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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5ea65d5f-c8c0-41d1-9aa6-bb603abcd638 | proportional-response-contextual-bandits-for | 2307.02108 | null | https://arxiv.org/abs/2307.02108v1 | https://arxiv.org/pdf/2307.02108v1.pdf | Proportional Response: Contextual Bandits for Simple and Cumulative Regret Minimization | Simple regret minimization is a critical problem in learning optimal treatment assignment policies across various domains, including healthcare and e-commerce. However, it remains understudied in the contextual bandit setting. We propose a new family of computationally efficient bandit algorithms for the stochastic contextual bandit settings, with the flexibility to be adapted for cumulative regret minimization (with near-optimal minimax guarantees) and simple regret minimization (with SOTA guarantees). Furthermore, our algorithms adapt to model misspecification and extend to the continuous arm settings. These advantages come from constructing and relying on "conformal arm sets" (CASs), which provide a set of arms at every context that encompass the context-specific optimal arm with some probability across the context distribution. Our positive results on simple and cumulative regret guarantees are contrasted by a negative result, which shows that an algorithm can't achieve instance-dependent simple regret guarantees while simultaneously achieving minimax optimal cumulative regret guarantees. | ['Emma Brunskill', 'Susan Athey', 'Ruohan Zhan', 'Sanath Kumar Krishnamurthy'] | 2023-07-05 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 2.96000361e-01 2.46013641e-01 -1.16730297e+00 -5.76271772e-01
-1.48709190e+00 -8.34359884e-01 7.26474375e-02 -4.34629880e-02
-2.12050870e-01 1.22009313e+00 5.18455684e-01 -8.34761262e-01
-1.19385135e+00 -4.96440440e-01 -1.00141037e+00 -8.05298924e-01
7.02552646e-02 6.69638932e-01 -7.03758895e-01 1.96094111e-01
6.03088029e-02 4.95545387e-01 -8.08025301e-01 4.08118844e-01
1.02632356e+00 1.04726088e+00 -1.57720029e-01 4.54120606e-01
-1.93817496e-01 6.11681342e-01 -2.02893734e-01 -6.48042560e-01
4.90294755e-01 -4.93952781e-01 -8.61239791e-01 1.38117364e-02
2.38668829e-01 -2.76290506e-01 -9.88329053e-02 9.05062079e-01
4.02628690e-01 1.55491367e-01 5.07033050e-01 -8.94415915e-01
-5.29592633e-01 8.90616715e-01 -7.89795160e-01 7.57853985e-02
2.85936952e-01 4.89618955e-03 1.24147141e+00 3.10238227e-02
2.51901150e-01 1.30397105e+00 8.31677973e-01 8.13859165e-01
-1.52065039e+00 -4.96552467e-01 5.19561708e-01 -2.66246766e-01
-7.87095249e-01 -4.83441114e-01 4.55736905e-01 -3.61483693e-01
4.97581273e-01 9.69709873e-01 6.49612725e-01 1.02867067e+00
-8.18621833e-03 1.12192130e+00 1.27700424e+00 -5.31753421e-01
5.88716269e-01 5.16674183e-02 4.26509589e-01 2.51638770e-01
4.07206893e-01 4.82203662e-01 -1.26283452e-01 -5.43056607e-01
7.92643487e-01 3.90867621e-01 -3.73983324e-01 -5.57242930e-01
-7.00592995e-01 1.12336493e+00 4.76846844e-01 -4.64992560e-02
-6.41301095e-01 5.40298939e-01 2.27940887e-01 2.75621831e-01
7.38276184e-01 8.87406617e-02 -8.43691707e-01 -3.88184711e-02
-8.47114325e-01 4.74764287e-01 8.22133422e-01 1.17104185e+00
4.88540322e-01 -3.55668366e-01 -9.44420397e-01 7.88712025e-01
7.25118518e-02 6.15321696e-01 -1.99836735e-02 -9.92538273e-01
8.90133679e-01 2.98570283e-02 8.19402099e-01 -4.69687760e-01
-5.71762145e-01 -9.70551670e-01 -7.04710960e-01 -2.06430376e-01
4.95712876e-01 -4.91286337e-01 -9.81500268e-01 2.01426435e+00
5.13025641e-01 -1.96265355e-02 -2.83588767e-01 9.00350928e-01
5.96484132e-02 2.06944451e-01 1.55407295e-01 -9.37458575e-01
1.19143450e+00 -8.35656345e-01 -9.49457407e-01 -6.76959336e-01
7.02654541e-01 -4.14083987e-01 8.14330876e-01 1.56518355e-01
-1.17350471e+00 3.25092077e-01 -4.81270224e-01 3.17750067e-01
1.74259305e-01 -3.83407593e-01 1.35382664e+00 1.39490032e+00
-5.86500645e-01 4.08753008e-01 -6.05427146e-01 -1.07671730e-01
9.67144608e-01 4.05696839e-01 4.27463325e-03 -3.13341588e-01
-6.25233233e-01 8.57785106e-01 1.56242102e-01 1.80671498e-01
-6.50063694e-01 -1.17439198e+00 -4.87274289e-01 1.70753717e-01
5.96002340e-01 -1.27545106e+00 1.63866484e+00 -1.50884783e+00
-1.55489838e+00 4.36077088e-01 -3.23926151e-01 -6.99657083e-01
9.30568874e-01 -1.92096636e-01 1.02374248e-01 -2.41022468e-01
5.87341860e-02 -5.67531250e-02 5.75990081e-01 -1.12518322e+00
-8.11412632e-01 -5.82474291e-01 4.24284130e-01 3.18510443e-01
-5.98397758e-03 -8.86753649e-02 -1.30078439e-02 -5.89039385e-01
2.61830036e-02 -9.70987558e-01 -7.35264480e-01 -3.71259987e-01
-3.43205273e-01 8.17180797e-02 -1.14750452e-01 -4.83641893e-01
1.23713994e+00 -1.82244074e+00 -5.97293563e-02 3.31071645e-01
-3.48620713e-01 -3.91655773e-01 -1.00390628e-01 2.67205298e-01
-1.93044439e-01 1.81922346e-01 -3.23794425e-01 -1.97328761e-01
2.65949547e-01 3.48586768e-01 -4.72522348e-01 7.01836407e-01
-4.71362621e-01 1.11096120e+00 -8.73470664e-01 -1.55075207e-01
1.30320966e-01 -2.96701819e-01 -9.35931623e-01 -1.64402366e-01
-3.95887911e-01 3.32523942e-01 -8.32479298e-01 4.64858174e-01
8.38069141e-01 -2.64190823e-01 5.10954499e-01 3.32471192e-01
2.13274181e-01 2.78278105e-02 -1.00824749e+00 1.51541007e+00
-6.24659836e-01 -1.24763325e-01 3.17713290e-01 -1.35571468e+00
3.71736288e-02 3.61860722e-01 5.85815370e-01 -5.82492173e-01
9.96860638e-02 1.31015450e-01 -2.40787864e-01 -3.89709085e-01
-5.48263565e-02 -8.02850246e-01 -9.82835442e-02 6.57412887e-01
-2.58412451e-01 2.94506997e-01 -3.44571650e-01 -9.89107117e-02
8.29853594e-01 -2.56521016e-01 2.84784883e-01 -6.14476144e-01
-1.09969974e-01 -6.94003478e-02 6.49701893e-01 1.52080095e+00
-6.94557577e-02 4.06978577e-01 5.79049289e-01 -4.97128695e-01
-6.68271780e-01 -9.32358563e-01 -3.83966744e-01 1.32998180e+00
-1.35826841e-01 4.39775407e-01 -6.37358487e-01 -8.34402382e-01
5.40499091e-01 9.32608008e-01 -1.02220654e+00 1.16248824e-01
-2.39522412e-01 -1.26639509e+00 -1.34863198e-01 3.91026914e-01
1.61879268e-02 -5.29302239e-01 -2.20724806e-01 3.88629645e-01
-1.82790816e-01 -6.17836654e-01 -8.47725630e-01 1.94648221e-01
-1.26773036e+00 -9.85811949e-01 -8.09605181e-01 -1.72447965e-01
5.55467069e-01 3.80422980e-01 9.26385164e-01 -4.04793561e-01
-2.24244315e-02 7.16168284e-01 -1.01362631e-01 -7.41327167e-01
1.41155705e-01 3.48547176e-02 -1.57159448e-01 2.81617165e-01
7.37103820e-02 -3.42587918e-01 -1.04412484e+00 6.33151233e-02
-7.22770214e-01 -3.52167226e-02 4.40327644e-01 1.01587200e+00
5.29828548e-01 -5.21331549e-01 8.72881651e-01 -1.45801091e+00
7.23340750e-01 -6.39600039e-01 -7.40945458e-01 5.44048131e-01
-7.02825427e-01 1.12123512e-01 1.38372421e-01 -3.99461031e-01
-1.28701997e+00 -1.17429178e-02 1.72651589e-01 -1.85381681e-01
2.15508357e-01 6.60570860e-01 1.00596145e-01 9.54239294e-02
6.92029595e-01 -7.82726258e-02 -4.15325500e-02 -7.06920266e-01
4.71792519e-01 5.40562510e-01 2.91133169e-02 -9.00396645e-01
1.46317214e-01 6.24558151e-01 5.88764809e-02 -9.00301486e-02
-1.67319727e+00 -2.95272917e-01 1.07903682e-01 1.23088874e-01
3.95538568e-01 -7.15952814e-01 -1.13436174e+00 -2.07496524e-01
-7.43128061e-01 -4.90126371e-01 -4.67376322e-01 6.95681453e-01
-1.14627135e+00 7.77031705e-02 -4.49668646e-01 -1.43636107e+00
-4.12964255e-01 -8.04537058e-01 8.07988882e-01 1.30635232e-01
-1.52823599e-02 -1.13070774e+00 8.01242515e-03 6.39941514e-01
3.42469156e-01 3.26822221e-01 1.03491020e+00 -3.52067977e-01
-5.86317301e-01 -2.24815696e-01 -1.30299762e-01 -6.62838146e-02
2.62236297e-01 -7.18401611e-01 -8.81114066e-01 -5.94814837e-01
7.33045768e-03 2.45950855e-02 7.57744551e-01 1.47397172e+00
1.41471434e+00 -9.50449705e-01 -5.83521545e-01 6.83639824e-01
1.48216248e+00 4.07528520e-01 3.08186889e-01 3.43487859e-01
1.19096890e-01 4.31086004e-01 5.25541246e-01 7.05942571e-01
2.59268451e-02 6.38518155e-01 4.61747706e-01 3.60224815e-03
4.61802840e-01 -1.08263873e-01 -6.27658665e-02 -1.62875876e-01
2.66662352e-02 -4.79214871e-03 -5.02516866e-01 8.12725902e-01
-2.44298553e+00 -1.11349988e+00 3.12496489e-03 2.57350087e+00
1.06016135e+00 -8.45985953e-03 3.65801185e-01 -3.24575961e-01
7.45449126e-01 -9.97340754e-02 -7.86792159e-01 -6.55666888e-01
4.33910564e-02 4.00129169e-01 9.95793045e-01 6.88455701e-01
-1.06920981e+00 5.86161494e-01 7.55687189e+00 1.13051808e+00
-5.32003641e-01 4.68936235e-01 1.08928788e+00 -8.02131414e-01
-6.20090187e-01 6.26769587e-02 -4.11505848e-01 5.19765913e-01
9.09550250e-01 -3.37097853e-01 8.40536654e-01 8.63811910e-01
4.11067367e-01 1.02478275e-02 -1.25894845e+00 9.37992454e-01
-7.29617536e-01 -1.62286925e+00 -2.91254133e-01 2.07421839e-01
1.35250819e+00 -1.34215146e-01 3.06026787e-01 1.58195511e-01
8.85699213e-01 -1.06770790e+00 6.87917948e-01 5.63216031e-01
8.95654917e-01 -1.12553871e+00 5.43070078e-01 4.93313491e-01
-3.19187313e-01 -6.29344702e-01 -2.34871030e-01 -1.45439908e-01
1.35745630e-01 8.21762621e-01 -4.06281590e-01 7.77548075e-01
4.82557833e-01 2.82372892e-01 4.19338554e-01 1.34702325e+00
8.40515271e-02 6.42653883e-01 -4.27051812e-01 3.23853344e-02
5.01705885e-01 -3.42486769e-01 2.59055287e-01 1.10936308e+00
3.41572076e-01 3.42099011e-01 3.05961482e-02 4.62442905e-01
2.67212894e-02 2.05914140e-01 -4.63589281e-01 3.47479314e-01
3.83004516e-01 7.88167119e-01 -3.24880362e-01 -1.55772462e-01
-2.96142906e-01 5.46548247e-01 2.64592707e-01 5.64586341e-01
-8.21769655e-01 1.71390012e-01 1.05095565e+00 -1.89147711e-01
3.47161949e-01 5.28779209e-01 -1.01887453e+00 -8.96647155e-01
6.13908768e-02 -6.91534579e-01 9.79005277e-01 -3.84504050e-01
-1.61648083e+00 -2.17301115e-01 1.51757762e-01 -8.28745604e-01
-8.98445919e-02 -4.17876661e-01 -2.69093245e-01 9.13390160e-01
-1.36305666e+00 -1.09001851e+00 4.35220540e-01 7.60136187e-01
2.41197169e-01 2.05164179e-01 6.53432369e-01 1.61253721e-01
-6.27543569e-01 9.03752148e-01 9.00194168e-01 -3.80361140e-01
2.89899141e-01 -1.21469450e+00 -4.30695474e-01 3.47980171e-01
-2.36513436e-01 6.06184542e-01 9.28049743e-01 -4.29321647e-01
-1.50706708e+00 -1.10930765e+00 3.99325728e-01 -6.11755431e-01
6.84895158e-01 3.52406763e-02 -1.96005046e-01 1.27050161e+00
-6.99535683e-02 -8.24571997e-02 7.90032983e-01 8.82994056e-01
-3.25740963e-01 -5.15379548e-01 -1.51415217e+00 6.81797385e-01
1.42297208e+00 -2.98406392e-01 -2.91569144e-01 1.00921571e+00
7.01944947e-01 -6.52688861e-01 -7.29940772e-01 4.10889208e-01
7.97470272e-01 -6.86475515e-01 9.95250881e-01 -1.33844447e+00
1.58703074e-01 3.87757003e-01 -2.07666457e-01 -1.30466533e+00
-5.45550525e-01 -1.01477158e+00 -1.70382544e-01 8.81846189e-01
5.59937596e-01 -1.03609800e+00 9.39573169e-01 1.10812140e+00
-5.77885984e-03 -7.03245282e-01 -1.46891844e+00 -8.61199796e-01
4.47143346e-01 -7.58597612e-01 8.91730547e-01 9.16849315e-01
1.86830744e-01 -1.30753726e-01 -7.11657405e-01 1.67577296e-01
7.89585173e-01 6.91093206e-01 3.79450053e-01 -8.83215785e-01
-6.70352340e-01 -6.39417529e-01 3.58138502e-01 -1.15934229e+00
-3.99398059e-02 -7.59824276e-01 -1.30146772e-01 -1.42059064e+00
8.47062230e-01 -7.54963279e-01 -6.94833696e-01 4.57152963e-01
-8.82972926e-02 -2.52976775e-01 6.01643808e-02 -1.02082342e-01
-4.91882235e-01 2.50080228e-01 1.27812481e+00 -3.35624248e-01
-1.99874312e-01 7.97190726e-01 -1.39656115e+00 2.52733916e-01
6.38693571e-01 -7.90656805e-01 -4.56286937e-01 -4.22120094e-01
3.29855353e-01 7.98722863e-01 1.68375984e-01 -1.61571398e-01
-8.55040699e-02 -9.78297889e-01 1.90772891e-01 -2.87242144e-01
3.79763165e-04 -1.00681603e+00 5.42143703e-01 5.80894291e-01
-5.82801104e-01 -4.13635641e-01 2.36762837e-01 1.03279078e+00
4.78812128e-01 -4.46579576e-01 7.06030726e-01 -2.61795610e-01
2.20736295e-01 5.46743631e-01 -1.06600128e-01 8.79706666e-02
1.05205202e+00 1.49767948e-02 -3.32984656e-01 -7.08141148e-01
-1.06650758e+00 3.93836439e-01 -2.02032384e-02 -4.99958135e-02
9.43837315e-02 -1.33429992e+00 -7.41400301e-01 -2.48500749e-01
-6.24331608e-02 -1.46165743e-01 7.19219983e-01 9.74805355e-01
-4.61165085e-02 8.73083830e-01 3.71402383e-01 -4.15793478e-01
-9.15748179e-01 9.39175248e-01 5.72582424e-01 -5.51733851e-01
-3.40660006e-01 7.16180801e-01 4.94154185e-01 -3.68475020e-01
2.41171673e-01 -2.66773999e-01 4.30461109e-01 -8.97448361e-02
5.65749586e-01 2.70911098e-01 1.25134736e-01 1.57461211e-01
-1.69814795e-01 2.67349094e-01 -3.83669348e-03 -1.91639796e-01
1.58457541e+00 -2.17772380e-01 -9.75976810e-02 2.41014473e-02
8.93424511e-01 -1.31272003e-01 -1.32879269e+00 -3.31912726e-01
2.54252136e-01 -7.28837907e-01 1.92423716e-01 -1.24005902e+00
-1.04398501e+00 2.57895350e-01 5.95544934e-01 4.19251770e-01
1.12024271e+00 -1.06615059e-01 2.53849775e-01 2.37588748e-01
6.46290123e-01 -1.03167367e+00 -6.59566283e-01 1.68965667e-01
8.55472624e-01 -1.08094943e+00 4.07911316e-02 -2.05466658e-01
-4.77690071e-01 5.44959366e-01 -4.93583530e-02 8.34126621e-02
7.66144454e-01 2.63098348e-02 -2.38361821e-01 1.30102575e-01
-8.01995039e-01 -2.47320220e-01 2.49463454e-01 4.47174549e-01
1.49969563e-01 6.68629706e-01 -7.15510786e-01 9.41120923e-01
-5.94174825e-02 2.11324498e-01 1.04534000e-01 8.34064543e-01
-2.60005623e-01 -9.71050203e-01 -5.48457205e-01 9.44162011e-01
-9.41109240e-01 -1.30031183e-01 6.21054731e-02 5.96421599e-01
-4.65644479e-01 9.47006702e-01 6.47494048e-02 3.18732202e-01
3.19639653e-01 8.44484195e-02 8.57956350e-01 -3.66259724e-01
-5.39165914e-01 2.58191049e-01 2.82334954e-01 -4.67603981e-01
-4.06775028e-01 -6.96103394e-01 -4.89232987e-01 -5.44528842e-01
-3.99075449e-01 3.47612083e-01 3.63563210e-01 1.13148355e+00
3.78867298e-01 3.34128171e-01 8.03256571e-01 -5.18713713e-01
-1.12739754e+00 -8.15380394e-01 -7.07716584e-01 3.12858820e-01
7.46003628e-01 -4.16665286e-01 -1.81758299e-01 -4.49799061e-01] | [4.535053253173828, 3.295076370239258] |
49514c1f-6332-442d-bdb3-57531b0e74c3 | llmva-gebc-large-language-model-with-video | 2306.10354 | null | https://arxiv.org/abs/2306.10354v1 | https://arxiv.org/pdf/2306.10354v1.pdf | LLMVA-GEBC: Large Language Model with Video Adapter for Generic Event Boundary Captioning | Our winning entry for the CVPR 2023 Generic Event Boundary Captioning (GEBC) competition is detailed in this paper. Unlike conventional video captioning tasks, GEBC demands that the captioning model possess an understanding of immediate changes in status around the designated video boundary, making it a difficult task. This paper proposes an effective model LLMVA-GEBC (Large Language Model with Video Adapter for Generic Event Boundary Captioning): (1) We utilize a pretrained LLM for generating human-like captions with high quality. (2) To adapt the model to the GEBC task, we take the video Q-former as an adapter and train it with the frozen visual feature extractors and LLM. Our proposed method achieved a 76.14 score on the test set and won the first place in the challenge. Our code is available at https://github.com/zjr2000/LLMVA-GEBC . | ['Feng Zheng', 'Teng Wang', 'Xiangchen Wang', 'Jinrui Zhang', 'Yunlong Tang'] | 2023-06-17 | null | null | null | null | ['video-captioning', 'boundary-captioning'] | ['computer-vision', 'computer-vision'] | [ 2.19052881e-01 2.50710130e-01 -3.10730964e-01 -2.27860257e-01
-1.33778441e+00 -5.97848892e-01 5.44819891e-01 -3.66373181e-01
-3.09843481e-01 7.81512678e-01 5.50284088e-01 -3.11774731e-01
5.90130627e-01 -1.26810521e-01 -1.29412150e+00 -2.03621790e-01
-3.66203859e-02 5.20761967e-01 1.50855571e-01 -9.08003747e-02
-1.00956462e-01 6.43042801e-03 -1.04509425e+00 8.53118658e-01
5.05395889e-01 1.10207736e+00 3.76342386e-01 1.05722034e+00
1.04200996e-01 8.66755724e-01 -5.92924118e-01 -5.88796914e-01
1.51743546e-01 -6.75102174e-01 -1.08863997e+00 -8.86865184e-02
5.08870244e-01 -2.11552680e-01 -6.58531308e-01 6.36839092e-01
5.01751781e-01 8.51636305e-02 5.35241365e-01 -1.65811169e+00
-9.40959454e-01 6.61770821e-01 -5.09325683e-01 5.30602932e-01
6.99433744e-01 2.75048524e-01 1.08051193e+00 -1.00738144e+00
9.00379717e-01 9.82795298e-01 3.97871882e-01 1.08458614e+00
-6.95796669e-01 -4.89159524e-01 3.55069578e-01 6.41651928e-01
-1.44880748e+00 -4.73015100e-01 5.01000226e-01 -4.87405986e-01
9.35471416e-01 4.29062337e-01 5.04811347e-01 1.75646818e+00
-1.22507460e-01 1.15913916e+00 3.89256358e-01 -2.21495658e-01
-8.06532428e-02 -1.02906145e-01 -9.91130844e-02 3.35278183e-01
-6.59675598e-02 3.22348103e-02 -5.55004776e-01 2.23470837e-01
6.26340449e-01 -6.27591491e-01 -7.18016505e-01 3.57613526e-02
-1.42287779e+00 5.76425195e-01 3.42706501e-01 1.23499297e-01
-3.77623081e-01 5.35520196e-01 5.01901507e-01 9.26823243e-02
3.66979271e-01 4.28823411e-01 -2.56270558e-01 -5.42469978e-01
-9.26793158e-01 1.62511170e-01 5.95205545e-01 1.18337786e+00
2.81360328e-01 -1.87219977e-01 -9.42379773e-01 7.45414555e-01
3.20267677e-01 5.50363541e-01 3.96182805e-01 -1.03062952e+00
9.23348904e-01 -1.59355313e-01 4.15417403e-01 -8.03703904e-01
-9.55338329e-02 -9.41239148e-02 -5.13843834e-01 -5.16432345e-01
1.02294140e-01 -4.14886087e-01 -1.12654305e+00 1.89038980e+00
-6.17533214e-02 7.83415198e-01 2.06060827e-01 1.05008423e+00
1.27921808e+00 1.40757978e+00 5.74149251e-01 -4.47175652e-02
1.41185188e+00 -1.43073547e+00 -7.93223560e-01 -5.18899560e-01
4.47910160e-01 -6.30661786e-01 9.87071872e-01 2.11712951e-03
-1.10607302e+00 -6.37210250e-01 -9.79727149e-01 -2.15900481e-01
6.48041517e-02 1.50663525e-01 4.16139811e-01 2.29023293e-01
-1.48479223e+00 1.31639525e-01 -5.23372650e-01 -3.28092098e-01
2.77545154e-01 3.39881219e-02 -3.97538960e-01 -1.45995677e-01
-1.64410675e+00 6.59432709e-01 4.81013387e-01 3.03401589e-01
-1.29316878e+00 -6.84975803e-01 -1.12924516e+00 1.20119929e-01
3.06701034e-01 -6.87384605e-01 1.81284797e+00 -1.27060115e+00
-1.23049641e+00 9.31328535e-01 -3.93321395e-01 -6.19146585e-01
6.95786119e-01 -4.62274790e-01 -4.79620576e-01 6.06892169e-01
1.72456264e-01 1.31612659e+00 7.49090374e-01 -1.21276534e+00
-4.65788186e-01 4.88725126e-01 1.54281408e-01 4.88546491e-01
2.70929724e-01 3.05830747e-01 -9.75080609e-01 -4.99316186e-01
-4.87941384e-01 -7.08062232e-01 3.71976309e-02 -3.52145821e-01
-3.11127424e-01 -1.78531200e-01 8.04186404e-01 -1.16257656e+00
1.14341640e+00 -2.31166339e+00 3.48671712e-02 -3.67957652e-01
-1.38091236e-01 4.16831613e-01 -5.46695590e-01 3.37910712e-01
-4.07583952e-01 4.23291475e-01 -1.08338900e-01 -6.68768942e-01
-1.08758919e-01 -6.45376891e-02 -5.65205574e-01 6.42784461e-02
6.06495142e-01 1.38957870e+00 -1.11188293e+00 -6.05721951e-01
1.00605935e-02 5.60494483e-01 -4.09395874e-01 4.45181966e-01
-5.11759460e-01 4.31979924e-01 -2.57267475e-01 4.21779931e-01
5.24560452e-01 -5.44587731e-01 -1.01257758e-02 -1.61416024e-01
2.25228384e-01 6.82368204e-02 -6.66072845e-01 1.73744488e+00
-2.45631382e-01 9.89386022e-01 -1.78794593e-01 -6.30483806e-01
6.36923194e-01 1.03864980e+00 3.89365792e-01 -6.80282652e-01
-6.77848458e-02 -1.31931715e-02 -5.53389609e-01 -5.91400623e-01
7.17151582e-01 6.62066862e-02 -3.63555759e-01 1.06947616e-01
1.45594001e-01 2.07978576e-01 2.09647790e-01 4.92564440e-01
1.03749716e+00 4.22557354e-01 1.64761260e-01 2.77024627e-01
5.80738306e-01 5.87172396e-02 6.71677172e-01 6.69345260e-01
-5.80072701e-01 1.35533655e+00 5.82295835e-01 -2.94604152e-01
-9.78981197e-01 -1.02223802e+00 4.16189879e-01 7.67870009e-01
2.66962707e-01 -4.35373724e-01 -7.72204995e-01 -7.45807648e-01
-5.74976861e-01 7.77617931e-01 -5.65186799e-01 -1.58312857e-01
-6.61780655e-01 -4.03726459e-01 6.02292836e-01 5.55148721e-01
5.86242676e-01 -1.49429429e+00 -3.14628094e-01 2.69171476e-01
-1.14836633e+00 -1.58608651e+00 -9.75985885e-01 -4.63350356e-01
-3.31983447e-01 -8.16904187e-01 -1.09183156e+00 -9.57890928e-01
3.07904512e-01 1.75618812e-01 1.21571517e+00 -7.00307414e-02
5.61394319e-02 5.81490338e-01 -7.38975644e-01 -2.05338612e-01
-4.54682559e-01 1.06689490e-01 -4.99704748e-01 1.50251821e-01
2.64230967e-01 -7.20629543e-02 -5.58840036e-01 2.15611398e-01
-6.40362442e-01 6.17567241e-01 3.76697063e-01 5.29336989e-01
6.33357704e-01 -8.50204229e-01 8.24269712e-01 -4.86275792e-01
5.12795210e-01 -6.60225272e-01 -3.85436743e-01 4.41850126e-01
8.56727064e-02 -3.64146173e-01 2.56704330e-01 -4.15836126e-01
-1.13990808e+00 -3.94199714e-02 -3.62707257e-01 -5.77302396e-01
-2.00134553e-02 4.56888944e-01 -3.44460636e-01 4.20199066e-01
3.77372026e-01 1.87502027e-01 -4.63961571e-01 -2.70948797e-01
3.74571979e-01 8.11380386e-01 8.95446479e-01 -5.67637324e-01
4.96443599e-01 2.09990770e-01 -5.41519105e-01 -5.29683173e-01
-9.79355991e-01 -5.86305082e-01 -1.57166779e-01 -5.82319915e-01
1.34186745e+00 -1.42716360e+00 -3.71462882e-01 4.17901337e-01
-1.56475997e+00 -7.21299887e-01 -2.24071145e-01 3.65972459e-01
-9.59355950e-01 3.48081052e-01 -7.02380598e-01 -4.28297788e-01
-4.84523147e-01 -1.01482356e+00 1.11595595e+00 5.72502494e-01
-2.04462290e-01 -7.34480977e-01 2.08690509e-01 6.65675163e-01
2.17534468e-01 3.52078170e-01 2.37905875e-01 -6.13132417e-01
-7.12205112e-01 -2.16147024e-02 -3.88431758e-01 2.57395327e-01
-1.76346213e-01 -1.87597685e-02 -9.29078698e-01 -1.65625796e-01
-3.67495537e-01 -4.22327548e-01 6.93513811e-01 4.65514451e-01
1.08151555e+00 -1.08131349e-01 -3.65812778e-01 5.42731106e-01
1.27429342e+00 3.62851888e-01 1.04936743e+00 3.32950532e-01
6.50133908e-01 2.03035906e-01 6.58938289e-01 2.18842670e-01
6.38623416e-01 8.20509732e-01 3.82915944e-01 -4.90473136e-02
-3.75473946e-01 -6.01852357e-01 8.42069864e-01 6.87642336e-01
1.09563395e-01 -8.22118163e-01 -9.42930162e-01 9.84519601e-01
-2.11501288e+00 -1.06760681e+00 -8.18830132e-02 1.71726489e+00
7.40896940e-01 -3.21997143e-02 -1.38074994e-01 -5.32387853e-01
1.16443646e+00 3.90146524e-01 -2.15571240e-01 -5.81661403e-01
-1.51888222e-01 -1.09613612e-01 2.10368127e-01 6.12568736e-01
-1.30183136e+00 1.14955544e+00 5.53799629e+00 6.94538295e-01
-9.85580742e-01 4.86333758e-01 9.34923112e-01 -2.23741331e-03
-1.30900458e-01 -9.34671313e-02 -8.48444402e-01 6.36877954e-01
1.58598387e+00 -2.71898687e-01 1.35777891e-01 5.15201986e-01
3.99245769e-01 1.16577394e-01 -1.08439887e+00 1.16206431e+00
3.91436011e-01 -1.35574460e+00 2.82566369e-01 -3.51748139e-01
7.41683781e-01 2.29170531e-01 -6.26383945e-02 7.04503596e-01
-5.87166063e-02 -1.14451313e+00 9.69732702e-01 5.86260259e-01
1.02901304e+00 -3.50629181e-01 7.97889292e-01 -1.04708597e-01
-1.20943236e+00 2.19635427e-01 -1.83443904e-01 1.26274869e-01
1.06094646e+00 7.44617656e-02 -8.78946424e-01 8.26435506e-01
6.27825201e-01 7.55862117e-01 -5.44501066e-01 1.54462349e+00
-4.89726424e-01 8.21890533e-01 -1.31366953e-01 3.08107764e-01
4.10324723e-01 2.00681254e-01 6.40297592e-01 1.29037797e+00
4.27525043e-01 3.29552405e-02 -1.99807137e-02 5.04485548e-01
-4.56382900e-01 -3.58214695e-03 -3.17290038e-01 -2.73783207e-01
2.68667400e-01 1.07362747e+00 -5.26146889e-01 -3.66937906e-01
-4.97178108e-01 1.32245612e+00 2.02190533e-01 6.70957685e-01
-1.39340961e+00 -6.97861686e-02 5.39564669e-01 -4.67489697e-02
5.29294133e-01 3.25610563e-02 3.02349091e-01 -1.40495789e+00
1.08185112e-02 -8.03274155e-01 5.03819585e-01 -1.49266970e+00
-1.00230134e+00 9.48416770e-01 2.34387055e-01 -1.07864153e+00
-2.62666374e-01 -4.40967977e-01 -5.86487174e-01 7.49623835e-01
-1.58240724e+00 -1.12118590e+00 -3.96233708e-01 4.81931359e-01
8.16275120e-01 2.11278558e-01 4.45784360e-01 5.53314507e-01
-5.50022542e-01 6.65892661e-01 -3.36911887e-01 3.86434764e-01
9.10578907e-01 -8.41119885e-01 8.68219137e-01 1.06670856e+00
2.93054014e-01 -2.14469999e-01 7.66801834e-01 -7.65929401e-01
-6.85791910e-01 -1.46990287e+00 1.35176253e+00 -6.02444232e-01
4.39126223e-01 -4.44288939e-01 -9.33619142e-01 1.15112948e+00
5.32365441e-01 2.43944861e-02 4.85947639e-01 -4.75135267e-01
-2.54356802e-01 2.05802739e-01 -7.73194611e-01 6.63510144e-01
1.07520235e+00 -5.10145187e-01 -7.33480334e-01 5.33188760e-01
1.21970558e+00 -6.10651672e-01 -5.39429665e-01 2.48783231e-01
1.07420102e-01 -2.79348969e-01 8.54157090e-01 -7.81784832e-01
6.18743718e-01 -4.08078820e-01 -1.49153978e-01 -9.95222688e-01
-1.54050872e-01 -1.00286222e+00 -2.46610120e-01 1.31208682e+00
7.25312114e-01 -1.16640091e-01 6.07332647e-01 5.62265217e-01
-3.35820645e-01 -3.98264050e-01 -1.06799734e+00 -7.56863415e-01
-1.72214717e-01 -5.03685892e-01 3.79018962e-01 5.94588876e-01
-1.49209008e-01 3.86667043e-01 -7.38962173e-01 1.90235332e-01
1.34878784e-01 -2.12629035e-01 5.14361382e-01 -7.28319347e-01
-2.66148299e-01 -5.31556644e-02 -2.33857229e-01 -1.30872989e+00
1.81463391e-01 -9.21115637e-01 3.87408257e-01 -1.86350560e+00
3.58900219e-01 -8.96966830e-02 -2.25332022e-01 6.04972303e-01
-3.00348669e-01 4.30732608e-01 6.25382841e-01 1.19334206e-01
-1.15387344e+00 6.65112317e-01 1.22114980e+00 -1.51031837e-01
1.49568934e-02 -1.73625335e-01 -7.37315595e-01 1.64148420e-01
9.39054012e-01 -4.58813041e-01 -3.85323375e-01 -5.71576118e-01
2.53452539e-01 4.60267961e-01 4.69365180e-01 -1.01329207e+00
-1.59777831e-02 -9.42730606e-02 1.20045975e-01 -3.70267719e-01
5.71940660e-01 -4.51368362e-01 4.06514198e-01 1.14480503e-01
-2.44022250e-01 2.50797689e-01 5.92047155e-01 5.23429513e-01
-4.60558891e-01 -3.72472107e-01 3.47148418e-01 -1.03380866e-01
-1.16912913e+00 4.67031211e-01 -4.29237783e-01 4.61524576e-01
1.21911395e+00 -5.83094358e-02 -5.60406923e-01 -9.38223958e-01
-8.65409195e-01 6.06231034e-01 3.19640845e-01 8.91069829e-01
6.19196236e-01 -1.38399684e+00 -1.03880322e+00 -3.48779827e-01
2.32534453e-01 -1.07516035e-01 6.44327521e-01 6.16836727e-01
-7.16773331e-01 6.16207540e-01 -2.48203591e-01 -4.91264254e-01
-1.02868772e+00 5.84081650e-01 3.99385303e-01 -3.71610492e-01
-6.32237494e-01 1.14217055e+00 4.30081278e-01 5.55984639e-02
7.06693679e-02 8.95435587e-02 -3.68903458e-01 3.80737036e-02
7.00645149e-01 -3.09102207e-01 -2.21573755e-01 -1.00736690e+00
-3.32225114e-01 1.87395543e-01 -1.82676658e-01 -3.77575248e-01
1.07065213e+00 -3.63589615e-01 4.09183264e-01 1.85330153e-01
1.26249194e+00 -4.56043810e-01 -1.62574434e+00 1.32444948e-01
9.10396501e-02 -8.03940296e-02 -2.56828964e-01 -9.83940244e-01
-8.66815150e-01 7.89920390e-01 3.80010933e-01 -3.56213063e-01
1.00722873e+00 2.47344896e-01 9.99577224e-01 -9.05658603e-02
2.10286006e-01 -7.69586325e-01 4.86373343e-02 5.66736042e-01
1.36017156e+00 -1.15674150e+00 -5.67944527e-01 -3.09268624e-01
-1.14960933e+00 6.71047270e-01 7.91736484e-01 1.14683256e-01
2.52429247e-01 -2.38825589e-01 2.94424653e-01 -3.55558190e-03
-1.06266224e+00 2.26258636e-02 4.40630823e-01 6.26801968e-01
9.97825116e-02 9.97123420e-02 -3.58810157e-01 7.85703123e-01
7.96569437e-02 2.35300839e-01 8.83490920e-01 6.56801462e-01
-9.82029513e-02 -9.44339633e-01 -1.15217336e-01 -3.32552083e-02
-5.82458436e-01 -2.86737561e-01 -2.51410455e-01 7.43559897e-01
-1.50916025e-01 1.03506887e+00 2.02027783e-01 -1.71281114e-01
2.68184900e-01 3.00278872e-01 1.68272272e-01 -7.16882765e-01
-4.96535689e-01 -2.32925147e-01 3.69859844e-01 -8.11694980e-01
-3.92734170e-01 -5.89832306e-01 -1.11289108e+00 1.77453965e-01
2.36539155e-01 5.64883828e-01 4.42030370e-01 5.95931411e-01
6.32364452e-01 5.18257499e-01 1.48323491e-01 -8.02262068e-01
-7.17246383e-02 -9.83709216e-01 -4.99780439e-02 5.63187122e-01
2.83305079e-01 -4.95683014e-01 -2.75903523e-01 4.82798427e-01] | [10.525189399719238, 0.6823262572288513] |
0e9d5949-54cc-4915-b2d3-7d70b12f45a5 | occluded-video-instance-segmentation | 2102.01558 | null | https://arxiv.org/abs/2102.01558v6 | https://arxiv.org/pdf/2102.01558v6.pdf | Occluded Video Instance Segmentation: A Benchmark | Can our video understanding systems perceive objects when a heavy occlusion exists in a scene? To answer this question, we collect a large-scale dataset called OVIS for occluded video instance segmentation, that is, to simultaneously detect, segment, and track instances in occluded scenes. OVIS consists of 296k high-quality instance masks from 25 semantic categories, where object occlusions usually occur. While our human vision systems can understand those occluded instances by contextual reasoning and association, our experiments suggest that current video understanding systems cannot. On the OVIS dataset, the highest AP achieved by state-of-the-art algorithms is only 16.3, which reveals that we are still at a nascent stage for understanding objects, instances, and videos in a real-world scenario. We also present a simple plug-and-play module that performs temporal feature calibration to complement missing object cues caused by occlusion. Built upon MaskTrack R-CNN and SipMask, we obtain a remarkable AP improvement on the OVIS dataset. The OVIS dataset and project code are available at http://songbai.site/ovis . | ['Serge Belongie', 'Song Bai', 'Alan Yuille', 'Philip H. S. Torr', 'Xiang Bai', 'Xinggang Wang', 'Yao Hu', 'Xiaoyu Liu', 'Yan Gao', 'Jiyang Qi'] | 2021-02-02 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 2.82019824e-01 5.82904443e-02 -5.54059744e-01 -4.26783592e-01
-6.76802754e-01 -6.79420829e-01 2.60969043e-01 -3.07513237e-01
-5.18811829e-02 3.42976928e-01 4.34861472e-03 5.50157353e-02
1.41334608e-01 -4.37488556e-01 -1.14152014e+00 -3.42537671e-01
1.04699887e-01 4.75986511e-01 6.62865341e-01 1.78031579e-01
1.37953356e-01 2.41505340e-01 -1.78685260e+00 6.41350389e-01
4.75121140e-01 1.14359093e+00 2.28895545e-01 8.17386746e-01
-1.15418583e-02 8.25196505e-01 -6.37522459e-01 -1.68860480e-01
4.37623113e-01 -9.46773663e-02 -9.30245876e-01 4.03465837e-01
1.02369773e+00 -5.97985625e-01 -9.50138330e-01 6.43663228e-01
-4.33681384e-02 -1.06082924e-01 3.40100855e-01 -1.47126675e+00
-5.85086524e-01 5.99425972e-01 -5.94737589e-01 7.28490114e-01
3.13457578e-01 4.82752085e-01 9.76788163e-01 -9.56978917e-01
8.28755498e-01 1.01859403e+00 3.97728533e-01 4.98219490e-01
-9.47777331e-01 -7.10560739e-01 5.99095643e-01 5.18875539e-01
-1.32932460e+00 -6.42360270e-01 3.94000202e-01 -5.64038277e-01
9.47797656e-01 2.22483963e-01 8.95393193e-01 1.18991399e+00
-3.00236791e-01 1.39533687e+00 5.26760042e-01 1.24852441e-01
5.24457507e-02 -1.24214895e-01 4.42039341e-01 4.47124213e-01
2.37287492e-01 9.43932086e-02 -7.36411572e-01 3.91347647e-01
7.92096972e-01 2.36002252e-01 -3.67192596e-01 -1.42284185e-01
-1.39838672e+00 4.28230971e-01 4.98535961e-01 9.32274014e-02
-1.96145251e-01 3.87979895e-01 2.65345216e-01 1.41916305e-01
4.19575661e-01 3.73395592e-01 -7.50882864e-01 -3.14693958e-01
-1.07096970e+00 2.60480911e-01 7.02081323e-01 1.39315653e+00
6.45273149e-01 -1.30296245e-01 -2.91632921e-01 5.74717999e-01
1.62988812e-01 4.14617777e-01 -1.32374018e-01 -1.48153496e+00
4.18200016e-01 5.03000498e-01 -1.32236863e-03 -5.96772850e-01
-1.76890910e-01 -3.15350622e-01 -2.35376030e-01 -2.42784262e-01
5.25073767e-01 9.51043069e-02 -1.21784341e+00 1.45769453e+00
3.47720146e-01 8.25552881e-01 -1.75340742e-01 1.11593962e+00
1.19638360e+00 7.28455722e-01 4.50032502e-02 -8.75840113e-02
1.44115996e+00 -1.24791574e+00 -6.17223978e-01 -5.13839543e-01
2.92029977e-01 -5.21632850e-01 9.04426396e-01 6.09163523e-01
-1.04021323e+00 -7.20348239e-01 -1.03140068e+00 -2.39192024e-01
-2.23058343e-01 9.64845419e-02 9.46477056e-01 2.76019871e-01
-7.83221960e-01 2.58724779e-01 -8.22921097e-01 -3.40687245e-01
8.71916533e-01 9.47288051e-02 -4.14571345e-01 -3.42038661e-01
-8.86007488e-01 3.87218624e-01 4.45195496e-01 2.05549464e-01
-1.49314737e+00 -9.33849216e-01 -7.86986768e-01 6.83161244e-02
1.04159117e+00 -3.29701215e-01 1.17389262e+00 -1.08126843e+00
-9.24911380e-01 9.25331175e-01 -3.72382522e-01 -5.52339733e-01
4.31308806e-01 -6.46020651e-01 -4.74735588e-01 5.49301803e-01
1.87384829e-01 1.07705116e+00 9.59884167e-01 -1.28980982e+00
-7.43466139e-01 -3.14375013e-01 3.19566429e-01 3.17071863e-02
1.14953466e-01 3.74096036e-02 -1.32380164e+00 -3.50994021e-01
4.28151280e-01 -1.02418864e+00 5.14725875e-03 1.69443786e-01
-3.47890735e-01 -2.01308146e-01 1.12907135e+00 -6.96892798e-01
9.48870361e-01 -2.31819129e+00 -4.40761484e-02 -2.46640697e-01
4.32303846e-01 4.24523234e-01 -3.32909197e-01 1.98821295e-02
-4.86700945e-02 1.09343946e-01 -1.85024124e-02 -2.08534896e-01
-9.31794718e-02 4.29489940e-01 -5.56816459e-01 3.68237793e-01
4.06148672e-01 1.09665942e+00 -7.68433154e-01 -4.03620601e-01
3.06709617e-01 3.01353455e-01 -7.69444823e-01 2.50373095e-01
-7.01545119e-01 5.32940269e-01 -3.26793224e-01 1.11889744e+00
7.28371978e-01 -4.81961280e-01 -5.98406233e-02 -2.11018711e-01
7.19839260e-02 1.97059706e-01 -1.18951797e+00 1.92387688e+00
1.08072922e-01 1.28197491e+00 -6.91548735e-02 -1.01057196e+00
4.72725600e-01 2.62168288e-01 5.64096808e-01 -6.56898379e-01
-8.09655711e-03 1.53368404e-02 -1.87287748e-01 -7.07058072e-01
6.13278389e-01 6.31605744e-01 1.73269629e-01 7.96144642e-03
1.61843106e-01 -6.96057221e-03 5.55571795e-01 4.71945763e-01
1.24242938e+00 3.49501997e-01 -1.98340893e-01 -2.26821098e-02
1.39989018e-01 1.31889552e-01 8.68017793e-01 1.01127398e+00
-4.74552602e-01 9.86393213e-01 6.93656862e-01 -7.03557730e-01
-1.00511312e+00 -1.16308331e+00 -3.11631531e-01 1.12500703e+00
6.23697579e-01 -5.10616541e-01 -8.04387331e-01 -6.50639892e-01
-2.18112413e-02 3.59715432e-01 -5.15329361e-01 1.63474143e-01
-4.72723484e-01 -3.95430833e-01 2.98101097e-01 8.32403719e-01
6.21842027e-01 -1.13994384e+00 -5.25762081e-01 5.04731052e-02
-5.12365460e-01 -1.69714761e+00 -4.62782532e-01 -5.34518249e-02
-5.90250611e-01 -1.46562564e+00 -3.59754592e-01 -5.98092139e-01
4.63097900e-01 7.57345974e-01 1.46400046e+00 2.44660988e-01
-6.50777876e-01 5.49584031e-01 -4.74429727e-01 -3.20777148e-01
1.45157240e-02 3.68777961e-02 -1.33050770e-01 -1.98718667e-01
6.33882821e-01 -3.49279344e-01 -8.01284969e-01 7.70950675e-01
-8.30068648e-01 2.12571308e-01 3.56410623e-01 3.84016275e-01
8.06471407e-01 -1.22786149e-01 3.95630449e-01 -7.43689895e-01
-4.15724963e-01 -5.54387867e-01 -6.99904859e-01 2.25194573e-01
-2.63041146e-02 -4.09252703e-01 -8.88726339e-02 -5.33773363e-01
-9.19108987e-01 9.86059606e-02 -2.49689873e-02 -8.05116653e-01
-4.29186672e-01 4.04620403e-03 -2.38720521e-01 2.90017784e-01
4.77057546e-01 2.39002220e-02 -4.58149254e-01 -3.19215864e-01
4.65047508e-01 6.37571216e-01 8.82204652e-01 -5.48730910e-01
5.25986314e-01 6.52829170e-01 -6.27685964e-01 -7.64234722e-01
-1.41218233e+00 -7.88897574e-01 -5.53589165e-01 -3.79824668e-01
1.10754275e+00 -1.42950714e+00 -8.66167068e-01 2.48641044e-01
-1.14017880e+00 -5.27939439e-01 -4.55786496e-01 3.01739246e-01
-5.88715494e-01 9.12648141e-02 -6.21005595e-01 -4.87558842e-01
4.96336073e-02 -1.20495796e+00 1.36245644e+00 3.90585870e-01
-1.01149350e-01 -2.14190573e-01 -4.03106898e-01 1.05491221e+00
6.97856322e-02 -4.35766131e-02 1.99777693e-01 -4.80629921e-01
-1.50470591e+00 -4.67626490e-02 -6.55519903e-01 2.22163081e-01
-1.92476660e-01 3.37012649e-01 -1.18569088e+00 -1.58530280e-01
-2.56463200e-01 -2.98989028e-01 1.10672581e+00 5.80718696e-01
1.60594761e+00 -2.53755860e-02 -3.77564967e-01 9.22523558e-01
1.11017191e+00 2.63933718e-01 8.90837669e-01 8.03753957e-02
8.84260774e-01 3.97710234e-01 8.77003849e-01 3.22281301e-01
3.17973435e-01 6.86210990e-01 5.39479613e-01 -1.00349031e-01
-3.49375010e-01 -2.31441766e-01 1.76324427e-01 5.35644829e-01
-4.34412062e-02 -4.16892022e-01 -8.60375345e-01 7.15580642e-01
-2.03733420e+00 -1.06058455e+00 -2.01942816e-01 1.79358637e+00
4.55857545e-01 3.98246974e-01 -5.36744222e-02 -1.91475824e-01
6.45464361e-01 3.88553709e-01 -8.84122014e-01 2.32275844e-01
-1.31477326e-01 -1.17921121e-01 5.43777287e-01 2.53134251e-01
-1.34635103e+00 1.27844834e+00 6.57310915e+00 6.87623620e-01
-8.88280571e-01 7.79798701e-02 7.33624339e-01 -3.58858019e-01
-5.99649502e-03 6.78122044e-02 -9.55795705e-01 4.85117286e-01
6.72149360e-01 1.90364510e-01 5.02138197e-01 9.73382413e-01
4.37281802e-02 -3.00594985e-01 -1.29022706e+00 1.10342598e+00
1.99105218e-01 -1.50931644e+00 -2.27959052e-01 2.96273399e-02
8.51531565e-01 3.99629712e-01 4.00925912e-02 4.26111549e-01
2.64138710e-02 -9.61384177e-01 7.77593136e-01 4.83823746e-01
7.75181472e-01 -2.27989152e-01 5.97230077e-01 5.90399839e-02
-1.22145188e+00 -1.13473544e-02 -3.53710413e-01 -3.19075026e-02
1.60144731e-01 4.34551686e-01 -5.11314929e-01 2.15722278e-01
1.00526142e+00 1.14555180e+00 -6.26394987e-01 1.14600992e+00
-2.74844974e-01 8.19046378e-01 -4.09577847e-01 5.28830290e-01
1.09612912e-01 -5.53984987e-03 4.67407763e-01 1.05112481e+00
-4.61205766e-02 3.01142454e-01 2.75604665e-01 1.01528132e+00
-3.06312352e-01 -4.62443382e-01 -4.28283364e-01 -1.29504994e-01
3.80010784e-01 1.18469214e+00 -9.67957377e-01 -7.07699120e-01
-4.49522942e-01 9.47638392e-01 3.56727801e-02 5.49685657e-01
-1.34795713e+00 1.89491734e-01 9.52558994e-01 5.55588268e-02
7.01097071e-01 -3.70715082e-01 -2.08124444e-01 -1.54411173e+00
2.27121741e-01 -8.77573311e-01 2.92276174e-01 -1.05950892e+00
-8.79526556e-01 3.40332448e-01 -2.22912827e-03 -1.14228034e+00
8.83868784e-02 -6.23591006e-01 -3.62882823e-01 1.28235547e-02
-1.41552818e+00 -9.94868159e-01 -9.13402319e-01 5.66931665e-01
1.23775220e+00 7.91644603e-02 3.02370608e-01 5.36215007e-01
-7.79306650e-01 2.77563334e-01 -2.31658697e-01 2.86574900e-01
5.57745337e-01 -9.09353733e-01 5.32696843e-01 9.03490722e-01
8.36652100e-01 2.41947219e-01 7.29575038e-01 -5.32179594e-01
-1.47693110e+00 -1.05662835e+00 3.23383361e-01 -8.65788579e-01
4.75083083e-01 -4.88277733e-01 -1.07538974e+00 1.04891729e+00
-4.27056551e-02 5.23110867e-01 4.19194102e-01 1.41742498e-01
-5.61863780e-01 -1.22221010e-02 -7.88033843e-01 5.62930107e-01
1.68835235e+00 -5.66635549e-01 -5.77108800e-01 6.20364189e-01
1.17424691e+00 -7.13410258e-01 -7.10623860e-01 6.54424191e-01
5.80618799e-01 -9.52305913e-01 1.13723922e+00 -7.31131077e-01
5.56088209e-01 -5.42936981e-01 -2.71035701e-01 -6.01646423e-01
-4.84758802e-02 -4.29126501e-01 -4.04635072e-01 1.09276867e+00
3.40778142e-01 -2.28056371e-01 1.12014198e+00 8.01250339e-01
-2.28207350e-01 -5.06947160e-01 -7.52670169e-01 -6.65256679e-01
-5.06173551e-01 -9.27430928e-01 4.65999842e-01 6.65603638e-01
-3.02371740e-01 8.39476660e-02 -3.07438374e-01 4.28947240e-01
5.22510469e-01 2.11665198e-01 1.03388405e+00 -9.60073829e-01
-2.58232117e-01 -8.32695141e-02 -5.26878655e-01 -1.46616983e+00
1.99920326e-01 -4.60782915e-01 3.69634591e-02 -1.49025655e+00
3.03265572e-01 -3.79409313e-01 -2.32108265e-01 5.70107996e-01
-1.62359223e-01 6.33211136e-01 4.34992492e-01 4.63931710e-01
-1.33487427e+00 4.47121084e-01 1.13050091e+00 -5.08232772e-01
-1.20673202e-01 -1.90167010e-01 -5.80944896e-01 9.12681639e-01
6.57852650e-01 -3.16680908e-01 -3.53264213e-01 -6.72270715e-01
-1.61002830e-01 1.56988725e-01 5.18071890e-01 -1.21370459e+00
1.73436269e-01 -9.78416204e-02 4.17020679e-01 -9.72974837e-01
5.78747511e-01 -8.08982909e-01 2.79570222e-01 9.53095704e-02
-2.39496052e-01 -3.91157180e-01 3.25659245e-01 6.49560332e-01
-2.91147411e-01 -1.65415555e-02 4.41201925e-01 -2.24292111e-02
-1.40603721e+00 6.60752296e-01 -2.10756302e-01 2.88628817e-01
1.12965286e+00 -3.32996279e-01 -7.52068579e-01 -1.89944148e-01
-8.80482793e-01 5.18264711e-01 3.79421324e-01 7.51151204e-01
6.34528160e-01 -8.33053708e-01 -6.24725878e-01 2.48030782e-01
4.03053701e-01 3.18606079e-01 5.82653522e-01 6.58421874e-01
-6.27745032e-01 3.34415197e-01 1.51813021e-02 -1.08513606e+00
-1.27663517e+00 6.43629849e-01 1.92768127e-01 2.48023018e-01
-8.05218935e-01 9.86688018e-01 5.65331578e-01 3.12999785e-02
3.98278028e-01 -4.42791551e-01 4.92287427e-02 -6.93183914e-02
6.72186792e-01 1.77916810e-01 -1.89576924e-01 -4.07945782e-01
-2.54842877e-01 4.64415967e-01 -1.69657946e-01 1.81291983e-01
1.21286917e+00 -1.56014591e-01 2.13540196e-01 3.29088807e-01
9.40563321e-01 -3.54691774e-01 -1.93285406e+00 -2.92921096e-01
-1.59884095e-01 -7.45391726e-01 -2.23090634e-01 -5.93368709e-01
-1.26970136e+00 7.90022492e-01 4.88562942e-01 1.31509304e-01
1.03756380e+00 5.62485099e-01 8.10981691e-01 3.92950207e-01
2.98274338e-01 -1.13423789e+00 1.92706764e-01 4.98213887e-01
8.11865747e-01 -1.57789230e+00 -2.00927611e-02 -8.23008060e-01
-7.82045782e-01 7.69665182e-01 9.64258909e-01 -3.22797522e-02
4.33645368e-01 1.27370700e-01 4.89388825e-03 -4.12262559e-01
-9.68965650e-01 -3.25122476e-01 2.06660360e-01 4.48403358e-01
5.42387068e-02 7.78002217e-02 2.66350150e-01 3.39271009e-01
-1.08134905e-02 9.61629972e-02 4.92840379e-01 5.21688223e-01
-3.95635635e-01 -4.24736708e-01 -1.90007180e-01 4.55051929e-01
-4.17751491e-01 2.87656803e-02 -2.66049523e-02 8.40433598e-01
2.70738959e-01 9.67322886e-01 4.07030970e-01 -1.53236002e-01
2.98562706e-01 -1.21104948e-01 3.39232326e-01 -5.63308775e-01
-2.15647221e-01 -7.39645399e-03 4.68113311e-02 -1.12210369e+00
-5.45018435e-01 -6.47776186e-01 -1.14093196e+00 -7.00036734e-02
-3.85781348e-01 -1.60769790e-01 6.18111789e-01 9.06502783e-01
3.66160989e-01 8.00590873e-01 2.33147427e-01 -9.98181224e-01
3.03782493e-01 -7.48984158e-01 -3.89508724e-01 4.87694114e-01
2.90141851e-01 -6.67813599e-01 -4.10538316e-01 3.37722033e-01] | [9.206570625305176, 0.08136147260665894] |
64d61393-401a-42d4-813a-7ea8f1832da3 | ginex-ssd-enabled-billion-scale-graph-neural | 2208.09151 | null | https://arxiv.org/abs/2208.09151v1 | https://arxiv.org/pdf/2208.09151v1.pdf | Ginex: SSD-enabled Billion-scale Graph Neural Network Training on a Single Machine via Provably Optimal In-memory Caching | Recently, Graph Neural Networks (GNNs) have been receiving a spotlight as a powerful tool that can effectively serve various inference tasks on graph structured data. As the size of real-world graphs continues to scale, the GNN training system faces a scalability challenge. Distributed training is a popular approach to address this challenge by scaling out CPU nodes. However, not much attention has been paid to disk-based GNN training, which can scale up the single-node system in a more cost-effective manner by leveraging high-performance storage devices like NVMe SSDs. We observe that the data movement between the main memory and the disk is the primary bottleneck in the SSD-based training system, and that the conventional GNN training pipeline is sub-optimal without taking this overhead into account. Thus, we propose Ginex, the first SSD-based GNN training system that can process billion-scale graph datasets on a single machine. Inspired by the inspector-executor execution model in compiler optimization, Ginex restructures the GNN training pipeline by separating sample and gather stages. This separation enables Ginex to realize a provably optimal replacement algorithm, known as Belady's algorithm, for caching feature vectors in memory, which account for the dominant portion of I/O accesses. According to our evaluation with four billion-scale graph datasets, Ginex achieves 2.11x higher training throughput on average (up to 2.67x at maximum) than the SSD-extended PyTorch Geometric. | ['Jae W. Lee', 'Sunhong Min', 'Yeonhong Park'] | 2022-08-19 | null | null | null | null | ['compiler-optimization'] | ['computer-code'] | [-2.83779263e-01 -4.52836156e-02 -5.98976314e-01 -1.61641002e-01
-2.24296615e-01 -3.13469678e-01 4.01493013e-01 3.65871191e-01
-4.96875733e-01 2.38934413e-01 -8.97596627e-02 -1.13977921e+00
-5.68371499e-03 -1.38285398e+00 -9.14893031e-01 -3.92890692e-01
-9.46415514e-02 6.53896570e-01 5.02334416e-01 -1.21354222e-01
9.72915068e-02 5.61053038e-01 -1.62329018e+00 -6.58462569e-02
8.81131232e-01 8.68304670e-01 2.98259705e-01 8.13775182e-01
-5.76148510e-01 7.88590252e-01 -6.46286249e-01 -4.36519295e-01
3.23318243e-01 -1.41858771e-01 -7.99278378e-01 -5.12336552e-01
6.32461071e-01 -6.47255719e-01 -7.60856807e-01 1.06311071e+00
4.95839536e-01 1.59057200e-01 1.47135064e-01 -1.26438856e+00
-5.39783955e-01 1.19434631e+00 -5.16661286e-01 1.78011030e-01
-3.35601419e-01 2.31285989e-01 9.37957466e-01 -1.94955811e-01
6.15165532e-01 1.00249231e+00 4.76724654e-01 3.75464290e-01
-9.78278458e-01 -4.20200735e-01 -1.23396456e-01 3.98146302e-01
-1.22160923e+00 -3.33926886e-01 4.47323859e-01 1.04982473e-01
1.51648891e+00 4.69476134e-01 6.99153900e-01 6.83388531e-01
3.46860677e-01 7.83148944e-01 6.00626886e-01 -3.73631239e-01
2.91108966e-01 -4.12444353e-01 5.23818433e-01 8.13399494e-01
6.77131176e-01 6.09820001e-02 -5.78996360e-01 -2.68163741e-01
5.45363665e-01 5.39924130e-02 -1.39345482e-01 -2.31173217e-01
-7.06959844e-01 7.42427826e-01 9.32464004e-01 2.48962581e-01
-2.56995767e-01 7.75237441e-01 1.00189054e+00 5.01190484e-01
3.22173268e-01 3.03373724e-01 -3.93673271e-01 -4.01967824e-01
-9.10153270e-01 -4.40906920e-02 1.15269375e+00 8.70520473e-01
7.77230263e-01 3.50985318e-01 3.78563814e-02 5.25230944e-01
2.06518412e-01 6.15256190e-01 4.26469505e-01 -5.90026855e-01
3.25758755e-01 9.38291848e-01 -9.26587701e-01 -8.29753280e-01
-4.23577815e-01 -6.84079111e-01 -1.11720848e+00 1.00270897e-01
3.28111351e-01 1.20619752e-01 -9.20028985e-01 1.56338859e+00
4.62886333e-01 1.97534636e-01 -2.99573064e-01 7.90451050e-01
8.19808483e-01 6.95565581e-01 -5.65544777e-02 9.79880020e-02
1.20943439e+00 -1.11942053e+00 -3.12648505e-01 -3.09461743e-01
1.23240376e+00 -3.66714478e-01 1.21196747e+00 2.76668727e-01
-7.82606363e-01 -3.36054981e-01 -1.36648118e+00 -3.56794477e-01
-3.91297340e-01 -3.24533552e-01 1.05431747e+00 6.81538999e-01
-1.45344532e+00 8.56491566e-01 -1.26878202e+00 -3.12758148e-01
2.25586593e-01 5.74125409e-01 -2.50742257e-01 -1.94575742e-01
-7.10417211e-01 5.21884322e-01 5.07166743e-01 -2.35536657e-02
-5.72171986e-01 -6.13330126e-01 -5.73488235e-01 4.36107546e-01
8.32758844e-01 -6.01486802e-01 1.00981426e+00 -5.26962876e-01
-1.46374631e+00 5.04509628e-01 3.29809636e-02 -7.70474851e-01
9.74463671e-02 -1.01688564e-01 -3.22073281e-01 -1.58596009e-01
-4.76531982e-01 1.93837181e-01 4.59853470e-01 -5.00327587e-01
-8.15757737e-03 -4.03719276e-01 -5.86171262e-02 1.40460804e-02
-7.84689009e-01 -2.22341549e-02 -7.49370635e-01 -3.16225052e-01
-1.74839646e-01 -9.92891788e-01 -1.14890978e-01 -1.12249911e-01
-6.13568902e-01 -4.08550203e-01 1.06091034e+00 -3.08045000e-01
1.55812562e+00 -2.10541153e+00 1.31860171e-02 4.41526115e-01
9.43246961e-01 7.30050862e-01 -1.70947760e-02 3.18965673e-01
3.76763731e-01 8.02380964e-02 1.23347253e-01 -2.90707856e-01
1.13578483e-01 4.22264487e-01 -3.16529453e-01 3.42988402e-01
-4.27240908e-01 1.26702058e+00 -6.63604617e-01 -4.44751918e-01
-2.34966986e-02 1.85614124e-01 -4.62945133e-01 2.25747257e-01
-5.75445890e-01 -3.94407570e-01 -2.50150472e-01 4.04056072e-01
7.11957157e-01 -8.94733429e-01 6.48239136e-01 1.34949654e-01
9.03882831e-02 5.92174470e-01 -9.08394992e-01 1.49581075e+00
-2.74462581e-01 7.61683106e-01 1.88963562e-01 -8.51950049e-01
8.86462450e-01 -3.42651576e-01 5.02120843e-03 -9.60380197e-01
1.22390620e-01 3.89842480e-01 1.00476168e-01 9.81447771e-02
8.42748702e-01 4.69962716e-01 -3.82135659e-02 8.26263130e-01
-1.14512123e-01 2.68482476e-01 1.93740636e-01 6.46766305e-01
1.75369298e+00 -3.29798877e-01 1.35635976e-02 -3.23840559e-01
1.10535778e-01 1.51653469e-01 1.97312832e-01 8.44549835e-01
-2.01696586e-02 -9.87804383e-02 8.07122111e-01 -5.89896381e-01
-9.15083170e-01 -8.01735997e-01 2.76292324e-01 1.55081379e+00
-5.67691699e-02 -1.07051790e+00 -9.23563123e-01 -7.54750252e-01
1.69016227e-01 5.89130580e-01 -2.64841259e-01 -5.04241526e-01
-8.87069404e-01 -5.56192338e-01 7.78093100e-01 4.84703898e-01
6.78406656e-01 -9.64548528e-01 -7.38074124e-01 1.42654374e-01
5.23207963e-01 -8.12636018e-01 -4.86348510e-01 4.45854634e-01
-1.04014111e+00 -9.60378230e-01 -8.10172036e-02 -3.94926131e-01
4.16649252e-01 4.45067763e-01 1.42319715e+00 7.68553495e-01
-1.84217364e-01 -1.87886685e-01 -1.43635657e-03 -1.33157801e-02
-7.34934866e-01 5.95521629e-01 -1.26583144e-01 -6.32560194e-01
4.41504627e-01 -7.24190295e-01 -4.28719252e-01 3.26115415e-02
-8.63659441e-01 3.07103813e-01 6.16241157e-01 7.68699408e-01
4.82449144e-01 -7.10376725e-02 1.07711628e-01 -1.30385923e+00
5.38853467e-01 -4.79481518e-01 -1.10260224e+00 2.13372305e-01
-1.19090211e+00 4.55044717e-01 1.13049209e+00 -3.48281324e-01
-4.58346009e-01 -3.98569316e-01 -7.87769929e-02 -7.19598949e-01
2.76369542e-01 7.97534823e-01 -6.49902001e-02 -9.05595422e-02
7.19358385e-01 1.66661277e-01 3.49220246e-01 -3.89103204e-01
3.97279441e-01 5.16808808e-01 5.58904052e-01 -6.25618815e-01
6.00989044e-01 -2.84304768e-02 2.91218877e-01 -6.85374796e-01
-2.07967147e-01 -1.34811059e-01 -1.14222124e-01 -2.22304221e-02
4.47082609e-01 -5.61258793e-01 -1.11958218e+00 7.56228864e-01
-8.90534818e-01 -1.04082584e+00 -6.79768473e-02 2.40892973e-02
4.27863859e-02 2.66457438e-01 -1.08417630e+00 -2.95950294e-01
-8.18042755e-01 -1.28328526e+00 7.51514137e-01 1.89131737e-01
1.41079754e-01 -7.68813491e-01 -1.04272380e-01 -9.45758354e-03
1.06263888e+00 -5.97461499e-02 1.22325170e+00 -8.11254442e-01
-1.13829255e+00 2.82765459e-02 -5.61776578e-01 1.36942819e-01
-4.36928093e-01 5.79877011e-03 -5.65677047e-01 -5.45806706e-01
-2.60052770e-01 -3.71432483e-01 8.83900583e-01 1.58869132e-01
1.38745666e+00 -2.51815140e-01 -4.88260001e-01 1.05066156e+00
1.57761097e+00 -2.53199637e-01 6.05951190e-01 2.82775741e-02
1.27945268e+00 -1.78013444e-01 1.06816962e-01 3.09206583e-02
3.42747420e-01 5.78871787e-01 7.23146439e-01 8.05242360e-02
-3.73000056e-01 -3.22046131e-01 4.12612259e-01 1.23098135e+00
2.68000573e-01 -6.06228471e-01 -1.17554891e+00 9.31933895e-02
-1.68501246e+00 -4.20055807e-01 -4.62969810e-01 2.38404965e+00
6.34513795e-01 3.43584657e-01 5.87839559e-02 -6.14341861e-03
5.72422028e-01 3.44587773e-01 -9.61837947e-01 -6.04651153e-01
1.74812198e-01 4.71749961e-01 9.74647045e-01 1.26558021e-01
-6.32396221e-01 1.00307596e+00 5.45215178e+00 1.12059069e+00
-1.72353864e+00 1.66034251e-01 6.15474403e-01 -3.48545536e-02
-2.78598696e-01 9.87263694e-02 -9.02029276e-01 5.64550638e-01
1.90473974e+00 -3.58859032e-01 8.15645278e-01 1.23895919e+00
-5.09602726e-01 -1.65054575e-01 -9.28628445e-01 8.85767341e-01
-2.44591802e-01 -1.68012786e+00 -1.83335960e-01 5.91130853e-01
3.46421629e-01 7.39702582e-01 -4.04042937e-02 6.32094145e-01
6.17987454e-01 -1.13684082e+00 4.64437932e-01 -3.51253599e-02
1.07282650e+00 -8.17300141e-01 6.27308905e-01 4.03974891e-01
-1.27701819e+00 1.20848849e-01 -5.37593365e-01 -6.06799014e-02
-2.01635789e-02 8.69130671e-01 -9.27765906e-01 4.04178321e-01
6.99845433e-01 2.82462388e-01 -9.36542392e-01 7.93571115e-01
5.46587519e-02 8.70214641e-01 -6.84620798e-01 -5.26924670e-01
5.50060645e-02 -1.41041711e-01 2.38171294e-01 9.04402614e-01
2.31035620e-01 -3.48806173e-01 -2.08322685e-02 8.21573317e-01
-6.98904276e-01 1.14443563e-02 -5.76305509e-01 -2.93975741e-01
7.79216468e-01 1.27900541e+00 -9.23570395e-01 -4.57386106e-01
-3.37594479e-01 8.81932497e-01 9.83239889e-01 -6.30567744e-02
-9.62833405e-01 -5.94108164e-01 4.65408623e-01 1.95492789e-01
3.25691491e-01 -5.29275656e-01 -3.46504480e-01 -8.61906350e-01
6.98052645e-02 -1.05123103e+00 3.24113250e-01 -3.15577149e-01
-8.06882083e-01 7.61627436e-01 -2.09014773e-01 -4.08584237e-01
-2.09272251e-01 -7.59863138e-01 -6.16481483e-01 7.13928401e-01
-1.08437538e+00 -8.52716982e-01 -5.02351880e-01 3.88736755e-01
-1.10632278e-01 -1.12066619e-01 7.63358891e-01 3.93764704e-01
-8.10105443e-01 9.64189708e-01 3.45341831e-01 4.07871604e-02
4.66148078e-01 -1.26275146e+00 1.04222298e+00 9.55527127e-01
1.63628489e-01 8.43948185e-01 4.39906001e-01 -8.22857797e-01
-2.38556218e+00 -1.05611372e+00 5.66760480e-01 2.73153391e-02
7.83315778e-01 -6.80165231e-01 -1.32496274e+00 7.11332560e-01
2.59820614e-02 3.95478666e-01 2.77507067e-01 5.76172888e-01
-6.14923120e-01 -3.45713675e-01 -6.27192497e-01 6.03761137e-01
1.23698592e+00 -5.34792304e-01 2.31655240e-01 4.77081001e-01
1.13010561e+00 -8.52749467e-01 -7.89094329e-01 3.13376784e-02
1.00226387e-01 -9.99171615e-01 6.68565929e-01 -4.07058626e-01
3.64652932e-01 6.33774474e-02 1.98697615e-02 -9.62319791e-01
-8.27857293e-03 -7.51755238e-01 -7.90332973e-01 8.60510528e-01
2.33507216e-01 -9.27578688e-01 1.24699128e+00 5.26130617e-01
-4.09167528e-01 -9.63587523e-01 -7.60280907e-01 -8.65951359e-01
-1.08322479e-01 -4.76268679e-01 9.71859992e-01 6.92708492e-01
-2.26372033e-01 5.92160821e-01 -9.94102284e-02 -1.59025520e-01
5.31507373e-01 2.03178123e-01 1.30507767e+00 -1.08961272e+00
-8.75777483e-01 -6.00607157e-01 -3.59533459e-01 -1.35656404e+00
2.57921159e-01 -1.35132134e+00 -2.58070707e-01 -1.35439301e+00
1.03680283e-01 -7.31976569e-01 -6.05249591e-02 7.31175065e-01
-1.53772697e-01 -1.77176505e-01 2.32888460e-01 1.66899562e-01
-7.06901193e-01 3.45110178e-01 8.87474418e-01 4.26239930e-02
-1.02308460e-01 -3.42852235e-01 -6.18351161e-01 1.73778802e-01
6.15926027e-01 -4.21641976e-01 -5.41780829e-01 -7.74229884e-01
4.62613970e-01 5.89327663e-02 1.50366321e-01 -1.08411837e+00
8.06809187e-01 7.00451583e-02 -1.78795591e-01 -5.25155067e-01
-1.47558586e-03 -4.42489028e-01 4.34056610e-01 6.36918306e-01
1.56781217e-03 4.57063675e-01 2.56609619e-01 4.92864668e-01
4.67740782e-02 7.40302429e-02 5.92421591e-01 2.48179466e-01
-6.62893713e-01 4.81580406e-01 -4.43555228e-03 1.43085748e-01
7.36902297e-01 1.22460745e-01 -1.09317601e+00 -1.57830924e-01
-3.02772522e-02 1.10796936e-01 7.60054290e-01 9.66291428e-02
4.91721153e-01 -9.79794085e-01 -4.04104829e-01 4.17145312e-01
-3.41636747e-01 2.54934162e-01 3.03159952e-01 7.54369676e-01
-9.64191735e-01 3.91967297e-01 5.69200888e-02 -5.26566744e-01
-1.33325040e+00 7.44810343e-01 2.24556699e-01 -8.18481684e-01
-8.06078255e-01 9.08850372e-01 -2.70796776e-01 -3.25373024e-01
6.72595808e-03 -2.90518761e-01 5.29249132e-01 -6.70968652e-01
4.49517339e-01 5.69569588e-01 5.24589896e-01 -6.60575703e-02
-2.11119950e-01 -1.28612921e-01 -2.56013870e-01 4.33688641e-01
1.27124214e+00 3.73430878e-01 -7.09905446e-01 1.40367925e-01
1.40770352e+00 -2.43551452e-02 -7.01075315e-01 -3.67606103e-01
-3.25744338e-02 -1.84981540e-01 4.65422243e-01 -3.67455810e-01
-1.38595259e+00 7.34871089e-01 2.64467388e-01 4.71658438e-01
9.87138927e-01 -2.08519939e-02 1.31770527e+00 5.52206814e-01
5.77991664e-01 -9.04718161e-01 -7.85532966e-02 8.07681501e-01
2.45456815e-01 -9.70918953e-01 2.88132161e-01 -3.45099717e-01
6.88060075e-02 9.75302815e-01 9.56427455e-01 -1.45528629e-01
5.67369521e-01 5.52646935e-01 -4.71578062e-01 -4.31147665e-01
-9.87976849e-01 2.98451751e-01 1.42691821e-01 1.47558704e-01
4.58278656e-02 4.08857018e-01 -1.43976197e-01 3.82359236e-01
-1.90262407e-01 -8.16540793e-02 3.95617068e-01 1.01762271e+00
-4.70513999e-01 -1.31015408e+00 1.02232240e-01 1.13086486e+00
-1.80073455e-01 -3.21210891e-01 -4.46901806e-02 8.50454509e-01
-6.62671983e-01 4.95145112e-01 2.91092902e-01 -8.23982477e-01
-4.57156636e-02 -7.19402432e-02 2.42415980e-01 -5.11573255e-01
-8.72640193e-01 -2.94329405e-01 8.93754363e-02 -8.85143995e-01
4.70558405e-01 -1.42176794e-02 -1.47690940e+00 -1.37835550e+00
-4.72134948e-01 6.21224083e-02 9.38121319e-01 5.47773957e-01
9.47105646e-01 7.17960656e-01 1.23105764e-01 -4.85908121e-01
-8.66573870e-01 -6.21547997e-01 -5.08503616e-01 -1.45132653e-02
-1.14277706e-01 -1.84565693e-01 -4.21805590e-01 -7.81023085e-01] | [7.018374919891357, 5.725513458251953] |
18c0224d-3fc7-4d7d-9a58-33a00c2aa51a | the-effect-of-heterogeneous-data-for | 1811.12254 | null | http://arxiv.org/abs/1811.12254v1 | http://arxiv.org/pdf/1811.12254v1.pdf | The Effect of Heterogeneous Data for Alzheimer's Disease Detection from Speech | Speech datasets for identifying Alzheimer's disease (AD) are generally
restricted to participants performing a single task, e.g. describing an image
shown to them. As a result, models trained on linguistic features derived from
such datasets may not be generalizable across tasks. Building on prior work
demonstrating that same-task data of healthy participants helps improve AD
detection on a single-task dataset of pathological speech, we augment an
AD-specific dataset consisting of subjects describing a picture with multi-task
healthy data. We demonstrate that normative data from multiple speech-based
tasks helps improve AD detection by up to 9%. Visualization of decision
boundaries reveals that models trained on a combination of structured picture
descriptions and unstructured conversational speech have the least out-of-task
error and show the most potential to generalize to multiple tasks. We analyze
the impact of age of the added samples and if they affect fairness in
classification. We also provide explanations for a possible inductive bias
effect across tasks using model-agnostic feature anchors. This work highlights
the need for heterogeneous datasets for encoding changes in multiple facets of
cognition and for developing a task-independent AD detection model. | ['Jekaterina Novikova', 'Frank Rudzicz', 'Aparna Balagopalan', 'Marzyeh Ghassemi'] | 2018-11-29 | null | null | null | null | ['alzheimer-s-disease-detection'] | ['medical'] | [ 3.93328071e-01 4.04781163e-01 1.16084710e-01 -9.81632531e-01
-1.18919134e+00 -4.13805753e-01 9.02170897e-01 2.06120044e-01
-7.05600381e-01 3.55333030e-01 8.32569182e-01 -1.44199416e-01
-4.99168076e-02 -1.99506834e-01 -2.93435931e-01 -1.26623064e-01
1.15263369e-02 5.60594261e-01 -1.59979914e-04 1.20325848e-01
-1.00341551e-02 2.22760499e-01 -1.34802210e+00 1.07872307e+00
7.44648218e-01 9.25451815e-01 3.10915351e-01 4.27237898e-01
-1.64770439e-01 7.11873055e-01 -8.59599173e-01 -6.08318567e-01
4.52654772e-02 -2.59294093e-01 -7.79815137e-01 2.67513633e-01
1.18169284e+00 -8.47553074e-01 -2.69055188e-01 7.76899457e-01
8.80710781e-01 -4.80153970e-02 7.13669896e-01 -1.30350792e+00
-1.24049795e+00 4.75829184e-01 1.64487783e-03 5.81656754e-01
3.27755183e-01 5.04479945e-01 1.02436340e+00 -6.61615789e-01
6.22946799e-01 1.68250787e+00 5.29453576e-01 8.49363387e-01
-1.55472696e+00 -7.30122089e-01 3.32423210e-01 3.56948584e-01
-7.26073503e-01 -1.09249985e+00 4.52339679e-01 -9.18688297e-01
9.71294463e-01 2.41516709e-01 6.51650786e-01 1.78814769e+00
-1.38863027e-01 5.25642633e-01 1.43355536e+00 -9.31290016e-02
1.39639765e-01 2.42428973e-01 6.16031766e-01 3.81847054e-01
3.72927368e-01 9.34822205e-03 -4.69193101e-01 -7.29124904e-01
1.76385909e-01 -2.82991379e-01 -1.68965131e-01 2.67970818e-03
-1.30948901e+00 1.00270700e+00 3.03188860e-01 1.91840470e-01
-1.66184574e-01 -1.51078850e-01 7.21302271e-01 4.16415811e-01
6.64009333e-01 5.81768870e-01 -2.65124321e-01 6.13940647e-03
-1.10336709e+00 3.77511233e-01 6.08879030e-01 5.34362853e-01
1.41840637e-01 2.15045433e-03 -6.76800728e-01 1.29584908e+00
2.41325647e-01 6.67772174e-01 7.04298198e-01 -1.24164140e+00
5.29231131e-01 5.49425006e-01 -6.18457049e-02 -5.73705852e-01
-7.73247063e-01 -2.75610685e-01 -2.28074014e-01 4.08530176e-01
5.46058059e-01 1.72115918e-02 -7.94287384e-01 1.92660677e+00
-2.20114902e-01 -6.96882248e-01 -1.90253362e-01 1.04525197e+00
8.33522797e-01 -4.25831005e-02 7.81226397e-01 -8.13469365e-02
1.84233284e+00 -5.97211659e-01 -5.63138187e-01 -8.91047835e-01
8.02977443e-01 -5.07904589e-01 1.56583273e+00 2.96853691e-01
-1.04453731e+00 -3.83587569e-01 -9.37144697e-01 -4.41323429e-01
-2.58136541e-01 1.81219354e-01 3.03819925e-01 9.33969975e-01
-1.22046375e+00 1.80037946e-01 -5.08242071e-01 -5.78981340e-01
9.37985778e-01 1.15864083e-01 -6.18593454e-01 -1.77711800e-01
-1.06207299e+00 1.28602171e+00 -8.43570158e-02 -3.38246375e-01
-8.50657642e-01 -8.15107048e-01 -6.38791859e-01 -1.36355922e-01
-1.31207064e-01 -9.39115047e-01 1.31802094e+00 -1.44254696e+00
-8.13688993e-01 1.35168183e+00 -2.35961884e-01 -5.65544486e-01
6.22300565e-01 -2.08449155e-01 -5.80173671e-01 4.75506723e-01
5.60551405e-01 9.33697939e-01 8.89518499e-01 -1.01339030e+00
-3.12706083e-01 -7.21478343e-01 -4.04071212e-02 5.68535067e-02
-5.53724110e-01 3.64041626e-01 5.30833364e-01 -6.73828900e-01
-2.32325986e-01 -8.96329284e-01 3.00093740e-01 6.70292497e-01
-2.59066433e-01 -8.10743421e-02 8.17962646e-01 -1.09904790e+00
9.27058697e-01 -2.08245540e+00 -3.83703768e-01 -2.03075930e-01
6.81226432e-01 1.03371441e-01 -2.71676034e-01 -7.56747127e-02
-1.77065700e-01 7.27468014e-01 -1.99267164e-01 -6.77861035e-01
2.88591802e-01 -4.06405926e-02 9.74868461e-02 5.01891494e-01
4.08006907e-01 7.31474280e-01 -6.14255786e-01 -5.89050531e-01
4.04490232e-02 2.87871510e-01 -6.17978871e-01 -1.16222493e-01
4.90590092e-03 4.01576549e-01 -1.58303380e-01 2.79139191e-01
5.60482681e-01 -2.81859487e-01 -1.47912040e-01 -1.93165496e-01
1.83453768e-01 6.66159511e-01 -2.95278758e-01 1.21154940e+00
-3.33157122e-01 8.15676868e-01 2.23962411e-01 -7.91094780e-01
6.81448698e-01 2.76601583e-01 1.44351646e-01 -9.59410012e-01
-9.41879973e-02 1.43852040e-01 6.38921142e-01 -8.15958321e-01
-5.89449927e-02 -3.67267251e-01 6.76980391e-02 6.61148310e-01
-1.44956157e-01 2.95879453e-01 1.20921833e-02 3.01934808e-01
1.27571952e+00 -6.20450079e-01 3.01315635e-01 -3.39365453e-01
2.87754178e-01 2.64594313e-02 2.78850406e-01 8.63999605e-01
-7.78957963e-01 7.95110822e-01 5.45318782e-01 -9.70449299e-02
-1.39998829e+00 -9.96990442e-01 -3.74617666e-01 1.28854060e+00
-8.60573411e-01 -4.32934821e-01 -5.61390042e-01 -6.20263219e-01
1.49336800e-01 1.33259141e+00 -7.24921584e-01 -3.94598871e-01
-2.26848453e-01 -7.51314640e-01 8.63106787e-01 4.84691978e-01
5.02970755e-01 -7.30927050e-01 -6.75593436e-01 -1.06395535e-01
-3.00255120e-01 -1.32102835e+00 -5.56986451e-01 -1.78805545e-01
-7.23372400e-01 -1.07548702e+00 -1.02403736e+00 -7.22390234e-01
4.34143692e-01 1.23361230e-01 1.14484334e+00 -1.26976550e-01
-2.09881946e-01 9.43729997e-01 -2.93801665e-01 -4.02691960e-01
-1.02553558e+00 -1.86756641e-01 -1.63168103e-01 -1.95245832e-01
6.92239583e-01 -3.26030493e-01 -7.33918309e-01 2.48006657e-01
-3.31713080e-01 3.81455146e-04 7.03767240e-01 7.57269740e-01
7.21462420e-04 -7.79401779e-01 7.26986885e-01 -5.78393400e-01
9.28821146e-01 -6.06619775e-01 4.32410657e-01 2.24709824e-01
-5.67031085e-01 -7.57862106e-02 1.84258074e-01 -7.04549313e-01
-1.06869686e+00 -1.73617363e-01 1.47644639e-01 -1.25970051e-01
-5.09156108e-01 -1.14301324e-01 -7.67700151e-02 2.21787333e-01
9.11874056e-01 -3.85531522e-02 6.84436798e-01 -3.46628219e-01
1.58813864e-01 9.74567056e-01 8.31909478e-02 -5.20421684e-01
2.01031253e-01 4.82344419e-01 -4.14368600e-01 -8.96739185e-01
-6.77582860e-01 -1.46481633e-01 -4.06544894e-01 -2.70443112e-01
1.17099524e+00 -9.84026492e-01 -6.45113230e-01 3.63107711e-01
-1.04978371e+00 -5.02770245e-01 -1.26892000e-01 6.24990344e-01
-5.12639999e-01 3.62891912e-01 -3.79366636e-01 -6.82106495e-01
-3.50436151e-01 -1.31945002e+00 1.17301357e+00 -4.87731189e-01
-1.00717247e+00 -7.51177967e-01 -2.32196867e-01 9.33238268e-01
4.59789693e-01 2.44359882e-03 1.27270865e+00 -1.32099676e+00
5.40208519e-02 -2.21447721e-02 -5.56015074e-01 5.01062632e-01
2.72735149e-01 -5.05981326e-01 -1.08855116e+00 -1.16961464e-01
2.33542264e-01 -8.05712700e-01 1.04816151e+00 4.98470485e-01
8.49462330e-01 -3.22099030e-01 -2.28198171e-01 -9.06632245e-02
6.11104429e-01 8.00276101e-02 4.01598811e-01 4.87520635e-01
4.32465672e-01 8.52495909e-01 -7.56555200e-02 1.45668820e-01
5.42448163e-01 7.15445757e-01 -4.40670736e-02 1.62624314e-01
-6.02182686e-01 2.00748190e-01 7.03940272e-01 1.46563902e-01
5.14970303e-01 2.56917626e-02 -1.04680467e+00 4.00352240e-01
-1.19647348e+00 -1.05995107e+00 -2.28597894e-01 1.97538030e+00
5.83868623e-01 1.45133168e-01 4.47518170e-01 -6.57389760e-02
9.24861133e-01 2.70483047e-01 -7.08868027e-01 -4.20099974e-01
-4.41630870e-01 -1.79554895e-01 3.07601988e-01 3.53038847e-01
-1.00249124e+00 3.56542408e-01 7.08417225e+00 2.91052282e-01
-8.47712815e-01 4.96963829e-01 9.36899304e-01 -6.29525959e-01
-3.68882746e-01 -4.27846462e-01 -4.44568127e-01 5.56194901e-01
1.28103638e+00 -9.94496271e-02 2.23717123e-01 7.99691439e-01
6.38641238e-01 -6.30676895e-02 -1.44865835e+00 8.49215567e-01
4.91033971e-01 -6.87089801e-01 4.73856896e-01 1.51145324e-01
-2.90602893e-02 1.25073045e-02 3.35060090e-01 6.57104254e-02
4.89392914e-02 -1.01351440e+00 1.06847847e+00 4.49545860e-01
8.03743899e-01 -6.35295082e-03 4.01002973e-01 -1.88207597e-01
-5.12101173e-01 -3.70448351e-01 -8.59175473e-02 -8.23822841e-02
2.06782073e-01 3.43790174e-01 -1.17768931e+00 -2.97027051e-01
6.78229570e-01 3.97724181e-01 -1.03326142e+00 8.10953379e-01
2.75641143e-01 7.00114906e-01 -1.75314844e-01 -9.93675143e-02
1.65722370e-02 2.65656054e-01 6.10417664e-01 1.39297056e+00
3.27893972e-01 -8.68641771e-03 -6.85059205e-02 1.11777031e+00
1.24158092e-01 1.80122271e-01 -6.26130164e-01 -4.18490082e-01
4.12192255e-01 8.97463858e-01 -4.74024475e-01 -2.81456500e-01
-9.28159356e-01 9.35294807e-01 4.54100490e-01 2.01857358e-01
-3.42633635e-01 2.69239306e-01 7.14939952e-01 4.01968151e-01
-1.37442216e-01 -1.96375579e-01 -7.30228186e-01 -6.87256515e-01
2.39687353e-01 -1.08942711e+00 5.72599113e-01 -1.25639212e+00
-1.78435850e+00 3.49781901e-01 3.67597304e-02 -7.89540529e-01
-7.77717084e-02 -6.92121685e-01 -3.25538009e-01 9.66213048e-01
-1.07091820e+00 -1.11958182e+00 -5.11822820e-01 6.09811187e-01
7.08934486e-01 -5.30191481e-01 8.92531335e-01 2.93935776e-01
-3.40751350e-01 6.88373268e-01 -3.81842524e-01 1.49716705e-01
1.30101061e+00 -1.13483882e+00 4.81557459e-01 3.44118208e-01
-2.10709199e-01 7.12491155e-01 6.33813977e-01 -8.37044179e-01
-6.14044428e-01 -8.56939077e-01 7.23804474e-01 -7.45686650e-01
7.77068019e-01 -3.79433930e-01 -9.33054268e-01 8.25989425e-01
-5.61816618e-03 -5.60472071e-01 1.02586412e+00 2.01738536e-01
-7.07981110e-01 2.94626434e-03 -1.40328312e+00 7.02762961e-01
1.41033053e+00 -9.12053108e-01 -8.99542689e-01 4.78570938e-01
6.67353451e-01 1.37044370e-01 -8.80392790e-01 -9.12336707e-02
6.93335533e-01 -9.13941920e-01 1.11608386e+00 -8.92850697e-01
4.71674621e-01 3.55087280e-01 -3.47690403e-01 -1.42123640e+00
-6.40510976e-01 -2.33574547e-02 4.20466483e-01 1.27236021e+00
6.00798070e-01 -8.86206210e-01 3.66341472e-01 1.23177564e+00
-4.04511750e-01 -7.19301179e-02 -1.15831983e+00 -6.38446808e-01
4.42026973e-01 -5.29545426e-01 1.52455077e-01 8.87229264e-01
-4.71649766e-02 2.35299662e-01 3.22375237e-03 3.26827615e-02
5.41034818e-01 -8.37084293e-01 1.87027737e-01 -1.53835166e+00
2.38409713e-01 -6.29684985e-01 -6.97339296e-01 -1.51780024e-01
6.36178434e-01 -1.35710335e+00 -4.64440256e-01 -1.64327669e+00
6.86827064e-01 -1.97371542e-01 1.02432832e-01 5.73489964e-01
-9.28943902e-02 -1.42875567e-01 3.66831273e-01 3.69578451e-01
-2.79460937e-01 3.77398729e-01 1.10811007e+00 -3.80453676e-01
4.06936370e-02 -2.54249036e-01 -1.25946069e+00 5.60870349e-01
8.77935350e-01 -5.30809522e-01 -6.62704527e-01 -5.10656655e-01
-1.87410563e-01 -6.13547683e-01 1.10669506e+00 -1.00263238e+00
-7.20732883e-02 1.80514738e-01 7.19013989e-01 1.41923577e-01
7.26680815e-01 -5.69591463e-01 -1.14629507e-01 4.61763352e-01
-8.75722706e-01 -5.98130263e-02 2.15569571e-01 5.45925975e-01
2.90591389e-01 -2.01224238e-01 7.87725210e-01 -5.53835690e-01
-4.27985698e-01 -2.20609277e-01 -6.36738718e-01 6.70323074e-01
5.72039545e-01 -2.40396962e-01 -1.02317512e+00 -5.09866118e-01
-1.23370850e+00 -4.01074253e-03 3.42972934e-01 5.28763652e-01
3.57086122e-01 -1.22912121e+00 -1.01598752e+00 -1.81641102e-01
3.44084442e-01 -8.82468164e-01 2.60452360e-01 8.86021137e-01
-7.89432749e-02 4.97368157e-01 -3.67900312e-01 -4.43779856e-01
-1.43086505e+00 2.82233536e-01 5.07529140e-01 3.60350043e-01
-8.80959213e-01 5.22679329e-01 5.42019546e-01 1.02333315e-02
1.53325945e-01 -2.70054668e-01 -2.48177256e-02 5.90262055e-01
8.13712776e-01 5.70030928e-01 9.67526138e-02 -7.14684188e-01
-5.02207756e-01 -6.24015741e-02 -4.68415976e-01 -5.44549108e-01
1.16967165e+00 -1.56216547e-01 2.20327124e-01 6.82325602e-01
1.24723983e+00 -3.35572988e-01 -1.10642087e+00 1.13030143e-01
-8.61282088e-03 -1.58609018e-01 1.00983046e-01 -1.21228158e+00
-6.59629703e-01 8.36801708e-01 1.09945595e+00 1.45382375e-01
5.59783518e-01 3.66028428e-01 4.92412388e-01 4.59426582e-01
-6.82826620e-04 -1.18631351e+00 2.83864170e-01 -3.81499305e-02
1.43054533e+00 -1.34325683e+00 4.60287146e-02 -2.51880772e-02
-1.03818345e+00 1.11874759e+00 4.58948523e-01 1.41722769e-01
3.90785187e-01 6.24673208e-03 6.66114688e-02 -2.58504599e-01
-7.05245793e-01 -1.36443004e-01 3.28514993e-01 1.19080043e+00
5.69629133e-01 5.62091805e-02 -4.80583578e-01 1.09163785e+00
-5.35770774e-01 -1.52146414e-01 5.60424685e-01 5.66339374e-01
-5.11922896e-01 -8.37054074e-01 -4.81064975e-01 1.25791800e+00
-4.66103762e-01 -1.84263483e-01 -8.91665041e-01 8.79172862e-01
-1.12158813e-01 1.14396465e+00 5.03362715e-01 -1.85894612e-02
3.81942362e-01 8.67271841e-01 2.09345832e-01 -6.87309563e-01
-6.12550676e-01 -4.00932074e-01 9.56450164e-01 -4.51477557e-01
-5.52179277e-01 -1.22992802e+00 -8.77929151e-01 -1.89992741e-01
3.89079452e-01 -4.68361408e-01 3.23712379e-01 8.75983477e-01
6.10211790e-01 4.85837311e-01 -1.89653546e-01 -6.26313329e-01
-5.74475110e-01 -1.19022882e+00 -3.33784938e-01 6.65454209e-01
5.37763953e-01 -8.53680313e-01 -6.32640541e-01 9.87226218e-02] | [11.088162422180176, 1.4616367816925049] |
7308487c-a128-4e84-8719-41f3d7625e2c | video-salient-object-detection-using | 1708.01447 | null | http://arxiv.org/abs/1708.01447v3 | http://arxiv.org/pdf/1708.01447v3.pdf | Video Salient Object Detection Using Spatiotemporal Deep Features | This paper presents a method for detecting salient objects in videos where
temporal information in addition to spatial information is fully taken into
account. Following recent reports on the advantage of deep features over
conventional hand-crafted features, we propose a new set of SpatioTemporal Deep
(STD) features that utilize local and global contexts over frames. We also
propose new SpatioTemporal Conditional Random Field (STCRF) to compute saliency
from STD features. STCRF is our extension of CRF to the temporal domain and
describes the relationships among neighboring regions both in a frame and over
frames. STCRF leads to temporally consistent saliency maps over frames,
contributing to the accurate detection of salient objects' boundaries and noise
reduction during detection. Our proposed method first segments an input video
into multiple scales and then computes a saliency map at each scale level using
STD features with STCRF. The final saliency map is computed by fusing saliency
maps at different scale levels. Our experiments, using publicly available
benchmark datasets, confirm that the proposed method significantly outperforms
state-of-the-art methods. We also applied our saliency computation to the video
object segmentation task, showing that our method outperforms existing video
object segmentation methods. | ['Trung-Nghia Le', 'Akihiro Sugimoto'] | 2017-08-04 | null | null | null | null | ['video-salient-object-detection'] | ['computer-vision'] | [ 3.92622948e-01 -4.96940285e-01 -3.62449557e-01 -4.64864075e-01
-6.87117040e-01 -3.69982392e-01 6.05414450e-01 3.28096092e-01
-5.20970941e-01 6.35679305e-01 2.65444756e-01 3.47330153e-01
-1.85531564e-02 -6.17553055e-01 -8.68860483e-01 -4.40898120e-01
-2.71258146e-01 -3.14263701e-01 1.29953754e+00 -5.75664602e-02
5.59452474e-01 3.90267551e-01 -1.81872976e+00 3.55331570e-01
7.94460952e-01 1.28292930e+00 8.77846599e-01 5.19552648e-01
1.41624346e-01 8.95886302e-01 -3.70010048e-01 1.85634062e-01
1.77316949e-01 -2.65600145e-01 -9.77039874e-01 3.17333400e-01
4.94620204e-01 -3.22899550e-01 -1.47683412e-01 1.09617817e+00
-5.31462319e-02 5.28990030e-01 3.09473097e-01 -1.19062912e+00
-5.74239612e-01 3.53923887e-01 -6.89471960e-01 8.33750904e-01
3.67758840e-01 -1.20595567e-01 1.02843797e+00 -1.06726134e+00
8.46939325e-01 1.15317023e+00 4.42730069e-01 1.11714944e-01
-1.03240633e+00 -2.62321353e-01 5.45401275e-01 3.88368249e-01
-1.25425684e+00 -5.22044599e-02 7.83930659e-01 -5.23171008e-01
7.88600564e-01 1.55291602e-01 7.19014943e-01 6.64914548e-01
3.09132040e-01 1.19097435e+00 1.04751921e+00 -2.80472249e-01
3.45143527e-01 -2.26370856e-01 1.42929211e-01 5.94291627e-01
-6.35003820e-02 -4.16123495e-02 -7.89131641e-01 -1.53052509e-02
1.06398070e+00 3.45203489e-01 -9.94299129e-02 -3.69196147e-01
-1.66651702e+00 7.98003554e-01 7.11288512e-01 5.14295816e-01
-5.89558303e-01 1.66516557e-01 2.91359961e-01 -2.81179756e-01
4.69915837e-01 1.79248422e-01 -4.18477714e-01 1.12315238e-01
-1.44931507e+00 3.89609933e-01 3.50266248e-02 1.02539790e+00
9.87191558e-01 -7.90180042e-02 -6.55526340e-01 5.81856787e-01
7.77751282e-02 4.17738616e-01 4.67665821e-01 -1.20416021e+00
9.92778838e-02 4.43782479e-01 3.79433811e-01 -1.29254794e+00
-2.68564731e-01 -1.85169205e-01 -3.99891317e-01 3.37033570e-02
1.93822876e-01 2.14934528e-01 -1.04701948e+00 1.64545643e+00
3.62719208e-01 6.52040541e-01 -1.99755728e-01 1.39818740e+00
8.57454062e-01 5.45452476e-01 3.85224491e-01 -1.33944079e-01
1.29736567e+00 -1.02469230e+00 -6.24928594e-01 -1.39250055e-01
-2.24571209e-02 -7.74722993e-01 6.70500278e-01 1.03656672e-01
-1.14188600e+00 -8.50698292e-01 -8.06364298e-01 -1.40772328e-01
-4.59774435e-01 1.79469049e-01 7.37859309e-01 -3.46003361e-02
-1.34106588e+00 5.64538181e-01 -8.76055896e-01 -3.84604394e-01
5.10842443e-01 3.01233917e-01 -1.21621296e-01 2.39126429e-01
-1.20515549e+00 6.87296271e-01 3.30614060e-01 -6.22648858e-02
-1.05648828e+00 -4.32115316e-01 -1.03914404e+00 5.37328571e-02
5.45007765e-01 -4.87667829e-01 1.13659251e+00 -1.27321327e+00
-1.07693636e+00 6.04692936e-01 -7.25649595e-01 -7.03036308e-01
1.72667325e-01 -3.30978602e-01 -2.11475402e-01 7.45446384e-01
6.41530395e-01 1.16443300e+00 1.12914026e+00 -9.85982060e-01
-1.07881093e+00 4.15006690e-02 1.26624957e-01 1.84972435e-01
6.37850016e-02 5.18031299e-01 -5.39169312e-01 -1.00540197e+00
2.38172963e-01 -6.36019170e-01 -2.67416239e-01 -1.58135176e-01
-3.32053989e-01 -3.32080632e-01 1.11206913e+00 -5.11145115e-01
1.09977734e+00 -1.97426057e+00 2.46164307e-01 -3.17181312e-02
2.92730987e-01 1.22799248e-01 -8.46864432e-02 -4.87889238e-02
8.38862136e-02 -3.16593647e-02 -3.10571223e-01 -3.30356866e-01
-2.99699366e-01 -9.42897871e-02 -3.74591321e-01 4.35654163e-01
5.86126983e-01 1.10640585e+00 -1.17067277e+00 -9.00736570e-01
4.82564270e-01 5.09657204e-01 -4.11772758e-01 6.53913571e-03
-2.71677524e-01 5.41483283e-01 -6.77955866e-01 7.57681668e-01
6.28855586e-01 -3.46552491e-01 -3.52802932e-01 -7.82320555e-03
-5.08250892e-01 1.93020657e-01 -9.68141913e-01 1.81487024e+00
1.11736625e-01 7.46528149e-01 -2.78431535e-01 -9.48201537e-01
8.84439528e-01 7.23310262e-02 7.62672126e-01 -5.78373015e-01
1.32598341e-01 4.92173135e-02 -5.98164022e-01 -3.32373142e-01
9.08150971e-01 3.04598093e-01 -6.42739013e-02 1.25827238e-01
3.87654662e-01 1.05977684e-01 4.01927412e-01 4.58044797e-01
7.89941549e-01 3.39035302e-01 2.99231797e-01 -5.89906752e-01
4.54394400e-01 1.56531241e-02 8.85861635e-01 8.25748563e-01
-6.22155607e-01 9.22638893e-01 2.06221163e-01 -5.55897832e-01
-6.70043409e-01 -1.04496503e+00 -5.00607342e-02 1.31524551e+00
7.92607486e-01 -2.72723913e-01 -8.83531392e-01 -7.46626973e-01
-2.11812593e-02 1.44345969e-01 -9.44438577e-01 2.37948835e-01
-5.91626287e-01 -3.59633803e-01 -1.16230264e-01 7.05912352e-01
6.30834818e-01 -1.35196900e+00 -1.30014002e+00 2.48888731e-01
-3.78904849e-01 -1.40483129e+00 -8.71922255e-01 -5.90465553e-02
-8.87467623e-01 -1.00102007e+00 -1.01346850e+00 -1.09237516e+00
4.10705775e-01 8.15304637e-01 1.02070713e+00 7.74052069e-02
-3.00533921e-01 1.90043122e-01 -5.47122300e-01 -8.16190913e-02
2.68636316e-01 -1.70249999e-01 -1.22112356e-01 2.77226180e-01
3.13072056e-01 -2.70226300e-01 -8.22414339e-01 4.39249188e-01
-9.09927547e-01 2.02731550e-01 3.06646526e-01 5.00331879e-01
8.86724353e-01 -6.60721958e-02 6.42309368e-01 -4.44472522e-01
9.14782807e-02 -4.85634595e-01 -7.03376174e-01 1.78444892e-01
1.03954963e-01 -1.02645300e-01 1.40222326e-01 -3.74615669e-01
-9.61943388e-01 2.49136910e-01 3.64118576e-01 -6.87879086e-01
-2.99531251e-01 2.83481479e-01 3.19145709e-01 -8.11980516e-02
2.24422827e-01 3.55082929e-01 -2.91994780e-01 -3.00085574e-01
1.40123382e-01 1.81444764e-01 7.59965837e-01 -2.64410824e-01
4.55770165e-01 6.97841346e-01 -1.66099086e-01 -6.90615654e-01
-1.01936734e+00 -6.43805206e-01 -1.04801667e+00 -3.74837667e-01
1.23308718e+00 -9.94604826e-01 -3.94501001e-01 4.68130559e-01
-1.21700644e+00 -1.33982778e-01 -2.21105143e-01 5.39342940e-01
-6.96384251e-01 2.93223590e-01 -5.63225091e-01 -6.78175509e-01
2.28531752e-03 -1.34965158e+00 1.53648114e+00 6.04164541e-01
-9.89542678e-02 -9.35100853e-01 -2.34793246e-01 -7.28178583e-03
4.96818215e-01 5.37660301e-01 2.55742192e-01 -3.36665690e-01
-9.72036481e-01 2.14458779e-01 -3.87425900e-01 6.56297132e-02
2.49849528e-01 1.45604700e-01 -7.84541845e-01 -9.28338096e-02
-5.09086885e-02 1.05643393e-02 1.29230225e+00 1.02642977e+00
1.13810527e+00 -6.45842105e-02 -5.00448406e-01 2.94052511e-01
1.48204660e+00 1.46722183e-01 3.88341069e-01 4.30790454e-01
6.20032966e-01 4.41526383e-01 1.17069554e+00 4.19530302e-01
5.12287438e-01 8.35065126e-01 4.41947192e-01 -3.98884952e-01
-6.57741725e-02 -1.01000637e-01 2.52958983e-01 3.37467313e-01
-1.31517917e-01 1.65156111e-01 -5.47740519e-01 1.05238593e+00
-2.05813265e+00 -1.06597126e+00 -7.94684961e-02 1.92213261e+00
7.54439473e-01 1.03966944e-01 4.51954424e-01 -7.34231388e-03
1.17465246e+00 2.97261447e-01 -4.31182593e-01 -1.45027682e-01
-3.49609375e-01 2.13661060e-01 4.45172101e-01 4.46600288e-01
-1.69159532e+00 1.31151950e+00 6.58481741e+00 6.69288397e-01
-1.26420486e+00 2.38220990e-01 5.82260132e-01 -6.85158325e-03
-1.03609934e-01 -3.63643616e-02 -7.41518855e-01 5.79207897e-01
3.74153048e-01 -8.12243521e-02 1.45315453e-01 9.44693327e-01
3.49867612e-01 -6.73077524e-01 -7.76087224e-01 6.86212897e-01
7.54935965e-02 -1.55746496e+00 -1.67403117e-01 -3.77691805e-01
1.11842048e+00 1.85728490e-01 2.15650067e-01 -1.53292418e-01
1.73391283e-01 -7.54837453e-01 1.26897335e+00 5.87669730e-01
3.67976546e-01 -6.59315467e-01 6.30560160e-01 -8.99947658e-02
-1.62163711e+00 -6.08167760e-02 -3.48463714e-01 -7.06563666e-02
2.37131864e-01 6.61877573e-01 -6.93339646e-01 3.25905710e-01
1.04863596e+00 1.24901748e+00 -8.36270869e-01 1.24029589e+00
-1.50098905e-01 3.34218711e-01 -1.80722982e-01 2.74482789e-03
4.23951268e-01 1.49972066e-01 6.31717503e-01 1.44290066e+00
1.35529786e-01 -2.82260813e-02 4.10614848e-01 9.75248992e-01
3.06284219e-01 -4.79877293e-02 -3.28956634e-01 2.99082786e-01
4.00854290e-01 1.23344111e+00 -1.31253731e+00 -6.54977858e-01
-3.80962163e-01 9.74357545e-01 6.68976083e-02 4.92534697e-01
-9.62285995e-01 -4.28936511e-01 6.17216647e-01 -4.31758165e-02
8.67235422e-01 -2.93972313e-01 -2.54964739e-01 -1.20697832e+00
-2.77239885e-02 -2.21002862e-01 3.02737355e-01 -7.76869416e-01
-8.06368113e-01 6.66028082e-01 1.58407897e-01 -1.35695446e+00
-1.45184889e-01 -9.12563726e-02 -5.89805841e-01 7.61621237e-01
-1.79762673e+00 -1.07877684e+00 -2.65741348e-01 8.16959739e-01
9.97611344e-01 1.20055884e-01 2.62927413e-01 -1.29768535e-01
-2.57307768e-01 5.76647967e-02 -2.20576480e-01 1.43502712e-01
4.01662111e-01 -1.18993807e+00 5.03048360e-01 1.33199227e+00
1.33899987e-01 5.68131864e-01 5.91416717e-01 -7.62104750e-01
-8.33172202e-01 -1.28120995e+00 9.80285704e-01 -4.54631716e-01
4.92912591e-01 -2.81798631e-01 -8.92258525e-01 5.44368982e-01
2.04675153e-01 4.89681333e-01 1.68722600e-01 -4.58837122e-01
-9.67680514e-02 1.14022851e-01 -1.15633309e+00 2.62572914e-01
9.66714442e-01 -5.24625063e-01 -7.64136434e-01 -3.29155521e-03
1.13537908e+00 -4.27702159e-01 -6.16197824e-01 5.49739599e-01
3.59633088e-01 -1.10322022e+00 9.75500345e-01 -2.00489104e-01
4.80791122e-01 -7.29660869e-01 -1.59242034e-01 -9.40232098e-01
-4.70536053e-01 -5.59186220e-01 -2.33278051e-02 8.85991275e-01
3.19711901e-02 -2.18088001e-01 4.91441309e-01 3.05059850e-01
-2.12245733e-01 -6.14199877e-01 -8.08412135e-01 -5.54752290e-01
-4.36721236e-01 -4.05444354e-01 4.90387529e-01 7.66700089e-01
-1.03444532e-01 -2.75408030e-01 -2.23548368e-01 2.53683954e-01
6.49163425e-01 6.77758813e-01 2.07692459e-01 -1.21303833e+00
7.61944205e-02 -3.70076090e-01 -6.37925148e-01 -1.05547965e+00
1.95809811e-01 -5.77742994e-01 3.45030367e-01 -1.41785049e+00
4.59792495e-01 -1.72990322e-01 -6.35584414e-01 4.69326109e-01
-4.78310376e-01 6.17702127e-01 3.79527658e-01 1.60690993e-01
-1.35023904e+00 4.34088171e-01 1.43963778e+00 -3.70711386e-02
-2.59759068e-01 -1.80917069e-01 -4.64085102e-01 7.65983045e-01
6.04964077e-01 -3.57848763e-01 -1.36438116e-01 -2.67029405e-01
-4.64441538e-01 -4.71191704e-02 6.82839990e-01 -1.16125953e+00
2.44444311e-01 -4.50405359e-01 5.61201990e-01 -8.14980686e-01
2.77139634e-01 -4.47167635e-01 -3.17753315e-01 2.07319453e-01
-4.09975231e-01 -7.56468102e-02 1.36155143e-01 5.86603284e-01
-5.86619258e-01 1.34369656e-01 9.22004759e-01 -3.71663757e-02
-1.36126697e+00 2.07533881e-01 -4.93276834e-01 -5.99769019e-02
1.14385641e+00 -2.40920693e-01 -3.09737585e-02 -2.27578044e-01
-8.90281558e-01 1.94288641e-01 5.37343979e-01 7.42582381e-01
9.61609662e-01 -1.25045884e+00 -3.97629559e-01 3.09530586e-01
-3.43268253e-02 -1.51192658e-02 2.72488266e-01 1.06108916e+00
-1.87724590e-01 6.61886275e-01 -4.52291429e-01 -1.03723896e+00
-1.22643030e+00 7.27359176e-01 1.16049070e-02 7.53067806e-02
-5.16243637e-01 9.39138949e-01 4.91056025e-01 3.70336533e-01
1.74735293e-01 -8.30825329e-01 -5.11759639e-01 -3.53075117e-02
6.30003989e-01 2.08421931e-01 -1.44020543e-01 -1.18934071e+00
-5.90957463e-01 8.18247259e-01 -7.89306406e-03 2.86184102e-02
1.20814025e+00 -4.05507565e-01 -8.58337339e-03 3.32975537e-01
1.18485272e+00 -1.61227480e-01 -1.78387153e+00 -4.24275637e-01
1.68067440e-01 -7.85762489e-01 1.06779605e-01 -5.19017518e-01
-1.24436212e+00 6.49594188e-01 5.45217931e-01 1.83356166e-01
1.42087841e+00 2.46729553e-01 8.13710690e-01 -2.61789203e-01
6.52553201e-01 -1.10098422e+00 3.07760239e-01 5.80976248e-01
7.65350223e-01 -1.41824043e+00 -9.43807364e-02 -6.32692039e-01
-1.03360987e+00 8.60231876e-01 4.31094378e-01 -5.25445282e-01
6.19705260e-01 -1.12244906e-02 -3.11141759e-01 4.30926681e-02
-5.70169091e-01 -6.79205894e-01 5.60221970e-01 4.88331884e-01
2.04830050e-01 -2.50464771e-02 -2.39928439e-01 5.62152922e-01
1.19800761e-01 2.47177422e-01 4.10453171e-01 1.15072441e+00
-7.96795189e-01 -7.23167837e-01 -3.13764155e-01 2.18672305e-01
-6.53267443e-01 -3.27343866e-02 -6.64074272e-02 5.15716136e-01
4.18026000e-01 9.95892167e-01 3.13950568e-01 -3.16846281e-01
-1.54378951e-01 -3.85094494e-01 2.41965085e-01 -6.16422951e-01
-4.46945906e-01 3.20778906e-01 -4.51909244e-01 -9.62658823e-01
-1.02688265e+00 -8.93022060e-01 -1.50436783e+00 3.61630470e-01
-2.55019814e-01 9.09716189e-02 5.12959957e-01 9.29004669e-01
4.34527069e-01 4.32587832e-01 6.25345826e-01 -1.42149770e+00
3.06408048e-01 -7.23278046e-01 -5.89338541e-01 4.79242355e-01
7.83212006e-01 -1.11576951e+00 -1.36617973e-01 4.78927284e-01] | [9.662165641784668, -0.2765635848045349] |
2f2d7a27-196d-44e6-b26e-bc11e358317b | rethinking-document-level-relation-extraction | 2306.08953 | null | https://arxiv.org/abs/2306.08953v1 | https://arxiv.org/pdf/2306.08953v1.pdf | Rethinking Document-Level Relation Extraction: A Reality Check | Recently, numerous efforts have continued to push up performance boundaries of document-level relation extraction (DocRE) and have claimed significant progress in DocRE. In this paper, we do not aim at proposing a novel model for DocRE. Instead, we take a closer look at the field to see if these performance gains are actually true. By taking a comprehensive literature review and a thorough examination of popular DocRE datasets, we find that these performance gains are achieved upon a strong or even untenable assumption in common: all named entities are perfectly localized, normalized, and typed in advance. Next, we construct four types of entity mention attacks to examine the robustness of typical DocRE models by behavioral probing. We also have a close check on model usability in a more realistic setting. Our findings reveal that most of current DocRE models are vulnerable to entity mention attacks and difficult to be deployed in real-world end-user NLP applications. Our study calls more attentions for future research to stop simplifying problem setups, and to model DocRE in the wild rather than in an unrealistic Utopian world. | ['Min Zhang', 'Shuai Zhang', 'Yequan Wang', 'Jing Li'] | 2023-06-15 | null | null | null | null | ['document-level-relation-extraction', 'relation-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [-1.00903884e-02 3.21529776e-01 -3.51593107e-01 -2.35491827e-01
-7.80867934e-01 -9.64541614e-01 6.17611051e-01 3.59385431e-01
-4.71608698e-01 7.01022685e-01 2.35802144e-01 -8.24505985e-01
-1.70636281e-01 -5.88991106e-01 -4.53475088e-01 1.07529595e-01
-7.72621110e-02 4.41065878e-01 4.05210823e-01 -1.36273980e-01
1.39153764e-01 5.41128337e-01 -1.07326925e+00 2.59639379e-02
6.97709441e-01 1.62698925e-01 -3.47222477e-01 6.56643450e-01
1.21497415e-01 7.11458504e-01 -8.49952400e-01 -1.04942632e+00
2.26000011e-01 1.11848332e-01 -1.20869303e+00 -4.01865721e-01
4.40154970e-01 -1.89162090e-01 -2.10554570e-01 1.15445757e+00
5.67889750e-01 -2.37266883e-01 3.30987841e-01 -1.41065359e+00
-7.61153817e-01 1.13434577e+00 -4.10159886e-01 4.51718181e-01
3.73306602e-01 1.51184350e-01 1.38000679e+00 -4.89235163e-01
8.17461610e-01 8.80083680e-01 8.69496822e-01 3.51248622e-01
-1.14297223e+00 -6.40841007e-01 3.01333636e-01 -7.11752772e-02
-1.47827017e+00 -7.60727823e-01 3.72100145e-01 -2.90153235e-01
1.24393189e+00 7.21071243e-01 1.09775160e-02 9.52654362e-01
1.02023054e-02 6.87803924e-01 9.14708495e-01 -5.36508143e-01
7.03600496e-02 3.40750247e-01 6.20626271e-01 2.78945029e-01
9.46504176e-01 -2.20769286e-01 -2.37886176e-01 -6.76305592e-01
2.64429867e-01 -4.62508529e-01 -2.99167484e-01 -3.03622454e-01
-9.27071750e-01 6.82175040e-01 -9.14278403e-02 6.10637963e-01
-2.62292743e-01 -1.16637237e-01 4.16778713e-01 9.89611149e-02
3.90517592e-01 8.13934982e-01 -9.44886506e-01 -3.13909292e-01
-9.62997794e-01 5.81628442e-01 1.21221733e+00 1.00149965e+00
2.19788507e-01 -3.54662836e-01 1.06451340e-01 4.77120548e-01
1.39310047e-01 1.25208884e-01 2.34185234e-01 -8.37323785e-01
6.61585927e-01 3.53142411e-01 3.69849384e-01 -1.12028289e+00
-2.78586358e-01 -4.71806288e-01 -2.03942388e-01 -3.12408596e-01
5.71500063e-01 -3.97861391e-01 -5.18067837e-01 1.58846426e+00
9.11804363e-02 2.74885982e-01 1.62953377e-01 4.97871011e-01
5.66138029e-01 2.78926224e-01 4.26771969e-01 -7.37936050e-02
1.58827102e+00 -4.83111531e-01 -6.59603298e-01 -3.89669031e-01
1.03736293e+00 -7.34968126e-01 9.96877074e-01 2.34460354e-01
-1.04895270e+00 1.35105208e-01 -9.74497855e-01 -6.95080981e-02
-5.40204167e-01 -3.63266990e-02 9.78164732e-01 1.14712787e+00
-6.68549180e-01 4.02475268e-01 -1.05996811e+00 -6.00792170e-01
1.12957887e-01 2.37446994e-01 -3.81582856e-01 1.62284508e-01
-1.35871768e+00 9.94949758e-01 2.19606340e-01 4.65236902e-02
-1.32580400e-01 -6.59372687e-01 -7.93188095e-01 1.77971900e-01
7.69979417e-01 -4.39794362e-01 1.41683340e+00 -2.08057493e-01
-9.38078165e-01 7.31413543e-01 -3.03930938e-01 -6.10325634e-01
1.93783000e-01 -4.06559139e-01 -7.39137471e-01 -1.56878412e-01
1.10798683e-02 2.58091167e-02 -1.95434645e-01 -1.30796790e+00
-4.77630287e-01 -2.66562462e-01 3.17325652e-01 -1.05610147e-01
-3.64518106e-01 7.11656511e-01 -3.68436158e-01 -5.78663766e-01
-2.01801270e-01 -7.91819334e-01 -3.06935400e-01 -6.53867304e-01
-6.47741079e-01 -1.05857454e-01 4.76682246e-01 -6.10439837e-01
1.88927245e+00 -1.92882395e+00 -4.74316388e-01 2.83120900e-01
3.95570636e-01 7.38073111e-01 5.59366681e-02 8.54882479e-01
-1.18662089e-01 9.05040681e-01 -7.00161234e-02 -1.82100553e-02
2.28531048e-01 -1.52665302e-01 -5.35921454e-01 3.30494642e-01
-8.55340287e-02 1.22780204e+00 -9.28937614e-01 -4.74474132e-01
-3.05980146e-01 2.82245189e-01 -6.63028240e-01 -3.22508872e-01
-2.16004297e-01 -2.03769892e-01 -7.47390270e-01 6.64388120e-01
6.05987251e-01 -3.58521134e-01 6.84775054e-01 -9.96339768e-02
-1.24461874e-01 7.20238864e-01 -1.18050301e+00 1.05328667e+00
-1.75265133e-01 4.09095824e-01 -1.12534903e-01 -5.59185743e-01
5.16722798e-01 3.62118602e-01 2.65679032e-01 -4.68129009e-01
-2.15033647e-02 1.98372230e-01 1.78649828e-01 -4.82068062e-01
9.57527220e-01 1.38643220e-01 -2.39791453e-01 5.17326772e-01
-4.08062994e-01 2.91747093e-01 1.70964435e-01 5.04940450e-01
1.31411207e+00 -2.08862811e-01 5.49098134e-01 -7.94930831e-02
2.85264462e-01 1.09833740e-01 6.52262568e-01 8.31970811e-01
-2.31756106e-01 1.81888938e-01 5.82712650e-01 -2.23927900e-01
-8.44709098e-01 -8.00486982e-01 -2.28149384e-01 7.93391466e-01
-2.18500346e-02 -9.88489211e-01 -8.79886985e-01 -1.16447306e+00
1.98728614e-03 1.09259641e+00 -1.56929746e-01 1.33823469e-01
-6.47824526e-01 -1.00701153e+00 1.17476439e+00 5.54320455e-01
2.92548478e-01 -8.15098107e-01 -7.02632070e-01 2.36958891e-01
-2.47090042e-01 -1.53166723e+00 -1.65840313e-01 -1.39128342e-01
-5.98753512e-01 -1.03189242e+00 -2.29024202e-01 -4.24148977e-01
3.27158511e-01 1.15296632e-01 1.30468643e+00 2.57814109e-01
-1.34961367e-01 2.80634612e-01 -4.72392321e-01 -3.03320587e-01
-2.72515059e-01 5.02858937e-01 -3.23175676e-02 -5.89279473e-01
1.12623715e+00 -6.70221925e-01 -2.89588660e-01 3.70855302e-01
-8.11698973e-01 -4.50611204e-01 5.42000234e-01 3.97846311e-01
-3.95679660e-02 1.50080711e-01 6.95639491e-01 -1.57671547e+00
9.91828799e-01 -6.68235421e-01 -5.50849259e-01 5.71871161e-01
-9.34798717e-01 -1.49385616e-01 4.34670359e-01 -2.94481158e-01
-8.93008173e-01 -2.54506737e-01 -4.10173625e-01 2.69081384e-01
-2.45748296e-01 9.10181582e-01 -3.33579510e-01 1.83434233e-01
7.44623005e-01 -2.17273101e-01 -5.73841870e-01 -6.14731014e-01
4.91696447e-01 9.63193059e-01 4.93757248e-01 -8.29873681e-01
8.95052731e-01 1.51239991e-01 -5.48693717e-01 -6.96947217e-01
-6.82493746e-01 -3.65796655e-01 -3.57213169e-01 5.03702223e-01
4.01143342e-01 -7.30029583e-01 -8.19853485e-01 2.54895210e-01
-9.23276126e-01 -4.02816117e-01 5.70262000e-02 2.35135138e-01
-2.26277933e-02 7.40805566e-01 -7.92774439e-01 -7.95633376e-01
-3.26975346e-01 -8.32499206e-01 7.88710773e-01 9.73129272e-02
-6.10962331e-01 -9.58822489e-01 2.80055642e-01 3.53049934e-01
3.33062857e-01 5.46185933e-02 8.80542815e-01 -1.27381003e+00
-4.85421240e-01 -4.07172441e-01 -2.53703445e-01 -2.28229091e-01
1.86192572e-01 2.41112843e-01 -9.21942174e-01 -1.90446079e-01
-3.34217697e-01 1.29122315e-02 3.73316765e-01 -1.10057712e-01
8.85955751e-01 -5.02921879e-01 -6.37832463e-01 3.29253376e-01
1.34632421e+00 1.53824985e-01 7.09670424e-01 4.89714891e-01
4.60329622e-01 4.56247211e-01 5.95107913e-01 1.21602088e-01
7.87688673e-01 7.94264853e-01 4.97679785e-02 5.21671996e-02
1.36409938e-01 -4.56877828e-01 3.02338004e-02 4.93628293e-01
3.35083827e-02 -7.95790851e-01 -1.13667262e+00 7.34296441e-01
-1.68950212e+00 -9.68944728e-01 -9.63104069e-02 2.07468152e+00
8.69401872e-01 3.51612061e-01 2.28731364e-01 9.03621390e-02
6.84207737e-01 -3.81309027e-03 -4.99960184e-02 -4.96925861e-01
1.02655217e-01 2.63873696e-01 7.86950409e-01 4.89857554e-01
-1.03350616e+00 1.23323393e+00 6.96662092e+00 4.20230240e-01
-1.11878097e+00 -1.45519963e-02 3.96149814e-01 1.57954156e-01
-3.77294004e-01 4.39669758e-01 -1.04106522e+00 3.92590106e-01
1.18987358e+00 -3.64329875e-01 1.92289144e-01 7.69623280e-01
-1.46884322e-01 4.63914871e-02 -1.09897506e+00 4.99398261e-01
-2.36523032e-01 -1.27361560e+00 -3.65264386e-01 3.40038478e-01
2.40233287e-01 3.00173387e-02 -6.60502389e-02 4.54445750e-01
8.24047625e-01 -1.00109053e+00 4.03270006e-01 1.37233377e-01
5.42962313e-01 -5.72131932e-01 7.97490299e-01 4.61973488e-01
-9.24938798e-01 1.12060048e-01 -1.43460259e-01 -5.43475784e-02
4.05983239e-01 4.77325469e-01 -1.00410795e+00 6.98742449e-01
5.46245754e-01 3.07787508e-01 -7.48625755e-01 1.06058037e+00
-7.90127814e-02 1.00517714e+00 -4.34263140e-01 3.67507823e-02
1.79531313e-02 4.61088985e-01 3.89561743e-01 1.55733263e+00
-1.24592945e-04 2.50824839e-01 -3.96672860e-02 4.92976487e-01
-1.61436900e-01 1.55686960e-01 -5.47272146e-01 -4.86322790e-01
9.42339659e-01 1.25577724e+00 -7.29053974e-01 -2.48618662e-01
-5.88055730e-01 6.70764804e-01 4.32444900e-01 4.26299870e-01
-7.68456757e-01 -5.59146643e-01 7.01136291e-01 1.58311546e-01
2.68376708e-01 -3.79374921e-01 -3.74019325e-01 -1.41643417e+00
9.62426737e-02 -1.15231228e+00 4.95940417e-01 -5.21554351e-01
-1.24518096e+00 4.46698934e-01 2.55168021e-01 -5.20677686e-01
-3.18522632e-01 -4.36000764e-01 -4.63551790e-01 8.19366038e-01
-1.48678744e+00 -9.51592267e-01 3.39690715e-01 1.03275992e-01
6.20539635e-02 3.26143891e-01 9.64863062e-01 6.91945314e-01
-8.07451785e-01 1.10334492e+00 -2.95587242e-01 5.50644994e-01
7.87573755e-01 -1.02464139e+00 1.13149023e+00 1.15721858e+00
3.83322924e-01 1.49548149e+00 1.01358414e+00 -9.36830938e-01
-1.24176824e+00 -7.73667872e-01 1.56347108e+00 -9.53103125e-01
8.32947075e-01 -5.68935037e-01 -1.10520005e+00 1.27360654e+00
1.17624909e-01 3.26471217e-02 7.24678934e-01 5.85386813e-01
-4.98482466e-01 3.33916545e-01 -1.29197192e+00 6.91519380e-01
1.07480872e+00 -4.24555153e-01 -8.08438718e-01 1.48641557e-01
5.99077940e-01 -4.21236932e-01 -1.02330792e+00 5.81847548e-01
7.26463556e-01 -6.84015632e-01 8.79606247e-01 -9.11328375e-01
3.84840705e-02 -1.76743552e-01 -9.73168239e-02 -7.98657477e-01
-1.27712414e-01 -7.63735950e-01 -2.26608410e-01 1.75007665e+00
6.84360266e-01 -8.97476375e-01 8.48909318e-01 1.19296443e+00
1.59999698e-01 -7.68414676e-01 -4.03983206e-01 -7.43907988e-01
2.49644905e-01 -3.93657625e-01 8.84060442e-01 1.34341431e+00
3.28387588e-01 3.29648882e-01 -1.58683151e-01 6.81082428e-01
2.18520030e-01 -1.54962316e-01 8.75903189e-01 -1.21142030e+00
-4.40719813e-01 -8.60267431e-02 -1.96200818e-01 -1.00448525e+00
9.36749130e-02 -7.23670185e-01 -2.10463092e-01 -1.45259404e+00
2.74007589e-01 -8.28887820e-01 -2.47811988e-01 7.31298208e-01
-2.42183939e-01 1.87696859e-01 9.43661109e-02 2.36562520e-01
-5.21352112e-01 -6.77755103e-02 5.47428310e-01 2.71216512e-01
-1.74303263e-01 4.40680310e-02 -1.34416139e+00 7.28582382e-01
7.31538832e-01 -6.17564797e-01 -4.05282438e-01 -3.29617828e-01
6.75686538e-01 -1.68755740e-01 2.13312715e-01 -5.14877975e-01
3.68575096e-01 -2.79830188e-01 -1.07715800e-01 -4.60845053e-01
-5.95560558e-02 -6.52264774e-01 6.43094033e-02 -6.64325953e-02
-2.43845254e-01 1.99456498e-01 2.66677916e-01 1.72252342e-01
9.75495726e-02 -3.20164472e-01 3.03247720e-01 -2.05877591e-02
-6.46485746e-01 9.87092033e-02 -2.74083078e-01 3.64850283e-01
9.01115060e-01 -1.94098935e-01 -7.97959387e-01 -2.95677274e-01
-5.25732756e-01 2.21304610e-01 6.89501047e-01 2.16907695e-01
4.73780967e-02 -6.09204054e-01 -4.96584624e-01 -1.93621248e-01
9.91282985e-02 -3.75766098e-01 -1.60803109e-01 5.87318301e-01
-3.31736565e-01 6.17358267e-01 2.76672453e-01 3.71646546e-02
-1.35177457e+00 7.39773035e-01 1.56443134e-01 -5.82104683e-01
-7.23669767e-01 5.47475636e-01 -2.22012132e-01 -4.94594395e-01
1.10834129e-01 -1.58326194e-01 -1.93561777e-01 -1.38702318e-01
5.10322988e-01 2.02757284e-01 2.90801466e-01 -4.75891382e-01
-6.45044148e-01 8.44164416e-02 -4.41320926e-01 -1.75983727e-01
1.36009812e+00 -1.58225119e-01 7.00738728e-02 9.40103233e-02
6.71285450e-01 1.08408463e+00 -5.99661231e-01 -1.37408823e-01
4.86892492e-01 -4.14011955e-01 -2.02184871e-01 -1.15113914e+00
-8.41293395e-01 3.84628773e-01 -7.06335455e-02 5.44300735e-01
7.88602293e-01 -1.58917792e-02 9.87207115e-01 5.28047383e-01
5.04101336e-01 -6.46410465e-01 -6.43173218e-01 4.48985994e-01
1.80359155e-01 -6.87250674e-01 3.53786319e-01 -7.40215778e-01
-5.62060058e-01 6.10231698e-01 4.06277925e-01 1.20597757e-01
5.79321027e-01 4.93507177e-01 1.34709805e-01 -3.19133461e-01
-7.02912211e-01 -6.54048398e-02 -7.15987533e-02 4.70826089e-01
8.41642857e-01 -7.86261111e-02 -5.79804301e-01 1.00796604e+00
-4.58293498e-01 4.78541702e-02 6.76337063e-01 1.12982821e+00
-2.15727076e-01 -1.53133178e+00 -2.51100082e-02 3.17186087e-01
-1.18859291e+00 -4.60371077e-01 -5.65462947e-01 1.16906571e+00
-1.98099315e-01 1.00171447e+00 -3.13727856e-01 -2.74295777e-01
4.18754488e-01 3.16297799e-01 3.24140996e-01 -8.60457897e-01
-7.26487458e-01 -3.69389772e-01 7.20306814e-01 -3.34094971e-01
-1.34810686e-01 -7.51197875e-01 -1.08269215e+00 -5.10980666e-01
-6.70345962e-01 4.55504060e-01 5.94072819e-01 8.52197051e-01
7.56291568e-01 1.25110656e-01 5.68012446e-02 4.81576100e-02
-4.88416106e-01 -6.18209779e-01 -3.09960753e-01 5.96473329e-02
-6.29082546e-02 -4.42217559e-01 -3.17900091e-01 -1.46181896e-01] | [9.376128196716309, 8.71340274810791] |
530e3b4b-04e4-4b9b-80d9-8a55f7c286f5 | spatio-temporal-deepkriging-for-interpolation | 2306.11472 | null | https://arxiv.org/abs/2306.11472v1 | https://arxiv.org/pdf/2306.11472v1.pdf | Spatio-temporal DeepKriging for Interpolation and Probabilistic Forecasting | Gaussian processes (GP) and Kriging are widely used in traditional spatio-temporal mod-elling and prediction. These techniques typically presuppose that the data are observed from a stationary GP with parametric covariance structure. However, processes in real-world applications often exhibit non-Gaussianity and nonstationarity. Moreover, likelihood-based inference for GPs is computationally expensive and thus prohibitive for large datasets. In this paper we propose a deep neural network (DNN) based two-stage model for spatio-temporal interpolation and forecasting. Interpolation is performed in the first step, which utilizes a dependent DNN with the embedding layer constructed with spatio-temporal basis functions. For the second stage, we use Long-Short Term Memory (LSTM) and convolutional LSTM to forecast future observations at a given location. We adopt the quantile-based loss function in the DNN to provide probabilistic forecasting. Compared to Kriging, the proposed method does not require specifying covariance functions or making stationarity assumption, and is computationally efficient. Therefore, it is suitable for large-scale prediction of complex spatio-temporal processes. We apply our method to monthly $PM_{2.5}$ data at more than $200,000$ space-time locations from January 1999 to December 2022 for fast imputation of missing values and forecasts with uncertainties. | ['Brian J Reich', 'Ying Sun', 'Pratik Nag'] | 2023-06-20 | null | null | null | null | ['imputation', 'gaussian-processes', 'imputation', 'imputation'] | ['computer-vision', 'methodology', 'miscellaneous', 'time-series'] | [-3.02772492e-01 -3.86522025e-01 9.39327255e-02 -4.62750465e-01
-8.34542453e-01 -1.07546061e-01 6.35797918e-01 2.18954295e-01
-2.58262187e-01 1.11841309e+00 6.98595271e-02 -7.17831969e-01
-3.00826281e-01 -1.34256160e+00 -1.03319323e+00 -9.88124132e-01
-3.66765052e-01 5.09170830e-01 -1.37545660e-01 2.04051390e-01
-5.59223890e-02 4.54115570e-01 -1.00562084e+00 -1.10362552e-01
1.03152394e+00 1.16834307e+00 2.43670061e-01 4.90723461e-01
-2.37665355e-01 7.67025650e-01 -1.92955047e-01 -3.63223732e-01
6.55148551e-02 -1.80019066e-01 -6.45034909e-02 -3.48074079e-01
-2.31107309e-01 -3.24025631e-01 -4.06861186e-01 8.33233953e-01
5.38087249e-01 5.01883149e-01 8.23597670e-01 -1.14565289e+00
-9.56439197e-01 4.26250130e-01 -7.31848836e-01 2.12569445e-01
-2.18850031e-01 1.41487554e-01 4.55429345e-01 -9.72435594e-01
-2.60830641e-01 1.43074930e+00 1.09610820e+00 8.11182261e-02
-1.40051115e+00 -6.81981862e-01 1.61497295e-01 -1.92372441e-01
-1.59342015e+00 -3.07901829e-01 4.89589334e-01 -6.74078524e-01
7.96567798e-01 -1.61682293e-01 4.17562574e-01 9.91680443e-01
8.54291439e-01 5.52187860e-01 1.00360811e+00 1.53190553e-01
4.13896620e-01 -3.62990260e-01 -2.23564550e-01 7.97960814e-03
4.05031778e-02 4.72604990e-01 -2.05840081e-01 -4.03347164e-01
1.14772999e+00 5.31821072e-01 1.36354730e-01 2.54231274e-01
-1.20667922e+00 8.74798656e-01 4.83134240e-01 -1.52612738e-02
-1.00705719e+00 7.03805327e-01 1.57265931e-01 8.48034471e-02
9.82704997e-01 -3.01802963e-01 -4.40706402e-01 -4.11047116e-02
-1.44646943e+00 3.56148779e-01 5.62367797e-01 8.30664277e-01
5.89498580e-01 5.08899152e-01 -4.06422377e-01 6.04989469e-01
6.00984216e-01 1.03667259e+00 1.46129638e-01 -7.79739439e-01
5.82195938e-01 -1.72141939e-01 6.60830319e-01 -1.18043661e+00
-3.36987138e-01 -4.63226765e-01 -1.47841644e+00 6.63093328e-02
2.52404302e-01 -7.59251356e-01 -9.59402025e-01 1.54023194e+00
1.25674307e-01 9.10594702e-01 4.98513132e-02 6.29915416e-01
2.27731764e-01 1.21342778e+00 5.54840028e-01 -4.06904221e-02
1.00435865e+00 -5.45475483e-01 -8.00150692e-01 5.03473505e-02
2.76252836e-01 -3.53727669e-01 5.76615632e-01 6.13872185e-02
-8.89846325e-01 -6.50951803e-01 -3.75288844e-01 1.27884194e-01
-3.04449111e-01 2.00838268e-01 4.40876365e-01 2.72514224e-01
-1.09081197e+00 8.24737310e-01 -1.31543219e+00 7.65473545e-02
3.08049262e-01 2.85561740e-01 -2.10202690e-02 1.24076039e-01
-1.56693649e+00 5.88019311e-01 4.35081571e-01 8.23401213e-01
-9.10566092e-01 -7.07281411e-01 -8.48793447e-01 4.68200356e-01
-2.19960406e-01 -6.79335773e-01 9.58674431e-01 -4.91036594e-01
-1.32626522e+00 -7.16754496e-02 -6.14796638e-01 -6.54096723e-01
4.72573161e-01 -1.00476228e-01 -7.98996508e-01 -3.99888992e-01
1.52023911e-01 1.69165492e-01 9.67867911e-01 -9.52018917e-01
-7.78404772e-01 -2.86242157e-01 -3.70628625e-01 -3.99436578e-02
1.84519440e-01 -2.40572002e-02 -7.33862594e-02 -8.81808877e-01
3.19208413e-01 -7.30289221e-01 -4.03407723e-01 -3.22655529e-01
-1.45315543e-01 -8.28788057e-02 4.25917625e-01 -1.26553690e+00
1.19316208e+00 -2.18164325e+00 -4.35496628e-01 1.69583410e-01
-2.65939504e-01 -1.87956035e-01 2.48809904e-02 6.93046033e-01
1.31337792e-01 9.80076119e-02 -6.55722737e-01 -7.19652236e-01
2.04926297e-01 3.86375219e-01 -7.28988886e-01 6.32876873e-01
3.53076339e-01 9.03122902e-01 -9.05722141e-01 -1.59219816e-01
4.32820737e-01 8.43530297e-01 2.27849260e-02 -1.97332650e-02
-2.54606992e-01 9.15793836e-01 -3.00000906e-01 5.18604934e-01
1.12262213e+00 -1.87592655e-01 -3.45929354e-01 4.36582178e-01
-3.64804775e-01 8.00482405e-04 -1.17704511e+00 1.18369842e+00
-8.60017478e-01 4.54763800e-01 -1.89059868e-01 -8.12683642e-01
1.08669746e+00 6.20835185e-01 2.92198867e-01 -4.85389203e-01
-2.79396057e-01 3.77395123e-01 -3.39616835e-01 -3.61543655e-01
4.66473341e-01 -4.49146867e-01 1.05594918e-01 1.38615280e-01
-2.49406099e-01 1.68038085e-01 -2.50445515e-01 -5.84686220e-01
6.74780548e-01 2.07201779e-01 -2.54427671e-01 -1.86040223e-01
2.94448584e-01 -2.52498865e-01 8.41181874e-01 6.35842562e-01
2.30398223e-01 5.18194020e-01 3.96181792e-01 -6.37656271e-01
-1.00213706e+00 -1.19462729e+00 -3.25839043e-01 7.25013852e-01
-2.63014346e-01 5.08769631e-01 -6.08903728e-02 1.03040691e-02
4.38417345e-01 1.14292490e+00 -5.99613547e-01 2.83645749e-01
-4.79683191e-01 -1.15872622e+00 4.11021173e-01 7.23047078e-01
5.80445051e-01 -1.03642380e+00 -2.74028461e-02 7.93632746e-01
-3.20690237e-02 -6.04720592e-01 -1.09069109e-01 5.95727153e-02
-1.18900239e+00 -4.08566386e-01 -1.09680581e+00 -4.29820240e-01
5.51534772e-01 -1.00525104e-01 8.71432722e-01 -6.68382764e-01
6.75814092e-01 -1.70569867e-01 1.42352805e-01 -4.30929184e-01
9.72551703e-02 -4.95975204e-02 -7.75761902e-02 3.21457803e-01
5.36614180e-01 -7.63590932e-01 -7.06153154e-01 1.16944708e-01
-8.52281451e-01 -2.46322468e-01 4.31119561e-01 8.89761269e-01
7.57917702e-01 5.14901400e-01 8.67618799e-01 -4.64636773e-01
6.64847732e-01 -1.04001892e+00 -9.13128734e-01 9.16483030e-02
-2.91705608e-01 -1.74003184e-01 6.09364927e-01 -2.00620949e-01
-1.40476632e+00 7.03806151e-03 -2.62344241e-01 -6.26886070e-01
-2.11778909e-01 1.19748342e+00 -1.82796959e-02 3.35691094e-01
1.55039340e-01 4.60728467e-01 -4.77679610e-01 -6.16720498e-01
7.43577406e-02 4.98830438e-01 5.84447324e-01 -5.77508152e-01
5.62100470e-01 6.44607544e-01 1.51811019e-01 -6.21891022e-01
-3.93253863e-01 -2.72028685e-01 -4.53159600e-01 1.58832103e-01
8.53330433e-01 -1.43007064e+00 -7.98082590e-01 7.27132082e-01
