e-01 1.41322957e-02 -3.58149379e-01
-1.28310061e+00 3.85242045e-01 2.32113019e-01 -1.85173899e-01
-3.46817702e-01 4.45203096e-01 1.40106082e-01 -2.63380915e-01
3.62093091e-01 -6.16837367e-02 -4.09636289e-01 -1.56612284e-02
7.48927474e-01 4.59194392e-01 -1.38887361e-01 -7.35407293e-01
-1.34370506e-01 -3.21144104e-01 -2.29184970e-01 6.24524541e-02
1.41449988e+00 -3.06793824e-02 -2.10319370e-01 7.17361212e-01
9.21815932e-01 -1.06171221e-01 -1.01513863e+00 1.54802024e-01
3.56634557e-01 -3.29088032e-01 -1.07078915e-02 -1.05498636e+00
-4.61688697e-01 1.20645893e+00 2.37888947e-01 1.01574194e+00
6.84858799e-01 -1.15848050e-01 3.40103209e-01 2.21638739e-01
-1.54463917e-01 -9.58075583e-01 1.11903504e-01 7.63665676e-01
8.89106035e-01 -1.42677736e+00 -1.21857315e-01 -3.04759085e-01
-9.37619448e-01 1.33673394e+00 7.40996540e-01 1.26778111e-01
2.97475785e-01 2.44136062e-02 2.54120767e-01 -3.80023092e-01
-9.21838820e-01 2.06228286e-01 3.34463924e-01 5.25424480e-01
1.05884516e+00 9.18669477e-02 -3.17933589e-01 7.12748766e-01
-5.14828146e-01 -1.96392834e-01 5.62613904e-01 3.97027135e-01
-6.22590959e-01 -7.90454388e-01 -4.81242865e-01 4.53067929e-01
-4.22305644e-01 -9.59922522e-02 -1.27380574e+00 9.03900862e-01
1.91913713e-02 1.40414667e+00 -1.78140685e-01 -7.13614464e-01
7.37899095e-02 3.19655150e-01 -1.81254491e-01 -5.95829964e-01
-1.01932478e+00 -4.41419572e-01 2.03444749e-01 -2.39799604e-01
-2.07096696e-01 -4.85665470e-01 -1.11149490e+00 -4.97038633e-01
-5.69045544e-01 6.29776061e-01 6.13649011e-01 1.48596644e+00
3.95878404e-01 2.36831546e-01 7.68561482e-01 -6.19005978e-01
-5.70808053e-01 -1.07108271e+00 -2.66422302e-01 5.18798828e-01
2.82110751e-01 -3.94729525e-01 -8.04078341e-01 -5.79590499e-02] | [9.290568351745605, 6.604339599609375] |
8a19635d-0f55-45d4-9d61-f1354ac74e56 | accelerated-mr-fingerprinting-with-low-rank | 2305.10651 | null | https://arxiv.org/abs/2305.10651v2 | https://arxiv.org/pdf/2305.10651v2.pdf | Accelerated MR Fingerprinting with Low-Rank and Generative Subspace Modeling | Magnetic Resonance (MR) Fingerprinting is an emerging multi-parametric quantitative MR imaging technique, for which image reconstruction methods utilizing low-rank and subspace constraints have achieved state-of-the-art performance. However, this class of methods often suffers from an ill-conditioned model-fitting issue, which degrades the performance as the data acquisition lengths become short and/or the signal-to-noise ratio becomes low. To address this problem, we present a new image reconstruction method for MR Fingerprinting, integrating low-rank and subspace modeling with a deep generative prior. Specifically, the proposed method captures the strong spatiotemporal correlation of contrast-weighted time-series images in MR Fingerprinting via a low-rank factorization. Further, it utilizes an untrained convolutional generative neural network to represent the spatial subspace of the low-rank model, while estimating the temporal subspace of the model from simulated magnetization evolutions generated based on spin physics. Here the architecture of the generative neural network serves as an effective regularizer for the ill-conditioned inverse problem without additional spatial training data that are often expensive to acquire. The proposed formulation results in a non-convex optimization problem, for which we develop an algorithm based on variable splitting and alternating direction method of multipliers.We evaluate the performance of the proposed method with numerical simulations and in vivo experiments and demonstrate that the proposed method outperforms the state-of-the-art low-rank and subspace reconstruction. | ['Bo Zhao', 'Lawrence L. Wald', 'Huihui Ye', 'Hengfa Lu'] | 2023-05-18 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 4.72804576e-01 -2.96411157e-01 3.28190960e-02 -2.51094908e-01
-8.90666366e-01 -1.49097472e-01 2.65306741e-01 -5.20218313e-01
-6.03386283e-01 7.67314792e-01 1.01456665e-01 -8.82454216e-02
-5.51613212e-01 -1.74669698e-01 -6.49171352e-01 -1.12554979e+00
-1.24957867e-01 4.32489038e-01 -1.94238514e-01 2.80868951e-02
2.06721440e-01 3.21755111e-01 -1.15699184e+00 6.94069192e-02
1.15403354e+00 8.19193602e-01 5.26236355e-01 3.29847008e-01
4.15003479e-01 6.28342450e-01 -8.23648497e-02 3.41487259e-01
-2.56651118e-02 -4.83130872e-01 -6.93528414e-01 -3.67592163e-02
3.27934295e-01 -4.15236950e-01 -8.09471428e-01 1.13658750e+00
7.05087245e-01 4.15627658e-01 4.83088613e-01 -7.69046128e-01
-5.82449079e-01 3.91750515e-01 -7.25866556e-01 5.29604495e-01
2.96345223e-02 -7.77013004e-02 4.15785581e-01 -1.18949425e+00
8.43442023e-01 7.30844378e-01 5.64454675e-01 2.69320816e-01
-1.55089772e+00 -6.56619012e-01 -2.36651495e-01 4.30557400e-01
-1.44165039e+00 -4.49575186e-01 1.13782299e+00 -6.73061907e-01
3.86159688e-01 1.72400087e-01 4.69227403e-01 1.02371967e+00
5.01108110e-01 5.20232737e-01 1.66127610e+00 -1.00329630e-01
-9.99185629e-03 -2.79238462e-01 2.15115219e-01 8.37272584e-01
9.28197578e-02 2.81219155e-01 -6.44483745e-01 -4.65652049e-01
1.03975582e+00 9.17595550e-02 -6.44239545e-01 -4.13991481e-01
-1.52913916e+00 7.05726862e-01 2.85225570e-01 5.66247046e-01
-7.49658287e-01 3.36091071e-02 8.63700882e-02 -1.38550922e-01
3.59856188e-01 2.98775047e-01 1.98106512e-01 2.08792120e-01
-1.41762793e+00 1.21417239e-01 3.68755639e-01 3.16745043e-01
4.74558592e-01 2.73698092e-01 -2.33916491e-01 8.46365035e-01
4.78908092e-01 6.14586592e-01 6.10085309e-01 -9.62354660e-01
9.64771211e-02 -4.32421528e-02 1.83315165e-02 -1.16629016e+00
-6.11266077e-01 -9.65724826e-01 -1.28357422e+00 -1.16879307e-01
3.17584246e-01 1.40912980e-02 -8.61163378e-01 1.90586841e+00
3.12857062e-01 5.89464307e-01 -2.35395253e-01 1.38762486e+00
5.72493315e-01 6.46189868e-01 -1.68680802e-01 -7.88318813e-01
1.18432915e+00 -8.14935088e-01 -1.05954909e+00 -7.35563561e-02
1.02820992e-01 -6.91188931e-01 6.69272125e-01 3.49649221e-01
-9.96799946e-01 -4.89015818e-01 -1.14009261e+00 1.86227515e-01
4.11212862e-01 1.62150666e-01 5.39892018e-01 2.69914538e-01
-8.67970765e-01 7.39614308e-01 -1.20942676e+00 2.40050048e-01
3.91049087e-02 3.44897389e-01 -5.18146336e-01 -3.67587268e-01
-1.28869653e+00 8.10258031e-01 1.59907162e-01 4.36732620e-01
-1.09487665e+00 -9.75381792e-01 -6.11690402e-01 -3.21994692e-01
1.98822215e-01 -7.79121280e-01 6.37463331e-01 -5.14281809e-01
-1.46575975e+00 5.61148465e-01 -2.32467443e-01 -3.72458994e-03
3.64999592e-01 -1.02992117e-01 -6.33775771e-01 5.90242028e-01
2.85560876e-01 4.86022718e-02 1.17576051e+00 -1.22410679e+00
2.18667999e-01 -6.00137591e-01 -4.38918173e-01 1.69725448e-01
-7.06205666e-02 -1.50226995e-01 -2.14246318e-01 -8.20775449e-01
7.88008869e-01 -1.13619423e+00 -4.30997729e-01 -2.70552367e-01
-3.06147486e-01 5.84270895e-01 6.01442516e-01 -1.12640846e+00
1.26172626e+00 -2.13255715e+00 4.13702220e-01 2.21561536e-01
4.92934167e-01 -3.64858806e-02 -5.10831550e-03 2.10670933e-01
-4.48133200e-01 -5.12114286e-01 -6.00911021e-01 -2.04020888e-02
-3.54051083e-01 1.26395160e-02 -1.33265033e-01 9.55123782e-01
-1.41128004e-01 6.45517111e-01 -1.09833586e+00 -4.37560588e-01
1.11147724e-01 8.92774940e-01 -5.54628730e-01 3.05807471e-01
3.88062388e-01 1.33816779e+00 -4.98008341e-01 2.90091664e-01
8.30540895e-01 -5.70991933e-01 4.28169042e-01 -7.95369208e-01
-2.77721792e-01 -1.46977842e-01 -1.28156197e+00 2.10734296e+00
-2.11075887e-01 1.02705397e-01 4.45529938e-01 -1.42563975e+00
5.40346622e-01 4.51860011e-01 1.05449629e+00 -6.84186399e-01
-9.83086601e-02 5.24284542e-01 4.23423275e-02 -6.46670163e-01
3.35279971e-01 -5.42283654e-01 2.37929553e-01 5.18085182e-01
5.10942899e-02 2.60430545e-01 1.22058257e-01 1.83881760e-01
9.71263230e-01 2.09299713e-01 -1.16903372e-01 -6.49090946e-01
7.40204632e-01 -3.16749930e-01 6.19770229e-01 7.26839066e-01
-3.64283915e-03 6.04910076e-01 6.79388493e-02 -2.86407262e-01
-1.00746405e+00 -9.27062213e-01 -6.80065453e-01 5.23168683e-01
9.34983119e-02 9.49723050e-02 -7.15607762e-01 -3.53649780e-02
-2.64436215e-01 2.96039701e-01 -4.77483243e-01 -8.14170763e-02
-9.25146163e-01 -1.33811641e+00 2.58370608e-01 4.37927507e-02
4.82099950e-01 -5.96019804e-01 -3.73512655e-01 6.24462008e-01
-7.67442048e-01 -1.21518409e+00 -5.66992700e-01 9.33304243e-03
-1.13596451e+00 -8.93375933e-01 -1.07264614e+00 -5.92410088e-01
8.28822851e-01 3.90781164e-01 7.35210717e-01 -2.11797699e-01
-2.66843736e-01 4.05886471e-01 3.83514282e-03 4.72013682e-01
-1.69218361e-01 -1.90140039e-01 2.82484800e-01 4.55330491e-01
-2.79684097e-01 -9.87605572e-01 -9.97057617e-01 4.54119653e-01
-1.09304941e+00 2.51283318e-01 7.39869893e-01 1.40066731e+00
9.52266872e-01 -1.32445753e-01 6.05407953e-01 -7.10913837e-01
4.01937902e-01 -6.21726215e-01 -4.38609511e-01 8.09116364e-02
-6.53306067e-01 4.54085290e-01 5.19681871e-01 -5.37474811e-01
-1.01929259e+00 9.89203528e-02 6.49019405e-02 -7.20940948e-01
3.14832300e-01 9.26656425e-01 7.75098577e-02 -4.67544973e-01
4.74169552e-01 7.97539532e-01 2.97835618e-01 -4.75332946e-01
2.58158803e-01 2.48139575e-01 8.46020222e-01 -7.92911530e-01
7.68091083e-01 7.36015320e-01 3.87070209e-01 -1.02731907e+00
-6.85440183e-01 -4.19975787e-01 -5.89471281e-01 -4.73057449e-01
8.24474573e-01 -7.94380546e-01 -5.50082862e-01 5.73520064e-01
-9.19861495e-01 8.07791203e-02 1.68601543e-01 1.07617688e+00
-7.80608892e-01 8.38062823e-01 -8.79520953e-01 -5.32012522e-01
-4.20534313e-01 -1.56842351e+00 7.23464370e-01 -7.57273659e-02
2.43205011e-01 -6.96508884e-01 1.99438274e-01 5.92948854e-01
5.57051122e-01 5.13886571e-01 9.68430340e-01 -2.43444555e-02
-6.82155252e-01 7.92022049e-02 -4.38995808e-02 1.16681106e-01
-2.13354871e-01 -6.99901700e-01 -7.16207147e-01 -5.24750769e-01
6.07547700e-01 -1.87754631e-02 7.17469633e-01 7.84306228e-01
1.16562045e+00 -7.99101666e-02 -2.58535326e-01 8.81315529e-01
1.37248099e+00 9.86735802e-03 4.94563162e-01 5.69143891e-02
9.23900127e-01 4.53419507e-01 4.56894457e-01 5.21879911e-01
2.82438308e-01 7.69489527e-01 1.12359613e-01 -1.66447625e-01
2.08592080e-02 -1.04485936e-01 1.48660615e-01 1.44250309e+00
-3.39598596e-01 4.34364527e-01 -8.44723701e-01 2.93657333e-01
-2.00699210e+00 -9.83352542e-01 -2.82521486e-01 2.31612754e+00
7.60830104e-01 -4.45151061e-01 -1.93082705e-01 7.08265752e-02
7.27521479e-01 2.78411239e-01 -7.52639115e-01 5.05332351e-01
-5.07397950e-02 2.04843268e-01 3.69286180e-01 5.20150542e-01
-1.00169671e+00 3.97035182e-01 5.97786045e+00 7.10104644e-01
-1.38629448e+00 5.39560437e-01 5.01277685e-01 4.20545861e-02
-3.36227953e-01 -1.44324094e-01 -2.01775312e-01 4.38327402e-01
8.96586299e-01 -8.98553133e-02 8.40726018e-01 3.19186687e-01
5.19625485e-01 -4.09209169e-02 -8.35555732e-01 1.32122564e+00
1.23226859e-01 -1.29059112e+00 -3.08160871e-01 1.89054102e-01
6.57831132e-01 1.28501996e-01 2.60179371e-01 -1.76927894e-02
-4.23405200e-01 -9.18922901e-01 6.12586260e-01 9.12098944e-01
9.42425489e-01 -5.63045263e-01 4.06672329e-01 3.94133776e-01
-9.61312056e-01 1.19954452e-01 -1.70662850e-01 3.46399009e-01
5.49467087e-01 9.76460695e-01 -4.32467349e-02 6.35283470e-01
3.63058031e-01 6.10901654e-01 -4.36790437e-02 8.22211027e-01
4.95719397e-03 5.73996603e-01 -2.48544708e-01 6.73059523e-01
1.74748793e-01 -6.95068359e-01 8.83513212e-01 8.75992775e-01
4.51399803e-01 6.12658739e-01 2.87440062e-01 1.09519804e+00
1.76413536e-01 -5.20466492e-02 -3.03434938e-01 -1.89752299e-02
4.91524227e-02 1.45334542e+00 -6.30780220e-01 -1.15406021e-01
-2.53679007e-01 9.54053640e-01 1.21582538e-01 7.28181541e-01
-8.04582596e-01 2.97834221e-02 1.74873933e-01 3.32381606e-01
8.56656488e-03 -5.44171154e-01 6.64651543e-02 -1.58629894e+00
1.89444512e-01 -9.34011281e-01 2.13126615e-01 -6.03555620e-01
-1.12541556e+00 6.10127926e-01 9.80898887e-02 -1.07477081e+00
-2.90267736e-01 -3.22285056e-01 -1.78304389e-01 9.90638375e-01
-1.55453229e+00 -1.02551401e+00 -2.21528053e-01 7.95486987e-01
-2.09238604e-02 9.15561989e-02 7.02946842e-01 8.95352364e-01
-5.85849881e-01 2.47252196e-01 4.77604151e-01 -1.13029040e-01
5.71453512e-01 -8.76947820e-01 -4.13034350e-01 9.18885231e-01
-2.60143101e-01 9.78406549e-01 6.92574859e-01 -6.30084336e-01
-1.88642597e+00 -7.37028420e-01 3.62394542e-01 1.23106614e-01
7.58796811e-01 -9.10263211e-02 -1.09056687e+00 3.44248623e-01
-1.09519802e-01 5.69680333e-01 6.85924053e-01 -2.71554321e-01
-3.73825431e-02 -7.01194331e-02 -1.17548811e+00 1.90047920e-01
8.62531841e-01 -8.27745259e-01 -4.19334143e-01 4.42666501e-01
2.34286442e-01 -5.29964089e-01 -1.22798085e+00 3.99562716e-01
7.23643720e-01 -7.27877855e-01 1.09731698e+00 -3.57334614e-01
4.01899904e-01 -5.73170185e-01 -1.37481600e-01 -1.32439721e+00
-7.43779778e-01 -7.39331007e-01 -3.93862724e-01 6.69623852e-01
1.14403330e-01 -5.63122630e-01 6.26497269e-01 4.95805115e-01
-3.10934216e-01 -7.44342387e-01 -1.12842119e+00 -6.19988799e-01
-7.24177286e-02 -1.17313229e-01 -1.47404000e-02 1.21371090e+00
-1.11431621e-01 3.35988283e-01 -7.20548630e-01 4.01931971e-01
1.32991993e+00 2.77186930e-01 8.16224739e-02 -8.14114392e-01
-5.96876860e-01 -2.10273489e-02 -1.10808194e-01 -1.09698534e+00
2.18108937e-01 -1.14040172e+00 1.11770570e-01 -1.12522352e+00
5.04572272e-01 -4.67186391e-01 -6.65360093e-01 -5.96175343e-02
-2.17813820e-01 2.17538223e-01 -4.40642424e-02 5.39743185e-01
-2.45083943e-01 7.42212176e-01 1.59444487e+00 -1.60243317e-01
4.18126173e-02 -3.69381547e-01 -3.90341848e-01 4.33584750e-01
2.55505890e-01 -5.46389163e-01 -4.56665248e-01 -2.95921534e-01
9.19325799e-02 8.08229625e-01 4.42762107e-01 -1.01609850e+00
3.07873070e-01 -4.59683686e-02 4.07564253e-01 -4.26504552e-01
3.48550469e-01 -6.50627732e-01 6.58657968e-01 5.99344909e-01
-1.53068304e-01 -4.31992859e-02 -8.91694799e-02 5.70974648e-01
-3.67850304e-01 -1.52211651e-01 1.06544852e+00 -1.64087474e-01
-3.24690282e-01 6.11390293e-01 -2.62107044e-01 -1.28623061e-02
4.37955558e-01 6.92557320e-02 -1.04283949e-03 -1.82727545e-01
-1.03310549e+00 -1.35568663e-01 1.07133813e-01 1.42503068e-01
8.49952877e-01 -1.48210013e+00 -8.02357554e-01 2.79639810e-01
-1.13479696e-01 -3.56002450e-01 9.77686107e-01 1.58304489e+00
-3.21111411e-01 3.28633726e-01 -3.39531541e-01 -8.03741574e-01
-5.65802395e-01 6.02615476e-01 4.79390025e-01 -6.40819609e-01
-7.57284820e-01 3.66303295e-01 4.52593029e-01 -4.62169737e-01
-3.87925208e-01 1.12177536e-01 -2.30756640e-01 -7.49500692e-02
5.53249955e-01 5.33899128e-01 1.73977707e-02 -1.04377341e+00
-3.95944953e-01 6.40296280e-01 5.74770980e-02 -3.34756643e-01
1.59100401e+00 -1.41502589e-01 -3.72094333e-01 2.63589025e-01
1.30726373e+00 -3.49659801e-01 -1.19727039e+00 -4.29350317e-01
-2.59314001e-01 -3.03361177e-01 7.41430938e-01 -4.12481606e-01
-1.18428195e+00 8.20276976e-01 9.58718240e-01 -4.82753873e-01
1.02361321e+00 -4.77541149e-01 9.04172003e-01 1.03456818e-01
6.42066896e-01 -9.00537372e-01 3.51942666e-02 3.77626330e-01
1.10763848e+00 -1.11932385e+00 2.77446568e-01 -3.30006868e-01
-2.92491049e-01 1.04746592e+00 1.28583014e-01 -2.77157426e-01
7.98480630e-01 9.69127379e-03 -1.49782836e-01 -4.46114480e-01
-1.87525287e-01 3.17616612e-01 5.67195892e-01 3.48636597e-01
4.58581567e-01 1.54941976e-01 -7.10317671e-01 5.73573530e-01
1.73429668e-01 2.15063483e-01 3.71427894e-01 6.56665385e-01
-5.29029332e-02 -9.56835985e-01 -5.86401105e-01 3.90824258e-01
-6.23239875e-01 -1.86195225e-01 5.12294292e-01 3.34613413e-01
-1.71787530e-01 6.26891255e-01 -4.02320832e-01 -9.57789421e-02
1.07286096e-01 -9.10876840e-02 7.78948486e-01 -1.69344410e-01
-2.19680950e-01 4.65612680e-01 -2.24979326e-01 -7.39321291e-01
-6.58939302e-01 -7.60012865e-01 -1.18066657e+00 2.49763466e-02
-2.29606971e-01 2.33668074e-01 7.36355782e-01 8.51132214e-01
3.51880252e-01 5.36996722e-01 7.19570458e-01 -1.11190748e+00
-6.53924167e-01 -9.00551438e-01 -8.80616069e-01 4.87538636e-01
4.08484429e-01 -9.09484029e-01 -1.66707546e-01 -1.09819159e-01] | [13.47022819519043, -2.390057325363159] |
77c776e3-46ee-4346-aeb8-c09be83d5f66 | generalization-of-the-dark-channel-prior-for | null | null | https://ieeexplore.ieee.org/abstract/document/8307410/ | https://ieeexplore.ieee.org/abstract/document/8307410/ | Generalization of the Dark Channel Prior for Single Image Restoration | Abstract— Images degraded by light scattering and absorption, such as hazy, sandstorm, and underwater images, often suffer color distortion and low contrast because of light traveling through turbid media. In order to enhance and restore such images, we first estimate ambient light using the depth-dependent color change. Then, via calculating the difference between the observed intensity and the ambient light, which we call the scene ambient light differential, scene transmission can be estimated. Additionally, adaptive color correction is incorporated into the image formation model (IFM) for removing color casts while restoring contrast. Experimental results on various degraded images demonstrate the new method outperforms other IFM-based methods subjectively and objectively. Our approach can be interpreted as a generalization of the common dark channel prior (DCP) approach to image restoration, and our method reduces to several DCP variants for different special cases of ambient lighting and turbid medium conditions. Index Terms— Haze removal, sandstorm, underwater, image restoration, transmission estimation, ambient light estimation. | ['IEEE', 'Fellow', 'and Pamela C. Cosman', 'Keming Cao', 'Yan-Tsung Peng'] | 2019-03-07 | null | null | null | ieee-transactions-on-image-processing-2019-3 | ['underwater-image-restoration'] | ['computer-vision'] | [ 7.09488988e-01 -3.92574310e-01 9.32761312e-01 -2.63718396e-01
-2.23992676e-01 -3.45756888e-01 2.52568215e-01 -3.31202090e-01
-5.17912865e-01 9.73489642e-01 1.06185153e-01 -5.62486462e-02
1.64777219e-01 -7.09656596e-01 -4.33956742e-01 -1.31886792e+00
2.43688270e-01 -2.33718846e-02 2.32380167e-01 -3.59385550e-01
3.68947238e-01 2.88084924e-01 -1.87256384e+00 -2.24746734e-01
1.44215095e+00 5.41525602e-01 6.23197973e-01 8.73937011e-01
-3.24299067e-01 7.64464021e-01 -6.00032747e-01 -1.24630228e-01
2.90605396e-01 -5.24770319e-01 -3.13341141e-01 4.24500287e-01
4.81406480e-01 -8.45861137e-01 -1.48326933e-01 1.48350763e+00
5.04558802e-01 3.64157945e-01 4.70177442e-01 -7.73094773e-01
-5.17068744e-01 -1.28501639e-01 -8.93748701e-01 1.88100517e-01
3.92827839e-01 2.73776233e-01 3.85285206e-02 -1.21134210e+00
2.99827874e-01 1.22048867e+00 4.22265559e-01 6.13010764e-01
-1.15650225e+00 -4.31971490e-01 -1.04521886e-01 3.09938639e-01
-1.13768220e+00 -6.01459503e-01 5.69296122e-01 -1.33477449e-01
3.86229664e-01 4.47090656e-01 8.81630957e-01 1.98439538e-01
3.37841511e-01 5.35562158e-01 1.88159585e+00 -6.94229841e-01
3.24289590e-01 3.94276790e-02 6.51600864e-03 5.73344886e-01
6.34714663e-01 5.19826822e-03 -4.20438766e-01 2.93179555e-03
3.92621934e-01 1.15683571e-01 -8.70000482e-01 2.59462714e-01
-4.16657001e-01 1.92654595e-01 1.67158112e-01 -2.62794197e-01
-2.36476496e-01 -1.25211403e-01 -4.09302711e-01 3.77213418e-01
7.93827713e-01 4.14593667e-02 2.41416804e-02 1.27716780e-01
-7.28673339e-01 -1.75717822e-03 7.88810015e-01 5.85151136e-01
1.23093712e+00 3.50190938e-01 1.97708577e-01 1.04809964e+00
7.70902991e-01 1.44084430e+00 1.38502494e-01 -1.32334137e+00
7.48328045e-02 1.70934916e-01 7.02742994e-01 -5.83251834e-01
-1.11017004e-01 4.81675230e-02 -8.20097089e-01 8.52207780e-01
1.65331632e-01 -1.22980863e-01 -1.20244551e+00 1.16628230e+00
4.99440193e-01 4.31931615e-01 6.02179229e-01 1.02871823e+00
6.83489263e-01 9.32550550e-01 -5.10755777e-01 -7.46080279e-01
9.93149221e-01 -6.11549914e-01 -1.16519523e+00 -3.69699895e-01
-1.87913418e-01 -1.04850960e+00 8.52265775e-01 6.69146478e-01
-1.47561145e+00 -1.05800003e-01 -1.20744598e+00 4.08062572e-03
2.03210814e-03 -3.20840538e-01 1.58113688e-01 8.29135478e-01
-1.30978477e+00 3.69426876e-01 -7.88098335e-01 -5.66017106e-02
-1.72091514e-01 1.87605157e-01 1.41469464e-01 -9.17675257e-01
-8.73714566e-01 1.00190961e+00 -2.94714093e-01 7.27304161e-01
-1.09323883e+00 -5.09385645e-01 -7.14734018e-01 -4.68195319e-01
6.83647320e-02 -4.84357774e-01 8.54283094e-01 -8.96998465e-01
-1.75438750e+00 7.06729054e-01 -5.04999757e-01 1.35418195e-02
2.82692730e-01 -4.43194926e-01 -4.26916122e-01 4.46837008e-01
-1.02120213e-01 -2.36181006e-01 1.10689330e+00 -2.26972294e+00
-4.85293746e-01 -4.80877936e-01 1.14016004e-01 6.97995186e-01
1.01905435e-01 -5.93595058e-02 -5.02801001e-01 9.30999815e-02
2.85242051e-01 -8.14044058e-01 -2.05794334e-01 2.85722792e-01
-4.14783895e-01 6.20996118e-01 9.90977883e-01 -7.92805076e-01
5.91473520e-01 -2.13524914e+00 -8.27458799e-02 9.84604061e-02
1.46540970e-01 3.73341113e-01 -2.17937499e-01 4.67730671e-01
4.49163169e-01 -5.05171955e-01 -7.82315373e-01 -6.12950623e-01
-4.81758475e-01 7.05134630e-01 6.99868798e-02 9.24425721e-01
-3.25399727e-01 4.56439219e-02 -1.01981783e+00 -4.45927620e-01
4.63301837e-01 7.85729051e-01 -2.33863279e-01 3.08403969e-01
1.92986429e-01 6.80456877e-01 3.49045433e-02 6.05790675e-01
1.63450372e+00 4.71937865e-01 3.34543511e-02 -9.02115554e-02
-7.32408166e-01 -3.98792684e-01 -1.20628870e+00 1.10515893e+00
-8.64140451e-01 8.32531989e-01 9.17706192e-01 -4.72817689e-01
9.52040434e-01 1.38382912e-01 1.51917383e-01 -6.87451720e-01
-8.46960023e-02 4.16201234e-01 -3.58044595e-01 -9.73696470e-01
6.72724187e-01 -4.86425996e-01 9.16646719e-01 1.69244394e-01
-6.11513078e-01 -4.95474964e-01 2.41687074e-02 2.44803250e-01
6.96426749e-01 3.63988504e-02 -1.99763328e-01 -3.01935375e-01
6.33217573e-01 -4.05365944e-01 5.92257023e-01 7.75038302e-01
-6.78039342e-02 7.30480850e-01 -1.74901307e-01 -2.18181670e-01
-7.65898466e-01 -1.15652287e+00 -2.02455223e-01 6.14561558e-01
1.00591278e+00 2.63656795e-01 -6.34468198e-01 4.34742659e-01
-2.41064340e-01 6.82269216e-01 -2.95260310e-01 -2.72046551e-02
-5.57861328e-01 -1.25555241e+00 7.28736073e-02 -3.00104946e-01
1.03878641e+00 -6.41346991e-01 -2.96326637e-01 1.47682443e-01
-5.89646578e-01 -1.15957642e+00 -2.56677438e-02 -7.93495029e-02
-9.29211259e-01 -1.22719455e+00 -8.33667755e-01 -6.52071357e-01
6.66031003e-01 9.54480171e-01 1.04207778e+00 5.14248550e-01
-4.53307897e-01 8.02685797e-01 -4.90661919e-01 -4.89057690e-01
-4.27149445e-01 -1.41287065e+00 8.68289452e-03 5.66176534e-01
1.18567236e-01 -6.52628660e-01 -1.25751090e+00 1.98406368e-01
-1.12935245e+00 1.15479581e-01 3.78264844e-01 5.69465995e-01
3.50118041e-01 3.36590797e-01 -3.45226139e-01 -8.06999147e-01
1.60487726e-01 -2.93238759e-01 -7.58205295e-01 -8.86159837e-02
-7.08429754e-01 -4.98043507e-01 2.57581264e-01 3.65594849e-02
-1.96534729e+00 -3.05274099e-01 2.57083684e-01 1.00618310e-01
-6.28623292e-02 2.67935008e-01 -1.67149872e-01 -5.30154467e-01
3.89180541e-01 6.37085676e-01 2.27121800e-01 -6.10629320e-01
1.42250042e-02 6.78547561e-01 5.93422055e-01 -1.49539247e-01
1.20898509e+00 1.33651412e+00 1.89568073e-01 -1.55095816e+00
-5.46154678e-01 -5.03471673e-01 -3.09282482e-01 -7.88176298e-01
1.03994298e+00 -1.08121359e+00 -7.16415524e-01 1.11449349e+00
-1.01494145e+00 -5.63712656e-01 8.17229748e-02 7.46793747e-01
-2.85112299e-02 8.83159339e-01 -9.28012252e-01 -1.53842080e+00
-1.62949234e-01 -1.06211913e+00 8.47361624e-01 5.79755545e-01
9.23380613e-01 -1.16297448e+00 1.41344056e-01 5.44148862e-01
6.51360273e-01 2.08098784e-01 5.92626810e-01 9.34354842e-01
-8.69990110e-01 1.63288668e-01 -4.20745790e-01 6.85916960e-01
4.56469744e-01 1.68333590e-01 -1.22521126e+00 -3.46342206e-01
3.74113142e-01 9.18273404e-02 9.65989113e-01 6.84229910e-01
4.26738441e-01 -1.57094091e-01 1.28240794e-01 8.72162580e-01
2.07947683e+00 8.99064019e-02 1.16235232e+00 5.50679147e-01
5.60615420e-01 5.72947979e-01 6.52853429e-01 6.76602960e-01
3.39409471e-01 4.17496473e-01 1.13779950e+00 -6.30489707e-01
-4.52848911e-01 7.25380898e-01 5.18729508e-01 7.77058721e-01
-8.10384095e-01 -4.45518225e-01 -3.86526167e-01 3.21354538e-01
-1.16843331e+00 -7.39111662e-01 -9.11386490e-01 2.31930065e+00
8.30195367e-01 -5.76582015e-01 -6.12927318e-01 2.07855880e-01
7.44554818e-01 -4.92209382e-02 -3.94377559e-01 -6.77143037e-02
-5.22718310e-01 1.71884716e-01 8.72988820e-01 1.15738177e+00
-4.43455011e-01 6.06778085e-01 6.00103283e+00 -1.82321351e-02
-8.60706747e-01 1.46562099e-01 -1.26025099e-02 2.42525503e-01
-6.30730093e-01 1.22430414e-01 -4.91935611e-01 5.21629691e-01
4.84322518e-01 2.42010012e-01 7.18510687e-01 -9.06453058e-02
9.56796050e-01 -8.64173770e-01 -2.55229980e-01 9.48170722e-01
2.32320771e-01 -4.24891919e-01 -8.24848339e-02 6.37916848e-02
9.73932624e-01 2.19254214e-02 6.45211041e-02 -3.62675607e-01
4.79006141e-01 -3.07331085e-01 4.14830565e-01 8.43880892e-01
5.79661727e-01 -2.36584902e-01 9.33295012e-01 -6.55270182e-03
-8.24768186e-01 -2.27433555e-02 -6.93202317e-01 -1.89469337e-01
4.38386708e-01 9.79874849e-01 -2.21021503e-01 4.87250030e-01
9.74268079e-01 5.90104461e-01 -3.06496084e-01 1.40772545e+00
-3.72220069e-01 5.43335259e-01 -2.30217844e-01 3.42736751e-01
5.29610738e-02 -1.05660307e+00 7.97578692e-01 8.93555105e-01
4.34752613e-01 5.87798178e-01 -9.43891555e-02 5.57385743e-01
3.33894789e-01 -2.29770392e-01 -1.36767790e-01 7.93797851e-01
1.80043802e-01 9.40714180e-01 -4.75600183e-01 -3.94591123e-01
-5.91346204e-01 1.34775233e+00 -7.67372429e-01 1.03042722e+00
-4.45941329e-01 -2.98185468e-01 7.81593323e-01 -9.68163386e-02
-1.81355506e-01 -2.84354270e-01 -4.04186398e-02 -1.31879425e+00
-6.46249205e-02 -5.43536127e-01 -1.69912159e-01 -1.37920964e+00
-9.44894373e-01 2.35610008e-01 -8.29175413e-02 -1.42223251e+00
7.52109528e-01 -7.07789958e-01 -7.13407099e-01 1.08391905e+00
-2.53350711e+00 -7.67500520e-01 -1.03340471e+00 9.50802267e-01
6.49808884e-01 4.13649768e-01 6.00680947e-01 4.38955247e-01
-4.25413877e-01 -2.46533886e-01 1.05358207e+00 -4.61920589e-01
7.41044998e-01 -1.44007409e+00 -4.92271125e-01 1.33569121e+00
-7.46105492e-01 2.51631469e-01 1.37043679e+00 -4.32362497e-01
-1.84794056e+00 -9.20716941e-01 1.68387726e-01 1.92058638e-01
3.81811291e-01 1.15632817e-01 -8.95848513e-01 2.86980599e-01
5.52110314e-01 3.30192186e-02 5.35542190e-01 -5.51661551e-01
2.15680487e-02 -4.44953918e-01 -1.35494936e+00 4.68739718e-01
5.23705542e-01 -2.21277490e-01 -1.44546852e-01 5.06765902e-01
3.40560466e-01 -3.36991459e-01 -5.82940340e-01 1.50901616e-01
6.04467928e-01 -1.33685279e+00 9.62107718e-01 3.30170035e-01
1.86618343e-01 -7.16569066e-01 -4.43626195e-01 -1.54944670e+00
-2.70995460e-02 -5.22086859e-01 2.47875318e-01 1.08648896e+00
1.85666084e-01 -9.60322618e-01 5.40684581e-01 5.99398553e-01
-6.25241816e-01 4.12296176e-01 -5.02211690e-01 -6.35811567e-01
-3.64173442e-01 3.30729201e-03 1.69019222e-01 6.55754805e-01
-5.72054923e-01 -8.87954757e-02 -8.77141953e-01 9.85836327e-01
1.67142224e+00 9.63971764e-02 8.05364192e-01 -1.40538859e+00
-1.80340156e-01 2.60225654e-01 -1.28953651e-01 -1.03061604e+00
-3.21478307e-01 -1.15722649e-01 7.76334703e-01 -2.01742840e+00
2.56862849e-01 -2.68340111e-01 7.30768144e-02 -6.41578808e-02
-3.49578798e-01 8.66921544e-01 5.86895756e-02 4.91901040e-01
-1.75501525e-01 5.35941720e-01 1.63872206e+00 -3.83000880e-01
-4.31544304e-01 3.36682564e-03 -1.63571388e-01 7.43868709e-01
5.39340913e-01 -2.21670717e-01 -2.30317086e-01 -9.37525809e-01
2.23556295e-01 1.20962434e-01 3.13447684e-01 -1.00778723e+00
1.32297963e-01 -3.76155823e-01 2.59213060e-01 -4.67250019e-01
9.08751547e-01 -9.74575639e-01 1.30940095e-01 6.02067888e-01
2.84391671e-01 -5.46323061e-01 -9.67026949e-02 8.59279513e-01
-3.01886141e-01 -5.13929784e-01 1.22626805e+00 -4.66946661e-01
-8.65946531e-01 6.25368655e-02 -9.66890752e-01 -3.18239957e-01
4.84740764e-01 -4.86836940e-01 -9.05511141e-01 -7.87058532e-01
-5.21798253e-01 -6.73694676e-03 7.34400630e-01 -4.13856328e-01
1.13496792e+00 -5.40111065e-01 -9.11834419e-01 -3.66521403e-02
-4.07000780e-02 -1.08499259e-01 5.15573382e-01 1.12883019e+00
-1.29693234e+00 -5.99845588e-01 1.38867363e-01 -5.18255949e-01
-1.58496475e+00 1.04350466e-02 4.55187142e-01 4.36268687e-01
-9.34228480e-01 7.69571543e-01 3.92400086e-01 1.00258037e-01
-3.62892561e-02 -4.30284366e-02 -2.17753887e-01 -4.43198889e-01
9.44712698e-01 8.63832593e-01 -8.23698193e-02 -6.76820815e-01
-5.24070598e-02 1.02122569e+00 3.91412705e-01 -5.04601188e-02
1.45446253e+00 -1.28507495e+00 -6.41844511e-01 4.46997792e-01
9.39739347e-01 1.93355232e-01 -1.48683465e+00 -1.11647621e-01
-7.35802710e-01 -1.00207770e+00 4.13988113e-01 -6.03384137e-01
-1.30674744e+00 7.32889771e-01 9.49011922e-01 4.32574064e-01
1.65470517e+00 -3.95153463e-01 6.83093727e-01 3.68930638e-01
2.11005718e-01 -1.00821173e+00 -1.58116105e-03 3.34597081e-01
4.45067614e-01 -1.22100639e+00 2.83751458e-01 -6.20551467e-01
-2.32105151e-01 1.23609340e+00 4.85011458e-01 9.11575183e-02
6.66778684e-01 4.79959041e-01 6.57083929e-01 -1.11957923e-01
-2.36206755e-01 -4.18397754e-01 -5.69448233e-01 8.81344736e-01
6.38919398e-02 -2.86341786e-01 -2.92673528e-01 -5.40796041e-01
3.92369926e-01 -2.37204522e-01 1.47287047e+00 8.76257002e-01
-1.06961548e+00 -6.67918503e-01 -9.61846471e-01 5.74879050e-02
-4.35398251e-01 -2.95303524e-01 1.84463724e-01 4.33878541e-01
3.26712839e-02 1.53870213e+00 -1.32114947e-01 1.23973683e-01
1.64886326e-01 -4.64220196e-01 4.72106636e-01 -3.84425819e-01
2.08429307e-01 2.37681895e-01 -9.04288441e-02 -2.83352703e-01
-1.37763321e+00 -4.22600895e-01 -1.03401458e+00 -2.64250875e-01
-5.41727901e-01 2.63461381e-01 9.77139533e-01 7.47313142e-01
-4.32090968e-01 2.49172121e-01 1.05723405e+00 -1.23155916e+00
2.85282940e-01 -1.01913023e+00 -1.41842675e+00 4.57544029e-01
8.83800387e-01 -6.49964869e-01 -1.18830156e+00 2.85967767e-01] | [10.781746864318848, -3.242655038833618] |
b7f0c350-d90d-42b9-a7bf-9aa029b3530e | deep-transfer-learning-for-cross-domain | 1807.07963 | null | http://arxiv.org/abs/1807.07963v2 | http://arxiv.org/pdf/1807.07963v2.pdf | Deep Transfer Learning for Cross-domain Activity Recognition | Human activity recognition plays an important role in people's daily life.
However, it is often expensive and time-consuming to acquire sufficient labeled
activity data. To solve this problem, transfer learning leverages the labeled
samples from the source domain to annotate the target domain which has few or
none labels. Unfortunately, when there are several source domains available, it
is difficult to select the right source domains for transfer. The right source
domain means that it has the most similar properties with the target domain,
thus their similarity is higher, which can facilitate transfer learning.
Choosing the right source domain helps the algorithm perform well and prevents
the negative transfer. In this paper, we propose an effective Unsupervised
Source Selection algorithm for Activity Recognition (USSAR). USSAR is able to
select the most similar $K$ source domains from a list of available domains.
After this, we propose an effective Transfer Neural Network to perform
knowledge transfer for Activity Recognition (TNNAR). TNNAR could capture both
the time and spatial relationship between activities while transferring
knowledge. Experiments on three public activity recognition datasets
demonstrate that: 1) The USSAR algorithm is effective in selecting the best
source domains. 2) The TNNAR method can reach high accuracy when performing
activity knowledge transfer. | ['Vincent W. Zheng', 'Meiyu Huang', 'Yiqiang Chen', 'Jindong Wang'] | 2018-07-20 | null | null | null | null | ['cross-domain-activity-recognition'] | ['computer-vision'] | [ 3.58717144e-01 -4.09866393e-01 -7.66874075e-01 -2.89067656e-01
-8.20003748e-01 -4.21291113e-01 3.70833129e-01 -1.76574484e-01
-3.03301156e-01 1.04885769e+00 3.77176523e-01 1.72377110e-01
-1.65944576e-01 -9.91112649e-01 -6.56317890e-01 -7.50739038e-01
2.17588488e-02 3.16192508e-01 2.79604644e-01 7.65761212e-02
1.35788754e-01 2.84825414e-01 -1.28955543e+00 2.99284369e-01
1.24841404e+00 1.09258711e+00 1.21170513e-01 -5.39509207e-02
-1.20326236e-01 1.14795995e+00 -7.48403490e-01 1.25513598e-01
1.70578673e-01 -9.11733449e-01 -9.72213328e-01 -8.80276114e-02
6.00774474e-02 -3.02080393e-01 -2.52164423e-01 7.44757891e-01
3.94154191e-01 4.61880922e-01 9.65811729e-01 -1.50977838e+00
-5.29616416e-01 3.20242137e-01 -4.44977552e-01 3.92058223e-01
5.36922216e-01 -3.50329676e-03 8.02638173e-01 -6.41722262e-01
5.30125201e-01 7.42331386e-01 5.95302820e-01 3.78068060e-01
-9.23200488e-01 -9.99056876e-01 6.46367967e-02 4.14328694e-01
-1.56977475e+00 -3.25424820e-01 9.99148667e-01 -5.02077758e-01
6.99657023e-01 -2.88308263e-02 7.85425246e-01 1.42544520e+00
-1.78568959e-01 1.12894142e+00 1.06146097e+00 -2.86670625e-01
3.75185341e-01 2.29412496e-01 4.10106815e-02 3.48389149e-01
1.45157367e-01 -1.77575737e-01 -9.93943691e-01 -6.34862110e-02
9.15619433e-01 2.88222790e-01 -2.96105981e-01 -4.30059284e-01
-1.43536723e+00 7.05004811e-01 4.75783646e-01 5.76079488e-01
-3.95718604e-01 -1.09490514e-01 1.82748020e-01 3.70643318e-01
3.87928069e-01 3.35412145e-01 -4.39088136e-01 -4.68674988e-01
-6.94003046e-01 -3.42835747e-02 6.81526959e-01 8.42240393e-01
1.03593969e+00 -2.48403996e-01 -3.14435303e-01 1.02449071e+00
1.51906118e-01 7.21157193e-01 5.78028440e-01 -8.11418295e-01
6.89061165e-01 9.97915149e-01 1.89672619e-01 -8.08189571e-01
2.63073365e-04 -2.55271107e-01 -7.33486950e-01 -1.41664654e-01
5.01491785e-01 -1.30837217e-01 -5.88442862e-01 1.83068967e+00
2.61667967e-01 5.62359333e-01 1.09362997e-01 8.16567183e-01
4.45356756e-01 7.53775716e-01 2.47057632e-01 -1.44797817e-01
1.11391747e+00 -8.60698879e-01 -5.25358558e-01 -4.62687045e-01
7.46417701e-01 -1.69816017e-01 1.09778225e+00 2.26826936e-01
-4.82129604e-01 -5.00905216e-01 -1.02043998e+00 2.52826989e-01
-3.49388450e-01 1.67742193e-01 6.97696030e-01 4.52538729e-01
-5.79367220e-01 4.85556066e-01 -7.67965317e-01 -5.38919032e-01
6.15541875e-01 2.94377327e-01 -5.28260648e-01 -1.64088011e-01
-1.42835164e+00 7.93434501e-01 5.00330567e-01 -4.16843742e-01
-8.39474916e-01 -5.33613086e-01 -7.88945436e-01 1.58983041e-02
3.65377009e-01 -4.21837300e-01 1.10031414e+00 -1.56203985e+00
-1.37231159e+00 5.42984545e-01 -2.19051421e-01 -2.67010480e-01
4.28324252e-01 -1.57780424e-01 -5.74864507e-01 2.70742804e-01
4.25063670e-01 4.65418637e-01 6.83612645e-01 -8.07884037e-01
-8.80413413e-01 -1.92307219e-01 -2.11986527e-01 5.75325847e-01
-7.81267047e-01 -2.59370744e-01 -4.51743245e-01 -7.47681916e-01
-1.23546757e-01 -8.29637051e-01 3.02919716e-01 -5.02296537e-02
1.36593893e-01 -4.69933331e-01 8.15545976e-01 -7.08803117e-01
1.17549670e+00 -2.17573047e+00 -5.10137938e-02 4.18798298e-01
-1.15187787e-01 2.52417296e-01 1.31398104e-02 3.29221874e-01
-8.21835771e-02 -2.70920664e-01 -1.95170328e-01 3.88537049e-01
-2.01128855e-01 3.28561157e-01 -2.96998266e-02 2.61160493e-01
7.53799379e-02 8.68551195e-01 -1.04398477e+00 -6.84364915e-01
1.13831721e-01 2.56865501e-01 -4.23812956e-01 3.23469341e-01
-2.50248495e-03 8.07848334e-01 -8.09831738e-01 6.18518889e-01
1.50007129e-01 -6.28032744e-01 1.78813085e-01 -1.83746621e-01
2.89195448e-01 3.66751552e-01 -1.21099043e+00 1.73618948e+00
-2.79905707e-01 5.67585170e-01 -4.99029130e-01 -1.21404445e+00
1.00794232e+00 2.74174601e-01 1.02499473e+00 -9.25717413e-01
-1.09815486e-02 1.41419768e-01 -1.31908447e-01 -4.34997201e-01
-8.23023543e-02 -6.14669621e-02 -2.48539254e-01 6.99285507e-01
2.85926908e-02 4.60415095e-01 -2.22105086e-02 9.16278362e-02
1.33028996e+00 3.05228740e-01 5.65252066e-01 1.89255085e-03
5.08396268e-01 1.41993985e-01 8.13004553e-01 5.36561370e-01
-5.84255278e-01 2.46164501e-01 1.35524064e-01 -2.37807557e-01
-5.21194041e-01 -1.23506474e+00 3.31844628e-01 1.41803789e+00
2.07099453e-01 -8.18849877e-02 -7.20543325e-01 -1.03874826e+00
-1.49409041e-01 5.18366337e-01 -7.79381335e-01 -5.52809238e-01
-6.54479384e-01 -2.69082695e-01 7.21426666e-01 9.85917866e-01
9.80858743e-01 -1.41525590e+00 -3.90901059e-01 7.01907501e-02
-7.78881788e-01 -6.58463657e-01 -8.36057007e-01 2.51158863e-01
-8.27372134e-01 -1.28743064e+00 -9.25467253e-01 -8.82388711e-01
7.44336069e-01 5.26518643e-01 9.44201410e-01 -2.24355221e-01
2.54818767e-01 3.62678617e-01 -6.32411778e-01 -3.07585001e-01
-3.16706002e-02 2.43033275e-01 8.63444954e-02 2.09888965e-01
9.32834625e-01 -6.44605875e-01 -6.72302604e-01 6.26766682e-01
-5.93425393e-01 -1.57589987e-01 7.32389331e-01 6.71955407e-01
4.32484359e-01 2.61340111e-01 8.47544372e-01 -7.58579731e-01
5.46148419e-01 -7.86561012e-01 -9.70404744e-02 3.79143745e-01
-6.62187278e-01 2.31914464e-02 5.77548504e-01 -8.04596663e-01
-1.20864582e+00 1.75793961e-01 1.86440453e-01 -4.11737144e-01
-2.25090399e-01 5.54510057e-01 -5.20706952e-01 1.46658540e-01
8.52735817e-01 4.86023188e-01 -2.74447091e-02 -3.74552041e-01
-5.34341782e-02 7.60155439e-01 2.50974447e-01 -6.10575795e-01
6.92981422e-01 4.75747943e-01 -6.22049987e-01 -7.22631574e-01
-9.66302395e-01 -7.11427867e-01 -7.42388606e-01 -2.34091535e-01
8.52036119e-01 -8.79226387e-01 -2.95593888e-01 5.48038960e-01
-6.88218534e-01 -5.75194716e-01 -3.27195376e-01 6.12325788e-01
-5.24275184e-01 2.55201310e-01 -2.47366369e-01 -5.39240599e-01
-2.58677930e-01 -7.94621229e-01 7.34169483e-01 3.81199419e-01
-5.24704397e-01 -1.09266734e+00 1.20475985e-01 6.02198482e-01
2.09234014e-01 4.38501425e-02 6.79108322e-01 -9.15082455e-01
-4.69702721e-01 5.07174358e-02 -5.32003716e-02 2.98799604e-01
7.19613135e-01 -6.17500365e-01 -7.97973514e-01 -2.50285804e-01
-2.93674558e-01 -4.00791287e-01 5.37045300e-01 2.65804559e-01
1.05919790e+00 -2.17407092e-01 -6.23818994e-01 3.12021345e-01
8.60092461e-01 6.75330877e-01 7.71422982e-01 4.40949380e-01
6.91050828e-01 5.28756380e-01 1.01685810e+00 2.85939068e-01
3.98322046e-01 7.33015597e-01 -2.49462754e-01 -6.40817210e-02
-5.57103679e-02 -5.29539883e-01 7.05217361e-01 7.26385176e-01
-7.87060559e-02 -3.21713015e-02 -9.78255928e-01 6.36547565e-01
-1.91682506e+00 -1.17506921e+00 4.23555940e-01 2.20120621e+00
1.06562626e+00 6.03365935e-02 3.31695229e-01 2.23734945e-01
5.59962690e-01 -2.26945579e-02 -8.45659971e-01 4.14813191e-01
1.33366778e-01 1.75169811e-01 4.15756255e-01 2.59175319e-02
-1.21343577e+00 7.10914314e-01 6.21041918e+00 9.69575107e-01
-9.66178179e-01 2.10155010e-01 3.85628015e-01 -4.77320515e-02
8.05703700e-02 -1.82095230e-01 -7.69481897e-01 7.40532041e-01
8.98179412e-01 -1.86019003e-01 3.63894880e-01 1.01769531e+00
2.25041732e-01 -1.43669575e-01 -1.24298096e+00 1.23142433e+00
2.27917358e-01 -1.05247891e+00 -3.24675478e-02 1.01096123e-01
6.14442885e-01 -1.92282110e-01 -3.15544903e-01 6.79467916e-01
4.24178362e-01 -9.31753039e-01 3.46754700e-01 4.40308988e-01
7.12506413e-01 -8.44994485e-01 4.71319020e-01 6.67831063e-01
-1.48843265e+00 -7.01821595e-02 -4.56700437e-02 4.63304669e-02
-7.31756585e-03 2.96187133e-01 -9.19064581e-01 4.02779669e-01
9.02722001e-01 1.10290062e+00 -5.48354626e-01 8.69560659e-01
-2.69980431e-01 7.94475675e-01 -1.31162494e-01 -6.40160218e-02
-4.48157489e-02 -5.00777006e-01 7.05253482e-02 1.09536624e+00
5.33099830e-01 1.76390529e-01 4.01083231e-01 5.19789457e-01
-2.63810307e-01 1.05447851e-01 -7.24003851e-01 -2.23382190e-01
5.88331997e-01 7.64647722e-01 -6.97665930e-01 -3.54629695e-01
-4.52346683e-01 1.15409124e+00 3.62931192e-01 3.51346195e-01
-9.37196314e-01 -3.49876761e-01 5.11034906e-01 2.26697683e-01
9.50238258e-02 6.26897514e-02 -1.91226467e-01 -1.29788744e+00
2.81533618e-02 -1.06048191e+00 9.38695014e-01 -6.44864798e-01
-1.48494148e+00 1.13991715e-01 1.17796935e-01 -1.78053689e+00
-2.39642933e-01 -4.10254806e-01 -4.08677489e-01 6.82430506e-01
-1.36071289e+00 -1.09356618e+00 -6.04290068e-01 1.10413063e+00
5.32227933e-01 -2.71704108e-01 7.05465198e-01 5.95636249e-01
-3.78448665e-01 5.32684147e-01 -1.15949012e-01 6.71290755e-01
9.78172839e-01 -1.10836506e+00 -1.29982010e-01 6.86223328e-01
1.64540932e-01 7.65197635e-01 9.83693451e-02 -7.57607937e-01
-1.12312090e+00 -1.21433032e+00 6.50766253e-01 -4.98857647e-01
3.45085353e-01 -1.00403326e-03 -1.11639738e+00 8.76544654e-01
-9.40943882e-02 -1.48749679e-01 1.22441947e+00 1.25892058e-01
-3.42466682e-01 -3.29215854e-01 -9.81737018e-01 5.09236872e-01
1.24179566e+00 -6.37438953e-01 -8.83386552e-01 2.60182500e-01
1.95298567e-01 1.71418898e-02 -1.03543675e+00 1.35685205e-01
5.00547528e-01 -6.26736462e-01 1.04587340e+00 -4.11557436e-01
1.84962288e-01 -4.93431866e-01 1.73108093e-02 -1.39730167e+00
-3.40948164e-01 -6.40589073e-02 -3.60965610e-01 1.23101437e+00
3.26043069e-01 -6.48670733e-01 1.00426114e+00 5.62762618e-01
2.49021694e-01 -3.03098202e-01 -8.15136790e-01 -1.03873217e+00
-1.56298876e-01 -1.74967170e-01 5.65593779e-01 1.39063299e+00
9.92352292e-02 4.83258218e-01 -5.29659927e-01 -7.74545968e-02
3.93530428e-01 1.72690615e-01 9.32997346e-01 -1.53052390e+00
-2.89578527e-01 -1.75045475e-01 -1.26299620e-01 -1.25002027e+00
2.75405586e-01 -8.82723510e-01 1.25403732e-01 -1.72499168e+00
2.84800589e-01 -6.11629009e-01 -7.20554471e-01 1.00654685e+00
-7.45047778e-02 7.33315945e-02 -3.08706462e-01 5.92566252e-01
-8.80147338e-01 5.83299398e-01 1.16420472e+00 -2.45232329e-01
-5.34760416e-01 1.34830534e-01 -7.64626801e-01 6.43833280e-01
8.47196043e-01 -3.88573468e-01 -7.81184196e-01 -6.14848807e-02
-1.33086488e-01 -2.12847013e-02 9.59893167e-02 -1.18671572e+00
1.90003037e-01 -5.70435941e-01 7.80120969e-01 -4.74781930e-01
3.43827546e-01 -1.01197267e+00 2.65107960e-01 4.91983622e-01
-2.05834165e-01 -5.51659644e-01 -1.95859864e-01 6.50197506e-01
-2.80312479e-01 -1.78410858e-02 8.13560724e-01 -5.51792495e-02
-1.14222109e+00 3.86716396e-01 -2.88764000e-01 8.58304873e-02
1.22582948e+00 -5.65969050e-01 -1.16566621e-01 -5.03536522e-01
-4.93081033e-01 1.01426817e-01 4.22951072e-01 5.97599149e-01
4.83190596e-01 -1.65255749e+00 -3.20315063e-01 1.34776220e-01
5.06538033e-01 -1.57484636e-01 -4.64399066e-03 8.89165461e-01
-1.67350829e-01 1.75736368e-01 -5.30286252e-01 -5.62597573e-01
-1.03384733e+00 3.29249382e-01 4.24727440e-01 -3.15875798e-01
-4.78038609e-01 5.05706191e-01 9.72541422e-02 -4.03635889e-01
1.13110445e-01 -5.63343987e-02 -4.54093605e-01 2.69212127e-01
6.29929900e-01 6.13989770e-01 1.85238961e-02 -5.84283173e-01
-5.56878150e-01 4.31918532e-01 5.40211722e-02 -1.00863613e-01
1.19078290e+00 5.92086054e-02 1.89717457e-01 5.05295932e-01
1.15611744e+00 -1.18412890e-01 -1.46853352e+00 -4.74803507e-01
2.04659760e-01 -5.23955345e-01 -2.44217619e-01 -7.57486761e-01
-9.29501057e-01 6.38984919e-01 5.96477449e-01 -1.77217513e-01
1.31385458e+00 -6.65937457e-03 9.15891469e-01 5.13550520e-01
6.86702728e-01 -1.36326730e+00 5.92861831e-01 4.10213947e-01
4.90207434e-01 -1.27832901e+00 -1.63443848e-01 -3.85241419e-01
-9.82991695e-01 6.01819098e-01 9.59571421e-01 7.71999061e-02
5.27188241e-01 -1.10281117e-01 9.46651846e-02 -1.22577384e-01
-3.31164747e-01 -1.99837953e-01 3.93405080e-01 8.77555609e-01
2.91769505e-01 4.38628234e-02 -1.14096291e-01 6.56636059e-01
1.58267230e-01 3.68153572e-01 -4.04284783e-02 1.25822341e+00
-5.38153231e-01 -1.20208764e+00 -4.46043938e-01 6.81397974e-01
-6.09169081e-02 3.01778167e-01 -4.63132650e-01 5.57404876e-01
1.30427629e-01 1.02158988e+00 -7.37403780e-02 -3.27678829e-01
4.05275106e-01 3.24808598e-01 3.68731320e-01 -6.78453207e-01
-3.73094678e-01 -7.72920549e-02 -9.48259607e-02 -6.33453131e-01
-6.57404065e-01 -5.86350262e-01 -1.33389080e+00 -3.31825912e-02
-8.90941247e-02 3.87438089e-01 -5.84784932e-02 1.06229532e+00
3.80677223e-01 3.70436639e-01 6.10125780e-01 -4.01758045e-01
-2.88120568e-01 -9.14027810e-01 -7.17212856e-01 8.61444056e-01
-9.86885205e-02 -1.07074046e+00 -8.82182717e-02 4.47576553e-01] | [8.02088737487793, 1.003119945526123] |
6786511b-0726-43f5-82c4-7461ebdd8442 | meta-self-learning-for-multi-source-domain | 2108.10840 | null | https://arxiv.org/abs/2108.10840v1 | https://arxiv.org/pdf/2108.10840v1.pdf | Meta Self-Learning for Multi-Source Domain Adaptation: A Benchmark | In recent years, deep learning-based methods have shown promising results in computer vision area. However, a common deep learning model requires a large amount of labeled data, which is labor-intensive to collect and label. What's more, the model can be ruined due to the domain shift between training data and testing data. Text recognition is a broadly studied field in computer vision and suffers from the same problems noted above due to the diversity of fonts and complicated backgrounds. In this paper, we focus on the text recognition problem and mainly make three contributions toward these problems. First, we collect a multi-source domain adaptation dataset for text recognition, including five different domains with over five million images, which is the first multi-domain text recognition dataset to our best knowledge. Secondly, we propose a new method called Meta Self-Learning, which combines the self-learning method with the meta-learning paradigm and achieves a better recognition result under the scene of multi-domain adaptation. Thirdly, extensive experiments are conducted on the dataset to provide a benchmark and also show the effectiveness of our method. The code of our work and dataset are available soon at https://bupt-ai-cz.github.io/Meta-SelfLearning/. | ['Wenli Zhou', 'Chuang Zhu', 'Shuhao Qiu'] | 2021-08-24 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 2.34335855e-01 -7.33525574e-01 -3.33300799e-01 -4.27114218e-01
-4.17100698e-01 -2.99211234e-01 6.18877351e-01 -1.28857061e-01
-2.58154452e-01 5.72300732e-01 -2.19170121e-03 2.60432325e-02
2.40601510e-01 -6.85044885e-01 -5.06753623e-01 -7.64255822e-01
6.25698447e-01 4.92605299e-01 2.37167090e-01 -2.25314766e-01
4.34275419e-01 1.03741929e-01 -1.46137965e+00 6.21303737e-01
1.19245780e+00 8.79942179e-01 3.65339637e-01 2.91150182e-01
-4.62387323e-01 6.28192544e-01 -6.42629564e-01 -3.82091880e-01
7.24532381e-02 -5.61911047e-01 -8.26870918e-01 4.85619813e-01
4.18776780e-01 -3.50369483e-01 -5.31603217e-01 1.01723230e+00
7.57043302e-01 6.68952018e-02 8.16999137e-01 -1.22846949e+00
-9.99289691e-01 4.46776986e-01 -7.93515086e-01 1.42721161e-01
-5.37618995e-02 -8.97586998e-03 6.01171374e-01 -1.10142255e+00
3.93386990e-01 9.88063216e-01 4.73727912e-01 7.61946142e-01
-7.38245189e-01 -8.84191215e-01 2.02399909e-01 5.41706741e-01
-1.25778449e+00 -4.09776270e-01 1.00216627e+00 -4.16684508e-01
5.27920485e-01 -1.94237113e-01 3.20556134e-01 1.42425001e+00
-5.98035529e-02 1.23639810e+00 1.20483422e+00 -7.08118320e-01
1.76695019e-01 3.07306558e-01 2.78091699e-01 5.42695761e-01
1.39955431e-01 -1.99458212e-01 -5.23193002e-01 1.88287571e-01
6.48572147e-01 2.72639006e-01 -1.22664608e-01 -4.30099964e-01
-1.19032967e+00 7.64702201e-01 -5.93397915e-02 5.90954065e-01
2.05696784e-02 -3.77066851e-01 6.60913587e-01 4.67331082e-01
5.41204512e-01 -1.30701348e-01 -6.08967900e-01 -9.18461829e-02
-6.34170651e-01 -8.08930472e-02 6.84993982e-01 1.01413441e+00
6.02008641e-01 2.77453452e-01 5.82225434e-02 1.52924430e+00
1.78328574e-01 5.95949888e-01 1.16478598e+00 -2.49448046e-01
7.19064057e-01 7.35770285e-01 -2.70918310e-01 -9.50747430e-01
-2.35711738e-01 -2.71874338e-01 -1.34697640e+00 6.83590025e-02
3.25677276e-01 -3.46380949e-01 -8.75760853e-01 1.31320369e+00
2.19042167e-01 1.75686061e-01 2.17892200e-01 7.16117322e-01
1.13521600e+00 7.40890265e-01 -1.71458926e-02 -8.92997608e-02
1.09941006e+00 -1.43311763e+00 -6.86884940e-01 -4.28541303e-01
6.72423959e-01 -8.47234786e-01 1.11205411e+00 6.17902517e-01
-7.15975404e-01 -7.28238106e-01 -1.10240984e+00 1.52301818e-01
-5.19383609e-01 3.53024721e-01 4.54408973e-01 6.17509544e-01
-5.22236407e-01 3.00316095e-01 -4.81370479e-01 -7.74854660e-01
6.64062262e-01 1.87682256e-01 -3.00448030e-01 -2.40897045e-01
-1.07615483e+00 7.77838647e-01 6.73811674e-01 -2.71768659e-01
-5.37200630e-01 -2.25208893e-01 -5.17289937e-01 -2.38244399e-01
3.31940442e-01 -4.19758648e-01 1.25544333e+00 -1.54684174e+00
-1.51451051e+00 9.94355679e-01 -8.76476839e-02 -1.20760761e-01
6.72514081e-01 -7.01344162e-02 -8.90414536e-01 -8.15669373e-02
-1.07620798e-01 3.63066196e-01 1.22434020e+00 -1.14262712e+00
-7.07493901e-01 -6.47590220e-01 -4.94453549e-01 2.69473046e-01
-1.00379598e+00 -1.08072639e-01 -5.79304755e-01 -9.85723913e-01
-5.99848144e-02 -8.10236275e-01 2.17416331e-01 -2.36805260e-01
-3.34586874e-02 -4.13909018e-01 1.18803132e+00 -4.28755641e-01
1.24300110e+00 -2.32395387e+00 -7.99599141e-02 -3.65109533e-01
5.20681106e-02 6.46278203e-01 -2.80589402e-01 4.79986310e-01
-2.64777839e-01 -6.60752803e-02 -2.19384596e-01 -1.06025487e-01
-1.30509451e-01 7.69723132e-02 -3.91042024e-01 3.12749386e-01
-7.89213851e-02 7.97548532e-01 -6.19641483e-01 -7.33074903e-01
3.37152421e-01 1.22350365e-01 -3.64235081e-02 5.87221310e-02
-1.73230574e-01 3.43700349e-01 -6.06486440e-01 8.53180468e-01
9.74805355e-01 -4.52070296e-01 3.29727866e-02 -1.14685193e-01
-9.52580478e-03 -2.24984020e-01 -1.27634442e+00 1.76279306e+00
-2.09911183e-01 5.31376779e-01 -2.11843789e-01 -1.44687319e+00
1.25210202e+00 1.62712172e-01 5.01158476e-01 -8.45959008e-01
2.67552704e-01 3.08211625e-01 -2.77937829e-01 -6.70116484e-01
3.71595919e-01 -1.47728354e-01 8.32319781e-02 5.60521603e-01
1.62576899e-01 1.28955483e-01 2.21897006e-01 -4.59063090e-02
7.82941759e-01 1.10812932e-01 2.78919429e-01 1.62489247e-02
7.35154450e-01 2.42312148e-01 6.08210623e-01 5.97934902e-01
-4.87758845e-01 5.57415843e-01 3.47338058e-02 -7.53654182e-01
-1.10976326e+00 -5.71938217e-01 -2.84963518e-01 1.31766462e+00
1.71651557e-01 -8.74066576e-02 -5.50510228e-01 -8.69711816e-01
-1.12111587e-02 3.85115087e-01 -4.87311929e-01 -2.88471907e-01
-4.07969892e-01 -1.07203770e+00 5.60981214e-01 5.78109264e-01
1.26548791e+00 -1.07082605e+00 8.68604481e-02 1.91793474e-03
-1.80973038e-01 -1.01372552e+00 -4.34206158e-01 8.86529163e-02
-1.21142471e+00 -8.95896196e-01 -1.12527752e+00 -1.33165610e+00
5.99524200e-01 5.24551868e-01 8.89417350e-01 7.49467164e-02
-1.13816828e-01 1.57586470e-01 -6.93650007e-01 -5.29984772e-01
-4.26417202e-01 2.13925466e-01 -2.78111864e-02 5.98168485e-02
8.38655949e-01 -3.08805794e-01 -3.22953105e-01 6.09657764e-01
-9.01497185e-01 2.48731330e-01 7.45241106e-01 1.17624748e+00
3.84931356e-01 3.34099293e-01 8.25422049e-01 -9.07320738e-01
5.92785597e-01 -4.82114911e-01 -4.41997319e-01 4.27507669e-01
-6.20081961e-01 -2.76488394e-01 9.00820971e-01 -5.63295186e-01
-1.27975535e+00 1.55982286e-01 9.51391906e-02 -1.34802595e-01
-5.35104215e-01 4.08231527e-01 -4.02181387e-01 -6.64668158e-02
6.64583802e-01 7.11621046e-01 -6.71901628e-02 -5.45957565e-01
-2.14475999e-03 1.32328987e+00 3.45787793e-01 -6.55950129e-01
5.91488123e-01 4.99341339e-01 -3.43016565e-01 -8.66722941e-01
-9.21485603e-01 -4.09855813e-01 -8.78884733e-01 -7.35246167e-02
4.99805212e-01 -8.75231743e-01 -3.29560012e-01 1.21557379e+00
-9.05892968e-01 -5.08657396e-01 1.19398870e-01 3.96319449e-01
-4.02363271e-01 7.95282185e-01 -3.38773221e-01 -4.56720263e-01
-3.32190275e-01 -8.42012405e-01 7.29215145e-01 3.53156388e-01
3.27699155e-01 -1.20628893e+00 1.48355246e-01 4.66172278e-01
2.13691860e-01 -1.40690655e-01 8.73682320e-01 -8.55584562e-01
-2.13176399e-01 -1.34898387e-02 -3.84055257e-01 5.93216300e-01
3.33805531e-01 -1.13499820e-01 -9.43928182e-01 -3.16692829e-01
-1.18611632e-02 -6.83211923e-01 9.85150695e-01 9.73948985e-02
1.45530224e+00 2.81516872e-02 -2.82703161e-01 6.39460444e-01
1.38270962e+00 4.27148968e-01 4.71795321e-01 8.22799921e-01
6.94395781e-01 4.08624589e-01 7.13866651e-01 7.51878500e-01
3.85221601e-01 5.42983294e-01 6.46674708e-02 -9.66096222e-02
-1.56826586e-01 -2.13678047e-01 2.39144281e-01 1.06867731e+00
1.69024795e-01 -4.20463264e-01 -1.15937805e+00 4.18487668e-01
-2.01435161e+00 -9.62124944e-01 -1.52517840e-01 2.05646968e+00
8.90802622e-01 -9.54773948e-02 2.08374932e-01 2.28321135e-01
1.02512002e+00 7.46336654e-02 -9.10929620e-01 -1.62275687e-01
-3.93068999e-01 -8.45953748e-02 1.81151450e-01 -9.31434855e-02
-1.46661317e+00 1.19012678e+00 5.86360264e+00 1.14352155e+00
-1.26685584e+00 6.37549907e-02 6.25708401e-01 1.41932383e-01
3.29016626e-01 -3.76609504e-01 -9.13605332e-01 7.44564891e-01
6.76814675e-01 -2.13509440e-01 2.45570332e-01 9.60493922e-01
-1.02144264e-01 1.49306446e-01 -8.45125556e-01 1.41329181e+00
4.74872053e-01 -1.16097069e+00 3.18100154e-01 -1.40009478e-01
9.58691835e-01 1.03407495e-01 2.03501269e-01 4.45129544e-01
1.92289755e-01 -8.18883777e-01 2.79202014e-01 2.88953304e-01
8.46967638e-01 -8.52382243e-01 5.92248201e-01 5.84355891e-01
-9.73648667e-01 -2.33278334e-01 -7.48756111e-01 1.43613294e-01
-4.91669208e-01 5.98714471e-01 -5.83393455e-01 4.75664496e-01
7.48881936e-01 1.30885327e+00 -7.06057608e-01 9.41427708e-01
1.60165522e-02 5.87693930e-01 1.00204140e-01 -2.33625919e-01
6.66355491e-02 -2.18000755e-01 1.09653205e-01 1.32038844e+00
1.66454345e-01 -9.01160985e-02 2.55109042e-01 4.97806400e-01
-2.87610739e-01 3.20984602e-01 -6.38159513e-01 -2.21421093e-01
4.20015723e-01 1.18322861e+00 -5.24061263e-01 -4.87641037e-01
-7.62433112e-01 1.21463501e+00 2.54206210e-01 2.95625120e-01
-6.35279655e-01 -5.59440851e-01 1.41387373e-01 -3.36849362e-01
2.86434621e-01 -1.93018854e-01 -6.45354390e-01 -1.55964100e+00
3.50228027e-02 -1.27865124e+00 5.61258733e-01 -6.61975801e-01
-1.60793114e+00 3.59987587e-01 -3.73830974e-01 -1.49800968e+00
1.17392160e-01 -8.03988516e-01 -3.89205933e-01 7.49394894e-01
-1.57146084e+00 -1.20036435e+00 -6.60806596e-01 9.35684919e-01
1.01837051e+00 -8.79562974e-01 7.44925320e-01 4.25152779e-01
-8.51295531e-01 8.40597689e-01 8.62952888e-01 5.87409794e-01
1.33718503e+00 -9.91934121e-01 3.79335284e-01 5.99662483e-01
-7.18860654e-03 2.84608394e-01 1.95129603e-01 -6.52595162e-01
-1.52640927e+00 -1.09186590e+00 7.60155261e-01 -4.81410384e-01
6.69961989e-01 -2.00911298e-01 -1.09169340e+00 5.78516662e-01
3.06343645e-01 -1.26936480e-01 8.61363351e-01 -5.04086278e-02
-3.73021394e-01 -3.02613288e-01 -1.07336044e+00 5.06078362e-01
8.68757784e-01 -1.77320257e-01 -7.56086469e-01 4.30845618e-01
2.56403953e-01 -2.43409827e-01 -6.65926993e-01 3.76194835e-01
4.65788066e-01 -9.76495802e-01 7.01070845e-01 -4.39519733e-01
6.78864241e-01 -1.07954986e-01 -1.36819988e-01 -1.32273078e+00
-4.44235086e-01 -8.95922035e-02 -2.71590576e-02 1.50111008e+00
1.08288713e-01 -7.24995077e-01 9.50770974e-01 1.28140584e-01
-6.00728057e-02 -4.97203618e-01 -7.06354380e-01 -1.01466763e+00
4.98295218e-01 -2.29863301e-01 6.25457048e-01 1.42743933e+00
-4.34665419e-02 5.33803403e-01 -4.90977973e-01 -2.69508332e-01
5.93823254e-01 3.21141809e-01 8.09602261e-01 -1.44928849e+00
-1.58748269e-01 -5.53692997e-01 -1.73223659e-01 -1.21394432e+00
3.05155635e-01 -8.86968553e-01 -2.09120154e-01 -1.35604930e+00
5.21441877e-01 -3.61918122e-01 -2.34982520e-01 5.03447831e-01
-9.47483629e-02 5.80991097e-02 9.76851732e-02 6.06981516e-01
-7.47013748e-01 7.80803323e-01 1.43420076e+00 -4.94968981e-01
-1.09004773e-01 -8.93819183e-02 -8.14388990e-01 8.01938415e-01
1.17155325e+00 -2.98844755e-01 -3.40982705e-01 -8.65369737e-01
-3.33149843e-02 -3.12138528e-01 7.34693855e-02 -9.27105665e-01
4.08651769e-01 -2.95697451e-01 7.35863984e-01 -7.42368817e-01
4.42049354e-02 -8.77781808e-01 -4.23551977e-01 3.29400957e-01
-2.26346120e-01 -1.12867460e-01 3.25672865e-01 5.17263055e-01
-3.25703442e-01 -4.11948651e-01 1.08943069e+00 -2.20185980e-01
-1.20875871e+00 4.00230736e-01 -2.53898591e-01 2.79105961e-01
9.25486505e-01 -3.70613337e-01 -5.26196957e-01 8.99356208e-04
-3.26752782e-01 1.42765313e-01 5.62756836e-01 7.19217002e-01
6.48985386e-01 -1.48039806e+00 -8.89897287e-01 2.72953987e-01
4.48614478e-01 -1.57875523e-01 4.14462924e-01 5.25874674e-01
-2.51131713e-01 3.52028131e-01 -4.51957166e-01 -6.09996736e-01
-1.29193985e+00 5.68348169e-01 2.26075217e-01 -7.84593299e-02
-6.43584192e-01 5.25576711e-01 9.08433422e-02 -6.74042523e-01
3.62467110e-01 2.45659024e-01 -2.58199543e-01 2.48158351e-03
7.40105450e-01 4.51281071e-01 1.19522989e-01 -5.05501568e-01
-2.35826537e-01 7.77618468e-01 -5.15890956e-01 1.44988537e-01
1.21029854e+00 -1.73760325e-01 3.16928551e-02 5.13498366e-01
1.03391635e+00 -4.07970428e-01 -1.08379066e+00 -6.26041949e-01
-6.55986890e-02 -5.12003601e-01 -3.86757888e-02 -1.03658748e+00
-1.00431454e+00 1.08000028e+00 7.51092315e-01 -8.91345888e-02
1.28730881e+00 -3.12573820e-01 9.72433388e-01 8.08299601e-01
1.39195025e-01 -1.71714962e+00 5.22257030e-01 8.33870113e-01
7.13780344e-01 -1.73511660e+00 2.92795571e-03 -2.33173613e-02
-9.29944456e-01 1.30770588e+00 1.00350583e+00 5.69162928e-02
6.19055808e-01 8.65372792e-02 2.37653881e-01 2.98140347e-01
-6.16782606e-01 -6.52181953e-02 -1.26574207e-02 8.41550827e-01
6.00297570e-01 -1.35967821e-01 -3.14922333e-01 7.27390945e-01
1.56386435e-01 2.61399150e-01 4.30055767e-01 1.03234816e+00
-6.95781648e-01 -1.52823162e+00 -4.83992577e-01 3.68833810e-01
-1.88409820e-01 -9.70341861e-02 -7.30332077e-01 6.43523455e-01
1.03772320e-01 1.00416791e+00 -1.03510872e-01 -4.86016572e-01
2.96779901e-01 3.03842425e-01 3.67879897e-01 -5.82705379e-01
-2.95764238e-01 7.10497703e-03 -3.71273190e-01 1.77001268e-01
-5.09446025e-01 -6.58006072e-01 -1.13668454e+00 -4.30838287e-01
-1.88942164e-01 -2.41915099e-02 7.06120670e-01 8.59389067e-01
4.00177360e-01 3.15944850e-01 8.57637405e-01 -4.44595635e-01
-7.11436033e-01 -1.08478701e+00 -7.02331722e-01 4.77672517e-01
8.27020928e-02 -5.70269585e-01 -1.48652852e-01 3.72640163e-01] | [11.833162307739258, 2.1425111293792725] |
3a3784f5-b7f3-47a9-a028-d8a0e6c77fb9 | mimo-mutual-integration-of-patient-journey | 2107.09288 | null | https://arxiv.org/abs/2107.09288v4 | https://arxiv.org/pdf/2107.09288v4.pdf | MIPO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning | Healthcare representation learning on the Electronic Health Records is crucial for downstream medical prediction tasks in health informatics. Many NLP techniques, such as RNN and self-attention, have been adapted to learn medical representations from hierarchical and time-stamped EHRs data, but fail when they lack either general or task-specific data. Hence, some recent works train healthcare representations by incorporating medical ontology, by self-supervised tasks like diagnosis prediction, but (1) the small-scale, monotonous ontology is insufficient for robust learning, and (2) critical contexts or dependencies underlying patient journeys are barely exploited to enhance ontology learning. To address the challenges, we propose a Transformer-based representation learning approach: Mutual Integration of Patient journey and medical Ontology (MIPO), which is a robust end-to-end framework. Specifically, the proposed method focuses on task-specific representation learning by a sequential diagnoses predictive task, which is also beneficial to the ontology-based disease typing task. To integrate information in the patient's visiting records, we further introduce a graph-embedding module, which can mitigate the challenge of data insufficiency in healthcare. In this way, MIPO creates a mutual integration to benefit both healthcare representation learning and medical ontology embedding. Such an effective integration is guaranteed by joint training over fused embeddings of the two modules, targeting both task-specific prediction and ontology-based disease typing tasks simultaneously. Extensive experiments conducted on two real-world benchmark datasets have shown MIPO consistently achieves better performance than state-of-the-art methods no matter whether the training data is sufficient or not. Also, MIPO derives more interpretable diagnose embedding results compared to its counterparts. | ['Clement Schlegel', 'Allison Clarke', 'Jing Jiang', 'Guodong Long', 'Chengqi Zhang', 'Sen Wang', 'Xueping Peng'] | 2021-07-20 | null | null | null | null | ['ontology-embedding'] | ['knowledge-base'] | [ 2.67825574e-01 5.38125932e-01 -4.79589015e-01 -3.53563249e-01
-5.86202085e-01 1.39096946e-01 1.47678688e-01 6.60966575e-01
-1.42910406e-01 4.36831146e-01 7.93293715e-01 -3.44260156e-01
-7.14903057e-01 -9.14410174e-01 -4.32169288e-01 -6.33742809e-01
-2.27038205e-01 6.58978105e-01 -1.77575454e-01 -2.42572755e-01
-4.42894578e-01 -9.33893546e-02 -1.30016983e+00 2.70125747e-01
1.20743525e+00 9.56938803e-01 1.53551385e-01 3.58915597e-01
-1.59791723e-01 1.07798529e+00 -1.55192465e-01 -3.72872084e-01
-1.92699119e-01 -3.75668675e-01 -8.57791722e-01 -1.56044513e-01
-3.25981915e-01 3.37124988e-02 -7.76400626e-01 8.63898516e-01
6.65996194e-01 -1.70211762e-01 6.49255097e-01 -1.01268709e+00
-1.08951497e+00 6.69117868e-01 4.34692428e-02 8.31103069e-04
4.96988803e-01 1.47855833e-01 1.03668523e+00 -1.97211653e-01
5.01567841e-01 1.10943520e+00 8.76754045e-01 7.60418773e-01
-9.18899536e-01 -4.11203027e-01 1.03288285e-01 2.74582922e-01
-1.16550338e+00 -1.44262582e-01 7.09779322e-01 -3.64143193e-01
1.03760195e+00 4.54977214e-01 6.27158642e-01 1.35415149e+00
3.17190260e-01 9.01375353e-01 5.74810326e-01 -2.04685614e-01
-2.03778632e-02 1.54586479e-01 2.54304081e-01 7.84521639e-01
2.27735832e-01 -4.53094486e-03 -8.34465623e-02 -3.28022450e-01
4.22973901e-01 8.07183146e-01 -6.43135428e-01 -1.70855120e-01
-1.41790605e+00 6.42104387e-01 8.41517329e-01 6.51515484e-01
-6.47449791e-01 -4.30059843e-02 7.05455899e-01 2.77306378e-01
4.82612252e-01 3.33195865e-01 -7.63773441e-01 8.19064751e-02
-4.47760820e-01 -2.05301885e-02 8.00481677e-01 8.36581707e-01
2.87403315e-01 -8.19428191e-02 -3.40587795e-01 7.38721371e-01
3.31220806e-01 1.35821834e-01 9.66056466e-01 -8.93597603e-02
6.45175457e-01 1.25165164e+00 -1.27106011e-01 -1.14614952e+00
-8.32409978e-01 -6.50697410e-01 -1.37332225e+00 -6.24292791e-01
-6.36733696e-02 -9.50658619e-02 -1.02053142e+00 1.79018044e+00
4.77958173e-01 5.83209634e-01 5.67793548e-01 7.51608908e-01
1.13725889e+00 4.64078724e-01 5.12925804e-01 -2.53710687e-01
1.78335023e+00 -9.04329956e-01 -1.14805353e+00 1.04288133e-02
1.24583268e+00 -2.51694232e-01 8.46326947e-01 -7.65833035e-02
-5.74829221e-01 -4.62802172e-01 -8.77456129e-01 -2.04291064e-02
-5.78726530e-01 -6.36676028e-02 7.78131187e-01 3.66523355e-01
-5.95355511e-01 5.52208424e-01 -7.75284171e-01 -4.79461819e-01
5.22732496e-01 3.57123137e-01 -4.82349128e-01 -5.09765923e-01
-1.74515676e+00 8.00951660e-01 6.79653704e-01 1.12230800e-01
-4.49220568e-01 -8.81232858e-01 -1.25801158e+00 3.36063623e-01
3.32800984e-01 -1.34513640e+00 7.23852158e-01 -4.64948595e-01
-1.15900409e+00 6.32723272e-01 -1.12010865e-02 -5.30651569e-01
2.98007905e-01 -1.94028735e-01 -8.79818082e-01 1.10363886e-02
-5.14222030e-03 1.06155373e-01 2.98152864e-01 -8.86466980e-01
-6.35916054e-01 -6.36794329e-01 -1.05962409e-02 1.93361402e-01
-8.89469683e-01 -4.59098369e-01 -5.02888978e-01 -7.04896152e-01
-2.01121252e-02 -5.65679610e-01 -5.69545329e-01 -4.23947871e-01
-4.59630102e-01 -5.22252858e-01 6.03547454e-01 -8.89234245e-01
1.68700004e+00 -2.14093351e+00 2.77068228e-01 -5.51983118e-02
4.16452229e-01 2.48177484e-01 -3.66128385e-02 5.31029403e-01
-1.55492470e-01 4.28406075e-02 -2.82798409e-01 -3.21000814e-01
-3.34072784e-02 5.35010338e-01 7.24957511e-03 2.50428528e-01
2.80258238e-01 1.27813613e+00 -1.22606254e+00 -5.61613619e-01
2.12349415e-01 8.20174277e-01 -5.69264948e-01 4.38423336e-01
1.21614337e-03 5.73501289e-01 -7.53130317e-01 8.73849750e-01
2.46837869e-01 -6.25966966e-01 4.30520296e-01 -4.58717793e-01
3.83915633e-01 2.97994196e-01 -6.77876174e-01 1.92074966e+00
-6.44995689e-01 -2.31955305e-01 -3.76551777e-01 -1.38502526e+00
7.25843310e-01 8.89151275e-01 9.23204958e-01 -7.32081711e-01
-4.75850403e-02 6.62975684e-02 -5.02926037e-02 -1.15730047e+00
-5.45846522e-02 -1.17805548e-01 -1.05945677e-01 -5.06658200e-03
1.50799870e-01 6.94958627e-01 -1.72738671e-01 2.47039739e-02
1.50927830e+00 -8.97453167e-03 6.20386541e-01 -8.42661317e-03
5.35875380e-01 -1.07395612e-01 8.96140873e-01 2.88649470e-01
-1.80981427e-01 2.70480007e-01 4.46030527e-01 -5.91327786e-01
-6.24638438e-01 -7.32027054e-01 -3.82499158e-01 8.09088767e-01
1.35709807e-01 -6.33578897e-01 -4.06632692e-01 -1.03899693e+00
2.10365564e-01 3.74434739e-01 -7.88811564e-01 -5.98205030e-01
-5.20637989e-01 -1.11911559e+00 4.72614646e-01 7.89507389e-01
6.70451969e-02 -1.11739182e+00 -3.98823410e-01 5.81963778e-01
-2.85372525e-01 -1.01863956e+00 -3.02252352e-01 1.77325323e-01
-1.05204606e+00 -1.32475579e+00 -6.92745090e-01 -9.52240288e-01
7.59106517e-01 -1.30057678e-01 1.03171992e+00 2.62346208e-01
-3.64264935e-01 2.98044533e-01 -5.98983645e-01 -2.02115759e-01
-1.51453048e-01 2.17013150e-01 5.25658354e-02 1.48471236e-01
6.18646622e-01 -6.44795477e-01 -8.06667686e-01 -4.31322940e-02
-1.03994775e+00 1.06339157e-01 9.18859005e-01 1.26136804e+00
6.10883892e-01 1.84543848e-01 1.06534410e+00 -1.29772544e+00
6.15510046e-01 -1.01893127e+00 1.09133393e-01 4.50720638e-01
-8.68085563e-01 1.98786914e-01 8.40873361e-01 -3.42535436e-01
-7.99536228e-01 -6.40887171e-02 -4.57416773e-01 -5.12406051e-01
-2.48751640e-01 9.50254321e-01 -3.53364348e-01 4.42294747e-01
4.85638767e-01 4.23366010e-01 -1.03112787e-01 -6.18428171e-01
1.72549084e-01 9.42380071e-01 3.95101100e-01 -1.63189352e-01
4.50080335e-01 2.25137264e-01 -1.13406762e-01 -2.92258441e-01
-7.94972718e-01 -7.43357241e-01 -3.75678658e-01 4.04775172e-01
1.15203202e+00 -9.49481130e-01 -9.55385089e-01 -6.45501688e-02
-9.12659168e-01 1.04463711e-01 -1.86801389e-01 6.62579656e-01
-3.98601115e-01 2.86253810e-01 -6.58653617e-01 -8.25897217e-01
-7.39793062e-01 -1.01677012e+00 1.07740796e+00 1.11645333e-01
-1.36673003e-01 -1.43764222e+00 3.95746380e-02 4.43534076e-01
3.01576674e-01 6.78876042e-01 1.41465664e+00 -9.02707577e-01
-1.94780707e-01 -4.39389914e-01 -2.90928125e-01 1.61996279e-02
6.18507683e-01 -7.34557033e-01 -8.36145103e-01 -2.87501335e-01
-5.01974821e-02 -9.90632176e-02 8.70017827e-01 9.99377593e-02
1.34717250e+00 -5.89372754e-01 -7.31392503e-01 7.81155884e-01
1.39796758e+00 1.97634652e-01 6.01873398e-01 1.36672959e-01
1.04635882e+00 6.40287936e-01 4.17455673e-01 3.78244936e-01
1.05510187e+00 5.85193455e-01 5.09365737e-01 -3.20735633e-01
7.93881789e-02 -3.40654582e-01 2.13682223e-02 1.27081656e+00
-4.31423225e-02 -1.45533442e-01 -1.04898465e+00 8.21446896e-01
-2.33358145e+00 -5.93698382e-01 3.67600247e-02 1.96200085e+00
9.16529536e-01 -9.43921283e-02 -9.51749384e-02 2.78276175e-01
3.96611989e-01 1.59801692e-02 -5.33675432e-01 -3.53041163e-04
2.43132949e-01 1.95530459e-01 2.89810002e-01 8.99177343e-02
-1.13801289e+00 5.35435617e-01 4.90804672e+00 6.40150189e-01
-8.10565770e-01 4.29445207e-01 3.67235333e-01 2.28583083e-01
-4.68458265e-01 -2.99412191e-01 -4.38876748e-01 7.76245475e-01
1.00422215e+00 1.93070546e-02 2.61019506e-02 7.80776978e-01
3.41020934e-02 7.22604454e-01 -1.28808999e+00 1.11427283e+00
-7.78593495e-02 -1.20003998e+00 1.55307591e-01 1.03604943e-01
3.75160903e-01 -2.26969808e-01 -7.17448071e-02 7.38455594e-01
1.24695189e-01 -1.17527103e+00 -1.26412079e-01 8.26760769e-01
8.45685840e-01 -6.76317751e-01 1.22735155e+00 3.63295943e-01
-1.47831321e+00 -3.94447774e-01 -2.44799778e-01 2.59206623e-01
2.11976916e-01 6.48556471e-01 -7.41891026e-01 1.50823331e+00
6.76838756e-01 1.18609500e+00 -3.37614864e-01 7.67486572e-01
-9.38522369e-02 5.73595047e-01 5.49102239e-02 2.73232132e-01
2.16419667e-01 -4.23382930e-02 1.45639405e-01 1.31675255e+00
2.74905801e-01 2.76208818e-01 4.32985306e-01 4.95768189e-01
-1.47688925e-01 4.01463747e-01 -8.25105131e-01 -2.69137472e-01
2.63047516e-01 1.17659342e+00 -1.50174320e-01 -4.81559515e-01
-4.74974126e-01 9.28907752e-01 3.56815457e-01 4.02334064e-01
-7.49115050e-01 -5.29444814e-01 8.43946755e-01 5.51129393e-02
1.17003284e-02 4.71289217e-01 -1.53065428e-01 -1.43533158e+00
-1.75614320e-02 -8.61197531e-01 8.11653972e-01 -1.93278179e-01
-1.48330379e+00 6.03310645e-01 -4.73488450e-01 -1.40327775e+00
-3.40773642e-01 -4.56434250e-01 -4.92671192e-01 5.98885000e-01
-1.89258397e+00 -1.49031413e+00 -2.85668820e-01 7.46619523e-01
2.18653321e-01 -1.33631334e-01 1.34369266e+00 9.17629361e-01
-7.55441785e-01 8.18502426e-01 -9.90351960e-02 3.18000555e-01
6.19818032e-01 -1.19818282e+00 -2.77322680e-02 3.31069529e-01
-2.18593389e-01 7.61782527e-01 2.52361894e-01 -6.56362534e-01
-1.56772196e+00 -1.56714880e+00 1.17055738e+00 -5.66257179e-01
3.52421910e-01 -6.83619827e-02 -1.23120713e+00 7.92427182e-01
-1.37065142e-01 2.33983859e-01 1.12591910e+00 3.89669061e-01
-2.68754661e-01 -1.74248904e-01 -1.08168769e+00 4.36376840e-01
1.30944681e+00 -5.35038412e-01 -8.44048202e-01 6.81883156e-01
1.20722413e+00 -3.25717598e-01 -1.53277838e+00 9.29213643e-01
4.30170327e-01 -4.21946794e-01 1.05368972e+00 -1.16717744e+00
4.67902005e-01 -2.83652455e-01 -5.94184399e-02 -1.20004523e+00
-5.51153839e-01 -2.44264632e-01 -7.45205939e-01 1.06981337e+00
1.61773011e-01 -8.67494464e-01 4.49503183e-01 3.21514547e-01
-2.74438411e-01 -1.47176504e+00 -7.89000034e-01 -5.08751333e-01
-3.37670058e-01 -1.07138179e-01 8.65977347e-01 1.50941586e+00
3.00678492e-01 3.72160822e-01 -5.57520926e-01 4.11558062e-01
3.73875797e-01 7.57568702e-02 4.28807676e-01 -1.44317532e+00
-3.39503676e-01 -1.56584650e-01 -7.94262469e-01 -7.57325470e-01
2.74242107e-02 -1.20654547e+00 -3.37806076e-01 -2.01917744e+00
1.84841409e-01 -5.65556288e-01 -9.83171761e-01 7.15047121e-01
-6.17366791e-01 -2.81558096e-01 -1.68086767e-01 2.33156160e-01
-6.77113414e-01 6.81860566e-01 1.23252761e+00 -3.41864198e-01
-2.11964250e-01 -1.73339680e-01 -1.01182210e+00 4.74264860e-01
5.31706095e-01 -5.61199367e-01 -4.59647506e-01 -4.52595115e-01
1.39767706e-01 3.70515019e-01 1.70390055e-01 -9.77995276e-01
2.83323050e-01 1.58453107e-01 1.84661239e-01 -1.79634050e-01
8.63502175e-02 -1.06226182e+00 2.05940321e-01 8.44342351e-01
-2.71090001e-01 -1.26070231e-01 -8.37999210e-02 1.05048573e+00
-4.43790615e-01 1.27355725e-01 3.20293069e-01 -8.13133046e-02
-5.83314002e-01 6.66759849e-01 1.73974201e-01 -8.90547875e-03
9.76598084e-01 4.30522859e-02 -9.41949561e-02 -4.47754785e-02
-8.26398373e-01 5.99177480e-01 6.00066083e-03 5.54915488e-01
5.87505698e-01 -1.48996603e+00 -6.29866302e-01 1.65686429e-01
5.93255639e-01 2.19885871e-01 5.96472263e-01 1.16001093e+00
-2.38597825e-01 6.16142452e-01 1.70365080e-01 -4.91267830e-01
-9.13207293e-01 1.07856345e+00 1.14085063e-01 -9.30888772e-01
-1.00005579e+00 4.96092975e-01 3.59549880e-01 -7.81388044e-01
3.02440703e-01 -6.58262014e-01 -6.27492011e-01 6.29308224e-02
5.85524678e-01 -1.14345200e-01 3.58658396e-02 -4.11961287e-01
-4.41525400e-01 5.44147015e-01 -1.22369960e-01 7.62645543e-01
1.46736586e+00 2.99741998e-02 2.02709660e-02 3.06438297e-01
1.21879506e+00 -4.30939734e-01 -5.97374260e-01 -5.30773640e-01
1.02152601e-01 -9.24451351e-02 -7.29174465e-02 -6.95986867e-01
-1.08359420e+00 9.13030624e-01 6.24235034e-01 2.85248816e-01
1.23002255e+00 -8.43467340e-02 1.31794810e+00 2.79370934e-01
2.02270150e-01 -6.80058658e-01 -1.00723222e-01 2.27708727e-01
5.87894917e-01 -1.27375805e+00 -2.67231584e-01 -3.36202860e-01
-6.31122768e-01 9.01057482e-01 4.97626543e-01 1.48603261e-01
8.07565033e-01 -6.29895627e-02 7.56847560e-02 -3.39372665e-01
-8.61064911e-01 -4.36151922e-01 4.03930485e-01 6.94403350e-01
5.51309049e-01 2.20041052e-01 -3.03489983e-01 1.16937113e+00
1.01247400e-01 2.85956293e-01 -2.31611520e-01 7.66540051e-01
-1.56433322e-02 -1.03133416e+00 -3.73357954e-03 6.52222872e-01
-5.70565820e-01 -2.69947648e-01 1.92492038e-01 5.72324395e-01
3.85858268e-01 7.26824164e-01 -2.67502576e-01 -6.83500469e-01
5.13135791e-01 2.93583721e-01 3.68493125e-02 -6.97302401e-01
-6.79144621e-01 -2.05313548e-01 -1.09239757e-01 -6.11002088e-01
-3.81894976e-01 -8.69810581e-03 -1.39899588e+00 5.72446696e-02
-2.15140611e-01 1.58866823e-01 2.17372343e-01 1.00923860e+00
8.03815484e-01 1.31839216e+00 3.72605473e-01 -2.30473787e-01
-5.30296147e-01 -1.00675261e+00 -4.29286867e-01 7.83230364e-01
4.13086832e-01 -6.26126170e-01 -1.16962545e-01 -9.81442034e-02] | [7.842689037322998, 6.507523059844971] |
f2cde338-e23b-4f15-aa9a-11a09fd79742 | background-suppression-network-for-weakly | 1911.09963 | null | https://arxiv.org/abs/1911.09963v1 | https://arxiv.org/pdf/1911.09963v1.pdf | Background Suppression Network for Weakly-supervised Temporal Action Localization | Weakly-supervised temporal action localization is a very challenging problem because frame-wise labels are not given in the training stage while the only hint is video-level labels: whether each video contains action frames of interest. Previous methods aggregate frame-level class scores to produce video-level prediction and learn from video-level action labels. This formulation does not fully model the problem in that background frames are forced to be misclassified as action classes to predict video-level labels accurately. In this paper, we design Background Suppression Network (BaS-Net) which introduces an auxiliary class for background and has a two-branch weight-sharing architecture with an asymmetrical training strategy. This enables BaS-Net to suppress activations from background frames to improve localization performance. Extensive experiments demonstrate the effectiveness of BaS-Net and its superiority over the state-of-the-art methods on the most popular benchmarks - THUMOS'14 and ActivityNet. Our code and the trained model are available at https://github.com/Pilhyeon/BaSNet-pytorch. | ['Youngjung Uh', 'Pilhyeon Lee', 'Hyeran Byun'] | 2019-11-22 | null | null | null | null | ['weakly-supervised-action-localization', 'weakly-supervised-temporal-action'] | ['computer-vision', 'computer-vision'] | [ 4.35380727e-01 -1.93723217e-01 -7.82183409e-01 -3.07768077e-01
-5.75142801e-01 -1.81660354e-01 4.79581386e-01 -3.60671818e-01
-4.83552426e-01 7.43907571e-01 2.98159033e-01 -9.13627446e-02
5.92692912e-01 -3.44521880e-01 -7.24907815e-01 -8.38428855e-01
-1.54311091e-01 -8.36403370e-02 9.38195586e-01 1.47820771e-01
5.03046717e-03 1.28648788e-01 -1.35757113e+00 8.92595410e-01
4.72599536e-01 1.18631041e+00 1.50606960e-01 6.59967005e-01
-3.15210409e-02 1.83132315e+00 -5.51135898e-01 -7.96530992e-02
3.87444496e-01 -7.36012936e-01 -6.25342786e-01 1.58928797e-01
6.61729574e-01 -5.55852354e-01 -7.35589087e-01 1.06245041e+00
3.31903875e-01 1.67449623e-01 2.79230982e-01 -1.55517197e+00
-2.64048487e-01 4.19283360e-01 -6.83297038e-01 7.60801792e-01
2.61828955e-02 2.10162461e-01 7.81164944e-01 -9.75591302e-01
5.57181597e-01 1.22616053e+00 5.66406846e-01 7.97212422e-01
-9.74859416e-01 -6.90879226e-01 6.39860630e-01 6.74182832e-01
-1.34769070e+00 -4.78810549e-01 7.75123119e-01 -4.55490112e-01
7.32174754e-01 1.46969467e-01 5.55989265e-01 1.33870995e+00
6.23242036e-02 1.23029470e+00 8.79626870e-01 -1.97480023e-01
2.94043690e-01 -1.98964134e-01 5.28889336e-02 9.39541280e-01
-5.12933498e-03 -1.82580411e-01 -8.16377044e-01 1.73514441e-01
9.94964182e-01 3.01052183e-01 -4.05641764e-01 -4.76320177e-01
-1.43023312e+00 4.89848286e-01 3.72540176e-01 3.71320248e-01
-2.75511980e-01 6.26856208e-01 6.28519237e-01 8.70016962e-02
7.32914388e-01 -1.86743990e-01 -5.21059752e-01 -2.38437951e-01
-1.12650394e+00 3.30506973e-02 4.15718317e-01 8.43296170e-01
6.12461150e-01 2.34770283e-01 -7.19097078e-01 6.88445628e-01
4.04688679e-02 1.85832113e-01 5.40726185e-01 -1.30820334e+00
4.35248524e-01 4.76939619e-01 1.77756220e-01 -7.32048929e-01
-2.24286512e-01 -3.98614585e-01 -6.75823450e-01 2.72989869e-01
5.91689825e-01 -1.31223783e-01 -1.17351604e+00 1.77475858e+00
2.72854447e-01 1.02417088e+00 -3.20357919e-01 1.08060002e+00
8.35012436e-01 6.64228857e-01 1.55940950e-01 -1.74336448e-01
1.08106232e+00 -1.75652158e+00 -7.81352639e-01 -4.03926313e-01
6.88141584e-01 -3.81909966e-01 9.43646550e-01 3.07704121e-01
-9.43636537e-01 -7.73990214e-01 -8.87609363e-01 4.75661159e-02
-1.40788838e-01 4.19794053e-01 6.11524045e-01 4.94129628e-01
-1.05151427e+00 6.19797945e-01 -1.24341524e+00 -2.45394275e-01
8.02414417e-01 1.61122411e-01 -3.12211990e-01 -3.73560041e-02
-1.07470167e+00 6.09128833e-01 4.38265413e-01 7.90907666e-02
-1.32799256e+00 -4.66307342e-01 -8.93170357e-01 4.16191928e-02
8.09816360e-01 -3.00014377e-01 1.36554599e+00 -1.41725409e+00
-1.42390561e+00 7.54090190e-01 -4.07703757e-01 -6.17530644e-01
6.74870491e-01 -3.08947057e-01 -3.96615595e-01 4.26906049e-01
2.51693219e-01 9.59363639e-01 8.88301015e-01 -9.85045910e-01
-1.03025746e+00 2.79418398e-02 2.15366453e-01 1.47633851e-01
-3.49822372e-01 1.55884281e-01 -8.39985251e-01 -8.12024653e-01
-5.54266870e-02 -8.02423179e-01 -2.57268518e-01 1.22180462e-01
-1.72962353e-01 -2.77697831e-01 8.15781713e-01 -5.80634177e-01
1.41182935e+00 -2.23088932e+00 -1.44495323e-01 -3.30684721e-01
1.68248266e-01 3.94578844e-01 -3.03382158e-01 -1.70347877e-02
-2.07104132e-01 -3.76364887e-02 -1.67650562e-02 -3.88172299e-01
-1.06515542e-01 1.99852288e-01 -1.00330345e-01 6.59400642e-01
3.40318799e-01 7.95003593e-01 -1.08256459e+00 -6.48703694e-01
4.22479093e-01 3.68541300e-01 -5.92325449e-01 8.80613625e-02
-3.37119192e-01 5.74569464e-01 -3.52219820e-01 1.00667083e+00
4.22300607e-01 -3.58398676e-01 3.58059332e-02 -1.79206327e-01
-1.38008585e-02 3.21559578e-01 -1.11247838e+00 1.87989128e+00
-7.79010728e-02 6.68681145e-01 2.49253511e-02 -1.12972093e+00
3.81933242e-01 3.34466398e-01 7.78534055e-01 -7.25403368e-01
1.04641058e-01 -2.19147094e-02 1.25954757e-02 -3.94889057e-01
7.90393576e-02 1.62050098e-01 1.26597732e-01 6.80520833e-02
2.64755040e-01 5.78314066e-01 5.76646566e-01 1.97429940e-01
1.47563159e+00 6.35887980e-01 9.80425626e-02 -1.17770366e-01
7.55309045e-01 -2.21834809e-01 1.25602734e+00 8.68759632e-01
-6.28117025e-01 6.98070467e-01 6.77861273e-01 -5.18476486e-01
-5.58402956e-01 -9.01763916e-01 1.42351985e-01 1.61456835e+00
2.43554816e-01 -6.55175269e-01 -7.67417848e-01 -1.11228848e+00
-3.16687673e-01 4.76231098e-01 -8.00925851e-01 -3.63652632e-02
-7.33269870e-01 -5.41176081e-01 3.73171061e-01 8.46084058e-01
5.13653696e-01 -1.24768591e+00 -7.01050818e-01 1.02908544e-01
-4.02123749e-01 -1.29001009e+00 -6.52287483e-01 3.14609230e-01
-7.71505356e-01 -1.11825621e+00 -8.48248482e-01 -7.10318029e-01
6.26704991e-01 3.50990862e-01 1.20196939e+00 1.30079910e-01
-4.89167385e-02 1.30772054e-01 -4.84898031e-01 -2.36861587e-01
-6.50083870e-02 -9.76601318e-02 -9.32590291e-02 2.74473846e-01
5.42379498e-01 -4.25249010e-01 -8.48127067e-01 6.39107287e-01
-7.61979640e-01 1.47163182e-01 4.60613281e-01 7.52677560e-01
6.50277793e-01 9.89290792e-03 3.35738987e-01 -7.84676671e-01
-1.42682314e-01 -3.40065569e-01 -4.45650995e-01 1.99678481e-01
5.40540926e-02 -3.79503042e-01 4.73158807e-01 -5.72411537e-01
-9.34368432e-01 3.80801708e-01 -8.95928890e-02 -8.35483968e-01
-2.67923832e-01 3.60659473e-02 -1.76025674e-01 2.58808844e-02
4.80761170e-01 2.99855739e-01 -2.84595639e-01 -3.87743264e-01
1.67478040e-01 2.63183415e-01 5.76131701e-01 -3.26974124e-01
4.24596757e-01 6.91318333e-01 -1.64438114e-01 -5.44667423e-01
-1.26615179e+00 -7.91186631e-01 -7.07368433e-01 -4.70733285e-01
9.71038818e-01 -1.32323492e+00 -3.18289608e-01 5.43382227e-01
-1.01902664e+00 -8.98387909e-01 -3.25097233e-01 4.64331746e-01
-6.45160377e-01 4.03905690e-01 -7.49444187e-01 -7.13310122e-01
8.06182772e-02 -1.11082792e+00 1.09255457e+00 5.80064803e-02
-4.13558446e-02 -7.94046700e-01 -2.10603058e-01 3.66693318e-01
2.22455382e-01 2.41514117e-01 2.00834125e-01 -4.75348324e-01
-6.22540891e-01 3.01663093e-02 -2.06053630e-01 6.37310982e-01
1.19537465e-01 -1.54993951e-01 -1.01503873e+00 -1.59920543e-01
-1.04705706e-01 -3.94714147e-01 1.42000091e+00 6.46913946e-01
1.49628103e+00 -1.97518870e-01 -3.39859426e-01 6.77518845e-01
1.12603247e+00 1.85437560e-01 7.30108917e-01 2.81508744e-01
8.49230587e-01 2.42249161e-01 7.83210158e-01 3.73819649e-01
1.69931442e-01 8.35649312e-01 5.48136413e-01 -3.48258168e-01
-4.60982293e-01 -7.20077828e-02 8.42810571e-01 3.59238297e-01
-2.11319670e-01 -2.94060916e-01 -7.43068159e-01 4.83601898e-01
-2.30369258e+00 -1.42897105e+00 -1.97770789e-01 1.93402660e+00
7.88207471e-01 3.37359250e-01 4.20948654e-01 -2.70009041e-02
9.02378976e-01 4.64077622e-01 -6.07333302e-01 3.24581683e-01
-9.09913331e-02 -1.90870743e-02 6.12250149e-01 3.28674883e-01
-1.67152929e+00 1.14407504e+00 5.49068308e+00 9.27901447e-01
-1.05921614e+00 6.59387290e-01 9.34420705e-01 -4.44033146e-01
4.04232949e-01 -6.99189156e-02 -8.93091023e-01 7.87159681e-01
7.90990472e-01 3.26085091e-01 1.05192967e-01 9.19406176e-01
3.99876475e-01 -2.81554937e-01 -1.12180316e+00 9.90567267e-01
2.20996976e-01 -1.34532702e+00 -1.96050614e-01 -3.43165517e-01
7.97358453e-01 2.65858263e-01 -2.27047965e-01 5.70738912e-01
1.51032656e-01 -7.00454056e-01 1.07246971e+00 3.84358138e-01
6.09900355e-01 -4.49775457e-01 6.65296078e-01 1.72881678e-01
-1.32974255e+00 -3.19119632e-01 -4.46364194e-01 -2.59961694e-01
2.84031659e-01 3.79778117e-01 -3.53228837e-01 1.52007297e-01
9.66536343e-01 1.17979383e+00 -6.64077640e-01 1.04301560e+00
-4.64408696e-01 9.97616172e-01 -1.35994568e-01 2.33642489e-01
5.03614485e-01 -5.82651934e-03 2.36734480e-01 1.50039279e+00
1.77885979e-01 7.15628862e-02 6.01907194e-01 6.26041949e-01
-2.97175776e-02 -2.12871194e-01 -2.59167999e-01 1.01390183e-01
8.51273537e-02 1.29703486e+00 -1.07220078e+00 -5.49157858e-01
-8.34840953e-01 1.18321025e+00 1.02945514e-01 4.77220178e-01
-1.31334114e+00 3.79627421e-02 6.86145902e-01 3.41357678e-01
5.03428578e-01 -1.78739857e-02 -2.53400765e-02 -1.43423378e+00
6.22566156e-02 -7.70510614e-01 4.87238020e-01 -7.02033281e-01
-1.02053666e+00 4.29023296e-01 -1.43502161e-01 -1.32134306e+00
1.52215943e-01 -6.68703616e-01 -7.10964918e-01 3.48115772e-01
-1.51486742e+00 -1.12031901e+00 -5.20048916e-01 7.62666881e-01
9.74454105e-01 -9.28845406e-02 2.99956828e-01 6.13907576e-01
-8.79079640e-01 4.41013038e-01 -1.84106693e-01 5.15902817e-01
8.09683263e-01 -1.23091471e+00 1.83205664e-01 1.23295081e+00
1.92648187e-01 1.25512600e-01 4.87989545e-01 -6.04179621e-01
-8.55102658e-01 -1.46781850e+00 6.27124786e-01 -5.52480042e-01
8.42615724e-01 -6.13534272e-01 -7.53481328e-01 7.05352485e-01
2.28341416e-01 6.53768241e-01 5.14784634e-01 -2.74437755e-01
-1.26352891e-01 -2.01165631e-01 -6.41625404e-01 5.54820597e-01
1.46080339e+00 -3.03255290e-01 -3.34171921e-01 6.19365454e-01
6.08377039e-01 -3.80352944e-01 -4.07830149e-01 4.98220831e-01
3.47199231e-01 -1.18404710e+00 9.28404510e-01 -4.30784732e-01
5.36666214e-01 -6.62688851e-01 -1.27292082e-01 -7.19874918e-01
-4.48870689e-01 -4.39036399e-01 -5.00126362e-01 1.12689614e+00
2.20102161e-01 -2.08625093e-01 1.11712933e+00 2.23253265e-01
-2.37701133e-01 -8.64525020e-01 -9.49406803e-01 -8.99625599e-01
-3.60080272e-01 -3.37719232e-01 6.61668107e-02 8.76123667e-01
-2.29980379e-01 2.43146732e-01 -6.41210258e-01 1.03043765e-02
3.85428071e-01 -2.52654374e-01 6.95858300e-01 -7.50666738e-01
-3.21691126e-01 -4.71542805e-01 -5.28885007e-01 -1.21028435e+00
3.98370236e-01 -6.47433817e-01 2.53927588e-01 -1.39509964e+00
2.89448410e-01 -1.29960701e-01 -9.48298991e-01 9.72586751e-01
-2.48122275e-01 6.70399725e-01 2.84627885e-01 1.32870838e-01
-1.36168957e+00 5.55842578e-01 1.01548111e+00 -2.08609283e-01
-7.19594359e-02 1.23669496e-02 -2.49154016e-01 9.79243636e-01
7.58467197e-01 -5.05361199e-01 -5.37924230e-01 -2.71333992e-01
-2.46264935e-01 5.14863618e-02 6.08401120e-01 -1.30702567e+00
1.73152864e-01 -2.51031607e-01 5.97800136e-01 -6.33440614e-01
3.69701058e-01 -7.05977261e-01 -1.07173711e-01 5.03838539e-01
-3.94874185e-01 -1.70095623e-01 3.08772139e-02 6.33592308e-01
-3.77209365e-01 -4.84531298e-02 8.68128181e-01 -1.36304319e-01
-1.33043838e+00 4.92167801e-01 -4.83697921e-01 -3.08569539e-02
1.18648362e+00 -3.43167841e-01 -2.94394910e-01 -2.99156189e-01
-8.06729853e-01 2.61770964e-01 4.94823366e-01 4.52506840e-01
4.39229608e-01 -1.40610468e+00 -6.15447521e-01 -4.44995277e-02
6.92527518e-02 -2.22170353e-01 2.81893671e-01 1.22480702e+00
-5.13949215e-01 3.50258976e-01 -1.84973821e-01 -6.63760364e-01
-1.26700270e+00 5.44627786e-01 3.88829380e-01 -2.98158914e-01
-6.57844365e-01 1.11206281e+00 6.84361875e-01 3.95381339e-02
6.29597664e-01 -4.30069327e-01 -9.97492373e-02 -9.71692502e-02
7.60804653e-01 3.45233768e-01 -2.08655119e-01 -6.63275540e-01
-5.94629526e-01 4.43081185e-02 2.82470826e-02 1.73098475e-01
1.22466087e+00 5.17490543e-02 7.09485337e-02 4.37583596e-01
9.26996469e-01 -3.24946523e-01 -1.87325406e+00 -2.84146369e-01
-5.22976592e-02 -5.87753057e-01 1.31065980e-01 -7.47147381e-01
-1.38064206e+00 8.48256767e-01 7.24201262e-01 -2.61729389e-01
1.34129655e+00 -6.69274554e-02 5.60786843e-01 1.80664763e-01
2.96015590e-01 -1.20785677e+00 5.28563380e-01 5.08230925e-01
6.33421004e-01 -1.31584001e+00 -1.04891859e-01 -5.00269234e-01
-6.71655893e-01 7.52030492e-01 1.05096769e+00 -1.20801233e-01
5.34230709e-01 3.03864151e-01 1.03615373e-01 1.24898128e-01
-8.18319440e-01 -4.05109525e-01 1.29946813e-01 4.77741539e-01
5.83040953e-01 -2.89143860e-01 -1.91119388e-01 4.41731364e-01
6.01404071e-01 1.68027282e-01 3.79117966e-01 1.02522600e+00
-4.87545818e-01 -1.04090810e+00 -2.04912990e-01 4.54311490e-01
-7.92820871e-01 -1.27524137e-01 -2.92217195e-01 5.64756632e-01
5.24667442e-01 8.62281203e-01 6.98427260e-02 -2.68974036e-01
1.46596422e-02 7.10956901e-02 4.14494038e-01 -7.59676337e-01
-2.88254023e-01 4.05556083e-01 8.54472667e-02 -1.12701261e+00
-9.45537210e-01 -7.48969674e-01 -1.10418105e+00 -2.72719301e-02
-2.01057851e-01 3.97054181e-02 1.12621076e-01 6.92476034e-01
2.77289808e-01 8.44568789e-01 3.52856606e-01 -1.17644691e+00
-1.55927211e-01 -9.60060775e-01 -5.37717819e-01 5.22373974e-01
2.92873114e-01 -8.51014018e-01 -2.52740324e-01 4.81780291e-01] | [8.491143226623535, 0.547023594379425] |
54808efa-aef3-49d6-b260-936cea2b633e | unsupervised-part-of-speech-tagging-in-noisy | null | null | https://aclanthology.org/W12-0601 | https://aclanthology.org/W12-0601.pdf | Unsupervised Part-of-Speech Tagging in Noisy and Esoteric Domains With a Syntactic-Semantic Bayesian HMM | null | ['William M. Darling', 'Michael J. Paul', 'Fei Song'] | 2012-04-01 | null | null | null | ws-2012-4 | ['unsupervised-part-of-speech-tagging'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01
7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01
7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00
-1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01
3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01
9.28529128e-02 -8.59008506e-02 -2.09758401e-01 9.22211111e-01
5.37373662e-01 4.49454933e-02 1.08379531e+00 -1.42548037e+00
-5.91619551e-01 6.10313773e-01 -4.01565880e-02 1.21627934e-01
1.03678203e+00 -5.39447069e-01 1.19919395e+00 -1.13026452e+00
7.20913768e-01 1.26888943e+00 8.66221786e-01 5.44149756e-01
-1.22286928e+00 -1.94712028e-01 -3.26822817e-01 -9.51717794e-02
-1.46558487e+00 -3.25250506e-01 4.25783843e-02 -2.08119690e-01
1.66093647e+00 1.26596653e+00 1.20609856e+00 1.01401424e+00
1.26658809e+00 8.34431887e-01 1.04267764e+00 -5.13792276e-01
3.35295945e-01 3.66983831e-01 1.54683650e-01 6.33519173e-01
8.40953708e-01 5.26628852e-01 -7.06372619e-01 -9.13127720e-01
9.33553874e-01 -2.94925272e-01 1.71355158e-01 -5.05680561e-01
-9.05919552e-01 6.91228509e-01 1.78732842e-01 3.83959889e-01
-1.39880210e-01 9.89067405e-02 1.26390755e-01 5.30987144e-01
-2.58292928e-02 6.47037446e-01 -9.11868811e-01 -1.33165747e-01
-8.71728659e-01 5.10332465e-01 1.25398111e+00 1.52653182e+00
1.24482810e-01 2.94908643e-01 -9.34252143e-02 3.17179203e-01
8.92314315e-01 1.01808000e+00 4.28362608e-01 -1.36146402e+00
-6.87414408e-02 1.72361732e-01 5.01781464e-01 -8.52631688e-01
-6.33224547e-01 -9.64177120e-03 -8.93263519e-01 4.49267089e-01
3.49161088e-01 4.57367361e-01 -8.02827001e-01 5.07305264e-01
4.33481112e-02 -2.34125629e-01 4.53833073e-01 5.55570945e-02
4.99930978e-01 3.76208365e-01 -1.34477139e-01 -5.73289394e-01
1.06082785e+00 -1.36716676e+00 -1.35299087e+00 2.33215362e-01
9.05734658e-01 -1.07320261e+00 4.35900748e-01 5.33875942e-01
-1.55548143e+00 -1.37560293e-01 -1.08699942e+00 1.78573877e-01
-7.27255583e-01 -3.14239264e-01 8.57801437e-01 1.43120694e+00
-1.60129595e+00 9.73287821e-01 -4.91727620e-01 6.59165755e-02
1.20568443e-02 8.24621081e-01 -2.64718989e-03 4.62812334e-01
-1.33193445e+00 1.08501506e+00 2.22979754e-01 -1.21242590e-01
-1.65216476e-01 -2.13068098e-01 -8.23704481e-01 -5.63443303e-01
-4.78693932e-01 -5.29636025e-01 1.44139910e+00 -2.59346128e-01
-1.65295815e+00 9.71794367e-01 -1.42069459e-01 -1.97814897e-01
6.14786744e-01 -1.28011424e-02 -8.31891418e-01 2.42498964e-01
-1.89849049e-01 5.76383233e-01 9.28263724e-01 -1.35132408e+00
-7.59897232e-01 -1.67359829e-01 -1.23336017e-01 2.66287565e-01
-1.25510961e-01 1.89734384e-01 2.11616129e-01 -1.12999000e-01
3.27147305e-01 -7.20919967e-01 -2.53068686e-01 -5.32041907e-01
-1.46512717e-01 -7.10518599e-01 7.70373225e-01 -4.51523662e-01
1.83705616e+00 -1.67618537e+00 -2.06720144e-01 3.98590982e-01
3.57815564e-01 -1.24705513e-03 2.40583986e-01 1.08380008e+00
-2.76906848e-01 7.32199550e-01 4.11965609e-01 -1.10722095e-01
2.24991128e-01 4.85861301e-01 -4.16602850e-01 3.05609167e-01
-1.29282743e-01 1.15307164e+00 -1.16605783e+00 -5.23096442e-01
4.11106765e-01 9.42391157e-02 -4.58732933e-01 4.79237735e-01
2.99364805e-01 1.47170946e-01 -3.56553018e-01 1.39399457e+00
1.15709066e+00 -1.31984919e-01 1.45911396e-01 5.30878425e-01
-3.79135728e-01 3.55090618e-01 -6.96863770e-01 1.05554795e+00
7.08333924e-02 5.00173986e-01 1.02364108e-01 -7.94621468e-01
3.33247900e-01 8.61540735e-01 4.37155962e-01 -9.67555881e-01
-2.26398129e-02 6.92409754e-01 1.12803578e-01 -6.07703328e-01
7.58228302e-01 3.94563079e-02 -3.64872098e-01 6.40070081e-01
-2.37588286e-01 -6.59476995e-01 9.64643434e-02 3.08100313e-01
6.22585893e-01 -5.18246442e-02 5.62923312e-01 -1.05613089e+00
7.86340594e-01 -1.82965681e-01 -1.81299388e-01 1.03415680e+00
-3.09923887e-01 3.19085121e-01 2.99841821e-01 -6.65102363e-01
-6.45341039e-01 -1.12307119e+00 -4.89381433e-01 1.30636716e+00
3.24267983e-01 -4.39044595e-01 -9.54439282e-01 -2.49762803e-01
1.77620783e-01 6.89606130e-01 -5.90509653e-01 3.84124845e-01
-5.03739953e-01 -8.32535863e-01 7.39044368e-01 3.45434904e-01
-5.07752821e-02 -1.33414865e+00 -6.58416986e-01 1.25490099e-01
-2.20292807e-01 -6.63697243e-01 -6.23428151e-02 4.48765576e-01
-1.35989368e+00 -5.18594682e-01 -6.66252747e-02 -8.20914626e-01
5.87345481e-01 2.46782884e-01 1.27047324e+00 5.39230824e-01
-2.31483161e-01 4.26904231e-01 -1.21292919e-01 -4.95818377e-01
-4.59671497e-01 -8.00336525e-02 5.28869390e-01 -5.87835789e-01
5.19427478e-01 -2.50617653e-01 -7.29350567e-01 5.37953973e-01
-6.88540697e-01 1.62748516e-01 1.79803044e-01 1.04410267e+00
1.35816500e-01 -9.34035778e-02 1.22507080e-01 -6.38007045e-01
8.72274399e-01 -1.69219792e-01 -3.78732830e-01 5.77745810e-02
-6.77108407e-01 -3.74140263e-01 3.21430594e-01 -3.25342178e-01
-1.01981449e+00 -4.87835288e-01 -9.82677937e-02 2.45538145e-01
1.11353043e-02 -1.46784872e-01 6.47139177e-02 -5.24923325e-01
8.02199244e-01 9.25758183e-02 1.99174434e-02 -6.80815242e-03
3.01039815e-01 7.09525108e-01 -6.82967342e-03 -6.68678164e-01
8.44880998e-01 4.91470337e-01 7.98524171e-02 -9.57177758e-01
-1.52186140e-01 -2.60129690e-01 -9.51962709e-01 -6.54426932e-01
6.56643391e-01 -6.78531289e-01 -9.10833478e-01 3.91110867e-01
-9.38691139e-01 -3.38627815e-01 -3.91645581e-01 4.25431967e-01
-1.01278400e+00 2.75717527e-02 -3.90154392e-01 -1.27895141e+00
-5.10977268e-01 -1.02017939e+00 9.43384409e-01 5.30070923e-02
-5.10597289e-01 -1.26927447e+00 5.87685481e-02 2.71537274e-01
1.81734428e-01 -1.73075795e-01 6.90226793e-01 -2.38256708e-01
-4.24233019e-01 -1.53791070e-01 2.34436691e-02 -1.39755070e-01
1.70832314e-02 4.95917559e-01 -9.81751978e-01 -5.31145096e-01
6.65065646e-02 -1.92070693e-01 -1.08835101e-01 6.52520418e-01
5.91872573e-01 -2.29931593e-01 -8.56000841e-01 5.40386558e-01
1.38545322e+00 3.85070026e-01 5.32770038e-01 7.28214979e-01
1.41836226e-01 5.53460240e-01 9.17806149e-01 4.63203549e-01
1.30579369e-02 3.28798652e-01 2.40537539e-01 1.49327129e-01
1.11720070e-01 -1.54819340e-01 3.77893507e-01 1.16112018e+00
-8.18235934e-01 -2.69281328e-01 -5.07867396e-01 4.42987174e-01
-1.72482407e+00 -1.40330648e+00 -4.32368398e-01 6.90478683e-01
6.25676990e-01 1.56016424e-01 -1.48347050e-01 3.35214496e-01
4.99015123e-01 -2.03574806e-01 -1.19133167e-01 -1.06291151e+00
-1.43546045e-01 3.15233678e-01 7.37729073e-01 1.00061214e+00
-7.20721722e-01 1.03317809e+00 1.29781246e+01 1.02230716e+00
2.21112028e-01 1.03134915e-01 5.16071796e-01 3.48020852e-01
-4.36954498e-01 -4.56139445e-02 -1.04416132e+00 2.72933897e-02
1.38140702e+00 -4.30666685e-01 6.85999811e-01 5.44219851e-01
3.44648361e-01 -4.23268199e-01 -1.26188684e+00 5.26221812e-01
9.73738134e-02 -1.40886843e+00 -2.83300440e-04 6.85225725e-01
7.73699820e-01 -5.08050561e-01 6.22419357e-01 3.24184299e-01
6.09259963e-01 -1.14389277e+00 8.60300779e-01 2.53660440e-01
1.03040910e+00 -6.05088234e-01 5.67372203e-01 1.68872893e-01
-1.14389896e+00 -2.20873043e-01 -8.77727985e-01 -1.00755692e+00
3.93533185e-02 -1.81779593e-01 -4.29956943e-01 3.48861217e-01
9.58353162e-01 2.99398601e-01 -3.93658698e-01 9.95779395e-01
-4.78694476e-02 1.04875881e-02 -2.71853864e-01 -4.48467314e-01
4.83122796e-01 -3.54241252e-01 4.66730654e-01 1.00164843e+00
2.48499006e-01 3.51035744e-01 -9.84472036e-02 4.01770771e-01
5.45058846e-01 3.29446048e-02 -1.19659424e+00 -1.78908288e-01
2.83276141e-01 9.16795909e-01 -4.83487815e-01 -4.22520459e-01
-2.00212970e-01 8.62069130e-01 -3.55488248e-02 5.01107454e-01
-6.11489356e-01 -4.35615242e-01 9.72222984e-01 -1.27327025e-01
-1.14700586e-01 -3.48497719e-01 -6.23769283e-01 -7.30352640e-01
-5.89872956e-01 -4.54965204e-01 5.93606755e-02 -5.53365827e-01
-1.39813089e+00 5.79277515e-01 -2.27688253e-02 -1.40553558e+00
-6.99901402e-01 -1.27676582e+00 -4.76714373e-01 4.92853165e-01
-1.11898029e+00 -1.10984349e+00 2.50124663e-01 4.52870727e-01
1.64141744e-01 -5.34416080e-01 1.39563632e+00 3.57715860e-02
1.00637585e-01 9.24474537e-01 6.69434488e-01 -7.37814724e-01
5.56605101e-01 -1.27867436e+00 5.68737745e-01 -1.37897313e-01
-4.31265175e-01 9.05828118e-01 6.28349900e-01 -5.39804697e-01
-1.41196322e+00 -3.66917729e-01 1.08350635e+00 -9.83769417e-01
6.55218959e-01 -3.86345625e-01 4.23767231e-02 7.88592756e-01
7.15902448e-01 -6.18741751e-01 8.21781039e-01 -1.83753878e-01
1.80774391e-01 5.75296998e-01 -1.39248300e+00 6.12354755e-01
1.66275799e+00 -4.63594139e-01 -6.25784039e-01 7.60327101e-01
8.13696027e-01 -6.94087505e-01 -1.30082703e+00 3.34633321e-01
8.65424156e-01 -8.75409484e-01 1.61978090e+00 -1.32660246e+00
-4.43697497e-02 2.81152606e-01 -2.61993498e-01 -9.32519078e-01
-5.96193194e-01 -1.23518765e+00 -5.33532679e-01 -5.83747849e-02
5.96577883e-01 -1.13057327e+00 3.42365682e-01 8.74560475e-01
-2.82833427e-01 -6.42737269e-01 -1.06996536e+00 -1.32016802e+00
-3.35779637e-02 -1.45572275e-01 4.90409225e-01 7.63798356e-01
6.83744550e-01 1.09839931e-01 -6.36873543e-02 -1.00294888e-01
5.33176839e-01 9.55312885e-03 4.41501856e-01 -1.34294486e+00
3.86843324e-01 -5.75816095e-01 -3.07655483e-01 -9.45992947e-01
-8.85957032e-02 -8.12076271e-01 -6.53862000e-01 -1.28511906e+00
-8.32044985e-03 -1.91056758e-01 -1.11109078e-01 -1.65725678e-01
3.67937148e-01 2.13746816e-01 1.20859891e-02 1.03788137e-01
-3.71160030e-01 6.18435517e-02 1.29639816e+00 6.91750320e-05
-1.62315920e-01 4.85058486e-01 -4.81304944e-01 7.84440815e-01
8.58408585e-02 -2.99253196e-01 -6.78878546e-01 6.11881316e-02
6.69384480e-01 4.61409837e-02 3.24159935e-02 -7.42885649e-01
5.37211418e-01 -3.75702560e-01 4.78586555e-01 -1.32223868e+00
1.30741090e-01 -9.61415648e-01 6.95283338e-02 9.40189242e-01
2.74610907e-01 1.20424610e-02 8.66204947e-02 5.39500564e-02
-1.44871444e-01 -5.70943117e-01 9.21121240e-01 -4.07591403e-01
-4.92852688e-01 -5.20386267e-03 -1.03226590e+00 8.97834301e-02
9.92593169e-01 -7.84614205e-01 -3.59281451e-01 -4.20183957e-01
-8.29068601e-01 -1.95836127e-02 6.50830388e-01 3.03609259e-02
7.30431557e-01 -1.51530886e+00 -2.30721906e-01 7.18729138e-01
-3.22939813e-01 -3.74400020e-01 -1.70157343e-01 6.58265352e-01
-1.32361674e+00 1.02442718e+00 -5.41665435e-01 -4.55340147e-01
-1.14228773e+00 4.78126436e-01 4.28307921e-01 -2.41845414e-01
-2.15481281e-01 1.11954463e+00 2.71224789e-02 -8.23025763e-01
1.85185194e-01 -9.21545625e-02 -7.54407048e-01 3.55081353e-03
6.88606799e-01 1.05194807e+00 -2.91290224e-01 -6.04341030e-01
-4.56784427e-01 6.65885091e-01 2.32151806e-01 -2.87484169e-01
9.17833567e-01 -2.19243199e-01 -9.89108324e-01 4.28274393e-01
8.38715494e-01 -1.36269778e-01 -3.69319022e-02 4.16855574e-01
1.25943512e-01 -8.02164078e-01 -4.32406247e-01 -3.55811834e-01
-1.85641110e-01 5.54822803e-01 5.34874737e-01 8.99602413e-01
8.57008278e-01 -3.02566767e-01 8.18335712e-01 9.66778398e-01
5.72402716e-01 -1.68019545e+00 -2.13140488e-01 6.89524531e-01
9.12339568e-01 -9.28350806e-01 5.44190466e-01 -7.27165341e-01
-4.14997995e-01 1.32979155e+00 4.68304873e-01 -1.55325383e-01
1.27306652e+00 5.74917436e-01 1.14069022e-02 -3.70670199e-01
-9.44949508e-01 1.12705544e-01 3.75366658e-01 1.11147714e+00
5.06513238e-01 5.07374525e-01 -9.66500878e-01 3.21953118e-01
-7.28706717e-01 -2.34555230e-01 4.90474731e-01 1.41972518e+00
-6.43810987e-01 -1.20391917e+00 -7.23931909e-01 4.61561680e-01
-5.49773455e-01 -1.16372630e-01 -5.28106689e-01 8.46754074e-01
-5.76629937e-02 1.49448860e+00 -1.97535474e-03 -5.03491640e-01
4.11356747e-01 1.41089618e-01 7.21762300e-01 -1.23501487e-01
-9.04846430e-01 4.15413082e-01 3.84890139e-01 -1.23056793e+00
-8.58632207e-01 -1.05834293e+00 -1.40667629e+00 -1.19437599e+00
-5.12782812e-01 1.89310342e-01 3.83317530e-01 3.90289724e-01
-2.06836104e-01 2.85260603e-02 9.80917513e-01 -1.07949340e+00
-3.90341938e-01 -9.39418614e-01 -1.00026262e+00 -7.84516707e-02
2.89751232e-01 -8.00943017e-01 -7.83523321e-01 2.83909619e-01] | [-7.365813255310059, 3.70330548286438] |
01914f63-60ea-4495-9ad6-e7997b488263 | joint-adaptive-neighbours-and-metric-learning | 1709.03656 | null | http://arxiv.org/abs/1709.03656v1 | http://arxiv.org/pdf/1709.03656v1.pdf | Joint Adaptive Neighbours and Metric Learning for Multi-view Subspace Clustering | Due to the existence of various views or representations in many real-world
data, multi-view learning has drawn much attention recently. Multi-view
spectral clustering methods based on similarity matrixes or graphs are pretty
popular. Generally, these algorithms learn informative graphs by directly
utilizing original data. However, in the real-world applications, original data
often contain noises and outliers that lead to unreliable graphs. In addition,
different views may have different contributions to data clustering. In this
paper, a novel Multiview Subspace Clustering method unifying Adaptive
neighbours and Metric learning (MSCAM), is proposed to address the above
problems. In this method, we use the subspace representations of different
views to adaptively learn a consensus similarity matrix, uncovering the
subspace structure and avoiding noisy nature of original data. For all views,
we also learn different Mahalanobis matrixes that parameterize the squared
distances and consider the contributions of different views. Further, we
constrain the graph constructed by the similarity matrix to have exact c (c is
the number of clusters) connected components. An iterative algorithm is
developed to solve this optimization problem. Moreover, experiments on a
synthetic dataset and different real-world datasets demonstrate the
effectiveness of MSCAM. | ['Xiangyang Luo', 'Nan Xu', 'Jiujun Wang', 'Yanqing Guo', 'Ran He'] | 2017-09-12 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-1.77713364e-01 -4.68139350e-01 2.20793001e-02 -2.56222636e-01
-5.11005223e-01 -6.95356548e-01 2.69313902e-01 -6.93041692e-03
1.78597867e-01 3.05750102e-01 3.14445496e-01 3.16413641e-01
-5.00226617e-01 -5.01035750e-01 -3.31480622e-01 -1.05983460e+00
1.91817239e-01 3.62129450e-01 6.09549507e-02 5.12062162e-02
3.21377665e-01 1.51430279e-01 -1.31200314e+00 9.46915448e-02
9.63953614e-01 5.81807315e-01 1.49205849e-01 2.11035788e-01
-7.62464032e-02 3.54456782e-01 -3.75919580e-01 -1.29331023e-01
3.05959135e-01 -5.20819008e-01 -1.85081542e-01 6.91725016e-01
1.86698139e-01 2.76907235e-01 -2.74144262e-01 1.34691679e+00
4.74562049e-01 1.90246314e-01 6.09215140e-01 -1.42815256e+00
-6.55354977e-01 4.19337839e-01 -1.00657737e+00 -5.82662523e-02
3.51961732e-01 -1.63800657e-01 9.66108918e-01 -1.06109881e+00
5.56811988e-01 1.27760148e+00 2.36617073e-01 1.70818016e-01
-1.38916135e+00 -4.88599479e-01 2.73444116e-01 4.97627139e-01
-1.65878296e+00 -2.59346426e-01 1.26685846e+00 -5.72483480e-01
-3.89171392e-02 3.09099466e-01 6.38455629e-01 9.12860453e-01
1.84438918e-02 6.50384009e-01 1.15415049e+00 -2.77251936e-02
3.13599825e-01 1.91631451e-01 3.99622209e-02 3.95628572e-01
4.56411093e-01 -4.37856942e-01 -2.22140357e-01 -3.60576034e-01
2.49767080e-01 5.35220504e-01 -6.22286677e-01 -1.13481152e+00
-1.34624624e+00 7.77563393e-01 2.24290997e-01 3.48402560e-01
-1.59793347e-01 -4.36593175e-01 3.86732876e-01 1.50417268e-01
1.81481287e-01 -1.93917066e-01 7.04411715e-02 4.00311261e-01
-5.95191240e-01 -2.41301775e-01 6.24636650e-01 9.50253010e-01
9.32906866e-01 7.87721351e-02 4.24136847e-01 8.87966931e-01
4.92668152e-01 5.56648374e-01 5.75816154e-01 -8.66096377e-01
6.49713397e-01 1.15336084e+00 -1.54742911e-01 -1.75019431e+00
-2.77644187e-01 -2.68431395e-01 -1.43768132e+00 4.52929270e-03
2.07525969e-01 3.49100083e-02 -4.17704850e-01 1.51034379e+00
5.86150348e-01 3.84255797e-01 2.42446363e-01 9.81280982e-01
7.34457195e-01 5.57797670e-01 -5.93644023e-01 -6.16712093e-01
8.31328094e-01 -5.14310122e-01 -6.84354126e-01 4.81138416e-02
2.89117396e-01 -6.95493400e-01 7.34085917e-01 6.15695953e-01
-5.26860178e-01 -5.31072676e-01 -1.21106493e+00 4.66931790e-01
-9.11072642e-02 7.53174499e-02 1.97160631e-01 4.46004450e-01
-6.77414298e-01 3.96772295e-01 -6.23017132e-01 -2.98454970e-01
1.15067801e-02 2.29944408e-01 -6.60840154e-01 -4.34400290e-01
-8.04288030e-01 2.63602644e-01 7.08711445e-01 1.49034619e-01
-4.33861196e-01 -1.30203590e-01 -7.72183537e-01 -1.98870718e-01
7.59792149e-01 -4.43611950e-01 2.22032011e-01 -1.01268756e+00
-9.32773292e-01 5.58638453e-01 -1.00791097e-01 2.25854591e-01
3.84332180e-01 2.39981517e-01 -7.12507546e-01 2.43545920e-01
1.90056071e-01 -2.33719975e-01 9.69795585e-01 -1.69854498e+00
-3.39380413e-01 -8.50722671e-01 -3.78138125e-01 3.63321543e-01
-3.15840304e-01 -4.61937666e-01 -6.51525319e-01 -4.35524911e-01
9.05023038e-01 -9.78742123e-01 -2.09421590e-01 -2.69375592e-01
-5.13788283e-01 -1.47567034e-01 1.11111164e+00 -5.58397174e-01
1.23683047e+00 -2.45867801e+00 8.62682104e-01 7.18770266e-01
4.79081511e-01 -7.55394027e-02 -1.52260186e-02 5.85736573e-01
-1.56191245e-01 -9.93156210e-02 -4.00309443e-01 3.99652533e-02
-3.60070825e-01 3.96979213e-01 6.90124556e-02 8.55850518e-01
-4.98282999e-01 1.60777539e-01 -9.64331567e-01 -6.16687536e-01
3.26838821e-01 2.84263551e-01 -1.81542307e-01 1.65908039e-01
2.89463937e-01 6.47255540e-01 -5.53582788e-01 4.83642429e-01
9.02041674e-01 -4.24489081e-01 4.86018419e-01 -4.30541307e-01
1.04660153e-01 -7.59920061e-01 -2.01805830e+00 1.79663253e+00
-5.73732518e-03 1.69930980e-01 3.09341371e-01 -1.28156292e+00
7.57934332e-01 2.78858691e-01 8.37838471e-01 -1.92197546e-01
6.35685548e-02 2.33098954e-01 -8.79791286e-03 -4.76589590e-01
4.83711734e-02 1.88324317e-01 2.58661747e-01 3.47037822e-01
-2.69469880e-02 1.90196335e-01 2.08299488e-01 4.72387403e-01
6.09428048e-01 -2.40682587e-01 5.30032635e-01 -1.98638663e-01
1.02747488e+00 -3.69591504e-01 9.19012964e-01 5.49416058e-02
-9.19034407e-02 8.11310828e-01 4.09439594e-01 -3.34975421e-01
-9.10341084e-01 -9.76966023e-01 8.52109417e-02 3.83806080e-01
6.02787554e-01 -6.28303885e-01 -5.99389613e-01 -7.27373540e-01
-1.01463288e-01 2.88115442e-01 -4.39806163e-01 -3.28169346e-01
-3.55855167e-01 -6.34104729e-01 -1.09016821e-01 1.14284933e-01
4.06483769e-01 -3.33445013e-01 1.52029702e-02 9.89800245e-02
-4.51165646e-01 -9.14466977e-01 -6.25163913e-01 -3.49695057e-01
-8.92730474e-01 -1.56840062e+00 -6.06866956e-01 -6.01974607e-01
9.06872392e-01 1.07860970e+00 6.53063715e-01 6.49075434e-02
-8.98182113e-03 6.85629547e-01 -4.95498657e-01 1.07168518e-01
-2.77756363e-01 -3.35177302e-01 4.36175048e-01 7.31608331e-01
3.29620272e-01 -9.62198913e-01 -5.52647650e-01 5.96766114e-01
-1.07495940e+00 -7.25144818e-02 4.28105384e-01 7.40548790e-01
9.13259566e-01 2.72028238e-01 4.26240772e-01 -7.27336466e-01
4.15019900e-01 -7.18807518e-01 -5.84827363e-01 4.26139802e-01
-7.73553312e-01 -1.49767650e-02 9.74286914e-01 -2.78619289e-01
-6.56994581e-01 1.33806080e-01 5.27152836e-01 -1.01894057e+00
-1.10374279e-01 5.39495647e-01 -7.53377676e-01 1.51236001e-02
3.60055208e-01 5.74375749e-01 2.28465006e-01 -3.19388747e-01
5.22188365e-01 6.38908446e-01 3.18941593e-01 -2.25374997e-01
1.08684075e+00 6.58882439e-01 1.17567316e-01 -9.28333521e-01
-6.32602572e-01 -7.10499406e-01 -8.57041001e-01 -4.12396908e-01
7.56452680e-01 -9.34724510e-01 -6.03826880e-01 2.66433656e-01
-7.68301308e-01 6.62175655e-01 1.55578330e-01 6.46465123e-01
-3.90001297e-01 1.01467693e+00 -5.83219677e-02 -6.51127934e-01
-6.04222082e-02 -1.09770799e+00 6.67237341e-01 1.73988611e-01
2.39700824e-01 -9.87066805e-01 1.86943710e-01 3.33255053e-01
-2.65734076e-01 5.49002111e-01 7.23504782e-01 -6.78561449e-01
-5.03100872e-01 -5.64416125e-02 -2.34778821e-02 3.07283998e-01
5.12010157e-01 7.30567724e-02 -6.24075472e-01 -7.09070563e-01
3.90266657e-01 1.35686263e-01 6.20536387e-01 2.55839288e-01
1.05016100e+00 -3.42471004e-01 -4.66374189e-01 6.18290126e-01
1.55888057e+00 2.53000438e-01 1.38739973e-01 4.10382226e-02
1.21560490e+00 7.18690634e-01 4.76273686e-01 6.44586146e-01
3.83026212e-01 4.27158177e-01 5.07197917e-01 3.34966302e-01
4.48470265e-01 -2.20899180e-01 2.24037677e-01 1.55423093e+00
-8.29130858e-02 -1.41786680e-01 -7.95765281e-01 3.32727015e-01
-2.14138889e+00 -1.06644797e+00 -3.69832635e-01 2.49446630e+00
1.33315355e-01 -8.90685916e-02 1.18626952e-01 2.97385305e-01
1.15299082e+00 4.22422051e-01 -6.90401316e-01 2.55486369e-01
-3.80319536e-01 -7.37757325e-01 1.91417068e-01 2.44787991e-01
-9.29160595e-01 3.13902438e-01 4.53056908e+00 7.58003235e-01
-7.95043051e-01 -5.82647435e-02 3.30863178e-01 7.09239691e-02
-4.99580622e-01 2.24108994e-01 -3.59422594e-01 5.73117375e-01
4.04715687e-01 -3.41409266e-01 6.77218199e-01 7.57735193e-01
2.29630858e-01 2.81636957e-02 -9.18025196e-01 1.42053246e+00
5.56695402e-01 -8.77891898e-01 2.69713640e-01 1.57694101e-01
8.36688757e-01 -2.37887695e-01 -1.66590735e-01 -2.12436333e-01
1.95080027e-01 -3.97825152e-01 3.23759735e-01 6.20826840e-01
5.21156847e-01 -1.07910240e+00 5.99830866e-01 6.68718159e-01
-1.51711011e+00 -1.29911855e-01 -6.04735970e-01 3.43980014e-01
6.81564361e-02 7.37742484e-01 -2.68199056e-01 1.17422688e+00
5.07973433e-01 1.40418780e+00 -8.01327825e-01 9.38068986e-01
-5.53504415e-02 2.79982686e-01 -1.43614784e-01 3.32162887e-01
1.53046967e-02 -1.11715794e+00 8.36318374e-01 4.25900131e-01
3.94088566e-01 3.03861797e-01 5.76941311e-01 5.68240047e-01
1.74247861e-01 3.81083220e-01 -9.69324827e-01 1.89770088e-01
5.67984581e-01 1.37076008e+00 -7.27363765e-01 -2.16312155e-01
-6.76263928e-01 1.05369425e+00 2.63610333e-01 4.99188393e-01
-5.53218365e-01 -1.44757718e-01 4.34631050e-01 -1.97329417e-01
1.27701283e-01 -3.73459488e-01 1.70080751e-01 -1.54152763e+00
2.12039411e-01 -1.01992488e+00 6.22454703e-01 -4.37652230e-01
-1.50220799e+00 2.38380387e-01 -1.03163376e-01 -1.85511589e+00
2.73180474e-02 -2.37186834e-01 -6.27993107e-01 4.27221119e-01
-8.70684385e-01 -9.44318950e-01 -4.99072224e-01 9.81758893e-01
4.20699060e-01 -3.80484253e-01 4.74760532e-01 2.18076691e-01
-7.49955893e-01 1.65448993e-01 8.54580224e-01 1.15969695e-01
7.19810128e-01 -1.23056865e+00 -2.14712858e-01 8.90325725e-01
4.58844751e-01 5.73205173e-01 5.16059458e-01 -5.49864590e-01
-1.74153423e+00 -9.00952220e-01 8.07081908e-02 -9.19870064e-02
6.70648634e-01 -2.92383045e-01 -1.07201135e+00 6.51271045e-01
1.73312470e-01 2.93335430e-02 1.00122583e+00 -6.74129725e-02
-4.11599070e-01 -3.30280513e-01 -8.72541964e-01 4.68380779e-01
8.92399848e-01 -3.94658804e-01 -3.84952724e-01 4.07764614e-01
4.09387738e-01 -9.12464634e-02 -9.20865715e-01 2.93179184e-01
1.98519692e-01 -1.32376051e+00 9.05332327e-01 -3.06186974e-01
5.01137506e-03 -7.81070650e-01 -3.70993555e-01 -1.70664608e+00
-5.04692793e-01 -2.17890471e-01 -1.52401373e-01 1.46336961e+00
-1.72866628e-01 -6.99776888e-01 5.57483613e-01 1.96327925e-01
1.20638736e-01 -4.47829664e-01 -9.68018115e-01 -7.13242888e-01
-3.27812046e-01 1.75677203e-02 3.45553130e-01 1.29608381e+00
-3.59470695e-02 5.63909948e-01 -5.03265917e-01 4.60383564e-01
1.17970622e+00 5.51029801e-01 8.05003703e-01 -1.59530532e+00
-1.69175491e-01 -2.24772498e-01 -6.94357812e-01 -5.12833476e-01
2.02951744e-01 -9.38482463e-01 -2.45538518e-01 -1.48722565e+00
5.09468555e-01 -1.77205101e-01 -2.03870833e-01 -1.90815538e-01
-3.89098614e-01 -2.77430356e-01 2.01956958e-01 6.95022106e-01
-7.68362582e-01 6.96914732e-01 1.09738338e+00 -2.06601083e-01
-1.79842114e-01 8.21347460e-02 -5.26710868e-01 8.38877380e-01
6.34171844e-01 -2.30407745e-01 -7.67270565e-01 -2.34434366e-01
5.15300483e-02 2.12755620e-01 6.69468343e-02 -1.15290093e+00
3.70049179e-01 -2.82015055e-01 3.94546419e-01 -7.69713521e-01
1.88555136e-01 -1.25648654e+00 5.24839044e-01 2.02807099e-01
1.22878432e-01 1.31337017e-01 -4.39353555e-01 1.30849314e+00
-4.77793187e-01 1.50983622e-02 7.99660921e-01 -3.31525952e-01
-5.84819615e-01 4.64890629e-01 -5.04273875e-03 1.62486434e-01
1.22377276e+00 -4.37397748e-01 8.61387700e-02 -4.32621121e-01
-8.01567078e-01 6.04970217e-01 7.22803950e-01 3.82024288e-01
8.72022271e-01 -1.76759541e+00 -7.25994468e-01 2.20301554e-01
4.30763185e-01 8.04920346e-02 5.08371055e-01 9.16581154e-01
-4.93901446e-02 4.17481810e-02 -2.97882203e-02 -9.17124391e-01
-1.42987156e+00 9.75430608e-01 6.94909245e-02 1.14845604e-01
-6.08528137e-01 2.30432197e-01 1.94683447e-01 -4.91547614e-01
-4.92776781e-02 3.26863736e-01 -5.87595105e-01 5.00422060e-01
4.04574722e-01 7.34798133e-01 -2.03504562e-01 -1.01653707e+00
-2.03492582e-01 9.86906350e-01 1.29481465e-01 9.50318351e-02
1.16383159e+00 -5.58533072e-01 -3.64763856e-01 8.32763910e-01
1.43021321e+00 1.68703422e-01 -9.63561952e-01 -4.90625560e-01
5.65800294e-02 -6.96583629e-01 -2.57365763e-01 9.09818709e-02
-1.29391539e+00 7.85508096e-01 5.95016956e-01 3.08165014e-01
1.12781966e+00 -1.53188244e-01 4.41805333e-01 3.18890810e-01
5.08957982e-01 -1.06156743e+00 1.69510126e-01 3.48770171e-02
6.73547447e-01 -1.36065698e+00 1.36732638e-01 -3.85082722e-01
-7.02740192e-01 1.12201750e+00 6.31612837e-01 -9.78665426e-02
6.34793937e-01 -4.55993265e-01 1.76857095e-02 -2.93414056e-01
-3.00653934e-01 5.04045151e-02 2.86174327e-01 4.04023260e-01
-2.69962568e-02 6.30301461e-02 -3.05186838e-01 2.41098821e-01
2.54716843e-01 -5.20456731e-01 6.32476687e-01 6.45876050e-01
-4.36582148e-01 -9.93238091e-01 -6.67373478e-01 4.25884247e-01
-5.79952411e-02 3.19987714e-01 -6.12684727e-01 5.10178685e-01
-1.17505044e-02 1.15505481e+00 -3.37545246e-01 -5.93031585e-01
3.09115350e-01 -7.98708275e-02 1.25656083e-01 -4.95930940e-01
3.62659059e-02 3.66227478e-01 -5.37587643e-01 -6.03245854e-01
-8.21765363e-01 -7.79910862e-01 -1.15192497e+00 -2.58699805e-01
-4.14632231e-01 5.14376819e-01 2.79174298e-01 7.64824927e-01
3.85079712e-01 9.71825719e-02 1.25239718e+00 -6.23826206e-01
-4.85081971e-01 -5.86486220e-01 -1.15676796e+00 7.39258289e-01
8.93956795e-02 -7.34973192e-01 -6.69500113e-01 -1.01092942e-01] | [8.181979179382324, 4.65186882019043] |
6b8ceb80-7bb9-48be-959f-d75dc5f788bb | research-on-attention-memory-networks-as-a | null | null | https://aclanthology.org/W16-5902 | https://aclanthology.org/W16-5902.pdf | Research on attention memory networks as a model for learning natural language inference | null | ['Jing Zhang', 'Zhuang Liu', 'Kaiyu Huang', 'Degen Huang'] | 2016-11-01 | null | null | null | ws-2016-11 | ['sentence-pair-modeling'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01
7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01
7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00
-1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01
3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01
9.28529128e-02 -8.59008506e-02 -2.09758401e-01 9.22211111e-01
5.37373662e-01 4.49454933e-02 1.08379531e+00 -1.42548037e+00
-5.91619551e-01 6.10313773e-01 -4.01565880e-02 1.21627934e-01
1.03678203e+00 -5.39447069e-01 1.19919395e+00 -1.13026452e+00
7.20913768e-01 1.26888943e+00 8.66221786e-01 5.44149756e-01
-1.22286928e+00 -1.94712028e-01 -3.26822817e-01 -9.51717794e-02
-1.46558487e+00 -3.25250506e-01 4.25783843e-02 -2.08119690e-01
1.66093647e+00 1.26596653e+00 1.20609856e+00 1.01401424e+00
1.26658809e+00 8.34431887e-01 1.04267764e+00 -5.13792276e-01
3.35295945e-01 3.66983831e-01 1.54683650e-01 6.33519173e-01
8.40953708e-01 5.26628852e-01 -7.06372619e-01 -9.13127720e-01
9.33553874e-01 -2.94925272e-01 1.71355158e-01 -5.05680561e-01
-9.05919552e-01 6.91228509e-01 1.78732842e-01 3.83959889e-01
-1.39880210e-01 9.89067405e-02 1.26390755e-01 5.30987144e-01
-2.58292928e-02 6.47037446e-01 -9.11868811e-01 -1.33165747e-01
-8.71728659e-01 5.10332465e-01 1.25398111e+00 1.52653182e+00
1.24482810e-01 2.94908643e-01 -9.34252143e-02 3.17179203e-01
8.92314315e-01 1.01808000e+00 4.28362608e-01 -1.36146402e+00
-6.87414408e-02 1.72361732e-01 5.01781464e-01 -8.52631688e-01
-6.33224547e-01 -9.64177120e-03 -8.93263519e-01 4.49267089e-01
3.49161088e-01 4.57367361e-01 -8.02827001e-01 5.07305264e-01
4.33481112e-02 -2.34125629e-01 4.53833073e-01 5.55570945e-02
4.99930978e-01 3.76208365e-01 -1.34477139e-01 -5.73289394e-01
1.06082785e+00 -1.36716676e+00 -1.35299087e+00 2.33215362e-01
9.05734658e-01 -1.07320261e+00 4.35900748e-01 5.33875942e-01
-1.55548143e+00 -1.37560293e-01 -1.08699942e+00 1.78573877e-01
-7.27255583e-01 -3.14239264e-01 8.57801437e-01 1.43120694e+00
-1.60129595e+00 9.73287821e-01 -4.91727620e-01 6.59165755e-02
1.20568443e-02 8.24621081e-01 -2.64718989e-03 4.62812334e-01
-1.33193445e+00 1.08501506e+00 2.22979754e-01 -1.21242590e-01
-1.65216476e-01 -2.13068098e-01 -8.23704481e-01 -5.63443303e-01
-4.78693932e-01 -5.29636025e-01 1.44139910e+00 -2.59346128e-01
-1.65295815e+00 9.71794367e-01 -1.42069459e-01 -1.97814897e-01
6.14786744e-01 -1.28011424e-02 -8.31891418e-01 2.42498964e-01
-1.89849049e-01 5.76383233e-01 9.28263724e-01 -1.35132408e+00
-7.59897232e-01 -1.67359829e-01 -1.23336017e-01 2.66287565e-01
-1.25510961e-01 1.89734384e-01 2.11616129e-01 -1.12999000e-01
3.27147305e-01 -7.20919967e-01 -2.53068686e-01 -5.32041907e-01
-1.46512717e-01 -7.10518599e-01 7.70373225e-01 -4.51523662e-01
1.83705616e+00 -1.67618537e+00 -2.06720144e-01 3.98590982e-01
3.57815564e-01 -1.24705513e-03 2.40583986e-01 1.08380008e+00
-2.76906848e-01 7.32199550e-01 4.11965609e-01 -1.10722095e-01
2.24991128e-01 4.85861301e-01 -4.16602850e-01 3.05609167e-01
-1.29282743e-01 1.15307164e+00 -1.16605783e+00 -5.23096442e-01
4.11106765e-01 9.42391157e-02 -4.58732933e-01 4.79237735e-01
2.99364805e-01 1.47170946e-01 -3.56553018e-01 1.39399457e+00
1.15709066e+00 -1.31984919e-01 1.45911396e-01 5.30878425e-01
-3.79135728e-01 3.55090618e-01 -6.96863770e-01 1.05554795e+00
7.08333924e-02 5.00173986e-01 1.02364108e-01 -7.94621468e-01
3.33247900e-01 8.61540735e-01 4.37155962e-01 -9.67555881e-01
-2.26398129e-02 6.92409754e-01 1.12803578e-01 -6.07703328e-01
7.58228302e-01 3.94563079e-02 -3.64872098e-01 6.40070081e-01
-2.37588286e-01 -6.59476995e-01 9.64643434e-02 3.08100313e-01
6.22585893e-01 -5.18246442e-02 5.62923312e-01 -1.05613089e+00
7.86340594e-01 -1.82965681e-01 -1.81299388e-01 1.03415680e+00
-3.09923887e-01 3.19085121e-01 2.99841821e-01 -6.65102363e-01
-6.45341039e-01 -1.12307119e+00 -4.89381433e-01 1.30636716e+00
3.24267983e-01 -4.39044595e-01 -9.54439282e-01 -2.49762803e-01
1.77620783e-01 6.89606130e-01 -5.90509653e-01 3.84124845e-01
-5.03739953e-01 -8.32535863e-01 7.39044368e-01 3.45434904e-01
-5.07752821e-02 -1.33414865e+00 -6.58416986e-01 1.25490099e-01
-2.20292807e-01 -6.63697243e-01 -6.23428151e-02 4.48765576e-01
-1.35989368e+00 -5.18594682e-01 -6.66252747e-02 -8.20914626e-01
5.87345481e-01 2.46782884e-01 1.27047324e+00 5.39230824e-01
-2.31483161e-01 4.26904231e-01 -1.21292919e-01 -4.95818377e-01
-4.59671497e-01 -8.00336525e-02 5.28869390e-01 -5.87835789e-01
5.19427478e-01 -2.50617653e-01 -7.29350567e-01 5.37953973e-01
-6.88540697e-01 1.62748516e-01 1.79803044e-01 1.04410267e+00
1.35816500e-01 -9.34035778e-02 1.22507080e-01 -6.38007045e-01
8.72274399e-01 -1.69219792e-01 -3.78732830e-01 5.77745810e-02
-6.77108407e-01 -3.74140263e-01 3.21430594e-01 -3.25342178e-01
-1.01981449e+00 -4.87835288e-01 -9.82677937e-02 2.45538145e-01
1.11353043e-02 -1.46784872e-01 6.47139177e-02 -5.24923325e-01
8.02199244e-01 9.25758183e-02 1.99174434e-02 -6.80815242e-03
3.01039815e-01 7.09525108e-01 -6.82967342e-03 -6.68678164e-01
8.44880998e-01 4.91470337e-01 7.98524171e-02 -9.57177758e-01
-1.52186140e-01 -2.60129690e-01 -9.51962709e-01 -6.54426932e-01
6.56643391e-01 -6.78531289e-01 -9.10833478e-01 3.91110867e-01
-9.38691139e-01 -3.38627815e-01 -3.91645581e-01 4.25431967e-01
-1.01278400e+00 2.75717527e-02 -3.90154392e-01 -1.27895141e+00
-5.10977268e-01 -1.02017939e+00 9.43384409e-01 5.30070923e-02
-5.10597289e-01 -1.26927447e+00 5.87685481e-02 2.71537274e-01
1.81734428e-01 -1.73075795e-01 6.90226793e-01 -2.38256708e-01
-4.24233019e-01 -1.53791070e-01 2.34436691e-02 -1.39755070e-01
1.70832314e-02 4.95917559e-01 -9.81751978e-01 -5.31145096e-01
6.65065646e-02 -1.92070693e-01 -1.08835101e-01 6.52520418e-01
5.91872573e-01 -2.29931593e-01 -8.56000841e-01 5.40386558e-01
1.38545322e+00 3.85070026e-01 5.32770038e-01 7.28214979e-01
1.41836226e-01 5.53460240e-01 9.17806149e-01 4.63203549e-01
1.30579369e-02 3.28798652e-01 2.40537539e-01 1.49327129e-01
1.11720070e-01 -1.54819340e-01 3.77893507e-01 1.16112018e+00
-8.18235934e-01 -2.69281328e-01 -5.07867396e-01 4.42987174e-01
-1.72482407e+00 -1.40330648e+00 -4.32368398e-01 6.90478683e-01
6.25676990e-01 1.56016424e-01 -1.48347050e-01 3.35214496e-01
4.99015123e-01 -2.03574806e-01 -1.19133167e-01 -1.06291151e+00
-1.43546045e-01 3.15233678e-01 7.37729073e-01 1.00061214e+00
-7.20721722e-01 1.03317809e+00 1.29781246e+01 1.02230716e+00
2.21112028e-01 1.03134915e-01 5.16071796e-01 3.48020852e-01
-4.36954498e-01 -4.56139445e-02 -1.04416132e+00 2.72933897e-02
1.38140702e+00 -4.30666685e-01 6.85999811e-01 5.44219851e-01
3.44648361e-01 -4.23268199e-01 -1.26188684e+00 5.26221812e-01
9.73738134e-02 -1.40886843e+00 -2.83300440e-04 6.85225725e-01
7.73699820e-01 -5.08050561e-01 6.22419357e-01 3.24184299e-01
6.09259963e-01 -1.14389277e+00 8.60300779e-01 2.53660440e-01
1.03040910e+00 -6.05088234e-01 5.67372203e-01 1.68872893e-01
-1.14389896e+00 -2.20873043e-01 -8.77727985e-01 -1.00755692e+00
3.93533185e-02 -1.81779593e-01 -4.29956943e-01 3.48861217e-01
9.58353162e-01 2.99398601e-01 -3.93658698e-01 9.95779395e-01
-4.78694476e-02 1.04875881e-02 -2.71853864e-01 -4.48467314e-01
4.83122796e-01 -3.54241252e-01 4.66730654e-01 1.00164843e+00
2.48499006e-01 3.51035744e-01 -9.84472036e-02 4.01770771e-01
5.45058846e-01 3.29446048e-02 -1.19659424e+00 -1.78908288e-01
2.83276141e-01 9.16795909e-01 -4.83487815e-01 -4.22520459e-01
-2.00212970e-01 8.62069130e-01 -3.55488248e-02 5.01107454e-01
-6.11489356e-01 -4.35615242e-01 9.72222984e-01 -1.27327025e-01
-1.14700586e-01 -3.48497719e-01 -6.23769283e-01 -7.30352640e-01
-5.89872956e-01 -4.54965204e-01 5.93606755e-02 -5.53365827e-01
-1.39813089e+00 5.79277515e-01 -2.27688253e-02 -1.40553558e+00
-6.99901402e-01 -1.27676582e+00 -4.76714373e-01 4.92853165e-01
-1.11898029e+00 -1.10984349e+00 2.50124663e-01 4.52870727e-01
1.64141744e-01 -5.34416080e-01 1.39563632e+00 3.57715860e-02
1.00637585e-01 9.24474537e-01 6.69434488e-01 -7.37814724e-01
5.56605101e-01 -1.27867436e+00 5.68737745e-01 -1.37897313e-01
-4.31265175e-01 9.05828118e-01 6.28349900e-01 -5.39804697e-01
-1.41196322e+00 -3.66917729e-01 1.08350635e+00 -9.83769417e-01
6.55218959e-01 -3.86345625e-01 4.23767231e-02 7.88592756e-01
7.15902448e-01 -6.18741751e-01 8.21781039e-01 -1.83753878e-01
1.80774391e-01 5.75296998e-01 -1.39248300e+00 6.12354755e-01
1.66275799e+00 -4.63594139e-01 -6.25784039e-01 7.60327101e-01
8.13696027e-01 -6.94087505e-01 -1.30082703e+00 3.34633321e-01
8.65424156e-01 -8.75409484e-01 1.61978090e+00 -1.32660246e+00
-4.43697497e-02 2.81152606e-01 -2.61993498e-01 -9.32519078e-01
-5.96193194e-01 -1.23518765e+00 -5.33532679e-01 -5.83747849e-02
5.96577883e-01 -1.13057327e+00 3.42365682e-01 8.74560475e-01
-2.82833427e-01 -6.42737269e-01 -1.06996536e+00 -1.32016802e+00
-3.35779637e-02 -1.45572275e-01 4.90409225e-01 7.63798356e-01
6.83744550e-01 1.09839931e-01 -6.36873543e-02 -1.00294888e-01
5.33176839e-01 9.55312885e-03 4.41501856e-01 -1.34294486e+00
3.86843324e-01 -5.75816095e-01 -3.07655483e-01 -9.45992947e-01
-8.85957032e-02 -8.12076271e-01 -6.53862000e-01 -1.28511906e+00
-8.32044985e-03 -1.91056758e-01 -1.11109078e-01 -1.65725678e-01
3.67937148e-01 2.13746816e-01 1.20859891e-02 1.03788137e-01
-3.71160030e-01 6.18435517e-02 1.29639816e+00 6.91750320e-05
-1.62315920e-01 4.85058486e-01 -4.81304944e-01 7.84440815e-01
8.58408585e-02 -2.99253196e-01 -6.78878546e-01 6.11881316e-02
6.69384480e-01 4.61409837e-02 3.24159935e-02 -7.42885649e-01
5.37211418e-01 -3.75702560e-01 4.78586555e-01 -1.32223868e+00
1.30741090e-01 -9.61415648e-01 6.95283338e-02 9.40189242e-01
2.74610907e-01 1.20424610e-02 8.66204947e-02 5.39500564e-02
-1.44871444e-01 -5.70943117e-01 9.21121240e-01 -4.07591403e-01
-4.92852688e-01 -5.20386267e-03 -1.03226590e+00 8.97834301e-02
9.92593169e-01 -7.84614205e-01 -3.59281451e-01 -4.20183957e-01
-8.29068601e-01 -1.95836127e-02 6.50830388e-01 3.03609259e-02
7.30431557e-01 -1.51530886e+00 -2.30721906e-01 7.18729138e-01
-3.22939813e-01 -3.74400020e-01 -1.70157343e-01 6.58265352e-01
-1.32361674e+00 1.02442718e+00 -5.41665435e-01 -4.55340147e-01
-1.14228773e+00 4.78126436e-01 4.28307921e-01 -2.41845414e-01
-2.15481281e-01 1.11954463e+00 2.71224789e-02 -8.23025763e-01
1.85185194e-01 -9.21545625e-02 -7.54407048e-01 3.55081353e-03
6.88606799e-01 1.05194807e+00 -2.91290224e-01 -6.04341030e-01
-4.56784427e-01 6.65885091e-01 2.32151806e-01 -2.87484169e-01
9.17833567e-01 -2.19243199e-01 -9.89108324e-01 4.28274393e-01
8.38715494e-01 -1.36269778e-01 -3.69319022e-02 4.16855574e-01
1.25943512e-01 -8.02164078e-01 -4.32406247e-01 -3.55811834e-01
-1.85641110e-01 5.54822803e-01 5.34874737e-01 8.99602413e-01
8.57008278e-01 -3.02566767e-01 8.18335712e-01 9.66778398e-01
5.72402716e-01 -1.68019545e+00 -2.13140488e-01 6.89524531e-01
9.12339568e-01 -9.28350806e-01 5.44190466e-01 -7.27165341e-01
-4.14997995e-01 1.32979155e+00 4.68304873e-01 -1.55325383e-01
1.27306652e+00 5.74917436e-01 1.14069022e-02 -3.70670199e-01
-9.44949508e-01 1.12705544e-01 3.75366658e-01 1.11147714e+00
5.06513238e-01 5.07374525e-01 -9.66500878e-01 3.21953118e-01
-7.28706717e-01 -2.34555230e-01 4.90474731e-01 1.41972518e+00
-6.43810987e-01 -1.20391917e+00 -7.23931909e-01 4.61561680e-01
-5.49773455e-01 -1.16372630e-01 -5.28106689e-01 8.46754074e-01
-5.76629937e-02 1.49448860e+00 -1.97535474e-03 -5.03491640e-01
4.11356747e-01 1.41089618e-01 7.21762300e-01 -1.23501487e-01
-9.04846430e-01 4.15413082e-01 3.84890139e-01 -1.23056793e+00
-8.58632207e-01 -1.05834293e+00 -1.40667629e+00 -1.19437599e+00
-5.12782812e-01 1.89310342e-01 3.83317530e-01 3.90289724e-01
-2.06836104e-01 2.85260603e-02 9.80917513e-01 -1.07949340e+00
-3.90341938e-01 -9.39418614e-01 -1.00026262e+00 -7.84516707e-02
2.89751232e-01 -8.00943017e-01 -7.83523321e-01 2.83909619e-01] | [-7.324551105499268, 3.7082862854003906] |
2f187e15-c208-451d-92d7-7d03e02662ba | testing-neural-programs | 1908.10711 | null | https://arxiv.org/abs/1908.10711v2 | https://arxiv.org/pdf/1908.10711v2.pdf | Testing Neural Program Analyzers | Deep neural networks have been increasingly used in software engineering and program analysis tasks. They usually take a program and make some predictions about it, e.g., bug prediction. We call these models neural program analyzers. The reliability of neural programs can impact the reliability of the encompassing analyses. In this paper, we describe our ongoing efforts to develop effective techniques for testing neural programs. We discuss the challenges involved in developing such tools and our future plans. In our preliminary experiment on a neural model recently proposed in the literature, we found that the model is very brittle, and simple perturbations in the input can cause the model to make mistakes in its prediction. | ['Md. Rafiqul Islam Rabin', 'Mohammad Amin Alipour', 'Ke Wang'] | 2019-08-25 | null | null | null | null | ['method-name-prediction'] | ['natural-language-processing'] | [-5.32145053e-02 1.16978034e-01 -2.33201474e-01 -5.03525138e-01
-2.28441909e-01 -3.48730147e-01 -3.10564101e-01 2.34546270e-02
1.05119079e-01 3.57760519e-01 -2.79104620e-01 -9.50902522e-01
4.71990615e-01 -1.02871776e+00 -1.22709966e+00 1.56916440e-01
-2.91149050e-01 -1.01301201e-01 4.26589072e-01 -1.60972670e-01
4.14324641e-01 2.12719217e-01 -1.55501544e+00 4.89566773e-01
8.03059399e-01 4.95451838e-01 -1.68729424e-01 1.06663430e+00
2.71023419e-02 1.30234814e+00 -9.33557153e-01 -4.43321854e-01
-1.21178247e-01 -2.42387354e-01 -9.14286315e-01 -5.81905842e-01
7.73281455e-02 -6.06296539e-01 -1.90567791e-01 1.62028432e+00
2.01405689e-01 -2.66054153e-01 1.07653067e-03 -1.54216647e+00
-5.26219070e-01 1.12297571e+00 -4.31202531e-01 3.31574589e-01
2.18291506e-01 3.18337679e-01 8.50633144e-01 -6.15580976e-01
3.98816913e-01 9.60132003e-01 1.27484059e+00 7.48707294e-01
-1.10169172e+00 -6.70214772e-01 -1.79704279e-01 7.50808790e-02
-1.12295401e+00 -6.04639292e-01 8.78650904e-01 -5.55121839e-01
1.77231693e+00 1.05666526e-01 4.10413504e-01 9.80527699e-01
7.69688427e-01 6.29377484e-01 5.25609910e-01 -6.31700814e-01
3.21637779e-01 -1.31105125e-01 5.39223194e-01 1.12900865e+00
4.42424744e-01 3.19382846e-01 -9.57791582e-02 -4.97951090e-01
4.50131148e-01 -2.35345230e-01 5.23016453e-02 3.07579078e-02
-7.75921643e-01 7.50683427e-01 2.73005545e-01 3.59741569e-01
-9.25439820e-02 8.70082319e-01 7.34663606e-01 5.37064970e-01
1.36410207e-01 8.01579893e-01 -5.94569206e-01 -5.65634847e-01
-1.03964114e+00 2.67069608e-01 8.59629631e-01 9.22722757e-01
3.76611203e-01 6.08916879e-01 2.39038795e-01 5.63589156e-01
3.16696167e-01 -1.83180094e-01 5.06336331e-01 -8.32733929e-01
2.31070638e-01 7.79711187e-01 -1.96287006e-01 -1.00811374e+00
-4.41729516e-01 -1.72819406e-01 -3.91894192e-01 4.25586998e-01
8.11240226e-02 -3.28892350e-01 -8.28714907e-01 1.56008554e+00
-3.04627895e-01 1.64762765e-01 -1.02051631e-01 3.19061011e-01
7.24932909e-01 3.78005594e-01 -1.38314292e-01 3.15333784e-01
7.04720199e-01 -7.65589595e-01 -5.31159341e-01 -4.08347160e-01
1.11938286e+00 -4.53138411e-01 1.13970912e+00 5.79843521e-01
-1.17825377e+00 -5.08677661e-01 -1.60088015e+00 3.76864821e-01
-2.20830813e-01 1.46605179e-01 7.93309271e-01 8.30657601e-01
-1.38716710e+00 1.04947984e+00 -1.43363690e+00 -1.75450653e-01
2.45759770e-01 7.14345098e-01 -1.71504542e-01 1.85974911e-01
-8.38352084e-01 7.93571651e-01 4.77094591e-01 8.56359005e-02
-9.59390640e-01 -1.62581682e-01 -9.35350776e-01 2.45472729e-01
2.52098203e-01 -2.75452971e-01 1.93269861e+00 -1.04755235e+00
-1.23728144e+00 5.29688001e-01 -1.54250532e-01 -5.96750498e-01
-2.90308241e-02 -1.20995112e-01 -7.12023735e-01 -5.70612431e-01
-2.67045200e-01 2.62489498e-01 4.52290237e-01 -6.81839824e-01
-3.22547406e-01 -8.70653167e-02 1.45884946e-01 -9.98970211e-01
-2.63724655e-01 4.98884499e-01 -1.43859997e-01 -4.90992904e-01
-1.40582070e-01 -1.02783620e+00 -2.47574076e-01 -3.25064063e-01
-5.94708204e-01 -9.72906351e-02 4.28147286e-01 -7.58121789e-01
1.38756871e+00 -2.04601240e+00 -2.60735512e-01 2.63691843e-01
3.39212239e-01 2.39537582e-01 -1.03182897e-01 1.81802645e-01
-3.29617590e-01 5.41050017e-01 -6.02407381e-02 1.21704906e-01
-4.63860519e-02 -1.17445536e-01 -2.80499190e-01 4.02385950e-01
5.42560577e-01 1.13011503e+00 -6.33715928e-01 -1.83482260e-01
-3.73814106e-01 -2.20148508e-02 -8.85469496e-01 3.83322626e-01
-5.55840433e-01 -3.03922176e-01 -3.16447794e-01 1.08190441e+00
2.92043239e-01 -1.02721117e-01 3.25676389e-02 2.35477746e-01
-1.53807839e-02 6.61192596e-01 -5.16504169e-01 1.07322979e+00
-3.43663454e-01 1.25806260e+00 -2.18997240e-01 -1.03215563e+00
1.04696560e+00 2.41008952e-01 -2.03074262e-01 -5.00043392e-01
2.24919826e-01 2.35561997e-01 6.55597687e-01 -6.82885110e-01
7.25157976e-01 2.04177737e-01 -1.73522070e-01 7.79455304e-01
9.36400816e-02 -2.46862904e-03 1.25223532e-01 -2.55021483e-01
1.74255240e+00 2.09307969e-01 2.82279670e-01 -1.84635043e-01
1.02509856e-01 3.56620312e-01 6.87939823e-01 8.08017731e-01
-2.56280452e-01 4.49473619e-01 1.17203271e+00 -7.52754986e-01
-1.32529318e+00 -3.41930985e-01 3.04779798e-01 9.95799720e-01
-5.48355877e-01 -6.41848326e-01 -1.20873070e+00 -8.23140323e-01
-1.24080718e-01 1.05198658e+00 -6.30591214e-01 -7.86220789e-01
-7.90008366e-01 -6.96513832e-01 1.28450465e+00 1.16171181e+00
6.53645396e-02 -1.31609893e+00 -8.57595682e-01 3.30623537e-01
4.36005384e-01 -5.69761813e-01 -1.20928943e-01 6.30246937e-01
-1.07015657e+00 -1.10030007e+00 1.49581969e-01 -7.59076715e-01
7.23819077e-01 -2.58456677e-01 1.51984024e+00 8.77316833e-01
-3.69032249e-02 -3.39312881e-01 -2.72077709e-01 -4.69451725e-01
-1.17382276e+00 1.01603217e-01 1.41255269e-02 -9.64266002e-01
6.78851128e-01 -4.90403831e-01 7.00473636e-02 2.14374080e-01
-8.31878901e-01 -1.53499439e-01 4.50970918e-01 9.49740767e-01
6.09840676e-02 4.93313164e-01 5.24121165e-01 -1.21791661e+00
1.09016204e+00 -5.90415955e-01 -9.04151022e-01 2.07310289e-01
-8.35657895e-01 2.87803769e-01 7.43375838e-01 -4.40661341e-01
-5.15874088e-01 3.61469537e-02 -6.68166280e-01 -3.13352942e-01
-1.98885612e-02 1.06615698e+00 -4.68227006e-02 -4.06880319e-01
9.88498747e-01 -9.18737352e-02 -1.02053471e-01 -3.66168678e-01
-4.44716424e-01 5.38393021e-01 5.14871001e-01 -7.46989012e-01
4.68095660e-01 -4.79369015e-01 -4.39107805e-01 -1.87843576e-01
-4.46230285e-02 4.71600980e-01 -3.97495419e-01 -2.06313506e-01
2.07062170e-01 -4.20021236e-01 -7.95857608e-01 5.63693821e-01
-1.46768391e+00 -6.84050441e-01 1.31718621e-01 -5.59832193e-02
-2.75719732e-01 1.41823784e-01 -8.29155922e-01 -5.50843298e-01
-3.21556836e-01 -1.71394289e+00 4.93776590e-01 1.31696627e-01
-8.92183483e-01 -7.95287073e-01 3.06077480e-01 -1.87759399e-01
6.42591298e-01 2.15192631e-01 1.35002589e+00 -8.26515973e-01
-4.28655624e-01 -6.32268548e-01 1.24558531e-01 6.65958405e-01
-1.97473049e-01 8.27818453e-01 -9.98618782e-01 -1.41157910e-01
6.53826520e-02 -2.75075585e-01 5.19589901e-01 2.28520125e-01
1.67507327e+00 -1.92779496e-01 -3.44863594e-01 5.52482367e-01
1.22014737e+00 5.41677952e-01 7.44298875e-01 4.04923528e-01
4.98022795e-01 3.48042220e-01 4.56523031e-01 5.91901317e-02
-4.96830530e-02 1.92130327e-01 7.35605896e-01 3.02201122e-01
3.42922300e-01 -1.59386694e-01 8.38380933e-01 7.37387598e-01
2.24074144e-02 -1.77412197e-01 -1.59944117e+00 4.73996252e-01
-1.67416108e+00 -5.60250998e-01 -7.53211603e-02 2.04550195e+00
8.26364398e-01 7.86467969e-01 -9.10323486e-02 2.16627240e-01
5.82456768e-01 -2.73406178e-01 -6.85439646e-01 -9.76383269e-01
4.09249485e-01 4.26605046e-01 3.52364719e-01 3.32894713e-01
-7.56751359e-01 8.34423423e-01 7.71609497e+00 1.88774019e-01
-1.26728559e+00 -3.76243591e-02 4.56900507e-01 -9.84021928e-03
-3.77098054e-01 1.17144115e-01 -5.44916272e-01 2.99622208e-01
1.54717052e+00 -3.50230068e-01 3.99459451e-01 1.51446092e+00
-1.70999885e-01 -2.32168436e-01 -1.59034169e+00 1.97544694e-01
1.12833478e-03 -1.37574875e+00 -5.42221725e-01 -7.51186609e-02
4.18270141e-01 3.13906461e-01 -2.10333526e-01 6.19734168e-01
6.25010788e-01 -1.40891600e+00 7.74770617e-01 3.80847067e-01
7.36000061e-01 -1.08317721e+00 1.15984380e+00 3.36504102e-01
-5.06871462e-01 -1.49878711e-01 -2.88783252e-01 -3.03197175e-01
-3.65009278e-01 6.62886798e-01 -1.10036981e+00 -3.62122416e-01
7.94204414e-01 4.69426751e-01 -9.65316176e-01 1.03177917e+00
-2.73179233e-01 8.50345671e-01 -7.59785846e-02 -4.31179315e-01
-2.27240607e-01 4.55692470e-01 1.78510338e-01 1.05777228e+00
4.77783203e-01 -4.49662060e-01 -1.64027110e-01 1.51343715e+00
-1.96731910e-01 -5.20485818e-01 -8.99511158e-01 -5.80461800e-01
3.13083947e-01 7.45286465e-01 -6.93185806e-01 -2.91844547e-01
-5.29825091e-01 4.81950104e-01 5.24412811e-01 9.80544984e-02
-9.43582475e-01 -6.86225593e-01 6.71386600e-01 7.19240354e-03
1.59666792e-01 -2.36894906e-01 -6.16157830e-01 -1.05235994e+00
3.26280117e-01 -1.20572042e+00 -7.51167089e-02 -9.20757890e-01
-6.18818223e-01 9.95810509e-01 -2.23031148e-01 -7.58463144e-01
-6.90595925e-01 -7.63872206e-01 -1.12550771e+00 8.28321397e-01
-8.48551452e-01 -4.28186685e-01 -3.66018116e-02 -1.25752494e-01
3.20833862e-01 -4.46339756e-01 8.71789753e-01 1.18672013e-01
-9.42996919e-01 9.76346135e-01 -2.06594571e-01 2.51695603e-01
3.04246455e-01 -1.00417781e+00 1.29442883e+00 1.30209351e+00
-1.75942004e-01 1.10872865e+00 9.97980475e-01 -8.45940948e-01
-1.59759557e+00 -1.09201038e+00 5.67192733e-01 -3.67156297e-01
8.34728420e-01 -3.02758217e-01 -1.25943244e+00 1.15588498e+00
1.16154432e-01 -6.36280477e-02 7.10999310e-01 3.43265831e-01
-4.07261133e-01 2.89523870e-01 -1.08551478e+00 5.61152041e-01
5.31328559e-01 -7.58110106e-01 -3.80148411e-01 1.91233560e-01
8.69483352e-01 -6.50757194e-01 -7.68219888e-01 5.09844959e-01
6.55352414e-01 -1.29026747e+00 3.19242299e-01 -9.07765448e-01
9.29356635e-01 -2.39421442e-01 4.78019491e-02 -1.36945701e+00
-2.00515538e-01 -4.33251351e-01 -5.91691554e-01 9.98931110e-01
6.65344000e-01 -5.17671764e-01 1.01305580e+00 9.63803887e-01
-3.75444740e-01 -7.88548768e-01 -4.55502510e-01 -5.70200622e-01
1.31754711e-01 -1.10103548e+00 7.69935906e-01 7.30992913e-01
4.54793930e-01 -3.10877757e-03 -1.36888653e-01 3.03845614e-01
-3.53667550e-02 -9.20849815e-02 5.82436860e-01 -1.08234513e+00
-7.72550523e-01 -6.37477398e-01 -4.55912918e-01 -3.72324795e-01
6.22828484e-01 -5.89806437e-01 3.53640109e-01 -6.75047398e-01
1.38597250e-01 -1.34584770e-01 -1.80973455e-01 9.42087531e-01
-4.96249087e-03 -7.51785338e-02 -3.43365729e-01 -1.55364498e-01
-1.98708147e-01 3.91544327e-02 3.31068516e-01 -3.29805881e-01
-1.47527799e-01 2.49739856e-01 -7.08634794e-01 7.84144819e-01
1.10701978e+00 -8.64789903e-01 -1.16726272e-01 -5.89200079e-01
9.57916200e-01 6.30339608e-02 2.78209597e-01 -1.17416382e+00
2.30184600e-01 3.38938572e-02 1.75051272e-01 -3.65913898e-01
-2.34119490e-01 -5.47225952e-01 1.63702369e-01 7.56573379e-01
-4.53254133e-01 6.81681216e-01 6.85803711e-01 3.42019275e-02
-3.24010521e-01 -8.51579964e-01 6.34030938e-01 -1.03773892e-01
-7.75285900e-01 -1.32962018e-01 -6.75730109e-01 -3.37269604e-01
7.54478276e-01 1.27337143e-01 -4.44183737e-01 -1.46207199e-01
-5.01853168e-01 2.50236783e-02 9.89494979e-01 4.80261296e-01
7.74684906e-01 -1.08849883e+00 -2.15390071e-01 5.49574196e-01
1.92935348e-01 -1.94816127e-01 -2.21666202e-01 6.11468852e-01
-8.91924262e-01 2.43604675e-01 -5.60072601e-01 -3.17274004e-01
-1.43876636e+00 7.20410764e-01 5.98820865e-01 -8.02540965e-03
-2.77803302e-01 7.13674307e-01 -4.22228694e-01 -7.23429203e-01
1.63559735e-01 -8.03659678e-01 3.30912694e-02 -7.20820963e-01
6.62740350e-01 7.06557631e-02 5.51519871e-01 8.14647973e-02
-4.01307702e-01 -9.36091319e-02 -1.97520390e-01 2.82291144e-01
1.46997595e+00 8.07925105e-01 -6.96655393e-01 7.40621865e-01
9.71242547e-01 -1.36857092e-01 -5.92052519e-01 3.93265709e-02
3.17710042e-01 -8.65645707e-02 2.58944750e-01 -6.75349772e-01
-1.10596871e+00 1.12102747e+00 2.76349157e-01 6.03837013e-01
1.06574082e+00 -2.90515959e-01 7.37788618e-01 7.54322886e-01
4.37634259e-01 -8.57700825e-01 -3.36754292e-01 9.01151180e-01
5.60967743e-01 -1.11477613e+00 -2.76911527e-01 -5.85663170e-02
-1.03216045e-01 1.45781767e+00 1.13829029e+00 -3.14484000e-01
4.69467968e-01 9.81033802e-01 -4.54536855e-01 -2.96307951e-01
-1.13075483e+00 5.60070753e-01 -5.50939096e-03 5.35342753e-01
9.99322534e-01 6.06028773e-02 3.51040274e-01 8.11508596e-01
-3.43584925e-01 3.83264005e-01 1.11992061e+00 1.21247089e+00
-4.41484690e-01 -1.05703604e+00 -5.15352547e-01 7.99572349e-01
-5.48340321e-01 -2.60730922e-01 -6.05382025e-01 6.96397603e-01
-2.20804021e-01 5.32682180e-01 -6.53023049e-02 -1.27024686e+00
2.55037665e-01 4.18643415e-01 4.03418541e-01 -8.51191103e-01
-6.74134076e-01 -4.96148765e-01 4.77943033e-01 -8.57975364e-01
1.90494567e-01 -6.76522732e-01 -1.16693985e+00 -6.47866189e-01
-3.89512837e-01 -1.52020916e-01 6.67013407e-01 7.72232234e-01
5.01689017e-01 9.53463674e-01 3.06518942e-01 -5.79912007e-01
-5.49740791e-01 -1.06783390e+00 -1.83147907e-01 -3.78752857e-01
5.93203068e-01 -4.07174438e-01 -1.65474713e-01 2.81314191e-04] | [7.522695064544678, 7.693163871765137] |
912dcf87-733b-4941-ad11-67824a8c143e | coupled-gradient-flows-for-strategic-non | 2307.01166 | null | https://arxiv.org/abs/2307.01166v2 | https://arxiv.org/pdf/2307.01166v2.pdf | Coupled Gradient Flows for Strategic Non-Local Distribution Shift | We propose a novel framework for analyzing the dynamics of distribution shift in real-world systems that captures the feedback loop between learning algorithms and the distributions on which they are deployed. Prior work largely models feedback-induced distribution shift as adversarial or via an overly simplistic distribution-shift structure. In contrast, we propose a coupled partial differential equation model that captures fine-grained changes in the distribution over time by accounting for complex dynamics that arise due to strategic responses to algorithmic decision-making, non-local endogenous population interactions, and other exogenous sources of distribution shift. We consider two common settings in machine learning: cooperative settings with information asymmetries, and competitive settings where a learner faces strategic users. For both of these settings, when the algorithm retrains via gradient descent, we prove asymptotic convergence of the retraining procedure to a steady-state, both in finite and in infinite dimensions, obtaining explicit rates in terms of the model parameters. To do so we derive new results on the convergence of coupled PDEs that extends what is known on multi-species systems. Empirically, we show that our approach captures well-documented forms of distribution shifts like polarization and disparate impacts that simpler models cannot capture. | ['Lillian Ratliff', 'Eric Mazumdar', 'Franca Hoffmann', 'Lauren Conger'] | 2023-07-03 | null | null | null | null | ['decision-making'] | ['reasoning'] | [ 1.75030023e-01 -1.39527932e-01 -5.27051911e-02 3.15436810e-01
-2.82333672e-01 -1.30488694e+00 7.62485027e-01 1.31804526e-01
-5.57055116e-01 9.49276567e-01 -5.34540974e-03 -4.05846506e-01
-5.18026114e-01 -5.19949257e-01 -9.12146032e-01 -1.16366971e+00
-2.00968206e-01 7.39380956e-01 -9.67633054e-02 -3.32214803e-01
2.00551853e-01 4.70606476e-01 -1.23107743e+00 -3.39632630e-01
8.00243616e-01 3.85177672e-01 -1.74274296e-01 1.09747517e+00
1.80144742e-01 9.50840890e-01 -7.51847386e-01 -3.09976667e-01
7.33476341e-01 -5.55224478e-01 -4.44849193e-01 1.51446655e-01
4.20648791e-02 -1.90326050e-01 -5.23015320e-01 1.00549948e+00
6.65091097e-01 -1.53310448e-01 1.10834861e+00 -1.45305014e+00
-7.32942462e-01 6.61497891e-01 -8.87542367e-01 5.38997114e-01
-1.81778282e-01 3.62161011e-01 7.65921712e-01 -1.43119559e-01
6.05879426e-01 1.20545352e+00 6.62278354e-01 5.86197197e-01
-1.65618336e+00 -6.24639153e-01 2.67664373e-01 -3.55617166e-01
-9.41904247e-01 -1.29390433e-01 5.66990018e-01 -1.00873864e+00
3.30234021e-01 1.95013523e-01 8.13400090e-01 1.13622332e+00
4.73493487e-01 5.58048248e-01 1.22425592e+00 -2.69209623e-01
4.36775953e-01 1.82308719e-01 6.57127276e-02 1.95857286e-01
6.51404321e-01 1.34796083e-01 -4.56754982e-01 -6.05752349e-01
7.50011563e-01 6.30091652e-02 -4.87627685e-01 -7.79865265e-01
-7.58616090e-01 7.11447239e-01 1.33951560e-01 5.09804934e-02
-5.57650328e-01 2.22591087e-01 -3.83317657e-02 6.46059453e-01
5.73319733e-01 6.65858567e-01 -4.43148166e-01 -1.85461089e-01
-5.07032752e-01 6.62389934e-01 1.11378181e+00 7.34079003e-01
6.68209076e-01 -2.93326020e-01 1.42873451e-02 3.75491798e-01
-6.49034679e-02 8.25744867e-01 1.38436452e-01 -1.20459282e+00
2.63752490e-01 2.80575991e-01 6.25215411e-01 -6.20164275e-01
-2.35406563e-01 -9.63945329e-01 -7.14106739e-01 2.62834206e-02
9.36486602e-01 -7.51862109e-01 -4.35891062e-01 2.39182401e+00
3.57874781e-01 3.75933945e-02 -5.25174253e-02 6.77592516e-01
-2.32417524e-01 6.57056093e-01 -2.04705372e-01 -6.18453026e-01
5.52758515e-01 -2.38058746e-01 -4.00262922e-01 -1.10834286e-01
7.65756369e-01 -3.11733365e-01 8.60499144e-01 2.80070543e-01
-1.23290491e+00 5.35289943e-01 -5.91757298e-01 5.43688476e-01
-2.06743449e-01 -7.24802554e-01 1.52162239e-01 4.56163049e-01
-1.22145092e+00 5.38245976e-01 -5.49832344e-01 -3.86562496e-01
4.55513865e-01 3.97296041e-01 1.85610086e-01 1.05340034e-01
-9.38092709e-01 5.96984088e-01 -6.04822159e-01 -1.21822283e-01
-1.05987668e+00 -1.33618343e+00 -6.72552781e-03 2.22241670e-01
7.12859333e-01 -1.03507388e+00 1.45334744e+00 -1.36698699e+00
-1.24989378e+00 5.45866907e-01 3.56481314e-01 -3.30274165e-01
8.51962030e-01 9.51397046e-02 5.24402738e-01 -3.82446021e-01
2.83257663e-02 1.06680980e-02 4.47400838e-01 -1.30980575e+00
-3.80821288e-01 -6.96213365e-01 2.72889137e-01 6.78023636e-01
-4.48072672e-01 -3.26169193e-01 3.86366785e-01 -4.01868820e-01
-7.28463888e-01 -1.46782184e+00 -3.12851727e-01 -1.04992472e-01
-2.95963675e-01 1.61880851e-01 6.69944406e-01 -3.99206486e-03
9.47063267e-01 -1.91014540e+00 7.55857587e-01 1.27720788e-01
4.30281132e-01 -2.23217090e-03 -2.61945784e-01 8.79166543e-01
3.50511581e-01 3.50491583e-01 -3.94878745e-01 -2.07871914e-01
2.43450537e-01 2.08177254e-01 -4.68638986e-01 6.82155013e-01
-3.00719440e-01 6.61358714e-01 -7.69891143e-01 1.46455511e-01
-4.87568021e-01 2.28193134e-01 -8.57438445e-01 2.57527572e-03
-1.64891422e-01 4.94765788e-01 -5.44554353e-01 1.55772343e-01
6.09447420e-01 -3.41986716e-01 4.06755835e-01 6.75909698e-01
-8.07783529e-02 -2.55347222e-01 -9.63808060e-01 1.05067074e+00
-3.66194725e-01 5.27710259e-01 7.59719431e-01 -9.80234861e-01
1.83425684e-04 -1.63509414e-01 5.78763962e-01 -1.70550033e-01
3.23636234e-01 1.41027540e-01 5.47089458e-01 -4.48100492e-02
2.93807089e-01 -3.66653502e-01 -2.57904172e-01 9.39551115e-01
4.64873463e-02 -1.87966645e-01 4.32581306e-02 6.65774405e-01
1.42998886e+00 -5.32168150e-01 9.65162143e-02 -8.36841404e-01
6.35038912e-02 1.34256080e-01 4.28353816e-01 1.28802824e+00
-3.21717381e-01 -7.48686343e-02 1.13438082e+00 2.40948331e-02
-1.18813479e+00 -1.01513934e+00 1.70622244e-02 1.27481449e+00
3.25117499e-01 -7.86293894e-02 -1.04223526e+00 -1.96954817e-01
6.63902104e-01 4.09164041e-01 -9.83574271e-01 -4.60782230e-01
-1.68086991e-01 -1.13249552e+00 4.20754194e-01 -1.44347707e-02
2.84892827e-01 -5.62972605e-01 -4.64349777e-01 -1.00823544e-01
3.10381174e-01 -4.36575323e-01 -9.73012507e-01 5.32806106e-02
-5.32234609e-01 -1.17663682e+00 -9.65808153e-01 -1.85637102e-01
4.12100941e-01 1.56711936e-01 8.73398244e-01 -2.66299069e-01
-3.72604251e-01 1.00680351e+00 2.56909966e-01 -6.02837980e-01
-5.44698715e-01 3.44554156e-01 3.43849152e-01 8.65456834e-02
-1.19985893e-01 -6.75173640e-01 -7.60706067e-01 2.23119587e-01
-1.11906862e+00 -2.62790203e-01 4.87967640e-01 6.66170895e-01
2.49132916e-01 -1.29848793e-01 7.44638622e-01 -1.08603370e+00
1.36717916e+00 -9.40180182e-01 -9.70247030e-01 3.27767611e-01
-6.33456826e-01 2.62162715e-01 7.22016573e-01 -8.08448851e-01
-1.16059148e+00 -2.30353311e-01 8.50869536e-01 -3.70578200e-01
2.09164485e-01 4.61246908e-01 2.26409305e-02 -2.02085227e-01
7.71099687e-01 2.88055360e-01 3.54600668e-01 -2.40528733e-01
5.06252468e-01 6.58590853e-01 2.69590080e-01 -9.12152112e-01
9.49059367e-01 3.56229514e-01 4.11585361e-01 -7.67819941e-01
-6.23617530e-01 7.90151581e-02 -4.63953406e-01 -3.49637777e-01
3.06581765e-01 -7.84178197e-01 -1.02973258e+00 8.78724873e-01
-7.86301732e-01 -9.69812453e-01 -6.42880082e-01 9.18021649e-02
-7.04523444e-01 -1.09811522e-01 -6.57373130e-01 -1.25569904e+00
1.22784264e-01 -7.86555171e-01 5.33185363e-01 3.45319241e-01
2.91745245e-01 -1.40034056e+00 7.51302302e-01 -1.00875363e-01
1.00600886e+00 1.69508040e-01 9.54511642e-01 -5.51273108e-01
-6.60137236e-01 1.21937536e-01 4.17998344e-01 -7.10239485e-02
-1.46361321e-01 8.25012848e-02 -4.12350118e-01 -3.35426599e-01
3.20070721e-02 -1.87699839e-01 6.52147353e-01 6.82543397e-01
8.16605449e-01 -8.10407341e-01 -4.64439988e-01 5.19897461e-01
1.24548948e+00 2.00543642e-01 -7.39723220e-02 9.69469622e-02
3.44522268e-01 7.00174034e-01 1.79226566e-02 7.31674790e-01
4.07474309e-01 4.18318480e-01 3.35534632e-01 7.91093186e-02
5.55082977e-01 -3.51005375e-01 2.74469614e-01 3.26896757e-01
1.47253186e-01 -6.32791638e-01 -9.59811330e-01 4.51947004e-01
-2.00620198e+00 -1.16946471e+00 9.42485780e-02 2.58995628e+00
9.64923024e-01 -8.76238197e-02 7.61706769e-01 -5.44300020e-01
8.52887630e-01 -1.93591639e-01 -1.27318275e+00 -2.86681682e-01
-3.05056840e-01 -1.66960865e-01 1.07540977e+00 7.62218893e-01
-5.04891396e-01 5.76752484e-01 7.34900379e+00 5.30931056e-01
-1.19285500e+00 2.17755005e-01 8.24637651e-01 -7.77124226e-01
-5.73340714e-01 -1.77931979e-01 -3.89938802e-01 4.91816729e-01
8.41888607e-01 -9.62320149e-01 9.85390127e-01 4.29270148e-01
1.57339171e-01 -4.61295731e-02 -1.00628948e+00 7.01863050e-01
-2.65467137e-01 -1.20537829e+00 -2.62898445e-01 8.68995368e-01
1.02131367e+00 6.41762018e-02 6.00252092e-01 6.27799109e-02
1.26290905e+00 -7.09560633e-01 6.39461219e-01 5.74127555e-01
6.23252988e-01 -9.23560619e-01 2.56285906e-01 8.61506462e-01
-4.79415745e-01 -6.15655363e-01 -3.70932929e-03 -5.08025765e-01
-1.43216372e-01 4.72567677e-01 -3.56911182e-01 -2.30951130e-01
2.32164487e-01 4.33148056e-01 -2.76345849e-01 7.79663205e-01
2.71023571e-01 7.54850268e-01 -5.29872775e-01 -3.19620632e-02
3.56487781e-02 -4.20123816e-01 8.41166079e-01 7.88478255e-01
1.89591646e-01 1.61067337e-01 -1.82851881e-01 1.06573439e+00
-3.78404051e-01 -1.89738572e-01 -9.75274801e-01 -2.63144046e-01
6.42741561e-01 1.20843589e+00 -3.19386601e-01 8.06883872e-02
2.29627732e-02 7.69802690e-01 3.12211245e-01 7.36344635e-01
-6.91410363e-01 -7.88467601e-02 1.12698233e+00 4.12545055e-01
5.65931424e-02 -9.77944806e-02 6.95512593e-02 -1.32921314e+00
-1.77809387e-01 -5.51154733e-01 2.88614690e-01 -3.10251325e-01
-1.59542477e+00 -1.27656544e-02 1.10438745e-02 -5.08884013e-01
-3.28476220e-01 -3.19786459e-01 -6.24330282e-01 7.34960556e-01
-1.04753697e+00 -4.35610861e-01 2.08131984e-01 5.13616741e-01
-8.19078684e-02 -4.54125516e-02 1.75740555e-01 2.91525070e-02
-9.05642927e-01 5.02466083e-01 9.35571015e-01 -4.45782512e-01
5.76514065e-01 -1.53050685e+00 -9.39121172e-02 7.84572184e-01
-2.44899854e-01 6.37042701e-01 8.80020440e-01 -4.50899780e-01
-1.69501472e+00 -9.21748579e-01 1.17932610e-01 -6.26839995e-01
9.65965211e-01 -7.79488683e-01 -6.26571417e-01 6.79237545e-01
1.56005040e-01 -1.37603104e-01 7.36875594e-01 -4.23466042e-02
-2.22547650e-01 -1.50304809e-01 -1.15326869e+00 5.53445935e-01
1.30010402e+00 -3.75842512e-01 1.89026266e-01 3.09038311e-01
3.64080280e-01 -1.22561656e-01 -4.71981019e-01 -1.73014894e-01
4.76551831e-01 -7.12972701e-01 5.81775308e-01 -9.41534340e-01
4.51031744e-01 3.81756872e-02 -1.00716896e-01 -1.85256815e+00
-3.56433123e-01 -1.31657887e+00 4.37439568e-02 9.89435136e-01
4.01342630e-01 -1.06108534e+00 5.76476932e-01 6.71265483e-01
3.67714673e-01 -7.40746796e-01 -8.58094156e-01 -6.49864018e-01
1.14044380e+00 5.34247100e-01 2.64073938e-01 8.54907930e-01
1.05188981e-01 4.33041751e-01 -3.35403025e-01 -3.08517404e-02
8.06571901e-01 -6.90410361e-02 9.23979938e-01 -1.23059678e+00
-5.31671762e-01 -7.83956349e-01 -4.86229062e-02 -1.02532482e+00
3.65630388e-01 -6.88579619e-01 -4.17361148e-02 -8.97512496e-01
7.64737546e-01 -4.98066336e-01 -1.86245665e-01 -1.09970383e-01
-2.58658342e-02 -1.65926665e-01 3.34093928e-01 6.26594424e-01
-6.45148695e-01 5.87750494e-01 1.11939394e+00 1.53603196e-01
-2.99755722e-01 1.15165442e-01 -1.26442885e+00 3.52906942e-01
5.36807299e-01 -6.16976559e-01 -6.11226380e-01 -2.64680833e-01
4.92517889e-01 2.16268077e-01 3.09438467e-01 -4.35482979e-01
4.90305573e-01 -7.48511314e-01 -1.47052273e-01 1.44170240e-01
-5.71124516e-02 -4.55581933e-01 2.29552910e-01 6.49986506e-01
-7.99920797e-01 1.27399534e-01 -1.04837827e-01 9.71393287e-01
4.91174608e-01 2.00133607e-01 8.32377791e-01 -6.33328827e-03
4.21201915e-01 5.46365917e-01 -9.95614171e-01 6.43138409e-01
1.31706381e+00 3.03841889e-01 -6.68093860e-01 -9.44647610e-01
-5.54629803e-01 5.06213784e-01 9.41800535e-01 -5.66487499e-02
-3.63693058e-01 -8.77023041e-01 -6.07019901e-01 -1.52904689e-01
-3.56612712e-01 -1.85855091e-01 2.41616607e-01 9.14219856e-01
-2.63378203e-01 7.54223838e-02 -1.45301998e-01 -4.39905226e-01
-9.42655444e-01 5.33404112e-01 1.10602129e+00 -2.35369861e-01
2.31764406e-01 6.87262356e-01 7.15888739e-01 -5.14399529e-01
2.83812676e-02 1.80995777e-01 3.53612244e-01 1.05753399e-01
1.09460711e-01 3.75205994e-01 -5.01167178e-01 -3.62024039e-01
-2.52435774e-01 3.00572604e-01 -8.78024995e-02 -5.01336694e-01
1.25940442e+00 -2.94280738e-01 -5.14511764e-02 6.58859789e-01
6.86823010e-01 2.11774051e-01 -1.65019774e+00 -4.62250352e-01
-4.01749998e-01 -3.09902936e-01 -1.54044911e-01 -9.20703828e-01
-1.07989562e+00 7.83072412e-01 1.87704518e-01 6.40653074e-01
9.18268263e-01 1.91524521e-01 3.42175402e-02 3.96534562e-01
1.64715081e-01 -8.55700374e-01 1.06242396e-01 3.34534138e-01
7.22799659e-01 -4.79329526e-01 -3.56352121e-01 6.29952997e-02
-4.05573666e-01 5.81703544e-01 5.79010546e-01 -3.19578201e-01
9.41333950e-01 6.37372434e-01 -2.71039158e-01 9.94953066e-02
-1.31001031e+00 9.36476439e-02 -3.38161647e-01 5.41460216e-01
2.37428080e-02 1.48001730e-01 -2.40412310e-01 6.59355104e-01
-1.38195053e-01 -2.41789967e-01 1.21333468e+00 8.77741873e-01
-3.70983183e-01 -1.03098536e+00 -1.80823326e-01 6.40533984e-01
-5.71957469e-01 9.71687734e-02 -1.16319525e+00 7.76286900e-01
-9.74446982e-02 6.12146199e-01 4.19597447e-01 2.14307494e-02
1.52818665e-01 -1.98885083e-01 6.84343934e-01 -4.65531886e-01
-6.20240510e-01 -8.22841749e-03 -5.73337913e-01 -1.45952165e-01
-1.57978386e-01 -1.06571496e+00 -5.13728023e-01 -8.58800411e-01
-3.70295823e-01 1.71073183e-01 2.70615637e-01 8.37583542e-01
7.91556001e-01 2.48136282e-01 7.83024907e-01 -5.86261451e-01
-1.13504982e+00 -8.83422196e-01 -1.05237591e+00 3.42476547e-01
6.52103186e-01 -6.00292504e-01 -9.91207600e-01 -3.43556970e-01] | [4.230816841125488, 2.626802682876587] |
c60ff0d9-dc4f-4d09-9118-a60604e6c848 | when-person-re-identification-meets-changing | 2003.04070 | null | https://arxiv.org/abs/2003.04070v3 | https://arxiv.org/pdf/2003.04070v3.pdf | When Person Re-identification Meets Changing Clothes | Person re-identification (ReID) is now an active research topic for AI-based video surveillance applications such as specific person search, but the practical issue that the target person(s) may change clothes (clothes inconsistency problem) has been overlooked for long. For the first time, this paper systematically studies this problem. We first overcome the difficulty of lack of suitable dataset, by collecting a small yet representative real dataset for testing whilst building a large realistic synthetic dataset for training and deeper studies. Facilitated by our new datasets, we are able to conduct various interesting new experiments for studying the influence of clothes inconsistency. We find that changing clothes makes ReID a much harder problem in the sense of bringing difficulties to learning effective representations and also challenges the generalization ability of previous ReID models to identify persons with unseen (new) clothes. Representative existing ReID models are adopted to show informative results on such a challenging setting, and we also provide some preliminary efforts on improving the robustness of existing models on handling the clothes inconsistency issue in the data. We believe that this study can be inspiring and helpful for encouraging more researches in this direction. The dataset is available on the project website: https://wanfb.github.io/dataset.html. | ['Yanwei Fu', 'Yang Wu', 'Xuelin Qian', 'Yixiong Chen', 'Fangbin Wan'] | 2020-03-09 | null | null | null | null | ['person-search'] | ['computer-vision'] | [ 1.59865543e-01 -1.92689776e-01 -1.31543010e-01 -5.01454711e-01
-1.50689840e-01 -4.60819870e-01 7.00915337e-01 -3.42159718e-01
-4.89188731e-01 7.46308804e-01 1.04723923e-01 1.26722813e-01
-1.07598133e-01 -5.25700808e-01 -7.13832021e-01 -7.19146609e-01
-1.33423924e-01 2.68257380e-01 2.50203192e-01 -2.66000241e-01
1.19967744e-01 3.23584646e-01 -1.57357800e+00 -1.30567119e-01
5.64515829e-01 4.82920259e-01 2.77667847e-02 4.39419448e-01
3.95002276e-01 4.18516278e-01 -8.23075831e-01 -8.25134635e-01
6.09055638e-01 -2.15604186e-01 -7.93803930e-01 2.23118708e-01
7.65931606e-01 -5.09765983e-01 -5.50067306e-01 1.12728727e+00
7.48206139e-01 2.42350921e-01 3.99936080e-01 -1.55436897e+00
-1.04030621e+00 3.99721980e-01 -6.89531565e-01 4.03596908e-01
6.06942832e-01 1.53503180e-01 4.41319436e-01 -4.65516686e-01
5.67069352e-01 1.40617502e+00 8.56768250e-01 1.19503534e+00
-7.17065036e-01 -8.16313267e-01 4.56678659e-01 4.99707997e-01
-1.54950583e+00 -5.93399167e-01 9.68730748e-01 -1.72734752e-01
3.60043854e-01 6.06713712e-01 5.66643536e-01 1.88388050e+00
-2.41684243e-01 9.26547885e-01 1.00193822e+00 -2.45375991e-01
-7.79053941e-02 4.86262232e-01 3.73266816e-01 6.34858847e-01
5.35382986e-01 1.00812860e-01 -1.50804043e-01 -2.12585077e-01
6.36871636e-01 1.79579005e-01 -5.44001937e-01 -3.18899512e-01
-1.22932923e+00 4.87717092e-01 4.04600263e-01 2.72603333e-01
-1.14547908e-02 -1.96625024e-01 2.79943675e-01 3.95332158e-01
4.57965940e-01 3.01811248e-01 -3.10435176e-01 7.47691318e-02
-5.03018618e-01 6.10948861e-01 8.06280613e-01 1.22863150e+00
2.04500407e-01 -8.86165872e-02 -1.61825009e-02 9.87597525e-01
1.06228413e-02 6.87332928e-01 2.82771826e-01 -7.52594471e-01
2.09129095e-01 4.23262417e-01 4.68341857e-01 -1.36591232e+00
-3.65143865e-01 -2.29158834e-01 -9.55374539e-01 -2.89343685e-01
7.69080162e-01 -2.09056482e-01 -6.32114887e-01 1.95835257e+00
5.15674889e-01 3.72419387e-01 -1.45579323e-01 1.14740205e+00
9.72351670e-01 4.34616864e-01 1.77411273e-01 -1.42254725e-01
1.62159538e+00 -9.28479493e-01 -7.63044894e-01 -1.72429219e-01
2.27243036e-01 -6.03989601e-01 7.77716637e-01 1.60363644e-01
-9.14764166e-01 -7.33955145e-01 -7.85792232e-01 1.94843426e-01
-6.70841217e-01 4.40982655e-02 8.58240843e-01 9.33639646e-01
-1.13345873e+00 2.68717408e-01 -4.14174646e-01 -1.06432962e+00
3.03028375e-01 3.91459852e-01 -4.44761276e-01 -3.72206450e-01
-1.38083732e+00 9.21000004e-01 -4.20674942e-02 3.65986258e-01
-5.80319166e-01 -3.96006495e-01 -9.00613368e-01 -5.65712512e-01
7.27905214e-01 -9.10584986e-01 9.89375591e-01 -9.78603661e-01
-9.34423447e-01 1.05888224e+00 -2.76168913e-01 -2.00532317e-01
8.33886147e-01 -1.41957000e-01 -7.10204661e-01 -1.49916261e-01
8.92310962e-02 6.61209345e-01 1.03369462e+00 -1.44556510e+00
-2.99031317e-01 -7.55423486e-01 2.23861217e-01 2.01325491e-02
-5.14166236e-01 3.23520213e-01 -7.23270953e-01 -9.49054539e-01
-3.32194477e-01 -1.19866478e+00 8.77579115e-03 -6.50630370e-02
-2.72855639e-01 -4.57507879e-01 7.41700411e-01 -6.41466558e-01
1.01944220e+00 -1.87584782e+00 7.28756785e-02 -1.82613775e-01
1.66074604e-01 5.50111771e-01 -2.28891179e-01 2.76745409e-01
-3.56624238e-02 2.01864108e-01 -9.95875448e-02 -4.76204544e-01
-1.69318676e-01 7.14726374e-02 4.43697721e-03 6.75513864e-01
1.18137106e-01 7.72271156e-01 -9.39492285e-01 -4.24136102e-01
-3.82745154e-02 5.63954055e-01 -5.38535230e-02 2.78022379e-01
3.43367934e-01 5.35418212e-01 -4.87842530e-01 8.98029864e-01
9.76722240e-01 1.46883158e-02 -5.18454649e-02 -3.13975483e-01
4.99776378e-02 -3.12010109e-01 -1.33517909e+00 1.21460831e+00
1.17585577e-01 5.12689888e-01 6.16057357e-03 -1.00602829e+00
7.36582696e-01 2.32400388e-01 3.39723110e-01 -4.77442145e-01
2.38452330e-01 -2.95135081e-01 -2.09436417e-02 -8.74696970e-01
6.69784606e-01 1.34281218e-01 -7.86546394e-02 2.14165568e-01
-3.56089294e-01 3.51501822e-01 3.27893883e-01 4.47866172e-02
9.00681734e-01 4.98037301e-02 7.39149675e-02 -3.32867146e-01
4.82823670e-01 -2.07867716e-02 5.39530218e-01 8.47224355e-01
-7.60237038e-01 4.60028440e-01 -3.09591647e-02 -8.81274283e-01
-9.80180264e-01 -8.33352268e-01 -2.16681004e-01 9.65950370e-01
5.47284305e-01 -8.69122148e-02 -8.78746152e-01 -6.76640749e-01
1.94683354e-02 3.22191566e-01 -8.96526039e-01 -1.37579232e-01
-7.11181700e-01 -1.19560516e+00 6.43255591e-01 5.52102387e-01
7.75752783e-01 -9.06494141e-01 -1.49235547e-01 -1.91615209e-01
-3.21761250e-01 -1.22032928e+00 -6.12706125e-01 -7.00324774e-01
-6.09997928e-01 -1.30033517e+00 -1.05549169e+00 -8.53999376e-01
9.57814753e-01 9.47460175e-01 8.34320724e-01 4.42396224e-01
-3.69052589e-01 6.63744986e-01 -4.81482476e-01 -6.33445084e-01
-1.13399349e-01 -1.00191928e-01 4.77983445e-01 1.54643580e-01
6.28961921e-01 1.52495587e-02 -6.82919502e-01 8.77458811e-01
-8.30830038e-01 -6.69705197e-02 1.09822743e-01 5.98751366e-01
-7.84511939e-02 2.29364887e-01 7.91962266e-01 -8.10722709e-01
7.03760028e-01 -5.16447484e-01 -3.65136266e-01 3.04711938e-01
-2.26632208e-01 -4.93066788e-01 2.89416909e-01 -8.60507846e-01
-9.67176676e-01 -3.21043611e-01 1.03964873e-01 -1.76250950e-01
-3.96317899e-01 -2.28489354e-01 -2.20435277e-01 -1.88454479e-01
4.05868530e-01 8.68235901e-02 -1.07254162e-01 -5.14845431e-01
-1.31920546e-01 7.48990417e-01 4.09211516e-01 -5.86869478e-01
1.01415694e+00 5.59202373e-01 -2.94543326e-01 -1.07450831e+00
-5.88755786e-01 -4.99818861e-01 -5.36380410e-01 -4.47600931e-01
6.64234877e-01 -1.00811863e+00 -8.32036734e-01 8.15260649e-01
-1.07827389e+00 -1.00452617e-01 2.09793657e-01 2.51607031e-01
-2.79996917e-03 7.47113764e-01 -4.74993467e-01 -9.75321114e-01
-1.03739791e-01 -9.71463919e-01 8.43852222e-01 3.89916450e-01
-3.08155447e-01 -1.09199190e+00 -1.17477670e-01 9.32317674e-01
4.78988141e-01 3.19934368e-01 3.66655588e-01 -4.40233886e-01
-4.08912212e-01 -5.37013650e-01 -2.44798630e-01 1.32906824e-01
3.12758505e-01 -1.41330644e-01 -1.07663596e+00 -6.65737987e-01
1.35126874e-01 -9.59713683e-02 9.18587685e-01 9.60979685e-02
1.22868633e+00 -3.46010834e-01 -4.94428813e-01 3.95912111e-01
1.02163756e+00 1.15988307e-01 4.28410351e-01 4.39279884e-01
7.82011271e-01 8.08429897e-01 4.66913313e-01 3.17322761e-01
8.61267209e-01 8.38086665e-01 1.79032028e-01 -1.57497793e-01
-2.52743989e-01 -9.58497226e-02 2.94285923e-01 4.11304921e-01
-6.47243500e-01 -5.97223938e-01 -8.37037683e-01 6.22768879e-01
-1.86444402e+00 -1.07675993e+00 -8.23608264e-02 2.29954600e+00
6.32299662e-01 -2.52995402e-01 4.93159771e-01 2.73947977e-02
1.09459734e+00 4.40096222e-02 -4.10698831e-01 1.15326978e-01
-7.09884539e-02 -4.58788961e-01 3.27359885e-01 2.13533744e-01
-1.31868184e+00 8.82477522e-01 5.90509987e+00 4.76521105e-01
-8.22111487e-01 1.11603085e-03 5.25953710e-01 6.54646903e-02
1.65327191e-01 -4.35791135e-01 -9.85415757e-01 6.86471164e-01
5.71099281e-01 5.26072122e-02 4.70005602e-01 7.44784653e-01
1.60527542e-01 -5.20148426e-02 -1.25069880e+00 1.17241287e+00
4.66412723e-01 -6.70745432e-01 4.90225405e-02 -7.44032934e-02
4.69500035e-01 -3.62637401e-01 4.50834967e-02 3.15613717e-01
-2.96585653e-02 -7.56913364e-01 3.12995821e-01 5.32181025e-01
4.37893867e-01 -4.67430353e-01 9.92991269e-01 3.24407220e-01
-9.72124636e-01 -1.40223920e-01 -6.79727852e-01 -1.40478730e-01
-1.35510549e-01 1.01079889e-01 -7.15170205e-01 5.37179053e-01
9.71328557e-01 6.96966827e-01 -9.50602114e-01 1.24626279e+00
1.29098922e-01 3.78878653e-01 -2.12903515e-01 -9.78092402e-02
-1.95330068e-01 3.27808559e-02 6.51674032e-01 1.16944814e+00
1.41682327e-01 2.08930224e-01 2.57822663e-01 6.43286943e-01
-1.60603106e-01 -1.50812551e-01 -8.98573399e-01 2.69879520e-01
3.24187607e-01 1.02068532e+00 -5.25503278e-01 -2.91851342e-01
-4.45751876e-01 1.09643245e+00 8.44016746e-02 4.82722491e-01
-9.19154406e-01 5.00466228e-02 6.33038104e-01 2.28622437e-01
-8.28427672e-02 -2.32079744e-01 1.27006456e-01 -1.42778099e+00
2.24743530e-01 -9.61308658e-01 5.43792069e-01 -6.24939919e-01
-1.63198805e+00 5.66841304e-01 3.97147924e-01 -1.08081961e+00
4.10612561e-02 -5.36712050e-01 -5.45861483e-01 4.98232663e-01
-1.56682408e+00 -1.39547145e+00 -5.76173782e-01 7.87584722e-01
8.23619843e-01 -2.16905877e-01 7.19798207e-01 4.44493234e-01
-1.15603817e+00 9.37238574e-01 -3.40868682e-01 4.35742736e-01
9.87436593e-01 -8.26161683e-01 4.64988083e-01 8.10006738e-01
-2.89453119e-01 9.91317570e-01 8.14861417e-01 -8.21945131e-01
-1.77594352e+00 -1.02538466e+00 8.02184284e-01 -9.86978471e-01
4.41377282e-01 -4.69358861e-01 -8.68084967e-01 6.56518459e-01
2.16162860e-01 -1.34051159e-01 6.73821568e-01 7.19218627e-02
-2.80584306e-01 -8.88253152e-02 -1.39671707e+00 7.29570329e-01
1.51404130e+00 -6.74635754e-04 -6.59587443e-01 3.58584464e-01
4.36806709e-01 -1.27988324e-01 -6.97002292e-01 4.88671541e-01
5.66179574e-01 -8.38581502e-01 1.16448593e+00 -6.22329175e-01
-1.07652895e-01 -2.54165649e-01 1.00196585e-01 -1.06457591e+00
-3.65318537e-01 -5.71845829e-01 1.24752941e-02 1.48945022e+00
-3.59142385e-02 -6.55710638e-01 6.51320040e-01 1.01491523e+00
3.93733889e-01 -4.95295376e-01 -5.98204494e-01 -9.71563578e-01
-1.85048819e-01 -2.33826682e-01 7.32153535e-01 1.20176101e+00
-2.28428781e-01 8.18208903e-02 -8.74987304e-01 5.22564530e-01
8.91145229e-01 -1.04632236e-01 1.09046149e+00 -1.13316369e+00
7.84055330e-03 -1.06407888e-01 -4.00218904e-01 -8.84559929e-01
7.27380514e-02 -4.83036995e-01 -2.35997841e-01 -1.21688640e+00
6.21062756e-01 -4.87958103e-01 -2.00840592e-01 3.11305642e-01
-4.90261853e-01 4.09564048e-01 3.74557614e-01 1.88990682e-01
-7.35086262e-01 3.04779261e-01 1.43902004e+00 -3.50328416e-01
5.39655946e-02 3.54304612e-01 -8.67369711e-01 7.11034238e-01
1.15818810e+00 -3.40782851e-01 -3.67823869e-01 -6.49950325e-01
-1.35440812e-01 -2.07109526e-01 7.56550729e-01 -9.21501994e-01
2.60044664e-01 -1.15427993e-01 6.39475524e-01 -7.02063786e-03
4.68503356e-01 -9.42097127e-01 4.94931377e-02 2.83265769e-01
-1.28099084e-01 2.04435080e-01 2.72855103e-01 5.53600729e-01
2.04770043e-01 -1.57801449e-01 5.98012805e-01 -4.26269174e-01
-9.69640493e-01 5.28546572e-01 -1.09505855e-01 -4.27397080e-02
1.08295155e+00 -4.55377728e-01 -3.54562521e-01 -4.23763245e-01
-4.82452393e-01 4.31919545e-01 6.77330971e-01 9.73240614e-01
6.37622416e-01 -1.11663604e+00 -8.19848061e-01 3.90354484e-01
1.91111937e-01 -3.69795203e-01 4.00663555e-01 5.60330510e-01
-1.29412673e-02 1.39139369e-01 -3.11252356e-01 -3.44368398e-01
-1.55543125e+00 8.71940732e-01 1.96770772e-01 1.86216742e-01
-5.61573923e-01 8.48331094e-01 8.84086341e-02 -3.69490325e-01
6.34235084e-01 7.98544139e-02 -3.68139356e-01 -9.10756513e-02
8.94746661e-01 6.10285401e-01 -3.44677240e-01 -8.50208104e-01
-5.00267684e-01 7.02312410e-01 -2.52333939e-01 5.60456872e-01
1.27800333e+00 -3.58438045e-01 -5.06512150e-02 5.73352948e-02
9.17712748e-01 -8.74988362e-02 -1.06576967e+00 -1.50851920e-01
-9.40018445e-02 -7.73989856e-01 -4.07937765e-01 -7.24263608e-01
-9.55664396e-01 3.66973162e-01 8.91831994e-01 3.61876935e-01
9.99905288e-01 4.35875393e-02 8.59325767e-01 5.17707050e-01
6.82944298e-01 -9.07891154e-01 -6.85766339e-02 1.22361228e-01
1.00694156e+00 -1.81677628e+00 7.38494024e-02 -5.05038977e-01
-6.49499893e-01 8.26532483e-01 8.37275147e-01 3.66932601e-02
3.76711190e-01 1.52266910e-02 1.19469650e-01 -1.11585706e-01
-3.03643703e-01 -1.95005909e-01 2.42212102e-01 1.02842093e+00
2.69402921e-01 1.01791456e-01 -1.30743057e-01 5.90931535e-01
-1.08113922e-01 -3.37708881e-03 5.03493547e-01 7.47543871e-01
-5.87240532e-02 -1.25729787e+00 -6.35105371e-01 4.76487987e-02
-3.75711530e-01 2.91129380e-01 -6.56766534e-01 1.08177626e+00
4.12428141e-01 1.13938987e+00 -1.61348864e-01 -3.92789721e-01
4.70020741e-01 -6.79172948e-02 5.80560744e-01 -2.49220997e-01
-4.53531235e-01 -3.04575175e-01 7.02979565e-02 -2.77778417e-01
-8.20605099e-01 -7.01260984e-01 -5.36653280e-01 -7.94457912e-01
-1.12998612e-01 -9.15627182e-02 4.73817587e-01 6.73313618e-01
1.04602262e-01 9.11470577e-02 5.19078672e-01 -9.07705605e-01
-4.78259534e-01 -9.46649492e-01 -3.36536109e-01 8.95678461e-01
3.97315502e-01 -8.12795877e-01 -2.77078182e-01 1.05015114e-01] | [14.579614639282227, 0.9926351308822632] |
d4e750b8-dc44-41ca-a205-ef97fc760d00 | generic-instance-search-and-re-identification | 1605.07104 | null | http://arxiv.org/abs/1605.07104v1 | http://arxiv.org/pdf/1605.07104v1.pdf | Generic Instance Search and Re-identification from One Example via Attributes and Categories | This paper aims for generic instance search from one example where the
instance can be an arbitrary object like shoes, not just near-planar and
one-sided instances like buildings and logos. First, we evaluate
state-of-the-art instance search methods on this problem. We observe that what
works for buildings loses its generality on shoes. Second, we propose to use
automatically learned category-specific attributes to address the large
appearance variations present in generic instance search. Searching among
instances from the same category as the query, the category-specific attributes
outperform existing approaches by a large margin on shoes and cars and perform
on par with the state-of-the-art on buildings. Third, we treat person
re-identification as a special case of generic instance search. On the popular
VIPeR dataset, we reach state-of-the-art performance with the same method.