-1.34024906e+00 -5.98307788e-01 -1.79242030e-01 1.01525664e+00
-5.30249655e-01 5.23496754e-02 -8.20276320e-01 -1.39130747e+00
-2.59902656e-01 -6.60934150e-01 1.08763599e+00 1.04808733e-01
1.76070914e-01 -1.59125316e+00 1.22601785e-01 -3.51469576e-01
6.93689287e-01 4.94108289e-01 6.59833550e-01 -2.32748985e-01
-5.27784467e-01 -4.03592080e-01 -3.53190213e-01 4.12256479e-01
2.72896618e-01 1.34711817e-01 -8.47531199e-01 -1.50518239e-01
2.94372678e-01 4.05199587e-01 9.20650303e-01 1.06053698e+00
1.32526207e+00 -3.43791991e-01 -1.63640559e-01 7.64967680e-01
1.51072907e+00 4.42671150e-01 7.06711709e-01 1.89498752e-01
7.74267495e-01 6.42563403e-01 4.76575553e-01 5.67223608e-01
6.16783142e-01 -3.53835262e-02 4.46445644e-01 -1.73062738e-02
7.26198316e-01 -4.95169073e-01 3.05141151e-01 6.50748432e-01
-2.32231691e-01 -4.18942273e-01 -1.27522314e+00 9.97450769e-01
-2.05255699e+00 -1.13263273e+00 -5.80433965e-01 2.48182821e+00
5.61887264e-01 -1.04483612e-01 -2.17771202e-01 -2.78060615e-01
8.54618609e-01 2.22784817e-01 -5.62030792e-01 -2.83642858e-01
-2.66141325e-01 4.90754098e-02 1.02132750e+00 6.23237431e-01
-1.32917154e+00 6.42636359e-01 5.50753212e+00 8.85697722e-01
-1.16078115e+00 3.00423592e-01 8.50704849e-01 2.02971339e-01
-3.71829897e-01 -1.04652718e-01 -8.39410126e-01 1.06724811e+00
1.46026456e+00 -2.84313820e-02 1.32967353e-01 6.42581165e-01
9.82755542e-01 -1.54126912e-01 -7.23410845e-01 8.14598024e-01
-7.33363390e-01 -1.19146562e+00 -2.40096956e-01 9.91769433e-02
1.02968526e+00 3.38819325e-01 4.26912576e-01 3.49216044e-01
7.75976479e-01 -1.28564322e+00 4.99656558e-01 1.23082829e+00
5.98741651e-01 -1.11318970e+00 1.08493161e+00 6.07618392e-01
-1.18412650e+00 -9.00034606e-02 -6.32841110e-01 -1.44359693e-01
7.47697294e-01 1.12874389e+00 -1.18649945e-01 5.63529015e-01
6.08407915e-01 7.58909166e-01 2.63533235e-01 1.27698517e+00
-3.98820877e-01 8.94396663e-01 -5.70161343e-01 3.17041636e-01
5.34198880e-01 -5.57545364e-01 2.52981782e-01 1.04392850e+00
1.16008735e+00 -7.00319633e-02 -3.55535895e-02 8.24500859e-01
1.09873526e-01 -2.56843925e-01 -4.80448127e-01 1.66272610e-01
5.97514629e-01 6.93075538e-01 -3.65160704e-01 -3.99006993e-01
-4.31499928e-01 7.83629358e-01 -2.57185727e-01 8.94934535e-01
-1.06819069e+00 -2.50866443e-01 4.77312058e-01 8.41600895e-02
4.82052952e-01 -6.79542542e-01 -5.09928107e-01 -1.04853082e+00
1.16116591e-02 -9.29292738e-02 7.33956546e-02 -8.05062294e-01
-1.76138258e+00 4.41135943e-01 -1.01155743e-01 -1.18007815e+00
-4.13347900e-01 -1.71967909e-01 -9.73504841e-01 1.89284098e+00
-1.89099574e+00 -1.31074047e+00 6.19857311e-02 5.28350592e-01
1.46212295e-01 2.67227441e-01 6.83874071e-01 4.50330108e-01
-4.71359581e-01 -7.98569247e-02 1.13957262e+00 5.17061651e-02
4.32634801e-01 -1.18672311e+00 1.17340124e+00 7.78540790e-01
-2.84128189e-01 6.20724380e-01 6.33841574e-01 -1.01706088e+00
-9.56385732e-01 -1.78864527e+00 1.31508589e+00 -1.12564310e-01
9.32955980e-01 -1.98645025e-01 -1.23602450e+00 8.75467002e-01
-9.29628760e-02 1.07699133e-01 5.51293910e-01 -8.84605274e-02
3.24846625e-01 -2.12156221e-01 -1.10895407e+00 3.17539632e-01
2.71017015e-01 -5.65237761e-01 -2.98468202e-01 3.14208806e-01
7.18550503e-01 -2.49341726e-01 -1.08364999e+00 3.75482917e-01
3.97201806e-01 -3.56814474e-01 8.05504858e-01 -4.15049583e-01
3.19697946e-01 -4.66614872e-01 -2.34407276e-01 -1.53831851e+00
-5.37546098e-01 -4.77779299e-01 -1.44420415e-01 1.28253603e+00
5.31512678e-01 -1.03211391e+00 6.90203846e-01 9.58201766e-01
-1.11149192e-01 -4.64094818e-01 -1.22335553e+00 -1.10376763e+00
4.40366030e-01 -6.98591053e-01 1.17310047e+00 9.15242255e-01
-7.19624460e-01 -3.23068917e-01 -7.81956315e-01 7.71571279e-01
7.50310957e-01 -1.92780606e-02 5.41735351e-01 -1.29060292e+00
-1.10389709e-01 3.35734822e-02 9.74078998e-02 -1.06228495e+00
1.15902849e-01 -2.73267120e-01 1.92108095e-01 -1.75745904e+00
-4.76177633e-01 -6.65718377e-01 -5.57898939e-01 2.75015056e-01
-9.35718268e-02 -1.91243127e-01 -3.25374365e-01 3.20708156e-01
2.01683119e-01 1.10351384e+00 8.88411105e-01 -1.10779628e-01
-2.80519724e-01 6.14180088e-01 -1.09368786e-01 6.42090857e-01
1.05241024e+00 -5.89553058e-01 -3.09663236e-01 -8.62594485e-01
3.66365492e-01 5.53788841e-01 7.35060215e-01 -1.07649457e+00
3.56412023e-01 -5.10868847e-01 5.74351311e-01 -1.10431182e+00
4.71103191e-01 -9.99700904e-01 7.46176779e-01 2.37149611e-01
-2.42539239e-03 2.59506911e-01 3.35068822e-01 9.02825117e-01
-5.39831102e-01 5.97017482e-02 2.92646080e-01 -1.02701606e-02
-4.98587817e-01 8.79626811e-01 -6.27952874e-01 -5.11330247e-01
6.96378112e-01 -4.79700556e-03 1.29920006e-01 -6.37115240e-01
-1.00004101e+00 5.79707026e-01 1.78355381e-01 -4.68993932e-02
4.95958239e-01 -1.50923097e+00 -8.74540508e-01 8.43086839e-02
-3.14473301e-01 3.81033093e-01 8.18416834e-01 9.34631824e-01
-5.86147964e-01 3.96033704e-01 1.02274127e-01 -5.09968877e-01
-1.70830503e-01 6.10259295e-01 2.04161763e-01 -1.52723327e-01
-2.49627933e-01 7.85127401e-01 1.44895822e-01 -5.04671216e-01
-1.89868331e-01 -6.76599324e-01 5.80298305e-02 1.35667473e-01
3.90744984e-01 5.55317938e-01 -8.47148448e-02 -6.99820638e-01
-2.72138007e-02 1.32508010e-01 7.02727735e-01 -4.43161577e-01
1.48257446e+00 -4.74049717e-01 -3.06349397e-01 1.08295047e+00
9.39008951e-01 -4.23078746e-01 -1.74101937e+00 -3.67875546e-01
1.32781520e-01 -4.26962435e-01 1.25258267e-01 -3.91319007e-01
-1.02116501e+00 1.41581094e+00 3.80774468e-01 2.22361356e-01
8.37660313e-01 -5.76816082e-01 9.78193045e-01 1.23777717e-01
2.49852210e-01 -9.02702808e-01 -9.10479248e-01 7.44291604e-01
7.99946785e-01 -1.22499943e+00 -4.04130965e-01 1.80427939e-01
-4.37487125e-01 9.14115846e-01 3.98152024e-02 -3.53957891e-01
1.13840151e+00 -4.44033816e-02 -2.51378387e-01 4.84738946e-02
-6.01625860e-01 2.80114561e-02 8.46406072e-02 5.74064136e-01
3.25876832e-01 5.12067258e-01 7.36261159e-02 6.43139005e-01
2.20673140e-02 2.77400225e-01 9.63297933e-02 7.08209515e-01
-2.65816003e-01 -6.63554192e-01 -6.19973004e-01 5.62085211e-01
-6.64317012e-01 -5.04303336e-01 7.25111306e-01 4.49096769e-01
6.17642142e-02 9.33748841e-01 4.08672035e-01 9.45003703e-02
7.82550648e-02 1.00205369e-01 -2.55920976e-01 -4.21019137e-01
-2.68056750e-01 2.75542945e-01 -1.97592631e-01 -8.52373093e-02
-2.27412790e-01 -1.04378581e+00 -1.02870750e+00 -8.73348892e-01
-1.12902164e-01 9.64568704e-02 7.79399276e-01 7.93536007e-01
4.46958363e-01 4.14918125e-01 6.28625870e-01 -1.02472794e+00
-4.32810962e-01 -1.10380447e+00 -1.07578802e+00 -2.27747887e-01
5.77357531e-01 -6.07228041e-01 -3.13774794e-01 3.82729061e-02] | [6.8524556159973145, 3.1675448417663574] |
ee505e68-3de2-45bf-92bf-7c02767fc75a | plume-efficient-3d-object-detection-from | 2101.06594 | null | https://arxiv.org/abs/2101.06594v3 | https://arxiv.org/pdf/2101.06594v3.pdf | PLUMENet: Efficient 3D Object Detection from Stereo Images | 3D object detection is a key component of many robotic applications such as self-driving vehicles. While many approaches rely on expensive 3D sensors such as LiDAR to produce accurate 3D estimates, methods that exploit stereo cameras have recently shown promising results at a lower cost. Existing approaches tackle this problem in two steps: first depth estimation from stereo images is performed to produce a pseudo LiDAR point cloud, which is then used as input to a 3D object detector. However, this approach is suboptimal due to the representation mismatch, as the two tasks are optimized in two different metric spaces. In this paper we propose a model that unifies these two tasks and performs them in the same metric space. Specifically, we directly construct a pseudo LiDAR feature volume (PLUME) in 3D space, which is then used to solve both depth estimation and object detection tasks. Our approach achieves state-of-the-art performance with much faster inference times when compared to existing methods on the challenging KITTI benchmark. | ['Raquel Urtasun', 'Ming Liang', 'Rui Hu', 'Bin Yang', 'Yan Wang'] | 2021-01-17 | null | null | null | null | ['3d-object-detection-from-stereo-images'] | ['computer-vision'] | [ 8.48908648e-02 -3.62951577e-01 -1.25061512e-01 -4.26586419e-01
-7.69095182e-01 -5.95698953e-01 6.56184673e-01 8.42856467e-02
-7.24331379e-01 3.74497771e-01 -3.48164439e-01 -3.09571624e-01
1.97660491e-01 -7.86806583e-01 -7.45937347e-01 -5.06920934e-01
3.88365448e-01 9.20758069e-01 7.39188969e-01 1.30310863e-01
6.31098032e-01 8.53080451e-01 -1.87457049e+00 -3.38631660e-01
8.27275097e-01 1.18062222e+00 5.20748794e-01 4.88971800e-01
-3.78135145e-01 1.93229750e-01 -1.61809191e-01 -5.99429607e-02
5.05371451e-01 9.50179845e-02 -3.56417745e-01 2.12038189e-01
4.82206494e-01 -5.56390405e-01 -7.89923072e-02 1.17055142e+00
3.70112449e-01 4.84436639e-02 6.74310446e-01 -1.32986951e+00
1.33964047e-01 -5.49459830e-02 -9.24161613e-01 -1.30663753e-01
3.29041839e-01 7.98260421e-02 8.53273928e-01 -1.10145497e+00
4.64149714e-01 1.41580141e+00 5.22480249e-01 4.13680762e-01
-1.40616131e+00 -7.06132531e-01 -1.81060787e-02 1.53877571e-01
-1.48987579e+00 -2.86363333e-01 7.36793518e-01 -5.11990368e-01
8.45089436e-01 -1.61694825e-01 6.44410491e-01 6.23068750e-01
2.26689391e-02 7.96450853e-01 1.11128855e+00 -7.84815177e-02
3.80102873e-01 2.18535542e-01 -1.15695499e-01 5.95353305e-01
4.45736080e-01 1.31097361e-01 -4.80015874e-01 7.70056769e-02
7.14794934e-01 8.14022347e-02 1.38566300e-01 -1.02456629e+00
-1.07948041e+00 9.73017633e-01 5.11143982e-01 -1.76508516e-01
-3.99333566e-01 9.22086909e-02 2.48805620e-02 -6.97649494e-02
4.93826151e-01 -3.01844552e-02 -1.04037210e-01 -9.70666483e-02
-8.20059955e-01 6.27200007e-01 6.57109082e-01 9.92118537e-01
1.09080982e+00 -5.05001307e-01 1.54014289e-01 6.59107387e-01
6.24355018e-01 7.40003347e-01 -9.86386836e-02 -1.05384493e+00
7.87052214e-01 7.79009998e-01 1.96127936e-01 -8.55347991e-01
-3.53732675e-01 -2.13944018e-01 -4.09758925e-01 6.83707118e-01
4.70599681e-01 3.28183085e-01 -9.32608008e-01 1.39754915e+00
9.11638975e-01 1.41797066e-01 -1.35013871e-02 1.14745569e+00
5.22069097e-01 4.96613532e-01 -2.86355317e-01 3.20980370e-01
1.28146887e+00 -7.60220408e-01 -1.37850612e-01 -6.97047830e-01
4.91762996e-01 -6.47494197e-01 6.02055967e-01 2.78515726e-01
-9.74033356e-01 -3.81614923e-01 -1.23778439e+00 -4.29970086e-01
-3.43547523e-01 -2.82823816e-02 2.45865062e-01 6.19485080e-01
-8.18882883e-01 4.10155952e-01 -9.17543948e-01 -4.25040811e-01
5.73440909e-01 3.98883224e-01 -3.44143957e-01 -3.80721182e-01
-5.37427545e-01 1.31589305e+00 3.58697504e-01 -2.65304223e-02
-7.78717279e-01 -5.07395685e-01 -1.12208211e+00 -2.98928171e-01
6.68442667e-01 -6.81829810e-01 1.24652898e+00 1.09430850e-01
-1.42048693e+00 1.10886276e+00 -2.78192639e-01 -4.77296233e-01
7.03764319e-01 -4.25553739e-01 3.47225726e-01 4.05775160e-02
3.25052112e-01 9.29955065e-01 7.55657017e-01 -1.17951345e+00
-9.78588104e-01 -9.23691809e-01 1.50149301e-01 4.03027892e-01
2.25050062e-01 -2.84522563e-01 -8.30478489e-01 1.04993775e-01
6.17626965e-01 -1.07897687e+00 -2.78006732e-01 5.25416374e-01
-3.75928789e-01 -3.70926440e-01 9.81957197e-01 -5.92952482e-02
4.49725330e-01 -1.94315231e+00 3.04759949e-01 4.16687056e-02
2.48658106e-01 2.17564881e-01 5.85152023e-02 1.63184281e-03
5.19693375e-01 -2.19884217e-01 -4.30357277e-01 -8.55286121e-01
7.22523034e-02 2.30472490e-01 -1.50261015e-01 6.53128028e-01
4.05934066e-01 7.07835555e-01 -9.92386162e-01 -6.97432935e-01
6.61687493e-01 5.25397122e-01 -5.22716820e-01 2.10206375e-01
-2.06842408e-01 2.90153623e-01 -6.91822529e-01 6.05758131e-01
9.87495899e-01 9.02598798e-02 -2.02966616e-01 1.90208182e-02
-4.44841266e-01 4.59383458e-01 -1.27246571e+00 1.92251766e+00
-3.41511726e-01 5.14184415e-01 1.67250484e-01 -9.87878263e-01
1.10193205e+00 -1.85734019e-01 4.89665151e-01 -5.34566343e-01
3.21057856e-01 3.33509982e-01 -2.85477877e-01 -1.93740115e-01
5.70634782e-01 -2.28324026e-01 -2.90089846e-03 2.03894839e-01
-3.35442662e-01 -9.62148666e-01 1.16857253e-01 -6.94751725e-05
1.02575612e+00 4.50347632e-01 2.77581364e-01 2.14343332e-02
5.89681804e-01 2.51213998e-01 5.70869803e-01 5.78004003e-01
-7.81523660e-02 6.54538095e-01 3.73812795e-01 -2.30848461e-01
-1.05475819e+00 -1.21524668e+00 -2.43744358e-01 1.20274842e-01
6.12517297e-01 -2.97712415e-01 -5.48793614e-01 -7.32997596e-01
5.47844410e-01 5.89029253e-01 -3.41195285e-01 -3.14993737e-03
-3.91507804e-01 -2.23684236e-01 1.72226414e-01 5.57561278e-01
4.65119660e-01 -5.93690515e-01 -1.26514721e+00 2.69239336e-01
1.17795199e-01 -1.50607502e+00 -1.76219031e-01 2.34081075e-01
-1.01985490e+00 -1.03609741e+00 -4.50626016e-01 -4.20662701e-01
5.25933802e-01 8.95626962e-01 8.17588806e-01 -8.15378577e-02
-3.92541707e-01 9.61855501e-02 -9.90342870e-02 -7.18231916e-01
-1.51429594e-01 6.89113587e-02 2.21471608e-01 -7.00063705e-02
6.21314526e-01 -4.63618696e-01 -5.18110991e-01 3.66279006e-01
-7.06710100e-01 1.03965737e-01 7.18399823e-01 4.95467484e-01
9.21657503e-01 -2.24745676e-01 2.35629138e-02 -5.77684343e-01
-1.34283649e-02 -2.87186712e-01 -1.34208262e+00 -2.85379589e-01
-4.65940475e-01 2.29834631e-01 1.83136076e-01 -2.29333416e-01
-6.20658398e-01 7.10688174e-01 -1.02122784e-01 -7.19717562e-01
-8.04159716e-02 2.07595751e-01 -1.55485809e-01 -1.85660630e-01
2.65787482e-01 8.50142762e-02 3.31036836e-01 -6.01715326e-01
1.67902693e-01 7.42761970e-01 3.77978504e-01 -2.53330350e-01
1.22897887e+00 8.41742396e-01 4.12585914e-01 -9.17976022e-01
-9.34655309e-01 -7.56483555e-01 -9.24887598e-01 -1.71125203e-01
8.52348030e-01 -9.13289785e-01 -7.08429873e-01 4.27945912e-01
-1.30948257e+00 -7.81975500e-03 -6.74795806e-02 6.87600851e-01
-6.41321242e-01 2.74183571e-01 -5.43822907e-02 -1.02972937e+00
-2.19549816e-02 -1.44078159e+00 1.58869720e+00 1.24481298e-01
1.56868041e-01 -4.47349012e-01 2.74522901e-02 4.44913864e-01
8.29017758e-02 1.84490547e-01 5.23536861e-01 -1.10499822e-01
-9.74268198e-01 -3.73885810e-01 -5.67426205e-01 1.59753188e-01
-3.88484895e-02 -2.55922496e-01 -1.03588665e+00 7.94446468e-03
7.18598291e-02 -1.81788266e-01 8.54979277e-01 1.87754929e-01
9.05354023e-01 4.60439146e-01 -5.80227435e-01 6.41735315e-01
1.36992311e+00 7.06870062e-03 4.49096799e-01 3.39457661e-01
5.96677482e-01 7.77319670e-01 1.01076436e+00 2.51877785e-01
6.86652005e-01 9.99555290e-01 8.52040410e-01 2.23220468e-01
2.94386502e-02 -2.39108771e-01 1.92702711e-01 3.87499809e-01
2.81813860e-01 1.14582270e-01 -9.81148481e-01 5.10650933e-01
-1.91041231e+00 -6.34651542e-01 -1.21972904e-01 2.36533999e+00
4.38436002e-01 4.42785501e-01 1.09371878e-01 2.37136304e-01
5.78441978e-01 -2.68386751e-02 -7.69391239e-01 6.68426305e-02
2.63948351e-01 1.16908848e-02 7.55224705e-01 4.72646177e-01
-9.57765520e-01 9.68360901e-01 5.43384504e+00 4.35780376e-01
-1.03969622e+00 2.76226755e-02 9.60417464e-02 -2.38821894e-01
-2.62323283e-02 2.30173945e-01 -1.21585274e+00 3.44809264e-01
4.78672177e-01 9.25506931e-03 2.07418695e-01 9.36859310e-01
1.64186016e-01 -5.96698940e-01 -1.26676977e+00 1.36217833e+00
1.81360155e-01 -1.05447710e+00 -1.48063183e-01 3.61158520e-01
2.48352438e-01 3.83452266e-01 -2.25155294e-01 7.00551495e-02
7.55803511e-02 -6.30368471e-01 9.62838113e-01 1.91194385e-01
6.55739963e-01 -6.40926600e-01 5.32054782e-01 8.00190449e-01
-1.37038815e+00 8.77744853e-02 -5.76067328e-01 -1.11315444e-01
3.35445851e-01 7.59467006e-01 -1.05981243e+00 3.77196878e-01
7.19897449e-01 6.99972510e-01 -3.94817531e-01 1.41194737e+00
-3.22446406e-01 -5.56146214e-03 -7.62692273e-01 -1.05964601e-01
1.59634918e-01 -3.78087461e-01 8.24257255e-01 6.46294415e-01
5.21233082e-01 -7.83343092e-02 3.68746519e-01 1.17273068e+00
5.26246019e-02 -2.64875978e-01 -8.72436523e-01 2.29279593e-01
6.14093959e-01 1.30385375e+00 -8.16153646e-01 -2.33746722e-01
-5.74035048e-01 7.82923818e-01 4.50096905e-01 -1.50839865e-01
-7.98516214e-01 -3.27923059e-01 9.14747477e-01 2.51983136e-01
4.58668590e-01 -6.67813718e-01 -2.96675175e-01 -1.09992969e+00
2.99851239e-01 -1.72392428e-01 -5.55470474e-02 -7.27581501e-01
-7.83086717e-01 2.89025128e-01 8.59365240e-02 -1.41646612e+00
-2.78088331e-01 -7.10987449e-01 -1.98717043e-01 9.47005928e-01
-1.95757079e+00 -8.55551302e-01 -5.88453472e-01 3.53736490e-01
6.46607161e-01 3.34817290e-01 3.02557379e-01 3.19200695e-01
-4.22150642e-01 -4.88378890e-02 -1.47766888e-01 -2.46895909e-01
3.73715162e-01 -1.14306414e+00 6.10937774e-01 8.03111136e-01
4.05274481e-02 1.08092591e-01 5.15484035e-01 -7.32888341e-01
-1.79837191e+00 -9.96004045e-01 8.50242436e-01 -6.25669301e-01
3.49999398e-01 -5.45070052e-01 -6.98590517e-01 3.30448925e-01
-4.56493765e-01 2.42849708e-01 -1.13776095e-01 -2.97897160e-01
-3.36529762e-01 -1.69470325e-01 -1.13483047e+00 3.65810513e-01
1.26351130e+00 -4.56071019e-01 -5.00233889e-01 2.73253042e-02
6.00031912e-01 -7.53153741e-01 -3.58535618e-01 3.94904554e-01
6.28005803e-01 -9.16503429e-01 1.07011521e+00 -4.28388640e-02
1.15219004e-01 -6.78904057e-01 -2.94054240e-01 -1.03368306e+00
1.26316354e-01 -2.97555745e-01 -1.42752454e-01 8.22723866e-01
1.12597883e-01 -6.63815916e-01 8.94383669e-01 2.78475314e-01
-6.54011145e-02 -5.98155439e-01 -1.15716112e+00 -9.87388909e-01
-3.14349890e-01 -7.51729071e-01 5.47163844e-01 2.34201550e-01
-5.13733745e-01 5.91720700e-01 6.77947327e-02 3.66262674e-01
1.13159931e+00 3.95033538e-01 1.17332661e+00 -1.66535068e+00
3.19939733e-01 -4.31416124e-01 -7.08281994e-01 -1.48082209e+00
1.29244596e-01 -8.27954888e-01 6.23049498e-01 -1.55348539e+00
1.42358735e-01 -7.94047475e-01 2.47987941e-01 1.57184228e-01
4.69504594e-04 4.28632081e-01 2.51403660e-01 1.48026004e-01
-3.84247184e-01 5.97118616e-01 8.86090219e-01 -1.53931335e-01
-2.11208731e-01 4.73752730e-02 -3.09796005e-01 7.59168088e-01
4.93432552e-01 -5.51101446e-01 -1.72156408e-01 -7.61094511e-01
-5.03369942e-02 1.14593655e-01 6.45728767e-01 -1.22480106e+00
3.21828246e-01 -1.99366763e-01 2.21195102e-01 -1.38281834e+00
8.33538055e-01 -9.43347156e-01 -1.73144594e-01 4.49946404e-01
2.14709982e-01 -3.56032886e-02 7.73930475e-02 5.61362803e-01
-2.02805400e-02 -2.91235656e-01 9.27814960e-01 -1.26526263e-02
-5.88753641e-01 5.13479292e-01 -3.42855463e-03 -2.80235797e-01
1.17725849e+00 -5.43798089e-01 1.49102971e-01 -1.26527071e-01
-1.83923587e-01 4.46796864e-01 7.39349961e-01 5.67044079e-01
7.80587316e-01 -1.27111626e+00 -6.89985335e-01 1.86641634e-01
1.14979312e-01 7.71451592e-01 -2.01905325e-01 8.72079313e-01
-5.21347344e-01 5.11279166e-01 -1.66011099e-02 -1.31281519e+00
-1.18258166e+00 4.54240650e-01 1.40079319e-01 -1.09836999e-02
-7.42661238e-01 6.14369154e-01 2.81103402e-01 -5.39335847e-01
3.00343901e-01 -3.79562616e-01 -2.96289977e-02 5.43913804e-02
5.18267214e-01 2.75443196e-01 1.93920091e-01 -7.39057779e-01
-6.15167379e-01 1.11470759e+00 7.36009851e-02 -2.63498694e-01
1.32401836e+00 -2.16756523e-01 1.75365314e-01 3.82569969e-01
1.24021745e+00 -2.95116544e-01 -1.51086187e+00 -4.51980829e-01
2.18432277e-01 -8.44766855e-01 2.19652131e-01 -3.85439023e-02
-8.98795247e-01 1.15802491e+00 5.52119493e-01 1.30947493e-02
7.26538539e-01 2.41387501e-01 8.72171938e-01 4.24339592e-01
6.64592981e-01 -9.79928434e-01 -3.03056724e-02 7.12808311e-01
6.52108908e-01 -1.52354658e+00 2.23514497e-01 -5.83807588e-01
-2.41812050e-01 9.36780870e-01 5.02504706e-01 -1.43906087e-01
3.09061438e-01 1.85617521e-01 -2.52182841e-01 -1.84422836e-01
-4.75888908e-01 -6.56721890e-01 2.24096298e-01 4.81199116e-01
-1.13240130e-01 -1.90075532e-01 -8.15931335e-02 -7.32185245e-02
-1.16193302e-01 -7.61029124e-02 1.50096491e-01 1.16497958e+00
-6.12235785e-01 -1.13776791e+00 -4.23987895e-01 3.60898197e-01
6.04773574e-02 2.70390391e-01 -3.80029440e-01 7.13871241e-01
3.98305170e-02 8.45609069e-01 1.79336548e-01 -4.31015164e-01
5.46794653e-01 -6.20295331e-02 6.88468635e-01 -8.58980775e-01
2.26104837e-02 -8.00646618e-02 -2.40820736e-01 -9.56077933e-01
-4.01785940e-01 -8.90818179e-01 -1.23517764e+00 -5.67825139e-02
-4.40364480e-01 -7.35661015e-02 1.29260492e+00 1.02229905e+00
3.00226212e-01 -3.02408934e-02 6.41128302e-01 -1.45665395e+00
-6.09577119e-01 -6.47691727e-01 -3.92259300e-01 2.35026926e-01
4.12849128e-01 -1.17347014e+00 -2.45489672e-01 -4.25847083e-01] | [7.839908599853516, -2.651840925216675] |
4f318ed9-9b35-457f-ab75-eb86030016e0 | s-3-track-self-supervised-tracking-with-soft | 2305.09981 | null | https://arxiv.org/abs/2305.09981v1 | https://arxiv.org/pdf/2305.09981v1.pdf | S$^3$Track: Self-supervised Tracking with Soft Assignment Flow | In this work, we study self-supervised multiple object tracking without using any video-level association labels. We propose to cast the problem of multiple object tracking as learning the frame-wise associations between detections in consecutive frames. To this end, we propose differentiable soft object assignment for object association, making it possible to learn features tailored to object association with differentiable end-to-end training. With this training approach in hand, we develop an appearance-based model for learning instance-aware object features used to construct a cost matrix based on the pairwise distances between the object features. We train our model using temporal and multi-view data, where we obtain association pseudo-labels using optical flow and disparity information. Unlike most self-supervised tracking methods that rely on pretext tasks for learning the feature correspondences, our method is directly optimized for cross-object association in complex scenarios. As such, the proposed method offers a reidentification-based MOT approach that is robust to training hyperparameters and does not suffer from local minima, which are a challenge in self-supervised methods. We evaluate our proposed model on the KITTI, Waymo, nuScenes, and Argoverse datasets, consistently improving over other unsupervised methods ($7.8\%$ improvement in association accuracy on nuScenes). | ['Felix Heide', 'Fahim Mannan', 'Fatemeh Azimi'] | 2023-05-17 | null | null | null | null | ['multiple-object-tracking'] | ['computer-vision'] | [-6.28533363e-02 -1.99283913e-01 -3.87805223e-01 -4.27578300e-01
-7.63048112e-01 -6.18474364e-01 6.09275579e-01 2.40254119e-01
-7.41236091e-01 6.99609697e-01 -3.09814394e-01 3.73245299e-01
-3.16165179e-01 -3.41655165e-01 -9.89954829e-01 -7.49868095e-01
-2.70878345e-01 6.47288799e-01 4.46569204e-01 1.77130595e-01
1.21405274e-01 4.47009087e-01 -1.71952021e+00 -9.16892141e-02
6.89469278e-01 1.07518828e+00 1.11841090e-01 6.44942284e-01
1.06652111e-01 5.27612329e-01 -2.05870673e-01 -4.13540661e-01
4.77275640e-01 -1.98320925e-01 -6.20159268e-01 3.51741314e-01
1.42324960e+00 -1.53523982e-01 -2.86327135e-02 1.01294386e+00
3.00010204e-01 1.72025070e-01 6.68956935e-01 -1.38565791e+00
-3.19871962e-01 1.28296241e-01 -9.28310931e-01 3.34577143e-01
-1.92937851e-02 1.96249753e-01 1.16994047e+00 -9.00438130e-01
7.32041538e-01 1.17677343e+00 7.70282745e-01 5.46492159e-01
-1.43072164e+00 -7.90812612e-01 4.02390063e-01 1.40653819e-01
-1.42190790e+00 -5.27880847e-01 5.74598670e-01 -8.26928854e-01
4.19865310e-01 1.19002853e-02 6.68396115e-01 7.31231332e-01
-6.98808357e-02 8.14952135e-01 9.18176055e-01 -3.30424428e-01
-5.39392307e-02 2.57921934e-01 2.74009526e-01 9.91308093e-01
4.98271376e-01 2.55885184e-01 -6.10806644e-01 -2.10370302e-01
7.00548053e-01 6.93249702e-02 1.11323610e-01 -1.03961372e+00
-1.41537225e+00 6.45180285e-01 5.23224354e-01 -5.33245429e-02
-2.24774972e-01 4.52137053e-01 1.55534729e-01 5.94286658e-02
4.78532791e-01 4.27246653e-02 -3.91323209e-01 2.53974289e-01
-8.09133410e-01 3.88852179e-01 4.12535220e-01 1.06915522e+00
1.13762701e+00 -7.01940283e-02 -2.89213687e-01 5.22422373e-01
5.86302400e-01 6.14071667e-01 1.57032952e-01 -9.20757294e-01
4.27382022e-01 4.75610465e-01 4.75014359e-01 -9.25473690e-01
-3.81229818e-01 -6.22850120e-01 -4.29041117e-01 4.15341496e-01
6.18134737e-01 -2.20279783e-01 -6.52250350e-01 1.93569195e+00
8.03400397e-01 5.50584137e-01 -4.55072196e-03 9.28956568e-01
6.05915070e-01 5.59093468e-02 2.04979464e-01 -3.83266747e-01
1.21975839e+00 -1.10786450e+00 -6.57328963e-01 -1.37859493e-01
6.64514959e-01 -6.28632545e-01 5.15488148e-01 2.51393276e-03
-9.53487694e-01 -7.72821069e-01 -8.70561719e-01 3.41426879e-01
-2.54071295e-01 2.62830734e-01 6.88320816e-01 4.94429946e-01
-7.80591369e-01 5.22687733e-01 -9.43271756e-01 -4.91817504e-01
5.57504594e-01 6.28394663e-01 -3.71460170e-01 3.11704725e-01
-5.51683486e-01 8.72449934e-01 5.50317168e-01 1.74802303e-01
-7.38455415e-01 -7.79131472e-01 -7.99036860e-01 -2.84256816e-01
5.74639320e-01 -7.64747441e-01 8.77996206e-01 -9.87839580e-01
-1.28440225e+00 9.95380402e-01 -2.51743942e-01 -4.73049551e-01
6.50371611e-01 -4.47735310e-01 -2.64518619e-01 3.64626460e-02
2.07440943e-01 9.09631431e-01 9.99981463e-01 -1.41415811e+00
-1.03882480e+00 -3.30711484e-01 7.79084787e-02 1.42071873e-01
-3.90429229e-01 1.46993380e-02 -5.51617622e-01 -5.43647110e-01
-5.00289835e-02 -1.23018610e+00 -1.54334292e-01 5.73282242e-01
-1.50198892e-01 -2.53739268e-01 1.07147110e+00 -1.39966652e-01
6.43540680e-01 -2.07490635e+00 3.42048883e-01 1.73528597e-01
2.26430491e-01 1.94047838e-01 -1.08727485e-01 -1.15354069e-01
2.57893443e-01 -3.54843199e-01 -3.58570218e-02 -7.10219145e-01
-9.91860628e-02 1.86981544e-01 1.30386636e-01 9.75626409e-01
3.49483281e-01 9.02728081e-01 -1.08241916e+00 -8.95266831e-01
3.43954682e-01 3.46343070e-01 -5.99561155e-01 3.10100019e-01
-3.91624510e-01 8.12227190e-01 -4.30255443e-01 5.40996075e-01
5.52473664e-01 -4.53262031e-01 -1.81335397e-02 -2.47290045e-01
-2.92220831e-01 -4.32911664e-01 -1.55519307e+00 1.83447194e+00
-2.95160890e-01 5.75428307e-01 -3.62235866e-02 -7.94930816e-01
7.50895858e-01 1.01527132e-01 8.40928257e-01 -2.58725792e-01
-1.30159257e-03 1.39152691e-01 9.94189992e-04 -3.49110126e-01
3.61538619e-01 3.59583385e-02 2.56181955e-01 1.71380192e-01
2.45722413e-01 3.59793127e-01 3.93183410e-01 3.17234360e-02
6.46655858e-01 5.15933454e-01 2.04457521e-01 -3.17265213e-01
6.72790051e-01 -9.52361338e-03 7.29533434e-01 6.66305423e-01
-1.68533280e-01 4.11320388e-01 -1.43917024e-01 -3.98837566e-01
-7.12448239e-01 -1.12155986e+00 -3.68276387e-01 1.06336987e+00
5.57718694e-01 -2.83260942e-01 -2.83335984e-01 -8.81130040e-01
2.70335495e-01 1.42258424e-02 -6.34048283e-01 1.86357886e-01
-7.26875365e-01 -6.49306655e-01 2.15158150e-01 4.99823987e-01
3.65017623e-01 -7.07769156e-01 -6.30735517e-01 2.37586215e-01
-9.31452885e-02 -1.38062727e+00 -7.21465826e-01 1.11889496e-01
-8.18462431e-01 -1.31263697e+00 -6.33726776e-01 -7.21968174e-01
7.78127909e-01 2.98541635e-01 8.27982306e-01 9.99458432e-02
-4.00870651e-01 7.67216325e-01 -1.05005220e-01 -3.35674673e-01
-5.81256822e-02 -5.67294545e-02 3.84011537e-01 5.62854111e-01
1.92060530e-01 -3.95536184e-01 -6.39167368e-01 5.89283168e-01
-5.02704263e-01 -1.37576044e-01 6.42757177e-01 7.38198161e-01
9.07006323e-01 -4.23329830e-01 4.59062666e-01 -6.42177999e-01
-4.06646222e-01 -3.59066635e-01 -1.09985185e+00 3.31889391e-01
-4.97499019e-01 1.44679070e-01 3.73692334e-01 -6.18254304e-01
-9.52424586e-01 6.64858222e-01 4.39342558e-01 -7.85403311e-01
-1.62506133e-01 -3.70620228e-02 6.24970812e-03 -5.53711057e-01
5.21961033e-01 -3.26198414e-02 1.88440494e-02 -4.15908486e-01
4.74749178e-01 1.72114834e-01 5.49731970e-01 -6.32241070e-01
1.27515650e+00 8.42878640e-01 2.37178862e-01 -7.42593944e-01
-1.17413700e+00 -9.21918511e-01 -1.08794117e+00 -5.25891542e-01
1.08784795e+00 -1.09572983e+00 -9.77931619e-01 1.97179422e-01
-1.02820146e+00 -7.90225565e-02 -2.80983925e-01 7.95449555e-01
-7.72158265e-01 4.64553952e-01 -6.81378469e-02 -9.18496311e-01
-1.43458754e-01 -9.65455592e-01 1.11271000e+00 3.59853029e-01
-1.15984855e-02 -1.14649558e+00 1.84017330e-01 2.77907640e-01
1.78273901e-01 4.44712669e-01 2.26991162e-01 -5.11169672e-01
-1.01329136e+00 -7.62624070e-02 -2.83705831e-01 1.30754933e-01
2.91771740e-01 -5.52044213e-02 -9.12582695e-01 -5.82801342e-01
-3.95547479e-01 -3.68842274e-01 7.98570752e-01 4.80297714e-01
9.96760964e-01 -1.54148459e-01 -7.06834555e-01 7.84157455e-01
1.46503031e+00 -1.57635108e-01 -2.88740993e-02 4.22629833e-01
9.63599980e-01 6.35368526e-01 1.04453194e+00 4.47888792e-01
4.93910044e-01 1.10409057e+00 5.93620718e-01 -1.40160739e-01
-2.33434975e-01 -5.55417389e-02 3.40315878e-01 3.02950799e-01
-2.09574431e-01 2.49113906e-02 -7.74999797e-01 6.22138679e-01
-2.30125546e+00 -1.00786793e+00 -3.53704661e-01 2.28389382e+00
6.96135223e-01 1.01039663e-01 4.19066519e-01 -5.64309597e-01
8.87324810e-01 -8.26699138e-02 -6.58541143e-01 5.25640845e-01
-9.03648958e-02 -1.59271613e-01 6.91307962e-01 4.62452024e-01
-1.58909214e+00 8.61077070e-01 5.29223013e+00 4.14988309e-01
-8.85626078e-01 1.24150924e-01 5.65668270e-02 -1.97882652e-01
2.36065537e-01 4.32416387e-02 -1.40548706e+00 3.13540459e-01
5.78994036e-01 4.16345373e-02 -4.14669774e-02 6.96029484e-01
1.20059386e-01 -6.30606487e-02 -1.36696613e+00 9.83164549e-01
9.27062407e-02 -1.33379352e+00 -2.10644841e-01 -2.87721753e-02
7.42908299e-01 -7.91477188e-02 1.52851388e-01 -7.20005203e-03
2.99747914e-01 -4.88513112e-01 7.72006691e-01 3.70201290e-01
3.67028296e-01 -3.38702828e-01 4.49563682e-01 1.39062494e-01
-1.57885110e+00 -2.77692545e-02 -6.32059947e-02 2.66280204e-01
2.02614561e-01 1.59706965e-01 -6.06876612e-01 7.27440476e-01
8.09575438e-01 1.07795119e+00 -6.89840555e-01 1.41644800e+00
2.32151300e-01 2.09560916e-01 -5.01611710e-01 2.65165299e-01
6.11390844e-02 -1.73120588e-01 7.11886823e-01 1.09269905e+00
2.57773045e-02 -2.03516498e-01 6.14923954e-01 5.92501342e-01
1.05825715e-01 8.60254690e-02 -3.55248719e-01 3.35810244e-01
2.02352837e-01 1.42978585e+00 -7.79105425e-01 -3.03142756e-01
-5.86956799e-01 7.11446166e-01 5.12589872e-01 1.87438205e-01
-1.01563346e+00 1.68537095e-01 7.74687111e-01 9.40143317e-02
6.40479207e-01 -2.58352458e-01 1.36182770e-01 -1.29577684e+00
1.78366303e-01 -3.76243323e-01 6.63757265e-01 -3.21182162e-01
-1.38268280e+00 3.02546769e-01 1.94258302e-01 -1.66666508e+00
-1.90441698e-01 -4.23803091e-01 -3.79008502e-01 4.70912784e-01
-1.83196461e+00 -1.54514968e+00 -3.40232044e-01 5.89126945e-01
3.14335376e-01 -6.18961416e-02 4.74166006e-01 5.17108381e-01
-5.02948880e-01 6.55864477e-01 1.46062329e-01 2.19492480e-01
1.03994572e+00 -1.22707534e+00 -3.98206227e-02 8.25834811e-01
4.38699752e-01 5.22385895e-01 7.91747630e-01 -6.62226081e-01
-1.42890966e+00 -1.47237313e+00 4.32775021e-01 -7.45610535e-01
7.75397599e-01 -3.16053122e-01 -7.50195146e-01 9.75710571e-01
-4.44831476e-02 7.37305403e-01 6.57119155e-01 2.43260473e-01
-2.38680765e-01 -2.79471368e-01 -9.07579124e-01 2.32061699e-01
1.34083056e+00 -3.93213592e-02 -3.40593636e-01 5.50674319e-01
4.73989666e-01 -5.91102064e-01 -9.72940683e-01 3.30642134e-01
6.30897462e-01 -4.90916371e-01 1.14533281e+00 -6.52049184e-01
-1.94377661e-01 -7.41726458e-01 -4.68862802e-02 -9.37283576e-01
-2.57178366e-01 -4.99481320e-01 -3.28704089e-01 1.40434718e+00
3.21989745e-01 -5.03449917e-01 9.23294425e-01 3.84079516e-01
-3.91066298e-02 -3.39964122e-01 -9.57709610e-01 -1.09506917e+00
-2.16282457e-01 1.99629087e-02 7.62074888e-02 9.01452541e-01
-4.87171441e-01 1.01736605e-01 -6.13955498e-01 6.81386232e-01
1.50268495e+00 5.53089678e-01 1.13957858e+00 -1.43623877e+00
-4.46475714e-01 -2.75039792e-01 -6.18228614e-01 -1.03257155e+00
4.53322083e-01 -9.60514545e-01 1.32578835e-01 -9.96034324e-01
4.02739674e-01 -7.78987825e-01 -2.86772490e-01 4.53173250e-01
-3.92772466e-01 4.14783686e-01 2.82734245e-01 3.04664850e-01
-1.24716794e+00 6.42527461e-01 1.07346487e+00 -2.53269881e-01
-1.32060394e-01 6.40774816e-02 -2.06682652e-01 6.71815038e-01
4.81693536e-01 -6.98631883e-01 -5.67326099e-02 -4.30642068e-01
-2.49537632e-01 -6.28272519e-02 8.40397596e-01 -1.06204760e+00
5.48483253e-01 -2.95893997e-01 3.27864736e-01 -7.93678105e-01
5.12369692e-01 -1.09156346e+00 2.13008583e-01 4.95033801e-01
-3.23059469e-01 -6.95869252e-02 1.89426765e-01 9.24487710e-01
-5.05770259e-02 -2.60440111e-02 9.74849701e-01 1.32257059e-01
-1.03984845e+00 7.74720907e-01 1.02896206e-01 1.55322999e-01
1.33549023e+00 -2.68873334e-01 -8.38949457e-02 1.34993479e-01
-7.84010231e-01 6.91339374e-01 5.15160441e-01 5.00205934e-01
2.78128177e-01 -1.43880427e+00 -6.75941169e-01 3.04376278e-02
4.70021427e-01 1.58391878e-01 2.00644985e-01 1.04689932e+00
5.17988242e-02 2.71457523e-01 -2.30476737e-01 -1.29620206e+00
-1.53102267e+00 4.80349243e-01 3.98664951e-01 -6.74571050e-03
-6.48143291e-01 7.10246205e-01 3.30008507e-01 -2.70936728e-01
3.60285759e-01 -3.68365012e-02 -2.37418741e-01 1.16570041e-01
2.98930496e-01 2.05042481e-01 -1.77914888e-01 -8.75785768e-01
-4.92082149e-01 1.05685472e+00 -2.13497862e-01 1.28582880e-01
1.22111845e+00 -2.89379090e-01 2.15990290e-01 4.46908504e-01
1.10333228e+00 -7.22386315e-02 -1.65341461e+00 -6.04314268e-01
2.26675749e-01 -7.02448547e-01 -7.62164816e-02 -3.76306832e-01
-1.19136524e+00 3.69275689e-01 1.06423533e+00 -2.47375786e-01
5.48987627e-01 2.66056091e-01 4.20029372e-01 4.36837822e-01
3.48357230e-01 -8.27043712e-01 3.35696936e-01 2.18512237e-01
4.85932142e-01 -1.81467557e+00 7.45923370e-02 -3.88191760e-01
-3.69464099e-01 9.37639058e-01 1.03939581e+00 -8.62673074e-02
6.04410768e-01 9.47270170e-02 6.59089833e-02 -2.42453456e-01
-5.55894136e-01 -6.08460307e-01 5.42461276e-01 5.81879199e-01
2.03044742e-01 -2.27426872e-01 -5.64285107e-02 -1.96997374e-01
2.13603392e-01 -1.27400815e-01 9.49029103e-02 9.57617462e-01
-3.61549079e-01 -1.14923990e+00 -4.80026662e-01 4.52840507e-01
-2.67527580e-01 3.14113438e-01 4.95662577e-02 9.66962159e-01
1.69167295e-01 6.93314314e-01 2.21007094e-01 -2.29269993e-02
3.93163800e-01 -1.57007292e-01 7.00002074e-01 -6.70818925e-01
-4.43357885e-01 -3.26218344e-02 -5.97508550e-02 -4.36638504e-01
-1.13305950e+00 -1.05121112e+00 -1.18862617e+00 2.32828576e-02
-6.97867453e-01 3.94625776e-03 3.62459004e-01 1.06035185e+00
2.20445782e-01 2.92060286e-01 5.02846420e-01 -1.06598568e+00
-3.11373264e-01 -5.13888538e-01 -2.50581235e-01 6.64592743e-01
6.88205659e-01 -1.22411549e+00 -2.14273065e-01 1.99975967e-01] | [6.417240142822266, -2.1082136631011963] |
560761cc-12ba-4484-97eb-4ce177e14f22 | making-language-models-better-tool-learners | 2305.13068 | null | https://arxiv.org/abs/2305.13068v1 | https://arxiv.org/pdf/2305.13068v1.pdf | Making Language Models Better Tool Learners with Execution Feedback | Tools serve as pivotal interfaces that enable humans to understand and reshape the world. With the advent of foundational models, AI systems can utilize tools to expand their capabilities and interact with the world. Existing tool learning methodologies, encompassing supervised fine-tuning and prompt engineering approaches, often induce language models to utilize tools indiscriminately, as complex problems often exceed their own competencies. However, introducing tools for simple tasks, which the models themselves can readily resolve, can inadvertently propagate errors rather than enhance performance. This leads to the research question: can we teach language models when and how to use tools? To meet this need, we propose Tool leaRning wIth exeCution fEedback (TRICE), a two-stage end-to-end framework that enables the model to continually learn through feedback derived from tool execution, thereby learning when and how to use tools effectively. Experimental results, backed by further analysis, show that TRICE can make the language model to selectively use tools by decreasing the model's dependency on tools while enhancing the performance. Code and datasets will be available in https://github.com/zjunlp/trice. | ['Ningyu Zhang', 'Huajun Chen', 'Honghao Gui', 'Shuofei Qiao'] | 2023-05-22 | null | null | null | null | ['prompt-engineering'] | ['natural-language-processing'] | [ 2.72909440e-02 4.62741554e-02 -2.64831632e-01 -2.28711843e-01
-2.10762754e-01 -8.83821249e-01 3.93999904e-01 -1.08794212e-01
-2.01595917e-01 2.42400363e-01 -2.16307789e-01 -6.41869307e-01
-2.88234279e-02 -8.18046451e-01 -5.98784626e-01 1.84484899e-01
2.14953184e-01 2.58034289e-01 2.50982314e-01 -2.77309269e-01
7.08119452e-01 4.89646286e-01 -1.87199187e+00 2.35034138e-01
1.50964749e+00 1.45827413e-01 3.50313336e-01 8.10562849e-01
-4.64941114e-01 1.47583318e+00 -6.24735951e-01 -2.05185071e-01
-3.40172276e-02 -2.30078325e-01 -8.68734539e-01 -1.92961901e-01
6.31837621e-02 -5.54281116e-01 7.41963983e-02 9.01194692e-01
1.21363543e-01 -8.81177932e-03 2.44735748e-01 -1.52806497e+00
-7.70282030e-01 8.83062184e-01 -2.32865438e-01 -2.14618593e-02
7.40909457e-01 4.97602820e-01 6.49555922e-01 -8.12873542e-01
5.92715621e-01 1.17941141e+00 7.64273763e-01 6.53182864e-01
-1.14986920e+00 -1.02678394e+00 6.96985424e-02 1.09688468e-01
-1.14963090e+00 -4.52297926e-01 9.90443230e-01 -8.77161086e-01
1.07274401e+00 4.32620555e-01 7.54781961e-01 9.07796562e-01
2.28220627e-01 9.61586952e-01 9.73627388e-01 -6.63172901e-01
-6.45870492e-02 4.43288594e-01 2.20183596e-01 9.60469663e-01
3.54401022e-01 4.02631223e-01 -8.08585465e-01 1.19470060e-01
7.26796925e-01 2.82813251e-01 -1.51350439e-01 -4.72939432e-01
-1.01610923e+00 4.22843874e-01 1.69989377e-01 5.76890230e-01
-2.05405459e-01 2.04330608e-01 2.23605171e-01 5.85455954e-01
-7.57200867e-02 1.34752381e+00 -5.28854311e-01 -7.64767945e-01
-7.42057323e-01 1.64022654e-01 9.44473803e-01 1.08819842e+00
8.07684839e-01 1.17925756e-01 8.92139692e-03 4.75107402e-01
2.67241925e-01 2.79101610e-01 3.95871699e-01 -1.08884752e+00
5.08649647e-02 1.17525268e+00 3.28082412e-01 -9.07784700e-01
-2.58085936e-01 -3.98805887e-01 -2.48205632e-01 4.61307019e-01
2.51427621e-01 -1.25531271e-01 -5.71240544e-01 1.53361547e+00
1.39848873e-01 -4.59731650e-03 -3.74383628e-01 6.00885630e-01
4.63447124e-01 3.66958201e-01 5.71356416e-02 1.08684316e-01
8.61667156e-01 -1.02649367e+00 -6.89758539e-01 -2.72238195e-01
1.32233107e+00 -6.57946706e-01 1.81181371e+00 7.62472808e-01
-1.01450574e+00 -7.11163044e-01 -1.19664872e+00 3.99181992e-02
-4.00112748e-01 -9.75490510e-02 8.11472476e-01 5.80153227e-01
-9.25410926e-01 9.15278316e-01 -1.21181536e+00 -1.11211747e-01
3.11930835e-01 1.14931189e-01 6.54344335e-02 -1.77878037e-01
-9.65390205e-01 1.10795772e+00 3.90009165e-01 -5.55525646e-02
-9.28781092e-01 -1.04823613e+00 -7.37088323e-01 -7.66592892e-03
6.22521102e-01 -7.01094091e-01 1.85351825e+00 -1.31238902e+00
-1.73101902e+00 4.23971474e-01 1.80362165e-01 6.26072958e-02
7.70391583e-01 -7.07997203e-01 -8.28298852e-02 -8.24102014e-02
1.69175521e-01 1.72277942e-01 4.88322586e-01 -1.37958705e+00
-5.49465477e-01 -9.27428380e-02 1.61871865e-01 -1.73242122e-01
-5.89010060e-01 1.50237352e-01 -2.14797810e-01 -3.92382115e-01
-3.62981170e-01 -8.63202631e-01 -5.98266497e-02 2.42751203e-02
-2.87004709e-01 -2.31905624e-01 6.41076744e-01 -5.40104747e-01
1.58172214e+00 -2.00621700e+00 9.47872996e-02 2.29139537e-01
6.70676589e-01 3.88430685e-01 -2.07493845e-02 6.96255028e-01
1.18846200e-01 4.69605923e-01 1.65787131e-01 3.48407254e-02
1.68268085e-01 -6.54390231e-02 1.69300959e-01 -1.29135296e-01
1.96855545e-01 8.41785133e-01 -1.18898499e+00 -3.05600494e-01
3.61988336e-01 1.30028859e-01 -7.37993896e-01 6.72850549e-01
-2.11137608e-01 3.79455119e-01 -5.74199021e-01 6.72638178e-01
2.20484391e-01 -4.36244965e-01 1.03858367e-01 3.16635728e-01
-3.28586191e-01 2.33570486e-01 -1.03614283e+00 1.59062040e+00
-1.03302920e+00 4.88844991e-01 3.40089276e-02 -5.04712641e-01
1.00525761e+00 1.22336082e-01 -3.68354581e-02 -7.62335718e-01
-4.25985418e-02 2.06218719e-01 3.87981951e-01 -8.37552369e-01
2.53501981e-01 2.33589336e-01 1.12130374e-01 6.57916486e-01
-1.06389552e-01 -3.53311926e-01 2.58835554e-01 2.22222582e-01
1.35852039e+00 4.42732990e-01 2.43118778e-01 8.60884711e-02
6.69817254e-02 2.13229388e-01 3.61112475e-01 9.15414095e-01
-5.98783493e-02 -3.11328173e-01 3.53404313e-01 -4.23481435e-01
-7.30099201e-01 -9.13352549e-01 3.52262378e-01 1.66272116e+00
-3.06428552e-01 -6.15595818e-01 -6.73314869e-01 -6.76480949e-01
-2.93837162e-03 1.27186120e+00 -4.76789892e-01 -5.77072263e-01
-3.38340282e-01 3.30514699e-01 3.76515657e-01 7.30630815e-01
3.86397660e-01 -1.29279387e+00 -1.17659199e+00 3.00446928e-01
2.52640992e-01 -6.28026903e-01 -4.08573031e-01 1.87628314e-01
-9.87957835e-01 -1.03592503e+00 -1.86695814e-01 -5.88848650e-01
6.43011510e-01 2.85602450e-01 1.44039428e+00 7.51624942e-01
-3.03706616e-01 6.60754204e-01 -4.66028839e-01 -6.17298186e-01
-7.90615380e-01 1.26039296e-01 -4.04530242e-02 -7.31514752e-01
4.09298331e-01 -9.94153440e-01 -2.36517012e-01 3.39657813e-01
-7.89649665e-01 6.44917905e-01 8.07551742e-01 6.92844510e-01
-6.80547729e-02 -1.38578609e-01 6.72144949e-01 -1.36451697e+00
8.67402315e-01 -2.48204350e-01 -7.32756793e-01 4.43611741e-01
-1.10025775e+00 1.96133956e-01 7.32008338e-01 -5.65664470e-01
-9.19651091e-01 -1.00355417e-01 3.09300900e-01 -5.46466947e-01
-2.97178086e-02 8.75046015e-01 -2.30676278e-01 -1.18896969e-01
9.04100180e-01 2.10345000e-01 -1.23816267e-01 -2.99208701e-01
4.89501268e-01 7.39374459e-01 3.73582333e-01 -1.01145744e+00
8.46560061e-01 -3.14678311e-01 -6.07874393e-01 -5.86622119e-01
-5.86847961e-01 -3.06798697e-01 -5.37817180e-01 -4.80774909e-01
3.45482707e-01 -4.95878547e-01 -8.24665606e-01 2.45675355e-01
-9.96024191e-01 -7.75955200e-01 -5.66307679e-02 9.69977975e-02
-4.03706759e-01 -1.02797471e-01 -3.72460395e-01 -9.93254721e-01
-1.86809495e-01 -1.15591598e+00 5.51318467e-01 3.77264112e-01
-7.94785500e-01 -9.53863740e-01 -3.50871719e-02 3.43867362e-01
6.76257908e-01 1.11861259e-01 1.07707310e+00 -7.26818681e-01
-7.44242907e-01 -3.45720291e-01 1.52608335e-01 3.95378709e-01
1.55583262e-01 3.17281723e-01 -8.42757165e-01 -6.42507570e-03
-1.07279032e-01 -4.36282903e-01 3.43724750e-02 -4.02340263e-01
1.42903244e+00 -4.32531059e-01 -3.16886008e-01 4.37746078e-01
9.13881838e-01 4.98281330e-01 2.38623470e-01 5.84574044e-01
7.98520625e-01 5.26526928e-01 5.72035491e-01 3.91020179e-01
2.78475732e-01 3.42931330e-01 1.31289616e-01 2.22683161e-01
5.10646142e-02 -7.75634229e-01 3.62611294e-01 1.01621413e+00
-3.39026772e-03 2.42563829e-01 -1.66810954e+00 4.80414987e-01
-1.69609225e+00 -5.85764289e-01 1.93272084e-01 2.10903573e+00
1.12036490e+00 4.84592438e-01 -3.40072624e-02 8.56452137e-02
2.16884002e-01 -3.81058455e-01 -7.16058910e-01 -5.31510651e-01
8.58946323e-01 1.80539340e-01 -7.30592683e-02 4.51120228e-01
-4.80346024e-01 1.11314571e+00 5.88977480e+00 3.61554086e-01
-1.13952851e+00 -2.20042542e-01 4.48621139e-02 2.07206085e-01
-5.50250947e-01 -1.31963305e-02 -2.72916496e-01 4.42331225e-01
1.20095170e+00 -5.20306051e-01 6.18965745e-01 1.28587449e+00
1.98460758e-01 1.56744905e-02 -1.72191370e+00 6.94302320e-01
-4.38142896e-01 -1.27896786e+00 1.07322447e-02 -4.51823503e-01
4.14831132e-01 -4.71327364e-01 -1.42331524e-02 7.80143976e-01
7.28957117e-01 -1.12514436e+00 7.17007637e-01 5.86187541e-01
6.59714639e-01 -8.46200824e-01 4.71087396e-01 9.09386337e-01
-8.38137388e-01 -6.71642050e-02 1.88353270e-01 -6.01164758e-01
-5.19740045e-01 3.73685770e-02 -1.33259344e+00 2.42951348e-01
5.34737051e-01 5.76397896e-01 -9.12577987e-01 9.32985842e-01
-5.92471063e-01 7.19154060e-01 9.10926089e-02 -4.51674819e-01
-3.79449934e-01 4.42022942e-02 3.17346066e-01 1.14060271e+00
2.36644328e-01 -8.20510313e-02 5.10530353e-01 1.21805358e+00
-5.95016079e-03 -5.40611371e-02 -9.03365850e-01 -6.62594676e-01
9.62896407e-01 1.00891125e+00 -4.87079233e-01 -2.07914844e-01
-2.13518396e-01 7.79590845e-01 5.27230740e-01 3.66902441e-01
-4.50815886e-01 -8.44464183e-01 4.77283597e-01 4.24214453e-01
-2.96952099e-01 -3.90218258e-01 -4.98205096e-01 -9.87488687e-01
-6.72973245e-02 -1.34353685e+00 -2.71305382e-01 -1.07482624e+00
-9.42826271e-01 2.63886184e-01 -1.10476173e-01 -9.59230602e-01
-3.58687460e-01 -4.34242606e-01 -6.64427042e-01 6.70161247e-01
-1.11727297e+00 -1.03519988e+00 -3.88855368e-01 1.57995820e-01
5.18699288e-01 -1.10201202e-01 9.20462787e-01 4.51408252e-02
-3.98545742e-01 7.95722842e-01 -2.05553770e-01 1.88053176e-01
5.16514599e-01 -1.23404598e+00 2.39744723e-01 7.19087362e-01
8.49174242e-03 1.10554528e+00 7.99552917e-01 -7.54238009e-01
-1.87041795e+00 -8.58534455e-01 5.79425514e-01 -9.18926060e-01
9.77460265e-01 -4.39632714e-01 -1.05474257e+00 8.47513020e-01
-7.39742890e-02 -2.90494174e-01 8.52837145e-01 5.27092993e-01
-6.57658577e-01 1.03354067e-01 -9.09809351e-01 6.97866917e-01
1.13757992e+00 -7.63105750e-01 -8.87812734e-01 1.13674492e-01
9.44998324e-01 -3.36842179e-01 -6.15422487e-01 1.42939270e-01
6.89433098e-01 -9.87337947e-01 5.36488593e-01 -7.85674036e-01
7.44868517e-01 -9.66812819e-02 2.37794355e-01 -1.42515230e+00
-1.33767545e-01 -1.00439370e+00 -3.82905662e-01 1.33752620e+00
3.79174262e-01 -5.65734327e-01 5.24978161e-01 8.88149202e-01
-2.41219863e-01 -6.82235599e-01 -2.98051924e-01 -8.73042166e-01
1.37978733e-01 -8.23672950e-01 6.15056038e-01 1.13299453e+00
2.08789378e-01 2.77225733e-01 1.01492852e-01 -1.85723957e-02
2.98722357e-01 -3.57190780e-02 1.04301631e+00 -1.49361992e+00
-5.58656335e-01 -6.77032292e-01 -6.19031116e-02 -7.46707737e-01
1.32721677e-01 -9.26626325e-01 1.64138302e-01 -1.37703824e+00
1.52364045e-01 -5.53904831e-01 -2.00389996e-01 6.27731264e-01
-2.04995468e-01 -5.76386273e-01 4.66297925e-01 3.13979715e-01
-8.46854448e-01 2.16724500e-01 1.35533226e+00 1.59773499e-01
-4.81808603e-01 4.48822938e-02 -9.31632996e-01 1.11384070e+00
6.90666974e-01 -4.54831332e-01 -5.25433481e-01 -5.04136443e-01
5.11169553e-01 4.51845117e-02 1.53208956e-01 -1.30790818e+00
3.14800769e-01 -4.36378211e-01 3.31744373e-01 9.22572538e-02
-2.72028714e-01 -9.94551182e-01 3.41897570e-02 5.96088707e-01
-6.21238589e-01 2.39252195e-01 4.20065910e-01 1.81807175e-01
2.64298972e-02 -2.82011360e-01 3.78305674e-01 -8.29507038e-02
-6.14898026e-01 -1.94422767e-01 -5.82111061e-01 -7.51100760e-03
1.03287697e+00 -2.36307710e-01 -2.28347391e-01 -2.47474030e-01
-5.47622085e-01 6.16989553e-01 6.23712659e-01 5.70504904e-01
6.17314219e-01 -9.61242080e-01 -2.02047020e-01 2.38155708e-01
4.06425685e-01 5.36811240e-02 -7.67601281e-02 4.47749883e-01
-5.07856965e-01 2.07993492e-01 -3.87458593e-01 -4.73109931e-01
-1.07879353e+00 7.09516704e-01 4.15891409e-01 -2.59820491e-01
-3.04897577e-01 9.45153117e-01 5.40264174e-02 -8.11340451e-01
3.62140954e-01 -5.32504141e-01 4.57218848e-02 -1.88676789e-01
7.30006099e-01 3.70420337e-01 -5.08511588e-02 4.96535659e-01
-1.12009153e-01 1.96626097e-01 -4.13738519e-01 1.67253930e-02
1.35456085e+00 2.32477739e-01 3.61013925e-03 9.44756389e-01
6.57564402e-01 -7.30644315e-02 -1.16584563e+00 -9.39041451e-02
5.39811254e-01 -5.51108420e-01 5.62953353e-02 -1.50949872e+00
-4.32813346e-01 1.16165495e+00 5.70874751e-01 3.25722396e-01
9.43911791e-01 -1.23864122e-01 3.22006553e-01 7.87258446e-01
6.15434527e-01 -1.07987690e+00 4.63925570e-01 4.34680939e-01
1.05197608e+00 -9.87666607e-01 -2.70340204e-01 -3.18831772e-01
-5.36152601e-01 1.21533287e+00 1.29559934e+00 -4.70542302e-03
3.81023049e-01 5.74331641e-01 1.38320372e-01 -1.72792271e-01
-9.04778719e-01 1.94610789e-01 -1.43432533e-02 8.38481426e-01
8.24134111e-01 1.03584081e-01 2.35440373e-01 8.58495295e-01
-3.39475334e-01 7.65183806e-01 6.08260870e-01 1.39691782e+00
-6.03808403e-01 -1.11725891e+00 -3.28507125e-01 4.64158714e-01
-9.59384739e-02 7.24785822e-03 -6.83275521e-01 9.15076017e-01
3.77178378e-02 7.86285639e-01 -2.38371074e-01 -6.75985754e-01
5.89410663e-01 4.77231741e-01 4.60195571e-01 -9.50942338e-01
-8.34784031e-01 -3.68348718e-01 5.54678738e-02 -7.34897554e-01
6.42547086e-02 -3.34261626e-01 -1.23739803e+00 -4.47187185e-01
-2.17223436e-01 2.96281397e-01 4.15183425e-01 7.94142663e-01
4.80914414e-01 9.24991012e-01 4.18375552e-01 -6.95219994e-01
-1.02349913e+00 -1.03038085e+00 -1.30534858e-01 1.05533794e-01
3.31415921e-01 -8.04646730e-01 -2.51573622e-01 6.14320785e-02] | [8.573668479919434, 7.445566654205322] |
89a32201-5f8a-4190-8a1a-5a231af87d4c | introducing-semantics-into-speech-encoders | 2211.08402 | null | https://arxiv.org/abs/2211.08402v1 | https://arxiv.org/pdf/2211.08402v1.pdf | Introducing Semantics into Speech Encoders | Recent studies find existing self-supervised speech encoders contain primarily acoustic rather than semantic information. As a result, pipelined supervised automatic speech recognition (ASR) to large language model (LLM) systems achieve state-of-the-art results on semantic spoken language tasks by utilizing rich semantic representations from the LLM. These systems come at the cost of labeled audio transcriptions, which is expensive and time-consuming to obtain. We propose a task-agnostic unsupervised way of incorporating semantic information from LLMs into self-supervised speech encoders without labeled audio transcriptions. By introducing semantics, we improve existing speech encoder spoken language understanding performance by over 10\% on intent classification, with modest gains in named entity resolution and slot filling, and spoken question answering FF1 score by over 2\%. Our unsupervised approach achieves similar performance as supervised methods trained on over 100 hours of labeled audio transcripts, demonstrating the feasibility of unsupervised semantic augmentations to existing speech encoders. | ['Wei Wang', 'Yizhou Sun', 'Hung-Yi Lee', 'Guan-Ting Lin', 'Alexei Baevski', 'Liang-Hsuan Tseng', 'Shang-Wen Li', 'Akshat Shrivastava', 'Zhaojiang Lin', 'Suyoun Kim', 'Changhan Wang', 'Shuyan Dong', 'Derek Xu'] | 2022-11-15 | null | null | null | null | ['spoken-language-understanding', 'entity-resolution', 'intent-classification', 'slot-filling', 'spoken-language-understanding'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'speech'] | [ 3.81212920e-01 7.08423853e-01 -2.74356872e-01 -9.25288022e-01
-1.49969733e+00 -3.06069553e-01 4.54028130e-01 1.22250564e-01
-5.68642557e-01 5.32922983e-01 8.46194208e-01 -2.79461384e-01
5.55396855e-01 -3.92859608e-01 -7.87218988e-01 4.61088158e-02
1.34830400e-01 6.73375010e-01 2.69534364e-02 -1.84038073e-01
-9.82228369e-02 -3.45934570e-01 -1.58505201e+00 5.04195213e-01
7.37930834e-01 1.04290366e+00 4.24902081e-01 8.22799742e-01
-8.47025752e-01 1.16228259e+00 -6.51568353e-01 8.58381838e-02
-4.36856896e-01 -5.74599087e-01 -1.26739085e+00 4.40242410e-01
2.09351435e-01 -3.24356318e-01 -4.08310235e-01 5.86288631e-01
3.19577098e-01 2.82876849e-01 4.01397347e-01 -1.05993533e+00
-7.17130482e-01 9.27382112e-01 1.36189237e-01 -3.67757827e-02
4.06385779e-01 -2.35399202e-01 1.26379788e+00 -1.12443554e+00
3.84829283e-01 1.21668220e+00 4.87906724e-01 7.94310689e-01
-1.21387196e+00 -5.45217395e-01 1.90269664e-01 5.93341738e-02
-1.43565071e+00 -1.34072244e+00 4.92130488e-01 -6.68029711e-02
1.63043320e+00 -1.55104343e-02 -6.97038621e-02 9.75949824e-01
-6.20128989e-01 1.09905005e+00 7.08339930e-01 -6.20412052e-01
5.03123045e-01 3.68143529e-01 2.33351767e-01 7.12274551e-01
-4.84645784e-01 -3.57763469e-01 -1.00095475e+00 8.77073035e-02
1.87710792e-01 -2.98027217e-01 -3.95827852e-02 2.47945450e-02
-9.15384352e-01 8.91337574e-01 3.40055823e-02 1.58257127e-01
-1.63401544e-01 2.45108470e-01 6.87094748e-01 3.50507408e-01
8.13569367e-01 3.83408368e-01 -7.17238247e-01 -5.25931597e-01
-1.20302343e+00 -2.92412668e-01 9.04719770e-01 1.04430997e+00
9.00407791e-01 4.42492962e-01 1.37009919e-01 1.42902458e+00
4.82535362e-01 8.58583450e-01 8.81902874e-01 -1.02939665e+00
5.19840598e-01 4.85716134e-01 -1.98215425e-01 -2.08459005e-01
-2.90924460e-01 -2.38777742e-01 -4.08977598e-01 -4.67434347e-01
-1.64841518e-01 -7.87828341e-02 -1.24279976e+00 1.79077041e+00
-9.99730229e-02 1.41353860e-01 6.24975681e-01 5.66866457e-01
9.96600330e-01 9.81796205e-01 4.66588378e-01 -9.35001671e-02
1.42916656e+00 -1.28113246e+00 -9.29696977e-01 -7.39700377e-01
9.43158388e-01 -5.92912793e-01 1.31759918e+00 -7.42481798e-02
-1.04288638e+00 -4.88813668e-01 -8.00653577e-01 -1.38410136e-01
-2.28310108e-01 3.13567877e-01 4.70754206e-01 7.29532242e-01
-1.22044182e+00 -1.19763901e-02 -9.51966286e-01 -5.32969296e-01
3.78476441e-01 1.99626401e-01 -4.16679889e-01 -2.80786678e-02
-1.20583820e+00 5.44119418e-01 2.03489572e-01 -4.53025252e-01
-1.24119067e+00 -7.30435610e-01 -1.31842351e+00 1.87996686e-01
3.63438457e-01 -1.72876328e-01 1.92225778e+00 -8.75087082e-01
-1.64585960e+00 8.45310450e-01 -8.98258448e-01 -7.88769245e-01
-4.66998130e-01 -2.22774357e-01 -5.32392561e-01 4.12984699e-01
3.04213643e-01 9.29354429e-01 6.45811498e-01 -9.38375950e-01
-5.27107894e-01 -2.18006328e-01 -3.88147175e-01 3.98652375e-01
-6.74002767e-01 2.06448391e-01 -1.33566886e-01 -4.53602672e-01
1.35999188e-01 -6.86946332e-01 -1.16914049e-01 -3.46870184e-01
-1.70702904e-01 -4.11311269e-01 9.01610017e-01 -8.05505753e-01
1.05069554e+00 -2.32425761e+00 -2.44987726e-01 -2.73779035e-01
-1.79827005e-01 2.56333053e-01 -4.48439986e-01 4.55584526e-01
1.93176359e-01 1.02989130e-01 -3.62662643e-01 -1.04945958e+00
1.04697332e-01 5.83079278e-01 -7.59700239e-01 -1.65151432e-01
4.14743185e-01 9.90872860e-01 -7.14464664e-01 -3.10192645e-01
1.33136421e-01 3.64660889e-01 -6.16557181e-01 5.57314932e-01
-4.52298552e-01 3.61256868e-01 -5.07485345e-02 5.80673158e-01
-7.51921013e-02 -4.56269234e-01 6.73477650e-02 3.48264098e-01
9.26802456e-02 1.40995359e+00 -8.36256564e-01 2.17742920e+00
-9.15604711e-01 5.66216648e-01 2.51626134e-01 -1.21783996e+00
1.05385649e+00 9.78833020e-01 1.61145419e-01 -7.05257416e-01
-2.41426155e-01 4.00475502e-01 -5.56084394e-01 -2.71184206e-01
4.35813397e-01 -4.07216907e-01 -2.06565842e-01 6.06816649e-01
7.12370217e-01 -3.02400351e-01 -2.22493246e-01 2.96163917e-01
1.24448240e+00 -2.44590685e-01 8.00045952e-02 6.38972670e-02
3.59942913e-01 1.73122615e-01 2.44151115e-01 6.49563730e-01
-7.82511085e-02 5.89469433e-01 -1.16430648e-01 5.77345043e-02
-1.02309215e+00 -1.04144239e+00 7.90067241e-02 1.60903919e+00
-4.00243223e-01 -6.56310081e-01 -9.05223548e-01 -5.02661586e-01
-2.01212600e-01 9.27325845e-01 9.76275071e-04 -2.45069280e-01
-2.96284527e-01 -1.35133058e-01 1.02231181e+00 8.91392648e-01
2.81910330e-01 -1.18735266e+00 2.12188847e-02 5.51859915e-01
-4.72324222e-01 -1.76064944e+00 -3.73375893e-01 4.61690217e-01
-8.27939868e-01 -3.71072501e-01 -7.60018229e-01 -9.76507485e-01
4.85912412e-01 1.91951111e-01 1.09551322e+00 -4.02095884e-01
-8.40652958e-02 5.94327450e-01 -5.95430851e-01 -4.22875315e-01
-7.52707779e-01 4.09588814e-01 2.60738760e-01 -1.22447088e-02
6.01830721e-01 -4.55870658e-01 -1.51545078e-01 2.15288535e-01
-7.09870756e-01 -6.65512308e-02 3.99313450e-01 8.13913286e-01
4.26814467e-01 -2.63955355e-01 1.30392385e+00 -5.87823570e-01
2.78042525e-01 -3.28356355e-01 -1.30914062e-01 3.85695547e-02
-6.54079020e-01 3.14140856e-01 6.95003927e-01 -7.01134279e-02
-1.36702776e+00 9.41070020e-02 -6.07154131e-01 -3.71469945e-01
-6.16917849e-01 4.63348359e-01 -1.76344901e-01 3.62185955e-01
5.67775488e-01 5.94251752e-01 2.98533976e-01 -7.25300074e-01
6.02894425e-01 1.31529486e+00 6.03749871e-01 -2.71542072e-01
3.77950400e-01 3.63112509e-01 -8.62948477e-01 -1.27827418e+00
-1.21632910e+00 -9.23898995e-01 -3.08467895e-01 1.94623485e-01
1.06758499e+00 -1.38959515e+00 -1.74765632e-01 2.14224130e-01
-1.02218008e+00 -4.80224788e-01 -5.51004648e-01 5.27612746e-01
-6.91154957e-01 2.32277259e-01 -8.57622504e-01 -1.09829819e+00
-3.80227774e-01 -8.74666631e-01 1.53897297e+00 -1.05902560e-01
-6.59703434e-01 -8.91623020e-01 -1.41823441e-01 1.04400289e+00
5.91885448e-01 -1.01476693e+00 7.82338619e-01 -1.05774331e+00
-3.94067436e-01 -1.00656047e-01 -5.80234639e-02 6.94018126e-01
1.55987516e-01 -8.65569770e-01 -1.38710165e+00 -1.12657152e-01
-2.84109443e-01 -8.66064548e-01 8.57903004e-01 1.23754531e-01
9.43899095e-01 -4.39817041e-01 -1.31895453e-01 2.55848944e-01
8.02796185e-01 3.14661086e-01 3.79036993e-01 -1.77653581e-02
5.65088272e-01 6.91632330e-01 2.74235338e-01 3.14564526e-01
6.57735467e-01 4.36501950e-01 -9.71281826e-02 -7.85479993e-02
-3.28049332e-01 -7.63488472e-01 7.06311524e-01 1.42781663e+00
8.36564720e-01 -1.36522010e-01 -1.01407754e+00 1.03521824e+00
-1.55124176e+00 -4.58237708e-01 3.78069192e-01 1.85800791e+00
1.18519866e+00 -8.70280117e-02 -1.71113074e-01 2.11273387e-01
4.50057507e-01 2.80213892e-01 -3.24018329e-01 -3.09571385e-01
-7.59922294e-03 5.04923701e-01 3.22489947e-01 7.61918366e-01
-1.05793726e+00 1.35689676e+00 6.66396809e+00 7.57961988e-01
-8.95644128e-01 3.99767876e-01 4.55034375e-01 -1.41429418e-05
-4.88103449e-01 1.37315184e-01 -9.18700635e-01 2.34420344e-01
1.84291005e+00 8.04601237e-02 3.44104916e-01 1.05507040e+00
2.96665013e-01 6.66906312e-02 -9.15485203e-01 9.80242491e-01
2.82858819e-01 -1.32219279e+00 1.07453577e-01 -3.15895170e-01
4.58199352e-01 4.08415318e-01 -2.55772263e-01 5.20554781e-01
3.88500571e-01 -1.17861319e+00 4.98062402e-01 5.95832877e-02
1.04386103e+00 -6.02171123e-01 5.77979743e-01 4.09903675e-01
-1.22602499e+00 4.43359949e-02 -7.40513727e-02 -1.69844925e-01
6.32255197e-01 3.38827223e-01 -1.40618861e+00 9.05736759e-02
5.54507136e-01 5.17723799e-01 -1.46918789e-01 3.04430962e-01
-3.01882654e-01 1.33622134e+00 -4.07834083e-01 2.47297827e-02
3.65212619e-01 4.44069415e-01 3.79674256e-01 1.31333685e+00
6.94027841e-02 1.13143802e-01 3.69487017e-01 6.73281908e-01
-3.42478722e-01 2.97745645e-01 -5.85965574e-01 -7.77807474e-01
6.87673330e-01 5.94655693e-01 -4.83712912e-01 -6.73414052e-01
-6.76561773e-01 1.24644434e+00 1.66875631e-01 3.66464674e-01
-2.49918878e-01 -4.73424196e-01 9.13444638e-01 3.85871995e-03
9.48828906e-02 -4.58002210e-01 -2.49893263e-01 -1.22400355e+00
-8.27836916e-02 -7.82012463e-01 1.28304154e-01 -8.10111105e-01
-1.06554484e+00 5.92545390e-01 -4.46371406e-01 -7.45412350e-01
-7.16806710e-01 -4.00126487e-01 -1.45018682e-01 7.40634263e-01
-1.64669418e+00 -1.04470623e+00 -2.68381462e-02 3.46202701e-01
1.24720216e+00 -6.28807425e-01 1.39632249e+00 4.69001263e-01
-2.43164256e-01 5.96994340e-01 -6.46671578e-02 3.53727788e-01
7.19468892e-01 -1.09196270e+00 7.15677738e-01 5.78490376e-01
6.08555734e-01 4.45066333e-01 3.05040270e-01 -3.87815922e-01
-1.20042789e+00 -1.15959847e+00 1.55389035e+00 -5.33999205e-01
6.57581925e-01 -7.33664572e-01 -1.08444870e+00 8.66003692e-01
1.83137044e-01 -1.62711516e-01 1.08976734e+00 4.30274546e-01
-5.88487267e-01 3.04508135e-02 -6.64209843e-01 2.34018639e-01
9.97576952e-01 -1.35592806e+00 -8.92638147e-01 2.95481950e-01
1.39240348e+00 2.73928884e-02 -6.14683807e-01 2.75949955e-01
2.93745156e-02 -2.73468584e-01 8.61733198e-01 -6.83793962e-01
1.80563524e-01 -1.84992969e-01 -6.05658948e-01 -1.16584241e+00
3.56884032e-01 -4.87539798e-01 2.75592655e-02 1.35106444e+00
7.13331223e-01 -4.96765673e-01 8.35364580e-01 3.65855366e-01
-5.05425930e-01 -3.38916749e-01 -1.07627344e+00 -7.86748886e-01
-2.27185115e-01 -8.31478655e-01 2.83536196e-01 8.92493367e-01
3.85271728e-01 8.46528649e-01 -2.01350957e-01 2.60501295e-01
4.49637562e-01 -2.35502392e-01 4.43372011e-01 -8.43897402e-01
-2.27092698e-01 -1.04090767e-02 -3.78278852e-01 -1.50387537e+00
8.30804169e-01 -1.12756670e+00 5.14763176e-01 -1.59112072e+00
-4.44163494e-02 -3.84186715e-01 -1.17030680e-01 9.40144241e-01
5.32665215e-02 2.23579213e-01 -1.26043946e-01 1.36110038e-01
-8.95471692e-01 1.00879836e+00 3.32140774e-01 -1.94366798e-01
-2.25056842e-01 -4.02327329e-01 -6.05741918e-01 6.87206924e-01
5.65774381e-01 -3.91636819e-01 -6.81030095e-01 -7.34936535e-01
-3.73288959e-01 2.34657928e-01 9.73817110e-02 -1.04015183e+00
2.99508423e-01 1.86946541e-01 -2.19471157e-01 -3.12107354e-01
6.58418536e-01 -3.89859229e-01 -5.49963534e-01 -3.71097922e-05
-9.47288096e-01 -4.90168601e-01 2.88726151e-01 3.87820393e-01
-8.32953155e-01 -1.61400229e-01 6.41200125e-01 -1.72421291e-01
-1.08256185e+00 -6.45140037e-02 -9.06276286e-01 4.77230012e-01
3.25096637e-01 1.21668100e-01 -1.09366715e-01 -1.07104111e+00
-8.86148512e-01 2.38107339e-01 -1.37171550e-02 7.53077269e-01
5.84499836e-01 -9.52410161e-01 -5.07293642e-01 4.42850173e-01
4.51307684e-01 -3.47431824e-02 2.05542669e-01 3.67338836e-01
6.23687655e-02 9.97482777e-01 4.57045346e-01 -6.14189088e-01
-1.12174761e+00 6.37667428e-04 1.06150202e-01 1.87098667e-01
-5.08423686e-01 1.18628454e+00 2.33873472e-01 -8.75151336e-01
5.62522829e-01 -2.78949797e-01 9.08875465e-02 -7.33841062e-02
6.22227907e-01 1.57827307e-02 2.60773063e-01 -5.79964578e-01
-5.41850150e-01 8.49147514e-02 3.26868407e-02 -6.63971126e-01
1.20429313e+00 -4.89696264e-01 3.35197806e-01 6.95240140e-01
1.42869914e+00 -1.13900952e-01 -9.70580459e-01 -5.71260929e-01
3.80406797e-01 1.39433727e-01 1.92021564e-01 -8.16407979e-01
-4.27642047e-01 1.06285334e+00 2.83235490e-01 -1.25436485e-01
8.61071348e-01 6.45260096e-01 1.37245822e+00 7.40498662e-01
3.49787354e-01 -1.33087564e+00 2.23287404e-01 9.33956623e-01
5.32725871e-01 -1.41608906e+00 -7.86502182e-01 -3.26559156e-01
-9.49734628e-01 6.50344610e-01 4.12091166e-01 2.63751924e-01
6.21965885e-01 5.55289388e-01 5.71994901e-01 -7.84939714e-03
-9.22304571e-01 -5.20726860e-01 1.93014547e-01 4.04401720e-01
6.65109217e-01 1.30456537e-01 2.40867272e-01 8.47677708e-01
-3.42745513e-01 -7.41738603e-02 1.50683299e-01 9.48900700e-01
-8.59547675e-01 -1.22553158e+00 -8.92414059e-03 2.40279958e-01
-3.98444086e-01 -6.11126065e-01 -2.50041425e-01 7.74498209e-02
-5.71179211e-01 1.33456457e+00 4.89651203e-01 -1.87885880e-01
3.24633002e-01 9.99881864e-01 -1.78569704e-01 -1.25107729e+00
3.69627178e-02 1.80581480e-01 5.76888442e-01 -5.95020533e-01
-3.29549968e-01 -4.13012475e-01 -1.88309288e+00 4.64109778e-01
-1.56760797e-01 5.76214075e-01 8.78839195e-01 1.18623292e+00
7.29374468e-01 5.26305199e-01 6.16949618e-01 -3.98835361e-01
-6.17514193e-01 -1.12041378e+00 -4.42837119e-01 1.26777723e-01
5.29659390e-01 -3.25215012e-01 -4.71925765e-01 3.88828248e-01] | [14.016847610473633, 6.992152690887451] |
0977af1a-a4b2-43e8-9d1a-a8d3c91a1b11 | a-point-cloud-generative-model-via-tree | 2107.09923 | null | https://arxiv.org/abs/2107.09923v1 | https://arxiv.org/pdf/2107.09923v1.pdf | A Point Cloud Generative Model via Tree-Structured Graph Convolutions for 3D Brain Shape Reconstruction | Fusing medical images and the corresponding 3D shape representation can provide complementary information and microstructure details to improve the operational performance and accuracy in brain surgery. However, compared to the substantial image data, it is almost impossible to obtain the intraoperative 3D shape information by using physical methods such as sensor scanning, especially in minimally invasive surgery and robot-guided surgery. In this paper, a general generative adversarial network (GAN) architecture based on graph convolutional networks is proposed to reconstruct the 3D point clouds (PCs) of brains by using one single 2D image, thus relieving the limitation of acquiring 3D shape data during surgery. Specifically, a tree-structured generative mechanism is constructed to use the latent vector effectively and transfer features between hidden layers accurately. With the proposed generative model, a spontaneous image-to-PC conversion is finished in real-time. Competitive qualitative and quantitative experimental results have been achieved on our model. In multiple evaluation methods, the proposed model outperforms another common point cloud generative model PointOutNet. | ['Shuqiang Wang', 'Yong liu', 'Yanyan Shen', 'Baiying Lei', 'Bowen Hu'] | 2021-07-21 | null | null | null | null | ['3d-shape-representation'] | ['computer-vision'] | [ 1.32199913e-01 5.75836360e-01 3.38009745e-01 -1.47187695e-01
-4.49587435e-01 -2.03477949e-01 4.58471715e-01 -2.80727953e-01
-1.10093750e-01 5.58602154e-01 -3.23614217e-02 -2.53113717e-01
-3.59785333e-02 -8.90004694e-01 -7.68245459e-01 -9.39214885e-01
2.30254129e-01 6.31271064e-01 -1.06572524e-01 -2.01959729e-01
1.07236862e-01 8.35523725e-01 -9.78682101e-01 -7.32065439e-02
9.06790495e-01 9.68813956e-01 5.13798416e-01 2.11627766e-01
-2.17513084e-01 4.16939497e-01 -4.16759729e-01 -3.15960467e-01
3.19248825e-01 -3.49967420e-01 -3.44092458e-01 2.28017733e-01
-1.99803337e-01 -3.27809602e-01 -5.45818806e-01 1.24216831e+00
5.11292398e-01 -2.83461511e-01 7.41253257e-01 -1.05939424e+00
-6.80846095e-01 3.63658428e-01 -4.21433628e-01 -4.14527059e-01
2.27877393e-01 1.93362743e-01 1.09091386e-01 -6.80625319e-01
5.72923481e-01 1.01711965e+00 5.85672200e-01 6.99875951e-01
-9.85130608e-01 -8.25494349e-01 -2.77902991e-01 -1.58125117e-01
-1.22951007e+00 -6.28809333e-02 1.35212862e+00 -5.12461841e-01
4.82007295e-01 7.01099485e-02 1.11392844e+00 1.00989485e+00
8.95565689e-01 5.23544967e-01 1.21694827e+00 -8.14516991e-02
5.08756116e-02 -2.81963255e-02 -4.36135620e-01 8.54312003e-01
2.43551254e-01 3.27326149e-01 -9.15289000e-02 9.67764854e-02
1.55111933e+00 6.89076245e-01 -4.93206292e-01 -5.50644994e-01
-1.28547144e+00 6.85583651e-01 1.03283346e+00 4.12112594e-01
-6.26895726e-01 2.44215459e-01 4.85209934e-02 4.44092155e-02
3.22948217e-01 3.30563784e-01 2.73721993e-01 5.70069700e-02
-7.04751492e-01 -1.63644910e-01 5.59003234e-01 1.28851557e+00
6.01950824e-01 4.05099392e-01 1.38671190e-01 3.50088596e-01
4.45225298e-01 5.69836259e-01 6.31202102e-01 -7.65234530e-01
2.43963823e-01 8.12462389e-01 -1.92636251e-01 -1.14967835e+00
-4.28196579e-01 -6.43670261e-01 -1.39435768e+00 3.45700175e-01
-3.52333903e-01 -8.21062848e-02 -1.29061818e+00 1.28273106e+00
1.80442318e-01 3.56055170e-01 1.04181834e-01 9.63115752e-01
1.26424825e+00 4.12170529e-01 -1.78572744e-01 -2.94817656e-01
1.05050647e+00 -6.12450838e-01 -7.36823440e-01 -2.86962003e-01
1.71682656e-01 -4.29987669e-01 6.25389993e-01 2.40556195e-01
-1.14503956e+00 -4.55098659e-01 -1.11327088e+00 2.87151318e-02
-4.49949093e-02 -1.09091163e-01 7.99837887e-01 2.83181727e-01
-1.08167565e+00 2.83218145e-01 -1.30212462e+00 1.09193519e-01
8.30350757e-01 4.62985873e-01 -7.27504492e-01 -2.62583077e-01
-7.74509370e-01 7.57048488e-01 2.71499157e-01 3.28577578e-01
-1.03807461e+00 -7.01687336e-01 -8.75714481e-01 -1.20012902e-01
-2.00257242e-01 -1.14020491e+00 7.85957634e-01 -4.19819176e-01
-1.66245222e+00 9.35362399e-01 1.62932858e-01 -2.69639432e-01
5.49493611e-01 7.04815313e-02 1.02884769e-02 2.66475290e-01
-1.31772697e-01 7.49712765e-01 8.59835923e-01 -1.54195714e+00
2.28643432e-01 -6.78537130e-01 -1.31805047e-01 3.18346620e-01
1.40455231e-01 -5.64032137e-01 -3.34447742e-01 -4.92083818e-01
7.96715379e-01 -8.33626449e-01 -5.26947975e-01 -7.03045055e-02
-4.84208643e-01 3.44280541e-01 6.98930919e-01 -6.50206923e-01
2.74823695e-01 -2.07245564e+00 3.77974689e-01 1.58889651e-01
5.70949614e-01 -8.31167176e-02 2.51598746e-01 2.19625205e-01
-2.83107087e-02 -4.50889319e-02 -4.73694533e-01 -4.47641551e-01
-3.95840734e-01 3.87869745e-01 -1.31155282e-01 5.68482935e-01
1.71758619e-03 1.43275607e+00 -8.96708012e-01 -5.42678416e-01
4.71847087e-01 6.80285156e-01 -6.32354140e-01 3.79493713e-01
1.26434773e-01 1.09525776e+00 -7.61194348e-01 6.56434000e-01
7.70033717e-01 -2.77149528e-01 -2.19089955e-01 -2.42326126e-01
3.15426111e-01 -1.89836115e-01 -4.23244387e-01 2.39595222e+00
-6.81560338e-01 2.12268680e-01 1.08755447e-01 -1.17921233e+00
1.15649056e+00 4.39434141e-01 7.43360162e-01 -4.42860663e-01
4.72942621e-01 3.53350848e-01 -1.30310193e-01 -3.62802237e-01
5.65887894e-03 -6.19512200e-01 -1.03770100e-01 -1.00258678e-01
2.05361649e-01 -8.69130671e-01 -8.56124222e-01 -3.32474709e-02
8.54659557e-01 1.74204543e-01 9.79816467e-02 -5.89575581e-02
3.43983293e-01 -1.81222204e-02 3.25201154e-01 1.80549204e-01
1.40205458e-01 8.70399773e-01 2.46339113e-01 -2.92769670e-01
-1.00411618e+00 -1.24399281e+00 -1.37760863e-01 -4.14976388e-01
4.17313010e-01 2.03379422e-01 -6.76819444e-01 -5.29862881e-01
-1.81165859e-01 5.82920849e-01 -5.41012883e-01 -5.83782554e-01
-5.57518244e-01 -4.61770147e-01 1.64198339e-01 4.13659036e-01
5.01163483e-01 -1.08234453e+00 -5.73191524e-01 3.80738646e-01
-1.97251603e-01 -1.06570780e+00 -1.21796831e-01 3.38213779e-02
-1.34308195e+00 -9.01691973e-01 -7.60155320e-01 -8.14807057e-01
1.08967054e+00 1.93180948e-01 7.73715138e-01 8.27567130e-02
-3.17456782e-01 1.57856032e-01 -3.24691772e-01 -5.56905389e-01
-5.02083600e-01 -2.89529234e-01 -1.12928502e-01 -1.17584124e-01
3.37437131e-02 -1.10664845e+00 -9.15626049e-01 5.20164818e-02
-9.85235453e-01 4.83305126e-01 1.09563601e+00 1.01486146e+00
8.64695966e-01 1.66315655e-03 4.85336304e-01 -7.84416199e-01
3.86045605e-01 -4.14671421e-01 -4.75197732e-01 -2.29336500e-01
-4.36930925e-01 -1.15052223e-01 5.06075084e-01 -2.54401386e-01
-8.01686466e-01 1.13030054e-01 -4.13997412e-01 -1.13645852e+00
-1.18850023e-01 3.87280047e-01 -3.84508997e-01 -2.95990974e-01
2.92646468e-01 7.21574068e-01 6.78327382e-01 -2.58887112e-01
2.98466533e-01 4.42199439e-01 7.92884827e-01 -1.81970760e-01
8.83739233e-01 6.00405455e-01 2.55935431e-01 -5.56488931e-01
-3.32277924e-01 -7.61784539e-02 -5.82018137e-01 -2.83929914e-01
1.14945161e+00 -9.07052279e-01 -5.55655599e-01 4.77146924e-01
-1.21948421e+00 -1.09840529e-02 -2.29741246e-01 7.62129068e-01
-8.37408781e-01 2.36510143e-01 -5.95192194e-01 -4.18897748e-01
-6.67322338e-01 -1.59039843e+00 1.35227489e+00 1.74322873e-01
2.80915499e-01 -9.00995374e-01 -2.66335905e-01 3.07556003e-01
3.45873713e-01 8.32344890e-01 9.49937701e-01 -2.12076262e-01
-9.49373603e-01 -5.85710764e-01 7.73773864e-02 2.78084546e-01
3.25656503e-01 -5.58400154e-01 -7.80777454e-01 -3.88365626e-01
6.30280733e-01 -9.20590851e-03 4.62132245e-01 6.20384037e-01
1.28526759e+00 -2.65749037e-01 -6.70839787e-01 1.07324672e+00
1.43114424e+00 3.81835848e-01 8.88346493e-01 -1.95679925e-02
9.02018309e-01 1.80354491e-01 1.24902897e-01 1.07713871e-01
1.46330670e-01 3.57906252e-01 9.69311118e-01 -1.47241771e-01
-2.03799382e-01 -5.93408346e-01 -2.47953180e-02 1.20498562e+00
-3.03818464e-01 1.44133925e-01 -7.23727942e-01 2.78641284e-01
-1.35229194e+00 -6.40200496e-01 9.21503156e-02 2.01982307e+00
5.43727696e-01 1.31473377e-01 -6.34157538e-01 6.92102984e-02
5.30489087e-01 -1.72067061e-01 -5.61641157e-01 3.15648615e-01
2.54795551e-01 3.54113042e-01 3.33951205e-01 2.45545149e-01
-5.39839625e-01 6.92988992e-01 5.35769033e+00 5.56616843e-01
-1.46889031e+00 3.10060114e-01 4.38091546e-01 9.08048674e-02
-5.94764709e-01 -2.34903634e-01 -2.13952944e-01 4.97852325e-01
4.11760300e-01 -2.95460690e-02 2.49198422e-01 8.06044161e-01
1.52697871e-02 3.01532626e-01 -1.02747500e+00 1.45765209e+00
7.16732144e-02 -1.47521281e+00 3.90561938e-01 3.56471092e-01
5.86832464e-01 -5.55758588e-02 -1.05220340e-02 1.64027493e-02
5.69692068e-02 -1.23779643e+00 6.26512587e-01 9.73237336e-01
1.16077948e+00 -8.14463675e-01 9.26660299e-01 6.96400762e-01
-6.24051273e-01 1.87378943e-01 -4.31048751e-01 3.35462302e-01
2.33825341e-01 6.25585735e-01 -9.43480551e-01 9.21300828e-01
5.20602822e-01 7.32066870e-01 -9.23225284e-02 9.94311154e-01
-2.87272215e-01 2.48031497e-01 -2.21877769e-01 1.57937139e-01
1.69893265e-01 -5.28694212e-01 7.86093175e-01 4.27588671e-01
6.67868495e-01 3.69094580e-01 -1.30667821e-01 1.27216649e+00
-1.52216688e-01 -1.36048168e-01 -1.07440615e+00 8.30451995e-02
2.69970059e-01 1.33449924e+00 -7.77120948e-01 -2.12747306e-02
-1.81540430e-01 9.29209471e-01 1.14054196e-01 2.06662789e-01
-7.00795949e-01 -1.58865765e-01 2.71233052e-01 3.40271831e-01
-1.26182318e-01 -4.36548978e-01 -3.94630313e-01 -1.11976647e+00
4.05253135e-02 -2.96140492e-01 -3.38259786e-01 -1.07979798e+00
-1.09167409e+00 8.85428607e-01 -6.80482462e-02 -1.57935524e+00
-2.66499311e-01 -5.02144933e-01 -7.27664888e-01 9.86428082e-01
-1.29780459e+00 -1.31635714e+00 -7.93937743e-01 7.55196810e-01
1.78820342e-01 -2.50112444e-01 8.93313408e-01 -8.93901065e-02
-5.47323227e-02 2.27795690e-01 -1.35782406e-01 2.11528108e-01
2.46522754e-01 -9.15710986e-01 1.35036469e-01 6.91048563e-01
-9.84912589e-02 5.54186940e-01 4.08171296e-01 -7.48465002e-01
-1.69708550e+00 -1.13104320e+00 2.95421239e-02 -3.05316538e-01
1.30739212e-01 -2.09083647e-01 -8.22448492e-01 7.52487540e-01
1.57901019e-01 1.46818146e-01 3.74265939e-01 -6.24015570e-01
3.20763022e-01 -2.15352792e-02 -1.36026096e+00 3.37091416e-01
1.21537781e+00 -2.82702833e-01 -8.29506576e-01 4.29669470e-01
9.89578009e-01 -9.11966741e-01 -1.21463740e+00 5.96042275e-01
1.85913309e-01 -7.61693835e-01 1.02695715e+00 -3.12071472e-01
7.24699199e-01 -2.05891341e-01 6.94270954e-02 -1.77708745e+00
-1.78527817e-01 -5.17665088e-01 1.12193301e-01 5.66418767e-01
-2.12643147e-02 -9.95704055e-01 8.82125854e-01 3.14435989e-01
-9.03297365e-01 -9.59061742e-01 -1.07177162e+00 -5.30281126e-01
2.44503364e-01 -2.21060887e-01 9.58682418e-01 7.82585144e-01
-1.50813907e-01 8.70541334e-02 3.64151299e-02 3.13396573e-01
7.97465920e-01 3.38473588e-01 5.85758209e-01 -1.22789347e+00
-5.97363375e-02 -3.79221290e-01 -1.04004800e+00 -9.88350391e-01
2.07371950e-01 -1.20660865e+00 -1.61939915e-02 -1.91296387e+00
2.33382918e-02 -6.46191180e-01 -1.62348792e-01 2.99123198e-01
1.16556354e-01 1.98358968e-01 -4.73198891e-02 5.08178532e-01
2.17943758e-01 8.59751523e-01 2.13985395e+00 -2.28535071e-01
-3.01211439e-02 7.06019811e-03 -5.47490895e-01 5.84905446e-01
5.49772680e-01 -2.71764129e-01 -5.68349063e-01 -5.85176528e-01
-3.33779156e-02 7.66923964e-01 7.47895062e-01 -1.09048915e+00
3.52375627e-01 6.18305318e-02 5.75441599e-01 -6.79996848e-01
5.77687681e-01 -1.24665380e+00 5.34164608e-01 5.64113081e-01
1.96339667e-01 -1.81047812e-01 9.21375155e-02 7.07889855e-01
-4.77077931e-01 1.40346572e-01 7.12320089e-01 -3.94461304e-01
-1.87449917e-01 8.77770007e-01 2.62843519e-01 -3.04057986e-01
1.14787292e+00 -4.72051650e-01 8.61788169e-02 -2.41129890e-01
-9.31669652e-01 -2.39793703e-01 6.24930203e-01 3.66869837e-01
1.28322077e+00 -1.73468077e+00 -6.10678256e-01 7.41364717e-01
9.34491828e-02 8.82875264e-01 5.47295511e-01 1.03639448e+00
-9.59107339e-01 4.75541592e-01 -5.27565241e-01 -9.88965750e-01
-4.71484542e-01 5.88060498e-01 6.28822923e-01 -1.11901306e-01
-1.14418399e+00 6.12189353e-01 5.00568926e-01 -4.66050625e-01
-2.61500329e-01 -4.31707770e-01 -1.20023444e-01 -6.52570248e-01
1.35606425e-02 -3.12292159e-01 1.79473653e-01 -5.26671171e-01
-1.60413370e-01 5.62968731e-01 2.79731333e-01 1.14376724e-01
1.57259357e+00 1.69753656e-01 -2.02563569e-01 1.15934014e-01
1.24560404e+00 -1.67432636e-01 -1.18404222e+00 -4.54642214e-02
-8.43144059e-01 -3.56585383e-01 3.34044129e-01 -3.39535922e-01
-1.65465653e+00 1.01811063e+00 4.82676268e-01 -1.02789253e-01
1.04361951e+00 1.02426797e-01 9.26037073e-01 2.34543439e-02
9.89251614e-01 -2.59547830e-01 -8.22969079e-02 -5.73545061e-02
1.23883462e+00 -1.03661323e+00 -1.78862944e-01 -4.97119665e-01
-4.53620464e-01 9.55731869e-01 4.54172462e-01 -4.17469710e-01
8.97718906e-01 2.69552648e-01 -1.14523999e-01 -5.85575044e-01
-2.97292054e-01 4.04429972e-01 2.63081491e-01 7.31378138e-01
-5.25932610e-02 3.55593562e-01 8.42153952e-02 6.52800143e-01
-5.69328368e-01 1.57845348e-01 4.33810294e-01 7.84787178e-01
7.25185871e-03 -8.34009647e-01 -1.91110149e-01 5.90862036e-01
-2.77883232e-01 -3.97083908e-02 3.22646163e-02 8.14672291e-01
-6.72366843e-02 5.45842588e-01 1.88529029e-01 -4.44245458e-01
2.98595995e-01 -1.46744356e-01 7.53505111e-01 -6.42616868e-01
-1.20855995e-01 1.24635518e-01 -5.57194233e-01 -6.29464865e-01
-3.17272037e-01 -4.01147306e-01 -1.43099284e+00 -4.58059367e-04
-1.78754926e-01 1.31366238e-01 9.40767050e-01 6.49883449e-01
4.20166016e-01 8.98283839e-01 6.70384407e-01 -1.11536443e+00
-3.12197328e-01 -1.07599640e+00 -6.65588558e-01 3.58191907e-01
1.84668809e-01 -9.75749016e-01 -1.65353164e-01 -1.31944148e-02] | [8.983933448791504, -3.587024688720703] |
b892112d-72ee-4c0c-8f7b-3c1305555cc3 | improving-multilingual-named-entity | 1707.02459 | null | http://arxiv.org/abs/1707.02459v1 | http://arxiv.org/pdf/1707.02459v1.pdf | Improving Multilingual Named Entity Recognition with Wikipedia Entity Type Mapping | The state-of-the-art named entity recognition (NER) systems are statistical
machine learning models that have strong generalization capability (i.e., can
recognize unseen entities that do not appear in training data) based on lexical
and contextual information. However, such a model could still make mistakes if
its features favor a wrong entity type. In this paper, we utilize Wikipedia as
an open knowledge base to improve multilingual NER systems. Central to our
approach is the construction of high-accuracy, high-coverage multilingual
Wikipedia entity type mappings. These mappings are built from weakly annotated
data and can be extended to new languages with no human annotation or
language-dependent knowledge involved. Based on these mappings, we develop
several approaches to improve an NER system. We evaluate the performance of the
approaches via experiments on NER systems trained for 6 languages. Experimental
results show that the proposed approaches are effective in improving the
accuracy of such systems on unseen entities, especially when a system is
applied to a new domain or it is trained with little training data (up to 18.3
F1 score improvement). | ['Jian Ni', 'Radu Florian'] | 2017-07-08 | improving-multilingual-named-entity-1 | https://aclanthology.org/D16-1135 | https://aclanthology.org/D16-1135.pdf | emnlp-2016-11 | ['multilingual-named-entity-recognition'] | ['natural-language-processing'] | [-4.85182405e-01 1.22866638e-01 -1.01886854e-01 -4.58693355e-01
-7.48217523e-01 -8.56419623e-01 4.77357626e-01 4.29941028e-01
-9.40181553e-01 1.24666345e+00 -6.11261539e-02 -2.54567742e-01
3.03583443e-01 -1.00634205e+00 -7.69240975e-01 -1.51975170e-01
1.43408328e-01 5.21978915e-01 4.36515391e-01 -5.08687854e-01
6.97992668e-02 3.72656375e-01 -1.20888376e+00 5.74459732e-02
1.55721581e+00 2.43588343e-01 3.11974168e-01 2.83371776e-01
-5.47099710e-01 6.36039257e-01 -6.37158155e-01 -7.62869596e-01
5.98052628e-02 -8.48627388e-02 -1.12300479e+00 -6.19528472e-01
2.18543559e-01 2.45008588e-01 1.62476953e-02 1.34272552e+00
4.37986255e-01 8.11460987e-02 5.37035167e-01 -8.36490571e-01
-1.05377388e+00 7.87513673e-01 -9.96788740e-02 1.74004942e-01
3.94047678e-01 -4.96439070e-01 7.24207282e-01 -1.20270407e+00
8.80187392e-01 8.26087654e-01 9.14582551e-01 6.74057364e-01
-8.66571903e-01 -6.30599499e-01 1.86443329e-04 1.83833674e-01
-1.70247912e+00 -3.79863411e-01 4.05455917e-01 -2.79678881e-01
1.05251908e+00 4.55038957e-02 -2.73660924e-02 8.21937382e-01
-8.12196508e-02 4.65658963e-01 1.00964034e+00 -7.96399176e-01
9.87719595e-02 7.47203350e-01 2.79127121e-01 6.11914575e-01
7.37889290e-01 -1.49469540e-01 -2.21559510e-01 -9.75039229e-02
4.33835655e-01 -4.13381934e-01 -2.39085853e-01 -2.15198189e-01
-1.24536121e+00 5.92197478e-01 4.91475165e-01 8.33131313e-01
-4.29075986e-01 -5.74254394e-01 3.94439250e-01 2.40446329e-01
4.30883139e-01 7.56381571e-01 -8.77215981e-01 7.92941079e-02
-5.89706838e-01 -1.20977908e-01 1.05805993e+00 1.12676656e+00
1.04319549e+00 -1.55973554e-01 3.58837694e-01 1.12787294e+00
6.75157532e-02 7.48235643e-01 7.96149909e-01 -1.04062654e-01
5.64300060e-01 8.04976821e-01 4.17144716e-01 -7.77180135e-01
-4.77589875e-01 -2.09677309e-01 -5.33555567e-01 -3.49659353e-01
1.94640741e-01 -5.40122926e-01 -9.60213900e-01 1.76040494e+00
4.48083729e-01 4.99606170e-02 8.24554145e-01 4.70642358e-01
1.03262532e+00 7.68743575e-01 5.63514173e-01 -2.40631979e-02
1.27871013e+00 -9.19830263e-01 -7.65472412e-01 -1.39524132e-01
1.16001749e+00 -8.82882714e-01 7.36710846e-01 -1.89044103e-02
-5.31509876e-01 -6.16324127e-01 -8.52206707e-01 5.15181683e-02
-1.17609537e+00 2.83382714e-01 3.82792413e-01 7.83051670e-01
-8.54894400e-01 4.55502063e-01 -6.61854148e-01 -9.65257347e-01
-3.27342629e-01 3.33071679e-01 -8.12484264e-01 -7.13098720e-02
-1.74679530e+00 1.33532631e+00 1.11146247e+00 1.98481083e-01
-4.04762328e-01 -3.97792578e-01 -1.01774788e+00 -4.62873541e-02
2.85676062e-01 -3.67186934e-01 1.07226050e+00 -9.62743700e-01
-9.75751519e-01 7.24776328e-01 -2.66318731e-02 -2.89087981e-01
5.55679575e-02 -2.99695522e-01 -1.32788467e+00 -1.18221357e-01
5.50373197e-01 4.30223644e-01 1.79676560e-03 -1.23401952e+00
-1.03782761e+00 -1.08149536e-01 4.47145253e-02 2.53884107e-01
-6.62477672e-01 2.11948410e-01 -1.97736040e-01 -5.11765361e-01
-1.48071960e-01 -9.00266051e-01 -2.93730229e-01 -8.70898068e-01
-3.35027426e-01 -3.61694157e-01 3.97181094e-01 -8.31474602e-01
1.38873959e+00 -1.92168498e+00 -2.71013945e-01 2.48558834e-01
-2.92047292e-01 6.35309815e-01 -5.48028462e-02 6.15572810e-01
-2.65317131e-02 5.14564037e-01 -4.80414703e-02 3.90526205e-01
-1.41919971e-01 1.69192970e-01 1.19737051e-02 -1.00964181e-01
1.67113185e-01 6.04495943e-01 -1.04335499e+00 -5.81579566e-01
-1.96170025e-02 3.01844656e-01 -1.95058331e-01 1.66559666e-01
1.35523751e-01 2.38019139e-01 -6.40064061e-01 5.86624503e-01
5.57264149e-01 -2.59884372e-02 5.22481084e-01 -3.00977290e-01
-3.52056652e-01 2.77649999e-01 -1.32716537e+00 1.58721137e+00
-5.98254979e-01 2.51682371e-01 -4.80812907e-01 -7.77175367e-01
1.07220984e+00 5.60355306e-01 -7.45434687e-02 -4.14260507e-01
-1.71422973e-01 7.80658126e-01 -1.69054151e-01 -6.02570355e-01
8.54039252e-01 -1.35689437e-01 -3.85922909e-01 3.80935445e-02
5.28403640e-01 5.55988371e-01 5.82115114e-01 -1.33847490e-01
8.31443071e-01 -1.75840445e-02 7.22275138e-01 -4.33390290e-01
9.42989409e-01 4.86997783e-01 9.23136294e-01 6.83547676e-01
-1.00349374e-01 -1.11786194e-01 -1.86807066e-01 -4.90684330e-01
-1.17952704e+00 -8.35052311e-01 -4.27436262e-01 1.08579850e+00
2.34546468e-01 -3.77977341e-01 -9.10080850e-01 -1.15659356e+00
-3.27336907e-01 7.99730003e-01 -3.62761229e-01 1.04278632e-01
-6.32041395e-01 -7.61871517e-01 9.47103620e-01 4.71605599e-01
5.90960085e-01 -1.14418900e+00 9.17548090e-02 4.92614567e-01
-3.94286215e-01 -1.33038974e+00 -1.79722920e-01 1.09186485e-01
-6.26590014e-01 -1.12325501e+00 -6.21532798e-01 -1.41286349e+00
1.00148761e+00 -1.35893337e-02 9.27962899e-01 -3.41722518e-02
2.20831171e-01 2.30586603e-01 -7.22906411e-01 -3.55251342e-01
-6.96333945e-01 5.39589107e-01 4.52872306e-01 -2.46293783e-01
9.52868164e-01 -8.46346393e-02 4.69505079e-02 5.52594781e-01
-9.19786572e-01 -2.21837401e-01 6.80331171e-01 9.75134015e-01
5.14879644e-01 1.50223866e-01 9.57253456e-01 -1.26071668e+00
5.54326117e-01 -6.33197486e-01 -4.88171548e-01 9.43316340e-01
-6.54602170e-01 3.69769573e-01 9.04239833e-01 -4.34277803e-01
-1.55881464e+00 8.31987560e-02 -2.21660495e-01 1.83038116e-01
-4.86023366e-01 8.74034226e-01 -2.96218932e-01 -2.79821306e-01
9.79262531e-01 8.88700113e-02 -8.64771247e-01 -7.91693985e-01
4.05703545e-01 9.72169816e-01 5.99897563e-01 -8.27360153e-01
8.40933561e-01 -9.75864455e-02 -5.30846655e-01 -7.38188684e-01
-8.68807673e-01 -6.63235068e-01 -1.00152540e+00 1.33362353e-01
8.72257769e-01 -1.19998050e+00 1.27509281e-01 3.68484110e-01
-1.07928455e+00 2.20854938e-01 1.18120588e-01 9.16069984e-01
7.86651000e-02 2.91635364e-01 -5.43461680e-01 -5.42802751e-01
-3.52436125e-01 -7.24929392e-01 5.17565608e-01 6.42230272e-01
5.82949780e-02 -1.38729703e+00 4.59242046e-01 9.99857262e-02
2.08791479e-01 2.26164721e-02 8.70349288e-01 -1.52786982e+00
-3.58950458e-02 -2.93872952e-01 1.50430769e-01 3.86460602e-01
2.92292327e-01 -1.09032109e-01 -7.75232553e-01 -1.50838718e-01
-3.86583537e-01 -2.21330836e-01 3.26466203e-01 -3.89398128e-01
2.55965173e-01 -2.28687435e-01 -4.89502162e-01 2.69948274e-01
1.66532755e+00 2.42437541e-01 4.33206350e-01 7.62361288e-01
7.39546001e-01 5.46856105e-01 8.38563442e-01 -1.31713852e-01
6.06369853e-01 4.29786295e-01 -1.87200144e-01 -2.92484552e-01
2.11840391e-01 -6.42116547e-01 2.92557776e-01 1.31093633e+00
-2.30168685e-01 -9.61342528e-02 -1.27188897e+00 8.68162334e-01
-1.65470004e+00 -8.26092839e-01 -1.44277722e-01 2.29914570e+00
1.14078987e+00 -1.72797237e-02 -2.81874329e-01 -5.02426744e-01
1.24881244e+00 -5.44248879e-01 -2.57265657e-01 -3.99742424e-01
-2.94410735e-01 2.59678423e-01 6.88309014e-01 3.64549100e-01
-1.29044890e+00 1.43369842e+00 5.79226971e+00 7.01652288e-01
-1.05312657e+00 1.54353291e-01 7.92152807e-02 7.73314595e-01
-1.39127553e-01 1.11704506e-01 -1.29567635e+00 2.78784007e-01
1.11962116e+00 -4.24764365e-01 6.49019331e-02 9.89905596e-01
-2.70242840e-01 9.73811448e-02 -9.50626791e-01 6.63922727e-01
1.95932031e-01 -1.02715969e+00 1.21579900e-01 -1.81008786e-01
9.12958801e-01 1.89735442e-01 -6.00783765e-01 7.96133101e-01
5.96990645e-01 -6.88631296e-01 3.94099385e-01 4.18666661e-01
7.06261694e-01 -8.21128070e-01 1.25009155e+00 5.42458415e-01
-1.21480346e+00 3.37699175e-01 -6.83694720e-01 3.99049640e-01
1.05574839e-01 2.51373380e-01 -1.04579091e+00 9.88132179e-01
8.07839036e-01 4.00949538e-01 -7.23941326e-01 1.13451779e+00
-6.29085481e-01 4.04441386e-01 -4.18028027e-01 -2.21717909e-01
9.65147913e-02 7.62030408e-02 3.32521617e-01 1.38036525e+00
5.40458620e-01 1.70434222e-01 2.53452331e-01 4.52317655e-01
-3.65443498e-01 8.23338568e-01 -7.66921639e-01 -5.91536984e-02
7.40973771e-01 1.28093517e+00 -3.98926407e-01 -5.33413410e-01
-6.90110922e-01 9.35042441e-01 6.92457497e-01 3.81196737e-01
-6.29307389e-01 -8.81647885e-01 2.88016796e-01 -2.97447771e-01
3.44031692e-01 6.81344373e-03 2.92503953e-01 -1.58480251e+00
7.95096308e-02 -7.13352144e-01 6.84850335e-01 -6.27957404e-01
-1.59214616e+00 1.07133675e+00 -1.74314961e-01 -1.16001308e+00
-3.39773864e-01 -5.90388775e-01 -2.85792619e-01 8.51423383e-01
-1.71196342e+00 -1.16550028e+00 -5.78875728e-02 5.90679169e-01
1.71349734e-01 -3.66920769e-01 1.20489597e+00 6.31749213e-01
-4.76186007e-01 6.99122488e-01 3.55251193e-01 6.97440803e-01
1.18321097e+00 -1.29147065e+00 3.85036409e-01 1.21885312e+00
3.16724062e-01 9.33702826e-01 5.41006982e-01 -9.33183253e-01
-8.70628834e-01 -1.14274836e+00 1.74718964e+00 -5.21812081e-01
7.76997566e-01 -1.81488872e-01 -1.15595198e+00 8.79007280e-01
2.41648629e-01 1.02499276e-02 1.10867560e+00 5.28449357e-01
-4.07991171e-01 6.71504065e-02 -1.29856491e+00 3.59650344e-01
7.05505073e-01 -5.70518374e-01 -1.25742126e+00 2.35251248e-01
5.54159880e-01 -3.52235585e-01 -1.30549812e+00 4.72153872e-01
1.83646113e-01 -3.72380525e-01 5.90312958e-01 -1.05351734e+00
-1.75165847e-01 -6.90284610e-01 -1.30849868e-01 -1.50023246e+00
-2.40648195e-01 -1.58246160e-01 3.61543924e-01 1.59056842e+00
1.01813757e+00 -6.32255673e-01 1.31527349e-01 7.07484066e-01
-1.03889860e-01 -9.05920193e-03 -6.39279127e-01 -1.10506427e+00
1.65456191e-01 8.78138468e-02 7.43863225e-01 1.68330264e+00
3.98194492e-01 6.44726098e-01 -3.29582781e-01 6.75811887e-01
2.58527786e-01 -2.18488231e-01 6.83047175e-01 -1.30523622e+00
2.17955768e-01 2.54494071e-01 -4.71304804e-01 -5.50510228e-01
3.13374996e-01 -9.04624999e-01 1.40916362e-01 -1.35343564e+00
1.62547708e-01 -7.48517394e-01 -6.09107137e-01 7.87715197e-01
-4.95510727e-01 -6.11372776e-02 -1.63447540e-02 3.33193749e-01
-7.77752519e-01 2.51710385e-01 5.76202035e-01 1.62918299e-01
-2.44100064e-01 -3.77746582e-01 -6.27238154e-01 8.41527939e-01
7.65168548e-01 -8.27455938e-01 1.15942009e-01 -5.22889555e-01
3.09924513e-01 -3.37069541e-01 -1.19132996e-01 -1.00874984e+00
4.39379185e-01 -1.54885575e-01 3.85451138e-01 -1.95966184e-01
-3.83005142e-01 -9.00413394e-01 3.60522300e-01 2.00209945e-01
-8.43834057e-02 1.87174112e-01 2.58877993e-01 3.59139562e-01
-5.29324293e-01 -6.56579614e-01 5.97650766e-01 -1.68444946e-01
-1.36801350e+00 8.72052386e-02 -1.22676179e-01 2.40698010e-01
1.06685364e+00 9.71076414e-02 -4.35131639e-01 2.97807306e-01
-7.63992786e-01 1.94801882e-01 6.36808813e-01 6.22363448e-01
6.03313632e-02 -1.59361076e+00 -7.62306988e-01 6.62462935e-02
5.82718492e-01 -3.05754274e-01 4.45054770e-02 2.67299175e-01
-6.94128990e-01 6.57547116e-01 -4.00661826e-01 -1.38921231e-01
-9.63877380e-01 6.58417583e-01 3.25256050e-01 -4.82221901e-01
-1.98264465e-01 4.04685915e-01 4.37210016e-02 -1.15098679e+00
-8.91362652e-02 6.83499034e-03 -7.26360917e-01 -1.27410963e-01
4.65651363e-01 9.08170342e-02 2.30085656e-01 -9.83800054e-01
-5.08268416e-01 4.61775362e-01 -2.27100909e-01 -9.92563926e-03
1.20759702e+00 -1.01378776e-01 -1.04426131e-01 4.50719953e-01
8.30803633e-01 4.85034794e-01 -1.36164948e-01 -5.35014868e-01
5.16162574e-01 -1.80890590e-01 -2.40172580e-01 -9.86400843e-01
-6.19898438e-01 3.71223211e-01 6.07913375e-01 1.80945620e-01
7.78976321e-01 -9.41349342e-02 7.77804315e-01 8.62124383e-01
7.83052981e-01 -1.42072368e+00 -7.83016384e-01 7.72031367e-01
2.39441365e-01 -1.42642832e+00 -3.63160223e-01 -4.71031278e-01
-5.62541842e-01 1.22234440e+00 8.81920516e-01 2.20387250e-01
6.22007668e-01 -1.14967637e-01 3.35414320e-01 1.21469712e-02
-4.19886589e-01 -3.58134866e-01 3.74827325e-01 6.17929101e-01
7.30495930e-01 1.87295005e-01 -7.92233169e-01 8.04575980e-01
-6.68077394e-02 -5.60904369e-02 5.09418488e-01 9.58184421e-01
-6.38181508e-01 -1.51219988e+00 -2.74559468e-01 1.19638316e-01
-6.70025349e-01 -4.85473782e-01 -2.74351120e-01 1.07349336e+00
2.62321055e-01 9.32049036e-01 -3.39056402e-01 -2.31861398e-01
6.11768901e-01 3.30680400e-01 7.98768401e-02 -8.35341156e-01
-6.02651179e-01 -3.86378288e-01 5.78290164e-01 -1.11161403e-01
-6.72842085e-01 -4.92926896e-01 -1.34477079e+00 -1.33361146e-01
-7.87087679e-01 8.98312509e-01 7.54589081e-01 1.01538813e+00
3.23897243e-01 -1.88296344e-02 5.51148057e-01 3.97141576e-02
-3.35509688e-01 -9.47326124e-01 -4.58327681e-01 4.67779160e-01
-2.41974831e-01 -5.15409946e-01 -1.61733702e-01 3.09865385e-01] | [9.788394927978516, 9.638354301452637] |
01128f28-e2fc-43ba-940b-3f9ee851e025 | corrpus-detecting-story-inconsistencies-via | 2212.10754 | null | https://arxiv.org/abs/2212.10754v3 | https://arxiv.org/pdf/2212.10754v3.pdf | CoRRPUS: Code-based Structured Prompting for Neurosymbolic Story Understanding | Story generation and understanding -- as with all NLG/NLU tasks -- has seen a surge in neurosymbolic work. Researchers have recognized that, while large language models (LLMs) have tremendous utility, they can be augmented with symbolic means to be even better and to make up for any flaws that the neural networks might have. However, symbolic methods are extremely costly in terms of the amount of time and expertise needed to create them. In this work, we capitalize on state-of-the-art Code-LLMs, such as Codex, to bootstrap the use of symbolic methods for tracking the state of stories and aiding in story understanding. We show that our CoRRPUS system and abstracted prompting procedures can beat current state-of-the-art structured LLM techniques on pre-existing story understanding tasks (bAbI Task 2 and Re^3) with minimal hand engineering. We hope that this work can help highlight the importance of symbolic representations and specialized prompting for LLMs as these models require some guidance for performing reasoning tasks properly. | ['Chris Callison-Burch', 'Lara J. Martin', 'Yijiang River Dong'] | 2022-12-21 | null | null | null | null | ['story-generation'] | ['natural-language-processing'] | [ 3.48536104e-01 7.30650783e-01 -1.18267305e-01 -4.22940999e-01
-8.27438593e-01 -6.51885092e-01 7.37978280e-01 2.42570713e-01
2.87121505e-01 6.34109497e-01 5.74816406e-01 -7.65683115e-01
-9.59893689e-02 -7.46165395e-01 -7.82647252e-01 1.56299815e-01
-2.42580716e-02 6.71460092e-01 2.76509553e-01 -6.59238994e-01
6.11913979e-01 7.16715455e-02 -1.58904862e+00 6.92686319e-01
7.25374103e-01 3.12574267e-01 8.44153911e-02 6.64988637e-01
-6.44549131e-01 1.77662969e+00 -8.58048975e-01 -3.27521950e-01
-3.08200985e-01 -5.95201790e-01 -1.17950654e+00 -2.74681956e-01
2.33052850e-01 -5.71186721e-01 -2.99316645e-01 4.95841533e-01
5.01402244e-02 9.11206454e-02 5.90859771e-01 -1.25561345e+00
-6.58489585e-01 1.19039524e+00 -1.99821472e-01 1.10350639e-01
8.29731584e-01 1.61172494e-01 7.10362256e-01 -4.85089868e-01
8.00908327e-01 1.23477018e+00 8.48078966e-01 6.80283487e-01
-1.13762379e+00 -6.05985999e-01 1.75619155e-01 1.97013378e-01
-1.04088306e+00 -7.22475886e-01 5.25030911e-01 -6.64369643e-01
1.63530207e+00 7.73071051e-02 7.64051795e-01 1.02592981e+00
-6.03135079e-02 9.74159300e-01 7.83379197e-01 -8.93123865e-01
1.26320473e-03 1.61677390e-01 2.17577890e-01 9.73768413e-01
1.49134481e-02 6.60418049e-02 -9.32983816e-01 5.54750524e-02
1.10922301e+00 -4.40038890e-01 -9.52262208e-02 -3.95373739e-02
-1.24008834e+00 8.77204657e-01 4.99756709e-02 5.52198648e-01
1.36762425e-01 6.01117134e-01 6.11337483e-01 1.38261318e-01
3.49706680e-01 8.62875342e-01 -1.87675416e-01 -8.23814571e-01
-1.16648221e+00 5.22945583e-01 1.15398085e+00 1.11794066e+00
1.09867550e-01 2.63118297e-01 -3.48032229e-02 7.45748580e-01
3.21756333e-01 -1.53183743e-01 4.09532994e-01 -1.15226746e+00
5.11594236e-01 7.79452741e-01 1.39083967e-01 -8.76800299e-01
-2.14113459e-01 -2.21720248e-01 -7.30943074e-03 4.00409520e-01
5.77046335e-01 2.20888387e-03 -5.94879568e-01 1.62071979e+00
-2.05839634e-01 3.10420334e-01 4.50085057e-03 4.93283510e-01
7.33028054e-01 6.85377955e-01 5.10462485e-02 2.63482600e-01
1.22757673e+00 -8.41050684e-01 -4.95462149e-01 -5.89682460e-01
1.08962846e+00 -4.81471777e-01 1.19203472e+00 5.15261948e-01
-1.39152181e+00 -2.70888478e-01 -1.27758896e+00 -1.34067655e-01
-4.18720067e-01 -9.93623883e-02 1.18540144e+00 4.80127513e-01
-9.66582775e-01 8.27852547e-01 -9.11042452e-01 -5.81860840e-01
3.18287581e-01 1.89256474e-01 -1.04041688e-01 -1.96104914e-01
-1.03777242e+00 1.33502030e+00 6.17499530e-01 -9.88003686e-02
-9.40296173e-01 -6.92795455e-01 -1.03241110e+00 1.42924320e-02
5.38261116e-01 -4.02807504e-01 1.81798160e+00 -7.50233591e-01
-1.23179924e+00 8.06283534e-01 -6.35989457e-02 -4.59218770e-01
2.62841761e-01 -5.61428726e-01 -2.54588276e-01 1.08637206e-01
3.55027974e-01 7.56183386e-01 3.44874054e-01 -1.03204548e+00
-3.19941342e-01 2.12421596e-01 5.28175831e-01 3.52513045e-02
1.03637606e-01 5.91944814e-01 1.00530043e-01 -7.37605870e-01
2.22128388e-02 -5.63483834e-01 -2.89351586e-02 -1.45638347e-01
-1.23204291e-01 -5.30381873e-03 6.32271767e-01 -8.97052348e-01
1.39726639e+00 -1.88687158e+00 -6.65070862e-02 -1.50213301e-01
-2.39751562e-02 2.41976947e-01 -1.40459659e-02 7.63619661e-01
-1.33377716e-01 3.75632375e-01 -1.67708814e-01 -1.23227730e-01
1.21348344e-01 3.30659360e-01 -6.57914996e-01 -5.12541737e-03
3.94519001e-01 9.17643368e-01 -9.57787335e-01 -5.92461944e-01
1.84086964e-01 3.18748295e-01 -5.94707489e-01 1.26597285e-01
-8.15855801e-01 2.32274786e-01 -1.64274603e-01 5.49755454e-01
-2.90131658e-01 -3.52618217e-01 -8.71775299e-03 5.99536657e-01
-8.68558437e-02 8.59234512e-01 -1.04458523e+00 1.96591985e+00
-5.29019058e-01 1.02210927e+00 -1.04356393e-01 -7.28244841e-01
7.33367622e-01 5.02940774e-01 -2.04318494e-01 -1.76933065e-01
1.08697675e-01 2.86594242e-01 1.24428784e-02 -6.40082777e-01
6.08293116e-01 -2.43598625e-01 -2.13717282e-01 8.90318036e-01
-1.04022084e-03 -5.60296953e-01 3.99216056e-01 5.57901800e-01
1.13837957e+00 8.14644873e-01 3.41267347e-01 -6.48223097e-03
8.34495053e-02 7.29530692e-01 1.18606733e-02 9.36905682e-01
3.61445040e-01 5.96994042e-01 7.52772927e-01 -3.08599740e-01
-1.06069064e+00 -5.08744359e-01 2.49981329e-01 1.35160482e+00
-3.25066417e-01 -6.37503564e-01 -8.53992343e-01 -3.46066624e-01
-3.15192640e-01 1.61750257e+00 -4.58206803e-01 -6.80403560e-02
-7.47419298e-01 -1.04550451e-01 1.12565339e+00 1.05459571e+00
1.10874824e-01 -1.28958261e+00 -1.06643331e+00 6.02436483e-01
-1.32798001e-01 -9.12754059e-01 3.83646972e-02 2.97377288e-01
-7.41402149e-01 -9.00059521e-01 -2.57466555e-01 -6.02330327e-01
6.12330019e-01 3.89425121e-02 1.25404727e+00 4.85874861e-01
-1.73219487e-01 4.59078252e-01 -7.84949303e-01 -6.36967778e-01
-9.19687927e-01 -9.09532979e-02 -4.54004705e-01 -8.97088230e-01
2.14239821e-01 -7.79117048e-01 9.24159586e-02 8.82652476e-02
-9.00017083e-01 8.17846298e-01 2.00139672e-01 6.17341161e-01
-1.47738293e-01 -1.27416551e-01 5.27075112e-01 -1.01230037e+00
9.28769052e-01 -3.65951955e-01 -3.33126247e-01 3.90569896e-01
-4.25492436e-01 1.97520465e-01 3.72248322e-01 -3.75862926e-01
-1.04207897e+00 -4.13477302e-01 9.73325316e-03 1.39920801e-01
-1.74473554e-01 8.80397558e-01 2.44654194e-01 7.94667080e-02
9.02891278e-01 6.51885569e-02 -1.70077696e-01 -3.01608652e-01
5.86340427e-01 4.49216545e-01 7.30320752e-01 -1.06783628e+00
5.19821465e-01 2.87810676e-02 -2.05897808e-01 -4.08413678e-01
-7.98258185e-01 -9.19348150e-02 -4.72990125e-01 -3.24800402e-01
5.59946358e-01 -8.51032138e-01 -2.92330176e-01 2.35146716e-01
-1.48649621e+00 -7.95893013e-01 -3.49582553e-01 8.46298784e-02
-8.58078301e-01 1.30667463e-01 -7.22645581e-01 -9.55333948e-01
4.97836294e-03 -9.34496343e-01 8.54664505e-01 1.75867513e-01
-1.23961425e+00 -8.31159770e-01 -9.56899822e-02 4.60083902e-01
5.07845521e-01 3.79574090e-01 1.47739458e+00 -9.52640355e-01
-5.79996526e-01 -3.02498281e-01 -1.51626378e-01 -1.10382289e-01
-4.61690962e-01 3.97054143e-02 -8.75692248e-01 3.87356192e-01
-2.32764781e-01 -7.47806966e-01 2.27772549e-01 6.22452646e-02
6.45783007e-01 -4.42377657e-01 -3.67334872e-01 1.96764767e-01
1.03011751e+00 4.98815179e-01 5.16299784e-01 5.62538803e-01
5.14256656e-01 6.82068348e-01 5.72590470e-01 3.88422221e-01
4.14876252e-01 5.32625616e-01 2.47417256e-01 1.76299125e-01
-1.61706731e-01 -6.55871034e-01 4.58163887e-01 4.33030069e-01
-8.59616250e-02 1.54089844e-02 -1.42591834e+00 6.99633062e-01
-2.20214701e+00 -1.07453728e+00 -1.27041698e-01 1.66180325e+00
1.04217041e+00 4.67732131e-01 -4.58523445e-02 3.47251922e-01
2.60879636e-01 2.11250544e-01 -2.75892824e-01 -7.37243950e-01
2.22651258e-01 3.12517911e-01 -9.70989838e-02 6.33846760e-01
-5.59676230e-01 1.16263819e+00 7.06440639e+00 6.47084117e-01
-7.70288050e-01 -5.01728393e-02 2.32297450e-01 -2.68099129e-01
-3.81260693e-01 3.51092488e-01 -6.57114685e-01 -3.29281054e-02
1.10715067e+00 -9.04238597e-02 7.65681148e-01 1.18445015e+00
1.12701535e-01 -4.59359288e-01 -1.65935338e+00 7.14216530e-01
2.69705653e-01 -1.78055549e+00 -1.00081369e-01 -4.93537694e-01
3.28465492e-01 -2.49543339e-01 -5.93874991e-01 7.45891690e-01
6.71117008e-01 -1.45285666e+00 1.33839464e+00 4.52995718e-01
6.62631035e-01 -6.35239065e-01 5.55581927e-01 6.71280563e-01
-9.33283210e-01 4.63653589e-03 9.60726440e-02 -7.46276140e-01
4.13983405e-01 -3.93091850e-02 -1.13456750e+00 1.78261533e-01
2.00832978e-01 4.58866596e-01 -7.14990973e-01 8.23527694e-01
-6.91593468e-01 7.43928730e-01 -2.48434097e-01 -2.62099355e-01
2.07118705e-01 3.21150959e-01 4.12167966e-01 1.40638435e+00
1.88982502e-01 3.92361164e-01 9.02171880e-02 1.10963356e+00
4.08075631e-01 -2.15195090e-01 -8.56829524e-01 -6.13782644e-01
3.75802785e-01 6.81778729e-01 -9.16776121e-01 -4.94627804e-01
-3.08600157e-01 6.65623069e-01 1.52383432e-01 1.59019172e-01
-8.63674760e-01 -4.07239020e-01 3.01913708e-01 4.86263812e-01
6.72286674e-02 -4.60574836e-01 -6.82154536e-01 -1.11008787e+00
-2.88372278e-01 -1.17737830e+00 1.15269199e-02 -1.50421119e+00
-6.53364778e-01 3.21432650e-01 5.28347075e-01 -6.60567462e-01
-9.91602361e-01 -4.87699687e-01 -8.31806779e-01 6.30784810e-01
-8.97353709e-01 -1.36090589e+00 -4.63546328e-02 1.67683855e-01
7.63997793e-01 -5.75528517e-02 1.01676023e+00 -1.69764131e-01
-7.55815208e-02 2.12195933e-01 -4.60960925e-01 2.58250117e-01
2.34978408e-01 -9.56808984e-01 7.64734924e-01 8.67324889e-01
2.65601993e-01 9.42351162e-01 9.86078382e-01 -7.00729609e-01
-1.05904913e+00 -3.91382635e-01 9.74316299e-01 -8.81236315e-01
9.15391088e-01 -5.25063217e-01 -8.73456120e-01 1.09367847e+00
2.13035613e-01 -7.34221458e-01 6.04275703e-01 2.54925877e-01
-4.20281082e-01 5.40816247e-01 -8.27847719e-01 7.98336685e-01
1.14817822e+00 -7.45394349e-01 -1.18814445e+00 4.15185601e-01
9.07261908e-01 -5.92600346e-01 -5.28056681e-01 3.36399525e-02
6.24762297e-01 -9.89145756e-01 7.39849508e-01 -8.18414092e-01
1.03572786e+00 -3.68111968e-01 -8.60489681e-02 -9.46299672e-01
5.22200279e-02 -7.50080049e-01 -2.11263254e-01 1.53012347e+00
5.44512272e-01 -2.99705625e-01 7.08151221e-01 1.13085294e+00
-3.69409919e-01 -7.69660473e-01 -6.26981854e-01 -6.17113113e-01
-6.90822974e-02 -1.18690932e+00 6.20420575e-01 8.87886405e-01
8.62868369e-01 3.76731992e-01 -6.09692074e-02 -2.99757898e-01
3.99438553e-02 -5.31367138e-02 8.21579218e-01 -1.05662382e+00
-7.66946733e-01 -5.99109948e-01 -7.14082047e-02 -7.70671248e-01
2.21821308e-01 -8.74948442e-01 1.08747348e-01 -1.94907224e+00
-3.81068960e-02 -1.77340969e-01 3.00488204e-01 1.02434242e+00
3.20072472e-01 -2.75233656e-01 4.31343496e-01 1.47736251e-01
-4.78377044e-01 6.73851697e-03 7.21014321e-01 -1.01129435e-01
-2.75632441e-01 -3.40379179e-01 -1.08474827e+00 1.10848534e+00
6.29522920e-01 -5.87335706e-01 -7.05312788e-01 -5.72563529e-01
6.93714082e-01 4.33152854e-01 4.97609198e-01 -1.28272605e+00
3.05197179e-01 -2.07787961e-01 1.36028990e-01 -3.64128798e-01
3.86692613e-01 -4.45273608e-01 2.19108492e-01 3.39254707e-01
-6.87292814e-01 -4.73839380e-02 6.18647695e-01 1.83473271e-03
-2.00509727e-01 -7.96103299e-01 1.99084550e-01 -5.37383080e-01
-8.03199172e-01 -6.43256009e-01 -7.14313090e-01 1.67409837e-01
9.88376021e-01 -5.88476181e-01 -5.63155472e-01 -7.69324064e-01
-4.85033751e-01 1.96091652e-01 5.82707047e-01 3.99108827e-01
4.81730014e-01 -9.20323491e-01 -4.39788431e-01 -2.72544958e-02
-4.03498940e-04 8.63960311e-02 -2.56547332e-01 4.41316217e-01
-7.81858385e-01 5.44953585e-01 -2.08209857e-01 -9.72630754e-02
-1.08435655e+00 3.79316628e-01 3.64528298e-02 -3.89021814e-01
-7.57399976e-01 1.02624309e+00 -3.72778401e-02 -2.00526237e-01
4.20670420e-01 -4.06734943e-01 -1.08965985e-01 8.63960907e-02
6.99274898e-01 2.68325418e-01 -2.01760277e-01 -1.48430526e-01
-3.55301887e-01 2.80537963e-01 -1.77520886e-01 -6.36907935e-01
1.69064677e+00 3.23094606e-01 -3.61745916e-02 6.36651158e-01
3.32690716e-01 -1.47006035e-01 -1.12989020e+00 1.84083983e-01
2.97476351e-01 -1.55767471e-01 -2.60797709e-01 -1.27267802e+00
-1.41733095e-01 9.85070229e-01 -3.49532455e-01 2.92027712e-01
6.84150875e-01 1.80693343e-01 6.58867121e-01 3.81022304e-01
7.83336699e-01 -8.78157079e-01 -5.13926484e-02 7.63277233e-01
1.16808701e+00 -7.16932714e-01 1.64257139e-01 -1.82843313e-01
-7.60785818e-01 1.29567671e+00 6.10265732e-01 -6.55165389e-02
5.26413806e-02 7.43085444e-01 -1.23642124e-01 -3.93054366e-01
-8.31530154e-01 6.95909932e-02 -2.87154138e-01 8.14968586e-01
7.23742008e-01 -2.40945891e-01 8.57867599e-02 7.76106596e-01
-5.68186522e-01 3.97745818e-01 7.08493590e-01 1.36484432e+00
-5.45248330e-01 -1.10052991e+00 -7.03326523e-01 3.66842121e-01
-3.15637529e-01 -4.41141695e-01 -6.23623312e-01 1.15014291e+00
7.48418048e-02 1.04685783e+00 -2.44197860e-01 -2.97503918e-01
1.55415550e-01 6.22473955e-01 7.76448071e-01 -1.13846219e+00
-8.82361829e-01 -2.41699010e-01 8.14872622e-01 -4.63181585e-01
-1.69161201e-01 -7.06752539e-01 -1.51506996e+00 -4.05353099e-01
-2.74843156e-01 -1.17896199e-01 4.45700973e-01 1.19791329e+00
1.19504973e-01 5.88094592e-01 -5.26510715e-01 -1.06120193e+00
-2.69024849e-01 -9.54978764e-01 -2.66794086e-01 -5.10869399e-02
-7.52300620e-02 -5.14406443e-01 3.58692929e-02 3.85257870e-01] | [11.162739753723145, 8.75307846069336] |
6639b24f-f20e-4389-96b5-fcf3fd1e386f | systematic-evaluation-of-cnn-advances-on-the | 1606.02228 | null | http://arxiv.org/abs/1606.02228v2 | http://arxiv.org/pdf/1606.02228v2.pdf | Systematic evaluation of CNN advances on the ImageNet | The paper systematically studies the impact of a range of recent advances in
CNN architectures and learning methods on the object categorization (ILSVRC)
problem. The evalution tests the influence of the following choices of the
architecture: non-linearity (ReLU, ELU, maxout, compatibility with batch
normalization), pooling variants (stochastic, max, average, mixed), network
width, classifier design (convolutional, fully-connected, SPP), image
pre-processing, and of learning parameters: learning rate, batch size,
cleanliness of the data, etc.
The performance gains of the proposed modifications are first tested
individually and then in combination. The sum of individual gains is bigger
than the observed improvement when all modifications are introduced, but the
"deficit" is small suggesting independence of their benefits. We show that the
use of 128x128 pixel images is sufficient to make qualitative conclusions about
optimal network structure that hold for the full size Caffe and VGG nets. The
results are obtained an order of magnitude faster than with the standard 224
pixel images. | ['Nikolay Sergievskiy', 'Jiri Matas', 'Dmytro Mishkin'] | 2016-06-07 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [ 3.48127820e-02 -2.19424181e-02 1.19487062e-01 -4.13277894e-01
1.82945311e-01 -5.34011483e-01 8.43122065e-01 3.75882566e-01
-1.07484281e+00 6.29418910e-01 1.72680438e-01 -4.78450477e-01
-5.35003960e-01 -5.42927742e-01 -5.12119114e-01 -7.01832891e-01
-2.99340874e-01 -2.05059811e-01 5.66058517e-01 -1.98974848e-01
5.06350160e-01 9.93070900e-01 -1.81446290e+00 2.76979417e-01
3.57167214e-01 1.26489758e+00 1.13957822e-01 7.87762940e-01
-1.45108148e-01 8.56860161e-01 -5.94214976e-01 -3.07236135e-01
6.44832253e-01 4.38039266e-02 -8.84103477e-01 2.07546968e-02
4.22557652e-01 -8.61857161e-02 -2.25910828e-01 9.64738548e-01
7.05103040e-01 6.61185607e-02 6.81951106e-01 -1.27559566e+00
-5.38621366e-01 7.05448151e-01 -3.50702077e-01 6.36237621e-01
-2.91148812e-01 3.28426063e-01 6.72941506e-01 -5.83559275e-01
6.03191435e-01 1.24660850e+00 8.05513144e-01 1.92919731e-01
-1.12421775e+00 -4.31312174e-01 2.01173156e-01 1.01407707e-01
-1.40715492e+00 -4.16597545e-01 1.49264425e-01 -3.61515462e-01
1.37962806e+00 1.37570530e-01 4.77654040e-01 6.57670498e-01
2.80095220e-01 5.30695096e-02 1.34389710e+00 -7.72784233e-01
1.79833516e-01 7.97443509e-01 5.24370730e-01 4.26035494e-01
7.61763394e-01 -1.21183954e-01 -1.00484036e-01 1.94298908e-01
5.80690742e-01 -2.47198001e-01 -1.40598148e-01 -1.37049094e-01
-1.02296829e+00 6.19965434e-01 6.87077045e-01 6.25383794e-01
-1.75131246e-01 2.67179668e-01 8.03456604e-01 7.49899209e-01
1.72676757e-01 6.82625115e-01 -7.22191930e-01 2.44671315e-01
-8.73079479e-01 2.03389913e-01 5.40146828e-01 1.05749285e+00
7.11772561e-01 2.31333792e-01 -2.45618790e-01 7.55157173e-01
-2.22682506e-02 -9.06094238e-02 7.66065478e-01 -8.47063839e-01
5.62723279e-01 6.52166963e-01 -7.98695236e-02 -8.23946834e-01
-7.50049889e-01 -4.37663049e-01 -6.03201330e-01 5.56892991e-01
7.27430463e-01 -4.68194485e-01 -9.76736963e-01 1.43679869e+00
-1.51319712e-01 -4.41344559e-01 1.40922191e-02 6.60277605e-01
9.34897959e-01 3.21561128e-01 3.75489712e-01 1.13792472e-01
1.40369904e+00 -8.44110906e-01 -5.30783236e-01 -2.16055080e-01
7.37581193e-01 -6.98329568e-01 7.89924741e-01 3.75759542e-01
-8.60546768e-01 -9.55929518e-01 -1.34589744e+00 -1.13361895e-01
-1.12422991e+00 4.63862091e-01 5.45283437e-01 9.50255215e-01
-1.21781600e+00 9.49215651e-01 -4.31264997e-01 -4.58943427e-01
4.63321179e-01 6.63613379e-01 -5.52589834e-01 1.72935665e-01
-8.56975198e-01 1.24259126e+00 8.94128382e-01 2.34326378e-01
-3.18808645e-01 -5.86202562e-01 -4.98060107e-01 3.17249596e-01
1.54504627e-01 -1.59963235e-01 7.39916801e-01 -1.41421103e+00
-1.27407956e+00 8.64514828e-01 4.35850978e-01 -7.59842873e-01
7.49324083e-01 8.05042386e-02 -2.23369524e-01 1.75662935e-02
-5.22048235e-01 1.12893987e+00 5.17851651e-01 -1.03183651e+00
-6.30506575e-01 -2.78770030e-01 1.91181868e-01 6.54025450e-02
-2.39766836e-01 2.11651951e-01 1.40572831e-01 -4.02532756e-01
-1.29155949e-01 -8.84220898e-01 -2.41352051e-01 8.49480648e-03
-2.07495287e-01 -1.98552310e-01 7.29357302e-01 -4.67996478e-01
8.69649649e-01 -2.39900112e+00 -4.02536154e-01 2.24267632e-01
-7.78388605e-02 4.11239415e-01 -2.10434809e-01 2.83609003e-01
-5.02054691e-01 2.74872720e-01 1.00969695e-01 -8.62597451e-02
-5.97036108e-02 4.19209078e-02 1.03829615e-01 4.87727016e-01
2.74008512e-01 7.94920146e-01 -1.57628909e-01 -4.70953494e-01
3.16694200e-01 5.78607678e-01 -3.20525765e-01 -3.29490364e-01
3.49536061e-01 -1.69833079e-01 -1.02794431e-01 2.10389659e-01
7.60810375e-01 3.89491245e-02 9.04056281e-02 -4.19380873e-01
-4.88939285e-01 2.14587539e-01 -1.55382788e+00 1.18207908e+00
-3.09732139e-01 9.80532289e-01 -2.77835988e-02 -9.38802063e-01
1.04186797e+00 2.38530919e-01 -4.56699776e-03 -6.00291371e-01
5.81915796e-01 2.12464765e-01 6.10344529e-01 -3.97715092e-01
4.60921437e-01 1.62697822e-01 5.00027001e-01 -2.24572308e-02
4.76468623e-01 2.80309677e-01 4.32781845e-01 -1.83004916e-01
8.86710048e-01 -1.88072965e-01 2.80828357e-01 -9.31288660e-01
5.48504531e-01 -2.15304315e-01 1.42805383e-01 8.28176916e-01
-3.33120048e-01 6.32791996e-01 9.97179568e-01 -3.37528378e-01
-1.16899085e+00 -5.77040911e-01 -2.44836047e-01 1.06203735e+00
-3.83068658e-02 -1.26398787e-01 -7.59503067e-01 -5.03381431e-01
6.67419508e-02 4.25003618e-01 -9.05390859e-01 -2.22358063e-01
-5.91139257e-01 -1.02342319e+00 6.86732590e-01 6.58461213e-01
7.13583469e-01 -1.27587271e+00 -1.08990586e+00 -8.18965808e-02
6.43036306e-01 -1.15927076e+00 2.37326160e-01 8.36793840e-01
-1.13248789e+00 -9.85698044e-01 -4.85731333e-01 -7.19876826e-01
5.75221419e-01 1.23519778e-01 8.72495115e-01 2.50529438e-01
-3.08761984e-01 1.65957734e-01 -2.99010128e-01 -4.70254362e-01
-7.28209093e-02 2.91945279e-01 -2.56517619e-01 -2.00226858e-01
2.50917107e-01 -3.41689646e-01 -6.96721435e-01 -2.64913719e-02
-9.98694777e-01 -3.18785548e-01 7.97112226e-01 5.06355226e-01
1.53005511e-01 2.17061877e-01 5.13617516e-01 -1.01509845e+00
6.29981697e-01 -6.25086576e-02 -4.75257218e-01 2.48521343e-01
-8.98083985e-01 1.26921684e-01 5.66518009e-01 -5.63669980e-01
-1.03953230e+00 -4.41301987e-03 -8.60509798e-02 -1.42239854e-01
-5.23126900e-01 1.42008424e-01 -2.36718524e-02 -3.84896398e-01
6.50967419e-01 -2.77722269e-01 -1.26789302e-01 -4.02063519e-01
2.58413374e-01 5.18955350e-01 1.96700364e-01 -1.81315765e-01
3.02768409e-01 3.47870171e-01 -7.64300898e-02 -9.42667246e-01
-8.34645703e-02 -2.02885628e-01 -9.82264340e-01 1.24704633e-02
9.41523671e-01 -7.89677143e-01 -5.70664704e-01 5.68533123e-01
-1.00635779e+00 -4.66490954e-01 -4.26106364e-01 4.82163489e-01
-5.76223247e-02 -3.95315140e-02 -6.90851450e-01 -5.67172170e-01
-3.08347672e-01 -1.23327756e+00 2.89549410e-01 3.59614402e-01
-3.37397605e-02 -8.87309551e-01 -5.26732326e-01 -1.09458603e-01
7.44348407e-01 1.29025161e-01 1.02304590e+00 -9.13493335e-01
-1.32337034e-01 -3.30699772e-01 -7.28244126e-01 8.01288664e-01
-8.41051042e-02 2.05524966e-01 -1.23634553e+00 -2.44464472e-01
-2.13911414e-01 -1.18125170e-01 1.09663117e+00 4.27325279e-01
1.22610044e+00 -2.19551668e-01 -1.67420805e-02 4.49839056e-01
1.83733368e+00 1.64717048e-01 9.44694936e-01 6.60038292e-01
4.12642270e-01 7.57941484e-01 -6.94197603e-03 1.91471279e-01
-9.03503150e-02 3.18629980e-01 4.67770845e-01 -1.89504549e-01
-3.81123751e-01 4.24917340e-01 5.05114980e-02 1.91170678e-01
-4.32489365e-01 -2.92587310e-01 -7.94567704e-01 3.80333930e-01
-1.28490877e+00 -7.08225906e-01 -2.21258745e-01 2.29043794e+00
1.92344949e-01 6.18064046e-01 1.22797489e-01 4.38136071e-01
7.17072129e-01 9.72983912e-02 4.41500843e-02 -9.55103338e-01
-3.80153596e-01 3.58189791e-01 1.25250876e+00 1.98411539e-01
-1.07271445e+00 5.85523427e-01 6.76871634e+00 7.71591783e-01
-1.50919402e+00 1.10129461e-01 8.28106880e-01 -5.34069948e-02
3.66863012e-01 -1.27030328e-01 -9.48089600e-01 3.58298689e-01
1.04884350e+00 3.48779678e-01 1.60668001e-01 8.17814946e-01
5.76706827e-02 -4.22564745e-01 -9.05393302e-01 7.18034446e-01
6.60773227e-03 -1.22079694e+00 -9.20676440e-02 8.38944986e-02
6.33334637e-01 2.47684926e-01 -7.82799572e-02 3.52569044e-01
-3.72490957e-02 -9.77237046e-01 1.03886199e+00 2.87912220e-01
3.41430455e-01 -7.17224002e-01 1.17068768e+00 -3.81768420e-02
-8.52253616e-01 -6.12433076e-01 -5.66645563e-01 -2.28074431e-01
-3.94482464e-01 3.08014899e-01 -3.98012549e-01 3.83327007e-01
8.82098913e-01 1.65627852e-01 -1.37675059e+00 1.15488279e+00
1.67724535e-01 5.18448651e-01 -4.53757942e-01 -1.16718985e-01
5.66262245e-01 1.03948087e-01 1.38973847e-01 1.84844375e+00
1.43604860e-01 -8.38094652e-02 -3.83625180e-01 6.08529747e-01
6.84892163e-02 2.28733942e-01 -4.19315040e-01 1.89149410e-01
4.48855788e-01 1.55971241e+00 -1.43355334e+00 -3.19878489e-01
-4.14154857e-01 5.82642555e-01 2.48271540e-01 3.18764240e-01
-4.66857553e-01 -8.10437322e-01 4.60403144e-01 3.67151946e-01
7.97939718e-01 -8.31654593e-02 -6.69892430e-01 -5.74913085e-01
4.06259038e-02 -7.31679499e-01 1.99008226e-01 -7.31929898e-01
-9.32189822e-01 6.79523230e-01 3.16100925e-01 -7.59324729e-01
2.47343138e-01 -1.25373006e+00 -9.61163342e-01 7.90254533e-01
-1.39331830e+00 -6.79143667e-01 -3.04090023e-01 2.83981681e-01
1.12337887e-01 -2.92522639e-01 3.99047136e-01 3.73635054e-01
-5.32754004e-01 6.55512631e-01 -1.60891265e-02 2.79280841e-01
4.11254883e-01 -1.12028944e+00 9.84189007e-03 7.82527268e-01
-1.50957257e-01 6.34754241e-01 6.82682395e-01 -1.27573431e-01
-8.49351227e-01 -9.85677898e-01 7.29556561e-01 1.47146999e-03
4.16092426e-01 -5.33416152e-01 -9.57124233e-01 5.16727626e-01
5.08980095e-01 1.99519858e-01 1.44074515e-01 -2.93178428e-02
-4.57026362e-01 -3.20147634e-01 -1.29920983e+00 4.67619956e-01
6.21789396e-01 -1.33873612e-01 -3.85953516e-01 -1.81059521e-02
5.08196473e-01 1.09517397e-02 -8.45774114e-01 2.69287288e-01
5.65533161e-01 -1.32063305e+00 9.62658882e-01 -5.32383263e-01
3.36353362e-01 -1.03295974e-01 -1.23080395e-01 -1.00475454e+00
-5.00279784e-01 8.83006305e-02 6.72723591e-01 1.38169789e+00
7.39798546e-01 -8.75028789e-01 5.96789360e-01 5.40821433e-01
-2.11713001e-01 -7.06692100e-01 -9.26659763e-01 -7.60485351e-01
3.36904407e-01 -3.04649502e-01 5.50413668e-01 8.32085907e-01
-2.01111630e-01 2.41352081e-01 4.82709259e-02 -2.03265354e-01
1.01696588e-01 -4.72941279e-01 4.77714896e-01 -1.23013186e+00
-1.63430795e-01 -8.37327123e-01 -8.75457466e-01 -2.35264540e-01
-1.86710030e-01 -5.98468125e-01 -1.94227904e-01 -1.42866075e+00
-2.53636092e-01 -5.24675786e-01 -5.57407975e-01 6.83090091e-01
7.88132623e-02 2.77882457e-01 5.19582748e-01 -9.94969234e-02
-2.79793888e-01 -2.59162840e-02 8.93475354e-01 4.22237292e-02
-2.74335980e-01 -2.07895607e-01 -5.37864625e-01 7.33315647e-01
9.47519958e-01 -2.96568424e-01 -2.60559112e-01 -4.72408354e-01
1.30810380e-01 -4.82573807e-01 4.80391890e-01 -1.30970442e+00
2.26862878e-01 2.91972190e-01 7.42456675e-01 -1.32963493e-01
9.51573327e-02 -8.16943288e-01 6.19596094e-02 6.17882073e-01
-7.29800582e-01 4.45110172e-01 6.19890869e-01 2.10878029e-01
-5.37873842e-02 -9.01584148e-01 1.06266415e+00 -2.19859436e-01
-7.66345024e-01 -2.63667345e-01 -5.19713283e-01 -2.12698177e-01
7.91398704e-01 -5.72944701e-01 -3.10908049e-01 8.95396248e-02
-7.08689392e-01 -1.59723401e-01 8.73234943e-02 4.90085304e-01
6.05065301e-02 -1.03524721e+00 -5.23772180e-01 3.52057070e-02
-6.39464930e-02 -4.77603108e-01 2.24138677e-01 9.21695650e-01
-8.60965252e-01 5.53090394e-01 -7.10975945e-01 -1.98199511e-01
-1.38044095e+00 6.36705577e-01 5.23901582e-01 -1.86763868e-01
-5.65207124e-01 7.70246208e-01 -6.30570948e-02 -2.67389655e-01
4.77409869e-01 -6.03563249e-01 -4.21767980e-01 3.27849329e-01
2.79197812e-01 6.75525606e-01 5.94444156e-01 -3.44360292e-01
-2.46426836e-01 3.51952344e-01 -2.07790434e-01 1.51560903e-01
1.34094214e+00 -3.12306918e-02 9.39505640e-03 4.14717048e-01
1.20083630e+00 -4.76104081e-01 -1.04004264e+00 3.19041163e-02
7.57341012e-02 -1.00799397e-01 2.00414911e-01 -7.11240828e-01
-1.23584247e+00 9.88465667e-01 1.07083499e+00 3.49227250e-01
9.60637748e-01 -3.23155463e-01 -1.14380747e-01 4.90626633e-01
3.32403705e-02 -1.34035850e+00 -3.56038332e-01 4.91309196e-01
8.48703444e-01 -9.97378170e-01 4.37340081e-01 -2.31319353e-01
-6.43657625e-01 1.35056841e+00 6.92823172e-01 -6.32765591e-01
8.47247005e-01 2.64198333e-01 -1.07116267e-01 -1.78562418e-01
-7.15863228e-01 -2.95357108e-01 9.25892591e-02 4.98589098e-01
5.91713190e-01 -2.31280595e-01 -5.05873799e-01 1.99848086e-01
-2.27270186e-01 -2.48554587e-01 5.70858121e-01 9.15532649e-01
-4.41335350e-01 -6.66126728e-01 -2.96983123e-01 6.86098874e-01
-6.26049757e-01 -1.06613040e-01 -3.20800722e-01 1.49082994e+00
8.23168635e-01 6.64637148e-01 4.28814173e-01 -1.66684493e-01
5.45530081e-01 1.31656364e-01 5.17162621e-01 -2.60640532e-01
-1.27113843e+00 -1.44284010e-01 -1.08876359e-02 -1.59360483e-01
-3.48450631e-01 -5.81843257e-01 -9.29686368e-01 -2.45879084e-01
-5.97790718e-01 -1.51337638e-01 9.01166260e-01 1.01555860e+00
2.13899672e-01 6.44135475e-01 6.31575659e-02 -9.49563682e-01
-4.78926957e-01 -1.34043217e+00 -6.49039090e-01 2.04162359e-01
3.41001339e-02 -6.55147135e-01 -5.33250391e-01 1.04646266e-01] | [8.639728546142578, 2.907447338104248] |
d2209a33-f2b9-4df0-9438-5cfbfb1a046b | variational-hierarchical-dialog-autoencoder | 2001.08604 | null | https://arxiv.org/abs/2001.08604v3 | https://arxiv.org/pdf/2001.08604v3.pdf | Variational Hierarchical Dialog Autoencoder for Dialog State Tracking Data Augmentation | Recent works have shown that generative data augmentation, where synthetic samples generated from deep generative models complement the training dataset, benefit NLP tasks. In this work, we extend this approach to the task of dialog state tracking for goal-oriented dialogs. Due to the inherent hierarchical structure of goal-oriented dialogs over utterances and related annotations, the deep generative model must be capable of capturing the coherence among different hierarchies and types of dialog features. We propose the Variational Hierarchical Dialog Autoencoder (VHDA) for modeling the complete aspects of goal-oriented dialogs, including linguistic features and underlying structured annotations, namely speaker information, dialog acts, and goals. The proposed architecture is designed to model each aspect of goal-oriented dialogs using inter-connected latent variables and learns to generate coherent goal-oriented dialogs from the latent spaces. To overcome training issues that arise from training complex variational models, we propose appropriate training strategies. Experiments on various dialog datasets show that our model improves the downstream dialog trackers' robustness via generative data augmentation. We also discover additional benefits of our unified approach to modeling goal-oriented dialogs: dialog response generation and user simulation, where our model outperforms previous strong baselines. | ['Sang-goo Lee', 'Walter Chang', 'Kang Min Yoo', 'Franck Dernoncourt', 'Trung Bui', 'Hanbit Lee'] | 2020-01-23 | null | https://aclanthology.org/2020.emnlp-main.274 | https://aclanthology.org/2020.emnlp-main.274.pdf | emnlp-2020-11 | ['user-simulation'] | ['natural-language-processing'] | [-2.55422413e-01 9.00710046e-01 -5.69834560e-03 -7.56243110e-01
-8.33367825e-01 -7.57837713e-01 9.37064886e-01 -3.56447190e-01
1.74632743e-01 7.55182683e-01 8.84105861e-01 -1.53985083e-01
1.35617897e-01 -5.99489033e-01 -3.63019317e-01 -5.74673414e-01
1.58568487e-01 1.10943699e+00 -6.09599389e-02 -7.45187759e-01
-1.63972154e-01 2.34884247e-02 -1.08546007e+00 3.14502329e-01
7.43139267e-01 6.01475298e-01 1.69679224e-01 9.49904442e-01
-4.20076072e-01 1.12826860e+00 -8.23985755e-01 -3.60726774e-01
-7.79569671e-02 -8.12785149e-01 -1.01113510e+00 5.04865348e-01
4.04051319e-02 -6.43091619e-01 -4.16029245e-01 8.14961791e-01
2.58070678e-01 4.63673830e-01 9.20129776e-01 -1.61986649e+00
-7.59634435e-01 8.94495010e-01 2.65730888e-01 -2.86921531e-01
3.14597964e-01 3.71573687e-01 1.35388446e+00 -6.70402348e-01
6.37317657e-01 1.95664895e+00 3.45013499e-01 1.32877362e+00
-1.48757803e+00 -2.68304288e-01 3.63366902e-01 -3.05564463e-01
-8.33892345e-01 -5.71781516e-01 9.58223939e-01 -6.67562306e-01
7.70985603e-01 7.96963945e-02 3.07025939e-01 1.75961089e+00
-1.04308926e-01 9.36206281e-01 7.92032719e-01 -2.57329881e-01
3.34971994e-01 3.93344939e-01 5.54197252e-01 7.76948869e-01
-6.00681961e-01 1.95670828e-01 -5.28950691e-01 -4.56131041e-01
5.87783754e-01 -1.38270646e-01 -2.99760755e-02 -3.40018690e-01
-9.02718663e-01 1.41504228e+00 2.54354298e-01 1.68800011e-01
-2.94782698e-01 -2.21259054e-02 1.41123265e-01 2.21031144e-01
3.87255520e-01 4.11759257e-01 -3.90574664e-01 9.12103131e-02
-4.40885127e-01 5.71436107e-01 1.16153598e+00 1.27574801e+00
6.88505173e-01 3.32013398e-01 -8.05425465e-01 9.07614529e-01
7.96442688e-01 3.20903391e-01 4.80345279e-01 -1.26265681e+00
3.72944742e-01 9.23038721e-01 2.98218369e-01 -2.09629744e-01
-4.40202445e-01 1.36277094e-01 -5.72920680e-01 2.42223702e-02
6.07052684e-01 -6.73652887e-01 -7.97999859e-01 2.23890734e+00
4.54628915e-01 -4.67590719e-01 5.58804095e-01 6.78387880e-01
1.16703784e+00 8.08699727e-01 3.91740918e-01 -1.94879413e-01
1.41234004e+00 -8.77610266e-01 -1.10735786e+00 -3.33494753e-01
3.27894986e-01 -4.41566378e-01 1.21378207e+00 -2.76881218e-01
-1.11575198e+00 -6.78353965e-01 -6.31893635e-01 -2.13277131e-01
-2.43523777e-01 6.32118285e-02 5.67355514e-01 4.96378213e-01
-1.16398275e+00 1.06623411e-01 -8.00463915e-01 -3.01468611e-01
-1.82079241e-01 3.84278029e-01 1.69439644e-01 6.72841668e-01
-1.38200557e+00 7.22154319e-01 3.63755614e-01 -9.80743170e-02
-1.30225492e+00 -4.10275400e-01 -1.15628648e+00 3.71951282e-01
3.39848131e-01 -6.93460166e-01 1.83851266e+00 -2.69269317e-01
-1.86597788e+00 4.48955715e-01 -3.44856322e-01 -5.83192825e-01
2.11543664e-01 -1.08830892e-01 5.42754643e-02 -2.16672868e-01
-2.46748880e-01 1.05971062e+00 8.45911980e-01 -1.50135064e+00
-4.38368887e-01 -3.45237076e-01 3.06110829e-01 3.36729705e-01
-3.48700017e-01 -1.57984674e-01 -1.20074637e-01 -2.23210603e-01
-1.03466459e-01 -1.11869466e+00 -4.57467109e-01 -7.30835319e-01
-7.67791092e-01 -6.70980692e-01 1.01884949e+00 -5.42275727e-01
1.00521719e+00 -1.94695032e+00 4.52076942e-01 -3.46120298e-01
4.38145101e-01 -1.46339461e-01 6.60487041e-02 5.34626722e-01
4.51770931e-01 -7.98279047e-02 -6.10803403e-02 -9.20677960e-01
6.47618353e-01 4.91972148e-01 -8.32376540e-01 -2.84605294e-01
2.12408423e-01 1.17241263e+00 -7.29060113e-01 -4.45693642e-01
4.15330380e-01 4.32384968e-01 -6.71376348e-01 1.01205420e+00
-1.13520801e+00 9.81243789e-01 -4.65711802e-01 2.94534355e-01
1.10149525e-01 -3.39055300e-01 6.26711249e-01 2.92740706e-02
2.00096443e-01 7.56700635e-01 -5.73280990e-01 1.55243206e+00
-5.17426550e-01 5.40592372e-01 3.41863990e-01 -5.09726226e-01
1.13835812e+00 7.48744726e-01 1.38574690e-01 -2.99887341e-02
3.25024515e-01 -4.07210439e-01 1.30530700e-01 -2.71667480e-01
6.88537836e-01 -1.74234122e-01 -5.96410871e-01 5.98442256e-01
7.00983763e-01 -3.19693625e-01 6.98532909e-02 5.59936523e-01
5.36207199e-01 4.36137915e-02 1.53654829e-01 -4.60529625e-02
3.76004308e-01 6.30259737e-02 3.32142800e-01 7.68112302e-01
-2.31919825e-01 1.92899927e-01 7.04382479e-01 -1.69352859e-01
-8.67368460e-01 -1.17891932e+00 1.90882385e-01 1.60870731e+00
-1.34034276e-01 -4.55999851e-01 -9.47660089e-01 -8.38334799e-01
-2.06060171e-01 1.15332460e+00 -5.14702916e-01 -1.38408944e-01
-6.82105958e-01 -7.97185242e-01 3.70124012e-01 5.87750375e-01
3.44802052e-01 -1.29720628e+00 -2.03543261e-01 3.51670384e-01
-4.49262828e-01 -1.30797398e+00 -5.55467188e-01 1.95559010e-01
-8.25436652e-01 -8.44173014e-01 -3.33782345e-01 -7.32894599e-01
2.96107680e-01 -2.25895673e-01 1.16182363e+00 -3.95776331e-01
1.82379633e-01 6.89874649e-01 -7.99299702e-02 -2.49586180e-01
-1.42339611e+00 7.47695044e-02 8.90022703e-03 -1.40828677e-02
4.48958874e-01 -5.63502610e-01 -1.61877707e-01 2.66590953e-01
-5.75567305e-01 8.14894661e-02 1.48504823e-01 1.18854690e+00
-2.96677947e-02 -5.11417747e-01 6.81392729e-01 -7.89086044e-01
1.26148796e+00 -4.20858681e-01 -7.18062222e-01 1.66996494e-01
-3.29112053e-01 4.75515336e-01 5.81425846e-01 -4.51051921e-01
-1.69132340e+00 2.18944833e-01 -2.47631609e-01 -4.12989140e-01
-5.61163902e-01 5.43516539e-02 -4.05587643e-01 5.75948417e-01
6.55018151e-01 3.30504239e-01 1.51578426e-01 -4.24656034e-01
8.60666871e-01 6.74760520e-01 5.06320715e-01 -7.09836841e-01
5.85449100e-01 1.92962691e-01 -2.80041397e-01 -9.47428405e-01
-8.45766068e-01 -2.43286476e-01 -5.93762100e-01 -1.63580880e-01
1.24856019e+00 -8.24895322e-01 -1.14608061e+00 1.33855835e-01
-1.55912185e+00 -6.31061971e-01 -4.05346572e-01 2.07832739e-01
-6.96164906e-01 1.56004369e-01 -9.77824092e-01 -1.40100658e+00
-3.67081523e-01 -1.32949007e+00 1.28448641e+00 3.17425877e-01
-6.39020026e-01 -1.47650838e+00 2.49030113e-01 5.09624422e-01
2.74335176e-01 9.97878164e-02 1.02840948e+00 -1.32562077e+00
-7.60481954e-01 1.92065388e-01 3.58292520e-01 2.49701247e-01
1.99542597e-01 -2.30322868e-01 -1.26243258e+00 -1.23145126e-01
3.88772935e-01 -7.61518955e-01 5.07622421e-01 4.43972707e-01
3.77365679e-01 -7.60669112e-01 -2.55030870e-01 1.57553166e-01
5.42797565e-01 4.25937712e-01 4.88704816e-02 -4.73796874e-01
5.71732402e-01 1.02444267e+00 4.22848582e-01 3.70124459e-01
6.73810184e-01 8.84981275e-01 4.00372654e-01 1.13604844e-01
-1.90327521e-02 -5.48457384e-01 6.07032418e-01 6.96888506e-01
4.06474769e-01 -4.84124482e-01 -6.86117172e-01 4.77435112e-01
-1.98312104e+00 -8.76210272e-01 -1.32293001e-01 1.59567678e+00
9.40631032e-01 7.03083128e-02 4.15390909e-01 -4.92866367e-01
6.11225069e-01 3.72993767e-01 -5.47093630e-01 -2.75660992e-01
1.28044248e-01 -1.54961720e-01 -2.57399678e-01 1.17705452e+00
-1.13587213e+00 1.42421710e+00 6.32284927e+00 2.54300326e-01
-5.77239633e-01 3.18079203e-01 4.66752619e-01 1.94944203e-01
-3.12225580e-01 6.34858832e-02 -1.37624800e+00 2.18451038e-01
1.02503705e+00 -1.36833236e-01 2.52682894e-01 1.05249357e+00
2.16357887e-01 5.08029461e-01 -1.52023864e+00 2.85401523e-01
-1.91676870e-01 -1.26957285e+00 1.08804658e-01 3.12900007e-01
6.03603244e-01 -4.23124999e-01 1.81030959e-01 9.15995240e-01
1.54334283e+00 -9.30788279e-01 3.23863864e-01 1.26045808e-01
2.90689111e-01 -2.09185898e-01 3.82579416e-01 7.22963095e-01
-9.11426365e-01 -8.02961513e-02 -1.17636099e-01 9.24258009e-02
4.89481241e-01 -1.13927439e-01 -1.68734527e+00 1.49923176e-01
9.55675393e-02 2.08815649e-01 -7.49102458e-02 -5.03742732e-02
-4.83428270e-01 6.38670027e-01 -7.98173845e-02 -2.30598286e-01
3.34330082e-01 -1.70195803e-01 7.12617934e-01 1.00008321e+00
-1.77173510e-01 1.37188777e-01 4.06967878e-01 1.55251729e+00
-4.49491702e-02 -2.43485510e-01 -7.24582255e-01 -1.98777691e-01
5.52911937e-01 1.17516387e+00 -2.02623203e-01 -3.93065840e-01
-4.67622988e-02 7.96641707e-01 3.52809161e-01 5.22228837e-01
-6.68165088e-01 3.27362299e-01 7.99837589e-01 -3.93612504e-01
-2.26445664e-02 -3.33734989e-01 -1.38554856e-01 -1.17787111e+00
-3.83528799e-01 -8.70286107e-01 4.61513281e-01 -5.25617659e-01
-1.28238142e+00 8.55703890e-01 2.03385085e-01 -6.82788491e-01
-1.39682674e+00 -4.43241864e-01 -7.58220553e-01 1.04153240e+00
-8.09798241e-01 -1.34359741e+00 -2.28901237e-01 7.19727933e-01
1.33715498e+00 -4.12532061e-01 1.16529310e+00 -4.47677553e-01
-3.47897857e-01 4.77733493e-01 -2.31696889e-01 3.62953603e-01
5.40476799e-01 -1.59402561e+00 5.53056598e-01 5.41928649e-01
2.45437905e-01 8.86035800e-01 9.25648391e-01 -7.42251039e-01
-9.94357407e-01 -1.03035557e+00 8.60111833e-01 -9.78504002e-01
6.43812001e-01 -9.57546771e-01 -9.33313131e-01 1.23641551e+00
6.14534259e-01 -9.14595842e-01 8.20290864e-01 4.36743021e-01
-1.63290352e-01 4.95715737e-01 -9.18004334e-01 8.12987924e-01
8.09638441e-01 -5.93695939e-01 -1.06279433e+00 5.58349252e-01
1.16145360e+00 -6.09580874e-01 -6.74200058e-01 1.80118665e-01
1.87759593e-01 -8.03886056e-01 1.03135645e+00 -1.09360492e+00
3.62320423e-01 2.30302006e-01 -2.22434029e-01 -1.33886492e+00
-2.19577365e-02 -1.09406936e+00 -5.76977193e-01 1.43264627e+00
3.79187524e-01 -1.97886571e-01 9.14808989e-01 9.54914749e-01
-3.23385775e-01 -2.75150269e-01 -7.24730551e-01 -3.77250820e-01
1.77184716e-01 -6.69071898e-02 4.65685368e-01 7.58954942e-01
2.21497431e-01 1.26468158e+00 -7.53929794e-01 2.62064457e-01
5.66277146e-01 3.97801578e-01 1.15559661e+00 -1.29735386e+00
-5.30114591e-01 -2.70920753e-01 2.57113576e-01 -1.49548721e+00
6.23990476e-01 -6.73136353e-01 3.40588689e-01 -1.41688025e+00
-9.09362212e-02 -1.30416363e-01 4.89605010e-01 3.76058549e-01
-4.36850429e-01 -5.85857868e-01 2.20639050e-01 2.60946959e-01
-5.22710264e-01 1.10148740e+00 1.07513952e+00 -2.12476134e-01
-7.09136844e-01 3.04318756e-01 -6.12633049e-01 8.61664116e-01
6.73676789e-01 -2.05121920e-01 -8.05269599e-01 -5.31529859e-02
-6.01537943e-01 6.74690425e-01 4.01123852e-01 -4.49544460e-01
3.12186122e-01 -2.57066548e-01 -3.02597620e-02 -5.90952635e-01
1.03204811e+00 -5.85586965e-01 -2.20516458e-01 2.89055675e-01
-9.62484419e-01 -3.76231045e-01 -1.30573371e-02 6.20200932e-01
-2.17117980e-01 -1.02493063e-01 7.08823681e-01 -2.59062320e-01
-3.33322972e-01 4.89125066e-02 -6.48613274e-01 1.96137324e-01
7.52096474e-01 3.77391040e-01 -3.83537173e-01 -1.07121789e+00
-1.36704433e+00 5.28772831e-01 7.97243714e-02 6.27538085e-01
4.10872787e-01 -1.11325860e+00 -5.36754251e-01 6.67267963e-02
-1.05576545e-01 -1.11204281e-01 2.58302242e-01 1.63612112e-01
2.82128930e-01 6.92789257e-01 -1.91602274e-03 -7.15235293e-01
-1.20899379e+00 5.32240689e-01 3.70987892e-01 -7.57362068e-01
-3.56591940e-01 9.50371087e-01 8.44108641e-01 -8.69877100e-01
6.06867135e-01 -2.31391490e-01 -4.51861888e-01 -1.05635766e-02
1.62094966e-01 -3.24671529e-02 -4.89161879e-01 -6.65699422e-01
1.41313165e-01 -2.74106741e-01 -2.32841551e-01 -5.06575763e-01
9.85362470e-01 -2.46478200e-01 3.06260914e-01 6.62427425e-01
7.51136303e-01 -1.57736406e-01 -1.68345749e+00 -3.22247505e-01
4.22264524e-02 8.14142749e-02 -4.15988535e-01 -6.97452784e-01
-4.79344517e-01 1.17263794e+00 3.32273483e-01 6.92218602e-01
4.86114264e-01 4.43423688e-01 7.06629097e-01 5.50502837e-01
1.07497685e-01 -8.53818476e-01 5.12800336e-01 8.54020238e-01
9.65654135e-01 -1.45146215e+00 -7.16319442e-01 -4.17307615e-01
-1.17776632e+00 8.90211701e-01 9.94885921e-01 1.76561132e-01
3.09387147e-01 2.48560607e-01 3.27044636e-01 -4.38951731e-01
-1.15621459e+00 -2.78346986e-01 2.11836189e-01 6.66304708e-01
5.15176654e-01 -1.37915555e-03 1.68213964e-01 8.33884478e-01
-5.35490155e-01 -5.67429900e-01 3.13881487e-01 5.68170190e-01
-5.02583921e-01 -1.33706510e+00 -3.66161793e-01 -3.87978256e-02
-6.26146123e-02 -1.03759736e-01 -9.48552907e-01 8.57025743e-01
-4.61952239e-01 1.31641912e+00 -4.85435780e-03 -4.16761965e-01
1.84871092e-01 8.75181079e-01 1.08797878e-01 -8.42706442e-01
-8.23189139e-01 3.24875504e-01 2.09191024e-01 -2.71074653e-01
-1.97236210e-01 -4.94810879e-01 -1.22620285e+00 2.98067220e-02
-1.58623457e-01 5.56393027e-01 4.63431716e-01 9.47899520e-01
2.52796143e-01 7.31211424e-01 5.35445571e-01 -6.28631651e-01
-1.14557874e+00 -1.42374682e+00 -2.73861349e-01 5.21523535e-01
3.87572169e-01 -6.68521523e-01 -3.29912841e-01 3.42129588e-01] | [12.810866355895996, 8.126664161682129] |
d7e8b046-cf75-4599-ad5a-4088ba33e865 | human-activity-recognition-using-self | 2304.14912 | null | https://arxiv.org/abs/2304.14912v1 | https://arxiv.org/pdf/2304.14912v1.pdf | Human Activity Recognition Using Self-Supervised Representations of Wearable Data | Automated and accurate human activity recognition (HAR) using body-worn sensors enables practical and cost efficient remote monitoring of Activity of DailyLiving (ADL), which are shown to provide clinical insights across multiple therapeutic areas. Development of accurate algorithms for human activity recognition(HAR) is hindered by the lack of large real-world labeled datasets. Furthermore, algorithms seldom work beyond the specific sensor on which they are prototyped, prompting debate about whether accelerometer-based HAR is even possible [Tong et al., 2020]. Here we develop a 6-class HAR model with strong performance when evaluated on real-world datasets not seen during training. Our model is based on a frozen self-supervised representation learned on a large unlabeled dataset, combined with a shallow multi-layer perceptron with temporal smoothing. The model obtains in-dataset state-of-the art performance on the Capture24 dataset ($\kappa= 0.86$). Out-of-distribution (OOD) performance is $\kappa = 0.7$, with both the representation and the perceptron models being trained on data from a different sensor. This work represents a key step towards device-agnostic HAR models, which can help contribute to increased standardization of model evaluation in the HAR field. | ['Niranjan Sridhar', 'Maximilien Burq'] | 2023-04-26 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 5.90000093e-01 2.24194191e-02 -7.48160601e-01 -4.23358262e-01
-1.04907906e+00 -1.63189188e-01 4.00586426e-01 4.46338564e-01
-6.52201056e-01 8.99844587e-01 5.75191081e-01 -2.35197261e-01
-1.93554893e-01 -4.43494380e-01 -4.23601180e-01 -5.71152151e-01
-4.05539840e-01 3.25561702e-01 1.92678301e-03 2.04789653e-01
-3.29748392e-01 3.38021517e-01 -1.30211461e+00 4.02663231e-01
5.07238746e-01 9.92584586e-01 -1.96980044e-01 7.57770836e-01
4.07655627e-01 1.08830214e+00 -6.46468103e-01 2.13354483e-01
-1.57553151e-01 -6.09212637e-01 -6.05878651e-01 9.57253650e-02
1.99293539e-01 -1.82844341e-01 8.33121222e-03 1.63391128e-01
8.12573910e-01 2.32560802e-02 5.02374232e-01 -9.50879753e-01
-1.74316794e-01 2.15674818e-01 -2.06759796e-01 3.81559521e-01
7.21360862e-01 2.41966441e-01 4.72978950e-01 -5.15350580e-01
3.12067121e-01 3.69345874e-01 1.20065069e+00 5.31589150e-01
-1.41178274e+00 -4.63589251e-01 -2.68000245e-01 6.16979003e-02
-1.43179643e+00 -4.59061623e-01 5.26230693e-01 -3.95386398e-01
1.42853415e+00 4.13955986e-01 1.27797866e+00 1.49627066e+00
6.93448007e-01 5.59405148e-01 1.20151556e+00 -2.56225705e-01
5.80641508e-01 -1.85479656e-01 6.13323599e-02 5.32460690e-01
6.37069523e-01 3.62372883e-02 -7.60448098e-01 -3.77733797e-01
7.82583535e-01 2.15066269e-01 -3.02976128e-02 -3.55512470e-01
-1.42561674e+00 4.07367378e-01 2.71428227e-01 4.91125405e-01
-8.28803778e-01 3.03651720e-01 4.05139029e-01 -1.54088452e-01
2.83026993e-01 3.61347526e-01 -5.02840519e-01 -7.46004045e-01
-1.16855431e+00 6.35704994e-02 6.48366034e-01 3.22097957e-01
4.85085174e-02 -3.83784398e-02 -3.90359014e-02 8.83029878e-01
4.19272840e-01 4.95647192e-01 9.53240812e-01 -9.66076493e-01
7.00881183e-02 8.38579476e-01 2.41232947e-01 -4.81646836e-01
-1.16090965e+00 -5.97714007e-01 -9.13921952e-01 7.80683979e-02
5.72812438e-01 -2.03052312e-01 -7.59547472e-01 1.54869568e+00
1.38062745e-01 2.55129248e-01 -5.40304370e-02 4.94235843e-01
6.11323774e-01 1.03055626e-01 6.07818365e-01 -3.24319214e-01
1.44658411e+00 -7.80498385e-01 -6.86848760e-01 -7.06951618e-01
1.15354812e+00 -2.02712044e-01 8.89732361e-01 6.99206352e-01
-8.93176794e-01 -4.49811548e-01 -1.40328991e+00 -2.40943525e-02
-4.11407769e-01 9.07475278e-02 7.62542963e-01 1.25285423e+00
-9.39535141e-01 5.48389435e-01 -1.57459462e+00 -6.95533693e-01
7.59472370e-01 5.45011640e-01 -4.58219975e-01 -1.27742723e-01
-8.52912784e-01 1.10864043e+00 1.89951539e-01 1.99781239e-01
-7.47688472e-01 -3.92363548e-01 -8.04319382e-01 -5.48891187e-01
-2.16655046e-01 -8.48699272e-01 1.17954600e+00 -7.24451840e-01
-1.37428296e+00 9.04028952e-01 -3.13781351e-02 -6.48060441e-01
4.77966309e-01 -3.58507454e-01 -9.04227734e-01 -8.66843536e-02
1.22411676e-01 3.06963533e-01 3.45549554e-01 -5.68285763e-01
-4.03579384e-01 -6.11531615e-01 -6.53374970e-01 1.43960699e-01
-9.45739523e-02 -1.56000867e-01 5.49866213e-03 -5.03004611e-01
5.53713925e-02 -9.77564812e-01 -4.58428174e-01 -2.12008320e-02
8.30315575e-02 -7.78769925e-02 2.45539397e-01 -9.78728414e-01
1.34630072e+00 -1.91208541e+00 -6.37760222e-01 8.59642178e-02
1.55946732e-01 2.61830270e-01 3.93380374e-01 3.89763564e-01
-2.58621365e-01 -3.32911104e-01 -3.31908226e-01 -2.55424917e-01
-2.97224611e-01 4.54947978e-01 4.50043827e-01 9.09893751e-01
-8.40843376e-03 1.13916016e+00 -1.10066724e+00 -4.04389203e-01
5.40892482e-01 6.33352280e-01 -2.68873602e-01 6.26420975e-02
2.10127383e-01 4.95799541e-01 -2.89572626e-01 8.06765199e-01
-4.97516803e-02 -7.28554785e-01 5.32096148e-01 7.07743876e-03
1.78268313e-01 1.83170021e-01 -1.14313030e+00 2.13648558e+00
-2.25134552e-01 2.66847402e-01 -5.13166845e-01 -9.73728716e-01
8.20062816e-01 4.67772871e-01 1.07637525e+00 -8.60449553e-01
2.11398676e-01 3.11376274e-01 1.02431634e-02 -5.79084456e-01
-7.69561008e-02 -3.50746840e-01 -2.88953573e-01 2.96095729e-01
3.04592162e-04 4.33436573e-01 -1.74197063e-01 -3.46965015e-01
1.81683993e+00 3.23186129e-01 8.11763704e-01 -2.09929213e-01
2.33008981e-01 2.28763223e-02 4.83993530e-01 8.80184472e-01
-6.50805473e-01 5.97769260e-01 -1.32630810e-01 -5.48385441e-01
-5.90124547e-01 -1.26287246e+00 -4.22123969e-01 8.90633464e-01
-2.88195282e-01 -4.81073201e-01 -5.28448820e-01 -5.24437189e-01
-6.06634915e-02 5.70527017e-01 -7.86332667e-01 -3.54486883e-01
-5.36624610e-01 -1.24135041e+00 8.88238728e-01 1.14600623e+00
4.34095323e-01 -1.06098449e+00 -1.26514280e+00 7.90030956e-01
-2.78150946e-01 -8.76829803e-01 1.28903538e-01 6.46535754e-01
-1.17700875e+00 -1.20715380e+00 -8.02363873e-01 -4.94234413e-01
3.22315991e-01 -2.75701284e-01 1.09373593e+00 -3.51162761e-01
-2.59554029e-01 6.73198879e-01 -1.24415070e-01 -4.88302797e-01
2.77358014e-02 7.96844512e-02 3.18415880e-01 -2.66476303e-01
8.55508327e-01 -6.56628907e-01 -9.94502842e-01 2.96560347e-01
-4.97348100e-01 -3.45251560e-01 7.62713134e-01 7.45633423e-01
8.44572008e-01 -3.59691590e-01 6.59750938e-01 -5.91971934e-01
5.91862261e-01 -6.22723341e-01 1.64078206e-01 -2.12110952e-02
-1.05725873e+00 -1.35907024e-01 3.50363672e-01 -5.53937256e-01
-6.64430499e-01 2.87596643e-01 -3.18347931e-01 1.04591481e-01
-2.88828731e-01 5.48567712e-01 9.70258266e-02 1.79947004e-01
1.26549029e+00 6.23121075e-02 -1.87039077e-02 -5.62563539e-01
-1.17496096e-01 7.11457372e-01 6.85598493e-01 -3.46935362e-01
4.55999114e-02 6.31972253e-01 6.65123612e-02 -1.06690025e+00
-5.85579157e-01 -7.53148615e-01 -3.98714751e-01 -9.73334461e-02
9.20100808e-01 -1.15428507e+00 -4.83691067e-01 5.65968275e-01
-4.25550967e-01 -8.37170005e-01 -6.69519722e-01 8.13845038e-01
-7.97197282e-01 1.58897310e-01 -3.62294942e-01 -9.45730090e-01
-4.85946506e-01 -7.10408747e-01 1.20423889e+00 2.38502566e-02
-1.11829913e+00 -9.61167037e-01 7.75090873e-01 7.05003858e-01
2.97197908e-01 7.61887193e-01 1.62584424e-01 -6.94140196e-01
1.13057584e-01 -5.84827304e-01 2.61063457e-01 3.32862109e-01
4.67880666e-01 -7.06935704e-01 -1.05511904e+00 -2.39749268e-01
7.96878710e-02 -3.33130628e-01 3.98110539e-01 6.90990508e-01
1.07175076e+00 -7.12643564e-02 -5.10510385e-01 3.03040266e-01
1.13547957e+00 3.23762119e-01 9.62888241e-01 4.45048779e-01
5.23143888e-01 -1.60607491e-02 2.79879361e-01 4.40822154e-01
4.65692848e-01 6.06982291e-01 3.01084630e-02 -3.65334839e-01
-1.72845587e-01 -2.03924939e-01 5.14653146e-01 4.77896601e-01
-3.38203967e-01 8.44517350e-02 -9.91251230e-01 5.71514368e-01
-1.79542291e+00 -9.08424497e-01 -1.99842080e-01 2.36481094e+00
5.99220216e-01 4.05018240e-01 6.23972356e-01 6.37661874e-01
8.25559497e-02 1.40638426e-01 -5.67072928e-01 -4.35754955e-01
4.30124216e-02 5.14270067e-01 7.27396309e-01 9.89312008e-02
-1.04516304e+00 1.27278611e-01 7.15791750e+00 3.02808911e-01
-8.87041748e-01 4.56126183e-01 5.05648255e-01 -3.36712033e-01
4.20427233e-01 -4.13405746e-01 -4.01667118e-01 6.39707565e-01
1.72611165e+00 2.37367153e-01 2.08010852e-01 8.64230335e-01
5.83475888e-01 -5.02439678e-01 -1.15541410e+00 1.24802387e+00
1.62725359e-01 -1.18473208e+00 -7.19224930e-01 2.40844652e-01
3.72815400e-01 4.57615077e-01 -4.78201717e-01 3.48797768e-01
1.17481556e-02 -1.19862282e+00 2.35220850e-01 7.26151407e-01
8.63395512e-01 -3.41148376e-01 8.44021618e-01 2.83808827e-01
-1.22671747e+00 -1.90707538e-02 3.84314358e-01 -4.22134578e-01
2.32477650e-01 4.61252779e-01 -1.08742690e+00 2.75380909e-01
8.73828411e-01 8.37751091e-01 -7.43808568e-01 9.75705981e-01
-4.84783947e-02 8.70567381e-01 -5.79782188e-01 -1.56415403e-01
-1.26080513e-01 2.85269439e-01 -1.24473870e-01 1.18914282e+00
1.04742251e-01 1.87563568e-01 1.27445549e-01 6.23719208e-02
1.78729728e-01 5.68769798e-02 -5.82772493e-01 -1.97596133e-01
-2.77834455e-03 7.90928543e-01 -7.08468199e-01 -2.37118125e-01
-5.22862732e-01 7.96692371e-01 -2.22691167e-02 4.20150310e-02
-6.68893099e-01 1.65847205e-02 4.46386516e-01 6.48144484e-01
-2.81596869e-01 -2.96638459e-01 -6.49817228e-01 -8.20386469e-01
5.25213033e-02 -7.83465505e-01 8.99984777e-01 -7.79198885e-01
-1.18901420e+00 -7.39702769e-03 -5.07354215e-02 -1.34530413e+00
-4.66383338e-01 -5.41500270e-01 -2.53285825e-01 5.96883535e-01
-8.30125988e-01 -9.65517163e-01 -5.44083774e-01 5.78720629e-01
3.05377096e-01 3.95445198e-01 1.27812648e+00 4.44763005e-01
-5.84901810e-01 4.36471760e-01 3.05604860e-02 1.63103193e-01
6.08695447e-01 -1.07166421e+00 1.28115684e-01 5.97277999e-01
-2.44349260e-02 5.70047379e-01 4.89745736e-01 -7.45791674e-01
-1.28116333e+00 -9.84684825e-01 9.92884934e-01 -1.10467899e+00
4.29739445e-01 -7.24894255e-02 -6.56322658e-01 8.77266824e-01
-2.67142564e-01 2.50550032e-01 1.42273760e+00 3.24832469e-01
2.34619781e-01 -2.22924605e-01 -1.33485901e+00 1.91567078e-01
1.26300216e+00 -4.99627769e-01 -7.35392213e-01 3.54681134e-01
-7.90126100e-02 -3.35908443e-01 -1.41622436e+00 5.48491359e-01
1.11583972e+00 -7.47783482e-01 1.04180086e+00 -6.58433914e-01
-7.83284307e-02 -1.89457357e-01 -1.85453430e-01 -8.97580564e-01
-3.21656674e-01 -3.53498667e-01 -5.37060618e-01 5.08015513e-01
2.23901168e-01 -6.57658517e-01 1.11800110e+00 7.46362686e-01
-1.63391396e-01 -8.38130414e-01 -1.18395901e+00 -8.35692346e-01
-3.35632622e-01 -9.15359855e-01 2.26581648e-01 9.41755056e-01
5.17934561e-01 2.96832472e-01 -4.39828545e-01 -8.61226842e-02
4.58646744e-01 -6.88745260e-01 5.71067572e-01 -1.33300197e+00
-3.98692608e-01 -1.40261441e-01 -8.19109797e-01 -5.84716856e-01
-5.60078979e-01 -6.25235796e-01 -1.11277148e-01 -1.86958432e+00
-2.65905093e-02 -2.12842718e-01 -8.58210027e-01 7.77672708e-01
2.08354220e-01 5.51284313e-01 -4.59408253e-01 1.93676203e-01
-6.91015184e-01 2.80519396e-01 6.00692511e-01 -6.29962087e-02
-5.20440042e-01 6.03761561e-02 -6.57260716e-01 7.36418784e-01
9.72226858e-01 -5.29709816e-01 -5.22367656e-01 6.89728409e-02
1.39282644e-01 -6.15498470e-03 5.02109587e-01 -1.71668053e+00
2.41480675e-02 -4.88697328e-02 1.00808191e+00 -3.43538582e-01
5.43133259e-01 -8.84236217e-01 6.04419231e-01 9.83989000e-01
-9.10543203e-02 1.01671256e-01 2.03656211e-01 6.41425490e-01
4.32797402e-01 2.90006787e-01 5.77192247e-01 -2.04480648e-01
-5.27612686e-01 1.49889616e-02 -5.92932463e-01 1.71959311e-01
8.09265137e-01 -6.44414723e-01 -1.91385731e-01 -1.44938022e-01
-1.08127856e+00 -1.11008644e-01 3.38013649e-01 3.78492981e-01
2.03968003e-01 -1.35008538e+00 -4.12626594e-01 1.37582272e-01
5.65687597e-01 -5.62624298e-02 2.19513461e-01 1.20451343e+00
-4.21045661e-01 3.58149588e-01 -2.46895000e-01 -7.77290881e-01
-8.18092763e-01 2.89653510e-01 5.79314709e-01 -3.99448395e-01
-7.37257481e-01 3.71864438e-01 -6.31902516e-01 -1.41380772e-01
2.76690155e-01 -6.17921352e-01 4.02428769e-02 -8.75537992e-02
6.83548629e-01 7.33891308e-01 4.26849693e-01 -4.23593372e-01
-8.90887201e-01 2.72866543e-02 3.21788877e-01 -1.35332435e-01
1.20389485e+00 2.34015405e-01 4.71713930e-01 8.49533975e-01
9.86508727e-01 -2.89197475e-01 -1.05201495e+00 1.88320756e-01
1.34414047e-01 3.40064019e-02 5.78750968e-02 -1.17591059e+00
-3.55192572e-01 4.04189229e-01 1.51157284e+00 -5.86807653e-02
1.03427815e+00 3.06680761e-02 7.23231137e-01 3.90317023e-01
4.57968175e-01 -1.32374847e+00 5.56915961e-02 -2.53666639e-01
3.98341179e-01 -1.17464375e+00 3.27314824e-01 2.10272029e-01
-3.82059515e-01 5.87221622e-01 1.81315392e-01 -5.88379130e-02
7.22829282e-01 6.12328835e-02 1.14181779e-01 -3.23889464e-01
-2.87574142e-01 2.03684196e-02 1.43881246e-01 8.75901461e-01
6.56184554e-01 4.46289480e-01 -6.62443578e-01 8.59367251e-01
9.61641409e-03 8.88367176e-01 1.98796779e-01 1.52066886e+00
-2.70931631e-01 -1.03794408e+00 -3.21687698e-01 1.06616831e+00
-3.95385861e-01 3.01575929e-01 -1.02306850e-01 6.97540283e-01
3.20893496e-01 1.11686206e+00 -1.73483208e-01 -2.86729664e-01
6.81291580e-01 5.59347570e-01 6.25298798e-01 -5.34680665e-01
-6.91907108e-01 -1.50754042e-02 6.40556753e-01 -9.81728315e-01
-7.10828483e-01 -1.01385772e+00 -1.16519094e+00 9.19623896e-02
2.83173949e-01 -1.59256458e-01 6.03199363e-01 1.04227757e+00
4.59148884e-01 6.42694175e-01 5.57135716e-02 -6.77452147e-01
-2.90886134e-01 -9.97346044e-01 -7.33806133e-01 2.80344129e-01
2.99217641e-01 -7.79251933e-01 -1.29838392e-01 1.13955989e-01] | [7.428934574127197, 0.7530152797698975] |
330b9415-6968-4b30-b4c6-414d11baa42f | mimo-is-all-you-need-a-strong-multi-in-multi | 2212.04655 | null | https://arxiv.org/abs/2212.04655v3 | https://arxiv.org/pdf/2212.04655v3.pdf | MIMO Is All You Need : A Strong Multi-In-Multi-Out Baseline for Video Prediction | The mainstream of the existing approaches for video prediction builds up their models based on a Single-In-Single-Out (SISO) architecture, which takes the current frame as input to predict the next frame in a recursive manner. This way often leads to severe performance degradation when they try to extrapolate a longer period of future, thus limiting the practical use of the prediction model. Alternatively, a Multi-In-Multi-Out (MIMO) architecture that outputs all the future frames at one shot naturally breaks the recursive manner and therefore prevents error accumulation. However, only a few MIMO models for video prediction are proposed and they only achieve inferior performance due to the date. The real strength of the MIMO model in this area is not well noticed and is largely under-explored. Motivated by that, we conduct a comprehensive investigation in this paper to thoroughly exploit how far a simple MIMO architecture can go. Surprisingly, our empirical studies reveal that a simple MIMO model can outperform the state-of-the-art work with a large margin much more than expected, especially in dealing with longterm error accumulation. After exploring a number of ways and designs, we propose a new MIMO architecture based on extending the pure Transformer with local spatio-temporal blocks and a new multi-output decoder, namely MIMO-VP, to establish a new standard in video prediction. We evaluate our model in four highly competitive benchmarks (Moving MNIST, Human3.6M, Weather, KITTI). Extensive experiments show that our model wins 1st place on all the benchmarks with remarkable performance gains and surpasses the best SISO model in all aspects including efficiency, quantity, and quality. We believe our model can serve as a new baseline to facilitate the future research of video prediction tasks. The code will be released. | ['Shuguang Cui', 'Xiaoguang Han', 'Xunlai Chen', 'Qian Chen', 'Chaofeng Chen', 'Yanran Li', 'Mengcheng Lan', 'Shuliang Ning'] | 2022-12-09 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [ 2.62039602e-01 -1.63857505e-01 -4.72862273e-01 -2.95835853e-01
-3.77656907e-01 -3.04946955e-02 4.95395690e-01 -4.35035408e-01
-9.88843888e-02 6.17042601e-01 2.59794861e-01 -5.09368122e-01
1.50706217e-01 -5.41983724e-01 -1.00116432e+00 -6.36057138e-01
-2.16471627e-01 -8.73730034e-02 8.35825324e-01 -4.55683291e-01
5.97155280e-02 4.62240819e-03 -1.34812403e+00 6.55066311e-01
4.59848851e-01 1.17250824e+00 4.38208908e-01 6.02238238e-01
3.47644985e-01 1.44349933e+00 -8.79022926e-02 -7.01136351e-01
4.11021858e-01 -5.15585363e-01 -5.99095166e-01 -9.12457481e-02
-1.72770508e-02 -6.43362343e-01 -7.53079534e-01 6.67439818e-01
3.37250888e-01 -1.41584069e-01 3.75464797e-01 -1.14270902e+00
-3.75908881e-01 5.95036328e-01 -5.02925694e-01 3.83177847e-01
2.13611886e-01 2.28166670e-01 1.01131809e+00 -9.66457963e-01
4.48561102e-01 9.15692210e-01 9.08971846e-01 4.56196785e-01
-7.61178374e-01 -6.56021953e-01 3.78504544e-01 5.92876315e-01
-1.40069354e+00 -6.80365026e-01 4.27340984e-01 -2.43201867e-01
1.15269768e+00 3.57878029e-01 6.77091300e-01 1.24634647e+00
5.18296123e-01 1.11556363e+00 8.13486814e-01 -2.84329504e-01
-4.73847352e-02 -1.63315296e-01 -3.01010549e-01 6.51920676e-01
-2.95338929e-01 4.50522751e-01 -7.72314847e-01 1.28149346e-01
6.34589314e-01 1.34986296e-01 -5.09680450e-01 -1.68390691e-01
-1.26221561e+00 4.64113206e-01 2.92805731e-01 1.17501117e-01
-4.19520885e-01 3.71685684e-01 3.93266350e-01 5.46075761e-01
4.34621334e-01 -9.76436734e-02 -4.55192357e-01 -4.15443599e-01
-1.34906697e+00 1.31311387e-01 5.92664599e-01 1.04285729e+00
3.97365630e-01 1.73235610e-01 -3.26782286e-01 7.19256759e-01
4.27667975e-01 9.66890678e-02 4.63421226e-01 -9.56060648e-01
6.76883519e-01 6.44790679e-02 -2.76830420e-02 -1.03054464e+00
-2.67658263e-01 -5.76585889e-01 -1.04356933e+00 5.24865948e-02
1.62990957e-01 -7.96841532e-02 -5.26842833e-01 1.54641950e+00
-9.41707462e-04 7.42359161e-01 1.89409163e-02 9.54690933e-01
4.73493695e-01 1.05496514e+00 -8.64410698e-02 -4.61093575e-01
9.84961152e-01 -1.62644112e+00 -3.18110675e-01 -3.35692197e-01
6.46350145e-01 -7.00034916e-01 6.78263664e-01 6.00527644e-01
-1.08047318e+00 -7.91030049e-01 -1.09114873e+00 1.79313198e-01
2.40699336e-01 2.11402979e-02 5.76925516e-01 4.92345214e-01
-1.20224428e+00 8.58840883e-01 -1.02208197e+00 -3.33061397e-01
2.57201284e-01 2.18392789e-01 -1.64731905e-01 -6.62753060e-02
-1.21822548e+00 7.03219593e-01 3.46494645e-01 1.23342469e-01
-8.73416901e-01 -6.41155422e-01 -5.13741255e-01 1.14697449e-01
5.17150939e-01 -7.46752322e-01 1.23289645e+00 -1.11683416e+00
-1.63397825e+00 3.53976637e-01 -3.54574949e-01 -9.68956351e-01
7.13764131e-01 -3.17436486e-01 -5.59256554e-01 1.03934621e-02
-2.40991727e-01 8.29685211e-01 8.37493360e-01 -9.10531640e-01
-9.63494182e-01 1.01982333e-01 2.54338861e-01 1.49428785e-01
-4.12598878e-01 1.31759301e-01 -8.95400167e-01 -9.87739921e-01
-5.85508980e-02 -1.05694771e+00 -3.93909395e-01 -3.98119725e-02
-2.38824666e-01 1.81784645e-01 4.24687564e-01 -6.44730449e-01
1.98482478e+00 -2.15034676e+00 1.26766816e-01 -2.33577073e-01
1.74527913e-01 3.72973442e-01 -1.52661586e-02 5.52711546e-01
-7.90888369e-02 1.17399432e-01 1.19093344e-01 -3.91639233e-01
-1.59405187e-01 7.06734136e-02 -6.04829609e-01 2.41744608e-01
-4.81288275e-03 9.57351744e-01 -8.47184777e-01 -4.44741994e-01
1.42077371e-01 3.85118634e-01 -8.46098483e-01 9.25447494e-02
-9.99115035e-02 3.86736900e-01 -2.99091607e-01 5.10656834e-01
4.74321932e-01 -5.66220582e-01 2.40545213e-01 -1.25307366e-01
-1.02253497e-01 3.33994925e-01 -9.82735336e-01 1.40653074e+00
-2.76294976e-01 7.77208030e-01 -4.70282614e-01 -1.16487980e+00
6.54354751e-01 6.01677060e-01 5.25375366e-01 -7.54817367e-01
-2.24825516e-01 1.94253460e-01 1.64769590e-02 -4.49565828e-01
6.49261534e-01 1.70255906e-03 2.01285422e-01 -6.10671975e-02
-1.59376904e-01 6.85088336e-01 9.98880714e-02 1.21910930e-01
1.23858690e+00 4.82363999e-01 4.35633063e-01 -4.72672954e-02
5.14847457e-01 -1.78893730e-01 9.47575510e-01 8.58079433e-01
-2.50952333e-01 7.79684246e-01 5.36292374e-01 -5.88988841e-01
-1.18982303e+00 -8.76243830e-01 1.46699309e-01 1.02569306e+00
2.82144785e-01 -7.66406357e-01 -4.72446561e-01 -6.29705131e-01
-4.75762814e-01 4.73778397e-01 -2.63382971e-01 -6.25811666e-02
-6.91801250e-01 -7.82043099e-01 5.58248460e-01 6.35519266e-01
5.45304298e-01 -8.27797472e-01 -6.29361868e-01 4.06484425e-01
-4.79525298e-01 -1.33686662e+00 -3.69597018e-01 -1.17830724e-01
-1.12438309e+00 -6.65334105e-01 -8.10239136e-01 -5.97599626e-01
6.96899667e-02 4.29030657e-01 1.25401855e+00 2.43269429e-01
6.32487953e-01 -1.12116776e-01 -6.50182903e-01 -9.49402526e-02
-5.07992446e-01 6.95042610e-02 5.71269393e-02 1.40507653e-01
1.67366356e-01 -6.03080034e-01 -7.24169016e-01 6.37233734e-01
-6.94785714e-01 7.66766429e-01 6.49226964e-01 9.69798505e-01
5.16927779e-01 -4.88128094e-03 5.62253714e-01 -6.08509839e-01
-4.12083119e-02 -8.38565946e-01 -3.07182848e-01 3.57388526e-01
-5.38113415e-01 -3.47603299e-02 9.53457355e-01 -3.18297833e-01
-9.32227433e-01 3.44338454e-02 -4.55539137e-01 -5.96824765e-01
2.08685458e-01 5.49734592e-01 1.07995488e-01 1.03044413e-01
3.11314702e-01 5.86520016e-01 -2.69653350e-01 -3.78399730e-01
-1.16394810e-01 4.82941747e-01 4.37355608e-01 -1.35056749e-01
5.78166366e-01 4.75344777e-01 5.28297834e-02 -6.40810907e-01
-6.57887757e-01 -3.08486551e-01 -2.89327741e-01 -3.81575137e-01
5.27092397e-01 -1.35413992e+00 -5.67897797e-01 4.48220372e-01
-1.05610371e+00 -5.16283691e-01 1.26768574e-01 5.18250346e-01
-6.29422307e-01 7.48976648e-01 -8.80235195e-01 -6.43151581e-01
-2.20852435e-01 -1.40320849e+00 7.86964297e-01 -3.88935804e-02
-3.13121974e-02 -7.36203015e-01 -2.04718590e-01 2.39472970e-01
4.63640690e-01 -2.32627109e-01 5.36000192e-01 -2.88923949e-01
-9.40081537e-01 1.09305672e-01 -9.43538100e-02 3.65954429e-01
-4.48719382e-01 -5.77223413e-02 -7.48629093e-01 -2.77571023e-01
-7.12319836e-02 -7.00765327e-02 1.12834394e+00 2.71360040e-01
1.29327476e+00 -1.61662892e-01 -4.29036736e-01 8.68714929e-01
1.48226976e+00 2.76892215e-01 1.05562782e+00 4.73582506e-01
3.92471522e-01 9.66789648e-02 7.39720166e-01 5.76653600e-01
6.15726352e-01 1.10223615e+00 5.63804507e-01 1.59801826e-01
-8.31165537e-02 -4.14719641e-01 7.09707022e-01 1.14955592e+00
-4.87174183e-01 -7.54644573e-01 -6.60850227e-01 3.83587807e-01
-2.12247515e+00 -1.20554101e+00 -2.34477982e-01 2.14018130e+00
5.10275602e-01 3.47523212e-01 2.71629971e-02 2.86248565e-01
4.34818417e-01 3.30412269e-01 -2.32407302e-01 -1.95814565e-01
-1.63234055e-01 -3.12782004e-02 6.18774295e-01 2.49020964e-01
-1.16002309e+00 9.13413644e-01 6.48158455e+00 1.15050542e+00
-1.17497003e+00 2.02727959e-01 9.86671507e-01 -4.09087129e-02
2.17291787e-02 1.62030667e-01 -9.54663157e-01 8.65849435e-01
1.27823949e+00 1.62938088e-01 4.54482555e-01 6.89502299e-01
3.85047823e-01 1.48934787e-02 -1.17003238e+00 1.22760034e+00
-1.56481683e-01 -1.60938740e+00 1.97681244e-02 1.51520148e-02
6.98929071e-01 3.01980376e-01 2.06609210e-03 5.13599992e-01
-2.16563627e-01 -8.43518674e-01 1.13133323e+00 8.06177080e-01
5.74199557e-01 -3.73136371e-01 6.17211640e-01 6.01289690e-01
-1.43244982e+00 -3.62459540e-01 -5.10917008e-01 -4.46625978e-01
5.97346306e-01 4.44216162e-01 -2.83777624e-01 6.99353456e-01
8.38736832e-01 1.01517355e+00 -3.94731909e-01 1.22220826e+00
7.22810850e-02 1.11858153e+00 -1.53772727e-01 2.52004772e-01
3.55218202e-01 4.94753644e-02 4.67341363e-01 1.29836202e+00
7.58849442e-01 1.58666670e-01 1.32845104e-01 3.17999035e-01
9.57497582e-03 -7.44251721e-03 -3.73308033e-01 3.84962201e-01
2.41655782e-01 6.78518355e-01 -5.38281798e-01 -4.80202198e-01
-9.17964995e-01 1.11045289e+00 8.68033543e-02 3.02800536e-01
-1.40313089e+00 1.72279015e-01 5.31304836e-01 1.26266778e-01
6.41491234e-01 -1.63495988e-01 -1.66214332e-01 -1.36655259e+00
2.98579156e-01 -9.77704763e-01 1.87932312e-01 -7.12191641e-01
-9.31432903e-01 7.11734533e-01 -1.33387789e-01 -1.74918723e+00
-4.94257957e-01 -4.51815546e-01 -4.79252726e-01 4.11696464e-01
-1.64431000e+00 -8.13448191e-01 -1.01577580e-01 3.39288414e-01
9.40969527e-01 -2.04145864e-01 4.92189020e-01 7.39695013e-01
-5.73877752e-01 7.17218339e-01 3.08166087e-01 -1.69092864e-02
6.45277977e-01 -5.83960176e-01 5.62307239e-01 1.07164669e+00
1.66003793e-01 6.73289225e-02 7.34817088e-01 -4.35865253e-01
-1.31049526e+00 -1.04059815e+00 1.20091021e+00 -2.93550611e-01
6.14255488e-01 -6.08656034e-02 -7.99888730e-01 7.66171098e-01
1.62447035e-01 1.94297105e-01 4.09270227e-01 -2.58204043e-01
-1.07627720e-01 -2.92203724e-01 -4.80957806e-01 8.11057746e-01
1.32467461e+00 -1.29479185e-01 -1.90744922e-01 1.51208937e-01
8.65881205e-01 -4.63759184e-01 -9.00964975e-01 6.79204404e-01
8.35750461e-01 -1.62685442e+00 1.15501904e+00 -4.06027645e-01
1.03546262e+00 -2.60823816e-01 -4.58805948e-01 -7.97113359e-01
-5.05524218e-01 -7.89534390e-01 -6.51064754e-01 9.56158519e-01
4.80198354e-01 -3.14235181e-01 8.17972422e-01 2.56486654e-01
-3.51076365e-01 -1.35675788e+00 -8.36360455e-01 -9.66744184e-01
-7.90787786e-02 -9.05335128e-01 5.47970176e-01 4.21601653e-01
-6.35207966e-02 2.35242859e-01 -1.22906899e+00 2.07280502e-01
2.72767007e-01 -5.08141369e-02 6.55417502e-01 -6.20835125e-01
-7.62178838e-01 -3.62742960e-01 -5.88762939e-01 -2.13416076e+00
-2.52000272e-01 -5.87296724e-01 -1.55447379e-01 -1.06290185e+00
3.08050275e-01 -4.11064446e-01 -3.51484865e-01 1.48093447e-01
-1.52674288e-01 5.04679024e-01 6.77079082e-01 5.03708184e-01
-9.71088409e-01 5.87903142e-01 9.91585612e-01 9.09078941e-02
8.34505707e-02 1.83279648e-01 -4.88588870e-01 8.29354763e-01
5.97656071e-01 -3.13345939e-01 -3.88955683e-01 -6.73169553e-01
3.42343539e-01 5.67324400e-01 4.21232074e-01 -1.42639709e+00
3.06156605e-01 -1.97184041e-01 2.30822086e-01 -5.17293811e-01
4.85738784e-01 -9.33692575e-01 5.58160067e-01 4.41658586e-01
-1.49411842e-01 1.94690987e-01 -8.63049924e-02 7.29161620e-01
-3.68321598e-01 -7.40283430e-02 6.25163734e-01 1.00404643e-01
-1.16657257e+00 5.14724314e-01 -4.08803672e-01 -1.86349392e-01
1.02609253e+00 -4.72370595e-01 -8.27131569e-02 -6.37223423e-01
-6.74148798e-01 2.01474413e-01 4.07887548e-01 5.26232183e-01
5.78134775e-01 -1.37374616e+00 -7.34088421e-01 6.09712265e-02
-4.51144055e-02 -5.17805755e-01 4.77018118e-01 1.18659163e+00
-6.03484511e-01 5.81517756e-01 -1.33440038e-02 -8.22913229e-01
-1.12767863e+00 7.26622701e-01 2.24654570e-01 -6.16386473e-01
-7.61758029e-01 6.58231914e-01 4.13574636e-01 1.37807205e-01
2.59356797e-01 -2.97834367e-01 -6.59338683e-02 -4.77185726e-01
6.40875638e-01 3.77432674e-01 -1.68956354e-01 -8.38723958e-01
-2.27683827e-01 3.05099189e-01 1.90620080e-01 2.32925117e-01
1.24294090e+00 -5.02279639e-01 2.28302449e-01 3.71691823e-01
1.05557013e+00 -3.42238456e-01 -1.52673686e+00 -3.01731646e-01
-1.97654292e-01 -5.77993035e-01 -2.44064242e-01 -5.03576279e-01
-1.28205240e+00 8.64501417e-01 3.94837350e-01 1.16791934e-01
1.51723170e+00 -2.75536925e-01 1.10187411e+00 1.67503953e-01
6.26782835e-01 -8.51621926e-01 -1.34289771e-01 7.18945742e-01
5.75029433e-01 -1.28444827e+00 -1.21325754e-01 -4.17746931e-01
-7.25855649e-01 1.21725595e+00 4.94131386e-01 -1.78229883e-02
8.58501613e-01 2.85175651e-01 -2.79615134e-01 3.25117648e-01
-1.32399762e+00 4.42596450e-02 2.62075424e-01 2.71657944e-01
6.13491654e-01 -2.57342085e-02 -2.80640036e-01 6.99547470e-01
-2.83047687e-02 3.90152007e-01 3.90900552e-01 6.65059865e-01
-3.80306810e-01 -1.12306476e+00 -2.64105380e-01 5.03866255e-01
-7.38071561e-01 -3.21474850e-01 3.91643494e-01 5.56585491e-01
1.56923130e-01 1.04465473e+00 -1.58604264e-01 -8.32132459e-01
4.49473895e-02 -1.28369883e-01 2.13165149e-01 -3.28102484e-02
-4.45954442e-01 9.15409848e-02 2.95048393e-02 -8.50424469e-01
-5.49153686e-01 -5.71980894e-01 -8.23809743e-01 -5.93968809e-01
-2.01583356e-01 -2.57301897e-01 1.52085260e-01 1.07493949e+00
5.59214175e-01 5.66486835e-01 6.51149213e-01 -8.91799748e-01
-5.76837838e-01 -7.22701669e-01 -1.87855676e-01 8.87809917e-02
2.03514233e-01 -4.82767880e-01 9.11587775e-02 2.47535959e-01] | [9.02558708190918, 0.23071494698524475] |
c59884a8-567b-463d-a2f3-2f57b4d693b8 | adaptive-wind-driven-optimization-trained | 1911.08942 | null | https://arxiv.org/abs/1911.08942v1 | https://arxiv.org/pdf/1911.08942v1.pdf | Adaptive Wind Driven Optimization Trained Artificial Neural Networks | This paper presents the application of a newly developed nature-inspired metaheuristic optimization method, namely the Adaptive Wind Driven Optimization (AWDO), to the training of feedforward artificial neural networks (NN) and presents a discussion into the future research of AWDO implementation in Deep Learning (DL). Application example of digit classification with MNIST dataset reveals interesting behavior of the derivative-free AWDO method compared to steepest descent method where results and future work on the implementation of AWDO in deep neural networks are discussed. | ['Zikri Bayraktar'] | 2019-11-20 | null | null | null | null | ['metaheuristic-optimization'] | ['methodology'] | [ 4.32572290e-02 -1.84595987e-01 2.37505585e-01 -4.63698417e-01
7.93445885e-01 -2.26444483e-01 3.35060060e-01 -1.77821234e-01
-7.76962876e-01 1.34653592e+00 -1.57398880e-01 -6.18946970e-01
-7.87052751e-01 -9.59877729e-01 -2.51114190e-01 -1.11791778e+00
-4.99416366e-02 5.57749212e-01 -2.89433300e-01 -7.78442502e-01
7.01459825e-01 1.20323670e+00 -1.74950731e+00 -8.32515284e-02
6.85398757e-01 1.10788202e+00 1.96527556e-01 9.50015724e-01
-3.58253330e-01 8.20574999e-01 -1.06617284e+00 1.43529832e-01
3.32845330e-01 -1.59942687e-01 -6.59704804e-01 -4.44280148e-01
-8.14951658e-02 4.67709601e-01 6.27778769e-02 8.23784292e-01
1.11253428e+00 9.46096539e-01 6.92359388e-01 -9.84658778e-01
-6.53045952e-01 5.71422577e-01 -1.04660116e-01 1.17855930e+00
-4.65099066e-01 -5.87277152e-02 3.48686814e-01 -5.12418807e-01
5.54461241e-01 1.17736197e+00 7.21176445e-01 9.15928423e-01
-5.50815582e-01 -4.72224534e-01 -1.92006916e-01 3.76702905e-01
-6.64938867e-01 -1.13208726e-01 1.16647995e+00 -9.68465358e-02
1.80739093e+00 2.65922755e-01 1.03159058e+00 7.38154173e-01
4.68568444e-01 8.10448289e-01 1.05167091e+00 -8.85965645e-01
1.00990593e+00 3.95075455e-02 6.19405091e-01 9.46415007e-01
7.19120324e-01 5.12472391e-01 -5.64271569e-01 4.31440808e-02
2.86699563e-01 -4.81325299e-01 3.92509907e-01 -6.89134523e-02
-3.50440323e-01 1.26331210e+00 5.23017287e-01 6.34177566e-01
-8.59595358e-01 1.31576166e-01 6.09898806e-01 4.27048177e-01
4.76624429e-01 1.01084566e+00 -8.89619231e-01 -2.36525550e-01
-8.68562460e-01 4.39572930e-01 9.48060930e-01 6.78176433e-02
3.02434832e-01 1.31424713e+00 2.40329847e-01 9.02422905e-01
3.82205486e-01 2.42064402e-01 1.14783156e+00 -4.69639331e-01
3.24320234e-02 6.38995290e-01 -3.02936852e-01 -7.33502686e-01
-7.25480795e-01 -1.14615583e+00 -6.43449664e-01 9.57220078e-01
5.93902990e-02 -1.17173493e+00 -1.15794384e+00 7.57063985e-01
3.97353828e-01 -1.97384059e-01 6.71532094e-01 8.15282226e-01
9.07505155e-01 4.97593462e-01 -2.69774020e-01 -1.16977841e-01
7.56275296e-01 -1.33077884e+00 -9.11432207e-01 -3.31394643e-01
4.99781549e-01 -5.15630662e-01 2.89456278e-01 8.85861337e-01
-9.88769114e-01 -6.16419733e-01 -1.53706479e+00 5.33786535e-01
-1.23636389e+00 -7.19426246e-03 1.14375615e+00 1.44197094e+00
-7.41889954e-01 1.19347048e+00 -9.56617475e-01 -3.15127105e-01
6.60206556e-01 9.51982796e-01 2.44812876e-01 3.63457471e-01
-9.10705984e-01 1.09279394e+00 1.00551486e+00 3.45601827e-01
-2.52177656e-01 -3.13778907e-01 -3.31937790e-01 -2.31477275e-01
-2.11458951e-01 -5.24483263e-01 8.41599524e-01 -1.39234030e+00
-2.20520926e+00 5.06407976e-01 -8.26597959e-02 -9.69104648e-01
3.68759520e-02 1.74442213e-02 -5.40899932e-01 -1.21241622e-01
-9.68538404e-01 5.35538614e-01 6.94913447e-01 -8.22238624e-01
-8.01393926e-01 -3.35708350e-01 -1.42839119e-01 -1.02501743e-01
-4.84744757e-01 -2.91837156e-01 7.03650057e-01 -9.65441287e-01
9.42388624e-02 -7.17505813e-01 -2.66107887e-01 -4.79850590e-01
-3.07310164e-01 -5.75688362e-01 1.03144157e+00 -1.69726267e-01
1.10484803e+00 -1.63757396e+00 2.67463237e-01 4.31870908e-01
-3.30900878e-01 9.05135393e-01 -1.12820514e-01 4.30494189e-01
-4.45195496e-01 -1.80715829e-01 4.91207391e-02 4.42650765e-02
-1.16447620e-01 8.58132422e-01 1.21173911e-01 6.33017421e-02
1.41624855e-02 6.75795197e-01 -7.41947055e-01 1.13430344e-01
1.88717455e-01 3.07783186e-01 -2.97285497e-01 -2.46219888e-01
7.09011452e-03 -3.37777644e-01 -5.84708452e-01 1.02687073e+00
7.45460629e-01 4.52636570e-01 -3.72082412e-01 -1.50052786e-01
-3.74188930e-01 -1.86534539e-01 -1.07661879e+00 1.16028214e+00
-3.69030625e-01 1.09296775e+00 -3.22592378e-01 -1.57534206e+00
1.42939401e+00 -1.03083529e-01 1.38449103e-01 -5.43537140e-01
6.02953017e-01 4.92546856e-01 5.93328774e-01 -7.26820648e-01
2.42802829e-01 7.61599168e-02 1.08468807e+00 3.06234837e-01
4.87219691e-01 1.90189257e-01 3.38024437e-01 -8.21398497e-01
8.47646832e-01 2.92672634e-01 1.18247032e-01 -8.00144434e-01
8.01343918e-01 6.55653298e-01 4.02887195e-01 7.54633307e-01
-3.27175975e-01 5.00859655e-02 -1.05848044e-01 -1.16848159e+00
-9.25499797e-01 -3.66733700e-01 -2.53211856e-01 1.01810384e+00
-6.49767339e-01 3.97297829e-01 -8.70925784e-01 -5.36982894e-01
2.33379096e-01 1.02821481e+00 -8.05244446e-01 -2.57697761e-01
-6.89948797e-01 -1.40958428e+00 6.85759485e-01 3.46490353e-01
9.40233946e-01 -1.47156000e+00 -1.29827678e+00 5.44052303e-01
8.63310874e-01 -2.98342198e-01 9.08151507e-01 1.09251761e+00
-1.58452415e+00 -6.82484686e-01 -7.25396514e-01 -8.74544561e-01
4.87959832e-01 -3.12034428e-01 9.95736063e-01 1.88485011e-01
-7.94945776e-01 1.04941636e-01 -5.67229509e-01 -1.31142747e+00
-2.68923402e-01 2.62189299e-01 4.94643413e-02 -4.53131229e-01
8.58143389e-01 -5.82883298e-01 -4.87711757e-01 -3.25691342e-01
-5.07654428e-01 -5.35235047e-01 3.80461097e-01 1.17217934e+00
1.31002441e-01 7.44061172e-01 5.69325566e-01 -7.55662620e-01
1.16325116e+00 -2.99794465e-01 -6.60813093e-01 2.14394063e-01
-9.33622003e-01 4.38534796e-01 6.16907954e-01 -5.92051268e-01
-8.87424111e-01 -1.35327443e-01 -4.03967351e-01 -4.20350015e-01
-1.44473493e-01 5.59634447e-01 6.42362714e-01 -6.91917837e-01
1.02036858e+00 1.54381022e-01 1.14078388e-01 -6.77523553e-01
-3.84896576e-01 5.14890313e-01 1.13319486e-01 -3.29431653e-01
5.48626781e-01 1.73454732e-01 4.70485151e-01 -1.02654529e+00
-3.70309830e-01 -9.70961973e-02 -2.26692349e-01 -2.37032011e-01
5.33438146e-01 -3.28869261e-02 -6.40404046e-01 7.40047574e-01
-8.90567124e-01 -1.85145587e-01 -5.94936907e-01 6.15565002e-01
-1.38353124e-01 -4.26210701e-01 -1.39923453e-01 -1.36405444e+00
-9.72912550e-01 -9.56011415e-01 -4.72649895e-02 8.19919169e-01
1.95307314e-01 -1.58769608e+00 4.72644158e-02 1.77011102e-01
6.36367500e-01 5.19045115e-01 1.21005547e+00 -9.77724671e-01
3.85146141e-01 -1.80808410e-01 4.46817994e-01 5.96447885e-01
2.15416357e-01 5.18514335e-01 -1.12192547e+00 -6.48714304e-02
2.85487711e-01 -3.86644840e-01 7.71705329e-01 9.01394308e-01
6.27034843e-01 -1.65505111e-01 -9.89502147e-02 7.32629061e-01
2.08115029e+00 1.01943254e+00 3.43768269e-01 1.54584968e+00
2.38069162e-01 4.13169503e-01 4.59862113e-01 5.55174887e-01
-5.91466367e-01 -5.23165911e-02 6.73122823e-01 1.02299578e-01
-3.57829109e-02 6.86435938e-01 -1.14554517e-01 5.74589491e-01
-5.65161169e-01 -5.91147721e-01 -8.44801545e-01 1.89495787e-01
-1.60444057e+00 -7.42296815e-01 -1.93070501e-01 1.34494519e+00
2.11381257e-01 4.29125577e-01 -3.87756079e-02 8.35242033e-01
4.56539959e-01 -1.10599957e-01 -9.28899169e-01 -1.51564145e+00
-4.38567817e-01 9.23785210e-01 7.73129523e-01 1.94518715e-01
-1.02891648e+00 7.66427279e-01 6.06782198e+00 8.16866457e-01
-1.65274107e+00 -1.96982205e-01 3.19462031e-01 -1.11920744e-01
1.95511922e-01 -5.72877467e-01 -9.46171224e-01 3.85858208e-01
1.00852764e+00 6.09580837e-02 7.88022757e-01 1.12443972e+00
3.16593140e-01 -3.40440750e-01 -3.92663956e-01 9.17645991e-01
-1.43697351e-01 -1.73004079e+00 9.06669721e-02 -8.22497681e-02
1.07481611e+00 5.05570173e-01 1.90317929e-02 2.44439505e-02
2.01307625e-01 -9.44841921e-01 6.17555857e-01 5.25147378e-01
-4.13664520e-01 -1.66966629e+00 1.21640038e+00 1.38650596e-01
-5.35141408e-01 -7.16190338e-01 -9.39516008e-01 -2.85076737e-01
-5.65292954e-01 6.55706108e-01 -5.99191129e-01 6.57966018e-01
8.20844591e-01 4.81106281e-01 -5.62048316e-01 1.35088646e+00
2.73265183e-01 6.33102834e-01 -3.73014897e-01 -9.26581502e-01
9.91387963e-01 -3.40876102e-01 8.27060997e-01 1.09797549e+00
1.71457946e-01 -1.62234604e-01 -6.97870374e-01 3.96645606e-01
4.41259235e-01 3.40378881e-02 -5.39936423e-01 -5.06377280e-01
3.63552600e-01 1.14385080e+00 -1.07179666e+00 -1.30714178e-01
3.38633239e-01 4.34263021e-01 1.64587647e-01 2.47705430e-01
-1.25376850e-01 -1.03826571e+00 5.43013215e-01 -8.57971787e-01
6.08945012e-01 -1.49550542e-01 -8.13545525e-01 -3.05069476e-01
-3.66283596e-01 -8.00102353e-01 3.04762721e-01 -7.74882078e-01
-1.02636349e+00 1.13514411e+00 -1.28175825e-01 -7.97372162e-01
-3.52905571e-01 -1.40603197e+00 -8.67256165e-01 1.00372648e+00
-1.49236310e+00 -2.97018349e-01 -9.12644118e-02 2.18796968e-01
9.35042262e-01 -1.38259614e+00 7.29136109e-01 -7.22251385e-02
-9.68368530e-01 4.07807022e-01 7.75673807e-01 -2.55661815e-01
-2.93655366e-01 -1.23389804e+00 1.72159418e-01 4.90368009e-01
-1.04500659e-01 4.40311223e-01 1.20405674e+00 -3.45858097e-01
-1.60305762e+00 -8.06193292e-01 5.69037318e-01 4.18710858e-01
3.88637334e-01 3.17027450e-01 -5.42875648e-01 -1.52405113e-01
7.64935553e-01 -2.86749303e-01 5.35773575e-01 -1.86514392e-01
7.10353911e-01 -2.92879760e-01 -1.70183516e+00 3.45814288e-01
5.28989255e-01 4.11861956e-01 -6.49722457e-01 5.76563537e-01
7.21031576e-02 -3.48946214e-01 -6.63621604e-01 5.28848767e-01
6.89870119e-01 -1.01508510e+00 1.00365150e+00 -9.47624505e-01
1.29273757e-01 1.27791747e-01 -9.75569785e-02 -1.61930001e+00
-9.59374383e-02 -6.17631316e-01 -4.02264118e-01 9.87690985e-01
2.90950835e-01 -1.00231683e+00 1.36771572e+00 1.83009222e-01
-9.42838863e-02 -1.38331413e+00 -1.13760710e+00 -9.11051512e-01
1.99341044e-01 -4.08662967e-02 5.39299607e-01 8.85247529e-01
-7.28670120e-01 -5.49743436e-02 3.37572426e-01 -1.57964960e-01
4.11006480e-01 -4.37526911e-01 -1.52682597e-02 -1.52034819e+00
-1.95254475e-01 -7.08958089e-01 -7.22893655e-01 -8.95836856e-03
5.43166064e-02 -5.17395735e-01 -5.02766728e-01 -1.40676773e+00
-9.30679083e-01 -3.50188136e-01 -5.43272078e-01 1.79993957e-01
1.80043533e-01 9.20780078e-02 3.28030512e-02 -1.28525823e-01
6.01508081e-01 4.32102531e-01 9.38043892e-01 -8.80140513e-02
-3.02857876e-01 3.97164196e-01 -1.04829349e-01 8.60766530e-01
1.28228199e+00 -8.06726873e-01 -7.27064848e-01 -5.60472369e-01
2.80180305e-01 -5.03692985e-01 -1.41119376e-01 -1.53010380e+00
2.10865319e-01 -2.02503670e-02 7.74940848e-01 -7.95435309e-01
1.01526223e-01 -8.24968398e-01 -2.67833203e-01 1.22678769e+00
-2.55387366e-01 7.49581873e-01 5.13654292e-01 2.58296132e-01
-1.02855772e-01 -1.40026903e+00 8.86653602e-01 -3.75994235e-01
-7.98944473e-01 -4.29123074e-01 -8.70110154e-01 -1.36874467e-01
9.72319305e-01 -1.00866377e+00 -2.06956699e-01 4.15719837e-01
-7.71362603e-01 -2.81745475e-03 -2.73168236e-01 1.89333141e-01
7.43536532e-01 -1.02401996e+00 -2.64791906e-01 2.03607708e-01
-8.67296755e-01 -3.36543530e-01 -2.68876076e-01 2.26295501e-01
-1.30947113e+00 5.60610890e-01 -1.02305925e+00 -9.31489915e-02
-1.34518492e+00 2.24273980e-01 1.18616629e+00 -8.65021944e-02
-3.59325916e-01 1.56079161e+00 -1.22348416e+00 -5.10128021e-01
5.99344850e-01 -6.28443211e-02 -9.10213888e-01 1.17410898e-01
1.54315054e-01 1.03800106e+00 8.53549123e-01 4.44547944e-02
-4.40673530e-01 5.29319167e-01 -5.80689721e-02 -8.50021839e-02
1.86289012e+00 5.54479957e-01 -1.08875163e-01 3.62709045e-01
1.07697296e+00 -7.66175568e-01 -7.71262825e-01 3.05137604e-01
3.89986843e-01 1.95692882e-01 8.64650726e-01 -1.28769553e+00
-1.62883079e+00 8.92487884e-01 1.45474291e+00 7.47724101e-02
1.40754676e+00 -1.30990708e+00 5.92424095e-01 1.58274138e+00
-1.09548315e-01 -1.59242773e+00 -2.26218596e-01 8.07973146e-01
6.23106718e-01 -9.13078904e-01 3.71288657e-01 4.48997766e-01
-3.48844856e-01 2.18178010e+00 6.43833876e-01 -9.03075457e-01
1.02631867e+00 4.92217958e-01 2.94259578e-01 -3.07910860e-01
-9.98147249e-01 -2.87495665e-02 4.31284085e-02 8.95106375e-01
2.35892519e-01 -5.39661586e-01 -5.99521875e-01 -2.10312396e-01
-3.11188996e-01 3.95205349e-01 3.06946456e-01 1.78596139e+00
-4.92035121e-01 -1.11209416e+00 -3.41440946e-01 4.57427382e-01
-6.86844289e-01 -2.30272397e-01 -5.46246588e-01 7.53533781e-01
3.89066756e-01 9.55919385e-01 2.22633868e-01 -3.86814892e-01
1.87745199e-01 1.13645062e-01 5.14732897e-01 -1.58962280e-01
-1.25928557e+00 -5.32043278e-01 2.60363638e-01 5.68121821e-02
-7.23413348e-01 -3.63930911e-01 -1.38495553e+00 -1.54750824e-01
-3.39893669e-01 5.97200334e-01 1.65790153e+00 1.32569587e+00
6.11384772e-02 1.11214197e+00 6.33716464e-01 -1.14463127e+00
-3.56273592e-01 -7.75890052e-01 -5.37245870e-01 -6.54869676e-01
6.28956497e-01 -8.22042048e-01 -3.51718634e-01 -3.93904984e-01] | [8.180985450744629, 3.279120683670044] |
349d1d26-ad02-4927-9c1f-a25e90c57cbb | deformable-medical-image-registration-setting | null | null | https://www.ncbi.nlm.nih.gov/pubmed/21568711 | https://pdfs.semanticscholar.org/0c5f/277d357e3667b18b5420fd660f221c935fcc.pdf | Deformable medical image registration: setting the state of the art with discrete methods | This review introduces a novel deformable image registration paradigm that exploits Markov random field formulation and powerful discrete optimization algorithms. We express deformable registration as a minimal cost graph problem, where nodes correspond to the deformation grid, a node's connectivity corresponds to regularization constraints, and labels correspond to 3D deformations. To cope with both iconic and geometric (landmark-based) registration, we introduce two graphical models, one for each subproblem. The two graphs share interconnected variables, leading to a modular, powerful, and flexible formulation that can account for arbitrary image-matching criteria, various local deformation models, and regularization constraints. To cope with the corresponding optimization problem, we adopt two optimization strategies: a computationally efficient one and a tight relaxation alternative. Promising results demonstrate the potential of this approach. Discrete methods are an important new trend in medical image registration, as they provide several improvements over the more traditional continuous methods. This is illustrated with several key examples where the presented framework outperforms existing general-purpose registration methods in terms of both performance and computational complexity. Our methods become of particular interest in applications where computation time is a critical issue, as in intraoperative imaging, or where the huge variation in data demands complex and application-specific matching criteria, as in large-scale multimodal population studies. The proposed registration framework, along with a graphical interface and corresponding publications, is available for download for research purposes (for Windows and Linux platforms) from http://www.mrf-registration.net. | ['Paragios N.', 'Komodakis N.', 'Sotiras A.', 'Glocker B.'] | 2011-08-15 | null | null | null | annual-review-of-biomedical-engineering-2011 | ['deformable-medical-image-registration', 'birl-cima'] | ['medical', 'medical'] | [ 4.24686730e-01 1.04842521e-01 -2.85927087e-01 -2.39134967e-01
-8.10838342e-01 -5.26105464e-01 3.53578240e-01 4.43305999e-01
-5.96306562e-01 7.08483279e-01 -8.10146332e-02 -9.04968604e-02
-5.35194993e-01 -6.01653039e-01 -3.30886632e-01 -1.00145769e+00
-2.18369558e-01 6.75865591e-01 1.67931691e-01 -3.88779104e-01
2.42254823e-01 8.15280914e-01 -1.06174672e+00 -3.16239238e-01
1.04685020e+00 7.53904641e-01 3.71746600e-01 1.76532447e-01
1.41324192e-01 -6.22079372e-02 -1.14808142e-01 -1.69444844e-01
2.83414453e-01 -2.73863345e-01 -7.71042407e-01 9.27314162e-02
2.41010055e-01 1.69102043e-01 -1.10926501e-01 1.20966101e+00
7.14339256e-01 3.07851791e-01 4.29795861e-01 -9.11972284e-01
-2.20829353e-01 2.14220151e-01 -4.22387064e-01 9.93478000e-02
5.05695224e-01 -1.58309445e-01 4.71058905e-01 -6.05733454e-01
7.27876961e-01 7.99557030e-01 6.70229316e-01 5.44025600e-01
-1.33787894e+00 -1.74688712e-01 3.48867774e-02 -2.12103590e-01
-1.35628867e+00 -2.44327456e-01 7.77722478e-01 -5.78634977e-01
5.40660858e-01 7.01422989e-01 5.82658947e-01 6.09365225e-01
7.71889865e-01 6.12974986e-02 1.23544300e+00 -3.41195762e-01
1.12418361e-01 -1.00028507e-01 3.02734673e-01 8.47867787e-01
4.15739566e-01 5.32180518e-02 -1.34812117e-01 -6.09980345e-01
9.40193474e-01 2.63529092e-01 -5.35957396e-01 -5.20806730e-01
-1.32871640e+00 7.32901931e-01 3.11364889e-01 6.98350966e-01
-5.23001194e-01 6.70330152e-02 1.50420919e-01 7.65339583e-02
3.93818468e-01 1.97398067e-01 2.91658547e-02 1.79259017e-01
-9.21360850e-01 1.46205917e-01 7.04761982e-01 6.73844576e-01
7.80053854e-01 -2.05107018e-01 1.18880555e-01 6.31007969e-01
4.73290503e-01 2.53439009e-01 6.49808228e-01 -7.44627357e-01
1.46461695e-01 3.90837997e-01 -1.05505489e-01 -1.11387503e+00
-7.40966618e-01 -3.21691692e-01 -1.18379354e+00 1.31246448e-01
2.57592857e-01 8.18556547e-02 -6.78909063e-01 1.54943144e+00
5.00845790e-01 2.11133078e-01 -2.09976152e-01 8.71569932e-01
9.17091012e-01 1.71784401e-01 5.30658104e-02 -6.42390132e-01
1.58121657e+00 -4.57631022e-01 -1.02270973e+00 1.28144652e-01
5.72695315e-01 -9.51702595e-01 5.20234346e-01 7.31808469e-02
-1.35314453e+00 -1.60389364e-01 -7.47909725e-01 9.21990722e-02
-2.36529365e-01 -1.95959006e-02 6.35319173e-01 5.83831608e-01
-1.36173236e+00 6.80369258e-01 -1.19393587e+00 -3.77299249e-01
6.30408749e-02 9.18759108e-01 -5.59633911e-01 1.01492874e-01
-9.86072600e-01 8.67134094e-01 1.19097814e-01 5.57152569e-01
-3.03955734e-01 -4.58541155e-01 -1.06721056e+00 -4.38321292e-01
2.50590473e-01 -8.06719303e-01 5.42836964e-01 -4.89478528e-01
-1.47045696e+00 1.27051520e+00 -1.02933221e-01 -5.15640080e-02
7.05787957e-01 1.26886368e-01 -1.79449916e-01 5.82357980e-02
-3.78657831e-03 2.44740605e-01 5.93949735e-01 -1.13104534e+00
1.94200188e-01 -5.88146865e-01 -9.04358402e-02 3.33868921e-01
3.27898189e-03 2.07543910e-01 -4.86989856e-01 -9.38897431e-01
5.21239281e-01 -1.23375046e+00 -7.45099127e-01 -7.04790503e-02
-3.39500159e-01 1.08613588e-01 3.95694166e-01 -6.80230856e-01
1.09139287e+00 -2.11684728e+00 4.79250729e-01 5.53929627e-01
3.03531915e-01 6.59953579e-02 1.60393909e-01 4.28836733e-01
-1.29086018e-01 1.21005051e-01 -6.09878480e-01 -3.20617110e-01
-2.45409131e-01 2.84351856e-01 3.93108428e-01 1.02056682e+00
-2.78704226e-01 9.25634742e-01 -7.96950042e-01 -5.68960309e-01
3.56619984e-01 7.43923247e-01 -2.34051481e-01 4.66194898e-02
2.96358883e-01 1.06999135e+00 -7.10223854e-01 4.59045708e-01
8.24304640e-01 -1.42346188e-01 4.20171678e-01 -4.45146561e-01
-3.25696707e-01 -2.64561802e-01 -1.46539330e+00 2.07250476e+00
-2.88376629e-01 7.36036599e-02 2.83536196e-01 -1.22538900e+00
1.08414483e+00 5.23241460e-01 1.10083199e+00 -3.31201047e-01
3.61237586e-01 4.28162873e-01 -2.34568328e-01 -5.19928932e-01
4.97686684e-01 -3.54841530e-01 3.25508267e-02 2.27890044e-01
-1.18618794e-01 -1.84628323e-01 1.15102701e-01 -1.41097024e-01
8.19595158e-01 9.55920517e-02 4.67898637e-01 -7.41317272e-01
7.80912638e-01 -1.47234842e-01 6.44533932e-01 4.60318774e-01
-8.63709003e-02 5.54445028e-01 2.94506490e-01 -5.15579164e-01
-3.62774283e-01 -7.91777611e-01 -5.85790455e-01 3.08957398e-01
5.91281593e-01 -7.56297410e-02 -6.19367063e-01 -3.82171065e-01
-1.59936592e-01 -8.48063007e-02 -5.74933112e-01 3.03338952e-02
-9.42481756e-01 -1.06830513e+00 6.86393082e-02 1.22351557e-01
5.53740226e-02 -8.77615869e-01 -3.86770248e-01 3.08842450e-01
-2.35605672e-01 -9.96574938e-01 -6.33085489e-01 -2.99928840e-02
-1.38751388e+00 -1.07833421e+00 -9.43158984e-01 -1.02801347e+00
1.07411861e+00 3.44106108e-02 9.75551128e-01 3.58469427e-01
-5.31015694e-01 6.62771523e-01 -1.96842745e-01 2.22564876e-01
-4.38382536e-01 -4.71937135e-02 5.94927073e-02 2.10423529e-01
-3.13318431e-01 -6.06912911e-01 -7.75647759e-01 4.96071041e-01
-1.05466628e+00 -3.27986538e-01 4.38055992e-01 7.64567375e-01
1.05603480e+00 -3.00768137e-01 3.16578299e-01 -1.07060766e+00
7.48293817e-01 -3.17636669e-01 -7.68811882e-01 2.97343671e-01
-5.96620560e-01 -1.56440213e-02 1.46346912e-01 -3.17178398e-01
-7.97043800e-01 3.13186437e-01 -3.22770357e-01 -1.96031079e-01
-1.68878034e-01 6.79760635e-01 -6.37360364e-02 -8.48775923e-01
5.31879127e-01 1.01015605e-01 3.38166296e-01 -4.98513073e-01
1.23453908e-01 1.69126227e-01 4.36727107e-01 -7.16977298e-01
6.96091652e-01 5.48639834e-01 4.05418128e-01 -7.06718802e-01
-8.79714787e-02 -4.91801769e-01 -9.14650142e-01 -1.63606733e-01
9.46103930e-01 -5.80419183e-01 -7.01873541e-01 3.69453967e-01
-1.02916849e+00 -1.32106081e-01 -3.73766005e-01 8.31716418e-01
-6.96905673e-01 6.07776523e-01 -5.85000575e-01 -2.99623638e-01
-4.06963915e-01 -1.72016609e+00 9.60812151e-01 2.35806495e-01
-3.00191846e-02 -1.66043556e+00 3.83973390e-01 1.11760072e-01
5.88236570e-01 1.01015246e+00 6.49984241e-01 -6.00291073e-01
-4.86552387e-01 -5.60575366e-01 2.54668415e-01 -5.27068637e-02
3.77358526e-01 -2.71643013e-01 -4.07924205e-01 -6.18840635e-01
1.90267473e-01 1.15054980e-01 4.24544573e-01 6.60540521e-01
1.00968421e+00 -1.20059259e-01 -5.03080428e-01 6.68693483e-01
1.63544750e+00 1.61932230e-01 5.65447092e-01 1.16135873e-01
6.49564922e-01 4.66891050e-01 5.70333362e-01 3.74970704e-01
2.78115004e-01 1.11766720e+00 4.94838417e-01 -4.67284739e-01
1.10001247e-02 4.58883375e-01 -2.98394524e-02 1.05289364e+00
-8.55372906e-01 1.09156907e-01 -8.21627915e-01 1.86783344e-01
-1.91427910e+00 -6.65459812e-01 -4.52508956e-01 2.63380313e+00
6.86409771e-01 -3.34569782e-01 -7.44790807e-02 -7.45935142e-02
9.27377224e-01 1.79464966e-01 -7.89133012e-02 -2.93340385e-01
8.73722807e-02 4.58372265e-01 4.08615410e-01 8.65327239e-01
-1.01079047e+00 4.04429227e-01 5.87778425e+00 6.84919178e-01
-1.14830756e+00 4.22068328e-01 3.08355987e-01 4.36845511e-01
-4.26437557e-01 8.04851130e-02 -5.49883008e-01 3.22410673e-01
4.83685315e-01 -1.72730088e-01 2.43977562e-01 1.93516299e-01
3.14269423e-01 -8.49102214e-02 -9.02928591e-01 9.82259750e-01
2.54451875e-02 -1.33826017e+00 -2.29735047e-01 4.37407017e-01
5.10257363e-01 -1.79342180e-01 -3.79784182e-02 -4.77793276e-01
-2.09547609e-01 -7.63223231e-01 2.75416255e-01 7.19247937e-01
9.44601238e-01 -3.37421805e-01 8.04377198e-01 5.53408265e-02
-1.37420285e+00 5.57658792e-01 -8.65490809e-02 3.77279729e-01
5.55748224e-01 6.18369043e-01 -2.07869038e-02 1.03033602e+00
2.91091859e-01 7.71310925e-01 -1.76055521e-01 1.30353892e+00
2.40140855e-02 -8.53431150e-02 -2.65329897e-01 2.77819455e-01
-7.79286548e-02 -7.03187525e-01 8.08127224e-01 1.08425856e+00
3.73686314e-01 3.16417664e-01 4.08994883e-01 6.48667455e-01
1.17052250e-01 3.98209900e-01 -6.19296074e-01 5.42415380e-01
5.71336858e-02 1.58023047e+00 -1.14295614e+00 8.65840912e-02
-2.94649601e-01 9.17119801e-01 3.34820747e-02 2.16996163e-01
-7.33003736e-01 -2.10315540e-01 2.70570427e-01 3.26473653e-01
-3.28826696e-01 -4.77548510e-01 -2.16836855e-02 -1.22583079e+00
1.65645033e-02 -5.99884033e-01 5.10071754e-01 -1.28627613e-01
-9.52574015e-01 9.11718905e-01 2.80780166e-01 -1.43332994e+00
-3.04098744e-02 -4.33579475e-01 -5.57051957e-01 9.23039377e-01
-1.47043514e+00 -1.14789176e+00 -5.81422985e-01 9.46136594e-01
8.07245970e-02 2.17327878e-01 1.03166533e+00 5.07397115e-01
-4.35064346e-01 5.42995512e-01 3.60092451e-03 -5.28819747e-02
6.57295883e-01 -1.05155146e+00 -2.52922744e-01 6.28035903e-01
-1.11217640e-01 6.92862749e-01 4.51647371e-01 -6.42912447e-01
-1.53212094e+00 -7.13989675e-01 8.12330127e-01 -2.01166555e-01
5.67851901e-01 -3.75314206e-02 -8.50937665e-01 6.57906175e-01
1.42297506e-01 5.58047056e-01 9.34814751e-01 -2.37976432e-01
4.68283087e-01 -4.82551679e-02 -1.40486550e+00 2.34499231e-01
8.60584855e-01 -6.05179518e-02 -3.44963342e-01 7.26287246e-01
1.90970138e-01 -1.00292146e+00 -1.58703005e+00 5.28089881e-01
4.32884246e-01 -7.14636385e-01 9.97704685e-01 -1.98542818e-01
-3.37801427e-01 -2.73735166e-01 9.14538801e-02 -1.01350915e+00
-3.35472047e-01 -1.12936580e+00 3.54386568e-01 8.20036471e-01
1.13475233e-01 -1.17955756e+00 4.18871015e-01 8.29852104e-01
-3.29107463e-01 -8.44121993e-01 -1.20828891e+00 -6.50406599e-01
-3.45509313e-02 2.44880151e-02 3.19521099e-01 1.15975797e+00
3.95264067e-02 -4.31339443e-01 -1.94008991e-01 2.78679579e-01
7.86123693e-01 1.17530540e-01 2.52253890e-01 -1.29538035e+00
-1.91604853e-01 -3.60863030e-01 -7.31315851e-01 -6.08105361e-01
1.03104651e-01 -1.07106483e+00 -1.19667426e-01 -1.52892411e+00
5.93616217e-02 -1.01078057e+00 -3.47936511e-01 5.13910890e-01
-4.05504517e-02 5.01535773e-01 7.61899054e-02 4.97591585e-01
-1.67786419e-01 2.40743160e-01 1.36579645e+00 8.62433314e-02
-4.55529213e-01 3.54664147e-01 -3.50112468e-01 6.10739052e-01
8.00820470e-01 -5.96586764e-01 -1.85246527e-01 -2.52506375e-01
1.25095889e-01 5.64997196e-01 3.25530171e-01 -7.06129909e-01
4.06811029e-01 -1.71827868e-01 -2.40179807e-01 -6.58889068e-03
2.81863034e-01 -9.37818408e-01 7.00132132e-01 8.28931332e-01
-1.04571669e-03 3.47535104e-01 1.19135924e-01 3.65254253e-01
-4.69243109e-01 -4.44851458e-01 9.25794184e-01 -1.81650698e-01
-3.15564752e-01 6.94243371e-01 -1.40493780e-01 -4.64532711e-02
1.17966843e+00 -3.38543057e-01 -2.20798794e-02 -7.70703480e-02
-1.48659575e+00 -1.02368109e-01 5.01244068e-01 1.42114043e-01
6.84037209e-01 -1.44164932e+00 -7.58867621e-01 1.71494558e-01
-1.28303349e-01 1.29741505e-01 3.80454004e-01 1.69694746e+00
-7.41549075e-01 1.92933809e-02 -2.04500198e-01 -7.38007486e-01
-1.25223672e+00 3.21161747e-01 5.24762332e-01 -4.79126394e-01
-7.03858137e-01 4.41419363e-01 1.82329088e-01 -2.89371580e-01
-1.06583729e-01 -2.89304227e-01 -3.18859011e-01 -8.23724568e-02
1.01261579e-01 3.64129335e-01 3.97023350e-01 -9.48827446e-01
-7.14042366e-01 1.20085537e+00 2.00233504e-01 1.75660029e-01
1.23868299e+00 -1.16534635e-01 -5.90811670e-01 1.33852839e-01
1.04594958e+00 1.56572968e-01 -6.99885845e-01 -2.30130047e-01
-2.60227859e-01 -3.13408166e-01 4.30211090e-02 -3.03873479e-01
-1.35217178e+00 5.19576848e-01 8.06597233e-01 2.40350172e-01
1.16929960e+00 -7.72449700e-03 3.42121691e-01 -2.03744292e-01
5.50208330e-01 -5.42110443e-01 -2.70451158e-01 1.78038746e-01
1.08996475e+00 -1.13468289e+00 3.12206745e-01 -7.86756992e-01
-2.65874743e-01 1.20391738e+00 3.38181145e-02 -3.24073702e-01
9.03000951e-01 3.88681829e-01 8.17902461e-02 -2.33699918e-01
1.01475276e-01 -3.24994535e-03 6.60368323e-01 4.35581297e-01
7.45833457e-01 1.06715053e-01 -1.07048059e+00 3.52858305e-01
1.48557261e-01 -5.32508940e-02 3.52853715e-01 9.46377218e-01
-5.26013151e-02 -1.64992738e+00 -4.36000735e-01 1.86410695e-01
-5.72991371e-01 9.08399671e-02 1.94911271e-01 7.86072195e-01
3.83344404e-02 5.85846663e-01 -2.11440027e-01 8.33639875e-02
4.04813111e-01 -3.94206554e-01 6.37577593e-01 -5.07850945e-01
-7.11126924e-01 3.60019028e-01 -2.31258258e-01 -8.03714395e-01
-8.78549516e-01 -8.65379870e-01 -1.30972517e+00 -8.74798596e-02
-4.97654676e-01 1.80277899e-01 7.96546102e-01 8.39673877e-01
2.83476949e-01 3.64732236e-01 5.92196584e-01 -1.22400689e+00
-2.12265059e-01 -5.28047323e-01 -6.33292198e-01 3.45961332e-01
2.04915717e-01 -8.17462623e-01 -1.84966937e-01 5.20581603e-02] | [13.993165016174316, -2.671675443649292] |
891021b4-71de-48e9-8fb3-e78bad3a75ec | idn-sum-a-new-dataset-for-interactive-digital | null | null | https://aclanthology.org/2022.creativesumm-29.1 | https://aclanthology.org/2022.creativesumm-29.1.pdf | IDN-Sum: A New Dataset for Interactive Digital Narrative Extractive Text Summarisation | Summarizing Interactive Digital Narratives (IDN) presents some unique challenges to existing text summarization models especially around capturing interactive elements in addition to important plot points. In this paper, we describe the first IDN dataset (IDN-Sum) designed specifically for training and testing IDN text summarization algorithms. Our dataset is generated using random playthroughs of 8 IDN episodes, taken from 2 different IDN games, and consists of 10,000 documents. Playthrough documents are annotated through automatic alignment with fan-sourced summaries using a commonly used alignment algorithm. We also report and discuss results from experiments applying common baseline extractive text summarization algorithms to this dataset. Qualitative analysis of the results reveals shortcomings in common annotation approaches and evaluation methods when applied to narrative and interactive narrative datasets. The dataset is released as open source for future researchers to train and test their own approaches for IDN text. | ['David E. Millard', 'Stuart E. Middleton', 'Ashwathy T. Revi'] | null | null | https://aclanthology.org/2022.creativesumm-1.1 | https://aclanthology.org/2022.creativesumm-1.1.pdf | coling-creativesumm-2022-10 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 6.63374901e-01 4.90452409e-01 -4.33068961e-01 -1.67192239e-02
-1.30875969e+00 -1.02354360e+00 1.09181786e+00 6.40103281e-01
-6.63309395e-02 7.82153010e-01 1.62544560e+00 3.17839120e-04
-3.17777812e-01 -3.44226539e-01 -2.60619938e-01 2.55136967e-01
-1.61968283e-02 6.99424088e-01 2.07821608e-01 -4.65762675e-01
9.10766244e-01 -2.26199552e-01 -1.29087019e+00 1.08795929e+00
9.03143287e-01 6.76329136e-02 -1.40821606e-01 1.30536544e+00
-3.12873751e-01 1.43117332e+00 -1.30408204e+00 -6.90064847e-01
-8.01654160e-02 -1.13577139e+00 -1.32976854e+00 -2.88086329e-02
8.12926054e-01 -3.88030171e-01 -3.82207185e-01 5.59093416e-01
7.74142802e-01 4.70547020e-01 9.23669279e-01 -1.12095916e+00
-2.79777825e-01 1.65184903e+00 -3.65870714e-01 4.87958282e-01
1.10312605e+00 -9.51663256e-02 1.39924896e+00 -3.52464736e-01
1.52736652e+00 1.08449829e+00 9.09155369e-01 3.87576789e-01
-1.25082839e+00 -2.33730569e-01 -1.78109780e-01 -1.64479073e-02
-7.35793829e-01 -6.52372062e-01 7.89663374e-01 -3.77593070e-01
1.19235241e+00 7.82943189e-01 1.07645583e+00 1.49569333e+00
-1.92617372e-01 1.09625340e+00 4.41770524e-01 -3.44287366e-01
-7.55003421e-04 -2.87627578e-01 2.95537442e-01 1.58033729e-01
3.23194772e-01 -1.06084621e+00 -1.00569069e+00 -2.66443104e-01
3.00494879e-01 -7.49745011e-01 -8.15588534e-02 3.94769877e-01
-1.44788003e+00 8.49094152e-01 -2.24076003e-01 4.17266369e-01
-2.48359576e-01 6.28503188e-02 1.28692877e+00 -2.48651896e-02
7.83535361e-01 8.84345651e-01 1.53328508e-01 -1.13403511e+00
-1.39129806e+00 1.10992765e+00 1.22297359e+00 1.09675646e+00
-4.21564840e-02 -1.26500025e-01 -8.04016292e-01 9.43006635e-01
-2.98902988e-01 6.02194741e-02 5.06027877e-01 -1.31158531e+00
1.21829462e+00 7.96039343e-01 -9.42229182e-02 -1.38046098e+00
-5.02166688e-01 -2.68852226e-02 -4.51842248e-01 -3.15084130e-01
2.29743466e-01 -2.00762749e-01 -1.87619299e-01 1.16017532e+00
-1.04260169e-01 -4.69118655e-01 2.46246681e-01 3.63637835e-01
1.83732355e+00 8.21233034e-01 -1.60427049e-01 -4.79654014e-01
1.31278968e+00 -1.07254326e+00 -1.21428323e+00 -2.10741699e-01
9.20426250e-01 -1.02372873e+00 1.33400393e+00 4.22890484e-01
-1.57435203e+00 -4.91859913e-02 -1.30976951e+00 -7.13838637e-01
-6.11335598e-02 1.30285248e-01 3.87767494e-01 4.94767934e-01
-9.31343019e-01 6.76202834e-01 -5.60360372e-01 -9.34997916e-01
7.06933498e-01 -2.39976376e-01 -1.39221653e-01 3.93404216e-01
-6.80614114e-01 5.99174976e-01 7.01441586e-01 -6.96998537e-01
-4.73117948e-01 -9.10943031e-01 -6.68092549e-01 -2.07202524e-01
4.88876849e-01 -3.65162820e-01 1.99576652e+00 -6.50561929e-01
-1.30678082e+00 9.52846348e-01 8.37524831e-02 -4.17784363e-01
7.21714616e-01 -6.37147248e-01 -7.67412111e-02 4.02384222e-01
5.73662579e-01 4.51820105e-01 -7.81344995e-02 -1.07356286e+00
-4.50411648e-01 3.65387499e-02 1.31432220e-01 5.19314408e-01
-4.25951123e-01 5.08632958e-01 -7.76332617e-02 -1.12786472e+00
-2.04496250e-01 -3.21783662e-01 3.96185257e-02 -7.65719771e-01
-1.09206033e+00 -1.01558566e-01 9.13349211e-01 -1.18549800e+00
1.97476912e+00 -1.57770061e+00 1.83358952e-01 -2.10286677e-01
3.50793630e-01 -1.62145525e-01 -8.05002463e-04 1.43626034e+00
2.41033971e-01 5.10881186e-01 -3.23577970e-01 -4.24973369e-01
1.81538284e-01 -1.10221416e-01 -3.42833400e-01 -1.71665587e-02
-2.91730613e-01 9.65614140e-01 -1.12041318e+00 -9.12757099e-01
-3.28180283e-01 -7.39716813e-02 -5.82899153e-01 -1.51756078e-01
-6.40330076e-01 9.82775912e-02 -3.87813270e-01 2.44980320e-01
6.29927814e-02 1.19183920e-01 1.59327716e-01 -7.98435807e-02
-5.01128733e-01 7.86877334e-01 -8.71978581e-01 2.09753299e+00
1.24971665e-01 1.72274530e+00 -5.60366511e-01 -5.12431502e-01
7.29753554e-01 4.56493676e-01 3.62996459e-01 -3.57977986e-01
1.97107404e-01 -1.48264756e-02 -2.14122772e-01 -7.99136698e-01
1.49951541e+00 4.11473006e-01 -7.55384564e-01 1.06645799e+00
3.00857276e-01 -5.91981828e-01 9.92043197e-01 1.13450730e+00
1.42375302e+00 -1.48174074e-02 7.97609210e-01 -8.23570788e-02
3.39013115e-02 9.43441868e-01 -7.98731521e-02 1.09465957e+00
2.26899192e-01 8.49758327e-01 1.17067063e+00 -2.12774143e-01
-1.47809350e+00 -6.77874565e-01 1.39919847e-01 1.07810533e+00
-1.89550236e-01 -1.66232419e+00 -1.18423975e+00 -5.17618418e-01
-6.00384295e-01 1.23295641e+00 -6.83452964e-01 2.38534421e-01
-8.07305753e-01 -4.86693114e-01 1.13842654e+00 4.23352659e-01
5.38400590e-01 -1.20445216e+00 -8.23312461e-01 2.46947557e-01
-9.19243038e-01 -8.32364738e-01 -4.23709035e-01 -2.66475260e-01
-6.67647183e-01 -1.15546978e+00 -1.58287361e-01 -4.26058620e-01
-6.37672544e-02 1.68068066e-01 1.34792280e+00 -5.09424545e-02
-1.43783584e-01 5.62158287e-01 -8.59181523e-01 -6.92452192e-01
-8.74760032e-01 7.65441656e-01 -4.00983691e-01 -8.74146879e-01
6.00592280e-03 -6.83262348e-01 -1.68136567e-01 -3.20319384e-01
-1.08485258e+00 6.31460190e-01 -5.90015315e-02 4.11803991e-01
6.78296015e-02 -1.73469961e-01 4.91753727e-01 -1.21370912e+00
1.49708045e+00 -3.87509018e-01 3.84522617e-01 -1.13532636e-02
9.60294157e-03 -4.80031550e-01 4.58382070e-01 -3.19048554e-01
-1.19105721e+00 -6.25669241e-01 -6.04444067e-04 5.59664488e-01
2.32637540e-01 7.46647656e-01 -9.35327560e-02 8.32016945e-01
1.06995130e+00 -2.59122163e-01 -3.55641633e-01 -5.02064288e-01
5.90970635e-01 6.05821311e-01 1.01299858e+00 -4.83218044e-01
4.79877502e-01 2.48385936e-01 -4.70978588e-01 -1.08766806e+00
-8.41167510e-01 -3.05377662e-01 -6.40559971e-01 -8.84165943e-01
7.24410176e-01 -5.78596354e-01 -3.72501642e-01 2.15527579e-01
-1.32167506e+00 -6.12040222e-01 -9.31506932e-01 1.31068766e-01
-8.82563472e-01 2.22472280e-01 -6.77441120e-01 -4.10141915e-01
-7.26109624e-01 -4.22878653e-01 8.08728278e-01 4.13794577e-01
-1.49878418e+00 -9.98272717e-01 6.07846856e-01 5.70342004e-01
-2.81749964e-02 8.85936379e-01 7.27342248e-01 -1.28229690e+00
1.62627310e-01 -2.74102509e-01 8.31382573e-02 -3.94314945e-01
-3.46873067e-02 6.29127920e-01 -6.30124748e-01 2.26136446e-01
-3.79809499e-01 -6.92890406e-01 7.31095910e-01 4.68840152e-01
5.93838274e-01 -1.12375593e+00 -3.09746474e-01 -2.03413535e-02
1.17144191e+00 7.55457059e-02 7.18775928e-01 8.72137189e-01
7.60108292e-01 5.10032296e-01 5.67760170e-01 9.78194535e-01
4.79806513e-01 3.86626780e-01 4.07262109e-02 3.30540895e-01
-3.50871801e-01 -6.24628007e-01 3.51495266e-01 1.09995127e+00
-3.19321007e-01 -8.95829022e-01 -1.08561194e+00 7.08318591e-01
-2.17069578e+00 -1.66007400e+00 -6.90276682e-01 1.26599967e+00
1.05916846e+00 2.97123730e-01 5.19533455e-01 5.05410075e-01
3.37059557e-01 7.26193845e-01 -1.25186339e-01 -7.58660913e-01
-3.58649284e-01 1.11056879e-01 -7.62656406e-02 2.85600305e-01
-9.82643783e-01 8.41954648e-01 6.57245016e+00 1.06002140e+00
-3.32629383e-01 1.46310598e-01 4.82989848e-01 -6.40285850e-01
-5.66163778e-01 1.09046005e-01 -4.21579689e-01 1.75063953e-01
9.33523238e-01 -7.97279179e-01 -4.48660515e-02 6.36195242e-01
5.50746322e-01 -4.88453239e-01 -9.99242842e-01 4.96681720e-01
5.25919855e-01 -1.91594684e+00 2.46047765e-01 -3.66502374e-01
1.15826130e+00 -4.74607587e-01 -3.94898385e-01 2.59251624e-01
5.91940761e-01 -9.36085522e-01 1.17347920e+00 4.96523201e-01
7.89013684e-01 -7.04410017e-01 6.18797004e-01 1.73455253e-01
-7.64619768e-01 2.23964632e-01 1.15380637e-01 -3.77865106e-01
5.46103776e-01 6.33533150e-02 -9.91560876e-01 6.45515978e-01
6.42299473e-01 1.10178530e+00 -9.34805036e-01 7.98690438e-01
-2.05045387e-01 8.71861756e-01 -6.96480125e-02 -3.62729102e-01
2.83022106e-01 -9.47644264e-02 1.21541953e+00 1.82487416e+00
1.14958890e-01 4.92677897e-01 3.90287116e-02 4.57292169e-01
-3.56282055e-01 4.78652239e-01 -6.26289308e-01 -4.91883695e-01
5.86249769e-01 1.15925348e+00 -1.44133306e+00 -6.05001390e-01
6.24239333e-02 8.34604383e-01 9.77729168e-03 1.13075338e-01
-7.37738729e-01 -5.77881336e-01 9.29512680e-02 3.60025197e-01
-8.65566805e-02 -5.53881489e-02 -7.82784462e-01 -8.03211808e-01
-9.81949717e-02 -1.15835142e+00 4.58053321e-01 -9.84312713e-01
-8.73787940e-01 5.23698330e-01 6.39873207e-01 -1.11570680e+00
-4.71799552e-01 2.85256207e-01 -1.10758233e+00 4.95280474e-02
-1.22759841e-01 -1.09831464e+00 -6.29042387e-01 -1.19449750e-01
1.21598232e+00 -5.39009422e-02 6.29385948e-01 -1.00907475e-01
-5.40683508e-01 2.99895972e-01 2.10590154e-01 3.27848762e-01
8.91594708e-01 -1.25737393e+00 7.43680656e-01 8.64776194e-01
1.98643297e-01 6.40276149e-02 1.21540761e+00 -9.86983716e-01
-7.24825144e-01 -8.18089962e-01 9.69033957e-01 -7.20604718e-01
8.14037859e-01 -1.93730667e-01 -6.52927995e-01 9.67614770e-01
1.17484093e+00 -1.24743724e+00 1.19309556e+00 -9.30158049e-02
1.77207485e-01 5.00608802e-01 -8.54672074e-01 9.41201627e-01
1.38096714e+00 -2.97535360e-01 -1.01719189e+00 5.27971625e-01
8.72692049e-01 -8.14274788e-01 -7.95188367e-01 -5.63159101e-02
6.09710276e-01 -8.27148974e-01 5.73550999e-01 -7.61127710e-01
1.65912819e+00 1.61266387e-01 1.89439449e-02 -1.26403558e+00
5.10288551e-02 -1.09335232e+00 1.92840900e-02 2.08563447e+00
3.44504684e-01 2.19267309e-01 5.90259194e-01 2.78029412e-01
-5.59013247e-01 -3.27291459e-01 -6.04444206e-01 -1.94208995e-01
-1.01978049e-01 -6.25432134e-01 2.42398471e-01 1.07169425e+00
7.59644568e-01 7.67942667e-01 -1.29246920e-01 -8.14567924e-01
3.16836894e-01 -3.46474677e-01 1.21928859e+00 -1.18600190e+00
2.19792902e-01 -9.58555579e-01 -1.67069122e-01 -5.43567300e-01
-1.18535168e-01 -9.41011548e-01 -7.97361135e-02 -2.31383944e+00
7.60320425e-01 3.17096800e-01 9.86570418e-01 3.11383247e-01
-4.31735739e-02 4.12337482e-01 3.34617347e-01 3.28021049e-01
-1.11049914e+00 3.89795840e-01 1.08596158e+00 -3.14057529e-01
-7.40300834e-01 -1.58804595e-01 -9.20495272e-01 8.28323603e-01
9.18406188e-01 -4.91900414e-01 -6.59306526e-01 -5.62294014e-02
6.59455717e-01 6.31870031e-02 5.41454218e-02 -1.14819264e+00
3.38649064e-01 -1.28030464e-01 1.92594469e-01 -1.28072083e+00
4.48216610e-02 2.09718138e-01 2.96610475e-01 -1.26594335e-01
-9.92012858e-01 2.70176828e-01 4.95064467e-01 8.57218206e-02
-1.09053202e-01 -5.95694482e-01 1.59111515e-01 -2.45506197e-01
-2.77402192e-01 -5.11678398e-01 -1.20427155e+00 7.74603426e-01
8.12255621e-01 -7.28925824e-01 -8.02047551e-01 -9.24254835e-01
-3.92063975e-01 2.61026293e-01 5.30689240e-01 3.24651986e-01
4.28360403e-01 -1.28546190e+00 -1.17087603e+00 -7.49672532e-01
-2.26905718e-02 1.23402804e-01 2.51192778e-01 5.10728776e-01
-1.08990645e+00 1.95882291e-01 -3.81062061e-01 -1.29737213e-01
-1.48485446e+00 -7.65352994e-02 -3.29671979e-01 -6.49556577e-01
-9.96227026e-01 5.10773122e-01 -4.86944169e-01 2.37251259e-02
5.03088012e-02 -2.72604525e-01 -7.69350708e-01 8.19932044e-01
8.66449296e-01 1.07118595e+00 -1.02070287e-01 -6.41047418e-01
9.14434940e-02 -6.90083280e-02 -9.50064138e-02 -7.80800521e-01
1.60252011e+00 -2.14094430e-01 -1.03680909e-01 8.77135396e-01
8.66848230e-01 4.05205578e-01 -6.96858823e-01 1.25291735e-01
4.88242477e-01 -5.17047979e-02 -3.99166942e-01 -7.85918236e-01
-2.07786307e-01 2.18083292e-01 -4.09112811e-01 6.67058647e-01
8.13248515e-01 1.60636872e-01 6.62474930e-01 2.77089894e-01
-5.13226986e-01 -1.30883014e+00 5.24042487e-01 6.35555923e-01
1.27295649e+00 -6.78949416e-01 7.44529784e-01 -1.95706725e-01
-9.81478691e-01 1.34490514e+00 5.86910784e-01 -3.50593254e-02
-3.41741070e-02 3.49843293e-01 -2.35671386e-01 -6.07793510e-01
-8.86215389e-01 3.45251262e-01 1.51336133e-01 4.65611190e-01
6.70790315e-01 -7.53813162e-02 -9.36448693e-01 1.20691371e+00
-1.18168855e+00 -3.31491202e-01 1.38384449e+00 9.93059993e-01
-2.76237756e-01 -7.41559327e-01 -4.15502250e-01 7.27120817e-01
-5.75799406e-01 3.42219844e-02 -1.26183200e+00 1.21773505e+00
-3.53083849e-01 9.41283405e-01 3.90432149e-01 -3.68339002e-01
5.16101480e-01 -1.18063651e-01 4.35233176e-01 -7.88415670e-01
-1.23063517e+00 -1.76429853e-01 1.05935192e+00 -2.55081952e-01
-7.61131465e-01 -1.29324019e+00 -1.22253990e+00 -7.62966216e-01
1.34171203e-01 3.00324410e-01 2.56301969e-01 7.97858417e-01
2.39062943e-02 7.62752593e-01 -9.10369456e-02 -1.14418602e+00
1.67851791e-01 -1.41899359e+00 -2.25532338e-01 3.09476733e-01
-1.35261074e-01 -1.65098403e-02 5.63159734e-02 5.54063559e-01] | [12.274041175842285, 9.494693756103516] |
e06022eb-d9ef-417a-96e7-d71ce94c0919 | deep-hurdle-networks-for-zero-inflated-multi | 2010.16040 | null | https://arxiv.org/abs/2010.16040v1 | https://arxiv.org/pdf/2010.16040v1.pdf | Deep Hurdle Networks for Zero-Inflated Multi-Target Regression: Application to Multiple Species Abundance Estimation | A key problem in computational sustainability is to understand the distribution of species across landscapes over time. This question gives rise to challenging large-scale prediction problems since (i) hundreds of species have to be simultaneously modeled and (ii) the survey data are usually inflated with zeros due to the absence of species for a large number of sites. The problem of tackling both issues simultaneously, which we refer to as the zero-inflated multi-target regression problem, has not been addressed by previous methods in statistics and machine learning. In this paper, we propose a novel deep model for the zero-inflated multi-target regression problem. To this end, we first model the joint distribution of multiple response variables as a multivariate probit model and then couple the positive outcomes with a multivariate log-normal distribution. By penalizing the difference between the two distributions' covariance matrices, a link between both distributions is established. The whole model is cast as an end-to-end learning framework and we provide an efficient learning algorithm for our model that can be fully implemented on GPUs. We show that our model outperforms the existing state-of-the-art baselines on two challenging real-world species distribution datasets concerning bird and fish populations. | ['Carla P. Gomes', 'Katherine Mills', 'Malin Pinsky', 'Michelle Stuart', 'Andrew Allyn', 'Di Chen', 'Jae Hee Lee', 'Junwen Bai', 'Shufeng Kong'] | 2020-10-30 | null | null | null | null | ['multi-target-regression'] | ['miscellaneous'] | [ 1.81253940e-01 -4.16060388e-01 -2.94071794e-01 -1.67019367e-01
-6.54852033e-01 -5.26646435e-01 5.99763274e-01 3.00441295e-01
-6.36007071e-01 8.42240036e-01 1.60003260e-01 -4.52355683e-01
-2.96486467e-01 -1.04333830e+00 -9.75556254e-01 -7.80561090e-01
-2.27107376e-01 4.96334165e-01 1.41613200e-01 -1.76162347e-01
5.97443320e-02 6.64052516e-02 -1.56987178e+00 -3.13236296e-01
1.02244115e+00 7.09437191e-01 2.45997533e-01 5.61111450e-01
1.55045569e-01 5.44825912e-01 -2.13019490e-01 -5.01400471e-01
2.92785794e-01 1.42363338e-02 -3.05773824e-01 -2.75531560e-01
5.09777248e-01 -3.50772530e-01 -2.00588685e-02 1.15674245e+00
4.47278678e-01 9.34755132e-02 1.04654872e+00 -1.54861248e+00
-4.39847648e-01 5.31925201e-01 -1.31867087e+00 4.46575060e-02
-2.62018830e-01 -5.38546108e-02 1.12287319e+00 -4.24633950e-01
1.25076741e-01 1.41731846e+00 8.62581551e-01 -4.37429994e-02
-1.67551374e+00 -6.88766062e-01 3.46202135e-01 1.22442663e-01
-1.37076986e+00 7.66446143e-02 5.56713760e-01 -8.74264836e-01
7.40033388e-01 3.31596404e-01 6.86495721e-01 1.01046622e+00
3.52099061e-01 6.20496750e-01 9.16764796e-01 6.03534319e-02
1.86833844e-01 -1.28069269e-02 -1.73705295e-02 3.27734053e-01
2.68587530e-01 3.41451496e-01 -2.59312302e-01 -2.48931482e-01
6.26086235e-01 2.69512445e-01 2.07864299e-01 -2.71629512e-01
-1.07049561e+00 1.04970610e+00 5.93846500e-01 4.76916134e-02
-4.48630989e-01 3.12226802e-01 2.75967896e-01 -1.70791402e-01
8.74169052e-01 3.47191258e-03 -5.49851894e-01 7.64512122e-02
-9.05786216e-01 6.11444831e-01 8.13521385e-01 5.31650245e-01
7.84826398e-01 -2.16248348e-01 -4.21392769e-02 1.08462930e+00
2.93404937e-01 8.67320597e-01 7.88076520e-02 -4.31025326e-01
3.14571857e-01 6.40855968e-01 3.17976236e-01 -1.28322411e+00
-5.16514361e-01 -7.31039941e-01 -1.39658260e+00 1.48742441e-02
6.22038245e-01 -3.04979682e-01 -4.84342754e-01 2.18932366e+00
5.21631956e-01 5.22043765e-01 -4.19455767e-01 8.11833858e-01
3.76949757e-01 9.80517328e-01 3.41692924e-01 -1.15655027e-01
1.22983575e+00 -9.07854080e-01 -5.15845358e-01 -4.21755552e-01
2.38809794e-01 -4.29145366e-01 9.56363678e-01 2.06456646e-01
-5.40689886e-01 -1.87605277e-01 -7.59505272e-01 -5.27386479e-02
-4.70312834e-01 1.68911457e-01 7.09480524e-01 2.71633804e-01
-9.11192536e-01 3.05291921e-01 -8.60573828e-01 -1.95230365e-01
3.78957301e-01 2.54655957e-01 -1.33402839e-01 2.45453984e-01
-1.01293015e+00 7.85515249e-01 -7.32364878e-02 1.62758633e-01
-8.56286228e-01 -1.02883816e+00 -6.61455631e-01 2.63763011e-01
3.90347749e-01 -6.72677219e-01 9.49448049e-01 -8.37718427e-01
-8.91368985e-01 8.86578202e-01 -5.03256097e-02 -3.40452135e-01
5.87215364e-01 -1.52056232e-01 -8.10397640e-02 -7.89882660e-01
5.01012385e-01 4.91951704e-01 5.88045061e-01 -9.58327889e-01
-9.93619621e-01 -6.20370030e-01 1.03462478e-02 8.64174068e-02
-4.88070756e-01 -5.80410622e-02 -2.42025509e-01 -8.20898652e-01
-2.92471081e-01 -8.95369172e-01 -6.96161866e-01 1.23101197e-01
-3.07907283e-01 -2.33557865e-01 1.41364813e-01 -5.90085387e-01
1.49784708e+00 -1.98091602e+00 4.48678076e-01 -1.22238532e-01
1.85359478e-01 -1.08558089e-01 -3.37970376e-01 3.87207150e-01
1.13452442e-01 8.43820050e-02 -6.11300409e-01 -3.09585929e-01
1.90263882e-01 1.74547091e-01 -3.45570296e-01 7.33687043e-01
1.23652354e-01 6.37341321e-01 -1.03048944e+00 -3.36752355e-01
5.39343804e-02 5.03779531e-01 -6.16344571e-01 2.73324192e-01
-1.70429677e-01 1.96889147e-01 -2.73686349e-01 5.10226429e-01
9.94978666e-01 -1.52245149e-01 1.94193423e-01 9.50717628e-02
-4.48798388e-01 -5.86939901e-02 -1.16761720e+00 1.37324548e+00
-4.57056016e-01 3.31972718e-01 2.53295787e-02 -1.02180612e+00
8.70334804e-01 -2.90553182e-01 5.32987893e-01 -4.87819821e-01
-1.67435274e-01 2.98762977e-01 -6.66044606e-03 -2.44911686e-01
3.91155511e-01 -4.94502872e-01 -4.83495802e-01 3.43873054e-01
-1.95249110e-01 -6.41188025e-02 1.25405133e-01 -3.04543138e-01
8.82897079e-01 1.35874063e-01 5.16347229e-01 -6.24426663e-01
2.47942001e-01 4.11154367e-02 7.76948571e-01 7.94507682e-01
-8.25168714e-02 1.45042181e-01 8.07951152e-01 -6.38893068e-01
-9.86716747e-01 -1.07817769e+00 -4.44947362e-01 1.37486172e+00
9.55187604e-02 -3.49471085e-02 -4.69696432e-01 -2.60954648e-01
3.62800121e-01 3.80340219e-01 -8.86981547e-01 -7.49731064e-03
-1.53267279e-01 -1.38787556e+00 4.47286218e-01 3.94607365e-01
1.28241152e-01 -6.02772772e-01 -5.34644425e-01 3.41166168e-01
-2.84381151e-01 -6.31705999e-01 -4.13074434e-01 3.53004247e-01
-5.88894486e-01 -9.45577860e-01 -7.24146247e-01 -6.26636326e-01
2.66655713e-01 2.58750647e-01 1.23305213e+00 -3.05601239e-01
-3.84965092e-01 -2.17209071e-01 3.14965993e-02 -4.03597474e-01
9.77712199e-02 3.16332817e-01 1.60793617e-01 1.67070851e-01
4.95205492e-01 -1.00621462e+00 -7.30415285e-01 3.06686282e-01
-9.96036768e-01 1.09461755e-01 6.30013108e-01 8.50863993e-01
7.80267537e-01 2.14521065e-02 8.02811444e-01 -6.27883494e-01
4.20513034e-01 -9.69328165e-01 -9.72342312e-01 2.84660310e-01
-3.92531380e-02 1.84160650e-01 7.03714609e-01 -7.63415337e-01
-6.60572052e-01 2.44154856e-01 -1.25668362e-01 -1.66398972e-01
1.99088901e-02 9.05261278e-01 -2.27098793e-01 2.31541619e-01
3.77839059e-01 1.92750264e-02 -1.67390049e-01 -5.42492628e-01
4.40038979e-01 7.37357736e-01 5.81846356e-01 -6.14113688e-01
7.48932481e-01 3.95032525e-01 3.66717041e-01 -8.73879552e-01
-9.00831163e-01 -4.94781375e-01 -7.73803353e-01 -1.55664250e-01
6.75708592e-01 -1.16796350e+00 -8.76961768e-01 8.13086092e-01
-1.05094802e+00 -6.27688706e-01 -1.16035879e-01 3.85646105e-01
-4.33991760e-01 2.25795180e-01 -6.23503774e-02 -1.03192043e+00
-2.63106197e-01 -9.73332405e-01 1.22340822e+00 1.01620667e-01
1.40683398e-01 -1.21514332e+00 6.80538058e-01 -2.36429963e-02
4.30595130e-01 6.53369308e-01 8.65223706e-01 -3.22122484e-01
-2.34188423e-01 -1.89753681e-01 -3.83873105e-01 6.24322426e-03
1.73929557e-01 -2.52729398e-03 -5.92955410e-01 -2.18087584e-01
-1.27742380e-01 -2.51361579e-01 1.10738266e+00 6.43808186e-01
1.24299812e+00 -4.79765773e-01 -3.45432848e-01 7.69431353e-01
1.54710245e+00 -3.86041999e-01 2.62972355e-01 2.70963043e-01
6.27414942e-01 9.46080685e-01 6.50600255e-01 8.18221748e-01
9.45597470e-01 8.03166270e-01 7.74071395e-01 -2.20071971e-01
4.23778236e-01 -4.28879648e-01 2.37461269e-01 3.24077129e-01
1.83749124e-01 -2.69762129e-01 -1.05106938e+00 8.66171658e-01
-2.20047569e+00 -9.94829059e-01 -6.57594860e-01 2.43709373e+00
7.81088710e-01 -3.62151027e-01 4.33982402e-01 -3.22654575e-01
7.23506212e-01 2.91307956e-01 -9.60531294e-01 -9.50920116e-03
-2.75969386e-01 -1.71615571e-01 7.54350007e-01 5.62394619e-01
-1.47676134e+00 8.01860750e-01 6.16435957e+00 1.06583560e+00
-1.13729548e+00 8.40769857e-02 7.78553665e-01 -2.97656447e-01
-2.79429793e-01 -5.75242713e-02 -7.92651653e-01 6.34591818e-01
7.62265563e-01 -3.31058949e-01 4.77001995e-01 7.20138133e-01
2.87185490e-01 -1.74737677e-01 -9.42753315e-01 8.59561682e-01
-2.09105864e-01 -6.21195436e-01 -2.00257882e-01 4.75914270e-01
6.84449434e-01 3.14343214e-01 3.30355346e-01 4.14972693e-01
6.80542707e-01 -1.03371787e+00 7.05561519e-01 5.04198968e-01
7.47210622e-01 -6.39264882e-01 4.13972527e-01 6.94927096e-01
-1.34368050e+00 -2.01400444e-01 -6.85306072e-01 -4.12729025e-01
9.43654403e-02 6.23303890e-01 -2.13194922e-01 2.96645433e-01
7.76243746e-01 9.57881927e-01 -5.44891596e-01 1.23517311e+00
-1.19241420e-02 6.07606173e-01 -8.65849018e-01 -8.84949863e-02
3.05476427e-01 -5.39717972e-01 3.59700829e-01 9.80550289e-01
5.66817105e-01 -1.58168390e-01 2.11906314e-01 1.06156433e+00
2.06211186e-03 2.37652913e-01 -5.23834288e-01 1.65058658e-01
2.11897522e-01 1.31933784e+00 -3.43406528e-01 1.41922712e-01
-5.23279011e-01 4.72875029e-01 5.81897557e-01 1.02506720e-01
-1.03577209e+00 5.24258986e-03 7.71898925e-01 2.79606551e-01
1.17798217e-01 -3.46110947e-02 -2.99602330e-01 -1.39614332e+00
-7.20000863e-02 -5.66630900e-01 4.68904167e-01 -2.66223580e-01
-1.84742486e+00 2.77815878e-01 -5.04832901e-02 -1.19207096e+00
-8.18115026e-02 -5.87624133e-01 -5.12440562e-01 9.67019498e-01
-1.88098025e+00 -1.54693568e+00 -1.58062607e-01 5.18298626e-01
2.47426853e-01 1.69987440e-01 6.40348732e-01 5.13490617e-01
-7.92795420e-01 3.84715080e-01 6.14181459e-01 -3.43134552e-01
6.07053876e-01 -1.38714957e+00 3.74386400e-01 8.28364372e-01
-2.39169925e-01 2.37753898e-01 6.66557610e-01 -6.86703980e-01
-1.33355987e+00 -1.35830975e+00 9.25652623e-01 -3.49441409e-01
1.13743448e+00 -5.50136387e-01 -8.13397229e-01 4.70765322e-01
-2.03129217e-01 6.18584678e-02 8.99891078e-01 3.81603569e-01
-3.04278195e-01 -3.20510566e-01 -8.54081988e-01 3.55521888e-01
7.68104374e-01 -8.05158466e-02 -1.04659274e-01 3.95789623e-01
4.58963662e-01 2.34686974e-02 -6.77402735e-01 3.07358116e-01
8.48255813e-01 -6.09400749e-01 9.59189713e-01 -7.67064512e-01
8.26121747e-01 -2.72404373e-01 -5.04156053e-01 -1.42811167e+00
-4.20235634e-01 -8.69588330e-02 7.62171224e-02 1.23549712e+00
4.02416468e-01 -4.84356791e-01 3.39735508e-01 4.38386291e-01
2.65293598e-01 -6.57699883e-01 -1.25285482e+00 -6.10516548e-01
5.91693461e-01 -2.39151493e-01 7.77208626e-01 7.77529895e-01
-2.67352909e-01 4.57725644e-01 -9.66698110e-01 3.93065333e-01
9.17405546e-01 3.72477651e-01 9.95481074e-01 -1.57138395e+00
-3.37392956e-01 -6.69773519e-01 -2.88999945e-01 -1.10196650e+00
2.93988138e-01 -6.27842188e-01 2.16244802e-01 -1.31407619e+00
6.91000581e-01 -4.57014650e-01 -7.29677975e-02 5.13848484e-01
-5.93675733e-01 1.26959860e-01 -1.68669522e-01 1.53202072e-01
-4.57299143e-01 9.15161192e-01 6.78699315e-01 -1.98905423e-01
-1.53167278e-01 2.72466332e-01 -8.73269916e-01 6.01830840e-01
4.74509627e-01 -7.06931949e-01 1.84930097e-02 -5.67477524e-01
5.28004467e-01 9.04521272e-02 5.44846356e-01 -6.82291448e-01
2.49321356e-01 -7.37472355e-01 -3.49113494e-02 -7.19105899e-01
1.90637827e-01 -8.84991050e-01 2.32228473e-01 4.17085886e-01
-1.05750576e-01 -1.23709150e-01 2.24830404e-01 7.34300017e-01
-4.42509614e-02 1.40277013e-01 8.59080851e-01 1.90591425e-01
-2.40832195e-01 5.96631885e-01 -3.77301633e-01 -1.09459907e-01
9.73556459e-01 4.57006752e-01 -3.37947756e-01 -1.51465997e-01
-3.56005460e-01 7.35547602e-01 3.40457290e-01 5.09051681e-01
1.28713906e-01 -1.23467886e+00 -1.12609446e+00 -2.69007273e-02
6.99890256e-02 6.64391592e-02 5.53791106e-01 8.35956335e-01
-3.24850589e-01 1.08009219e-01 -1.85889125e-01 -7.76720524e-01
-1.03171301e+00 6.26346350e-01 5.67763031e-01 -7.10719824e-01
7.53328297e-03 7.08720028e-01 6.42625868e-01 -9.40137506e-01
1.31810918e-01 -1.37280196e-01 -3.13588977e-01 3.89659464e-01
4.80039060e-01 3.14984769e-01 -4.22746360e-01 -8.68879557e-01
-3.09651047e-01 6.11445665e-01 1.48952261e-01 6.05456941e-02
1.98221445e+00 -1.42025217e-01 -4.87068087e-01 5.74454904e-01
1.05358696e+00 -3.62715989e-01 -1.38959384e+00 -2.58445412e-01
-1.54332891e-01 -5.90898871e-01 1.65361792e-01 -8.39018822e-01
-9.64325607e-01 1.10068631e+00 5.18302321e-01 1.71492949e-01
1.13848269e+00 -1.10447094e-01 4.43572640e-01 1.30494207e-01
2.21000358e-01 -6.68886244e-01 -2.64572799e-01 3.24134648e-01
9.64671791e-01 -1.38566422e+00 -8.90862048e-02 -7.06068575e-02
-3.55072737e-01 7.66233861e-01 6.07529581e-01 -1.99011579e-01
9.33755755e-01 3.75615239e-01 -3.92394453e-01 1.18908569e-01
-6.81766748e-01 -4.23920244e-01 2.59078890e-01 4.48768616e-01
2.24523574e-01 5.03231227e-01 -4.04961348e-01 1.01684749e+00
-3.39554518e-01 9.09981038e-03 3.96087229e-01 2.93756068e-01
-3.20421070e-01 -1.04617345e+00 -3.62045974e-01 5.89026630e-01
-4.88327026e-01 -8.20568800e-02 -2.70585537e-01 3.99109125e-01
1.17708437e-01 9.23217058e-01 1.97087884e-01 -2.14442238e-01
3.50426346e-01 -5.27593553e-01 3.50814909e-02 -3.69991452e-01
-5.01575768e-01 3.24571460e-01 -3.95580262e-01 -1.28004000e-01
-3.39560330e-01 -8.61846864e-01 -3.67929518e-01 -6.45213425e-01
-2.22015977e-01 -1.24764480e-01 7.17101872e-01 6.65365338e-01
1.47959918e-01 3.51340830e-01 6.82460606e-01 -9.40358758e-01
-7.64555156e-01 -1.13364697e+00 -8.09784770e-01 1.87271193e-01
4.54198360e-01 -7.12219298e-01 -3.67624968e-01 -2.45093435e-01] | [8.013596534729004, 4.043440341949463] |
52e58230-2b24-4eac-a476-aaedf669e503 | unsupervised-superpixel-generation-using-edge | 2211.15474 | null | https://arxiv.org/abs/2211.15474v2 | https://arxiv.org/pdf/2211.15474v2.pdf | Unsupervised Superpixel Generation using Edge-Sparse Embedding | Partitioning an image into superpixels based on the similarity of pixels with respect to features such as colour or spatial location can significantly reduce data complexity and improve subsequent image processing tasks. Initial algorithms for unsupervised superpixel generation solely relied on local cues without prioritizing significant edges over arbitrary ones. On the other hand, more recent methods based on unsupervised deep learning either fail to properly address the trade-off between superpixel edge adherence and compactness or lack control over the generated number of superpixels. By using random images with strong spatial correlation as input, \ie, blurred noise images, in a non-convolutional image decoder we can reduce the expected number of contrasts and enforce smooth, connected edges in the reconstructed image. We generate edge-sparse pixel embeddings by encoding additional spatial information into the piece-wise smooth activation maps from the decoder's last hidden layer and use a standard clustering algorithm to extract high quality superpixels. Our proposed method reaches state-of-the-art performance on the BSDS500, PASCAL-Context and a microscopy dataset. | ['Ender Konukoglu', 'Tianfei Zhou', 'Gustav Bredell', 'Jakob Geusen'] | 2022-11-28 | null | null | null | null | ['superpixels'] | ['computer-vision'] | [ 7.39631772e-01 2.46093497e-01 1.79922834e-01 -4.96374220e-01
-5.75250626e-01 -3.97988915e-01 3.95702392e-01 2.26096317e-01
-7.56432176e-01 6.39959693e-01 -4.04544584e-02 4.11445089e-02
-1.50606595e-02 -7.72162437e-01 -8.84045124e-01 -1.10304594e+00
4.58462872e-02 3.99782002e-01 4.97239530e-01 3.29334676e-01
4.58468437e-01 2.91796714e-01 -1.52589381e+00 4.10369903e-01
9.98191297e-01 9.07435477e-01 6.18460059e-01 8.04741383e-01
-1.22033991e-01 6.25169933e-01 -2.33606756e-01 -1.04387430e-02
3.34445357e-01 -4.94686723e-01 -7.75225222e-01 3.58965307e-01
6.82754397e-01 -2.11888850e-01 -5.45608215e-02 1.27173674e+00
4.92344230e-01 -1.85055912e-01 7.20345378e-01 -7.40773082e-01
-8.32650423e-01 5.97224712e-01 -6.84403121e-01 3.37395579e-01
-2.50827998e-01 2.51058429e-01 1.02131712e+00 -6.41003191e-01
1.03882182e+00 6.85452163e-01 5.98396957e-01 3.90457481e-01
-1.90222192e+00 -2.72106558e-01 -1.22408681e-01 8.96705836e-02
-1.24007738e+00 -3.49615097e-01 7.46786773e-01 -5.25098264e-01
8.73028815e-01 1.45526201e-01 5.41454136e-01 5.73897541e-01
-3.54768918e-03 6.01627827e-01 1.29412580e+00 -4.64871109e-01
3.95404875e-01 1.10014053e-02 -1.75597504e-01 6.40608251e-01
2.29567915e-01 1.67063028e-02 -4.45162088e-01 2.60689944e-01
9.62776959e-01 -9.55107659e-02 -4.65238810e-01 -6.04590595e-01
-1.40430915e+00 5.98809123e-01 7.50474334e-01 5.58120072e-01
-3.56090426e-01 1.66193500e-01 -4.31142561e-03 -8.97095874e-02
3.83322269e-01 6.78669274e-01 -3.89065236e-01 1.52245611e-01
-1.42602694e+00 -1.80155531e-01 2.49363288e-01 6.99413776e-01
1.10819101e+00 -1.98195934e-01 -1.78283811e-01 5.81232369e-01
-6.32342026e-02 -9.63617340e-02 3.63826841e-01 -1.37437761e+00
7.98196569e-02 7.89086282e-01 1.24404341e-01 -8.73421848e-01
-4.34019864e-01 -4.06385839e-01 -9.99019265e-01 4.54811275e-01
6.43103421e-01 2.03691293e-02 -1.29365516e+00 1.46740377e+00
2.64093041e-01 6.57556579e-02 -2.36359760e-01 1.11456037e+00
4.59241927e-01 4.65756565e-01 -1.41106710e-01 -2.72750437e-01
1.01279342e+00 -7.95941949e-01 -4.26613092e-01 -2.49343485e-01
5.27056813e-01 -7.49330759e-01 8.68618369e-01 4.16800857e-01
-1.17996526e+00 -6.69007182e-01 -1.09040439e+00 -3.58232915e-01
-5.17335057e-01 3.63059759e-01 5.98887503e-01 3.04088801e-01
-1.35832095e+00 8.74383509e-01 -8.61111522e-01 -1.56985357e-01
7.19567180e-01 6.50296628e-01 -4.14218307e-01 1.17896050e-01
-5.91258287e-01 5.28806090e-01 6.85718358e-01 -5.34214601e-02
-5.59280634e-01 -7.47096598e-01 -6.35504901e-01 2.57622674e-02
-2.99559627e-02 -6.37219131e-01 5.07458806e-01 -1.17550075e+00
-1.27166402e+00 1.14941931e+00 -7.75375515e-02 -5.56841791e-01
4.73063290e-01 2.14777052e-01 2.55489320e-01 5.08647382e-01
7.32655898e-02 1.44498158e+00 8.57852459e-01 -1.39252567e+00
-8.43078554e-01 -3.34932059e-01 -2.98540622e-01 7.26446658e-02
-2.58449852e-01 -2.41563380e-01 -6.03177309e-01 -5.68470478e-01
2.19383314e-01 -8.04682672e-01 -4.34925765e-01 2.79724181e-01
-5.07000387e-01 1.83229387e-01 6.83954179e-01 -4.84876394e-01
9.45362568e-01 -2.19100785e+00 5.81085145e-01 4.61944751e-02
3.63414198e-01 1.33675396e-01 -6.84818774e-02 -2.71780491e-01
-1.40898734e-01 1.63856000e-01 -6.26084507e-01 -4.09779429e-01
-2.50839978e-01 9.45662707e-02 3.34968686e-01 5.39081335e-01
3.78342181e-01 7.56534040e-01 -1.02003539e+00 -7.18246698e-01
5.69679797e-01 5.35587192e-01 -7.31107473e-01 1.37078568e-01
-2.03711390e-01 5.32750785e-01 -1.54511873e-02 1.72833264e-01
7.63982117e-01 -4.76608068e-01 1.38535634e-01 -4.90767360e-01
-2.79858679e-01 1.26275504e-02 -1.04794145e+00 1.86221671e+00
-2.11356148e-01 9.65726137e-01 2.80826092e-01 -1.04275513e+00
5.37966669e-01 2.46458501e-03 7.16754377e-01 -4.80806530e-01
-3.24263377e-03 1.77355096e-01 1.43356889e-01 -2.83278495e-01
3.75250041e-01 6.25124350e-02 3.45401257e-01 2.94165462e-01
2.66212851e-01 -1.41294897e-01 3.84685904e-01 1.37076378e-01
9.71430540e-01 2.48332232e-01 -5.85224926e-02 -6.08832836e-01
2.10197031e-01 3.30811031e-02 4.41489488e-01 4.96222436e-01
-7.79086128e-02 1.27895331e+00 5.70117950e-01 -3.79645586e-01
-1.51905286e+00 -1.04046404e+00 -3.28376442e-01 7.91832030e-01
1.57288849e-01 -1.20291151e-01 -1.03823066e+00 -4.24747616e-01
-2.51318455e-01 5.10533214e-01 -8.67974520e-01 2.84542665e-02
-4.59053636e-01 -1.16400564e+00 2.78556496e-01 4.10183787e-01
4.60725099e-01 -9.43935037e-01 -8.99594009e-01 1.98686019e-01
-2.10114211e-01 -1.20439088e+00 -5.86322665e-01 7.95176744e-01
-8.13017666e-01 -9.30979967e-01 -8.39357913e-01 -1.13363457e+00
1.42916679e+00 2.58810222e-01 1.00861740e+00 -8.64563808e-02
-7.86125243e-01 -1.99885070e-01 -2.55385876e-01 1.89251021e-01
-1.12579405e-01 -5.08810692e-02 -3.65058780e-01 7.44251087e-02
1.86178014e-01 -5.21768928e-01 -1.06237233e+00 1.10438652e-01
-1.13013661e+00 4.96618897e-01 7.79361784e-01 1.12840271e+00
9.82001364e-01 3.07630986e-01 -5.31954058e-02 -1.00506330e+00
1.73095495e-01 3.71268648e-03 -5.92766881e-01 1.26492590e-01
-5.96773446e-01 5.62843859e-01 7.05197453e-01 -3.12609822e-01
-9.44638491e-01 5.05666614e-01 1.00634448e-01 -2.55308360e-01
-1.74902901e-01 3.65988910e-02 -3.75796556e-02 -1.93005845e-01
6.25311196e-01 6.38070405e-01 -1.04865901e-01 -1.49720237e-01
5.48065007e-01 5.03898680e-01 6.76958680e-01 -1.83726132e-01
4.02932674e-01 8.47498953e-01 -1.13380000e-01 -9.38059151e-01
-4.34181750e-01 -6.49961650e-01 -1.04002869e+00 -4.94978763e-02
1.33542025e+00 -5.60732722e-01 -4.09450471e-01 4.74742085e-01
-1.02454579e+00 -6.31603837e-01 -3.42483014e-01 2.85436124e-01
-6.98784292e-01 5.04360795e-01 -6.89031839e-01 -4.15343225e-01
-2.05812231e-01 -1.25186086e+00 1.26044858e+00 3.14503878e-01
-1.15260251e-01 -9.18732107e-01 -3.08516592e-01 3.01114231e-01
2.68592358e-01 3.20880562e-01 8.73922169e-01 6.17906824e-02
-8.60432088e-01 1.12946101e-01 -6.81109905e-01 5.13245821e-01
2.13888824e-01 1.54159993e-01 -8.67964923e-01 -9.53724086e-02
-2.35317647e-01 -1.86820090e-01 1.25623274e+00 7.83346236e-01
1.21603751e+00 -3.41784835e-01 -2.24320322e-01 1.07822859e+00
1.76450408e+00 -2.03280151e-01 8.38873684e-01 2.85305619e-01
7.13260114e-01 6.24267280e-01 1.31116495e-01 1.77395388e-01
4.16783355e-02 5.70406139e-01 3.43230635e-01 -4.55993503e-01
-4.53961223e-01 9.74782836e-03 1.92949712e-01 4.25098002e-01
-1.57391667e-01 -1.90835092e-02 -6.85552478e-01 8.69847536e-01
-1.55553091e+00 -9.14233983e-01 -3.14352661e-01 2.21050763e+00
1.24027860e+00 3.25725317e-01 -9.59657505e-02 6.81476742e-02
7.69606113e-01 -4.57739085e-02 -4.84748721e-01 -2.76130494e-02
-3.83539826e-01 1.54671326e-01 1.12967312e+00 7.17791080e-01
-1.14696860e+00 1.19225454e+00 6.02590466e+00 7.85899341e-01
-1.21436262e+00 -1.62182629e-01 1.22090912e+00 -1.83626220e-01
-3.39492768e-01 -6.05260916e-02 -6.64959252e-01 7.06827104e-01
4.97626662e-01 4.02119309e-01 5.08388221e-01 4.90340322e-01
2.53385901e-01 -4.55342203e-01 -1.11717153e+00 1.03364527e+00
-4.12002429e-02 -1.58111668e+00 9.85267479e-03 1.97676748e-01
1.08769333e+00 2.01241449e-01 1.41877398e-01 -5.62210321e-01
4.14287865e-01 -1.23008418e+00 6.30417824e-01 5.25404692e-01
7.57072866e-01 -4.05806035e-01 6.78431273e-01 1.70518324e-01
-8.62549424e-01 1.66046470e-01 -5.40507793e-01 2.89670497e-01
-3.43194697e-03 7.29700089e-01 -6.96158051e-01 1.70126632e-01
8.36324990e-01 5.15439987e-01 -6.84102654e-01 1.00917697e+00
-6.36598170e-02 2.90338516e-01 -5.07820964e-01 1.80719540e-01
4.02253330e-01 -4.38172996e-01 1.85893461e-01 1.42787480e+00
8.16888213e-02 -4.91273627e-02 -2.33924270e-01 1.10315812e+00
2.61506718e-02 -1.73807979e-01 -2.76196152e-01 4.98249456e-02
1.59926191e-01 1.37421322e+00 -1.35653245e+00 -3.76165181e-01
-2.30273068e-01 1.34427929e+00 4.54320550e-01 4.12120044e-01
-6.48032904e-01 -2.18306497e-01 4.58582491e-01 3.56356382e-01
6.37092710e-01 -3.13167870e-01 -7.00529695e-01 -9.07860100e-01
-8.60443688e-04 -5.32633305e-01 8.48216563e-02 -8.37399542e-01
-8.43690515e-01 5.77150226e-01 -4.51128155e-01 -1.03936613e+00
1.31209821e-01 -6.71586633e-01 -4.67865705e-01 5.61454475e-01
-1.50832069e+00 -1.01733351e+00 -1.86372787e-01 1.94402501e-01
4.90481883e-01 3.73325646e-01 6.06557667e-01 2.19128817e-01
-3.40415031e-01 2.05663294e-01 3.45660567e-01 1.54954493e-01
6.35559201e-01 -1.67894948e+00 1.01961076e-01 9.41201568e-01
2.45101005e-01 4.67141598e-01 7.55019486e-01 -2.95989960e-01
-8.86724472e-01 -1.16165578e+00 8.44001830e-01 -3.36096585e-01
4.44484651e-01 -4.16464269e-01 -7.12837100e-01 2.19906300e-01
3.60442728e-01 2.02411219e-01 4.74813521e-01 -4.75247175e-01
-5.43607920e-02 -5.47100119e-02 -1.06380510e+00 5.80830097e-01
8.88085544e-01 -3.37745339e-01 -2.28663579e-01 2.19162673e-01
4.78122562e-01 -5.21572195e-02 -8.16920340e-01 1.49818674e-01
2.62194484e-01 -1.31633747e+00 1.08922541e+00 -3.19738120e-01
7.64080763e-01 -6.12678230e-01 1.39586180e-01 -1.14167547e+00
-6.16342604e-01 -4.47068512e-01 3.58689249e-01 9.60015237e-01
6.30259454e-01 6.95097609e-04 1.09084249e+00 4.92634386e-01
-1.73580050e-01 -7.05866516e-01 -8.59092534e-01 -3.68819475e-01
-8.53939131e-02 -4.55355756e-02 1.05394967e-01 7.14665174e-01
-1.77182123e-01 1.78057358e-01 -1.34585738e-01 3.21253389e-02
9.53395963e-01 2.27724537e-01 4.62926716e-01 -8.73306572e-01
-2.88191766e-01 -8.05155277e-01 -6.03839576e-01 -1.16456366e+00
-3.63559991e-01 -8.08094740e-01 4.50431287e-01 -1.69129765e+00
3.34024251e-01 -3.94157529e-01 -2.64865547e-01 2.93646514e-01
-3.36331725e-01 7.60329127e-01 -1.50443435e-01 1.21720292e-01
-8.67591798e-01 2.46521145e-01 1.39071381e+00 -2.99383670e-01
-2.04635337e-01 -5.63081205e-01 -5.57815552e-01 6.73639953e-01
6.65932238e-01 -2.66537905e-01 -2.72113115e-01 -7.28933990e-01
1.55551881e-01 -3.35584074e-01 3.35856736e-01 -1.18169832e+00
4.60939169e-01 -3.14143002e-02 6.61340833e-01 -2.15793416e-01
1.92019701e-01 -8.17539573e-01 -4.69434038e-02 2.50728160e-01
-5.44961214e-01 -4.93382990e-01 -1.23601288e-01 5.98339915e-01
-1.36808142e-01 -1.27786130e-01 1.17346883e+00 -2.97656864e-01
-6.63497984e-01 1.45441130e-01 -3.17831933e-01 -5.85970096e-02
1.13142192e+00 -7.12248087e-01 -1.57421991e-01 5.79185486e-02
-7.38989651e-01 4.24113572e-02 9.78292406e-01 5.56013696e-02
6.72324836e-01 -9.18416500e-01 -6.44921184e-01 4.07803059e-01
-1.16220135e-02 3.56395394e-01 2.64893949e-01 9.46864665e-01
-1.06670141e+00 3.37123007e-01 -5.04308701e-01 -9.00782347e-01
-1.13787115e+00 5.35656929e-01 3.55708420e-01 -3.67391482e-02
-5.58573306e-01 1.26618922e+00 4.13275421e-01 -4.37732833e-03
1.10717006e-01 -5.73489785e-01 9.23262816e-03 -1.00292042e-01
2.50370741e-01 1.52159585e-02 -1.09471299e-01 -6.80435479e-01
-1.74846560e-01 7.50823677e-01 -6.28859997e-02 5.65813631e-02
1.52224541e+00 -2.25088239e-01 -2.22335264e-01 2.05602035e-01
1.38170838e+00 -2.88974196e-01 -1.92137563e+00 5.93789592e-02
6.16030134e-02 -5.04345417e-01 5.16700506e-01 -4.16825056e-01
-1.30200565e+00 8.75691175e-01 7.96364307e-01 6.44015968e-02
1.18518198e+00 9.86598432e-02 6.77446723e-01 -1.24098726e-01
1.59679323e-01 -1.52350593e+00 1.68119907e-01 -6.24978505e-02
2.89516330e-01 -1.46495748e+00 1.18488483e-01 -4.86670017e-01
-5.07738233e-01 1.07061279e+00 4.67178643e-01 -3.18692386e-01
5.13363242e-01 4.43756074e-01 -7.30198771e-02 -1.22554213e-01
-6.02953434e-01 -3.59038591e-01 1.25792384e-01 6.89932764e-01
5.15612483e-01 3.24163474e-02 -2.57245600e-01 4.44319360e-02
-3.99198290e-03 -5.21381460e-02 5.36387444e-01 4.96210724e-01
-6.06666028e-01 -8.79459739e-01 -8.49249661e-02 5.91443956e-01
-4.31258857e-01 -2.47128665e-01 -3.45332861e-01 3.33847642e-01
6.01901650e-01 5.61817169e-01 5.37390590e-01 -2.05517232e-01
-3.01015824e-01 -1.25548661e-01 6.49106383e-01 -5.73279798e-01
-2.41908610e-01 2.69887775e-01 -2.87649810e-01 -3.50020230e-01
-5.88198185e-01 -6.11015439e-01 -1.29527628e+00 -3.23251747e-02
-2.96311915e-01 -5.53099588e-02 4.36991662e-01 7.71316588e-01
4.62518513e-01 5.20202875e-01 2.45306775e-01 -9.95272517e-01
-1.01058939e-02 -7.98990726e-01 -5.58323503e-01 6.79264426e-01
1.89267546e-01 -1.80020243e-01 -3.26577783e-01 8.05614412e-01] | [9.601276397705078, 0.4427917003631592] |
de4c9394-2119-46e0-9b89-d88c67fc5ea7 | avatargen-a-3d-generative-model-for | 2208.00561 | null | https://arxiv.org/abs/2208.00561v1 | https://arxiv.org/pdf/2208.00561v1.pdf | AvatarGen: a 3D Generative Model for Animatable Human Avatars | Unsupervised generation of clothed virtual humans with various appearance and animatable poses is important for creating 3D human avatars and other AR/VR applications. Existing methods are either limited to rigid object modeling, or not generative and thus unable to synthesize high-quality virtual humans and animate them. In this work, we propose AvatarGen, the first method that enables not only non-rigid human generation with diverse appearance but also full control over poses and viewpoints, while only requiring 2D images for training. Specifically, it extends the recent 3D GANs to clothed human generation by utilizing a coarse human body model as a proxy to warp the observation space into a standard avatar under a canonical space. To model non-rigid dynamics, it introduces a deformation network to learn pose-dependent deformations in the canonical space. To improve geometry quality of the generated human avatars, it leverages signed distance field as geometric representation, which allows more direct regularization from the body model on the geometry learning. Benefiting from these designs, our method can generate animatable human avatars with high-quality appearance and geometry modeling, significantly outperforming previous 3D GANs. Furthermore, it is competent for many applications, e.g., single-view reconstruction, reanimation, and text-guided synthesis. Code and pre-trained model will be available. | ['Jiashi Feng', 'Xinchao Wang', 'Zhongcong Xu', 'Guoxian Song', 'Yichun Shi', 'Hongyi Xu', 'Dingdong Yang', 'Zihang Jiang', 'Jianfeng Zhang'] | 2022-08-01 | null | null | null | null | ['3d-human-reconstruction'] | ['computer-vision'] | [ 1.84109032e-01 4.39708501e-01 2.11750448e-01 -7.63136074e-02
-4.27492380e-01 -7.19931960e-01 5.61193764e-01 -7.44186878e-01
3.83096278e-01 5.70504487e-01 2.02912807e-01 4.63546030e-02
5.54792345e-01 -9.42668438e-01 -8.30532789e-01 -6.58454657e-01
4.36213344e-01 7.03144014e-01 -1.25790656e-01 -5.16874313e-01
-5.07683098e-01 5.55286050e-01 -1.16842067e+00 -2.68933564e-01
7.98313737e-01 8.76942277e-01 -7.85924643e-02 6.00657821e-01
4.63255614e-01 3.72041553e-01 -5.57337523e-01 -7.03208566e-01
6.35342360e-01 -7.74755776e-01 -2.69606292e-01 3.94393057e-01
6.83762074e-01 -6.13337219e-01 -5.65206110e-01 5.33007085e-01
6.16008520e-01 2.50277191e-01 6.83740437e-01 -1.11162722e+00
-9.75432932e-01 1.80140287e-01 -6.44522011e-01 -8.14950764e-01
6.45814300e-01 4.91111934e-01 8.60033453e-01 -7.50402570e-01
9.47754443e-01 1.42378354e+00 5.55612147e-01 1.18144131e+00
-1.47636342e+00 -6.09495103e-01 -1.95449069e-02 -4.97373074e-01
-1.26648700e+00 -3.05626631e-01 1.13098419e+00 -4.76941109e-01
2.73601681e-01 4.09489244e-01 1.35518837e+00 1.64450383e+00
2.85500046e-02 7.18202233e-01 8.92525733e-01 -2.10888788e-01
4.10285816e-02 -3.25833410e-01 -8.37016404e-01 9.44922388e-01
-9.15735960e-03 1.97556973e-01 -3.57158154e-01 -1.25601679e-01
1.69796777e+00 1.11823484e-01 -2.70175159e-01 -9.33142126e-01
-1.46390748e+00 8.48864675e-01 4.41221446e-01 -3.06293309e-01
-5.49240649e-01 4.32781249e-01 2.09719539e-02 -8.71013254e-02
3.82898629e-01 5.90881348e-01 -1.30384803e-01 -2.32850816e-02
-6.42942488e-01 7.82747090e-01 5.39281011e-01 1.20139170e+00
4.63132173e-01 6.90997243e-01 -2.25671515e-01 6.21097684e-01
4.41802114e-01 1.03765893e+00 1.09559320e-01 -1.30207503e+00
2.89559811e-01 4.98700708e-01 -5.93743753e-04 -9.83310938e-01
-8.50918740e-02 -2.02183664e-01 -1.07439566e+00 5.24073184e-01
1.48289248e-01 -1.95402682e-01 -1.28236687e+00 1.98444152e+00
7.70827889e-01 2.65526492e-02 -8.23469535e-02 1.19253910e+00
9.71359253e-01 5.20245314e-01 -2.60010362e-01 -6.95083151e-03
1.26678860e+00 -1.02253807e+00 -7.06466317e-01 -4.09980007e-02
1.13802468e-02 -7.23538518e-01 1.17854357e+00 2.77455896e-01
-1.44083655e+00 -5.28019249e-01 -9.28958833e-01 -1.47889033e-01
3.67714524e-01 -1.98165953e-01 6.36721909e-01 5.50608158e-01
-9.24116373e-01 4.81008619e-01 -1.05642188e+00 -1.77563816e-01
2.26731479e-01 1.64046779e-01 -3.78829598e-01 1.02149829e-01
-1.01302564e+00 6.17753327e-01 -2.34467760e-01 -2.81712995e-03
-1.09331238e+00 -6.96360946e-01 -1.22577012e+00 -4.48526233e-01
1.78557962e-01 -1.40339255e+00 1.03187239e+00 -9.20739949e-01
-2.16622901e+00 9.40203965e-01 2.15743750e-01 -1.02364108e-01
9.74712133e-01 -6.15788884e-02 -1.74171224e-01 -3.67908441e-02
-1.50016516e-01 7.39908099e-01 1.22546184e+00 -1.63702297e+00
2.07688481e-01 -3.33978385e-01 -1.01889260e-02 4.10217673e-01
5.37068434e-02 -4.73844558e-01 -5.94098866e-01 -1.15891886e+00
2.17004523e-01 -1.33143103e+00 -3.38650048e-01 2.14150742e-01
-5.07092476e-01 3.79077613e-01 1.04069316e+00 -8.43737602e-01
6.03778422e-01 -1.79504097e+00 7.72065639e-01 3.31441581e-01
5.31058371e-01 6.60603866e-02 -1.76869944e-01 1.96160540e-01
9.96082500e-02 -2.37426143e-02 -2.00550690e-01 -5.06911874e-01
1.17912732e-01 2.30472863e-01 -8.80364105e-02 3.97997618e-01
2.02970536e-04 1.36121094e+00 -7.64326692e-01 -4.80521560e-01
3.34556907e-01 9.57966208e-01 -7.25092351e-01 6.11151278e-01
-2.17275217e-01 1.32700145e+00 -5.60634673e-01 6.79004788e-01
5.37011445e-01 -8.03485513e-03 1.11029841e-01 -3.76158446e-01
3.00293416e-01 -1.51176259e-01 -9.40559566e-01 2.00830173e+00
-5.55638075e-01 1.73313826e-01 3.68354648e-01 -4.76203918e-01
1.13479483e+00 3.86753201e-01 7.26475060e-01 -5.39899826e-01
3.10478777e-01 4.83944155e-02 -2.33295113e-01 -2.12947324e-01
3.98224086e-01 -3.33619535e-01 -1.66164055e-01 2.58982152e-01
-4.28411104e-02 -7.99583495e-01 -3.36253732e-01 5.53681366e-02
8.00797105e-01 7.19385624e-01 -1.79485753e-01 1.81424037e-01
1.18737526e-01 -3.34938228e-01 6.56166553e-01 7.09743649e-02
3.30876797e-01 1.27013814e+00 4.05399352e-02 -4.83953685e-01
-1.64653969e+00 -1.48196852e+00 1.46011263e-01 4.57970768e-01
3.17758292e-01 -2.42891222e-01 -8.47561538e-01 -4.81775135e-01
-1.12477019e-02 5.31861067e-01 -6.84086204e-01 -3.20661634e-01
-9.26542759e-01 -2.09668547e-01 4.38380629e-01 4.99044627e-01
3.18794489e-01 -1.01395118e+00 -5.02572715e-01 7.30291978e-02
-2.72575885e-01 -1.03821898e+00 -8.41346920e-01 -6.02774084e-01
-8.78267229e-01 -7.62927294e-01 -1.25559282e+00 -6.36656642e-01
8.69232059e-01 -9.65847969e-02 1.15831566e+00 -8.65269229e-02
-2.37713322e-01 5.89322329e-01 -3.22519660e-01 -1.81816667e-01
-7.56470084e-01 -1.67752907e-01 2.53758997e-01 7.92026445e-02
-6.55556023e-01 -9.69781220e-01 -9.27349269e-01 4.51092571e-01
-7.22481072e-01 6.84118271e-01 3.47276539e-01 8.73880148e-01
8.00100207e-01 -5.63217223e-01 2.64027566e-01 -6.96847498e-01
3.37310433e-01 -5.05827777e-02 -4.25097555e-01 -1.47365257e-02
-3.87534648e-01 -2.02063292e-01 6.88966930e-01 -7.36042500e-01
-1.04502618e+00 1.74679488e-01 -3.94692600e-01 -9.96220887e-01
7.66882971e-02 -1.49007082e-01 -5.03231764e-01 -1.16134383e-01
6.69319928e-01 1.17671393e-01 2.73464918e-01 -4.51277465e-01
6.92056000e-01 4.22755629e-02 6.25429988e-01 -7.79991984e-01
1.38308871e+00 4.25697118e-01 1.72834352e-01 -6.65189624e-01
-2.68995881e-01 3.11679929e-01 -5.86233377e-01 -3.00533473e-01
1.07252800e+00 -1.03428411e+00 -7.80298054e-01 5.81153810e-01
-1.04208839e+00 -7.54898548e-01 -6.06089592e-01 3.85414124e-01
-9.45878267e-01 3.34557295e-01 -6.80239797e-01 -5.67026019e-01
-6.02587342e-01 -1.10189641e+00 1.49673533e+00 8.02125707e-02
-3.93087417e-01 -8.75346363e-01 1.17493749e-01 6.58566654e-01
2.70673931e-01 1.18426692e+00 6.31935716e-01 2.74942786e-01
-5.13824284e-01 -1.84505329e-01 3.85104388e-01 2.71430761e-01
2.52189636e-01 1.56446155e-02 -6.14855111e-01 -5.03237247e-01
-3.02013606e-01 -4.44764346e-01 2.61885017e-01 3.38897556e-01
8.07786345e-01 -4.72666830e-01 -1.14615515e-01 1.11964273e+00
9.83191431e-01 -1.83349894e-03 6.06879652e-01 -2.27882102e-01
1.48891616e+00 4.91553754e-01 3.90982091e-01 5.01364708e-01
5.56533456e-01 1.07602000e+00 6.19256079e-01 -5.42229235e-01
-4.62858081e-01 -8.37132215e-01 3.44189256e-01 9.94630456e-01
-6.63970590e-01 -1.91477060e-01 -5.46046078e-01 2.06334978e-01
-1.63294911e+00 -7.19328821e-01 3.08740977e-02 2.21882343e+00
7.71901786e-01 -3.23933065e-01 3.82402122e-01 -2.85706967e-01
5.21042526e-01 7.93338194e-02 -8.79610658e-01 -1.52923331e-01
-9.05031264e-02 3.75105917e-01 1.78640276e-01 3.24853212e-01
-6.78010702e-01 1.04298127e+00 5.63416386e+00 3.61887276e-01
-1.16898215e+00 9.42535028e-02 3.87177944e-01 -1.89815059e-01
-7.79557347e-01 -1.20288327e-01 -2.74135709e-01 1.78773224e-01
2.20237046e-01 8.62165838e-02 5.29013336e-01 8.41796756e-01
2.60201573e-01 5.37354529e-01 -9.00775731e-01 1.16081846e+00
1.00062348e-01 -1.25428009e+00 4.21873957e-01 3.29706669e-01
1.11815810e+00 -5.55705547e-01 2.49627918e-01 -2.07314063e-02
4.85152543e-01 -1.13045669e+00 1.18240225e+00 5.29145062e-01
1.23759115e+00 -6.88827395e-01 2.26499602e-01 1.54328942e-01
-1.20618856e+00 4.95410293e-01 -4.57528941e-02 2.43027642e-01
6.17355645e-01 1.02360770e-01 -4.17645752e-01 6.56241655e-01
3.82754803e-01 5.44064522e-01 -1.89249799e-01 4.00526077e-01
-3.14217865e-01 3.74268740e-01 -2.17025489e-01 4.04379815e-01
-2.05946669e-01 -6.01593375e-01 7.93178976e-01 5.26309729e-01
5.70037901e-01 2.11825997e-01 3.92382473e-01 1.17126966e+00
-1.15638539e-01 8.31539109e-02 -7.56705821e-01 7.31940717e-02
2.27916688e-01 1.22460830e+00 -3.94493371e-01 -1.72601819e-01
-6.12026341e-02 1.39570582e+00 4.36467789e-02 4.16340232e-01
-1.14586532e+00 -1.69025455e-02 8.01044405e-01 5.43005764e-01
1.43883154e-01 -6.10296547e-01 -2.10019588e-01 -1.47772741e+00
6.35485072e-03 -1.14446318e+00 -2.22402260e-01 -8.52391303e-01
-1.09351647e+00 7.68401146e-01 -2.12352201e-01 -1.36018598e+00
-4.34378028e-01 -1.99007109e-01 -5.15888810e-01 9.41078901e-01
-6.71076477e-01 -1.95642197e+00 -5.46469271e-01 7.68955410e-01
3.51552844e-01 -1.08837627e-01 8.96954238e-01 3.21232304e-02
-3.31569761e-01 8.05726111e-01 -3.28656644e-01 1.60404310e-01
7.34168112e-01 -1.10914230e+00 8.31600666e-01 6.19133949e-01
2.04825681e-02 4.57943231e-01 7.15755463e-01 -8.22172284e-01
-1.89676762e+00 -1.14581692e+00 -1.13219738e-01 -7.12992370e-01
4.12996002e-02 -5.88256598e-01 -5.80679595e-01 7.51526415e-01
4.23967950e-02 8.86944532e-02 4.27567869e-01 -2.89215893e-01
-2.52127022e-01 1.93881672e-02 -1.25277567e+00 1.01782095e+00
1.47789359e+00 -2.16000900e-01 -2.41910264e-01 9.62024108e-02
9.07819688e-01 -1.04169512e+00 -1.22285092e+00 5.05348444e-01
8.17768693e-01 -6.33028626e-01 1.19223440e+00 -4.36040699e-01
5.82328320e-01 -3.96100521e-01 -3.24515556e-03 -1.42292893e+00
-3.55369180e-01 -1.15169072e+00 -4.26794797e-01 1.19342506e+00
1.70085087e-01 -3.93802613e-01 1.03194177e+00 7.38000512e-01
-2.11819351e-01 -7.19230711e-01 -6.22237086e-01 -7.88719475e-01
1.30443126e-01 -2.75693592e-02 8.74907374e-01 1.02673650e+00
-5.94218791e-01 4.09801900e-01 -9.97211933e-01 1.10333003e-02
8.43140185e-01 2.78862059e-01 1.43978262e+00 -1.07838047e+00
-5.48385620e-01 -1.07124411e-01 -4.28612769e-01 -1.26897717e+00
1.09589502e-01 -7.56221354e-01 3.62620018e-02 -1.58316171e+00
-1.93290263e-01 -6.67510688e-01 5.84426463e-01 3.48006666e-01
8.14246945e-03 5.49142122e-01 3.46821636e-01 2.22255185e-01
-4.22009602e-02 1.07399762e+00 2.23849750e+00 4.96077649e-02
-3.49847108e-01 4.58775684e-02 -5.44186354e-01 7.22868145e-01
5.42066216e-01 -6.19769003e-03 -5.71771741e-01 -5.23269475e-01
9.15820450e-02 3.73912990e-01 6.25389695e-01 -8.51203859e-01
-3.44681740e-01 -3.02820861e-01 6.52496874e-01 -1.57245144e-01
7.48531878e-01 -7.60777712e-01 9.05546010e-01 3.02229583e-01
-4.09090966e-02 1.98321939e-01 -1.32989615e-01 5.49741805e-01
8.53938758e-02 5.21217406e-01 1.03701866e+00 -3.23060155e-01
-1.01841286e-01 9.16419506e-01 1.16587944e-01 1.82270244e-01
9.86332536e-01 -3.57663095e-01 2.40713716e-01 -7.41441905e-01
-7.69953489e-01 -3.27490568e-02 1.27052319e+00 4.73577052e-01
7.08585560e-01 -1.86253917e+00 -7.89175987e-01 3.16547155e-01
-4.03934866e-02 5.93409121e-01 3.99752110e-01 5.73128700e-01
-7.56853759e-01 -2.23422945e-01 -3.52024555e-01 -5.97530603e-01
-1.17287481e+00 4.74884897e-01 3.21189493e-01 -1.76214706e-02
-1.09292531e+00 6.02920651e-01 5.37695348e-01 -8.38486075e-01
-7.95961693e-02 3.60327736e-02 2.59919584e-01 -5.37188113e-01
9.40129980e-02 2.28087112e-01 -2.81977236e-01 -9.82418895e-01
2.43916567e-02 9.55068707e-01 4.37964946e-01 -2.87042975e-01
1.28186154e+00 -4.03779745e-02 1.37750179e-01 1.81958839e-01
8.92042935e-01 3.73879224e-01 -1.75921631e+00 -4.18504961e-02
-1.06391907e+00 -5.71367145e-01 -3.55952322e-01 -5.56951046e-01
-1.51805806e+00 6.79395616e-01 3.22185934e-01 -2.35942408e-01
8.63989055e-01 -7.53386226e-03 1.28019357e+00 -7.50520006e-02
6.52024865e-01 -7.08968401e-01 4.65517402e-01 2.56122231e-01
1.28487253e+00 -8.46775293e-01 -7.57618174e-02 -6.23873949e-01
-8.76487672e-01 9.35780466e-01 8.09205115e-01 -2.24647775e-01
3.53262186e-01 2.47566774e-01 3.25242400e-01 -1.89888045e-01
-2.52050698e-01 2.28649765e-01 4.97647405e-01 1.01159453e+00
2.82027602e-01 3.13796729e-01 3.69504578e-02 4.29851234e-01
-6.48687243e-01 -3.72425675e-01 3.24029177e-01 5.92521310e-01
2.93140352e-01 -1.23913264e+00 -4.40194160e-01 3.30633111e-02
-1.34963825e-01 1.93187103e-01 -5.09993732e-01 6.82838500e-01
1.18678398e-01 5.11084378e-01 -1.21061616e-01 -5.80042899e-01
6.80577338e-01 -2.91325510e-01 8.38177621e-01 -5.62091529e-01
-4.07565564e-01 2.69708753e-01 -5.74576743e-02 -5.69519639e-01
-1.13521881e-01 -5.19580424e-01 -1.04094493e+00 -6.50393128e-01
-1.20455213e-01 -1.07509241e-01 5.25186419e-01 3.53847504e-01
4.65947419e-01 4.89528745e-01 6.73088253e-01 -1.39212894e+00
-3.20991099e-01 -5.98062098e-01 -6.07895911e-01 8.11976612e-01
1.65889874e-01 -8.04912388e-01 -1.01746522e-01 2.31697828e-01] | [12.049690246582031, -0.6578266620635986] |
df109661-f845-426d-a8e3-59a497f3c1a0 | edge-directionality-improves-learning-on | 2305.10498 | null | https://arxiv.org/abs/2305.10498v1 | https://arxiv.org/pdf/2305.10498v1.pdf | Edge Directionality Improves Learning on Heterophilic Graphs | Graph Neural Networks (GNNs) have become the de-facto standard tool for modeling relational data. However, while many real-world graphs are directed, the majority of today's GNN models discard this information altogether by simply making the graph undirected. The reasons for this are historical: 1) many early variants of spectral GNNs explicitly required undirected graphs, and 2) the first benchmarks on homophilic graphs did not find significant gain from using direction. In this paper, we show that in heterophilic settings, treating the graph as directed increases the effective homophily of the graph, suggesting a potential gain from the correct use of directionality information. To this end, we introduce Directed Graph Neural Network (Dir-GNN), a novel general framework for deep learning on directed graphs. Dir-GNN can be used to extend any Message Passing Neural Network (MPNN) to account for edge directionality information by performing separate aggregations of the incoming and outgoing edges. We prove that Dir-GNN matches the expressivity of the Directed Weisfeiler-Lehman test, exceeding that of conventional MPNNs. In extensive experiments, we validate that while our framework leaves performance unchanged on homophilic datasets, it leads to large gains over base models such as GCN, GAT and GraphSage on heterophilic benchmarks, outperforming much more complex methods and achieving new state-of-the-art results. | ['Michael Bronstein', 'Stephan Günnemann', 'Fabrizio Frasca', 'Francesco Di Giovanni', 'Bertrand Charpentier', 'Emanuele Rossi'] | 2023-05-17 | null | null | null | null | ['node-classification-on-non-homophilic'] | ['graphs'] | [-9.81522426e-02 4.99082178e-01 -4.24759537e-01 -2.82258689e-01