Fourth, we extend our method to search objects without restriction to the
specifically known category. We show that the combination of category-level
information and the category-specific attributes is superior to the alternative
method combining category-level information with low-level features such as
Fisher vector. | ['Shih-Fu Chang', 'Arnold W. M. Smeulders', 'Ran Tao'] | 2016-05-23 | null | null | null | null | ['instance-search'] | ['computer-vision'] | [ 3.55263725e-02 -1.57849118e-01 -2.27535799e-01 -3.02326262e-01
-9.41547453e-01 -7.86727130e-01 7.52223313e-01 1.44344360e-01
-4.26432729e-01 5.45789599e-01 -4.23276611e-02 2.23312423e-01
-6.96583569e-01 -7.48617947e-01 -7.36073375e-01 -5.76268911e-01
-2.09180247e-02 1.21039343e+00 5.20480275e-01 -2.79833943e-01
2.92682081e-01 5.39657176e-01 -2.08656406e+00 2.84411639e-01
7.27552772e-01 1.08282423e+00 5.07727414e-02 1.93543792e-01
-9.26878005e-02 5.13661001e-03 -5.62229633e-01 -8.04193497e-01
5.20035267e-01 2.28724964e-02 -1.02772343e+00 3.44229549e-01
1.24824464e+00 8.07026848e-02 -2.34616831e-01 9.30531442e-01
4.46331769e-01 2.60255188e-01 8.60938311e-01 -1.34741366e+00
-5.55868506e-01 1.76822379e-01 -4.78833318e-01 1.61339432e-01
5.04098952e-01 2.03991104e-02 1.34059179e+00 -1.01408947e+00
6.50776565e-01 1.37457812e+00 7.84613490e-01 3.27262551e-01
-1.52386558e+00 -3.98765177e-01 4.98494595e-01 5.89838803e-01
-1.67878342e+00 -1.69558093e-01 6.34061515e-01 -5.44627309e-01
7.83085644e-01 4.40839052e-01 4.37583983e-01 8.91681135e-01
-4.75198925e-01 8.66325200e-01 1.13196170e+00 -2.80876428e-01
5.16167097e-02 3.23993385e-01 4.83431160e-01 6.50105119e-01
3.69328141e-01 2.14818358e-01 -2.32659563e-01 -4.24580812e-01
4.54877019e-01 2.11603448e-01 -1.13255247e-01 -8.43416810e-01
-1.37413502e+00 8.00143778e-01 5.92398107e-01 2.14863345e-01
-3.59592348e-01 1.33357272e-01 2.28849947e-01 4.81007397e-02
3.72894883e-01 6.59790039e-01 -6.64473534e-01 1.72014058e-01
-1.15358579e+00 5.33039689e-01 8.77277970e-01 1.09242380e+00
8.14517021e-01 -5.15269458e-01 -4.32993889e-01 1.18951499e+00
-8.18492994e-02 5.00119507e-01 1.62642255e-01 -8.05269539e-01
2.92522490e-01 5.47574401e-01 2.17682704e-01 -7.97491372e-01
-4.66883808e-01 -8.82194102e-01 -4.53474104e-01 -3.75454389e-02
6.13293886e-01 4.29175198e-01 -1.21822011e+00 1.73980248e+00
4.83906269e-01 1.61445975e-01 -1.36294380e-01 1.00930536e+00
1.01671100e+00 1.65938914e-01 -7.85851777e-02 8.76007751e-02
2.01486087e+00 -1.26083195e+00 -1.59339771e-01 -3.66804540e-01
2.96031594e-01 -5.37057936e-01 9.21181202e-01 4.23717231e-01
-8.13886940e-01 -5.80370307e-01 -7.27773547e-01 1.45134300e-01
-7.98244536e-01 2.46177942e-01 6.87468708e-01 6.49844170e-01
-8.11592877e-01 5.11097908e-01 -1.83649942e-01 -7.99147427e-01
2.19713360e-01 5.80261886e-01 -6.43009305e-01 -2.91728497e-01
-1.10064173e+00 7.82811761e-01 3.53084087e-01 -3.37521523e-01
-6.13350451e-01 -7.99595058e-01 -6.50203586e-01 8.21741968e-02
8.97237420e-01 -9.61637855e-01 1.08016574e+00 -6.62325203e-01
-7.94963002e-01 1.19651651e+00 -2.60969728e-01 -2.10657164e-01
4.09169644e-01 -1.10016823e-01 -3.98086846e-01 1.25004396e-01
4.81269538e-01 4.51741993e-01 6.79637969e-01 -1.63331044e+00
-8.25131297e-01 -5.54122627e-01 3.92699033e-01 2.19965309e-01
-7.92261586e-02 -2.20736414e-01 -9.53853250e-01 -6.00908637e-01
2.75717437e-01 -1.15943015e+00 -1.67672828e-01 -9.59771313e-03
-5.05077779e-01 -5.22351980e-01 4.18731958e-01 -3.89605582e-01
1.03790295e+00 -1.97030485e+00 2.34621644e-01 4.64402288e-01
-9.33197737e-02 -7.45447278e-02 -9.80234891e-02 3.35310012e-01
-8.04156959e-02 1.02221467e-01 -1.40798926e-01 -3.19249839e-01
3.09007108e-01 2.68067330e-01 7.22512826e-02 3.73753846e-01
7.25046471e-02 8.44562113e-01 -9.66662824e-01 -6.18819892e-01
7.59408996e-02 1.97057649e-01 -5.72617292e-01 -2.72123933e-01
-1.71851024e-01 9.10295472e-02 -5.76754451e-01 9.63734567e-01
6.08473182e-01 -5.39238632e-01 -3.06658708e-02 -3.68549287e-01
2.69981146e-01 1.05102686e-02 -1.51958966e+00 1.51013243e+00
-2.83245504e-01 1.50506005e-01 -2.81872749e-02 -1.11490989e+00
6.74339712e-01 1.72015298e-02 5.42577684e-01 -6.04948282e-01
-5.08198440e-01 3.63231450e-01 -2.40804195e-01 -3.26563895e-01
4.28326070e-01 -6.84970394e-02 -3.55847746e-01 3.48933339e-02
1.41730964e-01 2.17398815e-02 3.08438629e-01 1.64698049e-01
9.05065477e-01 4.33314219e-02 3.00382555e-01 -3.82134736e-01
4.90733027e-01 2.05124542e-01 2.05436140e-01 1.19371438e+00
1.95750073e-01 7.15211391e-01 -1.51852015e-02 -4.82839108e-01
-8.37644100e-01 -1.07997406e+00 -5.31166613e-01 1.32009554e+00
4.96125698e-01 -3.97444576e-01 -5.08068621e-01 -8.18459034e-01
5.86551726e-01 5.01197696e-01 -7.87378907e-01 5.53778745e-02
-3.23785067e-01 -8.23461950e-01 1.88597977e-01 3.87926191e-01
5.60904324e-01 -7.91227937e-01 -2.59564430e-01 1.26809195e-01
-2.43559584e-01 -1.09213066e+00 -5.25501013e-01 1.43556222e-01
-5.94750524e-01 -1.22852349e+00 -8.39438021e-01 -7.23179400e-01
5.63866496e-01 1.79689705e-01 1.64181519e+00 2.47679755e-01
-5.13975382e-01 9.30334568e-01 -3.05096269e-01 -1.59427047e-01
3.53114337e-01 1.77762017e-01 1.44446403e-01 2.39507198e-01
4.90532875e-01 -2.90832460e-01 -7.70858765e-01 8.05922568e-01
-5.36764681e-01 -4.92182404e-01 5.89314401e-01 9.53121006e-01
8.09776604e-01 2.18100563e-01 3.25370520e-01 -8.73552084e-01
1.93119228e-01 -4.72308517e-01 -3.68615121e-01 4.48626697e-01
-6.99327767e-01 9.40068290e-02 1.82231024e-01 -8.07018936e-01
-5.20959258e-01 2.92062134e-01 1.12343408e-01 -4.16115731e-01
-3.39605987e-01 2.42511258e-01 -1.67637631e-01 -1.49498925e-01
6.00492716e-01 2.77407289e-01 -3.86087000e-01 -8.05444300e-01
2.60112107e-01 3.78517538e-01 7.05391765e-01 -8.01792920e-01
9.24900651e-01 5.31044960e-01 1.32658884e-01 -6.49486780e-01
-1.03974676e+00 -9.93103802e-01 -7.11120844e-01 -9.09864437e-03
8.54918897e-01 -8.45268548e-01 -8.32298994e-01 7.61096478e-02
-8.55033994e-01 -4.55254987e-02 -3.19336593e-01 3.20450276e-01
-5.62694311e-01 2.87799984e-01 -1.33159116e-01 -7.71322131e-01
-1.39603261e-02 -1.08479393e+00 1.66311121e+00 8.90355483e-02
1.55660570e-01 -7.43587077e-01 -1.44010916e-01 5.30471146e-01
1.48464397e-01 1.13082401e-01 7.44036317e-01 -1.21937656e+00
-8.61222088e-01 -3.79524320e-01 -1.96215481e-01 -1.79555029e-01
-3.73940319e-02 -4.13114041e-01 -8.36266458e-01 -3.58762026e-01
-4.32185173e-01 -5.64296916e-02 1.21295893e+00 2.68704325e-01
1.10665500e+00 -2.79762715e-01 -8.06961060e-01 5.14012158e-01
1.48934138e+00 -1.63130522e-01 5.08642256e-01 6.13484979e-01
5.99230587e-01 6.36420906e-01 9.18923676e-01 3.08096528e-01
6.11097693e-01 1.44472516e+00 5.88704288e-01 -8.44704807e-02
-1.26430228e-01 -9.27501451e-03 -2.76725978e-01 -2.24790528e-01
-3.06417972e-01 -8.75891075e-02 -8.51231039e-01 9.23399270e-01
-2.03518391e+00 -1.34782016e+00 2.89509026e-03 2.50809526e+00
5.08631051e-01 -1.21061821e-02 7.22105384e-01 5.48371859e-02
8.39805663e-01 -2.37409249e-01 -3.85340363e-01 2.25444943e-01
-6.38498962e-02 -6.44833818e-02 6.34086788e-01 3.44396681e-01
-1.54068363e+00 8.46632421e-01 6.47937632e+00 1.15349245e+00
-5.86310506e-01 1.06566370e-01 3.63609731e-01 -2.23273888e-01
-9.62485299e-02 -9.57946926e-02 -1.11414933e+00 5.78096986e-01
2.79649884e-01 2.31886849e-01 5.01685262e-01 1.10500169e+00
-5.61409771e-01 9.85630378e-02 -1.43264031e+00 1.18560541e+00
2.94694990e-01 -1.05380487e+00 -1.69001624e-01 3.99750471e-01
5.94851434e-01 -2.00968161e-01 1.98371917e-01 6.24393165e-01
1.87054709e-01 -9.23882306e-01 6.94487333e-01 6.76263988e-01
7.44879961e-01 -6.04649782e-01 6.08900130e-01 1.87797219e-01
-1.37454855e+00 -3.19299161e-01 -4.12878990e-01 4.53624099e-01
8.12909529e-02 3.39422524e-01 -5.84868789e-01 6.80199921e-01
9.74515319e-01 4.54366654e-01 -8.97441626e-01 1.52331829e+00
2.37002447e-01 2.52699137e-01 -7.88662493e-01 1.51531816e-01
3.30607265e-01 -7.99386948e-02 7.07968056e-01 1.27054226e+00
1.76154301e-01 1.20340332e-01 6.16146624e-01 6.14570856e-01
7.86348358e-02 1.15138084e-01 -5.33007562e-01 4.39777732e-01
4.04631913e-01 1.14644575e+00 -6.89660251e-01 -4.59491968e-01
-4.62430656e-01 1.04819250e+00 2.90389299e-01 3.27762395e-01
-7.42272735e-01 -5.22726551e-02 6.77616358e-01 4.24915791e-01
7.83609331e-01 2.08978385e-01 1.39566451e-01 -9.88222301e-01
3.32312107e-01 -8.09574485e-01 8.06226611e-01 -5.42420805e-01
-1.80318022e+00 4.89065677e-01 5.17370641e-01 -1.21324384e+00
-3.00625116e-01 -7.49781489e-01 -2.64588445e-01 6.06361091e-01
-1.51562750e+00 -1.47398591e+00 -3.79125655e-01 7.40107834e-01
6.69497430e-01 -2.85930008e-01 7.99944401e-01 5.22014916e-01
-9.47188511e-02 7.43240416e-01 1.54767111e-01 9.54474732e-02
7.83728600e-01 -1.33442330e+00 8.78656060e-02 3.64533037e-01
5.66286027e-01 7.13822484e-01 8.47059608e-01 -5.67112863e-01
-1.26182055e+00 -7.79705167e-01 7.76026666e-01 -9.57148254e-01
4.80784029e-01 -3.85642886e-01 -7.44682133e-01 5.86997032e-01
-3.47678512e-01 4.04873133e-01 3.54563713e-01 7.30448008e-01
-6.69855893e-01 -1.29348323e-01 -1.33229721e+00 3.25709820e-01
1.41330624e+00 -4.49486375e-01 -7.40385711e-01 8.03086042e-01
5.16119599e-01 -2.32968777e-01 -9.15057302e-01 5.71450889e-01
7.08867669e-01 -7.67279088e-01 1.74468172e+00 -7.16930926e-01
-1.68705713e-02 -3.93234849e-01 -4.66259122e-01 -1.06122720e+00
-7.00904071e-01 -1.97640777e-01 -1.86617859e-03 1.03006458e+00
5.00694990e-01 -5.99159479e-01 8.27520669e-01 6.39216185e-01
4.26550582e-02 -7.18578577e-01 -1.06213987e+00 -1.35828757e+00
-7.12708384e-02 -6.68690056e-02 7.59170353e-01 7.85789669e-01
-3.93394828e-01 3.06215107e-01 -4.44047272e-01 4.21336383e-01
9.03476238e-01 5.47240734e-01 7.62110531e-01 -1.79002464e+00
-6.36116982e-01 -4.65437353e-01 -7.19447970e-01 -1.11977148e+00
1.06886685e-01 -8.64215016e-01 -1.42229721e-01 -1.65051997e+00
6.71969533e-01 -7.86481440e-01 -3.93753976e-01 6.13743186e-01
-2.86595464e-01 5.94608545e-01 4.86428589e-01 3.48898202e-01
-9.30382729e-01 1.62008107e-01 1.03680277e+00 -4.16549146e-01
-5.65169938e-03 2.95474440e-01 -6.92892611e-01 4.54963803e-01
3.80824804e-01 -4.94259626e-01 -6.30813837e-02 -1.21874996e-01
6.86706528e-02 -1.45311728e-01 8.86710346e-01 -1.07093644e+00
2.46662140e-01 -1.54754996e-01 3.62563610e-01 -7.61049986e-01
8.57299387e-01 -1.19493997e+00 3.71378005e-01 2.58916974e-01
-1.60229996e-01 -2.39082217e-01 4.63655256e-02 8.45504582e-01
-9.13964286e-02 -3.30630779e-01 5.29582024e-01 -3.48029464e-01
-9.70568538e-01 4.43687379e-01 1.98037311e-01 7.23353624e-02
9.14893329e-01 -6.43673301e-01 -3.61170918e-01 -2.86111742e-01
-1.10543036e+00 2.57158667e-01 6.56846941e-01 4.87878263e-01
3.15027624e-01 -1.43234932e+00 -7.50757098e-01 -1.85549166e-02
6.36353314e-01 -2.92241216e-01 6.74737915e-02 7.93798447e-01
1.74187303e-01 5.10470390e-01 1.07002795e-01 -8.51509213e-01
-1.46920562e+00 1.10540855e+00 3.46477866e-01 -3.20292383e-01
-4.18879211e-01 7.92178810e-01 5.77656686e-01 -7.30949700e-01
2.53553003e-01 2.41141364e-01 -1.85889453e-01 2.48862937e-01
2.11090118e-01 1.49198905e-01 8.59002471e-02 -8.67475569e-01
-8.33085060e-01 1.02885950e+00 5.36417887e-02 1.16666108e-01
1.16124094e+00 1.24433739e-02 1.89077944e-01 2.00570643e-01
1.00598311e+00 -3.47203016e-02 -9.07537103e-01 -4.23903257e-01
1.11339375e-01 -6.63368404e-01 -2.98151523e-01 -1.12543225e+00
-1.06857634e+00 5.02511978e-01 7.49651134e-01 1.72937334e-01
9.16818023e-01 6.13404036e-01 2.51531631e-01 5.59408605e-01
7.72850931e-01 -1.08661056e+00 9.92628559e-02 2.94596374e-01
7.75554419e-01 -1.54142761e+00 2.75628775e-01 -5.62731743e-01
-6.43816769e-01 7.31720865e-01 4.82603550e-01 -1.72303841e-01
4.99237388e-01 -2.55850911e-01 -4.92722392e-01 -5.01893580e-01
-4.77512300e-01 -8.73897195e-01 9.50658023e-01 7.23019838e-01
-5.20843752e-02 8.82462636e-02 -1.22552946e-01 6.19861305e-01
-9.45434868e-02 -3.86229455e-01 -1.72056541e-01 6.36066079e-01
-4.44492906e-01 -1.08012438e+00 -6.17338121e-01 7.92562127e-01
-4.64415669e-01 -3.30840617e-01 -4.43197489e-01 1.17943048e+00
2.80770391e-01 8.43819857e-01 1.38797045e-01 -2.21551791e-01
6.36233509e-01 1.84901997e-01 6.87319934e-01 -6.52975082e-01
-7.31628120e-01 -1.77269429e-02 3.08832079e-01 -6.02861166e-01
-4.13656294e-01 -7.58343756e-01 -7.16922224e-01 4.47072610e-02
-4.95013505e-01 1.64885566e-01 5.64499855e-01 7.97664821e-01
3.81876439e-01 3.05314921e-02 2.47511715e-01 -9.54618394e-01
-5.91999173e-01 -7.13941872e-01 -6.93815887e-01 9.89951432e-01
3.85081917e-01 -1.33167422e+00 -4.48864222e-01 -1.84900269e-01] | [9.628376007080078, 1.1641433238983154] |
94463f7c-731c-44cd-b43d-eed2351de8fd | easyspider-a-no-code-visual-system-for | null | null | https://dl.acm.org/doi/abs/10.1145/3543873.3587345 | https://dl.acm.org/doi/pdf/10.1145/3543873.3587345 | EasySpider: A No-Code Visual System for Crawling the Web | The web is a treasure trove for data that is increasingly used by computer scientists for building large machine learning models as well as non-computer scientists for social studies or marketing analyses. As such, web-crawling is an essential tool for both computational and non-computational scientists to conduct research. However, most of the existing web crawler frameworks and software products either require professional coding skills without an easy-to-use graphic user interface or are expensive and limited in features. They are thus not friendly to newbies and inconvenient for complicated web-crawling tasks.
In this paper, we present an easy-to-use visual web crawler system, EasySpider, for designing and executing web crawling tasks without coding. The workflow of a new web crawling task can be visually programmed by following EasySpider’s visual wizard on the target webpages using an intuitive point-and-click interface. The generated crawler task can then be easily invoked locally or as a web service. Our EasySpider is cross-platform and flexible to adapt to different web-resources. It also supports advanced configuration for complicated tasks and extension. The whole system is open-sourced and transparent for free-access at GitHub, which avoids possible privacy leakage. | ['See-Kiong Ng', 'Jianwei Yin', 'Wenjie Feng', 'Naibo Wang'] | 2023-04-30 | null | null | null | acm-the-web-conference-2023-4 | ['data-integration', 'marketing'] | ['knowledge-base', 'miscellaneous'] | [-5.04635215e-01 -2.44577125e-01 -9.09167081e-02 -2.06997856e-01
-4.86972719e-01 -1.08286119e+00 5.09672165e-01 2.73638248e-01
-4.57200527e-01 3.03116232e-01 -5.26794076e-01 -8.37592125e-01
-1.11803394e-02 -9.71147835e-01 -2.65751153e-01 -4.30620939e-01
1.99635506e-01 3.15387428e-01 7.15078354e-01 -1.36943027e-01
1.01342551e-01 2.03655377e-01 -1.90273476e+00 2.59049863e-01
8.90235841e-01 6.59150958e-01 5.89378655e-01 3.63060027e-01
-6.06734574e-01 5.19293882e-02 -3.42932016e-01 -5.76963305e-01
3.60737830e-01 -2.58219719e-01 -4.46684182e-01 -3.14015090e-01
-2.58802503e-01 -3.35240923e-02 3.08417946e-01 1.14778936e+00
3.16743284e-01 -2.26852655e-01 6.92364350e-02 -1.42204452e+00
-3.69234622e-01 2.99389094e-01 -6.93777859e-01 -1.56926438e-01
5.45467317e-01 6.24854006e-02 6.92029357e-01 -7.46502995e-01
6.61837637e-01 8.05321157e-01 4.37745035e-01 1.54590651e-01
-8.85441065e-01 -8.40769053e-01 -1.73377723e-01 4.35550302e-01
-9.60079312e-01 -1.03251226e-01 6.95955336e-01 -4.52026904e-01
3.91808450e-01 6.47072554e-01 7.02032864e-01 1.16574240e+00
1.51506007e-01 2.53504485e-01 1.09326339e+00 -5.67165017e-01
5.24234891e-01 5.51845610e-01 4.51263368e-01 8.35293055e-01
8.25514734e-01 -5.53148985e-01 -4.02946025e-01 -4.31220472e-01
5.70350885e-01 3.05283308e-01 -1.22550584e-01 -6.65694833e-01
-9.23018932e-01 7.62854159e-01 8.40787143e-02 3.48498911e-01
-2.42920414e-01 -2.32690930e-01 3.80967975e-01 4.28933501e-01
2.25316048e-01 -1.31977752e-01 -3.95843327e-01 -5.20341635e-01
-4.15295869e-01 6.79476634e-02 1.24445784e+00 6.88655555e-01
7.00447500e-01 -4.55544412e-01 7.48393655e-01 9.87927675e-01
4.92377162e-01 3.18141758e-01 9.16488290e-01 -5.56279123e-01
2.30996639e-01 8.88495266e-01 1.85481608e-01 -8.87764931e-01
-3.93775791e-01 1.71992555e-02 -3.84266853e-01 7.06672311e-01
6.99955165e-01 -9.19164792e-02 -2.93075025e-01 9.12796497e-01
9.08875227e-01 -4.77801025e-01 -6.01312041e-01 8.56687605e-01
9.98250663e-01 3.34913313e-01 2.83417553e-01 5.84316216e-02
2.00568986e+00 -6.99351966e-01 -6.42760515e-01 -1.26500353e-01
6.34841800e-01 -1.05146241e+00 1.76978624e+00 4.26673979e-01
-8.10332775e-01 -1.12904467e-01 -9.61909771e-01 -6.27355799e-02
-1.17882276e+00 -1.14056282e-01 4.37818527e-01 8.07880938e-01
-7.36849487e-01 5.22037387e-01 -9.89997864e-01 -1.13775480e+00
2.01720744e-01 -1.65951490e-01 -5.35679042e-01 8.22915360e-02
-7.86473095e-01 8.45291436e-01 3.46756220e-01 -4.68433648e-01
-3.27980757e-01 -3.09078276e-01 -6.00859344e-01 2.58054912e-01
7.75160730e-01 -3.40472609e-01 1.17603850e+00 -6.41594231e-01
-1.35337913e+00 8.78329098e-01 3.58852148e-01 1.38649195e-01
7.26561666e-01 -1.49239212e-01 -3.84967357e-01 -8.35208222e-02
2.87213653e-01 -3.20893675e-01 7.31492460e-01 -1.30958140e+00
-5.49363852e-01 -5.78403115e-01 -1.50817215e-01 -2.51721106e-02
-7.79510617e-01 2.55917013e-01 -8.78298461e-01 -3.97604883e-01
-2.27191433e-01 -5.80793262e-01 -1.00975612e-03 3.48953068e-01
-2.70125300e-01 -7.58404359e-02 1.34400570e+00 -7.00575292e-01
1.39937723e+00 -2.01436520e+00 -7.01062202e-01 3.27392876e-01
1.90487489e-01 7.83347905e-01 -1.17136277e-02 9.15006995e-01
6.41884580e-02 4.01315838e-01 1.43616617e-01 2.27602884e-01
2.98656434e-01 -6.85004443e-02 6.12792134e-01 8.69086385e-02
-5.87926447e-01 4.16229576e-01 -1.05811012e+00 -6.02159441e-01
3.64331484e-01 4.44158345e-01 -3.56770270e-02 1.57482862e-01
-6.75450936e-02 -4.90580350e-02 -7.02403843e-01 5.79567075e-01
7.43767679e-01 -6.11381173e-01 5.16931832e-01 1.47822708e-01
-5.34878373e-01 1.96749061e-01 -1.19306743e+00 1.60550690e+00
-7.10982144e-01 3.14167708e-01 3.50403011e-01 -6.84904277e-01
1.04458976e+00 1.99559286e-01 2.40696907e-01 -5.07834315e-01
8.44882950e-02 2.94865727e-01 -4.13392454e-01 -9.03717041e-01
-1.31624952e-01 6.49665236e-01 2.71139979e-01 1.28739727e+00
-3.29032362e-01 4.78056848e-01 6.58994853e-01 1.52922481e-01
1.23558342e+00 5.70492327e-01 5.14548540e-01 -2.85363674e-01
5.48561931e-01 -1.02624316e-02 2.60822266e-01 4.91491169e-01
1.40803695e-01 1.35857508e-01 4.93468672e-01 -7.11555481e-01
-1.04966462e+00 -1.18269396e+00 -1.59687400e-01 1.39744985e+00
-1.40134677e-01 -1.00141001e+00 -1.02784669e+00 -1.03032923e+00
-1.80468798e-01 6.80114210e-01 -2.03563705e-01 4.03932631e-01
-2.00454950e-01 -4.52743113e-01 -1.09137543e-01 -5.98170161e-02
8.21786880e-01 -1.16268504e+00 -1.06350231e+00 1.88716725e-01
-3.55009586e-01 -8.18429649e-01 -2.53915191e-01 -1.27563223e-01
-4.62580621e-01 -1.49152446e+00 -5.90549529e-01 -9.21431720e-01
6.35604143e-01 7.29969203e-01 8.88893127e-01 3.05334240e-01
-6.28569424e-01 5.04380822e-01 -7.56075680e-01 -5.97037435e-01
-1.58427864e-01 3.68733048e-01 -3.82207781e-01 -1.68820426e-01
9.88069713e-01 -6.99807525e-01 -7.00013340e-01 5.15662432e-01
-1.05886734e+00 2.66114086e-01 1.96987838e-01 3.42625767e-01
8.70008171e-02 2.00292841e-01 5.39492786e-01 -1.11307955e+00
1.01903641e+00 -8.02540004e-01 -8.85848582e-01 2.91286409e-01
-9.58796024e-01 -1.75671563e-01 8.28323066e-01 -2.31945097e-01
-1.04409635e+00 5.81967607e-02 -1.77778631e-01 1.98830798e-01
-3.13819170e-01 6.00470483e-01 -2.13931769e-01 4.62382939e-03
5.73630273e-01 3.53776179e-02 3.93521428e-01 -1.14651573e+00
2.30639234e-01 1.23404670e+00 -2.30344143e-02 -3.64096910e-01
1.05228829e+00 4.35938984e-01 -3.43245298e-01 -7.77102113e-01
-1.26047477e-01 -4.65565920e-01 -4.79008108e-01 -4.65020657e-01
6.25492275e-01 -3.26367617e-01 -1.10484374e+00 3.67957860e-01
-8.12495768e-01 -1.72270551e-01 2.27093682e-01 7.85637498e-02
2.90692993e-03 7.29987621e-01 -2.01481596e-01 -4.70518619e-01
-5.36780000e-01 -8.47286224e-01 4.96207178e-01 4.63559985e-01
-1.03893705e-01 -9.99554634e-01 3.78427878e-02 3.29262316e-01
6.34171546e-01 2.82603562e-01 9.06266809e-01 -7.45041430e-01
-2.98128545e-01 -7.22053051e-01 -4.53280866e-01 -4.91902903e-02
2.16563731e-01 2.09603772e-01 -5.91029167e-01 -1.71195507e-01
-3.24557215e-01 -8.81552920e-02 7.56472424e-02 -3.11998338e-01
1.22660875e+00 -4.51049656e-01 -5.20373106e-01 2.12904483e-01
1.55050039e+00 2.25051001e-01 4.48459297e-01 1.02100611e+00
3.52509469e-01 8.72497678e-01 4.92210716e-01 7.97474921e-01
2.93303162e-01 5.95776021e-01 4.89387840e-01 -1.70436502e-01
3.89281839e-01 -1.62558287e-01 1.53354779e-01 5.41627705e-01
-1.59317046e-01 6.41138330e-02 -1.03706658e+00 8.41974169e-02
-2.04266787e+00 -6.86211646e-01 -4.93816197e-01 2.63107634e+00
6.81881607e-01 -1.23141475e-01 4.13231045e-01 2.21175142e-02
8.43678534e-01 -2.47589350e-01 -4.00535971e-01 -4.12323654e-01
6.88434124e-01 1.35548756e-01 2.45512053e-01 1.05587400e-01
-6.61930442e-01 6.43002987e-01 4.89636707e+00 7.57694304e-01
-8.60599756e-01 5.05715370e-01 4.24187817e-02 -6.98189139e-02
-1.74043223e-01 6.80796877e-02 -5.08755386e-01 6.43137753e-01
7.69356966e-01 -6.24142587e-01 6.71979725e-01 1.33811760e+00
6.00426495e-01 -1.93442464e-01 -7.37631083e-01 8.36999953e-01
-2.25955859e-01 -1.19155502e+00 -1.78604454e-01 2.53182828e-01
-1.42053172e-01 5.18826507e-02 -2.84262598e-01 -5.79628209e-03
6.40153408e-01 -3.80787969e-01 2.37150624e-01 -1.54971287e-01
7.63979375e-01 -4.83358830e-01 5.11181831e-01 3.33804280e-01
-1.14307594e+00 6.98829517e-02 -2.73336679e-01 9.47241560e-02
2.84002889e-02 5.87242663e-01 -4.49405849e-01 3.18096250e-01
1.40041828e+00 1.19603254e-01 -8.41664791e-01 1.34853208e+00
-6.32710382e-02 4.84264672e-01 -4.44178969e-01 -6.49584711e-01
-5.43546826e-02 -5.37772834e-01 2.24929959e-01 1.19821382e+00
5.03861129e-01 -2.81186223e-01 2.44596079e-02 3.41353893e-01
2.67842412e-01 7.88598120e-01 -7.17082918e-01 -2.01429322e-01
5.52822888e-01 1.90869772e+00 -1.11880648e+00 2.95615681e-02
-7.53597498e-01 7.45755613e-01 2.31255949e-01 3.19332004e-01
-7.28458047e-01 -1.14752662e+00 4.35112506e-01 8.55271339e-01
1.34542733e-01 -2.97148854e-01 -1.74834073e-01 -1.17623651e+00
1.40152469e-01 -9.72546160e-01 5.27324021e-01 -1.01778209e+00
-1.09367371e+00 6.69259727e-01 8.04864541e-02 -1.14379537e+00
-2.44450092e-01 -7.37209916e-01 -7.43238211e-01 4.50649083e-01
-1.10789621e+00 -1.13386118e+00 -9.29920316e-01 7.92777658e-01
2.29510054e-01 -7.60685131e-02 1.02149725e+00 2.72279501e-01
-6.75846577e-01 2.26269886e-01 5.82516074e-01 1.16754450e-01
7.49317050e-01 -1.04454029e+00 2.70904422e-01 7.05195189e-01
2.20316015e-02 8.19231689e-01 6.15583897e-01 -5.86566329e-01
-1.40009212e+00 -6.32668734e-01 1.00619912e+00 -2.57541895e-01
9.18950319e-01 -8.51905942e-01 -9.28924620e-01 4.82876509e-01
5.74588299e-01 -2.55244970e-01 1.09613109e+00 2.05504477e-01
-6.19431138e-01 -4.51013476e-01 -1.12365222e+00 9.91023302e-01
5.94430685e-01 3.39652114e-02 -1.13079116e-01 7.28817046e-01
1.32778376e-01 3.57828826e-01 -7.67046154e-01 -4.77087915e-01
7.93848813e-01 -1.20188248e+00 6.11827075e-01 -2.21558452e-01
1.44794703e-01 -2.14760467e-01 4.90510881e-01 -8.85567784e-01
-3.47509682e-01 -8.36173296e-01 3.27335626e-01 1.36845970e+00
3.95860910e-01 -1.13784409e+00 8.32789063e-01 7.90267825e-01
3.35562646e-01 -3.12774032e-01 -5.72116137e-01 -6.76962256e-01
-3.44435126e-01 -3.81221086e-01 8.04589331e-01 8.57952595e-01
7.86658943e-01 1.84853122e-01 1.15492254e-01 -2.88419545e-01
7.92932510e-01 -2.63745189e-02 1.05183840e+00 -1.59448028e+00
-3.11174154e-01 -3.50160420e-01 1.44440830e-01 -2.47018188e-01
-5.66331208e-01 -9.13015902e-01 -4.62435991e-01 -1.78180671e+00
1.35103196e-01 -3.49976152e-01 7.18920007e-02 9.25827444e-01
1.27753317e-01 3.37016225e-01 2.82345802e-01 4.97467577e-01
-6.46910071e-01 -1.85436368e-01 9.57194865e-01 3.57788950e-01
-1.83058694e-01 -3.39389630e-02 -7.62293220e-01 8.24256003e-01
1.02167213e+00 -7.75762677e-01 -3.80360007e-01 -1.63186733e-02
5.15083969e-01 -3.49349678e-01 2.59664387e-01 -9.06190991e-01
9.53516290e-02 -1.67224199e-01 1.67696342e-01 -2.93390810e-01
-1.19494006e-01 -1.23736560e+00 4.08728629e-01 5.63351750e-01
3.12780775e-02 1.79504976e-01 -7.90020078e-02 3.84059429e-01
2.82119334e-01 -8.08412731e-01 3.87458473e-01 -7.13413119e-01
-3.35194528e-01 1.25371352e-01 -7.42866993e-01 -1.09998710e-01
1.58483040e+00 -2.55536467e-01 -6.79507315e-01 -5.56170285e-01
-5.67011476e-01 1.80685326e-01 8.45911324e-01 5.87567091e-01
2.66346544e-01 -1.01986170e+00 -3.01350057e-01 4.51732308e-01
3.16991001e-01 -4.90146756e-01 -2.12283991e-02 2.59231240e-01
-9.81778562e-01 2.53446698e-01 -4.54349488e-01 -1.31068394e-01
-1.47043216e+00 7.22490907e-01 -3.72481078e-01 -1.99468032e-01
-8.68191421e-01 1.66162521e-01 -1.41270131e-01 -4.01757777e-01
2.07858011e-01 3.22489738e-01 -2.70409584e-01 1.46072675e-02
8.91333520e-01 6.03464365e-01 2.84907132e-01 -2.24335957e-02
-3.48838657e-01 5.14664650e-01 -1.52084783e-01 -1.28791600e-01
1.42382157e+00 -3.07565838e-01 -7.82329142e-02 3.68064225e-01
9.89363849e-01 -2.01097190e-01 -8.77397001e-01 1.02597229e-01
5.97911282e-03 -7.13837147e-01 -2.36683547e-01 -9.98080313e-01
-8.93606007e-01 7.40448952e-01 6.16597831e-01 9.23905969e-01
8.39192092e-01 -1.28259435e-01 6.78901911e-01 4.72002834e-01
7.06150413e-01 -1.24735796e+00 -4.65045497e-02 -2.36868903e-01
7.31526375e-01 -1.16135836e+00 6.38531595e-02 -5.36610186e-01
-4.79383677e-01 1.42675877e+00 4.66944575e-01 3.07695001e-01
1.21918726e+00 4.13051546e-01 5.71333706e-01 -2.23232493e-01
-7.60806978e-01 -1.97521433e-01 -2.73866028e-01 1.02949512e+00
5.14687777e-01 -3.08867931e-01 -1.20062268e+00 5.99821627e-01
-6.14392310e-02 4.29110169e-01 4.73359019e-01 1.16150379e+00
-4.34741676e-01 -1.54240775e+00 -5.18183053e-01 5.42952836e-01
-6.38168693e-01 1.10348091e-02 -1.76180840e-01 1.00091445e+00
-2.19969124e-01 1.05172539e+00 -4.70823608e-02 -2.46871889e-01
-6.61123544e-02 1.59741327e-01 -2.81353503e-01 -3.56181681e-01
-6.62505627e-01 7.27180243e-02 3.30972046e-01 -6.29026949e-01
1.98415756e-01 -5.91745973e-01 -1.04883492e+00 -6.46563888e-01
-1.63257644e-01 3.83312136e-01 1.38088655e+00 3.67723197e-01
7.51556098e-01 -1.26823798e-01 3.75851989e-01 -5.98071992e-01
-2.26667225e-01 -7.52829671e-01 -5.56278765e-01 4.26702976e-01
-7.11876333e-01 -5.31085372e-01 -2.87363052e-01 1.57245040e-01] | [9.370826721191406, 8.350706100463867] |
e5fbd2ef-7832-45c4-9de4-5371ee5e3717 | entities-dates-and-languages-zero-shot-on | 2204.05211 | null | https://arxiv.org/abs/2204.05211v1 | https://arxiv.org/pdf/2204.05211v1.pdf | Entities, Dates, and Languages: Zero-Shot on Historical Texts with T0 | In this work, we explore whether the recently demonstrated zero-shot abilities of the T0 model extend to Named Entity Recognition for out-of-distribution languages and time periods. Using a historical newspaper corpus in 3 languages as test-bed, we use prompts to extract possible named entities. Our results show that a naive approach for prompt-based zero-shot multilingual Named Entity Recognition is error-prone, but highlights the potential of such an approach for historical languages lacking labeled datasets. Moreover, we also find that T0-like models can be probed to predict the publication date and language of a document, which could be very relevant for the study of historical texts. | ['Daniel van Strien', 'Stefan Schweter', 'Enrique Manjavacas', 'Clémentine Fourrier', 'Javier de la Rosa', 'Christopher Akiki', 'Francesco De Toni'] | 2022-04-11 | null | https://aclanthology.org/2022.bigscience-1.7 | https://aclanthology.org/2022.bigscience-1.7.pdf | bigscience-acl-2022-5 | ['multilingual-named-entity-recognition'] | ['natural-language-processing'] | [-4.75446492e-01 8.76035318e-02 -5.08987010e-01 -3.97797018e-01
-1.13231301e+00 -9.37780917e-01 1.04372299e+00 3.24849337e-01
-8.12675238e-01 8.44742119e-01 3.93440932e-01 -7.05928445e-01
5.41087911e-02 -7.72310019e-01 -4.61485803e-01 -8.92997161e-02
-2.30947807e-01 5.25437176e-01 2.97478259e-01 -4.17986333e-01
3.10885966e-01 6.82596803e-01 -8.57305229e-01 3.56549948e-01
6.31083608e-01 2.44148582e-01 7.85463750e-02 3.22041631e-01
-7.82576799e-01 9.39503908e-01 -8.19135904e-01 -5.34209967e-01
-2.33461671e-02 7.26396069e-02 -1.05160725e+00 -5.76684624e-02
1.66582376e-01 2.51530498e-01 -8.54606703e-02 8.94405961e-01
5.02704501e-01 1.65453762e-01 5.22526503e-01 -7.71567583e-01
-5.91429412e-01 1.09994459e+00 -1.38346672e-01 4.42032069e-01
6.81856453e-01 -2.29537100e-01 9.74200606e-01 -9.47833002e-01
1.31258619e+00 9.30188656e-01 7.12390780e-01 3.56869817e-01
-8.62718105e-01 -4.36272293e-01 1.27881125e-01 1.15007065e-01
-1.31521082e+00 -6.21836483e-01 3.92124563e-01 -5.80497324e-01
1.22502565e+00 1.15938984e-01 2.80677170e-01 1.36918211e+00
1.45813763e-01 9.52520788e-01 1.28542602e+00 -9.43574786e-01
3.45097423e-01 3.61139327e-01 5.49836218e-01 3.03056538e-01
1.38741136e-01 5.86155392e-02 -6.58837855e-01 -3.45713526e-01
2.87229389e-01 -4.62559402e-01 -1.86950624e-01 1.59379125e-01
-1.52613378e+00 8.48362446e-01 -8.86415839e-02 1.01584208e+00
-4.00850058e-01 -5.01876771e-01 6.42210007e-01 4.29818928e-01
8.01774442e-01 9.59047735e-01 -9.42729115e-01 -3.50511163e-01
-1.35921526e+00 3.95321548e-02 1.35433578e+00 1.14805174e+00
4.76231307e-01 4.41057747e-03 -2.31029958e-01 6.60967946e-01
5.58564998e-02 3.29979777e-01 5.64033926e-01 -2.94905990e-01
3.94443154e-01 4.84139651e-01 3.62825215e-01 -5.11393547e-01
-8.25317919e-01 -2.73465127e-01 -1.52759731e-01 -3.97284597e-01
5.55792689e-01 -3.06923151e-01 -9.41487551e-01 1.42186451e+00
3.99154037e-01 -1.22213073e-01 4.13561285e-01 5.24099171e-01
7.09547698e-01 7.37834573e-01 2.70940572e-01 -5.38874805e-01
1.61900115e+00 -6.28754377e-01 -8.13391089e-01 -3.10701162e-01
9.36048508e-01 -9.40458536e-01 6.66373491e-01 1.35911241e-01
-5.65144122e-01 -1.54968366e-01 -4.65234339e-01 -5.18211089e-02
-8.38222742e-01 1.87461689e-01 7.63992548e-01 7.48907983e-01
-6.92271352e-01 3.36733133e-01 -8.18114877e-01 -9.73378897e-01
-2.83477634e-01 -6.77281991e-02 -2.74952084e-01 1.29068911e-01
-1.55842841e+00 1.00810802e+00 7.14833438e-01 -2.35066935e-01
-5.57159841e-01 -6.73231125e-01 -7.74747968e-01 -5.08798324e-02
5.23069024e-01 -2.70035341e-02 1.33705533e+00 -6.19679689e-01
-1.13476229e+00 1.04882634e+00 -1.49419278e-01 -4.92118657e-01
2.02083483e-01 -1.75083697e-01 -1.05275071e+00 3.25748622e-02
5.32287121e-01 1.12397686e-01 2.29155973e-01 -8.88113081e-01
-6.81954384e-01 -3.33573163e-01 -1.51801750e-01 -2.44391114e-01
-3.62609982e-01 7.71401823e-01 -2.56083965e-01 -8.90169382e-01
-2.13261530e-01 -7.95301974e-01 -4.42255855e-01 -6.63944244e-01
-2.86298990e-01 -3.67750466e-01 3.09253067e-01 -1.05615664e+00
1.47409070e+00 -2.04647040e+00 -4.32767212e-01 7.45602921e-02
-3.20916593e-01 8.83680061e-02 -7.56272674e-02 9.17099118e-01
4.41341810e-02 4.73920166e-01 8.56503397e-02 8.95273089e-02
3.85230295e-02 8.88323709e-02 -6.32521808e-01 3.27251226e-01
2.46226236e-01 8.32614958e-01 -1.05845547e+00 -6.11875415e-01
-1.76928923e-01 3.16794842e-01 4.93444316e-02 -1.14694752e-01
-2.22027227e-01 2.33013481e-01 -4.62807983e-01 6.93504989e-01
2.38212302e-01 -3.13141197e-02 5.36319911e-01 2.08394244e-01
-8.73443604e-01 5.99774480e-01 -9.07160878e-01 1.60740829e+00
-4.63393211e-01 7.74977088e-01 -2.40535349e-01 -4.82914388e-01
9.57185388e-01 6.97364688e-01 1.90802529e-01 -8.00796270e-01
-1.27520368e-01 4.27920520e-01 -4.37258095e-01 -4.64360774e-01
9.60988402e-01 -3.35580200e-01 -6.48479879e-01 3.37559998e-01
4.18250978e-01 2.62787670e-01 4.48819786e-01 8.40020403e-02
9.55234706e-01 2.70742267e-01 8.41540575e-01 -4.27956641e-01
3.61015260e-01 5.03184617e-01 6.11383140e-01 8.80779862e-01
-2.20578805e-01 2.28961140e-01 3.93869817e-01 -5.13998508e-01
-1.13147354e+00 -5.96734285e-01 -4.40116256e-01 1.33389997e+00
-3.91396642e-01 -3.68788123e-01 -3.33664447e-01 -7.90499270e-01
-4.47382838e-01 1.37070894e+00 -3.35135311e-01 5.47345579e-01
-6.49076939e-01 -7.38017499e-01 7.94129014e-01 2.88321614e-01
-1.53127566e-01 -1.06750512e+00 -5.26588857e-01 6.88374221e-01
-3.03246677e-01 -1.31000352e+00 -6.73747435e-02 5.56720376e-01
-6.13654912e-01 -7.21818388e-01 -1.08871841e+00 -8.24210703e-01
2.65540630e-01 -2.90586919e-01 1.16333044e+00 -6.25617146e-01
1.84749350e-01 4.86931056e-01 -6.90974116e-01 -5.35583079e-01
-5.70876539e-01 5.62125266e-01 2.45497134e-02 -2.10767254e-01
6.09954417e-01 -4.91084307e-02 -4.15899195e-02 4.10024412e-02
-6.59997404e-01 -3.41568440e-01 3.65427643e-01 6.24096334e-01
2.19557852e-01 -3.08780432e-01 6.32886231e-01 -1.08529997e+00
4.94272321e-01 -7.87539124e-01 -6.70384288e-01 8.17519963e-01
-5.25226593e-01 2.47740030e-01 4.72788632e-01 -5.54218471e-01
-1.36429536e+00 -1.32282570e-01 -1.71923384e-01 8.08612853e-02
-3.52156997e-01 8.71521235e-01 1.11844599e-01 2.41291910e-01
7.41320193e-01 2.57263154e-01 -8.22648883e-01 -7.22995043e-01
4.81697708e-01 7.35524893e-01 4.36107486e-01 -6.11490548e-01
4.76701051e-01 3.04840714e-01 -5.53580821e-01 -1.11909640e+00
-1.04118598e+00 -9.46002126e-01 -7.53055632e-01 -2.18934104e-01
8.19540560e-01 -1.04886544e+00 -5.15447073e-02 1.66147992e-01
-1.35544920e+00 -1.75768271e-01 -3.09514344e-01 7.06745982e-01
-2.61198848e-01 3.29827785e-01 -8.03878903e-01 -1.03503108e+00
-3.05594057e-01 -4.44081634e-01 9.53135490e-01 2.60855973e-01
-3.59743446e-01 -1.38239038e+00 4.76985067e-01 4.87590358e-02
1.87385812e-01 4.46872376e-02 8.25304329e-01 -1.59423041e+00
-1.98210344e-01 -1.99070528e-01 2.07471743e-01 -3.78800601e-01
-2.03391448e-01 -3.77836339e-02 -9.59135652e-01 -1.94077209e-01
5.33416532e-02 -1.43665075e-01 5.14086902e-01 -1.53676607e-03
7.64693618e-02 -5.13415813e-01 -4.31675553e-01 1.09163359e-01
1.46454418e+00 3.31124961e-01 3.60447407e-01 9.32515264e-01
3.37904751e-01 7.12482095e-01 7.51910925e-01 5.57392001e-01
5.26807964e-01 4.75768149e-01 -4.50044364e-01 -8.86790678e-02
2.36244559e-01 -1.48420215e-01 5.06159067e-01 1.10753429e+00
1.59690663e-01 -3.71872216e-01 -1.50414765e+00 1.14535093e+00
-1.45444036e+00 -1.20285738e+00 -2.13086545e-01 2.07512546e+00
1.02725363e+00 1.58740848e-01 5.32579347e-02 -3.67200732e-01
7.58050442e-01 3.45591038e-01 -9.95488744e-03 -3.49381775e-01
-3.88450325e-01 2.41686180e-01 7.77985394e-01 1.50103927e-01
-1.02729285e+00 1.18892670e+00 7.32076311e+00 8.46286535e-01
-1.21791160e+00 4.08330500e-01 3.57235283e-01 2.40792096e-01
-3.46523494e-01 5.21579742e-01 -1.17790413e+00 3.41798663e-01
1.64853096e+00 -5.10781586e-01 -4.37318310e-02 8.58853161e-01
1.22315802e-01 -1.97291598e-02 -8.87511909e-01 3.57757717e-01
1.14027619e-01 -1.14556456e+00 -2.53094137e-01 1.09736836e-02
6.99218750e-01 4.80397791e-01 -5.75748026e-01 6.09386265e-01
4.05780703e-01 -3.58049303e-01 9.63832021e-01 5.85859776e-01
5.48641443e-01 -6.43110514e-01 5.80396354e-01 6.10532105e-01
-1.02623177e+00 1.55076712e-01 -4.06190485e-01 1.89964682e-01
6.44257605e-01 6.55364096e-01 -1.18200386e+00 9.63810802e-01
3.73322487e-01 3.13342959e-01 -7.60129869e-01 1.10878646e+00
-1.84340656e-01 8.55599046e-01 -3.30106914e-01 -1.96522713e-01
3.34086627e-01 2.44120270e-01 6.41765416e-01 1.60981596e+00
4.58038628e-01 1.65471341e-02 2.87320405e-01 4.14356530e-01
1.72269061e-01 7.17186332e-01 -7.52861440e-01 -5.40694773e-01
3.97680074e-01 1.11765718e+00 -9.71031487e-01 -5.23910224e-01
-7.28013992e-01 8.89682949e-01 3.96428078e-01 3.38319808e-01
-3.33563298e-01 -3.83578539e-01 -7.43620247e-02 9.90368135e-05
4.80672300e-01 -4.79397058e-01 -1.05939731e-01 -1.59308124e+00
-1.81545302e-01 -8.88394117e-01 5.84383428e-01 -6.42750502e-01
-1.33512795e+00 7.63242245e-01 -1.69002041e-02 -1.14514804e+00
-3.00368428e-01 -6.68518484e-01 -4.30361003e-01 4.82966751e-01
-1.41608155e+00 -1.21256471e+00 5.58693051e-01 1.76106468e-01
5.81301272e-01 -2.00794354e-01 8.46632123e-01 1.98752746e-01
-4.45072651e-01 1.80888802e-01 2.89316207e-01 5.29803514e-01
8.22772086e-01 -1.13881040e+00 7.86522090e-01 1.09852386e+00
7.74019361e-01 8.04300785e-01 7.40227342e-01 -8.74250948e-01
-1.05364013e+00 -8.70996654e-01 1.99792302e+00 -5.87688804e-01
1.25508320e+00 -4.00148839e-01 -9.76939082e-01 7.78633356e-01
4.64561015e-01 -3.80277336e-01 9.94019806e-01 5.93847930e-01
-3.82779151e-01 3.85028571e-01 -7.66716480e-01 4.81335282e-01
6.57716453e-01 -7.80802846e-01 -1.12688684e+00 6.19469285e-01
7.76719928e-01 -1.33840963e-01 -1.20616603e+00 1.74289439e-02
3.67760420e-01 -4.41482484e-01 4.20216918e-01 -6.97930157e-01
-1.07492223e-01 8.14629868e-02 -1.06492497e-01 -1.11981940e+00
-9.62660462e-02 -5.84500074e-01 2.67061144e-01 1.79995430e+00
9.32314813e-01 -5.51173687e-01 8.73882547e-02 5.97227335e-01
1.77936107e-01 -4.78318967e-02 -1.07152915e+00 -1.02758694e+00
2.23504916e-01 -7.05660641e-01 4.79227662e-01 1.57960284e+00
2.23157778e-01 5.17579794e-01 -2.77552933e-01 1.58499300e-01
1.90662578e-01 -2.42527816e-02 3.55314344e-01 -1.19414592e+00
2.79108807e-02 -4.23251651e-02 -1.65882021e-01 -4.75423664e-01
5.72918177e-01 -8.80847931e-01 1.89800852e-03 -1.45148659e+00
3.78300920e-02 -5.46145439e-01 -3.44640464e-01 6.44915164e-01
-5.39380535e-02 -2.30021819e-01 2.80224651e-01 3.67860585e-01
-6.02943361e-01 1.24580160e-01 4.65359747e-01 6.09923750e-02
-2.54373133e-01 -1.04485884e-01 -2.76121169e-01 4.75518107e-01
4.90402013e-01 -8.64407599e-01 2.01891109e-01 -1.69500083e-01
5.80425918e-01 4.26936477e-01 -1.12651952e-01 -7.30993927e-01
3.70831013e-01 -3.92867595e-01 1.32270781e-02 -5.70412517e-01
-3.01225305e-01 -5.60313106e-01 3.51171046e-02 2.46525392e-01
-2.84589797e-01 2.78918505e-01 1.69913694e-01 4.17091787e-01
-3.65500838e-01 -4.54357654e-01 2.73933530e-01 -4.63045448e-01
-1.21890640e+00 -9.49847177e-02 -8.12087774e-01 3.57129902e-01
9.79668379e-01 1.38882235e-01 -3.82185221e-01 4.93985377e-02
-6.82115555e-01 9.20792893e-02 5.18777192e-01 5.81951618e-01
-5.56118004e-02 -1.04454565e+00 -7.39545822e-01 -1.97081119e-01
5.25048554e-01 -7.64350057e-01 3.65386046e-02 6.87645078e-01
-4.10467982e-01 7.40301490e-01 -7.35190213e-02 -2.39833772e-01
-7.10634470e-01 8.89415562e-01 -6.35109618e-02 -6.48914933e-01
-4.74160075e-01 4.08862501e-01 -1.52663216e-01 -8.03301096e-01
-2.27736384e-01 -2.32753693e-03 -4.93857265e-01 6.60768986e-01
3.90245527e-01 1.18112914e-01 4.73075695e-02 -7.62551606e-01
-7.15851486e-01 5.22623062e-02 -1.09148122e-01 -7.09447086e-01
1.39475322e+00 -1.95171222e-01 -9.66096576e-03 1.17612827e+00
9.09155488e-01 5.57764649e-01 -4.50119853e-01 -3.50756466e-01
1.13874209e+00 1.44082621e-01 -1.28381595e-01 -8.57348144e-01
-4.70047444e-01 4.08596307e-01 2.61081010e-01 2.51545250e-01
6.97551370e-01 1.91013381e-01 4.76189613e-01 4.68037337e-01
6.68295860e-01 -1.30757260e+00 -7.06166565e-01 8.47248256e-01
4.75352168e-01 -1.16858160e+00 -8.60225707e-02 -2.05348935e-02
-7.64192462e-01 1.13306940e+00 2.11373284e-01 4.97166991e-01
5.02855659e-01 2.17533007e-01 4.69601214e-01 -3.21715027e-01
-9.57935333e-01 -4.27993178e-01 3.17612410e-01 3.50556284e-01
8.27329397e-01 1.81130201e-01 -9.17746007e-01 5.66676736e-01
-2.73455769e-01 -2.62184199e-02 7.05514967e-01 1.30702758e+00
-3.18571329e-01 -1.22242939e+00 -4.15398210e-01 4.89780754e-02
-9.44129229e-01 -4.60801244e-01 -3.86764139e-01 9.23629582e-01
-1.75185546e-01 8.95195603e-01 -3.60960588e-02 8.17430466e-02
2.56965518e-01 7.38769412e-01 -6.82394532e-03 -7.73738086e-01
-1.05563664e+00 2.18655437e-01 6.47872031e-01 -1.92623273e-01
-5.75605631e-01 -7.84768760e-01 -9.93132889e-01 1.17132202e-01
-5.47715127e-01 4.46910203e-01 7.66839325e-01 1.08071446e+00
3.48398358e-01 -4.23295833e-02 5.46154320e-01 -2.20581800e-01
-3.79184097e-01 -9.92451668e-01 -4.99307662e-01 1.25093982e-01
-6.98089674e-02 -2.24121839e-01 -3.03925753e-01 1.42807141e-01] | [9.828740119934082, 9.693143844604492] |
2441a152-8af5-472c-b61d-3a37c39688fc | eaten-entity-aware-attention-for-single-shot | 1909.09380 | null | https://arxiv.org/abs/1909.09380v1 | https://arxiv.org/pdf/1909.09380v1.pdf | EATEN: Entity-aware Attention for Single Shot Visual Text Extraction | Extracting entity from images is a crucial part of many OCR applications, such as entity recognition of cards, invoices, and receipts. Most of the existing works employ classical detection and recognition paradigm. This paper proposes an Entity-aware Attention Text Extraction Network called EATEN, which is an end-to-end trainable system to extract the entities without any post-processing. In the proposed framework, each entity is parsed by its corresponding entity-aware decoder, respectively. Moreover, we innovatively introduce a state transition mechanism which further improves the robustness of entity extraction. In consideration of the absence of public benchmarks, we construct a dataset of almost 0.6 million images in three real-world scenarios (train ticket, passport and business card), which is publicly available at https://github.com/beacandler/EATEN. To the best of our knowledge, EATEN is the first single shot method to extract entities from images. Extensive experiments on these benchmarks demonstrate the state-of-the-art performance of EATEN. | ['Junyu Han', 'Errui Ding', 'Jingtuo Liu', 'Xiameng Qin', 'Jiaming Liu', 'He guo'] | 2019-09-20 | null | null | null | null | ['entity-extraction'] | ['natural-language-processing'] | [ 5.89184538e-02 -1.05076730e-01 -1.48361728e-01 -2.96404004e-01
-7.89499104e-01 -6.36464298e-01 6.67927861e-01 1.63773209e-01
-7.37634361e-01 4.82271314e-01 -5.34839965e-02 -2.59081244e-01
2.65929043e-01 -7.69900501e-01 -9.17645454e-01 -3.71709019e-01
3.24898005e-01 2.75833398e-01 2.22254544e-01 5.77916838e-02
3.01154047e-01 2.99805552e-01 -1.15122056e+00 3.91928524e-01
8.14947546e-01 1.11018968e+00 -1.85314436e-02 5.37148416e-01
-2.14507893e-01 7.70467758e-01 -6.17218554e-01 -1.33274055e+00
2.67752767e-01 -3.98191214e-02 -7.43543506e-01 6.19898327e-02
2.71298438e-01 -7.04700649e-01 -6.43737018e-01 1.15526688e+00
5.52866757e-01 -1.95391029e-01 5.45764685e-01 -1.26867545e+00
-1.15850067e+00 7.88895130e-01 -6.62558794e-01 1.35002807e-01
1.67393804e-01 2.44095564e-01 1.02703834e+00 -1.14764023e+00
8.50895166e-01 7.48790383e-01 3.97816211e-01 2.59531140e-01
-5.58190286e-01 -8.35558414e-01 1.52280360e-01 3.73637170e-01
-1.55323470e+00 -5.91651022e-01 5.12799203e-01 -2.95695961e-01
8.32058966e-01 9.19443071e-02 1.87853456e-01 1.20524752e+00
-2.25373302e-02 1.33946609e+00 8.30292284e-01 -3.00910413e-01
-5.02605364e-02 3.47659051e-01 3.77218992e-01 5.36542058e-01
5.88387668e-01 -3.75694335e-01 -5.67569852e-01 1.73257858e-01
5.25269270e-01 5.40832654e-02 -2.61442512e-01 -1.90258205e-01
-1.18850946e+00 4.10847217e-01 3.43813390e-01 5.37206195e-02
-5.21597683e-01 -2.93722749e-02 4.35961932e-01 6.45172782e-03
2.90777087e-01 2.21650526e-01 -3.10794562e-01 -1.70486778e-01
-8.10976148e-01 8.04388523e-02 9.00619924e-01 1.43176949e+00
4.60177213e-01 -2.34933242e-01 -2.48041600e-01 5.02747834e-01
2.39025056e-01 3.72169614e-01 2.79804498e-01 -1.63644865e-01
9.58005309e-01 7.33201146e-01 2.56253690e-01 -9.32434916e-01
-9.23255011e-02 -3.20489794e-01 -6.69157207e-01 -2.25317761e-01
4.04864669e-01 -4.61656183e-01 -9.24733400e-01 1.15042961e+00
3.89782757e-01 3.38723809e-01 3.38640250e-02 9.68072116e-01
1.04717088e+00 6.33114457e-01 1.36472091e-01 2.31880739e-01
1.72782838e+00 -1.17779100e+00 -8.55881691e-01 -1.53522059e-01
3.27855289e-01 -1.04583907e+00 5.09133577e-01 2.95068204e-01
-9.11924541e-01 -3.47820014e-01 -1.03252375e+00 -2.47488379e-01
-7.75292397e-01 5.79768121e-01 6.95685148e-01 5.63533425e-01
-3.21972013e-01 3.06591719e-01 -7.57215679e-01 -3.47998828e-01
6.27172410e-01 3.15726161e-01 -4.75334287e-01 -8.39115828e-02
-1.07012165e+00 8.31279576e-01 5.96800506e-01 4.29958284e-01
-5.66813231e-01 -3.58767480e-01 -8.73940408e-01 1.04024895e-01
7.26460516e-01 -4.48680639e-01 1.38286006e+00 -9.03047442e-01
-1.33126509e+00 9.69505548e-01 -1.81562211e-02 -4.43775266e-01
6.47348344e-01 -5.95898747e-01 -6.13386333e-01 1.56205535e-01
-6.85272645e-03 3.52756172e-01 7.90882826e-01 -9.40787792e-01
-6.88992858e-01 -3.14252973e-01 9.87312272e-02 -5.27614206e-02
-3.75858009e-01 5.09971559e-01 -1.06859434e+00 -7.88515091e-01
-3.34752619e-01 -8.19886088e-01 1.49405941e-01 -2.23750338e-01
-8.95243168e-01 -2.58589298e-01 4.65474576e-01 -1.03557634e+00
1.26905334e+00 -2.24126196e+00 -1.61290079e-01 7.65891606e-03
3.18984956e-01 5.13996363e-01 -1.59062847e-01 6.09851897e-01
1.28244072e-01 1.64521873e-01 -1.53313756e-01 -2.75304466e-01
5.42081416e-01 -4.09710199e-01 -3.48791182e-01 4.38586295e-01
6.26191854e-01 1.16385651e+00 -5.84179759e-01 -6.25776887e-01
7.91859627e-02 5.71610570e-01 -3.14646453e-01 2.61124283e-01
1.28893722e-02 -1.37612909e-01 -6.17379963e-01 9.18703675e-01
1.01138771e+00 -3.53188664e-01 7.25655407e-02 -2.52058864e-01
-1.11123785e-01 2.34333619e-01 -1.40023339e+00 1.46977293e+00
-1.46025464e-01 6.51222467e-01 -3.40751670e-02 -6.70463681e-01
7.55234599e-01 3.11305493e-01 1.77784398e-01 -6.73276901e-01
3.83809417e-01 3.04145813e-01 -1.46933466e-01 -5.41436493e-01
1.00783777e+00 3.97738665e-01 -3.38128775e-01 2.75664032e-01
3.52575630e-01 6.29601181e-01 4.46561068e-01 3.12153101e-01
1.05969763e+00 6.28620163e-02 2.61160523e-01 4.37766433e-01
3.67049158e-01 -1.25783235e-01 6.12912834e-01 6.68922901e-01
-2.34569609e-01 6.36559844e-01 3.70910287e-01 -2.43100315e-01
-1.10878205e+00 -9.09179330e-01 -8.58896673e-02 1.01745820e+00
4.29614246e-01 -3.80608052e-01 -9.15916085e-01 -7.10052848e-01
1.42291905e-02 5.13573885e-01 -3.43855500e-01 1.44942880e-01
-5.19896507e-01 -6.25900984e-01 7.04107165e-01 5.71299911e-01
9.06404197e-01 -1.22103822e+00 -2.84996092e-01 3.36035252e-01
-1.69195756e-01 -1.59742105e+00 -6.51145041e-01 -1.86741397e-01
-1.73106298e-01 -1.11418295e+00 -9.39393818e-01 -9.13081467e-01
6.14609122e-01 9.38007236e-02 9.60767269e-01 -6.46002265e-03
-3.14851314e-01 1.37072861e-01 -4.46637928e-01 -5.42096436e-01
-1.75056886e-02 5.23427188e-01 -4.14650589e-01 4.12803680e-01
1.00081992e+00 -1.90054886e-02 -7.57499516e-01 2.06005037e-01
-9.54041660e-01 2.05043808e-01 1.08291245e+00 5.60746431e-01
5.68381131e-01 -1.16188452e-01 5.53166449e-01 -1.17041767e+00
5.53910136e-01 -6.15913510e-01 -7.70393968e-01 4.56579626e-01
-3.80338490e-01 -1.54252976e-01 6.06254280e-01 -2.22696111e-01
-1.12446237e+00 1.37400731e-01 -7.95373991e-02 -2.10729942e-01
-3.70446116e-01 3.83935511e-01 -2.48482272e-01 2.46131063e-01
4.79952656e-02 2.75810421e-01 -7.41065621e-01 -7.23313570e-01
4.02168036e-01 1.18102336e+00 8.34533989e-01 -3.44051003e-01
9.10067618e-01 2.65969306e-01 -5.99611759e-01 -4.69289005e-01
-5.24081528e-01 -6.85852349e-01 -5.46416521e-01 7.68104866e-02
8.48501146e-01 -9.54290867e-01 -8.26924801e-01 1.00785613e+00
-1.34997988e+00 1.02100119e-01 1.15805052e-01 5.52177072e-01
-1.66094378e-01 3.20940554e-01 -9.27810967e-01 -7.07376420e-01
-6.57148540e-01 -1.02668500e+00 1.15102220e+00 6.18353248e-01
1.95596322e-01 -4.14678395e-01 -1.96631253e-01 5.46692610e-01
1.14183158e-01 1.88750997e-01 4.44013149e-01 -1.16296172e+00
-9.57295358e-01 -4.95670706e-01 -5.98254204e-01 1.33789659e-01
-1.53508812e-01 1.72488824e-01 -9.25038159e-01 -2.76176989e-01
-5.06808579e-01 -1.86598703e-01 1.00718999e+00 -1.58344090e-01
1.02968276e+00 -4.60357636e-01 -3.13692480e-01 7.08641827e-01
1.60546148e+00 2.16264635e-01 7.90039361e-01 6.19611204e-01
7.81221032e-01 3.98525685e-01 6.98180199e-01 5.72912216e-01
6.27777815e-01 5.35739899e-01 3.05653304e-01 -3.40541564e-02
-2.27127504e-02 -4.86761928e-01 3.09375912e-01 9.14728820e-01
5.84645979e-02 -6.66047096e-01 -9.88796592e-01 8.25954795e-01
-1.73616087e+00 -9.11739945e-01 -6.00130036e-02 1.91333485e+00
7.57515132e-01 1.79522648e-01 -1.12386845e-01 -2.40370184e-01
1.12320757e+00 1.28750592e-01 -6.65521562e-01 -1.04402438e-01
2.08072327e-02 3.18195611e-01 8.36753726e-01 1.53550776e-02
-1.49428248e+00 1.07285583e+00 4.76197863e+00 9.30557311e-01
-9.36493695e-01 -1.31610110e-01 5.30573010e-01 -2.81708855e-02
6.80972040e-02 -4.46772799e-02 -1.19917941e+00 8.96830857e-01
7.64734209e-01 -2.09178910e-01 2.03213453e-01 8.61956716e-01
-3.33149314e-01 1.61503553e-01 -1.02949393e+00 9.38305140e-01
1.41344547e-01 -1.19922626e+00 -1.31678164e-01 -1.12295104e-02
5.40042460e-01 1.72524340e-02 4.15779501e-02 4.77728784e-01
2.88930684e-01 -9.14241135e-01 7.29997456e-01 4.99404222e-01
7.61766255e-01 -8.80785704e-01 1.12099075e+00 9.16231945e-02
-1.21147621e+00 1.54589921e-01 -3.46476138e-01 5.44935584e-01
3.34075898e-01 3.89174610e-01 -7.69641876e-01 7.14799285e-01
5.98018587e-01 5.25708139e-01 -7.32237875e-01 1.42248273e+00
-3.70948970e-01 5.56030512e-01 -2.58198768e-01 -1.28570795e-01
2.27121711e-01 -1.73288379e-02 4.50853050e-01 1.56401813e+00
4.21752781e-01 1.68269873e-01 -1.47075653e-01 8.11238408e-01
-8.31212282e-01 2.92410523e-01 -3.97192657e-01 -3.62474352e-01
6.31389856e-01 1.50856328e+00 -6.72601342e-01 -4.03418332e-01
-7.60098815e-01 1.27271986e+00 2.94474572e-01 4.41053450e-01
-1.17194808e+00 -1.22924984e+00 3.00921172e-01 -1.77291185e-01
9.08072948e-01 8.17145500e-03 3.49282883e-02 -1.41948378e+00
5.19640088e-01 -1.09030533e+00 2.74695307e-01 -7.40357697e-01
-1.28605139e+00 5.71678758e-01 -3.73540491e-01 -1.26889467e+00
-3.73215564e-02 -7.71098912e-01 -5.43927848e-01 6.23696864e-01
-1.74893105e+00 -1.26300907e+00 -4.41888809e-01 4.64544147e-01
4.90799814e-01 -1.92813069e-01 4.65076953e-01 6.29232347e-01
-1.20730162e+00 8.91976774e-01 3.84397119e-01 1.05682075e+00
9.61988866e-01 -1.15207076e+00 9.16245222e-01 1.12339592e+00
1.76493183e-01 7.44009912e-01 3.22291881e-01 -5.05625904e-01
-1.48362279e+00 -1.17464495e+00 9.51903999e-01 -2.66399741e-01
7.64368355e-01 -5.40132999e-01 -8.83842647e-01 8.78710628e-01
5.51018238e-01 -5.75006008e-03 7.82390654e-01 -1.58501536e-01
-4.51574385e-01 -5.78472987e-02 -9.25961852e-01 6.17433727e-01
8.54793668e-01 -5.52105844e-01 -5.33219159e-01 1.62811100e-01
6.41060650e-01 -6.00707054e-01 -9.94089425e-01 2.61252552e-01
6.05094194e-01 -6.56159818e-01 6.77082479e-01 -5.46541989e-01
5.26744604e-01 -2.12419599e-01 1.19792975e-01 -9.73013759e-01
4.72969599e-02 -6.47787869e-01 -3.97535771e-01 1.91159010e+00
4.81491536e-01 -6.31623983e-01 7.77574837e-01 8.77154946e-01
5.49593568e-02 -6.73231125e-01 -6.69747114e-01 -6.03259087e-01
-2.08472297e-01 -1.33450955e-01 9.29457068e-01 9.42931414e-01
-1.49954647e-01 3.05858642e-01 -3.93495470e-01 2.97961324e-01
5.11426032e-01 3.22373539e-01 9.02523994e-01 -7.38677263e-01
-3.67046297e-01 -3.32771093e-01 -5.18236816e-01 -1.25625968e+00
-9.83764138e-03 -7.58817911e-01 1.45178018e-02 -1.54280293e+00
4.28839445e-01 -2.48495743e-01 -4.44567472e-01 3.90161812e-01
-3.47546071e-01 1.72700644e-01 4.86095399e-01 1.78212956e-01
-1.07413650e+00 3.50433528e-01 8.39059651e-01 -2.69222021e-01
1.68960914e-01 -1.00954056e-01 -7.72956133e-01 7.27446377e-01
1.02187407e+00 -6.56146586e-01 7.54911155e-02 -7.18328774e-01
1.41849756e-01 -2.59786487e-01 2.38882124e-01 -6.40212953e-01
5.12992084e-01 -1.60448831e-02 3.34916115e-01 -7.86231697e-01
6.70182705e-02 -7.55708098e-01 -1.16178997e-01 5.29106185e-02
-1.54656753e-01 9.91524309e-02 1.59559980e-01 5.58758438e-01
-3.53676200e-01 -4.99278456e-01 3.79610747e-01 1.19411364e-01
-1.14797568e+00 4.47907567e-01 -1.36319369e-01 1.97956145e-01
1.11680734e+00 -4.28623222e-02 -8.69483471e-01 -1.27232030e-01
-1.04468942e-01 3.01966310e-01 3.50251287e-01 5.57178497e-01
5.02486229e-01 -1.34671533e+00 -8.92969668e-01 -9.32984799e-02
2.74964422e-01 6.14349321e-02 2.94762045e-01 7.01503515e-01
-6.95519269e-01 5.42873800e-01 -1.60425514e-01 -1.25070915e-01
-1.22727048e+00 7.07192361e-01 6.30009547e-02 -4.52471763e-01
-4.12280470e-01 7.21388459e-01 1.47471428e-01 -2.34176710e-01
3.08800101e-01 -1.74501151e-01 -2.92678028e-01 1.95833761e-02
6.28418624e-01 2.55221099e-01 1.22092329e-01 -6.77382171e-01
-4.40612435e-01 4.69687164e-01 -6.83030069e-01 2.35582422e-02
1.33214593e+00 7.14980513e-02 9.77857560e-02 -4.98387888e-02
1.10780072e+00 1.36921972e-01 -1.15105653e+00 -4.39274549e-01
1.79738104e-01 -4.70171988e-01 -1.87361106e-01 -6.56218946e-01
-1.20889854e+00 8.54864836e-01 3.83429050e-01 -6.71626031e-02
1.08040857e+00 -2.74464607e-01 1.23010731e+00 5.65923691e-01
3.03097129e-01 -1.30269790e+00 -2.07004890e-01 2.20960438e-01
5.49043238e-01 -1.41034210e+00 8.76122387e-04 -4.00525063e-01
-7.74096787e-01 7.57978022e-01 7.86289454e-01 4.30452451e-02
3.34117442e-01 2.57487983e-01 -1.29079238e-01 -9.43643153e-02
-6.57510042e-01 -4.10321683e-01 2.02211037e-01 3.24060977e-01
4.26178128e-01 -4.74555083e-02 -2.97643870e-01 1.08636796e+00
2.00839322e-02 1.18865199e-01 4.40057546e-01 1.02943897e+00
-1.86675295e-01 -1.00856304e+00 -1.27350762e-01 2.94682860e-01
-1.00429535e+00 -3.83425981e-01 -5.99701345e-01 9.90823984e-01
5.96301034e-02 8.19614351e-01 3.63443643e-02 -2.19727516e-01
5.15975893e-01 1.37097254e-01 1.12271331e-01 -4.72679168e-01
-8.12807262e-01 -1.76742539e-01 1.57059103e-01 -2.36361742e-01
-1.71086654e-01 -4.40326095e-01 -1.23347485e+00 -5.40467024e-01
-6.73604608e-01 2.38025170e-02 7.04482138e-01 4.44781512e-01
6.53066158e-01 4.00307655e-01 5.59097707e-01 -3.61075401e-01
-4.06549960e-01 -9.01955128e-01 -4.71602619e-01 6.80592835e-01
1.23650558e-01 -2.97411531e-01 1.18808538e-01 2.32040390e-01] | [11.462964057922363, 2.3001677989959717] |
9e67e4b1-f085-42ca-8e1c-af28f638feb2 | linearized-optimal-transport-for-collider | 2008.08604 | null | https://arxiv.org/abs/2008.08604v1 | https://arxiv.org/pdf/2008.08604v1.pdf | Linearized Optimal Transport for Collider Events | We introduce an efficient framework for computing the distance between collider events using the tools of Linearized Optimal Transport (LOT). This preserves many of the advantages of the recently-introduced Energy Mover's Distance, which quantifies the "work" required to rearrange one event into another, while significantly reducing the computational cost. It also furnishes a Euclidean embedding amenable to simple machine learning algorithms and visualization techniques, which we demonstrate in a variety of jet tagging examples. The LOT approximation lowers the threshold for diverse applications of the theory of optimal transport to collider physics. | ['Katy Craig', 'Junyi Cheng', 'Tianji Cai', 'Nathaniel Craig'] | 2020-08-19 | null | null | null | null | ['jet-tagging'] | ['graphs'] | [-2.00011894e-01 -2.88867593e-01 -2.47400731e-01 -1.39694020e-01
-5.56941688e-01 -9.03405786e-01 9.65414703e-01 4.97117758e-01
-6.96943641e-01 6.48831546e-01 6.44873008e-02 -6.93932176e-01
-5.49004316e-01 -7.15852141e-01 -3.28732222e-01 -1.05279171e+00
-6.87854350e-01 5.80468774e-01 1.99228242e-01 -4.61990714e-01
3.41400385e-01 1.15423071e+00 -8.58567715e-01 -1.80408299e-01
4.32599597e-02 1.00850189e+00 -2.67034650e-01 8.45468462e-01
8.00377876e-02 6.16211653e-01 -2.38389328e-01 -5.21605551e-01
4.15762454e-01 -5.97604513e-01 -8.22523415e-01 -7.98628330e-01
-6.92970306e-02 2.48203769e-01 -8.46963584e-01 8.59814465e-01
3.18737030e-01 4.58847970e-01 7.80225694e-01 -1.30739582e+00
-6.39671147e-01 5.51723003e-01 -2.87322402e-01 1.03375149e+00
-1.34259742e-02 -9.18848291e-02 1.24614751e+00 -4.70929027e-01
1.00444400e+00 1.17892647e+00 6.67631865e-01 1.38620719e-01
-1.43812716e+00 -3.16465616e-01 -5.64930260e-01 7.63617977e-02
-1.13274884e+00 -2.24564075e-01 7.88918376e-01 -5.54955304e-01
1.02763784e+00 5.23828983e-01 7.98448741e-01 4.68022019e-01
1.18406510e+00 3.02277148e-01 8.06045055e-01 -4.80779916e-01
2.17452273e-01 -3.04970331e-02 1.63055524e-01 6.47193730e-01
2.35621661e-01 7.55698621e-01 -3.72017384e-01 -3.09551895e-01
5.48560858e-01 -3.37954253e-01 4.63637114e-02 -9.30794001e-01
-1.41774344e+00 1.01376879e+00 5.31346142e-01 3.96034628e-01
3.09662819e-01 2.77928680e-01 6.39602304e-01 5.20514488e-01
5.29306710e-01 1.07449627e+00 -2.99648345e-01 -1.30943984e-01
-6.33226633e-01 6.95283353e-01 5.65130293e-01 8.16383421e-01
4.61824059e-01 -1.75356463e-01 8.24190900e-02 -7.03931525e-02
-1.03015721e-01 3.73172104e-01 -1.62690908e-01 -8.55165422e-01
3.33209902e-01 1.00414842e-01 3.69753331e-01 -9.03401732e-01
-9.35321271e-01 -5.11637509e-01 -4.06751812e-01 3.52096289e-01
5.10622799e-01 7.31111094e-02 -6.80382475e-02 1.37380636e+00
4.66938585e-01 -2.29294032e-01 2.45579585e-01 8.11466575e-01
2.38407761e-01 8.04361105e-01 -8.15370455e-02 -2.64771074e-01
1.11787856e+00 -5.49419224e-01 -5.79302728e-01 2.39110500e-01
1.13882482e+00 -8.23979497e-01 5.39972842e-01 1.17070720e-01
-1.26158094e+00 -4.07821909e-02 -1.32288194e+00 -2.88701236e-01
-6.91128552e-01 -4.74001795e-01 1.21563172e+00 5.21556020e-01
-8.53832543e-01 1.15387940e+00 -9.42678154e-01 -3.51875097e-01
-1.40473083e-01 2.06435263e-01 -2.68097430e-01 4.64935631e-01
-8.37811887e-01 1.16493833e+00 3.59884441e-01 -1.48224473e-01
-2.44233400e-01 -1.11312890e+00 -6.11396968e-01 2.11246070e-02
-5.52196940e-03 -3.83937806e-01 1.37730217e+00 9.50230099e-03
-1.35115671e+00 5.12329280e-01 1.49450913e-01 -5.29970706e-01
3.67356688e-01 4.41951267e-02 -6.49874032e-01 2.75154620e-01
-2.62715220e-02 5.61394870e-01 2.13755965e-01 -4.86340076e-01
-2.84454048e-01 -1.02778569e-01 1.40930891e-01 -8.81542787e-02
1.00309417e-01 5.48467040e-01 1.08421743e-01 -6.82542145e-01
2.71215349e-01 -1.15770435e+00 -3.04101437e-01 -6.88338429e-02
-1.76457047e-01 -2.99791187e-01 7.55133748e-01 -3.64965856e-01
6.90246463e-01 -2.20539832e+00 6.53367698e-01 1.04872443e-01
1.60515860e-01 -2.45166227e-01 1.82484761e-01 8.07287216e-01
-6.39261603e-01 1.17123924e-01 -1.18538067e-02 1.82662994e-01
2.89101511e-01 -7.40133226e-02 -4.34880555e-01 8.34755421e-01
2.92358324e-02 1.08083403e+00 -1.15369844e+00 -2.51423806e-01
2.21641213e-01 2.86311418e-01 -6.74263060e-01 -1.97877333e-01
1.72860369e-01 5.76050878e-01 -2.10832849e-01 3.99560854e-02
5.67500651e-01 2.16185451e-01 -8.10870975e-02 -3.59137058e-01
-4.78977472e-01 3.96739870e-01 -8.04485023e-01 1.56099999e+00
-1.60120025e-01 8.72634232e-01 -7.91004673e-02 -1.15838730e+00
6.77390039e-01 5.20276353e-02 7.99868226e-01 -8.34736407e-01
1.71102330e-01 -1.02611646e-01 7.57860541e-02 -2.01293752e-01
8.35785151e-01 -5.13197720e-01 -7.30247319e-01 6.82340086e-01
4.25222367e-02 -3.76348168e-01 1.69118494e-01 5.18860400e-01
1.19966972e+00 -7.90954083e-02 2.32778832e-01 -8.83191705e-01
4.99055646e-02 3.46826673e-01 3.68249178e-01 7.06703544e-01
-2.85630941e-01 -7.59536028e-02 6.50012612e-01 -7.53422737e-01
-1.41699719e+00 -1.48917079e+00 -4.53256190e-01 7.43431568e-01
4.15620357e-01 -8.09357345e-01 -2.58759469e-01 -5.66003442e-01
6.66101053e-02 9.06812251e-01 -2.39919752e-01 -4.24226999e-01
-6.90134406e-01 -1.05158710e+00 3.97533238e-01 4.44764763e-01
-1.69746637e-01 -6.74586117e-01 -6.13868833e-01 -1.61565701e-03
-8.85477662e-03 -9.72972810e-01 -4.81727213e-01 4.40798014e-01
-8.08954000e-01 -9.31449711e-01 -4.86125238e-03 -2.24338368e-01
3.79184306e-01 2.11042061e-01 1.05071759e+00 -2.98919976e-01
-8.65872204e-01 2.59451121e-01 -1.66314304e-01 -3.25707942e-01
-4.30659562e-01 -1.68359935e-01 4.54710573e-01 -5.69630623e-01
3.10923368e-01 -3.03864330e-01 -3.88160497e-01 5.61302304e-02
-6.62839234e-01 -3.02503169e-01 2.06336945e-01 6.54082060e-01
4.25218403e-01 4.87243772e-01 7.70819932e-02 -1.66077852e-01
4.18265671e-01 -5.05343020e-01 -9.81785715e-01 -1.36496099e-02
-3.80377263e-01 5.69059670e-01 6.40338123e-01 -1.69676676e-01
-6.14705741e-01 -3.82561982e-01 2.37608299e-01 7.87553042e-02
4.65605170e-01 1.60587225e-02 1.52852550e-01 -6.13285482e-01
4.27333146e-01 -1.35803580e-01 -5.23904622e-01 -5.54265797e-01
7.37720668e-01 2.30684616e-02 6.86801612e-01 -5.13262391e-01
9.25977886e-01 5.91642976e-01 5.83450317e-01 -6.26247823e-01
-8.55804980e-01 -2.57263243e-01 -1.06940973e+00 3.54196467e-02
1.15271366e+00 -3.06572020e-01 -8.96730840e-01 -3.78958136e-01
-1.23461938e+00 1.33216977e-01 -8.39366198e-01 1.02835739e+00
-7.57424772e-01 1.27090916e-01 -6.25061393e-01 -5.68354189e-01
1.54791191e-01 -9.13796067e-01 8.46637785e-01 -1.06765456e-01
-1.82299733e-01 -1.11060643e+00 3.34739953e-01 -2.25923419e-01
2.32763648e-01 3.96804750e-01 1.58884919e+00 -4.24260616e-01
-6.34117842e-01 -3.53355408e-01 -2.04344779e-01 -2.23236993e-01
-5.07597089e-01 -3.58126223e-01 -1.39540613e-01 -5.30764937e-01
1.38724446e-01 1.65451542e-01 6.83294535e-01 1.75863668e-01
8.22120726e-01 -6.88078776e-02 -6.49550736e-01 7.87866294e-01
1.48323762e+00 4.38952297e-01 1.01655938e-01 3.27604741e-01
4.57952559e-01 3.50162417e-01 4.42545414e-01 4.56695035e-02
2.27545202e-02 1.15687227e+00 -5.64300828e-02 2.03411784e-02
1.10407345e-01 -8.96629468e-02 3.78658980e-01 1.11045790e+00
5.86558841e-02 -2.14554239e-02 -6.96405828e-01 2.89622784e-01
-1.69534230e+00 -1.21695900e+00 -1.46974042e-01 2.00894117e+00
2.99239546e-01 1.78377986e-01 1.00897983e-01 1.46425128e-01
3.39940488e-01 2.43926004e-01 -2.29267448e-01 -1.11428297e+00
1.58050299e-01 4.61940855e-01 1.01722562e+00 7.39303172e-01
-9.75084782e-01 7.33848751e-01 8.54337978e+00 5.69593608e-01
-7.42361963e-01 6.13988101e-01 -7.15084225e-02 -5.06127417e-01
-2.34590679e-01 3.76805842e-01 -6.99214816e-01 3.11620891e-01
1.16477573e+00 -6.34816051e-01 6.84581578e-01 5.19203842e-01
1.45470709e-01 2.02902406e-02 -1.41494405e+00 7.94667542e-01
-1.77922517e-01 -1.65253937e+00 -1.73100054e-01 5.85624352e-02
3.46546352e-01 1.14996759e-02 -1.22987315e-01 2.62906522e-01
4.34583127e-01 -6.14861369e-01 8.97031546e-01 3.95131022e-01
3.58880848e-01 -1.07943296e+00 4.74591225e-01 2.49802828e-01
-1.26146376e+00 -1.16448410e-01 -6.44834518e-01 -9.39558297e-02
4.54233527e-01 7.91932166e-01 -6.82162344e-01 7.67166674e-01
5.14610469e-01 2.08665028e-01 -4.25422281e-01 8.97442222e-01
3.34873229e-01 -6.12057187e-03 -5.23402333e-01 9.20677464e-03
4.05746430e-01 -5.85702777e-01 1.00465572e+00 1.20556247e+00
5.87083042e-01 1.74486376e-02 4.61368486e-02 8.66488278e-01
3.02461892e-01 9.05769914e-02 -1.16995335e+00 -3.45976114e-01
2.95883507e-01 1.20587003e+00 -1.15139639e+00 9.59100127e-02
-4.12988663e-02 8.15693021e-01 1.96619570e-01 9.15738195e-02
-9.11149681e-01 -9.14084613e-01 6.22780502e-01 -1.15337789e-01
3.39875728e-01 -1.03585339e+00 1.18450977e-01 -8.60675395e-01
-6.38464838e-02 5.07906750e-02 3.39053273e-01 -7.46537983e-01
-8.16388190e-01 4.20702666e-01 3.65818948e-01 -9.57961917e-01
-9.52528715e-02 -9.62555051e-01 -6.18184507e-01 5.65340400e-01
-9.72506642e-01 -6.43692791e-01 5.09520531e-01 7.23289400e-02
-2.89374795e-02 -1.58894747e-01 7.80006051e-01 1.15270257e-01
-5.81011415e-01 3.62365603e-01 5.88014424e-01 -1.73521191e-01
2.11834356e-01 -1.42238474e+00 6.15202248e-01 8.02741468e-01
4.04722571e-01 3.35230500e-01 1.24832952e+00 -3.78912777e-01
-1.89632607e+00 -7.41311133e-01 1.07518566e+00 -7.21137583e-01
1.18328536e+00 -8.58493090e-01 -2.20641732e-01 9.90085483e-01
1.09477468e-01 2.01884463e-01 4.38632280e-01 2.68471152e-01
-1.99713901e-01 -2.57541418e-01 -1.16058111e+00 6.53754592e-01
1.16745317e+00 -7.82673299e-01 -4.44671422e-01 7.94698536e-01
7.52851546e-01 -1.53993517e-01 -1.21497786e+00 3.46327484e-01
4.19315577e-01 -9.29518878e-01 1.17784917e+00 -9.12655652e-01
-2.28926525e-01 -1.12177558e-01 -1.35061324e-01 -1.55228496e+00
-5.10585010e-01 -1.23920572e+00 1.58751652e-01 7.26162493e-01
3.47289354e-01 -4.53293711e-01 4.22343910e-01 1.90941319e-01
7.93526694e-02 -5.80039382e-01 -1.35188675e+00 -1.29101062e+00
6.03651226e-01 -6.84196949e-01 6.15056813e-01 8.48879993e-01
6.43991888e-01 2.18419451e-02 3.64403985e-02 1.44023567e-01
6.88298345e-01 1.44504786e-01 1.53025344e-01 -1.08545887e+00
-2.34713882e-01 -4.89999920e-01 -1.02089870e+00 -8.33645225e-01
1.37158990e-01 -1.74688637e+00 -3.27609062e-01 -8.58731866e-01
5.07269874e-02 -7.16130793e-01 -6.69120669e-01 -1.24480933e-01
7.70648301e-01 1.98629394e-01 2.90125102e-01 7.39791393e-02
-7.77347505e-01 4.74036604e-01 9.48300838e-01 -1.50661813e-02
7.09355250e-02 -3.64798307e-01 -5.27792096e-01 6.49579048e-01
7.83664346e-01 -9.15681481e-01 -1.49732336e-01 -2.67710388e-01
5.05001903e-01 2.02661455e-01 4.63709325e-01 -1.05371833e+00
1.12038121e-01 -8.23417902e-02 4.04067129e-01 -5.36632299e-01
4.80156004e-01 -5.71917474e-01 1.56766951e-01 4.07870293e-01
-6.21487737e-01 8.38791430e-01 3.13147247e-01 4.74742293e-01
1.81306109e-01 -3.13503176e-01 8.68168354e-01 -6.93666562e-02
-4.35937077e-01 8.94622207e-02 -3.23369026e-01 1.12914704e-02
1.44268107e+00 4.95708168e-01 -3.61425638e-01 -3.40404883e-02
-7.09534883e-01 -3.82415690e-02 3.67877007e-01 5.24929464e-01
1.20540038e-01 -1.83762395e+00 -3.61498564e-01 7.88000673e-02
-1.40890330e-01 -9.41323996e-01 -8.35767314e-02 1.18014300e+00
-7.56522477e-01 9.18971300e-01 -1.47753209e-01 -4.95646209e-01
-8.68438721e-01 1.05017865e+00 3.35924476e-01 -3.79968733e-01
-1.17925286e+00 6.73431933e-01 -2.07435843e-02 -4.29538846e-01
-3.62551391e-01 -4.40987766e-01 4.55703467e-01 -2.38150030e-01
6.16227269e-01 6.81318581e-01 1.35647893e-01 -7.21548855e-01
-4.01796550e-01 5.54641962e-01 -2.84255240e-02 -3.06635112e-01
1.09947097e+00 -5.92939518e-02 -2.40311697e-01 6.92219496e-01
1.43255663e+00 2.11660206e-01 -6.25671327e-01 6.98949173e-02
3.65307719e-01 -3.84802580e-01 3.93709987e-01 -4.57142353e-01
-6.52878404e-01 9.97950792e-01 6.83992326e-01 5.86481631e-01
4.13064539e-01 3.48357797e-01 1.01347351e+00 8.29490662e-01
5.47783375e-01 -9.88344431e-01 -3.73779684e-01 3.94182384e-01
7.08083153e-01 -5.15300632e-01 1.07319959e-01 -2.37641543e-01
-1.62899747e-01 1.19955647e+00 -1.34231910e-01 -9.81476009e-02
8.90656292e-01 8.75520527e-01 -1.77676439e-01 -3.95575821e-01
-5.78680694e-01 1.23627931e-01 5.97239211e-02 2.19870433e-01
1.65762126e-01 1.53859451e-01 -4.14871097e-01 2.25035802e-01
-7.57238507e-01 -5.02160311e-01 5.37854850e-01 8.92424583e-01
-5.35549879e-01 -1.09885979e+00 -1.41829088e-01 5.85750565e-02
-3.66510928e-01 -4.51768711e-02 -1.96096912e-01 1.04458559e+00
6.49235547e-02 6.38972402e-01 4.24595565e-01 -3.14734071e-01
1.61131650e-01 4.39407647e-01 9.99616027e-01 -1.58576876e-01
-1.65131405e-01 -4.49816376e-01 -3.42485495e-02 -8.05452287e-01
-2.43520290e-01 -8.41584682e-01 -1.20184839e+00 -7.52975464e-01
-2.81633615e-01 6.45801425e-01 7.71405041e-01 1.15833950e+00
2.31239051e-01 4.77818429e-01 6.11811042e-01 -1.17003429e+00
-5.77841282e-01 -2.85764217e-01 -9.19731915e-01 1.94679230e-01
2.74962157e-01 -9.03617084e-01 -3.54497939e-01 -4.80875880e-01] | [15.6827392578125, 2.9224867820739746] |
b7ab3727-d52d-4d90-a13b-39d40bdfacdf | camel-tools-an-open-source-python-toolkit-for | null | null | https://aclanthology.org/2020.lrec-1.868 | https://aclanthology.org/2020.lrec-1.868.pdf | CAMeL Tools: An Open Source Python Toolkit for Arabic Natural Language Processing | We present CAMeL Tools, a collection of open-source tools for Arabic natural language processing in Python. CAMeL Tools currently provides utilities for pre-processing, morphological modeling, Dialect Identification, Named Entity Recognition and Sentiment Analysis. In this paper, we describe the design of CAMeL Tools and the functionalities it provides. | ['Nizar Habash', 'er', 'Mai Oudah', 'Salam Khalifa', 'Ossama Obeid', 'Go Inoue', 'Dima Taji', 'Bashar Alhafni', 'Alex Erdmann', 'Nasser Zalmout', 'Fadhl Eryani'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['arabic-sentiment-analysis', 'arabic-text-diacritization'] | ['natural-language-processing', 'natural-language-processing'] | [-8.85592163e-01 -5.80340385e-01 3.00843745e-01 -8.46453428e-01
-7.48170257e-01 -1.21405864e+00 3.68966639e-01 8.06353569e-01
-5.63072979e-01 5.84540963e-01 2.82327652e-01 -4.60392505e-01
3.52537453e-01 -1.05940139e+00 5.15563935e-02 -2.16963470e-01
-3.46167058e-01 4.22345966e-01 -1.10620804e-01 -8.12310219e-01
4.83386666e-01 7.64604151e-01 -1.25981951e+00 7.33484149e-01
9.58095670e-01 4.42220032e-01 -3.15554082e-01 8.26993644e-01
-1.03527367e+00 9.88851845e-01 -5.50682425e-01 -7.40583241e-01
7.31732473e-02 -1.57041788e-01 -1.17668283e+00 -1.02515824e-01
-1.95622459e-01 -2.44646907e-01 4.68857586e-01 8.62213969e-01
4.80955005e-01 -1.73776552e-01 5.09436727e-01 -1.31365252e+00
-6.57657027e-01 1.02089679e+00 -3.13114345e-01 -1.32140964e-01
1.38129008e+00 -8.73721540e-02 7.09168375e-01 -1.49934125e+00
9.48371649e-01 1.43532240e+00 1.13699555e+00 2.88434237e-01
-6.65884674e-01 -3.37515444e-01 -4.38157916e-01 3.79488501e-03
-1.48254347e+00 -6.35465741e-01 3.12899262e-01 -3.86457384e-01
9.72155571e-01 3.26321572e-01 2.74733752e-01 2.90991336e-01
1.01017676e-01 1.09397912e+00 1.49502623e+00 -1.05379653e+00
1.08483687e-01 2.71929085e-01 8.05274963e-01 9.74824250e-01
-1.71054095e-01 -1.00844300e+00 -8.28611195e-01 -5.49917221e-01
1.78802714e-01 -5.97966254e-01 4.25449431e-01 4.83735174e-01
-9.44489181e-01 1.00635719e+00 -2.19774246e-01 4.19042736e-01
-5.16235650e-01 -7.67098725e-01 7.27904260e-01 5.93390167e-01
2.99685806e-01 3.90697062e-01 -7.46869683e-01 -3.39387685e-01
-7.18103707e-01 2.07644522e-01 1.18176770e+00 1.39009953e+00
6.77143753e-01 2.15409920e-02 2.34230384e-01 9.60184216e-01
7.46288776e-01 5.57595253e-01 6.60971105e-01 -3.54101837e-01
1.65406644e-01 1.08303607e+00 2.66504586e-01 -8.28136802e-01
-5.95063984e-01 8.12108576e-01 1.51755452e-01 4.87282574e-02
8.97226632e-01 -4.22681689e-01 -6.63538635e-01 7.17540741e-01
5.34340382e-01 -1.12846637e+00 4.02577311e-01 2.73125619e-01
1.15246415e+00 8.49463105e-01 1.32426918e-01 1.38639987e-01
1.69401765e+00 -6.96170568e-01 -9.49394941e-01 -2.95508772e-01
7.52405643e-01 -1.66187477e+00 1.30236125e+00 3.37153226e-01
-1.18411899e+00 2.79016912e-01 -8.71107101e-01 -2.43516088e-01
-1.37488651e+00 3.13612819e-01 9.19198573e-01 1.49592984e+00
-9.46373701e-01 -2.27733571e-02 -8.02552998e-01 -9.33356404e-01
6.34553656e-02 2.34529153e-01 -6.09969497e-01 -7.17217997e-02
-1.01264572e+00 8.32127750e-01 4.45928067e-01 -1.02367848e-01
-8.93153399e-02 -2.49008000e-01 -1.38941777e+00 -5.07223845e-01
-2.06746787e-01 3.03096890e-01 1.61506701e+00 -6.58507705e-01
-1.70378280e+00 1.44889534e+00 -2.75218576e-01 -2.00145796e-01
1.24146752e-01 -2.73241550e-01 -6.98019087e-01 8.08525551e-03
3.91003698e-01 1.60331637e-01 4.77280080e-01 -8.31159174e-01
-5.74557781e-01 -6.13702595e-01 -1.49252579e-01 -2.12374385e-02
-2.78373748e-01 1.37155020e+00 -4.21331018e-01 -7.70174205e-01
-1.55590877e-01 -6.41076446e-01 -7.56939650e-02 -1.98193088e-01
-1.32230297e-01 -2.77137816e-01 2.18479022e-01 -1.35188901e+00
1.40518737e+00 -2.12576818e+00 -6.64842129e-01 4.30749089e-01
-3.50919604e-01 1.47227079e-01 -3.81514169e-02 1.10203350e+00
7.02148080e-02 1.51245490e-01 -2.70776123e-01 -6.37266561e-02
4.08947498e-01 7.65127018e-02 -1.81878328e-01 3.19321543e-01
4.55998093e-01 6.33551419e-01 -7.99034357e-01 -7.98990726e-01
2.27328539e-01 2.69817024e-01 -1.69169709e-01 4.95165326e-02
-8.05750955e-03 -1.64177373e-01 -1.23978689e-01 1.44016874e+00
1.13831282e+00 7.14226365e-01 3.67584586e-01 2.42018670e-01
-8.83786798e-01 4.97207284e-01 -1.59595037e+00 1.48396504e+00
-6.39960587e-01 4.47411150e-01 6.98673487e-01 9.85415801e-02
1.08115637e+00 3.08568120e-01 3.78252752e-02 -1.21342629e-01
3.70851934e-01 6.27298772e-01 -7.04732418e-01 -5.74072480e-01
6.43477798e-01 2.77553201e-01 -4.99538183e-01 8.95185351e-01
3.91231418e-01 -2.38106087e-01 8.47629666e-01 2.08917528e-01
4.95834768e-01 1.42309859e-01 1.02843249e+00 -2.24865079e-01
9.83912289e-01 3.74418944e-01 3.07451993e-01 3.38816434e-01
-3.29267979e-01 -1.11497743e-02 3.85578662e-01 -3.47686499e-01
-7.57763386e-01 -1.14737415e+00 -2.55115807e-01 1.81602728e+00
-7.85354376e-01 -9.74518657e-01 -1.15051484e+00 -5.48483849e-01
-2.07584038e-01 6.29432023e-01 -2.02632695e-01 6.75642371e-01
-5.94483793e-01 -1.10687220e+00 1.28342485e+00 1.58348680e-01
4.05108184e-01 -1.53927684e+00 -1.82946816e-01 3.16829532e-01
-5.62472418e-02 -9.82653856e-01 -3.08523357e-01 5.71243837e-02
-4.11595702e-01 -9.45511997e-01 -4.55934823e-01 -1.64495003e+00
5.78877985e-01 -2.88463831e-01 1.17175210e+00 -8.14829171e-02
-5.34546137e-01 4.39289063e-01 -6.53756738e-01 -7.82808363e-01
-5.97344041e-01 2.09246621e-01 -2.41866335e-02 -1.75926257e-02
1.05789304e+00 1.16630662e-02 3.59941900e-01 -5.51157072e-02
-9.88715649e-01 -5.18021345e-01 9.68915969e-02 2.40477189e-01
6.06260933e-02 -1.10236101e-01 4.25506592e-01 -8.46200883e-01
1.12959862e+00 -2.63176948e-01 -4.91970360e-01 6.19998932e-01
-1.42251298e-01 4.78734449e-02 6.59072638e-01 3.54402483e-01
-1.25475669e+00 5.01406610e-01 -9.62929010e-01 1.13843560e+00
-6.48887277e-01 9.79975700e-01 -4.04944330e-01 -2.76143402e-01
6.41109288e-01 3.30081642e-01 1.38513908e-01 -7.06248224e-01
5.00903606e-01 1.34749556e+00 1.21686831e-01 -6.40623748e-01
3.77578974e-01 3.21155310e-01 -1.01915574e+00 -1.36440372e+00
-5.28029025e-01 -4.66571033e-01 -1.08459079e+00 -1.78438917e-01
7.32624888e-01 -9.35361266e-01 -5.45513153e-01 1.29428661e+00
-1.05503964e+00 -1.00304127e-01 -1.42715216e-01 7.03333914e-02
-6.18434250e-02 2.69628048e-01 -1.09502435e+00 -8.83253753e-01
-8.40900838e-01 -7.03164458e-01 4.27348197e-01 4.18302923e-01
-4.87755507e-01 -1.14295530e+00 2.28255555e-01 1.51566807e-02
3.78681660e-01 -5.93959726e-03 7.47059762e-01 -1.00565314e+00
4.42169845e-01 -1.98525295e-01 -6.97200447e-02 2.52561867e-01
2.13020623e-01 5.83568513e-01 -7.25406051e-01 1.37537196e-01
-9.91511792e-02 -3.68121088e-01 -1.14538595e-01 -1.11569375e-01
2.82616705e-01 -4.12960559e-01 2.25529745e-01 3.02092403e-01
1.20489645e+00 2.62307912e-01 5.45267642e-01 9.42319930e-01
2.60316253e-01 9.89230514e-01 7.59871304e-01 7.31122971e-01
8.31422389e-01 -2.61102200e-01 -2.38180280e-01 2.04299986e-01
5.85632324e-01 2.30056882e-01 8.56423140e-01 1.20077324e+00
4.32821542e-01 4.65123743e-01 -1.65497863e+00 6.53918087e-01
-1.43838537e+00 -7.15270996e-01 -5.25785983e-01 1.54487312e+00
1.34399462e+00 -3.33674878e-01 5.69212496e-01 1.64843962e-01
4.91854459e-01 -2.63967693e-01 1.49013802e-01 -1.25642228e+00
-5.88856459e-01 7.33747303e-01 3.49612892e-01 7.85497010e-01
-1.29311478e+00 1.34259725e+00 7.11759329e+00 6.72862530e-01
-1.03393114e+00 4.48018424e-02 2.96645574e-02 5.89857996e-01
1.05996981e-01 -2.02874899e-01 -1.01748419e+00 1.58969089e-02
9.92742717e-01 -5.04512072e-01 4.35461521e-01 8.49079967e-01
1.74920827e-01 -1.46992892e-01 -5.39311945e-01 6.90809667e-01
4.13945168e-01 -1.04616988e+00 5.82779050e-02 -6.59174681e-01
4.74012494e-02 3.00484091e-01 -4.55963343e-01 2.02722594e-01
4.01478171e-01 -7.79519260e-01 8.77072334e-01 1.61328375e-01
2.92403936e-01 -1.07714057e+00 7.76649475e-01 -2.77966648e-01
-1.12820554e+00 1.12660088e-01 -2.07064152e-02 -7.81261995e-02
1.65804893e-01 3.38091075e-01 -8.59720409e-01 3.46625715e-01
1.00210130e+00 6.57106698e-01 -1.37690067e+00 1.17530632e+00
-4.68457699e-01 6.70822978e-01 -4.60876286e-01 -3.76916707e-01
2.40533873e-01 -4.80371952e-01 3.88810009e-01 2.21691132e+00
-1.18589578e-02 -1.75244138e-01 2.61252791e-01 9.80584994e-02
3.79406035e-01 1.22903812e+00 -3.37029606e-01 -3.39687705e-01
4.11755472e-01 1.42313087e+00 -1.22276819e+00 -1.16238207e-01
-4.29995686e-01 1.01037741e+00 -1.19420223e-01 -1.87462807e-01
-4.11692262e-01 -1.66425383e+00 6.78554833e-01 -1.27473608e-01
1.13091029e-01 -8.29536080e-01 -6.27343595e-01 -1.24346876e+00
-7.86535814e-02 -1.38243175e+00 8.34234893e-01 -8.60387862e-01
-1.49572885e+00 8.71266723e-01 -3.80199522e-01 -8.86896908e-01
-1.70112196e-02 -1.37216997e+00 -6.49183750e-01 6.43118799e-01
-9.85508204e-01 -1.51139212e+00 1.15775973e-01 8.87519240e-01
4.42292839e-01 -4.46603715e-01 1.47917998e+00 5.99584401e-01
-6.44161463e-01 5.51152050e-01 3.44616681e-01 8.21722865e-01
1.10400975e+00 -1.59568739e+00 4.40494776e-01 8.50635231e-01
-6.58930838e-02 1.04641151e+00 5.58891535e-01 -3.86927545e-01
-1.49690032e+00 -6.72639310e-01 1.67628288e+00 -2.77055681e-01
1.27879620e+00 -6.50564373e-01 -6.45831704e-01 8.74656320e-01
7.38580406e-01 -5.96095264e-01 1.43442726e+00 7.07321912e-02
-4.03227419e-01 -1.35344295e-02 -1.50905192e+00 6.30531788e-01
-1.01738706e-01 -6.99165940e-01 -8.25901031e-01 3.19151044e-01
7.85408616e-02 -1.52230442e-01 -9.36196387e-01 -7.12447703e-01
7.19465911e-01 -5.24238110e-01 6.03707492e-01 -4.98025954e-01
-7.32311979e-02 -5.05554855e-01 -1.13849983e-01 -1.18837881e+00
2.45456442e-01 -9.43518281e-01 6.79616272e-01 1.98353994e+00
8.99580598e-01 -7.04153597e-01 2.33963221e-01 6.06348574e-01
1.90790579e-01 -2.05545928e-02 -4.97043610e-01 -3.35648984e-01
2.35868305e-01 -8.49852085e-01 7.92282045e-01 1.24283135e+00
7.63817966e-01 3.61689776e-01 2.60906339e-01 1.89892158e-01
2.97246337e-01 -1.16003558e-01 3.88212979e-01 -8.66222918e-01
3.71205002e-01 -1.36721581e-01 -2.62262642e-01 -1.74068615e-01
3.20005603e-02 -7.54083455e-01 -7.30011985e-02 -1.63412392e+00
-6.46482885e-01 -4.07957166e-01 3.32447946e-01 1.09472752e+00
3.55488181e-01 4.05626446e-01 3.21301550e-01 -2.11511433e-01
-5.24565279e-01 1.84811223e-02 6.26148582e-02 -7.22422451e-02
-6.33440673e-01 -1.17073782e-01 -7.13635087e-01 1.17113721e+00
1.32315850e+00 -6.08767033e-01 5.15341103e-01 -5.33696175e-01
8.11322868e-01 -6.53225780e-01 -3.22231591e-01 -7.79570699e-01
-9.91852395e-03 -1.64386600e-01 2.70565450e-01 -7.32284784e-01
-2.29738235e-01 -4.99684542e-01 -6.71885729e-01 2.56315231e-01
1.01685651e-01 8.97121131e-01 6.38791263e-01 -7.62810230e-01
-3.05692852e-01 -8.42246771e-01 7.43711293e-01 -2.07879305e-01
-8.50555658e-01 -3.11820835e-01 -1.66733742e+00 1.83808610e-01
1.07791150e+00 3.15849967e-02 8.89712274e-02 -9.86798480e-02
-1.05050361e+00 4.02211905e-01 5.29471934e-01 4.72065508e-01
6.01471782e-01 -9.98372078e-01 -6.48058355e-01 4.39013660e-01
3.25258940e-01 -7.28984237e-01 -5.13996840e-01 3.72661650e-01
-1.72077882e+00 2.04551965e-01 -7.52687514e-01 2.53551245e-01
-1.69614697e+00 1.18241295e-01 6.22226037e-02 -1.32559091e-01
9.21845585e-02 7.90156901e-01 -1.01782620e+00 -1.46944451e+00
-3.68177332e-02 4.00714055e-02 -6.73410118e-01 6.78856611e-01
1.05579746e+00 6.00820303e-01 5.17153561e-01 -1.03151762e+00
-1.08517182e+00 3.72863740e-01 -1.95726424e-01 -5.78830600e-01
1.42034233e+00 -2.71414608e-01 -1.28723443e+00 8.73842418e-01
6.27881110e-01 8.76759827e-01 1.68097690e-01 1.95588082e-01
7.36821949e-01 2.79438384e-02 -4.47333306e-01 -9.25610721e-01
-3.16974968e-01 5.69186568e-01 4.21638787e-01 3.61541092e-01
9.77057695e-01 -2.97747105e-01 6.79838181e-01 7.17387736e-01
3.46019030e-01 -1.45979917e+00 -7.94426978e-01 1.47041762e+00
7.76579857e-01 -8.17521393e-01 -3.49063650e-02 -4.63792324e-01
-8.81039143e-01 1.37345695e+00 3.46403807e-01 2.10546982e-02
1.32032776e+00 6.81456029e-01 1.01386380e+00 -9.53620300e-02
-1.83472857e-02 -3.43537599e-01 -1.36731759e-01 8.18650186e-01
1.10553050e+00 -8.11482668e-02 -6.82926178e-01 1.00804210e+00
-8.43401670e-01 -2.04672173e-01 9.94590402e-01 1.78810632e+00
-3.60717058e-01 -1.45764160e+00 -1.29074001e+00 2.03086779e-01
-1.07301235e+00 -5.48675537e-01 -8.50114346e-01 7.03498483e-01
6.12236233e-03 1.19517124e+00 -1.99992359e-02 8.41878280e-02
2.72002995e-01 5.63592076e-01 4.13314968e-01 -7.19414592e-01
-1.23985386e+00 -1.52396515e-01 3.50765467e-01 -1.01904310e-01
-2.57795334e-01 -1.05187345e+00 -1.78766465e+00 -4.72923219e-01
7.99009111e-03 5.26799500e-01 9.47200358e-01 7.97207236e-01
2.33249903e-01 -3.53101104e-01 3.46573114e-01 -4.84669209e-01
6.41044825e-02 -8.60832930e-01 -8.08663964e-01 -9.18267146e-02
-1.92974554e-03 2.92640090e-01 -7.88127929e-02 4.94108498e-01] | [10.346405982971191, 10.369450569152832] |
2271f934-5aa3-4a6d-85f5-321622efb724 | monocular-visual-odometry-with-a-rolling | 1704.07163 | null | http://arxiv.org/abs/1704.07163v1 | http://arxiv.org/pdf/1704.07163v1.pdf | Monocular Visual Odometry with a Rolling Shutter Camera | Rolling Shutter (RS) cameras have become popularized because of low-cost
imaging capability. However, the RS cameras suffer from undesirable artifacts
when the camera or the subject is moving, or illumination condition changes.
For that reason, Monocular Visual Odometry (MVO) with RS cameras produces
inaccurate ego-motion estimates. Previous works solve this RS distortion
problem with motion prediction from images and/or inertial sensors. However,
the MVO still has trouble in handling the RS distortion when the camera motion
changes abruptly (e.g. vibration of mobile cameras causes extremely fast motion
instantaneously). To address the problem, we propose the novel MVO algorithm in
consideration of the geometric characteristics of RS cameras. The key idea of
the proposed algorithm is the new RS essential matrix which incorporates the
instantaneous angular and linear velocities at each frame. Our algorithm
produces accurate and robust ego-motion estimates in an online manner, and is
applicable to various mobile applications with RS cameras. The superiority of
the proposed algorithm is validated through quantitative and qualitative
comparison on both synthetic and real dataset. | ['Kuk-Jin Yoon', 'Chang-Ryeol Lee'] | 2017-04-24 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [ 1.21188410e-01 -6.06880665e-01 -2.75937617e-01 5.86647391e-02
-4.71090898e-02 -5.11417091e-01 4.47274208e-01 -6.28548682e-01
-3.37774247e-01 4.70191568e-01 2.18800027e-02 3.72722410e-02
1.25727341e-01 -3.33279103e-01 -7.02162683e-01 -7.42490947e-01
3.80259037e-01 -1.03476807e-01 3.39648217e-01 2.73887031e-02
5.19876540e-01 3.62658232e-01 -1.28463137e+00 -5.40502489e-01
7.78866231e-01 7.77171731e-01 5.81526220e-01 6.99143469e-01
3.39359015e-01 7.54875302e-01 -2.18525335e-01 6.73191175e-02
3.16291690e-01 -3.40112627e-01 -2.40293995e-01 5.60918152e-01
3.12128961e-01 -7.11495399e-01 -5.52795112e-01 1.18216622e+00
3.86984289e-01 2.36428723e-01 1.93173185e-01 -1.19050419e+00
-3.61566573e-01 -1.97858870e-01 -1.03046870e+00 2.28402987e-01
7.66838551e-01 5.58049455e-02 3.64983410e-01 -1.10565853e+00
7.49086857e-01 9.01671767e-01 5.88193297e-01 3.98111641e-01
-7.48170853e-01 -2.30216205e-01 -1.80396102e-02 5.16301453e-01
-1.38976228e+00 -7.14016676e-01 9.98784363e-01 -3.90725553e-01
5.26155472e-01 1.65640682e-01 5.29584229e-01 7.82353342e-01
4.29820538e-01 4.72998589e-01 7.04800546e-01 -2.11321309e-01
1.10149095e-02 1.88284978e-01 -7.27875605e-02 5.49967945e-01
7.63056576e-01 -1.34400159e-01 -4.84187126e-01 1.43205389e-01
9.59319830e-01 3.64027083e-01 -6.19340599e-01 -7.19994485e-01
-1.49130464e+00 2.58228421e-01 1.17396690e-01 -9.93789658e-02
-2.88448036e-01 -3.00821476e-03 1.59541681e-01 5.35134152e-02
8.07842165e-02 -6.39452338e-02 -2.00651474e-02 -4.91182148e-01
-6.51496768e-01 -1.79538757e-01 4.26464140e-01 1.08654201e+00
5.01600683e-01 3.48473668e-01 7.17475235e-01 7.03764796e-01
4.44292098e-01 7.19338417e-01 6.54466629e-01 -1.06522524e+00
7.72365570e-01 3.06068629e-01 5.50856173e-01 -1.50060892e+00
-3.30296516e-01 -1.92213789e-01 -7.91047633e-01 7.46505037e-02
2.35538289e-01 -1.89114049e-01 -3.43645126e-01 1.46944368e+00
5.82795620e-01 2.95988619e-01 1.34667056e-02 1.30377066e+00
5.51479816e-01 6.89312577e-01 -6.83603406e-01 -7.19716609e-01
9.30488527e-01 -7.90429473e-01 -1.25684988e+00 -4.06914502e-01
2.72726148e-01 -9.94136930e-01 8.75011623e-01 3.64870012e-01
-9.90175962e-01 -6.14053428e-01 -1.22107887e+00 4.22090031e-02
3.24555695e-01 3.71824265e-01 1.79172624e-02 4.91426408e-01
-7.73792028e-01 4.57089357e-02 -1.07781053e+00 -5.67364156e-01
-3.92791450e-01 3.27399164e-01 -4.08370227e-01 -1.18106239e-01
-8.91503990e-01 8.74107778e-01 1.24349734e-02 4.66781467e-01
-4.47962821e-01 -1.12030264e-02 -9.83223617e-01 -3.16369265e-01
6.66354954e-01 -6.26201093e-01 1.03811407e+00 -1.05816603e+00
-1.96245265e+00 4.43470657e-01 -4.82818395e-01 1.78436283e-02
6.85051978e-01 -4.14351344e-01 -4.42119807e-01 3.12640816e-01
-7.65963970e-03 -5.69103137e-02 9.67128456e-01 -1.03366506e+00
-2.73543596e-01 -5.09561658e-01 -3.79372984e-02 8.37446988e-01
-1.14185303e-01 -2.47798294e-01 -6.61749780e-01 -3.70349109e-01
6.08442605e-01 -1.24809945e+00 2.13653706e-02 -2.98952442e-02
-3.17831606e-01 5.43935716e-01 9.85120595e-01 -4.65430647e-01
1.13287544e+00 -2.27563930e+00 9.59375426e-02 -3.32925439e-01
-3.87421995e-02 2.24430725e-01 3.46415043e-01 3.30485225e-01
5.08352518e-02 -4.81880933e-01 1.57599837e-01 -1.03632338e-01
-6.46453083e-01 1.22532556e-02 -1.04797632e-01 9.52109396e-01
-3.90609443e-01 3.45334709e-01 -8.33670676e-01 -4.36478704e-01
6.15307450e-01 4.46350396e-01 -3.41519177e-01 1.96502686e-01
5.21289527e-01 6.45493686e-01 -4.48131233e-01 5.67587733e-01
9.48732436e-01 -9.45643783e-02 2.67906543e-02 -2.73859292e-01
-4.34667200e-01 1.10933155e-01 -1.68402410e+00 1.49029243e+00
-3.65709662e-01 9.35203493e-01 1.96769044e-01 -7.42778718e-01
7.62223184e-01 3.67697299e-01 5.35923362e-01 -4.05180365e-01
1.58722192e-01 2.59868503e-01 -2.71724790e-01 -8.42382014e-01
8.20015430e-01 -1.30457319e-02 3.83717865e-01 1.54057220e-01
-4.15703177e-01 -3.45384814e-02 1.73298959e-02 5.61148450e-02
6.90276027e-01 4.36571032e-01 7.60628164e-01 1.01870559e-01
8.13708365e-01 -2.84039807e-02 9.40576434e-01 1.90579146e-01
-3.58097941e-01 8.64349604e-01 -3.93397361e-02 -4.72527593e-01
-9.95124340e-01 -8.08777511e-01 5.46611510e-02 2.06165716e-01
9.54056859e-01 -5.42964153e-02 -5.82585633e-01 2.70970650e-02
-2.67527193e-01 1.80227235e-01 -1.41312584e-01 -1.91077694e-01
-7.18401551e-01 -6.79498434e-01 -3.39551233e-02 3.19282711e-01
7.41362274e-01 -3.71772796e-01 -7.18950272e-01 2.84726590e-01
-5.49614370e-01 -1.60072351e+00 -7.00280249e-01 -7.55128920e-01
-1.06631410e+00 -1.15303314e+00 -7.49866426e-01 -5.45150518e-01
9.10542190e-01 1.36853969e+00 2.54166067e-01 -2.68752068e-01
6.21138103e-02 4.10492927e-01 -2.35473841e-01 1.17348850e-01
-1.11882702e-01 -3.77251238e-01 5.96088052e-01 3.76627117e-01
3.91757973e-02 -1.96319476e-01 -7.61947572e-01 8.99683952e-01
-8.37479293e-01 2.98683345e-01 1.71017006e-01 5.97258091e-01
3.31942111e-01 1.24386445e-01 1.62441239e-01 -4.84355658e-01
2.05770016e-01 -3.30101758e-01 -9.03703988e-01 -1.03965119e-01
-4.99366909e-01 -2.54561871e-01 7.58794010e-01 -4.03383553e-01
-1.26453209e+00 2.70419598e-01 2.49883428e-01 -4.77946699e-01
1.68207392e-01 3.39595705e-01 -3.08011323e-01 -1.83214292e-01
4.44791704e-01 4.06850547e-01 1.56601548e-01 -2.52731264e-01
8.01736787e-02 7.03112781e-01 6.77789748e-01 1.22576244e-01
9.86552536e-01 9.20684218e-01 1.75793275e-01 -1.22221839e+00
-2.33844310e-01 -6.73146665e-01 -5.36224604e-01 -4.50706691e-01
6.66738808e-01 -1.13037825e+00 -7.80866206e-01 8.96331072e-01
-1.16764450e+00 2.56488711e-01 2.24868625e-01 1.09185600e+00
-4.79736865e-01 9.76587474e-01 -4.92586792e-01 -8.72918010e-01
3.63326520e-02 -1.39198756e+00 7.08789647e-01 5.26646256e-01
2.54295785e-02 -8.90638173e-01 1.38589844e-01 3.63441855e-01
3.12159322e-02 2.68459171e-01 8.64188280e-03 5.19080520e-01
-8.22787702e-01 -4.12609994e-01 -6.10398762e-02 1.36740685e-01
4.33571935e-01 3.23395640e-01 -5.68409741e-01 -4.03705895e-01
5.56012690e-01 4.00912344e-01 3.33421081e-01 5.96001089e-01
4.16939884e-01 -3.39017123e-01 -2.60912210e-01 8.17926526e-01
1.70813310e+00 4.57986802e-01 5.25070727e-01 6.50600851e-01
9.47146654e-01 3.65993351e-01 9.02893782e-01 5.63849568e-01
4.62659389e-01 1.03630006e+00 6.07010722e-01 2.28292957e-01
2.47719869e-01 -1.38571590e-01 8.37673247e-01 1.18448842e+00
-4.18073684e-01 -2.95404792e-01 -6.25800431e-01 4.21124488e-01
-1.80074060e+00 -1.00844479e+00 -5.95759034e-01 2.57641554e+00
2.55325109e-01 -9.80230793e-02 -1.47254705e-01 1.43196836e-01
1.03112054e+00 3.12920362e-01 -6.25333190e-01 -1.87743396e-01
3.05597130e-02 -8.56452107e-01 7.85941482e-01 5.96641600e-01
-7.04010308e-01 5.08322060e-01 5.39926147e+00 1.27726734e-01
-1.37475133e+00 8.95170774e-03 -2.52114814e-02 -1.64618507e-01
1.16834208e-01 1.61249742e-01 -8.08464289e-01 7.21151233e-01
3.92236680e-01 -1.41107827e-01 5.33678532e-01 8.79844964e-01
6.56520307e-01 -4.65183705e-01 -6.28101170e-01 1.44877863e+00
4.35443670e-01 -9.59900022e-01 -2.70002067e-01 3.51158902e-02
7.71836519e-01 -5.12951165e-02 9.99040008e-02 -4.13759023e-01
-4.85022068e-01 -1.50518909e-01 7.35233903e-01 5.30588746e-01
7.48335421e-01 -5.46885133e-01 7.54690707e-01 6.46082580e-01
-1.25070572e+00 -3.12711634e-02 -5.64638078e-01 -3.97664487e-01
5.11425555e-01 5.50018787e-01 -7.71421492e-01 6.71027720e-01
3.95514280e-01 9.79771674e-01 -2.71760821e-01 9.35184896e-01
-1.80817991e-01 1.45282462e-01 -3.23359042e-01 1.49116218e-01
9.44000948e-03 -6.33458495e-01 1.02137220e+00 5.62670112e-01
6.93592608e-01 2.82771081e-01 -1.33331150e-01 2.57412970e-01
2.07731634e-01 -6.27733842e-02 -9.63784516e-01 2.36667946e-01
3.42365861e-01 1.13763499e+00 -4.90144342e-01 -2.36808792e-01
-7.00509846e-01 1.16004467e+00 -2.91592598e-01 4.82973814e-01
-8.73920679e-01 -4.01730806e-01 5.39383650e-01 3.60239774e-01
2.67720949e-02 -7.90222168e-01 -1.43311501e-01 -1.76771522e+00
2.81379640e-01 -4.82056618e-01 -7.77953938e-02 -1.15051222e+00
-3.88943046e-01 3.26127827e-01 -2.25786150e-01 -2.00920939e+00
-3.09976131e-01 -3.15540254e-01 -5.63782096e-01 3.44127893e-01
-1.36961091e+00 -6.87902987e-01 -8.70315969e-01 5.49633026e-01
8.00219059e-01 1.70099698e-02 2.40272418e-01 4.18593585e-01
-8.54797959e-01 9.79880467e-02 5.84094107e-01 -2.05777124e-01
8.37167740e-01 -9.15556967e-01 2.78782785e-01 1.30550015e+00
-1.51342377e-01 8.00589979e-01 9.38299000e-01 -4.66910124e-01
-1.92120349e+00 -7.36471653e-01 6.54934347e-01 -3.14026445e-01
4.04984951e-01 -1.16471043e-02 -6.22587740e-01 7.52763450e-01
-6.52245358e-02 2.16879383e-01 1.34275168e-01 -6.42082095e-01
3.00298154e-01 -2.66486704e-01 -7.96714008e-01 5.23409247e-01
8.85274887e-01 -4.20197070e-01 -4.59236920e-01 8.43986794e-02
4.53249633e-01 -8.42680693e-01 -3.63783896e-01 1.41651705e-01
7.85150170e-01 -1.11904001e+00 9.35724139e-01 2.75962442e-01
7.77202845e-02 -8.07414353e-01 -8.22173711e-03 -1.16202283e+00
-1.21647291e-01 -8.80329072e-01 -1.09686613e-01 9.39379036e-01
-2.91083783e-01 -7.69757390e-01 6.15923643e-01 4.49028373e-01
1.94636453e-02 -1.63991958e-01 -8.23466301e-01 -8.05627167e-01
-9.73062038e-01 -1.50482014e-01 1.83335096e-01 1.01769030e+00
1.72068223e-01 2.98076183e-01 -8.93550456e-01 4.86958355e-01
7.09682286e-01 -2.58591305e-02 1.25377989e+00 -7.96148241e-01
-1.03641957e-01 2.19080046e-01 -7.21203625e-01 -1.50017738e+00
-3.86678785e-01 -9.95492190e-02 9.24052596e-02 -1.26762223e+00
9.74145234e-02 4.22837101e-02 9.48162898e-02 -5.15628040e-01
-3.46057862e-01 1.55152753e-01 1.32611424e-01 6.58568740e-01
-3.42418879e-01 5.36138654e-01 1.23938358e+00 3.03998917e-01
-3.90351832e-01 2.83265054e-01 -1.37886778e-01 9.96594965e-01
5.67323506e-01 -2.86331296e-01 -5.48060477e-01 -7.57833481e-01
4.39839065e-01 4.26563174e-01 2.72060633e-01 -1.04387772e+00
5.42227089e-01 -1.88976973e-01 1.72443911e-01 -6.02376580e-01
4.36236352e-01 -9.64471579e-01 4.94848222e-01 6.00302100e-01
4.06541288e-01 3.81773710e-01 -1.50301307e-01 7.42648482e-01
-3.41886789e-01 -1.94692552e-01 8.54118168e-01 -3.78224589e-02
-7.91516066e-01 1.71936065e-01 -4.37560827e-01 -3.15185189e-01
1.01461780e+00 -7.96983182e-01 -3.35984737e-01 -7.54895985e-01
-3.62788349e-01 -1.68523297e-01 1.01310134e+00 3.31616849e-01
7.30880797e-01 -1.35971880e+00 -2.07150593e-01 4.23234820e-01
3.27513628e-02 1.57090276e-01 4.46403444e-01 1.30805099e+00
-1.06904030e+00 5.11509478e-01 -2.00283110e-01 -8.85195971e-01
-1.28385675e+00 4.35380459e-01 1.73973054e-01 3.22628230e-01
-6.11434877e-01 2.98653334e-01 1.97892725e-01 3.94741409e-02
-1.95758343e-01 -4.09815878e-01 -1.71020955e-01 -2.29641691e-01
4.63447511e-01 8.84933233e-01 -1.14067398e-01 -1.07945204e+00
-3.91203344e-01 1.20257938e+00 2.08995983e-01 -2.94896871e-01
9.80006993e-01 -9.98674631e-01 1.72095656e-01 5.95712185e-01
1.24306238e+00 3.35751772e-01 -1.49103773e+00 -1.05220415e-01
-3.95824283e-01 -9.61846471e-01 -6.89490139e-02 1.72036871e-01
-8.69211853e-01 8.01608503e-01 4.77379650e-01 -7.12849349e-02
1.05734408e+00 -6.64853871e-01 8.85691464e-01 3.22540343e-01
5.99621236e-01 -1.13767946e+00 8.22691694e-02 3.11849207e-01
4.23059285e-01 -1.32429826e+00 4.13912326e-01 -5.23347557e-01
-7.55246103e-01 1.17444789e+00 4.92576241e-01 -1.64409608e-01
2.57009566e-01 -2.31423378e-01 1.23494744e-01 3.01018953e-01
-4.03100401e-01 3.07809860e-01 -4.30419222e-02 4.27947015e-01
8.54210258e-02 -2.06090540e-01 -3.83757502e-01 -2.07245901e-01
1.46926150e-01 6.12082109e-02 1.25889742e+00 9.50664520e-01
-4.68229353e-01 -6.69781089e-01 -7.83603489e-01 -1.97681233e-01
-4.16679710e-01 3.79615605e-01 6.45634532e-02 8.45219254e-01
-1.83019876e-01 9.37512338e-01 -9.07709897e-02 -2.87374735e-01
3.32605332e-01 -4.62161034e-01 3.22448432e-01 -3.31933111e-01
2.57718951e-01 3.06222081e-01 -2.16541141e-01 -6.64204001e-01
-6.04581535e-01 -8.32809389e-01 -1.24961603e+00 -3.84215355e-01
-6.98373795e-01 -3.98723111e-02 9.72326636e-01 7.01782882e-01
2.41798714e-01 1.50471088e-02 1.15079308e+00 -7.69371092e-01
-3.54519725e-01 -7.55510986e-01 -6.21702373e-01 4.66616333e-01
6.41344011e-01 -7.91366220e-01 -5.65076292e-01 3.49611640e-01] | [8.048389434814453, -2.1677029132843018] |
e22c4229-22dd-4c36-adaf-5cfde14fff08 | adir-adaptive-diffusion-for-image | 2212.03221 | null | https://arxiv.org/abs/2212.03221v1 | https://arxiv.org/pdf/2212.03221v1.pdf | ADIR: Adaptive Diffusion for Image Reconstruction | In recent years, denoising diffusion models have demonstrated outstanding image generation performance. The information on natural images captured by these models is useful for many image reconstruction applications, where the task is to restore a clean image from its degraded observations. In this work, we propose a conditional sampling scheme that exploits the prior learned by diffusion models while retaining agreement with the observations. We then combine it with a novel approach for adapting pretrained diffusion denoising networks to their input. We examine two adaption strategies: the first uses only the degraded image, while the second, which we advocate, is performed using images that are ``nearest neighbors'' of the degraded image, retrieved from a diverse dataset using an off-the-shelf visual-language model. To evaluate our method, we test it on two state-of-the-art publicly available diffusion models, Stable Diffusion and Guided Diffusion. We show that our proposed `adaptive diffusion for image reconstruction' (ADIR) approach achieves a significant improvement in the super-resolution, deblurring, and text-based editing tasks. | ['Raja Giryes', 'Tom Tirer', 'Shady Abu-Hussein'] | 2022-12-06 | null | null | null | null | ['deblurring'] | ['computer-vision'] | [ 6.01110697e-01 -1.75635651e-01 2.60679960e-01 -2.43378699e-01
-9.14471030e-01 -4.15671587e-01 8.12617362e-01 -3.25529099e-01
-5.08279979e-01 6.69678330e-01 6.73696816e-01 4.19891328e-02
-1.20741680e-01 -5.42409301e-01 -6.02829874e-01 -1.01427507e+00
3.34038734e-01 1.17316969e-01 3.96818817e-01 -2.30689749e-01
3.65076005e-01 4.29776371e-01 -1.27057362e+00 5.41875660e-01
1.09095943e+00 9.38146234e-01 7.65019894e-01 7.89712727e-01
2.47446215e-03 1.08237171e+00 -4.00379986e-01 -2.47069314e-01
2.23367080e-01 -7.43811369e-01 -6.14297271e-01 2.38730460e-01
5.12107968e-01 -6.57618761e-01 -6.30905747e-01 1.36226499e+00
6.14816129e-01 1.38655603e-01 6.85785055e-01 -4.91289854e-01
-1.54016626e+00 3.93221676e-01 -6.47163928e-01 5.07930636e-01
2.57486880e-01 1.96882263e-01 4.51529533e-01 -9.48471963e-01
1.21451104e+00 1.15173626e+00 5.30382335e-01 7.44190454e-01
-1.48642266e+00 -3.74015599e-01 -9.05388296e-02 3.41021717e-01
-1.21280289e+00 -1.00837815e+00 8.31845403e-01 -4.11843866e-01
7.52097130e-01 5.30819073e-02 2.03980789e-01 1.36430693e+00
2.49431416e-01 6.15926147e-01 1.49689317e+00 -5.17371476e-01
3.05923045e-01 1.20592020e-01 -2.44232029e-01 4.52989370e-01
-5.10941260e-02 2.93187439e-01 -6.90373361e-01 6.17134571e-02
9.51386511e-01 -1.86274186e-01 -7.48406529e-01 -3.03633302e-01
-1.30769444e+00 6.45935893e-01 3.67070705e-01 4.89487976e-01
-8.53076041e-01 1.45242605e-02 -4.18365709e-02 5.71000397e-01
1.00915301e+00 1.20821884e-02 3.94392498e-02 1.24824293e-01
-1.30048370e+00 1.13168821e-01 6.38383329e-01 6.05281651e-01
4.98936146e-01 1.70081094e-01 -3.54975969e-01 1.09914446e+00
2.53215075e-01 6.42118454e-01 6.52583778e-01 -1.45447254e+00
2.05105320e-01 -2.53287464e-01 3.02444518e-01 -9.98948634e-01
2.70419300e-01 -2.30489329e-01 -1.37855148e+00 4.14907098e-01
1.76378861e-01 3.31859775e-02 -1.12183332e+00 1.60021853e+00
7.20650926e-02 1.70950010e-01 2.46132612e-01 9.96635497e-01
5.84511876e-01 7.25449204e-01 -2.31743500e-01 -4.31659222e-01
7.72577047e-01 -1.06634808e+00 -9.82932866e-01 -1.76511467e-01
-8.77236277e-02 -9.79835808e-01 7.10723877e-01 6.63572848e-01
-1.28742564e+00 -5.89350343e-01 -9.92620111e-01 -2.42303595e-01
3.02368738e-02 4.63076942e-02 3.02925855e-02 3.60112458e-01
-1.63948691e+00 8.75482917e-01 -7.54260838e-01 -5.26638806e-01
4.08021808e-01 -2.66716152e-01 -5.05620718e-01 -6.63870037e-01
-9.24370408e-01 9.44595218e-01 -1.19437240e-01 -5.31708337e-02
-1.33374417e+00 -4.74036127e-01 -6.17926896e-01 -1.63284689e-01
2.75755197e-01 -7.36663818e-01 1.00003099e+00 -1.13432848e+00
-1.61915004e+00 8.53335083e-01 -4.32287455e-01 -4.97474700e-01
8.60588014e-01 -3.02226424e-01 -4.77763623e-01 3.61227006e-01
1.32067487e-01 5.58320999e-01 1.42821133e+00 -1.56268537e+00
-2.15039775e-01 -3.92482966e-01 -2.56530046e-01 9.25913677e-02
-2.24731132e-01 6.43207058e-02 -5.83496094e-01 -1.08192909e+00
1.50355771e-01 -6.93947613e-01 -3.29801083e-01 5.65911531e-01
-3.36387515e-01 3.50477219e-01 8.66493165e-01 -1.22308254e+00
1.04606843e+00 -2.20204973e+00 6.29496753e-01 1.16186686e-01
3.24489862e-01 3.11034173e-01 -5.66979587e-01 5.10613382e-01
-9.67104733e-03 -8.23566988e-02 -6.97661996e-01 -7.85347223e-01
-2.91126192e-01 1.95673704e-01 -4.55856830e-01 5.69326758e-01
-6.88329712e-02 6.71737671e-01 -8.16361964e-01 -3.08951348e-01
1.59749404e-01 8.93704891e-01 -4.67014968e-01 5.07990479e-01
-3.32398154e-02 8.44448447e-01 -7.27819502e-02 2.28638738e-01
8.27793121e-01 -1.22097418e-01 -5.25820861e-03 -4.49773669e-01
-1.06143892e-01 -2.30442688e-01 -1.14739215e+00 2.20280766e+00
-3.06441128e-01 7.07636237e-01 4.35419053e-01 -7.51972735e-01
7.41748810e-01 2.50385731e-01 3.15258503e-01 -9.84198987e-01
-2.75674254e-01 2.78196424e-01 -2.74910390e-01 -6.48522794e-01
5.82260907e-01 -1.45734340e-01 6.75731659e-01 7.32492805e-01
8.23789164e-02 -1.95634574e-01 2.52903819e-01 4.30238307e-01
1.15581739e+00 3.62042606e-01 2.15560850e-02 -2.26867750e-01
4.97487098e-01 -1.67242244e-01 2.02190459e-01 1.05797803e+00
-2.16952056e-01 1.19537640e+00 9.42711486e-04 -1.17592826e-01
-1.29662430e+00 -1.22508633e+00 2.20678583e-01 6.39664888e-01
8.74128118e-02 -1.18339688e-01 -1.00315225e+00 -3.63068700e-01
-4.21208382e-01 8.54201198e-01 -7.25408673e-01 -5.95861785e-02
-4.29401547e-01 -6.61504388e-01 3.81788611e-01 3.07346225e-01
9.03227746e-01 -9.50498521e-01 -2.42922634e-01 2.69902378e-01
-4.99693334e-01 -9.42803204e-01 -7.10015893e-01 -2.33091027e-01
-7.18432903e-01 -7.02564061e-01 -1.41725647e+00 -6.25736654e-01
6.68828309e-01 7.00424671e-01 9.55656350e-01 7.16396719e-02
1.97416004e-02 5.60742915e-01 -3.88427585e-01 2.31910273e-01
-8.19885135e-01 -3.50321710e-01 6.11099461e-03 4.08426404e-01
-1.19430095e-01 -7.83369601e-01 -8.82683873e-01 1.43518314e-01
-1.54859388e+00 -2.83714500e-03 7.31565118e-01 8.23366821e-01
7.12988913e-01 6.03597946e-02 3.80465746e-01 -8.07909191e-01
1.14460123e+00 -4.37558979e-01 -1.55120209e-01 3.12380701e-01
-8.13230276e-01 2.13655949e-01 4.50587928e-01 -6.42540812e-01
-1.56295824e+00 -1.71553731e-01 -3.40845108e-01 -6.60729825e-01
-5.43517917e-02 5.35799384e-01 1.03696205e-01 -1.98888481e-01
8.76466274e-01 6.88255608e-01 1.71539605e-01 -8.99014950e-01
7.35140204e-01 6.66502416e-01 8.52265239e-01 -4.35639918e-01
7.21036971e-01 8.92796218e-01 -3.92902315e-01 -9.73079026e-01
-6.91659212e-01 -2.08751678e-01 -6.24829471e-01 -1.20389044e-01
9.31596756e-01 -8.58250260e-01 1.51340552e-02 8.89369190e-01
-1.37616789e+00 -5.12656093e-01 -4.56447572e-01 2.82616287e-01
-5.67291200e-01 8.34498048e-01 -9.58570004e-01 -5.40494978e-01
-3.27993780e-01 -1.19519174e+00 9.09939528e-01 3.18916813e-02
3.21145840e-02 -1.02414930e+00 3.69295090e-01 1.12782873e-01
1.06389737e+00 -5.95985949e-02 7.31044054e-01 -8.99158418e-02
-5.49480498e-01 1.53345615e-01 -5.56386292e-01 8.87179077e-01
2.85940439e-01 -4.29581881e-01 -8.81329179e-01 -5.57447433e-01
5.17647266e-01 -2.15791181e-01 1.31684041e+00 5.52462935e-01
9.74411070e-01 -3.06016535e-01 -3.79416347e-02 6.85121357e-01
1.48433900e+00 -1.58368453e-01 1.03058493e+00 2.50450164e-01
5.02968669e-01 4.72886920e-01 8.09885785e-02 2.07340047e-01
3.10566813e-01 4.94641811e-01 1.90501899e-01 -9.82825905e-02
-7.90964603e-01 -2.10599318e-01 5.00098944e-01 1.01893353e+00
-3.32592338e-01 -4.26297456e-01 -5.62704086e-01 7.27674246e-01
-1.75165844e+00 -1.10940158e+00 1.49420962e-01 2.04979563e+00
9.42893922e-01 -1.83960050e-01 -4.64304119e-01 -2.14930832e-01
5.92667103e-01 6.31141067e-01 -6.29680097e-01 -1.87949580e-03
-5.26471913e-01 4.55309488e-02 2.73921728e-01 9.13973212e-01
-7.95902014e-01 8.10474217e-01 6.44676828e+00 8.86645913e-01
-1.03002775e+00 4.32195306e-01 6.56325281e-01 1.85576290e-01
-4.07254875e-01 -1.88509092e-01 -1.53761446e-01 2.25135580e-01
8.90915394e-01 -1.06059089e-01 9.27923441e-01 2.44350538e-01
5.50431430e-01 -2.79809088e-01 -8.77896309e-01 9.87403333e-01
5.74958384e-01 -1.44248843e+00 3.67454886e-01 -4.69082780e-02
1.06270313e+00 1.62404984e-01 3.16337526e-01 -2.20343798e-01
5.30317843e-01 -8.90291035e-01 8.77539694e-01 1.15607190e+00
9.06973183e-01 -2.19716907e-01 4.36612189e-01 4.53024954e-01
-7.23303556e-01 1.77518651e-01 -4.41517115e-01 3.93175960e-01
5.04690170e-01 8.43896627e-01 -4.54680026e-02 5.22844315e-01
8.86903763e-01 1.02132416e+00 -5.76623142e-01 9.15755987e-01
-2.93780595e-01 5.32485723e-01 1.63727850e-02 8.14444065e-01
-5.54854423e-02 -5.21683514e-01 8.04013848e-01 1.10372663e+00
6.34812176e-01 1.71274200e-01 -3.07876527e-01 1.19634473e+00
-8.43415633e-02 -2.62262046e-01 -6.32122099e-01 1.29469767e-01
1.13796014e-02 9.40391660e-01 -4.05210495e-01 -4.76412833e-01
-3.14393699e-01 1.88872945e+00 2.97259152e-01 7.94560194e-01
-5.22176027e-01 -3.19084823e-02 4.33660239e-01 -6.16170093e-02
5.38901865e-01 -4.42828655e-01 3.67173664e-02 -1.46205103e+00
-6.75602397e-03 -1.07939100e+00 -2.66227629e-02 -1.27256298e+00
-1.60664237e+00 9.75410342e-01 -1.45267010e-01 -9.53044832e-01
-1.90857396e-01 -2.37961933e-01 -2.80359775e-01 1.16824353e+00
-1.80624390e+00 -1.02613425e+00 -4.00123984e-01 7.72415221e-01
7.56501138e-01 7.41034895e-02 5.87881029e-01 4.82745886e-01
-1.86266065e-01 -4.70079388e-03 6.33781254e-01 -1.47313640e-01
1.00760102e+00 -1.10411012e+00 4.71507788e-01 1.15392935e+00
1.11596890e-01 5.00928819e-01 9.11878169e-01 -8.37679386e-01
-1.22707605e+00 -1.09142792e+00 7.76473939e-01 -2.59773046e-01
5.32343686e-01 8.34594890e-02 -1.27291512e+00 6.66945159e-01
6.31737173e-01 3.87471891e-03 2.11508781e-01 -7.35045671e-01
-3.35328132e-01 -2.68132016e-02 -1.22677004e+00 6.21847630e-01
1.16708195e+00 -7.15534627e-01 -4.68178391e-01 1.77071795e-01
5.25726080e-01 -3.70045573e-01 -7.58423924e-01 1.83758698e-02
3.28654528e-01 -1.28935802e+00 1.09015965e+00 -2.80002207e-01
7.64397085e-01 -2.81890720e-01 -3.50044847e-01 -1.82806647e+00
-4.49048728e-01 -6.56613111e-01 -2.00666279e-01 1.16795695e+00
2.00927839e-01 -5.07904649e-01 3.82159323e-01 5.03563464e-01
1.34694561e-01 -2.25636214e-01 -7.57006705e-01 -6.32513702e-01
1.21073835e-01 -1.99488550e-01 2.63114929e-01 8.06941450e-01
-7.41295815e-01 5.70066273e-02 -8.35168183e-01 1.01513155e-01
1.04328537e+00 -1.83084607e-01 4.87124205e-01 -7.52572715e-01
-4.74185050e-01 -2.42915452e-01 1.02219403e-01 -1.62223899e+00
-3.18052173e-02 -6.45256162e-01 1.76288888e-01 -1.75201213e+00
2.39304051e-01 -2.36095227e-02 -2.29311258e-01 4.75056134e-02
5.31593710e-03 5.61935544e-01 1.67528763e-01 7.28131890e-01
-3.62513065e-01 7.21720874e-01 1.46179509e+00 -4.05090392e-01
-1.68493874e-02 -3.82437795e-01 -7.34991193e-01 5.44310093e-01
4.63433832e-01 -4.65414613e-01 -4.19971675e-01 -1.13961601e+00
-1.73548032e-02 6.94344193e-02 4.91230965e-01 -8.08448553e-01
5.14014006e-01 -5.70603125e-02 3.53557140e-01 -1.24600731e-01
3.88542712e-01 -6.61676645e-01 3.12666118e-01 2.56684542e-01
-5.37748516e-01 8.10776651e-02 -1.08429782e-01 9.35560048e-01
-2.98340201e-01 -1.60307169e-01 1.04548168e+00 -2.82861799e-01
-7.12717175e-01 1.41316369e-01 -3.42446268e-01 -1.47321388e-01
6.23655379e-01 -1.65050477e-02 -5.17904818e-01 -9.40407574e-01
-9.21294272e-01 -4.26558822e-01 7.75379717e-01 3.66186887e-01
9.83900785e-01 -1.13732147e+00 -1.14436626e+00 2.85566866e-01
-1.21828109e-01 -3.46152812e-01 4.83893305e-01 1.01723254e+00
-5.53057194e-01 3.97330895e-03 -1.08011991e-01 -4.20840085e-01
-1.01877356e+00 7.12843657e-01 4.38097268e-01 -1.99706391e-01
-9.06706214e-01 6.04680955e-01 1.52089313e-01 -4.74100858e-02
-2.68216208e-02 -1.58552125e-01 -8.92393813e-02 -3.25667411e-01
8.51944029e-01 3.65026504e-01 -3.48463096e-02 -9.03195560e-01
9.57441628e-02 5.87804139e-01 -2.10864678e-01 -5.75176179e-01
1.50294626e+00 -7.30658531e-01 -3.70549828e-01 2.36035421e-01
9.46125925e-01 1.61318019e-01 -1.61316586e+00 -7.36163497e-01
-2.77043492e-01 -7.10158408e-01 4.56654310e-01 -1.03336537e+00
-1.34492958e+00 8.23751926e-01 9.04750109e-01 1.82644278e-01
1.29441726e+00 -1.88226536e-01 8.03670228e-01 6.70181066e-02
3.09101403e-01 -8.79772246e-01 1.83012083e-01 1.99799076e-01
1.41037440e+00 -1.22765684e+00 -2.16529351e-02 -1.95152819e-01
-6.81928217e-01 9.04677272e-01 1.21237822e-01 -1.99340791e-01
7.52225220e-01 -1.81981958e-02 3.06097478e-01 -5.51231727e-02
-6.19383574e-01 -5.87972961e-02 2.44353443e-01 8.52969885e-01
1.02935031e-01 -4.25686032e-01 -2.09559366e-01 1.73342153e-01
3.63755941e-01 2.97682792e-01 6.98859692e-01 7.96409190e-01
-3.57546717e-01 -9.70597208e-01 -4.32097048e-01 1.54283121e-01
-3.43162298e-01 -3.86772037e-01 -3.57151687e-01 1.39774218e-01
-1.11442812e-01 1.15077639e+00 -3.20852727e-01 -3.80583890e-02
2.08439320e-01 -2.97828794e-01 6.10654533e-01 -2.46749058e-01
-7.30523691e-02 1.98273659e-01 -1.02180034e-01 -7.00136125e-01
-9.33311284e-01 -5.65353870e-01 -5.72618544e-01 -3.74750823e-01
7.66518712e-02 -1.08400673e-01 6.50208235e-01 8.67151618e-01
5.80430925e-01 4.34319705e-01 3.54158133e-01 -1.18717194e+00
-6.79550231e-01 -1.08929193e+00 -8.13403368e-01 6.93724751e-01
5.95008254e-01 -2.98893750e-01 -4.41770643e-01 6.21884227e-01] | [11.648002624511719, -2.2320380210876465] |
6664b7ed-2252-43c9-a37c-e9d4b910c2bf | from-indoor-to-outdoor-unsupervised-domain | 2211.11155 | null | https://arxiv.org/abs/2211.11155v1 | https://arxiv.org/pdf/2211.11155v1.pdf | From Indoor To Outdoor: Unsupervised Domain Adaptive Gait Recognition | Gait recognition is an important AI task, which has been progressed rapidly with the development of deep learning. However, existing learning based gait recognition methods mainly focus on the single domain, especially the constrained laboratory environment. In this paper, we study a new problem of unsupervised domain adaptive gait recognition (UDA-GR), that learns a gait identifier with supervised labels from the indoor scenes (source domain), and is applied to the outdoor wild scenes (target domain). For this purpose, we develop an uncertainty estimation and regularization based UDA-GR method. Specifically, we investigate the characteristic of gaits in the indoor and outdoor scenes, for estimating the gait sample uncertainty, which is used in the unsupervised fine-tuning on the target domain to alleviate the noises of the pseudo labels. We also establish a new benchmark for the proposed problem, experimental results on which show the effectiveness of the proposed method. We will release the benchmark and source code in this work to the public. | ['Song Wang', 'Wei Feng', 'Ruize Han', 'Likai Wang'] | 2022-11-21 | null | null | null | null | ['gait-recognition'] | ['computer-vision'] | [ 5.71274385e-02 -6.58540905e-01 1.67371318e-01 -4.48151529e-01
-5.73394597e-01 2.79570166e-02 -9.31123495e-02 -4.15337384e-01
-2.13645369e-01 8.83004487e-01 1.50406212e-01 5.39275050e-01
-1.86955124e-01 -5.95673919e-01 -3.69505405e-01 -1.13782740e+00
-2.64201581e-01 4.32004899e-01 2.25928679e-01 1.60404757e-01
1.09134905e-01 4.41327132e-02 -1.38105094e+00 -3.19630414e-01
9.84999359e-01 9.66568589e-01 1.83386400e-01 4.38409388e-01
3.28404397e-01 5.60828984e-01 -5.77826619e-01 5.40760159e-02
-7.49500794e-03 -4.53239053e-01 -4.19861823e-01 4.77539212e-01
-7.44756907e-02 -4.61294830e-01 -4.30261910e-01 1.03613830e+00
7.44601965e-01 5.40378511e-01 8.89033794e-01 -1.39950216e+00
-4.18395698e-01 9.41714495e-02 -5.71433783e-01 1.20924130e-01
1.10513315e-01 3.25382322e-01 4.67723370e-01 -5.89638293e-01
3.54554772e-01 1.26417732e+00 6.65853560e-01 6.47329032e-01
-8.41591597e-01 -7.50073910e-01 1.45346463e-01 5.58924317e-01
-1.40109754e+00 -3.56894523e-01 1.03498638e+00 -4.69123870e-01
4.99421149e-01 -3.80981296e-01 2.89537668e-01 1.30420864e+00
1.99108049e-01 8.73646200e-01 1.02706766e+00 -2.08737820e-01
4.98221874e-01 -5.83923519e-01 2.76891232e-01 7.20590115e-01
6.00342751e-01 3.38406749e-02 -3.00615996e-01 1.01589553e-01
8.10224235e-01 -1.41469911e-01 -6.78294450e-02 -4.75740939e-01
-8.67029667e-01 4.33868498e-01 8.18860605e-02 2.28570938e-01
-6.16645701e-02 6.00513965e-02 5.73189735e-01 1.64717928e-01
3.32424760e-01 -1.75549522e-01 -2.46576503e-01 -3.49292904e-01
-6.99155092e-01 1.77080393e-01 5.06790400e-01 8.62466156e-01
7.06095040e-01 4.23054248e-01 -1.69799432e-01 1.06284225e+00
5.77798247e-01 7.25540638e-01 8.47263515e-01 -7.24603355e-01
3.89680773e-01 2.89465159e-01 2.40022093e-01 -1.18559027e+00
-5.31706572e-01 -2.23777786e-01 -1.03321445e+00 -3.58174816e-02
4.25391108e-01 -3.29770297e-01 -1.00980616e+00 1.83063304e+00
2.61723965e-01 8.02147686e-01 1.75958753e-01 1.09162176e+00
4.99322087e-01 4.79100704e-01 1.79734096e-01 -2.30453178e-01
9.16932404e-01 -9.27421510e-01 -6.78131402e-01 -4.48500872e-01
6.14212215e-01 -5.03299236e-01 1.04994249e+00 3.17910761e-01
-2.61635363e-01 -8.50932062e-01 -1.29072571e+00 2.21391827e-01
-2.51264989e-01 6.54665589e-01 3.18958253e-01 7.91574657e-01
-4.99619842e-01 8.33967268e-01 -1.18762207e+00 -7.28229702e-01
3.14424276e-01 3.29623669e-01 -2.79119909e-01 -3.76214355e-01
-1.29675460e+00 5.35158694e-01 6.09924555e-01 3.36055368e-01
-9.16802227e-01 5.37150428e-02 -1.09788072e+00 -1.88253194e-01
3.12477916e-01 -4.20942962e-01 7.30609596e-01 -4.67183173e-01
-1.59393108e+00 7.41834998e-01 8.84531364e-02 -2.65021741e-01
3.57924610e-01 -3.51700127e-01 -4.84282166e-01 -2.41252318e-01
6.05570436e-01 7.41100237e-02 1.09073436e+00 -9.58722651e-01
-4.15089041e-01 -5.48199475e-01 -4.99900103e-01 3.78071144e-02
-2.51142651e-01 -3.59361142e-01 -5.95527649e-01 -8.56109142e-01
-1.87200624e-02 -8.11554730e-01 -1.75774083e-01 -4.56289262e-01
-2.87003845e-01 -2.38409698e-01 1.00317633e+00 -7.84867108e-01
1.06946051e+00 -2.40321350e+00 1.17129698e-01 2.22422004e-01
-2.72515982e-01 1.43539488e-01 -5.83310761e-02 -6.35187253e-02
-6.45379396e-03 -3.83021146e-01 -7.56014943e-01 -5.02159417e-01
2.33046204e-01 5.18033504e-01 2.61429604e-02 4.70397174e-01
2.44298741e-01 2.48074397e-01 -1.07782841e+00 -7.83898830e-01
3.45397949e-01 2.28396244e-02 -4.47225302e-01 4.67737675e-01
3.89294587e-02 8.53825927e-01 -7.71804333e-01 9.11972642e-01
9.99110758e-01 -1.69454236e-02 1.77532136e-02 -2.79769778e-01
2.38961324e-01 -4.66139078e-01 -1.70181060e+00 1.81724656e+00
-1.58990055e-01 8.01080465e-02 -1.17091410e-01 -1.39542782e+00
1.20470130e+00 -1.23386182e-01 7.37036705e-01 -4.74372476e-01
2.85002738e-01 3.75956088e-01 -2.08141327e-01 -8.48200142e-01
5.81812337e-02 1.31194413e-01 -4.35824215e-01 1.48360446e-01
3.50930780e-01 2.21852530e-02 4.88624841e-01 -2.66257524e-01
9.86341655e-01 7.55987465e-01 1.51245996e-01 -1.54688954e-01
8.02199483e-01 -1.61540493e-01 1.08736575e+00 5.84037542e-01
-7.76095033e-01 6.47054732e-01 1.92975953e-01 -2.81182766e-01
-7.59683132e-01 -1.18494713e+00 -4.03084308e-02 6.97147608e-01
3.96925479e-01 -2.32918411e-01 -8.00215840e-01 -5.78381896e-01
-1.30344167e-01 2.62175351e-01 -3.52329344e-01 -5.56266487e-01
-6.47690713e-01 -1.09704447e+00 6.43155038e-01 8.13725829e-01
1.35345602e+00 -8.10183525e-01 -2.91052163e-01 3.56647879e-01
-5.23755074e-01 -1.24515522e+00 -5.74970722e-01 1.20685071e-01
-7.56932735e-01 -1.09944510e+00 -9.19511139e-01 -9.54516411e-01
5.08771718e-01 -1.77963644e-01 6.31250739e-01 -2.20074788e-01
-3.45411718e-01 6.37599528e-01 -2.85763681e-01 1.74150616e-02
2.54425406e-01 -1.27973303e-01 6.63607538e-01 3.39514047e-01
4.87566143e-01 -7.89123714e-01 -2.72213221e-01 4.99563694e-01
-5.14769554e-01 -3.48840505e-01 6.25322044e-01 1.08183885e+00
7.49523938e-01 4.40889567e-01 6.09559655e-01 -5.01878023e-01
6.32018626e-01 -5.29966712e-01 -4.22026426e-01 5.10352030e-02
-2.30018616e-01 2.97716171e-01 7.15208173e-01 -3.63036722e-01
-1.12530077e+00 3.18000644e-01 -6.87577873e-02 -5.00333309e-01
-5.02685010e-01 5.73219836e-01 -7.15460479e-01 -1.74173400e-01
3.88844699e-01 4.87668335e-01 -2.66903460e-01 -5.57061136e-01
-1.81100946e-02 6.71235740e-01 7.08007514e-01 -1.17708087e+00
8.31564307e-01 2.13413492e-01 1.57509178e-01 -1.04139161e+00
-4.39145535e-01 -5.54982662e-01 -7.07201600e-01 -4.73058820e-01
8.85863960e-01 -7.96787560e-01 -4.03986394e-01 9.83655334e-01
-7.62130558e-01 -2.95229048e-01 -4.42837179e-02 7.90692806e-01
-9.75074351e-01 8.95266294e-01 -5.27433276e-01 -7.36586571e-01
-1.31463125e-01 -1.15888250e+00 1.20952213e+00 5.53643107e-01
8.22588280e-02 -7.98341870e-01 3.71309251e-01 3.71353894e-01
5.45733310e-02 4.47919488e-01 6.56954706e-01 -4.35187757e-01
-3.71069998e-01 9.94188478e-04 -1.34323016e-01 4.28889453e-01
4.46331561e-01 -1.53819397e-01 -8.96037340e-01 -2.31816977e-01
1.62293598e-01 -5.24196088e-01 9.23612475e-01 4.74252582e-01
1.03986275e+00 2.11882219e-01 -2.02915624e-01 5.55604100e-01
1.25977325e+00 2.94491172e-01 6.00788116e-01 4.09289122e-01
6.63143575e-01 2.62978971e-01 1.10291278e+00 6.08804286e-01
2.42545024e-01 5.08579195e-01 2.41707396e-02 4.64788646e-01
3.74176763e-02 -1.62384942e-01 4.69370604e-01 1.21800935e+00
-3.81251484e-01 5.36789838e-03 -8.33227873e-01 4.74636883e-01
-2.23882151e+00 -5.67644298e-01 2.23469526e-01 2.12615585e+00
4.58316803e-01 1.31010652e-01 1.05524920e-01 2.09717497e-01
9.72316682e-01 1.95090666e-01 -7.46194124e-01 3.37642804e-02
1.29088849e-01 1.95550472e-01 2.78208762e-01 1.01729617e-01
-1.69281602e+00 7.71572173e-01 5.57909679e+00 1.01240480e+00
-8.92886102e-01 -1.43050328e-01 4.96281534e-01 5.69409490e-01
6.94675207e-01 -3.56471598e-01 -5.66562831e-01 7.96367705e-01
7.21774578e-01 -1.21526919e-01 2.80898243e-01 1.24492168e+00
4.11289066e-01 -5.08674197e-02 -9.78369236e-01 1.34777582e+00
1.34760113e-02 -5.12891412e-01 -1.39639392e-01 -2.78935850e-01
5.01357913e-01 -5.00672340e-01 2.30794270e-02 6.54320240e-01
2.25012854e-01 -5.86443663e-01 4.10283841e-02 6.79756761e-01
6.76183760e-01 -7.26540148e-01 9.51804578e-01 2.60378897e-01
-1.67095077e+00 -8.16547051e-02 -6.54999495e-01 -8.74491706e-02
-1.74189359e-01 5.21685243e-01 -1.52260840e-01 8.31711888e-01
7.54602551e-01 1.29022586e+00 -5.66495180e-01 1.32235873e+00
-2.00031683e-01 7.24353373e-01 -4.11704481e-01 3.68371755e-02
7.54112229e-02 -4.93462235e-01 5.27249396e-01 1.16575682e+00
4.32823032e-01 -4.69229184e-02 4.69837815e-01 8.52389812e-01
-6.28956407e-02 1.05230130e-01 -5.70650518e-01 -1.26407877e-01
1.64018422e-01 8.70151758e-01 -7.23327637e-01 -1.06049851e-01
-9.24813151e-02 1.13347995e+00 1.94772035e-01 3.03510696e-01
-8.38591278e-01 -5.41298509e-01 5.81590056e-01 -4.81433958e-01
1.85657516e-01 -5.03395319e-01 -1.12011284e-01 -1.57669914e+00
1.67854190e-01 -6.47863984e-01 4.68709469e-01 -6.10770941e-01
-1.55284917e+00 -1.66387364e-01 1.48192316e-01 -1.59430552e+00
-2.36383960e-01 -7.87903070e-01 -8.35635900e-01 4.86603290e-01
-1.15436721e+00 -9.93466258e-01 -5.17709672e-01 7.95830846e-01
5.20939171e-01 -2.08419144e-01 7.63697982e-01 8.11500549e-01
-9.89605546e-01 6.75563395e-01 6.28863573e-01 5.24987102e-01
1.07891917e+00 -1.03284776e+00 1.85885131e-01 1.08876562e+00
-2.95373768e-01 3.63453001e-01 6.37228131e-01 -8.40046883e-01
-1.24112868e+00 -1.30311704e+00 2.78487116e-01 -1.10358850e-03
7.21136689e-01 -1.35848850e-01 -7.35078156e-01 5.50544143e-01
-4.60164309e-01 6.59992024e-02 4.57789183e-01 -1.18395574e-01
1.66750532e-02 -2.94195324e-01 -1.41220331e+00 3.44903976e-01
1.08914149e+00 -2.31708676e-01 -5.47867835e-01 2.78533518e-01
4.68428254e-01 -4.10688937e-01 -8.23207796e-01 5.19348025e-01
4.26756412e-01 -5.11905432e-01 9.34441388e-01 -4.67848063e-01
1.70656085e-01 -6.00699723e-01 -2.40313128e-01 -1.48097014e+00
-4.09070998e-01 5.79126701e-02 -2.86204275e-02 1.36040354e+00
-5.18183470e-01 -4.73521024e-01 1.07049191e+00 4.25076842e-01
-1.91798151e-01 -1.42244264e-01 -1.09205413e+00 -1.06700373e+00
-9.26646888e-02 -3.64232361e-01 3.91966760e-01 8.22842062e-01
-1.85917750e-01 6.37661442e-02 -7.41851330e-01 3.56521338e-01
1.24943829e+00 -2.84599531e-02 7.01674819e-01 -1.14348054e+00
-3.21360320e-01 1.32308364e-01 -1.11993480e+00 -9.94103730e-01
1.84576809e-01 -2.68588096e-01 5.16206741e-01 -1.29429817e+00
-8.30732733e-02 -1.96859553e-01 -4.60429370e-01 2.30567977e-01
-2.99676985e-01 -1.12889605e-02 -1.17458098e-01 3.00058782e-01
-9.39795494e-01 8.54387105e-01 1.00897455e+00 -5.72254360e-01
-1.99285205e-02 8.71025771e-02 -1.58465058e-01 8.41095686e-01
9.81062829e-01 -3.92270535e-01 -4.92208511e-01 -2.81934112e-01
-7.82952130e-01 -2.51402706e-01 1.47113144e-01 -1.91755867e+00
1.68147787e-01 -2.65428513e-01 5.31963468e-01 -3.69072199e-01
3.49295169e-01 -7.28483260e-01 -2.40362287e-01 5.60921848e-01
8.40455070e-02 -5.05124211e-01 1.60764903e-01 9.15039778e-01
-2.56751090e-01 -1.95865124e-01 8.27681422e-01 -1.16787508e-01
-1.39120042e+00 4.73201364e-01 -3.93804669e-01 3.48887384e-01
7.81992376e-01 -2.85968840e-01 5.07860370e-02 -9.91289169e-02
-1.05273974e+00 4.27305490e-01 1.93532869e-01 1.37322173e-01
7.56491065e-01 -1.63655746e+00 -5.25840998e-01 2.63482064e-01
2.78945595e-01 1.65044013e-02 5.38393319e-01 4.88547027e-01
-4.95503843e-01 8.77652839e-02 -6.22097015e-01 -6.26704812e-01
-9.99274254e-01 3.99177015e-01 4.22836840e-01 -4.15965885e-01
-3.99039358e-01 4.57476526e-01 -1.39777949e-02 -5.62254608e-01
3.57834965e-01 -3.52245450e-01 -4.53472525e-01 -3.73809934e-01
2.51754463e-01 5.22099316e-01 -1.17415853e-01 -6.97410405e-01
-6.01358831e-01 9.31445360e-01 2.06146091e-01 3.11849713e-01
1.12425470e+00 -2.66115606e-01 6.27964064e-02 5.42602956e-01
1.01835799e+00 -4.65535700e-01 -1.31854594e+00 -1.75800323e-01
1.29370451e-01 -4.00460780e-01 -3.51469934e-01 -3.59589636e-01
-9.51793253e-01 8.35133791e-01 1.21500361e+00 -4.71089184e-01
1.18307328e+00 -6.29026532e-01 8.56723011e-01 8.15908790e-01
7.47028589e-01 -1.49367130e+00 3.22080523e-01 7.01776624e-01
3.73830557e-01 -1.30790651e+00 -1.54816359e-01 -3.70247126e-01
-5.19555151e-01 1.21869767e+00 9.27147985e-01 -4.86813635e-01
7.25777984e-01 1.78306341e-01 -2.40218967e-01 2.90116102e-01
-3.26749608e-02 -3.77686739e-01 -2.56831706e-01 1.25467968e+00
1.24403059e-01 1.56583339e-01 -2.36904770e-01 1.02228308e+00
2.86213666e-01 6.20341063e-01 2.99955398e-01 1.14075530e+00
-5.68857133e-01 -1.06694078e+00 -6.72545552e-01 3.15423876e-01
3.15687694e-02 4.20926243e-01 -3.14696804e-02 4.45454031e-01
5.14981925e-01 9.52165306e-01 -2.92600363e-01 -7.12467730e-01
3.48495603e-01 1.48940563e-01 4.44128394e-01 -3.66201639e-01
3.23679715e-01 -9.31872129e-02 1.07678726e-01 -5.14514327e-01
-5.78703225e-01 -5.51041901e-01 -1.34888256e+00 -8.41176361e-02
1.53542245e-02 2.35619918e-01 8.76513198e-02 1.12534082e+00
9.94854942e-02 5.23817480e-01 5.50045490e-01 -9.65050995e-01
-4.53988135e-01 -9.68301296e-01 -1.05459428e+00 4.54518288e-01
3.54240858e-03 -1.28320765e+00 -3.50463390e-01 2.48922348e-01] | [14.312410354614258, 1.4116252660751343] |
404afc3f-8860-48ef-b827-cfa8552765ae | joint-pruning-quantization-for-extremely | 2010.01892 | null | https://arxiv.org/abs/2010.01892v1 | https://arxiv.org/pdf/2010.01892v1.pdf | Joint Pruning & Quantization for Extremely Sparse Neural Networks | We investigate pruning and quantization for deep neural networks. Our goal is to achieve extremely high sparsity for quantized networks to enable implementation on low cost and low power accelerator hardware. In a practical scenario, there are particularly many applications for dense prediction tasks, hence we choose stereo depth estimation as target. We propose a two stage pruning and quantization pipeline and introduce a Taylor Score alongside a new fine-tuning mode to achieve extreme sparsity without sacrificing performance. Our evaluation does not only show that pruning and quantization should be investigated jointly, but also shows that almost 99% of memory demand can be cut while hardware costs can be reduced up to 99.9%. In addition, to compare with other works, we demonstrate that our pruning stage alone beats the state-of-the-art when applied to ResNet on CIFAR10 and ImageNet. | ['Shao-Yi Chien', 'Liang-Gee Chen', 'Jan P. Klopp', 'Sih-Sian Wu', 'Po-Hsiang Yu'] | 2020-10-05 | null | null | null | null | ['stereo-depth-estimation'] | ['computer-vision'] | [ 8.50372985e-02 2.24360511e-01 -3.23613808e-02 -5.22256434e-01
-2.07503870e-01 1.04571119e-01 4.05527711e-01 4.25725840e-02
-8.29415619e-01 5.45271933e-01 1.36216834e-01 -5.16316354e-01
1.48149937e-01 -9.84599710e-01 -7.17041850e-01 -4.39436138e-01
5.58762960e-02 1.69154610e-02 5.14503539e-01 -8.82663131e-02
5.34557849e-02 6.72589064e-01 -1.69432831e+00 4.68177795e-01
6.48079574e-01 1.37271428e+00 3.55855525e-01 4.20881093e-01
8.11008364e-02 9.95946705e-01 -5.82029641e-01 -5.08729398e-01
6.89714134e-01 5.46867512e-02 -5.02771258e-01 -2.58716762e-01
9.44305956e-01 -9.09193695e-01 -4.93710607e-01 1.24945319e+00
6.46610379e-01 -2.43026186e-02 2.52356738e-01 -6.99600697e-01
-8.03896561e-02 7.01545358e-01 -6.01499438e-01 4.78108078e-01
-3.01038682e-01 5.21336216e-03 9.35697734e-01 -7.71194994e-01
4.00883585e-01 1.10948288e+00 6.48744583e-01 5.20330250e-01
-1.04485846e+00 -9.93688881e-01 1.95757598e-01 2.61661530e-01
-1.22260118e+00 -7.07760513e-01 5.07820070e-01 -2.43237820e-02
1.49284494e+00 -9.68580022e-02 9.49123025e-01 6.98656082e-01
2.69389749e-01 5.92483521e-01 6.62094891e-01 -3.46084833e-01
5.23946226e-01 -1.72635302e-01 1.52877361e-01 8.61315429e-01
3.36799681e-01 1.54931471e-01 -6.04883194e-01 2.88570940e-01
8.80856097e-01 3.87127437e-02 -1.54417336e-01 4.11217697e-02
-9.09788251e-01 8.59498858e-01 7.97252178e-01 6.84789494e-02
-3.96930933e-01 4.08218026e-01 5.98416269e-01 2.54258633e-01
2.63760924e-01 1.90957382e-01 -2.96349496e-01 -1.11481905e-01
-1.45970929e+00 2.96866506e-01 7.00239897e-01 9.44993675e-01
6.44083560e-01 5.45404255e-01 -3.18390019e-02 8.34349334e-01
3.81746367e-02 2.37044334e-01 5.21254063e-01 -1.13362968e+00
5.21673858e-01 3.14970374e-01 -3.46686989e-01 -8.31538081e-01
-5.21162033e-01 -7.25989759e-01 -1.24214756e+00 4.87870634e-01
9.50001031e-02 -8.58632401e-02 -9.71581221e-01 1.56068063e+00
1.00594886e-01 3.11625242e-01 -1.14560403e-01 8.79940927e-01
9.49515581e-01 4.75946844e-01 9.85321999e-02 -1.29002184e-01
1.29478562e+00 -1.02974916e+00 -4.59215879e-01 -5.11961520e-01
8.07161331e-01 -4.79863852e-01 9.12678838e-01 6.51544869e-01
-1.25014091e+00 -6.39717817e-01 -1.54874945e+00 -4.69060719e-01
6.82493746e-02 2.24712715e-01 9.94083881e-01 5.76851249e-01
-1.18760717e+00 9.91631269e-01 -1.19582820e+00 -2.67918278e-02
8.33371997e-01 7.40950882e-01 -5.78536876e-02 -9.83824767e-03
-8.49584758e-01 7.70621717e-01 6.23648703e-01 -9.71366316e-02
-6.38528109e-01 -8.57112169e-01 -6.04680300e-01 4.84479904e-01
2.12373417e-02 -6.79970860e-01 1.40908754e+00 -6.44283950e-01
-1.58334315e+00 6.24976039e-01 -1.57680929e-01 -1.29730392e+00
3.32949013e-01 -3.07808101e-01 -7.88771734e-02 2.57444322e-01
-2.51471013e-01 1.46744239e+00 6.48196280e-01 -4.06975567e-01
-9.94655311e-01 -1.50265425e-01 3.23758543e-01 1.26804173e-01
-8.01963270e-01 -2.26168290e-01 -3.96113753e-01 -5.98751962e-01
2.81339169e-01 -7.01415837e-01 -3.37541044e-01 1.71304822e-01
1.21298258e-03 -5.41405240e-03 7.68256247e-01 -4.33667868e-01
1.18960786e+00 -2.07648802e+00 -2.13363484e-01 -1.04692847e-01
5.49063206e-01 4.47899312e-01 1.15850516e-01 -2.53030539e-01
1.63293809e-01 9.27053764e-03 3.13368142e-02 -6.09881103e-01
-1.48090407e-01 2.70760000e-01 -5.55307209e-01 3.80411416e-01
1.78005815e-01 6.77461445e-01 -5.52634716e-01 -3.39968592e-01
4.12264884e-01 6.37863219e-01 -1.15961218e+00 -2.21266627e-01
-9.74901766e-02 -8.58543962e-02 5.44748195e-02 5.15979230e-01
6.90934837e-01 -2.64850765e-01 1.10138260e-01 -4.58143651e-01
-6.87543526e-02 7.43532062e-01 -9.38282669e-01 1.53925192e+00
-5.37747443e-01 8.14716160e-01 8.84463340e-02 -9.94388223e-01
9.28702414e-01 5.00334576e-02 1.12080313e-01 -1.16778004e+00
3.65023851e-01 1.81314334e-01 3.13371778e-01 1.22652441e-01
7.25639105e-01 -7.95758888e-02 3.41805607e-01 -6.74100518e-02
1.89605981e-01 3.47152241e-02 2.53713012e-01 7.01883435e-02
1.15677369e+00 -2.59205490e-01 1.68347389e-01 -5.53421199e-01
1.36174768e-01 -6.17261380e-02 7.48719513e-01 5.40530264e-01
-1.70746490e-01 4.11896378e-01 5.78320384e-01 -5.21394789e-01
-1.21460819e+00 -7.70865202e-01 -3.29595298e-01 1.17651272e+00
-8.76533985e-02 -5.69238424e-01 -7.98663616e-01 -2.36395910e-01
-2.49367356e-01 5.75710237e-01 -2.19451353e-01 5.68681434e-02
-7.46642172e-01 -7.14282036e-01 4.20955479e-01 7.37171054e-01
9.61807609e-01 -8.34792018e-01 -1.13395870e+00 3.18615109e-01
3.72512698e-01 -1.49349594e+00 1.02985185e-02 5.86540699e-01
-1.38194430e+00 -6.41276777e-01 -3.62093896e-01 -9.16648388e-01
3.63651931e-01 3.52692753e-01 1.12236249e+00 5.50999753e-02
-1.35844857e-01 -4.45677429e-01 -1.66360781e-01 -2.71881610e-01
-1.88782327e-02 4.93531734e-01 7.53010660e-02 -6.75392628e-01
3.97335231e-01 -1.03956592e+00 -9.60041344e-01 -8.95539448e-02
-6.89748108e-01 3.27763021e-01 6.72491670e-01 7.68123925e-01
7.27371931e-01 1.43556863e-01 4.09533769e-01 -8.07269037e-01
2.50328958e-01 2.51347572e-02 -9.90671039e-01 -2.09675565e-01
-5.51547110e-01 2.35424638e-02 9.27107036e-01 -3.78527164e-01
-6.77008748e-01 2.39923701e-01 -6.56040728e-01 -6.53251171e-01
7.06508532e-02 2.39746273e-01 1.68867767e-01 -3.16486627e-01
6.84435606e-01 -1.99500114e-01 -3.00856739e-01 -4.10729349e-01
1.61660537e-01 3.65911245e-01 5.65017223e-01 -1.70933932e-01
3.64969581e-01 6.24694347e-01 2.81261802e-01 -8.72188747e-01
-8.64010930e-01 -1.96637064e-01 -4.94840622e-01 1.45238042e-01
5.31151652e-01 -1.34665191e+00 -8.22644472e-01 1.25482917e-01
-1.08133984e+00 -4.82823044e-01 -3.27279121e-01 5.28727770e-01
-3.57321054e-01 1.94464386e-01 -9.11418378e-01 -4.31751519e-01
-7.57009447e-01 -1.43057013e+00 8.73019636e-01 1.26627088e-01
-1.37293451e-02 -7.27451682e-01 -5.87856174e-01 -9.64216981e-03
4.92150307e-01 -2.20734300e-03 7.54392922e-01 -3.61730635e-01
-7.94246316e-01 2.03522161e-01 -6.26354933e-01 4.26808178e-01
-3.59717041e-01 -1.70178324e-01 -1.09874070e+00 -4.46933031e-01
1.98828921e-01 -4.73412097e-01 1.25269485e+00 5.38054347e-01
1.42644525e+00 -2.66019434e-01 5.81035055e-02 1.19344783e+00
1.46274590e+00 2.00242065e-02 7.48215020e-01 3.37964386e-01
5.97705364e-01 3.19993883e-01 3.53890955e-01 5.77795804e-01
2.37342529e-02 5.76639056e-01 5.94444394e-01 -4.49583530e-02
-3.96930933e-01 -8.75376314e-02 5.85219525e-02 1.09670997e+00
1.75149530e-01 7.06283897e-02 -8.96158397e-01 3.91905844e-01
-1.58028924e+00 -8.59186471e-01 8.66170973e-02 2.10042357e+00
8.17960918e-01 7.25637436e-01 -9.92123261e-02 4.12187994e-01
2.53408372e-01 2.91418612e-01 -5.81435978e-01 -5.64789653e-01
6.69853091e-02 8.20180774e-01 9.29327309e-01 3.87311429e-01
-1.06121993e+00 1.08958149e+00 6.57011938e+00 1.14025235e+00
-1.38814199e+00 1.54969037e-01 9.77855682e-01 -6.17496014e-01
1.26516372e-01 -2.36973405e-01 -1.39169288e+00 1.07150860e-01
1.20702517e+00 4.81997617e-02 2.36175984e-01 1.20267928e+00
1.09649092e-01 3.05094719e-02 -1.07851303e+00 1.22125685e+00
-4.36615705e-01 -1.66326141e+00 6.65690303e-02 1.91687673e-01
7.58647799e-01 5.58503926e-01 8.42148587e-02 3.58989209e-01
1.99308753e-01 -1.05503881e+00 8.32532167e-01 -1.25696972e-01
8.71417284e-01 -9.76834953e-01 6.80275857e-01 4.28364158e-01
-1.16711771e+00 -1.65809706e-01 -1.05823195e+00 -4.47182566e-01
1.00907028e-01 1.03823686e+00 -6.68998837e-01 -3.88902649e-02
8.23252439e-01 7.90099442e-01 -4.56155062e-01 1.12027943e+00
-3.09860975e-01 6.32756710e-01 -5.71365058e-01 -6.56849444e-02
4.00078833e-01 1.36171252e-01 -2.49332245e-02 1.38081717e+00
3.92511547e-01 1.73907727e-01 -1.49011001e-01 5.93179166e-01
-4.46147561e-01 5.55408420e-03 -3.34297270e-01 3.44820201e-01
6.55080676e-01 1.09671235e+00 -7.52406597e-01 -5.32530844e-01
-4.02523488e-01 7.86076128e-01 6.04114413e-01 -6.45844787e-02
-9.15357471e-01 -4.06031519e-01 7.97013164e-01 8.28339383e-02
7.23761857e-01 -3.92054558e-01 -7.41038442e-01 -1.11252737e+00
-1.55512378e-01 -6.99722588e-01 -1.60191923e-01 -3.90176684e-01
-5.10142624e-01 7.55472660e-01 -2.43784159e-01 -1.02117002e+00
-2.12769777e-01 -7.91399181e-01 -3.34811598e-01 6.73480630e-01
-1.74698818e+00 -6.41520441e-01 -4.24773544e-01 2.60107398e-01
6.24253154e-01 -5.27141765e-02 6.23580515e-01 5.68808079e-01
-5.38449109e-01 8.52823377e-01 6.03880882e-02 -3.69838551e-02
1.99196726e-01 -8.53370488e-01 7.12038159e-01 8.17532837e-01
1.48248732e-01 5.92536092e-01 4.94882852e-01 -3.58903646e-01
-1.16239619e+00 -1.25581884e+00 5.88936687e-01 4.41544265e-01
4.85816956e-01 -5.19582868e-01 -9.15244699e-01 3.95771921e-01
7.84394369e-02 2.10843951e-01 3.49439859e-01 7.58641139e-02
-3.41367781e-01 -4.24994797e-01 -1.10175920e+00 7.13728666e-01
1.32100451e+00 -2.22022355e-01 -3.38497460e-01 3.33380461e-01
8.72058511e-01 -7.20262289e-01 -8.33870769e-01 6.19639635e-01
6.08262122e-01 -1.41647100e+00 9.71094489e-01 -7.18048494e-03
6.95293248e-01 5.35200313e-02 -3.29220027e-01 -7.56687701e-01
-3.69168997e-01 -2.26120651e-01 -4.56077218e-01 8.98063838e-01
1.49265289e-01 -4.00463104e-01 1.40132749e+00 3.36260647e-01
-2.83340424e-01 -1.03803253e+00 -1.12882590e+00 -7.69349158e-01
-4.31940965e-02 -5.84359109e-01 3.92239064e-01 4.38187659e-01
-1.95734575e-01 3.44520628e-01 -1.85377136e-01 5.31767085e-02
6.31030977e-01 -1.65970296e-01 3.73992294e-01 -8.96794081e-01
-3.72179389e-01 -6.63465977e-01 -6.95158660e-01 -1.60127163e+00
1.46961240e-02 -7.46673048e-01 -2.01044261e-01 -1.36840570e+00
-1.00705624e-01 -4.74823892e-01 -1.38234019e-01 5.38750589e-01
3.93628269e-01 6.81817532e-01 2.94549584e-01 2.32106343e-01
-3.85911405e-01 6.90432608e-01 1.02019525e+00 -1.52796078e-02
-6.11499138e-02 -2.59742498e-01 -5.32829583e-01 8.80684197e-01
9.00562465e-01 -2.69615799e-01 -6.42895818e-01 -8.13167036e-01
6.25203252e-02 -1.36672527e-01 2.52121449e-01 -1.75325704e+00
3.99993807e-01 2.93357909e-01 3.24068993e-01 -7.70929813e-01
5.13330936e-01 -6.71208858e-01 -2.56889582e-01 7.58750200e-01
-1.38016492e-01 -2.65258998e-02 4.68176156e-01 2.66704470e-01
-3.31623167e-01 -3.36308479e-01 1.18915939e+00 2.09007096e-02
-8.12017560e-01 3.38039100e-01 -1.05599791e-01 -1.38013899e-01
7.05628335e-01 -3.24486256e-01 -4.97560859e-01 -1.28376603e-01
-5.03480971e-01 6.29071817e-02 2.81607717e-01 -1.32101893e-01
6.88422680e-01 -1.14741266e+00 -4.17501003e-01 2.38846868e-01
-4.05030310e-01 3.16109329e-01 1.11546844e-01 5.39177716e-01
-1.04296565e+00 7.54924893e-01 -3.57752115e-01 -6.17669642e-01
-1.27095008e+00 2.89357841e-01 2.47746125e-01 -3.54639590e-01
-9.10349607e-01 1.23904979e+00 1.65475726e-01 1.71508476e-01
6.82908177e-01 -8.05231571e-01 -8.82614776e-02 -1.70544758e-01
7.34483957e-01 3.06591570e-01 5.65940738e-01 -1.86362192e-01
-1.26874134e-01 2.88959146e-01 -2.51149863e-01 1.02156006e-01
1.40946102e+00 2.66612083e-01 1.54074505e-01 -1.06745660e-01
1.36113870e+00 -4.34974909e-01 -1.57598746e+00 -1.96350187e-01
-3.79064709e-01 -2.57543147e-01 6.98196590e-01 -3.46427679e-01
-1.48317194e+00 1.28220689e+00 7.59364784e-01 -3.77323441e-02
1.44169068e+00 -3.83390963e-01 1.00579035e+00 7.50697076e-01
5.14121115e-01 -9.68896568e-01 -1.58855036e-01 7.65492916e-01
5.65462112e-01 -1.03960311e+00 3.23532730e-01 -5.43069303e-01
-2.25534394e-01 1.06730807e+00 7.05109179e-01 -5.33803046e-01
7.18804777e-01 7.57959545e-01 -4.02952999e-01 -4.16615047e-03
-1.05164754e+00 -8.04887861e-02 -1.21665793e-02 3.00846815e-01
4.77143645e-01 -2.08807170e-01 -2.90885061e-01 1.91647395e-01
-8.58523071e-01 1.68289483e-01 2.96844631e-01 8.63271415e-01
-8.77758384e-01 -9.36371386e-01 1.00038789e-01 8.42792153e-01
-6.16109967e-01 -5.38601577e-01 3.24094087e-01 5.76699555e-01
3.08092624e-01 5.65998375e-01 4.76421446e-01 -6.48741961e-01
1.38293296e-01 -3.51582974e-01 5.42439759e-01 -6.46917224e-01
-7.67519295e-01 4.63562794e-02 7.96744823e-02 -6.52518570e-01
-2.73078591e-01 -1.38110578e-01 -1.17354715e+00 -8.33448529e-01
-3.11586916e-01 -2.71999717e-01 7.55394459e-01 6.65417731e-01
4.01470989e-01 8.88049603e-01 1.68173671e-01 -1.01012957e+00
-6.52243376e-01 -8.94113064e-01 -4.72093731e-01 -1.64950579e-01
1.89781263e-01 -6.06791019e-01 -1.90428972e-01 -8.31911415e-02] | [8.565573692321777, 3.007598876953125] |
40b11d38-f7c7-4dc6-a538-4580686b0516 | functional-magnetic-resonance-imaging-data | 2107.06104 | null | https://arxiv.org/abs/2107.06104v2 | https://arxiv.org/pdf/2107.06104v2.pdf | Functional Magnetic Resonance Imaging data augmentation through conditional ICA | Advances in computational cognitive neuroimaging research are related to the availability of large amounts of labeled brain imaging data, but such data are scarce and expensive to generate. While powerful data generation mechanisms, such as Generative Adversarial Networks (GANs), have been designed in the last decade for computer vision, such improvements have not yet carried over to brain imaging. A likely reason is that GANs training is ill-suited to the noisy, high-dimensional and small-sample data available in functional neuroimaging. In this paper, we introduce Conditional Independent Components Analysis (Conditional ICA): a fast functional Magnetic Resonance Imaging (fMRI) data augmentation technique, that leverages abundant resting-state data to create images by sampling from an ICA decomposition. We then propose a mechanism to condition the generator on classes observed with few samples. We first show that the generative mechanism is successful at synthesizing data indistinguishable from observations, and that it yields gains in classification accuracy in brain decoding problems. In particular it outperforms GANs while being much easier to optimize and interpret. Lastly, Conditional ICA enhances classification accuracy in eight datasets without further parameters tuning. | ['Bertrand Thirion', 'Hugo Richard', 'Badr Tajini'] | 2021-07-11 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 6.73697412e-01 3.44688922e-01 8.54963586e-02 -5.66350162e-01
-9.07431960e-01 -5.51516831e-01 6.83734179e-01 -3.58931035e-01
-3.92075092e-01 1.08365059e+00 4.66846168e-01 -1.92755312e-01
-6.83201198e-03 -5.52743018e-01 -7.58115351e-01 -7.35952199e-01
-7.10922405e-02 6.74656749e-01 -5.00025630e-01 1.15374610e-01
-1.82783321e-01 3.91499192e-01 -1.16431153e+00 1.30818725e-01
1.06241834e+00 6.81072354e-01 1.06144860e-01 4.70843524e-01
2.21282005e-01 6.10210598e-01 -6.01629674e-01 -2.56571889e-01
2.42772371e-01 -9.50690687e-01 -6.74740374e-01 1.05057746e-01
7.23675638e-02 -3.72326016e-01 -2.58732408e-01 8.58658791e-01
6.53215647e-01 2.45445147e-02 8.47940624e-01 -1.27380896e+00
-8.99899542e-01 5.77348709e-01 -3.92604291e-01 4.66572165e-01
-4.94879484e-02 3.17342997e-01 5.47181189e-01 -6.87925398e-01
6.69711292e-01 9.76369619e-01 4.11454201e-01 8.93379927e-01
-1.69723380e+00 -7.50481546e-01 -1.82444900e-01 -2.64570452e-02
-1.03806365e+00 -6.31876171e-01 8.91272008e-01 -6.29320741e-01
5.55411041e-01 9.04283375e-02 8.30400288e-01 1.80982089e+00
1.89919814e-01 6.51753128e-01 1.55302882e+00 -2.20646948e-01
4.72864091e-01 -1.39109716e-01 -1.01140119e-01 3.48941684e-01
1.49835601e-01 1.77318022e-01 -3.47274154e-01 -5.85189387e-02
9.43060756e-01 2.59203631e-02 -3.23570579e-01 -1.24047637e-01
-1.19633734e+00 1.02558076e+00 5.81943393e-01 3.53787810e-01
-8.20962489e-01 1.12676680e-01 2.49369085e-01 2.33214721e-01
6.65363491e-01 6.29295707e-01 -1.65053070e-01 -5.33450954e-02
-1.05056894e+00 1.32331371e-01 4.36622441e-01 5.52117407e-01
3.42880726e-01 5.49980342e-01 -9.89443511e-02 8.14164221e-01
-1.74326021e-02 6.08111739e-01 8.88591528e-01 -9.18638349e-01
2.34880149e-01 1.60606876e-01 -3.05858731e-01 -7.81849682e-01
-4.04524237e-01 -5.44647634e-01 -1.07109606e+00 1.45823509e-01
2.89861023e-01 -3.88641357e-01 -1.14033818e+00 2.23189878e+00
-1.90593731e-02 2.79809237e-02 7.69959204e-03 8.63328218e-01
5.18877089e-01 2.01422185e-01 2.34424427e-01 -7.11269304e-02
1.30464089e+00 -5.81048608e-01 -6.99250221e-01 -6.38670623e-01
2.78925061e-01 -1.66447267e-01 9.85564590e-01 2.65554339e-01
-1.13310945e+00 -3.06079954e-01 -8.88745368e-01 1.07279159e-01
-2.77513832e-01 -2.66372234e-01 1.05295992e+00 9.33968127e-01
-1.19342315e+00 1.70602903e-01 -1.22092056e+00 -2.13455066e-01
1.21541631e+00 3.32210600e-01 -6.78886533e-01 -2.26974010e-01
-9.66289639e-01 9.07675147e-01 2.12461174e-01 -4.59278561e-03
-1.23598874e+00 -8.32892060e-01 -8.04647565e-01 3.63080651e-02
1.49519503e-01 -1.04468977e+00 8.81904900e-01 -1.53134775e+00
-1.35882568e+00 8.57519746e-01 -1.30306020e-01 -6.22199357e-01
4.07897741e-01 1.43080831e-01 -2.71668106e-01 3.00612986e-01
8.74415115e-02 1.07484388e+00 1.10321820e+00 -1.15787077e+00
2.87861705e-01 -7.94615388e-01 -3.21153581e-01 7.77183399e-02
-1.57576352e-01 -1.74749251e-02 2.50316739e-01 -7.97113061e-01
1.42813623e-01 -9.86914933e-01 -3.52964133e-01 -2.53126502e-01
-4.22639996e-01 2.70314515e-01 3.89052331e-01 -8.65044951e-01
4.11838800e-01 -1.88373101e+00 1.70772925e-01 1.46203548e-01
4.55879450e-01 1.10681772e-01 -1.78110480e-01 -3.16799432e-02
-5.77837348e-01 3.46150160e-01 -5.61102509e-01 -2.99954683e-01
-2.02631623e-01 3.27185035e-01 -2.49577194e-01 4.55368251e-01
5.04288733e-01 1.39984679e+00 -7.75636554e-01 -1.58102706e-01
-1.46845177e-01 6.40054882e-01 -8.20768297e-01 2.77857959e-01
-1.10437935e-02 1.15254879e+00 -3.81873190e-01 5.34413218e-01
4.20771301e-01 -2.69295096e-01 1.23757176e-01 5.10602482e-02
3.22587758e-01 1.68494910e-01 -3.85894150e-01 1.87500024e+00
-2.70806611e-01 5.39659023e-01 -2.36345697e-02 -1.44897628e+00
6.19675994e-01 4.43581402e-01 5.22505760e-01 -7.59342611e-01
3.12725604e-01 4.92348596e-02 4.76417601e-01 -4.17188972e-01
-3.30675356e-02 -5.60063660e-01 4.79744598e-02 6.78270161e-01
3.17035973e-01 -2.42747128e-01 7.85297528e-03 1.75784811e-01
1.38195586e+00 -6.55837953e-02 1.00067616e-01 -1.84749410e-01
-9.67922993e-03 -1.00876018e-01 4.61998463e-01 7.02682137e-01
-1.79810226e-01 8.73796821e-01 5.99495709e-01 -1.53514072e-01
-1.22855663e+00 -1.12797713e+00 -1.71059012e-01 5.77878177e-01
-7.52336323e-01 1.01232164e-01 -1.16849136e+00 -5.61117768e-01
-3.38138968e-01 6.61890268e-01 -9.51185703e-01 -4.80433524e-01
-3.13878983e-01 -1.25168526e+00 7.23103642e-01 7.33054101e-01
3.99324149e-01 -1.27978826e+00 -5.57447910e-01 3.14594924e-01
-1.05695836e-01 -1.10653341e+00 -1.52300566e-01 3.23389649e-01
-1.10890913e+00 -9.24355567e-01 -8.66114497e-01 -4.73707289e-01
1.01428342e+00 -1.56979904e-01 1.17565370e+00 -2.02735692e-01
-4.65670347e-01 3.84025782e-01 -2.79890418e-01 -5.25760114e-01
-5.13477981e-01 4.54571098e-02 8.38219002e-02 1.81356102e-01
2.19111085e-01 -1.07742739e+00 -6.54928625e-01 -8.97972584e-02
-1.18695974e+00 1.46176979e-01 7.68657267e-01 1.14276385e+00
5.61658859e-01 -2.28111714e-01 9.89933193e-01 -1.15971041e+00
7.19082355e-01 -8.03685367e-01 -1.72339618e-01 -2.12550074e-01
-6.36970401e-01 7.17014670e-02 6.82707787e-01 -5.20450115e-01
-9.67026532e-01 -1.24020323e-01 -3.07512373e-01 -4.60574090e-01
-5.08212030e-01 6.39984250e-01 -5.15219326e-05 1.50222152e-01
8.64984572e-01 2.52006501e-01 3.56591135e-01 -3.54660720e-01
3.79904360e-01 3.24589491e-01 7.12870777e-01 -5.58181942e-01
6.26662672e-01 6.42646432e-01 -1.21028148e-01 -6.80793941e-01
-6.45126522e-01 3.47623467e-01 -6.45450175e-01 -5.15837744e-02
1.08430064e+00 -8.08390498e-01 -2.54572839e-01 3.89445901e-01
-6.95981145e-01 -5.83625972e-01 -4.37669009e-01 6.18461788e-01
-6.79266334e-01 -1.12945512e-01 -5.23868322e-01 -6.15193069e-01
-4.44574624e-01 -1.29641414e+00 7.46233582e-01 1.06111810e-01
-2.82843918e-01 -9.47909176e-01 -4.71465886e-02 4.78251964e-01
7.36892104e-01 5.93923330e-01 1.06312680e+00 -7.90633261e-01
-2.49757126e-01 -2.67355472e-01 -6.87187314e-02 5.18390596e-01
1.13088161e-01 -4.70964283e-01 -1.08954346e+00 -2.57314771e-01
3.09776276e-01 -6.22606993e-01 7.47501075e-01 5.83436072e-01
1.58411169e+00 -1.50533706e-01 -1.52610466e-01 6.85406744e-01
1.08366704e+00 3.98344070e-01 9.40800250e-01 -1.98788539e-01
6.35950089e-01 5.45972347e-01 -3.33182395e-01 -1.86174605e-02
3.78960729e-01 3.82972062e-01 2.01529175e-01 -3.49328309e-01
-2.40831345e-01 -1.27245024e-01 1.41365975e-01 7.57464230e-01
-1.13285117e-01 -7.03219846e-02 -1.03348231e+00 5.47742367e-01
-1.33014297e+00 -1.08615673e+00 1.69677526e-01 1.95195353e+00
9.36001897e-01 -3.71475480e-02 1.22446440e-01 8.83401409e-02
4.80906814e-01 -1.41298264e-01 -9.08426344e-01 -1.63853485e-02
-2.98176557e-01 7.59630620e-01 2.49505863e-01 1.63074583e-01
-7.21046090e-01 6.62549436e-01 6.77285051e+00 2.70466149e-01
-1.14087868e+00 6.37535632e-01 1.14106584e+00 -2.20539421e-01
-4.09709364e-01 -1.49405092e-01 1.34064797e-02 6.70269072e-01
1.20614612e+00 -4.15851362e-02 7.76538908e-01 6.75816357e-01
1.96826816e-01 -2.71109436e-02 -1.01057112e+00 1.00712979e+00
3.53816807e-01 -1.12232804e+00 -9.51605588e-02 2.45257601e-01
7.91637123e-01 2.61476159e-01 2.73510963e-01 1.58213720e-01
3.50900561e-01 -1.44663584e+00 5.81824183e-01 5.88333488e-01
9.29225564e-01 -7.96950817e-01 5.17222822e-01 3.70462954e-01
-3.04725379e-01 3.07796225e-02 -2.81280249e-01 1.75650179e-01
6.40346184e-02 7.93974578e-01 -7.01937258e-01 9.40287858e-02
4.86318678e-01 3.68805617e-01 -5.40950477e-01 7.21303225e-01
-3.11320931e-01 8.59762430e-01 -1.86187282e-01 4.01562065e-01
8.23297873e-02 -2.90835619e-01 3.72981966e-01 7.72299230e-01
2.30832726e-01 3.22441399e-01 1.85603462e-02 1.44325233e+00
-3.46250325e-01 -2.29619801e-01 -8.34548473e-01 -4.08143252e-01
2.36828446e-01 1.31370068e+00 -9.44040179e-01 -3.06947380e-01
-2.15095595e-01 1.00634611e+00 4.53211427e-01 4.08675700e-01
-7.60950744e-01 8.68353918e-02 4.15594399e-01 -6.71109511e-03
-6.04244024e-02 -2.10688874e-01 -5.45421064e-01 -1.24903953e+00
-7.83046037e-02 -1.11781287e+00 7.85735622e-02 -9.07555521e-01
-1.49784517e+00 8.43707740e-01 -6.67844266e-02 -5.29808939e-01
-4.54389840e-01 -3.79275829e-01 -5.70590734e-01 1.05196977e+00
-1.23952711e+00 -9.89898384e-01 -2.99078017e-01 7.24130392e-01
3.20225865e-01 -2.10782737e-01 1.12710571e+00 4.16799217e-01
-6.65568829e-01 4.57712561e-01 -3.39837335e-02 2.97523886e-01
5.33603132e-01 -1.16507626e+00 4.12664443e-01 9.07092631e-01
1.27343252e-01 7.61915445e-01 3.71915609e-01 -6.24393702e-01
-1.33220768e+00 -8.54476750e-01 2.37744272e-01 -4.50293601e-01
5.16004205e-01 -6.11860216e-01 -9.41913188e-01 9.14718688e-01
2.27806672e-01 1.39485344e-01 8.70397925e-01 -7.91662261e-02
-2.69421071e-01 2.18423024e-01 -1.40561891e+00 4.70678836e-01
1.09338677e+00 -6.26717508e-01 -5.31689465e-01 4.21339363e-01
3.08995605e-01 -2.73027301e-01 -9.13865983e-01 1.68529838e-01
2.71912098e-01 -7.14162111e-01 9.23484266e-01 -9.77097929e-01
6.81215167e-01 1.05933614e-01 3.07405367e-02 -1.82326627e+00
-2.87973762e-01 -4.67207909e-01 1.06743857e-01 1.08790231e+00
4.76643503e-01 -8.66629481e-01 8.76575410e-01 1.00817323e+00
-2.53058404e-01 -4.78253752e-01 -8.72313201e-01 -6.22920811e-01
3.66706342e-01 -3.91399831e-01 6.00201607e-01 1.12714922e+00
-2.76055574e-01 2.99928397e-01 -3.07307571e-01 -2.71874219e-01
6.18205726e-01 -1.02601409e-01 4.67776984e-01 -1.11507177e+00
-3.20720017e-01 -3.45282972e-01 -3.80522698e-01 -3.67114693e-01
4.22722191e-01 -1.23066866e+00 -4.25972454e-02 -1.29515791e+00
3.48081172e-01 -4.65637833e-01 -2.08463445e-01 7.87342727e-01
-3.22278261e-01 6.65196836e-01 9.51372758e-02 1.48466960e-01
6.57596365e-02 5.55098832e-01 1.22367883e+00 -4.65497673e-02
8.71476531e-03 -3.60834450e-01 -1.02598679e+00 4.87557679e-01
9.04803038e-01 -6.89182281e-01 -6.25178754e-01 -4.91928369e-01
-1.24565944e-01 8.02564397e-02 6.47713304e-01 -1.10018456e+00
-2.43743449e-01 2.39588886e-01 9.38080609e-01 9.31763127e-02
3.47654402e-01 -5.59924304e-01 5.31721711e-01 3.24372351e-01
-4.26449209e-01 1.51344135e-01 6.44878820e-02 4.33747560e-01
-5.08522652e-02 -7.14165047e-02 9.44773257e-01 -3.89216542e-01
-1.52160108e-01 4.34331626e-01 -5.08799613e-01 4.15013075e-01
8.12216938e-01 -2.75657680e-02 -1.91552266e-01 -4.50455606e-01
-9.31457400e-01 -2.30720773e-01 3.11781824e-01 2.93806821e-01
4.27255958e-01 -1.36481059e+00 -8.54528368e-01 5.09613693e-01
-2.77591258e-01 -1.40824094e-01 3.81235659e-01 1.08457553e+00
-1.22160874e-01 2.81286091e-01 -6.34369433e-01 -4.24322188e-01
-6.13710046e-01 4.65709299e-01 2.72855848e-01 -2.15782851e-01
-7.95408726e-01 7.18947768e-01 2.90300250e-01 -3.17262769e-01
-2.37410501e-01 -2.29201876e-02 -2.58927662e-02 2.50099599e-01
6.02004230e-01 -7.30301812e-02 2.39427075e-01 -4.46628600e-01
-1.36894062e-01 -1.95346951e-01 -4.57032286e-02 -3.41064602e-01
1.81635320e+00 1.93392172e-01 1.02134710e-02 1.63518772e-01
1.06781590e+00 -3.03141296e-01 -1.26270056e+00 -7.50545338e-02
-3.84673893e-01 -3.04739505e-01 3.48806173e-01 -1.00758135e+00
-1.62461638e+00 8.79557252e-01 5.59744537e-01 2.55932271e-01
1.19503057e+00 -1.62149563e-01 6.29619539e-01 7.42685944e-02
3.62339616e-01 -6.37269497e-01 7.29910508e-02 5.25068603e-02
8.86392772e-01 -1.11210644e+00 -3.17637861e-01 -7.78335892e-03
-8.28560591e-01 7.11994529e-01 4.50764477e-01 -2.97457844e-01
4.51113224e-01 4.32804674e-01 -4.64464277e-02 -4.91719961e-01
-3.87296677e-01 -1.32151306e-01 9.33117643e-02 9.67917979e-01
3.04633379e-01 1.22702464e-01 -6.45708740e-02 7.04635978e-01
-4.03044909e-01 4.11604196e-02 5.17170310e-01 7.90152609e-01
1.62923068e-01 -1.03392315e+00 -3.43253314e-01 1.05395770e+00
-7.19949305e-01 -3.37550551e-01 -3.40341419e-01 5.60003757e-01
-1.06364146e-01 7.60185838e-01 1.17208458e-01 3.33242230e-02
-6.14532083e-02 5.83537996e-01 6.29876971e-01 -6.72759354e-01
-3.73072147e-01 -6.08444447e-03 -1.30576342e-01 -4.91329640e-01
-4.37816590e-01 -8.32583010e-01 -9.95139956e-01 -1.51286751e-01
-1.08322546e-01 7.28461775e-04 9.13049519e-01 9.99131441e-01
5.45550764e-01 8.80259275e-01 4.64221001e-01 -1.04418826e+00
-2.85601556e-01 -1.08911252e+00 -6.12063885e-01 5.59493005e-01
1.24131396e-01 -7.85697699e-01 -2.93075144e-01 1.57905504e-01] | [14.128299713134766, -1.8259291648864746] |
5085ff2d-2365-4423-8a8a-c059b0f4bcd8 | neural-lens-modeling | 2304.04848 | null | https://arxiv.org/abs/2304.04848v1 | https://arxiv.org/pdf/2304.04848v1.pdf | Neural Lens Modeling | Recent methods for 3D reconstruction and rendering increasingly benefit from end-to-end optimization of the entire image formation process. However, this approach is currently limited: effects of the optical hardware stack and in particular lenses are hard to model in a unified way. This limits the quality that can be achieved for camera calibration and the fidelity of the results of 3D reconstruction. In this paper, we propose NeuroLens, a neural lens model for distortion and vignetting that can be used for point projection and ray casting and can be optimized through both operations. This means that it can (optionally) be used to perform pre-capture calibration using classical calibration targets, and can later be used to perform calibration or refinement during 3D reconstruction, e.g., while optimizing a radiance field. To evaluate the performance of our proposed model, we create a comprehensive dataset assembled from the Lensfun database with a multitude of lenses. Using this and other real-world datasets, we show that the quality of our proposed lens model outperforms standard packages as well as recent approaches while being much easier to use and extend. The model generalizes across many lens types and is trivial to integrate into existing 3D reconstruction and rendering systems. | ['Christoph Lassner', 'Noah Snavely', 'Aljaž Božič', 'Wenqi Xian'] | 2023-04-10 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xian_Neural_Lens_Modeling_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xian_Neural_Lens_Modeling_CVPR_2023_paper.pdf | cvpr-2023-1 | ['camera-calibration'] | ['computer-vision'] | [ 9.26332697e-02 -3.06740582e-01 4.75912988e-01 -5.33933520e-01
-3.52614731e-01 -9.06388819e-01 6.67818546e-01 -9.51032788e-02
-2.12472111e-01 2.10312933e-01 7.04964697e-02 -2.65261471e-01
-6.25157058e-02 -5.81264973e-01 -9.69946563e-01 -5.06301105e-01
2.64888257e-01 6.94316924e-01 3.64873677e-01 -5.65057322e-02
1.09739654e-01 8.82250786e-01 -1.71032214e+00 -1.66310310e-01
6.85712576e-01 9.63097990e-01 3.88071537e-01 7.81883419e-01
3.09126750e-02 2.55855471e-01 -3.81536394e-01 -5.22987187e-01
6.71498060e-01 -9.90136117e-02 -9.18240175e-02 3.08629334e-01
9.15115595e-01 -7.26796329e-01 -4.36191633e-02 8.86528134e-01
7.69280493e-01 -1.27898291e-01 3.82882297e-01 -7.15914965e-01
-2.11170107e-01 -8.40746015e-02 -1.61485180e-01 -5.01767933e-01
4.16400909e-01 2.43538678e-01 5.64520597e-01 -9.24023747e-01
6.12061441e-01 1.24463463e+00 6.71997190e-01 2.93931812e-01
-1.39415276e+00 -5.56970060e-01 1.14314845e-02 -3.02639455e-01
-1.19561231e+00 -5.80701649e-01 6.51219964e-01 -6.25294268e-01
8.30475211e-01 4.34087932e-01 1.10251260e+00 7.40856469e-01
6.39186502e-02 3.81462038e-01 1.11876893e+00 -4.67019558e-01
2.68277973e-01 2.13467389e-01 -1.56345695e-01 6.42120957e-01
4.14621234e-02 4.93699163e-01 -4.62917954e-01 -2.47312281e-02
1.04390562e+00 -2.81415969e-01 -5.27210414e-01 -7.54884243e-01
-1.13484287e+00 3.91855299e-01 5.15499830e-01 -2.69715607e-01
1.27283618e-01 2.67979443e-01 -2.47871488e-01 1.20786168e-01
6.41638577e-01 6.54494226e-01 -3.88373464e-01 -6.32707477e-02
-9.72893894e-01 4.87690359e-01 7.98592925e-01 1.16801500e+00
8.55353236e-01 -2.35851258e-01 3.55856754e-02 8.30372274e-01
5.11842549e-01 5.84579110e-01 -6.61055222e-02 -1.19859159e+00
1.03890948e-01 5.69232345e-01 1.32397950e-01 -7.86672354e-01
-4.36763644e-01 -3.58409703e-01 -5.56475639e-01 7.16240764e-01
3.32208544e-01 1.80017859e-01 -9.73334432e-01 1.34276521e+00
4.29290354e-01 5.76653302e-01 -2.70984650e-01 1.05137455e+00
8.86812747e-01 5.03256381e-01 -6.09490693e-01 7.30918907e-03
1.06597853e+00 -7.36941457e-01 -4.07698840e-01 -3.13578635e-01
1.84705228e-01 -1.35006952e+00 1.00925624e+00 6.21576488e-01
-1.42946708e+00 -4.58735615e-01 -1.18428862e+00 -4.84011173e-01
-7.64463022e-02 4.42125611e-02 6.64696753e-01 6.25175774e-01
-1.18970287e+00 5.51771462e-01 -9.58786190e-01 -9.19038355e-02
2.40025833e-01 5.54647744e-01 -8.23483523e-03 -1.51074484e-01
-4.06163037e-01 8.53548348e-01 3.53781544e-02 -1.47246581e-03
-7.72002816e-01 -1.09565139e+00 -6.37429655e-01 -4.96713854e-02
3.34585130e-01 -1.03460276e+00 1.28101087e+00 -4.80217367e-01
-1.90402246e+00 8.73482466e-01 -3.41423554e-03 -1.80572942e-01
6.48813546e-01 -4.86123919e-01 1.10015899e-01 -1.92151964e-01
-4.54229087e-01 4.66710210e-01 7.42674887e-01 -1.45493853e+00
-2.64562458e-01 -2.65318960e-01 4.93027866e-01 4.04441893e-01
-3.13278474e-02 1.36712473e-02 -1.27369130e+00 -3.31743449e-01
2.99976826e-01 -1.13446534e+00 -2.95449138e-01 5.34349144e-01
-4.89885181e-01 5.51346123e-01 5.78523576e-01 -4.07024622e-01
7.70862520e-01 -2.11893082e+00 3.56693357e-01 1.83667034e-01
1.84225827e-01 1.55865759e-01 -1.46976449e-02 1.22015499e-01
3.96186449e-02 -7.50485435e-02 -1.90817192e-01 -9.66624379e-01
8.26076195e-02 1.21167906e-01 -9.16958302e-02 3.69446099e-01
1.86548103e-02 4.69562352e-01 -5.35744190e-01 -7.38360360e-02
8.35673571e-01 9.66427982e-01 -9.34464335e-01 6.17483735e-01
-5.79135180e-01 6.83409512e-01 -1.13927796e-02 5.77027857e-01
8.55794430e-01 -1.73442572e-01 -5.70706688e-02 -4.04642016e-01
-3.42057467e-01 3.87572050e-01 -1.44359028e+00 1.95535922e+00
-7.32604563e-01 7.51417935e-01 1.08221792e-01 -2.09678873e-01
7.92106152e-01 6.20883964e-02 3.18315089e-01 -4.23547387e-01
2.13155344e-01 6.40578568e-02 -2.02049062e-01 -1.40746087e-01
4.38331902e-01 1.47293210e-01 3.39288175e-01 2.61510432e-01
-9.62303504e-02 -1.00947797e+00 -3.16194812e-04 -9.78548154e-02
6.47445440e-01 5.95182836e-01 1.36294618e-01 -2.09762588e-01
3.10390294e-01 -8.35484043e-02 2.18019813e-01 3.89284670e-01
7.54834652e-01 1.07431710e+00 2.00634822e-01 -1.94968328e-01
-1.22365236e+00 -1.17110813e+00 -4.72940475e-01 5.58804393e-01
1.74802750e-01 -5.30249953e-01 -7.40750670e-01 -1.09616444e-02
-4.11445983e-02 6.61280692e-01 -1.71775073e-01 1.02359727e-01
-5.68335235e-01 -7.00587869e-01 -2.11709011e-02 1.03388958e-01
3.77107799e-01 -2.78929800e-01 -7.37561584e-01 -1.19011907e-03
3.57994258e-01 -1.28228235e+00 -3.31582010e-01 5.27475476e-02
-9.85064268e-01 -1.09163678e+00 -3.11295152e-01 -2.52081156e-01
7.49985278e-01 4.37494814e-01 1.49208474e+00 2.74766892e-01
-1.18266530e-01 5.45460761e-01 5.83413988e-03 -5.85516632e-01
-4.86257643e-01 -2.38715544e-01 -6.12720363e-02 -1.74556419e-01
-1.94469750e-01 -7.95889080e-01 -6.79436862e-01 5.35358965e-01
-8.95449221e-01 5.22647083e-01 2.45076254e-01 4.81589198e-01
7.69030035e-01 -1.95465405e-02 -6.70689821e-01 -7.77608752e-01
3.29617739e-01 8.79415050e-02 -1.61665273e+00 1.18553462e-02
-7.07810819e-01 6.74142241e-02 4.45023090e-01 -3.92247260e-01
-9.94457960e-01 2.73656577e-01 -2.12243751e-01 -6.36831641e-01
-9.07364208e-03 2.68020809e-01 -3.70098591e-01 -5.22997558e-01
4.64192808e-01 -2.93804348e-01 -1.58576995e-01 -7.98798323e-01
2.61185616e-01 3.21070880e-01 6.09813511e-01 -4.45925623e-01
1.15780270e+00 4.79174137e-01 3.81738275e-01 -8.54049623e-01
-7.10247934e-01 -1.95541576e-01 -6.89718127e-01 -2.95098960e-01
7.49866068e-01 -1.05236542e+00 -7.46667087e-01 4.66601521e-01
-1.21398330e+00 -4.32638317e-01 -5.53773306e-02 6.30134940e-01
-4.20928806e-01 8.59029517e-02 -2.31907099e-01 -5.51630378e-01
1.70352533e-01 -1.66078031e+00 1.36255085e+00 2.65938014e-01
1.45909175e-01 -9.38269734e-01 1.26330003e-01 9.37232524e-02
4.86635864e-01 2.42441356e-01 7.57086575e-01 3.49942386e-01
-1.35939395e+00 -1.80028141e-01 -9.91930440e-02 4.23744142e-01
-1.20834023e-01 3.96259546e-01 -1.23695052e+00 -3.25071752e-01
1.58090413e-01 4.58961129e-02 5.31422615e-01 4.68402237e-01
1.25070381e+00 1.78882614e-01 -1.84976444e-01 1.49968636e+00
1.73438346e+00 -1.60189308e-02 7.71285057e-01 3.51649731e-01
8.58738661e-01 3.83871675e-01 3.59977305e-01 4.30967033e-01
3.40829581e-01 1.12321627e+00 8.09048891e-01 -2.00932935e-01
-2.38902614e-01 -2.70377416e-02 1.83559611e-01 7.51904488e-01
-3.12166065e-01 -2.76194781e-01 -7.67314315e-01 -1.26305893e-01
-1.45387220e+00 -4.77177233e-01 -2.73249477e-01 2.62900925e+00
7.17534244e-01 6.54572770e-02 -3.56165290e-01 -1.24105595e-01
1.45882636e-01 -1.19295148e-02 -4.64948773e-01 -9.47694108e-02
-1.17116205e-01 4.28015143e-01 6.58900917e-01 8.21941435e-01
-8.14766407e-01 7.22176433e-01 6.47911119e+00 1.90186948e-01
-1.18420482e+00 -1.20481357e-01 3.10091436e-01 -4.88499880e-01
-4.44613785e-01 2.68605977e-01 -8.59046042e-01 1.66370824e-01
7.03828752e-01 1.16744950e-01 8.20228219e-01 6.16383016e-01
3.00018877e-01 -2.53219575e-01 -1.53449738e+00 1.15485299e+00
9.67207104e-02 -1.42513967e+00 -2.22871915e-01 1.64286882e-01
6.23619318e-01 1.02654085e-01 5.04457355e-02 -1.90407708e-01
2.11313471e-01 -8.72992218e-01 7.94985950e-01 7.70927787e-01
8.33329618e-01 -6.00860715e-01 3.34592819e-01 2.89267868e-01
-7.68285215e-01 3.29736680e-01 -5.27521908e-01 9.94616300e-02
3.51603866e-01 8.40347230e-01 -7.81611979e-01 4.54860926e-01
7.71328986e-01 5.94943106e-01 -8.28525186e-01 1.46017504e+00
-2.34698147e-01 3.10370535e-01 -6.88419044e-01 2.21735090e-01
-1.37426600e-01 -5.82612693e-01 6.27160370e-01 9.38603699e-01
6.86172247e-01 -3.91890667e-02 2.49609700e-03 9.53977287e-01
-1.21478088e-01 -1.13636874e-01 -4.42942262e-01 4.02267754e-01
3.24154645e-01 1.13159835e+00 -4.41518277e-01 -1.88489282e-03
-5.08854389e-01 9.45452213e-01 8.37287307e-02 3.37897271e-01
-8.75972271e-01 1.73546225e-01 9.11085665e-01 3.87038499e-01
1.80890337e-01 -5.87693810e-01 -1.87221587e-01 -1.24241138e+00
4.51607630e-02 -6.12886250e-01 -3.45611721e-01 -1.31947303e+00
-9.21534300e-01 5.72520077e-01 2.86579758e-01 -1.37951362e+00
-2.23933101e-01 -9.59794700e-01 -2.91066855e-01 9.99070883e-01
-1.69437718e+00 -1.08033812e+00 -7.58840203e-01 5.71328044e-01
2.55024731e-01 -2.70685321e-03 8.13637555e-01 4.23726112e-01
-2.57300109e-01 1.94782913e-01 1.52808681e-01 -3.50377291e-01
8.09838831e-01 -1.33529973e+00 5.45837879e-01 9.11731303e-01
1.43961683e-01 6.05135739e-01 7.22049475e-01 -3.84615183e-01
-1.70186305e+00 -9.15110707e-01 2.42055491e-01 -6.74634516e-01
2.19462484e-01 -8.12509358e-01 -6.17478430e-01 6.01122975e-01
1.54531822e-01 -4.21402790e-02 5.31197369e-01 3.88497207e-03
-1.15785532e-01 -2.21393615e-01 -9.20806825e-01 7.31245935e-01
1.11477888e+00 -4.12336379e-01 -6.74267411e-02 4.94676471e-01
7.75400698e-01 -1.20685327e+00 -8.31322193e-01 2.94494629e-01
6.23498261e-01 -1.57771468e+00 1.31632161e+00 1.13908552e-01
4.34657544e-01 -5.99693418e-01 -3.09492707e-01 -1.31160700e+00
-2.35052377e-01 -8.11559141e-01 -5.74537404e-02 1.10051835e+00
1.78435430e-01 -4.16725218e-01 4.53616530e-01 6.35419667e-01
-5.00754952e-01 -3.58100772e-01 -7.52393186e-01 -4.59678471e-01
-2.46389821e-01 -8.29982340e-01 1.05000579e+00 5.78674912e-01
-9.38633680e-01 2.37333387e-01 -3.75695139e-01 6.75857484e-01
8.11991692e-01 1.60521269e-01 1.27302849e+00 -1.38740695e+00
-7.25937903e-01 -3.58719647e-01 -2.77498156e-01 -1.46070337e+00
-2.50721574e-01 -7.54069507e-01 -1.18751563e-02 -1.31470001e+00
-1.62306741e-01 -6.58384264e-01 4.88355905e-01 -1.44500747e-01
1.40822202e-01 4.48921591e-01 2.87346393e-01 2.26998657e-01
1.03728138e-01 2.98000425e-01 1.39257991e+00 2.00467989e-01
-3.31563920e-01 8.94128606e-02 -2.75874466e-01 9.08850610e-01
4.47431177e-01 -1.47857517e-01 -3.96393120e-01 -9.85646486e-01
6.31910980e-01 -3.97494398e-02 5.63732445e-01 -1.10153139e+00
1.71414107e-01 1.03255898e-01 5.14656544e-01 -7.81885862e-01
7.27376819e-01 -1.18785048e+00 6.39173031e-01 -9.46851969e-02
2.67173629e-03 -5.50169162e-02 2.99008578e-01 1.12581939e-01
2.05342218e-01 -2.57976323e-01 8.39461148e-01 -1.16496146e-01
-2.72780001e-01 5.16115665e-01 2.26911619e-01 -3.72583240e-01
6.50818825e-01 -2.65852213e-01 -1.87795386e-01 -2.99045205e-01
-4.53531981e-01 9.65868533e-02 1.18312943e+00 2.41620392e-01
6.02636218e-01 -1.31773734e+00 -5.11066437e-01 5.23352087e-01
2.06294984e-01 5.79068899e-01 -1.24003991e-01 5.44328570e-01
-9.43506181e-01 -1.51452115e-02 9.59930718e-02 -1.08181918e+00
-1.27380252e+00 4.02600408e-01 6.02371573e-01 8.59085843e-02
-7.00367868e-01 6.73893869e-01 1.37087330e-01 -4.85454440e-01
3.05979580e-01 -6.91652894e-01 1.01931386e-01 -3.54842752e-01
4.41307604e-01 9.48323235e-02 2.46223956e-01 -3.83510679e-01
-8.23373124e-02 1.11282420e+00 4.30833250e-01 -5.00459671e-02
1.53030515e+00 -1.90978721e-01 -2.72607893e-01 3.92275125e-01
1.00214469e+00 2.82149404e-01 -1.48268497e+00 -8.13241079e-02
-6.02515221e-01 -9.53195155e-01 4.56856787e-01 -7.79048383e-01
-1.13679278e+00 8.47397089e-01 7.90512860e-01 -2.13876981e-02
1.21248162e+00 -8.01124126e-02 4.78361219e-01 2.56227374e-01
3.18088144e-01 -7.13454962e-01 -3.00430030e-01 3.14483136e-01
9.72283781e-01 -1.09424174e+00 3.56809199e-01 -6.80275261e-01
3.42681296e-02 1.19809616e+00 3.69656056e-01 -1.49369255e-01
8.19165468e-01 7.06793427e-01 4.67073806e-02 -2.38203958e-01
-4.03740942e-01 -1.68426130e-02 6.74427867e-01 5.92575312e-01
4.61616457e-01 -2.30993956e-01 2.62041390e-01 -9.69076902e-02
-6.20513737e-01 -1.93627030e-01 5.76503336e-01 5.58601737e-01
-1.13553263e-01 -1.24702978e+00 -5.23218930e-01 2.34401450e-01
2.03693006e-02 -1.45610094e-01 -2.16801435e-01 6.29999876e-01
2.23850608e-01 6.59393907e-01 8.41428339e-02 -1.83238417e-01
5.92516541e-01 -4.87580955e-01 8.79694760e-01 -6.22070551e-01
-4.91276085e-01 5.57359636e-01 8.77789930e-02 -8.91596973e-01
-7.04700410e-01 -5.77427268e-01 -6.87289655e-01 -2.51249075e-01
-5.36717474e-01 -4.95295674e-01 1.02382970e+00 5.87605417e-01
2.09444150e-01 5.34034491e-01 5.42107940e-01 -1.40308774e+00
-1.61861137e-01 -6.16081238e-01 -2.85906732e-01 8.77322108e-02
3.11146766e-01 -8.22464764e-01 -3.83045644e-01 1.13112509e-01] | [9.526339530944824, -3.027308464050293] |
129d26f5-8453-4b22-8387-a0ea39b2390c | revisiting-learnable-affines-for-batch-norm | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yazdanpanah_Revisiting_Learnable_Affines_for_Batch_Norm_in_Few-Shot_Transfer_Learning_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yazdanpanah_Revisiting_Learnable_Affines_for_Batch_Norm_in_Few-Shot_Transfer_Learning_CVPR_2022_paper.pdf | Revisiting Learnable Affines for Batch Norm in Few-Shot Transfer Learning | Batch Normalization is a staple of computer vision models, including those employed in few-shot learning. Batch Normalization layers in convolutional neural networks are composed of a normalization step, followed by a shift and scale of these normalized features applied via the per-channel trainable affine parameters gamma and beta. These affine parameters were introduced to maintain the expressive powers of the model following normalization. While this hypothesis holds true for classification within the same domain, this work illustrates that these parameters are detrimental to downstream performance on common few-shot transfer tasks. This effect is studied with multiple methods on well-known benchmarks such as few-shot classification on miniImageNet and cross-domain few-shot learning (CD-FSL). Experiments reveal consistent performance improvements on CNNs with affine unaccompanied Batch Normalization layers; particularly in large domain-shift few-shot transfer settings. As opposed to common practices in few-shot transfer learning where the affine parameters are fixed during the adaptation phase, we show fine-tuning them can lead to improved performance. | ['Samira Ebrahimi Kahou', 'Eugene Belilovsky', 'Mohammad Havaei', 'Christian Desrosiers', 'Muawiz Chaudhary', 'Aamer Abdul Rahman', 'Moslem Yazdanpanah'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['cross-domain-few-shot', 'cross-domain-few-shot-learning'] | ['computer-vision', 'computer-vision'] | [ 4.80688423e-01 -8.74021873e-02 -8.96806642e-02 -6.35596454e-01
-6.37290835e-01 -4.06125516e-01 1.02225387e+00 -6.70670569e-02
-9.71300781e-01 5.16465664e-01 3.54838043e-01 5.09442277e-02
1.03233894e-02 -6.70626819e-01 -7.03850508e-01 -8.12872171e-01
-2.16399767e-02 -1.11256540e-02 5.10836899e-01 -5.93912423e-01
-4.85837832e-02 3.68622512e-01 -1.40265882e+00 3.21074724e-01
8.34995974e-03 9.38547015e-01 -2.92953134e-01 7.90653706e-01
-1.21261766e-02 8.62773836e-01 -4.98360693e-01 -5.21769345e-01
4.51490253e-01 -4.89445806e-01 -7.08617210e-01 -1.25008523e-01
7.31128335e-01 -4.58387703e-01 -4.52305347e-01 1.04779112e+00
7.93572247e-01 6.10704839e-01 9.38791037e-01 -1.25199962e+00
-5.57617903e-01 4.92141575e-01 -4.35438544e-01 7.39642739e-01
-3.77020448e-01 5.68933785e-01 9.76693213e-01 -7.87046731e-01
7.50073433e-01 1.17058277e+00 9.71372485e-01 6.55282199e-01
-1.34135294e+00 -7.48474836e-01 -1.76376313e-01 -1.24343829e-02
-1.13178146e+00 -8.52566063e-01 3.25942844e-01 -5.48076451e-01
1.42261720e+00 -3.47092271e-01 2.42233932e-01 1.34055305e+00
4.33060229e-01 5.79444468e-02 7.04298079e-01 -5.29469967e-01
4.39254105e-01 -4.77834828e-02 3.90400231e-01 3.78135681e-01
6.29693568e-02 8.72651637e-02 -5.50995290e-01 4.84238118e-02
6.80882752e-01 1.37536913e-01 6.69838786e-02 -4.44152892e-01
-9.96296346e-01 1.13607419e+00 3.98318082e-01 2.88135380e-01
-1.63683876e-01 3.64928842e-01 9.33419824e-01 7.66654253e-01
6.17748082e-01 4.21455711e-01 -4.65111285e-01 -2.50540465e-01
-8.08703363e-01 1.48169652e-01 8.01081777e-01 1.15164566e+00
9.28609073e-01 1.10361084e-01 -7.43486047e-01 9.83270943e-01
-3.66923451e-01 -8.49755928e-02 7.21328199e-01 -7.62062967e-01
3.80616844e-01 1.02707408e-01 -2.55101532e-01 -3.70408773e-01
-3.28370452e-01 -4.97113317e-01 -7.54339755e-01 2.76230723e-01
5.25473475e-01 -3.23850572e-01 -1.31139672e+00 1.88114357e+00
-7.01809451e-02 5.07873178e-01 -6.76411530e-03 6.41802967e-01
9.25434291e-01 3.70029539e-01 6.62199616e-01 1.58281133e-01
1.30307329e+00 -9.03754711e-01 -4.30465341e-01 -3.85762423e-01
9.93918478e-01 -7.02247083e-01 1.34684467e+00 -1.05042435e-01
-8.41282725e-01 -5.41974008e-01 -1.30521750e+00 -2.40974963e-01
-6.95921659e-01 -5.20152867e-01 3.96773785e-01 6.72767580e-01
-8.83033812e-01 8.69372129e-01 -8.21549118e-01 -6.59480035e-01
8.59206080e-01 2.73190945e-01 -4.69076544e-01 -3.31338108e-01
-1.32556283e+00 1.17136586e+00 5.22648394e-01 -5.59238851e-01
-8.84903312e-01 -1.21232975e+00 -9.32661533e-01 3.34262818e-01
7.69031644e-02 -7.39090204e-01 1.58890200e+00 -8.53931963e-01
-1.43834925e+00 1.12014449e+00 2.99754202e-01 -7.47127414e-01
5.90982139e-01 -1.08626902e-01 -2.46885180e-01 -4.41292487e-02
1.94154531e-02 9.07200694e-01 9.71468568e-01 -4.38825220e-01
-3.04731041e-01 -1.49071505e-02 1.99144140e-01 9.40336958e-02
-7.13372290e-01 2.91968644e-01 -2.92413950e-01 -4.94457453e-01
-4.75954801e-01 -6.38303280e-01 -2.53800899e-01 -6.36070874e-03
1.84303313e-01 7.52642453e-02 6.26125216e-01 -2.66850591e-01
7.62706578e-01 -2.22504783e+00 -2.44871035e-01 -1.37601057e-02
2.58765798e-02 3.63317460e-01 -4.80731070e-01 2.93089509e-01
-4.66140240e-01 -1.50455698e-01 -2.36371800e-01 -3.43328208e-01
3.99178565e-02 2.40502357e-01 -1.72432199e-01 7.54526079e-01
7.01597571e-01 1.23298264e+00 -7.85770297e-01 -1.79097086e-01
3.69914651e-01 5.52191198e-01 -6.08774483e-01 6.57496303e-02
6.57157376e-02 -5.69649488e-02 1.05423816e-01 2.02652156e-01
3.76616269e-01 -2.89824903e-01 -4.42935109e-01 -1.34431198e-01
9.23588872e-02 1.16652474e-01 -7.79115498e-01 1.84396839e+00
-3.98556441e-01 5.78977227e-01 -2.68613070e-01 -1.00752640e+00
8.05749059e-01 3.03678185e-01 2.44260579e-01 -7.16134846e-01
5.66704988e-01 -2.63881564e-01 2.95535624e-01 -3.14296901e-01
4.27836150e-01 -6.48396075e-01 -5.52400760e-02 3.70381922e-01
8.95008922e-01 -9.42942128e-02 2.59463757e-01 2.29297563e-01
1.47495866e+00 -9.14428234e-02 6.06395245e-01 -3.27201307e-01
-1.69158299e-02 3.79037741e-03 3.92054260e-01 9.22818899e-01
-4.86670583e-01 9.42703605e-01 5.36969543e-01 -2.87763685e-01
-1.48319137e+00 -9.45414007e-01 -1.47208065e-01 1.84863710e+00
-3.23471665e-01 -4.28039044e-01 -7.44691312e-01 -3.27883989e-01
-8.31144750e-02 7.16214716e-01 -1.12174582e+00 -7.02016950e-01
-3.19527268e-01 -1.08155501e+00 8.74034405e-01 7.30065167e-01
3.30087662e-01 -9.78082120e-01 -7.95749962e-01 3.30152363e-01