3.74678791e-01 -5.85903943e-01 1.01032770e+00 2.52946466e-01
2.86649968e-02 6.69812977e-01 3.63774300e-01 -8.03584397e-01
-4.38339114e-01 -1.57365155e+00 -9.10199106e-01 -5.54354191e-01
-6.44039154e-01 9.88768160e-01 3.76198590e-01 -5.12444198e-01
-7.35004246e-02 6.04439020e-01 -1.15177047e+00 1.90885216e-01
1.65108100e-01 5.76319516e-01 -4.65616614e-01 7.67119765e-01
-2.93187410e-01 1.24146450e+00 -2.44483769e-01 -5.50450146e-01
3.76450449e-01 -3.38221520e-01 -1.00520098e+00 -4.14284199e-01
5.45217216e-01 -1.15825258e-01 -9.73592281e-01 8.91941667e-01
1.85676843e-01 2.20149439e-02 5.64056635e-01 -1.60596180e+00
-8.71588409e-01 1.40973592e+00 -5.00683963e-01 7.17107430e-02
9.80640799e-02 7.01379105e-02 1.75077868e+00 -5.33155143e-01
1.00357854e+00 1.59340620e+00 8.75746191e-01 2.14135885e-01
-1.29766977e+00 -4.65966314e-01 3.69354665e-01 -2.53453366e-02
-1.14793324e+00 -6.22530431e-02 8.45379591e-01 -7.50964209e-02
1.15856850e+00 2.79337019e-01 9.09852564e-01 1.14637733e+00
2.98236430e-01 6.21857941e-01 8.03733647e-01 -1.88083872e-01
7.88850896e-03 -5.14269054e-01 4.88617688e-01 9.37320888e-01
7.46664166e-01 2.76871502e-01 -3.82990181e-01 -2.51711756e-01
7.49239445e-01 1.36207059e-01 -2.11812202e-02 -8.80781412e-01
-1.00326908e+00 9.59870458e-01 1.09838021e+00 4.15765047e-01
-1.07676946e-01 8.05863440e-01 6.14379644e-01 6.31089211e-01
5.58925867e-01 4.63229150e-01 -1.90327659e-01 1.47495851e-01
-4.67381924e-01 2.07031354e-01 1.31437552e+00 7.78636754e-01
7.53034532e-01 -2.38704495e-02 2.29568094e-01 5.23694694e-01
3.26283932e-01 1.31308079e-01 -6.86313063e-02 -6.04234517e-01
3.76411706e-01 1.04512155e+00 -6.33125961e-01 -1.53620136e+00
-7.95399368e-01 -7.05099583e-01 -1.02380323e+00 -1.82437211e-01
3.92708182e-01 7.97680989e-02 -1.03732133e+00 1.86470616e+00
-1.61744971e-02 1.60641059e-01 -1.89562291e-01 5.88807583e-01
9.98107672e-01 4.87616122e-01 -1.62877798e-01 3.03281963e-01
7.29708731e-01 -8.64944398e-01 -4.52685893e-01 -2.47007445e-01
1.05030012e+00 -2.61605352e-01 7.63306737e-01 2.27876216e-01
-9.94550228e-01 -2.38476624e-03 -1.04690385e+00 -1.12172663e-01
-8.56839716e-01 -9.04061913e-01 1.31344807e+00 6.77992523e-01
-1.67086744e+00 8.16995800e-01 -7.37377346e-01 -6.46632016e-01
3.60524893e-01 5.67308843e-01 -5.12309492e-01 -2.02296153e-01
-1.29088557e+00 6.42994583e-01 4.65487868e-01 7.28550255e-02
-7.22396612e-01 -6.52763605e-01 -9.87371802e-01 4.18169975e-01
5.30848145e-01 -8.97658050e-01 7.25410163e-01 -8.26343477e-01
-9.43786860e-01 8.71695220e-01 1.97463721e-01 -9.89753425e-01
4.79326010e-01 1.86533347e-01 -3.38108450e-01 7.48153999e-02
-1.94319323e-01 4.88136172e-01 1.11738622e-01 -1.30178165e+00
-1.17020369e-01 -2.26739645e-01 4.24430519e-01 -2.83776075e-02
-3.61813992e-01 -4.33511943e-01 -3.34640294e-01 -2.87530929e-01
1.32880300e-01 -1.02896595e+00 -5.67289516e-02 -2.33643517e-01
-8.08598161e-01 -4.19250607e-01 8.59371662e-01 9.78808478e-02
1.29089952e+00 -1.68915904e+00 5.59527501e-02 7.95965016e-01
1.05104184e+00 2.49668494e-01 -3.72959733e-01 9.30902064e-01
-3.54820222e-01 4.02002573e-01 -1.08559206e-01 -1.33274466e-01
5.40451668e-02 5.83218038e-01 -1.85547292e-01 6.34086788e-01
-5.17414398e-02 1.28463030e+00 -1.14007521e+00 -1.75557494e-01
2.44210020e-01 3.87886345e-01 -6.50762916e-01 -2.04692915e-01
-3.41310829e-01 -3.92141312e-01 1.27029583e-01 5.25026321e-01
5.20257175e-01 -9.48745728e-01 8.51618648e-01 4.12081480e-02
4.46613461e-01 5.67077339e-01 -1.11319554e+00 1.15106535e+00
-3.06625783e-01 7.84979939e-01 -4.40041199e-02 -1.16135025e+00
8.10573637e-01 -1.54014722e-01 5.50365806e-01 -7.42114782e-01
1.30423814e-01 1.99409977e-01 4.88801509e-01 2.98435129e-02
4.06865686e-01 -6.09471388e-02 1.45059690e-01 6.44863486e-01
1.22887209e-01 1.25911504e-01 4.04556066e-01 9.06715631e-01
1.63967633e+00 -3.32901776e-01 1.28667295e-01 -5.42372286e-01
9.83729679e-03 -2.42118344e-01 5.29000349e-02 9.87469375e-01
2.83474438e-02 4.74087179e-01 1.11095464e+00 -8.12315464e-01
-8.84437442e-01 -1.21706092e+00 1.51530340e-01 1.05154800e+00
1.29223645e-01 -8.40191364e-01 -3.02362949e-01 -8.67672324e-01
3.78583699e-01 4.30736005e-01 -1.00593650e+00 -3.63133848e-01
-6.58679008e-01 -1.06507218e+00 7.81692743e-01 5.35252929e-01
1.36232063e-01 -7.56603956e-01 2.68685848e-01 9.52112675e-02
1.05875514e-01 -9.70887065e-01 -3.92650105e-02 3.75683248e-01
-8.34649920e-01 -1.45200467e+00 -2.19249204e-01 -6.75570369e-01
5.41268528e-01 4.97420013e-01 1.61978006e+00 6.25127673e-01
6.28556907e-02 2.95144498e-01 -8.90873522e-02 -7.64976963e-02
-4.69270378e-01 4.91717458e-01 -1.14228025e-01 -2.71284968e-01
4.21485186e-01 -1.04907179e+00 -4.75766808e-01 1.30984470e-01
-8.93583119e-01 -9.83546674e-02 3.49502027e-01 9.00733829e-01
1.18612513e-01 3.08592394e-02 4.80236620e-01 -1.72154212e+00
8.11669230e-01 -8.06418121e-01 -4.38881069e-01 3.87146026e-02
-1.03562343e+00 1.73935205e-01 8.75282705e-01 1.18868304e-02
-4.32344407e-01 -5.72200477e-01 7.77526572e-03 -3.20763797e-01
3.65401953e-01 8.66734624e-01 2.18614727e-01 -3.42914313e-01
7.45883465e-01 -1.97393253e-01 2.69101746e-02 -2.51361933e-02
5.39973319e-01 3.84362265e-02 3.29459161e-01 -5.89414835e-01
7.12065935e-01 7.31421709e-01 6.64601743e-01 -6.28912866e-01
-4.61396933e-01 -3.09430897e-01 -4.56188977e-01 -8.85191262e-02
2.87208498e-01 -5.37766576e-01 -1.03764987e+00 3.87230873e-01
-9.01588321e-01 -6.46275878e-01 -1.29054755e-01 6.33759126e-02
-2.31091127e-01 5.38426340e-01 -1.01919806e+00 -5.34164667e-01
-1.34025842e-01 -7.62482405e-01 7.12849021e-01 -2.88201243e-01
-2.46083558e-01 -1.69709504e+00 2.50573128e-01 -1.13198869e-01
5.01555026e-01 3.99202734e-01 1.30133414e+00 -1.03699672e+00
-7.02863097e-01 -2.00856999e-01 -6.06114089e-01 -9.57121514e-03
4.93332138e-03 3.08474123e-01 -6.09156966e-01 -2.59534955e-01
-7.95954943e-01 -1.91417456e-01 1.21591318e+00 3.38917524e-01
7.61853576e-01 -3.63095701e-01 -5.42344093e-01 7.94247985e-01
1.50569761e+00 -2.23516807e-01 5.92800856e-01 2.19639868e-01
1.20348060e+00 4.41184372e-01 -3.18565041e-01 -1.89035535e-02
7.20478952e-01 1.58308655e-01 1.00681365e+00 -2.57205069e-01
-3.30510795e-01 -6.38735712e-01 8.98194015e-02 7.62442529e-01
-9.40925702e-02 -8.82427156e-01 -1.15100265e+00 7.40979552e-01
-2.02990937e+00 -8.22174668e-01 -6.28073812e-01 1.95493269e+00
2.98021615e-01 4.39301074e-01 2.65042454e-01 4.74645235e-02
6.72591686e-01 6.47469282e-01 -3.81410688e-01 -5.13269842e-01
-4.97574061e-01 2.84296900e-01 8.59469354e-01 6.91731393e-01
-8.14504802e-01 1.02880049e+00 6.81465149e+00 3.03666323e-01
-9.28860486e-01 -2.80390292e-01 6.03496611e-01 -2.62913316e-01
-7.54357457e-01 2.05937162e-01 -5.04091978e-01 2.76954800e-01
1.17439401e+00 -1.99519724e-01 6.39026701e-01 7.23910630e-01
-3.33139420e-01 2.88915247e-01 -1.48553848e+00 6.38854861e-01
-5.16335033e-02 -1.78619742e+00 3.35123926e-01 3.22869569e-01
7.61433840e-01 5.46488702e-01 -1.05415009e-01 3.44370246e-01
1.28629136e+00 -1.29209864e+00 1.65295184e-01 2.05671072e-01
3.50142956e-01 -7.13619769e-01 7.43648171e-01 -2.54991390e-02
-1.15972340e+00 4.80552129e-02 -2.88089275e-01 -2.87687302e-01
-4.57757935e-02 8.34365249e-01 -1.01691389e+00 7.40221143e-01
4.69699800e-01 9.79714811e-01 -6.61650240e-01 6.72258377e-01
-6.24166504e-02 7.11205482e-01 -5.67290187e-01 -3.18829894e-01
6.91630423e-01 -1.77697450e-01 5.58949053e-01 1.30143440e+00
-1.15434594e-01 -3.73778582e-01 -8.96543413e-02 8.64479125e-01
-6.82853758e-01 -2.42138412e-02 -1.30178583e+00 -4.20928121e-01
3.97164166e-01 1.06849837e+00 -9.80355263e-01 -3.19927424e-01
-3.81148934e-01 4.55731273e-01 7.71488011e-01 4.59049553e-01
-5.71624875e-01 -3.38052034e-01 4.13373828e-01 4.09489870e-01
1.38290614e-01 -2.23728240e-01 -2.84604847e-01 -1.02530968e+00
-1.73509642e-01 -7.80637681e-01 8.08308363e-01 -5.83403945e-01
-1.59637451e+00 4.31039274e-01 -1.39481351e-01 -4.24661785e-01
-2.57810682e-01 -8.43574286e-01 -6.24157190e-01 5.55052340e-01
-1.34208298e+00 -1.26408851e+00 -1.09725401e-01 5.31324387e-01
-2.58721143e-01 5.71173169e-02 4.02763695e-01 2.58102685e-01
-2.98703223e-01 4.33350712e-01 -1.83549196e-01 5.40572524e-01
5.69124103e-01 -1.59954965e+00 8.36775780e-01 8.77776861e-01
3.54466796e-01 1.04944873e+00 6.76154673e-01 -8.66518080e-01
-1.76524496e+00 -8.95533323e-01 8.96637380e-01 -4.03464466e-01
1.22299707e+00 -6.43224657e-01 -7.96223283e-01 1.32996643e+00
1.57642707e-01 3.60421509e-01 3.18038166e-01 8.02005708e-01
-7.44817019e-01 -7.46899545e-02 -7.23505199e-01 8.90364051e-01
1.76403594e+00 -5.30587733e-01 -1.00552037e-01 6.27458930e-01
9.28825259e-01 -3.01741153e-01 -8.28340530e-01 5.48756540e-01
4.44180161e-01 -1.35130394e+00 1.01546466e+00 -9.22777593e-01
5.93517780e-01 -4.67390008e-03 -1.55985475e-01 -1.39324236e+00
-5.14854312e-01 -6.77082598e-01 -3.76123279e-01 7.22203016e-01
4.57204074e-01 -1.06782770e+00 1.06451631e+00 3.80953074e-01
-1.27016619e-01 -8.47283065e-01 -6.70101941e-01 -7.42615461e-01
2.01261252e-01 -3.41638684e-01 6.57179296e-01 1.23351490e+00
2.20814615e-01 6.32752120e-01 -3.10532153e-01 -1.87241063e-02
5.93865991e-01 6.62060902e-02 9.18570817e-01 -1.53991127e+00
-3.14184010e-01 -9.46065843e-01 -6.82177424e-01 -9.19842362e-01
3.97283167e-01 -1.44486356e+00 -4.78560865e-01 -1.91800034e+00
1.91295400e-01 -3.93163741e-01 -2.12241411e-01 5.66975653e-01
1.12062991e-01 1.96616888e-01 1.10545821e-01 -1.05474658e-01
-6.08424127e-01 2.80972213e-01 1.11963975e+00 -2.63127834e-01
-1.31699100e-01 -2.14777857e-01 -8.43715012e-01 5.84008336e-01
5.81117094e-01 -3.33903015e-01 -6.69757783e-01 -3.27553004e-01
1.11581469e+00 -2.34366879e-01 5.31466126e-01 -6.79468453e-01
4.40642774e-01 8.15000162e-02 5.48672229e-02 -4.16687340e-01
2.92836223e-02 -6.77032173e-01 1.66655719e-01 5.77908039e-01
-4.15292978e-01 3.11151624e-01 -9.46340859e-02 8.65750015e-01
-1.01545230e-01 4.05983239e-01 5.21397531e-01 -2.92128593e-01
-3.73291165e-01 4.48108256e-01 -4.49719764e-02 1.08877584e-01
6.02644742e-01 -1.23850368e-01 -1.10243165e+00 -7.38168478e-01
-5.74173987e-01 3.73771340e-01 5.37935674e-01 3.38584095e-01
3.84521902e-01 -1.16674590e+00 -5.05185366e-01 1.02362685e-01
-1.32747218e-02 3.30848359e-02 -5.73464436e-04 9.52327371e-01
-7.55582511e-01 4.26065117e-01 2.13684700e-03 -5.63694596e-01
-1.03961790e+00 7.16350615e-01 6.13188624e-01 -6.18452668e-01
-8.48044991e-01 8.13604593e-01 4.71797258e-01 -8.49849463e-01
1.28398582e-01 -3.47328156e-01 2.92371988e-01 -9.94827971e-02
-6.17220625e-02 3.96852374e-01 3.09360713e-01 -2.87896246e-01
-3.60642225e-01 3.42221670e-02 -2.05380768e-01 1.95428923e-01
1.52313673e+00 1.84852242e-01 -6.00991666e-01 4.59975868e-01
1.30963385e+00 -7.09495554e-03 -6.29727960e-01 -1.69009686e-01
-3.79566401e-02 -1.06072150e-01 -1.46089047e-01 -6.31602347e-01
-1.31003928e+00 8.57034326e-01 -1.87792927e-01 8.87034357e-01
5.39548695e-01 1.23934157e-01 7.47683108e-01 6.91937268e-01
3.14774692e-01 -7.48754501e-01 -5.82245588e-02 7.54221141e-01
5.05777955e-01 -9.69299972e-01 1.02322988e-01 -4.90238339e-01
-1.37632743e-01 1.15556109e+00 5.33637285e-01 -5.00038266e-01
9.19793546e-01 2.65938818e-01 -2.73345411e-01 -8.31874907e-01
-9.99531388e-01 -1.91972423e-02 1.23236561e-03 5.59514105e-01
4.70140159e-01 1.67546615e-01 -9.96417329e-02 -2.90901661e-02
-3.66315365e-01 -3.04831475e-01 7.60020614e-01 5.01427770e-01
-1.12354882e-01 -9.66395259e-01 2.14871421e-01 8.01153600e-01
-2.48994350e-01 -4.59187657e-01 -1.10405779e+00 1.34650350e+00
-4.47026819e-01 7.24695921e-01 3.03693026e-01 -6.17360234e-01
5.45839816e-02 -1.82866812e-01 4.11114216e-01 -5.84229350e-01
-6.65197253e-01 -3.48109931e-01 5.47034264e-01 -7.03669846e-01
-2.56734580e-01 -7.65326917e-02 -1.18782234e+00 -1.12303960e+00
-1.05882123e-01 1.64053135e-03 2.12956592e-01 6.89417779e-01
4.52421516e-01 6.25480115e-01 2.74058729e-01 -5.07109344e-01
-1.62232131e-01 -6.64557159e-01 -7.31419921e-01 5.58941245e-01
3.56925547e-01 -6.06348276e-01 -7.29851723e-01 -7.35988438e-01] | [6.9644012451171875, 6.222329616546631] |
36f96dd1-2ee1-4ca9-94db-bc7c29c38b6c | domain-adaptation-using-silver-standard | 2307.03872 | null | https://arxiv.org/abs/2307.03872v1 | https://arxiv.org/pdf/2307.03872v1.pdf | Domain Adaptation using Silver Standard Labels for Ki-67 Scoring in Digital Pathology: A Step Closer to Widescale Deployment | Deep learning systems have been proposed to improve the objectivity and efficiency of Ki- 67 PI scoring. The challenge is that while very accurate, deep learning techniques suffer from reduced performance when applied to out-of-domain data. This is a critical challenge for clinical translation, as models are typically trained using data available to the vendor, which is not from the target domain. To address this challenge, this study proposes a domain adaptation pipeline that employs an unsupervised framework to generate silver standard (pseudo) labels in the target domain, which is used to augment the gold standard (GS) source domain data. Five training regimes were tested on two validated Ki-67 scoring architectures (UV-Net and piNET), (1) SS Only: trained on target silver standard (SS) labels, (2) GS Only: trained on source GS labels, (3) Mixed: trained on target SS and source GS labels, (4) GS+SS: trained on source GS labels and fine-tuned on target SS labels, and our proposed method (5) SS+GS: trained on source SS labels and fine-tuned on source GS labels. The SS+GS method yielded significantly (p < 0.05) higher PI accuracy (95.9%) and more consistent results compared to the GS Only model on target data. Analysis of t-SNE plots showed features learned by the SS+GS models are more aligned for source and target data, resulting in improved generalization. The proposed pipeline provides an efficient method for learning the target distribution without manual annotations, which are time-consuming and costly to generate for medical images. This framework can be applied to any target site as a per-laboratory calibration method, for widescale deployment. | ['April Khademi', 'Susan Done', 'Dimitrios Androutsos', 'Fei-Fei Liu', 'Wei Shi', 'Anthony Fyles', 'Melanie Dawe', 'Seyed Hossein Mirjahanmardi', 'Ngoc-Nhu Jennifer Nguyen', 'Amanda Dy'] | 2023-07-08 | null | null | null | null | ['domain-adaptation'] | ['methodology'] | [ 6.09951913e-01 -6.44891113e-02 -5.13018906e-01 -3.50287437e-01
-1.44253182e+00 -6.67581022e-01 2.75149584e-01 5.40578485e-01
-3.85265857e-01 1.00188482e+00 -1.52362175e-02 -4.11037236e-01
-2.09363267e-01 -6.90661669e-01 -6.56475663e-01 -9.44490016e-01
2.30057985e-01 9.90685940e-01 2.98812747e-01 3.75037305e-02
1.70574524e-02 3.39961767e-01 -8.89468849e-01 6.76177740e-01
8.85056138e-01 8.01333010e-01 2.14397177e-01 8.02832127e-01
-7.29661509e-02 4.60138083e-01 -6.88701630e-01 -1.56516641e-01
2.74347961e-01 -5.11192083e-01 -5.23354232e-01 -4.17081565e-01
2.25053906e-01 -3.53426963e-01 2.66357303e-01 8.09117198e-01
8.25654149e-01 -4.35475379e-01 1.01737416e+00 -1.24693227e+00
-5.65703452e-01 4.26271409e-01 -7.74847925e-01 8.67033228e-02
4.19654064e-02 4.31700826e-01 6.24909520e-01 -5.15321672e-01
7.82223701e-01 6.76737547e-01 9.14291084e-01 6.25804007e-01
-1.36355615e+00 -9.99879479e-01 -4.54546243e-01 -1.01994053e-01
-1.28052318e+00 -6.52584955e-02 3.91296387e-01 -7.16190517e-01
8.67795467e-01 1.31609663e-01 5.65991044e-01 1.24973595e+00
5.65930843e-01 4.61803615e-01 1.47132325e+00 -4.07754719e-01
4.52953339e-01 2.88634241e-01 1.03248484e-01 3.63611549e-01
3.78740907e-01 2.57229537e-01 -3.65879476e-01 -3.91416460e-01
8.14346254e-01 -2.00478286e-02 -1.05869673e-01 -2.02567354e-01
-1.00347459e+00 1.09172094e+00 3.46077383e-01 4.45812792e-01
-4.88388449e-01 -1.11534804e-01 4.84190583e-01 5.74662276e-02
3.07935745e-01 3.88977736e-01 -6.71600103e-01 -9.12954751e-03
-9.81331050e-01 8.21895376e-02 5.60492992e-01 6.18520021e-01
5.21981299e-01 -2.42975354e-01 -3.61691236e-01 1.02880549e+00
2.26859629e-01 6.42295420e-01 8.18882644e-01 -5.42267919e-01
1.54891044e-01 6.89831734e-01 9.70285386e-02 -5.46869695e-01
-9.13598955e-01 -8.14090848e-01 -6.94052815e-01 2.01023921e-01
6.83303118e-01 -1.00648090e-01 -1.34223044e+00 1.77423847e+00
1.90795481e-01 2.36739162e-02 2.34931141e-01 6.90023661e-01
9.52884793e-01 3.67941260e-01 5.44599235e-01 -1.09374866e-01
1.27014995e+00 -5.88423073e-01 -3.16731006e-01 -1.43616214e-01
1.05669677e+00 -7.08876491e-01 1.06612527e+00 4.06054914e-01
-6.94695830e-01 -2.29048371e-01 -1.09663165e+00 1.40309513e-01
-5.03251672e-01 2.59377241e-01 6.67871952e-01 9.36499655e-01
-1.12365508e+00 4.16382283e-01 -7.17971861e-01 -5.88070571e-01
6.17338836e-01 8.65439892e-01 -4.59736556e-01 -2.21016958e-01
-1.26920092e+00 9.04064119e-01 3.81462961e-01 -1.68652028e-01
-9.83159781e-01 -1.08024633e+00 -5.96984625e-01 -1.04075328e-01
-1.58558145e-01 -7.11928129e-01 1.22722721e+00 -9.77213144e-01
-1.54608488e+00 1.03858840e+00 8.49257782e-02 -1.97776705e-01
3.59837919e-01 3.93795669e-01 -3.31644088e-01 1.23225488e-01
3.97355884e-01 5.53636253e-01 3.39021057e-01 -1.05083573e+00
-8.43669057e-01 -4.72743422e-01 -3.51218492e-01 -1.34129778e-01
-1.21119563e-02 -8.29494447e-02 -4.84637059e-02 -4.40946043e-01
-3.22163522e-01 -1.01726544e+00 -1.47071987e-01 -1.95346653e-01
-1.59577027e-01 -1.97227016e-01 3.57297212e-01 -8.79331946e-01
9.34032917e-01 -1.98064971e+00 -5.94909906e-01 5.16749501e-01
1.68477565e-01 6.08489394e-01 -1.69890106e-01 1.09319821e-01
-4.19889778e-01 1.65756300e-01 -8.37717280e-02 3.14302027e-01
-1.32674858e-01 -1.77098632e-01 4.04748172e-01 3.58055323e-01
1.95827961e-01 9.05465126e-01 -1.09240389e+00 -3.61191750e-01
7.61109516e-02 3.02018911e-01 -5.07975936e-01 -3.00443843e-02
-7.52674823e-04 5.75974643e-01 -2.88326710e-01 7.29035795e-01
7.96208322e-01 -5.53596139e-01 3.28924596e-01 -2.60802865e-01
3.56141567e-01 5.33019230e-02 -9.07503426e-01 1.50758791e+00
-3.26286465e-01 1.56873882e-01 -2.93173730e-01 -6.28041685e-01
1.20349276e+00 2.19784975e-01 6.38697326e-01 -6.85333133e-01
2.26662278e-01 6.54191256e-01 3.03320557e-01 -2.24099413e-01
-8.99947509e-02 -6.02613449e-01 -1.58821847e-02 4.07352209e-01
2.13299900e-01 -3.05734873e-02 8.97259787e-02 -5.88001572e-02
1.33551764e+00 -7.70830140e-02 4.24167633e-01 -1.32945746e-01
4.39654768e-01 3.30487788e-01 6.59386814e-01 5.76444447e-01
-4.28528517e-01 6.50381267e-01 6.02712274e-01 -2.03332022e-01
-1.07334828e+00 -1.22691381e+00 -2.78657079e-01 8.55014503e-01
-4.03978109e-01 1.17955236e-02 -6.05103552e-01 -9.29684937e-01
-7.37982392e-02 5.62606156e-01 -7.46374726e-01 -2.99360693e-01
-8.97568241e-02 -1.03535259e+00 7.81955123e-01 7.25997925e-01
3.13493758e-01 -6.50158286e-01 -2.03010798e-01 3.45940262e-01
-4.60822769e-02 -5.30070901e-01 -4.56623763e-01 4.89404440e-01
-5.72491467e-01 -1.20396912e+00 -1.12955439e+00 -7.66270697e-01
6.32609844e-01 -2.83048689e-01 7.96718836e-01 -2.46336624e-01
-1.55853555e-01 1.21394433e-01 -2.43306875e-01 -9.42978978e-01
-6.65690601e-01 4.43506986e-02 -1.76928684e-01 -9.18211415e-02
9.39498067e-01 -1.19071007e-01 -8.39880586e-01 3.71015310e-01
-9.22236323e-01 -1.23618335e-01 1.08573198e+00 1.11559618e+00
8.09934318e-01 -2.82126427e-01 1.08544016e+00 -1.31299686e+00
4.93947774e-01 -5.12623608e-01 -3.48590523e-01 1.70551404e-01
-7.85757184e-01 -2.74237245e-01 4.12088215e-01 -6.50703251e-01
-9.48222935e-01 2.19816238e-01 -3.58374298e-01 -2.19553411e-01
-1.17808498e-01 7.02382684e-01 7.88532495e-02 -6.53482974e-02
1.12227333e+00 -3.76130790e-02 2.75232822e-01 -3.00739370e-02
-2.58143634e-01 1.03988957e+00 4.07549530e-01 -3.51117581e-01
3.06267291e-01 2.47888893e-01 1.88449353e-01 -4.10943806e-01
-5.18498182e-01 -5.90292037e-01 -4.18499917e-01 1.03404552e-01
8.10488045e-01 -7.29598403e-01 -1.72507241e-01 7.40815938e-01
-6.62662208e-01 -6.64047956e-01 -1.04357205e-01 7.34215260e-01
-3.92820865e-01 6.39709458e-02 -4.56723899e-01 -1.93231910e-01
-7.50522733e-01 -1.23990357e+00 1.07058692e+00 4.82805043e-01
-6.12675846e-01 -1.23343182e+00 4.19670612e-01 3.99205506e-01
5.65363348e-01 5.51901042e-01 1.09563339e+00 -1.23616743e+00
-6.58950880e-02 -5.47920644e-01 -3.51349831e-01 2.35759109e-01
4.31804895e-01 -5.73061816e-02 -9.50240076e-01 -2.06711814e-01
-2.72969812e-01 -2.28780225e-01 3.55280846e-01 7.17779279e-01
6.67709827e-01 1.06493294e-01 -5.39529860e-01 4.78782505e-01
1.37238383e+00 6.51786745e-01 6.80779636e-01 5.55108726e-01
2.08220735e-01 1.96523800e-01 6.09856009e-01 9.57228467e-02
2.92176515e-01 6.01623118e-01 -1.35475965e-02 -5.22467852e-01
-2.96525180e-01 -2.29486808e-01 7.22951218e-02 -1.94142964e-02
1.66910827e-01 -5.72201656e-03 -1.24853742e+00 6.47784770e-01
-1.35693467e+00 -5.65456331e-01 -3.83740187e-01 2.28640890e+00
1.04796052e+00 2.11864352e-01 2.14669034e-01 3.90127487e-02
6.90156162e-01 -6.45969868e-01 -6.91666901e-01 -5.15102029e-01
-1.10064382e-02 6.68710291e-01 6.15097463e-01 1.40505001e-01
-8.81816030e-01 5.73617041e-01 6.76372242e+00 8.49541545e-01
-1.59167588e+00 3.76727164e-01 8.35796356e-01 1.34298261e-02
-8.73837322e-02 -2.86413848e-01 -7.28515685e-01 5.35037220e-01
1.34160995e+00 -2.65303254e-01 -2.48008415e-01 7.10043490e-01
2.57448226e-01 -2.91083783e-01 -1.28992176e+00 7.78688610e-01
-3.33823487e-02 -1.49326432e+00 -9.74780172e-02 1.28007188e-01
7.60339797e-01 -3.77340498e-03 6.51595071e-02 5.17769814e-01
4.24752235e-01 -9.72443700e-01 -9.24642012e-03 3.34643930e-01
1.29214764e+00 -6.46161616e-01 1.44780731e+00 6.79926760e-03
-6.46835268e-01 1.93769962e-01 -1.24357790e-01 4.40140426e-01
-1.78782210e-01 6.04835570e-01 -1.59987676e+00 5.84971905e-01
6.14005685e-01 2.62130827e-01 -5.54637849e-01 1.08473372e+00
3.15635614e-02 6.59588695e-01 -3.22893173e-01 -4.67953160e-02
1.19558208e-01 1.28491491e-01 -6.33793175e-02 1.28412867e+00
3.55311632e-01 -8.52882490e-02 1.11799296e-02 6.19164228e-01
2.61635363e-01 3.34919184e-01 -2.93929189e-01 -2.80938804e-01
2.49811128e-01 1.25433886e+00 -9.35416043e-01 -3.02455962e-01
-3.29376608e-01 4.56126034e-01 -1.24693647e-01 2.19106391e-01
-7.79439569e-01 -4.88569319e-01 2.60175854e-01 4.43541467e-01
-7.56200850e-02 4.98464823e-01 -4.69106764e-01 -4.33264047e-01
-5.70937395e-01 -8.86650741e-01 9.65667129e-01 -7.41746426e-01
-1.44303501e+00 4.37082559e-01 -1.44006191e-02 -1.33205366e+00
-3.69963467e-01 -8.14736962e-01 -3.38483781e-01 1.23256922e+00
-1.30961525e+00 -1.38601375e+00 -3.74575377e-01 4.55705136e-01
1.21073015e-01 -2.13291809e-01 9.85954404e-01 4.24890906e-01
-2.78930962e-01 1.03631890e+00 3.07597429e-01 1.41892746e-01
1.14332449e+00 -1.08252692e+00 -4.13980901e-01 2.40284473e-01
-5.70066512e-01 6.47560298e-01 3.86222064e-01 -6.89086199e-01
-7.62037039e-01 -1.25491452e+00 8.15142274e-01 -3.82862777e-01
5.00128448e-01 1.53433144e-01 -8.31125796e-01 6.04037285e-01
-3.39764208e-01 -2.36401856e-01 1.49886179e+00 -7.34921172e-02
-1.62414283e-01 -2.43362248e-01 -1.72133863e+00 2.34483644e-01
2.86739707e-01 -2.33705610e-01 -3.58147532e-01 5.08124888e-01
2.08483323e-01 -6.90789759e-01 -1.22058845e+00 4.01953876e-01
6.60572231e-01 -6.81367695e-01 5.71204305e-01 -4.33184683e-01
4.96296495e-01 -5.19650757e-01 5.72664030e-02 -1.38734853e+00
-4.39035833e-01 2.42128205e-02 4.49872911e-01 1.17047834e+00
7.57381558e-01 -8.52125168e-01 1.04999268e+00 7.36716390e-01
-3.12786937e-01 -7.79979289e-01 -8.64781678e-01 -6.16572797e-01
2.33454779e-01 -9.45458934e-03 5.29628217e-01 9.22138929e-01
-1.20240189e-02 2.28348047e-01 1.24282539e-01 7.80712441e-02
3.23104769e-01 -3.10653299e-01 7.03796446e-01 -1.11861157e+00
-3.90383124e-01 -4.34567004e-01 -5.84529936e-01 -1.63667366e-01
-2.54694879e-01 -1.13520026e+00 -2.31474862e-01 -1.82500064e+00
5.72449327e-01 -6.07364893e-01 -5.87424219e-01 7.54841805e-01
-2.62425274e-01 6.11180544e-01 -1.85251847e-01 1.89235985e-01
3.27581540e-02 -2.51479477e-01 9.98359740e-01 -3.09510678e-01
-3.96637589e-01 7.67363161e-02 -1.07956243e+00 3.99181128e-01
7.76929080e-01 -5.50635576e-01 -4.82964814e-01 1.42134592e-01
1.95560008e-02 -2.02481464e-01 1.39191017e-01 -9.50652719e-01
-6.75453022e-02 -2.89362639e-01 8.67965043e-01 -5.52586854e-01
-5.90268448e-02 -6.62339807e-01 6.61397040e-01 7.93211818e-01
-1.24064885e-01 -4.23110873e-01 4.21371728e-01 1.77783236e-01
6.17439561e-02 -4.39797819e-01 1.04102266e+00 -5.00741042e-02
-4.52797532e-01 1.39175460e-01 -2.34086394e-01 -2.60422677e-01
1.26326716e+00 -4.29894000e-01 -6.11994147e-01 -3.09771776e-01
-9.59406376e-01 1.50403261e-01 4.13367897e-01 7.99175650e-02
2.72295773e-01 -1.33135593e+00 -6.76814497e-01 1.17023982e-01
3.85820627e-01 3.33141573e-02 5.62830687e-01 1.15891302e+00
-6.90418780e-01 6.46645427e-01 -4.22429204e-01 -9.71991241e-01
-1.10968769e+00 4.69862849e-01 4.47175682e-01 -7.69035637e-01
-1.36480749e-01 7.11842656e-01 5.77486634e-01 -5.10733306e-01
-2.08620876e-01 -2.86095530e-01 -2.17452526e-01 -1.24262730e-02
6.21680200e-01 2.61199832e-01 4.54829991e-01 -3.77109706e-01
-4.77934480e-01 4.97119367e-01 -4.06671464e-01 7.19509423e-02
1.27275074e+00 3.31514984e-01 2.90265620e-01 3.15437138e-01
1.06069982e+00 -1.57282352e-01 -8.53486419e-01 3.31452372e-03
9.09394585e-03 -1.68866083e-01 -1.08816914e-01 -1.50708675e+00
-7.92176545e-01 5.55476308e-01 1.18771136e+00 -4.48319405e-01
1.10749197e+00 -7.56949466e-03 4.96056497e-01 -2.77848691e-01
4.28838879e-01 -1.01797938e+00 3.15702744e-02 6.17865808e-02
6.45022929e-01 -1.07928932e+00 -1.99324593e-01 -2.59308338e-01
-7.39791572e-01 9.02557194e-01 5.31873047e-01 1.13977566e-01
4.42265451e-01 1.91289142e-01 3.30718517e-01 -1.44775942e-01
-4.96164411e-01 2.71425117e-02 1.07898310e-01 1.26880968e+00
6.45468652e-01 9.04324949e-02 -6.91152871e-01 1.05103600e+00
6.55932212e-03 6.74840450e-01 5.39494395e-01 9.11099255e-01
2.87342500e-02 -1.38543224e+00 -5.19533336e-01 1.09479725e+00
-5.95862806e-01 7.17430860e-02 -3.96434546e-01 9.27627683e-01
3.22046697e-01 8.32940400e-01 -2.61285692e-01 -3.36248577e-01
3.65821511e-01 3.34165394e-01 3.32225889e-01 -7.98266232e-01
-6.04959488e-01 3.15142781e-01 1.05336316e-01 -1.27145693e-01
-3.02828223e-01 -6.58291280e-01 -1.35919595e+00 -1.16538502e-01
-2.81380415e-01 1.15035728e-01 7.18382239e-01 8.00176680e-01
4.12824929e-01 5.95885098e-01 3.77456784e-01 -4.88791794e-01
-4.21201766e-01 -1.21985316e+00 -6.76836610e-01 3.86361539e-01
7.71729201e-02 -8.12804222e-01 -3.07172358e-01 3.72101739e-02] | [15.176328659057617, -2.8842287063598633] |
0c897ba9-264d-4f27-baa2-7e4ff8b6c2bf | multi-person-3d-pose-estimation-from | 2212.08731 | null | https://arxiv.org/abs/2212.08731v1 | https://arxiv.org/pdf/2212.08731v1.pdf | Multi-person 3D pose estimation from unlabelled data | Its numerous applications make multi-human 3D pose estimation a remarkably impactful area of research. Nevertheless, assuming a multiple-view system composed of several regular RGB cameras, 3D multi-pose estimation presents several challenges. First of all, each person must be uniquely identified in the different views to separate the 2D information provided by the cameras. Secondly, the 3D pose estimation process from the multi-view 2D information of each person must be robust against noise and potential occlusions in the scenario. In this work, we address these two challenges with the help of deep learning. Specifically, we present a model based on Graph Neural Networks capable of predicting the cross-view correspondence of the people in the scenario along with a Multilayer Perceptron that takes the 2D points to yield the 3D poses of each person. These two models are trained in a self-supervised manner, thus avoiding the need for large datasets with 3D annotations. | ['Luis J. Manso', 'George Vogiatzis', 'Pilar Bachiller', 'Daniel Rodriguez-Criado'] | 2022-12-16 | null | null | null | null | ['3d-pose-estimation'] | ['computer-vision'] | [-1.12748832e-01 1.50926067e-02 1.86090797e-01 -3.41591895e-01
-3.94321322e-01 -4.49326396e-01 3.90732139e-01 -3.16641256e-02
-4.42428380e-01 2.89001107e-01 1.02659263e-01 2.78048664e-01
1.36500314e-01 -6.01464748e-01 -7.10945666e-01 -3.90585005e-01
2.54533112e-01 8.91522527e-01 5.23045696e-02 -3.43441963e-03
2.24322993e-02 8.08924437e-01 -1.50617015e+00 -1.79662198e-01
2.42885083e-01 9.76338327e-01 -1.81337986e-02 7.51832008e-01
4.42663044e-01 1.33447289e-01 -5.69941223e-01 -5.57607174e-01
6.17450178e-01 -2.61010751e-02 -3.89225960e-01 7.62959719e-01
9.79834318e-01 -6.56662464e-01 -3.94457757e-01 7.87235439e-01
5.84010303e-01 1.05336137e-01 4.44300711e-01 -1.31349277e+00
-1.00048989e-01 -2.97478765e-01 -7.79134631e-01 -1.41659021e-01
1.06708121e+00 4.35493179e-02 6.00983202e-01 -8.48068237e-01
5.61401546e-01 1.34500754e+00 7.59806931e-01 4.69628841e-01
-9.20026422e-01 -2.00596854e-01 8.04309547e-02 -2.01554429e-02
-1.39643312e+00 -2.30857000e-01 1.05841494e+00 -5.98951340e-01
6.63385987e-01 6.27649724e-02 1.13029611e+00 1.20472097e+00
1.32406861e-01 4.65945929e-01 1.11554480e+00 -4.37997997e-01
-9.25736874e-02 2.80332118e-01 -6.66428134e-02 8.84273708e-01
4.03184980e-01 -1.13702536e-01 -5.49335897e-01 4.63564023e-02
1.03305948e+00 5.12337446e-01 -5.66307232e-02 -9.63234246e-01
-1.07329893e+00 3.97626221e-01 4.89104331e-01 -1.79144904e-01
-3.90847623e-01 8.65292177e-02 7.31392577e-02 -2.47584328e-01
3.68046135e-01 3.91110918e-03 -3.41579080e-01 2.76401937e-01
-6.00743890e-01 2.68604606e-01 5.20501256e-01 1.00978661e+00
7.10435510e-01 -2.70405680e-01 4.65026200e-01 4.33854282e-01
7.52622247e-01 6.06731594e-01 2.87408698e-02 -7.02350020e-01
8.21116865e-01 1.05855346e+00 1.95156038e-01 -1.27010131e+00
-7.72773027e-01 -4.25315142e-01 -8.82312655e-01 2.97893882e-01
7.44756043e-01 -2.24705219e-01 -6.37327015e-01 1.33462691e+00
7.17806518e-01 -3.22115660e-01 -3.14487427e-01 1.14561450e+00
7.26326883e-01 1.14481173e-01 -3.82339954e-01 1.77798599e-01
1.44020319e+00 -9.01964426e-01 -3.82667065e-01 -7.55042851e-01
-9.45230946e-03 -5.77894568e-01 4.48794425e-01 2.75361419e-01
-1.18268430e+00 -7.05389261e-01 -1.05366945e+00 -1.94684505e-01
-4.37152296e-01 3.49930942e-01 2.28642493e-01 6.54452205e-01
-8.31058085e-01 3.60280395e-01 -8.17799926e-01 -6.14664018e-01
9.32932496e-02 5.95295370e-01 -8.98281455e-01 -7.81762600e-02
-8.05221558e-01 1.19193661e+00 2.65737444e-01 6.05591357e-01
-4.43113863e-01 -1.13556772e-01 -1.01685357e+00 -2.05302253e-01
6.35879457e-01 -1.11043251e+00 7.77709186e-01 -7.41679490e-01
-1.22151971e+00 1.26135361e+00 -1.24644212e-01 4.63254489e-02
9.87027884e-01 -6.49105430e-01 -7.06383288e-02 1.69328213e-01
1.00800909e-01 4.77818012e-01 8.24506104e-01 -1.41707063e+00
-4.12744731e-01 -1.20787406e+00 1.32279694e-01 6.35200202e-01
2.25519910e-02 -2.65362740e-01 -9.35008168e-01 -2.25061119e-01
4.82191235e-01 -1.10056686e+00 -3.66308928e-01 1.97606176e-01
-6.85562611e-01 4.88680489e-02 6.17250800e-01 -7.49709010e-01
4.27154660e-01 -1.79334617e+00 5.23213744e-01 1.94238767e-01
3.89013469e-01 -2.32927874e-02 3.47624362e-01 2.69099772e-01
9.89358202e-02 -3.30646634e-01 3.24235022e-01 -7.75912702e-01
-8.39618146e-02 -9.52499285e-02 4.43810314e-01 8.64106417e-01
8.92429575e-02 6.62920415e-01 -6.11304700e-01 -5.17471492e-01
5.48366964e-01 7.63553679e-01 -2.68710405e-01 6.07189476e-01
1.77382484e-01 6.70025051e-01 -3.81579638e-01 6.89447820e-01
6.78326547e-01 -3.49947810e-01 1.82535052e-01 -4.52204287e-01
2.07030654e-01 -1.75535470e-01 -1.55179632e+00 1.75453448e+00
-2.58925110e-01 1.92908600e-01 1.07239470e-01 -5.93934476e-01
7.97839701e-01 4.11117405e-01 6.49444282e-01 -2.50855923e-01
3.21981579e-01 1.55721277e-01 -5.53191066e-01 -4.00718927e-01
4.33904707e-01 1.66848041e-02 -2.13474035e-01 2.82496870e-01
7.01153949e-02 2.59771258e-01 -1.24688119e-01 -2.22129628e-01
5.51102221e-01 3.86643380e-01 4.85844970e-01 4.52548176e-01
6.40985787e-01 -3.63137186e-01 3.93918157e-01 2.50809789e-01
-3.16575229e-01 8.14030468e-01 1.90657720e-01 -9.06445444e-01
-1.05043507e+00 -1.13193130e+00 3.67835104e-01 4.49487060e-01
4.70483720e-01 -2.66476572e-01 -6.39597058e-01 -7.84710228e-01
9.19968039e-02 -4.73953784e-02 -4.23329979e-01 1.77336648e-01
-7.57679045e-01 -4.34554398e-01 1.64701104e-01 5.47601461e-01
5.19061923e-01 -4.00972992e-01 -1.13572729e+00 -1.56834871e-01
-3.05482209e-01 -1.54627240e+00 -5.54548442e-01 3.98111828e-02
-7.75422096e-01 -1.40680623e+00 -7.94990003e-01 -5.76879919e-01
9.72222924e-01 5.17640293e-01 1.00574136e+00 -1.42804191e-01
-1.51110396e-01 6.72062039e-01 -4.16528583e-02 -1.48897335e-01
1.01980772e-02 -1.15454666e-01 3.97925526e-01 2.77118206e-01
4.99342680e-01 -5.42064726e-01 -6.08376861e-01 3.52481723e-01
-2.03024566e-01 1.90678373e-01 5.32587588e-01 4.58871424e-01
7.05232739e-01 -3.01600862e-02 -1.48484364e-01 -4.78013575e-01
4.97332737e-02 -6.11267388e-02 -5.44433773e-01 3.25133562e-01
-1.48886131e-04 -3.90453368e-01 4.38345343e-01 -1.97009042e-01
-7.35137284e-01 8.35148275e-01 1.16274253e-01 -5.61997354e-01
-6.84867918e-01 -1.27576804e-02 -5.46322048e-01 -5.74949458e-02
4.90287215e-01 4.70655151e-02 1.38588488e-01 -5.79926789e-01
2.36183360e-01 6.13938749e-01 4.95620638e-01 -1.16474070e-01
8.61224353e-01 5.80081880e-01 3.98921877e-01 -8.56937289e-01
-9.38898206e-01 -6.34611905e-01 -1.56172001e+00 -6.63441241e-01
1.19449008e+00 -1.27611923e+00 -9.47892666e-01 6.37963057e-01
-1.36656594e+00 3.46926719e-01 1.58990294e-01 6.01228416e-01
-5.67440093e-01 4.56182063e-01 -2.41653040e-01 -1.03046298e+00
-2.42600888e-01 -1.36425722e+00 1.46684110e+00 3.15124959e-01
-2.46976778e-01 -8.48054588e-01 -6.52345642e-02 9.28404570e-01
-3.75107378e-01 6.42106414e-01 2.46894613e-01 -3.65804970e-01
-6.87198997e-01 -7.14274764e-01 1.17450573e-01 5.69558814e-02
-4.03611641e-03 -4.04547453e-01 -1.00140762e+00 -3.36128086e-01
1.43436370e-02 -2.24981725e-01 2.26542830e-01 4.99291599e-01
5.66718638e-01 -1.11443147e-01 -3.71589571e-01 7.29619384e-01
1.30787551e+00 -3.19892436e-01 8.61306489e-03 4.63434517e-01
1.24007821e+00 8.19152653e-01 1.81586832e-01 4.63704079e-01
7.59718716e-01 9.83025789e-01 7.52144277e-01 -2.63663437e-02
2.09870227e-02 -3.65029335e-01 1.95995972e-01 5.51426530e-01
-4.55412567e-01 -9.40106511e-02 -8.73894274e-01 9.06174332e-02
-1.64348769e+00 -6.22763693e-01 -1.89421341e-01 2.37581682e+00
3.10489759e-02 9.39545259e-02 4.68923479e-01 1.06484018e-01
7.78939188e-01 1.36426136e-01 -4.66933638e-01 6.81508034e-02
9.33224708e-02 -4.72171575e-01 5.10831594e-01 2.62874573e-01
-1.36754620e+00 4.85213846e-01 5.67146063e+00 -1.31819472e-01
-7.25252092e-01 -3.11150670e-01 3.53987128e-01 -3.12216014e-01
3.15806776e-01 -1.41153574e-01 -1.00464618e+00 2.21222892e-01
4.90959525e-01 4.95663613e-01 1.46353215e-01 7.51460016e-01
-4.11286913e-02 -2.66573787e-01 -1.29762614e+00 1.39964473e+00
5.30839741e-01 -7.76390493e-01 -8.80033895e-02 3.01346153e-01
4.54039216e-01 -1.43587634e-01 -1.06823511e-01 -2.84578919e-01
-7.96295479e-02 -8.01835716e-01 8.74574304e-01 7.54192770e-01
4.92005169e-01 -7.49039173e-01 7.06646740e-01 8.11154306e-01
-1.08006430e+00 2.22826414e-02 -1.33773297e-01 -1.64508030e-01
4.77638125e-01 5.03715754e-01 -7.66567826e-01 8.51075411e-01
6.91304088e-01 7.61916339e-01 -6.96970701e-01 8.42491210e-01
-2.28030011e-01 -4.23043758e-01 -3.17129403e-01 1.77815735e-01
-2.08116472e-01 -3.35209906e-01 5.25947273e-01 6.88580573e-01
3.23299229e-01 -2.23306268e-01 5.62829375e-01 4.95365709e-01
9.97884646e-02 -2.27247924e-01 -7.18989670e-01 4.23087597e-01
1.07389510e-01 1.28431702e+00 -7.31832087e-01 -3.37279215e-02
-5.33971369e-01 1.19452405e+00 4.39802945e-01 1.26988426e-01
-6.07049763e-01 6.31543323e-02 4.08982366e-01 4.53297436e-01
1.01168580e-01 -6.13274813e-01 -2.04168797e-01 -1.33588123e+00
4.80876446e-01 -6.97203159e-01 2.97129571e-01 -9.37423170e-01
-1.28536332e+00 6.65551007e-01 7.20248595e-02 -1.45942414e+00
-4.00729537e-01 -8.15642774e-01 -6.35305867e-02 1.00110400e+00
-1.11653519e+00 -1.48453629e+00 -5.50717711e-01 5.14199138e-01
3.91322881e-01 -1.43047407e-01 7.01403379e-01 1.45159364e-01
-6.59531832e-01 3.40591341e-01 -3.92074347e-01 3.69820327e-01
7.03640878e-01 -1.24756956e+00 4.09380704e-01 8.56734812e-01
2.34400451e-01 5.39667308e-01 4.90592480e-01 -6.76556289e-01
-1.74977565e+00 -7.43261576e-01 1.00652385e+00 -9.47134078e-01
-7.11668166e-04 -5.19256175e-01 -2.66924769e-01 8.66237104e-01
-1.76930204e-01 1.34206027e-01 6.01826608e-01 1.32266209e-01
-3.28708202e-01 -1.35729074e-01 -9.68734920e-01 4.25107330e-01
1.01928031e+00 -4.78353113e-01 -5.28633654e-01 2.80484974e-01
1.84884086e-01 -9.80701625e-01 -8.00780535e-01 9.29773822e-02
6.94991350e-01 -1.24020660e+00 1.42906737e+00 -3.04341376e-01
2.26832286e-01 -3.03064883e-01 -2.03017786e-01 -1.10387135e+00
-9.27724317e-02 -1.90919474e-01 -3.73827577e-01 7.92678535e-01
-1.71034366e-01 -4.20987815e-01 1.07225621e+00 8.49056423e-01
3.74774009e-01 -5.82448483e-01 -9.74474132e-01 -4.68906045e-01
-5.64513087e-01 -1.21502392e-01 3.84646118e-01 3.77488256e-01
-3.15605283e-01 7.80021846e-01 -7.44560182e-01 5.49677491e-01
9.35213566e-01 1.54066026e-01 1.27047348e+00 -1.54586792e+00
-3.54829520e-01 -8.49863589e-02 -7.14819670e-01 -1.26189625e+00
-4.13105823e-02 -5.25252759e-01 -1.71848342e-01 -1.51387286e+00
2.10109904e-01 -2.30406802e-02 3.28706086e-01 7.57905319e-02
-1.79139584e-01 2.25486398e-01 3.49717468e-01 2.29153007e-01
-6.03805840e-01 4.01554137e-01 1.22641206e+00 4.48418371e-02
-3.45775671e-02 4.17702645e-01 -3.96959215e-01 1.18047965e+00
4.79482561e-01 -2.41614997e-01 -2.04984322e-01 -6.46731555e-01
3.77854049e-01 2.86254853e-01 8.75987887e-01 -1.12969124e+00
3.99673939e-01 4.63868938e-02 1.12895620e+00 -1.02914143e+00
9.98959839e-01 -1.24291921e+00 4.00347054e-01 3.16629022e-01
1.12670306e-02 4.55209225e-01 -1.36568651e-01 8.12360883e-01
8.41207281e-02 -2.86952127e-02 5.22118926e-01 -7.64932513e-01
-3.56565624e-01 5.38341403e-01 -4.84946696e-03 -1.25311539e-01
1.13220298e+00 -6.68739319e-01 2.70645350e-01 -3.72943431e-01
-8.21119070e-01 2.24268466e-01 6.55694306e-01 3.18950444e-01
6.99523389e-01 -1.30061924e+00 -4.28645283e-01 5.19789279e-01
3.97802591e-02 4.73170668e-01 4.17190760e-01 6.63819730e-01
-4.40375686e-01 3.83499920e-01 -3.27715784e-01 -7.82326937e-01
-1.50529516e+00 6.73907161e-01 4.97070968e-01 -1.77789882e-01
-6.37331724e-01 5.28239369e-01 -8.77979696e-02 -7.12767124e-01
2.80529708e-01 8.94199386e-02 -3.37156206e-01 7.17636198e-02
4.20917064e-01 5.99686742e-01 4.27801199e-02 -1.39673209e+00
-5.07773459e-01 1.11197114e+00 1.87782735e-01 -1.50120631e-01
1.42631984e+00 -4.12701011e-01 -6.29214430e-03 4.49586153e-01
1.21228898e+00 4.14239578e-02 -1.51203275e+00 -3.29470724e-01
-3.61097306e-01 -7.07799673e-01 -3.33488256e-01 -4.49139416e-01
-1.02742815e+00 1.03293693e+00 4.59157676e-01 9.35134962e-02
9.00136828e-01 -1.64600521e-01 6.23682559e-01 2.79091686e-01
4.73814666e-01 -1.07232749e+00 2.16358989e-01 2.77392715e-01
7.77359545e-01 -1.49820304e+00 3.06819469e-01 -5.14754176e-01
-4.97817904e-01 1.30230188e+00 7.56792188e-01 -1.78699657e-01
5.70214331e-01 -1.15700454e-01 2.02049941e-01 -2.97125190e-01
-1.98978007e-01 -1.36535630e-01 6.01442218e-01 7.12589920e-01
2.02564865e-01 1.31444400e-02 3.02447289e-01 1.90384537e-01
-1.98624507e-01 -2.69393802e-01 2.96317399e-01 7.81368613e-01
-8.72990936e-02 -9.97180402e-01 -8.29690397e-01 4.09211628e-02
-2.89743960e-01 6.32593393e-01 -6.50288284e-01 7.20291615e-01
3.48943681e-01 8.22320223e-01 -5.20136878e-02 -4.58045989e-01
7.70317972e-01 9.30531919e-02 7.82337546e-01 -5.58819473e-01
-5.42168081e-01 2.84177065e-01 -2.11532116e-01 -6.13883436e-01
-6.54723406e-01 -6.51584268e-01 -7.78598189e-01 -2.16268197e-01
-1.69923216e-01 -4.42183316e-01 6.18542671e-01 1.00319266e+00
2.35113591e-01 2.59485751e-01 5.53067327e-01 -1.41604161e+00
-4.84930992e-01 -5.69801033e-01 -6.01493418e-01 5.40305793e-01
4.57154721e-01 -8.32150161e-01 -9.86648574e-02 9.24529694e-03] | [7.056605815887451, -1.0324230194091797] |
c311ea2b-380b-4df1-8520-f213e39907b6 | discovering-new-intents-with-deep-aligned | 2012.08987 | null | https://arxiv.org/abs/2012.08987v7 | https://arxiv.org/pdf/2012.08987v7.pdf | Discovering New Intents with Deep Aligned Clustering | Discovering new intents is a crucial task in dialogue systems. Most existing methods are limited in transferring the prior knowledge from known intents to new intents. They also have difficulties in providing high-quality supervised signals to learn clustering-friendly features for grouping unlabeled intents. In this work, we propose an effective method, Deep Aligned Clustering, to discover new intents with the aid of the limited known intent data. Firstly, we leverage a few labeled known intent samples as prior knowledge to pre-train the model. Then, we perform k-means to produce cluster assignments as pseudo-labels. Moreover, we propose an alignment strategy to tackle the label inconsistency problem during clustering assignments. Finally, we learn the intent representations under the supervision of the aligned pseudo-labels. With an unknown number of new intents, we predict the number of intent categories by eliminating low-confidence intent-wise clusters. Extensive experiments on two benchmark datasets show that our method is more robust and achieves substantial improvements over the state-of-the-art methods. The codes are released at https://github.com/thuiar/DeepAligned-Clustering. | ['Rui Lyu', 'Ting-En Lin', 'Hua Xu', 'Hanlei Zhang'] | 2020-12-16 | null | null | null | null | ['text-clustering', 'open-intent-discovery', 'short-text-clustering'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 5.87759428e-02 3.39108258e-02 -3.70474577e-01 -1.02817762e+00
-8.60147834e-01 -6.25805140e-01 4.80519354e-01 -7.69619718e-02
-3.73233885e-01 5.87192237e-01 4.50347632e-01 9.59840119e-02
2.10982963e-01 -2.44345874e-01 -2.47271046e-01 -6.23995781e-01
1.17519379e-01 7.44668663e-01 -2.10202694e-01 -1.16871156e-01
5.23581564e-01 -2.89529920e-01 -1.26370144e+00 5.63938498e-01
1.24834192e+00 6.58976912e-01 3.21556479e-01 4.13728952e-01
-2.48316005e-01 7.65626788e-01 -5.93843758e-01 -2.42412955e-01
1.78373352e-01 -5.00318229e-01 -1.14914525e+00 4.01098132e-01
-1.09172575e-01 -3.24155539e-01 -2.19459727e-01 9.37970698e-01
2.08106324e-01 3.68479103e-01 6.59682751e-01 -1.32713187e+00
-4.47213918e-01 6.87735736e-01 -5.85810602e-01 -2.29644313e-01
5.30165732e-01 1.85577452e-01 1.23609507e+00 -9.63033020e-01
4.60607231e-01 1.02686512e+00 6.46329641e-01 7.38854587e-01
-9.98661399e-01 -6.12365842e-01 3.48822623e-01 4.48664576e-01
-1.37583470e+00 -6.05618060e-01 9.54526961e-01 -3.64607513e-01
5.91643870e-01 3.14497769e-01 2.16479152e-01 1.03618467e+00
-4.11086649e-01 1.21809089e+00 8.29951406e-01 -4.49839979e-01
2.78572083e-01 3.31654936e-01 6.06753111e-01 7.05550253e-01
-2.68799245e-01 -5.33026934e-01 -5.85532010e-01 -3.09944212e-01
1.54886231e-01 3.68690073e-01 -3.59209478e-01 -2.43100524e-01
-1.33531749e+00 9.70242739e-01 4.25140768e-01 4.20742750e-01
-2.16837376e-01 -2.87573874e-01 4.68344122e-01 1.44650131e-01
5.61780870e-01 5.50649047e-01 -6.88845396e-01 -1.49181664e-01
-6.35912955e-01 -1.50450259e-01 8.55677962e-01 9.80812907e-01
1.17225087e+00 -4.05133218e-01 -2.41393909e-01 1.12649822e+00
3.91502798e-01 2.64723562e-02 7.58989334e-01 -1.02055407e+00
6.16333902e-01 9.61094260e-01 1.58899292e-01 -7.92651474e-01
-5.67968130e-01 -2.25031987e-01 -9.06373024e-01 -5.42802513e-01
2.38753334e-01 -4.13574100e-01 -7.53652871e-01 1.61739278e+00
4.30953383e-01 2.79279649e-01 2.94703782e-01 8.79844964e-01
6.72031105e-01 7.60140300e-01 -3.07578593e-01 -4.16592360e-01
1.17732227e+00 -1.26449263e+00 -8.62008393e-01 -2.89038509e-01
7.91167140e-01 -7.16117084e-01 1.22406232e+00 3.18179399e-01
-4.47268397e-01 -5.66087484e-01 -6.98548913e-01 1.10982314e-01
-1.23632580e-01 5.15325129e-01 8.19931269e-01 4.62940812e-01
-8.46646905e-01 1.84190899e-01 -7.50196934e-01 -2.12565348e-01
1.42667294e-01 3.70761812e-01 -1.87833250e-01 -2.39656404e-01
-1.08160722e+00 4.67173517e-01 5.96989810e-01 -2.25790143e-02
-6.61858082e-01 -4.54954386e-01 -9.49997485e-01 -9.26120281e-02
6.20066702e-01 -3.89060766e-01 1.56290209e+00 -8.35517883e-01
-1.53085005e+00 6.13715410e-01 -5.85656822e-01 -1.35670885e-01
2.09028959e-01 -9.90858674e-02 -1.29007220e-01 5.38463481e-02
4.81966913e-01 8.45908701e-01 6.55133307e-01 -1.48981726e+00
-9.95414972e-01 -3.12602282e-01 -5.51026165e-02 6.83090448e-01
-6.53089941e-01 -3.11540335e-01 -7.20075548e-01 -3.88491482e-01
3.08214664e-01 -9.93941844e-01 -3.24367166e-01 -5.21633208e-01
-6.91459835e-01 -6.01100445e-01 8.83766174e-01 -5.05981863e-01
1.05844176e+00 -2.15293455e+00 2.15202391e-01 -1.05518259e-01
4.22873408e-01 1.13572098e-01 2.01155860e-02 3.84867162e-01
2.09167644e-01 -7.04862177e-03 -4.03602928e-01 -6.89852357e-01
9.83617753e-02 2.91201800e-01 -1.20585643e-01 2.08042771e-01
9.74938646e-02 6.57565594e-01 -1.06545341e+00 -4.38231111e-01
2.54610032e-01 3.76654901e-02 -5.45688510e-01 6.86957836e-01
-3.27077955e-01 7.65572727e-01 -4.18330461e-01 4.33730960e-01
5.49400985e-01 -4.93309349e-01 3.71465772e-01 -2.17368498e-01
4.44273353e-02 5.82711875e-01 -9.64529335e-01 2.10360169e+00
-5.28467298e-01 4.47368532e-01 5.14574498e-02 -1.22674859e+00
1.00717688e+00 2.60909110e-01 4.30456817e-01 -5.98493405e-02
2.34619770e-02 3.69093530e-02 -7.16179237e-02 -5.45896053e-01
5.78190863e-01 -5.02610020e-02 -4.69085097e-01 7.97185957e-01
3.05557847e-01 4.38837707e-02 2.22955868e-01 3.53553325e-01
9.54496861e-01 -3.21169555e-01 3.41082543e-01 6.05108701e-02
5.21368861e-01 1.67687848e-01 8.41741204e-01 6.20246768e-01
-2.16290623e-01 6.51318192e-01 3.16439241e-01 -5.55443287e-01
-5.95164716e-01 -6.84768915e-01 1.10701405e-01 1.34055007e+00
1.61697790e-01 -5.20039737e-01 -5.88951826e-01 -1.20130682e+00
-3.70191753e-01 8.20581436e-01 -4.91907299e-01 -2.27431282e-01
-3.34270716e-01 -9.71839309e-01 3.94383878e-01 3.16707641e-01
6.87881708e-01 -8.58626902e-01 -5.31555228e-02 1.68030903e-01
-7.94693887e-01 -9.05471861e-01 -7.06130147e-01 3.79160821e-01
-5.40777802e-01 -1.07036972e+00 -4.75048095e-01 -1.02295709e+00
9.55849648e-01 5.94026625e-01 7.80011535e-01 1.61243796e-01
3.89741431e-03 1.31671965e-01 -7.18914926e-01 4.69307788e-02
-3.52284938e-01 3.20462883e-01 2.73960441e-01 2.55745500e-01
7.39585280e-01 -2.09089503e-01 -3.83299738e-01 4.95055139e-01
-5.99875033e-01 3.43668938e-01 4.32283223e-01 1.11707854e+00
2.51865834e-01 2.95607060e-01 7.11982131e-01 -1.07395053e+00
7.23902345e-01 -6.53194070e-01 -5.20207211e-02 2.87074745e-01
-3.95890683e-01 1.09805211e-01 9.26784873e-01 -3.67827713e-01
-1.29342008e+00 3.97417158e-01 -1.74918965e-01 -3.13876688e-01
-5.43895006e-01 5.54117262e-01 -3.11420590e-01 3.53495419e-01
3.90147626e-01 3.17381054e-01 -2.28331760e-01 -5.99312603e-01
5.53441286e-01 1.24015617e+00 5.40771067e-01 -5.24840117e-01
4.71040457e-01 3.77668321e-01 -8.65527034e-01 -6.67864442e-01
-1.15679657e+00 -1.08834589e+00 -1.09086418e+00 -7.14706853e-02
6.79390013e-01 -9.62470531e-01 -3.41424286e-01 5.44265091e-01
-1.16469264e+00 -3.58633310e-01 -5.67460665e-04 4.91766781e-01
-4.20459658e-01 7.09437966e-01 -6.20643914e-01 -7.57637441e-01
-3.87145251e-01 -1.15577841e+00 9.01460409e-01 4.20390457e-01
-3.46760154e-01 -9.55950499e-01 4.46814932e-02 8.04101825e-01
-4.71853018e-02 -3.49522650e-01 5.73851049e-01 -1.31418490e+00
-2.15634704e-01 -1.79491058e-01 -1.40240431e-01 1.42693728e-01
7.15729654e-01 -2.27414414e-01 -9.48834777e-01 -2.58422703e-01
3.18222821e-01 -5.75016379e-01 8.81863952e-01 6.51004389e-02
1.00392711e+00 -6.11731052e-01 -4.41504508e-01 4.65443462e-01
7.99531102e-01 3.71099323e-01 1.00649506e-01 -1.00959495e-01
8.68690610e-01 6.92488551e-01 1.09160423e+00 7.76388645e-01
9.84727561e-01 7.25986063e-01 8.43899474e-02 -4.72762249e-02
2.12314457e-01 -1.42556697e-01 2.11188227e-01 1.32001889e+00
3.97238761e-01 -1.36749074e-01 -1.10876644e+00 5.91875792e-01
-2.17003775e+00 -6.62760735e-01 5.28755262e-02 1.93307853e+00
1.25932908e+00 -4.92853932e-02 -3.73220369e-02 -5.09903729e-02
1.09393120e+00 -7.94879720e-02 -6.73295856e-01 1.30155757e-01
3.73281777e-01 -4.47496355e-01 -7.81852603e-02 5.85611820e-01
-1.30530953e+00 1.25452578e+00 4.99186563e+00 7.39625573e-01
-7.76422441e-01 1.83479697e-01 8.24867725e-01 1.73224166e-01
-1.95370659e-01 1.08085290e-01 -9.54169512e-01 6.44048214e-01
4.98154521e-01 6.13411702e-02 3.46868396e-01 9.13052201e-01
1.21519312e-01 -6.56127855e-02 -1.09608138e+00 8.74445319e-01
5.05912602e-01 -1.00775492e+00 -1.29768506e-01 -2.28850856e-01
9.07253206e-01 -1.11962646e-01 -1.18281960e-01 6.16456568e-01
7.56768525e-01 -6.06429815e-01 6.89346418e-02 3.98402363e-01
5.73944151e-01 -6.96618497e-01 9.76702869e-01 7.25626886e-01
-1.03233361e+00 -5.43863028e-02 -4.79946017e-01 -2.39110053e-01
5.08344732e-02 5.17600536e-01 -1.35876429e+00 5.63777804e-01
5.79704344e-01 1.03709137e+00 -5.35873175e-01 8.73013735e-01
-4.56250042e-01 6.44388437e-01 -3.47501010e-01 -7.93988332e-02
2.82029629e-01 -1.95999444e-01 1.73593804e-01 1.07904065e+00
7.49651692e-04 2.33055636e-01 6.44277334e-01 7.44830489e-01
-2.30380774e-01 5.88125139e-02 -3.96591067e-01 1.65058196e-01
7.80100346e-01 1.48968208e+00 -7.51355350e-01 -4.52251881e-01
-4.26053077e-01 1.13289094e+00 5.07325172e-01 1.20094500e-01
-6.13411486e-01 -3.66709143e-01 4.75084722e-01 -5.55323124e-01
3.96910943e-02 -1.45131618e-01 -2.38916442e-01 -1.43871570e+00
-1.11730970e-01 -9.07347023e-01 6.10325992e-01 -5.22910297e-01
-1.56344938e+00 4.48268861e-01 -1.24556117e-01 -1.28545451e+00
-4.04852122e-01 -2.05599800e-01 -8.49323094e-01 4.50472414e-01
-1.28656268e+00 -1.01244247e+00 -4.81662720e-01 5.58393538e-01
1.24413657e+00 -4.12916332e-01 8.05724978e-01 3.79301310e-02
-7.95259655e-01 6.84754133e-01 2.66094446e-01 4.25140053e-01
1.16275001e+00 -1.31449664e+00 2.94418424e-01 6.31188273e-01
2.19464839e-01 7.46601701e-01 3.87107253e-01 -6.08583391e-01
-1.04713285e+00 -1.13940191e+00 7.07886040e-01 -3.15855742e-01
4.47774827e-01 -6.35344923e-01 -1.07488096e+00 7.08241820e-01
2.70650089e-01 -3.82709175e-01 1.30351818e+00 3.52690876e-01
-2.37377003e-01 1.66055530e-01 -9.42239106e-01 5.42514622e-01
6.94124520e-01 -2.68573552e-01 -9.48338568e-01 6.30680978e-01
1.00108433e+00 -3.31245422e-01 -6.97461247e-01 3.23680401e-01
1.91889510e-01 -8.23383510e-01 6.09865487e-01 -4.19081330e-01
1.39083564e-01 -3.67984861e-01 -1.17647238e-01 -1.41680336e+00
-2.76540041e-01 -5.79503655e-01 1.17532723e-01 1.46849728e+00
5.07173300e-01 -3.56971294e-01 8.65684807e-01 7.07601547e-01
-3.93207461e-01 -6.05277479e-01 -7.53349483e-01 -4.24835175e-01
-2.77635962e-01 -2.05749020e-01 4.66467619e-01 1.49058282e+00
5.99071324e-01 8.28324556e-01 -6.19127393e-01 9.60852951e-02
5.06709218e-01 3.85990888e-01 1.13809025e+00 -1.18716764e+00
-1.76752478e-01 -5.41772768e-02 -5.66687025e-02 -1.42200172e+00
4.89241511e-01 -8.48061264e-01 4.63809192e-01 -1.54901671e+00
4.91283178e-01 -7.00376153e-01 -1.70202315e-01 9.34840560e-01
-7.40181088e-01 1.23758335e-02 4.19960096e-02 6.15987122e-01
-1.39055741e+00 8.03564072e-01 7.20137656e-01 -2.80499220e-01
-5.60959518e-01 2.54746556e-01 -8.36058378e-01 8.80765080e-01
1.28133428e+00 -5.98956645e-01 -3.79030973e-01 -1.57117203e-01
-2.12083474e-01 -1.74391285e-01 -2.17041165e-01 -8.80747497e-01
5.46337426e-01 -2.39221260e-01 1.99553341e-01 -8.95617485e-01
3.82927984e-01 -6.14726424e-01 -3.51444155e-01 3.45719546e-01
-6.32125616e-01 -5.17106712e-01 -2.20252752e-01 5.18258989e-01
-2.54604965e-01 -5.63489497e-01 5.08141100e-01 -2.57413030e-01
-9.15835440e-01 3.27826366e-02 -4.58931953e-01 2.53478408e-01
9.32559550e-01 1.71722293e-01 -3.56879383e-01 -5.65127015e-01
-6.00216329e-01 8.57965469e-01 5.50259709e-01 5.59328735e-01
6.08504295e-01 -1.07025826e+00 -6.17285848e-01 1.52021766e-01
2.99090207e-01 3.58037889e-01 2.77910292e-01 6.09281123e-01
-7.85470605e-02 4.37604666e-01 4.98820096e-02 -7.96672344e-01
-1.30091059e+00 6.38618708e-01 -5.75637147e-02 -3.37027580e-01
-2.86800981e-01 9.23119307e-01 3.52999002e-01 -1.24069798e+00
4.17536587e-01 7.39516467e-02 -3.69568974e-01 6.02909625e-02
4.60582584e-01 1.11011863e-01 -1.47758141e-01 -5.40838540e-01
-3.26218724e-01 3.16481531e-01 -7.09687054e-01 1.22169122e-01
1.23317409e+00 -5.21211922e-01 -1.12083368e-01 7.64259398e-01
1.18801367e+00 -2.97437340e-01 -1.30577409e+00 -4.63919818e-01
2.07369298e-01 -4.41334128e-01 -3.29267710e-01 -7.08638370e-01
-9.04050350e-01 8.48303795e-01 1.84574813e-01 3.45961414e-02
9.32369649e-01 1.46778569e-01 7.29403377e-01 8.30859601e-01
2.78217763e-01 -1.15203905e+00 5.19447625e-01 6.74494565e-01
5.41767418e-01 -1.71205473e+00 -1.50286585e-01 -4.90149289e-01
-1.19060171e+00 7.91661918e-01 1.00536048e+00 3.46008539e-01
3.05723310e-01 -2.55856682e-02 4.15302664e-01 -6.37868717e-02
-9.50883806e-01 -2.14086324e-01 5.26698716e-02 6.64770901e-01
3.49701941e-01 5.97757436e-02 -1.55869246e-01 9.30073559e-01
-1.50809348e-01 -3.59570444e-01 6.99664116e-01 7.68328428e-01
-5.43624997e-01 -1.10847795e+00 -2.56705284e-01 7.14384437e-01
-1.16459303e-01 -2.02065885e-01 -7.73234427e-01 2.61058241e-01
2.68963049e-03 1.38778710e+00 -1.20008521e-01 -7.47602284e-01
-8.38393196e-02 3.41547787e-01 -1.85244069e-01 -1.16940117e+00
-4.81243283e-01 1.10676162e-01 2.55529699e-03 -1.49983004e-01
-4.69450593e-01 -6.16005778e-01 -1.35580075e+00 2.00927705e-02
-7.56691933e-01 6.64267182e-01 4.52626884e-01 9.63057697e-01
4.79250580e-01 3.05121720e-01 1.16727293e+00 -8.24371994e-01
-4.45271343e-01 -1.23645806e+00 -3.31569374e-01 5.14925241e-01
6.63455650e-02 -5.19152761e-01 -5.58694959e-01 2.39465520e-01] | [12.412796020507812, 7.468223571777344] |
bc6c523a-4879-41f8-be9e-67483ec92fe2 | dynamic-clustering-transformer-network-for | 2306.08073 | null | https://arxiv.org/abs/2306.08073v1 | https://arxiv.org/pdf/2306.08073v1.pdf | Dynamic Clustering Transformer Network for Point Cloud Segmentation | Point cloud segmentation is one of the most important tasks in computer vision with widespread scientific, industrial, and commercial applications. The research thereof has resulted in many breakthroughs in 3D object and scene understanding. Previous methods typically utilized hierarchical architectures for feature representation. However, the commonly used sampling and grouping methods in hierarchical networks are only based on point-wise three-dimensional coordinates, ignoring local semantic homogeneity of point clusters. Additionally, the prevalent Farthest Point Sampling (FPS) method is often a computational bottleneck. To address these issues, we propose a novel 3D point cloud representation network, called Dynamic Clustering Transformer Network (DCTNet). It has an encoder-decoder architecture, allowing for both local and global feature learning. Specifically, we propose novel semantic feature-based dynamic sampling and clustering methods in the encoder, which enables the model to be aware of local semantic homogeneity for local feature aggregation. Furthermore, in the decoder, we propose an efficient semantic feature-guided upsampling method. Our method was evaluated on an object-based dataset (ShapeNet), an urban navigation dataset (Toronto-3D), and a multispectral LiDAR dataset, verifying the performance of DCTNet across a wide variety of practical engineering applications. The inference speed of DCTNet is 3.8-16.8$\times$ faster than existing State-of-the-Art (SOTA) models on the ShapeNet dataset, while achieving an instance-wise mIoU of $86.6\%$, the current top score. Our method similarly outperforms previous methods on the other datasets, verifying it as the new State-of-the-Art in point cloud segmentation. | ['Jonathan Li', 'Linlin Xu', 'Jing Du', 'Dilong Li', 'Kyle Yilin Gao', 'Jun Zhou', 'Dening Lu'] | 2023-05-30 | null | null | null | null | ['point-cloud-segmentation', 'scene-understanding', 'clustering'] | ['computer-vision', 'computer-vision', 'methodology'] | [-6.76259547e-02 -3.28817099e-01 -2.84459114e-01 -4.32908058e-01
-6.25392973e-01 -2.45011151e-01 3.96738350e-01 6.98867664e-02
-2.58489579e-01 2.35948026e-01 -4.48382109e-01 -2.31588274e-01
-2.44010270e-01 -1.03954208e+00 -6.90050066e-01 -5.60149848e-01
-1.48430854e-01 7.60762691e-01 5.98642945e-01 -2.25876253e-02
3.33153009e-01 9.90698516e-01 -1.92923653e+00 4.88636829e-02
1.04320216e+00 1.35099113e+00 4.68838185e-01 2.64864057e-01
-5.19551218e-01 1.59756601e-01 -3.99743825e-01 1.96508016e-03
6.03251934e-01 1.06522106e-01 -5.94401240e-01 3.08839977e-01
7.33126402e-01 -1.77360505e-01 -7.69782588e-02 1.13134670e+00
4.58674133e-01 1.78562582e-01 4.52403426e-01 -1.34315348e+00
-3.21348011e-01 2.56845176e-01 -8.70809138e-01 -9.77534950e-02
-2.11494878e-01 8.32247287e-02 1.06745625e+00 -9.03347433e-01
4.93288606e-01 1.41992724e+00 8.43099654e-01 1.92606911e-01
-1.02724767e+00 -8.85815620e-01 3.04341316e-01 3.71285319e-01
-1.66276050e+00 8.72005448e-02 8.58962774e-01 -3.83384764e-01
1.15218055e+00 1.90394111e-02 1.01103842e+00 3.11449707e-01
-5.13030663e-02 7.54603624e-01 8.92532408e-01 -4.02831472e-02
4.74960387e-01 -2.71680862e-01 1.54314205e-01 6.49719417e-01
2.28437141e-01 1.16876245e-01 -4.85456914e-01 -1.52055517e-01
1.05637813e+00 2.40847692e-01 8.94934610e-02 -6.77940905e-01
-9.65651333e-01 8.75113487e-01 8.45537126e-01 8.87347311e-02
-3.65202934e-01 3.98596555e-01 2.48197094e-01 7.20329657e-02
5.28228879e-01 1.14369109e-01 -5.87043047e-01 -8.24448913e-02
-1.21250308e+00 2.12761402e-01 5.58704615e-01 1.38390243e+00
1.11190009e+00 9.35707241e-02 2.00612724e-01 9.87767637e-01
4.24645364e-01 6.39296055e-01 2.94112936e-02 -1.26801324e+00
2.51969010e-01 9.53550041e-01 -3.99229676e-01 -1.12672806e+00
-4.19042408e-01 -5.71232319e-01 -1.03211594e+00 3.60838085e-01
-2.84661539e-02 4.27390039e-01 -1.14250779e+00 1.26713812e+00
4.97919053e-01 4.43946779e-01 -2.90670007e-01 7.58174300e-01
7.01361179e-01 5.97081125e-01 -8.58779401e-02 1.59747034e-01
1.23471630e+00 -6.17905319e-01 -1.28695652e-01 -1.08265392e-01
3.05791408e-01 -5.33834934e-01 8.43953669e-01 4.10852283e-01
-8.95540059e-01 -8.47936392e-01 -1.05433428e+00 -1.89356953e-01
-2.47197479e-01 -8.19418132e-02 8.16162825e-01 5.99251568e-01
-1.10590875e+00 6.44852638e-01 -1.02705693e+00 -4.01052624e-01
7.72197127e-01 6.46551549e-01 7.45357713e-04 -3.23534280e-01
-6.33797169e-01 4.28110152e-01 2.74632841e-01 -3.64143215e-02
-5.41656375e-01 -9.41838443e-01 -7.11071551e-01 -4.32241969e-02
2.07452208e-01 -9.30649459e-01 9.76739109e-01 -5.02983630e-01
-1.31062043e+00 6.32811248e-01 -3.19379568e-01 -5.46364784e-01
2.30274394e-01 -1.16441369e-01 -2.24391326e-01 4.44300324e-01
4.60565060e-01 1.24660158e+00 7.85653472e-01 -1.46042013e+00
-1.02114201e+00 -6.96507335e-01 1.64613612e-02 3.26388955e-01
-8.63527805e-02 -5.01494229e-01 -6.95243835e-01 -2.53624856e-01
7.44399905e-01 -9.10167933e-01 -5.38956642e-01 4.57532465e-01
-3.13133478e-01 -4.13195997e-01 1.11906385e+00 -1.28149763e-01
8.30334723e-01 -2.35166788e+00 -2.09743664e-01 5.07399559e-01
3.93773764e-01 4.96670753e-02 1.16420597e-01 1.06954593e-02
1.99984327e-01 1.79801732e-01 -4.89201427e-01 -4.15743709e-01
-8.06835145e-02 4.33655620e-01 -2.53499467e-02 4.73689258e-01
1.32629469e-01 6.00453138e-01 -8.29998851e-01 -5.63857973e-01
6.89241350e-01 5.23341119e-01 -8.24015498e-01 -3.54651839e-01
-2.01081440e-01 2.28550315e-01 -5.70931494e-01 8.19742978e-01
1.03764963e+00 -2.65489250e-01 -2.01700747e-01 -4.15739179e-01
-3.52663606e-01 6.79062083e-02 -1.26650095e+00 2.01097202e+00
-3.69822294e-01 4.87793058e-01 -6.97584730e-03 -9.13827837e-01
1.10346937e+00 -2.81821042e-02 9.61172104e-01 -6.21212363e-01
8.26234743e-02 3.89473289e-01 -1.51221529e-01 -8.71482491e-02
7.20018864e-01 -2.21210942e-02 6.13568313e-02 -1.27491847e-01
-6.28354847e-02 -6.82645619e-01 5.95523790e-02 -1.57730002e-02
8.93479645e-01 -2.73493249e-02 1.08519737e-02 -4.98054951e-01
3.12533259e-01 4.06340629e-01 6.41626418e-01 7.22880960e-01
-1.66857541e-01 7.66030908e-01 4.57265824e-02 -2.85414338e-01
-9.11599398e-01 -1.10520637e+00 -4.15553659e-01 5.17688811e-01
5.66882312e-01 -2.99592942e-01 -6.37263417e-01 -4.27464992e-01
3.28565925e-01 6.62073612e-01 -8.76069069e-02 1.04580417e-01
-4.86193925e-01 -4.35616016e-01 4.05986279e-01 6.56155646e-01
9.49490130e-01 -6.08773112e-01 -7.16719389e-01 2.78618425e-01
-2.36475258e-03 -1.30774724e+00 -9.34072211e-02 1.18720636e-01
-1.49102259e+00 -1.12789893e+00 -2.79844373e-01 -7.46107876e-01
5.67235112e-01 6.85514390e-01 1.13982904e+00 8.45199898e-02
-2.72868335e-01 3.45559478e-01 -2.11360037e-01 -3.64885002e-01
1.77155331e-01 3.31983507e-01 3.47415134e-02 -3.84009093e-01
6.88435912e-01 -7.35535026e-01 -6.92788959e-01 3.99705350e-01
-7.40179002e-01 -1.25987139e-02 4.71470892e-01 5.57638943e-01
1.08427238e+00 3.40991795e-01 2.61758149e-01 -6.91713274e-01
3.53508592e-02 -2.84410268e-01 -7.45520949e-01 -2.91882336e-01
-4.76936281e-01 -2.67482162e-01 4.13485497e-01 1.53544456e-01
-6.18192136e-01 2.66674072e-01 -4.54704344e-01 -7.77674794e-01
-4.09511954e-01 2.30412334e-01 -2.21092403e-01 -2.65227050e-01
4.08025444e-01 1.87578022e-01 -2.53050216e-02 -4.46846753e-01
4.59356695e-01 5.79135656e-01 4.36986029e-01 -7.56992102e-01
8.65265727e-01 9.26502168e-01 2.72182137e-01 -1.28101814e+00
-5.40109456e-01 -9.00571883e-01 -9.13412452e-01 -1.36948079e-01
8.21507156e-01 -1.11717236e+00 -7.37351716e-01 4.63780999e-01
-1.11612904e+00 3.35756280e-02 -6.16160810e-01 3.96310478e-01
-6.87275350e-01 2.91127861e-01 -3.11946422e-01 -5.83650231e-01
-2.01759070e-01 -1.38512397e+00 1.31843901e+00 -1.89391412e-02
9.10426900e-02 -7.97521591e-01 -4.56178755e-01 2.18390524e-01
1.93899989e-01 2.83186376e-01 1.05406189e+00 -3.22092474e-01
-1.09906805e+00 8.86727944e-02 -5.77598274e-01 4.17088836e-01
4.93353270e-02 7.19704330e-02 -9.19905961e-01 -7.87402391e-02
4.34678420e-02 1.54727340e-01 7.54429400e-01 7.46118367e-01
1.48101604e+00 2.55110800e-01 -4.84970242e-01 8.76692235e-01
1.67268360e+00 2.38545656e-01 5.33989727e-01 1.69848889e-01
9.03927922e-01 3.04621190e-01 6.02540314e-01 3.38091165e-01
6.65634036e-01 6.11062527e-01 7.65213072e-01 -9.21434313e-02
-2.41691083e-01 -1.93917185e-01 -1.52241528e-01 1.06094146e+00
-9.75911841e-02 6.30529970e-02 -1.06311750e+00 6.83125556e-01
-1.71101546e+00 -7.26739407e-01 -4.23510790e-01 1.95974028e+00
5.08034766e-01 2.47481346e-01 -9.25601795e-02 2.46837422e-01
6.90696120e-01 1.33007720e-01 -6.97691202e-01 -1.39138788e-01
7.84049779e-02 4.79963839e-01 7.37502992e-01 2.11731046e-01
-1.11875224e+00 1.02186847e+00 5.59220505e+00 1.13506627e+00
-9.93012130e-01 2.99471561e-02 4.74795669e-01 5.26779182e-02
-2.59994417e-01 -6.02032095e-02 -1.09575665e+00 3.02393198e-01
6.02144063e-01 9.79556218e-02 1.10148571e-01 1.17098010e+00
1.65106997e-01 -1.79108649e-01 -8.88462543e-01 1.21697783e+00
-1.48146832e-02 -1.53959072e+00 1.71592161e-01 2.00735971e-01
6.72957659e-01 4.79339331e-01 -7.22357705e-02 1.12192869e-01
2.55963266e-01 -7.06513822e-01 7.91897535e-01 1.37629464e-01
9.85102713e-01 -9.94652331e-01 5.04472136e-01 3.72984022e-01
-1.68476164e+00 -3.70379202e-02 -6.61494434e-01 1.24513529e-01
1.85176685e-01 1.03526115e+00 -6.12146974e-01 6.84083223e-01
1.04059207e+00 1.10978675e+00 -3.34822774e-01 1.23465407e+00
5.92758283e-02 4.47771519e-01 -7.60926306e-01 2.16358840e-01
4.77630109e-01 -2.37883732e-01 5.84805906e-01 1.00475979e+00
5.07268965e-01 1.40158996e-01 5.31220019e-01 9.16741312e-01
-1.38031557e-01 -1.01838544e-01 -5.91154337e-01 4.25498754e-01
7.61561930e-01 1.04439557e+00 -1.04265058e+00 -2.79951900e-01
-3.51523846e-01 6.32417977e-01 -2.63445470e-02 1.03656985e-01
-6.87386990e-01 -2.94856250e-01 1.01686215e+00 8.17707852e-02
5.81269145e-01 -6.90981567e-01 -8.12521160e-01 -7.25728393e-01
-1.13290884e-01 -4.70513552e-01 2.30280384e-02 -4.37725186e-01
-1.31143820e+00 4.27192360e-01 1.79101601e-01 -1.61617780e+00
1.68114886e-01 -5.74892700e-01 -1.61774606e-01 6.08950913e-01
-1.84489214e+00 -1.03544617e+00 -5.09951591e-01 6.27137661e-01
8.27182531e-01 5.28227724e-02 4.34263766e-01 5.95309019e-01
-1.72279716e-01 1.43286958e-01 6.71296101e-03 -7.46321380e-02
1.86860025e-01 -1.11286616e+00 6.37417912e-01 6.32204711e-01
8.53259563e-02 4.87050533e-01 1.09678350e-01 -6.82479203e-01
-1.30244672e+00 -1.39965570e+00 5.90244770e-01 -1.22709870e-01
3.62286568e-01 -3.06365639e-01 -8.09413612e-01 2.65620977e-01
-2.64743567e-01 7.47532174e-02 4.03520852e-01 -6.93238080e-02
-2.22486407e-01 -2.98340231e-01 -1.34764862e+00 2.97918856e-01
1.50747132e+00 -4.19549257e-01 -3.02634954e-01 1.95989579e-01
9.58446383e-01 -6.42118454e-01 -9.81873631e-01 6.40302956e-01
3.24468553e-01 -1.09516394e+00 1.33562052e+00 1.20800495e-01
2.92679071e-01 -6.32021189e-01 -5.09509087e-01 -1.03445244e+00
-5.03162861e-01 -3.57123576e-02 -4.65980954e-02 9.38603580e-01
8.80785435e-02 -5.24458766e-01 1.09410596e+00 2.35328645e-01
-7.10594535e-01 -8.04696620e-01 -1.25575411e+00 -8.79229486e-01
-1.24189898e-01 -9.15681660e-01 9.13846076e-01 8.27985644e-01
-7.75992215e-01 3.05717122e-02 3.99118364e-01 4.41055894e-01
9.36336875e-01 2.80745804e-01 7.76374936e-01 -1.65907788e+00
2.33557284e-01 -6.57631218e-01 -8.14425886e-01 -1.53476858e+00
-1.19878231e-02 -1.00168610e+00 5.34943538e-03 -1.79022503e+00
-1.76689744e-01 -9.37892616e-01 1.83001328e-02 3.32178980e-01
2.34283626e-01 2.90718913e-01 3.50440085e-01 3.50769758e-01
-3.97163630e-01 6.56860054e-01 1.16506147e+00 -2.09435165e-01
-2.35498235e-01 1.21353596e-01 -4.91343677e-01 9.20776069e-01
7.53170371e-01 -4.51177150e-01 -4.21994567e-01 -6.23730779e-01
-8.05460960e-02 -3.52236003e-01 6.82037711e-01 -1.53151453e+00
4.86380309e-01 -1.29174113e-01 2.73145854e-01 -1.38862574e+00
5.94047964e-01 -1.23735452e+00 1.16101459e-01 3.70845735e-01
2.53686249e-01 2.70570647e-02 3.23811680e-01 5.90668201e-01
-3.19351375e-01 8.08524936e-02 8.88419807e-01 -2.61748582e-01
-1.17542303e+00 7.48455703e-01 9.04587731e-02 -1.23215104e-02
9.55435038e-01 -8.09924245e-01 -1.03315815e-01 1.06445760e-01
-4.01999176e-01 3.60080481e-01 5.65029025e-01 3.28471214e-01
9.05236363e-01 -1.22483802e+00 -4.24979597e-01 4.23731834e-01
2.59724315e-02 8.01246405e-01 3.15865785e-01 5.36257684e-01
-7.32114792e-01 2.76692867e-01 -3.30486968e-02 -1.41532528e+00
-8.47863972e-01 7.60742500e-02 2.63899744e-01 1.72021151e-01
-8.51823926e-01 8.11593950e-01 -3.02371588e-02 -5.22899091e-01
9.99406651e-02 -6.80240035e-01 6.31602407e-02 -4.33012880e-02
-7.74276182e-02 5.82528532e-01 2.84237683e-01 -5.82969368e-01
-3.67229640e-01 1.03055131e+00 1.41964614e-01 1.43212974e-01
1.36607945e+00 -5.71459755e-02 -2.59155899e-01 4.40360367e-01
1.20150328e+00 -4.00455564e-01 -1.23185909e+00 -2.55181074e-01
-6.76907524e-02 -7.08778560e-01 2.53344715e-01 -3.14803779e-01
-1.35102010e+00 1.02547848e+00 7.50129700e-01 2.01412052e-01
9.78068173e-01 -5.52059896e-02 9.70128000e-01 4.62991148e-01
9.54708815e-01 -1.15058792e+00 -2.70519376e-01 7.02943802e-01
4.73359793e-01 -1.08734572e+00 2.17731893e-01 -9.17117059e-01
-2.82898277e-01 9.60635781e-01 6.04897201e-01 -3.23239684e-01
1.05900180e+00 1.71449080e-01 -1.15074672e-01 -5.41982651e-01
-3.22714955e-01 -3.48798543e-01 1.64573967e-01 7.19987988e-01
9.47758555e-02 1.81151599e-01 1.63579732e-01 1.16187572e-01
-4.65324849e-01 -3.77739482e-02 1.01607461e-02 7.79841483e-01
-6.52463794e-01 -9.15726960e-01 -2.59551704e-01 7.29683340e-01
5.76779507e-02 -4.26764451e-02 3.36252786e-02 9.02746677e-01
4.18161422e-01 9.01888430e-01 4.80912983e-01 -4.36521471e-01
4.85616297e-01 -2.09226236e-01 3.24454963e-01 -7.71876931e-01
-5.24012029e-01 -1.50479348e-02 -3.15391093e-01 -7.61919916e-01
-7.10525990e-01 -8.35556149e-01 -1.64609361e+00 -3.23580056e-01
-3.72025013e-01 -5.74716181e-02 1.02288485e+00 7.35554457e-01
6.89855218e-01 6.05786979e-01 6.83422148e-01 -1.18185997e+00
-2.25585371e-01 -5.43460608e-01 -6.69916213e-01 1.54921889e-01
8.36845711e-02 -7.97330678e-01 -2.34057873e-01 -1.41880572e-01] | [7.90795373916626, -3.1311264038085938] |
0004a75e-29f9-4f2c-b050-aa6351312e62 | a-simple-framework-for-open-vocabulary | 2303.08131 | null | https://arxiv.org/abs/2303.08131v3 | https://arxiv.org/pdf/2303.08131v3.pdf | A Simple Framework for Open-Vocabulary Segmentation and Detection | We present OpenSeeD, a simple Open-vocabulary Segmentation and Detection framework that jointly learns from different segmentation and detection datasets. To bridge the gap of vocabulary and annotation granularity, we first introduce a pre-trained text encoder to encode all the visual concepts in two tasks and learn a common semantic space for them. This gives us reasonably good results compared with the counterparts trained on segmentation task only. To further reconcile them, we locate two discrepancies: $i$) task discrepancy -- segmentation requires extracting masks for both foreground objects and background stuff, while detection merely cares about the former; $ii$) data discrepancy -- box and mask annotations are with different spatial granularity, and thus not directly interchangeable. To address these issues, we propose a decoupled decoding to reduce the interference between foreground/background and a conditioned mask decoding to assist in generating masks for given boxes. To this end, we develop a simple encoder-decoder model encompassing all three techniques and train it jointly on COCO and Objects365. After pre-training, our model exhibits competitive or stronger zero-shot transferability for both segmentation and detection. Specifically, OpenSeeD beats the state-of-the-art method for open-vocabulary instance and panoptic segmentation across 5 datasets, and outperforms previous work for open-vocabulary detection on LVIS and ODinW under similar settings. When transferred to specific tasks, our model achieves new SoTA for panoptic segmentation on COCO and ADE20K, and instance segmentation on ADE20K and Cityscapes. Finally, we note that OpenSeeD is the first to explore the potential of joint training on segmentation and detection, and hope it can be received as a strong baseline for developing a single model for both tasks in open world. | ['Lei Zhang', 'Jianwei Yang', 'Jianfeng Gao', 'Chunyuan Li', 'Shilong Liu', 'Xueyan Zou', 'Feng Li', 'Hao Zhang'] | 2023-03-14 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [ 2.56299853e-01 1.85912266e-01 -1.57390833e-01 -2.18929201e-01
-1.10172343e+00 -9.18998957e-01 6.84852064e-01 -1.41185150e-01
-5.30222774e-01 3.67186457e-01 -1.41744483e-02 -4.19179797e-01
3.43205780e-01 -7.17908561e-01 -7.53515840e-01 -4.97613162e-01
9.50274169e-02 7.34531045e-01 6.13114238e-01 -9.43149105e-02
-1.56162515e-01 2.08660569e-02 -1.57329142e+00 3.29877138e-01
8.37261081e-01 9.94044363e-01 4.73549604e-01 6.66161716e-01
-3.56192768e-01 3.79710644e-01 -5.43128729e-01 -5.84456444e-01
6.61201417e-01 -6.75860718e-02 -8.72266889e-01 3.89077693e-01
1.09264660e+00 -3.83807242e-01 -2.19292209e-01 1.00462580e+00
3.91833961e-01 -1.68208823e-01 8.80384147e-01 -9.82528329e-01
-6.97645783e-01 5.02380967e-01 -8.51633132e-01 3.69529933e-01
-2.65512526e-01 3.83227885e-01 1.52461135e+00 -1.02084756e+00
7.27661490e-01 1.09610713e+00 6.71152532e-01 5.40214598e-01
-1.38252592e+00 -6.34101331e-01 4.44584459e-01 -2.74586827e-01
-1.53071904e+00 -3.46683234e-01 9.15508568e-02 -7.34437943e-01
9.87971604e-01 2.91509211e-01 5.25243759e-01 1.09321392e+00
-3.39363486e-01 1.05156183e+00 9.01841998e-01 -1.96245641e-01
2.70498823e-02 1.52133957e-01 2.57889539e-01 7.00561583e-01
3.43626440e-01 -5.23887053e-02 -1.04272753e-01 2.40236342e-01
7.20035017e-01 -1.53607354e-01 -2.34981775e-01 -2.39783421e-01
-1.25402594e+00 8.90632689e-01 4.20666903e-01 1.79730788e-01
3.10542703e-01 2.40120262e-01 4.88453805e-01 2.53172457e-01
9.09996271e-01 3.82349551e-01 -6.58692181e-01 2.96434522e-01
-1.31820321e+00 1.52568847e-01 6.66053951e-01 1.18138170e+00
9.13187265e-01 -8.56183991e-02 -3.60203356e-01 8.12548876e-01
2.93841511e-01 6.41135812e-01 5.74470460e-02 -7.24906683e-01
8.62699687e-01 4.67671245e-01 1.99024677e-01 -5.49838662e-01
-3.10053378e-01 -3.62358779e-01 -5.09964824e-01 6.34223409e-03
6.29685879e-01 -2.00871378e-01 -1.55686080e+00 1.50631297e+00
3.25623512e-01 4.06040281e-01 -2.32666191e-02 8.37688088e-01
1.03103888e+00 7.88126647e-01 8.25061053e-02 1.96893886e-01
1.77846944e+00 -1.17411137e+00 -4.28726882e-01 -7.62119770e-01
7.45408833e-01 -7.19359398e-01 1.14016783e+00 1.17722750e-01
-8.37248743e-01 -6.10020220e-01 -1.01496696e+00 -4.26418602e-01
-6.47580504e-01 2.53458261e-01 4.27846760e-01 6.29781783e-01
-1.02807665e+00 1.69186071e-01 -6.68530166e-01 -5.33114791e-01
6.39368832e-01 1.61350481e-02 -6.34240955e-02 8.54105428e-02
-1.14367747e+00 8.65440309e-01 5.41938186e-01 -1.32372990e-01
-9.15225446e-01 -8.11826110e-01 -1.04340887e+00 5.85481972e-02
6.83810830e-01 -5.08527398e-01 1.24026787e+00 -9.34061289e-01
-9.53161955e-01 1.39740336e+00 2.14313362e-02 -5.94662070e-01
6.37976170e-01 -1.84517533e-01 -2.58104473e-01 3.36955264e-02
5.51122427e-01 1.33349490e+00 1.01188672e+00 -1.25771809e+00
-1.02073109e+00 -3.00583184e-01 1.91273272e-01 3.12958717e-01
-5.19874915e-02 -7.74871185e-03 -9.76030171e-01 -6.91020370e-01
-8.31740424e-02 -6.52809739e-01 -2.01546699e-01 1.39861807e-01
-4.56416965e-01 -2.60858953e-01 8.01416814e-01 -6.75192297e-01
1.10080791e+00 -2.29402065e+00 1.37882113e-01 -8.05451795e-02
3.44393879e-01 4.50820416e-01 -2.83480346e-01 3.02924141e-02
1.20100848e-01 3.40231508e-01 -5.49538195e-01 -6.38576865e-01
1.25509456e-01 5.26269615e-01 -5.94368994e-01 4.71244007e-01
3.54640454e-01 1.04462361e+00 -8.01521242e-01 -6.11240625e-01
3.50586593e-01 2.10466236e-01 -4.59735215e-01 2.02572998e-02
-6.31439209e-01 2.73926765e-01 -1.31999046e-01 6.24853492e-01
8.10120821e-01 -3.96480471e-01 2.36717109e-02 1.38047308e-01
-2.08458528e-01 2.16173872e-01 -1.37479901e+00 1.72630632e+00
-3.07020932e-01 8.12065840e-01 4.17506963e-01 -9.31695342e-01
6.22959077e-01 2.17450798e-01 4.51412201e-02 -7.69421875e-01
9.07142535e-02 3.54929686e-01 -3.58110100e-01 -3.11505169e-01
5.17078876e-01 2.63090786e-02 -2.23239318e-01 1.68527275e-01
2.70845979e-01 -5.71863115e-01 3.86154145e-01 2.87352324e-01
6.58273399e-01 3.86173092e-02 2.27220863e-01 -4.35618550e-01
3.04342777e-01 9.40667838e-02 3.55574608e-01 1.02493358e+00
-2.67987162e-01 9.65580285e-01 5.11064172e-01 -4.52305526e-01
-1.06769335e+00 -1.15802550e+00 -3.66253376e-01 1.38823807e+00
2.41767451e-01 -3.95964712e-01 -8.58837366e-01 -8.75970483e-01
8.59773755e-02 5.55745482e-01 -7.60759771e-01 5.87047517e-01
-4.11189437e-01 -7.14518487e-01 8.49052846e-01 5.79681873e-01
5.29135406e-01 -7.93030918e-01 -4.27196026e-01 1.28591331e-02
-2.58253634e-01 -1.42354536e+00 -7.31148481e-01 4.80293274e-01
-4.46492910e-01 -1.09234083e+00 -8.22340906e-01 -9.61786687e-01
3.34463835e-01 5.53109348e-01 1.35471427e+00 4.48816642e-02
-5.78492403e-01 2.24230006e-01 -2.03579977e-01 -4.62254047e-01
-2.85200149e-01 3.56254548e-01 -4.51546133e-01 -1.40228763e-01
3.20994139e-01 -5.01075424e-02 -5.97414553e-01 3.26055974e-01
-1.00126588e+00 2.32728258e-01 2.81053007e-01 6.35931194e-01
5.48769295e-01 -3.58589143e-01 1.10360995e-01 -9.34884548e-01
6.76835105e-02 -4.32152569e-01 -1.00077903e+00 3.09758097e-01
-2.33755305e-01 -8.68217498e-02 2.74276078e-01 -1.16972260e-01
-8.71270418e-01 7.02896342e-02 -2.12286890e-01 -4.21451598e-01
-3.81929547e-01 1.62468571e-02 -1.09775551e-01 3.90493751e-01
5.49943268e-01 6.60889000e-02 -4.66598243e-01 -6.30246818e-01
9.07351017e-01 8.48321438e-01 5.61590016e-01 -4.03914124e-01
7.75489628e-01 8.46461952e-01 -6.17014647e-01 -8.78495455e-01
-1.17615926e+00 -9.35187161e-01 -7.34269321e-01 3.21198314e-01
1.38375473e+00 -1.38827777e+00 -1.70774218e-02 4.54644620e-01
-1.31420541e+00 -6.23322845e-01 -3.89468849e-01 -1.49475485e-01
-2.08703205e-01 3.69793713e-01 -5.43889761e-01 -5.87922692e-01
-3.62143099e-01 -1.24843121e+00 1.67682946e+00 -4.78084236e-02
-1.82836480e-03 -9.61622715e-01 1.09204976e-02 4.78822529e-01
9.76862311e-02 -1.59650207e-01 5.05549252e-01 -7.35267460e-01
-7.06085384e-01 1.41739622e-01 -9.17959988e-01 3.29685450e-01
-8.93157795e-02 -3.05194799e-02 -1.21324587e+00 -3.09318453e-01
-3.86825562e-01 -4.55582559e-01 1.47534072e+00 2.86625445e-01
1.02703023e+00 -3.44378292e-03 -4.78620559e-01 7.60725558e-01
1.41801143e+00 -2.01871395e-01 5.54094136e-01 2.48281121e-01
9.12052989e-01 6.36330485e-01 4.02457774e-01 2.56530046e-01
5.80385923e-01 7.19283283e-01 3.96342665e-01 -5.24953067e-01
-3.73067439e-01 -1.55478418e-01 2.62314916e-01 4.26487327e-01
2.26504549e-01 -4.72540617e-01 -1.04489946e+00 9.39951718e-01
-1.78637779e+00 -8.10967803e-01 -3.05012316e-01 2.01115537e+00
8.80697191e-01 1.80654764e-01 8.82263631e-02 -2.90658772e-01
7.37714291e-01 6.27963364e-01 -3.29668432e-01 -1.76347598e-01
-1.89542860e-01 4.61507380e-01 8.75390708e-01 7.68258512e-01
-1.61618507e+00 1.49716723e+00 6.27064848e+00 1.25390720e+00
-9.67348874e-01 4.85824317e-01 8.58209670e-01 -1.68859765e-01
-2.37319112e-01 -3.78991999e-02 -1.14611197e+00 3.97697419e-01
5.48446476e-01 6.28324568e-01 2.87641972e-01 7.68698335e-01
-6.68516755e-02 -1.79878399e-02 -9.63867188e-01 9.18125272e-01
4.80116978e-02 -1.41067159e+00 3.13129276e-02 -2.22780742e-03
8.76866877e-01 5.92626095e-01 -1.33360596e-02 5.16065419e-01
4.87194061e-01 -1.14632344e+00 8.83010030e-01 -2.87260264e-01
1.07578778e+00 -2.43568420e-01 4.65014338e-01 2.31976539e-01
-1.51248825e+00 7.88815990e-02 -3.57643366e-01 -3.26209981e-03
7.53045455e-02 3.52790862e-01 -5.61957181e-01 4.43119138e-01
6.69734657e-01 5.89146674e-01 -6.23149633e-01 7.19649553e-01
-4.18916970e-01 6.16409123e-01 -3.96528304e-01 3.52215171e-01
6.82680786e-01 -2.15703905e-01 4.27538425e-01 1.65036631e+00
1.53082479e-02 -1.98161870e-01 5.29866576e-01 1.05387223e+00
-1.59966141e-01 4.56859022e-02 -5.91168702e-01 1.68919235e-01
2.98482329e-01 1.30705655e+00 -1.12092412e+00 -7.19930947e-01
-8.88345599e-01 1.19123340e+00 4.59807098e-01 4.71503139e-01
-1.29697895e+00 -1.96148068e-01 8.43754828e-01 2.10645631e-01
8.73595476e-01 -1.63752884e-01 -5.23535848e-01 -1.51777208e+00
-1.67068228e-01 -7.24169016e-01 5.29286027e-01 -5.29678166e-01
-1.16887224e+00 4.06078726e-01 5.73371947e-02 -7.95763016e-01
2.63179868e-01 -8.65880907e-01 -4.96633977e-01 6.49539888e-01
-1.77561390e+00 -1.34145391e+00 -1.50596529e-01 4.27282780e-01
1.05298996e+00 2.55212843e-01 4.01749343e-01 3.89868647e-01
-7.46489763e-01 3.67064327e-01 9.09038708e-02 4.74156082e-01
7.54490197e-01 -1.59381175e+00 1.02853656e+00 1.11866903e+00
6.42854869e-01 1.62064806e-01 4.10249203e-01 -5.62644720e-01
-7.29450703e-01 -1.54710197e+00 8.31525922e-01 -7.95182586e-01
7.93436825e-01 -8.94077599e-01 -8.27975333e-01 8.96069229e-01
2.72553563e-01 2.15225086e-01 3.06354493e-01 6.12108856e-02
-5.29288888e-01 1.67146161e-01 -7.43381858e-01 5.89559853e-01
1.24695373e+00 -5.43818057e-01 -7.61836350e-01 4.50332344e-01
1.01881063e+00 -6.43748999e-01 -3.82964492e-01 2.33199939e-01
1.75762877e-01 -8.89633000e-01 1.07292008e+00 -2.94299841e-01
2.84833461e-01 -3.50776285e-01 -3.14535260e-01 -9.99193966e-01
-5.70942946e-02 -5.18310547e-01 2.37264141e-01 1.23113418e+00
6.26068532e-01 -5.04990041e-01 6.38535678e-01 2.53192723e-01
-2.44739547e-01 -4.68157738e-01 -1.04136777e+00 -7.45360732e-01
3.36278409e-01 -7.98487306e-01 4.30781752e-01 8.35906208e-01
-4.48658109e-01 5.61646163e-01 -3.57876867e-01 3.51985455e-01
3.37222248e-01 1.53469875e-01 9.12821710e-01 -1.15415204e+00
-2.91620970e-01 -4.76581603e-01 4.25500143e-03 -1.60561037e+00
8.98034796e-02 -1.01291215e+00 1.94892019e-01 -1.65345097e+00
2.91822672e-01 -4.33838069e-01 1.13717623e-01 6.06682420e-01
-3.75978708e-01 6.97298110e-01 2.87120849e-01 3.02925825e-01
-9.24399316e-01 3.07399064e-01 1.23815215e+00 -3.32648486e-01
-1.90731257e-01 -2.78624117e-01 -6.82588458e-01 7.64933109e-01
5.23308694e-01 -3.68100613e-01 -3.54784727e-01 -7.70553589e-01
9.56703275e-02 -2.45456114e-01 5.48375487e-01 -7.13884473e-01
-1.04323551e-01 9.64296088e-02 9.67382565e-02 -6.15268409e-01
1.78600058e-01 -6.65681064e-01 -4.00622845e-01 1.91550717e-01
1.32135702e-02 -3.95912260e-01 3.42708379e-01 7.36574352e-01
-6.44647479e-02 -1.46136269e-01 8.16200018e-01 -3.61347288e-01
-1.08000910e+00 4.03064013e-01 -3.84000838e-01 6.56797528e-01
9.65522945e-01 -2.76050061e-01 -4.75416303e-01 1.58532783e-02
-7.43121743e-01 6.22263432e-01 5.23793697e-01 4.59074974e-01
1.45287335e-01 -7.73035824e-01 -7.43572176e-01 3.08102310e-01
3.34425300e-01 4.36055094e-01 9.37506184e-02 7.42518246e-01
-6.66600466e-01 4.92819756e-01 3.28668833e-01 -9.01633263e-01
-1.05760753e+00 5.34819126e-01 4.64853942e-01 -5.33301890e-01
-6.83307052e-01 1.09831357e+00 8.58455002e-01 -6.11280203e-01
4.07656640e-01 -8.22091103e-01 3.74619178e-02 4.84594882e-01
4.90976751e-01 7.55822659e-02 5.24258949e-02 -5.84570110e-01
-3.42783123e-01 5.55430174e-01 -1.01463214e-01 -1.70992404e-01
8.97545636e-01 -3.22367877e-01 1.71255350e-01 3.54305387e-01
9.84557569e-01 -7.50513300e-02 -1.54034805e+00 -3.21052343e-01
-3.36368866e-02 -2.95781881e-01 6.84637437e-03 -6.76304460e-01
-9.96958673e-01 1.08835065e+00 4.70785707e-01 2.33674377e-01
7.33384192e-01 3.94947946e-01 7.88603187e-01 7.85079673e-02
6.64853156e-02 -1.19311726e+00 -3.99298631e-02 8.09465706e-01
5.49953103e-01 -1.56782687e+00 -1.48250058e-01 -6.71788514e-01
-6.88910723e-01 7.92989135e-01 7.00437665e-01 -1.05989359e-01
4.05225337e-01 3.23561698e-01 2.27114856e-01 -4.09453481e-01
-5.07152438e-01 -9.85068738e-01 5.08359909e-01 4.84288037e-01
3.64759088e-01 1.77969113e-01 1.11102723e-01 3.39374483e-01
1.16212331e-01 -3.59197497e-01 1.42827436e-01 5.81588745e-01
-6.74970925e-01 -8.39488506e-01 -4.21142995e-01 3.80343735e-01
-3.78095597e-01 -6.10182583e-01 -4.51500624e-01 1.10477018e+00
5.67924261e-01 8.12099695e-01 5.07309318e-01 4.70415503e-02
1.15390800e-01 1.33817613e-01 2.69610673e-01 -1.03055871e+00
-4.72012460e-01 3.22003484e-01 1.49501279e-01 -4.22467738e-01
-3.25318664e-01 -4.20115173e-01 -9.88975465e-01 -6.31308299e-04
-5.42985797e-01 -1.45163894e-01 2.20818192e-01 1.09080613e+00
2.62827486e-01 4.05850798e-01 4.87810001e-02 -9.70607817e-01
-3.66658062e-01 -8.65860999e-01 -6.38576865e-01 4.37814176e-01
3.91659081e-01 -5.88696957e-01 -4.39824224e-01 4.30999547e-02] | [9.658027648925781, 0.6628656983375549] |
38bb8e5d-d61b-4731-8dd5-84900a147cb1 | feature-relevance-analysis-to-explain-concept | 2301.08453 | null | https://arxiv.org/abs/2301.08453v1 | https://arxiv.org/pdf/2301.08453v1.pdf | Feature Relevance Analysis to Explain Concept Drift -- A Case Study in Human Activity Recognition | This article studies how to detect and explain concept drift. Human activity recognition is used as a case study together with a online batch learning situation where the quality of the labels used in the model updating process starts to decrease. Drift detection is based on identifying a set of features having the largest relevance difference between the drifting model and a model that is known to be accurate and monitoring how the relevance of these features changes over time. As a main result of this article, it is shown that feature relevance analysis cannot only be used to detect the concept drift but also to explain the reason for the drift when a limited number of typical reasons for the concept drift are predefined. To explain the reason for the concept drift, it is studied how these predefined reasons effect to feature relevance. In fact, it is shown that each of these has an unique effect to features relevance and these can be used to explain the reason for concept drift. | ['Juha Röning', 'Pekka Siirtola'] | 2023-01-20 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 2.76923716e-01 6.91574300e-03 -4.39581990e-01 -3.16576511e-01
1.42669395e-01 -3.07886630e-01 6.07964396e-01 7.45848298e-01
-3.35344136e-01 8.76593053e-01 -2.18358323e-01 -6.44877693e-03
-5.09090185e-01 -3.86554837e-01 -5.98763525e-01 -8.22414756e-01
-1.50711596e-01 5.42805493e-01 3.55866462e-01 -3.94479111e-02
4.79800940e-01 4.64369982e-01 -2.18692088e+00 2.61501402e-01
5.87872982e-01 8.25820744e-01 1.13778941e-01 5.40654123e-01
-4.70887423e-01 6.83793604e-01 -1.16958845e+00 4.47044343e-01
-1.10339381e-01 -7.75743425e-01 -7.36418545e-01 1.42896160e-01
1.05559431e-01 6.58978801e-03 6.28985882e-01 9.64177489e-01
1.90095335e-01 -4.61987741e-02 6.77093327e-01 -1.45177412e+00
2.62730896e-01 5.32833159e-01 -1.52823836e-01 6.15479112e-01
6.18162513e-01 -2.55874336e-01 6.22512639e-01 -5.01786590e-01
5.93674242e-01 1.09471297e+00 5.12111187e-01 7.69432247e-01
-9.73062098e-01 -6.30268693e-01 6.18272305e-01 4.47374672e-01
-1.21351159e+00 -2.50323534e-01 7.35892773e-01 -8.42281818e-01
7.49725997e-01 1.69559360e-01 9.96589661e-01 9.50765193e-01
5.84428549e-01 4.35640365e-01 7.52453327e-01 -7.22024143e-01
8.33314121e-01 5.50394952e-01 6.64584219e-01 2.87149876e-01
8.73843312e-01 2.00612813e-01 -8.63784015e-01 -2.41319910e-01
5.63636757e-02 -3.87837440e-02 -2.83059210e-01 -3.43645960e-01
-6.15364730e-01 5.39163828e-01 -9.91870612e-02 1.05250204e+00
-4.15795624e-01 9.05804932e-02 3.14510047e-01 4.49598640e-01
3.89833689e-01 4.23525184e-01 -6.42460823e-01 -5.79975188e-01
-7.14602351e-01 6.66772798e-02 7.92187750e-01 6.09478474e-01
8.39622140e-01 -7.71979392e-02 -8.52782205e-02 5.66245057e-02
2.82477409e-01 2.07436949e-01 1.03530276e+00 -3.44607681e-01
-1.02870382e-01 1.17911351e+00 5.19240141e-01 -4.11730886e-01
-5.06024897e-01 -4.96674150e-01 -2.64769763e-01 3.56642187e-01
6.71716690e-01 6.79085553e-02 -7.00264037e-01 1.64299273e+00
3.14380467e-01 8.50350857e-02 1.49079174e-01 5.97943902e-01
3.59140486e-01 3.00700486e-01 -8.50887746e-02 -8.27229857e-01
1.09298062e+00 -3.31053287e-01 -1.11795115e+00 -6.22852407e-02
8.83323669e-01 -2.97584832e-01 7.94337749e-01 5.70166945e-01
-6.46684170e-01 -6.88634515e-01 -1.35468709e+00 8.72522414e-01
-4.38021451e-01 -3.07676136e-01 5.00435710e-01 9.55625534e-01
-6.44049823e-01 8.49488437e-01 -8.92608821e-01 -8.01566720e-01
-1.61314216e-02 2.44996279e-01 2.15670437e-01 6.41786754e-02
-1.26902127e+00 9.69403446e-01 5.57557285e-01 4.73214872e-02
-7.79473841e-01 -4.71228391e-01 -3.36483985e-01 3.62806208e-03
1.72317609e-01 -4.37137783e-01 1.12144578e+00 -1.65985799e+00
-1.10264981e+00 4.37669754e-01 -5.76794803e-01 -5.81169486e-01
1.00498104e+00 -1.40502468e-01 -6.12579286e-01 -7.36906603e-02
9.48444679e-02 -4.23152186e-02 1.01032937e+00 -1.11493146e+00
-1.04462099e+00 -3.85441750e-01 -4.47454125e-01 -6.00732416e-02
-2.51524389e-01 -1.63797379e-01 7.90851861e-02 -1.29976973e-01
1.84911236e-01 -6.77018285e-01 1.17259130e-01 -6.85386837e-01
2.06899017e-01 -3.90711755e-01 9.13707376e-01 -1.00796454e-01
1.50816309e+00 -2.07344580e+00 -3.38221550e-01 4.88504738e-01
-3.72238532e-02 -7.74690658e-02 4.15033877e-01 4.44401652e-01
-2.43785948e-01 2.62606442e-01 -3.40135545e-01 1.49047881e-01
-3.57528120e-01 3.42168599e-01 -1.20372966e-01 3.09013337e-01
9.94080380e-02 2.20677733e-01 -9.89266574e-01 -4.60496992e-02
-1.03948995e-01 3.00055295e-01 1.35714859e-01 3.44725535e-03
-1.60130426e-01 4.53828394e-01 -4.29938734e-01 4.02959406e-01
3.20385844e-01 -1.44550329e-04 3.11648995e-01 3.81619245e-01
-5.98332249e-02 3.00772805e-02 -1.38531780e+00 8.19885254e-01
-2.44027954e-02 6.65865004e-01 -6.09945297e-01 -1.01056945e+00
1.25677431e+00 4.15123910e-01 6.75903797e-01 -5.28515458e-01
2.33711489e-03 4.80594963e-01 2.48194367e-01 -6.16154075e-01
2.22481355e-01 -1.66373372e-01 3.62042010e-01 5.55381596e-01
-3.87184799e-01 3.42151225e-01 3.85221660e-01 -1.55548424e-01
1.34021926e+00 1.63985342e-02 4.21290010e-01 -3.60879660e-01
9.00258958e-01 -4.53587249e-02 7.32071936e-01 8.31920564e-01
-2.87941068e-01 1.38959572e-01 5.32812715e-01 -6.39670372e-01
-4.77000862e-01 -6.80740416e-01 -1.32858053e-01 6.46360755e-01
3.09459656e-01 -4.14083719e-01 -6.08310282e-01 -7.57243276e-01
1.28221124e-01 9.60214019e-01 -1.01867533e+00 -5.64904273e-01
-1.72367349e-01 -8.86986673e-01 -1.47659719e-01 3.00659388e-01
1.77751914e-01 -1.10699368e+00 -1.38590622e+00 3.63496274e-01
1.11674264e-01 -4.18393433e-01 8.81886408e-02 5.86946309e-01
-1.37506640e+00 -1.46957231e+00 -1.51564449e-01 -1.62774041e-01
8.96301925e-01 2.13179328e-02 9.98360336e-01 4.03960556e-01
-8.84480029e-02 7.23060012e-01 -4.56923276e-01 -9.27467406e-01
-9.51801121e-01 -1.16494820e-02 2.54744172e-01 1.76964924e-01
7.74248183e-01 -1.40764385e-01 -1.22266874e-01 3.76964152e-01
-1.06825304e+00 -6.46413565e-01 2.33041599e-01 6.18588448e-01
2.97604591e-01 5.33577740e-01 8.80752563e-01 -9.52738702e-01
9.02978480e-01 -2.71450043e-01 -5.75361729e-01 5.19272745e-01
-1.76078236e+00 3.00339997e-01 2.80452762e-02 -5.18984437e-01
-9.25039530e-01 9.90517139e-02 6.30818367e-01 -1.47047564e-01
-2.86165327e-01 5.48764765e-01 -2.15321317e-01 4.86211479e-01
8.69534969e-01 2.22814232e-01 -3.69373336e-02 -4.26941782e-01
-2.52876490e-01 4.56686556e-01 -1.65396288e-01 -2.12025806e-01
3.78268540e-01 3.88208061e-01 2.14433610e-01 -7.34106064e-01
-5.00486612e-01 -6.31927013e-01 -8.30702424e-01 -5.92007220e-01
4.46490109e-01 -4.44136292e-01 -4.67736781e-01 4.56822306e-01
-1.04200709e+00 -1.46818891e-01 -6.83634937e-01 4.49340940e-01
-4.21587825e-01 7.35870376e-02 1.44168124e-01 -1.40630078e+00
-1.00566387e-01 -7.36075878e-01 3.18417519e-01 3.16805810e-01
-6.64716303e-01 -1.10872185e+00 1.35672152e-01 -1.99839205e-01
1.49893984e-01 2.35872477e-01 8.33510995e-01 -8.56052756e-01
-1.65686354e-01 -6.01847708e-01 5.28822601e-01 2.25753114e-01
6.03073776e-01 1.49240270e-01 -9.17379558e-01 -5.39343297e-01
4.31116760e-01 6.44308746e-01 8.99173260e-01 5.12938619e-01
2.55735487e-01 -7.46009573e-02 -5.62237918e-01 -2.09904626e-01
1.38862765e+00 8.69542301e-01 3.28801066e-01 7.48815298e-01
3.05913210e-01 6.98648095e-01 9.36900616e-01 4.91060287e-01
-1.95767075e-01 6.87954605e-01 2.81171083e-01 3.84324372e-01
2.40917102e-01 1.42030921e-02 7.01675057e-01 3.70589137e-01
9.38706398e-02 1.50393337e-01 -1.01828599e+00 6.01303279e-01
-2.14028907e+00 -8.47920537e-01 -3.48834783e-01 2.78824639e+00
4.92431134e-01 6.53909266e-01 3.60912770e-01 6.36340737e-01
8.97642076e-01 -4.25709277e-01 -7.02177823e-01 -4.86484200e-01
-6.11649454e-02 -3.94844621e-01 3.08935821e-01 6.79840684e-01
-6.67785466e-01 2.28556409e-01 6.18390560e+00 6.33898303e-02
-1.07841635e+00 4.66805957e-02 1.72028333e-01 -8.10645074e-02
-8.27323273e-02 2.22131401e-01 -8.90123367e-01 6.03522301e-01
1.15791333e+00 -5.45000970e-01 -1.08068466e-01 7.56991208e-01
4.14960980e-01 -7.65258133e-01 -1.52728188e+00 8.62409949e-01
2.41819054e-01 -7.59439409e-01 5.00180088e-02 8.97744205e-03
5.85225105e-01 -5.66825271e-01 -4.42762792e-01 -8.27979296e-02
-1.78340435e-01 -6.57216787e-01 8.87977958e-01 9.51348841e-01
-6.07608072e-02 -8.24318826e-01 1.15385318e+00 4.16786820e-01
-8.12640488e-01 -5.20723939e-01 -3.67593132e-02 -3.89367223e-01
-2.03144222e-01 8.95990312e-01 -1.16054881e+00 5.12820959e-01
8.75196457e-01 5.78782797e-01 -8.80455375e-01 1.19999027e+00
-4.35535520e-01 9.01266277e-01 -1.48729712e-01 -1.77937016e-01
-2.60742158e-01 1.57054111e-01 7.41555035e-01 8.96929860e-01
3.99983108e-01 -6.60384953e-01 -2.56460667e-01 6.64871454e-01
6.49449885e-01 1.19080208e-01 -5.47807038e-01 -7.89371878e-03
4.91801292e-01 6.37507021e-01 -1.15937293e+00 -4.65578735e-01
5.69679728e-03 7.74585545e-01 -4.17957127e-01 3.16660494e-01
-4.01865780e-01 -2.30100513e-01 3.40383947e-01 5.70664585e-01
3.61450940e-01 2.06894130e-01 -2.22646549e-01 -7.82551944e-01
1.51384711e-01 -4.35175687e-01 4.71142381e-01 -3.88296336e-01
-1.00824368e+00 3.37297440e-01 3.06763649e-01 -1.28120673e+00
-6.93932414e-01 -6.75261766e-02 -5.77874005e-01 9.53991294e-01
-1.30845141e+00 -4.18428332e-01 -4.41179156e-01 3.31359953e-01
3.67128283e-01 -2.96943843e-01 6.74665630e-01 -2.35451907e-01
-5.78829348e-01 1.81632087e-01 1.24166034e-01 -4.59463537e-01
6.01967573e-01 -1.32530797e+00 3.19377072e-02 8.57214391e-01
7.92335812e-03 4.63593394e-01 1.24832010e+00 -9.96719539e-01
-7.88701534e-01 -8.49569082e-01 1.07426536e+00 -7.04393327e-01
2.44509324e-01 -1.88876376e-01 -1.23349833e+00 3.39919060e-01
-3.15722436e-01 -3.96842390e-01 5.53139269e-01 4.26235609e-02
6.49203174e-03 -4.56220537e-01 -1.08960426e+00 7.17167407e-02
6.36697888e-01 -4.32532318e-02 -6.48136616e-01 4.29739244e-02
2.23858088e-01 1.21586993e-02 -4.75550532e-01 1.61177754e-01
5.63772917e-01 -1.03968251e+00 2.24488065e-01 -5.74856579e-01
-2.91091919e-01 -5.21871984e-01 2.29900643e-01 -1.17088842e+00
-1.31588101e-01 -5.68116367e-01 -3.70450050e-01 1.27194774e+00
4.81270045e-01 -9.68908489e-01 7.44603753e-01 5.21475315e-01
3.92509282e-01 -2.97899246e-01 -1.25407660e+00 -1.30567944e+00
-2.66576052e-01 -4.07269686e-01 7.21115589e-01 8.76088917e-01
1.36784121e-01 4.24125135e-01 9.55251083e-02 -8.54057297e-02
3.73600006e-01 -9.64025408e-02 5.91314912e-01 -2.00054240e+00
5.88200055e-02 -4.19148624e-01 -6.29506588e-01 -1.71015650e-01
-1.63606927e-01 -4.99857038e-01 7.32105747e-02 -1.43553758e+00
-6.38527945e-02 -8.37529227e-02 -7.13132620e-01 1.75704584e-01
-2.24131376e-01 -5.52181423e-01 1.23346888e-01 6.01614594e-01
-2.89342850e-01 4.67598327e-02 5.17192423e-01 -1.52742550e-01
-8.39479506e-01 4.88501251e-01 -2.83756286e-01 4.85177249e-01
7.74808228e-01 -9.56542611e-01 -6.94543242e-01 4.96929079e-01
6.86716974e-01 -2.43174285e-01 4.38223816e-02 -1.30495441e+00
1.52039886e-01 -5.92412055e-02 4.60890740e-01 -3.73899549e-01
-2.03736976e-01 -1.07985091e+00 2.87585050e-01 1.08282387e+00
-3.40435743e-01 8.10151100e-02 3.68293732e-01 8.82516026e-01
-1.45765170e-01 -8.57746184e-01 4.09491658e-01 -1.12123840e-01
-8.07211518e-01 -4.43754286e-01 -8.46003354e-01 -4.00555909e-01
1.20170796e+00 -8.05051386e-01 8.98942649e-02 -5.09654880e-01
-8.68944228e-01 1.78163886e-01 3.82397115e-01 6.88987017e-01
3.27380717e-01 -1.05102491e+00 -3.34991992e-01 2.16715932e-01
3.41789544e-01 -3.45467925e-01 1.07904769e-01 7.89461911e-01
7.18653649e-02 4.69716638e-01 -1.33466288e-01 -8.40123236e-01
-1.52482069e+00 6.81075871e-01 5.57527900e-01 -1.29186481e-01
-4.03288871e-01 3.69590759e-01 -2.10661530e-01 4.93119687e-01
4.32392448e-01 -6.38369918e-01 -5.28739512e-01 5.64876378e-01
6.32623196e-01 4.94954973e-01 4.68590617e-01 -3.57369959e-01
-7.21735895e-01 5.59509099e-01 -2.18266863e-02 -1.12065583e-01
1.09645545e+00 -3.17440569e-01 2.53666621e-02 1.33187413e+00
3.59735817e-01 -3.17580044e-01 -1.32640994e+00 3.79362702e-02
7.55907834e-01 -2.36787096e-01 -1.35726646e-01 -9.27580774e-01
-6.13472641e-01 5.30869424e-01 1.37915456e+00 6.21384978e-01
1.22061992e+00 -1.78588286e-01 -2.59511024e-01 4.21780139e-01
3.62794876e-01 -1.48588109e+00 -1.20365024e-02 1.86167136e-01
8.46901357e-01 -1.04786098e+00 1.37201324e-01 -9.73652527e-02
-3.68229955e-01 1.47463596e+00 5.31657040e-01 2.86089003e-01
8.16874444e-01 -1.03694774e-01 1.87475473e-01 -2.12909788e-01
-9.51415122e-01 -1.90942556e-01 5.00033796e-02 9.13898945e-01
3.71764898e-01 -1.60618778e-02 -8.02811146e-01 5.88863194e-01
9.37432647e-02 4.92623001e-01 8.29569757e-01 1.15036654e+00
-6.78100824e-01 -1.17026258e+00 -6.57418907e-01 3.18421692e-01
-9.70992520e-02 5.76404512e-01 -7.64629602e-01 9.07344222e-01
4.20823067e-01 1.12476170e+00 1.85058221e-01 -2.94569254e-01
5.66177905e-01 6.26917720e-01 3.55685800e-01 -6.43196762e-01
-7.46477544e-01 -1.72733456e-01 -9.36526805e-02 -4.44342047e-01
-5.59953272e-01 -1.04085326e+00 -1.43509078e+00 4.42685217e-01
-3.47609937e-01 5.39761484e-01 8.48891079e-01 1.21420443e+00
1.54078603e-01 6.37228727e-01 4.05249923e-01 -9.02587399e-02
-2.97379106e-01 -1.12448978e+00 -7.49214172e-01 5.89974880e-01
3.53477687e-01 -9.84471381e-01 -7.37567723e-01 1.71514347e-01] | [7.449616432189941, 3.2769885063171387] |
ab9cf34a-4ae1-4638-be13-81ce687584ac | teach-me-how-to-learn-a-perspective-review | 2307.03853 | null | https://arxiv.org/abs/2307.03853v1 | https://arxiv.org/pdf/2307.03853v1.pdf | Teach Me How to Learn: A Perspective Review towards User-centered Neuro-symbolic Learning for Robotic Surgical Systems | Recent advances in machine learning models allowed robots to identify objects on a perceptual nonsymbolic level (e.g., through sensor fusion and natural language understanding). However, these primarily black-box learning models still lack interpretation and transferability and require high data and computational demand. An alternative solution is to teach a robot on both perceptual nonsymbolic and conceptual symbolic levels through hybrid neurosymbolic learning approaches with expert feedback (i.e., human-in-the-loop learning). This work proposes a concept for this user-centered hybrid learning paradigm that focuses on robotic surgical situations. While most recent research focused on hybrid learning for non-robotic and some generic robotic domains, little work focuses on surgical robotics. We survey this related research while focusing on human-in-the-loop surgical robotic systems. This evaluation highlights the most prominent solutions for autonomous surgical robots and the challenges surgeons face when interacting with these systems. Finally, we envision possible ways to address these challenges using online apprenticeship learning based on implicit and explicit feedback from expert surgeons. | ['Antonio Krüger', 'Frank Kirchner', 'Michael Feld', 'Niko Kleer', 'Bilal Mahdy', 'Amr Gomaa'] | 2023-07-07 | null | null | null | null | ['sensor-fusion', 'natural-language-understanding'] | ['computer-vision', 'natural-language-processing'] | [ 3.28122601e-02 9.93074477e-01 -6.12672687e-01 -1.68896824e-01
-5.00697017e-01 -5.96269667e-01 1.72935545e-01 4.36906815e-01
-4.24525201e-01 5.20838797e-01 -2.18164504e-01 -4.41028178e-01
-6.24819040e-01 -4.62302566e-01 -8.02655160e-01 -5.44562399e-01
-2.76458323e-01 5.79690158e-01 -5.75011596e-02 -4.53825206e-01
1.77155644e-01 6.85318112e-01 -1.52416241e+00 4.57303047e-01
8.45036149e-01 5.66901267e-01 6.70979261e-01 8.18028927e-01
1.19027197e-01 1.07940316e+00 -1.99554563e-01 1.69474587e-01
5.73262572e-01 -6.08300231e-02 -8.43412519e-01 -1.84322506e-01
2.23510593e-01 -3.37450892e-01 -1.44582808e-01 1.06848907e+00
3.69285762e-01 -2.83533841e-01 4.39303070e-01 -1.38491511e+00
-3.72548431e-01 7.78615177e-01 1.51619062e-01 -4.57655936e-01
4.99989718e-01 2.50797123e-01 5.57619154e-01 -7.50822008e-01
8.10383797e-01 1.36556053e+00 6.85981512e-01 7.78841496e-01
-1.15975356e+00 -6.94418430e-01 5.13819307e-02 7.71925449e-02
-8.94857705e-01 8.15467462e-02 5.91449082e-01 -8.28901470e-01
5.11726439e-01 5.58940992e-02 1.00378668e+00 8.98549497e-01
7.47672021e-01 1.05577040e+00 1.04788494e+00 -5.75848937e-01
2.14263380e-01 5.26873112e-01 -6.02768362e-02 1.13575101e+00
3.20421875e-01 8.10200810e-01 -6.67055845e-01 9.50974599e-02
1.51738977e+00 3.31090778e-01 1.68856476e-02 -1.17914498e+00
-1.64279103e+00 6.25421882e-01 9.67566431e-01 2.70418286e-01
-4.81432855e-01 3.23509604e-01 2.36173943e-01 7.07781971e-01
-5.72686255e-01 1.30767012e+00 -6.03075087e-01 3.30910608e-02
-3.44054669e-01 -9.91301015e-02 1.03954983e+00 1.24954665e+00
8.36275876e-01 -2.62374163e-01 1.47635177e-01 4.72864836e-01
4.22561288e-01 1.05871381e-02 4.40250129e-01 -1.24285579e+00
-2.97828704e-01 5.24917126e-01 1.65951792e-02 -6.71184897e-01
-7.46585369e-01 -2.87310153e-01 -4.31753725e-01 1.03419447e+00
3.25578675e-02 -2.34639779e-01 -1.05044329e+00 1.38219047e+00
1.66871876e-01 1.49919866e-02 5.04509449e-01 1.03738236e+00
1.08777726e+00 1.61720172e-01 3.25662792e-01 4.08787429e-02
1.17369533e+00 -1.37857211e+00 -6.79017842e-01 -2.81276196e-01
9.68490541e-01 -4.70696867e-01 9.42450643e-01 9.93344605e-01
-8.12198102e-01 -5.90063214e-01 -1.20737827e+00 -5.54088876e-02
-5.31909943e-01 3.12048554e-01 8.25587213e-01 1.84350863e-01
-1.07941997e+00 7.05628812e-01 -1.26289773e+00 -6.90536976e-01
1.05518840e-01 8.64207149e-01 -5.10892153e-01 1.04043901e-01
-6.86824620e-01 1.49268150e+00 6.63067639e-01 -2.54479706e-01
-1.37102056e+00 -7.82324910e-01 -1.00216675e+00 -3.05516541e-01
3.93882036e-01 -8.48864615e-01 1.75188529e+00 -1.13239706e+00
-1.87979567e+00 8.64361167e-01 8.01893532e-01 -4.52083647e-01
4.50611711e-01 -6.16916418e-01 5.83543777e-02 3.08808059e-01
-1.74705788e-01 1.22478652e+00 5.32729089e-01 -1.71787834e+00
-4.59255069e-01 -1.37664080e-01 4.38931376e-01 4.75576848e-01
-2.95101106e-01 -4.97200131e-01 1.31930873e-01 -4.56538349e-01
3.15626442e-01 -1.37969232e+00 -6.39280677e-01 9.14726675e-01
4.54431139e-02 -1.23331413e-01 7.39416480e-01 -1.51644737e-01
4.21039730e-01 -2.17656660e+00 3.78106654e-01 2.38148659e-03
3.28306258e-01 1.14287697e-01 -1.41191289e-01 5.21169245e-01
-2.25727454e-01 -3.54209453e-01 1.67296126e-01 2.28870854e-01
-3.47960562e-01 5.17020702e-01 -7.92957842e-02 8.99018720e-02
6.26452416e-02 6.38981521e-01 -1.67679787e+00 -8.24267447e-01
5.22805035e-01 1.49302065e-01 -9.46836412e-01 5.64180613e-01
-1.10924885e-01 9.43896353e-01 -3.76192302e-01 6.08792782e-01
7.27241486e-02 4.58701476e-02 3.99834663e-01 -2.15749234e-01
-7.81062916e-02 -6.51217252e-02 -8.36085677e-01 2.30481863e+00
-8.69056582e-01 4.70708400e-01 4.15758669e-01 -1.01388550e+00
9.71082091e-01 5.61775088e-01 8.28578174e-01 -7.17824697e-02
1.77090913e-01 6.49788678e-01 4.60453659e-01 -8.95462692e-01
2.92684287e-01 -4.39385056e-01 -1.49006546e-01 -3.83786224e-02
5.85880280e-01 -8.30406427e-01 -2.70162225e-01 7.69553706e-02
1.07555246e+00 5.94606698e-01 6.32596731e-01 -5.08846343e-01
1.39034733e-01 5.88031709e-01 2.34776765e-01 8.29551041e-01
-3.28919888e-01 2.40288064e-01 1.45693496e-01 -5.87584317e-01
-5.24314165e-01 -1.51711047e+00 9.04421359e-02 1.13552010e+00
6.51918232e-01 -2.00217381e-01 -4.74338055e-01 -6.91047370e-01
2.67766625e-01 6.29513919e-01 -6.45747542e-01 -4.74850953e-01
-4.62701559e-01 2.73039252e-01 6.19559400e-02 5.73198617e-01
-2.00469375e-01 -1.36655760e+00 -1.11487794e+00 2.39798591e-01
3.50192338e-01 -8.60140502e-01 2.56240398e-01 5.52479982e-01
-1.33965075e+00 -1.15759158e+00 -4.88634408e-01 -1.28613758e+00
9.95458722e-01 2.14708373e-01 6.87368989e-01 -1.92449629e-01
-6.69286251e-01 1.02777040e+00 -5.10717154e-01 -9.92227435e-01
-8.59509647e-01 -7.05619082e-02 2.60118663e-01 -5.78987300e-01
-3.98936234e-02 -4.24503833e-01 -6.08887076e-01 2.69944996e-01
-7.65859723e-01 4.12171483e-01 1.51716185e+00 9.77297068e-01
4.46991384e-01 -6.12788081e-01 5.75449824e-01 -6.98020995e-01
6.46892369e-01 -4.94592130e-01 -2.36072913e-01 1.98541120e-01
-5.14869153e-01 1.12211600e-01 5.09502232e-01 -8.37743163e-01
-7.26782203e-01 7.63374209e-01 3.35636377e-01 -8.23370934e-01
-3.49113017e-01 5.29560745e-01 5.11111140e-01 -4.84743863e-01
1.21227562e+00 -1.23575166e-01 6.02124572e-01 -1.12832412e-01
4.29094970e-01 6.77267730e-01 6.86656356e-01 -6.39629722e-01
5.29786706e-01 1.79048851e-01 -9.95598882e-02 -6.96442664e-01
-6.03513241e-01 -4.38893884e-01 -8.69576931e-01 -5.98505616e-01
7.96601057e-01 -8.32290769e-01 -9.89961982e-01 2.24735518e-03
-1.01242518e+00 -6.16794050e-01 -6.25321209e-01 1.12708807e+00
-1.25289798e+00 -1.32226959e-01 -5.45547426e-01 -6.92810655e-01
-9.77438018e-02 -1.49252701e+00 1.03471160e+00 2.09057331e-01
-5.42051315e-01 -8.22775066e-01 1.11969970e-01 -9.69805289e-03
3.14886451e-01 2.25638449e-01 8.33216131e-01 -5.54069221e-01
-7.10830629e-01 -2.98070580e-01 1.46345496e-01 1.75567344e-01
3.43010992e-01 -4.92694974e-01 -5.14799595e-01 -4.33244079e-01
-1.56259686e-01 -6.78596973e-01 2.02897504e-01 7.38777444e-02
1.26788330e+00 -1.78683475e-01 -6.65548086e-01 1.24925286e-01
1.16862416e+00 2.47755021e-01 3.39473784e-01 1.33630782e-01
3.22094291e-01 8.46453011e-01 9.88752186e-01 -4.02174480e-02
2.35009357e-01 3.99406582e-01 7.66223729e-01 -2.04327539e-01
-9.45536569e-02 -2.50050813e-01 3.05250376e-01 9.19967949e-01
3.54053527e-02 6.11503124e-01 -1.22974944e+00 5.60788274e-01
-2.01640320e+00 -3.40174675e-01 4.01040316e-01 1.93626595e+00
9.85241532e-01 -4.62658182e-02 -3.80922109e-01 -2.28536621e-01
4.00246978e-01 -7.85017729e-01 -7.11557627e-01 -5.69696426e-01
6.12131357e-01 1.29351109e-01 4.40549523e-01 2.88798004e-01
-9.83144283e-01 1.03171086e+00 6.46035290e+00 2.80950844e-01
-1.34896314e+00 -6.11529425e-02 -5.91613501e-02 1.26118913e-01
3.02090019e-01 -2.17046849e-02 -1.62801906e-01 -1.03715993e-01
5.09733260e-01 -4.29533981e-02 2.57681608e-01 1.43785882e+00
-9.70723629e-02 -2.70586878e-01 -1.90490067e+00 1.26907063e+00
2.50944849e-02 -1.23894942e+00 2.03886931e-03 -1.58311531e-01
6.18120611e-01 -7.14539737e-02 3.89982201e-03 5.63411534e-01
4.28942502e-01 -1.03559077e+00 6.01987779e-01 8.70699406e-01
6.49085224e-01 -9.80969816e-02 5.74321330e-01 5.83291650e-01
-7.36622214e-01 -6.29002631e-01 1.08462639e-01 -8.90734643e-02
-2.46637851e-01 -1.30337417e-01 -1.35929191e+00 4.13955688e-01
6.42870545e-01 6.95145428e-01 -1.31715238e-01 1.27632856e+00
-2.65959024e-01 -2.28848606e-01 -8.42289031e-02 -2.81259567e-01
1.67282775e-01 2.04912066e-01 4.00059611e-01 1.18540263e+00
3.93621296e-01 3.05197150e-01 5.80228627e-01 5.26486635e-01
3.24982643e-01 1.02930665e-01 -8.42129707e-01 5.50271720e-02
8.19400325e-02 1.35303581e+00 -5.13107777e-01 -1.98289841e-01
-2.31206745e-01 6.83370709e-01 2.91126192e-01 -5.15359752e-02
-2.92355776e-01 -5.71885347e-01 5.54671168e-01 2.29771379e-02
-3.76795918e-01 -5.57331026e-01 -3.65830988e-01 -8.12005460e-01
-4.85834867e-01 -9.20647740e-01 1.25539094e-01 -9.55583394e-01
-1.11731160e+00 2.69400537e-01 1.95084825e-01 -1.90873992e+00
-4.29867327e-01 -1.27075946e+00 -1.88924491e-01 3.13963704e-02
-1.27068496e+00 -1.14961004e+00 -7.61076331e-01 5.16672373e-01
5.79299629e-01 -2.35259548e-01 1.23301685e+00 -2.17187390e-01
2.71708667e-01 2.88524002e-01 -1.81070656e-01 4.78462595e-03
1.05328965e+00 -1.07462490e+00 -6.98622048e-01 -1.94352403e-01
-2.21890613e-01 7.17307031e-01 6.67250752e-01 -4.68853533e-01
-1.80884266e+00 -7.16796100e-01 1.17694465e-02 -3.09527308e-01
5.32632232e-01 -2.28154778e-01 -4.68179107e-01 7.79565096e-01
2.62367934e-01 -1.96638063e-01 7.31749058e-01 -7.40982369e-02
-1.56562440e-02 -1.87500313e-01 -1.32512379e+00 9.88052249e-01
9.72590268e-01 -3.03101867e-01 -9.94704187e-01 5.88505089e-01
7.81652689e-01 -6.74595475e-01 -1.03189206e+00 9.21152234e-01
8.96045625e-01 -4.55004394e-01 7.92829692e-01 -6.12135410e-01
6.28393710e-01 -2.06849650e-01 2.69939154e-01 -1.50635481e+00
-1.70529142e-01 -5.61771452e-01 -5.35735115e-02 -5.05077727e-02
4.10704583e-01 -5.66026092e-01 7.02587187e-01 3.16159904e-01
-7.24396408e-01 -9.87518787e-01 -5.84538460e-01 -8.93450379e-01
2.17711195e-01 -1.06183961e-01 -1.35626137e-01 9.27456737e-01
9.44263756e-01 1.02896847e-01 7.59781227e-02 1.38883933e-01
2.89004415e-01 -5.85901402e-02 8.72335792e-01 -1.52039766e+00
-1.91154167e-01 -4.31032062e-01 -8.13410699e-01 -8.32872212e-01
-2.47829556e-02 -1.08219814e+00 6.66507900e-01 -1.74781334e+00
-4.90518101e-02 -5.99811077e-01 -2.61273563e-01 6.47278249e-01
4.51107264e-01 -2.81811386e-01 2.95376867e-01 4.44654852e-01
-4.59249735e-01 2.53328860e-01 1.63286293e+00 3.34758265e-03
-4.74310368e-01 -2.14774415e-01 -4.05932397e-01 1.02088583e+00
9.52627182e-01 -3.95657063e-01 -4.82257068e-01 -4.74831134e-01
2.86761746e-02 3.67027968e-01 4.96135920e-01 -1.28942513e+00
9.32516754e-01 -3.60847086e-01 1.94972567e-02 -1.71360418e-01
4.72032130e-01 -1.19667518e+00 -4.11915369e-02 1.25625694e+00
-6.10047460e-01 -3.66268009e-01 3.47974420e-01 5.23815334e-01
-2.68241584e-01 -2.85207808e-01 9.66293812e-01 -5.10632515e-01
-8.62662077e-01 -5.70270196e-02 -7.31568277e-01 -6.50451779e-01
1.52381647e+00 -2.59858549e-01 1.01361340e-02 -2.31110930e-01
-1.46033847e+00 3.05517584e-01 2.05007046e-01 8.29258978e-01
8.73584688e-01 -1.06983554e+00 -4.14280802e-01 2.64547586e-01
4.89815563e-01 3.05057783e-02 2.10964624e-02 8.29733074e-01
-1.01090133e+00 4.49464887e-01 -7.97594666e-01 -8.79052281e-01
-9.78902996e-01 7.01457322e-01 6.03342652e-01 5.99056156e-03
-4.22628224e-01 5.96786201e-01 4.32761252e-01 -1.31477630e+00
5.98048747e-01 -2.74282485e-01 -1.46582618e-01 -3.56494874e-01
-1.09744601e-01 9.12533402e-02 -1.64195538e-01 7.83932731e-02
-2.12745011e-01 4.62513775e-01 9.76406932e-02 -1.96204856e-01
1.07811630e+00 3.30153614e-01 -9.78773534e-02 6.59008622e-01
8.08669567e-01 -6.45594239e-01 -9.50444996e-01 -2.49320775e-01
2.05271408e-01 -1.80922046e-01 -5.48998378e-02 -1.11170030e+00
-6.09790027e-01 1.01226926e+00 1.01806438e+00 -1.65364504e-01
8.90447140e-01 2.15297416e-01 2.31955275e-01 1.11895108e+00
9.95502710e-01 -1.29534614e+00 7.59642720e-01 4.61408466e-01
1.42661500e+00 -1.42457044e+00 -8.12835619e-02 -6.10533178e-01
-5.96244395e-01 1.56061435e+00 1.08518505e+00 -3.65782708e-01
1.16674602e+00 4.09971893e-01 3.98300529e-01 -2.66831696e-01
-4.88332063e-01 8.20148289e-02 1.27275899e-01 7.30939686e-01
5.76917946e-01 3.27832252e-01 -3.06075841e-01 3.28714997e-01
-4.82833564e-01 3.89088631e-01 6.72307193e-01 1.53265607e+00
-5.60391605e-01 -9.53667939e-01 -2.54314959e-01 3.73624861e-01
5.53399995e-02 2.64404535e-01 -1.72810838e-01 8.33188951e-01
2.27977931e-01 8.90625000e-01 -1.61162928e-01 -3.95252734e-01
5.12968540e-01 -2.78725088e-01 8.14493358e-01 -1.18607593e+00
-7.38301992e-01 -2.21401289e-01 -2.24900067e-01 -8.18816543e-01
-4.07052398e-01 -3.72969031e-01 -1.56670773e+00 6.14661932e-01
-1.43761203e-01 3.04638967e-02 1.10827470e+00 5.05360603e-01
4.28170592e-01 8.23084056e-01 2.33896852e-01 -1.32787514e+00
-6.06170654e-01 -8.48616540e-01 -2.77462333e-01 8.71115481e-04
5.65073788e-01 -8.69140625e-01 1.51366303e-02 6.99683577e-02] | [14.040669441223145, -3.403916597366333] |
8ed86ca6-429a-49f1-a5e0-875ecb2ae98a | uncertainty-dtw-for-time-series-and-sequences | 2211.00005 | null | https://arxiv.org/abs/2211.00005v1 | https://arxiv.org/pdf/2211.00005v1.pdf | Uncertainty-DTW for Time Series and Sequences | Dynamic Time Warping (DTW) is used for matching pairs of sequences and celebrated in applications such as forecasting the evolution of time series, clustering time series or even matching sequence pairs in few-shot action recognition. The transportation plan of DTW contains a set of paths; each path matches frames between two sequences under a varying degree of time warping, to account for varying temporal intra-class dynamics of actions. However, as DTW is the smallest distance among all paths, it may be affected by the feature uncertainty which varies across time steps/frames. Thus, in this paper, we propose to model the so-called aleatoric uncertainty of a differentiable (soft) version of DTW. To this end, we model the heteroscedastic aleatoric uncertainty of each path by the product of likelihoods from Normal distributions, each capturing variance of pair of frames. (The path distance is the sum of base distances between features of pairs of frames of the path.) The Maximum Likelihood Estimation (MLE) applied to a path yields two terms: (i) a sum of Euclidean distances weighted by the variance inverse, and (ii) a sum of log-variance regularization terms. Thus, our uncertainty-DTW is the smallest weighted path distance among all paths, and the regularization term (penalty for the high uncertainty) is the aggregate of log-variances along the path. The distance and the regularization term can be used in various objectives. We showcase forecasting the evolution of time series, estimating the Fr\'echet mean of time series, and supervised/unsupervised few-shot action recognition of the articulated human 3D body joints. | ['Piotr Koniusz', 'Lei Wang'] | 2022-10-30 | null | null | null | null | ['few-shot-action-recognition', 'time-series-clustering'] | ['computer-vision', 'time-series'] | [ 1.85876355e-01 -9.47528258e-02 -3.95290926e-02 -3.09052050e-01
-8.56838644e-01 -3.45823377e-01 7.06380248e-01 -1.59795478e-01
-3.16348791e-01 5.00516772e-01 3.96509796e-01 2.80771911e-01
-6.85741305e-01 -5.89133263e-01 -6.77781582e-01 -8.62126529e-01
-6.24168396e-01 5.35490870e-01 4.08141255e-01 -1.46128759e-01
1.75014615e-01 5.32152772e-01 -1.40886652e+00 -8.65770802e-02
5.42546809e-01 1.16684794e+00 -1.04856053e-02 5.38814247e-01
-3.52535327e-03 6.29141748e-01 -4.30231482e-01 -3.77301455e-01
3.60599488e-01 -5.80128729e-01 -4.91988540e-01 2.57493377e-01
2.02522073e-02 -1.42538128e-02 -5.00774682e-01 9.93879914e-01
1.67443991e-01 7.51517236e-01 9.62674618e-01 -1.68048549e+00
-1.72615096e-01 4.04051542e-01 -5.60617805e-01 2.07515880e-01
4.86948401e-01 1.77882880e-01 7.44843543e-01 -5.40209413e-01
8.03639352e-01 1.42240012e+00 7.64318287e-01 3.55977535e-01
-1.14309180e+00 -2.14739218e-01 2.02038400e-02 3.59091133e-01
-1.33727813e+00 -1.05430782e-01 6.90752566e-01 -7.48531342e-01
5.56304872e-01 6.50711656e-02 6.86328650e-01 1.19777048e+00
7.59619713e-01 7.65206158e-01 6.90963268e-01 -4.15804423e-02
3.76919478e-01 -4.71266121e-01 -7.40734488e-02 5.22597551e-01
-4.81049210e-01 2.01246783e-01 -6.43491983e-01 -3.81303340e-01
4.88782465e-01 2.78343499e-01 -8.23101103e-02 -2.40596443e-01
-1.23386574e+00 5.84058523e-01 -1.18259534e-01 3.15458715e-01
-2.97947168e-01 2.12689489e-01 4.08809930e-01 3.28634769e-01
5.73117137e-01 5.01883216e-02 -3.90108287e-01 -5.14598846e-01
-8.12839270e-01 3.71275395e-01 6.14024580e-01 6.92614853e-01
6.06536508e-01 -1.62819892e-01 -2.86179423e-01 5.26320815e-01
3.06369275e-01 5.53099811e-01 5.61892092e-01 -1.26892221e+00
2.83765107e-01 2.44381353e-01 1.12817243e-01 -1.18381512e+00
-3.98322344e-01 2.35176384e-01 -7.81960547e-01 2.16579601e-01
8.62317264e-01 -5.63552305e-02 -5.18541873e-01 2.15546775e+00
4.04585779e-01 6.28371060e-01 -2.32281789e-01 6.51706278e-01
-1.48752723e-02 7.23586082e-01 -7.89623186e-02 -5.56333661e-01
1.13692701e+00 -4.50167567e-01 -7.81941772e-01 8.04261193e-02
3.83130103e-01 -6.24347806e-01 6.98371470e-01 3.00096869e-01
-1.07765591e+00 -5.55660307e-01 -7.90357828e-01 3.25999200e-01
-1.53737783e-01 -4.65424031e-01 -4.12933193e-02 1.87575802e-01
-6.26220345e-01 1.22170269e+00 -1.20124424e+00 -2.40397081e-01
-3.52744013e-02 -3.77194979e-03 -3.93776476e-01 1.19297951e-02
-1.21482301e+00 8.31810772e-01 1.84559658e-01 5.31994738e-02
-9.28394854e-01 -7.86143899e-01 -8.05363595e-01 -2.72642642e-01
5.44595182e-01 -3.23400885e-01 8.00109327e-01 -7.69729197e-01
-1.55565786e+00 4.49301928e-01 -2.45822389e-02 -5.80559909e-01
8.64057600e-01 -1.68985412e-01 -5.61791241e-01 -6.24795556e-02
4.65804897e-02 2.32190788e-01 1.18509507e+00 -5.87551177e-01
-4.16462332e-01 -4.80654866e-01 -2.67410874e-01 -1.89955756e-02
1.88941017e-01 -1.17003813e-01 -2.85230041e-01 -8.77484381e-01
2.72704124e-01 -1.20109487e+00 -1.14890218e-01 2.78247744e-01
-2.36694887e-01 -2.82114506e-01 7.85898209e-01 -8.60697985e-01
1.22032320e+00 -2.44425917e+00 3.51136982e-01 1.95405826e-01
-2.21648753e-01 -2.74578869e-01 -2.81329930e-01 5.02711594e-01
4.88104708e-02 -2.03367203e-01 -5.69280028e-01 -2.22067580e-01
5.23771048e-02 4.33901161e-01 -2.12195009e-01 8.96458507e-01
1.36839107e-01 4.94613320e-01 -1.06843722e+00 -4.13530916e-01
1.47681117e-01 5.69784045e-01 -8.02709013e-02 1.29519805e-01
-3.88562649e-01 6.32253826e-01 -4.85615373e-01 1.41165420e-01
3.70436609e-01 2.09879592e-01 -1.87159956e-01 -2.48279855e-01
-1.03116825e-01 -2.77362257e-01 -1.53660703e+00 1.91112792e+00
-4.97356104e-03 5.64676881e-01 -4.63695198e-01 -9.02072549e-01
1.03830123e+00 3.61830294e-01 1.15902722e+00 -3.04105818e-01
1.46648914e-01 5.35870008e-02 -2.07451552e-01 -7.48536527e-01
2.88025618e-01 -2.13846967e-01 -1.70445859e-01 4.69903350e-01
1.19576402e-01 -1.06018879e-01 2.17194036e-01 -1.28267584e-02
1.21777022e+00 4.20800745e-01 1.23983130e-01 -8.83967727e-02
4.64029968e-01 -3.88086855e-01 7.97255397e-01 4.77251023e-01
-4.41959471e-01 7.40624487e-01 6.47930801e-01 -3.97702724e-01
-1.05943036e+00 -1.26717138e+00 4.48145121e-02 6.32871151e-01
4.74141538e-02 -2.19148502e-01 -5.53780615e-01 -4.35592502e-01
8.44534785e-02 6.24415517e-01 -7.02770352e-01 -4.28621948e-01
-7.01119244e-01 -4.11587656e-01 4.43501294e-01 3.93980473e-01
2.84237534e-01 -7.05403984e-01 -6.69958889e-01 3.51842970e-01
-4.10255909e-01 -9.14474010e-01 -1.01337302e+00 -1.88515350e-01
-7.89336622e-01 -1.21479261e+00 -6.96444869e-01 -1.10631183e-01
1.34267136e-01 -2.30249614e-01 8.49804342e-01 -5.36862671e-01
-3.90788883e-01 8.77397478e-01 -2.13998839e-01 -9.14280489e-02
-2.91316837e-01 -7.55185723e-01 3.31895024e-01 5.77379525e-01
1.07600018e-01 -5.00576913e-01 -4.53522086e-01 5.83631814e-01
-7.72416651e-01 -5.39564431e-01 -2.10012287e-01 7.50848770e-01
9.59741056e-01 2.54608005e-01 8.05846241e-04 -1.21018710e-03
4.05533105e-01 -5.68516493e-01 -4.93301928e-01 5.00680447e-01
-1.94939137e-01 2.63822258e-01 2.39383772e-01 -9.48336840e-01
-1.04972148e+00 -1.43882334e-01 2.63844222e-01 -7.12542951e-01
-1.77135058e-02 3.91503781e-01 -1.69453006e-02 2.78672963e-01
4.02028084e-01 4.68493290e-02 3.13143522e-01 -2.90271759e-01
4.56477106e-01 2.23251805e-01 5.81915736e-01 -6.46758735e-01
4.64534163e-01 7.17514694e-01 4.64942455e-01 -8.90440702e-01
-4.69554573e-01 -3.43726665e-01 -6.64852023e-01 -6.68275356e-01
1.12634110e+00 -5.19030750e-01 -7.07941771e-01 6.30075395e-01
-1.05278051e+00 -2.53026783e-01 -7.62070179e-01 9.68717933e-01
-1.12517250e+00 5.74899495e-01 -4.42573011e-01 -1.04653728e+00
2.67303705e-01 -9.95310843e-01 8.48410606e-01 4.95230593e-02
-5.03382325e-01 -1.07289934e+00 5.67621052e-01 -3.43416184e-02
-9.06608179e-02 8.83164227e-01 7.82184124e-01 -6.23487353e-01
-2.37131983e-01 -4.68417078e-01 4.39204514e-01 4.97467458e-01
-2.23499686e-02 3.84401768e-01 -4.76027519e-01 -1.03751160e-01
4.51005608e-01 4.79379967e-02 5.57273090e-01 8.95426393e-01
8.60842049e-01 -2.06007391e-01 -1.32860467e-01 3.30904096e-01
1.14718711e+00 4.41987455e-01 5.62886596e-01 1.68012939e-02
3.13541472e-01 8.68264437e-01 9.51542616e-01 8.21370661e-01
8.74265507e-02 7.88977027e-01 3.90488535e-01 8.69061291e-01
2.17925474e-01 -2.27493495e-01 7.73741603e-01 6.61197662e-01
-3.38394284e-01 -1.96874246e-01 -8.39018047e-01 4.49415147e-01
-2.16636610e+00 -1.42902386e+00 -1.23815142e-01 2.70434761e+00
3.21584195e-01 8.06891248e-02 2.80859351e-01 1.00356437e-01
8.18980455e-01 4.07093823e-01 -6.41802549e-01 -1.50866836e-01
-7.36156330e-02 -1.83375940e-01 2.54320204e-01 6.68112934e-01
-9.29415464e-01 2.04456612e-01 6.06603003e+00 1.05795550e+00
-4.87033218e-01 4.73283790e-02 3.69225413e-01 -2.31438458e-01
1.59108620e-02 -8.62295628e-02 -4.58167702e-01 8.52704108e-01
1.03303623e+00 -4.30798262e-01 3.73849213e-01 5.94715774e-01
3.96429181e-01 -1.90647155e-01 -1.16189504e+00 7.12574184e-01
4.58051749e-02 -7.84520745e-01 -3.15011203e-01 9.42994505e-02
6.25902295e-01 -9.52624716e-03 1.34155586e-01 5.79180717e-02
2.24495366e-01 -6.41606867e-01 8.77038300e-01 1.24608779e+00
4.26662147e-01 -7.40772128e-01 4.06754553e-01 4.69804406e-01
-1.46815419e+00 -1.29091293e-01 -2.23892465e-01 1.58955514e-01
7.27076650e-01 6.78949833e-01 -1.24290697e-01 4.43230480e-01
4.93277013e-01 8.70412230e-01 4.50856760e-02 9.56620753e-01
1.83227018e-01 1.34028614e-01 -4.91722822e-01 2.42516175e-01
1.77394211e-01 -7.96426594e-01 1.05787933e+00 8.31812441e-01
7.29450464e-01 2.25141525e-01 2.95104742e-01 5.64754486e-01
4.46215183e-01 -3.08794528e-01 -5.35099328e-01 -4.92852181e-02
3.41443270e-01 6.58436537e-01 -5.53586185e-01 -1.86739981e-01
-2.92226434e-01 9.25444722e-01 -6.43255636e-02 3.13667089e-01
-9.96550441e-01 -1.00110648e-02 1.06143808e+00 1.05067331e-03
8.81292447e-02 -5.21299720e-01 2.68434901e-02 -9.25695956e-01
3.76605764e-02 -4.43470955e-01 6.47039413e-01 -6.60581291e-01
-1.52066696e+00 3.65103960e-01 3.91929835e-01 -1.67145193e+00
-4.10864085e-01 -3.91169488e-01 -5.68482518e-01 5.14166355e-01
-7.73861825e-01 -6.62443399e-01 4.38673720e-02 1.01277256e+00
6.59124732e-01 -7.21207857e-02 4.87492204e-01 1.25150457e-01
-3.47668588e-01 1.17880002e-01 3.42918932e-01 -1.85534105e-01
6.16770566e-01 -9.75824416e-01 1.46143585e-01 6.11580193e-01
1.92412697e-02 1.76390603e-01 1.02061605e+00 -9.11798358e-01
-1.13801003e+00 -8.22726846e-01 9.17907536e-01 -3.58219087e-01
1.10418403e+00 1.55039594e-01 -9.90060389e-01 6.19030774e-01
-4.34686720e-01 1.63705438e-01 5.46303809e-01 -3.88756096e-01
-2.36295745e-01 1.03272963e-03 -1.25488198e+00 5.17910540e-01
9.71941590e-01 -6.01935148e-01 -5.46939850e-01 3.85881573e-01
6.40200913e-01 -1.52356133e-01 -1.35011899e+00 3.73474538e-01
9.11987960e-01 -1.02600336e+00 9.74697769e-01 -6.28436029e-01
3.68230343e-01 -3.07494700e-01 -4.07899946e-01 -1.27108991e+00
-2.04739243e-01 -7.27246821e-01 -2.60480493e-01 1.14174581e+00
-5.52712269e-02 -3.00216347e-01 5.09504020e-01 8.51696968e-01
1.00869320e-01 -5.86972177e-01 -1.42869258e+00 -1.33163357e+00
-8.44564959e-02 -8.18025231e-01 5.27356148e-01 6.92590892e-01
6.23875521e-02 -4.79795277e-01 -7.20062137e-01 6.68352703e-03
9.24374700e-01 -3.73433679e-01 4.10643518e-01 -1.19058502e+00
-4.76268798e-01 -3.08687478e-01 -8.35710108e-01 -6.92458034e-01
2.61162400e-01 -5.11823654e-01 3.66807312e-01 -7.95565367e-01
-1.85707770e-02 1.06570870e-01 -1.38652816e-01 -1.81542620e-01
1.23603761e-01 -3.63503426e-01 3.17274272e-01 2.29493007e-01
-3.02457482e-01 7.94594288e-01 1.04159904e+00 -1.51832268e-01
-2.38450006e-01 2.81073928e-01 5.16094625e-01 1.01981437e+00
4.69616234e-01 -6.84719086e-01 -3.66676211e-01 9.45780054e-03
3.00235059e-02 5.75277865e-01 3.51816446e-01 -1.09039128e+00
1.72772780e-01 -4.73697662e-01 -1.51182130e-01 -6.34315073e-01
5.51654041e-01 -9.16267216e-01 7.92562664e-01 5.67598522e-01
-4.34691846e-01 -7.05915689e-03 -3.13302189e-01 1.12463427e+00
-2.52452999e-01 -2.69250304e-01 8.48051786e-01 -1.86406806e-01
-5.64243615e-01 5.24082303e-01 -5.17093003e-01 1.51931807e-01
1.08669674e+00 -2.53772616e-01 7.40397573e-02 -5.08130729e-01
-9.55085754e-01 1.57552913e-01 2.44806275e-01 3.87325287e-01
5.86267352e-01 -1.54498839e+00 -4.50998336e-01 -9.72322151e-02
-6.00106865e-02 -3.54312509e-01 5.74602485e-01 1.07727444e+00
1.77177042e-02 -1.71789780e-01 -2.53588021e-01 -6.81089103e-01
-1.18716693e+00 6.99606001e-01 3.55607659e-01 -2.96268404e-01
-6.18405581e-01 8.14679980e-01 -1.65030167e-01 -1.66905299e-01
2.62924641e-01 -2.32144758e-01 -1.07333742e-01 4.07642841e-01
5.84779739e-01 1.14864111e+00 -4.75947827e-01 -9.61474240e-01
-2.78927803e-01 9.66512442e-01 4.17929709e-01 -3.71657103e-01
1.11651480e+00 -4.10963655e-01 -1.01971433e-01 1.17834747e+00
1.47195876e+00 -6.40317559e-01 -1.86175513e+00 -3.06812286e-01
2.23631993e-01 -5.41006088e-01 -3.96579891e-01 -2.97642857e-01
-8.78262579e-01 6.05008185e-01 7.64703751e-01 1.60581648e-01
8.97728920e-01 -6.60665184e-02 7.83771098e-01 -6.89030066e-02
5.63069463e-01 -1.25167394e+00 2.27495477e-01 6.21377468e-01
8.86970520e-01 -1.07381642e+00 -9.28454027e-02 -7.42038414e-02
-7.38307059e-01 1.21900213e+00 -9.11624637e-03 -3.35669935e-01
1.14565682e+00 2.86777377e-01 -3.29788893e-01 -9.47290100e-03
-6.41205370e-01 -1.21352442e-01 4.31170940e-01 6.73507810e-01
1.18922301e-01 1.11678608e-01 -4.86695886e-01 3.25887799e-01
4.13186029e-02 8.88343379e-02 2.07012206e-01 7.28913009e-01
-4.16678995e-01 -6.96599007e-01 -4.48336005e-01 1.27912253e-01
-1.48886740e-01 4.96055067e-01 1.21188477e-01 5.31735539e-01
2.71848887e-01 8.67548227e-01 3.92345101e-01 -4.55055445e-01
4.66384202e-01 3.30575377e-01 3.59292686e-01 7.26194680e-02
-1.08023345e-01 3.42966393e-02 -1.24933362e-01 -9.80024219e-01
-5.61516523e-01 -1.18446541e+00 -1.09269845e+00 -4.29477394e-01
-1.55586839e-01 -1.71929374e-01 5.34414709e-01 1.25034547e+00
-6.40805960e-02 1.59184739e-01 7.43061423e-01 -9.22793806e-01
-8.13566744e-01 -7.85911620e-01 -1.13442934e+00 6.53083086e-01
1.87003538e-01 -7.37462163e-01 -7.19080746e-01 1.77605987e-01] | [7.419969081878662, 3.4414634704589844] |
3d33eb28-4097-4e99-bf63-98e3636f6e1b | learning-graph-enhanced-commander-executor | 2302.04094 | null | https://arxiv.org/abs/2302.04094v1 | https://arxiv.org/pdf/2302.04094v1.pdf | Learning Graph-Enhanced Commander-Executor for Multi-Agent Navigation | This paper investigates the multi-agent navigation problem, which requires multiple agents to reach the target goals in a limited time. Multi-agent reinforcement learning (MARL) has shown promising results for solving this issue. However, it is inefficient for MARL to directly explore the (nearly) optimal policy in the large search space, which is exacerbated as the agent number increases (e.g., 10+ agents) or the environment is more complex (e.g., 3D simulator). Goal-conditioned hierarchical reinforcement learning (HRL) provides a promising direction to tackle this challenge by introducing a hierarchical structure to decompose the search space, where the low-level policy predicts primitive actions in the guidance of the goals derived from the high-level policy. In this paper, we propose Multi-Agent Graph-Enhanced Commander-Executor (MAGE-X), a graph-based goal-conditioned hierarchical method for multi-agent navigation tasks. MAGE-X comprises a high-level Goal Commander and a low-level Action Executor. The Goal Commander predicts the probability distribution of goals and leverages them to assign each agent the most appropriate final target. The Action Executor utilizes graph neural networks (GNN) to construct a subgraph for each agent that only contains crucial partners to improve cooperation. Additionally, the Goal Encoder in the Action Executor captures the relationship between the agent and the designated goal to encourage the agent to reach the final target. The results show that MAGE-X outperforms the state-of-the-art MARL baselines with a 100% success rate with only 3 million training steps in multi-agent particle environments (MPE) with 50 agents, and at least a 12% higher success rate and 2x higher data efficiency in a more complicated quadrotor 3D navigation task. | ['Yu Wang', 'Huazhong Yang', 'Wei-Wei Tu', 'Chao Yu', 'Yuxiang Yang', 'Yiwen Sun', 'Shiyu Huang', 'Xinyi Yang'] | 2023-02-08 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [-2.14326575e-01 2.54033029e-01 -2.20030084e-01 2.72466838e-01
-8.30827773e-01 -4.94481117e-01 6.32604003e-01 1.51397675e-01
-5.67098200e-01 9.99392211e-01 8.36137533e-02 -4.55722958e-01
-4.16705638e-01 -9.94999588e-01 -7.48330355e-01 -7.75659084e-01
-5.62178671e-01 9.40707743e-01 2.59185374e-01 -5.55596650e-01
1.92645222e-01 1.56883761e-01 -1.39196706e+00 -4.37874377e-01
9.41616595e-01 5.79937696e-01 5.82213938e-01 8.79338384e-01
5.51887333e-01 9.05764699e-01 -6.73004329e-01 3.95506650e-01
4.94007498e-01 -6.01601064e-01 -6.77640855e-01 7.06133014e-03
-1.45758619e-03 -3.66027743e-01 -2.69936681e-01 9.68125522e-01
6.03213429e-01 6.92595780e-01 3.98356557e-01 -1.60074794e+00
-1.18655212e-01 4.84054208e-01 -5.98113000e-01 2.00643986e-02
3.24218988e-01 6.86921835e-01 1.12900054e+00 -2.61222184e-01
5.06215155e-01 1.38792837e+00 2.61854380e-01 5.33382654e-01
-1.01774800e+00 -5.53378224e-01 5.37191749e-01 -7.28357807e-02
-9.15374279e-01 1.21721439e-02 4.01530176e-01 -1.53330237e-01
1.39300311e+00 -1.87554032e-01 8.43929231e-01 8.90821457e-01
6.51533306e-01 5.22608757e-01 1.05730212e+00 -5.11239655e-02
6.54378295e-01 -6.58728540e-01 -9.93517190e-02 1.19918609e+00
1.78684279e-01 9.32697177e-01 -4.37959820e-01 -3.84144217e-01
8.36862743e-01 -2.93587834e-01 -1.29516032e-02 -5.19503057e-01
-1.17857206e+00 1.24804890e+00 7.16789782e-01 -1.75951511e-01
-8.41835976e-01 3.92603606e-01 1.05550528e-01 3.36434841e-01
-1.59639388e-01 1.07975578e+00 -4.46558654e-01 -3.13993067e-01
-2.34152257e-01 7.71751702e-01 9.68666553e-01 8.45215857e-01
7.77183890e-01 4.37029779e-01 9.44193900e-02 4.18980122e-01
4.24032927e-01 4.17654306e-01 2.29471445e-01 -1.34173453e+00
6.02525771e-01 5.15961766e-01 2.20602691e-01 -7.44991839e-01
-8.09669316e-01 -5.04328728e-01 -6.25944436e-01 1.09425092e+00
2.39319935e-01 -7.70356476e-01 -9.75880980e-01 2.00420570e+00
7.09182024e-01 1.48691967e-01 5.33057809e-01 1.03756249e+00
4.55189854e-01 8.15718293e-01 -4.96595129e-02 -1.40443489e-01
1.21439624e+00 -1.52679348e+00 -1.31889224e-01 -7.79643953e-01
6.76365197e-01 -9.87706333e-02 9.26315784e-01 3.01180720e-01
-1.04491055e+00 -3.82194966e-01 -1.12433386e+00 4.72388327e-01
-2.47123078e-01 -4.10541564e-01 8.13620389e-01 1.60790831e-01
-1.18923855e+00 4.96593952e-01 -1.08531010e+00 -1.86674833e-01
-4.11051884e-02 7.13198125e-01 -2.19738275e-01 5.98911792e-02
-1.15593088e+00 1.10858035e+00 6.26060665e-01 -3.15298289e-01
-1.64694285e+00 -4.24553365e-01 -1.16033602e+00 1.40508423e-02
9.61974382e-01 -9.38211977e-01 1.30593836e+00 -3.43110383e-01
-1.82742596e+00 -1.53243197e-02 4.34106708e-01 -5.96850812e-01
1.07210644e-01 1.06104389e-01 1.29353359e-01 -7.40733147e-02
3.98119003e-01 8.27022076e-01 6.92842066e-01 -1.29809451e+00
-1.15616024e+00 -3.12527925e-01 5.88000476e-01 1.05829549e+00
3.04276586e-01 -5.66929638e-01 -4.78533477e-01 -3.18168133e-01
-2.52395064e-01 -1.26145685e+00 -8.49616885e-01 -7.55312383e-01
-2.60503680e-01 -4.45516706e-01 6.10120416e-01 -1.83256000e-01
8.21186483e-01 -1.80111492e+00 8.40697527e-01 1.26992166e-01
4.66738015e-01 -1.64313316e-01 -6.11603439e-01 5.97674787e-01
3.82945508e-01 -1.66862011e-01 5.74754812e-02 -2.53414989e-01
2.06511959e-01 4.79382157e-01 9.07991454e-02 2.83734351e-01
-2.19592452e-01 7.90463030e-01 -1.25247777e+00 -2.17491165e-01
2.40932345e-01 5.70865721e-02 -8.99387658e-01 3.53994578e-01
-6.08844817e-01 5.55207372e-01 -8.42335165e-01 3.56859118e-01
6.71821609e-02 -3.84156585e-01 4.20838803e-01 2.64209986e-01
-1.98676169e-01 3.58557045e-01 -1.08598101e+00 1.59300387e+00
-4.53604460e-01 1.00391656e-01 4.70760614e-01 -7.10032642e-01
6.33351505e-01 -7.64722750e-02 7.35712826e-01 -7.90771842e-01
1.07917212e-01 -8.30089580e-03 3.39674056e-01 -1.10894814e-01
6.30213320e-01 2.13703677e-01 -5.20051062e-01 5.43883026e-01
-1.21257082e-01 -5.47574937e-01 5.19876420e-01 2.20579073e-01
1.48060334e+00 2.47776821e-01 4.62238878e-01 -1.19789742e-01
2.55841076e-01 4.78453636e-01 7.53538370e-01 1.24513125e+00
-2.42295101e-01 -1.20141119e-01 4.10886765e-01 -3.08611184e-01
-7.42250681e-01 -7.56367803e-01 7.65536904e-01 1.35637212e+00
5.55266082e-01 -5.44469297e-01 -4.43295896e-01 -8.09652448e-01
2.19410926e-01 8.74100149e-01 -5.83759844e-01 -2.48532429e-01
-7.43433118e-01 -7.86379516e-01 1.66897044e-01 2.29016140e-01
6.13087177e-01 -1.24000978e+00 -9.52612698e-01 4.81917262e-01
1.53988704e-01 -9.62127328e-01 -4.83558565e-01 5.99043131e-01
-4.96128649e-01 -1.29541302e+00 -1.45084202e-01 -8.26318502e-01
6.60188556e-01 1.06772728e-01 1.14642203e+00 1.71888441e-01
1.43084958e-01 4.54973280e-01 -3.49991262e-01 -1.35435924e-01
-5.71310937e-01 1.73105553e-01 1.79507315e-01 -7.54792809e-01
-1.70317054e-01 -4.32146966e-01 -3.29623818e-01 3.22093070e-01
-3.62083465e-01 2.40010560e-01 6.72646165e-01 1.13717949e+00
4.02480334e-01 4.22829241e-01 4.56649333e-01 -2.72556126e-01
9.69244719e-01 -4.38823968e-01 -1.23310900e+00 -1.56978965e-01
-5.99850297e-01 2.20301434e-01 9.32801366e-01 -4.51826602e-01
-5.33864677e-01 -6.03425764e-02 -3.15111168e-02 -2.04729304e-01
2.98703015e-02 7.78115094e-01 1.29540578e-01 -1.07765898e-01
5.68443775e-01 1.85596153e-01 4.26997155e-01 1.13822661e-01
2.73868948e-01 -3.36200856e-02 3.23607475e-01 -6.60363317e-01
8.37851048e-01 -1.60650209e-01 3.73855084e-01 -4.66617644e-01
-5.58984101e-01 3.30655947e-02 6.52720407e-02 -3.06389958e-01
8.58139277e-01 -9.27955449e-01 -1.30130339e+00 3.61304075e-01
-7.12155402e-01 -1.16975164e+00 -1.76959023e-01 7.33728170e-01
-8.82918417e-01 -6.32365793e-02 -6.31039381e-01 -6.02777898e-01
-2.11704507e-01 -1.58486843e+00 9.06233728e-01 4.71684605e-01
1.89003035e-01 -9.80094790e-01 2.36229688e-01 1.43884674e-01
3.26841325e-01 2.75359929e-01 1.00312150e+00 -5.73847771e-01
-8.61797035e-01 2.34778598e-01 2.25798815e-01 -2.95760959e-01
1.20574869e-01 -5.06551862e-01 -1.87967215e-02 -9.65807617e-01
-1.95667207e-01 -7.67689526e-01 4.62192088e-01 6.34339988e-01
3.82175148e-01 -3.11035007e-01 -5.48885882e-01 3.95000070e-01
1.33970845e+00 5.58199048e-01 4.98928390e-02 5.52742422e-01
5.34426391e-01 2.53811449e-01 1.02580929e+00 4.78783846e-01
9.59191740e-01 7.46795893e-01 1.06310487e+00 9.84035153e-03
-4.72042859e-02 -3.08308631e-01 6.54397368e-01 5.90329409e-01
-2.00344827e-02 -6.04629576e-01 -9.02916312e-01 1.79407209e-01
-2.15644002e+00 -5.96157610e-01 4.18409437e-01 2.06917381e+00
5.77609360e-01 3.33657950e-01 3.09453994e-01 -6.18832827e-01
3.71141106e-01 3.64057153e-01 -1.02899754e+00 -1.16808554e-02
2.05390751e-01 -3.27984691e-01 5.72431684e-01 1.16268253e+00
-1.02653229e+00 1.34367967e+00 5.74685383e+00 5.14555156e-01
-6.76980019e-01 -2.13309616e-01 1.43553138e-01 -6.20498434e-02
1.99705705e-01 6.94920719e-02 -1.08931756e+00 2.61426747e-01
6.98369861e-01 -2.53525406e-01 1.15059793e+00 8.77937675e-01
3.48014176e-01 -4.31438297e-01 -9.89021599e-01 6.94046617e-01
-4.99894731e-02 -1.27961290e+00 -1.01607949e-01 5.28578937e-01
7.00896382e-01 5.13404548e-01 -1.89776812e-02 9.26958680e-01
1.37859714e+00 -9.29235518e-01 4.33265716e-01 8.44093189e-02
1.86327428e-01 -9.60844457e-01 4.45647657e-01 7.25320518e-01
-1.30978239e+00 -4.35202807e-01 -2.94603616e-01 -3.75374705e-01
2.45140716e-01 -2.36586109e-01 -1.05810618e+00 6.52778566e-01
4.46509123e-01 5.43760061e-01 -2.39853859e-01 7.25334108e-01
-3.75423163e-01 1.92923203e-01 -5.18688619e-01 -4.66166228e-01
9.47553217e-01 -4.90254432e-01 1.03955996e+00 2.30830505e-01
2.25232497e-01 2.57213771e-01 1.10206783e+00 5.23524821e-01
2.10587367e-01 -3.16021323e-01 -6.88025415e-01 -1.44022837e-01
5.42606771e-01 1.23930109e+00 -5.45675516e-01 -2.86752045e-01
-3.12535435e-01 6.16131306e-01 7.37667680e-01 4.92233604e-01
-8.80795062e-01 -7.39281550e-02 9.12198126e-01 -5.19620895e-01
2.93242902e-01 -6.13783777e-01 2.07362697e-01 -6.89855635e-01
-4.81614143e-01 -1.34834266e+00 5.06240129e-01 -5.98812282e-01
-1.01599753e+00 7.30665565e-01 -1.83227986e-01 -9.93932188e-01
-9.23897266e-01 -4.95918721e-01 -5.32398999e-01 6.95061922e-01
-1.32477748e+00 -1.00700665e+00 -2.57162988e-01 5.01248896e-01
6.66199505e-01 -6.30125284e-01 9.34325159e-01 -2.45468512e-01
-6.30901873e-01 5.57406954e-02 -1.07999749e-01 -1.24407761e-01
1.39881179e-01 -1.53252482e+00 4.55730468e-01 7.09860802e-01
-2.10208833e-01 1.59364179e-01 7.85189152e-01 -1.03102338e+00
-1.86114943e+00 -9.75567818e-01 9.64137614e-02 -3.07626069e-01
6.94469571e-01 -2.03639120e-01 -2.91648239e-01 7.20732629e-01
3.52922171e-01 -3.08870375e-01 2.24829674e-01 1.45971611e-01
2.67707288e-01 2.15259731e-01 -7.80415356e-01 1.02826536e+00
9.42553043e-01 8.90482068e-02 -4.81114805e-01 4.73000318e-01
8.18485260e-01 -8.47035229e-01 -7.56556690e-01 4.24393594e-01
1.84445068e-01 -7.63547480e-01 9.28473234e-01 -7.46032715e-01
1.96752205e-01 -5.74631631e-01 -1.23261541e-01 -2.01241708e+00
-5.79358399e-01 -7.85848677e-01 -2.34988943e-01 3.10850501e-01
5.27247906e-01 -7.80425608e-01 9.48172629e-01 2.54743785e-01
-4.64904904e-01 -9.93609846e-01 -8.85335803e-01 -8.90705645e-01
4.39677052e-02 2.81210663e-03 6.19340122e-01 5.85806906e-01
7.51416609e-02 8.40172946e-01 -5.07500172e-01 7.14333117e-01
6.95750296e-01 2.84128785e-01 1.17161322e+00 -8.45067441e-01
-6.97032034e-01 -5.34920037e-01 3.25185061e-02 -1.40553463e+00
3.46435130e-01 -7.83117533e-01 3.36488634e-01 -1.89164639e+00
-1.78351179e-01 -6.27869368e-01 -2.82951817e-02 6.06689930e-01
-3.44374180e-01 -5.05288482e-01 4.97218519e-01 -9.88317654e-02
-9.74209905e-01 8.99496734e-01 1.60484385e+00 -1.78751394e-01
-7.02161968e-01 3.54537666e-02 -5.95472932e-01 6.66969538e-01
9.21583354e-01 -2.92587131e-01 -5.68144083e-01 -4.05466020e-01
1.01703800e-01 5.84048688e-01 3.26564431e-01 -1.13936615e+00
6.38807654e-01 -5.37982523e-01 1.72875375e-02 -4.38456595e-01
6.19072139e-01 -6.36170030e-01 -5.03665805e-02 9.03692961e-01
-1.89484388e-01 6.13702238e-01 1.06667720e-01 6.48330331e-01
1.43045913e-02 -1.00202724e-01 5.88524878e-01 -3.39194804e-01
-8.36576819e-01 5.07715881e-01 -6.12322986e-01 2.05055773e-01
1.19988859e+00 8.78114253e-02 -5.48554301e-01 -6.23909175e-01
-6.04570508e-01 1.19891238e+00 4.55688685e-01 4.49150115e-01
6.31019056e-01 -1.09070635e+00 -6.42928720e-01 1.30925015e-01
-1.88882306e-01 1.82403892e-01 -1.64406933e-02 6.44867778e-01
-1.23970672e-01 1.95356280e-01 -2.50348240e-01 -3.73204201e-01
-1.05686164e+00 5.31143904e-01 6.61238134e-01 -8.10730577e-01
-6.61679506e-01 7.59938359e-01 1.26452282e-01 -7.47948706e-01
2.22964346e-01 -1.16389751e-01 -2.65629917e-01 -2.68093050e-01
2.41235495e-01 3.87959510e-01 -5.43599904e-01 -3.19420427e-01
-2.73430169e-01 2.85801768e-01 -1.23020180e-01 -3.57866108e-01
1.35000730e+00 5.39316572e-02 7.93473348e-02 -1.43887773e-01
5.25192201e-01 -2.17398003e-01 -1.71495926e+00 1.69394806e-01
-2.87167698e-01 -7.23140985e-02 2.99027234e-01 -1.00716531e+00
-7.91890085e-01 5.03167585e-02 1.01892173e-01 3.47377509e-01
7.43697941e-01 -1.23579182e-01 5.45768559e-01 6.79051638e-01
9.98641372e-01 -1.18984413e+00 4.49014932e-01 1.17991066e+00
9.61950362e-01 -1.33639348e+00 9.12221745e-02 -1.35432810e-01
-8.18533421e-01 7.78056383e-01 1.22239935e+00 -2.43619606e-01
2.63228625e-01 2.59720176e-01 -1.44534335e-01 -5.18772006e-01
-1.15236938e+00 -5.12497306e-01 -1.87077820e-01 7.84032702e-01
-5.04666626e-01 9.34459567e-02 1.27933949e-01 2.85747349e-01
-4.32559699e-01 -5.62757969e-01 4.48194444e-01 1.13165748e+00
-6.98527753e-01 -1.03724384e+00 -2.77655065e-01 4.83697355e-01
2.17411757e-01 3.83232050e-02 -7.74275512e-02 1.09699893e+00
-1.98098958e-01 1.17393899e+00 -7.96445012e-02 -4.88933921e-01
3.34461600e-01 -5.15275538e-01 4.87664461e-01 -7.34295845e-01
-6.89486146e-01 1.35201976e-01 3.87007594e-01 -9.01425064e-01
-9.10811946e-02 -4.94622648e-01 -1.70201516e+00 -2.10229650e-01
-1.66702911e-01 4.97006774e-01 3.18598002e-01 8.30144525e-01
5.94861388e-01 8.74163270e-01 5.89531422e-01 -1.14242625e+00
-7.42784023e-01 -6.72451615e-01 -3.73302221e-01 -7.02521577e-02
4.95403975e-01 -1.04832518e+00 -3.17576677e-01 -7.28834331e-01] | [3.8151705265045166, 1.7958526611328125] |
c4ba690d-7a1e-4673-b795-f119a63ba8fe | capturing-localized-image-artifacts-through-a | 1711.04945 | null | http://arxiv.org/abs/1711.04945v1 | http://arxiv.org/pdf/1711.04945v1.pdf | Capturing Localized Image Artifacts through a CNN-based Hyper-image Representation | Training deep CNNs to capture localized image artifacts on a relatively small
dataset is a challenging task. With enough images at hand, one can hope that a
deep CNN characterizes localized artifacts over the entire data and their
effect on the output. However, on smaller datasets, such deep CNNs may overfit
and shallow ones find it hard to capture local artifacts. Thus some image-based
small-data applications first train their framework on a collection of patches
(instead of the entire image) to better learn the representation of localized
artifacts. Then the output is obtained by averaging the patch-level results.