6.10674679e-01 -1.20846832e+00 -5.38270295e-01 8.40837538e-01
-6.79582536e-01 -8.98519099e-01 -1.06980002e+00 -6.54777706e-01
3.93866569e-01 3.34292322e-01 1.18177462e+00 -2.30668649e-01
-5.96172690e-01 3.71125668e-01 -4.63828534e-01 -6.79225087e-01
-2.58498162e-01 4.70569670e-01 -1.39609426e-01 -2.50978082e-01
6.91982925e-01 -7.23820806e-01 -5.23322999e-01 1.52376607e-01
-1.08926177e+00 -2.35116869e-01 4.92577940e-01 1.10254180e+00
1.50865197e-01 -4.17479932e-01 5.62184572e-01 -1.07692456e+00
6.56571686e-01 -7.96319723e-01 -3.36836249e-01 -3.64539400e-02
-3.16580862e-01 -4.48574387e-02 5.74646592e-01 -7.70258248e-01
-1.05974042e+00 -6.62672520e-02 -6.47584200e-02 -7.34297156e-01
-4.16134715e-01 1.77846804e-01 2.60969102e-02 -2.47611642e-01
1.28865325e+00 -6.68265820e-02 -3.50527987e-02 -2.86527157e-01
6.48440480e-01 3.31488460e-01 5.40958583e-01 -2.75115639e-01
8.03136230e-01 4.28941220e-01 -1.31534755e-01 -9.90519226e-01
-8.67393672e-01 -7.85566866e-01 -9.52571571e-01 3.66787538e-02
9.74103510e-01 -1.14200306e+00 -1.07785985e-01 5.35256445e-01
-1.04952240e+00 -5.13314009e-01 -6.77195549e-01 4.44739431e-01
-6.61069751e-01 -2.62878984e-01 -7.13821054e-01 -2.09217981e-01
-3.02892208e-01 -8.33310485e-01 7.38403201e-01 1.84874088e-01
-3.86384159e-01 -1.00940275e+00 1.94635898e-01 -2.09100679e-01
7.86439419e-01 6.20999970e-02 9.75182056e-01 -1.08180392e+00
9.85509157e-02 -1.38302356e-01 -4.07129526e-01 4.84454334e-01
7.78399035e-02 -2.60765582e-01 -1.45538580e+00 -4.23492700e-01
-2.39924908e-01 -6.86122060e-01 1.24606800e+00 4.53529388e-01
7.80141413e-01 1.32891849e-01 3.29654515e-02 1.01642680e+00
1.41841364e+00 -1.16977602e-01 6.51951909e-01 4.82400507e-01
6.16997123e-01 3.21480602e-01 2.64903784e-01 2.97257543e-01
-1.24697477e-01 3.69679421e-01 2.03140154e-01 -5.99385947e-02
-3.49407256e-01 1.84919402e-01 1.26288906e-01 4.72555578e-01
-9.30927619e-02 2.18209431e-01 -9.65614736e-01 4.72039700e-01
-1.63852406e+00 -1.29225457e+00 3.78869385e-01 1.90109646e+00
8.62752259e-01 4.61044818e-01 1.49619579e-01 -1.15851551e-01
5.31066954e-01 4.33810025e-01 -6.40949428e-01 -4.94391531e-01
-2.17883751e-01 6.44750834e-01 9.33661520e-01 9.39451605e-02
-1.30277705e+00 1.15774453e+00 6.61737919e+00 7.49403596e-01
-1.20855999e+00 5.97215235e-01 6.62381411e-01 -6.21122837e-01
2.57075220e-01 -2.37479657e-01 -9.68367279e-01 9.75753441e-02
1.21503842e+00 -2.93736756e-01 2.58780092e-01 9.33236241e-01
-1.18749373e-01 1.64044246e-01 -1.28571975e+00 7.81434178e-01
4.32682298e-02 -1.44725847e+00 -1.94993019e-01 -1.36314005e-01
9.98474181e-01 6.08964086e-01 7.34607577e-02 7.93704867e-01
4.98564273e-01 -1.10856283e+00 5.03085792e-01 2.19117522e-01
9.38076377e-01 -8.45565736e-01 7.45295167e-01 8.03735554e-02
-7.87202537e-01 -1.53410971e-01 -7.87358463e-01 -2.22836077e-01
-6.07675835e-02 3.29656214e-01 -8.15595627e-01 1.72826294e-02
8.00721645e-01 6.88718319e-01 -5.21610260e-01 1.03338015e+00
6.21335804e-02 6.68581426e-01 -4.34180675e-03 1.91725731e-01
6.35847449e-01 9.83169749e-02 2.62310147e-01 1.59296978e+00
4.41383943e-02 6.66667372e-02 -3.25928986e-01 6.40289962e-01
-3.85268539e-01 4.33128774e-02 -6.98959053e-01 -1.55245364e-02
2.63982952e-01 1.34019947e+00 -7.65588760e-01 -4.66922969e-01
-7.85805106e-01 8.75681102e-01 4.47075278e-01 4.30049270e-01
-7.91217983e-01 -6.08648300e-01 8.87708187e-01 2.74071127e-01
5.50732851e-01 -3.76658365e-02 -3.54349673e-01 -1.09675550e+00
-4.28443372e-01 -6.76109672e-01 4.58373874e-01 -4.02761519e-01
-1.50890052e+00 3.36490750e-01 3.45016211e-01 -1.10857499e+00
-2.50181615e-01 -7.12840855e-01 -1.09549403e+00 1.04882705e+00
-1.55913997e+00 -1.25162125e+00 -3.32626522e-01 6.97801411e-01
6.04608059e-01 -4.54250216e-01 9.64775980e-01 3.07032108e-01
-4.66079265e-01 7.27591753e-01 1.11778669e-01 2.96919495e-01
1.25184417e+00 -9.88722503e-01 9.98768806e-01 8.05802405e-01
-1.07182570e-01 7.69660592e-01 9.12479281e-01 -2.99639732e-01
-8.70229602e-01 -1.40353906e+00 4.69169497e-01 -2.31283382e-01
9.45883453e-01 -5.18778801e-01 -1.21024978e+00 8.52606535e-01
3.71714830e-01 5.72561741e-01 8.28894973e-01 1.57614842e-01
-7.27403879e-01 1.25083014e-01 -9.65764403e-01 5.30132115e-01
1.20520258e+00 -5.96128523e-01 -7.37556636e-01 1.88772246e-01
7.82783926e-01 -1.87898889e-01 -8.75664115e-01 1.06392473e-01
6.40453577e-01 -6.90736055e-01 1.04111362e+00 -1.22271574e+00
6.55974627e-01 2.82005012e-01 -3.00264567e-01 -1.73086703e+00
-6.62057161e-01 -4.20084476e-01 1.74501106e-01 1.17702007e+00
1.61953673e-01 -5.42991817e-01 6.68210685e-01 6.12326920e-01
-2.14911178e-01 -3.66242260e-01 -8.56535792e-01 -1.01040637e+00
5.20756304e-01 -2.47181207e-01 4.47397053e-01 1.13825965e+00
-2.55514622e-01 5.01063824e-01 -1.65987924e-01 -3.16113740e-01
6.50199175e-01 -7.17293501e-01 8.81414235e-01 -1.19443738e+00
-3.15275282e-01 -5.41697323e-01 -6.19750559e-01 -2.30554208e-01
2.19371051e-01 -9.52522993e-01 8.81346464e-02 -9.97812152e-01
2.79471099e-01 -1.72200780e-02 -7.40259469e-01 7.16357350e-01
-1.04510330e-01 4.96091604e-01 2.06470981e-01 4.93604578e-02
-5.11362851e-01 5.11104822e-01 8.94123554e-01 -1.55786827e-01
-1.92002714e-01 -1.96744278e-01 -6.33903980e-01 7.40656972e-01
8.29733372e-01 -6.53862774e-01 -5.63286841e-01 -2.47013122e-01
-1.63577080e-01 -6.20063186e-01 5.04231513e-01 -1.11560667e+00
2.87441701e-01 -1.36030421e-01 5.56240141e-01 -1.04376927e-01
4.29588854e-01 -4.05810863e-01 -2.15911806e-01 3.21139604e-01
-5.33591628e-01 -2.01230749e-01 4.29011077e-01 3.86943549e-01
2.27954634e-03 -3.14027935e-01 1.32991195e+00 -1.45197064e-01
-1.16127229e+00 4.07186121e-01 -1.89645782e-01 3.82029623e-01
1.15124440e+00 -2.97688544e-01 -5.44395030e-01 -1.55529827e-01
-6.19644225e-01 -2.01653764e-01 3.52267683e-01 5.39891660e-01
2.71262616e-01 -1.21896696e+00 -6.16500914e-01 1.54898167e-01
6.47537470e-01 -3.35325241e-01 2.76824653e-01 8.06460202e-01
-2.10545063e-01 1.32546023e-01 -8.59253824e-01 -3.98861170e-01
-1.19343042e+00 3.22941184e-01 4.19998676e-01 6.99909357e-03
-7.64682591e-01 1.08657885e+00 2.97358900e-01 -3.25595945e-01
2.07273826e-01 -2.64002651e-01 1.04345046e-02 3.46379668e-01
6.45780265e-01 2.92396009e-01 4.56956744e-01 -5.20085812e-01
-1.76804319e-01 1.95934430e-01 -4.87458587e-01 -9.50707421e-02
1.76634574e+00 1.05558187e-01 4.77469057e-01 5.99836409e-01
1.50795913e+00 -7.78111875e-01 -1.64187634e+00 -5.24363816e-01
-7.92300776e-02 -1.92464087e-02 3.07905674e-01 -4.60915238e-01
-7.19198108e-01 1.17837834e+00 6.54232621e-01 -2.58435428e-01
7.94341147e-01 -1.28598139e-01 4.78900611e-01 4.63205099e-01
5.79843111e-02 -1.22089720e+00 3.60881649e-02 8.94452810e-01
5.05661964e-01 -1.47199392e+00 -1.64418459e-01 6.67294115e-02
-5.87294400e-01 9.80800211e-01 5.85188627e-01 -3.30121428e-01
7.26126373e-01 4.89625871e-01 -6.94305748e-02 -1.53024212e-01
-9.31267321e-01 -2.56787032e-01 1.99311867e-01 6.12433612e-01
5.67616165e-01 -3.13827664e-01 6.89569712e-02 5.37621439e-01
-9.18054730e-02 2.57699519e-01 3.98511887e-01 1.11689699e+00
-5.46609640e-01 -6.99030221e-01 -9.43538621e-02 7.11923480e-01
-4.90488112e-01 -4.12913263e-01 -5.03731593e-02 9.08390582e-01
-5.67283779e-02 5.62606812e-01 3.92999530e-01 -2.52392054e-01
4.46751535e-01 3.83222848e-01 5.09031832e-01 -1.02003229e+00
-8.45013976e-01 -2.87257254e-01 -1.27196768e-02 -5.20670116e-01
-2.30543226e-01 -3.87064338e-01 -9.84831035e-01 -3.37810159e-01
-3.54951955e-02 -3.33192825e-01 5.81564426e-01 1.14753449e+00
9.83489677e-02 8.60012233e-01 1.34956226e-01 -1.04754066e+00
-1.03302455e+00 -1.35838652e+00 -6.27714157e-01 6.96934164e-01
2.19327345e-01 -8.30791593e-01 -4.04934704e-01 3.25005412e-01] | [10.024577140808105, 2.7746808528900146] |
56104d8b-d95a-4191-b538-38b0b26d84e5 | tiny-hr-towards-an-interpretable-machine | 2208.07981 | null | https://arxiv.org/abs/2208.07981v1 | https://arxiv.org/pdf/2208.07981v1.pdf | Tiny-HR: Towards an interpretable machine learning pipeline for heart rate estimation on edge devices | The focus of this paper is a proof of concept, machine learning (ML) pipeline that extracts heart rate from pressure sensor data acquired on low-power edge devices. The ML pipeline consists an upsampler neural network, a signal quality classifier, and a 1D-convolutional neural network optimized for efficient and accurate heart rate estimation. The models were designed so the pipeline was less than 40 kB. Further, a hybrid pipeline consisting of the upsampler and classifier, followed by a peak detection algorithm was developed. The pipelines were deployed on ESP32 edge device and benchmarked against signal processing to determine the energy usage, and inference times. The results indicate that the proposed ML and hybrid pipeline reduces energy and time per inference by 82% and 28% compared to traditional algorithms. The main trade-off for ML pipeline was accuracy, with a mean absolute error (MAE) of 3.28, compared to 2.39 and 1.17 for the hybrid and signal processing pipelines. The ML models thus show promise for deployment in energy and computationally constrained devices. Further, the lower sampling rate and computational requirements for the ML pipeline could enable custom hardware solutions to reduce the cost and energy needs of wearable devices. | ['Nilanjan Ray', 'Ganesh Tata', 'Shailesh Nanisetty', 'Preetam Anbukarasu'] | 2022-08-16 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [ 4.24171329e-01 8.11413750e-02 -1.31349787e-01 -3.10343385e-01
-4.12057191e-01 -2.67436981e-01 -3.49695116e-01 4.85145390e-01
-5.96922576e-01 5.09234130e-01 -2.96969444e-01 -3.23304355e-01
3.58395308e-01 -6.30259037e-01 -5.07778883e-01 -3.62094074e-01
-3.68766606e-01 -1.91708773e-01 8.75892341e-02 5.56894004e-01
-9.12154987e-02 4.55823869e-01 -1.25881708e+00 7.64232874e-02
5.61752915e-01 1.66051698e+00 -1.16781391e-01 1.16376650e+00
5.33265173e-01 5.72777867e-01 -8.15131128e-01 1.01353444e-01
1.75750703e-01 -4.82561141e-01 -9.06247571e-02 -7.23635852e-01
2.57179946e-01 -8.07934523e-01 -2.24392220e-01 4.95285660e-01
1.34416819e+00 -4.07990724e-01 2.73787439e-01 -1.25464213e+00
2.49991119e-01 6.82788610e-01 -4.59287435e-01 4.82605487e-01
2.38938376e-01 1.74539745e-01 3.60046268e-01 -4.45352972e-01
-5.59355728e-02 6.15229130e-01 1.35746169e+00 2.39272922e-01
-1.24610889e+00 -8.13313782e-01 -8.18406403e-01 -1.95782213e-03
-1.61528087e+00 -6.34756505e-01 6.98130608e-01 -2.26858228e-01
1.50111425e+00 2.65987098e-01 1.06781709e+00 7.20170677e-01
8.12573195e-01 3.32902670e-01 1.02037811e+00 -2.85544544e-01
7.26783276e-01 2.91373968e-01 8.52665678e-02 6.78205907e-01
8.55415463e-01 -5.85809834e-02 -7.86916375e-01 -3.37433726e-01
8.15723181e-01 -8.20963159e-02 -5.20556122e-02 3.05101603e-01
-7.29404747e-01 3.01366210e-01 5.32932840e-02 -1.67109743e-02
-5.46646535e-01 5.64719558e-01 7.18094826e-01 -1.77915767e-01
2.07678691e-01 3.49679232e-01 -8.19137812e-01 -5.33599973e-01
-1.37328672e+00 -7.62298405e-02 1.18682468e+00 7.85569668e-01
1.32048115e-01 3.19469392e-01 -3.65184098e-01 7.24462569e-02
6.02114201e-01 7.49648273e-01 4.92338061e-01 -9.14453328e-01
8.04667845e-02 5.62220037e-01 -4.43811715e-02 -8.58558178e-01
-1.23996401e+00 -3.94615114e-01 -8.77679884e-01 -5.79117760e-02
3.16049993e-01 -7.26125658e-01 -5.15547574e-01 1.33315337e+00
2.23805368e-01 2.85066128e-01 -6.59786314e-02 7.77589500e-01
9.23948586e-01 5.01555443e-01 2.56337106e-01 -2.78607339e-01
1.69456112e+00 -4.55100089e-01 -7.80805051e-01 -2.31516108e-01
3.96244824e-01 -3.51815343e-01 7.40673423e-01 3.53680402e-01
-1.44228506e+00 -7.60624945e-01 -1.64852095e+00 -1.78527579e-01
1.74582362e-01 5.15644670e-01 5.83027184e-01 1.35016060e+00
-1.05738854e+00 9.34442759e-01 -1.27712393e+00 -2.42676005e-01
5.94034374e-01 5.80355585e-01 5.43351591e-01 5.67502499e-01
-1.03340256e+00 6.12836123e-01 2.35268205e-01 1.66353092e-01
-4.20493841e-01 -1.11348212e+00 -7.03505695e-01 4.34859782e-01
-4.64742571e-01 -7.25000978e-01 1.14831769e+00 -6.49175942e-01
-1.90986907e+00 5.93434215e-01 3.36221009e-02 -1.03022528e+00
3.04087192e-01 -3.61993819e-01 -6.65152907e-01 3.23112607e-01
-4.55075294e-01 3.02352041e-01 7.21399546e-01 -6.56026304e-02
-4.67902124e-01 -3.02798837e-01 -5.33443153e-01 -2.00927854e-01
-1.96097136e-01 -1.50006428e-01 -5.25074229e-02 -1.58304408e-01
-1.18362613e-01 -8.70756209e-01 -3.18992771e-02 2.63288409e-01
9.85081792e-02 3.60595524e-01 6.85553014e-01 -9.08060551e-01
1.37875211e+00 -2.03520751e+00 -6.19169533e-01 2.62129188e-01
1.49985582e-01 3.76300186e-01 6.61353767e-01 -6.19272627e-02
1.22766174e-01 -1.13662548e-01 6.50698841e-02 -3.91110890e-02
-2.42057472e-01 -1.52466506e-01 2.15748042e-01 7.50463426e-01
2.43460402e-01 8.04208100e-01 -4.45813745e-01 -3.00811201e-01
4.06743377e-01 8.70641053e-01 -3.66656363e-01 1.77455366e-01
3.78181100e-01 1.58095092e-01 -1.09240577e-01 7.11191535e-01
4.14710194e-01 -2.35124022e-01 4.25967783e-01 -9.24580157e-01
-1.26736835e-01 6.00484908e-01 -1.36394620e+00 1.85506904e+00
-3.94389242e-01 7.73850322e-01 2.52457917e-01 -6.18933380e-01
1.05642307e+00 5.43594003e-01 6.25728428e-01 -8.56788993e-01
7.39533961e-01 2.66898841e-01 8.40648562e-02 -8.14669132e-01
2.01816857e-01 -3.12908404e-02 -1.07748106e-01 2.19248205e-01
-3.36102508e-02 6.90240562e-02 -1.68602213e-01 -1.46274164e-01
1.34711993e+00 3.76892328e-01 3.64538610e-01 -6.09135628e-01
1.31271228e-01 -9.25187767e-02 8.26311588e-01 5.54226100e-01
-5.46627760e-01 8.77374262e-02 2.12062582e-01 -5.68394423e-01
-8.62925947e-01 -8.31647694e-01 -3.04550231e-01 6.65247321e-01
-7.93999881e-02 -6.48328960e-01 -7.40092814e-01 2.39430100e-01
2.45879032e-02 3.87860686e-01 5.30412085e-02 -3.00076902e-01
-5.68502307e-01 -8.66620898e-01 9.79558468e-01 9.88675654e-01
7.26291597e-01 -6.03407085e-01 -2.09256220e+00 4.89906311e-01
1.74754694e-01 -1.12974727e+00 -1.76398098e-01 4.79340822e-01
-1.20017457e+00 -7.29102492e-01 -1.82580668e-02 -2.45687291e-01
3.13820630e-01 -5.76049268e-01 1.03434932e+00 -3.09737921e-01
-9.89783287e-01 3.53970379e-01 4.94899079e-02 -9.74168241e-01
5.75385801e-02 -1.83556557e-01 3.37483793e-01 -3.88425380e-01
5.31324446e-01 -5.99121869e-01 -1.32233953e+00 -3.02420884e-01
-2.45628953e-01 -2.13917475e-02 7.40207493e-01 3.16503257e-01
4.76316839e-01 -3.67225520e-02 4.28931117e-01 -4.62529719e-01
4.24725592e-01 -2.83797204e-01 -7.35594690e-01 -2.46717677e-01
-1.06466019e+00 7.79822245e-02 6.43157005e-01 -4.10586208e-01
-5.66339076e-01 7.40343571e-01 -1.64066687e-01 -1.60538882e-01
1.92790866e-01 4.15336974e-02 -5.44531411e-03 1.14083074e-01
7.42244482e-01 -1.45737097e-01 1.32163316e-01 -1.84433788e-01
-1.73256516e-01 7.84717262e-01 7.21387208e-01 -1.93707049e-01
6.62930682e-02 8.20210427e-02 3.22797060e-01 -9.52766359e-01
-1.07654117e-01 -7.31105730e-02 -3.30742955e-01 -2.27795392e-01
9.76622403e-01 -1.41533673e+00 -1.17690313e+00 4.35436696e-01
-8.36520433e-01 -4.15803820e-01 -3.06109816e-01 6.53408408e-01
-2.58211732e-01 1.32862991e-02 -9.74984944e-01 -1.16236866e+00
-1.58250248e+00 -8.04188192e-01 7.91430950e-01 8.79345357e-01
-7.88355410e-01 -5.83394051e-01 -3.30501556e-01 2.15992019e-01
8.69793236e-01 6.29002571e-01 2.90726870e-01 -3.81316841e-01
-9.72140580e-02 -3.32434922e-01 -1.83903531e-03 1.89819410e-01
-4.27269079e-02 -1.25428200e-01 -1.33532500e+00 -4.00604934e-01
9.61036533e-02 -1.34862542e-01 1.18739463e-01 8.63932431e-01
1.17752850e+00 -1.34102762e-01 -4.76486921e-01 6.72856808e-01
1.52044725e+00 3.20654005e-01 6.13619924e-01 -1.73859209e-01
2.75761515e-01 -2.73085862e-01 1.52330860e-01 7.86204100e-01
1.08201653e-01 2.43124723e-01 4.38407473e-02 -3.93406838e-01
-4.51390781e-02 6.99428245e-02 3.93930912e-01 6.66707575e-01
1.14831790e-01 2.18652636e-01 -8.11435282e-01 1.87088341e-01
-1.30765045e+00 -6.31758392e-01 -2.65610605e-01 2.40597820e+00
9.20095503e-01 3.52984697e-01 3.05509955e-01 5.84351480e-01
4.70195293e-01 -3.31147790e-01 -9.22266364e-01 -7.73649514e-01
4.14414018e-01 9.52638209e-01 9.94134247e-01 4.01645079e-02
-9.21541035e-01 -7.78159127e-02 6.52953196e+00 7.58269848e-03
-1.38549304e+00 9.15594399e-02 6.87041879e-01 -5.25833964e-01
6.65048420e-01 -3.67651343e-01 -9.22539294e-01 7.05099940e-01
2.07113409e+00 -1.03402592e-01 9.58289132e-02 1.04471958e+00
3.89591277e-01 -3.42682600e-01 -1.20533323e+00 1.32856512e+00
-8.23931471e-02 -1.13655007e+00 -9.29720998e-01 -4.14244741e-01
-3.63135263e-02 3.97215337e-02 -5.38602412e-01 2.01801151e-01
-4.19001400e-01 -7.48061836e-01 5.15654027e-01 4.04729337e-01
1.17143595e+00 -7.43404210e-01 7.58298159e-01 1.42343670e-01
-1.55846143e+00 -1.74529299e-01 -5.82357608e-02 -5.91012061e-01
-1.21942842e-02 1.01601493e+00 -9.99640107e-01 1.21082805e-01
1.09854341e+00 2.08454773e-01 -3.48465800e-01 8.78590047e-01
1.20398007e-01 8.07226419e-01 -8.44365597e-01 -4.59767520e-01
-6.10100925e-01 2.72409230e-01 1.19366534e-01 1.58785665e+00
2.31864363e-01 3.28303188e-01 -7.11720495e-04 8.15815330e-01
7.10351840e-02 -2.56604642e-01 2.07613073e-02 2.88520772e-02
9.06714380e-01 1.67367995e+00 -7.73247004e-01 -4.90896225e-01
-3.58332545e-01 8.09288323e-01 -4.75712389e-01 -3.07259470e-01
-1.28445852e+00 -1.01320171e+00 4.74908352e-01 5.22348523e-01
-2.07851514e-01 -3.58032919e-02 -8.40626240e-01 -5.36611855e-01
1.50446370e-01 -3.51932764e-01 1.56932622e-01 -5.68945885e-01
-6.94971323e-01 1.92573249e-01 -3.58734012e-01 -8.73299122e-01
-7.22137168e-02 -5.83022475e-01 -5.22877455e-01 1.01230907e+00
-1.10534787e+00 -4.27310169e-01 -7.75953054e-01 2.29289621e-01
3.98618996e-01 3.09705377e-01 9.47413325e-01 6.17484272e-01
-9.50238943e-01 7.22152710e-01 -3.56671572e-01 2.12328777e-01
3.17552149e-01 -9.38001812e-01 3.02240282e-01 8.67240369e-01
-7.12763071e-01 5.71362495e-01 6.12887859e-01 -6.14770651e-01
-2.26870084e+00 -1.21931446e+00 5.72286069e-01 5.52665703e-02
2.67498225e-01 -4.72400814e-01 -5.34281433e-01 1.80216417e-01
7.10041970e-02 2.38098562e-01 8.43563557e-01 -3.94014627e-01
2.04359993e-01 -6.30606234e-01 -1.49656701e+00 2.24190250e-01
5.37685573e-01 -2.53632635e-01 -1.95331514e-01 -6.30628318e-02
3.48789960e-01 -7.64353693e-01 -1.39263940e+00 4.51539963e-01
1.19326460e+00 -5.36997437e-01 7.24774420e-01 2.06695184e-01
6.22497462e-02 -2.85335660e-01 1.86750308e-01 -7.00212359e-01
-3.27239871e-01 -8.18759680e-01 -8.36837888e-01 1.26334620e+00
4.07796204e-01 -5.25518179e-01 8.62905324e-01 1.15813410e+00
-3.62106040e-03 -6.91541970e-01 -9.12046969e-01 -4.46072906e-01
-7.74161816e-01 -3.36656600e-01 -1.86491087e-02 4.04019892e-01
3.39646459e-01 6.31511629e-01 -1.32310927e-01 2.43371427e-01
5.90459466e-01 -2.48841524e-01 2.12372437e-01 -1.22241437e+00
-5.46679616e-01 -1.40709147e-01 -5.55838227e-01 -5.09977043e-01
-7.42184758e-01 -4.57616508e-01 2.30184887e-02 -1.29622507e+00
-1.06424317e-01 -3.23895246e-01 -3.80906850e-01 6.42540097e-01
-2.33681917e-01 3.10157597e-01 -2.76625961e-01 -2.55069852e-01
-1.71763092e-01 -2.51144469e-01 1.38917580e-01 7.70207718e-02
-7.69434214e-01 -8.45749006e-02 -4.53590393e-01 6.47397399e-01
1.11726427e+00 -5.05276203e-01 -4.40643311e-01 -1.01512171e-01
1.83202371e-01 1.81803912e-01 3.19694579e-01 -1.71383512e+00
4.69482601e-01 6.27804756e-01 1.16710472e+00 -5.60656428e-01
3.27842116e-01 -1.00874829e+00 5.75185418e-01 1.34803498e+00
5.11211790e-02 1.50834158e-01 6.97780967e-01 1.59813955e-01
5.62391758e-01 3.80147785e-01 1.00178301e+00 1.79824650e-01
-2.04162180e-01 -1.75354198e-01 -4.14768577e-01 -1.25362396e-01
1.14598298e+00 -3.76636147e-01 -2.30865970e-01 2.21806213e-01
-4.58200246e-01 -6.20032847e-02 -1.05846360e-01 -3.70639749e-02
4.46179986e-01 -9.79435086e-01 -4.72812146e-01 4.85012621e-01
-4.26144391e-01 4.70185205e-02 3.28703016e-01 1.11793470e+00
-8.88090372e-01 3.04349661e-01 -5.27368665e-01 -7.38824368e-01
-1.31389785e+00 1.79770842e-01 6.12488866e-01 1.49379328e-01
-9.96086240e-01 6.12443149e-01 -1.01148498e+00 5.45779884e-01
4.09613431e-01 -8.21175396e-01 2.27535680e-01 -2.00280800e-01
8.58135521e-01 1.02813458e+00 3.46641272e-01 2.04147711e-01
-7.54842281e-01 3.88612241e-01 5.41444421e-01 2.26416454e-01
1.11434484e+00 9.11104605e-02 1.56374022e-01 5.84921777e-01
9.86845136e-01 -3.12941760e-01 -1.11610615e+00 2.14258507e-01
-5.91058508e-02 2.50502884e-01 4.73216504e-01 -8.96674097e-01
-1.12986255e+00 5.48104882e-01 1.44642079e+00 -3.04893591e-02
1.85088229e+00 -5.07551491e-01 9.56257105e-01 3.68312001e-02
1.57600358e-01 -1.27728593e+00 -4.92642492e-01 -1.57990471e-01
7.05140382e-02 -6.08425021e-01 4.48067099e-01 -1.37755007e-01
-1.05281577e-01 1.26385069e+00 5.05980074e-01 -2.17352107e-01
6.87453985e-01 1.27132511e+00 -2.60971487e-01 2.37491559e-02
-5.74211717e-01 3.86748970e-01 -2.84868348e-02 4.96741563e-01
8.09796154e-01 1.17604293e-01 -5.07024288e-01 9.39929426e-01
4.77764420e-02 8.05521905e-01 3.33533734e-01 1.28453887e+00
-4.23918486e-01 -4.53195810e-01 -2.31975853e-01 8.44517529e-01
-9.30426478e-01 -2.18499498e-03 7.77175799e-02 3.01119894e-01
2.92409748e-01 1.04575372e+00 3.93293172e-01 -5.01807034e-01
5.14409840e-01 2.45823339e-01 4.02793914e-01 -2.27626264e-01
-1.10343325e+00 9.04314592e-02 1.91498622e-01 -9.48376894e-01
-1.21518381e-01 -3.29797506e-01 -1.48000896e+00 -2.63203681e-01
-1.83138400e-01 -4.64635998e-01 1.24575841e+00 5.20550728e-01
1.03962159e+00 1.12018132e+00 2.85261363e-01 -6.79418504e-01
-2.97487527e-01 -9.65672612e-01 -6.31942987e-01 -2.62216359e-01
1.52401015e-01 -1.91918761e-01 -1.04983687e-01 3.82778138e-01] | [13.972968101501465, 3.1197683811187744] |
158209da-0002-4dc2-bcdf-b7b3d51bde0e | a-separation-logic-for-sequences-in-pointer | 2301.06237 | null | https://arxiv.org/abs/2301.06237v1 | https://arxiv.org/pdf/2301.06237v1.pdf | A separation logic for sequences in pointer programs and its decidability | Separation logic and its variants can describe various properties on pointer programs. However, when it comes to properties on sequences, one may find it hard to formalize. To deal with properties on variable-length sequences and multilevel data structures, we propose sequence-heap separation logic which integrates sequences into logical reasoning on heap-manipulated programs. Quantifiers over sequence variables and singleton heap storing sequence (sequence singleton heap) are new members in our logic. Further, we study the satisfiability problem of two fragments. The propositional fragment of sequence-heap separation logic is decidable, and the fragment with 2 alternations on program variables and 1 alternation on sequence variables is undecidable. In addition, we explore boundaries between decidable and undecidable fragments of the logic with prenex normal form. | ['Hanpin Wang', 'Yongzhi Cao', 'Zhao Jin', 'BoWen Zhang', 'Tianyue Cao'] | 2023-01-16 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 4.53224123e-01 2.53405690e-01 -6.28771245e-01 6.12104759e-02
-2.87306011e-01 -9.31294858e-01 2.40164503e-01 2.80308098e-01
-1.83900267e-01 1.00965357e+00 -1.56469103e-02 -1.21614671e+00
-2.77396739e-01 -1.29754806e+00 -6.76091969e-01 -5.67628324e-01
-7.96445251e-01 2.64731258e-01 9.46171284e-01 -1.60302326e-01
2.21339077e-01 4.74685907e-01 -1.69698751e+00 5.83713293e-01
6.07310712e-01 7.07880735e-01 -5.16116768e-02 7.77367353e-01
-5.29868960e-01 1.27267110e+00 -3.07951599e-01 -2.40415365e-01
2.81236898e-02 -1.55668512e-01 -1.28039348e+00 -4.19548959e-01
3.61822158e-01 2.61145607e-02 -1.17825300e-01 1.58751655e+00
-2.52079308e-01 -1.65018484e-01 1.94131076e-01 -1.97976208e+00
-6.28281593e-01 1.22788191e+00 -5.83096981e-01 1.98665977e-01
7.17138529e-01 2.31855288e-01 1.38978803e+00 4.26891595e-02
5.24742961e-01 1.33809292e+00 6.51959598e-01 5.51662028e-01
-1.66072094e+00 -4.89358418e-02 8.89941454e-02 5.68396330e-01
-1.29928339e+00 -2.40448311e-01 1.97030678e-01 -4.58504438e-01
1.40086663e+00 1.01403391e+00 3.82142216e-01 2.30768070e-01
4.39291418e-01 6.24077380e-01 1.25070751e+00 -7.45188653e-01
1.47995710e-01 1.22781634e-01 1.10833704e+00 7.72267163e-01
1.17224669e+00 1.99854106e-01 -4.66734804e-02 -5.94151199e-01
3.66261572e-01 -2.74476916e-01 -5.15918016e-01 -3.85351658e-01
-9.37429726e-01 6.98357344e-01 -2.85293043e-01 4.86600488e-01
1.67401448e-01 4.11594898e-01 8.54524434e-01 2.79816151e-01
-6.15745783e-01 1.43463880e-01 -2.82689452e-01 -6.67554587e-02
-7.18850315e-01 5.13280690e-01 1.39582372e+00 1.25094521e+00
7.33626366e-01 9.02405381e-02 -1.35852069e-01 -1.75619781e-01
1.95349440e-01 8.37089658e-01 1.85283199e-01 -1.04715538e+00
8.61176178e-02 5.09752750e-01 9.17929038e-02 -8.06441665e-01
-2.80442297e-01 3.67567390e-01 -2.93740541e-01 2.44050413e-01
1.64973319e-01 3.04444492e-01 -2.82451838e-01 1.93762362e+00
-4.83331941e-02 -7.14583099e-02 2.48910964e-01 3.74171942e-01
6.43878162e-01 8.34030807e-01 -1.65922269e-02 -8.76077712e-01
1.48213029e+00 -7.00277865e-01 -7.33032584e-01 -1.42489783e-02
9.77670550e-01 3.97716127e-02 6.60590053e-01 2.30188191e-01
-1.66302454e+00 -1.72213167e-02 -1.02091265e+00 -1.88287705e-01
-1.98615715e-01 -8.51945877e-01 9.02706683e-01 6.63990140e-01
-1.30663002e+00 5.90868406e-02 -8.13264012e-01 1.81892022e-01
-2.16409281e-01 6.79232836e-01 -3.81530583e-01 2.54014265e-02
-1.24733603e+00 8.59085262e-01 9.89844561e-01 -1.63420379e-01
-7.45383799e-01 -4.84014004e-01 -1.14103079e+00 3.49197835e-01
9.12153602e-01 -3.77672017e-01 1.34367335e+00 -7.10284472e-01
-1.15367472e+00 9.27490115e-01 -2.73058563e-01 -7.30543733e-01
2.85981577e-02 4.62104648e-01 -5.09042621e-01 -2.65823063e-02
1.20400414e-02 -1.35419071e-01 5.58272265e-02 -1.10319638e+00
-7.47409344e-01 -3.20867926e-01 9.52277780e-01 -5.45106232e-01
2.59712785e-01 2.64422387e-01 5.61580956e-02 1.88346505e-01
-1.19479820e-01 -9.12696958e-01 -1.40824495e-02 -1.89800352e-01
-2.52711564e-01 -1.60645634e-01 3.48320812e-01 -1.94266289e-01
1.61246896e+00 -2.26182556e+00 4.72141385e-01 3.48964006e-01
2.11384535e-01 2.35973015e-01 7.55954087e-02 2.86841750e-01
-1.94151700e-01 3.85457784e-01 -2.76588321e-01 7.83177793e-01
6.93822145e-01 5.40044725e-01 -5.60101211e-01 4.81152415e-01
-3.11357453e-02 9.64139819e-01 -9.15238202e-01 -9.14601266e-01
-2.38559648e-01 -3.17953616e-01 -1.10515034e+00 3.44176665e-02
-8.60634089e-01 -6.10267043e-01 -2.55715519e-01 6.37688279e-01
9.69811797e-01 -2.12850288e-01 8.88388991e-01 5.55473603e-02
-6.60830736e-01 4.63495791e-01 -1.50178969e+00 1.02814209e+00
-3.95898461e-01 3.39358985e-01 4.22904909e-01 -5.55086732e-01
2.91978210e-01 2.67099202e-01 -1.30774349e-01 -6.05977476e-01
-3.41435187e-02 2.35285103e-01 2.09291905e-01 -7.74363458e-01
7.75017023e-01 -6.83573663e-01 -5.13287902e-01 5.33083498e-01
-4.57844377e-01 4.50427458e-02 6.33873582e-01 2.21484661e-01
1.51965225e+00 -6.99115023e-02 5.43980896e-01 -6.53390348e-01
1.23542023e+00 2.03607336e-01 8.60159516e-01 7.35799670e-01
-1.24540329e-01 -4.30045217e-01 1.35791886e+00 -1.15217961e-01
-4.87571001e-01 -1.36222565e+00 -1.47704616e-01 1.05588686e+00
2.27745578e-01 -1.00762367e+00 -3.81767452e-01 -3.29895079e-01
1.31000355e-01 1.04313505e+00 -2.93958545e-01 -2.98901826e-01
-8.31235707e-01 -3.54791373e-01 8.47192347e-01 8.89446318e-01
3.16069603e-01 -8.10433149e-01 -1.18213785e+00 -1.02556646e-01
-1.52308390e-01 -9.43415880e-01 -3.89164090e-02 5.52378416e-01
-5.68545580e-01 -1.26260948e+00 4.50633168e-01 -5.72409391e-01
2.22475231e-01 4.08485383e-02 9.87968981e-01 2.72893041e-01
-2.96315905e-02 2.85709500e-01 1.37854076e-03 -1.76438361e-01
-4.91469294e-01 -2.52018631e-01 -4.41367865e-01 -8.37539017e-01
3.42597425e-01 -2.62788028e-01 3.91591519e-01 -7.28808790e-02
-1.16078830e+00 -3.21083963e-01 -1.81883395e-01 5.26556730e-01
4.20215905e-01 5.59157491e-01 -2.88571984e-01 -9.36659157e-01
3.24520290e-01 -4.00711507e-01 -1.15062344e+00 5.75568438e-01
-2.36717239e-02 5.51741898e-01 8.24805856e-01 -2.48962209e-01
-8.89921963e-01 -3.67402554e-01 3.68821949e-01 -2.99659893e-02
1.10278964e-01 8.73212695e-01 -6.34380519e-01 1.99551851e-01
3.74299139e-01 2.79802829e-01 1.19012006e-01 2.83300161e-01
2.36034244e-01 1.91760376e-01 8.44115615e-01 -1.39290512e+00
6.59325838e-01 4.69557226e-01 7.33122706e-01 -6.41681492e-01
-2.72132933e-01 5.71281575e-02 -2.35456601e-01 3.64007920e-01
6.12265289e-01 -2.60856986e-01 -1.37022710e+00 -4.35076281e-02
-1.19061625e+00 -5.45931399e-01 -3.65853310e-01 -1.84813425e-01
-1.01325226e+00 8.33167315e-01 -6.72289729e-01 -1.18125081e+00
1.82069719e-01 -1.33547664e+00 7.99910903e-01 -2.87144512e-01
-5.48565507e-01 -1.06683314e+00 2.41639957e-01 -2.45048955e-01
1.59449697e-01 1.86622605e-01 1.63773715e+00 -6.32247567e-01
-8.77045453e-01 -3.58538851e-02 -2.93788910e-01 1.23206735e-01
-4.51872587e-01 2.79044300e-01 -3.42638761e-01 -3.55333425e-02
3.17732394e-02 3.54190134e-02 3.16514462e-01 -2.20141988e-02
1.10517192e+00 -7.00499773e-01 -4.70887870e-01 1.72986299e-01
1.78134692e+00 2.46676266e-01 9.81766701e-01 6.16818309e-01
-5.09386286e-02 3.94243777e-01 5.86917639e-01 1.68782845e-01
2.16178969e-01 4.19091463e-01 2.50219434e-01 8.02106678e-01
2.19854772e-01 9.75639522e-02 4.98487175e-01 5.04598558e-01
-2.54280508e-01 8.04507658e-02 -1.25015545e+00 3.73893142e-01
-1.77430022e+00 -1.50402319e+00 -7.23032415e-01 2.29971552e+00
1.30701065e+00 3.97363394e-01 3.09114248e-01 5.41501880e-01
7.63298392e-01 1.49875786e-02 3.00563067e-01 -9.79137003e-01
-7.03725368e-02 4.03788924e-01 6.49857163e-01 1.16255307e+00
-6.05957270e-01 7.95963287e-01 6.91369534e+00 3.93484592e-01
-1.03584528e+00 1.65444583e-01 -5.59520781e-01 -4.67310585e-02
-7.45372713e-01 6.46778822e-01 -9.37166393e-01 4.63363230e-01
1.27013648e+00 -7.51931787e-01 4.85754728e-01 6.38046086e-01
-5.42651713e-01 -4.96925443e-01 -1.68374074e+00 2.62809277e-01
8.60724971e-02 -1.22336197e+00 -5.80495633e-02 1.01142153e-02
2.90046215e-01 -7.99736142e-01 -6.10186122e-02 5.10177433e-01
3.90816808e-01 -9.73733127e-01 1.17472661e+00 4.04211164e-01
6.22303009e-01 -6.19691610e-01 7.86347032e-01 2.53644943e-01
-1.14966476e+00 -3.76653045e-01 -1.29344225e-01 -5.26285112e-01
2.63533413e-01 2.53339529e-01 -2.62730956e-01 5.37420511e-01
2.91830391e-01 1.74989067e-02 -4.23048660e-02 1.07204771e+00
-2.34176755e-01 1.56798765e-01 -3.56663436e-01 5.00055663e-02
6.92130327e-02 -8.29336699e-03 4.88188267e-01 1.53110039e+00
2.21126117e-02 5.00987709e-01 7.97165409e-02 1.00395882e+00
5.52779794e-01 -5.55423856e-01 -5.47140360e-01 -2.46339627e-02
4.49061453e-01 4.34239954e-01 -6.11457944e-01 -5.58872461e-01
-6.45720422e-01 1.07374541e-01 1.69191509e-01 2.32325211e-01
-1.18030059e+00 -5.63907266e-01 5.31446218e-01 2.47523546e-01
3.78243655e-01 -3.88198316e-01 -4.32477653e-01 -1.01423657e+00
-4.91622537e-02 -1.03689790e+00 6.25019372e-01 -5.13369143e-01
-8.23499382e-01 2.31193304e-01 7.55679607e-01 -3.44912976e-01
-1.16986088e-01 -8.89065146e-01 -5.31970203e-01 8.49829316e-01
-1.40599680e+00 -7.39141762e-01 2.59804398e-01 3.82733852e-01
-3.16932440e-01 5.05983293e-01 8.84515703e-01 1.53682902e-01
-5.77635169e-01 4.61241752e-01 -4.12712008e-01 -4.22859490e-01
-3.03683914e-02 -1.11903429e+00 -4.07054842e-01 8.80866885e-01
-7.08483338e-01 1.18455780e+00 9.84899759e-01 -5.39288878e-01
-2.27511621e+00 -9.19082761e-01 1.20102596e+00 -1.93746820e-01
1.04598486e+00 1.28358513e-01 -1.21746707e+00 1.51195681e+00
1.90416262e-01 -7.20498040e-02 6.51944101e-01 2.02072725e-01
-7.80255914e-01 7.14131445e-02 -9.52853680e-01 6.46401286e-01
1.02242815e+00 -8.62378001e-01 -1.13404167e+00 -8.23023915e-02
6.74065292e-01 -4.01970923e-01 -7.51679838e-01 4.98321384e-01
4.59547460e-01 -9.26417887e-01 8.52929652e-01 -8.35295498e-01
2.79512644e-01 -7.38751292e-01 -7.35583246e-01 -3.50442261e-01
-5.26791394e-01 -4.95506436e-01 -5.50022066e-01 1.08782518e+00
2.15281487e-01 -7.90730715e-01 3.30123365e-01 8.37086618e-01
-1.45989284e-01 -4.10908431e-01 -8.44124556e-01 -1.42509091e+00
4.07069355e-01 -4.74267155e-01 8.02760065e-01 6.63599193e-01
1.14799476e+00 2.31515944e-01 2.49370173e-01 2.35823765e-01
5.02196789e-01 8.35096419e-01 4.50390041e-01 -1.00945270e+00
-6.44297063e-01 -9.63923514e-01 -5.74789345e-01 -3.83277148e-01
9.16956306e-01 -1.40577185e+00 1.89001173e-01 -1.31045306e+00
4.73592252e-01 -3.53706032e-01 2.40172759e-01 9.38209176e-01
3.30301285e-01 -2.71394849e-01 2.52914220e-01 -2.17283621e-01
-9.37110186e-01 4.85800952e-03 6.95413351e-01 -4.01825041e-01
8.26092809e-02 -2.28395954e-01 -3.93518925e-01 6.36762321e-01
4.57632929e-01 -2.63362706e-01 -3.82209092e-01 -4.57425602e-02
8.87293279e-01 7.72136450e-01 4.99202996e-01 -8.16956222e-01
2.68439084e-01 -1.03232038e+00 -1.04827714e+00 -3.71629059e-01
-8.04918483e-02 -9.38475609e-01 7.33361363e-01 9.18140233e-01
-4.81748849e-01 5.38834743e-02 5.71059823e-01 4.78356145e-03
-7.43032321e-02 -9.38348949e-01 5.94462931e-01 -1.92638010e-01
-8.29688609e-01 -3.05800617e-01 -7.74430394e-01 3.84268701e-01
1.49412823e+00 -2.37064287e-01 -7.45776415e-01 3.20560753e-01
-7.83961356e-01 2.65962660e-01 8.79595280e-01 -2.97737509e-01
2.21874237e-01 -1.16144621e+00 -3.65430899e-02 9.78082269e-02
-6.17070571e-02 -2.40155440e-02 1.47985831e-01 1.11178780e+00
-9.14112091e-01 8.39756489e-01 -3.70701969e-01 -1.73514754e-01
-1.65893936e+00 1.33029890e+00 8.07392523e-02 -1.48626044e-01
-1.64655894e-01 4.29118514e-01 1.40861109e-01 8.22803602e-02
2.31998846e-01 -6.10979021e-01 3.06507111e-01 -3.45757306e-01
8.94433439e-01 6.54350638e-01 -2.91270822e-01 -4.24235731e-01
-8.76844108e-01 1.24806054e-01 1.80738792e-02 -3.20100226e-02
1.05066884e+00 2.39216447e-01 -1.45602727e+00 7.17335165e-01
1.02077186e+00 2.72675991e-01 -6.84842318e-02 -1.06251024e-01
2.99661398e-01 -2.79769778e-01 -3.29706401e-01 -3.74035150e-01
-4.52650726e-01 7.37767518e-01 -2.24897996e-01 6.72249317e-01
1.01730287e+00 2.65498728e-01 4.60565448e-01 4.44092274e-01
7.28773415e-01 -4.82868165e-01 -6.16410255e-01 9.49458063e-01
5.10608256e-01 -2.11024880e-01 1.38498768e-01 -8.04155886e-01
-5.97077142e-03 1.09527504e+00 7.00780034e-01 2.12523788e-01
2.80269653e-01 1.32808530e+00 -7.83376038e-01 -4.17352393e-02
-1.04155779e+00 -4.93103676e-02 -4.03705150e-01 5.83004177e-01
5.55807590e-01 3.69235486e-01 -9.52432513e-01 8.86390626e-01
-2.31469497e-01 3.25006485e-01 1.00639582e+00 1.71882308e+00
-7.51624227e-01 -1.25361955e+00 -8.41334701e-01 3.26673165e-02
-4.94897217e-01 -2.10815981e-01 -5.09307720e-02 9.73511219e-01
1.68620422e-01 4.64684874e-01 1.75943375e-01 -1.85091533e-02
4.72976938e-02 9.52178165e-02 1.11944354e+00 -8.27639997e-01
-4.79239255e-01 -5.43216944e-01 4.72865790e-01 -3.79663587e-01
-3.97804976e-01 -7.30345666e-01 -1.88046515e+00 -9.82793093e-01
-4.31555450e-01 5.41309118e-01 -1.06031030e-01 7.38191307e-01
-2.11221129e-01 4.98648673e-01 -1.27785906e-01 -1.13914296e-01
-8.31735671e-01 -5.58954803e-03 -7.71109700e-01 -2.14169323e-01
4.35403734e-01 -4.21466857e-01 -3.98888052e-01 -1.31063089e-02] | [8.703289985656738, 6.807203769683838] |
43c8cd09-1a4d-4e19-8da8-ebeec139e3b6 | parallel-instance-query-network-for-named | 2203.10545 | null | https://arxiv.org/abs/2203.10545v1 | https://arxiv.org/pdf/2203.10545v1.pdf | Parallel Instance Query Network for Named Entity Recognition | Named entity recognition (NER) is a fundamental task in natural language processing. Recent works treat named entity recognition as a reading comprehension task, constructing type-specific queries manually to extract entities. This paradigm suffers from three issues. First, type-specific queries can only extract one type of entities per inference, which is inefficient. Second, the extraction for different types of entities is isolated, ignoring the dependencies between them. Third, query construction relies on external knowledge and is difficult to apply to realistic scenarios with hundreds of entity types. To deal with them, we propose Parallel Instance Query Network (PIQN), which sets up global and learnable instance queries to extract entities from a sentence in a parallel manner. Each instance query predicts one entity, and by feeding all instance queries simultaneously, we can query all entities in parallel. Instead of being constructed from external knowledge, instance queries can learn their different query semantics during training. For training the model, we treat label assignment as a one-to-many Linear Assignment Problem (LAP) and dynamically assign gold entities to instance queries with minimal assignment cost. Experiments on both nested and flat NER datasets demonstrate that our proposed method outperforms previous state-of-the-art models. | ['Yueting Zhuang', 'Weiming Lu', 'Fei Huang', 'Pengjun Xie', 'Guangwei Xu', 'Zeqi Tan', 'Xiaobin Wang', 'Yongliang Shen'] | 2022-03-20 | null | https://aclanthology.org/2022.acl-long.67 | https://aclanthology.org/2022.acl-long.67.pdf | acl-2022-5 | ['nested-named-entity-recognition', 'chinese-named-entity-recognition'] | ['natural-language-processing', 'natural-language-processing'] | [-1.11701470e-02 3.27631325e-01 -1.98075756e-01 -5.94956875e-01
-9.71579134e-01 -9.71899211e-01 1.81433320e-01 5.46974719e-01
-1.01955044e+00 7.95394599e-01 -1.03732750e-01 -1.88533515e-01
-1.82284657e-02 -1.35090649e+00 -8.87081444e-01 -4.96515222e-02
1.53812870e-01 9.44594622e-01 5.65925241e-01 -1.68998405e-01
1.44904926e-02 3.25663269e-01 -1.36392927e+00 2.23391771e-01
1.19736230e+00 8.88497174e-01 2.01933876e-01 5.20428300e-01
-8.74495268e-01 8.03425252e-01 -8.76946270e-01 -7.46718347e-01
-8.52699652e-02 -5.37698567e-02 -1.31707990e+00 -3.53026807e-01
3.71196270e-01 -9.29555669e-02 3.01205982e-02 1.06185126e+00
5.30976355e-01 2.80899167e-01 5.28919876e-01 -1.03923345e+00
-7.89550424e-01 8.31682920e-01 3.27473506e-03 1.20187923e-01
4.29591417e-01 -2.48684883e-01 1.44199955e+00 -1.07949519e+00
6.26169205e-01 1.01953995e+00 3.12627226e-01 6.02315366e-01
-1.13724375e+00 -6.53414726e-01 2.52030462e-01 1.18369713e-01
-1.62088275e+00 -2.73158163e-01 3.78581107e-01 -1.77749515e-01
1.25349200e+00 4.20411915e-01 1.02756016e-01 4.68291789e-01
-4.89806265e-01 1.02164423e+00 6.00104809e-01 -2.13859186e-01
2.45350525e-01 3.10185879e-01 5.33319771e-01 5.04874468e-01
2.72389412e-01 -4.41725761e-01 -2.56483048e-01 -2.84842938e-01
4.12005514e-01 -6.63148537e-02 -1.99521765e-01 2.30520684e-02
-1.20059824e+00 5.94892144e-01 5.55906951e-01 2.58834839e-01
-3.95308316e-01 -1.60525814e-01 3.91978562e-01 2.87501484e-01
6.07047230e-02 7.54618883e-01 -1.16521513e+00 2.03450561e-01
-5.65505445e-01 1.86951935e-01 1.42121816e+00 1.38991261e+00
1.30754817e+00 -5.63890398e-01 -4.18646753e-01 8.46770823e-01
1.45175308e-01 7.63321757e-01 3.38290930e-01 -5.23569047e-01
8.83106887e-01 1.09590065e+00 3.47015440e-01 -1.00870800e+00
-4.92169291e-01 -3.53857726e-01 -8.33410919e-01 -5.99899471e-01
3.16474229e-01 -5.17592549e-01 -6.98936045e-01 1.65848386e+00
6.41458273e-01 1.51183590e-01 2.77905822e-01 6.38684452e-01
1.19166493e+00 7.60778368e-01 5.51594794e-01 -9.22871530e-02
1.72637665e+00 -9.53181267e-01 -5.97799778e-01 -4.38380480e-01
9.74727392e-01 -4.55210745e-01 8.87290418e-01 2.29957383e-02
-7.84638941e-01 -4.15628642e-01 -4.20068234e-01 -3.42193186e-01
-9.51994538e-01 3.39295059e-01 4.30223048e-01 3.17506880e-01
-5.57532430e-01 1.35553584e-01 -6.53301060e-01 -2.26008669e-01
7.65641034e-02 6.47252083e-01 -4.07330483e-01 5.39188087e-02
-1.59985566e+00 6.71599686e-01 1.09394288e+00 2.09219486e-01
-4.32290971e-01 -6.61894143e-01 -1.07777464e+00 4.31595206e-01
8.99441719e-01 -7.73639798e-01 1.46456957e+00 -6.82145238e-01
-1.17201483e+00 7.34630048e-01 -6.16084874e-01 -2.38320768e-01
-5.10358363e-02 -3.36855173e-01 -6.27906322e-01 -4.43928838e-02
4.61733073e-01 5.04657805e-01 2.86451817e-01 -1.05510724e+00
-9.36862767e-01 -3.04933906e-01 4.93126392e-01 2.68355489e-01
-2.53837764e-01 1.20283172e-01 -6.71894610e-01 -2.33270973e-01
1.06380016e-01 -5.68807960e-01 -2.36368746e-01 -5.84164739e-01
-7.49152005e-01 -8.36111546e-01 3.44835639e-01 -4.37642097e-01
1.55023479e+00 -1.88888204e+00 -9.57378149e-02 1.33645281e-01
4.10786688e-01 1.89631805e-01 -1.64026171e-02 4.55783188e-01
2.03813463e-01 3.97755533e-01 -3.71865004e-01 -7.88275823e-02
3.30571115e-01 3.10593218e-01 -2.51697004e-01 -3.69624108e-01
5.18926740e-01 1.08990538e+00 -1.13893867e+00 -7.91437089e-01
-3.41288477e-01 8.93057510e-02 -5.16012371e-01 5.03665566e-01
-5.71001649e-01 1.75626293e-01 -9.93372500e-01 6.13451779e-01
6.99696302e-01 -5.77951014e-01 1.51392341e-01 -3.25632662e-01
6.88334778e-02 4.88173962e-01 -1.54240108e+00 1.36916351e+00
-6.01856411e-01 -2.33855750e-02 -9.96743366e-02 -1.02632403e+00
7.39081502e-01 4.91393983e-01 1.07977569e-01 -4.90040183e-01
-2.49081805e-01 4.48583126e-01 -4.19950217e-01 -8.63643169e-01
5.33145607e-01 -3.31864953e-02 -5.54269612e-01 3.03122997e-01
3.67494434e-01 2.80359149e-01 5.80214024e-01 2.42768899e-01
1.15360630e+00 -2.11811826e-01 3.18469256e-01 2.42025554e-01
7.25888729e-01 1.20011404e-01 9.83400643e-01 9.51274455e-01
1.47085115e-01 7.99480677e-02 3.41282159e-01 -2.78390348e-01
-5.19239187e-01 -9.75687087e-01 1.04593016e-01 1.63784397e+00
4.14972812e-01 -4.44133192e-01 -5.37751973e-01 -1.12188220e+00
-1.54427886e-01 7.05432296e-01 -2.02140644e-01 2.19782948e-01
-8.22217584e-01 -6.08106315e-01 6.56314552e-01 5.27142107e-01
5.80096126e-01 -1.54444861e+00 -4.77964789e-01 4.60914642e-01
-5.21439075e-01 -1.34277916e+00 -5.12060225e-01 2.82354414e-01
-6.08774424e-01 -1.10269260e+00 -3.92367691e-01 -1.12344217e+00
9.17924225e-01 -2.02008873e-01 1.61931372e+00 2.11668417e-01
6.53101504e-02 2.19352618e-01 -3.34011585e-01 -4.41328287e-01
-1.33025870e-01 8.05070698e-01 -2.87091494e-01 1.24595324e-02
9.70578969e-01 -2.08086133e-01 -4.68659043e-01 2.66210973e-01
-1.22457254e+00 -1.43433869e-01 8.06761563e-01 9.06341016e-01
7.77059257e-01 1.98019415e-01 7.27674961e-01 -1.57864368e+00
5.46582341e-01 -7.20961273e-01 -6.09724879e-01 8.91409218e-01
-3.10264379e-01 3.02817971e-01 9.16093349e-01 -3.44272435e-01
-1.20943797e+00 2.86491930e-01 -1.71879515e-01 1.34374589e-01
-5.81406772e-01 9.00761724e-01 -6.33868814e-01 3.75672907e-01
5.93565524e-01 3.52067053e-01 -8.67117763e-01 -5.44658124e-01
4.53483224e-01 7.25598097e-01 5.45804381e-01 -9.31046486e-01
8.28981400e-01 -1.01181582e-01 -4.42876607e-01 -7.33899415e-01
-1.40145683e+00 -8.54158103e-01 -8.17849100e-01 3.53839576e-01
9.91578281e-01 -8.20581079e-01 -7.24131167e-01 2.39888251e-01
-1.40805054e+00 -6.02028612e-03 -2.66688854e-01 2.62221783e-01
-1.79084782e-02 6.25222847e-02 -5.30465424e-01 -6.85509026e-01
-4.64367628e-01 -8.85730028e-01 1.19468677e+00 6.74654961e-01
1.27128944e-01 -9.85140800e-01 6.12751432e-02 3.03342134e-01
3.41517746e-01 -1.12835996e-01 9.70506132e-01 -1.46288431e+00
-8.00057828e-01 -3.72278422e-01 -2.53108501e-01 7.60148168e-02
7.90964738e-02 -4.35119122e-01 -7.19424784e-01 -7.09126964e-02
-2.45331809e-01 -5.33712804e-01 5.24104774e-01 -3.85205269e-01
1.06321383e+00 -6.82566345e-01 -5.04401088e-01 3.30925256e-01
1.35973251e+00 -8.39730874e-02 4.01782513e-01 2.65008688e-01
7.07688034e-01 6.00659251e-01 5.68071902e-01 2.23556519e-01
8.91133487e-01 3.28115344e-01 8.90310109e-02 -1.09507322e-01
4.92637306e-01 -4.69295472e-01 -8.38107169e-02 7.89728999e-01
2.65103072e-01 -3.38323087e-01 -1.01268303e+00 6.10371292e-01
-1.68709433e+00 -8.44966888e-01 6.91007674e-02 2.14989352e+00
1.24037313e+00 1.01279719e-02 -9.83915105e-02 -3.55923504e-01
8.15576911e-01 -3.06370467e-01 -8.20043504e-01 1.16663896e-01
-5.82242794e-02 3.36820751e-01 4.37790602e-01 3.03382784e-01
-1.21184993e+00 1.26903415e+00 5.08198261e+00 6.31766021e-01
-9.21634018e-01 -8.47983658e-02 2.67518044e-01 5.09527564e-01
-3.19005549e-01 3.41848105e-01 -1.40165865e+00 2.81979293e-01
8.64534736e-01 -3.65622431e-01 1.57726675e-01 7.89436758e-01
-3.82186621e-01 2.50659823e-01 -1.17575157e+00 6.71913445e-01
-1.62093610e-01 -9.51537251e-01 2.50004888e-01 -4.14603263e-01
3.56400609e-01 1.60607636e-01 -6.18734121e-01 1.05372250e+00
3.50123733e-01 -7.53282309e-01 3.04501861e-01 6.57298803e-01
4.41165388e-01 -6.35798037e-01 8.82482946e-01 8.42062771e-01
-1.49255502e+00 -5.06061204e-02 -6.40588462e-01 3.81144613e-01
2.30623871e-01 4.07379478e-01 -8.42812002e-01 7.74063051e-01
6.65495455e-01 2.15991616e-01 -6.35934949e-01 1.14348996e+00
-5.30993581e-01 5.60539484e-01 -7.89586961e-01 -3.21365982e-01
-6.00910261e-02 -1.35509726e-02 2.79547840e-01 1.44013846e+00
7.06696063e-02 5.58666229e-01 5.63001692e-01 1.01394856e+00
-6.32143497e-01 4.57701355e-01 -3.20519060e-01 -1.74348876e-01
1.02341461e+00 1.35814047e+00 -4.88444328e-01 -6.99602544e-01
-6.27331197e-01 1.01653087e+00 9.35428977e-01 4.92400020e-01
-5.80235243e-01 -1.13770330e+00 1.86459050e-01 -2.27751642e-01
4.36095059e-01 1.46997459e-02 1.55569598e-01 -1.59588575e+00
4.54147965e-01 -7.02287734e-01 8.42977345e-01 -4.22105134e-01
-1.62409008e+00 5.94392240e-01 -6.92275465e-02 -1.01548874e+00
-1.11580476e-01 -3.67004007e-01 -5.39630413e-01 7.67847061e-01
-1.73704398e+00 -1.02853513e+00 -2.78493285e-01 5.17827332e-01
3.69007170e-01 3.62910092e-01 1.09033167e+00 5.39576530e-01
-7.11197853e-01 8.86118889e-01 -2.49987572e-01 9.62751627e-01
6.96716666e-01 -1.58293498e+00 3.83947104e-01 6.33266628e-01
3.27990830e-01 9.66716707e-01 1.63124353e-01 -5.03041863e-01
-1.42160928e+00 -1.19319510e+00 1.54628098e+00 -6.02770448e-01
5.31751096e-01 -3.61045569e-01 -1.30061138e+00 8.69064510e-01
5.76820448e-02 2.84450442e-01 1.05468297e+00 3.32940757e-01
-4.38104331e-01 -4.05716058e-03 -9.89922702e-01 2.98753053e-01
8.62071574e-01 -5.49873233e-01 -9.73799467e-01 3.93138349e-01
1.08944666e+00 -3.93462628e-01 -1.17711449e+00 3.78417969e-01
7.53272399e-02 -4.31724936e-01 8.11783910e-01 -9.70618844e-01
-6.12808801e-02 -5.93314111e-01 1.97416954e-02 -1.01985490e+00
-1.96214337e-02 -3.38749290e-01 -2.85378754e-01 1.64756286e+00
1.09653926e+00 -7.84949243e-01 5.95351219e-01 1.24539745e+00
1.42726600e-01 -6.63025081e-01 -6.96868837e-01 -6.60600245e-01
2.41920706e-02 -1.21690586e-01 1.05522525e+00 1.09085119e+00
4.55821157e-02 9.95881736e-01 1.82083040e-01 8.31868231e-01
3.22994918e-01 3.86305988e-01 8.43188882e-01 -1.39420903e+00
-3.67550373e-01 -1.00034446e-01 -1.58125862e-01 -1.50633872e+00
3.33788812e-01 -1.02930200e+00 2.70746052e-01 -1.69122612e+00
5.11519276e-02 -9.23207998e-01 -3.94507080e-01 8.41299653e-01
-7.23942637e-01 -4.40504462e-01 3.20511051e-02 2.22555041e-01
-1.14225340e+00 3.98588747e-01 9.39396739e-01 -2.63937563e-01
-3.07810783e-01 6.29946887e-02 -5.99227369e-01 6.09083652e-01
5.58514237e-01 -8.50560546e-01 -3.66662621e-01 -8.35690618e-01
8.12862575e-01 3.06323677e-01 5.66621087e-02 -7.68180132e-01
8.49824607e-01 -3.18444699e-01 2.00336263e-01 -5.53784788e-01
-8.14753026e-02 -8.60413849e-01 -1.94278166e-01 -8.80808234e-02
-5.02069294e-01 5.70758283e-02 -9.39304009e-02 5.89796364e-01
-5.29836774e-01 -5.24125099e-01 2.81944126e-01 -3.76488388e-01
-7.95702457e-01 5.34765661e-01 1.22097470e-01 8.72213244e-01
7.61700928e-01 2.72538990e-01 -2.66598493e-01 1.31947175e-01
-8.60514939e-01 8.04938674e-01 5.67377098e-02 3.23012501e-01
3.83449674e-01 -1.06701040e+00 -6.93729579e-01 -3.30173434e-03
4.62822050e-01 8.56045067e-01 1.58712819e-01 4.58880335e-01
-2.79696196e-01 3.80469054e-01 4.97167259e-01 -3.97441596e-01
-8.10315847e-01 6.27460182e-01 4.00744826e-01 -6.60787463e-01
-2.66668499e-01 9.43468273e-01 2.17970505e-01 -1.27382731e+00
2.91028470e-01 -1.13392077e-01 -6.50361061e-01 5.72628528e-02
5.42174995e-01 -9.30733327e-03 6.20795973e-02 -4.40690905e-01
-2.54660368e-01 4.49286610e-01 -2.64097959e-01 1.60768270e-01
1.13202250e+00 -9.97009575e-02 -3.80857229e-01 4.07750189e-01
1.14378667e+00 1.05652265e-01 -4.28455740e-01 -6.22375131e-01
5.87421358e-01 4.14554682e-03 -5.10232627e-01 -7.95267582e-01
-8.96619499e-01 6.05437875e-01 5.69090135e-02 4.52493906e-01
1.17632449e+00 1.49730489e-01 1.11491418e+00 1.27865231e+00
4.27322894e-01 -1.11862338e+00 -5.06617427e-01 8.91125441e-01
3.23622048e-01 -1.41438329e+00 -4.25464362e-01 -5.60900390e-01
-4.11955595e-01 1.03573430e+00 1.13760459e+00 2.03325763e-01
6.71074092e-01 -5.83925564e-03 5.80575503e-02 -3.23856294e-01
-7.73800433e-01 -3.88931513e-01 4.07680303e-01 1.29942611e-01
5.13277471e-01 2.47295070e-02 -2.34868154e-01 9.53885376e-01
-8.74212310e-02 -6.24981858e-02 9.60179567e-02 1.17501640e+00
-5.08454025e-01 -1.35852623e+00 1.05068460e-01 6.45839334e-01
-6.09915137e-01 -3.95254999e-01 -3.21367621e-01 5.82721293e-01
1.88846648e-01 8.99974763e-01 -1.18662797e-01 -9.09437165e-02
7.97878563e-01 3.69238317e-01 -1.03351645e-01 -1.09016252e+00
-8.18957567e-01 -5.64727426e-01 1.90168753e-01 -1.94568813e-01
-2.69343197e-01 -1.40618250e-01 -1.82144976e+00 3.15362871e-01
-7.38217473e-01 8.07790518e-01 3.90820384e-01 1.11038804e+00
7.38889635e-01 2.29359031e-01 5.34908295e-01 7.82663003e-03
-7.15391934e-01 -8.70723665e-01 -3.92360598e-01 5.59309363e-01
2.19280228e-01 -3.45417649e-01 -2.98873574e-01 7.81274810e-02] | [9.569990158081055, 9.263631820678711] |
10b1af57-9b73-401d-bbd0-79cef5a51906 | fjmp-factorized-joint-multi-agent-motion | 2211.16197 | null | https://arxiv.org/abs/2211.16197v2 | https://arxiv.org/pdf/2211.16197v2.pdf | FJMP: Factorized Joint Multi-Agent Motion Prediction over Learned Directed Acyclic Interaction Graphs | Predicting the future motion of road agents is a critical task in an autonomous driving pipeline. In this work, we address the problem of generating a set of scene-level, or joint, future trajectory predictions in multi-agent driving scenarios. To this end, we propose FJMP, a Factorized Joint Motion Prediction framework for multi-agent interactive driving scenarios. FJMP models the future scene interaction dynamics as a sparse directed interaction graph, where edges denote explicit interactions between agents. We then prune the graph into a directed acyclic graph (DAG) and decompose the joint prediction task into a sequence of marginal and conditional predictions according to the partial ordering of the DAG, where joint future trajectories are decoded using a directed acyclic graph neural network (DAGNN). We conduct experiments on the INTERACTION and Argoverse 2 datasets and demonstrate that FJMP produces more accurate and scene-consistent joint trajectory predictions than non-factorized approaches, especially on the most interactive and kinematically interesting agents. FJMP ranks 1st on the multi-agent test leaderboard of the INTERACTION dataset. | ['Krzysztof Czarnecki', 'Eli-Henry Dykhne', 'Martin Ethier', 'Luke Rowe'] | 2022-11-27 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Rowe_FJMP_Factorized_Joint_Multi-Agent_Motion_Prediction_Over_Learned_Directed_Acyclic_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Rowe_FJMP_Factorized_Joint_Multi-Agent_Motion_Prediction_Over_Learned_Directed_Acyclic_CVPR_2023_paper.pdf | cvpr-2023-1 | ['motion-prediction'] | ['computer-vision'] | [-9.64484550e-03 3.07701766e-01 -9.92456079e-02 -6.85729265e-01
-6.28791332e-01 -4.60184693e-01 9.65025663e-01 -2.86724001e-01
-1.55543610e-01 7.33645141e-01 5.59763610e-01 -5.33100605e-01
-1.64210260e-01 -7.79010475e-01 -9.35683131e-01 -4.38335121e-01
-5.25905371e-01 1.01591444e+00 7.40699828e-01 -3.48592877e-01
1.58048257e-01 4.96959090e-01 -1.56956494e+00 3.03009421e-01
9.61639524e-01 1.68387949e-01 5.76649487e-01 1.27940416e+00
2.04287559e-01 1.42629814e+00 -2.60614485e-01 -4.49362665e-01
1.07203744e-01 -1.71290532e-01 -8.11524987e-01 8.67816508e-02
8.20346400e-02 -6.56648159e-01 -8.86976302e-01 5.77898443e-01
-1.11243308e-01 5.99553406e-01 8.99278402e-01 -2.06917286e+00
-3.49424519e-02 6.68637335e-01 -5.27828693e-01 2.56465375e-01
3.44191194e-01 6.99269116e-01 9.71460998e-01 -6.64267421e-01
9.60888565e-01 1.53776395e+00 1.83469251e-01 2.76175290e-01
-1.02645493e+00 -5.69716573e-01 7.54505396e-01 8.15576732e-01
-1.04353452e+00 -2.83719152e-01 6.66398823e-01 -8.40680540e-01
1.24763787e+00 3.32229994e-02 6.09445870e-01 1.23675275e+00
8.80534112e-01 1.22238016e+00 3.75272721e-01 3.80404264e-01
1.52855024e-01 -4.96254534e-01 2.62322724e-01 8.93627346e-01
-1.97062239e-01 3.31289500e-01 -5.30626237e-01 -1.94964528e-01
2.85076141e-01 -1.16136357e-01 9.89715457e-02 -4.06026095e-01
-1.47626293e+00 8.92236173e-01 2.39312321e-01 -5.49321532e-01
-7.02062786e-01 4.33353812e-01 1.48764759e-01 -1.35327838e-02
4.05246556e-01 2.49719936e-02 -3.32796961e-01 -3.56381923e-01
-4.10263807e-01 8.75832617e-01 8.75269234e-01 9.90594327e-01
1.06429136e+00 -8.03431645e-02 -2.98401862e-01 3.66515547e-01
4.50947136e-01 4.99210566e-01 -1.42401606e-01 -1.24637282e+00
7.07698703e-01 4.63446110e-01 3.80677909e-01 -1.07257891e+00
-6.42029405e-01 6.48308769e-02 -4.30916399e-01 2.98051447e-01
3.12009871e-01 -5.80128729e-01 -8.14068794e-01 1.71622646e+00
5.69714665e-01 8.33232284e-01 3.74456704e-01 8.61748457e-01
7.01793432e-01 1.08720732e+00 2.43291482e-01 1.57170489e-01
9.37399089e-01 -1.79095662e+00 -5.47741771e-01 -6.48003638e-01
6.78093672e-01 -3.73958439e-01 4.87963229e-01 1.51344299e-01
-6.91015005e-01 -6.32018387e-01 -7.38915503e-01 5.27153537e-02
-1.01686031e-01 -7.35385865e-02 8.13996732e-01 -2.12425888e-01
-1.09959781e+00 2.43095145e-01 -1.23117816e+00 1.06399441e-02
4.82503623e-02 1.69211224e-01 -4.37092692e-01 -2.12649822e-01
-9.68733370e-01 9.55999076e-01 3.67960721e-01 1.63568869e-01
-1.40866745e+00 -5.04734993e-01 -1.19251049e+00 -2.71840334e-01
4.23023731e-01 -7.58905292e-01 1.44906676e+00 -3.83099288e-01
-1.47680604e+00 1.00523852e-01 -6.00497723e-01 -7.07293212e-01
6.43970072e-01 -2.04127163e-01 -4.94130701e-01 -1.40601635e-01
4.61974174e-01 1.05906045e+00 6.62499666e-01 -1.28923368e+00
-1.37958193e+00 -8.87642428e-02 2.57550091e-01 5.43262720e-01
6.69651508e-01 -3.75240743e-01 -6.36773288e-01 -3.40464190e-02
-4.14209664e-01 -1.44055831e+00 -8.27555954e-01 -6.19074047e-01
-7.01557815e-01 -6.60154581e-01 1.05580962e+00 -7.64629126e-01
8.50509226e-01 -2.00560856e+00 5.08522868e-01 7.03578964e-02
3.98826540e-01 -4.01110768e-01 -4.34780806e-01 5.87660730e-01
2.78791934e-01 -5.76976717e-01 -2.01941982e-01 -5.49205542e-01
1.59192160e-01 4.80603576e-01 -3.75926435e-01 4.31139499e-01
1.26776189e-01 1.12849844e+00 -1.13913190e+00 -3.60585660e-01
3.59348625e-01 3.51790488e-01 -5.43539584e-01 3.62460494e-01
-6.67713344e-01 7.51838624e-01 -7.18843460e-01 2.65678484e-02
6.18880928e-01 -9.54994112e-02 9.53474417e-02 1.61264405e-01
-3.23050380e-01 1.04849696e-01 -9.96576488e-01 1.68587255e+00
-1.30811080e-01 9.17294204e-01 -1.91528350e-01 -5.03522277e-01
8.63220990e-01 -1.10044427e-01 5.70680797e-01 -5.64860463e-01
-3.45141411e-01 -2.48736039e-01 -2.38907542e-02 -6.05872989e-01
1.09881437e+00 3.66924047e-01 -4.11740392e-01 4.72006440e-01
-2.82900810e-01 -1.70742907e-03 5.36156535e-01 6.11115217e-01
1.39669728e+00 4.23346639e-01 -1.81274384e-01 6.81985617e-02
2.86043227e-01 6.09008551e-01 4.99297231e-01 7.20371008e-01
-3.47776622e-01 2.22732842e-01 6.63751602e-01 -6.70981407e-01
-8.69724154e-01 -1.08769298e+00 5.53603411e-01 1.20301664e+00
5.48386037e-01 -4.18060720e-01 -4.37573075e-01 -8.69476676e-01
3.83405164e-02 1.23843837e+00 -3.80791843e-01 -1.37911946e-01
-8.91130686e-01 -5.81834316e-01 1.53478652e-01 3.61461639e-01
1.82446167e-01 -1.14952099e+00 -7.68506110e-01 4.23387945e-01
-5.61193466e-01 -1.49227166e+00 -5.14186263e-01 -2.13350907e-01
-2.06812531e-01 -1.15369010e+00 -6.54185340e-02 -7.68935323e-01
4.55098957e-01 4.56008554e-01 1.14408219e+00 -4.23637956e-01
1.09768555e-01 3.98715794e-01 -2.11181149e-01 -2.27573097e-01
-6.03102207e-01 6.18278012e-02 -6.89156055e-02 1.22805640e-01
2.84599513e-01 -4.53852296e-01 -5.85372686e-01 4.07377303e-01
-2.65357733e-01 7.92654157e-01 4.43484217e-01 4.55229938e-01
5.26342392e-01 1.44451961e-01 4.39220220e-01 -6.88395500e-01
4.39084679e-01 -9.00881767e-01 -7.98493207e-01 -8.48628581e-02
5.46259340e-03 1.42216742e-01 6.15417719e-01 -1.29886419e-01
-1.38068652e+00 4.76964653e-01 -7.80080482e-02 -2.26782814e-01
-4.78435844e-01 5.31870604e-01 5.71983457e-02 3.83666843e-01
3.88211161e-01 2.82108009e-01 -1.69165805e-01 1.20520905e-01
6.22446477e-01 1.22457683e-01 8.27141166e-01 -3.90014082e-01
7.25824952e-01 5.23722410e-01 1.75320119e-01 -7.02635586e-01
-3.97133291e-01 -3.54792297e-01 -6.48771644e-01 -7.74412215e-01
1.12557948e+00 -1.03938639e+00 -1.03652906e+00 4.72180009e-01
-1.41962349e+00 -1.06115532e+00 5.56855872e-02 7.78795898e-01
-8.71499538e-01 2.46834904e-01 -4.77200240e-01 -8.52298200e-01
3.41650933e-01 -1.51458359e+00 1.16973960e+00 1.61155909e-02
-1.50558963e-01 -9.13919389e-01 5.08150816e-01 3.87948871e-01
-2.75505662e-01 4.56875324e-01 6.92663550e-01 -3.92408848e-01
-1.08277166e+00 -3.26223066e-03 -5.97577691e-02 -4.83901471e-01
-1.68406263e-01 3.54146451e-01 -4.70871627e-01 -1.00690447e-01
-7.07494855e-01 -3.25089619e-02 9.68399823e-01 6.12397611e-01
5.96365392e-01 -1.89275444e-01 -8.70631695e-01 3.20522696e-01
9.11296308e-01 4.75346386e-01 4.66535538e-01 1.95470110e-01
9.95405793e-01 1.01932263e+00 9.64360416e-01 4.22863334e-01
1.43948507e+00 6.75329924e-01 8.22964668e-01 1.08367428e-01
-1.45048842e-01 -4.95906293e-01 7.17257082e-01 4.35594976e-01
9.08597484e-02 -7.50310361e-01 -1.12321353e+00 7.36897767e-01
-2.46465206e+00 -1.05241823e+00 -7.82365978e-01 1.57922447e+00
2.29927171e-02 1.42883942e-01 3.20280045e-01 -6.28377736e-01
6.84520543e-01 4.29757684e-01 -8.86806667e-01 -2.46144518e-01
7.34382570e-02 -6.88969195e-01 4.54220921e-01 9.92224574e-01
-1.21621644e+00 1.27641284e+00 5.90999317e+00 4.23250496e-01
-4.64437038e-01 -9.85157564e-02 6.44085526e-01 8.47708508e-02
-2.38744497e-01 1.23442747e-01 -9.93113160e-01 3.03071856e-01
1.14631975e+00 -1.98546574e-02 3.52796525e-01 8.47081423e-01
5.64716041e-01 -3.13986093e-01 -1.15113413e+00 5.00736892e-01
-2.72697121e-01 -1.48543918e+00 3.75552326e-02 1.50984839e-01
8.77705097e-01 6.54308558e-01 -8.57520774e-02 5.48958659e-01
1.40196085e+00 -9.99604702e-01 8.28056693e-01 7.29133248e-01
1.04837634e-01 -1.03881669e+00 4.18447435e-01 7.36031353e-01
-1.55890477e+00 -8.35944563e-02 2.03075148e-02 -1.55748278e-01
9.51644778e-01 1.16727598e-01 -1.11337101e+00 7.76309013e-01
4.53747094e-01 1.12423313e+00 -3.14224392e-01 7.90352225e-01
-1.42457768e-01 4.62148160e-01 -2.51952767e-01 3.98771800e-02
7.39936411e-01 -4.31996495e-01 8.67698908e-01 1.17696214e+00
5.34435660e-02 1.45928279e-01 7.14332700e-01 5.94445288e-01
4.13528323e-01 -4.28849429e-01 -8.50107491e-01 2.50476331e-01
1.90229908e-01 1.28088939e+00 -6.36453867e-01 -3.04322153e-01
-5.38927197e-01 9.67644930e-01 5.85623324e-01 5.73569655e-01
-1.09181046e+00 1.18099034e-01 1.04840147e+00 -2.72387058e-01
2.93415993e-01 -7.02705681e-01 2.66724944e-01 -8.83607805e-01
-1.41029730e-01 -4.11209166e-01 3.65167767e-01 -7.57242501e-01
-9.15351570e-01 7.11186111e-01 3.27319592e-01 -1.04798830e+00
-9.13346767e-01 -2.99968183e-01 -9.10393953e-01 5.70113003e-01
-1.56402385e+00 -1.39233983e+00 -3.42438906e-01 6.23402536e-01
1.06498075e+00 -1.83703586e-01 4.18368250e-01 -8.92609805e-02
-7.14920759e-01 -1.69978887e-01 -1.72189593e-01 -1.42321333e-01
1.44576445e-01 -1.19358814e+00 1.14379930e+00 1.00878549e+00
8.49075615e-02 -2.59506196e-01 8.79041016e-01 -1.11099756e+00
-1.58255541e+00 -1.64565670e+00 8.04091096e-01 -5.92867732e-01
8.68407428e-01 -2.14654341e-01 -6.39740646e-01 1.19806194e+00
2.78581887e-01 -1.89970776e-01 3.15160513e-01 -1.72930345e-01
1.23339377e-01 3.69530886e-01 -5.74485421e-01 1.04955435e+00
1.26541364e+00 -1.09853424e-01 -2.75951922e-01 6.20143414e-01
9.19650793e-01 -5.27334809e-01 -3.85736227e-01 3.28912646e-01
5.25410295e-01 -8.83697391e-01 8.82210374e-01 -6.90429688e-01
5.77981532e-01 -5.09804845e-01 -8.54264945e-02 -1.57296693e+00
-5.31514704e-01 -6.68432951e-01 -2.00841352e-01 6.62593544e-01
7.75069118e-01 -4.31597739e-01 1.04441178e+00 5.72475910e-01
-5.52858472e-01 -4.76615071e-01 -7.98318028e-01 -3.53315771e-01
-3.57914537e-01 -8.41696918e-01 6.19822979e-01 3.86884570e-01
-1.08346216e-01 4.70104098e-01 -6.72076881e-01 6.93168283e-01
6.16144359e-01 2.65772611e-01 1.49925935e+00 -1.01656711e+00
-4.36208963e-01 -1.91198736e-01 -4.42598075e-01 -1.55400133e+00
8.10637653e-01 -8.27830315e-01 5.55214524e-01 -1.97313917e+00
1.57333985e-01 -1.73182026e-01 4.49068010e-01 2.13475525e-01
-2.01644182e-01 -2.63500333e-01 7.49481618e-02 1.51583463e-01
-9.71265733e-01 8.56697679e-01 1.39747190e+00 -2.79203117e-01
-4.38067913e-01 2.34844670e-01 2.39906888e-02 7.64456034e-01
6.52506530e-01 -3.18226039e-01 -8.24274123e-01 -4.54599887e-01
4.77096736e-02 7.02003717e-01 5.39444089e-01 -8.30540895e-01
6.23525441e-01 -7.32870102e-01 -1.33715063e-01 -1.37250400e+00
6.76415801e-01 -5.38882434e-01 5.20537794e-01 4.22730714e-01
-2.48390302e-01 3.74134451e-01 8.54540840e-02 1.04375863e+00
-9.51679423e-02 3.28957528e-01 5.81492446e-02 1.09534539e-01
-1.40273035e+00 7.31858432e-01 -9.77047026e-01 -1.88043848e-01
1.39382660e+00 -4.17972095e-02 -4.49490488e-01 -6.48145258e-01
-8.09039414e-01 1.03550398e+00 2.48958729e-02 7.47421563e-01
7.79323637e-01 -1.12990952e+00 -1.07844353e+00 3.65933664e-02
2.51113743e-01 3.25805873e-01 5.84801614e-01 6.25532866e-01
-4.40104872e-01 3.71163309e-01 -4.14304696e-02 -8.43537927e-01
-1.03531051e+00 3.74637306e-01 1.28861681e-01 -5.15179873e-01
-1.05735445e+00 7.96705604e-01 6.81281269e-01 -6.83836341e-01
-9.93612409e-02 -1.99257910e-01 -5.98946273e-01 -2.80458778e-01
4.32257324e-01 6.36640787e-01 -4.34160322e-01 -1.19986296e+00
-1.88450113e-01 1.17295124e-01 -2.14583710e-01 -3.35523814e-01
1.32602417e+00 -2.91373819e-01 1.21781997e-01 3.85876209e-01
8.91746163e-01 -3.90458196e-01 -2.19134331e+00 1.00550346e-01
-1.27826542e-01 -2.48055160e-01 -2.51626939e-01 -4.37450558e-01
-8.70135307e-01 3.51456404e-01 -8.65737945e-02 1.39590293e-01
3.66903275e-01 2.48277694e-01 9.42871392e-01 4.67884570e-01
5.15412807e-01 -7.41649568e-01 2.80770045e-02 8.42964649e-01
9.64381933e-01 -1.12825692e+00 -3.53789598e-01 -4.95909989e-01
-1.19851327e+00 8.48486185e-01 1.01766253e+00 -1.87668011e-01
5.96963286e-01 2.58791476e-01 -2.27121145e-01 -3.72650146e-01
-1.28923166e+00 -1.44296080e-01 2.64036417e-01 7.81890154e-01
-3.72531652e-01 5.08637309e-01 2.85183907e-01 2.55175561e-01
-5.61850727e-01 -3.43458623e-01 5.94828427e-01 6.01749599e-01
-3.82614255e-01 -6.30967677e-01 3.24431760e-03 4.17771846e-01
3.22229445e-01 3.52055341e-01 -1.67758554e-01 5.74817777e-01
-1.60980687e-01 1.31459987e+00 2.95803428e-01 -7.46575534e-01
3.02862555e-01 -2.50981241e-01 -2.49445513e-02 -3.53126168e-01
-9.75873172e-02 -3.02689284e-01 5.51612079e-01 -9.23882902e-01
-2.12577894e-01 -9.40004766e-01 -1.47850847e+00 -5.18000185e-01
1.08485460e-01 8.48362967e-02 4.57031399e-01 1.19874108e+00
6.04193628e-01 8.03397000e-01 6.01870596e-01 -1.38232160e+00
2.92314947e-01 -6.83788002e-01 -1.37243450e-01 2.21570522e-01
4.95182246e-01 -7.58266211e-01 -1.29568623e-02 1.61435977e-02] | [5.869652271270752, 0.8081386089324951] |
bdf0639e-f143-488c-839f-b864a5e1f66e | fusion-volumetric-object-level-slam | 1808.08378 | null | http://arxiv.org/abs/1808.08378v2 | http://arxiv.org/pdf/1808.08378v2.pdf | Fusion++: Volumetric Object-Level SLAM | We propose an online object-level SLAM system which builds a persistent and
accurate 3D graph map of arbitrary reconstructed objects. As an RGB-D camera
browses a cluttered indoor scene, Mask-RCNN instance segmentations are used to
initialise compact per-object Truncated Signed Distance Function (TSDF)
reconstructions with object size-dependent resolutions and a novel 3D
foreground mask. Reconstructed objects are stored in an optimisable 6DoF pose
graph which is our only persistent map representation. Objects are
incrementally refined via depth fusion, and are used for tracking,
relocalisation and loop closure detection. Loop closures cause adjustments in
the relative pose estimates of object instances, but no intra-object warping.
Each object also carries semantic information which is refined over time and an
existence probability to account for spurious instance predictions. We
demonstrate our approach on a hand-held RGB-D sequence from a cluttered office
scene with a large number and variety of object instances, highlighting how the
system closes loops and makes good use of existing objects on repeated loops.
We quantitatively evaluate the trajectory error of our system against a
baseline approach on the RGB-D SLAM benchmark, and qualitatively compare
reconstruction quality of discovered objects on the YCB video dataset.
Performance evaluation shows our approach is highly memory efficient and runs
online at 4-8Hz (excluding relocalisation) despite not being optimised at the
software level. | ['Stefan Leutenegger', 'Ronald Clark', 'Michael Bloesch', 'John McCormac', 'Andrew J. Davison'] | 2018-08-25 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [ 4.92217541e-01 1.88930199e-01 2.30779961e-01 -3.58310491e-01
-8.59789968e-01 -7.67171919e-01 3.36065382e-01 4.01717037e-01
-4.61769640e-01 3.89011025e-01 -3.38511527e-01 -1.32071093e-01
-2.15486035e-01 -5.22771537e-01 -1.16475296e+00 -3.48837793e-01
-3.92078549e-01 1.15232706e+00 1.09558213e+00 3.92556489e-02
2.77313858e-01 9.79141772e-01 -1.86432648e+00 3.68586332e-02
5.90136945e-01 1.28730977e+00 5.39926589e-01 1.02015376e+00
1.17896032e-03 6.28337860e-01 -5.77933669e-01 3.72959860e-03
6.81689739e-01 1.49578914e-01 -7.65453100e-01 7.09195137e-01
8.39352667e-01 -2.32612640e-01 -1.97905466e-01 9.10565794e-01
3.44455361e-01 1.89098790e-01 1.56444803e-01 -1.29778218e+00
2.63386816e-01 -8.37334320e-02 -5.04764020e-01 1.64427653e-01
6.92410886e-01 4.24349099e-01 4.77388620e-01 -1.11140370e+00
1.05629361e+00 1.12373722e+00 9.05923784e-01 5.05838394e-02
-1.39376044e+00 -3.74163449e-01 3.06132346e-01 4.83573973e-02
-1.57574511e+00 -4.68985081e-01 4.79177833e-01 -3.57852429e-01
1.30455720e+00 5.85572541e-01 9.99280810e-01 5.61822951e-01
3.56684774e-01 5.26937723e-01 9.27368820e-01 -2.43841782e-02
5.74278593e-01 -9.68991891e-02 -1.12122811e-01 9.99180079e-01
3.01590174e-01 -1.44818677e-02 -9.03895020e-01 -2.59177268e-01
9.29192185e-01 8.49801153e-02 -2.29453743e-01 -1.24525070e+00
-1.65473592e+00 2.33928710e-01 7.62767136e-01 -3.10372025e-01
-5.02307713e-01 4.87716556e-01 -9.91561487e-02 -6.19168719e-03
4.32664514e-01 1.65645793e-01 -6.24686956e-01 -2.31139600e-01
-1.08577442e+00 3.30933422e-01 5.84857404e-01 1.53525233e+00
1.13025522e+00 -4.23844308e-01 2.54223943e-01 2.23377571e-01
2.50833511e-01 6.10404193e-01 -1.59774572e-01 -1.36128247e+00
2.96627402e-01 7.79822588e-01 2.40467101e-01 -1.00122321e+00
-6.06464386e-01 -2.81045139e-01 -3.53036970e-01 4.54494655e-01
1.19283207e-01 6.16278350e-01 -1.32485795e+00 8.54685843e-01
9.33319390e-01 3.99402887e-01 -2.80686319e-01 9.69260275e-01
5.22976279e-01 3.58540118e-01 -4.43294704e-01 -1.67554915e-01
1.03909886e+00 -7.17210352e-01 -3.72499108e-01 -5.66581011e-01
4.04781371e-01 -6.01484716e-01 6.34800673e-01 6.02293432e-01
-1.07967913e+00 -4.45240200e-01 -1.06202769e+00 -2.29067072e-01
-1.67518914e-01 -4.26374048e-01 6.84031665e-01 3.67306560e-01
-1.10565114e+00 6.73073053e-01 -1.25643110e+00 -3.48562747e-01
6.49292409e-01 6.61897659e-01 -4.89919156e-01 -3.33290100e-01
-2.45072484e-01 7.84041226e-01 6.02267683e-01 2.23280817e-01
-1.03899980e+00 -8.35886478e-01 -8.50879252e-01 -6.33162618e-01
6.81717396e-01 -5.89214146e-01 1.01131904e+00 -4.58440781e-01
-1.02866173e+00 8.96367967e-01 -8.42467248e-02 -4.03838962e-01
7.49115765e-01 -9.46522057e-02 1.75734367e-02 1.03688367e-01
2.26051912e-01 8.55978906e-01 8.40397775e-01 -1.60945630e+00
-1.07967114e+00 -7.05456138e-01 -2.06867203e-01 3.80539000e-01
7.72834122e-01 -5.01645505e-01 -1.06634521e+00 -1.21330783e-01
1.25918722e+00 -1.12792695e+00 -5.48377693e-01 3.13407838e-01
-1.95970625e-01 2.73742616e-01 8.67096007e-01 -5.47786713e-01
7.33725607e-01 -2.00301194e+00 2.94902414e-01 5.10959446e-01
1.73875958e-01 -4.72012699e-01 2.73355514e-01 7.97004346e-03
4.76845473e-01 -3.36067885e-01 -5.14139116e-01 -6.06274068e-01
-9.68332440e-02 6.26592755e-01 -1.27673805e-01 1.00642323e+00
-9.38166901e-02 8.32211077e-01 -1.09367752e+00 -5.17722726e-01
5.73309720e-01 4.22018349e-01 -6.65515780e-01 4.39130776e-02
-5.08627534e-01 4.28169876e-01 -2.68519282e-01 1.19450498e+00
9.52375531e-01 -2.91565567e-01 5.14546335e-02 -1.97524801e-01
-1.88498437e-01 2.07304522e-01 -1.66184032e+00 2.44509077e+00
-1.78117417e-02 4.49803591e-01 1.07096061e-01 -2.17484534e-01
6.27071202e-01 -5.10266602e-01 5.15414357e-01 -5.46897948e-01
-6.66510314e-02 3.63352805e-01 -5.50615013e-01 7.55232712e-03
9.76585627e-01 3.74812633e-01 -1.63857490e-01 1.75369278e-01
8.21833238e-02 -7.33999252e-01 -2.19838142e-01 4.22184616e-01
1.62677586e+00 6.63502753e-01 1.62547752e-01 -1.68006212e-01
1.08859455e-02 6.66071832e-01 3.03548098e-01 8.64000976e-01
-1.29727364e-01 7.56782711e-01 -7.37654269e-02 -5.24631858e-01
-8.50857198e-01 -1.30417168e+00 -3.40421081e-01 6.88246071e-01
1.09950328e+00 -3.45367461e-01 -2.77152538e-01 -7.89531887e-01
4.37912822e-01 5.27861297e-01 -4.70971107e-01 9.49536636e-02
-5.92230976e-01 -3.43740493e-01 -4.08732798e-04 2.80239820e-01
3.04807633e-01 -9.27594244e-01 -1.41348195e+00 4.33810562e-01
-8.41589943e-02 -1.00276470e+00 -6.41379207e-02 8.06012571e-01
-1.10648501e+00 -1.18994868e+00 -8.27015489e-02 -4.77000892e-01
9.50163841e-01 3.88109803e-01 1.23032975e+00 2.78168917e-02
-7.48904169e-01 8.81513894e-01 -2.69126713e-01 -2.78978795e-01
-3.47784132e-01 -2.99803048e-01 2.26545796e-01 -3.52699935e-01
5.31830713e-02 -3.39830190e-01 -6.25148833e-01 4.66419339e-01
-5.76707065e-01 1.74913660e-01 4.13792014e-01 1.78802937e-01
1.34811604e+00 1.15623539e-02 -4.03823256e-01 -5.53807855e-01
-1.60403416e-01 -1.53011888e-01 -1.16772830e+00 8.05186927e-02
-5.34926116e-01 2.69269515e-02 -4.30929661e-01 -3.42121571e-01
-6.02922618e-01 7.46415675e-01 4.14302230e-01 -7.90661395e-01
-3.29306908e-03 -7.39316717e-02 -8.57183784e-02 -5.99466801e-01
7.13915408e-01 1.46308154e-01 -9.34708044e-02 -3.74180824e-01
4.27982062e-01 2.11392030e-01 9.26245868e-01 -3.94156814e-01
8.01450133e-01 6.93972051e-01 1.15245260e-01 -5.36232471e-01
-4.97030616e-01 -7.42074549e-01 -9.75783765e-01 -5.65754533e-01
6.87496185e-01 -1.00914001e+00 -7.82254100e-01 2.20625341e-01
-1.14667296e+00 -6.16934419e-01 -6.35844469e-01 2.57084131e-01
-8.29794586e-01 -1.25851901e-02 -9.75735262e-02 -8.22048187e-01
3.46706301e-01 -1.13324487e+00 1.63219047e+00 -2.03007698e-01
-7.12619815e-03 -4.28089440e-01 -7.80659392e-02 2.82171756e-01
-1.60311520e-01 6.16851509e-01 1.15460709e-01 -1.17387250e-01
-1.55316222e+00 -7.74379000e-02 -5.64109832e-02 -2.24147826e-01
-8.57951269e-02 -3.80959630e-01 -8.03830981e-01 -3.94312650e-01
-1.06198132e-01 -7.83592369e-03 6.22880697e-01 1.64269879e-01
1.05752707e+00 -8.15444514e-02 -7.73832619e-01 8.30352902e-01
1.59610522e+00 -1.17294922e-01 5.57998955e-01 4.20448691e-01
9.09168780e-01 2.96308517e-01 1.17704296e+00 5.15112817e-01
5.23072660e-01 6.49002850e-01 1.15828979e+00 8.79418999e-02
-1.45852983e-01 -1.15458362e-01 1.04605488e-01 3.47930342e-01
-5.95004000e-02 -1.07301131e-01 -1.18922174e+00 4.41539526e-01
-1.89001215e+00 -4.90711778e-01 -4.33356673e-01 2.48840880e+00
6.03620648e-01 4.14319485e-01 -7.89550468e-02 -5.50094759e-03
4.39571977e-01 1.28153497e-02 -8.57786596e-01 3.99640918e-01
5.67859877e-03 1.24097213e-01 1.31076097e+00 8.65448833e-01
-9.67325687e-01 1.09541118e+00 5.59263468e+00 3.33826154e-01
-6.09193087e-01 1.22391708e-01 6.11689165e-02 -5.02452374e-01
-1.23819998e-02 2.03533441e-01 -7.90029824e-01 1.57941550e-01
6.15791440e-01 3.81406933e-01 5.34729123e-01 9.85686004e-01
-2.58651406e-01 -9.11146045e-01 -1.31433213e+00 1.42476439e+00
9.71054658e-02 -1.60855722e+00 -3.19503337e-01 4.51357216e-01
5.82730293e-01 5.32038331e-01 -3.27616453e-01 -3.88999507e-02
2.63920963e-01 -5.70956230e-01 1.35839999e+00 5.89307368e-01
7.21604407e-01 -8.42474639e-01 3.80439162e-01 5.27003884e-01
-1.19687140e+00 4.79565971e-02 -3.76599818e-01 5.68715930e-02
2.03570306e-01 4.41356033e-01 -1.27863908e+00 5.59200048e-01
1.13175273e+00 5.55313289e-01 -8.63691390e-01 1.08955419e+00
7.81746879e-02 -1.06937811e-01 -7.82622814e-01 2.74036497e-01
1.17573388e-01 5.21107167e-02 8.11751127e-01 8.63119185e-01
1.86798811e-01 3.23562235e-01 3.81093889e-01 6.95664525e-01
1.92133263e-01 -4.48595911e-01 -3.46117258e-01 5.59586763e-01
5.34562111e-01 1.06124210e+00 -1.47107196e+00 -3.36997360e-01
2.14249358e-01 1.47025299e+00 1.09247595e-01 -3.69269447e-03
-9.57721293e-01 7.10049719e-02 6.66415274e-01 5.07475436e-01
5.71012676e-01 -5.39081931e-01 -1.55308709e-01 -8.81676912e-01
1.95145801e-01 -5.69354415e-01 1.72739640e-01 -1.05475855e+00
-6.17242694e-01 4.75461096e-01 -2.08322778e-02 -1.23427439e+00
-1.30131856e-01 -3.32761288e-01 5.19480467e-01 4.72544312e-01
-1.16434395e+00 -9.30575728e-01 -8.99976194e-01 6.11566007e-01
6.34995222e-01 4.44087863e-01 5.72989285e-01 1.27350986e-01
1.65545404e-01 -1.75548583e-01 -2.18005985e-01 -4.96051222e-01
2.50573367e-01 -1.22301793e+00 5.47175348e-01 8.60629499e-01
4.09910321e-01 2.49091357e-01 8.09520185e-01 -1.19215441e+00
-1.91548038e+00 -1.31320548e+00 3.14119071e-01 -1.17081034e+00
3.12584043e-01 -8.74047577e-01 -8.28934133e-01 9.46800172e-01
-5.78396380e-01 7.15556443e-01 -1.13911755e-01 -3.26870084e-01
5.47540374e-02 3.04584634e-02 -1.37741768e+00 1.69595882e-01
1.70826972e+00 -2.32072681e-01 -4.35516834e-01 6.54998004e-01
9.09531832e-01 -1.33171523e+00 -7.18001068e-01 6.30878389e-01
4.55364734e-01 -1.23291636e+00 1.20381010e+00 4.00770782e-03
-3.94437790e-01 -9.48318481e-01 -5.10425270e-01 -7.04283476e-01
-1.30095139e-01 -6.34273827e-01 -3.88384998e-01 6.10481620e-01
-3.52524072e-02 -2.73609221e-01 1.01143575e+00 6.17958844e-01
-3.98709565e-01 -5.13421953e-01 -1.31659722e+00 -8.56716275e-01
-1.12275040e+00 -9.49736059e-01 4.85225558e-01 6.17687106e-01
-7.05813229e-01 -1.82628050e-01 1.69572622e-01 8.88534129e-01
1.14747822e+00 1.32071301e-01 1.30175650e+00 -1.31285226e+00
-7.92184919e-02 9.58950259e-03 -8.69715333e-01 -1.23487830e+00
-2.83820719e-01 -8.47099245e-01 5.20580292e-01 -1.58765435e+00
-2.03122810e-01 -8.79551232e-01 1.32477000e-01 4.10363585e-01
2.05013722e-01 5.09481490e-01 5.33189438e-02 4.04124141e-01
-1.20855582e+00 1.87728643e-01 8.07397068e-01 3.87922712e-02
-5.58837116e-01 -2.04126373e-01 2.19159454e-01 8.26074243e-01
1.43173859e-01 -7.90684819e-01 -1.96285278e-01 -3.59393358e-01
3.28736484e-01 1.34537024e-02 9.17292356e-01 -1.45170414e+00
3.91238451e-01 -8.05305094e-02 7.28916705e-01 -1.49837601e+00
7.46012211e-01 -1.29900420e+00 7.62065113e-01 5.02418399e-01
2.16956690e-01 4.69330028e-02 2.94312000e-01 9.10153449e-01
4.49786901e-01 3.00036520e-01 5.21542847e-01 -4.21777219e-01
-1.21440935e+00 5.05767286e-01 1.54486626e-01 -3.11734140e-01
1.26732123e+00 -9.57273364e-01 2.19766498e-01 1.85599148e-01
-7.99180269e-01 2.48192370e-01 1.20030439e+00 5.05342960e-01
8.81744087e-01 -1.03369606e+00 -2.66471326e-01 5.36480188e-01
2.98547745e-01 1.02698576e+00 1.02524780e-01 8.36637259e-01
-1.01643467e+00 2.05605719e-02 1.40161574e-01 -1.57096910e+00
-1.19823444e+00 4.64151889e-01 2.75123537e-01 2.27326825e-01
-1.08049834e+00 1.15640521e+00 -1.38041347e-01 -4.06452626e-01
4.93311852e-01 -7.58488476e-01 6.95905268e-01 -7.83060268e-02
3.64019066e-01 5.13831258e-01 4.55024660e-01 -6.38805389e-01
-8.80282104e-01 5.46795189e-01 3.94080728e-01 -2.11379379e-01
1.40676856e+00 -4.49523062e-01 -2.24319294e-01 6.92413747e-01
7.88179517e-01 4.75441851e-02 -1.79947901e+00 -1.53298542e-01
1.10726535e-01 -8.66523564e-01 1.21118374e-01 -7.29113996e-01
-4.90665048e-01 2.07988933e-01 8.33780587e-01 -4.33318131e-02
7.52708673e-01 4.74699646e-01 3.45403880e-01 3.94180685e-01
1.42188609e+00 -9.29284990e-01 4.66327854e-02 3.55687857e-01
9.47971582e-01 -1.20072448e+00 6.40670955e-01 -3.92949879e-01
-3.33834380e-01 8.02712739e-01 5.01383662e-01 -1.01531498e-01
2.35897779e-01 3.99335027e-01 -2.89267957e-01 -5.34376383e-01
-2.75517792e-01 -1.46660745e-01 1.07981585e-01 6.22015774e-01
-6.32805467e-01 -1.39656007e-01 6.13440990e-01 -1.36605233e-01
-2.97108769e-01 -1.89272612e-01 2.93042958e-01 1.42354238e+00
-8.76261771e-01 -4.86004978e-01 -7.86638737e-01 3.35170060e-01
2.32392639e-01 2.28607103e-01 -2.06908733e-01 9.24358904e-01
2.91632742e-01 5.73389113e-01 4.00472373e-01 -3.03667158e-01
4.46191460e-01 -1.20410785e-01 9.66087162e-01 -7.91246295e-01
-4.97422904e-01 8.73225853e-02 -1.08489774e-01 -1.45321012e+00
-4.95264113e-01 -8.12593937e-01 -1.70048368e+00 -5.40412925e-02
-4.18705106e-01 -2.83986628e-01 1.12839937e+00 6.08704805e-01
6.37544870e-01 5.24826884e-01 2.50244260e-01 -1.57614756e+00
1.14567950e-01 -3.95421386e-01 -4.52277660e-01 2.57553589e-02
5.21808445e-01 -7.33347654e-01 -3.39249611e-01 1.54974371e-01] | [7.3467559814453125, -2.3643128871917725] |
ad0532bd-3c13-42c7-bc51-b1907b20c2cb | contour-detection-and-characterization-for | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Barranco_Contour_Detection_and_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Barranco_Contour_Detection_and_ICCV_2015_paper.pdf | Contour Detection and Characterization for Asynchronous Event Sensors | The bio-inspired, asynchronous event-based dynamic vision sensor records temporal changes in the luminance of the scene at high temporal resolution. Since events are only triggered at significant luminance changes, most events occur at the boundary of objects and their parts. The detection of these contours is an essential step for further interpretation of the scene. This paper presents an approach to learn the location of contours and their border ownership using Structured Random Forests on event-based features that encode motion, timing, texture, and spatial orientations. The classifier integrates elegantly information over time by utilizing the classification results previously computed. Finally, the contour detection and boundary assignment are demonstrated in a layer-segmentation of the scene. Experimental results demonstrate good performance in boundary detection and segmentation. | ['Cornelia Fermuller', 'Francisco Barranco', 'Yiannis Aloimonos', 'Ching L. Teo'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['contour-detection'] | ['computer-vision'] | [ 7.14848161e-01 -6.82061195e-01 1.60636678e-01 -4.74517524e-01
-3.09373826e-01 -5.85979879e-01 4.64658380e-01 5.82379818e-01
-4.81332570e-01 7.64484704e-01 -3.06977391e-01 1.80698082e-01
2.64950097e-02 -9.09216821e-01 -5.17640591e-01 -8.13029408e-01
-4.82940584e-01 6.91842288e-02 9.70005989e-01 3.38592291e-01
5.69868445e-01 8.37835670e-01 -1.94583607e+00 5.59366345e-01
3.55220288e-01 1.35273683e+00 2.83356816e-01 1.17106390e+00
-2.23892912e-01 8.14084530e-01 -5.77927351e-01 4.39299196e-01
1.42680919e-02 -4.60323840e-01 -3.93171668e-01 6.17096364e-01
3.73338968e-01 -1.52550980e-01 7.57882819e-02 8.71979356e-01
1.22596934e-01 -1.86896995e-02 6.41574740e-01 -1.11089480e+00
2.71861553e-02 -3.00646611e-02 -6.75814092e-01 4.73138720e-01
5.19731343e-01 5.29198050e-02 4.96506870e-01 -8.92285109e-01
8.88608754e-01 5.54940879e-01 6.92997515e-01 2.12249130e-01
-1.16069603e+00 -1.22556850e-01 1.68188483e-01 5.47626078e-01
-1.46911335e+00 -2.23736718e-01 1.02045476e+00 -6.43679559e-01
8.05334628e-01 3.23653996e-01 1.05167019e+00 5.25350332e-01
5.42326629e-01 5.30442774e-01 1.42805314e+00 -7.33662784e-01
6.48074150e-01 -3.74782860e-01 2.23283172e-01 8.66289198e-01
9.01046693e-02 1.98444039e-01 -9.00344253e-01 1.16050452e-01
9.82334316e-01 7.16492608e-02 -1.11170769e-01 -2.55434513e-01
-1.15241468e+00 2.47065678e-01 1.28718853e-01 2.39800066e-01
-6.25289202e-01 2.30133191e-01 9.25681666e-02 -1.44068494e-01
4.97732460e-01 -1.58717871e-01 -5.21928966e-01 -1.95777863e-02
-1.22289884e+00 -1.21895544e-01 5.73219180e-01 3.84712994e-01
9.97725189e-01 -2.16843039e-01 2.32682768e-02 1.13931537e-01
9.25647914e-02 4.45641428e-01 3.70524168e-01 -1.11657608e+00
-2.60675728e-01 6.92696154e-01 2.18007475e-01 -9.30309117e-01
-4.60556805e-01 3.90877247e-01 -4.69900906e-01 8.35947931e-01
7.04515934e-01 -2.15644687e-01 -1.20072019e+00 9.71099436e-01
6.37177229e-01 3.95322442e-01 -2.60787517e-01 6.87839806e-01
5.20346045e-01 7.65555501e-01 1.52447805e-01 -5.85925221e-01
1.37457705e+00 -1.17414601e-01 -9.33801293e-01 -6.61267042e-02
-2.05617845e-01 -7.95521796e-01 4.15247053e-01 4.13674176e-01
-9.47073460e-01 -5.60653865e-01 -9.30840790e-01 2.86416739e-01
-6.30199611e-01 1.28521740e-01 1.00999534e+00 4.32730556e-01
-9.48253751e-01 4.07663435e-01 -9.73101258e-01 -3.45345885e-01
2.61086434e-01 3.37007433e-01 -9.84290466e-02 2.57145643e-01
-5.59668899e-01 4.80823308e-01 3.19435030e-01 3.99078995e-01
-5.79003632e-01 -2.08630234e-01 -7.76307940e-01 -4.73106861e-01
7.29694739e-02 -2.72088528e-01 9.68810737e-01 -1.01144600e+00
-1.32235491e+00 1.13260484e+00 -6.52768493e-01 -5.92080295e-01
5.15155971e-01 3.38002324e-01 -2.52813995e-01 4.68344301e-01
-4.14585806e-02 6.22119486e-01 9.37770307e-01 -1.23846805e+00
-1.28229761e+00 -3.72249335e-01 -4.26974177e-01 3.05371508e-02
1.80297136e-01 -2.55065355e-02 -3.41912240e-01 -3.50562215e-01
6.19743884e-01 -2.67004371e-01 -2.97206581e-01 2.97321469e-01
-1.75570667e-01 -2.61314034e-01 9.86428559e-01 -4.59472090e-01
9.22183692e-01 -1.98228455e+00 -5.97632051e-01 2.70746022e-01
-1.26167517e-02 -2.99474299e-01 4.22459692e-01 1.22811437e-01
1.65591627e-01 -1.94788367e-01 -3.00275892e-01 1.69942230e-01
-6.83229804e-01 5.80717027e-02 -2.62157947e-01 6.71251774e-01
1.84933364e-01 7.00792432e-01 -6.79343522e-01 -9.28114593e-01
8.03514302e-01 3.21642011e-01 1.66249394e-01 -9.86410305e-03
-4.32847500e-01 5.26484430e-01 -4.80938733e-01 8.88797641e-01
4.88704771e-01 1.35069834e-02 1.67924557e-02 -2.56116867e-01
-7.39478469e-01 -1.28610030e-01 -1.33065426e+00 1.51205623e+00
-5.86731508e-02 1.12542236e+00 -2.24385962e-01 -7.08267808e-01
1.07912755e+00 2.30844140e-01 9.57120717e-01 -7.55954444e-01
1.41360909e-01 -8.72647017e-02 -4.18217510e-01 -7.14579523e-01
3.09227437e-01 2.03842893e-01 -1.03244342e-01 3.28616619e-01
-3.62675697e-01 -1.96857870e-01 3.20165336e-01 -3.77591223e-01
1.00855982e+00 3.39886189e-01 4.22873974e-01 1.23894162e-01
2.71352112e-01 3.61783922e-01 5.81292152e-01 7.70684481e-01
-4.62681741e-01 6.50838256e-01 -9.66557786e-02 -7.14223862e-01
-6.23570263e-01 -1.27470326e+00 -2.61473209e-01 8.24129820e-01
6.65731132e-01 2.22841218e-01 -6.22968435e-01 -1.21387430e-01
-1.85693473e-01 3.17538023e-01 -8.23487818e-01 2.95319349e-01
-7.34160423e-01 -8.43947411e-01 -9.42336172e-02 5.19867897e-01
7.09559262e-01 -1.33240628e+00 -1.50803149e+00 4.75273758e-01
-1.06465019e-01 -1.19397545e+00 -2.48196647e-01 4.98252004e-01
-1.07385147e+00 -1.11278498e+00 -2.25670859e-01 -1.02876699e+00
6.61629856e-01 1.38963416e-01 7.35031903e-01 -3.35218579e-01
-1.11733305e+00 6.90131128e-01 -5.28355539e-02 -4.56404388e-01
1.87597126e-01 -7.17303157e-01 -5.43053210e-01 6.19025588e-01
2.49028563e-01 -5.33487320e-01 -7.03647196e-01 2.79020965e-01
-4.23098028e-01 4.77904156e-02 4.16664891e-02 2.17145145e-01
1.10665333e+00 4.28407431e-01 -2.66748518e-02 -3.38718414e-01
-2.76918579e-02 -1.88896079e-02 -8.68093431e-01 3.65245730e-01
-7.79724866e-02 -3.35923433e-01 -5.41316520e-04 -6.13617003e-01
-1.27093709e+00 6.98698223e-01 5.28426170e-01 -9.65907127e-02
-6.40665412e-01 1.78660795e-01 1.37074724e-01 -1.47241235e-01
5.57571352e-01 3.01001251e-01 -3.81005764e-01 -3.75764444e-02
1.53719723e-01 4.04363841e-01 8.82903755e-01 -2.17802405e-01
1.82429016e-01 1.17312491e+00 6.15929905e-03 -1.11561775e+00
-4.92776811e-01 -6.69343829e-01 -9.30749655e-01 -9.20932651e-01
9.90194261e-01 -5.80603302e-01 -7.60951161e-01 8.50974798e-01
-1.37902868e+00 -5.39328218e-01 -6.06626272e-01 4.18317139e-01
-6.62095726e-01 1.39163667e-02 -4.22611326e-01 -1.38208258e+00
5.20443097e-02 -3.18741500e-01 9.66557980e-01 7.94597030e-01
-1.19604625e-01 -1.10302234e+00 -5.46035655e-02 3.44738327e-02
2.51666922e-03 7.91674316e-01 4.07220274e-01 8.28489065e-02
-1.08237731e+00 -3.59941840e-01 -5.62999249e-02 -1.88594148e-01
2.20314160e-01 7.05278635e-01 -1.08022308e+00 5.14925778e-01
6.14230298e-02 1.56996489e-01 8.53806376e-01 1.19682980e+00
1.13789618e+00 3.64411831e-01 -7.03788459e-01 4.93592858e-01
1.68780315e+00 8.06974232e-01 5.10004759e-01 1.57981083e-01
3.58545721e-01 7.34555304e-01 7.21672714e-01 6.50903106e-01
1.55062318e-01 3.94257605e-01 2.97968119e-01 -2.63870299e-01
-3.56369883e-01 2.16133930e-02 2.58310348e-01 1.76790819e-01
-1.41947702e-01 -1.43897161e-01 -9.22192335e-01 7.58176565e-01
-1.61349523e+00 -1.07755983e+00 -6.21697724e-01 2.11917233e+00
8.01211596e-01 1.85277522e-01 -2.91937068e-02 3.60325664e-01
1.13803649e+00 -3.81114371e-02 -6.61888242e-01 -3.47748339e-01
-4.46926624e-01 2.06459478e-01 7.31700778e-01 5.08278489e-01
-1.28405488e+00 8.67595553e-01 7.44044781e+00 4.42169100e-01
-1.37713730e+00 -1.80470824e-01 7.28140891e-01 2.52916992e-01
1.37294665e-01 2.67559346e-02 -7.55107284e-01 4.34704900e-01
5.20529687e-01 -4.96180952e-02 2.84546554e-01 2.15383202e-01
5.77327132e-01 -1.16655517e+00 -8.53929698e-01 8.61063898e-01
-2.69937012e-02 -1.25208402e+00 -4.02523190e-01 -2.98986346e-01
6.91586077e-01 -1.23402931e-01 -1.70821533e-01 -6.73954964e-01
1.94939181e-01 -6.25724196e-01 9.20947075e-01 9.91284430e-01
7.73977458e-01 -3.50769520e-01 1.35039628e-01 1.92164689e-01
-1.44037259e+00 -1.01407789e-01 1.85461785e-03 -4.12619621e-01
1.79667354e-01 8.02783668e-01 -8.50551426e-01 -6.30527809e-02
6.88590884e-01 5.70595086e-01 -5.07630706e-01 1.42090750e+00
-2.34756723e-01 5.80708563e-01 -5.52183747e-01 -1.51596218e-01
-2.39593178e-01 -1.99570969e-01 3.56544226e-01 1.18912840e+00
2.69246823e-03 2.78647155e-01 2.83517569e-01 7.40967333e-01
4.79539514e-01 -2.22771570e-01 -6.35567784e-01 3.17092478e-01
4.29458708e-01 1.26583576e+00 -1.58768892e+00 -1.48903400e-01
-2.18108281e-01 1.11115527e+00 -2.18301728e-01 5.93001366e-01
-6.61490440e-01 -2.39943922e-01 2.75127709e-01 5.35449907e-02
4.64614660e-01 -5.65089047e-01 -7.60890126e-01 -6.36763573e-01
1.79134414e-01 1.65258616e-01 2.95527637e-01 -8.66251767e-01
-1.00234389e+00 1.58119455e-01 -1.32698730e-01 -1.06160688e+00
-1.01630777e-01 -5.18466651e-01 -7.77939439e-01 6.47185564e-01
-1.24539673e+00 -1.07283771e+00 -5.19213676e-01 7.27692425e-01
6.97543681e-01 3.40336919e-01 7.05325484e-01 -2.77500987e-01
-2.69842416e-01 -3.61805677e-01 -1.45338709e-02 2.58579820e-01
2.33872801e-01 -1.19464254e+00 2.73453388e-02 1.06565118e+00
3.94137502e-01 -2.18756393e-01 5.62514305e-01 -8.85231018e-01
-1.05242503e+00 -8.58350694e-01 9.59161580e-01 -3.65656674e-01
4.86247748e-01 -4.03710961e-01 -6.39992893e-01 1.66851372e-01
-1.05900325e-01 3.56606275e-01 3.37208807e-01 -4.91127878e-01
2.21850574e-01 -4.41708595e-01 -1.02873969e+00 2.47796386e-01
8.07565868e-01 -5.75564802e-01 -4.30850804e-01 2.23692402e-01
9.38534513e-02 -4.25479174e-01 -3.54027450e-01 2.94536054e-01
7.00783193e-01 -1.17072594e+00 9.79813218e-01 9.76627320e-02
-3.70629057e-02 -5.63194513e-01 3.35223489e-02 -4.32221711e-01
8.79464298e-02 -5.20700753e-01 2.21221671e-01 1.13211513e+00
2.74655651e-02 -4.30243373e-01 1.03427410e+00 4.08729047e-01
4.25505728e-01 -2.95304060e-01 -1.18215203e+00 -3.32305253e-01
-8.03212881e-01 -6.93509817e-01 -6.78973719e-02 6.80269659e-01
-4.31474388e-01 -2.72645891e-01 2.57742018e-01 2.85285950e-01
9.11517560e-01 6.70003951e-01 1.63947716e-01 -1.39086521e+00
2.83743382e-01 -1.67968154e-01 -8.22080195e-01 -6.19708419e-01
-2.33154714e-01 -3.76668453e-01 4.02043968e-01 -1.56139207e+00
-6.62463382e-02 -3.81242156e-01 -3.40986878e-01 3.52219880e-01
-2.07868479e-02 4.70374435e-01 -2.65057266e-01 9.82861891e-02
-5.02821326e-01 -9.13063884e-02 9.90290463e-01 1.13171758e-02
-3.57045561e-01 -3.46295685e-02 3.65950793e-01 1.03083754e+00
8.32902312e-01 -3.42626035e-01 -1.11291423e-01 5.39172702e-02
-1.32069871e-01 3.12482685e-01 6.19995773e-01 -1.17723763e+00
8.25709879e-01 -4.24489319e-01 8.45569432e-01 -9.81012106e-01
3.74570072e-01 -9.28891540e-01 1.74088478e-01 3.55279714e-01
-1.86845288e-01 -8.74878392e-02 1.16056852e-01 8.81115556e-01
-1.04240775e-01 -1.57560527e-01 7.39032328e-01 -2.89807260e-01
-1.36565781e+00 4.18480746e-02 -9.28992808e-01 -2.45380089e-01
1.50451398e+00 -9.03603315e-01 -5.55588752e-02 -2.90330481e-02
-1.02237415e+00 -1.69613793e-01 3.62910748e-01 -6.12214431e-02
8.85921478e-01 -7.74329960e-01 -2.87782371e-01 5.92103004e-01
-1.41828224e-01 -7.67804980e-02 1.13855340e-01 6.22466743e-01
-7.87101507e-01 -2.42990963e-02 -4.56548184e-01 -1.04543889e+00
-1.44452858e+00 2.52653539e-01 6.09359622e-01 2.36460656e-01
-5.74303806e-01 8.02181721e-01 -4.02445681e-02 5.82463801e-01
2.08844364e-01 -5.25148451e-01 -3.14772427e-01 4.69853878e-01
3.37707192e-01 4.56289381e-01 -2.72186473e-02 -3.20549071e-01
-3.90432447e-01 1.04009974e+00 4.29174572e-01 -4.72916633e-01
9.27458048e-01 -4.74995315e-01 -2.42078528e-01 1.10202825e+00
7.33015418e-01 -9.12480503e-02 -1.75720465e+00 -9.31552649e-02
2.72678494e-01 -3.73489887e-01 2.50215292e-01 -8.78806055e-01
-8.04962516e-01 7.59130657e-01 9.19517636e-01 2.84241289e-01
1.51319337e+00 -1.63544014e-01 4.50608641e-01 -1.17228471e-01
6.46195590e-01 -1.18371689e+00 3.35472003e-02 2.56594479e-01
1.94032714e-01 -1.01130366e+00 1.94764584e-02 -7.04526246e-01
-3.27236056e-01 1.43565929e+00 2.76513308e-01 -1.05522200e-01
8.04532051e-01 7.91250169e-01 2.94500709e-01 -1.97049350e-01
-6.41986251e-01 -5.29484510e-01 1.45449311e-01 8.10218692e-01
1.61090195e-01 1.55057698e-01 -2.31116831e-01 -2.51028538e-01
2.87632257e-01 7.87351802e-02 4.54490602e-01 1.24783802e+00
-7.45797932e-01 -5.85940719e-01 -6.79743290e-01 1.70898691e-01
-2.71450967e-01 2.30957061e-01 -4.68056709e-01 5.99783897e-01
6.44910097e-01 1.11588526e+00 7.54520833e-01 2.43364759e-02
1.72366768e-01 1.09897405e-01 8.35567594e-01 -3.43552560e-01
-2.76113659e-01 3.23039383e-01 -1.79994866e-01 -6.54335201e-01
-8.88779998e-01 -1.02437556e+00 -1.80432725e+00 4.12112534e-01
-1.45059437e-01 -3.30352306e-01 1.15742159e+00 7.03768373e-01
-4.71430682e-02 4.31014270e-01 8.12169373e-01 -8.97296488e-01
4.27808464e-01 -3.03371012e-01 -8.00749063e-01 1.98438823e-01
4.84744608e-01 -4.33119476e-01 -3.55832160e-01 9.78781641e-01] | [8.67093563079834, -1.2675095796585083] |
e16c7d7a-9f29-4c63-910f-8c62e170799e | encoding-spatial-distribution-of | null | null | http://proceedings.neurips.cc/paper/2021/hash/c04c19c2c2474dbf5f7ac4372c5b9af1-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/c04c19c2c2474dbf5f7ac4372c5b9af1-Paper.pdf | Encoding Spatial Distribution of Convolutional Features for Texture Representation | Existing convolutional neural networks (CNNs) often use global average pooling (GAP) to aggregate feature maps into a single representation. However, GAP cannot well characterize complex distributive patterns of spatial features while such patterns play an important role in texture-oriented applications, e.g., material recognition and ground terrain classification. In the context of texture representation, this paper addressed the issue by proposing Fractal Encoding (FE), a feature encoding module grounded by multi-fractal geometry. Considering a CNN feature map as a union of level sets of points lying in the 2D space, FE characterizes their spatial layout via a local-global hierarchical fractal analysis which examines the multi-scale power behavior on each level set. This enables a CNN to encode the regularity on the spatial arrangement of image features, leading to a robust yet discriminative spectrum descriptor. In addition, FE has trainable parameters for data adaptivity and can be easily incorporated into existing CNNs for end-to-end training. We applied FE to ResNet-based texture classification and retrieval, and demonstrated its effectiveness on several benchmark datasets. | ['Yuhui Quan', 'Jinxiu Liang', 'Zhile Chen', 'Feng Li', 'Yong Xu'] | 2021-12-01 | null | https://openreview.net/forum?id=KnN6mh23cSX | https://openreview.net/pdf?id=KnN6mh23cSX | neurips-2021-12 | ['material-recognition', 'texture-classification'] | ['computer-vision', 'computer-vision'] | [ 2.36560404e-02 -3.58014941e-01 -1.16745960e-02 -4.33837563e-01
-4.38416928e-01 -4.19196457e-01 5.97494721e-01 6.06215298e-01
-3.10276628e-01 1.71803892e-01 1.65955514e-01 -2.15958413e-02
-5.25420070e-01 -1.49335253e+00 -6.35691762e-01 -8.13436866e-01
-5.83804131e-01 -5.18248230e-02 2.95487702e-01 -5.81054211e-01
2.61055738e-01 7.53161073e-01 -1.77768719e+00 5.52232325e-01
5.71760356e-01 1.60538208e+00 1.05845161e-01 3.16691607e-01
-1.46750927e-01 5.13036549e-01 -3.19701314e-01 -2.72040106e-02
1.59711048e-01 -1.05136773e-02 -6.99974298e-01 -1.08084166e-02
4.16199714e-01 3.68780486e-04 -5.72439730e-01 9.84813213e-01
4.61891234e-01 2.64211744e-01 8.95087659e-01 -6.77695453e-01
-9.15362477e-01 1.08944744e-01 -5.02145469e-01 4.66351718e-01
4.39004041e-02 -3.60295743e-01 1.06001770e+00 -7.84616709e-01
5.32563150e-01 1.21245944e+00 7.22598195e-01 -1.22680858e-01
-1.21423900e+00 -1.77109703e-01 -3.18253130e-01 3.26968074e-01
-1.65702367e+00 -1.34659618e-01 1.11634123e+00 -3.29731315e-01
8.94499063e-01 2.79872864e-01 7.90588379e-01 6.54838920e-01
6.53667212e-01 5.02254248e-01 1.22046554e+00 -2.70662218e-01
2.81141132e-01 -5.78064919e-01 -1.42620891e-01 1.01203501e+00
-9.87260267e-02 -2.45039925e-01 -7.08877861e-01 1.43607765e-01
1.15764248e+00 2.85773277e-02 -6.58645062e-03 -2.63278425e-01
-1.14714444e+00 6.96952581e-01 1.11392915e+00 4.01852399e-01
-3.41264457e-01 1.28044471e-01 5.51928520e-01 5.94218731e-01
6.53145969e-01 2.76960880e-01 -1.99312389e-01 7.09550455e-02
-6.18734241e-01 3.18579376e-01 2.17744634e-01 4.49223578e-01
1.02502108e+00 -9.80445072e-02 -2.22210899e-01 1.22159481e+00
-2.15727031e-01 4.50159758e-01 5.59535086e-01 -4.26259518e-01
1.97433606e-01 8.65488410e-01 -4.45265919e-01 -1.62780833e+00
-5.74705541e-01 -5.15410423e-01 -1.44772649e+00 -4.95597795e-02
1.90889657e-01 6.57160580e-01 -6.48106992e-01 1.43499029e+00
7.31717199e-02 -1.55391768e-01 -1.96035519e-01 9.31530595e-01
8.88697088e-01 4.88855541e-01 -1.64041832e-01 3.59568328e-01
1.59455919e+00 -3.77353847e-01 -1.83980465e-01 3.21315378e-01
5.01731873e-01 -5.77148736e-01 1.25980425e+00 1.70819312e-01
-6.72072113e-01 -7.96208143e-01 -1.28883576e+00 -2.32173681e-01
-7.37573028e-01 1.66013259e-02 7.06427276e-01 3.76111656e-01
-1.06505013e+00 8.56656969e-01 -5.45994759e-01 -2.31929749e-01
7.43608952e-01 1.92658275e-01 -5.37209511e-01 2.22085882e-02
-1.12700605e+00 4.10927504e-01 3.07455599e-01 2.41659805e-01
-2.11200342e-01 -2.99839139e-01 -8.38531196e-01 2.96024919e-01
-3.94132473e-02 -3.06768477e-01 5.89105606e-01 -8.73058379e-01
-1.33841443e+00 8.59375894e-01 1.13505162e-01 -2.82015949e-01
1.66028887e-01 1.82614237e-01 -5.55428624e-01 2.27725267e-01
1.65135995e-01 4.67929780e-01 8.95127594e-01 -7.29112029e-01
-4.00967807e-01 -4.53396767e-01 1.34794474e-01 4.67650173e-03
-5.46712995e-01 -3.11014116e-01 -4.38495958e-03 -9.90097165e-01
6.07808411e-01 -4.85554069e-01 8.29181224e-02 -3.82052921e-02
-3.33368182e-01 -4.22816485e-01 8.32005501e-01 -2.03199506e-01
1.21468163e+00 -2.27905798e+00 1.20571606e-01 5.49670696e-01
2.82884777e-01 -1.45288408e-01 -2.69252419e-01 4.39043015e-01
-9.31154704e-04 1.43416211e-01 -2.11327925e-01 2.26303264e-01
1.60203338e-01 -1.59834132e-01 -3.29771578e-01 5.24785042e-01
6.97712958e-01 1.06001329e+00 -6.46666646e-01 -1.78672016e-01
2.93106139e-01 4.63674128e-01 -4.19378370e-01 -1.16857670e-01
-1.15386128e-01 1.98431760e-01 -6.47099137e-01 8.53306055e-01
7.52505898e-01 -1.68523476e-01 -1.31294996e-01 -3.93374443e-01
-1.92088857e-01 -4.77235615e-02 -8.42558324e-01 1.49536324e+00
-7.71306098e-01 8.87609065e-01 -5.35715699e-01 -1.22704184e+00
1.35717142e+00 -2.17680797e-01 5.53985953e-01 -1.24176824e+00
1.51432544e-01 4.46568966e-01 -9.13283527e-02 -4.29993659e-01
7.23720014e-01 1.64917305e-01 -4.85550404e-01 2.96930790e-01
-4.88468744e-02 6.46321848e-02 -6.01610579e-02 -4.32983130e-01
1.09482920e+00 -2.51178086e-01 1.51275009e-01 -7.73388386e-01
5.66710770e-01 -2.62298524e-01 8.59196633e-02 5.86676002e-01
2.04507455e-01 7.59208560e-01 5.12675524e-01 -1.30071366e+00
-1.06472909e+00 -1.15633023e+00 -6.48272038e-01 1.16519618e+00
2.00160921e-01 -4.86375719e-01 -5.53365171e-01 -2.18148604e-01
6.91230148e-02 -3.77685905e-01 -9.94222224e-01 -1.59584329e-01
-5.91046691e-01 -7.00388908e-01 5.70730925e-01 3.77728790e-01
1.12008035e+00 -1.14391983e+00 -1.02819490e+00 4.35945541e-01
-2.48314843e-01 -8.59402180e-01 -3.53636771e-01 4.03445005e-01
-5.82134068e-01 -9.80227530e-01 -6.14555120e-01 -7.76832163e-01
4.36147273e-01 4.94190097e-01 9.88787532e-01 1.59529641e-01
-5.97642839e-01 2.17925921e-01 -5.78365028e-01 -9.95330736e-02
1.55900761e-01 3.38183016e-01 -1.38112321e-01 3.81027788e-01
2.88872153e-01 -8.24621379e-01 -8.08890998e-01 3.47664714e-01
-1.13118279e+00 -3.27444784e-02 4.95289445e-01 9.59078670e-01
9.34734225e-01 3.77177835e-01 5.40407598e-01 -2.34710097e-01
8.41827929e-01 -2.11326286e-01 -1.88568413e-01 2.07027823e-01
2.02735186e-01 2.49857843e-01 7.97295809e-01 -2.13076174e-01
-4.13934350e-01 -3.31368625e-01 -9.54049677e-02 -1.96134195e-01
-1.10880166e-01 8.73137772e-01 3.23735103e-02 -5.09052932e-01
7.70665288e-01 4.88353133e-01 -6.81576505e-02 -3.22370589e-01
1.27313793e-01 5.63121319e-01 3.77826273e-01 -7.56640494e-01
4.12662745e-01 5.90120494e-01 4.23964679e-01 -1.39675438e+00
-5.46515584e-01 -2.61234313e-01 -7.55479991e-01 -2.27131173e-01
6.56149924e-01 -8.34359348e-01 -7.73673236e-01 5.96649289e-01
-8.13708425e-01 -2.78631419e-01 -3.08948845e-01 7.37646967e-02
-7.64848888e-01 2.02084050e-01 -3.98290396e-01 -2.75902182e-01
-1.85861915e-01 -9.39269722e-01 1.39059067e+00 -1.58533435e-02
-7.43899196e-02 -9.35397148e-01 -2.61970535e-02 -3.29849035e-01
7.15613246e-01 6.33939981e-01 1.42799950e+00 1.07515529e-02
-4.66401279e-01 -2.86308736e-01 -4.67643529e-01 6.33328110e-02
2.84515351e-01 -1.07999416e-02 -8.37998152e-01 -2.30910733e-01
-3.39701325e-01 -4.30769116e-01 1.38362920e+00 3.78721803e-01
1.81642377e+00 -2.49456286e-01 1.48235783e-01 7.85167396e-01
1.60032916e+00 -3.06417495e-01 6.76230669e-01 6.10657811e-01
5.98404884e-01 5.63868999e-01 1.58830732e-01 4.97880191e-01
2.70658493e-01 8.05921912e-01 2.73818791e-01 -1.62003398e-01
-9.64757428e-02 -1.09220244e-01 -2.38228962e-02 7.83533037e-01
-4.97465998e-01 3.76897156e-02 -8.56561065e-01 2.54682064e-01
-1.66967797e+00 -9.76339996e-01 1.01015650e-01 1.96732068e+00
3.99093002e-01 1.17394060e-01 1.30002648e-01 3.16909462e-01
6.26627505e-01 5.70061505e-01 -3.50540012e-01 -4.87640589e-01
-5.53297102e-01 4.96359378e-01 5.10607004e-01 -4.58529666e-02
-1.37355030e+00 8.69318843e-01 5.26592350e+00 1.32438982e+00
-1.53005111e+00 -1.20875269e-01 7.75673389e-01 2.77469873e-01
-2.44363412e-01 -5.88632286e-01 -7.62915388e-02 2.28481025e-01
4.43323314e-01 -7.15831341e-03 4.03850377e-01 4.88728732e-01
2.87984926e-02 -3.83289456e-02 -5.13582766e-01 1.10901392e+00
-3.11575413e-01 -1.54595649e+00 3.71956408e-01 2.81308562e-01
5.73882341e-01 8.59153792e-02 4.49623793e-01 1.41883109e-04
-2.72360712e-01 -1.29767573e+00 9.31259274e-01 6.30231678e-01
1.21545470e+00 -9.94105637e-01 6.53728008e-01 -1.39478609e-01
-1.82184529e+00 -2.78208405e-01 -8.01821649e-01 -1.62952375e-02
-3.65038037e-01 6.01200461e-01 -1.05742700e-01 9.21023309e-01
8.42079818e-01 9.40027058e-01 -7.91884840e-01 8.12337279e-01
2.95237541e-01 1.52827829e-01 -3.04506272e-01 -1.19175777e-01
4.98216689e-01 -2.55681098e-01 1.34368226e-01 1.24523056e+00
5.32321453e-01 3.95154720e-03 1.59045339e-01 6.32776618e-01
-9.15831849e-02 4.42983449e-01 -7.29730725e-01 -2.19665710e-02
1.88255936e-01 1.29528153e+00 -1.32670522e+00 4.72562872e-02
-8.53485987e-02 7.19884336e-01 5.34303486e-01 1.94932252e-01
-4.47910130e-01 -6.46210551e-01 5.79238772e-01 2.59343982e-01
3.54392022e-01 -5.01552880e-01 -2.78390408e-01 -9.63341177e-01
2.27631286e-01 -5.95032871e-01 5.29574938e-02 -4.84450281e-01
-1.34668338e+00 9.74979103e-01 -2.94829845e-01 -1.56014180e+00
2.34840855e-01 -8.58795166e-01 -6.31689131e-01 7.71712899e-01
-1.38459206e+00 -1.27913177e+00 -4.85001385e-01 7.04577386e-01
3.62604886e-01 -2.48044327e-01 1.01550913e+00 6.29635304e-02
-2.15024635e-01 3.96245837e-01 2.40147963e-01 2.52669752e-01
3.02253783e-01 -1.15203381e+00 5.24228990e-01 2.55420476e-01
1.66176930e-01 4.26333129e-01 2.30909467e-01 -2.74898052e-01
-1.33922076e+00 -1.36407232e+00 6.23346686e-01 8.26050937e-02
6.72627032e-01 -5.48405349e-01 -9.05905485e-01 -2.29800195e-01
-2.02037573e-01 3.15234989e-01 6.40761793e-01 1.56691536e-01
-6.73356235e-01 -3.89278144e-01 -8.70526373e-01 4.48135376e-01
1.20066440e+00 -8.92256379e-01 -6.14774637e-02 2.28409827e-01
2.94582337e-01 -2.19381571e-01 -1.10662925e+00 5.10721147e-01
8.37010682e-01 -1.19184220e+00 8.67756009e-01 -4.49634403e-01
8.30692947e-01 -1.10261150e-01 -5.90983629e-01 -1.21416533e+00
-7.63213456e-01 -4.26486060e-02 2.05955237e-01 5.72426081e-01
-3.31908502e-02 -6.36120021e-01 4.93125707e-01 -4.83754426e-01
-1.40815780e-01 -1.14298093e+00 -1.28547084e+00 -7.88173616e-01
1.51369721e-01 -2.97878295e-01 9.18668389e-01 8.21238399e-01
-1.79264799e-01 -1.80724010e-01 2.05746368e-02 1.62910428e-02
4.52696621e-01 4.30703789e-01 4.29921836e-01 -1.43102849e+00
1.12288468e-01 -9.82848585e-01 -1.09295738e+00 -7.43193626e-01
1.72542781e-01 -1.08031178e+00 -9.55109894e-02 -1.21303988e+00
-3.12210228e-02 -6.27781987e-01 -6.18119657e-01 3.32169622e-01
3.36283058e-01 7.14768827e-01 -1.11917354e-01 2.87751436e-01
-5.97112834e-01 8.27425778e-01 1.31601429e+00 -4.30282652e-01
-1.20104821e-02 -3.03894818e-01 -4.27934557e-01 4.49664026e-01
9.20760632e-01 -6.25221655e-02 -1.13914900e-01 -4.71242219e-01
3.65579426e-01 -1.99026302e-01 7.16117024e-01 -1.40341699e+00
8.97434503e-02 7.96020553e-02 6.86631083e-01 -3.57587785e-01
6.96140304e-02 -4.94443715e-01 -1.54240485e-02 1.70476392e-01
-2.83899277e-01 1.44608423e-01 2.50585943e-01 4.90113765e-01
-5.81980586e-01 2.64783978e-01 6.41331613e-01 9.20194015e-03
-8.08049560e-01 5.60609221e-01 -3.59302640e-01 -3.16014498e-01
5.70593536e-01 -4.77254540e-01 -3.46262395e-01 -1.66628398e-02
-6.08948171e-01 -3.65118951e-01 4.72900838e-01 5.84748507e-01
8.15932930e-01 -1.74357975e+00 -4.98366892e-01 5.72038472e-01
3.84215832e-01 3.87111381e-02 7.33424604e-01 4.83973473e-01
-7.72724986e-01 3.48457068e-01 -8.21270287e-01 -8.33141148e-01
-9.06905830e-01 1.65814653e-01 4.71837550e-01 -8.27157870e-02
-8.28233898e-01 5.59266686e-01 2.03191429e-01 -1.37980178e-01
-7.83772096e-02 -4.32534814e-01 -3.67105126e-01 1.91068068e-01
3.73694450e-01 1.19322971e-01 3.77442330e-01 -8.81300032e-01
-1.21050894e-01 1.14223504e+00 3.73362526e-02 1.88281476e-01
1.53365695e+00 2.00069100e-02 -4.62388545e-01 5.61449468e-01
1.55504358e+00 -3.47564667e-01 -1.17057610e+00 -4.76383686e-01
5.21309525e-02 -4.00505275e-01 9.82232466e-02 -2.52994955e-01
-1.05378425e+00 9.92854416e-01 6.37974739e-01 7.53512442e-01
1.30455816e+00 1.37244845e-02 5.59823036e-01 6.88988209e-01
5.95319152e-01 -1.16300797e+00 2.77986139e-01 7.77968585e-01
1.20072722e+00 -9.13905144e-01 -1.53167933e-01 -3.33294034e-01
-1.13826632e-01 1.62151885e+00 8.08819756e-03 -5.72854459e-01
1.03350520e+00 -1.27846643e-01 -1.85341492e-01 -5.43681860e-01
-4.25857723e-01 -3.71317178e-01 6.97411895e-01 3.15148056e-01
3.82121414e-01 4.07799691e-01 7.20427791e-03 3.60246211e-01
-4.83682901e-01 -3.63707483e-01 1.73800677e-01 7.89998472e-01
-6.60976768e-01 -7.58624494e-01 -4.54507722e-03 7.65016496e-01
-2.78759003e-01 5.48651144e-02 -1.07170768e-01 5.28409362e-01
3.13282460e-01 5.30201197e-01 5.24376869e-01 -7.06532598e-01
4.34904188e-01 -3.29023242e-01 6.52619898e-01 -2.31188044e-01
-2.82178342e-01 -2.23991070e-02 -3.69783074e-01 -5.42722762e-01
-5.20906985e-01 -4.08666760e-01 -7.37767100e-01 -3.80578429e-01
1.99180529e-01 -1.07937075e-01 5.20204604e-01 7.69823611e-01
3.04976076e-01 7.48996973e-01 1.00943124e+00 -1.03253186e+00
-2.57752478e-01 -8.04035842e-01 -9.74505901e-01 4.20618415e-01
4.51188952e-01 -9.17223036e-01 1.22755161e-02 -1.80264503e-01] | [10.27814769744873, -0.25859883427619934] |
74e65524-7aa4-4c96-9237-111436373040 | a-framework-for-dynamically-meeting | 2306.14178 | null | https://arxiv.org/abs/2306.14178v1 | https://arxiv.org/pdf/2306.14178v1.pdf | A Framework for dynamically meeting performance objectives on a service mesh | We present a framework for achieving end-to-end management objectives for multiple services that concurrently execute on a service mesh. We apply reinforcement learning (RL) techniques to train an agent that periodically performs control actions to reallocate resources. We develop and evaluate the framework using a laboratory testbed where we run information and computing services on a service mesh, supported by the Istio and Kubernetes platforms. We investigate different management objectives that include end-to-end delay bounds on service requests, throughput objectives, cost-related objectives, and service differentiation. We compute the control policies on a simulator rather than on the testbed, which speeds up the training time by orders of magnitude for the scenarios we study. Our proposed framework is novel in that it advocates a top-down approach whereby the management objectives are defined first and then mapped onto the available control actions. It allows us to execute several types of control actions simultaneously. By first learning the system model and the operating region from testbed traces, we can train the agent for different management objectives in parallel. | ['Rolf Stadler', 'Forough Shahab Samani'] | 2023-06-25 | null | null | null | null | ['management'] | ['miscellaneous'] | [-2.98742115e-01 -1.97756752e-01 -2.21076876e-01 -4.25464243e-01
-4.15042818e-01 -5.79052985e-01 4.19813812e-01 -5.61283752e-02
-4.47012216e-01 9.90904570e-01 -4.47790772e-01 -6.77674234e-01
-5.29608727e-01 -6.15710974e-01 -4.86648589e-01 -7.36119628e-01
-9.10165250e-01 1.07160568e+00 6.32474124e-01 -1.05255939e-01
1.86186507e-01 9.20291305e-01 -1.68545735e+00 -8.65434930e-02
4.63436514e-01 9.93738472e-01 1.57431420e-02 1.16294849e+00
6.34654537e-02 7.00851381e-01 -1.10563207e+00 6.27170980e-01
3.29176515e-01 -9.86044928e-02 -6.93404496e-01 4.34238702e-01
-5.08176029e-01 -4.83537108e-01 -7.70628527e-02 3.19785446e-01
4.88163859e-01 1.81450799e-01 2.19908237e-01 -2.10687733e+00
5.14144897e-01 5.86672783e-01 -4.62256402e-01 3.98598671e-01
1.87095866e-01 4.21296924e-01 5.98380327e-01 1.13627404e-01
3.80289465e-01 1.13530779e+00 -5.17986007e-02 4.67074662e-01
-1.40162802e+00 -5.45855939e-01 5.36510885e-01 1.98660493e-01
-1.17082298e+00 -4.74793911e-01 1.15349218e-01 -2.08475981e-02
1.08388722e+00 3.41455936e-01 3.14789414e-01 4.72749293e-01
3.02503169e-01 3.56558830e-01 1.07670677e+00 -4.88793552e-01
8.00387561e-01 -2.39765216e-02 -4.55248117e-01 2.74500400e-01
-1.37031764e-01 1.95716381e-01 -1.29609357e-03 -3.24715495e-01
1.02256465e+00 -2.31027782e-01 -1.08834572e-01 -4.72943604e-01
-1.14901519e+00 3.84147823e-01 -9.51066464e-02 6.55411705e-02
-8.07605505e-01 3.50240618e-01 5.29388785e-01 7.65850306e-01
-1.66320741e-01 4.36544985e-01 -9.89068270e-01 -3.89148533e-01
-8.98937047e-01 7.35867023e-02 1.52666676e+00 1.06111777e+00
5.18146336e-01 3.03845465e-01 -1.00599863e-01 4.26282316e-01
2.19877914e-01 5.11051953e-01 1.31868175e-03 -1.58500361e+00
8.82641152e-02 -1.13350406e-01 6.69541180e-01 -5.28426953e-02
-4.43872511e-01 -1.55569822e-01 -1.66820362e-01 7.29426205e-01
1.75205931e-01 -7.40830183e-01 -7.61930406e-01 1.53343713e+00
5.17395377e-01 6.91038072e-01 1.49089992e-01 1.02086091e+00
-5.27295589e-01 7.88799107e-01 3.27680330e-03 -6.00320280e-01
1.18265307e+00 -9.16501999e-01 -5.01328707e-01 4.46994483e-01
3.66274416e-01 -6.61807835e-01 8.98799777e-01 4.43164051e-01
-1.37579429e+00 -1.63055107e-01 -9.79227602e-01 1.27649486e+00
-1.99194252e-01 -2.85902858e-01 2.21135914e-01 5.16477466e-01
-1.35392833e+00 5.96677661e-01 -9.68520582e-01 -4.90018517e-01
-3.40942353e-01 7.48489678e-01 3.21179271e-01 3.30172151e-01
-7.92428851e-01 7.64230490e-01 2.66130686e-01 -4.28985387e-01
-1.62347937e+00 -6.29053235e-01 -3.23740751e-01 3.45584482e-01
9.76713896e-01 -4.33419436e-01 1.84530890e+00 -8.05471539e-01
-2.11009359e+00 6.54318407e-02 5.52492797e-01 -1.60973102e-01
3.58522922e-01 2.11401209e-01 -7.03998387e-01 4.26129222e-01
-1.51783958e-01 1.21150970e-01 5.55115163e-01 -1.51685584e+00
-1.08334982e+00 2.97406524e-01 7.82975793e-01 2.07479507e-01
2.35740542e-02 3.63518685e-01 -2.12941051e-01 3.20001319e-02
-3.92937481e-01 -9.11612093e-01 -4.59603935e-01 -4.71437812e-01
1.31920829e-01 2.41078988e-01 6.99940503e-01 -2.35261191e-02
8.59743536e-01 -1.78334391e+00 2.02965029e-02 5.50309420e-01
-1.20309055e-01 -1.50988713e-01 -2.19984084e-01 9.23718154e-01
2.21963257e-01 1.69918820e-01 2.89284706e-01 2.21256129e-02
3.70313227e-01 6.84257746e-01 5.15522212e-02 2.70078331e-01
-4.81376201e-02 -1.91231206e-01 -8.51747632e-01 -3.20861787e-01
2.84806877e-01 2.10504532e-02 -6.41950786e-01 7.62898922e-01
-6.05544031e-01 6.62479699e-01 -5.89476943e-01 7.02778339e-01
5.31910777e-01 -1.58569306e-01 7.52764165e-01 4.31494385e-01
-5.09935737e-01 2.09466338e-01 -1.32630503e+00 1.07991302e+00
-1.12514198e+00 -2.10064109e-02 8.19550157e-01 -1.12665486e+00
3.90309662e-01 6.83797002e-01 1.07728887e+00 -6.95341706e-01
1.99364215e-01 2.80342221e-01 6.46463409e-02 -5.42453647e-01
-1.17603969e-02 -1.40526682e-01 -1.86458275e-01 8.69302869e-01
1.37257818e-02 9.26249325e-02 2.91011095e-01 -1.50967360e-01
1.36364639e+00 8.39467421e-02 1.08817056e-01 -4.01678145e-01
5.63279748e-01 -4.88134660e-03 5.17970204e-01 7.45930254e-01
-2.17195764e-01 -5.08740604e-01 9.28865135e-01 -2.99358159e-01
-1.04706895e+00 -1.26459146e+00 1.56423420e-01 1.29345000e+00
1.30228341e-01 1.50491983e-01 -6.22478008e-01 -4.78637904e-01
-3.01051326e-02 9.28418338e-01 -1.27548859e-01 1.63219452e-01
-6.10996068e-01 -3.15561384e-01 1.26921847e-01 1.30162001e-01
2.07327515e-01 -9.76326108e-01 -1.06060410e+00 7.19091296e-01
5.12696743e-01 -1.35163069e+00 -5.53458631e-01 1.77639604e-01
-6.44544661e-01 -1.10960507e+00 -4.17292640e-02 -3.90942365e-01
5.05702913e-01 2.06633896e-01 1.22285628e+00 1.42878085e-01
-2.64923155e-01 8.13947022e-01 -9.11024362e-02 -9.53253582e-02
-5.97050965e-01 1.48080410e-02 2.92406917e-01 -1.71774831e-02
-2.32608646e-01 -1.05242777e+00 -6.67814195e-01 5.77074647e-01
-1.02914870e+00 -1.97775230e-01 4.67771828e-01 4.56125200e-01
4.52099800e-01 3.68820488e-01 8.40809286e-01 -7.43271112e-01
7.35401213e-01 -7.39935040e-01 -1.24660170e+00 1.72001764e-01
-7.75945783e-01 3.70142572e-02 9.93324339e-01 -3.72315913e-01
-5.64885974e-01 -4.72602546e-01 2.48101484e-02 -4.91741210e-01
-3.84931296e-01 1.26851782e-01 -5.25830872e-02 -4.09666784e-02
1.07878864e-01 6.82978779e-02 1.53316990e-01 -1.38962060e-01
-7.66307637e-02 6.92623973e-01 9.94634777e-02 -1.23124444e+00
5.88083804e-01 1.67815655e-01 6.16935641e-03 -3.63344610e-01
-1.06486663e-01 -3.73477012e-01 -6.42362013e-02 -5.16736567e-01
6.44415393e-02 -5.00524521e-01 -1.65302479e+00 -2.28053704e-01
-5.65046668e-01 -6.72185004e-01 -1.71977937e-01 3.48118246e-01
-1.11034644e+00 -3.06053817e-01 -5.56246281e-01 -9.25709426e-01
-1.46246642e-01 -1.20991504e+00 7.45562375e-01 4.52011138e-01
3.27185184e-01 -8.75596166e-01 8.51035267e-02 -3.54964256e-01
9.11739886e-01 4.13762718e-01 8.44907343e-01 -5.28799891e-01
-7.34708846e-01 -5.80281625e-03 -4.88318168e-02 1.54365614e-01
1.93325803e-02 3.17052662e-01 -4.01859522e-01 -9.25538301e-01
-2.51486033e-01 -2.02247158e-01 -2.81837612e-01 9.76180285e-02
1.28452194e+00 -7.39902616e-01 -2.89965332e-01 2.77001679e-01
1.84780025e+00 5.97004950e-01 3.59069675e-01 6.15450501e-01
-2.81165000e-02 4.91259038e-01 8.31955492e-01 1.13764060e+00
4.88787442e-01 9.70966101e-01 8.36411595e-01 -3.77248637e-02
5.47946930e-01 4.36456561e-01 6.63786471e-01 5.18844247e-01
-3.19820978e-02 -3.46310019e-01 -7.82016277e-01 3.18746030e-01
-1.89073324e+00 -7.69650638e-01 6.65794253e-01 2.61455107e+00
4.98194724e-01 6.33539438e-01 6.43648624e-01 3.55577120e-03
5.08939743e-01 -3.57023358e-01 -7.36017406e-01 -8.34465086e-01
7.93313324e-01 2.96955287e-01 6.18414342e-01 5.39083242e-01
-4.56858963e-01 7.29774117e-01 6.65774918e+00 1.62345380e-01
-1.25962687e+00 -1.08251505e-01 2.60692000e-01 -2.33865827e-01
2.11503059e-01 1.65306449e-01 -3.54094774e-01 4.59173769e-01
1.96895075e+00 -5.79935312e-01 1.07492542e+00 8.90280545e-01
1.15427625e+00 -1.50649637e-01 -1.13603914e+00 3.06585580e-01
-6.94354653e-01 -1.07405007e+00 -2.71764040e-01 1.31053105e-01
4.50437397e-01 1.03616886e-01 -5.45572877e-01 5.80705285e-01
6.80786908e-01 -5.10789275e-01 5.18156111e-01 4.25598174e-01
5.93341947e-01 -1.19280863e+00 7.60295093e-01 4.52072173e-01
-1.01632106e+00 -2.32064128e-01 9.15149897e-02 -1.10420637e-01
2.97238469e-01 1.01154260e-01 -8.73848677e-01 3.75647217e-01
3.28968465e-01 -2.68966138e-01 5.88026904e-02 1.19235814e+00
2.48206988e-01 5.72100461e-01 -3.84317070e-01 7.49616027e-02
2.55256891e-01 -8.54236931e-02 3.72859418e-01 9.53249872e-01
1.91523582e-01 -8.67291689e-02 1.06667614e+00 5.41605234e-01
1.90389052e-01 -7.18414485e-02 7.93500468e-02 -5.41728213e-02
7.42174447e-01 1.39514768e+00 -8.08557451e-01 -4.34120297e-01
-4.01614457e-01 6.16748810e-01 -1.71220586e-01 7.14547336e-01
-1.21666372e+00 -5.79232156e-01 1.39171541e+00 1.56644836e-01
1.96215034e-01 -4.57012057e-01 3.52584988e-01 -7.74392307e-01
-2.54216075e-01 -1.09328306e+00 3.01253080e-01 -4.55939531e-01
-8.64668071e-01 5.98855257e-01 1.14602663e-01 -1.14279497e+00
-5.35703242e-01 -2.63908118e-01 -5.77983201e-01 7.28754222e-01
-1.89794743e+00 -2.66482443e-01 -7.28474334e-02 5.76743186e-01
5.81067145e-01 -8.51932690e-02 1.00819278e+00 3.54613125e-01
-7.84297466e-01 2.12208465e-01 1.78440556e-01 -5.37766218e-01
3.27767909e-01 -1.36239755e+00 5.48567995e-02 5.49874723e-01
-6.28058612e-01 1.54984389e-02 1.11097598e+00 1.17845014e-01
-1.52118826e+00 -6.48238897e-01 -5.44638671e-02 3.77202958e-01
1.09525037e+00 -1.43395916e-01 -7.03133643e-01 5.52288234e-01
4.74100441e-01 1.18102714e-01 5.33642828e-01 -1.92263663e-01
2.35235199e-01 -5.28199792e-01 -1.30230331e+00 5.36685407e-01
4.50207263e-01 3.03180087e-02 3.57189268e-01 5.29021204e-01
7.33375430e-01 -4.36226726e-01 -9.45758283e-01 7.45716467e-02
2.81778574e-01 -6.40088975e-01 2.60022879e-01 -9.69367564e-01
-4.06304836e-01 -4.60826457e-01 -3.11714798e-01 -1.54850233e+00
-7.95214921e-02 -9.66843724e-01 -1.84420019e-01 1.07514644e+00
3.32382351e-01 -7.40363061e-01 4.42820936e-01 4.53534842e-01
-1.26337186e-01 -7.71879494e-01 -9.63188350e-01 -9.61148322e-01
-3.92109036e-01 -4.01931517e-02 9.86297905e-01 4.84391332e-01
-6.11766279e-02 6.73488975e-02 -1.02374461e-02 9.15095747e-01
6.60298467e-01 3.75823915e-01 7.18712926e-01 -6.97098494e-01
-8.00074220e-01 -3.36452901e-01 -1.35186046e-01 -6.58709168e-01
1.66503757e-01 -2.14344993e-01 2.11067751e-01 -1.20884788e+00
-4.77541447e-01 -6.74540758e-01 -7.10380137e-01 3.77024740e-01
5.77658415e-01 -6.37728870e-01 1.54215679e-01 1.28753930e-01
-1.00860596e+00 2.97863334e-01 8.58891785e-01 3.27103943e-01
-3.83006990e-01 3.36968094e-01 -1.72615454e-01 2.85127699e-01
1.18330336e+00 -4.03568387e-01 -6.85961425e-01 -2.32825741e-01
-4.22319621e-01 9.51008916e-01 -3.51302214e-02 -1.30793417e+00
2.64456738e-02 -1.06966734e+00 -2.64345795e-01 1.64977401e-01
1.06669702e-01 -1.25695360e+00 2.20617160e-01 6.14301205e-01
-2.74345458e-01 6.45034432e-01 3.95618230e-01 4.22163457e-01
1.13453813e-01 2.54159749e-01 9.82352078e-01 -5.30966856e-02
-6.38491631e-01 4.36951488e-01 -8.79812360e-01 -8.71343464e-02
1.53375328e+00 3.18562180e-01 -1.26104027e-01 -6.34423196e-01
-9.42169547e-01 9.86178756e-01 4.42419171e-01 3.06269318e-01
3.21561068e-01 -8.15947056e-01 -6.09497786e-01 -1.01111703e-01
-4.62189727e-02 -7.36684680e-01 -1.20252036e-02 7.21449852e-01
-8.30517769e-01 1.56389669e-01 -5.81216097e-01 -5.97316325e-01
-1.00192368e+00 7.95102954e-01 9.10782278e-01 -3.57453346e-01
-3.55971843e-01 -4.38650139e-02 -4.14034903e-01 -2.14553729e-01
4.63375628e-01 7.00068772e-02 1.44625023e-01 -4.68628883e-01
5.71528137e-01 3.53972375e-01 -9.93625959e-04 6.65224809e-03
-4.02561605e-01 -1.12868406e-01 1.41307979e-03 -4.67129707e-01
1.37532198e+00 -3.46413851e-01 -1.40634701e-01 3.16598475e-01
5.63049436e-01 -3.70180130e-01 -1.41143990e+00 -4.83378880e-02
3.02185267e-01 -5.65664828e-01 2.96930104e-01 -9.88342166e-01
-1.21124148e+00 2.33863398e-01 5.72738409e-01 7.55637765e-01
1.53790951e+00 -4.60267216e-01 4.98420179e-01 5.29863015e-02
8.11830044e-01 -1.28772616e+00 4.54446524e-02 2.25517020e-01
4.36047137e-01 -5.42176247e-01 -4.30571854e-01 -2.70425081e-01
-4.07619119e-01 1.29051900e+00 8.93325150e-01 -2.12789983e-01
5.62538743e-01 8.73407841e-01 1.49391726e-01 -1.04461268e-01
-1.51599252e+00 -3.07071656e-01 -8.31633806e-01 4.92920578e-01
1.17241777e-02 4.10406172e-01 -2.64763385e-01 -1.91065341e-01
4.45973843e-01 8.81854072e-02 1.14192426e+00 1.28179586e+00
-6.31611168e-01 -1.48936927e+00 -4.58619356e-01 3.57252747e-01
-2.48153761e-01 4.41646665e-01 3.84569883e-01 9.62205052e-01
-2.19811559e-01 9.62860882e-01 1.39174998e-01 -1.37779310e-01
5.47403216e-01 -2.26677395e-02 2.55612522e-01 -6.54083610e-01
-4.24970746e-01 1.72645465e-01 3.24156225e-01 -8.38906109e-01
-1.60312161e-01 -6.10915959e-01 -1.66503346e+00 -6.38138473e-01
1.25325546e-01 5.07098973e-01 9.15622592e-01 8.37165594e-01
5.65871954e-01 1.29829550e+00 1.49607337e+00 -8.60208035e-01
-9.06760037e-01 -6.05927229e-01 -8.20759773e-01 -2.60569155e-01
3.51223201e-01 -8.65256429e-01 -4.66878206e-01 -3.10435534e-01] | [4.865922927856445, 2.049434185028076] |
02e8eb3c-e9ce-449d-bba7-82d1abc14722 | a-base-camp-for-scaling-ai | 1612.07896 | null | http://arxiv.org/abs/1612.07896v1 | http://arxiv.org/pdf/1612.07896v1.pdf | A Base Camp for Scaling AI | Modern statistical machine learning (SML) methods share a major limitation
with the early approaches to AI: there is no scalable way to adapt them to new
domains. Human learning solves this in part by leveraging a rich, shared,
updateable world model. Such scalability requires modularity: updating part of
the world model should not impact unrelated parts. We have argued that such
modularity will require both "correctability" (so that errors can be corrected
without introducing new errors) and "interpretability" (so that we can
understand what components need correcting).
To achieve this, one could attempt to adapt state of the art SML systems to
be interpretable and correctable; or one could see how far the simplest
possible interpretable, correctable learning methods can take us, and try to
control the limitations of SML methods by applying them only where needed. Here
we focus on the latter approach and we investigate two main ideas: "Teacher
Assisted Learning", which leverages crowd sourcing to learn language; and
"Factored Dialog Learning", which factors the process of application
development into roles where the language competencies needed are isolated,
enabling non-experts to quickly create new applications.
We test these ideas in an "Automated Personal Assistant" (APA) setting, with
two scenarios: that of detecting user intent from a user-APA dialog; and that
of creating a class of event reminder applications, where a non-expert
"teacher" can then create specific apps. For the intent detection task, we use
a dataset of a thousand labeled utterances from user dialogs with Cortana, and
we show that our approach matches state of the art SML methods, but in addition
provides full transparency: the whole (editable) model can be summarized on one
human-readable page. For the reminder app task, we ran small user studies to
verify the efficacy of the approach. | ['R. W. White', 'Z. Yang', 'C. J. C. Burges', 'T. Hart', 'S. Cucerzan', 'J. Lewis', 'A. Pastusiak'] | 2016-12-23 | null | null | null | null | ['dialog-learning'] | ['natural-language-processing'] | [ 6.73499927e-02 7.32935131e-01 -4.44216095e-02 -4.00712103e-01
-6.54015779e-01 -8.18100393e-01 6.36875272e-01 1.20392047e-01
-3.26075763e-01 7.36716449e-01 3.92681718e-01 -5.58115900e-01
-1.28337413e-01 -5.88564992e-01 -7.32861221e-01 -1.17650330e-01
1.73448101e-01 9.00662482e-01 3.17314863e-01 -4.13692296e-01
-3.83559316e-02 1.28880039e-01 -1.63979423e+00 6.03219867e-01
9.39189613e-01 3.43100250e-01 1.83533981e-01 8.20451081e-01
-5.30668318e-01 1.28493762e+00 -7.98938394e-01 -5.58311462e-01
4.57095355e-02 -3.24996382e-01 -1.32963085e+00 -1.38465622e-02
8.11406970e-02 -3.54252845e-01 4.07282114e-01 7.41221607e-01
2.65207112e-01 -9.95216705e-03 3.70278984e-01 -1.46914184e+00
-6.41176403e-01 1.02437937e+00 -6.98082820e-02 -2.32277676e-01
8.26953292e-01 4.31689799e-01 9.52110171e-01 -4.23767895e-01
6.11557186e-01 1.42694807e+00 6.88154101e-01 1.07231927e+00
-1.38388586e+00 -4.70145762e-01 1.91542044e-01 -8.52847025e-02
-6.66259825e-01 -6.19416296e-01 5.43003023e-01 -7.98376024e-01
1.12123084e+00 6.07325733e-01 4.50924039e-01 1.18641043e+00
-1.36955649e-01 8.26148748e-01 1.16033947e+00 -6.99396491e-01
4.93381917e-01 8.20060015e-01 3.57809842e-01 7.76386321e-01
5.86678199e-02 -2.16104269e-01 -6.58780813e-01 -4.44958150e-01
3.29597026e-01 -9.89892781e-02 -9.45552737e-02 -2.90884972e-01
-1.06696534e+00 7.70917118e-01 -1.13535747e-01 5.90598583e-01
-1.33034676e-01 -1.44810811e-01 2.21172139e-01 4.84540641e-01
4.27919149e-01 9.67394888e-01 -9.35468435e-01 -4.89584178e-01
-4.24089640e-01 2.47877032e-01 1.53619814e+00 9.09534514e-01
9.15086150e-01 -2.94687301e-01 -1.07478037e-01 8.46266687e-01
4.46138024e-01 2.66101301e-01 8.64958525e-01 -1.18148732e+00
1.53901383e-01 1.06257141e+00 2.06472382e-01 -4.78684247e-01
-3.79531145e-01 3.13532352e-01 -2.91898906e-01 3.12490940e-01
6.39982820e-01 -5.56839108e-01 -6.82193816e-01 1.89374816e+00
4.43194002e-01 -1.51348770e-01 3.60624380e-02 4.02273655e-01
7.25061774e-01 4.47907150e-01 2.51496047e-01 -8.97652954e-02
1.55297422e+00 -7.90086329e-01 -5.40426731e-01 -4.28073436e-01
1.01197064e+00 -4.57674116e-01 1.48246515e+00 4.60847914e-01
-1.08136630e+00 -3.50367188e-01 -7.76394427e-01 -1.74625114e-01
-5.45334876e-01 -1.32443592e-01 7.20138729e-01 8.23038757e-01
-1.06800973e+00 3.80237967e-01 -7.20757127e-01 -6.61682367e-01
8.83518904e-02 3.90286654e-01 -3.26095790e-01 3.24450701e-01
-1.08765638e+00 1.07447517e+00 1.22628778e-01 -4.86633420e-01
-8.31649125e-01 -7.44125903e-01 -7.72096872e-01 9.40575972e-02
6.23655438e-01 -8.75355005e-01 1.88281918e+00 -1.43700624e+00
-1.82593131e+00 8.35724056e-01 -1.86604738e-01 -4.11236137e-01
5.13371706e-01 -3.11237395e-01 2.44606938e-03 -3.96433353e-01
4.25845310e-02 5.38598299e-01 6.02113783e-01 -1.28564250e+00
-6.82912171e-01 -4.35655683e-01 5.67482412e-01 1.96829159e-02
-5.69926441e-01 2.83199012e-01 1.57526340e-02 -2.43345410e-01
-4.98292774e-01 -9.57303762e-01 -3.09012145e-01 -2.35079750e-01
-2.04576731e-01 -5.12930095e-01 6.53916240e-01 -6.19410813e-01
1.23835063e+00 -1.92533588e+00 2.32531846e-01 5.49070910e-02
5.20962119e-01 3.89756411e-01 -1.37102827e-01 5.08542657e-01
4.34359200e-02 4.43751067e-01 -1.58544630e-01 -4.83521163e-01
2.51891673e-01 2.73762494e-01 -2.53900528e-01 -3.21608692e-01
1.09086223e-01 7.97223330e-01 -1.04161894e+00 -3.10838550e-01
-2.88991979e-03 2.48345241e-01 -8.01441491e-01 5.06393611e-01
-7.37764597e-01 3.80877912e-01 -4.40519065e-01 2.20874593e-01
1.29444569e-01 -3.76355499e-01 4.63081062e-01 1.68029457e-01
-1.45432338e-01 5.23164213e-01 -1.40233362e+00 1.37495935e+00
-8.72752428e-01 4.13771182e-01 3.26402366e-01 -7.07076907e-01
7.02435672e-01 5.27533352e-01 3.35049331e-01 -2.42397115e-01
-1.42105579e-01 6.02239072e-02 -6.64572697e-04 -8.61346304e-01
1.44675091e-01 -3.53817679e-02 -3.79552171e-02 9.67884243e-01
1.34048939e-01 -1.93054438e-01 -1.36725875e-02 4.24507320e-01
1.51142001e+00 2.93753371e-02 6.45205140e-01 -1.29229814e-01
4.59175885e-01 1.17372915e-01 3.71480942e-01 8.91018629e-01
-1.59206644e-01 1.52215973e-01 6.55496299e-01 -6.35481894e-01
-6.56789780e-01 -7.88142979e-01 3.37979525e-01 1.82665443e+00
-4.53056544e-01 -5.67512870e-01 -1.00597107e+00 -9.98665392e-01
-1.18720800e-01 1.00793147e+00 -4.74123985e-01 -1.64344236e-02
-5.23141563e-01 -2.95718163e-01 5.02309501e-01 3.57250810e-01
3.14185828e-01 -1.40392292e+00 -8.18411529e-01 2.91745991e-01
-1.52856514e-01 -9.49452579e-01 -2.27994621e-01 2.29082823e-01
-4.91948754e-01 -9.76768374e-01 -1.82530820e-01 -6.02998197e-01
5.08881927e-01 6.73812479e-02 1.35660219e+00 3.80883723e-01
-9.66227576e-02 9.83290493e-01 -3.22713405e-01 -7.90945470e-01
-1.02129102e+00 1.63603798e-01 9.83005464e-02 -8.68027434e-02
7.40881383e-01 -7.07248747e-01 -1.01238579e-01 8.85257050e-02
-9.04800355e-01 2.44782701e-01 5.13939917e-01 7.00953543e-01
-2.68026441e-01 -3.19922149e-01 6.89917922e-01 -1.41253328e+00
1.05500674e+00 -4.73029584e-01 -4.68238205e-01 4.66762424e-01
-6.84523702e-01 3.21097821e-01 7.88833261e-01 -6.53056324e-01
-1.25339317e+00 4.75390643e-01 -1.78178608e-01 1.40628189e-01
-6.63955390e-01 2.62840331e-01 -3.63679737e-01 9.03542712e-02
1.04701805e+00 -2.57576089e-02 8.57440755e-02 -6.33217931e-01
7.62730479e-01 9.81835306e-01 2.87370175e-01 -9.90694165e-01
7.66898751e-01 7.42465854e-02 -6.48386657e-01 -8.27101231e-01
-6.89784408e-01 -3.38716775e-01 -6.25998676e-01 -3.33171375e-02
8.94544840e-01 -7.04283535e-01 -1.07851350e+00 1.07380174e-01
-1.42915690e+00 -7.54124105e-01 -5.84636986e-01 1.95485242e-02
-5.64515710e-01 1.69210538e-01 -5.92043281e-01 -9.55677569e-01
-2.83484638e-01 -1.09512711e+00 9.20605838e-01 2.81836420e-01
-9.94304895e-01 -1.14736974e+00 3.83117467e-01 6.55931473e-01
6.29080534e-01 -3.31978917e-01 1.13457310e+00 -1.40235114e+00
-3.24682772e-01 -6.22705668e-02 1.64305061e-01 3.67191255e-01
2.17597067e-01 -1.39332354e-01 -1.29504383e+00 8.12990367e-02
1.80434927e-01 -4.68672276e-01 1.29343405e-01 -2.34775990e-01
9.07934368e-01 -9.22622383e-01 -3.31590980e-01 -9.06955302e-02
7.35575974e-01 2.39443734e-01 4.03624058e-01 2.33230174e-01
6.13955736e-01 8.76415193e-01 1.50651306e-01 3.40590239e-01
8.49830449e-01 7.38815963e-01 -3.86718847e-02 3.66390720e-02
4.09510471e-02 -4.23506409e-01 4.74667579e-01 6.52647614e-01
7.53438920e-02 -3.21985222e-04 -1.17144489e+00 5.83275676e-01
-2.10575843e+00 -8.46448600e-01 1.80216148e-01 2.12453198e+00
1.44984686e+00 -2.34341379e-02 3.53354454e-01 -1.68930218e-01
2.30049953e-01 -2.50623941e-01 -3.29988390e-01 -6.34860158e-01
5.01751244e-01 2.34055161e-01 -6.97769746e-02 1.00063908e+00
-8.55604768e-01 9.13836300e-01 6.03716946e+00 3.97967041e-01
-8.69949758e-01 4.47658360e-01 5.00653625e-01 2.34578088e-01
-5.35348833e-01 2.36471340e-01 -6.99687302e-01 4.82512712e-01
1.04367936e+00 -1.09011330e-01 6.63090348e-01 1.09716725e+00
1.48818195e-01 -6.31637052e-02 -1.68319011e+00 5.65604627e-01
-6.22437447e-02 -1.24218035e+00 -6.79969415e-02 -2.10896775e-01
3.81294787e-01 -1.51476189e-01 -3.45678061e-01 6.88549936e-01
1.00806952e+00 -9.95975137e-01 3.43097687e-01 5.37938297e-01
2.63938516e-01 -8.07847530e-02 4.11183745e-01 1.02887666e+00
-7.61438251e-01 -2.37533852e-01 7.05865473e-02 -3.21901858e-01
-8.92804787e-02 3.17505389e-01 -1.24398696e+00 -1.65249798e-02
6.61504090e-01 1.89009741e-01 -7.10055053e-01 6.01883054e-01
-2.73718297e-01 6.57134712e-01 -3.78179342e-01 -3.83620054e-01
-2.64285207e-01 5.01824692e-02 5.69527447e-01 1.31437218e+00
3.37161799e-03 2.02617362e-01 2.04052821e-01 1.09677351e+00
-6.27364814e-02 1.29982010e-01 -8.99539709e-01 3.75907049e-02
5.11594772e-01 1.33715880e+00 -2.50769526e-01 -5.23023725e-01
-4.48870867e-01 7.85859525e-01 4.53514934e-01 2.71800399e-01
-2.83063233e-01 -2.07164481e-01 5.53672194e-01 2.48070627e-01
-1.33788407e-01 9.57311168e-02 -3.51187676e-01 -1.20226955e+00
-6.97853975e-03 -1.56366444e+00 2.45342165e-01 -7.40584016e-01
-1.39461052e+00 4.47781026e-01 1.04346238e-01 -5.38025141e-01
-6.63391113e-01 -4.92057949e-01 -6.97073877e-01 6.12203121e-01
-8.41366291e-01 -1.19050431e+00 -2.51548797e-01 4.72552270e-01
7.17785299e-01 -1.62607372e-01 1.13196778e+00 1.10222943e-01
-3.62832606e-01 5.43014884e-01 -5.08888900e-01 2.12147180e-02
8.48403394e-01 -1.45364153e+00 3.10027033e-01 6.47369683e-01
2.65576243e-01 9.17144835e-01 8.23816657e-01 -7.05109298e-01
-1.45826745e+00 -7.69916534e-01 1.31328321e+00 -1.34656751e+00
7.59249330e-01 -6.80519402e-01 -1.12392688e+00 1.10619128e+00
2.96985418e-01 -3.55069250e-01 5.73224664e-01 3.99803340e-01
-4.57183033e-01 -2.50416156e-02 -1.20308185e+00 6.81353688e-01
9.39941108e-01 -6.12070858e-01 -8.17064583e-01 6.39693439e-01
1.04521477e+00 -1.38927430e-01 -6.67872727e-01 1.50028780e-01
4.74705458e-01 -1.00281501e+00 7.02792883e-01 -1.07241762e+00
2.15118527e-01 -1.73018605e-01 1.17785320e-01 -1.35763013e+00
-5.33062033e-02 -1.09437394e+00 -3.38057458e-01 1.55429924e+00
7.05944002e-01 -8.98290455e-01 6.19541287e-01 1.44530094e+00
-3.31921056e-02 -5.25041759e-01 -7.15667069e-01 -4.79260713e-01
1.20701909e-01 -5.67998588e-01 8.69183540e-01 9.97436106e-01
5.63248515e-01 8.14205647e-01 -2.86679655e-01 -1.98015999e-02
2.29687560e-02 -3.89365494e-01 1.29217684e+00 -1.43461752e+00
-7.41748214e-01 -3.47079068e-01 3.64211611e-02 -1.05971718e+00
2.75521636e-01 -6.04100525e-01 2.05198795e-01 -1.31207895e+00
3.07607412e-01 -5.19287050e-01 2.41591886e-01 9.96357143e-01
-2.38439143e-01 -3.51187557e-01 3.33462238e-01 1.73052818e-01
-7.96268165e-01 -7.46841310e-03 5.57895720e-01 -1.24630138e-01
-6.49505436e-01 2.15629876e-01 -9.84524846e-01 1.09332919e+00
5.85803986e-01 -4.24978107e-01 -5.55025697e-01 -3.93144190e-01
5.13278425e-01 -1.20415412e-01 3.65073144e-01 -8.40270936e-01
5.94014168e-01 -2.67899692e-01 -1.34645730e-01 2.88182259e-01
9.78289843e-02 -9.79914784e-01 5.24856783e-02 2.49879897e-01
-6.84920192e-01 6.47301525e-02 2.07564190e-01 2.79299945e-01
1.59307301e-01 -4.69875544e-01 4.65407819e-01 -3.69950175e-01
-4.80977863e-01 -1.83134824e-01 -7.73519337e-01 1.01636350e-01
1.07999861e+00 1.59001082e-01 -5.02083659e-01 -7.96459198e-01
-5.23639679e-01 1.53331622e-01 4.13203210e-01 4.93701875e-01
2.21138820e-01 -7.28758454e-01 -4.41840023e-01 2.51908362e-01
1.11392066e-01 -5.50427362e-02 -2.79737860e-01 6.55364692e-01
-1.40066773e-01 2.19816759e-01 9.98912454e-02 -3.05567145e-01
-1.24237859e+00 6.04345739e-01 4.38595921e-01 -3.78273070e-01
-2.32194111e-01 7.08993018e-01 1.40896127e-01 -8.80251348e-01
5.40358603e-01 -3.52754772e-01 -2.71075428e-01 -7.52690360e-02
7.32231855e-01 1.96256056e-01 -3.86950374e-02 -1.32857889e-01
-2.82378048e-01 1.42808676e-01 -1.26395419e-01 -1.90661088e-01
1.25989103e+00 -1.67246640e-01 -3.10269654e-01 6.32361352e-01
5.34148455e-01 2.85078198e-01 -8.08393478e-01 -2.88790971e-01
3.21271062e-01 -1.55513603e-02 -5.92546284e-01 -1.21471155e+00
-3.83538842e-01 8.78536165e-01 4.45326328e-01 7.41568685e-01
9.18576598e-01 2.89170742e-01 6.24907672e-01 7.47189641e-01
4.39435035e-01 -9.83495295e-01 1.92732498e-01 5.46364069e-01
7.18275189e-01 -1.55993712e+00 -9.25575122e-02 -1.08719945e-01
-8.15018296e-01 1.10685670e+00 9.31546569e-01 4.28250223e-01
4.76122290e-01 5.29074311e-01 2.74978071e-01 -2.79537946e-01
-1.14584029e+00 -2.54127551e-02 1.41647488e-01 7.95176685e-01
6.80691957e-01 1.54494405e-01 -7.36357644e-02 9.96129453e-01
-2.97922075e-01 1.13651104e-01 5.22512078e-01 8.94306839e-01
-4.37858194e-01 -1.30649269e+00 -2.58458167e-01 3.11435759e-01
-2.58515060e-01 -1.53308073e-02 -9.87740755e-01 7.01687098e-01
1.77630082e-01 1.21962142e+00 -3.46823305e-01 -4.59248573e-01
4.01529878e-01 6.52269423e-01 1.20159268e-01 -1.08474183e+00
-9.73175585e-01 -3.69641095e-01 3.50582004e-01 -6.41719759e-01
-3.27581406e-01 -4.27014679e-01 -1.05869472e+00 -2.00131208e-01
-1.70685783e-01 2.35039741e-01 5.80506384e-01 1.28573620e+00
4.84083802e-01 1.47630632e-01 3.71099204e-01 -5.96882761e-01
-7.29288161e-01 -9.44923162e-01 9.94719937e-02 5.30042350e-01
2.92749912e-01 -2.05915198e-01 -4.92298156e-01 3.83737713e-01] | [12.648484230041504, 7.887949466705322] |
6d6f1fe0-f7c8-4674-b577-cb086eba9e4b | babyai-towards-grounded-language-learning | 2004.07200 | null | https://arxiv.org/abs/2004.07200v2 | https://arxiv.org/pdf/2004.07200v2.pdf | Zero-Shot Compositional Policy Learning via Language Grounding | Despite recent breakthroughs in reinforcement learning (RL) and imitation learning (IL), existing algorithms fail to generalize beyond the training environments. In reality, humans can adapt to new tasks quickly by leveraging prior knowledge about the world such as language descriptions. To facilitate the research on language-guided agents with domain adaption, we propose a novel zero-shot compositional policy learning task, where the environments are characterized as a composition of different attributes. Since there are no public environments supporting this study, we introduce a new research platform BabyAI++ in which the dynamics of environments are disentangled from visual appearance. At each episode, BabyAI++ provides varied vision-dynamics combinations along with corresponding descriptive texts. To evaluate the adaption capability of learned agents, a set of vision-dynamics pairings are held-out for testing on BabyAI++. Unsurprisingly, we find that current language-guided RL/IL techniques overfit to the training environments and suffer from a huge performance drop when facing unseen combinations. In response, we propose a multi-modal fusion method with an attention mechanism to perform visual language-grounding. Extensive experiments show strong evidence that language grounding is able to improve the generalization of agents across environments with varied dynamics. | ['Yining Zhang', 'Jingkang Wang', 'Tianshi Cao', 'Sivabalan Manivasagam'] | 2020-04-15 | null | null | null | null | ['grounded-language-learning'] | ['natural-language-processing'] | [-2.66652912e-01 -4.75838065e-01 5.59423938e-02 -2.47841254e-01
-4.79834050e-01 -7.63494492e-01 9.26882565e-01 -4.58138168e-01
-6.99335098e-01 8.24407101e-01 1.25991791e-01 2.32695807e-02
1.29269943e-01 -5.53249419e-01 -8.79482508e-01 -8.70359838e-01
-9.09982100e-02 6.71050847e-01 3.95001769e-02 -4.92315769e-01
-6.73682839e-02 3.09030414e-01 -1.67713177e+00 4.18407679e-01
8.08948398e-01 5.47956467e-01 6.09298706e-01 8.11175466e-01
-1.52082890e-01 8.49779069e-01 -7.36728311e-01 -1.75458238e-01
5.73563457e-01 -5.66498399e-01 -3.58911902e-01 1.78838044e-01
3.49120677e-01 -5.20020068e-01 -6.68700457e-01 9.42643881e-01
6.49393082e-01 4.69765306e-01 4.36607122e-01 -1.51551437e+00
-1.38197112e+00 6.26802921e-01 -2.63519824e-01 1.25673592e-01
5.58551431e-01 1.04900587e+00 6.45944595e-01 -8.18213582e-01
8.15969706e-01 1.61364150e+00 3.34447891e-01 1.10621452e+00
-1.28822875e+00 -8.69130433e-01 7.05277383e-01 3.91146243e-01
-1.08409798e+00 -3.65378678e-01 7.35697567e-01 -3.77768815e-01
1.04564977e+00 -1.25603706e-01 9.12478447e-01 1.88858807e+00
1.55293331e-01 1.07178259e+00 1.42727709e+00 -1.61477774e-01
3.97273451e-01 2.22668946e-01 -4.46135730e-01 8.05404723e-01
3.09247132e-02 6.31001234e-01 -8.73669863e-01 1.26262188e-01
7.01148868e-01 -1.91944484e-02 -1.29112571e-01 -6.47112072e-01
-1.55357218e+00 6.47812247e-01 4.73602682e-01 2.74797808e-03
-2.61554599e-01 2.75055647e-01 4.46104765e-01 7.17308521e-01
1.54313505e-01 7.72785544e-01 -4.53346312e-01 -7.40761161e-02
-1.59411281e-01 5.26290059e-01 5.21857262e-01 1.13100529e+00
9.02582407e-01 3.97553116e-01 -3.69044036e-01 6.03601456e-01
1.41316622e-01 9.32155132e-01 8.62402439e-01 -1.09790647e+00
3.73832285e-01 4.93635476e-01 7.93622509e-02 -5.91020703e-01
-3.12671304e-01 -3.03445935e-01 -6.63981199e-01 4.92737830e-01
3.44524920e-01 -3.52440715e-01 -1.02607226e+00 2.15451908e+00
2.51051307e-01 3.66304606e-01 5.99418342e-01 9.07987118e-01
7.31384218e-01 6.80887580e-01 1.84384346e-01 -1.62474275e-01
9.77125525e-01 -1.24360657e+00 -6.18798256e-01 -4.29484069e-01
3.86686921e-01 -3.20123851e-01 1.61841154e+00 3.14266175e-01
-8.58341694e-01 -6.96815968e-01 -7.92104244e-01 3.87316108e-01
-4.63544220e-01 -1.71015754e-01 7.15161443e-01 2.20817685e-01
-1.11869860e+00 2.12170213e-01 -6.84285879e-01 -5.64508319e-01
2.24441409e-01 1.65926695e-01 -4.26293194e-01 -6.11336455e-02
-1.03348041e+00 9.74351346e-01 5.30449867e-01 -2.77088702e-01
-1.45901465e+00 -3.93976897e-01 -8.81170571e-01 -4.22648072e-01
7.07716286e-01 -9.68560457e-01 1.46085835e+00 -1.36189294e+00
-1.93639851e+00 6.15381360e-01 2.49514431e-01 -4.43232238e-01
6.01514101e-01 -1.07816443e-01 -3.10988098e-01 -1.51540739e-02
4.28340808e-02 8.57069612e-01 1.08348513e+00 -1.59561133e+00
-7.55872250e-01 -2.16721088e-01 4.00654495e-01 6.60017252e-01
-3.00665528e-01 -3.56941193e-01 -3.12793434e-01 -6.36042416e-01
-4.91607755e-01 -1.12415946e+00 -2.55173981e-01 -4.46234085e-02
-7.85654634e-02 -2.99188286e-01 8.56496453e-01 -2.78600395e-01
6.84680223e-01 -2.21582270e+00 5.10463953e-01 -2.94697404e-01
4.42236476e-02 2.18638971e-01 -6.37388706e-01 4.61229801e-01
3.80958855e-01 -1.95194319e-01 1.93806794e-02 -4.80421811e-01
2.82245219e-01 6.95980549e-01 -5.14289975e-01 2.26186901e-01
9.01097953e-02 1.14038467e+00 -1.20954347e+00 -2.67572463e-01
1.93478793e-01 1.37983829e-01 -6.08073711e-01 4.81648743e-01
-7.72290528e-01 8.35954666e-01 -5.06415486e-01 5.46127081e-01
3.02991748e-01 -1.35829672e-01 -5.88397086e-02 2.04787225e-01
2.79394947e-02 -2.79924333e-01 -9.05251980e-01 2.04052901e+00
-4.12988842e-01 4.85138565e-01 -4.47817184e-02 -8.68246973e-01
7.29060829e-01 1.74130470e-01 2.41640642e-01 -1.01840591e+00
-1.59832835e-01 -1.34227261e-01 1.47034124e-01 -8.59470367e-01
3.30577731e-01 -1.41108096e-01 -2.10437506e-01 4.50653613e-01
1.52135074e-01 -3.07875961e-01 3.86610553e-02 2.06147611e-01
9.80385244e-01 6.88418746e-01 1.77724004e-01 1.33143365e-01
1.48252487e-01 2.24291116e-01 6.59184098e-01 1.10453033e+00
-4.68456656e-01 2.20087543e-01 -3.68572287e-02 -6.64364576e-01
-1.11857224e+00 -1.24572361e+00 4.08853203e-01 1.61537981e+00
3.47960889e-01 -1.12471469e-01 -5.57330191e-01 -7.15562344e-01
8.89139250e-02 8.41283679e-01 -7.53145158e-01 -4.28411961e-01
-4.89696890e-01 -5.49355447e-01 5.44732690e-01 5.82426906e-01
6.80788398e-01 -1.57079780e+00 -9.88212109e-01 1.09798141e-01
-1.78230461e-02 -1.21414375e+00 -4.30127025e-01 -3.18765789e-02
-3.59120905e-01 -7.74753869e-01 -7.21031427e-01 -7.32267499e-01
4.54595774e-01 4.00162041e-01 9.97950494e-01 -1.77843213e-01
-1.02212198e-01 1.05432916e+00 -5.41332722e-01 -4.07416344e-01
-6.36808395e-01 -3.15561056e-01 5.77756524e-01 -4.56411466e-02
1.02402925e-01 -5.08792520e-01 -5.04160941e-01 1.64787322e-01
-7.70399928e-01 3.21098775e-01 7.97809243e-01 9.49901044e-01
3.80281568e-01 -2.46907055e-01 6.07545614e-01 -3.52501869e-01
8.24624658e-01 -4.28014547e-01 -5.57310402e-01 5.51278114e-01
-4.19921577e-01 2.87526965e-01 8.30541551e-01 -1.23783827e+00
-1.26166105e+00 1.23955291e-02 4.18912381e-01 -7.51747966e-01
-3.72194529e-01 2.90586829e-01 7.45878071e-02 -1.42246541e-02
8.64178658e-01 5.18038750e-01 1.45545542e-01 -1.20860964e-01
7.91254044e-01 3.91981065e-01 6.42651439e-01 -8.60007286e-01
9.85422015e-01 4.13435876e-01 -3.59780252e-01 -5.77674091e-01
-6.04935408e-01 -1.16665833e-01 -2.24865705e-01 -3.42630267e-01
8.87667835e-01 -1.02880323e+00 -8.35318923e-01 5.96114039e-01
-8.93599451e-01 -9.94843602e-01 -4.01908606e-01 5.10866225e-01
-9.32331562e-01 1.93102747e-01 -3.21302712e-01 -7.88711607e-01
8.02510381e-02 -1.30738664e+00 8.63658905e-01 3.65183890e-01
9.80829075e-02 -9.39895451e-01 3.32167298e-01 1.27029300e-01
4.57309186e-01 1.61018983e-01 7.58506060e-01 -6.33914948e-01
-5.93290687e-01 3.28935802e-01 1.35295153e-01 1.11399941e-01
1.25830144e-01 -1.25625297e-01 -8.70147467e-01 -6.22591496e-01
-2.15482280e-01 -8.53388667e-01 7.58856535e-01 6.67942092e-02
1.05509341e+00 -4.14260209e-01 -1.24464974e-01 6.96211457e-01
1.16032457e+00 4.15399134e-01 1.34742379e-01 6.47677302e-01
6.67393208e-01 4.29783255e-01 5.25708616e-01 6.68878675e-01
6.73396587e-01 6.72045052e-01 5.60961723e-01 1.57162905e-01
-2.80473560e-01 -5.17509639e-01 9.59412694e-01 5.90868056e-01
5.85656241e-03 -3.24157536e-01 -6.82040751e-01 3.62431347e-01
-2.06141973e+00 -1.28076911e+00 7.66868472e-01 1.90742683e+00
7.78979063e-01 -1.95744604e-01 2.54795790e-01 -7.21011519e-01
3.18437278e-01 3.47320661e-02 -1.20689726e+00 -1.36522710e-01
-4.97599214e-01 -2.96106547e-01 1.22024298e-01 4.21330392e-01
-8.33230615e-01 1.27943742e+00 6.56869841e+00 6.12628937e-01
-1.19553244e+00 -3.67337931e-03 2.49970585e-01 -2.15385348e-01
-4.00926709e-01 -1.54611826e-01 -6.02148652e-01 3.65449995e-01
5.36861777e-01 -1.74019709e-01 1.02177811e+00 7.39677787e-01
8.60875249e-02 -1.48856044e-02 -1.10734487e+00 1.13037157e+00
2.91036427e-01 -1.22948146e+00 2.53798276e-01 -1.73157990e-01
9.29770052e-01 2.62242436e-01 5.42619586e-01 8.84838581e-01
9.96209323e-01 -1.00182569e+00 8.57547641e-01 7.15632498e-01
6.40564382e-01 -3.47435862e-01 2.69954562e-01 7.20308781e-01
-8.06749105e-01 -6.24786973e-01 -2.75276482e-01 -1.76789150e-01
-6.36298731e-02 -5.81530392e-01 -9.05122757e-01 3.53382409e-01
8.34853709e-01 7.51678407e-01 -5.43066621e-01 7.48186648e-01
-2.13524848e-01 2.70237476e-01 -1.12203896e-01 -1.96345523e-01
4.46887910e-01 -1.33061692e-01 7.53160655e-01 9.81004357e-01
4.19846475e-01 2.96701789e-02 6.86123848e-01 8.55704904e-01
7.55643845e-02 -1.67766467e-01 -1.06374764e+00 -1.15551740e-01
4.28753078e-01 8.94120693e-01 -3.38670135e-01 -4.73922253e-01
-5.90324521e-01 1.13228714e+00 6.75651610e-01 9.24171090e-01
-7.34727919e-01 4.20868635e-01 7.61823595e-01 -3.28654021e-01
1.61718249e-01 -3.98397952e-01 1.92422420e-01 -1.49098539e+00
-2.62194633e-01 -1.32201409e+00 2.06905916e-01 -1.04890013e+00
-1.67260695e+00 6.76370144e-01 2.84219146e-01 -1.19334364e+00
-4.62398112e-01 -6.63302183e-01 -3.93372655e-01 4.84565735e-01
-1.34722328e+00 -1.37092686e+00 -3.76098871e-01 9.83887136e-01
1.05274010e+00 -9.70740080e-01 8.62791002e-01 -2.97390580e-01
-5.49014688e-01 5.20445704e-01 1.07457444e-01 -4.22240794e-02
9.73068595e-01 -1.31601262e+00 2.39549041e-01 6.85877562e-01
3.25939238e-01 4.29616332e-01 9.92271185e-01 -5.73277831e-01
-1.73927176e+00 -1.05188274e+00 2.99841203e-02 -5.35249114e-01
6.76645875e-01 -3.00371289e-01 -8.66630912e-01 8.30804586e-01
6.21191025e-01 6.03071488e-02 4.78507638e-01 -1.54592127e-01
-5.47467113e-01 -3.75533774e-02 -1.02025580e+00 1.10055482e+00
1.35581315e+00 -5.64594030e-01 -7.28253126e-01 3.54871064e-01
1.05297685e+00 -4.15271610e-01 -3.95026505e-01 2.83160269e-01
4.06678408e-01 -8.62890959e-01 9.31045532e-01 -9.11755800e-01
9.53760445e-02 -4.44519222e-01 -4.51560438e-01 -1.81533337e+00
-3.73562217e-01 -8.62113595e-01 -1.55957654e-01 8.43492508e-01
9.92588922e-02 -7.65172720e-01 3.78995806e-01 3.51488411e-01
-7.96842799e-02 -3.97097439e-01 -7.68111825e-01 -1.05298698e+00
1.90800995e-01 -2.36602262e-01 6.34639382e-01 8.97478819e-01
-1.08668081e-01 3.58020484e-01 -5.66849828e-01 1.24497198e-01
5.03324747e-01 1.95489913e-01 1.17541409e+00 -7.15528488e-01
-7.10871816e-01 -5.33692122e-01 -6.03123344e-02 -9.95924294e-01
5.57928860e-01 -7.60962725e-01 2.06237406e-01 -1.38948047e+00
2.85896242e-01 -4.16494310e-01 -3.92484486e-01 6.40829206e-01
-2.09665015e-01 -5.85005730e-02 5.33942759e-01 3.70208979e-01
-9.57242131e-01 9.68218267e-01 1.67171335e+00 -3.80308360e-01
-4.41525400e-01 -1.97830409e-01 -5.13381958e-01 6.78119659e-01
8.29654813e-01 -1.14251345e-01 -8.36263895e-01 -7.51074791e-01
2.50443101e-01 -5.74502721e-03 5.47452688e-01 -9.21915054e-01
2.73903131e-01 -7.73500562e-01 2.94350743e-01 -1.80998117e-01
4.23429251e-01 -6.32266879e-01 -4.13836911e-02 3.31389248e-01
-5.29359579e-01 4.97868717e-01 3.88572246e-01 8.17109585e-01
1.67047560e-01 9.30685699e-02 5.00453472e-01 -4.04357910e-01
-1.33378744e+00 4.78506207e-01 -4.61639285e-01 9.87876356e-02
1.23669744e+00 -2.40578637e-01 -5.48173070e-01 -5.27379155e-01
-6.80749416e-01 5.07915616e-01 7.51764774e-01 6.58410549e-01
7.72669077e-01 -1.37093067e+00 -7.12234974e-01 2.72722781e-01
4.30070549e-01 -8.22499469e-02 3.60845685e-01 4.20535803e-01
-1.24089152e-01 -1.49920480e-02 -6.27429664e-01 -5.66127777e-01
-9.43495393e-01 1.09154475e+00 4.44423974e-01 7.44805932e-02
-8.11559916e-01 9.03467894e-01 6.00069642e-01 -6.27910733e-01
3.38008344e-01 -1.69880658e-01 -7.78151229e-02 -2.67117023e-01
5.15087366e-01 -5.90316765e-02 -7.22038984e-01 -5.44800878e-01
-1.17418274e-01 4.87376422e-01 -3.78329009e-02 -5.28756261e-01
1.19679618e+00 -2.61825979e-01 3.83135736e-01 7.66404152e-01
5.67953050e-01 -2.03749701e-01 -2.11344862e+00 -5.96965909e-01
-2.70371944e-01 -3.58838022e-01 -4.30408597e-01 -1.09083152e+00
-7.35418618e-01 6.93379760e-01 6.87327564e-01 -1.89207308e-02
1.04593670e+00 9.38352048e-02 3.77642274e-01 8.76640081e-01
5.50135493e-01 -1.24679339e+00 8.53441715e-01 6.21897578e-01
1.19867146e+00 -1.50644648e+00 -3.69241536e-01 4.87706125e-01
-1.26541245e+00 7.55048275e-01 1.11458910e+00 -1.49228975e-01
6.18076995e-02 2.50614047e-01 3.26545864e-01 1.05362043e-01
-1.24657559e+00 -4.51796174e-01 7.09844530e-02 1.23639309e+00
-2.13953495e-01 1.76067680e-01 5.09232581e-01 2.25325257e-01
-2.26098925e-01 -3.34829152e-01 4.01782781e-01 6.56419158e-01
-5.34043550e-01 -9.75656509e-01 -3.45643073e-01 -8.77009332e-02
2.38254145e-01 2.11259499e-01 -3.57168943e-01 8.81361365e-01
2.19714791e-01 7.85301566e-01 -8.43119696e-02 -4.42455798e-01
3.55602205e-01 4.33657654e-02 6.61741614e-01 -6.25340462e-01
-3.57451379e-01 -1.53417766e-01 -2.20364884e-01 -7.03413606e-01
-3.93178046e-01 -7.47544765e-01 -1.30822349e+00 -2.09707379e-01
3.19274187e-01 -2.23403975e-01 3.32519948e-01 1.03061485e+00
3.16115975e-01 6.89024985e-01 4.59935069e-01 -9.71380651e-01
-8.83290529e-01 -7.39889443e-01 -1.56907290e-01 7.42973447e-01
6.95601463e-01 -1.00326204e+00 -1.90898702e-01 8.93000141e-02] | [4.374476432800293, 0.9175587296485901] |
41eccd79-e72a-4be2-b817-c32a64c12b6d | mparrottts-multilingual-multi-speaker-text-to | 2305.11926 | null | https://arxiv.org/abs/2305.11926v1 | https://arxiv.org/pdf/2305.11926v1.pdf | MParrotTTS: Multilingual Multi-speaker Text to Speech Synthesis in Low Resource Setting | We present MParrotTTS, a unified multilingual, multi-speaker text-to-speech (TTS) synthesis model that can produce high-quality speech. Benefiting from a modularized training paradigm exploiting self-supervised speech representations, MParrotTTS adapts to a new language with minimal supervised data and generalizes to languages not seen while training the self-supervised backbone. Moreover, without training on any bilingual or parallel examples, MParrotTTS can transfer voices across languages while preserving the speaker-specific characteristics, e.g., synthesizing fluent Hindi speech using a French speaker's voice and accent. We present extensive results on six languages in terms of speech naturalness and speaker similarity in parallel and cross-lingual synthesis. The proposed model outperforms the state-of-the-art multilingual TTS models and baselines, using only a small fraction of supervised training data. Speech samples from our model can be found at https://paper2438.github.io/tts/ | ['Vineet Gandhi', 'Niranjan Pedanekar', 'Saiteja Kosgi', 'Vishal Tambrahalli', 'Neil Shah'] | 2023-05-19 | null | null | null | null | ['text-to-speech-synthesis', 'speech-synthesis'] | ['speech', 'speech'] | [ 1.40440892e-02 2.93486804e-01 -2.67108351e-01 -6.31452322e-01
-1.23050988e+00 -8.14122736e-01 5.12577176e-01 -3.56696159e-01
5.10618382e-04 6.91458821e-01 4.51222539e-01 -7.24591315e-01
4.92049366e-01 -3.07789534e-01 -8.87077451e-01 -4.36321050e-01
1.70397937e-01 7.48007596e-01 5.43568842e-02 -6.27281427e-01
-6.58506155e-01 2.13278249e-01 -1.14094853e+00 3.52494091e-01
9.25836265e-01 3.94751489e-01 6.55843318e-01 6.30924940e-01
-1.56592116e-01 4.56659079e-01 -7.27449656e-01 -5.02831459e-01
1.62902400e-01 -6.55251563e-01 -8.94589424e-01 9.92961377e-02
6.92300200e-01 -3.82037126e-02 -4.04504061e-01 7.28788495e-01
6.99431241e-01 2.15205271e-03 4.78672326e-01 -9.73825753e-01
-8.35775673e-01 1.28176308e+00 8.84265527e-02 1.24731801e-01
3.39371711e-01 2.81265199e-01 1.15802610e+00 -1.14319181e+00
7.24455714e-01 1.56210995e+00 3.80779743e-01 8.62785757e-01
-1.53125346e+00 -8.51466298e-01 1.62134930e-01 -8.90516937e-02
-1.42314577e+00 -1.22504210e+00 6.74423099e-01 -6.81267977e-02
1.11383426e+00 3.38412225e-01 3.29502642e-01 1.59257698e+00
-1.76495478e-01 1.12088943e+00 1.10785651e+00 -5.82853377e-01
-7.73864388e-02 3.16012263e-01 -4.43505824e-01 5.16699910e-01
-5.23309469e-01 1.54286996e-01 -7.32346714e-01 3.22444797e-01
4.58213151e-01 -7.14760542e-01 -3.07926685e-01 -2.45555844e-02
-1.62768066e+00 5.78396261e-01 1.74027160e-01 6.86418772e-01
-1.13759674e-01 -3.01776767e-01 6.06022239e-01 8.09043884e-01
5.68819523e-01 2.05806851e-01 -8.99976611e-01 -4.42070961e-02
-9.42289054e-01 -2.75326781e-02 7.49379218e-01 1.47853351e+00
7.05011845e-01 8.45093489e-01 -8.12442079e-02 1.31226087e+00
9.15225148e-02 1.16391456e+00 7.34042466e-01 -8.00780416e-01
7.92592406e-01 -1.16254248e-01 -3.32027495e-01 -5.93273938e-02
-7.62906484e-03 -5.15661538e-01 -6.66073859e-01 -1.81662142e-01
1.28237545e-01 -4.69347745e-01 -8.94623101e-01 1.94694638e+00
1.47134542e-01 -1.59181505e-01 5.93808174e-01 4.58010077e-01
1.06156909e+00 1.16276908e+00 -1.78389281e-01 -3.97700191e-01
1.06344819e+00 -1.48213601e+00 -9.00388658e-01 -3.62670004e-01
4.87241268e-01 -1.09741271e+00 1.50914299e+00 1.02155685e-01
-1.35497010e+00 -7.94689238e-01 -9.37230647e-01 -4.38881246e-03
-4.28722173e-01 2.57660240e-01 1.13312848e-01 8.36008608e-01
-1.48952997e+00 2.36729130e-01 -6.21048272e-01 -5.01835525e-01
-1.28071621e-01 9.92885605e-02 -4.70876366e-01 4.44326252e-02
-1.37081075e+00 9.12702024e-01 2.94276059e-01 -2.42298529e-01
-1.26988935e+00 -6.36888087e-01 -1.04116678e+00 -1.47740662e-01
2.68530458e-01 -4.12220716e-01 1.83160865e+00 -1.14660239e+00
-2.33594322e+00 7.18056798e-01 -4.45536554e-01 -3.78836870e-01
2.74205625e-01 -5.61571121e-02 -9.58115458e-01 -4.11195271e-02
3.49373430e-01 7.20776379e-01 9.13286150e-01 -1.19725251e+00
-5.63971877e-01 3.28426361e-02 -4.50826675e-01 4.26753491e-01
-2.44490653e-01 3.64558965e-01 -2.49424383e-01 -8.59009504e-01
-2.34955698e-01 -1.08169115e+00 2.54029464e-02 -7.22657084e-01
-6.24565721e-01 -2.09868774e-01 7.13342071e-01 -9.19042647e-01
9.43122268e-01 -2.16637588e+00 4.56255287e-01 -2.26549357e-01
-6.50712073e-01 3.15568089e-01 -4.24911141e-01 7.99040616e-01
-5.82961589e-02 7.81865567e-02 -3.38135779e-01 -7.28973687e-01
5.43325804e-02 3.47219527e-01 -4.90526080e-01 8.31055716e-02
1.99461192e-01 8.04321647e-01 -9.86086726e-01 -2.23235935e-01
2.53627062e-01 3.19028974e-01 -3.04295123e-01 4.76007283e-01
-2.83802450e-01 1.01479638e+00 8.34130347e-02 5.36172092e-01
2.64884055e-01 2.71238416e-01 3.76614422e-01 1.38580859e-01
-3.74337077e-01 1.02174497e+00 -8.07953715e-01 2.11379957e+00
-1.12370396e+00 4.21249509e-01 3.57106835e-01 -6.99153841e-01
9.39226747e-01 9.03097093e-01 -2.05812100e-02 -6.47633314e-01
-5.19281588e-02 7.99725711e-01 6.03850298e-02 -1.74979120e-01
4.10092980e-01 -3.11045170e-01 -2.71595806e-01 3.16949964e-01
6.95582211e-01 -5.94803333e-01 1.90582976e-01 7.56110102e-02
6.55081987e-01 -2.38679573e-02 3.13318938e-01 -4.62423533e-01
6.33491635e-01 -1.79765418e-01 5.14360309e-01 3.67387563e-01
-3.07281092e-02 5.11550307e-01 -2.79679567e-01 5.75876422e-02
-9.04976845e-01 -1.50141072e+00 -1.00615516e-01 1.56383657e+00
-4.98205066e-01 -5.27637005e-01 -8.27875018e-01 -6.52414560e-01
-2.51954019e-01 1.07023668e+00 -3.28233428e-02 1.66425556e-01
-8.24176550e-01 -1.04291022e-01 8.62781048e-01 1.35761678e-01
1.76650241e-01 -1.39017844e+00 4.84187275e-01 3.23297620e-01
-4.91106063e-01 -1.32484019e+00 -9.99651790e-01 2.28768513e-01
-6.57634318e-01 -3.36087793e-01 -9.73901689e-01 -1.26032388e+00
2.16869861e-01 1.49957076e-01 1.22834909e+00 -5.01666188e-01
3.66261184e-01 2.85720170e-01 -3.18754435e-01 -1.81691095e-01
-1.45780468e+00 6.91000044e-01 4.96600926e-01 -1.65105406e-02
-1.40295923e-01 -6.25170887e-01 8.33872333e-02 3.33798170e-01
-5.65399647e-01 1.53210372e-01 6.19269073e-01 7.60556638e-01
4.57153708e-01 -2.92639464e-01 1.05490184e+00 -6.99698985e-01
4.76916373e-01 -4.16495204e-01 -4.72799271e-01 2.19294354e-01
-2.04471752e-01 1.52501184e-03 1.11616731e+00 -5.22803128e-01
-1.24924684e+00 -6.50640205e-02 -5.60027838e-01 -2.73571402e-01
-3.71117443e-01 2.67969787e-01 -5.35834253e-01 4.11640555e-01
6.90761209e-01 5.78133464e-01 -1.58269167e-01 -6.90175116e-01
7.73138463e-01 1.19960725e+00 5.91013253e-01 -6.44849360e-01
8.59035254e-01 -1.88166335e-01 -7.87238836e-01 -1.16483653e+00
-5.71995854e-01 -2.64538437e-01 -7.54389048e-01 -4.26824726e-02
6.21583462e-01 -1.24330199e+00 -1.84098944e-01 6.14482820e-01
-1.15283668e+00 -6.90777183e-01 -3.85950208e-01 4.46975291e-01
-7.51424372e-01 2.59798784e-02 -7.95976877e-01 -5.63738286e-01
-4.35445040e-01 -1.31966949e+00 1.14833462e+00 -2.25063249e-01
-2.78166920e-01 -9.96555984e-01 2.51636386e-01 4.38073486e-01
5.95029235e-01 -4.54992324e-01 7.93344438e-01 -7.93266535e-01
-2.87915349e-01 3.66313219e-01 5.11670947e-01 9.00724888e-01
6.73001707e-01 -1.96047664e-01 -1.08496308e+00 -5.62377870e-01
-1.76095784e-01 -6.02921903e-01 3.22930396e-01 1.14313826e-01
3.95229489e-01 -6.44034207e-01 9.72624198e-02 5.89193881e-01
7.27223694e-01 1.43697113e-01 1.01298079e-01 -1.49970572e-03
6.99464023e-01 7.60929346e-01 1.83654174e-01 -1.94483235e-01
6.33783996e-01 8.11059773e-01 -1.07025363e-01 -1.42575905e-01
-7.29303122e-01 -5.51034153e-01 1.12356782e+00 2.03225851e+00
4.01298314e-01 -1.32023454e-01 -8.19588065e-01 8.65006268e-01
-1.33235037e+00 -6.87146187e-01 1.49168253e-01 2.22614717e+00
1.46746874e+00 -1.44754618e-01 2.61992484e-01 -2.06125956e-02
8.92722368e-01 3.67303163e-01 -3.69229585e-01 -6.77424669e-01
-4.04110134e-01 3.77540588e-01 2.28996903e-01 9.32971179e-01
-7.75524616e-01 1.71704245e+00 6.23173428e+00 1.06592655e+00
-1.33636045e+00 4.84525293e-01 3.65319848e-01 -6.54473603e-02
-5.46545208e-01 -6.83349296e-02 -9.07418787e-01 2.11196631e-01
1.55136740e+00 -3.03723395e-01 8.85648906e-01 6.38499439e-01
2.43462130e-01 7.64847815e-01 -1.13482153e+00 6.53713167e-01
1.26969054e-01 -1.07405090e+00 2.71396309e-01 -3.56260121e-01
8.39534700e-01 6.38498366e-01 1.71686321e-01 6.34357691e-01
6.78640664e-01 -9.54424441e-01 1.15987062e+00 -2.29941890e-01
1.22364795e+00 -6.06740713e-01 1.86673060e-01 3.91366273e-01
-1.20695150e+00 3.48257214e-01 2.96005402e-02 3.99354249e-01
3.99343103e-01 8.13564509e-02 -1.35099983e+00 9.03545618e-01
5.27131081e-01 7.76840508e-01 -4.32077169e-01 2.95299888e-01
-4.48995918e-01 1.12732005e+00 -2.39230677e-01 2.61236340e-01
2.10535884e-01 -8.10438395e-02 8.47356260e-01 1.51227176e+00
5.51673412e-01 -5.18770099e-01 4.06063557e-01 5.34318507e-01
-2.36236557e-01 7.54348099e-01 -6.77578926e-01 -1.31863013e-01
6.88957810e-01 9.18832004e-01 -6.68878183e-02 -3.96621644e-01
-5.29426932e-01 1.19927835e+00 3.87484938e-01 6.29529834e-01
-2.75631517e-01 -2.54111588e-01 5.96382499e-01 1.31667033e-01
3.07376921e-01 -3.48410994e-01 1.16657734e-01 -1.21089578e+00
-5.38603477e-02 -1.51172721e+00 -2.79363729e-02 -6.31369770e-01
-1.39212322e+00 1.33950806e+00 -1.38855189e-01 -1.11566734e+00
-7.37883270e-01 -3.45385939e-01 -4.62214142e-01 1.13455677e+00
-1.54634488e+00 -1.65847898e+00 4.76675868e-01 8.37724030e-01
1.10709786e+00 -8.42456698e-01 1.19444776e+00 2.22333536e-01
-4.22406614e-01 8.24740291e-01 1.77887052e-01 1.08806148e-01
1.08973634e+00 -1.20368981e+00 1.05577934e+00 8.36635590e-01
4.49395955e-01 4.53446448e-01 7.11687922e-01 -4.95049328e-01
-1.13731301e+00 -1.28516507e+00 1.28785038e+00 -1.78136215e-01
9.33709621e-01 -7.69260705e-01 -8.41821492e-01 1.14070189e+00
9.86520290e-01 -1.76931709e-01 6.96223736e-01 3.33044291e-01
-4.30861533e-01 -3.52604926e-01 -8.54536116e-01 9.98365879e-01
1.17749631e+00 -8.26361477e-01 -6.32559299e-01 4.50024992e-01
1.19302607e+00 -3.59295398e-01 -8.47022891e-01 2.52403229e-01
2.08799258e-01 -6.63896143e-01 7.66675293e-01 -4.41301823e-01
-1.39216110e-01 -2.08410978e-01 -4.33139831e-01 -2.01852751e+00
-1.79112121e-01 -1.18920290e+00 3.44402730e-01 1.54638815e+00
8.89353752e-01 -8.20785999e-01 3.55797186e-02 -4.59133774e-01
-7.83178091e-01 -9.25106928e-02 -1.30519009e+00 -1.32281327e+00
5.73014081e-01 -3.66223097e-01 7.80549586e-01 1.03418434e+00
5.72163835e-02 8.47570181e-01 -5.78683674e-01 3.61440808e-01
3.74745429e-01 -1.33247092e-01 8.72390151e-01 -6.87391698e-01
-4.99496460e-01 -3.21388006e-01 1.90706521e-01 -1.15445650e+00
7.36446500e-01 -1.48690307e+00 3.16671789e-01 -1.16618466e+00
-4.36545491e-01 -4.21457618e-01 -9.00859088e-02 6.93844497e-01
4.52337079e-02 -2.81634405e-02 3.57978195e-01 8.33464935e-02
-2.35997081e-01 9.13866997e-01 1.38543868e+00 -2.86429793e-01
-4.16037709e-01 1.39212877e-01 -4.84936327e-01 3.97974849e-01
1.06814075e+00 -4.81949896e-01 -5.38785040e-01 -7.12828279e-01
-4.93726522e-01 3.52484137e-01 -3.68934691e-01 -9.37683582e-01
6.43606186e-02 -1.18993469e-01 -2.31014445e-01 -2.05914319e-01
4.84166771e-01 -4.95355219e-01 1.22019120e-01 1.56362250e-01
-3.83918762e-01 2.21016690e-01 4.60856587e-01 -4.45137508e-02
-4.27276254e-01 -6.12642504e-02 8.19117248e-01 -1.41990349e-01
-2.97477871e-01 1.43108249e-01 -7.31143594e-01 2.18753695e-01
5.14910579e-01 3.66680145e-01 -2.85191089e-01 -5.32825887e-01
-7.49042988e-01 -2.02322137e-02 3.94076973e-01 9.53025222e-01
3.55362177e-01 -1.58468223e+00 -1.23826659e+00 5.80388546e-01
2.82573819e-01 -2.56097943e-01 7.79414102e-02 3.73734742e-01
-7.83639029e-02 6.19805992e-01 -1.28912227e-02 -6.14444077e-01
-1.11903954e+00 3.00061226e-01 3.12631726e-01 -7.94397108e-03
-3.84741992e-01 7.04391360e-01 2.24663824e-01 -1.41645658e+00
1.17484778e-01 -3.07387233e-01 2.04779282e-01 -3.07895601e-01
3.10398430e-01 5.55707328e-02 1.61847308e-01 -1.26954651e+00
-3.96336436e-01 2.29293391e-01 -1.65613696e-01 -6.96201563e-01
1.01744127e+00 -4.64160353e-01 1.51535586e-01 1.00030291e+00
1.16381359e+00 6.21993601e-01 -9.26192224e-01 -5.36523938e-01
-1.78108007e-01 -8.43033046e-02 -1.17065139e-01 -1.00127351e+00
-9.60140586e-01 7.49265969e-01 2.05732718e-01 5.04802652e-02
7.69114316e-01 2.63133973e-01 1.09534657e+00 3.86117309e-01
3.78467143e-01 -1.05814219e+00 -1.14637680e-01 1.00431848e+00
1.36529315e+00 -1.24857724e+00 -7.79357195e-01 -3.85412157e-01
-1.07429314e+00 7.96945810e-01 3.03207755e-01 2.38833308e-01
5.96573830e-01 3.67348045e-01 7.10086823e-01 4.87052679e-01
-8.54559720e-01 -1.72755048e-01 2.31205747e-01 7.70561278e-01
7.83464313e-01 5.14954090e-01 3.07084650e-01 3.84056717e-01
-1.01448405e+00 -5.11810482e-01 2.68749475e-01 5.28248906e-01
-2.29962513e-01 -1.56920409e+00 -2.78614700e-01 -1.95186213e-01
-3.85576278e-01 -6.03579521e-01 -2.65486240e-01 4.89577174e-01
-1.73786074e-01 1.27992249e+00 -1.79865897e-01 -3.02523285e-01
5.04895926e-01 4.40742701e-01 3.07942778e-01 -9.80437934e-01
-7.62694597e-01 3.76320690e-01 4.61437702e-01 -1.99546546e-01
-2.37936914e-01 -8.65540981e-01 -1.09056413e+00 -2.30386272e-01
3.28178182e-02 3.02557737e-01 7.32553303e-01 8.46495271e-01
2.94932753e-01 6.42442644e-01 9.51795459e-01 -9.37565088e-01
-4.73843336e-01 -1.31266236e+00 -4.10025746e-01 4.04919907e-02
6.56448841e-01 -4.48304452e-02 -3.80428642e-01 2.08786815e-01] | [14.619803428649902, 6.91439962387085] |
426a61d3-5cd1-43db-bf34-f3c93b81691f | multimodal-sentiment-analysis-using | 1806.06228 | null | http://arxiv.org/abs/1806.06228v1 | http://arxiv.org/pdf/1806.06228v1.pdf | Multimodal Sentiment Analysis using Hierarchical Fusion with Context Modeling | Multimodal sentiment analysis is a very actively growing field of research. A
promising area of opportunity in this field is to improve the multimodal fusion
mechanism. We present a novel feature fusion strategy that proceeds in a
hierarchical fashion, first fusing the modalities two in two and only then
fusing all three modalities. On multimodal sentiment analysis of individual
utterances, our strategy outperforms conventional concatenation of features by
1%, which amounts to 5% reduction in error rate. On utterance-level multimodal
sentiment analysis of multi-utterance video clips, for which current
state-of-the-art techniques incorporate contextual information from other
utterances of the same clip, our hierarchical fusion gives up to 2.4% (almost
10% error rate reduction) over currently used concatenation. The implementation
of our method is publicly available in the form of open-source code. | ['N. Majumder', 'D. Hazarika', 'A. Gelbukh', 'E. Cambria', 'S. Poria'] | 2018-06-16 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 4.99658614e-01 7.38804564e-02 1.67710081e-01 -4.93812382e-01
-1.30895448e+00 -6.38908267e-01 6.41671121e-01 5.39693654e-01
-5.65107524e-01 4.50457662e-01 4.31843847e-01 1.04877621e-01
3.23017269e-01 -3.47812712e-01 -4.16333705e-01 -7.60908306e-01
2.28762105e-01 -3.34183685e-02 2.62100279e-01 -4.46730733e-01
1.57844752e-01 1.18648164e-01 -1.77206457e+00 7.37655401e-01
3.63662809e-01 1.31234026e+00 -1.76096380e-01 1.00840878e+00
-2.11036637e-01 7.68881202e-01 -3.39284152e-01 -6.87246382e-01
-1.04884501e-03 -4.14469391e-01 -7.11292028e-01 1.79726824e-01
3.93111318e-01 -1.50629878e-01 5.49071431e-02 1.04132986e+00
4.69154537e-01 2.32839242e-01 2.77296692e-01 -1.08974278e+00
-5.34214675e-02 6.67916298e-01 -5.29984832e-01 -3.46167274e-02
8.40856731e-01 -5.10841072e-01 1.06927001e+00 -9.78682578e-01
4.02080446e-01 1.09944761e+00 4.30347174e-01 2.58433700e-01
-9.88303602e-01 -3.80018592e-01 5.13662025e-02 5.19886836e-02
-1.25355053e+00 -6.99282885e-01 7.41404474e-01 -3.82570744e-01
1.01406085e+00 3.70875865e-01 3.46058965e-01 7.51464128e-01
1.00083731e-01 8.94690275e-01 1.17415154e+00 -6.60555184e-01
4.73425873e-02 2.97635406e-01 2.73672730e-01 5.97277761e-01
-3.08258176e-01 -4.22662020e-01 -8.19319129e-01 -2.28133276e-01
2.38731056e-01 8.71125385e-02 1.18460365e-01 -5.76445237e-02
-1.20294833e+00 8.15065205e-01 5.15937917e-02 4.85512227e-01
-4.85663921e-01 -5.33451233e-03 6.26510918e-01 3.60849112e-01
5.21026433e-01 2.51299758e-02 -3.36244404e-01 -6.37722313e-01
-9.60846663e-01 3.72889042e-01 6.70321882e-01 6.76675797e-01
7.55329728e-01 -2.49938160e-01 2.84225196e-01 9.37706292e-01
3.37720990e-01 4.27681476e-01 4.21822697e-01 -1.02411878e+00
4.71571684e-01 6.61972761e-01 3.92077118e-02 -8.97233903e-01
-6.67154133e-01 8.25453028e-02 -4.24993962e-01 -6.34341985e-02
2.62285888e-01 -6.41045630e-01 -6.69696152e-01 1.55914140e+00
4.24987882e-01 -1.61827728e-01 3.62676919e-01 7.45093703e-01
9.56120312e-01 6.85993314e-01 -4.67606783e-02 -3.67262810e-01
1.78849375e+00 -8.60353410e-01 -8.93029988e-01 1.13083944e-01
4.71773714e-01 -1.16266215e+00 5.16293049e-01 5.05777180e-01
-1.27930856e+00 -3.81398052e-01 -1.00565958e+00 4.95310239e-02
-5.62472522e-01 7.89771695e-03 5.75944126e-01 9.39868987e-01
-1.08825338e+00 1.06735311e-01 -7.96276152e-01 -4.89067733e-01
9.54870656e-02 6.52714908e-01 -7.60997593e-01 -5.21081574e-02
-1.05097091e+00 6.79022849e-01 9.90709066e-02 -4.65564169e-02
-2.80031413e-01 -3.90157402e-01 -1.14050829e+00 5.06840199e-02
6.04914904e-01 -3.05345684e-01 1.44712770e+00 -1.05675042e+00
-1.68986344e+00 5.76939225e-01 -6.42876565e-01 -3.01373512e-01
1.83007866e-01 -4.61179703e-01 -6.76617801e-01 3.59133899e-01
-2.08213285e-01 7.73641765e-01 8.07607830e-01 -1.17847979e+00
-9.10072863e-01 -3.32991689e-01 2.84871578e-01 3.48453373e-01
-5.99126577e-01 5.56599855e-01 -5.70897460e-01 -5.38602412e-01
1.13620870e-01 -1.15212131e+00 -2.49241054e-01 -5.10728121e-01
1.35632679e-02 -4.40682843e-02 8.30285490e-01 -5.92733741e-01
1.17667806e+00 -2.18690300e+00 4.26821619e-01 7.86392242e-02
1.57995634e-02 2.57879794e-02 -1.24946706e-01 8.47853422e-01
5.27733676e-02 5.46311168e-03 -2.74789453e-01 -8.53486776e-01
2.95301527e-02 -1.29775390e-01 -1.08785793e-01 4.12242472e-01
2.24394575e-01 4.97479677e-01 -7.60981202e-01 -3.91026795e-01
4.16465998e-01 5.96714973e-01 -4.17949438e-01 7.86775798e-02
2.33661979e-01 4.05438066e-01 -2.19968975e-01 7.72211492e-01
6.24768376e-01 1.41218334e-01 2.08827555e-01 -4.17861938e-01
-2.48334795e-01 -1.75847888e-01 -1.26087654e+00 1.83250701e+00
-3.45673680e-01 8.15188944e-01 2.10773293e-02 -7.76690543e-01
7.17951119e-01 6.90419734e-01 6.14539862e-01 -4.68147010e-01
5.00111878e-01 -4.14162502e-03 -2.19806075e-01 -5.66438675e-01
1.10539794e+00 -3.75491261e-01 -6.61519825e-01 3.08862269e-01
5.11658907e-01 -9.64805633e-02 4.59277421e-01 3.29341054e-01
9.83439088e-01 -1.08066469e-01 3.40513170e-01 2.76012331e-01
7.84815669e-01 -1.94521785e-01 1.73024356e-01 4.67131376e-01
-2.07334444e-01 7.03400135e-01 6.34677291e-01 -1.36752561e-01
-7.82247603e-01 -5.36970198e-01 -4.03607562e-02 1.35974145e+00
-1.49568394e-02 -7.52986133e-01 -8.04681897e-01 -6.28266096e-01
-2.92021096e-01 3.17350626e-01 -7.76495516e-01 3.34581792e-01
-3.05391341e-01 -6.45365655e-01 5.43737948e-01 5.70365012e-01
2.01887056e-01 -7.43569911e-01 -6.03702128e-01 5.58097549e-02
-2.95839876e-01 -1.40961266e+00 -1.28382623e-01 1.23161212e-01
-6.41161740e-01 -6.94716871e-01 -7.14845240e-01 -4.15662140e-01
4.24524248e-01 2.50754297e-01 6.62223279e-01 -5.42909838e-02
2.60902226e-01 5.96428096e-01 -9.62561548e-01 -3.79483432e-01
-3.23729515e-01 -6.45867512e-02 -3.79077457e-02 4.05045152e-01
3.16422343e-01 -2.03381911e-01 -3.05398554e-01 2.21409217e-01
-1.11771071e+00 -2.00752854e-01 3.87553155e-01 7.57943869e-01
3.40386331e-01 1.38157621e-01 5.62622607e-01 -8.79880607e-01
4.81911629e-01 -6.23660326e-01 -4.16107327e-01 9.40549672e-02
-2.22372543e-02 -2.24218503e-01 4.56012666e-01 -1.49788484e-01
-1.25571811e+00 4.14822370e-01 -3.34668875e-01 -3.65055174e-01
-5.46317339e-01 7.95584440e-01 1.28953099e-01 -2.07673952e-01
5.74581660e-02 -6.40465245e-02 -4.72504161e-02 -2.87535250e-01
5.53403914e-01 7.86034107e-01 5.90805292e-01 -3.05511415e-01
1.64113194e-01 5.25659323e-01 -6.04424402e-02 -9.62032795e-01
-5.03979623e-01 -7.91737556e-01 -5.70065379e-01 -5.31021416e-01
8.52634907e-01 -1.05253434e+00 -8.96663964e-01 4.40122366e-01
-9.07231152e-01 2.72213876e-01 2.57987916e-01 5.85743248e-01
-4.28654999e-01 4.46045369e-01 -5.42739570e-01 -1.02159631e+00
-8.91408250e-02 -1.39941585e+00 1.45495880e+00 3.79428506e-01
-3.14290762e-01 -9.34619963e-01 1.89757496e-01 7.94862568e-01
2.69510239e-01 2.07710654e-01 4.13876742e-01 -8.91163349e-01
1.08045362e-01 -6.52915657e-01 -3.36262770e-02 3.42593729e-01
6.73729777e-02 1.37710243e-01 -1.13979590e+00 -1.41925216e-01
-8.93817632e-04 -3.96426886e-01 8.56823564e-01 2.42617533e-01
3.23143244e-01 2.46479362e-01 -7.88102448e-02 -1.56204313e-01
1.38048375e+00 3.25347602e-01 5.58296561e-01 4.36101779e-02
5.93758464e-01 9.37521040e-01 7.74481714e-01 6.91460371e-01
5.74372530e-01 7.05368936e-01 3.55070561e-01 -5.20282350e-02
2.25415751e-01 3.21555257e-01 5.60786188e-01 1.06449795e+00
-2.75811613e-01 -2.90858358e-01 -7.85685718e-01 5.93558848e-01
-2.13312411e+00 -1.00641549e+00 -1.10493675e-01 1.91982281e+00
4.64798063e-01 -4.18316647e-02 4.26417381e-01 3.26606929e-01
5.41827202e-01 1.13918670e-01 1.19428955e-01 -7.72906780e-01
-1.72485098e-01 2.79527828e-02 2.15658605e-01 6.52761579e-01