Such an approach ignores the spatial correlation among patches and how various
patch locations affect the output. It also fails in cases where few patches
mainly contribute to the image label. To combat these scenarios, we develop the
notion of hyper-image representations. Our CNN has two stages. The first stage
is trained on patches. The second stage utilizes the last layer representation
developed in the first stage to form a hyper-image, which is used to train the
second stage. We show that this approach is able to develop a better mapping
between the image and its output. We analyze additional properties of our
approach and show its effectiveness on one synthetic and two real-world vision
tasks - no-reference image quality estimation and image tampering detection -
by its performance improvement over existing strong baselines. | ['Parag Shridhar Chandakkar', 'Baoxin Li'] | 2017-11-14 | null | null | null | null | ['image-quality-estimation'] | ['computer-vision'] | [ 4.78555471e-01 -1.52185351e-01 2.26750627e-01 -9.28528905e-02
-9.97717857e-01 -3.70377481e-01 3.67575198e-01 1.88912243e-01
-3.60151343e-02 5.04813373e-01 1.21679366e-01 6.04564324e-02
2.27513716e-01 -7.84842193e-01 -1.31793559e+00 -8.76887858e-01
2.21356809e-01 -2.75896490e-01 4.55065817e-01 -6.42368272e-02
3.66638750e-01 4.85448927e-01 -1.44684076e+00 5.97120225e-01
7.66710401e-01 1.06740105e+00 3.19506943e-01 5.34256041e-01
2.54610300e-01 9.90775704e-01 -9.82407451e-01 -7.10026994e-02
3.75035942e-01 -3.56325358e-01 -4.39436138e-01 3.25224876e-01
8.45189571e-01 -3.50507110e-01 -2.95560181e-01 1.30462098e+00
4.86466616e-01 -6.91702664e-02 4.36612278e-01 -1.03394043e+00
-7.74330318e-01 3.89566600e-01 -8.63843441e-01 1.61991656e-01
4.77263518e-03 5.15515029e-01 8.09531510e-01 -9.01922405e-01
7.01581478e-01 9.86791968e-01 8.95608604e-01 3.02620083e-01
-1.44653034e+00 -4.80655074e-01 -1.53504580e-01 3.93560939e-02
-1.15078294e+00 -2.02440068e-01 9.37910914e-01 -4.35636550e-01
8.17498565e-01 -2.68868692e-02 3.38637084e-01 1.28856134e+00
1.82552949e-01 5.92777312e-01 1.15734661e+00 -3.18595916e-01
2.24744797e-01 5.01999371e-02 2.57229060e-01 4.29193676e-01
4.34689701e-01 7.49758109e-02 -3.67999732e-01 -8.02655593e-02
8.59026551e-01 1.20895050e-01 -4.60975289e-01 -1.77601695e-01
-1.13425565e+00 6.14712358e-01 8.92643392e-01 4.55258459e-01
-4.93758500e-01 3.98564845e-01 4.25618142e-02 2.87895352e-01
3.79025072e-01 5.92562556e-01 -3.05208415e-01 3.03199172e-01
-1.23707175e+00 3.60487133e-01 3.28700125e-01 6.44166708e-01
1.11841023e+00 -1.08243547e-01 -2.56906748e-01 9.83104229e-01
-2.29052797e-01 2.77547032e-01 2.30398312e-01 -8.89337957e-01
4.28307503e-01 6.42602801e-01 2.68392891e-01 -1.08446157e+00
-2.72230834e-01 -6.31529450e-01 -9.77544069e-01 5.55884898e-01
6.31030917e-01 4.14843895e-02 -1.25643313e+00 1.74492669e+00
-9.55983810e-03 2.22063303e-01 -3.42545331e-01 9.47801054e-01
4.16391075e-01 6.84181690e-01 -4.87214699e-02 8.24901238e-02
1.24729490e+00 -1.02957058e+00 -4.06525522e-01 -3.31908226e-01
3.58954519e-01 -7.71502912e-01 1.20016193e+00 5.62867165e-01
-1.18905127e+00 -8.00437927e-01 -1.35112619e+00 -2.54690707e-01
-4.16108608e-01 2.45014533e-01 1.54491395e-01 3.67638439e-01
-1.16234767e+00 8.79334927e-01 -5.44362009e-01 -2.66835272e-01
8.38106334e-01 1.54852182e-01 -2.79702783e-01 -2.96009183e-01
-9.17444468e-01 8.21873426e-01 -6.95900321e-02 -6.63504843e-03
-1.16747272e+00 -8.11413646e-01 -6.23765409e-01 2.18371153e-01
1.78207487e-01 -6.56192601e-01 8.32819939e-01 -1.44121027e+00
-9.73879933e-01 7.30733573e-01 2.79938206e-02 -3.06653440e-01
5.71910620e-01 -4.69742082e-02 -5.63170500e-02 3.50665636e-02
1.81432471e-01 5.53912103e-01 1.15048015e+00 -1.65961158e+00
-3.68606567e-01 -3.15713435e-01 1.23748988e-01 -2.53362805e-01
-2.74853557e-01 -1.16661713e-01 -6.18007243e-01 -8.47672701e-01
-1.03565536e-01 -8.40824425e-01 -1.86876133e-01 1.54468581e-01
-4.63551760e-01 1.30132511e-01 7.05584884e-01 -5.01873195e-01
8.70835721e-01 -2.39186835e+00 -7.97697008e-02 2.69084066e-01
3.03252220e-01 3.28966022e-01 -5.32479584e-01 4.33061749e-01
-2.76481867e-01 1.06345624e-01 -4.47971553e-01 -4.97774333e-01
-2.58967847e-01 -1.28412209e-02 -3.22407275e-01 5.63703954e-01
7.21053243e-01 9.82696176e-01 -7.30282664e-01 -2.37264678e-01
2.99816430e-01 6.41019523e-01 -3.47547203e-01 2.83946574e-01
-2.20572323e-01 3.17404360e-01 -1.79776743e-01 5.29650390e-01
1.04123139e+00 -4.89776731e-01 -2.29862511e-01 -3.91601890e-01
1.98338628e-02 8.94808322e-02 -1.06746030e+00 1.70074284e+00
-5.22096515e-01 9.86729205e-01 -1.69149950e-01 -1.00150144e+00
7.00378001e-01 9.33765024e-02 4.58764583e-01 -8.93554688e-01
1.04799747e-01 2.42545664e-01 -1.54949635e-01 -6.84950173e-01
3.03100765e-01 -5.11892233e-03 2.65513510e-01 5.05470037e-01
9.43574682e-02 1.16451971e-01 -9.31016654e-02 1.80831209e-01
1.52538931e+00 5.01870587e-02 4.36613485e-02 -1.77937597e-01
2.29433790e-01 -7.32020438e-02 3.52231711e-01 9.78282928e-01
-1.11161888e-01 1.35037661e+00 6.34172618e-01 -6.13450944e-01
-1.31563604e+00 -9.77285385e-01 -1.78566530e-01 6.71919167e-01
3.73916417e-01 -1.61773026e-01 -9.77393687e-01 -8.13196361e-01
7.44827697e-03 3.96756649e-01 -1.12553525e+00 -2.56315082e-01
-5.07828951e-01 -8.12115014e-01 4.53783333e-01 4.62329388e-01
5.01168191e-01 -1.20669484e+00 -6.56168878e-01 1.03629433e-01
-2.42990196e-01 -9.70045626e-01 -3.05843621e-01 3.31348449e-01
-6.65946960e-01 -1.11735702e+00 -8.37053359e-01 -6.48606956e-01
9.13849890e-01 5.09931564e-01 1.28169167e+00 5.71343780e-01
-3.39210302e-01 -1.61759034e-01 -3.02476525e-01 -1.79546386e-01
-3.98450762e-01 -3.23987365e-01 -4.47831631e-01 3.14804494e-01
5.83873466e-02 -5.97517610e-01 -9.45031524e-01 1.56941950e-01
-1.25172937e+00 -1.12193517e-01 9.19946849e-01 9.68337715e-01
4.25780147e-01 1.97734818e-01 3.46315831e-01 -9.40467417e-01
2.67100632e-01 -5.32274723e-01 -3.92689705e-01 1.58125326e-01
-3.15968812e-01 1.07444316e-01 7.14626014e-01 -6.62190318e-01
-8.41674507e-01 1.33189365e-01 -1.04596522e-02 -5.76910615e-01
-1.37918562e-01 1.85830861e-01 -2.86628813e-01 -1.97604001e-01
9.79484975e-01 6.57890365e-02 -2.34390482e-01 -6.03641570e-01
9.01035443e-02 4.87884074e-01 6.35182321e-01 -3.84252459e-01
8.41298819e-01 5.66272140e-01 -7.74657577e-02 -6.84320271e-01
-7.32824206e-01 -2.76575029e-01 -4.62188900e-01 -1.92486972e-01
7.56011724e-01 -8.38889301e-01 -2.57127255e-01 5.79933226e-01
-1.36469233e+00 -4.97649580e-01 -3.96759182e-01 -7.00132847e-02
-3.64675373e-01 2.88374573e-01 -4.15190935e-01 -5.14910340e-01
9.81816091e-03 -1.36368108e+00 1.35798144e+00 8.19232464e-02
1.01605512e-01 -6.88403606e-01 4.45269644e-02 1.66521326e-01
5.72471142e-01 3.80378753e-01 9.66608524e-01 -2.64700443e-01
-8.22291195e-01 -2.28433833e-01 -6.42275810e-01 6.63227320e-01
6.32574782e-02 -5.35164354e-03 -1.39836931e+00 -2.59600729e-01
1.03774980e-01 -3.52239192e-01 1.14235997e+00 4.36575323e-01
1.44710863e+00 -1.18270308e-01 -1.46241933e-01 5.58992565e-01
1.88838160e+00 -2.63670146e-01 1.27019048e+00 4.64146733e-01
7.70507455e-01 5.67877352e-01 1.99433297e-01 1.01445980e-01
-1.16502784e-01 7.66041279e-01 6.31716728e-01 -5.70335567e-01
-6.81790352e-01 -2.52044857e-01 4.15821612e-01 1.67777404e-01
8.80651847e-02 -3.66425455e-01 -6.64804935e-01 6.93917692e-01
-1.71303260e+00 -8.59820426e-01 -1.67028949e-01 2.23503542e+00
7.03519940e-01 8.64263028e-02 -5.77843077e-02 2.40963191e-01
7.12592542e-01 1.37930155e-01 -5.61119974e-01 -3.36239673e-02
-4.23178554e-01 2.79469669e-01 4.55202609e-01 2.00642541e-01
-9.58383560e-01 6.14484727e-01 6.11715651e+00 8.46155882e-01
-1.27433932e+00 2.57264823e-01 9.60975766e-01 4.69730087e-02
-3.15410346e-01 -4.72232662e-02 -1.19706742e-01 6.47127151e-01
7.27942944e-01 5.25657594e-01 3.80779684e-01 5.88403642e-01
2.70520359e-01 -3.12634945e-01 -1.14643884e+00 1.01060963e+00
4.20768261e-02 -1.37781429e+00 1.40469417e-01 1.65505379e-01
1.08846569e+00 9.77931321e-02 1.98567927e-01 -2.07413346e-01
7.17640445e-02 -1.16744435e+00 8.55706692e-01 5.12726605e-01
7.23726809e-01 -4.24407065e-01 8.05362463e-01 2.09312782e-01
-7.95226634e-01 -1.56611025e-01 -6.56710446e-01 1.40687153e-01
-1.98481590e-01 8.97408307e-01 -4.44776058e-01 3.42860192e-01
8.37598562e-01 7.11062372e-01 -9.80724633e-01 1.17796683e+00
-5.28276637e-02 5.74973285e-01 -1.66972190e-01 4.36258614e-01
1.32372916e-01 6.31864043e-03 4.24218714e-01 1.19953251e+00
4.22101706e-01 -2.51846761e-01 -2.21263289e-01 1.26950967e+00
-3.91371965e-01 -1.16664939e-01 -7.38809168e-01 2.63735682e-01
1.16077021e-01 1.26788414e+00 -7.56304264e-01 -4.12521005e-01
-6.46496296e-01 1.12362385e+00 4.75537002e-01 5.24363399e-01
-8.28876495e-01 -3.24295789e-01 5.72688282e-01 3.39380443e-01
3.41221660e-01 8.43278244e-02 -6.38299644e-01 -1.21582294e+00
1.72337785e-01 -1.15535176e+00 8.38759094e-02 -1.07706380e+00
-1.24078059e+00 6.51359558e-01 -4.35693294e-01 -1.41671836e+00
3.34576331e-02 -5.34149408e-01 -7.17423856e-01 8.62505972e-01
-1.51608121e+00 -1.23785138e+00 -5.79703808e-01 6.63890123e-01
5.69648087e-01 3.11880559e-01 5.05115092e-01 3.69217962e-01
-5.43123901e-01 5.80446005e-01 3.48493941e-02 2.03818187e-01
7.67712057e-01 -1.26429486e+00 3.54058683e-01 1.29373586e+00
3.27136993e-01 4.59116727e-01 7.72132218e-01 -5.51656961e-01
-1.16589546e+00 -1.25548255e+00 5.93720257e-01 -6.72387838e-01
4.76252317e-01 -3.43486309e-01 -1.18640757e+00 4.08268601e-01
3.26711893e-01 4.58467394e-01 4.01882753e-02 -2.59178638e-01
-8.18201005e-01 -2.44682565e-01 -9.94545698e-01 3.41671377e-01
7.66255796e-01 -6.28604054e-01 -2.20340222e-01 2.18123496e-01
4.65248197e-01 -8.71451050e-02 -6.37013495e-01 2.80191272e-01
4.69591230e-01 -1.33235037e+00 1.00253236e+00 -3.29191238e-01
9.18988466e-01 -4.20527011e-01 -1.44457370e-02 -1.36305904e+00
-2.72370785e-01 -5.40647268e-01 2.42099971e-01 1.07717502e+00
5.13528287e-01 -3.19347382e-01 5.23893714e-01 3.93332511e-01
-1.07203081e-01 -5.28472483e-01 -5.31790316e-01 -6.51191831e-01
1.57719865e-01 -4.70265120e-01 5.95258534e-01 7.18428493e-01
-4.02728826e-01 1.14598274e-01 -4.99899238e-01 4.24062610e-01
7.37081409e-01 1.55910388e-01 7.79510140e-01 -9.53328550e-01
-2.96551347e-01 -3.54339242e-01 -4.08911645e-01 -8.17031264e-01
-2.45915055e-01 -5.03194332e-01 3.25467348e-01 -1.26864839e+00
5.52527070e-01 -3.90251935e-01 -5.51662505e-01 5.24728417e-01
-4.42018151e-01 8.44046772e-01 1.68049619e-01 3.21021378e-01
-4.47321147e-01 7.46734962e-02 1.20239770e+00 -4.48659718e-01
4.42446209e-03 -3.08033675e-01 -5.89353144e-01 4.18401092e-01
5.76684475e-01 -6.77100718e-01 -1.67446300e-01 -6.27367616e-01
2.18161866e-01 -1.83113202e-01 8.28204215e-01 -1.27421808e+00
3.51081379e-02 1.50763527e-01 7.13365614e-01 -2.44355187e-01
1.42441571e-01 -8.67250800e-01 3.08279216e-01 2.94551283e-01
-5.03570139e-01 -2.61590958e-01 9.49686319e-02 5.14472783e-01
-2.80581802e-01 -2.40249828e-01 1.12341022e+00 -1.25602394e-01
-2.55104870e-01 2.36329228e-01 -1.73535824e-01 -2.02969924e-01
7.20003486e-01 -1.83688194e-01 -5.34236848e-01 -3.91735941e-01
-3.97233278e-01 -2.47380868e-01 8.68519425e-01 3.53180438e-01
6.88751221e-01 -1.27219105e+00 -6.92717075e-01 4.17578995e-01
3.12353134e-01 -4.90899794e-02 2.72981197e-01 8.25620174e-01
-4.76126611e-01 -1.13771893e-01 -3.99061143e-01 -7.17979014e-01
-9.82728362e-01 7.68023252e-01 4.63993758e-01 -1.91871986e-01
-8.05688500e-01 6.78962469e-01 6.48817301e-01 2.39995927e-01
2.27068663e-01 -5.04363060e-01 -3.30496915e-02 -2.24343866e-01
6.38367474e-01 1.06556118e-01 3.32941115e-01 -6.79112911e-01
-7.32327849e-02 7.30022073e-01 4.67338599e-02 7.92261958e-02
1.44780743e+00 -1.33801907e-01 -8.44586492e-02 2.83994794e-01
1.57686639e+00 8.62535760e-02 -1.52859771e+00 -1.46315217e-01
-1.19258478e-01 -7.57636189e-01 1.97325557e-01 -8.72189164e-01
-1.45989501e+00 1.11651719e+00 7.27345109e-01 3.68444115e-01
1.49066854e+00 2.54084240e-03 8.83164048e-01 -6.64466992e-02
1.85855046e-01 -8.22068870e-01 4.50821638e-01 2.44745612e-02
1.05309355e+00 -1.33436763e+00 -1.87856123e-01 -2.37604395e-01
-4.09825265e-01 1.20553517e+00 4.33926821e-01 -4.68076617e-01
4.47223246e-01 3.28750312e-01 2.00331613e-01 -3.20083320e-01
-6.60509169e-01 -2.62252748e-01 2.21408144e-01 5.34741819e-01
1.63573518e-01 -2.66672939e-01 1.71585172e-01 4.55245912e-01
2.04468742e-01 4.17393968e-02 7.15722978e-01 6.58409536e-01
-2.33707935e-01 -9.76864994e-01 -4.94595379e-01 3.60507131e-01
-6.25654161e-01 -9.12993103e-02 -5.22142112e-01 6.17067754e-01
5.19206226e-01 1.01735067e+00 -1.45603679e-02 -5.57492316e-01
4.68266368e-01 -2.29095563e-01 4.67071474e-01 -4.68336672e-01
-8.14696968e-01 2.06270337e-01 -3.24144185e-01 -1.01060736e+00
-3.67282599e-01 -5.32949865e-01 -8.20851743e-01 -7.96422958e-02
-4.31793004e-01 -2.55283713e-01 7.16242433e-01 6.74347758e-01
3.57305855e-01 5.82928538e-01 7.92717040e-01 -1.12559760e+00
-4.19703037e-01 -1.02626944e+00 -5.63503385e-01 9.75273669e-01
8.09002221e-01 -5.49920321e-01 -5.45747578e-01 2.38647744e-01] | [11.511083602905273, -1.8133447170257568] |
f1d04e16-c69e-45c9-b3f5-ffc2a4d0fb73 | explicit-syntactic-guidance-for-neural-text | 2306.11485 | null | https://arxiv.org/abs/2306.11485v2 | https://arxiv.org/pdf/2306.11485v2.pdf | Explicit Syntactic Guidance for Neural Text Generation | Most existing text generation models follow the sequence-to-sequence paradigm. Generative Grammar suggests that humans generate natural language texts by learning language grammar. We propose a syntax-guided generation schema, which generates the sequence guided by a constituency parse tree in a top-down direction. The decoding process can be decomposed into two parts: (1) predicting the infilling texts for each constituent in the lexicalized syntax context given the source sentence; (2) mapping and expanding each constituent to construct the next-level syntax context. Accordingly, we propose a structural beam search method to find possible syntax structures hierarchically. Experiments on paraphrase generation and machine translation show that the proposed method outperforms autoregressive baselines, while also demonstrating effectiveness in terms of interpretability, controllability, and diversity. | ['Yongjing Yin', 'Yue Zhang', 'Shuming Shi', 'Wei Bi', 'Jianhao Yan', 'Leyang Cui', 'Yafu Li'] | 2023-06-20 | null | null | null | null | ['paraphrase-generation', 'text-generation', 'machine-translation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 6.95005059e-01 5.16376913e-01 -1.42641783e-01 -5.09923041e-01
-8.08406234e-01 -7.59455144e-01 8.28981221e-01 -7.03420863e-03
1.63843334e-01 9.72777486e-01 9.24629748e-01 -7.56883085e-01
3.01626086e-01 -1.13408518e+00 -6.25759184e-01 -3.96730810e-01
3.67126077e-01 7.33229756e-01 -2.39521340e-01 -4.55734640e-01
3.53623718e-01 -1.19083166e-01 -1.12278044e+00 6.30274236e-01
1.24376273e+00 3.47471118e-01 5.85073233e-01 6.86924398e-01
-6.70441270e-01 8.46855223e-01 -4.83383119e-01 -5.05520701e-01
-1.51885757e-02 -1.26497889e+00 -9.08815205e-01 9.30322334e-02
-3.63396034e-02 -1.84565589e-01 1.41745210e-01 7.94925690e-01
1.98719632e-02 2.22585183e-02 6.35451436e-01 -6.87333107e-01
-9.62475538e-01 1.31459439e+00 -1.28537774e-01 -3.05875205e-02
8.00543129e-01 1.04680452e-02 1.40262556e+00 -1.05865383e+00
7.17541158e-01 1.35658944e+00 8.37315544e-02 8.33030164e-01
-1.46855271e+00 -3.87175024e-01 3.96328807e-01 -2.45233133e-01
-9.12767351e-01 -3.01802874e-01 8.19169700e-01 -4.87414956e-01
1.26489282e+00 8.30485523e-02 6.89625561e-01 1.39666128e+00
4.49703276e-01 7.00862706e-01 9.33721483e-01 -8.99236917e-01
2.69282460e-01 -3.02856743e-01 2.52624840e-01 7.37716496e-01
1.12555489e-01 5.19978814e-02 -6.63161874e-01 -2.47492760e-01
5.83655179e-01 -6.64957702e-01 4.22859490e-02 4.44194004e-02
-1.32235777e+00 1.26870501e+00 1.16749838e-01 1.98592782e-01
-5.00415444e-01 1.03063092e-01 2.01958612e-01 2.02941805e-01
2.71924019e-01 6.56352043e-01 -2.63281226e-01 -1.63176283e-02
-8.35218728e-01 5.38621843e-01 8.37674141e-01 1.30155504e+00
6.22066975e-01 2.30764285e-01 -3.87970388e-01 6.83989763e-01
4.72670555e-01 5.47205567e-01 8.21398258e-01 -5.87817490e-01
9.09067094e-01 5.59184074e-01 -6.68264329e-02 -6.57164037e-01
-2.02369809e-01 -1.72833383e-01 -6.65261447e-01 -5.31260550e-01
2.06137314e-01 -4.93574500e-01 -8.34914684e-01 1.99247193e+00
7.90383220e-02 -2.24522278e-01 3.50188196e-01 5.55006564e-01
6.63629711e-01 1.13581431e+00 2.61359304e-01 -4.32319969e-01
1.24573326e+00 -1.00859404e+00 -6.79233372e-01 -5.67170560e-01
6.23478353e-01 -7.15696216e-01 1.12878728e+00 2.02312708e-01
-1.47053814e+00 -6.26882851e-01 -8.81958365e-01 -1.72854260e-01
9.87615213e-02 2.12089345e-01 5.57511210e-01 4.95198578e-01
-1.05093896e+00 4.22627062e-01 -6.10730827e-01 -5.13652042e-02
-1.70738250e-01 -1.78854596e-02 7.29049891e-02 2.49466926e-01
-1.44536722e+00 5.35073638e-01 8.12031388e-01 -1.51039526e-01
-6.63815737e-01 -3.69690478e-01 -1.08615458e+00 2.41254032e-01
6.18204214e-02 -1.27866113e+00 1.46605396e+00 -1.03515244e+00
-1.67898118e+00 4.90219533e-01 -7.24003077e-01 -5.42052984e-01
7.44799152e-02 -1.35624334e-01 5.99876866e-02 -1.76186562e-01
3.64397168e-01 7.24926710e-01 7.81901956e-01 -1.09869182e+00
-8.76064479e-01 -1.96144894e-01 -2.09016144e-01 4.50038612e-01
1.00590535e-01 3.24489959e-02 -1.19512737e-01 -9.77461934e-01
2.76176184e-01 -9.71879423e-01 -3.87684196e-01 -9.69914973e-01
-6.49426043e-01 -4.58551168e-01 -4.25328612e-02 -8.62821758e-01
1.72627079e+00 -1.42261243e+00 5.90389431e-01 2.16596946e-01
-1.99152455e-01 -2.09401637e-01 -4.33129847e-01 8.32324266e-01
-1.80215299e-01 3.58052790e-01 -2.08647668e-01 -2.28180841e-01
8.71437043e-02 4.35470119e-02 -1.03797400e+00 -5.31764865e-01
3.36614162e-01 1.27337623e+00 -1.12379920e+00 -2.67066389e-01
-1.29912987e-01 2.83255074e-02 -6.33566499e-01 4.76871431e-01
-6.59020185e-01 2.58311659e-01 -6.30470276e-01 3.34745377e-01
-3.70597839e-02 -3.21180999e-01 8.00964475e-01 3.60380918e-01
-2.85714236e-03 1.22315609e+00 -6.53792739e-01 1.68416822e+00
-5.86853206e-01 2.70427674e-01 -4.27328110e-01 -6.20740533e-01
1.11883795e+00 2.77518272e-01 -3.05687338e-01 -6.40027404e-01
-8.94394889e-02 1.67286739e-01 2.56166339e-01 -2.93974429e-01
7.47459948e-01 -4.06740010e-01 -5.27743459e-01 8.43491554e-01
-1.35585040e-01 -2.19782054e-01 5.08208275e-01 4.28424031e-01
7.98720837e-01 4.88778502e-01 6.73182905e-01 -1.60805762e-01
4.67437565e-01 7.69506469e-02 4.45721120e-01 7.57044256e-01
4.61468875e-01 3.83028775e-01 4.97672111e-01 -3.60830784e-01
-1.15112329e+00 -1.23253882e+00 5.12188673e-01 1.34593642e+00
-2.73427725e-01 -7.28593707e-01 -1.08957481e+00 -5.59393048e-01
-4.27965999e-01 1.45280981e+00 -4.58762079e-01 4.80898209e-02
-1.19245768e+00 -6.34585202e-01 4.15655375e-01 5.98292589e-01
5.57052642e-02 -1.56360805e+00 -5.56696117e-01 6.39042616e-01
-8.12439322e-01 -8.77071917e-01 -6.52910233e-01 -9.04020369e-02
-9.38459575e-01 -6.30172551e-01 -2.23525852e-01 -1.02733171e+00
7.05259204e-01 5.47165237e-03 1.18377244e+00 1.85264945e-01
3.01205814e-01 -3.19855183e-01 -4.69075531e-01 -1.87410563e-01
-1.10181296e+00 4.05649424e-01 -1.71502709e-01 -3.25764060e-01
1.76200047e-01 -5.41436791e-01 -2.49322370e-01 -2.13264078e-01
-6.20674193e-01 8.07623506e-01 6.42295778e-01 9.64498281e-01
5.16972601e-01 -4.13657993e-01 7.68641174e-01 -9.68593001e-01
1.16798604e+00 -5.15327334e-01 -4.91812468e-01 4.86503750e-01
-6.98192358e-01 4.59661365e-01 1.06679165e+00 -8.30948576e-02
-1.45929027e+00 1.77512348e-01 -1.14592262e-01 3.47467422e-01
-3.39837074e-01 7.59489894e-01 -2.14540631e-01 8.17405462e-01
7.42642581e-01 8.33176255e-01 -2.37633735e-01 -1.46708399e-01
7.59345889e-01 4.20778573e-01 4.55755353e-01 -9.93218958e-01
8.19014847e-01 -3.37996721e-01 -6.03414439e-02 -5.88221312e-01
-6.63782179e-01 2.03138813e-01 -5.58944345e-01 6.24891557e-02
9.36557770e-01 -8.06912780e-01 -2.31087402e-01 -1.26519995e-02
-1.51314771e+00 -4.01465654e-01 -1.11567512e-01 3.52563933e-02
-8.15209150e-01 5.26182234e-01 -6.90513074e-01 -6.82855725e-01
-7.47512400e-01 -1.11549520e+00 1.21222222e+00 1.53791964e-01
-8.37150931e-01 -8.79396439e-01 2.20730960e-01 3.83713394e-01
1.89189062e-01 3.97662520e-02 1.64844608e+00 -7.04789340e-01
-7.72600114e-01 2.57403404e-01 1.04426891e-01 -1.48020014e-01
1.84387878e-01 -1.10238716e-01 -3.31916988e-01 2.32031588e-02
-1.65736288e-01 -2.65345842e-01 4.54337686e-01 2.93575227e-01
5.07425070e-01 -7.20938027e-01 -2.65047342e-01 5.06627619e-01
9.85229194e-01 5.20395815e-01 5.91001451e-01 2.50305504e-01
5.33598244e-01 8.23491871e-01 4.27201658e-01 2.24286973e-01
5.63232720e-01 4.30106014e-01 -8.69672522e-02 3.34300697e-01
-7.00062588e-02 -1.08397138e+00 6.71932161e-01 9.78371859e-01
2.63662845e-01 -6.30704224e-01 -9.45015550e-01 4.66917217e-01
-1.89300120e+00 -1.03005564e+00 -1.89945474e-01 1.82495344e+00
1.05942309e+00 1.17078207e-01 3.48009169e-03 -1.34941489e-01
6.46532953e-01 1.47937387e-01 -9.74748954e-02 -7.66274273e-01
-1.02492146e-01 3.22698355e-01 -2.40673095e-01 8.12751830e-01
-5.79876423e-01 1.51627767e+00 6.82742643e+00 5.20613015e-01
-7.53903449e-01 -3.80826801e-01 6.12405777e-01 3.57265100e-02
-8.09502959e-01 4.05448854e-01 -1.03424203e+00 5.49028575e-01
9.78822231e-01 -5.50801218e-01 6.40574634e-01 6.91943109e-01
4.75545645e-01 2.46262461e-01 -1.19859028e+00 5.67141831e-01
1.85957730e-01 -1.51385617e+00 7.82217860e-01 -9.63294972e-03
7.63583004e-01 -3.78121525e-01 -3.73396039e-01 4.21696931e-01
6.76920831e-01 -1.01978409e+00 8.92933011e-01 2.37688124e-01
5.08852661e-01 -5.88313758e-01 7.53404796e-02 7.76001036e-01
-1.13817644e+00 -1.15614623e-01 -1.94622993e-01 -3.08478624e-01
6.70683742e-01 2.75300443e-01 -1.09625590e+00 5.46193063e-01
-1.85014933e-01 2.59985209e-01 -3.36207777e-01 2.20943615e-01
-1.08942461e+00 9.36371803e-01 2.01554924e-01 -4.07677621e-01
3.07278275e-01 -6.09956264e-01 5.96256673e-01 1.42724574e+00
5.72080910e-01 3.97074431e-01 2.99483120e-01 1.18145978e+00
-1.00575648e-01 4.22608703e-01 -5.23978055e-01 -1.28353089e-01
6.50369883e-01 9.62959409e-01 -5.93526840e-01 -5.21159947e-01
-2.20917329e-01 1.09714115e+00 5.38861334e-01 4.27460253e-01
-6.12930596e-01 -2.48992339e-01 2.68254995e-01 3.03667933e-02
3.43062162e-01 -2.62756377e-01 -6.61807537e-01 -1.17109263e+00
-4.56396416e-02 -1.23875284e+00 3.17882806e-01 -9.76933122e-01
-9.62087214e-01 9.45516348e-01 5.02029434e-02 -6.51060879e-01
-1.03524315e+00 -1.93037122e-01 -7.98015952e-01 1.25107968e+00
-1.05938876e+00 -1.21108270e+00 1.35457546e-01 4.99719940e-02
1.21414137e+00 -3.19470227e-01 1.00457919e+00 -5.65100789e-01
-4.85484868e-01 5.32686889e-01 -3.05269212e-01 1.40510648e-01
2.02772006e-01 -1.32894564e+00 1.24895191e+00 1.26579738e+00
3.37396830e-01 1.15029633e+00 5.33417523e-01 -1.10940802e+00
-1.06123686e+00 -1.19195569e+00 1.77965426e+00 -3.68117869e-01
5.75022876e-01 -5.94561458e-01 -7.55812526e-01 8.30840766e-01
4.87497061e-01 -1.19228709e+00 7.36761034e-01 6.35448657e-03
-3.27036262e-01 5.18299580e-01 -5.92361927e-01 9.71024334e-01
1.19973063e+00 -3.31133395e-01 -1.22968912e+00 2.59333998e-01
8.56548011e-01 -4.24847871e-01 -2.81276435e-01 5.32024428e-02
5.20948589e-01 -7.04292715e-01 5.59580505e-01 -9.19984639e-01
1.06689358e+00 -1.07960552e-01 -1.30237430e-01 -1.51941645e+00
-7.13122487e-01 -1.14070415e+00 -1.64062753e-01 1.13756955e+00
9.07538354e-01 -2.31561333e-01 7.74362445e-01 5.79419672e-01
-3.17949891e-01 -6.78943753e-01 -4.67456460e-01 -6.02467120e-01
2.64560997e-01 -2.74133921e-01 9.12908792e-01 5.28623879e-01
4.78800893e-01 1.37710667e+00 -3.61959964e-01 -2.35478282e-01
4.13732797e-01 6.14331543e-01 7.77468383e-01 -8.94183755e-01
-6.21759057e-01 -5.27018130e-01 5.03519952e-01 -1.55428255e+00
4.38459396e-01 -1.24542177e+00 4.25818861e-01 -1.77123451e+00
2.28483528e-01 -1.44645676e-01 3.69301289e-01 3.95583630e-01
-6.86031878e-01 -4.29280728e-01 2.60879964e-01 2.58495450e-01
-8.63268599e-02 5.48943281e-01 1.01573253e+00 1.62244156e-01
-4.32704717e-01 -6.73524067e-02 -1.05310178e+00 5.04362464e-01
9.21419322e-01 -4.11134601e-01 -7.81118512e-01 -5.64911842e-01
5.71156085e-01 5.79570949e-01 -2.64498860e-01 -4.72522050e-01
-7.48678371e-02 -5.54236531e-01 1.76915482e-01 -4.53227311e-01
-3.52175497e-02 -1.54588809e-02 1.55441403e-01 4.27943051e-01
-8.19819987e-01 4.95740533e-01 -1.28419355e-01 3.90942037e-01
3.21215950e-02 -3.43862236e-01 4.21035469e-01 -1.73113450e-01
-3.18305284e-01 -9.68399867e-02 -8.38930726e-01 1.76782191e-01
6.50070190e-01 -1.28491580e-01 -2.88279563e-01 -5.25519311e-01
-5.68870306e-01 6.70542493e-02 3.26501191e-01 6.74849868e-01
6.22846723e-01 -1.25432682e+00 -9.22656000e-01 3.97819847e-01
-7.65357092e-02 -1.06771603e-01 -2.44822815e-01 -1.13808557e-01
-3.73422861e-01 7.51036465e-01 6.70169443e-02 -1.15178451e-01
-1.09435356e+00 4.16453421e-01 4.15886231e-02 -8.08921933e-01
-3.83192420e-01 8.11902344e-01 5.54906368e-01 -1.89388588e-01
-1.17518567e-01 -5.35362780e-01 -7.89473280e-02 -2.15435922e-01
7.26557136e-01 4.24865931e-02 -2.10996881e-01 -5.82921565e-01
1.53736062e-02 2.90003389e-01 -2.54264653e-01 -5.39611220e-01
8.63146722e-01 -2.55907893e-01 -2.38911137e-01 1.26485735e-01
6.02278590e-01 -4.97804210e-02 -7.73382783e-01 -1.26151755e-01
3.86397004e-01 -5.87716624e-02 -4.53906089e-01 -9.34484720e-01
-2.81409085e-01 6.48231030e-01 -2.97345757e-01 1.83343425e-01
9.30380166e-01 -6.09715246e-02 1.08731842e+00 4.41672474e-01
3.04782420e-01 -8.90335262e-01 1.73286200e-01 1.08255661e+00
1.04507911e+00 -6.32255971e-01 -3.77226233e-01 -5.54365456e-01
-7.71033406e-01 1.17017853e+00 6.33622825e-01 -6.83618486e-02
8.97321478e-03 1.60612106e-01 -6.08659349e-02 4.41520363e-02
-1.31225646e+00 3.00661456e-02 2.49741077e-01 5.58090329e-01
9.34389889e-01 1.01286165e-01 -7.42847443e-01 8.57138574e-01
-8.38692129e-01 -2.57809758e-01 5.51129043e-01 6.77569389e-01
-6.43524826e-01 -1.62084985e+00 -2.14297295e-01 2.71870613e-01
-1.63875014e-01 -6.61305606e-01 -8.29924464e-01 3.36684614e-01
-1.54550582e-01 1.22461593e+00 -7.45543391e-02 -2.32274085e-01
3.14494520e-01 5.62778294e-01 5.21583021e-01 -1.19969594e+00
-8.01092684e-01 3.29324782e-01 4.26277578e-01 -3.40876371e-01
2.20408365e-01 -7.63335586e-01 -1.51965380e+00 6.94289207e-02
-9.82351825e-02 3.27618212e-01 3.94821852e-01 9.89800990e-01
4.34663057e-01 4.04921561e-01 6.94310784e-01 -5.59633851e-01
-6.31703675e-01 -9.98845696e-01 1.07639745e-01 3.55113566e-01
-2.21509114e-02 1.98050812e-02 -2.07699183e-02 4.45269793e-01] | [11.719158172607422, 9.001250267028809] |
826511c3-1d52-487d-90e8-5627bf2fc264 | efficient-conditional-gan-transfer-with | 2102.06696 | null | https://arxiv.org/abs/2102.06696v2 | https://arxiv.org/pdf/2102.06696v2.pdf | Efficient Conditional GAN Transfer with Knowledge Propagation across Classes | Generative adversarial networks (GANs) have shown impressive results in both unconditional and conditional image generation. In recent literature, it is shown that pre-trained GANs, on a different dataset, can be transferred to improve the image generation from a small target data. The same, however, has not been well-studied in the case of conditional GANs (cGANs), which provides new opportunities for knowledge transfer compared to unconditional setup. In particular, the new classes may borrow knowledge from the related old classes, or share knowledge among themselves to improve the training. This motivates us to study the problem of efficient conditional GAN transfer with knowledge propagation across classes. To address this problem, we introduce a new GAN transfer method to explicitly propagate the knowledge from the old classes to the new classes. The key idea is to enforce the popularly used conditional batch normalization (BN) to learn the class-specific information of the new classes from that of the old classes, with implicit knowledge sharing among the new ones. This allows for an efficient knowledge propagation from the old classes to the new ones, with the BN parameters increasing linearly with the number of new classes. The extensive evaluation demonstrates the clear superiority of the proposed method over state-of-the-art competitors for efficient conditional GAN transfer tasks. The code is available at: https://github.com/mshahbazi72/cGANTransfer | ['Luc van Gool', 'Ajad Chhatkuli', 'Danda Pani Paudel', 'Zhiwu Huang', 'Mohamad Shahbazi'] | 2021-02-12 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Shahbazi_Efficient_Conditional_GAN_Transfer_With_Knowledge_Propagation_Across_Classes_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Shahbazi_Efficient_Conditional_GAN_Transfer_With_Knowledge_Propagation_Across_Classes_CVPR_2021_paper.pdf | cvpr-2021-1 | ['conditional-image-generation'] | ['computer-vision'] | [ 4.16248888e-01 2.87899494e-01 -1.64119169e-01 -2.08230987e-01
-6.86294198e-01 -5.84083021e-01 7.00639307e-01 -1.81393087e-01
-2.47393742e-01 1.27613461e+00 9.48824957e-02 9.87397805e-02
2.14212656e-01 -1.14793277e+00 -9.27442670e-01 -1.31996381e+00
4.29774612e-01 4.53961730e-01 6.70472160e-02 -2.03987658e-01
-1.22961052e-01 4.04263675e-01 -1.30875742e+00 1.50610641e-01
1.02165151e+00 7.81997204e-01 2.80763209e-01 4.54273969e-01
-1.23791778e-02 8.74205172e-01 -7.86812425e-01 -6.29442394e-01
1.60399616e-01 -8.77396464e-01 -7.34884262e-01 -1.60153687e-01
2.59265125e-01 -1.52786061e-01 -1.68778971e-01 8.73889267e-01
4.40789431e-01 1.75563991e-01 7.78211713e-01 -1.29574358e+00
-8.66479814e-01 5.76318562e-01 -5.07539034e-01 -1.27928048e-01
-2.33472317e-01 7.02342242e-02 7.96012282e-01 -6.59818769e-01
5.31497121e-01 8.26556742e-01 3.85971367e-01 9.41967964e-01
-1.17106616e+00 -1.02887523e+00 1.42947763e-01 2.26583168e-01
-1.30352008e+00 -2.06483662e-01 8.24980080e-01 -4.21146661e-01
2.69268990e-01 1.83819681e-01 7.01861918e-01 1.29098713e+00
-2.12871358e-01 8.16396296e-01 1.27650702e+00 -4.98986483e-01
1.31635800e-01 4.81245488e-01 -2.42660552e-01 4.27890867e-01
7.78890103e-02 4.76262420e-02 -5.60847223e-01 1.93035454e-01
7.01957703e-01 -1.21455854e-02 -5.51027238e-01 -2.89882571e-01
-9.68930960e-01 1.02189600e+00 8.50473821e-01 5.78411222e-01
-4.40165579e-01 2.04459235e-01 6.46284968e-02 1.85100839e-01
6.72560513e-01 3.30162019e-01 -5.98238349e-01 9.45847109e-02
-7.82545209e-01 1.12663642e-01 5.35228193e-01 9.35505986e-01
1.21032286e+00 1.44625425e-01 -4.61822093e-01 7.06202984e-01
4.80591282e-02 4.24430370e-01 4.78622824e-01 -5.22011101e-01
5.67351282e-01 4.15971160e-01 -1.55997083e-01 -6.68556988e-01
2.55216151e-01 -7.29953051e-01 -1.21178722e+00 2.48375550e-01
3.80310714e-01 -2.99350262e-01 -1.06334102e+00 2.14760280e+00
3.15273672e-01 4.26917583e-01 2.68802583e-01 3.64756227e-01
6.36324525e-01 7.76056826e-01 8.02128837e-02 -1.70448348e-01
9.57737982e-01 -1.11299384e+00 -5.83875299e-01 -3.07204664e-01
4.53996718e-01 -7.47850716e-01 8.83071005e-01 1.61215857e-01
-9.32966709e-01 -7.18608737e-01 -9.33466554e-01 1.87666640e-01
-5.05268514e-01 1.17351301e-01 7.05284655e-01 6.60535097e-01
-9.67042744e-01 3.25354755e-01 -6.60804629e-01 -1.83885083e-01
9.22743618e-01 2.26334214e-01 -2.62898892e-01 -2.53197134e-01
-1.20867419e+00 6.78758144e-01 5.67217588e-01 3.73617746e-02
-1.06740010e+00 -9.35010612e-01 -6.49145961e-01 4.63638641e-02
2.64015585e-01 -9.07243133e-01 9.48099017e-01 -1.57692468e+00
-1.73137009e+00 7.05316067e-01 2.93687526e-02 -2.72281468e-01
6.17629766e-01 -2.03383654e-01 4.65574116e-02 -5.32319807e-02
1.34501547e-01 8.36098373e-01 1.09863746e+00 -1.42676687e+00
-4.53106850e-01 -1.70730218e-01 1.35932982e-01 2.76768148e-01
-4.76593226e-01 -4.15344626e-01 -4.58989948e-01 -1.06441927e+00
-3.41455996e-01 -9.63064730e-01 2.00073980e-02 -2.87591279e-01
-5.58457792e-01 -6.52586743e-02 6.78817511e-01 -5.40155411e-01
7.76081741e-01 -2.18806720e+00 5.06453156e-01 1.40431747e-01
-2.91523174e-05 3.99274915e-01 -9.47364643e-02 3.11997354e-01
-2.70674050e-01 -6.66639954e-02 -4.87685949e-01 -3.83093804e-01
-1.60785690e-01 3.08802783e-01 -3.94131869e-01 1.95856839e-02
2.59163439e-01 1.04884160e+00 -8.43184590e-01 -1.51849389e-01
8.83959085e-02 7.61676431e-01 -5.03441930e-01 5.12439847e-01
-1.97285190e-01 1.08301067e+00 -3.97532225e-01 1.97946683e-01
8.31361592e-01 -1.85513005e-01 -5.34188794e-03 -1.35090902e-01
3.13949585e-01 -1.40675390e-02 -7.83965647e-01 1.61329734e+00
-5.41547596e-01 3.96863610e-01 -2.21969694e-01 -1.16721451e+00
7.73505151e-01 4.24892306e-01 1.70830145e-01 -4.89443809e-01
4.69797775e-02 1.43251330e-01 -1.71665668e-01 3.22203822e-02
6.21259250e-02 -6.22702062e-01 1.32102668e-01 2.35785022e-01
4.37369615e-01 -2.56769389e-01 9.77708772e-02 1.90674528e-01
6.26239538e-01 1.23197109e-01 2.58398503e-01 1.33202016e-01
5.50715983e-01 -3.79478812e-01 5.43072164e-01 5.48945248e-01
3.94999921e-01 6.23517096e-01 2.88067669e-01 6.23450009e-03
-6.18035972e-01 -1.16697443e+00 1.51351690e-01 1.01689613e+00
-1.28188595e-01 -1.10643946e-01 -9.27803993e-01 -1.06028199e+00
-2.21879154e-01 8.47578108e-01 -9.47674692e-01 -4.85457629e-01
-4.69725281e-01 -8.47721815e-01 4.65401202e-01 7.07847834e-01
7.35315919e-01 -1.21066236e+00 -2.57315785e-01 4.43431251e-02
-2.10060611e-01 -1.07613349e+00 -3.43353540e-01 2.07359672e-01
-8.10152411e-01 -8.50474775e-01 -1.25296807e+00 -7.82145441e-01
1.00287545e+00 -2.12254018e-01 1.00123882e+00 7.39940861e-03
3.74239534e-02 4.76778269e-01 -5.19507587e-01 -4.88432974e-01
-5.61199009e-01 2.58460969e-01 -3.15529823e-01 5.12055755e-01
-9.55343246e-02 -8.01016569e-01 -4.84963387e-01 2.62177289e-01
-1.06007612e+00 4.67417836e-01 7.45799720e-01 9.78996813e-01
6.68114722e-01 2.20438257e-01 8.37557495e-01 -1.24403822e+00
1.63414627e-01 -4.60177183e-01 -4.07334596e-01 2.62233227e-01
-5.24274409e-01 5.06203212e-02 7.98590600e-01 -4.52026039e-01
-1.50633764e+00 -8.74926150e-03 -2.56345153e-01 -3.06091905e-01
-2.41952300e-01 4.07172590e-01 -4.58835423e-01 -4.98590693e-02
4.91135985e-01 4.51534539e-01 -3.29112798e-01 -4.20522362e-01
7.21772850e-01 2.19587386e-01 4.89477962e-01 -7.37597287e-01
1.03529644e+00 4.94607598e-01 -1.03982560e-01 -4.62188780e-01
-8.91367674e-01 6.03193566e-02 -7.36484170e-01 -2.67267860e-02
9.82422292e-01 -9.63747501e-01 -1.09772861e-01 8.95125031e-01
-1.10471892e+00 -7.85592854e-01 -8.42728257e-01 3.54126126e-01
-6.49921358e-01 6.07994664e-03 -4.98538792e-01 -4.52963740e-01
-3.01528066e-01 -9.94771779e-01 7.63588190e-01 5.56540251e-01
1.62076309e-01 -1.16499567e+00 9.47119072e-02 4.20131236e-01
5.02730072e-01 4.21665817e-01 9.98356342e-01 -5.63151360e-01
-7.38509953e-01 -2.30141670e-01 -1.03987060e-01 7.95735359e-01
4.87948984e-01 -3.74159187e-01 -1.04841661e+00 -4.25589949e-01
1.43922921e-02 -3.05159926e-01 1.10185122e+00 2.68550396e-01
1.02667964e+00 -2.74356335e-01 -3.12822014e-01 5.57223082e-01
1.37439287e+00 8.06545615e-02 9.08043981e-01 -2.32419167e-02
9.47496593e-01 3.35625291e-01 3.64689916e-01 2.49120936e-01
1.77536309e-01 7.26895154e-01 3.63400906e-01 -2.93604732e-01
-5.14218986e-01 -3.23012143e-01 3.38945627e-01 8.25495660e-01
-3.72784168e-01 -4.29806709e-01 -5.59110105e-01 5.12414753e-01
-1.57400441e+00 -7.10415244e-01 2.60700524e-01 2.34054065e+00
1.20974636e+00 -7.04201981e-02 -2.10736752e-01 -4.15270403e-02
8.49036574e-01 9.30682644e-02 -3.98214370e-01 9.26696509e-02
-1.50108039e-01 7.45200753e-01 2.69902408e-01 4.45680171e-01
-9.24082518e-01 1.03589737e+00 5.08785677e+00 1.09694648e+00
-1.17123652e+00 5.49217165e-01 8.01293433e-01 8.10477212e-02
-2.29889989e-01 4.08380143e-02 -7.51778185e-01 4.57731754e-01
6.71088636e-01 -1.85868591e-01 3.86693984e-01 5.56190908e-01
-3.42240214e-01 -5.49525246e-02 -1.09125233e+00 7.15964198e-01
1.56321377e-01 -1.31865513e+00 3.41343105e-01 -3.67939589e-03
1.19009626e+00 -1.57089382e-01 2.11004242e-01 3.91668379e-01
3.28399181e-01 -9.00196791e-01 5.08302093e-01 6.52919114e-01
8.01660478e-01 -9.21409011e-01 8.85352731e-01 2.66968966e-01
-1.01397324e+00 1.34777427e-01 -2.21434429e-01 1.43957257e-01
5.40343821e-02 7.93388605e-01 -7.59522259e-01 9.34662640e-01
5.84998429e-01 7.56325901e-01 -6.65692866e-01 6.99876964e-01
-8.23317289e-01 8.40202749e-01 -1.55183394e-02 4.96071368e-01
3.20989601e-02 -2.66622752e-01 2.89310306e-01 9.43080425e-01
4.56536502e-01 -7.86950663e-02 -2.69597411e-01 9.93261516e-01
-4.44927007e-01 1.27953738e-01 -5.32395184e-01 6.48144633e-02
4.92286496e-02 1.12359345e+00 -6.19459629e-01 -5.02509475e-01
-2.90773511e-01 1.17837000e+00 3.69518876e-01 5.99670351e-01
-9.73567903e-01 -3.35309476e-01 3.62275898e-01 3.13370973e-02
5.87674677e-01 1.90131947e-01 -3.42977867e-02 -1.12084234e+00
8.67245197e-02 -6.77445769e-01 5.29188097e-01 -7.52545595e-01
-1.48239100e+00 6.18242383e-01 -1.42424135e-02 -1.05216718e+00
-5.62269762e-02 -2.63618439e-01 -7.95843840e-01 9.92764115e-01
-1.72852778e+00 -1.45823443e+00 -4.52837914e-01 8.05415332e-01
3.48639399e-01 -2.98018605e-01 9.95673656e-01 4.20184642e-01
-4.22401994e-01 8.55041265e-01 1.56068206e-01 2.14852020e-01
7.71990001e-01 -1.22338331e+00 1.65367755e-03 9.04834390e-01
1.64724335e-01 2.76717871e-01 3.81188154e-01 -5.06834865e-01
-7.51071930e-01 -1.30290961e+00 5.31100333e-01 -3.69882137e-01
3.02296460e-01 -3.29178244e-01 -9.12402987e-01 8.62684906e-01
5.26204348e-01 2.13357024e-02 8.24083924e-01 -1.59524664e-01
-5.07526398e-01 -3.86247844e-01 -9.89975214e-01 3.90603542e-01
7.41093278e-01 -3.44876111e-01 -2.01943427e-01 3.05779159e-01
6.71771228e-01 -2.03424886e-01 -7.03939855e-01 5.04945695e-01
1.92497417e-01 -9.14498925e-01 9.40211654e-01 -4.57008243e-01
5.56012630e-01 -3.24746698e-01 -2.17302726e-03 -1.73628676e+00
-6.78948462e-02 -4.00900692e-01 -4.24505472e-02 1.69487762e+00
3.72571498e-01 -9.81420457e-01 7.27458656e-01 2.55752146e-01
-9.65578854e-02 -6.25432491e-01 -9.21505988e-01 -7.90184021e-01
4.78493929e-01 -6.11706525e-02 6.10528886e-01 1.09150732e+00
-5.11778355e-01 4.31203544e-01 -5.61393499e-01 -9.01814364e-03
3.53386909e-01 1.79994047e-01 8.36649895e-01 -1.05550909e+00
-5.63173831e-01 -3.48016798e-01 -4.25467402e-01 -9.26529706e-01
2.84011096e-01 -1.12379003e+00 1.57580785e-02 -1.46042025e+00
3.24902683e-01 -7.86773443e-01 -5.12531579e-01 8.15813541e-01
-4.42743331e-01 4.74145412e-01 3.91420007e-01 -1.75906729e-03
-1.40628189e-01 9.00356472e-01 1.53044868e+00 -2.86803722e-01
-8.99216980e-02 3.04589629e-01 -8.68587911e-01 4.42438871e-01
1.04791498e+00 -6.99492395e-01 -6.90214336e-01 -3.00016880e-01
1.74464583e-01 -2.75933236e-01 3.97205502e-01 -1.14950824e+00
5.80011271e-02 -3.57715823e-02 4.65187788e-01 -1.80843204e-01
3.97980124e-01 -8.26232374e-01 2.94463903e-01 3.65844429e-01
-1.82076782e-01 -4.41217005e-01 2.62932032e-01 6.58138335e-01
-3.95358652e-01 -2.86297381e-01 1.01732397e+00 -7.95506909e-02
-3.22843224e-01 4.07844573e-01 -1.26156881e-01 1.65296301e-01
1.09669185e+00 1.15807034e-01 -2.22458556e-01 -4.78004962e-01
-8.25668931e-01 -2.09191754e-01 3.13061297e-01 3.28227997e-01
3.20114493e-01 -1.49088120e+00 -8.72421741e-01 2.65733689e-01
4.50250022e-02 2.85111159e-01 3.95561635e-01 8.69440615e-01
-7.03971833e-02 8.50875676e-02 -4.22407389e-01 -3.64297986e-01
-9.84932125e-01 5.23778379e-01 3.04754108e-01 -5.43413460e-01
-3.41150969e-01 1.10650647e+00 9.37994361e-01 -3.77037346e-01
-8.95302892e-02 5.32006733e-02 -1.08353801e-01 1.77538112e-01
3.66989940e-01 1.73781335e-01 8.50855410e-02 -4.58892941e-01
-3.61447632e-02 5.93164265e-01 -2.12571144e-01 5.52833974e-02
1.34645700e+00 1.29643351e-01 -2.48663411e-01 3.11813831e-01
1.10596454e+00 6.74412623e-02 -1.23530257e+00 -2.86512047e-01
-5.10883987e-01 -3.31987888e-01 -2.46526301e-01 -9.47255373e-01
-1.74476576e+00 9.83275294e-01 5.30355215e-01 -9.77713838e-02
1.57341421e+00 2.51944721e-01 6.54862165e-01 -1.45901978e-01
3.57606471e-01 -7.31163681e-01 2.41227433e-01 3.14034134e-01
9.63625491e-01 -9.63068783e-01 -2.35476926e-01 -6.32913411e-01
-6.71556115e-01 8.90673995e-01 6.67138278e-01 2.36235019e-02
6.12201810e-01 -5.48589490e-02 8.62354692e-03 3.63587178e-02
-3.45502973e-01 -2.06064194e-01 2.15781882e-01 7.12385118e-01
2.56175011e-01 3.23153973e-01 6.42986596e-02 5.50333202e-01
-6.93335058e-03 6.98985457e-02 2.55406111e-01 6.70130551e-01
2.41703048e-01 -1.51011348e+00 -1.55225813e-01 3.88282418e-01
-2.64267176e-01 -3.07120144e-01 -3.41074049e-01 7.10643053e-01
4.71810549e-01 6.60004735e-01 -1.57642484e-01 -1.23546295e-01
1.31002069e-01 3.29519182e-01 6.65980637e-01 -7.52258182e-01
-4.96655703e-01 -1.80572480e-01 -3.57909352e-01 -1.00994393e-01
-7.21237361e-01 -5.34040928e-01 -9.82823730e-01 2.95186993e-02
-4.11779732e-01 3.19391847e-01 3.66043627e-01 9.17810142e-01
2.99771428e-01 7.55496383e-01 6.01584792e-01 -8.19499969e-01
-1.68668572e-02 -9.61892664e-01 -4.96435702e-01 4.83651906e-01
1.55361533e-01 -7.23847508e-01 -4.39065993e-01 3.46023887e-01] | [11.625799179077148, -0.2488868087530136] |
028a55e5-ebef-4396-a016-d35409458c87 | learning-to-model-multimodal-semantic | 2211.07289 | null | https://arxiv.org/abs/2211.07289v1 | https://arxiv.org/pdf/2211.07289v1.pdf | Learning to Model Multimodal Semantic Alignment for Story Visualization | Story visualization aims to generate a sequence of images to narrate each sentence in a multi-sentence story, where the images should be realistic and keep global consistency across dynamic scenes and characters. Current works face the problem of semantic misalignment because of their fixed architecture and diversity of input modalities. To address this problem, we explore the semantic alignment between text and image representations by learning to match their semantic levels in the GAN-based generative model. More specifically, we introduce dynamic interactions according to learning to dynamically explore various semantic depths and fuse the different-modal information at a matched semantic level, which thus relieves the text-image semantic misalignment problem. Extensive experiments on different datasets demonstrate the improvements of our approach, neither using segmentation masks nor auxiliary captioning networks, on image quality and story consistency, compared with state-of-the-art methods. | ['Thomas Lukasiewicz', 'Bowen Li'] | 2022-11-14 | null | null | null | null | ['story-visualization'] | ['computer-vision'] | [ 5.00668526e-01 2.08636925e-01 1.97694421e-01 -4.11558062e-01
-5.85860312e-01 -6.02073908e-01 8.65786731e-01 -4.34240043e-01
6.09653443e-02 6.69338286e-01 3.78290802e-01 2.63193429e-01
2.29594082e-01 -7.82595098e-01 -7.86605477e-01 -6.11161947e-01
7.12202907e-01 4.03647095e-01 4.22573984e-01 -3.28365415e-01
1.33790761e-01 -7.49400780e-02 -1.54941988e+00 7.55543292e-01
1.01983035e+00 6.66388690e-01 6.47955954e-01 6.55102551e-01
-3.58940363e-01 9.03046548e-01 -7.36022830e-01 -7.63192117e-01
5.14391065e-02 -1.03020215e+00 -6.76184833e-01 5.52133322e-01
6.87495172e-01 -2.68313348e-01 -3.02553177e-01 1.27207828e+00
4.71033990e-01 6.19736053e-02 5.28679609e-01 -1.46333086e+00
-9.78180647e-01 8.38498652e-01 -8.93353581e-01 -4.07778062e-02
4.62496698e-01 4.61097181e-01 7.68021405e-01 -5.43459177e-01
9.95096266e-01 1.45521975e+00 3.51910710e-01 8.40389609e-01
-1.41951203e+00 -4.98061299e-01 5.23191392e-01 2.71572024e-01
-1.07489645e+00 -4.72058088e-01 9.74109888e-01 -3.97130400e-01
5.59521914e-01 2.12300614e-01 8.25287640e-01 1.62143242e+00
7.97630101e-02 7.76703775e-01 1.27428782e+00 -1.94504246e-01
1.48714826e-01 1.18115425e-01 -5.20367563e-01 5.73668122e-01
8.69543180e-02 -2.41294637e-01 -7.29277372e-01 3.81863296e-01
9.78666544e-01 -1.55944675e-01 -3.39044124e-01 -3.90668660e-01
-1.20657253e+00 7.29816139e-01 2.63952911e-01 3.78899425e-01
-2.63149172e-01 2.95764059e-01 2.48302370e-01 1.53964395e-02
3.86815637e-01 5.41570604e-01 -8.11517388e-02 5.34252310e-03
-1.08559871e+00 4.08014268e-01 4.01588649e-01 1.04725611e+00
4.20705140e-01 3.18386555e-01 -6.11199915e-01 8.01115692e-01
8.34591091e-02 5.24520099e-01 3.31743777e-01 -9.10377562e-01
7.35069215e-01 5.33613384e-01 -5.67854159e-02 -1.20300794e+00
-2.44549856e-01 -3.07490230e-01 -9.90994632e-01 1.53266937e-01
3.06375682e-01 -1.71469018e-01 -1.15767527e+00 2.09225750e+00
3.11822414e-01 3.45696747e-01 1.11708164e-01 1.09099793e+00
9.81115460e-01 7.22388685e-01 1.54960603e-01 -3.83765437e-02
1.40595138e+00 -1.32168257e+00 -9.25610125e-01 -6.29387736e-01
3.05011664e-02 -7.06593812e-01 1.35638380e+00 6.38945475e-02
-1.39060271e+00 -6.41138971e-01 -9.30318236e-01 -4.49545756e-02
-1.32241905e-01 -8.64425451e-02 3.45709741e-01 4.25574630e-01
-1.13665271e+00 2.80510277e-01 -5.50256968e-01 -3.15313220e-01
4.76757944e-01 -3.06690037e-01 -6.01655617e-02 -5.43017723e-02
-1.10731876e+00 8.03622782e-01 5.33316016e-01 -2.06119101e-02
-9.90231872e-01 -6.68824017e-01 -9.66996551e-01 1.26629055e-01
2.87686467e-01 -1.33555877e+00 1.10389090e+00 -1.43684053e+00
-1.66662514e+00 9.09330308e-01 -2.97205206e-02 -1.43995702e-01
8.60864699e-01 7.42249715e-04 -2.59804100e-01 2.02064827e-01
1.91712871e-01 1.11556232e+00 9.01736617e-01 -1.74109280e+00
-3.50105941e-01 -1.83878005e-01 1.52903542e-01 6.13334894e-01
-2.13041082e-01 -2.21224446e-02 -8.49706769e-01 -9.20170605e-01
-6.86552525e-02 -7.94375956e-01 -3.40650648e-01 -2.31115341e-01
-6.15187883e-01 2.75446475e-01 8.42688620e-01 -7.07951188e-01
9.12902117e-01 -1.90924215e+00 6.42848849e-01 -3.23200345e-01
8.04088786e-02 -2.58810580e-01 -3.75381947e-01 2.21313164e-01
9.73679498e-02 7.28802979e-02 -3.55116069e-01 -8.75878453e-01
-1.58915818e-01 2.57366747e-01 -3.85234833e-01 7.74883255e-02
1.68711647e-01 1.22143817e+00 -9.56861258e-01 -7.49667525e-01
3.73429060e-01 6.40268922e-01 -3.33409071e-01 4.05870467e-01
-6.98987603e-01 8.31839263e-01 -3.23042572e-01 4.14450496e-01
7.63971746e-01 -4.63462651e-01 2.36154005e-01 -2.89989680e-01
3.35060269e-01 -1.00884186e-02 -1.00182021e+00 2.28738189e+00
-5.08088112e-01 7.29233146e-01 -6.89004213e-02 -7.49166369e-01
7.75438547e-01 1.20397992e-01 1.90124616e-01 -1.05139911e+00
1.30072653e-01 -2.64189452e-01 -3.71900529e-01 -5.99182725e-01
6.80281162e-01 -1.86451778e-01 -2.10754037e-01 4.04084057e-01
-1.22169917e-02 -4.33684051e-01 1.87343821e-01 1.83705226e-01
4.76270288e-01 3.95429343e-01 -2.74901092e-01 9.77479592e-02
1.74149707e-01 -9.11535472e-02 3.70387912e-01 7.39545763e-01
2.43273973e-01 1.30965745e+00 6.45897925e-01 -1.80043653e-01
-1.26191604e+00 -1.14975965e+00 1.96259126e-01 8.00316215e-01
6.29997611e-01 -1.89536244e-01 -1.11505318e+00 -5.65399349e-01
-5.44442475e-01 1.06194532e+00 -6.56595647e-01 -4.95162141e-03
-5.64413905e-01 -7.59439528e-01 2.08121315e-01 4.99430954e-01
7.65037358e-01 -1.21999168e+00 -8.49196494e-01 4.75782016e-03
-5.94637692e-01 -1.42937613e+00 -5.03878951e-01 -3.44560832e-01
-5.21175802e-01 -8.33512843e-01 -8.84211242e-01 -5.77898443e-01
7.72394419e-01 2.21567839e-01 1.39127672e+00 -1.58160284e-01
-1.45779714e-01 2.62334615e-01 -3.62010717e-01 1.87555496e-02
-5.59432149e-01 5.48303388e-02 -6.07335091e-01 1.01965360e-01
-4.75354254e-01 -6.72524929e-01 -7.87951648e-01 1.03506044e-01
-1.21146619e+00 1.02821469e+00 3.35032254e-01 8.29304338e-01
5.47693908e-01 2.03814339e-02 4.10106301e-01 -9.50656116e-01
5.91325283e-01 -4.16926265e-01 -3.77760410e-01 6.19788527e-01
-3.95471931e-01 -1.69456005e-02 7.08022416e-01 -3.90791059e-01
-1.38132417e+00 4.11229162e-03 1.35053620e-01 -6.25019193e-01
-9.00485814e-02 6.71859905e-02 -5.55811524e-01 3.94529641e-01
1.94611371e-01 4.73150820e-01 -2.31305838e-01 -9.21973437e-02
9.58716631e-01 2.14918956e-01 8.40931356e-01 -5.63379526e-01
5.95455408e-01 5.87992847e-01 -2.86899090e-01 -2.01633543e-01
-8.52727473e-01 1.49643838e-01 -5.45141935e-01 -5.72561681e-01
1.20743060e+00 -9.41066325e-01 -2.55155832e-01 6.70437992e-01
-1.51806128e+00 -4.56166387e-01 -4.45952564e-01 -9.50547829e-02
-8.96521032e-01 1.46303669e-01 -4.51576144e-01 -4.97983485e-01
-3.21371257e-01 -1.28994274e+00 1.36736155e+00 5.27334809e-01
-2.21395761e-01 -1.01120043e+00 -4.47932854e-02 5.77124774e-01
3.34874749e-01 7.46912479e-01 8.62340152e-01 -5.44082895e-02
-7.42363393e-01 4.31846827e-01 -3.59415591e-01 -6.44295067e-02
1.40469268e-01 -3.99728492e-02 -9.03749764e-01 -2.09044918e-01
-4.22398699e-03 -2.76978105e-01 8.06095660e-01 4.01687503e-01
1.25536883e+00 -5.78746736e-01 -1.86280474e-01 7.08490670e-01
1.51836240e+00 1.13800168e-01 9.37988460e-01 4.06223327e-01
1.01322472e+00 7.42571056e-01 3.57859701e-01 3.54790449e-01
5.55263519e-01 8.49837720e-01 5.29039145e-01 -4.34898645e-01
-5.50627887e-01 -6.66729331e-01 1.33469969e-01 4.67381269e-01
3.18379641e-01 -7.98634887e-01 -6.18480086e-01 5.13120711e-01
-2.17423534e+00 -1.16832495e+00 4.11360525e-02 1.79583633e+00
8.65182281e-01 5.78771420e-02 1.18556015e-01 -3.55055481e-01
8.35074306e-01 5.48387170e-01 -6.78520977e-01 -3.36570978e-01
-5.87047398e-01 -2.11721957e-01 1.54305220e-01 2.73176491e-01
-7.97784865e-01 1.13442004e+00 6.36207771e+00 8.60195100e-01
-1.12062764e+00 3.70133847e-01 1.06527102e+00 -3.68929625e-01
-8.48523676e-01 -1.45761203e-02 -4.54652220e-01 7.48707771e-01
4.41033930e-01 4.26653661e-02 3.80232036e-01 5.74423075e-01
2.48675808e-01 -1.20245069e-01 -9.15477514e-01 1.01411664e+00
4.89427835e-01 -1.75369906e+00 4.11014825e-01 -4.86837864e-01
1.32453144e+00 -4.66879815e-01 3.76209438e-01 -8.00417736e-02
4.71626908e-01 -1.14985251e+00 1.33107865e+00 8.07168305e-01
8.70931029e-01 -7.45543897e-01 3.76922429e-01 6.09909818e-02
-1.08922660e+00 1.60899520e-01 1.10265613e-01 2.85746157e-01
7.20052600e-01 2.42216200e-01 -4.67356831e-01 5.62110484e-01
6.81904972e-01 7.41015375e-01 -7.48064995e-01 5.33001661e-01
-4.35710549e-01 7.69731030e-02 1.05729677e-01 1.34059057e-01
1.91223457e-01 -1.63943276e-01 4.32638377e-01 1.09644437e+00
2.54205048e-01 -8.69887546e-02 -3.28464285e-02 1.45025897e+00
-6.80917129e-02 -1.72438085e-01 -4.48900014e-01 4.71439585e-02
4.60697979e-01 1.13471007e+00 -9.90644515e-01 -4.01494056e-01
-2.08924487e-01 1.55233753e+00 2.25383580e-01 4.90046471e-01
-1.02526939e+00 1.19688652e-01 5.14924407e-01 3.31272006e-01
2.85522163e-01 -7.10746720e-02 -7.98426807e-01 -1.15632820e+00
2.02053964e-01 -9.34040606e-01 2.60610670e-01 -1.29386175e+00
-1.22961307e+00 7.78465629e-01 1.95586398e-01 -1.13794863e+00
-3.23321909e-01 1.69892088e-01 -8.33236635e-01 6.55548871e-01
-1.20674753e+00 -1.52288377e+00 -4.74503547e-01 5.73225081e-01
9.08424616e-01 8.64555500e-03 3.82017255e-01 -3.69503046e-03
-5.52008390e-01 6.03084803e-01 -9.41257477e-02 -1.88755885e-01
5.44556975e-01 -1.06137156e+00 5.74093461e-01 1.08144808e+00
2.65094817e-01 3.20586786e-02 8.97451282e-01 -7.64476359e-01
-1.11537659e+00 -1.20381486e+00 4.14698035e-01 -4.19039518e-01
3.55383337e-01 -5.64189494e-01 -7.59424984e-01 4.65273291e-01
7.93786585e-01 -4.15960193e-01 2.33186454e-01 -3.78812224e-01
-3.90185237e-01 1.28974497e-01 -1.07419789e+00 9.66352940e-01
1.30992377e+00 -3.64949524e-01 -3.69560093e-01 2.38865614e-01
9.89285052e-01 -7.73435652e-01 -4.12320614e-01 3.25120986e-01
3.56265366e-01 -1.22013354e+00 9.61664855e-01 -4.00467932e-01
1.09219062e+00 -4.15345758e-01 1.09720364e-01 -1.36974561e+00
-1.39528945e-01 -6.24937952e-01 1.39986664e-01 1.54884529e+00
2.57610112e-01 -1.83731690e-01 6.76173806e-01 6.00473404e-01
-7.45730177e-02 -6.41815126e-01 -9.08956766e-01 -4.01020586e-01
-6.21171482e-02 -1.43980920e-01 8.08209777e-01 9.88459587e-01
-2.35016629e-01 5.54384112e-01 -7.85594046e-01 -1.91954169e-02
5.00784338e-01 4.41995978e-01 8.61820459e-01 -5.47304809e-01
-4.46863294e-01 -7.31594145e-01 -3.01638395e-01 -8.72492492e-01
1.51127920e-01 -7.07220197e-01 6.90042451e-02 -1.82632983e+00
5.96042454e-01 -1.97715342e-01 1.62643552e-01 3.80967975e-01
-4.83165473e-01 2.40564242e-01 6.01156652e-01 7.70572647e-02
-8.04092765e-01 8.96529973e-01 1.74120319e+00 -2.97144473e-01
-1.26598552e-01 -6.60126328e-01 -9.15046692e-01 5.32477021e-01
4.95701462e-01 -3.59467119e-01 -8.37903023e-01 -8.96609128e-01
3.03718120e-01 3.50363374e-01 6.66909754e-01 -8.78545523e-01
2.58708864e-01 -4.06274915e-01 5.61703146e-01 -5.19240201e-01
4.99017328e-01 -5.96917331e-01 6.75010920e-01 2.32390255e-01
-4.78266865e-01 1.91826716e-01 1.38667136e-01 7.09609389e-01
-2.07479924e-01 6.67517036e-02 9.34250414e-01 -1.40179232e-01
-6.32875085e-01 1.39072254e-01 -4.46973890e-02 2.81267315e-01
1.24593163e+00 -3.91110808e-01 -4.95296031e-01 -5.84903955e-01
-5.26134133e-01 3.98503155e-01 8.54857087e-01 8.01042259e-01
7.47862339e-01 -1.41125870e+00 -8.84815753e-01 -1.47743698e-03
3.01559493e-02 1.89730108e-01 8.47952306e-01 4.85792786e-01
-2.42330238e-01 -4.59430255e-02 -3.78011793e-01 -7.62917817e-01
-1.12284923e+00 4.22994733e-01 4.39837784e-01 -3.45383614e-01
-7.99517632e-01 9.13307905e-01 6.22806966e-01 -3.08573265e-02
4.11977060e-02 5.30746020e-02 -1.25358298e-01 -6.93220319e-03
4.01500434e-01 -1.17444560e-01 -3.01932216e-01 -5.86244643e-01
-8.37071911e-02 6.97635770e-01 -2.95755640e-02 -3.55533659e-01
1.24026406e+00 -5.56234658e-01 1.52070358e-01 4.16098833e-01
8.14489305e-01 -3.41875672e-01 -1.74112475e+00 -6.50891885e-02
-5.40089786e-01 -7.43107796e-01 -3.83899599e-01 -8.50560129e-01
-1.56643867e+00 6.25489295e-01 5.60709238e-01 -1.26885436e-02
1.23556674e+00 2.83635974e-01 8.92028093e-01 -3.57451022e-01
1.32753819e-01 -1.06334078e+00 6.41660631e-01 1.05855234e-01
1.14542651e+00 -1.16101420e+00 -1.66600689e-01 -3.16891313e-01
-1.26574755e+00 9.62321818e-01 1.01644886e+00 1.72434039e-02
6.86223013e-03 2.81733811e-01 1.31298108e-02 -2.48877376e-01
-7.68271506e-01 -3.19585502e-02 2.18779415e-01 7.13109732e-01
1.97211727e-01 -2.48983707e-02 -1.40444621e-01 6.25154614e-01
-3.47333461e-01 -3.60129207e-01 5.03108799e-01 4.81131077e-01
5.34936115e-02 -9.28240776e-01 -3.46072376e-01 7.56231397e-02
-8.81378204e-02 -1.78501993e-01 -4.45910245e-01 5.26454151e-01
2.46604607e-01 9.48892474e-01 2.52706647e-01 -2.86699504e-01
2.41723299e-01 -1.48698956e-01 7.21358299e-01 -4.25738931e-01
-4.84809250e-01 6.39573634e-02 2.41839215e-02 -5.30463398e-01
-6.49690032e-01 -6.81177139e-01 -1.18180120e+00 -1.65502965e-01
-2.39170454e-02 -3.14857066e-01 5.41451275e-01 8.85878086e-01
4.58851576e-01 1.03543460e+00 6.02328360e-01 -7.99414456e-01
-6.24129735e-02 -7.51986384e-01 -2.77492434e-01 7.81734109e-01
-9.57862567e-03 -5.14514863e-01 -1.35023430e-01 4.23137128e-01] | [11.176928520202637, 0.4622081518173218] |
976ccc4e-106a-4820-910f-d25795f45428 | towards-hiding-adversarial-examples-from | 1812.02843 | null | https://arxiv.org/abs/1812.02843v2 | https://arxiv.org/pdf/1812.02843v2.pdf | Fooling Network Interpretation in Image Classification | Deep neural networks have been shown to be fooled rather easily using adversarial attack algorithms. Practical methods such as adversarial patches have been shown to be extremely effective in causing misclassification. However, these patches are highlighted using standard network interpretation algorithms, thus revealing the identity of the adversary. We show that it is possible to create adversarial patches which not only fool the prediction, but also change what we interpret regarding the cause of the prediction. Moreover, we introduce our attack as a controlled setting to measure the accuracy of interpretation algorithms. We show this using extensive experiments for Grad-CAM interpretation that transfers to occluding patch interpretation as well. We believe our algorithms can facilitate developing more robust network interpretation tools that truly explain the network's underlying decision making process. | ['Hamed Pirsiavash', 'Vipin Pillai', 'Akshayvarun Subramanya'] | 2018-12-06 | fooling-network-interpretation-in-image | http://openaccess.thecvf.com/content_ICCV_2019/html/Subramanya_Fooling_Network_Interpretation_in_Image_Classification_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Subramanya_Fooling_Network_Interpretation_in_Image_Classification_ICCV_2019_paper.pdf | iccv-2019-10 | ['network-interpretation'] | ['computer-vision'] | [ 7.55555451e-01 9.06868815e-01 1.35426521e-01 -2.79957503e-01
-5.47688544e-01 -1.17944133e+00 4.87619609e-01 -3.39417815e-01
-1.47763163e-01 7.17440546e-01 -3.29530478e-01 -8.23977590e-01
2.43413046e-01 -8.65157366e-01 -1.27489471e+00 -6.58002913e-01
-1.22348122e-01 3.80419016e-01 1.50227189e-01 -4.46017608e-02
2.20095534e-02 6.26393735e-01 -8.83096635e-01 5.68859518e-01
3.96863937e-01 5.39077878e-01 -7.50154018e-01 1.00041437e+00
5.01675665e-01 9.48938191e-01 -1.35631227e+00 -8.92460763e-01
6.47331953e-01 -4.57295269e-01 -9.42912638e-01 -2.19147474e-01
1.00636268e+00 -6.61130846e-01 -4.06936020e-01 1.32990944e+00
1.48300290e-01 -2.09092423e-01 2.88386196e-01 -1.71107376e+00
-5.89879990e-01 9.38908935e-01 -1.94161937e-01 2.38693610e-01
-3.55981961e-02 4.19250846e-01 7.97513485e-01 -1.57407597e-01
5.94829082e-01 1.42484331e+00 8.70305121e-01 9.73421752e-01
-1.34313846e+00 -9.56872463e-01 1.00185338e-03 -4.71852571e-02
-1.14093208e+00 -3.56717110e-01 8.36366892e-01 -1.33625075e-01
2.84545541e-01 6.46308720e-01 4.17569548e-01 1.70011616e+00
1.59590021e-01 6.35071456e-01 1.09060371e+00 -3.25023472e-01
2.52407581e-01 1.12839237e-01 1.23681650e-01 7.84454882e-01
3.89122486e-01 4.15083349e-01 8.47243145e-02 -6.01106346e-01
9.69444513e-01 -1.65721446e-01 -3.76367629e-01 -3.82998176e-02
-8.71930897e-01 1.03705657e+00 6.77196324e-01 -1.06090829e-01
2.50071362e-02 7.10289717e-01 3.95875663e-01 4.34339374e-01
1.82423249e-01 7.73277164e-01 -6.36273623e-01 2.63685107e-01
-3.25927705e-01 2.96723962e-01 9.45145547e-01 5.67566752e-01
4.14164186e-01 3.37679327e-01 1.32652134e-01 2.00477704e-01
3.78074385e-02 4.45343226e-01 7.34443963e-03 -1.33671641e+00
1.18460208e-01 2.51191556e-01 1.27477003e-02 -1.35280740e+00
-2.21147500e-02 -3.85610729e-01 -5.68093061e-01 9.40219402e-01
8.49356592e-01 -6.11862898e-01 -9.00823176e-01 1.91967809e+00
5.53981848e-02 4.34636265e-01 9.66965854e-02 8.24456275e-01
2.91350603e-01 3.60566489e-02 5.18516302e-02 5.94271958e-01
1.04647255e+00 -7.35558689e-01 -3.03487718e-01 -2.45831445e-01
5.97384274e-01 -4.53025758e-01 8.46969306e-01 3.82875055e-01
-9.21407998e-01 -2.31296018e-01 -1.34737694e+00 2.58558363e-01
-4.60452467e-01 -3.48972499e-01 8.08745801e-01 1.16225088e+00
-8.26343775e-01 8.41674984e-01 -8.49124908e-01 2.66672164e-01
1.16004372e+00 5.94234288e-01 -3.74256104e-01 2.61563689e-01
-1.38716149e+00 7.73066044e-01 2.65751958e-01 -6.79933419e-03
-1.29988444e+00 -7.54943132e-01 -6.33288503e-01 1.60830114e-02
3.00207525e-01 -5.50296545e-01 1.11807334e+00 -1.78119767e+00
-9.34240699e-01 9.04391587e-01 2.52664611e-02 -9.74944055e-01
9.68535662e-01 6.35855123e-02 -4.57098544e-01 3.72460932e-01
-2.97458023e-01 6.23903215e-01 1.13896215e+00 -1.55908358e+00
-3.42232794e-01 -3.39843005e-01 7.90183902e-01 -2.68331826e-01
-1.92174807e-01 -1.99061170e-01 2.49388412e-01 -7.90764272e-01
-2.48835355e-01 -1.07663083e+00 -3.20071757e-01 4.75719184e-01
-8.58541012e-01 4.10335213e-01 1.23200369e+00 -5.73432684e-01
5.69504619e-01 -2.29842663e+00 -5.41264474e-01 5.17195880e-01
6.96570933e-01 5.81720889e-01 -5.95479906e-02 -2.18668759e-01
-2.70545036e-01 8.57197642e-01 -3.03624153e-01 -2.16403842e-01
3.03970482e-02 6.18743956e-01 -9.13773298e-01 6.65414274e-01
4.20621723e-01 1.08338189e+00 -6.89031899e-01 3.80233303e-02
-1.11760199e-02 5.93919694e-01 -4.85986620e-01 -1.02873661e-01
-2.52218813e-01 3.45788121e-01 -3.01239431e-01 3.96571070e-01
7.61241317e-01 -4.93933782e-02 7.67777339e-02 1.18157282e-01
8.76361966e-01 1.18955135e-01 -7.43315578e-01 8.17476928e-01
-1.98755726e-01 1.15464854e+00 1.84300631e-01 -8.76059473e-01
5.81710339e-01 2.56789684e-01 -3.76420557e-01 -1.66838154e-01
2.64395684e-01 -1.29309282e-01 3.62502605e-01 -1.78891852e-01
2.88535416e-01 -1.38523027e-01 2.49468591e-02 6.42481446e-01
-3.80038232e-01 2.41481811e-01 -6.74959838e-01 2.74502307e-01
1.43325222e+00 -2.93470532e-01 -2.15911344e-01 -1.17896974e-01
1.25033587e-01 3.18598926e-01 4.01511014e-01 1.31358099e+00
-3.90151381e-01 7.98185170e-01 9.53315854e-01 -7.23252535e-01
-1.25033307e+00 -1.26392138e+00 -1.98757946e-02 6.24017000e-01
1.48665309e-01 1.21104822e-01 -1.07189882e+00 -1.44635046e+00
1.60401344e-01 7.90763974e-01 -1.15582395e+00 -5.01003206e-01
-4.75157917e-01 -5.32249033e-01 1.61974156e+00 7.00591028e-01
5.49736857e-01 -8.49409938e-01 -3.93490136e-01 -1.94762915e-01
2.87365288e-01 -1.12410355e+00 -1.98233813e-01 2.11694688e-01
-7.18032479e-01 -1.40785730e+00 -1.17238909e-02 -4.34977323e-01
1.31710398e+00 4.44590896e-02 1.13256538e+00 8.22985113e-01
-1.37427419e-01 2.72220820e-01 -8.08217302e-02 -7.05960214e-01
-1.18237603e+00 -2.62284800e-02 -2.28907481e-01 -1.62133664e-01
1.60854653e-01 -6.55063570e-01 -4.58887309e-01 5.47682226e-01
-1.24600995e+00 -2.75727510e-01 2.86996186e-01 8.26721191e-01
1.91605330e-01 2.31095389e-01 4.54288423e-01 -1.85865617e+00
5.46455741e-01 -3.56124938e-01 -5.34570932e-01 8.84684622e-02
-5.44770837e-01 -2.08565369e-01 1.02550995e+00 -8.30371857e-01
-6.33738339e-01 -1.96076810e-01 -1.76017538e-01 -7.17633009e-01
-5.86335063e-01 -1.32911384e-01 -2.30424345e-01 -6.58921957e-01
1.00486743e+00 -2.34154046e-01 1.53541118e-01 5.46809146e-03
1.09365523e-01 1.52784899e-01 7.05892682e-01 -4.97817636e-01
1.36540723e+00 8.25122178e-01 1.32952318e-01 -2.99173445e-01
-8.07002604e-01 5.89228630e-01 -3.16266507e-01 3.47802825e-02
5.71222425e-01 -6.87033057e-01 -8.63362193e-01 4.72750664e-01
-1.31306279e+00 -5.52527130e-01 -2.22821757e-01 -3.16041082e-01
-8.96865875e-02 1.97944015e-01 -6.23221576e-01 -5.40132165e-01
-9.44543537e-03 -1.16166115e+00 6.55114949e-01 6.54864535e-02
-5.05463362e-01 -1.46386182e+00 -3.97528082e-01 4.78632331e-01
3.27323467e-01 7.67978907e-01 9.23509061e-01 -1.24943399e+00
-6.75058126e-01 -4.65991110e-01 -1.28440112e-01 6.05287313e-01
-1.00691542e-01 2.04531491e-01 -1.49829507e+00 -1.74704626e-01
-3.04365903e-02 -9.14507955e-02 6.67069852e-01 -8.13480914e-02
1.44348013e+00 -7.84600556e-01 -2.48353079e-01 7.24654436e-01
1.27015090e+00 1.79073647e-01 1.15151191e+00 4.06832457e-01
1.04211259e+00 2.66970813e-01 -8.57477775e-04 -2.47457981e-01
-2.76834130e-01 4.45754796e-01 7.38480330e-01 -5.73222399e-01
-3.21499351e-03 -3.40936214e-01 3.48069280e-01 -4.19938505e-01
1.63877025e-01 -2.84303248e-01 -8.29116046e-01 1.61785111e-01
-1.61060357e+00 -1.00952697e+00 -2.20602959e-01 1.79445565e+00
6.41506255e-01 6.91369295e-01 -1.22254953e-01 3.06355655e-01
6.92052066e-01 2.14803573e-02 -5.16596437e-01 -8.16853762e-01
-1.54201701e-01 4.84659910e-01 8.82496893e-01 7.33402371e-01
-1.14502621e+00 8.56391847e-01 6.98586035e+00 6.90623045e-01
-9.48021293e-01 8.44276771e-02 1.06425643e+00 8.74829441e-02
-4.45367932e-01 3.41200419e-02 -2.78993815e-01 4.27715957e-01
7.50602186e-01 5.21080568e-02 5.37608743e-01 1.01827669e+00
-6.47662580e-02 1.87395856e-01 -1.27262712e+00 2.89606243e-01
7.28812069e-02 -1.41754568e+00 1.70915529e-01 1.97635427e-01
6.74455523e-01 -4.28605348e-01 3.51461083e-01 6.17900901e-02
7.16270626e-01 -1.43511653e+00 5.77719510e-01 5.17089758e-03
4.49693114e-01 -9.26134288e-01 8.55338812e-01 6.06891625e-02
-2.58680969e-01 2.22743765e-01 -3.94597113e-01 -2.80229628e-01
-5.04343688e-01 3.82780939e-01 -1.26778948e+00 -9.28326994e-02
3.38846594e-01 8.56062546e-02 -7.23213077e-01 5.06798506e-01
-8.38998199e-01 1.09483361e+00 -1.94419682e-01 3.22341621e-01
2.15422109e-01 2.76920646e-01 7.02419758e-01 8.39195192e-01
-3.39300990e-01 -1.90229088e-01 -2.18216017e-01 1.33071935e+00
-4.03817505e-01 -7.52771020e-01 -1.03539884e+00 -1.20751619e-01
3.55247378e-01 8.83429945e-01 -7.83649325e-01 -2.74011552e-01
1.64990462e-02 1.09589493e+00 9.06335562e-02 3.76991421e-01
-1.11838758e+00 -1.04411528e-01 1.04697144e+00 -5.60445413e-02
2.71368176e-01 8.99789110e-02 -7.37772405e-01 -8.93061876e-01
-8.14483911e-02 -1.29976797e+00 1.70607328e-01 -6.71803117e-01
-1.19051397e+00 5.75196981e-01 -4.19754535e-01 -8.64135325e-01
-2.50368435e-02 -6.63674653e-01 -9.66374159e-01 7.59141028e-01
-1.08667111e+00 -1.09131062e+00 -7.69262463e-02 5.19563437e-01
1.29551724e-01 -9.87299979e-02 1.11333501e+00 -1.88132692e-02
-7.12491274e-01 1.22315383e+00 -2.26468146e-01 8.73023927e-01
4.51112866e-01 -1.36155868e+00 8.01026225e-01 1.13018084e+00
3.13844591e-01 7.17730522e-01 8.71016264e-01 -4.59478527e-01
-1.15031981e+00 -1.28706956e+00 1.34526432e-01 -1.02023947e+00
7.99233317e-01 -4.77473885e-01 -1.13503551e+00 1.14256799e+00
-7.19866678e-02 7.05107674e-02 7.01672018e-01 -5.11809310e-04
-7.96377182e-01 2.02842459e-01 -1.55538189e+00 8.60177696e-01
8.66235673e-01 -7.83215404e-01 -4.99842644e-01 2.96202928e-01
8.08849871e-01 -6.40241563e-01 -4.87281859e-01 1.99766785e-01
4.97218668e-01 -9.51005995e-01 1.15350676e+00 -1.08686304e+00
7.59409189e-01 -1.98647410e-01 2.92214639e-02 -1.19439614e+00
1.64863244e-01 -6.44053519e-01 2.34998111e-02 1.01164341e+00
7.82648504e-01 -1.09272695e+00 1.08215237e+00 9.23600733e-01
3.05925250e-01 -4.34624940e-01 -7.71352470e-01 -5.07234216e-01
2.94909507e-01 -5.46907604e-01 6.37416720e-01 1.41950274e+00
-4.53553319e-01 -2.89755389e-02 -2.39279255e-01 8.35764408e-01
6.42347395e-01 -2.98276752e-01 8.89185667e-01 -8.69396985e-01
-6.11281276e-01 -3.01809341e-01 -7.46851265e-01 -4.86778319e-01
4.28716898e-01 -6.99532092e-01 -1.70632839e-01 -5.86139441e-01
-1.80434048e-01 -5.06501317e-01 -1.51740640e-01 8.04843903e-01
-3.28390956e-01 7.59195745e-01 1.12737358e-01 5.55498786e-02
-2.44810045e-01 -3.72181892e-01 1.24378383e+00 -3.90837371e-01
4.17348742e-01 1.95891514e-01 -9.89212155e-01 8.70419621e-01
1.00069320e+00 -7.83843517e-01 -3.46808195e-01 -5.48411489e-01
2.69918710e-01 -4.21855003e-01 1.09916008e+00 -7.92210042e-01
-1.48028865e-01 1.26573578e-01 6.57285035e-01 3.27862680e-01
8.94291401e-02 -1.04013443e+00 2.10569352e-01 6.07404053e-01
-6.76522255e-01 -9.29960757e-02 4.50247347e-01 5.29330373e-01
3.81562300e-02 -2.99783885e-01 8.11842680e-01 -7.39240199e-02
-4.28956270e-01 7.77216107e-02 -3.05454731e-01 1.14553191e-01
9.81044531e-01 -6.79273456e-02 -7.95955241e-01 -6.27434850e-01
-9.06431317e-01 1.31670032e-02 8.75402033e-01 5.30773588e-02
4.49712932e-01 -9.22199965e-01 -3.23988825e-01 3.45243156e-01
-4.02486473e-01 -3.63566875e-02 -7.38730952e-02 3.25191230e-01
-8.10964227e-01 -2.82371730e-01 -1.94599643e-01 -2.65394241e-01
-1.49355769e+00 4.45720345e-01 7.51101434e-01 4.34635282e-02
-6.06682539e-01 8.06579590e-01 2.11772248e-01 -2.50846654e-01
3.43304724e-01 -8.73934254e-02 3.62073690e-01 -4.77149963e-01
6.48709714e-01 1.59153566e-01 -1.39356285e-01 -1.54248640e-01
-1.83047220e-01 -1.97315425e-01 -2.11520836e-01 1.64469108e-01
9.89289880e-01 2.52715051e-01 2.77374331e-02 -1.03466302e-01
1.14546311e+00 9.52283517e-02 -1.33595538e+00 2.06217736e-01
-3.68562698e-01 -5.12441993e-01 -1.02451503e-01 -1.15801907e+00
-1.29554093e+00 7.94889569e-01 5.20004988e-01 6.25742435e-01
1.02499926e+00 -2.81856567e-01 7.51747072e-01 5.46996176e-01
2.76402295e-01 -3.70589852e-01 -3.65009993e-01 1.78130299e-01
5.67130387e-01 -1.30761755e+00 -1.44392416e-01 -5.43132484e-01
-2.62719840e-01 1.11432636e+00 6.25217021e-01 -4.66143638e-01
2.84581602e-01 6.88370645e-01 6.76064968e-01 -1.33509055e-01
-5.52652478e-01 4.66628522e-01 -1.01662889e-01 8.81489098e-01
-2.21226424e-01 2.90314496e-01 4.47370470e-01 4.14618701e-01
-4.63009059e-01 -2.66856194e-01 8.61411393e-01 7.68143415e-01
8.07528943e-02 -1.11081862e+00 -6.94100559e-01 2.68248111e-01
-9.15506303e-01 -1.53583109e-01 -1.04111612e+00 1.13840842e+00
1.99750572e-01 7.01879203e-01 1.78739931e-02 -6.51267350e-01
1.45624414e-01 -1.35514783e-02 3.63588989e-01 -3.73076409e-01
-9.56048965e-01 -5.96863151e-01 2.85150409e-01 -5.28032243e-01
-2.99100392e-02 -2.65599161e-01 -1.14512956e+00 -6.92020178e-01
-8.17933455e-02 -1.74897745e-01 3.65246981e-01 1.07028234e+00
2.63550490e-01 6.73928559e-01 7.61452556e-01 -3.73485774e-01
-5.87323964e-01 -5.43771744e-01 -2.81331033e-01 7.04233468e-01
5.18725932e-01 -2.45782942e-01 -1.03143370e+00 1.23686098e-01] | [5.732962608337402, 7.822439670562744] |
96602cf3-d1dc-4faa-9846-710690724cbe | nuno-a-general-framework-for-learning | 2305.18694 | null | https://arxiv.org/abs/2305.18694v2 | https://arxiv.org/pdf/2305.18694v2.pdf | NUNO: A General Framework for Learning Parametric PDEs with Non-Uniform Data | The neural operator has emerged as a powerful tool in learning mappings between function spaces in PDEs. However, when faced with real-world physical data, which are often highly non-uniformly distributed, it is challenging to use mesh-based techniques such as the FFT. To address this, we introduce the Non-Uniform Neural Operator (NUNO), a comprehensive framework designed for efficient operator learning with non-uniform data. Leveraging a K-D tree-based domain decomposition, we transform non-uniform data into uniform grids while effectively controlling interpolation error, thereby paralleling the speed and accuracy of learning from non-uniform data. We conduct extensive experiments on 2D elasticity, (2+1)D channel flow, and a 3D multi-physics heatsink, which, to our knowledge, marks a novel exploration into 3D PDE problems with complex geometries. Our framework has reduced error rates by up to 60% and enhanced training speeds by 2x to 30x. The code is now available at https://github.com/thu-ml/NUNO. | ['Jun Zhu', 'Ze Cheng', 'Hang Su', 'Chengyang Ying', 'Zhongkai Hao', 'Songming Liu'] | 2023-05-30 | null | null | null | null | ['operator-learning'] | ['miscellaneous'] | [-2.38446400e-01 -4.74785298e-01 7.61291683e-02 2.37191096e-01
-7.43548572e-01 -4.51690584e-01 5.38695678e-02 2.26989537e-01
-2.37007454e-01 1.02860308e+00 -6.50282651e-02 -6.13746464e-01
-8.58122557e-02 -8.78730178e-01 -7.03218281e-01 -5.93745708e-01
-4.93114889e-01 4.58068013e-01 -4.21498157e-02 2.37018277e-04
1.14496611e-01 7.46389508e-01 -1.26483905e+00 -2.35998463e-02
9.98548627e-01 1.18903720e+00 -2.07268104e-01 8.47101808e-01
3.20840552e-02 5.68300009e-01 -1.62286744e-01 4.16680932e-01
4.99366730e-01 -3.33028257e-01 -8.74603391e-01 -2.22503006e-01
4.13085788e-01 -4.37167287e-01 -1.97288796e-01 7.23576069e-01
6.33996606e-01 5.67969799e-01 7.73613572e-01 -1.03481293e+00
-4.81804073e-01 1.22106783e-01 -5.67433655e-01 1.91391498e-01
1.21290751e-01 1.21049888e-01 8.37637663e-01 -9.52427506e-01
4.73379016e-01 1.17502153e+00 1.15358114e+00 3.42401415e-01
-1.37641001e+00 -6.86711788e-01 -2.80395716e-01 -3.15389216e-01
-1.30825627e+00 -8.68212730e-02 7.91263580e-01 -6.95137560e-01
6.88114703e-01 2.39502952e-01 6.84132576e-01 6.93219543e-01
6.37799859e-01 6.60772443e-01 1.27585363e+00 -1.19848453e-01
4.63125318e-01 -2.16965199e-01 -1.88360572e-01 7.38979638e-01
-5.68904318e-02 2.95378745e-01 -4.20364529e-01 -5.49227953e-01
1.44502914e+00 -1.71065941e-01 -4.87395406e-01 -4.03830022e-01
-8.38852286e-01 1.03592110e+00 4.62916940e-01 1.83481142e-01
-2.96528578e-01 1.54641002e-01 5.77824056e-01 4.07487363e-01
8.60543668e-01 8.54893386e-01 -7.36942470e-01 -4.04443771e-01
-7.88687289e-01 5.97387254e-01 1.01984262e+00 5.86332381e-01
8.54667723e-01 2.83568084e-01 3.22472513e-01 7.04228997e-01
-1.26527905e-01 3.30236763e-01 1.87319592e-01 -1.39259803e+00
2.05953568e-02 4.12505001e-01 2.13704973e-01 -1.00289464e+00
-4.25228000e-01 -2.84403563e-01 -1.35331178e+00 6.67653143e-01
5.36935806e-01 -5.57104230e-01 -6.35310709e-01 1.40391552e+00
7.50698447e-01 4.98951137e-01 -1.05554208e-01 9.93238509e-01
2.57801265e-01 8.55324328e-01 -7.59253725e-02 -8.53157490e-02
8.27087462e-01 -9.43955302e-01 -3.79620820e-01 3.86422873e-01
8.66677046e-01 -6.92821681e-01 1.26544607e+00 3.61203849e-01
-1.20620167e+00 -6.42538071e-01 -8.24167609e-01 -1.73791468e-01
-2.78343111e-01 -4.37019110e-01 8.02994311e-01 7.36894459e-02
-9.18624759e-01 1.21753657e+00 -1.22893846e+00 -4.07161266e-02
6.28734887e-01 2.66099155e-01 -1.41662747e-01 -2.10518725e-02
-1.06752312e+00 6.73134804e-01 9.36418399e-02 -2.06248119e-01
-6.79799199e-01 -1.75326180e+00 -8.44851434e-01 -4.39454764e-02
1.97619721e-01 -6.84397399e-01 1.11863720e+00 -4.98808801e-01
-1.67500401e+00 3.32895070e-01 3.99639457e-02 -1.35232627e-01
6.07638955e-01 -3.40492040e-01 -1.52583513e-02 -1.99749246e-02
1.54167831e-01 2.41799608e-01 5.72660089e-01 -1.19580877e+00
-3.02997231e-01 -5.91392964e-02 -3.87290940e-02 2.40140989e-01
-2.72156805e-01 -3.44796270e-01 1.65360495e-01 -8.81345332e-01
-1.49370715e-01 -9.85041022e-01 -4.85522747e-01 6.01381719e-01
-1.58343107e-01 -1.60529807e-01 1.07370830e+00 -6.33889318e-01
9.24610794e-01 -2.20716190e+00 1.93702489e-01 1.32449374e-01
6.45302117e-01 1.57759026e-01 2.52307653e-01 3.64745170e-01
8.97082314e-02 -2.84206476e-02 -7.49069154e-01 -2.63806999e-01
-8.36610720e-02 3.26631427e-01 -1.30494013e-01 5.62841296e-01
2.42135048e-01 6.17341876e-01 -9.91008520e-01 -4.21434492e-01
2.21428335e-01 5.22428751e-01 -7.65611589e-01 3.26199412e-01
-2.02581719e-01 9.82599854e-01 -6.05521202e-01 6.28285110e-01
8.48047197e-01 -5.86423039e-01 -5.49889654e-02 1.21478830e-02
-3.51350874e-01 1.29618153e-01 -1.37354970e+00 1.90141988e+00
-9.58945036e-01 5.89844584e-01 6.49026334e-01 -1.07409227e+00
7.17398524e-01 3.77522498e-01 9.52945113e-01 -5.13801754e-01
4.18012813e-02 4.29653049e-01 -2.39706561e-01 -3.00368786e-01
3.78784448e-01 -3.70633006e-01 -5.42503111e-02 4.42092270e-01
-2.65785992e-01 -4.84130591e-01 -1.70799077e-03 -2.24180371e-01
1.31401205e+00 1.66997507e-01 1.24382898e-01 -8.96259427e-01
3.20735574e-01 2.11710557e-01 6.08853579e-01 4.31650847e-01
8.04164913e-03 4.03624535e-01 5.15898228e-01 -7.17460334e-01
-1.12355459e+00 -1.17688262e+00 -5.24547994e-01 7.68532395e-01
1.10803485e-01 -2.40699232e-01 -4.00193125e-01 -2.30898723e-01
6.00657940e-01 2.97795326e-01 -6.47523642e-01 1.74299315e-01
-9.29081857e-01 -5.03785908e-01 4.97611761e-01 7.08470821e-01
5.59914708e-01 -7.99924552e-01 -5.15545785e-01 4.12086457e-01
3.60453814e-01 -9.66917038e-01 -5.78646421e-01 2.75564641e-01
-1.14376378e+00 -1.08089662e+00 -7.69592285e-01 -7.78022647e-01
6.08748436e-01 -2.12568924e-01 1.15575910e+00 5.81047349e-02
-4.60579395e-01 2.56216079e-01 1.12294988e-03 -4.50839996e-02
-4.58811224e-01 2.40075052e-01 2.00152576e-01 -3.04819673e-01
-4.20666665e-01 -9.70005631e-01 -5.35671294e-01 1.05065413e-01
-7.72878408e-01 6.77575842e-02 1.68701708e-01 9.51471269e-01
6.43552780e-01 2.63461560e-01 4.44793284e-01 -7.97704101e-01
7.56324947e-01 -6.85873568e-01 -6.88930452e-01 -3.51024687e-01
-4.80103910e-01 -8.04736465e-03 1.08455920e+00 -6.97622836e-01
-8.48226130e-01 -3.46184112e-02 -2.84290582e-01 -7.84367025e-01
-1.09868035e-01 4.59739715e-01 3.18960398e-01 -5.68133116e-01
7.32114613e-01 -1.84559330e-01 1.16349449e-02 -6.36716723e-01
-4.75129969e-02 3.38366628e-01 3.01624388e-01 -1.24694073e+00
6.76580429e-01 3.41100156e-01 4.85394061e-01 -9.91082251e-01
-4.31808859e-01 -3.20058405e-01 -5.19487798e-01 -2.72603352e-02
7.84773469e-01 -6.57458961e-01 -8.19798470e-01 6.66869998e-01
-9.68544185e-01 -1.13599992e+00 -5.00868678e-01 4.79745299e-01
-4.59377170e-01 2.21403375e-01 -1.11149490e+00 -5.85020363e-01
-3.03160012e-01 -1.06070006e+00 9.61536646e-01 7.90168270e-02
-3.78451794e-01 -1.60252309e+00 2.55350769e-01 -1.50384203e-01
7.45673299e-01 7.72135615e-01 1.09665847e+00 -4.80593182e-02
-2.23978549e-01 1.97229251e-01 -2.06931889e-01 3.99197698e-01
3.48317653e-01 -2.04681367e-01 -5.90003371e-01 -4.83421713e-01
1.12504967e-01 -5.68791151e-01 4.97213304e-01 3.51660401e-01
1.55697596e+00 -1.50316477e-01 -3.48289013e-01 8.47696006e-01
1.56985879e+00 -1.21133633e-01 -7.10603595e-02 -2.24478811e-01
8.70374322e-01 9.28948075e-02 2.82951593e-01 7.10048199e-01
2.94255942e-01 4.49102551e-01 7.28189796e-02 -4.01649177e-01
-1.12823889e-01 -1.00204989e-01 -9.75224525e-02 9.39809144e-01
-4.85689491e-02 2.46579014e-02 -1.12985301e+00 5.02823412e-01
-1.60475838e+00 -2.41911396e-01 -2.08418280e-01 1.91078234e+00
1.20266902e+00 2.96056569e-02 -4.94816154e-02 1.17039075e-02
3.20521683e-01 5.23898453e-02 -8.92773151e-01 -6.22013807e-01
3.30072969e-01 7.78240800e-01 5.08791983e-01 9.10999060e-01
-1.16837621e+00 6.28155589e-01 5.83777809e+00 7.44773626e-01
-1.48911726e+00 1.17627539e-01 6.54322445e-01 5.69366887e-02
-9.68179107e-02 -3.29696238e-01 -3.60602736e-01 5.38970411e-01
8.19598496e-01 -1.56860888e-01 6.26322150e-01 8.81691098e-01
2.83390969e-01 1.92406084e-02 -1.00130105e+00 8.82583141e-01
-4.87507671e-01 -1.56053972e+00 -3.38181347e-01 6.88182339e-02
1.05438483e+00 3.68132070e-02 -1.98841289e-01 3.46650064e-01
4.46227342e-01 -1.14600158e+00 1.33627445e-01 1.54025391e-01
9.42361891e-01 -6.13997221e-01 3.77244532e-01 3.13603878e-01
-1.49025989e+00 3.02180648e-01 -1.77668989e-01 -4.72688377e-01
2.33583987e-01 7.95219600e-01 -3.76737505e-01 5.58322608e-01
8.45416844e-01 9.52416062e-01 1.92271888e-01 8.35633278e-01
3.35799694e-01 6.32730603e-01 -5.75734437e-01 1.68889657e-01
3.15274984e-01 -2.88972199e-01 4.75253642e-01 1.03477359e+00
5.14942586e-01 3.15414578e-01 6.03004277e-01 9.09674168e-01
-2.38794684e-01 7.07232803e-02 -6.01780295e-01 2.71823376e-01
2.98946023e-01 1.03854656e+00 -5.67029774e-01 -1.01512656e-01
-2.99544215e-01 8.54017437e-01 3.31874281e-01 4.90621030e-01
-7.45289087e-01 -3.83975089e-01 1.18985331e+00 2.36745432e-01
1.96336731e-01 -7.28164375e-01 -6.05030537e-01 -9.40658748e-01
-4.84422296e-02 -6.70787573e-01 2.48368695e-01 -4.02756691e-01
-1.62112093e+00 4.02842224e-01 -6.78963261e-04 -1.08504534e+00
7.12736323e-02 -9.46153462e-01 -5.54832339e-01 1.06930816e+00
-1.50180542e+00 -7.10203588e-01 -2.86153138e-01 5.20686865e-01
3.35893810e-01 2.46883526e-01 1.00629485e+00 5.12812674e-01
-2.25455478e-01 3.17185909e-01 4.12266254e-01 2.41391689e-01
4.45291370e-01 -1.36534095e+00 4.23017949e-01 4.37140107e-01
-5.08748710e-01 2.44327590e-01 5.27328968e-01 -6.79940641e-01
-1.59584641e+00 -1.05832911e+00 3.99592400e-01 -1.39368847e-01
8.41128647e-01 -3.64654452e-01 -1.52209532e+00 4.14546907e-01
2.30230093e-02 5.10245144e-01 6.05716348e-01 -5.29798195e-02
-1.56475080e-03 -5.63595332e-02 -1.33286583e+00 4.73417938e-01
1.00140178e+00 -4.07187968e-01 -3.09177977e-03 3.37255031e-01
6.17972672e-01 -1.07303238e+00 -1.57738316e+00 6.98851705e-01
3.23128402e-01 -5.45748293e-01 9.28237855e-01 -6.08741164e-01
6.43099010e-01 -1.91835910e-01 -3.33983335e-03 -1.52582645e+00
-2.72522718e-01 -7.84015656e-01 -3.70298356e-01 7.27317929e-01
1.12113088e-01 -9.78702605e-01 7.79960513e-01 5.11536598e-01
-3.51940513e-01 -1.36849880e+00 -1.17430520e+00 -7.07475603e-01
8.10675442e-01 -4.14778411e-01 3.82514060e-01 1.29652536e+00
-1.39091790e-01 -1.42773047e-01 -1.91445917e-01 2.03744188e-01
6.02760017e-01 2.20081314e-01 5.72008669e-01 -1.21152318e+00
-4.52454418e-01 -5.43399990e-01 -8.16524103e-02 -1.30153549e+00
3.57328445e-01 -9.02542412e-01 1.05633147e-01 -1.20091784e+00
-4.54744905e-01 -1.14216554e+00 1.17712412e-02 3.47306401e-01
-5.04198410e-02 2.35751018e-01 -2.45555416e-01 9.27136093e-02
-8.37219059e-02 6.38860226e-01 1.76887417e+00 2.08715960e-01
-4.07457054e-01 -7.47092813e-02 -3.12558115e-01 6.19565070e-01
1.20132649e+00 -2.91983932e-01 -3.32524151e-01 -5.53837895e-01
-1.76857099e-01 3.56254607e-01 2.24381909e-01 -1.19275260e+00
2.13255897e-01 -3.51901174e-01 3.91929388e-01 -6.11479655e-02
3.16152781e-01 -6.17289662e-01 -4.09710295e-02 5.15697479e-01
-3.16418052e-01 3.61354262e-01 7.64387310e-01 2.04555258e-01
-6.85203969e-02 2.66161203e-01 9.81844306e-01 -2.81347960e-01
-3.47462714e-01 6.00977302e-01 -4.15344179e-01 5.89911580e-01
8.13878953e-01 1.64152876e-01 5.78727536e-02 -1.43108651e-01
-4.62481260e-01 3.05672765e-01 5.85375547e-01 -2.45694276e-02
4.14260030e-01 -1.47227323e+00 -6.92339838e-01 4.96056497e-01
-4.64248151e-01 6.72912061e-01 3.36591810e-01 6.95225358e-01
-9.79737997e-01 1.35853477e-02 -2.70775050e-01 -6.17122889e-01
-7.33346999e-01 3.03897828e-01 4.95457441e-01 -4.45035815e-01
-8.54121447e-01 7.93611705e-01 2.01089047e-02 -6.49563909e-01
-4.77456711e-02 -6.66604578e-01 5.65902531e-01 -3.54245275e-01
1.37649998e-01 7.80251741e-01 5.35793304e-02 -1.04235239e-01
-1.12898536e-01 6.84792221e-01 3.11736792e-01 6.47967160e-02
1.31373668e+00 3.88434023e-01 -2.14917094e-01 6.33167505e-01
1.52559817e+00 -1.01071130e-02 -1.66163015e+00 -2.03496754e-01
-3.64654183e-01 -4.31817770e-01 9.18291286e-02 -4.42419887e-01
-1.16241872e+00 7.65732646e-01 2.56832540e-01 3.46069902e-01
1.05208635e+00 -1.72900453e-01 1.39627171e+00 1.01569630e-01
2.15784162e-01 -8.16942096e-01 7.90274367e-02 6.53987169e-01
9.05702353e-01 -1.09156787e+00 8.16277694e-03 -4.02398497e-01
-1.62887529e-01 1.14481366e+00 7.79339254e-01 -4.89081949e-01
1.20996320e+00 9.21732187e-01 1.34652361e-01 7.91146383e-02
-4.12335783e-01 3.16141754e-01 5.18135503e-02 3.33075881e-01
4.61630017e-01 5.58110476e-02 4.49541956e-02 -8.90344847e-03
-1.05147026e-01 1.12829559e-01 3.01826209e-01 1.23688114e+00
-3.83064635e-02 -1.19550610e+00 -2.75403738e-01 5.50081313e-01
-3.06730688e-01 4.43913974e-02 4.18874830e-01 8.81297827e-01
5.26777208e-02 3.83701682e-01 2.26159096e-01 -1.56474337e-01
1.78841546e-01 8.46068114e-02 3.03889215e-01 -4.21008974e-01
-3.46802354e-01 -3.08182448e-01 -1.76570460e-01 -5.18863201e-01
-1.51558340e-01 -8.13832879e-01 -1.58131969e+00 -7.78546929e-01
2.91314691e-01 3.14060688e-01 2.70370752e-01 7.15252101e-01
5.46937108e-01 6.99919701e-01 5.76327503e-01 -1.21501410e+00
-4.87790167e-01 -7.48019993e-01 -6.63194895e-01 2.86384940e-01
6.26590312e-01 -8.35102499e-01 -4.77281630e-01 -1.45131871e-01] | [6.509576797485352, 3.408738136291504] |
07a778e1-f29e-4bde-a25b-029c0a201f07 | a-bayesian-model-for-generative-transition | 1506.04334 | null | http://arxiv.org/abs/1506.04334v2 | http://arxiv.org/pdf/1506.04334v2.pdf | A Bayesian Model for Generative Transition-based Dependency Parsing | We propose a simple, scalable, fully generative model for transition-based
dependency parsing with high accuracy. The model, parameterized by Hierarchical
Pitman-Yor Processes, overcomes the limitations of previous generative models
by allowing fast and accurate inference. We propose an efficient decoding
algorithm based on particle filtering that can adapt the beam size to the
uncertainty in the model while jointly predicting POS tags and parse trees. The
UAS of the parser is on par with that of a greedy discriminative baseline. As a
language model, it obtains better perplexity than a n-gram model by performing
semi-supervised learning over a large unlabelled corpus. We show that the model
is able to generate locally and syntactically coherent sentences, opening the
door to further applications in language generation. | ['Jan Buys', 'Phil Blunsom'] | 2015-06-13 | a-bayesian-model-for-generative-transition-1 | https://aclanthology.org/W15-2108 | https://aclanthology.org/W15-2108.pdf | ws-2015-8 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [ 2.24076211e-01 6.76047206e-01 -7.81571418e-02 -6.09215021e-01
-1.55825174e+00 -6.68183446e-01 7.61223018e-01 9.73847806e-02
-2.94890046e-01 9.72672999e-01 6.49917006e-01 -6.93515122e-01
2.33034521e-01 -9.32216883e-01 -7.49540031e-01 -7.51226962e-01
1.08689800e-01 1.30511057e+00 3.60307425e-01 -1.58093825e-01
1.17445067e-02 1.77458212e-01 -1.16449916e+00 1.91091523e-01
8.43627632e-01 1.41335040e-01 6.67063773e-01 1.10510993e+00
-2.55081117e-01 6.63621902e-01 -5.38769782e-01 -5.25754154e-01
-3.71737301e-01 -8.13239813e-01 -8.58843029e-01 -1.27126426e-01
-7.53210783e-02 1.22784777e-02 6.93437532e-02 9.12966251e-01
4.31375057e-01 4.01074216e-02 5.57522535e-01 -4.45905596e-01
-3.86168540e-01 1.20051479e+00 4.43565883e-02 3.06154460e-01
2.92320579e-01 -3.70483696e-01 1.34800887e+00 -7.25531518e-01
7.16412425e-01 1.68400061e+00 4.41689193e-01 7.49226511e-01
-1.44478273e+00 -1.81616396e-01 4.40618336e-01 -4.29448456e-01
-8.36378574e-01 -4.26742107e-01 3.95126581e-01 -1.14516392e-01
1.37488472e+00 2.57485867e-01 5.24433315e-01 1.34624541e+00
4.77870166e-01 9.38165724e-01 1.02679169e+00 -9.13735986e-01
5.10936618e-01 -2.79923677e-01 1.42717302e-01 8.22251439e-01
2.88422614e-01 -4.00230996e-02 -5.21717966e-01 -5.76226532e-01
5.35882056e-01 -6.16822004e-01 1.95357293e-01 -9.84270945e-02
-8.93162251e-01 1.16084158e+00 -1.93464637e-01 3.02117318e-01
-2.82106042e-01 4.54826206e-01 3.49665806e-02 -2.91221142e-01
7.77911901e-01 2.17125267e-01 -6.00291252e-01 -3.90684456e-01
-9.40233588e-01 3.06007296e-01 1.11292827e+00 9.46919978e-01
5.27042687e-01 1.30942807e-01 -3.36507648e-01 6.75366759e-01
7.62689412e-01 7.68825471e-01 3.73495400e-01 -8.15322876e-01
4.75035965e-01 -2.20210641e-03 6.05767705e-02 -1.52467370e-01
-2.96188891e-01 -1.74704760e-01 -4.28858638e-01 -1.85700461e-01
2.37736672e-01 -4.66722220e-01 -1.34204543e+00 1.80685878e+00
3.55752736e-01 -3.12513635e-02 1.43222883e-01 3.00924778e-01
3.42656553e-01 8.50372732e-01 6.24783576e-01 -5.50488889e-01
1.47324789e+00 -9.16777849e-01 -7.57670105e-01 -6.49884403e-01
5.26234508e-01 -7.76374280e-01 4.84985262e-01 2.32501939e-01
-1.30335391e+00 -3.54038775e-01 -6.13289356e-01 -4.97713163e-02
1.71472386e-01 4.35093977e-02 1.14507699e+00 1.08548498e+00
-1.29095101e+00 7.35237658e-01 -1.42405069e+00 -3.79008114e-01
1.26648964e-02 3.98944050e-01 -2.42882967e-02 7.38740414e-02
-9.91782665e-01 7.49400198e-01 6.39966547e-01 -2.99078561e-02
-8.50121677e-01 -1.04816012e-01 -1.06243527e+00 -1.78742837e-02
8.87266397e-02 -9.80271161e-01 1.70878708e+00 -4.99694616e-01
-2.03424144e+00 4.53139603e-01 -7.72529721e-01 -6.12186968e-01
8.95773992e-02 -2.71914124e-01 -2.66725812e-02 -9.62963477e-02
1.66750073e-01 7.28554547e-01 6.64169729e-01 -1.04986572e+00
-7.79244065e-01 -1.77326918e-01 -3.85363400e-01 2.39241809e-01
3.24880123e-01 3.34320068e-01 -4.93467450e-01 -5.78927994e-01
3.25995654e-01 -1.06254041e+00 -8.60270083e-01 -1.27786028e+00
-5.69540858e-01 -3.27449083e-01 1.82432055e-01 -7.47770011e-01
1.18000925e+00 -1.60052121e+00 2.75602490e-01 -2.42888406e-02
-2.13729009e-01 2.73286663e-02 -1.21398874e-01 7.10478067e-01
2.08206579e-01 2.04762653e-01 -3.88301194e-01 -7.34028935e-01
5.60269579e-02 7.47846544e-01 -5.07295787e-01 -1.09136306e-01
3.85189891e-01 1.05015266e+00 -1.06454921e+00 -5.12506247e-01
-8.37088600e-02 4.16223735e-01 -6.58740580e-01 2.68684596e-01
-5.68171740e-01 4.13261175e-01 -7.16898501e-01 4.66878057e-01
2.92460531e-01 -5.94033748e-02 9.18841183e-01 5.19029617e-01
-1.91487186e-03 1.04599023e+00 -7.19758749e-01 1.66383207e+00
-4.81560141e-01 1.74750015e-01 -5.60191981e-02 -6.76184356e-01
9.68850911e-01 3.15821081e-01 -1.75572917e-01 -1.53759733e-01
9.58589185e-03 1.34702548e-01 3.95883173e-02 -7.00306743e-02
5.04523158e-01 -4.68261629e-01 -4.71018910e-01 4.51328039e-01
4.90115047e-01 -3.18400472e-01 4.85090971e-01 2.87833661e-01
1.26031065e+00 6.36459231e-01 3.41178805e-01 -2.84086168e-01
1.50172729e-02 3.91393788e-02 7.14948654e-01 1.16353881e+00
3.28441083e-01 5.17740309e-01 5.45390785e-01 -2.60488629e-01
-9.66641605e-01 -1.27799511e+00 -8.30649808e-02 1.49222934e+00
-3.96090835e-01 -6.75855279e-01 -9.57987666e-01 -8.30798328e-01
-4.00056034e-01 1.15797579e+00 -4.85358149e-01 1.78509414e-01
-9.71775234e-01 -1.51841080e+00 4.09516066e-01 7.51652777e-01
-1.09642752e-01 -1.30613720e+00 -1.02251403e-01 7.58030474e-01
-3.14271718e-01 -1.17074287e+00 1.72846243e-02 7.13659763e-01
-1.26520360e+00 -6.74947441e-01 -2.28219122e-01 -7.90885746e-01
6.44444108e-01 -3.99995714e-01 1.44779420e+00 -3.10718268e-01
1.18262425e-01 6.69076741e-02 -4.48179007e-01 -2.39119411e-01
-1.22596705e+00 2.82587618e-01 -1.58973664e-01 -5.08607924e-01
2.73714393e-01 -5.39712489e-01 -1.45009726e-01 -2.71855474e-01
-4.83409107e-01 1.40064016e-01 8.46253037e-01 1.09271586e+00
6.74496770e-01 -2.70174295e-01 2.04748958e-01 -1.36626863e+00
6.93800807e-01 -4.23002541e-01 -5.93853951e-01 1.47997931e-01
-6.06216729e-01 5.59233844e-01 4.27975833e-01 -8.78925472e-02
-1.58297193e+00 3.27335566e-01 -8.42704833e-01 5.16788661e-01
-4.37234968e-01 1.78822517e-01 -2.51645714e-01 4.01382744e-01
2.99579144e-01 2.89148957e-01 -5.87120473e-01 -6.80954397e-01
7.15517998e-01 3.55415136e-01 3.55142891e-01 -8.35971177e-01
7.78078377e-01 1.98292330e-01 1.11819701e-02 -6.32523596e-01
-1.01187408e+00 -1.50136769e-01 -8.93196642e-01 4.11008835e-01
1.06554544e+00 -9.03707266e-01 -1.53463155e-01 1.17726706e-01
-1.33894372e+00 -4.49389249e-01 -2.73051858e-01 6.19997621e-01
-6.78008437e-01 5.63572228e-01 -1.04229248e+00 -1.10039043e+00
-4.07721460e-01 -7.99598038e-01 1.49553764e+00 1.16437398e-01
-2.90743232e-01 -1.26930082e+00 7.34387636e-01 2.51322240e-01
-4.31083376e-03 -1.56259522e-01 1.04146135e+00 -8.76214504e-01
-6.36307299e-01 -7.62751922e-02 3.17195982e-01 1.53784811e-01
-1.93795577e-01 8.11750889e-02 -8.32506657e-01 2.84902263e-03
-1.98119313e-01 -1.57805815e-01 1.18415380e+00 6.28438294e-01
3.66991967e-01 -3.79414529e-01 -6.00659370e-01 3.00344229e-01
1.17711604e+00 1.54600084e-01 5.19569039e-01 1.02525540e-01
5.46798527e-01 2.80533820e-01 4.74763364e-01 2.76734531e-01
5.91173530e-01 4.08926964e-01 1.84314579e-01 3.36840928e-01
1.31317064e-01 -7.71459043e-01 6.04399264e-01 1.08632791e+00
-1.57061860e-01 -5.37057340e-01 -9.97670472e-01 6.47065580e-01
-1.93062937e+00 -8.99265170e-01 -3.74740243e-01 1.89219213e+00
9.46293831e-01 4.35360670e-01 5.05896285e-02 -1.19572885e-01
5.60537696e-01 1.86994761e-01 2.03874826e-01 -8.42944086e-01
-2.66799238e-02 8.58839571e-01 4.11338210e-01 1.01573527e+00
-1.11082137e+00 1.57536149e+00 8.05310440e+00 6.22607887e-01
-3.18777382e-01 3.47465307e-01 5.65576553e-01 2.24548191e-01
-6.60880506e-01 5.08506835e-01 -1.51061797e+00 3.26320231e-01
1.57985210e+00 1.43411651e-01 1.67452902e-01 8.44669223e-01
9.99090075e-02 -3.01617444e-01 -8.81473541e-01 1.24025099e-01
8.05885047e-02 -1.23255992e+00 4.68380265e-02 8.24397877e-02
6.19449496e-01 2.55828261e-01 -3.02780688e-01 4.92094249e-01
1.40643704e+00 -8.58566701e-01 6.77733064e-01 1.78504989e-01
1.11235328e-01 -7.41080165e-01 6.55171990e-01 7.33550489e-01
-8.97785664e-01 1.78540666e-02 -5.06733477e-01 -2.01707825e-01
7.70095706e-01 7.51145661e-01 -1.02055860e+00 3.92148793e-01
3.74680221e-01 3.22635733e-02 -3.94714892e-01 6.20399415e-01
-8.64514828e-01 1.20677519e+00 -4.48065311e-01 -2.87482917e-01
3.36197585e-01 -2.53604710e-01 5.64847827e-01 1.61851418e+00
4.25356656e-01 1.40339732e-01 3.36075783e-01 5.93203545e-01
1.35539860e-01 1.27837956e-01 -3.54811370e-01 -1.25684053e-01
4.97202665e-01 1.16129398e+00 -9.71452415e-01 -4.40879375e-01
2.39345320e-02 1.09977877e+00 5.52257240e-01 1.62928462e-01
-5.65645397e-01 9.98884067e-02 2.61729330e-01 -3.34178239e-01
7.93297589e-01 -4.86993521e-01 -1.31786540e-01 -1.04643583e+00
-1.91830710e-01 -5.22336781e-01 2.95090318e-01 -4.44962054e-01
-8.99604023e-01 8.79222214e-01 1.19617522e-01 -3.38279217e-01
-1.07760596e+00 -5.52207232e-01 -6.19389474e-01 9.22245800e-01
-1.30518389e+00 -1.20337284e+00 4.74680871e-01 -1.32394478e-01
6.85197890e-01 6.02276996e-02 1.31946921e+00 -3.81100297e-01
-6.54139400e-01 2.46453077e-01 1.50675431e-01 7.04368725e-02
2.22030386e-01 -1.72045338e+00 1.21589887e+00 1.09935975e+00
6.81821048e-01 6.91625893e-01 9.65148747e-01 -8.60132873e-01
-1.18675447e+00 -1.04418123e+00 1.52148449e+00 -7.37274826e-01
6.17835641e-01 -7.29809999e-01 -7.55554616e-01 1.00951993e+00
2.58255601e-01 -3.97159815e-01 8.29541802e-01 5.46436012e-01
-6.40895441e-02 4.30226833e-01 -6.77466273e-01 4.00582045e-01
9.49705720e-01 -1.01307854e-01 -8.38263094e-01 5.99028170e-01
6.30748987e-01 -5.64975619e-01 -5.18471897e-01 1.68356523e-01
2.66311556e-01 -9.15517688e-01 6.98298216e-01 -7.14510798e-01
2.50548214e-01 2.13313580e-01 -1.35919943e-01 -1.29603505e+00
-6.88302934e-01 -9.87747073e-01 -2.23923221e-01 1.30088675e+00
7.57933676e-01 -3.74167085e-01 8.24374020e-01 4.40876991e-01
-2.36996353e-01 -5.76336801e-01 -1.04130483e+00 -5.43161809e-01
2.36957580e-01 -5.73972225e-01 3.41262907e-01 1.93988830e-01
-1.29044533e-01 8.76681268e-01 -3.15212071e-01 3.06146622e-01
7.98842609e-01 1.67390615e-01 3.96183521e-01 -1.23397470e+00
-8.30340147e-01 -2.61301585e-02 6.29859045e-02 -1.39227152e+00
4.50965643e-01 -8.35571647e-01 5.53503990e-01 -1.77362919e+00
3.15263540e-01 -4.63301122e-01 1.30888700e-01 5.14300585e-01
-4.95826274e-01 1.19854145e-01 -1.97703633e-02 -5.48222549e-02
-5.85071802e-01 5.11919737e-01 7.69657135e-01 3.31354380e-01
-2.18475685e-01 2.72767216e-01 -6.86936915e-01 9.62141037e-01
8.09608638e-01 -8.75901461e-01 -1.25374869e-01 -3.87959868e-01
3.00813228e-01 2.57453859e-01 -4.32735961e-03 -6.28723443e-01
-1.88299660e-02 -4.29794714e-02 2.88518757e-01 -5.97480953e-01
2.77028561e-01 8.61599892e-02 3.02186143e-02 3.72998714e-01
-2.16321707e-01 8.36949274e-02 2.53804903e-02 7.96018004e-01
-5.05426303e-02 -6.05574250e-01 5.32206595e-01 -4.01135117e-01
-4.42926258e-01 -5.12124002e-02 -8.76756728e-01 1.18754521e-01
5.89127183e-01 2.89905012e-01 -3.45881023e-02 -2.39264965e-01
-1.02813041e+00 -1.68316305e-01 1.74721241e-01 2.99137950e-01
3.24175358e-01 -9.93696451e-01 -7.67438710e-01 1.04088269e-01
-3.73850375e-01 -8.19899794e-03 -8.70342106e-02 3.13184738e-01
-4.17987496e-01 7.01659381e-01 3.47492278e-01 -4.26629961e-01
-1.15344656e+00 1.80962861e-01 4.49502990e-02 -9.42377448e-01
-6.45625174e-01 9.82077599e-01 -4.11543287e-02 -4.76555854e-01
-1.10634223e-01 -3.68191212e-01 1.95004586e-02 -2.11363792e-01
4.07653719e-01 -5.92180081e-02 -2.64318809e-02 -4.35180008e-01
-3.21984112e-01 7.17387572e-02 -1.32260621e-01 -6.16345048e-01
1.37068009e+00 -1.40792564e-01 -2.02249587e-01 4.26489979e-01
4.22924459e-01 3.34467322e-01 -1.18597817e+00 1.26067013e-01
2.35893846e-01 -1.41829075e-02 -9.68542323e-03 -7.50767112e-01
-2.94963121e-01 7.96821296e-01 2.08759997e-02 1.60783350e-01
5.55817306e-01 3.72374147e-01 8.32303524e-01 4.41261053e-01
5.42820871e-01 -9.75546062e-01 -2.65076995e-01 8.16196024e-01
2.58951336e-01 -9.24807310e-01 -2.29871064e-01 -5.63713551e-01
-6.84437454e-01 9.94545162e-01 1.33780301e-01 -1.53251722e-01
3.18998247e-01 7.86437571e-01 3.37246992e-02 1.47553876e-01
-1.23584390e+00 -4.39792395e-01 1.22472554e-01 7.74792135e-01
6.24187887e-01 2.89989322e-01 -4.54349697e-01 7.04308033e-01
-3.72674733e-01 -3.67745906e-01 3.11537534e-01 8.43746126e-01
-8.07665944e-01 -1.86068571e+00 -2.31838852e-01 9.59101692e-02
-7.49040067e-01 -6.11378908e-01 -1.49910867e-01 5.95656872e-01
-8.31431001e-02 9.65553820e-01 3.54549326e-02 2.37621348e-02
-6.96097389e-02 5.78599095e-01 8.10235918e-01 -1.11257935e+00
-4.29542691e-01 5.52458227e-01 6.30462110e-01 -3.39960098e-01
-1.43490061e-01 -9.71297443e-01 -1.18463540e+00 1.01953134e-01
-3.47608030e-01 5.08887649e-01 6.13461792e-01 1.11603010e+00
1.97265819e-01 4.36259955e-01 4.86254573e-01 -8.80919516e-01
-6.81473434e-01 -1.07886803e+00 -4.19626564e-01 -7.05519468e-02
-2.55982667e-01 -2.98057497e-01 -2.19000369e-01 1.21471460e-03] | [10.388066291809082, 9.625964164733887] |
f9dd149b-9aad-4732-bb3b-7aef34f364d1 | learning-embedding-of-3d-models-with-quadric | 1907.10250 | null | https://arxiv.org/abs/1907.10250v1 | https://arxiv.org/pdf/1907.10250v1.pdf | Learning Embedding of 3D models with Quadric Loss | Sharp features such as edges and corners play an important role in the perception of 3D models. In order to capture them better, we propose quadric loss, a point-surface loss function, which minimizes the quadric error between the reconstructed points and the input surface. Computation of Quadric loss is easy, efficient since the quadric matrices can be computed apriori, and is fully differentiable, making quadric loss suitable for training point and mesh based architectures. Through extensive experiments we show the merits and demerits of quadric loss. When combined with Chamfer loss, quadric loss achieves better reconstruction results as compared to any one of them or other point-surface loss functions. | ['Sung-Eui Yoon', 'Nitin Agarwal', 'M Gopi'] | 2019-07-24 | null | null | null | null | ['3d-shape-representation'] | ['computer-vision'] | [-2.78702229e-01 4.57543842e-02 -1.05748810e-01 -2.44661033e-01
-7.93358922e-01 -1.84997067e-01 6.01374447e-01 2.35132668e-02
-3.39508168e-02 3.61680329e-01 -1.01471663e-01 7.73407072e-02
-1.52534455e-01 -8.82649839e-01 -1.01826155e+00 -1.91103190e-01
-2.78939337e-01 5.55403650e-01 4.83521312e-01 -2.33089045e-01
3.49657625e-01 7.28363156e-01 -1.28775668e+00 2.00208709e-01
6.97879672e-01 1.29470170e+00 -6.11685291e-02 2.31263056e-01
9.22627673e-02 4.10162896e-01 -1.45635501e-01 -6.58418179e-01
3.79009396e-01 1.84985161e-01 -5.81759334e-01 2.15705801e-02
7.39233792e-01 -2.58239686e-01 -2.55257338e-01 8.65152836e-01
2.98075229e-01 -1.76517949e-01 1.01718485e+00 -1.08995998e+00
-4.27511990e-01 -2.14293316e-01 -6.52948439e-01 -3.51845711e-01
6.05799973e-01 -2.04126798e-02 1.19852555e+00 -1.51364899e+00
6.80038869e-01 1.58147049e+00 1.27428746e+00 1.00139931e-01
-1.35094142e+00 -4.68212277e-01 -2.42602810e-01 1.33966699e-01
-1.60506392e+00 -1.93709716e-01 1.13794124e+00 -3.10798049e-01
8.31843197e-01 2.50971168e-01 6.66680157e-01 8.09501648e-01
6.93170309e-01 9.27836597e-01 7.65438080e-01 -1.67389601e-01
-6.80390513e-03 -8.75817239e-02 -3.83953571e-01 8.10644150e-01
7.13173673e-02 3.76946300e-01 -4.40696031e-01 -3.35563540e-01
1.42949367e+00 9.89951193e-02 -1.98995575e-01 -9.49214160e-01
-1.03419006e+00 7.58305848e-01 8.81160200e-01 -1.78830937e-01
-3.85510623e-01 4.84987766e-01 1.04955524e-01 3.46186966e-01
5.99747479e-01 2.63784647e-01 -2.06642404e-01 6.31093094e-03
-7.06192613e-01 4.44877625e-01 6.42218888e-01 1.06162703e+00
9.71420169e-01 -1.13905184e-01 1.13535315e-01 9.69271660e-01
6.82622373e-01 6.26425683e-01 -3.23299557e-01 -1.25997508e+00
3.42492878e-01 7.96563745e-01 5.69077097e-02 -1.51348579e+00
-3.79114300e-01 -1.07981831e-01 -8.45586658e-01 6.04640126e-01
1.24228097e-01 5.63903213e-01 -8.02898705e-01 1.23121357e+00
2.80321449e-01 3.27196866e-01 -4.77960885e-01 1.01010776e+00
7.30778873e-01 3.80599856e-01 -2.77250171e-01 3.81491482e-01
1.07588649e+00 -4.66800094e-01 -3.10656965e-01 -2.77356859e-02
2.55596757e-01 -9.14220095e-01 1.08715498e+00 3.60607237e-01
-1.44710529e+00 -5.89516759e-01 -1.04618180e+00 -4.30831850e-01
1.15525082e-01 -2.43222520e-01 5.95188320e-01 2.11178184e-01
-9.57052112e-01 9.43241239e-01 -1.00165999e+00 1.71870708e-01
5.97281039e-01 3.72445524e-01 -3.26669961e-01 -1.39272451e-01
-8.93973112e-01 1.07177663e+00 -2.46352807e-01 -1.43636465e-01
-6.73569202e-01 -1.04758143e+00 -1.00234711e+00 5.31345941e-02
6.44796565e-02 -9.90724504e-01 1.05328226e+00 -4.77077693e-01
-1.42019844e+00 9.80161309e-01 -3.79670002e-02 -2.68067181e-01
8.28017294e-01 -3.14612776e-01 1.17357627e-01 2.38827154e-01
4.01173346e-02 6.57611489e-01 1.00360870e+00 -1.49972379e+00
-1.40748501e-01 -6.62852824e-01 8.54353085e-02 1.18328154e-01
2.12656781e-01 -5.63972235e-01 -5.00238955e-01 -6.43694937e-01
4.77864325e-01 -7.23946154e-01 -3.66087765e-01 8.30529094e-01
-3.38320971e-01 -4.34683353e-01 7.27384448e-01 -6.43021226e-01
8.21222603e-01 -2.42202353e+00 -3.81884188e-03 4.43739712e-01
3.17304909e-01 -5.45830131e-02 -3.11341472e-02 4.49953169e-01
3.32805783e-01 9.55212265e-02 -4.10610735e-01 -3.67133796e-01
1.36088759e-01 2.11414725e-01 4.17983420e-02 5.72934508e-01
4.57292199e-01 8.92676473e-01 -6.10263646e-01 -4.04164076e-01
3.24502409e-01 9.23371851e-01 -6.81053638e-01 5.73444068e-02
-1.09052114e-01 1.32170230e-01 -6.60573006e-01 6.56901896e-01
9.87591505e-01 -2.65122592e-01 -4.12895590e-01 -3.86481524e-01
2.31620505e-01 4.46752459e-01 -1.19691837e+00 1.69989777e+00
-6.03033423e-01 3.34557086e-01 2.65109926e-01 -5.18881202e-01
1.24852979e+00 2.04016566e-01 4.96403962e-01 -6.49244487e-01
4.54716198e-02 4.54524040e-01 -5.12122571e-01 -9.13370808e-04
2.55999744e-01 -4.24083531e-01 2.27572266e-02 -1.41313914e-02
-2.18231976e-01 -8.64929199e-01 -3.73397380e-01 1.35969026e-02
7.99787164e-01 2.41470903e-01 3.04628998e-01 -3.14047635e-01
3.86989802e-01 -1.41719297e-01 4.78369355e-01 1.48462743e-01
5.34290671e-02 1.00858212e+00 4.40264374e-01 -6.61958039e-01
-1.26332772e+00 -1.61332273e+00 -6.11440659e-01 2.38385990e-01
6.27625227e-01 -4.45832253e-01 -2.83408612e-01 -4.95059013e-01
6.51873469e-01 4.02447015e-01 -3.59562337e-01 -2.10022941e-01
-6.60923541e-01 6.74926788e-02 2.23194495e-01 8.44187558e-01
4.25595462e-01 -5.61569035e-01 -4.98923570e-01 6.47282749e-02
1.32495850e-01 -8.33515823e-01 -6.34079874e-01 -1.19440809e-01
-1.24706161e+00 -1.20559204e+00 -4.84446019e-01 -7.52324522e-01
6.11503124e-01 2.60059893e-01 1.41394949e+00 9.83349606e-02
-1.35749012e-01 4.26141739e-01 -9.58605781e-02 -2.77898014e-01
-2.22486690e-01 -3.40702713e-01 -2.44858935e-01 -1.38736248e-01
1.33135408e-01 -7.06262290e-01 -8.02650630e-01 5.45478582e-01
-5.77018559e-01 -8.45519304e-02 4.80531335e-01 7.23032773e-01
1.02615643e+00 -1.62522778e-01 2.42996365e-01 -6.82236373e-01
4.66219813e-01 -1.96255684e-01 -5.22672117e-01 -1.71141744e-01
-4.01154071e-01 -3.14213894e-02 4.76061642e-01 7.26062059e-02
-5.29941201e-01 4.28084023e-02 -4.11716193e-01 -9.56894994e-01
2.95565128e-01 3.58364701e-01 -5.08483499e-02 -3.95151645e-01
5.97560823e-01 1.44755933e-03 2.64851511e-01 -8.25451255e-01
1.75439268e-01 3.51872176e-01 2.60699153e-01 -4.59725648e-01
7.24522650e-01 7.60059953e-01 4.25621599e-01 -1.00778043e+00
-5.12643516e-01 -3.90296429e-01 -7.11883903e-01 -6.50662705e-02
5.58380663e-01 -7.99693644e-01 -7.86844313e-01 2.74756968e-01
-1.29467678e+00 -6.96228519e-02 -4.07399893e-01 3.92145425e-01
-9.70981896e-01 4.60682869e-01 -6.18601620e-01 -6.40978813e-01
-2.23230347e-01 -1.04075086e+00 1.42943501e+00 -3.30553412e-01
-1.82308942e-01 -1.08383346e+00 -6.09816574e-02 1.15398020e-01
2.75189787e-01 5.89348257e-01 9.55398619e-01 1.01513326e-01
-9.61410642e-01 -5.39040625e-01 -2.74902493e-01 5.02966166e-01
-1.26957908e-01 1.46236882e-01 -6.77406490e-01 -3.21615875e-01
4.49780636e-02 -2.91073561e-01 7.52581358e-01 3.94858569e-01
1.18648696e+00 -4.41415422e-02 -2.68317997e-01 9.15851772e-01
1.56268895e+00 -2.38208398e-01 8.02678466e-01 2.16459915e-01
7.56907046e-01 3.50051492e-01 5.23428082e-01 5.51585972e-01
6.42336607e-01 7.84085512e-01 7.76906073e-01 -8.97471905e-02
-2.42470652e-01 -5.69886744e-01 7.24357814e-02 7.35261738e-01
-2.77123988e-01 3.05461407e-01 -8.66300523e-01 2.35754937e-01
-1.62275541e+00 -6.89208686e-01 -3.16868633e-01 2.31614566e+00
5.58775663e-01 3.06164920e-01 -5.45777120e-02 1.30943075e-01
2.73716092e-01 1.18654199e-01 -6.21946871e-01 -4.44079220e-01
-1.16036348e-01 2.94416428e-01 3.92126560e-01 5.73405802e-01
-1.06344092e+00 8.24251235e-01 7.46301174e+00 8.60204041e-01
-1.06068254e+00 -1.64860070e-01 3.86014670e-01 2.28061020e-01
-7.11202025e-01 -1.30798683e-01 -5.12996018e-01 3.26487511e-01
3.68301988e-01 -8.70694444e-02 2.79652864e-01 9.63427901e-01
1.18481614e-01 1.81806207e-01 -1.38664329e+00 1.06963348e+00
-1.37003601e-01 -1.38973618e+00 2.66095728e-01 1.51908711e-01
6.37547016e-01 3.86774987e-02 1.90616697e-01 1.27830217e-02
8.37785676e-02 -1.17595232e+00 8.10666978e-01 6.20087504e-01
8.79971325e-01 -8.37752879e-01 5.28995335e-01 3.52459848e-01
-1.27095604e+00 3.46417576e-01 -7.41882205e-01 4.48025353e-02
2.52974033e-01 5.73441803e-01 -6.91497326e-01 4.85940397e-01
7.43104517e-01 1.01536679e+00 -1.95799440e-01 1.32748091e+00
-1.35937154e-01 1.57044426e-01 -5.75369835e-01 2.65047848e-01
2.90830970e-01 -4.95395452e-01 7.90217221e-01 9.11494195e-01
2.25496307e-01 -1.92322716e-01 2.19791323e-01 1.21922386e+00
-2.21460760e-01 1.03588134e-01 -8.86705935e-01 5.08083284e-01
5.81106901e-01 7.75267541e-01 -2.48250514e-01 5.19495644e-02
-5.21013737e-01 8.22719276e-01 3.92288268e-01 2.39879847e-01
-6.47191584e-01 -2.13834286e-01 8.52657318e-01 7.21932650e-01
3.95083070e-01 -6.09591722e-01 -7.84109831e-01 -8.16975534e-01
2.99594402e-01 -4.44527030e-01 -1.69468205e-02 -7.18997478e-01
-1.61234307e+00 3.92214864e-01 -2.83645112e-02 -1.53969514e+00
1.01674519e-01 -6.96151793e-01 -6.51738286e-01 8.35497499e-01
-1.61611116e+00 -1.20336306e+00 -2.90661603e-01 6.54967487e-01
3.88317198e-01 2.61187106e-01 6.18733108e-01 2.24537879e-01
1.14810757e-01 5.60077190e-01 -6.68470711e-02 -1.55247331e-01
4.01302904e-01 -1.26833487e+00 5.64769566e-01 1.94952324e-01
2.94563863e-02 4.85916257e-01 2.73737043e-01 -4.89438623e-01
-1.63486218e+00 -8.58617902e-01 6.08670354e-01 -5.70365608e-01
3.22532028e-01 -1.70310289e-01 -1.07347775e+00 5.28411746e-01
-1.36264116e-01 2.77738459e-03 2.80709743e-01 1.19215706e-02
-3.16937774e-01 -6.26836121e-02 -1.22189653e+00 4.81091797e-01
1.02544248e+00 -6.29137814e-01 -6.16542339e-01 1.24191344e-01
5.04145920e-01 -5.06297648e-01 -1.30111003e+00 9.01036203e-01
5.16436040e-01 -1.21767092e+00 1.51304269e+00 -2.26008341e-01
5.04384875e-01 -1.94583341e-01 -2.35929653e-01 -1.25802219e+00
-4.33351517e-01 -5.51519156e-01 -1.13698542e-01 5.70630789e-01
4.02216911e-01 -4.04301018e-01 9.70556378e-01 4.61967200e-01
-4.06163484e-01 -1.27961516e+00 -1.26774347e+00 -9.01543498e-01
3.11938494e-01 -5.27418792e-01 5.70900857e-01 6.44544423e-01
-2.03358293e-01 1.90482005e-01 -3.25683355e-01 -6.49603084e-02
7.81229794e-01 1.41202345e-01 8.87329459e-01 -1.39626670e+00
1.11562192e-01 -5.84517241e-01 -8.15110981e-01 -1.42347610e+00
-7.80332312e-02 -1.09160662e+00 -2.11188614e-01 -1.71056366e+00
-2.52574116e-01 -7.70249367e-01 2.44958196e-02 1.51671499e-01
-5.58647066e-02 4.25145984e-01 1.42682359e-01 2.88516104e-01
-1.83150932e-01 9.54667985e-01 1.93583739e+00 -1.08393773e-01
-1.54305279e-01 1.52630851e-01 -1.74766943e-01 1.11489797e+00
3.59436899e-01 -1.77181318e-01 -1.07637018e-01 -6.41985893e-01
1.60117596e-01 1.51034668e-01 5.64334154e-01 -8.75604689e-01
1.09417036e-01 1.69688743e-02 4.94098872e-01 -8.13972950e-01
8.10463667e-01 -9.97029483e-01 1.99881077e-01 2.85801023e-01
3.82880867e-02 -2.45996024e-02 -1.42594352e-02 5.58899939e-01
-4.56368268e-01 4.33245338e-02 1.11346793e+00 -1.66529089e-01
-4.70267445e-01 6.57682121e-01 2.16079369e-01 1.02740467e-01
1.02840567e+00 -8.05641592e-01 3.13447684e-01 -2.84561604e-01
-7.48103917e-01 2.20020652e-01 8.72904181e-01 3.56882751e-01
1.26724899e+00 -1.72125196e+00 -8.45770538e-01 4.60587472e-01
-1.14205880e-02 4.03435141e-01 -1.36257997e-02 9.57726181e-01
-1.02608800e+00 1.14318922e-01 -1.92445353e-01 -9.49296415e-01
-8.83412242e-01 2.98069954e-01 4.46961880e-01 4.73044533e-03
-9.38891590e-01 7.76209414e-01 4.03308511e-01 -5.52573621e-01
2.91086972e-01 -4.29846227e-01 2.26179615e-01 -3.83252442e-01
8.16772282e-02 7.30753303e-01 9.28461999e-02 -5.14520466e-01
-5.06615996e-01 1.02670240e+00 2.75205553e-01 2.27288470e-01
1.32085967e+00 1.54169187e-01 -5.05272038e-02 3.70397747e-01
1.46674955e+00 -1.12962894e-01 -1.44656849e+00 -1.56616732e-01
-4.44892086e-02 -1.00354230e+00 1.91416796e-02 -4.38897967e-01
-8.83921742e-01 1.03129840e+00 3.17643702e-01 1.47653446e-01
7.46747375e-01 1.78098097e-01 1.12198842e+00 -5.70852682e-02
8.17504406e-01 -7.44225502e-01 2.92228401e-01 5.03720999e-01
1.38900661e+00 -1.22198784e+00 1.27455397e-02 -9.78170037e-01
-4.97557551e-01 1.06120336e+00 5.58451593e-01 -9.79706168e-01
1.10468018e+00 1.94493085e-01 2.43792925e-02 -3.86875868e-01
-6.32982731e-01 6.05446249e-02 7.87028551e-01 5.75922012e-01
3.59020382e-01 1.27094626e-01 -3.57215345e-01 1.89249769e-01
-3.25564384e-01 -1.48664981e-01 -8.22588429e-02 7.84415364e-01
-4.65464473e-01 -1.03367555e+00 -3.28365594e-01 5.15182674e-01
-8.80681574e-02 2.36180246e-01 -3.99665892e-01 8.55694532e-01
-1.30086288e-01 4.84814644e-01 1.42959744e-01 -4.69450176e-01
9.68009412e-01 -4.21049088e-01 6.73208594e-01 -5.29101789e-01
-3.85738283e-01 1.44668385e-01 -7.94673190e-02 -1.07696319e+00
-1.09014452e-01 -5.47831237e-01 -1.36361766e+00 -5.33591449e-01
-2.47333840e-01 -8.71936828e-02 5.50312340e-01 4.13637638e-01
5.51625192e-01 -9.15236585e-03 9.07104075e-01 -1.10474825e+00
-9.32515800e-01 -6.89373434e-01 -6.84543252e-01 7.33775795e-01
2.13287354e-01 -9.89669323e-01 -3.43112856e-01 -1.78769827e-01] | [8.528017044067383, -3.539512872695923] |
255b9171-e802-41ee-a113-582b69d5a7d1 | improving-sentence-similarity-estimation-for | 2302.12490 | null | https://arxiv.org/abs/2302.12490v1 | https://arxiv.org/pdf/2302.12490v1.pdf | Improving Sentence Similarity Estimation for Unsupervised Extractive Summarization | Unsupervised extractive summarization aims to extract salient sentences from a document as the summary without labeled data. Recent literatures mostly research how to leverage sentence similarity to rank sentences in the order of salience. However, sentence similarity estimation using pre-trained language models mostly takes little account of document-level information and has a weak correlation with sentence salience ranking. In this paper, we proposed two novel strategies to improve sentence similarity estimation for unsupervised extractive summarization. We use contrastive learning to optimize a document-level objective that sentences from the same document are more similar than those from different documents. Moreover, we use mutual learning to enhance the relationship between sentence similarity estimation and sentence salience ranking, where an extra signal amplifier is used to refine the pivotal information. Experimental results demonstrate the effectiveness of our strategies. | ['Sujian Li', 'Wenjie Li', 'Ruifeng Yuan', 'Shichao Sun'] | 2023-02-24 | null | null | null | null | ['unsupervised-extractive-summarization', 'extractive-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.89021683e-01 2.28734180e-01 -5.26749790e-01 -4.99619335e-01
-1.00304961e+00 -3.19855303e-01 5.83650410e-01 8.42012048e-01
-4.92548615e-01 6.78440213e-01 1.06237924e+00 2.93538153e-01
-2.71521658e-01 -5.11596262e-01 -3.17210346e-01 -4.78950411e-01
1.77950606e-01 -2.46293783e-01 9.24451277e-02 -1.95173562e-01
9.85332847e-01 -1.06395088e-01 -1.21816707e+00 5.61540127e-01
1.20215547e+00 4.43171442e-01 6.57129169e-01 7.74917960e-01
-2.69371212e-01 1.00987864e+00 -9.06796098e-01 -1.32381111e-01
-4.30797897e-02 -8.63010049e-01 -7.16634154e-01 4.98232245e-02
5.42173982e-01 -2.20958233e-01 -1.34256393e-01 1.18141472e+00
7.98476398e-01 2.79039681e-01 7.47947037e-01 -6.39963686e-01
-4.38585758e-01 1.22902226e+00 -7.42899418e-01 5.99888027e-01
4.66259778e-01 -2.25431085e-01 1.48780119e+00 -8.18384767e-01
3.87239009e-01 1.10594773e+00 3.68103802e-01 2.76193619e-01
-9.76172268e-01 -2.64987826e-01 3.80721837e-01 2.29681522e-01
-1.04420555e+00 -6.23958170e-01 1.31109393e+00 -5.11857271e-02
8.13260257e-01 4.66948926e-01 4.94357526e-01 7.65447795e-01
2.64315277e-01 1.16555643e+00 7.27842927e-01 -6.37343764e-01
1.64364621e-01 1.28711656e-01 4.64834452e-01 3.76042426e-01
3.39607239e-01 -5.53502738e-01 -8.23003173e-01 -3.73153687e-02
1.51623800e-01 3.36797237e-02 -3.11487705e-01 8.59966129e-02
-1.16840959e+00 8.82095993e-01 3.40234905e-01 5.62560558e-01
-5.11535585e-01 -1.58630922e-01 6.32731378e-01 1.26827329e-01
7.48062611e-01 8.84868264e-01 -2.66271740e-01 -7.71173388e-02
-1.35936856e+00 2.86550764e-02 5.23449600e-01 6.82141900e-01
7.26227939e-01 4.69539948e-02 -6.76513970e-01 8.77426147e-01
2.00174764e-01 3.88314664e-01 7.37647355e-01 -9.23651040e-01
8.08249474e-01 6.26590371e-01 -3.39481294e-01 -1.33563828e+00
-2.69585431e-01 -5.60001969e-01 -7.98157990e-01 -4.65961635e-01
-2.91202307e-01 -3.45086098e-01 -3.66956502e-01 1.55641556e+00
-1.78950831e-01 5.99432811e-02 3.42190087e-01 8.36681485e-01
1.19018722e+00 7.56410599e-01 -7.82124698e-02 -7.92837739e-01
1.14324224e+00 -9.24079061e-01 -9.93280590e-01 -2.77356476e-01
6.92129135e-01 -9.06096578e-01 1.14602363e+00 -8.45278949e-02
-1.38447785e+00 -4.98009354e-01 -1.24369335e+00 -2.06603840e-01
1.29299611e-01 2.89040953e-01 3.75179708e-01 3.64731133e-01
-8.44458580e-01 6.07529283e-01 -3.25449884e-01 -1.48483649e-01
3.34234864e-01 1.33457392e-01 1.24164030e-01 4.16025639e-01
-1.31170905e+00 7.61841178e-01 6.95143342e-01 -1.95576817e-01
-2.49185607e-01 -6.56568050e-01 -9.03534293e-01 4.33632314e-01
4.19378191e-01 -9.56976116e-01 1.17368484e+00 -1.00386643e+00
-1.37778175e+00 6.27637863e-01 -4.90626812e-01 -5.05791783e-01
-1.02985743e-02 -4.83678043e-01 -4.10101786e-02 5.43204188e-01
2.93506652e-01 5.72807908e-01 8.24070156e-01 -1.08945274e+00
-7.39258707e-01 -2.24678963e-01 6.97150975e-02 7.69479811e-01
-7.82514930e-01 -1.72587875e-02 -2.10758165e-01 -9.39481735e-01
2.12641507e-01 -2.88332790e-01 -3.11411768e-01 -7.67094970e-01
-6.47995949e-01 -2.91248649e-01 6.12481415e-01 -4.94672447e-01
1.76153088e+00 -1.91655540e+00 1.84823841e-01 -5.82042821e-02
2.67428488e-01 -9.33310669e-03 -1.78599626e-01 6.26612484e-01
1.39364809e-01 1.69489801e-01 -3.33059698e-01 -2.27093890e-01
-1.42891124e-01 -3.20352465e-01 -5.99145412e-01 1.83395557e-02
2.52921075e-01 7.61227727e-01 -1.25119781e+00 -1.00249410e+00
9.59546678e-03 4.59016301e-02 -5.34366488e-01 6.69457540e-02
3.84057201e-02 5.36638014e-02 -5.43577194e-01 2.05123976e-01
3.87456238e-01 -9.58720446e-02 1.54209539e-01 -5.25911272e-01
-1.42767638e-01 5.79558372e-01 -7.19860196e-01 1.64579844e+00
-3.83938640e-01 8.77947211e-01 -3.27080727e-01 -9.61991489e-01
9.20271516e-01 2.12486722e-02 4.34554726e-01 -6.04019225e-01
1.89850062e-01 -1.04373530e-01 -1.03332959e-01 -5.51268280e-01
1.21025753e+00 -2.30413198e-01 -2.91027486e-01 5.46854138e-01
-8.20276290e-02 -4.56864059e-01 4.15156454e-01 6.87178016e-01
9.00810122e-01 -3.09769332e-01 6.27905011e-01 -3.36409390e-01
6.60588205e-01 -1.93778723e-01 4.97694492e-01 9.15875435e-01
-3.35060135e-02 7.55961001e-01 5.63071132e-01 2.32889131e-01
-8.27058911e-01 -9.55640912e-01 1.84799507e-01 1.16851068e+00
4.81934726e-01 -9.30635571e-01 -7.51214802e-01 -8.30514789e-01
-3.51892829e-01 1.06896257e+00 -2.78367132e-01 -5.97485840e-01
-5.79566658e-01 -5.68016112e-01 4.04734045e-01 4.08415943e-01
3.89710665e-01 -9.28284645e-01 -5.99839866e-01 -8.62924531e-02
-5.04503131e-01 -7.33608007e-01 -9.49661076e-01 -5.47051206e-02
-1.03727412e+00 -7.78650999e-01 -6.88041866e-01 -1.01040745e+00
7.93052077e-01 7.24655509e-01 9.92386222e-01 -1.44873664e-01
2.31802076e-01 3.55833828e-01 -5.50935566e-01 -4.95370775e-01
-2.52122104e-01 4.69415575e-01 7.98908621e-02 -1.64519787e-01
3.40693772e-01 -4.46584135e-01 -4.84819025e-01 -3.09323877e-01
-9.42846477e-01 1.80238605e-01 8.15010071e-01 7.21909344e-01
2.55189121e-01 6.11545667e-02 9.42821681e-01 -8.35886955e-01
1.39405835e+00 -2.65890121e-01 1.90389961e-01 2.61039078e-01
-5.35559773e-01 3.03982526e-01 6.46344006e-01 -2.48236343e-01
-1.30965579e+00 -2.69527912e-01 1.21910773e-01 -8.03716630e-02
2.21002862e-01 9.39514339e-01 -1.68783039e-01 6.66321874e-01
4.62337404e-01 4.22499955e-01 -1.94850475e-01 -1.23170301e-01
4.38779116e-01 7.59496093e-01 5.02180398e-01 -3.85992050e-01
5.54280877e-01 2.05728322e-01 -2.24339694e-01 -1.02978730e+00
-1.36098731e+00 -6.25516832e-01 -6.70022368e-01 -3.14152926e-01
6.02865040e-01 -8.53486419e-01 -3.83842945e-01 -1.42352404e-02
-1.22621465e+00 3.00845295e-01 -5.12872934e-01 6.55851245e-01
-3.14315319e-01 9.08212483e-01 -4.33301926e-01 -8.04250896e-01
-8.89323235e-01 -5.70352852e-01 1.03464758e+00 5.06216466e-01
-5.21325409e-01 -9.79165733e-01 8.77512097e-02 2.39539713e-01
1.87324286e-01 1.47412047e-02 7.05270767e-01 -6.99968755e-01
-8.44479576e-02 -1.45822108e-01 -1.31591275e-01 3.43870938e-01
6.10048294e-01 -6.54707551e-02 -7.67727673e-01 -1.22067787e-01
3.84688437e-01 -1.11184314e-01 1.31878352e+00 7.20491767e-01
8.52610230e-01 -7.01400638e-01 -1.94279641e-01 2.35282388e-02
1.03211963e+00 -1.36002749e-01 4.76778656e-01 1.13319181e-01
6.86323762e-01 7.95633793e-01 8.80360723e-01 7.06730545e-01
2.48747349e-01 3.61197114e-01 -8.25207010e-02 4.95418683e-02
2.04069018e-02 -2.71805376e-01 6.27257526e-01 1.55181503e+00
2.36273006e-01 -2.13136077e-01 -4.16864157e-01 4.91756201e-01
-2.00934601e+00 -1.49987531e+00 -1.02521144e-01 1.98106003e+00
1.07548559e+00 6.20689034e-01 -1.29423574e-01 1.48819163e-01
9.99528766e-01 6.12313092e-01 -3.50152969e-01 -3.89100611e-01
-3.55606496e-01 -2.78147072e-01 1.19729854e-01 6.53061032e-01
-1.12819040e+00 8.83967876e-01 6.47218323e+00 9.89211440e-01
-8.44299674e-01 -3.30735028e-01 5.22637844e-01 -3.98181438e-01
-7.27846622e-01 1.31377369e-01 -6.40263736e-01 6.15052879e-01
6.41965091e-01 -8.06091428e-01 -2.89649636e-01 5.74094415e-01
7.50051618e-01 -3.19633573e-01 -9.15766120e-01 8.38737130e-01
6.04090989e-01 -1.30618834e+00 3.14216137e-01 -3.72192383e-01
8.74880195e-01 -3.84417146e-01 3.84898111e-02 3.34974229e-01
-7.96613097e-02 -4.26321328e-01 5.44508457e-01 7.57280111e-01
1.56287387e-01 -9.38570619e-01 8.42000008e-01 5.70673585e-01
-1.07481515e+00 1.18141726e-01 -4.48763072e-01 -3.78635019e-01
3.41218561e-01 9.23681557e-01 -8.84374022e-01 6.94659293e-01
1.02283217e-01 1.06834257e+00 -7.99638569e-01 7.84342587e-01
-4.05296564e-01 7.23863959e-01 1.12352587e-01 -4.07517046e-01
1.97214752e-01 -2.23398179e-01 9.50818717e-01 1.48318040e+00
5.49632423e-02 1.00053862e-01 1.95331275e-01 5.18380940e-01
-2.06001997e-01 4.76842701e-01 -5.52609563e-01 2.47562565e-02
5.67412674e-01 1.25899327e+00 -7.01232970e-01 -6.32227838e-01
-1.21755049e-01 9.41900074e-01 1.66958526e-01 5.00982106e-02
-5.53862274e-01 -6.78176284e-01 8.34666118e-02 -1.90498069e-01
2.14460179e-01 -9.39055383e-02 -6.24727368e-01 -1.27329659e+00
8.88020173e-02 -4.83033717e-01 2.96645492e-01 -7.21671402e-01
-1.28786826e+00 3.30913603e-01 1.11922756e-01 -1.47862446e+00
-2.57797182e-01 1.73981741e-01 -1.00002885e+00 4.61789131e-01
-1.41295934e+00 -6.61243796e-01 3.25517878e-02 -6.06546700e-02
1.05114961e+00 -2.02859446e-01 4.60169733e-01 -1.68256536e-01
-6.34107709e-01 4.59802806e-01 5.75913265e-02 -1.15369797e-01
8.73270035e-01 -1.36049581e+00 -2.47983951e-02 1.04155266e+00
1.20182946e-01 7.65196681e-01 9.40678596e-01 -7.66929507e-01
-9.80327249e-01 -9.25632358e-01 1.29331899e+00 -1.12334631e-01
4.09129232e-01 5.19601330e-02 -7.66674995e-01 1.87747940e-01
7.71069288e-01 -8.00176978e-01 1.04337275e+00 2.01766923e-01
-2.61253156e-02 -1.41524240e-01 -7.33901143e-01 9.20066953e-01
8.17007840e-01 -5.63807666e-01 -1.30250168e+00 1.32884309e-01
9.43433940e-01 -2.66176425e-02 -5.83236217e-01 3.56905133e-01
3.64002883e-01 -7.29838967e-01 7.73251712e-01 -5.01673102e-01
9.38768983e-01 -2.82179773e-01 -9.03490186e-02 -1.48460305e+00
-4.78747666e-01 -4.79679823e-01 -2.34184310e-01 1.63430250e+00
3.65109205e-01 -1.13267645e-01 6.82431161e-01 2.43558735e-01
-3.77677321e-01 -6.34401798e-01 -4.29635376e-01 -5.06124139e-01
-2.04448864e-01 -4.41107433e-03 2.39896297e-01 8.92212093e-01
6.43303216e-01 1.26788485e+00 -3.96524847e-01 -8.37130100e-02
4.43423271e-01 3.56147379e-01 5.83064914e-01 -1.03746986e+00
-2.15212293e-02 -8.26928914e-01 -9.49891880e-02 -1.25696504e+00
4.16680813e-01 -1.02645659e+00 3.06546807e-01 -1.84919834e+00
8.27076554e-01 4.70618308e-01 -5.05928159e-01 1.61608867e-02
-9.11979496e-01 -2.38675326e-01 3.84016097e-01 3.00736904e-01
-1.11864734e+00 9.28178132e-01 1.12000775e+00 -5.14642000e-01
-4.61398304e-01 7.84789212e-03 -1.19748557e+00 7.72056460e-01
1.04900026e+00 -5.34105599e-01 -6.53975308e-01 -3.44145745e-01
2.56201416e-01 -1.53809324e-01 -1.73206419e-01 -8.69588196e-01
5.57124853e-01 -1.58212110e-01 2.12744206e-01 -1.06358647e+00
-3.27691361e-02 -4.22957987e-01 -6.68256402e-01 3.61366719e-01
-1.07126713e+00 -4.89190500e-03 -4.84106801e-02 5.71531475e-01
-4.07392412e-01 -7.42461205e-01 4.43757236e-01 6.59045577e-03
-4.99215990e-01 -2.83061922e-01 -5.35531402e-01 3.04145396e-01
6.34114623e-01 -1.62902623e-01 -2.12307259e-01 -5.76153576e-01
-1.59113705e-01 3.19788069e-01 2.14571089e-01 4.14250940e-01
9.35291946e-01 -1.26364529e+00 -1.16685390e+00 -2.88376838e-01
4.47358526e-02 -1.59302950e-01 2.88482279e-01 6.47095859e-01
3.30940075e-02 4.41641957e-01 1.13866113e-01 -4.93356615e-01
-1.50762844e+00 2.98382938e-01 -3.32278192e-01 -4.12402928e-01
-4.40660655e-01 5.62915981e-01 7.08520785e-02 -6.12392314e-02
5.47686107e-02 -2.29950860e-01 -6.95969701e-01 6.07232273e-01
7.08166420e-01 4.74185526e-01 -1.75659448e-01 -5.58670878e-01
-2.42598817e-01 4.77426350e-01 -4.71854299e-01 -1.88116103e-01
1.14111042e+00 -4.18953598e-01 -1.74644619e-01 5.78282356e-01
1.22100210e+00 3.66720915e-01 -9.38906550e-01 -3.21897030e-01
3.53071958e-01 -3.85068744e-01 2.93305486e-01 -3.04826409e-01
-6.53233826e-01 5.18059731e-01 -1.83259137e-02 3.25450271e-01
1.19577217e+00 -5.00639193e-02 6.97097182e-01 6.66915357e-01
-1.46459103e-01 -1.48257923e+00 5.63269973e-01 6.77041650e-01
9.90394771e-01 -1.25376284e+00 6.03215992e-01 -4.11830604e-01
-9.83534634e-01 1.12720835e+00 4.61592019e-01 -1.47373781e-01
2.95473695e-01 2.93284450e-02 -1.43652499e-01 -1.28789186e-01
-7.65879750e-01 -2.45786428e-01 5.00723481e-01 1.66924030e-01
7.21230805e-01 -1.58307657e-01 -9.14198399e-01 9.20950711e-01
-3.93581092e-01 -3.87857735e-01 7.21485078e-01 9.54450965e-01
-1.01821959e+00 -7.74335325e-01 -2.12082624e-01 7.48290896e-01
-4.31428581e-01 -4.67886627e-01 -6.37723804e-01 5.90038784e-02
-3.78823847e-01 1.23650515e+00 -5.62480465e-03 -3.66779357e-01
2.77065337e-01 -2.50849962e-01 3.07171702e-01 -9.36496437e-01
-6.45257413e-01 2.77835190e-01 1.19485281e-01 4.87415679e-02
-7.07474053e-01 -7.50653863e-01 -1.38995433e+00 8.54363292e-02
-6.12110138e-01 4.75113064e-01 4.04276609e-01 9.70656276e-01
4.42167372e-01 8.40004146e-01 1.09744787e+00 -7.92612851e-01
-5.73365808e-01 -1.19474733e+00 -4.53192085e-01 2.92288870e-01
3.43727887e-01 -3.60722169e-02 -4.19801652e-01 1.38922572e-01] | [12.572571754455566, 9.54356575012207] |
91d1c8e6-fa91-4aac-955b-3f33f856ef80 | processing-and-normalizing-hashtags | null | null | https://aclanthology.org/R15-1015 | https://aclanthology.org/R15-1015.pdf | Processing and Normalizing Hashtags | null | ['Thierry Declerck', 'Piroska Lendvai'] | 2015-09-01 | processing-and-normalizing-hashtags-1 | https://aclanthology.org/R15-1015 | https://aclanthology.org/R15-1015.pdf | ranlp-2015-9 | ['rumour-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] | [-7.206403732299805, 3.7356035709381104] |
a0079327-7ad3-47c6-9c40-ccd8e35163b6 | xeroalign-zero-shot-cross-lingual-transformer | 2105.02472 | null | https://arxiv.org/abs/2105.02472v2 | https://arxiv.org/pdf/2105.02472v2.pdf | XeroAlign: Zero-Shot Cross-lingual Transformer Alignment | The introduction of pretrained cross-lingual language models brought decisive improvements to multilingual NLP tasks. However, the lack of labelled task data necessitates a variety of methods aiming to close the gap to high-resource languages. Zero-shot methods in particular, often use translated task data as a training signal to bridge the performance gap between the source and target language(s). We introduce XeroAlign, a simple method for task-specific alignment of cross-lingual pretrained transformers such as XLM-R. XeroAlign uses translated task data to encourage the model to generate similar sentence embeddings for different languages. The XeroAligned XLM-R, called XLM-RA, shows strong improvements over the baseline models to achieve state-of-the-art zero-shot results on three multilingual natural language understanding tasks. XLM-RA's text classification accuracy exceeds that of XLM-R trained with labelled data and performs on par with state-of-the-art models on a cross-lingual adversarial paraphrasing task. | ['Ignacio Iacobacci', 'Milan Gritta'] | 2021-05-06 | null | https://aclanthology.org/2021.findings-acl.32 | https://aclanthology.org/2021.findings-acl.32.pdf | findings-acl-2021-8 | ['multilingual-nlp'] | ['natural-language-processing'] | [ 1.28577694e-01 2.64059603e-01 -4.04470623e-01 -4.69168365e-01
-1.47997689e+00 -7.56769419e-01 1.00969768e+00 -1.29320785e-01
-6.34115994e-01 9.14648592e-01 4.95291680e-01 -6.95408106e-01
5.75922549e-01 -4.89336193e-01 -1.02872777e+00 -1.18506044e-01
6.88153267e-01 1.04027224e+00 -2.94304490e-01 -7.40149856e-01
-1.64810598e-01 -1.64402112e-01 -5.65848768e-01 5.34780264e-01
1.13883436e+00 2.83890814e-01 3.07516217e-01 3.58819246e-01
-5.43157935e-01 6.36405766e-01 -3.12522352e-01 -1.00734949e+00
2.29276508e-01 -4.83007640e-01 -1.04559720e+00 -4.16788727e-01
7.56408989e-01 1.63697258e-01 -2.55208880e-01 9.67795730e-01
5.87580144e-01 -2.11644694e-01 7.36025035e-01 -1.18036139e+00
-1.26324403e+00 1.19683397e+00 -4.34252471e-01 2.99839526e-01
4.83856976e-01 1.12287372e-01 1.20920658e+00 -1.27851915e+00
9.50266123e-01 1.39058554e+00 9.39517200e-01 6.89566016e-01
-1.64634395e+00 -7.72945762e-01 -2.09824294e-02 1.42803848e-01
-1.14624596e+00 -6.22731864e-01 5.93216181e-01 -2.15920761e-01
1.51970398e+00 -1.64739005e-02 2.61412654e-02 1.66779149e+00
4.80928719e-01 9.21035290e-01 1.35927665e+00 -8.80653977e-01
-3.19829404e-01 4.90598857e-01 2.19221935e-02 5.15542448e-01
-5.25296964e-02 -1.52956266e-02 -7.00930357e-01 3.81551348e-02
7.03735426e-02 -5.61589479e-01 -4.56410527e-01 -3.28435719e-01
-1.45306802e+00 1.04326534e+00 2.33829737e-01 6.74737394e-01
1.52456760e-01 -1.40985996e-01 9.17578995e-01 8.71727645e-01
9.21371818e-01 1.03369939e+00 -8.16063106e-01 -6.26601279e-02
-8.53149951e-01 -1.20071530e-01 7.22127974e-01 1.05802691e+00
6.09028578e-01 1.35286346e-01 -2.55985688e-02 1.28735161e+00
-9.91531238e-02 6.06386721e-01 1.02488577e+00 -4.34267461e-01
1.29622245e+00 3.81392390e-01 -1.84294254e-01 -4.22508627e-01
-3.07204481e-02 -2.67999023e-01 -7.31441259e-01 -2.09576398e-01
4.71973509e-01 -2.51640286e-02 -6.65752649e-01 1.90780628e+00
-3.20535392e-01 -2.02011973e-01 7.90855706e-01 4.37891990e-01
6.50798440e-01 8.58130813e-01 7.42377862e-02 2.03045592e-01
1.25558090e+00 -1.38119602e+00 -5.81064165e-01 -8.90438676e-01
1.14055574e+00 -1.06720436e+00 1.80200362e+00 -1.33591443e-01
-9.25825953e-01 -6.91365719e-01 -1.02873194e+00 -3.89412493e-01
-8.37040842e-01 1.63070560e-01 2.51974374e-01 5.01934469e-01
-9.00248230e-01 3.47718120e-01 -4.79236633e-01 -7.22651958e-01
1.40856445e-01 -1.66299999e-01 -7.89421201e-01 -5.26970565e-01
-1.73781025e+00 1.68125701e+00 3.53098243e-01 -3.52729559e-01
-8.39825630e-01 -1.09314930e+00 -1.26751399e+00 -2.22541064e-01
-1.32070497e-01 -5.23345232e-01 1.23851061e+00 -1.05461740e+00
-1.44899869e+00 1.58990479e+00 -1.40624136e-01 -6.20529234e-01
5.99810481e-01 -5.67070127e-01 -4.68412638e-01 -3.47418368e-01
6.41280055e-01 7.31937408e-01 7.40967095e-01 -1.07125854e+00
-2.89634556e-01 -4.72560041e-02 -3.31727058e-01 3.65694642e-01
-2.86429375e-01 2.52761066e-01 -6.68037683e-02 -7.48053849e-01
-5.15313387e-01 -7.86853731e-01 -1.10304303e-01 -4.99597788e-01
-3.53463173e-01 -3.46239716e-01 6.26580000e-01 -9.32772398e-01
6.53358161e-01 -1.68289340e+00 3.44289571e-01 -5.21033108e-01
-4.47881252e-01 5.16811728e-01 -6.64761484e-01 7.46666729e-01
-4.48835820e-01 7.35944360e-02 -2.88031131e-01 -7.44567573e-01
7.95139000e-02 4.17476922e-01 -8.67287159e-01 2.52273619e-01
3.70891333e-01 1.35296297e+00 -1.14949453e+00 -3.11188221e-01
2.75111228e-01 1.53452322e-01 -1.93336159e-01 3.64613235e-01
-1.59922227e-01 4.22398478e-01 -5.82445413e-02 2.53875017e-01
2.46522129e-01 -5.16424999e-02 1.24171987e-01 -1.03783160e-01
4.08182628e-02 8.28112900e-01 -1.43455431e-01 2.31787801e+00
-1.30119681e+00 8.55686486e-01 -2.65052766e-01 -1.07051182e+00
9.56211984e-01 5.62128961e-01 5.05467393e-02 -7.97590196e-01
1.55004431e-02 6.33769631e-01 -1.39458701e-02 -2.82259136e-01
6.04290545e-01 -3.68029803e-01 -5.72375655e-01 7.60851562e-01
4.98503029e-01 -5.98857284e-01 -6.55700490e-02 2.51274288e-01
7.69413590e-01 4.90986228e-01 5.39488971e-01 -6.05665922e-01
6.73016846e-01 2.60493159e-01 1.11748576e-01 6.92038298e-01
-9.20668617e-02 4.09018874e-01 -3.60807702e-02 -4.51108247e-01
-1.46712899e+00 -1.09315562e+00 -2.06305027e-01 1.29207695e+00
-1.92929551e-01 -2.03429475e-01 -7.51577735e-01 -9.46741521e-01
1.26687698e-02 1.42040789e+00 -6.48916185e-01 -2.04620436e-01
-6.90480292e-01 -6.90165818e-01 9.70138073e-01 3.08224827e-01
3.33944917e-01 -1.13995290e+00 4.94517758e-02 4.52149123e-01
-6.41900957e-01 -1.46432590e+00 -8.95602286e-01 3.83798897e-01
-4.46643084e-01 -7.52696693e-01 -7.13190496e-01 -1.09789312e+00
7.14249849e-01 -7.45763704e-02 1.70617151e+00 -6.86159790e-01
-9.14311185e-02 1.32381245e-01 -3.14601034e-01 -2.39660844e-01
-1.15301263e+00 5.45420945e-01 2.14108363e-01 -3.64131063e-01
7.93273211e-01 -3.97565871e-01 1.69594824e-01 2.43780062e-01
-4.65406030e-01 1.96975768e-01 4.75555480e-01 1.13723528e+00
3.26288730e-01 -6.47246659e-01 8.76201212e-01 -1.05611002e+00
9.32329297e-01 -2.96925902e-01 -2.12703213e-01 6.87316954e-01
-5.79446256e-01 3.93665433e-01 1.25208271e+00 -6.14621043e-01
-8.55137348e-01 -3.28545630e-01 -1.69371516e-01 -3.06863517e-01
9.12991241e-02 2.65835881e-01 -2.45418787e-01 2.35976651e-01
8.73930752e-01 5.23130953e-01 -1.12893522e-01 -4.93517518e-01
1.01424241e+00 9.10899639e-01 7.11944103e-01 -8.20107102e-01
7.34894514e-01 -3.07921544e-02 -6.40505016e-01 -5.80683291e-01
-1.16720748e+00 -2.20726952e-01 -9.16295230e-01 2.49122858e-01
8.84399474e-01 -1.15707731e+00 3.25606048e-01 3.74754608e-01
-1.56003726e+00 -5.95474005e-01 -3.20218831e-01 2.60314733e-01
-5.77144265e-01 7.37078860e-02 -6.58306360e-01 -1.33125171e-01
-7.38261044e-01 -1.13466144e+00 1.09045565e+00 -4.10482407e-01
-6.19969249e-01 -1.47835088e+00 4.97712374e-01 5.69862425e-01
4.23586428e-01 -3.12759191e-01 1.12522817e+00 -1.06623006e+00
2.43725851e-02 -1.42910570e-01 -1.33438304e-01 6.03271902e-01
3.31447721e-01 -7.00444162e-01 -8.20978701e-01 -5.14779389e-01
-6.60668388e-02 -9.66740310e-01 6.19248688e-01 -3.77203263e-02
5.25985360e-01 -3.24346393e-01 -3.18046182e-01 5.91965377e-01
1.25063658e+00 -2.91013360e-01 5.26332915e-01 5.77436686e-01
7.37029731e-01 5.20236075e-01 4.85580623e-01 -5.48929036e-01
5.40398240e-01 7.81912029e-01 -7.38003179e-02 -2.68771678e-01
-2.41543770e-01 -8.10931623e-01 7.37500191e-01 1.60715771e+00
6.30174875e-01 -3.11521858e-01 -9.32049334e-01 8.57789874e-01
-1.62043822e+00 -6.93732738e-01 1.27971113e-01 2.02282929e+00
1.43025398e+00 1.47610396e-01 -4.74250853e-01 -3.32112491e-01
5.48330545e-01 4.02089149e-01 -4.20530081e-01 -9.13592756e-01
-3.13147545e-01 3.98526251e-01 5.51004946e-01 8.70308280e-01
-9.25416827e-01 1.58284676e+00 6.43413877e+00 1.04395509e+00
-1.01672173e+00 6.30001009e-01 4.06484663e-01 1.78318962e-01
-4.23958987e-01 4.51357365e-02 -1.06381893e+00 4.80882734e-01
1.17545950e+00 -4.86694276e-01 4.89373773e-01 8.36883843e-01
-7.78613761e-02 3.85050446e-01 -1.51778269e+00 7.88274765e-01
6.08689249e-01 -1.14214122e+00 3.37947994e-01 -3.84026200e-01
9.80880380e-01 6.57560527e-01 -3.93029302e-02 8.33152890e-01
8.98162067e-01 -1.26214552e+00 3.71459901e-01 8.96873549e-02
1.14972150e+00 -5.87285459e-01 7.23514974e-01 4.09934133e-01
-9.18492436e-01 5.19985855e-01 -6.06730819e-01 9.59407166e-02
5.13702452e-01 2.02450767e-01 -9.09038484e-01 6.71558321e-01
4.12251234e-01 8.65653217e-01 -5.26530445e-01 -1.56796817e-02
-4.79872674e-01 5.54867864e-01 -9.56863165e-03 1.05517216e-01
4.60931987e-01 -1.91020906e-01 5.16334295e-01 1.48148179e+00
1.08823292e-01 -6.60256982e-01 2.63137341e-01 7.76919544e-01
-5.54254055e-01 5.56770265e-01 -1.13793659e+00 -1.73463985e-01
3.81051600e-01 9.13843393e-01 9.99064147e-02 -4.85195041e-01
-8.10414493e-01 1.36750615e+00 8.95433903e-01 8.81728604e-02
-6.74179971e-01 -3.71052235e-01 5.90857267e-01 -1.10029317e-01
-8.71429071e-02 -1.57826513e-01 -1.50333688e-01 -1.50934649e+00
-1.64153397e-01 -1.37617373e+00 1.26365572e-01 -9.09547150e-01
-1.73823309e+00 9.18965518e-01 -2.85209924e-01 -1.17839885e+00
-4.61979717e-01 -6.75242901e-01 -6.02048695e-01 1.36497319e+00
-1.64969611e+00 -1.74198318e+00 2.42442265e-01 4.63596880e-01
1.19294727e+00 -8.68965685e-01 1.31171727e+00 2.34519124e-01
-3.18351567e-01 9.72362697e-01 3.92934680e-01 3.91495705e-01
1.32281387e+00 -1.26565146e+00 1.18116009e+00 9.89685357e-01
5.86711287e-01 4.22683269e-01 7.18790412e-01 -4.29324985e-01
-1.00639832e+00 -1.29377294e+00 1.59847796e+00 -9.97239351e-01
1.25375044e+00 -6.31686747e-01 -1.03934491e+00 1.18363237e+00
7.99525559e-01 -1.30962402e-01 6.98409140e-01 2.00113744e-01
-8.12590837e-01 5.26875444e-02 -8.48964810e-01 8.23965013e-01
7.68853724e-01 -1.15535164e+00 -1.38017619e+00 5.65865040e-01
9.31646347e-01 -1.89748168e-01 -8.21303129e-01 1.60978138e-01
3.18159342e-01 -3.81816357e-01 8.53115618e-01 -1.15308285e+00
8.70616972e-01 1.83380201e-01 -2.56779253e-01 -2.05346394e+00
8.85503218e-02 -5.65358758e-01 5.62308788e-01 1.35728812e+00
8.22984040e-01 -8.92002165e-01 2.63915598e-01 8.91451910e-02
-1.73203841e-01 -5.07570088e-01 -1.19319916e+00 -1.05904436e+00
9.30800080e-01 -3.98869127e-01 1.77693039e-01 1.49715447e+00
3.07512879e-01 1.06620932e+00 -5.27826130e-01 -2.28364706e-01
5.83796799e-01 8.54199901e-02 9.91466343e-01 -5.90961933e-01
-4.09042209e-01 -3.84093374e-01 -2.97279237e-03 -1.24268901e+00
9.82524872e-01 -1.79014730e+00 6.91043139e-02 -1.44720936e+00
1.56191349e-01 -3.21387380e-01 -6.70575500e-02 6.58579350e-01
-3.29319775e-01 3.08179229e-01 1.31927967e-01 1.72651976e-01
-1.64309919e-01 7.19564319e-01 1.08136928e+00 -4.76860195e-01
6.60635605e-02 -4.20354903e-01 -5.68974137e-01 5.77898145e-01
7.57161856e-01 -5.87496340e-01 -2.91650802e-01 -9.46587980e-01
-2.28913464e-02 -1.67255640e-01 -8.50527361e-02 -5.44184029e-01
-1.58050612e-01 9.43553895e-02 -3.72728482e-02 -2.43059069e-01
2.40168363e-01 -5.74062943e-01 -2.56300807e-01 5.24970353e-01
-7.27908731e-01 3.59521866e-01 3.64638269e-01 5.64936996e-02
-3.83627355e-01 -3.74565095e-01 8.69136035e-01 -2.59768426e-01
-5.17460644e-01 -7.04437420e-02 -2.12166518e-01 7.64296114e-01
6.44845903e-01 2.63090402e-01 -6.29034936e-01 -2.91562259e-01
-3.89809668e-01 3.67660046e-01 4.49841022e-01 1.13874149e+00
8.05224776e-02 -1.47128594e+00 -1.18054140e+00 2.35994324e-01
4.47244585e-01 -4.33518469e-01 -3.49431783e-01 4.65869874e-01
-2.61874259e-01 8.42746675e-01 -2.56307662e-01 -4.33769047e-01
-1.05673563e+00 5.58766425e-01 5.33065021e-01 -9.01796401e-01
-5.45674264e-01 8.57971907e-01 1.09190919e-01 -1.27643883e+00
-2.84668177e-01 -5.16476780e-02 2.34775931e-01 -1.11320131e-01
2.81703293e-01 -9.90484655e-02 3.38166095e-02 -7.39201784e-01
-3.66654068e-01 5.58597505e-01 -4.10025269e-01 -3.16051960e-01
1.19710624e+00 -2.22257882e-01 -9.19416100e-02 7.79062927e-01
1.43870151e+00 2.28526071e-01 -7.88656294e-01 -5.60307860e-01
1.23818442e-01 -1.86807901e-01 -3.33153009e-01 -9.27099943e-01
-5.58970749e-01 1.08185887e+00 1.35665581e-01 -2.95839846e-01
4.86005664e-01 7.83726498e-02 1.00594020e+00 3.74141335e-01
5.58790267e-01 -1.04441035e+00 2.45359130e-02 9.20175195e-01
1.15910327e+00 -1.58388233e+00 -2.39992544e-01 -4.98653986e-02
-8.67706597e-01 8.50966752e-01 6.05883062e-01 -1.12971425e-01
3.53084534e-01 2.06057921e-01 6.22596264e-01 2.78987855e-01
-8.05558026e-01 1.03153981e-01 2.90627569e-01 6.12459898e-01
6.81505620e-01 3.81254479e-02 -2.78876036e-01 4.06218082e-01
-7.30059326e-01 -3.00217271e-01 2.30160967e-01 4.29558188e-01
-6.40235916e-02 -1.56199050e+00 -1.40410632e-01 1.76472455e-01
-4.69637603e-01 -7.09905088e-01 -3.51128995e-01 9.55811083e-01
-1.96481541e-01 7.25786805e-01 9.54216905e-03 -6.31675646e-02
3.26634943e-01 6.54026449e-01 5.65901399e-01 -7.72890687e-01
-6.56213760e-01 -5.68703890e-01 3.98959249e-01 -2.39494860e-01
-9.58062857e-02 -4.55068082e-01 -7.28467703e-01 -4.77858931e-02
-1.41543597e-01 1.79555252e-01 5.79261184e-01 1.19644904e+00
1.83988079e-01 3.80075425e-01 4.80259627e-01 -6.28391087e-01
-8.17661166e-01 -1.33976805e+00 -7.64146596e-02 7.53705442e-01
9.57713127e-02 -2.76983321e-01 -3.81146997e-01 9.52869728e-02] | [11.165385246276855, 9.96558952331543] |
4dc818ac-424c-4faf-b769-bba1f1a70936 | deblursr-event-based-motion-deblurring-under | 2303.08977 | null | https://arxiv.org/abs/2303.08977v1 | https://arxiv.org/pdf/2303.08977v1.pdf | DeblurSR: Event-Based Motion Deblurring Under the Spiking Representation | We present DeblurSR, a novel motion deblurring approach that converts a blurry image into a sharp video. DeblurSR utilizes event data to compensate for motion ambiguities and exploits the spiking representation to parameterize the sharp output video as a mapping from time to intensity. Our key contribution, the Spiking Representation (SR), is inspired by the neuromorphic principles determining how biological neurons communicate with each other in living organisms. We discuss why the spikes can represent sharp edges and how the spiking parameters are interpreted from the neuromorphic perspective. DeblurSR has higher output quality and requires fewer computing resources than state-of-the-art event-based motion deblurring methods. We additionally show that our approach easily extends to video super-resolution when combined with recent advances in implicit neural representation. The implementation and animated visualization of DeblurSR are available at https://github.com/chensong1995/DeblurSR. | ['QiXing Huang', 'Chandrajit Bajaj', 'Chen Song'] | 2023-03-15 | null | null | null | null | ['deblurring', 'video-super-resolution'] | ['computer-vision', 'computer-vision'] | [ 5.69309831e-01 -5.16904175e-01 3.77678454e-01 4.60251160e-02
-1.92220181e-01 -7.59821951e-01 6.48969889e-01 -5.64745009e-01
-5.50582707e-01 1.07856905e+00 4.58481580e-01 3.58317673e-01
2.58690536e-01 -6.05061173e-01 -8.94090354e-01 -8.32415283e-01
1.44655496e-01 -2.36189365e-01 6.55651867e-01 -1.07922377e-02
6.82154715e-01 4.74757582e-01 -1.46063268e+00 5.38919091e-01
3.69220018e-01 7.32137024e-01 5.70110381e-01 1.08244133e+00
1.28568813e-01 7.40372539e-01 -5.34289241e-01 2.88202226e-01
-2.03613229e-02 -7.81921864e-01 -6.39471531e-01 -4.05961633e-01
4.14539486e-01 -2.32031584e-01 -8.95111322e-01 1.10484636e+00
3.23109269e-01 -3.97869274e-02 4.39398199e-01 -9.44760144e-01
-1.22865701e+00 2.65767187e-01 -6.75021470e-01 9.64113295e-01
3.38770926e-01 1.89906880e-01 2.25659475e-01 -9.11256552e-01
1.18589497e+00 1.17776644e+00 7.22693264e-01 9.93838549e-01
-1.63794160e+00 -5.90364754e-01 -3.74801345e-02 3.35295945e-01
-1.26429522e+00 -6.57207847e-01 4.05518740e-01 -5.33830285e-01
1.10827255e+00 2.13122129e-01 8.63105237e-01 1.20001745e+00
7.59861410e-01 3.61597985e-01 1.32391775e+00 6.00010715e-02
4.26274180e-01 -7.20806003e-01 2.42625669e-01 3.76627713e-01
3.93797696e-01 2.79028177e-01 -1.05310190e+00 4.20446582e-02
1.71285152e+00 1.04948156e-01 -7.20305026e-01 7.06563368e-02
-1.45833170e+00 1.54713184e-01 3.33196998e-01 4.38106000e-01
-2.00306848e-01 1.00403833e+00 -8.74327794e-02 2.34487951e-01
2.76225924e-01 4.20858562e-01 8.16577375e-02 -2.68324405e-01
-1.15385759e+00 2.80181855e-01 3.39180529e-01 6.39560640e-01
5.80106139e-01 3.09474677e-01 -1.36892021e-01 5.19086123e-01
-7.63652697e-02 2.72549123e-01 6.60029173e-01 -1.91000128e+00
-4.56936985e-01 -3.25079355e-03 3.04356754e-01 -7.04294622e-01
-1.11249886e-01 7.83228949e-02 -7.66756952e-01 5.65058112e-01
4.98282939e-01 3.78250070e-02 -1.01619518e+00 1.77011180e+00
-3.07548821e-01 7.66726792e-01 4.85859849e-02 1.23539591e+00
7.62324095e-01 8.09384048e-01 -1.07711621e-01 -1.43402457e-01
1.25440693e+00 -6.08244121e-01 -8.67635965e-01 -3.04835290e-01
-7.18250945e-02 -3.77900600e-01 5.20180941e-01 4.17034447e-01
-1.49062443e+00 -1.69040456e-01 -1.07922184e+00 -4.42646354e-01
-2.30296716e-01 -2.64310092e-01 4.30735379e-01 3.39523394e-04
-1.61059725e+00 9.13479805e-01 -1.24215984e+00 -4.73970383e-01
4.79092389e-01 2.19235376e-01 -3.95836920e-01 8.04388970e-02
-1.02825356e+00 8.69885802e-01 -9.44797248e-02 -1.55641094e-01
-9.13749337e-01 -7.93520153e-01 -5.32702684e-01 -1.41058475e-01
-3.44553113e-01 -1.04456854e+00 1.22456861e+00 -1.03984618e+00
-1.51544523e+00 1.16966963e+00 -5.97324312e-01 -6.44516468e-01
3.52692217e-01 -5.47918007e-02 -5.10844849e-02 5.10610461e-01
-3.23924601e-01 1.10998499e+00 8.11205029e-01 -1.14161837e+00
-2.19992965e-01 -3.28037292e-01 -4.95077342e-01 1.05178222e-01
1.55882522e-01 7.34726759e-03 -3.00933868e-01 -1.03327882e+00
6.68384582e-02 -6.90417588e-01 -9.77891237e-02 5.63556850e-01
2.22671568e-01 4.82707322e-01 1.00994492e+00 -5.56050420e-01
9.92064536e-01 -1.98475945e+00 6.94206238e-01 -5.41027546e-01
4.66416538e-01 -6.98060021e-02 -2.53305256e-01 2.43493944e-01
-2.79801160e-01 -1.50780324e-02 -6.84344590e-01 -3.47756520e-02
-6.54124796e-01 1.31402746e-01 -5.27448833e-01 7.82074034e-01
1.75309330e-01 1.12149847e+00 -1.07552302e+00 -2.42530592e-02
1.32397637e-01 8.88965547e-01 -4.73125070e-01 -7.77635649e-02
5.29026762e-02 8.39545429e-01 -6.11040294e-02 4.74145651e-01
8.05742800e-01 -2.77267903e-01 -2.02257171e-01 -3.03275406e-01
-7.03959525e-01 -7.52669200e-02 -7.08992958e-01 2.04063272e+00
-8.03947821e-02 1.34288132e+00 2.78596848e-01 -5.05162179e-01
7.64606893e-01 8.38271156e-02 3.37244093e-01 -4.16002065e-01
5.88062294e-02 2.39739254e-01 -3.26076239e-01 -2.18723983e-01
7.36118436e-01 -1.54807614e-02 8.69479701e-02 4.35570210e-01
1.86144188e-02 -3.75890106e-01 6.01765178e-02 2.84703851e-01
1.53553426e+00 5.92560768e-01 1.70261532e-01 -5.26928127e-01
-7.86574930e-02 -1.38865216e-02 4.64158982e-01 6.44553304e-01
-2.86169738e-01 1.13754308e+00 1.68839559e-01 -4.03310180e-01
-1.19328105e+00 -1.37108743e+00 -2.44934976e-01 5.32379150e-01
5.53664088e-01 1.62159763e-02 -9.90820527e-01 4.12168205e-01
-4.83494028e-02 5.76724052e-01 -9.59145904e-01 -1.13724716e-01
-6.26863122e-01 -7.43032694e-01 6.08769059e-01 5.44765294e-01
4.13085788e-01 -1.19552374e+00 -1.31026506e+00 3.64899963e-01
-2.22481206e-01 -8.43587279e-01 -6.35271430e-01 2.59686291e-01
-9.87078369e-01 -5.60742676e-01 -1.36817670e+00 -6.59519434e-01
6.34814799e-01 5.37727594e-01 8.78694594e-01 -6.59818649e-02
-8.29462349e-01 2.91940033e-01 -1.47196949e-01 2.28760213e-01
-2.01427728e-01 -5.53886056e-01 -2.39756089e-02 -3.37001920e-01
1.94029376e-01 -1.12432063e+00 -9.09135818e-01 3.19259048e-01
-1.24688506e+00 4.31773245e-01 1.18686564e-01 4.69423354e-01
7.30419934e-01 -6.73377037e-01 3.95812899e-01 -5.14732003e-01
5.40127397e-01 -4.03229296e-01 -5.54623127e-01 -1.55257404e-01
-1.78572506e-01 2.99900860e-01 2.45225102e-01 -7.80016541e-01
-9.94588554e-01 -1.37918396e-02 2.93948114e-01 -5.49879491e-01
-8.24884474e-02 -1.31921887e-01 5.91106355e-01 -3.53836954e-01
7.51010239e-01 4.58739370e-01 -1.82420284e-01 -2.72187650e-01
2.97562361e-01 3.94723952e-01 1.12782145e+00 -2.60265887e-01
3.98812562e-01 1.17894506e+00 -1.27161399e-01 -9.50203896e-01
-1.22339472e-01 -7.97671676e-02 -5.27162075e-01 -4.39511448e-01
8.94461632e-01 -7.68226683e-01 -5.69705725e-01 1.13578069e+00
-1.49604273e+00 -6.71303988e-01 -4.01330411e-01 2.40530416e-01
-9.67486739e-01 2.32346416e-01 -1.13991916e+00 -5.34921944e-01
-1.24302886e-01 -8.36655796e-01 9.27016616e-01 5.45620203e-01
-5.39606690e-01 -6.27732933e-01 4.60206062e-01 -2.35041186e-01
6.81041121e-01 3.14603716e-01 3.67028713e-01 3.20264190e-01
-9.24756169e-01 4.82553393e-01 -4.35073435e-01 -3.95997524e-01
1.56051680e-01 1.88455105e-01 -1.00334799e+00 -9.68748257e-02
9.50885937e-02 -7.99875557e-02 1.37675214e+00 9.90208685e-01
1.11090016e+00 -1.24913573e-01 -4.68120992e-01 9.40813243e-01
1.42927933e+00 2.96848416e-01 1.21768701e+00 3.88901353e-01
5.25921702e-01 3.44656855e-01 1.49879139e-02 4.74994838e-01
3.41987126e-02 5.92117667e-01 3.46253484e-01 1.99853837e-01
-6.89903498e-01 -7.86930174e-02 4.64078248e-01 7.98475504e-01
-3.27856839e-01 -1.57979861e-01 -5.76961994e-01 7.06932366e-01
-1.94246495e+00 -1.27405298e+00 -1.86786458e-01 2.07989073e+00
1.00081360e+00 -3.76189649e-01 -1.72154605e-01 -2.66767979e-01
1.05351758e+00 1.37661010e-01 -9.95084882e-01 -2.97665179e-01
-5.68346620e-01 1.27870113e-01 7.87848294e-01 7.01243281e-01
-5.51245749e-01 1.18334544e+00 7.23388481e+00 3.29686791e-01
-1.19204140e+00 2.13340715e-01 3.82944882e-01 -2.88140208e-01
-5.59131861e-01 1.50100607e-03 -5.25494635e-01 4.88773167e-01
9.64004695e-01 -3.84079278e-01 9.75305319e-01 -3.31928884e-03
5.60071230e-01 -4.00909275e-01 -9.66696858e-01 1.40074670e+00
-1.07140653e-01 -1.88404286e+00 -3.36611532e-02 -1.41189069e-01
7.42209315e-01 2.36512005e-01 2.79997200e-01 -5.92439413e-01
5.11688530e-01 -1.12789369e+00 1.05427194e+00 1.05548131e+00
9.56905544e-01 -3.01779121e-01 -6.61809975e-03 -1.29419163e-01
-1.23191202e+00 1.99975699e-01 -5.54588616e-01 -1.51959389e-01
4.61390615e-01 4.57313269e-01 -2.58692428e-02 -1.50239930e-01
1.11321867e+00 1.23695302e+00 -3.12431663e-01 1.11556792e+00
1.36265740e-01 1.70722976e-01 1.30867260e-02 2.36548960e-01
-2.10288361e-01 -6.96277469e-02 9.69927847e-01 1.28286958e+00
7.48608410e-01 3.96681696e-01 -9.13612306e-01 1.51096654e+00
-4.47617322e-02 -6.81458116e-01 -5.31409025e-01 -2.11793482e-01
7.27294326e-01 1.06460106e+00 -8.78508568e-01 -3.42718989e-01
3.76345254e-02 1.63495278e+00 1.79603428e-01 6.52751446e-01
-6.84716940e-01 -4.09633934e-01 8.59514296e-01 1.98902920e-01
4.34515238e-01 -4.22421455e-01 -3.79343987e-01 -1.49410498e+00
-5.06868958e-01 -3.61623943e-01 -9.96486992e-02 -1.70187569e+00
-1.04245710e+00 6.50440037e-01 -1.47706524e-01 -1.04123425e+00
-9.04128104e-02 -5.26032567e-01 -4.53157604e-01 8.63986909e-01
-1.39910781e+00 -5.16182721e-01 -5.91858089e-01 3.95398885e-01
6.29418552e-01 6.56973660e-01 6.01051688e-01 -8.98037627e-02
-2.90834874e-01 -1.85930625e-01 1.51113838e-01 -3.25666815e-01
8.24312389e-01 -9.74744201e-01 9.36517537e-01 1.04026043e+00
-2.03721717e-01 4.65616882e-01 1.04007232e+00 -7.00812221e-01
-1.29955709e+00 -1.01809525e+00 2.56319284e-01 -3.41451019e-01
7.73589790e-01 -9.43531170e-02 -1.19986594e+00 6.76614761e-01
5.05281329e-01 3.26150507e-01 1.79233253e-01 -1.06047630e+00
-3.20123434e-01 2.83948779e-01 -1.23074567e+00 9.16768491e-01
1.41013205e+00 -5.27818203e-01 -6.88734591e-01 -3.85259807e-01
5.25755405e-01 -3.37837517e-01 -7.04777241e-01 -7.09223822e-02
7.21339405e-01 -1.02197278e+00 8.10737014e-01 -7.32396394e-02
4.97646987e-01 -6.75677776e-01 -3.41334827e-02 -1.35405743e+00
-5.00880480e-01 -9.33696032e-01 -1.68484002e-01 7.81042397e-01
-4.37762477e-02 -7.38047063e-01 5.57518780e-01 4.57480341e-01
-7.62323365e-02 -1.31263986e-01 -1.04271305e+00 -6.75436556e-01
6.47119135e-02 1.34550139e-01 -5.41863739e-02 8.48954380e-01
7.59516880e-02 -4.10198689e-01 -2.75874645e-01 -4.86819856e-02
8.61352503e-01 1.62532672e-01 -5.43072447e-02 -1.00050938e+00
-3.17576796e-01 -3.96891445e-01 -5.83267212e-01 -1.22622359e+00
-2.24046335e-01 -7.26850629e-01 2.69905984e-01 -1.42560363e+00
4.57354963e-01 1.77801490e-01 -2.60717124e-01 4.90461975e-01
2.66414165e-01 7.35653639e-01 1.54428184e-01 6.48090005e-01
-3.29498142e-01 2.64890134e-01 1.37493181e+00 2.11498633e-01
-2.11584434e-01 -7.32691765e-01 -4.51722860e-01 7.41407335e-01
6.82746351e-01 -5.66698968e-01 1.16562307e-01 -7.67377973e-01
8.66935626e-02 1.83419377e-01 6.80642605e-01 -1.02864778e+00
4.60923254e-01 -1.07730485e-01 4.31257069e-01 -2.90034890e-01
5.89940727e-01 -2.87248701e-01 7.73145616e-01 7.13402689e-01
-4.97061938e-01 4.08441663e-01 3.69976878e-01 6.12434268e-01
3.11105959e-02 -1.52582660e-01 1.21121526e+00 -5.30332148e-01
-8.91904235e-01 7.41915172e-03 -1.20156825e+00 1.66735813e-01
7.79183865e-01 -5.64672351e-01 -9.71448720e-01 -3.94347847e-01
-8.05995047e-01 -3.66873950e-01 1.29900217e+00 2.09401682e-01
1.00508726e+00 -1.18602192e+00 -4.24595207e-01 -1.06269896e-01
-3.41236562e-01 -3.38569880e-01 3.15498173e-01 7.86896646e-01
-8.69434953e-01 1.98241994e-01 -9.55868542e-01 -5.19848943e-01
-1.14886808e+00 3.35999936e-01 4.65560377e-01 4.25356120e-01
-7.19659984e-01 9.62054431e-01 4.83962268e-01 5.44002950e-01
-2.38252282e-01 -5.93772769e-01 3.37537937e-02 -2.21021101e-01
9.09768581e-01 4.93623078e-01 -3.45548660e-01 -5.73996127e-01
-5.16518533e-01 9.66020644e-01 1.69707909e-01 -6.66375041e-01
1.48966718e+00 -5.61381757e-01 -2.88876563e-01 7.24158406e-01
8.68460774e-01 -1.02887504e-01 -2.08208466e+00 1.04704887e-01
-1.97321311e-01 -3.38876933e-01 2.75645971e-01 -6.16143465e-01
-1.03466415e+00 9.89351571e-01 6.16982102e-01 -1.18976809e-01
1.14634717e+00 3.33565027e-02 8.87278736e-01 -9.04479176e-02
4.60110277e-01 -5.99222183e-01 1.59385458e-01 3.59042108e-01
1.03615999e+00 -3.59245539e-01 -2.19145447e-01 -2.85421968e-01
-4.58442241e-01 1.23081088e+00 3.98416996e-01 -7.52713323e-01
2.73255914e-01 1.01725411e+00 -1.35952055e-01 9.78021920e-02
-1.14630330e+00 8.76970664e-02 -2.59233147e-01 7.14167535e-01
3.25249106e-01 -3.55248928e-01 -1.68715179e-01 3.39507580e-01
1.86120331e-01 6.64269149e-01 1.12729037e+00 9.53566551e-01
-6.57989681e-01 -6.52647555e-01 -4.32147712e-01 -4.91314419e-02
-5.22169054e-01 -3.12945187e-01 -4.39832866e-01 1.91940799e-01
-2.15594515e-01 4.25947636e-01 4.79836494e-01 -1.50695696e-01
-1.02322474e-01 -2.14425996e-01 1.09541190e+00 -3.73159051e-01
-3.01686883e-01 1.17290035e-01 -3.57834011e-01 -8.78151119e-01
-7.39969134e-01 -7.02064872e-01 -1.62052751e+00 -2.82190502e-01
2.46450275e-01 -2.63084948e-01 5.03805876e-01 3.99269015e-01
7.59517550e-01 6.14240468e-01 -1.17284343e-01 -1.46480417e+00
2.90005028e-01 -4.65541571e-01 -5.35578310e-01 7.28422105e-02
7.10397184e-01 -5.11905789e-01 -6.02546334e-01 8.08953285e-01] | [8.737509727478027, -1.2484973669052124] |
8e415248-09cf-4a5c-9eb0-7b031b508c21 | bilinear-value-networks | 2204.13695 | null | https://arxiv.org/abs/2204.13695v3 | https://arxiv.org/pdf/2204.13695v3.pdf | Bilinear value networks | The dominant framework for off-policy multi-goal reinforcement learning involves estimating goal conditioned Q-value function. When learning to achieve multiple goals, data efficiency is intimately connected with the generalization of the Q-function to new goals. The de-facto paradigm is to approximate Q(s, a, g) using monolithic neural networks. To improve the generalization of the Q-function, we propose a bilinear decomposition that represents the Q-value via a low-rank approximation in the form of a dot product between two vector fields. The first vector field, f(s, a), captures the environment's local dynamics at the state s; whereas the second component, {\phi}(s, g), captures the global relationship between the current state and the goal. We show that our bilinear decomposition scheme substantially improves data efficiency, and has superior transfer to out-of-distribution goals compared to prior methods. Empirical evidence is provided on the simulated Fetch robot task-suite and dexterous manipulation with a Shadow hand. | ['Pulkit Agrawal', 'Ge Yang', 'Zhang-Wei Hong'] | 2022-04-28 | null | null | null | null | ['multi-goal-reinforcement-learning'] | ['methodology'] | [-5.90132624e-02 -9.21132639e-02 -2.32992813e-01 -7.85396770e-02
-8.43826234e-01 -5.13225496e-01 3.46185923e-01 1.77959517e-01
-5.27446508e-01 8.49319160e-01 4.17733103e-01 -1.24170348e-01
-5.26121259e-01 -5.46123505e-01 -9.16124165e-01 -7.85343945e-01
-5.32030404e-01 4.79546100e-01 -1.35059893e-01 -5.70498645e-01
3.88694435e-01 2.29969531e-01 -1.19158471e+00 -1.01612873e-01
5.27995646e-01 1.08756602e+00 2.55916566e-01 7.94036269e-01
3.13026041e-01 9.62060392e-01 -4.75041598e-01 1.65539399e-01
7.41579592e-01 -2.65425235e-01 -6.41623080e-01 -1.96006954e-01
2.73124397e-01 -5.33747673e-01 -5.26667774e-01 1.32140756e+00
3.04488510e-01 7.03649700e-01 6.92188144e-01 -1.40190959e+00
-4.68197227e-01 2.76158631e-01 -3.68069112e-01 -9.76263285e-02
2.20656395e-01 3.74176174e-01 1.17027414e+00 -5.00076234e-01
4.85331267e-01 1.44893026e+00 6.67443991e-01 2.58396566e-01
-1.53412700e+00 -2.96463579e-01 3.55771869e-01 6.62598982e-02
-1.09282506e+00 -7.05245212e-02 3.99913371e-01 -6.31593287e-01
9.01377916e-01 -2.07973167e-01 7.32696116e-01 9.46751416e-01
8.74189973e-01 6.66380465e-01 1.43742204e+00 -8.24621972e-03
5.69590151e-01 -3.34986955e-01 1.52812980e-03 8.85581136e-01
-9.02782530e-02 7.79962063e-01 -7.04638660e-01 -3.76731634e-01
1.04510128e+00 1.44659299e-02 -2.65721291e-01 -9.59775329e-01
-1.02159798e+00 8.54950786e-01 3.65242302e-01 4.39505503e-02
-7.89126933e-01 7.22406328e-01 3.21655273e-01 6.10079050e-01
2.02428460e-01 4.47267711e-01 -4.05080467e-01 -5.56712508e-01
-4.06271100e-01 8.68032932e-01 9.56648767e-01 9.34559524e-01
9.80091095e-01 9.59899575e-02 -2.72417307e-01 4.62848127e-01
2.60370463e-01 4.71196771e-01 2.93192089e-01 -1.44754004e+00
5.45985699e-01 1.32534474e-01 6.05006754e-01 -6.99279845e-01
-5.94773889e-01 -5.30705273e-01 -5.07141054e-01 7.48644531e-01
9.24081028e-01 -4.46350962e-01 -8.60262156e-01 2.14531374e+00
1.65369540e-01 -1.87557638e-01 1.19415589e-01 9.20865476e-01
-3.27734798e-02 4.75820512e-01 2.82694120e-02 -2.07308277e-01
7.60976672e-01 -5.80668986e-01 -7.37351298e-01 -5.57992756e-01
3.56170982e-01 -3.24270308e-01 1.02058458e+00 4.32751715e-01
-1.14048302e+00 -4.30462569e-01 -9.39222455e-01 -6.21698275e-02
-1.93856224e-01 -2.66699940e-01 7.06392229e-01 2.42945030e-02
-1.11979723e+00 1.00581729e+00 -9.83286321e-01 -6.73261285e-02
1.57095566e-01 3.97234201e-01 -3.84873807e-01 4.29717377e-02
-1.15781271e+00 1.43505633e+00 5.04540682e-01 -2.18144402e-01
-1.55042017e+00 -4.90179181e-01 -7.53941953e-01 1.22708566e-01
5.94163477e-01 -5.22885740e-01 1.46984661e+00 -4.98755991e-01
-1.80098712e+00 1.17561288e-01 2.41402224e-01 -3.74844074e-01
3.34236294e-01 -2.75533974e-01 2.23979875e-01 -1.22223437e-01
2.95576990e-01 3.92495275e-01 1.08790672e+00 -1.21435177e+00
-7.06571162e-01 -6.68065906e-01 2.29844257e-01 6.18550599e-01
-9.94579569e-02 -5.44952154e-01 1.17341921e-01 -3.70602131e-01
1.62583679e-01 -9.85257685e-01 -7.02326715e-01 2.30845772e-02
3.00373938e-02 -2.21848398e-01 2.90788889e-01 -8.39068532e-01
8.94039512e-01 -1.99820626e+00 6.67691410e-01 4.10946786e-01
2.05572024e-01 -4.51426595e-01 -2.37092108e-01 6.77462876e-01
-9.77662019e-03 -6.12003088e-01 -7.54618496e-02 -2.54581928e-01
4.22451824e-01 4.37583208e-01 -2.86376774e-01 7.80859590e-01
-4.96996120e-02 7.91364372e-01 -1.24706936e+00 -5.76412212e-03
-3.56226899e-02 8.76525789e-02 -7.56617010e-01 1.66797742e-01
-3.77129138e-01 3.03069860e-01 -6.35463297e-01 2.34809294e-01
1.72002032e-01 -1.13490306e-01 4.03155357e-01 -8.63192379e-02
-2.33204395e-01 1.93910569e-01 -1.33453083e+00 2.16986346e+00
-3.53590548e-01 4.82866727e-02 4.91730243e-01 -1.15587962e+00
7.67039120e-01 9.65109691e-02 9.09389138e-01 -7.05133557e-01
2.39598215e-01 -2.12599542e-02 5.80818951e-02 -3.12512606e-01
8.13476324e-01 -3.33903760e-01 -3.18455756e-01 3.38710994e-01
4.73448783e-01 -4.42770749e-01 3.94845933e-01 7.88012333e-03
1.30760622e+00 8.19543600e-01 4.57925677e-01 -5.54858208e-01
-2.04052031e-01 1.78687900e-01 5.27141213e-01 1.08956301e+00
-4.94507462e-01 6.90795034e-02 7.31810272e-01 -2.19986454e-01
-9.75471377e-01 -1.24628317e+00 1.66354761e-01 1.41798413e+00
8.81447867e-02 -2.56594479e-01 -4.69376683e-01 -3.76337022e-01
5.20812988e-01 6.53309107e-01 -7.62461185e-01 -3.84600013e-01
-5.20579338e-01 -4.00369793e-01 8.02017078e-02 5.06471515e-01
2.37254977e-01 -6.44264162e-01 -8.55286241e-01 6.60837412e-01
-2.76177645e-01 -5.74381709e-01 -7.12703407e-01 8.09422314e-01
-9.74584639e-01 -8.50315452e-01 -5.43605685e-01 -3.33619386e-01
2.93399394e-01 -1.51387215e-01 9.64978039e-01 -6.57669067e-01
8.99848416e-02 7.63653398e-01 7.43205845e-02 -2.13200986e-01
-1.49683982e-01 -5.25371969e-01 5.09134829e-01 -2.22087651e-01
-3.60426195e-02 -7.61362374e-01 -2.99915373e-01 2.63743903e-02
-6.42812192e-01 -1.89522833e-01 5.25614917e-01 1.26173210e+00
6.42810822e-01 -7.61242062e-02 2.66541928e-01 -1.09218828e-01
1.00362003e+00 -3.78767937e-01 -9.29730058e-01 -1.48929283e-02
-5.19617915e-01 4.85574484e-01 5.84084213e-01 -7.67366469e-01
-8.50579381e-01 1.79436624e-01 1.57021359e-01 -5.76609254e-01
2.95414627e-01 7.71996975e-01 2.33703494e-01 -1.22788385e-01
9.12088633e-01 1.73770383e-01 4.11529690e-01 -4.78437185e-01
7.85958767e-01 1.65763766e-01 6.81658626e-01 -1.08653736e+00
5.43431640e-01 2.04803824e-01 3.85500103e-01 -4.94934916e-01
-6.77098095e-01 -3.89738709e-01 -2.59469301e-01 -3.56605172e-01
5.64356625e-01 -8.82215440e-01 -1.36442769e+00 3.80862981e-01
-8.19634438e-01 -8.22725892e-01 -5.98240376e-01 7.69073248e-01
-1.43109691e+00 1.15794629e-01 -5.64478755e-01 -9.66211855e-01
9.79646444e-02 -1.05068588e+00 8.55598509e-01 -2.41267607e-02
6.76999465e-02 -8.37884307e-01 2.82364428e-01 -7.35385641e-02
4.06686366e-01 1.62021667e-01 9.11526084e-01 -2.82786757e-01
-5.01346767e-01 7.66174942e-02 8.39240775e-02 4.29078668e-01
-3.71045060e-02 -8.41985762e-01 -5.46576798e-01 -7.93931305e-01
3.87631387e-01 -7.37848580e-01 5.57205796e-01 6.32848620e-01
7.06260085e-01 -5.06732285e-01 -3.39748263e-02 3.84479403e-01
1.46976721e+00 3.11044514e-01 3.00085008e-01 2.57444173e-01
4.72649992e-01 3.40734303e-01 9.75568533e-01 9.09489989e-01
4.71225590e-01 5.44347227e-01 7.08072424e-01 4.40820038e-01
2.62733221e-01 -3.92004848e-01 7.69297898e-01 3.82067055e-01
-2.84513891e-01 1.27697676e-01 -7.65926778e-01 3.86308312e-01
-2.14248061e+00 -9.04525638e-01 4.54336971e-01 2.42272425e+00
7.66969919e-01 1.78598434e-01 1.87528327e-01 -5.02225459e-01
1.99685872e-01 5.59984557e-02 -1.07814348e+00 -4.56883237e-02
3.75572354e-01 2.94069856e-01 7.70566106e-01 8.30910444e-01
-9.42594409e-01 5.93500257e-01 7.23104477e+00 7.57600248e-01
-7.97790289e-01 1.82156503e-01 3.44569944e-02 -2.65548766e-01
4.28073034e-02 -4.26032841e-02 -5.98728180e-01 2.05711529e-01
7.18309522e-01 -3.82733196e-01 1.03641713e+00 9.32928145e-01
1.00738123e-01 -3.87496382e-01 -1.28416216e+00 7.64546394e-01
-1.13204852e-01 -8.40455830e-01 -4.93128747e-01 3.51474941e-01
5.91098011e-01 2.55418301e-01 2.53108233e-01 8.15578163e-01
1.13389194e+00 -8.88601124e-01 9.59756076e-01 6.90906584e-01
6.96571648e-01 -6.01555228e-01 2.92689413e-01 5.44948578e-01
-1.05665636e+00 -5.63956201e-01 -3.00920874e-01 -4.44832951e-01
-5.42149097e-02 1.17311083e-01 -6.80042922e-01 3.55713278e-01
5.05110681e-01 6.16575360e-01 3.65085751e-02 6.42538011e-01
3.07554398e-02 -4.32636263e-03 -5.37764549e-01 -1.98365793e-01
5.74084342e-01 -7.20649838e-01 7.50549674e-01 5.33678532e-01
1.93131819e-01 1.58601075e-01 6.89531982e-01 9.07157063e-01
3.53298903e-01 -4.52646054e-02 -7.80921638e-01 -3.17655087e-01
-7.31744692e-02 9.96850729e-01 -6.36823997e-02 -1.60946384e-01
-2.29609400e-01 6.71838343e-01 8.16217124e-01 6.94719136e-01
-4.73814875e-01 -1.41281813e-01 1.21506453e+00 -2.50597537e-01
1.57758713e-01 -7.02726543e-01 -8.70587081e-02 -1.00939679e+00
-1.44857902e-03 -1.11270118e+00 2.84366161e-01 -5.86293638e-01
-1.23903430e+00 -1.50239989e-02 1.89578205e-01 -1.10700214e+00
-5.98637283e-01 -8.42926025e-01 -2.33335365e-02 1.12249637e+00
-9.69778955e-01 -8.31845164e-01 6.17808923e-02 6.74705207e-01
2.98516508e-02 -1.99130580e-01 8.97769570e-01 -4.23835441e-02
-1.00010060e-01 1.17794506e-01 6.20056987e-01 -2.34621689e-01
4.92826074e-01 -1.51331317e+00 7.14570358e-02 5.36237955e-01
-2.47219026e-01 6.60992801e-01 1.05221975e+00 -7.78843403e-01
-1.80596852e+00 -7.23408222e-01 -1.38347046e-02 -3.24375719e-01
1.06142354e+00 -1.52160555e-01 -6.57503366e-01 9.69431102e-01
-1.09202027e-01 -1.06068879e-01 6.98560178e-02 3.27117056e-01
-1.01718463e-01 -1.19378321e-01 -1.08110046e+00 7.11580813e-01
9.23332870e-01 -6.39503717e-01 -6.09201014e-01 3.37057143e-01
2.41604820e-01 -7.09634662e-01 -1.16148734e+00 2.06102625e-01
7.21833408e-01 -7.44205415e-01 8.95511389e-01 -1.02480757e+00
3.58237147e-01 -2.23059028e-01 -5.20493805e-01 -1.95457685e+00
-6.53491318e-01 -7.57993281e-01 -4.46482450e-01 2.48953238e-01
-1.12159267e-01 -6.23107076e-01 7.36710012e-01 5.28031349e-01
-3.02446753e-01 -8.56242478e-01 -1.18438721e+00 -9.40837920e-01
4.05814052e-01 -1.88862309e-01 1.74806774e-01 4.91525829e-01
3.86738479e-01 2.52388537e-01 -5.83523154e-01 1.02102891e-01
9.13476169e-01 1.61446556e-01 7.27306604e-01 -8.79737556e-01
-6.62338257e-01 -4.68456447e-01 -2.29550079e-01 -1.45673227e+00
1.60217881e-01 -8.02932382e-01 5.99094450e-01 -1.51441455e+00
3.84751260e-02 -3.62318009e-01 -4.41358685e-01 5.67727447e-01
-1.24982595e-02 -4.03932065e-01 2.97794104e-01 -1.23171054e-01
-4.69692737e-01 9.14158881e-01 1.65128958e+00 -1.22843727e-01
-3.52189034e-01 -1.46941930e-01 -5.51136315e-01 7.19949782e-01
7.03460455e-01 -4.56006110e-01 -2.88124412e-01 -2.36098021e-01
8.49661380e-02 6.56361997e-01 2.17423961e-01 -1.00961113e+00
2.97129780e-01 -7.00439572e-01 1.88160017e-01 -3.30967963e-01
7.61694193e-01 -6.96081936e-01 1.45747066e-01 7.29870498e-01
-4.15151566e-01 1.76011786e-01 1.65244102e-01 8.18035066e-01
-1.37021467e-02 -1.28985554e-01 7.03057110e-01 -3.20363104e-01
-5.60039878e-01 4.55752105e-01 -4.38459188e-01 2.38769278e-01
6.35107219e-01 2.39739940e-01 -3.17939758e-01 -6.70033872e-01
-7.80430496e-01 4.56402928e-01 2.41930515e-01 2.48790070e-01
5.38952708e-01 -1.47921348e+00 -4.87449288e-01 6.95635751e-02
-1.51982859e-01 -2.41310447e-01 1.88103959e-01 7.18091488e-01
4.46730927e-02 1.62822038e-01 -6.84454381e-01 -3.35618854e-01
-6.80607200e-01 3.40036631e-01 6.03686392e-01 -6.86636746e-01
-4.32721585e-01 7.14963436e-01 1.81070119e-01 -4.32016999e-01
3.41150612e-01 -4.14458811e-01 -4.89047877e-02 -1.45540357e-01
2.12713748e-01 4.78547871e-01 -2.44475275e-01 -3.91610414e-01
-2.86268499e-02 1.71774492e-01 9.74285901e-02 -6.19756281e-01
1.34987020e+00 5.38251884e-02 -7.86876604e-02 7.54794121e-01
9.87201035e-01 -6.77716374e-01 -1.94673789e+00 -3.45254064e-01
-1.91367656e-01 -4.72665459e-01 1.91117480e-01 -8.89809251e-01
-5.37133396e-01 5.21492958e-01 7.55098045e-01 1.40672892e-01
6.81185842e-01 -3.50246310e-01 2.87714690e-01 9.41589475e-01
9.34526503e-01 -1.50233197e+00 5.08165479e-01 1.10326052e+00
1.15693760e+00 -1.03152585e+00 7.67872902e-03 2.99097449e-01
-6.73945725e-01 1.01593614e+00 5.21452844e-01 -2.59793043e-01
4.84695584e-01 2.83014953e-01 -1.25471391e-02 -2.15661392e-01
-5.98567009e-01 -3.42746675e-01 1.57049760e-01 6.77493453e-01
-9.52086747e-02 2.84816653e-01 5.96860088e-02 2.72856593e-01
-2.02544838e-01 5.25180772e-02 4.38043475e-01 1.16048157e+00
-5.35378754e-01 -9.40769553e-01 -3.70469540e-01 7.01970994e-01
-2.18143612e-01 8.82089362e-02 3.22026640e-01 6.80826724e-01
-1.73527628e-01 8.33847523e-01 -8.90873969e-02 -2.50066340e-01
5.19728482e-01 9.32865515e-02 9.72608745e-01 -6.08480215e-01
-3.92746717e-01 -3.13367173e-02 -3.77031080e-02 -1.12856650e+00
-7.12154880e-02 -8.09325933e-01 -1.11299324e+00 -2.07501173e-01
-3.64742689e-02 9.14094821e-02 7.95846164e-01 9.05002832e-01
7.12713599e-02 6.04708970e-01 3.94336760e-01 -1.04517150e+00
-1.53525531e+00 -1.07990563e+00 -9.88582373e-01 5.02290964e-01
6.89120770e-01 -1.23761153e+00 -2.75433958e-01 -3.12813908e-01] | [4.2092742919921875, 1.9114960432052612] |
2fe62cde-7e4e-4224-b447-2b09c2bba98e | adaptive-multi-view-subspace-clustering-for | null | null | https://ui.adsabs.harvard.edu/abs/2020PaReL.130..299Y/abstract | https://ui.adsabs.harvard.edu/abs/2020PaReL.130..299Y/abstract | Adaptive multi-view subspace clustering for high-dimensional data, | With the rapid development of multimedia technologies, we frequently confront with high-dimensional data and multi-view data, which usually contain redundant features and distinct types of features. How to efficiently cluster such kinds of data is still a great challenge. Traditional multi-view subspace clustering aims to determine the distribution of views by extra empirical parameters and search the optimal projection matrix by eigenvalue decomposition, which is impractical for real-world applications. In this paper, we propose a new adaptive multi-view subspace clustering method to integrate heterogenous data in the low-dimensional feature space. Concretely, we extend K-means clustering with feature learning to handle high-dimensional data. Besides, for multi-view data, we evaluate the weights of distinct views according to their compactness of the cluster structure in the low-dimensional subspace. We apply the proposed method to four benchmark datasets and compare it with several widely used clustering algorithms. Experimental results demonstrate the effectiveness of the proposed method. | ['Yan Fei ; Wang Xiaodong ; Zeng Zhiqiang ; Hong Chaoqun'] | 2020-02-01 | null | null | null | pattern-recognition-letters-2020-2 | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-4.58139956e-01 -9.20591354e-01 1.25320880e-02 -1.52557909e-01
-4.67721164e-01 -7.85200059e-01 2.23468721e-01 -2.33392134e-01
-2.73677021e-01 5.46183996e-02 4.78088468e-01 8.85025412e-02
-5.40312529e-01 -5.31999707e-01 -3.75173390e-02 -1.01513314e+00
1.85156748e-01 5.63712418e-01 1.01703823e-01 1.31408364e-01
2.96377063e-01 2.81169444e-01 -1.62595093e+00 3.37068975e-01
7.98502088e-01 6.42063141e-01 2.32723460e-01 2.88135111e-01
3.42933498e-02 -8.02260488e-02 -3.51140082e-01 3.29630300e-02
2.75999904e-01 -4.48480397e-01 -3.80105197e-01 6.89069390e-01
3.87305133e-02 3.43414694e-02 7.63319433e-03 1.17344415e+00
6.01094007e-01 2.17770129e-01 6.70212328e-01 -1.34176779e+00
-1.48969114e-01 1.50494903e-01 -1.03328371e+00 2.43711844e-01
1.41313136e-01 -2.23198682e-01 7.92167127e-01 -1.17100644e+00
5.82011402e-01 1.26535618e+00 2.28683487e-01 2.30216563e-01
-1.23036063e+00 -4.65295821e-01 2.32791528e-01 6.12865925e-01
-1.66252220e+00 -3.16281140e-01 1.07518148e+00 -5.96119165e-01
2.55608857e-01 2.67881036e-01 5.82022309e-01 6.81020498e-01
-1.47151113e-01 6.01946175e-01 1.13740742e+00 -4.97254767e-02
3.01248252e-01 2.28135556e-01 9.18425247e-02 3.31173688e-01
3.02355856e-01 -3.61111194e-01 -1.12785578e-01 -4.24336910e-01
3.25133026e-01 5.64503908e-01 -5.02275407e-01 -1.16308045e+00
-1.46338296e+00 8.18277299e-01 -2.62509584e-02 4.01257843e-01
-3.17525953e-01 -6.24123871e-01 4.64606166e-01 1.40023408e-02
1.51240855e-01 -1.73731178e-01 -2.62470514e-01 1.99657865e-02
-7.91186333e-01 -1.15467675e-01 5.98056674e-01 1.00473273e+00
7.44294643e-01 -2.06669956e-01 3.85984480e-01 9.73324776e-01
3.53590131e-01 3.99456918e-01 6.79022372e-01 -7.82716334e-01
5.57197034e-01 9.60715652e-01 1.09335966e-02 -1.36718428e+00
-4.32490259e-01 -1.81781992e-01 -1.33573830e+00 -6.75560236e-02
2.43462279e-01 -1.20451346e-01 -3.38608533e-01 1.25345945e+00
6.50729179e-01 1.06075309e-01 3.31317753e-01 1.19771409e+00
5.32113194e-01 7.12909222e-01 -3.76866668e-01 -6.85501397e-01
1.13660896e+00 -7.18635380e-01 -5.38806677e-01 2.98821092e-01
4.03329879e-01 -7.88213730e-01 9.70147491e-01 5.32347620e-01
-6.92529619e-01 -4.93686825e-01 -8.75170469e-01 4.98039603e-01
-2.08797138e-02 3.37292463e-01 2.12009668e-01 5.40877104e-01
-5.99486887e-01 5.59213459e-02 -7.18608856e-01 -4.35474128e-01
3.59128006e-02 3.27041417e-01 -6.75115168e-01 -4.08910394e-01
-8.15612078e-01 1.84799805e-01 6.67123914e-01 3.80229168e-02
-3.89458627e-01 -3.45453978e-01 -5.33977270e-01 9.62582156e-02
7.11844981e-01 -5.81198275e-01 5.14203906e-01 -6.07022047e-01
-9.57506895e-01 4.96108979e-01 -2.96003193e-01 4.66362447e-01
2.81861097e-01 -1.93181206e-02 -7.04858720e-01 2.15437159e-01
1.29307017e-01 -2.02823699e-01 7.71797359e-01 -1.55486298e+00
-6.51015341e-01 -9.02439594e-01 -4.40447479e-01 6.84986949e-01
-7.20531404e-01 -1.53688088e-01 -8.11659276e-01 -2.43472740e-01
6.67190254e-01 -9.32217777e-01 -3.87706548e-01 -4.12541687e-01
-4.03100848e-01 -8.00606981e-02 1.28892434e+00 -2.91220665e-01
1.31289124e+00 -2.55989957e+00 7.88109958e-01 5.12471020e-01
3.92247021e-01 -2.18947791e-02 1.67668670e-01 5.33917546e-01
-1.47080436e-01 -1.57060608e-01 -1.12433784e-01 1.25424042e-01
-2.37194791e-01 5.65919019e-02 3.83959301e-02 6.76733613e-01
-5.55607378e-01 8.20894241e-02 -7.84699142e-01 -6.99682057e-01
2.97697663e-01 3.76816750e-01 -5.95134199e-01 2.13586822e-01
4.33674723e-01 4.76625860e-01 -7.32540309e-01 4.53580797e-01
7.66923010e-01 -4.60868835e-01 4.36482519e-01 -4.66556728e-01
-6.97739869e-02 -8.12633157e-01 -1.85564935e+00 1.63193333e+00
-1.32905588e-01 2.39407341e-03 2.92649746e-01 -1.07504964e+00
7.06956625e-01 2.15644404e-01 8.65593612e-01 2.65905284e-04
1.42854765e-01 1.86204866e-01 1.20865360e-01 -4.84690845e-01
3.49870235e-01 -2.92833328e-01 -5.97543567e-02 4.76428121e-01
-1.12575218e-01 3.59672546e-01 1.90875992e-01 2.20537499e-01
6.05413854e-01 -3.88463825e-01 4.35278863e-01 -2.93055266e-01
9.94406760e-01 -2.73734987e-01 8.31190050e-01 -8.87202695e-02
-8.17410201e-02 7.98435271e-01 4.56547707e-01 -3.38629365e-01
-8.82362187e-01 -9.16721702e-01 5.23017421e-02 7.32992351e-01
3.93599749e-01 -5.45629501e-01 -5.98638415e-01 -6.85531437e-01
-1.23552077e-01 2.68917114e-01 -2.91947335e-01 -8.81239176e-02
-3.04315567e-01 -9.91558909e-01 -2.65970051e-01 2.30284065e-01
3.26610088e-01 -4.01494145e-01 -4.62058276e-01 1.25433311e-01
-3.50742877e-01 -1.02824759e+00 -6.83909059e-01 -1.51553005e-01
-9.65107799e-01 -1.21840978e+00 -7.86538422e-01 -7.54930556e-01
8.73116136e-01 9.48038340e-01 6.94978714e-01 -2.02620290e-02
-1.84941739e-01 5.74088454e-01 -5.27448833e-01 1.55928701e-01
-7.49122724e-02 3.31367068e-02 5.06081641e-01 5.12379587e-01
5.12918591e-01 -7.14518487e-01 -7.47433066e-01 8.24867070e-01
-1.07939911e+00 -9.27500650e-02 4.89161670e-01 8.46756935e-01
8.34219277e-01 7.33585954e-01 3.94749850e-01 -7.61675239e-01
5.27297676e-01 -5.87371230e-01 -5.69449067e-01 3.64308417e-01
-6.09938085e-01 -8.69189054e-02 8.90642285e-01 -2.54037440e-01
-8.44461799e-01 3.52320462e-01 3.43063742e-01 -8.53726983e-01
-2.96733797e-01 5.69682479e-01 -8.30231667e-01 2.65303969e-01
2.54273146e-01 5.69956720e-01 1.16577446e-01 -6.11537635e-01
5.53474128e-01 8.09863627e-01 2.96170592e-01 -4.87216055e-01
8.81990254e-01 6.76918447e-01 8.85009170e-02 -8.55498970e-01
-5.16934574e-01 -1.03052115e+00 -1.05603087e+00 -2.25857034e-01
7.87648261e-01 -1.01402307e+00 -6.31264567e-01 2.55310178e-01
-6.56064868e-01 5.92015982e-01 1.35978013e-01 7.76624560e-01
-5.13053954e-01 9.24381852e-01 -1.03785738e-01 -4.72069532e-01
2.54866364e-03 -1.17014074e+00 7.69182920e-01 8.16727504e-02
2.14040533e-01 -8.28050971e-01 2.09543660e-01 4.20916140e-01
-9.77029055e-02 1.80948794e-01 1.00096726e+00 -6.88319206e-01
-1.90460637e-01 -1.16457954e-01 -8.72819964e-03 3.06464378e-02
3.76849234e-01 -1.52921990e-01 -4.99797523e-01 -6.45245790e-01
3.82770509e-01 -8.32447186e-02 6.22163057e-01 3.04954946e-01
1.20325375e+00 -1.05153635e-01 -4.74972814e-01 7.03018248e-01
1.59292567e+00 3.14923346e-01 2.20217884e-01 1.62233666e-01
9.65075076e-01 8.26673448e-01 7.09853172e-01 9.29317832e-01
3.14767778e-01 6.48680747e-01 1.89779148e-01 1.86621308e-01
6.03872120e-01 2.31116768e-02 3.45893465e-02 1.58806348e+00
-4.82479930e-02 -1.33980706e-01 -8.91562521e-01 4.59540874e-01
-2.00374222e+00 -1.21538532e+00 -2.40032554e-01 2.30263710e+00
2.69557506e-01 -2.50197917e-01 4.63051051e-01 3.32123071e-01
1.04811943e+00 6.46923259e-02 -5.70859611e-01 2.45022163e-01
-7.63012469e-02 -8.16000581e-01 8.10756311e-02 1.15018031e-02
-1.28082097e+00 5.02587020e-01 5.54353905e+00 7.21903682e-01
-9.35434937e-01 -6.55634180e-02 5.29001474e-01 -2.14602217e-01
-2.42630869e-01 -1.53177097e-01 -7.06955969e-01 5.93427718e-01
6.71201646e-01 -4.06490505e-01 4.91037130e-01 7.84330368e-01
1.70689091e-01 -4.94826473e-02 -9.44154561e-01 1.56467581e+00
3.14244330e-01 -8.12818170e-01 1.29494607e-01 2.10950509e-01
8.26055646e-01 -3.13989937e-01 3.69399078e-02 -3.82982492e-02
-4.53091823e-02 -5.55782676e-01 1.36195749e-01 2.30934456e-01
6.19171858e-01 -1.17268932e+00 4.92359459e-01 6.64082170e-01
-1.35711443e+00 -2.12783456e-01 -6.67204559e-01 2.80425817e-01
1.52251318e-01 6.14753067e-01 -2.84572095e-01 8.78245294e-01
7.11885154e-01 1.06355178e+00 -5.39114416e-01 1.15420663e+00
4.47768480e-01 2.42682993e-01 -2.86163092e-01 3.14599693e-01
1.43988743e-01 -9.37210023e-01 5.65832496e-01 6.77775741e-01
6.39976621e-01 5.00722885e-01 5.30824244e-01 3.11030447e-01
3.25543225e-01 5.09014070e-01 -7.17983127e-01 8.30304250e-02
4.87265557e-01 1.58147120e+00 -1.08115697e+00 -2.77253568e-01
-8.41990590e-01 1.06858110e+00 5.46274669e-02 2.79140919e-01
-4.59141552e-01 -1.03436247e-01 4.71841395e-01 -2.58321892e-02
5.27216792e-01 -2.76905447e-01 1.90804247e-02 -1.52795184e+00
1.32186413e-01 -1.00835276e+00 6.31429672e-01 -3.26683551e-01
-1.53516316e+00 4.26242441e-01 2.44960412e-02 -1.97194159e+00
-1.89514667e-01 -2.79918581e-01 -3.75028938e-01 6.15010142e-01
-8.98379922e-01 -7.82068729e-01 -3.53067279e-01 1.04507887e+00
4.31669533e-01 -5.60039878e-01 6.96760893e-01 3.54808658e-01
-6.81916475e-01 2.17984706e-01 9.10409927e-01 7.90942013e-02
7.88566172e-01 -1.15921581e+00 -2.89179355e-01 6.53153360e-01
1.50006205e-01 6.27259314e-01 4.67686236e-01 -3.92658919e-01
-1.81300676e+00 -9.76197898e-01 2.25870088e-01 -1.67500272e-01
5.00821531e-01 -3.23763341e-01 -9.54488814e-01 3.85627300e-01
8.33558887e-02 7.62673095e-02 1.37975919e+00 2.70245790e-01
-2.94893205e-01 -1.75407529e-01 -1.00827420e+00 4.27508503e-01
6.89728260e-01 -2.36386031e-01 -4.16028976e-01 2.90065199e-01
2.88983703e-01 -3.77098769e-02 -1.09284699e+00 2.21489385e-01
4.58431929e-01 -1.25431895e+00 9.59858179e-01 -5.36096931e-01
1.10964917e-01 -8.11059356e-01 -4.76307780e-01 -1.62478876e+00
-7.15831399e-01 -1.65855721e-01 1.63668394e-01 1.32413220e+00
-1.66174531e-01 -3.65459710e-01 9.23719645e-01 2.90744364e-01
1.75269961e-01 -6.19014859e-01 -9.17339921e-01 -7.76952744e-01
-2.27602273e-01 1.76992148e-01 4.71303046e-01 1.12815845e+00
1.15062222e-01 6.31582260e-01 -4.59094971e-01 3.36497337e-01
9.61602807e-01 7.74682879e-01 7.46712267e-01 -1.46715713e+00
-4.16292578e-01 -2.69203693e-01 -7.14641511e-01 -5.61637640e-01
9.88626853e-02 -7.02789783e-01 -1.79653913e-01 -1.40327287e+00
8.18808138e-01 -1.84852675e-01 -4.08758998e-01 -1.71199426e-01
-4.18134660e-01 -1.43538296e-01 1.98900178e-01 5.93032420e-01
-9.33863521e-01 7.76789069e-01 1.00759220e+00 2.14442778e-02
-3.48632812e-01 4.70788740e-02 -6.91364527e-01 7.97725081e-01
6.04594886e-01 -1.77090093e-01 -7.63946354e-01 -2.17214316e-01
-9.49241146e-02 3.64240021e-01 -8.17995146e-02 -1.10860908e+00
3.44721079e-01 -2.73877800e-01 5.87425292e-01 -8.31185460e-01
1.33511826e-01 -1.36957622e+00 3.82003665e-01 2.83390969e-01
1.98064700e-01 2.06913307e-01 -2.06738770e-01 9.73339081e-01
-5.18389106e-01 1.21304356e-01 8.03913474e-01 -1.41625598e-01
-8.69395196e-01 3.63794863e-01 -3.10467094e-01 4.65962440e-02
1.34675300e+00 -2.14079738e-01 2.16111615e-02 -3.57019961e-01
-8.77504468e-01 4.33471054e-01 7.94529557e-01 3.98197681e-01
8.21815908e-01 -1.71780479e+00 -5.27001679e-01 3.54646742e-01
4.47167993e-01 1.90642234e-02 7.03649521e-01 7.85948575e-01
-1.18598945e-01 1.71511635e-01 -3.17010760e-01 -8.33415687e-01
-1.67101097e+00 1.14458370e+00 -1.92680165e-01 5.92487603e-02
-5.69258392e-01 2.00985298e-01 4.11484480e-01 -3.76920164e-01
-1.46314949e-02 3.76267970e-01 -6.56687140e-01 3.93065721e-01
5.59454858e-01 5.12755215e-01 -2.28082761e-01 -1.01219356e+00
-3.06168437e-01 9.95483220e-01 -7.27972239e-02 -6.75521269e-02
1.45208585e+00 -4.54957306e-01 -3.48718286e-01 7.26425409e-01
1.48579705e+00 -4.99839336e-02 -9.80592430e-01 -3.09761435e-01
1.21837752e-02 -6.37847304e-01 -1.13177031e-01 -6.90197200e-02
-1.25868261e+00 9.53890622e-01 6.81769729e-01 7.57071152e-02
1.34709871e+00 -1.27924478e-03 5.01029432e-01 3.75911742e-01
4.73731041e-01 -1.08782780e+00 3.59573513e-02 1.14621505e-01
5.45859635e-01 -1.17895937e+00 2.03922525e-01 -5.56272149e-01
-8.11614513e-01 9.97361362e-01 6.90924525e-01 7.91844912e-03
9.16151583e-01 -6.40632734e-02 9.06552896e-02 -2.84431309e-01
-6.13158762e-01 8.22027996e-02 2.75306940e-01 4.24818844e-01
1.00156300e-01 1.17863603e-01 -4.09455925e-01 5.79421699e-01
1.39300048e-01 -4.52977240e-01 6.20505273e-01 7.87286401e-01
-4.63725597e-01 -1.14964187e+00 -8.10320914e-01 5.27034998e-01
-2.33792827e-01 3.89666617e-01 -3.22681099e-01 5.46058476e-01
-1.13431774e-01 9.28543150e-01 -5.73849343e-02 -6.51047409e-01
3.02165836e-01 1.37592629e-01 6.89062774e-02 -4.67867762e-01
-9.78590026e-02 7.25415707e-01 -5.96007228e-01 -4.04441804e-01
-6.20792150e-01 -1.05334902e+00 -1.07001114e+00 -1.25288650e-01
-1.37923017e-01 3.76604348e-01 4.00229096e-01 5.30514657e-01
5.18833995e-01 9.85103846e-02 1.19699359e+00 -6.10350668e-01
-6.18697345e-01 -4.89708781e-01 -1.08275211e+00 6.77250624e-01
-7.89516047e-02 -7.26277947e-01 -3.41337770e-01 1.05060264e-01] | [8.151823997497559, 4.589442729949951] |
945ae92e-6395-4412-aeb1-4e6b564d7d58 | self-move-and-other-move-quantum-categorical | 2210.04451 | null | https://arxiv.org/abs/2210.04451v1 | https://arxiv.org/pdf/2210.04451v1.pdf | Self-move and Other-move: Quantum Categorical Foundations of Japanese | The purpose of this work is to contribute toward the larger goal of creating a Quantum Natural Language Processing (QNLP) translator program. This work contributes original diagrammatic representations of the Japanese language based on prior work that accomplished on the English language based on category theory. The germane differences between the English and Japanese languages are emphasized to help address English language bias in the current body of research. Additionally, topological principles of these diagrams and many potential avenues for further research are proposed. Why is this endeavor important? Hundreds of languages have developed over the course of millennia coinciding with the evolution of human interaction across time and geographic location. These languages are foundational to human survival, experience, flourishing, and living the good life. They are also, however, the strongest barrier between people groups. Over the last several decades, advancements in Natural Language Processing (NLP) have made it easier to bridge the gap between individuals who do not share a common language or culture. Tools like Google Translate and DeepL make it easier than ever before to share our experiences with people globally. Nevertheless, these tools are still inadequate as they fail to convey our ideas across the language barrier fluently, leaving people feeling anxious and embarrassed. This is particularly true of languages born out of substantially different cultures, such as English and Japanese. Quantum computers offer the best chance to achieve translation fluency in that they are better suited to simulating the natural world and natural phenomenon such as natural speech. Keywords: category theory, DisCoCat, DisCoCirc, Japanese grammar, English grammar, translation, topology, Quantum Natural Language Processing, Natural Language Processing | ['Ryder Dale Walton'] | 2022-10-10 | null | null | null | null | ['culture'] | ['speech'] | [-1.40431687e-01 -7.77310319e-03 1.59052275e-02 -1.29397318e-01
-1.70204222e-01 -7.59107172e-01 8.34929407e-01 2.81126827e-01
-4.40326482e-01 7.41746068e-01 5.48606694e-01 -7.18150020e-01
1.03892334e-01 -1.00782156e+00 -1.44925252e-01 -2.32085496e-01
1.72695085e-01 3.23572129e-01 -1.41589507e-01 -1.03495908e+00
4.03085947e-01 3.21844369e-01 -1.14065492e+00 -1.20196663e-01
1.08488321e+00 1.71537489e-01 9.29479301e-02 4.50282633e-01
-3.39292824e-01 5.29362798e-01 -2.97079355e-01 -5.95252812e-01
-7.28813112e-02 -7.14763939e-01 -1.02325296e+00 -3.74485224e-01
7.82343298e-02 1.72537521e-01 -2.45469183e-01 1.24099064e+00
1.93486661e-01 1.04511596e-01 5.30057609e-01 -8.23177278e-01
-1.07122159e+00 5.27148247e-01 -1.77342117e-01 9.61041227e-02
6.58797443e-01 3.10079232e-02 8.61841679e-01 -6.10436559e-01
8.40412498e-01 1.45274234e+00 6.83409750e-01 5.39189517e-01
-1.12672174e+00 -2.56399721e-01 -3.61380309e-01 -9.41594616e-02
-1.47332871e+00 -1.37060255e-01 4.53896344e-01 -3.92021537e-01
1.11798728e+00 1.36407539e-01 1.00010800e+00 8.21370721e-01
8.19385648e-01 -1.58273935e-01 1.29556906e+00 -8.95452678e-01
7.41383154e-03 3.15044343e-01 1.73070297e-01 8.98622096e-01
3.79252344e-01 7.45906457e-02 -5.66851497e-01 -1.40786141e-01
7.52105534e-01 -2.31689677e-01 -3.35317031e-02 1.27896979e-01
-1.84939766e+00 7.33578980e-01 1.74035013e-01 9.19261098e-01
-1.69144809e-01 1.47499323e-01 3.51772100e-01 5.75008690e-01
1.25823900e-01 4.60492879e-01 -8.06294382e-02 -7.29716480e-01
-4.44602102e-01 1.80436254e-01 9.72277343e-01 7.58926570e-01
5.32654762e-01 -2.86924273e-01 5.59039593e-01 5.26158273e-01
3.98547322e-01 6.76204622e-01 3.82166803e-01 -9.06935871e-01
2.66835511e-01 4.72422719e-01 8.05464238e-02 -1.43184519e+00
-4.67600137e-01 -1.42132625e-01 -7.67521977e-01 -2.14279294e-02
6.02250040e-01 -1.18017100e-01 -5.41630566e-01 1.82081568e+00
-3.81185040e-02 -6.93109810e-01 3.49302828e-01 9.24988329e-01
3.72723103e-01 9.47115421e-01 2.15630710e-01 -3.53696058e-03
1.72600687e+00 -4.10165280e-01 -6.62098289e-01 3.02749593e-02
9.21528697e-01 -1.07897437e+00 1.28812313e+00 2.64029384e-01
-9.68780994e-01 -2.90137023e-01 -9.73044217e-01 -3.52946997e-01
-7.24316716e-01 -4.10995156e-01 9.34844315e-01 1.17299545e+00
-1.18608427e+00 6.51152432e-01 -9.22280490e-01 -1.03921568e+00
-3.81390303e-02 2.75973380e-01 -4.08369720e-01 1.82065085e-01
-1.58961284e+00 1.36976337e+00 1.35553032e-01 5.35172364e-03
4.21055853e-02 -1.96614131e-01 -6.13398135e-01 -3.17963332e-01
3.22468579e-02 -8.63822162e-01 1.00078237e+00 -8.93399060e-01
-1.53965580e+00 9.44271684e-01 -1.91647574e-01 -3.14326227e-01
1.91160098e-01 1.66066736e-01 -6.69891655e-01 4.21235431e-03
3.08316499e-01 6.60976529e-01 -1.30814919e-02 -6.10422075e-01
-4.08015788e-01 -2.42889941e-01 1.35751382e-01 3.80326778e-01
-2.06695423e-01 4.85527992e-01 5.18109985e-02 -5.12836814e-01
2.30965286e-01 -9.01616037e-01 -1.56613082e-01 -1.64523125e-01
-1.27638549e-01 -2.90582418e-01 3.59048724e-01 -6.17771864e-01
9.46605682e-01 -2.04611683e+00 1.26912564e-01 9.57698151e-02
2.56278634e-01 -2.55687416e-01 2.84915209e-01 1.07934058e+00
2.75483996e-01 7.03428686e-01 -2.20218465e-01 2.59258658e-01
2.30567396e-01 1.67607069e-01 -2.70619184e-01 5.59959412e-01
-1.45456776e-01 8.44254315e-01 -1.15742052e+00 -5.12611449e-01
-5.75358011e-02 4.82869267e-01 -3.87380093e-01 -5.22999883e-01
1.70533583e-01 4.87500787e-01 -3.25035155e-01 5.16293764e-01
2.09269956e-01 -1.95581168e-02 3.06267858e-01 2.97363281e-01
-7.43749619e-01 6.44290268e-01 -7.91190624e-01 1.67099798e+00
-5.87073505e-01 8.15059245e-01 -3.98549549e-02 -7.07773745e-01
7.34898567e-01 3.91105264e-01 -3.24920863e-02 -8.19433570e-01
2.22841203e-01 5.98734796e-01 5.96172392e-01 -2.49389350e-01
8.62063289e-01 -7.19096005e-01 -4.49089408e-01 6.98569059e-01
-2.83141702e-01 -7.37585902e-01 3.71893048e-01 4.38374549e-01
5.45883060e-01 1.36043102e-01 4.80947912e-01 -7.35332370e-01
2.16772735e-01 3.28879416e-01 4.98250395e-01 4.38946187e-01
-5.10327041e-01 3.45340192e-01 4.60346967e-01 -5.94685137e-01
-1.32324648e+00 -1.29270363e+00 -2.48612702e-01 7.14195848e-01
4.17261392e-01 -5.56353390e-01 -6.59260750e-01 1.65002301e-01
-5.62166452e-01 8.75404179e-01 -1.08148649e-01 -1.05294161e-01
-4.04954940e-01 -5.67457139e-01 6.56786203e-01 6.10424466e-02
6.18173003e-01 -9.20258701e-01 -7.25992322e-01 2.29049593e-01
-3.92989606e-01 -1.10072446e+00 -3.73913467e-01 -2.37991050e-01
-7.98347652e-01 -4.05715883e-01 -5.51040769e-01 -8.92765641e-01
5.26145995e-01 1.73464000e-01 9.18135464e-01 6.14492968e-03
-1.77749380e-01 4.86248463e-01 -2.92149931e-01 -3.75894815e-01
-8.61205876e-01 -7.35022202e-02 6.01016819e-01 -6.16154671e-01
4.67599243e-01 -5.34231424e-01 -2.42409348e-01 1.85323268e-01
-7.14097500e-01 3.68972600e-01 1.41068727e-01 6.19935453e-01
-5.23414053e-02 1.04563177e-01 3.46653342e-01 -3.99053007e-01
8.60487998e-01 -3.72684479e-01 -1.56509787e-01 2.83817798e-01
-4.35276449e-01 -1.36094019e-01 7.21676767e-01 -2.20466256e-02
-1.04080963e+00 -7.26600885e-01 9.92351994e-02 8.25538278e-01
-9.84392874e-03 6.78388417e-01 1.83769554e-01 1.14130467e-01
7.58202851e-01 9.92046222e-02 1.11572787e-01 2.21455395e-02
4.69516397e-01 8.43475819e-01 2.88123548e-01 -7.06842899e-01
5.90968549e-01 5.10130048e-01 -4.94678430e-02 -1.55072391e+00
2.59521138e-03 -2.77660582e-02 -8.07997406e-01 -2.01717183e-01
1.06930101e+00 -6.74196005e-01 -5.33451140e-01 3.49003434e-01
-1.30655110e+00 -1.60638064e-01 -2.13430107e-01 7.63667524e-01
-3.91207963e-01 4.70288694e-01 -6.63472176e-01 -6.53824747e-01
-1.06756598e-01 -1.02312875e+00 6.76186919e-01 4.07987207e-01
-6.31525040e-01 -1.38470829e+00 1.24187000e-01 2.37462088e-01
5.66676199e-01 1.88581139e-01 1.22313094e+00 -1.27428830e-01
-2.91651607e-01 1.11885004e-01 -1.41969755e-01 -2.89132614e-02
3.39215636e-01 2.06697926e-01 -2.11706802e-01 3.75043117e-02
1.56556331e-02 -2.64856666e-01 2.64755517e-01 -1.71303093e-01
-3.28119621e-02 -1.23828933e-01 -2.99632937e-01 5.12535907e-02
1.19846010e+00 3.51777405e-01 6.93415403e-01 3.51148009e-01
3.72261226e-01 8.20168972e-01 1.71228737e-01 -1.57163829e-01
8.85587096e-01 6.08541965e-01 -3.70163530e-01 5.76061197e-02
-6.03314899e-02 -1.90966666e-01 5.69834054e-01 1.55240119e+00
-3.44573140e-01 1.19178347e-01 -1.45310771e+00 4.38066959e-01
-1.36908960e+00 -9.94566560e-01 -2.80117124e-01 2.15426230e+00
7.43114233e-01 2.27291182e-01 1.09087966e-01 -2.30587780e-01
6.29047155e-01 -1.81707352e-01 7.64550567e-02 -1.13750994e+00
-2.43493944e-01 1.01371601e-01 3.03552955e-01 5.99722207e-01
-6.47298694e-01 1.32234085e+00 6.37657833e+00 4.26780164e-01
-1.34174454e+00 1.31323844e-01 4.25301462e-01 5.39941430e-01
-4.79742795e-01 5.49388945e-01 -3.79608005e-01 1.83273569e-01
1.26959097e+00 -7.01167285e-01 8.02095234e-01 1.51492760e-01
5.51907420e-01 -1.69082522e-01 -7.18764663e-01 9.08797622e-01
-1.29495099e-01 -1.20981741e+00 -2.16961771e-01 2.45241657e-01
5.14678299e-01 1.49843454e-01 1.43562965e-02 6.50048852e-02
3.57674807e-01 -1.01480567e+00 9.56671834e-01 5.51029325e-01
6.40408695e-01 -4.70847607e-01 4.60425526e-01 2.23548889e-01
-8.30241799e-01 1.86242849e-01 -4.34844136e-01 -8.10948670e-01
3.62476110e-01 3.99123192e-01 -5.10775983e-01 5.55659294e-01
4.42632496e-01 2.87790269e-01 -3.02864850e-01 5.05139410e-01
-5.92783913e-02 5.55417538e-01 -6.08021915e-01 -6.09651387e-01
3.42001379e-01 -7.44886994e-01 7.28429556e-01 9.85649288e-01
4.68380064e-01 1.84372589e-01 -9.22621787e-02 9.85797644e-01
1.77572981e-01 3.66891176e-01 -9.45270658e-01 -7.27913499e-01
4.20797288e-01 9.11767244e-01 -1.33492494e+00 -2.07644969e-01
-5.47476768e-01 9.63749886e-01 -1.34242652e-02 9.22779664e-02
-6.09460771e-01 -5.41307688e-01 4.93485570e-01 9.47594792e-02
-4.86339241e-01 -9.58697200e-01 -5.43777585e-01 -1.33182204e+00
-2.96417564e-01 -1.06688356e+00 -1.22742802e-01 -6.11255109e-01
-1.01499319e+00 4.49198008e-01 -7.81747606e-03 -7.39235520e-01
-1.69798031e-01 -5.99764884e-01 -4.61691916e-01 1.04335308e+00
-4.76554573e-01 -1.06790888e+00 3.87502611e-01 4.35166061e-03
1.73473150e-01 -2.39072125e-02 1.11438489e+00 1.17559843e-01
-2.44200364e-01 1.82262853e-01 2.22439811e-01 5.29232062e-03
4.15654063e-01 -1.07168150e+00 7.32471824e-01 8.07148278e-01
4.08511162e-01 1.39263177e+00 7.74134040e-01 -6.72836840e-01
-1.50784099e+00 -2.29398862e-01 1.49061239e+00 -5.33550501e-01
1.03195512e+00 -6.36756718e-01 -4.17711347e-01 3.92139912e-01
4.71219718e-01 -6.98636234e-01 6.48063242e-01 3.34903687e-01
-2.36523956e-01 2.25758031e-01 -9.45895255e-01 1.23961198e+00
1.08526194e+00 -9.84332383e-01 -8.94680440e-01 2.84969240e-01
7.86798298e-01 -1.85377095e-02 -7.12286055e-01 -2.42840752e-01
8.49631965e-01 -8.65744710e-01 5.05979538e-01 -3.23501348e-01
2.51629233e-01 -3.37789804e-01 -2.04481333e-01 -1.25070512e+00
-1.43925577e-01 -1.02485895e+00 9.87421215e-01 1.15320492e+00
6.16084814e-01 -1.19355428e+00 3.20566595e-01 8.11846852e-01
-8.10356811e-02 -3.02916467e-01 -1.03317547e+00 -7.20026851e-01
7.68562973e-01 -5.26740074e-01 3.63762051e-01 1.07825041e+00
8.13890100e-01 6.03875399e-01 -4.62739095e-02 -3.59398454e-01
3.50495279e-01 -9.74041820e-02 5.32152414e-01 -1.14960241e+00
3.19193453e-02 -6.64761364e-01 -7.19022989e-01 -6.25921130e-01
-1.80598006e-01 -1.00836289e+00 -3.36254925e-01 -1.68327034e+00
1.62775144e-01 -3.50100756e-01 3.01870614e-01 1.82646915e-01
3.49050969e-01 3.08546215e-01 4.01896209e-01 4.06091452e-01
-2.13654667e-01 3.02759349e-01 1.24281430e+00 1.21000424e-01
-3.83252591e-01 -5.15219927e-01 -1.11899698e+00 7.77523160e-01
9.77659822e-01 -3.00981700e-01 -2.07805067e-01 -4.12257165e-01
7.64231086e-01 1.05902296e-03 2.69476265e-01 -9.81035590e-01
1.84491277e-01 -3.08321714e-01 -9.44091380e-02 -1.31034568e-01
1.35306418e-01 -4.51545328e-01 2.48965517e-01 7.69729078e-01
1.36626540e-02 3.81675839e-01 7.38791525e-02 9.57988799e-02
-1.37895569e-02 4.64590564e-02 6.55706584e-01 -3.32222760e-01
-5.44785559e-01 -3.42168719e-01 -9.68966186e-01 -1.84588358e-02
8.52654576e-01 -2.85250753e-01 -5.46680570e-01 -5.21775246e-01
-4.66594279e-01 -2.74045113e-02 1.02399361e+00 5.07879734e-01
1.31854221e-01 -1.01013422e+00 -4.42129910e-01 -2.17132702e-01
-1.80894762e-01 -6.17487133e-01 7.30800480e-02 9.05520737e-01
-1.22675192e+00 8.61832798e-01 -4.60591853e-01 -1.09740712e-01
-6.97473824e-01 3.21175694e-01 2.21157506e-01 7.47440383e-02
-7.05956936e-01 4.81564522e-01 -5.37960208e-04 -6.37656569e-01
-4.32529241e-01 -3.87081563e-01 1.04777761e-01 -2.01152563e-01
4.13822412e-01 4.77991581e-01 -2.99425483e-01 -1.16881537e+00
-5.70860922e-01 6.25451386e-01 1.96832448e-01 -7.02050865e-01
9.03426349e-01 -4.86865729e-01 -7.91941464e-01 8.74321342e-01
9.63718176e-01 4.13836211e-01 -2.02830359e-01 3.89699697e-01
-5.94803095e-02 -5.02855629e-02 -3.02787066e-01 -6.97720528e-01
1.03699110e-01 9.73897874e-01 4.62241858e-01 6.08774483e-01
5.20683408e-01 -5.84738292e-02 7.85084128e-01 3.73966724e-01
8.12065661e-01 -1.22378731e+00 -2.94854522e-01 7.93814600e-01
5.04162371e-01 -8.63473773e-01 -9.15282741e-02 -4.45384055e-01
-4.76278722e-01 1.32865918e+00 7.63801634e-02 1.59325272e-01
5.96823335e-01 -1.16720006e-01 2.10062012e-01 -2.92402238e-01
-3.87417048e-01 -1.62753925e-01 -2.46567532e-01 6.14955127e-01
9.25048113e-01 4.71213907e-01 -1.21306336e+00 1.84398130e-01
-7.54997969e-01 -1.16645180e-01 9.00634766e-01 9.23102140e-01
-4.69168454e-01 -1.48017097e+00 -7.25631356e-01 -1.04094908e-01
-5.73591590e-01 -3.55779320e-01 -7.28078067e-01 8.96168232e-01
2.23508880e-01 1.22008741e+00 9.55429450e-02 -3.30064058e-01
-5.05309887e-02 2.17672274e-01 6.82871938e-01 -5.69084704e-01
-3.94406289e-01 -3.23171467e-01 2.57957518e-01 5.54937311e-02
-2.90336579e-01 -6.98717713e-01 -1.71224892e+00 -8.73085022e-01
-1.39319971e-01 5.85220873e-01 7.75847912e-01 9.56487536e-01
3.13339174e-01 7.27168918e-02 8.13180953e-02 -5.92429578e-01
-2.73223460e-01 -7.18176186e-01 -5.01658082e-01 -1.40866697e-01
-1.52838588e-01 -4.57752377e-01 -1.26032084e-01 8.31962228e-02] | [10.449748992919922, 9.936261177062988] |
d354314f-fe9d-423f-9ed5-6fdc91172ed5 | tvlt-textless-vision-language-transformer | 2209.14156 | null | https://arxiv.org/abs/2209.14156v2 | https://arxiv.org/pdf/2209.14156v2.pdf | TVLT: Textless Vision-Language Transformer | In this work, we present the Textless Vision-Language Transformer (TVLT), where homogeneous transformer blocks take raw visual and audio inputs for vision-and-language representation learning with minimal modality-specific design, and do not use text-specific modules such as tokenization or automatic speech recognition (ASR). TVLT is trained by reconstructing masked patches of continuous video frames and audio spectrograms (masked autoencoding) and contrastive modeling to align video and audio. TVLT attains performance comparable to its text-based counterpart on various multimodal tasks, such as visual question answering, image retrieval, video retrieval, and multimodal sentiment analysis, with 28x faster inference speed and only 1/3 of the parameters. Our findings suggest the possibility of learning compact and efficient visual-linguistic representations from low-level visual and audio signals without assuming the prior existence of text. Our code and checkpoints are available at: https://github.com/zinengtang/TVLT | ['Mohit Bansal', 'Yixin Nie', 'Jaemin Cho', 'Zineng Tang'] | 2022-09-28 | null | null | null | null | ['multimodal-sentiment-analysis', 'multimodal-sentiment-analysis'] | ['computer-vision', 'natural-language-processing'] | [ 2.05138221e-01 -1.11417770e-01 -3.72416556e-01 -3.66455227e-01
-1.31049335e+00 -6.32714689e-01 7.78456748e-01 1.20717995e-01
-5.48636079e-01 3.07133704e-01 3.68420869e-01 -4.83865976e-01
3.99110466e-01 -1.49954319e-01 -8.90970230e-01 -4.80336905e-01
2.87765712e-01 -3.18466611e-02 -1.10949971e-01 1.25763074e-01
8.35364759e-02 1.68346390e-01 -1.77737093e+00 7.28685260e-01
1.51963934e-01 1.17129517e+00 3.05029631e-01 1.20094121e+00
-2.18991175e-01 1.34249413e+00 -3.55491519e-01 -2.68732727e-01
-1.34628981e-01 -2.10809648e-01 -7.41181314e-01 2.94725120e-01
6.57001376e-01 -4.42862988e-01 -7.90471554e-01 6.90955222e-01
4.70053464e-01 3.81262489e-02 4.79074180e-01 -1.35724866e+00
-7.49368846e-01 3.71145248e-01 -5.74717581e-01 1.03456490e-01
6.84920669e-01 2.74098396e-01 1.03037047e+00 -1.38208926e+00
4.80677515e-01 1.32252371e+00 3.19225341e-01 5.93701303e-01
-1.00268662e+00 -4.72080022e-01 1.63795173e-01 5.58738172e-01
-1.51006830e+00 -1.09728158e+00 4.68663394e-01 -4.90374118e-01
1.16108263e+00 3.10271442e-01 3.56421322e-01 1.31254673e+00
-3.68938260e-02 1.02390921e+00 8.91706049e-01 -5.66471279e-01
-4.38895151e-02 2.65308768e-01 -4.09450335e-03 1.00406122e+00
-3.53586197e-01 -1.38037145e-01 -1.07669604e+00 -9.13420990e-02
5.23977995e-01 -1.03439912e-02 -3.81992310e-01 -1.18916752e-02
-1.33628511e+00 7.47855604e-01 -1.33359030e-01 1.04672663e-01
-2.68651009e-01 5.45717359e-01 5.02452016e-01 4.13223952e-01
3.64625365e-01 -2.42405638e-01 -2.17248887e-01 -1.59905106e-01
-1.14732671e+00 -3.93130898e-01 3.39401156e-01 1.05497730e+00
8.53984535e-01 4.33356941e-01 -3.17623883e-01 7.81130791e-01
5.82262516e-01 7.87892222e-01 7.05301821e-01 -1.21831465e+00
4.15855318e-01 5.46676852e-02 -1.93290502e-01 -5.74791312e-01
-1.65230960e-01 1.97562516e-01 -4.99633372e-01 -7.72651881e-02
1.83176279e-01 -6.64039180e-02 -1.12985277e+00 1.51616454e+00
7.12075233e-02 1.99470595e-01 8.23857561e-02 7.86481142e-01
1.33080554e+00 8.96569014e-01 -5.94317988e-02 -2.06109837e-01
1.56098270e+00 -8.39078426e-01 -7.32348084e-01 -2.08810896e-01
2.81397730e-01 -1.02127945e+00 1.31583178e+00 3.71351689e-01
-1.32920384e+00 -5.76469898e-01 -7.41530538e-01 -6.77272379e-01
-2.66417801e-01 4.31393713e-01 4.53065366e-01 5.27760744e-01
-1.38545680e+00 -1.61000296e-01 -9.36445713e-01 -4.00801212e-01
3.35158527e-01 1.31678373e-01 -5.09409308e-01 -1.52161151e-01
-8.26416790e-01 4.99210805e-01 -6.37730062e-02 1.19209580e-01
-1.29685831e+00 -4.72638816e-01 -1.21728528e+00 -9.09245461e-02
3.63324821e-01 -6.90723598e-01 1.38334930e+00 -1.18309712e+00
-1.71112657e+00 1.05796432e+00 -7.17275381e-01 -5.25629401e-01
2.24316895e-01 -1.24795124e-01 -2.32236892e-01 7.40760386e-01
-1.38374954e-01 9.42321301e-01 1.55411899e+00 -9.58417892e-01
-4.06451911e-01 -1.23900011e-01 -2.08802056e-02 3.20352763e-01
-3.15860629e-01 2.00449303e-01 -1.01059866e+00 -5.81879139e-01
-1.66085020e-01 -6.64102256e-01 3.05859506e-01 2.57208526e-01
-2.26889223e-01 -3.60892564e-02 7.52117157e-01 -9.90487814e-01
7.32699692e-01 -2.25626397e+00 5.06536737e-02 -1.51746660e-01
2.89018273e-01 7.85642266e-02 -5.42686403e-01 5.49891889e-01
-2.16832682e-02 -1.83715299e-01 1.07826822e-01 -6.85982406e-01
4.14484330e-02 1.12498268e-01 -6.55092418e-01 6.23320878e-01
7.65085667e-02 1.02468836e+00 -5.46597719e-01 -7.29447007e-01
5.89150131e-01 8.32007170e-01 -4.51863974e-01 2.97059081e-02
-3.82889777e-01 2.62153894e-01 -1.39570758e-01 9.60871518e-01
2.30625361e-01 -4.00966108e-01 1.24254115e-01 -5.40204287e-01
-6.29117936e-02 3.12599182e-01 -8.08720767e-01 1.93456221e+00
-6.56662524e-01 1.49912655e+00 3.58324975e-01 -9.85686362e-01
4.61408556e-01 5.89678526e-01 4.41023380e-01 -8.88847291e-01
4.78522331e-02 -3.10538292e-01 -7.94462919e-01 -7.82615006e-01
5.96239507e-01 2.11174726e-01 2.01836638e-02 3.54964525e-01
4.98626113e-01 -6.52491078e-02 -1.19491875e-01 4.32905674e-01
8.88949692e-01 1.26166251e-02 2.63798404e-02 5.21217704e-01
5.09716451e-01 -1.13938853e-01 -4.41139303e-02 5.92635751e-01
4.69622128e-02 7.07964361e-01 2.81318158e-01 1.36388823e-01
-8.66854787e-01 -1.23806012e+00 7.87031054e-02 1.50511968e+00
-1.64748594e-01 -7.74359763e-01 -5.20986795e-01 -1.51949257e-01
-2.40440145e-01 6.74863398e-01 -2.46250957e-01 7.77569637e-02
-7.74113908e-02 -1.80986524e-01 8.32111001e-01 3.20741773e-01
2.21901849e-01 -7.52095938e-01 -3.50928992e-01 -2.79930264e-01
-5.94396353e-01 -1.33389592e+00 -6.75360024e-01 -8.32086205e-02
-6.05341971e-01 -8.86144459e-01 -7.52341032e-01 -7.89824486e-01
5.04679918e-01 5.32151401e-01 8.39690983e-01 -2.21212715e-01
-5.89862823e-01 1.32315075e+00 -2.99270451e-01 -9.92843956e-02
-1.80550173e-01 -4.28422749e-01 -3.92777435e-02 2.70183027e-01
1.33744746e-01 -3.15830350e-01 -5.24808407e-01 9.68984663e-02
-9.19766784e-01 1.28716439e-01 3.93452376e-01 8.74557018e-01
8.87815297e-01 -3.32281291e-01 1.18685812e-01 -3.04244667e-01
4.45132703e-01 -2.47719690e-01 -6.31603599e-01 3.12946945e-01
-2.06368834e-01 -7.89370015e-02 2.47521266e-01 -6.64894342e-01
-7.86311388e-01 1.11223444e-01 -1.08999528e-01 -1.00652599e+00
-1.71851590e-01 4.50757772e-01 4.22652476e-02 4.31746664e-03
4.52864587e-01 5.87871552e-01 9.58172008e-02 -3.32388818e-01
6.70141220e-01 9.14157152e-01 8.08176935e-01 -3.12834829e-01
5.17386436e-01 7.33736217e-01 -4.27485794e-01 -1.35192561e+00
-5.62341809e-01 -5.61468303e-01 -1.03288844e-01 -4.12021577e-01
9.35152650e-01 -1.43369508e+00 -9.24987495e-01 3.06512177e-01
-1.07844269e+00 -4.26715344e-01 -2.70294070e-01 7.44732261e-01
-6.60227060e-01 5.38106740e-01 -6.65445626e-01 -7.29248345e-01
-4.02027220e-01 -1.02553880e+00 1.38562250e+00 3.36325914e-02
-4.88216653e-02 -6.84985220e-01 -1.91420078e-01 8.32248509e-01
1.58412382e-01 -3.20123523e-01 6.76931322e-01 -2.43981987e-01
-6.54160500e-01 -3.31565067e-02 -2.43270710e-01 4.60874707e-01
-5.43402694e-02 1.05723828e-01 -1.53422236e+00 -4.87151414e-01
-1.22623898e-01 -7.36067593e-01 1.04062140e+00 6.40306294e-01
1.24035978e+00 -4.16024208e-01 -5.06080547e-03 6.88015640e-01
1.24969256e+00 -5.71478605e-02 4.59019482e-01 -1.54157400e-01
7.52112150e-01 4.97517318e-01 3.15261155e-01 4.87835199e-01
6.46265507e-01 5.98880887e-01 4.26857710e-01 -1.65481493e-01
-3.83957237e-01 -2.83665597e-01 8.29736948e-01 9.61617231e-01
2.24742308e-01 -2.93121487e-01 -7.85872757e-01 6.56789064e-01
-1.66688120e+00 -1.14344633e+00 1.96985275e-01 2.13511062e+00
6.52940214e-01 -2.68720716e-01 3.48644592e-02 -9.29362476e-02
6.50175035e-01 3.15177917e-01 -6.30947769e-01 -3.01335633e-01
-1.77217454e-01 2.22453013e-01 4.68485653e-01 7.98795044e-01
-9.56390142e-01 9.97139931e-01 6.42276859e+00 8.54951203e-01
-1.41290402e+00 4.89737362e-01 4.25788641e-01 -6.57008410e-01
-3.61501157e-01 -5.28769344e-02 -3.22088391e-01 1.72425687e-01
1.10495460e+00 6.73320843e-03 7.22652793e-01 3.70309651e-01
4.60332185e-01 -1.71152249e-01 -1.05552638e+00 1.64168036e+00
4.40461814e-01 -1.48065579e+00 2.02155694e-01 -1.75912470e-01
1.65379882e-01 4.40278739e-01 4.45023358e-01 1.76807553e-01
-1.07920974e-01 -1.18898106e+00 1.06831670e+00 6.54363394e-01
1.16703987e+00 -4.22001123e-01 1.51159391e-01 -1.28905056e-03
-1.27805889e+00 2.01741699e-02 -1.72404885e-01 2.53503084e-01
-5.39965965e-02 1.32622570e-01 -8.33046734e-01 4.02562559e-01
8.98939013e-01 7.62878120e-01 -5.45821011e-01 5.67049205e-01
-4.78005335e-02 8.88307154e-01 -2.69006133e-01 2.51704395e-01
-3.48303504e-02 2.34076351e-01 5.22764921e-01 1.26640916e+00
2.69768387e-01 -8.91233832e-02 7.97165856e-02 5.12083292e-01
-1.62811995e-01 3.82234082e-02 -5.35038352e-01 -3.83911043e-01
5.10257244e-01 1.10865700e+00 -3.36703062e-01 -3.28030169e-01
-7.96296716e-01 1.07854176e+00 -9.71410796e-02 6.67965233e-01
-8.71744096e-01 -2.83913851e-01 5.99690557e-01 -4.66187857e-02
5.25735855e-01 -3.55180949e-01 1.47097453e-01 -1.41572940e+00
6.95128813e-02 -9.32785392e-01 4.17793542e-01 -1.29798102e+00
-8.76209736e-01 4.67215240e-01 -1.42486945e-01 -1.24550867e+00
-4.32583660e-01 -5.68974376e-01 -1.87503904e-01 6.35958493e-01
-1.54282486e+00 -1.34528232e+00 -2.82929897e-01 1.31889427e+00
7.35417485e-01 -2.60638297e-01 7.43919849e-01 3.23627442e-01
-3.73897016e-01 6.78106010e-01 5.38155548e-02 1.80761218e-01
9.02084291e-01 -7.51651764e-01 -2.50090398e-02 6.32362783e-01
5.33579171e-01 3.18278909e-01 6.67801440e-01 -1.28206015e-01
-2.06889582e+00 -1.04181731e+00 8.34513724e-01 -2.84595132e-01
7.58228302e-01 -5.19094586e-01 -4.82635647e-01 9.13855314e-01
5.60642362e-01 4.01453897e-02 7.35922992e-01 -2.27458209e-01
-7.04695404e-01 -2.11798415e-01 -7.33355463e-01 5.01057625e-01
4.82968330e-01 -1.47877955e+00 -4.80797917e-01 5.00994802e-01
7.45910048e-01 -3.64011854e-01 -6.59043252e-01 -6.97199032e-02
6.13034368e-01 -6.32480621e-01 1.10206878e+00 -2.34079704e-01
1.47225618e-01 -3.93129259e-01 -5.77815890e-01 -6.71809316e-01
2.85509169e-01 -7.67318726e-01 -1.39011905e-01 1.18385565e+00
2.94359922e-01 -4.89754736e-01 4.26988423e-01 1.45721674e-01
2.67966818e-02 -2.30939403e-01 -1.07917809e+00 -3.57548386e-01
-5.09442627e-01 -8.34227920e-01 -4.00831178e-02 8.81316602e-01
7.13887438e-02 4.83324826e-01 -5.29080093e-01 3.27414572e-01
5.95474243e-01 9.61516425e-02 5.77845812e-01 -5.82289577e-01
-4.13195670e-01 -2.05765978e-01 -3.17605168e-01 -1.10136104e+00
2.87529081e-01 -9.20775473e-01 -4.37529199e-02 -1.64092267e+00
5.65497950e-02 4.48384702e-01 -1.76363304e-01 8.45763624e-01
4.77121294e-01 4.68565613e-01 2.14238942e-01 1.15140654e-01
-8.53394032e-01 5.90863585e-01 8.69123340e-01 -5.88373482e-01
5.48470877e-02 -2.24320024e-01 -6.19588494e-01 6.09686315e-01
5.29000819e-01 -1.73331499e-01 -5.50304174e-01 -7.03118145e-01
1.19255535e-01 5.47177553e-01 8.33078742e-01 -6.43732250e-01
3.89407456e-01 -3.15099629e-03 3.46961439e-01 -6.70004725e-01
9.80812430e-01 -5.12860656e-01 -9.81436670e-02 9.56160799e-02
-5.51617086e-01 8.25622082e-02 4.53805327e-01 4.91785288e-01
-4.71652091e-01 5.99391870e-02 4.81919974e-01 1.05522228e-02
-7.67900586e-01 1.48382410e-01 -1.04322553e+00 -5.60257174e-02
5.35497189e-01 -2.17820525e-01 -4.44019377e-01 -9.80365336e-01
-8.82943749e-01 1.89114764e-01 2.63948858e-01 4.61086482e-01
1.07136548e+00 -1.08424485e+00 -6.66786373e-01 1.61317065e-01
1.20996945e-01 -4.66063410e-01 4.78888720e-01 8.29569399e-01
-2.57425070e-01 6.11111879e-01 1.10306896e-01 -8.37159157e-01
-1.62958121e+00 4.69498366e-01 2.84269333e-01 4.01014954e-01
-6.10546947e-01 8.26064289e-01 3.55033427e-01 6.49513863e-03
6.14647686e-01 -3.09643418e-01 -4.61768620e-02 2.30228588e-01
6.39594913e-01 5.18593043e-02 8.71967897e-03 -7.12576449e-01
-4.56436634e-01 5.07127643e-01 1.66109297e-02 -5.04930437e-01
1.04570317e+00 -5.76826036e-01 6.18948452e-02 7.23525167e-01
1.23038208e+00 3.83002721e-02 -1.21151984e+00 -3.86008054e-01
-4.21123624e-01 -1.63478896e-01 4.39631045e-01 -4.09491330e-01
-1.04001546e+00 1.16601145e+00 7.12812781e-01 -1.54220358e-01
1.25267863e+00 3.10923398e-01 5.46319604e-01 5.59804678e-01
3.04430816e-03 -1.03702176e+00 3.67363244e-01 4.54953194e-01
7.89817274e-01 -1.08068287e+00 -7.81466290e-02 4.22053412e-02
-7.94590414e-01 9.11342204e-01 1.33863613e-01 2.91929841e-01
4.69726294e-01 2.82891512e-01 2.55866319e-01 1.53786791e-02
-1.32543015e+00 -4.52431738e-01 4.82928276e-01 6.14085913e-01
3.41662586e-01 -2.10007623e-01 4.33602631e-01 2.25407630e-01
-1.75020751e-02 -1.10819846e-01 4.26742017e-01 9.10600960e-01
-3.34653735e-01 -6.63297832e-01 -5.11830628e-01 1.74985155e-01
-4.67232436e-01 -4.72903132e-01 -1.98949844e-01 4.26300049e-01
-3.03355187e-01 1.21174026e+00 2.33587176e-01 -4.24655944e-01
-6.62609190e-02 2.22441345e-01 7.19236374e-01 -4.19240981e-01
-3.20828676e-01 5.02134562e-01 1.01478517e-01 -8.50603521e-01
-6.65631831e-01 -6.43146813e-01 -1.14485526e+00 -2.05691710e-01
-3.77440676e-02 -9.29283351e-02 9.05685484e-01 9.46463585e-01
4.46393698e-01 3.34932923e-01 5.11801302e-01 -9.74431098e-01
-9.41089913e-02 -6.55627549e-01 -2.51626670e-01 -3.58340815e-02
9.24464345e-01 -2.82423526e-01 -2.94318795e-01 6.31928861e-01] | [10.39376163482666, 1.1021649837493896] |
29b3c3f0-6d09-480f-99c0-45e7f1772807 | what-does-the-failure-to-reason-with | 2305.19597 | null | https://arxiv.org/abs/2305.19597v1 | https://arxiv.org/pdf/2305.19597v1.pdf | What does the Failure to Reason with "Respectively" in Zero/Few-Shot Settings Tell Us about Language Models? | Humans can effortlessly understand the coordinate structure of sentences such as "Niels Bohr and Kurt Cobain were born in Copenhagen and Seattle, respectively". In the context of natural language inference (NLI), we examine how language models (LMs) reason with respective readings (Gawron and Kehler, 2004) from two perspectives: syntactic-semantic and commonsense-world knowledge. We propose a controlled synthetic dataset WikiResNLI and a naturally occurring dataset NatResNLI to encompass various explicit and implicit realizations of "respectively". We show that fine-tuned NLI models struggle with understanding such readings without explicit supervision. While few-shot learning is easy in the presence of explicit cues, longer training is required when the reading is evoked implicitly, leaving models to rely on common sense inferences. Furthermore, our fine-grained analysis indicates models fail to generalize across different constructions. To conclude, we demonstrate that LMs still lag behind humans in generalizing to the long tail of linguistic constructions. | ['Anders Søgaard', 'Daniel Hershcovich', 'Seolhwa Lee', 'Ruixiang Cui'] | 2023-05-31 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [ 2.62631088e-01 6.59820676e-01 -3.13981213e-02 -5.99729896e-01
-6.23496473e-01 -6.52121663e-01 1.09433687e+00 4.68022436e-01
-4.60748732e-01 9.49256361e-01 6.22502327e-01 -6.74554527e-01
-2.16289967e-01 -9.16546285e-01 -7.18516648e-01 -1.55872285e-01
2.43089750e-01 7.32881129e-01 1.57716200e-01 -4.77497876e-01
1.53426737e-01 8.03637281e-02 -1.44408894e+00 2.78515637e-01
8.82595479e-01 5.29434264e-01 3.53305608e-01 4.71048683e-01
-5.63781083e-01 1.33704674e+00 -7.15872884e-01 -6.17292583e-01
-2.08146259e-01 -4.00546610e-01 -1.18502438e+00 -2.91606098e-01
4.94146407e-01 9.39345583e-02 -2.23553479e-02 9.32149947e-01
1.13014571e-01 2.68394768e-01 7.71094322e-01 -9.14480925e-01
-8.83973658e-01 1.19354737e+00 3.55746783e-02 2.80779541e-01
5.28920710e-01 1.89167321e-01 1.44327462e+00 -7.36571848e-01
7.91311085e-01 1.53274620e+00 6.06843650e-01 6.47027493e-01
-1.38013041e+00 -2.57893622e-01 2.91287750e-01 3.39091688e-01
-1.05302334e+00 -3.32980037e-01 5.58340371e-01 -4.44005609e-01
1.14563882e+00 1.68728799e-01 3.72725934e-01 1.32602704e+00
5.55388331e-02 7.40372300e-01 1.21310163e+00 -7.77581394e-01
2.70828694e-01 5.18705323e-02 5.96184433e-01 5.41284084e-01
3.73051405e-01 2.08703317e-02 -5.46708465e-01 -7.01722205e-02
2.55344719e-01 -3.04299802e-01 -2.34601144e-02 -2.08323728e-02
-1.34752452e+00 8.59982252e-01 2.46783614e-01 5.67572713e-01
-9.73063707e-02 2.91261315e-01 4.03581709e-01 3.49895030e-01
2.52575338e-01 9.50603306e-01 -7.62335479e-01 -1.04139864e-01
-5.88046074e-01 4.18108970e-01 1.11677575e+00 1.03487444e+00
6.88977420e-01 -1.42106742e-01 2.25150213e-01 5.87238193e-01
1.31702706e-01 4.29210544e-01 4.51147497e-01 -1.22527099e+00
3.69465739e-01 3.03016782e-01 4.12883401e-01 -7.66009629e-01
-3.86255622e-01 -1.63384646e-01 -5.28973401e-01 -1.35899782e-01
7.16685474e-01 -2.36507893e-01 -6.50258243e-01 2.00168991e+00
-2.63917726e-02 1.97577193e-01 5.29630542e-01 5.36824882e-01
9.04670060e-01 6.07675970e-01 6.34335995e-01 -1.52944133e-01
1.37726212e+00 -4.81544495e-01 -5.60331464e-01 -7.59766877e-01
8.49921346e-01 -2.35088110e-01 1.52460861e+00 -3.87341753e-02
-1.27954721e+00 -4.64102745e-01 -1.00585043e+00 -4.64104950e-01
-6.09259188e-01 -2.91502029e-01 8.37779284e-01 1.32634416e-01
-7.62315154e-01 4.76991594e-01 -6.58864141e-01 -4.98581886e-01
1.51098922e-01 -5.59971072e-02 -1.88193724e-01 4.23662066e-02
-1.68503010e+00 1.40038836e+00 6.16719067e-01 -2.65521314e-02
-6.99932039e-01 -7.63930857e-01 -1.16200364e+00 9.77641419e-02
5.90009451e-01 -7.31571674e-01 1.37726855e+00 -8.97561371e-01
-1.15416718e+00 1.32143760e+00 -3.10899466e-01 -7.87926018e-01
2.14672610e-01 -2.90962994e-01 -3.80008191e-01 -1.60564423e-01
3.30017298e-01 5.40926099e-01 2.87666112e-01 -1.22765338e+00
-4.44184273e-01 -2.28973538e-01 7.45495081e-01 -5.70489280e-02
2.66887128e-01 1.25616401e-01 4.59928870e-01 -4.37083036e-01
-5.64826699e-03 -6.37804806e-01 9.51780826e-02 -2.27377802e-01
-5.33224761e-01 -5.87725818e-01 7.89169669e-02 -4.19253230e-01
1.01553404e+00 -1.88298380e+00 1.51886433e-01 -2.91196741e-02
2.53739506e-01 7.75035471e-02 1.04887247e-01 5.89619875e-01
-8.07804838e-02 4.10672247e-01 -1.81716532e-01 -2.82395855e-02
4.06728506e-01 8.00350845e-01 -5.05297124e-01 1.97510398e-03
7.08872527e-02 1.07187855e+00 -1.18412220e+00 -4.22769725e-01
2.49439791e-01 -1.08905196e-01 -5.25934279e-01 -7.96459708e-03
-7.66543627e-01 2.01890543e-01 -2.32235625e-01 2.20288381e-01
1.35940805e-01 -6.25962615e-01 4.59589988e-01 -1.39137238e-01
-2.46317871e-02 9.78290737e-01 -7.94326246e-01 1.75706708e+00
-7.99681306e-01 4.91576672e-01 -2.09119439e-01 -1.00638807e+00
6.73498154e-01 2.81590641e-01 -5.22519827e-01 -3.97341907e-01
1.29437268e-01 2.52119392e-01 3.52259964e-01 -7.58976698e-01
2.03454733e-01 -7.80233443e-01 -5.34300148e-01 3.30171347e-01
8.99793133e-02 -3.00652802e-01 3.57576996e-01 4.25439417e-01
9.11911547e-01 2.12028503e-01 7.66188920e-01 -5.66128731e-01
4.40740019e-01 1.18972406e-01 5.40689707e-01 1.04431593e+00
1.38890073e-01 6.85618892e-02 5.84395051e-01 -5.22473752e-01
-1.01056385e+00 -1.49768758e+00 -3.42414439e-01 1.33252704e+00
2.15695754e-01 -5.19662559e-01 -5.06185293e-01 -3.80542487e-01
-2.22863227e-01 1.83152163e+00 -6.81997180e-01 6.57557920e-02
-5.59769630e-01 -2.29589164e-01 5.44732451e-01 6.12717211e-01
3.64136547e-01 -1.33263659e+00 -7.82352328e-01 3.38232130e-01
-2.74496645e-01 -1.06453872e+00 2.10861206e-01 2.77968854e-01
-4.37984973e-01 -9.55347717e-01 1.20999724e-01 -7.41350830e-01
3.62880111e-01 -3.62908185e-01 1.59154975e+00 2.72107214e-01
-3.04577470e-01 2.05752268e-01 -1.55374318e-01 -6.34739161e-01
-7.58960783e-01 4.49044369e-02 -1.67563677e-01 -6.14882648e-01
7.01753259e-01 -7.38268852e-01 7.82537907e-02 -1.54187813e-01
-7.08006263e-01 2.01046780e-01 3.53276618e-02 9.37792301e-01
-1.19780200e-02 -2.59983897e-01 6.12861514e-01 -1.47422218e+00
5.41689456e-01 -5.90182006e-01 -3.69565308e-01 5.62247992e-01
-1.44868031e-01 4.56146121e-01 6.21978760e-01 -1.78467572e-01
-1.54330504e+00 -7.00607598e-01 -5.25782034e-02 2.78389961e-01
-3.80929828e-01 7.13088870e-01 -2.45635390e-01 8.28931630e-01
9.11771655e-01 4.56074104e-02 -1.68347239e-01 -3.63781124e-01
6.51979327e-01 3.66881073e-01 6.32412076e-01 -1.35125232e+00
7.15990603e-01 3.47420931e-01 -1.24196209e-01 -8.59694779e-01
-1.79743981e+00 -4.89496626e-02 -6.63063705e-01 2.95325369e-01
1.05022871e+00 -8.71383607e-01 -6.55014932e-01 9.95268971e-02
-1.35199869e+00 -5.03785431e-01 -4.51406300e-01 2.24196672e-01
-8.16245377e-01 1.55281201e-01 -7.12123811e-01 -6.27000213e-01
8.02183524e-02 -6.37878716e-01 7.45113194e-01 -2.58989539e-02
-9.59859252e-01 -1.66638923e+00 -1.76157862e-01 2.08537996e-01
3.01901400e-01 2.60295153e-01 1.71468186e+00 -1.02737164e+00
-4.16388422e-01 1.63949221e-01 -1.20328255e-01 1.80108637e-01
1.95103675e-01 -1.47618130e-01 -8.91474903e-01 2.00607777e-01
5.59790172e-02 -6.89924955e-01 5.38745642e-01 -5.25108837e-02
9.56408978e-01 -5.76510549e-01 -1.50141686e-01 1.38259292e-01
1.58928549e+00 -1.70498878e-01 5.50418735e-01 3.50914657e-01
3.78211379e-01 6.52003109e-01 3.75896752e-01 1.75493956e-01
7.16123581e-01 1.82514012e-01 -2.79554520e-02 3.67349118e-01
-6.31363243e-02 -5.95677853e-01 3.16546746e-02 6.37769103e-01
-7.47931004e-02 -1.97369143e-01 -1.14387286e+00 8.15800726e-01
-1.84745955e+00 -1.27556908e+00 1.64811566e-01 1.79126835e+00
1.37312555e+00 5.13687193e-01 -4.00714666e-01 -2.32115224e-01
4.44096178e-01 3.53206277e-01 -2.76012152e-01 -4.80740756e-01
-2.32700437e-01 5.09019613e-01 2.99325027e-02 9.74389255e-01
-7.00393558e-01 1.21505237e+00 6.05056095e+00 5.69764555e-01
-5.29187381e-01 1.25894845e-01 1.97623089e-01 5.94307594e-02
-8.33768964e-01 4.48599428e-01 -7.27817059e-01 3.18588704e-01
1.20157492e+00 -2.87594885e-01 3.34120154e-01 5.11450231e-01
4.56424383e-03 -1.20631494e-01 -1.53528500e+00 4.65132624e-01
2.00633779e-02 -1.63056219e+00 1.29830435e-01 -3.78282785e-01
5.67952514e-01 -6.27999008e-02 -4.33408558e-01 4.28745687e-01
8.80599260e-01 -1.20338917e+00 8.60164165e-01 6.76729441e-01
4.62068588e-01 -4.12244588e-01 6.73138618e-01 6.68641686e-01
-6.78591847e-01 -1.56040797e-02 -5.24366796e-01 -5.77849627e-01
2.67435342e-01 2.46935666e-01 -5.37926555e-01 1.31280303e-01
4.71413024e-02 5.52441120e-01 -3.63103360e-01 1.69176698e-01
-8.48228812e-01 6.72979653e-01 -1.99544594e-01 -3.81876379e-01
3.65789831e-01 -2.47613154e-02 3.70237470e-01 1.19982350e+00
-1.75019521e-02 4.72547472e-01 2.76752025e-01 1.26815188e+00
3.91969495e-02 -3.04741174e-01 -5.98879576e-01 3.60140428e-02
6.98488176e-01 7.11161613e-01 -2.01359808e-01 -7.63601363e-01
-5.54347277e-01 4.97642219e-01 5.74579120e-01 4.29367512e-01
-6.44053161e-01 -2.26501495e-01 6.54654622e-01 2.59715438e-01
-1.76352561e-02 -1.58213720e-01 -3.21021527e-01 -1.26915407e+00
-6.37640432e-02 -6.65892065e-01 1.79767385e-01 -1.15939200e+00
-1.55772996e+00 1.71718150e-01 4.87219095e-01 -4.99434680e-01
-7.03692079e-01 -8.98973465e-01 -8.02289367e-01 7.67950237e-01
-1.41902220e+00 -1.32517695e+00 1.95283238e-02 2.10794881e-01
7.57208586e-01 1.62552729e-01 1.06876767e+00 -3.66416007e-01
-6.20381683e-02 1.94602713e-01 -3.37431371e-01 3.90487015e-01
4.14991140e-01 -1.45848012e+00 5.18925846e-01 5.63859165e-01
2.01666161e-01 1.23149800e+00 1.10909367e+00 -3.94181401e-01
-9.45648909e-01 -7.08162189e-01 1.47969782e+00 -7.65976667e-01
1.17676795e+00 -3.52891982e-01 -1.10943663e+00 1.17824697e+00
4.73492146e-01 -1.54963672e-01 8.02139282e-01 6.47498131e-01
-7.52398729e-01 2.14035928e-01 -9.62282062e-01 8.06012273e-01
1.29715955e+00 -8.69115174e-01 -1.65007937e+00 4.98885006e-01
9.51042295e-01 -1.57225445e-01 -7.43117452e-01 1.28786221e-01
1.65128335e-01 -8.57456565e-01 7.84813285e-01 -1.23457849e+00
6.70530021e-01 -2.63997138e-01 -4.04128730e-01 -1.27162158e+00
-8.01362470e-02 -3.42152059e-01 2.20516816e-01 1.04074681e+00
8.15053105e-01 -5.38887858e-01 1.50904253e-01 1.02324724e+00
-4.19530496e-02 -4.06354636e-01 -8.38725567e-01 -6.68649495e-01
4.38776642e-01 -6.94676220e-01 4.70718354e-01 9.07209814e-01
5.19117057e-01 8.12226832e-01 6.97166696e-02 2.09123585e-02
5.42663217e-01 2.24341020e-01 5.76642215e-01 -1.37986827e+00
-5.33668637e-01 -1.48757368e-01 -3.00489932e-01 -9.39744949e-01
7.71658599e-01 -1.09649742e+00 1.65109769e-01 -1.55647469e+00
2.64008135e-01 -5.41082382e-01 8.31643268e-02 5.04192293e-01
-2.00613916e-01 -3.21345657e-01 2.45693684e-01 -5.20653315e-02
-5.71351051e-01 1.80267051e-01 8.89131546e-01 -5.23786210e-02
3.30133557e-01 -3.41357619e-01 -8.86068523e-01 1.36828935e+00
6.08977318e-01 -1.08775988e-01 -6.42159641e-01 -6.73478484e-01
6.37316346e-01 1.03749767e-01 6.76329851e-01 -6.13647103e-01
2.28895098e-01 -5.10348558e-01 4.15039025e-02 -8.52744281e-02
2.44239584e-01 -4.46315795e-01 -1.12431347e-01 1.93434447e-01
-9.60693061e-01 2.76525710e-02 1.30903989e-01 4.29136783e-01
-9.29925963e-02 -5.58838308e-01 7.70435393e-01 -6.94382071e-01
-1.01551247e+00 -2.28279144e-01 -3.06694001e-01 9.33746755e-01
6.08714998e-01 1.21456847e-01 -4.58880961e-01 -3.85350227e-01
-1.18009090e+00 6.53065518e-02 4.40245241e-01 2.60766864e-01
4.03455913e-01 -8.42308700e-01 -6.95955455e-01 -1.13328084e-01
2.41852358e-01 3.92556787e-02 5.61291492e-03 4.23383474e-01
-4.12831545e-01 2.94575214e-01 2.11444825e-01 -2.61071950e-01
-6.20839775e-01 6.15050375e-01 2.64526516e-01 -1.40380576e-01
-6.90797091e-01 9.96066809e-01 4.24811959e-01 -6.14117444e-01
2.53869854e-02 -6.33754909e-01 1.74236536e-01 -6.43824413e-02
5.11667490e-01 -1.10984765e-01 -3.35714161e-01 -2.75865972e-01
-3.22919935e-01 2.84991920e-01 -1.64557502e-01 -9.45058689e-02
1.22742212e+00 -3.25504869e-01 -2.83355981e-01 1.13172436e+00
9.46993768e-01 9.47566554e-02 -8.51153970e-01 -4.19480115e-01
5.25584936e-01 -1.08129501e-01 -5.59428334e-01 -9.70196724e-01
2.94831749e-02 1.03369093e+00 -3.18679154e-01 2.03004971e-01
4.18977618e-01 5.44993103e-01 5.73590577e-01 8.59475791e-01
5.72484970e-01 -1.03598046e+00 -1.92461401e-01 7.68498480e-01
7.58208930e-01 -1.09258986e+00 -1.28400698e-01 -2.19210356e-01
-6.02579236e-01 9.70529199e-01 5.41288257e-01 -1.61390841e-01
4.35323238e-01 3.34902555e-01 2.10282635e-02 -3.30265701e-01
-1.18728518e+00 -1.81648359e-01 -1.77231506e-01 6.08429968e-01
6.20898843e-01 2.29310215e-01 -1.83786973e-01 4.30581182e-01
-7.04932809e-01 -2.99616903e-01 6.94261849e-01 7.14539587e-01
-6.77469671e-01 -7.92335808e-01 1.46104582e-02 3.33488673e-01
-3.05850595e-01 -6.20756626e-01 -2.09785283e-01 1.19375348e+00
2.07372084e-01 8.30233097e-01 2.25986153e-01 3.28167140e-01
3.69229130e-02 4.76295829e-01 6.24607027e-01 -1.05154645e+00
-2.39549324e-01 -4.19713318e-01 6.76845372e-01 -1.26015082e-01
-6.42392278e-01 -5.32500923e-01 -1.59407043e+00 -3.90466779e-01
-8.74772593e-02 2.25058243e-01 2.45048061e-01 1.68304014e+00
-7.49391243e-02 4.85222518e-01 -1.09282404e-01 -2.65592963e-01
-8.40965211e-01 -9.46195424e-01 -4.72805083e-01 6.27696812e-01
3.23080420e-01 -5.98872006e-01 -7.41268039e-01 1.28077134e-01] | [10.197457313537598, 8.371746063232422] |
346ccbd4-5121-4c24-92ec-f08ec26cec42 | fea2fea-exploring-structural-feature | 2106.13061 | null | https://arxiv.org/abs/2106.13061v4 | https://arxiv.org/pdf/2106.13061v4.pdf | Fea2Fea: Exploring Structural Feature Correlations via Graph Neural Networks | Structural features are important features in a geometrical graph. Although there are some correlation analysis of features based on covariance, there is no relevant research on structural feature correlation analysis with graph neural networks. In this paper, we introuduce graph feature to feature (Fea2Fea) prediction pipelines in a low dimensional space to explore some preliminary results on structural feature correlation, which is based on graph neural network. The results show that there exists high correlation between some of the structural features. An irredundant feature combination with initial node features, which is filtered by graph neural network has improved its classification accuracy in some graph-based tasks. We compare differences between concatenation methods on connecting embeddings between features and show that the simplest is the best. We generalize on the synthetic geometric graphs and certify the results on prediction difficulty between structural features. | ['Rex Ying', 'Jiaqing Xie'] | 2021-06-24 | null | null | null | null | ['structual-feature-correlation'] | ['graphs'] | [-2.20002308e-01 3.23007107e-01 8.98050442e-02 -3.83871853e-01
3.13218981e-01 -3.55353206e-01 5.56149721e-01 4.71223086e-01
4.99859415e-02 2.47661546e-01 3.53653550e-01 -4.21854138e-01
-7.47849047e-01 -1.31782472e+00 -2.17858016e-01 -4.76157099e-01
-8.33993018e-01 2.48453692e-01 1.13253914e-01 -7.09364831e-01
3.96618396e-01 8.77083659e-01 -1.36952031e+00 1.79920971e-01
3.55498463e-01 8.72219443e-01 -1.48642555e-01 8.44646931e-01
-3.20688874e-01 6.23645306e-01 -1.58407047e-01 -4.92913514e-01
3.64873856e-01 -5.91943227e-02 -9.93423641e-01 -2.30801508e-01
6.95700943e-01 3.53805900e-01 -4.33134496e-01 8.86768341e-01
3.21062863e-01 1.05186306e-01 1.17581308e+00 -1.65046144e+00
-1.26803446e+00 7.18856990e-01 -2.63068825e-01 1.93592906e-01
5.97331822e-01 -2.73957342e-01 1.50674081e+00 -9.23732579e-01
8.31957221e-01 1.26861846e+00 1.24756360e+00 4.18821648e-02
-9.61702168e-01 -1.94407046e-01 -1.59890264e-01 2.99557626e-01
-1.29346263e+00 1.69700712e-01 1.12905252e+00 -5.03645718e-01
1.46614230e+00 3.42303246e-01 9.84511137e-01 5.92584074e-01
6.41558468e-01 2.74759561e-01 7.98804581e-01 -5.55329740e-01
-4.65664655e-01 -6.27318993e-02 6.16708815e-01 1.36010647e+00
4.14671510e-01 1.80341437e-01 -2.18413591e-01 -1.40684888e-01
6.83463693e-01 2.29131505e-02 1.23163452e-02 -6.10547841e-01
-9.85583544e-01 1.19086075e+00 8.84263337e-01 6.83398068e-01
-2.27232128e-02 2.20797136e-01 4.02033299e-01 7.51339912e-01
5.12443244e-01 8.69129956e-01 -5.48393786e-01 1.53412297e-01
-3.46429855e-01 -5.54123148e-02 9.02471244e-01 7.70790040e-01
1.00660229e+00 2.62930334e-01 8.78654420e-02 7.42303967e-01
3.82490575e-01 1.84264794e-01 2.07001269e-01 -3.39140683e-01
1.72729313e-01 1.05487788e+00 -9.16522443e-01 -1.99815631e+00
-1.06046641e+00 -3.38438034e-01 -1.00122774e+00 3.15668583e-01
1.86234355e-01 -1.20745189e-02 -7.25705147e-01 1.29984140e+00
6.09961525e-02 -5.48583269e-02 -1.24710672e-01 5.79840422e-01
1.49868441e+00 4.01982874e-01 -2.21738562e-01 4.10176396e-01
9.83852744e-01 -9.07887399e-01 -4.74499911e-01 4.41321015e-01
1.29579830e+00 -7.75526762e-01 8.12308967e-01 1.54690444e-01
-4.90739077e-01 -7.55191267e-01 -1.14811325e+00 -3.42388265e-03
-1.12206006e+00 1.56291835e-02 1.45662010e+00 9.10147011e-01
-1.37924922e+00 1.33118272e+00 -4.06846732e-01 -8.81506085e-01
1.88402712e-01 6.99700594e-01 -9.10516858e-01 2.79734790e-01
-1.06746948e+00 9.08866107e-01 5.97588181e-01 -1.36648849e-01
5.95243908e-02 -3.80369246e-01 -1.01482797e+00 3.14528681e-02
-1.04860663e-01 -6.34383380e-01 1.58703327e-01 -5.95773518e-01
-1.28586018e+00 5.29579639e-01 3.93085510e-01 -3.10970366e-01
-5.07274382e-02 2.82783270e-01 -7.20659971e-01 -9.68175009e-02
-2.86538929e-01 6.13203347e-01 6.25668705e-01 -8.32146883e-01
-2.33337790e-01 -4.10670675e-02 5.64024150e-02 -1.26070436e-02
-5.26027441e-01 -1.34402394e-01 2.85806179e-01 -5.50473988e-01
4.30193841e-01 -9.02633965e-01 -1.23045124e-01 -1.78105548e-01
-6.48117244e-01 -6.09855890e-01 1.01782250e+00 -2.80812889e-01
1.16132283e+00 -1.82167661e+00 -1.61342368e-01 6.64084017e-01
5.53609192e-01 -2.15401854e-02 -4.74768728e-01 1.00685918e+00
-6.52651548e-01 5.61204970e-01 1.40947476e-01 1.21988356e-01
-2.71048397e-02 6.36072531e-02 6.90231547e-02 5.20383060e-01
6.15339279e-01 1.15822673e+00 -6.98208392e-01 -3.57896298e-01
2.81510800e-01 5.60653985e-01 -7.55337656e-01 -4.31157555e-03
2.84074008e-01 -3.22233401e-02 -5.33153236e-01 5.93801796e-01
6.96488082e-01 -2.78537333e-01 -6.43747076e-02 -6.16381824e-01
1.73452258e-01 9.83570516e-02 -1.06269777e+00 1.50665569e+00
8.12955142e-04 6.86670899e-01 -6.06767893e-01 -1.31129920e+00
1.41844308e+00 -1.94345504e-01 5.79669952e-01 -2.62312800e-01
2.45888010e-01 -6.84479475e-02 6.53055429e-01 -6.01235449e-01
6.44385159e-01 1.88512921e-01 1.02249555e-01 2.60869384e-01
4.08266336e-01 -2.71077663e-01 3.11586201e-01 4.83013004e-01
1.44154751e+00 -8.67763758e-02 2.24689141e-01 -6.42945290e-01
6.15375221e-01 -9.04851258e-02 -1.20132267e-01 4.83928084e-01
1.95843745e-02 8.53828371e-01 6.67711973e-01 -8.38556349e-01
-9.49605286e-01 -1.01060617e+00 8.49950910e-02 9.76427972e-01
-1.40974715e-01 -1.27253354e+00 -2.38940850e-01 -9.98264849e-01
1.98733464e-01 1.67940170e-01 -9.91403997e-01 -2.94916898e-01
-3.45971942e-01 -6.01023376e-01 3.77229035e-01 4.39784199e-01
2.57150382e-01 -9.06759024e-01 1.20440759e-01 -3.80856171e-02
8.25064659e-01 -7.15331554e-01 -7.98407197e-02 2.73457229e-01
-9.23106194e-01 -1.36080360e+00 2.23499864e-01 -1.01111078e+00
6.75253153e-01 2.05455616e-01 1.21690702e+00 7.50338554e-01
-4.57232505e-01 4.87923354e-01 -6.32240951e-01 -1.21977977e-01
-3.17652851e-01 3.72327894e-01 5.47839962e-02 -4.29934770e-01
2.68971473e-01 -6.81231499e-01 -1.75582677e-01 -8.19398239e-02
-4.45680261e-01 -8.00073668e-02 5.05209267e-01 7.37521887e-01
1.71814650e-01 3.56576711e-01 2.11212605e-01 -8.94682109e-01
1.02612877e+00 -3.94982249e-01 -9.22851190e-02 5.88041358e-02
-7.13715792e-01 4.22136217e-01 7.77926087e-01 -9.14465040e-02
-2.27800816e-01 -6.25582933e-02 -2.33364314e-01 -1.49629846e-01
-6.20182529e-02 8.79462719e-01 2.05293909e-01 -5.62235475e-01
6.70416236e-01 -2.44456500e-01 2.76613653e-01 -2.78500855e-01
5.50352633e-01 7.01796860e-02 -2.00777084e-01 -4.29750651e-01
9.57444072e-01 -1.14570567e-02 7.24392295e-01 -1.03038645e+00
-3.22411716e-01 -1.09776795e-01 -1.04431605e+00 -1.71004757e-01
8.38628292e-01 -3.19198668e-01 -7.60960877e-01 1.15891464e-01
-8.60866308e-01 1.76272959e-01 -1.94895953e-01 5.73579729e-01
-4.45184648e-01 4.76914614e-01 -6.32699370e-01 -3.42289656e-01
-2.61495799e-01 -9.59286451e-01 5.51855743e-01 -3.42653021e-02
-2.25911200e-01 -1.48947084e+00 2.38782629e-01 -2.40527615e-01
6.11228108e-01 4.79832500e-01 1.21606648e+00 -1.09107041e+00
-3.87564182e-01 -4.00018811e-01 -4.19340551e-01 1.64990097e-01
3.12775999e-01 6.17684722e-01 -4.81956899e-01 -2.75591969e-01
-5.86665213e-01 -1.30071891e-02 9.14001584e-01 3.32394540e-01
1.03707802e+00 8.56075622e-03 -3.70950729e-01 6.59282506e-01
1.50791764e+00 -1.32485881e-01 6.04338408e-01 2.66990393e-01
1.25924814e+00 6.42160654e-01 1.47793278e-01 -3.17178369e-02
3.61142635e-01 2.87062079e-01 4.91280466e-01 -2.47413754e-01
-4.21688110e-01 -2.35951841e-01 1.80035651e-01 1.32912624e+00
-4.03854787e-01 -1.48180068e-01 -9.33699191e-01 3.87952402e-02
-1.54711950e+00 -8.29505682e-01 -8.65609705e-01 1.59101212e+00
-7.93098100e-03 1.31190196e-01 1.52098894e-01 2.18717128e-01
6.86933279e-01 3.47659856e-01 2.56287545e-01 -1.03012359e+00
-2.26333782e-01 4.67860430e-01 5.03009200e-01 4.05658424e-01
-1.26255643e+00 1.06257272e+00 7.28408527e+00 6.14030719e-01
-9.93553221e-01 -2.32849166e-01 2.83361912e-01 5.56488693e-01
-4.01580364e-01 1.28080189e-01 -4.80673522e-01 3.52716260e-02
8.55987370e-01 -2.88661979e-02 2.08663359e-01 8.98962557e-01
-4.45405692e-01 3.17814708e-01 -1.18230152e+00 9.08225060e-01
2.48379886e-01 -1.48238063e+00 2.60768026e-01 1.65039495e-01
4.54471827e-01 1.67883217e-01 8.47708359e-02 4.05954748e-01
4.13296044e-01 -1.45944989e+00 2.35891715e-02 6.37488186e-01
2.96586901e-01 -8.44735324e-01 7.56307662e-01 -3.66176307e-01
-1.59464824e+00 7.49674365e-02 -8.62192392e-01 -5.04283845e-01
-1.22700900e-01 7.16531038e-01 -1.09623337e+00 9.79865909e-01
5.80765605e-01 1.30277479e+00 -1.29034269e+00 9.34249997e-01
-1.40798062e-01 3.11938286e-01 -2.78797209e-01 -4.23859626e-01
3.20989072e-01 -6.02295339e-01 4.43665713e-01 1.19762552e+00
5.31791747e-01 -2.19401747e-01 2.71848857e-01 7.71880388e-01
1.93988502e-01 6.42702222e-01 -1.46067548e+00 -2.47872099e-01
8.31243582e-03 1.79538488e+00 -1.16950333e+00 -1.28936497e-02
-5.52583337e-01 5.86124420e-01 6.26333296e-01 -1.78007543e-01
-6.45100236e-01 -7.11715341e-01 4.97655779e-01 2.14975588e-02
1.52948946e-01 -5.83412945e-01 -2.10920483e-01 -8.66101682e-01
-2.86638200e-01 -2.58085728e-01 4.38356936e-01 -8.18017423e-01
-1.69177032e+00 7.28737891e-01 -1.49499089e-01 -1.18832374e+00
5.78375086e-02 -1.23836029e+00 -1.02038181e+00 5.31626940e-01
-9.54478800e-01 -1.58967233e+00 -1.73333108e-01 5.34219623e-01
-1.97956905e-01 -6.08273566e-01 1.01091874e+00 8.92871916e-02
-3.93606007e-01 4.85024452e-01 -1.63230404e-01 4.62131560e-01
4.38119203e-01 -1.38617992e+00 6.23593688e-01 4.70983058e-01
5.72219491e-01 5.64676166e-01 3.77461255e-01 -8.53992164e-01
-1.64734066e+00 -9.73170996e-01 7.97932088e-01 -6.05485559e-01
9.49098229e-01 -3.23445916e-01 -8.27692866e-01 5.88427484e-01
3.91278863e-01 3.51335466e-01 7.39452541e-01 5.28496444e-01
-4.48401242e-01 2.37486169e-01 -1.00722814e+00 4.77964103e-01
1.52106583e+00 -4.67207551e-01 -6.00629926e-01 2.90051699e-01
7.32745409e-01 1.69442311e-01 -1.35452282e+00 6.64177001e-01
4.32800025e-01 -1.05765438e+00 1.10183418e+00 -1.00199246e+00
4.29085642e-01 -1.25996456e-01 -3.57500017e-01 -1.59581745e+00
-9.15804684e-01 -1.12255231e-01 2.29374766e-01 1.11326432e+00
7.13986158e-01 -8.84928942e-01 1.00872517e+00 2.72211283e-01
-1.76676601e-01 -8.21004093e-01 -5.08839726e-01 -1.08167922e+00
3.04971784e-01 -4.37334329e-01 5.86094677e-01 1.45620382e+00
5.65100722e-02 6.46312177e-01 -7.18503371e-02 -9.05500203e-02
1.25294909e-01 1.07208185e-01 8.61045957e-01 -1.87449944e+00
8.46821964e-02 -6.00835204e-01 -1.43893921e+00 -8.22523013e-02
3.67267132e-01 -1.74323916e+00 -6.67698145e-01 -1.79220200e+00
5.55567490e-03 -4.98467267e-01 -3.28784287e-01 4.50300485e-01
3.98149416e-02 9.28500220e-02 1.72532752e-01 -3.38898957e-01
-3.26175272e-01 4.14411873e-01 1.26292694e+00 -1.90924451e-01
-8.87968093e-02 -4.90782529e-01 -4.78052616e-01 7.10617721e-01
8.96937966e-01 -3.97179097e-01 -4.72237319e-01 -2.05138251e-01
5.12591004e-01 -4.49198514e-01 2.24676002e-02 -1.34352028e+00
5.66904210e-02 1.85670614e-01 5.96032560e-01 -6.32069230e-01
1.67414382e-01 -7.84118176e-01 3.06168914e-01 5.25495410e-01
-1.53358504e-01 6.51500165e-01 -9.36359391e-02 3.83801699e-01
-1.61972344e-01 -3.19292754e-01 3.43443751e-01 -1.16039932e-01
-8.36030304e-01 5.40720940e-01 -1.49682745e-01 -2.76304662e-01
7.32102931e-01 -5.05081832e-01 -4.20605510e-01 -2.45145172e-01
-9.55001175e-01 -2.74084151e-01 3.86846900e-01 6.72106147e-01
7.40960538e-01 -1.63261354e+00 -5.79850972e-01 4.49104905e-01
8.11140612e-02 -5.94641507e-01 -9.56601053e-02 7.93899834e-01
-6.41321480e-01 2.54094601e-01 -5.70843756e-01 -5.80185294e-01
-1.43740129e+00 7.94201910e-01 1.69379890e-01 -2.78557450e-01
-5.28023243e-01 1.02258790e+00 -3.86781365e-01 -8.74178886e-01
-3.24569583e-01 -4.02046293e-01 -7.15199590e-01 2.88746297e-01
-1.47962689e-01 4.27935719e-01 1.89973727e-01 -9.16424930e-01
-4.32852298e-01 1.08658862e+00 6.99088648e-02 6.01414442e-01
1.81090057e+00 2.18176231e-01 -6.41116738e-01 2.26954147e-01
1.82809651e+00 1.64807588e-01 -3.02052706e-01 2.04895109e-01
1.73341587e-01 -4.72016066e-01 -7.61220828e-02 -3.23143899e-01
-1.49121034e+00 7.60547817e-01 7.61000574e-01 9.17059898e-01
5.46642482e-01 -5.04918471e-02 2.63177872e-01 8.18498313e-01
3.03955764e-01 -8.79746497e-01 1.93853781e-01 9.70742941e-01
1.00599968e+00 -1.18665385e+00 3.67221892e-01 -9.17049944e-01
-3.39985192e-01 1.72941637e+00 6.43005729e-01 -9.27130878e-01
1.32344115e+00 -3.15607898e-02 -3.33293438e-01 -8.96581709e-01
-5.31255424e-01 -4.40477908e-01 5.98081887e-01 8.95861208e-01
8.13454330e-01 1.34383202e-01 -3.81828845e-01 2.46446773e-01
-7.95147657e-01 -6.98581338e-01 5.78545928e-01 7.03269064e-01
-3.60325247e-01 -1.38106012e+00 7.99857378e-02 9.75974381e-01
-2.51435041e-01 -3.82899135e-01 -6.99003518e-01 1.19574261e+00
1.18352227e-01 7.53807485e-01 1.55260295e-01 -1.05679619e+00
2.67265558e-01 4.65274556e-03 6.18755400e-01 -6.72291338e-01
-8.09978426e-01 -5.66452503e-01 4.67842788e-01 -5.66960990e-01
-4.48264122e-01 -2.32586160e-01 -1.16520560e+00 -5.11406958e-01
-6.30028605e-01 1.22417293e-01 6.83679402e-01 6.14696681e-01
4.30625468e-01 6.21869504e-01 6.45380259e-01 -8.53855312e-01
2.33208850e-01 -9.88768280e-01 -7.92075634e-01 6.74848557e-01
-5.98297454e-02 -8.34342837e-01 -4.34033900e-01 -4.53579873e-01] | [6.999556541442871, 6.104860782623291] |
6ad2b708-0a81-4c4f-907c-bc83f4daa973 | benchmarking-evaluation-metrics-for-code | 2211.16319 | null | https://arxiv.org/abs/2211.16319v1 | https://arxiv.org/pdf/2211.16319v1.pdf | Benchmarking Evaluation Metrics for Code-Switching Automatic Speech Recognition | Code-switching poses a number of challenges and opportunities for multilingual automatic speech recognition. In this paper, we focus on the question of robust and fair evaluation metrics. To that end, we develop a reference benchmark data set of code-switching speech recognition hypotheses with human judgments. We define clear guidelines for minimal editing of automatic hypotheses. We validate the guidelines using 4-way inter-annotator agreement. We evaluate a large number of metrics in terms of correlation with human judgments. The metrics we consider vary in terms of representation (orthographic, phonological, semantic), directness (intrinsic vs extrinsic), granularity (e.g. word, character), and similarity computation method. The highest correlation to human judgment is achieved using transliteration followed by text normalization. We release the first corpus for human acceptance of code-switching speech recognition results in dialectal Arabic/English conversation speech. | ['Ahmed Ali', 'Nizar Habash', 'Sunayana Sitaram', 'Hamdy Mubarak', 'Shammur Chowdhury', 'Oumnia Chellah', 'Amir Hussein', 'Injy Hamed'] | 2022-11-22 | null | null | null | null | ['transliteration'] | ['natural-language-processing'] | [ 1.98997438e-01 9.58868023e-03 3.94210145e-02 -6.53759539e-01
-9.80677426e-01 -8.72031271e-01 6.76041245e-01 1.15080558e-01
-4.43199337e-01 3.81431580e-01 4.04851377e-01 -7.21396923e-01
1.27944410e-01 7.54361600e-02 -2.66972691e-01 -2.84276128e-01
2.65066892e-01 4.37922627e-01 2.04359040e-01 -4.75952983e-01
6.10803187e-01 2.63092488e-01 -1.65109992e+00 7.32768655e-01
1.23246944e+00 5.17420650e-01 4.02473450e-01 6.82954490e-01
-2.34825477e-01 9.03578281e-01 -8.59171093e-01 -6.59860730e-01
-9.51537862e-02 -5.92457116e-01 -1.22662938e+00 9.89565402e-02
4.96713966e-01 2.05756590e-01 4.33600284e-02 1.24026239e+00
5.16218662e-01 -2.25207023e-02 6.88783467e-01 -9.54753101e-01
-9.14766908e-01 7.94592023e-01 2.29466885e-01 4.14450914e-01
9.14948523e-01 7.06961825e-02 8.45756769e-01 -1.25218046e+00
6.58563375e-01 1.22292471e+00 6.00382268e-01 4.87769336e-01
-1.01791131e+00 -3.95511210e-01 -9.04863700e-02 3.62537205e-01
-1.67428756e+00 -1.11412823e+00 1.87228009e-01 -9.02422369e-01
1.41896474e+00 5.95038295e-01 1.26808614e-01 9.29549336e-01
-2.17235759e-01 5.51743507e-01 1.30325806e+00 -1.15893149e+00
1.50242731e-01 6.15081429e-01 2.30150223e-01 7.19657123e-01
-2.80741423e-01 -1.45587519e-01 -6.43769979e-01 -1.00915127e-01
1.68076053e-01 -7.68675745e-01 -4.44721609e-01 -3.59684862e-02
-1.41973042e+00 6.68657243e-01 -3.75368863e-01 6.73879504e-01
1.48867890e-01 -4.62782562e-01 6.63088977e-01 7.92540014e-01
4.66632284e-02 5.31130970e-01 -5.00033557e-01 -6.27726436e-01
-9.29148972e-01 -4.70959514e-01 9.78625894e-01 1.33594394e+00
5.16757071e-01 1.26276016e-01 -1.31746784e-01 1.60638189e+00
3.96552831e-01 7.45671213e-01 9.52327013e-01 -7.95453668e-01
4.33967203e-01 2.45858833e-01 -1.59504101e-01 -9.16571498e-01
-9.26320720e-03 -6.39654100e-02 -1.39807761e-01 -7.18664527e-02
3.92813712e-01 2.48258486e-01 -6.43357694e-01 1.33800578e+00
-2.15459049e-01 -6.60705090e-01 2.30731666e-01 6.33355677e-01
7.09228516e-01 5.92251122e-01 -2.01337129e-01 -2.68238038e-01
1.40466630e+00 -1.30277860e+00 -7.27162480e-01 -2.19191775e-01
7.08238780e-01 -1.42623460e+00 1.60322928e+00 3.61000746e-01
-9.68506217e-01 -5.88213980e-01 -9.49862301e-01 -7.46928342e-03
-4.96438086e-01 5.88892698e-01 2.19305515e-01 1.27160192e+00
-1.31805408e+00 -4.00080383e-02 -5.56434095e-01 -8.19963396e-01
-4.04950410e-01 2.19585717e-01 -4.82174009e-01 1.87734798e-01
-1.07330930e+00 1.23233950e+00 1.51285185e-02 -3.85044008e-01
-6.59228325e-01 7.47649819e-02 -8.53027761e-01 -1.42077684e-01
-2.24054247e-01 1.04809418e-01 1.48966122e+00 -1.42323053e+00
-1.81031954e+00 1.58771789e+00 -2.30377778e-01 -5.84136769e-02
2.75957346e-01 3.80538732e-01 -1.16110539e+00 -1.30590647e-01
1.99791789e-01 3.55039328e-01 6.40471101e-01 -1.13514185e+00
-8.25522602e-01 -1.98522471e-02 -3.74240079e-03 1.00518584e-01
-3.37866455e-01 8.72251511e-01 -4.02595282e-01 -8.48598540e-01
8.85503367e-03 -1.14714599e+00 4.77980316e-01 -3.37905139e-01
-1.63417771e-01 -1.89340904e-01 1.48436204e-01 -1.18497479e+00
1.62052965e+00 -2.32624388e+00 6.67039081e-02 1.62015513e-01
-2.45535612e-01 3.53860050e-01 -1.56517699e-01 6.28523886e-01
-6.85126856e-02 4.80060317e-02 -5.24976015e-01 -3.22138995e-01
2.28339687e-01 1.81744233e-01 -1.67411789e-01 4.12634373e-01
-5.32044508e-02 4.76176262e-01 -7.58648694e-01 -5.11230946e-01
-3.86564322e-02 3.40084583e-01 -4.58129644e-01 1.94719762e-01
3.51429760e-01 1.87960282e-01 1.61059514e-01 8.59634161e-01
3.49424094e-01 1.78065777e-01 3.71035725e-01 2.33022466e-01
-5.22055387e-01 8.92613590e-01 -8.91193926e-01 1.65023601e+00
-8.67988527e-01 9.57767189e-01 2.08008394e-01 -6.58320427e-01
9.44895506e-01 3.80044401e-01 -3.54413033e-01 -6.98893845e-01
-1.72792345e-01 8.68473411e-01 4.37855273e-01 -6.04699969e-01
6.63164139e-01 2.17742041e-01 -1.89577684e-01 3.16837698e-01
2.32436404e-01 -2.10069135e-01 2.04034612e-01 -9.94463935e-02
8.25903177e-01 -3.70783508e-01 7.41601944e-01 -8.33649158e-01
1.18695140e+00 6.06090538e-02 1.72249302e-01 5.65974236e-01
-4.42782521e-01 6.16392612e-01 5.04145503e-01 -1.27989904e-03
-9.98641312e-01 -9.10238743e-01 -2.94827193e-01 1.51266241e+00
-1.66989058e-01 -3.76084179e-01 -1.07388723e+00 -7.17858434e-01
-2.39554018e-01 8.69080663e-01 -2.74711072e-01 2.40496323e-02
-5.94304681e-01 -1.80734262e-01 1.07399070e+00 2.00351164e-01
-4.50263172e-02 -9.68168736e-01 -7.96709359e-02 2.09932420e-02
-3.83369863e-01 -1.16032171e+00 -9.44002986e-01 5.40664271e-02
-2.09151492e-01 -8.64684582e-01 -5.16384423e-01 -1.30515277e+00
6.11667991e-01 3.73778284e-01 1.17593312e+00 4.16081578e-01
1.57905325e-01 4.78705257e-01 -8.19427013e-01 2.34151736e-01
-1.27460837e+00 1.38698235e-01 1.80402309e-01 -1.59885898e-01
4.56680834e-01 -4.98822592e-02 -1.22979388e-01 7.85958230e-01
-6.25129938e-01 -3.56749356e-01 5.72502434e-01 7.70835698e-01
3.49600315e-02 -6.58215165e-01 3.31164181e-01 -5.43367505e-01
9.97192383e-01 -3.01788926e-01 -4.59682167e-01 7.42898464e-01
-6.05276525e-01 6.12836666e-02 6.01917922e-01 -2.65226692e-01
-9.45275784e-01 1.39285058e-01 -4.08741295e-01 2.79058367e-01
-2.93950409e-01 5.04813433e-01 -7.83979893e-02 -3.29194665e-01
9.52926159e-01 5.39717019e-01 -6.51878342e-02 -4.37051386e-01
3.72692138e-01 1.54905939e+00 6.33762956e-01 -8.66887867e-01
2.87137538e-01 -1.09172508e-01 -1.08593440e+00 -1.10949993e+00
-7.34546185e-02 -5.85724056e-01 -8.51473629e-01 -3.46106291e-01
8.41070652e-01 -9.00601208e-01 -3.20113182e-01 5.50711215e-01
-1.39724970e+00 -2.78125018e-01 1.93976074e-01 5.27246892e-01
-3.84746343e-01 7.07755983e-01 -7.52344847e-01 -5.23964524e-01
-4.08244729e-02 -1.71231914e+00 9.82513070e-01 -5.04980564e-01
-6.25704587e-01 -8.68027091e-01 2.10387722e-01 7.46261239e-01
5.86572647e-01 -5.48061013e-01 9.25148427e-01 -9.05777574e-01
-1.25323907e-01 1.28499165e-01 -1.09731443e-01 7.03115702e-01
1.64404392e-01 3.57208997e-01 -9.36918378e-01 -1.69025153e-01
-3.20955589e-02 -2.53634095e-01 3.64205748e-01 -1.61419377e-01
4.46719408e-01 -3.69661003e-01 1.78851411e-01 2.20884994e-01
8.57019365e-01 6.01391315e-01 5.44883490e-01 2.97478348e-01
4.69198078e-01 8.65054607e-01 4.17766601e-01 1.12023480e-01
5.60190082e-01 9.17095304e-01 -3.32969666e-01 4.66461629e-01
-4.00449902e-01 1.44795686e-01 9.80150402e-01 2.04039884e+00
2.21058711e-01 -1.70939043e-01 -1.69295526e+00 5.38173139e-01
-1.41428816e+00 -6.29384398e-01 -2.82212317e-01 2.35407162e+00
9.30533290e-01 -7.47928172e-02 1.70389056e-01 6.83452981e-03
1.06726027e+00 -1.21386528e-01 2.79312372e-01 -8.61598253e-01
-2.88938046e-01 -3.74484472e-02 1.27704814e-01 1.13093817e+00
-6.68964922e-01 1.21048462e+00 6.80111980e+00 1.03577554e+00
-1.24705720e+00 4.70263064e-01 2.63532251e-01 4.96044457e-01
-2.86725640e-01 9.35880467e-02 -7.29136229e-01 6.10983193e-01
1.12079132e+00 -5.18700592e-02 9.06814814e-01 7.98061490e-01
-1.70959443e-01 1.03617184e-01 -1.12972653e+00 1.09196424e+00
6.19752109e-01 -9.27783191e-01 -1.20022714e-01 -3.10878575e-01
6.63394570e-01 2.90963054e-01 -1.99089631e-01 4.04110253e-01
1.63205877e-01 -8.86493921e-01 1.30120230e+00 1.97784215e-01
1.03340971e+00 -4.58539635e-01 5.50429344e-01 -1.52794188e-02
-1.05844593e+00 6.84751272e-02 4.30428907e-02 -8.36115330e-03
-1.02112256e-01 1.63810074e-01 -7.96075523e-01 1.45272940e-01
3.53170455e-01 3.46614599e-01 -8.83197427e-01 7.27852345e-01
-2.25463301e-01 6.03547931e-01 -2.81228591e-02 -2.95519263e-01
4.36161235e-02 -1.71007320e-01 3.34499151e-01 1.93913066e+00
5.25004864e-01 -3.30063403e-01 6.77836835e-02 5.22872984e-01
2.35577926e-01 7.32702672e-01 -3.30943555e-01 -9.36005265e-02
9.99826610e-01 8.46281469e-01 -8.77231300e-01 -4.11000431e-01
-6.64029717e-01 1.13775945e+00 3.82383466e-01 6.98618963e-02
-4.99017745e-01 -6.10194504e-01 6.92529142e-01 -1.61219403e-01
-2.65015475e-02 -3.72107536e-01 -1.19403124e-01 -1.21738505e+00
2.27723554e-01 -1.69633603e+00 1.44427374e-01 -6.21402860e-01
-1.11486280e+00 1.25422740e+00 -3.32807869e-01 -1.29930043e+00
-1.97998598e-01 -8.21673572e-01 -2.65283614e-01 7.01736391e-01
-1.01693499e+00 -7.73906767e-01 -1.66154459e-01 3.56956273e-01
8.33179832e-01 -7.37810135e-01 9.51586068e-01 8.15677524e-01
-4.42138344e-01 8.94854844e-01 1.08457305e-01 1.78125948e-01
9.26015496e-01 -1.18138587e+00 6.15752518e-01 7.78787136e-01
2.32697725e-01 8.98178101e-01 6.48318589e-01 -3.64875376e-01
-9.55078304e-01 -5.03034830e-01 1.49879813e+00 -7.30717063e-01
9.39836502e-01 -4.97509032e-01 -7.49695837e-01 4.14982080e-01
4.19078290e-01 -3.69114131e-01 8.79873395e-01 8.26832801e-02
-6.08705699e-01 7.06688613e-02 -8.84798646e-01 5.14306366e-01
9.58648682e-01 -1.41333842e+00 -6.61435544e-01 3.82214129e-01
5.50746858e-01 -2.23647654e-01 -1.05150557e+00 4.48057288e-03
5.36440551e-01 -1.06175756e+00 5.62530756e-01 -2.67231882e-01
1.55142307e-01 -4.55841869e-01 -5.64514518e-01 -1.39957452e+00
-1.90388948e-01 -6.48904085e-01 6.83856726e-01 1.32815456e+00
8.52940083e-01 -6.31544411e-01 -4.29012813e-02 4.07414407e-01
-7.73132145e-01 -2.03261152e-01 -9.99714136e-01 -1.03805268e+00
1.97837949e-01 -4.50595319e-01 4.60893571e-01 1.45799422e+00
8.50311637e-01 2.88406491e-01 -1.52898103e-01 -2.30030324e-02
3.84140685e-02 -2.19166011e-01 5.82711339e-01 -8.17358434e-01
-2.22206414e-01 -9.37712848e-01 -6.73146486e-01 -1.03095543e+00
4.71939772e-01 -1.19965303e+00 2.84770578e-01 -1.02193236e+00
-5.52886026e-03 -2.52395958e-01 1.53836340e-01 3.39759350e-01
9.03443769e-02 -1.71735790e-02 1.24710962e-01 3.83762658e-01
-6.67163730e-01 3.74670178e-01 5.80491960e-01 -6.03844933e-02
5.39814495e-02 -2.27038458e-01 -2.98368812e-01 4.25610542e-01
8.27530801e-01 -4.96127397e-01 -8.57224837e-02 -6.65456057e-01
1.73932567e-01 1.67954788e-01 -2.02302590e-01 -1.10311675e+00
3.40598613e-01 -1.69519465e-02 -6.56474888e-01 1.44591466e-01
-7.64397308e-02 -5.00313282e-01 -2.55070888e-02 5.46512246e-01
-5.19555271e-01 4.75366831e-01 -9.87845939e-03 -1.03632331e-01
-5.87029815e-01 -6.23287857e-01 8.96402240e-01 3.11299984e-04
-7.00032771e-01 -4.87292916e-01 -1.07620180e+00 2.29942098e-01
6.05359912e-01 -3.24200511e-01 -4.65106666e-01 -3.06871444e-01
-7.02455461e-01 -2.66304880e-01 8.33996952e-01 9.10717070e-01
2.13585660e-01 -1.25375974e+00 -7.25847125e-01 3.00377369e-01
7.03429937e-01 -1.13856959e+00 -2.94584453e-01 9.16215897e-01
-9.46763456e-01 7.72833705e-01 -2.93666393e-01 -4.28487033e-01
-1.36362052e+00 2.30086029e-01 3.91471148e-01 3.06252867e-01
2.11496443e-01 6.83144510e-01 -3.18929523e-01 -1.07326198e+00
2.69368023e-01 -4.25476879e-01 -1.07730299e-01 5.16566867e-03
3.05266798e-01 5.35632968e-01 5.47165275e-01 -1.33952641e+00
-7.23636746e-01 4.11416531e-01 5.31018749e-02 -3.77002209e-01
6.59994245e-01 -3.28326643e-01 -4.99652982e-01 5.17673731e-01
1.22992384e+00 6.78473353e-01 -4.28395987e-01 -4.00901400e-02
5.91173708e-01 -4.97518182e-01 -3.53097171e-01 -8.04450333e-01
-6.42152548e-01 6.99785233e-01 6.87190294e-01 5.08918822e-01
5.25986493e-01 1.01290010e-01 4.59018737e-01 6.18176699e-01
4.60164994e-01 -1.58023036e+00 -2.18632832e-01 1.12357998e+00
1.06541598e+00 -1.27192700e+00 -5.41926146e-01 -4.76412416e-01
-7.93464243e-01 1.11777806e+00 4.08126801e-01 5.14949501e-01
6.39022589e-01 1.66967690e-01 6.20925128e-01 -1.43394889e-02
-5.25279880e-01 -2.40898639e-01 5.11937916e-01 6.37889504e-01
1.20510852e+00 2.90957212e-01 -7.14311779e-01 2.75525182e-01
-6.79574788e-01 -6.53699219e-01 5.53947806e-01 7.24713087e-01
-6.35086536e-01 -1.27176452e+00 -4.12333131e-01 1.50020689e-01
-3.43323708e-01 -5.04476368e-01 -6.20638013e-01 4.48758721e-01
3.37820798e-02 1.31179798e+00 -1.80116087e-01 -5.32963276e-01
4.30387527e-01 2.72728026e-01 2.80758500e-01 -8.16530228e-01
-8.18204939e-01 -1.86665878e-01 5.68550110e-01 -3.12834442e-01
-3.34084302e-01 -1.09678686e+00 -1.02186191e+00 -1.82478726e-01
-2.87133723e-01 3.61178815e-01 9.01475489e-01 8.70193958e-01
2.29551122e-01 5.38393483e-02 4.64228690e-01 -3.16849679e-01
-5.13789475e-01 -1.12871361e+00 -1.88937306e-01 4.36553270e-01
2.33975753e-01 -3.03339690e-01 -7.19389498e-01 3.69130820e-01] | [14.369465827941895, 7.030169486999512] |
13dad4df-317c-49aa-afde-d41acad70026 | segment-phrase-table-for-semantic | 1509.08075 | null | http://arxiv.org/abs/1509.08075v1 | http://arxiv.org/pdf/1509.08075v1.pdf | Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing | We introduce Segment-Phrase Table (SPT), a large collection of bijective
associations between textual phrases and their corresponding segmentations.