-1.49921322e+00 7.23598719e-01 6.49068165e+00 5.75908124e-01
-1.12622535e+00 1.33749127e-01 2.38822684e-01 -1.88958794e-01
-4.67912890e-02 6.69894367e-02 -6.69174612e-01 3.57330233e-01
1.22901642e+00 1.49861857e-01 7.35685080e-02 5.07305622e-01
1.16029717e-01 -7.01018512e-01 -8.39858353e-01 9.78782773e-01
4.68373775e-01 -1.21537399e+00 -2.64854878e-01 1.05536415e-03
7.47866452e-01 -6.84212148e-02 2.32043657e-02 1.98461279e-01
-5.46306260e-02 -6.22188509e-01 7.96513200e-01 4.72223133e-01
3.06264311e-01 -1.04222643e+00 1.30870616e+00 -3.71997096e-02
-1.30322778e+00 1.59957353e-02 1.88204035e-01 -9.39878076e-02
5.97114742e-01 4.73835438e-01 -4.70845371e-01 9.91450667e-01
7.24903822e-01 5.69652855e-01 -5.70263267e-01 7.20271409e-01
2.80469000e-01 4.57418561e-01 -2.36015275e-01 9.41766053e-02
2.28044808e-01 3.04370327e-03 4.76276040e-01 1.62366509e+00
2.83387899e-01 2.25295603e-01 1.79282099e-01 -1.96904331e-01
1.22000566e-02 2.89801300e-01 -5.27036190e-01 -1.43844128e-01
1.15264803e-01 1.46879792e+00 -8.46009970e-01 -5.40153682e-01
-8.92129302e-01 9.03523564e-01 3.15842293e-02 4.86467443e-02
-7.00911760e-01 -5.74114263e-01 3.16588849e-01 -6.85584307e-01
6.77067101e-01 -1.44415110e-01 -8.64730999e-02 -1.11369228e+00
-5.34417899e-03 -8.09037089e-01 4.08270448e-01 -6.39850020e-01
-1.01312673e+00 8.44545007e-01 9.61687192e-02 -1.25979042e+00
-4.85704064e-01 -5.50931334e-01 -2.86853224e-01 5.36985815e-01
-1.09032249e+00 -1.29220843e+00 -1.67615563e-01 6.17210746e-01
6.06134176e-01 -1.85520843e-01 1.13332129e+00 4.56070453e-01
-4.67687398e-01 6.42515659e-01 -1.08625524e-01 -7.68100098e-02
7.79123127e-01 -1.00414121e+00 -1.33648813e-01 7.10902870e-01
1.68072224e-01 4.86148089e-01 9.46957529e-01 -2.56227195e-01
-1.57124388e+00 -4.82775301e-01 1.05288363e+00 -3.28641593e-01
1.02304816e+00 -3.12625200e-01 -8.15853894e-01 4.10817057e-01
7.83024788e-01 -3.00587296e-01 1.27898824e+00 1.58480927e-01
-3.48706990e-01 -5.00662476e-02 -1.12427902e+00 4.58577454e-01
1.24977499e-01 -6.14621818e-01 -5.82605004e-01 -3.58244292e-02
6.77316844e-01 -4.08607572e-01 -1.19948483e+00 5.08552313e-01
8.61073852e-01 -1.13954127e+00 5.34866035e-01 -1.18441917e-01
4.41202015e-01 -4.08128023e-01 -8.00827146e-01 -1.10643578e+00
2.98304200e-01 -6.56230450e-01 -1.94877777e-02 1.42159832e+00
4.38499361e-01 -4.50203687e-01 6.00414157e-01 5.39501190e-01
-1.38396487e-01 -7.37871349e-01 -8.15913916e-01 -1.52012199e-01
-3.67567182e-01 -8.38463545e-01 2.62030780e-01 6.86142266e-01
6.98251903e-01 4.12326306e-01 -5.25278032e-01 8.48632380e-02
3.16868782e-01 -1.44269550e-02 7.01077580e-01 -7.54976451e-01
-3.15946899e-02 -4.26953614e-01 -6.84709013e-01 -6.57305956e-01
1.72096878e-01 -3.86453927e-01 6.88098744e-02 -1.11648154e+00
3.01775992e-01 2.13049114e-01 -4.25905168e-01 6.10495806e-01
-8.71967897e-02 8.31315398e-01 5.87719738e-01 -2.11617097e-01
-8.58009219e-01 3.99810612e-01 5.13252616e-01 1.51975928e-02
-1.49324134e-01 -1.72048256e-01 -7.31113493e-01 8.17702532e-01
6.10923767e-01 -3.52855623e-01 -1.70082852e-01 -1.22437671e-01
1.58085629e-01 3.14934522e-01 4.51174844e-03 -8.64794910e-01
3.89627934e-01 4.59484719e-02 1.74507841e-01 -7.56971717e-01
9.65479672e-01 -9.39251840e-01 1.05229780e-01 -1.35109946e-01
-2.43143156e-01 1.89038083e-01 5.62558651e-01 4.50710207e-01
-8.34399879e-01 -2.65036076e-01 3.93740565e-01 1.82704538e-01
-7.43109882e-01 -2.85777003e-01 -8.17729175e-01 -6.34546816e-01
9.83402133e-01 4.85796221e-02 -2.86419004e-01 -6.79112911e-01
-9.29540217e-01 7.97858238e-02 3.41263324e-01 4.19413000e-01
5.85742176e-01 -1.17763221e+00 -5.95881701e-01 -6.88840374e-02
2.31438532e-01 -6.67570114e-01 5.85105777e-01 1.25646269e+00
-1.09230042e-01 4.49240178e-01 -5.83601333e-02 -6.38394058e-01
-1.83815718e+00 2.66418397e-01 -1.46786600e-01 -1.22939259e-01
-1.68427210e-02 6.67583525e-01 -6.30026236e-02 -1.19531482e-01
6.68063685e-02 2.06389334e-02 -5.28659701e-01 5.66045463e-01
7.76626766e-01 3.90990764e-01 4.03471023e-01 -1.28355145e+00
-5.86250484e-01 6.34547055e-01 -1.89964771e-01 -6.33277595e-01
1.20974267e+00 -3.74191225e-01 -2.73492783e-01 7.75743663e-01
1.42675710e+00 3.21046948e-01 -7.87088037e-01 -6.35466203e-02
-1.95444226e-01 -3.54157925e-01 1.26165003e-01 -6.07525349e-01
-8.38191688e-01 6.04775131e-01 4.99870718e-01 5.61992347e-01
1.48091245e+00 2.34782830e-01 6.09935641e-01 3.30150545e-01
1.85003862e-01 -8.14535379e-01 -2.23732680e-01 5.27917802e-01
7.78873861e-01 -1.45071185e+00 1.44524246e-01 -3.86044890e-01
-1.08904409e+00 1.11775076e+00 7.40450574e-04 -1.36534110e-01
7.09096909e-01 4.20066655e-01 2.48390824e-01 -1.45940691e-01
-8.10148299e-01 -3.42354119e-01 4.17429268e-01 1.24553047e-01
7.59255946e-01 1.10216454e-01 -4.18535352e-01 6.35699034e-01
-1.97112653e-02 -1.83248952e-01 5.61064363e-01 1.34108758e+00
-2.95710534e-01 -1.45704877e+00 -6.06764615e-01 1.76322430e-01
-8.78903329e-01 -3.78010832e-02 -3.01776320e-01 4.97928977e-01
-1.09564744e-01 1.45509875e+00 -1.16782193e-03 -7.42893517e-01
4.29094166e-01 2.49845967e-01 4.08778995e-01 -3.85733008e-01
-8.03560734e-01 6.77112997e-01 3.43659312e-01 -6.00275517e-01
-1.24370861e+00 -1.23325849e+00 -1.21651030e+00 -2.28700206e-01
-3.21533203e-01 6.95150644e-02 1.01439762e+00 1.15354180e+00
2.89007038e-01 3.39392394e-01 6.99900627e-01 -1.44475806e+00
2.12806240e-01 -9.33074951e-01 -3.26280028e-01 3.00879002e-01
5.31495273e-01 -6.84749365e-01 -1.96986899e-01 3.82165253e-01] | [13.159317970275879, 5.2041144371032715] |
c7896eee-52f3-4d4b-ad5e-9d53b2137439 | otfpf-optimal-transport-based-feature-pyramid | 2205.04684 | null | https://arxiv.org/abs/2205.04684v2 | https://arxiv.org/pdf/2205.04684v2.pdf | OTFPF: Optimal Transport-Based Feature Pyramid Fusion Network for Brain Age Estimation with 3D Overlapped ConvNeXt | Chronological age of healthy brain is able to be predicted using deep neural networks from T1-weighted magnetic resonance images (T1 MRIs), and the predicted brain age could serve as an effective biomarker for detecting aging-related diseases or disorders. In this paper, we propose an end-to-end neural network architecture, referred to as optimal transport based feature pyramid fusion (OTFPF) network, for the brain age estimation with T1 MRIs. The OTFPF consists of three types of modules: Optimal Transport based Feature Pyramid Fusion (OTFPF) module, 3D overlapped ConvNeXt (3D OL-ConvNeXt) module and fusion module. These modules strengthen the OTFPF network's understanding of each brain's semi-multimodal and multi-level feature pyramid information, and significantly improve its estimation performances. Comparing with recent state-of-the-art models, the proposed OTFPF converges faster and performs better. The experiments with 11,728 MRIs aged 3-97 years show that OTFPF network could provide accurate brain age estimation, yielding mean absolute error (MAE) of 2.097, Pearson's correlation coefficient (PCC) of 0.993 and Spearman's rank correlation coefficient (SRCC) of 0.989, between the estimated and chronological ages. Widespread quantitative experiments and ablation experiments demonstrate the superiority and rationality of OTFPF network. The codes and implement details will be released on GitHub: https://github.com/ZJU-Brain/OTFPF after final decision. | ['Cheng Zhuo', 'Yiyu Shi', 'Qianqian Yang', 'Xunzhao Yin', 'Le Xue', 'Shunjie Dong', 'Yalin Wang', 'Yanyan Huang', 'Yu Fu'] | 2022-05-10 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [-4.20058936e-01 -1.21587785e-02 -8.76907334e-02 -4.41218436e-01
-2.13095710e-01 3.29564571e-01 2.79393822e-01 1.48103416e-01
-6.97259486e-01 8.15728307e-01 2.65730858e-01 -2.48076692e-02
-2.35896468e-01 -6.71366513e-01 -4.92672205e-01 -6.72119617e-01
-9.18890774e-01 1.93229288e-01 2.80563831e-01 9.49493647e-02
1.24795355e-01 3.61001849e-01 -1.48470712e+00 -4.96109463e-02
1.32185149e+00 1.45782709e+00 3.56753081e-01 3.08108807e-01
2.60893285e-01 5.56534171e-01 -2.84414262e-01 -4.56536055e-01
-2.53736414e-02 2.87699372e-01 -5.97081661e-01 -5.35338402e-01
2.09660292e-01 -6.66101098e-01 -5.89377403e-01 1.04054618e+00
8.03634107e-01 -2.12105438e-01 8.20513427e-01 -1.58150184e+00
-7.67383456e-01 5.77431321e-01 -8.27317715e-01 5.21264851e-01
-2.14344621e-01 -1.87919009e-02 3.81820589e-01 -8.77540350e-01
3.02501023e-01 1.02802336e+00 9.55664277e-01 5.61707020e-01
-5.38956821e-01 -1.02612102e+00 7.68398046e-02 7.52108932e-01
-1.29018235e+00 -1.42775819e-01 2.81677037e-01 -5.42879820e-01
6.39105141e-01 -2.00167358e-01 1.14683270e+00 8.37287247e-01
9.91369188e-01 7.92591453e-01 1.29691195e+00 1.12309523e-01
2.57913675e-02 -7.59333134e-01 2.04876527e-01 9.46523130e-01
4.21625942e-01 1.90405786e-01 -6.66185915e-01 2.25994792e-02
9.15208459e-01 6.82052746e-02 -2.26084918e-01 -4.75772396e-02
-1.49625540e+00 5.28387666e-01 9.10673082e-01 4.12621409e-01
-7.03030527e-01 2.73601204e-01 5.17114699e-01 1.46647573e-01
7.63781250e-01 -2.35952601e-01 -5.65116465e-01 1.09589703e-01
-9.45564151e-01 2.72723824e-01 3.63726355e-02 5.12854278e-01
2.92103469e-01 7.46154189e-02 -2.58077234e-01 9.78188932e-01
2.13695869e-01 6.92468882e-01 8.46585393e-01 -1.00298476e+00
-5.76235950e-02 3.77613962e-01 -3.58790904e-01 -6.14430428e-01
-9.56990898e-01 -6.06516957e-01 -1.22043312e+00 1.18714333e-01
5.07900357e-01 -2.82914430e-01 -9.95144784e-01 1.93599987e+00
2.57824928e-01 -6.99590147e-02 -3.30925912e-01 8.35513353e-01
8.37261856e-01 2.40899101e-01 2.81839937e-01 -2.38818124e-01
1.78689063e+00 -7.88233578e-01 -4.56792235e-01 -1.39918283e-01
5.25273979e-01 -3.25238138e-01 5.84738672e-01 3.35758984e-01
-1.07506633e+00 -4.96302307e-01 -8.84259343e-01 1.46585345e-01
-3.46845001e-01 3.63611728e-01 7.92746365e-01 5.41806519e-01
-1.35746169e+00 7.20675886e-01 -1.16305208e+00 -2.89582253e-01
7.95565307e-01 6.55929327e-01 -6.57818913e-01 -1.97636783e-01
-1.43803811e+00 8.73339295e-01 4.42701131e-01 2.49482378e-01
-9.46449995e-01 -1.16245413e+00 -5.18699408e-01 -2.00163946e-01
-2.37319693e-01 -1.24775255e+00 1.05699503e+00 -4.33520883e-01
-7.93247044e-01 6.77715302e-01 -1.17095523e-01 -6.09427750e-01
4.39226449e-01 -5.09814143e-01 -4.56695735e-01 4.31187898e-01
3.87183100e-01 1.14657795e+00 6.22698784e-01 -6.23869359e-01
-5.82165837e-01 -9.49675679e-01 -3.06645334e-01 -3.08009144e-03
-5.32373726e-01 2.60277152e-01 2.40714233e-02 -5.96799850e-01
1.93819150e-01 -6.18270159e-01 -5.76352999e-02 3.98321450e-01
-4.28080149e-02 -5.05786717e-01 5.77827871e-01 -1.38234174e+00
1.09706819e+00 -1.56950212e+00 -9.02126431e-02 -6.98224604e-02
7.88851202e-01 -1.91745609e-02 5.99973351e-02 -1.53762579e-01
-2.87474245e-01 -1.96470097e-01 -2.00651556e-01 -5.94891608e-02
-1.69660762e-01 -2.12276846e-01 6.45869315e-01 6.28837168e-01
-1.21244013e-01 1.05365634e+00 -8.06770563e-01 -6.26535177e-01
-1.14546500e-01 6.50848985e-01 -2.00357482e-01 -1.26535013e-01
5.02059281e-01 4.28439766e-01 -4.71977621e-01 7.79434323e-01
7.06952155e-01 2.36727782e-02 -3.87967050e-01 -5.01692951e-01
-2.27753267e-01 -5.15730679e-01 -4.89841819e-01 1.77617621e+00
-2.14661002e-01 2.91956842e-01 -1.31483329e-02 -8.17286730e-01
1.09151912e+00 1.65440097e-01 8.91609848e-01 -9.43952084e-01
4.96161789e-01 4.07763779e-01 2.81144470e-01 -3.64679664e-01
-1.41295001e-01 -3.20881903e-02 2.20652848e-01 3.24997157e-01
2.10260272e-01 4.94727880e-01 4.14557308e-01 1.97427690e-01
1.15351284e+00 -5.39073488e-03 1.15599483e-01 -6.43616736e-01
7.61892378e-01 -7.03651965e-01 7.65228391e-01 3.55536789e-01
-8.22575986e-01 2.69338697e-01 4.52692777e-01 -5.82854986e-01
-1.13692248e+00 -1.22775996e+00 -3.62540424e-01 8.04300904e-01
-2.21733585e-01 -1.85671717e-01 -1.06188321e+00 -6.08295262e-01
-6.27266662e-03 2.41946504e-01 -8.47042143e-01 -2.90335447e-01
-4.10957843e-01 -1.01915848e+00 5.44279456e-01 9.26841021e-01
1.02390385e+00 -7.94779420e-01 -4.96419311e-01 -4.65060174e-02
-4.54398334e-01 -9.17146742e-01 -4.92471159e-01 -6.16268516e-02
-1.16222703e+00 -9.06189084e-01 -1.53600812e+00 -6.80025578e-01
7.52651036e-01 -2.02954203e-01 8.33898723e-01 1.43083796e-01
-2.03818858e-01 2.79512644e-01 -2.48555854e-01 -4.41532582e-01
1.32084846e-01 2.36291498e-01 5.54666400e-01 -3.02445203e-01
1.94962740e-01 -1.11973035e+00 -1.33446908e+00 2.21461460e-01
-4.59527999e-01 1.85901746e-01 9.42060351e-01 7.40392089e-01
2.95503497e-01 -1.21367633e-01 8.82627249e-01 -1.63878977e-01
5.25586605e-01 -3.97769362e-01 -2.78221637e-01 1.38512149e-01
-7.81878948e-01 -9.55247059e-02 3.39976788e-01 -4.15562510e-01
-7.56768405e-01 -1.76342919e-01 -3.45869750e-01 -2.94718921e-01
-1.91365797e-02 3.77789378e-01 -7.77544007e-02 -4.61818390e-02
3.76490474e-01 2.83732921e-01 3.28128368e-01 -3.40795249e-01
-2.92852875e-02 5.15982389e-01 7.79862046e-01 -6.14184201e-01
4.48997885e-01 1.91116482e-01 4.74606566e-02 -5.20614326e-01
-5.91791332e-01 5.95968105e-02 -8.20118487e-01 -7.42814362e-01
9.10962284e-01 -1.09781909e+00 -7.11228073e-01 1.28068531e+00
-9.31250274e-01 -3.48038107e-01 3.38903308e-01 8.55609059e-01
-4.70417678e-01 5.06522894e-01 -8.85128736e-01 -5.35033405e-01
-1.22321367e+00 -1.04806626e+00 7.64453769e-01 4.06679958e-01
4.55023423e-02 -8.79809856e-01 -4.78577077e-01 5.10056734e-01
5.37980199e-01 3.59619141e-01 1.09258306e+00 -4.09902573e-01
1.50965238e-02 -2.47925743e-01 -6.18129909e-01 3.96523118e-01
-1.82719320e-01 -2.88343042e-01 -6.19432330e-01 -3.76453727e-01
-2.12187782e-01 -5.58413453e-02 7.73124933e-01 1.06913185e+00
1.21133888e+00 1.08657986e-01 -2.15176493e-01 3.58130813e-01
1.17533958e+00 2.23309368e-01 8.96032274e-01 5.16106248e-01
5.58291495e-01 4.51215923e-01 4.74270970e-01 4.91792053e-01
7.46896803e-01 1.51173890e-01 5.72116792e-01 7.21658245e-02
-3.62416387e-01 2.76043177e-01 4.60429162e-01 1.18455029e+00
-4.80273545e-01 4.62887615e-01 -1.18310356e+00 6.70630813e-01
-1.62237775e+00 -6.55256748e-01 -3.02646905e-01 2.02336812e+00
5.17534912e-01 2.94598669e-01 3.19354028e-01 2.29754359e-01
1.08148491e+00 -2.75127552e-02 -7.41761029e-01 -6.37619570e-02
-3.72888856e-02 1.04143955e-01 6.58481538e-01 4.82797511e-02
-9.62198198e-01 3.41593146e-01 5.20808077e+00 8.09879601e-01
-1.04048729e+00 5.38916111e-01 9.26196277e-01 3.99535708e-02
1.84070677e-01 -3.60616237e-01 -4.37472582e-01 6.94138050e-01
9.38854396e-01 -4.49403316e-01 2.85291612e-01 5.77840924e-01
2.19886482e-01 -3.05499703e-01 -5.94706833e-01 8.61550510e-01
-6.46654665e-02 -8.59612584e-01 -2.56947339e-01 1.59476236e-01
3.77749622e-01 2.57680237e-01 1.07290849e-01 3.27237159e-01
-1.07626952e-01 -7.57000089e-01 7.02980042e-01 9.84373033e-01
8.53021920e-01 -7.98875630e-01 9.44340050e-01 1.31582692e-01
-1.43570280e+00 -3.84974509e-01 -1.94714308e-01 -8.35655928e-02
1.22427113e-01 1.09838915e+00 -2.48078540e-01 6.65316999e-01
1.28241968e+00 7.62665629e-01 -8.34478498e-01 1.34144771e+00
-2.22951874e-01 1.98093772e-01 -1.57186657e-01 5.52690960e-02
-3.09129562e-02 1.42197907e-01 9.83577892e-02 6.04411781e-01
7.43976355e-01 -2.05170121e-02 -2.13378057e-01 5.41298568e-01
9.38091427e-02 1.73160046e-01 -5.95375954e-04 2.35805735e-01
4.39430833e-01 1.41643643e+00 -9.13929343e-01 -2.35175848e-01
-2.63100952e-01 5.23795843e-01 2.15174988e-01 3.01483157e-03
-8.90925229e-01 -4.40939039e-01 3.67618650e-01 2.43219480e-01
2.76440289e-02 -2.85636455e-01 -2.96309680e-01 -9.92707133e-01
6.74111247e-02 -4.77612197e-01 2.73815691e-01 -1.23680007e+00
-1.43390536e+00 6.19142056e-01 1.25422463e-01 -7.54268110e-01
2.35502794e-01 -6.35995507e-01 -7.04572380e-01 8.05100262e-01
-1.27995968e+00 -1.44187164e+00 -4.45923805e-01 5.39812386e-01
2.74224162e-01 -2.67667949e-01 5.90179145e-01 5.90032876e-01
-7.25007117e-01 4.87227023e-01 3.78390104e-02 2.32829913e-01
6.52930140e-01 -1.22442293e+00 1.38016045e-01 9.62503970e-01
-8.84471536e-01 5.84995568e-01 4.14074391e-01 -8.38950396e-01
-8.69685352e-01 -1.07212317e+00 8.38739097e-01 4.60352860e-02
8.78138363e-01 -3.23624611e-02 -7.50261068e-01 3.11511219e-01
7.34813660e-02 8.82321969e-02 4.61927712e-01 7.39867613e-02
-3.19585621e-01 -4.64620650e-01 -1.18994308e+00 3.85428369e-01
1.20268440e+00 -4.01709005e-02 -3.24351430e-01 2.12264121e-01
6.49196327e-01 -1.33524209e-01 -1.75522089e+00 8.69533896e-01
1.11067533e+00 -1.00176668e+00 1.08140278e+00 3.39209922e-02
5.61174572e-01 -1.96406856e-01 1.68154508e-01 -1.19991779e+00
-5.06100595e-01 1.02019668e-01 -3.70651960e-01 1.08768964e+00
3.48022170e-02 -7.33485281e-01 6.46749556e-01 4.25753832e-01
-4.95266080e-01 -1.38695931e+00 -1.19795454e+00 -8.39325249e-01
5.38449407e-01 -3.51008385e-01 6.86722279e-01 5.43075919e-01
-3.81658226e-01 -4.02598642e-02 -2.08106995e-01 1.88540906e-01
9.01063442e-01 -5.62780738e-01 6.95234463e-02 -1.58186281e+00
4.77308482e-01 -6.28712416e-01 -6.83757842e-01 -3.44732225e-01
1.34446606e-01 -8.84615839e-01 -3.91231626e-01 -1.92282903e+00
3.56201887e-01 -2.46302679e-01 -7.62494981e-01 6.52294278e-01
-6.85379133e-02 2.46950924e-01 -6.35785982e-02 1.20144486e-01
-4.57541972e-01 7.92705655e-01 1.51888418e+00 -2.51137495e-01
2.87223041e-01 -1.89777285e-01 -5.10293126e-01 6.70044720e-01
1.17105544e+00 -3.16562504e-01 -3.39272618e-01 -3.90860975e-01
-1.41286194e-01 -5.02585322e-02 4.69225854e-01 -1.61640704e+00
3.14576209e-01 3.33547235e-01 1.01249325e+00 -8.31854224e-01
2.85020083e-01 -3.64002168e-01 5.03485277e-02 1.18039036e+00
2.11254880e-01 3.86604488e-01 -9.58702564e-02 3.87830772e-02
1.42270075e-02 -5.91790788e-02 7.26290107e-01 -6.08488172e-02
-6.90081537e-01 9.60514009e-01 -5.79341948e-01 -2.78600872e-01
9.29654241e-01 -2.68011481e-01 -5.74088395e-01 -8.02794024e-02
-8.88129115e-01 4.22735780e-01 1.24663696e-01 3.86707306e-01
7.57234812e-01 -1.60749209e+00 -8.04106891e-01 -1.26205817e-01
5.88637851e-02 -3.92502487e-01 8.49157929e-01 1.59512043e+00
-4.86674160e-01 1.99836329e-01 -8.66598189e-01 -5.22540212e-01
-1.15927815e+00 3.70721698e-01 4.08495992e-01 -3.95222813e-01
-5.89203715e-01 7.68039107e-01 2.41647616e-01 -4.01837170e-01
1.03887938e-01 -2.63216317e-01 -3.43276143e-01 8.59145969e-02
6.83033288e-01 6.55194402e-01 3.20458300e-02 -6.88255370e-01
-5.02773464e-01 5.41544259e-01 -4.09647584e-01 1.29591584e-01
1.58634806e+00 -1.61825582e-01 -4.69000101e-01 1.91761538e-01
9.60452616e-01 -7.00739384e-01 -1.11192143e+00 -1.52487814e-01
-1.59145683e-01 4.48653251e-02 2.76704252e-01 -8.59388530e-01
-1.60403883e+00 9.07255769e-01 1.41265965e+00 -2.05601171e-01
1.42514729e+00 2.55762432e-02 1.32808590e+00 9.11957212e-03
5.54578662e-01 -9.53112602e-01 1.10227287e-01 4.24060166e-01
8.40658963e-01 -7.82858193e-01 2.63228476e-01 -1.38714939e-01
-3.39415550e-01 1.20699370e+00 8.58803511e-01 8.35918728e-03
9.23997998e-01 6.38152286e-03 -9.45616141e-02 -9.38088745e-02
-4.75623965e-01 3.33547383e-03 1.99975908e-01 9.26721752e-01
4.90387917e-01 1.24419428e-01 -8.50925922e-01 1.11452484e+00
-4.57172394e-01 2.73856014e-01 2.14381620e-01 7.63229728e-01
-5.84014535e-01 -9.20785010e-01 -3.13221812e-01 1.05644715e+00
-4.95124549e-01 -5.55702560e-02 2.80060172e-01 5.97640336e-01
6.04958773e-01 4.60030615e-01 -1.01959724e-02 -4.88309413e-01
-6.84054894e-03 5.81321903e-02 5.82594812e-01 1.22631624e-01
-3.76451045e-01 -1.43977001e-01 -6.48625568e-02 -5.04180789e-01
-5.79612851e-01 -7.99607515e-01 -1.33180833e+00 -4.43040162e-01
-7.19547123e-02 -2.01426353e-02 7.20225930e-01 9.09899592e-01
2.56257594e-01 6.42207623e-01 3.74110281e-01 -8.48554850e-01
8.85959715e-02 -1.17339373e+00 -7.24315584e-01 -1.05011150e-01
-8.64202753e-02 -1.16134763e+00 1.44015849e-01 -6.33683577e-02] | [14.09220027923584, -1.5637959241867065] |
b5159bf7-a486-46ff-bdac-164157c99dcf | probabilistic-forecasting-with-temporal | 1906.04397 | null | https://arxiv.org/abs/1906.04397v3 | https://arxiv.org/pdf/1906.04397v3.pdf | Probabilistic Forecasting with Temporal Convolutional Neural Network | We present a probabilistic forecasting framework based on convolutional neural network for multiple related time series forecasting. The framework can be applied to estimate probability density under both parametric and non-parametric settings. More specifically, stacked residual blocks based on dilated causal convolutional nets are constructed to capture the temporal dependencies of the series. Combined with representation learning, our approach is able to learn complex patterns such as seasonality, holiday effects within and across series, and to leverage those patterns for more accurate forecasts, especially when historical data is sparse or unavailable. Extensive empirical studies are performed on several real-world datasets, including datasets from JD.com, China's largest online retailer. The results show that our framework outperforms other state-of-the-art methods in both accuracy and efficiency. | ['Zizhuo Wang', 'Yixiong Chen', 'Yitian Chen', 'Yanfei Kang'] | 2019-06-11 | null | null | null | null | ['probabilistic-time-series-forecasting'] | ['time-series'] | [-3.85928601e-01 -4.89423484e-01 -5.50903440e-01 -7.29234397e-01
-5.93029082e-01 -4.19182420e-01 7.41778851e-01 -1.65682316e-01
1.96566612e-01 7.92339146e-01 7.86246181e-01 -5.60461342e-01
-2.55991340e-01 -1.05832362e+00 -1.11115456e+00 -6.45493269e-01
-7.55730927e-01 2.09101498e-01 -3.07124555e-01 -9.06958506e-02
1.31411359e-01 4.44244295e-01 -1.34470093e+00 6.67763501e-02
6.85303152e-01 1.25315499e+00 2.11431429e-01 1.86625510e-01
-1.80515736e-01 1.23332214e+00 -4.07980323e-01 -2.01067820e-01
-1.09460808e-01 1.97106943e-01 -1.11136101e-01 -2.27495909e-01
-2.45497078e-01 -5.85701585e-01 -6.61752760e-01 5.96500754e-01
1.71205848e-01 6.40300214e-02 8.69110882e-01 -1.10604870e+00
-1.18883586e+00 1.23453116e+00 -7.96464622e-01 4.85733986e-01
-5.97549900e-02 -4.06986028e-02 9.77897525e-01 -7.91696250e-01
7.64478445e-02 1.49707472e+00 9.16254044e-01 -1.70904636e-01
-1.24310124e+00 -1.03405070e+00 5.36226451e-01 1.68692350e-01
-1.15286040e+00 -5.37373573e-02 8.53516996e-01 -4.43940014e-01
8.41411173e-01 -3.29772145e-01 6.00722842e-02 1.51289403e+00
9.04934585e-01 8.44378710e-01 9.42542136e-01 1.58698112e-01
1.37549877e-01 -3.07832211e-01 1.74657539e-01 4.46043201e-02
-5.57151511e-02 4.79673594e-01 -3.49084020e-01 -2.74707854e-01
8.05450022e-01 5.25225580e-01 6.70372471e-02 1.04424737e-01
-1.14457560e+00 1.11802280e+00 5.39837718e-01 1.98960543e-01
-1.05459332e+00 5.19167721e-01 4.79224324e-01 -1.14814535e-01
1.03774095e+00 -5.01891375e-02 -7.93640196e-01 -2.36202478e-02
-9.62272167e-01 3.50745320e-01 9.17426348e-01 7.89534152e-01
4.46543694e-01 5.51834941e-01 -2.97623396e-01 5.76730907e-01
3.34836364e-01 6.93466127e-01 2.92690635e-01 -5.22391200e-01
4.42353398e-01 3.00626785e-01 2.72908807e-01 -1.22372341e+00
-6.82156920e-01 -3.18892598e-01 -1.31850743e+00 -5.43336928e-01
4.19061817e-02 -5.36718130e-01 -1.01069260e+00 1.75464761e+00
-1.39243249e-02 1.06542313e+00 -9.16717574e-02 6.31773889e-01
8.25140715e-01 1.22304916e+00 3.12784255e-01 -3.05344045e-01
1.18676674e+00 -4.26088214e-01 -1.00310886e+00 8.58193561e-02
2.36587420e-01 -5.57955980e-01 4.53598261e-01 2.34139875e-01
-5.47265947e-01 -4.90839869e-01 -6.83523297e-01 2.99527258e-01
-5.03418744e-01 3.20845574e-01 1.06657350e+00 1.10158101e-01
-8.16064596e-01 6.06447816e-01 -8.79746675e-01 3.84178571e-02
3.85590613e-01 9.40673649e-02 -6.50464222e-02 -2.48397529e-01
-1.67886674e+00 6.38835490e-01 3.40594769e-01 5.44848621e-01
-1.08059943e+00 -1.12957525e+00 -8.06711137e-01 4.94676530e-01
-1.73398703e-02 -3.15145582e-01 1.19301963e+00 -4.90512401e-01
-1.35163689e+00 -1.36380747e-01 -8.54189396e-02 -8.02054286e-01
1.17616445e-01 -2.58114904e-01 -9.81449366e-01 -5.64471960e-01
2.66580824e-02 3.79797518e-02 6.60294652e-01 -9.95280683e-01
-6.19194448e-01 -2.55518377e-01 -1.93763033e-01 -3.77018481e-01
-8.72950330e-02 1.65475279e-01 -1.69694021e-01 -1.09543180e+00
-2.59640276e-01 -7.53204942e-01 -3.38956356e-01 -8.15696657e-01
-4.67512310e-01 -6.96462393e-01 7.81916618e-01 -9.57591653e-01
1.42555499e+00 -2.10874200e+00 -2.27441669e-01 2.33189002e-01
-7.81976506e-02 -3.53026986e-01 -8.73463899e-02 7.89622724e-01
-1.38260201e-01 6.79930449e-02 6.07429212e-03 -2.80599833e-01
1.00911312e-01 4.57910508e-01 -1.29944301e+00 4.49735373e-01
3.91163558e-01 1.04500711e+00 -6.49971128e-01 8.34554955e-02
2.18153119e-01 8.46062005e-01 -5.34890080e-03 1.09971084e-01
-2.83577144e-01 4.25613195e-01 -3.94236505e-01 7.24122584e-01
9.11788940e-01 -5.23841381e-01 3.07061523e-01 2.46875845e-02
-1.01777390e-01 5.35497189e-01 -9.99826431e-01 1.18209338e+00
-7.37448275e-01 4.60062385e-01 -4.06309247e-01 -1.06362426e+00
1.18820286e+00 3.17102700e-01 5.36636472e-01 -6.19223952e-01
-6.10137470e-02 3.26353796e-02 -3.99540067e-01 -1.26165792e-01
6.76102221e-01 8.52011293e-02 -4.00900364e-01 3.90104622e-01
3.34400637e-03 5.03397942e-01 -2.53749005e-02 -9.06820819e-02
8.05979848e-01 2.24891409e-01 1.49936348e-01 -3.50852191e-01
8.22118893e-02 -3.48726958e-01 8.45711350e-01 6.81302428e-01
2.84826010e-01 2.57564098e-01 7.62641191e-01 -9.72138822e-01
-7.66277134e-01 -1.07830667e+00 -3.44901532e-01 1.12469387e+00
-1.52685210e-01 -1.43127486e-01 1.36569634e-01 -5.37219346e-01
4.92296278e-01 1.03960085e+00 -1.01273334e+00 4.68503758e-02
-5.02360880e-01 -1.17752528e+00 2.90396661e-01 1.06735432e+00
1.75475463e-01 -1.28565478e+00 1.34005576e-01 6.62415147e-01
-3.80217582e-02 -9.40954745e-01 -2.79254615e-01 2.13882819e-01
-8.51217687e-01 -6.56536222e-01 -5.30182123e-01 -3.74623746e-01
6.83791339e-02 7.96498209e-02 1.32091486e+00 -7.56229937e-01
2.30950028e-01 1.07245237e-01 -1.21629842e-01 -6.11516953e-01
-3.30173448e-02 1.54024094e-01 7.07276240e-02 4.20543067e-02
7.07597792e-01 -8.55715692e-01 -6.47593856e-01 2.78201550e-01
-9.32964206e-01 -4.74843949e-01 5.51747084e-01 9.57588971e-01
5.83155274e-01 4.46230143e-01 1.11233366e+00 -6.78053439e-01
7.21721292e-01 -1.44257414e+00 -8.65012228e-01 1.93035468e-01
-5.91519713e-01 3.52370813e-02 6.43546641e-01 -6.27985537e-01
-1.19489551e+00 -1.70791700e-01 2.75247216e-01 -6.48323953e-01
-8.99170190e-02 1.16134822e+00 3.66907299e-01 8.11848342e-01
1.31227478e-01 2.86116123e-01 -4.40911621e-01 -6.08578742e-01
4.63254720e-01 5.26313961e-01 5.97888112e-01 -3.70661557e-01
3.74621451e-01 4.35912699e-01 -1.86354652e-01 -2.44256943e-01
-8.86689842e-01 -3.10849041e-01 -2.91313380e-01 -4.01250273e-03
5.53583741e-01 -1.38910699e+00 -7.93255687e-01 4.51292604e-01
-1.14263391e+00 -2.36899540e-01 9.57743898e-02 7.67851472e-01
-3.62395108e-01 -3.08769733e-01 -8.64747107e-01 -1.04689682e+00
-3.62375200e-01 -8.00665736e-01 1.13219166e+00 2.68556982e-01
9.68775824e-02 -1.33833694e+00 2.44381204e-01 -2.82769084e-01
6.58941507e-01 3.25976908e-01 9.73901749e-01 -6.57757878e-01
-3.78462970e-01 -3.19818109e-01 -3.95978719e-01 1.17239967e-01
3.11611682e-01 2.28911608e-01 -8.43516767e-01 -1.61526166e-02
-4.07672435e-01 2.02694535e-02 1.12600267e+00 9.79847312e-01
1.58491874e+00 -5.58545232e-01 -4.31850493e-01 5.97893059e-01
1.18181920e+00 2.89583772e-01 7.26216376e-01 1.63055137e-01
7.68369198e-01 5.83256304e-01 3.93609613e-01 9.10954654e-01
1.01094544e+00 3.48555177e-01 4.48880374e-01 1.51855692e-01
4.66602087e-01 -6.35840654e-01 4.50053573e-01 8.13472033e-01
-2.19487641e-02 -4.42135602e-01 -9.93459523e-01 8.04969907e-01
-2.16218400e+00 -1.14359951e+00 -3.97939645e-02 1.85951209e+00
5.95210612e-01 -1.38273641e-01 7.78488591e-02 -5.44187605e-01
8.22886467e-01 4.69137996e-01 -8.43180001e-01 -2.79328614e-01
-3.26117843e-01 1.17248502e-02 7.64563859e-01 -1.32928118e-01
-1.50630867e+00 7.51369417e-01 6.97631359e+00 7.43548930e-01
-1.21365094e+00 -6.85572550e-02 1.14373219e+00 8.09063539e-02
-5.28094828e-01 -2.10834458e-01 -8.66201103e-01 9.06777442e-01
1.57047391e+00 -1.85468674e-01 3.52107495e-01 8.32985044e-01
2.63724029e-01 2.43787497e-01 -7.81669497e-01 7.08083391e-01
-3.04983824e-01 -1.73886347e+00 -1.66073099e-01 1.36805773e-01
1.04261982e+00 3.54396373e-01 4.31859642e-01 8.09309244e-01
1.07544780e+00 -1.29809952e+00 3.09302062e-01 1.09010315e+00
4.97720093e-01 -1.22373593e+00 1.07861376e+00 8.15063417e-02
-1.32691801e+00 -2.63891339e-01 -2.60141045e-01 -7.38982260e-02
4.68884796e-01 1.02050805e+00 -5.67279875e-01 7.20490694e-01
1.18138635e+00 1.29174423e+00 7.46627375e-02 5.92802882e-01
-1.08558245e-01 1.13115156e+00 -3.36669415e-01 2.89143957e-02
3.00932884e-01 -2.38666505e-01 6.51065856e-02 1.00279260e+00
7.27496743e-01 -2.32593209e-01 1.51816621e-01 9.11423743e-01
-2.48314604e-01 -4.81570810e-02 -7.77264476e-01 -1.63500175e-01
6.11540675e-01 1.14685619e+00 -2.34329566e-01 -2.79518723e-01
-5.53731143e-01 4.25493956e-01 2.98911959e-01 6.54522896e-01
-1.07467091e+00 1.06499996e-02 7.43950963e-01 -2.17906997e-01
8.05309951e-01 -3.15695703e-01 -2.27071434e-01 -1.14411080e+00
-2.04370305e-01 -4.50222969e-01 4.90952730e-01 -7.71038890e-01
-2.16917396e+00 3.79187346e-01 1.11995168e-01 -1.05699492e+00
-7.74901211e-01 -4.71725523e-01 -9.05382037e-01 9.50563431e-01
-1.78674877e+00 -1.43452299e+00 2.56609023e-01 5.07826746e-01
3.35788071e-01 -2.36874416e-01 7.23834872e-01 1.25551715e-01
-8.24776053e-01 1.88978687e-01 6.29832804e-01 1.40592158e-01
5.43000937e-01 -1.09914422e+00 6.24749422e-01 7.78430700e-01
3.93975750e-02 7.23988533e-01 4.08401608e-01 -8.31352532e-01
-1.37707412e+00 -1.37644005e+00 8.93835664e-01 -2.45153159e-01
1.22341263e+00 -3.00705552e-01 -9.94806588e-01 9.25034463e-01
1.80420846e-01 6.03837334e-02 6.35840833e-01 6.94466054e-01
-6.26922190e-01 -3.56398106e-01 -7.45521069e-01 2.67296463e-01
3.80785495e-01 -3.51703137e-01 -3.29903454e-01 4.06656325e-01
9.56650972e-01 -2.42316112e-01 -1.29809892e+00 5.58035314e-01
6.50433004e-01 -5.10783970e-01 8.72712553e-01 -6.19586706e-01
8.78590345e-01 -6.31443262e-02 -3.55791658e-01 -1.52114785e+00
-6.64921403e-01 -5.45321047e-01 -6.84121668e-01 1.53230166e+00
4.02182251e-01 -8.58852923e-01 4.19937111e-02 3.94532233e-01
7.96850249e-02 -7.77143657e-01 -9.65341926e-01 -6.42272651e-01
2.97248185e-01 -7.90526092e-01 1.38197505e+00 1.05110407e+00
-4.84386653e-01 7.54147395e-03 -9.64827895e-01 5.37376702e-01
2.35715091e-01 5.76253831e-01 5.17475903e-01 -1.34410334e+00
1.10812619e-01 -3.58591586e-01 -1.61299244e-01 -7.46494830e-01
7.26738095e-01 -3.75571668e-01 -7.72472322e-02 -1.29031706e+00
-1.25103481e-02 -5.00519872e-01 -1.00786352e+00 4.96581823e-01
-1.30174935e-01 -1.28280967e-01 -1.56395033e-01 3.21370870e-01
-2.19126642e-01 9.40290987e-01 6.86068177e-01 -1.23780534e-01
-1.55867040e-01 2.90314913e-01 -7.07528889e-01 4.74238098e-01
1.00364506e+00 -2.65326887e-01 -2.68738091e-01 -2.84344912e-01
3.78965110e-01 2.67617881e-01 3.99239868e-01 -3.95838290e-01
-2.29857564e-02 -3.86771798e-01 6.61125362e-01 -1.22814965e+00
-9.20209736e-02 -7.16115057e-01 2.78366178e-01 -1.33525133e-01
-4.16042119e-01 4.73240286e-01 4.73039955e-01 9.97377694e-01
-4.05071199e-01 5.50217152e-01 -1.04216821e-01 2.33320281e-01
-9.22042251e-01 7.21441746e-01 -2.75724351e-01 -4.24275547e-01
8.28040659e-01 5.28479517e-01 -4.67781663e-01 -7.82591641e-01
-3.07183951e-01 4.54541594e-01 -3.86749089e-01 8.95228982e-01
3.81834626e-01 -1.73913419e+00 -8.49807322e-01 1.02574617e-01
3.22201289e-02 -2.41344497e-01 7.06391394e-01 8.64967227e-01
3.09150033e-02 7.39384115e-01 1.58293158e-01 -5.55813074e-01
-1.56741872e-01 8.54615271e-01 6.90608378e-03 -5.82066119e-01
-5.82536280e-01 4.24606413e-01 4.61160392e-01 -6.03722155e-01
1.41992405e-01 -7.73604691e-01 -5.68048418e-01 2.48783186e-01
7.74180233e-01 2.93684572e-01 -1.80843487e-01 -5.12868464e-01
-3.28476191e-01 8.52650627e-02 -1.49427757e-01 1.97051764e-01
1.88145423e+00 -2.72686213e-01 -4.49677296e-02 1.00059307e+00
1.01635778e+00 -4.56235230e-01 -1.64462519e+00 -4.90199506e-01
7.06568882e-02 -1.35421589e-01 4.38605964e-01 -8.10064316e-01
-1.27286863e+00 5.91255546e-01 4.21868622e-01 5.58663785e-01
1.14613426e+00 -2.42381804e-02 7.29824007e-01 -2.72364113e-02
2.72027731e-01 -7.72164762e-01 -5.44775546e-01 7.11060822e-01
1.01827896e+00 -1.40486646e+00 -3.47574241e-02 1.94386780e-01
-8.19648743e-01 1.20469904e+00 2.88703084e-01 -6.36669874e-01
1.17644119e+00 3.36320370e-01 -1.70910820e-01 -1.03171393e-01
-1.16466892e+00 -2.92307809e-02 6.47720873e-01 4.46827739e-01
6.53228104e-01 5.88576138e-01 5.24061359e-02 9.45705056e-01
-1.39840260e-01 8.45481083e-02 2.91766435e-01 4.52942789e-01
3.58100235e-01 -5.56594431e-01 -2.57499188e-01 7.19225109e-01
-7.00843394e-01 -3.23952317e-01 3.88295591e-01 9.11130607e-01
-3.16363394e-01 8.75309110e-01 6.83807015e-01 -3.00190061e-01
1.14131160e-01 -1.80021599e-02 -2.77606428e-01 -6.08809330e-02
-2.83975571e-01 3.01637083e-01 -1.35896787e-01 -6.77212417e-01
-5.90075254e-01 -8.45829368e-01 -8.64215434e-01 -6.16631925e-01
-1.32425830e-01 -3.35716546e-01 6.52276695e-01 9.01334584e-01
7.58154094e-01 9.11102891e-01 1.07505500e+00 -1.01218021e+00
-5.31156838e-01 -1.39664698e+00 -8.85532498e-01 5.37010431e-02
5.08879840e-01 -9.96210396e-01 -2.95584589e-01 -1.19324520e-01] | [6.906900882720947, 3.1170542240142822] |
6bb99e08-6885-4e37-80b4-386a591ecdbd | provable-benefits-of-general-coverage | 2304.12886 | null | https://arxiv.org/abs/2304.12886v2 | https://arxiv.org/pdf/2304.12886v2.pdf | What can online reinforcement learning with function approximation benefit from general coverage conditions? | In online reinforcement learning (RL), instead of employing standard structural assumptions on Markov decision processes (MDPs), using a certain coverage condition (original from offline RL) is enough to ensure sample-efficient guarantees (Xie et al. 2023). In this work, we focus on this new direction by digging more possible and general coverage conditions, and study the potential and the utility of them in efficient online RL. We identify more concepts, including the $L^p$ variant of concentrability, the density ratio realizability, and trade-off on the partial/rest coverage condition, that can be also beneficial to sample-efficient online RL, achieving improved regret bound. Furthermore, if exploratory offline data are used, under our coverage conditions, both statistically and computationally efficient guarantees can be achieved for online RL. Besides, even though the MDP structure is given, e.g., linear MDP, we elucidate that, good coverage conditions are still beneficial to obtain faster regret bound beyond $\widetilde{O}(\sqrt{T})$ and even a logarithmic order regret. These results provide a good justification for the usage of general coverage conditions in efficient online RL. | ['Volkan Cevher', 'Luca Viano', 'Fanghui Liu'] | 2023-04-25 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [ 7.28318393e-02 4.92517650e-01 -5.80112159e-01 -1.00847870e-01
-9.00801837e-01 -7.90580273e-01 -5.01556098e-02 1.76035836e-01
-5.15307665e-01 1.31939852e+00 -2.11193025e-01 -6.31392956e-01
-7.28433311e-01 -9.10599768e-01 -8.15683782e-01 -9.28613245e-01
-4.29577440e-01 3.43012214e-01 1.22729670e-02 -8.91248807e-02
2.42336392e-01 5.33003747e-01 -1.59826839e+00 -4.55287248e-01
1.04106569e+00 1.36782360e+00 2.09935322e-01 5.45206904e-01
-2.63583004e-01 7.32097805e-01 -4.42563117e-01 -4.20049131e-01
5.76277971e-01 -6.66183949e-01 -8.85463417e-01 2.12362543e-01
-3.26604187e-01 -6.29426479e-01 -1.76725924e-01 1.03085196e+00
4.29463804e-01 2.53667623e-01 5.15202522e-01 -1.24941516e+00
-8.42030421e-02 8.97392035e-01 -6.47705078e-01 2.61898637e-01
4.54192102e-01 1.82328373e-01 1.04494631e+00 3.69321220e-02
4.96081740e-01 9.46503282e-01 3.05581361e-01 5.03322423e-01
-9.52111125e-01 -5.43162882e-01 5.23947537e-01 1.47552475e-01
-9.41357613e-01 -2.96761483e-01 5.30521333e-01 6.07266277e-03
5.29681027e-01 2.21616805e-01 7.96189249e-01 8.36181402e-01
2.54373014e-01 1.15678716e+00 1.33439136e+00 -4.56180602e-01
6.79049373e-01 3.84301580e-02 -8.20724107e-03 5.84123075e-01
5.04338443e-01 3.45090508e-01 -3.88592601e-01 -2.58205179e-03
8.88530195e-01 -5.30296676e-02 -2.69398570e-01 -5.17483771e-01
-7.56649971e-01 8.79578471e-01 3.47211622e-02 1.36733383e-01
-3.53332400e-01 2.07842484e-01 3.51440817e-01 6.95354939e-01
1.89766645e-01 3.49302262e-01 -2.60321140e-01 -6.09870553e-01
-8.22807491e-01 2.63874501e-01 9.74808753e-01 1.33920956e+00
6.58846974e-01 1.09416619e-02 -3.37404370e-01 2.46936157e-01
-1.35925692e-02 5.20288944e-01 8.96356651e-04 -1.25830150e+00
8.43356550e-01 1.88457713e-01 7.96977401e-01 -5.44016957e-01
-3.51074457e-01 -7.26922929e-01 -5.53359866e-01 5.21283448e-02
8.03327739e-01 -4.98752952e-01 -2.66171873e-01 1.78106737e+00
3.85178745e-01 -3.52717608e-01 1.77193329e-01 7.40510166e-01
-2.58746505e-01 5.03415227e-01 -3.21394116e-01 -9.38409448e-01
8.69435251e-01 -5.01760900e-01 -8.85052443e-01 -1.39787653e-02
7.19603419e-01 -1.66016608e-01 1.23776329e+00 5.93555927e-01
-1.35360527e+00 2.20715025e-04 -1.09185541e+00 4.42950010e-01
8.68044868e-02 -2.23948926e-01 9.61320281e-01 9.60421205e-01
-7.79336512e-01 8.65171492e-01 -8.59500885e-01 -2.10807160e-01
3.68638009e-01 1.83802783e-01 1.62992448e-01 -2.01553434e-01
-9.33051586e-01 6.39269829e-01 3.37566763e-01 1.09891012e-01
-1.16645634e+00 -4.15007859e-01 -4.93753970e-01 1.23053819e-01
1.05108285e+00 -3.19203258e-01 1.34066248e+00 -7.49904990e-01
-1.76050591e+00 3.46055418e-01 -3.94708328e-02 -7.20540404e-01
1.10569060e+00 -1.00561976e-01 6.66533113e-02 4.48110610e-01
-2.52417654e-01 7.23015144e-02 6.18609667e-01 -1.06428874e+00
-8.90749276e-01 -4.10443604e-01 8.19315851e-01 3.87366325e-01
-2.21142754e-01 -2.26100951e-01 9.47350115e-02 8.51727463e-03
1.94699550e-03 -7.28436470e-01 -5.34948409e-01 -1.14738263e-01
-7.77823254e-02 -3.36035341e-01 -2.79181916e-02 -4.20386076e-01
1.06362891e+00 -2.01158953e+00 -3.51366140e-02 2.65957177e-01
-9.26478803e-02 -2.39539117e-01 2.29107868e-02 7.68565178e-01
3.69857490e-01 1.07331954e-01 -1.08656198e-01 -1.13673039e-01
3.33522558e-01 3.44223887e-01 -3.31577003e-01 7.48309135e-01
-2.04314366e-01 6.68622017e-01 -9.57991123e-01 -4.04710144e-01
8.66243243e-02 -3.26177329e-01 -5.52285433e-01 4.83160205e-02
-3.61133456e-01 5.80944479e-01 -7.03502119e-01 6.14916444e-01
5.02601147e-01 -1.35643512e-01 3.13110232e-01 3.73655498e-01
-1.73761651e-01 1.49955750e-01 -1.45337403e+00 1.28361762e+00
-7.05582023e-01 1.35357097e-01 4.00568604e-01 -1.17677724e+00
6.33994579e-01 3.33765931e-02 5.94253421e-01 -7.43349195e-01
2.43808091e-01 1.53953105e-01 -1.11679211e-01 -4.50153559e-01
3.05664301e-01 -3.54896545e-01 -5.52472323e-02 5.99771202e-01
-1.48941264e-01 1.53457209e-01 3.95340532e-01 -1.91073313e-01
1.16467214e+00 3.05198669e-01 3.34074914e-01 -5.38271070e-01
2.24238470e-01 -9.63463709e-02 6.72230780e-01 1.13255656e+00
-4.71761495e-01 -3.01983822e-02 1.03814995e+00 -4.27363161e-03
-9.48355854e-01 -8.46934259e-01 -2.86138547e-03 1.04481459e+00
3.45240176e-01 -8.96961614e-03 -4.94346380e-01 -6.74197316e-01
-8.25005490e-03 8.64998221e-01 -6.57175720e-01 9.56405252e-02
-1.75960287e-01 -6.96940660e-01 4.19269383e-01 4.28996503e-01
5.26161075e-01 -6.62053287e-01 -9.39003050e-01 3.17957968e-01
-1.46053001e-01 -8.59628201e-01 -1.16444841e-01 5.97738326e-01
-9.49952900e-01 -1.12323833e+00 -7.35502601e-01 -1.84843257e-01
5.49845576e-01 2.95051813e-01 6.67184055e-01 -3.36207241e-01
1.97715074e-01 7.02292383e-01 -6.22285426e-01 -3.62918854e-01
-1.57127097e-01 -6.06334880e-02 1.56913511e-02 -2.17827693e-01
-2.83313453e-01 -6.52966797e-01 -7.51543105e-01 2.97857076e-01
-8.13861430e-01 -1.49945915e-01 7.41609037e-01 6.17793739e-01
6.44214988e-01 3.11072230e-01 7.71464288e-01 -6.71444893e-01
5.08271337e-01 -2.84327090e-01 -9.26055670e-01 4.09655601e-01
-8.31039965e-01 3.47304016e-01 9.27068055e-01 -3.64922434e-01
-1.00688052e+00 -2.53413439e-01 -2.20533818e-01 -4.01726514e-01
8.72859359e-02 4.08783972e-01 -2.05717355e-01 3.44110020e-02
3.59733373e-01 6.14251435e-01 1.48763210e-01 -1.94641754e-01
2.17872351e-01 3.00911397e-01 -1.94112182e-01 -8.89698446e-01
5.73299468e-01 5.62139153e-01 3.31194639e-01 -5.83088338e-01
-9.65038478e-01 -1.46477953e-01 -2.97815979e-01 -3.26039374e-01
3.53368610e-01 -5.26325524e-01 -1.27505445e+00 -1.11822501e-01
-4.58164901e-01 -6.59415722e-01 -7.56191194e-01 4.79972750e-01
-1.26111686e+00 5.66344917e-01 -5.35061061e-01 -1.82523131e+00
8.54545608e-02 -8.63738835e-01 5.49855232e-01 2.04519123e-01
4.66494381e-01 -8.23050737e-01 -2.04763845e-01 1.80753410e-01
1.71823874e-01 3.35429907e-01 7.01347530e-01 -3.64045501e-01
-6.22932613e-01 1.09461725e-01 -5.45677058e-02 3.12226504e-01
4.98113669e-02 -2.85498708e-01 -4.76949602e-01 -6.93252206e-01
1.70045450e-01 -4.20973659e-01 6.48697376e-01 4.25478041e-01
1.38878489e+00 -8.41030657e-01 -6.27350807e-02 3.84647340e-01
1.70316899e+00 4.80672270e-01 5.60419321e-01 3.83142501e-01
-3.30500342e-02 7.23468721e-01 1.23983479e+00 1.16783381e+00
2.44463563e-01 2.02463865e-01 6.53335214e-01 4.03955251e-01
5.70262432e-01 -4.23545331e-01 5.73557913e-01 3.72709811e-01
-3.23683560e-01 -2.08773166e-01 -4.69720393e-01 4.66465443e-01
-1.72818387e+00 -9.01880324e-01 2.58294374e-01 2.81613731e+00
8.99254322e-01 2.25592881e-01 4.57119554e-01 2.73853809e-01
7.09251881e-01 8.03652778e-02 -8.86510909e-01 -6.06714964e-01
-8.81180167e-02 1.36895791e-01 1.02954936e+00 4.47156966e-01
-4.92004275e-01 5.46199739e-01 6.30994749e+00 1.28644276e+00
-7.55986691e-01 1.74567938e-01 6.64987803e-01 -3.59593451e-01
-4.59814310e-01 2.46312991e-01 -8.99706602e-01 4.24926847e-01
9.54223514e-01 -3.22734803e-01 7.64576137e-01 1.03504622e+00
4.19651598e-01 -5.29170275e-01 -1.02389801e+00 8.89350116e-01
-3.65441680e-01 -8.86821806e-01 -3.39832753e-01 5.59665680e-01
5.81479788e-01 -6.21008337e-01 -1.80383876e-01 5.50230682e-01
4.16414976e-01 -8.90480220e-01 7.17469871e-01 3.91727149e-01
7.96638370e-01 -1.37229395e+00 6.85833156e-01 7.46494114e-01
-9.30399179e-01 -4.71762538e-01 -5.55890620e-01 -2.52049059e-01
1.28128260e-01 6.85536504e-01 -4.44950253e-01 1.06414700e+00
4.58537042e-01 4.18222398e-01 1.27480283e-01 9.17250633e-01
-2.37595230e-01 5.05334616e-01 -5.48400283e-01 -4.74826962e-01
3.61766666e-01 -4.30747539e-01 4.16755468e-01 7.13768780e-01
2.96094656e-01 1.20289803e-01 1.12243809e-01 5.70745885e-01
3.74335140e-01 9.10904258e-02 -3.91678482e-01 -2.37713933e-01
4.23189849e-01 9.57366645e-01 -8.54179800e-01 -7.66227115e-03
-8.72579664e-02 8.58113229e-01 3.04635346e-01 2.68789142e-01
-1.18648887e+00 -2.47110456e-01 4.83108133e-01 2.22693589e-02
5.28426945e-01 -4.13826138e-01 -2.14631915e-01 -9.29500043e-01
2.24671289e-01 -6.55578852e-01 4.43977863e-01 -7.17909858e-02
-1.20414758e+00 -4.08035070e-02 1.49685636e-01 -1.33957732e+00
-1.89378321e-01 -3.86059105e-01 -3.15149158e-01 3.55488300e-01
-1.78015685e+00 -5.33133268e-01 2.00649455e-01 6.85439944e-01
3.75036359e-01 3.83019418e-01 4.05850202e-01 8.04967582e-02
-5.56004703e-01 8.15101266e-01 5.77989519e-01 -5.18708587e-01
2.19847053e-01 -1.25532877e+00 -5.56341290e-01 6.87242925e-01
-3.03034693e-01 3.41376066e-01 9.45235312e-01 -4.11873937e-01
-1.81964302e+00 -8.20054114e-01 9.07418132e-02 4.46072333e-02
7.79150188e-01 -2.10264608e-01 -2.70762712e-01 5.23008466e-01
-1.07449986e-01 -4.10283238e-01 5.88427365e-01 2.50043213e-01
2.38389939e-01 -3.33686203e-01 -1.34219098e+00 7.75778472e-01
1.45774686e+00 -7.48798773e-02 -2.31739506e-01 3.07940006e-01
6.84051394e-01 -3.01138431e-01 -1.01843405e+00 3.08064729e-01
5.68272710e-01 -1.20019007e+00 5.30151665e-01 -5.56678474e-01
3.09833527e-01 1.65865168e-01 -2.43400618e-01 -1.03219175e+00
1.34785861e-01 -1.01972306e+00 -3.80445689e-01 1.10456824e+00
3.62575561e-01 -8.74723971e-01 8.07894707e-01 6.16372883e-01
-4.21013171e-03 -1.17178845e+00 -1.35424209e+00 -1.43384302e+00
2.69046605e-01 -5.65105498e-01 4.26732391e-01 5.30436575e-01
2.28791341e-01 -2.50189960e-01 -4.83464599e-01 3.67968194e-02
6.04207754e-01 3.99104059e-01 5.97258747e-01 -7.58512020e-01
-7.99713731e-01 -2.71054238e-01 2.49766916e-01 -1.53838658e+00
-4.17613126e-02 -4.30255264e-01 2.04675198e-02 -1.53336298e+00
2.26893052e-02 -9.01398182e-01 -3.28490525e-01 3.66016448e-01
2.40951955e-01 -5.53890944e-01 4.60272640e-01 1.38599515e-01
-9.15344238e-01 8.89169157e-01 1.65671349e+00 3.38555843e-01
-2.13764220e-01 4.60429758e-01 -7.08351195e-01 3.86033982e-01
1.01931834e+00 -2.57248074e-01 -5.98539710e-01 2.34700181e-02
4.97376472e-01 8.83732557e-01 -8.83028954e-02 -9.04453456e-01
-3.29237878e-02 -7.82705605e-01 -6.93899691e-02 -5.82919478e-01
2.30251700e-01 -8.73169065e-01 -5.91199920e-02 7.81273067e-01
-4.69288498e-01 -3.13021511e-01 -5.02241701e-02 9.54160631e-01
3.62662256e-01 -7.31720746e-01 5.35550237e-01 -1.76830426e-01
-2.04590291e-01 3.06753248e-01 -4.17209923e-01 5.59169315e-02
1.25933063e+00 -4.45868194e-01 -6.78188652e-02 -6.52343631e-01
-6.12004519e-01 5.00229418e-01 2.42680669e-01 -2.66422659e-01
2.11323425e-01 -8.68250668e-01 -2.82103360e-01 -3.06867689e-01
-3.26986730e-01 -7.05912039e-02 4.27889973e-01 1.25277531e+00
-2.30685011e-01 4.46053088e-01 -1.89000532e-01 -2.84474730e-01
-6.68730795e-01 7.39246845e-01 2.21844569e-01 -5.84955037e-01
-6.10856116e-01 4.53222066e-01 -1.69984564e-01 1.15233690e-01
4.68124211e-01 -6.12771809e-01 8.82653147e-02 2.67922389e-03
2.99353898e-01 6.83513224e-01 -1.96394935e-01 4.60866570e-01
-1.53897554e-01 1.11401811e-01 2.86718398e-01 -3.73111546e-01
1.22359276e+00 -6.61378682e-01 3.85180414e-01 6.49684906e-01
6.27926171e-01 2.51493901e-01 -1.80080819e+00 7.34279398e-03
-1.73755586e-01 -4.21793520e-01 -2.69982070e-01 -6.60567641e-01
-9.72418368e-01 6.92103565e-01 4.04350221e-01 6.71168983e-01
1.18722808e+00 -9.90768746e-02 5.71292818e-01 5.24212897e-01
1.18085921e+00 -1.26518500e+00 -2.08610281e-01 4.20149297e-01
6.51770055e-01 -9.17606294e-01 -5.98343313e-02 -3.58257711e-01
-5.49112856e-01 1.09348583e+00 2.83343285e-01 -3.02444816e-01
2.27019653e-01 2.34278470e-01 -7.48942196e-01 3.02300811e-01
-7.21541286e-01 -6.72904611e-01 -6.04014158e-01 4.18129265e-01
6.77184958e-04 2.16452479e-01 -7.93736279e-01 7.26634562e-01
-2.19349533e-01 -1.48952417e-02 6.62508368e-01 9.85289156e-01
-6.56648993e-01 -1.03715289e+00 -2.42498279e-01 5.52869022e-01
-5.91919184e-01 1.94759145e-01 6.23818934e-02 9.64621067e-01
-2.43029654e-01 1.19258738e+00 -1.41101614e-01 -2.35435683e-02
-1.09905787e-02 -1.50302872e-01 8.97593439e-01 -2.01590165e-01
-1.43822581e-01 -1.36644933e-02 3.57575238e-01 -6.47978246e-01
-4.99824584e-01 -6.93298817e-01 -1.11952090e+00 -5.64133704e-01
-5.10717690e-01 3.39245081e-01 5.38516521e-01 1.27107811e+00
1.14324644e-01 2.68536210e-01 8.96414101e-01 -3.71726751e-01
-1.17941725e+00 -8.05957496e-01 -1.05830276e+00 -3.39710206e-01
1.37421846e-01 -7.44544387e-01 -6.65596604e-01 -5.25308311e-01] | [4.3876190185546875, 2.8723256587982178] |
a917c8a9-0798-4b81-975c-ee67e46cb018 | malware-traffic-classification-evaluation-of | 2010.11627 | null | https://arxiv.org/abs/2010.11627v2 | https://arxiv.org/pdf/2010.11627v2.pdf | Malware Traffic Classification: Evaluation of Algorithms and an Automated Ground-truth Generation Pipeline | Identifying threats in a network traffic flow which is encrypted is uniquely challenging. On one hand it is extremely difficult to simply decrypt the traffic due to modern encryption algorithms. On the other hand, passing such an encrypted stream through pattern matching algorithms is useless because encryption ensures there aren't any. Moreover, evaluating such models is also difficult due to lack of labeled benign and malware datasets. Other approaches have tried to tackle this problem by employing observable meta-data gathered from the flow. We try to augment this approach by extending it to a semi-supervised malware classification pipeline using these observable meta-data. To this end, we explore and test different kind of clustering approaches which make use of unique and diverse set of features extracted from this observable meta-data. We also, propose an automated packet data-labeling pipeline to generate ground-truth data which can serve as a base-line to evaluate the classifiers mentioned above in particular, or any other detection model in general. | ['Juan Caballero', 'Syed Muhammad Kumail Raza'] | 2020-10-22 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [ 2.90360481e-01 -1.95705369e-01 -7.30199516e-02 -2.99516976e-01
-3.74965966e-01 -8.24375570e-01 9.08358455e-01 5.87798417e-01
-3.31592113e-01 5.53566039e-01 -2.81557024e-01 -6.76913619e-01
-2.05765665e-01 -1.18146980e+00 -2.74911225e-01 -6.38188064e-01
-2.58855104e-01 6.48289621e-01 5.51333606e-01 -1.70323923e-01
3.15131813e-01 8.05861890e-01 -1.81441212e+00 6.69626951e-01
4.32548314e-01 9.94798541e-01 -1.27382517e-01 6.54425383e-01
-4.07954603e-01 8.04225206e-01 -7.19962239e-01 -7.70327389e-01
4.54948634e-01 -3.33394915e-01 -8.34991992e-01 2.90328879e-02
3.09830934e-01 -2.04879999e-01 -1.43857554e-01 1.25994766e+00
-1.80612966e-01 -5.01039565e-01 5.92022181e-01 -1.71848238e+00
3.50966454e-01 5.52153289e-01 -1.50231853e-01 1.86112508e-01
1.94257081e-01 3.60618919e-01 9.20764327e-01 1.70735672e-01
7.76885331e-01 1.02918279e+00 6.18525803e-01 4.96546745e-01
-1.33208513e+00 -5.34259379e-01 -2.51383275e-01 3.38505089e-01
-8.32871556e-01 -2.27532372e-01 7.59216785e-01 -5.49518704e-01
5.58782399e-01 5.75135231e-01 5.16153514e-01 1.39350033e+00
-9.30769145e-02 3.59739184e-01 1.42644870e+00 -2.78428257e-01
2.27417737e-01 7.11234629e-01 3.80606860e-01 6.66116238e-01
6.27718270e-01 3.78591150e-01 -5.78052476e-02 -5.62619746e-01
-8.58082250e-03 1.08145669e-01 -2.09471107e-01 -3.38645071e-01
-9.49885905e-01 6.71153665e-01 -2.71524396e-02 5.52337229e-01
-2.86820829e-01 -1.63605437e-01 8.13920557e-01 8.00574958e-01
3.57462645e-01 5.70502281e-01 -5.29757559e-01 -2.60925442e-01
-9.64124918e-01 1.18455574e-01 1.24823654e+00 4.58462566e-01
1.10378671e+00 -1.67354092e-01 2.04827145e-01 1.74337253e-01
1.67148292e-01 2.42879853e-01 1.01032414e-01 -6.16680622e-01
5.37845314e-01 7.91926146e-01 -2.17298806e-01 -1.23274291e+00
-2.18145669e-01 -3.04969937e-01 -5.45452774e-01 5.49630642e-01
8.50933909e-01 6.49890769e-03 -4.68946248e-01 1.46999025e+00
3.19272727e-01 3.54130834e-01 1.61777029e-03 5.37398219e-01
2.32331619e-01 3.77396643e-01 -1.23037286e-01 -2.56387532e-01
1.51069367e+00 -4.78340179e-01 -3.72590959e-01 4.64797139e-01
7.15975940e-01 -5.15748084e-01 7.41800487e-01 5.97111881e-01
-2.61328369e-01 -3.45644951e-01 -9.40857112e-01 5.44656813e-01
-8.39247823e-01 -3.01058739e-01 4.67042506e-01 1.27916515e+00
-7.38330245e-01 8.91079843e-01 -6.99817657e-01 -4.08346564e-01
4.07538384e-01 3.82693410e-01 -6.18474782e-01 1.39124945e-01
-8.78567040e-01 5.86904705e-01 5.86884499e-01 -3.67641509e-01
-9.60688353e-01 -6.33515954e-01 -5.35117388e-01 1.04416668e-01
5.57873905e-01 -3.19841683e-01 8.75413001e-01 -1.02862251e+00
-1.12952197e+00 9.38822806e-01 4.05373722e-02 -6.53841078e-01
7.14179397e-01 3.72274190e-01 -5.26852190e-01 3.36028814e-01
-2.72262841e-01 1.67297050e-01 1.14591253e+00 -1.45953202e+00
-7.39548922e-01 -4.67237175e-01 2.33568892e-01 -7.81117141e-01
-6.89275205e-01 3.09682369e-01 1.59722522e-01 -4.50867176e-01
-4.80964243e-01 -9.38504577e-01 -1.54880762e-01 -3.41615736e-01
-6.79659009e-01 9.68736857e-02 1.53938413e+00 -4.16168541e-01
1.04727042e+00 -1.92128479e+00 -3.99384409e-01 6.28738701e-01
4.89250690e-01 7.47134328e-01 -1.33534983e-01 5.52416503e-01
-1.54728577e-01 4.89456952e-01 -2.04594284e-01 -4.75948483e-01
-2.65471339e-02 1.87520847e-01 -6.88623846e-01 2.96251357e-01
4.48173165e-01 4.97269660e-01 -9.49537992e-01 -4.86757725e-01
5.12339771e-01 3.96344841e-01 -4.66825992e-01 7.50627443e-02
-4.81662899e-01 6.80172086e-01 -3.88436139e-01 3.20872545e-01
7.37244427e-01 1.21434435e-01 3.17673832e-01 -8.72283950e-02
-1.77444652e-01 4.82964098e-01 -1.10842085e+00 1.05841172e+00
-4.94046479e-01 7.51959682e-01 -4.48985584e-03 -1.23804116e+00
9.48757708e-01 4.04974490e-01 8.41841996e-01 -2.99507797e-01
3.75848621e-01 3.37224156e-01 6.63672984e-02 -4.06310648e-01
2.09143803e-01 -1.73109416e-02 1.00146249e-01 8.12301099e-01
4.34488244e-02 2.46008903e-01 3.81590754e-01 -5.19014001e-02
1.76994944e+00 -2.52283156e-01 7.99948052e-02 -2.34208822e-01
1.18087661e+00 2.01840177e-01 4.83067542e-01 7.88890898e-01
-1.24582037e-01 2.77626038e-01 1.02554238e+00 -7.62532711e-01
-1.08321691e+00 -8.62838686e-01 1.69367641e-02 3.43173385e-01
-1.24336347e-01 -9.09207880e-01 -8.99622500e-01 -1.34510946e+00
-7.14974403e-02 3.47740382e-01 -4.88812476e-01 -9.28074270e-02
-5.90174496e-01 -6.56555653e-01 8.36076379e-01 -5.25117442e-02
5.44719815e-01 -1.07736957e+00 -8.31302524e-01 1.06668152e-01
-9.00105387e-02 -1.42802072e+00 3.94456714e-01 1.91799685e-01
-7.60042310e-01 -1.55703449e+00 2.79751569e-01 -2.31874436e-01
5.61222851e-01 1.07132241e-01 1.10117197e+00 5.61496496e-01
-5.43211818e-01 1.79158613e-01 -6.53515995e-01 -5.25068879e-01
-9.92806613e-01 1.51733667e-01 -4.19539213e-02 5.49415290e-01
6.44812346e-01 -7.80921638e-01 -1.81120411e-01 3.49094808e-01
-1.10422564e+00 -5.22458017e-01 4.17731017e-01 3.93111974e-01
1.25528693e-01 6.71389878e-01 3.32539439e-01 -1.10869980e+00
5.24889052e-01 -5.96020043e-01 -8.83633494e-01 2.67893225e-01
-3.15501392e-01 4.17241268e-02 1.20124078e+00 -3.28571409e-01
-6.85510874e-01 1.05721140e-02 -3.11641097e-01 -4.41051662e-01
-6.80937648e-01 4.22452390e-02 -4.64058518e-01 -1.26951694e-01
5.61261594e-01 9.65387821e-02 1.58952042e-01 -3.83819818e-01
1.00328419e-02 9.90596652e-01 1.23366788e-01 -4.93521035e-01
1.41042447e+00 8.77381921e-01 3.91815066e-01 -1.03827918e+00
-3.81426364e-01 -4.93440717e-01 -6.57067657e-01 -1.51627377e-01
6.56516612e-01 -9.75743011e-02 -1.00396192e+00 3.64507586e-01
-1.08778775e+00 -1.05856970e-01 -3.27164590e-01 3.35381895e-01
-3.44035268e-01 8.39375973e-01 -4.08061892e-01 -9.06653821e-01
-8.12706500e-02 -1.27845502e+00 9.85785902e-01 -3.94241333e-01
-2.42909729e-01 -9.78321016e-01 1.61397472e-01 2.73389697e-01
3.62333864e-01 3.31826031e-01 9.10360932e-01 -1.08802485e+00
-7.23271310e-01 -4.78280723e-01 -1.92672566e-01 5.54174602e-01
3.56745154e-01 3.58640701e-01 -1.12236714e+00 -2.18812093e-01
3.21946502e-01 -4.50008735e-02 7.30305672e-01 -4.17030454e-01
1.27355075e+00 -3.48343372e-01 -2.84940034e-01 5.21066964e-01
1.45975566e+00 -2.58706175e-02 6.69891536e-01 3.92982841e-01
3.90195489e-01 1.12987828e+00 4.69588697e-01 3.12221497e-01
5.12958923e-03 7.84652650e-01 5.54509103e-01 2.49631748e-01
-5.90011757e-03 -7.46255741e-02 3.48338485e-01 4.00880545e-01
4.88783158e-02 -1.63838699e-01 -9.22419608e-01 1.78136155e-01
-1.46748614e+00 -1.52304232e+00 -6.13348484e-01 2.27620292e+00
3.07049632e-01 4.76380676e-01 3.80611628e-01 9.15908396e-01
5.43748975e-01 6.54992368e-03 2.70824611e-01 -4.53641683e-01
9.59312618e-02 3.34359169e-01 4.79949325e-01 3.13847899e-01
-1.26365829e+00 4.53445107e-01 4.85348749e+00 8.76540959e-01
-1.43615627e+00 1.57950565e-01 3.79936695e-01 4.12711352e-01
-2.79551655e-01 4.26211417e-01 -7.97242641e-01 8.83261204e-01
1.23198795e+00 1.87818199e-01 4.82218802e-01 7.57855356e-01
1.30764663e-01 2.30799709e-02 -1.20298064e+00 8.15284431e-01
-1.03021003e-01 -1.22396433e+00 3.78454067e-02 5.27708411e-01
2.50639111e-01 -1.65455356e-01 -1.66005284e-01 2.00559378e-01
8.01702365e-02 -7.37855434e-01 4.39203888e-01 3.48936349e-01
4.95478421e-01 -5.94153643e-01 6.91409171e-01 5.80069661e-01
-1.17902279e+00 -3.51631701e-01 -1.75256386e-01 9.35918987e-02
-1.62416492e-02 7.71283209e-01 -9.80539203e-01 7.83944130e-01
4.48710829e-01 4.44986522e-01 -7.74028122e-01 1.07447731e+00
-9.83220711e-02 8.61216366e-01 -4.43122268e-01 1.38636202e-01
6.36347681e-02 -1.90921411e-01 7.71438062e-01 1.36240709e+00
1.87871948e-01 -5.27160525e-01 2.83370644e-01 8.46906483e-01
1.03066027e-01 2.63874251e-02 -9.65214670e-01 -1.55669525e-01
3.13252807e-01 1.68272877e+00 -1.08043647e+00 -3.14216107e-01
-3.46698225e-01 5.98096430e-01 3.82263511e-02 -1.06268860e-01
-7.85722911e-01 -3.52074176e-01 8.50626230e-01 3.04128975e-01
7.45223984e-02 -2.40485281e-01 -6.17720634e-02 -1.31716681e+00
6.34534284e-02 -1.10635114e+00 3.32880199e-01 -3.60808261e-02
-1.34559703e+00 8.14326346e-01 3.79497670e-02 -1.46327341e+00
-3.30104172e-01 -8.28934729e-01 -7.69779205e-01 4.10016090e-01
-1.51308894e+00 -1.19591641e+00 -4.29124087e-01 6.67318463e-01
4.69209440e-02 -4.31574613e-01 7.61774242e-01 4.79316920e-01
-5.47344983e-01 3.66817683e-01 -2.77827889e-01 3.52977306e-01
3.27196807e-01 -1.01991868e+00 2.91807503e-01 1.00256979e+00
5.19565701e-01 5.30053020e-01 7.27077603e-01 -6.82347059e-01
-1.31775558e+00 -1.23572743e+00 6.92436039e-01 -6.71799421e-01
8.92383754e-01 -6.70566082e-01 -8.77466917e-01 4.70926642e-01
-2.85431258e-02 1.66105963e-02 6.07794523e-01 -9.14004967e-02
-6.82699502e-01 -3.57127339e-01 -1.37001669e+00 2.33605534e-01
9.52465057e-01 -7.03028381e-01 -3.38224202e-01 2.11232737e-01
3.51724327e-01 4.18109834e-01 -7.86788225e-01 5.01938701e-01
2.03806520e-01 -1.43025696e+00 7.42342353e-01 -8.55411649e-01
1.14693604e-01 -5.04843116e-01 -1.05902627e-01 -9.32057679e-01
3.30037594e-01 -7.14270592e-01 -1.00033924e-01 1.65858376e+00
2.11413324e-01 -1.00871742e+00 1.15637040e+00 1.33077204e-01
2.71171689e-01 -3.88369530e-01 -1.02198803e+00 -1.12058949e+00
-2.79261768e-01 -8.91793966e-01 9.44609106e-01 1.17002726e+00
-3.06449775e-02 -9.12496150e-02 -7.09995478e-02 1.44568041e-01
8.81039679e-01 1.44104391e-01 1.07559049e+00 -1.53238606e+00
-4.04659748e-01 -4.95092928e-01 -1.01284051e+00 -2.17326298e-01
5.64191461e-01 -9.34989333e-01 -4.64188099e-01 -8.32253456e-01
-4.31068651e-02 -9.92838800e-01 -2.06789114e-02 3.41651022e-01
2.99674720e-01 3.35835278e-01 3.85636330e-01 2.01843649e-01
-3.08174729e-01 -1.71428267e-02 4.10108268e-01 -8.66813436e-02
1.04841024e-01 1.47915691e-01 -2.80930907e-01 5.45310259e-01
1.06186879e+00 -7.31292546e-01 -4.49051172e-01 1.21504895e-01
1.38341010e-01 -1.49720624e-01 8.00228715e-01 -1.27546024e+00
-3.92826311e-02 -4.36342061e-02 -1.53533041e-01 -3.19972873e-01
2.32431963e-01 -1.30012119e+00 2.04692468e-01 7.51546681e-01
6.52915463e-02 1.18546464e-01 -1.59290463e-01 5.08835495e-01
-3.47692162e-01 -5.41984558e-01 6.63942933e-01 -2.88459271e-01
-4.90186006e-01 3.01318228e-01 -5.10502815e-01 -2.07168050e-02
1.38904965e+00 -3.89761031e-01 -4.75918978e-01 -1.91177949e-01
-3.14531744e-01 -2.35807180e-01 8.46855581e-01 4.24497932e-01
2.51025945e-01 -8.73031199e-01 -5.22444904e-01 4.89503920e-01
8.84568989e-02 -7.13975430e-01 -9.65769663e-02 9.06439364e-01
-7.20531762e-01 4.24966753e-01 -4.45786387e-01 -6.18187785e-01
-1.57518995e+00 1.06599569e+00 2.58376330e-01 -5.84874690e-01
-3.11342061e-01 1.00154683e-01 -4.66941476e-01 -5.63421786e-01
3.69542055e-02 -6.42405078e-02 -1.50865257e-01 1.96636721e-01
6.14278972e-01 3.91011506e-01 3.77323955e-01 -7.41530538e-01
-3.26889068e-01 2.91327536e-01 1.29245088e-01 1.48862889e-02
1.18209279e+00 1.84262186e-01 -4.66085941e-01 3.83491255e-02
1.49080312e+00 4.05449390e-01 -5.79113662e-01 1.85545266e-01
5.95083773e-01 -8.73537719e-01 -4.73711818e-01 -1.57378912e-01
-1.15838051e+00 1.11519980e+00 4.88972813e-01 9.21828926e-01
1.09647167e+00 -4.08209115e-01 6.28862500e-01 4.94969845e-01
7.51701236e-01 -6.46871507e-01 -8.22160989e-02 2.32950494e-01
1.61338765e-02 -1.12448132e+00 -2.74125814e-01 -6.97085798e-01
-1.97170839e-01 1.49830151e+00 3.69224459e-01 -2.44382229e-02
7.57058024e-01 4.13801849e-01 7.08034933e-02 -1.03951499e-01
-8.47166955e-01 -3.94899249e-01 -2.25847840e-01 7.70653963e-01
-3.72331357e-03 -1.35092497e-01 -2.82141000e-01 -8.82008895e-02
-2.97917128e-01 -9.01411399e-02 6.86074674e-01 8.92984867e-01
-2.04746738e-01 -1.93738377e+00 -5.19723892e-01 6.76450729e-01
-6.47899628e-01 3.08149844e-01 -5.67185223e-01 7.08211899e-01
4.42054749e-01 1.27537894e+00 -1.65279374e-01 -8.62277627e-01
2.31182743e-02 9.52496678e-02 2.73648471e-01 -4.97064054e-01
-9.81971443e-01 -6.76463664e-01 3.01106036e-01 -5.17001331e-01
-3.66059393e-01 -8.05664062e-01 -7.38672495e-01 -5.37704587e-01
-2.42373616e-01 3.36260349e-01 8.27419937e-01 8.97530735e-01
2.48398498e-01 1.96723983e-01 9.99015212e-01 -5.24302781e-01
-6.51633263e-01 -4.36996460e-01 -1.85600862e-01 8.76838267e-01
2.75015593e-01 -4.93985116e-01 -3.47135663e-01 -8.45899507e-02] | [5.250848770141602, 7.244238376617432] |
de028dcb-5152-4768-a557-59125e54a185 | analysing-the-impact-of-audio-quality-on-the | 2305.01965 | null | https://arxiv.org/abs/2305.01965v1 | https://arxiv.org/pdf/2305.01965v1.pdf | Analysing the Impact of Audio Quality on the Use of Naturalistic Long-Form Recordings for Infant-Directed Speech Research | Modelling of early language acquisition aims to understand how infants bootstrap their language skills. The modelling encompasses properties of the input data used for training the models, the cognitive hypotheses and their algorithmic implementations being tested, and the evaluation methodologies to compare models to human data. Recent developments have enabled the use of more naturalistic training data for computational models. This also motivates development of more naturalistic tests of model behaviour. A crucial step towards such an aim is to develop representative speech datasets consisting of speech heard by infants in their natural environments. However, a major drawback of such recordings is that they are typically noisy, and it is currently unclear how the sound quality could affect analyses and modelling experiments conducted on such data. In this paper, we explore this aspect for the case of infant-directed speech (IDS) and adult-directed speech (ADS) analysis. First, we manually and automatically annotated audio quality of utterances extracted from two corpora of child-centred long-form recordings (in English and French). We then compared acoustic features of IDS and ADS in an in-lab dataset and across different audio quality subsets of naturalistic data. Finally, we assessed how the audio quality and recording environment may change the conclusions of a modelling analysis using a recent self-supervised learning model. Our results show that the use of modest and high audio quality naturalistic speech data result in largely similar conclusions on IDS and ADS in terms of acoustic analyses and modelling experiments. We also found that an automatic sound quality assessment tool can be used to screen out useful parts of long-form recordings for a closer analysis with comparable results to that of manual quality annotation. | ['Okko Räsänen', 'Alejandrina Cristia', 'María Andrea Cruz Blandón'] | 2023-05-03 | null | null | null | null | ['language-acquisition'] | ['natural-language-processing'] | [ 2.59155363e-01 2.05468953e-01 5.96773088e-01 -6.21587753e-01
-8.55758011e-01 -4.69146311e-01 6.07972622e-01 4.36145157e-01
-7.00256348e-01 2.00473055e-01 4.99305248e-01 -2.90532619e-01
-3.56604993e-01 -4.67229068e-01 -7.45488882e-01 -4.56710100e-01
-2.14088559e-01 5.30109525e-01 4.92900699e-01 -5.01901954e-02
9.17806253e-02 3.02947581e-01 -2.18086505e+00 4.71057743e-01
5.37159204e-01 5.12467742e-01 6.95714474e-01 1.20900023e+00
-1.12110674e-01 7.20382273e-01 -7.21297145e-01 -3.16302776e-01
-1.02423348e-01 -4.69490558e-01 -8.92398953e-01 -1.35776803e-01
2.81745881e-01 -3.02307934e-01 2.55000949e-01 7.47950435e-01
8.84815514e-01 3.92056108e-02 6.24383450e-01 -8.47515106e-01
-1.73956424e-01 1.01079440e+00 3.96980375e-01 3.23607087e-01
7.18817413e-01 5.11236303e-02 8.25721443e-01 -7.20219314e-01
2.98741102e-01 1.20402408e+00 6.38828635e-01 7.01128006e-01
-1.55886900e+00 -5.33940136e-01 -6.54895902e-02 3.92779708e-02
-1.16009712e+00 -1.10271537e+00 6.11671388e-01 -7.38118291e-01
1.06502450e+00 1.98752701e-01 9.46860671e-01 1.17135513e+00
-4.59819347e-01 4.62864399e-01 1.27026951e+00 -9.95416343e-01
4.04767036e-01 6.36888444e-01 1.04066886e-01 2.38724455e-01
-2.32295960e-01 4.88870919e-01 -7.25503206e-01 -2.91686822e-02
3.86288166e-01 -9.80871260e-01 -2.67954946e-01 -1.37887895e-01
-9.54956889e-01 5.89967251e-01 -2.52356917e-01 8.47222328e-01
-1.91558659e-01 -1.07299231e-01 5.82492769e-01 5.73252141e-01
3.79303813e-01 5.37243307e-01 -6.98131919e-01 -8.14583421e-01
-8.93619299e-01 2.07504511e-01 9.19499338e-01 8.61766994e-01
2.82685727e-01 2.47353278e-02 4.53960687e-01 1.47831976e+00
6.01463437e-01 3.47584903e-01 6.80130124e-01 -9.17341173e-01
2.93468624e-01 5.43808676e-02 -2.15050936e-01 -6.95947945e-01
-4.48349506e-01 -3.68700549e-02 -3.04105096e-02 1.88280120e-01
7.13800550e-01 -6.30264729e-02 -5.50077736e-01 1.94913054e+00
1.84375525e-01 -2.84628898e-01 2.03834251e-01 5.01902044e-01
8.90907526e-01 5.53652048e-01 2.11092710e-01 -4.13130254e-01
1.06265748e+00 -4.63109851e-01 -6.30921781e-01 -3.67633738e-02
8.28078568e-01 -9.32872772e-01 1.44111371e+00 8.44104588e-01
-1.41185224e+00 -7.66292751e-01 -9.72606540e-01 2.74479717e-01
-2.03733295e-01 -2.10672200e-01 3.47276032e-01 1.12199950e+00
-1.19145811e+00 7.66288400e-01 -9.51670825e-01 -4.41322833e-01
-3.35775204e-02 2.99273193e-01 -4.19303566e-01 3.13023806e-01
-1.05092633e+00 9.40749943e-01 3.28896642e-01 -2.08684891e-01
-8.99260461e-01 -5.99536717e-01 -9.74218667e-01 -1.76201746e-01
-1.20862342e-01 1.23843856e-01 1.77840066e+00 -1.11820614e+00
-1.54930961e+00 1.01914299e+00 2.49721497e-01 -2.07403541e-01
2.18456060e-01 -2.52253264e-01 -6.34009361e-01 1.95355415e-01
-7.36993626e-02 3.48749578e-01 5.34821570e-01 -1.20187867e+00
-6.38494968e-01 -2.20916465e-01 -1.80686533e-01 4.48657349e-02
-1.84179932e-01 6.96783960e-01 -2.61289738e-02 -5.94455361e-01
1.02190852e-01 -6.20014608e-01 1.69125140e-01 -3.53879482e-01
2.17163697e-01 -1.17331840e-01 -8.04854110e-02 -7.62930155e-01
1.14734244e+00 -2.19771957e+00 -1.05457895e-01 1.67884499e-01
-2.27994815e-01 2.28406027e-01 -1.34705529e-01 6.38984144e-01
-1.76338822e-01 1.15899861e-01 5.86564541e-02 -4.08363193e-01
9.21256319e-02 1.51082426e-01 -1.76705942e-02 2.66409248e-01
1.92216069e-01 2.88876176e-01 -8.74055624e-01 -6.27911150e-01
2.03650311e-01 5.41691363e-01 -6.96645141e-01 6.63865983e-01
2.17049159e-02 5.85184932e-01 5.85204586e-02 3.77329260e-01
4.13087383e-02 7.47568429e-01 -1.12662464e-01 1.62565172e-01
-4.34680909e-01 1.00541723e+00 -1.14399147e+00 1.48368001e+00
-7.65128672e-01 7.56833851e-01 2.70690203e-01 -9.37874079e-01
8.87740433e-01 7.69750476e-01 1.65034253e-02 -8.12711775e-01
2.53079385e-01 4.64796484e-01 6.64898336e-01 -8.69611502e-01
6.09571412e-02 -3.96965355e-01 3.57623279e-01 4.67918128e-01
3.61354142e-01 -7.14181840e-01 2.07833856e-01 -3.05520236e-01
8.70280087e-01 1.30466431e-01 -8.08671396e-03 -5.08277595e-01
4.88997847e-01 -3.55606288e-01 2.05346674e-01 6.17627621e-01
-6.41812310e-02 6.79120004e-01 1.59942552e-01 -7.98238888e-02
-1.02543664e+00 -1.06659222e+00 -5.17531991e-01 1.38095284e+00
-6.55159771e-01 -5.09548247e-01 -1.04105151e+00 -3.05511616e-02
-5.42391896e-01 1.03212488e+00 -3.11362714e-01 -1.48047417e-01
-6.37722135e-01 -3.27233404e-01 6.83260918e-01 4.45651859e-01
-4.01806325e-01 -1.53668141e+00 -8.85459483e-01 5.94688296e-01
1.80596337e-01 -9.01987433e-01 4.64202538e-02 4.40348625e-01
-6.41092479e-01 -8.47155869e-01 -4.10423785e-01 -1.02317202e+00
2.11547121e-01 -3.64148825e-01 1.11437356e+00 2.11093053e-01
6.02574013e-02 7.09322155e-01 -7.55991995e-01 -9.51798141e-01
-1.30967927e+00 -2.35625982e-01 4.96097863e-01 -4.18771803e-01
4.68448281e-01 -9.57520664e-01 -3.11651807e-02 2.34009027e-01
-8.06793571e-01 -1.05942123e-01 2.83168465e-01 7.87860513e-01
1.16215780e-01 2.58514822e-01 8.07953537e-01 -5.30968249e-01
6.92785323e-01 -7.93550089e-02 -5.04135251e-01 4.09176201e-02
-3.75107765e-01 -4.01677229e-02 6.48079097e-01 -6.90056503e-01
-9.41451490e-01 -1.42428860e-01 -8.16145241e-01 1.58854827e-01
-8.48556638e-01 3.22353661e-01 -3.05918366e-01 2.10530147e-01
7.81735003e-01 8.70679393e-02 6.51884228e-02 -6.80187702e-01
-1.05272003e-01 1.16031945e+00 5.43821394e-01 -9.17700171e-01
5.40079474e-01 -3.72798622e-01 -6.51038408e-01 -1.31867480e+00
-2.69102663e-01 -9.91797447e-02 -7.28239238e-01 -3.21264923e-01
6.15709960e-01 -7.16489613e-01 -3.17935348e-01 6.60309970e-01
-1.02365088e+00 -7.39402533e-01 -3.19644004e-01 9.75307882e-01
-9.17144299e-01 9.57619920e-02 -3.40241224e-01 -1.33808458e+00
-6.86231107e-02 -1.23405361e+00 8.43930602e-01 -6.21276908e-02
-7.22720206e-01 -9.63797331e-01 3.84152651e-01 4.33623433e-01
3.09949964e-01 -2.60382354e-01 1.07883537e+00 -1.09624588e+00
2.36076027e-01 -3.97418253e-02 5.28949320e-01 8.23618293e-01
9.64329243e-02 3.54290694e-01 -1.57387292e+00 -2.43039042e-01
3.10562760e-01 -7.27011085e-01 1.07996844e-01 1.92415282e-01
6.17138565e-01 -6.01233318e-02 3.16570729e-01 2.13910341e-02
9.66169775e-01 6.09053433e-01 3.00513923e-01 5.02627268e-02
1.15909390e-01 1.53393853e+00 7.08238125e-01 -6.68118596e-02
1.19496115e-01 8.17334116e-01 -1.29677251e-01 2.44251609e-01
4.82061319e-03 -4.15473998e-01 5.63860297e-01 1.40454161e+00
5.92254512e-02 7.80906677e-02 -1.07887542e+00 1.03702343e+00
-1.06411850e+00 -8.37223351e-01 -2.40143463e-02 2.42299151e+00
1.14086187e+00 6.08618557e-01 5.59705853e-01 1.00395525e+00
4.46258873e-01 -3.16116303e-01 2.82279402e-01 -1.06903839e+00
3.13898951e-01 5.03488898e-01 -3.24198306e-01 5.63229680e-01
-5.20656347e-01 4.55288202e-01 6.28255367e+00 5.59604466e-01
-7.43839800e-01 3.48210670e-02 3.38448465e-01 -1.49478197e-01
-2.93410659e-01 -3.53091747e-01 -4.53676105e-01 4.80019510e-01
1.57725179e+00 9.18495134e-02 5.13154507e-01 6.57347322e-01
5.47780156e-01 -3.05990160e-01 -1.72174823e+00 6.66236281e-01
-1.87129393e-01 -5.54057419e-01 -5.48924804e-01 -3.09785992e-01
1.87594667e-01 1.39789023e-02 -3.30853730e-01 3.80852282e-01
-8.14294070e-02 -1.08940625e+00 1.24417436e+00 3.20188642e-01
6.74949110e-01 -5.62192142e-01 5.69556713e-01 7.29283988e-01
-9.31189775e-01 4.36815843e-02 -1.44517496e-01 -4.70752984e-01
1.24949820e-01 2.20334440e-01 -1.04505217e+00 -5.61226979e-02
9.76541817e-01 -2.31328178e-02 -5.69358885e-01 9.46572065e-01
9.96706355e-03 1.18366730e+00 -5.76260328e-01 -3.36931318e-01
-8.58504102e-02 2.55232543e-01 4.28091884e-01 1.43958592e+00
8.77336711e-02 5.93371019e-02 -5.13038516e-01 6.70065403e-01
7.20015705e-01 5.07414937e-01 -6.99092865e-01 -3.09679180e-01
6.40365362e-01 7.42969811e-01 -5.83627701e-01 -2.19984222e-02
-6.67491019e-01 2.31519297e-01 2.14784548e-01 -1.35630533e-01
-2.99768984e-01 -1.04306497e-01 4.16269511e-01 3.41538548e-01
1.99538153e-02 -5.06152995e-02 -7.10056201e-02 -6.50109291e-01
6.70023933e-02 -1.14702332e+00 -7.63619170e-02 -6.32379949e-01
-1.11441207e+00 6.37359679e-01 4.96353537e-01 -8.58095109e-01
-7.21232057e-01 -5.51579475e-01 -7.62750566e-01 1.01270425e+00
-7.33573616e-01 -5.56506038e-01 1.38984218e-01 7.35720098e-02
7.78859258e-01 -2.10353822e-01 1.02187753e+00 3.52373570e-01
-1.91989437e-01 5.83511889e-01 -1.59477130e-01 -4.13635187e-02
3.16632777e-01 -1.40180075e+00 6.18311405e-01 6.24847114e-01
5.60221076e-01 5.53645253e-01 1.10134161e+00 -2.84776121e-01
-7.05315053e-01 -4.27927345e-01 9.30590749e-01 -5.36507785e-01
6.24154985e-01 -6.10710859e-01 -1.19558847e+00 1.32844329e-01
6.19157180e-02 -4.28964436e-01 7.93016613e-01 -2.64620166e-02
1.36056110e-01 9.32179317e-02 -1.15789378e+00 2.84043729e-01
1.13443160e+00 -6.77408278e-01 -1.09800541e+00 -1.21911332e-01
8.18116248e-01 -6.43760487e-02 -1.05535650e+00 4.25351948e-01
8.76739323e-01 -1.24934638e+00 6.13515675e-01 -4.25590456e-01
2.51430959e-01 1.50084883e-01 -2.19695061e-01 -1.41363811e+00
4.35638651e-02 -4.57955122e-01 4.41660047e-01 1.78620195e+00
5.89119494e-01 -3.91744941e-01 3.44797105e-01 6.65583909e-01
-3.09149891e-01 -7.53954113e-01 -8.73010814e-01 -5.19216537e-01
3.53331029e-01 -1.19371772e+00 4.58755881e-01 4.16448534e-01
1.03451334e-01 1.65970996e-01 2.31295630e-01 1.47496253e-01
2.10229740e-01 -5.76292813e-01 6.69299662e-01 -1.38123453e+00
-5.10792136e-01 -3.30314040e-01 -6.40267134e-01 -4.57987964e-01
4.82420810e-02 -4.50811982e-01 3.83670360e-01 -1.00864375e+00
-3.59789133e-01 -5.47277510e-01 5.37957624e-02 2.57479232e-02
1.13793097e-01 -1.24519452e-01 1.31652743e-01 -2.52131701e-01
1.27339065e-01 2.77877599e-01 6.44960821e-01 2.60682821e-01
-4.41724896e-01 5.11108875e-01 -4.52301770e-01 1.03839922e+00
6.93251312e-01 -6.11547351e-01 -7.76859760e-01 -4.63835955e-01
1.20278783e-01 -9.09130834e-03 7.17826933e-02 -1.20195508e+00
-2.70353481e-02 1.11690171e-01 5.35680838e-02 -3.67799252e-01
1.73330322e-01 -9.26955640e-01 1.76818475e-01 3.30522090e-01
-7.22694814e-01 -9.24369544e-02 3.83203924e-01 -1.43016443e-01
-1.71440393e-01 -8.48554075e-01 8.59107375e-01 -7.69974738e-02
-3.31192672e-01 -5.65922201e-01 -7.40344346e-01 1.60252243e-01
5.73002279e-01 -4.09554690e-01 2.82645762e-01 -3.84795904e-01
-8.70960236e-01 -1.02458827e-01 5.97185969e-01 5.20218790e-01
3.72346103e-01 -1.01596797e+00 -6.99853837e-01 7.13254392e-01
2.11479723e-01 1.74849197e-01 -3.05221099e-02 6.99153781e-01
-5.59189379e-01 3.55790168e-01 -6.17558137e-02 -6.04839027e-01
-1.57993770e+00 3.20965618e-01 3.61069471e-01 4.53436077e-01
-3.66393030e-01 9.45829988e-01 1.21382542e-01 -5.59164703e-01
7.42589414e-01 -6.59456968e-01 -4.77724284e-01 1.61555722e-01
7.06615210e-01 4.11939979e-01 3.70180756e-01 -9.26115155e-01
-2.65501738e-01 4.10980046e-01 5.12755178e-02 -5.86981535e-01
1.44055831e+00 -8.37641507e-02 1.79562375e-01 1.11995864e+00
1.19286156e+00 3.08174968e-01 -8.48652124e-01 -6.36921078e-02
2.06814662e-01 -2.51857042e-01 -9.73769650e-02 -6.53778315e-01
-4.70878214e-01 9.62223709e-01 6.25391841e-01 6.77647531e-01
1.19927394e+00 2.46914268e-01 2.37597644e-01 1.90669075e-01
4.53209579e-01 -1.19032538e+00 -2.21380174e-01 1.74801037e-01
1.14545548e+00 -9.67035115e-01 -3.74231309e-01 -2.63460964e-01
-4.61681664e-01 1.00076854e+00 5.02990603e-01 3.54330719e-01
5.84517777e-01 5.04515588e-01 3.55135262e-01 -1.19731396e-01
-1.04262698e+00 -1.27380177e-01 3.46720308e-01 1.00369394e+00
7.99669445e-01 -9.73085836e-02 -3.09939742e-01 8.79352033e-01
-1.00038588e+00 -1.77126989e-01 5.08838236e-01 9.44488883e-01
-4.74473089e-01 -1.12977386e+00 -5.85541964e-01 4.73949552e-01
-7.28625119e-01 -4.90223542e-02 -4.95418817e-01 7.80539155e-01
3.32311094e-01 1.32619977e+00 -9.45537314e-02 -5.20921409e-01
6.50471926e-01 5.04829943e-01 6.09531820e-01 -1.11528778e+00
-7.49317050e-01 1.45192549e-01 5.80657840e-01 -3.29157323e-01
-6.14387393e-01 -1.04403436e+00 -1.08106673e+00 3.14144462e-01
-7.49122024e-01 4.25678730e-01 9.38028157e-01 1.02508354e+00
-5.25567532e-01 5.28187156e-01 6.47135913e-01 -8.71946275e-01
-4.76390570e-01 -1.26514518e+00 -5.92883706e-01 1.50678694e-01
4.42710131e-01 -5.77598810e-01 -7.52348006e-01 1.99344724e-01] | [14.34271240234375, 6.3614912033081055] |
a15220f8-ae79-424f-859c-c582a22afaa1 | unsupervised-pre-training-of-graph | 2207.10603 | null | https://arxiv.org/abs/2207.10603v1 | https://arxiv.org/pdf/2207.10603v1.pdf | Unsupervised pre-training of graph transformers on patient population graphs | Pre-training has shown success in different areas of machine learning, such as Computer Vision, Natural Language Processing (NLP), and medical imaging. However, it has not been fully explored for clinical data analysis. An immense amount of clinical records are recorded, but still, data and labels can be scarce for data collected in small hospitals or dealing with rare diseases. In such scenarios, pre-training on a larger set of unlabelled clinical data could improve performance. In this paper, we propose novel unsupervised pre-training techniques designed for heterogeneous, multi-modal clinical data for patient outcome prediction inspired by masked language modeling (MLM), by leveraging graph deep learning over population graphs. To this end, we further propose a graph-transformer-based network, designed to handle heterogeneous clinical data. By combining masking-based pre-training with a transformer-based network, we translate the success of masking-based pre-training in other domains to heterogeneous clinical data. We show the benefit of our pre-training method in a self-supervised and a transfer learning setting, utilizing three medical datasets TADPOLE, MIMIC-III, and a Sepsis Prediction Dataset. We find that our proposed pre-training methods help in modeling the data at a patient and population level and improve performance in different fine-tuning tasks on all datasets. | ['Anees Kazi', 'Nassir Navab', 'Chantal Pellegrini'] | 2022-07-21 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 4.95797306e-01 3.33668023e-01 -2.38424256e-01 -4.57214445e-01
-7.94237077e-01 -1.57175690e-01 1.69387892e-01 8.97897661e-01
-2.95540839e-01 5.83805919e-01 3.23631227e-01 -6.89029992e-01
-3.05520326e-01 -8.02052319e-01 -5.21112859e-01 -5.29636741e-01
-5.26673853e-01 9.72963214e-01 5.21377511e-02 -3.33375037e-02
-3.77828449e-01 2.57494330e-01 -8.97450507e-01 8.52375150e-01
8.65036964e-01 7.42893934e-01 -4.31523025e-02 4.94998008e-01
1.26010105e-01 1.11284864e+00 -2.30912089e-01 -2.91767716e-01
1.32215336e-01 -4.40072000e-01 -7.37377524e-01 -1.93765759e-02
5.49881086e-02 1.86012655e-01 -2.22526073e-01 5.35290658e-01
9.32720006e-01 -2.11547658e-01 6.67644799e-01 -9.72504675e-01
-3.96151066e-01 8.02336037e-01 -4.26221162e-01 -1.10142855e-02
-4.14834023e-02 2.78178006e-01 7.76115954e-01 -4.40130740e-01
6.79595828e-01 1.14745343e+00 1.14180279e+00 6.06208801e-01
-1.38499987e+00 -5.61117172e-01 -1.55820966e-01 4.48517129e-02
-1.21052003e+00 4.32007201e-03 5.90938509e-01 -6.70092285e-01
9.00886476e-01 1.37270734e-01 4.97917891e-01 1.24450278e+00
4.56467092e-01 6.08058751e-01 1.13000131e+00 -3.35030705e-01
2.05400974e-01 -6.56874403e-02 8.73197019e-02 9.00736272e-01
1.79867998e-01 8.49522203e-02 -4.00291294e-01 -5.29249907e-01
3.35156739e-01 3.83323401e-01 -1.01799905e-01 -4.52202916e-01
-1.40378392e+00 9.97103333e-01 4.18330133e-01 4.06000614e-01
-4.64452922e-01 -1.76394254e-01 6.92079425e-01 3.26407790e-01
7.35988677e-01 4.34369832e-01 -6.83735669e-01 3.31253499e-01
-9.27725077e-01 -1.00888833e-01 7.60726213e-01 6.00983977e-01
3.45621854e-01 -2.37437248e-01 -4.58321333e-01 8.19692194e-01
1.64888546e-01 2.94507235e-01 6.91831648e-01 -2.30114982e-01
6.91582143e-01 1.02843857e+00 -5.66493988e-01 -9.00205076e-01
-1.23504686e+00 -7.81940103e-01 -1.37311411e+00 -2.21588731e-01
3.00453156e-01 -1.76099449e-01 -1.15829003e+00 1.82051396e+00
2.94743299e-01 3.02729487e-01 2.38388121e-01 6.99313223e-01
8.45050097e-01 2.08553270e-01 3.45088124e-01 -6.77378327e-02
1.50185621e+00 -7.66582966e-01 -4.98827219e-01 -2.05028415e-01
1.44306505e+00 -2.68667668e-01 9.64662015e-01 4.76286650e-01
-6.29429102e-01 -1.37733936e-01 -5.78464806e-01 1.52323708e-01
-4.16197360e-01 1.17402904e-01 5.72285354e-01 4.01431680e-01
-9.22894955e-01 6.39750183e-01 -9.92315710e-01 -6.86237693e-01
7.82775760e-01 5.80404997e-01 -5.36112368e-01 -3.99630815e-01
-1.25290871e+00 7.53287852e-01 4.62963045e-01 -9.73665342e-02
-7.25858867e-01 -9.37635422e-01 -7.83451259e-01 5.73307499e-02
3.69480968e-01 -1.27936792e+00 6.71715379e-01 -5.16794443e-01
-1.02278352e+00 1.02188134e+00 2.97591776e-01 -7.19124198e-01
5.45700908e-01 2.38256156e-01 -4.35210794e-01 5.64510822e-02
-4.14480902e-02 4.00540233e-01 5.07579386e-01 -1.06594741e+00
-1.20997630e-01 -8.01904142e-01 -4.51335341e-01 -1.48766786e-01
-5.15729249e-01 -7.26075321e-02 -4.79736459e-03 -7.62526810e-01
-2.59402275e-01 -1.06370795e+00 -8.10328066e-01 -4.20517176e-01
-6.07207358e-01 1.19012102e-01 4.99636739e-01 -7.27909565e-01
1.20995820e+00 -2.04000521e+00 9.21445861e-02 4.14887309e-01
6.86406910e-01 4.41477805e-01 -3.37939531e-01 6.63909674e-01
-2.69445777e-01 1.72348976e-01 -6.25019014e-01 -5.40341496e-01
-3.94723117e-01 2.31335431e-01 1.97152853e-01 3.58169734e-01
4.64418083e-01 1.12288845e+00 -9.84292865e-01 -7.30234146e-01
6.46418780e-02 4.23013270e-01 -9.11846936e-01 3.23654741e-01
-1.18745290e-01 9.60393727e-01 -4.03515190e-01 5.99774778e-01
3.41340601e-01 -8.98315072e-01 4.57388818e-01 -1.55673832e-01
4.79805291e-01 3.66552956e-02 -6.56377614e-01 1.79580009e+00
-5.98255932e-01 1.38189435e-01 -6.23707473e-02 -1.40728879e+00
7.02474535e-01 4.82769251e-01 1.03100216e+00 -6.37064397e-01
2.54494637e-01 1.36107787e-01 4.12508130e-01 -7.31490195e-01
-9.32388455e-02 -5.79468906e-01 3.35399881e-02 4.23438340e-01
2.44540386e-02 2.13320106e-01 3.21021900e-02 1.97783187e-01
1.64568341e+00 -5.17796218e-01 3.17780226e-01 -1.17128193e-01
3.09367120e-01 2.10853785e-01 4.86873031e-01 7.70624220e-01
-4.26376909e-02 7.93909311e-01 4.76676732e-01 -3.88090700e-01
-7.73201108e-01 -6.41555548e-01 -2.82657266e-01 9.84250903e-01
-4.82766658e-01 -6.05081439e-01 -5.17332315e-01 -8.68238747e-01
2.67937511e-01 1.91828504e-01 -7.62505651e-01 -5.77731967e-01
-4.10226971e-01 -1.30869007e+00 6.85321450e-01 4.92804945e-01
2.26901863e-02 -1.10867715e+00 -3.49231511e-01 4.32016820e-01
-1.22043053e-02 -1.39066303e+00 -1.63841844e-01 5.18036604e-01
-1.11656213e+00 -1.20977938e+00 -5.59690952e-01 -8.06003511e-01
6.26730740e-01 -2.92079836e-01 1.18697536e+00 2.10362136e-01
-5.85976422e-01 2.57359505e-01 -4.26379144e-01 -5.10698736e-01
-8.13485682e-01 4.40195024e-01 5.85661680e-02 3.07306916e-01
2.77283013e-01 -5.87615609e-01 -5.95615208e-01 1.63813159e-02
-1.15511918e+00 2.10700482e-01 9.37647879e-01 1.20488739e+00
4.79319751e-01 -2.26363629e-01 7.54832268e-01 -1.58092117e+00
6.08518720e-01 -7.96877921e-01 -2.82015651e-02 2.52338052e-01
-7.70415485e-01 1.71660990e-01 8.24485600e-01 -5.11739492e-01
-4.12380219e-01 1.05598688e-01 -1.84104696e-01 -5.06690383e-01
-2.03241631e-01 1.17418432e+00 1.50931671e-01 1.21139072e-01
9.13672030e-01 -7.82939568e-02 4.16971743e-01 -5.05876184e-01
4.40158918e-02 7.38251984e-01 1.94302395e-01 -3.54402840e-01
5.18282294e-01 4.69238222e-01 4.70120400e-01 -4.39685285e-01
-9.21608269e-01 -6.31999850e-01 -6.07003570e-01 3.04515123e-01
8.88913512e-01 -9.21024740e-01 -5.85273564e-01 3.76133889e-01
-7.36600041e-01 -6.54558539e-01 -3.05387646e-01 6.52316630e-01
-3.81819040e-01 2.93858618e-01 -5.76541960e-01 -2.97463119e-01
-6.34048522e-01 -1.13738263e+00 1.26847386e+00 -4.25997525e-01
-1.68536991e-01 -1.47357249e+00 3.63110572e-01 4.81917590e-01
5.33184826e-01 5.71310282e-01 1.30266881e+00 -1.30164802e+00
-3.59491967e-02 -3.64400238e-01 -1.66844606e-01 2.76169121e-01
3.99141133e-01 -5.76258957e-01 -8.28206658e-01 -5.13679683e-01
-2.77057976e-01 -5.31038642e-01 1.03648627e+00 3.68993372e-01
1.37030172e+00 -2.51397640e-01 -5.86571336e-01 7.42010832e-01
1.19527459e+00 -1.34270564e-01 2.82089740e-01 -9.10030305e-03
1.03247857e+00 7.70196438e-01 9.13267136e-02 5.28419137e-01
6.73710763e-01 5.56313634e-01 3.94562066e-01 -6.45137429e-01
-8.08457732e-02 -1.61322385e-01 1.69577114e-02 9.27666724e-01
1.35880813e-01 -2.10465625e-01 -1.58770347e+00 6.50318205e-01
-2.01801872e+00 -5.32923460e-01 -1.28544912e-01 2.08649707e+00
1.11784232e+00 -4.27973494e-02 2.04129979e-01 -3.54097001e-02
4.98757631e-01 -3.31097364e-01 -5.37265837e-01 -7.91535228e-02
-1.71192244e-01 4.41192836e-01 5.00778794e-01 2.14255899e-01
-1.07576597e+00 7.43656754e-01 5.43312407e+00 6.42672598e-01
-1.37746096e+00 3.77965093e-01 8.82487535e-01 2.62237396e-02
-1.18425533e-01 -1.16273068e-01 -3.34168881e-01 4.16097939e-01
1.23010588e+00 1.51741967e-01 7.92110860e-02 5.36661863e-01
3.51843387e-01 2.73795664e-01 -1.29793298e+00 9.75958526e-01
-4.07127254e-02 -1.38336647e+00 1.24085255e-01 2.01931491e-01
5.75020134e-01 3.42450738e-01 1.85505599e-02 4.21110690e-01
3.77045184e-01 -1.32018912e+00 -3.76754552e-02 2.67794102e-01
8.11546564e-01 -2.72388279e-01 9.86360788e-01 5.10114133e-01
-7.52596259e-01 -1.47469640e-01 -5.10010161e-02 2.28639007e-01
3.14236097e-02 1.05160820e+00 -1.49519587e+00 9.14105058e-01
4.67938066e-01 9.22706544e-01 -7.93221056e-01 9.54403818e-01
2.87968457e-01 9.99528289e-01 -1.82213560e-01 2.95659572e-01
-6.30709305e-02 -5.48573285e-02 1.87772781e-01 1.36128604e+00
1.22955084e-01 1.57452896e-01 5.43278813e-01 5.97186685e-01
-1.07769467e-01 5.01042366e-01 -7.93848395e-01 -1.37909144e-01
-1.53376207e-01 1.40597343e+00 -6.28843546e-01 -4.13636625e-01
-3.26802135e-01 4.50182706e-01 3.43311310e-01 2.20236659e-01
-6.96899116e-01 3.83621342e-02 2.18175482e-02 4.06970739e-01
-1.62426859e-01 6.84288815e-02 -4.16825086e-01 -1.38531673e+00
-3.29549044e-01 -1.12499750e+00 8.62329125e-01 -3.03762168e-01
-1.68496895e+00 7.84649253e-01 -1.60375714e-01 -1.29101133e+00
-3.40290517e-01 -8.12102139e-01 -3.68274271e-01 6.28892779e-01
-1.63288403e+00 -1.40603924e+00 -3.49924028e-01 7.30779350e-01
-8.14570487e-02 -3.54916245e-01 8.94093096e-01 6.02407455e-01
-6.54599428e-01 6.47644162e-01 -1.03806645e-01 4.09251004e-01
1.01225245e+00 -1.09133244e+00 1.79822911e-02 4.14654881e-01
-5.27638309e-02 5.97943306e-01 2.32130095e-01 -6.88108981e-01
-1.30257726e+00 -1.60240209e+00 7.08055794e-01 -3.54593188e-01
6.93723321e-01 -4.81951863e-01 -1.09173274e+00 6.29207075e-01
-6.75937161e-02 3.84852767e-01 1.07712901e+00 2.77742088e-01
-2.79978961e-01 -9.03712213e-02 -1.16878688e+00 3.07741255e-01
1.06183684e+00 -3.91533464e-01 -2.37754330e-01 7.58254409e-01
6.47874594e-01 -3.40083063e-01 -1.35738719e+00 9.21641350e-01
1.34417966e-01 -6.05087936e-01 8.85890663e-01 -1.30021358e+00
4.67907786e-01 1.67465299e-01 -4.18026149e-02 -1.62916303e+00
-2.16207951e-01 -5.36892474e-01 1.69209652e-02 8.27113509e-01
6.39314830e-01 -9.60629165e-01 7.31301188e-01 3.57048064e-01
-3.13428402e-01 -1.10584950e+00 -7.37384260e-01 -5.90264499e-01
2.92821735e-01 -3.47361237e-01 4.19695646e-01 1.30243444e+00
1.57583430e-01 5.24678707e-01 -2.85535157e-01 1.06338859e-01
4.09545451e-01 5.49639314e-02 6.89384937e-01 -1.43285549e+00
-5.16052842e-01 -2.20682681e-01 -4.76075530e-01 -1.80121094e-01
1.35330647e-01 -1.44320941e+00 -2.49377221e-01 -1.77450562e+00
5.10072708e-01 -9.06191826e-01 -5.24902105e-01 1.06000054e+00
-4.33440655e-01 1.69931978e-01 -9.58314016e-02 1.74247429e-01
-4.89121497e-01 3.48130405e-01 1.16252530e+00 -3.27806741e-01
-3.19105536e-01 -4.38236967e-02 -5.31153560e-01 4.53080118e-01
8.63494396e-01 -7.52860606e-01 -2.90950686e-01 -1.66720122e-01
1.74212217e-01 3.09897870e-01 3.75946283e-01 -9.54292357e-01
4.11748216e-02 7.66881090e-03 1.60890277e-02 -1.61027193e-01
-5.33311181e-02 -8.17129731e-01 7.22558349e-02 8.44084561e-01
-4.38075632e-01 2.17627808e-01 3.25134516e-01 5.50163627e-01
-3.20516020e-01 3.41619343e-01 5.99820077e-01 -1.11797146e-01
-2.20322028e-01 5.07064164e-01 -1.18229993e-01 4.71338779e-01
8.92322838e-01 1.74567893e-01 -4.17744488e-01 -1.83181241e-01
-1.06435823e+00 4.21302259e-01 1.88622430e-01 3.61959904e-01
5.05356014e-01 -9.93049264e-01 -1.17032683e+00 3.02019984e-01
4.73251402e-01 2.23068967e-01 3.63589495e-01 1.51130903e+00
-5.00964940e-01 3.94554913e-01 5.93119077e-02 -9.86913502e-01
-1.13575745e+00 8.69828522e-01 2.71635622e-01 -1.02274847e+00
-5.98313987e-01 3.03213865e-01 3.07657301e-01 -8.45420957e-01
4.61654440e-02 -6.01798832e-01 -1.67153478e-01 5.37786186e-02
8.31018761e-02 -1.41148001e-01 6.02586865e-01 -4.29295182e-01
-4.98879701e-01 4.33723360e-01 -3.68056595e-02 2.71521747e-01
1.55773664e+00 2.99442440e-01 -2.29112357e-01 3.19282353e-01
1.40766454e+00 -1.00751504e-01 -4.88943547e-01 -4.81808335e-01
1.45639896e-01 1.47837594e-01 -1.03892788e-01 -8.85598421e-01
-1.22016835e+00 1.02156889e+00 5.23856819e-01 1.50940090e-01
1.27020252e+00 1.58586204e-02 5.27916312e-01 3.53664249e-01
3.60517263e-01 -4.93781686e-01 1.47452295e-01 3.54431450e-01
5.65434337e-01 -1.44403207e+00 -1.83676347e-01 -4.31670398e-01
-8.69295418e-01 8.44504774e-01 3.04788738e-01 1.84438318e-01
8.99619877e-01 3.29396009e-01 2.19159126e-01 -4.31705236e-01
-9.47700262e-01 -2.70253003e-01 4.02469516e-01 5.58811009e-01
4.59044486e-01 1.89929783e-01 -4.24923114e-02 7.39246309e-01
7.01651424e-02 3.25698704e-01 4.58991140e-01 8.66120160e-01
9.84636471e-02 -1.25799692e+00 -1.70165554e-01 8.40002477e-01
-6.83522642e-01 -5.81564248e-01 -3.63095820e-01 6.87214613e-01
1.44810319e-01 7.87066698e-01 -2.17170283e-01 -5.76254129e-01
3.54194999e-01 2.44142503e-01 2.51444936e-01 -1.04279768e+00
-9.47775126e-01 -7.64235407e-02 7.96058923e-02 -4.44209129e-01
-4.79036123e-01 -3.88210893e-01 -1.27994359e+00 1.03528515e-01
-1.71104997e-01 1.10184394e-01 3.38357717e-01 8.38645041e-01
9.79690135e-01 9.27410185e-01 5.35353303e-01 -2.14787304e-01
-6.18057013e-01 -1.04118586e+00 -3.45881522e-01 7.23753870e-01
4.16196734e-01 -4.75382358e-01 -1.58740163e-01 -3.24274078e-02] | [7.947263240814209, 6.437857627868652] |
98760b5b-3b4b-4e85-827e-a9337bb45917 | weakly-semi-supervised-neural-topic-models | null | null | https://openreview.net/forum?id=BJlRKgkDwN | https://openreview.net/pdf?id=BJlRKgkDwN | WEAKLY SEMI-SUPERVISED NEURAL TOPIC MODELS | We consider the problem of topic modeling in a weakly semi-supervised setting. In this scenario, we assume that the user knows a priori a subset of the topics she wants the model to learn and is able to provide a few exemplar documents for those topics. In addition, while each document may typically consist of multiple topics, we do not assume that the user will identify all its topics exhaustively.
Recent state-of-the-art topic models such as NVDM, referred to herein as Neural Topic Models (NTMs), fall under the variational autoencoder framework. We extend NTMs to the weakly semi-supervised setting by using informative priors in the training objective. After analyzing the effect of informative priors, we propose a simple modification of the NVDM model using a logit-normal posterior that we show achieves better alignment to user-desired topics versus other NTM models. | ['Bing Xiang', 'Feng Nan', 'Ran Ding', 'Ramesh Nallapati', 'Ian Gemp'] | 2019-03-13 | null | null | null | iclr-workshop-lld-2019 | ['topic-models'] | ['natural-language-processing'] | [-6.38680384e-02 6.94402218e-01 -5.12833476e-01 -5.50633967e-01
-8.93049359e-01 -4.37295705e-01 1.08928800e+00 4.76813838e-02
-2.31273934e-01 5.50353944e-01 3.97378594e-01 -1.55141696e-01
-6.67753210e-03 -8.18297565e-01 -8.80015433e-01 -6.17492676e-01
1.48008972e-01 1.15882683e+00 9.74388942e-02 6.70319647e-02
-8.07559341e-02 -2.28404343e-01 -1.39334989e+00 -1.07293218e-01
9.40812409e-01 4.79832262e-01 5.73916793e-01 3.90220821e-01
-2.77466238e-01 3.14653635e-01 -5.16560674e-01 -3.85851383e-01
-8.84328261e-02 -9.46364403e-02 -7.69887924e-01 5.43309033e-01
2.81082302e-01 -4.97730881e-01 -2.51084954e-01 9.38168645e-01
-2.54491647e-03 3.32451284e-01 1.22007608e+00 -1.33231699e+00
-4.94835466e-01 9.14933383e-01 -4.29452211e-01 -2.27008134e-01
1.83207057e-02 -4.24957871e-01 1.38280642e+00 -8.08245122e-01
7.21275389e-01 1.27655220e+00 2.21434012e-01 5.99854708e-01
-1.62152457e+00 -3.73147428e-01 6.09358847e-01 -1.45942152e-01
-1.21384203e+00 -1.20518349e-01 9.56114888e-01 -6.10050321e-01
3.36696267e-01 -1.51940778e-01 3.52824003e-01 1.50905097e+00
-3.98468971e-02 1.25440776e+00 8.76723051e-01 -3.52272660e-01
6.29881859e-01 8.57538819e-01 5.93685746e-01 7.31434748e-02
2.84066617e-01 -4.23905998e-01 -6.14045024e-01 -7.91990459e-01
7.34137774e-01 1.74780056e-01 -2.55514830e-01 -1.01535916e+00
-9.32771504e-01 1.39522851e+00 -9.08670574e-02 1.17921390e-01
-5.88448822e-01 2.57866457e-02 7.00913146e-02 4.97171935e-03
9.63489115e-01 1.61624700e-01 -5.01235425e-01 3.53871547e-02
-1.42115474e+00 5.19720852e-01 1.13484609e+00 1.25682831e+00
9.38204825e-01 -6.00917153e-02 -1.47228315e-01 8.10061336e-01
7.74851024e-01 3.21381569e-01 3.87673199e-01 -8.98812890e-01
2.33774319e-01 2.68542729e-02 5.53647757e-01 -2.86548167e-01
4.31335345e-02 -5.36012232e-01 -4.12507266e-01 -2.14971721e-01
1.31982148e-01 -1.85773417e-01 -1.00727665e+00 1.90872145e+00
3.93155992e-01 2.51131892e-01 1.62083670e-01 6.65173650e-01
3.75276327e-01 1.18503475e+00 2.98596919e-01 -4.08186197e-01
1.27535152e+00 -8.43666732e-01 -7.99234569e-01 -2.35002592e-01
1.59537882e-01 -3.97823751e-01 1.10928440e+00 5.71605384e-01
-1.05446565e+00 -3.58123690e-01 -8.06488395e-01 -5.11198938e-02
-1.81681976e-01 9.58277509e-02 6.57338440e-01 7.09619105e-01
-1.15298283e+00 4.27246511e-01 -1.27184868e+00 -5.83852053e-01
2.26042420e-01 2.25630730e-01 7.52587020e-02 -9.88011360e-02
-1.16988957e+00 6.78089023e-01 4.16078150e-01 -4.95461106e-01
-1.63037574e+00 -7.31228113e-01 -8.94334495e-01 3.08846146e-01
5.85408330e-01 -7.85158217e-01 1.75090909e+00 -6.41103864e-01
-1.40802240e+00 4.87566859e-01 -7.17318892e-01 -7.10511446e-01
3.12258780e-01 -4.65540171e-01 2.04596650e-02 1.61136374e-01
1.59250036e-01 8.31295252e-01 1.16209435e+00 -1.62371755e+00
-6.79658175e-01 -8.57298747e-02 2.11848333e-01 4.07323301e-01
-6.16991758e-01 -1.48871616e-01 -6.84539318e-01 -5.13920009e-01
3.93070765e-02 -8.77567291e-01 -1.83568642e-01 -1.27451792e-01
-5.83435655e-01 -6.70750976e-01 1.12528825e+00 -2.89273828e-01
9.37796414e-01 -1.93198395e+00 3.04937780e-01 2.01869562e-01
3.47778887e-01 -2.89891303e-01 2.44954258e-01 5.67988098e-01
2.50639379e-01 7.82024413e-02 -3.46892595e-01 -1.20154381e+00
4.07830566e-01 4.62104887e-01 -8.92620087e-01 4.84168530e-01
-1.94591627e-01 3.97765040e-01 -6.57423913e-01 -5.98740876e-01
7.54857212e-02 6.20330453e-01 -6.08571172e-01 3.18290830e-01
-8.29402208e-01 1.43467132e-02 -7.44255424e-01 1.55622646e-01
5.94366968e-01 -6.05948627e-01 1.66115001e-01 2.71591276e-01
1.48062512e-01 4.47862774e-01 -1.13583994e+00 1.61173904e+00
-4.25085306e-01 6.43726051e-01 2.14453265e-01 -8.72836590e-01
5.69254398e-01 7.18301177e-01 3.90606642e-01 5.14924824e-01
-1.47780925e-01 -2.12227121e-01 -4.27809000e-01 2.02820990e-02
8.44642401e-01 -3.26161265e-01 1.08193882e-01 9.52434719e-01
5.99648416e-01 -3.05004288e-02 7.64996484e-02 7.34429061e-01
3.31677884e-01 2.28787363e-01 2.07095563e-01 -3.91578645e-01
-3.26346159e-01 -7.00330511e-02 3.18084300e-01 1.22257936e+00
2.02189595e-01 7.69802570e-01 5.01906455e-01 2.85168767e-01
-1.20314813e+00 -1.33382356e+00 -4.52081680e-01 1.33874190e+00
-1.31573826e-01 -6.17978752e-01 -7.52756715e-01 -5.74183404e-01
-1.78364545e-01 1.17743504e+00 -6.89980388e-01 3.34492385e-01
7.34479129e-02 -7.16488779e-01 -2.78677866e-02 3.25510323e-01
-6.72834143e-02 -6.65051520e-01 -3.72945011e-01 2.83867955e-01
-1.65680066e-01 -7.61196375e-01 -3.44482332e-01 3.83524597e-01
-1.06187809e+00 -4.76078868e-01 -1.23126292e+00 -6.86504483e-01
5.36751449e-01 9.70945731e-02 1.10048532e+00 -6.51715517e-01
4.30694371e-01 7.10978806e-01 -2.36809999e-01 -6.99525774e-01
-3.71771693e-01 2.82916963e-01 2.23307431e-01 7.27027953e-02
4.77346867e-01 -6.08447731e-01 -3.51964861e-01 9.79103986e-03
-1.01177740e+00 4.69967946e-02 4.20049131e-01 7.01549709e-01
5.78797996e-01 3.31556350e-01 2.57182956e-01 -1.46197379e+00
6.26602650e-01 -9.29706097e-01 -4.85258132e-01 1.44250706e-01
-6.99572027e-01 1.39513522e-01 2.97912955e-01 -8.31468403e-01
-1.26791716e+00 -2.99638033e-01 1.69613764e-01 -5.29039502e-01
-5.14815927e-01 6.92515731e-01 -2.64246345e-01 6.48864567e-01
3.72939289e-01 4.08361882e-01 -2.68368483e-01 -7.19052196e-01
5.31216562e-01 7.22701073e-01 1.89130202e-01 -7.81806052e-01
8.03676784e-01 6.35022283e-01 -5.02079308e-01 -1.01418793e+00
-9.67139721e-01 -7.33844161e-01 -4.04401898e-01 2.27896068e-02
7.00997949e-01 -1.26123691e+00 -2.82732844e-01 1.74665853e-01
-1.23589182e+00 -3.88830870e-01 -3.23191494e-01 6.08320892e-01
-8.16070557e-01 3.35568964e-01 -5.26228726e-01 -1.16554272e+00
-7.99627155e-02 -1.19793892e+00 1.20275640e+00 1.17492139e-01
-3.59355360e-01 -1.32405162e+00 6.55244067e-02 -5.09070493e-02
3.97588372e-01 -3.18317890e-01 1.10130775e+00 -1.30015588e+00
-4.98100281e-01 -1.19024776e-01 2.68501341e-01 2.73632288e-01
-4.29654196e-02 -2.56826997e-01 -1.16254425e+00 -2.83571005e-01
3.89549941e-01 -2.81681180e-01 1.05007541e+00 8.92805457e-01
8.64105582e-01 -4.26770329e-01 -6.39288723e-01 9.43799168e-02
1.20383012e+00 -8.52836818e-02 1.65562063e-01 1.18585333e-01
4.49607819e-01 7.29545951e-01 3.84458393e-01 5.19307554e-01
8.41549754e-01 5.38166344e-01 2.74874508e-01 -1.26070511e-02
7.12700844e-01 -4.67977256e-01 3.66408497e-01 4.30822611e-01
3.50039124e-01 -6.79271340e-01 -7.30083942e-01 8.10645759e-01
-1.89791632e+00 -6.27741635e-01 2.90800214e-01 2.06441975e+00
9.34939682e-01 2.39852354e-01 3.05386245e-01 -2.64764041e-01
6.47719741e-01 3.77535522e-01 -5.85712135e-01 9.99006480e-02
7.61560723e-02 -8.42328295e-02 2.05744952e-01 6.31646097e-01
-1.30710125e+00 9.89665270e-01 6.61692715e+00 8.21717560e-01
-6.58619821e-01 3.91965210e-01 6.02965355e-01 -1.75209567e-02
-7.62045741e-01 3.01143914e-01 -1.21082139e+00 3.33129466e-01
1.21721184e+00 -2.77636856e-01 -4.37077694e-02 1.38849580e+00
2.37980425e-01 -1.43803686e-01 -1.40820980e+00 4.38245893e-01
1.53216243e-01 -9.01407599e-01 2.98346668e-01 4.23201412e-01
8.86766315e-01 -4.53012660e-02 3.29556465e-01 4.52546179e-01
7.92524040e-01 -7.17349946e-01 7.59816766e-01 5.24185419e-01
2.40604833e-01 -6.30422592e-01 4.09128338e-01 7.42592514e-01
-5.49183369e-01 1.90674260e-01 -6.08579159e-01 3.00108880e-01
5.02419174e-01 7.36842155e-01 -9.24049079e-01 2.63463914e-01
4.64451402e-01 6.44164264e-01 5.40992469e-02 8.87826443e-01
-1.65466964e-01 1.32761204e+00 -6.25194848e-01 6.14479417e-03
3.88904005e-01 -2.18011528e-01 7.39554465e-01 9.00866032e-01
3.89373362e-01 -2.46761441e-02 4.41968262e-01 1.18576765e+00
-2.57457569e-02 5.64244092e-02 -3.95921320e-01 -4.47604433e-02
4.85815376e-01 9.19159472e-01 -4.50084925e-01 -6.61668479e-01
-6.09676182e-01 9.02012825e-01 6.87904060e-02 7.93008626e-01
-3.48655522e-01 4.08429801e-02 5.88425636e-01 2.25217551e-01
4.40758646e-01 -2.39643827e-01 1.52542964e-02 -1.29716802e+00
-2.31626630e-01 -5.95420420e-01 4.26853448e-01 -8.90004098e-01
-1.31481767e+00 4.10666108e-01 7.38330662e-01 -7.98700452e-01
-6.85084343e-01 -3.60567927e-01 -7.86219597e-01 1.03059137e+00
-1.45844138e+00 -1.20097756e+00 3.37424874e-01 3.93632382e-01
9.34815288e-01 -9.81742591e-02 7.73654878e-01 -4.89862591e-01
-2.04113066e-01 9.05040726e-02 4.41978335e-01 -2.38376901e-01
7.39602983e-01 -1.66924882e+00 3.84269983e-01 5.76470673e-01
4.84755486e-01 1.10744834e+00 1.28587162e+00 -6.50608063e-01
-8.55200589e-01 -1.10197043e+00 9.58352685e-01 -5.17925441e-01
5.65149903e-01 -7.23971665e-01 -1.09260929e+00 1.28113163e+00
3.78788620e-01 -5.40623009e-01 9.17969942e-01 6.33443952e-01
-9.35054943e-02 4.45718348e-01 -8.11393023e-01 5.09007990e-01
1.62654340e-01 -5.21038353e-01 -8.64803195e-01 5.96414566e-01
1.12454295e+00 -1.34224549e-01 -7.13019907e-01 -1.28368601e-01
1.69058815e-01 -5.07622123e-01 7.80906796e-01 -7.37298369e-01
2.79859483e-01 -2.86849272e-02 -2.49378517e-01 -1.44936585e+00
-1.02625929e-01 -6.76006496e-01 -6.58187151e-01 1.34526861e+00
5.28487146e-01 -3.85140955e-01 1.15594649e+00 9.89271104e-01
3.85132805e-02 -4.73498374e-01 -8.04877996e-01 -5.17376006e-01
3.61608267e-01 -7.20791459e-01 3.51404786e-01 7.93643117e-01
9.16922614e-02 2.73421973e-01 -6.34909511e-01 3.83946717e-01
9.54473913e-01 1.23667136e-01 7.09297478e-01 -1.58185053e+00
-6.99331164e-01 -1.76460251e-01 2.48163581e-01 -1.76267242e+00
4.12260741e-01 -4.45210367e-01 2.15456083e-01 -1.59017944e+00
5.89609504e-01 -2.58647054e-01 -1.65275335e-01 2.10697770e-01
-2.27682233e-01 -3.83898526e-01 -1.63058743e-01 4.47112113e-01
-7.42860734e-01 9.02855754e-01 5.69547713e-01 -6.08578697e-02
-3.33306521e-01 5.67354798e-01 -9.28857207e-01 8.43120217e-01
4.94308889e-01 -6.95054352e-01 -7.59752810e-01 -2.81983316e-01
-7.49242678e-02 1.33344024e-01 1.55411750e-01 -4.47649062e-01
5.14332891e-01 -2.34916523e-01 1.13467261e-01 -9.67606544e-01
7.41116464e-01 -6.17396891e-01 -2.03104854e-01 -1.96539447e-01
-8.59073460e-01 -6.29082143e-01 -1.46261826e-01 1.06376815e+00
-2.08637699e-01 -4.91004020e-01 4.04821634e-01 -1.64426416e-01
-2.95235902e-01 5.51072598e-01 -8.76506627e-01 -1.34019136e-01
7.18825102e-01 6.90634698e-02 1.50269777e-01 -1.13156068e+00
-9.90836382e-01 4.66104567e-01 4.45289642e-01 1.49210304e-01
3.90929669e-01 -9.49624002e-01 -5.89813769e-01 -2.22040266e-01
2.37221614e-01 2.49246180e-01 2.58605629e-01 4.30603027e-01
4.58066374e-01 7.87724316e-01 6.43055320e-01 -6.22696519e-01
-8.61034989e-01 5.93937635e-01 -3.63728143e-02 -2.83746421e-01
-5.95291734e-01 6.52979136e-01 8.11254561e-01 -3.71080786e-01
5.69260895e-01 1.03553664e-02 -3.35900366e-01 2.56946720e-02
3.36711228e-01 9.97233242e-02 -3.35529864e-01 -4.56393182e-01
1.24698088e-01 -7.34022111e-02 -5.77136397e-01 -7.20048249e-01
1.35349321e+00 -3.45574468e-01 2.62412965e-01 9.18360293e-01
9.81840789e-01 -3.10561568e-01 -1.48155749e+00 -7.24727631e-01
-5.11107668e-02 5.43459766e-02 2.99919516e-01 -4.22177613e-01
-5.22346377e-01 9.90102589e-01 1.77035227e-01 3.47714901e-01
6.18462265e-01 4.52607304e-01 3.42361212e-01 5.15585423e-01
2.74584740e-01 -9.84816134e-01 -1.10701926e-01 2.99514174e-01
4.44686264e-01 -1.20913231e+00 -9.70623940e-02 -2.81552613e-01
-9.02283072e-01 9.57835555e-01 3.03982496e-01 -9.04533267e-02
1.11463761e+00 4.09397073e-02 -9.47386026e-02 -1.93495229e-01
-1.13399744e+00 -3.65961902e-02 4.18965250e-01 6.96549773e-01
3.31873387e-01 4.15707640e-02 3.48388590e-02 8.72456491e-01
-2.85867870e-01 -3.28489065e-01 7.37153053e-01 7.87126362e-01
-7.88095474e-01 -1.09172702e+00 -2.48220131e-01 6.00950778e-01
-6.11494839e-01 -2.68993378e-01 -8.80011171e-02 7.65039206e-01
-4.82358277e-01 9.34885442e-01 2.46003345e-01 1.22547470e-01
-2.56234080e-01 4.48123604e-01 -1.82616934e-01 -1.13287413e+00
2.33206991e-02 7.87644446e-01 -2.08117783e-01 2.92455573e-02
-3.48457903e-01 -1.01890421e+00 -7.26364374e-01 6.50621355e-02
-4.43489999e-01 6.97495162e-01 8.43677878e-01 1.09158599e+00
6.54888451e-02 1.06614500e-01 4.79687989e-01 -8.29766452e-01
-7.89447308e-01 -1.42660487e+00 -1.01383197e+00 1.44414574e-01
4.76632833e-01 -6.15612805e-01 -4.37025815e-01 3.47950846e-01] | [10.365682601928711, 6.9236249923706055] |
816f42b0-ad29-4d17-a95b-6f99348e621b | robustness-out-of-the-box-compositional | 2012.00558 | null | https://arxiv.org/abs/2012.00558v1 | https://arxiv.org/pdf/2012.00558v1.pdf | Robustness Out of the Box: Compositional Representations Naturally Defend Against Black-Box Patch Attacks | Patch-based adversarial attacks introduce a perceptible but localized change to the input that induces misclassification. While progress has been made in defending against imperceptible attacks, it remains unclear how patch-based attacks can be resisted. In this work, we study two different approaches for defending against black-box patch attacks. First, we show that adversarial training, which is successful against imperceptible attacks, has limited effectiveness against state-of-the-art location-optimized patch attacks. Second, we find that compositional deep networks, which have part-based representations that lead to innate robustness to natural occlusion, are robust to patch attacks on PASCAL3D+ and the German Traffic Sign Recognition Benchmark, without adversarial training. Moreover, the robustness of compositional models outperforms that of adversarially trained standard models by a large margin. However, on GTSRB, we observe that they have problems discriminating between similar traffic signs with fine-grained differences. We overcome this limitation by introducing part-based finetuning, which improves fine-grained recognition. By leveraging compositional representations, this is the first work that defends against black-box patch attacks without expensive adversarial training. This defense is more robust than adversarial training and more interpretable because it can locate and ignore adversarial patches. | ['Alan Yuille', 'Chenglin Yang', 'Adam Kortylewski', 'Christian Cosgrove'] | 2020-12-01 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 5.16199172e-01 -1.88293710e-01 -2.19450817e-01 -2.06597924e-01
-7.97738910e-01 -1.32039070e+00 7.12595344e-01 -5.14150739e-01
-4.58172522e-02 4.56654489e-01 -1.79095849e-01 -8.38096559e-01
3.54244187e-02 -8.67217183e-01 -1.26032710e+00 -7.77986705e-01
-1.00577716e-02 1.29664421e-01 5.95607698e-01 -5.37167013e-01
1.26047328e-01 9.87850130e-01 -1.46794152e+00 6.81463301e-01
5.51659465e-01 7.90743172e-01 -7.01429427e-01 1.14642179e+00
3.56675863e-01 9.76560652e-01 -1.24307406e+00 -7.35238492e-01
8.76494229e-01 -2.60717124e-01 -6.67305768e-01 -2.06882626e-01
1.44753814e+00 -4.57950056e-01 -7.60826468e-01 1.04963791e+00
4.61489171e-01 -1.86111301e-01 2.96828657e-01 -1.44908583e+00
-9.54863131e-01 2.58569300e-01 -3.19288909e-01 1.32813960e-01
6.61326423e-02 8.42978597e-01 7.32182682e-01 -2.28170350e-01
5.26973069e-01 1.60641956e+00 9.20188785e-01 9.58464861e-01
-1.33831632e+00 -7.72145987e-01 1.84619382e-01 2.44086504e-01
-1.13154507e+00 -3.20910215e-01 7.49098718e-01 -6.85943887e-02
8.79172385e-01 8.56825233e-01 6.04380853e-02 1.62507200e+00
3.41510117e-01 6.55901551e-01 1.55916488e+00 -3.00802469e-01
-4.79935072e-02 -1.17059976e-01 6.04321919e-02 6.06278241e-01
2.75971591e-01 9.84979153e-01 -3.29789221e-02 -5.78774571e-01
5.55533946e-01 -1.97836086e-01 -2.93332100e-01 -3.26765060e-01
-8.50517869e-01 7.14334130e-01 6.71722651e-01 9.60250944e-02
4.62076142e-02 5.27106047e-01 5.77892601e-01 7.71607459e-01
6.68279901e-02 7.55257368e-01 -7.22324014e-01 2.53396213e-01
-6.11294746e-01 3.74252826e-01 8.27873409e-01 4.22105014e-01
5.05777776e-01 5.09969831e-01 -1.52188301e-01 4.52821136e-01
-5.68091981e-02 1.22493267e+00 1.43413082e-01 -9.88688886e-01
3.28064710e-01 1.71712458e-01 -2.22275048e-01 -1.18185031e+00
-1.73133358e-01 -4.76002872e-01 -5.86845756e-01 1.22060490e+00
8.46071899e-01 -2.71764576e-01 -1.31683910e+00 1.85935235e+00
2.72405624e-01 3.13340366e-01 8.30563828e-02 7.23520637e-01
3.40112507e-01 4.25213248e-01 -4.28019091e-03 5.83408415e-01
1.30056894e+00 -7.94998229e-01 -1.80919185e-01 -2.07517147e-01
3.24018776e-01 -8.78441215e-01 1.07397377e+00 3.87044698e-01
-5.38610816e-01 -6.77239597e-01 -1.39540839e+00 3.58195871e-01
-8.04974318e-01 -3.84937376e-01 4.74303573e-01 1.66999280e+00
-8.63016903e-01 6.13033652e-01 -5.90291142e-01 -1.75783277e-01
6.51725113e-01 3.39273334e-01 -5.82525492e-01 -9.45754200e-02
-1.28417301e+00 9.98037100e-01 -1.28570557e-01 -1.41852766e-01
-1.22643125e+00 -7.93468952e-01 -5.62456906e-01 -1.91216543e-01
3.55150372e-01 -5.59052527e-01 8.66959751e-01 -1.31253743e+00
-1.49608266e+00 8.82679701e-01 2.62836784e-01 -9.98166859e-01
7.35982537e-01 4.78358306e-02 -8.14199865e-01 2.45073304e-01
-3.75003457e-01 6.61406219e-01 1.63371909e+00 -1.43838954e+00
-2.85306156e-01 -2.32710168e-02 5.16450763e-01 -4.09307927e-01
-4.77922410e-02 2.40650326e-01 1.99326292e-01 -1.11381984e+00
-5.62790692e-01 -1.26699853e+00 -8.38880017e-02 4.10658002e-01
-4.12698448e-01 2.16017157e-01 1.60663736e+00 -3.76858652e-01
5.11977732e-01 -2.29201984e+00 -4.84950423e-01 2.97243804e-01
8.32213387e-02 1.03619742e+00 -7.21167922e-01 1.64354950e-01
-3.13028038e-01 5.69576383e-01 -2.63812631e-01 1.54889971e-01
3.12948942e-01 4.88929003e-01 -1.25919509e+00 7.49992549e-01
4.95595902e-01 1.11872125e+00 -5.63115478e-01 6.53640404e-02
2.45964602e-01 6.16802156e-01 -6.69663608e-01 -8.14146623e-02
-3.12309891e-01 3.36807162e-01 -1.42925888e-01 8.16315293e-01
1.03952050e+00 2.81435788e-01 -2.15561464e-01 -1.56981677e-01
3.12960118e-01 5.38640916e-02 -1.00083673e+00 7.98403561e-01
-1.99220657e-01 1.02955711e+00 8.45345259e-02 -7.86244333e-01
7.70067513e-01 9.08935294e-02 -1.12985797e-01 -6.97025955e-01
-4.24470529e-02 3.15145552e-01 9.93144140e-02 -6.89223260e-02
2.87052244e-01 3.72475162e-02 -2.15263411e-01 4.85882074e-01
-1.95436701e-01 -1.31826922e-01 -1.39837876e-01 -2.59768944e-02
1.55864298e+00 8.83574560e-02 -1.87804267e-01 5.80787286e-02
5.05046368e-01 -5.82921207e-02 5.27594328e-01 1.42928815e+00
-4.16133106e-01 6.70827746e-01 4.68073100e-01 -7.98286378e-01
-9.27678704e-01 -1.27815425e+00 -8.28741267e-02 1.11821508e+00
1.20501615e-01 -2.42794473e-02 -7.31208503e-01 -1.35504389e+00
3.40421766e-01 5.23037851e-01 -9.50620830e-01 -5.14358759e-01
-9.96973455e-01 -5.65699697e-01 1.63760793e+00 6.55712485e-01
7.10710883e-01 -9.01610076e-01 -3.16826403e-01 -1.95548654e-01
2.30969995e-01 -9.82417762e-01 -4.26048219e-01 1.22918986e-01
-5.40055156e-01 -1.39786804e+00 -4.37103719e-01 -4.40250665e-01
6.38740778e-01 2.48055369e-01 1.08595908e+00 4.57329184e-01
-4.24726158e-01 4.42193627e-01 -3.57472330e-01 -3.59702617e-01
-8.53229523e-01 -2.04751447e-01 -6.91753328e-02 1.07526831e-01
1.31122708e-01 -3.91344339e-01 -3.98334295e-01 9.21127200e-01
-1.18740404e+00 -8.56540442e-01 6.33461535e-01 1.01884305e+00
3.51113021e-01 2.17863441e-01 1.92715257e-01 -9.55314577e-01
4.81377751e-01 5.68944076e-03 -5.83078623e-01 2.24815279e-01
-2.12195233e-01 2.10064817e-02 1.10764539e+00 -8.71871889e-01
-7.74946392e-01 -2.15345308e-01 -4.55056608e-01 -5.57788253e-01
-6.42191648e-01 -3.83753151e-01 -3.04613739e-01 -1.07044089e+00
1.35132802e+00 2.73729749e-02 -1.79492638e-01 -2.64916718e-01
6.33797884e-01 2.87059873e-01 8.21279526e-01 -8.60270560e-01
1.61503685e+00 7.06891716e-01 2.12016985e-01 -7.77166605e-01
-4.51739848e-01 2.46423274e-01 -2.25774288e-01 8.18859488e-02
6.05717659e-01 -4.14004505e-01 -7.71927595e-01 8.22786510e-01
-1.01502299e+00 -6.59873903e-01 -5.91299534e-01 -1.71450615e-01
-5.83200812e-01 6.01228237e-01 -6.25128031e-01 -2.56073117e-01
-3.35388235e-03 -1.12051535e+00 8.39069426e-01 -1.02276251e-01
-1.16333053e-01 -6.53213739e-01 4.67838272e-02 4.23156977e-01
8.20547223e-01 6.00226820e-01 8.02507699e-01 -8.05332065e-01
-8.80528390e-01 -6.64604962e-01 -1.60719693e-01 8.04772258e-01
2.71634553e-02 2.56928742e-01 -1.17148745e+00 -4.51701701e-01
1.59730259e-02 -4.47701603e-01 9.97553289e-01 -1.76679064e-02
1.07267034e+00 -7.30001628e-01 -9.78773162e-02 9.12505329e-01
1.11785388e+00 5.65220825e-02 1.11111927e+00 6.40423715e-01
6.37465596e-01 3.60633969e-01 3.14465880e-01 -2.11018860e-01
-2.29767725e-01 7.21692026e-01 7.16848254e-01 -4.01081741e-01
-5.99565387e-01 -2.45851383e-01 5.08601367e-01 -4.10743594e-01
1.40725642e-01 -5.03386676e-01 -6.22929633e-01 1.82075158e-01
-1.36315978e+00 -1.41302860e+00 2.99654379e-02 2.05091691e+00
6.55258894e-01 4.54749793e-01 1.67458147e-01 4.64647472e-01
7.27756202e-01 5.93201637e-01 -6.29220724e-01 -8.52326095e-01
-6.95505202e-01 6.19681656e-01 1.07988560e+00 5.58274686e-01
-1.54870450e+00 1.19059420e+00 6.70070267e+00 1.04336607e+00
-1.38144124e+00 6.20324425e-02 4.88325179e-01 2.91356653e-01
-1.68620467e-01 4.90166359e-02 -5.90478957e-01 3.75572324e-01
8.07825863e-01 5.08398414e-01 5.79278767e-01 9.70767975e-01
-3.14691216e-01 5.66731930e-01 -8.21974099e-01 3.27396482e-01
2.38256708e-01 -1.41805160e+00 1.86575815e-01 2.10405290e-02
9.14724350e-01 4.85706143e-02 7.15001225e-01 4.24566388e-01
8.08821559e-01 -1.17166853e+00 6.47987723e-01 8.76398385e-02
6.43720090e-01 -7.11981177e-01 5.33247411e-01 -4.98469323e-02
-9.31045175e-01 -1.41280785e-01 -4.37955797e-01 1.98238596e-01
-1.70273691e-01 8.98349732e-02 -4.35466558e-01 2.17192367e-01
5.85270584e-01 1.01134494e-01 -8.96021366e-01 8.68742406e-01
-6.94677591e-01 9.66901064e-01 -2.89233029e-01 3.38917047e-01
2.96221077e-01 5.83360314e-01 8.10813308e-01 1.18117356e+00
-2.23341361e-01 -4.12923664e-01 1.68863997e-01 7.52024293e-01
-1.68795943e-01 -4.90844369e-01 -8.21599782e-01 2.01553062e-01
3.27516645e-01 8.31214845e-01 -4.30811763e-01 -2.10831299e-01
-8.27330574e-02 1.01928031e+00 -2.84143854e-02 5.16495705e-01
-1.11162603e+00 -6.03957772e-01 1.23248041e+00 -9.09763724e-02
7.39623368e-01 -2.69998252e-01 -2.63913393e-01 -9.82593715e-01
-4.49107774e-03 -1.83711565e+00 3.91475081e-01 -4.32163775e-01
-1.49349868e+00 6.74153686e-01 -3.83435935e-01 -1.27777648e+00
-4.64823022e-02 -1.01894736e+00 -8.27629030e-01 6.52153075e-01
-1.33294153e+00 -1.51691616e+00 1.69478878e-01 9.20398533e-01
7.76172727e-02 -2.80550897e-01 8.48883748e-01 1.48650423e-01
-3.06008250e-01 1.27967691e+00 -5.73297963e-02 5.53065360e-01
9.81037557e-01 -1.11600900e+00 1.05638766e+00 1.31073785e+00
3.19687605e-01 6.36541963e-01 7.43994594e-01 -6.13662720e-01
-1.41098714e+00 -1.29732430e+00 3.36383224e-01 -8.64260197e-01
8.06767642e-01 -2.68281132e-01 -9.82332528e-01 6.11387193e-01
9.99844521e-02 5.65604508e-01 5.08340955e-01 -2.51117855e-01
-1.52033961e+00 -3.02450061e-01 -1.62383604e+00 7.78715134e-01
9.05649424e-01 -9.36039329e-01 -6.37750447e-01 3.35840106e-01
8.81511688e-01 -4.76102710e-01 -5.99907756e-01 5.63536465e-01
6.61717474e-01 -9.33559358e-01 1.46952331e+00 -9.17793095e-01
-1.39657959e-01 -6.96498692e-01 -3.50066692e-01 -1.10610116e+00
-2.87713051e-01 -9.40285683e-01 -6.40557781e-02 1.02499115e+00
3.40586513e-01 -1.19729495e+00 8.77065122e-01 2.88335025e-01
4.65699509e-02 -2.44274825e-01 -8.68111730e-01 -1.43194056e+00
5.67230225e-01 -4.30900455e-01 9.25265551e-01 9.04237807e-01
-9.13526177e-01 -4.43276078e-01 -6.44910097e-01 7.32267916e-01
7.45741129e-01 -5.22546750e-03 1.33670688e+00 -7.45473623e-01
-6.58042252e-01 -5.85759342e-01 -9.82788980e-01 -8.95202816e-01
3.35129410e-01 -4.79073405e-01 -9.82095376e-02 -5.96463799e-01
-5.50007999e-01 -4.15519685e-01 -3.28874588e-01 8.02555859e-01
-2.29365528e-01 9.07654166e-01 4.06753212e-01 7.64339268e-02
-2.15232536e-01 -4.68336046e-02 1.07473457e+00 -6.86793268e-01
1.65770277e-01 -1.54614141e-02 -6.91265047e-01 5.93105912e-01
9.40341532e-01 -7.25721776e-01 -1.17306389e-01 -4.40311909e-01
-1.14522114e-01 -6.67290390e-01 9.11221504e-01 -1.19116616e+00
-7.48040751e-02 -1.86131328e-01 2.87019938e-01 -2.25602686e-01
2.33550146e-01 -8.19708705e-01 -3.56065147e-02 7.07356751e-01
-2.67933756e-01 -9.84772518e-02 5.69379091e-01 6.97346032e-01
5.92780299e-03 1.20022476e-01 1.09237194e+00 1.14770979e-01
-5.77550471e-01 3.12725455e-01 -4.91945356e-01 1.99695781e-01
9.70544338e-01 -3.11692148e-01 -1.16021085e+00 -1.36982709e-01
-3.63054305e-01 -4.11711693e-01 7.92400360e-01 5.39051056e-01
3.72272462e-01 -1.20135748e+00 -7.91180372e-01 5.24121284e-01
-7.42069781e-02 -7.27811337e-01 3.50339442e-01 3.88755113e-01
-7.99689293e-01 2.02743530e-01 -4.10252452e-01 -5.37925661e-01
-1.59786987e+00 9.70465541e-01 6.70704246e-01 -1.82593152e-01
-4.22091335e-01 9.36762094e-01 2.08000630e-01 -7.10356951e-01
2.66226351e-01 -1.83199525e-01 4.52255815e-01 -6.43714547e-01
6.45228922e-01 3.39232147e-01 7.67843947e-02 -7.76862741e-01
-4.86645818e-01 8.37706745e-01 -1.19440340e-01 2.16571927e-01
9.17823732e-01 4.64706630e-01 2.66999155e-02 -5.46530962e-01
1.29122674e+00 5.00809073e-01 -1.44499421e+00 2.97178719e-02
-3.55878413e-01 -8.94883096e-01 -3.58354568e-01 -1.09181201e+00
-1.23441792e+00 8.64083588e-01 8.11277330e-01 4.03921008e-01
1.06147230e+00 -4.61873829e-01 8.60651553e-01 6.49282873e-01
1.77953959e-01 -4.92467791e-01 5.15967421e-02 5.95260322e-01
8.20931971e-01 -9.53858972e-01 -2.12026790e-01 -2.78632432e-01
-2.54728436e-01 9.95715022e-01 5.68756878e-01 -5.46939492e-01
4.11734283e-01 3.80624771e-01 5.65868080e-01 1.27284795e-01
-5.74754238e-01 -1.71820179e-01 3.81957233e-01 1.22235036e+00
-2.92479634e-01 1.61946028e-01 2.47337803e-01 -1.20730408e-01
-2.46914878e-01 -5.93346477e-01 3.14529479e-01 1.00478148e+00
-2.11818546e-01 -1.61931932e+00 -1.07611108e+00 -9.80581120e-02
-6.74759388e-01 -1.40131740e-02 -9.10441577e-01 8.59025896e-01
3.33552718e-01 1.09607196e+00 -4.00010884e-01 -7.08719611e-01
4.43158686e-01 -1.58110276e-01 5.12261689e-01 -7.12970644e-02
-9.73355770e-01 -6.19319320e-01 1.69948027e-01 -8.67937446e-01
-1.13608446e-02 -2.79113948e-01 -6.10073507e-01 -7.82602966e-01
-1.73034847e-01 -2.79668480e-01 4.99435723e-01 7.45140970e-01
4.90941852e-01 4.45843458e-01 9.16135013e-01 -6.94255292e-01
-8.55347395e-01 -2.66178578e-01 -9.41454491e-04 6.12663150e-01
6.68472767e-01 -4.00842369e-01 -8.49443793e-01 -2.43828252e-01] | [5.557440757751465, 7.911334991455078] |
c76ab8d0-eb79-4ab9-8017-cc964e970c74 | vifi-loc-multi-modal-pedestrian-localization | 2211.12021 | null | https://arxiv.org/abs/2211.12021v1 | https://arxiv.org/pdf/2211.12021v1.pdf | ViFi-Loc: Multi-modal Pedestrian Localization using GAN with Camera-Phone Correspondences | In Smart City and Vehicle-to-Everything (V2X) systems, acquiring pedestrians' accurate locations is crucial to traffic safety. Current systems adopt cameras and wireless sensors to detect and estimate people's locations via sensor fusion. Standard fusion algorithms, however, become inapplicable when multi-modal data is not associated. For example, pedestrians are out of the camera field of view, or data from camera modality is missing. To address this challenge and produce more accurate location estimations for pedestrians, we propose a Generative Adversarial Network (GAN) architecture. During training, it learns the underlying linkage between pedestrians' camera-phone data correspondences. During inference, it generates refined position estimations based only on pedestrians' phone data that consists of GPS, IMU and FTM. Results show that our GAN produces 3D coordinates at 1 to 2 meter localization error across 5 different outdoor scenes. We further show that the proposed model supports self-learning. The generated coordinates can be associated with pedestrian's bounding box coordinates to obtain additional camera-phone data correspondences. This allows automatic data collection during inference. After fine-tuning on the expanded dataset, localization accuracy is improved by up to 26%. | ['HongSheng Lu', 'Marco Gruteser', 'Kristin Dana', 'Hansi Liu'] | 2022-11-22 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [-1.48966551e-01 -9.90330726e-02 -7.04196915e-02 -5.05561292e-01
-1.28052890e+00 -7.50265718e-01 6.10684037e-01 2.77580973e-02
-3.53912085e-01 1.08886719e+00 2.67729402e-01 -6.00595400e-02
4.84972566e-01 -1.10162807e+00 -1.09695399e+00 -6.07857406e-01
2.85473734e-01 3.81956369e-01 1.60110280e-01 9.43780988e-02
-2.07153678e-01 4.45994288e-01 -1.42350149e+00 -7.97498673e-02
6.87432766e-01 1.06940186e+00 -6.85663819e-02 1.04087782e+00
1.29304469e-01 4.29096341e-01 -9.53231633e-01 -6.81611836e-01
4.04437184e-01 2.03048661e-02 5.42781502e-02 -7.44354501e-02
6.88424230e-01 -7.06948042e-01 -7.56528258e-01 9.11278486e-01
5.97534239e-01 -4.97430302e-02 3.33426416e-01 -1.87749636e+00
-4.69152182e-01 1.90835163e-01 -3.56054187e-01 -9.25314054e-02
5.97941697e-01 4.10149723e-01 3.35785389e-01 -5.94295084e-01
1.14526637e-01 9.95749712e-01 1.29992795e+00 5.15021145e-01
-9.89092708e-01 -6.71469688e-01 -1.35474987e-02 1.90597624e-01
-1.85163653e+00 -5.89227915e-01 6.71807647e-01 -2.05414787e-01
6.04106307e-01 3.57143849e-01 6.84395194e-01 1.53375781e+00
3.27330045e-02 5.15194178e-01 5.09865165e-01 -3.89476796e-03
3.15715253e-01 2.80211210e-01 -3.87576610e-01 3.35133046e-01
4.57876593e-01 1.10761918e-01 -4.56588864e-01 8.05537216e-03
7.25171268e-01 3.78688753e-01 -2.83554439e-02 9.40720923e-03
-1.40058672e+00 6.85826719e-01 6.42355025e-01 -1.05435930e-01
-4.18112427e-01 6.00009143e-01 -3.94745097e-02 -2.45572984e-01
3.16114545e-01 -1.71116859e-01 -1.05414115e-01 -4.20334548e-01
-9.15027738e-01 6.48164004e-02 4.16965187e-01 1.64216292e+00
8.78978133e-01 1.87534448e-02 4.43414524e-02 3.52925688e-01
3.09251934e-01 1.33964145e+00 9.31144282e-02 -1.06152332e+00
9.12914395e-01 4.56775725e-01 3.67720306e-01 -1.29238153e+00
-2.40806177e-01 -1.90932006e-01 -1.16353083e+00 -1.14231788e-01
4.67466503e-01 -7.65446126e-01 -6.64605141e-01 1.69820702e+00
3.90824348e-01 7.61476815e-01 9.09933373e-02 8.03318202e-01
7.14511573e-01 6.27847433e-01 4.53379303e-02 3.43645871e-01
1.15734816e+00 -5.37327588e-01 -5.31955779e-01 -4.46291745e-01
5.00591338e-01 -3.57302099e-01 4.41133946e-01 -1.20625176e-01
-8.87247205e-01 -8.45545411e-01 -9.19093490e-01 5.67907207e-02
-7.30774701e-01 2.50356317e-01 3.31080735e-01 1.01523328e+00
-1.25729358e+00 -4.91785109e-02 -8.79594207e-01 -4.65287417e-01
5.25064886e-01 5.49763680e-01 -4.01551723e-01 -1.31587163e-01
-1.26802552e+00 7.62825489e-01 3.78147587e-02 1.04670569e-01
-6.06789470e-01 -8.53515267e-01 -9.67396498e-01 -1.50676340e-01
-2.29049418e-02 -1.01831961e+00 9.75692928e-01 -5.82089484e-01
-1.16988122e+00 2.10516185e-01 -3.85558963e-01 -6.89367652e-01
6.76823556e-01 -1.94161654e-01 -7.48700976e-01 9.68990549e-02
5.65827549e-01 1.06483531e+00 6.44585133e-01 -1.38774991e+00
-1.06283343e+00 -3.31259459e-01 4.99680787e-02 -3.90965119e-02
-9.51206032e-03 -5.40678799e-01 -6.34020567e-01 -3.51901025e-01
-1.30300984e-01 -1.06285012e+00 -2.46254981e-01 -4.37037572e-02
-5.99016428e-01 2.25924060e-01 1.03308654e+00 -7.41035402e-01
7.79768705e-01 -1.93963182e+00 -5.20730853e-01 3.18116426e-01
1.81687474e-01 6.86611980e-02 1.18588224e-01 1.20588169e-01
2.85291761e-01 -1.13862239e-01 1.40282452e-01 -8.42577636e-01
1.19522050e-01 2.35993102e-01 -1.69854522e-01 5.85614026e-01
-2.21980028e-02 1.17093730e+00 -9.19018269e-01 -4.32642400e-01
9.04164553e-01 1.08195877e+00 -5.57022929e-01 4.80315946e-02
2.73655057e-01 8.35204184e-01 -3.55155945e-01 7.48140574e-01
9.01072681e-01 -5.21233305e-02 -2.99472541e-01 -3.11042398e-01
-8.29770695e-03 -3.36854383e-02 -1.26817381e+00 1.58773494e+00
-5.35226107e-01 7.85156250e-01 -2.27496430e-01 -5.38396120e-01
7.17451215e-01 2.87599772e-01 5.84641278e-01 -6.94649339e-01
3.39636877e-02 -1.34128809e-01 -7.48162746e-01 -9.68697667e-02
6.81800961e-01 4.69657481e-01 -5.01526237e-01 -4.80855592e-02
-2.17633843e-01 2.95527190e-01 -2.06837371e-01 1.44335628e-01
1.21189106e+00 -1.58768430e-01 3.65743879e-03 3.51110399e-01
4.59684789e-01 -3.90858948e-02 4.54582542e-01 8.68257165e-01
-2.24836737e-01 8.40837598e-01 -1.06211960e-01 -4.53815073e-01
-1.15325475e+00 -1.40519464e+00 1.16772518e-01 4.63020086e-01
3.05828154e-01 -4.10264492e-01 -9.44454968e-01 -6.01505339e-01
1.46594644e-01 8.31629157e-01 -4.32272226e-01 -1.43690333e-01
-6.46376908e-01 -4.69646960e-01 9.13997054e-01 9.19608414e-01
1.05394268e+00 -2.03286931e-01 -4.71702605e-01 2.04523265e-01
-5.21583080e-01 -1.45557785e+00 -5.71242273e-01 -5.09149611e-01
-2.32864007e-01 -1.00915253e+00 -7.68185198e-01 -2.70813316e-01
9.44134295e-01 5.33210576e-01 8.44865143e-01 -2.74679124e-01
1.47705823e-01 8.89844954e-01 -5.30947857e-02 -2.54017144e-01
-2.06215650e-01 8.24766457e-02 2.86249638e-01 2.50592023e-01
7.03068078e-01 -5.16860843e-01 -9.33406949e-01 4.31322932e-01
-1.63768932e-01 -5.45908324e-02 2.19810769e-01 3.01037610e-01
5.50215721e-01 1.49577349e-01 4.03291643e-01 -3.36406678e-01
-1.27489846e-02 -8.19350541e-01 -8.41637969e-01 -2.18689982e-02
-5.98723851e-02 -4.54594970e-01 6.68251157e-01 -2.72156566e-01
-8.57567787e-01 5.02510548e-01 -2.83042014e-01 -5.16056538e-01
-6.78432763e-01 -2.37872839e-01 -6.00209892e-01 -4.64535393e-02
3.77284676e-01 2.31847510e-01 -3.55042368e-01 -9.43710804e-02
5.48733711e-01 8.64917696e-01 1.03110313e+00 -1.72957450e-01
9.90702748e-01 6.88267827e-01 3.12988535e-02 -8.36062253e-01
-3.67582858e-01 -3.38185579e-01 -7.12041795e-01 -3.96346301e-01
1.12799060e+00 -1.47988760e+00 -1.16274011e+00 4.64674085e-01
-1.28966308e+00 -7.70055577e-02 -1.53589681e-01 6.13740325e-01
-4.27396923e-01 9.00977477e-02 -1.49879187e-01 -8.59777331e-01
2.15129890e-02 -1.10379624e+00 1.60370100e+00 3.98488373e-01
-1.41480818e-01 -1.10890734e+00 -1.39943093e-01 4.68787313e-01
4.71085012e-01 4.77865130e-01 -5.90982847e-02 -2.28928149e-01
-1.10987759e+00 -7.55927265e-01 -1.06949165e-01 6.72618523e-02
2.17363223e-01 -3.61181527e-01 -9.87550199e-01 -1.95795923e-01
-4.72710609e-01 4.31657374e-01 4.05908823e-01 5.57259619e-01
1.03636003e+00 -6.08834028e-01 -7.51627803e-01 8.38373780e-01
1.32118869e+00 1.22298114e-01 6.95688486e-01 1.15737841e-01
1.14241493e+00 1.04320034e-01 2.60364741e-01 4.86423403e-01
1.16882193e+00 9.40134883e-01 5.73565662e-01 -1.02730058e-02
-3.32388490e-01 -7.73420870e-01 2.70543188e-01 3.60402614e-01
4.08604695e-03 -4.89749640e-01 -8.47843587e-01 5.70201993e-01
-1.81600547e+00 -1.24215221e+00 -2.58192688e-01 2.34553409e+00
2.57534295e-01 -4.17544246e-02 3.34171712e-01 -3.27584371e-02
9.96669352e-01 -1.31162658e-01 -5.24792492e-01 3.11159253e-01
-1.05114266e-01 -2.90732235e-01 1.27428198e+00 7.61317134e-01
-1.20067704e+00 8.37094903e-01 5.95797920e+00 4.29804385e-01
-8.35446119e-01 2.56145984e-01 6.40375793e-01 -1.79897755e-01
-1.69580579e-01 -2.32944444e-01 -1.26949966e+00 1.24769306e+00
1.26161540e+00 2.90536612e-01 4.24914390e-01 1.00044203e+00
1.87427446e-01 -2.71144331e-01 -1.06825721e+00 1.40771961e+00
1.85026839e-01 -1.55077779e+00 -3.93761784e-01 3.14944953e-01
7.72594094e-01 1.82585403e-01 5.57989925e-02 2.39648953e-01
4.64252144e-01 -8.76067460e-01 6.59035623e-01 7.58384049e-01
9.11210954e-01 -9.88562882e-01 8.35220993e-01 4.44351494e-01
-1.52156293e+00 -6.27429504e-03 -2.37603173e-01 9.99313667e-02
6.10398710e-01 4.90339488e-01 -9.95759428e-01 3.62159878e-01
7.44364321e-01 6.77932203e-01 -8.36743414e-01 8.09312344e-01
-1.86175629e-01 3.84396553e-01 -6.90944612e-01 1.50660202e-01
7.51024261e-02 -1.02787882e-01 4.27758038e-01 1.01226580e+00
8.76842558e-01 -6.40384257e-02 7.33729899e-02 7.81416059e-01
-7.11366907e-02 -6.89977407e-01 -1.05843902e+00 7.13341892e-01
1.13197362e+00 9.97360170e-01 -4.00388122e-01 -4.73867536e-01
-5.25322497e-01 1.07641387e+00 -1.37598217e-01 6.05847180e-01
-1.43945241e+00 -1.99060544e-01 1.12588906e+00 3.30632746e-01
3.27635974e-01 -3.72039407e-01 -3.75940979e-01 -1.10628998e+00
-1.08155347e-02 -3.53632987e-01 -7.83323646e-02 -8.48556519e-01
-1.03433239e+00 2.07884625e-01 -1.10197738e-01 -1.34481227e+00
-5.08348942e-01 -1.60453588e-01 -4.54299748e-01 7.78356075e-01
-1.13677871e+00 -1.60763752e+00 -6.72223330e-01 8.75469744e-01
2.24786773e-01 -3.40337455e-01 7.11824417e-01 7.81034112e-01
-4.73881871e-01 9.36796367e-01 2.40565345e-01 6.29496157e-01
4.67455328e-01 -1.15814090e+00 8.64514291e-01 1.08616316e+00
2.73947753e-02 3.75094026e-01 5.01976967e-01 -7.59146810e-01
-1.51971352e+00 -1.76525867e+00 8.94165039e-01 -1.08897173e+00
2.02184409e-01 -4.95754182e-01 -1.56060904e-01 1.13056195e+00
1.51950875e-02 3.34369719e-01 6.03584886e-01 -2.87998855e-01
-6.39367998e-02 -5.82507968e-01 -1.49002957e+00 6.49953067e-01
9.60727036e-01 -5.75016499e-01 -1.04460180e-01 2.70414263e-01
5.55795968e-01 -5.85912287e-01 -8.64245832e-01 7.04386160e-02
3.19255203e-01 -9.06573176e-01 1.48253453e+00 -9.78124049e-03
-1.84129402e-01 -6.34106100e-01 -5.89113355e-01 -1.34452713e+00
-1.91864353e-02 -5.62703073e-01 -2.95765042e-01 1.45715380e+00
2.41888426e-02 -8.60104799e-01 9.56766427e-01 8.76794994e-01
-5.41581847e-02 -9.47779119e-02 -1.40233135e+00 -5.36481738e-01
-3.38568002e-01 -8.39077711e-01 1.32134676e+00 5.99671304e-01
-4.62516218e-01 3.21173221e-01 -5.96348226e-01 8.71613920e-01
9.87591028e-01 -5.05012691e-01 1.39876699e+00 -8.78251553e-01
1.02681622e-01 -1.63945258e-01 -8.23346853e-01 -1.33501494e+00
4.72861230e-02 -4.18645710e-01 -3.76318544e-02 -1.54335368e+00
-3.17861259e-01 -5.67350924e-01 2.85277426e-01 2.56311804e-01
6.83783069e-02 5.97135961e-01 1.04883060e-01 -1.20171994e-01
-7.77263641e-01 2.60752529e-01 6.18935943e-01 -3.50717485e-01
2.55463598e-03 4.48226303e-01 -6.40887439e-01 7.11966813e-01
9.26226854e-01 -1.35442495e-01 -3.17072153e-01 -6.66701734e-01
7.37432986e-02 1.35650605e-01 1.07563698e+00 -1.70618498e+00
6.30381942e-01 1.70179784e-01 1.03735554e+00 -1.04136169e+00
8.54591191e-01 -1.29237545e+00 5.90613067e-01 1.28608599e-01
1.90206602e-01 2.81076759e-01 1.35729298e-01 6.83414280e-01
1.33064345e-01 3.86932701e-01 4.21633303e-01 3.02188173e-02
-6.58836126e-01 4.81193483e-01 -3.21966916e-01 -1.95178628e-01
1.37786067e+00 -4.65188682e-01 -3.96468312e-01 -7.83143580e-01
-4.01068181e-01 2.44649485e-01 5.82854629e-01 3.68850112e-01
6.08580112e-01 -1.89789355e+00 -4.56702918e-01 4.70545650e-01
-4.82744016e-02 2.34439209e-01 3.59562099e-01 6.01348341e-01
-4.36277688e-01 7.64568210e-01 7.24709928e-02 -9.92474318e-01
-9.80218887e-01 4.74764407e-01 2.91976005e-01 4.32527632e-01
-4.68666822e-01 5.22836745e-01 -2.24514648e-01 -3.47130030e-01
2.56389320e-01 -4.23754811e-01 2.11528704e-01 -2.17728660e-01
6.91860318e-01 7.11745799e-01 -1.59568235e-01 -1.27367866e+00
-5.92988849e-01 6.67043507e-01 4.58921969e-01 -1.49508461e-01
9.84765351e-01 -6.86004519e-01 5.32245696e-01 8.82996991e-02
1.27620745e+00 2.26134583e-01 -1.68768990e+00 -6.75366744e-02
-5.18403590e-01 -6.59915626e-01 -1.58485532e-01 -5.34293532e-01
-1.19481361e+00 6.79642558e-01 7.77996123e-01 8.91909972e-02
8.43457758e-01 1.86693259e-02 1.19763613e+00 2.71607697e-01
7.84188390e-01 -1.00112307e+00 -4.16301847e-01 1.67090937e-01
2.33632877e-01 -1.59365046e+00 -3.44972789e-01 -1.49440005e-01
-5.05404472e-01 6.94913507e-01 6.26811743e-01 7.74178207e-02
5.69293857e-01 3.91994417e-01 6.23066314e-02 3.66181612e-01
-2.24160001e-01 -2.72837847e-01 8.23855307e-03 1.35992730e+00
-2.49607876e-01 2.71200329e-01 8.13233376e-01 6.03683770e-01
-5.77095866e-01 -2.86622643e-01 2.28051275e-01 5.68514347e-01
-2.15678588e-01 -7.96244621e-01 -8.32355678e-01 1.69039249e-01
2.10078321e-02 1.20253675e-01 -2.70660128e-02 7.16489434e-01
5.94470143e-01 1.26954198e+00 4.84944850e-01 -6.10376239e-01
2.43336990e-01 -1.82288557e-01 2.68411428e-01 1.64018348e-02
-1.28625363e-01 -3.66452724e-01 -1.29962593e-01 -7.49354541e-01
-3.41804206e-01 -7.25379169e-01 -1.02924109e+00 -1.03577793e+00
1.08622521e-01 -4.65712100e-02 8.46936405e-01 8.63898933e-01
7.46745825e-01 4.60903496e-01 6.89606845e-01 -1.10015941e+00
4.55173850e-02 -6.12916648e-01 -7.58768469e-02 2.35936448e-01
5.87062657e-01 -5.03737330e-01 -3.42566110e-02 2.41599396e-01] | [6.79966402053833, 0.4938651919364929] |
8a405f7f-7d1e-4ea5-9157-322aa43759c5 | deep-abstract-q-networks | 1710.00459 | null | http://arxiv.org/abs/1710.00459v2 | http://arxiv.org/pdf/1710.00459v2.pdf | Deep Abstract Q-Networks | We examine the problem of learning and planning on high-dimensional domains
with long horizons and sparse rewards. Recent approaches have shown great
successes in many Atari 2600 domains. However, domains with long horizons and
sparse rewards, such as Montezuma's Revenge and Venture, remain challenging for
existing methods. Methods using abstraction (Dietterich 2000; Sutton, Precup,
and Singh 1999) have shown to be useful in tackling long-horizon problems. We
combine recent techniques of deep reinforcement learning with existing
model-based approaches using an expert-provided state abstraction. We construct
toy domains that elucidate the problem of long horizons, sparse rewards and
high-dimensional inputs, and show that our algorithm significantly outperforms
previous methods on these domains. Our abstraction-based approach outperforms
Deep Q-Networks (Mnih et al. 2015) on Montezuma's Revenge and Venture, and
exhibits backtracking behavior that is absent from previous methods. | ['Christopher Grimm', 'Stefanie Tellex', 'Melrose Roderick'] | 2017-10-02 | null | null | null | null | ['montezumas-revenge'] | ['playing-games'] | [-3.31427395e-01 3.83770823e-01 -6.14138246e-01 1.12794377e-01
-8.69062841e-01 -6.27939999e-01 7.74984360e-01 -1.86672192e-02
-4.83377844e-01 1.53338122e+00 2.32653365e-01 -5.72067916e-01
-5.85748971e-01 -7.34821916e-01 -7.72201836e-01 -4.43003803e-01
-7.34256089e-01 8.76801014e-01 2.63327032e-01 -6.59551382e-01
3.09667021e-01 2.76079655e-01 -1.34575927e+00 -2.54572555e-02
6.59240425e-01 8.33510578e-01 1.24272086e-01 7.75450170e-01
3.50564033e-01 1.08405423e+00 -5.23451447e-01 7.16243088e-02
6.22974336e-01 -1.53047740e-01 -1.07043076e+00 8.39026049e-02
-7.05183148e-02 -8.04583132e-01 -4.95916516e-01 7.70989835e-01
3.91560316e-01 2.95342684e-01 4.72457737e-01 -1.23388731e+00
-5.19585371e-01 7.53513932e-01 -5.29795706e-01 4.40256715e-01
2.07883805e-01 5.99336267e-01 1.07820606e+00 -4.29420948e-01
6.78351462e-01 1.51989186e+00 4.95248020e-01 5.95346689e-01
-1.16074824e+00 -3.65029842e-01 2.71841288e-01 2.37048179e-01
-5.17472744e-01 -2.12082148e-01 3.77365887e-01 -3.76439691e-01
1.16800392e+00 -2.50760585e-01 8.35725904e-01 1.20478559e+00
3.30095619e-01 1.20060015e+00 1.25584555e+00 -2.82041252e-01
5.91081500e-01 -5.05952060e-01 -2.61733174e-01 5.05789399e-01
9.50907320e-02 9.27124560e-01 -2.85600662e-01 -1.99520439e-01
9.18212354e-01 1.81733593e-01 2.92969167e-01 -6.99738622e-01
-1.32998455e+00 1.24843168e+00 3.80872518e-01 -2.10960910e-01
-7.05998123e-01 4.57871497e-01 4.36581492e-01 7.48329699e-01
2.03509498e-02 8.81319940e-01 -5.41939139e-01 -3.83012950e-01
-7.17146277e-01 1.02114701e+00 8.75643432e-01 1.12204647e+00
4.29622859e-01 2.75718242e-01 -2.23863497e-01 3.21700335e-01
-1.26053795e-01 3.90371710e-01 3.43704313e-01 -1.71015787e+00
7.31870890e-01 2.58929818e-03 9.39595520e-01 -3.35229576e-01
-8.25315475e-01 -3.24210256e-01 -5.04750788e-01 5.93208492e-01
6.85980439e-01 -7.24792659e-01 -9.27540481e-01 1.82390141e+00
2.18055025e-01 -1.30772501e-01 6.51738465e-01 9.48948562e-01
-4.61379960e-02 7.08877563e-01 -2.37623289e-01 -1.80926293e-01
8.82894754e-01 -1.34536779e+00 -4.89960432e-01 -5.46132743e-01
7.66616404e-01 -2.06079885e-01 9.06166971e-01 6.33188665e-01
-1.52279162e+00 -1.65838957e-01 -8.44088435e-01 1.53197587e-01
-1.27056539e-01 -3.27214956e-01 1.00376225e+00 1.72142029e-01
-1.02656281e+00 9.84285355e-01 -9.70182121e-01 -2.27001056e-01
5.42292655e-01 3.67529988e-01 1.81476668e-01 -2.60956705e-01
-1.51581454e+00 1.18316936e+00 5.86578667e-01 -1.03219740e-01
-1.77679408e+00 -2.28374928e-01 -7.30242133e-01 2.53930628e-01
1.16576147e+00 -4.66851383e-01 2.00791931e+00 -6.80495918e-01
-1.69766200e+00 1.52376726e-01 5.93897700e-01 -9.63172495e-01
4.97193635e-01 -3.29032153e-01 -8.36775154e-02 9.56464112e-02
1.76794380e-01 7.80481756e-01 5.71593761e-01 -6.90232992e-01
-8.13050807e-01 -2.35779062e-01 8.31261873e-01 5.08999109e-01
-1.62930209e-02 -3.50396037e-01 4.53509599e-01 -3.44916314e-01
-4.57525671e-01 -1.03240728e+00 -7.76490688e-01 -1.57090470e-01
-1.20518692e-01 -2.51938194e-01 2.50141412e-01 -2.77966022e-01
7.49779880e-01 -1.72140992e+00 3.39357257e-01 -1.48868173e-01
1.39930785e-01 2.30157316e-01 -3.81424963e-01 8.66926670e-01
1.60841689e-01 -9.23095718e-02 -8.74962658e-02 9.06850547e-02
2.62468070e-01 6.53671265e-01 -2.09680453e-01 2.23138824e-01
1.76430374e-01 9.85987246e-01 -1.39299500e+00 -1.44044548e-01
-5.67088872e-02 -4.54348445e-01 -7.75726497e-01 8.20061341e-02
-8.46343040e-01 1.53312445e-01 -6.14071965e-01 6.93411946e-01
1.17211916e-01 -3.64511460e-01 3.49807203e-01 7.88216472e-01
-2.51267582e-01 3.62523854e-01 -1.19160175e+00 1.75231302e+00
-3.36355448e-01 3.19369823e-01 2.56300658e-01 -1.11926782e+00
6.10081375e-01 4.01177824e-01 3.55560839e-01 -9.79902506e-01
-4.32085954e-02 3.54169995e-01 1.23854473e-01 -4.36472952e-01
8.65529537e-01 -5.08091688e-01 -2.96631277e-01 4.55874801e-01
-1.21580809e-01 -4.46357459e-01 5.81230700e-01 2.05806911e-01
1.37417138e+00 3.33643049e-01 2.29627594e-01 -3.87284100e-01
-2.28848264e-01 5.41643918e-01 6.46755278e-01 1.04842901e+00
-3.93628716e-01 1.44126222e-01 1.18795478e+00 -6.27651989e-01
-1.33555114e+00 -9.77708340e-01 1.71439767e-01 9.96474206e-01
1.06832102e-01 -1.79430678e-01 -2.98506558e-01 -5.55076540e-01
3.97114992e-01 6.99155688e-01 -8.04953098e-01 -5.08594066e-02
-2.46670812e-01 -3.21543932e-01 3.66532862e-01 6.19584620e-01
3.44685763e-01 -1.26373601e+00 -1.06107020e+00 6.40475094e-01
1.80633038e-01 -6.53189480e-01 -3.68541032e-02 5.18698990e-01
-1.11110926e+00 -9.06984448e-01 -1.09744596e+00 -3.50057364e-01
1.46902744e-02 -1.18321866e-01 1.25543904e+00 -3.94611269e-01
-1.26952067e-01 3.90166759e-01 -1.42333820e-01 -4.38861489e-01
-3.18354845e-01 1.28453553e-01 1.87845498e-01 -9.03978765e-01
-8.15822333e-02 -4.24510390e-01 -5.67138374e-01 1.96677536e-01
-7.93586791e-01 -1.34948492e-01 5.99475384e-01 1.46622288e+00
2.65661776e-01 -1.46879122e-01 1.04109752e+00 -7.95863748e-01
9.42892373e-01 -8.29539478e-01 -1.19376278e+00 -5.00705913e-02
-6.60233736e-01 2.69008338e-01 7.29309261e-01 -4.55385178e-01
-9.07720327e-01 -1.79349601e-01 2.41398245e-01 -4.40160275e-01
-1.15023525e-02 5.73025107e-01 5.54930210e-01 2.91869849e-01
8.58041227e-01 -1.22982584e-01 1.21753775e-01 -3.08232248e-01
3.05034161e-01 2.53634840e-01 2.86252022e-01 -1.05770826e+00
5.80971658e-01 2.25745633e-01 2.43508711e-01 -1.53085768e-01
-9.45788085e-01 5.58733419e-02 -2.08920985e-01 1.75672129e-01
5.95053732e-01 -9.90125895e-01 -9.24162388e-01 2.19122231e-01
-8.33948553e-01 -8.91424417e-01 -8.64035249e-01 5.08378386e-01
-1.36505568e+00 1.08076088e-01 -9.60191011e-01 -9.61967111e-01
3.15449923e-01 -1.09813595e+00 8.25178862e-01 3.71275753e-01
5.51769175e-02 -8.28669488e-01 4.09493864e-01 7.06525519e-03
4.62556034e-01 2.70089716e-01 7.26845980e-01 -4.62551475e-01
-7.90637970e-01 2.84638971e-01 1.61928367e-02 -4.22250740e-02
-2.48364225e-01 -5.12930274e-01 -4.72475737e-01 -5.06791711e-01
-2.90680885e-01 -1.14617121e+00 5.55327117e-01 5.55631757e-01
9.48958397e-01 -4.42702711e-01 2.01967321e-02 2.00235069e-01
1.19980645e+00 4.99748826e-01 4.44212914e-01 9.73424971e-01
-9.91892889e-02 5.36985993e-01 1.18771303e+00 8.94501388e-01
4.02924538e-01 4.36220020e-01 9.48859274e-01 5.72593398e-02
5.17864347e-01 -2.28409559e-01 3.61403257e-01 -8.26236978e-03
-1.11081548e-01 -2.08825648e-01 -9.32680130e-01 9.42428827e-01
-2.22246099e+00 -1.20890141e+00 5.41773379e-01 1.97000682e+00
7.93725610e-01 5.10802269e-01 6.60877883e-01 -1.27529874e-01
3.18534970e-01 1.75779104e-01 -1.11750114e+00 -8.74349535e-01
1.40793368e-01 6.20467551e-02 5.82790375e-01 5.01399875e-01
-8.81194890e-01 1.07392943e+00 7.03212643e+00 4.70987797e-01
-4.90125686e-01 -1.53463855e-01 4.86998916e-01 -4.69381511e-01
-1.78593159e-01 1.26321718e-01 -7.03007877e-01 2.39159390e-01
1.18875480e+00 -4.10226136e-01 8.34776282e-01 1.01770639e+00
1.25114039e-01 -3.27470809e-01 -1.39617360e+00 4.08367097e-01
-5.55685878e-01 -1.38540852e+00 -6.59056187e-01 2.50242829e-01
1.11163974e+00 2.03908741e-01 2.09076717e-01 9.69226539e-01
1.36104333e+00 -1.11461163e+00 5.42899430e-01 1.55511990e-01
4.99888718e-01 -9.49035466e-01 5.65314114e-01 7.72531152e-01
-6.26433790e-01 -7.58097589e-01 -5.42963028e-01 -7.53354728e-01
2.00064376e-01 1.89806506e-01 -8.42396736e-01 4.54409480e-01
5.86416066e-01 8.01660001e-01 8.74137878e-02 8.07388365e-01
-1.48498774e-01 3.53296667e-01 -4.56268162e-01 -2.34479576e-01
1.05602610e+00 -1.74197420e-01 3.74788493e-01 4.97333348e-01
1.98697835e-01 1.15171850e-01 5.94340026e-01 8.94405484e-01
1.36937872e-01 -5.49276650e-01 -9.08682942e-01 -3.86787087e-01
3.55441839e-01 7.15907335e-01 -4.19361889e-01 -5.07234454e-01
-3.04505855e-01 3.67990881e-01 4.47181702e-01 4.97245252e-01
-6.41103208e-01 -2.68321306e-01 6.47804916e-01 -1.45950392e-01
6.01217389e-01 -3.27975899e-01 1.04240142e-01 -1.07683837e+00
-1.71316907e-01 -1.20416307e+00 5.93707204e-01 -6.78256452e-01
-9.91353333e-01 1.40613899e-01 2.32118264e-01 -1.15409911e+00
-9.38286781e-01 -4.61447448e-01 -3.19192737e-01 6.67487741e-01
-1.79147589e+00 -7.19410062e-01 4.33427662e-01 6.52583301e-01
8.68298054e-01 -2.20921442e-01 6.22418284e-01 -1.24468245e-01
-4.05269772e-01 -3.07427086e-02 7.04653680e-01 -3.42120171e-01
3.00115347e-01 -1.46774685e+00 5.30431747e-01 5.68476439e-01
-4.30399895e-01 2.39283308e-01 6.68203413e-01 -6.83998644e-01
-1.62093556e+00 -7.19605982e-01 1.47033215e-01 -3.06324959e-01
1.04755604e+00 -1.22609869e-01 -5.29790878e-01 1.03240967e+00
3.52245867e-01 -1.73105314e-01 -9.35541615e-02 3.14841688e-01
-2.85444818e-02 2.09219277e-01 -1.04964435e+00 7.16835380e-01
1.03227019e+00 -2.13772774e-01 -8.94326389e-01 6.46033108e-01
7.43365943e-01 -6.96664870e-01 -8.25755715e-01 4.03119586e-02
4.16499645e-01 -1.01693118e+00 9.41594422e-01 -1.19189239e+00
7.78191984e-01 2.95621634e-01 -5.26464544e-03 -1.73228323e+00
-3.97873819e-01 -9.50276852e-01 -3.56250852e-01 4.66945797e-01
3.05844814e-01 -5.67885816e-01 8.53770852e-01 3.57840270e-01
-1.85883835e-01 -8.82472277e-01 -8.87135684e-01 -1.26260543e+00
7.09552646e-01 2.23967463e-01 6.41271174e-01 6.32186055e-01
3.06012899e-01 2.00844243e-01 -8.01171064e-01 -1.72890961e-01
7.50087738e-01 3.02067459e-01 6.29615068e-01 -1.05349231e+00
-5.25056601e-01 -2.40066648e-01 1.11895829e-01 -1.13838434e+00
1.88629776e-01 -5.25182545e-01 1.18228227e-01 -1.60101628e+00
-2.95276493e-02 -5.70066750e-01 -1.98854148e-01 6.05120599e-01
2.81707138e-01 -5.06843984e-01 4.19639558e-01 5.96063361e-02
-9.76023018e-01 8.54869485e-01 1.61722612e+00 -4.58742678e-02
-1.31859779e-01 1.26190886e-01 -5.90506196e-01 5.46955764e-01
1.00035703e+00 -4.02008533e-01 -5.60725868e-01 -3.98195207e-01
4.75835443e-01 9.86970544e-01 1.84878692e-01 -8.73421311e-01
6.75702840e-02 -7.91238010e-01 8.66079181e-02 -4.70990002e-01
5.23715079e-01 -6.73454702e-01 -2.07185164e-01 1.05853581e+00
-6.14408314e-01 4.46703881e-01 3.16559345e-01 8.38635087e-01
-7.18472227e-02 -2.54543275e-01 6.96736634e-01 -4.73159790e-01
-7.69503653e-01 2.32862458e-01 -7.24470973e-01 5.09593248e-01
1.18010569e+00 9.96180251e-02 -4.42199737e-01 -7.54509389e-01
-9.86681521e-01 9.97474253e-01 3.07053030e-01 2.24476844e-01
3.70653391e-01 -1.20418060e+00 -5.08911729e-01 -1.73938453e-01
-2.32471451e-01 2.81482577e-01 5.52317500e-02 6.68644309e-01
-2.00103804e-01 4.00888771e-01 -8.28891933e-01 -2.35556766e-01
-4.62720186e-01 8.58166754e-01 3.49567086e-01 -9.32646394e-01
-6.26530170e-01 3.22443873e-01 1.06286211e-02 -4.96985972e-01
2.78457791e-01 -4.58597660e-01 -2.94524413e-02 1.10729635e-01
2.03438044e-01 5.65039396e-01 -3.83039534e-01 3.63220334e-01
-1.78064741e-02 2.57952809e-02 -2.98924297e-01 -3.64479333e-01
1.66849720e+00 -1.90993696e-02 7.29910970e-01 4.37851280e-01
5.21832526e-01 -7.65463591e-01 -1.75851905e+00 -3.13574135e-01
2.29325160e-01 -4.90120083e-01 -2.70888448e-01 -9.81683254e-01
-7.09807515e-01 1.13011527e+00 3.27197045e-01 3.76058757e-01
7.34422803e-01 -2.11558163e-01 7.75775075e-01 9.64514732e-01
7.59436965e-01 -1.48022616e+00 3.99708569e-01 9.44448769e-01
7.18549192e-01 -1.33219194e+00 -4.52582240e-02 5.66002488e-01
-8.47000897e-01 1.04799700e+00 7.74837077e-01 -3.26766044e-01
2.47052312e-02 3.33054930e-01 -3.97550523e-01 -2.03192368e-01
-1.40069282e+00 -3.94827425e-01 -5.34639418e-01 7.07683206e-01
-3.77816767e-01 1.77243099e-01 -8.85291472e-02 2.55984426e-01
-3.32177915e-02 3.56347591e-01 9.95474875e-01 1.34609294e+00
-6.69749558e-01 -9.46148515e-01 -4.13030744e-01 5.77333748e-01
-3.72139633e-01 6.19995072e-02 -1.10842325e-01 1.07616758e+00
-5.28737962e-01 6.64317429e-01 5.23868762e-02 1.40446171e-01
2.52283901e-01 -8.37188736e-02 5.09060383e-01 -7.76757002e-01
-5.81625640e-01 1.53714180e-01 4.12717134e-01 -7.72834897e-01
-1.68819442e-01 -8.15514565e-01 -1.14468694e+00 -4.14291769e-01
-2.43342724e-02 3.36558819e-01 3.58962238e-01 9.23599184e-01
2.39172682e-01 5.41761100e-01 6.73970520e-01 -9.61773038e-01
-1.57963955e+00 -6.32422030e-01 -9.37733352e-01 5.09198159e-02
7.98409343e-01 -9.73801732e-01 -2.76217103e-01 -6.23014987e-01] | [4.045840263366699, 1.830581784248352] |
47bb35d7-3787-4e63-a1c5-4cea2012fa97 | sarcasm-detection-building-a-contextual | null | null | https://aclanthology.org/W16-4313 | https://aclanthology.org/W16-4313.pdf | Sarcasm Detection : Building a Contextual Hierarchy | The conundrum of understanding and classifying sarcasm has been dealt with by the traditional theorists as an analysis of a sarcastic utterance and the ironic situation that surrounds it. The problem with such an approach is that it is too narrow, as it is unable to sufficiently utilize the two indispensable agents in making such an utterance, viz. the speaker and the listener. It undermines the necessary context required to comprehend a sarcastic utterance. In this paper, we propose a novel approach towards understanding sarcasm in terms of the existing knowledge hierarchy between the two participants, which forms the basis of the context that both agents share. The difference in relationship of the speaker of the sarcastic utterance and the disparate audience found on social media, such as Twitter, is also captured. We then apply our model on a corpus of tweets to achieve significant results and consequently, shed light on subjective nature of context, which is contingent on the relation between the speaker and the listener. | ['Navjyoti Singh', 'Taradheesh Bali'] | 2016-12-01 | null | null | null | ws-2016-12 | ['lexical-analysis'] | ['natural-language-processing'] | [-2.56364364e-02 5.08091748e-01 -2.41214707e-01 -2.35269099e-01
-1.18278794e-01 -4.14974779e-01 8.56858134e-01 5.88803947e-01
-2.61358529e-01 2.37040982e-01 1.06425858e+00 -4.60618675e-01
-7.45234406e-03 -6.85427845e-01 -9.41433460e-02 -5.12343824e-01
7.39776254e-01 3.20226789e-01 1.81839854e-01 -6.81545556e-01
4.56760883e-01 -2.70050585e-01 -1.45751429e+00 4.01442975e-01
3.91120791e-01 6.34808540e-01 2.72569120e-01 1.38590410e-01
-3.47108454e-01 1.56077397e+00 -6.85721874e-01 -4.39789444e-01
-2.84503639e-01 -9.96557772e-01 -1.25390780e+00 3.47716033e-01
-2.04557836e-01 -1.59326941e-02 1.93139538e-01 9.71973658e-01
2.31932238e-01 -2.39537835e-01 4.36109602e-01 -6.66875482e-01
-3.42705131e-01 9.94370937e-01 -1.58783764e-01 1.23321325e-01
4.48983192e-01 -3.15122336e-01 1.01565278e+00 -4.61929947e-01
6.83989406e-01 1.17289352e+00 7.76544750e-01 4.77235883e-01
-7.17479944e-01 7.64598772e-02 2.30276790e-02 2.12777704e-01
-9.59981918e-01 -5.01293302e-01 1.04383194e+00 -8.75803292e-01
3.69057685e-01 5.14283061e-01 8.25005531e-01 1.17223585e+00
-1.36191979e-01 2.95017481e-01 9.27409112e-01 -8.98811400e-01
2.18017623e-01 7.01055467e-01 5.36602914e-01 -7.30067194e-02
-1.03091463e-01 -4.28226173e-01 -5.80328465e-01 -2.57075936e-01
1.81918234e-01 -1.69198379e-01 -2.28276819e-01 2.39782818e-02
-8.39573562e-01 1.16694033e+00 -2.78506428e-02 9.74643588e-01
-3.82550448e-01 -3.68132055e-01 6.90484464e-01 4.51825857e-01
4.45663095e-01 2.21160620e-01 3.68723236e-02 -3.90207708e-01
-9.15422142e-01 6.80052117e-02 1.20532811e+00 -3.29104136e-03
2.73843378e-01 -3.09253871e-01 3.45960289e-01 5.83320498e-01
6.48393571e-01 2.42427036e-01 5.48701167e-01 -7.12441206e-01
4.16892171e-02 1.08487487e+00 3.17005128e-01 -1.46404779e+00
-2.01036081e-01 -3.83472025e-01 -5.62945232e-02 -7.18550012e-02
6.38768196e-01 -1.48185238e-01 5.36395311e-02 1.81527400e+00
5.19257128e-01 -2.28783250e-01 3.92149121e-01 1.03097355e+00
1.02298927e+00 3.30319196e-01 9.07695144e-02 -4.92556095e-01
1.60433519e+00 -7.45216727e-01 -9.95873690e-01 -3.19811940e-01
8.68555069e-01 -1.02157319e+00 1.20215535e+00 7.27041662e-02
-1.25807929e+00 -1.63439989e-01 -1.18824804e+00 -7.71226436e-02
2.18574014e-02 -4.34739105e-02 2.45015472e-01 4.93531346e-01
-5.08860886e-01 2.48224676e-01 -4.02287692e-01 -6.01822376e-01
-2.01333314e-01 -1.98314577e-01 2.40025252e-01 4.74446893e-01
-1.19222915e+00 1.11615729e+00 9.96883884e-02 2.54830033e-01
-2.76586294e-01 -2.63049126e-01 -5.22620261e-01 -1.20942064e-01
6.16106808e-01 -4.52498466e-01 1.24483216e+00 -1.69523978e+00
-1.43469274e+00 1.10442221e+00 -8.69398862e-02 -4.19846922e-01
6.44214749e-01 -9.84351635e-02 -4.72532064e-01 1.00015327e-01
1.35342866e-01 -3.70658100e-01 4.81909186e-01 -1.39531195e+00
-3.90253216e-01 -6.08527303e-01 2.89150029e-01 3.48376870e-01
-1.76834330e-01 5.56136906e-01 1.52886525e-01 -2.66928047e-01
3.34026545e-01 -8.14614475e-01 2.26108417e-01 -7.19620645e-01
-3.66815805e-01 -2.50665247e-01 1.08009219e+00 -4.38797444e-01
1.45150876e+00 -2.42829633e+00 6.92320813e-04 -2.73379963e-02
4.31743562e-01 1.81487441e-01 5.76172054e-01 1.30320084e+00
6.54532164e-02 6.07802533e-03 1.85943216e-01 -1.68699607e-01
9.30468142e-02 2.59705096e-01 -6.88695908e-01 5.55586219e-01
-4.40270811e-01 4.64818835e-01 -9.32586730e-01 -3.89646113e-01
-2.27197092e-02 5.87276220e-01 -2.73240179e-01 1.60004944e-01
-2.21051425e-01 4.61570650e-01 -7.25261748e-01 1.27758011e-01
1.73547447e-01 -3.16817880e-01 8.43484879e-01 -6.52683601e-02
-5.15571833e-01 7.81210840e-01 -7.39078701e-01 1.19311512e+00
-3.18230391e-01 6.16727829e-01 4.02506530e-01 -1.05010867e+00
1.03136873e+00 7.37248838e-01 2.71260232e-01 -4.57691044e-01
6.53463244e-01 2.54039139e-01 2.91733295e-01 -6.93821609e-01
4.88199115e-01 -6.76294684e-01 -2.69668132e-01 9.52494740e-01
-5.88637769e-01 -6.91018924e-02 7.91704580e-02 4.87955600e-01
5.17150164e-01 -8.19215253e-02 5.16190231e-01 -6.06009305e-01
6.99319124e-01 1.34745657e-01 4.32476670e-01 3.11484247e-01
-2.04673350e-01 1.72850415e-01 7.47188091e-01 -6.75156176e-01
-8.73397112e-01 -6.54824853e-01 9.63265449e-03 9.29129720e-01
3.51867229e-01 -5.28648913e-01 -9.76214826e-01 -2.97513038e-01
-5.08018970e-01 9.35910165e-01 -5.54690421e-01 1.03039458e-01
-5.29111683e-01 -5.10492623e-01 3.11916769e-01 -1.79485157e-02
2.42060825e-01 -8.79105449e-01 -1.61418116e+00 3.02218974e-01
-6.57696962e-01 -8.97614598e-01 8.87511373e-02 9.30344779e-03
-4.70306128e-01 -1.22138858e+00 3.79163384e-01 -6.41201794e-01
4.50017303e-01 2.05007777e-01 1.23999250e+00 4.94598687e-01
2.96795219e-01 2.53558606e-01 -6.91612840e-01 -6.16904020e-01
-1.07643247e+00 -3.51499677e-01 -2.39610031e-01 2.71831639e-02
6.65223539e-01 -6.68849528e-01 -3.14089507e-01 2.93938756e-01
-9.91492808e-01 3.29523563e-01 -1.34494584e-02 5.11903048e-01
-1.60453960e-01 4.04333100e-02 7.56972730e-01 -1.09109902e+00
7.61264145e-01 -7.50056148e-01 1.85394943e-01 1.69734299e-01
-3.69707346e-01 -3.44409227e-01 3.48023683e-01 -2.37331405e-01
-1.27955353e+00 -5.35323322e-01 -4.51744720e-03 3.52395535e-01
4.01728339e-02 8.00921261e-01 -3.22530456e-02 6.57849669e-01
6.33903444e-01 -9.17122588e-02 3.66668016e-01 -3.90141845e-01
2.61301368e-01 1.00051701e+00 5.01151979e-01 -4.33565140e-01
3.53639066e-01 7.18657672e-01 -5.28397381e-01 -9.81350183e-01
-1.30829036e+00 -5.49713075e-01 -4.48327094e-01 -4.04118031e-01
8.09746265e-01 -7.48782516e-01 -8.60763192e-01 1.54537156e-01
-1.29194069e+00 -9.04075578e-02 -4.68719333e-01 4.60933477e-01
-6.09476984e-01 4.73202884e-01 -5.75789273e-01 -1.02172518e+00
-1.72288716e-01 -9.00198519e-01 3.15612793e-01 8.40453357e-02
-9.32474136e-01 -1.40853596e+00 2.62808800e-01 9.01331782e-01
3.12222809e-01 4.08228725e-01 1.13343644e+00 -9.29334462e-01
1.53748736e-01 -6.88645914e-02 1.70793027e-01 1.91045374e-01
2.96371490e-01 -6.57565892e-02 -8.25911343e-01 2.53468379e-02
1.04053688e+00 -7.71390378e-01 1.40230760e-01 3.66118327e-02
1.02918729e-01 -6.07473850e-01 1.50798351e-01 -3.10399801e-01
1.40765846e+00 1.36821195e-01 4.81889069e-01 3.53644133e-01
1.06643200e-01 1.32410061e+00 4.17573333e-01 6.66623592e-01
6.11763299e-01 6.47556007e-01 3.89081687e-01 2.24405937e-02
-4.52999212e-02 -2.63582319e-01 3.84075850e-01 1.20527577e+00
1.43859927e-02 7.59482011e-02 -9.32991922e-01 5.42255580e-01
-2.26619291e+00 -1.14911199e+00 -5.79490304e-01 1.85414159e+00
8.62413704e-01 -1.73419788e-02 1.30892754e-01 4.11953807e-01
5.46362579e-01 5.45817077e-01 1.28473029e-01 -7.43954122e-01
-3.83912250e-02 -4.73634571e-01 -3.71342182e-01 7.77033269e-01
-5.17713666e-01 7.08276927e-01 6.27684069e+00 4.90349531e-01
-1.09958196e+00 2.89132684e-01 3.56539369e-01 8.65717381e-02
-5.45571327e-01 3.63227308e-01 -3.01115215e-01 3.43571573e-01
8.86170030e-01 -1.89189151e-01 1.18314236e-01 5.75067163e-01
7.25710750e-01 -4.61899787e-01 -9.56227958e-01 4.36725527e-01
3.49850833e-01 -1.11451817e+00 -3.34109813e-01 -8.47071186e-02
3.20100367e-01 -2.55583256e-01 -9.68616605e-02 -2.61617601e-01
1.65891498e-01 -9.77081835e-01 1.16402543e+00 9.91344079e-02
-3.79558019e-02 -1.80068359e-01 1.00736070e+00 7.54382730e-01
-6.23534918e-01 -1.11764245e-01 8.69529769e-02 -8.21794212e-01
4.09790993e-01 4.55025822e-01 -7.32999563e-01 3.62544954e-01
1.52633190e-01 3.68392110e-01 -1.22077830e-01 3.13075967e-02
-3.60945672e-01 8.15905213e-01 -1.29898917e-02 -1.10000782e-01
3.53209913e-01 -4.15761828e-01 7.28270769e-01 8.62681925e-01
-1.56596690e-01 1.76339582e-01 2.82978266e-01 8.48660171e-01
5.21009743e-01 5.55417895e-01 -4.22217667e-01 -8.28429163e-02
4.25271183e-01 1.03916276e+00 -7.40868986e-01 -2.97893643e-01
-5.40446162e-01 3.41115326e-01 2.01654419e-01 1.33799270e-01
-6.34121358e-01 4.91068244e-01 7.74309561e-02 4.71502781e-01
-2.76941974e-02 2.53148060e-02 -3.38985741e-01 -8.79918516e-01
3.99860442e-02 -9.79731679e-01 1.66841120e-01 -4.85336691e-01
-1.11775649e+00 4.41687971e-01 -9.58603099e-02 -5.97011983e-01
-3.14553082e-01 -2.01916546e-01 -9.92279828e-01 7.29893386e-01
-1.08357859e+00 -1.47743356e+00 -1.59334525e-01 3.23711872e-01
4.17820215e-01 2.52653271e-01 8.03770065e-01 1.81680843e-02
-2.65025795e-01 -1.62135616e-01 -2.90872157e-01 -4.05804031e-02
3.24894667e-01 -8.18306148e-01 -5.14107347e-01 5.83675742e-01
8.18266869e-02 6.61328733e-01 1.31723356e+00 -4.69337851e-01
-9.98036623e-01 -4.34667850e-03 1.69194746e+00 -6.50687575e-01
1.16696179e+00 -6.10863678e-02 -8.30613971e-01 3.82604390e-01
5.17419457e-01 -6.80447519e-01 1.13012552e+00 4.17368978e-01
-3.20976913e-01 3.37684512e-01 -8.95380557e-01 4.94023353e-01
6.96853399e-01 -5.25073707e-01 -1.30479467e+00 1.03242964e-01
4.52601373e-01 5.39934635e-02 -5.95220685e-01 1.69893011e-01
4.90625203e-01 -1.54186702e+00 4.97314215e-01 -5.77578545e-01
7.12069929e-01 -8.05890858e-02 -3.82217795e-01 -8.66459548e-01
3.64198148e-01 -4.66074258e-01 3.75379622e-01 1.32318366e+00
2.36256599e-01 -4.64470685e-01 5.63697994e-01 5.82924008e-01
-3.02268360e-02 -6.56004012e-01 -7.20286489e-01 -1.28436863e-01
1.37859210e-01 -4.69081134e-01 4.03768867e-01 1.13034213e+00
8.36589873e-01 6.80795014e-01 -2.93827891e-01 -3.48644674e-01
-1.10153323e-02 3.72669727e-01 8.20767105e-01 -1.28695357e+00
-4.10558581e-01 -2.67338753e-01 -5.94501495e-02 -8.11172843e-01
-2.93153878e-02 -5.06838202e-01 -5.97184151e-02 -1.38031447e+00
4.55529094e-01 -4.05768394e-01 1.44494295e-01 -8.00026488e-03
2.15652660e-01 -3.67709063e-02 3.58812481e-01 6.42049909e-01
-4.85712796e-01 3.29266191e-01 9.37530041e-01 3.40670913e-01
-3.87989998e-01 -2.28934690e-01 -1.05717719e+00 1.46365142e+00
8.00482035e-01 -3.92468989e-01 -6.71162844e-01 -1.81686744e-01
6.18339479e-01 1.62113771e-01 3.84104550e-01 -4.31912363e-01
9.91444439e-02 -2.47051567e-01 -5.82974792e-01 -3.34348708e-01
2.84473240e-01 -8.41076553e-01 5.39802253e-01 6.24294579e-01
-4.65447903e-01 4.12085541e-02 -4.18611228e-01 1.52046219e-01
-5.18689513e-01 -5.81220150e-01 6.17506564e-01 -2.48937055e-01
-2.41038069e-01 -7.51195908e-01 -4.71691459e-01 2.61542559e-01
9.39533770e-01 -4.20995831e-01 -5.36612332e-01 -6.30101740e-01
-8.74125123e-01 -1.46036088e-01 8.38480353e-01 8.93581510e-02
1.81534424e-01 -7.94573665e-01 -7.39142001e-01 -1.02878906e-01
-8.91491175e-02 -2.98263311e-01 2.85201043e-01 1.17906260e+00
-3.74188155e-01 1.44708216e-01 -1.00311497e-02 -2.45700821e-01
-1.43422771e+00 3.77231449e-01 2.01382309e-01 -2.89181709e-01
-6.14467144e-01 2.06461132e-01 4.49294925e-01 -2.85043661e-02
-3.23426649e-02 8.38549249e-03 -6.63312733e-01 4.44882661e-01
5.57525396e-01 2.64517099e-01 -3.73062640e-01 -1.27332866e+00
-8.87162462e-02 3.36157709e-01 3.36395651e-01 -2.52841353e-01
1.19013941e+00 -7.53993213e-01 -5.24296582e-01 1.07142150e+00
7.77609050e-01 5.06863952e-01 -6.41402185e-01 -2.52044439e-01
1.84494540e-01 -4.45985883e-01 -1.36403278e-01 -6.98467612e-01
-2.82618850e-01 6.39566839e-01 -1.53279975e-01 9.67797220e-01
6.25074148e-01 4.26900208e-01 6.91034436e-01 -1.02759369e-01
3.54233496e-02 -1.38857889e+00 2.80633777e-01 3.60475600e-01
7.26159513e-01 -1.02247536e+00 -2.34064665e-02 -7.57744431e-01
-7.87740827e-01 1.13060892e+00 2.81488806e-01 1.49930254e-01
5.96550941e-01 2.08944485e-01 7.06945002e-01 -7.03774333e-01
-8.49179268e-01 -3.98195274e-02 -1.97999999e-01 1.47349551e-01
7.65744448e-01 2.28510261e-01 -1.22380471e+00 7.07579315e-01
-5.75587928e-01 -1.74446553e-01 8.67035687e-01 7.02290297e-01
-7.01286435e-01 -1.06197822e+00 -3.55429709e-01 -3.27594608e-01
-8.68324101e-01 1.43900365e-01 -8.96114111e-01 7.49502838e-01
3.76883417e-01 1.42634869e+00 4.75503020e-02 -1.38588503e-01
2.64133629e-03 -2.91848499e-02 4.51873317e-02 -6.81427777e-01
-9.54326510e-01 2.63652653e-01 6.72003865e-01 5.51002063e-02
-1.05183256e+00 -5.67164958e-01 -1.27692938e+00 -3.75586808e-01
-3.52876872e-01 7.24584997e-01 7.30047703e-01 1.43483055e+00
-1.21606551e-01 -8.31257831e-03 5.59865654e-01 -2.13369355e-01
-7.50334620e-01 -8.35685551e-01 -5.01435876e-01 8.57375324e-01
9.63782147e-02 -3.70756835e-01 -5.11971891e-01 2.83683359e-04] | [9.265239715576172, 10.436478614807129] |
38e76646-8984-4f6f-88d2-a60f3baec537 | reconstruction-and-quantification-of-3d-iris | 2006.05179 | null | https://arxiv.org/abs/2006.05179v1 | https://arxiv.org/pdf/2006.05179v1.pdf | Reconstruction and Quantification of 3D Iris Surface for Angle-Closure Glaucoma Detection in Anterior Segment OCT | Precise characterization and analysis of iris shape from Anterior Segment OCT (AS-OCT) are of great importance in facilitating diagnosis of angle-closure-related diseases. Existing methods focus solely on analyzing structural properties identified from the 2D slice, while accurate characterization of morphological changes of iris shape in 3D AS-OCT may be able to reveal in addition the risk of disease progression. In this paper, we propose a novel framework for reconstruction and quantification of 3D iris surface from AS-OCT imagery. We consider it to be the first work to detect angle-closure glaucoma by means of 3D representation. An iris segmentation network with wavelet refinement block (WRB) is first proposed to generate the initial shape of the iris from single AS-OCT slice. The 3D iris surface is then reconstructed using a guided optimization method with Poisson-disk sampling. Finally, a set of surface-based features are extracted, which are used in detecting of angle-closure glaucoma. Experimental results demonstrate that our method is highly effective in iris segmentation and surface reconstruction. Moreover, we show that 3D-based representation achieves better performance in angle-closure glaucoma detection than does 2D-based feature. | ['Jinkui Hao', 'Jiang Liu', 'Huazhu Fu', 'Yitian Zhao', 'Yan Hu', 'Xiulan Zhang', 'Yanwu Xu', 'Fei Li'] | 2020-06-09 | null | null | null | null | ['iris-segmentation'] | ['medical'] | [ 2.13432476e-01 -1.88128844e-01 -3.76209840e-02 4.66501229e-02
-3.77678096e-01 -2.59464771e-01 1.94826014e-02 2.34059855e-01
-3.24242353e-01 4.49610472e-01 1.74024984e-01 -4.53091592e-01
-4.53094423e-01 -6.92266762e-01 -1.87878497e-02 -7.08687067e-01
-2.35447541e-01 5.71583986e-01 1.25680506e-01 6.85107782e-02
5.30546665e-01 8.73749375e-01 -1.80459321e+00 -7.40760515e-05
1.21691048e+00 1.13654721e+00 9.28000733e-02 7.23136783e-01
1.06320053e-01 -2.78957784e-01 -1.74726218e-01 -1.75263695e-02
4.39131379e-01 -5.73666573e-01 -7.33256638e-01 5.86889803e-01
5.09267390e-01 -4.38087463e-01 5.97423494e-01 1.20092452e+00
6.58055425e-01 -2.21416354e-01 8.26929152e-01 -5.57499863e-02
-1.23666776e-02 -1.88884512e-01 -7.60722816e-01 3.40597779e-01
3.66167933e-01 1.83738336e-01 6.27113819e-01 -8.60712171e-01
4.53806639e-01 8.59838188e-01 3.88370544e-01 3.79754514e-01
-1.44152200e+00 -3.16173017e-01 -4.50567514e-01 -2.15640687e-03
-1.21290529e+00 -2.70373315e-01 5.02559662e-01 -9.93314683e-01
4.70252424e-01 5.33550620e-01 1.48646510e+00 1.43314332e-01
1.16448380e-01 5.53391218e-01 1.76781917e+00 -6.46167696e-01
-1.04341827e-01 -2.34324798e-01 1.89326972e-01 7.29016781e-01
5.29176295e-01 4.12362397e-01 2.14487175e-03 -2.06340507e-01
1.12054753e+00 -1.89427838e-01 -6.04247391e-01 -1.12175234e-02
-1.04414916e+00 3.79332066e-01 1.51284873e-01 3.24516982e-01
-6.01395667e-01 -3.14539462e-01 -5.88455703e-03 -6.40213257e-03
8.73378217e-01 6.69153810e-01 -2.22922526e-02 -1.77669853e-01
-1.09504199e+00 8.62113237e-02 1.37927204e-01 1.80926993e-01
4.93080199e-01 -3.36608738e-01 -1.68827891e-01 7.96180010e-01
5.39499104e-01 4.27203268e-01 4.96297091e-01 -4.91902322e-01
-2.02713832e-01 1.03868484e+00 9.96787995e-02 -4.68793273e-01
-4.22594607e-01 -6.57818139e-01 -8.72264802e-01 4.69714373e-01
4.07821238e-01 3.71761806e-02 -1.10561514e+00 4.80955452e-01
6.08392596e-01 3.55907977e-01 -4.06374961e-01 1.13585377e+00
6.67198241e-01 1.27121106e-01 -4.99505877e-01 -5.18990755e-01
1.47799027e+00 -3.19974244e-01 -3.37615639e-01 3.43108088e-01
4.85305727e-01 -1.08536816e+00 6.97069228e-01 6.16577446e-01
-1.33004248e+00 -3.65449011e-01 -5.05808175e-01 1.67155758e-01
2.91429460e-01 6.50120914e-01 2.42877975e-01 6.38366699e-01
-9.04712200e-01 4.51200396e-01 -9.29565072e-01 -2.49181703e-01
4.20592993e-01 4.55095768e-01 -1.73825085e-01 1.39948383e-01
-3.16081315e-01 6.27587676e-01 1.40257612e-01 2.27069706e-01
1.01133641e-02 -6.09230995e-01 -6.56669796e-01 -4.15218264e-01
-1.00314967e-01 -9.50723350e-01 7.37231970e-01 -4.63183016e-01
-1.49840593e+00 1.34562373e+00 -7.20291317e-01 -3.50420386e-01
1.40400574e-01 5.20947687e-02 -1.41445190e-01 5.49582005e-01
-1.04413144e-01 -7.35630393e-02 1.12001133e+00 -1.25803924e+00
-7.96922743e-01 -9.69296575e-01 -3.52420211e-01 -9.51069593e-02
1.67419072e-02 2.90254682e-01 -2.88811713e-01 -4.51251119e-01
7.57471383e-01 -7.78710008e-01 -3.62169355e-01 -8.09321553e-02
-5.57312787e-01 -8.79057571e-02 2.22288862e-01 -8.02418172e-01
1.55543625e+00 -1.97624505e+00 7.43322149e-02 4.45863813e-01
6.83427453e-01 8.17176700e-01 1.29835427e-01 3.17112468e-02
-6.97361007e-02 4.58612233e-01 -1.77880302e-01 -3.35036486e-01
-5.20380020e-01 -2.84176677e-01 2.48150639e-02 7.14413822e-01
2.74553776e-01 6.38972342e-01 -6.21633828e-01 -6.39276564e-01
3.32013845e-01 4.77546394e-01 -5.44167399e-01 -2.02403981e-02
-1.70525655e-01 9.59084630e-01 -6.66496515e-01 8.87753606e-01
8.34470451e-01 -2.96064764e-01 -1.59646228e-01 -1.86785504e-01
-5.06448507e-01 3.73464487e-02 -1.04921508e+00 1.02183437e+00
-4.14525121e-01 3.39186400e-01 9.22892466e-02 -6.32853806e-01
9.37896252e-01 3.92043889e-01 5.39920747e-01 -3.25367987e-01
2.46944368e-01 6.90577984e-01 3.21496874e-01 -6.66682303e-01
1.16049506e-01 -2.43501931e-01 8.23890388e-01 3.33804697e-01
-4.83279765e-01 -2.51989871e-01 2.46730551e-01 -6.60501182e-01
3.75296503e-01 -2.20923070e-02 5.15916348e-01 -7.91451484e-02
8.40188205e-01 -1.17278330e-01 4.92677063e-01 1.43580601e-01
-9.56144184e-02 6.28945827e-01 5.02956629e-01 -6.43498540e-01
-8.82816076e-01 -7.15543866e-01 -1.05681205e+00 -5.47228158e-01
7.28710815e-02 -3.69227618e-01 -6.50052369e-01 -2.72554338e-01
7.43250400e-02 2.25701369e-02 -3.31484765e-01 6.36571825e-01
-4.68397379e-01 -1.15219188e+00 -1.58148602e-01 -1.79951191e-01
4.29243147e-01 -3.22690248e-01 -5.44575155e-01 8.95342380e-02
1.15421824e-02 -7.25780725e-01 -8.37847963e-02 -9.80503738e-01
-1.54922283e+00 -1.55517578e+00 -9.74557161e-01 -7.11516023e-01
1.14127171e+00 1.37150362e-01 7.62834191e-01 4.98064131e-01
-8.31442595e-01 2.65513271e-01 -2.80626625e-01 -4.11815375e-01
-2.86608666e-01 -5.61196089e-01 4.79652323e-02 7.99630225e-01
3.67835909e-01 -6.28853321e-01 -1.12717319e+00 3.71846795e-01
-4.39135492e-01 4.32408601e-02 6.25255167e-01 6.14213765e-01
1.21062744e+00 2.82453597e-01 -2.43433520e-01 -6.26994550e-01
6.45283699e-01 3.68947349e-02 -8.91274869e-01 -1.28834322e-01
-8.20067525e-01 -4.20007139e-01 6.53295070e-02 -8.48203152e-02
-5.96651375e-01 -1.46816045e-01 -3.57601680e-02 -3.65094781e-01
-3.81171733e-01 6.37083352e-01 5.67798913e-01 -4.20621246e-01
7.22428381e-01 4.44091231e-01 5.05199969e-01 -9.91890252e-01
-2.74050921e-01 9.29278374e-01 5.29920757e-02 -3.93319577e-01
5.89443862e-01 8.87711883e-01 6.27784014e-01 -1.30749679e+00
-7.14887619e-01 -1.17951179e+00 -6.35577440e-01 -2.60245800e-01
8.32469225e-01 -5.64662635e-01 -1.06444800e+00 8.25628996e-01
-9.70956087e-01 5.78067601e-02 -3.82119387e-01 1.09053218e+00
-4.25840110e-01 6.38900876e-01 -5.51812291e-01 -9.86826658e-01
-4.04501468e-01 -1.48671281e+00 1.24043775e+00 1.85184196e-01
2.11445943e-01 -9.65960562e-01 3.26626599e-01 8.00088048e-01
1.97685495e-01 3.71859103e-01 1.00122571e+00 7.33994693e-02
-7.44770110e-01 -3.79016817e-01 -2.71088809e-01 6.72076225e-01
1.06248364e-01 4.03339684e-01 -6.44920230e-01 -3.30274373e-01
2.49287307e-01 2.74462789e-01 8.06559265e-01 1.17973387e+00
1.04737890e+00 -1.00442417e-01 -3.84420961e-01 1.07540655e+00
1.77427292e+00 -1.30883157e-01 8.69558394e-01 2.65673846e-01
3.10039550e-01 6.30986571e-01 7.16648400e-01 5.96756339e-01
-1.30966678e-01 7.69070029e-01 5.27647555e-01 -4.17952597e-01
-4.92011636e-01 1.65438935e-01 -2.13660806e-01 6.99563026e-01
-1.09321034e+00 3.42492551e-01 -1.16038096e+00 6.47364497e-01
-1.32444799e+00 -5.89832366e-01 -8.61808479e-01 2.72225761e+00
9.02008057e-01 -1.47685379e-01 3.48162740e-01 1.85704917e-01
8.54285717e-01 -3.56589884e-01 -2.11355686e-01 1.30411685e-01
1.08941607e-02 7.91343212e-01 2.48493388e-01 7.30674386e-01
-8.37697804e-01 6.01305783e-01 5.71273899e+00 7.57634521e-01
-1.33752835e+00 -2.55283207e-01 3.55202645e-01 -1.02938324e-01
-8.46187696e-02 2.74831146e-01 -1.13415682e+00 4.53308523e-01
3.68545473e-01 5.31044155e-02 9.60793793e-02 -8.46993849e-02
7.34115243e-01 -4.92173791e-01 -4.78817850e-01 1.14877784e+00
-2.47309715e-01 -1.36024714e+00 1.96642041e-01 8.18100989e-01
7.09711790e-01 -7.90293440e-02 1.31455153e-01 -9.53640342e-01
-4.79046285e-01 -1.04076326e+00 -1.39885619e-01 1.14794207e+00
1.34892225e+00 -4.78732765e-01 9.46796298e-01 1.70611098e-01
-1.02802694e+00 6.70995414e-02 -4.41131815e-02 -2.77126078e-02
1.12253465e-01 1.12957871e+00 -1.16795468e+00 3.84695351e-01
3.79320920e-01 1.03719258e+00 -4.82317060e-01 1.97863328e+00
-1.99464798e-01 7.96154797e-01 -1.95717677e-01 2.51977384e-01
-2.68220156e-01 -8.87111068e-01 1.35078311e+00 5.42440474e-01
5.77015877e-01 2.80820757e-01 -1.75068080e-02 9.70651209e-01
4.97140139e-01 5.12758672e-01 -4.44100976e-01 -8.85567516e-02
1.15583420e-01 1.03998387e+00 -7.01266527e-01 4.94880341e-02
-2.99854189e-01 2.33981833e-01 -4.52572018e-01 3.45584780e-01
1.38536155e-01 -5.07132150e-02 7.73766279e-01 1.02075243e+00
1.00349942e-02 -3.76891583e-01 -2.91095585e-01 -1.11542189e+00
8.83488730e-02 -7.24419653e-01 -1.28686517e-01 -5.89650452e-01
-9.63960171e-01 4.99967694e-01 -3.36853027e-01 -1.63891280e+00
5.06108291e-02 -8.17687869e-01 -5.78056335e-01 1.37205696e+00
-1.77489340e+00 -1.26914585e+00 -1.55395597e-01 5.30270159e-01
4.98045608e-02 -2.41195738e-01 8.27459335e-01 8.91713127e-02
-6.83975458e-01 3.39880027e-02 5.30353896e-02 5.78927100e-02
3.44718218e-01 -1.30603099e+00 3.42474761e-03 8.30415428e-01
-2.69745171e-01 7.27672398e-01 4.33033854e-01 -9.59828913e-01
-1.04865146e+00 -7.40119040e-01 9.47555244e-01 -2.01330781e-01
3.92842710e-01 3.59628230e-01 -5.53849280e-01 3.03359121e-01
-4.15600479e-01 1.32686188e-02 9.45511520e-01 2.00363070e-01
2.48129189e-01 -5.02860695e-02 -9.24742520e-01 4.93278623e-01
6.49304092e-01 -2.42594063e-01 -4.30842608e-01 5.22461653e-01
7.30426908e-02 -5.29277623e-01 -1.28692472e+00 6.46341026e-01
4.75848585e-01 -1.27284372e+00 8.22699726e-01 -5.80918133e-01
2.40638182e-01 -4.30341184e-01 3.28790814e-01 -9.33722019e-01
-1.24513768e-01 -1.09758210e+00 8.34180340e-02 5.64024448e-01
8.49426687e-02 -9.25603926e-01 9.30109739e-01 1.32417139e-02
-1.53330982e-01 -1.08861184e+00 -9.09230590e-01 -5.46379685e-01
-2.09680840e-01 -1.63664460e-01 2.94343889e-01 5.21814704e-01
-3.03250313e-01 -2.55634189e-01 3.99474591e-01 4.15821135e-01
9.31086898e-01 6.03951097e-01 7.09516406e-01 -1.91784823e+00
-1.27304941e-02 -5.67075849e-01 -9.90180612e-01 -8.89652669e-01
-2.30462089e-01 -7.40273356e-01 -6.31141424e-01 -1.64282548e+00
3.04057915e-02 -6.10768795e-01 7.64031187e-02 -6.77093565e-02
-3.25350510e-03 5.03639698e-01 -3.03653479e-01 7.06349492e-01
4.87481147e-01 1.00370921e-01 2.13733482e+00 2.68082082e-01
-5.62930644e-01 8.37250233e-01 -2.65461296e-01 8.61320674e-01
5.46177387e-01 -7.24210292e-02 -2.30249554e-01 8.60876366e-02
4.07729089e-01 2.09315553e-01 7.40886748e-01 -8.04831386e-01
2.33787652e-02 -2.69038174e-02 -4.05678432e-03 -5.83221316e-01
2.70702451e-01 -4.58863705e-01 -1.25844583e-01 4.11977887e-01
2.83539742e-01 -6.55983031e-01 1.04230598e-01 4.52228576e-01
-5.55926383e-01 -4.59757894e-01 1.03525937e+00 -2.08310589e-01
-4.03015874e-02 5.62979937e-01 -1.45968363e-01 1.28003331e-02
8.13886344e-01 -7.44510472e-01 -8.78018737e-02 2.71110177e-01
-1.16907012e+00 -2.54573196e-01 7.78824985e-01 -5.57656169e-01
1.02127945e+00 -7.30344832e-01 -1.20340228e+00 6.69611573e-01
2.71030068e-02 3.84410143e-01 2.44447425e-01 1.53676009e+00
-1.06539118e+00 5.21127641e-01 -1.74954847e-01 -9.63756323e-01
-1.71029997e+00 2.75293307e-04 7.30955839e-01 -2.37469599e-01
-8.05478036e-01 9.24135625e-01 -4.21433225e-02 2.64659435e-01
-1.55905560e-01 -5.90585291e-01 -5.75551152e-01 2.56418288e-01
5.70504963e-01 3.20834279e-01 1.33944541e-01 -7.44359195e-01
2.57612795e-01 1.47429848e+00 -1.45186722e-01 4.57559638e-02
1.19644010e+00 -2.30281323e-01 -8.72270882e-01 1.87157035e-01
6.49544597e-01 5.26591361e-01 -7.24441707e-01 -2.61254281e-01
-4.11585897e-01 -9.20786738e-01 7.52254903e-01 -5.91278791e-01
-8.42688322e-01 9.91674960e-01 6.93916500e-01 3.13808560e-01
1.52442110e+00 -2.65378445e-01 9.52262104e-01 -4.37220603e-01
3.23537260e-01 -6.15193784e-01 -3.61161500e-01 -1.32821769e-01
8.91655087e-01 -1.03521228e+00 1.39899433e-01 -7.85238326e-01
7.64397830e-02 1.41348636e+00 -6.36667153e-03 -3.47593576e-02
9.53620076e-01 -3.99393290e-01 -4.01611477e-02 -5.63230872e-01
-7.40312561e-02 -9.18503702e-01 8.73988688e-01 5.29370308e-01
4.38090414e-01 1.63609117e-01 -6.98946416e-01 1.44839892e-02
-2.18274698e-01 1.20233044e-01 5.81310511e-01 5.05491972e-01
-6.39407814e-01 -1.37492418e+00 -5.22678077e-01 8.08740735e-01
-6.85190201e-01 -3.77953917e-01 -7.19585717e-02 5.31618536e-01
2.79073328e-01 7.87759721e-01 1.87089652e-01 -6.27863035e-02
1.28152996e-01 -1.68863088e-01 7.51040697e-01 -1.08715820e+00
-3.32754254e-01 6.47372723e-01 3.61346640e-02 -4.49908167e-01
-6.89430058e-01 -6.74449444e-01 -6.95916355e-01 1.00310259e-01
-5.28022528e-01 -1.71504095e-02 9.25290048e-01 5.54225504e-01
4.72206354e-01 -3.50198261e-02 7.94794023e-01 -5.13533354e-01
-2.29506716e-01 -7.97402382e-01 -1.31454396e+00 1.11946814e-01
9.17489111e-01 -5.73125124e-01 -6.67222381e-01 5.71704991e-02] | [15.752470970153809, -3.9671237468719482] |
b2a86ab3-fc36-496d-8abe-7b701078d2f7 | using-explicit-discourse-connectives-in | null | null | https://aclanthology.org/I17-1049 | https://aclanthology.org/I17-1049.pdf | Using Explicit Discourse Connectives in Translation for Implicit Discourse Relation Classification | Implicit discourse relation recognition is an extremely challenging task due to the lack of indicative connectives. Various neural network architectures have been proposed for this task recently, but most of them suffer from the shortage of labeled data. In this paper, we address this problem by procuring additional training data from parallel corpora: When humans translate a text, they sometimes add connectives (a process known as \textit{explicitation}). We automatically back-translate it into an English connective and use it to infer a label with high confidence. We show that a training set several times larger than the original training set can be generated this way. With the extra labeled instances, we show that even a simple bidirectional Long Short-Term Memory Network can outperform the current state-of-the-art. | ['Raphael Rubino', 'Frances Yung', 'Wei Shi', 'Vera Demberg'] | 2017-11-01 | using-explicit-discourse-connectives-in-1 | https://aclanthology.org/I17-1049 | https://aclanthology.org/I17-1049.pdf | ijcnlp-2017-11 | ['implicit-discourse-relation-classification'] | ['natural-language-processing'] | [ 5.57002783e-01 8.78895700e-01 -4.23863262e-01 -6.45863712e-01
-7.33282387e-01 -6.67743802e-01 7.29630113e-01 8.68022144e-02
-5.36388397e-01 1.26372027e+00 3.25110823e-01 -6.28945529e-01
4.49016452e-01 -7.09609985e-01 -8.40323389e-01 -3.05118978e-01
3.39136384e-02 9.69181061e-01 1.51492730e-01 -2.71692812e-01
5.24153262e-02 -1.21869549e-01 -9.06903148e-01 7.60596871e-01
1.04216456e+00 7.85660625e-01 1.51483834e-01 4.17179674e-01
-3.99153262e-01 1.26033604e+00 -8.18768680e-01 -7.18251586e-01
-1.27514780e-01 -6.11612737e-01 -1.52307546e+00 -1.89778060e-01
8.61619413e-03 -5.35598278e-01 -5.41070476e-03 1.02754760e+00
9.71022099e-02 -9.07185581e-03 7.05860913e-01 -9.87880707e-01
-8.63271475e-01 1.15874243e+00 -2.20989347e-01 6.37288839e-02
4.21918958e-01 -3.82733434e-01 1.21090674e+00 -9.83427048e-01
8.13836932e-01 1.20257056e+00 4.73990768e-01 5.80160797e-01
-1.22250724e+00 -4.83933300e-01 4.02734488e-01 5.02183080e-01
-1.09332466e+00 -4.94941086e-01 8.13056827e-01 -1.73434928e-01
1.31296396e+00 4.10113364e-01 4.61038321e-01 1.35054708e+00
-4.00252312e-01 8.80502641e-01 1.29211795e+00 -9.04691756e-01
-8.34200252e-03 1.16501428e-01 5.53106904e-01 6.85266197e-01
-1.93246955e-03 -5.58989197e-02 -5.31613290e-01 1.91009805e-01
3.83450747e-01 -6.26819670e-01 -5.05324960e-01 3.32037151e-01
-1.17154384e+00 7.84494162e-01 5.40928185e-01 5.68756461e-01
3.66399996e-02 -3.17478068e-02 4.33468521e-01 6.19447827e-01
7.09545135e-01 7.00430453e-01 -5.38288116e-01 -3.60664092e-02
-7.15787292e-01 -7.07656965e-02 1.26651895e+00 1.11223102e+00
5.45409322e-01 -2.08340868e-01 8.45253281e-03 8.85423303e-01
8.03517736e-03 -3.21921632e-02 4.78281856e-01 -9.78536546e-01
8.61452520e-01 5.78561485e-01 3.14929374e-02 -8.71763706e-01
-3.22043657e-01 -2.26549253e-01 -8.97146165e-01 -1.00841522e-01
5.67443490e-01 -3.56857151e-01 -5.30089736e-01 1.98970008e+00
1.33616582e-01 2.32124757e-02 2.85707414e-01 8.38184834e-01
8.24949622e-01 7.88327932e-01 8.27704929e-03 -5.79923332e-01
1.14401686e+00 -1.28591144e+00 -9.40872490e-01 -5.33676267e-01
7.57773459e-01 -5.36443770e-01 9.42542672e-01 4.20444131e-01
-1.04202771e+00 -4.07160431e-01 -1.21389055e+00 -2.93952018e-01
-2.38936603e-01 1.03396147e-01 7.90438294e-01 2.22382650e-01
-7.81206727e-01 5.25673330e-01 -7.41272151e-01 -1.06958427e-01
1.74261466e-01 3.24896961e-01 -4.58954722e-01 2.19185241e-02
-1.40329206e+00 1.43968272e+00 8.44244123e-01 4.19178903e-01
-3.75901133e-01 -1.04432881e-01 -9.35702741e-01 -4.60345745e-02
7.96253920e-01 -6.94463849e-01 1.50880015e+00 -1.31513166e+00
-1.81578994e+00 1.10982370e+00 -2.81043619e-01 -5.28319299e-01
5.21159708e-01 -6.11610949e-01 -1.16245352e-01 -5.31489886e-02
5.34632467e-02 4.37308848e-01 5.85631669e-01 -1.20259988e+00
-4.81505543e-01 -6.95476914e-03 4.44638044e-01 1.42818749e-01
-4.00310934e-01 2.29452506e-01 -1.47015035e-01 -6.67414844e-01
1.42783016e-01 -9.40979779e-01 5.52671291e-02 -2.71136820e-01
-7.07281411e-01 -6.50269210e-01 4.78031367e-01 -7.12007761e-01
1.12562478e+00 -1.73955727e+00 5.60532987e-01 -1.83779493e-01
1.49317324e-01 4.91893172e-01 9.15873609e-03 1.33508891e-01
-9.55277234e-02 3.17826301e-01 -4.21987295e-01 -3.64904165e-01
-2.10990217e-02 5.84214151e-01 -5.48401833e-01 1.25237659e-01
5.21380126e-01 8.59543443e-01 -1.06174648e+00 -6.38633847e-01
-3.29511017e-01 -4.03661802e-02 -3.86384100e-01 4.43814844e-01
-6.56253338e-01 4.15488958e-01 -1.45345345e-01 2.61804432e-01
3.17348212e-01 -4.97320801e-01 9.05196488e-01 -7.00003877e-02
1.65712640e-01 1.03661609e+00 -8.01589012e-01 1.60782504e+00
-6.13100231e-01 8.67370427e-01 3.27362604e-02 -1.53069794e+00
7.73464561e-01 6.02985978e-01 -2.02969193e-01 -5.20094216e-01
2.74419159e-01 4.80087966e-01 3.91142279e-01 -6.53115034e-01
3.43230575e-01 -4.53351855e-01 5.07003143e-02 7.13160574e-01
1.08079001e-01 3.79269756e-02 3.93725157e-01 2.05262881e-02
9.73097205e-01 3.04947585e-01 3.00187558e-01 -4.17270437e-02
6.65749550e-01 3.43093872e-02 6.79249585e-01 3.89841467e-01
2.37843782e-01 4.31879163e-01 6.77693009e-01 -3.39308381e-01
-9.20944691e-01 -7.94975519e-01 -3.64701450e-02 1.09444964e+00
-1.03359483e-01 -4.77687091e-01 -7.08280385e-01 -9.22197521e-01
-3.92688781e-01 9.86047685e-01 -4.39428329e-01 -2.37789899e-02
-1.29851723e+00 -7.98995316e-01 6.60878778e-01 7.33073413e-01
7.45340705e-01 -1.15749502e+00 -3.34952593e-01 3.76517773e-01
-7.25285470e-01 -1.35587347e+00 -5.19447140e-02 5.14242411e-01
-7.36095965e-01 -1.04534650e+00 -3.48096788e-01 -1.21992481e+00
8.85819316e-01 -3.15457553e-01 1.45061588e+00 3.80186707e-01
3.79659832e-01 -5.90759158e-01 -5.03517926e-01 -1.56210601e-01
-8.06233048e-01 3.51859331e-01 -1.33758619e-01 -2.96412081e-01
3.52706969e-01 -5.95843196e-01 9.73869041e-02 -9.23785288e-03
-5.40927887e-01 5.47426999e-01 5.03045201e-01 1.35038245e+00
4.16642010e-01 -1.74401551e-01 8.26848924e-01 -1.41661835e+00
8.03178310e-01 -3.94030452e-01 -3.70065600e-01 3.48926604e-01
-5.04225731e-01 3.25602025e-01 7.60131001e-01 -6.00635588e-01
-1.19968319e+00 -2.75645584e-01 -1.45945475e-01 1.27205238e-01
-1.34169096e-02 9.87153232e-01 -4.48746905e-02 4.89768744e-01
6.07458055e-01 -1.30783215e-01 -1.59182712e-01 -4.80338722e-01
6.12632036e-01 8.02618325e-01 6.27319515e-01 -9.79614437e-01
5.22037983e-01 -6.71020076e-02 -2.05034316e-01 -5.49975991e-01
-1.59193254e+00 7.25742355e-02 -8.55130374e-01 3.22294012e-02
8.62865925e-01 -6.51189566e-01 -3.76797646e-01 1.87705413e-01
-1.77273154e+00 -7.21318007e-01 -1.46100223e-01 4.24764007e-01
-4.55157101e-01 1.89367756e-01 -1.17620623e+00 -6.09159946e-01
-3.42352420e-01 -8.63359153e-01 4.59325492e-01 -1.28726155e-01
-7.57789493e-01 -9.72259343e-01 -5.80498017e-02 4.13227379e-01
2.98385173e-01 1.29117996e-01 1.28406775e+00 -8.95062506e-01
-4.48469907e-01 1.31986707e-01 -3.47925901e-01 5.63733160e-01
2.43671343e-01 -1.06572397e-01 -8.84279907e-01 -5.86573873e-03
2.74523556e-01 -8.31446707e-01 7.58966386e-01 -2.44955003e-01
9.45871413e-01 -7.93914020e-01 -3.65240097e-01 3.03017825e-01
7.87033439e-01 1.02377764e-03 3.13531458e-01 4.37925816e-01
5.26297510e-01 7.20361471e-01 5.20131171e-01 -3.41154963e-01
4.36756968e-01 4.94371921e-01 1.46090165e-01 -2.09849756e-02
-2.50790060e-01 -2.45299917e-02 3.68019193e-01 1.50679743e+00
-2.32523113e-01 -3.38380456e-01 -9.49634194e-01 4.10610318e-01
-1.89293802e+00 -8.94667029e-01 -2.63799667e-01 1.65361512e+00
1.69655871e+00 5.28158247e-01 -3.88442248e-01 4.52045262e-01
6.22259855e-01 -7.90304765e-02 -2.22571820e-01 -4.25349385e-01
-2.13065043e-01 3.59432191e-01 -1.58239827e-01 8.96516740e-01
-1.09945452e+00 1.06087077e+00 6.56249380e+00 5.55644691e-01
-1.15832734e+00 2.47904047e-01 8.37614357e-01 2.29694963e-01
-9.52233821e-02 6.65769279e-02 -6.43052101e-01 2.68357962e-01
1.07056618e+00 -4.82201017e-02 3.10192466e-01 6.72695041e-01
-4.55797970e-01 -2.06053197e-01 -1.74062264e+00 6.17428541e-01
3.09989244e-01 -1.36768198e+00 -5.03952103e-03 -3.22205335e-01
5.04056633e-01 -1.60175860e-01 -3.99775118e-01 5.64685047e-01
3.78130138e-01 -1.21170330e+00 7.65984595e-01 1.75202057e-01
1.01701283e+00 -2.99641490e-01 9.04572368e-01 7.03785181e-01
-7.42624760e-01 9.50259417e-02 -1.25080809e-01 -6.07676089e-01
2.46423438e-01 5.02637923e-01 -1.01635110e+00 4.81459230e-01
2.03801453e-01 5.34977436e-01 -3.70625168e-01 4.38180506e-01
-1.05725229e+00 8.59184146e-01 -6.05376005e-01 -1.86336160e-01
1.51550621e-02 -2.75046498e-01 4.56977040e-01 1.21318305e+00
8.53141844e-02 3.11721206e-01 1.22769915e-01 1.04439950e+00
-5.78174829e-01 -1.32104263e-01 -4.97735858e-01 2.41767317e-02
4.77618515e-01 1.01201630e+00 -5.85103095e-01 -6.41829967e-01
-3.93074989e-01 1.13693309e+00 1.09298241e+00 3.22399080e-01
-6.48233116e-01 -1.20983794e-01 -4.10127975e-02 -2.41542742e-01
-1.11017421e-01 -3.01713914e-01 -2.07671717e-01 -1.16651690e+00
3.83810073e-01 -8.60630929e-01 2.69374788e-01 -7.89853096e-01
-1.49129164e+00 9.34804082e-01 6.70829490e-02 -8.79013419e-01
-7.26957500e-01 -8.21354032e-01 -4.78071094e-01 7.85462201e-01
-1.39554429e+00 -1.00033247e+00 -4.95889187e-02 1.94236800e-01
6.47093177e-01 -7.15220720e-02 1.35562313e+00 3.65413487e-01
-4.54024971e-01 4.97361571e-01 -3.02593410e-01 6.97195530e-01
7.19289124e-01 -1.35536337e+00 1.43233299e-01 8.01144600e-01
3.74873012e-01 8.52608919e-01 7.28529274e-01 -4.01008904e-01
-9.31784391e-01 -9.65194225e-01 1.36197054e+00 -5.03613353e-01
8.44103336e-01 -3.80295962e-01 -1.04459333e+00 1.22108936e+00
7.19357967e-01 1.40276190e-03 7.33906448e-01 4.27500129e-01
-3.97517830e-01 8.28078091e-02 -7.15071261e-01 6.98866487e-01
1.17644298e+00 -6.32354140e-01 -1.36063969e+00 5.54248571e-01
9.04740810e-01 -6.66264236e-01 -6.76438451e-01 4.51895982e-01
6.64423481e-02 -6.92009211e-01 5.95170021e-01 -8.22570205e-01
8.21250319e-01 -1.97716057e-01 -1.34165108e-01 -1.39606977e+00
-1.44991800e-01 -6.03874922e-01 -4.37520236e-01 1.39277732e+00
8.58651876e-01 -5.61484575e-01 5.49846053e-01 6.67397916e-01
-2.63168812e-01 -7.78539479e-01 -1.10168755e+00 -6.90992713e-01
3.46107572e-01 -2.00014085e-01 2.37556696e-01 1.21813893e+00
6.26984000e-01 1.32890224e+00 -2.26394370e-01 -3.73085886e-01
1.43439785e-01 3.90300304e-01 4.41095024e-01 -1.12612057e+00
-3.96347314e-01 -2.78303623e-01 1.89174891e-01 -1.39902008e+00
7.36618876e-01 -1.29468369e+00 1.48345590e-01 -1.54161596e+00
4.75699194e-02 -8.46971691e-01 1.05678581e-01 7.04084873e-01
-4.08873498e-01 1.70944795e-01 1.32607996e-01 1.72994703e-01
-4.92103249e-01 6.24463558e-01 1.06402266e+00 -3.75658363e-01
4.21466418e-02 -2.03191698e-01 -5.30341327e-01 9.65379655e-01
8.09132814e-01 -5.55890679e-01 -3.06867152e-01 -8.05575192e-01
4.79679912e-01 2.26700455e-01 3.62288207e-02 -6.59513593e-01
1.30717650e-01 -7.31091499e-02 1.32475853e-01 -3.22550416e-01
5.77118456e-01 -6.84036791e-01 -1.35731369e-01 3.03918302e-01
-7.00475454e-01 2.36318842e-01 -6.69057742e-02 4.06036794e-01
-5.30800045e-01 -5.44083834e-01 2.89280742e-01 -3.20200711e-01
-3.79240692e-01 -3.80734861e-01 -4.92257595e-01 3.15359473e-01
7.69235015e-01 4.03518498e-01 -7.54981995e-01 -1.55673116e-01
-8.43891859e-01 1.71694770e-01 9.71096903e-02 2.17815831e-01
3.79805475e-01 -1.30853033e+00 -8.30838680e-01 -1.06941268e-01
-2.47137204e-01 5.56090713e-01 -6.07149184e-01 9.00560856e-01
-4.58246052e-01 3.25946420e-01 5.79662845e-02 -3.46796274e-01
-1.16196680e+00 5.55556715e-01 2.17116356e-01 -5.58715820e-01
-7.13618159e-01 1.01154041e+00 -1.79765478e-01 -5.18160582e-01
1.92586958e-01 -4.18542147e-01 -3.09567958e-01 1.39592454e-01
3.47610891e-01 -2.13938415e-01 9.99712944e-02 -5.06414235e-01
-1.46958038e-01 8.95838365e-02 -4.16407436e-02 -1.29491553e-01
1.51580822e+00 5.94781488e-02 -5.32239735e-01 6.63447142e-01
9.91221786e-01 2.81140348e-03 -7.22865045e-01 -3.87886763e-01
4.14080769e-01 -9.84338205e-03 -4.75596219e-01 -8.48310471e-01
-8.28583419e-01 9.35978532e-01 -2.85158157e-01 3.66901517e-01
8.50229621e-01 -1.01787085e-02 6.91506863e-01 9.50423121e-01
3.18233162e-01 -1.20375586e+00 1.39935702e-01 1.01399136e+00
1.03286088e+00 -1.39691544e+00 3.61737907e-02 -7.63833284e-01
-4.00238216e-01 1.26671326e+00 7.94052958e-01 -1.99316770e-01
4.11167592e-01 4.73091662e-01 6.22679405e-02 -1.55985672e-02
-9.56466317e-01 5.43810688e-02 -1.08462989e-01 5.36851466e-01
9.85415876e-01 2.07669735e-02 -5.75506449e-01 8.63006771e-01
-4.91278887e-01 -5.18251350e-03 5.29274821e-01 7.56430566e-01
-1.21601552e-01 -1.41905224e+00 -1.36553049e-01 3.93724024e-01
-5.38873494e-01 -2.99887210e-01 -6.02253675e-01 7.36684859e-01
-7.21531222e-03 1.04107261e+00 3.70006892e-03 -1.84020102e-01
4.49493378e-02 4.24546927e-01 7.29768813e-01 -8.13458443e-01
-5.70743322e-01 -3.70255321e-01 1.12601995e+00 -7.87613988e-02
-7.72578716e-01 -3.73484999e-01 -1.47263038e+00 -2.53727108e-01
-4.76051748e-01 3.16104591e-01 1.29425064e-01 1.38034725e+00
-9.66296121e-02 7.65521049e-01 5.79011291e-02 -7.19603896e-01
-6.76365376e-01 -1.42803681e+00 1.38982043e-01 5.95071197e-01
3.00917834e-01 -7.11258888e-01 -2.14351490e-01 4.40365493e-01] | [10.673955917358398, 9.179931640625] |
d6cb7d40-008c-402a-9e0f-b6ec1a1a1302 | towards-lingua-franca-named-entity | 1912.01389 | null | https://arxiv.org/abs/1912.01389v2 | https://arxiv.org/pdf/1912.01389v2.pdf | Towards Lingua Franca Named Entity Recognition with BERT | Information extraction is an important task in NLP, enabling the automatic extraction of data for relational database filling. Historically, research and data was produced for English text, followed in subsequent years by datasets in Arabic, Chinese (ACE/OntoNotes), Dutch, Spanish, German (CoNLL evaluations), and many others. The natural tendency has been to treat each language as a different dataset and build optimized models for each. In this paper we investigate a single Named Entity Recognition model, based on a multilingual BERT, that is trained jointly on many languages simultaneously, and is able to decode these languages with better accuracy than models trained only on one language. To improve the initial model, we study the use of regularization strategies such as multitask learning and partial gradient updates. In addition to being a single model that can tackle multiple languages (including code switch), the model could be used to make zero-shot predictions on a new language, even ones for which training data is not available, out of the box. The results show that this model not only performs competitively with monolingual models, but it also achieves state-of-the-art results on the CoNLL02 Dutch and Spanish datasets, OntoNotes Arabic and Chinese datasets. Moreover, it performs reasonably well on unseen languages, achieving state-of-the-art for zero-shot on three CoNLL languages. | ['Taesun Moon', 'Jian Ni', 'Radu Florian', 'Parul Awasthy'] | 2019-11-19 | null | null | null | null | ['cross-lingual-ner'] | ['natural-language-processing'] | [-3.16018254e-01 1.06086813e-01 -3.00063699e-01 -1.99871659e-01
-1.04986203e+00 -6.13116264e-01 4.72133189e-01 3.37381124e-01
-8.94110739e-01 9.59435821e-01 1.44349366e-01 -4.19946879e-01
1.37286276e-01 -5.32723010e-01 -7.03926742e-01 -1.95549294e-01
1.29373714e-01 8.10821593e-01 3.15377086e-01 -2.88612753e-01
-1.55999407e-01 4.91382927e-01 -1.17526066e+00 5.61522007e-01
9.92905319e-01 6.26610577e-01 4.40097034e-01 3.60529631e-01
-5.70750475e-01 1.10513830e+00 -5.42000294e-01 -9.81890321e-01
1.33291259e-02 1.01065589e-02 -9.66137707e-01 -1.73736185e-01
-2.66192295e-02 1.79909468e-01 -1.38945788e-01 9.07730341e-01
5.01029551e-01 -2.22465545e-01 3.03490460e-01 -8.55587840e-01
-5.96924067e-01 1.03915954e+00 -4.56201702e-01 -1.19333461e-01
3.61258507e-01 -3.80241841e-01 9.12291586e-01 -1.17111337e+00
1.04013658e+00 1.12485826e+00 5.48799515e-01 3.56008738e-01
-1.01740241e+00 -7.12942302e-01 1.20481461e-01 1.25781775e-01
-1.72462249e+00 -6.64757192e-01 3.83487880e-01 -4.79927838e-01
1.49366319e+00 -2.40762383e-02 1.11926831e-01 1.04738510e+00
1.10239953e-01 9.89804268e-01 9.86259878e-01 -8.05072308e-01
-9.28008407e-02 5.81915498e-01 2.34782659e-02 6.95797265e-01
8.49831477e-02 -1.42573088e-01 -6.96829081e-01 1.22637982e-02
7.46027380e-02 -4.98585790e-01 -1.18060753e-01 -2.78304935e-01
-1.26134133e+00 7.23421812e-01 -3.76840383e-02 7.24100232e-01
-2.05123276e-01 -4.11239713e-01 5.12272537e-01 4.43223119e-01
4.73416358e-01 3.85162801e-01 -1.16209793e+00 -2.51647353e-01
-9.59669709e-01 -2.18451712e-02 1.38102221e+00 1.29311955e+00
7.46399879e-01 -6.33101910e-02 -9.93777514e-02 1.10696745e+00
2.46130973e-01 3.94934475e-01 6.41304910e-01 -1.96031645e-01
9.99514341e-01 7.43925035e-01 -4.11891341e-02 -4.14922655e-01
-6.85256422e-01 -3.51313829e-01 -7.08465576e-01 -2.18267024e-01
4.58609998e-01 -4.21001285e-01 -8.25148165e-01 1.61469209e+00
2.26172954e-01 -3.16540956e-01 5.39029360e-01 2.42363468e-01
8.08114529e-01 6.68088913e-01 1.22441210e-01 -3.09340864e-01
1.41831708e+00 -9.12590802e-01 -9.33157563e-01 -4.28404391e-01
9.46041405e-01 -1.04165411e+00 7.07419276e-01 6.05445743e-01
-7.99888313e-01 -5.33917904e-01 -9.05767143e-01 -2.43797466e-01
-9.71962273e-01 5.93447924e-01 4.60011005e-01 7.63470590e-01
-8.55054259e-01 2.26085901e-01 -8.99526238e-01 -5.65819025e-01
9.26650017e-02 3.15475941e-01 -5.97490728e-01 -2.21449062e-01
-1.33924127e+00 1.24081326e+00 9.14990425e-01 6.44549280e-02
-6.13814294e-01 -4.48024571e-01 -9.55126107e-01 -2.34949380e-01
7.28004634e-01 -1.68518871e-01 8.28993976e-01 -5.38998306e-01
-1.37951291e+00 9.67804849e-01 -2.73052722e-01 -5.15592277e-01
4.30848926e-01 -3.85973454e-01 -7.53654838e-01 -4.93953258e-01
1.32425204e-01 4.93276864e-01 4.62770849e-01 -1.12555480e+00
-5.95094383e-01 -3.93146455e-01 -1.37478277e-01 -7.22706243e-02
-3.24898958e-01 7.39175200e-01 -9.52539265e-01 -5.73145449e-01
-1.58033535e-01 -1.00355697e+00 -7.63246417e-02 -6.13354325e-01
-6.66992068e-01 -3.92467439e-01 4.16878849e-01 -1.24909270e+00
1.60025573e+00 -2.15353155e+00 3.18389297e-01 -2.48017982e-02
-1.78240642e-01 5.11148453e-01 -1.72237456e-01 5.91397226e-01
-4.09946032e-02 1.60905913e-01 -3.53094876e-01 -3.83220047e-01
-1.88767299e-01 1.89472824e-01 3.74593399e-03 2.72457838e-01
6.29794657e-01 8.81854892e-01 -5.89356542e-01 -5.00968516e-01
-2.14955032e-01 4.63597655e-01 -4.50174272e-01 1.90816358e-01
-4.20155711e-02 3.34940076e-01 -3.56015116e-01 8.28387439e-01
5.64828277e-01 -7.21576437e-02 5.50358295e-01 -8.11846778e-02
-4.33098912e-01 1.82956129e-01 -1.48059571e+00 1.84334695e+00
-7.85180867e-01 3.83450478e-01 2.13630330e-02 -8.48875940e-01
1.08119094e+00 5.54911137e-01 2.50361204e-01 -6.72231436e-01
-5.72828017e-02 7.21042395e-01 1.22214437e-01 -7.85373747e-01
5.08689165e-01 4.46576700e-02 -5.13531268e-01 2.09965244e-01
5.30874431e-01 3.41542102e-02 6.12820446e-01 8.94781854e-03
9.61603105e-01 3.22798043e-01 6.10397577e-01 -9.62621495e-02
8.17373335e-01 -7.87755698e-02 6.26897633e-01 5.02960205e-01
1.83914304e-01 3.26461941e-01 4.05625969e-01 -2.23854870e-01
-8.46549273e-01 -7.64898777e-01 -3.68269026e-01 1.34342873e+00
-1.83035284e-01 -6.30623162e-01 -5.91242909e-01 -7.92326331e-01
2.36052517e-02 9.67416048e-01 -2.51660854e-01 6.19038865e-02
-7.85863519e-01 -9.66848552e-01 8.51192772e-01 2.92200446e-01
3.45267326e-01 -1.36910772e+00 -1.30063444e-01 6.20363176e-01
-3.15931708e-01 -1.50548899e+00 -6.37217537e-02 7.63331831e-01
-4.67047125e-01 -1.10938525e+00 -5.81799746e-01 -8.23711336e-01
2.47720942e-01 -5.54081500e-01 1.28031957e+00 -2.11562246e-01
-3.98589149e-02 8.90923366e-02 -4.96288031e-01 -5.47282338e-01
-6.62762344e-01 7.24944830e-01 -1.03007302e-01 6.11675940e-02
6.70124173e-01 -6.83029518e-02 4.56249088e-01 8.38768110e-02
-9.29597795e-01 -1.61153436e-01 7.97614753e-01 8.59280825e-01
3.92497420e-01 -2.37966210e-01 5.91138482e-01 -1.31715941e+00
4.65420097e-01 -6.14974797e-01 -5.68850160e-01 8.91447365e-01
-5.49538612e-01 4.52069759e-01 5.11000812e-01 -2.87799984e-01
-1.25712562e+00 2.56685466e-01 -3.73736084e-01 -2.29069199e-02
-1.44486353e-01 9.14617896e-01 -4.44621176e-01 1.46664619e-01
5.51717877e-01 1.40024513e-01 -4.56782937e-01 -9.53209996e-01
4.00815159e-01 1.02968776e+00 1.78587511e-01 -6.70034230e-01
4.85600829e-01 -8.82208347e-02 -5.84318459e-01 -1.06022012e+00
-8.19817960e-01 -5.03993928e-01 -1.24880064e+00 1.73589766e-01
1.09197187e+00 -1.20945907e+00 -1.86286837e-01 7.17037499e-01
-1.45728242e+00 -2.09319338e-01 -6.06841072e-02 4.88996565e-01
-1.35838404e-01 9.31255445e-02 -6.95485950e-01 -6.98283851e-01
-1.05397165e-01 -1.24289966e+00 1.05120027e+00 -3.99397640e-03
-1.16082057e-01 -1.05284429e+00 1.81920573e-01 2.10057214e-01
2.57225037e-01 -1.28202066e-01 1.06911731e+00 -1.18910873e+00
-4.45954621e-01 -2.24024594e-01 -2.13739917e-01 3.56278479e-01
-2.15034895e-02 -7.59785548e-02 -8.01863730e-01 -3.89817834e-01
-9.27754417e-02 -5.90711415e-01 8.49515736e-01 -2.58901566e-01
6.80549443e-01 -7.45777562e-02 -3.77009213e-01 5.12008965e-01
1.51527143e+00 3.19550723e-01 4.99598354e-01 3.77481401e-01
7.61363029e-01 8.25634778e-01 4.96949941e-01 2.55073279e-01
7.74696171e-01 7.35205352e-01 8.34579095e-02 -2.27196347e-02
-5.54128364e-02 -1.18780546e-01 4.21289980e-01 1.30721891e+00
8.81196037e-02 -3.45840991e-01 -1.32826102e+00 6.42689526e-01
-1.73884296e+00 -3.36746752e-01 -8.45821276e-02 2.26864743e+00
1.16926360e+00 7.09145591e-02 -2.14564651e-01 -2.06543103e-01
5.56761801e-01 3.68863456e-02 -3.16997200e-01 -3.70002568e-01
-5.62033236e-01 4.70424324e-01 5.92250347e-01 4.13248599e-01
-1.29966843e+00 1.41829550e+00 5.70390749e+00 8.40663254e-01
-1.07407355e+00 4.77183431e-01 4.28803027e-01 3.04101676e-01
-1.51858523e-01 1.97506905e-01 -1.42650378e+00 3.24156493e-01
1.23913038e+00 -5.95087595e-02 4.82306331e-01 6.69071198e-01
-2.75951505e-01 -1.11990966e-01 -1.03138137e+00 7.92638779e-01
2.50338823e-01 -9.00813639e-01 -1.13044225e-01 -6.47819787e-02
7.48200357e-01 5.80696881e-01 -5.64266622e-01 7.94366896e-01
3.76414418e-01 -8.33284736e-01 7.91625023e-01 6.19866610e-01
7.98047483e-01 -7.72270501e-01 9.44757521e-01 6.80770576e-01
-1.05694079e+00 -3.47767211e-02 -3.54835510e-01 4.55482751e-01
1.80198342e-01 4.28794205e-01 -5.82663834e-01 8.19872856e-01
6.60795748e-01 6.14014089e-01 -9.31467116e-01 9.32631493e-01
-3.86041194e-01 3.24052066e-01 -3.27837646e-01 -4.86331992e-03
2.20277184e-03 -5.03860898e-02 2.97679752e-01 1.57559848e+00
4.76981401e-01 -2.72763610e-01 3.98784786e-01 4.56123114e-01
-3.49838585e-01 6.21876359e-01 -6.71440959e-01 -1.79788783e-01
4.01372015e-01 1.10538507e+00 -4.01932985e-01 -4.17201340e-01
-8.05923879e-01 9.66925144e-01 7.92662919e-01 4.18016136e-01
-5.58531404e-01 -6.09143734e-01 5.24989963e-02 -1.62025392e-01
4.98206556e-01 -3.13715070e-01 3.57760787e-02 -1.48889780e+00
1.55778006e-01 -1.13119328e+00 4.40399915e-01 -4.69564825e-01
-1.10800993e+00 1.01050198e+00 -1.42445579e-01 -9.68593478e-01
-4.64348167e-01 -8.32133591e-01 1.18732704e-02 9.25564468e-01
-1.68415308e+00 -1.24706256e+00 2.41445974e-01 6.47264063e-01
4.41632122e-01 -6.42087579e-01 1.00012064e+00 7.29235232e-01
-8.00253928e-01 6.50029898e-01 2.91173786e-01 5.52772701e-01
1.10831904e+00 -1.17317450e+00 1.19870320e-01 9.75692749e-01
5.99379241e-01 6.59892142e-01 2.78882921e-01 -7.46720970e-01
-1.33300078e+00 -9.75964129e-01 1.69307184e+00 -4.89120603e-01
9.40734625e-01 -5.47780156e-01 -1.03027403e+00 7.17603564e-01
4.17964339e-01 -3.25115062e-02 7.23424613e-01 4.45659369e-01
-2.89374173e-01 -5.50445616e-02 -7.95530617e-01 1.65854022e-01
6.92429900e-01 -7.56339848e-01 -5.81735134e-01 4.16579098e-01
6.82943344e-01 -5.83105803e-01 -1.16499746e+00 3.46036047e-01
3.09681505e-01 -6.60191417e-01 6.55966580e-01 -8.31079781e-01
1.60924971e-01 -2.16418922e-01 -2.09736496e-01 -1.19092345e+00
8.45165849e-02 -4.04503584e-01 -2.04248548e-01 1.66763580e+00
1.13590205e+00 -4.83376741e-01 3.71694416e-01 4.35976118e-01
-2.70247553e-02 -5.63501656e-01 -1.18427646e+00 -7.54205108e-01
2.31331140e-01 -6.63175285e-01 4.93617237e-01 1.09520066e+00
-1.88538149e-01 7.19876945e-01 -4.80295569e-01 5.21814153e-02
1.27400473e-01 -8.56348574e-02 6.00833416e-01 -1.22258413e+00
-1.79846615e-01 -1.34041160e-01 -8.60616788e-02 -7.65332341e-01
5.83797872e-01 -1.20545435e+00 3.79672199e-02 -1.38826227e+00
-4.13793288e-02 -6.04333758e-01 -2.92755336e-01 9.10792410e-01
-1.52850643e-01 -7.60345310e-02 3.96481663e-01 1.05966441e-01
-6.15186512e-01 4.04614419e-01 6.14312947e-01 -1.52297840e-01
-3.90923560e-01 3.44170220e-02 -5.57942092e-01 6.40642703e-01
3.88173401e-01 -8.21063757e-01 1.71771929e-01 -6.72821701e-01
5.72917521e-01 1.49414778e-01 -3.26536775e-01 -9.96761620e-01
4.77248162e-01 2.67588764e-01 2.24661216e-01 -5.53691685e-01
8.76962990e-02 -8.21802258e-01 3.95416319e-02 3.18608761e-01
-3.14355157e-02 1.92197248e-01 3.32721472e-01 3.21653366e-01
-6.32004201e-01 -5.75022817e-01 4.86107647e-01 -2.08008036e-01
-9.89977479e-01 5.48603460e-02 -3.03553492e-01 3.98218483e-01
7.96289265e-01 1.97465360e-01 -1.21933341e-01 2.28538513e-01
-8.91594708e-01 3.23690206e-01 1.33658648e-01 8.02863061e-01
2.16674045e-01 -1.10822034e+00 -9.91528988e-01 2.99160302e-01
4.23372209e-01 -1.99392974e-01 -1.15927368e-01 7.82615781e-01
-4.58925635e-01 6.35248065e-01 1.74607337e-01 -4.41582441e-01
-1.02930295e+00 7.16236353e-01 9.09541920e-02 -9.23969567e-01
-8.56095739e-03 8.09693158e-01 -3.21135670e-01 -1.11185610e+00
1.87527671e-01 -2.27527320e-01 -6.14200056e-01 6.17254317e-01
2.53002077e-01 4.62786257e-02 5.93197644e-01 -8.05262566e-01
-5.26541889e-01 4.81969565e-01 -1.52930826e-01 -6.17384315e-02
1.44209945e+00 -2.33026780e-02 -4.85695213e-01 8.15180123e-01
1.15808177e+00 5.56005180e-01 -5.85035026e-01 -5.17987728e-01
7.51344621e-01 4.27493006e-02 -2.63954014e-01 -1.02406001e+00
-8.85414660e-01 7.74236381e-01 4.00656223e-01 9.90938116e-03
8.98612022e-01 5.67018501e-02 5.38723946e-01 6.33580267e-01
6.14526391e-01 -1.26697731e+00 -3.72588456e-01 1.07291472e+00
6.98465824e-01 -1.47639716e+00 -1.80622190e-01 -2.33582467e-01
-7.50612736e-01 1.21426845e+00 4.07486558e-01 3.39149624e-01
9.37890410e-01 4.91068482e-01 6.21328764e-02 4.67179567e-02
-7.17504799e-01 -3.94276768e-01 4.78359669e-01 3.89274269e-01
9.58011210e-01 -6.42364100e-02 -4.05400604e-01 8.41618598e-01
-9.96623412e-02 -1.68185607e-02 3.22529763e-01 1.02354681e+00
-4.71558645e-02 -1.72612441e+00 -1.62964568e-01 4.45928842e-01
-7.73128510e-01 -4.56024766e-01 -3.56907815e-01 1.10131037e+00
4.03801680e-01 8.70289385e-01 -2.17525214e-01 -1.59775779e-01
6.10786021e-01 5.99857807e-01 1.97369769e-01 -9.01215136e-01
-9.17970598e-01 1.03267603e-01 5.05428731e-01 -3.48415166e-01
-4.39620495e-01 -7.01382935e-01 -1.02011847e+00 1.25375018e-01
-4.65378463e-01 2.60625899e-01 8.34294975e-01 1.18251932e+00
2.34862790e-01 5.04137218e-01 3.64036232e-01 -2.45268226e-01
-3.30961227e-01 -1.15307248e+00 -3.95167440e-01 6.20271564e-02
-1.50828566e-02 -4.16624814e-01 -5.59508801e-03 6.23790063e-02] | [10.06914234161377, 9.691272735595703] |
122cadd1-135f-4c3e-aad4-23b84a83f8d4 | consistency-driven-sequential-transformers | 2204.00656 | null | https://arxiv.org/abs/2204.00656v1 | https://arxiv.org/pdf/2204.00656v1.pdf | Consistency driven Sequential Transformers Attention Model for Partially Observable Scenes | Most hard attention models initially observe a complete scene to locate and sense informative glimpses, and predict class-label of a scene based on glimpses. However, in many applications (e.g., aerial imaging), observing an entire scene is not always feasible due to the limited time and resources available for acquisition. In this paper, we develop a Sequential Transformers Attention Model (STAM) that only partially observes a complete image and predicts informative glimpse locations solely based on past glimpses. We design our agent using DeiT-distilled and train it with a one-step actor-critic algorithm. Furthermore, to improve classification performance, we introduce a novel training objective, which enforces consistency between the class distribution predicted by a teacher model from a complete image and the class distribution predicted by our agent using glimpses. When the agent senses only 4% of the total image area, the inclusion of the proposed consistency loss in our training objective yields 3% and 8% higher accuracy on ImageNet and fMoW datasets, respectively. Moreover, our agent outperforms previous state-of-the-art by observing nearly 27% and 42% fewer pixels in glimpses on ImageNet and fMoW. | ['James J. Clark', 'Chetan L. Srinidhi', 'Samrudhdhi B. Rangrej'] | 2022-04-01 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Rangrej_Consistency_Driven_Sequential_Transformers_Attention_Model_for_Partially_Observable_Scenes_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Rangrej_Consistency_Driven_Sequential_Transformers_Attention_Model_for_Partially_Observable_Scenes_CVPR_2022_paper.pdf | cvpr-2022-1 | ['hard-attention'] | ['methodology'] | [ 2.38663703e-01 3.50583464e-01 -1.13043441e-02 -4.47779417e-01
-5.04078209e-01 -2.94383198e-01 2.70552754e-01 -3.67869847e-02
-6.02155983e-01 8.62645328e-01 -2.45829001e-01 5.73271401e-02
2.24273512e-03 -8.52339029e-01 -9.81285393e-01 -9.86046553e-01
1.83118582e-01 4.74875212e-01 2.41200313e-01 1.51394248e-01
-1.90967858e-01 4.92624372e-01 -1.44910216e+00 -7.09506497e-02
8.23873281e-01 1.24894476e+00 8.40674222e-01 6.42046452e-01
3.24912310e-01 1.11877775e+00 -5.19723356e-01 1.42294139e-01
4.65240419e-01 -4.13055122e-01 -6.38135552e-01 5.30193269e-01
7.41669357e-01 -6.90028846e-01 -2.50969589e-01 1.07553196e+00
1.37264788e-01 3.73688459e-01 4.42861527e-01 -1.06197417e+00
-4.38118577e-01 5.09444296e-01 -7.47176051e-01 1.07731037e-01
-4.68219407e-02 5.96817732e-01 8.87343705e-01 -4.54845577e-01
5.78786314e-01 8.45170021e-01 3.27793390e-01 2.07207814e-01
-1.08708704e+00 -4.65005964e-01 8.32824588e-01 3.57285321e-01
-1.25635374e+00 -9.38319694e-03 8.76937091e-01 -4.34830934e-01
7.98314869e-01 -4.47047465e-02 8.64296079e-01 7.26688087e-01
2.87854284e-01 1.04534221e+00 1.07216954e+00 -1.99029967e-01
4.26824450e-01 8.48294124e-02 -1.64838091e-01 8.73508334e-01
-4.35119271e-02 2.00174563e-02 -3.51817429e-01 3.70351374e-01
7.37734795e-01 3.48894566e-01 -5.00490785e-01 -3.73147935e-01
-1.10708082e+00 7.36305416e-01 9.15936947e-01 -2.78951973e-02
-8.37360859e-01 1.88789666e-01 -2.47486308e-01 -9.97397080e-02
5.95701277e-01 6.59272492e-01 -3.70862365e-01 2.04786211e-01
-1.07056451e+00 -9.84119773e-02 3.08432430e-01 8.28110933e-01
1.02765024e+00 3.75564367e-01 1.72814071e-01 5.48969865e-01
1.16921969e-01 6.99160516e-01 2.56003588e-01 -1.00681853e+00
1.03235006e-01 6.29475594e-01 1.81995213e-01 -8.06257725e-01
-4.68447983e-01 -6.70837939e-01 -8.52789581e-01 6.58572197e-01
3.25732946e-01 -3.07604879e-01 -1.10491490e+00 1.69237566e+00
3.66441607e-01 2.76238889e-01 1.52670279e-01 1.19943011e+00
4.08254385e-01 8.97562861e-01 8.28908980e-02 -1.03622630e-01
9.66105759e-01 -1.09214973e+00 -3.17278087e-01 -7.63213098e-01
2.05755219e-01 -3.81880850e-01 1.14759409e+00 5.41602612e-01
-9.46517527e-01 -6.28830552e-01 -1.17609072e+00 1.39823362e-01
-1.35768726e-01 4.67386931e-01 6.86150610e-01 6.08235132e-03
-8.32902789e-01 4.78027463e-01 -9.26231563e-01 -1.62615374e-01
5.73279023e-01 9.83254910e-02 -1.69996411e-01 -2.94794142e-01
-6.84284806e-01 8.11234236e-01 4.93104994e-01 5.60804792e-02
-1.37545419e+00 -8.08683574e-01 -9.30141330e-01 2.41397783e-01
6.46131992e-01 -5.70862591e-01 1.02288818e+00 -1.17073679e+00
-1.41056883e+00 6.29521668e-01 5.77994063e-02 -8.05882990e-01
4.42052871e-01 -3.33255798e-01 2.02320311e-02 2.61656642e-01
9.83658731e-02 1.19107687e+00 8.09522033e-01 -1.25749469e+00
-1.00892651e+00 -2.40130618e-01 5.42873383e-01 5.31729996e-01
-3.24634276e-02 -7.15866566e-01 -2.97755241e-01 -1.07163824e-01
3.31904069e-02 -9.08320487e-01 -3.68456692e-01 2.55925566e-01
-3.70527148e-01 4.15186659e-02 7.74434149e-01 -4.65889722e-01
6.54791057e-01 -2.04955125e+00 1.96421131e-01 -2.14787558e-01
2.16357067e-01 1.03004903e-01 -1.47456810e-01 -1.94163155e-02
2.16909140e-01 -3.30625594e-01 -3.87822926e-01 -4.01496559e-01
-4.02546018e-01 4.08800960e-01 -5.31141758e-01 5.16856432e-01
1.56400695e-01 7.04651773e-01 -1.12029421e+00 -3.33059341e-01
7.10405767e-01 4.09448177e-01 -4.85628694e-01 3.36191684e-01
-6.51075363e-01 5.09783387e-01 -3.94157946e-01 7.06116796e-01
6.68694317e-01 -4.80108827e-01 1.02506168e-01 -9.41875055e-02
-2.80759245e-01 -3.52630615e-02 -7.70904779e-01 1.80288899e+00
-9.57260251e-01 8.91712010e-01 -1.57575071e-01 -8.58097911e-01
8.79739225e-01 -3.34522650e-02 3.66785198e-01 -5.62951505e-01
4.38021831e-02 -1.64029866e-01 -7.57972747e-02 -3.78409505e-01
5.02900362e-01 1.40559986e-01 2.29359597e-01 2.57128179e-01
6.70127273e-02 -2.53716350e-01 5.50151952e-02 -4.08449508e-02
1.02303457e+00 2.54980475e-01 2.86962837e-01 -8.05295259e-02
2.97526211e-01 2.67900050e-01 7.52537906e-01 8.96869123e-01
-7.72745684e-02 8.04319263e-01 3.42063189e-01 -8.15012515e-01
-1.05391991e+00 -9.37261581e-01 -2.23536789e-02 8.37602973e-01
4.58706230e-01 1.40327677e-01 -7.23199308e-01 -8.61732721e-01
-2.91542798e-01 8.52403998e-01 -6.68947220e-01 1.40185151e-02
-3.04517835e-01 -9.35453713e-01 1.42119274e-01 6.01995945e-01
1.06932378e+00 -1.35095012e+00 -1.46916735e+00 1.30609870e-01
-2.62702346e-01 -1.11483836e+00 -2.67777562e-01 3.05892378e-01
-4.99794275e-01 -1.15362358e+00 -5.40451765e-01 -5.24706483e-01
8.79413724e-01 2.91499972e-01 1.19643366e+00 1.27353191e-01
-5.51793277e-01 1.45434335e-01 -3.02695096e-01 -3.56407553e-01
1.35863349e-01 2.20807821e-01 -2.38364279e-01 3.41008161e-03
3.59924249e-02 -5.09650171e-01 -7.93931842e-01 6.03181869e-02
-7.57396221e-01 3.77071053e-01 9.05525744e-01 9.75263953e-01
8.14411998e-01 2.56250888e-01 1.44642875e-01 -8.36955070e-01
-2.04001382e-01 -4.44994539e-01 -1.05051780e+00 2.31074437e-01
-5.20286918e-01 4.40967195e-02 6.73937082e-01 -4.98334289e-01
-1.25013816e+00 3.70069623e-01 -7.16813132e-02 -6.69779897e-01
-3.70060861e-01 3.70969355e-01 4.46389709e-03 1.60191394e-02
5.82493067e-01 4.29142416e-01 -2.17062354e-01 -1.18467890e-01
7.85785094e-02 2.59600490e-01 5.82946241e-01 -2.23654658e-01
5.62877059e-01 6.55321002e-01 -5.27069382e-02 -8.76280487e-01
-1.38113654e+00 -4.26282227e-01 -4.69775945e-01 -3.90975893e-01
9.32754576e-01 -1.03849554e+00 -8.66286457e-01 4.42531168e-01
-1.11308944e+00 -9.30631697e-01 -5.31156242e-01 5.45331359e-01
-5.83328724e-01 -9.09457728e-02 -2.19450504e-01 -9.28700686e-01
-2.56172985e-01 -1.24037945e+00 1.11401224e+00 3.35429758e-01
6.34123906e-02 -9.30364072e-01 -1.51095122e-01 1.14035986e-01
2.93403029e-01 4.80693310e-01 5.40704370e-01 -1.08337201e-01
-1.09219861e+00 -8.44342485e-02 -3.26105505e-01 2.61571258e-01
-8.92806947e-02 -2.99841285e-01 -1.07155252e+00 -1.65131718e-01
-4.36779968e-02 -5.44989109e-01 1.02758634e+00 7.32724726e-01
1.36835241e+00 -2.90712267e-01 -2.35454887e-01 8.80040765e-01
1.73566997e+00 4.08110052e-01 5.19286394e-01 4.96896893e-01
5.68364024e-01 5.15031755e-01 9.36110020e-01 6.44617975e-01
1.95067331e-01 5.49675584e-01 1.15700734e+00 -2.68051594e-01
-3.48656178e-01 -2.42717475e-01 2.23470956e-01 7.17991963e-02
1.00901552e-01 -5.50813258e-01 -7.25803912e-01 8.37012768e-01
-1.96867144e+00 -8.30020428e-01 3.05493176e-01 2.12150955e+00
5.00161469e-01 8.62600133e-02 -3.67003083e-01 -3.01011354e-01
2.52232134e-01 5.24497688e-01 -1.13504362e+00 3.56409729e-01
-2.14537680e-01 -2.43049964e-01 8.57005477e-01 8.84152830e-01
-1.05915046e+00 1.04984641e+00 5.62266636e+00 5.77663600e-01
-1.29737508e+00 -1.03992432e-01 9.63049293e-01 -2.70961434e-01
3.91640030e-02 -1.18378233e-02 -9.63407815e-01 4.15202528e-01
2.98218757e-01 2.39639610e-01 7.68801510e-01 1.32338667e+00
9.46648940e-02 -5.95499933e-01 -1.07863081e+00 9.12277460e-01
3.04402411e-01 -1.21781635e+00 -2.86095053e-01 -1.40287196e-02
9.45010304e-01 3.50323528e-01 2.34988257e-01 8.13128874e-02
7.13925004e-01 -7.64366508e-01 8.08022976e-01 3.83482516e-01
5.92009604e-01 -6.39333487e-01 9.52644408e-01 5.15570402e-01
-9.04877484e-01 -1.58324718e-01 -5.89679301e-01 -1.29857764e-01
2.34533399e-01 7.33070612e-01 -1.10838115e+00 3.43443215e-01
8.55832756e-01 8.36506963e-01 -4.20936674e-01 8.99308860e-01
-6.39632821e-01 4.73092765e-01 -4.89807516e-01 4.15379442e-02
5.40789604e-01 -1.60874218e-01 5.00320971e-01 6.76692843e-01
3.53144288e-01 2.10881442e-01 5.20277441e-01 9.63918805e-01
5.03044110e-03 -5.40874183e-01 -4.40005273e-01 3.99209827e-01
4.00652707e-01 1.37812865e+00 -6.70377672e-01 -5.50532043e-01
-1.48299947e-01 9.52105343e-01 4.16677564e-01 5.02479374e-01
-8.44884336e-01 -2.09854662e-01 7.23533452e-01 7.48601109e-02
3.11870247e-01 -1.02666197e-02 -2.60130137e-01 -1.09596467e+00
-1.46807998e-01 -5.07852554e-01 2.88975000e-01 -1.26328242e+00
-8.69526863e-01 1.13801706e+00 -1.09940963e-02 -1.15479994e+00
-3.97661954e-01 -4.58862454e-01 -6.02313995e-01 7.10648000e-01
-1.88465953e+00 -1.06970489e+00 -8.53759885e-01 3.97718608e-01
9.91201043e-01 -4.23964374e-02 5.22252023e-01 -1.15087651e-01
-4.44074482e-01 4.06835340e-02 -7.99936131e-02 -5.87017350e-02
3.76627624e-01 -1.32277811e+00 6.53257892e-02 9.53939557e-01
2.28979290e-01 -3.74362953e-02 8.83422613e-01 -4.08442646e-01
-8.27480137e-01 -1.43532443e+00 4.36489493e-01 -1.94969267e-01
3.50567758e-01 -4.07271236e-02 -7.46577144e-01 9.31384504e-01
3.67601722e-01 1.55635044e-01 -4.06726114e-02 -5.96953183e-02
2.38200892e-02 -3.57445627e-01 -1.09862363e+00 6.26054049e-01
7.63682246e-01 -5.61239384e-03 -1.77984372e-01 5.25343537e-01
6.00730419e-01 -6.39325500e-01 -4.73277956e-01 3.87867033e-01
3.04325312e-01 -1.06018603e+00 8.02384496e-01 -3.07215191e-02
5.92944205e-01 -5.38344264e-01 -7.68139660e-02 -1.65404630e+00
-2.93426156e-01 -1.38962373e-01 9.13866088e-02 7.51154482e-01
3.52469176e-01 -6.67076945e-01 1.00162697e+00 3.75982553e-01
-3.14711183e-01 -8.16405118e-01 -6.79922223e-01 -6.11658990e-01
-4.16260839e-01 -2.30372787e-01 5.11130810e-01 6.21934474e-01
-4.99813795e-01 6.87625408e-02 -6.49375439e-01 7.35637367e-01
8.44117224e-01 5.04451752e-01 6.71057940e-01 -9.04531777e-01
-4.71955955e-01 -1.79808453e-01 -2.01357722e-01 -1.35139990e+00
1.87413424e-01 -4.10674959e-01 5.43892205e-01 -1.47293317e+00
2.04758257e-01 -4.54563767e-01 -1.46486938e-01 8.60367417e-01
-9.10909555e-04 2.98323333e-01 3.14429849e-01 1.13802455e-01
-8.13100457e-01 7.25977123e-01 1.07595849e+00 -4.13891584e-01
-2.01410890e-01 -1.47545040e-01 -3.45329076e-01 8.85388851e-01
7.80392408e-01 -4.29025680e-01 -6.09015822e-01 -7.28164732e-01
6.88371956e-02 3.64328213e-02 5.15337884e-01 -1.37768328e+00
2.30487093e-01 -4.82560694e-01 7.47831166e-01 -6.67984664e-01
6.88581347e-01 -9.08845723e-01 6.47490695e-02 4.91443366e-01
-3.78875494e-01 -2.44868353e-01 1.23770475e-01 5.53012490e-01
-2.03414574e-01 -2.22506300e-01 9.50580359e-01 -3.64535123e-01
-9.83686090e-01 5.41968346e-01 -2.38133460e-01 -2.36613840e-01
1.20111704e+00 1.91619433e-02 -3.31990510e-01 -3.62610132e-01
-4.88850236e-01 4.38039273e-01 5.74148655e-01 1.86016992e-01
7.05714822e-01 -9.96317565e-01 -4.97382581e-01 3.38668764e-01
-9.34286043e-03 6.45105779e-01 5.37961662e-01 6.64525211e-01
-6.18771970e-01 3.07094008e-01 -2.64533281e-01 -7.22879052e-01
-1.17018259e+00 4.59163278e-01 3.16995025e-01 -3.58728021e-01
-7.94260561e-01 7.38903224e-01 8.21863115e-01 -1.66261315e-01
2.07617119e-01 -4.72238660e-01 1.86552573e-02 -2.88224041e-01
5.72592556e-01 3.38078174e-03 -2.04372332e-01 -2.33394697e-01
-1.13075137e-01 5.65423787e-01 -1.27850235e-01 -2.31428258e-02
1.53949869e+00 -2.74732471e-01 3.53091985e-01 4.25596803e-01
7.15616703e-01 -4.29998934e-01 -2.32977962e+00 -1.97857812e-01
-6.68625653e-01 -5.29666305e-01 5.30645490e-01 -1.06603420e+00
-1.27625036e+00 9.09167588e-01 4.38903749e-01 -2.56330837e-02
1.37893021e+00 -2.18302924e-02 6.63923860e-01 4.78354514e-01
4.42495555e-01 -9.05852020e-01 1.40947670e-01 3.51817042e-01
8.32291961e-01 -1.50199640e+00 7.51730204e-02 -1.50604993e-01
-9.42313373e-01 8.32803428e-01 9.11552906e-01 -3.27882022e-01
3.59484226e-01 7.43999332e-02 -3.61087243e-03 -7.98591897e-02
-9.42166805e-01 -2.56409347e-01 1.22335583e-01 5.48928678e-01
-1.51169285e-01 3.83225121e-02 4.81160462e-01 5.42527549e-02
-1.95796154e-02 -1.72298074e-01 5.79853714e-01 5.10735035e-01
-6.88430071e-01 -4.85397607e-01 -2.68568248e-01 2.55470991e-01
-1.94370255e-01 -1.41966686e-01 3.05210501e-02 8.26075673e-01
2.28701517e-01 7.57346809e-01 3.70284796e-01 -1.08467229e-01
-1.46912178e-02 -2.94895381e-01 2.78038114e-01 -5.56809902e-01
3.60686891e-02 -9.01824534e-02 -3.66116345e-01 -5.61522841e-01
-4.16118979e-01 -4.79523659e-01 -1.11429894e+00 -2.01175883e-01
-2.11211354e-01 -9.96931568e-02 6.88055038e-01 9.46383357e-01
2.87417620e-01 8.72373521e-01 5.67983389e-01 -1.12116587e+00
-2.22597599e-01 -1.01619446e+00 -7.24980235e-01 9.51182917e-02
5.20133078e-01 -6.91445887e-01 -3.10702860e-01 2.08629936e-01] | [9.554837226867676, 0.13894721865653992] |
77f4c4ea-4487-4d85-91b9-f1db1412f367 | sharp-attention-for-sequence-to-sequence | null | null | https://openreview.net/forum?id=UvNXZgJAOAP | https://openreview.net/pdf?id=UvNXZgJAOAP | Sharp Attention for Sequence to Sequence Learning | Attention mechanism has been widely applied to tasks that output some sequence from an input image. Its success comes from the ability to align relevant parts of the encoded image with the target output. However, most of the existing methods fail to build clear alignment because the aligned parts are unable to well represent the target. In this paper we seek clear alignment in attention mechanism through a \emph{sharpener} module. Since it deliberately locates the target in an image region and refines representation to be target-specific, the alignment and interpretability of attention can be significantly improved. Experiments on synthetic handwritten digit as well as real-world scene text recognition datasets show that our approach outperforms the mainstream ones such as soft and hard attention. | ['Hua Liu', 'Pei Zhang'] | 2021-09-29 | null | null | null | null | ['scene-text-recognition', 'hard-attention'] | ['computer-vision', 'methodology'] | [ 6.03018403e-01 5.08662052e-02 -2.66153691e-03 -5.25251806e-01
-4.54253942e-01 -5.57500243e-01 7.53918052e-01 -1.97647855e-01
-2.56794721e-01 5.40799260e-01 3.20839584e-01 7.63533711e-02
4.61988486e-02 -4.63588655e-01 -8.06425512e-01 -6.30885720e-01
6.99292839e-01 3.02478552e-01 2.51075536e-01 -2.58298963e-01
5.56188107e-01 1.92116067e-01 -1.18079448e+00 6.91421509e-01
7.25471497e-01 8.64047110e-01 7.13303566e-01 6.51638269e-01
-2.19750941e-01 8.61612618e-01 -8.86903763e-01 -3.75834644e-01
2.26130337e-01 -6.15442276e-01 -9.18959856e-01 3.07993323e-01
5.63699484e-01 -1.21101886e-01 -2.86064684e-01 1.36449194e+00
4.18240577e-01 -1.50161922e-01 7.01312423e-01 -9.53278661e-01
-1.40528262e+00 7.57201016e-01 -8.16425145e-01 4.05730665e-01
2.54951417e-01 1.07959256e-01 9.13642347e-01 -1.01032388e+00
4.17655259e-01 1.28714371e+00 2.25447297e-01 4.90446210e-01
-9.25361514e-01 -5.74276030e-01 4.31434363e-01 3.69981021e-01
-1.18504965e+00 -4.00201768e-01 7.99693704e-01 -1.73562378e-01
9.65941727e-01 3.37007016e-01 1.93425760e-01 1.07817674e+00
3.18649828e-01 8.55411768e-01 1.03806043e+00 -5.43377936e-01
-2.09161073e-01 2.67256834e-02 2.61060536e-01 2.91474372e-01
-5.90116829e-02 -6.03491962e-02 -4.05619174e-01 5.18366337e-01
7.63592005e-01 -2.24779397e-02 -6.37283027e-01 1.02353096e-01
-1.30207479e+00 3.86224806e-01 8.19320381e-01 5.24471641e-01
-4.88313138e-01 2.16436051e-02 7.15631917e-02 1.84091985e-01
9.24471468e-02 5.37498057e-01 -2.36725062e-01 7.76258633e-02
-8.63994777e-01 -1.30260050e-01 2.62442172e-01 9.25845087e-01
5.33706427e-01 2.18255118e-01 -5.29836237e-01 7.66288519e-01
2.36445487e-01 3.65454674e-01 8.52568507e-01 -4.27042693e-01
8.35658491e-01 8.84756207e-01 -3.92616056e-02 -1.08098948e+00
3.33948545e-02 -5.13508916e-01 -8.29835892e-01 3.94243419e-01
2.64567375e-01 3.60382013e-02 -1.39690709e+00 1.59538376e+00
-2.58243233e-01 4.68415432e-02 2.51504719e-01 1.12793863e+00
8.89168918e-01 9.46885943e-01 -1.17430091e-03 2.00222760e-01
1.19430649e+00 -1.39676368e+00 -7.74295211e-01 -8.65940452e-01
-6.66629290e-03 -9.49672937e-01 1.18505251e+00 2.32299805e-01
-1.13089180e+00 -1.11953819e+00 -1.24116695e+00 -2.02558547e-01
-2.34580100e-01 2.59862334e-01 1.46587431e-01 2.39708111e-01
-8.13280284e-01 3.98036122e-01 -3.51720840e-01 -3.65386426e-01
5.53741932e-01 4.74983096e-01 -5.75183153e-01 -9.38568115e-02
-9.49136615e-01 1.07696950e+00 6.23042524e-01 4.30596083e-01
-9.05564606e-01 -1.27920419e-01 -7.60762811e-01 3.11503112e-01
1.23843700e-01 -4.56975430e-01 1.18933308e+00 -1.92725992e+00
-1.50672209e+00 8.06786239e-01 2.72237440e-03 -2.84101725e-01
3.29061776e-01 -3.43118578e-01 -2.97213763e-01 1.33043975e-01
-1.02925964e-01 8.68626297e-01 1.21864402e+00 -1.25227261e+00
-6.84430003e-01 -3.20611775e-01 -3.97205949e-02 4.15532529e-01
-2.70346284e-01 1.77339554e-01 -5.08387506e-01 -9.02612984e-01
1.33672684e-01 -4.16147351e-01 -1.39171630e-01 -2.18429789e-01
-4.80254084e-01 1.02153430e-02 1.22423470e+00 -6.57970905e-01
1.01126671e+00 -2.09258938e+00 3.36529613e-01 -1.43348157e-01
2.75909975e-02 5.32106817e-01 -3.39557558e-01 3.83737057e-01
-3.35888654e-01 1.97887078e-01 -2.39152417e-01 -1.10084191e-01
-2.53769815e-01 2.68880963e-01 -6.16354883e-01 2.96164185e-01
6.46185160e-01 1.13946998e+00 -6.64168060e-01 -3.95398170e-01
2.53162533e-01 3.63959402e-01 -2.83322752e-01 3.90015602e-01
-1.72534257e-01 3.68034512e-01 -3.27405035e-01 5.52736640e-01
6.00078762e-01 -3.23774248e-01 -8.91468301e-02 -2.64413714e-01
3.37003469e-02 9.20419693e-02 -8.96384358e-01 1.38132346e+00
-1.23395346e-01 8.53472888e-01 -9.91117880e-02 -1.13719070e+00
1.09893382e+00 1.24105960e-01 -1.04173265e-01 -8.60763729e-01
2.68863589e-01 -1.31203383e-01 3.70670289e-01 -7.39800394e-01
6.06528819e-01 2.97506060e-03 8.53704363e-02 2.91112989e-01
5.07953614e-02 9.03387612e-04 -1.32249996e-01 -5.23972325e-02
7.06167221e-01 3.96403968e-01 3.61358762e-01 -2.36931950e-01
6.92241490e-01 -5.85638396e-02 3.21526319e-01 7.73932934e-01
-1.60971582e-01 1.05902207e+00 4.20719653e-01 -3.64377856e-01
-1.30877531e+00 -6.84221268e-01 6.16031550e-02 1.07108903e+00
3.65068346e-01 -1.15554102e-01 -8.54444504e-01 -8.20237398e-01
-3.73544663e-01 6.39729381e-01 -9.29440200e-01 -1.38745680e-01
-8.26658487e-01 -4.26657617e-01 4.83842403e-01 7.84425378e-01
7.67057061e-01 -1.51093805e+00 -7.80753076e-01 1.18030310e-01
-3.05316187e-02 -9.03396845e-01 -6.03827715e-01 2.98520952e-01
-7.04900265e-01 -8.74522090e-01 -9.46035743e-01 -1.23249376e+00
1.11739814e+00 1.73996508e-01 8.08118343e-01 1.29284695e-01
-8.71975645e-02 -1.70360550e-01 -4.57200468e-01 -4.22518760e-01
-3.66227686e-01 -1.13638960e-01 -4.70314085e-01 2.34371811e-01
2.93743432e-01 -3.39760929e-02 -3.54549944e-01 2.99660534e-01
-9.53416824e-01 4.63198841e-01 8.70212734e-01 1.06375706e+00
3.15568417e-01 -7.64922723e-02 4.90515620e-01 -7.22338140e-01
7.47136474e-01 -1.84699386e-01 -3.24345112e-01 3.88023704e-01
-3.52893889e-01 2.14709476e-01 7.19859302e-01 -5.92480659e-01
-1.07013607e+00 1.77470341e-01 1.95111032e-03 -3.95409584e-01
-2.04540297e-01 5.75556695e-01 -2.00428694e-01 1.96983024e-01
4.96764809e-01 4.81520563e-01 -1.76634386e-01 -3.75585943e-01
1.05944805e-01 8.90500844e-01 8.60911846e-01 -4.50230390e-01
6.20817661e-01 1.98350638e-01 -4.54944462e-01 -4.26842868e-01
-7.01432288e-01 -6.92133084e-02 -7.39678741e-01 -9.67692286e-02
8.29575241e-01 -4.23229098e-01 -3.35961163e-01 4.90035385e-01
-1.37024260e+00 -2.07832977e-01 -1.55541360e-01 2.69461811e-01
-3.94479632e-01 4.11499113e-01 -3.49893063e-01 -7.20882237e-01
-3.39106202e-01 -1.43991065e+00 1.19774210e+00 5.44237196e-01
-2.22940132e-01 -6.28755391e-01 -2.69581467e-01 2.87220255e-02
3.67844790e-01 -1.61804426e-02 9.18464005e-01 -7.62842238e-01
-4.57019925e-01 -2.05753818e-01 -4.82259661e-01 4.89722371e-01
3.47060800e-01 3.09571415e-01 -1.08809412e+00 -5.11485264e-02
9.62689817e-02 -3.10995847e-01 9.63723361e-01 2.81443030e-01
1.18472338e+00 -4.54488367e-01 -2.82360613e-01 3.83562416e-01
1.33725441e+00 4.66467619e-01 1.02348161e+00 3.67808640e-01
5.28893888e-01 5.42178810e-01 5.45576692e-01 9.73478109e-02
-7.87180513e-02 6.33067846e-01 6.42733753e-01 -3.45785320e-01
-3.27762663e-01 -2.19521463e-01 4.61532921e-01 5.62867522e-01
6.94629624e-02 -6.25193357e-01 -9.59536433e-01 5.23999989e-01
-1.99775755e+00 -1.11103010e+00 -4.97514978e-02 1.97931015e+00
9.27862287e-01 3.77803653e-01 -3.46210837e-01 2.05865487e-01
1.07523191e+00 1.06175378e-01 -2.87512600e-01 -5.21089017e-01
-5.10678887e-01 2.76561886e-01 1.44069359e-01 5.51123440e-01
-9.97176707e-01 1.07624292e+00 6.87537479e+00 6.98137522e-01
-1.32616293e+00 -2.28947312e-01 8.89032662e-01 1.75028503e-01
-3.81138884e-02 -2.72630677e-02 -7.73872375e-01 5.10430396e-01
5.58710515e-01 -1.51488855e-01 1.62814781e-01 6.87868178e-01
-4.65882979e-02 -5.51440800e-03 -1.29162216e+00 9.78492439e-01
3.47809404e-01 -1.29042029e+00 3.59495133e-01 -1.85641229e-01
7.38383710e-01 -3.23002994e-01 3.26272249e-01 2.50206351e-01
-7.45867342e-02 -1.71877158e+00 1.04738820e+00 6.95069909e-01
6.69916391e-01 -5.19532859e-01 8.84725153e-01 4.72137034e-01
-8.81765187e-01 -1.64856762e-01 -5.28918922e-01 -3.25389326e-01
-2.92170737e-02 -4.75703739e-02 -9.21194017e-01 3.15597653e-01
6.18372560e-01 7.56481528e-01 -9.44075048e-01 1.08021343e+00
-4.56541449e-01 4.79090542e-01 4.66397926e-02 -1.23791292e-01
6.24853969e-01 -8.04345403e-03 3.87084723e-01 1.29863501e+00
2.92374969e-01 8.43904689e-02 -1.21441752e-01 1.03403735e+00
-8.64265040e-02 1.54926941e-01 -5.98134100e-01 -1.45724028e-01
8.69507194e-02 1.06394017e+00 -5.60127556e-01 -5.22762239e-01
-2.88515687e-01 1.31577563e+00 2.80867994e-01 3.79928708e-01
-1.02602172e+00 -5.75609207e-01 2.42716119e-01 -2.09560648e-01
5.74821532e-01 2.82104313e-02 -5.22776663e-01 -8.90289068e-01
-1.64109224e-03 -1.11569142e+00 3.42625111e-01 -1.37567079e+00
-1.03388119e+00 9.71521437e-01 -2.47357041e-01 -1.06540084e+00
3.87077453e-04 -7.14284003e-01 -7.89700747e-01 1.21557224e+00
-1.24879456e+00 -1.20919359e+00 -4.46447819e-01 4.70607698e-01
1.18405485e+00 -2.01762319e-01 5.75883508e-01 8.99743736e-02
-7.12173343e-01 5.18069208e-01 -1.43953502e-01 2.68801093e-01
7.83122838e-01 -1.08601272e+00 3.87850553e-01 1.07491040e+00
3.19554955e-01 6.82307422e-01 7.44315147e-01 -6.36626482e-01
-9.24445033e-01 -8.79568040e-01 7.71287322e-01 -4.92137164e-01
3.60885322e-01 -1.15415923e-01 -1.04421186e+00 7.78997779e-01
1.03789985e+00 -7.99290538e-02 1.56202003e-01 -5.48149168e-01
-3.38930011e-01 -5.40220588e-02 -9.22576368e-01 7.64924467e-01
7.15916514e-01 -3.01185817e-01 -1.03818727e+00 5.19550070e-02
4.18069959e-01 -5.61428666e-01 -3.67481858e-01 5.27366400e-01
3.83066684e-01 -8.93617809e-01 7.29014337e-01 -8.99351537e-01
9.22212660e-01 -4.78309512e-01 -1.46358564e-01 -1.16050148e+00
-6.76171899e-01 -3.75503451e-01 7.60233030e-02 1.46179795e+00
5.12256265e-01 -2.08687842e-01 4.44420606e-01 4.77529138e-01
-3.20013911e-01 -5.80092967e-01 -7.28218377e-01 -4.32143688e-01
-4.30251798e-03 -9.97640342e-02 5.11267722e-01 8.30515921e-01
-1.46347415e-02 6.93547487e-01 -4.00821000e-01 2.11020902e-01
1.59605175e-01 2.84672529e-01 5.58487594e-01 -7.62909353e-01
-2.66487449e-01 -8.15817416e-01 -5.13886213e-01 -1.19837570e+00
-4.15585451e-02 -7.69066274e-01 2.12799698e-01 -1.67565155e+00
3.41786176e-01 -1.40501365e-01 -4.73993212e-01 7.58171201e-01
-4.06597525e-01 4.59310144e-01 3.53933364e-01 2.27660298e-01
-4.85239953e-01 4.21002686e-01 1.37574971e+00 -4.74071354e-01
8.95858929e-02 -3.27415943e-01 -7.76046336e-01 7.13936508e-01
8.43640387e-01 -2.99979836e-01 -1.90828264e-01 -8.09049845e-01
-5.97318821e-02 -2.63787955e-01 3.48831654e-01 -9.32545066e-01
1.27192721e-01 -4.19118613e-01 8.94008994e-01 -5.56694746e-01
1.28449813e-01 -9.09059525e-01 3.67370062e-02 4.14999276e-01
-6.60136938e-01 2.70684212e-01 2.84738272e-01 2.97776639e-01
-4.36826169e-01 -6.82499886e-01 1.02199221e+00 -1.79224744e-01
-9.64999497e-01 -1.12285782e-02 -1.26248792e-01 -6.20100349e-02
1.13790894e+00 -6.06331289e-01 -5.85796118e-01 -3.58625531e-01
-5.15077353e-01 8.72903466e-02 3.90260071e-01 7.03676820e-01
7.99965978e-01 -1.29686356e+00 -8.58178973e-01 3.07722807e-01
9.89020318e-02 -6.97214231e-02 4.72116210e-02 6.86509132e-01
-4.10886437e-01 5.25231063e-01 -5.83500147e-01 -6.28080428e-01
-1.31065822e+00 8.09533358e-01 3.72529447e-01 7.81224295e-02
-6.42773032e-01 9.74759758e-01 6.02912068e-01 1.65697828e-01
4.57529098e-01 -5.35009384e-01 -3.58481467e-01 -3.28900009e-01
6.96367323e-01 -1.40587673e-01 -3.19132596e-01 -9.33940947e-01
-3.59216034e-01 8.07611465e-01 -2.38024935e-01 -2.04887614e-03
1.32881880e+00 -1.45035699e-01 -6.40275627e-02 2.11288512e-01
9.57687140e-01 5.24227880e-02 -1.53344929e+00 -8.49519819e-02
2.71378960e-02 -5.78116119e-01 -1.32992074e-01 -1.02300894e+00
-1.16313720e+00 1.02669346e+00 4.57931191e-01 1.70401648e-01
1.25982022e+00 -1.35229945e-01 3.67064595e-01 3.08786541e-01
-1.26142845e-01 -1.01243484e+00 4.00339872e-01 5.72911441e-01
1.36646008e+00 -1.16725469e+00 -2.33694240e-01 -9.63153169e-02
-9.97512281e-01 1.36841035e+00 1.14769459e+00 -2.25310177e-01
1.33660793e-01 2.74722546e-01 1.50725737e-01 -4.88057546e-02
-6.85990870e-01 -1.90869436e-01 4.65160787e-01 6.00514948e-01
4.98810142e-01 -5.19813299e-01 -1.07728913e-02 7.32692122e-01
-2.95175631e-02 -2.07872614e-01 4.93341535e-01 6.71453714e-01
-6.53059781e-01 -9.65395868e-01 -5.99089503e-01 1.56450525e-01
-6.19476080e-01 -2.24133506e-01 -7.75738537e-01 6.43100977e-01
2.11336434e-01 6.49684668e-01 1.72666073e-01 -2.68675923e-01
2.34242007e-01 4.23860364e-02 5.41902959e-01 -6.80791914e-01
-8.19287241e-01 1.84812382e-01 -2.06636906e-01 -1.81488469e-01
-3.65245461e-01 -2.09407598e-01 -1.38104641e+00 2.83203542e-01
-3.80704910e-01 -8.95371065e-02 3.64939511e-01 8.64578068e-01
2.91909426e-01 7.90697932e-01 4.04797643e-01 -7.12624073e-01
-5.04287779e-01 -1.18275738e+00 -7.78849125e-02 6.39890611e-01
3.73637199e-01 -3.58710676e-01 9.62474272e-02 3.94556999e-01] | [10.863228797912598, 1.8702524900436401] |
fa23224e-f48c-4e4d-8299-8ed7da348a80 | color-texture-classification-based-on | 1906.11010 | null | https://arxiv.org/abs/1906.11010v1 | https://arxiv.org/pdf/1906.11010v1.pdf | Color Texture Classification Based on Proposed Impulse-Noise Resistant Color Local Binary Patterns and Significant Points Selection Algorithm | The main aim of this paper is to propose a color texture classification approach which uses color sensor information and texture features jointly. High accuracy, low noise sensitivity and low computational complexity are specified aims for our proposed approach. One of the efficient texture analysis operations is local binary patterns. The proposed approach includes two steps. First, a noise resistant version of color local binary patterns is proposed to decrease sensitivity to noise of LBP. This step is evaluated based on combination of color sensor information using AND operation. In second step, a significant points selection algorithm is proposed to select significant LBP. This phase decreases final computational complexity along with increasing accuracy rate. The Proposed approach is evaluated using Vistex, Outex, and KTH TIPS2a data sets. Our approach has been compared with some state of the art methods. It is experimentally demonstrated that the proposed approach achieves highest accuracy. In two other experiments, result show low noise sensitivity and low computational complexity of the proposed approach in comparison with previous versions of LBP. Rotation invariant, multi resolution, general usability are other advantages of our proposed approach. In the present paper, a new version of LBP is proposed originally, which is called Hybrid color local binary patterns. It can be used in many image processing applications to extract color and texture features jointly. Also, a significant point selection algorithm is proposed for the first time to select key points of images. | ['Shervan Fekri-Ershad', 'Farshad Tajeripour'] | 2019-06-26 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 4.01694626e-01 -8.69203091e-01 -8.10733512e-02 -8.47586095e-02
-3.90491903e-01 -1.02782018e-01 2.23378599e-01 1.87049821e-01
-5.47164798e-01 7.63567209e-01 -3.46404761e-01 1.87336832e-01
-4.67938274e-01 -1.02204812e+00 -1.39115617e-01 -1.00309014e+00
9.14171860e-02 -5.91948535e-03 7.23808229e-01 -1.58754945e-01
9.89062250e-01 8.93521965e-01 -1.86182916e+00 3.22923660e-01
5.60542107e-01 1.33383238e+00 3.48476380e-01 6.64717853e-01
-6.04715608e-02 6.43493414e-01 -3.25202644e-01 3.09578091e-01
2.62532651e-01 -3.17918479e-01 -7.83289075e-01 2.26037502e-01
1.42856538e-01 -9.28050280e-02 5.00578940e-01 9.55004394e-01
5.94087124e-01 4.28680629e-01 6.19104624e-01 -8.09377134e-01
-3.28473240e-01 -6.55574352e-02 -9.65123177e-01 4.12512660e-01
1.82549521e-01 -2.60792345e-01 4.06006455e-01 -8.15134764e-01
5.40385127e-01 1.06292439e+00 7.16926396e-01 -2.58556992e-01
-1.16751707e+00 -5.88363886e-01 -5.48687398e-01 6.49020433e-01
-1.64097416e+00 -1.16358161e-01 1.01312006e+00 -1.35174960e-01
7.15627670e-01 4.71782088e-01 5.49327314e-01 2.26048261e-01
6.26943886e-01 6.08282425e-02 2.21677828e+00 -9.58928585e-01
1.52150169e-01 9.35460404e-02 4.68380392e-01 1.09497392e+00
4.07895386e-01 -1.06160805e-01 -4.16729361e-01 -1.13874279e-01
5.53453326e-01 5.16900793e-02 8.25949237e-02 -1.58527821e-01
-7.78960943e-01 4.15608913e-01 1.42988384e-01 7.67926514e-01
-4.49137002e-01 -5.74688846e-03 3.29104483e-01 7.24090487e-02
1.69982929e-02 -1.25133514e-01 -1.13569789e-01 -4.60591882e-01
-7.23990858e-01 -3.34366821e-02 4.68360960e-01 3.46615255e-01
8.75768900e-01 -1.72009736e-01 1.11293226e-01 1.28567421e+00
1.66720763e-01 7.05211163e-01 5.24679720e-01 -5.64688504e-01
-1.63079929e-02 5.11269331e-01 1.95933565e-01 -1.81206322e+00
-3.87952715e-01 -2.05562320e-02 -6.88395143e-01 5.28599679e-01
1.58701286e-01 3.29523355e-01 -1.03266740e+00 9.04981375e-01
3.37982833e-01 -1.22235063e-02 -3.56820673e-02 7.02138960e-01
5.58183372e-01 7.72589207e-01 -3.02873775e-02 -1.08019710e-01
1.68365562e+00 -4.59712327e-01 -7.21876264e-01 4.05105382e-01
-9.12198424e-02 -1.47359538e+00 8.03184092e-01 7.50887275e-01
-5.12272418e-01 -8.67958963e-01 -1.16229880e+00 3.32138896e-01
-5.88988841e-01 6.02239072e-01 3.30570459e-01 7.72654653e-01
-6.40945852e-01 4.92258012e-01 -8.77709568e-01 -1.04841852e+00
-5.47524244e-02 5.86775243e-01 -5.22785783e-01 1.54592434e-03
-6.27479970e-01 8.42348576e-01 3.85951906e-01 1.99312031e-01
2.07320869e-01 2.14067265e-01 -5.60320556e-01 -1.46898767e-02
7.67150447e-02 2.30918139e-01 6.34922028e-01 -8.52695465e-01
-1.77919829e+00 4.68094647e-01 -4.06398535e-01 -1.11380801e-01
-6.66537415e-03 1.10365331e-01 -4.96689051e-01 6.19226098e-01
-1.14279218e-01 1.87032878e-01 6.97742999e-01 -1.15506887e+00
-9.03585792e-01 -3.63959193e-01 -5.88728666e-01 1.36926189e-01
-1.04258999e-01 1.07728682e-01 -4.08010334e-01 -5.17036736e-01
6.60533071e-01 -8.71985555e-01 2.02422962e-01 -2.48427391e-01
1.42524904e-02 2.54365392e-02 1.32071412e+00 -7.68038452e-01
1.03398192e+00 -1.99125946e+00 -5.67769825e-01 9.56915200e-01
-5.54610193e-01 2.99616188e-01 1.72434956e-01 4.10059810e-01
1.39379531e-01 -1.04988724e-01 -3.78652960e-02 3.64796698e-01
-3.91790271e-01 1.28835976e-01 1.48740038e-01 5.17274678e-01
1.36196747e-01 1.04756512e-01 -1.20070353e-01 -8.70505333e-01
5.59856594e-01 6.23746216e-01 -9.20930058e-02 -3.72471511e-01
5.98964572e-01 2.01944172e-01 -3.65789413e-01 8.30382288e-01
1.12694788e+00 3.23151201e-01 1.16393052e-01 -8.74694705e-01
-3.88093710e-01 -2.85966516e-01 -1.68224311e+00 7.56824970e-01
-3.97426367e-01 2.47279033e-01 -7.88036510e-02 -1.40444016e+00
1.52744496e+00 2.07212716e-01 6.97883129e-01 -1.04992831e+00
3.57902467e-01 4.35173810e-01 -3.10406294e-02 -7.28498042e-01
4.78803307e-01 -2.70740129e-02 3.16985518e-01 1.99805766e-01
-1.72589049e-01 1.18226364e-01 2.78838187e-01 -5.70423424e-01
6.66116178e-01 4.21082348e-01 6.73374653e-01 -4.97513711e-01
1.11592555e+00 2.26897582e-01 5.56920290e-01 6.29518926e-01
-3.73097986e-01 1.34592161e-01 2.82989770e-01 -4.34842318e-01
-8.33066046e-01 -5.02886176e-01 -4.65208530e-01 5.50138056e-01
5.70424438e-01 -1.16534410e-02 -4.17845428e-01 -3.06445926e-01
-1.22062735e-01 -2.17648029e-01 -5.45406222e-01 2.54451543e-01
-8.43025744e-01 -7.61800826e-01 1.44838631e-01 4.01182882e-02
1.19080436e+00 -9.99227345e-01 -1.00164676e+00 8.19789842e-02
-1.75019614e-02 -8.97787631e-01 1.83789328e-01 6.97936863e-02
-1.03774726e+00 -1.00548089e+00 -5.07131875e-01 -1.02517390e+00
6.27085030e-01 3.52735311e-01 3.82835329e-01 1.93767175e-01
-6.43826544e-01 2.39790946e-01 -7.84277737e-01 -1.87377483e-01
5.83289377e-02 -1.57815427e-01 -2.64345169e-01 4.04387712e-01
4.70788211e-01 -3.23386759e-01 -7.32481003e-01 4.79791582e-01
-6.84297144e-01 -2.73860514e-01 9.47616339e-01 1.02561176e+00
1.05235505e+00 6.66359782e-01 1.49621338e-01 -6.07465863e-01
4.80172366e-01 1.08043831e-02 -7.28575110e-01 3.42467338e-01
-3.57475549e-01 2.44727761e-01 7.03146160e-01 -1.51604056e-01
-1.20865500e+00 1.07369632e-01 -6.87913001e-02 4.19775009e-01
-3.77331764e-01 5.27103603e-01 2.83806413e-01 -8.62588048e-01
4.71101195e-01 4.34127092e-01 2.22873881e-01 -7.29059815e-01
-5.13977647e-01 9.88657713e-01 2.28155613e-01 -8.22814047e-01
3.30255330e-01 6.27975285e-01 7.12422848e-01 -1.19310784e+00
1.44620463e-01 -5.82358897e-01 -5.78576744e-01 -1.94627553e-01
6.96191728e-01 -1.73701599e-01 -9.43874598e-01 7.40191698e-01
-5.09843111e-01 2.19186589e-01 4.03816044e-01 5.92928231e-01
-4.18521404e-01 6.11905634e-01 -3.09119821e-01 -1.04091048e+00
-5.30085206e-01 -1.32539248e+00 6.39210463e-01 3.22533876e-01
1.69731125e-01 -5.83674490e-01 -1.87133461e-01 1.03551365e-01
6.95378125e-01 4.61803466e-01 6.97159171e-01 -1.02699935e-01
-2.67667770e-01 -4.34763163e-01 -4.56021249e-01 2.99807966e-01
6.90325022e-01 2.77286768e-01 -3.16302568e-01 5.58160432e-03
-7.55507424e-02 -7.01195821e-02 7.16517568e-01 2.62760162e-01
1.12541115e+00 -7.93173611e-02 -3.67044479e-01 2.21586466e-01
2.16840363e+00 8.42905283e-01 6.84442997e-01 7.01876938e-01
1.97363868e-01 3.63318384e-01 1.15924728e+00 3.92791927e-01
-6.35068119e-02 7.82236040e-01 -2.85457894e-02 -2.32925504e-01
-2.05121994e-01 2.89186299e-01 9.62002873e-02 8.06957662e-01
-3.82757604e-01 2.17378467e-01 -9.11171973e-01 3.66844773e-01
-1.62655044e+00 -1.04188216e+00 -1.21237457e-01 2.17575264e+00
4.84692991e-01 -9.48112383e-02 3.20909149e-03 7.11367726e-01
7.89114356e-01 -1.62556484e-01 1.79332376e-01 -9.38185096e-01
-1.43428314e-02 1.19826865e+00 7.15246201e-01 5.04762292e-01
-1.32710409e+00 6.82205021e-01 4.62706184e+00 1.04268205e+00
-1.79824460e+00 1.00472994e-01 5.85316420e-01 5.92784047e-01
6.03421926e-01 7.55154788e-02 -5.63421547e-01 5.61041355e-01
4.39421535e-01 5.18222213e-01 2.13416085e-01 6.06402814e-01
2.58136809e-01 -1.03605747e+00 1.46005349e-02 1.16706884e+00
2.86071766e-02 -9.67223346e-01 -1.94832366e-02 -5.56271859e-02
5.64089954e-01 -5.64381421e-01 7.93888941e-02 -3.12585115e-01
-4.06097084e-01 -7.09537268e-01 2.71401525e-01 8.89169276e-01
5.81695557e-01 -1.10482275e+00 1.26491952e+00 -2.12052539e-01
-1.44746256e+00 -1.35850310e-01 -4.08655107e-01 -2.07011569e-02
-3.49189192e-01 3.43155116e-01 -5.74934125e-01 7.12606966e-01
9.04202998e-01 3.31555754e-01 -6.22544587e-01 1.32750666e+00
3.29762578e-01 5.26656151e-01 -6.58509851e-01 -3.83458257e-01
1.66646212e-01 -3.09121937e-01 1.77879691e-01 1.25109684e+00
7.31069088e-01 1.65503040e-01 1.75481334e-01 3.56394082e-01
6.40273392e-01 9.19566929e-01 -3.79244775e-01 2.57541150e-01
4.06066418e-01 1.23849738e+00 -1.31036329e+00 -3.77313077e-01
-3.01161557e-01 9.06967998e-01 -1.81103081e-01 4.86714542e-02
-4.89024818e-01 -1.16132081e+00 -1.00075379e-01 7.07545057e-02
5.85941851e-01 -4.29751426e-01 -2.90130645e-01 -6.16630912e-01
-2.16834322e-01 -8.76148820e-01 3.61044884e-01 -5.95018625e-01
-7.32258618e-01 4.45854843e-01 6.91917539e-02 -1.32567954e+00
3.60022545e-01 -8.95537198e-01 -3.33503783e-01 9.67485964e-01
-1.19792271e+00 -1.32678413e+00 -5.30084312e-01 6.96182251e-01
3.41409028e-01 -1.44403636e-01 6.09580040e-01 2.01415390e-01
-4.00084496e-01 4.31118757e-01 3.15443873e-01 -9.14186388e-02
8.31865132e-01 -7.44201899e-01 -6.21558726e-01 1.12077272e+00
-2.99729109e-01 7.38586545e-01 5.61865568e-01 -8.12425256e-01
-1.23533511e+00 -3.93143356e-01 5.52726984e-01 5.28837621e-01
7.58208632e-02 8.17935616e-02 -4.82122838e-01 -2.30044592e-02
1.27453849e-01 7.18799010e-02 4.67281252e-01 -3.68680865e-01
1.99492902e-01 -8.54397237e-01 -1.54905987e+00 2.59619713e-01
7.20393062e-02 -2.10670501e-01 -2.60154217e-01 -1.49938911e-01
-5.75690567e-01 -2.48758584e-01 -9.52280223e-01 5.57105362e-01
1.09742892e+00 -1.11400044e+00 7.38678038e-01 3.47018152e-01
-1.02057355e-02 -7.44236290e-01 -3.88115883e-01 -5.77903569e-01
-3.38492393e-01 -2.11994499e-01 5.44049203e-01 1.07504272e+00
-1.31726265e-04 -7.26135194e-01 6.62321627e-01 7.19969571e-02
3.36943090e-01 -7.21425295e-01 -8.26848209e-01 -6.22598350e-01
-5.28321564e-01 -3.04088872e-02 3.08673531e-01 6.45448029e-01
-8.16973597e-02 -2.13453680e-01 -4.15442646e-01 1.05705671e-01
6.56599283e-01 3.15794617e-01 6.56157315e-01 -1.11096084e+00
-1.94421098e-01 -2.14775145e-01 -9.02739227e-01 -3.09090942e-01
-6.42989457e-01 -3.18368793e-01 -5.16458303e-02 -1.36996794e+00
2.35649750e-01 -7.77430415e-01 -7.49023080e-01 5.64133525e-01
-7.74401566e-03 9.74139810e-01 3.73357944e-02 1.19371012e-01
-2.47545633e-02 -9.03232619e-02 1.07273412e+00 6.83752298e-02
-3.95359457e-01 -2.18169093e-01 -1.29139081e-01 4.48089361e-01
1.11712515e+00 -2.73654819e-01 -1.93970948e-01 1.54643923e-01
-1.90694749e-01 -1.60969943e-01 2.62250513e-01 -1.41019702e+00
-1.48218911e-04 -4.41606402e-01 6.12839997e-01 -5.75011134e-01
4.29190546e-01 -1.07924652e+00 1.64183766e-01 8.03493917e-01
2.69054383e-01 2.15089917e-01 1.76949114e-01 1.74348995e-01
-3.83200765e-01 -2.57808864e-01 1.18204165e+00 -1.00604326e-01
-1.14820945e+00 -3.48447084e-01 -4.32338029e-01 -9.80809927e-01
1.09042645e+00 -8.86885345e-01 -3.24719965e-01 -2.23111697e-02
-6.38599396e-01 -6.42036200e-01 3.55896950e-01 1.76991615e-02
5.72336912e-01 -1.32311249e+00 -2.10973635e-01 3.31580609e-01
5.09556271e-02 -9.80392694e-01 3.42179954e-01 1.36905003e+00
-1.38527513e+00 4.53903735e-01 -1.07149398e+00 -5.67622304e-01
-1.83445799e+00 1.22540213e-01 6.72297180e-02 -8.16631764e-02
-3.93636703e-01 3.12787801e-01 -7.04871356e-01 1.24623902e-01
-2.75415689e-01 -4.37986583e-01 -5.73423266e-01 -2.77197182e-01
1.78545401e-01 6.83437645e-01 3.32259357e-01 -9.88463998e-01
-5.77958345e-01 1.47818673e+00 1.64773032e-01 -3.06821167e-01
1.10728824e+00 -7.35293180e-02 -6.45833433e-01 1.57363638e-01
1.18387330e+00 6.29345834e-01 -3.97990286e-01 1.42040670e-01
-1.00222360e-02 -8.50387156e-01 -2.93120020e-03 -9.85125482e-01
-7.45316565e-01 7.82027662e-01 1.45611918e+00 -9.52052176e-02
1.57454371e+00 -7.48431563e-01 6.59001589e-01 3.70095074e-01
5.33360839e-01 -1.35825610e+00 -1.83248773e-01 3.15425903e-01
6.22286499e-01 -1.10553563e+00 3.28368098e-01 -3.94891143e-01
-3.76584142e-01 1.57693601e+00 6.46803021e-01 -4.08244491e-01
7.97258079e-01 2.12619588e-01 6.17038272e-02 9.61483568e-02
-1.82844788e-01 -3.69326055e-01 1.87871277e-01 5.04528403e-01
4.17452663e-01 -5.63492738e-02 -1.35107505e+00 1.58878610e-01
1.63623720e-01 2.78713435e-01 3.44207227e-01 1.48446345e+00
-9.25098419e-01 -1.41251552e+00 -9.95758057e-01 4.09621090e-01
-8.72145593e-01 3.13498676e-01 1.61049236e-02 9.47982252e-01
5.79895139e-01 1.01517665e+00 -6.82173148e-02 -6.22082055e-01
2.71670688e-02 1.02413639e-01 5.58020353e-01 1.73659652e-01
-2.70057559e-01 2.21697837e-01 -7.03302994e-02 -5.72364509e-01
-8.69422793e-01 -3.55354518e-01 -1.10510898e+00 -2.10433990e-01
-3.10500711e-01 4.09363806e-01 1.12865973e+00 6.63421333e-01
4.01081026e-01 7.50337690e-02 4.51967239e-01 -5.29033363e-01
5.45781963e-02 -8.55027378e-01 -5.91171503e-01 4.97630864e-01
6.11066110e-02 -9.69074547e-01 1.05658308e-01 1.13254130e-01] | [10.377471923828125, -0.3667498528957367] |
0501a39c-dfb5-48e0-b84f-50928fd5fbfd | example-based-synthesis-of-static-analysis | 2204.08643 | null | https://arxiv.org/abs/2204.08643v1 | https://arxiv.org/pdf/2204.08643v1.pdf | Example-based Synthesis of Static Analysis Rules | Static Analysis tools have rules for several code quality issues and these rules are created by experts manually. In this paper, we address the problem of automatic synthesis of code quality rules from examples. We formulate the rule synthesis problem as synthesizing first order logic formulas over graph representations of code. We present a new synthesis algorithm RhoSynth that is based on Integer Linear Programming-based graph alignment for identifying code elements of interest to the rule. We bootstrap RhoSynth by leveraging code changes made by developers as the source of positive and negative examples. We also address rule refinement in which the rules are incrementally improved with additional user-provided examples. We validate RhoSynth by synthesizing more than 30 Java code quality rules. These rules have been deployed as part of a code review system in a company and their precision exceeds 75% based on developer feedback collected during live code-reviews. Through comparisons with recent baselines, we show that current state-of-the-art program synthesis approaches are unable to synthesize most of these rules. | ['Srinivasan Sengamedu SHS', 'Pranav Garg'] | 2022-04-19 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 3.55701149e-01 8.46840024e-01 -5.88937998e-01 -4.51067567e-01
-7.65142202e-01 -6.64349020e-01 3.06515753e-01 4.96752679e-01
5.73012769e-01 2.37839222e-01 1.32460192e-01 -8.67577493e-01
-1.32318527e-01 -8.09639692e-01 -9.39217210e-01 6.51399612e-01
-6.01542182e-02 8.42366077e-04 5.68300903e-01 -4.29437459e-01
5.20030499e-01 -3.62823531e-02 -1.65376163e+00 8.29275846e-01
1.19584441e+00 3.41825157e-01 -4.28118438e-01 9.08645630e-01
-1.81351662e-01 1.18550587e+00 -3.71525973e-01 -8.17502260e-01
4.43973392e-01 -5.35453916e-01 -9.16277230e-01 4.73573394e-02
5.23805916e-01 -1.90893620e-01 4.08208549e-01 1.40185189e+00
-1.99327648e-01 -6.55150473e-01 1.02184333e-01 -1.77198005e+00
-5.35033941e-01 1.19226074e+00 -4.12304342e-01 -2.08084911e-01
7.47226119e-01 1.79145113e-01 1.45287585e+00 -5.83593130e-01
9.75945532e-01 1.06304109e+00 7.37439513e-01 6.64528430e-01
-1.53095770e+00 -3.89084846e-01 4.17560190e-02 -1.39204472e-01
-1.09179831e+00 -6.43536627e-01 8.29887688e-01 -9.31009591e-01
1.90822768e+00 4.74399984e-01 6.94879293e-01 4.13115561e-01
5.53568244e-01 1.97407171e-01 8.63589048e-01 -9.26634789e-01
1.35634646e-01 3.62896949e-01 4.66490299e-01 1.18092155e+00
8.50480437e-01 -1.80261291e-03 -1.97565377e-01 -7.99353898e-01
3.18111002e-01 -6.02816284e-01 -1.28278434e-01 -5.75881243e-01
-9.31167483e-01 7.25271821e-01 -9.52611715e-02 4.37426209e-01
1.70858860e-01 5.73841631e-01 3.43521714e-01 5.40081620e-01
5.53270951e-02 9.84081984e-01 -8.33596766e-01 -3.34441990e-01
-8.61057341e-01 2.78081238e-01 1.30768228e+00 1.33745074e+00
1.05139279e+00 1.28142044e-01 1.50233209e-02 4.21618134e-01
7.76689231e-01 3.22850585e-01 1.26951486e-01 -1.08536887e+00
5.22578597e-01 1.58991790e+00 7.73861706e-02 -1.28390110e+00
5.65381311e-02 -2.11885035e-01 3.76195759e-01 5.75453639e-01
-1.23064056e-01 2.05760468e-02 -3.98620307e-01 1.29363430e+00
5.36004156e-02 -2.99410135e-01 -8.12009722e-02 8.53238851e-02
4.32815999e-01 2.48909250e-01 -4.36176240e-01 -4.75029886e-01
8.61628294e-01 -8.24482560e-01 -4.69814509e-01 -2.16967121e-01
1.01982427e+00 -4.73557293e-01 1.34072030e+00 8.72951508e-01
-1.09516478e+00 -1.26149714e-01 -1.34673309e+00 3.65114391e-01
-6.83503225e-02 2.18050152e-01 6.08493209e-01 1.01059222e+00
-1.36158812e+00 5.17983556e-01 -7.46749222e-01 3.17359716e-02
1.34139553e-01 5.16946554e-01 -1.41845703e-01 1.95863068e-01
-5.99218428e-01 6.80899560e-01 2.01736361e-01 -4.15772229e-01
-6.51460707e-01 -1.02845335e+00 -1.14182258e+00 -6.20243028e-02
6.15671754e-01 -3.76648694e-01 1.69823635e+00 -1.09734392e+00
-1.29377353e+00 9.86513555e-01 -4.98441830e-02 -3.26772332e-01
1.58999756e-01 -6.07719794e-02 -8.29705298e-01 -3.75687808e-01
3.47448468e-01 -3.30034904e-02 5.51719248e-01 -1.51285350e+00
-6.82590365e-01 1.61104754e-01 5.93875468e-01 -7.79882729e-01
-1.63092285e-01 4.11385268e-01 -3.90991837e-01 -2.33106688e-01
-5.48334539e-01 -8.46242905e-01 -2.11770698e-01 -2.74766117e-01
-3.14147443e-01 -1.91276744e-02 3.89162123e-01 -7.29435682e-01
2.03021240e+00 -1.82902157e+00 6.54213950e-02 7.32708693e-01
3.87667209e-01 3.53563093e-02 -1.45934686e-01 4.13184702e-01
-1.84233114e-01 8.37760925e-01 -2.97257960e-01 5.69159865e-01
4.19188678e-01 6.51927851e-03 -2.77270138e-01 1.13385558e-01
5.11996329e-01 8.76126230e-01 -1.26265216e+00 -5.61022043e-01
-1.82862982e-01 -2.83420503e-01 -1.20624745e+00 3.42903240e-03
-9.32558358e-01 -4.87056613e-01 -1.36743054e-01 6.39761925e-01
3.29764485e-01 -3.58863831e-01 7.15047598e-01 -1.32329054e-02
-1.18997023e-01 4.94187206e-01 -1.20338869e+00 1.51525366e+00
-6.89226449e-01 2.48084992e-01 -2.52695829e-01 -1.82773665e-01
1.02356756e+00 1.86180517e-01 -2.95503922e-02 -3.66004288e-01
-1.84152901e-01 3.74871910e-01 2.60706872e-01 -7.39562690e-01
2.62334079e-01 1.18180208e-01 -3.75357687e-01 7.59893119e-01
-8.29352513e-02 -6.80915833e-01 8.12864900e-01 2.95610249e-01
1.70149410e+00 3.78047585e-01 9.73581851e-01 -3.49081963e-01
7.39540398e-01 5.51927388e-01 8.02102327e-01 5.81501961e-01
2.03581631e-01 1.98140606e-01 1.30628824e+00 -3.12265038e-01
-1.00791728e+00 -5.39808929e-01 2.69379854e-01 7.34395981e-01
-3.54072124e-01 -1.63820851e+00 -9.09135699e-01 -1.27957273e+00
-1.33977666e-01 1.06442940e+00 -6.86007738e-01 -2.95154870e-01
-7.24146008e-01 -1.87612832e-01 5.83387911e-01 3.69578898e-01
-1.31758273e-01 -8.44835699e-01 -9.38176394e-01 4.15293992e-01
2.26482242e-01 -9.01429892e-01 -3.72076839e-01 -1.44810662e-01
-6.05501235e-01 -1.82545030e+00 5.92940092e-01 -4.18700635e-01
8.36838126e-01 -3.44516963e-01 1.78157043e+00 1.02628732e+00
-3.16718638e-01 5.53075373e-01 -5.12375236e-01 -3.65551114e-01
-1.41536009e+00 -2.15373002e-02 -4.18125808e-01 -5.84789515e-01
3.76193643e-01 -3.97211820e-01 1.61050737e-01 2.35785306e-01
-8.53618383e-01 1.25675565e-02 3.46419603e-01 5.83600998e-01
2.39770427e-01 2.30155259e-01 3.22121918e-01 -1.54548013e+00
1.00671113e+00 -2.91492641e-01 -1.25040233e+00 9.12702501e-01
-1.08823180e+00 4.58117038e-01 6.37460768e-01 -2.37998646e-02
-8.72859240e-01 1.68037072e-01 4.21755575e-02 2.12922469e-01
2.40216807e-01 8.01734209e-01 -1.23781867e-01 -4.32621211e-01
1.38245595e+00 -4.62740570e-01 -4.09447849e-01 1.04455754e-01
3.09088200e-01 4.04364556e-01 1.86152726e-01 -1.14739656e+00
1.01602221e+00 -2.68963575e-01 -2.33405620e-01 -1.69094391e-02
-2.28601843e-01 9.45526436e-02 -2.25365132e-01 -1.83610886e-01
2.98852086e-01 -3.93003762e-01 -2.81769603e-01 -1.61953628e-01
-1.28110480e+00 -6.90554857e-01 -3.89681816e-01 -3.40805054e-01
-5.20000994e-01 3.27979296e-01 -2.71115124e-01 -8.08553696e-01
-2.05559179e-01 -1.39043438e+00 9.70184922e-01 -2.19409168e-01
-8.24685037e-01 -6.59915864e-01 6.17067695e-01 -1.55954110e-02
2.54418969e-01 5.43890178e-01 1.47068405e+00 -4.26655114e-01
-4.65521306e-01 -6.41411021e-02 1.12917319e-01 3.82017136e-01
3.95930290e-01 1.02313852e+00 -4.57232803e-01 -4.89828782e-03
-2.71664262e-01 -7.01401308e-02 -1.84052721e-01 -3.52006882e-01
7.77957916e-01 -7.63079107e-01 -2.74148434e-01 9.66112986e-02
1.69533622e+00 3.09249818e-01 5.12550592e-01 4.61479932e-01
5.25953054e-01 4.98899519e-01 6.83176160e-01 3.74334961e-01
3.02757621e-01 6.24520838e-01 5.16008079e-01 5.36419451e-01
-9.11216289e-02 -1.55521616e-01 6.69570804e-01 7.88138747e-01
5.91606051e-02 3.62627387e-01 -1.59960604e+00 9.03084815e-01
-1.92209494e+00 -5.91854334e-01 -5.20306647e-01 1.86196959e+00
1.37884951e+00 5.18063188e-01 1.46932855e-01 4.19695735e-01
3.23921055e-01 -4.92356807e-01 -1.62414089e-01 -8.74139488e-01
5.69820881e-01 3.76965821e-01 1.41744643e-01 8.54061782e-01
-4.75514442e-01 7.67162859e-01 6.94614220e+00 2.93248594e-01
-8.31059217e-01 -8.74655396e-02 -5.61400577e-02 3.60335499e-01
-1.11896062e+00 7.32149541e-01 -7.38669693e-01 3.98398452e-02
9.86421824e-01 -7.52626836e-01 7.64047921e-01 1.18862772e+00
-1.57049000e-01 -3.85764404e-03 -1.61939061e+00 4.44264203e-01
1.36186510e-01 -1.58441901e+00 -1.06306322e-01 -2.91293889e-01
1.25318968e+00 -3.27922881e-01 -3.95071507e-01 2.92440116e-01
9.06072497e-01 -9.21684265e-01 8.87312353e-01 4.41864669e-01
7.47531056e-01 -5.74541092e-01 4.22498107e-01 1.66172817e-01
-1.03342044e+00 -1.36834756e-01 1.25104055e-01 -1.64978728e-01
-3.31530869e-01 7.97019660e-01 -1.25079715e+00 6.09253645e-01
6.45764232e-01 7.80318320e-01 -1.21734166e+00 8.55272293e-01
-7.10520208e-01 6.28744960e-01 -4.95034829e-02 9.29403305e-03
-4.22974616e-01 1.85384497e-01 1.78924412e-01 1.51073837e+00
2.23660097e-01 -2.46866733e-01 -1.21899828e-01 1.42028952e+00
-1.02495784e-02 6.99227154e-02 -6.79919183e-01 -2.51664013e-01
2.38487706e-01 1.21832001e+00 -5.66708505e-01 -3.67943317e-01
-6.70247912e-01 6.90822452e-02 2.64498055e-01 2.25206375e-01
-7.56018639e-01 -6.57526433e-01 4.70839500e-01 2.63102591e-01
2.44373322e-01 -9.38442536e-03 -4.24955875e-01 -1.10649705e+00
4.55099285e-01 -1.69648623e+00 8.96242335e-02 -6.87781990e-01
-8.21149528e-01 6.77897513e-01 2.99048692e-01 -1.02033281e+00
-6.02034509e-01 -5.79676270e-01 -7.21826553e-01 3.55991095e-01
-1.15756059e+00 -7.59165823e-01 -1.69429272e-01 -1.10761933e-02
1.54748425e-01 -1.12901315e-01 7.16489375e-01 1.51947096e-01
-1.28018960e-01 5.89724302e-01 -1.04823792e+00 -2.46538416e-01
4.00016457e-01 -1.49701059e+00 7.58032143e-01 1.45598543e+00
3.00502349e-02 1.26864433e+00 1.00614870e+00 -1.09991300e+00
-1.47229946e+00 -1.16483521e+00 1.02440655e+00 -6.81750357e-01
1.13156247e+00 -4.93256450e-01 -9.23264503e-01 1.02661717e+00
2.27672428e-01 1.09946758e-01 5.20426095e-01 -9.66420919e-02
-7.00562596e-01 -1.08686760e-01 -1.03786969e+00 6.47000849e-01
1.17306828e+00 -7.15033293e-01 -6.70321822e-01 2.81062871e-01
7.43843794e-01 -3.76663417e-01 -1.10437500e+00 5.72926700e-01
2.60886073e-01 -1.10254931e+00 3.47152859e-01 -6.80751383e-01
8.37762773e-01 -9.78201628e-01 -1.02139272e-01 -1.17077637e+00
-6.90970570e-02 -1.12843716e+00 -4.33107078e-01 1.24112236e+00
9.85969245e-01 -4.17285919e-01 3.75628024e-01 9.42374885e-01
-2.49287501e-01 -6.95625961e-01 -2.59780407e-01 -7.69772232e-01
-1.64165273e-01 -7.32012510e-01 9.20901358e-01 9.68889594e-01
9.47318137e-01 2.07300663e-01 -1.28199225e-02 -1.19963244e-01
2.03581676e-01 2.30627880e-01 1.03521085e+00 -1.10163498e+00
-9.33276892e-01 -4.93734151e-01 -3.83420885e-01 -1.37332857e-01
3.21948946e-01 -1.01180732e+00 8.28240290e-02 -1.70024419e+00
1.74308151e-01 -1.82161480e-01 2.90134460e-01 1.06116879e+00
4.17599604e-02 -2.69939154e-01 -8.33239034e-02 -2.55390227e-01
-6.87850595e-01 -2.40258008e-01 6.30601168e-01 -2.85721362e-01
-2.51095563e-01 -2.66361535e-01 -1.09140420e+00 7.19029427e-01
6.71451569e-01 -7.56436706e-01 -6.53495193e-01 -2.70681888e-01
1.59472191e+00 2.60797106e-02 8.57931674e-02 -9.08815324e-01
1.86910525e-01 -4.64528978e-01 -6.66118562e-01 1.19117290e-01
-6.93457961e-01 -1.06877685e+00 7.43025899e-01 6.67721093e-01
-4.54283208e-01 3.49298924e-01 3.81212592e-01 1.13924593e-01
-1.31347001e-01 -7.80808449e-01 3.59792203e-01 -1.17283195e-01
-7.49127328e-01 -2.70605743e-01 -3.56702358e-01 2.44090647e-01
1.18884015e+00 7.55481981e-03 -8.13957810e-01 9.60540026e-02
-2.76211947e-01 3.70223857e-02 1.09159923e+00 6.00537062e-01
5.30649424e-01 -1.19758666e+00 -4.93897378e-01 2.13998064e-01
7.09922493e-01 -6.26769662e-01 -6.89148486e-01 6.97946131e-01
-7.43208349e-01 1.26170889e-01 -1.22276455e-01 -3.81147861e-01
-1.31060171e+00 7.02911258e-01 4.27648515e-01 -4.79782164e-01
-1.52004376e-01 2.13824093e-01 -3.32159936e-01 -6.18787646e-01
-3.06931943e-01 -1.12727284e+00 3.84062901e-02 -6.87061727e-01
4.41869438e-01 1.91067383e-01 5.41850209e-01 -1.10285193e-01
-7.83418059e-01 6.35976553e-01 1.53667301e-01 -1.01004049e-01
1.45842421e+00 6.02867484e-01 -6.58028662e-01 5.08990169e-01
7.20576584e-01 6.76659584e-01 -6.87691927e-01 2.05182210e-01
4.78782296e-01 -5.55444002e-01 -3.35179359e-01 -1.02516067e+00
-9.33497727e-01 2.68246621e-01 -4.34500445e-03 4.32221681e-01
8.95318389e-01 -1.29685163e-01 6.92274496e-02 5.57310700e-01
7.60838747e-01 -9.31968987e-01 7.10355565e-02 3.69160503e-01
1.08800364e+00 -9.68103111e-01 2.17696652e-01 -9.94742692e-01
-2.73417264e-01 1.38630438e+00 8.34473670e-01 -2.68177778e-01
3.78273159e-01 8.34694147e-01 -1.75476447e-01 -4.15665209e-01
-1.09412277e+00 3.32743436e-01 3.82328659e-01 6.65907502e-01
7.46995807e-01 -5.44360951e-02 -5.12395978e-01 8.08017313e-01
-2.00935856e-01 5.35885096e-01 1.08330965e+00 1.42307103e+00
-2.07814321e-01 -1.75529456e+00 -2.32567862e-01 6.96969569e-01
-4.21466380e-01 -2.76744455e-01 -8.47789526e-01 8.30569625e-01
1.84811860e-01 1.10446882e+00 -9.05351579e-01 -7.32826531e-01
6.45921707e-01 -8.92050266e-02 8.66190374e-01 -1.32234633e+00
-1.19262314e+00 -4.42846656e-01 9.88410532e-01 -8.52483332e-01
-3.55275363e-01 -6.67483091e-01 -1.34276366e+00 2.10420281e-01
-3.68933171e-01 2.49865755e-01 3.58678848e-01 6.96717262e-01
4.53053921e-01 7.45946467e-01 1.96090862e-01 -8.23966116e-02
-3.85105789e-01 -2.37522855e-01 -5.29628480e-03 1.78788930e-01
1.73040241e-01 -5.03376424e-01 -3.33789766e-01 7.04588652e-01] | [7.989375591278076, 7.557650566101074] |
eee391c9-5b1f-41d1-ab67-d61396d0bcbc | ontology-guided-semantic-composition-for-zero | 2006.16917 | null | https://arxiv.org/abs/2006.16917v1 | https://arxiv.org/pdf/2006.16917v1.pdf | Ontology-guided Semantic Composition for Zero-Shot Learning | Zero-shot learning (ZSL) is a popular research problem that aims at predicting for those classes that have never appeared in the training stage by utilizing the inter-class relationship with some side information. In this study, we propose to model the compositional and expressive semantics of class labels by an OWL (Web Ontology Language) ontology, and further develop a new ZSL framework with ontology embedding. The effectiveness has been verified by some primary experiments on animal image classification and visual question answering. | ['Jeff Z. Pan', 'Freddy Lecue', 'Jiaoyan Chen', 'Huajun Chen', 'Yuxia Geng'] | 2020-06-30 | null | null | null | null | ['ontology-embedding'] | ['knowledge-base'] | [ 2.56505936e-01 3.30854625e-01 -4.82822299e-01 -5.66711605e-01
1.38354346e-01 -1.10526025e-01 6.68185353e-01 3.27405572e-01
-4.20272917e-01 5.75012803e-01 1.25998631e-01 1.70344003e-02
-3.94396603e-01 -1.08049333e+00 -3.75405669e-01 -2.24211931e-01
-2.29339987e-01 7.11214170e-03 6.19916022e-01 -3.54788244e-01
2.99280323e-02 1.36952354e-02 -2.26635337e+00 6.31322145e-01
5.14509618e-01 1.29019761e+00 -3.52349170e-02 3.74942482e-01
-7.67171144e-01 1.61072183e+00 -2.55552262e-01 -5.08064747e-01
-1.81397587e-01 -2.20350817e-01 -9.78370190e-01 5.42608611e-02
3.45718384e-01 -1.21783745e-02 -3.27529132e-01 1.28838849e+00
3.14769328e-01 3.73500437e-01 6.69623554e-01 -1.79000914e+00
-1.05613148e+00 3.24987441e-01 3.79479080e-02 2.44766161e-01
3.68765533e-01 -5.99625111e-02 1.29226327e+00 -7.78549671e-01
9.72021163e-01 1.42126656e+00 6.30569637e-01 8.31078053e-01
-9.30348754e-01 -5.88292360e-01 1.37539312e-01 1.10554039e+00
-1.29461074e+00 -1.61517262e-01 9.07405436e-01 -6.70777798e-01
8.55995119e-01 7.15586543e-02 5.20102262e-01 1.10218751e+00
1.24796137e-01 8.25464070e-01 9.08661067e-01 -5.54346144e-01
4.50677782e-01 3.37810427e-01 8.50691319e-01 9.44114208e-01
8.19520429e-02 1.98030937e-02 -5.03287077e-01 -1.57502174e-01
1.09801680e-01 2.43621722e-01 -6.05848767e-02 -7.44135857e-01
-6.27141893e-01 1.15310109e+00 5.51738560e-01 4.34867859e-01
1.20700993e-01 -1.47582695e-01 7.07559168e-01 3.98681402e-01
5.18790424e-01 3.94075155e-01 -4.25690830e-01 3.99321258e-01
-1.66728511e-01 -3.47161405e-02 6.90815270e-01 1.26715815e+00
8.84266138e-01 -8.94745886e-02 -1.06507689e-01 1.10547423e+00
3.00499886e-01 1.07456613e-02 1.03917074e+00 -8.68977129e-01
-3.42212439e-01 1.14392483e+00 -4.09705579e-01 -8.62958014e-01
-3.33566695e-01 8.44499469e-02 -4.70745474e-01 2.91031122e-01
-2.28650749e-01 2.26115286e-01 -9.03002441e-01 1.64428210e+00
4.81588721e-01 4.53346044e-01 4.83039618e-01 6.27769947e-01
1.54275048e+00 6.57097638e-01 6.36786103e-01 -4.72948374e-03
1.63092947e+00 -1.10158241e+00 -1.12057889e+00 -1.28432006e-01
6.84553862e-01 -2.14359879e-01 1.21989083e+00 -9.68196243e-03
-1.64002374e-01 -5.75186372e-01 -1.30814624e+00 -3.18838000e-01
-1.25021863e+00 -2.70037949e-01 8.66178691e-01 3.86902571e-01
-5.33860326e-01 6.13341749e-01 -2.09659457e-01 -6.60655379e-01
7.72302687e-01 -4.30082977e-02 -5.51790416e-01 -1.23257503e-01
-1.54714811e+00 9.65170562e-01 9.15519893e-01 -5.51097333e-01
-7.21995115e-01 -6.83606207e-01 -1.23021984e+00 1.32046252e-01
5.84494054e-01 -2.79964924e-01 9.89149570e-01 -1.05236602e+00
-1.38470089e+00 1.15283191e+00 2.33012185e-01 -4.38348413e-01
-1.96300119e-01 2.17405394e-01 -8.21566939e-01 1.76319629e-01
1.54607520e-01 5.94392538e-01 6.97617173e-01 -1.05434382e+00
-7.61761785e-01 -6.24314666e-01 4.27991986e-01 -9.60186869e-02
-7.93025017e-01 -1.40002176e-01 1.82780158e-02 -5.32810867e-01
-2.20881015e-01 -5.37789583e-01 -1.50080137e-02 3.57213229e-01
2.04665184e-01 -8.04982841e-01 9.32750881e-01 -2.53066808e-01
1.13974583e+00 -2.24645114e+00 -8.97988528e-02 -1.63085684e-01
2.74787039e-01 3.75291318e-01 -2.37745658e-01 2.90839732e-01
-2.74367779e-01 -3.51319984e-02 -1.64079025e-01 3.88332993e-01
9.14012492e-02 5.83659589e-01 -4.35542822e-01 7.23573640e-02
3.46711576e-02 9.88091588e-01 -1.14624834e+00 -9.94015753e-01
2.99067467e-01 1.16824552e-01 -4.55694824e-01 4.02388811e-01
-3.51997614e-01 -3.41879398e-01 -4.09856141e-01 9.60527480e-01
2.53074795e-01 -3.53817523e-01 1.10827349e-01 -4.38878357e-01
1.93023294e-01 -4.14454699e-01 -7.87693858e-01 1.65292454e+00
-2.45466530e-01 5.86010039e-01 -6.67883515e-01 -1.11880910e+00
6.40937567e-01 4.73426610e-01 5.09406865e-01 -7.84756243e-01
2.94707865e-01 -1.09276347e-01 -1.86045051e-01 -1.26775527e+00
-7.35637620e-02 -4.32159513e-01 2.77346298e-02 -1.10573918e-01
8.92242432e-01 8.72024000e-02 2.55850077e-01 3.77822411e-03
8.07175398e-01 1.90376937e-01 8.30094934e-01 -2.67865270e-01
6.40737653e-01 1.75051585e-01 5.86411595e-01 4.01073217e-01
-5.53824544e-01 -1.00761093e-02 4.82082069e-01 -8.32323015e-01
-8.98546815e-01 -9.83949840e-01 -5.15407979e-01 1.39218044e+00
4.60710168e-01 -5.72867513e-01 -5.04385889e-01 -8.14371467e-01
3.89498435e-02 8.79801691e-01 -8.47455859e-01 -6.26491010e-01
3.28513682e-01 -3.96508902e-01 3.60992074e-01 3.73475522e-01
4.39189374e-01 -1.23785770e+00 -7.55861223e-01 2.10179731e-01
1.20362476e-01 -1.12701631e+00 1.64434984e-01 1.42969340e-01
-6.31378770e-01 -1.52092075e+00 -2.84312427e-01 -1.06984472e+00
4.52254474e-01 5.73591255e-02 1.00144649e+00 -8.29557236e-03
-7.26244867e-01 5.18314660e-01 -7.50645518e-01 -6.38392389e-01
1.32886842e-01 -3.09508771e-01 -3.83387282e-02 1.84607834e-01
8.90159667e-01 -4.08825070e-01 -2.07373559e-01 1.41905934e-01
-1.17607021e+00 -4.73743416e-02 4.14182022e-02 9.19083178e-01
4.77386236e-01 1.70447618e-01 7.74780571e-01 -1.27672541e+00
5.33286989e-01 -5.99542499e-01 -4.47408020e-01 6.75251842e-01
-6.86668158e-01 1.51874170e-01 5.17996192e-01 -5.94106197e-01
-9.78455842e-01 -7.30516464e-02 -8.84694532e-02 -6.84511244e-01
-2.28542015e-01 4.74953890e-01 -3.98130804e-01 -2.19564438e-01
6.51375055e-01 -3.30825597e-02 5.20105474e-02 -4.81454968e-01
6.19808137e-01 7.71880507e-01 3.24839234e-01 -3.99685502e-01
2.80121326e-01 5.58602393e-01 -8.38815123e-02 -9.68464494e-01
-1.44759071e+00 -6.10030890e-01 -5.19414604e-01 -3.77453208e-01
1.18544376e+00 -6.94897890e-01 -8.79566789e-01 1.43218175e-01
-9.66245055e-01 1.22498013e-01 -6.24821246e-01 3.79749835e-01
-5.87418318e-01 2.07556367e-01 -3.92266095e-01 -7.07277417e-01
-1.46599665e-01 -6.39636219e-01 5.95470011e-01 2.30729759e-01
-9.01702642e-02 -8.48883390e-01 1.86097950e-01 2.52490938e-01
1.37465298e-01 1.86762303e-01 1.51056588e+00 -9.69784856e-01
-1.11543015e-01 -1.53881982e-01 -2.93854177e-01 4.13642019e-01
9.49505866e-02 -2.11851269e-01 -1.19080997e+00 -1.26927032e-03
9.33105573e-02 -5.40752888e-01 7.13785589e-01 -6.74796179e-02
1.40744376e+00 -3.18012089e-01 -2.07864746e-01 4.22003180e-01
1.80688226e+00 3.72287035e-01 4.97139364e-01 3.16381127e-01
5.75310946e-01 8.92701924e-01 5.94887972e-01 2.97005296e-01
4.30940509e-01 3.60580683e-01 5.18553376e-01 4.87389266e-01
-1.81152537e-01 -4.54510331e-01 -1.16871230e-01 8.04119647e-01
1.33326352e-01 -2.31682323e-02 -8.63322735e-01 5.45699477e-01
-2.05998206e+00 -1.16527200e+00 5.12954369e-02 1.69677651e+00
7.88297534e-01 -1.01244766e-02 -3.72636318e-01 -9.60610434e-02
7.23482788e-01 2.52082199e-01 -5.73874533e-01 -3.28905523e-01
-9.65484306e-02 3.65926623e-01 2.86946982e-01 3.78767282e-01
-1.36018181e+00 1.13169539e+00 6.88536739e+00 1.02723789e+00
-6.99585438e-01 3.64124775e-01 1.55188486e-01 2.87027121e-01
-5.72531372e-02 1.33205429e-01 -6.46311224e-01 3.35251123e-01
7.70430684e-01 -5.73517382e-01 1.22346491e-01 1.22817898e+00
-5.19587815e-01 1.85810015e-01 -1.11588120e+00 1.10372078e+00
4.36079025e-01 -1.39582515e+00 4.89665121e-01 -3.70975018e-01
5.52101314e-01 -2.81165421e-01 -3.31246734e-01 8.02100599e-01
2.17855275e-01 -8.31873536e-01 4.36368287e-01 8.33094060e-01
6.31623149e-01 -6.32670701e-01 7.06730127e-01 4.73008364e-01
-1.27828205e+00 -5.70428610e-01 -8.26021910e-01 -1.04274303e-01
-3.14939231e-01 -4.99255136e-02 -3.07003170e-01 5.19802570e-01
9.15546954e-01 1.05337405e+00 -7.14580595e-01 9.34789836e-01
-3.44410688e-01 3.95464838e-01 1.40652284e-01 -1.54724762e-01
2.33249739e-01 1.95238255e-02 1.65644184e-01 8.74053478e-01
-8.66367966e-02 5.09890556e-01 2.48735696e-01 6.65846229e-01
-1.44462734e-01 4.79841530e-01 -1.10748541e+00 -2.17451215e-01
1.26584411e-01 1.02815247e+00 -4.30059582e-01 -6.78338706e-01
-7.73194373e-01 6.86363995e-01 3.12512696e-01 2.57199317e-01
-7.73127913e-01 -7.76411116e-01 6.81265712e-01 -1.36299595e-01
4.62446883e-02 3.70268226e-01 2.49376390e-02 -1.42718041e+00
-3.17360461e-01 -5.08814454e-01 8.44992161e-01 -1.11841679e+00
-1.84495485e+00 4.84120578e-01 -1.66585501e-02 -1.41735899e+00
1.28514707e-01 -8.97173107e-01 -3.48419338e-01 2.29550853e-01
-1.73077035e+00 -1.36909449e+00 -3.44971299e-01 7.38459706e-01
7.40061939e-01 -6.15410268e-01 1.22732449e+00 4.85156715e-01
-3.34838957e-01 1.76922083e-01 -1.04760699e-01 1.31231859e-01
4.10443515e-01 -9.93253291e-01 -4.62217063e-01 2.24979043e-01
2.83307314e-01 3.52765650e-01 5.52356422e-01 -4.87973005e-01
-9.93301392e-01 -1.02241552e+00 1.08926129e+00 -3.54353338e-01
9.37820137e-01 -1.12988651e-01 -1.02795792e+00 6.62658989e-01
2.70669341e-01 4.17546630e-01 1.15127754e+00 -9.45050344e-02
-7.41007566e-01 -8.22938904e-02 -1.18821073e+00 4.74337399e-01
1.11187780e+00 -9.15532351e-01 -1.24544740e+00 2.62386560e-01
1.13850594e+00 3.56425345e-01 -8.52510393e-01 7.05213964e-01
6.22541606e-01 -4.57723200e-01 1.09589672e+00 -1.38870299e+00
6.06256187e-01 -2.71195710e-01 -5.80537736e-01 -1.03701794e+00
-4.02473569e-01 2.95404494e-01 -8.31715167e-02 1.09199786e+00
-9.37703028e-02 -4.29458380e-01 6.23612046e-01 4.42237645e-01
3.48699465e-02 -5.89902043e-01 -8.60293090e-01 -9.49556768e-01
-2.22334743e-01 -3.01380664e-01 5.58387935e-01 1.34747314e+00
3.19375813e-01 6.00838900e-01 -3.92420620e-01 -8.89647380e-02
8.43102932e-01 1.72043279e-01 2.15065211e-01 -1.73770964e+00
1.40580788e-01 -2.45687440e-01 -1.14778650e+00 -2.64371336e-01
7.14297235e-01 -1.27595901e+00 -1.23571895e-01 -1.36882997e+00
2.91993171e-01 9.22761634e-02 -7.49704063e-01 7.52737701e-01
-9.09817293e-02 3.02223973e-02 7.56187811e-02 -1.33034214e-02
-9.41365004e-01 8.71218443e-01 9.04135406e-01 -5.12167454e-01
3.63110512e-01 -3.79663110e-01 -4.07971442e-01 1.08990514e+00
5.54895401e-01 -4.85366315e-01 -8.78813922e-01 -9.55955386e-02
1.30187675e-01 -9.26003382e-02 4.64897782e-01 -1.09656286e+00
2.32932732e-01 -3.38729501e-01 8.13246965e-02 -1.80464700e-01
3.89497250e-01 -1.34579170e+00 -1.06359079e-01 3.18470985e-01
-7.83838332e-01 -5.90379536e-01 3.27545442e-02 7.64906406e-01
-4.43433642e-01 -7.63776064e-01 9.20781255e-01 -6.27666861e-02
-1.76500237e+00 5.99895895e-01 -2.43203565e-01 2.51977444e-01
1.44906986e+00 -5.45211732e-02 -2.76166946e-01 9.80459899e-02
-1.06102240e+00 3.58810723e-01 1.99604094e-01 8.07609081e-01
7.65079260e-01 -1.75898826e+00 -7.66039193e-02 2.44849652e-01
1.07431149e+00 -8.08720350e-01 2.99213707e-01 1.42337810e-02
-4.18390483e-01 2.80470371e-01 -6.29604161e-01 -1.28258348e-01
-1.27194369e+00 1.04527521e+00 3.03225666e-01 1.37602255e-01
-7.43838310e-01 7.96845675e-01 2.55575389e-01 -7.15120018e-01
4.04613495e-01 2.29337201e-01 -9.49200213e-01 3.06478411e-01
6.24507368e-01 2.58330405e-01 -3.90075982e-01 -6.62164330e-01
-3.32413346e-01 5.98901451e-01 3.21614832e-01 3.44980359e-01
1.37596905e+00 1.24961799e-02 -2.20337406e-01 1.06639779e+00
1.51643252e+00 -7.11044490e-01 -8.27460647e-01 -5.57517290e-01
6.31133854e-01 -5.11766493e-01 -5.24347760e-02 -4.46112573e-01
-7.37140715e-01 1.09244919e+00 1.10210502e+00 2.86798865e-01
6.42572224e-01 1.93002373e-01 6.19445801e-01 6.96094573e-01
5.23994207e-01 -1.36467755e+00 2.82851160e-02 4.70757961e-01
5.65289676e-01 -1.36780846e+00 -1.34535804e-01 -5.25825262e-01
-5.97108543e-01 1.20732296e+00 7.90808797e-01 -9.08160880e-02
1.26398563e+00 -1.96095213e-01 -1.05430476e-01 -5.57394028e-01
-1.06254184e+00 -7.08490849e-01 2.92189658e-01 6.38760149e-01
1.99613467e-01 6.81332871e-02 -4.26077753e-01 7.72611022e-01
2.78338253e-01 3.79095227e-01 8.93031508e-02 1.12817216e+00
-9.22242403e-01 -1.07298779e+00 2.79224038e-01 5.59173286e-01
-1.73910141e-01 1.44285932e-02 -2.12696314e-01 3.54058415e-01
5.85306346e-01 8.40511739e-01 -2.49142814e-02 -4.40074533e-01
4.18194056e-01 7.01169133e-01 2.29000747e-01 -8.49910498e-01
-6.89414144e-02 -7.00382292e-01 -1.04437575e-01 -6.09636247e-01
-5.62575758e-01 1.48317352e-01 -1.27688861e+00 1.76043883e-01
-2.36364126e-01 1.78814791e-02 4.54184353e-01 9.75369096e-01
1.73731625e-01 6.13341153e-01 5.34713805e-01 2.53366400e-02
-3.85545462e-01 -6.20622277e-01 -8.09269965e-01 8.05595636e-01
3.69374342e-02 -1.04551911e+00 -4.47020680e-01 3.65078777e-01] | [10.023003578186035, 2.453230142593384] |
de1bf8dc-95e7-4647-897d-39e8926b9ff5 | mvtn-learning-multi-view-transformations-for | 2212.13462 | null | https://arxiv.org/abs/2212.13462v1 | https://arxiv.org/pdf/2212.13462v1.pdf | MVTN: Learning Multi-View Transformations for 3D Understanding | Multi-view projection techniques have shown themselves to be highly effective in achieving top-performing results in the recognition of 3D shapes. These methods involve learning how to combine information from multiple view-points. However, the camera view-points from which these views are obtained are often fixed for all shapes. To overcome the static nature of current multi-view techniques, we propose learning these view-points. Specifically, we introduce the Multi-View Transformation Network (MVTN), which uses differentiable rendering to determine optimal view-points for 3D shape recognition. As a result, MVTN can be trained end-to-end with any multi-view network for 3D shape classification. We integrate MVTN into a novel adaptive multi-view pipeline that is capable of rendering both 3D meshes and point clouds. Our approach demonstrates state-of-the-art performance in 3D classification and shape retrieval on several benchmarks (ModelNet40, ScanObjectNN, ShapeNet Core55). Further analysis indicates that our approach exhibits improved robustness to occlusion compared to other methods. We also investigate additional aspects of MVTN, such as 2D pretraining and its use for segmentation. To support further research in this area, we have released MVTorch, a PyTorch library for 3D understanding and generation using multi-view projections. | ['Bernard Ghanem', 'Silvio Giancola', 'Faisal AlZahrani', 'Abdullah Hamdi'] | 2022-12-27 | null | null | null | null | ['3d-shape-retrieval', '3d-shape-recognition', '3d-classification'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.70670912e-01 -4.64538336e-01 2.33124286e-01 -5.85938692e-01
-8.77662897e-01 -9.99253511e-01 8.17300439e-01 -3.58390808e-01
1.23959966e-01 -1.97575599e-01 -1.00727789e-01 -3.40638846e-01
2.49581978e-01 -9.42543685e-01 -8.59609187e-01 -4.41394329e-01
2.09986299e-01 1.03667212e+00 2.49283060e-01 -1.65042460e-01
2.38526955e-01 1.28548348e+00 -1.65108252e+00 7.34255731e-01
1.92897871e-01 9.66167927e-01 -1.83074772e-02 7.99745381e-01
-2.77285546e-01 -1.84115052e-01 -2.13963568e-01 -4.79591578e-01
6.87221467e-01 3.25616628e-01 -6.18377030e-01 3.65005821e-01
1.26911867e+00 -5.53776145e-01 7.84162059e-02 6.95422173e-01
8.76020491e-01 -8.77956972e-02 6.59320474e-01 -1.03184330e+00
-6.49108827e-01 -3.98842357e-02 -6.89185262e-01 3.44962999e-02
5.37684441e-01 1.01368912e-01 9.32231665e-01 -1.61233354e+00
7.63905823e-01 1.65221107e+00 6.13403380e-01 5.45677900e-01
-1.15782487e+00 -6.41552567e-01 1.94381595e-01 -2.69809127e-01
-1.08572316e+00 -5.08108854e-01 8.45083952e-01 -4.75361794e-01
1.35639620e+00 4.01930004e-01 8.34771931e-01 9.30286705e-01
2.00661674e-01 9.47107077e-01 1.12541008e+00 -7.27573559e-02
-1.75796509e-01 -1.72673419e-01 -3.80029231e-01 6.81277692e-01
-1.74814373e-01 2.22669899e-01 -3.69884968e-01 -3.66900533e-01
1.06244457e+00 1.82653740e-02 2.56000292e-02 -1.17610073e+00
-9.69474494e-01 5.22366703e-01 3.45423281e-01 -1.04405746e-01
-2.58239985e-01 -3.55550721e-02 3.21191013e-01 2.63041735e-01
9.92153823e-01 7.11532906e-02 -7.17726767e-01 6.91820905e-02
-6.63690567e-01 2.85244793e-01 7.47685552e-01 1.02995694e+00
5.57874858e-01 1.54384062e-01 2.21446440e-01 9.97051477e-01
5.61157882e-01 8.64875376e-01 -2.51639992e-01 -1.04427958e+00
7.74513781e-01 9.00628328e-01 -2.31060296e-01 -8.53523016e-01
-4.36632901e-01 -3.40273201e-01 -6.17170274e-01 5.45667350e-01
2.05281392e-01 2.44612262e-01 -1.22524953e+00 1.22493970e+00
6.51339471e-01 1.19085237e-01 -4.67626899e-02 9.27746236e-01
1.25899756e+00 3.75660539e-01 -4.60657656e-01 4.59357560e-01
1.08869231e+00 -8.10931921e-01 1.77501008e-01 -1.17814243e-01
4.07438993e-01 -1.08947527e+00 1.05137801e+00 4.32649732e-01
-1.56654394e+00 -4.77889150e-01 -8.39787364e-01 -9.24598873e-02
-4.13249433e-01 3.25827263e-02 4.61286008e-01 6.12350047e-01
-1.15545297e+00 6.76392078e-01 -1.05867016e+00 -2.03760654e-01
7.68840134e-01 4.49128062e-01 -5.17835975e-01 -2.74286419e-01
-3.67902577e-01 7.76576757e-01 -1.19909234e-01 -1.03390531e-03
-8.79229367e-01 -1.10701847e+00 -8.17798316e-01 -1.64645180e-01
6.59429953e-02 -1.16549110e+00 1.13291371e+00 -3.82935166e-01
-1.34910762e+00 1.49603117e+00 -8.29801708e-02 1.91455171e-01
5.13928175e-01 -9.48041379e-02 -7.61559606e-02 2.77698576e-01
3.66949588e-02 7.71172285e-01 7.86768377e-01 -1.67830181e+00
-3.91897827e-01 -8.59695971e-01 1.30487487e-01 5.65975964e-01
1.28627777e-01 3.99263240e-02 -8.31448197e-01 -2.62115657e-01
7.07724392e-01 -1.11869895e+00 9.96718183e-03 2.22762704e-01
-3.28722984e-01 -1.75116971e-01 1.17250133e+00 -3.72001946e-01
2.00489461e-01 -1.89584470e+00 2.02021122e-01 3.48875135e-01
2.51583457e-01 2.77798742e-01 -1.25774905e-01 3.42913598e-01
-1.54141068e-01 1.46197215e-01 3.68271694e-02 -7.20962405e-01
-1.35195956e-01 2.65827358e-01 -2.30212703e-01 5.06049156e-01
-1.11546695e-01 9.87462401e-01 -5.03646970e-01 -5.64830340e-02
5.71585774e-01 8.02741647e-01 -5.78693807e-01 9.49153379e-02
-3.02798748e-01 6.15632296e-01 -2.91130275e-01 8.45978618e-01
9.65742707e-01 -5.68624735e-01 -2.22829849e-01 -3.65379244e-01
2.44683530e-02 1.95226848e-01 -1.15175605e+00 2.00023055e+00
-6.83805466e-01 2.04809308e-01 1.65953025e-01 -4.44172055e-01
9.76645112e-01 3.40340734e-01 6.99718058e-01 -2.91427225e-01
-8.83929953e-02 2.28928268e-01 -3.19696844e-01 -1.50500238e-01
2.37753868e-01 -6.72940984e-02 3.99685413e-01 6.37526035e-01
-9.30875689e-02 -7.24048972e-01 -1.72315493e-01 4.88724448e-02
5.61821580e-01 4.88012940e-01 -4.29424271e-02 6.38598353e-02
4.72896606e-01 -2.74806529e-01 7.64119700e-02 2.26492226e-01
2.80507982e-01 1.07382190e+00 7.84543008e-02 -8.07973683e-01
-1.20279050e+00 -1.29171813e+00 -2.23503262e-01 9.60268378e-01
-7.51406327e-02 -1.86397702e-01 -3.78797174e-01 -8.65048170e-01
2.64400154e-01 5.59305608e-01 -2.79568404e-01 3.34063560e-01
-7.04875946e-01 -4.33320314e-01 4.24838036e-01 7.41620481e-01
3.15088242e-01 -8.79856765e-01 -6.89615369e-01 -1.00693703e-01
2.65591919e-01 -1.44059348e+00 -6.45671189e-01 -8.41593370e-02
-1.35353124e+00 -1.20171165e+00 -6.97738707e-01 -5.66200674e-01
8.34917545e-01 6.69252992e-01 1.56136966e+00 3.59465778e-02
-1.71139161e-03 1.05482721e+00 -4.88043353e-02 -4.53876108e-01
-4.42808986e-01 8.45101401e-02 -1.51684910e-01 -2.14402512e-01
2.28334993e-01 -1.05493033e+00 -6.27284527e-01 5.40053129e-01
-6.38535380e-01 1.83188826e-01 2.69468635e-01 4.70813930e-01
1.05257487e+00 -6.37330174e-01 -1.63710807e-02 -9.72231627e-01
2.95472682e-01 -1.67345498e-02 -7.82721817e-01 2.10470259e-01
-4.09630120e-01 -3.28211486e-01 5.20455241e-01 -5.28888702e-02
-8.07888865e-01 3.08861881e-01 -5.49473166e-01 -1.13258541e+00
-5.86724520e-01 5.95067665e-02 -3.30238968e-01 -4.36496526e-01
4.04068619e-01 3.52375567e-01 2.38963619e-01 -6.78193867e-01
4.50646728e-01 3.72506440e-01 1.02026731e-01 -5.48656166e-01
8.04984868e-01 9.23161805e-01 1.12683296e-01 -8.19352090e-01
-9.16322768e-01 -5.10681629e-01 -1.05328572e+00 -2.31043279e-01
7.33163595e-01 -9.97047722e-01 -7.40214884e-01 5.98725021e-01
-1.26972866e+00 -8.27463940e-02 -9.61407498e-02 1.29455507e-01
-8.24957967e-01 3.24548423e-01 -4.98207062e-01 -4.45188314e-01
-6.41813815e-01 -1.39822853e+00 1.79196703e+00 -5.94658814e-02
1.47840589e-01 -1.18085396e+00 -8.62929896e-02 5.38872600e-01
1.49456680e-01 3.27872485e-01 9.68952596e-01 -6.37442887e-01
-8.01767290e-01 2.11475953e-03 -1.57288194e-01 2.31887981e-01
4.04468328e-02 1.09614842e-01 -1.25340974e+00 -6.23021603e-01
-3.27104405e-02 -2.54913658e-01 5.51749825e-01 4.65272993e-01
1.17506897e+00 2.39466980e-01 -3.55185002e-01 1.22109103e+00
1.40973485e+00 1.60196438e-01 3.70325297e-01 6.97843730e-02
1.14613485e+00 4.06562030e-01 2.07789987e-01 2.64361680e-01
5.53418458e-01 8.50912571e-01 7.65557408e-01 -2.96833426e-01
-1.42515272e-01 -2.32084900e-01 -6.36836067e-02 1.06224322e+00
-1.94227159e-01 -4.49995808e-02 -1.13567173e+00 2.60981500e-01
-1.23243737e+00 -7.76335657e-01 -2.96360761e-01 2.03167939e+00
1.79874435e-01 2.12229833e-01 2.11531863e-01 -1.96007654e-01
2.82618582e-01 2.61527807e-01 -7.98331439e-01 -4.93335545e-01
-7.87990764e-02 1.47701561e-01 3.03191096e-01 4.51256067e-01
-1.15206456e+00 9.28311467e-01 6.01133823e+00 4.97550756e-01
-1.28208864e+00 9.61466599e-03 6.40199900e-01 -3.42813171e-02
-5.56979179e-01 -2.60846078e-01 -9.82038736e-01 -1.89327091e-01
3.45498919e-01 4.10912454e-01 4.00697887e-01 8.81612182e-01
9.08931047e-02 2.76665777e-01 -1.32755673e+00 1.26656115e+00
3.73736590e-01 -1.37398183e+00 4.06117499e-01 2.03526333e-01
7.94415832e-01 6.40260160e-01 2.17813239e-01 -9.99656320e-02
2.80893862e-01 -8.82940114e-01 4.80229497e-01 2.95009613e-01
9.26305950e-01 -7.75235832e-01 2.50204355e-01 5.52208722e-01
-1.35065722e+00 4.22826976e-01 -4.43849355e-01 4.58129734e-01
2.79777378e-01 3.88261586e-01 -8.94410014e-01 7.92906880e-01
6.32655561e-01 9.36257958e-01 -5.80851614e-01 9.14374352e-01
8.83480236e-02 2.81583034e-02 -5.59137046e-01 4.37606990e-01
2.76699126e-01 -2.75769472e-01 8.21494997e-01 8.63495409e-01
4.38655704e-01 1.45007288e-02 3.90320212e-01 9.77755308e-01
8.88388157e-02 -2.88284961e-02 -1.16641796e+00 4.40351665e-01
1.85877711e-01 1.25457942e+00 -9.63648438e-01 -2.58431256e-01
-5.52805722e-01 7.53833711e-01 2.70062804e-01 1.52535424e-01
-4.17987913e-01 3.45619559e-01 6.06841445e-01 2.53793389e-01
6.36029243e-01 -4.99855310e-01 -5.71797192e-01 -1.14919209e+00
6.85243085e-02 -8.94331992e-01 2.57824600e-01 -9.98507261e-01
-1.55381072e+00 7.62113750e-01 2.72620618e-01 -1.51347685e+00
-1.00491077e-01 -8.36308360e-01 -3.44446778e-01 8.96660328e-01
-1.46097445e+00 -1.55122459e+00 -1.95911795e-01 5.09011984e-01
8.63776922e-01 -3.56047153e-01 1.00378823e+00 3.70058805e-01
2.61312407e-02 3.37110192e-01 -1.68666661e-01 7.60255521e-03
4.29696620e-01 -1.48956394e+00 1.14457476e+00 3.92805755e-01
6.00971043e-01 3.97036761e-01 1.47853017e-01 -6.36790097e-01
-1.75263476e+00 -9.74831522e-01 3.68534237e-01 -9.38333809e-01
2.03554835e-02 -3.80601048e-01 -8.42458844e-01 7.39818573e-01
6.01884760e-02 2.76085198e-01 5.67310750e-01 8.07669312e-02
-4.57882136e-01 5.23699224e-02 -1.05088341e+00 4.28415060e-01
1.34674060e+00 -5.35049975e-01 -3.38383943e-01 2.69345671e-01
3.97695661e-01 -1.27168906e+00 -1.03409457e+00 5.95072806e-01
6.79203093e-01 -1.39428782e+00 1.53357410e+00 -6.09802306e-01
5.12369573e-01 -4.47933823e-02 -2.88620502e-01 -1.58445382e+00
-5.69903143e-02 -1.57393187e-01 -3.07836384e-02 8.67238879e-01
4.03812379e-01 -5.57800949e-01 1.16245091e+00 5.10130346e-01
-4.05368596e-01 -1.26000655e+00 -8.65507424e-01 -6.27849698e-01
2.52367169e-01 -6.14993155e-01 7.67295182e-01 7.77392745e-01
-9.49733257e-01 4.34181780e-01 6.85339496e-02 3.55953038e-01
6.71548069e-01 7.56044984e-01 1.10255742e+00 -1.35211754e+00
-2.16999933e-01 -3.73448730e-01 -2.28799880e-01 -1.36544108e+00
1.20032132e-01 -1.34275579e+00 -4.39290613e-01 -1.72447944e+00
-2.77411323e-02 -6.01168156e-01 1.97196141e-01 3.03492486e-01
2.40867928e-01 3.85888666e-01 3.80915999e-01 1.66401505e-01
-2.68032521e-01 2.78753132e-01 2.08450890e+00 6.68637455e-02
-2.98558682e-01 5.32268643e-01 -2.33810186e-01 1.10820842e+00
6.78103566e-01 -2.48851508e-01 -5.23721457e-01 -8.29488993e-01
1.60005748e-01 3.00928503e-01 5.29521883e-01 -5.78405321e-01
1.01651259e-01 1.97916850e-02 7.01647341e-01 -1.54169989e+00
9.25699294e-01 -1.00208044e+00 1.22500874e-01 8.67743567e-02
8.17901492e-02 6.74574673e-01 3.17067891e-01 3.61652195e-01
7.35468119e-02 5.72519526e-02 6.89810753e-01 -6.21521235e-01
-3.15594584e-01 6.83143616e-01 1.11239322e-01 1.84537008e-01
8.05884242e-01 -5.96393883e-01 -5.18042408e-02 -3.07555467e-01
-9.89577413e-01 1.78990141e-01 6.41235232e-01 5.64535558e-01
1.00521040e+00 -1.51237297e+00 -6.66817904e-01 6.09359443e-01
-1.07045352e-01 4.06109005e-01 1.36934862e-01 6.53402686e-01
-5.78675926e-01 2.76748329e-01 -1.43578857e-01 -1.25744295e+00
-1.64028299e+00 2.65251666e-01 4.88054067e-01 -1.94972441e-01
-9.79899108e-01 7.96545208e-01 2.54352897e-01 -1.05405903e+00
1.09217197e-01 -3.41712505e-01 -9.05137435e-02 -8.98484141e-02
7.30031207e-02 2.80866295e-01 5.25555134e-01 -7.72584796e-01
-2.58821905e-01 1.10219240e+00 -1.61244273e-02 -5.24407141e-02
1.74699843e+00 1.64885476e-01 -1.14707746e-01 5.40537357e-01
1.41157424e+00 3.01152803e-02 -1.22582126e+00 7.45107532e-02
-3.27549547e-01 -6.90997720e-01 -1.91453308e-01 -7.20536351e-01
-1.40848172e+00 1.18828344e+00 6.31970048e-01 6.91862851e-02
7.67440736e-01 2.37603355e-02 9.78922665e-01 2.96145648e-01
3.99869233e-01 -5.96115887e-01 4.32026275e-02 8.27063203e-01
1.14423990e+00 -1.20061898e+00 1.03199385e-01 -6.37526035e-01
-3.38888466e-01 1.27492702e+00 9.05441761e-01 -3.02415758e-01
8.66304874e-01 3.68157923e-01 3.34783435e-01 -7.35335588e-01
-8.32739770e-01 1.17320091e-01 7.23262727e-01 5.33991158e-01
3.38005424e-01 -3.34884934e-02 4.86694783e-01 -1.65794998e-01
-3.67951810e-01 -5.39288878e-01 1.55904144e-01 7.25889087e-01
6.05570152e-02 -1.29143524e+00 -4.80860025e-01 6.16432369e-01
-4.19316977e-01 1.07087806e-01 -6.07777238e-01 7.05579579e-01
-1.53178781e-01 2.85036236e-01 1.16425805e-01 -3.60906869e-01
8.25009823e-01 1.74737483e-01 8.26796412e-01 -9.60835159e-01
-8.15431833e-01 4.07052070e-01 -2.78267823e-02 -7.14728296e-01
-5.92200279e-01 -8.41060340e-01 -9.36991274e-01 -2.20935047e-01
-2.89783061e-01 -5.15705407e-01 7.45951056e-01 7.61941791e-01
6.52274907e-01 2.08717033e-01 6.59572244e-01 -1.39823782e+00
-6.20587409e-01 -4.08794254e-01 -2.24600270e-01 4.09643382e-01
-9.43323947e-04 -6.63083613e-01 -1.69761807e-01 -1.72683284e-01] | [8.251294136047363, -3.5346157550811768] |
2780cf57-aedc-42cb-a3cb-8bb2c45282dd | batch-constrained-distributional | 2012.08984 | null | https://arxiv.org/abs/2012.08984v1 | https://arxiv.org/pdf/2012.08984v1.pdf | Batch-Constrained Distributional Reinforcement Learning for Session-based Recommendation | Most of the existing deep reinforcement learning (RL) approaches for session-based recommendations either rely on costly online interactions with real users, or rely on potentially biased rule-based or data-driven user-behavior models for learning. In this work, we instead focus on learning recommendation policies in the pure batch or offline setting, i.e. learning policies solely from offline historical interaction logs or batch data generated from an unknown and sub-optimal behavior policy, without further access to data from the real-world or user-behavior models. We propose BCD4Rec: Batch-Constrained Distributional RL for Session-based Recommendations. BCD4Rec builds upon the recent advances in batch (offline) RL and distributional RL to learn from offline logs while dealing with the intrinsically stochastic nature of rewards from the users due to varied latent interest preferences (environments). We demonstrate that BCD4Rec significantly improves upon the behavior policy as well as strong RL and non-RL baselines in the batch setting in terms of standard performance metrics like Click Through Rates or Buy Rates. Other useful properties of BCD4Rec include: i. recommending items from the correct latent categories indicating better value estimates despite large action space (of the order of number of items), and ii. overcoming popularity bias in clicked or bought items typically present in the offline logs. | ['Gautam Shroff', 'Lovekesh Vig', 'Pankaj Malhotra', 'Priyanka Gupta', 'Diksha Garg'] | 2020-12-16 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-2.51330316e-01 -3.01107436e-01 -7.90466011e-01 -5.08817077e-01
-7.53037810e-01 -5.98900855e-01 5.90455651e-01 -2.30609160e-02
-5.99681020e-01 8.16233635e-01 4.54739332e-01 -5.55774212e-01
-3.12403649e-01 -6.82296336e-01 -9.07395840e-01 -3.82230848e-01
-5.40265441e-01 7.24383116e-01 -5.65521838e-03 -2.54955053e-01
1.45234108e-01 -1.74309164e-02 -1.56851482e+00 2.07999170e-01
7.72084594e-01 1.16775095e+00 2.26568997e-01 8.99598300e-01
-1.29972801e-01 9.16981518e-01 -3.34679574e-01 -9.27167684e-02
5.37615240e-01 -4.72107708e-01 -4.09779876e-01 -1.18715040e-01
3.41979235e-01 -9.98955667e-01 -3.89816016e-01 5.55079877e-01
4.58380222e-01 6.87120855e-01 4.66883928e-01 -1.04749167e+00
-9.86943066e-01 9.39183712e-01 -2.90080398e-01 2.61754096e-01
3.43753219e-01 3.80206019e-01 1.47278738e+00 -5.07427275e-01
1.44379213e-01 1.19694722e+00 2.98555911e-01 6.03110433e-01
-1.48080945e+00 -4.84900326e-01 8.45004559e-01 3.28570567e-02
-7.02295065e-01 -2.75436521e-01 3.18165630e-01 -3.55982155e-01
8.08099627e-01 1.38184071e-01 4.82430607e-01 1.90150928e+00
-1.66322619e-01 1.31494725e+00 1.03000629e+00 -1.25712290e-01
7.20116079e-01 2.53955901e-01 1.88724622e-01 8.18225965e-02
1.48197562e-01 4.78839040e-01 -4.92379516e-01 -4.16976273e-01
9.91229773e-01 4.37818646e-01 -8.35006405e-03 -5.30114949e-01
-8.05273116e-01 1.03817642e+00 1.78866357e-01 -2.10134521e-01
-7.46709943e-01 2.59281725e-01 1.99327052e-01 6.68656945e-01
4.23858613e-01 5.01248419e-01 -9.29199994e-01 -5.41571796e-01
-7.78523564e-01 6.88487172e-01 1.00913429e+00 1.07934237e+00
4.29816037e-01 2.09382504e-01 -6.79861963e-01 8.22192788e-01
3.34652394e-01 6.95514560e-01 7.29976654e-01 -9.20016944e-01
5.82465827e-01 -7.57508650e-02 1.01701570e+00 -2.90193677e-01
-1.77709192e-01 -5.09341300e-01 -2.21117195e-02 -6.97171390e-02
6.37427151e-01 -3.61170143e-01 -7.51545310e-01 1.99642956e+00
1.18613377e-01 -2.97423825e-02 -3.05007875e-01 8.88562500e-01
2.51786232e-01 4.15513068e-01 1.52310252e-01 -2.17642978e-01
6.08273804e-01 -9.06285346e-01 -4.23870414e-01 -6.51273653e-02
5.46356201e-01 -2.24401325e-01 1.86298668e+00 7.64467716e-01
-1.17108917e+00 -4.23438966e-01 -5.04008114e-01 3.07774216e-01
-5.24286591e-02 3.84376943e-02 7.44825065e-01 6.02232695e-01
-8.79504561e-01 7.72903144e-01 -8.52303922e-01 -2.52823830e-01
3.76487941e-01 4.31853920e-01 5.67688823e-01 1.78963859e-02
-1.03843284e+00 3.05204451e-01 -2.19487548e-01 -2.29569495e-01
-1.34493446e+00 -8.32689703e-01 -3.13381135e-01 1.87044397e-01
9.49358642e-01 -2.82843381e-01 2.01944304e+00 -1.18727863e+00
-2.03729033e+00 -2.84215994e-02 2.15386882e-01 -7.48113096e-01
8.18828762e-01 -8.19270253e-01 -4.26124752e-01 -4.25157845e-01
-2.45076925e-01 8.08858126e-02 8.70758295e-01 -1.03431189e+00
-8.72559845e-01 -1.86666086e-01 3.41493279e-01 2.75182068e-01
-3.51242602e-01 -4.47456568e-01 -5.94471171e-02 -5.34424722e-01
-7.25296319e-01 -1.03850484e+00 -2.18207046e-01 -3.40787649e-01
-2.59028189e-02 -4.84334409e-01 5.48325658e-01 -4.25227702e-01
1.25734544e+00 -1.84546876e+00 -2.67482430e-01 2.84324616e-01
-1.71888858e-01 1.62523523e-01 -3.95638674e-01 6.15202844e-01
4.42113489e-01 1.62899196e-02 6.24523044e-01 -2.76777864e-01
5.02972484e-01 3.68541360e-01 -6.84922814e-01 2.66945094e-01
-5.20896912e-01 9.29265618e-01 -1.12181079e+00 1.75901458e-01
1.46211103e-01 -8.29610825e-02 -1.10604715e+00 6.54100060e-01
-8.45050097e-01 6.15526259e-01 -4.52506483e-01 3.41666192e-01
4.35706615e-01 -3.51436257e-01 4.48772937e-01 3.81166399e-01
-3.87266576e-02 7.73819029e-01 -1.04486036e+00 1.46641755e+00
-9.71447349e-01 -8.61275718e-02 6.07991442e-02 -8.17277372e-01
4.39385891e-01 1.57158375e-01 5.64181685e-01 -1.11764038e+00
-1.02816507e-01 2.95242462e-02 -2.92171650e-02 -4.40445721e-01
4.78727102e-01 1.24613352e-01 8.49155262e-02 9.95713592e-01
1.55989408e-01 4.98822421e-01 -6.73096552e-02 3.55055690e-01
1.00615156e+00 3.93676400e-01 1.02254450e-01 -1.08731717e-01
-6.71482384e-02 -5.24166822e-01 4.17408228e-01 1.71941173e+00
-5.86844757e-02 -4.19587828e-02 4.77640301e-01 -1.79104686e-01
-9.79616940e-01 -1.29322731e+00 1.74099386e-01 2.17885566e+00
-4.74211052e-02 -2.33837485e-01 -1.86695233e-01 -9.76203859e-01
3.94219160e-01 1.14673126e+00 -6.17712796e-01 -1.62931040e-01
-2.66089529e-01 -3.73765200e-01 -2.24770475e-02 6.61337793e-01
-2.71066036e-02 -1.14400125e+00 -8.65173489e-02 5.03656805e-01
2.06258923e-01 -7.73566127e-01 -8.57082605e-01 1.44372687e-01
-8.94097269e-01 -6.87371612e-01 -6.19670212e-01 -1.43995121e-01
3.05426925e-01 2.25552380e-01 1.27353978e+00 -2.56864160e-01
1.77874297e-01 9.16865468e-01 -5.74904561e-01 -2.85114586e-01
-4.59069712e-03 -6.83841035e-02 4.66653109e-01 1.06323443e-01
3.08128893e-01 -6.94014192e-01 -1.13866210e+00 5.32322526e-01
-6.83793545e-01 -4.84028667e-01 5.84152937e-01 9.69695032e-01
3.02763224e-01 -3.68151903e-01 9.45806623e-01 -1.32761705e+00
1.08370411e+00 -9.52358902e-01 -6.96004391e-01 1.51425049e-01
-1.07435203e+00 1.44041255e-01 1.06185472e+00 -9.79780912e-01
-1.10530353e+00 -4.75419492e-01 -2.59436741e-02 -3.78070503e-01
-1.37081861e-01 1.98192328e-01 1.49000332e-01 4.80071247e-01
7.80956507e-01 1.72693104e-01 -5.33481315e-02 -7.63398826e-01
7.00371087e-01 7.44227052e-01 1.11442581e-01 -1.02723062e+00
3.11952025e-01 2.09512174e-01 -6.26831412e-01 -4.93255883e-01
-1.17288125e+00 -5.74680090e-01 -1.24292128e-01 -6.43317848e-02
1.82168558e-01 -7.49383211e-01 -1.13166773e+00 1.25582024e-01
-2.95465618e-01 -1.19621491e+00 -6.90227687e-01 8.18872213e-01
-9.25248504e-01 2.97229625e-02 -7.75984764e-01 -1.32775331e+00
-2.56412566e-01 -8.06664407e-01 7.36042380e-01 1.91525146e-01
9.16062761e-03 -1.23970056e+00 2.30651662e-01 8.98126662e-02
7.28570700e-01 -4.85282421e-01 7.35376775e-01 -1.06376445e+00
-3.42931986e-01 -2.06462696e-01 1.71173871e-01 5.15810668e-01
1.20935433e-01 -4.04451311e-01 -7.91814506e-01 -5.88031590e-01
-2.73128003e-01 -8.82926941e-01 6.35014474e-01 6.14859104e-01
1.56357372e+00 -7.68506229e-01 1.17465958e-01 2.06167772e-01
1.09000158e+00 4.84994389e-02 8.28714296e-02 -7.12833181e-02
2.96865582e-01 3.52725178e-01 7.48054504e-01 1.01788318e+00
3.48666608e-01 6.84709966e-01 4.53102916e-01 2.85467625e-01
4.00617927e-01 -1.04849231e+00 7.28244603e-01 3.15053314e-01
-1.07845329e-02 -4.65130240e-01 -8.18595737e-02 3.17455202e-01
-2.31958103e+00 -1.15719497e+00 3.13131899e-01 2.93014407e+00
9.01466489e-01 2.75177419e-01 9.09916997e-01 -5.54743588e-01
3.20047408e-01 -8.69737118e-02 -1.28347266e+00 -4.95863020e-01
3.66530061e-01 2.74955273e-01 8.12983751e-01 4.28928375e-01
-8.08476448e-01 6.46967232e-01 6.76188469e+00 7.00380802e-01
-1.00285268e+00 1.58890888e-01 4.86036628e-01 -8.26407552e-01
-4.67953444e-01 -2.39730895e-01 -9.74764347e-01 6.47322655e-01
1.19868541e+00 2.32350789e-02 1.03347802e+00 1.05945528e+00
6.04464173e-01 1.73481293e-02 -1.50501668e+00 8.26900125e-01
-3.00182223e-01 -9.53310490e-01 -2.45241851e-01 5.96048951e-01
8.98755968e-01 2.83449411e-01 5.27414143e-01 1.15693593e+00
1.32611978e+00 -8.10164750e-01 7.18011737e-01 6.93722248e-01
5.26081741e-01 -4.93263274e-01 3.58530581e-01 8.07848811e-01
-6.34244919e-01 -6.86567128e-01 -4.12658423e-01 -1.38548285e-01
5.87088503e-02 4.27139640e-01 -7.63803542e-01 -2.20311321e-02
4.98821288e-01 7.99984753e-01 -1.36122584e-01 9.12079215e-01
-7.95492083e-02 1.45874238e+00 -4.01372731e-01 -3.44501525e-01
4.10970002e-01 -1.88608363e-01 2.06733942e-01 9.81636047e-01
2.43685588e-01 -1.12114400e-01 6.31499767e-01 8.15986454e-01
-2.05053717e-01 1.80846110e-01 -2.88395166e-01 -1.49792656e-01
3.38552892e-01 1.09194529e+00 -4.46919799e-02 -2.33812138e-01
-5.99932969e-01 6.76616371e-01 4.28665221e-01 6.19987249e-01
-8.52553368e-01 2.44402945e-01 7.33683169e-01 3.87828082e-01
7.45263100e-01 -3.30913007e-01 2.82770753e-01 -1.23755121e+00
-2.74676561e-01 -9.23839092e-01 5.32501101e-01 -2.71884471e-01
-1.77145636e+00 -5.99040203e-02 2.39583477e-02 -1.05344486e+00
-5.64326942e-01 -4.64109629e-01 -6.86460912e-01 6.83753729e-01
-1.39156258e+00 -6.54881239e-01 2.48859227e-01 7.61232257e-01
8.16814661e-01 -7.37323165e-02 5.27616382e-01 2.99469024e-01
-4.08428758e-01 9.97478664e-01 7.71110594e-01 -4.13302600e-01
8.66832972e-01 -1.61038339e+00 2.93344676e-01 1.20862730e-01
3.11180294e-01 7.02429295e-01 6.80105269e-01 -5.28339326e-01
-1.59110951e+00 -8.81316006e-01 -3.50732021e-02 -6.28421545e-01
8.93864453e-01 -7.62344182e-01 -7.10254312e-01 8.12638462e-01
-2.02146828e-01 1.54321697e-02 8.14652681e-01 7.73440599e-01
-3.14866304e-01 -1.13055944e-01 -9.16082919e-01 7.13221133e-01
1.24289250e+00 -3.87521863e-01 -1.05572321e-01 5.27653217e-01
7.62916386e-01 -1.16758540e-01 -7.11842418e-01 -1.55497283e-01
8.09494376e-01 -6.88953459e-01 8.78862917e-01 -1.30949104e+00
-4.95317057e-02 3.22798640e-01 -1.74214989e-01 -1.40616047e+00
-3.61512482e-01 -9.26988542e-01 -6.04025126e-01 9.06894267e-01
5.42204320e-01 -4.32228982e-01 8.11580539e-01 8.22831213e-01
2.17497423e-01 -7.70915687e-01 -3.64737660e-01 -9.07594800e-01
2.82463450e-02 -5.05263865e-01 4.58936423e-01 3.89630556e-01
-8.36610794e-02 2.37272874e-01 -1.03292537e+00 -1.46775305e-01
6.32916451e-01 3.32956851e-01 1.05465710e+00 -1.02325845e+00
-1.06676292e+00 -1.76915362e-01 4.96720642e-01 -1.89671528e+00
-1.58230793e-02 -5.55625618e-01 2.26269513e-01 -1.14380133e+00
-9.26903859e-02 -7.40304410e-01 -8.43494058e-01 2.47125074e-01
1.15175933e-01 -3.29352945e-01 -3.15152667e-02 3.48666102e-01
-1.20838797e+00 7.16550589e-01 1.15587914e+00 3.39372188e-01
-6.42153680e-01 8.44296873e-01 -8.34680438e-01 3.98443758e-01
7.11086273e-01 -3.49289864e-01 -8.66143823e-01 -3.77086876e-03
3.83880198e-01 1.62182942e-01 -1.95393544e-02 -4.37850237e-01
-2.79406309e-02 -7.05848634e-01 1.66211888e-01 -3.12602401e-01
1.38280794e-01 -5.63644826e-01 -4.84850109e-01 6.84056729e-02
-1.13083637e+00 -2.78458506e-01 -2.63139278e-01 1.33177245e+00
5.13486981e-01 1.46412486e-02 4.15037751e-01 -1.56685829e-01
-4.69389230e-01 6.31742656e-01 -4.27689999e-01 3.34977269e-01
6.24819398e-01 8.03709030e-02 -1.85938761e-01 -1.07951927e+00
-9.44409132e-01 4.99435812e-01 1.97956692e-02 5.68838716e-01
2.43100271e-01 -1.10506558e+00 -3.59348923e-01 5.05545586e-02
1.02590628e-01 -4.24343258e-01 1.63351312e-01 7.33806789e-01
2.58137882e-01 4.66830760e-01 7.55370595e-03 -2.96576887e-01
-6.04286373e-01 6.07370198e-01 1.77455574e-01 -4.78386462e-01
-4.35642570e-01 6.38835430e-01 2.52780408e-01 -5.64997554e-01
7.17506528e-01 -4.81965303e-01 -7.06761032e-02 -2.25561216e-01
4.35654938e-01 5.18974721e-01 -8.03393647e-02 1.52537048e-01
2.55937725e-01 -3.47402543e-01 -5.22247553e-01 -3.33709955e-01
1.29104412e+00 -3.19273204e-01 8.22341740e-01 8.72953713e-01
7.63848126e-01 1.39373496e-01 -1.88035703e+00 -6.28658950e-01
-5.55680133e-02 -7.53639281e-01 1.61397442e-01 -1.24927998e+00
-7.30408669e-01 4.30669397e-01 5.59286773e-01 5.10174990e-01
5.15867829e-01 4.01557982e-02 9.41373169e-01 4.92313564e-01
5.63179433e-01 -1.49740708e+00 3.62039000e-01 3.12104195e-01
5.87698877e-01 -1.40610886e+00 -1.02533184e-01 5.56500316e-01
-9.88904893e-01 6.61537230e-01 7.18901098e-01 -3.96177351e-01
1.01240361e+00 -1.83383688e-01 -6.60757273e-02 3.06762785e-01
-1.26829433e+00 -1.99495211e-01 1.90768912e-01 5.11367261e-01
5.33702195e-01 2.86265582e-01 -2.97885329e-01 9.76695061e-01
3.07002570e-02 1.33465409e-01 3.45315486e-01 8.70131910e-01
-4.85479027e-01 -1.31885242e+00 1.46396086e-01 1.04823351e+00
-4.35364574e-01 -2.30160225e-02 -3.89298797e-02 4.33534056e-01
-3.77849579e-01 9.34181929e-01 9.58426204e-03 -2.82275140e-01
3.91179681e-01 -1.08322062e-01 3.71121168e-01 -7.78698444e-01
-5.45451939e-01 2.17928439e-01 1.65982582e-02 -8.51641476e-01
5.06148823e-02 -7.14851558e-01 -8.65491450e-01 -2.59988397e-01
-4.17155415e-01 3.07637930e-01 5.86465657e-01 7.52311647e-01
4.99576479e-01 1.19234994e-01 9.79680479e-01 -9.07784879e-01
-1.65716660e+00 -1.05222666e+00 -9.79118764e-01 5.92257619e-01
4.31900799e-01 -7.30538130e-01 -5.78782439e-01 -5.16025245e-01] | [4.145132541656494, 2.328749895095825] |
df452f45-07e0-40b6-89c5-2ac7947a2177 | eamm-one-shot-emotional-talking-face-via | 2205.15278 | null | https://arxiv.org/abs/2205.15278v3 | https://arxiv.org/pdf/2205.15278v3.pdf | EAMM: One-Shot Emotional Talking Face via Audio-Based Emotion-Aware Motion Model | Although significant progress has been made to audio-driven talking face generation, existing methods either neglect facial emotion or cannot be applied to arbitrary subjects. In this paper, we propose the Emotion-Aware Motion Model (EAMM) to generate one-shot emotional talking faces by involving an emotion source video. Specifically, we first propose an Audio2Facial-Dynamics module, which renders talking faces from audio-driven unsupervised zero- and first-order key-points motion. Then through exploring the motion model's properties, we further propose an Implicit Emotion Displacement Learner to represent emotion-related facial dynamics as linearly additive displacements to the previously acquired motion representations. Comprehensive experiments demonstrate that by incorporating the results from both modules, our method can generate satisfactory talking face results on arbitrary subjects with realistic emotion patterns. | ['Xun Cao', 'Feng Xu', 'Wayne Wu', 'Qianyi Wu', 'Kaisiyuan Wang', 'Hang Zhou', 'Xinya Ji'] | 2022-05-30 | null | null | null | null | ['talking-face-generation'] | ['computer-vision'] | [-9.34938788e-02 2.16154858e-01 1.25022128e-01 -3.95694464e-01
-6.85900211e-01 -2.18582496e-01 5.96079886e-01 -1.07737410e+00
3.72949213e-01 4.48360264e-01 6.37821794e-01 4.62104082e-01
8.13066140e-02 -3.20471972e-01 -4.97732490e-01 -7.18540668e-01
1.11030741e-02 -6.69850186e-02 -5.40737987e-01 -3.65970075e-01
-6.36742786e-02 3.42730075e-01 -1.86711049e+00 3.69543970e-01
6.48174763e-01 9.34481919e-01 -1.61146253e-01 8.14514279e-01
2.91770920e-02 1.02898014e+00 -5.07070243e-01 -2.62273818e-01
-9.56787467e-02 -8.06283832e-01 -7.36168623e-01 4.43533838e-01
2.57632852e-01 -5.24458587e-01 -3.31165344e-01 6.77652776e-01
8.21641266e-01 3.77457231e-01 7.99609900e-01 -1.46898127e+00
-7.96195626e-01 1.52955756e-01 -5.47512770e-01 -2.84073591e-01
7.78948247e-01 1.76575497e-01 5.74908435e-01 -1.35929585e+00
1.00108373e+00 1.63238716e+00 6.43686950e-01 1.16005838e+00
-1.15099943e+00 -7.13694930e-01 1.88342348e-01 3.13896477e-01
-1.51593661e+00 -8.25389564e-01 1.41782534e+00 -3.95784557e-01
5.67375481e-01 1.61041245e-01 9.07675087e-01 1.44589424e+00
8.55271891e-02 8.59741986e-01 6.47403121e-01 -1.32114485e-01
1.83385119e-01 -8.89309198e-02 -5.49940705e-01 4.80107874e-01
-7.16466367e-01 6.45913258e-02 -8.19454253e-01 -9.77232307e-02
6.89090967e-01 -3.32802683e-01 -2.74155438e-01 -2.95995563e-01
-9.65273857e-01 7.85446465e-01 6.03593923e-02 2.30677381e-01
-7.57018268e-01 3.96240652e-01 3.01776081e-01 1.26756102e-01
7.09183335e-01 -7.96257108e-02 1.40702590e-01 -3.63466501e-01
-1.13785231e+00 4.51856613e-01 5.07428944e-01 8.72990847e-01
5.56218147e-01 5.78002930e-01 -1.84089407e-01 7.42276490e-01
3.92534763e-01 4.92262870e-01 4.68231052e-01 -1.53546667e+00
-2.57915854e-01 6.78130686e-02 2.02041239e-01 -1.40381992e+00
-2.64571100e-01 1.78842887e-01 -8.99241626e-01 1.08000979e-01
-1.42842159e-01 -3.91012460e-01 -6.79167211e-01 2.06103826e+00
6.70651197e-01 7.28605747e-01 1.68776110e-01 1.11946714e+00
1.02950490e+00 9.44585621e-01 1.38903052e-01 -7.02119827e-01
1.09794700e+00 -7.09667385e-01 -1.37800753e+00 3.39827836e-01
3.23081106e-01 -6.91274166e-01 9.60908115e-01 5.10011613e-01
-1.32837749e+00 -7.53385782e-01 -6.53743327e-01 -4.73447551e-04
2.80702621e-01 1.50862306e-01 5.76164126e-01 3.83327693e-01
-1.20172262e+00 4.11302447e-01 -7.34477937e-01 -9.27751064e-02
3.00541371e-01 1.70126960e-01 -3.68987143e-01 1.84703663e-01
-1.14592445e+00 5.23328662e-01 -2.99881309e-01 2.88709015e-01
-1.15377879e+00 -8.78542840e-01 -9.51885283e-01 -1.26663387e-01
-2.17938283e-03 -7.99637079e-01 1.38438082e+00 -1.47243893e+00
-2.08570862e+00 5.33043265e-01 -7.45567560e-01 1.10393114e-01
4.02614027e-01 -3.55411321e-01 -4.73249316e-01 6.62882030e-01
-2.57195920e-01 1.40129602e+00 1.33792579e+00 -1.61556971e+00
-5.14506102e-02 -2.37510633e-03 -3.83486122e-01 2.01015562e-01
-5.37169337e-01 1.81956664e-01 -3.26795846e-01 -1.05725658e+00
-9.75539237e-02 -9.90234256e-01 -7.86309168e-02 2.60318339e-01
-2.97051936e-01 -2.04710975e-01 1.31037068e+00 -7.04992354e-01
1.14084756e+00 -2.29380202e+00 4.87854093e-01 1.14621874e-02
-4.28405497e-03 3.07681151e-02 -3.25481057e-01 2.73206323e-01
-4.01966989e-01 -5.45641333e-02 -1.38809845e-01 -6.29601121e-01
-7.81059191e-02 1.48073910e-03 -5.29288530e-01 2.56376922e-01
6.54215395e-01 9.95216429e-01 -7.71909773e-01 -5.87931573e-01
9.89885777e-02 1.37121046e+00 -1.13558602e+00 1.80786014e-01
-9.98007432e-02 9.47521567e-01 -3.09139758e-01 5.54159105e-01
6.95924222e-01 1.98578984e-01 5.96349053e-02 -2.19656989e-01
9.85725224e-02 -2.23591357e-01 -1.11129439e+00 1.89546669e+00
-4.09539193e-01 6.84418559e-01 3.21955085e-01 -6.33391857e-01
8.48375261e-01 9.04486120e-01 9.45577562e-01 -1.83720067e-01
2.81042218e-01 -1.28426924e-01 -2.70648152e-01 -8.43062580e-01
3.87230009e-01 -6.45422280e-01 1.24778017e-01 1.89365432e-01
2.45438337e-01 -3.61248046e-01 -3.09691876e-01 1.60803497e-01
6.46507561e-01 4.79823858e-01 -2.08891749e-01 -1.54023515e-02
7.66999662e-01 -4.06810105e-01 5.79567790e-01 -1.62261531e-01
-2.77902097e-01 7.92693019e-01 3.25209916e-01 -1.91240668e-01
-6.03181362e-01 -9.37580585e-01 8.06038678e-02 9.82193291e-01
-1.19553305e-01 -5.21077931e-01 -1.09408772e+00 -1.93542451e-01
-2.22261921e-01 5.57094276e-01 -6.54724240e-01 -4.00833130e-01
-5.32927275e-01 -2.85823554e-01 4.26378757e-01 5.24583042e-01
2.75852382e-01 -1.35569334e+00 -3.89721334e-01 2.52834588e-01
-7.58516788e-01 -1.00805950e+00 -7.87186801e-01 -8.17682326e-01
-5.14535308e-01 -4.61920112e-01 -1.06311846e+00 -7.67476678e-01
5.30667663e-01 2.00144649e-01 5.90996146e-01 -2.97018439e-01
-4.46788043e-01 9.64064300e-01 -2.42059931e-01 -2.96035677e-01
-2.94643551e-01 -5.91353893e-01 5.62104106e-01 6.85305536e-01
1.05283342e-01 -9.43076074e-01 -7.29682028e-01 1.35292947e-01
-8.36749017e-01 -2.31424365e-02 6.99974000e-02 6.85021579e-01
3.79775614e-01 -1.95339620e-01 9.67608750e-01 -1.00037470e-01
5.87425292e-01 -3.78139436e-01 7.36927688e-02 -3.10024619e-01
7.23025277e-02 -2.06775472e-01 4.28313673e-01 -9.69435573e-01
-1.48898375e+00 3.27605695e-01 -3.46247554e-01 -1.17644668e+00
-1.78341851e-01 2.47988328e-01 -4.49341446e-01 -6.67693019e-02
3.18460464e-01 1.89298421e-01 2.85016507e-01 -5.18447123e-02
7.54759967e-01 4.55822647e-01 9.34506774e-01 -6.28487051e-01
7.06559360e-01 8.56388986e-01 -1.19714200e-01 -1.25011194e+00
-3.97111773e-01 -2.01310858e-01 -4.93623614e-01 -8.41441393e-01
1.08069468e+00 -1.04596698e+00 -1.23013282e+00 5.26374578e-01
-1.25356376e+00 -8.77802745e-02 -2.33678013e-01 4.92762059e-01
-1.04686594e+00 4.27049369e-01 -7.43863881e-01 -1.23609853e+00
-2.95916975e-01 -9.97567892e-01 1.36286950e+00 -1.71205625e-02
-6.87321126e-01 -6.92594349e-01 9.12215412e-02 1.85940459e-01
3.60863537e-01 4.53604549e-01 4.36041236e-01 2.34733820e-01
-3.08164954e-01 1.54060507e-02 3.92595589e-01 3.06757450e-01
7.71751404e-02 3.81026566e-01 -1.20339489e+00 -5.63124232e-02
1.33675188e-01 -5.39494574e-01 5.59656262e-01 6.57071829e-01
1.14986336e+00 -5.10533094e-01 -1.16919227e-01 6.26171589e-01
7.10034549e-01 1.24925554e-01 8.14499021e-01 -4.26527977e-01
6.14082456e-01 1.21870792e+00 5.54560483e-01 8.07479560e-01
3.50008488e-01 7.34226823e-01 2.92911738e-01 1.46295071e-01
-7.56775448e-03 -3.97850901e-01 7.53871739e-01 1.18789697e+00
-4.07869846e-01 1.12510491e-02 -4.90965843e-01 5.38151920e-01
-1.58766758e+00 -1.44830739e+00 1.98787495e-01 1.62417984e+00
5.76329291e-01 -4.60720330e-01 2.34114349e-01 1.19356431e-01
5.51924586e-01 8.35979655e-02 -5.35661638e-01 -2.55380005e-01
1.40946452e-02 1.58708781e-01 -7.92955756e-01 5.39389610e-01
-9.10085618e-01 1.10082591e+00 6.69144487e+00 7.00405598e-01
-1.40040016e+00 -1.24482065e-01 4.87349510e-01 -4.77722853e-01
-6.22202456e-01 -1.54049203e-01 -4.39413309e-01 2.39453688e-01
9.20752347e-01 -4.00890052e-01 2.69108742e-01 8.84208560e-01
8.06422710e-01 5.02533257e-01 -8.28144372e-01 1.21051049e+00
2.44969785e-01 -1.08537650e+00 4.23810840e-01 1.67373437e-02
5.78881621e-01 -9.68766510e-01 3.56270224e-01 2.79337227e-01
-9.31009352e-02 -1.17790008e+00 8.57442498e-01 8.59001696e-01
9.96480525e-01 -1.05464101e+00 1.62398160e-01 2.75797933e-01
-1.34213495e+00 -2.45006382e-02 -5.81946634e-02 1.20574929e-01
6.77605510e-01 2.22275674e-01 -4.00955468e-01 5.36128879e-01
6.57739341e-01 8.00531805e-01 2.45631188e-01 3.71449709e-01
2.96120252e-02 5.94965875e-01 -8.23784918e-02 2.80557275e-01
9.49928537e-03 -1.79874346e-01 7.36921489e-01 1.03941274e+00
7.41813779e-01 6.18431330e-01 -1.07246183e-01 8.82860601e-01
7.43026733e-02 2.00138763e-01 -7.55667448e-01 -1.01002604e-01
2.78469145e-01 1.45329428e+00 -3.08361828e-01 -2.07362056e-01
-1.81284085e-01 1.26043618e+00 -1.46470338e-01 5.75738609e-01
-1.03362179e+00 -9.98670310e-02 1.18634307e+00 2.38701981e-02
2.23164871e-01 -1.77105203e-01 3.61346394e-01 -1.13587904e+00
-1.02149181e-01 -8.42243195e-01 -4.42999415e-02 -1.18521237e+00
-9.73047435e-01 5.41787565e-01 -1.25118181e-01 -1.35768795e+00
-6.12351894e-01 -3.59002322e-01 -8.11438620e-01 5.28635919e-01
-1.16463673e+00 -1.27195787e+00 -3.66506577e-01 9.27563667e-01
5.88312089e-01 6.44782111e-02 8.28920960e-01 2.65479445e-01
-3.27317327e-01 5.98990023e-01 -5.66972554e-01 -1.09177209e-01
8.22902083e-01 -5.11520505e-01 -3.57435532e-02 3.82446527e-01
-9.96292382e-02 6.40409768e-01 8.90897155e-01 -4.79248464e-01
-1.56384933e+00 -1.07583869e+00 6.18061960e-01 -3.84212047e-01
3.13932300e-01 -2.77437329e-01 -1.02499902e+00 6.05640650e-01
3.79309654e-01 2.38238387e-02 8.49854648e-01 -4.92249936e-01
-8.80546719e-02 1.06921144e-01 -1.16480100e+00 7.84245789e-01
1.31455874e+00 -5.31924963e-01 -4.34311956e-01 -1.59650892e-02
7.29658186e-01 -1.27942339e-01 -8.54644477e-01 5.67115724e-01
6.64082348e-01 -6.91087425e-01 1.08559465e+00 -7.36274958e-01
7.85012662e-01 -2.06056371e-01 -8.58930945e-02 -1.32953250e+00
-2.35403165e-01 -1.23298967e+00 -3.88987929e-01 1.45082319e+00
-1.04725145e-01 -1.49003878e-01 6.53636277e-01 3.58254671e-01
-6.11850694e-02 -5.24819314e-01 -9.77439821e-01 -4.80601579e-01
2.31927738e-01 -6.76513851e-01 4.52486426e-01 1.19659984e+00
2.18491316e-01 1.74397990e-01 -9.35898483e-01 6.76669031e-02
3.78388256e-01 -6.88434318e-02 1.03947687e+00 -1.02231729e+00
-1.68979317e-01 -3.44614297e-01 -4.74291205e-01 -9.21445251e-01
6.82135224e-01 -5.59203506e-01 3.04998636e-01 -1.16010869e+00
8.66216049e-02 3.27118725e-01 1.75266534e-01 2.32673571e-01
-2.14963108e-01 3.31142992e-01 1.44294307e-01 -4.77794111e-02
-2.52983868e-01 1.29271579e+00 1.54895079e+00 1.76681131e-01
-2.82829881e-01 -3.82211298e-01 -6.23807132e-01 1.02075458e+00
3.01683009e-01 -4.78173494e-02 -6.69830918e-01 1.14990093e-01
-6.79669157e-02 5.04038990e-01 5.69603145e-01 -9.68040586e-01
4.98739593e-02 -2.19178945e-01 4.29196805e-01 -5.17842352e-01
9.13893521e-01 -5.35060346e-01 3.92495543e-01 8.33230019e-02
-3.43922019e-01 -7.01251030e-02 3.08938056e-01 5.01272678e-01
-4.02899206e-01 4.47732210e-01 7.70911038e-01 1.28814548e-01
-3.81005168e-01 4.14774388e-01 -8.17937315e-01 -2.16954455e-01
1.09895396e+00 -1.66015044e-01 2.85315841e-01 -1.30967224e+00
-1.22127044e+00 1.03869587e-01 1.86981186e-01 5.51875055e-01
8.70194733e-01 -1.84380531e+00 -8.20048213e-01 2.18780667e-01
-2.80467272e-02 -2.69739300e-01 8.60752344e-01 8.07251573e-01
-9.37171802e-02 1.26458958e-01 -2.19540000e-01 -6.41523242e-01
-1.32890046e+00 6.67158782e-01 2.20701233e-01 4.26747829e-01
-7.29802430e-01 8.62606227e-01 4.58669364e-01 -4.27536070e-01
1.25135884e-01 1.00679241e-01 -2.68923819e-01 2.50879169e-01
7.12510169e-01 2.42037475e-01 -4.68395025e-01 -1.23833263e+00
-2.57704943e-01 9.46903467e-01 3.69189560e-01 -6.37310445e-01
1.22956157e+00 -1.72253728e-01 1.60635605e-01 4.61393714e-01
1.24967158e+00 1.23901367e-01 -1.53285825e+00 3.84056509e-01
-4.38293219e-01 -3.03317726e-01 -1.83807164e-01 -1.26650155e-01
-1.15476727e+00 1.19382596e+00 3.98891777e-01 -2.82247573e-01
1.50587833e+00 -1.89983577e-01 8.96287382e-01 2.93801595e-02
2.09678099e-01 -9.74065542e-01 7.13525176e-01 2.73237318e-01
1.23860443e+00 -8.09365451e-01 -5.48491836e-01 -7.10626483e-01
-1.03827596e+00 9.64902937e-01 6.32323623e-01 -2.60606501e-02
7.87657976e-01 2.62212485e-01 2.68969893e-01 -6.84287027e-02
-1.14450097e+00 3.12029757e-02 3.55096191e-01 7.60577023e-01
5.53332508e-01 -2.68479556e-01 -2.10648015e-01 7.71236241e-01
-4.34634715e-01 4.66332674e-01 3.61025304e-01 6.97817862e-01
-2.09120557e-01 -6.71945572e-01 -5.59924603e-01 -2.68845588e-01
-3.60355437e-01 2.00452343e-01 -6.15473688e-01 6.14120126e-01
-1.00254565e-01 1.01393878e+00 1.08295396e-01 -5.91393054e-01
2.61338919e-01 6.04454756e-01 5.32932699e-01 -8.60296190e-02
-1.17718138e-01 4.74093974e-01 -1.91360921e-01 -7.32429624e-01
-5.99386632e-01 -6.62830174e-01 -1.44371474e+00 -4.65797275e-01
3.17722559e-02 2.02116519e-02 3.94839883e-01 6.51153207e-01
8.68319631e-01 5.22374213e-01 7.61371851e-01 -1.56798375e+00
-1.35280848e-01 -9.73436654e-01 -3.79120976e-01 5.74147880e-01
3.99788320e-01 -8.80861878e-01 -5.65905511e-01 6.20501935e-01] | [13.104826927185059, -0.34004727005958557] |
7513a0fe-862d-4bb1-a22d-8c59e183879d | rank-position-forecasting-in-car-racing | 2010.01707 | null | https://arxiv.org/abs/2010.01707v2 | https://arxiv.org/pdf/2010.01707v2.pdf | Rank Position Forecasting in Car Racing | Forecasting is challenging since uncertainty resulted from exogenous factors exists. This work investigates the rank position forecasting problem in car racing, which predicts the rank positions at the future laps for cars. Among the many factors that bring changes to the rank positions, pit stops are critical but irregular and rare. We found existing methods, including statistical models, machine learning regression models, and state-of-the-art deep forecasting model based on encoder-decoder architecture, all have limitations in the forecasting. By elaborative analysis of pit stops events, we propose a deep model, RankNet, with the cause effects decomposition that modeling the rank position sequence and pit stop events separately. It also incorporates probabilistic forecasting to model the uncertainty inside each sub-model. Through extensive experiments, RankNet demonstrates a strong performance improvement over the baselines, e.g., MAE improves more than 10% consistently, and is also more stable when adapting to unseen new data. Details of model optimization, performance profiling are presented. It is promising to provide useful forecasting tools for the car racing analysis and shine a light on solutions to similar challenging issues in general forecasting problems. | ['Judy Qiu', 'Ohno Yoshiyuki', 'Takuya Araki', 'Fugang Wang', 'Selahattin Akkas', 'Jiayu Li', 'Bo Peng'] | 2020-10-04 | null | null | null | null | ['carracing-v0'] | ['playing-games'] | [-1.95688874e-01 -1.20193303e-01 -5.05691648e-01 -1.13975620e+00
-1.04678082e+00 -2.78495461e-01 7.39046574e-01 -4.14256603e-01
1.83049053e-01 6.99137330e-01 5.55541754e-01 -3.71484101e-01
-1.71301305e-01 -6.90225422e-01 -1.12053144e+00 -6.69100285e-01
-4.80580293e-02 7.71883368e-01 4.18597579e-01 -4.81957078e-01
5.56032538e-01 3.43508154e-01 -2.05025172e+00 6.89644217e-01
6.10506058e-01 1.37234390e+00 1.98435828e-01 5.18105209e-01
-1.08328104e-01 1.29222214e+00 -3.82704228e-01 -6.57251894e-01
1.17175482e-01 2.62967348e-01 -3.25315684e-01 -4.12013084e-01
5.34558892e-01 -5.83932877e-01 -8.87242138e-01 4.46209133e-01
7.42211491e-02 3.72095108e-01 5.88993192e-01 -1.68682098e+00
-5.36994338e-01 1.06439722e+00 -2.66357243e-01 2.32557610e-01
-1.61405846e-01 8.51844177e-02 8.79040658e-01 -7.09872723e-01
1.48199230e-01 1.44054580e+00 9.14846480e-01 5.41469336e-01
-6.06797457e-01 -8.16439748e-01 6.41136289e-01 8.50588560e-01
-1.00049174e+00 -3.51746202e-01 8.10376346e-01 -6.81156874e-01
8.11796069e-01 2.68430352e-01 4.56052944e-02 1.34362900e+00
8.54233980e-01 1.17078876e+00 8.73426497e-01 2.06927761e-01
-1.55877635e-01 -1.14421815e-01 5.03390133e-01 3.42242539e-01
-2.54944235e-01 7.87008762e-01 -8.34997356e-01 2.09778368e-01
4.36777733e-02 1.93910494e-01 5.94838381e-01 2.66914606e-01
-1.09746850e+00 6.70987666e-01 1.95954651e-01 -8.05316195e-02
-2.63136268e-01 8.92439902e-01 3.52893859e-01 -1.68568030e-01
7.39281833e-01 1.49584889e-01 -9.36021268e-01 -5.91930926e-01
-1.14137650e+00 6.29214227e-01 5.38200319e-01 1.26566672e+00
4.68575209e-01 2.21773103e-01 -7.25496233e-01 6.29895806e-01
3.09041560e-01 5.76153219e-01 1.46410257e-01 -1.00851727e+00
4.48290318e-01 -1.72410682e-02 9.18402821e-02 -1.14510202e+00
-6.43355668e-01 -5.46689153e-01 -5.51763177e-01 -1.76589012e-01
1.16025306e-01 -2.20979542e-01 -1.20354760e+00 1.73876333e+00
-1.84293911e-01 8.09241176e-01 -3.57988566e-01 6.20638967e-01
9.28921878e-01 1.13443041e+00 2.41132706e-01 1.56608075e-01
1.19858348e+00 -1.28036523e+00 -1.03196871e+00 -5.00990033e-01
2.35370502e-01 -8.42006683e-01 5.48201621e-01 5.15452147e-01
-8.53037894e-01 -9.46743667e-01 -8.46660733e-01 1.70616042e-02
-6.13119245e-01 3.58388513e-01 9.20705736e-01 4.87318456e-01
-7.58858263e-01 7.20431089e-01 -9.28742886e-01 3.66638243e-01
1.71194047e-01 2.52223730e-01 2.74385154e-01 -5.54589890e-02
-1.60741508e+00 1.22747493e+00 2.22930819e-01 6.81287706e-01
-1.28266144e+00 -9.06440079e-01 -8.46899152e-01 9.49180406e-03
5.18786788e-01 -4.61722940e-01 1.58907545e+00 -3.31789911e-01
-1.58410013e+00 1.28763095e-01 -7.47893512e-01 -6.18275702e-01
3.04653376e-01 -4.08286929e-01 -1.00638974e+00 -8.51718664e-01
4.90490571e-02 6.16084039e-01 5.68401933e-01 -1.19281530e+00
-1.25999868e+00 9.34008416e-03 -3.51197243e-01 -2.35719487e-01
3.00428659e-01 -2.30149888e-02 -5.95865607e-01 -5.72682977e-01
-1.45193666e-01 -1.11554110e+00 -4.35788870e-01 -1.06421614e+00
-4.52388555e-01 -5.16446948e-01 5.86250782e-01 -8.04160953e-01
1.66817975e+00 -1.97978365e+00 -4.09151852e-01 1.13993809e-01
1.90230235e-02 -9.44444388e-02 -4.18613628e-02 3.50195646e-01
2.14056641e-01 -5.87466024e-02 2.92573869e-01 -3.93080831e-01
4.30558801e-01 4.19865698e-01 -9.17355001e-01 5.71282655e-02
2.83375055e-01 1.09756815e+00 -9.07101333e-01 -1.16650127e-01
2.60322601e-01 3.17519754e-01 -3.78090262e-01 2.24930570e-01
-3.99621844e-01 3.21990967e-01 -5.28635323e-01 7.28416920e-01
8.64988387e-01 3.96150872e-02 -3.17585945e-01 -2.90627718e-01
-3.27573121e-01 6.12774551e-01 -8.92184794e-01 1.11474895e+00
-5.07710576e-01 9.71651495e-01 -5.61621308e-01 -7.20306873e-01
1.11325097e+00 -5.50205931e-02 3.89193922e-01 -4.73643601e-01
8.36926326e-02 9.79906842e-02 -1.67193457e-01 -4.56271082e-01
1.13637996e+00 -5.04113454e-03 -5.14513016e-01 -2.96876639e-01
-1.97536826e-01 8.23848471e-02 3.06630045e-01 2.10259333e-02
9.25504863e-01 2.24522740e-01 -5.57056189e-01 2.51098685e-02
2.36133635e-01 6.70805946e-02 9.86739874e-01 7.13253558e-01
-1.02641203e-01 5.94356596e-01 6.80729628e-01 -8.90869379e-01
-7.27637112e-01 -8.86859894e-01 -4.04997706e-01 1.42191827e+00
2.91517317e-01 -3.86683226e-01 -2.08395854e-01 -5.73992968e-01
3.27094615e-01 1.47248793e+00 -8.34184825e-01 -2.67538041e-01
-8.14716756e-01 -9.82919037e-01 2.94262379e-01 1.05989730e+00
3.32525909e-01 -8.08176875e-01 5.99122234e-03 4.43504900e-01
-1.93698779e-01 -1.21328139e+00 -3.99082452e-01 2.17346698e-01
-6.02877378e-01 -8.17395329e-01 -1.46149442e-01 -4.63566363e-01
1.70827076e-01 -1.11745290e-01 1.49934363e+00 -2.80311167e-01
2.12146536e-01 5.07871760e-03 -6.06787913e-02 -6.55474961e-01
-4.31085616e-01 1.57853276e-01 -1.62356475e-03 5.18833175e-02
6.60896480e-01 -7.08480030e-02 -4.84399021e-01 8.59394312e-01
-5.25812268e-01 1.95977047e-01 5.49900830e-01 7.78231800e-01
5.49956799e-01 2.51730770e-01 7.38246024e-01 -1.06490135e+00
3.82784516e-01 -8.49759996e-01 -7.64642477e-01 6.15378246e-02
-9.88822579e-01 1.80622950e-01 3.71365309e-01 -2.21331120e-01
-1.43868804e+00 7.39280730e-02 -4.52886879e-01 -2.96127617e-01
-1.96599633e-01 4.24933016e-01 8.38698521e-02 7.22042918e-01
2.29231179e-01 3.79015170e-02 -3.30015838e-01 -4.32871848e-01
5.95483899e-01 4.94713485e-01 7.24401355e-01 -4.35455561e-01
5.80153763e-01 2.99032032e-01 -2.95304433e-02 -7.15571120e-02
-1.19175351e+00 -4.14737672e-01 -3.40033650e-01 -5.09433568e-01
6.84776306e-01 -1.01779592e+00 -7.97348797e-01 4.26986963e-01
-1.20164442e+00 -2.66360492e-01 4.98249568e-02 4.09816861e-01
-5.14824748e-01 -2.09186509e-01 -7.30175197e-01 -9.66020405e-01
2.43011862e-01 -1.46917176e+00 1.30231643e+00 1.11712568e-01
9.38713457e-03 -7.91402161e-01 -4.19400483e-02 5.32422304e-01
6.18223965e-01 3.51815999e-01 7.77129054e-01 -5.53221464e-01
-8.65676403e-01 -6.24033689e-01 5.05980477e-02 2.37054527e-01
-3.31083685e-01 3.21377516e-01 -9.95255053e-01 2.93107450e-01
-5.01074612e-01 1.73417896e-01 1.31935203e+00 7.85678506e-01
1.73226368e+00 -1.30781993e-01 -7.28434324e-01 3.93861353e-01
7.48354137e-01 5.05429447e-01 7.80027568e-01 2.44136617e-01
6.61605299e-01 7.72823334e-01 1.06295860e+00 4.38702345e-01
8.89097035e-01 7.83818483e-01 6.51825130e-01 1.19215272e-01
1.74462721e-02 -6.15910172e-01 5.87955713e-01 8.79794538e-01
2.60139704e-02 -5.60889304e-01 -9.04258728e-01 5.84258139e-01
-2.26436305e+00 -1.08626187e+00 -5.42483628e-01 1.78427970e+00
4.07744199e-01 2.79226691e-01 -6.61587268e-02 -2.98631877e-01
6.79839849e-01 3.41977209e-01 -7.43692636e-01 -5.16192794e-01
-9.39296037e-02 -4.86946523e-01 9.45989966e-01 4.14364845e-01
-1.26956713e+00 1.16162658e+00 7.40045118e+00 1.05422378e+00
-7.56420910e-01 7.48083144e-02 1.35529673e+00 -1.72680974e-01
-4.46397036e-01 -7.58906379e-02 -1.59294367e+00 9.95833635e-01
1.42639697e+00 1.44756883e-01 3.86968642e-01 1.12273860e+00
2.64058858e-01 1.20793171e-01 -1.20439124e+00 7.90506005e-01
1.01369480e-02 -1.53313780e+00 -2.18158200e-01 -1.44377232e-01
9.32095766e-01 3.98714751e-01 5.53001821e-01 9.35322165e-01
7.05538511e-01 -1.08382535e+00 8.83335114e-01 9.53675508e-01
4.57186371e-01 -1.02884853e+00 1.18967104e+00 3.73286486e-01
-1.01022744e+00 -3.71434122e-01 -4.73273039e-01 2.75063869e-02
7.92889416e-01 9.27093327e-01 -6.09852552e-01 2.93526769e-01
7.35683441e-01 9.31673408e-01 -4.28636223e-01 6.94910526e-01
-2.31985286e-01 1.02022481e+00 -1.92406267e-01 -8.64840522e-02
3.73960108e-01 1.87725887e-01 2.89165705e-01 1.24748099e+00
6.49267137e-01 -2.39091888e-02 5.51838502e-02 6.69027388e-01
1.36095315e-01 -4.12520170e-01 -4.08795267e-01 2.44326264e-01
3.77388418e-01 1.05466568e+00 -1.48529381e-01 -4.06254351e-01
-2.74131030e-01 3.41917574e-01 -1.09113395e-01 2.16882050e-01
-1.50789666e+00 5.75875491e-02 8.60577941e-01 3.32161814e-01
3.84889483e-01 -2.30810046e-01 -3.37743551e-01 -7.51926005e-01
-2.01205820e-01 -6.12972498e-01 2.82816857e-01 -8.87179017e-01
-1.38934505e+00 8.90239954e-01 3.27855527e-01 -1.27996874e+00
-6.34801924e-01 -8.42623591e-01 -6.22978210e-01 4.65824395e-01
-1.82627356e+00 -1.15944088e+00 -3.80164683e-02 5.28611876e-02
9.57983553e-01 -4.45194632e-01 1.84383363e-01 3.68588358e-01
-6.40246689e-01 6.36860132e-01 3.09573203e-01 -3.26706797e-01
6.61903918e-01 -1.15320194e+00 8.00447226e-01 7.68146396e-01
-3.82551923e-02 2.81938881e-01 8.64199996e-01 -8.64367783e-01
-1.28524518e+00 -1.30477989e+00 1.40300381e+00 -1.16201127e+00
6.90082133e-01 -3.27850342e-01 -5.44435918e-01 7.20763445e-01
6.56007379e-02 -2.68784940e-01 4.65951204e-01 5.30779600e-01
-1.42681107e-01 -4.51811373e-01 -6.36461616e-01 3.80384177e-01
8.40901077e-01 -9.85365883e-02 -4.44447577e-01 4.99864787e-01
9.45305288e-01 -7.25270569e-01 -6.71767473e-01 6.69632137e-01
6.39303684e-01 -8.05005968e-01 8.97317231e-01 -7.20009685e-01
9.05229509e-01 -3.83093268e-01 -2.17422217e-01 -1.34781229e+00
-7.96579182e-01 -7.08400965e-01 -4.07125741e-01 1.25430632e+00
7.88500190e-01 -4.16660339e-01 7.35106766e-01 1.08847106e+00
-7.59937167e-01 -9.08346534e-01 -1.02072895e+00 -6.80063844e-01
8.34585875e-02 -1.27745998e+00 1.31235111e+00 3.26994926e-01
-4.47525561e-01 1.63842514e-01 -1.08862615e+00 4.03918207e-01
2.27679387e-01 -7.73499999e-03 7.89139807e-01 -1.08982468e+00
-9.59148481e-02 -5.40240228e-01 -3.26225966e-01 -1.52199662e+00
5.16189814e-01 -6.29904389e-01 5.39645970e-01 -1.28437173e+00
-9.09386203e-02 -5.27893066e-01 -7.22878277e-01 2.83960372e-01
-1.92105234e-01 -1.70658320e-01 9.82925817e-02 2.94089280e-02
-8.20306003e-01 6.90049946e-01 1.04317880e+00 -2.32789993e-01
1.12003043e-01 6.11151099e-01 -7.13572621e-01 6.75355911e-01
7.81910360e-01 -4.62115794e-01 -2.55454361e-01 -6.29921734e-01
5.55200696e-01 8.78458470e-02 1.74355984e-01 -9.07922029e-01
3.57630700e-01 -3.33272219e-01 2.98495591e-01 -1.48822415e+00
3.84890020e-01 -8.33290637e-01 3.12352151e-01 6.16049245e-02
-5.86070478e-01 4.07493085e-01 5.05230725e-01 9.80131209e-01
-3.99651617e-01 4.98435088e-02 9.72609520e-02 4.04006720e-01
-1.28829348e+00 5.36534250e-01 -6.43565297e-01 -3.67823064e-01
8.01154077e-01 6.22263439e-02 -3.74554932e-01 -6.82793677e-01
-5.52792907e-01 7.65442431e-01 -4.18865263e-01 1.07107615e+00
4.16339248e-01 -1.61090899e+00 -8.27848196e-01 1.14362881e-01
1.33524209e-01 -2.20087901e-01 5.31189978e-01 5.80072343e-01
-7.66750500e-02 8.44910860e-01 2.64093131e-01 -4.82312560e-01
-6.94411814e-01 5.91592610e-01 2.63757794e-03 -4.90912050e-01
-9.53318626e-02 9.69145417e-01 1.60647556e-01 -4.06392664e-01
4.00634497e-01 -7.51366973e-01 -4.15918678e-01 8.13829303e-02
5.62871218e-01 8.05446863e-01 2.09922776e-01 -7.32310653e-01
-3.37389529e-01 3.67474675e-01 -1.07267790e-01 2.70179242e-01
1.37594867e+00 -1.92989692e-01 1.24315314e-01 7.08975375e-01
8.94854069e-01 -2.95441538e-01 -1.71950877e+00 -9.08339769e-02
2.00041771e-01 -5.06770432e-01 4.13961738e-01 -1.21183097e+00
-1.28043950e+00 7.99146295e-01 6.69244230e-01 -3.94684039e-02
8.28231990e-01 4.30366509e-02 1.29708242e+00 2.23553315e-01
3.23054224e-01 -1.31592083e+00 -4.33709234e-01 9.34504747e-01
8.74932706e-01 -1.57383120e+00 -2.69112080e-01 -1.99532807e-01
-9.53847110e-01 1.06128573e+00 6.60030961e-01 1.29232213e-01
1.06523860e+00 4.26184237e-01 4.03490141e-02 -2.59672523e-01
-1.43308544e+00 -1.99760161e-02 7.38613546e-01 2.96571195e-01
4.41574812e-01 5.83024919e-01 4.85801771e-02 1.19648719e+00
-4.23678160e-01 -3.53839844e-02 9.79836434e-02 2.84517407e-01
-3.54838729e-01 -1.05261242e+00 -2.44510815e-01 7.46866345e-01
-3.33108693e-01 -2.12401614e-01 2.22951576e-01 6.25737786e-01
5.01267791e-01 1.27690411e+00 3.49064201e-01 -8.84572566e-01
5.17244518e-01 1.40446723e-01 -1.99982941e-01 -2.47616246e-01
-5.61825573e-01 -1.94034442e-01 4.03673261e-01 -8.06333482e-01
1.56691641e-01 -8.10305774e-01 -1.01557994e+00 -7.26240635e-01
-3.36937934e-01 -7.46446401e-02 9.36236084e-01 8.65013003e-01
5.38969457e-01 1.00507116e+00 7.44133413e-01 -8.39576662e-01
-4.86041278e-01 -9.63290691e-01 -4.13693845e-01 2.03933820e-01
2.73701400e-01 -9.50696111e-01 -4.33275491e-01 1.17411107e-01] | [6.546148300170898, 1.706516146659851] |
14ea5221-f450-4156-a168-ba4201189f56 | self-supervised-visual-representation-2 | 2205.15288 | null | https://arxiv.org/abs/2205.15288v2 | https://arxiv.org/pdf/2205.15288v2.pdf | Self-Supervised Visual Representation Learning with Semantic Grouping | In this paper, we tackle the problem of learning visual representations from unlabeled scene-centric data. Existing works have demonstrated the potential of utilizing the underlying complex structure within scene-centric data; still, they commonly rely on hand-crafted objectness priors or specialized pretext tasks to build a learning framework, which may harm generalizability. Instead, we propose contrastive learning from data-driven semantic slots, namely SlotCon, for joint semantic grouping and representation learning. The semantic grouping is performed by assigning pixels to a set of learnable prototypes, which can adapt to each sample by attentive pooling over the feature and form new slots. Based on the learned data-dependent slots, a contrastive objective is employed for representation learning, which enhances the discriminability of features, and conversely facilitates grouping semantically coherent pixels together. Compared with previous efforts, by simultaneously optimizing the two coupled objectives of semantic grouping and contrastive learning, our approach bypasses the disadvantages of hand-crafted priors and is able to learn object/group-level representations from scene-centric images. Experiments show our approach effectively decomposes complex scenes into semantic groups for feature learning and significantly benefits downstream tasks, including object detection, instance segmentation, and semantic segmentation. Code is available at: https://github.com/CVMI-Lab/SlotCon. | ['Xiaojuan Qi', 'Xiangyu Zhang', 'Anlin Zheng', 'Bingchen Zhao', 'Xin Wen'] | 2022-05-30 | null | null | null | null | ['unsupervised-semantic-segmentation', 'unsupervised-pre-training'] | ['computer-vision', 'methodology'] | [ 3.82358670e-01 7.53058717e-02 -3.78249496e-01 -5.86219966e-01
-7.55422115e-01 -3.64663720e-01 5.99809825e-01 2.49659851e-01
-4.99064475e-01 2.84893543e-01 5.53582683e-02 -8.13192129e-02
-5.17556705e-02 -7.81818509e-01 -7.56682158e-01 -6.92301571e-01
8.93952027e-02 3.31943393e-01 4.23878968e-01 1.13958120e-01
3.36483657e-01 5.10442019e-01 -1.85938990e+00 3.91567022e-01
8.52128327e-01 1.16338730e+00 7.32330382e-01 3.84097964e-01
-3.74477923e-01 5.49886882e-01 -3.74267548e-01 1.34879962e-01
2.80498922e-01 -3.11681747e-01 -8.97787929e-01 7.40716279e-01
4.09617454e-01 8.27743262e-02 -1.48373889e-02 1.10187948e+00
1.75372884e-01 3.82100850e-01 6.12347484e-01 -1.11287248e+00
-5.55577815e-01 3.06625366e-01 -6.63266122e-01 1.23817697e-01
-8.55293125e-02 2.80616313e-01 1.32589269e+00 -1.12804389e+00
4.94823933e-01 1.46166611e+00 3.06897014e-01 5.21823227e-01
-1.46655083e+00 -4.14289564e-01 7.14426219e-01 1.43811330e-01
-1.38855588e+00 -4.90650386e-01 9.77872550e-01 -6.01000071e-01
7.64664948e-01 1.01076335e-01 6.32418633e-01 6.86786354e-01
-3.88535231e-01 1.27307236e+00 9.33303356e-01 -3.73345941e-01
3.69995326e-01 6.51104823e-02 1.84784740e-01 6.47933960e-01
1.38772562e-01 -7.98808224e-03 -3.46669734e-01 1.63237751e-01
9.29637849e-01 2.95222163e-01 -2.06206888e-01 -7.83228040e-01
-1.13765430e+00 8.65551114e-01 8.53615642e-01 2.25275815e-01
-3.83236349e-01 1.12723552e-01 2.58488327e-01 -4.33684774e-02
5.86995959e-01 4.35992122e-01 -6.30535722e-01 4.20556813e-01
-9.76315677e-01 4.68891859e-02 3.05546880e-01 9.91871834e-01
1.45115995e+00 -7.70448819e-02 -2.86174446e-01 9.88072276e-01
5.12922943e-01 2.28067160e-01 5.61082602e-01 -8.41191232e-01
2.87661821e-01 8.79073739e-01 -2.04764426e-01 -7.29953766e-01
-4.61965978e-01 -3.97067040e-01 -5.03183246e-01 1.81174338e-01
2.57847279e-01 1.92808174e-02 -1.54757726e+00 1.67166853e+00
3.72629166e-01 2.19618082e-01 -9.63499472e-02 1.05853224e+00
9.47052002e-01 5.60011268e-01 4.99737710e-01 1.91262633e-01
1.43045807e+00 -1.19514120e+00 -2.29315892e-01 -5.60640454e-01
4.51570541e-01 -6.30033016e-01 1.31168139e+00 5.21205738e-02
-9.82429206e-01 -7.91139781e-01 -9.47853267e-01 -2.45537192e-01
-4.19585019e-01 2.35099241e-01 7.82839477e-01 4.10868734e-01
-1.02576220e+00 2.65889525e-01 -1.00145555e+00 -3.92047524e-01
8.33203375e-01 3.54507565e-01 -1.89788267e-01 -1.62622854e-01
-6.68823719e-01 4.49159205e-01 7.66331911e-01 -6.48709908e-02
-1.05951273e+00 -6.03689909e-01 -1.13771701e+00 1.68216959e-01
6.13511443e-01 -7.34080493e-01 1.12101173e+00 -1.26560736e+00
-1.41832030e+00 9.98328447e-01 -1.39026001e-01 -1.96966127e-01
8.35184082e-02 -1.76372916e-01 6.81389794e-02 3.15745831e-01
2.65958637e-01 1.19732404e+00 9.90481853e-01 -1.54345059e+00
-7.92997062e-01 -3.61574203e-01 2.29547694e-01 3.75052989e-01
-2.51473635e-01 -2.20240414e-01 -9.00532424e-01 -7.02008843e-01
5.08077800e-01 -7.19991744e-01 -5.59502721e-01 1.47818759e-01
-3.95932764e-01 -2.44785339e-01 7.85489082e-01 -2.87022859e-01
8.38001847e-01 -2.40134501e+00 1.08992554e-01 2.20258012e-01
1.08677395e-01 1.36900529e-01 -3.76834184e-01 7.30080903e-02
1.02004617e-01 1.81285404e-02 -4.20796543e-01 -5.28514147e-01
-1.14195846e-01 3.25411707e-01 -1.02101192e-01 3.45153272e-01
5.47455788e-01 9.41122353e-01 -9.61469412e-01 -5.68072021e-01
4.59391654e-01 2.43584901e-01 -7.87087500e-01 3.25507671e-01
-6.25177979e-01 4.85800415e-01 -6.71611309e-01 6.83598340e-01
6.04441464e-01 -5.09227157e-01 2.30148554e-01 -1.78754359e-01
-3.21654007e-02 3.29678774e-01 -1.15448105e+00 1.90319490e+00
-5.32917559e-01 3.50897670e-01 9.49023478e-03 -1.46459031e+00
9.82908249e-01 -5.76193854e-02 3.68937373e-01 -6.13341033e-01
7.79152513e-02 -1.21775158e-02 -2.75915146e-01 -3.89177680e-01
3.84798884e-01 -8.66465420e-02 -1.24850839e-01 2.40049899e-01
5.02347469e-01 -3.61020178e-01 1.43753529e-01 9.64913890e-02
6.20843709e-01 3.59330475e-01 3.43105823e-01 -3.22497010e-01
5.26002109e-01 7.75506198e-02 7.43280530e-01 7.53718138e-01
-1.04857147e-01 8.35995376e-01 3.02958578e-01 -2.31121719e-01
-7.19910502e-01 -1.34993017e+00 -1.07019506e-01 1.52691436e+00
4.54934955e-01 -3.05045873e-01 -4.71579999e-01 -7.77604878e-01
4.61851433e-02 5.05151212e-01 -5.60403347e-01 -1.52782604e-01
-3.93844426e-01 -6.31611526e-01 -8.01482871e-02 6.93282425e-01
4.66387808e-01 -1.18814349e+00 -6.16076410e-01 1.66621581e-01
6.82789832e-02 -8.85878384e-01 -4.12807673e-01 4.34200644e-01
-9.72534716e-01 -9.82208490e-01 -5.49831510e-01 -9.40170944e-01
9.94650185e-01 6.60422206e-01 1.04184246e+00 2.06421167e-01
-5.64010561e-01 5.75518429e-01 -3.67357403e-01 -2.53953874e-01
1.49925038e-01 7.91848153e-02 -4.66570139e-01 2.54748702e-01
2.69589186e-01 -4.64748800e-01 -7.46623993e-01 2.18173668e-01
-1.04866135e+00 1.94285482e-01 7.85016179e-01 8.94701898e-01
8.54033649e-01 -1.91265434e-01 5.52672446e-01 -1.01463377e+00
1.07021131e-01 -5.18584073e-01 -6.86823606e-01 1.77610517e-01
-3.26813221e-01 1.52421877e-01 3.91722798e-01 -2.73635983e-01
-1.16680884e+00 5.03753901e-01 1.20696798e-01 -4.34771687e-01
-4.88588035e-01 4.45857137e-01 -5.20767748e-01 1.79721370e-01
5.61505258e-01 2.23097309e-01 -6.32763356e-02 -4.83034402e-01
8.53400946e-01 4.49476182e-01 5.30694664e-01 -8.49307775e-01
6.56615555e-01 5.84592521e-01 -4.20312881e-01 -7.76191175e-01
-1.05718029e+00 -8.83203804e-01 -8.07061076e-01 2.57904734e-02
1.04974866e+00 -1.13211489e+00 -2.58890182e-01 3.45375955e-01
-8.43559921e-01 -4.19753641e-01 -6.14043891e-01 4.56434667e-01
-7.82880008e-01 3.30516994e-01 -2.96026736e-01 -5.67205429e-01
2.23802924e-02 -1.10844409e+00 1.26461458e+00 4.27364200e-01
1.04855536e-03 -1.10300100e+00 -3.20996702e-01 3.63228887e-01
2.24024564e-01 1.19738571e-01 9.10824955e-01 -5.59992790e-01
-8.35324705e-01 -2.54319645e-02 -4.38797176e-01 5.07992923e-01
3.03213060e-01 -1.98022664e-01 -1.10348284e+00 -2.92026877e-01
-2.59899437e-01 -4.99878138e-01 1.22953308e+00 4.89904016e-01
1.64534175e+00 -1.59270450e-01 -4.59875345e-01 7.51929224e-01
1.43392980e+00 3.81453931e-02 3.07625353e-01 3.48937362e-01
7.71920264e-01 7.33111203e-01 7.05657184e-01 5.62006474e-01
5.24489641e-01 5.16962051e-01 3.98958802e-01 -4.12643403e-01
-2.49042317e-01 -2.83345938e-01 -5.89839416e-03 2.80569613e-01
1.25035733e-01 -9.64347273e-02 -7.90614009e-01 7.51955986e-01
-2.04642868e+00 -5.82659960e-01 3.17751259e-01 1.96855915e+00
7.78957307e-01 6.01852462e-02 1.44501686e-01 -1.94351390e-01
8.26655865e-01 2.69416153e-01 -7.07251668e-01 2.15516686e-02
1.14015117e-02 2.81097531e-01 4.10876632e-01 3.37354094e-01
-1.38638222e+00 1.34401584e+00 5.46291208e+00 8.20252240e-01
-1.23443627e+00 7.82893002e-02 6.14375532e-01 1.75031573e-02
-3.92911404e-01 1.59604073e-01 -6.52908742e-01 2.25368813e-01
2.79393047e-01 -5.22849299e-02 1.68863863e-01 1.00278997e+00
1.47101700e-01 -1.32366285e-01 -1.18573332e+00 8.70782375e-01
8.66272114e-03 -1.23340869e+00 2.96623558e-01 -1.29612416e-01
7.59783626e-01 -9.71776724e-04 2.22775981e-01 3.21788192e-01
5.03710568e-01 -8.68383706e-01 7.84669220e-01 4.27869290e-01
4.50375289e-01 -4.72110152e-01 3.82727057e-01 1.71481490e-01
-1.27398992e+00 -1.53300047e-01 -5.18785894e-01 -2.91167423e-02
-3.76857780e-02 6.33523643e-01 -8.88487458e-01 4.92159367e-01
5.83101034e-01 1.06960917e+00 -6.80843711e-01 1.18611574e+00
-4.29964274e-01 5.93674242e-01 -2.09980622e-01 2.94384152e-01
4.61718172e-01 -8.12308714e-02 1.89721897e-01 1.21709895e+00
8.04045722e-02 6.27824441e-02 8.12567413e-01 9.79105830e-01
5.60094863e-02 1.18569955e-01 -2.10004017e-01 6.60434142e-02
4.57534522e-01 1.41198540e+00 -1.05277610e+00 -5.38760543e-01
-5.13207674e-01 8.10001552e-01 5.21045864e-01 5.72591126e-01
-3.99595141e-01 -1.90754548e-01 5.90007544e-01 6.61771521e-02
6.38517082e-01 -3.00253540e-01 -5.12682974e-01 -1.16935527e+00
-1.40386105e-01 -5.05896926e-01 6.49456263e-01 -4.91685301e-01
-1.33916879e+00 3.70412946e-01 1.29726201e-01 -1.18500769e+00
-3.85049544e-02 -6.46915197e-01 -6.96008027e-01 7.18873858e-01
-1.71605182e+00 -1.37025082e+00 -3.37243915e-01 7.62142479e-01
7.87618399e-01 -6.83426857e-02 5.09209931e-01 5.42163551e-02
-4.89773005e-01 3.35231602e-01 -1.43928215e-01 -1.39429746e-02
4.91056234e-01 -1.37046182e+00 1.77715302e-01 7.64090300e-01
2.88355410e-01 5.07260203e-01 3.50781620e-01 -3.49956304e-01
-9.86992180e-01 -1.37012458e+00 3.93295169e-01 -1.93223208e-01
5.07234514e-01 -4.65930969e-01 -9.83449459e-01 6.33953452e-01
-1.30733296e-01 3.61065716e-01 7.43506908e-01 2.67733365e-01
-4.99168128e-01 -5.69230393e-02 -9.68168259e-01 5.28875172e-01
1.08995748e+00 -4.92676109e-01 -6.93367243e-01 3.69385660e-01
6.59364939e-01 -5.16413003e-02 -5.45139611e-01 4.43608850e-01
1.15905806e-01 -7.13184774e-01 1.15876627e+00 -5.31596959e-01
1.47247046e-01 -5.64662158e-01 -2.17724875e-01 -1.19079447e+00
-5.25072873e-01 -4.72200103e-02 2.72288412e-01 1.24940407e+00
3.69114012e-01 -5.22977114e-01 8.69131804e-01 3.93777788e-01
-4.47523147e-01 -6.19416595e-01 -6.74773276e-01 -7.98928797e-01
-2.29772776e-02 -4.16658700e-01 4.18323010e-01 8.18955958e-01
-2.90783703e-01 3.95021707e-01 -4.46037240e-02 3.62887472e-01
5.82464874e-01 4.08600867e-01 8.17671478e-01 -1.23437822e+00
-3.42800111e-01 -5.42702794e-01 -4.87131894e-01 -1.33820009e+00
2.31067076e-01 -1.15790844e+00 4.56992567e-01 -1.62814116e+00
2.03278482e-01 -7.55774379e-01 -5.99526167e-01 7.91774571e-01
-4.52089578e-01 2.03929543e-01 4.62623775e-01 2.43048951e-01
-1.04337025e+00 6.52114511e-01 1.36086452e+00 -2.92533934e-01
-3.93609375e-01 4.03993810e-03 -8.92868221e-01 7.78639555e-01
7.90063739e-01 -3.27259451e-01 -6.24618530e-01 -4.20068830e-01
-4.05314744e-01 -3.78717214e-01 5.38251221e-01 -8.63104522e-01
1.42441720e-01 -3.67766917e-01 4.06082511e-01 -4.74371076e-01
3.17941159e-01 -5.84861577e-01 -3.06415856e-01 2.40984276e-01
-3.78242880e-01 -5.31054676e-01 1.53000131e-01 6.14689708e-01
-3.97018433e-01 -2.21523941e-01 8.42986941e-01 -4.36547399e-01
-1.33498883e+00 4.22645688e-01 -3.85098577e-01 3.35753858e-02
9.91063833e-01 -3.55037838e-01 -1.18944407e-01 -1.89081486e-02
-9.59353030e-01 4.37359720e-01 4.75870609e-01 5.84781826e-01
6.73793316e-01 -1.09625614e+00 -4.68270242e-01 3.69197488e-01
4.71586734e-01 4.41788673e-01 3.32615048e-01 4.55980897e-01
-1.54310897e-01 1.44774750e-01 -2.62724280e-01 -1.02237904e+00
-9.92520213e-01 7.11737096e-01 1.10992454e-01 1.53377037e-02
-5.74469686e-01 1.07802474e+00 9.62500453e-01 -5.90557098e-01
1.54213548e-01 -4.14647579e-01 -2.31770933e-01 1.49008423e-01
2.29439735e-01 -9.43661705e-02 -1.22938976e-01 -4.83627439e-01
-2.59048283e-01 5.84878862e-01 -2.39643574e-01 3.60178277e-02
1.40389585e+00 -3.68082643e-01 2.50911042e-02 3.64177018e-01
1.18216991e+00 -3.57994258e-01 -1.76897109e+00 -6.15636706e-01
3.43098432e-01 -5.20458817e-01 5.90717085e-02 -5.62534153e-01
-1.22640252e+00 9.47424114e-01 5.08876443e-01 -1.62958652e-01
1.25879705e+00 4.71939802e-01 3.70693296e-01 3.00826281e-01
2.04350412e-01 -1.11708677e+00 5.15274465e-01 4.53970462e-01
6.99623823e-01 -1.37604046e+00 -1.24826990e-01 -6.81703329e-01
-6.06574595e-01 9.66957271e-01 7.74888575e-01 -2.62104958e-01
6.58389688e-01 -6.87229112e-02 1.79556414e-01 -2.35098869e-01
-5.64153731e-01 -7.86503673e-01 5.30851662e-01 7.33942270e-01
3.27640772e-01 1.95191011e-01 -5.65706529e-02 5.80680311e-01
9.10649374e-02 -3.05457771e-01 2.00668886e-01 1.04964364e+00
-8.57217371e-01 -1.12566340e+00 -3.15909535e-01 4.37328398e-01
-5.05207926e-02 -4.90553789e-02 -8.33041593e-02 6.53872728e-01
4.12147194e-01 8.12396109e-01 3.71083796e-01 -9.06879529e-02
2.31015027e-01 -8.58153850e-02 2.96656489e-01 -1.23446858e+00
-2.30719864e-01 2.69917965e-01 -2.31465816e-01 -5.75955033e-01
-6.02576971e-01 -6.93572581e-01 -1.30675220e+00 5.08622766e-01
-1.47064030e-01 -5.85970236e-04 5.07686138e-01 9.22343731e-01
3.70674878e-01 5.28082907e-01 6.32986367e-01 -1.14599979e+00
-2.23569855e-01 -7.15505898e-01 -5.54096520e-01 6.60926998e-01
3.63627702e-01 -8.29918325e-01 -2.11135432e-01 2.74119526e-01] | [9.639455795288086, 1.0197519063949585] |
d42141bc-b038-46c6-b719-3ac1063a6358 | beyond-one-glance-gated-recurrent | 1811.10914 | null | http://arxiv.org/abs/1811.10914v3 | http://arxiv.org/pdf/1811.10914v3.pdf | Beyond One Glance: Gated Recurrent Architecture for Hand Segmentation | As mixed reality is gaining increased momentum, the development of effective
and efficient solutions to egocentric hand segmentation is becoming critical.