Leveraging recent progress in object recognition and natural language
semantics, we show how we can successfully build a high-quality segment-phrase
table using minimal human supervision. More importantly, we demonstrate the
unique value unleashed by this rich bimodal resource, for both vision as well
as natural language understanding. First, we show that fine-grained textual
labels facilitate contextual reasoning that helps in satisfying semantic
constraints across image segments. This feature enables us to achieve
state-of-the-art segmentation results on benchmark datasets. Next, we show that
the association of high-quality segmentations to textual phrases aids in richer
semantic understanding and reasoning of these textual phrases. Leveraging this
feature, we motivate the problem of visual entailment and visual paraphrasing,
and demonstrate its utility on a large dataset. | ['Yejin Choi', 'Ali Farhadi', 'Santosh Kumar Divvala', 'Fereshteh Sadeghi', 'Hamid Izadinia'] | 2015-09-27 | segment-phrase-table-for-semantic-1 | http://openaccess.thecvf.com/content_iccv_2015/html/Izadinia_Segment-Phrase_Table_for_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Izadinia_Segment-Phrase_Table_for_ICCV_2015_paper.pdf | iccv-2015-12 | ['visual-entailment'] | ['reasoning'] | [ 5.89507520e-01 1.77361548e-01 -5.90444744e-01 -5.91041923e-01
-1.04517913e+00 -1.06114912e+00 6.87468171e-01 3.47315758e-01
-1.39720112e-01 5.17362058e-01 4.08412784e-01 -2.81037122e-01
1.81235701e-01 -5.01772285e-01 -1.13566828e+00 -2.20509991e-01
1.78430483e-01 4.95135963e-01 4.72834736e-01 1.43464198e-02
2.96574563e-01 2.24878073e-01 -1.40290117e+00 6.80310726e-01
6.62832081e-01 9.27357197e-01 2.67442852e-01 4.26535428e-01
-4.71496552e-01 6.93439722e-01 -1.93174362e-01 -8.31234396e-01
2.71896958e-01 -2.16323853e-01 -1.15219033e+00 5.17676234e-01
1.05359328e+00 -1.66871846e-01 2.61921575e-03 9.77218032e-01
-1.21275209e-01 1.20594576e-02 6.13616347e-01 -1.40622365e+00
-9.46271539e-01 7.16753840e-01 -8.70765448e-01 -1.91997904e-02
5.66447377e-01 1.38000220e-01 1.80633414e+00 -9.77376580e-01
1.05349946e+00 1.30794966e+00 6.35253191e-01 3.82839561e-01
-1.55612218e+00 -3.84257406e-01 2.82827169e-01 2.07302332e-01
-1.11001170e+00 -1.54647723e-01 5.24527848e-01 -4.55609381e-01
1.12260354e+00 2.10309908e-01 9.59481597e-01 8.77994120e-01
-6.46799058e-02 1.12170351e+00 1.14703143e+00 -4.65816587e-01
-7.12535158e-02 6.76383451e-02 3.69316995e-01 1.09634256e+00
1.56015128e-01 -2.97142506e-01 -8.49352002e-01 2.17108175e-01
6.93607271e-01 -1.15253083e-01 -2.20220789e-01 -5.13955653e-01
-1.37890279e+00 7.28753269e-01 6.77403986e-01 6.58076331e-02
-2.83387750e-02 4.84460831e-01 4.71345454e-01 -5.65762669e-02
2.67248690e-01 6.60883844e-01 -2.69586414e-01 -3.42447711e-05
-7.43524194e-01 1.91826805e-01 7.26022363e-01 1.40117884e+00
9.36792433e-01 -5.65082490e-01 -3.39550614e-01 7.53521144e-01
1.69731155e-01 6.69659317e-01 -8.96741450e-02 -1.39392269e+00
4.69720244e-01 6.38254881e-01 6.66803261e-03 -1.03617358e+00
-1.62294939e-01 -5.84918298e-02 -3.78915489e-01 -2.27687225e-01
4.01373327e-01 5.44495702e-01 -1.03731489e+00 1.91969979e+00
9.57584009e-02 2.05669910e-01 -1.23099133e-01 6.54562771e-01
7.35819042e-01 3.96681279e-01 1.67619944e-01 7.17748627e-02
1.84738302e+00 -1.22301424e+00 -5.33709764e-01 -4.44035798e-01
3.89032006e-01 -6.92514598e-01 1.58306885e+00 1.55486930e-02
-9.94650304e-01 -3.00203204e-01 -8.40068579e-01 -5.51374435e-01
-4.37830657e-01 -2.57109135e-01 7.35916615e-01 3.23296934e-01
-1.06444490e+00 2.11895600e-01 -6.76670790e-01 -6.29618883e-01
9.06554878e-01 3.03322058e-02 -4.64982897e-01 -3.18091452e-01
-7.85259306e-01 8.00366044e-01 2.83859015e-01 -2.49458537e-01
-6.04970098e-01 -1.00764191e+00 -9.87049937e-01 -7.98928291e-02
7.56903470e-01 -1.05624175e+00 1.47532153e+00 -7.53139436e-01
-7.49932647e-01 1.60452533e+00 -6.51756048e-01 -7.09371686e-01
3.13438535e-01 -1.93803385e-01 1.25564024e-01 7.64241636e-01
6.83642209e-01 1.25262821e+00 7.90733635e-01 -1.50608861e+00
-8.34574997e-01 -2.75436729e-01 3.43668312e-01 2.71517515e-01
-1.65985197e-01 -1.31814465e-01 -1.00641394e+00 -6.08430684e-01
1.41254157e-01 -8.92670631e-01 -2.00890731e-02 5.19584715e-01
-7.21156359e-01 -2.87029237e-01 6.90372407e-01 -4.44142610e-01
8.37620199e-01 -2.08616519e+00 1.47821575e-01 2.98221350e-01
4.30750191e-01 -2.60377407e-01 -1.70269534e-01 2.44660437e-01
1.93671972e-01 5.00781059e-01 -5.73777556e-01 -5.12053132e-01
4.19140756e-01 7.65251994e-01 -7.87182748e-01 -4.11661118e-02
4.64413673e-01 1.57146084e+00 -9.66982603e-01 -9.29166496e-01
1.02329217e-01 -4.16047163e-02 -4.96493071e-01 -1.17072694e-01
-7.24012673e-01 1.92543149e-01 -5.05133867e-01 7.97530949e-01
4.11155581e-01 -7.25211442e-01 -4.96089980e-02 -4.28472638e-01
2.40309224e-01 1.72247320e-01 -6.41980588e-01 2.04562283e+00
-2.86290199e-01 7.49166906e-01 -1.43760204e-01 -7.77243078e-01
4.13862973e-01 -4.82101142e-02 3.89104277e-01 -8.13592136e-01
-8.92059729e-02 1.26717240e-01 -5.69029272e-01 -5.81950009e-01
5.53771257e-01 -3.06105763e-01 -2.54137248e-01 7.61934817e-01
-4.28415798e-02 -7.89549828e-01 5.15974343e-01 8.95708799e-01
8.24552894e-01 2.19047725e-01 3.74724537e-01 -2.53731996e-01
1.28340155e-01 5.25028825e-01 1.50925651e-01 1.01666844e+00
-1.90026060e-01 5.80980480e-01 6.75081849e-01 -2.49129519e-01
-1.37814641e+00 -1.26416874e+00 -1.31602302e-01 1.32781458e+00
5.36721766e-01 -5.10756314e-01 -7.11824238e-01 -6.37725353e-01
1.35956109e-01 5.09504497e-01 -7.77686000e-01 1.84637398e-01
-4.18160379e-01 -2.92463601e-01 5.73566139e-01 1.01442432e+00
4.54089880e-01 -1.05984819e+00 -6.26945198e-01 -1.87345222e-01
-4.30967212e-01 -1.84573233e+00 -7.40304351e-01 2.27298476e-02
-5.63759685e-01 -1.02611029e+00 -5.31485081e-01 -1.09212768e+00
6.87106013e-01 5.11416554e-01 1.76788199e+00 2.77859658e-01
-4.40809160e-01 7.57663786e-01 -3.05455953e-01 -2.23864004e-01
-3.65363628e-01 -1.55491203e-01 -4.49944764e-01 -3.63487273e-01
2.36782134e-01 -3.85686338e-01 -3.15557212e-01 2.65610635e-01
-9.51882303e-01 5.36030114e-01 4.66845483e-01 7.12812364e-01
9.52104747e-01 -3.93929511e-01 2.75418669e-01 -1.18625796e+00
3.84882957e-01 -1.06562980e-01 -4.71197903e-01 6.25593960e-01
-3.48097891e-01 2.57686526e-01 1.75320163e-01 -1.22320764e-01
-9.38773930e-01 4.87072729e-02 5.71564361e-02 -2.03024536e-01
-1.77754104e-01 4.13418144e-01 1.47834480e-01 4.99256402e-02
4.57726270e-01 8.83137956e-02 -1.87360093e-01 -3.78270522e-02
1.11369073e+00 2.25015596e-01 1.02484584e+00 -1.07746398e+00
7.98691750e-01 8.91708076e-01 9.53157470e-02 -5.39337099e-01
-1.59436703e+00 -7.75137901e-01 -7.37745881e-01 6.14280105e-02
1.32470655e+00 -9.15188015e-01 -6.44701242e-01 6.71497732e-02
-1.26985550e+00 -3.02795947e-01 -4.57954079e-01 -1.49358660e-01
-9.32637632e-01 4.32666302e-01 -6.17696702e-01 -3.61015111e-01
-1.47269487e-01 -1.10194635e+00 1.49085248e+00 1.21381097e-01
-3.43780726e-01 -9.61668372e-01 -3.15876931e-01 7.72062242e-01
-1.93137571e-01 1.54572248e-01 1.24938035e+00 -3.88607621e-01
-1.07810080e+00 3.22405338e-01 -8.86149228e-01 -3.00235897e-02
-1.19987957e-01 -7.87479803e-02 -8.26554656e-01 2.82988269e-02
-3.79000038e-01 -7.38066792e-01 1.04595780e+00 2.89737731e-01
1.08762860e+00 -1.17357023e-01 -4.24071223e-01 5.27786434e-01
1.45698190e+00 -2.94533998e-01 3.65504414e-01 3.48274022e-01
1.07155681e+00 6.44251108e-01 7.01700032e-01 4.14138436e-02
6.51929140e-01 5.32273352e-01 2.79233903e-01 -3.52648139e-01
-3.64938140e-01 -5.00869691e-01 -2.55758196e-01 3.90085220e-01
2.40277797e-01 -6.95865527e-02 -1.10331237e+00 7.99335897e-01
-2.05832267e+00 -8.46395314e-01 -1.90864161e-01 1.62223518e+00
1.19057083e+00 3.09883863e-01 1.28032699e-01 -2.84628689e-01
4.79582310e-01 2.13639095e-01 -6.41219199e-01 -3.98605257e-01
-2.65565455e-01 2.79556364e-01 5.68913460e-01 5.89308619e-01
-1.00378382e+00 1.36325133e+00 6.84212780e+00 9.37311232e-01
-5.17846525e-01 -5.11956513e-02 4.83489901e-01 1.67473495e-01
-8.35011244e-01 3.45838070e-02 -6.52703226e-01 -7.03167692e-02
2.54029632e-01 -1.72761843e-01 4.99002337e-01 6.02484882e-01
-8.01767707e-02 -2.30310157e-01 -1.45086002e+00 9.54216003e-01
2.52692044e-01 -1.75630558e+00 4.02464777e-01 -2.38018587e-01
1.01747775e+00 -1.13196902e-01 1.24353968e-01 -1.11779101e-01
4.25457120e-01 -1.12695611e+00 9.71474290e-01 1.88582972e-01
8.29359829e-01 -4.78046268e-01 4.47715968e-01 -7.49331042e-02
-1.47705793e+00 1.62926123e-01 -1.71730697e-01 2.88011402e-01
2.80315727e-01 3.49558890e-01 -9.46341813e-01 4.79265451e-01
5.71871758e-01 1.21268582e+00 -7.07661331e-01 7.21882284e-01
-6.63409293e-01 3.27331901e-01 -2.85670608e-01 7.82893151e-02
4.89318520e-01 -3.56323668e-03 3.64532709e-01 1.46395147e+00
-3.10641319e-01 1.67681158e-01 3.93997729e-01 1.21768498e+00
-4.96479690e-01 -9.07210931e-02 -6.66362107e-01 -2.57662475e-01
4.94492054e-01 1.07818007e+00 -1.15015781e+00 -6.06784999e-01
-5.49579501e-01 1.10279787e+00 4.82826978e-01 5.15918314e-01
-8.34740341e-01 7.14040175e-02 6.35895014e-01 -2.14836642e-01
5.09591997e-01 -2.45536461e-01 -9.22560036e-01 -1.11553359e+00
2.03908920e-01 -7.42943764e-01 2.95205742e-01 -1.22217298e+00
-1.54338801e+00 2.33222350e-01 8.93647000e-02 -8.36332440e-01
-9.80708525e-02 -7.11752295e-01 -1.84154555e-01 5.39392292e-01
-1.64287829e+00 -1.62756801e+00 -2.83083051e-01 6.72290146e-01
8.17343056e-01 3.52348655e-01 7.48203516e-01 -6.39050230e-02
-1.38617188e-01 4.62896109e-01 -3.69447827e-01 2.14345768e-01
7.01504827e-01 -1.54270971e+00 7.30471671e-01 8.34561110e-01
7.79712617e-01 7.26069033e-01 7.49644816e-01 -5.15911341e-01
-1.22551513e+00 -9.59638536e-01 6.46019161e-01 -9.12005723e-01
1.12030613e+00 -4.73786980e-01 -8.12611699e-01 9.31835234e-01
2.77170271e-01 2.25409545e-04 5.07754683e-01 2.38235191e-01
-1.05321240e+00 3.02595824e-01 -8.98916125e-01 8.07670891e-01
1.34522152e+00 -1.05107093e+00 -1.04252934e+00 3.74076307e-01
1.31999886e+00 -5.03687561e-01 -6.37939394e-01 1.81993976e-01
7.49590635e-01 -7.46566594e-01 1.35361075e+00 -7.60216236e-01
1.01524365e+00 -1.80747211e-01 -4.43563551e-01 -8.64041746e-01
7.53367618e-02 -3.74635398e-01 2.41039336e-01 1.12938297e+00
6.79260254e-01 -3.11537325e-01 8.03456724e-01 9.06957328e-01
-2.19023764e-01 -8.24852049e-01 -5.29793441e-01 -6.50444746e-01
-3.25524956e-02 -7.21119523e-01 3.06433856e-01 8.77179921e-01
1.08128183e-01 4.50702548e-01 -1.12945311e-01 2.66921725e-02
7.58735001e-01 6.22541666e-01 5.96267819e-01 -8.39360178e-01
-2.88640082e-01 -4.56502795e-01 -3.53627622e-01 -1.34528720e+00
4.21276242e-01 -1.05041778e+00 2.99082160e-01 -1.82579505e+00
8.08787167e-01 -3.22470129e-01 2.98645645e-02 7.70833135e-01
-2.26315826e-01 9.26997542e-01 5.02986491e-01 2.66339719e-01
-1.07710814e+00 7.47115761e-02 1.64290702e+00 -2.95376241e-01
1.36686981e-01 -6.19427860e-01 -1.00997663e+00 8.50313902e-01
4.19722944e-01 -3.89218271e-01 -4.58154649e-01 -7.58248866e-01
3.96421731e-01 -9.18597654e-02 6.87432230e-01 -3.44234824e-01
2.30735332e-01 -3.43907773e-01 -6.38014823e-02 -6.49918735e-01
3.28724265e-01 -7.90322661e-01 -4.27987188e-01 7.43652508e-02
-6.33357227e-01 -1.11859076e-01 3.12342167e-01 8.40623260e-01
-1.71195492e-01 -9.24540758e-02 6.23473883e-01 -3.30761105e-01
-1.18983662e+00 2.49635532e-01 1.78473964e-02 6.36254549e-01
9.61274147e-01 -3.39084566e-01 -7.06345022e-01 -2.02498943e-01
-7.61330545e-01 4.99801159e-01 7.96594501e-01 2.14560285e-01
6.09962165e-01 -1.23012292e+00 -4.52272832e-01 6.55947998e-02
6.22451365e-01 9.19722989e-02 -1.22506507e-02 8.72936487e-01
-4.32705253e-01 4.44971383e-01 -2.30754077e-01 -1.04182088e+00
-1.34729886e+00 4.28561360e-01 -5.86724281e-02 -3.00004985e-02
-8.82709920e-01 1.13376796e+00 4.31771487e-01 -9.43671167e-03
3.00648779e-01 -7.18335390e-01 2.53335923e-01 -4.85203229e-02
3.00477237e-01 -2.22971335e-01 -2.95639187e-01 -4.73649770e-01
-3.77126098e-01 1.01306689e+00 -1.23916343e-01 -4.16366428e-01
1.07074261e+00 -4.80651826e-01 -2.89814204e-01 4.37581331e-01
1.13599908e+00 7.95315802e-02 -1.26505780e+00 -4.40692425e-01
2.83079445e-01 -6.06711149e-01 -4.17854965e-01 -8.82276952e-01
-5.65126538e-01 8.34018290e-01 -1.30928978e-01 2.58214623e-01
1.03374445e+00 7.04541445e-01 1.08272958e+00 6.59369707e-01
4.06887144e-01 -7.77468562e-01 4.95422125e-01 4.64797854e-01
7.07700789e-01 -1.41193163e+00 -1.23963945e-01 -9.25940514e-01
-9.56588447e-01 8.18356872e-01 3.06856781e-01 -2.53187194e-02
1.70926809e-01 3.25620770e-01 1.65814627e-02 -3.66157621e-01
-7.08802760e-01 -5.59673250e-01 5.96923769e-01 6.75707638e-01
1.74839303e-01 9.85062197e-02 6.01866245e-02 2.59415209e-01
-2.72137612e-01 -1.82112008e-01 4.10985947e-01 7.65665114e-01
-6.21641338e-01 -9.53305781e-01 -1.34714618e-01 3.78668308e-01
-2.07205668e-01 -4.71327692e-01 -6.67235732e-01 8.42334509e-01
-5.66307940e-02 7.96341419e-01 1.79334491e-01 7.21602747e-03
1.75057814e-01 -1.25792697e-02 7.35657215e-01 -9.11416829e-01
-2.38905966e-01 -4.96961251e-02 2.09013551e-01 -8.01412165e-01
-7.20934749e-01 -4.39465374e-01 -1.70703650e+00 -1.85504541e-01
6.49477243e-02 -2.39020750e-01 4.04686302e-01 1.28116155e+00
9.69286263e-02 3.83687109e-01 3.00817993e-02 -4.59252954e-01
-1.70542493e-01 -3.22179824e-01 -5.42727113e-01 9.17232275e-01
3.81930977e-01 -4.96662229e-01 -1.12701058e-01 7.19746351e-01] | [10.547661781311035, 1.4418561458587646] |
73e13ed0-3360-4936-828f-6b6d5f181afd | a-knowledge-augmented-neural-network-model | null | null | https://aclanthology.org/C18-1049 | https://aclanthology.org/C18-1049.pdf | A Knowledge-Augmented Neural Network Model for Implicit Discourse Relation Classification | Identifying discourse relations that are not overtly marked with discourse connectives remains a challenging problem. The absence of explicit clues indicates a need for the combination of world knowledge and weak contextual clues, which can hardly be learned from a small amount of manually annotated data. In this paper, we address this problem by augmenting the input text with external knowledge and context and by adopting a neural network model that can effectively handle the augmented text. Experiments show that external knowledge did improve the classification accuracy. Contextual information provided no significant gain for implicit discourse relations, but it did for explicit ones. | ['Yugo Murawaki', 'Yudai Kishimoto', 'Sadao Kurohashi'] | 2018-08-01 | a-knowledge-augmented-neural-network-model-1 | https://aclanthology.org/C18-1049 | https://aclanthology.org/C18-1049.pdf | coling-2018-8 | ['implicit-discourse-relation-classification'] | ['natural-language-processing'] | [ 2.49598205e-01 8.92884791e-01 -4.23994929e-01 -3.37336898e-01
-3.44661504e-01 -6.71788335e-01 9.29001272e-01 5.58691740e-01
-5.30834556e-01 1.35206103e+00 5.95939159e-01 -5.39083302e-01
-7.52291083e-02 -8.69937778e-01 -4.14985657e-01 -2.35053390e-01
-3.44578959e-02 4.29179370e-01 5.87611377e-01 -5.92338145e-01
2.77993530e-01 6.12764210e-02 -1.19342911e+00 7.96602964e-01
1.02679443e+00 6.80686235e-01 1.10783629e-01 4.17955190e-01
-5.92005253e-01 1.77091599e+00 -1.16061568e+00 -2.21394762e-01
-2.50035226e-01 -6.35915935e-01 -1.50829291e+00 -1.88130215e-01
5.61559908e-02 -3.10413241e-01 -1.91949159e-01 7.45838404e-01
9.70230922e-02 2.15977311e-01 5.61581433e-01 -7.23527431e-01
-7.49562085e-01 1.01818740e+00 -1.07894108e-01 5.02094090e-01
7.49330282e-01 -2.38653809e-01 1.14930356e+00 -4.71717834e-01
1.02974272e+00 1.23286569e+00 5.21932602e-01 3.57896477e-01
-1.13794470e+00 -6.77412227e-02 6.06812954e-01 5.56008220e-01
-9.25048053e-01 -2.26558864e-01 1.01515353e+00 -5.36452174e-01
1.30002332e+00 4.69378203e-01 4.68074352e-01 1.09954000e+00
-5.47497988e-01 5.55267751e-01 1.13710117e+00 -9.91681874e-01
-7.15465546e-02 2.89233804e-01 6.41560078e-01 4.65676278e-01
5.42514697e-02 -7.08517730e-02 -3.89216006e-01 -4.16804664e-02
5.51315844e-01 -7.87899852e-01 -4.13758963e-01 2.12657645e-01
-1.19875884e+00 7.79848337e-01 6.02370441e-01 1.02265906e+00
-7.97129646e-02 -2.82046556e-01 5.63663363e-01 6.77034318e-01
6.57466352e-01 1.25575757e+00 -5.28569162e-01 -2.31171936e-01
-4.08249885e-01 2.19293348e-02 1.16046476e+00 6.49137080e-01
6.96485877e-01 -2.88214050e-02 -1.92478120e-01 7.34001160e-01
-2.05858916e-01 3.94022949e-02 3.66044283e-01 -1.09510696e+00
6.71084464e-01 1.20164883e+00 2.19169185e-01 -1.31425714e+00
-6.06668115e-01 1.95759013e-01 -3.44489336e-01 -9.89048034e-02
9.44738984e-01 -4.89332944e-01 -4.19981897e-01 1.79150283e+00
3.36865246e-01 -1.91321716e-01 3.73833925e-01 7.57059693e-01
1.10033107e+00 5.43334126e-01 4.19693664e-02 -4.72675264e-01
1.26161444e+00 -9.61355329e-01 -1.28048325e+00 -4.05024409e-01
1.01363230e+00 -7.12830901e-01 9.93295312e-01 -1.92274563e-02
-9.36171591e-01 -3.05859983e-01 -9.97603536e-01 -2.58792013e-01
-5.22512197e-01 2.89702881e-02 6.84978902e-01 2.84126490e-01
-6.11581981e-01 4.23616529e-01 -5.59990048e-01 -1.44510329e-01
1.14463829e-01 2.49740928e-01 -4.40642387e-01 1.99323922e-01
-1.56299376e+00 1.59518421e+00 1.08532310e+00 1.12324700e-01
4.23061242e-03 -2.42998749e-01 -1.02347028e+00 -8.38045329e-02
1.08727145e+00 -2.27925301e-01 1.24837625e+00 -1.34298408e+00
-1.59049380e+00 8.51525426e-01 -2.33401075e-01 -4.40186679e-01
3.91423434e-01 -5.34649730e-01 -3.62681508e-01 2.66710639e-01
-1.29996896e-01 1.46846950e-01 3.14512372e-01 -1.37952292e+00
-4.35198814e-01 -1.95552379e-01 7.12903917e-01 2.52185017e-01
-3.85058224e-01 2.90915459e-01 9.12500471e-02 -7.13917017e-01
8.25720578e-02 -6.53396010e-01 -1.04073286e-01 -6.12308323e-01
-5.12932956e-01 -6.98019743e-01 7.54520833e-01 -8.71335387e-01
1.47891283e+00 -1.83551705e+00 2.60645628e-01 1.50475204e-01
2.10582510e-01 5.31324267e-01 1.52274802e-01 4.47094798e-01
-2.55313423e-03 2.23869547e-01 -3.45770381e-02 3.94399643e-01
-6.21493198e-02 4.69862878e-01 -2.59037584e-01 -8.35916102e-02
4.64675963e-01 9.47109461e-01 -1.12319922e+00 -5.13477504e-01
-5.22608170e-03 1.16658047e-01 -3.68003815e-01 3.17626178e-01
-8.09669912e-01 3.94362330e-01 -5.25320292e-01 2.84218043e-01
9.28602554e-03 -5.03603578e-01 7.89648175e-01 4.99504842e-02
1.21414199e-01 9.28192556e-01 -1.02481961e+00 1.08354080e+00
-2.44262069e-01 1.09463882e+00 1.52754514e-02 -1.19725275e+00
1.00869226e+00 6.29930317e-01 -1.43808112e-01 -7.10317254e-01
3.89125913e-01 1.66706577e-01 4.12178904e-01 -1.14385378e+00
5.60983062e-01 -1.88689619e-01 -8.03011954e-02 5.20425320e-01
-1.35930121e-01 1.57569036e-01 3.82758170e-01 5.08950129e-02
9.29389417e-01 -7.23402798e-02 6.92769527e-01 -7.83360973e-02
7.19278812e-01 4.44884330e-01 5.32249689e-01 4.97681260e-01
9.15326253e-02 1.01372391e-01 8.90430450e-01 -4.13312644e-01
-6.55582190e-01 -3.38267654e-01 -2.32876055e-02 1.19937396e+00
1.82617866e-02 -6.58714414e-01 -6.50650680e-01 -9.72140133e-01
-3.21826965e-01 6.90786719e-01 -7.16978312e-01 2.24253953e-01
-1.11839116e+00 -5.83378613e-01 4.86074060e-01 7.05559313e-01
4.58636254e-01 -1.23619246e+00 -7.17997193e-01 2.47850627e-01
-5.37463486e-01 -1.35366714e+00 1.76293403e-01 4.39508677e-01
-6.60260975e-01 -1.49935985e+00 -9.13544670e-02 -9.08405185e-01
6.68273270e-01 -1.41491935e-01 1.18191159e+00 6.15055740e-01
4.31646019e-01 -1.06121592e-01 -7.04982758e-01 -1.59969464e-01
-7.72230804e-01 3.47599059e-01 -2.95585215e-01 -4.68098223e-01
6.07676148e-01 -3.52792531e-01 1.65093198e-01 1.20853111e-01
-6.59496844e-01 1.64934546e-02 1.78932086e-01 1.20163465e+00
-1.82448447e-01 -1.62562177e-01 9.66599703e-01 -1.29609168e+00
9.96478260e-01 -3.08209717e-01 -2.60651618e-01 3.17965925e-01
-1.78801671e-01 2.10914999e-01 4.27865595e-01 -6.04174376e-01
-1.44113839e+00 -2.47869000e-01 -1.65330976e-01 3.53645742e-01
-3.81289721e-01 1.07582676e+00 -1.82211608e-01 2.48635516e-01
9.81791615e-01 -5.33126950e-01 -2.50484884e-01 -5.35384774e-01
4.71202403e-01 6.03899837e-01 5.46328306e-01 -1.00307763e+00
4.03896272e-01 -1.57647356e-01 -2.87744433e-01 -8.75858963e-01
-1.35065711e+00 -1.66565433e-01 -9.74175811e-01 -7.67660141e-03
9.43611026e-01 -5.18778741e-01 -3.89214873e-01 -6.41112179e-02
-1.47309148e+00 -6.46078110e-01 -4.29172873e-01 5.02251506e-01
-1.91537008e-01 4.27252948e-01 -7.49143541e-01 -6.17751658e-01
2.47863889e-01 -6.92121983e-01 7.91975558e-02 8.57319981e-02
-9.38475788e-01 -1.25582576e+00 2.36838218e-02 4.58753049e-01
3.52913886e-01 5.78265309e-01 1.21930981e+00 -1.18917871e+00
-4.64513838e-01 -9.74452645e-02 -1.14719011e-01 2.46611029e-01
4.97677296e-01 -1.50945798e-01 -1.12611353e+00 2.85856247e-01
1.35488525e-01 -6.44857347e-01 5.91336191e-01 -1.16840675e-01
7.20385075e-01 -8.32604647e-01 -3.13397110e-01 -7.48870708e-03
8.37840497e-01 3.64364177e-01 4.89605218e-01 6.99942529e-01
6.26911342e-01 9.54820752e-01 4.14336175e-01 5.21256663e-02
3.56949925e-01 6.40624821e-01 3.17011252e-02 8.96025263e-03
-1.99315295e-01 -4.67178114e-02 1.83305386e-02 7.83257723e-01
-4.39460427e-01 -1.01187050e-01 -1.15878963e+00 6.51115179e-01
-1.97474670e+00 -1.08066583e+00 -2.60133743e-01 1.48148930e+00
1.67806387e+00 3.60433161e-01 -9.00324658e-02 5.00754297e-01
5.83584249e-01 1.71046779e-01 -1.72436997e-01 -4.02580202e-01
-4.05801505e-01 -4.37611304e-02 -1.89460978e-01 9.64672804e-01
-1.19294453e+00 9.46810842e-01 7.08028984e+00 4.51428205e-01
-1.03093731e+00 3.04642208e-02 5.33291638e-01 3.68447214e-01
-3.12394053e-01 5.96440732e-02 -4.58532244e-01 7.18295053e-02
7.61666000e-01 -2.02712193e-01 1.49924606e-01 8.16115141e-01
-2.37501085e-01 -2.75613606e-01 -1.32252371e+00 2.38809690e-01
6.88619092e-02 -1.54813075e+00 -2.74136126e-01 -2.62910396e-01
7.21880317e-01 -3.79040539e-01 -6.20308340e-01 4.56918597e-01
3.54795665e-01 -1.11387658e+00 3.42982590e-01 3.01283598e-01
3.27176780e-01 -2.76330322e-01 1.07985067e+00 5.34133136e-01
-6.16492450e-01 2.89291758e-02 -2.13275440e-02 -8.65555346e-01
-1.93499029e-03 1.68408394e-01 -1.16317725e+00 5.40249228e-01
2.18326479e-01 3.19664985e-01 -7.03268051e-01 5.53647518e-01
-8.85275900e-01 7.18027234e-01 -4.73195642e-01 -2.49888480e-01
1.84592545e-01 2.31297150e-01 5.71495891e-01 1.33651423e+00
-2.99417049e-01 4.66716170e-01 4.33817118e-01 6.50158465e-01
-1.10413760e-01 1.15092114e-01 -7.82104611e-01 -2.30935216e-02
5.37147284e-01 7.04016566e-01 -7.84102738e-01 -5.67530274e-01
-4.26679492e-01 4.37559187e-01 6.80619359e-01 4.60059077e-01
-1.66325763e-01 -3.06144327e-01 1.52803892e-02 -9.82320681e-03
9.24649090e-02 -1.64842412e-01 -4.97948945e-01 -1.17196417e+00
2.28029549e-01 -9.52426374e-01 5.72309136e-01 -5.95086277e-01
-1.34626305e+00 8.20200980e-01 3.12243879e-01 -6.81381762e-01
-6.92194700e-01 -7.13273287e-01 -7.14013338e-01 7.61766315e-01
-1.46208823e+00 -9.64343369e-01 -2.20015824e-01 4.06781882e-01
3.36656958e-01 -1.23137720e-01 1.11469877e+00 -1.66719481e-02
-1.83141574e-01 4.40092295e-01 -2.67050117e-01 6.70244157e-01
7.28059411e-01 -1.50504923e+00 -1.78195596e-01 7.14583933e-01
2.68324409e-02 7.67039716e-01 8.99877191e-01 -5.95271170e-01
-8.25562596e-01 -5.13360500e-01 1.40328145e+00 -7.58856654e-01
9.58034694e-01 9.06214714e-02 -1.50557232e+00 9.40402210e-01
8.12128663e-01 -1.81766167e-01 8.02138865e-01 7.04311013e-01
-4.70972508e-01 4.19711202e-01 -8.69961441e-01 5.35515606e-01
7.74112582e-01 -6.74494505e-01 -1.64833748e+00 2.36435980e-01
1.05068231e+00 -7.33589649e-01 -8.47563624e-01 4.61577117e-01
-3.75991017e-02 -3.57810318e-01 7.99530923e-01 -9.14187670e-01
6.28829837e-01 -3.26166153e-01 -2.26155221e-01 -1.19176328e+00
1.38647646e-01 -4.44837183e-01 -5.23193300e-01 1.45532739e+00
8.84286523e-01 -3.89508158e-01 5.90080917e-01 9.13605928e-01
-1.61866948e-01 -4.74170715e-01 -8.65047514e-01 -7.29891002e-01
3.46273243e-01 -1.37783244e-01 4.23645377e-01 1.59088182e+00
1.13326347e+00 9.98891234e-01 -1.40795842e-01 -2.56562769e-01
-2.55535454e-01 2.21426606e-01 5.77784479e-01 -1.22668934e+00
-1.52163535e-01 -3.69122297e-01 -2.13423267e-01 -8.14494431e-01
5.28776944e-01 -8.88755679e-01 -1.18027382e-01 -1.82220685e+00
-2.07094073e-01 -4.44752365e-01 1.01091005e-01 6.93245411e-01
-6.33901417e-01 -6.82902560e-02 1.16168283e-01 -3.59816141e-02
-6.48540139e-01 4.32276100e-01 1.17578626e+00 -2.25170940e-01
-4.75146115e-01 -2.36460775e-01 -7.34148204e-01 1.19355297e+00
9.32257175e-01 -2.79509366e-01 -4.85591292e-01 -5.48411191e-01
4.63488936e-01 1.71279728e-01 1.80862740e-01 -5.40954411e-01
2.41797909e-01 -4.37026918e-01 2.57614672e-01 -4.47573990e-01
4.19660658e-01 -6.42092645e-01 -3.47112149e-01 1.16185769e-01
-7.43351579e-01 7.56804943e-02 2.84998327e-01 3.61597925e-01
-5.81430614e-01 -4.89832908e-01 2.46828243e-01 -2.99355000e-01
-6.79377973e-01 -8.43442082e-01 -5.88787615e-01 4.58955884e-01
9.88733649e-01 -2.25630607e-02 -8.05242598e-01 -1.84933364e-01
-1.12805021e+00 2.21411794e-01 2.73698598e-01 3.48528117e-01
2.95338631e-01 -1.29672003e+00 -6.70266628e-01 -1.90931052e-01
-1.09689720e-01 1.54880539e-01 -3.23991567e-01 4.99896944e-01
-4.12319273e-01 3.56982350e-01 -7.21206143e-02 -3.93019259e-01
-1.49658048e+00 6.91146970e-01 2.32425436e-01 -4.85937774e-01
-7.86041498e-01 5.79741240e-01 -2.83952743e-01 -3.88986856e-01
4.13346708e-01 -5.49293995e-01 -8.59996140e-01 4.50645983e-01
6.51393771e-01 -4.17367183e-02 -8.85605887e-02 -6.73451662e-01
-1.13436967e-01 2.29233474e-01 -5.81828393e-02 -2.47744080e-02
1.47658718e+00 -1.74750656e-01 -3.57587904e-01 7.93504894e-01
6.53959751e-01 1.42829701e-01 -6.83063745e-01 -6.66656554e-01
8.15082073e-01 -4.29745764e-01 -2.04940036e-01 -1.05863750e+00
-5.69741368e-01 6.51159704e-01 -1.95247531e-01 8.35736752e-01
7.14862108e-01 2.88906336e-01 3.18004400e-01 1.12982213e+00
1.42984260e-02 -1.23382056e+00 2.72934705e-01 1.17607069e+00
1.01572192e+00 -1.38524032e+00 1.60107240e-02 -6.84983134e-01
-6.58072531e-01 1.27105248e+00 1.02469945e+00 2.06267759e-02
4.56304789e-01 3.93410772e-01 3.03606391e-01 -4.00403529e-01
-8.49341691e-01 -2.70630121e-01 2.10842103e-01 6.47187948e-01
8.48964036e-01 -3.88832986e-02 -4.63027686e-01 7.65232444e-01
-3.32356274e-01 -4.93627004e-02 6.23324513e-01 1.12159348e+00
-5.36966801e-01 -1.15083706e+00 -5.00818074e-01 3.14673632e-01
-5.73906183e-01 -2.43543517e-02 -9.26140845e-01 1.06216013e+00
1.31490737e-01 1.04913342e+00 -7.82970116e-02 -1.81158826e-01
2.75837392e-01 4.36405838e-01 4.21178550e-01 -7.37351239e-01
-8.47553670e-01 -1.79725155e-01 1.19971371e+00 2.49667373e-03
-9.09385264e-01 -3.58800352e-01 -1.25766277e+00 -2.33960539e-01
-6.16792321e-01 3.91814321e-01 5.69006950e-02 1.43772531e+00
-1.77713364e-01 7.90660739e-01 1.47846609e-01 -4.95677024e-01
-3.21582973e-01 -1.23446345e+00 1.64129645e-01 5.30789793e-01
5.37133753e-01 -6.26316071e-01 -2.87402242e-01 2.81059802e-01] | [10.803590774536133, 9.261039733886719] |
7b6c1e47-03c2-4f81-b34f-127c0ba02662 | joint-graphical-models-for-date-selection-in | null | null | https://aclanthology.org/P15-1154 | https://aclanthology.org/P15-1154.pdf | Joint Graphical Models for Date Selection in Timeline Summarization | null | ['Giang Tran', 'Katja Markert', 'Eelco Herder'] | 2015-07-01 | joint-graphical-models-for-date-selection-in-1 | https://aclanthology.org/P15-1154 | https://aclanthology.org/P15-1154.pdf | ijcnlp-2015-7 | ['timeline-summarization'] | ['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] | [-7.446542739868164, 3.658743143081665] |
6d962044-75a0-46fb-b912-188cd955fb23 | training-graph-neural-networks-by-graphon | 2109.01918 | null | https://arxiv.org/abs/2109.01918v1 | https://arxiv.org/pdf/2109.01918v1.pdf | Training Graph Neural Networks by Graphon Estimation | In this work, we propose to train a graph neural network via resampling from a graphon estimate obtained from the underlying network data. More specifically, the graphon or the link probability matrix of the underlying network is first obtained from which a new network will be resampled and used during the training process at each layer. Due to the uncertainty induced from the resampling, it helps mitigate the well-known issue of over-smoothing in a graph neural network (GNN) model. Our framework is general, computationally efficient, and conceptually simple. Another appealing feature of our method is that it requires minimal additional tuning during the training process. Extensive numerical results show that our approach is competitive with and in many cases outperform the other over-smoothing reducing GNN training methods. | ['Lizhen Lin', 'Yihao Fang', 'Ziqing Hu'] | 2021-09-04 | null | null | null | null | ['graphon-estimation'] | ['graphs'] | [ 3.78861219e-01 8.22859168e-01 -3.43524843e-01 -1.77333638e-01
1.97000112e-02 -3.05902928e-01 6.07536137e-01 8.03977996e-02
-3.45667481e-01 9.53260779e-01 -1.99985921e-01 -5.20147860e-01
-3.79379034e-01 -1.12612486e+00 -1.17688727e+00 -5.63502371e-01
-2.94922411e-01 4.52523410e-01 3.98566537e-02 9.83330160e-02
-1.03359930e-01 7.13434756e-01 -1.06363034e+00 -4.88192350e-01
8.37936223e-01 9.67667580e-01 8.98525193e-02 5.36967039e-01
-1.11405984e-01 9.90037203e-01 -3.43890905e-01 -5.25875151e-01
2.55626082e-01 -3.95843506e-01 -8.09649169e-01 2.37528428e-01
3.90224785e-01 -1.96787521e-01 -7.33358741e-01 1.37537718e+00
6.29253685e-02 5.35412729e-01 4.02167827e-01 -1.19535732e+00
-3.68493259e-01 1.14548898e+00 -5.11632204e-01 -7.40396306e-02
-3.71579617e-01 -2.19679564e-01 9.69178140e-01 -7.59847164e-01
6.74779475e-01 1.17594337e+00 1.11472917e+00 3.75979215e-01
-1.67106199e+00 -6.08700395e-01 3.50916266e-01 -1.87832564e-01
-1.50420678e+00 -3.05890828e-01 9.79076862e-01 -2.35941723e-01
5.44783533e-01 -1.62486985e-01 7.86068261e-01 8.27890635e-01
-2.42728449e-04 5.35892606e-01 7.44960606e-01 -4.66171861e-01
3.50111991e-01 4.82215770e-02 3.44134092e-01 1.12117493e+00
6.25420213e-01 6.84214160e-02 -8.20381343e-02 -1.88872710e-01
1.12269390e+00 -1.61722470e-02 -2.79512852e-01 -8.32713604e-01
-5.03493965e-01 9.41793859e-01 7.97425389e-01 8.29911083e-02
-5.15258789e-01 5.32312691e-01 3.04061651e-01 3.60037476e-01
8.04272473e-01 1.23600788e-01 -1.20981157e-01 3.33995879e-01
-9.49965775e-01 -8.05336088e-02 1.11484456e+00 6.87781692e-01
1.16772771e+00 4.99877781e-01 3.51262510e-01 7.94018209e-01
3.26315701e-01 1.18406983e-02 3.33777629e-02 -8.75927866e-01
5.37133813e-01 6.32216454e-01 -4.32443470e-01 -1.09500420e+00
-4.61301357e-01 -8.01257670e-01 -1.35886836e+00 3.70404482e-01
5.89652896e-01 -4.87441540e-01 -1.14710593e+00 1.90716398e+00
1.71211198e-01 5.57251275e-01 -1.60796091e-01 4.76573557e-01
6.01666868e-01 5.82331717e-01 -9.12522059e-03 -8.45749006e-02
6.32819712e-01 -8.69644344e-01 -4.12492841e-01 -3.70376915e-01
6.39130116e-01 -1.69527065e-02 4.98890013e-01 1.53888121e-01
-1.13195240e+00 -4.95727241e-01 -1.17578614e+00 2.71915644e-01
-3.88565809e-01 3.82399149e-02 8.43427718e-01 7.60101974e-01
-1.41756535e+00 1.33958972e+00 -8.85666668e-01 -3.07346106e-01
4.37680185e-01 6.10946536e-01 -3.70678693e-01 -1.40390098e-01
-1.19779885e+00 7.03028381e-01 9.23914373e-01 3.00223917e-01
-7.09820688e-01 -6.20831430e-01 -9.82843637e-01 2.49833718e-01
7.22547531e-01 -6.93494439e-01 9.78020310e-01 -1.10402799e+00
-1.73586154e+00 2.49993250e-01 7.13540018e-02 -9.04202580e-01
3.77278835e-01 9.72456038e-02 -4.18397598e-02 1.09270178e-01
-3.56914043e-01 4.35440689e-01 9.16560054e-01 -1.19968116e+00
3.99921574e-02 -1.69363752e-01 2.56182551e-01 7.53343850e-02
-6.38361787e-03 -5.56974292e-01 -6.50892615e-01 -6.18422210e-01
1.89522162e-01 -1.11252892e+00 -5.35441756e-01 -1.51557609e-01
-7.54846394e-01 -2.11824074e-01 5.48379600e-01 -6.32446885e-01
1.00998831e+00 -1.92964637e+00 1.42115623e-01 8.76972497e-01
7.67422616e-01 4.01051700e-01 -3.11879873e-01 5.22904932e-01
-4.56291437e-01 2.65336722e-01 -1.10935502e-01 -3.77241284e-01
-2.20118836e-01 4.02769685e-01 -1.28009245e-01 6.15623295e-01
1.61378607e-01 8.91183138e-01 -8.75421166e-01 -2.41070181e-01
2.82387376e-01 6.40217900e-01 -4.92334604e-01 8.66766926e-03
-2.86282688e-01 2.09650278e-01 -3.24823320e-01 -6.41260413e-04
6.53781235e-01 -7.77430415e-01 6.22120857e-01 -1.67478025e-01
3.81375402e-01 4.05584276e-01 -1.28987634e+00 1.28927982e+00
-4.77940679e-01 6.41060650e-01 1.45286813e-01 -1.46207893e+00
8.23807657e-01 8.33857954e-02 2.39502788e-01 -7.49807507e-02
2.51029581e-01 -1.24904484e-01 8.02101716e-02 9.67818201e-02
2.39129871e-01 9.78601631e-03 4.10353184e-01 5.77234149e-01
4.11760479e-01 1.61250755e-01 4.37947541e-01 5.92916429e-01
1.02630651e+00 2.68235635e-02 4.16781902e-01 -3.76884133e-01
4.21351641e-01 -4.28332120e-01 4.10572529e-01 9.98827696e-01
2.53223956e-01 2.15938941e-01 9.02597666e-01 -3.06815207e-01
-9.47522104e-01 -1.01707268e+00 1.68237239e-01 7.24818766e-01
-3.28306407e-01 -3.53160053e-01 -1.07167947e+00 -9.56327558e-01
-3.63402106e-02 5.33994615e-01 -8.18848610e-01 -3.15949500e-01
-5.47256112e-01 -4.43661124e-01 4.11678463e-01 5.36361456e-01
3.63467634e-01 -7.15559423e-01 4.15286243e-01 3.47075045e-01
2.93054670e-01 -1.05432487e+00 -2.04961881e-01 2.13892773e-01
-1.34249640e+00 -1.06512773e+00 -6.26809299e-01 -8.60707223e-01
1.06882870e+00 1.42149702e-01 1.12050843e+00 3.59418869e-01
2.07624137e-01 5.92804812e-02 1.97477564e-01 -1.14858173e-01
-6.74292266e-01 3.32613051e-01 -1.50805516e-02 5.80677390e-02
9.19524953e-03 -9.18176293e-01 -5.13433628e-02 -2.12093458e-01
-8.73591125e-01 1.75342649e-01 4.44508672e-01 8.42371821e-01
4.03539807e-01 4.81502235e-01 5.15311718e-01 -1.31485617e+00
7.98205793e-01 -4.83375221e-01 -1.02190769e+00 1.57812998e-01
-8.46082449e-01 3.04454952e-01 1.08365583e+00 -3.59165132e-01
-8.27345252e-01 8.10107961e-02 1.96771517e-01 -4.28435802e-01
6.48829490e-02 8.81826162e-01 6.20699599e-02 -4.19656456e-01
4.83192533e-01 -1.54688284e-01 3.48508000e-01 -4.49894428e-01
5.15739560e-01 -1.07145004e-01 4.14001465e-01 -2.56080151e-01
1.20845497e+00 2.61041939e-01 4.34270710e-01 -8.43782306e-01
-8.40256512e-01 1.39218390e-01 -6.12186551e-01 -3.21454704e-01
4.21926945e-01 -7.74121404e-01 -6.85866356e-01 5.84307194e-01
-1.08550048e+00 -5.04884601e-01 -2.20568493e-01 6.03474140e-01
-1.92079574e-01 3.50743800e-01 -8.34922194e-01 -7.95062900e-01
-2.38518253e-01 -6.92460060e-01 2.07923204e-01 3.11286539e-01
1.21256277e-01 -1.74871802e+00 -1.39352024e-01 -1.94594800e-01
2.41406664e-01 2.91350693e-01 9.99127626e-01 -7.50713229e-01
-7.25156665e-01 -5.20895123e-01 -6.59502208e-01 6.20761037e-01
1.26111269e-01 1.08965553e-01 -7.00930953e-01 -4.65819776e-01
-1.60269618e-01 -2.49770463e-01 1.02508426e+00 6.43556356e-01
1.26217937e+00 -2.99508840e-01 -3.95704955e-01 5.90867937e-01
1.59952056e+00 -2.70903707e-01 3.56541276e-01 -5.07386737e-02
1.13278103e+00 4.90038306e-01 -2.02768981e-01 1.03087746e-01
2.30361834e-01 1.19743191e-01 5.31843722e-01 -2.57363826e-01
-1.23022936e-01 -5.01541615e-01 1.11329198e-01 9.76732373e-01
-1.87931970e-01 -4.15102690e-01 -8.17133367e-01 2.40901679e-01
-1.88301361e+00 -6.35634422e-01 -2.30001495e-03 2.14944410e+00
6.97657287e-01 4.77966607e-01 4.44045700e-02 7.42133260e-02
1.00875258e+00 4.39123988e-01 -5.49334288e-01 -2.29293138e-01
1.40172914e-01 2.52714187e-01 8.70088041e-01 9.28661823e-01
-8.60508859e-01 8.69792461e-01 6.49976110e+00 6.51704252e-01
-7.92448997e-01 -2.99223185e-01 5.91790199e-01 1.48427203e-01
-2.96398737e-02 8.73015076e-02 -6.14826620e-01 1.11081026e-01
1.14275968e+00 -4.81945723e-01 9.52726722e-01 8.34400594e-01
1.32648736e-01 9.68723074e-02 -1.14385796e+00 4.95665044e-01
-6.95984066e-02 -1.65157068e+00 -1.94024239e-02 2.36520037e-01
7.32525349e-01 4.41531658e-01 -2.72880852e-01 3.21802765e-01
1.09221709e+00 -9.44284379e-01 2.22225428e-01 5.18254459e-01
6.21393263e-01 -9.95728016e-01 5.70700824e-01 3.06543261e-01
-1.32494926e+00 2.85617203e-01 -4.73182201e-01 -1.71023995e-01
-1.49738602e-02 1.19809175e+00 -1.09170127e+00 6.12663388e-01
2.34722927e-01 7.92726636e-01 -4.54573452e-01 8.86138618e-01
-3.73973072e-01 8.27568054e-01 -5.06996036e-01 -1.42059833e-01
3.87452245e-01 -5.72152972e-01 4.60708529e-01 8.25765371e-01
8.01582485e-02 -2.85713941e-01 2.81329870e-01 9.67861772e-01
-5.71365237e-01 -2.16261938e-01 -9.46287811e-01 -3.39089930e-01
4.43532497e-01 1.33159399e+00 -9.78927433e-01 -6.01706564e-01
-3.47586691e-01 5.10643482e-01 9.58201110e-01 7.58451045e-01
-6.64119124e-01 -5.33955872e-01 1.54415816e-01 -1.07935965e-01
4.57153261e-01 -1.58886760e-01 2.93009188e-02 -9.72445846e-01
5.23793511e-02 -5.16475737e-01 1.73133880e-01 -4.42488462e-01
-1.18875039e+00 6.62100375e-01 -1.22687705e-01 -7.06197321e-01
-5.60092628e-01 -7.67777443e-01 -6.73315585e-01 9.58150804e-01
-1.41877317e+00 -6.95178330e-01 -1.60156772e-01 3.40891868e-01
-4.31594485e-03 1.84992757e-02 5.35170138e-01 1.44490823e-01
-8.17092717e-01 4.25886095e-01 2.39550054e-01 4.25143510e-01
-9.79313510e-04 -1.37999165e+00 8.66250634e-01 1.07455873e+00
8.84297416e-02 7.77174354e-01 6.44340932e-01 -7.89524078e-01
-1.13534296e+00 -1.28680491e+00 6.65151894e-01 4.35810536e-02
1.07391989e+00 -4.08782929e-01 -1.08852625e+00 9.52542245e-01
7.33914524e-02 1.24997556e-01 3.40098172e-01 3.77123088e-01
-2.98818678e-01 -4.88736369e-02 -9.46358323e-01 6.97156489e-01
1.02722275e+00 -5.49270451e-01 -2.64961809e-01 2.99205452e-01
7.11242735e-01 -3.58947009e-01 -9.30123866e-01 3.27434063e-01
2.04096764e-01 -8.08270574e-01 8.57893348e-01 -5.88383138e-01
1.77191913e-01 4.93192747e-02 3.20742875e-01 -1.62295711e+00
-4.62214828e-01 -8.70899022e-01 -4.67975318e-01 1.07983816e+00
4.62546676e-01 -1.01783180e+00 1.33509314e+00 5.76821148e-01
2.08284572e-01 -5.90866208e-01 -6.30335391e-01 -9.59976971e-01
2.79701184e-02 -4.15709376e-01 4.79490727e-01 9.12250221e-01
-2.25625768e-01 6.12636685e-01 -3.39185327e-01 2.63800919e-01
1.03172660e+00 -2.12458730e-01 7.55349696e-01 -1.68298972e+00
-5.05948007e-01 -3.89821589e-01 -2.03757405e-01 -1.12946904e+00
4.63631272e-01 -1.15531898e+00 -1.73792541e-01 -1.72507119e+00
-9.81965587e-02 -3.43511999e-01 -3.86500567e-01 4.09961849e-01
-7.85264373e-02 1.07385460e-02 1.32860318e-01 -1.04009375e-01
-3.28698814e-01 3.55616391e-01 1.11584032e+00 1.68305725e-01
-1.70608759e-01 3.48264545e-01 -5.80787122e-01 1.05348647e+00
9.35150802e-01 -5.74483871e-01 -7.30636597e-01 -1.74921319e-01
3.31054360e-01 2.22654387e-01 5.50616264e-01 -1.00613797e+00
2.05519363e-01 2.35909387e-01 2.51818448e-01 -5.59212387e-01
2.81425029e-01 -8.04248154e-01 1.04563490e-01 6.55473590e-01
-2.43324175e-01 -9.25650448e-02 1.46248013e-01 8.67746592e-01
1.53134629e-01 -5.02731800e-01 7.98685491e-01 -2.45157108e-02
-2.20558465e-01 5.50593078e-01 -3.87754291e-01 1.28276169e-01
4.51077968e-01 -2.07414433e-01 -2.64148027e-01 -6.26805782e-01
-7.74468720e-01 1.21685984e-02 2.81971097e-01 4.28629257e-02
2.67408043e-01 -1.26061606e+00 -3.45833361e-01 1.60847977e-01
-4.16779459e-01 1.94746733e-01 1.42322136e-02 6.80182457e-01
-3.14835519e-01 1.53326884e-01 1.46328300e-01 -2.85359859e-01
-8.79496634e-01 6.20525599e-01 4.85846102e-01 -6.29376531e-01
-8.92595172e-01 7.81894982e-01 4.13200334e-02 -6.05516374e-01
5.45499086e-01 -3.98725450e-01 7.45341554e-03 -3.02942723e-01
2.41604567e-01 5.01427710e-01 -5.54491319e-02 -2.49783516e-01
-8.00565034e-02 1.91399306e-01 -2.86374360e-01 1.15758039e-01
1.40015292e+00 -3.47659513e-02 -2.90288687e-01 3.87586355e-01
1.27988493e+00 -2.04383045e-01 -1.48157930e+00 -7.13517129e-01
5.10780141e-02 9.86848399e-02 2.97995448e-01 -3.00425947e-01
-1.54844439e+00 5.85229814e-01 7.88592547e-03 8.84259820e-01
8.66501033e-01 -9.81462747e-02 3.83411258e-01 8.72309864e-01
5.52613363e-02 -1.07915688e+00 -3.33317131e-01 5.60140014e-01
5.12110472e-01 -8.84179533e-01 2.18243673e-01 -5.56137979e-01
-2.51662694e-02 1.17114842e+00 4.75422800e-01 -7.26319969e-01
9.54493999e-01 9.01665464e-02 -2.35614628e-01 -2.21245050e-01
-6.96528971e-01 9.52914879e-02 3.32179010e-01 6.74009860e-01
2.10318342e-01 -1.75595745e-01 7.12681562e-02 1.14509948e-01
-1.74937561e-01 1.58564802e-02 7.37913489e-01 4.83351350e-01
-3.80659312e-01 -9.10495758e-01 1.86262175e-01 7.01982260e-01
-3.72410893e-01 -2.81432420e-01 -4.00149405e-01 1.07944357e+00
-4.33150768e-01 6.37165606e-01 -8.30224752e-02 -2.91592419e-01
2.56764460e-02 -1.03922181e-01 3.61315668e-01 -5.04109383e-01
-2.47587159e-01 -1.89993486e-01 4.44284201e-01 -5.87939918e-01
-3.17960948e-01 -1.84310228e-01 -1.18676841e+00 -4.91581440e-01
-6.33789837e-01 2.18389586e-01 6.76096022e-01 1.04841256e+00
3.04748923e-01 7.55693793e-01 5.83252728e-01 -1.04900134e+00
-4.51626271e-01 -7.71472514e-01 -8.14193547e-01 9.43175703e-02
3.25781554e-01 -6.50218964e-01 -6.59090161e-01 -4.21886206e-01] | [6.9846272468566895, 6.060612678527832] |
4bfa7494-80bb-4a01-b5a0-98a64e6c7f74 | nir-prompt-a-multi-task-generalized-neural | 2212.00229 | null | https://arxiv.org/abs/2212.00229v2 | https://arxiv.org/pdf/2212.00229v2.pdf | NIR-Prompt: A Multi-task Generalized Neural Information Retrieval Training Framework | Information retrieval aims to find information that meets users' needs from the corpus. Different needs correspond to different IR tasks such as document retrieval, open-domain question answering, retrieval-based dialogue, etc., while they share the same schema to estimate the relationship between texts. It indicates that a good IR model can generalize to different tasks and domains. However, previous studies indicate that state-of-the-art neural information retrieval (NIR) models, e.g, pre-trained language models (PLMs) are hard to generalize. Mainly because the end-to-end fine-tuning paradigm makes the model overemphasize task-specific signals and domain biases but loses the ability to capture generalized essential signals. To address this problem, we propose a novel NIR training framework named NIR-Prompt for retrieval and reranking stages based on the idea of decoupling signal capturing and combination. NIR-Prompt exploits Essential Matching Module (EMM) to capture the essential matching signals and gets the description of tasks by Matching Description Module (MDM). The description is used as task-adaptation information to combine the essential matching signals to adapt to different tasks. Experiments under in-domain multi-task, out-of-domain multi-task, and new task adaptation settings show that NIR-Prompt can improve the generalization of PLMs in NIR for both retrieval and reranking stages compared with baselines. | ['Xueqi Cheng', 'HuaWei Shen', 'Liang Pang', 'Shicheng Xu'] | 2022-12-01 | null | null | null | null | ['open-domain-question-answering'] | ['natural-language-processing'] | [ 1.80955499e-01 -3.01740915e-01 -4.04716820e-01 -3.61612886e-01
-1.08111465e+00 -4.61001337e-01 8.42079639e-01 -1.29926145e-01
-4.29568082e-01 1.64336249e-01 3.43157560e-01 1.38267577e-01
-4.66090739e-01 -2.19620377e-01 -1.69176117e-01 -2.83232689e-01
4.46683288e-01 6.73894227e-01 5.95599115e-02 -7.49820888e-01
3.39080513e-01 1.05739810e-01 -1.20665634e+00 9.90901887e-01
8.42935741e-01 1.20551181e+00 6.71792090e-01 4.32225645e-01
-5.54132998e-01 5.13999999e-01 -7.67623484e-01 -1.08742200e-01
1.66928843e-01 -3.24295789e-01 -1.16659939e+00 -5.22735000e-01
3.86919677e-01 -2.84843713e-01 -5.77337146e-01 7.57931948e-01
9.74655271e-01 6.66721940e-01 9.32978511e-01 -9.44569051e-01
-1.16884446e+00 6.72533810e-01 -5.90457261e-01 3.24984938e-01
5.25675535e-01 -1.92645237e-01 1.10298622e+00 -9.25080001e-01
4.87875551e-01 1.51063335e+00 3.70445848e-01 1.02163768e+00
-1.09293640e+00 -9.49780405e-01 3.04662943e-01 2.41084054e-01
-1.20330918e+00 -4.17616010e-01 9.72062588e-01 -1.54693171e-01
8.57321918e-01 4.10706133e-01 -2.40053549e-01 1.64928412e+00
3.11107561e-02 1.24952447e+00 8.54711890e-01 -3.82620245e-01
-1.90056130e-01 3.58278692e-01 6.91200614e-01 1.66317588e-03
-2.66194493e-01 -1.44395486e-01 -7.98889041e-01 -1.05005033e-01
2.66832113e-01 1.89861536e-01 -4.10781413e-01 1.77764758e-01
-1.33866930e+00 6.52467847e-01 5.56400180e-01 7.19811440e-01
-2.85831809e-01 -3.20508897e-01 6.56906605e-01 8.24176133e-01
5.45799971e-01 8.02110076e-01 -7.20728159e-01 4.44354028e-01
-8.43518078e-01 9.02800858e-02 5.70572019e-01 9.40252423e-01
7.73548722e-01 -1.92720920e-01 -1.05454731e+00 1.59104681e+00
3.86411399e-01 5.73175669e-01 1.16846824e+00 -4.96295184e-01
5.98142803e-01 5.85989058e-01 -2.77827740e-01 -8.67404938e-01
-8.00416887e-01 -6.47049487e-01 -1.09191000e+00 -7.88389444e-01
4.17164937e-02 -8.96978658e-03 -7.82820463e-01 1.80536652e+00
-6.19690120e-02 -8.25135857e-02 2.79095113e-01 1.10992444e+00
1.42160404e+00 8.49526584e-01 1.74872547e-01 -6.01621382e-02
1.59273756e+00 -9.92063820e-01 -8.56801033e-01 -5.56978226e-01
5.73834658e-01 -7.32039213e-01 1.44193482e+00 2.52717823e-01
-7.26908624e-01 -9.44096327e-01 -8.21850419e-01 -3.61968040e-01
-4.71395195e-01 3.11535299e-01 4.24350709e-01 2.22544029e-01
-7.77551770e-01 1.86030269e-01 1.65255796e-02 -4.56454486e-01
-3.91783677e-02 1.55244723e-01 -1.71347603e-01 -2.58105218e-01
-1.87527573e+00 9.38863218e-01 6.35786533e-01 8.89806822e-02
-8.02403092e-01 -8.29247773e-01 -5.33065796e-01 1.88266531e-01
1.94880933e-01 -8.07656348e-01 1.34924364e+00 -9.59015131e-01
-1.60150945e+00 9.41278994e-01 1.33129582e-01 -2.91930903e-02
-1.64235793e-02 -3.70070308e-01 -7.28509784e-01 1.74963802e-01
8.50484222e-02 7.68537045e-01 1.03156972e+00 -1.12610447e+00
-2.62732536e-01 -4.60486710e-01 -1.32650882e-01 5.44930279e-01
-6.69385970e-01 2.02478692e-01 -5.30420601e-01 -6.18924737e-01
5.42289391e-02 -7.58669615e-01 3.18946362e-01 -5.80494761e-01
-4.46699440e-01 -7.73567021e-01 6.66624248e-01 -6.13559246e-01
1.39074719e+00 -2.27331591e+00 4.47904058e-02 3.56093049e-02
-3.34997848e-02 3.14183593e-01 -8.08348775e-01 6.03410840e-01
-8.42434317e-02 -6.67055249e-02 3.83570075e-01 -1.76503181e-01
2.31984153e-01 -1.60774246e-01 -4.73888248e-01 -1.85933392e-02
-1.16373356e-02 9.86727655e-01 -9.00125563e-01 -4.40841913e-01
-1.24155760e-01 2.57247180e-01 -1.61034063e-01 2.57691354e-01
-3.04051399e-01 5.64423263e-01 -8.94888997e-01 4.31696355e-01
5.47017932e-01 -3.78490627e-01 -7.55290240e-02 -5.39672077e-01
4.62669969e-01 4.76323009e-01 -8.83420646e-01 2.33900046e+00
-6.63693070e-01 5.85888267e-01 -2.01095697e-02 -1.26233971e+00
1.13781416e+00 4.63208139e-01 4.38474834e-01 -1.36966372e+00
2.02859238e-01 1.39176875e-01 -1.79718196e-01 -5.96750498e-01
7.07594752e-01 -7.05372691e-02 -2.94138849e-01 6.38668060e-01
3.61083001e-01 1.39926732e-01 -1.23179182e-02 3.38604838e-01
9.12165225e-01 -4.44630384e-02 4.56876196e-02 -1.88343450e-01
6.80615246e-01 -5.36579527e-02 7.02576265e-02 1.03369784e+00
7.12847337e-02 6.00514710e-01 -1.43368095e-01 -7.74215087e-02
-4.66198206e-01 -7.83900678e-01 -3.02881837e-01 2.14738274e+00
2.00802028e-01 7.55499629e-03 -2.81706482e-01 -6.68446124e-01
-2.83953417e-02 7.50590086e-01 -4.51798320e-01 -7.27033496e-01
-3.15439612e-01 -6.96312666e-01 5.01787961e-01 3.13005634e-02
6.15822732e-01 -1.39033163e+00 -6.63470551e-02 1.50639325e-01
-6.94050252e-01 -1.07408404e+00 -6.73550546e-01 8.91811326e-02
-8.43892455e-01 -7.47230709e-01 -1.15243959e+00 -8.22290599e-01
1.97205752e-01 7.65981555e-01 1.21298373e+00 -6.90092295e-02
1.27719343e-02 6.77136064e-01 -5.65234244e-01 -3.10602129e-01
-3.62540185e-01 5.99886060e-01 2.89268093e-03 2.54081666e-01
7.56555855e-01 -3.57474685e-02 -6.01520181e-01 4.67129856e-01
-1.18627286e+00 -1.27162829e-01 8.76320243e-01 1.03895843e+00
1.72740996e-01 -3.37462902e-01 1.04539287e+00 -7.69327044e-01
1.49881935e+00 -6.73512459e-01 3.46870609e-02 7.84966886e-01
-6.47047102e-01 3.76741260e-01 4.99254048e-01 -9.85111535e-01
-1.31472754e+00 -4.88736302e-01 1.96758047e-01 -3.83019239e-01
3.99559513e-02 7.56072700e-01 1.66291352e-02 2.58472204e-01
1.18924665e+00 3.44074309e-01 -1.66444466e-01 -8.43543351e-01
4.37291771e-01 1.27281022e+00 4.11321282e-01 -8.96830022e-01
5.13484299e-01 -4.54628235e-03 -4.48411494e-01 -6.00201726e-01
-1.33219945e+00 -1.17895103e+00 -4.70766872e-01 -1.34038657e-01
5.74016809e-01 -1.09009826e+00 -6.62284732e-01 1.45686075e-01
-1.30460417e+00 -2.14244619e-01 -1.16586648e-02 5.54713726e-01
-2.08240375e-01 2.41019145e-01 -5.57674527e-01 -5.31305730e-01
-8.91342938e-01 -8.66278768e-01 1.37005389e+00 1.81552067e-01
-3.12151909e-01 -9.21106339e-01 3.10679823e-01 5.43678999e-01
7.55263209e-01 -5.74098408e-01 1.05958211e+00 -1.26357222e+00
2.66337935e-02 -1.18563212e-01 -4.46689904e-01 3.47620904e-01
-1.79283712e-02 -7.37551808e-01 -1.34084272e+00 -3.12210590e-01
7.38464221e-02 -6.14379048e-01 1.23056114e+00 2.45667532e-01
1.05650759e+00 -2.82583237e-01 -4.45868313e-01 2.12343097e-01
1.05691206e+00 4.01043892e-02 6.05563521e-01 4.32914823e-01
4.91801798e-01 9.20507014e-01 5.69006324e-01 1.62586138e-01
2.46578947e-01 9.56929505e-01 -1.91167608e-01 -7.42326723e-03
-2.77494907e-01 -1.09808348e-01 6.13618433e-01 9.60128248e-01
5.32346666e-01 -1.77352875e-01 -7.91105270e-01 3.65583628e-01
-1.70360696e+00 -9.86857533e-01 1.07604861e-01 2.06554770e+00
1.26639438e+00 -3.15407097e-01 -2.16672808e-01 -2.74346173e-01
5.90940535e-01 1.00396574e-01 -7.21437395e-01 -2.53555149e-01
-9.37915314e-03 9.22713876e-02 8.90269876e-02 1.63005859e-01
-8.28045487e-01 9.25400853e-01 5.61515522e+00 1.31249237e+00
-1.16137290e+00 3.47862661e-01 2.84351498e-01 9.97890607e-02
-4.80252594e-01 -3.57539356e-01 -9.34309959e-01 2.63949245e-01
8.92871499e-01 -1.56518161e-01 5.23732007e-01 5.70234179e-01
-1.29386753e-01 2.63817132e-01 -1.23185825e+00 1.23619056e+00
3.13771516e-01 -6.94249630e-01 5.18328190e-01 -4.84601974e-01
5.45859933e-01 3.23170006e-01 1.79288343e-01 1.03947854e+00
2.27792636e-02 -7.60560274e-01 3.74354541e-01 7.42355049e-01
8.01122308e-01 -1.37017056e-01 7.04855978e-01 6.58953965e-01
-8.29168200e-01 -1.46429002e-01 -6.21033251e-01 3.86442512e-01
-2.01546311e-01 2.70969540e-01 -8.61752987e-01 7.44060040e-01
5.51362813e-01 6.61309719e-01 -6.69312537e-01 6.20563507e-01
3.17799859e-02 1.66081965e-01 -8.77731759e-03 -3.77834700e-02
1.83629423e-01 -8.67653731e-03 3.44211340e-01 1.32524753e+00
1.76492780e-01 8.29312801e-02 1.00834422e-01 7.86732852e-01
-3.13688904e-01 3.20567071e-01 -4.55649585e-01 -8.25679824e-02
4.75348562e-01 1.31487513e+00 -2.10555419e-02 -3.22587371e-01
-4.62890178e-01 1.12326181e+00 2.67236471e-01 8.96778166e-01
-2.52879173e-01 -3.89420092e-01 3.66132259e-01 -2.00707808e-01
-3.89020830e-01 1.24306805e-01 -5.78129292e-03 -1.21061659e+00
-1.66067541e-01 -1.14241672e+00 7.77643919e-01 -1.02694035e+00
-1.70898151e+00 6.15396857e-01 2.44750738e-01 -1.31397378e+00
-3.44066411e-01 -5.15626848e-01 -2.53633112e-01 1.06223416e+00
-1.97105813e+00 -1.04588628e+00 -4.54869062e-01 9.78172123e-01
9.22878861e-01 -6.18776023e-01 8.64569724e-01 4.31910813e-01
-5.77339470e-01 7.76336789e-01 3.53322446e-01 1.52345836e-01
1.41155243e+00 -8.01140904e-01 -2.28344753e-01 3.58639002e-01
2.76385695e-01 7.77920902e-01 2.03315243e-01 -2.41708800e-01
-1.57615042e+00 -8.35172951e-01 9.06776130e-01 -3.87970567e-01
4.49630618e-01 -3.04189265e-01 -1.16837025e+00 4.54885811e-01
4.30047989e-01 -3.29225749e-01 6.64444327e-01 5.34920096e-01
-7.19021738e-01 -4.57543433e-01 -8.01291049e-01 4.04980183e-01
8.05664599e-01 -9.58188772e-01 -8.51523101e-01 6.55481517e-01
8.22789431e-01 -1.49612203e-01 -7.91258276e-01 3.40378106e-01
5.93982577e-01 -3.00297409e-01 1.26901889e+00 -8.33296478e-01
9.92252901e-02 1.41483605e-01 -2.25043878e-01 -1.42533660e+00
-5.54323256e-01 -5.13225675e-01 5.70873246e-02 1.18989491e+00
3.71038049e-01 -5.08169055e-01 1.95363667e-02 4.37944710e-01
-2.30463803e-01 -7.12320432e-02 -7.02041566e-01 -7.93129802e-01
2.78372802e-02 -4.29741353e-01 3.86915445e-01 1.23926258e+00
2.80980825e-01 1.21430933e+00 -3.77707899e-01 -1.73589066e-01
1.90389782e-01 7.42021650e-02 6.84614241e-01 -1.67180765e+00
-1.64010286e-01 -6.95061147e-01 4.09430802e-01 -1.52478790e+00
6.08148694e-01 -1.46033990e+00 5.65839857e-02 -1.62730217e+00
4.63026553e-01 -5.36927044e-01 -9.40948904e-01 6.27301693e-01
-3.00513834e-01 -1.14675835e-01 2.72672325e-01 7.39148736e-01
-1.00296748e+00 6.52677178e-01 1.39570737e+00 -6.45977914e-01
-5.01661658e-01 7.41323754e-02 -1.03670478e+00 6.18800186e-02
6.90352798e-01 -6.05343819e-01 -5.77524602e-01 -6.45094097e-01
3.47184986e-01 2.20659405e-01 1.32153511e-01 -7.62272775e-01
3.28914791e-01 1.84807777e-02 3.41973662e-01 -5.58598697e-01
2.45753944e-01 -7.84590900e-01 -2.32034028e-01 4.21682745e-02
-1.11876726e+00 -1.88638628e-01 3.16123694e-01 4.94284451e-01
-3.81361246e-01 -5.06917477e-01 4.01044369e-01 -1.23956844e-01
-8.75166893e-01 2.52346069e-01 -4.56868038e-02 3.54717642e-01
2.12492406e-01 5.95776998e-02 -5.42114675e-01 -4.55109984e-01
-3.67807925e-01 5.20676911e-01 -4.54649568e-01 1.08010757e+00
6.76717639e-01 -1.39693689e+00 -1.09048676e+00 -1.19786281e-02
6.60381913e-01 -2.51676947e-01 4.83711302e-01 8.56251359e-01
3.08158368e-01 7.80969203e-01 4.81965356e-02 -6.38108552e-01
-9.87725794e-01 5.70886970e-01 2.62489259e-01 -6.91221833e-01
-3.99743974e-01 6.65062606e-01 5.39098024e-01 -7.63295352e-01
4.13159460e-01 -8.94791111e-02 -7.01335728e-01 4.24863577e-01
7.83029556e-01 2.05135614e-01 2.22948179e-01 -3.68702352e-01
-1.60989240e-01 6.04619801e-01 -4.51085299e-01 -1.33358061e-01
9.40358400e-01 -3.19604874e-01 2.07657181e-02 4.87339437e-01
1.45687103e+00 -5.27173102e-01 -5.34844577e-01 -1.06335199e+00
1.98024854e-01 6.99546263e-02 3.68217140e-01 -1.28382158e+00
-8.87593389e-01 1.01347864e+00 6.78319395e-01 4.44822125e-02
1.24966788e+00 3.83739546e-02 8.99425685e-01 9.11413550e-01
1.82931364e-01 -1.48918331e+00 4.71013159e-01 7.91106284e-01
1.27687955e+00 -1.34637606e+00 -1.60906494e-01 1.95015132e-01
-8.46919000e-01 1.19366169e+00 5.48206329e-01 2.64037967e-01
4.71688449e-01 -4.61255133e-01 1.09899923e-01 -2.66667873e-01
-7.81266868e-01 -3.05323392e-01 1.00091910e+00 5.12741387e-01
5.56955278e-01 -1.90556675e-01 -2.82157093e-01 8.78562033e-01
2.23259762e-01 -1.43979982e-01 -1.36066437e-01 3.99621487e-01
-5.25201976e-01 -9.30316448e-01 -4.00462925e-01 4.37886029e-01
-3.37111682e-01 -2.98861146e-01 -4.92487311e-01 5.42314351e-01
-5.22857368e-01 1.21405530e+00 -2.11533085e-01 -4.46779221e-01
4.96863604e-01 4.38550532e-01 1.94958404e-01 -7.71664441e-01
-8.81477177e-01 1.38591468e-01 1.25826914e-02 -4.42241520e-01
-5.23408055e-01 -1.20330758e-01 -9.52645183e-01 2.35757276e-01
-4.84860092e-01 2.22009584e-01 6.07888520e-01 9.77893054e-01
5.34341455e-01 6.56606793e-01 7.24707842e-01 -4.59543914e-01
-1.02992427e+00 -1.69593167e+00 -5.79146028e-01 8.28390360e-01
3.02095324e-01 -5.80694199e-01 -2.60679275e-01 -2.52385199e-01] | [11.220016479492188, 7.920525550842285] |
72590d73-fdb7-4fa5-b900-a29482571ebd | safe-policy-optimization-with-local | 2111.04894 | null | https://arxiv.org/abs/2111.04894v1 | https://arxiv.org/pdf/2111.04894v1.pdf | Safe Policy Optimization with Local Generalized Linear Function Approximations | Safe exploration is a key to applying reinforcement learning (RL) in safety-critical systems. Existing safe exploration methods guaranteed safety under the assumption of regularity, and it has been difficult to apply them to large-scale real problems. We propose a novel algorithm, SPO-LF, that optimizes an agent's policy while learning the relation between a locally available feature obtained by sensors and environmental reward/safety using generalized linear function approximations. We provide theoretical guarantees on its safety and optimality. We experimentally show that our algorithm is 1) more efficient in terms of sample complexity and computational cost and 2) more applicable to large-scale problems than previous safe RL methods with theoretical guarantees, and 3) comparably sample-efficient and safer compared with existing advanced deep RL methods with safety constraints. | ['Yanan Sui', 'Yunyue Wei', 'Akifumi Wachi'] | 2021-11-09 | null | http://proceedings.neurips.cc/paper/2021/hash/adf7e293599134777339fdc40ddfa818-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/adf7e293599134777339fdc40ddfa818-Paper.pdf | neurips-2021-12 | ['safe-exploration'] | ['robots'] | [-1.52079269e-01 3.79935086e-01 -6.92321897e-01 2.08197087e-01
-9.92179751e-01 -4.40652966e-01 4.26032543e-01 2.73199081e-01
-6.39020741e-01 1.30574298e+00 8.02930631e-03 -5.27109921e-01
-2.87112862e-01 -7.03550220e-01 -9.80514109e-01 -7.84408748e-01
-9.47754681e-01 1.87603265e-01 1.58423617e-01 -2.11804941e-01
1.49343565e-01 5.06213844e-01 -1.22166181e+00 -6.58889949e-01
7.21019864e-01 1.15479720e+00 -1.83196008e-01 4.05557662e-01
9.42452192e-01 9.83771563e-01 -2.21973225e-01 4.51398522e-01
6.08684182e-01 -2.72615075e-01 -9.64438260e-01 -3.09845120e-01
-2.93572932e-01 -6.41029954e-01 -3.61865938e-01 1.00954127e+00
3.21678460e-01 5.87931931e-01 1.67286426e-01 -1.54420578e+00
-1.58630207e-01 5.30984163e-01 -4.07932878e-01 -2.93246895e-01
1.50926232e-01 6.29085004e-01 8.82585347e-01 -7.41184354e-02
4.31955725e-01 1.20495498e+00 4.49609220e-01 1.01253450e+00
-1.32292569e+00 -6.47681236e-01 5.40789545e-01 1.12417899e-02
-1.21113694e+00 -3.15360039e-01 4.18623328e-01 -1.14698231e-01
1.14521801e+00 1.26600161e-01 8.94450307e-01 1.15316188e+00
7.26489484e-01 8.45613241e-01 1.13228846e+00 -7.15541989e-02
9.14096415e-01 -2.00396687e-01 -3.23929548e-01 7.40176082e-01
2.85072803e-01 1.24963748e+00 -3.19935977e-01 -3.30696881e-01
6.05424404e-01 -2.56908536e-01 -1.03375711e-01 -7.87828505e-01
-1.11816764e+00 1.26899743e+00 4.61817265e-01 -2.92914033e-01
-2.41671279e-01 9.12629366e-01 5.42192638e-01 6.16731167e-01
1.90770060e-01 8.04068804e-01 -5.72019219e-01 -1.31850392e-01
-3.97259027e-01 6.83795691e-01 8.24415445e-01 8.95589471e-01
3.15939993e-01 6.58579290e-01 -4.04187664e-03 8.61215517e-02
4.50962514e-01 5.57836056e-01 1.32292196e-01 -1.38439178e+00
1.34990126e-01 -1.17407758e-02 7.13214874e-01 -4.07438874e-01
-6.17905617e-01 -3.95165235e-01 -4.79036570e-01 1.02999675e+00
4.66483943e-02 -5.37090659e-01 -4.37443525e-01 2.07275176e+00
4.84246433e-01 1.02660477e-01 3.79153162e-01 6.99827909e-01
-1.33258000e-01 5.29287815e-01 8.35912973e-02 -8.56111944e-01
6.80174530e-01 -7.34274328e-01 -7.05661654e-01 -3.48817229e-01
5.78217506e-01 1.17609084e-01 1.13627255e+00 6.02691710e-01
-1.03120971e+00 -1.50782922e-02 -1.38517475e+00 5.02064824e-01
-1.15336567e-01 -6.73058987e-01 8.47287774e-01 5.82746983e-01
-8.28707457e-01 8.91966224e-01 -1.00912988e+00 1.65122394e-02
4.91547883e-01 6.88741446e-01 2.64595426e-03 4.85837668e-01
-1.18749750e+00 1.14672422e+00 4.13545728e-01 -1.56255066e-01
-2.08713555e+00 -5.33945262e-01 -1.15988553e+00 -1.72232315e-01
1.09482324e+00 -3.06832403e-01 1.61128318e+00 -2.71171123e-01
-1.87602842e+00 -9.27048698e-02 3.69222462e-01 -9.87315774e-01
6.14195168e-01 -5.86271048e-01 -1.84283078e-01 -1.22993521e-01
-8.10173303e-02 5.35440266e-01 9.11693394e-01 -1.14137959e+00
-7.59425938e-01 -1.70023903e-01 1.81408644e-01 1.80321231e-01
-1.21810302e-01 -3.20418328e-01 5.48626304e-01 -3.61202478e-01
-6.82527542e-01 -1.05380476e+00 -9.74538982e-01 2.12332249e-01
-1.88726157e-01 -2.59205967e-01 8.10242176e-01 -3.58357400e-01
1.02149045e+00 -1.77327788e+00 4.83293906e-02 4.18265224e-01
-4.40431349e-02 1.34169728e-01 -2.60401368e-01 4.33693320e-01
3.62520695e-01 -9.28448588e-02 -3.31279397e-01 4.34049079e-03
2.00451583e-01 4.00005698e-01 -7.64446259e-01 9.71321046e-01
-1.69590004e-02 8.99184406e-01 -1.34387660e+00 -3.29577148e-01
2.30052590e-01 7.71751255e-02 -6.52259409e-01 4.30700988e-01
-6.31319582e-01 5.93511283e-01 -7.09234238e-01 4.99624908e-01
1.68516293e-01 3.83680224e-01 1.28516480e-01 5.66376448e-01
-2.44088143e-01 2.00395301e-01 -9.66500461e-01 1.39802885e+00
-5.40635347e-01 2.80348063e-01 3.03752959e-01 -8.32830131e-01
8.23518693e-01 3.87948215e-01 6.04042530e-01 -9.76445556e-01
3.08574885e-01 5.11202253e-02 -3.86034280e-01 -3.29702497e-01
1.55890048e-01 -3.65601838e-01 -5.47421396e-01 5.42814553e-01
-3.74790609e-01 -5.14804542e-01 -1.50863424e-01 -1.61531717e-01
1.27534640e+00 5.38447201e-01 5.66400349e-01 -6.27346992e-01
2.72897899e-01 -6.88209757e-02 7.14154065e-01 8.54738653e-01
-5.37888408e-01 -4.79017615e-01 7.96994925e-01 -5.68717420e-01
-8.14521670e-01 -1.03319848e+00 5.17184064e-02 8.55112255e-01
3.31263632e-01 -2.47944534e-01 -5.83674312e-01 -1.06754458e+00
4.59728271e-01 1.09412062e+00 -8.90495837e-01 -5.33006310e-01
-5.82605720e-01 -1.26763314e-01 4.11448985e-01 6.30087376e-01
1.20791152e-01 -1.19096959e+00 -1.46431053e+00 2.33553112e-01
4.34409618e-01 -5.49182653e-01 -5.47373116e-01 6.98781908e-01
-8.56033206e-01 -1.16265428e+00 1.05591379e-01 -3.04985374e-01
5.37909806e-01 -1.66137502e-01 7.67972529e-01 -1.09569609e-01
-2.17655763e-01 4.57762361e-01 -6.56335987e-03 -5.28764963e-01
-3.10991824e-01 -3.25245231e-01 5.39617896e-01 -5.06178379e-01
-3.91627997e-01 -3.21610540e-01 -4.53436047e-01 1.85363010e-01
-8.31325054e-01 -4.72622663e-01 2.40509734e-01 9.82801974e-01
7.38450110e-01 1.37605026e-01 9.79207635e-01 -4.09186989e-01
8.77894580e-01 -3.91335905e-01 -1.45017552e+00 1.21857807e-01
-1.02969265e+00 6.30984187e-01 8.47920597e-01 -4.92427230e-01
-7.53105819e-01 3.50696504e-01 -2.89617032e-02 -3.93339574e-01
2.33816326e-01 1.46150872e-01 -7.64709562e-02 -4.44898516e-01
6.82906330e-01 5.23552448e-02 3.73095185e-01 -8.81873295e-02
3.68771940e-01 1.04781277e-01 2.36190870e-01 -8.93599868e-01
8.41721654e-01 3.63690913e-01 4.24803168e-01 -3.86124849e-01
-7.32429862e-01 2.41469756e-01 -1.41705900e-01 -4.74705696e-02
4.65323389e-01 -6.84385836e-01 -1.57619584e+00 -1.57074288e-01
-4.75054353e-01 -1.04222620e+00 -8.68272901e-01 3.64291728e-01
-1.30940270e+00 3.32765490e-01 -4.28644270e-01 -1.38193309e+00
-4.42574680e-01 -1.08119452e+00 7.59970963e-01 -7.24199638e-02
-1.60074532e-01 -8.84102762e-01 4.38725859e-01 -3.23068202e-01
3.34741712e-01 6.52019739e-01 6.12332702e-01 -2.23041609e-01
-4.29594010e-01 8.28048065e-02 4.26744163e-01 2.45814398e-01
-1.12579986e-01 -3.98722112e-01 -5.38506866e-01 -9.75846410e-01
2.11090282e-01 -9.33097422e-01 6.71272397e-01 3.56295258e-01
1.29013228e+00 -1.05438900e+00 -2.63247371e-01 4.35300797e-01
1.38804090e+00 4.84155446e-01 3.49347949e-01 5.55959523e-01
1.82056338e-01 4.78639066e-01 1.38168097e+00 9.23669100e-01
1.44869685e-01 4.84470427e-01 1.09805906e+00 3.45919907e-01
5.75976908e-01 -5.21655262e-01 8.68931890e-01 -4.05190215e-02
3.68563354e-01 1.94592029e-01 -5.22063434e-01 7.10950732e-01
-2.40656948e+00 -7.85907507e-01 6.79754257e-01 2.47731352e+00
1.06088626e+00 3.54161829e-01 4.70459372e-01 -1.07262529e-01
8.28595832e-02 -4.63011861e-02 -1.23956978e+00 -8.46030653e-01
3.46505582e-01 1.19921446e-01 8.34949255e-01 8.66608858e-01
-1.16024148e+00 9.04798150e-01 7.51801920e+00 7.69765556e-01
-7.34622419e-01 5.66980662e-03 4.88357633e-01 -5.55979133e-01
-2.82006443e-01 -3.41632515e-02 -5.27232647e-01 2.08740532e-01
1.04667127e+00 -4.23005909e-01 9.64662373e-01 1.27265644e+00
2.99787730e-01 -2.32904136e-01 -1.42464006e+00 4.57384735e-01
-4.93014961e-01 -9.61520255e-01 -5.85211277e-01 9.25578922e-02
7.65902698e-01 -1.01912297e-01 2.70121276e-01 5.15421569e-01
1.00641179e+00 -1.38073075e+00 9.86261368e-01 4.04515378e-02
6.38649404e-01 -1.45275688e+00 3.68435353e-01 6.40010118e-01
-1.03045011e+00 -7.18560159e-01 -2.04799458e-01 -1.75430819e-01
2.48167347e-02 -8.10096115e-02 -7.27542937e-01 1.24406293e-01
6.95828855e-01 5.11025310e-01 1.53807364e-02 7.56765366e-01
-6.57132387e-01 3.93892944e-01 -3.82165253e-01 -3.26176584e-01
6.50474250e-01 9.08171386e-02 5.56542337e-01 6.08241081e-01
1.54772937e-01 -2.90890425e-01 5.32212496e-01 8.18439484e-01
3.86610299e-01 -4.44215506e-01 -1.00697672e+00 4.13837694e-02
5.31103134e-01 8.93909633e-01 -2.74953723e-01 9.38326940e-02
3.18513550e-02 2.80797452e-01 4.87678975e-01 1.90934613e-01
-9.77618396e-01 -3.03260297e-01 1.04148114e+00 -2.76167274e-01
3.22184533e-01 -4.16452289e-01 -1.95953146e-01 -5.82534134e-01
-1.84105411e-01 -9.64089572e-01 6.32972062e-01 -1.25471547e-01
-9.59182918e-01 4.26144361e-01 4.84870747e-02 -1.20682323e+00
-6.31571174e-01 -3.80754203e-01 -2.93546140e-01 2.72859454e-01
-1.58653915e+00 -9.30105031e-01 3.72891635e-01 8.47982705e-01
3.67975503e-01 -1.50840640e-01 9.30492938e-01 -4.60043311e-01
-6.61969721e-01 6.70872271e-01 1.48499742e-01 -6.04945600e-01
3.04053068e-01 -1.27531111e+00 2.03385636e-01 8.62409055e-01
-4.07519937e-01 3.57318580e-01 1.01585627e+00 -7.74117529e-01
-1.93850183e+00 -1.32921946e+00 4.94874865e-02 -3.49558383e-01
8.38835776e-01 -3.33037198e-01 -4.69100088e-01 7.56918609e-01
1.53713465e-01 2.16646209e-01 2.33209595e-01 -3.40719298e-02
-3.10492635e-01 -2.93186277e-01 -1.55356753e+00 8.83419454e-01
9.67353940e-01 -3.52074116e-01 -3.37544978e-01 2.70001441e-01
1.10149395e+00 -3.83525312e-01 -9.20655847e-01 6.09220684e-01
3.88179809e-01 -4.30472612e-01 8.43805015e-01 -8.37431312e-01
-2.11857304e-01 -3.46114546e-01 4.77452250e-03 -1.43727756e+00
-2.93432266e-01 -1.33065236e+00 -7.92494774e-01 3.33944619e-01
2.12885767e-01 -9.01222467e-01 5.06428897e-01 5.46727479e-01
-1.94842622e-01 -1.20657849e+00 -1.42573011e+00 -1.35959256e+00
4.20394510e-01 -4.67044890e-01 5.94022334e-01 5.46734869e-01
7.59995043e-01 -1.48208052e-01 -8.79081964e-01 2.28024960e-01
1.01445329e+00 9.97729450e-02 3.68476808e-01 -6.82340503e-01
-3.45057547e-01 -4.77141857e-01 2.04313830e-01 -6.25635028e-01
5.72249711e-01 -3.51570189e-01 5.75907946e-01 -1.14757800e+00
-1.93770424e-01 -5.43805659e-01 -5.74440241e-01 8.87187123e-01
1.62688255e-01 -3.71060669e-01 -6.25556558e-02 -2.98740178e-01
-1.00971961e+00 1.06473494e+00 1.22545505e+00 -1.26254931e-01
-3.46487403e-01 1.10252306e-01 -7.06198454e-01 4.91938591e-01
1.02569759e+00 -3.54868054e-01 -7.75472045e-01 3.85239422e-02
3.43057722e-01 3.81868392e-01 3.43388319e-01 -9.10678566e-01
-3.08866221e-02 -9.35927451e-01 -4.21318971e-02 -3.26644063e-01
1.81000635e-01 -1.02549636e+00 -1.57810897e-01 1.36848199e+00
-7.99183369e-01 -1.22510895e-01 4.08434570e-01 7.96014309e-01
3.03436041e-01 -8.36447552e-02 1.08925259e+00 2.95913424e-02
-6.31512880e-01 5.58341086e-01 -5.71798563e-01 1.37035638e-01
1.65010917e+00 3.33913535e-01 -2.91914493e-01 -4.43259478e-01
-3.61281961e-01 8.95359099e-01 4.28550094e-01 5.04213750e-01
1.04058564e+00 -1.37603843e+00 -2.89167941e-01 1.31906748e-01
-1.15235053e-01 -5.17656915e-02 -1.79044098e-01 5.91219306e-01
-1.02570444e-01 5.42752326e-01 -2.93758333e-01 -6.83772936e-02
-8.63907754e-01 1.28826225e+00 4.03109550e-01 -3.35791737e-01
-7.42523968e-01 5.72292686e-01 9.10725594e-02 -2.22213402e-01
8.14266205e-01 -3.16372126e-01 1.60890803e-01 -4.75450844e-01
6.46212220e-01 5.34995139e-01 -4.71751064e-01 2.50541568e-02
-4.25005525e-01 2.46248066e-01 1.56779110e-01 -3.12263787e-01
1.45985532e+00 7.66052231e-02 1.89367756e-01 2.18891710e-01
8.05739582e-01 -3.81464273e-01 -2.09637952e+00 -2.34283544e-02
5.18212542e-02 -4.90893811e-01 1.06566012e-01 -6.89201474e-01
-9.12815332e-01 6.03482068e-01 5.97553313e-01 1.16000064e-01
1.03589857e+00 -2.00062022e-01 8.44224215e-01 7.09127128e-01
9.41556096e-01 -1.42948377e+00 2.35517219e-01 4.80513394e-01
1.09844422e+00 -1.07147908e+00 1.02621600e-01 2.18604028e-01
-8.01921964e-01 7.00761139e-01 8.37751448e-01 -4.33265239e-01
5.67105234e-01 6.19953394e-01 -4.20561761e-01 6.62940815e-02
-1.22041905e+00 -1.53750956e-01 -8.80891383e-02 8.01267087e-01
-3.35093319e-01 2.35875517e-01 -1.23992376e-01 3.99905950e-01
1.70997664e-01 -1.54678412e-02 3.56159270e-01 1.24250221e+00
-7.23818839e-01 -1.02734530e+00 -5.83045930e-02 1.10364951e-01
-2.77023882e-01 3.89878511e-01 3.31934504e-02 7.56622374e-01
-2.12366313e-01 9.87512887e-01 -3.47907782e-01 -3.96598607e-01
2.12021455e-01 -4.44515824e-01 6.48898542e-01 -2.84728020e-01
-4.46949184e-01 -4.72891033e-02 2.55651921e-01 -1.41456437e+00
1.00852676e-01 -7.11102247e-01 -1.57073581e+00 -2.40292177e-01
-1.31655470e-01 2.05633685e-01 3.43036085e-01 8.86907518e-01
2.02851325e-01 4.46811080e-01 1.08736169e+00 -6.66390121e-01
-1.49360740e+00 -2.49156564e-01 -5.74389338e-01 -1.17903449e-01
9.99680817e-01 -7.84582138e-01 -2.78484404e-01 -6.52650297e-01] | [4.527355194091797, 2.1679186820983887] |
897840a7-5978-4051-8556-059786a03462 | language-agnostic-code-switching-in-end-to | 2210.08992 | null | https://arxiv.org/abs/2210.08992v2 | https://arxiv.org/pdf/2210.08992v2.pdf | Language-agnostic Code-Switching in Sequence-To-Sequence Speech Recognition | Code-Switching (CS) is referred to the phenomenon of alternately using words and phrases from different languages. While today's neural end-to-end (E2E) models deliver state-of-the-art performances on the task of automatic speech recognition (ASR) it is commonly known that these systems are very data-intensive. However, there is only a few transcribed and aligned CS speech available. To overcome this problem and train multilingual systems which can transcribe CS speech, we propose a simple yet effective data augmentation in which audio and corresponding labels of different source languages are concatenated. By using this training data, our E2E model improves on transcribing CS speech. It also surpasses monolingual models on monolingual tests. The results show that this augmentation technique can even improve the model's performance on inter-sentential language switches not seen during training by 5,03% WER. | ['Alexander Waibel', 'Juan Hussain', 'Christian Huber', 'Enes Yavuz Ugan'] | 2022-10-17 | null | null | null | null | ['sequence-to-sequence-speech-recognition'] | ['speech'] | [ 3.52748662e-01 1.61774885e-02 -7.34912157e-02 -3.76995772e-01
-1.31010854e+00 -8.52390051e-01 6.81894600e-01 -4.59664539e-02
-6.63203955e-01 6.13789380e-01 3.13943416e-01 -9.02935445e-01
6.01120889e-01 -5.17705129e-03 -8.53626490e-01 -3.15394431e-01
1.38441578e-01 6.80248559e-01 1.35609612e-01 -5.94652772e-01
-8.54278952e-02 2.02377394e-01 -1.36546874e+00 6.20332539e-01
1.14697421e+00 5.23977697e-01 4.81407583e-01 7.44330168e-01
-4.13657814e-01 6.19728923e-01 -7.93952763e-01 -4.37316328e-01
9.76024568e-02 -6.91933572e-01 -8.89753044e-01 -9.86152738e-02
6.08142555e-01 1.94592252e-01 -2.22757131e-01 1.02400017e+00
5.91315150e-01 5.94100282e-02 2.84730583e-01 -7.40539134e-01
-9.23303246e-01 1.16292107e+00 -9.94321108e-02 4.15420771e-01
7.13855445e-01 -1.59694701e-01 8.85433257e-01 -1.34186029e+00
6.48048341e-01 1.26950431e+00 4.28565234e-01 8.72094095e-01
-1.33031774e+00 -8.39712203e-01 2.27402613e-01 1.09881207e-01
-1.53536725e+00 -1.17771816e+00 5.39307773e-01 -2.88796574e-01
1.44649005e+00 4.42344517e-01 3.70211422e-01 1.36695695e+00
-1.67189717e-01 1.01978815e+00 1.28323913e+00 -9.33129966e-01
-7.91210085e-02 3.85990143e-01 -2.05587015e-01 2.98425883e-01
-5.22278130e-01 3.32770169e-01 -8.92483950e-01 3.92477334e-01
2.44005442e-01 -7.15765595e-01 -3.97796392e-01 1.71273738e-01
-1.54782593e+00 5.46005309e-01 1.47480726e-01 7.74836779e-01
-3.16255987e-02 -3.22047144e-01 5.68966389e-01 9.45049882e-01
6.18247151e-01 5.08900106e-01 -6.08127296e-01 -3.19487393e-01
-9.94454086e-01 -3.68222177e-01 7.13270962e-01 1.19797683e+00
2.13150233e-01 5.24090946e-01 -1.39785424e-01 1.47271013e+00
5.12314960e-02 8.28721702e-01 9.34275508e-01 -4.72935289e-01
8.74377549e-01 2.30322659e-01 -3.94454032e-01 -2.73246676e-01
3.93477418e-02 -5.90954363e-01 -6.25173450e-01 -2.51407683e-01
2.22319663e-01 -1.45149752e-01 -1.06526411e+00 1.79286194e+00
-2.94941396e-01 1.16464168e-01 3.41118842e-01 5.50669014e-01
5.34576893e-01 9.72512603e-01 7.17248991e-02 -2.61981070e-01
9.40677702e-01 -1.29521680e+00 -8.96765351e-01 -4.47680771e-01
7.06885159e-01 -1.09478033e+00 1.30208468e+00 4.02788490e-01
-1.29794407e+00 -7.03944266e-01 -9.95528102e-01 -8.22065175e-02
-5.60709178e-01 2.76796818e-01 1.38676152e-01 7.58928478e-01
-1.46203470e+00 9.73622128e-02 -6.57301366e-01 -4.56771851e-01
-1.08208515e-01 3.17266196e-01 -5.78611791e-01 -1.32234216e-01
-1.35734093e+00 1.12991524e+00 4.17419106e-01 -1.25713587e-01
-1.07608020e+00 -5.52518189e-01 -7.70842135e-01 -4.00527418e-02
2.11577013e-01 -5.04572392e-02 1.54397678e+00 -1.33887219e+00
-2.05755496e+00 1.05795562e+00 -4.29120988e-01 -3.22586268e-01
3.81980747e-01 -1.99164346e-01 -1.05836642e+00 -1.91618487e-01
-9.67738554e-02 7.35447526e-01 6.44170403e-01 -1.10270846e+00
-8.01399648e-01 -1.33700073e-01 -5.22910833e-01 3.38676333e-01
-4.10947442e-01 5.76972663e-01 -2.77334750e-01 -8.08254719e-01
-5.37069403e-02 -1.14068508e+00 1.95130736e-01 -6.88260436e-01
-4.17124242e-01 -3.36989045e-01 4.52227116e-01 -1.18470573e+00
1.38101184e+00 -2.43688607e+00 3.28530014e-01 6.81679398e-02
-4.69121605e-01 7.74198472e-01 -4.76462543e-01 5.41149437e-01
-3.04191172e-01 1.50210708e-01 -4.94876176e-01 -6.51414692e-01
-1.76666051e-01 1.99393511e-01 -3.61772656e-01 4.30332236e-02
3.12586576e-01 6.91645503e-01 -8.47142398e-01 9.95129347e-03
1.18078671e-01 3.95442486e-01 -4.29178476e-01 3.16918969e-01
7.11202435e-03 8.65837753e-01 3.95658642e-01 4.97907668e-01
2.25543573e-01 3.09113562e-01 3.14461976e-01 5.70009530e-01
-4.11950856e-01 1.00417030e+00 -8.82396221e-01 1.90982294e+00
-7.89069414e-01 8.91406417e-01 5.97752966e-02 -9.33603168e-01
9.37628090e-01 8.56519818e-01 -1.54981881e-01 -8.06569517e-01
-4.03432176e-02 8.51297736e-01 3.57548416e-01 -1.67975932e-01
2.95731455e-01 -1.08487353e-01 -5.78171238e-02 6.85003996e-02
2.87161648e-01 -2.32730865e-01 8.95791575e-02 3.55389365e-03
9.52324390e-01 -1.16608560e-01 1.97159022e-01 -3.93204182e-01
8.49025965e-01 -9.14428383e-02 2.86335647e-01 5.25168240e-01
3.24092805e-02 5.72251260e-01 -6.20492958e-02 4.56270464e-02
-1.06665444e+00 -1.17392325e+00 -1.10518122e-02 1.46204960e+00
-4.24170524e-01 -3.00208509e-01 -8.43767762e-01 -4.32177931e-01
-2.85743326e-01 1.12799931e+00 -1.03683509e-01 -1.12340704e-01
-8.44991505e-01 -1.88205883e-01 8.86397719e-01 4.05818135e-01
2.18094602e-01 -1.12484884e+00 3.23072106e-01 4.94359761e-01
-5.96902907e-01 -1.39191222e+00 -7.21041679e-01 3.34177852e-01
-6.04841828e-01 -4.54218477e-01 -8.67899954e-01 -1.19329607e+00
4.57832456e-01 2.77230740e-01 1.18309093e+00 -5.48730977e-02
3.48156869e-01 1.06524611e-02 -6.00521266e-01 -2.02015996e-01
-1.21215558e+00 5.30080199e-01 3.63321006e-01 2.17653089e-03
4.37973499e-01 -4.40149724e-01 1.41225487e-01 1.10481955e-01
-6.15179121e-01 -6.45589828e-03 7.16243327e-01 7.76923478e-01
2.82730907e-01 -5.36448002e-01 8.14644694e-01 -7.71218300e-01
7.23025560e-01 -3.69508296e-01 -4.65751559e-01 5.10831535e-01
-5.82660615e-01 1.00068435e-01 8.67338955e-01 -5.47215462e-01
-1.09152710e+00 1.36733130e-01 -5.10342121e-01 -2.68071771e-01
-3.37768614e-01 5.94491899e-01 -9.19131413e-02 2.65159383e-02
6.18791521e-01 4.57589746e-01 -3.27636749e-01 -7.05777764e-01
3.36183131e-01 1.41048026e+00 7.27491796e-01 -5.04307210e-01
4.84616667e-01 -3.17329824e-01 -8.68859470e-01 -1.10761845e+00
-5.03143311e-01 -5.72075963e-01 -7.27279484e-01 -1.10203482e-01
7.75591731e-01 -1.18994975e+00 -2.30923891e-01 5.69771767e-01
-1.42409682e+00 -5.08829176e-01 -6.33132225e-03 6.10268831e-01
-2.81663418e-01 5.85508309e-02 -6.49617851e-01 -6.97863817e-01
-1.06806166e-01 -1.27208471e+00 9.24561203e-01 -4.20949489e-01
-2.44516537e-01 -8.38463068e-01 2.80951560e-01 4.28958148e-01
6.51459575e-01 -5.26686907e-01 9.06408727e-01 -9.36011791e-01
-3.35361540e-01 8.64944533e-02 2.67200947e-01 8.30547273e-01
1.26366317e-01 -1.59560945e-02 -1.11990452e+00 -5.21175623e-01
-3.09361517e-01 -3.16303581e-01 5.25148451e-01 -5.83524667e-02
7.61336446e-01 -1.99677140e-01 -9.91991833e-02 3.77745837e-01
1.00080705e+00 6.74381852e-01 5.20705581e-01 4.57870252e-02
5.87539136e-01 6.97654068e-01 7.33385608e-02 -3.58840078e-01
3.28799665e-01 9.36074495e-01 -1.41832024e-01 -3.89331616e-02
-5.27285933e-01 -5.28309524e-01 1.01064074e+00 2.10348034e+00
2.84438521e-01 -3.79777342e-01 -1.27855778e+00 8.14972579e-01
-1.34727979e+00 -6.43622637e-01 -1.76675171e-01 2.36278582e+00
1.35792732e+00 1.18414372e-01 -9.21707973e-02 1.40067846e-01
8.90365899e-01 -4.21615019e-02 -1.70801774e-01 -7.48872459e-01
-4.29506332e-01 5.31999528e-01 3.59154969e-01 9.98477697e-01
-6.86812043e-01 1.49655974e+00 6.64688253e+00 9.50327098e-01
-1.32693148e+00 5.26370943e-01 2.86519855e-01 1.54856056e-01
-4.85345244e-01 -4.15027849e-02 -8.72411370e-01 5.36648393e-01
1.70051837e+00 2.55710948e-02 9.33795094e-01 4.90761101e-01
-2.92111058e-02 9.53745544e-02 -1.30598319e+00 1.01263511e+00
2.80952424e-01 -9.42902088e-01 8.93759653e-02 -3.64349693e-01
8.25586855e-01 5.51139414e-01 7.65003711e-02 7.53320396e-01
5.24586976e-01 -1.00205672e+00 9.50639844e-01 -9.28111374e-02
1.29005015e+00 -6.52498662e-01 5.32451153e-01 4.09792721e-01
-1.01528919e+00 -1.84122697e-02 1.40674142e-02 1.14226766e-01
2.84665495e-01 2.48069555e-01 -1.04259849e+00 4.13972378e-01
3.90809715e-01 4.38563317e-01 -7.26114631e-01 8.48173559e-01
-4.32088166e-01 1.06244242e+00 -2.02984408e-01 1.03110820e-01
2.38178208e-01 1.03193056e-02 6.02897644e-01 1.65159202e+00
5.04039407e-01 -2.34088331e-01 6.15222426e-03 2.97515899e-01
-3.39701563e-01 3.51165116e-01 -7.63538122e-01 -2.09334314e-01
8.07791352e-01 5.03154039e-01 -4.04875338e-01 -4.04626399e-01
-5.06913960e-01 1.37478220e+00 4.76472318e-01 4.35502082e-01
-3.74936461e-01 -4.09006417e-01 4.90629107e-01 -1.34455428e-01
-6.27913373e-03 -5.27057171e-01 -7.34883994e-02 -1.18636703e+00
8.56133923e-02 -1.38587201e+00 -1.40045524e-01 -6.88508391e-01
-1.12426937e+00 1.22637081e+00 -3.29675794e-01 -1.18197715e+00
-4.40347999e-01 -5.52461803e-01 -1.10089444e-01 8.88348997e-01
-1.49436367e+00 -1.06143916e+00 2.93346167e-01 5.54209828e-01
1.00591886e+00 -7.56520152e-01 1.11227953e+00 8.58743310e-01
-4.06456798e-01 9.32128072e-01 4.16165620e-01 2.66379148e-01
9.53440547e-01 -1.30345953e+00 8.19663525e-01 1.08499360e+00
7.32196391e-01 5.96552491e-01 5.30301332e-01 -4.68378425e-01
-8.61926138e-01 -8.08386207e-01 1.67384076e+00 -5.09654284e-01
7.04827905e-01 -8.42564285e-01 -9.06841874e-01 8.64914894e-01
7.15202332e-01 -2.36143902e-01 7.26380587e-01 1.83348432e-01
-4.27791357e-01 -1.46920860e-01 -6.02423251e-01 7.91242301e-01
1.25105309e+00 -1.10078859e+00 -7.59172022e-01 2.84004986e-01
1.03945410e+00 -3.90300900e-01 -5.95359504e-01 1.86270893e-01
1.33978069e-01 -6.24202967e-01 6.07914269e-01 -7.00919271e-01
1.75439388e-01 -1.30085707e-01 -4.00688261e-01 -2.03683209e+00
-9.40008555e-03 -8.14612925e-01 5.02098799e-01 1.40270758e+00
8.93570364e-01 -6.28870726e-01 -1.95734520e-02 1.27289249e-02
-7.90313542e-01 -8.38642791e-02 -1.28098714e+00 -1.22207916e+00
3.99653971e-01 -4.77696776e-01 3.33992153e-01 1.37652946e+00
1.61909908e-01 6.70886397e-01 -1.63461864e-01 6.06930777e-02
6.70432821e-02 -4.84877676e-01 3.04536670e-01 -1.05322850e+00
-1.48319021e-01 -4.61487591e-01 -1.46640390e-02 -1.08352304e+00
6.34438336e-01 -1.42957807e+00 4.17771190e-01 -9.40236986e-01
-2.84241468e-01 -5.74218154e-01 -3.12838465e-01 4.55167353e-01
-1.00647300e-01 4.25201394e-02 4.05820638e-01 1.81903124e-01
-2.65855879e-01 6.04794741e-01 8.36469591e-01 -4.89665009e-02
-2.43339971e-01 -8.53695869e-02 -3.23476762e-01 2.53554016e-01
7.85625875e-01 -6.62903547e-01 -2.68799812e-01 -9.81287181e-01
4.73607611e-03 1.83115333e-01 -2.61194646e-01 -1.14555705e+00
3.10915798e-01 2.94837058e-01 -1.93161577e-01 -2.16424212e-01
1.57292292e-01 -7.73324549e-01 1.11552991e-01 3.47882658e-01
-7.08438158e-01 3.99705589e-01 3.34391445e-01 1.88296095e-01
-6.72339261e-01 -1.23972110e-01 7.83505559e-01 -7.11550843e-03
-5.72670817e-01 -1.88413382e-01 -8.48090172e-01 2.46355176e-01
7.01873600e-01 1.12607509e-01 -1.26410350e-01 -4.36884940e-01
-7.60856926e-01 -1.52627937e-02 1.13690563e-01 8.63678217e-01
3.49697858e-01 -1.40763497e+00 -1.08211172e+00 6.23690367e-01
2.23188385e-01 -3.46127450e-01 -1.65093035e-01 7.92961121e-01
-3.35870743e-01 8.45554352e-01 -9.03696716e-02 -5.80849349e-01
-1.21675098e+00 2.69566327e-01 3.63515317e-01 -4.39400487e-02
-9.74280164e-02 1.21732032e+00 -2.41558924e-01 -1.04422235e+00
3.46807629e-01 -2.03917652e-01 -4.20958176e-02 -6.44669607e-02
3.51797909e-01 1.90830842e-01 5.68136930e-01 -8.45455468e-01
-4.51637834e-01 2.80649841e-01 -2.47693449e-01 -6.63599610e-01
9.91363704e-01 -2.83587813e-01 2.69437768e-02 8.64347398e-01
1.14958990e+00 4.88574624e-01 -8.04002464e-01 -4.37577665e-01
3.71897191e-01 -1.61051989e-01 -1.45291969e-01 -1.06779516e+00
-8.08242738e-01 1.16279590e+00 6.72891438e-01 1.76547602e-01
8.43825579e-01 1.27111701e-02 9.79158759e-01 4.70872670e-01
4.45056140e-01 -1.39365196e+00 -2.19437778e-01 1.05429792e+00
1.09333253e+00 -1.28286970e+00 -8.98689926e-01 -3.18019927e-01
-7.79070258e-01 7.79435277e-01 4.44313645e-01 1.05370007e-01
5.01606286e-01 2.75101393e-01 4.64460403e-01 4.29091543e-01
-8.08069766e-01 -2.16587335e-01 2.42028698e-01 4.96666729e-01
8.65243196e-01 2.81646132e-01 -3.20413798e-01 1.08349942e-01
-5.67202091e-01 -3.25374305e-01 4.10225242e-01 6.73381627e-01
-2.51513958e-01 -1.55137670e+00 -2.67296433e-01 2.05544308e-02
-6.41125917e-01 -6.62498415e-01 -5.03862798e-01 5.78478098e-01
-1.43968061e-01 1.28746104e+00 1.08829685e-01 -4.84271526e-01
4.44314539e-01 6.24837697e-01 1.57269537e-01 -9.81820703e-01
-7.76231050e-01 9.57833529e-02 3.01412612e-01 -3.47448140e-01
-3.02584440e-01 -6.84712946e-01 -9.34420884e-01 -1.67182639e-01
-3.05539876e-01 2.62660176e-01 9.00981307e-01 9.87324238e-01
2.13312477e-01 6.54687703e-01 7.12156355e-01 -5.41652858e-01
-5.40876985e-01 -1.25204456e+00 -2.54145086e-01 2.17200220e-01
6.83576763e-01 -2.31374905e-01 -4.13193792e-01 3.22904259e-01] | [14.365903854370117, 6.939558982849121] |
f364a510-76af-4d4b-895a-d539a7adaa10 | stochastically-dominant-distributional | 1905.07318 | null | https://arxiv.org/abs/1905.07318v4 | https://arxiv.org/pdf/1905.07318v4.pdf | Stochastically Dominant Distributional Reinforcement Learning | We describe a new approach for managing aleatoric uncertainty in the Reinforcement Learning (RL) paradigm. Instead of selecting actions according to a single statistic, we propose a distributional method based on the second-order stochastic dominance (SSD) relation. This compares the inherent dispersion of random returns induced by actions, producing a more comprehensive and robust evaluation of the environment's uncertainty. The necessary conditions for SSD require estimators to predict accurate second moments. To accommodate this, we map the distributional RL problem to a Wasserstein gradient flow, treating the distributional Bellman residual as a potential energy functional. We propose a particle-based algorithm for which we prove optimality and convergence. Our experiments characterize the algorithm performance and demonstrate how uncertainty and performance are better balanced using an \textsc{ssd} policy than with other risk measures. | ['Michal Lyskawinski', 'Xiaohu Li', 'John D. Martin', 'Brendan Englot'] | 2019-05-17 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/3862-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/3862-Paper.pdf | icml-2020-1 | ['distributional-reinforcement-learning'] | ['methodology'] | [-1.35047594e-02 2.59822041e-01 -1.55338660e-01 -2.20771909e-01
-1.01010036e+00 -4.95963961e-01 7.39723921e-01 2.08353609e-01
-9.09339964e-01 1.14248025e+00 1.79372475e-01 -3.54405969e-01
-8.43952417e-01 -7.37890959e-01 -5.92839718e-01 -1.07076442e+00
-2.17580438e-01 4.71625149e-01 7.81886652e-02 1.34475753e-01
6.10274136e-01 5.93448520e-01 -1.65351987e+00 -3.36029887e-01
1.15102816e+00 1.08893704e+00 2.17456073e-01 7.74211645e-01
2.96239904e-03 7.19337225e-01 -7.66569853e-01 -1.91553310e-01
3.98460239e-01 -5.58821976e-01 -4.63338733e-01 -1.46337703e-01
-3.41110118e-02 -3.43953550e-01 2.33555898e-01 1.45759523e+00
7.09300339e-01 5.63704908e-01 1.10737836e+00 -1.24390936e+00
-2.86975324e-01 7.29411781e-01 -5.11135638e-01 2.47719511e-01
1.29375264e-01 1.13297343e-01 9.92722988e-01 -4.26068872e-01
3.70768040e-01 1.53037786e+00 4.32042599e-01 5.16917944e-01
-1.42699766e+00 -1.40383646e-01 3.29280078e-01 -1.97822571e-01
-1.06717467e+00 -1.54501557e-01 3.90209764e-01 -3.68582428e-01
5.63958108e-01 3.99620473e-01 3.33323389e-01 8.31076860e-01
6.30141914e-01 8.71666670e-01 1.31986320e+00 -4.49766487e-01
9.43660617e-01 1.25705585e-01 -1.31878763e-01 4.97069269e-01
5.89541137e-01 6.15507603e-01 -2.07845122e-01 -3.27211887e-01
4.85941887e-01 -2.60396391e-01 -2.03910731e-02 -9.05528784e-01
-6.03550375e-01 1.02856123e+00 -9.43443328e-02 3.34105305e-02
-6.37276828e-01 5.83593905e-01 6.04954101e-02 1.54901311e-01
6.05159104e-01 5.47429085e-01 -2.07209021e-01 -2.96544909e-01
-7.11540937e-01 6.72223628e-01 9.23484683e-01 5.47490895e-01
3.55061591e-01 7.18794316e-02 -5.13038754e-01 4.25600111e-01
7.54729450e-01 8.20923209e-01 1.41130686e-01 -1.34178543e+00
3.53204727e-01 -8.70364755e-02 6.86805427e-01 -7.83454120e-01
-2.99789757e-01 -4.79154855e-01 -2.24654853e-01 7.00307608e-01
6.41436517e-01 -4.05054748e-01 -5.86734593e-01 1.94076467e+00
2.48559818e-01 -1.04452498e-01 1.21371672e-01 5.70789754e-01
-4.53213274e-01 5.13736010e-01 2.14085862e-01 -6.57652199e-01
7.11800992e-01 -3.36307764e-01 -8.64005148e-01 1.99468181e-01
4.13353384e-01 -2.38515228e-01 9.34614897e-01 4.22902346e-01
-1.19164586e+00 -3.47416964e-03 -9.80037928e-01 6.94798172e-01
-2.00318366e-01 -4.07782108e-01 7.17786178e-02 8.39535952e-01
-1.01679385e+00 1.22615528e+00 -9.02778029e-01 6.31513447e-02
3.27728331e-01 1.93631008e-01 3.51471514e-01 4.03264463e-01
-1.06457484e+00 1.20899189e+00 3.49868685e-01 2.56782547e-02
-1.14124846e+00 -6.82826757e-01 -7.37253010e-01 8.83872360e-02
4.80047226e-01 -2.90423155e-01 1.34020448e+00 -3.40350866e-01
-1.82224655e+00 1.47754207e-01 3.11568856e-01 -7.83581495e-01
1.07425964e+00 -4.61658537e-01 1.34909138e-01 1.12046108e-01
5.57167679e-02 1.76457360e-01 8.65887046e-01 -1.41201830e+00
-6.42473757e-01 -2.84551740e-01 -2.19272282e-02 3.48845214e-01
-3.47789116e-02 -1.09070607e-01 4.59244847e-01 -3.99660587e-01
-2.41024837e-01 -9.00969148e-01 -6.07168913e-01 -4.61260170e-01
-3.45702887e-01 -2.76200265e-01 2.45556176e-01 -2.46594921e-01
1.48366070e+00 -1.83772361e+00 2.23697305e-01 5.70097387e-01
-3.97832431e-02 -1.99845266e-02 7.02867582e-02 5.01162827e-01
3.85626107e-01 2.87356645e-01 -5.67442656e-01 -2.07247019e-01
5.88836670e-01 1.81229427e-01 -4.31338727e-01 7.32157528e-01
9.43498686e-02 3.80156517e-01 -1.16092634e+00 -3.47602904e-01
1.01608627e-01 1.01606496e-01 -6.91418052e-01 1.27303272e-01
-3.65310907e-01 1.88236400e-01 -8.39825511e-01 1.16795264e-01
5.18457055e-01 2.49230698e-01 1.33347869e-01 4.30856109e-01
-3.74112517e-01 -8.39268193e-02 -1.39175260e+00 1.09872890e+00
-4.30915564e-01 -2.64453003e-04 1.50997251e-01 -7.85311282e-01
7.71991372e-01 -1.64522246e-01 6.17950022e-01 -2.94241935e-01
2.62261808e-01 3.86439472e-01 -2.11557254e-01 -2.97673643e-01
4.49597746e-01 -3.73598725e-01 -2.30332643e-01 6.98376358e-01
-5.55784069e-02 -4.52143282e-01 1.83535680e-01 4.54512388e-02
1.01992166e+00 4.39461321e-01 3.77989709e-02 -1.01226735e+00
1.58307016e-01 -3.26720297e-01 4.37784582e-01 1.10703170e+00
-4.62439597e-01 4.35153693e-02 7.88995862e-01 7.37643316e-02
-6.71615064e-01 -1.50252664e+00 -3.32105696e-01 9.73997355e-01
7.21757784e-02 -4.83686477e-02 -8.12811255e-01 -8.86813700e-01
6.14134789e-01 1.38099039e+00 -7.13000834e-01 -3.84253860e-01
-1.17881052e-01 -8.94523203e-01 2.21113920e-01 3.01035583e-01
2.60969669e-01 -7.81321168e-01 -1.07916331e+00 2.67522573e-01
2.91626662e-01 -2.43473127e-01 -4.13058221e-01 5.31385362e-01
-6.95123136e-01 -9.72259045e-01 -7.22971797e-01 1.29600391e-01
5.04938722e-01 -3.99332613e-01 9.51828659e-01 -6.04659736e-01
-4.26694602e-02 9.86467063e-01 -6.94318190e-02 -7.51655459e-01
-3.70975465e-01 -5.67710340e-01 5.38062513e-01 -1.11025147e-01
1.35837048e-01 -2.21082345e-01 -5.58526158e-01 9.26535577e-04
-8.38444650e-01 -9.47068274e-01 3.97526830e-01 7.88513362e-01
6.09906971e-01 4.50357348e-01 6.60833955e-01 -6.34263277e-01
1.07655096e+00 -3.81863117e-01 -1.09272552e+00 2.31279388e-01
-9.90202188e-01 6.62877142e-01 2.00069025e-01 -2.97562957e-01
-1.44614673e+00 -1.45471349e-01 3.17587346e-01 -3.13864261e-01
1.32184431e-01 2.60106087e-01 6.78599924e-02 1.37614205e-01
5.03398240e-01 1.49400523e-02 1.61478564e-01 -3.23401988e-01
5.69320500e-01 4.59832072e-01 1.80040285e-01 -1.08388138e+00
6.33545697e-01 3.52515101e-01 2.17684090e-01 -7.01951563e-01
-9.07816768e-01 -3.68360244e-02 -2.35717446e-01 -4.06224072e-01
1.04756439e+00 -3.30932140e-01 -1.04077625e+00 1.65746942e-01
-7.50605226e-01 -3.54043037e-01 -8.86640847e-01 9.01384711e-01
-1.23277640e+00 1.78183123e-01 -3.49314451e-01 -1.84660780e+00
1.75801575e-01 -1.10225260e+00 9.19514120e-01 1.44936994e-01
7.16347396e-02 -1.11371255e+00 5.23656309e-01 -1.58879399e-01
4.24895644e-01 2.75133252e-01 6.11037254e-01 -7.52798736e-01
-3.96168351e-01 1.13597766e-01 1.17523044e-01 5.28879166e-01
-2.45686442e-01 3.71520631e-02 -7.60441184e-01 -3.20810527e-01
4.64227796e-01 -2.66366452e-01 1.07136905e+00 6.65833533e-01
9.30414975e-01 -4.43208694e-01 2.57860310e-02 2.02914447e-01
1.46543705e+00 5.27509868e-01 2.46277645e-01 5.41098177e-01
1.37218416e-01 8.36335897e-01 8.89131248e-01 9.40950155e-01
6.92110807e-02 2.17045441e-01 6.27529204e-01 7.17029810e-01
5.61841428e-01 -4.58712757e-01 5.12029827e-01 2.95976430e-01
4.64332327e-02 -1.80577040e-01 -8.29133570e-01 3.52504462e-01
-1.84771132e+00 -1.34108651e+00 3.14408928e-01 2.55236697e+00
8.19011867e-01 2.11167291e-01 1.25658497e-01 -1.57416150e-01
7.65552640e-01 1.00708626e-01 -7.73121536e-01 -5.89476764e-01
1.02566488e-01 -3.69599415e-03 9.87407088e-01 8.93230617e-01
-1.04012108e+00 4.51317489e-01 7.78878975e+00 1.03704929e+00
-3.71271610e-01 -1.71192765e-01 5.91140151e-01 -2.33279228e-01
-7.43642151e-01 -2.79756516e-01 -8.37285221e-01 6.97204530e-01
1.05082417e+00 -4.82673049e-01 3.26539397e-01 8.67331266e-01
3.50325167e-01 -5.91685832e-01 -9.48063195e-01 4.41024393e-01
-2.72207588e-01 -8.81476104e-01 -2.93263793e-01 3.72631788e-01
8.20146441e-01 -2.31072217e-01 3.67152333e-01 4.02852565e-01
1.03530276e+00 -9.06987488e-01 9.08991814e-01 9.47188318e-01
3.72257769e-01 -1.19085801e+00 5.82187951e-01 4.70980883e-01
-6.98597193e-01 -4.06582415e-01 -4.37650353e-01 7.01736733e-02
-6.11893553e-03 6.32459402e-01 -3.76228362e-01 4.01263326e-01
4.50003028e-01 7.89931491e-02 -5.09154089e-02 9.63941991e-01
-1.16204783e-01 2.66782314e-01 -5.09341538e-01 -3.84683102e-01
4.10764903e-01 -6.28802061e-01 8.61802757e-01 9.11934137e-01
4.95893925e-01 -1.24642193e-01 2.85865843e-01 9.56717074e-01
1.94792166e-01 -4.66359966e-02 -6.86125934e-01 -2.12256923e-01
5.39322555e-01 7.09131718e-01 -7.22331762e-01 -9.40512270e-02
1.13381520e-01 4.90407735e-01 1.84993550e-01 3.65893304e-01
-7.39786506e-01 -4.14541423e-01 7.26400733e-01 -4.04607952e-01
2.98361897e-01 -5.39641827e-02 -9.18630511e-02 -8.63057137e-01
6.65531633e-03 -5.26351094e-01 4.20038104e-01 -1.78249672e-01
-1.41403854e+00 2.55214840e-01 5.04303336e-01 -9.83267665e-01
-5.63119173e-01 -6.07514262e-01 -5.25809884e-01 7.43212819e-01
-1.37858498e+00 -2.02045932e-01 6.86879158e-01 2.96532243e-01
1.46638602e-01 -1.11420706e-01 5.12283623e-01 -2.68517464e-01
-5.05761623e-01 1.86106026e-01 7.30031788e-01 -6.58001363e-01
3.89242172e-01 -1.89645410e+00 -3.16953987e-01 7.26707935e-01
-3.01890969e-01 3.42806131e-01 1.27796829e+00 -7.91282237e-01
-1.27655184e+00 -9.87677932e-01 3.62900585e-01 -6.64387405e-01
1.06802166e+00 4.88808602e-02 -3.88603568e-01 4.07750845e-01
-6.59360811e-02 -2.91840523e-01 3.42405528e-01 -5.96821665e-05
-6.73273578e-02 2.65580695e-02 -1.54187465e+00 5.27707815e-01
8.74415457e-01 -4.47391719e-01 -7.10075974e-01 2.30048284e-01
6.23344421e-01 -3.11504528e-02 -8.30434203e-01 4.03463900e-01
3.50561291e-01 -1.03225124e+00 6.44885123e-01 -7.43053198e-01
1.19314834e-01 -1.34843290e-01 -3.98325771e-01 -1.66866875e+00
-1.35413438e-01 -9.26132381e-01 -2.53069475e-02 9.82728183e-01
2.96428651e-01 -7.73970187e-01 6.18735790e-01 7.03227222e-01
4.15649414e-02 -8.88882875e-01 -1.09910703e+00 -1.34268677e+00
5.22361100e-01 -5.75564325e-01 3.56887788e-01 4.27228123e-01
1.04920790e-01 -3.68960410e-01 -1.74877629e-01 1.31855095e-02
1.27347910e+00 -8.57886747e-02 7.06716031e-02 -1.05679703e+00
-4.36435252e-01 -8.17566276e-01 -2.17526719e-01 -6.40875041e-01
3.06782424e-01 -6.32754207e-01 5.61940849e-01 -1.34721875e+00
1.16044998e-01 -3.96087646e-01 -7.37508416e-01 -1.74177319e-01
-1.02199882e-01 -4.36917067e-01 3.35417420e-01 -7.48017728e-02
-8.63385439e-01 9.79663312e-01 1.03823614e+00 8.85037258e-02
-3.55131060e-01 3.38017553e-01 -5.39669275e-01 9.54465508e-01
9.44269955e-01 -4.84483063e-01 -6.38300896e-01 -1.04094520e-01
2.65496194e-01 1.39462173e-01 1.49392664e-01 -8.57558012e-01
3.93794738e-02 -4.69438612e-01 2.27075025e-01 -4.81681496e-01
6.17029965e-02 -6.38377070e-01 -8.60126093e-02 6.75517380e-01
-9.04618621e-01 2.72055436e-02 3.21993083e-02 1.03612959e+00
1.15739018e-01 -5.71624875e-01 9.34066653e-01 6.67962134e-02
-1.03723690e-01 1.78238288e-01 -5.80535471e-01 2.77365774e-01
1.25027597e+00 3.30338031e-01 -2.44498804e-01 -4.91211325e-01
-7.92981863e-01 4.87067372e-01 2.79154032e-01 -6.77816942e-02
5.57070613e-01 -1.28735018e+00 -6.60611689e-01 -2.16481805e-01
-2.36253813e-01 -4.28730369e-01 9.12041217e-02 7.31795132e-01
-2.19257057e-01 2.99018174e-01 -8.67913216e-02 -3.40565979e-01
-5.30967057e-01 6.28093779e-01 5.14093757e-01 -4.54106569e-01
-2.14341938e-01 9.02561665e-01 1.24108903e-01 -3.13007206e-01
5.30306220e-01 -1.36353210e-01 -1.36419609e-01 2.92807400e-01
5.57117224e-01 8.19042206e-01 -3.68581325e-01 -1.55514166e-01
-3.55258793e-01 2.45824918e-01 3.48245531e-01 -7.54480064e-01
1.25193155e+00 -3.03765863e-01 1.23973824e-01 6.07542992e-01
8.24695528e-01 1.50266185e-01 -1.78320801e+00 8.71077776e-02
5.59000611e-01 -5.68640590e-01 2.16477141e-01 -8.14431310e-01
-6.32462680e-01 6.17146194e-01 6.06727958e-01 6.21851206e-01
7.23523200e-01 -2.57066429e-01 1.29871234e-01 4.49968219e-01
5.63336730e-01 -1.52434576e+00 -4.38368618e-02 4.11444902e-01
9.43897188e-01 -7.78797925e-01 8.52325186e-02 2.49731213e-01
-8.81728470e-01 8.38392377e-01 2.09552929e-01 -3.26064795e-01
9.15296614e-01 4.06221509e-01 -2.04077780e-01 1.39445633e-01
-7.57585406e-01 -6.33379579e-01 5.10413237e-02 6.36023760e-01
-6.51823208e-02 2.42356852e-01 -5.84615290e-01 2.55947173e-01
-2.94739958e-02 -3.19852203e-01 5.08422315e-01 9.60283518e-01
-8.55174422e-01 -9.92053986e-01 -2.40163788e-01 5.25280595e-01
-6.31875515e-01 1.74791977e-01 1.82468668e-02 5.71459711e-01
-3.00304174e-01 1.08733106e+00 1.08430222e-01 -1.37598231e-01
3.30420375e-01 1.00147083e-01 5.40168941e-01 -4.78326201e-01
-8.00776109e-02 2.80628562e-01 5.03581800e-02 -8.85748208e-01
-3.04977804e-01 -1.03561270e+00 -1.05420375e+00 -1.67398840e-01
-1.71973646e-01 6.21235847e-01 8.36701214e-01 8.31365108e-01
9.31019783e-02 5.35861969e-01 8.43935609e-01 -7.96555102e-01
-1.89914989e+00 -5.89853466e-01 -1.13494647e+00 7.26508275e-02
3.14921707e-01 -1.01470888e+00 -9.32439804e-01 -6.20180726e-01] | [4.287740230560303, 2.633284091949463] |
777c8adb-6ceb-4a92-a15e-19a741d4e9db | classification-with-costly-features-as-a | 1909.02564 | null | https://arxiv.org/abs/1909.02564v1 | https://arxiv.org/pdf/1909.02564v1.pdf | Classification with Costly Features as a Sequential Decision-Making Problem | This work focuses on a specific classification problem, where the information about a sample is not readily available, but has to be acquired for a cost, and there is a per-sample budget. Inspired by real-world use-cases, we analyze average and hard variations of a directly specified budget. We postulate the problem in its explicit formulation and then convert it into an equivalent MDP, that can be solved with deep reinforcement learning. Also, we evaluate a real-world inspired setting with sparse training dataset with missing features. The presented method performs robustly well in all settings across several distinct datasets, outperforming other prior-art algorithms. The method is flexible, as showcased with all mentioned modifications and can be improved with any domain independent advancement in RL. | ['Viliam Lisý', 'Tomáš Pevný', 'Jaromír Janisch'] | 2019-09-05 | null | null | null | null | ['classification-with-costly-features'] | ['miscellaneous'] | [ 5.58732390e-01 2.04543591e-01 -7.33186126e-01 -4.83592033e-01
-9.47004437e-01 -5.88769674e-01 6.74373269e-01 -2.21111532e-02
-7.06459224e-01 1.29338813e+00 -1.75198227e-01 3.54495384e-02
-5.37099957e-01 -6.32885277e-01 -8.77653658e-01 -7.72357702e-01
-3.70382890e-02 1.00093460e+00 -2.60991633e-01 -1.34350270e-01
2.62087733e-01 5.50710320e-01 -1.49114394e+00 8.91420916e-02
5.87698638e-01 1.32910776e+00 3.04732680e-01 3.89234036e-01
5.56167066e-02 7.67627358e-01 -8.25256228e-01 -1.00862496e-01
5.87136328e-01 -1.84295431e-01 -9.63738859e-01 5.09429216e-01
3.62380207e-01 -4.79368389e-01 8.08319002e-02 6.48821175e-01
6.18153334e-01 2.71858722e-01 7.45605886e-01 -1.26248729e+00
-4.21884477e-01 3.87662917e-01 -4.93433565e-01 -3.17733996e-02
4.08499986e-01 1.79432616e-01 1.07201874e+00 -4.60616857e-01
5.89940369e-01 1.12719786e+00 2.70334810e-01 4.83274281e-01
-1.42959380e+00 -5.59024155e-01 4.18787658e-01 1.72829539e-01
-8.67441773e-01 -3.95732760e-01 8.34503353e-01 -3.31970453e-01
9.14994299e-01 -7.53802434e-02 4.88033533e-01 1.51363099e+00
-2.48897895e-01 1.22763848e+00 1.42734718e+00 -3.01895797e-01
7.33171940e-01 7.41363764e-02 9.22833458e-02 4.59244281e-01
2.20662355e-01 1.23959750e-01 -4.82004583e-01 -1.22561440e-01
4.95974839e-01 9.76931676e-02 -1.70774400e-01 -6.67714179e-01
-9.78061795e-01 1.12875581e+00 2.20598474e-01 2.03251511e-01
-4.75638598e-01 2.72997506e-02 2.59697855e-01 6.17467165e-01
3.40522140e-01 5.05897641e-01 -7.53601909e-01 -1.18739516e-01
-1.23788512e+00 4.59614336e-01 9.96476889e-01 1.04396868e+00
8.58931720e-01 3.38728279e-02 -2.04316005e-01 7.44059741e-01
-1.20990805e-01 4.32947427e-01 6.75442815e-01 -1.14764893e+00
4.10772562e-01 9.67574269e-02 6.42858386e-01 -6.44313931e-01
-5.60281456e-01 -6.79390073e-01 -8.35819602e-01 1.50198951e-01
7.18062162e-01 -2.43348956e-01 -6.75905108e-01 1.94999003e+00
2.72595197e-01 3.01889896e-01 3.93756218e-02 8.73569250e-01
2.44345903e-01 3.10899496e-01 -2.84912556e-01 -3.95246208e-01
1.13080096e+00 -9.28837359e-01 -7.49600708e-01 -5.75458884e-01
1.53400764e-01 -4.95987386e-01 1.25512397e+00 1.08783865e+00
-9.55570757e-01 -4.45166141e-01 -1.23943198e+00 -7.92463198e-02
-2.86790133e-01 2.57428259e-01 8.55696321e-01 6.80102289e-01
-8.21093380e-01 8.84170353e-01 -6.72008097e-01 -2.06627429e-01
4.23396409e-01 5.31728745e-01 -1.98520020e-01 -4.63683844e-01
-1.04041469e+00 7.58172274e-01 3.66630256e-01 1.98653474e-01
-1.16887295e+00 -7.56445467e-01 -8.08416963e-01 -1.74042985e-01
8.15588892e-01 -7.04621553e-01 1.46215951e+00 -1.18251717e+00
-1.97463429e+00 6.78129613e-01 2.71243090e-03 -6.26147509e-01
9.07258987e-01 -3.92462283e-01 -2.39758775e-01 1.41907975e-01
-1.75833981e-02 4.22843635e-01 1.30814350e+00 -1.09899640e+00
-4.87005293e-01 -2.35382259e-01 5.41790247e-01 3.64680178e-02
-1.80979297e-01 -3.43921572e-01 -6.95022121e-02 -6.62749887e-01
-3.91361147e-01 -9.52934742e-01 -3.01638454e-01 -1.12060748e-01
-1.52836159e-01 -9.09728780e-02 7.49584794e-01 -3.33348483e-01
6.95161581e-01 -1.93434286e+00 2.75581777e-01 -3.77033018e-02
-3.78600173e-02 3.23345065e-02 -3.50778550e-01 5.21836102e-01
1.27797559e-01 -2.12900639e-01 -5.22728860e-01 -5.48078120e-01
3.11618656e-01 6.56000614e-01 -2.99922258e-01 6.67738020e-01
3.69083613e-01 6.12586439e-01 -9.99043226e-01 -1.71738371e-01
1.71627551e-01 1.00053228e-01 -6.76895857e-01 2.15291798e-01
-5.47213256e-01 5.43335378e-01 -5.74462116e-01 5.38333058e-01
7.47861385e-01 -4.17060912e-01 3.35328102e-01 1.11856461e-01
2.58123338e-01 9.96147245e-02 -1.57186210e+00 2.12413239e+00
-9.10844862e-01 3.51924360e-01 1.68270141e-01 -1.71304286e+00
7.44488716e-01 -9.09443852e-03 5.12829363e-01 -5.94929755e-01
4.35861899e-03 1.81560069e-01 -8.47282410e-02 -4.64448214e-01
2.68956423e-01 -2.52090335e-01 -1.53172702e-01 6.26300097e-01
3.96413028e-01 -1.71868190e-01 3.31099659e-01 -1.82884291e-01
1.15359485e+00 1.52584955e-01 5.62592149e-01 -2.60805488e-01
3.80486548e-01 -2.64743507e-01 4.85365719e-01 9.97034371e-01
1.65392347e-02 4.00440276e-01 6.15787327e-01 -2.09983677e-01
-7.99031615e-01 -9.08904850e-01 -2.50677526e-01 1.09954834e+00
-1.01144843e-01 8.68860260e-02 -5.23522139e-01 -8.74899924e-01
1.38010740e-01 6.37887120e-01 -6.97744668e-01 2.80580837e-02
-4.23626900e-01 -8.71946037e-01 6.85441494e-02 2.99643278e-01
5.22903025e-01 -1.08411074e+00 -8.33914042e-01 3.14612210e-01
1.12309404e-01 -1.30514836e+00 -7.11392090e-02 5.12177467e-01
-7.33472824e-01 -1.08929741e+00 -8.78989697e-01 -4.92401809e-01
3.31712812e-01 -1.31245583e-01 1.35601223e+00 -1.95420578e-01
-3.71527761e-01 6.61499321e-01 -4.86014187e-01 -4.30089295e-01
4.52970667e-03 3.41123194e-01 9.97457653e-02 2.36943826e-01
3.14642936e-02 -5.77834070e-01 -5.93267024e-01 7.05926940e-02
-8.88443589e-01 -4.17282790e-01 7.56118238e-01 1.15758669e+00
6.66172326e-01 7.38578215e-02 9.31161046e-01 -1.09146595e+00
5.84300220e-01 -6.39072180e-01 -6.59407973e-01 -1.59402229e-02
-5.00744522e-01 4.09945041e-01 9.25914407e-01 -6.11837804e-01
-8.91757548e-01 2.88633227e-01 2.46682111e-03 -3.03075761e-01
-2.82288134e-01 3.28895032e-01 -3.89078468e-01 -2.24715602e-02
4.55625176e-01 1.98877528e-01 2.48448439e-02 -5.47100484e-01
3.35958064e-01 5.94639003e-01 4.28494252e-02 -9.87594903e-01
6.06010556e-01 5.07594943e-01 1.38801202e-01 -8.19223046e-01
-1.17300332e+00 -2.89639771e-01 -6.92626774e-01 2.63276994e-01
2.17638344e-01 -8.98437679e-01 -8.64703119e-01 1.76717937e-01
-7.62561262e-01 -7.32047439e-01 -6.68681085e-01 5.09277046e-01
-1.16829193e+00 2.30687723e-01 -2.94819444e-01 -8.09707522e-01
1.04297824e-01 -1.08851647e+00 1.14184129e+00 -1.09518535e-01
6.92866221e-02 -8.61501932e-01 8.72412175e-02 3.35467607e-01
2.99851209e-01 1.88062772e-01 6.69022799e-01 -8.04036617e-01
-6.00616097e-01 6.35918379e-02 -1.88534763e-02 6.45239174e-01
1.27572551e-01 -4.19089615e-01 -1.16868126e+00 -5.33026874e-01
1.78728029e-01 -9.40533340e-01 9.27162051e-01 4.32315171e-01
1.54401302e+00 -4.65767294e-01 1.25102296e-01 4.86362815e-01
1.38219905e+00 -1.00238942e-01 2.57125705e-01 3.62818360e-01
1.00295059e-01 6.50187612e-01 8.97806466e-01 9.74757910e-01
1.70485035e-01 7.03049600e-01 7.05845118e-01 8.46140552e-03
2.62632430e-01 4.33683135e-02 2.40271822e-01 2.75856644e-01
4.01783437e-02 -2.24108860e-01 -4.44866717e-01 5.04934192e-01
-1.99445963e+00 -1.06252789e+00 4.92565364e-01 2.14602113e+00
8.53635311e-01 -6.69454336e-02 4.20616537e-01 4.31068271e-01
2.63597846e-01 4.02412355e-01 -1.01946747e+00 -3.67098510e-01
-1.46167129e-01 7.46249974e-01 5.67475736e-01 3.91229868e-01
-1.12148118e+00 6.13667190e-01 7.24309111e+00 1.04859865e+00
-1.18809795e+00 1.28326371e-01 6.19713604e-01 -5.24781942e-01
-1.17384255e-01 -2.93486744e-01 -6.67307675e-01 4.43150818e-01
8.73554945e-01 -2.72328816e-02 9.03610408e-01 9.68042552e-01
7.30756968e-02 -1.97042018e-01 -1.66798997e+00 1.13652885e+00
-3.13065760e-02 -1.13271832e+00 -3.21409971e-01 1.13584280e-01
5.72411895e-01 -7.35913217e-02 4.88252014e-01 5.92968106e-01
4.75750923e-01 -1.10277879e+00 5.74178815e-01 3.23112547e-01
7.21245408e-01 -8.47873449e-01 6.67886615e-01 6.72125995e-01
-6.65213227e-01 -3.86278570e-01 -5.63768864e-01 -4.25936401e-01
-2.52889186e-01 8.56213450e-01 -7.39223063e-01 7.67942846e-01
4.04799700e-01 1.01173842e+00 -2.43403032e-01 8.24077547e-01
-2.60770738e-01 5.07453024e-01 -4.31919843e-01 -8.76052007e-02
3.88741344e-01 -1.80946484e-01 3.52699608e-02 1.23302639e+00
3.57187241e-01 -2.07709029e-01 3.12895894e-01 7.37670124e-01
-2.16576070e-01 7.25004748e-02 -5.19550741e-01 2.27536812e-01
3.35553795e-01 1.29060805e+00 -3.45629066e-01 -1.62803322e-01
-3.82202089e-01 1.05624127e+00 5.70021749e-01 4.35244858e-01
-8.03050876e-01 -1.12943925e-01 6.49835587e-01 -1.22788444e-01
6.90093756e-01 5.18239709e-03 1.00797877e-01 -1.24575531e+00
1.09739281e-01 -1.00631654e+00 4.33850974e-01 -2.66491532e-01
-1.64907336e+00 2.11894363e-01 1.30803585e-01 -1.21399772e+00
-6.42710626e-01 -8.40699434e-01 -8.58920738e-02 5.41555226e-01
-2.16871357e+00 -8.03434312e-01 3.75497937e-02 9.25910234e-01
5.53026795e-01 -1.68733388e-01 7.98397779e-01 3.66777360e-01
-7.56901979e-01 6.51991308e-01 3.84179473e-01 -3.01728040e-01
6.68191969e-01 -1.38287127e+00 -3.49309653e-01 3.80712569e-01
1.48843527e-01 2.66237378e-01 7.16711581e-01 -4.73489910e-02
-1.57424366e+00 -1.08363080e+00 3.10177743e-01 -2.96445191e-01
7.85290241e-01 -6.80143893e-01 -4.90415543e-01 7.03254938e-01
2.47741252e-01 3.37698609e-01 6.34309769e-01 2.12989539e-01
-2.32029304e-01 -2.13543504e-01 -1.61485422e+00 -3.38846855e-02
1.10743904e+00 -3.06147635e-01 -5.79894423e-01 5.69854617e-01
5.69022059e-01 -4.41645145e-01 -8.77133429e-01 2.37965763e-01
4.01129037e-01 -9.02040184e-01 1.13053381e+00 -6.81898534e-01
3.12728077e-01 1.39625743e-01 -3.16268235e-01 -1.42601705e+00
-9.09416284e-03 -6.70436978e-01 -3.29660207e-01 9.77099061e-01
3.05248410e-01 -5.31155229e-01 9.03200626e-01 4.24387246e-01
2.15130672e-01 -7.36904502e-01 -1.03014112e+00 -1.15842319e+00
1.80420235e-01 -4.49176729e-01 7.15768814e-01 9.43276346e-01
-3.04342330e-01 1.80560231e-01 -7.44913101e-01 1.48777857e-01
7.04845548e-01 4.64026868e-01 7.74194777e-01 -1.15672183e+00
-9.02942717e-01 -2.47462735e-01 -1.03441544e-01 -1.32439482e+00
6.00004852e-01 -6.43636107e-01 -8.63812715e-02 -1.08089757e+00
6.36402741e-02 -5.94307601e-01 -4.12939608e-01 4.24433470e-01
1.93397284e-01 -2.86542065e-02 3.48678499e-01 2.57742242e-03
-6.33727431e-01 6.82664275e-01 1.17125070e+00 -3.22585583e-01
-6.14401475e-02 3.60385835e-01 -6.25819921e-01 6.41151547e-01
8.56890798e-01 -5.00589669e-01 -7.18981385e-01 -2.21397966e-01
1.44620627e-01 1.66391402e-01 2.52021104e-01 -1.00592875e+00
-1.87496588e-01 -4.94448662e-01 3.66775423e-01 -3.27631116e-01
5.65266609e-01 -1.08293438e+00 -2.76699275e-01 3.64964068e-01
-5.53311646e-01 -3.37821662e-01 4.47083823e-02 6.48293078e-01
-1.51517391e-01 -5.86204529e-01 7.89427817e-01 -1.53339654e-01
-4.84067053e-01 4.01245624e-01 -1.13676637e-01 3.81422698e-01
1.10346842e+00 -6.44965544e-02 -1.11242354e-01 -3.33316594e-01
-7.84492373e-01 1.67435616e-01 2.27585763e-01 3.53601128e-02
3.45360398e-01 -1.12522590e+00 -5.90502381e-01 1.54230297e-01
3.71775031e-02 8.67014825e-02 1.74886599e-01 6.46856844e-01
2.85101999e-02 3.32501709e-01 -3.48072678e-01 -5.23798943e-01
-5.34142673e-01 9.57303703e-01 1.27010018e-01 -6.45163715e-01
-5.00757813e-01 4.41785991e-01 -2.10634153e-02 -5.23295999e-01
4.70766485e-01 -5.36810040e-01 -7.00752810e-02 2.33826816e-01
6.05927587e-01 2.35073268e-01 2.27462813e-01 7.70858005e-02
-1.39827237e-01 5.61819255e-01 -4.84439405e-03 -1.03251062e-01
1.58508039e+00 -3.17312814e-02 4.11961585e-01 6.16961300e-01
1.11775875e+00 -1.62961394e-01 -1.57381725e+00 -3.33064556e-01
-5.99156246e-02 -3.94222796e-01 -3.25260647e-02 -9.31639731e-01
-1.25860476e+00 9.16008890e-01 3.89654219e-01 6.75644502e-02
1.31707406e+00 -1.30967647e-01 5.29997587e-01 8.08096051e-01
6.37918651e-01 -1.18489587e+00 2.73896813e-01 3.97024125e-01
9.82496381e-01 -1.62013745e+00 1.57606095e-01 4.16860059e-02
-6.82048917e-01 9.66648519e-01 2.81006008e-01 -5.41538715e-01
5.91928363e-01 4.01826113e-01 -5.36300242e-01 1.88963294e-01
-7.83210158e-01 -2.25479245e-01 1.02422073e-01 8.99874806e-01
5.77456169e-02 -5.72686680e-02 -2.25783557e-01 7.06188083e-01
-1.78234726e-01 3.50513697e-01 4.64356959e-01 8.95641267e-01
-2.67843127e-01 -1.31286836e+00 -2.17747226e-01 5.32370746e-01
-3.85403633e-01 1.66986465e-01 -2.10930064e-01 1.06831324e+00
9.86349955e-02 8.34946990e-01 4.54411507e-02 1.11242652e-01
1.61062643e-01 -1.45814076e-01 8.40895832e-01 -5.71794868e-01
-3.53038847e-01 -4.02626581e-03 6.31660894e-02 -8.76529634e-01
-8.73950899e-01 -8.97836983e-01 -9.62076187e-01 -1.41801715e-01
1.29965246e-01 -2.03314081e-01 5.64944983e-01 1.10584939e+00
2.59130359e-01 3.79188836e-01 7.39917696e-01 -1.03439009e+00
-8.92357647e-01 -8.50229263e-01 -8.51842523e-01 3.23371083e-01
6.56950533e-01 -9.13036108e-01 -4.20313150e-01 -2.29888916e-01] | [4.155403137207031, 2.206609010696411] |
675af2f5-1f8e-4c75-a9bf-86d32fd5f544 | cad-pu-a-curvature-adaptive-deep-learning | 2009.04660 | null | https://arxiv.org/abs/2009.04660v1 | https://arxiv.org/pdf/2009.04660v1.pdf | CAD-PU: A Curvature-Adaptive Deep Learning Solution for Point Set Upsampling | Point set is arguably the most direct approximation of an object or scene surface, yet its practical acquisition often suffers from the shortcoming of being noisy, sparse, and possibly incomplete, which restricts its use for a high-quality surface recovery. Point set upsampling aims to increase its density and regularity such that a better surface recovery could be achieved. The problem is severely ill-posed and challenging, considering that the upsampling target itself is only an approximation of the underlying surface. Motivated to improve the surface approximation via point set upsampling, we identify the factors that are critical to the objective, by pairing the surface approximation error bounds of the input and output point sets. It suggests that given a fixed budget of points in the upsampling result, more points should be distributed onto the surface regions where local curvatures are relatively high. To implement the motivation, we propose a novel design of Curvature-ADaptive Point set Upsampling network (CAD-PU), the core of which is a module of curvature-adaptive feature expansion. To train CAD-PU, we follow the same motivation and propose geometrically intuitive surrogates that approximate discrete notions of surface curvature for the upsampled point set. We further integrate the proposed surrogates into an adversarial learning based curvature minimization objective, which gives a practically effective learning of CAD-PU. We conduct thorough experiments that show the efficacy of our contributions and the advantages of our method over existing ones. Our implementation codes are publicly available at https://github.com/JiehongLin/CAD-PU. | ['Yuan Gao', 'Ke Chen', 'Kui Jia', 'Xian Shi', 'Jiehong Lin'] | 2020-09-10 | null | null | null | null | ['point-set-upsampling'] | ['computer-vision'] | [ 9.05622989e-02 2.28717849e-01 -1.40990287e-01 -2.38993652e-02
-1.00690162e+00 -2.53789216e-01 3.42396945e-01 -1.00539990e-01
6.09497055e-02 4.20519799e-01 6.59955069e-02 -2.83779670e-02
-1.81362350e-02 -1.11687005e+00 -1.03775382e+00 -4.78633970e-01
-1.08553153e-02 2.78036028e-01 1.12303503e-01 -3.54405761e-01
3.46275330e-01 8.82565796e-01 -1.53760254e+00 2.77023707e-02
1.06742585e+00 9.55508530e-01 1.20923445e-01 4.68721837e-01
1.02072276e-01 1.66872609e-02 -1.41326830e-01 -2.68038005e-01
6.94352448e-01 -1.87182352e-01 -7.28498995e-01 2.95783319e-02
6.38061345e-01 -5.79138756e-01 -2.38737032e-01 1.14588606e+00
2.61908054e-01 2.03202888e-01 7.91925967e-01 -9.32612717e-01
-5.87100267e-01 3.73938791e-02 -7.14362800e-01 -4.15141374e-01
2.51104653e-01 5.43663763e-02 1.03432393e+00 -1.39962673e+00
5.71279824e-01 1.05369210e+00 1.00877702e+00 5.91809273e-01
-1.23535216e+00 -4.79887605e-01 -1.97477378e-02 -2.90514618e-01
-1.54839849e+00 -4.46154416e-01 1.33757412e+00 -2.75100350e-01
2.63233334e-01 4.35950249e-01 7.21395373e-01 6.57538772e-01
-1.06495403e-01 9.38843548e-01 5.88817060e-01 -3.23547035e-01
3.00777525e-01 1.49047837e-01 -1.89377606e-01 6.79969609e-01
2.14424103e-01 4.09590751e-02 -7.19080344e-02 -4.53248680e-01
1.52265191e+00 3.97322834e-01 -5.46655774e-01 -7.86821246e-01
-8.77465189e-01 9.41614270e-01 9.12118971e-01 2.57056713e-01
-3.61927420e-01 1.98790178e-01 9.01562870e-02 1.79858357e-01
6.98755145e-01 5.09373963e-01 -3.34742129e-01 1.12087518e-01
-9.95940506e-01 5.09227276e-01 5.24359703e-01 9.45193589e-01
1.18833077e+00 -2.27504700e-01 1.34255335e-01 8.12489629e-01
3.60466540e-01 5.34825563e-01 1.86019868e-01 -1.11356354e+00
4.21308219e-01 6.77757442e-01 3.24674666e-01 -1.39752197e+00
3.42175132e-03 -1.21244676e-01 -9.07828093e-01 4.32775557e-01
4.67333764e-01 9.89257544e-02 -6.30457461e-01 1.52264762e+00
6.52890086e-01 3.88767868e-01 -2.00092196e-01 1.07076919e+00
3.37310582e-01 6.20575905e-01 -4.48329151e-01 2.75551435e-03
1.12668180e+00 -7.14809835e-01 -2.28832006e-01 -2.53074896e-02
4.99456495e-01 -6.59184337e-01 1.42600429e+00 1.47141427e-01
-1.25207138e+00 -4.15253669e-01 -1.05861688e+00 -2.46219188e-01
7.43246600e-02 4.27849218e-02 5.82673848e-01 2.67210931e-01
-8.88458908e-01 1.12209725e+00 -9.51711714e-01 1.26655415e-01
8.16620350e-01 2.71671623e-01 -1.82187378e-01 -1.25366136e-01
-8.89879107e-01 4.75775898e-01 -6.85091764e-02 3.69327739e-02
-5.35090923e-01 -1.09434807e+00 -8.15679014e-01 1.90838408e-02
2.27354541e-01 -7.79989123e-01 1.10795772e+00 -9.51967120e-01
-1.49248254e+00 6.89534485e-01 -1.86450452e-01 -2.72206932e-01
6.73080623e-01 -3.87354732e-01 2.00074553e-01 3.29461277e-01
-5.32468818e-02 3.63736123e-01 1.11218834e+00 -1.52452934e+00
-3.43763977e-01 -4.69274282e-01 -3.96063700e-02 2.27212444e-01
-2.35227466e-01 -5.62806487e-01 -3.43811721e-01 -7.59339273e-01
2.95586407e-01 -6.14012420e-01 -5.36870062e-01 5.56167483e-01
-4.27359641e-01 -1.99442101e-03 9.53242004e-01 -4.85768110e-01
8.95740390e-01 -2.27397633e+00 1.04163960e-02 3.94514084e-01
2.95672387e-01 2.32158124e-01 4.17899014e-03 4.21731621e-01
7.81181082e-03 1.54586554e-01 -5.83105624e-01 -4.08956379e-01
-2.51877993e-01 2.58266740e-02 -6.09495044e-01 6.50782406e-01
3.52190584e-01 8.53975356e-01 -9.79120195e-01 -1.01907566e-01
3.21252912e-01 6.99613690e-01 -7.98027813e-01 1.51015744e-01
-2.71449327e-01 4.06379640e-01 -9.14963186e-01 6.33702874e-01
1.00046945e+00 -1.49073333e-01 -3.37371886e-01 -4.30491418e-01
-5.16026877e-02 -4.11004461e-02 -1.40592432e+00 1.62981117e+00
-4.76309955e-01 1.87978640e-01 2.03852341e-01 -8.15838218e-01
1.05589545e+00 6.59308657e-02 6.66719794e-01 -2.00705424e-01
-1.68047287e-02 5.59164405e-01 -5.33069730e-01 -2.40616947e-01
4.40999687e-01 -2.99355119e-01 2.32042119e-01 7.35722035e-02
-5.36089897e-01 -6.33722246e-01 -5.27983665e-01 1.95561834e-02
8.32952559e-01 7.40826428e-02 3.38275850e-01 -2.71648705e-01
6.57470584e-01 -7.89601952e-02 4.50307608e-01 1.74754903e-01
-4.80131656e-02 8.88460994e-01 2.96282709e-01 -4.38075811e-01
-1.40132213e+00 -8.89313340e-01 -4.57061857e-01 4.13632810e-01
4.43715155e-01 -1.65845335e-01 -8.27382326e-01 -5.81273913e-01
2.79433966e-01 5.76768696e-01 -5.44607878e-01 -4.38683331e-02
-7.89731145e-01 -2.03491047e-01 2.74447531e-01 4.38082963e-01
3.48356038e-01 -7.27346301e-01 -4.92087334e-01 -1.25858299e-02
1.07911706e-01 -5.91787040e-01 -7.81374633e-01 -4.57387596e-01
-1.27636600e+00 -1.15490985e+00 -1.06091380e+00 -8.02009523e-01
1.08256292e+00 3.83744895e-01 9.01634634e-01 3.05822045e-01
5.00397049e-02 2.16389164e-01 -1.43305808e-01 -2.14665309e-01
-3.17704052e-01 -1.90869235e-02 9.27636772e-02 1.55372277e-01
-7.82353058e-02 -8.55620146e-01 -1.02077579e+00 3.48059028e-01
-9.45670426e-01 8.70988518e-02 4.47444469e-01 8.86145771e-01
9.65433180e-01 -6.44712821e-02 5.36493540e-01 -8.71162295e-01
3.51838738e-01 -3.34895819e-01 -6.01088703e-01 -2.78946459e-01
-3.94148648e-01 -5.77552579e-02 8.87873650e-01 -3.27925205e-01
-5.88845849e-01 1.89043269e-01 -5.12575865e-01 -1.03128076e+00
3.27993669e-02 6.44076467e-02 -1.17945962e-01 -4.27120000e-01
6.19004846e-01 1.58534691e-01 3.77474189e-01 -5.85381985e-01
4.87497926e-01 3.97060722e-01 3.41160238e-01 -5.74601769e-01
1.06394279e+00 9.95447040e-01 7.68944696e-02 -1.01213646e+00
-7.41672754e-01 -5.54432809e-01 -4.67972547e-01 -6.79326579e-02
2.91390479e-01 -6.43647254e-01 -5.29918611e-01 2.68774718e-01
-1.05534732e+00 -2.97383279e-01 -7.94081569e-01 8.50147605e-02
-9.23205853e-01 5.32187521e-01 -4.66135859e-01 -8.11109006e-01
-5.59727371e-01 -1.11538112e+00 1.33197689e+00 1.26774358e-02
1.54035515e-03 -1.00725865e+00 1.61242709e-01 8.94104987e-02
2.53828824e-01 5.47589600e-01 6.94024324e-01 -3.26295167e-01
-7.10531890e-01 -3.43483061e-01 -8.06813911e-02 5.56243122e-01
2.40489841e-01 3.70482728e-02 -8.71356487e-01 -5.15184760e-01
2.83053368e-01 -1.69726878e-01 5.99469304e-01 5.47870100e-01
1.34736514e+00 -6.17110014e-01 -5.00759304e-01 8.97393346e-01
1.73398495e+00 -4.73559648e-01 7.03851759e-01 2.09601864e-01
8.51710379e-01 5.22556603e-01 7.30153739e-01 3.80493283e-01
1.63782090e-01 6.11159623e-01 7.10560143e-01 -1.91885695e-01
-2.31623556e-02 -5.56455135e-01 2.08660096e-01 7.85710394e-01
-1.55430153e-01 3.35308671e-01 -6.50918424e-01 6.77949786e-01
-1.61512661e+00 -7.42828846e-01 -2.23214865e-01 2.54187751e+00
8.55925024e-01 -1.14682332e-01 1.88645661e-01 2.00269297e-01
5.64692855e-01 1.06670432e-01 -6.02161109e-01 -6.57415763e-02
2.45520100e-01 1.88541532e-01 4.46612090e-01 7.27485418e-01
-9.06623662e-01 7.12608516e-01 5.71140862e+00 1.08570778e+00
-1.13866162e+00 -2.64379680e-01 6.62988722e-01 1.50498629e-01
-7.11064935e-01 1.09583149e-02 -6.30698621e-01 5.21076798e-01
5.27014434e-01 -1.38442859e-01 4.32040542e-01 1.13678956e+00
2.80736953e-01 3.78662437e-01 -1.08298516e+00 7.82859802e-01
-3.13588351e-01 -1.58174562e+00 1.82124794e-01 1.35508418e-01
7.99634993e-01 9.30500850e-02 8.53842646e-02 -1.77685004e-02
-3.94108333e-02 -9.19608951e-01 6.24502838e-01 5.45977354e-01
1.09375894e+00 -8.69965374e-01 3.53726000e-01 6.13008618e-01
-1.15154088e+00 1.34257630e-01 -5.22124290e-01 1.83701962e-01
1.56746358e-01 8.06716323e-01 -7.03895450e-01 6.28904879e-01
4.41643238e-01 7.77638614e-01 -4.59469259e-02 8.42693806e-01
-3.41378972e-02 3.89614254e-01 -5.98939002e-01 2.00735539e-01
5.60196377e-02 -5.09544969e-01 8.70734513e-01 8.20708632e-01
3.41273785e-01 1.27405107e-01 9.20203254e-02 1.21375632e+00
-2.08718404e-01 3.52348536e-01 -8.79448891e-01 3.87509614e-01
5.98859549e-01 1.19419706e+00 -3.36487889e-01 -1.51220128e-01
-3.15522611e-01 7.94233859e-01 5.21748543e-01 2.43709609e-01
-5.29960454e-01 -3.83710504e-01 7.76730657e-01 6.72657907e-01
3.11639875e-01 -1.94618687e-01 -5.46546161e-01 -1.13928938e+00
3.00708145e-01 -7.92081177e-01 -8.73218775e-02 -5.01487970e-01
-1.44361877e+00 2.69590139e-01 -2.14191362e-01 -1.60112309e+00
6.05617426e-02 -4.10873324e-01 -7.63838947e-01 9.93167222e-01
-1.61846268e+00 -1.16801429e+00 -3.10458690e-01 4.70389545e-01
5.98269165e-01 2.80594736e-01 6.16382480e-01 2.94389397e-01
-2.32969671e-01 6.03213489e-01 1.74050808e-01 1.07857779e-01
3.38835776e-01 -1.10464883e+00 5.17553568e-01 7.64374733e-01
-1.77047685e-01 7.76452661e-01 5.65500200e-01 -6.55423164e-01
-1.53499353e+00 -1.21830225e+00 2.60994703e-01 -4.83712852e-01
4.85850692e-01 -2.95695186e-01 -1.28186560e+00 5.85606873e-01
-5.68000138e-01 2.00942487e-01 1.39064476e-01 -3.71176511e-01
-5.06184902e-03 -5.28234690e-02 -1.55184472e+00 8.03231061e-01
8.42725337e-01 -3.46283644e-01 -5.34830749e-01 3.22105020e-01
8.36289704e-01 -5.54667175e-01 -1.06311500e+00 6.15040720e-01
3.24219227e-01 -9.34502423e-01 1.28707492e+00 -2.65958786e-01
6.18097961e-01 -3.32277060e-01 -1.39039591e-01 -1.29838967e+00
-3.44893575e-01 -8.49905729e-01 -5.20734906e-01 1.01710021e+00
1.42981425e-01 -8.33413720e-01 1.17630291e+00 4.77247477e-01
-2.42663622e-01 -1.48355436e+00 -9.46008146e-01 -6.32589817e-01
4.73182440e-01 -3.63015682e-01 7.76533604e-01 9.86825824e-01
-2.22201318e-01 -1.59676205e-02 -1.14489973e-01 4.01811063e-01
7.01806903e-01 2.26152375e-01 9.47422147e-01 -1.16358650e+00
-1.64330915e-01 -3.81470561e-01 -3.27681065e-01 -1.58891249e+00
-2.47427374e-01 -7.98643649e-01 4.10217457e-02 -1.32434559e+00
-2.30114773e-01 -9.50575590e-01 2.17160001e-01 1.05443358e-01
-2.30655596e-01 1.53424725e-01 -8.34322348e-02 6.66022003e-01
1.75446883e-01 9.11094546e-01 1.80591130e+00 1.31288305e-01
-4.33759004e-01 3.46279919e-01 -7.75869668e-01 1.00079679e+00
6.39356673e-01 -8.83031189e-02 -3.20449203e-01 -2.62989253e-01
4.95992601e-02 9.94482711e-02 4.34645325e-01 -8.60274076e-01
5.74293025e-02 -1.92891195e-01 2.07496494e-01 -5.42840540e-01
4.67989713e-01 -9.80432391e-01 -3.26806642e-02 3.55060190e-01
-1.58730388e-01 -3.85956019e-01 -3.90203893e-02 6.16878927e-01
-1.69377830e-02 -2.48609811e-01 1.10892355e+00 -5.86578920e-02
-1.16824992e-01 8.27421069e-01 3.04625630e-01 -2.29431409e-02
1.02497125e+00 -3.58251572e-01 2.50289828e-01 -2.00361624e-01
-3.06099892e-01 1.81150865e-02 1.05617881e+00 4.41344827e-02
9.41150367e-01 -1.55961215e+00 -6.41200006e-01 5.51507354e-01
1.09822871e-02 7.49998629e-01 1.42275356e-02 7.05268383e-01
-8.45653713e-01 -9.11834538e-02 2.16533214e-01 -5.35791397e-01
-7.42460787e-01 5.86402297e-01 5.27056694e-01 3.60672325e-02
-1.11646914e+00 7.20985234e-01 2.24568412e-01 -4.91263211e-01
3.99665162e-02 -7.05957532e-01 1.57238767e-02 -4.88405973e-01
5.16220450e-01 6.12770975e-01 -5.64562120e-02 -4.91783440e-01
-4.23834063e-02 9.32693303e-01 8.47311467e-02 1.62339061e-01
1.30039918e+00 2.77447030e-02 -3.98672670e-02 2.61268049e-01
1.39454579e+00 4.43149477e-01 -1.49490118e+00 -1.40805975e-01
-3.62483889e-01 -8.07381332e-01 6.13112375e-02 1.53408170e-01
-1.05147254e+00 6.83026552e-01 2.04161346e-01 2.64684111e-01
9.20791626e-01 -1.63809955e-02 1.20611000e+00 1.40888289e-01
4.95513529e-01 -7.20722795e-01 -1.19126126e-01 2.32735857e-01
1.13619077e+00 -1.01170051e+00 2.56168157e-01 -8.04546773e-01
-2.31139839e-01 1.03896582e+00 4.05708015e-01 -8.60536814e-01
7.77982295e-01 6.31769747e-02 -2.34821662e-01 -4.38210696e-01
-1.82099566e-01 2.68093705e-01 4.31295455e-01 3.05736929e-01
8.78830701e-02 4.55855206e-02 -8.26414078e-02 3.17162454e-01
-2.14786723e-01 5.70772737e-02 2.63943076e-01 7.18865693e-01
-5.36841214e-01 -8.98977697e-01 -4.84522700e-01 5.38208961e-01
-3.13191593e-01 5.02509400e-02 -2.67477408e-02 6.78923607e-01
-1.22097090e-01 2.52430439e-01 5.64295389e-02 -1.96137935e-01
6.43691540e-01 -3.90670717e-01 2.37644583e-01 -4.97320861e-01
-1.15672335e-01 -1.37949735e-01 -3.44878167e-01 -7.87617028e-01
-1.10596985e-01 -6.72050357e-01 -1.40934289e+00 -3.29479605e-01
-2.76472300e-01 1.13962121e-01 4.41606581e-01 4.98060435e-01
5.43595552e-01 6.66425303e-02 1.14681160e+00 -1.26060891e+00
-9.65764344e-01 -7.24168718e-01 -5.24747908e-01 6.84379697e-01
6.18199348e-01 -6.13281131e-01 -6.93620563e-01 -1.39175266e-01] | [8.373880386352539, -3.5390121936798096] |
70468d1b-d7a5-4dad-8618-14608c9e0c11 | source-class-selection-with-label-propagation | null | null | https://breckon.org/toby/publications/papers/wang21pda.pdf | https://breckon.org/toby/publications/papers/wang21pda.pdf | Source Class Selection with Label Propagation for Partial Domain Adaptation | In traditional unsupervised domain adaptation problems, the target domain is assumed to share the same set of classes as the source domain. In practice, there exist situations where target-domain data are from only a subset of source-domain classes and it is not known which classes the target-domain data belong to since they are unlabeled. This problem has been formulated as Partial Domain Adaptation (PDA) in the literature and is a challenging task due to the negative transfer issue (i.e. source-domain data belonging to the irrelevant classes harm the domain adaptation).