Traditional segmentation techniques typically follow a one-shot approach, where
the image is passed forward only once through a model that produces a
segmentation mask. This strategy, however, does not reflect the perception of
humans, who continuously refine their representation of the world. In this
paper, we therefore introduce a novel gated recurrent architecture. It goes
beyond both iteratively passing the predicted segmentation mask through the
network and adding a standard recurrent unit to it. Instead, it incorporates
multiple encoder-decoder layers of the segmentation network, so as to keep
track of its internal state in the refinement process. As evidenced by our
results on standard hand segmentation benchmarks and on our own dataset, our
approach outperforms these other, simpler recurrent segmentation techniques, as
well as the state-of-the-art hand segmentation one. Furthermore, we demonstrate
the generality of our approach by applying it to road segmentation, where it
also outperforms other baseline methods. | ['Mathieu Salzmann', 'Joachim Hugonot', 'Kaicheng Yu', 'Wei Wang', 'Pascal Fua'] | 2018-11-27 | null | null | null | null | ['road-segementation', 'hand-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.70803714e-01 3.44541818e-01 -1.85049295e-01 -1.17387801e-01
-6.39355004e-01 -6.17046654e-01 4.99190778e-01 -2.01547220e-01
-4.97905910e-01 6.10541582e-01 4.38571423e-01 -2.19670255e-02
2.89261788e-01 -7.24978685e-01 -5.51706970e-01 -4.29346770e-01
1.79839462e-01 8.26932371e-01 6.65348411e-01 -3.30698460e-01
1.84785903e-01 3.92644584e-01 -1.26051545e+00 4.51743230e-02
7.39587843e-01 5.18827498e-01 3.22884560e-01 7.47757912e-01
1.20689802e-01 1.02472055e+00 -2.70269036e-01 -4.70022470e-01
8.13942701e-02 -5.60407937e-01 -1.43213069e+00 1.78371891e-01
1.05115004e-01 -5.28104544e-01 -4.60602015e-01 8.35315526e-01
3.80060732e-01 3.07429552e-01 5.09791374e-01 -7.38632441e-01
-3.35585833e-01 8.59301031e-01 -8.49486709e-01 4.58048992e-02
4.74632680e-01 1.83449671e-01 1.09798515e+00 -5.82375050e-01
1.13065851e+00 9.63846207e-01 5.86957335e-01 6.51851296e-01
-1.24791062e+00 -2.73563951e-01 6.92026675e-01 -1.36290491e-02
-1.30604279e+00 -3.96935076e-01 6.50044560e-01 -4.47163343e-01
1.00515759e+00 -2.74692643e-02 1.03242862e+00 1.01644051e+00
-6.65688366e-02 1.23816526e+00 9.78993475e-01 -4.30232614e-01
9.85199437e-02 -1.33720487e-01 2.82603443e-01 7.87684739e-01
-5.07732332e-02 -1.95702583e-01 -3.14967632e-01 3.93461913e-01
1.06020975e+00 -2.91685700e-01 -2.88583219e-01 -5.22093654e-01
-1.24540842e+00 4.02514607e-01 5.20891905e-01 4.88359183e-01
-7.10773885e-01 4.87969041e-01 2.51713693e-01 -1.64584905e-01
4.30054277e-01 3.12590390e-01 -2.20435962e-01 -2.50343531e-01
-1.73138571e+00 3.95053983e-01 9.13354576e-01 8.46318543e-01
6.42329574e-01 -1.76430717e-01 -3.10057908e-01 4.74515736e-01
3.53536874e-01 -1.49642572e-01 2.63485521e-01 -1.12888992e+00
4.00143743e-01 5.79324543e-01 1.84518114e-01 -5.53029776e-01
-5.71129382e-01 -5.03730297e-01 -3.98408532e-01 2.34141424e-01
7.54828990e-01 -1.48876637e-01 -1.35910594e+00 1.66559637e+00
1.60531223e-01 1.48244739e-01 -1.41342059e-01 1.14723217e+00
5.16895711e-01 3.50089967e-01 1.85911000e-01 4.12671082e-02
1.07838583e+00 -1.30259633e+00 -7.45602131e-01 -4.16548342e-01
5.07384121e-01 -6.65214419e-01 1.07918000e+00 5.58327436e-01
-1.45299053e+00 -3.89367163e-01 -1.02461529e+00 -2.11904615e-01
-1.70279562e-01 -4.37522046e-02 6.23787880e-01 6.28370106e-01
-1.28595209e+00 7.97740519e-01 -1.19792736e+00 -5.49598873e-01
7.28813946e-01 3.39092046e-01 -9.84263644e-02 1.01761848e-01
-8.09704661e-01 9.44839895e-01 3.34137172e-01 3.12547475e-01
-7.91925490e-01 -4.07960504e-01 -7.17483401e-01 -1.32445963e-02
6.02773428e-01 -8.12280595e-01 1.58935750e+00 -9.30561066e-01
-1.86386871e+00 8.86811256e-01 -4.77952808e-01 -4.63429362e-01
9.07296956e-01 -6.18007720e-01 1.36373788e-01 1.43134862e-01
-4.74140719e-02 1.01489699e+00 6.93561673e-01 -1.48630714e+00
-6.70709670e-01 -3.70201409e-01 2.44621262e-01 2.35868290e-01
3.38038296e-01 7.14045018e-02 -8.24161828e-01 -4.86436278e-01
3.84297729e-01 -1.05024648e+00 -7.47405291e-01 -4.32346404e-01
-7.39382982e-01 -6.89293891e-02 6.21931791e-01 -9.43460464e-01
1.25898492e+00 -1.75182486e+00 5.38148284e-01 4.44425702e-01
3.85772556e-01 9.17659476e-02 6.63781539e-02 2.68230110e-01
9.87460241e-02 9.08291638e-02 -5.69044828e-01 -7.25738823e-01
1.42705971e-02 1.34376392e-01 -2.81861603e-01 4.60540920e-01
-3.87124717e-02 1.24843919e+00 -9.87910509e-01 -6.35824978e-01
3.25624049e-01 6.51111662e-01 -7.77354717e-01 -3.42323594e-02
-4.68612701e-01 7.66125441e-01 -3.06854814e-01 4.27170753e-01
3.34185094e-01 -3.83744240e-01 2.40528494e-01 -4.63408493e-02
-2.97275364e-01 2.85182089e-01 -1.14666891e+00 2.15822029e+00
-3.48371953e-01 5.89367926e-01 -1.62019834e-01 -7.93649137e-01
3.01899999e-01 5.13040483e-01 4.45946068e-01 -7.27186263e-01
3.03154200e-01 1.64398968e-01 -6.96216747e-02 -3.21221560e-01
8.06023359e-01 -2.24117078e-02 7.77872875e-02 6.51132882e-01
2.53992956e-02 -8.57310891e-02 3.03264409e-01 2.99146801e-01
7.48674512e-01 8.37569356e-01 1.15395762e-01 -3.19511592e-02
4.50607449e-01 2.38925174e-01 3.18642884e-01 7.87210763e-01
-1.84536710e-01 1.08303487e+00 5.55814385e-01 -9.98662561e-02
-9.41852748e-01 -9.37680125e-01 2.36716643e-01 1.06590855e+00
2.32105315e-01 -3.58919322e-01 -1.31646466e+00 -6.18275225e-01
-5.35454869e-01 7.04181552e-01 -6.25827253e-01 3.01737338e-01
-9.62005615e-01 -3.93622279e-01 4.42941755e-01 8.23822021e-01
6.67071402e-01 -1.32117617e+00 -1.08007014e+00 4.50780421e-01
-3.56806248e-01 -1.17827475e+00 -4.07148123e-01 -5.08701541e-02
-8.79032791e-01 -9.97470617e-01 -1.03008235e+00 -7.23156691e-01
4.43861961e-01 3.12827677e-02 1.10550678e+00 2.01151595e-02
-2.07280234e-01 4.71123010e-01 -1.87539399e-01 -6.77400827e-02
-1.96181431e-01 6.55027211e-01 -5.23364902e-01 -1.14180312e-01
3.96637879e-02 -5.28433919e-01 -8.59805524e-01 7.34627172e-02
-5.37374020e-01 2.93647170e-01 5.11242032e-01 3.99619192e-01
6.48736298e-01 -3.54827076e-01 3.81287664e-01 -1.19144166e+00
5.71738243e-01 -1.86589167e-01 -3.92074794e-01 2.59759754e-01
-3.80432069e-01 -1.10901922e-01 1.71242267e-01 -1.08793400e-01
-1.44243002e+00 4.27256376e-01 -3.27963263e-01 -2.89286762e-01
-9.83823538e-02 5.02953053e-01 3.30890007e-02 1.37004942e-01
5.16905248e-01 1.34041950e-01 -1.37640104e-01 -5.44898570e-01
7.65276670e-01 3.52051437e-01 8.65813017e-01 -2.84230053e-01
5.63057482e-01 5.87466359e-01 -3.07324052e-01 -8.10699344e-01
-8.68771613e-01 -4.58712369e-01 -1.15289915e+00 -3.58798504e-01
1.22071159e+00 -5.82411647e-01 -5.74046493e-01 5.90422094e-01
-1.42469752e+00 -8.47365677e-01 -6.04921937e-01 3.44355285e-01
-8.49710226e-01 3.33278090e-01 -8.60112190e-01 -7.84479082e-01
-1.27899840e-01 -1.34421444e+00 1.08303344e+00 2.44287997e-01
-6.73029065e-01 -8.83561671e-01 1.85619250e-01 2.96602398e-01
3.82146746e-01 1.66855827e-01 5.56998074e-01 -3.68486345e-01
-7.14364409e-01 -2.03707442e-01 -2.64382571e-01 -1.47801116e-02
-1.08925007e-01 2.62290649e-02 -1.19177437e+00 -1.00440167e-01
-1.40767276e-01 -1.26468286e-01 9.69375551e-01 5.58670998e-01
1.16084075e+00 3.11980024e-03 -4.65502560e-01 5.23698926e-01
1.12983024e+00 2.68868748e-02 9.25247312e-01 2.76685029e-01
9.83349919e-01 7.38990009e-01 3.86838734e-01 1.01955451e-01
7.72024453e-01 6.17807209e-01 3.54703456e-01 -2.91882515e-01
-2.86557496e-01 -2.59763777e-01 -1.97572056e-02 6.93810463e-01
-7.37078547e-01 -3.12596440e-01 -1.04179132e+00 5.55734634e-01
-2.04051733e+00 -1.05970848e+00 -9.42833126e-02 2.05317450e+00
6.72945559e-01 1.94000185e-01 4.20922518e-01 3.29262406e-01
5.27694046e-01 3.54780257e-01 -5.22378623e-01 -3.37371886e-01
1.79156482e-01 6.04896486e-01 4.57305312e-01 6.51482701e-01
-1.18459690e+00 1.64902282e+00 7.07374573e+00 3.07725549e-01
-1.08218074e+00 1.79679185e-01 3.86335820e-01 5.94310574e-02
-1.59958288e-01 1.41782969e-01 -5.11377275e-01 4.49825823e-03
5.23750067e-01 1.21770121e-01 6.46701992e-01 5.69151819e-01
2.20531374e-01 -3.97544056e-01 -1.14331245e+00 7.18444824e-01
6.15465920e-03 -1.14551103e+00 -1.88424930e-01 7.54036307e-02
6.48540080e-01 1.07493520e-01 -1.17498010e-01 1.32126555e-01
5.01649201e-01 -1.26777971e+00 1.10183263e+00 5.93698621e-01
4.41392511e-01 -5.68106890e-01 4.51680630e-01 3.77648562e-01
-1.24989057e+00 2.24555224e-01 1.59969583e-01 -1.31040365e-01
6.57882571e-01 9.24634039e-02 -5.66747665e-01 4.44948733e-01
4.56048369e-01 7.31135905e-01 -4.87879395e-01 1.09257221e+00
-5.02422512e-01 6.80871964e-01 -1.76093102e-01 4.30670768e-01
4.14747745e-01 -1.13019153e-01 5.32459676e-01 1.33451140e+00
-1.85777918e-01 9.19853225e-02 2.25738600e-01 9.49891329e-01
5.62428832e-02 1.07942924e-01 -3.55231494e-01 -9.79203824e-03
1.32580057e-01 1.01658010e+00 -1.30660331e+00 -4.13549066e-01
-3.65378410e-01 1.27936530e+00 2.36633047e-01 6.30463064e-01
-7.76964426e-01 -3.13198119e-01 2.88465291e-01 1.39685839e-01
4.52143967e-01 -4.32569116e-01 -4.49294746e-01 -9.90951419e-01
-6.15520552e-02 -7.34291911e-01 2.75919527e-01 -5.57833910e-01
-6.35908782e-01 6.48508191e-01 -3.97678502e-02 -7.58138895e-01
-4.35036153e-01 -1.08802803e-01 -5.05357265e-01 9.06055927e-01
-1.48376703e+00 -1.28426278e+00 -4.09546345e-01 4.89363432e-01
7.83741772e-01 4.18219507e-01 6.35823190e-01 1.62571490e-01
-6.88425064e-01 1.99221015e-01 -6.24777913e-01 2.03312278e-01
2.92203158e-01 -1.20657837e+00 8.92147958e-01 1.00911057e+00
2.86140710e-01 6.93234503e-01 7.06766546e-01 -7.52298653e-01
-9.92615700e-01 -7.51963198e-01 7.02534497e-01 -3.93139213e-01
4.74479675e-01 -2.57210970e-01 -1.06856489e+00 1.18413424e+00
4.03331876e-01 -1.53596848e-01 1.96742207e-01 1.79175064e-01
-7.22726807e-02 4.53185618e-01 -8.82484257e-01 7.39371896e-01
1.38264632e+00 -5.16411304e-01 -6.93663061e-01 7.28616416e-02
5.06956160e-01 -8.42444122e-01 -4.83663052e-01 2.25073397e-01
8.65807950e-01 -1.10734689e+00 8.26117396e-01 -5.31060338e-01
4.57138717e-01 -1.66709736e-01 1.06528521e-01 -1.08262324e+00
-2.40281373e-01 -7.51342058e-01 -1.71726480e-01 9.70320702e-01
4.99407500e-01 -4.99011785e-01 1.26318359e+00 5.43134868e-01
-1.28893137e-01 -8.04438233e-01 -7.50645459e-01 -3.26916277e-01
5.08650802e-02 -5.92669487e-01 4.05657411e-01 6.31473660e-01
-4.43589091e-02 2.84202695e-01 -1.27114490e-01 -7.60400072e-02
6.54701114e-01 1.10372063e-02 7.92634666e-01 -1.11914408e+00
-2.11073980e-01 -6.65129364e-01 -2.41609529e-01 -1.48368382e+00
1.04949765e-01 -7.83701837e-01 2.93213606e-01 -2.12874866e+00
1.09877028e-02 -2.60139763e-01 -8.10978413e-02 3.92689466e-01
-2.34662406e-02 5.28175116e-01 3.81715566e-01 3.07784319e-01
-5.40830374e-01 2.01968446e-01 1.54128981e+00 9.36950222e-02
-6.66954517e-01 1.11282475e-01 -5.71543396e-01 1.08820140e+00
6.82248890e-01 -1.66205153e-01 -4.58957583e-01 -4.06164557e-01
1.28698394e-01 4.25176024e-02 4.29505169e-01 -1.05146468e+00
4.11695987e-01 5.46375103e-02 2.29442656e-01 -8.31185520e-01
4.41897631e-01 -6.10811234e-01 8.30006674e-02 4.84514713e-01
-2.13324472e-01 -1.27406493e-01 -1.31101266e-01 3.25989664e-01
-9.50373560e-02 -1.70950130e-01 7.03772247e-01 -3.97323608e-01
-8.56531382e-01 1.58058897e-01 -4.35494781e-01 7.39924312e-02
9.72128689e-01 -7.45837927e-01 7.05915177e-03 -3.38732153e-01
-1.09726179e+00 4.04523373e-01 5.73981225e-01 2.53609031e-01
4.31922644e-01 -6.80901885e-01 -4.32953775e-01 -5.60886189e-02
-4.22483504e-01 4.57432032e-01 2.20641643e-01 1.15110445e+00
-7.31813073e-01 4.51415420e-01 3.04316543e-02 -6.39460444e-01
-9.15611744e-01 3.18751782e-01 5.72361529e-01 -2.91842133e-01
-1.17063737e+00 8.23497474e-01 2.56554663e-01 -1.46127224e-01
4.27957535e-01 -4.66524601e-01 -4.22702163e-01 3.28774750e-01
2.92310655e-01 5.59891105e-01 -1.02158353e-01 -7.39601612e-01
-2.13208750e-01 7.72327542e-01 -2.28223816e-01 -5.89117527e-01
1.25289369e+00 -1.59350976e-01 -1.37590200e-01 6.30601466e-01
7.00260043e-01 -6.75546005e-02 -1.43857074e+00 -4.44406196e-02
2.24956363e-01 -2.28687346e-01 -2.17377190e-02 -8.74915242e-01
-1.21038079e+00 7.54125953e-01 3.10342997e-01 1.78115852e-02
8.94771397e-01 5.04118204e-02 9.79649365e-01 1.36800706e-01
4.61299181e-01 -1.18668973e+00 5.86669752e-03 6.56200707e-01
6.99200273e-01 -9.07181501e-01 -8.01218376e-02 -6.54375076e-01
-7.13280082e-01 8.99694979e-01 4.95277286e-01 -3.22439551e-01
4.57664758e-01 1.78481534e-01 1.80431679e-01 -4.22798157e-01
-2.21086532e-01 -5.85696399e-01 8.93482864e-02 3.34761560e-01
5.72337389e-01 -8.82943869e-02 -2.62419969e-01 4.21149999e-01
-4.21945930e-01 4.64422524e-01 4.43411022e-01 1.01424265e+00
-3.21361572e-01 -9.88936961e-01 -9.65093970e-02 2.86569059e-01
-4.08793151e-01 1.36497661e-01 -4.19538110e-01 1.07663739e+00
-1.04987249e-01 6.36140823e-01 3.54610980e-02 -2.16667876e-01
5.16432464e-01 2.28858471e-01 7.62173712e-01 -7.82120049e-01
-7.63384104e-01 1.13874651e-01 -4.13038284e-02 -8.82410467e-01
-6.11491084e-01 -8.14390898e-01 -1.58635736e+00 -3.57719101e-02
-2.93678343e-01 -1.88073993e-01 5.90136051e-01 1.19884515e+00
-5.61825447e-02 8.85493279e-01 1.34495616e-01 -1.43947101e+00
-1.84579954e-01 -7.73989797e-01 -3.24877769e-01 1.42444121e-02
2.71693081e-01 -6.09175622e-01 1.64093122e-01 3.41658071e-02] | [9.350666046142578, 0.16243773698806763] |
0b70e0a2-950b-42a5-8573-5abf21b68c63 | imdiffusion-imputed-diffusion-models-for | 2307.00754 | null | https://arxiv.org/abs/2307.00754v1 | https://arxiv.org/pdf/2307.00754v1.pdf | ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection | Anomaly detection in multivariate time series data is of paramount importance for ensuring the efficient operation of large-scale systems across diverse domains. However, accurately detecting anomalies in such data poses significant challenges. Existing approaches, including forecasting and reconstruction-based methods, struggle to address these challenges effectively. To overcome these limitations, we propose a novel anomaly detection framework named ImDiffusion, which combines time series imputation and diffusion models to achieve accurate and robust anomaly detection. The imputation-based approach employed by ImDiffusion leverages the information from neighboring values in the time series, enabling precise modeling of temporal and inter-correlated dependencies, reducing uncertainty in the data, thereby enhancing the robustness of the anomaly detection process. ImDiffusion further leverages diffusion models as time series imputers to accurately capturing complex dependencies. We leverage the step-by-step denoised outputs generated during the inference process to serve as valuable signals for anomaly prediction, resulting in improved accuracy and robustness of the detection process. We evaluate the performance of ImDiffusion via extensive experiments on benchmark datasets. The results demonstrate that our proposed framework significantly outperforms state-of-the-art approaches in terms of detection accuracy and timeliness. ImDiffusion is further integrated into the real production system in Microsoft and observe a remarkable 11.4% increase in detection F1 score compared to the legacy approach. To the best of our knowledge, ImDiffusion represents a pioneering approach that combines imputation-based techniques with time series anomaly detection, while introducing the novel use of diffusion models to the field. | ['Dongmei Zhang', 'QIngwei Lin', 'Saravan Rajmohan', 'Shilin He', 'Bowen Li', 'Ruomeng Ding', 'Yudong Liu', 'Minghua Ma', 'Chaoyun Zhang', 'Yuhang Chen'] | 2023-07-03 | null | null | null | null | ['imputation', 'anomaly-detection', 'imputation', 'time-series-anomaly-detection', 'imputation'] | ['computer-vision', 'methodology', 'miscellaneous', 'time-series', 'time-series'] | [-1.24085911e-01 -7.16114402e-01 3.17761511e-01 -1.26774102e-01
-6.33748412e-01 -5.36911845e-01 6.64084852e-01 4.77268368e-01
-1.63740560e-01 2.60550112e-01 1.26950443e-01 -3.89298379e-01
-3.27514142e-01 -9.32523549e-01 -4.57756966e-01 -7.18460739e-01
-5.40027082e-01 1.04781866e-01 1.46287248e-01 -1.25342816e-01
4.30540591e-01 6.24633074e-01 -1.49445033e+00 5.03376313e-02
1.05764496e+00 1.37290895e+00 -5.37615180e-01 4.13127214e-01
-2.87973464e-01 7.97585607e-01 -7.23319292e-01 -5.63137643e-02
3.65680605e-01 -6.83156922e-02 -1.47695672e-02 -1.91302970e-01
-1.33460373e-01 -4.40034330e-01 -2.52806365e-01 6.82032108e-01
2.21806645e-01 2.22132921e-01 5.33359349e-01 -1.38906384e+00
-3.71099174e-01 6.13738969e-02 -7.83447862e-01 6.55155420e-01
2.91214138e-01 2.85134554e-01 6.81547582e-01 -7.06984997e-01
1.56125594e-02 7.09807158e-01 9.33621168e-01 2.78360192e-02
-1.28866184e+00 -6.80913270e-01 2.71360099e-01 2.37968743e-01
-1.23374307e+00 -3.32564890e-01 9.76282895e-01 -4.99420613e-01
1.03397274e+00 4.40652579e-01 3.52396697e-01 1.19696605e+00
3.90613943e-01 7.19797909e-01 7.06553102e-01 -1.47551149e-01
4.69745636e-01 -5.68698704e-01 1.12320095e-01 2.26853579e-01
1.15438357e-01 1.43808767e-01 -5.32821000e-01 -7.30302274e-01
6.01037323e-01 7.90500641e-01 1.24897949e-01 1.17584772e-01
-1.09179628e+00 4.47628915e-01 1.09703742e-01 3.05415899e-01
-9.35587585e-01 -1.22007512e-01 6.48653924e-01 4.69946116e-01
8.83781016e-01 1.79842383e-01 -3.72595698e-01 -4.51476187e-01
-1.12189937e+00 2.82812476e-01 5.97081065e-01 5.75031340e-01
2.07882121e-01 5.18037021e-01 -3.64017963e-01 7.65001178e-01
2.38861233e-01 5.07724881e-01 5.01842618e-01 -7.31712937e-01
5.05938172e-01 9.10389185e-01 4.09260951e-02 -1.23372579e+00
-3.23718131e-01 -7.20886528e-01 -1.12916636e+00 1.27018645e-01
4.47407693e-01 -1.08764775e-01 -7.29267776e-01 1.58566797e+00
3.73358309e-01 9.55155492e-01 -3.68655287e-02 3.92809778e-01
-1.18549988e-01 6.82860017e-01 -3.66353914e-02 -3.02782357e-01
7.77129710e-01 -5.46406746e-01 -7.03384459e-01 9.31789503e-02
4.90533501e-01 -7.63128698e-01 5.60211182e-01 5.53159535e-01
-7.22805023e-01 -2.61955738e-01 -7.80000389e-01 7.00731516e-01
-2.24015459e-01 -2.94917285e-01 4.14436698e-01 3.62100840e-01
-5.72295547e-01 5.91889620e-01 -1.27684999e+00 -1.62154764e-01
4.73509848e-01 5.47663420e-02 -2.23516986e-01 8.16550180e-02
-9.05861616e-01 5.32563269e-01 -5.22030443e-02 2.81249344e-01
-7.21492708e-01 -9.49730992e-01 -6.29109323e-01 -1.00881457e-01
3.81968379e-01 -3.02287102e-01 1.07261062e+00 -5.34803212e-01
-9.10962820e-01 1.37963206e-01 -4.39939797e-01 -8.37546766e-01
6.55766070e-01 -3.99981618e-01 -1.04302478e+00 -2.64109254e-01
1.25885531e-01 -5.73420763e-01 1.09501386e+00 -7.42249072e-01
-6.71040714e-01 -5.66443086e-01 -6.65735543e-01 -4.45712060e-01
-4.25744236e-01 -8.08060169e-02 -2.30655357e-01 -1.24166358e+00
3.36358637e-01 -6.12610221e-01 -3.83772820e-01 -1.98690176e-01
-2.33826295e-01 -1.25667289e-01 1.20094562e+00 -9.53450203e-01
1.67215073e+00 -2.36728621e+00 -3.66700441e-01 7.06728876e-01
1.81195021e-01 2.61685401e-01 5.53441569e-02 8.22656214e-01
3.13846916e-02 -2.97297984e-01 -5.59317291e-01 -5.47189534e-01
-9.35806111e-02 4.26326931e-01 -8.58215213e-01 6.30764067e-01
4.11924392e-01 6.49905026e-01 -7.23656356e-01 1.91849679e-01
4.13745373e-01 3.13359737e-01 -4.70364749e-01 3.88433725e-01
-1.04203030e-01 7.22143769e-01 -4.40933585e-01 9.58024919e-01
6.55820608e-01 1.04962796e-01 -3.00376922e-01 2.29584187e-01
-1.40939474e-01 -5.89241758e-02 -1.23675931e+00 1.39693153e+00
-3.97874534e-01 3.80662918e-01 -1.95147544e-01 -1.14277816e+00
1.23722339e+00 3.10094386e-01 8.90127540e-01 -9.91325140e-01
-2.78996021e-01 3.52945834e-01 -2.48553842e-01 -4.18079197e-01
3.61830473e-01 3.32156390e-01 -8.64107087e-02 5.84359288e-01
-3.51161987e-01 4.92614150e-01 -5.74908219e-04 7.22677559e-02
1.58424926e+00 1.64993733e-01 1.70148373e-01 1.69032604e-01
5.47348559e-01 -1.29103333e-01 7.26841092e-01 8.04320335e-01
-1.38936087e-01 4.09725219e-01 3.97232085e-01 -6.02526307e-01
-9.22465026e-01 -1.22245145e+00 -1.06273107e-01 6.99863911e-01
-4.11874801e-01 -3.81101310e-01 -3.77406925e-01 -6.61142051e-01
3.65483552e-01 8.71005714e-01 -6.07042372e-01 -1.21265627e-01
-5.80624998e-01 -1.06520188e+00 6.72528505e-01 6.95338309e-01
3.51708740e-01 -9.29191589e-01 -3.13583672e-01 6.23992980e-01
-2.33920783e-01 -8.52708876e-01 -1.74912319e-01 -1.01614967e-01
-1.25274038e+00 -1.01255596e+00 -3.56369495e-01 6.74477816e-02
5.81727207e-01 1.77796520e-02 8.67826283e-01 4.54088487e-02
-2.44659469e-01 3.85106355e-01 -3.47891957e-01 -4.95730966e-01
-2.49317005e-01 -2.35944793e-01 2.25370377e-01 4.35170233e-01
6.50322139e-01 -1.08303308e+00 -6.62401378e-01 3.11051786e-01
-1.13539422e+00 -7.41379142e-01 4.16104019e-01 7.57953227e-01
5.34668803e-01 2.20031410e-01 9.25787270e-01 -6.12821996e-01
7.17027605e-01 -1.21182060e+00 -7.46311247e-01 -8.54286030e-02
-9.67319012e-01 -6.55836314e-02 7.68910050e-01 -3.38288337e-01
-7.89322853e-01 -2.27002442e-01 -2.39644364e-01 -5.34951568e-01
-2.93442160e-01 6.62584722e-01 3.90645355e-01 2.47008726e-01
4.61371154e-01 5.72612524e-01 1.77212879e-01 -7.78579235e-01
-8.81219432e-02 6.22956932e-01 8.47546041e-01 -6.21835589e-01
8.29062462e-01 6.82795048e-01 5.21799847e-02 -7.36468852e-01
-4.82008994e-01 -8.01228285e-01 -3.80735934e-01 -1.55412078e-01
1.59894437e-01 -9.27936316e-01 -5.32886147e-01 8.26784849e-01
-8.74463975e-01 1.37005478e-01 -3.08774829e-01 2.22848520e-01
-9.99074504e-02 4.43705738e-01 -4.37692076e-01 -1.23337507e+00
-4.26555783e-01 -5.39397359e-01 9.97246563e-01 -1.81072444e-01
-4.17934239e-01 -1.11104953e+00 2.88689733e-01 -1.00835401e-03
8.31656814e-01 8.34752798e-01 6.34322047e-01 -1.04462695e+00
-5.07917881e-01 -7.38114238e-01 1.01678498e-01 4.53165174e-01
2.10924208e-01 1.02042109e-01 -9.10353661e-01 -1.61836937e-01
2.47207358e-01 5.28255522e-01 6.52585983e-01 2.19952241e-01
1.32617545e+00 -2.13557601e-01 -5.93179204e-02 4.97465879e-01
1.23488295e+00 1.95770144e-01 7.19577074e-01 6.44034386e-01
4.62563455e-01 3.21073681e-01 5.82892060e-01 9.81436729e-01
3.34805012e-01 5.34114659e-01 5.91850996e-01 3.56423855e-02
4.96825308e-01 -1.36750981e-01 5.33795595e-01 9.28896964e-01
-8.06858167e-02 4.62607713e-03 -1.10838449e+00 8.21718752e-01
-2.13107944e+00 -1.22425652e+00 -4.77891922e-01 2.37551975e+00
3.17411453e-01 2.66010702e-01 2.21232906e-01 5.74413955e-01
2.92516977e-01 1.35695890e-01 -7.37610579e-01 -3.40572506e-01
-3.43050323e-02 1.57785341e-01 2.51090676e-01 2.58301124e-02
-1.06386399e+00 2.67364264e-01 6.14551449e+00 6.70804977e-01
-1.02493703e+00 -7.87101686e-03 4.13402170e-01 -1.12557694e-01
-1.91270914e-02 -2.48182952e-01 -3.97886068e-01 8.21266472e-01
1.23637176e+00 -2.09247634e-01 3.49084824e-01 7.57900894e-01
6.06777072e-01 1.72258653e-02 -8.03801715e-01 7.09424198e-01
-1.41298831e-01 -1.12099588e+00 -1.97856948e-01 1.40603393e-01
7.00628936e-01 1.99635148e-01 6.07160814e-02 2.28743598e-01
1.41440947e-02 -7.86798537e-01 3.93016905e-01 9.77218807e-01
8.87134075e-02 -9.98897970e-01 9.35439408e-01 4.91711795e-01
-1.23079765e+00 -2.95960873e-01 1.44095421e-01 -3.83004218e-01
2.25455001e-01 1.31428206e+00 -4.90149558e-01 7.81355739e-01
8.00112784e-01 9.97667193e-01 -3.95637870e-01 1.09907842e+00
7.71116614e-02 1.05297327e+00 -5.35785496e-01 5.82868814e-01
7.69935921e-02 -3.85787398e-01 8.33042741e-01 9.30136323e-01
7.16669202e-01 -2.98278630e-01 1.59055591e-01 7.23133862e-01
1.63903117e-01 -6.62921220e-02 -6.54499710e-01 1.90023948e-02
5.36120236e-01 9.75800991e-01 -2.76410818e-01 -1.01049148e-01
-5.71032763e-01 8.31666112e-01 -1.68415178e-02 4.52498645e-01
-8.65255117e-01 -2.82623380e-01 9.12136853e-01 8.02407712e-02
2.27675214e-01 -5.44355869e-01 -5.11020482e-01 -1.20459938e+00
4.99300361e-01 -9.33460832e-01 6.49148047e-01 -1.92458808e-01
-1.83107519e+00 4.77590233e-01 -2.83417284e-01 -1.58814180e+00
-6.05382085e-01 -1.94414675e-01 -9.67836916e-01 9.88827944e-01
-1.30670810e+00 -9.71825540e-01 -2.85548419e-01 8.07654142e-01
4.83937383e-01 -2.22323075e-01 8.67276728e-01 3.83198261e-01
-8.23738337e-01 3.23030680e-01 6.58362150e-01 4.99104187e-02
5.83609581e-01 -1.11227810e+00 9.32125092e-01 1.44447923e+00
6.67561069e-02 6.84585929e-01 4.52349782e-01 -7.61927724e-01
-1.37559688e+00 -1.35981619e+00 6.36925101e-01 -6.20934725e-01
1.01897168e+00 -1.18273064e-01 -1.39779830e+00 5.18150091e-01
-2.54725695e-01 1.38299868e-01 8.23902249e-01 2.18871459e-01
-3.48744929e-01 -3.16199392e-01 -1.19402170e+00 4.60674167e-01
7.60543287e-01 -5.28714538e-01 -6.14396513e-01 6.04667375e-03
2.86947757e-01 -2.57027298e-01 -1.03313041e+00 4.73577529e-01
4.50741112e-01 -9.95729625e-01 9.95702744e-01 -3.99902999e-01
2.51075327e-01 -4.72688675e-01 -2.55232900e-01 -1.21186435e+00
-7.10941330e-02 -6.10765457e-01 -8.99964213e-01 1.46181798e+00
9.24548954e-02 -1.06915605e+00 3.70024621e-01 5.71575105e-01
3.83731425e-02 -6.22622669e-01 -1.03210330e+00 -8.90519738e-01
-3.24308783e-01 -1.08253324e+00 9.17552590e-01 1.02317119e+00
-3.56906503e-01 -5.07014692e-01 -5.16275525e-01 5.82223177e-01
8.83976400e-01 5.37978560e-02 8.50929797e-01 -1.40603733e+00
-2.80965120e-01 -1.74932703e-01 -5.86735487e-01 -5.70441306e-01
-1.13004096e-01 -5.16931951e-01 -2.64093190e-01 -1.06578338e+00
-4.42392677e-01 -4.64367062e-01 -7.37554789e-01 4.66028094e-01
-1.14973940e-01 2.85703331e-01 -3.06982160e-01 5.42845845e-01
-3.06751460e-01 5.64301670e-01 4.66442555e-01 1.92470893e-01
-3.49495351e-01 4.33587313e-01 -4.18750674e-01 6.57991827e-01
9.56651330e-01 -5.43557525e-01 -1.99484721e-01 -3.48612875e-01
-7.16561899e-02 -1.28918633e-01 4.80320901e-01 -1.20150506e+00
3.18590641e-01 -1.19979307e-01 5.21413326e-01 -7.37921834e-01
-7.46315531e-03 -1.05774271e+00 2.65300542e-01 3.23232889e-01
1.56092241e-01 6.94713593e-01 4.94058400e-01 1.03997219e+00
-4.43171442e-01 3.66058916e-01 1.75480098e-01 3.21394861e-01
-8.05281758e-01 2.90425658e-01 -4.61984009e-01 -8.98960009e-02
1.13124478e+00 1.90043915e-02 -2.32144043e-01 -3.94008785e-01
-6.62523806e-01 3.20193261e-01 3.48254681e-01 5.57484746e-01
5.57543457e-01 -1.27282631e+00 -7.41204619e-01 7.05114067e-01
2.07394883e-01 -1.24327905e-01 3.60194057e-01 1.19365931e+00
-2.51775593e-01 -9.60702151e-02 -2.73965206e-02 -8.85158598e-01
-8.95484746e-01 4.80180889e-01 4.33705263e-02 -3.32099020e-01
-8.06453764e-01 1.10386588e-01 -4.46119905e-01 -3.12372714e-01
2.42149428e-01 -2.93731153e-01 9.80031118e-02 -3.24853584e-02
8.03828597e-01 7.39453793e-01 4.95422423e-01 -3.55598360e-01
-3.70495170e-01 2.83862799e-01 -7.83284530e-02 2.08664164e-02
1.59599304e+00 -9.29836556e-02 -2.60723144e-01 6.04379773e-01
7.01566875e-01 2.71605551e-01 -1.24123073e+00 -4.01073933e-01
4.89976585e-01 -6.07757449e-01 1.57282292e-03 -7.94170797e-01
-8.51599455e-01 6.33370757e-01 5.90521157e-01 4.56715941e-01
1.57617366e+00 -5.33023357e-01 1.00724626e+00 1.48518220e-01
2.58100986e-01 -8.29476058e-01 -1.04922086e-01 4.53055292e-01
7.35825837e-01 -1.09118509e+00 -1.17687412e-01 7.25388750e-02
-4.60579753e-01 9.03301775e-01 3.34497780e-01 -2.88628846e-01
6.28417253e-01 4.29529428e-01 1.65495396e-01 5.90826422e-02
-8.04844737e-01 1.02349564e-01 4.09729004e-01 4.89059150e-01
1.67037949e-01 -8.17282349e-02 -2.37730592e-02 6.15471542e-01
3.30906987e-01 -1.04178064e-01 1.44151479e-01 1.09663367e+00
-9.80225056e-02 -1.19621837e+00 -6.63315177e-01 7.24456608e-01
-6.01687014e-01 -2.50652358e-02 1.25236493e-02 3.56278330e-01
-1.65037259e-01 1.14948976e+00 2.96912909e-01 -3.06248039e-01
6.00924253e-01 3.05973530e-01 -3.15059900e-01 -1.92199601e-03
-7.12898374e-01 4.18984741e-02 -2.42184535e-01 -9.34535861e-01
-1.17596418e-01 -8.85993958e-01 -1.03745186e+00 -5.62761188e-01
4.66705114e-02 6.77832589e-02 7.30241954e-01 1.01367164e+00
8.56920600e-01 7.56915867e-01 8.69445801e-01 -5.20749509e-01
-7.39682019e-01 -8.25889885e-01 -4.05727059e-01 5.64318180e-01
6.40586972e-01 -4.48224872e-01 -4.98986065e-01 -3.28037828e-01] | [7.333265781402588, 2.73496150970459] |
f3235234-0033-404a-92a7-5dee2a94fe35 | chore-contact-human-and-object-reconstruction | 2204.02445 | null | https://arxiv.org/abs/2204.02445v2 | https://arxiv.org/pdf/2204.02445v2.pdf | CHORE: Contact, Human and Object REconstruction from a single RGB image | Most prior works in perceiving 3D humans from images reason human in isolation without their surroundings. However, humans are constantly interacting with the surrounding objects, thus calling for models that can reason about not only the human but also the object and their interaction. The problem is extremely challenging due to heavy occlusions between humans and objects, diverse interaction types and depth ambiguity. In this paper, we introduce CHORE, a novel method that learns to jointly reconstruct the human and the object from a single RGB image. CHORE takes inspiration from recent advances in implicit surface learning and classical model-based fitting. We compute a neural reconstruction of human and object represented implicitly with two unsigned distance fields, a correspondence field to a parametric body and an object pose field. This allows us to robustly fit a parametric body model and a 3D object template, while reasoning about interactions. Furthermore, prior pixel-aligned implicit learning methods use synthetic data and make assumptions that are not met in the real data. We propose a elegant depth-aware scaling that allows more efficient shape learning on real data. Experiments show that our joint reconstruction learned with the proposed strategy significantly outperforms the SOTA. Our code and models are available at https://virtualhumans.mpi-inf.mpg.de/chore | ['Gerard Pons-Moll', 'Bharat Lal Bhatnagar', 'Xianghui Xie'] | 2022-04-05 | null | null | null | null | ['object-reconstruction'] | ['computer-vision'] | [ 2.7960277e-01 3.3440709e-01 4.9044693e-01 -3.9604607e-01
-1.7974657e-01 -3.4114170e-01 6.4501244e-01 -2.0957412e-01
-3.4819344e-01 5.1628757e-01 -5.5347908e-02 3.0903965e-01
1.0646520e-01 -6.5261972e-01 -9.7772545e-01 -6.7107272e-01
2.1559815e-01 1.2218819e+00 4.2068335e-01 -8.9370832e-02
-4.7298688e-02 6.6997153e-01 -1.7065209e+00 2.3472607e-01
6.5797526e-01 8.0703449e-01 3.7124595e-01 7.8198093e-01
-2.4535337e-02 5.3239095e-01 -2.9471067e-01 -3.6162192e-01
5.5651605e-01 -3.1575036e-01 -5.6303716e-01 5.0767440e-01
6.5246129e-01 -5.1524389e-01 -2.9237971e-01 6.3123178e-01
4.3673936e-01 2.7861318e-01 7.1812582e-01 -1.3866643e+00
-2.7559501e-01 -2.2002722e-01 -4.6798047e-01 -7.5898540e-01
6.5791112e-01 3.3296150e-01 4.4039127e-01 -8.4749407e-01
8.6066955e-01 1.5528593e+00 6.9598162e-01 8.5850239e-01
-1.4565173e+00 -1.9085968e-01 8.5529089e-02 3.7180007e-02
-1.3890820e+00 -5.3116721e-01 8.3891916e-01 -6.3715202e-01
8.6710882e-01 4.6547011e-01 1.0564440e+00 1.0941586e+00
-7.1770437e-02 5.2674407e-01 1.0782884e+00 -5.6623518e-01
2.6398844e-01 1.6224571e-01 -6.9233976e-02 7.8224874e-01
3.1447780e-01 -5.7424739e-02 -5.9593540e-01 -2.8473020e-01
1.4551672e+00 1.6645500e-01 -3.5526863e-01 -1.3084714e+00
-1.3855935e+00 3.4441280e-01 3.2584095e-01 -1.4268152e-01
-5.0783342e-01 4.0876371e-01 -1.8754753e-01 -1.7462611e-01
1.6931297e-01 4.1786335e-02 -4.7845775e-01 1.4108612e-01
-5.9363395e-01 6.9998908e-01 9.6951020e-01 1.0254987e+00
9.2310357e-01 -2.9620862e-01 2.3948963e-01 2.6956749e-01
5.3228146e-01 6.9740760e-01 -1.8329883e-01 -1.5599273e+00
5.7198390e-02 6.5982538e-01 6.2032694e-01 -9.8312801e-01
-2.7915710e-01 -7.7511668e-02 -6.4680034e-01 7.7545530e-01
6.3745445e-01 1.9411515e-01 -9.3990409e-01 1.6283687e+00
8.2323503e-01 1.8130749e-01 -1.5334058e-01 1.1701684e+00
5.8627635e-01 3.5195538e-01 -5.7084270e-02 5.6376901e-02
1.3498641e+00 -8.0466574e-01 -6.3376147e-01 -3.5363552e-01
3.6741498e-01 -5.0997323e-01 1.0489581e+00 4.8331550e-01
-1.3701552e+00 -4.6261534e-01 -8.9583278e-01 -5.1965338e-01
-1.2549040e-01 -6.0455009e-02 5.6810391e-01 1.6416405e-01
-9.7143281e-01 4.8865011e-01 -1.0571084e+00 -6.2348706e-01
1.4225645e-01 4.1888151e-01 -5.6859386e-01 6.4535461e-02
-5.7579571e-01 1.0482180e+00 1.6004270e-01 1.1042296e-01
-7.0541584e-01 -3.5939661e-01 -9.0331262e-01 -3.4935015e-01
5.9548479e-01 -1.3961318e+00 1.2581806e+00 -9.5174652e-01
-1.4796137e+00 1.2884271e+00 -1.4519893e-01 -2.7757004e-01
9.2916769e-01 -4.9621215e-01 2.8657261e-01 1.3104175e-01
-2.8474799e-01 8.3904582e-01 7.6425022e-01 -1.8153154e+00
6.1576519e-02 -7.6360190e-01 1.8090609e-01 5.8690536e-01
4.2827269e-01 -4.9502710e-01 -5.9333050e-01 -3.9063317e-01
4.9045432e-01 -9.7170550e-01 -2.1103765e-01 9.5708662e-01
-2.1280387e-01 1.8820870e-01 7.8077042e-01 -7.3720121e-01
2.9845020e-01 -1.8844706e+00 7.0253283e-01 1.7078647e-01
3.1821746e-01 6.4193286e-02 5.8809031e-02 2.3279707e-01
2.1676455e-01 -3.7690544e-01 -5.9844077e-01 -8.3143079e-01
3.0595231e-01 6.5752894e-01 -1.2560461e-01 7.0609593e-01
-1.5218396e-01 8.8638520e-01 -6.9807225e-01 -6.2830490e-01
2.8227746e-01 9.9001545e-01 -5.3026074e-01 5.8033359e-01
-4.6737769e-01 8.5520291e-01 -2.9221228e-01 4.9908918e-01
8.1166840e-01 -1.5154921e-01 2.2432277e-01 -2.6997647e-01
5.9133638e-02 3.7391821e-03 -1.5212708e+00 2.1131806e+00
-1.7884538e-01 -6.7615139e-05 6.2682098e-01 -8.7122607e-01
7.0928299e-01 4.0898517e-01 4.1633075e-01 -4.3965095e-01
1.1535637e-01 1.2708682e-01 -3.0809727e-01 -5.5804622e-01
8.9629173e-02 -3.1202093e-01 2.1107227e-01 3.0837682e-01
-7.8015983e-02 -7.8413033e-01 -3.4308988e-01 1.0267407e-01
8.3345157e-01 1.0626334e+00 1.9297387e-01 -4.3039504e-02
2.7028823e-01 -1.5302762e-01 5.7511538e-01 3.5322601e-01
-6.1788815e-03 8.4590238e-01 1.2916881e-01 -7.1502084e-01
-1.1239272e+00 -1.4152019e+00 -5.7292636e-03 5.6293178e-01
4.0576914e-01 -1.7210729e-01 -9.5739627e-01 -3.2441291e-01
1.8190336e-01 7.3173589e-01 -6.5766907e-01 1.3938597e-01
-7.9247069e-01 -6.6352345e-02 6.2014718e-02 4.0785807e-01
3.5703772e-01 -1.0358665e+00 -1.2642485e+00 -7.5271308e-02
-3.6855420e-01 -1.1167730e+00 -2.5151816e-01 -1.1698615e-01
-8.6191618e-01 -1.1520436e+00 -7.2911417e-01 -5.2800399e-01
8.4727812e-01 1.0093537e-01 1.2271525e+00 1.3626249e-01
-6.1178440e-01 9.9857146e-01 4.5215316e-02 -2.8252739e-01
-2.4043050e-01 -6.4959794e-01 2.2525643e-01 2.1729623e-01
1.0197104e-02 -8.0057693e-01 -5.9088248e-01 4.0871292e-01
-7.4632233e-01 6.5457505e-01 2.8871337e-01 3.1883323e-01
9.0940166e-01 -4.9250251e-01 -3.0861551e-01 -6.4570624e-01
-1.0712433e-01 -1.3546003e-01 -5.1987344e-01 3.2744348e-01
9.1143280e-02 5.6578897e-02 -1.3686121e-01 -6.6164619e-01
-1.2424731e+00 5.7932711e-01 2.6037738e-01 -6.6288632e-01
-4.9769363e-01 -2.7129436e-01 -6.0904139e-01 3.4715962e-02
7.3677814e-01 -6.3864008e-02 1.0694477e-01 -6.9819218e-01
4.5081249e-01 8.3369166e-02 7.4054986e-01 -8.7509817e-01
8.7066507e-01 8.2908988e-01 3.1121194e-01 -9.8623681e-01
-4.2138499e-01 -2.0101887e-01 -1.3026488e+00 -2.3291138e-01
1.0434419e+00 -7.3274475e-01 -8.8464099e-01 5.3572053e-01
-1.5288824e+00 -7.2521275e-01 -6.5291744e-01 3.8615081e-01
-1.1146119e+00 4.5581755e-01 -2.7068704e-01 -1.1012279e+00
1.0758246e-02 -9.5789272e-01 1.4226294e+00 -1.3333145e-01
-5.7172257e-01 -8.7009364e-01 1.0956011e-01 4.4111192e-01
1.4139351e-01 9.2079496e-01 4.7741276e-01 3.0562722e-03
-8.5705525e-01 -3.1316765e-02 2.2392372e-02 8.4618144e-02
4.8141178e-02 -2.2831136e-01 -9.4930220e-01 -1.4857766e-01
3.1082800e-01 -3.0683520e-01 4.0640861e-01 2.2158547e-01
9.0236759e-01 -3.6923730e-01 -4.7892392e-01 7.8162140e-01
1.2068803e+00 -1.2529011e-01 6.0636789e-01 -9.4360868e-03
1.0054771e+00 1.1594719e+00 4.7627828e-01 4.9279347e-01
4.8413622e-01 1.0235679e+00 6.9619036e-01 -2.6092466e-02
-2.8977525e-01 -2.4481747e-01 2.7714220e-01 4.6843782e-01
-5.3682518e-01 -1.8984297e-02 -1.0805105e+00 2.4000795e-01
-2.0496740e+00 -7.0399243e-01 -3.8406745e-01 2.3950162e+00
6.9581515e-01 -3.4598589e-02 8.7110415e-02 5.3460691e-02
3.7099713e-01 -4.2524847e-01 -7.5427532e-01 -2.0608988e-02
-1.1513768e-01 2.0038357e-01 1.6993682e-01 7.4122334e-01
-7.3428106e-01 7.3216534e-01 5.4959688e+00 1.4150804e-01
-6.7297578e-01 1.6255566e-01 4.5301944e-02 -2.1280228e-01
-2.7824384e-01 1.8424411e-01 -4.5750299e-01 -1.1212700e-01
4.7211561e-01 5.6214124e-02 5.7426870e-01 5.6459618e-01
6.6586941e-02 -2.0980774e-01 -1.4704906e+00 1.2442596e+00
1.4962965e-01 -8.1741637e-01 3.4861304e-02 1.8246107e-01
4.0905863e-01 -4.3541503e-01 -1.5230079e-01 -1.2693587e-01
3.6107369e-02 -8.6571878e-01 1.2755312e+00 1.1480848e+00
6.7346627e-01 -1.2770404e-01 1.5977706e-01 8.2308447e-01
-1.0683655e+00 3.3159345e-01 -1.0620792e-01 -3.3859313e-01
3.4447536e-01 4.2189160e-01 -6.7445815e-01 2.5713882e-01
7.3959851e-01 4.1253123e-01 -2.1620806e-01 7.9992044e-01
-2.3540500e-01 -3.2976032e-03 -5.9801376e-01 4.3217745e-01
-3.4344169e-01 -4.9556199e-01 7.1455932e-01 9.2647582e-01
2.3292170e-01 4.9379227e-01 1.4638190e-01 1.3093547e+00
2.9625729e-01 -1.5064396e-01 -5.7048494e-01 1.9126241e-01
-1.1811056e-01 1.0178132e+00 -7.0445991e-01 -2.9961184e-01
-5.4423753e-02 1.3377520e+00 3.7421623e-01 5.3660983e-01
-8.5719645e-01 1.2567976e-01 6.5719891e-01 8.7951863e-01
-1.8093834e-03 -7.5646210e-01 -1.1029636e-01 -1.3647965e+00
2.9409990e-01 -4.8915339e-01 -1.3170172e-01 -1.2217189e+00
-1.0140063e+00 3.4097490e-01 4.7440541e-01 -1.0598325e+00
-8.6376749e-02 -8.0354691e-01 -2.0412156e-01 9.2205703e-01
-9.3627471e-01 -1.4537181e+00 -6.7323625e-01 7.0840710e-01
3.2369456e-01 5.5049574e-01 9.3391752e-01 -1.3839725e-01
7.5064227e-02 -9.5462063e-03 -4.9181420e-01 -2.1223760e-01
7.2152567e-01 -1.3403363e+00 3.4250975e-01 3.3609498e-01
-4.5385301e-02 5.8447760e-01 7.5688237e-01 -7.4982053e-01
-1.6874118e+00 -5.8658290e-01 4.4116789e-01 -8.6221570e-01
-6.6662477e-03 -8.6486083e-01 -1.0781486e+00 1.0042833e+00
-6.6639028e-02 2.8766254e-01 2.3409587e-01 -3.0161282e-01
-2.5145850e-01 1.0101318e-01 -1.4493990e+00 6.6291267e-01
1.5010388e+00 -4.0804651e-01 -7.4583983e-01 3.5222951e-01
6.2170750e-01 -8.2515198e-01 -4.9137452e-01 4.2551550e-01
8.8131458e-01 -1.2179078e+00 1.3100425e+00 -3.0173737e-01
-1.5862569e-01 -4.4190598e-01 -3.9639589e-01 -8.6818504e-01
-6.0719877e-02 -5.8784348e-01 -5.2441168e-01 7.0234662e-01
-2.6714087e-01 -6.9409758e-01 7.8007102e-01 1.1088142e+00
5.4580662e-02 -5.2287501e-01 -8.4919274e-01 -5.7918668e-01
-1.2417511e-01 -3.3102745e-01 4.0974468e-01 8.5698509e-01
-4.4872692e-01 1.0649578e-02 -3.8493153e-01 3.2605198e-01
1.1936250e+00 -3.9250240e-02 1.2378970e+00 -1.6406847e+00
-5.6646639e-01 -4.0400133e-02 -3.1664905e-01 -1.1613992e+00
1.7055890e-01 -4.9301064e-01 2.0273481e-01 -1.7145735e+00
6.0364679e-02 -4.3509930e-01 4.1716883e-01 4.9735263e-01
2.7755925e-01 1.6844253e-01 1.2563549e-01 2.0656157e-01
-4.4344893e-01 6.4392668e-01 1.5636836e+00 1.2931785e-01
-1.0572548e-01 -1.2864764e-01 5.7638448e-02 1.2098296e+00
5.5837834e-01 -2.7624476e-01 -3.4510657e-01 -6.1364394e-01
2.7704149e-01 1.3351122e-01 1.0957421e+00 -1.0782577e+00
2.1197090e-01 -2.4403825e-01 4.8920029e-01 -5.3327721e-01
1.1293917e+00 -1.1882081e+00 8.9849877e-01 4.4003302e-01
-2.2112073e-02 -2.1217002e-01 1.6645026e-01 5.1273352e-01
4.4522563e-01 5.2723154e-02 5.6821066e-01 -5.1894730e-01
-4.0977928e-01 3.2751888e-01 6.1940905e-02 -1.4832744e-01
8.7342334e-01 -4.6604660e-01 2.6620236e-01 -3.4415349e-01
-1.2552693e+00 -5.2175228e-03 1.1420794e+00 2.0785511e-01
8.9037204e-01 -1.3500696e+00 -6.6802877e-01 4.6106273e-01
-1.3497886e-01 6.4643359e-01 3.3934823e-01 7.6626718e-01
-8.4005076e-01 9.8021589e-02 -2.6474631e-01 -8.4511113e-01
-1.2358279e+00 4.4986936e-01 5.9444219e-01 2.8479129e-01
-8.3384961e-01 6.6284072e-01 7.5621170e-01 -9.1172826e-01
3.4965703e-01 -5.0156403e-01 4.0259910e-01 -5.1046473e-01
2.9511350e-01 5.8851266e-01 -2.8544724e-01 -1.0350974e+00
-2.7605313e-01 1.0942817e+00 6.4963424e-01 -4.1126192e-01
1.1990894e+00 -1.6321024e-01 -2.1733007e-01 7.4639231e-01
8.1135952e-01 -4.4930119e-02 -1.6536102e+00 -4.0454808e-01
-4.2550784e-01 -6.0871291e-01 -2.7517289e-01 -7.7750391e-01
-5.7043856e-01 1.0709009e+00 5.3605938e-01 -1.0670141e-01
8.6832064e-01 1.4669903e-01 5.2549064e-01 4.4849652e-01
7.4709541e-01 -7.6132798e-01 2.0360014e-01 2.2521046e-01
1.4337394e+00 -1.0285449e+00 1.3027479e-01 -6.5666002e-01
-4.2173624e-01 8.9566630e-01 7.3027611e-01 -1.4296280e-01
5.9299552e-01 3.4143445e-01 -6.0752932e-02 -2.4650127e-01
-4.0770888e-01 -5.1519990e-02 4.4632137e-01 8.0824709e-01
1.1542469e-01 2.1099547e-01 2.1177302e-01 3.7828672e-01
-1.4161976e-01 -5.9063982e-02 2.0050700e-01 1.0943248e+00
-3.4397298e-01 -1.1429224e+00 -8.2816482e-01 -2.2498456e-01
2.4233446e-01 4.8442066e-01 -6.1291456e-01 7.9727012e-01
6.2785017e-01 3.3752728e-01 1.9533105e-01 1.2884736e-01
6.4012814e-01 1.7992549e-01 1.2494603e+00 -6.4503664e-01
-4.6780575e-02 1.3180709e-01 -3.0990514e-01 -8.2724065e-01
-7.1381533e-01 -8.2139474e-01 -1.6837937e+00 -9.4128035e-02
6.6115461e-02 -1.9799855e-01 7.9328078e-01 7.5579363e-01
3.0564088e-01 1.7500727e-01 -1.3437130e-01 -1.8050125e+00
-3.0112785e-01 -6.8811262e-01 -5.6552571e-01 6.4793706e-01
4.4056070e-01 -1.0500915e+00 -3.7068939e-01 4.0108955e-01] | [7.017403602600098, -1.2282941341400146] |
93d8faaf-efac-495e-8289-7367c2e8fa78 | oimnet-prototypical-normalization-and | 2207.10320 | null | https://arxiv.org/abs/2207.10320v1 | https://arxiv.org/pdf/2207.10320v1.pdf | OIMNet++: Prototypical Normalization and Localization-aware Learning for Person Search | We address the task of person search, that is, localizing and re-identifying query persons from a set of raw scene images. Recent approaches are typically built upon OIMNet, a pioneer work on person search, that learns joint person representations for performing both detection and person re-identification (reID) tasks. To obtain the representations, they extract features from pedestrian proposals, and then project them on a unit hypersphere with L2 normalization. These methods also incorporate all positive proposals, that sufficiently overlap with the ground truth, equally to learn person representations for reID. We have found that 1) the L2 normalization without considering feature distributions degenerates the discriminative power of person representations, and 2) positive proposals often also depict background clutter and person overlaps, which could encode noisy features to person representations. In this paper, we introduce OIMNet++ that addresses the aforementioned limitations. To this end, we introduce a novel normalization layer, dubbed ProtoNorm, that calibrates features from pedestrian proposals, while considering a long-tail distribution of person IDs, enabling L2 normalized person representations to be discriminative. We also propose a localization-aware feature learning scheme that encourages better-aligned proposals to contribute more in learning discriminative representations. Experimental results and analysis on standard person search benchmarks demonstrate the effectiveness of OIMNet++. | ['Bumsub Ham', 'Junghyup Lee', 'Donghyeon Baek', 'Youngmin Oh', 'SangHoon Lee'] | 2022-07-21 | null | null | null | null | ['person-search'] | ['computer-vision'] | [-1.33661777e-01 -2.63863295e-01 1.89521089e-02 -5.59167862e-01
-4.40889031e-01 -4.41374749e-01 8.96973014e-01 1.93914771e-02
-8.18976760e-01 5.48610508e-01 4.25272077e-01 4.10396427e-01
-5.43607166e-03 -9.14955258e-01 -5.34404218e-01 -5.72777152e-01
1.00385450e-01 7.20405102e-01 2.19437346e-01 2.60138754e-02
-3.47869322e-02 5.83319366e-01 -1.73927307e+00 -2.08105817e-02
6.56620681e-01 6.67280734e-01 -1.39726177e-01 4.33696270e-01
-3.16787548e-02 2.90594250e-01 -9.42292273e-01 -6.98815823e-01
4.56672579e-01 1.41657284e-03 -6.11043036e-01 -6.18613660e-02
8.08608413e-01 -4.45757687e-01 -8.18170130e-01 9.59766448e-01
8.27039242e-01 4.84132916e-01 9.95375693e-01 -1.35974324e+00
-9.41998959e-01 1.71977401e-01 -7.76693404e-01 5.15914738e-01
6.45926952e-01 3.11325639e-01 6.97351456e-01 -1.17724288e+00
2.24019542e-01 1.71186042e+00 7.97334254e-01 7.84514368e-01
-1.24086893e+00 -8.32513273e-01 3.42026800e-01 2.55518615e-01
-2.02855897e+00 -3.12679619e-01 4.99484837e-01 -3.90599728e-01
6.48898602e-01 4.40168679e-01 6.12518370e-01 1.43133247e+00
-3.36788923e-01 1.13480818e+00 6.79786325e-01 -3.42434824e-01
-1.47380263e-01 3.73562276e-01 4.72508371e-01 5.07033467e-01
5.76181531e-01 2.07628086e-01 -4.32035863e-01 -4.05866653e-01
8.16079915e-01 3.18003774e-01 -2.51538575e-01 -5.66357851e-01
-1.12722862e+00 6.47098124e-01 7.52737164e-01 9.52376872e-02
-3.72914582e-01 -2.50698347e-02 3.75968367e-01 -1.21506944e-01
1.17654324e-01 5.64788617e-02 1.83412388e-01 4.22513902e-01
-7.87072182e-01 6.07692540e-01 5.61014891e-01 1.15581894e+00
9.15420711e-01 -3.81728172e-01 -9.31800246e-01 9.98944759e-01
2.28813320e-01 7.77489007e-01 5.41140556e-01 -3.23524356e-01
4.71705467e-01 8.68478298e-01 4.56724674e-01 -1.14576328e+00
-4.38646227e-01 -5.03881454e-01 -9.18844223e-01 -3.06079686e-01
4.76560026e-01 2.17338204e-01 -9.38515306e-01 1.80557179e+00
3.27649087e-01 2.53794670e-01 -9.14337412e-02 1.29762387e+00
1.16706133e+00 3.60383332e-01 3.36610675e-01 4.08357829e-01
1.63453174e+00 -9.95594740e-01 -3.15891653e-01 -3.98882508e-01
4.54654992e-02 -3.90797347e-01 9.04875040e-01 -1.96574941e-01
-7.85803437e-01 -8.61111164e-01 -7.85214543e-01 6.09560171e-03
-6.54308558e-01 3.60834152e-01 4.45734322e-01 9.95290160e-01
-1.00058746e+00 2.44113982e-01 -3.52536500e-01 -7.95221269e-01
4.39940840e-01 4.73872066e-01 -4.36237961e-01 -2.63728321e-01
-1.07166386e+00 7.41477370e-01 3.21199596e-01 3.21433954e-02
-6.93437696e-01 -5.42396545e-01 -8.88111949e-01 1.36366919e-01
2.10033640e-01 -1.04079163e+00 8.19769621e-01 -6.16633654e-01
-8.07932317e-01 1.20770752e+00 -3.98908973e-01 -4.12611485e-01
7.07035005e-01 -2.59040505e-01 -4.89396274e-01 -9.51512754e-02
5.78065813e-01 7.85640299e-01 7.25745618e-01 -1.37246442e+00
-6.97494626e-01 -4.67613727e-01 -1.04916804e-01 2.47312978e-01
-5.14175713e-01 3.57941806e-01 -1.02892375e+00 -7.64861763e-01
-1.37723191e-02 -7.00877070e-01 -3.20035338e-01 1.05593346e-01
-5.57888746e-01 -6.53308332e-01 3.25401068e-01 -5.12042701e-01
9.88400161e-01 -1.94668984e+00 -9.03356150e-02 5.92095196e-01
3.45851451e-01 1.93328544e-01 -3.32081825e-01 2.31686518e-01
1.28041610e-01 -1.54432923e-01 1.44172341e-01 -7.52838910e-01
2.39634037e-01 1.46856129e-01 -2.38080278e-01 7.32690930e-01
1.79465190e-01 1.10604441e+00 -9.41986799e-01 -5.54860413e-01
2.27690712e-01 5.83983362e-01 -2.98352659e-01 2.10828468e-01
5.44672132e-01 4.04548764e-01 -4.77700591e-01 8.30402315e-01
8.81451845e-01 -1.29728526e-01 -2.69490004e-01 -2.41184235e-01
-5.39810061e-02 -1.55683085e-01 -1.38230729e+00 1.30046856e+00
1.58529073e-01 3.17623675e-01 -1.41213879e-01 -8.31972122e-01
1.04582989e+00 -9.43675265e-02 4.71524358e-01 -7.16486275e-01
7.24189952e-02 -9.73668024e-02 -5.34380078e-01 -1.69032559e-01
6.64623141e-01 4.12236691e-01 -2.55021334e-01 2.95909315e-01
4.10090312e-02 5.09241223e-01 1.70328051e-01 1.31122306e-01
7.32535779e-01 -2.08581626e-01 3.71776283e-01 -2.67332286e-01
9.07487690e-01 -3.39432210e-01 5.68921685e-01 1.46564925e+00
-6.31485522e-01 7.08019078e-01 -1.39499694e-01 -7.85944760e-01
-9.17629898e-01 -1.45784187e+00 -2.44570211e-01 1.38007808e+00
5.97011626e-01 -3.56010050e-01 -7.44098663e-01 -7.63266623e-01
3.68712813e-01 3.40073049e-01 -7.62095630e-01 -1.38587251e-01
-7.40329087e-01 -8.74430299e-01 8.37700129e-01 6.84399307e-01
5.97692609e-01 -8.11143100e-01 -3.50465864e-01 4.38457355e-02
-2.70167708e-01 -9.02566373e-01 -7.85582721e-01 -3.06034148e-01
-6.14093803e-02 -1.24285710e+00 -1.45041466e+00 -8.86143565e-01
9.43167150e-01 6.64722145e-01 9.82184768e-01 1.12037830e-01
-6.71902418e-01 1.01357305e+00 -1.59356207e-01 -3.90619189e-01
2.14217246e-01 -1.72595590e-01 6.67273998e-01 2.94603497e-01
9.86724854e-01 -2.01163992e-01 -8.40302050e-01 7.44648278e-01
-4.17419851e-01 -3.91287208e-01 4.66980159e-01 8.53818536e-01
6.35828495e-01 -1.76748738e-01 3.66126001e-01 -2.82882690e-01
5.42676270e-01 -4.53305930e-01 -2.88297385e-01 5.20677567e-01
-2.79235095e-01 -1.88748822e-01 2.04929098e-01 -6.15629494e-01
-8.01787496e-01 -6.98540658e-02 1.54350400e-01 -4.19041127e-01
-3.21016222e-01 -2.57351905e-01 -2.33126685e-01 -3.07733864e-01
8.59642148e-01 5.27990758e-01 -3.63492429e-01 -5.28158069e-01
3.72044981e-01 6.23381197e-01 1.02083755e+00 -8.55598032e-01
1.10210848e+00 6.68906927e-01 -3.63378048e-01 -6.35424614e-01
-6.35065913e-01 -1.10045218e+00 -7.77380228e-01 -2.07286373e-01
7.05337763e-01 -1.12836993e+00 -8.12342167e-01 2.46430188e-01
-1.12552130e+00 3.06557536e-01 -3.45171809e-01 4.35779810e-01
-2.58984536e-01 6.92140698e-01 -2.53586441e-01 -1.06114793e+00
-5.00921547e-01 -7.60893404e-01 1.26992202e+00 7.76687622e-01
-1.35424167e-01 -5.72914779e-01 1.11056499e-01 2.62407392e-01
3.30608726e-01 9.04048905e-02 2.72447050e-01 -9.09385264e-01
-4.44832265e-01 -5.99882066e-01 -7.51583397e-01 -4.28708643e-02
9.58178788e-02 -6.50601566e-01 -1.18143666e+00 -7.29328215e-01
-5.23167551e-01 -1.08121306e-01 1.12150669e+00 1.02011427e-01
1.16386890e+00 -3.35617930e-01 -9.12569165e-01 7.03115463e-01
1.00420511e+00 -3.59656364e-01 4.33716744e-01 5.60030580e-01
6.58083498e-01 6.53530061e-01 3.04676235e-01 6.90815568e-01
7.33260095e-01 9.03997958e-01 1.14680380e-01 -2.53662288e-01
-3.54591638e-01 -4.69909787e-01 5.09767756e-02 -5.36867045e-02
-4.09956634e-01 -1.96097955e-01 -8.47638190e-01 6.32474840e-01
-1.96341145e+00 -1.04518270e+00 4.96238582e-02 2.34483862e+00
5.34430265e-01 -2.89642513e-01 6.02860093e-01 -1.48468047e-01
1.13980877e+00 -1.45593777e-01 -5.68764746e-01 5.11914134e-01
-4.00194377e-01 -4.88104187e-02 5.77047765e-01 5.07060140e-02
-1.49788749e+00 9.09930646e-01 6.12821960e+00 6.83683038e-01
-4.98415828e-01 5.32409698e-02 2.27673978e-01 -6.56133294e-02
5.67719787e-02 -3.97705317e-01 -1.31934094e+00 5.31677723e-01
2.88499385e-01 -3.81051511e-01 2.51780510e-01 1.03848290e+00
-8.62553492e-02 2.96758682e-01 -1.26628625e+00 1.58138609e+00
4.26379353e-01 -8.54473054e-01 3.01462203e-01 -6.75222725e-02
5.83591580e-01 -2.59889275e-01 1.08098596e-01 6.54228866e-01
3.18418831e-01 -1.09368801e+00 7.99565554e-01 8.04124177e-01
5.45456588e-01 -7.28834033e-01 8.66912305e-01 3.38755608e-01
-1.66862369e+00 -2.99667567e-01 -7.95101583e-01 3.36529702e-01
1.49358124e-01 9.58301798e-02 -9.13226426e-01 6.02668524e-01
1.12113690e+00 6.03886724e-01 -1.08545721e+00 1.68062150e+00
3.84237170e-02 4.56562564e-02 -5.04425526e-01 1.60452370e-02
-3.54301371e-02 2.89938003e-02 6.90779269e-01 1.70234859e+00
2.12435886e-01 3.82910017e-04 7.50953078e-01 1.21588087e+00
9.08387601e-02 1.63789123e-01 -4.61748958e-01 4.80065644e-01
5.81464767e-01 1.01992774e+00 -5.51103950e-01 -3.34605247e-01
-4.47474986e-01 1.20820844e+00 4.02480900e-01 6.68453574e-01
-8.25416803e-01 -1.68898284e-01 8.61783624e-01 1.60833523e-01
9.35330018e-02 -4.46674973e-02 6.53485283e-02 -1.25100386e+00
1.48907721e-01 -4.29508150e-01 8.38194907e-01 -3.00950795e-01
-1.96425986e+00 4.02357996e-01 2.05146536e-01 -1.06335175e+00
-5.31697683e-02 -5.77961981e-01 -7.04722643e-01 1.10304165e+00
-1.52461112e+00 -1.60111046e+00 -6.78838491e-01 9.43956316e-01
3.16889375e-01 -4.13957715e-01 6.34480357e-01 4.82186168e-01
-8.46482217e-01 1.35674727e+00 -2.13565543e-01 5.74713171e-01
8.77975643e-01 -1.09055948e+00 5.80582738e-01 7.85618484e-01
5.01617379e-02 1.16603601e+00 4.94544238e-01 -7.19500542e-01
-1.16653192e+00 -1.25327516e+00 7.52157509e-01 -7.84641087e-01
2.69311517e-01 -4.61598694e-01 -8.37242007e-01 6.29269302e-01
-3.91086131e-01 2.05172300e-01 7.62888432e-01 1.22943051e-01
-6.01383984e-01 -4.75152880e-02 -1.33862650e+00 5.76406598e-01
1.44147873e+00 -5.14001429e-01 -5.51927388e-01 4.37887967e-01
2.94290543e-01 -1.36312053e-01 -5.17474174e-01 3.04455012e-01
5.28883338e-01 -8.92488658e-01 1.83018517e+00 -6.45526528e-01
-6.03773773e-01 -4.96627748e-01 -2.42058367e-01 -7.77306616e-01
-7.43625998e-01 -2.59838372e-01 -9.30062830e-02 1.40421057e+00
-1.76649809e-01 -6.77783787e-01 9.54553187e-01 7.53350616e-01
2.88346887e-01 -3.46439630e-01 -1.10598886e+00 -1.05440581e+00
-1.43120974e-01 2.07261797e-02 8.94395292e-01 6.21515155e-01
-4.33479786e-01 -1.09977834e-01 -5.16015112e-01 5.11666119e-01
1.04019368e+00 -1.25917733e-01 1.21489275e+00 -1.41594207e+00
-9.08829793e-02 -5.77178001e-01 -8.60070407e-01 -1.34534562e+00
2.19164610e-01 -9.28564191e-01 -1.60951761e-03 -1.47350180e+00
7.52459168e-01 -5.73751867e-01 -4.42342252e-01 5.47815800e-01
-5.23225546e-01 2.34822944e-01 2.20878035e-01 5.86427689e-01
-7.31117845e-01 7.03392267e-01 7.04918623e-01 -5.26869833e-01
-9.48824957e-02 2.97104061e-01 -7.04860091e-01 6.41858339e-01
3.75677973e-01 -1.53215379e-01 8.78310390e-03 -3.80893022e-01
-4.58065689e-01 -7.44895697e-01 1.11769950e+00 -1.21544206e+00
5.69186985e-01 6.47434443e-02 1.11488998e+00 -7.88544714e-01
6.05444849e-01 -6.19509757e-01 -1.45741045e-01 2.81276911e-01
-1.02679156e-01 -5.90212345e-02 -2.10764864e-03 8.46410930e-01
7.13867042e-03 -5.81710525e-02 6.11747921e-01 -2.18897656e-01
-1.02621710e+00 5.94919622e-01 5.80577329e-02 -1.14105791e-01
9.02293742e-01 -4.65498567e-01 -2.99553156e-01 -2.10059524e-01
-4.56629157e-01 5.10139465e-01 5.22277594e-01 5.53532898e-01
6.73480332e-01 -1.50419247e+00 -8.07677388e-01 3.30269426e-01
4.47083831e-01 -2.59140462e-01 1.64495528e-01 4.54118431e-01
-4.71208021e-02 4.93801862e-01 -6.51947856e-02 -8.15081894e-01
-1.14561152e+00 7.05124736e-01 4.59322870e-01 -3.56866047e-02
-8.25800478e-01 1.05066657e+00 6.62998378e-01 -6.97095037e-01
6.33238196e-01 2.27432817e-01 -5.65481842e-01 1.26002245e-02
9.78701472e-01 4.30091441e-01 -3.85066509e-01 -1.05595338e+00
-7.14231491e-01 7.01259196e-01 -1.79026499e-01 1.49930537e-01
8.88593137e-01 -1.89420283e-01 1.38471320e-01 -2.08280817e-01
8.47297549e-01 -6.84884340e-02 -1.16439128e+00 -6.68660522e-01
1.16643406e-01 -7.55550504e-01 -4.78954613e-01 -5.14035046e-01
-7.47346997e-01 4.32213932e-01 9.71662939e-01 -1.31496981e-01
6.74737036e-01 2.56469280e-01 6.96711063e-01 5.32529891e-01
6.66052043e-01 -9.41459119e-01 1.29833952e-01 3.24885637e-01
8.57887387e-01 -1.31495786e+00 -2.50400888e-04 -3.89554232e-01
-4.68604773e-01 9.67925906e-01 8.93419206e-01 -3.60764144e-03
2.79706031e-01 -3.05665374e-01 -2.63405532e-01 -3.53987627e-02
2.04874575e-01 -6.46865606e-01 6.46299005e-01 1.14909410e+00
-1.50552183e-01 1.69316456e-01 1.08209617e-01 1.22361958e+00
-2.16327339e-01 -2.70838767e-01 -1.32778853e-01 6.25174105e-01
-5.06273329e-01 -8.89241219e-01 -9.24664199e-01 2.42154926e-01
1.23140849e-01 1.07274458e-01 -5.34646213e-01 6.71991825e-01
3.90143484e-01 8.35943162e-01 1.46182433e-01 -3.17627639e-01
7.34037697e-01 -4.92776483e-02 3.80957335e-01 -4.06630516e-01
-4.69017297e-01 -4.24569100e-01 -2.02443510e-01 -5.10525048e-01
-1.78283066e-01 -6.49568379e-01 -9.16929245e-01 -3.34096938e-01
-7.28106499e-02 5.42771965e-02 3.19247484e-01 7.37279236e-01
2.37390876e-01 1.36312455e-01 3.47012460e-01 -1.22714651e+00
-6.84811711e-01 -7.83659756e-01 -3.57859135e-01 5.93665779e-01
2.19055891e-01 -1.09701920e+00 -1.71204537e-01 -2.45551124e-01] | [14.788959503173828, 0.8610552549362183] |
3fea7485-f329-483b-8ce8-3ce9ebd28f55 | neighborhood-collective-estimation-for-noisy | 2208.03207 | null | https://arxiv.org/abs/2208.03207v1 | https://arxiv.org/pdf/2208.03207v1.pdf | Neighborhood Collective Estimation for Noisy Label Identification and Correction | Learning with noisy labels (LNL) aims at designing strategies to improve model performance and generalization by mitigating the effects of model overfitting to noisy labels. The key success of LNL lies in identifying as many clean samples as possible from massive noisy data, while rectifying the wrongly assigned noisy labels. Recent advances employ the predicted label distributions of individual samples to perform noise verification and noisy label correction, easily giving rise to confirmation bias. To mitigate this issue, we propose Neighborhood Collective Estimation, in which the predictive reliability of a candidate sample is re-estimated by contrasting it against its feature-space nearest neighbors. Specifically, our method is divided into two steps: 1) Neighborhood Collective Noise Verification to separate all training samples into a clean or noisy subset, 2) Neighborhood Collective Label Correction to relabel noisy samples, and then auxiliary techniques are used to assist further model optimization. Extensive experiments on four commonly used benchmark datasets, i.e., CIFAR-10, CIFAR-100, Clothing-1M and Webvision-1.0, demonstrate that our proposed method considerably outperforms state-of-the-art methods. | ['Yizhou Yu', 'Feng Liu', 'Guanbin Li', 'Jichang Li'] | 2022-08-05 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 2.24636495e-01 -2.88285762e-01 -5.79090901e-02 -7.17942834e-01
-1.50390911e+00 -5.96972406e-01 3.32623929e-01 2.23280728e-01
-3.79425853e-01 8.21494401e-01 1.81169078e-01 -4.29446101e-02
-1.92811400e-01 -3.04725260e-01 -5.18013239e-01 -1.10234392e+00
3.89809519e-01 1.75286919e-01 -9.10090581e-02 3.68664861e-01
1.27995759e-01 1.23378374e-01 -1.53675449e+00 1.77570164e-01
9.00142848e-01 1.29619241e+00 6.60982579e-02 2.95797616e-01
-7.12584853e-02 9.25297260e-01 -6.31054699e-01 -1.84830382e-01
1.53754547e-01 -2.98894584e-01 -6.16585433e-01 1.75727874e-01
5.10677397e-01 1.33410580e-02 8.27649608e-02 1.52959752e+00
5.88584900e-01 4.06568497e-01 4.82006669e-01 -1.06607282e+00
-7.11366832e-01 6.96676016e-01 -5.80765307e-01 9.76637453e-02
-2.35621497e-01 2.33714059e-01 9.12543833e-01 -1.17364550e+00
2.72343457e-01 1.34636140e+00 8.93097341e-01 7.39127219e-01
-1.54074419e+00 -9.40914392e-01 3.27307642e-01 -3.64034288e-02
-1.75007284e+00 -6.27993286e-01 7.71538317e-01 -3.37850630e-01
2.09157214e-01 3.25167865e-01 1.02626672e-02 1.17651880e+00
-1.71921030e-01 5.59432447e-01 1.26833582e+00 -3.02150607e-01
5.39312243e-01 1.73882529e-01 6.54370070e-01 2.98329055e-01
1.94311306e-01 7.10757077e-02 -4.85770375e-01 -5.17714202e-01
1.12728499e-01 -7.50818700e-02 -3.50555867e-01 1.70696732e-02
-1.10009348e+00 5.82294941e-01 4.82117265e-01 -5.07582389e-02
-3.06037873e-01 1.22168727e-01 5.10134101e-01 1.41349792e-01
7.86637604e-01 1.96831524e-01 -5.90867817e-01 1.31815463e-01
-8.62878978e-01 2.42279410e-01 2.54993320e-01 8.18118513e-01
7.94520557e-01 -1.17084973e-01 -4.45182800e-01 1.21366167e+00
4.61674929e-01 5.32890916e-01 4.18987811e-01 -9.72730875e-01
5.79422951e-01 3.25926065e-01 1.73938051e-01 -9.56706822e-01
-3.69051158e-01 -8.86086166e-01 -1.15642679e+00 -9.90946069e-02
1.98796555e-01 -1.18420266e-01 -1.08576572e+00 1.86311018e+00
5.51605284e-01 5.42170346e-01 1.32925883e-01 9.03810441e-01
8.83721113e-01 3.84992689e-01 5.08274138e-01 -2.25577384e-01
9.81061697e-01 -9.07514453e-01 -7.07612574e-01 -2.24335417e-01
9.08356249e-01 -8.18571150e-01 1.10072780e+00 5.13675809e-01
-4.72751796e-01 -7.21258461e-01 -6.11203671e-01 1.14721499e-01
-1.56591445e-01 4.43270206e-01 2.78595626e-01 5.85146725e-01
-6.29023731e-01 6.60204947e-01 -5.62394440e-01 6.92994222e-02
7.91846037e-01 1.83892339e-01 -3.45001966e-01 -3.27988595e-01
-9.14215088e-01 4.27313089e-01 4.32169080e-01 4.27780479e-01
-8.88842881e-01 -7.39820659e-01 -7.53091514e-01 3.52563299e-02
5.83630621e-01 -3.34352732e-01 1.28784359e+00 -7.69801617e-01
-9.17693079e-01 6.21467173e-01 -3.34222764e-01 -3.03000331e-01
4.56560045e-01 -2.51325279e-01 -5.53704977e-01 -4.78095651e-01
2.81169325e-01 5.18308043e-01 7.69585192e-01 -1.67427623e+00
-9.64048266e-01 -4.54192042e-01 -5.40580988e-01 -1.99006405e-02
-3.19635093e-01 -2.14030921e-01 -2.86512554e-01 -8.74779522e-01
7.29288518e-01 -8.08216333e-01 -3.54137033e-01 -2.95108140e-01
-6.98093235e-01 -5.07355690e-01 9.12093520e-01 -3.76314521e-01
1.30582476e+00 -2.17785692e+00 -3.95379722e-01 5.81080496e-01
4.91317630e-01 4.65734661e-01 -4.51895565e-01 -2.00203136e-01
-1.51783422e-01 3.51494640e-01 -1.46288089e-02 -8.05112481e-01
-1.83863178e-01 1.67343870e-01 -3.68957788e-01 5.93016982e-01
1.94909483e-01 6.04437470e-01 -1.07853544e+00 -3.94307554e-01
2.77284067e-02 3.91225576e-01 -1.87753931e-01 -4.45747748e-02
1.74389258e-02 6.16693318e-01 -3.54449481e-01 7.99150229e-01
1.02777636e+00 -2.49919072e-01 -7.91578069e-02 -4.37311172e-01
2.12261170e-01 3.01588297e-01 -1.65566111e+00 1.16481769e+00
-2.72220790e-01 2.08329514e-01 1.24066435e-01 -8.10514748e-01
1.13604331e+00 1.32693753e-01 1.37949511e-01 -5.18566072e-01
2.41486058e-01 2.67227024e-01 -4.48798656e-01 -4.85818535e-01
2.80333191e-01 4.05161828e-02 -1.78364553e-02 2.56049901e-01
-1.72247719e-02 2.78480887e-01 2.09983010e-02 -8.31891596e-03
8.83845806e-01 -1.87920734e-01 8.35773200e-02 -3.28583062e-01
5.77423215e-01 -3.32763672e-01 1.18015087e+00 1.19759512e+00
-5.42434216e-01 8.01740170e-01 2.88988143e-01 -3.32306832e-01
-7.42803991e-01 -9.48920131e-01 -2.41182387e-01 8.77143860e-01
2.60042965e-01 -4.16327685e-01 -7.79799879e-01 -1.09706187e+00
-2.15621945e-02 7.69607544e-01 -6.12249434e-01 -2.87147790e-01
-2.95022160e-01 -1.03348696e+00 5.30139923e-01 5.27171314e-01
4.28600132e-01 -7.17896819e-01 1.96337789e-01 1.40312286e-02
-4.08032358e-01 -8.80329728e-01 -6.05776250e-01 5.93795121e-01
-7.17812538e-01 -9.46384490e-01 -1.54142007e-01 -8.78574967e-01
9.23785985e-01 7.10216343e-01 1.02181196e+00 4.26939100e-01
6.12544753e-02 -1.54365271e-01 -4.21118915e-01 -2.23207340e-01
-2.42721751e-01 -4.38804142e-02 3.61712933e-01 1.67594582e-01
6.28883243e-01 -3.94815981e-01 -3.42916518e-01 5.72783351e-01
-8.67307901e-01 -2.63029754e-01 3.34701061e-01 1.22272694e+00
1.22417784e+00 4.09479618e-01 8.77080023e-01 -1.24363184e+00
5.14447510e-01 -7.30609298e-01 -5.65096378e-01 4.31082964e-01
-6.85687959e-01 2.45071715e-03 8.90992761e-01 -6.02881074e-01
-1.04617858e+00 3.05777311e-01 -1.11833490e-01 -3.51005822e-01
-3.33781719e-01 3.03249389e-01 -4.36111152e-01 -3.06743272e-02
8.75739694e-01 1.54387623e-01 -4.17023331e-01 -8.70795965e-01
2.70989299e-01 1.01679409e+00 7.10611045e-01 -5.47674954e-01
8.15679669e-01 3.11422467e-01 -9.79574919e-02 -5.42274415e-01
-1.49211550e+00 -7.79188752e-01 -5.22920549e-01 4.97939624e-02
3.41451943e-01 -1.07271135e+00 -5.34439504e-01 5.30320644e-01
-8.87142718e-01 -6.88884482e-02 -3.02705586e-01 3.91013891e-01
-9.91899818e-02 3.06318641e-01 -4.40236807e-01 -9.64653552e-01
-3.26382190e-01 -1.21570575e+00 1.19033778e+00 4.17421699e-01
-1.11007623e-01 -7.26792336e-01 -1.67125404e-01 5.12639880e-01
2.72907346e-01 -2.52142139e-02 7.97486007e-01 -9.49371457e-01
-3.61855179e-01 -2.90280402e-01 -3.61132324e-01 8.26924086e-01
1.98021129e-01 -1.93586171e-01 -1.35621524e+00 -2.86977053e-01
7.29171783e-02 -5.04894137e-01 1.06722820e+00 2.74552166e-01
1.50346398e+00 -6.16076030e-02 -3.27144146e-01 6.31880105e-01
1.34909952e+00 -2.47252155e-02 3.17241371e-01 1.73456162e-01
8.33325028e-01 2.69111544e-01 7.89248526e-01 1.56242937e-01
1.50856704e-01 3.97872061e-01 3.81511956e-01 1.18646085e-01
-2.05019295e-01 -4.01361912e-01 -1.01503789e-01 7.47080207e-01
5.07387698e-01 -2.26187885e-01 -6.78735018e-01 4.71122205e-01
-1.96472251e+00 -6.16441488e-01 -2.81232655e-01 2.27598381e+00
7.39865601e-01 9.48491991e-02 -3.50498199e-01 3.42666924e-01
9.21725869e-01 -8.14086199e-02 -6.20387971e-01 3.83703768e-01
-3.76749545e-01 -2.82965153e-01 4.83637661e-01 4.73660111e-01
-1.57603264e+00 9.10166562e-01 5.65605068e+00 1.39139915e+00
-8.33007455e-01 2.50009030e-01 1.23736763e+00 -1.25697777e-01
-2.04953551e-01 -3.48553360e-02 -1.15253425e+00 6.65777385e-01
6.23637795e-01 2.37332314e-01 4.06578630e-01 1.05634654e+00
4.67200041e-01 -2.10270047e-01 -8.88229132e-01 1.18251038e+00
-2.47171223e-02 -1.11201990e+00 -1.18611820e-01 -2.93150514e-01
1.02621543e+00 -3.59552354e-02 1.19126268e-01 4.61466938e-01
3.58129531e-01 -1.02643275e+00 7.39838898e-01 7.34469712e-01
4.11224037e-01 -8.90011847e-01 1.14592230e+00 5.91862082e-01
-9.97412443e-01 -2.77541727e-01 -6.46898687e-01 2.72294551e-01
-2.21597433e-01 1.25981450e+00 -5.07911563e-01 2.45977774e-01
9.85409737e-01 5.61804831e-01 -7.47723639e-01 1.19073999e+00
-2.30090499e-01 9.85596776e-01 -3.35449606e-01 1.60747960e-01
2.78141275e-02 -1.85144413e-02 2.99912006e-01 9.95478451e-01
1.92649975e-01 1.01505868e-01 4.29013491e-01 8.04474413e-01
-4.34158951e-01 4.88549098e-02 -1.25446737e-01 4.09515589e-01
8.71240973e-01 1.41565037e+00 -6.88488364e-01 -3.42482775e-01
-1.84807464e-01 6.18245780e-01 4.93685812e-01 4.35606837e-01
-6.71566606e-01 -3.43545675e-01 6.99515700e-01 -2.30413929e-01
3.49501893e-02 1.74992278e-01 -7.12084174e-01 -1.08744133e+00
1.56031415e-01 -8.89553785e-01 2.39295959e-01 -5.63667715e-01
-1.77943122e+00 6.65960729e-01 -5.92122316e-01 -1.21965671e+00
2.74750322e-01 -1.23431750e-01 -4.30449843e-01 1.04544806e+00
-1.53662455e+00 -1.04526162e+00 -5.26185393e-01 1.82339966e-01
6.37067139e-01 6.46534702e-03 6.60771430e-01 4.37353969e-01
-8.05917263e-01 9.09405231e-01 6.30230546e-01 1.39202759e-01
9.09871638e-01 -1.13947284e+00 3.25794607e-01 9.35673416e-01
5.03167100e-02 5.66063881e-01 4.83802170e-01 -8.95068884e-01
-6.89090014e-01 -1.70796251e+00 1.07565236e+00 -4.49020505e-01
4.93377626e-01 -4.30746078e-01 -1.25768709e+00 4.26948547e-01
-7.00601697e-01 6.94447637e-01 5.42678475e-01 3.36712487e-02
-4.93143946e-01 -4.96402800e-01 -1.38311052e+00 4.75837648e-01
1.08529544e+00 -3.46220315e-01 -1.17671065e-01 6.40841246e-01
7.75774598e-01 -3.72566968e-01 -6.68370008e-01 4.53403473e-01
1.48342028e-01 -7.21857011e-01 7.63394713e-01 -5.67964256e-01
3.47117223e-02 -6.08528793e-01 -5.03781617e-01 -1.46980608e+00
-4.70111787e-01 -4.01611984e-01 7.92304948e-02 1.79176331e+00
3.99954557e-01 -3.78001511e-01 8.59541357e-01 5.64700902e-01
-9.78164002e-02 -7.03398228e-01 -9.17221248e-01 -6.91400945e-01
-2.38904059e-01 -6.69254899e-01 7.09977269e-01 8.91800940e-01
-5.61864138e-01 2.30310410e-01 -6.21478021e-01 4.18319076e-01
9.60213125e-01 -3.67779940e-01 6.37080014e-01 -1.34378815e+00
-9.85936075e-03 -7.08061084e-02 -1.23012193e-01 -9.75059628e-01
2.53018469e-01 -7.37432361e-01 6.26764357e-01 -1.01829123e+00
2.45934710e-01 -9.51376379e-01 -6.54802442e-01 6.51674211e-01
-6.24582231e-01 6.06444180e-01 -4.90299240e-02 4.54204500e-01
-1.06670892e+00 6.53964996e-01 1.00420666e+00 -1.09097160e-01
-3.89650241e-02 3.09708834e-01 -9.90707934e-01 8.10759842e-01
8.07500362e-01 -1.00526822e+00 -3.58347565e-01 -3.37165356e-01
1.19239632e-02 -4.91921186e-01 3.47241849e-01 -1.08366835e+00
1.68360263e-01 -1.68430060e-01 5.20062268e-01 -5.86024642e-01
-1.94347017e-02 -9.14072871e-01 -1.00259043e-01 9.14310664e-02
-8.01123381e-01 -2.44705871e-01 -1.50833130e-01 9.47092891e-01
-1.79744050e-01 -5.75014472e-01 1.06427133e+00 1.19082130e-01
-6.74378395e-01 1.75227821e-01 -1.51029423e-01 1.92730531e-01
7.70804703e-01 3.17957520e-01 -5.99031806e-01 -1.18596599e-01
-8.81027222e-01 5.45645118e-01 1.93471313e-01 3.72064233e-01
4.53972995e-01 -1.43667340e+00 -6.83946550e-01 3.27167720e-01
3.72580439e-01 2.25044280e-01 5.25225878e-01 7.18333304e-01
6.79240525e-02 1.82021514e-01 5.85497677e-01 -7.98699141e-01
-1.45897257e+00 3.84617507e-01 3.50470722e-01 -2.30428100e-01
-4.50884104e-01 1.09473968e+00 -8.99242461e-02 -7.59145081e-01
6.87747955e-01 -2.62643307e-01 -1.34057269e-01 -2.77306229e-01
7.64946163e-01 5.42770267e-01 4.63256925e-01 -7.63717115e-01
-3.15815628e-01 5.44292092e-01 -1.56433299e-01 4.61404294e-01
1.13298357e+00 -5.42698741e-01 -6.91946000e-02 3.93841177e-01
1.17325163e+00 -1.23661093e-01 -1.33784747e+00 -8.23521793e-01
2.20598370e-01 -5.75552583e-01 2.87652731e-01 -9.96498585e-01
-1.15673065e+00 5.16850293e-01 9.26030815e-01 2.55820453e-02
1.25175428e+00 -9.73815992e-02 7.40577579e-01 5.25265992e-01
1.48437873e-01 -1.26758659e+00 -1.23863600e-01 5.62763989e-01
3.83691847e-01 -1.72915065e+00 -2.86022455e-01 -4.48258519e-01
-4.82161105e-01 7.59587824e-01 6.61898375e-01 5.49173765e-02
7.66407192e-01 1.31488353e-01 4.07948405e-01 9.81367156e-02
-7.12097645e-01 -8.48259963e-03 3.51225525e-01 6.55507922e-01
1.00958973e-01 6.54593706e-02 -1.56107411e-01 1.18339086e+00
7.37082511e-02 -5.17070517e-02 2.08737820e-01 6.78394377e-01
-6.37756228e-01 -8.46169174e-01 -7.23399222e-01 7.58805752e-01
-4.79880214e-01 -1.80864930e-01 -2.00655628e-02 2.96653539e-01
6.36423290e-01 1.40469205e+00 -1.41911224e-01 -6.62378430e-01
4.63605970e-01 1.31589502e-01 -2.46608555e-01 -6.11034811e-01
-5.77477217e-01 2.29119033e-01 -1.17913902e-01 -3.66733789e-01
-1.97307080e-01 -4.73983109e-01 -1.03802717e+00 -1.44894242e-01
-7.05492437e-01 4.03394222e-01 6.91564977e-01 1.13112330e+00
3.26372236e-01 4.60530400e-01 7.20565021e-01 -7.41957843e-01
-1.02903426e+00 -1.04831469e+00 -7.02947676e-01 6.43236935e-01
5.60917318e-01 -6.12043262e-01 -7.76455641e-01 3.33697721e-02] | [9.387531280517578, 3.892132520675659] |
2c976eda-e567-4be7-b8bc-f235291a1ba2 | mixture-of-linear-models-co-supervised-by | 2108.04035 | null | https://arxiv.org/abs/2108.04035v1 | https://arxiv.org/pdf/2108.04035v1.pdf | Mixture of Linear Models Co-supervised by Deep Neural Networks | Deep neural network (DNN) models have achieved phenomenal success for applications in many domains, ranging from academic research in science and engineering to industry and business. The modeling power of DNN is believed to have come from the complexity and over-parameterization of the model, which on the other hand has been criticized for the lack of interpretation. Although certainly not true for every application, in some applications, especially in economics, social science, healthcare industry, and administrative decision making, scientists or practitioners are resistant to use predictions made by a black-box system for multiple reasons. One reason is that a major purpose of a study can be to make discoveries based upon the prediction function, e.g., to reveal the relationships between measurements. Another reason can be that the training dataset is not large enough to make researchers feel completely sure about a purely data-driven result. Being able to examine and interpret the prediction function will enable researchers to connect the result with existing knowledge or gain insights about new directions to explore. Although classic statistical models are much more explainable, their accuracy often falls considerably below DNN. In this paper, we propose an approach to fill the gap between relatively simple explainable models and DNN such that we can more flexibly tune the trade-off between interpretability and accuracy. Our main idea is a mixture of discriminative models that is trained with the guidance from a DNN. Although mixtures of discriminative models have been studied before, our way of generating the mixture is quite different. | ['Jia Li', 'Lin Lin', 'Beomseok Seo'] | 2021-08-05 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 1.80335879e-01 4.50085133e-01 -3.53151619e-01 -6.34155035e-01
-1.77481189e-01 -4.83071804e-01 4.46115941e-01 7.74870738e-02
-1.38444126e-01 6.86607480e-01 2.45318264e-01 -8.08445990e-01
-4.74134743e-01 -7.37038076e-01 -5.62718868e-01 -6.86848581e-01
4.01882201e-01 5.55358469e-01 -5.59440032e-02 -1.04540616e-01
1.61332786e-01 5.49604535e-01 -1.33878374e+00 -4.46196124e-02
8.59990180e-01 9.17903304e-01 3.06607097e-01 2.47785211e-01
-3.42693657e-01 5.99450767e-01 -3.32397223e-01 -3.66073370e-01
1.42633319e-01 -6.68744862e-01 -5.92837751e-01 -1.31200790e-01
-1.77332699e-01 -1.77901715e-01 -1.66009590e-02 1.02772427e+00
-1.97181050e-02 -1.03391506e-01 6.55280173e-01 -1.24001276e+00
-7.16759026e-01 7.43799210e-01 -5.01787663e-01 -1.42969713e-01
-2.46698260e-01 1.38581172e-01 1.04269266e+00 -4.10823464e-01
2.72394776e-01 1.34812117e+00 5.22719204e-01 5.28461516e-01
-1.53121352e+00 -5.84105670e-01 1.82056278e-01 1.33337677e-01
-1.09986675e+00 -8.66322443e-02 6.62495553e-01 -5.31718314e-01
5.24776042e-01 3.15963238e-01 7.30552673e-01 1.10877049e+00
2.05024704e-01 4.90242481e-01 1.14557374e+00 -4.46289152e-01
3.29827785e-01 4.24432844e-01 8.60534012e-02 4.12425339e-01
7.66216993e-01 9.70560834e-02 -1.71310678e-01 1.43270520e-02
1.00766587e+00 2.91335702e-01 -1.95960552e-01 -2.82822162e-01
-9.38235462e-01 1.07695591e+00 4.43628550e-01 6.65602505e-01
-4.02092874e-01 1.27820387e-01 1.56914294e-01 2.91975617e-01
4.09267515e-01 7.92284310e-01 -8.12359273e-01 -2.62855887e-01
-9.94415581e-01 2.39033058e-01 7.52460957e-01 3.28891277e-01
8.26650262e-01 9.44298655e-02 2.83011138e-01 6.70134842e-01
4.78736609e-01 1.15049101e-01 5.93100250e-01 -9.02640045e-01
-3.65946367e-02 7.89934933e-01 -6.10957444e-02 -9.75163639e-01
-4.84228909e-01 -4.93270576e-01 -9.53874946e-01 2.84517705e-01
7.38779128e-01 -1.31537989e-01 -8.23864460e-01 1.80193150e+00
-6.01800792e-02 -1.91104993e-01 -2.28031442e-01 1.17558229e+00
5.14312506e-01 5.62249541e-01 1.18988223e-01 2.09371790e-01
1.29699457e+00 -6.19847417e-01 -4.74437863e-01 -4.83411431e-01
6.97373807e-01 -4.45301265e-01 9.99479711e-01 4.35523450e-01
-6.39751554e-01 -5.32336295e-01 -9.38441932e-01 -1.83945075e-01
-3.03055286e-01 -3.46284322e-02 8.63660336e-01 4.65514123e-01
-7.57094622e-01 8.73624802e-01 -1.04783952e+00 -4.86390173e-01
3.60255778e-01 3.90875846e-01 -3.24122012e-01 1.48041509e-02
-1.05842519e+00 1.10120046e+00 4.36343104e-01 2.00379819e-01
-3.68475795e-01 -4.37505633e-01 -6.33427680e-01 3.72572988e-01
3.57462555e-01 -8.49000633e-01 1.08458281e+00 -1.43562305e+00
-1.12119222e+00 5.02859175e-01 -2.63768375e-01 -6.00922763e-01
4.26661223e-01 -3.44070867e-02 -2.37471327e-01 -4.06996429e-01
-4.23699096e-02 6.00282669e-01 4.69091833e-01 -1.11984181e+00
-3.26952279e-01 -4.94966209e-01 1.57065809e-01 -2.60190457e-01
-9.01774392e-02 -1.81664243e-01 -4.82000224e-02 -5.60910344e-01
3.34217668e-01 -9.94371176e-01 -4.10979569e-01 1.58016272e-02
-5.67442536e-01 -2.16744319e-02 7.74708211e-01 -5.43695807e-01
1.00458264e+00 -1.95608270e+00 4.66596801e-03 2.31569022e-01
5.36064148e-01 2.87000358e-01 2.25593161e-04 2.72886813e-01
-3.40962350e-01 5.44582367e-01 -1.22017488e-01 -2.16562271e-01
-7.41876708e-03 4.77283448e-01 -3.74531955e-01 2.21939936e-01
4.41668212e-01 8.83651257e-01 -6.84253871e-01 -4.26436998e-02
3.21651936e-01 6.29720986e-01 -4.81017262e-01 2.78073624e-02
-1.82063863e-01 5.27876019e-01 -6.26805902e-01 1.69175372e-01
3.73798788e-01 -4.26551640e-01 2.83515692e-01 4.88007627e-02
3.01939137e-02 5.84831238e-01 -1.02998292e+00 1.09775400e+00
-3.70926768e-01 1.11970150e+00 -2.52340525e-01 -1.60108232e+00
9.44252551e-01 2.86291391e-01 3.79675508e-01 -5.82112849e-01
2.37777457e-01 3.29248726e-01 5.51167607e-01 -4.46534514e-01
4.04738784e-01 -7.11637318e-01 2.67600268e-01 5.07837713e-01
-2.60843396e-01 1.30887806e-01 -1.61511064e-01 -2.99478322e-01
9.25468326e-01 1.50508240e-01 4.09046322e-01 -1.73264131e-01
1.14165224e-01 8.62184465e-02 8.01811695e-01 6.20456815e-01
7.17981532e-02 6.68664813e-01 7.69994676e-01 -6.62733018e-01
-1.28255153e+00 -7.90887117e-01 -2.97955036e-01 7.38315821e-01
-1.43683687e-01 -1.97132260e-01 -6.16971433e-01 -5.26171684e-01
8.77218321e-02 9.21242893e-01 -6.86377525e-01 -3.02360773e-01
-3.33778292e-01 -7.06267834e-01 3.00559282e-01 6.70001686e-01
3.10679018e-01 -8.95156503e-01 -7.41631031e-01 1.77647486e-01
-5.02499267e-02 -1.00866199e+00 2.10310683e-01 4.38647836e-01
-1.21415687e+00 -9.04671550e-01 -3.77050757e-01 -2.54023343e-01
6.80732131e-01 3.20773631e-01 1.09060144e+00 2.32148051e-01
3.02262185e-03 -7.00702425e-03 -2.11141482e-01 -8.98093462e-01
-7.35395849e-01 3.24261338e-02 1.36624247e-01 -2.19553888e-01
7.45429039e-01 -6.64768755e-01 -4.43874836e-01 3.81296158e-01
-1.17830026e+00 2.88330287e-01 8.55543494e-01 7.25697756e-01
3.53605568e-01 1.59572124e-01 7.70424843e-01 -9.18677509e-01
5.08457303e-01 -5.33305347e-01 -3.94527912e-01 1.16113998e-01
-8.06658328e-01 4.93296266e-01 6.24773979e-01 -4.70656633e-01
-6.37500942e-01 -2.36568734e-01 -1.41226426e-01 -3.35949838e-01
-3.89659882e-01 7.95033276e-01 -2.15802640e-01 3.76632750e-01
5.86186230e-01 -2.54405523e-03 1.65564060e-01 -6.55567288e-01
1.71242133e-01 5.32320201e-01 1.09273501e-01 -5.40684760e-01
7.15284288e-01 2.92196691e-01 6.28029257e-02 -7.86803782e-01
-8.32889378e-01 -4.23354143e-03 -4.89196688e-01 1.69060640e-02
8.38163376e-01 -5.45329273e-01 -4.72236007e-01 -2.07344308e-01
-1.11192501e+00 -2.87732035e-01 -3.74564588e-01 6.52555466e-01
-3.68890643e-01 9.95680988e-02 -2.58493960e-01 -7.96582282e-01
1.97886154e-01 -1.27801192e+00 7.08831906e-01 3.43275100e-01
-7.20936418e-01 -1.34574044e+00 -2.35173211e-01 4.05609816e-01
6.00383580e-01 2.69959152e-01 1.25977182e+00 -9.83328938e-01
-5.72423756e-01 -3.62950832e-01 -3.40467483e-01 6.40309274e-01
3.39494914e-01 1.10395238e-01 -1.08523786e+00 6.41367659e-02
1.91207841e-01 -7.15081468e-02 7.91761816e-01 6.92772985e-01
1.42022383e+00 -3.44754577e-01 -3.03344309e-01 1.82631597e-01
1.23618495e+00 2.94752598e-01 6.07611418e-01 4.31891620e-01
6.13601446e-01 9.33390200e-01 1.68790907e-01 6.04674220e-02
3.12799960e-01 7.62712538e-01 4.69549954e-01 -2.79708475e-01
2.31010363e-01 -2.38635167e-01 1.96218401e-01 4.92314696e-01
-3.65343302e-01 -1.68278202e-01 -9.28409815e-01 3.12073499e-01
-2.13106394e+00 -9.87844408e-01 -1.50627658e-01 2.25871468e+00
4.71842200e-01 4.33960050e-01 1.37018576e-01 2.73591876e-01
5.03477871e-01 -1.90098017e-01 -5.64345777e-01 -6.74453259e-01
-2.17762142e-02 -1.35215327e-01 3.64590824e-01 2.81318188e-01
-5.32182992e-01 3.97108555e-01 5.73032045e+00 6.00259483e-01
-1.35217118e+00 -2.19733030e-01 1.07941198e+00 1.09757930e-01
-5.44219792e-01 3.11863571e-01 -5.79615176e-01 4.21558589e-01
9.52805519e-01 -1.87068596e-01 3.67320299e-01 9.20398772e-01
7.24635959e-01 -7.59866014e-02 -1.48882103e+00 7.67888963e-01
-4.80048239e-01 -1.23635995e+00 -9.54019092e-03 5.56392431e-01
4.28223461e-01 -1.64434351e-02 -2.07263492e-02 2.75501251e-01
1.70802385e-01 -1.41422391e+00 7.29501665e-01 4.86861169e-01
2.74938762e-01 -4.69181389e-01 9.31477964e-01 7.78333008e-01
-6.27139509e-01 -3.73409800e-02 -5.41115046e-01 -5.24619043e-01
-1.17576778e-01 9.03654516e-01 -8.83484900e-01 2.49003023e-01
4.14177179e-01 3.59332085e-01 -3.40652704e-01 9.46629286e-01
-2.36686379e-01 9.67992008e-01 -3.04557115e-01 -4.43120226e-02
3.66709352e-01 -3.37375641e-01 3.89222920e-01 9.89646375e-01
5.15572548e-01 -5.90099618e-02 -2.02570826e-01 1.36856735e+00
1.62178859e-01 -1.86693668e-01 -8.00829530e-01 -4.26521033e-01
2.77187020e-01 1.24448383e+00 -7.76937544e-01 -7.42281526e-02
-5.20177066e-01 3.86618674e-01 1.78695962e-01 3.13357055e-01
-7.28244901e-01 8.27944055e-02 7.88153410e-01 5.49403310e-01
1.48820251e-01 -2.87823856e-01 -7.03709841e-01 -1.06305254e+00
6.46504089e-02 -9.04848278e-01 -4.24529426e-02 -7.08493650e-01
-1.14543414e+00 4.45120931e-01 9.32099484e-03 -9.24189508e-01
-5.31025171e-01 -8.10255289e-01 -5.74545920e-01 9.95357931e-01
-1.26503658e+00 -8.13466012e-01 -1.37585774e-01 -1.77983776e-01
3.81433398e-01 5.43793216e-02 7.68527687e-01 7.93200359e-02
-6.26812041e-01 1.74402311e-01 1.91645294e-01 1.99031994e-01
4.51346517e-01 -1.20391595e+00 3.31495821e-01 6.16143584e-01
4.69656169e-01 8.69011581e-01 1.14662921e+00 -3.38385671e-01
-1.09162414e+00 -7.82668829e-01 9.17250752e-01 -5.45606256e-01
5.85559785e-01 -1.95509717e-01 -1.20156884e+00 7.34496951e-01
-1.17927335e-01 -1.96005672e-01 7.47873664e-01 4.56155747e-01
-3.72929484e-01 -2.33339444e-01 -9.64345396e-01 6.70512557e-01
4.55521196e-01 -2.86462128e-01 -4.47191685e-01 -7.89837614e-02
5.18729329e-01 -5.81040382e-02 -7.01975524e-01 2.54045844e-01
7.03359902e-01 -1.16873670e+00 6.29831433e-01 -8.27620208e-01
7.39561260e-01 -1.78049162e-01 -1.47585452e-01 -1.34911084e+00
-3.41332644e-01 -1.93786174e-01 1.22776091e-01 1.21603024e+00
5.38095295e-01 -8.08929682e-01 8.93445492e-01 1.24349666e+00
7.68203214e-02 -1.05414259e+00 -7.09437728e-01 -8.45589697e-01
2.00522363e-01 -9.48574126e-01 7.67063737e-01 9.15070176e-01
-1.32252276e-01 5.96782744e-01 -1.91528514e-01 -3.53628732e-02
3.55960429e-01 1.38920769e-01 7.89358020e-01 -1.59821737e+00
-4.79342103e-01 -7.89372504e-01 -5.23048043e-01 -1.04603398e+00
1.40469112e-02 -6.13579988e-01 -1.46134481e-01 -1.63762259e+00
3.38362336e-01 -6.06444418e-01 -2.68201947e-01 6.73518240e-01
-1.42105147e-01 7.45471194e-02 2.45563373e-01 2.96855599e-01
7.67574608e-02 2.56795019e-01 1.12636948e+00 2.49502156e-02
-2.25416929e-01 9.04658511e-02 -1.31497049e+00 9.37260270e-01
8.31776559e-01 -6.13159895e-01 -4.86807197e-01 -4.22483146e-01
5.08121133e-01 -1.97868291e-02 7.42558420e-01 -7.90072322e-01
-2.19217554e-01 -3.56964916e-01 3.65293145e-01 -4.69837412e-02
1.42168730e-01 -9.26398039e-01 3.90590310e-01 3.92095596e-01
-3.67218316e-01 -3.37649509e-02 1.99827597e-01 4.91933346e-01
-1.96779162e-01 -3.65671486e-01 6.24079525e-01 -6.47712275e-02
-3.81678879e-01 3.20789292e-02 -2.86995262e-01 -1.88813776e-01
6.30114675e-01 -3.91201466e-01 -2.49337226e-01 -7.65346050e-01
-5.99125862e-01 -1.63542926e-02 5.93240976e-01 5.44623554e-01
2.96287119e-01 -1.07761431e+00 -5.15853524e-01 8.51551741e-02
-7.37518743e-02 8.48159790e-02 5.01490124e-02 8.64020705e-01
-2.43938789e-01 7.17082143e-01 -5.06256633e-02 -4.68035400e-01
-8.01804245e-01 3.68715852e-01 2.62069076e-01 -2.83837050e-01
-6.28931224e-01 3.76539946e-01 6.01423919e-01 -2.16098040e-01
-3.54116270e-03 -6.60950363e-01 4.91480306e-02 -2.74969518e-01
4.11189586e-01 1.97775379e-01 -1.58169210e-01 -3.45714748e-01
-1.90865725e-01 2.06605479e-01 4.99903820e-02 1.84249625e-01
1.63597882e+00 5.05132861e-02 -1.05736842e-02 6.84844017e-01
9.46728170e-01 -2.82649189e-01 -1.14797497e+00 -5.78323156e-02
1.12089582e-01 -2.75702685e-01 9.96127203e-02 -7.41062701e-01
-1.04432809e+00 1.19687438e+00 2.60605693e-01 7.75870264e-01
9.08234954e-01 1.02742903e-01 5.77074885e-01 3.29286546e-01
8.51779506e-02 -9.14041936e-01 -2.10404858e-01 1.19299859e-01
6.63997233e-01 -1.49709404e+00 9.84833464e-02 4.59014028e-02
-5.77759206e-01 1.40943384e+00 2.90237397e-01 6.32588342e-02
6.09319866e-01 1.08135283e-01 5.32622002e-02 -1.67223439e-01
-6.70741141e-01 -4.34955582e-02 4.17128265e-01 3.63446742e-01
7.44209111e-01 9.72267687e-02 -2.82240778e-01 8.00515652e-01
-3.68964463e-01 7.39691332e-02 4.67643648e-01 2.87517190e-01
-5.25708199e-01 -1.25378537e+00 -3.08040202e-01 5.78528225e-01
-5.89420140e-01 1.62999123e-01 -4.36734855e-01 1.04450071e+00
2.38449648e-01 9.40485895e-01 5.48215173e-02 -4.56834704e-01
2.87011843e-02 1.09548703e-01 1.25158012e-01 -5.93905210e-01
-1.43858179e-01 -7.72528201e-02 9.17778239e-02 -3.42922062e-01
-4.24165517e-01 -5.19858062e-01 -1.13437116e+00 -5.21712780e-01
-3.77345830e-01 1.40487224e-01 8.51297498e-01 1.41675854e+00
3.30402434e-01 5.03418386e-01 3.80375415e-01 -6.32323086e-01
-5.07619798e-01 -8.65596056e-01 -6.18515134e-01 2.32893288e-01
3.26583952e-01 -6.79820955e-01 -3.37739021e-01 -1.28049061e-01] | [8.767579078674316, 5.535050868988037] |
ef74abfb-9800-4523-934a-73defb59ee55 | content-extraction-and-lexical-analysis-from | null | null | https://aclanthology.org/W18-6118 | https://aclanthology.org/W18-6118.pdf | Content Extraction and Lexical Analysis from Customer-Agent Interactions | In this paper, we provide a lexical comparative analysis of the vocabulary used by customers and agents in an Enterprise Resource Planning (ERP) environment and a potential solution to clean the data and extract relevant content for NLP. As a result, we demonstrate that the actual vocabulary for the language that prevails in the ERP conversations is highly divergent from the standardized dictionary and further different from general language usage as extracted from the Common Crawl corpus. Moreover, in specific business communication circumstances, where it is expected to observe a high usage of standardized language, code switching and non-standard expression are predominant, emphasizing once more the discrepancy between the day-to-day use of language and the standardized one. | ['Sergiu Nisioi', 'Anca Bucur', 'Liviu P. Dinu'] | 2018-11-01 | null | null | null | ws-2018-11 | ['lexical-analysis'] | ['natural-language-processing'] | [-2.78102428e-01 3.29066664e-01 -1.84254646e-01 -1.38240322e-01
-3.18353355e-01 -8.88451874e-01 9.93444443e-01 4.38781321e-01
-6.49505079e-01 7.58820236e-01 5.97988009e-01 -5.05564511e-01
-2.86661774e-01 -6.76394999e-01 -7.24217668e-02 -3.60713333e-01
4.14609998e-01 6.53540492e-01 -5.73927024e-03 -9.14300859e-01
3.41206014e-01 5.41624784e-01 -1.25539398e+00 2.86788166e-01
3.48321259e-01 7.56501436e-01 7.61076987e-01 1.26192361e-01
-1.09742451e+00 1.12873328e+00 -7.83914387e-01 -6.42226934e-01
2.08043411e-01 -2.20823050e-01 -1.05454791e+00 1.28705159e-01
-2.15262935e-01 2.05486685e-01 -3.67722288e-02 1.20256615e+00
5.23718670e-02 -8.72535557e-02 4.41780418e-01 -7.36592054e-01
-4.00568575e-01 1.08359063e+00 -6.04955703e-02 2.84797072e-01
5.92294395e-01 -1.65990874e-01 1.12677705e+00 -6.87294304e-01
1.57140696e+00 8.52939308e-01 3.92290473e-01 3.41164991e-02
-8.69001806e-01 -4.35666203e-01 7.99104422e-02 -1.85433552e-01
-1.84972000e+00 -3.58790874e-01 5.07476330e-01 -7.56037652e-01
1.36258519e+00 1.25686616e-01 6.82946086e-01 1.37329888e+00
3.44467193e-01 8.65520816e-03 7.05385804e-01 -9.46403027e-01
1.35784671e-01 9.60319877e-01 3.05914402e-01 7.35726431e-02
6.65863812e-01 -3.36691439e-01 -2.17129529e-01 -8.49217176e-02
2.09700391e-01 -1.65477231e-01 -2.96502531e-01 -2.32765138e-01
-9.67432916e-01 9.14698422e-01 -5.88438511e-01 1.30387676e+00
-9.36062157e-01 -5.07584751e-01 7.92716384e-01 4.59457904e-01
1.26708955e-01 5.26555240e-01 -7.85171628e-01 -6.53697252e-01
-5.83345532e-01 1.93656117e-01 1.49959314e+00 1.43827760e+00
5.56859434e-01 2.05795723e-03 3.40385765e-01 7.55480766e-01
4.99788016e-01 4.13395852e-01 7.09410548e-01 -7.40439415e-01
4.84507889e-01 6.37438238e-01 4.26035494e-01 -1.03563929e+00
-4.56528634e-01 -6.51184857e-01 -5.17407842e-02 -3.29383761e-01
4.87678528e-01 -1.20135434e-01 -1.39614925e-01 1.26906252e+00
-8.69562402e-02 -6.99252129e-01 5.90191841e-01 2.57994115e-01
8.03723693e-01 3.11751813e-01 3.27498853e-01 -6.90979481e-01
1.70942831e+00 -6.25600576e-01 -1.21545827e+00 -1.99530944e-01
8.52130771e-01 -9.38619494e-01 1.08738887e+00 3.79009277e-01
-8.27947080e-01 -5.99656552e-02 -1.13207698e+00 9.98448282e-02
-8.26829553e-01 -3.10116440e-01 4.09921169e-01 8.21146190e-01
-6.14562511e-01 -6.84736576e-03 -1.92522466e-01 -9.47377503e-01
-2.60189861e-01 -3.52476954e-01 -3.33361357e-01 1.61937639e-01
-1.19804919e+00 1.30788851e+00 8.61099362e-01 -1.98894873e-01
-2.77243733e-01 -2.72511750e-01 -6.35397136e-01 2.01170355e-01
9.41344976e-01 8.30454305e-02 1.38965321e+00 -8.58339906e-01
-1.51216662e+00 9.66641545e-01 2.27495819e-01 -5.40533543e-01
1.37066215e-01 1.38009250e-01 -1.10104513e+00 -6.15161769e-02
2.39449680e-01 -2.27653101e-01 3.06215256e-01 -1.22496557e+00
-1.14433825e+00 -1.02985367e-01 1.77603364e-01 -7.57956877e-02
-1.83413640e-01 5.25671542e-01 -3.04480225e-01 -4.41589236e-01
-2.06272423e-01 -4.98106211e-01 -1.51906274e-02 -1.14007545e+00
8.69331881e-02 -2.44105667e-01 9.65703204e-02 -9.06155348e-01
1.82464945e+00 -2.15879941e+00 -2.62679271e-02 6.34495974e-01
3.24579477e-01 -3.67043987e-02 1.37557447e-01 1.39806056e+00
1.31239414e-01 4.27641124e-01 4.09449160e-01 2.75135100e-01
3.04489255e-01 4.31656688e-01 -5.10524690e-01 2.36086175e-01
-4.51895475e-01 6.47693753e-01 -8.48455191e-01 -4.69745338e-01
-1.02200266e-02 3.02665025e-01 -6.88587874e-02 -3.24650377e-01
-1.16786420e-01 2.25473866e-01 -5.88213861e-01 6.87350631e-01
1.33115396e-01 -1.13218635e-01 9.36717391e-01 -9.70290229e-02
-7.61353612e-01 6.62114680e-01 -1.10233116e+00 1.56475914e+00
-8.48133504e-01 5.88217318e-01 2.64941692e-01 -6.00671709e-01
1.00280678e+00 6.20538175e-01 4.88484234e-01 -1.03586185e+00
4.54906970e-01 4.61893260e-01 1.28858253e-01 -6.30233109e-01
9.01345015e-01 7.89212342e-03 -3.25205088e-01 1.57308578e-01
2.35487297e-01 -8.29113349e-02 7.50185251e-01 -6.13744669e-02
8.82396162e-01 -1.32989332e-01 1.19688165e+00 -6.35644734e-01
8.18400443e-01 3.86425644e-01 4.25030977e-01 3.64733785e-01
1.16519192e-02 -1.90598845e-01 7.23738790e-01 -2.37033129e-01
-9.58044469e-01 -4.15692240e-01 -3.13142449e-01 8.48999858e-01
-3.06876209e-02 -1.13566363e+00 -6.65243149e-01 -3.82667154e-01
-5.39632261e-01 1.46574807e+00 1.03489853e-01 5.99762619e-01
-5.09817362e-01 -5.14373444e-02 2.63496786e-01 -2.64613301e-01
1.47549808e-01 -1.23425531e+00 -6.90878332e-01 5.20657957e-01
-3.19686651e-01 -1.74867177e+00 -8.58520642e-02 1.96335435e-01
-1.12888932e-01 -8.98645282e-01 -3.95599939e-02 -6.60127044e-01
4.22166809e-02 -4.84527051e-02 1.35230672e+00 -1.59882251e-02
1.53884336e-01 5.46919644e-01 -8.96303475e-01 -7.82670617e-01
-1.03003740e+00 3.40508044e-01 -2.66411990e-01 -2.43897691e-01
9.13794935e-01 -2.54839510e-01 9.08793602e-03 -8.52568671e-02
-7.97067881e-01 -4.02437449e-01 3.66887063e-01 2.71344036e-01
5.28885759e-02 3.04381549e-01 5.71452618e-01 -8.59509349e-01
1.04175901e+00 -7.12228298e-01 -6.22440934e-01 2.20443144e-01
-1.03862846e+00 -1.84405684e-01 4.49364424e-01 3.94995790e-03
-1.20900404e+00 -6.06786549e-01 -2.34295443e-01 4.52462256e-01
-3.45175594e-01 9.82965946e-01 -1.17628016e-01 3.20762902e-01
4.62929606e-01 1.12674631e-01 -1.40763626e-01 -5.38960397e-01
2.48141691e-01 1.11224627e+00 8.11365247e-02 -2.98660934e-01
4.55068201e-01 2.54829079e-01 -5.77460885e-01 -1.34906089e+00
-3.38430941e-01 -6.76849425e-01 -4.14744407e-01 -2.57465452e-01
1.06094873e+00 -8.25961888e-01 -5.50615847e-01 -1.68057352e-01
-1.35330701e+00 -2.18237370e-01 -7.30250716e-01 7.30964243e-01
-4.38432246e-01 3.18532944e-01 -2.65771985e-01 -9.65295374e-01
-1.39419228e-01 -1.03017640e+00 5.66691041e-01 3.42036335e-04
-8.29900861e-01 -1.10014617e+00 5.00904247e-02 2.66584158e-01
4.07024235e-01 -1.49952874e-01 1.08122313e+00 -1.36899424e+00
-2.13221177e-01 -1.88050136e-01 3.30810472e-02 1.98792428e-01
2.22273782e-01 -3.13175656e-02 -3.19474906e-01 -4.39995863e-02
3.32055777e-01 1.62436381e-01 -3.89637381e-01 -2.57079601e-01
9.44201574e-02 -2.82483667e-01 -1.49946570e-01 -1.99617092e-02
1.82316101e+00 8.55745852e-01 6.17281139e-01 1.08651710e+00
-7.00845942e-02 1.06431150e+00 6.55262470e-01 5.83337784e-01
2.99717158e-01 8.71308386e-01 -1.18659042e-01 5.79693794e-01
2.14644760e-01 -1.08010188e-01 2.80287445e-01 1.32954192e+00
-3.48050930e-02 -2.35120595e-01 -1.20298553e+00 5.51057220e-01
-1.61859727e+00 -9.74059284e-01 -2.09215298e-01 1.96462858e+00
6.41920447e-01 2.51711518e-01 -1.22199766e-01 -3.96649212e-01
4.80581820e-01 6.50556982e-02 4.65361506e-01 -5.60933173e-01
-2.39070117e-01 3.53088439e-01 9.17232275e-01 6.01696730e-01
-3.48864347e-01 8.02745044e-01 7.04644775e+00 9.40212011e-01
-7.63730705e-01 4.60206985e-01 -3.01213980e-01 1.78309962e-01
-3.78555626e-01 1.28564358e-01 -1.04419148e+00 5.55072486e-01
1.29176462e+00 -7.65211284e-01 6.39613748e-01 9.57972229e-01
2.08096534e-01 3.74041088e-02 -7.27827787e-01 1.06016195e+00
6.09358773e-02 -1.41028273e+00 -2.04786152e-01 4.13812429e-01
2.86046982e-01 3.19990516e-01 -7.33328760e-01 5.67254305e-01
2.98647553e-01 -6.86698437e-01 1.18974411e+00 3.50137353e-01
3.39738160e-01 -3.68565112e-01 1.12045860e+00 4.89223719e-01
-1.12471807e+00 -1.30994514e-01 -1.84066907e-01 4.94981743e-02
5.15270770e-01 1.08201504e-01 -7.43900537e-01 8.29533756e-01
5.36503851e-01 1.07694522e-01 -1.44917637e-01 1.41877428e-01
1.09630460e-02 1.26230553e-01 -1.43618770e-02 -2.72038519e-01
4.49087918e-01 -8.92018795e-01 8.02651942e-01 1.50068486e+00
4.77664769e-01 -2.41069272e-01 -2.04136465e-02 4.85147655e-01
1.68092251e-01 8.95008564e-01 -8.68286550e-01 -5.64509988e-01
6.94551647e-01 1.27066898e+00 -9.19300079e-01 -3.18815380e-01
-1.12066650e+00 5.20661414e-01 -1.31424591e-01 4.92595434e-01
-5.58786869e-01 -4.85369891e-01 6.05942965e-01 1.63786411e-01
6.95099652e-01 -4.09182221e-01 -8.42965394e-02 -9.38018024e-01
1.06256403e-01 -1.15401542e+00 1.00029722e-01 -4.91453201e-01
-1.10611618e+00 9.58348572e-01 2.17864230e-01 -9.13409233e-01
-5.27339399e-01 -6.85014427e-01 5.82631528e-02 8.15769911e-01
-1.30750132e+00 -7.76843071e-01 -1.15243047e-01 2.94422984e-01
6.00058913e-01 -8.44689012e-01 9.63527560e-01 5.70855021e-01
-2.34599769e-01 -3.31268944e-02 1.33298144e-01 -1.18979499e-01
2.87182927e-01 -8.85539889e-01 5.51834628e-02 5.02898276e-01
2.71632135e-01 9.87422228e-01 1.15965748e+00 -7.86043346e-01
-1.22392464e+00 -4.41435993e-01 1.52241707e+00 -3.13585192e-01
1.42713165e+00 -1.87804744e-01 -5.64948559e-01 8.73682499e-01
9.96717930e-01 -8.94408226e-01 7.73427963e-01 -1.00132339e-02
-7.53331929e-02 -9.33908001e-02 -1.08554232e+00 7.78403640e-01
1.02719867e+00 -5.92157423e-01 -1.01623535e+00 3.32487524e-01
7.36806035e-01 9.48072318e-03 -1.18620002e+00 -1.62667677e-01
5.36249578e-01 -7.25573003e-01 5.49537122e-01 -4.17652339e-01
-2.24305674e-01 -6.54934719e-02 -7.01262236e-01 -1.00048852e+00
-1.17742963e-01 -9.52829182e-01 3.48356843e-01 1.60371840e+00
6.09137714e-01 -1.07281268e+00 2.13525131e-01 5.66641390e-01
-1.87525712e-02 -6.75351694e-02 -9.28281486e-01 -8.50562572e-01
-4.36340831e-03 -6.12501085e-01 8.00444305e-01 1.07703924e+00
4.37374979e-01 6.36505306e-01 1.30538821e-01 -3.23340923e-01
2.33674981e-02 -2.36533046e-01 7.50561774e-01 -1.31903076e+00
-5.21604531e-02 -6.09994352e-01 -2.09642187e-01 -8.48142087e-01
1.71375021e-01 -8.53391409e-01 -2.78224707e-01 -1.44387984e+00
-3.02790016e-01 -1.67665973e-01 1.59122884e-01 -3.93301338e-01
9.21534777e-01 -3.68261337e-01 3.54095399e-01 2.38234103e-01
-1.75952122e-01 3.39457169e-02 7.95541465e-01 1.64761826e-01
-3.04300815e-01 -4.09451693e-01 -1.16225338e+00 8.68013382e-01
6.65682256e-01 -5.19505560e-01 -3.90123218e-01 2.23174859e-02
9.34421778e-01 -8.81266296e-02 -3.46423417e-01 -5.52685440e-01
1.36724878e-02 -5.64149678e-01 -6.15653992e-01 -3.85432512e-01
-1.09604530e-01 -1.57002139e+00 6.50179148e-01 2.28403911e-01
7.39272907e-02 2.43934169e-01 -8.63864943e-02 1.00724787e-01
-3.27642113e-01 -1.05410135e+00 2.11607650e-01 -3.92575115e-01
-1.03863478e+00 -3.01410586e-01 -1.04937911e+00 1.46787718e-01
1.09715462e+00 -4.41322058e-01 -3.06249946e-01 -2.80020118e-01
-6.64462149e-01 -2.22954214e-01 5.21714687e-01 3.41434330e-01
-5.05536236e-02 -1.04568410e+00 -5.27985394e-01 4.54964070e-03
3.49135816e-01 -7.06822991e-01 -7.16221035e-02 9.56965268e-01
-9.17390466e-01 9.51329172e-01 -3.10375571e-01 6.13265000e-02
-8.00629556e-01 6.58135056e-01 7.58981630e-02 -5.18076837e-01
-7.97917485e-01 -1.92035556e-01 -1.06958605e-01 -1.19449645e-01
1.92856863e-01 -5.18728793e-01 -6.76372766e-01 5.71382761e-01
5.02422154e-01 3.46429110e-01 2.35627204e-01 -1.03445518e+00
-3.15011322e-01 2.08137274e-01 7.22511113e-02 -2.97916710e-01
1.18071413e+00 -8.31125855e-01 -3.60214740e-01 9.73592222e-01
9.00267601e-01 7.65075147e-01 -1.66408449e-01 -2.00484276e-01
7.16791809e-01 -2.08415896e-01 -2.16886342e-01 -5.13922989e-01
-6.35898530e-01 2.74183899e-02 2.62786031e-01 1.10071659e+00
5.66575050e-01 1.88305184e-01 5.76895356e-01 6.20403051e-01
7.29996085e-01 -1.76367950e+00 -6.35948122e-01 6.84959710e-01
1.18157005e+00 -5.74385643e-01 -1.29187360e-01 -6.89287961e-01
-9.42935944e-01 1.25158989e+00 7.09391832e-02 3.09992552e-01
1.01524866e+00 5.28319001e-01 3.65360737e-01 -6.30436838e-01
-5.91282666e-01 -4.82525170e-01 -4.51115847e-01 6.50091588e-01
5.32004952e-01 -3.24409977e-02 -1.17793071e+00 7.98884630e-01
-5.01966178e-01 8.67392868e-03 8.36148977e-01 9.38463628e-01
-2.48237506e-01 -1.36430478e+00 1.21795367e-02 5.86027652e-02
-9.65053439e-01 -1.30904734e-01 -2.92151839e-01 1.45066309e+00
2.23951891e-01 1.08278692e+00 1.77513808e-01 -1.54912900e-02
7.23172188e-01 5.68493366e-01 2.12727383e-01 -7.28541970e-01
-8.79082739e-01 2.11942419e-01 8.66147816e-01 -3.87855411e-01
-6.65309548e-01 -6.52548730e-01 -1.25113428e+00 -2.28738338e-01
-3.06540728e-01 5.06597579e-01 1.00698709e+00 1.10650170e+00
3.19064744e-02 5.67368031e-01 7.69891366e-02 -1.64305329e-01
-4.98102635e-01 -1.11821604e+00 -1.10501719e+00 4.14673239e-01
-2.57792085e-01 -6.61956549e-01 -3.21083665e-01 1.68925390e-01] | [10.045528411865234, 9.652057647705078] |
3006f487-591e-4063-b843-3c095efb5f44 | leveraging-adaptive-color-augmentation-in | 2011.00148 | null | https://arxiv.org/abs/2011.00148v1 | https://arxiv.org/pdf/2011.00148v1.pdf | Leveraging Adaptive Color Augmentation in Convolutional Neural Networks for Deep Skin Lesion Segmentation | Fully automatic detection of skin lesions in dermatoscopic images can facilitate early diagnosis and repression of malignant melanoma and non-melanoma skin cancer. Although convolutional neural networks are a powerful solution, they are limited by the illumination spectrum of annotated dermatoscopic screening images, where color is an important discriminative feature. In this paper, we propose an adaptive color augmentation technique to amplify data expression and model performance, while regulating color difference and saturation to minimize the risks of using synthetic data. Through deep visualization, we qualitatively identify and verify the semantic structural features learned by the network for discriminating skin lesions against normal skin tissue. The overall system achieves a Dice Ratio of 0.891 with 0.943 sensitivity and 0.932 specificity on the ISIC 2018 Testing Set for segmentation. | ['Abdullah Thabit', 'Prem Prasad', 'Anindo Saha'] | 2020-10-31 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 4.80220348e-01 1.16807990e-01 -4.87559974e-01 -4.00901407e-01
-4.87419635e-01 -9.10641670e-01 1.72504753e-01 9.53583047e-03
-5.01967072e-01 5.60304999e-01 -2.29848444e-01 -4.92987543e-01
1.57242045e-01 -5.71887314e-01 -3.85278046e-01 -7.56219506e-01
1.02290295e-01 -1.90270901e-01 -2.82728896e-02 1.33609146e-01
7.72000849e-02 7.34701276e-01 -1.15367281e+00 4.04454499e-01
1.34857404e+00 1.07149398e+00 -3.06302160e-01 8.47833216e-01
2.80398391e-02 4.32988554e-01 -5.70879459e-01 -5.24908483e-01
2.93612152e-01 -5.04110336e-01 -7.24211574e-01 1.79205045e-01
6.34600163e-01 -3.86472493e-01 -3.15065563e-01 1.36632216e+00
5.53460777e-01 -3.81969839e-01 6.77369416e-01 -9.80139613e-01
-6.38574958e-01 2.14743745e-02 -1.07604468e+00 5.17926179e-02
1.43668652e-01 4.42714185e-01 5.80949485e-01 -4.21286196e-01
7.91755199e-01 7.58185029e-01 5.34743369e-01 9.35368896e-01
-1.13058150e+00 -4.58565265e-01 -1.07201144e-01 -4.70317602e-02
-1.26277840e+00 2.70283278e-02 4.41209584e-01 -3.49941224e-01
2.63338804e-01 7.95635998e-01 9.89742935e-01 1.03099859e+00
1.68384731e-01 8.11424196e-01 1.58130157e+00 -3.32022607e-01
1.79988831e-01 1.41950563e-01 -2.87896041e-02 9.84621704e-01
3.69616210e-01 8.12198818e-02 -1.71373580e-02 8.89585093e-02
1.11638641e+00 -4.32350561e-02 -1.69770733e-01 9.80895758e-02
-6.06176496e-01 6.56718135e-01 7.46987462e-01 -6.43443987e-02
-1.88346803e-01 3.71573046e-02 2.09502831e-01 9.35092010e-03
3.41609448e-01 5.10024548e-01 -1.39071094e-02 3.31056751e-02
-6.32003307e-01 -2.91209072e-01 3.69089097e-01 4.21903729e-01
1.89684033e-01 -6.82688504e-02 -3.00452381e-01 9.10315573e-01
-1.11593045e-01 4.47685778e-01 1.83065534e-01 -9.33286369e-01
-1.15737967e-01 9.78135526e-01 -1.30468130e-01 -5.74169993e-01
-6.02295935e-01 -2.25130051e-01 -1.01384294e+00 4.75241661e-01
6.48196459e-01 -3.24041724e-01 -1.55272996e+00 1.34765458e+00
4.13157672e-01 -2.57621676e-01 -2.68817693e-01 1.21612179e+00
5.42653441e-01 1.21152386e-01 5.07825851e-01 1.06769688e-01
1.24273062e+00 -7.26621032e-01 -6.63521945e-01 -1.72128722e-01
5.68030000e-01 -6.01448536e-01 1.09423149e+00 4.02271271e-01
-9.29837286e-01 -2.61917323e-01 -8.74107182e-01 -4.20045247e-03
-2.26471245e-01 4.90781605e-01 8.66289616e-01 8.30498934e-01
-9.14226294e-01 3.17100346e-01 -1.00681436e+00 -4.19917673e-01
1.01892066e+00 3.30643237e-01 -3.66779417e-01 -1.79009169e-01
-8.89331162e-01 7.03704536e-01 6.95706811e-03 2.13106185e-01
-6.01998210e-01 -7.48445809e-01 -5.19910336e-01 -2.25655347e-01
-4.84293103e-02 -3.08643878e-01 7.70227134e-01 -1.32479751e+00
-1.39954734e+00 1.20245898e+00 9.62415896e-03 -3.83229375e-01
7.85326242e-01 -1.41618913e-02 -4.39364195e-01 5.35586894e-01
-2.49053061e-01 9.02595699e-01 4.99520630e-01 -1.09766710e+00
-5.72066069e-01 -4.13978964e-01 1.45731464e-01 1.82194233e-01
-3.45841587e-01 5.61607294e-02 -6.25260592e-01 -4.12060320e-01
-1.47361487e-01 -1.08166623e+00 -4.94568110e-01 7.87524760e-01
-8.77320528e-01 2.91894823e-01 6.40717149e-01 -9.24989104e-01
9.68270242e-01 -2.05200362e+00 -2.10525855e-01 6.41284704e-01
4.96661901e-01 5.65201402e-01 -2.98387498e-01 -1.33953974e-01
-1.47756293e-01 5.08772373e-01 -2.63854951e-01 3.04014742e-01
-4.12009835e-01 -2.24690750e-01 1.92250788e-01 5.26445627e-01
6.78077221e-01 1.08079469e+00 -7.71493018e-01 -6.16684377e-01
4.87349868e-01 7.32738376e-01 -1.70272529e-01 3.67701761e-02
5.65321185e-03 2.61065394e-01 -2.46567696e-01 1.06452608e+00
8.91421914e-01 -3.55461359e-01 4.40505147e-01 -3.76907289e-01
2.66036242e-01 -2.74452090e-01 -7.45214462e-01 1.53147793e+00
-3.56839359e-01 9.33905184e-01 1.99785262e-01 -3.57222259e-01
6.32831991e-01 -8.61364305e-02 5.17206788e-01 -7.87247360e-01
2.08013490e-01 -1.14894286e-01 4.02147889e-01 -6.52637064e-01
5.11822291e-02 5.31754680e-02 5.21957338e-01 1.40041158e-01
-3.74497741e-01 -5.08245081e-02 1.96504191e-01 8.02438408e-02
8.59481990e-01 -1.23645507e-01 -8.22813213e-02 -1.50067672e-01
2.16267537e-02 9.79653224e-02 2.73389846e-01 2.58701473e-01
-4.79276389e-01 8.18029046e-01 8.30968261e-01 -1.94725603e-01
-9.81338322e-01 -1.29176819e+00 -4.60926116e-01 5.93669772e-01
2.62097865e-01 1.18456952e-01 -1.01857090e+00 -8.97536218e-01
-3.39439549e-02 3.47142845e-01 -1.08498704e+00 -1.21629097e-01
-1.38059407e-01 -9.61394191e-01 7.43164361e-01 7.74688661e-01
4.63311076e-01 -5.00753105e-01 -5.17585576e-01 -3.98181677e-01
1.13352790e-01 -8.77278209e-01 -3.02951753e-01 -9.83397663e-02
-5.41055977e-01 -1.61563611e+00 -8.83621037e-01 -6.75027430e-01
1.31517470e+00 4.11064290e-02 5.89157581e-01 1.61527887e-01
-1.25772238e+00 -9.49472282e-03 -7.90989771e-02 -2.57597923e-01
-2.70118475e-01 -1.54336140e-01 -3.41428190e-01 -2.95284875e-02
3.29237640e-01 6.58447444e-02 -1.02541041e+00 1.85441047e-01
-1.04195023e+00 3.03230196e-01 7.96180844e-01 8.90747249e-01
7.82704473e-01 -1.54909506e-01 6.24434985e-02 -1.18363976e+00
6.96569741e-01 -1.02159441e-01 -4.99246955e-01 5.16708791e-01
-5.35857439e-01 -4.31183547e-01 5.65962851e-01 -4.43816751e-01
-1.08984280e+00 3.31539929e-01 2.80730966e-02 -1.95126399e-01
-2.46276438e-01 2.49708548e-01 1.80721849e-01 -4.20738459e-01
9.80206966e-01 -8.60270560e-02 2.66237736e-01 -2.39121318e-02
2.87273914e-01 6.73038900e-01 3.99224818e-01 -9.98508036e-02
4.02583539e-01 5.80982029e-01 2.13247791e-01 -8.03046286e-01
-8.30522716e-01 -2.02046156e-01 -6.32289529e-01 -3.89775366e-01
1.03580356e+00 -7.37676620e-01 -6.78482354e-01 6.08147144e-01
-7.57808447e-01 -4.83888358e-01 -2.83371598e-01 2.39396751e-01
1.80493936e-01 3.60896736e-01 -8.39053690e-01 -7.66351402e-01
-2.97411919e-01 -1.00678205e+00 8.91198218e-01 7.87406147e-01
-3.46361488e-01 -1.14229655e+00 -1.76163688e-01 2.77720362e-01
3.35087985e-01 9.44743991e-01 7.48022079e-01 -2.05489069e-01
-1.07692532e-01 -6.18300617e-01 -7.28782356e-01 5.26266038e-01
4.57568139e-01 7.90654242e-01 -1.13107717e+00 -1.45918041e-01
-8.01350057e-01 -4.53613192e-01 8.78551602e-01 6.09150648e-01
1.80342364e+00 -1.20798558e-01 -2.54617721e-01 8.51176679e-01
1.50967801e+00 2.87654787e-01 7.47430086e-01 -9.13075060e-02
7.34219909e-01 9.24464524e-01 4.89374340e-01 7.18405992e-02
-1.38039857e-01 -2.55229082e-02 5.05519986e-01 -1.05641246e+00
-4.07502860e-01 -1.94626063e-01 -2.64071226e-01 -5.36569357e-02
-2.16367915e-01 -1.39012828e-01 -9.95237291e-01 5.21169603e-01
-1.14048159e+00 -4.58736360e-01 -3.49114507e-01 2.02081537e+00
9.55307901e-01 6.41833246e-02 1.84627131e-01 -1.20108172e-01
9.50497091e-01 -1.83616415e-01 -7.84391999e-01 -3.76503676e-01
-3.58290195e-01 6.01439953e-01 7.65908301e-01 4.36725467e-01
-1.12596917e+00 8.43592882e-01 6.70925379e+00 6.31317735e-01
-1.47435594e+00 -4.01770890e-01 1.27344382e+00 -2.05946296e-01
-3.22863221e-01 -5.03143251e-01 -4.19938043e-02 4.76384759e-01
5.23781896e-01 2.36679703e-01 2.04782158e-01 6.42129779e-01
1.55268356e-01 -2.70306557e-01 -7.61990070e-01 8.08681071e-01
-2.22849607e-01 -1.41275811e+00 4.34284396e-02 1.53766438e-01
9.83717084e-01 -2.61936426e-01 6.79006398e-01 -4.83830154e-01
3.28007996e-01 -1.59330308e+00 -1.54927462e-01 5.05057275e-01
1.64919686e+00 -7.63087869e-01 7.72997737e-01 -3.97351116e-01
-5.43237746e-01 2.09301546e-01 -1.24442309e-01 3.59893113e-01
-2.61522502e-01 5.15326679e-01 -8.81806433e-01 2.24980563e-02
5.23032069e-01 4.04555321e-01 -8.92678022e-01 1.10129225e+00
-3.70617718e-01 7.40125358e-01 -2.58257210e-01 -2.97105223e-01
2.83606082e-01 -1.48800761e-01 -1.63162351e-02 1.30276179e+00
-1.47767499e-01 -7.76119232e-02 -4.16977763e-01 9.15764928e-01
-2.81037670e-02 -3.75069515e-03 -4.16413754e-01 -3.02192450e-01
3.39715302e-01 1.64490175e+00 -8.04496944e-01 3.64726223e-02
-8.78877863e-02 1.18759799e+00 -4.46944311e-02 6.29691422e-01
-8.16237628e-01 -7.09803522e-01 6.71365619e-01 1.80580616e-01
-4.64091808e-01 1.93627417e-01 -7.89474905e-01 -7.84554243e-01
-1.37306109e-01 -6.87527001e-01 2.85740644e-01 -5.86868107e-01
-1.20434356e+00 4.58115131e-01 -5.37463307e-01 -1.05413139e+00
1.97896466e-01 -1.10232627e+00 -8.10520291e-01 8.72126222e-01
-1.38458395e+00 -1.22512698e+00 -7.62419939e-01 4.30837154e-01
-1.21237338e-02 3.16292271e-02 8.31402481e-01 -4.48179990e-02
-9.63470876e-01 8.37998152e-01 1.49937376e-01 4.50516045e-01
7.41549850e-01 -1.60361159e+00 3.00309062e-01 7.96702027e-01
-3.44535202e-01 5.69047511e-01 3.49497646e-01 -4.36409980e-01
-1.08326578e+00 -1.08621073e+00 -1.37396418e-02 -5.43988198e-02
6.38821781e-01 -1.74399614e-01 -6.63567245e-01 2.14258865e-01
6.60354570e-02 1.29650638e-01 1.11597800e+00 5.68378754e-02
-4.12208438e-01 -1.49481565e-01 -1.50668299e+00 8.78284395e-01
6.23125613e-01 -4.95725960e-01 2.75548637e-01 5.62496603e-01
2.47191325e-01 -6.06465220e-01 -9.98590827e-01 3.82400364e-01
7.90101230e-01 -7.19981313e-01 8.02185774e-01 -7.78570414e-01
8.21605146e-01 3.87181551e-03 2.98184574e-01 -1.10456431e+00
-3.10086887e-02 -3.39903593e-01 3.32951248e-01 8.97880375e-01
5.26333869e-01 -4.03820276e-01 1.30328166e+00 8.37488949e-01
1.80419028e-01 -1.02770734e+00 -7.97330439e-01 -2.01696411e-01
2.37612888e-01 -8.83029625e-02 3.64038162e-02 9.31679070e-01
-1.28802881e-01 -1.86900377e-01 -1.76525898e-02 -1.70048580e-01
6.68731391e-01 -1.08209632e-01 3.54101568e-01 -8.66516709e-01
1.20021671e-01 -6.34163022e-01 -3.28886777e-01 -5.19959569e-01
-2.14767873e-01 -7.21130252e-01 -4.34243679e-01 -1.54939806e+00
3.14595759e-01 -3.49254012e-01 -5.15529931e-01 6.77185237e-01
-3.83029163e-01 6.36200726e-01 -1.21881709e-01 -1.80786505e-01
-2.21594647e-01 -1.38040796e-01 1.73809338e+00 -3.60852480e-01
-1.35623038e-01 -1.83924716e-02 -9.47229981e-01 6.57873452e-01
1.14525056e+00 9.93927345e-02 -2.87634611e-01 -3.57717931e-01
-8.77315998e-02 -2.54631549e-01 5.97559571e-01 -5.87530315e-01
5.58808371e-02 -4.45464879e-01 9.80016947e-01 -1.87260836e-01
1.68114737e-01 -5.81738830e-01 -2.38356754e-01 9.39563096e-01
-6.75054610e-01 -3.82457852e-01 3.92033309e-01 3.49829286e-01
-1.27063125e-01 8.37887749e-02 1.26833856e+00 4.54833126e-03
-5.73152781e-01 2.38209024e-01 -3.79167348e-01 -7.45533556e-02
1.28039134e+00 -2.78253824e-01 -7.21381783e-01 -1.81129292e-01
-5.82597613e-01 2.60836750e-01 6.36629522e-01 1.36423305e-01
7.75524378e-01 -1.03999388e+00 -6.08023465e-01 2.32121557e-01
1.96435034e-01 -8.52738321e-02 5.83388150e-01 8.20939720e-01
-1.20392025e+00 9.43700373e-02 -5.89890242e-01 -6.05099142e-01
-1.35203528e+00 4.64747660e-02 6.99232399e-01 4.41300422e-02
-3.00782323e-01 1.14005411e+00 9.16643292e-02 -9.14733335e-02
1.47484317e-01 -2.13263839e-01 3.86122055e-02 -3.17206234e-01
4.15619284e-01 4.34069484e-01 -2.00950652e-01 -9.64931026e-02
-2.44419068e-01 4.81241852e-01 -2.83484966e-01 1.02885738e-01
9.33393717e-01 1.87031046e-01 -1.42338514e-01 -7.20765442e-02
1.00408864e+00 -2.25058347e-02 -1.43345404e+00 3.20997030e-01
-3.76874238e-01 -6.08238459e-01 6.75893724e-02 -1.54030132e+00
-1.26145124e+00 1.11642110e+00 1.24745214e+00 2.62974888e-01
1.30595386e+00 -3.34994823e-01 6.81941688e-01 -2.85940766e-02
-4.27155137e-01 -1.39630568e+00 -3.20603512e-02 -2.26715684e-01
5.47649264e-01 -1.35278320e+00 4.57127355e-02 -8.03350687e-01
-1.03414357e+00 1.21541858e+00 1.08490455e+00 -1.10543534e-01
2.65163928e-01 5.02598226e-01 4.57009643e-01 -1.34227142e-01
-4.10366505e-01 -1.23757087e-01 5.86895645e-01 8.96762133e-01
5.73937416e-01 3.15764993e-01 -4.40899432e-01 1.18946940e-01
8.79199505e-02 -1.89853057e-01 3.95880967e-01 6.05581403e-01
-1.69638231e-01 -8.48179281e-01 1.01295173e-01 7.75160909e-01
-7.37708449e-01 1.14458926e-01 -9.50158238e-01 1.08761740e+00
5.92875807e-03 6.57947421e-01 2.77903855e-01 -5.00258207e-01
-4.00806107e-02 -3.49613994e-01 5.92849970e-01 -3.47179443e-01
-5.17476678e-01 1.38092667e-01 7.58856684e-02 -5.40892124e-01
-3.20877224e-01 -1.69251189e-01 -1.22161067e+00 -1.63220957e-01
-2.05571279e-01 -2.91157633e-01 9.10237968e-01 3.85997385e-01
1.27611578e-01 5.99886358e-01 7.04496026e-01 3.72148119e-02
-2.63222128e-01 -7.44975090e-01 -7.92499006e-01 4.73776579e-01
2.23133057e-01 -1.54953986e-01 -1.57747999e-01 5.54852597e-02] | [15.641011238098145, -2.9485630989074707] |
f3c6ea26-c337-48b5-b9e0-0d217e375229 | linguistically-informed-self-attention-for | 1804.08199 | null | http://arxiv.org/abs/1804.08199v3 | http://arxiv.org/pdf/1804.08199v3.pdf | Linguistically-Informed Self-Attention for Semantic Role Labeling | Current state-of-the-art semantic role labeling (SRL) uses a deep neural
network with no explicit linguistic features. However, prior work has shown
that gold syntax trees can dramatically improve SRL decoding, suggesting the
possibility of increased accuracy from explicit modeling of syntax. In this
work, we present linguistically-informed self-attention (LISA): a neural
network model that combines multi-head self-attention with multi-task learning
across dependency parsing, part-of-speech tagging, predicate detection and SRL.
Unlike previous models which require significant pre-processing to prepare
linguistic features, LISA can incorporate syntax using merely raw tokens as
input, encoding the sequence only once to simultaneously perform parsing,
predicate detection and role labeling for all predicates. Syntax is
incorporated by training one attention head to attend to syntactic parents for
each token. Moreover, if a high-quality syntactic parse is already available,
it can be beneficially injected at test time without re-training our SRL model.
In experiments on CoNLL-2005 SRL, LISA achieves new state-of-the-art
performance for a model using predicted predicates and standard word
embeddings, attaining 2.5 F1 absolute higher than the previous state-of-the-art
on newswire and more than 3.5 F1 on out-of-domain data, nearly 10% reduction in
error. On ConLL-2012 English SRL we also show an improvement of more than 2.5
F1. LISA also out-performs the state-of-the-art with contextually-encoded
(ELMo) word representations, by nearly 1.0 F1 on news and more than 2.0 F1 on
out-of-domain text. | ['Andrew McCallum', 'Patrick Verga', 'David Weiss', 'Daniel Andor', 'Emma Strubell'] | 2018-04-23 | linguistically-informed-self-attention-for-1 | https://aclanthology.org/D18-1548 | https://aclanthology.org/D18-1548.pdf | emnlp-2018-10 | ['predicate-detection', 'semantic-role-labeling-predicted-predicates'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.81689668e-01 6.11959338e-01 -4.76445138e-01 -6.77799404e-01
-1.33836520e+00 -7.07946062e-01 3.84670794e-01 5.75850546e-01
-8.77059877e-01 7.89757550e-01 6.43098116e-01 -5.25568485e-01
1.93906963e-01 -6.94562852e-01 -1.01056409e+00 -3.52122158e-01
-3.39394854e-03 6.73175514e-01 2.62284786e-01 -3.32222402e-01
-3.08109939e-01 8.15733746e-02 -1.43489361e+00 6.86378360e-01
5.74918270e-01 8.93054187e-01 2.10182250e-01 6.50987327e-01
-5.04676998e-01 8.76496673e-01 -7.51899183e-01 -2.34389484e-01
-9.87185091e-02 -8.19754526e-02 -1.06754971e+00 -3.36670280e-01
4.92475688e-01 2.47227997e-02 3.01252324e-02 7.59710312e-01
5.90449750e-01 4.70949300e-02 1.00439660e-01 -7.30711162e-01
-1.06262863e+00 1.07859361e+00 -2.39398271e-01 3.27351987e-01
3.61033916e-01 -9.92380977e-02 1.75939906e+00 -8.08781207e-01
6.29175723e-01 1.43783915e+00 6.32153571e-01 9.23720241e-01
-1.26412034e+00 -6.58363461e-01 3.60750824e-01 -7.62174651e-02
-8.67827594e-01 -3.44538212e-01 5.72694004e-01 -5.51394969e-02
1.60475218e+00 -8.89518261e-02 2.88071692e-01 9.71659184e-01
-3.03072594e-02 1.08145475e+00 9.29359674e-01 -6.82097793e-01
9.67787802e-02 -2.93804348e-01 5.36908090e-01 7.70841002e-01
3.03784639e-01 -1.41168445e-01 -5.15933096e-01 -2.19673198e-02
2.86879927e-01 -4.30925697e-01 9.66044739e-02 2.23099634e-01
-9.63414013e-01 1.05198359e+00 3.21056664e-01 5.03440559e-01
-1.87533975e-01 3.39657187e-01 6.31914675e-01 1.88609749e-01
7.25247324e-01 5.91534913e-01 -1.12424374e+00 -1.80854544e-01
-7.59459257e-01 4.02550101e-02 5.63606083e-01 5.42592943e-01
4.46528673e-01 1.75249651e-01 -2.14181662e-01 1.11446941e+00
2.93427080e-01 3.74515921e-01 6.83638632e-01 -7.57175624e-01
4.54378426e-01 6.75426185e-01 -1.92936271e-01 -8.09084922e-02
-6.03175163e-01 -4.63033974e-01 -2.36195415e-01 -7.32391104e-02
5.40207624e-01 -2.65497267e-01 -1.17502618e+00 2.14078569e+00
1.71979159e-01 -2.89114341e-02 4.81135994e-01 5.15866399e-01
1.04414666e+00 7.50555873e-01 7.40752220e-01 1.60873890e-01
1.64784992e+00 -1.05308127e+00 -6.31959021e-01 -7.24573851e-01
1.07113969e+00 -5.26805460e-01 1.23885357e+00 1.03251211e-01
-1.08975363e+00 -6.74779415e-01 -9.58305657e-01 -4.29302573e-01
-4.38676566e-01 8.89253393e-02 8.13432992e-01 6.36274815e-01
-1.15996993e+00 5.29125154e-01 -7.80448437e-01 -2.53645867e-01
6.74986422e-01 5.59306920e-01 -6.10821247e-01 -3.51145655e-01
-1.48725069e+00 8.99183750e-01 5.66010833e-01 -2.77476728e-01
-9.22826648e-01 -1.06468463e+00 -1.42892540e+00 1.13033481e-01
3.68338138e-01 -3.30395848e-01 1.51856315e+00 -8.32491875e-01
-1.32957673e+00 1.13610482e+00 -3.30313265e-01 -6.53935611e-01
-1.80432454e-01 -5.77196836e-01 -5.71070671e-01 -8.34844112e-02
4.32108968e-01 8.74369919e-01 2.31181800e-01 -9.87212181e-01
-7.86461234e-01 -1.95576519e-01 3.46757114e-01 2.01050892e-01
-1.07717194e-01 4.42166150e-01 -2.69450128e-01 -5.33418238e-01
-2.12945044e-01 -6.15412712e-01 -2.23176017e-01 -2.86864340e-01
-4.68782112e-02 -8.08476031e-01 4.76971507e-01 -6.93119466e-01
1.07206607e+00 -2.22475958e+00 -2.34249845e-01 -4.00023550e-01
-1.36906743e-01 6.03414059e-01 -5.24487913e-01 7.57649317e-02
-3.67902666e-01 4.93072808e-01 -4.18502510e-01 -6.68003857e-01
-7.93724507e-02 6.52621448e-01 -4.76888269e-02 4.02790964e-01
7.22871959e-01 8.46381247e-01 -1.13603687e+00 -2.82011956e-01
1.00655124e-01 3.82850915e-01 -7.72000492e-01 1.59304634e-01
-4.42150563e-01 2.75091290e-01 -1.18035391e-01 6.38185501e-01
2.89975345e-01 -2.10713863e-01 4.57680494e-01 9.73344967e-02
-1.08075172e-01 1.14476514e+00 -7.35164940e-01 1.81395483e+00
-9.05079067e-01 3.35707307e-01 1.71940178e-02 -1.18474841e+00
7.37314999e-01 4.42876488e-01 3.45963508e-01 -8.31816852e-01
1.01120122e-01 3.58546406e-01 3.39135408e-01 -8.34401976e-03
2.74791777e-01 -2.63002187e-01 -4.91961181e-01 2.90174454e-01
5.60905278e-01 2.87167847e-01 3.72927785e-01 -9.60379317e-02
1.33923352e+00 2.49399289e-01 4.43832099e-01 -4.65963900e-01
6.61628842e-01 1.40323222e-01 8.19740474e-01 6.35878205e-01
-1.35929137e-01 3.68959844e-01 4.31605279e-01 -3.69132340e-01
-8.01858544e-01 -8.43771935e-01 -1.98290512e-01 1.77886701e+00
-2.16868192e-01 -3.63554448e-01 -6.20347261e-01 -1.05087197e+00
3.27654071e-02 1.04839587e+00 -6.63095534e-01 8.20670649e-02
-1.08385229e+00 -8.73026192e-01 8.98024321e-01 8.83957922e-01
2.97902226e-01 -1.46755004e+00 -4.37482536e-01 6.58879519e-01
1.49424613e-01 -1.32527673e+00 -1.10121213e-01 7.06649721e-01
-5.50737977e-01 -1.00776196e+00 -3.80992085e-01 -1.06329858e+00
4.02098566e-01 -3.71276379e-01 1.36634529e+00 9.52517837e-02
-7.79775754e-02 -9.70463380e-02 -5.80616474e-01 -3.27528626e-01
-4.84473109e-01 1.47887006e-01 -1.95252851e-01 -2.83331394e-01
4.64756101e-01 -3.20329875e-01 -1.71927050e-01 -1.70985401e-01
-5.50048769e-01 -1.18760347e-01 5.55416465e-01 1.14039814e+00
7.61479974e-01 -1.09789692e-01 6.84225440e-01 -1.33666980e+00
2.08200470e-01 -5.23162305e-01 -4.30266887e-01 1.13249220e-01
-3.59693199e-01 3.45784724e-01 8.53165150e-01 -2.34250233e-01
-1.24052465e+00 7.22855181e-02 -7.53602624e-01 8.21680203e-03
-4.22173679e-01 3.99211496e-01 -4.43258703e-01 4.81692135e-01
5.50500989e-01 -7.33268857e-02 -3.52328658e-01 -7.51142740e-01
6.19968474e-01 4.87154305e-01 4.65081483e-01 -8.42289388e-01
3.58185887e-01 2.07384825e-01 -3.13841164e-01 -6.36067092e-01
-1.62405419e+00 -5.66250265e-01 -5.48656762e-01 5.57818174e-01
1.23058653e+00 -1.10291743e+00 -4.22940642e-01 2.55373806e-01
-1.23991466e+00 -8.15480530e-01 -4.82434064e-01 1.41452953e-01
-3.02863181e-01 2.07196479e-03 -8.63184452e-01 -5.70875585e-01
-4.49913561e-01 -9.40333903e-01 1.19534767e+00 -5.61943799e-02
-2.97146022e-01 -1.16245866e+00 -5.41107245e-02 5.51866353e-01
2.72663504e-01 -4.41390872e-02 1.12314034e+00 -1.29160881e+00
-4.61937897e-02 5.62203005e-02 -2.47703999e-01 5.27226090e-01
5.81745133e-02 -6.01256967e-01 -1.19620895e+00 -1.96460158e-01
-4.16431963e-01 -4.72940415e-01 9.80035603e-01 2.42059857e-01
1.15325689e+00 -1.30443066e-01 -2.15455875e-01 4.48993504e-01
1.35918045e+00 3.41312230e-01 2.82857150e-01 3.36438745e-01
7.19151556e-01 4.54108119e-01 6.91284895e-01 -4.94949929e-02
5.07039428e-01 3.83713752e-01 3.19409043e-01 -1.97809324e-01
-5.81458926e-01 -3.89230490e-01 5.73475957e-01 5.31854272e-01
4.00288433e-01 -5.08456051e-01 -9.89486933e-01 9.80911374e-01
-1.58592534e+00 -6.39996946e-01 -4.95900288e-02 1.81725693e+00
1.15704656e+00 4.87487495e-01 -1.79835975e-01 2.37902910e-01
5.66973090e-01 4.59740967e-01 -3.53180379e-01 -7.95805216e-01
-1.27826869e-01 9.80384827e-01 8.23215663e-01 7.24876404e-01
-1.44306779e+00 1.60690510e+00 5.97147751e+00 8.70192468e-01
-9.73057389e-01 6.49540603e-01 6.30035639e-01 9.43583548e-02
-3.79977733e-01 7.12154731e-02 -1.44215453e+00 3.05759519e-01
1.39050651e+00 1.74799085e-01 1.21120229e-01 1.02406061e+00
-2.04326063e-01 -5.87873198e-02 -1.06444979e+00 5.39004683e-01
7.75378719e-02 -1.30544770e+00 -6.72803670e-02 -1.83026060e-01
4.47095454e-01 4.95741040e-01 -3.66657734e-01 8.42154264e-01
8.19675863e-01 -1.11184978e+00 7.79206634e-01 -4.31365669e-01
1.11497402e+00 -6.85173333e-01 9.27563190e-01 2.96375960e-01
-1.27364874e+00 -2.08491534e-01 -3.48957688e-01 -3.29139590e-01
3.81369233e-01 5.60007930e-01 -8.27341914e-01 4.29486662e-01
5.94626009e-01 7.07725942e-01 -4.33270037e-01 3.97672951e-01
-9.66714561e-01 1.21826673e+00 -3.53169620e-01 -1.30983949e-01
5.85688770e-01 6.29275084e-01 2.90454209e-01 1.49633479e+00
-4.61136475e-02 8.39174092e-02 4.78679180e-01 4.38476115e-01
-3.82997692e-01 2.46598765e-01 -3.08080435e-01 -1.32150456e-01
4.27518666e-01 1.00306988e+00 -5.15852213e-01 -4.95111495e-01
-6.03554726e-01 8.76286864e-01 7.18634367e-01 -1.07790485e-01
-6.45417213e-01 -2.35843599e-01 9.46856201e-01 1.17470585e-01
4.66943413e-01 -9.21776518e-02 -2.44905949e-01 -8.54422271e-01
-3.29797089e-01 -6.33092701e-01 6.66490138e-01 -5.46531379e-01
-1.29444230e+00 6.14858985e-01 -7.11776689e-02 -4.99662787e-01
-1.42438173e-01 -1.01271820e+00 -4.09410655e-01 9.80212152e-01
-2.02716637e+00 -1.40367508e+00 4.30295765e-01 2.20625684e-01
8.73086393e-01 -8.74667838e-02 1.20151305e+00 4.80041802e-01
-4.42348152e-01 7.68203318e-01 -2.97965199e-01 5.54675400e-01
6.42804086e-01 -1.55399203e+00 8.38908672e-01 9.30835783e-01
1.70290396e-01 5.48856914e-01 2.38946289e-01 -6.57752097e-01
-9.63312685e-01 -1.29646266e+00 1.58327913e+00 -5.55998743e-01
8.75653148e-01 -5.68030655e-01 -9.20728803e-01 9.32291269e-01
1.84698388e-01 4.24341053e-01 9.43090498e-01 5.19609451e-01
-4.65681374e-01 1.06103845e-01 -1.09284103e+00 1.52287215e-01
1.22547603e+00 -4.99299616e-01 -1.06427932e+00 2.97623128e-01
1.30957770e+00 -4.38469738e-01 -7.94403970e-01 5.09512782e-01
2.01555163e-01 -4.24750447e-01 7.97137141e-01 -7.98141897e-01
4.66270208e-01 -1.47106543e-01 -4.52585965e-01 -1.16696608e+00
-3.98262382e-01 -2.70995528e-01 2.36041233e-01 1.33380735e+00
8.54754329e-01 -5.58074832e-01 5.94892025e-01 3.10673177e-01
-6.87847555e-01 -7.61939287e-01 -1.00413704e+00 -7.71817386e-01
3.25967252e-01 -8.36593509e-01 3.71794701e-01 9.22363818e-01
-2.18100742e-01 7.75766253e-01 2.12456472e-02 2.25762412e-01
3.61700892e-01 -1.14609875e-01 1.73562735e-01 -1.27750671e+00
-4.44902271e-01 -1.72501028e-01 -7.88620859e-02 -9.51557100e-01
9.05269980e-01 -1.37672186e+00 2.41943136e-01 -1.74330616e+00
-1.04484752e-01 -7.00330138e-01 -5.82082272e-01 1.25591636e+00
-2.91490078e-01 1.78554162e-01 1.69101059e-01 -3.73396397e-01
-6.63365602e-01 3.33177418e-01 8.75141203e-01 5.50309531e-02
2.98718102e-02 -4.92547840e-01 -1.03762925e+00 8.70249629e-01
8.38411748e-01 -8.22310567e-01 -2.08872437e-01 -7.67992735e-01
-5.77137747e-04 -9.19607878e-02 4.66975570e-02 -8.95503044e-01
-2.26282790e-01 1.61188513e-01 3.07389051e-01 -3.12567919e-01
4.19829875e-01 -5.01101553e-01 -5.09015918e-01 3.94502044e-01
-5.14236569e-01 -4.21162434e-02 5.89864016e-01 3.74034852e-01
-1.85028076e-01 -4.99026626e-01 7.60264099e-01 -3.45888138e-01
-1.08425510e+00 4.36417051e-02 -4.30237770e-01 5.48009872e-01
5.87429583e-01 1.38949603e-01 -4.31801379e-01 1.52571470e-01
-9.06945229e-01 2.82584816e-01 2.73207966e-02 5.34506142e-01
1.63599521e-01 -9.66140985e-01 -7.47606337e-01 4.79499787e-01
1.15676016e-01 2.63884634e-01 1.09115556e-01 1.44821703e-01
-3.30512255e-01 4.79276270e-01 2.09714338e-01 -3.45488757e-01
-1.20000494e+00 2.66657710e-01 3.91383655e-02 -8.42634439e-01
-4.27435935e-01 1.29180026e+00 2.20480636e-01 -7.80165017e-01
-5.98813780e-02 -5.18649518e-01 -3.52208525e-01 3.79139259e-02
5.53525984e-01 -1.78631708e-01 1.59439653e-01 -7.08838344e-01
-7.28224635e-01 3.65038782e-01 -8.99102986e-02 -1.03095509e-01
1.57784390e+00 4.21318263e-01 -5.57007128e-03 2.92821437e-01
1.15712643e+00 3.23457032e-01 -1.08701801e+00 -2.37590969e-01
3.64115179e-01 5.41741922e-02 1.31051108e-01 -1.28108716e+00
-8.04985762e-01 1.04744613e+00 2.40677014e-01 -2.48666093e-01
7.93016791e-01 4.00650471e-01 1.02224100e+00 2.59220988e-01
2.96611637e-01 -8.91435027e-01 -4.05482426e-02 1.04630280e+00
5.65484881e-01 -1.20768023e+00 -3.11759323e-01 -4.73993838e-01
-5.47650576e-01 6.04086936e-01 6.31583512e-01 -2.90312648e-01
6.88118398e-01 5.41537344e-01 8.49053413e-02 -4.28711772e-02
-8.44167233e-01 -5.10404587e-01 1.82665989e-01 4.62093979e-01
8.59154880e-01 2.41661489e-01 -3.34883034e-01 1.01147342e+00
-2.37732023e-01 -3.56019497e-01 1.53550506e-01 1.00415564e+00
-6.01148367e-01 -1.54163086e+00 5.47871739e-02 2.63468415e-01
-1.16398215e+00 -6.38722420e-01 -5.87925781e-03 9.05187011e-01
3.36444318e-01 9.51181531e-01 3.46453726e-01 6.46306798e-02
4.16034997e-01 5.43700933e-01 2.85175323e-01 -1.44438171e+00
-8.06194127e-01 -3.68918851e-02 7.97524750e-01 -6.06988072e-01
-4.73491132e-01 -6.05218410e-01 -1.88838315e+00 2.86520928e-01
-8.13749135e-02 5.20933494e-02 6.04043067e-01 1.08033586e+00
2.58837938e-01 7.10638046e-01 4.49751616e-02 -5.76348245e-01
-2.36461475e-01 -9.71636176e-01 -2.11272418e-01 3.77292097e-01
2.62192637e-01 -6.54345036e-01 -2.27318913e-01 -1.80474848e-01] | [10.376916885375977, 9.512594223022461] |
ceb5a5e0-292d-4a67-9dd2-4eb2d9b9aab6 | abinet-autonomous-bidirectional-and-iterative | 2211.10578 | null | https://arxiv.org/abs/2211.10578v2 | https://arxiv.org/pdf/2211.10578v2.pdf | ABINet++: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Spotting | Scene text spotting is of great importance to the computer vision community due to its wide variety of applications. Recent methods attempt to introduce linguistic knowledge for challenging recognition rather than pure visual classification. However, how to effectively model the linguistic rules in end-to-end deep networks remains a research challenge. In this paper, we argue that the limited capacity of language models comes from 1) implicit language modeling; 2) unidirectional feature representation; and 3) language model with noise input. Correspondingly, we propose an autonomous, bidirectional and iterative ABINet++ for scene text spotting. Firstly, the autonomous suggests enforcing explicitly language modeling by decoupling the recognizer into vision model and language model and blocking gradient flow between both models. Secondly, a novel bidirectional cloze network (BCN) as the language model is proposed based on bidirectional feature representation. Thirdly, we propose an execution manner of iterative correction for the language model which can effectively alleviate the impact of noise input. Finally, to polish ABINet++ in long text recognition, we propose to aggregate horizontal features by embedding Transformer units inside a U-Net, and design a position and content attention module which integrates character order and content to attend to character features precisely. ABINet++ achieves state-of-the-art performance on both scene text recognition and scene text spotting benchmarks, which consistently demonstrates the superiority of our method in various environments especially on low-quality images. Besides, extensive experiments including in English and Chinese also prove that, a text spotter that incorporates our language modeling method can significantly improve its performance both in accuracy and speed compared with commonly used attention-based recognizers. | ['Yongdong Zhang', 'Chenggang Yan', 'Yuxin Wang', 'Hongtao Xie', 'Zhendong Mao', 'Shancheng Fang'] | 2022-11-19 | null | null | null | null | ['text-spotting', 'scene-text-recognition'] | ['computer-vision', 'computer-vision'] | [ 4.41240102e-01 -5.52012026e-01 -1.17138557e-01 -3.23922545e-01
-1.29527792e-01 -2.46864319e-01 7.74530470e-01 -3.79052073e-01
-5.69138885e-01 1.75848156e-01 4.17794943e-01 -4.31975663e-01
2.46971041e-01 -8.14007938e-01 -8.07994127e-01 -4.74943131e-01
7.93752193e-01 2.91789144e-01 2.22432181e-01 -1.87711015e-01
5.01187027e-01 4.82521981e-01 -1.26372719e+00 5.45300364e-01
9.96713996e-01 9.44598198e-01 7.03625739e-01 6.73541248e-01
-6.73132956e-01 1.35300732e+00 -2.83923835e-01 -4.65395659e-01
1.80021212e-01 -4.11363304e-01 -6.74163342e-01 3.78887802e-01
6.46055937e-01 -5.32444835e-01 -8.10372114e-01 1.11600077e+00
5.52663863e-01 -2.99658421e-02 5.65142989e-01 -8.16561043e-01
-1.23497534e+00 7.59396791e-01 -7.21320212e-01 7.13490769e-02
1.54498026e-01 3.57610583e-01 1.11255014e+00 -1.22969198e+00
3.08784693e-01 1.28129828e+00 5.34867644e-01 4.63291824e-01
-1.06614745e+00 -4.83202010e-01 5.29291332e-01 4.86629218e-01
-1.40260208e+00 -5.09884477e-01 8.52877140e-01 -3.56129467e-01
1.19238508e+00 3.01163912e-01 5.36800385e-01 1.13109350e+00
-1.71487369e-02 1.39682651e+00 8.12972844e-01 -6.39832199e-01
-2.95469940e-01 2.07515761e-01 3.04861814e-01 9.17293966e-01
-4.46414687e-02 -2.41818428e-01 -5.10457456e-01 4.51705933e-01
8.52303803e-01 2.37195641e-01 -3.44357222e-01 -1.26526117e-01
-1.22651649e+00 6.38512969e-01 5.86783111e-01 3.23598564e-01
-1.98606566e-01 2.83101350e-01 5.99712491e-01 1.07895710e-01
2.40332305e-01 1.12049684e-01 -4.97632921e-02 -8.24801847e-02
-9.84215021e-01 -3.28290194e-01 4.23388124e-01 9.58459496e-01
5.68092525e-01 4.42490548e-01 -3.94222528e-01 1.31160223e+00
3.73682469e-01 8.26211095e-01 6.97469711e-01 -1.70671239e-01
6.82106018e-01 9.00172174e-01 -4.08911675e-01 -1.07837105e+00
-1.09074079e-01 -3.92907888e-01 -1.19556272e+00 -1.85124472e-01
7.60923028e-02 2.96621382e-01 -1.11322427e+00 1.32656598e+00
-1.99634343e-01 2.36107826e-01 7.78495241e-03 1.11038399e+00
9.52508807e-01 8.11917186e-01 -1.13959305e-01 2.17310801e-01
1.37468910e+00 -1.49844921e+00 -7.97483981e-01 -4.77195382e-01
7.36142397e-01 -9.29884851e-01 1.60028875e+00 2.51301557e-01
-8.32779944e-01 -6.63773298e-01 -9.87490594e-01 -5.97699821e-01
-3.81865442e-01 6.88252866e-01 4.08009470e-01 5.54004014e-01
-9.72216427e-01 1.14686921e-01 -6.05570555e-01 -6.21259272e-01
4.00214642e-01 2.45161578e-01 -1.62116364e-01 -2.29314700e-01
-1.04828894e+00 7.93495238e-01 3.54941070e-01 4.66721684e-01
-6.74391568e-01 -2.37523347e-01 -8.90034318e-01 3.04571360e-01
3.35240126e-01 -6.08925402e-01 8.54570210e-01 -1.33608222e+00
-1.69772077e+00 7.25358188e-01 -3.33123833e-01 -4.94889289e-01
6.19479239e-01 -3.66286308e-01 -3.57500106e-01 5.81804812e-02
-4.66913357e-02 7.32213497e-01 8.43040049e-01 -9.74869967e-01
-6.39798224e-01 -6.59613609e-02 -1.67364940e-01 5.35078943e-01
-8.42303455e-01 1.03404559e-01 -9.31694627e-01 -7.88045645e-01
1.77910209e-01 -4.10715967e-01 5.01025468e-03 1.28656536e-01
-6.13560617e-01 -2.34768003e-01 1.06076908e+00 -7.02632368e-01
1.35515523e+00 -2.15138054e+00 4.49824706e-02 1.56702884e-02
2.19048858e-01 3.87080938e-01 -3.09913337e-01 2.32378691e-01
1.35581121e-01 1.32213503e-01 -9.69558507e-02 -4.11390811e-01
1.12496719e-01 1.59315005e-01 -7.46039987e-01 3.66875172e-01
1.73926845e-01 1.21473801e+00 -4.28344995e-01 -5.13936818e-01
6.00128293e-01 3.79249543e-01 -5.95704317e-01 1.23828463e-03
-2.22965702e-01 -2.47229680e-01 -3.44469368e-01 5.33799231e-01
6.40356004e-01 -3.86526465e-01 -3.77043411e-02 -4.20661509e-01
-2.50737935e-01 3.17265034e-01 -9.45406616e-01 1.61581028e+00
-4.77896959e-01 9.43915904e-01 -1.87658973e-03 -1.09839118e+00
1.03549516e+00 -4.71805148e-02 -2.61409041e-02 -1.10168421e+00
3.32339555e-01 7.01776221e-02 -8.45992044e-02 -5.62260687e-01
7.09555030e-01 2.15536594e-01 1.86804846e-01 3.35414380e-01
-7.48406649e-02 9.49144512e-02 8.35627317e-02 3.08930814e-01
7.40270436e-01 1.15278075e-02 -9.21140686e-02 -3.12078297e-01
8.99160147e-01 -1.43093079e-01 2.39339471e-01 9.41857517e-01
-1.37329608e-01 6.06996059e-01 2.44929507e-01 -5.58153629e-01
-1.07211268e+00 -6.51601732e-01 4.10531648e-03 1.31641555e+00
3.79403859e-01 -3.40569675e-01 -5.83334804e-01 -5.34039319e-01
-1.77719399e-01 5.90767026e-01 -4.94563669e-01 -1.46784723e-01
-5.96884966e-01 -6.00331008e-01 9.50868368e-01 6.38996661e-01
1.06774509e+00 -1.11516547e+00 -2.50422895e-01 6.41020313e-02
-2.28960395e-01 -1.33389473e+00 -8.49456251e-01 8.82023126e-02
-5.57693005e-01 -6.65085137e-01 -7.31174171e-01 -1.19899440e+00
7.59919107e-01 5.26452899e-01 6.88460469e-01 2.97677159e-01
-1.77493662e-01 4.09695655e-01 -3.93888205e-01 2.63460930e-02
-1.45765170e-01 1.26577348e-01 -1.33711055e-01 4.10006166e-01
5.62108457e-01 -2.01598778e-01 -4.43044394e-01 2.19411954e-01
-9.56394553e-01 7.19507337e-01 7.39044070e-01 1.13386118e+00
3.10816735e-01 -1.78232163e-01 7.99988806e-02 -7.71072388e-01
6.58733368e-01 5.02559766e-02 -5.39614975e-01 5.29129446e-01
-5.22791982e-01 -1.14121445e-01 9.38792109e-01 -5.01963735e-01
-1.03036392e+00 1.02762468e-01 -2.02735111e-01 -5.01524866e-01
-1.29779577e-01 5.23763597e-01 -2.60547101e-01 -1.88588500e-01
3.86957675e-01 1.02659869e+00 -1.27505124e-01 -4.34478849e-01
4.27391738e-01 9.16297138e-01 4.73395020e-01 -5.70886016e-01
6.91314399e-01 5.20110667e-01 -4.72503006e-01 -1.18108404e+00
-6.08577311e-01 -3.39413762e-01 -6.06287599e-01 -8.31831172e-02
7.93135941e-01 -9.90457058e-01 -8.80875289e-01 8.19729328e-01
-1.25298309e+00 -4.36710835e-01 1.21279053e-01 3.12921971e-01
-3.17376047e-01 7.60347843e-01 -8.21652293e-01 -6.81054235e-01
-5.37125468e-01 -1.31078875e+00 1.03906476e+00 1.53491974e-01
3.23393375e-01 -1.00570381e+00 -4.18050140e-01 3.94154787e-01
5.39142370e-01 -6.23910367e-01 8.86100471e-01 -4.08596754e-01
-7.41066039e-01 2.53124759e-02 -9.32781816e-01 3.42259526e-01
1.51993660e-02 -6.43479079e-02 -1.08398628e+00 -1.54945582e-01
-1.34457394e-01 -3.70251715e-01 1.20380950e+00 2.81501114e-01
1.32802033e+00 -2.67933697e-01 -1.42754450e-01 9.37058449e-01
1.34622502e+00 1.22872345e-01 8.32681060e-01 4.02040243e-01
1.33820522e+00 3.79082769e-01 7.08064139e-02 2.31270880e-01
4.25444156e-01 5.84341049e-01 1.66606694e-01 -4.58199114e-01
-4.35039580e-01 -4.44328398e-01 5.77209353e-01 1.09410322e+00
5.22498846e-01 -3.91803890e-01 -1.03892279e+00 4.43173081e-01
-1.96325541e+00 -9.14172947e-01 -1.88676462e-01 1.74827588e+00
7.29971290e-01 1.75429910e-01 -3.22311580e-01 -6.20422140e-02
8.40849817e-01 2.37131104e-01 -5.79904377e-01 -3.85386974e-01
-6.56879604e-01 -2.18987480e-01 4.93813664e-01 5.90183139e-01
-1.04855001e+00 1.50755000e+00 5.52268457e+00 1.25937951e+00
-1.72220123e+00 -1.54351652e-01 7.29321957e-01 1.43794209e-01
-3.16249818e-01 -1.15869395e-01 -9.68291283e-01 4.99017566e-01
3.20992142e-01 -4.51190099e-02 5.90211093e-01 7.47889638e-01
3.11306536e-01 2.18396783e-01 -1.01546001e+00 1.23178566e+00
4.91222858e-01 -1.46804619e+00 6.76904142e-01 -1.74880341e-01
5.65131903e-01 1.82295516e-01 1.22633085e-01 3.31549019e-01
1.65575027e-01 -1.14450705e+00 1.07244945e+00 3.72432500e-01
9.65536296e-01 -4.06990826e-01 4.23178196e-01 4.83233869e-01
-1.34334278e+00 -1.23184565e-02 -5.16709208e-01 -1.37668580e-01
-2.34223753e-02 4.14581865e-01 -7.47823179e-01 3.25008750e-01
4.53943610e-01 1.08788502e+00 -7.09650934e-01 8.86795700e-01
-1.92682594e-01 8.09454620e-01 -3.97066832e-01 -3.59310359e-01
5.82233250e-01 -2.65597224e-01 2.88197130e-01 1.55114877e+00
5.51482625e-02 -2.16058210e-01 2.98818648e-01 1.13710415e+00
-2.17203990e-01 3.77213120e-01 -6.65333211e-01 -2.28771135e-01
2.66030043e-01 1.07331312e+00 -8.65273058e-01 -4.51996356e-01
-5.75139344e-01 1.30313373e+00 3.65823090e-01 5.71301579e-01
-7.84390092e-01 -5.73294282e-01 2.05698237e-01 -1.91771179e-01
3.91072035e-01 -2.52875090e-01 -6.77198172e-01 -1.56828165e+00
1.49506778e-01 -9.24971163e-01 5.84679469e-02 -8.92569542e-01
-1.25909770e+00 6.78462088e-01 -6.85108006e-01 -1.07618034e+00
4.18584108e-01 -9.27914679e-01 -5.94093561e-01 8.71396065e-01
-1.64285779e+00 -1.47179389e+00 -3.17970186e-01 7.41922021e-01
1.09160280e+00 -3.16758066e-01 4.28752005e-01 4.91581321e-01
-8.22572649e-01 7.88906217e-01 1.36363268e-01 5.85152864e-01
6.38216674e-01 -9.25663412e-01 5.20839453e-01 1.16738009e+00
4.31824148e-01 5.66087127e-01 1.97075188e-01 -5.98321140e-01
-1.67498279e+00 -1.13536489e+00 8.29457402e-01 -2.75375009e-01
7.66975105e-01 -8.07322025e-01 -9.88006473e-01 6.06239736e-01
3.38812619e-01 -2.14271963e-01 2.36646324e-01 -1.21549256e-01
-5.04974008e-01 -1.41674012e-01 -5.12296021e-01 1.03483796e+00
1.03905690e+00 -7.91319489e-01 -4.42303687e-01 3.07839423e-01
7.08310068e-01 -3.41829449e-01 -9.17932242e-02 1.43642083e-01
5.34218550e-01 -8.27716649e-01 7.38931477e-01 -3.66005778e-01
5.87043762e-01 -3.83855313e-01 -2.84998745e-01 -7.76963651e-01
-5.28718174e-01 -2.86550879e-01 2.22208679e-01 1.31206429e+00
3.50965917e-01 -5.96037090e-01 7.46899903e-01 2.80177772e-01
-1.50494277e-01 -6.11823857e-01 -6.96661651e-01 -4.61162776e-01
8.26377347e-02 -5.53201795e-01 2.80348718e-01 1.07525635e+00
-2.54831940e-01 6.64462924e-01 -7.01264739e-01 -8.71494263e-02
3.24279755e-01 1.37179017e-01 6.55384898e-01 -7.76413620e-01
-1.99193388e-01 -9.08245265e-01 -2.72180259e-01 -1.96161723e+00
3.53942692e-01 -1.03831387e+00 9.03702229e-02 -1.57161474e+00
4.86313790e-01 -3.42073351e-01 -1.29660904e-01 5.50988436e-01
-1.74405187e-01 2.46502921e-01 4.17071998e-01 2.85082698e-01
-7.27666676e-01 9.27012146e-01 1.31009912e+00 -5.97491562e-01
7.74140209e-02 -5.54812729e-01 -6.21684134e-01 6.46172285e-01
4.31240857e-01 -1.95677504e-02 -3.58935356e-01 -1.18299437e+00
1.90417022e-01 -2.78803051e-01 4.19197321e-01 -7.36168742e-01
7.57239342e-01 -1.12362325e-01 4.51136470e-01 -6.68609738e-01
1.74909785e-01 -9.23091173e-01 -5.32151639e-01 3.02800953e-01
-4.79369789e-01 -4.14558873e-02 2.43136689e-01 3.95549983e-01
-2.88612932e-01 -1.40406057e-01 7.31866419e-01 2.67449263e-02
-1.02609348e+00 1.77882731e-01 -4.96035039e-01 -9.99257788e-02
5.21113276e-01 -5.41765988e-01 -4.99336392e-01 -3.64890456e-01
-2.95919210e-01 3.87170166e-01 4.29334044e-01 5.46849549e-01
8.73455226e-01 -1.29893601e+00 -6.91317797e-01 5.43738782e-01
4.45162319e-02 -2.33253807e-01 2.73939848e-01 9.26820397e-01
-7.53661513e-01 6.17339730e-01 7.80986473e-02 -7.00713992e-01
-1.10754085e+00 3.90285134e-01 5.33561468e-01 -1.04191199e-01
-7.62459993e-01 8.90045702e-01 6.38420820e-01 -3.97183329e-01
5.86552739e-01 -3.20060879e-01 -1.42141312e-01 -1.90202579e-01
5.25906622e-01 4.87169484e-03 -7.50628710e-02 -7.98977196e-01
-3.03166986e-01 8.52528870e-01 -4.85825092e-01 1.36826470e-01
9.33503330e-01 -3.86667758e-01 -2.29243696e-01 3.10573518e-01
1.05370808e+00 -1.78070113e-01 -1.06856632e+00 -5.04288733e-01
-4.76057865e-02 -3.50813150e-01 3.07250738e-01 -9.00348902e-01
-1.06258678e+00 1.36094391e+00 4.75993842e-01 -7.57322907e-02
1.17853439e+00 -5.34235060e-01 7.65450060e-01 8.01642954e-01
-1.46675810e-01 -1.06352139e+00 1.90112367e-01 1.05083597e+00
8.76504242e-01 -1.25455654e+00 -2.84914523e-01 -2.08788827e-01
-6.78120673e-01 1.27010429e+00 8.72992337e-01 -1.08306251e-01
3.53965670e-01 3.93880934e-01 2.57070094e-01 -5.76002663e-03
-7.57269263e-01 -2.57240236e-01 3.09436738e-01 1.69957891e-01
4.78222370e-01 -2.13438690e-01 3.57069001e-02 5.13752222e-01
-1.24536688e-02 -9.98792499e-02 3.89683336e-01 6.07849658e-01
-6.04720235e-01 -8.29754472e-01 -3.09375018e-01 5.03548682e-01
-2.37852380e-01 -7.34342754e-01 -5.34983575e-01 4.35295880e-01
-1.40328392e-01 6.57025397e-01 8.85241777e-02 -3.98651302e-01
2.45272830e-01 -1.86382569e-02 1.93827063e-01 -4.11375940e-01
-4.89269733e-01 3.26566696e-01 -2.33356968e-01 -1.98654145e-01
-3.81369144e-02 -1.60986379e-01 -1.13978755e+00 -3.90382469e-01
-4.59366679e-01 -1.74617782e-01 5.38745165e-01 9.56848502e-01
3.46211255e-01 6.21695757e-01 5.37510991e-01 -5.86069465e-01
-4.36405331e-01 -9.60961819e-01 -4.43690598e-01 3.56963962e-01
2.42353424e-01 -2.99994916e-01 -2.31305718e-01 2.35752314e-01] | [11.85139274597168, 2.142874002456665] |
8b6e69c3-1e5a-478a-ba44-33f71a794f17 | evidential-deep-learning-for-open-set-action | 2107.10161 | null | https://arxiv.org/abs/2107.10161v2 | https://arxiv.org/pdf/2107.10161v2.pdf | Evidential Deep Learning for Open Set Action Recognition | In a real-world scenario, human actions are typically out of the distribution from training data, which requires a model to both recognize the known actions and reject the unknown. Different from image data, video actions are more challenging to be recognized in an open-set setting due to the uncertain temporal dynamics and static bias of human actions. In this paper, we propose a Deep Evidential Action Recognition (DEAR) method to recognize actions in an open testing set. Specifically, we formulate the action recognition problem from the evidential deep learning (EDL) perspective and propose a novel model calibration method to regularize the EDL training. Besides, to mitigate the static bias of video representation, we propose a plug-and-play module to debias the learned representation through contrastive learning. Experimental results show that our DEAR method achieves consistent performance gain on multiple mainstream action recognition models and benchmarks. Code and pre-trained models are available at {\small{\url{https://www.rit.edu/actionlab/dear}}}. | ['Yu Kong', 'Qi Yu', 'Wentao Bao'] | 2021-07-21 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Bao_Evidential_Deep_Learning_for_Open_Set_Action_Recognition_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Bao_Evidential_Deep_Learning_for_Open_Set_Action_Recognition_ICCV_2021_paper.pdf | iccv-2021-1 | ['open-set-action-recognition'] | ['computer-vision'] | [ 4.28379208e-01 9.50983614e-02 -4.52761531e-01 -3.99121732e-01
-7.14694440e-01 -2.98934639e-01 6.03740454e-01 -6.87486827e-01
-1.42479882e-01 5.31762362e-01 3.05534393e-01 -1.12295724e-01
-9.49676090e-04 -2.19125330e-01 -9.88793969e-01 -7.20931590e-01
1.63285434e-01 2.72334814e-01 5.15291728e-02 1.62977144e-01
1.04077592e-01 2.66353041e-01 -1.38799572e+00 3.70244414e-01
5.94648421e-01 1.16077805e+00 -7.02929646e-02 6.09719515e-01
2.33957276e-01 1.52558255e+00 -5.17955542e-01 -2.51264602e-01
5.57130992e-01 -6.12074375e-01 -6.99331999e-01 7.27751136e-01
6.30268633e-01 -7.09213376e-01 -9.35754955e-01 1.06282318e+00
2.42554277e-01 3.14735472e-01 5.35560071e-01 -1.69319117e+00
-8.06138992e-01 4.02357191e-01 -5.87673008e-01 3.82722378e-01
2.56074011e-01 5.59924841e-01 7.81638920e-01 -7.51988113e-01
3.62534076e-01 1.13170242e+00 3.31656039e-01 9.14367259e-01
-8.35167766e-01 -8.65914583e-01 7.53860533e-01 5.16399801e-01
-1.10867250e+00 -7.66024590e-01 8.33610058e-01 -4.67730492e-01
4.89969283e-01 1.27425194e-01 6.35938466e-01 1.75487518e+00
-1.62792221e-01 1.37227726e+00 1.00530505e+00 -8.05220082e-02
2.56205261e-01 -2.77502596e-01 1.18335322e-01 6.28573120e-01
2.08552599e-01 9.88048464e-02 -5.08830607e-01 1.02434076e-01
1.11252999e+00 5.49527645e-01 -2.61832744e-01 -5.49596369e-01
-1.12283921e+00 4.89775181e-01 8.39465633e-02 -1.16318710e-01
-4.84088272e-01 5.33843398e-01 3.98293972e-01 1.87106565e-01
2.78909445e-01 6.33352473e-02 -4.59583908e-01 -4.71066982e-01
-5.48732162e-01 8.06784537e-03 4.94340420e-01 8.05139005e-01
5.18484712e-01 2.29731560e-01 -3.11312258e-01 6.55319691e-01
1.42805308e-01 5.88001966e-01 5.93874216e-01 -1.29028440e+00
4.92516160e-01 5.77638328e-01 2.57316053e-01 -7.47987568e-01
3.20417918e-02 -2.79260457e-01 -8.31749618e-01 1.94288477e-01
5.31484783e-01 -4.56554629e-02 -9.15588677e-01 1.68857872e+00
3.21544260e-01 8.64969015e-01 1.13319963e-01 9.63843465e-01
4.40182894e-01 5.01305580e-01 2.49963850e-02 -2.25756451e-01
9.05561268e-01 -1.20540476e+00 -6.67277753e-01 -5.16649723e-01
5.72100997e-01 -1.47909254e-01 1.12762618e+00 5.50469041e-01
-7.56314039e-01 -5.60804129e-01 -9.10558701e-01 1.10131085e-01
9.87671986e-02 4.77857262e-01 6.16660833e-01 2.58263826e-01
-5.09142578e-01 4.92513001e-01 -1.34065485e+00 -1.73385277e-01
9.58253324e-01 1.96107417e-01 -4.94087666e-01 -2.84407496e-01
-8.50546062e-01 5.35164297e-01 2.39825636e-01 2.75757968e-01
-1.32106531e+00 -3.82640183e-01 -9.41784382e-01 -1.57633021e-01
9.81676877e-01 -3.00785899e-01 1.41209495e+00 -1.52234423e+00
-1.50596070e+00 7.54432738e-01 5.31035149e-03 -6.70489371e-01
7.49086857e-01 -6.89158738e-01 -4.12698448e-01 1.94719657e-01
3.52553204e-02 2.66630888e-01 1.16212702e+00 -1.09996581e+00
-3.77893060e-01 -3.06242406e-01 4.21635956e-01 -7.99014270e-02
-2.85904288e-01 -1.84199706e-01 -5.64390302e-01 -7.60214031e-01
-9.88417342e-02 -1.18872893e+00 -2.28652611e-01 1.89251602e-01
-2.51553476e-01 -2.29294091e-01 8.87799442e-01 -6.93343341e-01
1.26090050e+00 -2.30885887e+00 -2.72306576e-02 -2.10295781e-01
8.53761733e-02 4.21696931e-01 -1.81867987e-01 9.36615914e-02
-2.97323704e-01 -2.49531597e-01 -1.57884493e-01 -2.33991116e-01
2.14583218e-01 5.92990637e-01 -5.06400287e-01 7.10644484e-01
2.99610823e-01 9.64686334e-01 -1.03423083e+00 -3.92806381e-01
3.30186635e-01 3.04262489e-01 -4.51555818e-01 2.52164125e-01
-4.31686074e-01 6.34117663e-01 -6.67632699e-01 8.20310712e-01
4.73457932e-01 -6.52511656e-01 2.17059940e-01 5.07903174e-02
3.77725035e-01 -3.04933880e-02 -1.50653958e+00 1.66363668e+00
-1.59899011e-01 4.84770983e-01 -1.65719315e-01 -1.41047418e+00
6.92914128e-01 2.25922078e-01 7.59271085e-01 -5.38060546e-01
1.96108848e-01 -1.96739659e-01 4.93959375e-02 -5.44447660e-01
2.79145151e-01 2.86511123e-01 -7.07620895e-03 6.01249933e-01
7.25820735e-02 3.80880922e-01 9.11979526e-02 1.87334761e-01
1.40538216e+00 5.65896869e-01 2.79773295e-01 2.77709454e-01
4.97356385e-01 -2.69243538e-01 1.10292447e+00 7.51189768e-01
-6.06128752e-01 5.66076458e-01 6.24982357e-01 -6.05657220e-01
-5.56130111e-01 -9.32213068e-01 2.17395872e-01 1.07623494e+00
2.49525338e-01 -2.63638914e-01 -6.44790292e-01 -1.09874094e+00
-1.16093412e-01 4.75656331e-01 -8.29101562e-01 -3.89027715e-01
-6.39461339e-01 -3.13636184e-01 3.26853544e-01 8.76106143e-01
6.78406417e-01 -1.18115485e+00 -6.50142014e-01 -2.85784360e-02
-2.47762993e-01 -1.21326256e+00 -5.24114549e-01 -1.12213701e-01
-6.57860756e-01 -1.36085963e+00 -4.75477934e-01 -3.96198988e-01
7.22424626e-01 3.90743405e-01 1.02776849e+00 1.16518259e-01
-1.49116486e-01 8.45463216e-01 -5.41634262e-01 -3.92706871e-01
-2.49026731e-01 -4.16086942e-01 2.21262872e-01 5.36794007e-01
6.60483897e-01 -4.93573993e-01 -8.23674262e-01 5.20998657e-01
-9.57288861e-01 5.40177897e-02 6.98215365e-01 9.25777435e-01
7.20574737e-01 -1.35228857e-02 4.05450374e-01 -6.96878910e-01
-6.13297559e-02 -5.50333798e-01 -5.06684542e-01 2.49614432e-01
-4.58552361e-01 -9.75671485e-02 3.90033841e-01 -9.36948538e-01
-9.90655243e-01 4.08312142e-01 2.47017011e-01 -1.10938680e+00
-3.02408427e-01 2.15586647e-01 -5.32482386e-01 3.48887563e-01
3.29123318e-01 3.82813126e-01 6.26209453e-02 -4.36965764e-01
1.64852515e-01 4.58741784e-01 6.39189422e-01 -6.80717707e-01
7.08125770e-01 6.95951104e-01 -2.92054236e-01 -4.02627975e-01
-1.35440063e+00 -3.29919368e-01 -6.96360230e-01 -5.04062712e-01
6.68725431e-01 -1.23858619e+00 -4.09269303e-01 7.09735394e-01
-8.00874531e-01 -7.77119815e-01 -5.68980813e-01 6.46800756e-01
-8.44587266e-01 4.02575076e-01 -4.51860070e-01 -7.98796058e-01
4.28326465e-02 -9.94536459e-01 1.08646405e+00 3.25030893e-01
-1.28646165e-01 -6.77223980e-01 4.71161865e-02 7.29168296e-01
-6.48604035e-02 2.38050804e-01 1.80776343e-01 -7.74716318e-01
-8.87574971e-01 -3.19096684e-01 6.38307184e-02 9.18359458e-01
2.86248684e-01 -5.32377809e-02 -9.59765613e-01 -2.83767581e-01
-1.23543344e-01 -6.14763975e-01 9.02046323e-01 3.46366495e-01
1.59241140e+00 -3.45132470e-01 -1.26518756e-01 4.70073760e-01
8.93328011e-01 1.59549251e-01 9.10641253e-01 2.33521044e-01
7.36495137e-01 2.36939579e-01 9.03080642e-01 8.98795187e-01
2.01789200e-01 4.39964920e-01 5.18775284e-01 1.31393880e-01
-7.59180859e-02 -5.25182605e-01 8.12524974e-01 3.81654203e-01
-1.48771971e-01 -3.23010474e-01 -6.42248631e-01 4.57700163e-01
-2.27653337e+00 -1.45245838e+00 1.78453207e-01 2.06624842e+00
6.50683165e-01 1.51746392e-01 1.67592570e-01 6.68335259e-02
6.76268697e-01 4.67896014e-01 -1.05365908e+00 3.07163894e-01
-1.24232829e-01 8.87998343e-02 3.98239762e-01 6.40164018e-02
-1.44477642e+00 8.72562408e-01 5.33061886e+00 6.63370609e-01
-1.09247851e+00 2.27515429e-01 6.25770748e-01 -3.12874585e-01
2.54404157e-01 6.91579804e-02 -6.15038753e-01 6.27361476e-01
7.37041533e-01 -7.44151697e-02 2.47857735e-01 1.09250569e+00
2.43926346e-01 6.73087686e-02 -1.44518197e+00 1.26069951e+00
1.54234037e-01 -1.17425394e+00 -3.60456742e-02 1.80390716e-01
7.38553703e-01 -6.70988392e-03 6.26091361e-02 7.61216104e-01
5.48435390e-01 -8.75307381e-01 6.85573697e-01 7.92483449e-01
6.45854652e-01 -1.56797335e-01 3.13274503e-01 3.61617416e-01
-1.03307152e+00 -3.88566494e-01 -1.64418548e-01 -3.70528817e-01
1.89149100e-03 1.58542067e-01 -2.88469732e-01 2.99918741e-01
6.34406805e-01 1.35913765e+00 -5.44246733e-01 6.93937361e-01
-3.74944657e-01 9.56430435e-01 3.38019878e-02 3.82151335e-01
6.93292916e-02 -2.81066954e-01 3.25981021e-01 7.64188945e-01
1.16560169e-01 2.59291559e-01 4.79426205e-01 5.93705177e-01
-1.54623523e-01 -3.93553883e-01 -5.23712873e-01 -4.12571192e-01
2.08203316e-01 9.30782378e-01 -4.34820026e-01 -4.21079159e-01
-6.48821890e-01 1.20463049e+00 3.11356783e-01 4.49572057e-01
-1.18194628e+00 2.87711471e-01 9.37094390e-01 6.63589016e-02
5.54987967e-01 -1.85260788e-01 2.89856583e-01 -1.57038915e+00
4.09385622e-01 -1.15478039e+00 5.94305515e-01 -6.97417200e-01
-1.27755558e+00 8.22273344e-02 -1.50845191e-02 -1.67488647e+00
-3.03068280e-01 -6.80055320e-01 -5.50865591e-01 1.94441408e-01
-1.14529169e+00 -1.09534240e+00 -4.24668223e-01 7.95714736e-01
8.12998056e-01 -2.71085829e-01 4.49691921e-01 3.59010279e-01
-8.92856836e-01 5.37287414e-01 1.28598623e-02 5.91225982e-01
6.08707249e-01 -9.45461273e-01 2.08382294e-01 9.74283338e-01
3.95121187e-01 2.68827826e-01 4.80305344e-01 -5.29347956e-01
-1.62254119e+00 -1.30098343e+00 8.80708992e-02 -7.94263840e-01
9.32337284e-01 -2.55006611e-01 -9.80927646e-01 1.26105344e+00
-1.13272838e-01 5.90305090e-01 7.11436272e-01 -1.69398323e-01
-5.27648270e-01 -1.60674155e-01 -6.72526062e-01 5.29726267e-01
1.43586326e+00 -5.50345719e-01 -5.44924438e-01 4.70188439e-01
4.43706572e-01 -4.03468698e-01 -6.99323058e-01 5.23495674e-01
6.91512644e-01 -7.89126813e-01 8.91538978e-01 -1.06285262e+00
5.79019725e-01 -3.14659476e-01 -2.82704502e-01 -9.52903271e-01
-1.89515486e-01 -6.30622387e-01 -7.45240808e-01 1.11409032e+00
9.66077298e-03 -5.49886525e-01 9.66037214e-01 7.39700258e-01
1.05360290e-02 -6.57450914e-01 -9.33064580e-01 -1.08335066e+00
-2.52079785e-01 -4.59936112e-01 2.95690805e-01 9.11924183e-01
-3.42558473e-01 1.48672894e-01 -9.20241475e-01 2.74311125e-01
5.65200746e-01 1.93926558e-01 1.16829908e+00 -9.35677469e-01
-7.43691266e-01 -1.12470403e-01 -5.81469536e-01 -1.55694926e+00
5.58357775e-01 -4.75464106e-01 1.20942466e-01 -1.26068628e+00
3.60283464e-01 -1.97183877e-01 -7.30557323e-01 6.00716710e-01
-1.77177802e-01 -6.82123676e-02 2.66133040e-01 2.80221254e-01
-1.22509599e+00 9.41610813e-01 1.13384581e+00 -4.19089675e-01
1.41173020e-01 1.41730919e-01 -6.41517699e-01 9.19473648e-01
7.51532495e-01 -4.17013466e-01 -6.73779011e-01 -5.11844933e-01
-2.76437312e-01 6.44169897e-02 7.37296104e-01 -1.04892147e+00
-2.23302990e-02 -6.56766474e-01 3.38697135e-01 -4.79801059e-01
6.05836511e-01 -8.21841240e-01 7.97743127e-02 3.04679453e-01
-4.15292889e-01 -2.14570552e-01 -8.11680108e-02 9.32089865e-01
-1.34557202e-01 -1.77568365e-02 6.48229599e-01 -1.36459738e-01
-1.04069364e+00 7.71769762e-01 -1.96512178e-01 2.80559331e-01
1.16841841e+00 -2.41062924e-01 -4.98443604e-01 -5.69315374e-01
-6.84584200e-01 3.69526863e-01 4.34328169e-01 5.79028428e-01
6.52287900e-01 -1.35030079e+00 -5.54572225e-01 1.39394803e-02
3.53879094e-01 -2.93721911e-02 5.74099362e-01 9.40769494e-01
-6.07280917e-02 -8.11143965e-03 -1.94351286e-01 -5.08163452e-01
-1.23483574e+00 6.54590011e-01 4.23474193e-01 -1.75968513e-01
-8.73264074e-01 7.38923013e-01 4.59394813e-01 -2.42869288e-01
5.66556156e-01 -1.57529354e-01 7.11545572e-02 -3.19717079e-01
7.36047208e-01 3.36406022e-01 -4.10914153e-01 -6.88989520e-01
-4.09793228e-01 5.22111952e-02 -1.39365584e-01 2.25011200e-01
1.38003588e+00 -5.85715845e-03 4.23033029e-01 6.45654559e-01
9.17760074e-01 -2.73225784e-01 -2.01111269e+00 -4.71417814e-01
-1.42492414e-01 -7.21663713e-01 -1.55675292e-01 -5.97214639e-01
-1.20330441e+00 6.87778711e-01 6.21345401e-01 -2.08223388e-01
1.04212666e+00 1.10526215e-02 6.57759845e-01 5.77955246e-01
1.99395567e-01 -1.34273326e+00 5.89211464e-01 3.82368326e-01
9.89673555e-01 -1.51096117e+00 3.41586098e-02 -5.50358072e-02
-9.08862829e-01 7.67389476e-01 1.01216984e+00 -3.00843984e-01
6.43274546e-01 6.99907541e-02 1.25778556e-01 -1.62868753e-01
-9.88065064e-01 -1.57861754e-01 -4.39176112e-02 4.68677312e-01
-4.86011468e-02 4.29009199e-02 1.27322927e-01 8.10416579e-01
3.53247046e-01 3.48307312e-01 6.14400029e-01 1.24041498e+00
-6.21274859e-02 -8.30561280e-01 -1.62049681e-01 4.04119760e-01
-2.84100771e-01 2.63871163e-01 -3.85889381e-01 7.28058279e-01
9.67516527e-02 7.86876738e-01 1.35069713e-01 -5.27898192e-01
2.28679001e-01 1.26841679e-01 5.31923175e-01 -5.09613335e-01
1.09519623e-01 -1.99480742e-01 -1.81152560e-02 -9.17461872e-01
-6.63127124e-01 -9.24915195e-01 -1.16637826e+00 -2.64812466e-02
-1.13620602e-01 -1.70376197e-01 1.56136617e-01 1.03899026e+00
5.13842702e-01 5.47357619e-01 7.34829664e-01 -7.61659265e-01
-9.63326871e-01 -9.77444589e-01 -6.24936581e-01 6.85409427e-01
2.91628033e-01 -1.05920899e+00 -4.53861862e-01 4.57980990e-01] | [8.505770683288574, 0.7048065066337585] |
2e2322be-a51a-4ee1-9993-1049357a2f06 | discover-and-mitigate-unknown-biases-with | 2207.10077 | null | https://arxiv.org/abs/2207.10077v2 | https://arxiv.org/pdf/2207.10077v2.pdf | Discover and Mitigate Unknown Biases with Debiasing Alternate Networks | Deep image classifiers have been found to learn biases from datasets. To mitigate the biases, most previous methods require labels of protected attributes (e.g., age, skin tone) as full-supervision, which has two limitations: 1) it is infeasible when the labels are unavailable; 2) they are incapable of mitigating unknown biases -- biases that humans do not preconceive. To resolve those problems, we propose Debiasing Alternate Networks (DebiAN), which comprises two networks -- a Discoverer and a Classifier. By training in an alternate manner, the discoverer tries to find multiple unknown biases of the classifier without any annotations of biases, and the classifier aims at unlearning the biases identified by the discoverer. While previous works evaluate debiasing results in terms of a single bias, we create Multi-Color MNIST dataset to better benchmark mitigation of multiple biases in a multi-bias setting, which not only reveals the problems in previous methods but also demonstrates the advantage of DebiAN in identifying and mitigating multiple biases simultaneously. We further conduct extensive experiments on real-world datasets, showing that the discoverer in DebiAN can identify unknown biases that may be hard to be found by humans. Regarding debiasing, DebiAN achieves strong bias mitigation performance. | ['Chenliang Xu', 'Anthony Hoogs', 'Zhiheng Li'] | 2022-07-20 | null | null | null | null | ['facial-attribute-classification'] | ['computer-vision'] | [ 3.81872505e-01 2.67694443e-01 -3.34402233e-01 -6.11606717e-01
-1.63712859e-01 -5.67454278e-01 3.24031651e-01 -3.41599286e-01
-4.03117925e-01 9.68653381e-01 -9.07961354e-02 -2.84062207e-01
9.26029831e-02 -7.27551162e-01 -8.21415901e-01 -8.76702428e-01
2.28068173e-01 3.38948846e-01 3.06518059e-02 -1.85455963e-01
2.35831007e-01 2.32225180e-01 -1.57297099e+00 3.90589297e-01
1.02848351e+00 1.03985751e+00 -3.12019169e-01 2.56984085e-01
2.55105346e-01 8.07025611e-01 -7.99318492e-01 -6.22254372e-01
5.40689111e-01 -3.14581841e-01 -7.80913949e-01 -2.56427288e-01
1.02814972e+00 -6.68169677e-01 -8.18952024e-02 1.42502558e+00
4.38730299e-01 -5.22843659e-01 5.63572764e-01 -1.80296385e+00
-1.15534317e+00 7.74492025e-01 -6.91852093e-01 -3.60982828e-02
-1.82931185e-01 1.75303385e-01 9.09110367e-01 -5.00321150e-01
3.71239811e-01 1.52618897e+00 7.99648583e-01 1.20972514e+00
-1.34902000e+00 -1.32214606e+00 4.58814710e-01 8.45591426e-02
-1.13778734e+00 -4.38294560e-01 8.76891971e-01 -3.22983563e-01
3.19078803e-01 3.69977266e-01 2.83166915e-01 1.80904269e+00
-8.92252661e-03 8.48718405e-01 1.72190428e+00 -2.59245574e-01
2.84810901e-01 5.37096739e-01 5.97089708e-01 5.76972544e-01
5.03325582e-01 5.55863082e-01 -6.07110858e-01 -3.05892766e-01
5.38725555e-01 5.12366295e-02 -2.32491285e-01 -2.71136433e-01
-1.26636815e+00 6.58366203e-01 7.47691035e-01 -9.62382779e-02
-1.22855619e-01 9.05550420e-02 2.42378905e-01 5.33704102e-01
4.87138957e-01 6.24309301e-01 -4.87248749e-01 4.47795928e-01
-6.61683083e-01 1.77623346e-01 5.71959794e-01 9.60452497e-01
1.01408255e+00 -1.92473888e-01 -1.89171657e-01 8.55469346e-01
-1.30303741e-01 7.31946409e-01 2.91396022e-01 -7.76069939e-01
1.83136776e-01 8.26060832e-01 2.56060570e-01 -1.10283506e+00
-3.76671821e-01 -3.79626453e-01 -1.21416855e+00 3.71444464e-01
7.09789157e-01 -3.52222145e-01 -1.11203039e+00 2.03448200e+00
2.83212453e-01 -1.13898866e-01 -1.68889225e-01 1.23203707e+00
6.73997045e-01 9.93892550e-02 1.86581742e-02 2.28060469e-01
1.34984326e+00 -8.80325079e-01 -6.77859843e-01 -6.07781351e-01
3.67096812e-01 -2.61281103e-01 1.11854923e+00 7.08815038e-01
-5.45718670e-01 -4.60186988e-01 -1.26079893e+00 9.77809578e-02
-4.04275775e-01 1.33425876e-01 7.41880834e-01 8.92089725e-01
-8.74256670e-01 4.34415817e-01 -3.77305955e-01 -1.43952757e-01
7.88413227e-01 4.02371615e-01 -2.28036389e-01 -1.94812015e-01
-1.34299707e+00 7.88017690e-01 1.48674309e-01 1.25358552e-02
-1.34071898e+00 -7.47324586e-01 -5.03275633e-01 -2.03896180e-01
6.30581081e-01 -5.97587287e-01 8.75166476e-01 -1.90322781e+00
-8.63849759e-01 1.20431340e+00 -4.18055505e-02 -3.18922251e-01
8.51233721e-01 -3.37049633e-01 -4.74419326e-01 -2.22261786e-01
9.64548215e-02 8.96705687e-01 1.13777971e+00 -1.63355863e+00
-5.90910614e-01 -5.78554451e-01 4.72658038e-01 -2.67872185e-01
-7.11783051e-01 -1.80890143e-01 1.11866899e-01 -7.87870347e-01
-1.12943607e-03 -1.01853907e+00 7.18027651e-02 3.19896668e-01
-8.49144280e-01 -5.53278737e-02 9.21804070e-01 -3.77294898e-01
7.72298217e-01 -2.02765584e+00 -3.89139168e-02 1.60849363e-01
4.74363297e-01 2.24506453e-01 -2.51121283e-01 -2.76890814e-01
-2.95904577e-01 3.69152457e-01 -1.39246196e-01 -1.71765342e-01
-1.89252034e-01 3.02615494e-01 -5.37706673e-01 6.09212279e-01
2.38976687e-01 6.40696168e-01 -9.67051864e-01 -4.29675937e-01
-3.35430503e-01 2.66796470e-01 -5.30933976e-01 2.32229486e-01
-8.92528519e-02 2.70562470e-01 -1.64569438e-01 1.04158735e+00
9.56657469e-01 -1.97296664e-01 2.45191500e-01 -2.38542750e-01
2.31801391e-01 8.74295738e-03 -1.19531202e+00 1.00294530e+00
-1.72485724e-01 3.59555066e-01 3.10091823e-01 -8.54281008e-01
1.00368536e+00 2.95541342e-02 3.32854898e-03 -7.43877590e-01
1.83775380e-01 1.93210468e-01 -6.57648221e-02 -3.31685901e-01
3.05935800e-01 -2.59051651e-01 5.64074330e-03 6.35485470e-01
-5.53089455e-02 4.21622187e-01 -1.98386133e-01 1.54038385e-01
9.59086299e-01 -2.08585858e-01 -5.80361113e-02 -5.11692405e-01
3.31713110e-01 4.55991644e-03 9.55629468e-01 1.13993239e+00
-5.01148224e-01 5.83266854e-01 6.80422604e-01 -6.55311704e-01
-9.22127664e-01 -7.00979590e-01 -1.58323079e-01 1.41979027e+00
4.30870801e-01 1.34931087e-01 -7.43555069e-01 -1.44382644e+00
3.97505432e-01 4.67145532e-01 -1.20487154e+00 -2.87249148e-01
-2.49605536e-01 -1.08310175e+00 7.13564336e-01 6.72514796e-01
5.53314686e-01 -8.15628171e-01 -5.07642925e-01 -2.81308681e-01
-2.53349304e-01 -7.92330265e-01 -3.79227668e-01 3.16281497e-01
-7.26615250e-01 -1.30514610e+00 -5.67147255e-01 -4.11728233e-01
1.20043993e+00 3.62351179e-01 1.15549898e+00 7.46682167e-01
-1.18101917e-01 8.01915899e-02 -1.20998792e-01 -7.67895639e-01
-4.32165056e-01 1.56563520e-02 1.66227266e-01 1.77901760e-01
5.84558964e-01 -4.24404800e-01 -7.42761731e-01 9.30734754e-01
-7.97804594e-01 5.29493019e-02 7.25725651e-01 1.17448020e+00
1.03870444e-01 -3.12694535e-02 9.20774460e-01 -1.50728023e+00
3.34519595e-01 -5.90025663e-01 -6.06888533e-01 2.42805973e-01
-1.06004977e+00 -2.01946385e-02 3.20842803e-01 -9.22413647e-01
-1.00655711e+00 -1.18542492e-01 2.51120597e-01 -2.26194128e-01
-4.46691513e-01 -1.32020563e-01 -3.82211357e-01 -1.37697145e-01
1.05288112e+00 -1.96982682e-01 -6.18298203e-02 -4.88097221e-01
1.39575139e-01 8.17989051e-01 5.25363326e-01 -8.97366583e-01
8.16959441e-01 7.76826084e-01 -1.88231319e-01 -7.22131208e-02
-1.41020584e+00 -7.22415000e-02 -7.42511690e-01 -2.54246473e-01
5.34595013e-01 -9.40158784e-01 -6.59665406e-01 6.93280637e-01
-1.02465403e+00 -3.14761847e-01 9.88251492e-02 -7.56991431e-02
1.12291854e-02 9.37249735e-02 -4.51202750e-01 -7.98880041e-01
-2.49730259e-01 -1.03452802e+00 5.96541166e-01 2.22100809e-01
-4.41990852e-01 -7.12218523e-01 -2.90307194e-01 6.50366902e-01
5.78133821e-01 2.97403932e-01 1.11647832e+00 -7.65355527e-01
-3.32555681e-01 3.12997377e-03 -4.87305045e-01 5.91911018e-01
1.66708708e-01 -2.17396334e-01 -1.54865408e+00 -4.44398552e-01
-2.88024426e-01 -6.81155920e-01 1.01557481e+00 -9.42624807e-02
1.43337083e+00 -5.24554729e-01 -3.42482835e-01 5.45570791e-01
1.22324681e+00 1.02289490e-01 5.39076507e-01 6.69155657e-01
7.84216225e-01 8.15380275e-01 4.80754197e-01 1.70669824e-01
3.83266389e-01 2.19152197e-01 9.39419568e-01 -5.48710465e-01
-1.83468536e-01 -2.83422798e-01 2.91058987e-01 5.72154373e-02
-1.05103865e-01 -1.59041137e-02 -8.63998771e-01 8.25382352e-01
-1.76174021e+00 -6.29350007e-01 -1.06012143e-01 2.00561190e+00
1.19056511e+00 1.88257501e-01 1.95211306e-01 1.73380435e-01
8.15845728e-01 -1.87591352e-02 -1.03847408e+00 -1.47685111e-01
-3.66484046e-01 -2.09007591e-01 6.18832171e-01 1.19114064e-01
-1.09646344e+00 7.60183454e-01 6.71352100e+00 2.62899637e-01
-1.29117632e+00 1.85101777e-01 1.05543923e+00 -3.43761027e-01
-4.02544200e-01 8.06573927e-02 -8.12792182e-01 5.38687527e-01
3.06124330e-01 3.96938115e-01 7.01990306e-01 9.98454332e-01
-3.86894166e-01 -5.58373481e-02 -1.55258119e+00 8.46270025e-01
2.20670074e-01 -9.25300539e-01 1.11226380e-01 -1.50738796e-02
8.81725371e-01 -2.17005178e-01 3.84160042e-01 3.85866165e-01
6.43002629e-01 -1.24106717e+00 1.04404199e+00 3.48145306e-01
8.23816180e-01 -7.76637852e-01 8.35101962e-01 4.99605596e-01
-8.62338468e-02 -4.45722669e-01 -4.34283644e-01 -1.90878227e-01
-5.75550973e-01 9.96698856e-01 -5.99173605e-01 2.45914802e-01
1.17917168e+00 4.86958116e-01 -7.90612459e-01 5.24421751e-01
-6.74632609e-01 7.02998877e-01 2.74679270e-02 9.49340984e-02
-3.32898498e-02 1.41005307e-01 3.02376956e-01 1.04771161e+00
1.58823747e-02 -7.31820390e-02 -5.79256378e-02 1.27215195e+00
-3.70503873e-01 -3.07752132e-01 -4.67571586e-01 1.80049181e-01
5.91665924e-01 1.28204668e+00 -3.83396208e-01 -3.78039688e-01
-2.38448232e-01 7.82631099e-01 4.95928705e-01 3.94729376e-01
-6.47009194e-01 -2.54181892e-01 8.55063915e-01 1.38145655e-01
-1.33033648e-01 3.18602622e-01 -5.58415890e-01 -1.08869457e+00
-1.14576370e-01 -1.53515518e+00 5.50370455e-01 -8.30203354e-01
-1.82799101e+00 4.22148436e-01 -4.95264418e-02 -7.64806151e-01
3.71484607e-01 -7.73109138e-01 -4.04974043e-01 8.71840835e-01
-1.82845485e+00 -1.28800738e+00 -6.58641338e-01 6.48370802e-01
5.87684549e-02 -3.77556346e-02 5.51003337e-01 3.31105173e-01
-9.03904796e-01 8.59482646e-01 -4.95542854e-01 3.78579646e-01
1.41622126e+00 -1.37944901e+00 -3.36208604e-02 7.11061597e-01
-2.49772400e-01 7.69097686e-01 6.89753890e-01 -6.57970011e-01
-1.23703659e+00 -1.08171833e+00 4.33091313e-01 -6.34634614e-01
4.40324455e-01 -6.36010408e-01 -1.07899809e+00 8.79871786e-01
8.97783190e-02 1.27408668e-01 5.58535993e-01 5.65791070e-01
-8.46919596e-01 -3.84615242e-01 -1.40728927e+00 5.26814342e-01
1.19457221e+00 -3.78933519e-01 -4.31170911e-01 1.73542291e-01
3.86844903e-01 -3.66425544e-01 -5.17786026e-01 4.71138477e-01
5.39297938e-01 -1.07937956e+00 8.52772593e-01 -7.35345840e-01
6.23605251e-01 -2.46705145e-01 4.72706929e-02 -1.46398771e+00
-2.08952889e-01 -3.25019240e-01 1.36914805e-01 1.58229995e+00
4.19020057e-01 -9.57229316e-01 7.59133935e-01 8.36458862e-01
3.85687321e-01 -4.86851573e-01 -6.88502610e-01 -7.98649549e-01
2.14548916e-01 -1.72060728e-01 1.19623172e+00 1.56054294e+00
-3.90503883e-01 2.29633421e-01 -9.07602072e-01 4.39741015e-01
9.77477551e-01 1.40886143e-01 8.99452329e-01 -1.38704097e+00
-4.02184166e-02 -3.07493091e-01 1.29121527e-01 -8.68879139e-01
2.89829701e-01 -7.92157590e-01 2.38200396e-01 -7.30841696e-01
6.68124855e-01 -9.78884399e-01 -6.55121505e-01 1.20964217e+00
-4.91625607e-01 2.48490691e-01 -1.31775320e-01 2.05608130e-01
-4.19746280e-01 2.02707246e-01 1.19264352e+00 -5.24725080e-01
2.71439880e-01 -2.48322576e-01 -1.37141156e+00 8.49964738e-01
6.45290971e-01 -9.50714350e-01 -1.91317141e-01 -6.51354611e-01
5.42995036e-01 -4.58600193e-01 8.41498137e-01 -6.83936298e-01
1.00964211e-01 -3.20041388e-01 5.62399507e-01 -2.76595831e-01
-5.56924567e-02 -7.83108890e-01 -1.04584612e-01 2.77993888e-01
-4.73615050e-01 -1.77876130e-01 5.86752556e-02 4.65321392e-01
5.33340164e-02 -2.06074700e-01 8.68086934e-01 -1.36905834e-01
-5.74749589e-01 1.06349677e-01 3.90087557e-03 3.98480520e-02
8.04974377e-01 1.10495776e-01 -1.01953876e+00 -6.52805865e-02
-2.79503167e-01 3.82235050e-01 6.93621993e-01 4.75752383e-01
3.51656497e-01 -1.31313694e+00 -6.73075438e-01 2.55785823e-01
3.04637671e-01 4.91532274e-02 7.12985843e-02 7.54403591e-01
9.53702629e-02 -1.44377053e-01 -4.98679429e-01 -6.27515435e-01
-1.47060561e+00 8.41597795e-01 5.61927319e-01 3.83179747e-02
-3.44481558e-01 9.80407238e-01 4.82617080e-01 -6.19431734e-01
4.27670836e-01 -8.15132260e-02 1.89337563e-02 4.63708378e-02
6.43673360e-01 3.28969717e-01 -1.63061218e-03 -2.76885360e-01
-4.62338626e-01 3.50607961e-01 -2.83689886e-01 3.30843210e-01
1.32521033e+00 -8.47428292e-02 -2.79898733e-01 2.10268125e-01
6.52365565e-01 -6.43976256e-02 -1.32960379e+00 -4.29798961e-01
-2.14335565e-02 -6.97502494e-01 2.12334812e-01 -1.44247091e+00
-1.42488766e+00 8.99415910e-01 7.68521726e-01 4.90911677e-02
1.26980102e+00 -2.11053416e-01 5.89992523e-01 3.67779583e-01
4.57650036e-01 -1.16485500e+00 2.90831774e-01 2.75296032e-01
9.19434726e-01 -1.64947045e+00 4.40749489e-02 -2.46931821e-01
-6.71630144e-01 8.94016981e-01 1.15308905e+00 1.40314430e-01
4.09310848e-01 4.14061755e-01 6.13464296e-01 -1.51455015e-01
-8.31670284e-01 1.77693874e-01 3.11755747e-01 6.04186237e-01
1.75076555e-02 4.71820347e-02 1.45635471e-01 8.75480592e-01
-8.40036049e-02 -1.44597813e-01 4.55027223e-01 8.07146609e-01
-5.15315011e-02 -9.00864363e-01 -8.83056819e-01 3.19906563e-01
-3.87782544e-01 1.25726968e-01 -9.73992527e-01 6.01113915e-01
6.37550533e-01 9.43062067e-01 -7.68980980e-02 -6.11628175e-01
3.77545029e-01 -7.89114758e-02 2.49042749e-01 -4.25828427e-01
-7.71584511e-01 -5.22647977e-01 1.00434981e-01 -6.46275163e-01
-4.34720427e-01 -3.42113286e-01 -7.42488682e-01 -4.01244313e-01
-4.21704948e-01 -2.54924417e-01 5.21162570e-01 8.44411850e-01
1.84059680e-01 4.72834766e-01 5.93591511e-01 -3.99056524e-01
-8.74380469e-01 -9.11121845e-01 -4.68523920e-01 8.43043983e-01
7.12011576e-01 -8.19548428e-01 -6.00193441e-01 -1.69320162e-02] | [9.162840843200684, 4.195428371429443] |
60247de3-127c-473d-83e3-d9c4f1dbeee3 | supervised-pretraining-can-learn-in-context | 2306.14892 | null | https://arxiv.org/abs/2306.14892v1 | https://arxiv.org/pdf/2306.14892v1.pdf | Supervised Pretraining Can Learn In-Context Reinforcement Learning | Large transformer models trained on diverse datasets have shown a remarkable ability to learn in-context, achieving high few-shot performance on tasks they were not explicitly trained to solve. In this paper, we study the in-context learning capabilities of transformers in decision-making problems, i.e., reinforcement learning (RL) for bandits and Markov decision processes. To do so, we introduce and study Decision-Pretrained Transformer (DPT), a supervised pretraining method where the transformer predicts an optimal action given a query state and an in-context dataset of interactions, across a diverse set of tasks. This procedure, while simple, produces a model with several surprising capabilities. We find that the pretrained transformer can be used to solve a range of RL problems in-context, exhibiting both exploration online and conservatism offline, despite not being explicitly trained to do so. The model also generalizes beyond the pretraining distribution to new tasks and automatically adapts its decision-making strategies to unknown structure. Theoretically, we show DPT can be viewed as an efficient implementation of Bayesian posterior sampling, a provably sample-efficient RL algorithm. We further leverage this connection to provide guarantees on the regret of the in-context algorithm yielded by DPT, and prove that it can learn faster than algorithms used to generate the pretraining data. These results suggest a promising yet simple path towards instilling strong in-context decision-making abilities in transformers. | ['Emma Brunskill', 'Ofir Nachum', 'Chelsea Finn', 'Yash Chandak', 'Aldo Pacchiano', 'Annie Xie', 'Jonathan N. Lee'] | 2023-06-26 | null | null | null | null | ['decision-making'] | ['reasoning'] | [ 4.45366681e-01 5.25294006e-01 -3.64591599e-01 -3.54262352e-01
-1.05166352e+00 -7.09006608e-01 6.83518887e-01 -2.06851900e-01
-3.44432086e-01 9.41250920e-01 8.76200125e-02 -6.73427641e-01
-4.88615900e-01 -6.80272341e-01 -1.05946529e+00 -9.01327133e-01
-1.70586482e-01 1.08668876e+00 -1.24722563e-01 1.05734877e-01
1.69973895e-01 2.07326531e-01 -1.40177929e+00 2.35465720e-01
7.71095157e-01 1.02021492e+00 4.41119164e-01 6.63155437e-01
7.84740970e-02 8.42439413e-01 -1.06162123e-01 -4.08358276e-01
4.09160584e-01 -3.81067872e-01 -9.27817762e-01 1.34444267e-01
-2.45981887e-02 -3.44097048e-01 -7.70250037e-02 6.93973362e-01
3.40506315e-01 3.13869834e-01 8.42721105e-01 -1.01806879e+00
-4.55117971e-01 1.09981573e+00 -2.58946270e-01 3.43333393e-01
1.87748551e-01 4.87511486e-01 1.38362265e+00 -6.27521813e-01
5.06449163e-01 1.43089628e+00 4.81856614e-01 7.69856870e-01
-1.79339159e+00 -5.75672448e-01 4.98410136e-01 1.30726784e-01
-6.50356412e-01 -3.71785969e-01 5.07616282e-01 -4.40173894e-01
9.22365367e-01 -9.83142108e-03 1.02853596e+00 1.73139262e+00
1.32427722e-01 1.37342441e+00 1.50708961e+00 -4.15271819e-01
9.42412496e-01 -3.83117199e-02 -1.15521014e-01 5.64754963e-01
2.04359010e-01 6.05322540e-01 -7.12437451e-01 -2.69428760e-01
6.31647825e-01 3.67271788e-02 3.27439643e-02 -1.00244117e+00
-1.01194549e+00 8.97271633e-01 1.13282040e-01 -7.24058691e-03
-2.95557529e-01 4.67987478e-01 2.42956787e-01 5.72035849e-01
3.62157702e-01 8.27128589e-01 -7.78122604e-01 -2.74984866e-01
-6.29409432e-01 4.05221194e-01 1.00402594e+00 8.63642514e-01
7.65602469e-01 4.98134494e-02 -5.48664153e-01 4.54511493e-01
1.12644909e-02 5.07159770e-01 6.14330292e-01 -1.26824963e+00
3.63431990e-01 -1.04478672e-01 3.34583789e-01 -8.04561377e-02
-3.05070430e-01 -7.31155992e-01 -4.34184879e-01 1.43924907e-01
4.68100429e-01 -4.49729383e-01 -7.90765107e-01 2.11848855e+00
4.34303164e-01 3.89185436e-02 1.97183087e-01 4.33358252e-01
-4.45658565e-01 6.63105845e-01 8.64759609e-02 -5.71173787e-01
9.42370117e-01 -8.05331230e-01 -4.40920800e-01 -6.56206250e-01
6.48084342e-01 -1.39689609e-01 1.20937324e+00 7.78887987e-01
-1.21369767e+00 -3.01649392e-01 -8.93663228e-01 2.18322784e-01
3.86411399e-02 -3.13939929e-01 9.92651165e-01 5.85740030e-01
-1.05238092e+00 9.70386624e-01 -9.22081590e-01 -3.08244288e-01
7.55805135e-01 1.88921660e-01 1.68841720e-01 -8.81049111e-02
-9.78412867e-01 1.06113124e+00 5.15119910e-01 -1.80589497e-01
-1.82552338e+00 -5.77031672e-01 -5.03794849e-01 2.82017440e-01
1.13023818e+00 -8.57032776e-01 2.09175897e+00 -1.10816622e+00
-1.90729034e+00 5.63531935e-01 -9.46372375e-02 -9.71243024e-01
5.65382898e-01 -2.47519165e-01 2.18459606e-01 -1.42980441e-01
7.24312663e-02 4.73583102e-01 1.20227849e+00 -8.95752490e-01
-9.23959553e-01 -4.38453645e-01 2.58188844e-01 3.28493804e-01
-8.67516398e-02 -4.49349433e-01 1.51040539e-01 -4.25886810e-01
-1.59585074e-01 -1.02015650e+00 -3.48528683e-01 -2.75609076e-01
-3.38522106e-01 -4.51208830e-01 4.15946633e-01 -1.55874535e-01
9.05068099e-01 -1.74971962e+00 2.46312618e-01 9.26152095e-02
-2.72479691e-02 -4.98444168e-03 -9.26878154e-02 5.48363030e-01
1.68498561e-01 1.17451645e-01 -2.62180865e-01 -5.09254992e-01
4.41583693e-01 6.94503009e-01 -7.47616053e-01 1.95769608e-01
2.72295475e-02 1.06689775e+00 -1.16500354e+00 -1.66483074e-01
-9.66088008e-03 -2.86269844e-01 -7.60837376e-01 2.95409322e-01
-9.60422575e-01 4.38207090e-01 -7.82742083e-01 5.94480455e-01
8.17953348e-02 -3.75296205e-01 6.05417371e-01 4.64932799e-01
3.72988731e-01 4.44495440e-01 -9.80779350e-01 1.71784186e+00
-7.38877952e-01 4.00913596e-01 6.45466521e-02 -1.47247624e+00
4.53829318e-01 1.73116282e-01 1.42030805e-01 -5.68283021e-01
1.39265120e-01 1.25478640e-01 1.02221429e-01 -4.40517873e-01
7.96569604e-03 -5.43770730e-01 -1.52572423e-01 8.20617616e-01
3.72008055e-01 -1.96146563e-01 1.27932712e-01 6.81297630e-02
1.16580403e+00 4.48172450e-01 3.74546915e-01 -2.18294144e-01
-1.34352192e-01 -1.11508608e-01 6.39810801e-01 1.53916991e+00
-2.19450668e-02 -1.56419307e-01 7.20279574e-01 -4.03080046e-01
-9.39298034e-01 -1.25847197e+00 -3.91562209e-02 1.60506999e+00
-4.43097413e-01 -2.69059062e-01 -5.29938817e-01 -8.25568736e-01
1.31191805e-01 1.05564082e+00 -9.43428397e-01 -3.13535243e-01
-1.84094355e-01 -5.87229908e-01 1.34138495e-01 5.38498282e-01
5.50848305e-01 -9.61873889e-01 -8.03533673e-01 4.64621842e-01
4.89920862e-02 -8.10724735e-01 -3.45199108e-01 7.58346558e-01
-1.23774195e+00 -1.07970774e+00 -5.52310824e-01 -3.84182602e-01
2.04879865e-01 -1.59576342e-01 1.11478400e+00 -5.83369672e-01
2.23867991e-03 7.80287266e-01 -2.37638671e-02 -5.59627652e-01
-4.08297390e-01 2.06261143e-01 2.57311016e-01 4.23614122e-03
2.63134360e-01 -9.93333757e-01 -4.88908589e-01 2.66041875e-01
-5.35231411e-01 1.88435420e-01 8.93976152e-01 1.13594055e+00
5.41276991e-01 -1.66294321e-01 5.17811716e-01 -1.10392988e+00
8.25602174e-01 -5.25379837e-01 -9.03865755e-01 4.27185178e-01
-9.78820562e-01 7.13358343e-01 5.58735728e-01 -7.38164008e-01
-1.26741207e+00 5.07637821e-02 3.19322467e-01 -4.20949548e-01
5.78778982e-02 5.23251414e-01 2.56753080e-02 3.12319875e-01
7.52622724e-01 3.67707640e-01 -7.38731101e-02 -4.96671498e-01
4.46905077e-01 2.13164285e-01 1.94259658e-01 -1.31341028e+00
6.72757149e-01 2.65461624e-01 1.37164578e-01 -2.77517200e-01
-1.55100107e+00 -5.39960191e-02 -2.33804181e-01 5.50606176e-02
6.53691113e-01 -8.58595371e-01 -8.65661204e-01 1.81752726e-01
-5.62361598e-01 -1.16952121e+00 -7.28462756e-01 3.09036523e-01
-1.27526331e+00 -4.83633578e-02 -4.01817143e-01 -1.36420333e+00
-9.44925621e-02 -8.23998332e-01 8.95461977e-01 6.62279725e-02
-1.06879435e-02 -1.02887630e+00 2.22108290e-01 2.54609793e-01
3.22644860e-01 -8.76620114e-02 9.61158693e-01 -8.76060426e-01
-8.64278138e-01 4.11245465e-01 3.34176302e-01 3.03028911e-01
-1.35779604e-01 -3.91253233e-01 -1.02383769e+00 -4.93842363e-01
2.55958378e-01 -8.06109965e-01 9.86522973e-01 3.76544446e-01
1.38654482e+00 -6.65491462e-01 -3.57010484e-01 5.95635355e-01
1.06365955e+00 2.41746202e-01 1.28797635e-01 2.77723581e-01
8.60976502e-02 4.03821766e-01 7.11040258e-01 5.85875094e-01
1.27988711e-01 5.24244666e-01 2.63411105e-01 6.02154672e-01
5.36696076e-01 -7.99643159e-01 5.83758116e-01 8.28173310e-02
4.23324183e-02 2.69557834e-02 -7.99552917e-01 4.99456078e-01
-2.15904045e+00 -1.08091867e+00 8.52984548e-01 2.39867234e+00
1.24410522e+00 3.91094208e-01 2.67139465e-01 -2.32134610e-01
2.52054900e-01 -6.10973984e-02 -1.35132468e+00 -5.69709122e-01
1.42119527e-01 6.18710399e-01 4.25875634e-01 4.85945076e-01
-8.68715882e-01 1.02157366e+00 6.81821823e+00 1.01719916e+00
-7.03943491e-01 2.86338091e-01 8.35425675e-01 -3.95750821e-01
-3.40736747e-01 2.89897799e-01 -8.48881483e-01 1.71852395e-01
1.15896297e+00 -3.42661947e-01 8.90086591e-01 1.22111166e+00
7.99609274e-02 -2.56033421e-01 -1.73045743e+00 7.20159709e-01
-3.97189230e-01 -1.40259612e+00 -5.95844164e-02 3.50075722e-01
8.24527502e-01 1.59375165e-02 3.21045965e-01 9.26690459e-01
1.26376164e+00 -9.51149762e-01 7.43112743e-01 3.54399800e-01
6.35614216e-01 -6.92399919e-01 2.23873079e-01 8.48479807e-01
-5.34043431e-01 -7.93953896e-01 -4.32319313e-01 -3.01812440e-01
-1.44923344e-01 4.18728799e-01 -1.25317931e+00 6.93612471e-02
5.49616456e-01 6.30303144e-01 -2.52066582e-01 7.50228465e-01
-5.69943309e-01 8.20066631e-01 -5.10786712e-01 -2.57776350e-01
4.74950999e-01 -2.27338597e-01 4.41234469e-01 8.43684673e-01
3.34351212e-01 1.24074034e-01 3.84530485e-01 8.78677011e-01
-3.39486226e-02 -2.89257497e-01 -7.89599955e-01 -1.47650272e-01
3.97935867e-01 8.75587165e-01 -2.08536983e-01 -4.43979174e-01
1.93262339e-01 7.32953668e-01 7.96527863e-01 4.78343844e-01
-5.72387993e-01 3.58723372e-01 6.80003285e-01 -9.45824832e-02
6.65273666e-01 3.30367847e-03 -1.19161546e-01 -1.25671589e+00
-2.41436616e-01 -1.02704239e+00 6.10318065e-01 -9.65370595e-01
-1.35829103e+00 -5.85284270e-02 3.23931098e-01 -6.79607511e-01
-7.11294353e-01 -5.90486169e-01 -4.30733263e-01 4.87809896e-01
-1.23016918e+00 -7.84031451e-01 4.56038535e-01 6.51993275e-01
7.85374284e-01 -1.37413889e-02 6.42157793e-01 -6.05743408e-01
-5.64424157e-01 4.78010148e-01 5.09877026e-01 -3.37549716e-01
4.07542974e-01 -1.56405663e+00 2.00482383e-01 6.85932279e-01
2.81036198e-01 5.29478073e-01 8.03228021e-01 -3.72198701e-01
-1.66397250e+00 -9.33711708e-01 3.38307917e-01 -6.13219380e-01
8.74588490e-01 -5.68794370e-01 -4.72240329e-01 1.31757426e+00
2.29137037e-02 -4.03991789e-01 5.30361712e-01 6.77381873e-01
-3.36667836e-01 -3.46962512e-01 -1.10696018e+00 7.79298425e-01
1.43389535e+00 -5.28675854e-01 -1.05357003e+00 5.56109488e-01
6.54506028e-01 -4.30624038e-01 -4.31024164e-01 2.38744100e-03
4.50054526e-01 -9.01615083e-01 9.54969347e-01 -1.03140485e+00
3.57403457e-01 3.73741120e-01 -1.89567745e-01 -1.51294136e+00
-3.38963300e-01 -1.31247818e+00 -7.40077674e-01 7.62794495e-01
5.22311509e-01 -8.95752251e-01 7.18762159e-01 6.08312547e-01
-2.24990789e-02 -1.01922750e+00 -1.09205103e+00 -8.83688807e-01
3.69339466e-01 -6.07551157e-01 4.76337612e-01 4.61880386e-01
4.39871028e-02 5.75249970e-01 -4.73320514e-01 -1.32538095e-01
9.57380176e-01 3.54603082e-01 7.14633226e-01 -1.20732760e+00
-1.04115713e+00 -3.96304727e-01 4.77700293e-01 -1.42325556e+00
2.26251081e-01 -8.24289680e-01 1.68099210e-01 -1.11360347e+00
3.20842087e-01 -6.54891789e-01 -3.73424262e-01 5.84178090e-01
5.78735583e-02 -6.05298996e-01 1.67422980e-01 1.64329246e-01
-7.65293002e-01 7.39980876e-01 1.39202440e+00 -6.53903484e-02
-1.92301512e-01 5.36878824e-01 -1.05821681e+00 7.19058752e-01
7.87099659e-01 -6.21389389e-01 -7.25004971e-01 -1.62586227e-01
5.39033651e-01 2.09209397e-01 1.58700943e-01 -8.58406425e-01
6.82866722e-02 -6.21623755e-01 4.08799589e-01 -3.10322642e-01
3.48036528e-01 -5.57599068e-01 -5.98052852e-02 4.73040819e-01
-8.59379113e-01 -2.95369416e-01 -5.00464775e-02 1.23296380e+00
4.94213939e-01 -3.33990365e-01 6.20774865e-01 -5.62544703e-01
-5.41711986e-01 4.49743420e-01 -7.34398901e-01 4.56295311e-01
9.14557815e-01 -3.36931087e-02 5.42878592e-03 -5.63050628e-01
-1.06999648e+00 5.80218196e-01 2.63381630e-01 -1.12742104e-01
3.59248906e-01 -9.52230990e-01 -3.71409297e-01 -6.54741079e-02
4.19452004e-02 1.42275184e-01 3.37051600e-02 7.51281977e-01
3.62989575e-01 4.53700542e-01 4.23262548e-03 -5.94180822e-01
-6.64190888e-01 9.27700579e-01 5.50062537e-01 -1.01656592e+00
-4.25273716e-01 7.99256206e-01 3.53686780e-01 -3.90871197e-01
3.44168305e-01 -6.37064338e-01 2.48743400e-01 2.75924206e-02
4.18258935e-01 -3.64818349e-02 -2.74073184e-01 5.82889795e-01
1.49768710e-01 7.48094395e-02 -2.44334221e-01 -5.94072461e-01
1.32593596e+00 -9.05571580e-02 4.04834539e-01 8.09113622e-01
6.47675514e-01 -3.90328467e-01 -1.90589452e+00 -5.66250086e-01
2.08872959e-01 -3.28644544e-01 9.43324820e-04 -1.16756749e+00
-5.26277125e-01 9.61902499e-01 3.55314910e-01 1.58107594e-01
8.93931806e-01 1.13655008e-01 3.88304353e-01 1.29510927e+00
7.88673222e-01 -1.34708619e+00 3.55249465e-01 6.34705722e-01
7.30290294e-01 -1.08913922e+00 -2.26977423e-01 3.15267622e-01
-6.86600089e-01 8.89179647e-01 4.31874305e-01 -1.06978126e-01
5.56569099e-01 4.93669026e-02 -6.77556276e-01 1.52893588e-02
-1.49825227e+00 -3.41656566e-01 -3.03999066e-01 8.77556860e-01
-3.08073372e-01 9.99863073e-02 3.17784280e-01 4.99041677e-01
-1.28688768e-01 2.51032561e-01 3.71686935e-01 1.01927912e+00
-6.49128735e-01 -1.08313644e+00 -8.60116929e-02 6.99144423e-01
-3.08595270e-01 -7.13599548e-02 -2.81855851e-01 6.49576187e-01
-2.31000245e-01 7.38387585e-01 -1.09924331e-01 -1.25039011e-01
-1.00448122e-02 4.53518838e-01 7.93486118e-01 -7.22119391e-01
-3.82256985e-01 -1.98617056e-02 2.92760164e-01 -7.50421107e-01
-1.06837429e-01 -9.51809287e-01 -6.02247715e-01 -1.89886242e-01
-9.62696373e-02 2.74958909e-01 2.97497094e-01 1.22076476e+00
3.64821851e-01 2.59431928e-01 7.08080828e-01 -8.78331840e-01
-1.48691273e+00 -9.93603349e-01 -5.78875542e-01 6.02503791e-02
5.20175815e-01 -7.84961045e-01 -5.92515469e-01 -2.85488278e-01] | [4.142365455627441, 2.0284292697906494] |
ad0cd79a-3687-45d7-b976-675f56f45b31 | random-forest-for-dissimilarity-based-multi | 2007.08377 | null | https://arxiv.org/abs/2007.08377v1 | https://arxiv.org/pdf/2007.08377v1.pdf | Random Forest for Dissimilarity-based Multi-view Learning | Many classification problems are naturally multi-view in the sense their data are described through multiple heterogeneous descriptions. For such tasks, dissimilarity strategies are effective ways to make the different descriptions comparable and to easily merge them, by (i) building intermediate dissimilarity representations for each view and (ii) fusing these representations by averaging the dissimilarities over the views. In this work, we show that the Random Forest proximity measure can be used to build the dissimilarity representations, since this measure reflects similarities between features but also class membership. We then propose a Dynamic View Selection method to better combine the view-specific dissimilarity representations. This allows to take a decision, on each instance to predict, with only the most relevant views for that instance. Experiments are conducted on several real-world multi-view datasets, and show that the Dynamic View Selection offers a significant improvement in performance compared to the simple average combination and two state-of-the-art static view combinations. | ['Robert Sabourin', 'Simon Bernard', 'Laurent Heutte', 'Hongliu Cao'] | 2020-07-16 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [ 1.11317284e-01 -1.47599280e-01 -2.68615484e-01 -6.58966601e-01
-7.85055518e-01 -6.95238590e-01 9.26310837e-01 5.45550883e-01
9.02369022e-02 5.38275301e-01 4.18734670e-01 2.60928720e-01
-4.19644654e-01 -8.24453115e-01 -1.05655920e-02 -8.35085392e-01
9.45968851e-02 9.19015169e-01 4.64240462e-01 -1.50001347e-01
4.34528470e-01 4.61743325e-01 -2.09248662e+00 7.52633274e-01
7.40639269e-01 7.19281793e-01 2.08745658e-01 3.24822694e-01
-2.40860090e-01 7.33508289e-01 -3.61960083e-01 -4.45656210e-01
2.72595763e-01 -5.00501990e-01 -8.23935568e-01 4.68091130e-01
5.29787362e-01 -3.90742496e-02 2.45669991e-01 9.72174883e-01
3.88608873e-01 3.50077450e-01 9.57103074e-01 -1.23604691e+00
-3.47060263e-01 3.66451144e-01 -7.59267569e-01 1.40565589e-01
6.50230706e-01 -4.81520146e-01 1.26996171e+00 -9.83960927e-01
1.04421198e+00 1.31652987e+00 4.10967886e-01 3.11216086e-01
-1.51992416e+00 -2.74315357e-01 5.16109288e-01 4.62951690e-01
-1.28195763e+00 -3.46448869e-01 8.86482477e-01 -5.79258323e-01
9.92769599e-01 4.57221746e-01 5.51104188e-01 9.51299548e-01
1.74753577e-01 5.74191868e-01 1.56137002e+00 -3.52256685e-01
2.11779177e-01 5.52031100e-01 2.80619562e-01 5.54276943e-01
2.67238617e-01 -2.93635309e-01 -3.66416395e-01 -5.21973372e-01
1.36625797e-01 3.59233081e-01 -3.09318185e-01 -1.04268992e+00
-1.21111810e+00 9.70420182e-01 4.09348667e-01 4.49079335e-01
-4.24954474e-01 -6.19561434e-01 6.09886467e-01 4.69244391e-01
5.83581388e-01 5.30382752e-01 -1.89481720e-01 2.02013552e-01
-7.37981319e-01 2.22129226e-01 8.15318882e-01 7.52378821e-01
7.74563491e-01 -5.12277126e-01 7.92257935e-02 1.20531023e+00
6.29555359e-02 2.75754146e-02 5.46825469e-01 -8.06482732e-01
5.98412037e-01 7.70787776e-01 -1.64370522e-01 -1.07871211e+00
-2.90147722e-01 -1.49623275e-01 -1.00583863e+00 2.97121227e-01
3.79679427e-02 3.45549285e-01 -8.72895837e-01 1.67605960e+00
3.84117514e-01 -3.39463472e-01 1.36997625e-01 6.24398530e-01
8.98480237e-01 4.81208920e-01 -2.91270494e-01 -4.10289407e-01
1.23183501e+00 -9.68907058e-01 -3.95075917e-01 1.05089359e-01
4.23359513e-01 -6.45222485e-01 4.22359705e-01 4.94790852e-01
-9.73512530e-01 -7.53254592e-01 -1.24199080e+00 1.92497984e-01
-6.54820740e-01 -2.05791190e-01 3.65128100e-01 2.75326043e-01
-9.74685729e-01 7.17434704e-01 -5.59496641e-01 -6.28740191e-01
7.73669779e-02 2.89994717e-01 -9.59058404e-01 -2.74872661e-01
-7.46974587e-01 1.03873229e+00 4.71231967e-01 -4.00999784e-01
-5.77220738e-01 -3.54035079e-01 -8.25883806e-01 -2.28200890e-02
4.06212509e-01 -9.38818038e-01 6.34128690e-01 -9.92006481e-01
-8.13082576e-01 1.12625599e+00 -2.49381095e-01 -9.33909342e-02
4.81304526e-01 1.97491139e-01 -5.58220446e-01 1.50960907e-01
4.10568923e-01 5.63608468e-01 6.67876482e-01 -1.88998282e+00
-8.45297575e-01 -7.05629706e-01 3.77839893e-01 5.20689845e-01
-1.20600417e-01 -1.88172877e-01 -4.17701155e-01 -4.64822054e-01
6.44273639e-01 -1.03270710e+00 -3.33967805e-01 -9.86748636e-02
-3.44557464e-01 -1.37322232e-01 7.18707204e-01 -4.01557833e-01
1.10612202e+00 -1.93915915e+00 7.59918034e-01 3.58726710e-01
5.05360663e-01 -1.79000031e-02 5.83890714e-02 7.52365589e-01
-2.81870514e-01 2.78664455e-02 -1.41845167e-01 -4.08541560e-01
-2.99997598e-01 2.96092212e-01 -5.25145121e-02 3.25966656e-01
-2.24390879e-01 9.97220725e-02 -8.41821432e-01 -4.77393866e-01
1.93536118e-01 2.34104738e-01 -3.74570191e-01 2.39448905e-01
3.24768424e-01 2.31002912e-01 -3.33085269e-01 3.34129125e-01
7.41020620e-01 -9.82919410e-02 5.95357656e-01 -3.79823238e-01
1.06928818e-01 2.10381001e-01 -1.36458671e+00 1.56593001e+00
-5.81456721e-01 3.67547780e-01 -2.49550089e-01 -1.09607482e+00
1.00280893e+00 3.33085299e-01 6.61880016e-01 -2.65992969e-01
-1.63001135e-01 1.71152592e-01 -4.81580049e-02 -3.82962078e-01
4.19463634e-01 -4.60162878e-01 -1.18998565e-01 6.80554509e-01
2.30258435e-01 5.99554228e-03 4.12183732e-01 2.26602972e-01
6.77645564e-01 -8.78320411e-02 1.05785012e+00 -1.75288677e-01
7.51108766e-01 -2.61144489e-01 3.91809881e-01 4.27552283e-01
6.19173646e-02 9.63235974e-01 5.75569212e-01 -6.93682730e-01
-8.83489609e-01 -1.28594446e+00 -5.04355729e-02 7.82430410e-01
2.41693616e-01 -5.96622884e-01 -3.92314643e-01 -1.35222447e+00
8.00503865e-02 6.25655532e-01 -9.23578143e-01 -3.73559110e-02
-1.91487640e-01 -4.94304687e-01 -1.54836372e-01 5.92479110e-01
3.32369804e-01 -6.06817365e-01 -4.51037645e-01 1.83270305e-01
-3.24911207e-01 -7.34612584e-01 -5.75135536e-02 4.53045785e-01
-1.02870488e+00 -9.61064398e-01 -6.14629149e-01 -5.58959603e-01
7.54674196e-01 7.68262446e-01 1.28606272e+00 -1.62341036e-02
6.82262853e-02 2.22537816e-01 -5.18490851e-01 -9.98066962e-02
-4.35482860e-01 1.50325093e-02 2.72154480e-01 1.07596107e-01
3.52024049e-01 -8.12233031e-01 -2.32754439e-01 4.82948840e-01
-9.16362107e-01 1.21234432e-01 3.88041764e-01 9.91896391e-01
9.57688689e-01 5.00636958e-02 4.96386707e-01 -1.25814247e+00
3.86354774e-01 -5.39609671e-01 -1.80745393e-01 6.49214029e-01
-7.45201170e-01 2.11058006e-01 6.71900153e-01 -8.70543718e-02
-9.66920257e-01 -6.92792013e-02 9.28740278e-02 -5.50276816e-01
-3.11954170e-01 4.94358420e-01 -2.53225476e-01 2.14194924e-01
5.05511820e-01 8.31556097e-02 3.99797633e-02 -5.87261319e-01
4.17547286e-01 6.73660696e-01 6.81148097e-02 -2.74567544e-01
5.29943109e-01 6.42231584e-01 -6.21958897e-02 -5.71413755e-01
-7.72723079e-01 -7.64465034e-01 -1.22566378e+00 -1.83694720e-01
7.72286594e-01 -8.69658470e-01 -1.03226006e-01 2.55344734e-02
-8.06102097e-01 6.01593912e-01 -1.97606862e-01 4.66209888e-01
-6.03700638e-01 3.34304124e-01 -8.66757110e-02 -3.83402646e-01
7.69395828e-02 -1.25129831e+00 9.68587399e-01 5.65605760e-02
-4.02852356e-01 -1.15945756e+00 3.95393759e-01 3.84065092e-01
3.09874993e-02 3.96611780e-01 1.05084562e+00 -1.12049997e+00
-1.46220267e-01 -1.20169729e-01 -1.46153718e-01 3.11533809e-01
5.99598408e-01 6.35017753e-02 -1.15734112e+00 -4.86087114e-01
2.40550026e-01 -7.07209781e-02 1.07728970e+00 2.42831588e-01
9.55388784e-01 -3.68388579e-03 -6.44073546e-01 4.84861672e-01
1.79876328e+00 2.59562224e-01 4.03227925e-01 2.50518531e-01
8.58400702e-01 8.84100676e-01 7.07066774e-01 5.28598487e-01
4.24497098e-01 9.13930535e-01 3.20676148e-01 6.96522044e-03
5.86976893e-02 -2.12334413e-02 3.91493961e-02 1.13397801e+00
-4.22085702e-01 -2.90862828e-01 -6.17316484e-01 4.56044823e-01
-1.89143836e+00 -1.43287349e+00 -1.26643116e-02 2.40181541e+00
1.29814222e-01 1.68452591e-01 4.26338881e-01 1.01601087e-01
8.07607651e-01 4.39460427e-01 -2.58161873e-01 -7.30854154e-01
-1.02588553e-02 -2.76673019e-01 -2.20319768e-03 3.78291845e-01
-1.20570588e+00 2.98669547e-01 6.30624628e+00 4.63903248e-01
-8.67886007e-01 -1.79165285e-02 4.81416732e-01 -4.38186862e-02
-6.55236661e-01 2.26356629e-02 -4.96534735e-01 1.74429968e-01
5.17011344e-01 -3.65719765e-01 1.76476628e-01 8.28577280e-01
-3.02789479e-01 -2.73446441e-01 -1.36094427e+00 8.69616032e-01
4.94254410e-01 -1.13282037e+00 5.20308554e-01 2.61213213e-01
9.36961770e-01 -2.13626385e-01 -1.77900136e-01 7.34354630e-02
2.03722581e-01 -6.04530573e-01 5.07050693e-01 6.42238200e-01
4.19374555e-01 -1.12028885e+00 8.67107809e-01 3.66320997e-01
-1.57652688e+00 -2.25793794e-02 -4.17555928e-01 1.90245956e-01
-5.42811155e-02 5.28529823e-01 -5.92447400e-01 1.31476176e+00
5.87649465e-01 1.09034169e+00 -8.19857955e-01 8.62288356e-01
2.38766342e-01 -2.47497723e-01 3.78978997e-02 2.97407985e-01
1.27346843e-01 -3.82855743e-01 5.55996716e-01 9.75531459e-01
4.31324035e-01 -1.16300382e-01 4.01252747e-01 4.55210716e-01
2.39153132e-01 1.63508251e-01 -1.23929226e+00 3.69502544e-01
2.87973493e-01 1.22669661e+00 -9.64170516e-01 -7.22009659e-01
-6.85867786e-01 1.19779503e+00 4.33980525e-01 -2.44669262e-02
-6.97875738e-01 -2.63098270e-01 8.12051415e-01 -1.23167761e-01
6.29518270e-01 2.62995124e-01 -1.25457287e-01 -1.33641028e+00
1.34601802e-01 -9.24616933e-01 8.66311908e-01 -6.13712430e-01
-1.77843463e+00 9.26271558e-01 4.33634013e-01 -1.97973394e+00
-5.31699896e-01 -2.95964330e-01 -3.32928181e-01 8.14975083e-01
-1.06929195e+00 -1.02970648e+00 -1.82791218e-01 5.04630625e-01
7.16593683e-01 -1.97599456e-01 1.10144520e+00 3.39131542e-02
-1.93739325e-01 2.48584747e-01 2.86330372e-01 -2.89366633e-01
8.21902394e-01 -1.57302272e+00 1.10670634e-01 3.81021947e-01
4.15388852e-01 6.01602316e-01 6.90838277e-01 -4.18087363e-01
-8.24645042e-01 -8.14620554e-01 9.98804510e-01 -4.78821278e-01
3.18164825e-01 -1.40030056e-01 -1.05181968e+00 5.42562544e-01
3.66787642e-01 -3.96009348e-02 1.10724771e+00 2.19875857e-01
-5.93379319e-01 -2.70958543e-01 -1.34911883e+00 3.72061372e-01
9.23304319e-01 -6.06841624e-01 -8.69023681e-01 1.10537529e-01
4.64487784e-02 -4.64551616e-03 -1.12331069e+00 3.46250683e-01
7.94413865e-01 -1.79565752e+00 1.00575435e+00 -5.41012406e-01
5.83862126e-01 -3.71388555e-01 -5.22815943e-01 -1.81728160e+00
-6.24868393e-01 1.81835935e-01 1.73540220e-01 1.54909718e+00
4.86528218e-01 -6.31015241e-01 3.57328326e-01 2.05285683e-01
2.41776541e-01 -9.29281592e-01 -7.62200534e-01 -8.49416316e-01
-2.42994964e-01 9.69271213e-02 5.93792319e-01 1.16728497e+00
7.22239390e-02 5.16764283e-01 -2.22046837e-01 2.14070976e-02
5.10116041e-01 7.75356412e-01 5.99999607e-01 -1.64914465e+00
-3.53490323e-01 -3.58340383e-01 -8.75542879e-01 -3.81520808e-01
6.40564635e-02 -1.02121282e+00 -1.78226411e-01 -1.75226891e+00
7.87382901e-01 -3.00749451e-01 -4.21183020e-01 1.06241249e-01
-4.13350761e-01 8.91709551e-02 3.39904010e-01 3.60657930e-01
-5.16509831e-01 2.06328303e-01 1.07357001e+00 -2.47510284e-01
-1.20641805e-01 2.87267745e-01 -8.24039638e-01 8.30216765e-01
4.39085782e-01 -6.04774475e-01 -8.07699978e-01 -8.64989609e-02
-1.57337174e-01 2.77115136e-01 -7.43249506e-02 -1.18355393e+00
-6.46818653e-02 -8.53747651e-02 5.25197387e-01 -6.83110058e-01
4.98451561e-01 -1.03434896e+00 5.79853058e-01 3.09201717e-01
-4.44160312e-01 5.18065214e-01 -2.42306098e-01 7.49680936e-01
-6.03250623e-01 -2.20049039e-01 8.63932014e-01 -4.78197038e-01
-8.23898137e-01 1.16580501e-01 -2.49715626e-01 -1.29335374e-01
1.25177336e+00 -3.91586423e-01 -1.35870680e-01 -4.32882279e-01
-1.06633329e+00 -8.78602918e-03 8.59390080e-01 4.98640835e-01
5.32175660e-01 -1.63414657e+00 -6.11581087e-01 2.24078804e-01
6.40595853e-01 -4.96634990e-01 2.22062409e-01 6.52005076e-01
-2.09957659e-01 4.13638279e-02 -6.55543208e-01 -7.80396283e-01
-1.88316059e+00 9.18786287e-01 1.11401610e-01 -7.02926338e-01
-5.58847308e-01 5.93541503e-01 3.90752077e-01 -3.86159062e-01
-8.18873271e-02 -7.47148469e-02 -7.42207885e-01 9.02769685e-01
5.49685895e-01 3.65905523e-01 3.46423864e-01 -8.87461782e-01
-5.99632204e-01 8.49445522e-01 -3.94619435e-01 -1.75188586e-01
1.46247971e+00 -3.61984909e-01 -1.80277452e-01 9.14031744e-01
1.49362230e+00 -6.42403215e-02 -6.70217931e-01 -2.05978096e-01
-5.90632334e-02 -7.87270367e-01 -2.98146933e-01 -5.03478765e-01
-1.28473723e+00 7.51455128e-01 5.27165532e-01 7.33109176e-01
1.29326129e+00 1.46717399e-01 1.66997120e-01 3.14854741e-01
5.14814019e-01 -8.27870965e-01 -1.29618108e-01 1.64526716e-01
9.99247193e-01 -1.22852588e+00 5.55052400e-01 -4.82347310e-01
-1.12126195e+00 1.32278967e+00 5.98667681e-01 -1.20123841e-01
6.99168086e-01 -1.58541292e-01 7.22460970e-02 -4.35850441e-01
-1.14260960e+00 -3.32116693e-01 4.65225428e-01 6.59139395e-01
4.75669712e-01 2.07696632e-01 -3.53788972e-01 5.47922030e-02
1.34114459e-01 -5.35317242e-01 4.13270682e-01 9.64586020e-01
-3.29665571e-01 -1.36711860e+00 -2.73538738e-01 7.96915948e-01
-1.98687658e-01 3.90509635e-01 -7.75722086e-01 7.83927262e-01
2.33778939e-01 8.43995214e-01 1.19431674e-01 -7.08700001e-01
5.42290628e-01 1.89694554e-01 5.43733716e-01 -7.62781739e-01
-6.94667459e-01 -2.63517108e-02 3.10998589e-01 -5.27995408e-01
-7.66788542e-01 -9.79604959e-01 -6.28555238e-01 -1.70081049e-01
-3.89679432e-01 5.50565822e-03 3.99544686e-01 9.08988714e-01
1.90248549e-01 4.45415378e-01 1.22414339e+00 -1.08771658e+00
-4.20609713e-01 -6.83873713e-01 -8.29127073e-01 8.56758833e-01
1.66278824e-01 -1.10515404e+00 -4.17070180e-01 -1.02268644e-01] | [8.46896743774414, 4.4792070388793945] |
35174381-66e9-4c9c-a99f-202b58798717 | knowledge-graph-based-waveform-recommendation | 2202.01926 | null | https://arxiv.org/abs/2202.01926v1 | https://arxiv.org/pdf/2202.01926v1.pdf | Knowledge Graph Based Waveform Recommendation: A New Communication Waveform Design Paradigm | Traditionally, a communication waveform is designed by experts based on communication theory and their experiences on a case-by-case basis, which is usually laborious and time-consuming. In this paper, we investigate the waveform design from a novel perspective and propose a new waveform design paradigm with the knowledge graph (KG)-based intelligent recommendation system. The proposed paradigm aims to improve the design efficiency by structural characterization and representations of existing waveforms and intelligently utilizing the knowledge learned from them. To achieve this goal, we first build a communication waveform knowledge graph (CWKG) with a first-order neighbor node, for which both structured semantic knowledge and numerical parameters of a waveform are integrated by representation learning. Based on the developed CWKG, we further propose an intelligent communication waveform recommendation system (CWRS) to generate waveform candidates. In the CWRS, an improved involution1D operator, which is channel-agnostic and space-specific, is introduced according to the characteristics of KG-based waveform representation for feature extraction, and the multi-head self-attention is adopted to weigh the influence of various components for feature fusion. Meanwhile, multilayer perceptron-based collaborative filtering is used to evaluate the matching degree between the requirement and the waveform candidate. Simulation results show that the proposed CWKG-based CWRS can automatically recommend waveform candidates with high reliability. | ['Jun Wang', 'Qihang Peng', 'Yundi Guan', 'Tianfu Qi', 'Wei Huang'] | 2022-01-24 | null | null | null | null | ['intelligent-communication'] | ['time-series'] | [-1.35566041e-01 -4.09410745e-01 1.04454853e-01 -4.29215312e-01
-4.12849873e-01 -2.73659468e-01 -1.26409337e-01 1.16640359e-01
2.17098176e-01 2.69472003e-01 3.65897804e-01 -2.10315585e-01
-1.11512661e+00 -9.61223364e-01 5.10829166e-02 -7.13658512e-01
-1.07744537e-01 8.69474933e-02 -2.29392853e-02 -4.34286356e-01
4.94553119e-01 7.38701224e-01 -1.36883461e+00 1.96392596e-01
1.20360923e+00 1.39668584e+00 4.48594779e-01 2.61987388e-01
-3.28115076e-01 4.01236296e-01 -8.34805012e-01 -3.60648543e-01
-1.82265341e-02 -4.14919734e-01 -7.30674788e-02 -2.30611227e-02
-4.39528018e-01 9.85284448e-02 -4.42305326e-01 8.73643279e-01
1.10872018e+00 3.36629421e-01 4.70911980e-01 -1.24794829e+00
-6.43229783e-01 1.09602737e+00 -2.06965283e-01 6.61096126e-02
1.86787888e-01 -2.89840370e-01 9.51618671e-01 -8.22182536e-01
9.64648053e-02 9.55948353e-01 6.40141964e-01 -1.22020833e-01
-6.05752110e-01 -7.83663154e-01 3.26201409e-01 9.01163816e-01
-1.83327508e+00 -2.64709622e-01 1.53577042e+00 -2.63576388e-01
4.39677626e-01 3.54708612e-01 7.54079580e-01 2.46735930e-01
-7.03467205e-02 4.84658629e-01 4.67967093e-01 -4.59562808e-01
2.66569406e-01 1.06188782e-01 2.23547652e-01 4.61589485e-01
2.19632074e-01 3.93621624e-03 -3.88927460e-01 -2.03101203e-01
3.23317409e-01 -1.24704838e-01 -4.27209795e-01 -8.50341469e-02
-4.78552192e-01 6.65457010e-01 3.30321431e-01 6.29321575e-01
-4.07624245e-01 -3.48484039e-01 1.65848389e-01 2.42763653e-01
2.47865915e-02 4.30713266e-01 -1.89415604e-01 1.15585551e-01
-8.67465973e-01 -1.49477348e-01 9.23747480e-01 8.28864396e-01
6.22682631e-01 7.19244897e-01 -1.94613963e-01 8.54551911e-01
4.86840814e-01 6.77009344e-01 5.26291370e-01 -4.40352261e-01
2.68834829e-01 4.64031965e-01 -1.37872338e-01 -1.84490693e+00
-5.88464081e-01 -1.03954065e+00 -7.50686109e-01 -2.74000406e-01
-3.39949608e-01 -4.28103030e-01 -4.02032554e-01 1.31538236e+00
1.33176640e-01 4.20660853e-01 2.15841740e-01 9.19110775e-01
9.96485412e-01 8.35819304e-01 -1.36089787e-01 -5.89675188e-01
9.58029926e-01 -4.81523752e-01 -9.22674716e-01 3.91636521e-01
2.97130257e-01 -7.62412250e-01 3.30540627e-01 6.95616722e-01
-6.97416663e-01 -7.75072515e-01 -1.23195040e+00 8.05701554e-01
-1.37205794e-01 5.40643930e-01 4.71983641e-01 9.39506471e-01
-6.57062829e-01 5.11250913e-01 1.84550788e-02 -5.40080927e-02
1.55871570e-01 1.81148693e-01 3.80877674e-01 8.99623707e-02
-1.48996794e+00 9.18243825e-01 5.35158575e-01 3.53377163e-01
-4.08409178e-01 -8.54492843e-01 -5.50634503e-01 3.19702119e-01
2.79210091e-01 -2.36087129e-01 6.92313254e-01 -5.23236454e-01
-1.68921375e+00 -3.28579456e-01 2.01221019e-01 -2.66871125e-01
-6.56165481e-02 3.54142040e-01 -1.62032115e+00 2.94600487e-01
-5.28689921e-01 -4.42129225e-01 1.04398680e+00 -1.35477424e+00
-7.35795319e-01 -6.13856614e-02 -1.90491349e-01 1.54943630e-01
-7.08603919e-01 1.32866995e-03 -2.97063112e-01 -8.36483121e-01
1.30573243e-01 -3.12964022e-01 -8.53772461e-02 -4.08640414e-01
-1.65874854e-01 -1.42487943e-01 8.25682938e-01 -7.80699313e-01
1.86569262e+00 -2.18438029e+00 5.64481393e-02 1.22097754e+00
1.28567278e-01 5.90952277e-01 -1.76200077e-01 6.96294606e-01
6.49882928e-02 -2.51715302e-01 2.94455681e-02 3.57933998e-01
3.54396664e-02 4.32550423e-02 -1.75786734e-01 9.68079716e-02
-1.14208236e-01 3.74089152e-01 -7.02569485e-01 -4.93050396e-01
2.80956119e-01 3.00601363e-01 -4.57421541e-01 3.41979980e-01
2.88114622e-02 1.71784595e-01 -8.20047200e-01 5.19721329e-01
7.34125316e-01 8.47695512e-04 3.08559358e-01 -1.00426626e+00
-3.31279814e-01 -3.09798717e-01 -1.55473053e+00 1.45569742e+00
-7.03989267e-01 1.50928974e-01 1.20951481e-01 -1.31747043e+00
1.61692786e+00 3.58476371e-01 5.22948086e-01 -7.02734351e-01
4.85171437e-01 1.88495100e-01 2.06874028e-01 -8.70556951e-01
1.64266109e-01 3.22658807e-01 1.69232905e-01 3.65673780e-01
-4.26651873e-02 -5.50446920e-02 1.40155300e-01 4.26412635e-02
9.13906574e-01 -2.32249856e-01 1.00406513e-01 -5.52718528e-02
9.96165991e-01 -2.13744730e-01 7.89284408e-01 4.96491611e-01
3.75311375e-01 2.79795200e-01 -1.80856735e-01 -1.20615914e-01
-4.33044225e-01 -7.09922314e-01 -8.76555443e-02 8.13449621e-01
4.26103145e-01 -3.95475596e-01 -1.93411469e-01 -2.67859548e-01
8.50754529e-02 9.66192126e-01 -3.76173928e-02 -5.88229358e-01
-2.81532854e-01 -5.88795483e-01 5.38960338e-01 2.35097468e-01
3.88390720e-01 -7.56354332e-01 -1.49272278e-01 6.85890853e-01
1.01564422e-01 -6.49639964e-01 -2.46852234e-01 -1.51614189e-01
-3.82866591e-01 -7.02132523e-01 -5.01848221e-01 -5.81740499e-01
5.05243957e-01 3.26855093e-01 4.04903024e-01 4.15624321e-01
-9.74913910e-02 5.32375455e-01 -1.06810331e+00 -5.57512045e-01
-7.64708072e-02 -1.42420784e-01 7.44541511e-02 6.88199520e-01
-9.56532136e-02 -1.04618371e+00 -5.08879483e-01 3.26706707e-01
-6.95404828e-01 -3.00924540e-01 8.92471910e-01 4.15031999e-01
1.40393198e-01 7.74781764e-01 1.19852173e+00 -4.22170728e-01
1.07569408e+00 -6.72206402e-01 -5.24084687e-01 5.47491848e-01
-7.99997509e-01 -2.93546617e-01 1.05766702e+00 -4.36124414e-01
-1.29140210e+00 -2.68705815e-01 -2.15629414e-01 -5.23234844e-01
2.05865353e-01 1.21032631e+00 -6.45805895e-01 -4.87976849e-01
4.29624498e-01 4.45519120e-01 -1.04479797e-01 -6.21259391e-01
5.36561012e-01 9.75097001e-01 4.06583607e-01 -6.42194271e-01
1.06297886e+00 1.09665364e-01 -1.17105484e-01 -7.83197939e-01
-5.74671388e-01 -2.89418697e-01 -2.19401196e-01 -5.98978817e-01
2.99784034e-01 -4.57009554e-01 -8.98312032e-01 1.05930373e-01
-1.12480617e+00 6.05500281e-01 2.02011578e-02 1.00015450e+00
-6.15581982e-02 3.96036923e-01 -1.10372126e-01 -1.01241851e+00
-5.68213820e-01 -7.75484681e-01 2.02956170e-01 5.92175782e-01
1.10842049e-01 -6.99636817e-01 -3.71013582e-01 2.90618930e-02
7.94079363e-01 -1.42263025e-01 1.10006738e+00 -9.71236408e-01
-3.10440689e-01 -2.76507378e-01 -2.56018519e-01 2.78135389e-01
1.14210822e-01 -3.27910259e-02 -6.07545376e-01 8.20043124e-03
-4.70276587e-02 4.20641840e-01 3.62729430e-01 1.91997409e-01
1.26731980e+00 -1.84905812e-01 -1.83251038e-01 5.50118744e-01
1.43561637e+00 9.82019067e-01 6.27676487e-01 -1.25917211e-01
6.48750782e-01 5.38389862e-01 5.41484177e-01 9.56541061e-01
3.92355919e-01 5.23304701e-01 7.83900172e-02 1.33741170e-01
4.33964953e-02 4.47728019e-03 -5.78935556e-02 1.52336431e+00
-8.72710198e-02 -4.99461442e-01 -3.43353629e-01 2.36254603e-01
-1.83568037e+00 -9.99686718e-01 9.17054787e-02 1.88853228e+00
5.45774043e-01 1.21330008e-01 -1.66445926e-01 6.68702960e-01
9.59722042e-01 -5.30073345e-02 -3.48556042e-01 -4.15076375e-01
-1.92831107e-03 2.93013752e-01 1.51517838e-01 2.97391694e-02
-6.05203390e-01 2.80060381e-01 4.70959330e+00 1.50897419e+00
-1.29221094e+00 -1.40991047e-01 -2.42807925e-01 3.45749468e-01
-6.79950237e-01 8.73053297e-02 -5.46241820e-01 6.60601139e-01
6.28970742e-01 -8.30109298e-01 5.24010837e-01 5.70424020e-01
5.40755093e-01 5.37014902e-01 -3.24073195e-01 1.28335059e+00
3.83237302e-01 -1.40545702e+00 1.98329791e-01 -3.88611376e-01
3.46216917e-01 -7.13395953e-01 -1.79128721e-01 1.88623101e-01
-7.81177357e-02 -5.71675062e-01 6.01137698e-01 1.24082458e+00
2.77201384e-01 -9.98455048e-01 7.64829099e-01 1.70014694e-01
-1.64282131e+00 -5.44429719e-01 -3.16205174e-01 1.93699583e-01
4.43053186e-01 9.85797405e-01 -5.71768582e-01 1.53221178e+00
3.69501293e-01 4.75011110e-01 -2.18778327e-01 1.74745917e+00
-1.43126875e-01 8.46882045e-01 -2.81105667e-01 -2.67011255e-01
-1.92313850e-01 -3.04832667e-01 6.41073108e-01 9.68068540e-01
8.72311831e-01 6.70404017e-01 4.52215791e-01 6.30604684e-01
2.18337089e-01 5.79592645e-01 3.88960913e-02 -3.60997617e-02
1.11171877e+00 1.53432465e+00 -5.37619591e-01 -1.73357010e-01
-3.09940964e-01 2.45615821e-02 -2.12951154e-01 2.98392534e-01
-8.41300964e-01 -9.60385740e-01 4.02484462e-02 -1.52044356e-01
6.25694454e-01 -2.74308845e-02 -9.68982205e-02 -5.85999966e-01
-2.08426207e-01 -5.88493347e-01 4.42598850e-01 -9.19147670e-01
-1.52158833e+00 6.54849768e-01 -1.11899972e-01 -1.74447250e+00
1.08982645e-01 -8.48799869e-02 -1.02046514e+00 6.62609875e-01
-1.47384953e+00 -1.03915882e+00 -3.55352163e-01 6.87130213e-01
4.59240749e-02 -5.67706287e-01 7.39214301e-01 7.99102724e-01
-5.88235974e-01 9.08903837e-01 1.17340796e-01 -1.12680770e-01
2.26375550e-01 -5.57660997e-01 -5.55049658e-01 6.97815716e-01
1.62219301e-01 5.70553839e-01 5.84551990e-01 -6.13271356e-01
-1.65244758e+00 -9.95889425e-01 4.15786743e-01 5.68303823e-01
7.54228830e-01 2.90555567e-01 -8.44359875e-01 -2.01545790e-01
-9.03635100e-02 -1.29983738e-01 9.71436560e-01 -1.16103418e-01
-1.45255074e-01 -7.53066659e-01 -1.15554869e+00 5.15119672e-01
8.94289792e-01 -1.45776048e-01 -6.56956017e-01 8.65901113e-02
7.77312398e-01 -6.10515289e-03 -1.28343487e+00 6.46734595e-01
6.93583131e-01 -4.87081081e-01 9.89672959e-01 -1.85985029e-01
-2.71081805e-01 -7.86232591e-01 -2.34714523e-01 -1.59485042e+00
-5.91032207e-01 -6.34860992e-01 -5.07684238e-02 1.48483121e+00
4.76701230e-01 -4.92216736e-01 3.88288021e-01 5.41007519e-02
-6.22339308e-01 -6.26118124e-01 -6.71357453e-01 -7.80524373e-01
-7.53596485e-01 -7.78516650e-01 1.17414165e+00 8.55491579e-01
2.40649611e-01 2.69288659e-01 -5.51513910e-01 5.88863909e-01
5.19510031e-01 5.90499818e-01 4.10822123e-01 -1.48613405e+00
-4.56062585e-01 -4.71230567e-01 -5.85674524e-01 -6.34414315e-01
-7.57289529e-02 -9.75743711e-01 -2.61706710e-01 -1.61662638e+00
-5.43955803e-01 -6.10199988e-01 -6.28217816e-01 1.49774790e-01
2.36764237e-01 -9.79299396e-02 2.54098147e-01 1.34309372e-02
-5.36822557e-01 6.59298837e-01 1.05243659e+00 -2.00128138e-01
-3.25837195e-01 4.31215435e-01 -8.90076458e-01 6.54626489e-01
7.06525147e-01 -2.93248892e-01 -7.41168380e-01 -2.67770201e-01
1.90643504e-01 3.98781091e-01 -1.62297100e-01 -1.13028538e+00
7.63596237e-01 -1.26757503e-01 3.20536703e-01 -8.37107062e-01
2.62837648e-01 -1.20852029e+00 3.10650766e-01 2.13372827e-01
-1.70506090e-01 -3.59068692e-01 -4.02003601e-02 6.18409634e-01
-1.09029748e-01 -4.59758580e-01 2.06302032e-01 3.51839483e-01
-9.92845535e-01 3.12718660e-01 -4.68021065e-01 -3.05576712e-01
7.76238739e-01 -1.67124391e-01 -2.11803228e-01 -6.21368170e-01
-8.63177717e-01 2.97603786e-01 -5.53549588e-01 2.95115381e-01
9.96615887e-01 -1.35163701e+00 -7.10563660e-01 1.67854458e-01
3.02395344e-01 -7.61290729e-01 6.71496689e-01 6.22889519e-01
-3.16930227e-02 -5.26262037e-02 1.25126559e-02 -1.67488620e-01
-9.88214374e-01 2.97110558e-01 -1.81089784e-03 1.86588198e-01
-3.95165682e-01 5.04709601e-01 -7.12111235e-01 -1.79734781e-01
2.62097359e-01 1.93440676e-01 -9.93690133e-01 3.05196643e-01
6.15520597e-01 5.65943897e-01 2.95307785e-01 -4.93730217e-01
-3.49173933e-01 7.59363174e-01 3.08621645e-01 1.11454099e-01
1.26682186e+00 -2.36071110e-01 -4.64441888e-02 -1.11037321e-01
9.47862089e-01 4.04448569e-01 -4.03645396e-01 -3.52855951e-01
-5.52653484e-02 -4.53973323e-01 2.64434546e-01 -9.43469107e-01
-1.54854071e+00 3.82500261e-01 5.05346358e-01 3.57583195e-01
1.44098413e+00 -4.70791310e-01 9.53507602e-01 5.18410504e-01
5.99437714e-01 -1.16649640e+00 -2.81841010e-01 2.64919907e-01
9.26625252e-01 -3.45371872e-01 1.98074803e-01 -4.33029294e-01
-4.91904885e-01 1.37194920e+00 2.43164986e-01 8.64092261e-03
1.19676268e+00 3.44630629e-01 -1.07077099e-01 -5.49781322e-02
-3.76302779e-01 -5.34432769e-01 5.99615455e-01 7.76379168e-01
-1.15541361e-01 1.16575710e-01 -7.98886478e-01 1.50249255e+00
-3.11145753e-01 -9.60108452e-03 2.95446575e-01 7.52432406e-01
-8.71413529e-01 -1.15983784e+00 -4.31659281e-01 7.22431719e-01
1.49603173e-01 3.75099741e-02 1.48546979e-01 5.51037192e-02
4.37625915e-01 1.55026901e+00 -3.53066057e-01 -1.22089779e+00
6.03724062e-01 -2.75568247e-01 2.70673364e-01 -2.89268166e-01
-3.62815738e-01 1.90563053e-01 1.94285870e-01 -6.69923425e-02
-3.37122202e-01 -1.64157271e-01 -1.53337550e+00 -1.08028710e-01
-7.18333900e-01 7.98040390e-01 8.23393643e-01 1.12306535e+00
6.02104723e-01 8.67552221e-01 1.23352599e+00 -5.98530054e-01
-5.06640136e-01 -7.32924521e-01 -8.49157691e-01 -2.10664291e-02
-4.21479315e-01 -8.48521709e-01 -4.09503549e-01 -2.55318195e-01] | [10.087170600891113, 5.589885711669922] |
b4b5a842-1db8-44cf-8d28-8c1d29d61a65 | cardiac-segmentation-from-lge-mri-using-deep | 1906.07347 | null | https://arxiv.org/abs/1906.07347v2 | https://arxiv.org/pdf/1906.07347v2.pdf | Cardiac Segmentation from LGE MRI Using Deep Neural Network Incorporating Shape and Spatial Priors | Cardiac segmentation from late gadolinium enhancement MRI is an important task in clinics to identify and evaluate the infarction of myocardium. The automatic segmentation is however still challenging, due to the heterogeneous intensity distributions and indistinct boundaries in the images. In this paper, we propose a new method, based on deep neural networks (DNN), for fully automatic segmentation. The proposed network, referred to as SRSCN, comprises a shape reconstruction neural network (SRNN) and a spatial constraint network (SCN). SRNN aims to maintain a realistic shape of the resulting segmentation. It can be pre-trained by a set of label images, and then be embedded into a unified loss function as a regularization term. Hence, no manually designed feature is needed. Furthermore, SCN incorporates the spatial information of the 2D slices. It is formulated and trained with the segmentation network via the multi-task learning strategy. We evaluated the proposed method using 45 patients and compared with two state-of-the-art regularization schemes, i.e., the anatomically constraint neural network and the adversarial neural network. The results show that the proposed SRSCN outperformed the conventional schemes, and obtained a Dice score of 0.758(std=0.227) for myocardial segmentation, which compares with 0.757(std=0.083) from the inter-observer variations. | ['Xiahai Zhuang', 'Lingchao Xu', 'Qing Ye', 'Qian Yue', 'Xinzhe Luo'] | 2019-06-18 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 3.49974871e-01 6.35806099e-02 1.46429285e-01 -2.41634190e-01
-7.33370245e-01 -3.74125957e-01 2.73841582e-02 -2.20171567e-02
-7.28915751e-01 7.98385203e-01 -1.20231025e-01 -2.03836828e-01
-5.34766242e-02 -4.57877696e-01 -5.10856271e-01 -1.10406363e+00
1.50147170e-01 4.29655373e-01 4.76664335e-01 1.79656029e-01
9.71340612e-02 5.21464169e-01 -7.10440278e-01 -1.85701009e-02
1.10816479e+00 1.04497409e+00 2.68069535e-01 2.35133588e-01
1.40841022e-01 7.88415015e-01 -4.24709976e-01 -1.22230068e-01
1.81543022e-01 -6.84312582e-01 -7.37836719e-01 7.97370821e-02
-3.40666473e-02 -2.26480544e-01 -2.09113985e-01 1.09470332e+00
8.29303920e-01 1.11688115e-01 6.42798066e-01 -6.94902956e-01
-4.21309203e-01 3.95399541e-01 -6.90772295e-01 3.42542708e-01
-4.18127716e-01 -1.55153219e-02 2.58535147e-01 -5.57424545e-01
6.31662130e-01 6.00754380e-01 8.46600056e-01 5.57322323e-01
-1.09783673e+00 -5.41272461e-01 -2.76298910e-01 -1.16921484e-01
-1.38317823e+00 -3.58467065e-02 1.01324511e+00 -6.11933529e-01
2.36633360e-01 1.33321270e-01 5.59653223e-01 6.33281648e-01
5.15479624e-01 5.10485530e-01 1.23490739e+00 -2.48750344e-01
7.19122961e-02 -1.47617422e-02 6.19692961e-03 6.44902170e-01
2.71171093e-01 1.77703589e-01 4.14940596e-01 -6.17532954e-02
1.10688698e+00 2.40305141e-02 -4.34297889e-01 -4.92668629e-01
-1.05590796e+00 8.10290813e-01 6.55219793e-01 5.80513120e-01
-4.37489152e-01 -8.47223476e-02 6.95183158e-01 -2.08855957e-01
5.80941796e-01 1.58704028e-01 -1.53275952e-01 3.26386094e-01
-1.00299835e+00 4.73101437e-03 5.21121323e-01 4.96766388e-01
3.44644576e-01 2.85772532e-01 -2.98784167e-01 8.59448433e-01
3.52579921e-01 2.89770484e-01 7.43231118e-01 -9.10553753e-01
2.52605438e-01 3.80683035e-01 -1.33959427e-01 -1.08501554e+00
-6.14854813e-01 -7.34773159e-01 -1.29089594e+00 2.35677198e-01
4.94408071e-01 -4.71135944e-01 -9.72715199e-01 1.63227642e+00
3.51545334e-01 1.94992751e-01 8.93423855e-02 1.26037204e+00
9.53548551e-01 3.55330050e-01 2.88172096e-01 -4.31809157e-01
9.38383698e-01 -1.10188472e+00 -9.29870486e-01 -1.22529842e-01
7.19101787e-01 -6.90278530e-01 6.39651120e-01 2.97686070e-01
-1.17686141e+00 -5.03158510e-01 -9.94378746e-01 4.07681286e-01
1.87100753e-01 2.26883069e-01 3.20681185e-01 6.04341149e-01
-9.65632617e-01 7.73706555e-01 -1.04391944e+00 1.24212064e-01
4.96780276e-01 4.87094074e-01 -2.60030389e-01 2.51138061e-02
-1.24372041e+00 8.40259194e-01 6.19241238e-01 4.97148305e-01
-7.29550600e-01 -6.00704610e-01 -8.36101472e-01 -2.21073136e-01
2.91083157e-01 -4.62664545e-01 9.80623066e-01 -1.25904500e+00
-1.51947176e+00 9.77993786e-01 2.70193666e-01 -2.53803253e-01
7.35421062e-01 1.56583250e-01 -2.12635383e-01 5.70918679e-01
1.55308709e-01 4.38702941e-01 5.11831939e-01 -1.38966596e+00
-1.02643028e-01 -3.20853502e-01 -2.23363116e-01 1.74966514e-01
1.77836999e-01 2.59301037e-01 -4.25048113e-01 -9.79875684e-01
4.32884395e-01 -1.03154480e+00 -5.64298749e-01 6.28376007e-02
-5.07010400e-01 2.35049829e-01 6.58904672e-01 -1.08416319e+00
1.09210992e+00 -2.04073215e+00 9.43927169e-02 4.03750718e-01
2.99947590e-01 5.41115284e-01 9.94961262e-02 -3.27832013e-01
-2.10176945e-01 9.83524248e-02 -8.13448250e-01 -2.69540008e-02
-4.92073148e-01 1.39145851e-01 5.88633120e-01 8.02283704e-01
-8.55834186e-02 8.57108414e-01 -8.41196597e-01 -7.62588561e-01
2.90598720e-01 5.25647581e-01 -3.38728219e-01 3.02633464e-01
2.03281388e-01 9.65621650e-01 -5.08002520e-01 4.97615039e-01
9.23901737e-01 -1.24576665e-01 2.96860963e-01 -3.11554134e-01
-1.11703366e-01 -5.78741610e-01 -9.81571078e-01 1.70546269e+00
-2.23199278e-01 1.60834476e-01 1.92023069e-01 -1.22108853e+00
9.47423697e-01 6.89348340e-01 9.37623084e-01 -6.11858666e-01
5.51421106e-01 5.37379563e-01 3.01161528e-01 -7.08564997e-01
-2.63061613e-01 -6.10108197e-01 5.96000217e-02 3.72317910e-01
-8.06948319e-02 1.42391492e-02 5.37512414e-02 -1.75476328e-01
8.03831756e-01 9.41557214e-02 5.34062348e-02 -4.40538913e-01
8.58412445e-01 -1.67124212e-01 9.95509565e-01 4.66592550e-01
-5.04727602e-01 9.55687404e-01 4.65933084e-01 -5.64162195e-01
-1.00097907e+00 -9.10886288e-01 -4.36233431e-01 2.08173141e-01
2.43780300e-01 2.92207658e-01 -1.10226357e+00 -8.26941550e-01
-3.53607684e-01 1.98859185e-01 -4.11831975e-01 -2.30078697e-01
-8.66812825e-01 -1.01476455e+00 6.29034162e-01 6.53356314e-01
7.31557786e-01 -1.25017476e+00 -5.35515010e-01 4.00726885e-01
-3.70135009e-01 -1.02218831e+00 -6.07782722e-01 1.06833763e-01
-1.00588429e+00 -9.99306083e-01 -1.14239645e+00 -9.55582500e-01
8.47343326e-01 -1.76704109e-01 9.77769852e-01 1.89907804e-01
-1.92035392e-01 -1.24383740e-01 -1.88908234e-01 -7.04834238e-02
-5.58637917e-01 -5.66082112e-02 -1.63050547e-01 1.19064920e-01
-9.55620781e-02 -6.21100664e-01 -8.41510296e-01 4.20029819e-01
-1.10928464e+00 -1.95451677e-02 7.78739929e-01 1.03120995e+00
9.88214076e-01 -2.08425056e-02 7.02299953e-01 -1.23920119e+00
3.30001503e-01 -3.46434504e-01 -5.31410635e-01 2.40098625e-01
-5.37395477e-01 -2.88695544e-01 6.64721429e-01 -3.87578666e-01
-1.11047161e+00 4.05677438e-01 -3.13747197e-01 -4.96753156e-01
-1.50550753e-01 5.77206969e-01 -1.60742149e-01 -5.60989022e-01
3.98209900e-01 1.46985158e-01 1.98766828e-01 -2.70113915e-01
-1.81229741e-04 4.99591738e-01 5.81293046e-01 -4.21484441e-01
4.16853100e-01 3.74132007e-01 1.22449674e-01 -5.05622745e-01
-8.62551689e-01 -3.85434210e-01 -8.97372663e-01 -2.71541625e-01
1.21077096e+00 -6.74304783e-01 -3.12816769e-01 8.52221727e-01
-1.23244464e+00 -4.50574189e-01 -1.71004668e-01 7.63949931e-01
-6.68339074e-01 6.80591941e-01 -9.46442425e-01 -4.04722989e-01
-6.34490907e-01 -1.34990895e+00 3.59275192e-01 2.50104904e-01
1.93050817e-01 -1.34732318e+00 8.22550729e-02 4.87531334e-01
5.80553710e-01 8.84797871e-01 8.39724243e-01 -6.30609572e-01
-2.90603757e-01 -1.90729111e-01 -4.55593377e-01 7.90586531e-01
1.34861857e-01 -3.38783234e-01 -7.33463109e-01 -3.51072222e-01
5.41316211e-01 -3.33608478e-01 6.88393474e-01 9.62680638e-01
1.43447185e+00 2.38958653e-03 -1.43869400e-01 9.31627035e-01
1.73280120e+00 6.16558552e-01 7.32029915e-01 2.46344671e-01
8.75150263e-01 3.73068273e-01 4.20117408e-01 2.20843464e-01
9.16447584e-03 4.37397599e-01 4.52071190e-01 -6.78088427e-01
-6.43465593e-02 3.02335680e-01 -3.75555366e-01 9.44972515e-01
-3.44870180e-01 -1.32022351e-01 -1.04983139e+00 3.19199711e-01
-1.79182470e+00 -6.14501834e-01 -3.29329908e-01 1.97814286e+00
8.27398241e-01 1.83739126e-01 -2.03014821e-01 9.65804607e-03
9.66162145e-01 2.05505248e-02 -5.05727470e-01 -3.49827647e-01
5.27347326e-02 3.63738626e-01 5.81656933e-01 4.42049146e-01
-1.43102527e+00 5.32808006e-01 5.57261896e+00 8.31770003e-01
-1.27410579e+00 3.33055705e-01 8.59611928e-01 4.38788503e-01
4.45544422e-02 -2.89410532e-01 -2.41776332e-01 6.04067028e-01
7.86576569e-01 1.36578828e-01 4.73013930e-02 4.33437198e-01
4.43249494e-01 -2.65361872e-02 -4.97835279e-01 7.36940801e-01
2.56651103e-01 -1.08092797e+00 -2.07806036e-01 -2.47822583e-01
8.58492732e-01 -9.44867283e-02 -2.19268635e-01 8.98537338e-02
-3.39900136e-01 -9.05209959e-01 4.12234783e-01 6.17604434e-01
9.46419656e-01 -8.26533437e-01 1.30594718e+00 3.95988226e-01
-9.73522127e-01 2.10644603e-01 -1.80610731e-01 2.96983391e-01
3.11358720e-01 5.63712299e-01 -2.97023386e-01 6.92967415e-01
3.70868921e-01 5.80855608e-01 -1.87322751e-01 1.21269858e+00
-1.89212114e-01 4.52018976e-01 -1.19929433e-01 4.10573781e-01
4.49510038e-01 -5.46583951e-01 4.11139011e-01 1.10857129e+00
1.63397163e-01 1.66500345e-01 4.18349564e-01 8.34448695e-01
-2.29414143e-02 3.89760524e-01 -2.88523644e-01 4.78190869e-01
-3.68914530e-02 1.38552606e+00 -1.07204247e+00 -2.63146281e-01
-3.25552672e-01 9.49418604e-01 5.15225194e-02 2.36035526e-01
-9.67298150e-01 -4.71006006e-01 -2.84234196e-01 9.15258154e-02
5.69633842e-02 1.90772101e-01 -3.64689767e-01 -9.40594196e-01
7.74835944e-02 -6.37655318e-01 2.99565524e-01 -5.80898464e-01
-1.25310552e+00 9.75068569e-01 -3.86126228e-02 -1.21386528e+00
1.31903425e-01 -4.21408415e-01 -7.93931723e-01 1.16027701e+00
-1.59162450e+00 -1.01834726e+00 -4.40427512e-01 4.24587667e-01
2.39032730e-01 4.12006266e-02 7.09520578e-01 7.05206692e-01
-7.21568286e-01 4.38374013e-01 1.80235907e-01 3.75313699e-01
6.23133063e-01 -1.25768876e+00 -3.89881372e-01 7.79056668e-01
-6.04041636e-01 1.85805947e-01 2.05657631e-01 -7.12297976e-01
-4.62344587e-01 -1.37908173e+00 7.39189446e-01 2.18481824e-01
3.45551401e-01 1.51763216e-01 -1.10073042e+00 4.84200090e-01
2.27742985e-01 6.56328917e-01 6.46690369e-01 -6.75473034e-01
4.80722994e-01 -2.57557482e-02 -1.42684639e+00 1.51031911e-01
5.00662625e-01 -9.38863307e-02 -4.33022141e-01 3.04875463e-01
6.00389183e-01 -8.31046343e-01 -1.14467800e+00 7.24224031e-01
4.28242773e-01 -8.67329776e-01 9.80833411e-01 -2.58947462e-01
4.94756788e-01 -2.09190592e-01 2.54580200e-01 -1.17548633e+00
-4.46065426e-01 -2.32110485e-01 3.08846772e-01 9.63731766e-01
3.34438801e-01 -5.09684682e-01 9.62975085e-01 6.16644621e-01
-5.80848694e-01 -1.02037811e+00 -9.98542070e-01 -5.74185371e-01
3.44054133e-01 3.03865653e-02 2.25315373e-02 1.02189898e+00
-5.15165031e-01 6.09223284e-02 -2.68859327e-01 7.89709166e-02
8.25985610e-01 -1.82325110e-01 -1.23504572e-01 -1.10041988e+00
-1.36876374e-01 -2.16506764e-01 -2.89178401e-01 -6.07468724e-01
2.45501056e-01 -1.03990591e+00 1.27435848e-01 -1.54870033e+00
2.85318017e-01 -8.52685332e-01 -7.48815954e-01 3.60415399e-01
-1.90107316e-01 4.85724896e-01 7.84650445e-02 3.10190439e-01
-3.39622468e-01 4.83785540e-01 1.87793231e+00 -1.10238582e-01
-2.85351098e-01 3.98217022e-01 -4.63954359e-01 1.03396034e+00
1.11085355e+00 -6.90073848e-01 -3.84349793e-01 -3.54325831e-01
-3.89733493e-01 4.56169754e-01 3.92981321e-01 -1.12910712e+00
1.18664064e-01 2.13634238e-01 4.17674035e-01 -6.61880732e-01
-2.08272729e-02 -1.00490582e+00 1.90474242e-01 8.14600289e-01
-2.50026256e-01 -7.64429569e-02 9.99777466e-02 2.15755701e-01
-4.13779825e-01 -5.51484764e-01 1.14047492e+00 -4.72548604e-01
-2.12626249e-01 4.83904779e-01 -3.04748148e-01 3.44554245e-01
1.14187658e+00 -1.57718077e-01 1.42328024e-01 1.59675330e-01
-1.08853972e+00 6.90755248e-02 1.16935987e-02 -2.56457925e-01
7.59046793e-01 -1.41917109e+00 -6.63208723e-01 8.44521224e-02
-3.69711727e-01 3.73756856e-01 6.11311972e-01 1.40376472e+00
-1.04012549e+00 9.10209566e-02 -3.20887685e-01 -7.19534338e-01
-1.02700782e+00 4.35210913e-01 8.08110058e-01 -6.67531610e-01
-7.34537125e-01 5.41689873e-01 1.93148345e-01 -5.40147722e-01
1.07876085e-01 -3.51584554e-01 -4.12384570e-01 -2.27453753e-01
1.12887949e-01 2.92417586e-01 1.00411609e-01 -7.25399137e-01
-3.41615736e-01 8.61804903e-01 1.05318360e-01 2.34685123e-01
1.20323694e+00 2.62231417e-02 -3.08597773e-01 1.32845253e-01
1.23962617e+00 -3.24301153e-01 -1.26338553e+00 -1.55299395e-01
-1.69795647e-01 -1.90905452e-01 3.77317429e-01 -8.77747834e-01
-1.58824527e+00 7.76090980e-01 1.03760397e+00 -8.77563953e-02
1.16696632e+00 -4.30164993e-01 1.00994503e+00 -1.26864985e-01
2.10731868e-02 -9.05363739e-01 -6.08933009e-02 2.24576607e-01
7.60048032e-01 -1.35212851e+00 -1.16124719e-01 -5.99185169e-01
-8.56432676e-01 1.17114985e+00 5.78851044e-01 -3.83357406e-01
8.13966691e-01 2.35158533e-01 4.32856143e-01 -1.08343609e-01
1.21941969e-01 1.86324835e-01 2.53684133e-01 4.77299035e-01
4.67449725e-01 -3.83879468e-02 -7.51973569e-01 8.57990861e-01
4.96456891e-01 2.54537493e-01 2.83888400e-01 7.92622149e-01
-1.46442175e-01 -1.07693875e+00 -2.50284314e-01 3.11369866e-01
-8.96090448e-01 7.74992257e-02 3.28855395e-01 8.57823312e-01
6.02741301e-01 6.13022745e-01 -2.13763714e-01 -2.52092294e-02
3.66002321e-01 -2.55618602e-01 3.21217179e-01 -5.08170187e-01
-8.45319629e-01 4.25673932e-01 -2.27744624e-01 -3.25121433e-01
-7.27542698e-01 -4.95098472e-01 -1.42719626e+00 1.39906526e-01
-3.82946372e-01 1.68209493e-01 4.35842931e-01 8.71478856e-01
-2.10583240e-01 8.89503419e-01 8.86118829e-01 -8.40200245e-01
-5.52256167e-01 -8.69819224e-01 -7.94379473e-01 4.99158949e-01
8.12839493e-02 -5.18879056e-01 -3.57587546e-01 5.76041900e-02] | [14.397568702697754, -2.4551761150360107] |
6c79e348-aa32-4195-97a0-099e7caaaf37 | meta-song-evaluation-for-chord-recognition | null | null | https://arxiv.org/abs/1109.0420 | https://arxiv.org/pdf/1109.0420 | Meta-song evaluation for chord recognition | We present a new approach to evaluate chord recognition systems on songs which do not have full annotations. The principle is to use online chord databases to generate high accurate "pseudo annotations" for these songs and compute "pseudo accuracies" of test systems. Statistical models that model the relationship between "pseudo accuracy" and real performance are then applied to estimate test systems' performance. The approach goes beyond the existing evaluation metrics, allowing us to carry out extensive analysis on chord recognition systems, such as their generalizations to different genres. In the experiments we applied this method to evaluate three state-of-the-art chord recognition systems, of which the results verified its reliability. | ['Raul Santos-Rodriguez', 'Tijl De Bie', 'Yizhao Ni', 'Matt Mcvicar'] | 2011-09-02 | null | null | null | tbd-2011-9 | ['chord-recognition'] | ['audio'] | [-8.37796256e-02 -3.93491387e-01 -1.17196171e-02 -2.62636721e-01
-1.04223192e+00 -1.12287140e+00 4.16795164e-01 5.38956262e-02
-4.51393574e-01 6.64392173e-01 -1.36011481e-01 7.30449036e-02
-4.14438158e-01 -6.23894334e-01 -1.48647949e-01 -3.23967755e-01
-4.38790619e-01 5.24176419e-01 8.37546527e-01 -5.26714027e-01
8.64170313e-01 2.93371022e-01 -1.72753525e+00 4.68167514e-01
3.24014276e-01 9.73112226e-01 -1.42122447e-01 1.30239165e+00
6.47978261e-02 7.55357206e-01 -1.51858866e+00 -3.08088392e-01
2.52240866e-01 -5.90545774e-01 -1.19270134e+00 -3.59336108e-01
3.23518097e-01 -3.80892232e-02 -1.39433116e-01 9.31336641e-01
5.47510445e-01 1.05765417e-01 6.40365720e-01 -1.15391707e+00
-3.17131370e-01 9.58151817e-01 2.48561352e-01 1.75074488e-01
9.49146628e-01 9.64615680e-03 1.31308532e+00 -5.76301038e-01
5.75792313e-01 5.45951486e-01 9.41349804e-01 4.61402357e-01
-1.17015970e+00 -7.27109969e-01 -4.42860007e-01 4.51660007e-01
-1.74089599e+00 -4.48767841e-01 6.40421867e-01 -5.08855283e-01
8.30487251e-01 7.12284505e-01 9.32657659e-01 6.66713238e-01
-5.71680404e-02 6.83261573e-01 1.06901634e+00 -5.69395840e-01
2.70663947e-01 1.18341193e-01 2.31598437e-01 3.66511464e-01
-1.95528135e-01 2.95018107e-01 -9.94743288e-01 -1.88466534e-01
6.99925959e-01 -8.53982449e-01 -3.98041576e-01 1.84253622e-02
-1.29977059e+00 5.23357272e-01 -1.43113015e-02 6.79103851e-01
-5.34655415e-02 -6.67471662e-02 8.84371817e-01 7.01197326e-01
1.47625983e-01 1.30642414e+00 -5.60492933e-01 -8.82318020e-01
-1.42923594e+00 6.01309538e-01 1.35333335e+00 7.84860671e-01
2.26345420e-01 7.08364472e-02 -3.41529399e-01 9.52364445e-01
-2.00927228e-01 -3.39428782e-02 8.43697727e-01 -1.12154710e+00
-8.29028413e-02 5.37303090e-01 -1.75142456e-02 -7.37394631e-01
-1.83810368e-01 -4.60785598e-01 -3.16543519e-01 3.19600016e-01
7.14042664e-01 3.83988678e-01 -5.12655377e-01 1.65301335e+00
-1.84415236e-01 1.73526645e-01 4.70198579e-02 8.16537559e-01
5.99600613e-01 2.45986998e-01 -3.74358028e-01 -2.99405545e-01
1.02409434e+00 -7.73954093e-01 -4.33032244e-01 8.51729810e-01
6.26922727e-01 -1.20553315e+00 1.44237995e+00 1.15716755e+00
-9.64409053e-01 -8.90705705e-01 -1.32933033e+00 4.53161061e-01
-3.87084901e-01 1.20288253e-01 4.97394651e-01 9.41897035e-01
-1.03436077e+00 1.20669556e+00 -4.25425112e-01 1.13339335e-01
-3.14569861e-01 4.17865902e-01 -1.00347266e-01 8.39717627e-01
-1.29503155e+00 6.38593912e-01 5.37228227e-01 -2.39009485e-01
-1.18186402e+00 -5.96885502e-01 -9.36559737e-02 -6.88012242e-02
3.92583609e-01 8.21702480e-02 1.87445176e+00 -8.70262563e-01
-1.75875854e+00 8.18816781e-01 4.82150733e-01 -2.13685319e-01
5.14952481e-01 -2.39361376e-01 -5.93670547e-01 -1.75525993e-01
-8.93714651e-02 1.79024696e-01 4.08483505e-01 -8.42047334e-01
-6.04033768e-01 4.53803390e-02 1.55902997e-01 -4.20392342e-02
-3.96766067e-01 1.47749916e-01 -5.26783586e-01 -8.91976535e-01
3.82836238e-02 -1.04764462e+00 1.21136464e-01 -6.40995383e-01
-5.21734118e-01 -3.48977327e-01 4.33247417e-01 -6.00330353e-01
1.76012266e+00 -1.87620890e+00 1.44077778e-01 4.95274067e-01
-2.69445956e-01 2.55651921e-01 -7.06988201e-02 5.62159419e-01
2.11158246e-01 -4.80750750e-04 -1.34206817e-01 7.70482570e-02
2.12693065e-02 1.37065724e-01 -4.13149297e-01 3.47829871e-02
-3.12621117e-01 2.81875879e-01 -8.03467691e-01 -5.33247292e-01
-7.52358511e-02 5.35260513e-02 -5.62253296e-01 4.87314761e-01
-2.68224657e-01 2.86979556e-01 -8.60697925e-02 7.19045937e-01
1.04829436e-02 2.14388832e-01 1.92880243e-01 1.38986111e-02
-3.22679520e-01 5.83516657e-01 -1.62273467e+00 1.63637900e+00
1.96357090e-02 6.40447617e-01 -4.83956277e-01 -6.34647369e-01
1.30905628e+00 5.86381972e-01 4.28341210e-01 -2.69531757e-01
-4.34610946e-03 4.59542036e-01 2.42614344e-01 -9.59148556e-02
1.02948153e+00 2.06545055e-01 -4.45644617e-01 4.07519192e-01
4.06545520e-01 -3.88125002e-01 6.22557044e-01 -3.86077464e-02
1.16425216e+00 3.92371237e-01 3.06810111e-01 -3.98910969e-01
9.29259419e-01 1.71640530e-01 4.84065652e-01 9.27609682e-01
-7.95841515e-02 4.41846877e-01 4.86579776e-01 -2.74936259e-01
-1.04393792e+00 -1.20012617e+00 -1.65954500e-01 1.28271246e+00
-2.16546997e-01 -1.06057072e+00 -9.12395775e-01 -6.05930805e-01
-2.33545780e-01 1.73852712e-01 -4.75295216e-01 -1.33333027e-01
-3.89303297e-01 -2.67749518e-01 1.50627959e+00 6.13772511e-01
2.42360324e-01 -1.39136469e+00 -5.90178907e-01 4.57792789e-01
-7.23592425e-03 -5.99014163e-01 -3.47857147e-01 7.80916959e-02
-6.61192358e-01 -1.50341976e+00 -4.76450533e-01 -6.21842921e-01
-2.52510428e-01 -3.76728952e-01 1.41532278e+00 4.55260545e-01
-4.13491100e-01 2.21826315e-01 -7.06886649e-01 -4.18751240e-01
-7.47003138e-01 4.84641522e-01 5.70738792e-01 -4.30014610e-01
2.39106357e-01 -6.96892381e-01 -2.68682003e-01 8.02418709e-01
-6.84804976e-01 -3.72368962e-01 2.65400589e-01 8.23768675e-01
5.97704530e-01 -7.71146193e-02 8.68591905e-01 -9.88766253e-01
1.03703785e+00 4.18609791e-02 -7.50553310e-01 4.17099416e-01
-1.07600713e+00 -1.01935770e-03 7.77677357e-01 -8.76861215e-01
-3.62913638e-01 -6.67401031e-02 -3.66271079e-01 -3.27170879e-01
2.07791533e-02 4.13201749e-01 4.27328199e-02 -2.75380552e-01
9.45056021e-01 3.33192825e-01 -3.33672881e-01 -7.52468646e-01
-2.70817801e-03 9.15740848e-01 1.16815591e+00 -9.09635007e-01
5.44337690e-01 -4.06592071e-01 -1.47389114e-01 -7.58784592e-01
-6.07832849e-01 -5.68837404e-01 -7.73028970e-01 -7.58143485e-01
3.14157039e-01 -4.18899089e-01 -1.08934879e+00 4.68682468e-01
-7.31506348e-01 -2.97481149e-01 -5.11308908e-01 6.61698878e-01
-1.02744055e+00 4.03563440e-01 -8.94602239e-01 -9.30275202e-01
-4.13131267e-01 -9.05855775e-01 7.16148913e-01 1.82106271e-01
-6.88106835e-01 -6.35545194e-01 7.65192747e-01 -4.08446118e-02
2.82653153e-01 -1.83488443e-01 5.55480361e-01 -9.99182045e-01
-2.56345093e-01 -3.20255905e-01 2.90789962e-01 7.10011780e-01
-7.09847733e-02 4.66308922e-01 -1.17128801e+00 -1.97684675e-01
-3.34442377e-01 -4.16533947e-01 3.12033743e-01 -1.28212288e-01
1.27362335e+00 8.42696801e-02 1.98870629e-01 4.53024417e-01
1.16536522e+00 2.72589713e-01 6.34066761e-01 4.34529126e-01
1.94328174e-01 3.79272759e-01 1.05395484e+00 4.56874102e-01
-2.56714106e-01 1.28924537e+00 -2.44011730e-02 4.99281138e-01
-9.71545652e-02 -4.26679134e-01 4.42398667e-01 1.35194445e+00
-6.10917151e-01 -4.29306366e-02 -8.32757533e-01 4.13742751e-01
-1.50479376e+00 -1.06591594e+00 -2.00688854e-01 2.43340468e+00
1.04181230e+00 4.56669778e-01 6.79795444e-01 1.19520831e+00
4.32296872e-01 -1.56315893e-01 -1.20575838e-01 -5.66761613e-01
3.78467073e-03 6.60701990e-01 9.72695500e-02 2.54287481e-01
-9.37309384e-01 1.07278621e+00 8.32520580e+00 1.09051561e+00
-8.58583212e-01 -1.62662476e-01 -2.12948266e-02 1.14330932e-01
2.37554625e-01 1.59646526e-01 -6.62469685e-01 1.46762386e-01
1.26720858e+00 -3.62959713e-01 5.61974943e-01 9.36908543e-01
-2.71279246e-01 1.43957242e-01 -1.26045954e+00 7.14674234e-01
-4.85086534e-03 -1.29295754e+00 -5.12603030e-04 -2.40598638e-02
5.30491292e-01 -2.74212956e-01 -4.16204900e-01 6.19449973e-01
1.63865551e-01 -6.76284671e-01 9.24811363e-01 7.28264928e-01
1.14687932e+00 -8.10064077e-01 6.78469837e-01 2.22312510e-01
-1.31640446e+00 1.17176563e-01 -2.97829300e-01 -3.39031816e-01
-1.69093743e-01 4.91389260e-02 -1.13833940e+00 5.09863734e-01
7.18365073e-01 2.45952696e-01 -8.66208911e-01 1.32146609e+00
-2.09495217e-01 8.23587477e-01 -2.16306508e-01 -3.73426765e-01
-1.77301481e-01 1.39324233e-01 5.61846018e-01 1.42317462e+00
3.43396276e-01 -1.84847191e-01 7.74716884e-02 6.09150529e-01
1.59572929e-01 6.73788965e-01 -2.25077569e-01 -1.40936002e-01
6.25687301e-01 1.08419788e+00 -8.20377767e-01 -4.71123636e-01
1.41008139e-01 7.67816484e-01 5.56636490e-02 -2.85253793e-01
-6.81318104e-01 -6.75601065e-01 4.31699127e-01 2.31585670e-02
-1.13912091e-01 -1.96892470e-01 -1.30351380e-01 -9.21502590e-01
-2.68157989e-01 -1.19395554e+00 6.01297438e-01 -6.50202155e-01
-1.14303517e+00 7.84944236e-01 4.31491658e-02 -1.65871024e+00
-6.73759341e-01 -6.81664646e-01 -5.59198320e-01 6.20474875e-01
-4.76084054e-01 -6.50783777e-01 -2.02712253e-01 5.90311408e-01
4.75448370e-01 -7.07311332e-01 1.55201077e+00 4.11859691e-01
7.55923092e-02 9.98826981e-01 -1.18967198e-01 3.65634382e-01
7.61575222e-01 -1.53095543e+00 2.72625357e-01 4.45843101e-01
9.38236713e-01 3.34189981e-01 7.74862587e-01 -3.52337688e-01
-1.00205052e+00 -4.10194576e-01 7.05377400e-01 -6.38338864e-01
6.02551341e-01 -1.97305605e-01 -1.03210342e+00 1.95870087e-01
-2.67028153e-01 -4.60894525e-01 1.23848319e+00 5.58127224e-01
-5.81010282e-01 -1.57724202e-01 -7.28203475e-01 2.63231814e-01
1.02162290e+00 -8.36980760e-01 -7.77473032e-01 5.18133789e-02
3.97361815e-01 -2.88784295e-01 -1.35861707e+00 6.07548833e-01
1.05955923e+00 -1.02758968e+00 7.25499928e-01 -7.16204047e-01
8.73264223e-02 -4.44224238e-01 -3.80822241e-01 -1.15233493e+00
-5.60409017e-02 -5.00040889e-01 -6.04219362e-02 1.37995172e+00
5.78532994e-01 3.40386927e-02 9.20159221e-01 -6.97627664e-02
-2.44918957e-01 -3.58104408e-01 -9.63601589e-01 -1.11806917e+00
-1.46790832e-01 -8.53162050e-01 6.48258030e-01 9.92157459e-01
5.51738024e-01 1.53131977e-01 -4.13991451e-01 -1.93746492e-01
3.96013618e-01 2.11179480e-01 1.13797009e+00 -1.55293405e+00
-9.37659681e-01 -6.72712088e-01 -1.02457428e+00 -6.38057590e-01
-8.90983939e-02 -7.52533317e-01 1.87264234e-01 -4.33503509e-01
-8.72440562e-02 -4.87776190e-01 -7.80115426e-01 2.43719488e-01
2.13831663e-01 8.17590415e-01 2.50147939e-01 4.08755451e-01
-7.63932705e-01 2.98331715e-02 7.47573078e-01 1.64526314e-01
-4.39566344e-01 3.83036196e-01 -2.71810800e-01 6.49797440e-01
7.27184653e-01 -5.35496473e-01 -2.49900535e-01 2.61560708e-01
1.78894043e-01 3.15036923e-01 -1.89531688e-02 -1.81586146e+00
2.28477076e-01 3.71129997e-02 2.44531587e-01 -4.75757331e-01
2.66634554e-01 -2.92528510e-01 2.18776315e-01 4.17076349e-01
-5.99131048e-01 2.78517574e-01 7.96434358e-02 1.59927234e-01
-5.96938848e-01 -4.87304807e-01 5.12417912e-01 1.24023497e-01
-6.34651482e-01 -1.70745298e-01 -3.00639749e-01 -4.76080365e-02
7.58259833e-01 -1.94025993e-01 2.66790297e-02 -4.28196669e-01
-1.05780506e+00 -3.15940261e-01 5.10378778e-01 5.31278133e-01
5.14152467e-01 -1.29240942e+00 -6.98388577e-01 4.42317724e-01
3.83012861e-01 -8.61213803e-01 -3.26379500e-02 4.99591291e-01
-8.49133849e-01 3.09994698e-01 -5.36386788e-01 -5.53651214e-01
-1.67556667e+00 3.04459363e-01 2.87510186e-01 -4.14449364e-01
-2.73769021e-01 7.26439297e-01 -6.24941826e-01 -5.12966812e-01
4.45623875e-01 -1.19095229e-01 -3.70915443e-01 -5.57352193e-02
6.57208681e-01 3.50618839e-01 1.51707157e-01 -5.18343210e-01
-2.23668337e-01 3.90092850e-01 2.13218689e-01 -4.91123348e-01
9.53110635e-01 5.01384258e-01 1.14301637e-01 1.10184729e+00
5.69332182e-01 3.03344339e-01 -5.32923579e-01 -3.25515307e-02
5.55187881e-01 -7.22562432e-01 -3.76574934e-01 -9.38147962e-01
-6.10704303e-01 5.91238976e-01 5.80407381e-01 7.34902442e-01
1.25162435e+00 -2.29139552e-01 2.58714765e-01 6.17065489e-01
7.76458800e-01 -1.39546180e+00 -1.06660262e-01 7.01048195e-01
8.22919846e-01 -5.82481325e-01 -9.00303423e-02 -2.17612624e-01
-2.80529827e-01 1.34458852e+00 5.23531735e-01 -3.39761496e-01
5.44256568e-01 4.17764336e-01 5.91626540e-02 3.87330242e-02
-9.50847507e-01 -3.72122135e-03 5.52027822e-01 2.52639025e-01
8.32159996e-01 4.13183570e-01 -7.89357901e-01 1.02705145e+00
-9.53485310e-01 2.71783620e-01 6.14586115e-01 6.59582734e-01
-3.80169570e-01 -1.49097335e+00 -4.03863519e-01 4.50103819e-01
-7.39078641e-01 2.59954929e-01 -9.54378247e-01 9.75521147e-01
1.29207388e-01 9.00263906e-01 -3.04725409e-01 -1.34079885e+00
7.31025219e-01 3.67525905e-01 6.39782608e-01 -4.34745938e-01
-1.10225737e+00 3.54817742e-03 3.80350232e-01 -4.04426634e-01
-3.41350287e-01 -6.67476237e-01 -1.00877190e+00 -4.58357155e-01
-5.88474810e-01 8.74407947e-01 6.78617477e-01 6.21981978e-01
-4.06518161e-01 6.07672393e-01 6.97487414e-01 -6.80818200e-01
-7.76183426e-01 -1.20143807e+00 -1.03374887e+00 6.09874487e-01
-3.43535572e-01 -5.16845345e-01 -2.73373365e-01 1.91399753e-02] | [15.907193183898926, 5.284383296966553] |
a227d73d-792e-495a-a33d-11daa832d78f | extended-multilingual-protest-news-detection | 2211.11360 | null | https://arxiv.org/abs/2211.11360v1 | https://arxiv.org/pdf/2211.11360v1.pdf | Extended Multilingual Protest News Detection -- Shared Task 1, CASE 2021 and 2022 | We report results of the CASE 2022 Shared Task 1 on Multilingual Protest Event Detection. This task is a continuation of CASE 2021 that consists of four subtasks that are i) document classification, ii) sentence classification, iii) event sentence coreference identification, and iv) event extraction. The CASE 2022 extension consists of expanding the test data with more data in previously available languages, namely, English, Hindi, Portuguese, and Spanish, and adding new test data in Mandarin, Turkish, and Urdu for Sub-task 1, document classification. The training data from CASE 2021 in English, Portuguese and Spanish were utilized. Therefore, predicting document labels in Hindi, Mandarin, Turkish, and Urdu occurs in a zero-shot setting. The CASE 2022 workshop accepts reports on systems developed for predicting test data of CASE 2021 as well. We observe that the best systems submitted by CASE 2022 participants achieve between 79.71 and 84.06 F1-macro for new languages in a zero-shot setting. The winning approaches are mainly ensembling models and merging data in multiple languages. The best two submissions on CASE 2021 data outperform submissions from last year for Subtask 1 and Subtask 2 in all languages. Only the following scenarios were not outperformed by new submissions on CASE 2021: Subtask 3 Portuguese \& Subtask 4 English. | ['Erdem Yörük', 'Fatih Beyhan', 'Aaqib Javid', 'Francielle Vargas', 'Milena Slavcheva', 'Tadashi Nomoto', 'Niklas Stoehr', 'Hansi Hettiarachchi', 'Yaoyao Dai', 'Benjamin Radford', 'Alaeddin Selçuk Gürel', 'Onur Uca', 'Fırat Duruşan', 'Osman Mutlu', 'Ali Hürriyetoğlu'] | 2022-11-21 | null | null | null | null | ['event-extraction', 'document-classification', 'sentence-classification'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-2.30013639e-01 6.51942939e-02 3.53954434e-02 -3.23501468e-01
-1.63447118e+00 -9.97455657e-01 9.40667927e-01 4.53107536e-01
-8.86545181e-01 1.17863834e+00 6.20016277e-01 -2.20403939e-01
2.54666418e-01 -4.24305350e-01 -7.41481185e-01 -1.56988576e-01
5.01369946e-02 8.06644261e-01 4.27259058e-01 -4.49932218e-01
-8.64071921e-02 1.96532950e-01 -1.30569506e+00 1.05147064e+00
5.64938903e-01 5.31779706e-01 1.72452480e-01 7.99737215e-01
5.46051152e-02 8.79432857e-01 -7.54291415e-01 -5.94375849e-01
-1.72574759e-01 -4.39119488e-01 -1.27654147e+00 -4.59352672e-01
2.84259737e-01 2.11274236e-01 -2.39385307e-01 7.76121318e-01
9.11496878e-01 1.21678688e-01 2.94685483e-01 -8.29667449e-01
-1.92032367e-01 1.19462252e+00 -3.19204926e-01 6.85541630e-01
9.28684056e-01 -1.39816597e-01 1.03453887e+00 -1.12829792e+00
1.44142997e+00 1.22112453e+00 6.70836031e-01 3.86068791e-01
-9.08331633e-01 -5.77957332e-01 6.73025995e-02 5.36260068e-01
-1.23111129e+00 -6.98626518e-01 4.22554404e-01 -5.74239910e-01
1.73916996e+00 4.93278444e-01 3.10223967e-01 1.61368263e+00
2.07215101e-01 1.11037779e+00 9.34758365e-01 -6.86107695e-01
1.23827234e-02 5.05629838e-01 4.99537826e-01 1.74000293e-01
-7.84040242e-02 8.88268128e-02 -9.47950125e-01 -2.20943004e-01
-3.74572664e-01 -7.17071235e-01 -2.91516811e-01 3.33462745e-01
-1.35413647e+00 7.97133386e-01 -2.20002055e-01 6.21353447e-01
-3.37882966e-01 -5.10863304e-01 1.20251465e+00 6.45941079e-01
6.08209670e-01 3.84149373e-01 -9.30301726e-01 -3.29878241e-01
-9.70102251e-01 4.70921904e-01 1.15625691e+00 1.07382476e+00
1.64209157e-01 -3.47040474e-01 -5.80525458e-01 9.68190610e-01
4.67789695e-02 4.16441917e-01 7.77082205e-01 -5.22669852e-01
1.19476271e+00 2.47691467e-01 2.85866529e-01 -3.11038882e-01
-7.89056659e-01 -3.19631308e-01 -1.58962891e-01 -4.53071922e-01
4.18366432e-01 -5.06511509e-01 -5.30722618e-01 1.80131555e+00
2.38177419e-01 -4.82624203e-01 5.70645869e-01 6.04133368e-01
1.39522564e+00 6.94733322e-01 9.07959118e-02 -5.02908647e-01
1.75067091e+00 -1.07857943e+00 -7.31787682e-01 -1.98917642e-01
1.03997934e+00 -1.27897310e+00 7.35072553e-01 3.66629422e-01
-1.21576023e+00 -5.28178155e-01 -9.28033948e-01 -9.83618200e-02
-3.99196476e-01 1.89716503e-01 1.02842912e-01 3.99506569e-01
-7.23307252e-01 2.57889152e-01 -7.24390566e-01 -8.70138049e-01
-3.59405428e-01 2.64571477e-02 -4.44019794e-01 8.93956572e-02
-1.90423691e+00 1.10321450e+00 8.70706558e-01 -4.12125945e-01
-7.59402633e-01 -5.40053129e-01 -1.08227324e+00 -7.66241327e-02
3.11546087e-01 -1.26429880e-02 1.55746627e+00 -4.02177393e-01
-9.69639957e-01 1.41495192e+00 3.86962891e-02 -7.48908997e-01
6.75032198e-01 -2.46898219e-01 -9.66591418e-01 -7.46117830e-02
6.12873018e-01 3.31753165e-01 1.84361171e-02 -6.49019241e-01
-9.84791934e-01 -2.47123793e-01 -2.05489665e-01 1.94946095e-01
-1.85630143e-01 8.54579329e-01 -1.47759482e-01 -4.97095555e-01
-4.23817098e-01 -7.83172011e-01 2.41119489e-01 -1.32081950e+00
-6.98919415e-01 -5.86034417e-01 6.68126404e-01 -1.06905723e+00
1.36967278e+00 -2.15942168e+00 -2.21918091e-01 -3.89986724e-01
-4.66424525e-01 2.22826134e-02 -1.86063841e-01 6.88378870e-01
-4.76205200e-01 -1.40796512e-01 2.90237576e-01 -3.12441021e-01
2.07293928e-01 -1.43636778e-01 -4.41517949e-01 9.88310724e-02
2.24977180e-01 6.52558267e-01 -9.94911313e-01 -6.77312732e-01
-9.74084958e-02 -9.73087773e-02 -4.00144875e-01 -1.57394245e-01
-3.93820219e-02 2.69671649e-01 -1.53329179e-01 4.89056855e-01
3.60487968e-01 7.45080784e-02 5.10980308e-01 -1.18078798e-01
-6.79109395e-01 1.05107701e+00 -1.20716083e+00 1.67519021e+00
-3.39307219e-01 7.38539040e-01 -9.68846232e-02 -8.21091592e-01
8.46959054e-01 8.82919967e-01 5.76621354e-01 -6.81738496e-01
-4.98624220e-02 5.29988885e-01 -4.46026623e-02 -7.09851444e-01
6.69271231e-01 1.15566375e-02 -7.59782732e-01 3.73307735e-01
4.90961432e-01 8.14992711e-02 1.00163233e+00 3.82372677e-01
1.18061697e+00 7.00191781e-02 5.80159664e-01 -3.83548051e-01
5.24211824e-01 3.08885187e-01 7.88883090e-01 7.87899852e-01
-2.58530527e-01 4.83789623e-01 4.31665570e-01 -3.33689362e-01
-8.02632630e-01 -9.12455499e-01 -4.47188288e-01 1.36595523e+00
-4.37412471e-01 -8.30766618e-01 -5.93284309e-01 -9.54185605e-01
-3.00796062e-01 1.07048440e+00 -4.29021120e-01 1.38135299e-01
-8.32866251e-01 -9.36694264e-01 1.01725507e+00 3.78713965e-01
5.68921924e-01 -1.49920273e+00 -6.29612744e-01 6.30833685e-01
-1.03328395e+00 -1.30895030e+00 -7.78117776e-01 5.27497172e-01
-2.04827920e-01 -1.26998734e+00 -4.79968607e-01 -9.55360293e-01
-3.28055650e-01 -3.98860872e-01 1.34025657e+00 -7.63505936e-01
-3.65222067e-01 3.33037376e-01 -4.90273267e-01 -5.76916218e-01
-6.24477327e-01 4.19280469e-01 1.12308741e-01 -3.54396790e-01
8.35312903e-01 2.71581858e-02 5.40250763e-02 3.12706530e-02
-4.45378900e-01 -1.80874124e-01 2.93932617e-01 8.82045090e-01
3.17801744e-01 -4.13144857e-01 8.08320224e-01 -8.54045928e-01
4.81245130e-01 -6.77375019e-01 -2.16851458e-01 3.56000543e-01
-1.46802142e-01 -1.41772270e-01 4.73924488e-01 -2.74960488e-01
-1.32100511e+00 -1.81851778e-02 -2.60909915e-01 2.61416376e-01
-3.08420926e-01 7.72445440e-01 -1.17420919e-01 8.27202439e-01
1.03168106e+00 5.60032250e-03 -5.38651705e-01 -6.62603259e-01
-1.30077735e-01 9.18358386e-01 6.28327668e-01 -5.52520514e-01
1.03372857e-01 -9.49167237e-02 -6.67030752e-01 -8.25909793e-01
-1.09806883e+00 -6.09900594e-01 -5.39153218e-01 -1.04120634e-01
1.01645851e+00 -1.06997108e+00 -3.40312332e-01 4.32143897e-01
-1.39165032e+00 -1.84034050e-01 -5.35482645e-01 8.47149074e-01
-4.04615581e-01 1.69474587e-01 -9.68028128e-01 -4.78234529e-01
-5.53260803e-01 -9.13906276e-01 1.08636177e+00 -6.95359930e-02
-5.88834226e-01 -8.66148293e-01 6.97160304e-01 4.62111562e-01
-1.55223325e-01 1.29057094e-01 6.59372151e-01 -1.45163047e+00
3.73713166e-01 -2.70759016e-01 1.36362985e-01 1.62275061e-01
-3.28088343e-01 -2.25694016e-01 -8.33435357e-01 -5.42162299e-01
-5.65671660e-02 -8.65590155e-01 9.89120781e-01 1.36881292e-01
2.54213065e-01 -5.32687157e-02 -4.05199200e-01 -4.50671688e-02
9.02842760e-01 4.00511235e-01 3.25830042e-01 7.12071717e-01
2.98282001e-02 7.26876140e-01 8.89722705e-01 4.65499550e-01
5.71446300e-01 9.98566985e-01 -1.03326902e-01 3.75262618e-01
-2.22762555e-01 1.64493062e-02 1.02930188e+00 9.33673501e-01
1.80630699e-01 -2.99134046e-01 -1.10482681e+00 9.61943626e-01
-1.96748030e+00 -1.30878592e+00 -4.13687676e-01 2.10905123e+00
1.18411744e+00 2.27626592e-01 2.03188509e-01 -3.16694565e-03
8.42041850e-01 3.81702408e-02 -1.62253335e-01 -5.39335787e-01
-4.60109830e-01 2.26601258e-01 -6.65833279e-02 4.64340746e-01
-1.53606498e+00 1.06179631e+00 5.75330067e+00 6.36601627e-01
-8.92384231e-01 4.40308273e-01 2.29581103e-01 -5.61190546e-01
2.99096704e-02 1.05914781e-02 -1.45743167e+00 6.81265295e-01
1.44082260e+00 -3.06448251e-01 -1.51440546e-01 5.16918480e-01
5.33325821e-02 3.97919863e-02 -1.11110985e+00 7.07885981e-01
2.16224447e-01 -1.23243999e+00 -3.08345199e-01 -2.29545966e-01
6.60113275e-01 6.84452891e-01 -5.04624605e-01 1.08536434e+00
4.21945274e-01 -5.41427910e-01 1.01629984e+00 3.38721752e-01
5.59149921e-01 -7.27802932e-01 1.10255039e+00 4.85019624e-01
-1.10625660e+00 4.68290225e-02 -6.32608831e-02 2.85010099e-01
5.15400290e-01 4.09069866e-01 -8.68053079e-01 7.85993576e-01
1.01247311e+00 6.33104503e-01 -4.75238860e-01 8.16445231e-01
-3.45861524e-01 8.01167548e-01 -3.94531250e-01 1.13431001e-02
2.03450650e-01 5.02210557e-01 1.01602077e+00 1.80696106e+00
3.05544347e-01 -2.39327505e-01 4.30755585e-01 2.93883651e-01
-1.20784491e-01 4.59548742e-01 -4.53248560e-01 2.60161251e-01
6.16214633e-01 1.17844772e+00 -2.88041532e-01 -4.05111074e-01
-4.46984529e-01 7.90352643e-01 3.94470811e-01 1.54508531e-01
-7.60248661e-01 -6.05554044e-01 8.95327702e-02 -1.34199470e-01
2.14220583e-01 2.61207283e-01 3.77285898e-01 -1.33906317e+00
7.85221010e-02 -8.22373509e-01 1.18046677e+00 -4.27873045e-01
-1.20737839e+00 9.03921306e-01 9.06242132e-02 -1.06260347e+00
-9.09212112e-01 -5.04534841e-01 -4.01350200e-01 6.86024964e-01
-1.00644970e+00 -1.08424366e+00 2.18135640e-01 7.67661214e-01
7.40417063e-01 -5.14612496e-01 1.01121879e+00 7.76912570e-01
-5.42772651e-01 6.14794970e-01 2.04128072e-01 3.65560919e-01
1.47603965e+00 -1.19156051e+00 1.62491411e-01 7.15578675e-01
4.06463683e-01 6.16076104e-02 7.10594654e-01 -7.27372050e-01
-5.71088314e-01 -1.08664548e+00 1.92414618e+00 -4.73075867e-01
8.10867310e-01 -3.33256811e-01 -4.28573757e-01 1.04962647e+00
4.73123312e-01 -3.74143094e-01 7.29393423e-01 3.35231692e-01
-1.87410235e-01 1.58563673e-01 -9.99709249e-01 2.29854852e-01
7.64689267e-01 -5.56624830e-01 -9.48526084e-01 8.11769426e-01
5.41157842e-01 -6.41571343e-01 -1.08921516e+00 2.78364271e-01
1.25871822e-01 -4.61067736e-01 7.03733206e-01 -1.06715333e+00
4.17743355e-01 1.43803343e-01 -3.42767626e-01 -1.16557920e+00
-9.73022357e-02 -5.87797463e-01 -5.97541220e-02 1.49887037e+00
7.95409858e-01 -4.36147571e-01 1.40127853e-01 9.48554196e-04
-5.09912252e-01 -1.88006684e-01 -1.29440975e+00 -8.80149961e-01
2.88260013e-01 -5.35908043e-01 2.81140339e-02 1.12932944e+00
2.87437588e-01 8.50160182e-01 -2.05076382e-01 -1.15436658e-01
3.28084588e-01 2.54079700e-01 3.39151025e-01 -1.13062024e+00
-3.07362348e-01 -1.76288873e-01 -4.53195050e-02 -4.08361882e-01
2.37308025e-01 -1.38479567e+00 1.06050754e-02 -1.32523406e+00
5.76205134e-01 5.66844791e-02 -2.71569371e-01 6.93820596e-01
-1.16111189e-01 2.14409307e-01 2.43808240e-01 3.09765309e-01
-7.97370672e-01 2.68503606e-01 4.90593016e-01 -1.52090728e-01
-2.83706665e-01 -6.11446938e-03 -4.40982789e-01 7.15972781e-01
6.79292858e-01 -6.92502081e-01 2.57648975e-01 -3.24741036e-01
2.14682072e-01 2.31109828e-01 1.75245032e-01 -9.77396369e-01
2.02862635e-01 1.02842771e-01 2.07710162e-01 -9.89373207e-01
5.47539145e-02 -2.02134550e-01 -2.29916498e-02 5.08952856e-01
-3.90050530e-01 3.93680185e-02 4.30627316e-01 8.71506780e-02
-5.41714191e-01 -5.30514181e-01 5.95294058e-01 -1.79537222e-01
-8.07897508e-01 -3.51975173e-01 -8.16930056e-01 6.89230382e-01
1.22963619e+00 3.27289581e-01 -5.16492784e-01 -6.93494305e-02
-1.50609207e+00 3.39408994e-01 -2.49348283e-01 6.73062623e-01
1.33820713e-01 -1.31052661e+00 -1.42848766e+00 -1.64652407e-01
2.10230306e-01 -5.79066932e-01 2.97306478e-01 1.07702756e+00
-1.17969163e-01 8.88830483e-01 -3.51630850e-03 -3.31830025e-01
-1.67498553e+00 2.47772485e-01 1.98770761e-01 -1.17650568e+00
-2.99062103e-01 6.73364460e-01 -2.31860682e-01 -8.65464568e-01
-7.89705943e-03 2.11350992e-01 -4.63419467e-01 6.76592648e-01
5.63771129e-01 3.20174098e-01 6.48895502e-01 -6.48664832e-01
-8.46168637e-01 -9.39398855e-02 -4.85351503e-01 -4.96730208e-01
1.22916138e+00 5.22239618e-02 4.53409292e-02 6.14763081e-01
9.64600682e-01 2.95442462e-01 -4.03259546e-01 -1.68838426e-01
5.19556701e-01 5.13899565e-01 -3.43555719e-01 -1.33769608e+00
-5.04636824e-01 6.34117901e-01 2.45151058e-01 2.72946447e-01
7.61976302e-01 4.11509186e-01 6.98368907e-01 4.85335231e-01
5.26610732e-01 -1.51375329e+00 -1.97580084e-01 1.33947992e+00
1.08180070e+00 -1.04601371e+00 -4.57867742e-01 2.71528997e-02
-7.39818394e-01 9.76149857e-01 5.31695426e-01 2.20798656e-01
5.33849061e-01 3.20483953e-01 -8.52046385e-02 -1.70674935e-01
-1.11378193e+00 -7.20386878e-02 3.66393715e-01 5.33902235e-02
9.18728888e-01 4.93139327e-02 -8.08549702e-01 1.28073251e+00
-5.14457583e-01 -2.43663087e-01 3.30631047e-01 1.12428784e+00
-2.06844717e-01 -1.07105112e+00 -3.63007188e-01 3.49041849e-01
-7.53821790e-01 -3.79489690e-01 -6.73783004e-01 1.18645477e+00
1.67920336e-01 1.01817501e+00 6.52854815e-02 -2.04727098e-01
7.47030854e-01 6.11116648e-01 3.81734431e-01 -8.58978927e-01
-1.16205609e+00 -1.80235859e-02 9.94795918e-01 -4.50366050e-01
-2.53690213e-01 -1.33104777e+00 -1.25368989e+00 6.16869284e-03
-1.26002654e-01 5.77505946e-01 3.66877049e-01 1.04204774e+00
2.91421384e-01 5.00101984e-01 3.43260288e-01 -3.41197759e-01
-6.84651554e-01 -1.57830083e+00 -4.26341891e-01 4.04752254e-01
-1.68398336e-01 -3.87544423e-01 -2.59677202e-01 9.08119902e-02] | [9.140772819519043, 9.719822883605957] |
264adccf-3df6-4367-b834-1c858d211ba4 | template-free-prompt-tuning-for-few-shot-ner | 2109.13532 | null | https://arxiv.org/abs/2109.13532v3 | https://arxiv.org/pdf/2109.13532v3.pdf | Template-free Prompt Tuning for Few-shot NER | Prompt-based methods have been successfully applied in sentence-level few-shot learning tasks, mostly owing to the sophisticated design of templates and label words. However, when applied to token-level labeling tasks such as NER, it would be time-consuming to enumerate the template queries over all potential entity spans. In this work, we propose a more elegant method to reformulate NER tasks as LM problems without any templates. Specifically, we discard the template construction process while maintaining the word prediction paradigm of pre-training models to predict a class-related pivot word (or label word) at the entity position. Meanwhile, we also explore principled ways to automatically search for appropriate label words that the pre-trained models can easily adapt to. While avoiding complicated template-based process, the proposed LM objective also reduces the gap between different objectives used in pre-training and fine-tuning, thus it can better benefit the few-shot performance. Experimental results demonstrate the effectiveness of the proposed method over bert-tagger and template-based method under few-shot setting. Moreover, the decoding speed of the proposed method is up to 1930.12 times faster than the template-based method. | ['Xuanjing Huang', 'Qi Zhang', 'Linyang Li', 'Yiding Tan', 'Tao Gui', 'Xin Zhou', 'Ruotian Ma'] | 2021-09-28 | null | https://aclanthology.org/2022.naacl-main.420 | https://aclanthology.org/2022.naacl-main.420.pdf | naacl-2022-7 | ['few-shot-ner'] | ['natural-language-processing'] | [ 2.76299149e-01 7.24787712e-02 -2.02548541e-02 -3.27976555e-01
-1.05602801e+00 -4.49894756e-01 4.51744139e-01 2.18835607e-01
-9.82565939e-01 9.07402039e-01 2.05667838e-01 -3.60321552e-01
-1.21178657e-01 -8.08252871e-01 -3.56264770e-01 -8.34400296e-01
3.30632657e-01 3.93069178e-01 3.94333065e-01 -1.13976099e-01
2.26413861e-01 9.78853330e-02 -1.40194654e+00 -2.52854884e-01
9.69961584e-01 6.92991495e-01 7.94791400e-01 3.18059027e-01
-4.46212858e-01 3.69169652e-01 -4.80309069e-01 -4.97595161e-01
-3.74109223e-02 -4.52890784e-01 -8.14769804e-01 -6.78950772e-02
1.87817991e-01 1.14636511e-01 -2.69929051e-01 1.00285041e+00
9.60339487e-01 8.03007126e-01 6.24590814e-01 -4.35516417e-01
-2.85249829e-01 6.74209535e-01 -3.81261766e-01 3.93244326e-01
2.42096428e-02 -8.09627399e-03 1.23461282e+00 -1.06405330e+00
5.79454124e-01 8.57662857e-01 4.01487619e-01 7.01613605e-01
-7.25674689e-01 -4.79431540e-01 1.49964720e-01 4.66171950e-01
-1.53311408e+00 -7.10483968e-01 5.15899360e-01 -2.04210475e-01
1.04196620e+00 2.19235152e-01 1.54772371e-01 7.94795334e-01
-1.64080918e-01 6.58527017e-01 8.17977786e-01 -7.99147189e-01
3.08191925e-01 1.27705187e-01 3.36553901e-01 6.85115635e-01
1.09116003e-01 -4.24659550e-01 -3.14981848e-01 -2.43729986e-02
2.12702468e-01 -1.00173794e-01 -1.86934382e-01 1.76761299e-01
-1.03405774e+00 7.28193581e-01 -1.75430492e-01 6.54282868e-01
-3.70586306e-01 -1.20507434e-01 5.73514521e-01 -1.45816192e-01
3.87788862e-01 4.30755228e-01 -6.17159426e-01 -3.18840802e-01
-1.19943869e+00 -1.65740326e-01 5.96039355e-01 1.28440821e+00
8.96444976e-01 1.37501080e-02 -7.93493927e-01 1.13529646e+00
7.73149803e-02 2.01841027e-01 7.19054163e-01 -4.05202240e-01
5.97202003e-01 1.07115552e-01 1.61108524e-01 -7.06916153e-01
-4.13556486e-01 -6.77264571e-01 -7.31036782e-01 -6.84779167e-01
2.22563192e-01 -4.59906667e-01 -9.13344562e-01 1.61159205e+00
4.36878204e-01 4.03150499e-01 -5.81317507e-02 5.86857915e-01
6.25990272e-01 9.58007038e-01 3.87449294e-01 -7.09780991e-01
1.63218701e+00 -1.14472747e+00 -1.01170981e+00 -4.59067851e-01
1.10944366e+00 -8.44357669e-01 1.15147579e+00 7.56508783e-02
-9.74895835e-01 -5.49888730e-01 -9.44429576e-01 -7.25486083e-03
-3.39602113e-01 1.79632172e-01 4.37787265e-01 6.05069697e-01
-5.20708203e-01 6.72905862e-01 -4.35957789e-01 -4.95193481e-01
8.07155669e-02 2.53662944e-01 -3.65904495e-02 -7.26338625e-02
-1.66457188e+00 1.06344855e+00 7.70458102e-01 1.38053671e-01
-5.60322225e-01 -4.55399960e-01 -8.02974463e-01 4.75374132e-01
6.89415753e-01 -2.99939752e-01 1.40353847e+00 -1.47943661e-01
-1.43586493e+00 5.46919107e-01 -5.66877067e-01 -2.11655006e-01
6.17160313e-02 1.34615246e-02 -4.64599729e-01 -1.40028507e-01
1.36159956e-01 4.00226891e-01 5.19059002e-01 -6.79817438e-01
-9.34789002e-01 1.52562529e-01 -1.67135999e-03 3.69444728e-01
-7.27246344e-01 1.04508944e-01 -6.28305972e-01 -5.04100204e-01
-6.54990375e-02 -5.53609967e-01 -3.37064624e-01 -6.49626911e-01
-3.30983520e-01 -6.71851099e-01 3.48554164e-01 -5.38259506e-01
1.75404882e+00 -2.06912017e+00 -2.44453385e-01 -1.64723784e-01
-2.67543972e-01 6.31294668e-01 -1.89240649e-01 6.40599430e-01
3.12523782e-01 2.52239823e-01 -1.14879087e-01 -4.57867026e-01
1.53874114e-01 1.67341202e-01 -2.58274287e-01 2.86705971e-01
2.59442814e-02 7.99612105e-01 -1.07058275e+00 -9.86269534e-01
2.42489561e-01 9.49311405e-02 -2.43902668e-01 2.17797846e-01
-1.48006037e-01 7.11361468e-02 -4.31252927e-01 4.59829062e-01
4.94883597e-01 -3.89074311e-02 1.20165840e-01 -1.63487718e-01
-1.98189765e-01 6.38122559e-01 -1.21370065e+00 1.80522943e+00
-8.83220792e-01 2.06509426e-01 -3.96234959e-01 -9.81092453e-01
9.91141379e-01 6.15296543e-01 1.36686802e-01 -6.72678888e-01
2.67521292e-01 1.15736231e-01 -5.92337623e-02 -7.32215762e-01
6.42531216e-01 -4.48362172e-01 -2.70423561e-01 3.30994099e-01
3.70935440e-01 3.41080725e-01 5.49671531e-01 -6.92506582e-02
1.00696003e+00 1.24746911e-01 5.79426050e-01 -1.33535177e-01
7.23073125e-01 -1.70424551e-01 9.55551505e-01 8.01886857e-01
-1.38688877e-01 2.83911228e-01 -2.80653927e-02 -1.03538655e-01
-9.71056044e-01 -5.05750418e-01 -2.26253256e-01 1.38953507e+00
1.94181338e-01 -4.75830197e-01 -7.12883770e-01 -6.95159495e-01
-6.32414997e-01 1.09743333e+00 -2.11022601e-01 -3.34562659e-02
-7.05025792e-01 -8.01528394e-01 6.73676312e-01 3.32399696e-01
2.78160870e-01 -1.12104571e+00 -3.92220438e-01 6.77955270e-01
-3.57118040e-01 -1.05595696e+00 -8.60057712e-01 3.85494113e-01
-7.86556304e-01 -5.25286555e-01 -7.88625062e-01 -1.15046382e+00
6.43822491e-01 3.40847135e-01 7.50714242e-01 1.68945789e-01
-1.20646983e-01 -2.38998577e-01 -6.97314858e-01 -1.59806594e-01
5.44675924e-02 5.07874191e-01 5.14696948e-02 5.93598280e-03
5.66942215e-01 -4.24049705e-01 -4.91559058e-01 2.36311108e-01
-7.28635848e-01 4.93423678e-02 6.43413007e-01 1.08056760e+00
5.35626948e-01 1.33230120e-01 8.19695652e-01 -1.00738728e+00
5.45844197e-01 -4.87181515e-01 -3.92069310e-01 6.93547249e-01
-7.28399396e-01 2.58203775e-01 8.94302964e-01 -6.24064803e-01
-1.27565384e+00 1.56781763e-01 -3.77427042e-01 8.96075070e-02
-1.33830205e-01 5.51577747e-01 -3.20687383e-01 2.34216288e-01
3.80922645e-01 4.84485060e-01 -7.90713191e-01 -7.98575759e-01
4.37880069e-01 8.85162294e-01 3.52595150e-01 -6.55789435e-01
8.67544770e-01 -1.83744416e-01 -2.50231415e-01 -7.22213864e-01
-1.08867335e+00 -7.93356121e-01 -6.39229834e-01 -8.33183601e-02
8.31573308e-01 -7.48074532e-01 -4.36386794e-01 -9.28042736e-03
-1.45048451e+00 1.80868819e-01 -1.35126084e-01 5.17693281e-01
-2.84279317e-01 6.28656447e-01 -5.15324056e-01 -1.00059235e+00
-7.39434958e-01 -8.64468515e-01 7.93433428e-01 5.43702006e-01
-9.04512852e-02 -9.30695713e-01 2.85352077e-02 3.78223509e-02
3.02833825e-01 -4.37646180e-01 1.08536756e+00 -1.10149944e+00
-2.34492838e-01 -3.16663235e-01 -9.28010121e-02 -4.35632467e-02
1.69223901e-02 -5.53803146e-01 -1.03080213e+00 -1.92510128e-01
2.13021692e-02 -2.64391482e-01 6.79283023e-01 8.24787244e-02
9.49375510e-01 -3.81925851e-01 -2.99678475e-01 4.12468165e-01
1.54129839e+00 4.56593633e-01 4.77912515e-01 4.18862432e-01
5.17098188e-01 6.33420289e-01 1.09437764e+00 5.78694046e-01
2.19528362e-01 8.01936030e-01 -9.18713436e-02 4.62800302e-02
1.20195307e-01 -4.21827614e-01 4.14819233e-02 1.40215218e+00
4.06066962e-02 -5.27673185e-01 -7.10172296e-01 6.61490500e-01
-1.84250665e+00 -9.68342721e-01 1.12921730e-01 2.20433068e+00
1.11563218e+00 2.09079593e-01 -2.41590977e-01 -2.86336448e-02
1.04602623e+00 2.96638548e-01 -3.43612403e-01 -3.59401613e-01
8.53972510e-02 4.65212137e-01 5.38112223e-01 5.28297603e-01
-1.05789304e+00 1.33335936e+00 5.29152870e+00 1.61896360e+00
-7.36619592e-01 4.70719188e-01 3.65440875e-01 -2.21319031e-02
-1.84848949e-01 3.20405096e-01 -1.41425478e+00 5.17275274e-01
1.03558993e+00 -5.21300375e-01 2.71746904e-01 7.03745008e-01
4.69843477e-01 -5.64967804e-02 -9.15964365e-01 8.37901831e-01
2.45432686e-02 -1.14361584e+00 -1.09145753e-01 -1.50270209e-01
5.99944711e-01 -3.37922394e-01 -4.85645950e-01 7.14499891e-01
-8.26659352e-02 -6.86315656e-01 5.31508029e-01 3.59725684e-01
7.55973577e-01 -7.47613251e-01 8.16665769e-01 7.59349585e-01
-1.29007614e+00 1.10586025e-01 -8.83572459e-01 -1.15517758e-01
5.04623652e-01 6.19409561e-01 -9.11689639e-01 6.01948321e-01
1.44769773e-01 2.37919167e-01 -2.17107505e-01 1.28125501e+00
-4.38172787e-01 7.22989678e-01 -9.77579281e-02 -4.64848340e-01
4.83384460e-01 -4.22320962e-02 3.08970064e-01 1.56206536e+00
5.79113126e-01 3.30263197e-01 1.47074029e-01 3.43450129e-01
-9.93575677e-02 6.04807258e-01 -2.29452372e-01 -8.49526748e-02
1.04846108e+00 1.60741115e+00 -7.46958315e-01 -4.08893853e-01
-4.23682600e-01 9.41315591e-01 4.18485761e-01 2.64928967e-01
-8.73760521e-01 -9.30901110e-01 9.69224423e-02 -1.89761341e-01
5.16372740e-01 -2.20808893e-01 -1.58455838e-02 -1.07508731e+00
-5.85724190e-02 -4.86046970e-01 2.53814787e-01 -4.83037829e-01
-1.09980977e+00 7.20133603e-01 -1.81346983e-01 -1.31054151e+00
-2.17221469e-01 -2.15828329e-01 -8.92328441e-01 9.48838651e-01
-1.51498950e+00 -8.82982910e-01 1.24628551e-01 1.83405727e-01
1.06061995e+00 1.62173539e-01 9.06162798e-01 5.64372420e-01
-8.51155937e-01 8.26686680e-01 2.60356665e-01 5.52502796e-02
8.22684348e-01 -9.95967984e-01 2.58945584e-01 1.09059823e+00
2.33667716e-01 6.65876687e-01 6.90369427e-01 -5.76925337e-01
-1.11992598e+00 -9.93913651e-01 1.61781001e+00 1.45297244e-01
5.37799835e-01 -3.77499431e-01 -8.45140338e-01 3.16625595e-01
2.86547542e-01 -2.47243792e-01 7.41099119e-01 2.52979696e-01
1.29995227e-01 -1.07965916e-01 -8.19961667e-01 6.23491526e-01
9.36337531e-01 -3.49557817e-01 -8.94007564e-01 4.88870561e-01
9.62489724e-01 -2.29565755e-01 -7.03772485e-01 2.51390010e-01
2.51118392e-01 -3.01067472e-01 6.60247803e-01 -4.81854886e-01
9.00671259e-02 -3.14240903e-01 -3.04911938e-02 -1.19628680e+00
-6.18945718e-01 -6.84994698e-01 -1.46081552e-01 1.82904613e+00
5.84168375e-01 -2.92205662e-01 5.18365979e-01 6.19328201e-01
-2.78737187e-01 -9.03540194e-01 -1.10139632e+00 -9.70559537e-01
-1.68418378e-01 -1.40124708e-01 5.54435730e-01 8.46167743e-01
2.73131698e-01 5.83811104e-01 -7.34340489e-01 4.97265011e-02
3.31603557e-01 -1.46383464e-01 2.29477793e-01 -9.18364584e-01
-4.67930287e-01 -1.81836635e-01 -8.55569635e-03 -1.34735644e+00
1.06126361e-01 -9.11548078e-01 7.38962293e-01 -1.51949656e+00
2.54127681e-01 -6.20099008e-01 -6.69116795e-01 6.44540012e-01
-6.36289477e-01 -1.59288883e-01 2.73987412e-01 1.73539296e-01
-8.53125751e-01 6.58650100e-01 1.03285277e+00 -4.45796326e-02
-1.09914429e-01 -3.37100439e-02 -4.93008941e-01 4.73165512e-01
7.24339783e-01 -8.93015027e-01 -3.52930963e-01 -4.67703640e-01
8.63433406e-02 1.03122279e-01 -3.27978969e-01 -8.21266532e-01
6.64876878e-01 -2.56716967e-01 -3.75143699e-02 -5.70869982e-01
1.90478086e-01 -5.68472207e-01 -1.86836332e-01 1.81120306e-01
-3.58299226e-01 -1.55202225e-01 9.44559369e-03 5.25451720e-01
-3.53740901e-02 -1.22193754e+00 5.79665601e-01 -1.68361679e-01
-9.78005588e-01 3.96198094e-01 -3.83818775e-01 2.99149334e-01
9.27036643e-01 -1.09256409e-01 -1.47660539e-01 -7.84354061e-02
-4.88830417e-01 2.83143729e-01 4.74086776e-02 3.29293698e-01
3.29100639e-01 -1.15660322e+00 -4.18707967e-01 -1.68702587e-01
1.17261931e-01 -3.90879298e-03 4.40856308e-01 8.14565539e-01
-5.74983954e-02 6.01239085e-01 2.63950437e-01 -1.07971221e-01
-1.05226469e+00 6.29921198e-01 -9.91781205e-02 -5.31318545e-01
-5.99665880e-01 8.70025754e-01 -3.83907221e-02 -2.32158080e-01
4.66385394e-01 2.09439352e-01 -4.09573853e-01 2.90780216e-01
6.10145688e-01 4.01836932e-01 1.38234541e-01 -4.35976982e-01
-3.71947348e-01 5.95308661e-01 -2.42259532e-01 -1.80748940e-01
1.18807054e+00 -4.67306703e-01 1.26108199e-01 4.32862014e-01
9.68782485e-01 1.17838392e-02 -8.19446921e-01 -5.16967118e-01
5.35036504e-01 -1.63616806e-01 1.18196040e-01 -5.65199494e-01
-5.57664037e-01 1.07643902e+00 3.24039876e-01 -6.19377531e-02
1.01613200e+00 -1.96173579e-01 1.27560377e+00 6.00408018e-01
4.64231014e-01 -1.44091964e+00 -3.61634880e-01 7.25473642e-01
5.82266822e-02 -1.04183221e+00 -2.41296723e-01 -3.43196422e-01
-4.09088254e-01 1.09760618e+00 6.77959323e-01 2.88044304e-01
3.36995393e-01 -3.48411426e-02 -1.37396082e-01 1.76738262e-01
-8.36103737e-01 -5.26524484e-01 2.80468673e-01 2.57916987e-01
5.59632301e-01 -1.35302335e-01 -1.08207381e+00 8.37598205e-01
6.10274784e-02 -5.04222475e-02 3.22507173e-01 8.79716992e-01
-1.01910019e+00 -1.35219216e+00 2.35357247e-02 3.85652483e-01
-5.01854360e-01 -6.04844511e-01 2.95025319e-01 3.73557150e-01
3.24488640e-01 1.13696623e+00 -1.61927223e-01 -3.80418837e-01
2.32284963e-01 5.88228226e-01 1.83814839e-01 -9.76963282e-01
-3.72550905e-01 3.05885941e-01 3.55701834e-01 6.64124191e-02
-2.85445184e-01 -4.26584512e-01 -1.38458431e+00 7.11699650e-02
-9.39413786e-01 5.88575900e-01 4.75424588e-01 1.39167070e+00
1.50077686e-01 3.96691680e-01 7.15921402e-01 -5.90775073e-01
-7.37115860e-01 -1.33995247e+00 -5.04275262e-01 1.45126656e-01
-2.48162612e-01 -6.47820175e-01 -2.64061153e-01 -1.17626749e-01] | [9.741789817810059, 9.416905403137207] |
ccc914cf-0366-442b-ae94-22dd06b9956e | the-brain-tumor-segmentation-brats-challenge | 2305.09011 | null | https://arxiv.org/abs/2305.09011v5 | https://arxiv.org/pdf/2305.09011v5.pdf | The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn) | Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions. | ['Dominic LaBella', 'Maruf Adewole', 'Jeff Rudie', 'Evan Calabrese', 'Byrone Cole', 'Maria Diaz', 'Syed Muhammad Anwar', 'Jan Kirschke', 'James Eddy', 'Keyvan Farahani', 'Ahmed W. Moawad', 'Anahita Fathi Kazerooni', 'Anastasia Janas', 'Verena Chung', 'Benedikt Wiestler', 'Juan Eugenio Iglesias', 'Bjoern Menze', 'Marius George Linguraru', 'Spyridon Bakas', 'Ujjwal Baid', 'Thomas Yu', 'Mariam Aboian', 'Udunna Anazodo', 'Jake Albrecht', 'Andras Jakab', 'Priscila Crivellaro', 'Meyer Errol Colak', 'Javier Villanueva', 'Soonmee Cha', 'Christopher Hess', 'John Mongan', 'Suyash Mohan', 'Abhishek Mahajan', 'Marc André Weber', 'Arash Nazeri', 'Mikhail Milchenko', 'Daniel Marcus', 'Pamela Lamontagne', 'Aikaterini Kotrotsou', 'Rivka R. Colen', 'Roland Wiest', 'Hassan M. Fathallah-Shaykh', 'Michel Bilello', 'Justin Kirby', 'John Freymann', 'Christos Davatzikos', 'Zeke Meier', 'Elaine Johanson', 'Ariana Familiar', 'Zhifan Jiang', 'Xinyang Liu', 'Chunhao Wang', 'Zachary Reitman', 'Walter Wiggins', 'Farouk Dako', 'Russell Takeshi Shinohara', 'Timothy Bergquist', 'Felix Meissen', 'Ivan Ezhov', 'Marie Piraud', 'Koen van Leemput', 'Florian Kofler', 'Gian Marco Conte', 'Hongwei Bran Li'] | 2023-05-15 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical'] | [ 7.58866668e-01 3.03871315e-02 1.18625462e-01 -3.88402760e-01
-9.42436397e-01 -3.77477318e-01 6.98361516e-01 2.92005902e-03
-6.70323133e-01 8.04141343e-01 3.26355010e-01 -4.71395791e-01
-2.29912415e-01 -4.08959389e-01 -2.53768057e-01 -6.22167766e-01
-5.64107001e-02 8.64144206e-01 3.40436786e-01 5.32132797e-02
7.86176249e-02 5.86484432e-01 -1.23389518e+00 3.93921405e-01
1.06315315e+00 7.84262359e-01 6.06292844e-01 6.88049912e-01
-5.04268520e-02 6.91317141e-01 -1.64327517e-01 -2.22483240e-02
2.48268053e-01 -5.64663589e-01 -1.03145766e+00 3.32811236e-01
2.08260119e-01 -3.13167602e-01 -1.28450125e-01 1.08603489e+00
7.07014322e-01 1.18468828e-01 5.74126422e-01 -1.11982632e+00
-1.04370177e-01 7.20727265e-01 -4.87481058e-01 6.43568277e-01
-3.04931160e-02 5.01166999e-01 1.31104127e-01 -6.76320374e-01
9.70650256e-01 5.13975680e-01 4.37236905e-01 6.65551424e-01
-1.15788782e+00 -4.45478529e-01 -3.50842118e-01 3.70431632e-01
-9.47697759e-01 -3.86476845e-01 2.98721611e-01 -6.39937162e-01
8.58798325e-01 3.91867042e-01 7.81495988e-01 7.73571908e-01
4.18626517e-01 6.70417309e-01 1.45357442e+00 -3.60626429e-01
1.89234957e-01 -3.81117642e-01 1.33549068e-02 6.31609976e-01
2.04767466e-01 1.98676243e-01 -1.77005425e-01 -5.45290811e-03
5.32079637e-01 -3.47787663e-02 -6.50056779e-01 -2.58047700e-01
-1.74886012e+00 5.27795732e-01 4.62946147e-01 8.40974629e-01
-7.43322730e-01 2.65358686e-02 4.71904337e-01 -6.52213767e-02
2.89204836e-01 3.37636799e-01 6.60284907e-02 -2.14865133e-01
-1.54111016e+00 1.55396938e-01 1.06531762e-01 6.09000146e-01
3.31622027e-02 4.58907001e-02 -1.47289813e-01 8.44259202e-01
1.29001692e-01 2.97880530e-01 1.15841949e+00 -9.16081131e-01
-7.71318153e-02 3.01710755e-01 -2.32224196e-01 -4.52271819e-01
-7.57545590e-01 -3.86558414e-01 -8.51070702e-01 3.61629277e-01
5.59459269e-01 -1.22322142e-02 -1.44239914e+00 1.27277684e+00
2.44099140e-01 2.44210184e-01 -6.63447529e-02 1.01966345e+00
8.63151133e-01 -1.66045092e-02 2.31589943e-01 -3.80439758e-01
1.24504471e+00 -1.07094693e+00 -7.59061277e-01 -9.16053504e-02
9.02452230e-01 -8.01290452e-01 7.20783293e-01 3.10732573e-01
-1.53218782e+00 -4.98605426e-04 -8.86157870e-01 3.21639001e-01
-3.18085015e-01 -2.94034541e-01 5.47002673e-01 7.29795456e-01
-1.19191265e+00 3.00251514e-01 -1.29760456e+00 -1.64180100e-01
5.08817017e-01 5.38173318e-01 -5.55823386e-01 -3.66016507e-01
-9.63563144e-01 1.39020514e+00 4.46451128e-01 3.55886221e-02
-8.57497573e-01 -1.31325221e+00 -6.18554413e-01 -5.47898889e-01
1.76838264e-01 -6.72933817e-01 1.40230060e+00 -8.69771183e-01
-1.06618989e+00 1.21122921e+00 -1.31656200e-01 -6.52701378e-01
9.83358383e-01 5.61124086e-01 -4.83746171e-01 4.98590738e-01
1.20453134e-01 1.03984630e+00 6.03863478e-01 -1.08650970e+00
-5.86254895e-01 -6.66107774e-01 -5.40786147e-01 2.24564686e-01
4.03219283e-01 1.93580076e-01 -2.43862435e-01 -4.91560072e-01
1.55232444e-01 -1.12741411e+00 -4.71439332e-01 -3.13544154e-01
-3.25538754e-01 5.59815109e-01 8.27898026e-01 -1.17266154e+00
7.91003585e-01 -1.77480519e+00 1.39262190e-03 2.33073890e-01
5.07433355e-01 1.96837485e-01 3.16861086e-02 -3.93027306e-01
-4.65439945e-01 -2.30267248e-03 -7.77666032e-01 7.02230856e-02
-3.76355588e-01 -1.12999611e-01 2.58523256e-01 5.69599330e-01
-2.67069280e-01 1.16095352e+00 -8.33801627e-01 -6.48167431e-01
5.68750262e-01 4.22183335e-01 -3.01634818e-01 -9.71126482e-02
8.50804895e-03 1.18693578e+00 -4.08396758e-02 6.06562793e-01
5.48585296e-01 -1.62967950e-01 9.54233781e-02 -3.01651865e-01
-2.21187025e-01 -2.07201064e-01 -6.60900414e-01 1.83623755e+00
-2.82791525e-01 4.47927713e-01 3.77881795e-01 -9.32391405e-01
3.93388510e-01 6.44127667e-01 1.27960992e+00 -8.89533222e-01
3.61960769e-01 5.36431491e-01 6.54240310e-01 -4.94185865e-01
1.47902682e-01 -3.20024878e-01 6.75811052e-01 5.31300783e-01
-1.76771358e-01 -6.59244180e-01 4.79285002e-01 7.65348896e-02
1.28217959e+00 -1.49945334e-01 -8.34281817e-02 -3.77262682e-01
5.01114130e-01 4.77215379e-01 2.21747875e-01 3.73046696e-01
-7.39916444e-01 9.01631236e-01 4.67965119e-02 -3.31844360e-01
-1.23459852e+00 -9.94677663e-01 -4.36344713e-01 3.61027032e-01
-2.57136583e-01 1.33631408e-01 -1.07543004e+00 -5.58456302e-01
-5.09225130e-01 7.84601033e-01 -7.51025200e-01 3.61604728e-02
-5.69093347e-01 -1.15048981e+00 4.98371214e-01 2.08052963e-01
3.73055607e-01 -1.20531952e+00 -1.20007241e+00 5.67981839e-01
-5.87450206e-01 -1.20718896e+00 -4.89403993e-01 5.82828000e-02
-9.68945801e-01 -1.15379214e+00 -1.44365108e+00 -6.04219079e-01
8.19788396e-01 6.81465343e-02 1.01245201e+00 1.33056358e-01
-8.09375525e-01 5.28060198e-01 -2.01255947e-01 -3.33398312e-01
-6.46671593e-01 9.47868600e-02 -2.38231927e-01 -2.50052720e-01
2.78393291e-02 -4.11327869e-01 -8.09521973e-01 2.31310919e-01
-1.44166374e+00 6.28559470e-01 5.48384488e-01 9.15116429e-01
7.64765561e-01 -2.56464809e-01 3.78485382e-01 -8.34353328e-01
7.77503729e-01 -3.53443116e-01 -3.98419023e-01 4.47583169e-01
-4.66373295e-01 -1.32661432e-01 1.33648530e-01 -2.95510888e-01
-1.01763749e+00 1.07605897e-01 -2.33414203e-01 -2.37152532e-01
-4.02459323e-01 6.54184818e-01 2.98021495e-01 -3.38744402e-01
6.21445775e-01 4.51891571e-01 3.14377576e-01 1.84064731e-01
1.42665625e-01 3.86179328e-01 8.37414980e-01 -3.34538847e-01
2.62244344e-01 6.20734692e-01 1.33360222e-01 -7.42104948e-01
-3.57745677e-01 -2.41701066e-01 -8.57697189e-01 -6.19294286e-01
1.02743483e+00 -3.47075552e-01 -1.03621557e-01 5.54291904e-01
-7.91022003e-01 -5.90983689e-01 -1.70957446e-01 6.23894036e-01
-7.28775799e-01 3.32450479e-01 -4.33598340e-01 -1.23249732e-01
-7.08006382e-01 -2.27753854e+00 6.64255619e-01 2.20359519e-01
-2.18848199e-01 -1.13070798e+00 -9.81230065e-02 6.17662549e-01
9.67725515e-01 6.18235767e-01 9.46408391e-01 -4.59615022e-01
-3.09413642e-01 -3.90937971e-03 -2.26957291e-01 -1.39220413e-02
2.99664736e-01 -1.13404743e-01 -6.35503948e-01 -1.90989062e-01
-1.75302431e-01 -2.06125036e-01 5.54737747e-01 1.04514492e+00
1.16883230e+00 4.87785131e-01 -1.74701497e-01 6.39604568e-01
1.18440938e+00 5.20918846e-01 6.37304127e-01 3.90119106e-01
4.54345137e-01 7.70974755e-01 1.57926366e-01 3.39874960e-02
3.07288289e-01 5.36496878e-01 4.13530290e-01 -3.00824255e-01
-4.85530466e-01 3.95945460e-01 -2.50377774e-01 8.25452328e-01
-2.05434993e-01 3.84021193e-01 -1.55908716e+00 7.02512383e-01
-1.41316986e+00 -7.92961597e-01 -4.25898761e-01 2.09044027e+00
8.52697432e-01 -2.75937878e-02 4.21651527e-02 1.03560336e-01
6.78050578e-01 -3.68855894e-01 -7.53206789e-01 2.38964818e-02
-5.38821518e-02 2.54504710e-01 6.19855404e-01 4.76143926e-01
-6.92836285e-01 6.26680255e-01 6.88215017e+00 5.60702205e-01
-1.54275537e+00 5.04484653e-01 9.90309536e-01 -9.39778686e-02
-2.92653620e-01 -3.92435700e-01 -1.32741943e-01 5.08682966e-01
1.13576758e+00 -3.49327743e-01 5.99707186e-01 3.33593041e-01
4.05100137e-01 -5.56922495e-01 -8.02522242e-01 9.18358028e-01
1.65967233e-02 -1.52915752e+00 -3.91498327e-01 2.34798014e-01
7.69511521e-01 4.77260798e-01 2.30723783e-01 -4.23960350e-02
2.62131751e-01 -1.31273293e+00 6.31128132e-01 4.49352652e-01
1.13926876e+00 -5.78094065e-01 5.16865313e-01 1.45599350e-01
-7.70010173e-01 3.50070119e-01 2.17315599e-01 7.83365667e-01
4.73511100e-01 5.83544493e-01 -1.09858191e+00 4.80760396e-01
5.66810250e-01 2.85120249e-01 -5.16916215e-01 1.43038750e+00
1.42018765e-01 4.78312105e-01 -2.12129116e-01 6.13675416e-01
4.12417173e-01 -2.76979029e-01 4.21092927e-01 1.06032169e+00
2.81513602e-01 2.66792983e-01 8.60970691e-02 7.06056356e-01
1.76518708e-01 7.08369762e-02 -3.35806042e-01 2.69676037e-02
-1.61785278e-02 1.47459137e+00 -1.35377812e+00 -4.69921142e-01
-4.71474797e-01 7.85978854e-01 -1.02003932e-01 1.22678265e-01
-7.31793046e-01 2.09587887e-01 1.30577892e-01 3.51709634e-01
-3.15795332e-01 -1.83642924e-01 -5.71132541e-01 -9.29520726e-01
-5.07935166e-01 -1.08467090e+00 2.94298798e-01 -8.42879236e-01
-7.72628307e-01 8.95374775e-01 1.74723014e-01 -7.73631573e-01
-3.84204358e-01 -5.30912042e-01 -4.99110132e-01 1.04427052e+00
-1.48120487e+00 -1.22580481e+00 -5.87193608e-01 7.40784764e-01
4.14833486e-01 -1.09129317e-01 6.88608706e-01 5.04963100e-01
-3.83539051e-01 2.24480465e-01 -5.69450632e-02 -1.90720990e-01
6.00802541e-01 -1.13038647e+00 -4.76529822e-02 7.71901965e-01
-3.28549415e-01 2.23493367e-01 6.13510907e-01 -7.17758536e-01
-9.61706042e-01 -9.60829735e-01 4.36212450e-01 -1.47614732e-01
6.81458712e-01 4.55602288e-01 -7.38380134e-01 6.98125541e-01
2.98881739e-01 2.40074515e-01 7.58529723e-01 -9.64140773e-01
4.24399287e-01 4.08065557e-01 -1.66299248e+00 5.80725610e-01
6.02594912e-01 -9.20971259e-02 -4.07624066e-01 4.50871468e-01
1.86641067e-01 -8.91755641e-01 -1.09997869e+00 6.49529636e-01
3.27651262e-01 -9.12774086e-01 9.25456166e-01 -2.95552015e-01
5.63807964e-01 -1.40972003e-01 9.59580466e-02 -1.54638219e+00
-4.53771725e-02 -2.24075302e-01 3.60230148e-01 4.28052574e-01
4.16415602e-01 -6.42091274e-01 7.99619317e-01 1.27022731e+00
-4.11693454e-01 -6.27813637e-01 -1.07945514e+00 -2.82244831e-01
3.29430848e-01 -5.13722420e-01 5.00152171e-01 1.03646433e+00
-1.15161642e-01 -4.64421958e-01 9.42203328e-02 -2.28927761e-01
7.46862948e-01 -2.14691237e-01 1.49640679e-01 -8.68038118e-01
1.42853716e-02 -9.66009378e-01 -2.93548375e-01 -1.31573841e-01
2.44728867e-02 -1.31352437e+00 -4.15885411e-02 -1.70049894e+00
3.80124778e-01 -4.88646030e-01 -2.66266167e-01 4.60194200e-01
-5.47040403e-02 4.66034710e-01 1.09140322e-01 2.34077916e-01
-1.02721177e-01 1.94696277e-01 1.84536028e+00 -2.80298740e-01
9.62632596e-02 -2.25998893e-01 -2.91462392e-01 6.83694601e-01
9.36894357e-01 -2.79251844e-01 -4.15355235e-01 -3.73903692e-01
-5.68193018e-01 4.29589719e-01 2.72828043e-01 -1.12195015e+00
3.81705999e-01 -9.77808312e-02 4.43848878e-01 -6.57441318e-01
6.16517179e-02 -8.87307465e-01 5.66361666e-01 8.48011732e-01
-2.75218964e-01 3.56076807e-01 2.24634230e-01 -2.41076425e-01
-3.09441656e-01 -3.33445102e-01 1.23447502e+00 -4.82862383e-01
-6.13913119e-01 5.90387404e-01 -5.99580407e-01 1.15074962e-01
1.36561739e+00 -3.25453848e-01 -1.18641660e-01 -1.66405082e-01
-1.07575643e+00 4.87823300e-02 4.40246403e-01 1.87260762e-01
7.12947786e-01 -1.05879664e+00 -9.85919356e-01 7.20360037e-03
-9.84659865e-02 -1.19002633e-01 6.63044274e-01 1.70915520e+00
-9.72942829e-01 5.48079431e-01 -7.18558073e-01 -6.34006381e-01
-1.22234190e+00 1.68044299e-01 8.60068083e-01 -5.98268449e-01
-8.27279747e-01 4.65009272e-01 -3.29599641e-02 -5.66934764e-01
-6.21352717e-02 -3.28764379e-01 -6.38254061e-02 -3.18690658e-01
7.97555268e-01 1.55805454e-01 5.82024872e-01 -9.86135483e-01
-3.15054625e-01 -1.93407703e-02 -2.15585098e-01 -5.47510624e-01
1.26802528e+00 1.31115308e-02 -1.07397422e-01 -9.32662264e-02
7.46587396e-01 -4.85243678e-01 -9.74329829e-01 -1.13839917e-01
1.92413434e-01 -2.80792594e-01 6.02142632e-01 -1.19398010e+00
-1.50678003e+00 7.75764942e-01 9.48949397e-01 -9.34912488e-02
1.15088642e+00 -4.11773741e-01 8.99962246e-01 -4.81557339e-01
4.48444575e-01 -8.48389268e-01 -4.35728133e-01 2.78309077e-01
8.45628202e-01 -1.17298925e+00 -2.16251403e-01 -3.22638065e-01
-7.52753258e-01 1.01832545e+00 4.70540226e-01 4.18739617e-01
5.50804138e-01 6.97414756e-01 2.81689197e-01 -2.60727197e-01
-4.19414669e-01 -1.24306373e-01 3.44744354e-01 8.15156043e-01
7.49405205e-01 2.38149479e-01 -4.87540156e-01 2.33341902e-01
-2.48073846e-01 2.58663028e-01 5.55339634e-01 9.74264085e-01
-9.08970386e-02 -1.06828320e+00 -4.94697720e-01 7.85790980e-01
-6.72735155e-01 -1.19630724e-01 2.85415426e-02 4.93676543e-01
9.20225978e-02 7.25096464e-01 -2.64193177e-01 1.99341461e-01
1.66701913e-01 2.11543329e-02 7.56782353e-01 -3.47291410e-01
-7.31453240e-01 2.59561669e-02 -1.76834136e-01 -4.10982162e-01
-5.43489456e-01 -7.88785040e-01 -1.54047692e+00 -2.34749019e-01
6.17874973e-02 -5.96507341e-02 1.18216383e+00 1.00181174e+00
1.30660996e-01 1.00988936e+00 -5.69293238e-02 -1.02784681e+00
-1.14877792e-02 -8.48040938e-01 -4.04605895e-01 4.83818173e-01
-9.83951166e-02 -6.08478546e-01 5.43822385e-02 3.50382119e-01] | [14.276727676391602, -2.3931586742401123] |
2445309f-aecc-4f3d-bf0a-4daf5a597108 | ldc-net-a-unified-framework-for-localization | 2110.04727 | null | https://arxiv.org/abs/2110.04727v1 | https://arxiv.org/pdf/2110.04727v1.pdf | LDC-Net: A Unified Framework for Localization, Detection and Counting in Dense Crowds | The rapid development in visual crowd analysis shows a trend to count people by positioning or even detecting, rather than simply summing a density map. It also enlightens us back to the essence of the field, detection to count, which can give more abundant crowd information and has more practical applications. However, some recent work on crowd localization and detection has two limitations: 1) The typical detection methods can not handle the dense crowds and a large variation in scale; 2) The density map heuristic methods suffer from performance deficiency in position and box prediction, especially in high density or large-size crowds. In this paper, we devise a tailored baseline for dense crowds location, detection, and counting from a new perspective, named as LDC-Net for convenience, which has the following features: 1) A strong but minimalist paradigm to detect objects by only predicting a location map and a size map, which endows an ability to detect in a scene with any capacity ($0 \sim 10,000+$ persons); 2) Excellent cross-scale ability in facing a large variation, such as the head ranging in $0 \sim 100,000+$ pixels; 3) Achieve superior performance in location and box prediction tasks, as well as a competitive counting performance compared with the density-based methods. Finally, the source code and pre-trained models will be released. | ['Xuelong Li', 'Yuan Yuan', 'Junyu Gao', 'Tao Han', 'Qi Wang'] | 2021-10-10 | null | null | null | null | ['visual-crowd-analysis'] | ['computer-vision'] | [-5.58957815e-01 -5.85254848e-01 2.92723954e-01 -7.88498223e-02
-2.86222547e-01 -3.82135123e-01 6.44247115e-01 1.89528003e-01
-8.72281194e-01 8.83734286e-01 3.01380187e-01 -1.63630620e-01
3.43725622e-01 -1.02043676e+00 -3.33390832e-01 -4.44189072e-01
-4.01466876e-01 9.49251115e-01 1.00104046e+00 -3.26314032e-01
2.98288167e-01 5.75966239e-01 -1.66320682e+00 -2.33752318e-02
7.80164778e-01 9.34673369e-01 4.72942978e-01 9.76002634e-01
-2.21384645e-01 1.13057971e+00 -1.02549672e+00 -5.91101825e-01
1.94893867e-01 -4.65904213e-02 -2.25923777e-01 -2.91083932e-01
5.70110857e-01 -6.65013433e-01 -3.76406491e-01 6.99861288e-01
1.03046703e+00 8.99750888e-02 7.65945554e-01 -1.24544787e+00
-6.46670997e-01 2.25755833e-02 -1.20539379e+00 8.30793619e-01
8.38485956e-01 3.35020036e-01 3.42843920e-01 -9.87881005e-01
1.73370019e-01 1.40261900e+00 1.06652212e+00 4.59858090e-01
-5.36444187e-01 -5.94577730e-01 1.09027572e-01 -1.38176814e-01
-1.86060095e+00 -3.36670786e-01 4.66242358e-02 -7.26811647e-01
1.08783722e+00 2.91372627e-01 8.49450290e-01 7.33545840e-01
-2.20079571e-01 6.74759686e-01 8.93461347e-01 -2.32656464e-01
2.67531216e-01 1.02141157e-01 -2.63404191e-01 7.50489950e-01
6.21621788e-01 -9.05707777e-02 -5.41957617e-01 -2.57905155e-01
7.92251706e-01 2.40269288e-01 3.62619422e-02 2.31931046e-01
-1.16505623e+00 8.14955294e-01 6.89998925e-01 7.04085156e-02
-1.08380631e-01 1.89997807e-01 3.49578977e-01 -4.68154848e-01
5.01939654e-01 2.12350205e-01 1.69511080e-01 -3.52886945e-01
-1.08688951e+00 6.25086069e-01 6.55181527e-01 1.26344228e+00
8.40841115e-01 -3.60066518e-02 -6.68649912e-01 5.99646270e-01
2.01044660e-02 1.28421521e+00 2.92755455e-01 -8.49558711e-01
1.00617790e+00 6.75954938e-01 6.89516008e-01 -1.48585546e+00
-8.56602848e-01 2.49878198e-01 -9.21524823e-01 1.74453795e-01
7.17121124e-01 -4.42283809e-01 -5.09834588e-01 1.24574840e+00
3.19302797e-01 1.19345851e-01 -5.66582561e-01 1.02674961e+00
9.72707868e-01 6.07553840e-01 2.08010469e-02 1.32345352e-02
1.53858590e+00 -7.00629473e-01 -4.97423083e-01 -7.24262357e-01
5.30735910e-01 -4.51448023e-01 1.05130696e+00 -1.13519013e-01
-1.08843672e+00 -7.31964111e-01 -7.22649455e-01 -1.93452269e-01
-7.08466411e-01 1.57478794e-01 7.30538428e-01 8.19083929e-01
-1.18503559e+00 4.09306660e-02 -5.51915228e-01 -6.00843549e-01
6.90989971e-01 3.19061607e-01 -6.33560941e-02 -1.19389489e-01
-9.58760083e-01 1.05681336e+00 2.12140992e-01 -2.95761943e-01
-3.66649151e-01 -5.43572962e-01 -1.00058877e+00 -1.43038705e-01
1.66544139e-01 -6.95499659e-01 8.43648255e-01 -1.44022688e-01
-8.81746948e-01 9.62254345e-01 -7.48105168e-01 -4.03294683e-01
1.08380306e+00 -9.66255888e-02 -2.55554646e-01 -3.45937349e-02
9.53225017e-01 8.42273355e-01 2.60001659e-01 -1.24903929e+00
-1.40779006e+00 -4.42264795e-01 -1.22139759e-01 1.19950876e-01
-2.54189730e-01 3.76153260e-01 -5.50510526e-01 -3.12764704e-01
-1.82625845e-01 -5.22807181e-01 -2.17250735e-01 8.61565117e-03
-2.40084529e-01 -3.60156208e-01 7.30851591e-01 -4.64389235e-01
1.47582042e+00 -1.79330289e+00 -6.36457324e-01 6.00741385e-03
7.31553018e-01 3.46318841e-01 3.62211645e-01 3.72641683e-01
6.90289915e-01 3.33353244e-02 1.94199756e-01 -7.01066077e-01
-1.45838521e-02 -3.52983885e-02 -1.65681139e-01 6.58633292e-01
1.45122269e-02 1.01972246e+00 -1.31622159e+00 -1.10273182e+00
4.89043027e-01 2.95357257e-01 -3.53101104e-01 1.60223097e-01
3.65829945e-01 3.18599999e-01 -1.95451900e-01 1.00505018e+00
1.02443695e+00 -4.27470684e-01 -3.70941728e-01 3.96199256e-01
-5.95056653e-01 -5.13196647e-01 -1.37295651e+00 9.36012626e-01
-9.75278243e-02 7.95606256e-01 -1.58407286e-01 -2.82551110e-01
9.41279769e-01 -3.95233221e-02 2.98886746e-01 -7.19706178e-01
1.10066077e-02 3.17543864e-01 -4.67569321e-01 -4.82768118e-01
1.08867764e+00 3.54141928e-02 -2.76815951e-01 9.90254134e-02
-4.55421776e-01 -1.01305321e-02 7.13219643e-01 4.00496215e-01
1.07574642e+00 -4.25774425e-01 5.32840312e-01 -1.19385973e-01
1.99842513e-01 8.46162960e-02 4.48170722e-01 1.28693926e+00
-7.51102805e-01 8.84927452e-01 3.94428521e-01 -6.77663028e-01
-9.75353003e-01 -1.04901016e+00 -2.67654732e-02 1.43798625e+00
4.51613992e-01 -4.28249650e-02 -6.17535710e-01 -3.36923897e-01
3.86962116e-01 1.30457446e-01 -6.85507476e-01 6.16318822e-01
-9.24881101e-01 -9.70431864e-01 7.70115614e-01 1.06142855e+00
8.55857015e-01 -8.81498516e-01 -5.50408185e-01 -1.29957974e-01
-4.87743139e-01 -1.23430705e+00 -5.96520483e-01 -3.49794954e-01
-3.07665199e-01 -9.17384386e-01 -1.29922080e+00 -7.11970568e-01
5.94228864e-01 8.60905588e-01 1.30946648e+00 3.17744732e-01
-1.70830920e-01 2.09246963e-01 -2.41319835e-01 -1.00244045e+00
3.28401387e-01 -8.07402804e-02 3.62729311e-01 -3.03280354e-01
8.98345709e-01 -1.21056095e-01 -8.96750271e-01 5.94599128e-01
-1.83234021e-01 -3.34949970e-01 1.81456685e-01 3.06309313e-01
3.02039444e-01 -2.02551171e-01 5.33039093e-01 -4.69160229e-01
6.23360693e-01 -6.45627379e-01 -7.10939884e-01 -3.45955640e-02
-7.86382705e-02 -6.89840019e-01 4.52216566e-01 -3.82086843e-01
-8.78988981e-01 1.29457012e-01 -1.22217471e-02 8.16721544e-02
-1.59093186e-01 -3.08635414e-01 9.68493149e-02 -4.19009365e-02
1.14386976e+00 2.37228200e-01 -2.46081561e-01 -1.03970960e-01
1.20720267e-01 6.92966223e-01 7.13003218e-01 -3.50047708e-01
6.98210001e-01 1.21300852e+00 -6.34037256e-02 -8.14493775e-01
-6.47097945e-01 -1.01091695e+00 -9.38124418e-01 -3.47297490e-01
9.00444031e-01 -1.30805624e+00 -1.27078915e+00 5.43763220e-01
-1.29499483e+00 -2.09629938e-01 -1.96936443e-01 3.59698594e-01
-3.24148983e-01 6.97081685e-01 -5.67735672e-01 -1.52202272e+00
-6.75069764e-02 -7.02276886e-01 1.48558557e+00 7.02437580e-01
-1.07266389e-01 -9.82195854e-01 3.20987180e-02 1.73160821e-01
5.92126310e-01 3.81796062e-01 -1.32676885e-02 -4.37305123e-01
-5.24268627e-01 -5.70381522e-01 -7.39499688e-01 -2.67610729e-01
-3.14181238e-01 -1.96140707e-01 -1.04516888e+00 -2.47764096e-01
-6.35446429e-01 -1.67660013e-01 9.39141273e-01 7.18090713e-01
9.87431288e-01 1.84969942e-03 -8.48034620e-01 5.53191602e-01
1.18472087e+00 -1.43517449e-01 7.34205186e-01 3.51260424e-01
8.13302040e-01 5.16877353e-01 5.00777304e-01 1.00590038e+00
9.90026653e-01 5.39181113e-01 3.99146795e-01 -3.61567318e-01
-5.47043495e-02 -5.03963470e-01 -6.62286058e-02 3.60652775e-01
-8.26526523e-01 -4.40602422e-01 -1.12273800e+00 6.21457517e-01
-1.96914518e+00 -1.31250107e+00 -4.60243464e-01 2.12978482e+00
3.26765656e-01 -7.65895173e-02 9.49749053e-01 -2.83616018e-02
1.23391247e+00 2.09294498e-01 -8.57204720e-02 2.83161849e-01
-3.28054398e-01 -3.67604911e-01 8.61092448e-01 3.99295002e-01
-1.19723272e+00 1.00807965e+00 7.20913506e+00 1.04288185e+00
-5.84819198e-01 1.38945460e-01 5.64121485e-01 -2.14785054e-01
5.10271370e-01 -4.77993965e-01 -1.48572958e+00 1.14013267e+00
2.78346479e-01 2.40815833e-01 1.83344394e-01 9.89570439e-01
2.73606002e-01 -7.32975304e-01 -7.30587065e-01 1.43481791e+00
2.15018407e-01 -1.31488371e+00 -1.87776133e-01 1.90791786e-01
7.01986134e-01 8.54406953e-02 -1.88028663e-01 5.62571347e-01
4.66651380e-01 -1.15106559e+00 1.03057945e+00 5.42773902e-01
9.17665124e-01 -5.65190077e-01 1.12138331e+00 8.04225266e-01
-1.57958913e+00 -2.61611640e-01 -1.05042803e+00 -6.25005960e-01
6.28897786e-01 4.70292360e-01 -7.82600462e-01 -5.75914457e-02
1.02726102e+00 2.81918079e-01 -7.89432824e-01 1.40310144e+00
1.42568752e-01 -1.49405259e-03 -5.31358659e-01 -6.83895528e-01
9.92465019e-02 1.21838480e-01 2.79460669e-01 1.87845576e+00
6.18394852e-01 1.84331778e-02 4.91889685e-01 5.93603671e-01
1.48208126e-01 -2.22926792e-02 -8.60879481e-01 8.11981082e-01
1.03275895e+00 1.00756884e+00 -1.00105524e+00 -4.62271929e-01
-5.50841630e-01 6.99018478e-01 4.94179815e-01 3.07392597e-01
-1.08068955e+00 -2.76266545e-01 1.89627528e-01 8.48396122e-01
2.05262646e-01 -2.20917702e-01 -4.33839440e-01 -8.50102186e-01
1.08506940e-01 -2.09367156e-01 3.89449209e-01 -7.13844240e-01
-1.53790927e+00 4.11718696e-01 1.73878849e-01 -1.19256175e+00
-5.27030677e-02 -8.09159815e-01 -8.83362889e-01 8.57014239e-01
-1.41995311e+00 -1.15874076e+00 -9.70595300e-01 6.73018098e-01
2.05142155e-01 -3.68419945e-01 3.23224306e-01 5.26611745e-01
-3.93027544e-01 6.36133432e-01 -1.76030714e-02 5.71089208e-01
5.58179021e-01 -1.51625264e+00 5.61937273e-01 7.88646519e-01
-3.82651687e-01 2.71982431e-01 4.01975721e-01 -8.30922544e-01
-7.58509278e-01 -1.13315964e+00 1.15187144e+00 -1.27540970e+00
3.34149867e-01 -5.61071694e-01 -4.29730803e-01 4.81345683e-01
-3.84327739e-01 5.38974047e-01 3.73036385e-01 4.97291200e-02
-1.25074210e-02 1.95505023e-02 -1.27397871e+00 4.59782332e-01
1.20944977e+00 -1.25133455e-01 -1.03178330e-01 5.64687312e-01
2.59123713e-01 -6.16999447e-01 -3.19976717e-01 1.90329701e-02
4.56635296e-01 -1.45471263e+00 1.27316761e+00 3.80237512e-02
9.97684970e-02 -4.28638786e-01 -2.03106746e-01 -7.97645509e-01
-6.71636581e-01 -4.58101332e-01 -2.88816333e-01 1.18026483e+00
2.44596571e-01 -7.32266843e-01 9.11577761e-01 5.45823634e-01
7.65482113e-02 -5.26932657e-01 -1.07808220e+00 -9.42179501e-01
-2.19506584e-02 -2.29248002e-01 8.56523573e-01 5.26339829e-01
4.97154072e-02 1.23344421e-01 -6.64017498e-01 1.62423387e-01
4.34622139e-01 -4.30175006e-01 1.36310446e+00 -1.16123831e+00
1.08333483e-01 -4.37883377e-01 -7.24236727e-01 -1.61135066e+00
-4.38416898e-01 -2.93657154e-01 1.67703539e-01 -1.80749059e+00
5.53565204e-01 -6.38756812e-01 4.74915326e-01 -7.43572041e-02
-5.68388224e-01 4.99017358e-01 2.32960433e-01 5.14938474e-01
-1.25688553e+00 1.22531846e-01 1.03130221e+00 9.92418304e-02
-3.28963250e-02 2.14008912e-01 -6.30618870e-01 9.84733641e-01
5.07498503e-01 -2.94406861e-01 1.03948124e-01 -5.94791710e-01
4.23569947e-01 -1.53642133e-01 5.33778429e-01 -1.49353516e+00
5.94483972e-01 -2.04542205e-01 9.65714037e-01 -8.47132981e-01
5.30625403e-01 -2.84813464e-01 -4.66634512e-01 4.54387516e-01
5.23277223e-01 2.72844046e-01 -8.07492286e-02 6.81585312e-01
5.74594736e-02 -2.52724677e-01 7.63429403e-01 -5.51740170e-01
-8.79911125e-01 4.15192485e-01 -2.20530808e-01 3.69797826e-01
1.08229041e+00 -8.61546516e-01 -8.82209599e-01 -4.82847571e-01
-1.58094779e-01 4.97336119e-01 6.19670093e-01 -1.90358539e-03
3.23818177e-01 -1.38148499e+00 -9.63533998e-01 -4.73176502e-03
2.60230184e-01 2.48933643e-01 1.78388506e-01 8.93868923e-01
-1.01615310e+00 3.66407603e-01 5.01286089e-02 -8.18378925e-01
-9.27105486e-01 6.77666187e-01 2.52295911e-01 -1.42287463e-01
-4.01290059e-01 1.08074009e+00 3.26732874e-01 -2.77450830e-01
3.21610063e-01 -7.98802748e-02 -3.08873862e-01 8.07759240e-02
1.16512299e+00 1.26420057e+00 -4.12914455e-01 -1.09373593e+00
-6.59325242e-01 6.25467837e-01 5.11947274e-01 1.13825954e-01
7.81521201e-01 -3.63128752e-01 1.29282251e-01 2.38150343e-01
6.94957018e-01 3.65071952e-01 -1.41025794e+00 -1.85492620e-01
-2.14595079e-01 -1.02024102e+00 -5.84592223e-01 -3.23707104e-01
-5.97268879e-01 8.09412241e-01 6.42527282e-01 5.28629005e-01
4.80550766e-01 3.36864293e-01 7.55444765e-01 2.44795546e-01
7.42796600e-01 -1.32336211e+00 3.05204868e-01 7.41494596e-01
6.27328098e-01 -1.90257883e+00 2.11272642e-01 -3.42322737e-01
-8.93535674e-01 6.53210461e-01 9.07144666e-01 -9.96529460e-02
5.34327149e-01 6.22831762e-01 -4.38065678e-02 -4.09826100e-01
3.04785911e-02 -7.74224401e-01 7.83832371e-03 1.46422458e+00
2.67282158e-01 2.62423217e-01 1.48813978e-01 5.98152995e-01
-3.66720676e-01 -2.23101541e-01 1.89017519e-01 7.37603068e-01
-1.09784722e+00 -7.98042491e-02 -1.03299189e+00 6.36163712e-01
-3.32478076e-01 -5.75181916e-02 -2.79553264e-01 9.28148448e-01
6.33522868e-01 1.22855520e+00 4.69445318e-01 -1.79446369e-01
6.39943480e-01 -5.35187662e-01 2.23377436e-01 -5.28300881e-01
-3.02999318e-01 -3.79115045e-01 -1.32544696e-01 -3.04926693e-01
-2.90815830e-01 -6.57316923e-01 -1.14537477e+00 -1.09151435e+00
-4.33333367e-01 -1.94720596e-01 1.77795634e-01 8.04096878e-01
1.26662076e-01 -4.95215692e-02 3.02614570e-01 -1.52696311e+00
-1.06796637e-01 -1.27699399e+00 -9.10834193e-01 3.66699427e-01
1.26544341e-01 -9.39479351e-01 -3.59957814e-01 -1.56543404e-01] | [8.357792854309082, -0.36806297302246094] |
781b86ec-b3c1-4550-8853-03dc9ec3bb40 | granger-causality-for-compressively-sensed | 2210.11420 | null | https://arxiv.org/abs/2210.11420v1 | https://arxiv.org/pdf/2210.11420v1.pdf | Granger Causality for Compressively Sensed Sparse Signals | Compressed sensing is a scheme that allows for sparse signals to be acquired, transmitted and stored using far fewer measurements than done by conventional means employing Nyquist sampling theorem. Since many naturally occurring signals are sparse (in some domain), compressed sensing has rapidly seen popularity in a number of applied physics and engineering applications, particularly in designing signal and image acquisition strategies, e.g., magnetic resonance imaging, quantum state tomography, scanning tunneling microscopy, analog to digital conversion technologies. Contemporaneously, causal inference has become an important tool for the analysis and understanding of processes and their interactions in many disciplines of science, especially those dealing with complex systems. Direct causal analysis for compressively sensed data is required to avoid the task of reconstructing the compressed data. Also, for some sparse signals, such as for sparse temporal data, it may be difficult to discover causal relations directly using available data-driven/ model-free causality estimation techniques. In this work, we provide a mathematical proof that structured compressed sensing matrices, specifically Circulant and Toeplitz, preserve causal relationships in the compressed signal domain, as measured by Granger Causality. We then verify this theorem on a number of bivariate and multivariate coupled sparse signal simulations which are compressed using these matrices. We also demonstrate a real world application of network causal connectivity estimation from sparse neural spike train recordings from rat prefrontal cortex. | ['Nithin Nagaraj', 'Aditi Kathpalia'] | 2022-09-23 | null | null | null | null | ['connectivity-estimation', 'quantum-state-tomography'] | ['graphs', 'medical'] | [ 9.16276991e-01 -1.84016883e-01 -2.38484032e-02 -1.38531523e-02
-1.02647930e-01 -4.30737585e-01 5.53180873e-01 2.45794952e-02
-8.58460888e-02 1.23936474e+00 3.83034199e-01 -3.05344194e-01
-6.99198186e-01 -5.05673349e-01 -6.33314967e-01 -9.07397151e-01
-5.83682239e-01 3.58877778e-01 -1.31702453e-01 3.51309590e-02
4.44254696e-01 4.61901367e-01 -1.15663576e+00 -7.31214806e-02
6.96656406e-01 7.74979949e-01 4.11805660e-01 3.96080226e-01
2.78569758e-01 9.54438925e-01 -9.32583064e-02 1.55406177e-01
8.41701496e-03 -9.82337952e-01 -3.40202808e-01 -2.79449135e-01
-2.77480394e-01 1.25104166e-03 -8.73278558e-01 1.24772537e+00
1.40290916e-01 -9.92415026e-02 4.37417120e-01 -1.00065827e+00
-6.42918110e-01 8.97222102e-01 -7.36195385e-01 5.80910861e-01
4.36991364e-01 -1.76742360e-01 6.78020716e-01 -7.08811164e-01
5.97192645e-01 1.10160577e+00 2.91664928e-01 2.54191682e-02
-1.51649129e+00 -7.43347466e-01 -3.96131665e-01 4.49820876e-01
-1.12677062e+00 -6.66541755e-01 9.23177242e-01 -4.49159205e-01
5.51295221e-01 1.77960247e-01 8.30115020e-01 1.02063251e+00
7.49166906e-01 2.84067929e-01 1.42705059e+00 -4.54079658e-01
4.24866915e-01 -5.08545637e-01 2.82376483e-02 4.00837302e-01
5.39530277e-01 4.27118003e-01 -1.06712019e+00 -3.71176988e-01
1.05520391e+00 1.06698528e-01 -6.06506228e-01 -2.79066741e-01
-1.72126555e+00 7.81980753e-01 1.47695839e-01 5.85551679e-01
-7.67101467e-01 2.81084627e-01 4.03799079e-02 5.16556621e-01
-5.54275438e-02 1.76830843e-01 1.79255798e-01 -6.67150924e-03
-1.16518688e+00 4.82328702e-03 8.25131834e-01 6.81998253e-01
1.62330568e-01 2.77775079e-01 2.95015216e-01 2.81336039e-01
3.61478537e-01 8.55457842e-01 4.75825757e-01 -1.19691050e+00
-1.60070658e-01 -1.29259422e-01 -9.05553252e-02 -1.14141715e+00
-2.23197505e-01 -4.20208186e-01 -1.63454032e+00 -3.64459902e-01
1.02403052e-01 6.06185645e-02 -2.10228890e-01 1.78078508e+00
-2.68593766e-02 6.24330997e-01 -6.96461946e-02 9.13901269e-01
7.82798827e-02 4.52225268e-01 -3.31030905e-01 -1.01639080e+00
1.15486586e+00 1.01677224e-01 -1.09692764e+00 -1.73347026e-01
-5.90842552e-02 -7.35324919e-01 2.15062156e-01 6.71834767e-01
-1.05454695e+00 2.69938516e-03 -9.83407438e-01 3.48703533e-01
4.05928969e-01 -4.14896935e-01 8.61319005e-01 2.73085535e-01
-8.56219888e-01 8.48455369e-01 -9.85914707e-01 -2.67946869e-01
5.28242469e-01 1.85022295e-01 -5.44977963e-01 -5.45963645e-01
-1.22014678e+00 5.96812904e-01 4.83759008e-02 -2.45158672e-02
-1.17643034e+00 -5.59986532e-01 -3.82174045e-01 7.76610849e-03
-3.30792926e-02 -8.37670147e-01 6.41460121e-01 -3.48297238e-01
-1.10716784e+00 3.56590748e-01 -4.84809726e-01 -8.21151793e-01
-4.10448834e-02 1.72088131e-01 -4.48377103e-01 4.18078661e-01
1.79467142e-01 -9.28190202e-02 1.06322610e+00 -8.44197094e-01
1.41402125e-01 -6.30408883e-01 -4.96644855e-01 -2.28306219e-01
-3.38111371e-02 -1.49041578e-01 4.81514126e-01 -5.72372139e-01
8.40407014e-01 -8.71419847e-01 -4.40624326e-01 -1.51972726e-01
-4.03966516e-01 3.93699914e-01 8.89108777e-01 -5.39432883e-01
8.30238938e-01 -2.17053962e+00 5.77057242e-01 4.05971169e-01
5.27732015e-01 -2.52562046e-01 -2.32542790e-02 9.29979026e-01
-4.20964926e-01 -1.17500842e-01 -6.50530577e-01 5.79818338e-02
-5.33228397e-01 2.30709925e-01 -6.07951880e-01 1.00734556e+00
2.40565743e-02 6.10806406e-01 -1.06067753e+00 -2.76563793e-01
1.22888334e-01 6.24262750e-01 -4.72942173e-01 -1.86645016e-02
1.31617159e-01 1.13234174e+00 -5.02584755e-01 3.41793716e-01
2.23940432e-01 -6.74061060e-01 3.80611688e-01 -5.18806763e-02
-1.04006507e-01 1.46027118e-01 -1.08065224e+00 1.89671934e+00
-2.60357857e-01 8.90544176e-01 2.50072539e-01 -1.47943115e+00
6.28002107e-01 6.92916274e-01 8.60546172e-01 -8.02357018e-01
3.54057550e-02 1.63744703e-01 3.97764385e-01 -4.91903841e-01
2.57520437e-01 -5.63118696e-01 2.02685997e-01 7.82095313e-01
-1.87551662e-01 -9.20629576e-02 2.10162792e-02 7.65065014e-01
1.60064292e+00 -4.87650126e-01 4.62083966e-01 -5.08618712e-01
1.14452921e-01 -8.72266144e-02 3.73177618e-01 5.60264170e-01
-1.52325794e-01 3.73814374e-01 2.84065068e-01 7.62415817e-03
-1.11115146e+00 -1.04317057e+00 -3.59749019e-01 9.31727290e-02
1.30602598e-01 -8.83928984e-02 -3.05278212e-01 5.87025464e-01
-2.12676972e-01 5.70383191e-01 -4.23639208e-01 -2.05021635e-01
-3.07154030e-01 -7.26764858e-01 3.78620982e-01 -1.23535804e-02
2.99889088e-01 -8.54652762e-01 -7.08062410e-01 4.63960588e-01
-2.32790962e-01 -1.24914658e+00 4.56022657e-02 2.64395863e-01
-1.24625313e+00 -1.03926361e+00 -5.16209543e-01 -2.34680682e-01
5.11566103e-01 4.97129887e-01 7.04104483e-01 -1.42736375e-01
-3.76783073e-01 2.99666643e-01 -1.66728809e-01 1.56099414e-02
-2.46404022e-01 -6.62469566e-01 4.25330758e-01 4.11410965e-02
9.69660189e-03 -1.35464358e+00 -4.83407885e-01 9.22760367e-02
-9.48207080e-01 4.24604416e-02 6.15370452e-01 9.99230206e-01
6.89875841e-01 3.28221142e-01 7.46622324e-01 -6.10702455e-01
8.02358925e-01 -8.12515914e-01 -4.34209079e-01 -1.57099664e-01
-3.77850920e-01 7.63471797e-02 5.39508939e-01 -3.02340746e-01
-6.70815885e-01 -1.38864860e-01 3.64142835e-01 -6.09157205e-01
9.46670324e-02 1.08895504e+00 1.98963031e-01 3.47148776e-02
7.61807919e-01 4.15580601e-01 3.55240345e-01 -1.48592561e-01
1.25926822e-01 2.64050245e-01 5.77438116e-01 -4.41259503e-01
6.13935173e-01 8.65470171e-01 6.00302219e-01 -1.19344401e+00
-3.79709721e-01 -3.69171917e-01 -4.09094989e-01 -1.12720586e-01
5.45349002e-01 -7.99472332e-01 -7.41100073e-01 1.03896014e-01
-1.09919322e+00 2.64756177e-02 -3.11938077e-01 8.93638611e-01
-6.39604270e-01 2.52015680e-01 -7.76225269e-01 -9.60809350e-01
8.83136913e-02 -8.18940580e-01 6.99689984e-01 -1.10987037e-01
-3.17774624e-01 -8.18489969e-01 1.39368057e-01 1.20919116e-01
6.43504858e-01 2.70110309e-01 9.90760267e-01 -9.60157216e-02
-8.18887949e-01 -2.53668189e-01 -2.62703584e-03 -1.46344468e-01
9.07705650e-02 -2.95004517e-01 -6.62603378e-01 -2.75419563e-01
6.68178380e-01 -9.38458145e-02 7.40325570e-01 7.78664112e-01
8.28824639e-01 -2.74858922e-01 -5.83161950e-01 2.10724249e-01
1.29768789e+00 2.62304187e-01 7.33281553e-01 -5.41468501e-01
6.22714341e-01 6.29855037e-01 9.49027240e-02 5.64202666e-01
-7.59164840e-02 2.74099022e-01 4.01310712e-01 4.87176985e-01
-6.04694895e-02 -3.23607653e-01 1.65516809e-01 1.38704538e+00
-1.87302440e-01 2.53387913e-02 -6.38191283e-01 5.55401742e-01
-1.59297621e+00 -1.45165181e+00 -5.40484905e-01 2.28608918e+00
7.29758501e-01 -1.46937758e-01 -3.43535691e-01 5.89730322e-01
6.96979284e-01 -1.13929056e-01 -7.39539325e-01 2.31387571e-01
-3.74735028e-01 3.34692299e-01 4.78006333e-01 4.79961932e-01
-2.47225091e-01 4.14424419e-01 6.33643579e+00 3.79195869e-01
-9.65654016e-01 3.74619991e-01 2.40840226e-01 -9.90305245e-02
-7.49587715e-01 4.04012799e-01 1.38316546e-02 3.79106194e-01
1.15206313e+00 -4.73134965e-01 9.69387829e-01 1.39080954e-03
7.36571968e-01 -4.87491071e-01 -1.05725229e+00 1.32928777e+00
-4.18616176e-01 -1.57832813e+00 -2.18041688e-01 3.79907995e-01
8.00808191e-01 1.40786201e-01 -1.15567110e-01 -5.98318398e-01
3.30936611e-01 -1.18282866e+00 4.81405884e-01 6.63793683e-01
7.98743427e-01 -3.93539488e-01 2.74067670e-01 6.36649609e-01
-7.21201181e-01 -7.25300089e-02 -2.91970164e-01 -5.64742684e-01
7.05247521e-01 1.61231613e+00 -3.09595019e-01 3.93067151e-01
5.01134336e-01 1.11904097e+00 2.88173884e-01 9.45098341e-01
-1.52926296e-01 1.01469958e+00 -4.41100329e-01 1.75217494e-01
-2.99800873e-01 -4.27812845e-01 9.93140399e-01 3.29656959e-01
5.98117411e-01 7.10999489e-01 -4.49042112e-01 1.09479260e+00
1.35111645e-01 -5.26321948e-01 -1.06233239e+00 -6.29679084e-01
7.82076538e-01 8.25365663e-01 -8.61284792e-01 4.66373563e-02
-8.05751160e-02 6.72877491e-01 -3.09097946e-01 4.94822145e-01
-4.63831037e-01 2.21596316e-01 2.96166092e-01 3.44309360e-01
-1.03544968e-03 -7.73842871e-01 -3.84931505e-01 -1.23710489e+00
-3.73108327e-01 -8.80392671e-01 -1.25491098e-01 -7.15335727e-01
-1.24982548e+00 3.33577901e-01 2.30435058e-02 -1.04242980e+00
-2.79377490e-01 4.24259454e-02 -3.49562079e-01 9.34868157e-01
-1.25518250e+00 -3.26913565e-01 7.09616616e-02 9.63925183e-01
2.57884506e-02 -1.48199588e-01 8.32851708e-01 3.27000290e-01
-3.37196559e-01 -3.22516710e-01 1.86326042e-01 -2.88329631e-01
3.31186831e-01 -6.06466830e-01 -1.60150453e-01 9.78505850e-01
3.52472782e-01 9.84005868e-01 1.13722599e+00 -8.33147526e-01
-1.96216726e+00 -5.33322752e-01 6.59650326e-01 2.53659755e-01
1.11212611e+00 -1.66093826e-01 -7.35635221e-01 5.33921480e-01
2.43481204e-01 1.89242229e-01 7.75184453e-01 -6.31365627e-02
-1.13843707e-02 1.05658561e-01 -1.10943723e+00 3.37921858e-01
1.06479681e+00 -6.68038785e-01 -5.17038882e-01 4.81937379e-01
4.08549726e-01 8.41479152e-02 -9.97535527e-01 4.28221077e-02
5.76318562e-01 -9.86599982e-01 8.16389859e-01 -1.45806670e-01
6.91355884e-01 -4.01847273e-01 -5.23146570e-01 -1.40699279e+00
-4.46876615e-01 -8.61313164e-01 -2.06909373e-01 5.25777102e-01
-1.31163225e-01 -8.61775100e-01 5.22897124e-01 5.09076938e-02
2.57464629e-02 -2.79565871e-01 -1.27482986e+00 -6.13994539e-01
-2.69134194e-01 -4.37285811e-01 1.14184082e-01 1.28695381e+00
3.51633966e-01 5.54826021e-01 -4.11224484e-01 3.24385494e-01
1.31802118e+00 1.52006879e-01 5.95157850e-04 -1.36995709e+00
-5.73541045e-01 -1.73982829e-01 -5.28691053e-01 -9.16760504e-01
-1.27883330e-02 -8.72573495e-01 -1.28087342e-01 -1.33444953e+00
1.63165152e-01 -7.19420850e-01 -1.62585095e-01 1.23571910e-01
5.02080500e-01 2.17773795e-01 1.99370552e-03 8.11110795e-01
-7.61635676e-02 7.03426898e-01 1.41775298e+00 -2.94355210e-02
2.57581592e-01 -2.67848402e-01 -5.31358123e-01 4.30164516e-01
4.83081311e-01 -7.47126758e-01 -6.53725266e-01 -2.78809935e-01
6.91452861e-01 1.07351851e+00 6.75341308e-01 -1.02503240e+00
6.58326864e-01 -1.94756702e-01 4.95857850e-04 -3.33810091e-01
2.26728231e-01 -9.53874290e-01 7.95863748e-01 5.49424171e-01
-2.65395433e-01 2.58935858e-02 -1.22646742e-01 9.60407794e-01
-5.02282202e-01 -7.90364593e-02 5.15305400e-01 -1.01607762e-01
-3.02758813e-01 1.90585911e-01 -4.38343614e-01 -1.32743269e-01
8.34678829e-01 9.62858722e-02 -3.06219488e-01 -5.71501672e-01
-7.15849757e-01 -9.91437882e-02 1.98977873e-01 -1.17447175e-01
9.47604239e-01 -1.21968269e+00 -6.61964178e-01 3.58479291e-01
-4.06622589e-01 -2.03354210e-01 5.29705346e-01 1.55765700e+00
-7.42418766e-02 6.38868570e-01 -2.79897839e-01 -8.10009062e-01
-7.18085051e-01 5.10724902e-01 -1.63189247e-01 -6.58087293e-03
-5.99712610e-01 4.84551787e-01 -3.20315398e-02 4.57450002e-01
-3.83353919e-01 -3.32339969e-03 1.26899958e-01 -1.49898916e-01
7.17869759e-01 2.45731026e-01 -7.18167275e-02 -4.19775575e-01
-3.07234019e-01 1.83481410e-01 4.31879938e-01 -4.04616535e-01
1.61685169e+00 -2.58375794e-01 -7.55245686e-01 8.36813271e-01
8.80158961e-01 1.95572004e-02 -1.04347551e+00 -2.40166187e-01
-1.82390362e-01 -2.34356806e-01 3.82610202e-01 -3.66096735e-01
-1.13251877e+00 9.46024179e-01 3.34356159e-01 7.36434579e-01
1.10901129e+00 1.04314432e-01 5.63615024e-01 3.08618158e-01
9.79943752e-01 -2.84596533e-01 -8.21137950e-02 2.61737287e-01
8.89201343e-01 -8.29485536e-01 1.77257046e-01 -2.60279119e-01
-6.20926963e-03 7.05190957e-01 -6.36479378e-01 -3.99486959e-01
9.99704301e-01 5.77088952e-01 -7.73171306e-01 -4.84120339e-01
-8.61670673e-01 4.17147487e-01 -1.18005686e-01 5.09010553e-01
4.20391858e-01 2.85765082e-01 -4.32387620e-01 1.80254549e-01
-1.79378808e-01 3.31207961e-01 8.39920640e-01 8.05099785e-01
-2.90712148e-01 -1.02883804e+00 -5.71360469e-01 7.54086435e-01
-3.64173651e-01 -5.16896009e-01 2.52829082e-02 1.94994912e-01
-5.34649372e-01 1.08032358e+00 -9.42885503e-02 -1.56273276e-01
-3.15991968e-01 -1.89852670e-01 8.17191958e-01 -4.02039528e-01
3.51601064e-01 1.21783100e-01 -1.97477341e-01 -6.40804470e-01
-9.49498951e-01 -1.14040363e+00 -1.30022681e+00 -5.02475858e-01
-2.25167260e-01 2.57704049e-01 7.76867628e-01 1.21201909e+00
3.02837491e-01 6.47073209e-01 4.06113446e-01 -7.87367225e-01
-2.99858689e-01 -7.86758006e-01 -9.53570962e-01 9.98271815e-03
4.66123223e-01 -6.66028261e-01 -4.06897336e-01 2.60835767e-01] | [6.9334821701049805, 4.245969772338867] |
edb0adec-e136-4019-8256-592322ea47be | poseformerv2-exploring-frequency-domain-for | 2303.17472 | null | https://arxiv.org/abs/2303.17472v1 | https://arxiv.org/pdf/2303.17472v1.pdf | PoseFormerV2: Exploring Frequency Domain for Efficient and Robust 3D Human Pose Estimation | Recently, transformer-based methods have gained significant success in sequential 2D-to-3D lifting human pose estimation. As a pioneering work, PoseFormer captures spatial relations of human joints in each video frame and human dynamics across frames with cascaded transformer layers and has achieved impressive performance. However, in real scenarios, the performance of PoseFormer and its follow-ups is limited by two factors: (a) The length of the input joint sequence; (b) The quality of 2D joint detection. Existing methods typically apply self-attention to all frames of the input sequence, causing a huge computational burden when the frame number is increased to obtain advanced estimation accuracy, and they are not robust to noise naturally brought by the limited capability of 2D joint detectors. In this paper, we propose PoseFormerV2, which exploits a compact representation of lengthy skeleton sequences in the frequency domain to efficiently scale up the receptive field and boost robustness to noisy 2D joint detection. With minimum modifications to PoseFormer, the proposed method effectively fuses features both in the time domain and frequency domain, enjoying a better speed-accuracy trade-off than its precursor. Extensive experiments on two benchmark datasets (i.e., Human3.6M and MPI-INF-3DHP) demonstrate that the proposed approach significantly outperforms the original PoseFormer and other transformer-based variants. Code is released at \url{https://github.com/QitaoZhao/PoseFormerV2}. | ['Chen Chen', 'Pichao Wang', 'Mengyuan Liu', 'Ce Zheng', 'Qitao Zhao'] | 2023-03-30 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhao_PoseFormerV2_Exploring_Frequency_Domain_for_Efficient_and_Robust_3D_Human_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhao_PoseFormerV2_Exploring_Frequency_Domain_for_Efficient_and_Robust_3D_Human_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-human-pose-estimation', 'human-dynamics'] | ['computer-vision', 'computer-vision'] | [-9.59497318e-02 -4.89425600e-01 -1.63151115e-01 4.18863334e-02
-6.83997631e-01 -2.78405309e-01 2.45066136e-01 -3.07310313e-01
-4.70911771e-01 4.31780279e-01 3.67083609e-01 3.48635733e-01
1.13653354e-01 -3.50045770e-01 -7.04851389e-01 -5.70071638e-01
-3.38701963e-01 1.33615702e-01 5.47604740e-01 -2.90687591e-01
-1.98698491e-01 1.77848935e-01 -1.38770211e+00 2.35898532e-02
4.44884747e-01 1.17998052e+00 3.84743601e-01 6.64853871e-01
4.48888659e-01 5.03603041e-01 -4.88134086e-01 -1.99363753e-01
3.59187484e-01 -2.26135328e-01 -4.76944804e-01 1.65334329e-01
3.23004693e-01 -5.16229093e-01 -7.83483624e-01 9.54443693e-01
8.94574583e-01 2.06516564e-01 2.51769274e-01 -1.07878864e+00
-2.62540519e-01 2.19417185e-01 -8.42371225e-01 5.70581079e-01
6.42276883e-01 3.25765580e-01 7.36879826e-01 -1.20118368e+00
5.58746457e-01 1.48016667e+00 8.27642262e-01 4.10528481e-01
-1.05794919e+00 -7.54617274e-01 1.72468647e-01 4.47467178e-01
-1.46444809e+00 -3.90264988e-01 8.14935863e-01 -2.84049004e-01
7.85496294e-01 9.51903909e-02 8.71062756e-01 1.37623167e+00
3.66946042e-01 1.06581473e+00 8.20098221e-01 -1.35848880e-01
-8.86159912e-02 -6.28323913e-01 -2.11544320e-01 8.33235085e-01
-4.54531983e-02 6.63889125e-02 -9.44033146e-01 -4.33640517e-02
1.32555795e+00 1.39920816e-01 -5.60764194e-01 -4.31943804e-01
-1.48825526e+00 5.54358542e-01 6.33323073e-01 2.25873217e-01
-5.24929047e-01 4.33806092e-01 6.18384361e-01 2.88177550e-01
3.59000474e-01 2.52112970e-02 -3.72066081e-01 -1.83606893e-01
-7.99337864e-01 4.51873541e-01 3.01543236e-01 9.24295545e-01
2.12533861e-01 1.58616491e-02 -2.23391473e-01 7.35818207e-01
2.92581409e-01 5.48026741e-01 6.19762778e-01 -1.01381195e+00
5.37194490e-01 1.31007835e-01 1.67127877e-01 -1.26359510e+00
-5.17630398e-01 -5.58826208e-01 -9.29547131e-01 -1.73223317e-01
5.88460326e-01 -4.19516042e-02 -7.38290548e-01 1.84373307e+00
6.05575025e-01 2.11086765e-01 -4.04925615e-01 1.37245142e+00
6.91036582e-01 5.07465303e-01 -1.30312843e-02 -6.98569939e-02
1.57287419e+00 -9.96157050e-01 -6.96792424e-01 -2.76700109e-01
1.38597101e-01 -8.59762907e-01 9.50424731e-01 4.07325655e-01
-1.18111241e+00 -1.06717420e+00 -1.03131402e+00 -2.93768104e-02
2.61218578e-01 1.88849241e-01 5.45219123e-01 2.18623504e-01
-7.99808383e-01 6.59534633e-01 -1.25819206e+00 -3.42632771e-01
1.66965291e-01 4.09083605e-01 -5.04311502e-01 8.84677693e-02
-1.44988179e+00 5.80323040e-01 1.38881415e-01 3.67675871e-01
-8.35017204e-01 -4.32527184e-01 -8.88341367e-01 -1.61862686e-01
7.02515125e-01 -8.47490430e-01 1.28413439e+00 -5.41189611e-01
-1.50013781e+00 4.40461844e-01 -1.17216490e-01 -3.27954799e-01
6.99840844e-01 -8.98547649e-01 -1.10491328e-01 4.80934680e-01
8.84529725e-02 4.58084852e-01 1.19893622e+00 -6.03060901e-01
-5.83037436e-01 -5.61154902e-01 9.65126827e-02 4.82856989e-01
-4.37889516e-01 -1.00232489e-01 -9.40672636e-01 -1.10378444e+00
2.53730476e-01 -9.71883476e-01 -1.90510854e-01 2.49611244e-01
-1.48862839e-01 -3.08596939e-01 7.04356313e-01 -6.92898273e-01
1.24297786e+00 -2.16286874e+00 4.86218095e-01 -1.32393524e-01
2.41635531e-01 3.31884652e-01 -1.44559415e-02 3.54547292e-01
1.38739288e-01 -4.17142034e-01 2.12766171e-01 -3.03513318e-01
-1.30509287e-01 4.76866178e-02 -3.41210552e-02 8.40288579e-01
8.80954117e-02 8.49964976e-01 -8.80906284e-01 -6.80590987e-01
3.84421140e-01 7.68214583e-01 -6.14346743e-01 2.96732634e-01
1.53856754e-01 6.48919225e-01 -6.76262617e-01 6.75233781e-01
4.10592288e-01 -3.67716283e-01 1.36265129e-01 -5.62509418e-01
1.40595034e-01 9.56155136e-02 -1.38540471e+00 2.19504857e+00
-2.63027936e-01 2.96036422e-01 1.69865727e-01 -9.52598929e-01
5.96880019e-01 5.66350460e-01 7.58839607e-01 -5.70574045e-01
2.78521806e-01 1.59380257e-01 -1.78074270e-01 -5.08262336e-01
2.47405216e-01 1.60013273e-01 -1.60555273e-01 -2.91158948e-02
2.74820387e-01 2.91485131e-01 2.05662683e-01 -6.21803738e-02
1.07009792e+00 5.97168565e-01 4.32319969e-01 -2.09119841e-01
5.88739276e-01 -4.53232169e-01 7.27534890e-01 4.76624072e-01
-5.15629351e-01 6.33322418e-01 9.94915515e-02 -4.80017483e-01
-8.01884949e-01 -1.31415987e+00 -2.30937880e-02 1.15517771e+00
3.98380131e-01 -5.61620176e-01 -6.50988102e-01 -5.66963494e-01
9.00904983e-02 -2.35392869e-01 -5.20921350e-01 -2.61112273e-01
-1.03204131e+00 -4.71067905e-01 5.63828647e-01 8.68109286e-01
6.40785933e-01 -8.17713320e-01 -1.19620347e+00 3.99534106e-01
-6.92472637e-01 -1.23940456e+00 -1.02227569e+00 -1.49226055e-01
-8.92906964e-01 -1.06757498e+00 -1.13871932e+00 -7.04573810e-01
2.70844400e-01 3.58188987e-01 8.85318816e-01 4.54636291e-02
-4.53394353e-01 3.98854285e-01 -3.96505952e-01 -3.33256237e-02
2.70139754e-01 -4.28979397e-02 4.37223047e-01 -9.62932408e-03
5.70210554e-02 -5.69699824e-01 -9.94729400e-01 5.87798774e-01
-5.61424434e-01 -6.70200437e-02 5.85325778e-01 1.08077300e+00
4.51593608e-01 -6.90679997e-02 3.15025747e-01 -5.91226071e-02
2.89474994e-01 -1.00363441e-01 -2.69008189e-01 -5.26669845e-02
-9.97877866e-02 -8.06274638e-02 5.14470577e-01 -6.64642751e-01
-8.60881805e-01 2.04751030e-01 -2.26657957e-01 -8.60673845e-01
7.60988668e-02 3.35398972e-01 -6.88592568e-02 7.88256526e-02
4.76807833e-01 2.88264275e-01 2.06616461e-01 -5.86655617e-01
8.45632777e-02 2.33450428e-01 8.93615305e-01 -5.77201664e-01
7.94640839e-01 4.23440546e-01 2.44455487e-02 -7.72616148e-01
-6.98240995e-01 -6.94108844e-01 -7.63447106e-01 -4.54761863e-01
7.39444017e-01 -1.23713231e+00 -8.15983891e-01 6.21625245e-01
-1.02491558e+00 -3.65043469e-02 1.91632956e-02 8.45496833e-01
-6.65775180e-01 7.20747232e-01 -9.52929378e-01 -7.00271308e-01
-4.38638210e-01 -1.24960339e+00 1.32073104e+00 2.13465746e-02
-4.28613037e-01 -6.06127143e-01 -1.76073730e-01 2.54823267e-01
1.58026859e-01 3.99615556e-01 3.69479775e-01 -1.27071023e-01
-2.65686721e-01 -2.56294072e-01 4.33184206e-02 2.22394213e-01
1.44356847e-01 -5.40683508e-01 -7.23064184e-01 -6.22086525e-01
9.63848159e-02 -4.81921852e-01 7.87123263e-01 5.01486719e-01
9.32450473e-01 -1.49461001e-01 -2.49545217e-01 5.75906873e-01
1.04104614e+00 -8.52527842e-02 3.69890004e-01 2.01042905e-01
6.08165920e-01 4.37617898e-01 8.77043366e-01 6.18065536e-01
2.73896784e-01 1.17228520e+00 3.14698100e-01 -1.02315538e-01
-3.51898611e-01 -2.45199502e-01 6.85382783e-01 8.35293949e-01
-5.71242929e-01 4.29441631e-02 -6.10501468e-01 3.62507522e-01
-2.05392814e+00 -9.26636517e-01 1.53569952e-01 2.15165973e+00
8.02536011e-01 2.34405473e-01 4.99753267e-01 3.30141932e-01
6.30403399e-01 2.87690938e-01 -5.67784607e-01 4.10907388e-01
7.99690038e-02 1.69502288e-01 1.86191604e-01 1.97492421e-01
-1.41210794e+00 6.89828992e-01 5.46244669e+00 9.25803900e-01
-9.43890870e-01 2.16818497e-01 2.05075175e-01 -4.33399916e-01
5.24484158e-01 -4.04789448e-01 -6.98118508e-01 4.93568808e-01
5.03007531e-01 2.60649100e-02 1.72073022e-01 7.67195225e-01
1.59964219e-01 7.53105953e-02 -1.02395725e+00 1.14322054e+00
-2.50330698e-02 -8.47947240e-01 -2.47284234e-01 -7.81676546e-02
1.81674823e-01 -8.12551975e-02 7.34214187e-02 1.55688375e-01
-2.49170259e-01 -7.93151379e-01 1.04372060e+00 3.40402842e-01
7.70348489e-01 -7.53264606e-01 6.96914971e-01 3.13409686e-01
-1.81869555e+00 -1.12319835e-01 -2.28390694e-01 -1.70229480e-01
2.95740098e-01 5.09654164e-01 -3.87195230e-01 5.82343698e-01
1.15409315e+00 8.47186685e-01 -3.03084582e-01 8.12664092e-01
-2.16951549e-01 3.99623930e-01 -4.54922467e-01 1.63238108e-01
5.58875464e-02 2.69064635e-01 8.03903461e-01 1.16526854e+00
3.22339147e-01 1.86193988e-01 6.14860535e-01 3.47815424e-01
1.92619532e-01 -1.02897435e-01 -2.21741423e-01 2.05888256e-01
3.56933892e-01 1.09587133e+00 -6.43875599e-01 -2.13884383e-01
-3.73707086e-01 1.16283894e+00 1.83878586e-01 2.87095189e-01
-1.09247780e+00 -3.29802275e-01 7.50907481e-01 2.42847607e-01
4.98889148e-01 -5.88485241e-01 2.65355408e-01 -1.31969810e+00
3.34078968e-01 -1.00088429e+00 5.37719309e-01 -4.00641322e-01
-1.11190867e+00 4.62740779e-01 8.28005373e-02 -1.33634329e+00
-3.22003931e-01 -5.14329195e-01 -1.01444460e-01 5.53403795e-01
-9.45030212e-01 -1.04727709e+00 -3.12151700e-01 8.45895767e-01
8.59067857e-01 1.77019194e-01 6.53380573e-01 5.16680241e-01
-4.38778013e-01 6.75719202e-01 -3.54369164e-01 2.55448431e-01
8.56825411e-01 -9.16987419e-01 4.69584227e-01 8.75337005e-01
1.18347339e-01 6.89110577e-01 8.25508714e-01 -6.90159321e-01
-1.74476016e+00 -8.17689061e-01 5.73104262e-01 -3.37679297e-01
4.45241511e-01 -4.94094849e-01 -6.87189162e-01 4.96064216e-01
-6.12088442e-02 3.10121268e-01 3.13765973e-01 -9.85547081e-02
-2.30323315e-01 -1.32883847e-01 -7.41388679e-01 4.97103423e-01
1.41274023e+00 -4.16811675e-01 -6.66712284e-01 1.94245398e-01
5.69843590e-01 -7.19844878e-01 -1.05201483e+00 6.86413586e-01
9.38530028e-01 -9.44479108e-01 1.35309470e+00 -1.61690876e-01
1.74175113e-01 -4.24418598e-01 -2.23153874e-01 -9.89250064e-01
-7.34309971e-01 -7.12731123e-01 -5.73553503e-01 7.16886640e-01
-3.15741777e-01 -2.53988564e-01 5.74117184e-01 -1.10993177e-01
4.80156541e-02 -9.78819013e-01 -1.23282206e+00 -8.38713884e-01
-3.64156365e-01 -3.53684336e-01 2.46844113e-01 4.17414159e-01
-4.01196741e-02 3.48476410e-01 -8.42258513e-01 1.83758572e-01
8.03002119e-01 4.86888774e-02 8.10970366e-01 -9.54390645e-01
-6.48989141e-01 -1.85752362e-01 -6.71387911e-01 -1.58716774e+00
-2.81438053e-01 -4.50805366e-01 1.76870421e-01 -1.05128956e+00
1.49344921e-01 1.01446383e-01 -3.20606798e-01 4.52327967e-01
-3.41409534e-01 5.08591294e-01 4.54171449e-01 3.42784137e-01
-6.10860050e-01 7.29070008e-01 1.40569973e+00 -1.64677191e-03
3.16242054e-02 5.38480543e-02 -6.68233857e-02 8.93066287e-01
4.36439842e-01 -1.72376946e-01 -4.10947919e-01 -5.04423440e-01
-2.25870684e-01 3.23406875e-01 7.10999727e-01 -1.28952754e+00
1.86477512e-01 1.39369726e-01 8.73396575e-01 -6.54381394e-01
6.49366736e-01 -6.34117126e-01 1.51101172e-01 8.20403278e-01
-1.35958999e-01 3.54030728e-01 -4.60176915e-03 7.26433635e-01
-1.92310557e-01 3.80580246e-01 8.27760577e-01 -2.76404798e-01
-9.13293481e-01 5.26571751e-01 -2.29454964e-01 5.71702458e-02
8.33778322e-01 -1.77828982e-01 2.60821670e-01 -3.74753714e-01
-7.28929579e-01 2.36647815e-01 2.97990173e-01 6.59145772e-01
8.05698276e-01 -1.51867402e+00 -6.24083400e-01 2.85157949e-01
-1.67401567e-01 -1.12734944e-01 4.25825387e-01 1.26418555e+00
-2.56642133e-01 4.18320060e-01 -2.82519162e-01 -9.11408067e-01
-1.31196284e+00 4.86688584e-01 1.12289183e-01 -4.22847599e-01
-1.07903719e+00 1.00917363e+00 3.50863785e-01 4.16816957e-03
4.93212879e-01 -2.54946232e-01 -4.61607873e-02 -2.43934561e-02
6.12058699e-01 4.26315993e-01 -7.99155757e-02 -8.40044737e-01
-6.26182735e-01 7.89164543e-01 1.05658911e-01 7.24660978e-02
1.09202290e+00 -3.11716229e-01 3.18043947e-01 2.81996578e-01
1.18613899e+00 -2.34887853e-01 -1.55420375e+00 -4.67409700e-01
-2.65667409e-01 -5.81536710e-01 -1.03598632e-01 -2.25932807e-01
-1.21591663e+00 8.63431752e-01 7.29842007e-01 -3.11226457e-01
1.34214389e+00 -3.55951898e-02 1.21905220e+00 1.52712375e-01
6.85064912e-01 -1.07631731e+00 6.01473868e-01 3.61357361e-01
1.02593672e+00 -1.05173612e+00 2.52763718e-01 -4.19609547e-01
-3.60224098e-01 1.09135699e+00 7.14598298e-01 -2.59295583e-01
6.19271338e-01 1.52599931e-01 -6.28716871e-02 -1.19894981e-01
-5.52921355e-01 -1.29006207e-01 5.46089888e-01 3.83714348e-01
5.49350679e-01 -1.42116532e-01 -2.89049685e-01 6.74036980e-01
-1.33990183e-01 6.26541302e-02 -1.20041616e-01 1.02426720e+00
-3.49307060e-01 -8.54963660e-01 -6.72783494e-01 6.59219250e-02
-5.96584678e-01 2.35495970e-01 4.10622135e-02 8.96382809e-01
1.46740437e-01 8.22721779e-01 -2.20169246e-01 -6.24725819e-01
5.70447981e-01 -2.29671761e-01 7.24182189e-01 -2.82468110e-01
-3.89927387e-01 7.45086551e-01 -8.30785930e-02 -1.08982384e+00
-6.42262816e-01 -7.54447043e-01 -1.19541216e+00 -1.14388056e-01
-3.02821070e-01 -6.57644868e-02 5.04091084e-02 7.30230451e-01
3.08242559e-01 6.16007626e-01 3.92666340e-01 -1.36114287e+00
-9.03791487e-01 -1.04609847e+00 -5.35189509e-01 4.08476204e-01
4.44171757e-01 -1.17016399e+00 4.68523204e-02 9.30210575e-02] | [7.174097537994385, -0.6407365798950195] |
81a2b44a-097b-4274-9ac9-5152e0bd2c5f | an-efficient-keyframes-selection-based | null | null | https://aclanthology.org/2021.icon-main.29 | https://aclanthology.org/2021.icon-main.29.pdf | An Efficient Keyframes Selection Based Framework for Video Captioning | Describing a video is a challenging yet attractive task since it falls into the intersection of computer vision and natural language generation. The attention-based models have reported the best performance. However, all these models follow similar procedures, such as segmenting videos into chunks of frames or sampling frames at equal intervals for visual encoding. The process of segmenting video into chunks or sampling frames at equal intervals causes encoding of redundant visual information and requires additional computational cost since a video consists of a sequence of similar frames and suffers from inescapable noise such as uneven illumination, occlusion and motion effects. In this paper, a boundary-based keyframes selection approach for video description is proposed that allow the system to select a compact subset of keyframes to encode the visual information and generate a description for a video without much degradation. The proposed approach uses 3 4 frames per video and yields competitive performance over two benchmark datasets MSVD and MSR-VTT (in both English and Hindi). | ['Sivaji Bandyopadhyay', 'Thoudam Doren Singh', 'Salam Michael Singh', 'Loitongbam Sanayai Meetei', 'Alok Singh'] | null | null | null | null | icon-2021-12 | ['video-description'] | ['computer-vision'] | [ 3.62171799e-01 -1.41317725e-01 -3.42010707e-01 -2.11136326e-01
-7.21858442e-01 -5.18088281e-01 6.11733556e-01 1.89737111e-01
-4.28759634e-01 8.43778968e-01 3.41381937e-01 1.52925104e-01
3.73787582e-01 -4.89228696e-01 -6.58474505e-01 -7.92306244e-01
-1.35620171e-02 1.17486052e-01 4.84003156e-01 1.02304988e-01
4.88133579e-01 3.51430476e-01 -1.82225716e+00 5.04013300e-01
4.61002439e-01 9.50601280e-01 7.44202435e-01 7.22076714e-01
-3.72431815e-01 1.05302334e+00 -6.61323071e-01 -1.25759020e-01
1.86340332e-01 -7.05088854e-01 -8.74599278e-01 5.63208580e-01
5.40906012e-01 -5.04961669e-01 -5.11693954e-01 9.50672388e-01
4.96393174e-01 5.22543550e-01 6.21488273e-01 -1.30591822e+00
-5.34873664e-01 5.17002285e-01 -7.20804155e-01 4.21028048e-01
8.11453044e-01 -8.88681319e-03 8.59274268e-01 -8.87375653e-01
9.91948962e-01 1.24158192e+00 2.33461112e-01 7.52077997e-01
-1.02580678e+00 -4.38699365e-01 1.70856550e-01 6.49206638e-01
-1.48547280e+00 -4.91750598e-01 6.08033419e-01 -3.72122079e-01
9.71871376e-01 3.87238204e-01 8.39850605e-01 9.55805779e-01
2.69296736e-01 9.40629780e-01 4.53305215e-01 -4.00694162e-01
2.78939992e-01 7.71969706e-02 -6.50856569e-02 3.91931742e-01
8.17280561e-02 -2.45947972e-01 -5.59686184e-01 -3.60229686e-02
8.65896583e-01 1.87278375e-01 -5.83265722e-01 -1.57001197e-01
-1.41815841e+00 8.13349068e-01 2.24519029e-01 1.39814392e-01
-6.99341118e-01 8.76436159e-02 6.93157136e-01 1.84910328e-04
4.43903767e-02 1.66753069e-01 1.11138113e-01 -1.51164189e-01
-1.23733425e+00 4.62795347e-01 2.94638395e-01 1.41929817e+00
6.75870478e-01 2.86763132e-01 -5.98370433e-01 8.43476951e-01
9.44012925e-02 3.80759090e-01 5.60744524e-01 -1.11233735e+00
6.20711207e-01 2.20156655e-01 2.50372946e-01 -1.18144250e+00
7.26376772e-02 2.30519190e-01 -8.62082839e-01 -2.82212477e-02
9.52209532e-02 -9.30992514e-02 -1.04234517e+00 1.51521039e+00
1.11371621e-01 2.08930895e-01 1.79880381e-01 1.23166287e+00
1.12461567e+00 1.34774303e+00 2.80063838e-01 -6.15377903e-01
1.36630344e+00 -9.72150981e-01 -9.49861646e-01 5.42387590e-02
-1.27971977e-01 -9.82694030e-01 8.00096810e-01 2.46211290e-01
-1.50924540e+00 -8.02654564e-01 -1.01002395e+00 -1.44015417e-01
1.18258052e-01 -7.49013349e-02 2.07522690e-01 6.75314432e-03
-9.65611577e-01 2.78444260e-01 -4.26244617e-01 -4.60655123e-01
1.36419699e-01 2.66108543e-01 -5.23137569e-01 -2.39988983e-01
-1.01071513e+00 5.06731987e-01 5.82729936e-01 -9.61470976e-02
-1.16626549e+00 -2.88748771e-01 -1.05709994e+00 9.13570002e-02
3.24572891e-01 -5.26351094e-01 1.09944105e+00 -1.27897096e+00
-1.17079353e+00 5.45396090e-01 -5.10103703e-01 -6.10820830e-01
3.81916583e-01 -1.29666016e-01 -1.47179738e-01 5.17685235e-01
5.36258705e-02 1.15955222e+00 1.08699322e+00 -1.10920286e+00
-9.13385034e-01 2.86417380e-02 1.01294205e-01 4.49925572e-01
-7.38066956e-02 1.79236785e-01 -8.81191194e-01 -8.53024960e-01
-4.35587130e-02 -9.01876926e-01 -5.30527160e-02 -2.48025239e-01
-2.57653326e-01 -1.54312640e-01 9.43709791e-01 -6.15530193e-01
1.35400236e+00 -2.06075120e+00 3.52610767e-01 -3.27823788e-01
3.88855115e-02 3.30512643e-01 -1.13094814e-01 5.20752013e-01
2.42042611e-03 1.87769476e-02 -6.32148013e-02 -1.17860869e-01
-3.42120826e-01 1.18534066e-01 -2.42181286e-01 3.03897887e-01
1.60546958e-01 4.68028903e-01 -8.59389246e-01 -7.99471736e-01
4.04534489e-01 6.15715802e-01 -5.98082662e-01 5.15597463e-01
-2.48611569e-01 3.51411700e-01 -2.60879546e-01 5.18284857e-01
6.67592525e-01 9.84362364e-02 -1.45656988e-02 -4.69139308e-01
-1.88575044e-01 -6.84936419e-02 -1.15845656e+00 1.60553133e+00
-1.98815420e-01 1.09038067e+00 -3.12554568e-01 -9.33231950e-01
8.71167779e-01 6.29027545e-01 4.07124072e-01 -5.10883033e-01
1.66059643e-01 -2.89976865e-01 -2.18993187e-01 -8.03945124e-01
7.33710170e-01 4.34232429e-02 3.17105651e-02 2.95785576e-01
3.61791588e-02 1.17737129e-01 5.60182035e-01 2.10876703e-01
9.71847832e-01 9.64683518e-02 5.34770012e-01 7.32358918e-02
7.27555573e-01 9.95889306e-02 8.44088793e-01 6.26593351e-01
-2.98735112e-01 1.05800462e+00 2.65717715e-01 -6.34162366e-01
-1.51821220e+00 -9.44836378e-01 2.09876239e-01 8.48861396e-01
4.42660123e-01 -5.55089176e-01 -7.09085763e-01 -3.65423173e-01
-3.57023746e-01 6.86383784e-01 -4.88374949e-01 -2.13223379e-02
-7.05318749e-01 -2.48381406e-01 1.45014152e-01 4.04321402e-01
6.66610539e-01 -1.41216922e+00 -9.11786914e-01 3.26314062e-01
-5.92356682e-01 -1.16238081e+00 -8.06823730e-01 -2.10362256e-01
-5.58247924e-01 -8.51008415e-01 -1.18113875e+00 -1.10492694e+00
8.22434545e-01 6.34568334e-01 1.01015675e+00 5.60257137e-02
-3.96011829e-01 6.65355474e-02 -7.16120660e-01 -6.52734563e-02
-3.67517471e-01 -3.26414198e-01 -1.80317625e-01 6.76408559e-02
1.89026564e-01 -3.72638255e-02 -6.81161702e-01 2.25943193e-01
-9.69393492e-01 4.40433770e-01 3.34699601e-01 8.57541680e-01
8.27385247e-01 6.13972507e-02 3.05927515e-01 -5.85886717e-01
3.90532970e-01 -5.54410756e-01 -4.43927616e-01 1.16089895e-01
9.31971669e-02 -5.36908731e-02 8.47773433e-01 -3.89613092e-01
-1.05901134e+00 2.40699172e-01 2.34187976e-01 -7.23107159e-01
-2.37919286e-01 1.89571887e-01 1.30748246e-02 2.32225686e-01
2.22739607e-01 7.18697369e-01 -1.61380649e-01 -2.10243747e-01
2.20047191e-01 7.55303144e-01 5.94487011e-01 -1.97822690e-01
4.55484003e-01 2.58449852e-01 -3.06633204e-01 -1.05996180e+00
-2.84078121e-01 -4.85943705e-01 -4.76336241e-01 -4.11442518e-01
1.12254179e+00 -9.75000739e-01 -4.28829551e-01 2.51524955e-01
-1.43764150e+00 1.75005034e-01 4.04080674e-02 4.98017073e-01
-7.01517999e-01 5.73279798e-01 -5.20159721e-01 -7.32513428e-01
-3.73847187e-01 -1.54383349e+00 9.35681999e-01 4.63354647e-01
-2.89367378e-01 -5.18311977e-01 -2.61426359e-01 1.76963985e-01
1.57437250e-01 2.84437805e-01 7.56617069e-01 -2.95520365e-01
-7.18327880e-01 -9.47669074e-02 -2.54030824e-01 2.91244268e-01
2.48597085e-01 1.89739034e-01 -4.65496153e-01 -3.12794596e-01
-3.60172510e-01 -2.02092662e-01 7.62393415e-01 4.84181970e-01
1.06410456e+00 -4.02857423e-01 -1.90623865e-01 4.97946918e-01
1.59673977e+00 7.94864058e-01 8.27385366e-01 2.81449616e-01
6.10168695e-01 4.56828952e-01 1.00739539e+00 6.69348896e-01
1.79766029e-01 8.22824836e-01 3.47901702e-01 2.27874368e-02
-1.69607714e-01 -9.25486237e-02 3.09751302e-01 5.81659377e-01
-2.82570809e-01 -6.34587884e-01 -6.54464662e-01 7.66125560e-01
-1.94902444e+00 -1.53898096e+00 4.37303893e-02 2.38458824e+00
5.84607244e-01 -1.50126308e-01 2.15967640e-01 1.47884741e-01
1.11748266e+00 2.41527766e-01 -3.86032701e-01 -6.02606893e-01
-3.58533822e-02 -2.83426106e-01 3.12434614e-01 3.57543230e-01
-1.00588512e+00 9.69023705e-01 6.43707275e+00 7.30455339e-01
-1.09322345e+00 -2.43671104e-01 6.37174606e-01 -2.50612348e-01
-6.10549711e-02 -9.87567604e-02 -8.45893860e-01 6.97230577e-01
6.88852668e-01 -3.40904713e-01 2.83238530e-01 6.62782788e-01
6.22875690e-01 -4.17094648e-01 -9.34782207e-01 1.33367825e+00
3.55345905e-01 -1.40362048e+00 6.59890652e-01 -3.92762393e-01
7.47705877e-01 -4.02651250e-01 -9.04714316e-02 -5.66550680e-02
-1.70441762e-01 -9.40901220e-01 1.05894053e+00 3.43100011e-01
7.40388989e-01 -9.61422801e-01 8.46421480e-01 5.82003072e-02
-1.33867133e+00 -8.57169852e-02 -7.07069576e-01 1.01207122e-01
3.76815945e-01 2.94311419e-02 -7.61408150e-01 3.01649243e-01
7.27421820e-01 7.87910759e-01 -2.87684470e-01 1.24634898e+00
1.57388017e-01 4.36446607e-01 1.56228691e-01 -8.54941271e-03
4.24628586e-01 -1.55766889e-01 7.29150712e-01 1.36239505e+00
4.95554596e-01 3.19044530e-01 3.15582305e-01 5.25532842e-01
1.78742200e-01 2.79833347e-01 -6.79897785e-01 -5.08046802e-03
5.76701164e-01 1.06114519e+00 -8.15477014e-01 -6.75686657e-01
-6.27332211e-01 1.23341811e+00 3.37960981e-02 3.88269722e-01
-1.14731252e+00 -4.36840773e-01 4.75972235e-01 2.73604542e-01
6.37356222e-01 -9.62554365e-02 3.27751279e-01 -1.04152203e+00
-1.01386599e-01 -9.11137700e-01 3.57701540e-01 -9.37450588e-01
-6.19156301e-01 9.68017161e-01 2.18344614e-01 -1.57793832e+00
-5.38719833e-01 -1.58908684e-02 -3.98013860e-01 6.18849158e-01
-1.24048412e+00 -7.22682774e-01 -7.24290967e-01 7.01820970e-01
1.39977884e+00 -3.22867364e-01 4.37196612e-01 3.01631570e-01
-4.88315195e-01 3.03021401e-01 2.82856170e-02 7.08178282e-02
5.95747828e-01 -1.10273683e+00 2.87444055e-01 7.70592034e-01
2.01559272e-02 3.49020988e-01 9.95183825e-01 -6.15282178e-01
-1.21519208e+00 -1.11739028e+00 9.68037605e-01 3.01519901e-01
4.07026559e-02 -1.07365705e-01 -7.94976115e-01 4.55787629e-01
6.12149835e-01 -6.54627308e-02 4.85729933e-01 -7.90732563e-01
-1.46775572e-02 -1.38259521e-02 -9.90177691e-01 7.58527935e-01
8.13006878e-01 -1.22471295e-01 -4.68301982e-01 1.71557069e-01
5.70024490e-01 -2.84527481e-01 -5.03126025e-01 1.14530854e-01
4.62255687e-01 -1.16643476e+00 8.41315627e-01 -3.35505664e-01
5.48441529e-01 -5.16704798e-01 -3.09755802e-01 -9.76950645e-01
-3.87127966e-01 -7.72521675e-01 1.88844174e-01 1.39091492e+00
1.13849483e-01 1.34657085e-01 5.94410956e-01 4.27266270e-01
9.01776701e-02 -4.77269322e-01 -5.73493421e-01 -5.46427488e-01
-5.85372508e-01 -1.38813788e-02 2.38882646e-01 6.38843536e-01
-8.03098530e-02 3.27261090e-01 -7.85114467e-01 -1.67858824e-01
6.95363343e-01 8.50387961e-02 6.90595210e-01 -7.64700294e-01
2.22558565e-02 -1.66824050e-02 -7.19322979e-01 -9.66948867e-01
1.30626664e-01 -5.63570142e-01 3.09722364e-01 -1.62171900e+00
6.37568891e-01 3.65124382e-02 -3.78013775e-02 2.31612727e-01
-1.89040124e-01 4.05115098e-01 5.43963790e-01 3.88001740e-01
-8.12588573e-01 3.69447052e-01 1.07620776e+00 -2.28910059e-01
-1.91565961e-01 -3.09196264e-01 -2.06554651e-01 5.37461400e-01
5.10262311e-01 -3.16269994e-01 -7.91629076e-01 -4.71655279e-01
-3.57477218e-01 5.97650230e-01 2.19613522e-01 -1.09979689e+00
1.06044091e-01 -2.79921204e-01 5.82919180e-01 -6.97473943e-01
3.81334424e-01 -7.67810106e-01 5.50887465e-01 3.51736873e-01
-5.51366150e-01 4.37748194e-01 2.01902241e-01 5.10019422e-01
-5.50135791e-01 -3.91094357e-01 1.02332735e+00 -4.41839188e-01
-1.33975923e+00 2.97638774e-01 -7.12454975e-01 -6.64676130e-02
1.41578865e+00 -5.75527608e-01 2.29960270e-02 -6.90839827e-01
-7.33293593e-01 2.24954896e-02 4.68316942e-01 6.41458929e-01
9.06357050e-01 -1.51667702e+00 -8.58215332e-01 7.27838948e-02
1.80301089e-02 -1.72637090e-01 4.41834420e-01 3.34784359e-01
-9.35721576e-01 5.75509608e-01 -6.27935350e-01 -6.99760616e-01
-1.76306355e+00 6.73796117e-01 -1.34153113e-01 1.45536482e-01
-7.60265350e-01 7.12977588e-01 4.21026438e-01 7.94829547e-01
3.27450693e-01 -1.53412133e-01 -5.81559002e-01 2.29179308e-01
8.24631989e-01 3.20563555e-01 -3.87614191e-01 -1.24330223e+00
-2.40764439e-01 6.80744350e-01 -3.36607486e-01 -1.00930855e-01
1.05944002e+00 -5.16006887e-01 1.94003224e-01 2.79177070e-01
1.37720048e+00 -3.40254307e-01 -1.31570780e+00 -7.75939599e-02
-2.52616018e-01 -7.54228711e-01 -4.21566628e-02 -2.84873635e-01
-1.12331474e+00 6.67829692e-01 3.81151795e-01 1.16132095e-01
1.28844202e+00 -1.45575345e-01 9.79031205e-01 -9.45714936e-02
3.69834542e-01 -1.00547171e+00 1.78328022e-01 1.57342345e-01
1.01479042e+00 -1.16873205e+00 -1.26908626e-02 -3.40029031e-01
-1.08425152e+00 1.11637342e+00 6.30602479e-01 -1.98419303e-01
4.92899865e-02 9.60583314e-02 5.17444015e-02 1.29760385e-01
-1.02057087e+00 -1.86500281e-01 2.71292359e-01 7.51355827e-01
5.50505817e-01 -2.88645595e-01 -6.76592708e-01 1.86088294e-01
1.78948611e-01 -9.52934027e-02 7.83024430e-01 8.72894704e-01
-6.57737911e-01 -7.57181346e-01 -4.99981076e-01 1.30381942e-01
-6.24782801e-01 -4.65440750e-02 -1.53716505e-01 6.60279155e-01
1.33004814e-01 9.28118825e-01 3.19522768e-01 -2.02907756e-01
8.18138048e-02 -1.05880015e-01 4.01218951e-01 -5.61480224e-01
-4.14202482e-01 4.39031869e-01 -5.89624606e-02 -4.84938413e-01
-5.30346572e-01 -7.13199854e-01 -1.46869910e+00 -2.30521575e-01
-1.01600960e-01 2.93594599e-01 3.80699873e-01 6.18152976e-01
2.39254892e-01 3.64523709e-01 6.07376277e-01 -1.15436649e+00
-5.21051213e-02 -7.78631866e-01 -5.31422079e-01 7.48036623e-01
4.89552408e-01 -4.94041145e-01 -5.56557141e-02 7.33294904e-01] | [10.448274612426758, 0.5220648050308228] |
bda534f6-68f6-4f89-9a27-d0158165834f | busem-at-semeval-2017-task-4a-sentiment | null | null | https://aclanthology.org/S17-2131 | https://aclanthology.org/S17-2131.pdf | BUSEM at SemEval-2017 Task 4A Sentiment Analysis with Word Embedding and Long Short Term Memory RNN Approaches | This paper describes our approach for SemEval-2017 Task 4: Sentiment Analysis in Twitter. We have participated in Subtask A: Message Polarity Classification subtask and developed two systems. The first system uses word embeddings for feature representation and Support Vector Machine, Random Forest and Naive Bayes algorithms for classification of Twitter messages into negative, neutral and positive polarity. The second system is based on Long Short Term Memory Recurrent Neural Networks and uses word indexes as sequence of inputs for feature representation. | ['Murat Saraclar', 'Arzucan Ozgur', 'Deger Ayata'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['subjectivity-analysis'] | ['natural-language-processing'] | [ 5.15651293e-02 2.17656910e-01 -5.54199457e-01 -5.77006042e-01
-1.28503412e-01 -4.45599586e-01 9.88480091e-01 6.70048535e-01
-7.15450346e-01 7.32130706e-01 7.68694699e-01 -6.58642471e-01
3.72527748e-01 -8.54349494e-01 5.50823845e-03 -3.05535197e-01
-9.92527977e-02 5.14565587e-01 2.45159697e-02 -9.48487997e-01
7.28055596e-01 1.17673084e-01 -1.51565111e+00 7.78015435e-01
4.70777601e-02 1.27607071e+00 -5.84290802e-01 1.06750774e+00
-6.75706685e-01 1.21501911e+00 -5.30801594e-01 -3.30595613e-01
-1.58010468e-01 -6.18752055e-02 -1.14402199e+00 -5.58014691e-01
-1.43346623e-01 4.42464352e-01 1.48180827e-01 6.72628164e-01
6.52581751e-01 1.01621002e-01 1.01063991e+00 -1.21378028e+00
-6.21893823e-01 7.33257473e-01 -3.47989500e-01 5.22225738e-01
7.85341263e-01 -3.91106129e-01 1.26375079e+00 -1.14406252e+00
7.00788379e-01 1.06791914e+00 9.93388951e-01 4.57227290e-01
-8.46560240e-01 -6.93103254e-01 1.73964977e-01 3.70520890e-01
-6.26309097e-01 -2.08751142e-01 7.29651749e-01 -6.49535358e-01
1.65853000e+00 2.21672192e-01 1.00119257e+00 1.30090272e+00
6.54797971e-01 8.22025418e-01 1.37650716e+00 -2.87017554e-01
1.87664255e-01 6.89335763e-01 1.02128124e+00 1.05612352e-01
-2.64090635e-02 -2.27520257e-01 -5.83485961e-01 -4.50658858e-01
-4.90352988e-01 -1.60631746e-01 1.41750723e-01 1.45817995e-01
-9.01238441e-01 1.28663886e+00 5.00691295e-01 4.95641440e-01
-5.77395916e-01 -2.98595518e-01 9.37557280e-01 9.33905661e-01
9.33392763e-01 7.76663065e-01 -9.98641789e-01 -3.09285019e-02
-6.68520331e-01 3.37146521e-01 1.21788800e+00 1.99044436e-01
5.44355273e-01 1.44605279e-01 -1.91978693e-01 9.64919567e-01
5.95043480e-01 5.82035840e-01 1.37674308e+00 1.15403540e-01
3.88161808e-01 8.47517967e-01 3.61060277e-02 -1.34237516e+00
-8.05246890e-01 1.16363615e-01 -4.91392076e-01 1.11510092e-02
-2.87541270e-01 -6.86118424e-01 -1.20325947e+00 1.02910650e+00
-8.55001435e-02 -6.95884973e-02 5.77839553e-01 3.82445276e-01
1.50195956e+00 1.13964701e+00 2.97483057e-01 -3.52350622e-01
1.46542072e+00 -7.41069376e-01 -7.65110910e-01 -3.80741954e-01
8.31761599e-01 -9.42456424e-01 8.77128303e-01 3.94963264e-01
-7.57116199e-01 -1.04894087e-01 -1.14807820e+00 9.55618620e-02
-1.48809707e+00 -4.92951155e-01 6.29583776e-01 7.32986152e-01
-1.27606499e+00 2.71618843e-01 -2.18181103e-01 -1.68669492e-01
1.29779920e-01 6.93201303e-01 -3.94336909e-01 7.96064854e-01
-1.74625707e+00 1.16100740e+00 -1.51789645e-02 2.41550617e-02
-2.19466314e-01 -2.37362534e-01 -1.35333705e+00 -2.40613252e-01
-5.93645692e-01 -1.83228448e-01 1.43557239e+00 -1.31779110e+00
-1.79609096e+00 1.18830740e+00 -4.88334805e-01 -5.75703442e-01
-1.64295256e-01 -3.94287854e-02 -8.12323987e-01 -3.75854343e-01
1.15125537e-01 2.62507796e-01 8.51360500e-01 -5.92327714e-01
-4.66243774e-01 -3.55257690e-01 -4.63361531e-01 2.46688604e-01
-4.01880413e-01 4.57050443e-01 4.73684698e-01 -4.94860351e-01
-1.44005120e-01 -1.01307666e+00 -4.26043332e-01 -1.29041612e+00
-3.53079438e-01 -6.08285964e-01 9.36099172e-01 -2.31796578e-01
1.14016509e+00 -1.74670851e+00 -1.80043057e-01 5.88810503e-01
1.92924757e-02 2.12534010e-01 -7.71599263e-02 5.76152027e-01
-7.05458283e-01 2.72562504e-01 2.37983033e-01 -2.18869895e-01
-1.60965145e-01 -1.85777903e-01 -8.42733145e-01 2.83180296e-01
1.14631601e-01 8.62546146e-01 -6.05261266e-01 -2.99986333e-01
8.37649181e-02 5.48239231e-01 -1.97514817e-01 7.00536296e-02
3.39533426e-02 -2.34385818e-01 -2.99272627e-01 5.22173166e-01
4.30170327e-01 8.12691748e-02 2.30053559e-01 -5.01653142e-02
-4.93337363e-01 7.96157539e-01 -7.64010847e-01 4.70578223e-01
-6.86015189e-01 1.10417807e+00 -4.20061946e-01 -8.55256438e-01
1.10548723e+00 5.30574501e-01 1.45850897e-01 -7.96055138e-01
7.33983159e-01 1.74076900e-01 -3.86813581e-01 -4.44665015e-01
1.04367638e+00 -5.07717609e-01 -5.36781609e-01 4.50169355e-01
1.04552649e-01 -2.28071824e-01 9.56149325e-02 1.49435341e-01
5.95314324e-01 -4.19867456e-01 5.23876131e-01 -2.26658180e-01
7.19876409e-01 3.04947179e-02 8.60581249e-02 3.24928492e-01
-1.37991339e-01 4.77249891e-01 1.05116129e+00 -8.39481294e-01
-5.39182425e-01 -4.66219991e-01 -8.10055509e-02 1.64804959e+00
-1.87134892e-01 -5.45998633e-01 -8.34689066e-02 -6.69786572e-01
-1.57849789e-01 8.14334691e-01 -1.18222797e+00 -7.25666881e-02
-3.76197666e-01 -1.05970407e+00 2.72107542e-01 3.23017895e-01
-2.19530120e-01 -1.60433018e+00 -2.70286232e-01 3.45500447e-02
1.13276787e-01 -6.12812221e-01 1.46257535e-01 7.16082394e-01
-5.55279553e-01 -9.33327258e-01 -3.54510754e-01 -1.10011446e+00
2.56318778e-01 -1.63655519e-01 1.23467743e+00 -4.37239975e-01
2.04871804e-01 4.59912084e-02 -5.40642858e-01 -1.02271390e+00
-9.59944949e-02 3.81434619e-01 1.08654939e-01 1.44074727e-02
9.28013802e-01 -2.51937181e-01 -3.69502395e-01 -9.36534256e-03
-5.60894847e-01 -6.14593446e-01 2.35058978e-01 6.59970105e-01
2.33666763e-01 -7.00768530e-01 6.68884099e-01 -1.35979140e+00
1.45263708e+00 -9.41989481e-01 -1.40222833e-01 -4.10989344e-01
-8.93908501e-01 2.17179190e-02 5.07849932e-01 -1.39415234e-01
-6.25620604e-01 -1.77177116e-01 -7.89619625e-01 4.86537188e-01
3.08492512e-01 9.15818036e-01 5.64755440e-01 2.16159463e-01
8.84660542e-01 -1.21389022e-02 -7.59191513e-02 3.00958641e-02
1.94802240e-01 1.21827900e+00 -2.55084008e-01 5.06603777e-01
2.07659811e-01 2.66497970e-01 -5.43330252e-01 -9.62817311e-01
-9.88787651e-01 -7.95774102e-01 -1.86027288e-01 -3.23819578e-01
1.10882044e+00 -9.92398858e-01 -6.94929481e-01 5.66142976e-01
-9.30108309e-01 -2.76328120e-02 -1.76534325e-01 3.67127061e-01
-2.14005768e-01 -3.82332921e-01 -7.10626364e-01 -5.98431826e-01
-9.60560679e-01 -8.03665221e-01 7.05497801e-01 1.25677854e-01
-9.95509803e-01 -1.24384570e+00 9.21334803e-01 -4.99723293e-02
6.42340541e-01 7.43504241e-02 6.20532393e-01 -1.40702212e+00
8.53406608e-01 -8.36913586e-01 7.87311718e-02 3.89327496e-01
-1.48975477e-01 1.86082527e-01 -1.10137975e+00 1.65169075e-01
-1.33657157e-01 -7.42890179e-01 1.29489100e+00 4.97107983e-01
6.92563772e-01 -2.93039501e-01 -2.98673332e-01 1.48149550e-01
1.01997685e+00 3.52668576e-02 6.47679090e-01 8.19001973e-01
3.53331029e-01 7.40669489e-01 6.42085910e-01 2.33124822e-01
7.18627930e-01 5.42895570e-02 3.55310977e-01 -4.93752398e-02
6.35916352e-01 3.89281549e-02 7.24791944e-01 9.51408446e-01
2.81392515e-01 -1.72099873e-01 -1.23334217e+00 7.21305132e-01
-1.70249677e+00 -1.10402620e+00 -5.34023821e-01 1.50206172e+00
6.50666773e-01 4.30814832e-01 2.23662391e-01 3.96735251e-01
4.87239957e-01 7.72449791e-01 -1.97649607e-03 -1.68676126e+00
-2.44974583e-01 2.66926914e-01 5.58862329e-01 7.86158144e-01
-1.32882702e+00 1.01639664e+00 7.31040382e+00 2.12564290e-01
-1.59425306e+00 7.91300833e-02 7.02624321e-01 2.37165345e-03
-3.95672441e-01 -2.75294662e-01 -7.59703338e-01 2.92443514e-01
1.61787105e+00 -1.88062072e-01 -4.74495888e-01 1.07999003e+00
-2.87896376e-02 -9.66872647e-02 -4.71708000e-01 7.10063040e-01
3.30614477e-01 -1.50237358e+00 1.56767383e-01 -4.06389922e-01
5.90691447e-01 8.52070272e-01 4.03117627e-01 8.33064258e-01
3.93635154e-01 -1.17518997e+00 5.04201651e-01 3.17616403e-01
3.98190618e-01 -9.81189132e-01 1.39457178e+00 -1.62667572e-01
-8.54459465e-01 -1.31829113e-01 -6.26982972e-02 -6.69244647e-01
1.82040840e-01 5.26620626e-01 -1.06170821e+00 -2.87757099e-01
7.03723490e-01 1.17479122e+00 -5.94240606e-01 2.19048291e-01
-1.89307183e-01 8.52077365e-01 2.10614614e-02 -8.13959897e-01
6.03318036e-01 2.53393240e-02 4.29278463e-01 1.80842042e+00
-3.46819192e-01 -4.20681655e-01 -1.19463652e-01 -5.22856414e-01
-2.21327871e-01 6.01375043e-01 -8.45750928e-01 -1.38150021e-01
-4.83600087e-02 1.43095148e+00 -8.06947231e-01 -6.31386697e-01
-2.18742386e-01 4.80630696e-01 1.12986006e-01 9.31332409e-02
-3.63113552e-01 -8.49915326e-01 5.00863433e-01 1.10809460e-01
2.75087088e-01 3.07098299e-01 -5.68563581e-01 -1.13398898e+00
-5.40939271e-01 -7.42829680e-01 4.80081141e-01 -6.91977382e-01
-1.18914437e+00 1.22073579e+00 -4.53905791e-01 -9.09237742e-01
-5.98225713e-01 -7.55665481e-01 -1.09435606e+00 7.48517871e-01
-1.72690701e+00 -9.22023237e-01 1.13628663e-01 6.55117989e-01
4.00275230e-01 -6.01482749e-01 1.12552118e+00 7.67650232e-02
-2.96264917e-01 2.38000274e-01 -7.96909556e-02 2.55942643e-02
7.86170065e-01 -1.42256773e+00 5.08133411e-01 -4.27503996e-02
-2.60686487e-01 4.58581835e-01 1.01826692e+00 -4.87777948e-01
-9.33811128e-01 -1.05273890e+00 1.91371334e+00 -5.57369292e-01
1.09606135e+00 -4.20253009e-01 -3.32385391e-01 7.53713310e-01
6.57872021e-01 -3.15302193e-01 1.43296456e+00 5.29021800e-01
-4.06941593e-01 1.23238318e-01 -9.34794962e-01 4.99140382e-01
-2.44179770e-01 -6.65932357e-01 -9.96663690e-01 4.75203693e-01
6.55434370e-01 -9.05865221e-04 -5.36081612e-01 3.23244631e-01
8.65148306e-01 -6.00641966e-01 6.46262228e-01 -9.07578409e-01
7.63658047e-01 -1.39630148e-02 -8.12164769e-02 -1.49218309e+00
9.29765031e-02 -3.54598671e-01 1.97952330e-01 6.82987988e-01
1.33688152e+00 -9.17722344e-01 7.94264793e-01 4.91451733e-02
2.68999785e-01 -1.05225956e+00 -5.98877430e-01 2.75314420e-01
1.07787572e-01 -9.03393745e-01 2.01657131e-01 1.10432446e+00
4.27462518e-01 1.33989215e+00 -3.77015352e-01 -4.59012270e-01
-4.77107733e-01 2.64250666e-01 5.94937444e-01 -1.24462867e+00
4.36921775e-01 -6.40240133e-01 -6.94266379e-01 -4.18223679e-01
4.73456860e-01 -9.45953190e-01 -2.47389510e-01 -1.61003506e+00
-2.07937926e-01 1.36430845e-01 -6.19996011e-01 3.92218441e-01
1.33218169e-01 6.96233988e-01 -4.70218249e-02 -2.21426889e-01
-8.47456157e-01 4.45780694e-01 2.18665287e-01 -2.14640662e-01
-4.52858180e-01 4.27437574e-01 -1.04826701e+00 1.00619125e+00
1.16203606e+00 -7.27999985e-01 -1.00223541e-01 1.43246874e-01
1.48810637e+00 -4.71125811e-01 -3.05722833e-01 -2.18482718e-01
-3.51684429e-02 1.02064848e-01 3.65386814e-01 -9.08555984e-01
3.23530644e-01 -5.25929034e-01 -6.75088942e-01 4.95068431e-01
-6.81788206e-01 5.94050527e-01 1.29824907e-01 3.42430860e-01
-6.98286712e-01 -1.69308752e-01 5.26323795e-01 -3.14410515e-02
-6.10558093e-01 7.74255171e-02 -1.35807800e+00 -1.84433296e-01
8.30363095e-01 -2.71751255e-01 -1.62580058e-01 -5.35745025e-01
-8.74603271e-01 5.46778262e-01 -8.59908480e-03 7.78263748e-01
8.11540425e-01 -1.07913291e+00 -6.29096627e-01 2.93977290e-01
3.12073529e-01 -7.56883442e-01 -1.77358493e-01 7.91478276e-01
-5.08446515e-01 4.50070083e-01 -1.43085539e-01 -9.99768823e-02
-1.59481227e+00 1.75618008e-01 1.87899396e-01 -5.39413989e-01
1.61557853e-01 1.11333883e+00 -5.57789266e-01 -9.58057463e-01
-1.26609817e-01 -1.78199098e-01 -1.67364848e+00 1.17943704e+00
7.97092080e-01 1.60635188e-01 2.20452100e-01 -1.08183026e+00
-5.98060787e-01 5.65063000e-01 -3.52097809e-01 -2.36857831e-01
1.53892958e+00 1.69231772e-01 -5.41320324e-01 1.26275229e+00
1.73024857e+00 2.45990589e-01 3.08799744e-01 2.15428561e-01
4.08998400e-01 1.90860271e-01 1.95397720e-01 -7.82991707e-01
-5.32721519e-01 7.07678795e-01 6.08188570e-01 9.18340385e-01
6.93142116e-01 -1.93510666e-01 6.73597515e-01 6.94297493e-01
-5.12654960e-01 -1.48062384e+00 -2.76894331e-01 1.40151131e+00
7.35318005e-01 -1.56179512e+00 -2.56364476e-02 3.40236515e-01
-1.19684875e+00 1.16318405e+00 2.49821499e-01 -5.43712378e-01
1.25378621e+00 9.61689278e-02 6.45089567e-01 -6.60992622e-01
-1.28133309e+00 -9.73328874e-02 3.27518553e-01 3.20295691e-01
1.08634496e+00 2.02059641e-01 -7.17374682e-01 6.05084836e-01
-7.50227332e-01 -3.41867298e-01 7.84738362e-01 9.97339010e-01
-5.34832180e-01 -8.54786992e-01 3.17847505e-02 8.17154884e-01
-1.02474880e+00 -3.38403434e-01 -9.84293818e-01 3.68676335e-01
-2.08508387e-01 1.07599831e+00 1.17460936e-01 -8.16921115e-01
4.73786265e-01 1.42109737e-01 -4.33942199e-01 -7.38755524e-01
-1.28074729e+00 -5.36746025e-01 6.14933789e-01 -4.72257048e-01
-5.34357667e-01 -5.73898554e-01 -1.13623798e+00 -3.94107960e-02
-3.14995021e-01 7.23186553e-01 1.34898138e+00 8.41978550e-01
3.05520684e-01 3.00553441e-01 1.05472898e+00 -9.71395850e-01
-1.86821222e-01 -1.35518408e+00 -3.06423783e-01 1.60012230e-01
7.84369409e-01 -3.08970571e-01 -6.09986007e-01 -2.75906473e-01] | [11.141216278076172, 7.035920143127441] |
7c0cc368-ddc3-4a2f-93b1-55eae0d32ea9 | robust-subspace-recovery-layer-for | 1904.00152 | null | https://arxiv.org/abs/1904.00152v2 | https://arxiv.org/pdf/1904.00152v2.pdf | Robust Subspace Recovery Layer for Unsupervised Anomaly Detection | We propose a neural network for unsupervised anomaly detection with a novel robust subspace recovery layer (RSR layer). This layer seeks to extract the underlying subspace from a latent representation of the given data and removes outliers that lie away from this subspace. It is used within an autoencoder. The encoder maps the data into a latent space, from which the RSR layer extracts the subspace. The decoder then smoothly maps back the underlying subspace to a "manifold" close to the original inliers. Inliers and outliers are distinguished according to the distances between the original and mapped positions (small for inliers and large for outliers). Extensive numerical experiments with both image and document datasets demonstrate state-of-the-art precision and recall. | ['Chieh-Hsin Lai', 'Dongmian Zou', 'Gilad Lerman'] | 2019-03-30 | null | https://openreview.net/forum?id=rylb3eBtwr | https://openreview.net/pdf?id=rylb3eBtwr | iclr-2020-1 | ['unsupervised-anomaly-detection-with-specified-5', 'unsupervised-anomaly-detection-with-specified-4', 'unsupervised-anomaly-detection-with-specified-7', 'unsupervised-anomaly-detection-with-specified-6', 'unsupervised-anomaly-detection-with-specified'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-7.80890733e-02 9.96572431e-03 1.16083892e-02 -2.11650357e-01
-7.78980017e-01 -5.03850222e-01 3.73941839e-01 -1.16283081e-01
-1.44846007e-01 1.69720918e-01 5.50570190e-01 1.30447417e-01
-1.47644579e-01 -2.46613473e-01 -9.81420159e-01 -8.85418773e-01
-1.84068158e-01 5.74445128e-01 -3.65566462e-01 1.37441367e-01
3.54294598e-01 8.60700727e-01 -1.30283427e+00 2.51286775e-01
8.11418056e-01 7.54722714e-01 -4.99579757e-01 5.26764691e-01
2.52206653e-01 3.58783424e-01 -4.84953135e-01 1.88366786e-01
7.75326610e-01 -9.09468159e-02 -1.42365023e-01 4.81956065e-01
6.48095548e-01 -6.87518358e-01 -9.41426635e-01 1.38830221e+00
1.46336302e-01 1.85914040e-01 9.14404511e-01 -1.12445915e+00
-7.00892985e-01 1.86570197e-01 -5.35270572e-01 -1.41218856e-01
3.46654028e-01 -1.59697384e-01 7.70235479e-01 -1.51964808e+00
5.35562813e-01 9.17732060e-01 7.07699060e-01 4.05302703e-01
-1.29541755e+00 -3.92458171e-01 -1.34470001e-01 -2.53415585e-01
-1.73936427e+00 -7.59884000e-01 9.11857069e-01 -7.99202919e-01
9.08785820e-01 2.58131325e-01 3.39717358e-01 1.14324844e+00
2.86973119e-01 9.04418528e-01 3.13093603e-01 -2.83721417e-01
4.32088763e-01 -1.51359200e-01 2.20634609e-01 4.70859319e-01
3.69133025e-01 3.61332327e-01 -6.64409101e-01 -4.37560946e-01
8.84067535e-01 4.49742347e-01 -3.61220807e-01 -9.30613518e-01
-1.18438661e+00 9.34982717e-01 3.27853858e-01 1.35548741e-01
-6.20654464e-01 -1.82246156e-02 1.48490384e-01 2.77873129e-01
3.73261392e-01 4.24916059e-01 -3.97653505e-02 2.50424713e-01
-1.50212276e+00 -8.33686441e-02 7.14526713e-01 9.62882400e-01
5.51624417e-01 5.67655325e-01 1.50129110e-01 8.09380949e-01
6.24721348e-01 3.93921703e-01 7.11262047e-01 -1.11122572e+00
2.81136423e-01 8.29238713e-01 9.93814319e-02 -1.22345710e+00
-8.50290582e-02 -6.20527506e-01 -1.31942725e+00 3.01356792e-01
2.64481246e-01 1.52070180e-01 -1.03574073e+00 1.14050806e+00
2.00750027e-02 5.55036783e-01 1.49170458e-01 1.11871076e+00
4.20728534e-01 8.50820720e-01 -6.27316415e-01 -1.38435304e-01
6.52592123e-01 -6.80273533e-01 -4.44818139e-01 -4.95369583e-01
4.43654865e-01 -5.81443548e-01 6.40665770e-01 5.82942128e-01
-1.00785136e+00 -3.74063432e-01 -1.38565540e+00 -4.96493466e-02
-2.23257527e-01 6.53981566e-01 -2.05389485e-02 -1.25117823e-01
-1.06655908e+00 8.72834086e-01 -1.05141079e+00 -3.76226872e-01
1.19753152e-01 3.39651257e-01 -6.97488606e-01 1.54131457e-01
-6.81270182e-01 4.26499486e-01 4.69022483e-01 3.40732515e-01
-1.03212702e+00 -2.32711896e-01 -1.02295840e+00 -1.25546788e-03
3.57400812e-03 -3.78898919e-01 5.92476606e-01 -1.14619386e+00
-1.25658023e+00 7.86095500e-01 -2.47017443e-01 -5.90867519e-01
4.96821493e-01 -7.71121681e-01 -6.75073445e-01 1.25672091e-02
1.87197253e-01 3.54064882e-01 1.66273463e+00 -1.32113981e+00
-5.40494323e-02 -5.95545590e-01 -9.95246053e-01 1.43726349e-01
-3.75246257e-01 5.25157638e-02 -6.34757400e-01 -1.10446966e+00
1.25550532e+00 -1.00441551e+00 -1.76530257e-01 -1.81553677e-01
-8.56844425e-01 4.77714762e-02 9.31075037e-01 -1.07592702e+00
1.28180754e+00 -2.59091711e+00 4.15030032e-01 8.18272471e-01
2.81100571e-01 -4.33509871e-02 -9.72673520e-02 4.06818122e-01
-7.87848830e-01 -4.18595195e-01 -3.20011705e-01 -2.31846496e-01
-1.49140060e-01 1.14619419e-01 -9.41140771e-01 1.06904471e+00
-1.43152937e-01 4.75329876e-01 -6.84671998e-01 1.49784416e-01
2.19945267e-01 3.54644984e-01 -6.21268690e-01 3.54168892e-01
6.03895374e-02 3.71032655e-01 -4.56685647e-02 6.84191465e-01
6.40977919e-01 -8.52449238e-02 -2.56946474e-01 -1.10782005e-01
4.96090669e-03 8.43749568e-02 -1.64865685e+00 1.56317592e+00
5.48589528e-01 7.82079339e-01 -8.02110955e-02 -7.52223551e-01
1.16139185e+00 2.75861025e-01 6.09186411e-01 3.10962070e-02
-8.60107597e-03 3.67538184e-01 -2.38705590e-01 -1.89852700e-01
6.40966773e-01 3.52463454e-01 -6.54041395e-02 2.47380957e-01
1.98370218e-01 4.80313540e-01 -1.40226513e-01 3.18643838e-01
9.45768297e-01 3.39636430e-02 2.96010196e-01 -1.62186876e-01
6.09358847e-01 -2.55991280e-01 6.85312331e-01 7.05023050e-01
-1.25892714e-01 9.65658844e-01 3.35885704e-01 -7.10164130e-01
-1.27076268e+00 -1.59258950e+00 8.24784487e-02 6.77942455e-01
-7.97028318e-02 -3.18102092e-01 -6.65161610e-01 -6.03183806e-01
3.39522034e-01 8.61736894e-01 -4.29685682e-01 -5.32096028e-01
-4.29617226e-01 -1.33291215e-01 4.31907564e-01 6.51018023e-01
1.01462945e-01 -8.14707041e-01 1.77283539e-04 5.47945872e-02
-4.09175921e-03 -7.75637567e-01 -4.70790446e-01 9.95793343e-02
-1.38186729e+00 -8.79140258e-01 -5.03088892e-01 -7.20076442e-01
1.32658303e+00 1.07679628e-02 7.02580571e-01 -8.65938738e-02
5.89702949e-02 1.77842960e-01 9.56488848e-02 2.56841094e-03
-6.23514056e-01 -4.43836927e-01 7.56342888e-01 3.96266222e-01
8.74436975e-01 -4.44384396e-01 -4.70393628e-01 2.49038205e-01
-7.97877014e-01 -4.11176175e-01 3.87195796e-01 8.78167868e-01
7.46375203e-01 -2.24720943e-03 -1.25355855e-01 -3.64901453e-01
4.10201699e-01 -5.44190168e-01 -9.33853269e-01 -2.47946590e-01
-3.54401290e-01 1.78977594e-01 7.69510984e-01 -1.46117374e-01
-3.16309273e-01 3.88143480e-01 2.02852786e-01 -1.17950273e+00
-5.72345793e-01 1.79747254e-01 -4.41325083e-02 4.34559494e-01
6.95531964e-01 5.79111695e-01 -1.65221654e-02 -7.74267972e-01
3.37902427e-01 8.82468581e-01 1.19904470e+00 -2.56249636e-01
1.31085265e+00 6.97069764e-01 -4.55472678e-01 -8.39048147e-01
-6.39695287e-01 -8.22109640e-01 -1.23473370e+00 -2.83492991e-04
6.06739342e-01 -1.22664249e+00 -6.63824528e-02 4.24324960e-01
-9.23086822e-01 2.54581392e-01 -7.74530828e-01 7.26162136e-01
-6.05652571e-01 5.05177081e-01 -6.53709292e-01 -6.82263970e-01
-3.59042645e-01 -9.22419786e-01 1.16756046e+00 3.50228436e-02
-4.97133881e-01 -5.47492027e-01 1.89483643e-01 -9.93576944e-02
-1.33775070e-01 2.37921506e-01 7.17309892e-01 -1.15054357e+00
-5.70993781e-01 -8.35779548e-01 2.30976358e-01 7.53417373e-01
-1.33503884e-01 1.06707454e-01 -9.59456384e-01 -8.55462074e-01
2.18120709e-01 2.73663908e-01 1.10029769e+00 4.18257028e-01
1.17580152e+00 -5.62371969e-01 -5.02291620e-01 1.12377536e+00
1.38396776e+00 1.14582822e-01 6.43239498e-01 5.53204238e-01
9.59345043e-01 2.19447523e-01 2.17280716e-01 3.43629330e-01
-3.62038165e-01 1.47400722e-01 3.13228935e-01 1.02364212e-01
2.72193730e-01 -4.35712606e-01 8.44425678e-01 1.32091212e+00
4.19237435e-01 1.66850686e-01 -1.02497280e+00 7.93244004e-01
-1.98164058e+00 -7.66983151e-01 1.01974607e-01 2.63022256e+00
3.56918693e-01 2.15822041e-01 -1.59316525e-01 2.41667926e-01
7.58572698e-01 2.14931205e-01 -8.81737888e-01 -2.52618283e-01
-1.14708208e-01 -3.95776331e-01 5.08802056e-01 6.11887336e-01
-1.36349165e+00 9.70528066e-01 6.68584538e+00 3.10305685e-01
-8.47965837e-01 -4.97407049e-01 2.32924312e-01 -1.22514650e-01
5.12261223e-03 2.91927699e-02 -5.27786672e-01 4.10881162e-01
7.99394429e-01 1.93995401e-01 7.75029361e-01 1.07167912e+00
3.93203162e-02 3.70547056e-01 -1.53098965e+00 1.06185532e+00
3.55356693e-01 -1.05122495e+00 2.40964368e-01 5.45997508e-02
8.21434140e-01 4.64193851e-01 1.88501924e-01 1.12228885e-01
3.42208773e-01 -1.06663835e+00 3.95459324e-01 1.08447385e+00
7.25759625e-01 -8.46463025e-01 5.63243032e-01 5.06902874e-01
-5.35135031e-01 -1.64079040e-01 -7.36382782e-01 1.76087007e-01
-3.18491071e-01 6.91739500e-01 -8.98769557e-01 1.51810959e-01
7.42062986e-01 1.09104860e+00 -5.13270497e-01 9.94589448e-01
-2.31524825e-01 5.12652934e-01 -4.83090281e-01 8.32354128e-01
8.25357884e-02 -8.11919034e-01 1.45499647e+00 8.52972269e-01
6.10408902e-01 -2.78378636e-01 1.00939922e-01 1.01652563e+00
-2.34175161e-01 -1.55049682e-01 -1.09765375e+00 -4.81502935e-02
6.78344011e-01 9.63039517e-01 -4.39725161e-01 -4.85818714e-01
-1.05438478e-01 1.37112772e+00 2.38012522e-01 8.37832451e-01
-2.57133693e-01 -3.35645080e-01 8.48119140e-01 -4.75196615e-02
2.43665174e-01 -4.16327924e-01 -3.95202428e-01 -1.49675584e+00
3.52780968e-02 -1.09512997e+00 5.13273597e-01 -9.08515930e-01
-1.39107990e+00 3.72718334e-01 -4.70537186e-01 -1.90800762e+00
-6.23468220e-01 -6.67442322e-01 -6.27250373e-01 6.72255635e-01
-6.87440932e-01 -5.52855134e-01 -1.96600139e-01 6.09209955e-01
2.67923683e-01 -9.28719699e-01 1.04416811e+00 -1.29888773e-01
-7.45317698e-01 5.91781914e-01 1.20048010e+00 6.64731801e-01
6.27554536e-01 -1.29970765e+00 6.58762932e-01 1.42041647e+00
4.60829645e-01 1.13183784e+00 6.75608039e-01 -7.72999942e-01
-1.45250356e+00 -1.26425207e+00 6.97929561e-01 -6.56518102e-01
5.56468248e-01 -2.90595859e-01 -1.17033470e+00 1.26765382e+00
-1.05609842e-01 5.56535199e-02 6.68119788e-01 -4.49728370e-02
-7.01639831e-01 -7.07043335e-02 -9.71443594e-01 8.42725158e-01
5.57980001e-01 -9.14790154e-01 -9.87157345e-01 2.04267547e-01
4.52775091e-01 -6.00629151e-01 -7.41203189e-01 2.81744778e-01
3.27810556e-01 -9.92529988e-01 1.18991816e+00 -6.81726813e-01
2.35360220e-01 -7.09773481e-01 -3.46161038e-01 -1.43461347e+00
-4.43953037e-01 -6.84632540e-01 -8.86926591e-01 7.77702034e-01
-7.87142888e-02 -4.71811503e-01 9.58021104e-01 3.80794287e-01
-2.93409318e-01 -3.96643758e-01 -9.64078546e-01 -8.27308655e-01
-6.35130182e-02 -4.74913210e-01 5.39838612e-01 1.14146483e+00
-1.03377484e-01 -4.05066535e-02 -3.34959090e-01 7.89376616e-01
9.38335419e-01 7.60685652e-02 8.10339272e-01 -1.36206400e+00
-4.11674716e-02 -1.64999053e-01 -5.64953566e-01 -1.13834798e+00
2.08321497e-01 -1.07451630e+00 2.09952787e-01 -1.20108759e+00
-9.81667489e-02 3.73888761e-01 -6.45237923e-01 3.06805700e-01
2.05028281e-01 3.08105946e-02 -4.01923172e-02 8.20848644e-01
-4.36286509e-01 6.94972396e-01 2.99906850e-01 -1.02065489e-01
-4.81228352e-01 -2.07327977e-01 -4.45885628e-01 1.39408755e+00
5.71256161e-01 -5.82994103e-01 2.66368091e-01 -4.74405855e-01
-5.59440106e-02 -6.66163787e-02 3.17120433e-01 -1.39039576e+00
2.88217127e-01 2.70720720e-01 1.12490916e+00 -1.22043633e+00
1.41147718e-01 -1.19646347e+00 -1.04682148e-01 2.87275612e-01
-3.40462297e-01 1.29788760e-02 1.14843577e-01 7.77646363e-01
-2.86906898e-01 -1.26224965e-01 6.46874845e-01 3.76271576e-01
-4.92486030e-01 1.85212031e-01 -2.08008528e-01 -3.66306752e-01
7.61173189e-01 -5.24758637e-01 8.44064876e-02 -5.17435253e-01
-1.02046371e+00 6.31583035e-02 7.22029626e-01 6.47331059e-01
1.11972582e+00 -1.79185259e+00 -7.48415232e-01 1.23123872e+00
2.90633500e-01 7.89514482e-02 -1.23568110e-01 6.16208375e-01
-4.96869951e-01 3.35332364e-01 6.26827925e-02 -1.01327229e+00
-8.72172177e-01 7.76412010e-01 4.17421997e-01 1.86382338e-01
-9.89405036e-01 6.39566779e-01 6.71538175e-04 -5.93456686e-01
5.97279906e-01 -2.67612070e-01 6.34912997e-02 -2.11894184e-01
4.55194980e-01 5.86208522e-01 -1.04869843e-01 -1.13874984e+00
-2.91696072e-01 3.77912194e-01 -1.03880323e-01 7.12025389e-02
1.31451607e+00 6.12427220e-02 -3.55374068e-01 5.86675167e-01
1.52720261e+00 1.87629312e-01 -1.44780505e+00 -3.36325735e-01
4.66456503e-01 -5.68292797e-01 3.83162573e-02 -3.24350268e-01
-8.07457566e-01 5.99778950e-01 7.63125777e-01 -1.52398258e-01
9.89574611e-01 -2.43134886e-01 7.23669171e-01 6.03749156e-01
-6.37072250e-02 -1.40615726e+00 6.31008223e-02 8.08264732e-01
1.09833550e+00 -8.78948748e-01 5.31889014e-02 2.60763317e-01
-3.64782035e-01 1.27319849e+00 4.26462173e-01 -8.67536306e-01
6.08840346e-01 7.38331154e-02 1.75941393e-01 -3.35620612e-01
-2.21051499e-01 3.07913303e-01 8.43426585e-01 3.68517488e-01
1.28675848e-01 -7.14605004e-02 4.62175101e-01 4.76005286e-01
-2.93570757e-01 -3.06308389e-01 4.29834217e-01 4.06479657e-01
-6.09754741e-01 -4.29930210e-01 -9.78708088e-01 4.93992686e-01
-1.24952532e-01 -2.11311039e-03 -5.00317216e-01 4.61378366e-01
-1.95556298e-01 4.71144915e-01 4.79357183e-01 -4.23393726e-01
4.09921169e-01 4.08755779e-01 -1.85439721e-01 -4.25634801e-01
-6.99565187e-02 7.24720120e-01 -5.10669172e-01 -9.64367509e-01
4.10347462e-01 -7.15543151e-01 -1.44964933e+00 5.56443259e-02
-1.27951786e-01 1.18138000e-01 4.77085829e-01 6.29689574e-01
5.34973919e-01 1.17079556e-01 7.21120179e-01 -6.65148079e-01
-9.21578050e-01 -8.51422608e-01 -7.83935010e-01 6.54109359e-01
7.97852874e-01 -1.80867806e-01 -6.41578376e-01 1.10468887e-01] | [7.662403106689453, 2.4048454761505127] |
af97b5e7-2bdf-45ed-a1ae-44b764e342e2 | diva-domain-invariant-variational | 1905.10427 | null | https://arxiv.org/abs/1905.10427v2 | https://arxiv.org/pdf/1905.10427v2.pdf | DIVA: Domain Invariant Variational Autoencoders | We consider the problem of domain generalization, namely, how to learn representations given data from a set of domains that generalize to data from a previously unseen domain. We propose the Domain Invariant Variational Autoencoder (DIVA), a generative model that tackles this problem by learning three independent latent subspaces, one for the domain, one for the class, and one for any residual variations. We highlight that due to the generative nature of our model we can also incorporate unlabeled data from known or previously unseen domains. To the best of our knowledge this has not been done before in a domain generalization setting. This property is highly desirable in fields like medical imaging where labeled data is scarce. We experimentally evaluate our model on the rotated MNIST benchmark and a malaria cell images dataset where we show that (i) the learned subspaces are indeed complementary to each other, (ii) we improve upon recent works on this task and (iii) incorporating unlabelled data can boost the performance even further. | ['Christos Louizos', 'Max Welling', 'Maximilian Ilse', 'Jakub M. Tomczak'] | 2019-05-24 | null | null | null | null | ['rotated-mnist'] | ['computer-vision'] | [ 3.72468680e-01 2.33231083e-01 -7.02277618e-03 -3.99773568e-01
-4.73996490e-01 -7.85584331e-01 8.28568101e-01 5.05967438e-02
-2.01312840e-01 1.13376915e+00 3.44180316e-01 -9.48796922e-04
-2.58276105e-01 -4.98646349e-01 -7.19513834e-01 -1.02287865e+00
2.02972949e-01 1.01269007e+00 2.85894811e-01 -2.26109266e-01
-1.09585769e-01 5.79578459e-01 -1.43318486e+00 1.67470455e-01
8.01217914e-01 5.48428953e-01 1.31213948e-01 3.27473670e-01
2.71936357e-01 6.63453698e-01 -4.23199445e-01 1.05410755e-01
3.52440774e-01 -5.08069515e-01 -1.08603287e+00 6.20570242e-01
2.22242728e-01 -1.53310016e-01 -3.43841106e-01 9.57769871e-01
3.12793076e-01 3.94620270e-01 1.20927453e+00 -1.25721216e+00
-8.85012507e-01 9.47203487e-02 -1.87528849e-01 7.65490979e-02
4.96918859e-04 -2.05057696e-01 7.75370061e-01 -5.89003503e-01
1.30721939e+00 1.11575508e+00 2.84983873e-01 8.38900864e-01
-1.62293601e+00 -2.53668755e-01 2.48747483e-01 -4.82516080e-05
-1.27051961e+00 -2.02754185e-01 8.54783118e-01 -8.24706197e-01
7.28667617e-01 -1.79838464e-01 2.26679057e-01 1.67853594e+00
8.21914598e-02 7.08055198e-01 1.35197842e+00 -3.10187608e-01
7.82005668e-01 3.69934916e-01 1.55360907e-01 1.71972066e-01
2.36350477e-01 6.16943240e-02 -2.73183554e-01 -2.63413101e-01
7.77759194e-01 5.10589369e-02 -5.31913042e-01 -1.20288134e+00
-1.19241130e+00 1.14508343e+00 3.91104698e-01 5.62044442e-01
-4.94242758e-01 -3.50635052e-01 3.35122079e-01 3.50448847e-01
6.31358445e-01 3.30753773e-01 -6.05342209e-01 3.48136336e-01
-8.47867370e-01 2.59353548e-01 9.66246963e-01 1.01026428e+00
8.19217682e-01 1.92864001e-01 -3.82204093e-02 7.98218668e-01
1.68624800e-02 4.06590760e-01 6.79583669e-01 -8.32887053e-01
5.55017255e-02 4.82400090e-01 1.39777377e-01 -3.92489791e-01
-3.39657813e-01 -5.05557120e-01 -8.75130355e-01 1.91076890e-01
4.76276636e-01 -1.90982640e-01 -1.19096649e+00 2.01458549e+00
3.22729588e-01 3.06079179e-01 4.33041066e-01 7.68607497e-01
6.56381428e-01 4.60314274e-01 1.65648479e-02 -2.39903584e-01
9.80361998e-01 -5.94609797e-01 -5.40788412e-01 -2.57628679e-01
4.33341265e-01 -3.13706607e-01 4.99036938e-01 5.13060927e-01
-5.35245836e-01 -4.66660917e-01 -1.10218990e+00 -9.58653763e-02
-5.72498977e-01 -1.35856479e-01 4.16566789e-01 4.80999202e-01
-1.12852657e+00 6.68170571e-01 -1.11385202e+00 -8.47336650e-01
3.67376626e-01 2.98648000e-01 -6.56844318e-01 -3.08044344e-01
-1.09582400e+00 7.60204315e-01 6.91451311e-01 -4.93805289e-01
-1.23017120e+00 -4.82541770e-01 -9.53392327e-01 -1.13368355e-01
3.13442379e-01 -7.90874541e-01 9.63204682e-01 -1.02661169e+00
-1.25665486e+00 9.70193684e-01 -1.91650733e-01 -4.85877424e-01
3.88484865e-01 1.27228573e-01 -2.91855901e-01 1.78501606e-01
1.41175985e-01 7.08848059e-01 9.22501862e-01 -1.49739695e+00
-2.40044370e-01 -7.95037210e-01 -1.89602971e-02 1.16176300e-01
-3.10855627e-01 -5.95976174e-01 -1.47098871e-02 -7.55584478e-01
1.34877369e-01 -1.24529243e+00 -3.60414326e-01 -9.22652557e-02
-1.46158203e-01 -2.61326224e-01 1.06901836e+00 -6.98994398e-01
5.97168565e-01 -2.14592147e+00 7.10840404e-01 1.44503444e-01
1.67535856e-01 2.35246480e-01 -3.64394225e-02 5.17002463e-01
-3.55447978e-01 -9.14451405e-02 -7.00062513e-01 -1.68222323e-01
-2.44606305e-02 8.73960793e-01 -5.47457755e-01 6.33064747e-01
3.54450583e-01 7.49226332e-01 -9.43816781e-01 -1.36693835e-01
1.07099138e-01 7.24512935e-01 -6.58877969e-01 1.64017886e-01
-3.32484454e-01 1.00435293e+00 -4.79142696e-01 2.72348255e-01
7.91278481e-01 -4.55444753e-01 4.78928328e-01 1.78480759e-01
3.15900743e-01 -5.55142388e-02 -1.31114328e+00 1.87682104e+00
-1.91358089e-01 4.97085661e-01 1.29616186e-01 -1.50154734e+00
8.77918720e-01 5.11968017e-01 4.51584846e-01 -2.70605654e-01
-8.37783441e-02 3.04853171e-01 -5.68610914e-02 -3.40425819e-01
2.50487357e-01 -6.02427602e-01 7.15094358e-02 3.81307662e-01
8.00048351e-01 4.25982065e-02 6.52611405e-02 1.69927552e-01
8.74110758e-01 2.81507641e-01 5.57596028e-01 -7.28289425e-01
4.34619993e-01 -3.15676033e-02 6.34144127e-01 7.12997675e-01
-2.33726680e-01 7.49955356e-01 5.04519284e-01 -3.04112196e-01
-1.15681493e+00 -1.32157707e+00 -5.69654465e-01 1.02732491e+00
4.56754491e-02 1.37933180e-01 -6.47948325e-01 -7.49044120e-01
7.92095065e-02 6.27399981e-01 -9.46706772e-01 -4.70029041e-02
-3.35591763e-01 -5.56664824e-01 9.47353244e-02 7.90418327e-01
1.76084816e-01 -8.62358510e-01 -4.04775888e-01 1.59239292e-01
1.02130391e-01 -1.19730771e+00 2.40888204e-02 4.76988703e-01
-1.01864672e+00 -9.61828113e-01 -1.15778947e+00 -8.58631670e-01
6.29913688e-01 -9.31614935e-02 1.12809718e+00 -4.48994309e-01
-5.26259579e-02 7.69222677e-01 -3.99346828e-01 -4.44115222e-01
-6.31479919e-01 3.63236703e-02 3.46641272e-01 9.88592282e-02
4.14882958e-01 -8.50337207e-01 -3.60715479e-01 3.07384521e-01
-1.27498245e+00 -4.34673816e-01 2.76719362e-01 1.18583667e+00
6.69631779e-01 -6.55455189e-03 7.70806849e-01 -1.24247456e+00
3.21663588e-01 -7.71662056e-01 -4.71287400e-01 1.77095592e-01
-3.40021074e-01 3.62062603e-01 6.06225550e-01 -5.63158453e-01
-1.12090099e+00 1.95788920e-01 5.92452884e-02 -5.45120955e-01
-7.31807590e-01 2.96248078e-01 -2.32654229e-01 1.53725088e-01
9.64422107e-01 3.37765187e-01 3.25804502e-02 -6.92694724e-01
3.93706918e-01 6.93535566e-01 6.18888557e-01 -6.94019854e-01
7.48501420e-01 1.00252533e+00 1.25329390e-01 -1.13426709e+00
-5.48134744e-01 -6.80725396e-01 -1.06291974e+00 3.66051644e-01
1.03827393e+00 -9.39324439e-01 -3.31651084e-02 -1.49314143e-02
-1.05207133e+00 -1.77951530e-01 -4.98767883e-01 5.95704675e-01
-8.41694117e-01 4.90340918e-01 -3.80737305e-01 -5.55768788e-01
5.64073920e-02 -1.19440162e+00 1.08488238e+00 8.31878632e-02
-5.36620766e-02 -1.51805830e+00 4.59419131e-01 -2.14488860e-02
1.92486212e-01 4.28056598e-01 9.25972581e-01 -1.37302184e+00
-3.17878067e-01 1.17098860e-01 1.57485500e-01 5.47387838e-01
1.78569078e-01 -5.74910879e-01 -1.23190117e+00 -6.24384999e-01
3.44298631e-01 -4.23272967e-01 1.13503873e+00 3.15101057e-01
8.86610270e-01 4.52771597e-02 -5.16433299e-01 4.55181867e-01
1.37577319e+00 2.94773728e-01 4.54178005e-01 4.15610150e-02
4.01986361e-01 6.70380533e-01 2.52066761e-01 3.85156065e-01
7.41610453e-02 6.07409537e-01 4.11698997e-01 -1.25386149e-01
8.13724548e-02 -1.12603270e-01 2.16337547e-01 4.25361067e-01
-4.62302454e-02 -4.37998503e-01 -9.01486874e-01 1.02037883e+00
-1.83245885e+00 -7.81733096e-01 1.98998377e-01 2.02235961e+00
5.62213600e-01 -2.93988794e-01 2.35333115e-01 -1.48016121e-03
7.13510871e-01 -2.41793226e-02 -8.43776226e-01 -3.02602947e-01
-3.00901175e-01 5.29275835e-01 1.24379836e-01 2.89580464e-01
-1.34848440e+00 7.51174867e-01 5.99020004e+00 5.98060369e-01
-1.06889009e+00 1.65518790e-01 3.51099491e-01 2.97362059e-01
-2.52136350e-01 1.00727584e-02 -4.49888259e-01 1.74813047e-01
7.41717339e-01 -4.75312062e-02 3.56716603e-01 1.05223167e+00
-4.13933069e-01 3.09473455e-01 -1.57881188e+00 7.39610851e-01
1.35292470e-01 -1.00612378e+00 2.59735197e-01 3.37129444e-01
1.01622307e+00 1.25951082e-01 1.68943748e-01 4.22494471e-01
4.99326319e-01 -1.12560606e+00 1.44122601e-01 3.29096615e-01
4.01115865e-01 -7.01840758e-01 6.13900423e-01 5.46744287e-01
-5.82638860e-01 7.99933672e-02 -5.99616110e-01 1.66031241e-01
6.56609843e-03 3.69144380e-01 -9.31472719e-01 8.45623672e-01
4.90270287e-01 1.02097094e+00 -4.45044279e-01 8.08666289e-01
-2.23591074e-01 5.75477958e-01 -2.64675200e-01 4.86730844e-01
2.42449582e-01 -1.83529422e-01 8.07485044e-01 1.01760280e+00
3.61621946e-01 7.61465356e-02 1.86664715e-01 9.95351255e-01
-1.64364763e-02 -4.44922335e-02 -9.72678959e-01 -3.80518883e-02
6.51571080e-02 9.58323181e-01 -6.74125195e-01 -2.97160000e-01
-4.37691391e-01 1.19655204e+00 2.97360301e-01 8.16761732e-01
-3.42482597e-01 -8.49948898e-02 6.07185841e-01 -1.52629942e-01
7.28597641e-01 -7.13493675e-02 1.16905347e-01 -1.48923218e+00
3.94152757e-03 -6.81696892e-01 6.15044177e-01 -6.05372965e-01
-1.71335530e+00 6.21003628e-01 1.92787498e-01 -1.26402938e+00
-6.65516734e-01 -1.01649511e+00 -3.24340723e-02 8.83325875e-01
-1.40691829e+00 -1.16057110e+00 1.95897017e-02 7.79402673e-01
4.85252887e-01 -3.47329706e-01 1.03680992e+00 -8.82439613e-02
-2.06070840e-01 1.57219350e-01 7.55571961e-01 1.65548891e-01
7.39104390e-01 -1.38818002e+00 4.48379805e-03 6.63794100e-01
3.48163098e-01 7.93900430e-01 8.63720477e-01 -5.51612258e-01
-9.08550918e-01 -1.14229238e+00 5.13364673e-01 -6.50471151e-01
5.15210807e-01 -5.62994123e-01 -1.33685029e+00 1.10143220e+00
2.63556987e-01 2.17566356e-01 9.67924654e-01 1.97843209e-01
-5.64156055e-01 3.54801357e-01 -1.26699352e+00 1.51161045e-01
8.55341315e-01 -5.13368964e-01 -8.90108705e-01 5.08798897e-01
4.67918366e-01 -2.90507555e-01 -9.62426662e-01 3.79398555e-01
2.66419381e-01 -9.69623446e-01 9.56377804e-01 -1.05276048e+00
3.08739901e-01 -2.38855883e-01 -5.14470220e-01 -1.59874475e+00
-2.80254334e-01 -1.65773317e-01 -6.31279573e-02 1.10605073e+00
4.80210595e-02 -7.01172590e-01 5.77274024e-01 4.17251974e-01
-2.53780689e-02 -5.09548962e-01 -1.18111956e+00 -1.17558169e+00
7.34224617e-01 1.15128024e-03 4.83995587e-01 1.25783598e+00
-2.25648969e-01 4.70404923e-01 -3.87498707e-01 2.47702807e-01
6.04967713e-01 2.90486187e-01 4.78125840e-01 -1.61794972e+00
-5.55232227e-01 1.91993099e-02 -6.56339765e-01 -1.02626133e+00
4.85729665e-01 -1.12547338e+00 -2.46675402e-01 -1.32909477e+00
3.75845939e-01 -1.02659566e-02 -3.21318448e-01 4.16363478e-01
6.46963939e-02 7.79996738e-02 -5.30708060e-02 2.08894506e-01
-5.68978310e-01 7.13743329e-01 1.02272344e+00 -1.39433086e-01
-2.01141536e-01 -1.20032854e-01 -7.95532465e-01 6.18806660e-01
6.34292662e-01 -4.38308477e-01 -6.52463853e-01 -3.43784958e-01
-4.32380229e-01 4.36647460e-02 5.75934708e-01 -8.98640513e-01
-5.73416464e-02 -2.09926412e-01 5.08345306e-01 -2.42412284e-01
3.71771663e-01 -7.88351536e-01 3.29484493e-02 2.91625589e-01
-1.66300401e-01 -4.25098121e-01 1.99784249e-01 9.54233706e-01
-1.92744061e-01 -2.40001321e-01 9.93851423e-01 -1.31260350e-01
-9.09802794e-01 1.79687619e-01 -2.56509393e-01 2.08525538e-01
1.08296061e+00 -1.73089594e-01 -2.39880204e-01 -4.53245699e-01
-1.11364436e+00 1.23909034e-01 7.92583466e-01 3.79592359e-01
3.86548370e-01 -1.28378880e+00 -8.14358175e-01 2.38641098e-01
4.51736331e-01 1.29071847e-01 3.02757561e-01 5.49691141e-01
-1.67265639e-01 5.59210181e-01 -3.78485829e-01 -9.63135660e-01
-8.34593058e-01 1.07249510e+00 2.98113674e-01 -3.07659835e-01
-5.03732979e-01 4.85373735e-01 8.02624881e-01 -6.49895668e-01
-6.32868856e-02 -8.41916725e-02 -4.31064904e-01 8.40706825e-02
2.88309634e-01 3.07434380e-01 9.43080261e-02 -1.04798222e+00
-4.71556097e-01 4.41254646e-01 -2.66791999e-01 -1.31714240e-01
1.65093029e+00 4.01051380e-02 1.18915200e-01 4.99631494e-01
1.35189652e+00 -3.59585553e-01 -1.29317105e+00 -4.89301205e-01
6.60800859e-02 -1.33251905e-01 -1.88947797e-01 -5.57754457e-01
-7.84507334e-01 1.06894147e+00 6.22284710e-01 1.07058614e-01
1.17870462e+00 3.49913836e-01 2.74450719e-01 2.72445679e-01
3.99121314e-01 -1.04287505e+00 8.92121941e-02 5.32853425e-01
8.82255852e-01 -1.16106045e+00 -9.69087034e-02 -5.69368489e-02
-8.24059546e-01 9.60281312e-01 1.56638384e-01 -3.94227266e-01
6.78609133e-01 -2.11173400e-01 -9.34847072e-02 -1.22491263e-01
-5.86017787e-01 -3.80318463e-01 3.65879238e-01 1.20397782e+00
2.63134181e-01 -5.76465279e-02 -7.28824884e-02 5.73515356e-01
1.73623607e-01 6.66214749e-02 5.12138069e-01 1.05304813e+00
-1.89585835e-01 -1.32144237e+00 -3.13960016e-01 1.55421585e-01
-3.85155946e-01 2.85007417e-01 -4.96254921e-01 9.75472331e-01
7.89651573e-02 7.67822504e-01 -5.27484566e-02 5.44892810e-02
1.27338454e-01 4.41678882e-01 5.06257534e-01 -9.05377090e-01
1.89427566e-02 1.17905512e-01 -3.91124308e-01 -2.60508984e-01
-6.42110109e-01 -8.49020779e-01 -1.03603280e+00 3.77821714e-01
6.58780187e-02 2.16349706e-01 4.49565649e-01 9.16247964e-01
3.98318261e-01 3.37826669e-01 3.34990352e-01 -8.71853292e-01
-7.19405591e-01 -8.85854602e-01 -1.01292562e+00 9.24357116e-01
6.48446679e-01 -1.06515098e+00 -3.89504731e-01 2.57142544e-01] | [10.216826438903809, 2.9519152641296387] |
77de2ef2-9a5a-457c-90ea-81397f51bf29 | cluster-analysis-with-deep-embeddings-and | 2109.12714 | null | https://arxiv.org/abs/2109.12714v2 | https://arxiv.org/pdf/2109.12714v2.pdf | Cluster Analysis with Deep Embeddings and Contrastive Learning | Unsupervised disentangled representation learning is a long-standing problem in computer vision. This work proposes a novel framework for performing image clustering from deep embeddings by combining instance-level contrastive learning with a deep embedding based cluster center predictor. Our approach jointly learns representations and predicts cluster centers in an end-to-end manner. This is accomplished via a three-pronged approach that combines a clustering loss, an instance-wise contrastive loss, and an anchor loss. Our fundamental intuition is that using an ensemble loss that incorporates instance-level features and a clustering procedure focusing on semantic similarity reinforces learning better representations in the latent space. We observe that our method performs exceptionally well on popular vision datasets when evaluated using standard clustering metrics such as Normalized Mutual Information (NMI), in addition to producing geometrically well-separated cluster embeddings as defined by the Euclidean distance. Our framework performs on par with widely accepted clustering methods and outperforms the state-of-the-art contrastive learning method on the CIFAR-10 dataset with an NMI score of 0.772, a 7-8% improvement on the strong baseline. | ['Ali Jannesari', 'John Just', 'Jansel Herrera-Gerena', 'Ramakrishnan Sundareswaran'] | 2021-09-26 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [ 3.16636339e-02 4.84190546e-02 -8.39832723e-02 -4.27965611e-01
-1.21978521e+00 -5.68243325e-01 9.86074150e-01 4.17921633e-01
-6.83774173e-01 1.08707257e-01 3.51604253e-01 2.53266633e-01
-3.72189671e-01 -3.29028070e-01 -5.68297088e-01 -1.11519563e+00
-2.54531950e-01 7.76182652e-01 -3.15270185e-01 2.98847765e-01
3.56226146e-01 2.30260938e-01 -1.65264952e+00 2.24468857e-01
8.87257040e-01 7.33976543e-01 -4.64552231e-02 6.14076853e-01
4.69047517e-01 6.69278800e-01 -2.04014376e-01 -4.20203388e-01
1.36500821e-01 -2.89552450e-01 -6.89430058e-01 1.57075763e-01
7.00052261e-01 1.61460698e-01 -2.07453668e-01 7.98045933e-01
3.60487223e-01 1.32142782e-01 1.17980230e+00 -1.26173806e+00
-8.92011464e-01 5.36261797e-01 -8.45322549e-01 -5.85189238e-02
-1.96191564e-01 2.43220236e-02 1.54199243e+00 -1.10522676e+00
4.57263529e-01 1.17908311e+00 6.67619705e-01 3.39843482e-01
-1.76219738e+00 -5.46852052e-01 2.73071090e-03 1.85444623e-01
-1.39430225e+00 -2.56736040e-01 8.81651580e-01 -8.07417393e-01
7.82622457e-01 -8.48375410e-02 1.35818914e-01 8.20060551e-01
-8.10975432e-02 7.47501731e-01 1.04642582e+00 -6.49507761e-01
3.59785110e-01 1.65645376e-01 1.76096737e-01 7.43821800e-01
4.16784614e-01 1.59284160e-01 -2.99281716e-01 1.84490774e-02
4.43655133e-01 3.06430250e-01 1.48029877e-02 -1.08370304e+00
-1.41878366e+00 1.28009224e+00 8.67043376e-01 2.33951479e-01
-3.12622756e-01 4.10212874e-01 3.35458159e-01 -1.02432288e-01
6.53807640e-01 6.22687399e-01 -2.49153167e-01 1.10444456e-01
-1.24367869e+00 1.43703803e-01 4.54898894e-01 3.42170566e-01
8.49391997e-01 -2.96857834e-01 -5.61362356e-02 7.18241036e-01
5.29575706e-01 1.41778767e-01 3.43862772e-01 -1.21487355e+00
2.39146188e-01 6.02219701e-01 -1.78974465e-01 -8.66104603e-01
-1.86351538e-01 -6.75162673e-01 -7.72917449e-01 6.83687091e-01
2.76413321e-01 8.28153193e-02 -7.50354111e-01 2.09211493e+00
-1.33010373e-02 4.44187194e-01 5.28819598e-02 8.50493431e-01
3.93113434e-01 4.45437789e-01 5.45687899e-02 8.28741118e-02
1.16771197e+00 -1.16744125e+00 -4.02317196e-01 5.92360310e-02
5.32980204e-01 -6.69487715e-01 9.89366174e-01 5.11575699e-01
-1.05574393e+00 -6.54442191e-01 -1.26598716e+00 -1.68501452e-01
-3.75529110e-01 3.04621011e-01 6.52592957e-01 5.58961511e-01
-1.20608914e+00 7.07087159e-01 -9.94690895e-01 -2.34585017e-01
7.38756478e-01 4.07655150e-01 -4.81155872e-01 -1.93621650e-01
-4.88788009e-01 6.62303925e-01 3.51256222e-01 -2.69258231e-01
-8.30586553e-01 -8.25784981e-01 -9.45263207e-01 2.13369668e-01
7.91383013e-02 -7.57376373e-01 6.44094646e-01 -7.55733848e-01
-1.26217127e+00 1.16895199e+00 -1.80027839e-02 -5.93278110e-01
4.56529111e-01 -3.11513424e-01 -4.86188382e-02 4.84771430e-01
2.78627694e-01 1.07784688e+00 8.28478038e-01 -1.71848619e+00
-3.09592783e-01 -6.91122532e-01 -1.52752012e-01 3.55316162e-01
-3.84335041e-01 -3.61857891e-01 -2.76517063e-01 -5.62923491e-01
1.05810143e-01 -1.06898272e+00 -3.33945662e-01 1.76795751e-01
-2.92167425e-01 -4.36361104e-01 6.97056711e-01 -4.06764358e-01
8.84089410e-01 -2.34021878e+00 3.91006798e-01 2.24827021e-01
7.72957683e-01 -2.68234592e-02 -1.26878947e-01 4.01141256e-01
-4.41425711e-01 1.79366134e-02 -5.19273221e-01 -9.08453763e-01
2.32275099e-01 -1.32663712e-01 -6.33584559e-02 8.17637026e-01
3.14461857e-01 8.36913168e-01 -1.08472562e+00 -2.36660287e-01
3.97078067e-01 6.90783143e-01 -8.65376770e-01 9.18627083e-02
3.05455513e-02 1.79588839e-01 3.20707351e-01 4.99130376e-02
4.77479964e-01 -5.30563295e-01 1.50048852e-01 -1.99998528e-01
-3.39983543e-03 4.13904227e-02 -8.22135866e-01 2.29735112e+00
-3.24114233e-01 9.06147182e-01 -1.54822230e-01 -1.39189851e+00
7.76422441e-01 2.28225484e-01 5.49505711e-01 -2.41158634e-01
-7.26403370e-02 -7.12659061e-02 -1.45535115e-02 -1.93076417e-01
2.16849968e-01 -5.46890758e-02 -1.21363804e-01 5.65854847e-01
4.04621929e-01 2.38778576e-01 -4.55485508e-02 5.66431701e-01
9.85172868e-01 9.51012000e-02 -2.90423147e-02 -5.25384963e-01
2.72426009e-01 -1.09977052e-01 2.29124308e-01 4.64052290e-01
-2.04090297e-01 9.28427458e-01 5.79177916e-01 -4.00876030e-02
-1.03596044e+00 -1.66942954e+00 -5.13686091e-02 1.03004324e+00
-1.87558606e-02 -4.40533876e-01 -7.25849330e-01 -7.34632909e-01
4.15478647e-02 6.35657966e-01 -9.19775009e-01 -3.39582413e-01
-3.84104937e-01 -6.27935350e-01 3.41888785e-01 6.74483001e-01
2.52819210e-01 -7.29134083e-01 -2.75250912e-01 -5.91137521e-02
-2.98904553e-02 -9.16261792e-01 -4.37909573e-01 4.21879798e-01
-9.50863481e-01 -1.13325131e+00 -5.95827222e-01 -1.12491798e+00
7.12782800e-01 4.68502969e-01 1.24890304e+00 -2.27012232e-01
-5.11550307e-01 4.59218770e-01 -2.29033887e-01 -3.84905376e-02
-5.63527225e-03 -3.30783874e-02 6.73455894e-02 3.91462892e-02
6.88681424e-01 -5.66514075e-01 -9.50832129e-01 -7.85962492e-02
-8.47152293e-01 -2.66919971e-01 7.19921112e-01 9.53785241e-01
4.74245369e-01 -1.04549862e-01 5.87463975e-01 -7.78627396e-01
3.89056355e-01 -5.48839808e-01 -3.88428599e-01 1.82770014e-01
-9.23329175e-01 3.51838380e-01 4.96480137e-01 -2.43995771e-01
-6.89522266e-01 2.37597495e-01 3.45541388e-01 -8.32300246e-01
-3.47871184e-01 3.16636026e-01 -9.88795757e-02 3.75553071e-01
8.20246160e-01 3.69609706e-02 4.32123810e-01 -3.60194325e-01
1.10439444e+00 4.45225388e-01 7.76363432e-01 -5.28494060e-01
8.61984968e-01 8.24839175e-01 -2.53066838e-01 -6.69252038e-01
-7.83996463e-01 -8.75791311e-01 -1.02719939e+00 1.31772041e-01
1.29162300e+00 -1.19908941e+00 -6.81940138e-01 4.62822616e-02
-9.29989815e-01 5.61097264e-02 -2.93539792e-01 7.58441925e-01
-7.11616099e-01 4.04346883e-01 -5.27449906e-01 -4.90834445e-01
-1.05764613e-01 -1.18327463e+00 1.18810308e+00 -5.70370816e-02
-1.41703978e-01 -1.20426428e+00 3.80727619e-01 6.57589674e-01
1.05623841e-01 6.40727639e-01 1.02574205e+00 -6.72930360e-01
-5.73822439e-01 -1.44966424e-01 -3.61355990e-01 5.45242131e-01
-8.37194994e-02 2.42220592e-02 -1.22209442e+00 -5.47974586e-01
-2.21432388e-01 -5.28180480e-01 1.60507059e+00 5.37484646e-01
1.18714297e+00 -1.43110186e-01 -2.23438814e-01 5.05064726e-01
1.79626846e+00 -2.39194170e-01 5.65332949e-01 1.03091104e-02
8.68073046e-01 6.71746194e-01 1.42508484e-02 1.88804656e-01
5.30499458e-01 4.93040860e-01 5.61357796e-01 -1.35078114e-02
-1.37730062e-01 -1.42847806e-01 2.36902654e-01 8.47283423e-01
9.05243009e-02 1.53097436e-01 -8.07487607e-01 7.77130842e-01
-1.97383797e+00 -1.19459450e+00 -3.78174940e-03 2.29873157e+00
5.54256558e-01 9.80837345e-02 2.51421362e-01 3.10529083e-01
6.97437584e-01 1.92581490e-01 -3.49759370e-01 -1.83556348e-01
1.13666102e-01 3.28929543e-01 1.71851039e-01 5.27608871e-01
-1.37270844e+00 7.97026157e-01 5.53101444e+00 6.07754230e-01
-9.12739038e-01 1.70700148e-01 6.79143012e-01 -2.13711053e-01
-2.47839406e-01 -7.97076896e-02 -2.12557524e-01 3.51816326e-01
8.20574403e-01 1.75596833e-01 3.28938365e-01 8.05612087e-01
-5.31055816e-02 1.97241858e-01 -1.55425298e+00 1.28540111e+00
4.38953429e-01 -1.59906399e+00 -1.93901397e-02 4.45482105e-01
9.63097274e-01 2.91131943e-01 6.77128732e-01 1.54067159e-01
4.09742862e-01 -1.32110822e+00 5.01880646e-01 3.68732810e-01
6.79691732e-01 -9.41188753e-01 4.63592261e-01 2.62582675e-02
-9.84429061e-01 1.92677956e-02 -2.40675732e-01 1.37391882e-02
-4.00145382e-01 6.23468101e-01 -6.60810590e-01 3.54304194e-01
6.10461414e-01 1.05637527e+00 -6.89833045e-01 1.11519039e+00
-1.42846063e-01 8.09156656e-01 -2.61277352e-02 3.13515335e-01
5.22486448e-01 -3.91691834e-01 2.52638012e-01 1.21962094e+00
-1.97939500e-01 -3.59906733e-01 2.22636443e-02 1.19230354e+00
-3.22707862e-01 -2.95210660e-01 -6.22168601e-01 1.57197081e-02
4.93523419e-01 1.40800810e+00 -7.42590189e-01 -2.35414714e-01
-2.60479182e-01 1.19396865e+00 5.71356475e-01 3.68466705e-01
-7.54308879e-01 -3.76754045e-01 7.65445232e-01 -1.78096205e-01
6.46458983e-01 -3.03069443e-01 -4.78767067e-01 -1.11967695e+00
-2.24302523e-02 -4.78413969e-01 4.10674989e-01 -4.06869560e-01
-1.70391095e+00 4.71373439e-01 -9.85889509e-02 -1.31653750e+00
-1.67380124e-01 -5.52657843e-01 -8.43163967e-01 4.82840270e-01
-1.39946842e+00 -1.24896073e+00 -2.22447067e-01 5.04461288e-01
3.67694676e-01 -2.77954191e-01 1.01014149e+00 2.03635395e-01
-5.45464396e-01 7.37725914e-01 5.20283043e-01 3.82873982e-01
7.06959844e-01 -1.71742892e+00 -1.17064463e-02 7.61529326e-01
8.16843629e-01 7.83669114e-01 5.70745349e-01 7.84502178e-02
-7.91146278e-01 -1.08434033e+00 7.81186640e-01 -7.55813181e-01
5.08885562e-01 -6.60191536e-01 -7.43350744e-01 5.61063647e-01
6.21842146e-01 2.27535427e-01 1.15612888e+00 3.94685984e-01
-1.03093660e+00 -7.70252571e-02 -1.03172886e+00 3.87048602e-01
7.97024429e-01 -6.35791421e-01 -7.52858818e-01 4.10227060e-01
7.67602146e-01 4.20588940e-01 -8.74379635e-01 1.07625246e-01
4.94884521e-01 -1.12638438e+00 1.09308827e+00 -5.29567063e-01
6.62989497e-01 -3.08095694e-01 -4.00659949e-01 -1.39244640e+00
-8.02776277e-01 -3.95711273e-01 2.24967003e-02 1.01413000e+00
3.95994335e-01 -3.11272681e-01 1.02925301e+00 2.14967951e-01
-4.33216617e-02 -9.65794384e-01 -6.86621666e-01 -7.69799531e-01
4.38149244e-01 -1.81754395e-01 -2.53257900e-02 1.08918774e+00
7.40649784e-03 6.96081460e-01 1.28077250e-02 2.07187235e-01
1.33199358e+00 1.03492603e-01 5.30653298e-01 -1.60572481e+00
-2.23310247e-01 -7.72659659e-01 -7.41802752e-01 -8.13379943e-01
3.47961277e-01 -1.23003614e+00 -9.83573124e-02 -1.52632225e+00
5.40472209e-01 -3.69614005e-01 -7.44389832e-01 1.28884837e-01
-1.21690609e-01 4.08692122e-01 2.98738182e-01 3.35966200e-01
-1.07268798e+00 7.97650576e-01 5.72745442e-01 -2.81997263e-01
4.82396707e-02 -4.50947553e-01 -9.00322735e-01 7.03890145e-01
6.97611690e-01 -6.32190585e-01 -3.96211505e-01 -4.26402688e-01
-1.33162394e-01 -3.99256766e-01 6.96082771e-01 -1.18086624e+00
2.99176693e-01 4.86461669e-01 5.42048037e-01 -7.25769937e-01
5.34798086e-01 -7.31785595e-01 -4.76537883e-01 4.56152231e-01
-6.89344168e-01 -1.65452454e-02 -2.53992110e-01 9.12204146e-01
-2.99299926e-01 1.89981520e-01 9.23646450e-01 4.21432406e-02
-6.08817637e-01 2.21867815e-01 9.49298069e-02 1.39753833e-01
1.18886209e+00 -3.27860147e-01 -2.71724254e-01 -1.08059943e-01
-7.37160027e-01 1.54317081e-01 5.13964593e-01 4.47303683e-01
7.86857545e-01 -1.41458952e+00 -9.61458743e-01 2.22633377e-01
4.07285661e-01 -1.18491411e-01 -6.89834580e-02 7.59093344e-01
-4.11178112e-01 2.67286569e-01 -6.16777688e-02 -9.75769699e-01
-1.09741640e+00 5.88390350e-01 1.21263377e-01 -2.98186004e-01
-5.64439118e-01 1.19173753e+00 2.48467639e-01 -5.72269380e-01
4.70400363e-01 -1.53338388e-01 -9.50472355e-02 2.02821329e-01
2.94120073e-01 4.15379852e-01 -3.91163081e-02 -4.99053925e-01
-5.05830586e-01 6.33743763e-01 -2.68238485e-01 -2.42674172e-01
1.58728373e+00 7.47970715e-02 -8.62056389e-02 5.38239717e-01
2.00298190e+00 -2.08010569e-01 -1.56195009e+00 -3.72335225e-01
1.73155010e-01 -2.73449630e-01 1.14538729e-01 -7.72906899e-01
-1.04043877e+00 1.07571685e+00 8.84327173e-01 -9.39261541e-02
7.18057573e-01 4.30357605e-01 5.18935740e-01 2.35469386e-01
-5.94263896e-03 -1.06144953e+00 8.14623296e-01 7.00341910e-02
6.60914004e-01 -1.59276748e+00 3.60594727e-02 -6.01160666e-03
-8.25647295e-01 8.28781068e-01 3.19046080e-01 -8.75078380e-01
8.98726702e-01 -1.28724828e-01 1.46642821e-02 -4.26341683e-01
-8.24143171e-01 -3.38604599e-01 5.52303433e-01 6.16670907e-01
4.66668069e-01 2.39851728e-01 5.79139702e-02 2.89987713e-01
-1.10463813e-01 -5.39807916e-01 2.08603695e-01 3.11376661e-01
-5.23343027e-01 -8.17980766e-01 -1.37111172e-01 4.25586671e-01
-3.37954849e-01 4.40764837e-02 -2.75777400e-01 7.31505454e-01
2.31137544e-01 1.08213413e+00 4.04404253e-01 -4.99846160e-01
-2.21673727e-01 1.78086071e-03 5.79688191e-01 -6.85687184e-01
-3.13861430e-01 8.59968178e-03 -5.31915665e-01 -5.66468239e-01
-7.52086341e-01 -8.58495235e-01 -1.35538518e+00 -2.23395620e-02
-2.90975422e-01 1.62015840e-01 7.35396743e-01 7.76487350e-01
3.75723541e-01 3.06903034e-01 8.45260799e-01 -9.85738754e-01
-6.48283184e-01 -7.36177146e-01 -5.20887256e-01 8.98947299e-01
4.30325627e-01 -7.25314379e-01 -5.90347469e-01 7.67852962e-02] | [9.371991157531738, 3.074007749557495] |
c1761790-3d2b-4f4c-a625-06536680d578 | synthstrip-skull-stripping-for-any-brain | 2203.09974 | null | https://arxiv.org/abs/2203.09974v2 | https://arxiv.org/pdf/2203.09974v2.pdf | SynthStrip: Skull-Stripping for Any Brain Image | The removal of non-brain signal from magnetic resonance imaging (MRI) data, known as skull-stripping, is an integral component of many neuroimage analysis streams. Despite their abundance, popular classical skull-stripping methods are usually tailored to images with specific acquisition properties, namely near-isotropic resolution and T1-weighted (T1w) MRI contrast, which are prevalent in research settings. As a result, existing tools tend to adapt poorly to other image types, such as stacks of thick slices acquired with fast spin-echo (FSE) MRI that are common in the clinic. While learning-based approaches for brain extraction have gained traction in recent years, these methods face a similar burden, as they are only effective for image types seen during the training procedure. To achieve robust skull-stripping across a landscape of imaging protocols, we introduce SynthStrip, a rapid, learning-based brain-extraction tool. By leveraging anatomical segmentations to generate an entirely synthetic training dataset with anatomies, intensity distributions, and artifacts that far exceed the realistic range of medical images, SynthStrip learns to successfully generalize to a variety of real acquired brain images, removing the need for training data with target contrasts. We demonstrate the efficacy of SynthStrip for a diverse set of image acquisitions and resolutions across subject populations, ranging from newborn to adult. We show substantial improvements in accuracy over popular skull-stripping baselines -- all with a single trained model. Our method and labeled evaluation data are available at https://w3id.org/synthstrip. | ['Malte Hoffmann', 'Bruce Fischl', 'Adrian V. Dalca', 'Jocelyn S. Mora', 'Andrew Hoopes'] | 2022-03-18 | null | null | null | null | ['skull-stripping'] | ['medical'] | [ 5.34402311e-01 -1.08470200e-02 9.36979651e-02 -6.08621359e-01
-9.26108539e-01 -4.26529467e-01 4.01276499e-01 -6.84885830e-02
-7.04221308e-01 5.60629666e-01 1.14564091e-01 -1.77348763e-01
-2.07812980e-01 -2.49231875e-01 -5.90206087e-01 -5.20348370e-01
-3.86158496e-01 5.97012043e-01 5.18720627e-01 -8.55234638e-03
-1.43055357e-02 5.89199662e-01 -1.09843516e+00 1.92682311e-01
7.10403502e-01 7.38256335e-01 2.39042401e-01 2.85799950e-01
1.05492122e-01 4.06172007e-01 -1.99250013e-01 -2.96742618e-01
3.41943860e-01 -2.74803072e-01 -8.23266387e-01 7.76378298e-03
6.75291538e-01 -3.90126228e-01 -3.44275117e-01 1.04080725e+00
8.09602976e-01 4.32574470e-03 5.34661114e-01 -8.15358102e-01
-4.37696487e-01 6.21598899e-01 -8.03590834e-01 8.80842626e-01
-1.71918809e-01 3.67596656e-01 3.50525826e-01 -9.12837923e-01
8.36560905e-01 6.09308839e-01 8.69515955e-01 6.69888914e-01
-1.50686538e+00 -8.25385094e-01 -2.15235010e-01 8.64123777e-02
-1.09377396e+00 -6.40315473e-01 4.82975930e-01 -7.40840971e-01
7.13060439e-01 6.16760924e-02 7.29451954e-01 9.36959982e-01
3.27673167e-01 5.38427234e-01 1.66360247e+00 -1.72764938e-02
9.54168439e-02 -4.06650990e-01 1.13682359e-01 4.25064325e-01
2.26588994e-01 -1.85167506e-01 -3.95267636e-01 -3.32349613e-02
9.16638792e-01 -1.93380434e-02 -5.42483747e-01 -5.61210096e-01
-1.49792624e+00 3.94897103e-01 3.34386617e-01 4.00483578e-01
-5.61836123e-01 -1.48699462e-01 3.90278518e-01 -1.37035055e-02
3.94464076e-01 4.40219402e-01 -1.18967161e-01 1.87126756e-01
-1.50188935e+00 2.44019493e-01 3.31490159e-01 6.86058223e-01
1.88714981e-01 2.19713971e-02 -6.51914254e-02 1.05573988e+00
-1.14622362e-01 4.62176412e-01 8.53686690e-01 -8.65631580e-01
1.39802888e-01 2.32185990e-01 -3.35971057e-01 -4.73978013e-01
-9.21978354e-01 -6.71746135e-01 -7.94163883e-01 2.87038445e-01
6.29546046e-01 -1.24375634e-01 -1.30762887e+00 1.60328162e+00
4.93273139e-01 2.96305749e-03 -5.47276258e-01 1.16886139e+00
6.48873627e-01 -7.22520202e-02 9.05801803e-02 -1.92504987e-01
1.25301182e+00 -6.56270266e-01 -3.90643984e-01 -4.00615841e-01
4.66885120e-01 -4.37843531e-01 1.16610444e+00 4.15015221e-01
-1.32462871e+00 1.75837770e-01 -8.99220228e-01 1.46539167e-01
-5.79115488e-02 -5.10111034e-01 6.21592104e-01 5.73014736e-01
-1.09212351e+00 5.96263766e-01 -1.22540128e+00 -6.18015826e-02
9.03283894e-01 4.43618238e-01 -8.00563157e-01 -2.68081665e-01
-7.34652400e-01 1.14257991e+00 9.37941298e-02 -9.38482489e-03
-8.86575282e-01 -1.37817848e+00 -4.69407350e-01 -3.54459673e-01
4.58080024e-01 -5.77023268e-01 1.27869427e+00 -9.23044264e-01
-1.03980327e+00 1.09379888e+00 1.38181001e-01 -5.39502323e-01
6.68724298e-01 5.73547371e-02 -3.97331208e-01 5.80580235e-01
1.51410297e-01 6.05141103e-01 9.71543849e-01 -1.03948796e+00
7.79390037e-02 -6.92761421e-01 -4.46590930e-01 8.27746931e-03
1.30422145e-01 4.50753540e-01 -2.12991089e-01 -5.87503374e-01
3.96824062e-01 -8.99077296e-01 -2.66683131e-01 -4.37269397e-02
-2.10569978e-01 6.74346983e-01 3.88818383e-01 -1.09364545e+00
8.02607358e-01 -1.81694651e+00 -5.93406446e-02 3.01586151e-01
6.35017097e-01 2.42008433e-01 2.51774285e-02 -2.53563374e-01
-4.91511375e-01 2.91178562e-02 -6.99346066e-01 -1.12492658e-01
-2.96776146e-01 -1.92863271e-01 2.11386591e-01 7.99399555e-01
-5.32761998e-02 8.68364990e-01 -9.03671801e-01 -6.15153551e-01
3.78143713e-02 5.61405540e-01 -5.87025225e-01 -1.32460386e-01
3.49730462e-01 9.88063455e-01 -1.77744448e-01 4.10155922e-01
5.50776124e-01 -1.57567203e-01 2.16764569e-01 -2.27816164e-01
-1.10261656e-01 5.69305792e-02 -6.34955704e-01 1.97103095e+00
-2.50900328e-01 3.12851638e-01 5.82769156e-01 -9.64313507e-01
4.77875441e-01 4.01104957e-01 8.80118430e-01 -8.35424900e-01
2.59949178e-01 4.58365798e-01 6.58726752e-01 -5.14785230e-01
-2.11513996e-01 -7.28325248e-01 5.74266493e-01 4.96295929e-01
3.37102264e-01 -4.11535174e-01 3.03736359e-01 4.91973758e-02
1.39473581e+00 5.28099611e-02 -1.35318175e-01 -5.60378015e-01
3.49115245e-02 -4.93034460e-02 5.21681249e-01 4.73569542e-01
-4.80378062e-01 1.05693007e+00 2.43271410e-01 -4.51996177e-01
-1.26469910e+00 -1.28020751e+00 -5.81116498e-01 5.89776337e-01
-4.51325953e-01 5.37407361e-02 -1.14646399e+00 -6.03084505e-01
-2.33230636e-01 3.58604878e-01 -6.37795150e-01 5.19840000e-03
-6.65798247e-01 -1.22413039e+00 5.06241977e-01 2.90176272e-01
2.74824589e-01 -1.03146172e+00 -1.02378440e+00 3.14770669e-01
-6.43060505e-02 -1.30438912e+00 -5.48154235e-01 2.54043192e-01
-1.03825748e+00 -1.08286572e+00 -1.15472448e+00 -5.84111750e-01
7.40666091e-01 -2.50002127e-02 1.22868228e+00 6.92091435e-02
-7.23229706e-01 3.68728489e-01 -5.31435162e-02 -3.12402546e-01
-1.72711015e-01 1.23635419e-02 1.15426712e-01 -7.86176026e-02
-5.95666617e-02 -9.92749810e-01 -9.72551644e-01 1.57449171e-01
-1.04945338e+00 3.37153018e-01 8.15171003e-01 6.90725267e-01
7.98667669e-01 -2.78379858e-01 6.57519937e-01 -1.03136408e+00
5.09403288e-01 -5.72378516e-01 -3.38898391e-01 1.23989224e-01
-4.35561687e-01 -1.13209657e-01 4.14983541e-01 -5.99282384e-01
-8.09255600e-01 -2.20914066e-01 -2.05669969e-01 -3.24930549e-01
-3.23321164e-01 4.31109905e-01 1.37986064e-01 -3.49376321e-01
8.06088388e-01 3.07567507e-01 3.31066996e-01 -4.15987194e-01
-1.04566542e-02 2.29522541e-01 8.91938329e-01 -5.05819917e-01
5.25782347e-01 6.91990376e-01 3.67916301e-02 -7.97941148e-01
-6.68020248e-01 -1.41198635e-01 -1.00129414e+00 -2.98595816e-01
8.80340755e-01 -4.70691979e-01 -2.23185703e-01 4.89513189e-01
-5.43158710e-01 -7.69793510e-01 -1.33326337e-01 7.71688700e-01
-4.25547272e-01 2.00569823e-01 -5.31072378e-01 -3.62560511e-01
-7.22006202e-01 -1.53741372e+00 7.23426461e-01 9.38184187e-02
-3.07382256e-01 -9.18241978e-01 -7.02556521e-02 4.82891649e-01
7.48913527e-01 5.82684815e-01 1.04184508e+00 -6.73109770e-01
-1.45314232e-01 2.00073123e-02 -4.94381785e-02 4.31143343e-01
9.08010006e-02 -3.15180182e-01 -7.67354548e-01 -1.72937617e-01
1.24739654e-01 -4.04431969e-01 5.98380268e-01 7.23486066e-01
1.18108702e+00 1.79725289e-01 3.26474681e-02 9.05123651e-01
1.03169680e+00 1.06322609e-01 4.67812181e-01 3.30705523e-01
4.91533995e-01 7.95071542e-01 -4.25131209e-02 -7.83747509e-02
3.84727001e-01 5.96700490e-01 1.45352870e-01 -3.62703264e-01
-5.46189606e-01 1.75623387e-01 -1.12729028e-01 8.54604959e-01
-1.32555068e-01 6.01345122e-01 -1.47766209e+00 6.64911568e-01
-1.15954864e+00 -8.21431100e-01 -1.12787187e-02 2.34690380e+00
1.11011803e+00 2.06329197e-01 3.72861981e-01 -6.53674081e-02
5.37389815e-01 -6.41490147e-02 -8.56669664e-01 1.29522398e-01
-1.09419115e-01 5.69498420e-01 6.47668779e-01 2.86852926e-01
-9.38646615e-01 5.58933496e-01 6.98207140e+00 3.44611883e-01
-1.61405146e+00 6.41717792e-01 6.68476045e-01 -5.30510128e-01
-2.18444794e-01 -2.55814701e-01 -1.77460909e-01 4.80955273e-01
9.56912458e-01 -4.84579951e-02 6.13244891e-01 4.55502570e-01
2.62299955e-01 -1.34861276e-01 -9.29665685e-01 9.99916673e-01
8.14417377e-02 -1.19102263e+00 -3.80446851e-01 -7.43951723e-02
5.18461704e-01 5.79138100e-01 1.07920960e-01 -2.46173725e-03
1.11752385e-02 -1.26961613e+00 7.17335463e-01 3.94156814e-01
1.11369002e+00 -3.95910621e-01 3.99839044e-01 1.13736399e-01
-5.92245638e-01 1.84337407e-01 1.81830488e-02 4.68784392e-01
2.36466959e-01 7.86297321e-01 -7.04248428e-01 2.47794941e-01
9.10386026e-01 2.47961283e-01 -6.06273711e-01 1.32558727e+00
5.98815568e-02 6.84117436e-01 -4.26131278e-01 7.39816546e-01
4.05652933e-02 -3.20782900e-01 4.70803678e-01 1.11916757e+00
2.19904900e-01 3.67025405e-01 -1.31962329e-01 9.29348826e-01
-7.23665506e-02 1.42790213e-01 -3.63529980e-01 1.13564968e-01
2.60030210e-01 1.56939650e+00 -1.11521912e+00 -2.51959920e-01
-4.75298882e-01 5.31168878e-01 3.13943356e-01 3.48762572e-01
-6.40372038e-01 -4.29016277e-02 2.06687272e-01 8.35059166e-01
-2.63122823e-02 -3.17058653e-01 -3.91240984e-01 -1.13054252e+00
8.87442902e-02 -1.14949334e+00 5.55194430e-02 -7.49692082e-01
-1.04992592e+00 8.01630139e-01 4.45015281e-01 -5.82752645e-01
9.71286267e-04 -4.13353860e-01 -6.84798300e-01 7.93078303e-01
-1.46145737e+00 -9.25555706e-01 -2.72278607e-01 6.55399024e-01
2.34844968e-01 1.40336365e-01 5.28817534e-01 6.33273005e-01
-5.26390314e-01 3.62786323e-01 -1.16062373e-01 9.84758213e-02
7.72868156e-01 -1.17247176e+00 3.28870505e-01 8.76900613e-01
-3.76131088e-02 8.50523353e-01 5.94122231e-01 -6.05606019e-01
-1.20812035e+00 -8.35837781e-01 1.64583072e-01 -2.33724177e-01
8.23749185e-01 -3.71041834e-01 -1.18573403e+00 8.05473626e-01
-1.09362163e-01 5.24688363e-01 6.62275612e-01 -1.52371645e-01
-3.24596733e-01 5.10976762e-02 -1.27000964e+00 5.44687986e-01
1.13414395e+00 -3.23197216e-01 -6.25065804e-01 3.51873726e-01
8.67996365e-02 -6.54372573e-01 -1.27572072e+00 4.80535746e-01
7.15923905e-01 -1.01857257e+00 9.91920888e-01 -4.70137328e-01
3.57713938e-01 -1.29330922e-02 3.24874640e-01 -1.44755495e+00
-2.32788205e-01 -4.99702811e-01 1.73837721e-01 7.63506413e-01
4.13272142e-01 -6.76599026e-01 5.94214857e-01 9.33759987e-01
-4.34600919e-01 -8.86586010e-01 -1.04313707e+00 -6.79410458e-01
5.28003991e-01 -3.50355744e-01 5.38176537e-01 9.71849740e-01
-1.41375810e-01 4.37501669e-02 6.01526536e-02 -1.02470666e-01
1.04434526e+00 -1.43533051e-01 3.58980805e-01 -1.21408522e+00
-1.95106998e-01 -6.29656136e-01 -3.45117599e-01 -3.47982436e-01
1.95627734e-01 -1.28034532e+00 -1.95391830e-02 -1.52517724e+00
3.50210577e-01 -6.33570850e-01 -2.86910415e-01 6.38847291e-01
-5.51152974e-02 5.46659648e-01 -7.33678136e-03 1.94129139e-01
-1.19144306e-01 2.10836276e-01 1.64514709e+00 4.11901111e-03
8.82917345e-02 -4.29477751e-01 -4.82372940e-01 9.20232892e-01
7.88936496e-01 -5.64256191e-01 -5.94868422e-01 -6.02006912e-01
-2.93517739e-01 3.57081927e-02 4.79845315e-01 -1.16261744e+00
6.28614333e-03 1.01835005e-01 5.11701047e-01 -1.06003471e-02
1.48928389e-01 -6.02752864e-01 3.49468261e-01 2.74306327e-01
-1.94724619e-01 2.41963968e-01 1.96033895e-01 -1.80439264e-01
9.58555415e-02 -7.68680051e-02 1.17762709e+00 -3.41320157e-01
-2.91834116e-01 6.35797620e-01 -1.70928210e-01 5.96314371e-01
7.57755697e-01 -2.22828746e-01 -2.39576280e-01 -9.54053551e-02
-9.61634636e-01 -2.34328900e-02 5.71531296e-01 1.78842649e-01
6.10815942e-01 -9.20273185e-01 -8.74946535e-01 1.75464183e-01
-2.75422662e-01 7.17043579e-02 5.91235518e-01 1.85576487e+00
-5.67978442e-01 1.15385607e-01 -6.53769374e-01 -5.73515654e-01
-9.31751847e-01 2.86553204e-01 7.65025914e-01 -1.46159053e-01
-1.22197580e+00 6.32911325e-01 4.85650152e-01 -4.38895226e-01
-1.95145309e-01 -3.17070484e-01 1.10386297e-01 -3.00743312e-01
7.07017958e-01 2.44339257e-02 5.71046770e-01 -8.57455015e-01
-5.08082330e-01 2.76617348e-01 -2.35779971e-01 -2.68479377e-01
1.63557172e+00 8.72197077e-02 5.17720059e-02 2.64473379e-01
9.10655200e-01 -1.42724559e-01 -1.42383146e+00 -1.94760546e-01
1.52337313e-01 -3.48659396e-01 4.15320992e-01 -8.83804739e-01
-1.52969646e+00 8.96415949e-01 7.70935953e-01 -3.26926768e-01
9.46584344e-01 -1.29716977e-01 8.65602732e-01 -3.30284774e-01
5.94063580e-01 -7.90872157e-01 -3.13692272e-01 1.42744198e-01
9.31673110e-01 -1.09814239e+00 5.80233373e-02 -1.24231614e-01
-6.92229807e-01 7.86494553e-01 3.50240409e-01 -1.08667538e-02
5.75766325e-01 7.94109166e-01 2.38279402e-01 -4.85148787e-01
-3.90549153e-01 2.49432418e-02 2.87404776e-01 8.14565778e-01
5.34974694e-01 -1.13625601e-01 -2.11798236e-01 6.31602943e-01
-3.88783336e-01 6.79926127e-02 4.92486000e-01 9.68580484e-01
-1.90756693e-01 -8.68005395e-01 -4.03673202e-01 8.98884535e-01
-9.05359685e-01 -2.54098982e-01 -4.41838764e-02 7.73079336e-01
1.85843855e-02 5.26863277e-01 -9.60133299e-02 1.37784272e-01
3.05327117e-01 2.58821040e-01 9.52203870e-01 -7.21536040e-01
-6.37403727e-01 5.63382208e-02 -1.59916192e-01 -6.06435120e-01
-2.95548528e-01 -8.98388267e-01 -1.52460682e+00 -1.78629950e-01
9.76051204e-04 -1.55084997e-01 8.02550912e-01 9.99413073e-01
2.38312498e-01 6.25314355e-01 5.60750952e-03 -1.05422342e+00
-3.23739350e-01 -9.34999287e-01 -7.87240028e-01 6.51953340e-01
2.34477982e-01 -9.32552099e-01 -3.07967719e-02 1.65156990e-01] | [14.028482437133789, -2.3145992755889893] |
e081e86e-8818-47be-b1c4-4b3213605cbf | 190600546 | 1906.00546 | null | https://arxiv.org/abs/1906.00546v1 | https://arxiv.org/pdf/1906.00546v1.pdf | Rethinking Loss Design for Large-scale 3D Shape Retrieval | Learning discriminative shape representations is a crucial issue for large-scale 3D shape retrieval. In this paper, we propose the Collaborative Inner Product Loss (CIP Loss) to obtain ideal shape embedding that discriminative among different categories and clustered within the same class. Utilizing simple inner product operation, CIP loss explicitly enforces the features of the same class to be clustered in a linear subspace, while inter-class subspaces are constrained to be at least orthogonal. Compared to previous metric loss functions, CIP loss could provide more clear geometric interpretation for the embedding than Euclidean margin, and is easy to implement without normalization operation referring to cosine margin. Moreover, our proposed loss term can combine with other commonly used loss functions and can be easily plugged into existing off-the-shelf architectures. Extensive experiments conducted on the two public 3D object retrieval datasets, ModelNet and ShapeNetCore 55, demonstrate the effectiveness of our proposal, and our method has achieved state-of-the-art results on both datasets. | ['Zhaoqun Li', 'Cheng Xu', 'Biao Leng'] | 2019-06-03 | null | null | null | null | ['3d-shape-retrieval', '3d-object-retrieval'] | ['computer-vision', 'computer-vision'] | [-3.12928289e-01 -5.83628304e-02 -2.75374293e-01 -3.80456835e-01
-7.95512319e-01 -7.80484438e-01 6.72345102e-01 2.54351735e-01
-1.69532120e-01 1.33913994e-01 4.16049138e-02 -8.14417377e-02
-4.04199094e-01 -7.07976520e-01 -4.34460640e-01 -8.11648369e-01
-2.67016720e-02 5.67647040e-01 2.45946288e-01 1.05636835e-01
1.81284726e-01 1.10656929e+00 -1.32252347e+00 6.77832067e-02
6.17916703e-01 1.37476480e+00 -9.50174332e-02 -5.67790717e-02
-1.19696856e-01 6.66688606e-02 -2.50364840e-01 -3.68798763e-01
5.26811659e-01 4.38239902e-01 -4.57743794e-01 1.50964214e-02
6.76523209e-01 -2.38480493e-01 -4.88371849e-01 9.23685849e-01
7.38933921e-01 3.30871828e-02 1.11739099e+00 -1.20142078e+00
-1.06245625e+00 -1.03946529e-01 -5.27174890e-01 -2.63231665e-01
1.69191599e-01 -1.07143559e-01 1.38731956e+00 -1.47616172e+00
6.33188784e-01 1.35683405e+00 4.04959708e-01 1.98308691e-01
-1.21414673e+00 -4.93127525e-01 1.95974141e-01 1.95221454e-01
-1.75140274e+00 -2.77858377e-01 1.09082603e+00 -3.18747997e-01
7.98737347e-01 3.52431476e-01 6.14855886e-01 5.71635842e-01
-7.96107277e-02 1.15996957e+00 5.15271008e-01 -9.83911604e-02
1.13532364e-01 1.05755739e-01 -5.04195504e-02 6.53205335e-01
3.88879091e-01 -1.46450952e-01 -1.09852299e-01 -4.77246374e-01
8.57806504e-01 2.01778680e-01 -3.38099033e-01 -1.43597686e+00
-1.04644680e+00 8.75941932e-01 8.04424405e-01 4.00985569e-01
-9.75630954e-02 1.07769206e-01 2.31063977e-01 1.96132928e-01
5.22602737e-01 6.01008162e-02 -2.22124547e-01 2.16923624e-01
-7.64959931e-01 3.23140264e-01 6.36182189e-01 1.14521015e+00
6.83943391e-01 -3.16489756e-01 -1.67019263e-01 1.08434725e+00
7.27798283e-01 7.69255817e-01 9.06301960e-02 -5.94756067e-01
3.50835323e-01 9.73464012e-01 -2.00127382e-02 -1.32560718e+00
-2.77646333e-01 -4.52401102e-01 -7.19857812e-01 9.07430723e-02
2.48718441e-01 6.40484750e-01 -6.25941634e-01 1.45984876e+00
4.36896741e-01 1.89157277e-01 -2.09428445e-01 1.17625773e+00
9.60766554e-01 3.61057162e-01 -3.16738337e-01 1.96547166e-01
1.14278412e+00 -7.65144110e-01 -2.33247831e-01 1.75809026e-01
5.39404035e-01 -9.65549529e-01 1.02156293e+00 -6.33987561e-02
-9.41056848e-01 -3.82160097e-01 -1.13876688e+00 -2.33622670e-01
-4.02498960e-01 3.00494790e-01 7.69582570e-01 5.64830720e-01
-9.51734543e-01 5.27994812e-01 -7.88201869e-01 -2.44789660e-01
4.98544633e-01 2.39021361e-01 -5.25953591e-01 -1.88489467e-01
-6.03477120e-01 5.97082555e-01 5.15853018e-02 -2.71806307e-02
-5.91031551e-01 -8.03132772e-01 -7.52944112e-01 1.54991433e-01
1.45178586e-01 -7.15064049e-01 6.96556389e-01 -1.99367121e-01
-1.28974223e+00 1.16596901e+00 -8.36693347e-02 9.76699889e-02
3.55791181e-01 -1.93160027e-01 -2.63374478e-01 2.45772526e-01
-7.38570392e-02 4.72094923e-01 8.91680717e-01 -1.50086832e+00
2.53374167e-02 -6.16453052e-01 1.00958064e-01 1.46846935e-01
-6.46074176e-01 -2.13542283e-01 -7.79506087e-01 -6.24134541e-01
6.63360775e-01 -9.75545645e-01 3.92185636e-02 7.39822209e-01
-1.97754025e-01 -7.39723146e-01 9.48208511e-01 -2.16695875e-01
1.16758049e+00 -2.40310645e+00 1.22989036e-01 5.76742291e-01
1.62696138e-01 3.04447681e-01 -3.79918188e-01 3.63226444e-01
1.35581437e-02 -6.06115237e-02 -1.73982024e-01 -3.01630437e-01
5.07173955e-01 1.24477595e-01 -3.01673889e-01 6.57963932e-01
3.27058196e-01 9.87429678e-01 -8.84613335e-01 -4.46176708e-01
2.67915398e-01 7.32105911e-01 -7.21735179e-01 1.26160055e-01
1.08648323e-01 -6.99112639e-02 -7.59363711e-01 8.92931461e-01
1.11953259e+00 -2.78660327e-01 -4.93922085e-02 -5.10699928e-01
2.26801753e-01 1.99221849e-01 -1.36779344e+00 1.93864512e+00
-2.34037220e-01 1.74058080e-01 -2.24354081e-02 -1.06727004e+00
1.28740728e+00 -1.66082941e-02 6.09852493e-01 -4.68669593e-01
2.07243264e-02 4.79420215e-01 -4.22034591e-01 -1.42783239e-01
4.58385646e-01 3.17385018e-01 8.56713504e-02 4.38456029e-01
5.50908260e-02 -3.96217823e-01 -1.60029992e-01 -4.68116440e-02
7.11198926e-01 1.97865963e-01 6.83313608e-02 -3.91592443e-01
7.27162838e-01 -5.92837155e-01 5.01287341e-01 4.89562571e-01
-2.32478589e-01 8.93976092e-01 2.17343226e-01 -4.59391356e-01
-9.74125028e-01 -1.47995055e+00 -5.43942869e-01 5.83180070e-01
5.05991578e-01 -4.13357049e-01 -1.78575143e-01 -9.47260857e-01
5.36767602e-01 2.83426374e-01 -3.77395660e-01 -1.97268248e-01
-4.83331263e-01 -2.47711301e-01 3.97967786e-01 5.14761031e-01
2.96491057e-01 -5.94456851e-01 -2.08922312e-01 -3.05562792e-03
2.71740913e-01 -8.53421271e-01 -8.24497879e-01 -1.83140635e-01
-8.99771154e-01 -1.17975652e+00 -8.94695759e-01 -1.01503813e+00
9.03094590e-01 6.21414959e-01 9.81776774e-01 1.63889110e-01
-3.85996193e-01 6.74524307e-01 -3.97218764e-01 -1.05480365e-01
2.54615247e-01 9.54520106e-02 -5.00619486e-02 1.60354078e-01
5.17265856e-01 -6.43729210e-01 -1.01358724e+00 4.61441159e-01
-9.60457206e-01 -5.31210005e-01 4.90585715e-01 8.47376287e-01
8.01756442e-01 -3.98642808e-01 3.51030111e-01 -2.52969891e-01
4.03848082e-01 -2.82659084e-01 -4.66489643e-01 4.93798763e-01
-5.32192051e-01 5.90770617e-02 2.57685870e-01 -3.10374111e-01
-4.66980457e-01 5.67297405e-03 4.18894216e-02 -8.57580721e-01
5.88793717e-02 3.13092947e-01 -4.11576092e-01 -3.84169310e-01
1.67455226e-01 3.12580049e-01 9.28388685e-02 -8.24427128e-01
5.84584594e-01 5.72059572e-01 6.21390864e-02 -7.49425411e-01
1.01076424e+00 7.63241470e-01 1.81285858e-01 -7.91874826e-01
-5.13303459e-01 -6.62775993e-01 -7.41552234e-01 7.77271986e-02
3.94528329e-01 -8.77261817e-01 -7.81677961e-01 2.17219606e-01
-1.08389163e+00 3.31276029e-01 -1.91779479e-01 3.64171535e-01
-4.75694031e-01 6.72820568e-01 -2.97907740e-01 -6.39553368e-01
-5.52776396e-01 -9.14775193e-01 1.46531451e+00 1.57699570e-01
1.15647383e-01 -9.28347945e-01 2.80248467e-02 6.89079687e-02
4.54295933e-01 7.65438750e-02 1.10211229e+00 -6.92767382e-01
-7.15647221e-01 -5.12081861e-01 -6.06250405e-01 3.22345257e-01
1.97001219e-01 7.79889897e-02 -8.98857057e-01 -7.31465280e-01
-2.49158353e-01 -2.83166140e-01 1.01992834e+00 1.72930285e-01
1.33422542e+00 -3.55052203e-02 -4.17338431e-01 6.51061952e-01
1.46896148e+00 -7.32013509e-02 5.65843642e-01 4.78806309e-02
5.49621761e-01 3.44675392e-01 5.82020819e-01 4.15223479e-01
3.83856177e-01 9.64380741e-01 4.14687037e-01 -1.15266107e-01
-1.49121299e-01 -3.42718989e-01 9.33477581e-02 1.01228988e+00
7.03977346e-02 3.02360039e-02 -5.26765704e-01 6.00857258e-01
-1.94599354e+00 -7.75178611e-01 2.33275563e-01 2.39534569e+00
5.29330075e-01 -3.24132144e-02 -1.35379061e-01 -6.85270205e-02
4.06715631e-01 3.89496237e-01 -5.16322374e-01 -1.75566778e-01
-3.57944101e-01 2.57100165e-01 2.42536366e-01 3.30735415e-01
-1.38665497e+00 7.74123192e-01 5.75511456e+00 1.30636799e+00
-1.08361113e+00 1.27870245e-02 3.15385222e-01 -9.32103395e-02
-7.07233846e-01 3.19375768e-02 -6.24838114e-01 1.04062378e-01
-3.30391712e-02 -2.39646375e-01 6.44190237e-02 9.59148586e-01
-8.05456340e-02 4.18124259e-01 -1.21163249e+00 1.18955588e+00
2.15313435e-01 -1.11257422e+00 4.09432918e-01 8.75719637e-02
6.92318916e-01 -8.58199503e-03 1.65303111e-01 1.61950484e-01
-2.72083551e-01 -8.50051403e-01 6.53460205e-01 4.88904506e-01
7.42060184e-01 -7.55776525e-01 5.18120944e-01 -1.69748522e-03
-1.61572933e+00 2.58280218e-01 -8.99372697e-01 3.56152356e-01
2.83603091e-02 7.71006346e-01 -5.66042900e-01 7.77833581e-01
4.50724751e-01 9.22563136e-01 -6.07158184e-01 1.38213158e+00
-1.31775603e-01 -1.84547305e-02 -4.55004811e-01 -1.96172729e-01
2.41585553e-01 -5.10509431e-01 9.09194946e-01 1.00034356e+00
3.52869183e-01 -9.75705385e-02 3.13732386e-01 9.23630357e-01
-3.05650793e-02 4.63559777e-01 -8.04836333e-01 -3.46601382e-02
5.10176063e-01 1.20110631e+00 -4.29362476e-01 -7.07766339e-02
-5.47863007e-01 9.25900102e-01 3.78370792e-01 4.34487402e-01
-6.12163365e-01 -5.62390566e-01 1.08494484e+00 5.23962379e-02
6.88569784e-01 -4.44148272e-01 -2.58868605e-01 -1.25987256e+00
4.16090757e-01 -3.45860183e-01 3.19629848e-01 -4.75810468e-01
-1.68437505e+00 3.45894396e-01 -2.11441562e-01 -1.73013771e+00
3.65752757e-01 -9.82429683e-01 -4.58386660e-01 7.09909439e-01
-1.72569335e+00 -1.38993526e+00 -9.16726664e-02 6.98429286e-01
-5.92480786e-03 -1.90717295e-01 1.02686143e+00 5.91026127e-01
-8.68070796e-02 8.56216729e-01 3.86225849e-01 1.10856377e-01
8.03577781e-01 -9.91827846e-01 -3.46805947e-03 2.50042289e-01
3.74709934e-01 7.87250817e-01 2.66805803e-03 -2.38333195e-01
-1.66856706e+00 -9.23351705e-01 9.34150338e-01 -3.28731298e-01
5.83549261e-01 -3.68459791e-01 -9.03228164e-01 2.69183040e-01
-1.24289483e-01 2.29537264e-01 7.63340712e-01 1.52732162e-02
-8.38969529e-01 -2.77227223e-01 -1.08796930e+00 4.36379373e-01
1.30780780e+00 -7.42076218e-01 -6.73468530e-01 2.71869987e-01
5.92500091e-01 -2.12026313e-01 -1.16808951e+00 7.91137397e-01
7.74218023e-01 -8.12650919e-01 1.39562881e+00 -6.30010843e-01
1.19634099e-01 -5.44404209e-01 -5.02737939e-01 -1.05100715e+00
-5.11793673e-01 -1.68683633e-01 -1.65756360e-01 1.14900851e+00
3.33512306e-01 -7.27459371e-01 7.05627978e-01 5.17080247e-01
-1.80683061e-01 -1.10237253e+00 -1.17322612e+00 -1.10248125e+00
3.22289199e-01 -2.43409738e-01 7.90113509e-01 8.30074608e-01
-9.26159769e-02 -8.28771666e-02 -1.93520915e-02 1.97563186e-01
8.00157189e-01 7.37165809e-01 7.14018643e-01 -1.39213157e+00
-1.23250827e-01 -8.87002349e-01 -9.31021035e-01 -1.53958595e+00
2.14452654e-01 -1.42916048e+00 -4.68323261e-01 -1.40248334e+00
3.85746568e-01 -8.40371013e-01 -6.17693067e-01 5.38406074e-01
8.72977450e-02 4.16111380e-01 3.33237261e-01 2.23677561e-01
-6.31661594e-01 1.25648010e+00 1.41549873e+00 -5.88708639e-01
1.29246106e-02 -1.05572887e-01 -4.95814234e-01 5.25762796e-01
3.57487231e-01 -1.74974173e-01 -2.49504432e-01 -6.23202145e-01
1.42697215e-01 -4.72204298e-01 3.66336316e-01 -5.27589321e-01
1.58196077e-01 5.25777638e-02 3.44399124e-01 -8.59433413e-01
5.21809638e-01 -1.16309631e+00 6.89177774e-04 2.26975009e-01
7.12006241e-02 -3.34334493e-01 1.93356723e-01 4.82527912e-01
-3.56988043e-01 -1.48269802e-01 6.50051236e-01 3.18683982e-01
-4.64819670e-01 6.76635563e-01 4.78939235e-01 -7.41678625e-02
9.59213853e-01 -2.48478070e-01 -1.34584352e-01 -1.54127449e-01
-6.20494545e-01 2.65521795e-01 5.85832238e-01 7.09562182e-01
9.63023186e-01 -2.08432341e+00 -7.42470384e-01 3.81830066e-01
5.38378954e-01 -1.16700128e-01 -4.54496853e-02 6.59243047e-01
-3.76272917e-01 6.53799653e-01 1.24255374e-01 -9.42737281e-01
-1.06752467e+00 4.57844526e-01 1.14084542e-01 -1.33577555e-01
-5.45311987e-01 6.77370012e-01 2.94753402e-01 -8.03998530e-01
3.02387983e-01 -1.15573592e-01 3.77409197e-02 6.40447810e-02
4.25309896e-01 2.79950947e-01 1.63872108e-01 -5.43086469e-01
-7.00688899e-01 1.12497675e+00 1.47387996e-01 3.91217113e-01
1.55159688e+00 4.60588150e-02 -2.41656139e-01 1.81250870e-01
1.77499032e+00 2.87602842e-02 -1.10718727e+00 -6.15539730e-01
-2.53377445e-02 -8.39469671e-01 -1.08337037e-01 -3.53938550e-01
-1.19707608e+00 7.84036517e-01 6.58004403e-01 3.07760425e-02
9.20373499e-01 3.22985411e-01 7.23483026e-01 5.29577136e-01
6.68062747e-01 -9.75997627e-01 1.60687819e-01 5.13486445e-01
1.33186042e+00 -1.03880715e+00 1.21249720e-01 -5.96348763e-01
-1.84423655e-01 1.18954337e+00 3.36348593e-01 -5.63596129e-01
1.13110471e+00 -1.76244423e-01 -1.03000350e-01 -2.93923348e-01
-4.62537199e-01 -2.03891829e-01 9.50832844e-01 3.67052495e-01
4.74898815e-01 2.01804906e-01 -5.74519098e-01 4.40209448e-01
1.69946879e-01 -4.83289540e-01 -2.61243135e-01 6.69066131e-01
-3.45408887e-01 -1.27417195e+00 -1.21547967e-01 2.33877271e-01
7.76950791e-02 1.15414053e-01 -3.83637249e-01 5.40625036e-01
-1.77102298e-01 5.51646531e-01 1.09613881e-01 -9.84501988e-02
6.43202126e-01 1.20555453e-01 6.96319044e-01 -3.38721037e-01
-8.31015594e-03 2.56842107e-01 -2.85741329e-01 -7.71393299e-01
-3.59222025e-01 -6.02380216e-01 -1.16338336e+00 -8.53353813e-02
-4.66117859e-01 -6.26359209e-02 5.76228797e-01 5.25717914e-01
6.77010894e-01 -1.42021954e-01 9.33130920e-01 -9.16149080e-01
-8.30556750e-01 -7.20173538e-01 -7.26120591e-01 7.90818512e-01
-4.07458879e-02 -1.08011377e+00 -4.12873775e-01 -3.78852665e-01] | [8.152656555175781, -3.8590548038482666] |
f715ca18-f34f-4875-9b66-716e9b57bc02 | foreground-guided-facial-inpainting-with | 2105.03342 | null | https://arxiv.org/abs/2105.03342v1 | https://arxiv.org/pdf/2105.03342v1.pdf | Foreground-guided Facial Inpainting with Fidelity Preservation | Facial image inpainting, with high-fidelity preservation for image realism, is a very challenging task. This is due to the subtle texture in key facial features (component) that are not easily transferable. Many image inpainting techniques have been proposed with outstanding capabilities and high quantitative performances recorded. However, with facial inpainting, the features are more conspicuous and the visual quality of the blended inpainted regions are more important qualitatively. Based on these facts, we design a foreground-guided facial inpainting framework that can extract and generate facial features using convolutional neural network layers. It introduces the use of foreground segmentation masks to preserve the fidelity. Specifically, we propose a new loss function with semantic capability reasoning of facial expressions, natural and unnatural features (make-up). We conduct our experiments using the CelebA-HQ dataset, segmentation masks from CelebAMask-HQ (for foreground guidance) and Quick Draw Mask (for missing regions). Our proposed method achieved comparable quantitative results when compare to the state of the art but qualitatively, it demonstrated high-fidelity preservation of facial components. | ['Moi Hoon Yap', 'Kevin Walker', 'Vincent Drouard', 'Connah Kendrick', 'Jireh Jam'] | 2021-05-07 | null | null | null | null | ['facial-inpainting', 'foreground-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.60512227e-01 2.46217057e-01 9.77680087e-02 -4.92305905e-01
-5.27648866e-01 -2.11252853e-01 4.34272051e-01 -4.97693956e-01
-2.25266039e-01 8.92160833e-01 1.16145939e-01 5.05592525e-01
1.04961619e-01 -7.16112733e-01 -8.83734584e-01 -8.11163604e-01
1.59564223e-02 -1.18162639e-01 -2.09065340e-02 -3.62076491e-01
7.40365610e-02 9.86211061e-01 -1.80519557e+00 4.48472083e-01
8.23370814e-01 1.26495564e+00 -1.37065157e-01 2.90778667e-01
-8.81246924e-02 8.58687818e-01 -4.50528622e-01 -7.04577863e-01
5.46731830e-01 -6.18115664e-01 -6.59216940e-01 4.46980327e-01
8.70945573e-01 -6.16247773e-01 -3.91723156e-01 1.18235493e+00
3.34865719e-01 -9.69184041e-02 6.50303900e-01 -1.68398893e+00
-7.03961849e-01 -2.50166450e-02 -1.03183925e+00 -2.47411460e-01
3.47126693e-01 9.73898545e-02 4.49407011e-01 -1.03573132e+00
7.95090377e-01 1.66956747e+00 5.80384612e-01 7.56942928e-01
-1.20001519e+00 -1.03848350e+00 -7.84306601e-02 5.35486192e-02
-1.30576217e+00 -6.91660583e-01 1.15500188e+00 -1.60140887e-01
3.41660082e-01 4.26521838e-01 7.42690265e-01 9.67360556e-01
2.55091339e-01 5.52026093e-01 1.38477230e+00 -3.25123250e-01
-3.00903264e-02 2.60506898e-01 -5.07731795e-01 1.06400895e+00
-9.68153551e-02 2.59813517e-01 -5.23658574e-01 6.08702656e-03
1.05029798e+00 2.72017539e-01 -4.40764338e-01 -1.04068197e-01
-6.66802585e-01 6.01924658e-01 4.48999256e-01 1.31362706e-01
-3.58694732e-01 3.12904626e-01 1.73661470e-01 3.85679185e-01
6.14778519e-01 -6.31772801e-02 -2.08114013e-01 1.58258468e-01
-1.33284307e+00 3.66180867e-01 3.45550537e-01 9.65369105e-01
9.95786965e-01 2.79105157e-01 -3.02053720e-01 8.82939637e-01
2.33745545e-01 4.48831856e-01 2.31277421e-01 -1.26828229e+00
5.37829176e-02 5.59511662e-01 3.09787504e-02 -1.38937068e+00
-1.08383790e-01 4.41440456e-02 -9.40949678e-01 8.01419675e-01
1.08873732e-01 -4.34150286e-02 -1.04591453e+00 1.73877358e+00
5.48328459e-01 5.67033567e-05 -1.41689956e-01 9.65041518e-01
9.85220313e-01 6.43637180e-01 1.50817126e-01 -3.48088145e-01
1.30644000e+00 -1.05650640e+00 -1.15663135e+00 2.30494216e-02
-4.15675230e-02 -9.77290869e-01 9.48232889e-01 3.24557006e-01
-1.45498157e+00 -6.64042115e-01 -7.94968009e-01 -3.49754363e-01
-1.36000693e-01 1.58939585e-01 5.16576052e-01 5.89959860e-01
-1.23232698e+00 7.42277265e-01 -4.02724326e-01 -2.82749653e-01
7.34371722e-01 2.46964455e-01 -9.65912044e-01 3.38523276e-02
-9.29535508e-01 6.94724798e-01 1.70574397e-01 2.76841640e-01
-1.00660586e+00 -7.46991396e-01 -9.75143015e-01 7.06683025e-02
6.20814599e-02 -3.14389855e-01 9.24588025e-01 -1.76584613e+00
-1.74913919e+00 1.06371617e+00 4.49706465e-02 7.80517906e-02
9.02478099e-01 -2.22917736e-01 -2.65994787e-01 5.94938576e-01
2.83483751e-02 1.18612695e+00 1.35849881e+00 -1.46134472e+00
-4.15966123e-01 -2.90227532e-01 -1.60459816e-01 5.06412834e-02
-2.40810975e-01 3.05866599e-01 -2.74046212e-01 -9.36076224e-01
-1.00259990e-01 -5.73497593e-01 1.97260365e-01 9.30322468e-01
-5.21787256e-02 1.89045221e-01 1.20421636e+00 -9.93956983e-01
8.54314208e-01 -2.28735352e+00 3.04505397e-02 -4.13949303e-02
2.55534828e-01 3.08066159e-01 -2.35419974e-01 3.28210384e-01
-1.55951053e-01 3.61443684e-02 -5.23163617e-01 -5.68199337e-01
-3.70377183e-01 2.84250140e-01 -1.74651995e-01 5.84872901e-01
6.60841763e-01 8.26162219e-01 -5.79719722e-01 -7.64202356e-01
2.00818360e-01 7.34253883e-01 -6.34698510e-01 3.25967073e-01
-6.68849722e-02 3.58009100e-01 -1.44591063e-01 1.09358740e+00
1.37952542e+00 3.70748550e-01 -3.20784837e-01 -4.37801063e-01
3.27430107e-02 -7.14016020e-01 -9.18916941e-01 1.55993950e+00
-2.97065288e-01 6.48625135e-01 6.47288859e-01 -5.07961214e-01
1.02922809e+00 2.07895711e-01 4.61599201e-01 -7.97152221e-01
3.00714761e-01 1.50832102e-01 -4.51882124e-01 -7.55110800e-01
4.80546951e-01 -4.98009235e-01 4.47423875e-01 6.49486631e-02
2.05788866e-01 -2.94306695e-01 -1.46245390e-01 -1.74651295e-02
5.48997402e-01 2.97487468e-01 -4.19369154e-02 -2.59999245e-01
4.49593961e-01 -3.81584883e-01 4.87126946e-01 -7.55799562e-02
-3.38540792e-01 1.13003719e+00 6.65064454e-01 -3.92556638e-01
-9.93570685e-01 -9.16060269e-01 -7.95620531e-02 8.02823782e-01
1.68247640e-01 -9.00198296e-02 -1.14397275e+00 -5.70706129e-01
3.49881761e-02 2.57003725e-01 -1.02334619e+00 -3.07669193e-01
-4.33482289e-01 -3.83048505e-01 6.99955940e-01 3.41014057e-01
9.51475263e-01 -1.32661688e+00 -4.42513913e-01 1.23965815e-02
-7.68104866e-02 -1.15854049e+00 -6.32973075e-01 -3.51035148e-01
-8.63766432e-01 -1.03161240e+00 -1.05191767e+00 -9.98807430e-01
8.83275568e-01 9.63216051e-02 8.59869480e-01 2.78486252e-01
-6.17554843e-01 1.51162399e-02 -1.15411781e-01 -1.30807742e-01
-3.02659303e-01 -5.91225326e-01 -2.26788312e-01 5.53378880e-01
-2.79648125e-01 -6.98795438e-01 -7.70532489e-01 1.48368627e-01
-1.41152322e+00 2.01275155e-01 6.87779427e-01 8.55652750e-01
3.86909187e-01 -9.45048928e-02 3.24703634e-01 -7.56909132e-01
4.15261239e-01 -2.24468887e-01 -3.08174193e-01 1.03907198e-01
-2.31745914e-01 -2.51941562e-01 4.37165141e-01 -4.36062455e-01
-1.36255467e+00 -1.19652368e-01 -3.13663661e-01 -7.71732032e-01
-1.63522512e-01 -2.15099722e-01 -3.81922007e-01 -5.23406088e-01
3.18784863e-01 2.72472560e-01 4.83980566e-01 -4.17438060e-01
3.57748359e-01 5.19860506e-01 5.09081900e-01 -6.46258593e-01
6.79496169e-01 7.51223564e-01 1.85689941e-01 -8.74783337e-01
-4.05951291e-01 8.78927112e-02 -4.91698623e-01 -4.49655294e-01
7.10064828e-01 -8.36438119e-01 -6.01213634e-01 7.11882651e-01
-1.25700498e+00 -1.42582014e-01 -3.18008631e-01 -3.68482731e-02
-5.96354783e-01 3.99515033e-01 -7.69630671e-01 -7.80708492e-01
-4.65546072e-01 -1.12224841e+00 1.36736536e+00 4.20804769e-01
5.04283234e-02 -5.85580230e-01 -3.05051774e-01 2.49299899e-01
4.74027574e-01 9.80988920e-01 8.10595751e-01 2.69105911e-01
-4.59739208e-01 8.35377648e-02 -6.04951024e-01 6.83945119e-01
4.15285230e-01 4.05198157e-01 -1.24768722e+00 -1.87954649e-01
-1.35597466e-02 -4.12686169e-01 6.54596448e-01 1.46855742e-01
1.25129128e+00 -6.22016490e-01 -1.20682493e-01 7.76504874e-01
1.35236955e+00 8.96360278e-02 1.13792562e+00 -7.60615692e-02
5.70112288e-01 9.90284920e-01 6.46587431e-01 3.75466228e-01
-2.38461107e-01 5.85921288e-01 4.84261066e-01 -6.59230173e-01
-4.52820510e-01 -3.98988932e-01 4.85853553e-01 4.39486802e-01
-2.68385828e-01 9.85183716e-02 -1.49098575e-01 4.72647429e-01
-1.56871986e+00 -8.81588817e-01 3.19676958e-02 1.74555337e+00
1.15445769e+00 -3.12422246e-01 1.04949409e-02 1.09713174e-01
6.40018284e-01 4.04916331e-02 -1.38571754e-01 -4.68613625e-01
-2.54048288e-01 5.42692482e-01 1.06911935e-01 5.38404524e-01
-7.79227972e-01 1.05550492e+00 6.23378801e+00 1.31872618e+00
-1.22887468e+00 2.24312797e-01 1.12580383e+00 -1.14819698e-01
-2.24665046e-01 -2.54045963e-01 -4.63836521e-01 6.16278052e-01
3.28893036e-01 1.94948718e-01 1.33435681e-01 6.96257889e-01
2.72528768e-01 -1.11809529e-01 -7.84495175e-01 1.16540217e+00
2.66418159e-01 -1.21720302e+00 4.55982924e-01 -1.42310008e-01
9.05221701e-01 -8.18677425e-01 2.92336404e-01 -3.56363729e-02
-3.71929586e-01 -1.32382405e+00 9.80940163e-01 7.99465239e-01
1.22225726e+00 -1.18003261e+00 5.02069890e-01 -1.26105011e-01
-8.67893815e-01 1.28991678e-01 -4.19778794e-01 -1.71057582e-02
-3.33396569e-02 5.65960705e-01 -3.24177086e-01 3.57525826e-01
8.04684341e-01 5.18216014e-01 -6.03260100e-01 6.69182777e-01
-1.95335731e-01 2.23036259e-01 -6.34463578e-02 3.34829360e-01
1.53418377e-01 -3.71552348e-01 2.17915893e-01 1.06787217e+00
3.37698907e-01 2.14205742e-01 -2.30218619e-01 1.21725595e+00
-3.30166012e-01 3.10145229e-01 -6.69552267e-01 7.17359781e-02
8.22615921e-02 1.57650423e+00 -6.58948541e-01 -2.34469265e-01
-2.80651033e-01 1.36200535e+00 1.76377669e-01 2.40768179e-01
-1.03262556e+00 -4.61983114e-01 8.13490510e-01 5.55057824e-01
-1.90749438e-03 1.11860700e-01 -1.93452416e-03 -8.25888395e-01
5.43411933e-02 -8.83606434e-01 -1.82090014e-01 -9.67391789e-01
-1.09571564e+00 8.11587572e-01 4.09678137e-03 -1.01180565e+00
7.76215941e-02 -4.67664719e-01 -5.23191750e-01 7.46981323e-01
-1.62030351e+00 -1.48399568e+00 -7.70301700e-01 8.16963553e-01
5.02425551e-01 -2.10580174e-02 5.14255106e-01 5.80252171e-01
-3.03210109e-01 8.41574848e-01 -1.72449067e-01 1.00276031e-01
8.07219267e-01 -6.51195347e-01 -8.43059793e-02 6.61102176e-01
-3.32157671e-01 3.73531997e-01 5.52957475e-01 -5.18777370e-01
-1.04646087e+00 -1.28512931e+00 6.14324808e-01 1.06619328e-01
4.97495458e-02 -2.62486696e-01 -8.06967795e-01 4.79948372e-01
5.50118566e-01 2.65786052e-01 2.81307697e-01 -8.18389595e-01
-2.39384174e-01 -3.27770621e-01 -1.81494927e+00 6.43391967e-01
1.05874622e+00 -2.12217316e-01 -2.45304555e-01 1.01427644e-01
6.37829185e-01 -2.03405529e-01 -6.14367187e-01 4.92622465e-01
6.81297064e-01 -1.33319914e+00 6.88099205e-01 -2.70324230e-01
6.82105958e-01 -3.92085701e-01 -9.13858712e-02 -1.00512731e+00
-1.30489692e-01 -9.59139824e-01 1.71391547e-01 1.57048154e+00
-9.80498418e-02 -4.20511335e-01 7.69675016e-01 5.80446303e-01
6.98152781e-02 -7.13103473e-01 -8.27124596e-01 -4.55530941e-01
-7.32390657e-02 1.80599734e-01 6.33649468e-01 8.58910680e-01
-5.16330242e-01 -1.89931929e-01 -5.97635090e-01 -2.53656834e-01
6.60580099e-01 -2.68292781e-02 6.26579165e-01 -9.48254168e-01
1.16257325e-01 -3.60982001e-01 -5.68629444e-01 -4.43543762e-01
1.20884962e-01 -4.62964296e-01 -1.71257138e-01 -1.05320990e+00
2.01267749e-01 -1.93800077e-01 1.47846267e-01 7.60753810e-01
-3.37786451e-02 8.12693238e-01 2.96925783e-01 2.10797917e-02
-2.48122757e-04 8.57006311e-01 1.83313227e+00 -9.38927680e-02
1.34737283e-01 -4.06208009e-01 -6.73303902e-01 7.31469989e-01
6.34569645e-01 -4.27586645e-01 -2.81364024e-01 -1.63053870e-01
-2.32710123e-01 1.20870776e-01 5.53151608e-01 -1.03576028e+00
-1.50623336e-01 -3.19756866e-01 8.15693676e-01 -2.79327661e-01
6.60935342e-01 -9.35959756e-01 4.48740631e-01 4.29911226e-01
-1.78618670e-01 -3.64060737e-02 4.13618028e-01 3.01606506e-01
-5.22700071e-01 -1.68900359e-02 1.39715660e+00 -1.15005761e-01
-6.05759859e-01 5.01842737e-01 -1.16157167e-01 -1.96761236e-01
1.25677574e+00 -4.50453490e-01 7.68308863e-02 -4.65924203e-01
-5.21847665e-01 -3.15801531e-01 6.45111740e-01 3.34004879e-01
1.00455952e+00 -1.53532743e+00 -7.74015427e-01 6.53996468e-01
-1.03010423e-01 -4.52702343e-02 3.42782736e-01 6.75018370e-01
-1.09569776e+00 -1.88021958e-01 -9.58236396e-01 -3.95114005e-01
-1.39943075e+00 4.55550998e-01 3.14741969e-01 2.68469304e-01
-4.59192246e-01 8.35621476e-01 5.87878227e-01 -1.27643228e-01
2.58094043e-01 -3.33226681e-01 1.14383344e-02 7.38773271e-02
7.16175258e-01 3.90219808e-01 -3.63143757e-02 -8.11385334e-01
-1.68472245e-01 8.04836512e-01 7.65317585e-03 -3.33538093e-02
1.29709125e+00 -9.45953503e-02 -4.84920442e-01 -2.64243662e-01
1.42081785e+00 1.96088701e-01 -1.62025368e+00 1.18124299e-01
-4.89327610e-01 -9.20607686e-01 -1.04528859e-01 -6.77151024e-01
-1.76805198e+00 8.69806468e-01 8.17785084e-01 -3.12477022e-01
1.55174422e+00 -3.14181715e-01 9.43711340e-01 -3.92600358e-01
3.21687967e-01 -8.95715058e-01 3.25912684e-01 -9.71724540e-02
1.31082284e+00 -1.09375107e+00 -1.53445706e-01 -7.61831105e-01
-5.59755385e-01 1.24460316e+00 6.90467775e-01 -4.07191932e-01
6.29649520e-01 3.99839818e-01 1.15491942e-01 -1.02489248e-01
-3.68701249e-01 6.14224598e-02 2.21415505e-01 7.20492959e-01
3.28363657e-01 -1.98955953e-01 -2.82913893e-01 4.19935971e-01
4.39731404e-03 9.49736759e-02 3.64472598e-01 8.10858548e-01
-3.28901082e-01 -7.87950635e-01 -5.05397975e-01 6.45529777e-02
-5.42498410e-01 -1.93974860e-02 -4.58664149e-01 1.06465530e+00
4.82861519e-01 8.05461228e-01 -4.14567478e-02 -1.46367639e-01
1.97448999e-01 -1.85178354e-01 8.58562410e-01 -2.51316190e-01
-4.91899759e-01 1.82613894e-01 -1.53425634e-01 -9.11567390e-01
-4.76679474e-01 -2.29003802e-01 -1.10193944e+00 -6.51412308e-01
2.38547046e-02 -1.06891379e-01 5.38404465e-01 4.93723065e-01
4.02069509e-01 3.67457330e-01 6.82635486e-01 -9.96419191e-01
-3.57130505e-02 -9.50287998e-01 -9.78623986e-01 7.01963782e-01
4.17118937e-01 -8.70724678e-01 -3.58650416e-01 3.97722572e-01] | [12.714282989501953, -0.1074012815952301] |
f78d2cc2-98ab-4ea5-b5fb-bb594d22fc92 | language-models-and-automated-essay-scoring | 1909.09482 | null | https://arxiv.org/abs/1909.09482v1 | https://arxiv.org/pdf/1909.09482v1.pdf | Language models and Automated Essay Scoring | In this paper, we present a new comparative study on automatic essay scoring (AES). The current state-of-the-art natural language processing (NLP) neural network architectures are used in this work to achieve above human-level accuracy on the publicly available Kaggle AES dataset. We compare two powerful language models, BERT and XLNet, and describe all the layers and network architectures in these models. We elucidate the network architectures of BERT and XLNet using clear notation and diagrams and explain the advantages of transformer architectures over traditional recurrent neural network architectures. Linear algebra notation is used to clarify the functions of transformers and attention mechanisms. We compare the results with more traditional methods, such as bag of words (BOW) and long short term memory (LSTM) networks. | ['Pedro Uria Rodriguez', 'Christopher M. Ormerod', 'Amir Jafari'] | 2019-09-18 | null | null | null | null | ['automated-essay-scoring'] | ['natural-language-processing'] | [-2.00576901e-01 -1.57219827e-01 3.83096412e-02 -2.16120332e-01
-6.28065586e-01 -4.40533787e-01 5.54381430e-01 2.28939712e-01
-5.18858254e-01 6.55990422e-01 6.81670249e-01 -7.74168670e-01
-2.35175073e-01 -8.40696514e-01 -3.30217004e-01 -2.28380308e-01
3.04522336e-01 3.17056149e-01 -1.50433242e-01 -7.00087249e-01
6.51725650e-01 4.16844845e-01 -1.19158721e+00 5.55980980e-01
7.46514499e-01 8.59375596e-01 -1.68521389e-01 9.57171381e-01
-7.44359732e-01 1.63848591e+00 -9.21073794e-01 -9.14819598e-01
-2.01492012e-01 -1.85950771e-01 -9.61615026e-01 -9.07558203e-01
5.78031480e-01 -1.70552477e-01 -8.45772326e-01 8.15551758e-01
5.49786031e-01 3.45216632e-01 6.87037945e-01 -6.84653461e-01
-1.42558575e+00 1.04328048e+00 -3.38552386e-01 4.43847567e-01
3.76181692e-01 -2.56213427e-01 1.41435599e+00 -1.00785851e+00
1.62952945e-01 1.03525913e+00 1.17708147e+00 4.76050496e-01
-7.00468957e-01 -7.29435921e-01 2.45739147e-01 4.74734515e-01
-9.25416768e-01 -2.37335503e-01 7.14151740e-01 -3.08291346e-01
1.46609521e+00 8.80393609e-02 7.26751029e-01 1.33978796e+00
8.74636114e-01 1.05044210e+00 1.09788895e+00 -8.93391192e-01
-1.33821577e-01 -1.26036435e-01 1.32800722e+00 1.08427691e+00
9.47337821e-02 -4.02366882e-03 -1.17864037e+00 -1.97231546e-01
6.90271497e-01 -1.69135090e-02 -8.84396257e-04 2.35864088e-01
-8.84282112e-01 9.12079453e-01 3.99365067e-01 6.18821383e-01
-3.89965385e-01 4.15753752e-01 7.04019904e-01 5.58835566e-01
4.51087087e-01 7.56532431e-01 -4.36921805e-01 -2.91012406e-01
-1.12439549e+00 4.53000627e-02 7.88571894e-01 7.05677032e-01
5.64177558e-02 5.75871050e-01 -6.90921843e-01 9.57419515e-01
1.51071846e-01 -3.95982899e-02 1.26807785e+00 -3.81353974e-01
4.82755244e-01 5.56931257e-01 -3.01719606e-01 -1.12994254e+00
-4.09658492e-01 -8.38560939e-01 -9.75583017e-01 -1.52712047e-01
2.26625532e-01 1.23640217e-01 -9.34432328e-01 1.32031024e+00
-9.55319166e-01 4.06647809e-02 1.44673154e-01 4.81808901e-01
1.29744470e+00 9.39265847e-01 3.24563384e-01 3.16510350e-01
1.42786753e+00 -1.40277624e+00 -1.01812863e+00 -4.03797954e-01
7.64588892e-01 -5.39812386e-01 1.28583157e+00 4.01889324e-01
-1.51146412e+00 -6.80545032e-01 -1.15318131e+00 -6.80298388e-01
-9.45170820e-01 5.21705389e-01 7.78950214e-01 7.92463660e-01
-1.33604300e+00 8.04253280e-01 -7.76791453e-01 -2.06757024e-01
1.21054403e-01 4.03964698e-01 1.20375469e-01 4.96745855e-01
-1.69318950e+00 1.37100410e+00 4.23035771e-02 4.22508180e-01
-5.16135633e-01 -8.04317296e-01 -8.89578760e-01 5.80740392e-01
-3.32951367e-01 -6.31792784e-01 1.71273005e+00 -7.38944769e-01
-2.01844621e+00 7.24822164e-01 -2.77336448e-01 -8.48028183e-01
-1.50090024e-01 -5.15286565e-01 -3.15555423e-01 -1.65221974e-01
-4.59367394e-01 2.02458724e-01 1.99106514e-01 -3.71618718e-01
-1.51753291e-01 -1.55064493e-01 -1.10540502e-01 -3.51125561e-03
-9.18131649e-01 6.16170049e-01 2.79354900e-02 -8.76373768e-01
-2.28510469e-01 -5.90943038e-01 -2.21942291e-02 -4.81745929e-01
-1.72223866e-01 -7.09706306e-01 1.88019812e-01 -1.11286938e+00
1.85429740e+00 -1.57528031e+00 8.47287476e-02 1.57873139e-01
2.83937782e-01 2.28782535e-01 -2.90773898e-01 4.73056376e-01
-3.22800934e-01 -2.32244320e-02 9.41023827e-02 -5.26432693e-01
3.12709063e-01 -1.03052139e-01 -8.08543086e-01 4.44425968e-03
-2.71648586e-01 1.48736608e+00 -5.93225181e-01 -1.56172454e-01
-5.98893911e-02 4.58382040e-01 5.67545556e-02 1.42531723e-01
4.55967821e-02 -4.48778391e-01 -2.00243041e-01 5.82191050e-01
-1.89731345e-02 -2.09999517e-01 -9.34189633e-02 1.61547452e-01
-1.89811051e-01 1.13501775e+00 -5.78222156e-01 1.72831929e+00
-6.08647168e-01 1.20160317e+00 -3.97388786e-01 -6.11444712e-01
1.19361055e+00 5.05334139e-01 -1.46956831e-01 -7.46720195e-01
2.38850564e-01 1.57129765e-01 -9.55457985e-02 -9.29704681e-02
1.01185262e+00 -7.81946182e-02 -2.64657766e-01 8.91327977e-01
3.60300869e-01 2.03439415e-01 2.46762931e-01 4.56886351e-01
9.97036755e-01 -1.34321779e-01 4.09247518e-01 -3.64080936e-01
7.91297019e-01 -2.67315239e-01 1.85157821e-01 1.01533222e+00
-3.68446074e-02 3.65324765e-01 5.93894899e-01 -7.35735357e-01
-9.89415824e-01 -9.20981944e-01 2.76022226e-01 1.55175173e+00
-7.11928666e-01 -8.72894883e-01 -4.71470237e-01 -2.85499424e-01
-3.49250853e-01 1.00220811e+00 -6.14402831e-01 -2.01874569e-01
-6.02289438e-01 -7.18549669e-01 1.14909959e+00 9.55703616e-01
5.89981258e-01 -1.34455895e+00 -5.41468501e-01 3.27434093e-01
-8.90657306e-02 -6.17129862e-01 -3.20991933e-01 2.23094776e-01
-7.61497855e-01 -6.03966832e-01 -7.63516724e-01 -8.84710789e-01
1.08402930e-01 -1.64449394e-01 1.54972911e+00 1.11521959e-01
-1.45147428e-01 2.59765863e-01 3.42411473e-02 -6.30187571e-01
-4.21954431e-02 4.60923880e-01 2.00623590e-02 -4.12139177e-01
9.06813979e-01 -3.30490977e-01 -1.10800289e-01 -4.53134149e-01
-4.86783862e-01 8.17846060e-02 5.93333006e-01 1.03232837e+00
-1.00915842e-01 -3.43820959e-01 4.24104929e-01 -7.24026978e-01
1.77660513e+00 5.33499494e-02 -4.03978795e-01 6.66757941e-01
-7.13164389e-01 1.31274134e-01 5.61941326e-01 -2.38534287e-01
-9.96528208e-01 -4.24377054e-01 -1.64192513e-01 -1.09444611e-01
3.46720427e-01 8.97267342e-01 5.94857872e-01 -3.08003455e-01
5.95989048e-01 4.85058099e-01 -3.60986859e-01 -4.61120605e-01
1.80018485e-01 8.34926248e-01 5.14744043e-01 -6.19059205e-01
2.23680466e-01 -3.12929541e-01 -4.02085066e-01 -2.83784270e-01
-1.18990409e+00 -2.00494483e-01 -8.17662120e-01 -1.17536530e-01
4.69667435e-01 -7.91996121e-01 -9.16541219e-01 5.37525713e-01
-1.43112051e+00 -7.59250894e-02 -1.35141969e-01 4.14100170e-01
-2.42384851e-01 1.75751269e-01 -1.59291708e+00 -8.59856308e-01
-1.07544279e+00 -9.17150676e-01 7.04381287e-01 4.63489801e-01
-5.10008037e-01 -1.28141642e+00 3.51963103e-01 2.61161357e-01
7.20799983e-01 -4.39787179e-01 1.13257074e+00 -7.94143558e-01
-3.02990098e-02 -2.98216701e-01 -1.58037454e-01 2.89942443e-01
-5.81474006e-01 -2.44647190e-02 -9.78466272e-01 -5.57376258e-03
1.00074030e-01 -3.89565557e-01 1.34390128e+00 3.94577652e-01
1.57144630e+00 -2.61297286e-01 1.32717401e-01 3.56198639e-01
1.07638550e+00 1.03099076e-02 7.05996215e-01 5.06926060e-01
5.53985536e-01 5.44466674e-01 -2.25586444e-01 -1.31087787e-02
4.65094805e-01 1.18823498e-01 -3.83337677e-01 1.05151823e-02
1.02813937e-01 -3.96285951e-01 7.92334080e-01 1.51624668e+00
-7.25589693e-02 -1.86442703e-01 -1.17439592e+00 5.14409959e-01
-1.88097584e+00 -8.82887065e-01 -2.10281476e-01 1.68601918e+00
9.30054307e-01 1.90183103e-01 -1.67175591e-01 1.89597622e-01
5.07564902e-01 3.36043149e-01 -5.15327565e-02 -1.29463923e+00
-2.29278073e-01 9.61531937e-01 4.23898041e-01 6.07398570e-01
-8.34900379e-01 1.04904151e+00 7.97150946e+00 7.55609989e-01
-9.32357490e-01 1.54242337e-01 6.75636828e-01 -2.10917607e-01
-1.68211609e-01 -3.98048252e-01 -8.79103959e-01 1.88429207e-01
1.72525477e+00 -2.79885173e-01 2.60218829e-01 6.24143600e-01
-1.95009679e-01 3.93357724e-01 -9.29537833e-01 9.48648453e-01
4.84553635e-01 -1.57367885e+00 3.67517024e-01 -4.70542580e-01
3.48076344e-01 7.06335306e-02 2.43331909e-01 8.15491319e-01
6.54100537e-01 -1.50779676e+00 4.91386265e-01 9.68510330e-01
5.62510967e-01 -8.17108929e-01 8.70881677e-01 1.00025407e-03
-1.02560329e+00 -3.89532685e-01 -5.90246201e-01 -6.13077223e-01
-1.56098634e-01 4.16694015e-01 -2.01104462e-01 4.85227346e-01
6.56270266e-01 8.82767379e-01 -8.57050121e-01 8.14321995e-01
-6.56153858e-01 8.77774656e-01 1.36345208e-01 -5.79801798e-01
5.76335609e-01 -1.92825779e-01 6.86357692e-02 1.51879418e+00
2.86813617e-01 1.14026770e-01 -3.83717209e-01 9.54209805e-01
-3.85796219e-01 1.22547634e-01 -3.40406537e-01 -3.33954006e-01
2.44067103e-01 1.28964627e+00 -1.89641014e-01 -5.71853459e-01
-4.16133136e-01 7.71170974e-01 7.00671315e-01 3.79983068e-01
-5.93574226e-01 -8.70680213e-01 3.75847101e-01 -2.35594198e-01
-2.20148519e-01 -4.80735481e-01 -8.14679682e-01 -1.36459386e+00
-2.93985903e-01 -8.42430353e-01 6.52943611e-01 -1.22968698e+00
-1.62717128e+00 6.13598764e-01 -4.97532547e-01 -5.70091963e-01
-1.39824048e-01 -1.19298327e+00 -1.06682110e+00 1.07842302e+00
-1.57615709e+00 -1.05362344e+00 -1.23111941e-01 4.25471216e-01
6.11750007e-01 -7.84676909e-01 1.29238212e+00 1.13324866e-01
-7.52921879e-01 8.49454343e-01 3.08219612e-01 6.18189454e-01
6.43078864e-01 -1.35138023e+00 6.28324747e-01 6.95404053e-01
4.11278218e-01 1.15761721e+00 2.86382437e-01 -3.51188600e-01
-1.12846851e+00 -6.69167995e-01 1.77141559e+00 -5.27730882e-01
1.18025339e+00 -6.05707765e-01 -8.86634469e-01 1.10840821e+00
7.97774017e-01 -5.22098124e-01 9.50748801e-01 5.30909717e-01
-7.06513703e-01 -7.28059486e-02 -6.12859905e-01 7.86752999e-01
6.12158775e-01 -9.30362165e-01 -1.16176033e+00 1.98038653e-01
4.67878997e-01 -2.52345443e-01 -7.41347611e-01 2.43731648e-01
8.03080380e-01 -7.22602189e-01 9.44633365e-01 -1.02303040e+00
7.81676054e-01 2.59810477e-01 4.20032859e-01 -1.48913383e+00
-8.45067680e-01 -7.37173617e-01 -3.51402193e-01 8.78773987e-01
5.16364217e-01 -6.49788320e-01 5.86635530e-01 7.22123623e-01
-2.76854753e-01 -9.12111819e-01 -7.09227860e-01 -4.47802246e-01
6.03489876e-01 -4.02849734e-01 5.70792735e-01 8.31490695e-01
6.79944396e-01 6.77223325e-01 -2.37484872e-01 -5.19947529e-01
1.91028103e-01 7.22304061e-02 9.02566835e-02 -1.24761987e+00
4.42682579e-03 -1.01038957e+00 -2.26612702e-01 -9.66579199e-01
5.63893914e-01 -1.01485097e+00 -4.45379168e-01 -1.61389863e+00
3.00321877e-01 1.65550038e-01 -9.06783164e-01 7.46458292e-01
-1.09315114e-02 1.67440355e-01 1.31688640e-01 -7.04852585e-03
-3.41612905e-01 6.77549243e-01 6.31077945e-01 -3.36369812e-01
5.14305383e-02 -2.65524685e-01 -1.02945304e+00 6.13374770e-01
9.89824891e-01 -2.77558506e-01 -5.92331849e-02 -8.18000376e-01
6.62855983e-01 -1.55207247e-01 1.09935857e-01 -6.91991746e-01
9.00386333e-01 3.50315958e-01 7.39114046e-01 -7.39512861e-01
2.62375474e-01 -1.96046799e-01 -5.61933339e-01 5.00649929e-01
-1.04099631e+00 6.66933298e-01 3.49202722e-01 3.16853561e-02
-4.12673324e-01 -4.92800474e-01 4.36813265e-01 -4.17105764e-01
-3.01301092e-01 -6.53536841e-02 -8.22401226e-01 -5.45890152e-01
4.17116016e-01 6.01530448e-03 -5.90159595e-01 -5.05547404e-01
-5.50935090e-01 2.07962841e-01 -4.24549460e-01 7.15883553e-01
7.97887504e-01 -1.22734785e+00 -7.81108618e-01 1.88463733e-01
-1.88559175e-01 -7.56751895e-01 -5.40434495e-02 7.08094239e-01
-5.99134982e-01 1.31466091e+00 -3.65763187e-01 2.81113118e-01
-1.39324677e+00 4.44006249e-02 7.33627200e-01 -9.82160628e-01
-4.80402559e-01 9.96200085e-01 -7.62512088e-02 -7.11395562e-01
5.20323992e-01 -3.02414149e-01 -8.59323680e-01 -2.23554671e-02
7.18069673e-01 4.55812156e-01 4.14394796e-01 -6.31554723e-02
-9.92761329e-02 3.08947474e-01 -4.56108153e-01 -7.97115713e-02
1.42027509e+00 3.62043530e-01 -6.59120917e-01 8.91214848e-01
6.00341380e-01 -1.02027714e-01 -9.74452309e-03 -2.82182544e-01
4.07760233e-01 1.14336498e-01 4.06448305e-01 -1.17288625e+00
-8.07938516e-01 1.36745882e+00 2.89897501e-01 -1.23215549e-01
8.02904248e-01 -6.72199607e-01 9.73785579e-01 7.79169500e-01
-5.73207401e-02 -1.34219635e+00 -2.44059116e-02 1.25635707e+00
9.26827371e-01 -4.77254301e-01 -5.36885113e-02 2.64392436e-01
-4.83240366e-01 1.75014114e+00 7.07323611e-01 -4.63370979e-01
5.82830369e-01 4.32028085e-01 -7.07009211e-02 -4.24213588e-01
-1.14439833e+00 3.69417042e-01 6.61747217e-01 -2.13480666e-01
1.12655461e+00 -7.31493384e-02 -5.45258224e-01 1.24645078e+00
-8.44167709e-01 7.15166777e-02 8.08914959e-01 7.24785268e-01
-2.99340278e-01 -9.17146206e-01 -3.52281809e-01 6.55709743e-01
-8.92907679e-01 -7.23289967e-01 -8.53863358e-01 4.03196067e-01
-5.78006029e-01 6.78519309e-01 1.53795034e-01 -2.69803077e-01
1.42853945e-01 7.25075424e-01 4.34420735e-01 -5.82967937e-01
-1.57457173e+00 -5.91723382e-01 1.48395613e-01 -2.30258986e-01
2.25463603e-02 -3.34556311e-01 -1.12437582e+00 -5.63187718e-01
-3.64938080e-01 2.80502886e-01 7.64778376e-01 9.25653160e-01
3.54071647e-01 9.43230987e-01 -7.26550370e-02 -3.25055212e-01
-7.31988430e-01 -1.40823185e+00 -4.61253166e-01 -3.60663742e-01
1.01387873e-01 -1.28221184e-01 -2.00522020e-01 -2.42701098e-01] | [11.273510932922363, 9.317023277282715] |
57aa923c-94a6-4faf-a9fc-24cdf33a728d | knowledge-transfer-driven-few-shot-class | 2306.10942 | null | https://arxiv.org/abs/2306.10942v1 | https://arxiv.org/pdf/2306.10942v1.pdf | Knowledge Transfer-Driven Few-Shot Class-Incremental Learning | Few-shot class-incremental learning (FSCIL) aims to continually learn new classes using a few samples while not forgetting the old classes. The key of this task is effective knowledge transfer from the base session to the incremental sessions. Despite the advance of existing FSCIL methods, the proposed knowledge transfer learning schemes are sub-optimal due to the insufficient optimization for the model's plasticity. To address this issue, we propose a Random Episode Sampling and Augmentation (RESA) strategy that relies on diverse pseudo incremental tasks as agents to achieve the knowledge transfer. Concretely, RESA mimics the real incremental setting and constructs pseudo incremental tasks globally and locally, where the global pseudo incremental tasks are designed to coincide with the learning objective of FSCIL and the local pseudo incremental tasks are designed to improve the model's plasticity, respectively. Furthermore, to make convincing incremental predictions, we introduce a complementary model with a squared Euclidean-distance classifier as the auxiliary module, which couples with the widely used cosine classifier to form our whole architecture. By such a way, equipped with model decoupling strategy, we can maintain the model's stability while enhancing the model's plasticity. Extensive quantitative and qualitative experiments on three popular FSCIL benchmark datasets demonstrate that our proposed method, named Knowledge Transfer-driven Relation Complementation Network (KT-RCNet), outperforms almost all prior methods. More precisely, the average accuracy of our proposed KT-RCNet outperforms the second-best method by a margin of 5.26%, 3.49%, and 2.25% on miniImageNet, CIFAR100, and CUB200, respectively. Our code is available at https://github.com/YeZiLaiXi/KT-RCNet.git. | ['Xueming Qian', 'Guoshuai Zhao', 'Yaxiong Wang', 'Ye Wang'] | 2023-06-19 | null | null | null | null | ['class-incremental-learning', 'few-shot-class-incremental-learning', 'incremental-learning'] | ['computer-vision', 'methodology', 'methodology'] | [ 1.41588926e-01 1.40455186e-01 -3.03827465e-01 -7.97609165e-02
-4.88876760e-01 -3.82198393e-01 5.55006683e-01 -5.74958101e-02
-4.14936543e-01 1.09231222e+00 -7.86941350e-02 6.13618568e-02
-3.87834311e-01 -8.08289409e-01 -1.01230443e+00 -1.01536465e+00
1.57318518e-01 3.99613023e-01 5.61627746e-01 -3.08719665e-01
1.80104882e-01 6.02698186e-03 -1.29596579e+00 9.03508812e-02
1.24904096e+00 9.78432596e-01 3.20351154e-01 1.78346202e-01
-2.95792110e-02 9.33997989e-01 -3.28808308e-01 -4.48263496e-01
-8.39132816e-03 -5.52246749e-01 -7.91303754e-01 -2.59871453e-01
1.06377841e-03 -1.99393719e-01 -5.44023156e-01 1.01026821e+00
5.35883009e-01 2.82255709e-01 4.55113381e-01 -1.33347952e+00
-1.05024767e+00 8.38086009e-01 -3.21760207e-01 9.73110721e-02
5.12731187e-02 1.69938013e-01 8.69815826e-01 -9.51122165e-01
6.99486196e-01 8.76731336e-01 6.28196061e-01 7.64308751e-01
-1.08712828e+00 -8.77417028e-01 4.66457605e-01 6.45490646e-01
-1.47153544e+00 -3.53470504e-01 8.53538156e-01 -2.01592043e-01
6.37585878e-01 -3.45354415e-02 7.70948470e-01 1.14076316e+00
-1.98830113e-01 1.02187943e+00 1.13031745e+00 -2.28074193e-01
3.46009582e-01 2.74142772e-01 2.55392581e-01 6.75993025e-01
1.10753447e-01 -4.34192084e-02 -5.63452840e-01 7.23987967e-02
7.03014672e-01 2.14858547e-01 -4.66286093e-01 -4.07716721e-01
-1.07733548e+00 4.03135240e-01 6.29311860e-01 4.89278048e-01
-2.55486637e-01 1.92341860e-02 3.93606693e-01 3.94395441e-01
3.35866213e-01 2.52146661e-01 -7.09266782e-01 -6.80768415e-02
-4.73579794e-01 -7.88645912e-03 4.49557394e-01 1.21500862e+00
9.33982193e-01 -6.44252524e-02 -4.41366822e-01 9.13494945e-01
-9.54946056e-02 1.88177869e-01 7.10556090e-01 -6.92321718e-01
4.31616277e-01 7.91929305e-01 -1.66996419e-02 -8.07473540e-01
-2.06865668e-01 -8.41140807e-01 -9.44546163e-01 -2.66788363e-01
2.07016304e-01 -5.98055013e-02 -6.53824508e-01 2.03322124e+00
3.50065768e-01 6.68369591e-01 1.87945470e-01 5.15570045e-01
7.49847233e-01 6.79993212e-01 9.39744934e-02 -6.15610719e-01
9.93814647e-01 -1.04764032e+00 -5.18862009e-01 -6.94220066e-02
5.80919147e-01 -1.67899176e-01 1.18107498e+00 3.33751112e-01
-9.23984289e-01 -6.57056153e-01 -1.04938567e+00 2.32500941e-01
-2.82173991e-01 1.77626327e-01 5.07599652e-01 2.00032234e-01
-7.92983651e-01 6.08132005e-01 -6.70135856e-01 -1.23134419e-01
8.61409605e-01 1.50965497e-01 -2.73921132e-01 -6.82164058e-02
-1.36263430e+00 6.66414201e-01 6.86926305e-01 1.17427334e-01
-9.11501765e-01 -1.01911128e+00 -3.53403002e-01 1.80957183e-01
5.47418714e-01 -5.49320400e-01 1.10255754e+00 -9.62392628e-01
-1.66499388e+00 5.60783923e-01 -2.37400644e-02 -4.32341903e-01
5.47060847e-01 -2.26722583e-01 -1.49997056e-01 5.78723475e-02
-1.98159099e-01 5.50558686e-01 6.51602209e-01 -1.20832598e+00
-5.65751672e-01 -2.36384645e-01 1.53955817e-01 4.82681781e-01
-6.82960570e-01 -5.43092608e-01 -5.95032454e-01 -6.44231081e-01
-1.12416089e-01 -8.82976830e-01 6.76142499e-02 -1.62543207e-01
-2.23579064e-01 -5.45698822e-01 6.03672326e-01 -4.87509370e-01
1.32799315e+00 -1.96875978e+00 3.62967432e-01 -1.36867911e-01
2.42091432e-01 6.63403809e-01 -9.74277183e-02 2.81680197e-01
-1.18164502e-01 -1.25037625e-01 -4.27023262e-01 -1.09748766e-01
-2.25009561e-01 5.10035045e-02 -2.63301432e-01 1.52798384e-01
2.95527019e-02 1.20527744e+00 -1.13436413e+00 -2.69330829e-01
1.93572678e-02 3.55623394e-01 -4.18074638e-01 1.05729148e-01
-3.77728343e-01 5.08034289e-01 -4.22167569e-01 4.55880076e-01
6.14927649e-01 -3.30535561e-01 1.74641624e-01 -2.27615148e-01
2.68347003e-02 -3.61197926e-02 -8.38811517e-01 1.95967591e+00
-3.26590508e-01 2.56110460e-01 -4.95976806e-01 -1.40075338e+00
1.00004947e+00 3.65893245e-01 3.83729607e-01 -9.32637930e-01
4.99369726e-02 2.21328899e-01 -2.04795110e-03 -3.24712574e-01
-1.28889456e-02 -5.69859184e-02 2.34755173e-01 3.02933693e-01
3.28821570e-01 4.06359613e-01 3.34351994e-02 3.78476858e-01
1.05875552e+00 2.72347778e-01 2.93917984e-01 -9.26344246e-02
8.14673841e-01 -2.72295237e-01 9.54337537e-01 5.75599015e-01
-4.18712437e-01 2.65177518e-01 4.48944777e-01 -2.40489125e-01
-6.64690316e-01 -1.10566342e+00 1.87258665e-02 1.03429639e+00
3.77964139e-01 -1.14514008e-01 -6.12774730e-01 -8.67992103e-01
-1.89704463e-01 8.11619341e-01 -7.61065066e-01 -7.75100648e-01
-5.01755655e-01 -7.92149484e-01 3.86443079e-01 5.63622892e-01
9.76023436e-01 -1.16560435e+00 -2.08382055e-01 2.73416489e-01
-2.15498179e-01 -8.37455273e-01 -4.36378062e-01 4.26416062e-02
-8.72579992e-01 -1.19694829e+00 -8.58967364e-01 -9.68867481e-01
7.44983971e-01 2.49274105e-01 7.17235565e-01 1.41805634e-01
-1.77332669e-01 2.71953106e-01 -5.45462310e-01 -1.79717585e-01
-1.01345766e-03 2.82020003e-01 7.92901143e-02 1.89627156e-01
2.95037717e-01 -1.01924324e+00 -8.03015530e-01 2.71753818e-01
-7.72529483e-01 4.46302801e-01 7.10407257e-01 1.11651599e+00
6.45311475e-01 6.43233210e-02 1.22283256e+00 -9.93498564e-01
4.76684898e-01 -6.69162929e-01 -4.40499246e-01 6.38387918e-01
-8.55869770e-01 -7.35295340e-02 9.47786391e-01 -8.12748551e-01
-1.41364837e+00 -1.20316163e-01 9.09050852e-02 -6.16944015e-01
1.06090315e-01 4.23803538e-01 -2.84107536e-01 -6.95126411e-03
6.52330041e-01 7.49425113e-01 -2.90699005e-01 -3.31461519e-01
4.65438575e-01 4.23748791e-01 4.85077590e-01 -6.57112062e-01
7.99295962e-01 3.36348116e-01 -4.15807426e-01 -4.83994991e-01
-1.05840313e+00 -1.28160417e-01 -6.64572001e-01 -1.79628536e-01
4.71271098e-01 -8.52125704e-01 -8.24565887e-01 9.37304080e-01
-9.87123430e-01 -7.32826471e-01 -4.54260290e-01 3.96708220e-01
-5.59193313e-01 -5.25662536e-03 -6.39631152e-01 -6.13209605e-01
-4.88065422e-01 -7.64219940e-01 5.54882526e-01 5.45376718e-01
3.82900417e-01 -1.01150942e+00 1.69101357e-01 2.80156791e-01
5.11489749e-01 6.04549609e-03 1.09416246e+00 -6.76572561e-01
-5.35043418e-01 1.03313938e-01 -3.39769214e-01 4.24260318e-01
2.47975573e-01 -4.64558095e-01 -1.06787455e+00 -2.59567112e-01
-1.69386268e-01 -6.52575910e-01 9.89239037e-01 1.14638314e-01
1.43481052e+00 -2.76503831e-01 -2.86003888e-01 5.98065078e-01
1.37943888e+00 4.96438980e-01 7.97852218e-01 2.39476651e-01
6.35476470e-01 2.93519288e-01 6.72342837e-01 3.04849178e-01
5.00714123e-01 4.77476031e-01 1.98641837e-01 3.52502286e-01
-2.86524951e-01 -5.17775118e-01 2.72161990e-01 1.27273333e+00
-2.94159919e-01 7.94566981e-03 -6.46862209e-01 5.18000007e-01
-2.13780451e+00 -1.02657890e+00 4.51184928e-01 2.28567529e+00
1.22914827e+00 1.52298912e-01 -2.00474277e-01 -3.50141525e-03
7.28275418e-01 -2.20229458e-02 -1.00908208e+00 1.49703994e-01
-4.91411649e-02 1.21620476e-01 1.71127558e-01 3.03172588e-01
-8.66268456e-01 1.18630183e+00 4.41734743e+00 1.25302267e+00
-9.37370598e-01 2.82487869e-01 7.17602789e-01 -9.60386246e-02
-1.80817351e-01 5.04418388e-02 -8.19544256e-01 5.06318271e-01
6.56737328e-01 -5.38470149e-01 6.59109175e-01 7.25594461e-01
-1.54722571e-01 1.25827491e-01 -9.97823715e-01 9.28412080e-01
4.85760719e-02 -1.37957573e+00 5.32142967e-02 -3.43937486e-01
8.92361939e-01 -1.80866793e-01 7.79426619e-02 9.28843379e-01
1.82600647e-01 -6.46231174e-01 4.84149784e-01 9.16587949e-01
8.38006914e-01 -8.10945034e-01 5.01066267e-01 5.51121831e-01
-1.28645205e+00 -1.69553623e-01 -4.56048042e-01 6.81083351e-02
-1.57178640e-01 4.81458098e-01 -4.61150795e-01 6.20700181e-01
6.64568067e-01 9.37419951e-01 -6.50191844e-01 1.01908982e+00
-4.47843671e-01 7.84535408e-01 -3.51600312e-02 -4.82345223e-02
-5.86180650e-02 -2.75490433e-01 3.72693032e-01 8.65265727e-01
1.64104924e-01 3.48418117e-01 -1.01392688e-02 8.33420813e-01
-4.95917708e-01 1.22699313e-01 -4.17742550e-01 9.70995799e-02
9.01404023e-01 1.12361073e+00 -5.39760113e-01 -4.53705043e-01
-2.54510552e-01 1.15266955e+00 8.18008363e-01 5.74749470e-01
-9.87296700e-01 -7.10944176e-01 3.03797930e-01 -2.74876863e-01
2.89533049e-01 1.41253278e-01 4.00224887e-02 -1.31802058e+00
2.59380281e-01 -6.84338212e-01 2.68415391e-01 -7.46220410e-01
-1.32686710e+00 6.00257933e-01 -1.02150530e-01 -1.17852247e+00
8.46360028e-02 -1.92962393e-01 -8.25942159e-01 5.61652184e-01
-1.60139012e+00 -1.24500453e+00 -5.77327251e-01 8.35611105e-01
5.68482876e-01 -3.32496166e-01 6.92707062e-01 4.79449570e-01
-8.46784353e-01 9.74746466e-01 2.30143666e-01 7.32467994e-02
6.53718293e-01 -1.01217854e+00 1.15324982e-01 5.52284777e-01
-2.92289350e-02 6.42234445e-01 1.95930988e-01 -7.02553332e-01
-1.34509516e+00 -1.37692428e+00 4.64384586e-01 -1.97460085e-01
7.36378074e-01 -3.54968756e-01 -1.27035248e+00 7.48281121e-01
-6.67585731e-02 9.32802185e-02 3.72928292e-01 4.50645275e-02
-4.93617058e-01 -4.83924657e-01 -8.92219245e-01 6.09019458e-01
1.46568036e+00 -4.49257225e-01 -6.30243242e-01 3.58746231e-01
1.12006044e+00 -1.30251348e-01 -8.36776495e-01 4.10242856e-01
5.43083429e-01 -6.61937177e-01 8.78092349e-01 -5.76675653e-01
2.64112234e-01 -2.39315122e-01 8.16854760e-02 -1.40827847e+00
-5.44869900e-01 -4.45954382e-01 -4.25835878e-01 1.49721456e+00
2.60288507e-01 -1.00531852e+00 7.08330870e-01 1.86666310e-01
-1.54104665e-01 -1.07613850e+00 -8.88219059e-01 -9.84997571e-01
1.94105014e-01 -2.46942252e-01 4.54350293e-01 1.13380325e+00
-2.13115774e-02 4.55514163e-01 -3.39563459e-01 -7.60468915e-02
6.20586932e-01 7.81865045e-02 5.06527841e-01 -1.16356075e+00
-4.13005054e-01 -4.23022807e-01 -1.28665358e-01 -9.92428064e-01
1.83197230e-01 -1.09453619e+00 -1.77156880e-01 -1.39199126e+00
4.46034700e-01 -8.67518783e-01 -7.96065331e-01 7.84494519e-01
-4.08315957e-01 -4.28395569e-02 1.23493686e-01 3.50043714e-01
-9.28552985e-01 1.10042191e+00 1.32596707e+00 -4.87668552e-02
-5.04266381e-01 3.14190388e-02 -9.22045171e-01 5.54595232e-01
9.63086843e-01 -4.26915348e-01 -9.41552043e-01 -2.88267225e-01
1.72673002e-01 -1.68619469e-01 3.06942403e-01 -1.01176822e+00
5.80808699e-01 -1.47947371e-01 3.17844093e-01 -3.54887575e-01
2.27605432e-01 -5.70519984e-01 -6.04649000e-02 5.70404351e-01
-4.16563481e-01 -4.86805111e-01 5.49427383e-02 8.28239739e-01
-3.06601543e-02 1.91655904e-02 7.65292048e-01 5.33812563e-04
-7.05783010e-01 7.20218241e-01 1.14075944e-01 2.56382465e-01
1.27607346e+00 7.40101039e-02 -6.99230015e-01 -5.28749712e-02
-7.23999381e-01 4.73858356e-01 1.54467970e-01 4.96090174e-01
7.34122872e-01 -1.52535415e+00 -6.56988978e-01 3.85692273e-03
2.23955214e-01 1.53134421e-01 6.25917852e-01 1.04006732e+00
-1.43568709e-01 3.86609435e-01 -1.88850984e-01 -3.28098565e-01
-7.96752751e-01 6.76367223e-01 3.46402228e-01 -3.79764318e-01
-6.27581477e-01 9.33359683e-01 3.32516551e-01 -5.73570192e-01
3.97761554e-01 -5.09549789e-02 -2.22649753e-01 5.75567111e-02
6.12811506e-01 4.34636205e-01 -9.43715335e-04 -9.23300982e-02
-2.58991778e-01 4.59869504e-01 -3.61941725e-01 2.07189322e-01
1.43027091e+00 -1.27437875e-01 3.87701429e-02 7.47274578e-01
1.09419334e+00 -4.63079989e-01 -1.37385070e+00 -7.07877994e-01
-1.08972281e-01 -5.73335961e-02 -2.74765581e-01 -9.22089458e-01
-1.13329911e+00 8.49752307e-01 6.28875196e-01 -2.95600921e-01
1.29762018e+00 -4.03356515e-02 8.20624173e-01 5.91886103e-01
5.99948406e-01 -1.06964457e+00 3.28193486e-01 4.44898903e-01
8.62332582e-01 -1.13637328e+00 -1.19619995e-01 -1.75810516e-01
-6.81013882e-01 7.74774790e-01 9.42588449e-01 -1.31836802e-01
6.54112935e-01 -3.00737381e-01 -4.51341718e-01 3.26424353e-02
-1.10232854e+00 -8.58394504e-02 1.52228579e-01 5.49769282e-01
1.25082701e-01 -2.44243443e-03 -3.65872175e-01 1.04381013e+00
1.12977490e-01 3.11750740e-01 9.99694094e-02 7.19216287e-01
-4.53614801e-01 -9.19305384e-01 1.15664601e-01 4.09275681e-01
7.70921856e-02 -1.54333353e-01 -1.41029343e-01 7.52575755e-01
2.57746398e-01 5.49580216e-01 -1.52008384e-01 -5.46846330e-01
2.45516732e-01 1.94602311e-01 5.24357021e-01 -4.77207541e-01
-3.25587571e-01 -1.26239493e-01 -3.29904139e-01 -3.99727851e-01
-2.81459808e-01 -4.68350023e-01 -1.43472636e+00 -6.04835823e-02
-4.17581558e-01 2.92140990e-01 2.20631093e-01 1.01290607e+00
4.65471238e-01 7.76607931e-01 7.42338836e-01 -3.51437241e-01
-4.23769951e-01 -9.97760117e-01 -4.59374100e-01 2.50282109e-01
-2.31784284e-02 -7.55472541e-01 -2.02714384e-01 7.08356574e-02] | [9.802639961242676, 3.4110050201416016] |
86b49603-4f23-46bb-8db5-4c73655b1dcd | privacy-preserving-representations-are-not-1 | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chelani_Privacy-Preserving_Representations_Are_Not_Enough_Recovering_Scene_Content_From_Camera_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chelani_Privacy-Preserving_Representations_Are_Not_Enough_Recovering_Scene_Content_From_Camera_CVPR_2023_paper.pdf | Privacy-Preserving Representations Are Not Enough: Recovering Scene Content From Camera Poses | Visual localization is the task of estimating the camera pose from which a given image was taken and is central to several 3D computer vision applications. With the rapid growth in the popularity of AR/VR/MR devices and cloud-based applications, privacy issues are becoming a very important aspect of the localization process. Existing work on privacy-preserving localization aims to defend against an attacker who has access to a cloud-based service. In this paper, we show that an attacker can learn about details of a scene without any access by simply querying a localization service. The attack is based on the observation that modern visual localization algorithms are robust to variations in appearance and geometry. While this is in general a desired property, it also leads to algorithms localizing objects that are similar enough to those present in a scene. An attacker can thus query a server with a large enough set of images of objects, e.g., obtained from the Internet, and some of them will be localized. The attacker can thus learn about object placements from the camera poses returned by the service (which is the minimal information returned by such a service). In this paper, we develop a proof-of-concept version of this attack and demonstrate its practical feasibility. The attack does not place any requirements on the localization algorithm used, and thus also applies to privacy-preserving representations. Current work on privacy-preserving representations alone is thus insufficient. | ['Zuzana Kukelova', 'Fredrik Kahl', 'Torsten Sattler', 'Kunal Chelani'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['visual-localization'] | ['computer-vision'] | [-3.21554802e-02 -3.84816617e-01 3.09761129e-02 -1.98715568e-01
-8.16086471e-01 -1.31948113e+00 3.27834845e-01 4.13614213e-01
-5.43586850e-01 2.80405730e-01 -4.87056732e-01 -4.95593280e-01
1.93597436e-01 -6.85216010e-01 -8.74160528e-01 -8.44619930e-01
-1.58916906e-01 3.89064789e-01 3.45443487e-01 1.30211934e-01
4.34587717e-01 1.40867281e+00 -1.05550683e+00 -2.21552670e-01
3.21286805e-02 1.23220909e+00 9.88255441e-02 9.67408538e-01
2.09802151e-01 4.44159478e-01 -7.80186892e-01 -3.39847475e-01
6.47056878e-01 6.28376454e-02 -5.68385839e-01 1.25239149e-01
4.47955787e-01 -6.20945632e-01 -3.74528915e-01 1.36105728e+00
3.61990660e-01 4.58781943e-02 1.20334007e-01 -1.73849547e+00
-3.89773786e-01 -2.11111307e-01 -6.18374169e-01 2.56080449e-01
6.62002504e-01 -2.47486830e-01 6.11736238e-01 -3.83009225e-01
7.03885972e-01 7.50992954e-01 3.86579692e-01 4.22759473e-01
-1.14069676e+00 -6.50652647e-01 6.16728105e-02 1.43724337e-01
-1.95270228e+00 -3.99362087e-01 6.09916151e-01 -7.83430338e-02
4.59293216e-01 6.86520040e-01 1.88629359e-01 7.31503546e-01
1.90647408e-01 3.25339586e-01 1.05335546e+00 -2.94076115e-01
4.94198203e-01 5.87505817e-01 -1.24033801e-01 4.32799906e-01
6.41477466e-01 -3.79664451e-02 -3.59468162e-01 -9.32603598e-01
8.64847541e-01 2.59344906e-01 -5.85073948e-01 -1.28352177e+00
-8.52791727e-01 8.21258128e-01 4.53312516e-01 -3.04690246e-02
-2.53066063e-01 3.07981193e-01 3.17581505e-01 3.80769670e-01
2.01760918e-01 2.25510478e-01 -4.17809576e-01 2.42043491e-02
-6.65531814e-01 2.10569456e-01 1.00461757e+00 1.24132907e+00
7.50618637e-01 -3.92015934e-01 6.44423246e-01 -1.72904462e-01
1.89351469e-01 6.59201324e-01 -9.68687162e-02 -9.59355772e-01
1.37193903e-01 1.30633727e-01 4.77913201e-01 -1.50522745e+00
1.69617578e-01 8.78763422e-02 -4.27084148e-01 4.35038388e-01
3.82138520e-01 -7.65174180e-02 -3.01398844e-01 1.77883387e+00
5.68083525e-01 3.49557519e-01 6.46018982e-02 9.77201104e-01
1.08066224e-01 5.30481339e-01 -2.81251460e-01 -1.76563740e-01
1.45926392e+00 -2.79777288e-01 -3.15462738e-01 8.03726073e-03
3.15061659e-01 -6.78997338e-01 3.01248729e-01 2.88849235e-01
-5.79481065e-01 -5.67178912e-02 -1.08891106e+00 8.80055353e-02
-6.12180233e-01 -2.39609271e-01 4.82656300e-01 1.04113507e+00
-1.20053697e+00 1.29863590e-01 -8.90237212e-01 -7.96499848e-01
2.53275812e-01 7.49085963e-01 -7.90129423e-01 -2.30699465e-01
-6.95950687e-01 7.96353877e-01 5.34427948e-02 -2.43871704e-01
-8.53963673e-01 -2.75486201e-01 -7.48319447e-01 4.19508026e-04
4.73834574e-01 -4.45479959e-01 1.07874286e+00 -7.68109322e-01
-8.67179632e-01 8.99037480e-01 -2.58041859e-01 -6.85884833e-01
7.13866889e-01 1.90966111e-02 -2.69451082e-01 5.91142058e-01
2.54566520e-01 2.27966204e-01 1.04266715e+00 -1.39712727e+00
-7.43324161e-01 -8.26478541e-01 3.81140083e-01 7.58622065e-02
-1.63972422e-01 2.54703790e-01 -5.07470548e-01 -1.00752570e-01
2.95064121e-01 -1.11172879e+00 -3.94602180e-01 5.31414270e-01
-2.64514297e-01 2.82780826e-01 1.39823306e+00 -4.02585447e-01
3.90142918e-01 -2.43601966e+00 -5.86479366e-01 6.86434984e-01
2.44019061e-01 6.09365590e-02 2.08098814e-01 4.97252882e-01
4.72648293e-02 2.93508708e-01 2.98152357e-01 -4.56415713e-01
-6.91773593e-02 1.68645814e-01 -7.42734194e-01 1.44818377e+00
-3.79949629e-01 3.38579535e-01 -7.91072428e-01 -2.26863474e-01
4.10086960e-01 6.63444817e-01 -4.79432434e-01 1.44600257e-01
1.12003572e-01 5.46308160e-01 -6.45424128e-01 5.97065628e-01
1.15294671e+00 -1.25537440e-01 1.34174004e-01 9.93380994e-02
-1.87195484e-02 5.63490838e-02 -1.23553908e+00 1.56423020e+00
-3.66559505e-01 5.78728616e-01 5.08052945e-01 -7.27242827e-01
8.22176993e-01 5.39337456e-01 5.82047522e-01 -5.99798188e-02
2.22379029e-01 2.04821423e-01 -5.29096842e-01 3.48469801e-02
5.36410511e-01 4.09512311e-01 -2.68381655e-01 5.90755820e-01
-4.72134471e-01 1.32540733e-01 -5.57064891e-01 2.35620186e-01
1.32470965e+00 -2.67754495e-01 3.45021605e-01 4.84055430e-02
4.91183758e-01 -3.86191048e-02 3.09232533e-01 1.10349727e+00
-3.62957001e-01 4.86917078e-01 2.31793895e-01 -3.59177858e-01
-9.09475148e-01 -1.11490846e+00 6.92505483e-03 5.82750082e-01
7.06838429e-01 -5.03581643e-01 -5.45298636e-01 -8.28509092e-01
1.39820695e-01 4.05111790e-01 -3.91643792e-01 -6.64447248e-02
-2.80130893e-01 9.55299661e-02 4.82225448e-01 1.80884510e-01
4.76514071e-01 -4.44046974e-01 -1.10928893e+00 -1.66194990e-01
-4.45494661e-03 -1.26969731e+00 -5.80040395e-01 7.00494321e-03
-5.84020257e-01 -1.29039443e+00 -3.21696103e-01 -4.65265661e-01
1.11127436e+00 9.79453266e-01 6.11491263e-01 1.01292737e-01
-1.74583673e-01 1.01781237e+00 -2.47823521e-01 -3.58638525e-01
-2.06602305e-01 -1.49930596e-01 2.83679545e-01 2.54446357e-01
6.13142431e-01 -5.72149098e-01 -5.07083774e-01 2.94528395e-01
-7.72159219e-01 -8.25822353e-01 1.60996079e-01 1.71395242e-01
6.91189289e-01 3.92032713e-01 -2.22258553e-01 -7.30490506e-01
2.87517458e-01 -3.93573195e-01 -1.22896540e+00 1.18574135e-01
-3.16752702e-01 -3.58126372e-01 4.75214392e-01 -5.10341585e-01
-2.55655110e-01 6.25673950e-01 2.45532542e-01 -9.08724070e-01
-5.07769048e-01 -7.72757977e-02 -5.25875211e-01 -8.30065787e-01
2.06274241e-01 4.09903675e-01 5.95839396e-02 -3.64363700e-01
3.96177649e-01 6.90567315e-01 6.04288459e-01 -3.54337782e-01
1.14556301e+00 9.50717151e-01 3.07618320e-01 -9.55785513e-01
-3.13127115e-02 -7.90988207e-01 -3.50537837e-01 7.55094588e-02
5.87983608e-01 -8.44634771e-01 -1.38375366e+00 4.57059033e-02
-1.37900519e+00 5.38814068e-01 -2.37606496e-01 2.28355765e-01
-6.62279725e-01 6.19491816e-01 -4.69854325e-02 -9.99111891e-01
8.44778866e-02 -1.29568863e+00 8.27683747e-01 1.44692615e-01
2.16725189e-03 -7.15741277e-01 -2.82862842e-01 1.25283495e-01
3.86068255e-01 4.40348119e-01 4.04671758e-01 -1.03288710e+00
-1.12625885e+00 -1.04438972e+00 -1.44038320e-01 4.51373272e-02
1.55612826e-01 -3.50068927e-01 -9.39904749e-01 -7.38640964e-01
4.89886612e-01 1.51191100e-01 3.74739151e-03 2.69389540e-01
1.07934034e+00 -6.38717055e-01 -4.34974521e-01 7.37845540e-01
1.65414155e+00 2.27469072e-01 4.45425361e-01 4.85615492e-01
5.50120115e-01 3.71253401e-01 6.12760484e-01 4.47783768e-01
2.44037539e-01 8.54234040e-01 8.31384540e-01 6.54613674e-02
7.43300259e-01 -3.44437242e-01 2.17479274e-01 -1.80420503e-01
3.70578289e-01 -1.82696432e-01 -4.92260069e-01 3.32953632e-01
-1.77174473e+00 -7.37407684e-01 1.67168677e-01 2.62533879e+00
1.15171023e-01 -2.70968139e-01 5.73142916e-02 -2.76628613e-01
8.74785185e-01 1.78261012e-01 -7.27311611e-01 -5.06120443e-01
1.48647532e-01 -7.75455460e-02 1.39655304e+00 4.21113908e-01
-1.25676394e+00 7.34265685e-01 5.52612734e+00 4.29165512e-01
-1.22572029e+00 2.27866992e-01 4.14320767e-01 2.88809761e-02
3.82881202e-02 3.91319692e-01 -7.02468395e-01 2.76241302e-01
8.21945369e-01 -4.77406859e-01 4.53975827e-01 1.45247030e+00
1.84536632e-02 -2.43609071e-01 -1.15126991e+00 1.17821097e+00
3.36270720e-01 -1.27099884e+00 -2.70325691e-01 7.21122801e-01
1.18303686e-01 -4.09175083e-02 2.43345007e-01 -4.68880683e-01
2.92038321e-01 -7.42311001e-01 7.34266520e-01 -1.19874127e-01
7.94923306e-01 -1.15535820e+00 7.28909791e-01 6.18144989e-01
-1.23051453e+00 1.17783040e-01 -6.67847991e-01 3.30450416e-01
5.94919771e-02 4.07166369e-02 -9.11089242e-01 4.69507664e-01
8.47946525e-01 1.66043073e-01 -5.56996584e-01 1.13359070e+00
-2.17390180e-01 2.16480643e-01 -7.26755440e-01 2.51838230e-02
-1.69167167e-03 -1.57300532e-01 8.60706985e-01 7.74803698e-01
3.78404349e-01 9.50463936e-02 2.84605712e-01 6.27722144e-01
-1.13758340e-01 -4.59699659e-04 -1.25572634e+00 2.07306102e-01
8.15039933e-01 1.04504395e+00 -8.03877115e-01 1.88826308e-01
-3.98443907e-01 1.42611682e+00 -1.01191521e-01 1.91332221e-01
-6.94442570e-01 -3.65780711e-01 1.11830688e+00 3.12579304e-01
5.46028793e-01 -6.18184626e-01 5.27626090e-02 -1.10912979e+00
2.75475420e-02 -8.63957942e-01 4.13671553e-01 -5.75969696e-01
-9.73804474e-01 4.35704917e-01 -1.15264408e-01 -1.31884122e+00
-1.59444332e-01 -3.52374882e-01 -2.29156494e-01 8.68906319e-01
-1.33824897e+00 -1.04269803e+00 -1.49811655e-02 1.06392741e+00
-1.25506073e-01 9.52240825e-03 1.14117920e+00 9.04492438e-02
2.67247371e-02 6.05529785e-01 3.20326760e-02 3.53444517e-01
5.67016363e-01 -9.12885368e-01 3.66101235e-01 1.34025466e+00
6.94945753e-01 1.01036036e+00 7.67005324e-01 -5.08880973e-01
-1.87842059e+00 -9.06769097e-01 8.57443750e-01 -6.45844162e-01
5.14982402e-01 -6.74554944e-01 -5.69605827e-01 9.00858819e-01
-1.74289018e-01 4.24741954e-01 5.61797440e-01 -4.12382156e-01
-5.71642280e-01 -1.96325868e-01 -1.67405498e+00 4.88909870e-01
4.18290049e-01 -9.67918992e-01 -7.26459324e-02 4.57229674e-01
5.78077257e-01 -4.41066891e-01 -4.12440896e-01 -7.84389749e-02
2.49866828e-01 -9.58480656e-01 1.12665331e+00 -3.57734054e-01
-7.04175353e-01 -6.91975415e-01 -6.93452537e-01 -6.75054312e-01
1.39349818e-01 -8.78855288e-01 -1.04607791e-01 1.19668186e+00
-1.72244266e-01 -9.78986740e-01 1.18830860e+00 9.90557909e-01
7.58166552e-01 1.17986109e-02 -1.45194852e+00 -8.46307755e-01
-3.77574056e-01 -4.13370073e-01 6.81916356e-01 7.81956553e-01
-4.07392502e-01 -2.92377979e-01 -3.62407625e-01 1.37753689e+00
8.73895049e-01 6.11534305e-02 1.15193093e+00 -8.97790909e-01
-1.13361459e-02 2.63151735e-01 -1.11491060e+00 -1.08594918e+00
1.36007413e-01 -5.16024828e-01 -9.20713842e-02 -9.12584186e-01
-1.24080621e-01 -4.69540864e-01 -2.12062061e-01 3.11036527e-01
5.13877153e-01 3.18286568e-01 3.41971248e-01 4.27778363e-01
-4.98251975e-01 -8.88701007e-02 3.24379593e-01 7.65290260e-02
7.08281100e-02 6.05093539e-01 -6.86181426e-01 6.65673971e-01
8.36650431e-01 -7.64883399e-01 -5.00778675e-01 -1.49876595e-01
-9.94616002e-02 1.15052782e-01 7.85334945e-01 -8.35722029e-01
6.75683677e-01 -2.44164076e-02 2.71267653e-01 -5.26268303e-01
5.98786175e-01 -1.81090117e+00 3.58189583e-01 5.07462680e-01
-2.93577071e-02 4.00692552e-01 -3.43283862e-02 9.04978812e-01
5.73596805e-02 -1.72716975e-01 7.31532276e-01 -1.38661474e-01
-7.49738932e-01 5.44641912e-01 -2.63804555e-01 -5.36584854e-01
1.42841935e+00 -4.48867291e-01 -2.08209410e-01 -8.94460082e-01
-4.73110080e-01 -9.95770544e-02 1.25482070e+00 1.99417055e-01
7.46450126e-01 -9.16898131e-01 -1.43868819e-01 3.02132934e-01
2.50055641e-01 -1.96018904e-01 -1.49648383e-01 5.74573338e-01
-8.06476474e-01 2.61489958e-01 3.43065076e-02 -5.70817292e-01
-1.70400262e+00 1.05578446e+00 1.52784318e-01 2.65343159e-01
-7.04243660e-01 7.17396319e-01 4.33178730e-02 -3.00616007e-02
4.33250397e-01 1.69883624e-01 3.94387335e-01 -3.76215756e-01
7.93249428e-01 9.53144804e-02 -2.47517470e-02 -1.00707757e+00
-8.52950871e-01 4.16621029e-01 -1.98968992e-01 -2.64930934e-01
1.03270078e+00 -4.91205424e-01 -2.18849912e-01 -7.40677193e-02
1.41939235e+00 6.21825099e-01 -1.13300848e+00 -1.06840461e-01
1.34620396e-03 -1.17720652e+00 1.82240759e-03 -7.33685791e-02
-1.25002897e+00 6.16493583e-01 8.20925176e-01 3.74346346e-01
1.01199555e+00 1.04822107e-01 6.06097341e-01 4.02574748e-01
1.25237334e+00 -5.42494535e-01 -4.04591203e-01 -8.49630907e-02
3.51977527e-01 -9.57870007e-01 1.81098416e-01 -5.11289954e-01
-4.21446383e-01 8.77349794e-01 1.13081269e-01 -3.84675741e-01
6.86246753e-01 3.95434171e-01 2.31269240e-01 1.46355398e-03
-2.34736815e-01 1.51843682e-01 -2.80370951e-01 1.09650230e+00
-5.19036174e-01 -4.34054397e-02 2.67740279e-01 1.56629860e-01
-5.38219884e-02 -3.17120224e-01 6.27316654e-01 1.30598664e+00
-2.84602135e-01 -1.17921221e+00 -7.54730523e-01 -1.71940401e-01
-6.00387633e-01 2.59336710e-01 -5.04217803e-01 6.80143058e-01
-3.23910378e-02 1.11082292e+00 -6.47709891e-02 -2.34477177e-01
1.57819852e-01 -3.47220719e-01 1.55779809e-01 -5.45298755e-01
-3.31323206e-01 -7.17381760e-02 -3.79962176e-01 -9.79694068e-01
-1.74188137e-01 -8.85374665e-01 -8.99093091e-01 -6.97871268e-01
-4.44636732e-01 3.99580866e-01 1.12467015e+00 2.70828962e-01
4.28494632e-01 -2.70822257e-01 1.11909735e+00 -6.75170660e-01
-5.98579526e-01 1.17040344e-01 -9.23744440e-01 4.99176681e-02
6.33319736e-01 -3.21120501e-01 -6.22143209e-01 -1.40754685e-01] | [7.59715461730957, -2.138594150543213] |
2dfa0c92-da35-48d5-9f1d-493bccde167c | automatic-code-generation-from-sketches-of | 2103.05704 | null | https://arxiv.org/abs/2103.05704v1 | https://arxiv.org/pdf/2103.05704v1.pdf | Automatic code generation from sketches of mobile applications in end-user development using Deep Learning | A common need for mobile application development by end-users or in computing education is to transform a sketch of a user interface into wireframe code using App Inventor, a popular block-based programming environment. As this task is challenging and time-consuming, we present the Sketch2aia approach that automates this process. Sketch2aia employs deep learning to detect the most frequent user interface components and their position on a hand-drawn sketch creating an intermediate representation of the user interface and then automatically generates the App Inventor code of the wireframe. The approach achieves an average user interface component classification accuracy of 87,72% and results of a preliminary user evaluation indicate that it generates wireframes that closely mirror the sketches in terms of visual similarity. The approach has been implemented as a web tool and can be used to support the end-user development of mobile applications effectively and efficiently as well as the teaching of user interface design in K-12. | ['Edson C. Vargas Júnior', 'Jean C. R. Hauck', 'Aldo von Wangenheim', 'Christiane Gresse von Wangenheim', 'Daniel Baulé'] | 2021-03-09 | null | null | null | null | ['component-classification'] | ['natural-language-processing'] | [ 2.14052215e-01 -1.49275929e-01 -8.65666121e-02 -4.31700982e-02
-5.14059365e-01 -7.64403522e-01 4.38683629e-01 1.14423595e-01
2.29195114e-02 -6.47810996e-02 -1.85433909e-01 -9.34745371e-01
-3.14856291e-01 -8.00606549e-01 -5.36026180e-01 2.26606689e-02
3.52290928e-01 4.99071032e-01 3.39385033e-01 -1.54923037e-01
6.28720462e-01 5.61657786e-01 -1.95240462e+00 6.85975790e-01
4.23552841e-01 8.53147507e-01 5.29773235e-01 9.88179743e-01
-6.94486380e-01 3.62544328e-01 -6.07010365e-01 -5.58992088e-01
7.00122565e-02 -2.79104233e-01 -7.24296212e-01 -3.68115716e-02
7.23957360e-01 -3.29899639e-01 2.03920648e-01 6.75544679e-01
4.09232974e-01 -2.24211365e-01 6.34652972e-01 -1.10562038e+00
-1.49194583e-01 3.42245579e-01 -3.53169978e-01 -3.38650942e-01
9.02869105e-01 -3.80762964e-01 1.03321612e+00 -1.30307245e+00
8.20254445e-01 7.61730850e-01 1.18381500e+00 2.83738583e-01
-1.16839778e+00 -7.95544982e-01 -2.42850482e-01 1.14274748e-01
-1.48559129e+00 -1.04323454e-01 9.50020373e-01 -8.65366101e-01
8.88898134e-01 5.11829555e-01 1.15562165e+00 6.50391340e-01
2.35694960e-01 8.95495057e-01 6.73516929e-01 -1.00896966e+00
3.55834484e-01 3.33152324e-01 3.19068044e-01 9.26971138e-01
-1.71064824e-01 -4.05226082e-01 -1.36668295e-01 -2.94401884e-01
1.16037071e+00 2.63114005e-01 1.21348068e-01 -6.72601104e-01
-8.10941219e-01 6.40650451e-01 -3.67374681e-02 4.89010602e-01
-3.20587426e-01 1.98579535e-01 3.08072656e-01 3.90306711e-01
8.50186199e-02 5.04718542e-01 -4.25221980e-01 -6.81665301e-01
-1.08987176e+00 5.85842967e-01 9.52744544e-01 1.15597725e+00
5.11825562e-01 -1.84800342e-01 2.12465510e-01 9.78462875e-01
3.39944065e-01 1.21827140e-01 3.01028937e-01 -9.27092850e-01
1.97561964e-01 1.05045283e+00 -3.77466604e-02 -1.01863468e+00
1.22713976e-01 1.42245397e-01 -2.48178318e-01 1.04431748e+00
5.22781610e-01 1.16924793e-01 -7.01497793e-01 8.28399003e-01
1.65509745e-01 -1.35917425e-01 -5.27175248e-01 2.39242315e-01
8.99622798e-01 6.07959747e-01 1.55199364e-01 4.01483804e-01
1.52874029e+00 -6.88504875e-01 -6.18337154e-01 2.74652243e-01
4.84505385e-01 -1.15926385e+00 1.60355771e+00 8.40008199e-01
-1.18044066e+00 -9.78332758e-01 -1.37135208e+00 -3.91890341e-03
-4.95680422e-01 7.02359200e-01 5.47955453e-01 1.06115901e+00
-7.68300116e-01 8.44007432e-01 -4.94868815e-01 -2.14987844e-01
5.69658101e-01 3.40196759e-01 -4.34913129e-01 1.67120904e-01
-4.64980364e-01 6.85317278e-01 -7.30700186e-03 -5.78816056e-01
-2.25408822e-01 -1.29821491e+00 -6.12335026e-01 2.39865884e-01
-3.36442553e-02 -4.01001304e-01 1.59148252e+00 -1.07514012e+00
-1.72875071e+00 9.10746038e-01 2.23741561e-01 2.13249072e-01
6.21420860e-01 -4.89834398e-01 -8.32845643e-02 -2.37651661e-01
-1.58823580e-01 3.90982717e-01 9.02885556e-01 -1.12077069e+00
-9.29333150e-01 -6.69829994e-02 1.95047095e-01 3.61403376e-02
-2.50451982e-01 9.57635604e-03 -6.84714198e-01 -6.68453157e-01
-4.25574332e-02 -7.98295915e-01 2.05528542e-01 3.73693198e-01
1.50550589e-01 -5.58643878e-01 1.04458392e+00 -9.09113884e-01
1.68907952e+00 -1.90341198e+00 -1.58485010e-01 5.79014540e-01
1.58793062e-01 5.02807438e-01 1.38151631e-01 6.77534878e-01
-5.09649098e-01 3.91606987e-02 2.22660199e-01 -1.84509084e-01
1.07558846e-01 -2.91174799e-01 -2.41475597e-01 -1.06635734e-01
-4.01618838e-01 4.98260468e-01 -8.95363152e-01 -1.93423763e-01
5.95225930e-01 4.48280007e-01 -4.54164416e-01 5.36412358e-01
-1.23449981e-01 -3.88714105e-01 -8.89014974e-02 4.85988319e-01
5.09985864e-01 4.98858392e-02 4.82009798e-01 -2.13347986e-01
-2.15315327e-01 1.77207321e-01 -1.33433712e+00 1.86650383e+00
-1.00233364e+00 1.11618614e+00 -2.69085288e-01 -6.05136693e-01
1.14557457e+00 3.90464216e-01 2.70954639e-01 -4.96776134e-01
-8.30511004e-02 2.41612807e-01 -2.00063795e-01 -7.72249341e-01
3.25409293e-01 2.44778484e-01 1.17908016e-01 9.50592458e-01
1.09860286e-01 -3.33592951e-01 -1.03790641e-01 3.36782411e-02
9.92648661e-01 6.49115682e-01 5.50616980e-01 -2.72526622e-01
5.33320785e-01 -1.54815301e-01 -4.20449913e-01 6.43202484e-01
4.80628937e-01 7.02588260e-01 4.32227015e-01 -9.91641581e-01
-1.37485898e+00 -1.02805102e+00 2.61709094e-01 1.37986374e+00
-4.91795331e-01 -9.50526953e-01 -1.04317725e+00 -6.65350378e-01
-3.50411206e-01 6.15338087e-01 -4.53164369e-01 2.21615821e-01
-6.21685028e-01 4.48424965e-01 1.75450057e-01 5.57384610e-01
-2.33119261e-02 -1.35355330e+00 -9.77696776e-01 2.45467857e-01
3.28530103e-01 -4.88871276e-01 -3.33905637e-01 -1.82182491e-01
-6.76263511e-01 -1.11926365e+00 -9.77983832e-01 -1.15475392e+00
9.52138066e-01 1.13709547e-01 1.26542068e+00 5.05848765e-01
-6.24005020e-01 5.38753271e-01 -4.15694088e-01 -4.72560257e-01
-5.55129468e-01 2.24997163e-01 -3.23878646e-01 -4.40507889e-01
3.60647321e-01 -7.65838325e-01 -4.72980082e-01 5.04598357e-02
-7.50872612e-01 5.17419219e-01 4.20035988e-01 5.20030200e-01
2.47790128e-01 5.40973544e-02 1.49603516e-01 -1.10794950e+00
1.01850736e+00 -7.18747377e-02 -6.59326851e-01 3.94112021e-01
-5.69813490e-01 -1.58499226e-01 5.23449302e-01 -6.21158600e-01
-6.62011266e-01 4.73251194e-01 -7.05653846e-01 -3.15835565e-01
-3.11485589e-01 5.74603677e-01 -1.93973526e-01 -1.90360829e-01
6.52314365e-01 1.08279072e-01 -1.35382086e-01 -6.91520870e-01
2.38855436e-01 9.04487729e-01 4.15130734e-01 -5.55546224e-01
6.72539830e-01 -2.09352508e-01 -3.81286293e-01 -1.05480123e+00
-6.31477684e-02 -4.03359562e-01 -8.35066080e-01 -4.58699435e-01
4.85560983e-01 -5.49944460e-01 -6.69457734e-01 3.61471534e-01
-1.53948474e+00 -4.53356951e-01 7.60317594e-02 2.18705810e-03
-4.33279604e-01 8.29239711e-02 2.88574938e-02 -7.18939126e-01
-4.86101210e-01 -1.14969897e+00 9.44933772e-01 8.60555768e-02
-1.11214519e+00 -8.39755654e-01 3.13425064e-01 8.84825960e-02
2.37029985e-01 4.40393277e-02 1.40232146e+00 -5.00495493e-01
-3.02252769e-01 -6.14537477e-01 -1.11917138e-01 2.89872512e-02
2.03346178e-01 5.15976489e-01 -9.30982232e-01 1.17834553e-01
-6.38247550e-01 1.04974352e-01 -1.90118819e-01 5.74338213e-02
1.31612885e+00 -3.09661716e-01 -4.18141097e-01 3.59257162e-01
1.50465715e+00 7.05940068e-01 7.70489931e-01 2.48838827e-01
7.19287872e-01 5.95577717e-01 4.01840001e-01 4.69941378e-01
1.56816125e-01 9.84418511e-01 -4.22130115e-02 7.76230544e-02
-1.33514538e-01 -6.39984608e-01 1.32736908e-02 6.67614877e-01
-1.54482841e-01 3.99944365e-01 -1.08007193e+00 4.05431151e-01
-1.57081449e+00 -9.22424078e-01 -2.58872420e-01 2.21199608e+00
7.48880982e-01 2.42864102e-01 3.77864063e-01 7.72418857e-01
4.25705165e-01 -5.32923877e-01 1.42960370e-01 -7.85219491e-01
1.02681828e+00 1.01833010e+00 -6.39238432e-02 6.00534141e-01
-8.13804984e-01 7.94712305e-01 6.51268196e+00 9.53638077e-01
-1.28254032e+00 -9.54049528e-02 3.71624738e-01 4.60519195e-01
-3.49136949e-01 -3.12835574e-01 -4.48945463e-01 4.37791884e-01
6.79384828e-01 1.24230096e-02 5.10805130e-01 1.38708270e+00
-8.45457986e-02 1.52910026e-02 -1.31933045e+00 1.41068912e+00
-2.15323851e-01 -1.93210363e+00 1.91860907e-02 -2.45791137e-01
4.49227065e-01 -7.37610757e-01 -2.58372664e-01 3.61480266e-01
1.71190966e-03 -1.11615837e+00 9.77893233e-01 4.98430967e-01
1.32316720e+00 -9.23504472e-01 4.00777280e-01 1.04592487e-01
-1.43225777e+00 1.26659825e-01 6.79125451e-03 -3.61357212e-01
-4.23755676e-01 -1.22250402e-02 -1.23327851e+00 -5.70319667e-02
7.63888001e-01 1.13789722e-01 -7.62977481e-01 1.32257688e+00
-4.15319428e-02 3.70474398e-01 4.68692444e-02 -3.88199836e-01
-2.58675843e-01 -1.63560688e-01 3.56563739e-02 1.57161355e+00
7.33807027e-01 -4.79210019e-02 -1.44806102e-01 8.66909921e-01
2.26282507e-01 2.32238293e-01 -7.58462489e-01 -1.96093246e-01
7.64386356e-01 1.40281749e+00 -1.10880685e+00 -4.34922844e-01
-2.88034558e-01 1.05404639e+00 -3.08439974e-02 1.86463267e-01
-4.88362044e-01 -1.28871000e+00 7.13434696e-01 6.72950387e-01
5.11456132e-01 -2.10867122e-01 -6.23855650e-01 -3.11623693e-01
-3.66271771e-02 -1.04750288e+00 -3.52147043e-01 -1.22126961e+00
-5.60825646e-01 7.29722440e-01 -1.74572408e-01 -1.29753184e+00
-4.85228479e-01 -9.56243038e-01 -1.24686790e+00 9.44931805e-01
-1.70562431e-01 -1.28247619e+00 -7.23800898e-01 2.69977421e-01
9.19765472e-01 -5.17095327e-01 1.17477143e+00 4.99122769e-01
5.77609651e-02 7.47411847e-01 -2.55439997e-01 1.06866255e-01
1.70343682e-01 -1.49196661e+00 9.99879539e-01 2.75274873e-01
4.75612730e-01 9.77661908e-01 6.63043320e-01 -5.48764884e-01
-1.44059539e+00 -5.33055902e-01 9.48021531e-01 -6.01536393e-01
3.73608589e-01 -8.15884113e-01 -7.42103398e-01 2.50272602e-01
2.63796240e-01 -4.21571523e-01 9.52477157e-01 8.68284609e-03
-3.69148761e-01 1.66630242e-02 -8.36577713e-01 7.77787447e-01
8.43812943e-01 -8.86752903e-01 -7.55708158e-01 -9.46692824e-02
1.33192867e-01 -4.48265046e-01 -7.14763880e-01 -1.57512113e-01
1.47649336e+00 -1.00422490e+00 1.12305605e+00 -2.95171082e-01
4.76421863e-01 -1.67079464e-01 2.57188737e-01 -9.36961174e-01
-1.80329800e-01 -8.39230478e-01 -8.62415880e-02 1.16721535e+00
2.77997315e-01 2.74801582e-01 1.10012293e+00 4.78351086e-01
5.87521791e-02 -7.90534914e-01 -4.65700626e-01 -1.26935303e-01
-3.43662888e-01 -9.06548560e-01 6.30295098e-01 7.81194508e-01
2.87001878e-01 3.01729470e-01 3.98841910e-02 -4.12137240e-01
2.92799268e-02 -2.34591085e-02 1.27360523e+00 -1.73547614e+00
-2.02240273e-01 -8.29884589e-01 -3.06881726e-01 -8.17249179e-01
-1.21496402e-01 -6.22104764e-01 -1.15479253e-01 -1.46769369e+00
-3.07884336e-01 -6.76655889e-01 3.80876929e-01 3.17233562e-01
2.49759302e-01 2.64716864e-01 1.16069563e-01 -4.63690199e-02
-1.35205269e-01 -3.54742289e-01 6.92451775e-01 -5.78930005e-02
-3.64393413e-01 6.23872459e-01 -5.29580414e-01 9.39053237e-01
3.17158312e-01 -2.64616221e-01 -7.07612932e-01 -2.37479489e-02
5.36052465e-01 -8.38923305e-02 3.12579647e-02 -1.29559124e+00
4.34862264e-02 3.34563144e-02 6.04531586e-01 -7.90252686e-01
-1.26815170e-01 -1.28861332e+00 5.07076740e-01 4.77310091e-01
-3.16934854e-01 5.57143629e-01 2.32799068e-01 -1.04370490e-01
2.61357516e-01 -8.46858025e-01 4.09999639e-01 -2.10408971e-01
-6.32096410e-01 -6.27131835e-02 -8.90326619e-01 -5.70318341e-01
8.38787794e-01 -7.37333000e-01 4.34338689e-01 -4.19223934e-01
-5.39187789e-01 -7.82206953e-01 4.40183163e-01 6.30350053e-01
8.77610624e-01 -1.54741669e+00 6.99703991e-02 7.77889371e-01
1.28748938e-01 -3.83070886e-01 -1.69190481e-01 -7.05425860e-03
-1.31345034e+00 3.03018510e-01 -7.11836994e-01 -3.42933774e-01
-1.85826969e+00 2.31055051e-01 6.64356872e-02 -4.60428521e-02
-7.31905460e-01 7.46089935e-01 -2.22345501e-01 -3.58537495e-01
6.79503143e-01 -4.81249571e-01 -3.58005673e-01 1.26648873e-01
1.01222992e+00 5.30205309e-01 3.85560751e-01 -7.87689462e-02
-1.06527835e-01 7.47995377e-01 8.83904696e-02 -2.53676057e-01
1.30239010e+00 3.94138277e-01 1.97068855e-01 3.85455340e-01
1.24491799e+00 2.41555914e-01 -1.06208694e+00 2.93538570e-01
3.26732159e-01 -4.90957707e-01 -1.79615617e-01 -8.38541746e-01
-5.39356828e-01 1.14561045e+00 9.75272775e-01 2.93840855e-01
6.28159344e-01 -4.00586098e-01 6.00947797e-01 4.57955331e-01
2.66778469e-01 -1.14577377e+00 3.49091917e-01 4.96381730e-01
1.00434303e+00 -6.81195617e-01 1.02404483e-01 -2.78436244e-01
-1.77080259e-01 1.68142998e+00 6.21974349e-01 -2.58719206e-01
6.71594620e-01 4.99496192e-01 1.81741312e-01 -3.00505459e-01
-4.04200077e-01 3.05363834e-01 6.94868982e-01 8.40347588e-01
1.00492680e+00 -2.38100085e-02 -2.90320814e-01 7.78332829e-01
-2.57037371e-01 4.33934987e-01 2.78458625e-01 9.92376387e-01
-2.30484292e-01 -1.44058275e+00 -2.08457828e-01 2.43915334e-01
-4.56451654e-01 -1.30609363e-01 -5.30597568e-01 8.09874535e-01
2.48322800e-01 1.97859645e-01 2.86310703e-01 -7.99617171e-01
3.45603257e-01 3.17395717e-01 5.96230328e-01 -7.28258371e-01
-7.56020248e-01 -1.77446574e-01 -1.07382126e-01 -3.35152656e-01
3.12549144e-01 -1.49927184e-01 -9.84140337e-01 -2.76534915e-01
-2.77140699e-02 8.79312456e-02 1.14114916e+00 8.03779244e-01
3.07659447e-01 3.96742105e-01 2.51475543e-01 -1.20861375e+00
-4.15985137e-02 -8.18526328e-01 1.61246415e-02 2.38629907e-01
5.90917170e-02 -4.36702907e-01 3.89882803e-01 4.12016332e-01] | [8.172926902770996, 7.209803104400635] |
61bf5435-6527-46e8-903b-e70bacd356f6 | region2vec-community-detection-on-spatial | 2210.08041 | null | https://arxiv.org/abs/2210.08041v1 | https://arxiv.org/pdf/2210.08041v1.pdf | Region2Vec: Community Detection on Spatial Networks Using Graph Embedding with Node Attributes and Spatial Interactions | Community Detection algorithms are used to detect densely connected components in complex networks and reveal underlying relationships among components. As a special type of networks, spatial networks are usually generated by the connections among geographic regions. Identifying the spatial network communities can help reveal the spatial interaction patterns, understand the hidden regional structures and support regional development decision-making. Given the recent development of Graph Convolutional Networks (GCN) and its powerful performance in identifying multi-scale spatial interactions, we proposed an unsupervised GCN-based community detection method "region2vec" on spatial networks. Our method first generates node embeddings for regions that share common attributes and have intense spatial interactions, and then applies clustering algorithms to detect communities based on their embedding similarity and spatial adjacency. Experimental results show that while existing methods trade off either attribute similarities or spatial interactions for one another, "region2vec" maintains a great balance between both and performs the best when one wants to maximize both attribute similarities and spatial interactions within communities. | ['Song Gao', 'Wen Ye', 'Jiawei Zhu', 'Yunlei Liang'] | 2022-10-10 | null | null | null | null | ['community-detection'] | ['graphs'] | [-6.23667479e-01 -7.83996359e-02 -3.38728994e-01 3.54276178e-03
4.80118036e-01 -8.25373530e-01 8.34158957e-01 7.32927382e-01
-1.33779794e-01 1.75424471e-01 7.00826883e-01 -5.20366013e-01
-4.17085469e-01 -1.40535176e+00 -9.90900025e-02 -4.45377707e-01
-8.12416852e-01 3.25302213e-01 4.93597209e-01 -1.23272829e-01
2.05996707e-01 7.02826679e-01 -1.19438601e+00 -1.02022566e-01
1.11155570e+00 2.45784566e-01 2.51390547e-01 5.61896026e-01
-3.93706262e-01 7.12273896e-01 -1.37046799e-01 -2.08970800e-01
1.13189764e-01 -2.34285608e-01 -7.02563703e-01 -5.27125783e-02
-2.50091761e-01 8.04704875e-02 -8.16485405e-01 1.07841384e+00
2.90869892e-01 9.01626870e-02 9.05201852e-01 -1.45545912e+00
-9.28932905e-01 8.85724068e-01 -9.53863561e-01 5.39907634e-01
1.84396297e-01 8.27256367e-02 1.48054361e+00 -8.10794890e-01
7.26717591e-01 1.25438619e+00 8.05765986e-01 -1.67215541e-01
-1.27498949e+00 -6.46679819e-01 1.95051938e-01 -7.46494010e-02
-1.89283407e+00 -3.87813337e-02 8.50800633e-01 -9.12450969e-01
9.48764384e-01 2.30068326e-01 9.09038246e-01 6.69163525e-01
6.83076531e-02 4.07355607e-01 5.64898908e-01 -1.00314721e-01
2.84721870e-02 -2.97347680e-02 -7.69300982e-02 5.66501498e-01
4.63150173e-01 -1.88148126e-01 -2.10372970e-01 -2.96823800e-01
1.05511367e+00 3.83658022e-01 -1.53819650e-01 -4.63121474e-01
-1.36618495e+00 1.19227815e+00 1.26637661e+00 7.58528590e-01
-3.63936275e-01 2.57857949e-01 1.96642414e-01 2.03499466e-01
6.54350698e-01 4.75566745e-01 3.44727576e-01 3.10566217e-01
-1.04066539e+00 8.93304572e-02 5.52618861e-01 7.01682329e-01
1.06880474e+00 -2.26691172e-01 1.38370201e-01 9.29192901e-01
4.20430988e-01 2.30436504e-01 1.35927558e-01 -6.14154279e-01
5.33338964e-01 1.42985618e+00 -3.89460742e-01 -1.98272836e+00
-4.44882184e-01 -3.66374075e-01 -1.30846810e+00 -1.95058584e-01
1.92488328e-01 -5.69449961e-02 -4.63018447e-01 1.45968783e+00
3.10192138e-01 1.73773229e-01 -1.94794744e-01 8.67475748e-01
8.24065149e-01 7.08200455e-01 -4.62615527e-02 4.17680144e-01
1.08528149e+00 -5.82683742e-01 -4.61682737e-01 -1.54474825e-01
5.69318712e-01 -4.40000087e-01 5.83696127e-01 -5.65825462e-01
-5.97332478e-01 -1.89077511e-01 -8.90877008e-01 3.33757460e-01
-7.23754227e-01 -2.50303119e-01 1.02449024e+00 2.96848953e-01
-1.36509573e+00 4.58009750e-01 -4.94311899e-01 -7.05734432e-01
5.11036634e-01 1.70224831e-01 -5.20724595e-01 -6.88907504e-02
-1.15277112e+00 4.10678983e-01 3.20640594e-01 -2.96852639e-04
-4.67460871e-01 -5.21845281e-01 -9.59686697e-01 4.36760932e-01
2.04351004e-02 -2.64023840e-01 2.34309763e-01 -7.86799431e-01
-3.85785043e-01 6.64200604e-01 3.68181430e-02 -8.85712206e-02
1.74714252e-01 5.80759108e-01 -6.64740324e-01 2.71507919e-01
5.09370923e-01 7.97511637e-01 1.25764057e-01 -1.06175423e+00
-5.40328443e-01 -1.12461500e-01 2.58877147e-02 7.82518908e-02
-7.69082010e-01 3.06073786e-03 -4.95621502e-01 -7.62910962e-01
6.12917304e-01 -7.84087956e-01 -5.65408945e-01 1.03574410e-01
-6.24256015e-01 -3.27145845e-01 7.59833992e-01 -3.14033538e-01
1.71524096e+00 -2.00726652e+00 1.86745882e-01 9.39925492e-01
9.33409095e-01 -1.57801077e-01 -3.23329449e-01 9.38674450e-01
-8.22051242e-02 5.75631976e-01 -1.82293251e-01 2.94827998e-01
-1.56575680e-01 1.11774258e-01 8.71898383e-02 5.89853942e-01
4.87793922e-01 9.64038253e-01 -1.30915332e+00 -5.92197597e-01
7.60932863e-02 4.99302894e-01 -6.19254947e-01 4.83243959e-03
2.05431968e-01 1.12262718e-01 -4.88059044e-01 4.07156706e-01
6.87182426e-01 -5.51996231e-01 7.12646782e-01 1.74356207e-01
-3.01380932e-01 -3.50663811e-02 -1.34957576e+00 1.10859811e+00
-1.20245479e-02 1.17435145e+00 1.87080011e-01 -1.02667415e+00
1.04886556e+00 1.28812656e-01 6.00689292e-01 -4.94484723e-01
-2.10166574e-01 -3.13282087e-02 3.28191638e-01 -3.87221575e-01
4.08258170e-01 3.97297502e-01 4.57788594e-02 7.59037793e-01
-2.51524419e-01 3.26963723e-01 1.56266406e-01 9.43288803e-01
1.35087526e+00 -7.35426307e-01 4.69765157e-01 -7.41617680e-01
3.03287119e-01 9.35301036e-02 5.28266728e-01 3.70077610e-01
-1.19484358e-01 6.37990475e-01 1.00158310e+00 -5.32715023e-01
-1.12514758e+00 -1.13774920e+00 1.81697458e-01 8.46584380e-01
2.14326844e-01 -4.08435851e-01 -2.09288150e-01 -4.12857294e-01
3.87863696e-01 -2.03666732e-01 -8.58898699e-01 2.16328755e-01
-3.99635226e-01 -5.81404507e-01 5.26898265e-01 5.77246845e-01
2.81111240e-01 -8.88262808e-01 9.52254161e-02 2.22058922e-01
-2.15465620e-01 -7.66757667e-01 -7.34803259e-01 -1.49099603e-01
-7.26633430e-01 -1.33523250e+00 -7.43759811e-01 -1.05310297e+00
1.00444221e+00 9.86586630e-01 1.17775238e+00 6.95613801e-01
-2.13836312e-01 1.44468322e-01 -5.36889315e-01 -2.06054188e-02
-8.96171853e-02 2.60198146e-01 2.15100005e-01 1.46735281e-01
4.11646366e-01 -1.20961642e+00 -7.81810582e-01 3.43937933e-01
-7.78622508e-01 -1.60679668e-01 5.60563385e-01 5.02565265e-01
4.67048176e-02 5.15741825e-01 4.16732550e-01 -7.19715238e-01
8.29607010e-01 -1.16744566e+00 -4.66223329e-01 1.96052969e-01
-6.94143772e-01 -6.08454831e-02 2.81688958e-01 -2.77099729e-01
-4.45860624e-01 -2.15238199e-01 4.19433117e-01 -2.45469913e-01
6.21377826e-02 9.46174204e-01 -1.87719673e-01 1.06092952e-01
5.58898032e-01 2.12226540e-01 2.63042795e-03 -2.69047678e-01
5.10583043e-01 5.58140457e-01 2.00679556e-01 -3.32462460e-01
9.90733147e-01 5.49030721e-01 -2.11701810e-01 -1.11678731e+00
1.06005400e-01 -9.19068933e-01 -1.06585908e+00 -2.86935210e-01
9.61375117e-01 -1.02589500e+00 -6.44872189e-01 4.44042310e-02
-1.00698674e+00 -5.85397258e-02 2.39136472e-01 3.96196276e-01
3.28479886e-01 4.90504563e-01 -6.48666918e-01 -6.23594940e-01
2.39856914e-02 -6.77849710e-01 3.80768359e-01 1.63909227e-01
-3.08977574e-01 -1.59591126e+00 5.29322863e-01 -1.30270869e-01
5.61983824e-01 3.65465969e-01 1.06536627e+00 -4.73965704e-01
-7.28556395e-01 -1.20475024e-01 -8.06023657e-01 -4.86570299e-01
5.10580719e-01 2.65229434e-01 -5.86777925e-01 -3.10830683e-01
-1.11838150e+00 5.13978064e-01 9.64634299e-01 4.04682368e-01
7.79056430e-01 -6.18621171e-01 -7.13757217e-01 5.38527191e-01
1.45716369e+00 -1.28186360e-01 6.23346210e-01 2.54621834e-01
1.15314937e+00 9.61679995e-01 -1.23182975e-01 4.03346807e-01
4.97212768e-01 2.82207191e-01 5.42516470e-01 -4.60392594e-01
6.11563511e-02 -4.47006106e-01 6.96836337e-02 9.95886743e-01
-1.70912445e-01 -2.38823071e-01 -1.47358406e+00 1.22850561e+00
-1.88321173e+00 -1.44909191e+00 -5.14270842e-01 1.85422456e+00
5.28027534e-01 1.28608003e-01 4.78098363e-01 -1.54179603e-01
1.13620448e+00 5.07993877e-01 -2.95511216e-01 -6.67457208e-02
-5.30426741e-01 -1.88182086e-01 5.57793379e-01 4.75399971e-01
-1.06346178e+00 9.72368598e-01 6.41583443e+00 5.49439967e-01
-8.13613415e-01 -1.48765638e-01 5.63628674e-01 8.90607908e-02
-9.15031016e-01 7.39970133e-02 -1.88694686e-01 3.51733953e-01
5.56003749e-01 -2.34231099e-01 3.34418863e-01 6.72219634e-01
4.59187776e-01 1.88273534e-01 -6.62701488e-01 7.08106458e-01
-4.47370082e-01 -1.87180102e+00 1.57935679e-01 6.77972853e-01
9.82461810e-01 2.70805866e-01 -9.06907097e-02 -1.82178989e-01
9.68161047e-01 -1.43579066e+00 3.33827138e-01 1.31620884e-01
6.86103523e-01 -9.02087271e-01 4.82587039e-01 1.86045885e-01
-2.20151258e+00 -1.86869279e-01 -4.55471009e-01 -2.49924734e-01
-1.20806940e-01 7.19601989e-01 -8.23318303e-01 3.48924100e-01
7.39949644e-01 1.14409351e+00 -7.15986013e-01 1.03358495e+00
-2.11524174e-01 5.33527732e-01 -1.64172456e-01 -1.95957810e-01
7.32692659e-01 -8.23899388e-01 5.25657594e-01 1.45284092e+00
1.69829413e-01 -3.63467559e-02 2.66805202e-01 1.22343135e+00
-4.93570752e-02 6.37031272e-02 -1.22474623e+00 -4.49077338e-01
1.07227457e+00 1.45751595e+00 -1.37843060e+00 -1.25432163e-02
-4.17820960e-01 6.51743889e-01 5.38415909e-01 4.05448467e-01
-5.09059727e-01 -6.06355965e-01 1.04969311e+00 6.68805778e-01
3.33419263e-01 -6.82361186e-01 -8.24127644e-02 -8.67434323e-01
-2.98019469e-01 -4.53729600e-01 3.09002668e-01 -5.01708806e-01
-1.40862727e+00 4.42942053e-01 -2.15726867e-01 -1.37802184e+00
2.38961518e-01 -1.82740182e-01 -1.25991416e+00 7.59803593e-01
-1.21947598e+00 -1.26561618e+00 -2.45475501e-01 6.21876061e-01
9.05351341e-02 -2.36870959e-01 5.83605349e-01 2.04708368e-01
-5.58302760e-01 3.38940114e-01 4.70075995e-01 8.88290882e-01
4.48759459e-02 -1.47864366e+00 6.79760158e-01 1.03102195e+00
3.34968179e-01 9.90696371e-01 1.00088961e-01 -9.25512016e-01
-1.03315794e+00 -1.23728359e+00 1.19721067e+00 -2.19535872e-01
1.09864867e+00 -5.36547780e-01 -9.25619841e-01 5.22885263e-01
3.62944782e-01 -8.70840997e-02 8.88344705e-01 4.93002892e-01
-7.40780711e-01 -1.20830182e-02 -8.54438603e-01 8.67707372e-01
1.15191591e+00 -8.14401746e-01 -8.93269777e-02 2.39926487e-01
7.09984243e-01 5.87088346e-01 -1.02656460e+00 -9.80186164e-02
3.27006102e-01 -8.50083411e-01 1.08092308e+00 -5.00750601e-01
5.47800303e-01 -3.84812653e-01 -9.23153162e-02 -1.49828231e+00
-1.06502604e+00 -3.36096078e-01 3.69453430e-01 1.40201437e+00
5.89904130e-01 -5.60327530e-01 9.88907337e-01 1.62546307e-01
3.34711134e-01 -3.46333325e-01 -6.79140866e-01 -6.16338193e-01
1.92982346e-01 -1.33068830e-01 8.95396888e-01 1.72044766e+00
2.79352218e-01 4.47221071e-01 -3.91296670e-02 3.87587637e-01
5.60696840e-01 1.04738861e-01 5.98118126e-01 -1.66733634e+00
2.98577994e-01 -9.78132844e-01 -7.52635658e-01 -6.98030591e-01
-4.37752232e-02 -1.08883011e+00 -4.38622862e-01 -1.74371719e+00
5.49244046e-01 -8.86806071e-01 -1.69691473e-01 3.32485437e-01
-1.42098829e-01 1.09366171e-01 -2.12894440e-01 4.12052363e-01
-5.35650492e-01 3.21917892e-01 8.11897337e-01 -5.33003926e-01
-5.20970702e-01 -3.42184961e-01 -8.60238552e-01 4.48032111e-01
5.55544138e-01 -2.79386222e-01 -4.75824893e-01 -4.83045548e-01
6.30444288e-01 -1.84182793e-01 2.94315159e-01 -9.98963833e-01
5.50593913e-01 -3.70207131e-01 3.88959676e-01 -6.60994768e-01
-2.16910020e-01 -9.06645000e-01 4.37938422e-01 5.53469658e-01
-1.73530266e-01 3.74151021e-01 -1.73924163e-01 9.18440044e-01
-3.33867103e-01 4.28936034e-01 5.69128513e-01 -2.16431409e-01
-8.41960549e-01 5.37804544e-01 -8.69292140e-01 -1.42154574e-01
9.55322742e-01 -5.69868445e-01 -3.24615002e-01 -5.07580936e-01
-5.37163973e-01 6.27241910e-01 7.08216250e-01 5.36062717e-01
8.97023320e-01 -1.67253399e+00 -9.04661357e-01 1.05786592e-01
3.41651827e-01 -1.28393769e-01 6.19627014e-02 6.79367125e-01
-1.03529155e+00 2.55784214e-01 -1.86976105e-01 -5.88244259e-01
-1.39452004e+00 5.57240665e-01 2.14878649e-01 -2.57302791e-01
-6.52570903e-01 9.80013669e-01 2.66924292e-01 -6.01676226e-01
3.53087559e-02 -8.93041566e-02 -6.63784087e-01 5.08560658e-01
4.20370251e-01 2.75085658e-01 -4.54235941e-01 -8.70101690e-01
-5.41283607e-01 4.99266714e-01 1.65811837e-01 7.74307176e-02
1.68795335e+00 -1.65212393e-01 -5.62318265e-01 1.98926941e-01
1.30172241e+00 2.53365953e-02 -8.15452874e-01 -4.05786693e-01
4.23976988e-01 -6.16089761e-01 -9.09013115e-03 3.04947179e-02
-1.58737075e+00 9.45271075e-01 2.39960209e-01 7.42377222e-01
7.22595513e-01 2.30339363e-01 5.17748117e-01 1.17617808e-02
7.88730904e-02 -7.72494435e-01 4.10636038e-01 4.17568803e-01
4.81596470e-01 -1.22226524e+00 3.89758527e-01 -5.82067013e-01
-5.41872144e-01 9.18497920e-01 5.93884706e-01 -2.78997272e-01
1.22344148e+00 -5.24324924e-02 -2.64811337e-01 -8.12483966e-01
-3.58663023e-01 -5.42581677e-01 4.54943419e-01 8.67110789e-01
4.12870198e-01 5.01819789e-01 5.18577062e-02 1.05014712e-01
-3.90747823e-02 -9.15767074e-01 4.16539073e-01 5.61985373e-01
-6.70005858e-01 -8.93595695e-01 -3.00873756e-01 5.25044620e-01
-1.81539521e-01 -3.07885259e-01 -8.27563286e-01 8.36865127e-01
4.06212471e-02 9.89084601e-01 5.88689506e-01 -6.73465669e-01
-6.54468238e-02 -6.25341117e-01 -2.77951866e-01 -6.65131569e-01
-5.56017697e-01 -7.94371869e-03 -1.50661767e-01 -3.12968999e-01
-3.27188343e-01 -5.24500668e-01 -1.04796720e+00 -9.87193763e-01
-1.86470941e-01 3.45239550e-01 2.29087874e-01 5.15336514e-01
4.70386177e-01 2.72711933e-01 1.02975178e+00 -7.66588926e-01
4.35419738e-01 -8.81773591e-01 -1.01220810e+00 3.94033998e-01
1.14540890e-01 -6.20659649e-01 -3.64326626e-01 -4.02722925e-01] | [7.186811447143555, 6.014355182647705] |
aa4247a3-c0bb-411d-8bb7-32d3a0cf3e10 | joint-english-spelling-error-correction-and | null | null | https://aclanthology.org/C12-1144 | https://aclanthology.org/C12-1144.pdf | Joint English Spelling Error Correction and POS Tagging for Language Learners Writing | null | ['Mamoru Komachi', 'Tomoya Mizumoto', 'Yuji Matsumoto', 'Keisuke Sakaguchi'] | 2012-12-01 | joint-english-spelling-error-correction-and-1 | https://aclanthology.org/C12-1144 | https://aclanthology.org/C12-1144.pdf | coling-2012-12 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01
7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01
7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00
-1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01
3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01
9.28529128e-02 -8.59008506e-02 -2.09758401e-01 9.22211111e-01
5.37373662e-01 4.49454933e-02 1.08379531e+00 -1.42548037e+00
-5.91619551e-01 6.10313773e-01 -4.01565880e-02 1.21627934e-01
1.03678203e+00 -5.39447069e-01 1.19919395e+00 -1.13026452e+00
7.20913768e-01 1.26888943e+00 8.66221786e-01 5.44149756e-01
-1.22286928e+00 -1.94712028e-01 -3.26822817e-01 -9.51717794e-02
-1.46558487e+00 -3.25250506e-01 4.25783843e-02 -2.08119690e-01
1.66093647e+00 1.26596653e+00 1.20609856e+00 1.01401424e+00
1.26658809e+00 8.34431887e-01 1.04267764e+00 -5.13792276e-01
3.35295945e-01 3.66983831e-01 1.54683650e-01 6.33519173e-01
8.40953708e-01 5.26628852e-01 -7.06372619e-01 -9.13127720e-01
9.33553874e-01 -2.94925272e-01 1.71355158e-01 -5.05680561e-01
-9.05919552e-01 6.91228509e-01 1.78732842e-01 3.83959889e-01
-1.39880210e-01 9.89067405e-02 1.26390755e-01 5.30987144e-01
-2.58292928e-02 6.47037446e-01 -9.11868811e-01 -1.33165747e-01
-8.71728659e-01 5.10332465e-01 1.25398111e+00 1.52653182e+00
1.24482810e-01 2.94908643e-01 -9.34252143e-02 3.17179203e-01
8.92314315e-01 1.01808000e+00 4.28362608e-01 -1.36146402e+00
-6.87414408e-02 1.72361732e-01 5.01781464e-01 -8.52631688e-01
-6.33224547e-01 -9.64177120e-03 -8.93263519e-01 4.49267089e-01
3.49161088e-01 4.57367361e-01 -8.02827001e-01 5.07305264e-01
4.33481112e-02 -2.34125629e-01 4.53833073e-01 5.55570945e-02
4.99930978e-01 3.76208365e-01 -1.34477139e-01 -5.73289394e-01
1.06082785e+00 -1.36716676e+00 -1.35299087e+00 2.33215362e-01
9.05734658e-01 -1.07320261e+00 4.35900748e-01 5.33875942e-01
-1.55548143e+00 -1.37560293e-01 -1.08699942e+00 1.78573877e-01
-7.27255583e-01 -3.14239264e-01 8.57801437e-01 1.43120694e+00
-1.60129595e+00 9.73287821e-01 -4.91727620e-01 6.59165755e-02
1.20568443e-02 8.24621081e-01 -2.64718989e-03 4.62812334e-01
-1.33193445e+00 1.08501506e+00 2.22979754e-01 -1.21242590e-01
-1.65216476e-01 -2.13068098e-01 -8.23704481e-01 -5.63443303e-01
-4.78693932e-01 -5.29636025e-01 1.44139910e+00 -2.59346128e-01
-1.65295815e+00 9.71794367e-01 -1.42069459e-01 -1.97814897e-01
6.14786744e-01 -1.28011424e-02 -8.31891418e-01 2.42498964e-01
-1.89849049e-01 5.76383233e-01 9.28263724e-01 -1.35132408e+00
-7.59897232e-01 -1.67359829e-01 -1.23336017e-01 2.66287565e-01
-1.25510961e-01 1.89734384e-01 2.11616129e-01 -1.12999000e-01
3.27147305e-01 -7.20919967e-01 -2.53068686e-01 -5.32041907e-01
-1.46512717e-01 -7.10518599e-01 7.70373225e-01 -4.51523662e-01
1.83705616e+00 -1.67618537e+00 -2.06720144e-01 3.98590982e-01
3.57815564e-01 -1.24705513e-03 2.40583986e-01 1.08380008e+00
-2.76906848e-01 7.32199550e-01 4.11965609e-01 -1.10722095e-01
2.24991128e-01 4.85861301e-01 -4.16602850e-01 3.05609167e-01
-1.29282743e-01 1.15307164e+00 -1.16605783e+00 -5.23096442e-01
4.11106765e-01 9.42391157e-02 -4.58732933e-01 4.79237735e-01
2.99364805e-01 1.47170946e-01 -3.56553018e-01 1.39399457e+00
1.15709066e+00 -1.31984919e-01 1.45911396e-01 5.30878425e-01
-3.79135728e-01 3.55090618e-01 -6.96863770e-01 1.05554795e+00
7.08333924e-02 5.00173986e-01 1.02364108e-01 -7.94621468e-01
3.33247900e-01 8.61540735e-01 4.37155962e-01 -9.67555881e-01
-2.26398129e-02 6.92409754e-01 1.12803578e-01 -6.07703328e-01
7.58228302e-01 3.94563079e-02 -3.64872098e-01 6.40070081e-01
-2.37588286e-01 -6.59476995e-01 9.64643434e-02 3.08100313e-01
6.22585893e-01 -5.18246442e-02 5.62923312e-01 -1.05613089e+00
7.86340594e-01 -1.82965681e-01 -1.81299388e-01 1.03415680e+00
-3.09923887e-01 3.19085121e-01 2.99841821e-01 -6.65102363e-01
-6.45341039e-01 -1.12307119e+00 -4.89381433e-01 1.30636716e+00
3.24267983e-01 -4.39044595e-01 -9.54439282e-01 -2.49762803e-01
1.77620783e-01 6.89606130e-01 -5.90509653e-01 3.84124845e-01
-5.03739953e-01 -8.32535863e-01 7.39044368e-01 3.45434904e-01
-5.07752821e-02 -1.33414865e+00 -6.58416986e-01 1.25490099e-01
-2.20292807e-01 -6.63697243e-01 -6.23428151e-02 4.48765576e-01
-1.35989368e+00 -5.18594682e-01 -6.66252747e-02 -8.20914626e-01
5.87345481e-01 2.46782884e-01 1.27047324e+00 5.39230824e-01
-2.31483161e-01 4.26904231e-01 -1.21292919e-01 -4.95818377e-01
-4.59671497e-01 -8.00336525e-02 5.28869390e-01 -5.87835789e-01
5.19427478e-01 -2.50617653e-01 -7.29350567e-01 5.37953973e-01
-6.88540697e-01 1.62748516e-01 1.79803044e-01 1.04410267e+00
1.35816500e-01 -9.34035778e-02 1.22507080e-01 -6.38007045e-01
8.72274399e-01 -1.69219792e-01 -3.78732830e-01 5.77745810e-02
-6.77108407e-01 -3.74140263e-01 3.21430594e-01 -3.25342178e-01
-1.01981449e+00 -4.87835288e-01 -9.82677937e-02 2.45538145e-01
1.11353043e-02 -1.46784872e-01 6.47139177e-02 -5.24923325e-01
8.02199244e-01 9.25758183e-02 1.99174434e-02 -6.80815242e-03
3.01039815e-01 7.09525108e-01 -6.82967342e-03 -6.68678164e-01
8.44880998e-01 4.91470337e-01 7.98524171e-02 -9.57177758e-01
-1.52186140e-01 -2.60129690e-01 -9.51962709e-01 -6.54426932e-01
6.56643391e-01 -6.78531289e-01 -9.10833478e-01 3.91110867e-01
-9.38691139e-01 -3.38627815e-01 -3.91645581e-01 4.25431967e-01
-1.01278400e+00 2.75717527e-02 -3.90154392e-01 -1.27895141e+00
-5.10977268e-01 -1.02017939e+00 9.43384409e-01 5.30070923e-02
-5.10597289e-01 -1.26927447e+00 5.87685481e-02 2.71537274e-01
1.81734428e-01 -1.73075795e-01 6.90226793e-01 -2.38256708e-01
-4.24233019e-01 -1.53791070e-01 2.34436691e-02 -1.39755070e-01
1.70832314e-02 4.95917559e-01 -9.81751978e-01 -5.31145096e-01
6.65065646e-02 -1.92070693e-01 -1.08835101e-01 6.52520418e-01
5.91872573e-01 -2.29931593e-01 -8.56000841e-01 5.40386558e-01
1.38545322e+00 3.85070026e-01 5.32770038e-01 7.28214979e-01
1.41836226e-01 5.53460240e-01 9.17806149e-01 4.63203549e-01
1.30579369e-02 3.28798652e-01 2.40537539e-01 1.49327129e-01
1.11720070e-01 -1.54819340e-01 3.77893507e-01 1.16112018e+00
-8.18235934e-01 -2.69281328e-01 -5.07867396e-01 4.42987174e-01
-1.72482407e+00 -1.40330648e+00 -4.32368398e-01 6.90478683e-01
6.25676990e-01 1.56016424e-01 -1.48347050e-01 3.35214496e-01
4.99015123e-01 -2.03574806e-01 -1.19133167e-01 -1.06291151e+00
-1.43546045e-01 3.15233678e-01 7.37729073e-01 1.00061214e+00
-7.20721722e-01 1.03317809e+00 1.29781246e+01 1.02230716e+00
2.21112028e-01 1.03134915e-01 5.16071796e-01 3.48020852e-01
-4.36954498e-01 -4.56139445e-02 -1.04416132e+00 2.72933897e-02
1.38140702e+00 -4.30666685e-01 6.85999811e-01 5.44219851e-01
3.44648361e-01 -4.23268199e-01 -1.26188684e+00 5.26221812e-01
9.73738134e-02 -1.40886843e+00 -2.83300440e-04 6.85225725e-01
7.73699820e-01 -5.08050561e-01 6.22419357e-01 3.24184299e-01
6.09259963e-01 -1.14389277e+00 8.60300779e-01 2.53660440e-01
1.03040910e+00 -6.05088234e-01 5.67372203e-01 1.68872893e-01
-1.14389896e+00 -2.20873043e-01 -8.77727985e-01 -1.00755692e+00
3.93533185e-02 -1.81779593e-01 -4.29956943e-01 3.48861217e-01
9.58353162e-01 2.99398601e-01 -3.93658698e-01 9.95779395e-01
-4.78694476e-02 1.04875881e-02 -2.71853864e-01 -4.48467314e-01
4.83122796e-01 -3.54241252e-01 4.66730654e-01 1.00164843e+00
2.48499006e-01 3.51035744e-01 -9.84472036e-02 4.01770771e-01
5.45058846e-01 3.29446048e-02 -1.19659424e+00 -1.78908288e-01
2.83276141e-01 9.16795909e-01 -4.83487815e-01 -4.22520459e-01
-2.00212970e-01 8.62069130e-01 -3.55488248e-02 5.01107454e-01
-6.11489356e-01 -4.35615242e-01 9.72222984e-01 -1.27327025e-01
-1.14700586e-01 -3.48497719e-01 -6.23769283e-01 -7.30352640e-01
-5.89872956e-01 -4.54965204e-01 5.93606755e-02 -5.53365827e-01
-1.39813089e+00 5.79277515e-01 -2.27688253e-02 -1.40553558e+00
-6.99901402e-01 -1.27676582e+00 -4.76714373e-01 4.92853165e-01
-1.11898029e+00 -1.10984349e+00 2.50124663e-01 4.52870727e-01
1.64141744e-01 -5.34416080e-01 1.39563632e+00 3.57715860e-02
1.00637585e-01 9.24474537e-01 6.69434488e-01 -7.37814724e-01
5.56605101e-01 -1.27867436e+00 5.68737745e-01 -1.37897313e-01
-4.31265175e-01 9.05828118e-01 6.28349900e-01 -5.39804697e-01
-1.41196322e+00 -3.66917729e-01 1.08350635e+00 -9.83769417e-01
6.55218959e-01 -3.86345625e-01 4.23767231e-02 7.88592756e-01
7.15902448e-01 -6.18741751e-01 8.21781039e-01 -1.83753878e-01
1.80774391e-01 5.75296998e-01 -1.39248300e+00 6.12354755e-01
1.66275799e+00 -4.63594139e-01 -6.25784039e-01 7.60327101e-01
8.13696027e-01 -6.94087505e-01 -1.30082703e+00 3.34633321e-01
8.65424156e-01 -8.75409484e-01 1.61978090e+00 -1.32660246e+00
-4.43697497e-02 2.81152606e-01 -2.61993498e-01 -9.32519078e-01
-5.96193194e-01 -1.23518765e+00 -5.33532679e-01 -5.83747849e-02
5.96577883e-01 -1.13057327e+00 3.42365682e-01 8.74560475e-01
-2.82833427e-01 -6.42737269e-01 -1.06996536e+00 -1.32016802e+00
-3.35779637e-02 -1.45572275e-01 4.90409225e-01 7.63798356e-01
6.83744550e-01 1.09839931e-01 -6.36873543e-02 -1.00294888e-01
5.33176839e-01 9.55312885e-03 4.41501856e-01 -1.34294486e+00
3.86843324e-01 -5.75816095e-01 -3.07655483e-01 -9.45992947e-01
-8.85957032e-02 -8.12076271e-01 -6.53862000e-01 -1.28511906e+00
-8.32044985e-03 -1.91056758e-01 -1.11109078e-01 -1.65725678e-01
3.67937148e-01 2.13746816e-01 1.20859891e-02 1.03788137e-01
-3.71160030e-01 6.18435517e-02 1.29639816e+00 6.91750320e-05
-1.62315920e-01 4.85058486e-01 -4.81304944e-01 7.84440815e-01
8.58408585e-02 -2.99253196e-01 -6.78878546e-01 6.11881316e-02
6.69384480e-01 4.61409837e-02 3.24159935e-02 -7.42885649e-01
5.37211418e-01 -3.75702560e-01 4.78586555e-01 -1.32223868e+00
1.30741090e-01 -9.61415648e-01 6.95283338e-02 9.40189242e-01
2.74610907e-01 1.20424610e-02 8.66204947e-02 5.39500564e-02
-1.44871444e-01 -5.70943117e-01 9.21121240e-01 -4.07591403e-01
-4.92852688e-01 -5.20386267e-03 -1.03226590e+00 8.97834301e-02
9.92593169e-01 -7.84614205e-01 -3.59281451e-01 -4.20183957e-01
-8.29068601e-01 -1.95836127e-02 6.50830388e-01 3.03609259e-02
7.30431557e-01 -1.51530886e+00 -2.30721906e-01 7.18729138e-01
-3.22939813e-01 -3.74400020e-01 -1.70157343e-01 6.58265352e-01
-1.32361674e+00 1.02442718e+00 -5.41665435e-01 -4.55340147e-01
-1.14228773e+00 4.78126436e-01 4.28307921e-01 -2.41845414e-01
-2.15481281e-01 1.11954463e+00 2.71224789e-02 -8.23025763e-01
1.85185194e-01 -9.21545625e-02 -7.54407048e-01 3.55081353e-03
6.88606799e-01 1.05194807e+00 -2.91290224e-01 -6.04341030e-01
-4.56784427e-01 6.65885091e-01 2.32151806e-01 -2.87484169e-01
9.17833567e-01 -2.19243199e-01 -9.89108324e-01 4.28274393e-01
8.38715494e-01 -1.36269778e-01 -3.69319022e-02 4.16855574e-01
1.25943512e-01 -8.02164078e-01 -4.32406247e-01 -3.55811834e-01
-1.85641110e-01 5.54822803e-01 5.34874737e-01 8.99602413e-01
8.57008278e-01 -3.02566767e-01 8.18335712e-01 9.66778398e-01
5.72402716e-01 -1.68019545e+00 -2.13140488e-01 6.89524531e-01
9.12339568e-01 -9.28350806e-01 5.44190466e-01 -7.27165341e-01
-4.14997995e-01 1.32979155e+00 4.68304873e-01 -1.55325383e-01
1.27306652e+00 5.74917436e-01 1.14069022e-02 -3.70670199e-01
-9.44949508e-01 1.12705544e-01 3.75366658e-01 1.11147714e+00
5.06513238e-01 5.07374525e-01 -9.66500878e-01 3.21953118e-01
-7.28706717e-01 -2.34555230e-01 4.90474731e-01 1.41972518e+00
-6.43810987e-01 -1.20391917e+00 -7.23931909e-01 4.61561680e-01
-5.49773455e-01 -1.16372630e-01 -5.28106689e-01 8.46754074e-01
-5.76629937e-02 1.49448860e+00 -1.97535474e-03 -5.03491640e-01
4.11356747e-01 1.41089618e-01 7.21762300e-01 -1.23501487e-01
-9.04846430e-01 4.15413082e-01 3.84890139e-01 -1.23056793e+00
-8.58632207e-01 -1.05834293e+00 -1.40667629e+00 -1.19437599e+00
-5.12782812e-01 1.89310342e-01 3.83317530e-01 3.90289724e-01
-2.06836104e-01 2.85260603e-02 9.80917513e-01 -1.07949340e+00
-3.90341938e-01 -9.39418614e-01 -1.00026262e+00 -7.84516707e-02
2.89751232e-01 -8.00943017e-01 -7.83523321e-01 2.83909619e-01] | [-7.377369403839111, 3.770484209060669] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.