We address the PDA problem by detecting the outlier classes in the source domain progressively. As a result, the PDA is boiled down to an easier unsupervised domain adaptation problem which can be solved without the issue of negative transfer. Specifically, we employ the locality preserving projection to learn a latent common subspace in which a label propagation algorithm is used to label the target-domain data. The outlier classes can be detected if no target-domain data are labeled as these classes. We remove the detected outlier classes from the source domain and repeat the process for multiple iterations until convergence. Experimental results on commonly used datasets Office31 and Office-Home demonstrate our proposed method achieves state-of-the-art performance with an average accuracy of 98.1\% and 75.4\% respectively. | ['Toby P.', 'Qian; Breckon', 'Wang'] | 2021-09-01 | null | null | null | icip-2021-9 | ['partial-domain-adaptation'] | ['methodology'] | [ 5.00032008e-01 4.25034910e-02 -2.61016488e-01 -4.15014774e-01
-7.91163206e-01 -5.63345909e-01 3.60320151e-01 1.49369732e-01
-3.03530306e-01 1.08552659e+00 -4.70340699e-02 2.46644959e-01
-8.66286457e-02 -5.62486529e-01 -5.16280472e-01 -1.04911566e+00
2.81262010e-01 7.54131317e-01 4.68285024e-01 7.10131088e-03
1.75588608e-01 2.31756955e-01 -1.34078169e+00 2.13586658e-01
1.16488934e+00 7.51377165e-01 8.23964179e-02 1.15095615e-01
-3.07445705e-01 5.29443800e-01 -6.82261407e-01 7.59182721e-02
5.25676489e-01 -5.64251482e-01 -6.06606901e-01 5.03708422e-01
4.09135282e-01 -5.21567166e-02 1.26990587e-01 1.38086510e+00
3.67444038e-01 4.47934091e-01 8.72397065e-01 -1.52856147e+00
-5.78457177e-01 -1.02962896e-01 -8.97998095e-01 1.90997183e-01
-4.01023403e-02 -4.59393382e-01 5.37448168e-01 -1.15071821e+00
7.03318179e-01 9.56128895e-01 3.00624669e-01 6.60340130e-01
-1.37345421e+00 -1.05718327e+00 3.77201259e-01 3.40033956e-02
-1.48136330e+00 -4.30593818e-01 1.11186802e+00 -6.34214461e-01
4.25037980e-01 -1.71916813e-01 -6.20591752e-02 9.71928716e-01
-1.98509187e-01 4.18951839e-01 1.04151511e+00 -4.16410416e-01
5.49019277e-01 6.25239909e-01 3.24691147e-01 1.35297254e-01
2.96371102e-01 4.41795252e-02 -4.18458909e-01 -2.65453458e-01
5.92156351e-01 1.88848585e-01 -1.47623122e-01 -1.00118983e+00
-1.01384568e+00 8.33939672e-01 3.55404615e-01 1.39121547e-01
-3.93304408e-01 -7.24219382e-01 3.06798071e-01 4.58769828e-01
5.88017583e-01 3.36476080e-02 -5.48661590e-01 2.60618806e-01
-7.54221261e-01 5.69995902e-02 7.17235208e-01 1.24503386e+00
1.07067513e+00 -5.80927879e-02 3.15939009e-01 1.21438372e+00
2.55802065e-01 4.83470380e-01 5.86421311e-01 -6.64817452e-01
7.25894272e-01 8.77949238e-01 4.34245259e-01 -7.69794822e-01
-1.09087057e-01 -5.86602926e-01 -7.66127586e-01 3.09880406e-01
7.04066575e-01 -1.24166980e-01 -1.04013085e+00 1.77025259e+00
6.96661770e-01 3.94397527e-01 4.58662510e-01 9.31862056e-01
5.30920804e-01 6.49081111e-01 1.03657857e-01 -4.80665118e-01
8.60391557e-01 -8.64613831e-01 -6.07840002e-01 -4.85185385e-01
5.93723059e-01 -9.39139187e-01 9.79961038e-01 3.03263396e-01
-5.04503310e-01 -7.05301225e-01 -1.12143040e+00 2.96722829e-01
-4.20241088e-01 -1.56224947e-02 4.11970681e-03 4.19325739e-01
-4.62154895e-01 2.74005324e-01 -5.16149998e-01 -8.10413659e-01
4.74270999e-01 4.99505937e-01 -5.08507729e-01 -2.73201019e-01
-9.21666503e-01 5.10840714e-01 7.54374325e-01 -3.73360157e-01
-7.14032471e-01 -5.87841809e-01 -5.27431071e-01 -2.74025679e-01
4.92936790e-01 -2.68454492e-01 9.86781001e-01 -1.55405569e+00
-1.22232687e+00 9.48756278e-01 -3.51562053e-01 3.69314626e-02
4.12568450e-01 -1.49258360e-01 -9.04430389e-01 -8.63655731e-02
5.23872495e-01 1.60026893e-01 1.06828499e+00 -1.51694679e+00
-1.11620986e+00 -6.37905121e-01 -4.55519885e-01 5.34916461e-01
-5.34625113e-01 -2.08620906e-01 -4.41835076e-01 -7.29852021e-01
6.29392147e-01 -1.12423944e+00 1.04145825e-01 -1.15466401e-01
-1.97794542e-01 -1.23328365e-01 1.34658706e+00 -4.97065336e-01
1.01479280e+00 -2.45719814e+00 3.15139107e-02 3.49219739e-01
1.10447019e-01 2.97343284e-01 -3.67804095e-02 1.97787777e-01
-4.70195562e-01 -3.21501046e-01 -5.35411477e-01 -3.92148420e-02
-2.59699225e-01 2.73436129e-01 -4.81204897e-01 5.96528351e-01
7.27543905e-02 4.53880876e-02 -9.66856420e-01 -4.87769425e-01
1.42855540e-01 1.54330507e-01 -4.92890596e-01 2.96677679e-01
6.69542253e-02 8.78775835e-01 -6.17793620e-01 5.90793610e-01
1.05613089e+00 -1.82497486e-01 1.72702491e-01 7.61925951e-02
1.37014329e-01 -6.72857985e-02 -1.57517338e+00 1.58024883e+00
-2.33040638e-02 9.84848216e-02 -4.16149348e-02 -1.18418276e+00
1.18775749e+00 2.73854524e-01 5.44001818e-01 -3.80141318e-01
-2.62064248e-01 4.17752206e-01 3.36973630e-02 -2.74315596e-01
2.34802812e-01 -5.48182070e-01 -8.94615278e-02 2.71418095e-01
1.26643270e-01 3.00353676e-01 -9.12428275e-02 -9.59500745e-02
7.81882763e-01 2.28492692e-01 5.00722826e-01 -3.37669760e-01
7.05935180e-01 3.57642084e-01 1.01628447e+00 5.48372030e-01
-6.04658544e-01 6.70872033e-01 5.68154342e-02 -1.07136831e-01
-1.02769840e+00 -1.55821919e+00 -1.29732773e-01 1.23136783e+00
2.51792848e-01 1.34461030e-01 -4.35440421e-01 -1.03275990e+00
-1.08233146e-01 5.25167108e-01 -4.33733702e-01 -2.78271168e-01
-5.14519513e-01 -6.41956270e-01 3.89535464e-02 3.61093402e-01
6.48621738e-01 -9.42623734e-01 -1.23918980e-01 3.27244312e-01
-2.94383705e-01 -8.56070578e-01 -3.10330033e-01 3.36678147e-01
-1.14478981e+00 -1.16932392e+00 -1.02822459e+00 -1.30515909e+00
1.07428098e+00 2.72168338e-01 8.46900165e-01 -4.59067672e-01
4.05335218e-01 -2.97424216e-02 -2.75671870e-01 -3.07519972e-01
-2.62366146e-01 4.25465172e-03 3.00689191e-01 3.24919850e-01
9.31058407e-01 -6.10592008e-01 -3.30237001e-01 5.62479317e-01
-8.40198696e-01 -4.24571276e-01 4.32276636e-01 9.70333457e-01
8.59121323e-01 5.06458938e-01 9.71141696e-01 -1.38058662e+00
4.20413315e-01 -8.59253645e-01 -4.72045243e-01 6.79831877e-02
-5.65198779e-01 -2.73231000e-01 9.11422193e-01 -7.44815469e-01
-1.25577664e+00 3.74288112e-01 5.13022006e-01 -4.42047119e-01
-6.51927412e-01 2.99851894e-01 -5.77643454e-01 2.14425147e-01
8.60210061e-01 4.00712818e-01 -2.19219133e-01 -5.93579590e-01
1.11575238e-02 8.48598242e-01 5.23284137e-01 -5.38489997e-01
1.14848661e+00 6.07140958e-01 -3.32488656e-01 -9.15164232e-01
-8.35258007e-01 -1.02774513e+00 -8.14079046e-01 1.20661102e-01
6.40031457e-01 -1.06965089e+00 2.53659695e-01 3.58503878e-01
-7.43673623e-01 4.41186726e-02 -4.99640644e-01 5.80469131e-01
-4.04867053e-01 5.81206083e-01 6.75448477e-02 -6.64333165e-01
5.37916161e-02 -9.03076351e-01 7.21984386e-01 3.40842187e-01
-3.14979523e-01 -8.73085678e-01 7.37595037e-02 5.38874231e-02
6.18396066e-02 2.55659670e-01 1.04125357e+00 -1.12321258e+00
-2.00950101e-01 -2.12131917e-01 -1.03050388e-01 4.98547912e-01
5.99116504e-01 -6.99863732e-01 -9.96592343e-01 -5.61502755e-01
2.92511970e-01 5.99063747e-03 4.89031613e-01 1.57376200e-01
6.80118203e-01 -1.69306546e-01 -6.04165494e-01 3.94849956e-01
1.51225388e+00 5.17292976e-01 2.75895715e-01 3.58890563e-01
6.95132136e-01 7.30873406e-01 9.71218944e-01 4.27371949e-01
2.96002086e-02 6.27879620e-01 1.89444423e-02 -7.84661844e-02
4.17388082e-02 -2.99166679e-01 2.78543711e-01 7.91744530e-01
4.11963761e-01 -8.29386339e-02 -1.04451060e+00 8.43922973e-01
-2.01064610e+00 -6.89612448e-01 -7.00681433e-02 2.64597392e+00
7.53204942e-01 2.10950047e-01 2.22260192e-01 2.00825036e-01
1.15529144e+00 -1.75332233e-01 -9.51396346e-01 -1.41576165e-02
-2.88702548e-02 -4.31726761e-02 3.50975364e-01 3.53895754e-01
-1.44798565e+00 8.88110638e-01 5.10670662e+00 5.60281098e-01
-9.83130515e-01 1.38246000e-01 3.11750948e-01 1.01380713e-01
2.65579581e-01 1.30464390e-01 -8.50604117e-01 5.43882787e-01
6.11997664e-01 -1.84903115e-01 2.55955189e-01 1.00135648e+00
7.96059147e-02 -3.37158442e-02 -1.22314394e+00 9.56446052e-01
9.17369053e-02 -4.00461346e-01 -7.62970895e-02 1.25619574e-02
9.47033823e-01 -1.17981032e-01 2.05746200e-02 4.88228559e-01
2.47239336e-01 -4.95201647e-01 2.34154552e-01 2.71748573e-01
8.47783148e-01 -9.33337152e-01 5.84055126e-01 6.89359307e-01
-1.04097700e+00 -2.38688558e-01 -6.67685032e-01 5.35492525e-02
-2.26130396e-01 6.52974725e-01 -9.25244153e-01 3.75245631e-01
8.28462720e-01 9.45455253e-01 -3.11766654e-01 1.02985692e+00
-6.89127669e-02 5.56760550e-01 -3.46006393e-01 6.36394978e-01
-1.34310545e-02 -5.03982902e-01 6.66765273e-01 7.27680147e-01
4.40746605e-01 1.63333252e-01 5.19340754e-01 6.10685647e-01
-5.34835719e-02 3.03598911e-01 -8.20709586e-01 2.46888354e-01
6.18182063e-01 7.60454357e-01 -6.84624016e-01 -5.36200047e-01
-5.71272135e-01 1.27953064e+00 2.30859876e-01 7.40209043e-01
-5.83458722e-01 -5.83566606e-01 6.63290262e-01 8.98406655e-02
3.09289902e-01 1.25054210e-01 -3.07946384e-01 -1.36660361e+00
2.03114942e-01 -8.41563165e-01 7.83626735e-01 -4.36007470e-01
-1.70736682e+00 4.36604202e-01 -8.10659975e-02 -1.80925691e+00
-5.84918186e-02 -2.74208426e-01 -2.78902084e-01 1.02635205e+00
-1.67328227e+00 -8.58807385e-01 -3.67681086e-01 1.13962269e+00
5.33038735e-01 -4.38686073e-01 9.84299242e-01 4.74740624e-01
-4.46820319e-01 5.50179839e-01 7.86082268e-01 1.49311364e-01
1.20855093e+00 -1.18647087e+00 -3.25636327e-01 1.00134349e+00
-2.23785773e-01 5.49671054e-01 5.44202566e-01 -8.19806278e-01
-6.33844674e-01 -1.50674069e+00 1.02286458e+00 -3.41617703e-01
5.00752985e-01 -3.39304715e-01 -1.16348505e+00 8.33486021e-01
-1.35784179e-01 2.26348847e-01 9.13803458e-01 -5.02973907e-02
-3.24445486e-01 -2.45522141e-01 -1.46438217e+00 3.75418305e-01
8.58647048e-01 -3.99416924e-01 -8.87082458e-01 4.27350134e-01
3.12527329e-01 -1.33936644e-01 -7.38438547e-01 2.59002328e-01
6.18912987e-02 -6.62257135e-01 9.62777913e-01 -5.20282269e-01
6.02391995e-02 -8.26115191e-01 -3.12139452e-01 -1.38023663e+00
-3.07449788e-01 -9.49485302e-02 -3.26582752e-02 1.47564590e+00
1.87746257e-01 -8.54631960e-01 9.90879238e-01 5.41625559e-01
1.25527605e-01 6.56667054e-02 -1.00326681e+00 -1.00687730e+00
2.12432802e-01 5.05744852e-02 4.54277813e-01 1.34724987e+00
-1.59835771e-01 5.64538240e-01 -1.86847448e-01 5.44434726e-01
9.32750583e-01 4.27772075e-01 7.38890588e-01 -1.66131139e+00
2.22010761e-02 1.56187817e-01 -1.43681422e-01 -1.05273533e+00
1.89615563e-01 -9.52965438e-01 1.59425557e-01 -1.11180449e+00
1.58876538e-01 -6.70752347e-01 -7.36488581e-01 3.83781910e-01
-1.11207284e-01 1.34103850e-01 1.05927503e-02 7.71598995e-01
-5.14651239e-01 4.79240924e-01 8.74965191e-01 -5.70260696e-02
-6.20095551e-01 8.41415450e-02 -6.58404052e-01 9.05453801e-01
8.68378997e-01 -9.39182520e-01 -6.74050748e-01 -2.45998651e-01
-4.80276793e-01 -1.20077610e-01 9.08026844e-02 -1.23800743e+00
1.31464347e-01 -2.75694281e-01 6.08436525e-01 -6.22930646e-01
2.61350870e-01 -1.43188453e+00 -1.70056596e-02 2.12119088e-01
-1.56899184e-01 -3.87046963e-01 1.00293778e-01 9.45784986e-01
-3.58020216e-01 -1.10863574e-01 1.17867470e+00 3.38656977e-02
-1.12377989e+00 2.53880173e-01 -1.95907295e-01 2.77375817e-01
1.35789680e+00 -5.96202314e-01 -1.20634012e-01 -1.22037992e-01
-1.06193554e+00 5.84220961e-02 6.81950510e-01 5.26440918e-01
5.74324310e-01 -1.45596063e+00 -6.26436591e-01 4.81930047e-01
6.45074606e-01 1.44219935e-01 1.98178664e-02 4.97621566e-01
-8.21860414e-03 1.52810514e-01 -2.45141760e-01 -8.28970671e-01
-1.14180291e+00 7.61298418e-01 1.38000607e-01 -1.11786798e-01
-5.91122746e-01 5.83600461e-01 7.15622306e-01 -7.45863676e-01
2.30892360e-01 1.08781420e-01 -3.92859936e-01 1.12172887e-01
2.50199109e-01 2.83886254e-01 -2.94928234e-02 -9.47276413e-01
-5.75964928e-01 7.20549166e-01 -1.98075950e-01 1.67318061e-01
1.25597870e+00 -3.75366241e-01 4.46293987e-02 5.63416660e-01
1.28900433e+00 -6.86660260e-02 -1.29836857e+00 -7.79824615e-01
2.92713344e-01 -4.96384859e-01 -3.78700644e-01 -8.53246868e-01
-8.29013765e-01 7.81754136e-01 1.17463124e+00 -2.58661091e-01
1.44416177e+00 -1.51635244e-01 5.69699347e-01 2.12151095e-01
2.51259059e-01 -1.47731733e+00 3.06324568e-04 5.29935241e-01
5.79800665e-01 -1.45779681e+00 -1.76857173e-01 -5.35462797e-01
-7.10639417e-01 8.30167234e-01 8.54520500e-01 -3.35584819e-01
7.86347806e-01 -2.48620361e-01 2.38374829e-01 9.36418697e-02
-2.16888338e-01 -4.99681244e-03 1.92768946e-01 9.21576977e-01
2.11380005e-01 1.18219726e-01 -1.15371764e-01 6.74552083e-01
1.03278525e-01 1.18055530e-01 3.22774917e-01 1.00413692e+00
-5.57904661e-01 -1.23922420e+00 -6.80039763e-01 3.99574161e-01
-3.23230684e-01 1.21607035e-01 -2.99219191e-01 8.37302923e-01
3.62173915e-01 1.04530120e+00 -8.01082402e-02 -9.87690836e-02
5.04051507e-01 4.94498491e-01 -2.87789553e-02 -1.04336309e+00
-1.37669474e-01 3.16815436e-01 -2.69938082e-01 -2.64137059e-01
-4.32591915e-01 -6.01800144e-01 -1.38119948e+00 1.10855363e-01
-2.73138434e-01 2.96515375e-01 1.60691679e-01 7.59930611e-01
3.25689942e-01 2.45314151e-01 6.07222676e-01 -2.10501209e-01
-5.74362755e-01 -7.76094615e-01 -8.95926833e-01 8.55406940e-01
4.01309967e-01 -8.60684395e-01 -3.55479658e-01 3.62645417e-01] | [10.369848251342773, 3.0295941829681396] |
339f1b32-d90e-4f2f-8854-b51d38c420e6 | open-vocabulary-panoptic-segmentation-with-1 | 2303.04803 | null | https://arxiv.org/abs/2303.04803v4 | https://arxiv.org/pdf/2303.04803v4.pdf | Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models | We present ODISE: Open-vocabulary DIffusion-based panoptic SEgmentation, which unifies pre-trained text-image diffusion and discriminative models to perform open-vocabulary panoptic segmentation. Text-to-image diffusion models have the remarkable ability to generate high-quality images with diverse open-vocabulary language descriptions. This demonstrates that their internal representation space is highly correlated with open concepts in the real world. Text-image discriminative models like CLIP, on the other hand, are good at classifying images into open-vocabulary labels. We leverage the frozen internal representations of both these models to perform panoptic segmentation of any category in the wild. Our approach outperforms the previous state of the art by significant margins on both open-vocabulary panoptic and semantic segmentation tasks. In particular, with COCO training only, our method achieves 23.4 PQ and 30.0 mIoU on the ADE20K dataset, with 8.3 PQ and 7.9 mIoU absolute improvement over the previous state of the art. We open-source our code and models at https://github.com/NVlabs/ODISE . | ['Shalini De Mello', 'Xiaolong Wang', 'Wonmin Byeon', 'Arash Vahdat', 'Sifei Liu', 'Jiarui Xu'] | 2023-03-08 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xu_Open-Vocabulary_Panoptic_Segmentation_With_Text-to-Image_Diffusion_Models_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xu_Open-Vocabulary_Panoptic_Segmentation_With_Text-to-Image_Diffusion_Models_CVPR_2023_paper.pdf | cvpr-2023-1 | ['panoptic-segmentation', 'open-vocabulary-panoptic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 7.21310526e-02 -5.53076081e-02 -3.31153989e-01 -8.06735530e-02
-1.14828157e+00 -1.17108202e+00 9.18044627e-01 -2.19443694e-01
-2.35728219e-01 1.64697900e-01 2.43601918e-01 -2.62934536e-01
1.94210649e-01 -8.42450619e-01 -6.12185121e-01 -6.54591024e-01
1.84725881e-01 9.24339712e-01 3.79396856e-01 -1.78789526e-01
8.02550837e-02 2.77256519e-02 -1.10903144e+00 6.88164011e-02
9.51977789e-01 1.02494395e+00 2.38569960e-01 8.42919767e-01
-4.77356851e-01 5.81923723e-01 -4.61974233e-01 -4.22697634e-01
5.79142034e-01 -2.95163155e-01 -9.00551975e-01 3.56006324e-01
1.09532118e+00 -8.78768116e-02 -4.50958252e-01 1.03231263e+00
3.50144863e-01 6.01675734e-02 1.03956294e+00 -8.44052374e-01
-1.19213188e+00 5.01429200e-01 -7.98977375e-01 3.41108441e-01
-1.34211257e-01 4.85201418e-01 1.44913471e+00 -7.84591556e-01
1.14859223e+00 1.22934091e+00 4.32123303e-01 5.15872419e-01
-1.40670776e+00 -9.02841926e-01 5.39904125e-02 -3.38766962e-01
-1.63821316e+00 -2.07697242e-01 2.70879596e-01 -8.55145276e-01
8.08909833e-01 2.83077240e-01 6.76433623e-01 1.03266442e+00
1.75809339e-01 9.26391602e-01 1.25330853e+00 8.01628828e-03
-6.33070022e-02 -5.42717315e-02 5.84865585e-02 7.38657415e-01
3.50042135e-02 -1.24587014e-01 -1.08722307e-01 1.11034252e-01
7.64222860e-01 -1.41463995e-01 -3.89010832e-02 -2.74109066e-01
-1.25612783e+00 1.15441215e+00 7.56639719e-01 1.50228828e-01
-1.75181940e-01 2.92047381e-01 3.59293133e-01 1.31521106e-01
1.21497250e+00 5.97124279e-01 -3.90276879e-01 1.23247176e-01
-1.45573807e+00 2.32645631e-01 7.62516856e-01 1.05900800e+00
7.98523366e-01 8.95732418e-02 -1.90589264e-01 1.15995336e+00
3.14432025e-01 1.04048944e+00 3.17177325e-01 -1.13663208e+00
4.80218649e-01 3.64125401e-01 -3.29682529e-01 -6.67126358e-01
-3.33374068e-02 -3.89641196e-01 -5.85359097e-01 -2.59202212e-01
2.51769185e-01 -1.24086015e-01 -1.75489461e+00 1.24563348e+00
1.90299958e-01 2.51045465e-01 7.59932995e-02 7.64481902e-01
9.64208305e-01 1.34360409e+00 1.67366788e-01 1.83716550e-01
1.42721343e+00 -1.44653440e+00 -5.61067343e-01 -4.20662016e-01
3.46949697e-01 -1.06930614e+00 1.10848570e+00 2.12089345e-01
-6.78630531e-01 -4.49903756e-01 -8.07665765e-01 -1.84826568e-01
-6.18473113e-01 -3.12861323e-01 5.23557484e-01 5.45848131e-01
-1.32923138e+00 1.61587328e-01 -4.55214173e-01 -5.37273645e-01
7.86036193e-01 -5.15576303e-02 -1.66012138e-01 -2.22947091e-01
-8.96714807e-01 3.69427234e-01 4.42671388e-01 -6.45979881e-01
-1.33349741e+00 -9.15781677e-01 -9.22418475e-01 -2.40316644e-01
5.95832288e-01 -5.03711283e-01 1.34923339e+00 -9.35031235e-01
-1.17968214e+00 1.28957462e+00 1.65672749e-02 -6.17622793e-01
5.08558214e-01 -2.32257739e-01 -3.16171467e-01 6.18941188e-01
5.20112097e-01 1.73366463e+00 1.10573637e+00 -1.10963273e+00
-5.51424861e-01 -1.26048982e-01 -8.06465372e-02 1.56466588e-01
-1.96143463e-01 4.41622222e-04 -1.00853920e+00 -1.04480875e+00
-3.83109897e-02 -1.32073116e+00 -4.56810266e-01 8.25216621e-02
-4.19828206e-01 -2.12927610e-01 7.46695995e-01 -7.42131650e-01
1.02238870e+00 -2.04133940e+00 1.28662437e-01 5.91636309e-03
5.00343978e-01 1.76071838e-01 -3.28234404e-01 1.91348448e-01
2.80810028e-01 5.64838946e-01 -6.00044250e-01 -3.72292131e-01
-6.15526922e-02 3.38940471e-01 -6.42930269e-01 3.15069348e-01
2.04426542e-01 1.23659372e+00 -7.88186133e-01 -7.33211219e-01
3.30480367e-01 3.81747037e-01 -5.50248146e-01 -7.93777779e-02
-8.87651265e-01 5.24416506e-01 -3.83488476e-01 5.69795012e-01
7.28738189e-01 -5.93841434e-01 -1.93509236e-01 2.98212737e-01
-4.01305482e-02 3.42166610e-02 -6.82380617e-01 1.97148550e+00
-4.44681376e-01 1.14471757e+00 -4.33009192e-02 -4.02763009e-01
8.78512979e-01 6.35799468e-02 4.17131364e-01 -6.03229165e-01
-4.18307260e-03 3.25392157e-01 -4.07351255e-01 -1.22818269e-01
9.29131746e-01 -2.04431694e-02 -2.20879614e-01 3.34543467e-01
3.87710661e-01 -9.65941310e-01 4.62436497e-01 6.73584759e-01
7.33527124e-01 -2.92187501e-02 -1.11795582e-01 -5.51860452e-01
2.36928880e-01 3.12953264e-01 1.59454912e-01 7.88640678e-01
-2.26304874e-01 1.19711196e+00 2.99505681e-01 -2.56108850e-01
-1.20826149e+00 -1.34658039e+00 -3.64051223e-01 1.05467629e+00
2.03680739e-01 -6.28115058e-01 -9.16441321e-01 -6.84769332e-01
-8.21771845e-02 5.56926370e-01 -5.72021008e-01 4.87210751e-01
-1.29434481e-01 -7.86773503e-01 8.07229221e-01 1.81071997e-01
5.54370999e-01 -8.71705413e-01 -7.20007196e-02 -3.49026062e-02
-3.50394815e-01 -1.39811110e+00 -7.84781575e-01 -1.15353242e-01
-6.80961668e-01 -8.43010545e-01 -1.37672091e+00 -9.37955976e-01
4.14875060e-01 3.62438440e-01 1.34521043e+00 -3.25185299e-01
-3.72128934e-01 4.85298485e-01 -3.90365124e-01 -1.63233623e-01
-5.29821277e-01 4.05400425e-01 -5.04932463e-01 6.24051951e-02
2.94515759e-01 -1.98837861e-01 -7.73203492e-01 3.19754213e-01
-1.23962212e+00 1.81811705e-01 2.90267110e-01 3.85182112e-01
9.16400194e-01 -2.72646785e-01 2.20448244e-03 -8.69494498e-01
4.16234046e-01 -7.04431713e-01 -8.03181946e-01 1.48629993e-01
-6.49819553e-01 -3.42090949e-02 1.28684267e-01 -4.31967705e-01
-8.86717081e-01 -1.45723820e-01 -2.75281280e-01 -6.42966270e-01
-1.68938026e-01 2.17367426e-01 4.90265459e-01 1.46671996e-01
7.12463915e-01 2.72548378e-01 -3.32188159e-01 -4.17102993e-01
1.10201263e+00 9.11900699e-01 4.23721045e-01 -3.57463747e-01
7.93342531e-01 9.21338856e-01 -6.01079047e-01 -1.05877638e+00
-1.09064043e+00 -8.27482164e-01 -5.70924222e-01 -5.09309173e-02
1.41512954e+00 -1.45409083e+00 2.96208978e-01 6.60555780e-01
-8.29683244e-01 -8.64026368e-01 -4.50173616e-01 -1.36509195e-01
-5.08317769e-01 4.28169131e-01 -7.87693679e-01 -3.14718425e-01
-6.17080271e-01 -1.23869908e+00 1.44302905e+00 1.72561556e-01
-1.58873677e-01 -1.08756697e+00 2.08838969e-01 7.03465760e-01
2.31681630e-01 5.75372130e-02 3.62498373e-01 -5.58130085e-01
-7.77392387e-01 8.42385739e-02 -5.84873676e-01 5.11391163e-01
-1.04158267e-01 9.01982188e-02 -9.49637353e-01 -1.76682442e-01
-4.19529587e-01 -6.19945109e-01 1.36246300e+00 6.31311059e-01
8.44796360e-01 -5.49858473e-02 -3.15402806e-01 9.23223495e-01
1.46127129e+00 -1.67522952e-01 7.42948711e-01 1.68401256e-01
9.76753175e-01 5.25844336e-01 4.92714852e-01 4.66309004e-02
4.72573847e-01 4.68156666e-01 1.04785502e-01 -1.36650190e-01
-5.58354259e-01 -4.96185273e-01 3.53992224e-01 9.65084434e-01
5.44175029e-01 -6.21598959e-01 -1.33299279e+00 1.04612291e+00
-1.43577230e+00 -6.57044649e-01 -4.28504050e-01 1.61409724e+00
9.25547302e-01 1.45872116e-01 -1.46519884e-01 -4.83488262e-01
5.99368036e-01 9.10874486e-01 -4.11212832e-01 -2.66217947e-01
-4.52329755e-01 3.66749167e-01 9.33901608e-01 6.80516243e-01
-1.42045581e+00 1.81478202e+00 6.08707571e+00 1.59256554e+00
-1.22422910e+00 6.25953019e-01 1.01899099e+00 -9.21307430e-02
-4.50587720e-01 2.12900415e-01 -9.70628381e-01 3.80053669e-01
1.05914235e+00 -1.32417062e-03 3.02709371e-01 6.35277629e-01
-2.24395275e-01 -1.41232550e-01 -4.62620944e-01 9.82939482e-01
3.26433003e-01 -1.64409137e+00 4.27960873e-01 2.54661918e-01
1.56132197e+00 9.66676950e-01 3.51957083e-01 2.35380352e-01
7.63347566e-01 -1.11069894e+00 8.03059697e-01 7.46828765e-02
1.17849767e+00 -2.60663360e-01 2.13823274e-01 3.08015831e-02
-1.17443156e+00 2.38154203e-01 -2.34643027e-01 3.42189699e-01
2.70709425e-01 4.68456686e-01 -5.25094867e-01 3.30707967e-01
7.95353353e-01 1.04816508e+00 -8.15141022e-01 6.87118530e-01
-3.87861043e-01 9.99904990e-01 -6.20151401e-01 2.91924000e-01
1.01710665e+00 -4.63310003e-01 6.07246757e-01 1.44668078e+00
7.65039921e-02 -1.87303886e-01 3.91666412e-01 1.04905427e+00
-3.92160952e-01 3.32740605e-01 -7.53311932e-01 -3.83675337e-01
1.88733265e-02 1.19481039e+00 -1.12811625e+00 -6.87112093e-01
-3.42537999e-01 1.22093797e+00 -9.91046708e-03 3.43285501e-01
-7.66678512e-01 -1.52871311e-01 6.52139902e-01 3.82515565e-02
7.59016335e-01 -3.79519254e-01 -2.64887273e-01 -1.39267874e+00
-3.51732582e-01 -7.65900195e-01 5.47555149e-01 -9.79587078e-01
-1.32692289e+00 6.48639321e-01 1.15634417e-02 -8.04297924e-01
6.24832101e-02 -5.26144683e-01 -2.82734215e-01 6.75920427e-01
-1.54202771e+00 -1.48454309e+00 -1.36866197e-01 4.64592099e-01
1.39838552e+00 1.67323519e-02 5.10817230e-01 1.50805339e-01
-2.90403038e-01 1.66206598e-01 5.61935484e-01 3.24948847e-01
6.23292744e-01 -1.28872299e+00 1.12006032e+00 9.20021236e-01
8.05845737e-01 1.86037749e-01 4.30625290e-01 -9.12867069e-01
-8.13343763e-01 -1.36550868e+00 7.40738988e-01 -7.87151873e-01
1.17608058e+00 -6.73624277e-01 -7.09894478e-01 7.77252197e-01
5.43415964e-01 1.05035052e-01 5.10138750e-01 -1.83778957e-01
-6.83594406e-01 1.01810805e-01 -6.48798466e-01 8.80308926e-01
9.98616517e-01 -7.45976210e-01 -5.78486025e-01 6.47473931e-01
1.20322502e+00 -3.34781021e-01 -9.40507829e-01 8.43925476e-02
1.96658641e-01 -5.02105296e-01 1.01292503e+00 -1.03987373e-01
6.69273317e-01 -1.05910646e-02 -2.65864849e-01 -1.16729391e+00
-1.17988385e-01 -8.45723152e-01 3.41686368e-01 1.18525958e+00
6.85777366e-01 -5.51205277e-01 5.29866219e-01 -2.58484297e-02
1.02733344e-01 -4.53672647e-01 -7.29661822e-01 -7.90621340e-01
7.15115845e-01 -7.70747066e-01 1.32046729e-01 8.71098816e-01
-6.26966774e-01 6.69285119e-01 -3.05195034e-01 -3.48306783e-02
4.71276850e-01 1.53203323e-01 7.60628879e-01 -1.04608679e+00
5.41557837e-03 -5.52014887e-01 -1.62788272e-01 -1.60466814e+00
3.00791264e-01 -1.26815486e+00 -5.71645722e-02 -1.55110991e+00
2.04616144e-01 -7.24062026e-01 -4.82952856e-02 1.47558033e-01
-4.23583761e-02 9.87990260e-01 4.75147605e-01 5.98273218e-01
-8.94654334e-01 5.36717415e-01 1.37525105e+00 -7.11876929e-01
-1.88012496e-01 -5.20344615e-01 -7.17392385e-01 5.41716099e-01
1.09858477e+00 -7.03819633e-01 -5.53288758e-01 -8.10295820e-01
1.44217074e-01 -4.87910658e-01 3.53265256e-01 -1.01869297e+00
-4.54461649e-02 -2.76742056e-02 -8.92586485e-02 -4.99512345e-01
3.91350269e-01 -3.00925910e-01 -1.69873778e-02 1.83602974e-01
-4.21104729e-01 -2.17760995e-01 4.14139867e-01 7.80943215e-01
-1.37707546e-01 -1.15970373e-01 7.95804501e-01 -3.95881593e-01
-1.08877015e+00 6.49290860e-01 -5.08779585e-01 8.28359902e-01
9.13425505e-01 1.18891094e-02 -4.48207915e-01 -4.79768962e-01
-6.37648582e-01 4.16487843e-01 6.89880788e-01 8.62372220e-01
3.91905218e-01 -7.64595866e-01 -9.02380705e-01 3.91524434e-02
2.88563460e-01 1.31480888e-01 1.53435886e-01 6.63760245e-01
-1.03199804e+00 6.22335494e-01 1.80782124e-01 -1.00653982e+00
-1.02668738e+00 4.19029832e-01 2.75376260e-01 -3.96637440e-01
-7.78004587e-01 1.01968455e+00 7.88842380e-01 -5.90932667e-01
-7.23100603e-02 -3.18556607e-01 -2.38884781e-02 2.15486884e-01
3.22876573e-01 -1.07663363e-01 -3.90259773e-01 -9.24439788e-01
6.48255423e-02 9.46622372e-01 -2.34514147e-01 -4.78968114e-01
1.16279125e+00 -3.69794399e-01 -7.36910775e-02 4.85201657e-01
1.33307970e+00 3.88351940e-02 -1.45680380e+00 -3.62693578e-01
-1.13029651e-01 -3.70421231e-01 3.86618972e-01 -8.42657566e-01
-1.18697798e+00 1.09865582e+00 6.51901603e-01 7.07419664e-02
6.99208319e-01 4.74420369e-01 1.00954747e+00 1.34448215e-01
1.62256762e-01 -1.20743251e+00 1.71051145e-01 5.68559885e-01
7.54078329e-01 -1.40247059e+00 1.62986554e-02 -6.11087561e-01
-1.02679884e+00 6.88857198e-01 2.93996304e-01 -3.72156054e-01
6.99329793e-01 -8.57857801e-03 4.94566441e-01 -5.31099975e-01
-6.09814703e-01 -5.74126482e-01 4.29179907e-01 5.17580926e-01
3.46843898e-02 3.76011997e-01 1.78366423e-01 -1.04903787e-01
-1.61865965e-01 -2.90871143e-01 3.61390054e-01 3.81878108e-01
-4.33166116e-01 -6.80648267e-01 -3.23685557e-01 4.45800900e-01
-5.96934199e-01 -6.26424551e-01 -4.74372804e-01 7.18935370e-01
-2.50733793e-02 9.62154627e-01 5.54302573e-01 -2.93746069e-02
-2.46733755e-01 4.38822806e-02 8.15380067e-02 -7.40411818e-01
-4.84036535e-01 3.80697429e-01 -3.17465402e-02 -3.73278737e-01
-3.47492129e-01 -5.01176000e-01 -9.71989512e-01 -2.45411903e-01
-2.63301343e-01 1.38978229e-03 5.51339447e-01 5.44159889e-01
3.90270770e-01 2.56400138e-01 2.07245395e-01 -7.06541240e-01
-1.38982639e-01 -9.73847151e-01 -5.37486970e-01 4.96041805e-01
2.46311173e-01 -3.01046520e-01 -3.29399973e-01 2.78853744e-01] | [9.692018508911133, 0.7392117977142334] |
91cc9392-b63f-4f3f-aacc-bc247dc6329e | pixel-by-pixel-mean-opinion-score-pmos-for-no | 2206.06541 | null | https://arxiv.org/abs/2206.06541v1 | https://arxiv.org/pdf/2206.06541v1.pdf | Pixel-by-pixel Mean Opinion Score (pMOS) for No-Reference Image Quality Assessment | Deep-learning based techniques have contributed to the remarkable progress in the field of automatic image quality assessment (IQA). Existing IQA methods are designed to measure the quality of an image in terms of Mean Opinion Score (MOS) at the image-level (i.e. the whole image) or at the patch-level (dividing the image into multiple units and measuring quality of each patch). Some applications may require assessing the quality at the pixel-level (i.e. MOS value for each pixel), however, this is not possible in case of existing techniques as the spatial information is lost owing to their network structures. This paper proposes an IQA algorithm that can measure the MOS at the pixel-level, in addition to the image-level MOS. The proposed algorithm consists of three core parts, namely: i) Local IQA; ii) Region of Interest (ROI) prediction; iii) High-level feature embedding. The Local IQA part outputs the MOS at the pixel-level, or pixel-by-pixel MOS - we term it 'pMOS'. The ROI prediction part outputs weights that characterize the relative importance of region when calculating the image-level IQA. The high-level feature embedding part extracts high-level image features which are then embedded into the Local IQA part. In other words, the proposed algorithm yields three outputs: the pMOS which represents MOS for each pixel, the weights from the ROI indicating the relative importance of region, and finally the image-level MOS that is obtained by the weighted sum of pMOS and ROI values. The image-level MOS thus obtained by utilizing pMOS and ROI weights shows superior performance compared to the existing popular IQA techniques. In addition, visualization results indicate that predicted pMOS and ROI outputs are reasonably aligned with the general principles of the human visual system (HVS). | ['Jayoon Koo', 'Ilhyun Cho', 'Namuk Kim', 'Anant Baijal', 'Cheul-hee Hahm', 'Wook-Hyung Kim'] | 2022-06-14 | null | null | null | null | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 3.46019149e-01 -3.25803310e-01 -5.15524447e-02 -2.38087326e-01
-7.57160068e-01 -1.43750787e-01 1.96616605e-01 4.34953034e-01
-2.59036362e-01 4.20351416e-01 1.27323449e-01 5.94617836e-02
-1.54144779e-01 -1.25242186e+00 -3.91363025e-01 -9.58499670e-01
5.81608228e-02 -4.82322663e-01 3.77179921e-01 -1.05839847e-02
4.54793125e-01 6.06292665e-01 -1.63165116e+00 3.37835759e-01
8.22446167e-01 1.70632422e+00 2.24180799e-02 6.79311454e-01
-4.36122082e-02 7.05487072e-01 -7.70484865e-01 -7.02608703e-03
1.88018471e-01 -5.28928995e-01 -5.01000941e-01 1.39328942e-01
3.84743959e-01 -2.76681095e-01 -1.92537978e-01 1.24041080e+00
4.69466358e-01 -9.42252651e-02 6.78447604e-01 -1.30838656e+00
-4.24240142e-01 -8.85315761e-02 -7.20990777e-01 3.73379588e-01
2.73441613e-01 1.79477677e-01 1.09730494e+00 -7.98930526e-01
2.43910730e-01 1.10886216e+00 3.72492790e-01 -2.24241063e-01
-1.12145674e+00 -3.20480049e-01 -2.08947957e-01 4.51355755e-01
-1.40477192e+00 7.79882073e-02 9.96294916e-01 -5.38403153e-01
7.88875997e-01 1.99190393e-01 6.59027159e-01 2.48429760e-01
7.69191146e-01 5.75411856e-01 1.46826494e+00 -3.99107724e-01
3.51259142e-01 6.01858608e-02 2.95869121e-03 6.98216021e-01
3.74172106e-02 1.50150791e-01 -5.32917321e-01 1.10599779e-01
8.69106948e-01 -6.90396801e-02 -2.10452989e-01 -1.96296200e-01
-1.17853963e+00 5.68582058e-01 8.91424417e-01 3.85915011e-01
-7.40799665e-01 6.95164427e-02 1.89842865e-01 3.16926330e-01
6.78419992e-02 1.26470953e-01 -1.74565628e-01 1.47393420e-01
-1.12485659e+00 -1.05846442e-01 3.78634781e-01 2.08170548e-01
1.12633920e+00 1.04292601e-01 -3.79788190e-01 8.25034022e-01
3.72616887e-01 4.51283842e-01 3.52781832e-01 -1.00417805e+00
2.63374418e-01 1.08653843e+00 1.05236031e-01 -1.50441396e+00
-3.00874829e-01 -4.59230125e-01 -8.99659097e-01 9.31617200e-01
2.92235494e-01 1.40465856e-01 -7.52564132e-01 1.63367486e+00
2.98755050e-01 -1.94386616e-01 -1.01445094e-01 8.95288289e-01
8.43319178e-01 1.14808261e+00 8.50077569e-02 -1.71475202e-01
1.50188661e+00 -9.32292163e-01 -7.23417580e-01 -3.91751751e-02
9.73042250e-02 -8.70492220e-01 1.07815635e+00 3.67031723e-01
-1.28130054e+00 -1.10301852e+00 -1.36335421e+00 8.15418661e-02
-6.17473662e-01 3.50664973e-01 -9.58508998e-02 4.30902064e-01
-1.36706376e+00 4.79809344e-01 -3.31348777e-01 -1.96623996e-01
2.02279910e-01 3.27595830e-01 -3.00356477e-01 1.32774457e-01
-1.15742075e+00 8.70360017e-01 1.28530115e-01 8.09362307e-02
-8.47712457e-01 -4.23275024e-01 -8.96045744e-01 4.08142567e-01
3.12359985e-02 -5.28478861e-01 6.26689792e-01 -1.37457502e+00
-1.31118464e+00 6.98097765e-01 -2.64282018e-01 -4.31463420e-02
1.32338703e-01 3.09944898e-01 -5.95342159e-01 4.48993295e-01
5.61136119e-02 8.65719736e-01 1.07706738e+00 -1.32585454e+00
-9.04554605e-01 -3.70160878e-01 2.95121074e-01 2.45622024e-01
-4.50855613e-01 -9.78352875e-02 -4.56917226e-01 -6.57946229e-01
1.94074944e-01 -3.35248023e-01 8.84098038e-02 4.80056643e-01
-7.36673474e-02 -2.46882930e-01 8.24552357e-01 -8.08855116e-01
1.44728303e+00 -2.43405414e+00 -1.38689861e-01 4.08237636e-01
1.82087570e-01 2.44813740e-01 -2.83196747e-01 2.70694375e-01
-2.90221453e-01 -2.31414102e-02 -2.83068925e-01 3.22054654e-01
-2.66186774e-01 -2.68909276e-01 3.21721882e-01 3.10868561e-01
5.73225796e-01 7.46574938e-01 -6.94904804e-01 -7.74464667e-01
6.33652806e-01 5.96949518e-01 -1.67277277e-01 1.91138536e-01
2.63545632e-01 7.05570206e-02 -3.05436790e-01 7.56157041e-01
7.82931507e-01 -2.05046237e-01 -2.57220924e-01 -7.50825942e-01
-2.99816996e-01 -4.05827817e-03 -1.35970879e+00 1.12497473e+00
-4.62164432e-01 7.54149437e-01 6.77024871e-02 -8.75719190e-01
1.14141297e+00 2.80991584e-01 6.67458594e-01 -1.11610794e+00
1.90866947e-01 8.74025449e-02 -3.44829890e-03 -2.92950451e-01
4.83264327e-01 -3.77072133e-02 -1.25056226e-03 3.18927705e-01
-3.11369803e-02 1.54339135e-01 5.82361184e-02 1.15849584e-01
9.65692759e-01 -2.03575548e-02 6.36447728e-01 -2.32255757e-01
9.66914594e-01 -8.42149332e-02 3.57838452e-01 3.93237025e-01
-5.89115858e-01 5.65774560e-01 6.25208914e-01 -2.87762851e-01
-1.06242788e+00 -1.33403981e+00 -3.06853265e-01 6.76609814e-01
5.16205132e-01 -8.77096578e-02 -8.87778819e-01 -4.23081011e-01
-5.47958165e-02 1.65186033e-01 -6.38300478e-01 -2.46720970e-01
-2.56821930e-01 -4.51639563e-01 -6.67418540e-02 5.36398828e-01
8.59695017e-01 -1.41643870e+00 -6.31642401e-01 1.71121344e-01
-1.30412668e-01 -8.67400467e-01 -5.00016153e-01 -9.33356211e-02
-7.18267381e-01 -8.32791746e-01 -7.54728019e-01 -9.54476655e-01
8.62392306e-01 2.74259806e-01 1.01477826e+00 6.70322552e-02
-3.86260837e-01 1.24295637e-01 -2.09781021e-01 -5.65581955e-02
-1.15964957e-01 -5.06120861e-01 -2.40294561e-01 4.74770695e-01
-3.56178242e-03 -4.07588094e-01 -1.03173578e+00 3.86453331e-01
-1.00267184e+00 -8.32645595e-02 9.63651061e-01 7.10509300e-01
9.88684177e-01 6.07039690e-01 4.88063335e-01 -2.55796432e-01
5.59069455e-01 -1.23536125e-01 -5.28614402e-01 3.11169416e-01
-5.18554628e-01 -2.85945952e-01 5.58744371e-01 -1.07424580e-01
-9.09961820e-01 -1.43387258e-01 -1.37759879e-01 -5.28564453e-02
-1.50394440e-01 5.26495457e-01 -4.84494150e-01 -2.69400179e-01
4.86540228e-01 2.75527745e-01 -1.19340360e-01 4.72165383e-02
1.19161382e-01 9.38075423e-01 4.64211047e-01 -1.30431578e-01
5.83185494e-01 4.10203636e-01 7.68159926e-02 -7.14091778e-01
-5.10091305e-01 -4.55830723e-01 -6.06225729e-01 -6.65438354e-01
1.07515919e+00 -8.44141424e-01 -7.33416378e-01 5.35018504e-01
-8.68781745e-01 -2.66060103e-02 -4.44788039e-01 3.65418524e-01
-4.36968297e-01 2.15083212e-01 -5.47309160e-01 -6.59545600e-01
-5.41585684e-01 -1.39432871e+00 1.04248321e+00 5.66067636e-01
-1.28154069e-01 -7.80015230e-01 -1.91947535e-01 3.86019826e-01
3.66766214e-01 4.06795204e-01 1.16138017e+00 2.23533422e-01
-6.51380241e-01 -3.16150188e-01 -6.89530551e-01 8.24415922e-01
1.79428428e-01 8.55672956e-02 -9.68757570e-01 -1.51392519e-01
-3.89472172e-02 -4.05123085e-02 6.12290382e-01 8.19430709e-01
1.19303429e+00 -8.40202197e-02 1.15159504e-01 3.35920334e-01
1.81417060e+00 4.14816469e-01 9.67653453e-01 3.10152501e-01
4.17247564e-01 4.36794579e-01 7.55054295e-01 2.56981581e-01
9.86056253e-02 8.09748650e-01 5.58573961e-01 -6.53649211e-01
-4.68969852e-01 -9.28834751e-02 5.80623150e-01 8.28228235e-01
2.79905707e-01 -1.72646865e-01 -6.31303310e-01 6.47239983e-01
-1.50637484e+00 -7.59226561e-01 -1.09280258e-01 2.21038747e+00
5.23862004e-01 1.76155165e-01 1.31260872e-01 6.30505443e-01
6.96028769e-01 3.57919693e-01 -5.99893093e-01 -8.36440325e-01
-1.48092359e-01 1.60777196e-01 2.13557452e-01 4.30686831e-01
-9.68303919e-01 3.62926751e-01 5.95817566e+00 1.04172814e+00
-1.14343846e+00 -1.59415118e-02 9.02804971e-01 1.54380411e-01
-2.18234465e-01 -7.20763654e-02 -2.88677603e-01 6.62801981e-01
6.26367211e-01 4.14073132e-02 2.73378551e-01 6.72758698e-01
3.63198727e-01 -5.37103474e-01 -7.42743611e-01 1.08682382e+00
6.06366172e-02 -9.84002709e-01 2.48255253e-01 1.21731468e-01
6.98584259e-01 -4.47766662e-01 2.61506915e-01 -1.10276073e-01
-4.00535226e-01 -9.20549572e-01 7.76811481e-01 6.24324858e-01
9.44874704e-01 -8.01048636e-01 8.94945443e-01 -2.93334424e-02
-1.44438243e+00 -1.18643828e-01 -3.03575873e-01 1.15760632e-01
-5.41347414e-02 8.02360237e-01 -3.17918807e-01 4.32830721e-01
9.06074822e-01 5.52398145e-01 -5.93823493e-01 9.51201737e-01
-1.73634067e-01 3.47750455e-01 -1.53655726e-02 1.39348775e-01
2.63910025e-01 -3.09772760e-01 2.75141835e-01 1.05515838e+00
3.97163242e-01 -5.28194383e-02 -1.99998036e-01 9.88474846e-01
-1.15889341e-01 3.53157371e-01 -1.64994553e-01 1.57104403e-01
2.64344990e-01 1.47894752e+00 -7.27654099e-01 -4.84968752e-01
-5.60714960e-01 9.63770568e-01 -1.94311589e-01 3.78307432e-01
-6.13514543e-01 -7.00464666e-01 4.52556074e-01 2.49669895e-01
2.66080111e-01 9.33189169e-02 -4.97341573e-01 -5.56800306e-01
2.40713820e-01 -7.69468367e-01 3.39776337e-01 -1.08103585e+00
-1.12830365e+00 5.85481405e-01 -3.60283673e-01 -1.51566005e+00
2.68972784e-01 -6.55278146e-01 -8.35737467e-01 1.14837956e+00
-1.58446991e+00 -8.88658166e-01 -6.46007180e-01 6.90270185e-01
3.67238700e-01 1.73265263e-01 6.21505380e-01 3.49932164e-01
-6.22711062e-01 5.60797989e-01 2.71774139e-02 1.80844486e-01
4.59620237e-01 -1.23563480e+00 -1.92635909e-01 8.31479371e-01
-1.66784972e-01 2.07169831e-01 3.34108353e-01 -2.55838454e-01
-9.27637875e-01 -7.35749006e-01 9.28867877e-01 4.23741862e-02
3.95789266e-01 1.27764240e-01 -8.30440462e-01 -1.08157180e-01
1.56439602e-01 1.43742755e-01 3.74103367e-01 -4.21964288e-01
-5.00085317e-02 -7.98137665e-01 -1.52962422e+00 3.64894718e-01
2.73982137e-01 -7.34507382e-01 -2.58031160e-01 -1.62092179e-01
3.83154422e-01 1.42419264e-01 -1.22736979e+00 4.56891000e-01
7.40831256e-01 -1.20036304e+00 1.01762044e+00 3.33078265e-01
6.21034145e-01 -8.40006590e-01 -1.19362727e-01 -1.05109954e+00
-6.41274273e-01 2.76985645e-01 5.92121333e-02 1.18092155e+00
4.54770803e-01 -2.71456361e-01 4.85459656e-01 1.93857744e-01
1.52987644e-01 -9.97036994e-01 -8.51617873e-01 -5.25903583e-01
-3.02743226e-01 -2.72978485e-01 5.81751525e-01 6.39858723e-01
-1.84073612e-01 1.20453499e-01 -1.24426506e-01 2.81173855e-01
5.57886243e-01 1.44603983e-01 2.96997428e-01 -8.61589551e-01
-3.39215025e-02 -6.45707369e-01 -9.43307221e-01 -5.40156424e-01
-5.31902492e-01 -5.73682427e-01 -8.51177350e-02 -1.89124727e+00
4.21304196e-01 -1.47896364e-01 -9.40276206e-01 2.85967320e-01
-2.02290028e-01 6.54802382e-01 2.16035768e-01 1.73180759e-01
-3.89607430e-01 2.74471313e-01 1.46322358e+00 -3.65647584e-01
-1.13879442e-01 -3.26810271e-01 -4.20560449e-01 5.68391204e-01
8.51733506e-01 -2.33277127e-01 -1.46965414e-01 -1.41900659e-01
8.57393593e-02 9.09426063e-02 4.57538128e-01 -1.42632926e+00
9.59731713e-02 -1.72694325e-01 8.44135344e-01 -6.49350822e-01
2.94691980e-01 -8.50092471e-01 4.73673902e-02 5.20417809e-01
-2.29335546e-01 2.51472592e-02 -3.20898704e-02 2.86556959e-01
-7.67891288e-01 -3.08577679e-02 1.13391650e+00 8.98497030e-02
-9.74923432e-01 1.35830432e-01 -2.07691178e-01 -6.11208141e-01
1.16615200e+00 -6.69275582e-01 -1.86893418e-01 -4.31647718e-01
-5.40023983e-01 -1.51232123e-01 4.77760285e-01 1.46323800e-01
9.67000961e-01 -1.57463598e+00 -4.76616740e-01 4.65191722e-01
2.95550674e-01 -4.71686333e-01 4.24925983e-01 9.64392304e-01
-6.09471500e-01 5.09632453e-02 -6.35486484e-01 -6.46542430e-01
-1.28217494e+00 4.72337902e-01 3.31409872e-01 -3.91490161e-01
-1.71012521e-01 5.18433630e-01 3.87037724e-01 1.92485794e-01
1.42906025e-01 -2.22679064e-01 -5.47450900e-01 1.51018083e-01
6.63722515e-01 4.24757212e-01 -6.85219280e-03 -1.09383607e+00
-3.67312312e-01 1.06565654e+00 4.03137863e-01 -1.41105607e-01
9.84111071e-01 -3.67985398e-01 -3.34194541e-01 3.91028672e-01
1.45297325e+00 -1.27105147e-01 -1.24288058e+00 -2.08296239e-01
-3.61812860e-01 -5.21006584e-01 4.49072182e-01 -1.09936416e+00
-1.31280053e+00 1.23243320e+00 1.27430952e+00 1.65019944e-01
1.83695507e+00 -2.35256493e-01 9.00324643e-01 -2.87546456e-01
2.05659688e-01 -1.12627399e+00 3.93258601e-01 1.59514963e-03
7.77046025e-01 -1.11096239e+00 1.06009990e-01 -8.44810307e-02
-6.08124435e-01 1.03580499e+00 3.98381323e-01 -1.03778988e-01
7.45094121e-01 4.62444648e-02 2.43980333e-01 -1.61398232e-01
-2.23895520e-01 -1.97432175e-01 7.58725941e-01 4.38280374e-01
3.18168610e-01 1.72174349e-01 -3.71206939e-01 3.50984514e-01
1.31735086e-01 -8.78611356e-02 2.29278117e-01 6.91008687e-01
-7.64981568e-01 -8.74052346e-01 -5.70977211e-01 5.54423273e-01
-4.65130955e-01 4.43339795e-02 -2.03131780e-01 3.95534605e-01
5.52150309e-01 1.17510545e+00 9.78731140e-02 -6.33619785e-01
3.59461784e-01 -3.60681117e-01 2.40762010e-01 -1.55934021e-01
-5.77389896e-01 -4.42857854e-03 -4.23518509e-01 -8.06916416e-01
-5.01513660e-01 -2.39642993e-01 -1.21727788e+00 -3.01683664e-01
-2.07795322e-01 -9.66868252e-02 7.96924353e-01 6.26063466e-01
5.90670444e-02 6.94931090e-01 8.93996298e-01 -6.78198814e-01
1.58636689e-01 -7.94312835e-01 -8.09471190e-01 5.28922498e-01
4.17394489e-01 -3.57961953e-01 -4.12901163e-01 5.29764481e-02] | [11.736309051513672, -1.935028076171875] |
de20d434-667c-43e0-8ee5-45ac130dee1e | domain-concretization-from-examples | 2011.09034 | null | https://arxiv.org/abs/2011.09034v1 | https://arxiv.org/pdf/2011.09034v1.pdf | Domain Concretization from Examples: Addressing Missing Domain Knowledge via Robust Planning | The assumption of complete domain knowledge is not warranted for robot planning and decision-making in the real world. It could be due to design flaws or arise from domain ramifications or qualifications. In such cases, existing planning and learning algorithms could produce highly undesirable behaviors. This problem is more challenging than partial observability in the sense that the agent is unaware of certain knowledge, in contrast to it being partially observable: the difference between known unknowns and unknown unknowns. In this work, we formulate it as the problem of Domain Concretization, an inverse problem to domain abstraction. Based on an incomplete domain model provided by the designer and teacher traces from human users, our algorithm searches for a candidate model set under a minimalistic model assumption. It then generates a robust plan with the maximum probability of success under the set of candidate models. In addition to a standard search formulation in the model-space, we propose a sample-based search method and also an online version of it to improve search time. We tested our approach on IPC domains and a simulated robotics domain where incompleteness was introduced by removing domain features from the complete model. Results show that our planning algorithm increases the plan success rate without impacting the cost much. | ['Yu Zhang', 'Piyush Rajesh Medikeri', 'Akshay Sharma'] | 2020-11-18 | null | null | null | null | ['known-unknowns'] | ['miscellaneous'] | [ 4.75965410e-01 9.91305590e-01 -2.28513449e-01 -1.39518201e-01
-4.72075343e-01 -5.59270263e-01 4.89310592e-01 2.00091861e-02
-1.91733599e-01 1.09312534e+00 -3.04682758e-02 -3.21134478e-01
-7.00137794e-01 -8.58156025e-01 -7.51829207e-01 -3.03359985e-01
1.10721968e-01 1.04149354e+00 5.73713422e-01 -2.40503088e-01
2.74127066e-01 4.80812788e-01 -1.63963604e+00 -1.76966801e-01
9.95123148e-01 4.73202169e-01 5.49856305e-01 2.52201319e-01
1.12655267e-01 7.78012276e-01 -6.26070261e-01 2.70555705e-01
5.13539255e-01 -4.74929988e-01 -1.23156643e+00 6.26729012e-01
-1.40129283e-01 -4.48345035e-01 -1.75479148e-02 1.20358717e+00
7.10660219e-02 -5.73469214e-02 5.05360663e-01 -1.60511124e+00
6.96347887e-03 7.51442671e-01 -3.36482860e-02 -4.69456613e-01
8.54667485e-01 2.33074129e-01 6.80471718e-01 -2.00539276e-01
7.55278766e-01 1.38704073e+00 1.64669782e-01 5.89951873e-01
-1.39147854e+00 -4.94226575e-01 3.31612855e-01 2.67664284e-01
-1.44697094e+00 -2.16690347e-01 6.41260386e-01 -6.05346799e-01
8.98405910e-01 1.63329188e-02 5.21347165e-01 8.45945954e-01
1.39248565e-01 6.09026492e-01 1.22828090e+00 -7.06295729e-01
5.99819303e-01 4.70856100e-01 5.96252419e-02 6.13943040e-01
5.12386858e-01 5.84699869e-01 -3.20600063e-01 -2.01989964e-01
8.46083760e-01 -4.12207812e-01 -1.63160235e-01 -8.39391470e-01
-9.83125508e-01 6.13323331e-01 -3.15193772e-01 1.55084819e-01
-4.49738979e-01 -1.69088468e-02 -1.56591818e-01 7.24031985e-01
-2.33076304e-01 9.32622552e-01 -6.27876997e-01 -1.23756967e-01
-4.81135041e-01 4.20066684e-01 1.32514548e+00 1.38236201e+00
9.64962959e-01 2.97351796e-02 4.17211950e-01 2.54864454e-01
3.55936438e-01 2.48747662e-01 3.04928899e-01 -1.23435056e+00
2.44614080e-01 9.94314790e-01 7.16852784e-01 -6.63906395e-01
-5.33317745e-01 -2.29983732e-01 5.19515648e-02 5.63890576e-01
5.49948573e-01 -2.11386085e-01 -9.18008626e-01 1.80486000e+00
5.83621323e-01 1.20458461e-01 2.82584220e-01 7.97175705e-01
1.83838770e-01 5.16634047e-01 -3.62384230e-01 -5.70059240e-01
1.11015618e+00 -6.18789256e-01 -7.65700817e-01 -5.06401837e-01
6.86795294e-01 -2.59573877e-01 8.91692460e-01 8.44308794e-01
-9.25780475e-01 -2.27933273e-01 -1.27578652e+00 5.05381227e-01
-6.41060323e-02 -1.78637818e-01 3.85720998e-01 5.89792430e-01
-7.69349277e-01 5.37483931e-01 -8.75606060e-01 -4.87228453e-01
-6.82379827e-02 8.03852916e-01 -3.66043329e-01 -2.09106028e-01
-9.71620381e-01 1.27587521e+00 8.21951509e-01 -1.33521751e-01
-1.39732647e+00 -3.71956199e-01 -9.14168894e-01 -2.13170517e-02
1.23912287e+00 -4.31587815e-01 1.67552483e+00 -9.69485044e-01
-1.76284587e+00 1.56301811e-01 1.62207950e-02 -5.90616643e-01
7.60325074e-01 2.89529096e-03 -1.78382784e-01 -1.39788270e-01
2.47350894e-02 3.45014185e-01 7.30059087e-01 -1.34167302e+00
-8.94779027e-01 -2.06414923e-01 6.33238494e-01 1.77537188e-01
1.84450075e-01 -3.27693909e-01 -1.44353556e-02 1.23922519e-01
5.30013978e-01 -1.21365523e+00 -6.18764043e-01 -2.66569227e-01
-2.83686012e-01 -2.32713923e-01 6.46357358e-01 -2.28048801e-01
1.05335939e+00 -1.88346267e+00 1.68590084e-01 5.31553566e-01
-6.92026019e-02 -8.16147774e-02 -2.90533453e-01 7.62037992e-01
1.08658168e-02 2.17655804e-02 -2.80156970e-01 3.77151668e-01
1.21768236e-01 7.19818354e-01 -2.72889972e-01 4.79790509e-01
2.11236686e-01 2.01354101e-01 -9.97339964e-01 -4.91941184e-01
2.25710660e-01 -2.54401147e-01 -6.65024102e-01 4.06852178e-02
-9.20105934e-01 5.53020895e-01 -7.21370816e-01 2.83074975e-01
5.08672953e-01 7.56102875e-02 5.95482707e-01 5.39950907e-01
-1.45038605e-01 4.15273398e-01 -1.92151630e+00 1.55784512e+00
-5.74792206e-01 8.66865963e-02 8.62237066e-02 -1.01763093e+00
9.12969947e-01 5.49071729e-01 3.71351212e-01 -4.74392027e-01
-1.18265022e-02 2.97751427e-01 3.38142991e-01 -6.13171935e-01
3.49419713e-01 -2.06801295e-01 -1.96281120e-01 5.33095479e-01
1.37752770e-02 -5.25937796e-01 1.08770676e-01 -1.34048700e-01
1.24376845e+00 3.17188650e-01 8.18082869e-01 -3.26456368e-01
4.61613089e-01 5.51137626e-01 8.65692019e-01 8.61784518e-01
1.12046860e-02 1.06954738e-01 8.59210849e-01 -4.18361872e-01
-8.17206442e-01 -5.47820985e-01 1.45436138e-01 4.59356308e-01
3.84259015e-01 -4.46507543e-01 -5.89653552e-01 -7.31810510e-01
-3.71864080e-01 1.09453666e+00 -2.30125934e-01 -2.28924528e-01
-4.63544071e-01 7.23196119e-02 2.01252416e-01 1.76655781e-02
2.02804536e-01 -1.13957024e+00 -1.23579192e+00 5.14314175e-01
8.19242501e-04 -9.50146258e-01 1.73540656e-02 4.14615542e-01
-7.55324364e-01 -1.37955308e+00 2.37222128e-02 -7.61929810e-01
8.17157030e-01 -1.79711580e-01 7.85643756e-01 7.12919459e-02
1.47285789e-01 4.90388453e-01 -3.09640646e-01 -7.13993669e-01
-9.32901323e-01 -1.01542741e-01 1.41040578e-01 -4.04775590e-01
1.72651678e-01 -4.60723490e-01 2.04625592e-01 5.13694823e-01
-8.39196205e-01 4.83368412e-02 5.73764384e-01 8.55336070e-01
4.36744153e-01 1.02663839e+00 5.78904569e-01 -8.63834262e-01
7.81458020e-01 -2.41844788e-01 -1.20625651e+00 2.44756296e-01
-8.56275856e-01 5.49409389e-01 4.97662246e-01 -7.00066864e-01
-1.11099112e+00 7.21006334e-01 5.25702000e-01 -5.18590733e-02
-7.11480618e-01 7.31782317e-01 -5.93987763e-01 1.35347292e-01
7.77784705e-01 2.38101453e-01 7.51051307e-02 -3.30386788e-01
4.30843979e-02 3.89032036e-01 1.62210897e-01 -9.80997026e-01
9.41326916e-01 4.82873246e-02 3.54661673e-01 -7.09590435e-01
-5.35675287e-01 -1.86472461e-01 -6.98631465e-01 -1.10178497e-02
3.38562816e-01 -6.59284949e-01 -6.82187080e-01 -5.80159239e-02
-1.12367332e+00 -5.12283385e-01 -4.63933259e-01 4.66262490e-01
-1.00704706e+00 2.21497625e-01 8.54855403e-02 -1.18508255e+00
5.67732751e-01 -1.53334284e+00 5.49656093e-01 1.39192849e-01
-4.38716143e-01 -6.40294373e-01 1.52869243e-02 -4.86484244e-02
-1.04945106e-02 3.12290847e-01 1.08061719e+00 -1.04965973e+00
-7.65346527e-01 -2.02236012e-01 3.67772311e-01 5.30423895e-02
1.26533166e-01 -3.48969758e-01 -6.24545455e-01 -2.04943419e-01
1.86879203e-01 -1.84496313e-01 -9.87245217e-02 1.44439384e-01
6.39341652e-01 -8.20183337e-01 -3.64663422e-01 -3.33296895e-01
1.43640041e+00 7.70739853e-01 3.81346673e-01 5.13533175e-01
2.16890965e-02 9.90020275e-01 1.04100013e+00 7.36691356e-01
2.47335806e-01 8.21397364e-01 5.87561011e-01 4.41621006e-01
1.57550454e-01 -4.59301084e-01 2.62100607e-01 -3.29119228e-02
2.23986596e-01 3.27421315e-02 -1.31970501e+00 8.13539445e-01
-2.07488227e+00 -8.24511766e-01 1.21476620e-01 2.31484318e+00
7.92841673e-01 2.66549557e-01 2.53415227e-01 3.50929081e-01
6.59609556e-01 -6.00494862e-01 -6.34179771e-01 -3.57275963e-01
3.97157073e-01 -3.61489691e-02 4.89123732e-01 9.30358529e-01
-6.47073328e-01 8.58389676e-01 5.68414736e+00 5.54136157e-01
-7.03268647e-01 -1.62927940e-01 -2.77443826e-01 2.40510762e-01
-2.49881849e-01 6.73812211e-01 -7.28598416e-01 3.03570405e-02
7.84774125e-01 -4.06096756e-01 5.86613894e-01 1.12724555e+00
2.73651928e-01 -4.66115862e-01 -1.68592763e+00 3.72435719e-01
-2.46059969e-01 -8.70695889e-01 -2.26963669e-01 4.84771609e-01
6.20202184e-01 -5.15883088e-01 -2.43733957e-01 2.95867711e-01
7.49587834e-01 -9.24876034e-01 1.08446610e+00 1.60031006e-01
3.30167413e-01 -8.72704327e-01 6.53942287e-01 1.23688781e+00
-8.34050119e-01 -5.12780547e-01 -1.50676593e-01 -6.26071513e-01
1.70289483e-02 8.67774636e-02 -1.65984523e+00 6.63146079e-01
7.82896504e-02 3.60181481e-01 -1.70467302e-01 1.08588958e+00
-6.01270914e-01 3.32490504e-01 -5.87903202e-01 -7.14032799e-02
2.05690026e-01 -5.06296530e-02 8.22278023e-01 6.26785219e-01
1.76335916e-01 2.80244291e-01 6.09640539e-01 9.52992439e-01
6.26163781e-01 -4.55525279e-01 -9.27924216e-01 1.69116661e-01
8.42257619e-01 6.10590577e-01 -5.74527442e-01 -2.06516773e-01
-2.21007735e-01 3.57286215e-01 -8.15376043e-02 3.78369272e-01
-5.82569718e-01 -1.74130470e-01 2.66253650e-01 1.03756882e-01
3.46534029e-02 -1.66819960e-01 -1.49746880e-01 -6.86040938e-01
-8.09799209e-02 -1.22814941e+00 3.31221372e-01 -4.23826933e-01
-7.50007749e-01 3.64192307e-01 5.89981019e-01 -1.38495851e+00
-6.27541184e-01 -3.39361995e-01 -2.66988546e-01 5.70444882e-01
-1.30943382e+00 -6.88458979e-01 1.00429049e-02 5.08436322e-01
7.37340689e-01 -4.55928855e-02 7.51691163e-01 -2.56092966e-01
-2.55829215e-01 8.21837112e-02 -6.42368615e-01 -5.50729156e-01
2.64774233e-01 -1.13479698e+00 -1.47042692e-01 9.28680539e-01
-1.17127270e-01 5.36881924e-01 1.37744570e+00 -7.53087997e-01
-1.46540797e+00 -9.23485935e-01 8.12370896e-01 -4.13117200e-01
6.21008933e-01 -1.63914546e-01 -7.31110930e-01 9.56142843e-01
-1.10824011e-01 -5.20443559e-01 8.15005228e-02 -1.40469268e-01
9.22967121e-02 2.20979199e-01 -1.48744023e+00 6.64776862e-01
1.05007219e+00 -1.25878841e-01 -1.00269449e+00 3.71732056e-01
9.14618552e-01 -4.48358595e-01 -6.89823091e-01 3.46818179e-01
2.92448819e-01 -5.70508718e-01 5.00868917e-01 -6.72155857e-01
1.25098124e-01 -6.71753407e-01 -1.07323527e-01 -1.31001782e+00
-2.24245504e-01 -7.96865821e-01 -1.38563029e-02 9.39055026e-01
6.84405804e-01 -6.42044663e-01 9.12379503e-01 1.15622783e+00
-3.24816465e-01 -4.91571009e-01 -1.16922987e+00 -1.24003363e+00
-1.02860637e-01 -5.65509140e-01 9.44982409e-01 6.90058231e-01
4.72141504e-01 2.70666629e-01 -2.33965009e-01 7.05883145e-01
4.23202395e-01 2.53540009e-01 9.26813960e-01 -1.45406723e+00
-4.44190949e-01 4.20482568e-02 -1.12718724e-01 -9.02128875e-01
4.58300829e-01 -3.28677833e-01 5.14920175e-01 -1.74940240e+00
-2.75527298e-01 -6.48063123e-01 2.01802537e-01 7.34244287e-01
7.00575829e-01 -9.21678662e-01 2.93636143e-01 -2.10939180e-02
-4.85870242e-01 2.42100716e-01 1.44230235e+00 -2.49584615e-01
-7.50361621e-01 2.84471542e-01 -4.72254246e-01 9.89938319e-01
7.72384763e-01 -6.68523431e-01 -9.57671106e-01 -1.73440710e-01
3.34136128e-01 6.91768765e-01 1.39081657e-01 -1.06598473e+00
3.87614965e-01 -9.68206048e-01 -4.43102717e-01 -3.28830391e-01
3.71516287e-01 -1.58038139e+00 6.04391932e-01 9.18337166e-01
-3.47152323e-01 -2.65289158e-01 8.18470940e-02 6.31882250e-01
-6.81172013e-02 -7.75576651e-01 4.42924380e-01 -2.97847897e-01
-1.05817676e+00 -2.04445288e-01 -6.87069178e-01 -3.40089291e-01
1.39330471e+00 -5.55193007e-01 -9.20414999e-02 -3.91273767e-01
-7.54801333e-01 3.93585175e-01 5.66827774e-01 1.86093196e-01
5.53830206e-01 -7.01194406e-01 -3.53286803e-01 2.29610160e-01
2.11060926e-01 3.98958892e-01 -2.59327471e-01 5.90397239e-01
-1.66945502e-01 4.97648835e-01 -1.87923282e-01 -3.32403302e-01
-1.05205679e+00 7.75031626e-01 3.30724597e-01 -2.08866164e-01
-4.48673844e-01 2.19736457e-01 1.45055473e-01 -5.86046338e-01
4.67461735e-01 -6.53188705e-01 -4.01488632e-01 -3.65896553e-01
2.39850178e-01 3.54463279e-01 -6.68370426e-02 -2.28499398e-01
-4.56120461e-01 3.18090767e-01 1.98844507e-01 -4.51404065e-01
1.25400770e+00 -2.14845091e-01 2.39665061e-01 1.93193957e-01
1.87476173e-01 -1.40944272e-01 -1.22276437e+00 -2.84491003e-01
5.44341147e-01 -3.65342319e-01 -2.36499324e-01 -1.06367719e+00
-3.57855052e-01 3.93483937e-01 2.58482993e-01 2.94110268e-01
9.69685853e-01 1.96824312e-01 1.06040724e-01 8.07193279e-01
1.12273741e+00 -1.23703766e+00 3.37534845e-02 6.48541272e-01
8.69092822e-01 -9.82523024e-01 1.31177127e-01 -5.08464515e-01
-7.03643858e-01 1.00697505e+00 9.52500224e-01 -1.84202660e-02
2.61685789e-01 3.92167419e-01 -2.40890995e-01 -7.78671280e-02
-1.04169548e+00 -2.03940913e-01 -1.34343103e-01 9.57333565e-01
-2.55780220e-01 3.15882042e-02 -3.84240657e-01 6.75847292e-01
-3.44066441e-01 3.23878318e-01 1.24631977e+00 1.33333063e+00
-8.60170424e-01 -1.43603945e+00 -5.87761819e-01 1.25800237e-01
1.84872318e-02 6.50625288e-01 -5.24982572e-01 1.07708502e+00
2.02723235e-01 1.35920608e+00 -3.52385044e-01 -3.49023074e-01
5.77485025e-01 -1.25410454e-02 5.68541884e-01 -1.17360938e+00
-5.35398051e-02 9.36095715e-02 5.47057927e-01 -6.05472863e-01
-2.81821936e-01 -5.45493841e-01 -1.44807971e+00 1.89818412e-01
-6.56941175e-01 3.67780954e-01 5.88482857e-01 1.22440588e+00
2.04754379e-02 3.28235835e-01 3.65656257e-01 -3.78185302e-01
-1.11371970e+00 -7.13015556e-01 -5.27217686e-01 -9.43519250e-02
3.35303605e-01 -8.49418819e-01 -1.37009829e-01 8.49089175e-02] | [4.447085380554199, 1.3993568420410156] |
769bdcc3-0cd0-4820-8950-60de8c8a5cb6 | universal-embeddings-for-spatio-temporal | 2011.06165 | null | https://arxiv.org/abs/2011.06165v1 | https://arxiv.org/pdf/2011.06165v1.pdf | Universal Embeddings for Spatio-Temporal Tagging of Self-Driving Logs | In this paper, we tackle the problem of spatio-temporal tagging of self-driving scenes from raw sensor data. Our approach learns a universal embedding for all tags, enabling efficient tagging of many attributes and faster learning of new attributes with limited data. Importantly, the embedding is spatio-temporally aware, allowing the model to naturally output spatio-temporal tag values. Values can then be pooled over arbitrary regions, in order to, for example, compute the pedestrian density in front of the SDV, or determine if a car is blocking another car at a 4-way intersection. We demonstrate the effectiveness of our approach on a new large scale self-driving dataset, SDVScenes, containing 15 attributes relating to vehicle and pedestrian density, the actions of each actor, the speed of each actor, interactions between actors, and the topology of the road map. | ['Raquel Urtasun', 'Ersin Yumer', 'Abbas Sadat', 'Wenjie Luo', 'Eric Kee', 'Sean Segal'] | 2020-11-12 | null | null | null | null | ['temporal-tagging'] | ['natural-language-processing'] | [-2.15264305e-01 6.72535077e-02 -3.04001898e-01 -6.45546913e-01
-5.31422079e-01 -5.97705245e-01 7.13701725e-01 7.38271594e-01
-7.92475581e-01 6.53533459e-01 3.76136988e-01 -3.12472403e-01
-1.17997393e-01 -1.28645635e+00 -7.62344718e-01 -6.22082353e-01
-5.04686058e-01 4.63703781e-01 7.94683158e-01 4.57964242e-02
-2.13157028e-01 7.76048183e-01 -1.58739877e+00 -2.54759192e-01
3.63947004e-01 9.84170437e-01 1.35159299e-01 8.08999360e-01
-1.84872121e-01 7.09072709e-01 -1.46471992e-01 -3.21649730e-01
1.43053075e-02 3.35881591e-01 -4.76116627e-01 -2.03237295e-01
3.66822988e-01 -4.50579971e-01 -8.84214461e-01 6.12877548e-01
1.41672075e-01 1.63553342e-01 6.74522161e-01 -1.61752248e+00
3.20754386e-02 4.88440663e-01 -1.60879284e-01 5.46731591e-01
6.65992079e-03 2.01572269e-01 9.93005276e-01 -2.72191674e-01
4.26693588e-01 1.07538211e+00 5.51349938e-01 1.14029415e-01
-1.05570066e+00 -6.16861999e-01 3.20824027e-01 3.49539429e-01
-1.48191404e+00 -3.24151397e-01 5.45409024e-01 -6.33499503e-01
5.99377632e-01 1.95962209e-02 7.45650351e-01 8.40113282e-01
8.13066661e-02 5.46405911e-01 5.73183179e-01 1.39597848e-01
4.77851301e-01 9.29536298e-02 9.00599211e-02 6.40506208e-01
4.72562164e-02 2.36595243e-01 -4.41401005e-01 -1.59566343e-01
2.69396842e-01 2.68597484e-01 5.91520548e-01 -6.88307583e-01
-1.19919288e+00 7.59836435e-01 6.39277160e-01 -9.36609283e-02
-3.91974211e-01 5.59769273e-01 4.25487906e-01 -6.46254495e-02
5.60112059e-01 -1.34080648e-01 -5.27079940e-01 -3.13724339e-01
-5.67837715e-01 3.80625963e-01 5.23930013e-01 8.52536917e-01
1.34103394e+00 -2.50756323e-01 -1.84524804e-01 3.77529889e-01
5.31880893e-02 8.85622084e-01 -3.88893008e-01 -1.24866295e+00
5.96921980e-01 6.20659649e-01 3.49812746e-01 -7.74434745e-01
-5.09392023e-01 1.53803140e-01 -6.11952186e-01 2.01986670e-01
4.09119010e-01 -2.89849252e-01 -7.17753828e-01 1.79468799e+00
5.22363365e-01 6.63070560e-01 -3.26579362e-02 7.42629051e-01
4.61770713e-01 7.45703757e-01 8.33127737e-01 3.56814414e-01
1.26089489e+00 -2.77658463e-01 -3.08730841e-01 -3.11028093e-01
6.47075891e-01 -5.77208288e-02 4.65095520e-01 -3.98914516e-01
-5.05760431e-01 -4.76633340e-01 -6.07762158e-01 1.60132185e-01
-1.06143820e+00 -4.74544354e-02 7.17305958e-01 3.35437357e-01
-8.78202200e-01 3.94836128e-01 -8.08191776e-01 -4.83806789e-01
5.35693228e-01 3.64067286e-01 -5.70146084e-01 -1.72587499e-01
-1.32852232e+00 7.70582438e-01 3.70756775e-01 -3.55919093e-01
-1.02087700e+00 -9.06827629e-01 -1.27128053e+00 2.47683581e-02
4.46470737e-01 -4.62796450e-01 7.73998499e-01 -2.64248461e-01
-8.36556554e-01 7.90571988e-01 -3.04710835e-01 -6.90560162e-01
3.19004476e-01 -1.20187765e-02 -8.73548627e-01 -1.98127568e-01
5.06342888e-01 7.71425486e-01 4.44912612e-01 -1.02631366e+00
-1.36786115e+00 -4.19128627e-01 4.49954450e-01 -9.40124169e-02
-1.75794244e-01 -2.65392393e-01 -4.69356894e-01 -1.47145599e-01
-2.53503829e-01 -9.19853568e-01 -4.76047963e-01 6.76663890e-02
-1.17609777e-01 -5.43204665e-01 1.00994039e+00 -3.01277906e-01
1.15124941e+00 -2.64522123e+00 -3.15799147e-01 2.43145093e-01
2.75526077e-01 6.27421439e-02 -1.72957316e-01 2.94095457e-01
3.57815981e-01 -4.21379693e-02 -1.16877221e-01 -1.23462103e-01
1.07491583e-01 7.48930633e-01 -1.57383591e-01 4.80406553e-01
3.68113786e-01 9.14505422e-01 -1.29828608e+00 -6.84934914e-01
7.27969825e-01 5.56486368e-01 -2.00840265e-01 1.56299531e-01
-1.31931552e-03 2.02465340e-01 -7.31045902e-01 2.48514891e-01
5.12113512e-01 3.32689285e-01 -1.28102198e-01 -1.40453521e-02
-4.98582959e-01 2.92800248e-01 -1.23154044e+00 1.17186368e+00
-6.07572854e-01 7.45310068e-01 -1.47914276e-01 -7.73056209e-01
7.57602453e-01 8.59593824e-02 9.57349002e-01 -8.39541256e-01
-1.16925329e-01 -2.65380234e-01 -5.89283049e-01 -5.12913287e-01
4.47862625e-01 3.81571740e-01 -6.66394770e-01 3.94879460e-01
-1.27291530e-01 1.73032254e-01 3.77526045e-01 3.10167789e-01
1.34155464e+00 -2.58415103e-01 1.11068256e-01 -1.28086671e-01
3.76625955e-01 9.38837901e-02 3.94769490e-01 5.46220481e-01
-3.38156402e-01 -3.35618891e-02 5.08494794e-01 -8.13298523e-01
-1.16789889e+00 -1.77698326e+00 -6.33396804e-02 1.30727196e+00
3.24152350e-01 -3.61888826e-01 -3.57313246e-01 -7.76687443e-01
5.54201901e-01 5.80429435e-01 -7.69652426e-01 -1.34931877e-01
-6.12646520e-01 -2.74312407e-01 3.02135646e-01 7.13959277e-01
3.48931432e-01 -6.52154863e-01 -6.87326849e-01 3.74087125e-01
-7.61782452e-02 -1.49007463e+00 -3.12953651e-01 2.08073154e-01
-5.60024440e-01 -1.08219862e+00 -6.16067797e-02 -5.62482238e-01
5.65906107e-01 1.64124489e-01 9.71323252e-01 -3.91169161e-01
-2.22319737e-01 3.79799426e-01 -3.92674729e-02 -3.27450782e-01
-1.76034331e-01 3.04632396e-01 -3.75829898e-02 1.60084233e-01
6.63731039e-01 -4.51122999e-01 -4.66850370e-01 3.76775086e-01
-4.14715528e-01 -1.11712426e-01 3.29423547e-01 7.34684989e-02
6.60351992e-01 2.60306746e-01 2.19441175e-01 -5.34934521e-01
6.44838484e-03 -9.87232149e-01 -6.69187665e-01 -9.27351788e-02
-9.10458565e-02 1.52796760e-01 3.67912084e-01 -3.23220789e-01
-5.40195525e-01 5.30197322e-01 -6.20003529e-02 -8.49623233e-02
-6.33549809e-01 2.01509949e-02 -4.02741104e-01 4.02671605e-01
1.57096446e-01 -8.74979198e-02 -3.72357249e-01 -2.81287491e-01
5.87240696e-01 5.94451964e-01 9.03149068e-01 -3.71609211e-01
9.00540531e-01 7.69152462e-01 1.52990431e-01 -7.50729203e-01
-7.38188028e-01 -9.08708632e-01 -9.41843092e-01 -4.28944081e-01
1.09745705e+00 -1.15698469e+00 -9.28463936e-01 2.78308034e-01
-9.05655622e-01 -5.43758988e-01 -5.02317071e-01 5.90679884e-01
-4.31874394e-01 -2.76713133e-01 -2.35044807e-01 -7.63563097e-01
3.89719009e-01 -7.24601805e-01 1.18671119e+00 1.05285555e-01
-1.26162082e-01 -1.12704003e+00 1.66905239e-01 -4.31870706e-02
1.78202644e-01 4.11075622e-01 8.29822600e-01 -2.45555967e-01
-6.55046582e-01 -2.42746443e-01 -4.09945428e-01 5.45858555e-02
1.01652510e-01 -7.33902380e-02 -7.91547298e-01 1.59606356e-02
-1.19279289e+00 1.75055444e-01 8.62307787e-01 3.90410632e-01
1.02748060e+00 -1.75047830e-01 -7.89366424e-01 4.45878893e-01
1.36632478e+00 -1.50597498e-01 4.79370743e-01 1.90911531e-01
7.83489048e-01 5.38508296e-01 6.55671299e-01 6.96485043e-01
1.08818424e+00 7.17476785e-01 6.37952030e-01 -2.49901921e-01
-1.98478289e-02 -4.86860663e-01 2.89312094e-01 1.53702632e-01
-8.29632301e-03 -1.73084050e-01 -9.76929963e-01 1.10374916e+00
-1.78342295e+00 -1.07149589e+00 -3.61168742e-01 2.14153910e+00
7.06078932e-02 1.18031710e-01 4.88538653e-01 -4.39039618e-02
5.47040284e-01 3.78044218e-01 -6.31189466e-01 -2.70895958e-01
4.47397493e-02 -7.15993941e-02 1.36714935e+00 5.54710448e-01
-1.50697875e+00 1.00859916e+00 6.79175806e+00 3.43707740e-01
-6.73825800e-01 2.49779984e-01 4.29183602e-01 -6.87809959e-02
-2.18759537e-01 6.96628019e-02 -9.16272521e-01 8.04724574e-01
1.19576347e+00 5.45774512e-02 3.01008582e-01 7.44147718e-01
3.69973212e-01 -3.61462295e-01 -8.57804239e-01 5.62838316e-01
-3.69407475e-01 -1.25037968e+00 -3.07361722e-01 2.98103362e-01
4.11559254e-01 3.49702537e-01 -1.62443668e-01 2.55520254e-01
9.36421156e-01 -7.01099336e-01 7.02346742e-01 4.71763968e-01
7.75123596e-01 -9.86849785e-01 5.78495264e-01 2.56255716e-01
-1.63160825e+00 -3.54584545e-01 -1.30414248e-01 1.66928302e-02
4.28555340e-01 5.38764179e-01 -6.88102543e-01 8.78182277e-02
9.18356359e-01 8.03695083e-01 -7.08911538e-01 7.33685255e-01
-9.21099707e-02 5.64956009e-01 -6.03841662e-01 1.39804512e-01
4.72531199e-01 -2.93624904e-02 4.41517532e-01 1.15946949e+00
2.63186753e-01 1.69764459e-01 3.48395824e-01 4.60071176e-01
2.39568707e-02 -3.11588079e-01 -1.01496100e+00 3.63447487e-01
6.47896349e-01 1.21482921e+00 -2.73639232e-01 -3.30542266e-01
-3.94978464e-01 4.60855216e-01 3.26372385e-01 4.68163699e-01
-1.04699624e+00 -1.82994336e-01 1.43520939e+00 6.83073282e-01
5.24529040e-01 -4.69498873e-01 -1.08700022e-02 -4.89696860e-01
-1.98817760e-01 1.69197232e-01 4.81566042e-01 -5.68626642e-01
-9.08369660e-01 5.17045893e-02 2.03222767e-01 -9.56832886e-01
-2.82300502e-01 -4.17808414e-01 -5.78495979e-01 5.03796220e-01
-1.61543310e+00 -1.17621541e+00 -3.53149205e-01 7.73140669e-01
1.36219010e-01 -1.39115438e-01 4.95548159e-01 5.83725572e-01
-3.49332839e-01 9.79308560e-02 3.99202555e-02 4.41190362e-01
4.67315108e-01 -1.09638095e+00 7.08178341e-01 6.83231413e-01
6.68125227e-02 -2.10552081e-01 5.91682673e-01 -5.06392300e-01
-1.13725245e+00 -1.64814174e+00 1.19239819e+00 -6.84531629e-01
9.42865133e-01 -5.45302212e-01 -5.20609677e-01 5.84717929e-01
-3.65138650e-01 4.89775270e-01 4.35263783e-01 2.14848474e-01
-2.70687461e-01 -6.58389866e-01 -1.00254238e+00 4.22815889e-01
1.18120337e+00 -6.10810280e-01 -2.45223716e-01 5.54728508e-02
5.29762626e-01 3.84967811e-02 -8.05235922e-01 3.12237442e-01
3.42737585e-01 -6.78976119e-01 1.27946615e+00 -5.29045701e-01
-8.11200142e-02 -4.08104390e-01 -2.85335541e-01 -1.24834871e+00
-5.44661760e-01 -1.36236614e-02 -8.93740654e-02 1.28077877e+00
2.43775770e-01 -5.68625033e-01 6.80940747e-01 6.34546220e-01
-4.99045514e-02 -3.00655335e-01 -1.36813962e+00 -6.66168153e-01
-1.00663453e-01 -8.42864633e-01 1.15599835e+00 5.97619712e-01
-4.23981011e-01 1.44247547e-01 -2.95575380e-01 5.72540224e-01
6.36116803e-01 -2.35049009e-01 1.01659787e+00 -1.53618360e+00
3.60638589e-01 -1.82824716e-01 -9.17959988e-01 -7.41235316e-01
5.54108381e-01 -6.39404476e-01 -6.35856688e-02 -1.58084142e+00
1.28483191e-01 -9.20004845e-01 -3.81523609e-01 6.56310737e-01
5.74057661e-02 2.47751638e-01 -1.16848923e-01 -3.10380667e-01
-8.81848991e-01 2.17279211e-01 7.00252235e-01 -3.86570901e-01
9.83294919e-02 3.62108164e-02 -1.13862745e-01 5.07052243e-01
8.78272116e-01 -7.35615015e-01 -2.74134606e-01 -3.89982700e-01
1.45618379e-01 4.02579866e-02 9.07659292e-01 -1.20606983e+00
1.72390088e-01 -4.73984748e-01 3.06187242e-01 -8.73084664e-01
4.64970291e-01 -1.16304231e+00 2.52935439e-01 1.94417238e-01
-1.88615397e-01 3.73052731e-02 2.24084988e-01 7.80318677e-01
-1.02606907e-01 2.42782637e-01 4.99954313e-01 1.87963754e-01
-1.33116341e+00 8.45725834e-01 -6.27077162e-01 1.71305969e-01
1.51392448e+00 -1.95372343e-01 -2.24515833e-02 -1.99609891e-01
-5.93224704e-01 6.07693911e-01 4.71633822e-01 6.98975682e-01
2.82387614e-01 -1.50698423e+00 -7.27597952e-01 2.62845337e-01
5.50056100e-01 -2.52422541e-01 3.17800522e-01 3.86291623e-01
-1.26471430e-01 2.10964188e-01 -1.77296832e-01 -5.81506550e-01
-1.15875709e+00 6.73218727e-01 1.47535130e-01 -7.07755610e-02
-6.50694847e-01 1.92683548e-01 5.79596572e-02 -3.88004005e-01
2.00884640e-02 -2.00313210e-01 -1.81374401e-01 1.44573942e-01
3.51576656e-01 5.62680066e-01 -2.37541735e-01 -1.18494809e+00
-6.38415992e-01 5.44371903e-01 5.85554302e-01 2.50383150e-02
1.23147953e+00 -3.75952244e-01 3.05607498e-01 5.01721859e-01
1.32185507e+00 -1.70401663e-01 -1.64708698e+00 -4.89149928e-01
-1.03655882e-01 -4.94804651e-01 1.85087427e-01 -4.06516314e-01
-1.09172559e+00 8.21840525e-01 8.53137732e-01 1.76914614e-02
6.35225832e-01 6.19894624e-01 9.59656119e-01 2.21508697e-01
6.88383877e-01 -1.27851105e+00 -4.14953113e-01 5.72956204e-01
-2.48091407e-02 -1.34822035e+00 -1.69888601e-01 -7.53957629e-02
-7.07066834e-01 6.83548033e-01 3.53339076e-01 3.96034271e-02
9.56350267e-01 4.86607403e-01 -1.77352726e-02 -1.67004690e-01
-6.64826035e-01 -8.21907222e-01 1.56435892e-02 8.39809358e-01
-1.92422628e-01 6.22095764e-01 6.23510480e-01 -8.65977332e-02
-6.33093491e-02 -2.21101493e-01 4.53851074e-02 6.36844456e-01
-6.40738130e-01 -8.25954258e-01 -1.25049964e-01 5.28927803e-01
9.65900943e-02 3.42211246e-01 6.12292774e-02 7.77030408e-01
7.86120832e-01 9.14723217e-01 8.31811666e-01 -3.76394153e-01
5.67696750e-01 -4.82657067e-02 5.57779334e-02 -3.19225103e-01
-3.83844748e-02 -7.65953481e-01 1.79290354e-01 -7.16242611e-01
-2.64733195e-01 -9.87108290e-01 -1.25580680e+00 -6.09522462e-01
4.03228879e-01 1.03714414e-01 7.48986602e-01 8.91498625e-01
3.64924550e-01 3.22224647e-01 7.55259156e-01 -6.74828351e-01
2.29621977e-01 -5.06828368e-01 -5.89683533e-01 6.61229670e-01
6.45607293e-01 -8.56196463e-01 -7.91046768e-02 1.24139860e-01] | [6.268782615661621, 0.6191445589065552] |
52079f47-befd-439d-9bd2-bd4abd8702fc | parsing-r-cnn-for-instance-level-human | 1811.12596 | null | http://arxiv.org/abs/1811.12596v1 | http://arxiv.org/pdf/1811.12596v1.pdf | Parsing R-CNN for Instance-Level Human Analysis | Instance-level human analysis is common in real-life scenarios and has
multiple manifestations, such as human part segmentation, dense pose
estimation, human-object interactions, etc. Models need to distinguish
different human instances in the image panel and learn rich features to
represent the details of each instance. In this paper, we present an end-to-end
pipeline for solving the instance-level human analysis, named Parsing R-CNN. It
processes a set of human instances simultaneously through comprehensive
considering the characteristics of region-based approach and the appearance of
a human, thus allowing representing the details of instances. Parsing R-CNN is
very flexible and efficient, which is applicable to many issues in human
instance analysis. Our approach outperforms all state-of-the-art methods on
CIHP (Crowd Instance-level Human Parsing), MHP v2.0 (Multi-Human Parsing) and
DensePose-COCO datasets. Based on the proposed Parsing R-CNN, we reach the 1st
place in the COCO 2018 Challenge DensePose Estimation task. Code and models are
public available. | ['Lu Yang', 'Qing Song', 'Zhihui Wang', 'Ming Jiang'] | 2018-11-30 | parsing-r-cnn-for-instance-level-human-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Yang_Parsing_R-CNN_for_Instance-Level_Human_Analysis_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Yang_Parsing_R-CNN_for_Instance-Level_Human_Analysis_CVPR_2019_paper.pdf | cvpr-2019-6 | ['multi-human-parsing', 'human-part-segmentation', 'human-parsing'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.51756853e-02 3.84702206e-01 3.05451900e-01 -4.58943576e-01
-7.95325935e-01 -3.11318785e-01 2.38177687e-01 7.98552670e-03
-3.64867806e-01 4.00910884e-01 2.01077729e-01 4.77254033e-01
3.23118061e-01 -5.83203852e-01 -1.06722999e+00 -3.02327812e-01
-1.91341117e-02 1.22962880e+00 5.05620718e-01 -3.33870381e-01
-3.69773597e-01 4.32101518e-01 -1.64694273e+00 5.72646737e-01
4.07393366e-01 1.04602623e+00 1.33822009e-01 1.05293572e+00
1.45824000e-01 6.67694151e-01 -7.10530818e-01 -7.77448297e-01
2.21601069e-01 -2.02749342e-01 -1.01400495e+00 3.56485307e-01
6.97725236e-01 -4.05203789e-01 -1.84536606e-01 5.87993443e-01
8.31338644e-01 -4.70680941e-04 5.57203650e-01 -1.31355107e+00
-1.65190458e-01 5.11736274e-01 -8.84303749e-01 5.96240759e-02
7.53306687e-01 3.36772084e-01 6.63042247e-01 -8.15845013e-01
8.30503404e-01 1.62268758e+00 8.77180457e-01 7.25388110e-01
-5.00160635e-01 -5.41981041e-01 3.24164152e-01 1.06022872e-01
-9.88865793e-01 -3.71042155e-02 5.88302553e-01 -6.72830820e-01
7.30719805e-01 1.23918653e-01 1.10563481e+00 1.27052152e+00
-3.11707929e-02 1.58853877e+00 9.67606485e-01 -1.38918787e-01
-5.56996949e-02 -2.39660144e-01 2.47497737e-01 9.48761880e-01
1.11826330e-01 -1.84136316e-01 -3.94599348e-01 7.19851032e-02
6.88928306e-01 -2.18946859e-02 3.77202965e-02 -4.07826960e-01
-1.33657384e+00 4.51790631e-01 6.97800636e-01 -8.99400637e-02
-5.76401711e-01 3.12595934e-01 5.59978843e-01 -7.03944504e-01
3.55386406e-01 1.02012113e-01 -7.00796962e-01 4.74699326e-02
-9.41695690e-01 8.68478835e-01 6.96167707e-01 1.30813670e+00
3.75466853e-01 -4.89044964e-01 -5.51871598e-01 4.91484910e-01
1.66634992e-01 4.43921804e-01 5.90089932e-02 -9.22711015e-01
7.71426320e-01 5.91559589e-01 1.51349798e-01 -7.01247752e-01
-9.43160594e-01 -3.23844433e-01 -7.37448752e-01 -1.47878766e-01
7.53710270e-01 -3.37995112e-01 -1.22860789e+00 1.34614515e+00
1.01551759e+00 -1.46328546e-02 -2.44021878e-01 1.07372868e+00
1.47720969e+00 6.48454309e-01 5.30207217e-01 3.04406494e-01
2.02268648e+00 -1.57277322e+00 -6.27242982e-01 -5.77837586e-01
2.00125709e-01 -4.87443894e-01 7.69171178e-01 3.43599498e-01
-1.35508657e+00 -8.29495192e-01 -5.93996882e-01 -4.85631526e-01
-5.40322006e-01 4.80387241e-01 7.28036880e-01 3.33193302e-01
-5.85564435e-01 3.91180903e-01 -7.21168041e-01 -5.37396848e-01
7.12205529e-01 2.08664626e-01 -6.55499756e-01 -1.15730837e-01
-1.02364802e+00 6.26742840e-01 5.94189286e-01 6.95229769e-01
-9.21800554e-01 -6.27474904e-01 -1.04680741e+00 -1.72589391e-01
6.45012677e-01 -1.05435956e+00 1.35565090e+00 -7.30020285e-01
-1.14141858e+00 1.41040254e+00 -3.97421606e-02 -3.14172864e-01
1.22905624e+00 -7.76781142e-01 1.44835273e-02 1.54198185e-01
2.86253661e-01 9.59930778e-01 7.03672469e-01 -1.25938654e+00
-7.58298874e-01 -6.22117221e-01 1.16623389e-02 1.04497440e-01
6.68803215e-01 3.09440017e-01 -1.17565763e+00 -3.82573962e-01
-1.16579510e-01 -1.03609216e+00 -4.44755942e-01 -1.62582636e-01
-9.08256650e-01 -3.87592703e-01 5.84442794e-01 -1.11423528e+00
8.29231918e-01 -1.78983641e+00 5.65653622e-01 -2.05191165e-01
4.39186424e-01 2.77112126e-01 7.03555420e-02 2.30877116e-01
6.11149147e-02 -8.64600539e-02 -4.44501787e-01 -8.53875935e-01
1.78540185e-01 -2.84967409e-03 4.01381224e-01 5.08748055e-01
2.53403068e-01 1.38692510e+00 -6.76792443e-01 -1.04776216e+00
3.35028052e-01 5.66720366e-01 -3.80536348e-01 6.59450710e-01
-3.70739847e-01 7.02188492e-01 -4.75237846e-01 1.00669038e+00
7.94812381e-01 -2.60826260e-01 5.82516892e-03 -3.74735504e-01
1.41492069e-01 -5.34904063e-01 -1.17084515e+00 1.69422841e+00
-1.44988343e-01 2.39018321e-01 2.65515864e-01 -4.55151677e-01
1.84270144e-01 1.60271809e-01 3.90158147e-01 -3.87245744e-01
2.94174880e-01 -9.22560766e-02 -2.24709824e-01 -7.20558286e-01
3.78074527e-01 3.35139602e-01 -4.33536083e-01 -8.02922472e-02
1.71660677e-01 4.85209413e-02 3.37153405e-01 2.18471318e-01
7.35538602e-01 5.26701748e-01 4.94053423e-01 -3.01959105e-02
3.25122625e-01 1.93642583e-02 5.77852726e-01 5.71668804e-01
-6.52923226e-01 1.12826490e+00 6.50317788e-01 -8.71965230e-01
-1.04713714e+00 -8.42946768e-01 7.54505843e-02 1.34064543e+00
1.95528641e-01 -3.65748495e-01 -1.37333989e+00 -7.62042880e-01
-7.82134756e-03 1.51701614e-01 -1.13635349e+00 6.00155354e-01
-1.06318927e+00 -5.17394185e-01 3.61631483e-01 9.33511794e-01
7.03019440e-01 -1.64505231e+00 -9.85127449e-01 1.82503060e-01
-5.46554923e-01 -1.69648266e+00 -4.00809437e-01 3.53537574e-02
-2.78648496e-01 -1.44995177e+00 -9.82236922e-01 -6.94495499e-01
5.06440699e-01 -2.61930227e-01 1.62672651e+00 4.87460569e-02
-9.21144724e-01 5.47826588e-01 -3.79861116e-01 -6.94151759e-01
8.03169981e-02 2.36587808e-01 -2.95300961e-01 -1.01239301e-01
4.71646264e-02 5.12505360e-02 -8.30700934e-01 3.26219678e-01
-5.32634199e-01 2.21091628e-01 6.51398838e-01 4.00284827e-01
1.02867317e+00 -1.71855927e-01 -9.21669379e-02 -1.30222392e+00
1.27656329e-02 -3.97435099e-01 -3.00709844e-01 5.71930289e-01
5.33777833e-01 -3.35888982e-01 2.86506176e-01 -2.68863618e-01
-1.03060782e+00 7.19633698e-01 -3.23619425e-01 -3.84655952e-01
-6.81069195e-01 -2.05760658e-01 -6.56572402e-01 2.60579407e-01
3.59205961e-01 -1.25794113e-01 -4.52664793e-01 -3.97641420e-01
5.16686976e-01 1.44615695e-01 8.13607395e-01 -8.15769851e-01
6.91856682e-01 3.65517646e-01 -6.63692458e-03 -5.81900299e-01
-1.29532826e+00 -6.43204868e-01 -1.18017578e+00 -5.96652508e-01
1.81528354e+00 -1.16086674e+00 -9.80327785e-01 8.19408298e-01
-1.46782327e+00 -5.13728559e-01 -5.87257333e-02 -7.41788521e-02
-5.55814803e-01 1.20996490e-01 -7.84798563e-01 -7.90886164e-01
-3.62547159e-01 -1.26507103e+00 1.95677161e+00 3.87818515e-01
-1.05284788e-01 -6.72598422e-01 -1.24193035e-01 1.08182180e+00
-1.23967409e-01 1.01713562e+00 2.20923081e-01 -5.05952239e-01
-5.77243865e-01 -3.92861247e-01 -8.59753639e-02 -1.68906927e-01
-7.06282496e-01 6.96035996e-02 -1.11444640e+00 -5.86416759e-02
-6.01310372e-01 -5.66609800e-01 8.29463661e-01 6.82188451e-01
1.50674045e+00 -1.72388896e-01 -4.75425303e-01 6.60414517e-01
8.86309147e-01 -4.49688941e-01 6.86781585e-01 1.37548536e-01
1.34915411e+00 1.03754151e+00 9.20546293e-01 5.14735579e-01
7.04109669e-01 7.49808550e-01 6.03990734e-01 -5.23695529e-01
-1.18254602e-01 -3.85366380e-01 -9.72679928e-02 1.93036661e-01
-4.89823014e-01 -3.28513920e-01 -1.07608402e+00 3.24359238e-01
-2.00520134e+00 -9.58463430e-01 -5.56212842e-01 1.55691421e+00
1.88485771e-01 3.15305181e-02 8.64414573e-01 -3.66369128e-01
1.04171669e+00 1.62101954e-01 -5.31978726e-01 5.73482737e-02
3.31111476e-02 1.14410169e-01 3.67587656e-01 1.23003319e-01
-1.69943607e+00 1.16435516e+00 5.83179617e+00 5.95130563e-01
-3.84660780e-01 2.31378555e-01 8.70566130e-01 -4.40625101e-02
4.64697957e-01 -5.06006241e-01 -1.02676904e+00 3.36460978e-01
3.80229384e-01 5.42396784e-01 2.80554835e-02 1.18204045e+00
-1.70609355e-01 -1.58690780e-01 -1.35030806e+00 1.13313413e+00
1.16886981e-01 -9.53936815e-01 -1.10707849e-01 4.83940020e-02
5.20515144e-01 -2.69259363e-01 -1.59729272e-01 6.51389420e-01
2.77436022e-02 -1.19948304e+00 1.24565220e+00 5.55182338e-01
3.73768657e-01 -9.14773762e-01 1.01263499e+00 5.05300701e-01
-1.67930222e+00 -8.23094323e-03 -1.53064653e-01 2.61368215e-01
4.59988296e-01 4.32829201e-01 -4.94345933e-01 5.73122084e-01
1.24229693e+00 4.11827445e-01 -9.19683754e-01 7.65183151e-01
-2.77282119e-01 8.94951969e-02 -1.67296186e-01 1.49666995e-01
1.55849457e-01 1.95667028e-01 2.57774979e-01 1.53130412e+00
-4.14513871e-02 5.12080133e-01 6.57172680e-01 6.20590627e-01
-1.46451175e-01 -5.03003746e-02 -2.04696819e-01 2.50363559e-01
1.15252241e-01 1.61637223e+00 -9.71436679e-01 -5.17428458e-01
-2.48527005e-02 1.23775625e+00 5.64743578e-01 1.93456579e-02
-1.24554682e+00 3.84935997e-02 3.68604124e-01 4.73357916e-01
5.07915020e-01 3.85999121e-02 1.02151737e-01 -9.41299856e-01
3.27516735e-01 -9.11866128e-01 5.71166575e-01 -8.53308737e-01
-1.21427202e+00 7.32281625e-01 3.11124921e-01 -8.67452800e-01
-2.47967929e-01 -7.43473172e-01 -5.09901404e-01 4.88141179e-01
-1.06120539e+00 -1.95456946e+00 -7.74470210e-01 6.34554744e-01
8.45427394e-01 1.83602065e-01 4.07191783e-01 1.49719313e-01
-9.11319017e-01 5.66076934e-01 -8.12538385e-01 5.92338502e-01
3.10242385e-01 -1.42573678e+00 7.93164015e-01 7.70245433e-01
-1.33067816e-01 2.83960134e-01 7.05136597e-01 -8.75515997e-01
-1.24376798e+00 -1.21016037e+00 5.98956823e-01 -1.01863468e+00
1.82503730e-01 -7.34622240e-01 -5.98346114e-01 8.64387631e-01
1.59040585e-01 4.70184743e-01 4.15130407e-01 1.14874922e-01
-2.55521148e-01 2.93774277e-01 -1.25628734e+00 3.88482004e-01
1.39932597e+00 -1.45163476e-01 -3.50866258e-01 8.20190370e-01
7.76472807e-01 -1.14760208e+00 -9.23150480e-01 5.14819682e-01
5.74495733e-01 -1.13263524e+00 1.28374827e+00 -7.33566821e-01
7.47409225e-01 -2.77885437e-01 9.70712900e-02 -7.61856139e-01
-4.53861654e-01 -4.08721507e-01 -3.70921046e-01 1.05363071e+00
2.39764228e-01 4.73718978e-02 8.62247705e-01 7.18249977e-01
-5.71305938e-02 -9.74028230e-01 -7.44956434e-01 -3.13001156e-01
-8.46880488e-03 -2.78932601e-01 5.60039341e-01 4.30804759e-01
-5.32849967e-01 4.72597256e-02 -6.08678699e-01 4.73970771e-01
1.08190775e+00 -1.14028074e-01 1.37921357e+00 -1.13838172e+00
-5.34136653e-01 -1.65568069e-01 -4.41430598e-01 -8.10931981e-01
2.41766945e-01 -2.53573447e-01 2.31455848e-01 -1.80552161e+00
5.82043052e-01 2.57105865e-02 2.95551121e-01 4.18188632e-01
-6.80748641e-01 2.77209610e-01 6.36030197e-01 9.47123542e-02
-1.16425693e+00 2.42614433e-01 1.51653016e+00 -4.47598659e-02
1.00803204e-01 1.60789222e-01 -2.00474754e-01 9.48132575e-01
4.16279674e-01 -3.15092832e-01 1.36779115e-01 -3.23035777e-01
2.38786619e-02 2.95003742e-01 7.36660719e-01 -1.34185529e+00
1.98302284e-01 7.60150850e-02 9.82857645e-01 -1.09993112e+00
5.71407855e-01 -6.90784812e-01 2.87425131e-01 4.59696919e-01
-1.35956913e-01 1.72059789e-01 3.95399109e-02 6.21977568e-01
-9.27718170e-03 9.49197486e-02 8.27111185e-01 -7.50468254e-01
-9.04021502e-01 6.74393117e-01 2.27402359e-01 4.27969724e-01
1.41799593e+00 -1.80642262e-01 -1.27463222e-01 -3.83241288e-02
-1.07049143e+00 6.43625915e-01 2.20790401e-01 3.93784344e-01
3.98943573e-01 -9.71539915e-01 -8.92291963e-01 -2.45131016e-01
2.61334538e-01 6.40732229e-01 8.67410421e-01 6.76140070e-01
-9.06556547e-01 1.17911048e-01 -2.57240266e-01 -7.95133829e-01
-1.37363625e+00 5.39883912e-01 5.01433372e-01 -5.64403236e-01
-8.13509583e-01 1.09962058e+00 5.08653045e-01 -6.65561497e-01
3.81416053e-01 -2.62186199e-01 -2.18810648e-01 5.68806753e-02
6.10507131e-01 4.46959615e-01 -2.16389611e-01 -1.09779871e+00
-6.63215399e-01 8.80309761e-01 9.35352221e-02 1.69838145e-01
1.24829614e+00 2.42513567e-01 5.57226390e-02 1.88931525e-01
1.05411553e+00 -2.31320366e-01 -1.49193156e+00 1.54610306e-01
-3.89299095e-01 -1.33171380e-01 -6.25432312e-01 -8.10603023e-01
-1.26816332e+00 1.03567600e+00 5.11234820e-01 -1.65579483e-01
7.40762532e-01 5.10278046e-01 9.89554584e-01 5.79910614e-02
5.81743419e-01 -1.37252641e+00 1.60509109e-01 3.68161738e-01
9.78447735e-01 -1.49458265e+00 1.71149373e-01 -8.12224865e-01
-1.04766512e+00 9.76503551e-01 1.20112026e+00 -9.42633823e-02
3.66405487e-01 1.57135040e-01 -8.58670771e-02 -5.86177886e-01
-1.70155942e-01 -4.35684472e-01 5.64452231e-01 7.24676132e-01
3.92897189e-01 3.95185649e-01 6.31274357e-02 8.01302016e-01
-3.79434854e-01 -3.22338432e-01 -5.85488323e-03 8.40438962e-01
-2.50764281e-01 -5.38578689e-01 -7.62103140e-01 5.11587635e-02
-4.15118784e-01 4.04339075e-01 -6.36319160e-01 1.25704563e+00
6.01521194e-01 5.94539702e-01 8.57708137e-03 -1.16531268e-01
8.02315414e-01 -9.41924527e-02 6.15542889e-01 -5.31202495e-01
-1.02448404e+00 -5.03184572e-02 1.48943260e-01 -1.08912969e+00
-5.13916612e-01 -7.57100105e-01 -1.30992353e+00 -2.12050095e-01
8.53167176e-02 -3.67462009e-01 5.29256999e-01 1.13677549e+00
5.31018376e-02 8.09162676e-01 -1.17098084e-02 -1.79142332e+00
2.22603474e-02 -9.35538471e-01 -3.99383366e-01 7.32396007e-01
3.11496276e-02 -7.86256194e-01 -1.21756336e-02 1.82420593e-02] | [8.38000774383545, -0.1571657955646515] |
3bf0969e-26e5-4f2e-bc3b-1f5e3f1d4056 | object-based-bayesian-full-waveform-inversion | 2305.06646 | null | https://arxiv.org/abs/2305.06646v1 | https://arxiv.org/pdf/2305.06646v1.pdf | Object based Bayesian full-waveform inversion for shear elastography | We develop a computational framework to quantify uncertainty in shear elastography imaging of anomalies in tissues. We adopt a Bayesian inference formulation. Given the observed data, a forward model and their uncertainties, we find the posterior probability of parameter fields representing the geometry of the anomalies and their shear moduli. To construct a prior probability, we exploit the topological energies of associated objective functions. We demonstrate the approach on synthetic two dimensional tests with smooth and irregular shapes. Sampling the posterior distribution by Markov Chain Monte Carlo (MCMC) techniques we obtain statistical information on the shear moduli and the geometrical properties of the anomalies. General affine-invariant ensemble MCMC samplers are adequate for shapes characterized by parameter sets of low to moderate dimension. However, MCMC methods are computationally expensive. For simple shapes, we devise a fast optimization scheme to calculate the maximum a posteriori (MAP) estimate representing the most likely parameter values. Then, we approximate the posterior distribution by a Gaussian distribution found by linearization about the MAP point to capture the main mode at a low computational cost. | ['Andrea Gutierrez', 'Elena Cebrian', 'Ana Carpio'] | 2023-05-11 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [ 2.03823462e-01 7.24388883e-02 2.35192135e-01 -1.02125645e-01
-7.97573507e-01 -4.29697245e-01 7.11482882e-01 9.36319679e-02
-2.51997828e-01 6.76641762e-01 8.18554834e-02 -1.47887051e-01
-4.15622264e-01 -7.56521106e-01 -5.49713492e-01 -1.03813601e+00
-3.20343524e-01 8.74386251e-01 4.05592978e-01 2.75683761e-01
5.18147707e-01 4.79713798e-01 -1.01918352e+00 -1.73371390e-01
8.87944460e-01 7.33907402e-01 -4.36349846e-02 6.74000740e-01
4.82200831e-02 1.55820310e-01 -1.57624990e-01 -4.22619224e-01
-8.15806389e-02 -4.72078443e-01 -7.66030371e-01 1.17711581e-01
-6.54900372e-02 -5.22844136e-01 2.85179496e-01 1.22900856e+00
3.46171021e-01 2.04351678e-01 1.36855495e+00 -5.88312864e-01
4.27424535e-02 2.85623103e-01 -4.33647931e-01 -4.74378131e-02
1.69665888e-01 8.96151066e-02 7.29462206e-01 -1.06239974e+00
8.70087981e-01 9.65795636e-01 8.33758235e-01 1.94425970e-01
-1.72074640e+00 3.66144739e-02 -3.93230498e-01 5.66604221e-03
-1.37911808e+00 -3.54698986e-01 1.04760909e+00 -8.74769092e-01
2.65529364e-01 2.21658677e-01 5.59542954e-01 9.73640144e-01
4.24771458e-01 2.34591156e-01 1.14094245e+00 -4.30223197e-01
6.81993783e-01 -2.02153131e-01 -1.04550533e-01 8.08155417e-01
4.09896553e-01 -4.55799922e-02 -2.50653386e-01 -7.75711954e-01
8.65060806e-01 -3.67422938e-01 -1.78192139e-01 -6.18339241e-01
-1.30074739e+00 7.45582998e-01 -1.71082333e-01 1.16519682e-01
-5.68024278e-01 2.25765139e-01 2.34506056e-02 -2.84044921e-01
4.59936649e-01 3.02436978e-01 -1.44573957e-01 -6.41998500e-02
-1.06016266e+00 4.04774368e-01 8.42897475e-01 1.57875448e-01
6.12655580e-01 -9.35964137e-02 1.50478393e-01 5.19039512e-01
6.74817622e-01 8.44108939e-01 -1.39101624e-01 -1.27955830e+00
1.01068541e-01 8.59838650e-02 3.49800467e-01 -1.09022856e+00
-1.78354427e-01 -1.76438227e-01 -8.36329401e-01 2.18188971e-01
9.44314063e-01 -2.26553261e-01 -6.87230766e-01 1.41725540e+00
5.04296362e-01 3.10167760e-01 -2.90916026e-01 7.24193573e-01
8.72994587e-03 5.17821908e-01 -1.25181884e-01 -4.74999487e-01
1.22794259e+00 -2.99165938e-02 -3.41996014e-01 1.10207073e-01
3.87337178e-01 -8.61607015e-01 3.72141153e-01 1.76635638e-01
-1.11333203e+00 2.88326107e-02 -8.23830724e-01 4.97434974e-01
2.32585520e-01 1.99545279e-01 1.77431963e-02 6.20898306e-01
-6.28538251e-01 1.15346503e+00 -1.40969408e+00 1.31578982e-01
2.64788568e-01 -5.87713756e-02 7.60402754e-02 2.82387793e-01
-6.59705818e-01 8.27113867e-01 2.18792826e-01 2.69619107e-01
-8.07272255e-01 -6.25186622e-01 -5.91571569e-01 -1.98253125e-01
1.38172098e-02 -5.97276390e-01 7.00262189e-01 -2.65549541e-01
-1.67785180e+00 7.42548823e-01 -1.61541671e-01 -1.76961645e-01
6.12519383e-01 1.90581873e-01 5.90230450e-02 4.63966250e-01
-1.64283141e-01 1.34395927e-01 1.09230113e+00 -1.30018055e+00
5.18451869e-01 -1.01793543e-01 -6.58613801e-01 -2.26223499e-01
2.95536309e-01 -1.67849645e-01 7.79127330e-02 -6.70055985e-01
8.72230947e-01 -9.98905063e-01 -4.42673832e-01 1.42002016e-01
-5.46789289e-01 3.47813606e-01 2.09145144e-01 -8.67929041e-01
7.94832826e-01 -2.15206695e+00 4.57039237e-01 8.42232823e-01
2.76657581e-01 -9.91844386e-02 4.58360970e-01 2.83115476e-01
1.84159026e-01 -4.45752069e-02 -7.45193481e-01 -2.37803638e-01
-3.79730724e-02 1.95677504e-01 -2.37510234e-01 9.49661255e-01
3.01838994e-01 5.34929812e-01 -7.11375833e-01 -6.83787346e-01
1.19410940e-01 4.45942074e-01 -7.88932800e-01 1.33392274e-01
-3.03385675e-01 9.25779641e-01 -8.98811221e-01 3.81295204e-01
1.01145983e+00 -3.51574570e-01 3.96856368e-01 -2.73847371e-01
-5.07869907e-02 -6.48849532e-02 -1.45069206e+00 1.17376828e+00
-3.71771380e-02 2.03487456e-01 2.78831601e-01 -9.15848672e-01
9.94737029e-01 2.75532067e-01 5.83324671e-01 1.70750946e-01
1.97842360e-01 6.33162856e-01 -2.52634566e-02 -5.45952141e-01
6.41403571e-02 -3.94618332e-01 8.32813680e-02 6.31172419e-01
-6.30250573e-03 -4.06513810e-01 -1.37257308e-01 -1.24436453e-01
8.13480794e-01 3.34697425e-01 1.77139416e-01 -6.71724856e-01
5.87328553e-01 -4.28381473e-01 3.50762814e-01 5.92483938e-01
1.17327515e-02 8.20051491e-01 9.43186224e-01 -4.34924304e-01
-1.38885820e+00 -1.38237131e+00 -7.29402184e-01 1.24903150e-01
-1.35499746e-01 -1.08475119e-01 -9.69230950e-01 -1.43773109e-01
-4.53083962e-02 5.30845642e-01 -4.40250725e-01 1.23156257e-01
-7.62025774e-01 -1.26877618e+00 1.44352406e-01 -3.03023383e-02
2.31494457e-01 -7.68253207e-01 -6.48159981e-01 1.90519109e-01
-2.84245759e-01 -9.50693130e-01 -1.46608770e-01 -2.78582424e-01
-1.20703673e+00 -8.93895984e-01 -8.46497834e-01 -2.58815646e-01
9.92820442e-01 -7.89608300e-01 8.07308495e-01 -5.75076751e-02
-1.71349376e-01 9.65128839e-02 1.60368815e-01 2.69335628e-01
-7.95108378e-01 -3.41012001e-01 1.07650369e-01 3.21368426e-01
-2.84059316e-01 -7.35523224e-01 -7.35613585e-01 4.40291345e-01
-5.95952451e-01 -1.65732309e-01 2.62452096e-01 6.57557547e-01
8.20525289e-01 -8.42350870e-02 1.77020401e-01 -5.01156092e-01
3.11705589e-01 -3.72268945e-01 -8.22224498e-01 1.02823868e-01
-3.84069592e-01 6.08684421e-01 1.80587277e-01 -4.05673623e-01
-1.14660060e+00 -2.14676792e-03 -3.03335309e-01 -2.85345525e-01
-2.22415894e-01 4.31695133e-01 2.48682603e-01 -1.94065273e-01
5.26306272e-01 2.84224361e-01 2.45936498e-01 -6.38863027e-01
1.01215087e-01 2.16812164e-01 4.65866387e-01 -1.24329078e+00
5.22194564e-01 6.97221816e-01 7.63058424e-01 -9.68682230e-01
-4.61285263e-01 9.36169177e-02 -6.21501505e-01 -3.87260139e-01
9.15873289e-01 -2.54471451e-01 -8.38629067e-01 6.88782632e-01
-1.12402833e+00 -3.90883870e-02 -2.32903510e-01 8.28467309e-01
-8.75128329e-01 7.20073104e-01 -4.45712388e-01 -1.10494554e+00
-1.06556565e-01 -1.40220392e+00 1.09000432e+00 -9.34285820e-02
-2.62584478e-01 -1.16070986e+00 3.88328016e-01 3.39118787e-03
3.85187656e-01 6.85181856e-01 1.23100412e+00 -2.87094474e-01
-7.38891244e-01 -3.76946449e-01 1.45439906e-02 -4.56583872e-02
-2.81587809e-01 5.06119013e-01 -7.82443583e-01 -1.27946153e-01
2.67885745e-01 3.16493481e-01 7.06862390e-01 1.03155386e+00
1.11607051e+00 -2.20391855e-01 -4.68050301e-01 5.57493329e-01
1.45059407e+00 -1.66700512e-01 4.33056623e-01 -1.74294010e-01
3.36041093e-01 7.26615846e-01 1.44693673e-01 6.00055456e-01
-4.29164991e-02 5.59358239e-01 1.48768395e-01 5.26188314e-01
4.13504280e-02 -2.41687909e-01 1.83030203e-01 8.69259477e-01
-4.27452296e-01 2.26831108e-01 -1.12819779e+00 4.34316516e-01
-1.48646533e+00 -7.31997788e-01 -1.03321604e-01 2.55034494e+00
8.64671171e-01 3.58662665e-01 5.28179035e-02 -6.71518296e-02
7.62398183e-01 3.35045233e-02 -3.40468854e-01 3.19977850e-02
1.54995337e-01 1.03534974e-01 3.97623003e-01 9.24251199e-01
-7.88212240e-01 1.58431828e-01 6.97308826e+00 6.54229820e-01
-7.11858690e-01 -1.14513271e-01 5.12187660e-01 2.74109870e-01
-6.11961722e-01 3.00606191e-01 -6.91904068e-01 8.37448418e-01
9.25118804e-01 7.06015080e-02 2.07563311e-01 3.22885513e-01
1.39299527e-01 -4.01274502e-01 -7.08551526e-01 5.71667671e-01
-4.47616756e-01 -1.42656708e+00 -2.87460446e-01 3.33830893e-01
7.31609344e-01 -4.53203917e-02 -9.06806290e-02 -5.63386142e-01
-2.47710035e-04 -5.70430100e-01 5.64021528e-01 1.22275078e+00
7.68076062e-01 -6.11227572e-01 7.26980209e-01 3.69878322e-01
-8.81227195e-01 3.08100969e-01 -3.37276578e-01 2.52161860e-01
5.16434610e-01 1.10450101e+00 -7.98997343e-01 1.24402687e-01
4.04391587e-01 2.80958444e-01 -2.40303487e-01 8.14295828e-01
-9.59797110e-03 7.70464242e-01 -7.83840179e-01 -4.43261582e-03
-2.39757404e-01 -8.57628167e-01 1.11276460e+00 7.95131266e-01
5.38508058e-01 -7.00459853e-02 -1.04678117e-01 1.19412339e+00
3.50005001e-01 -5.09665348e-02 -3.39548409e-01 -9.10723060e-02
4.86911029e-01 9.90038633e-01 -1.30472302e+00 3.17005850e-02
1.48850501e-01 3.17032307e-01 -2.34145708e-02 4.41126972e-01
-3.91812384e-01 -9.56254154e-02 2.86855727e-01 2.82957435e-01
4.55827892e-01 -6.22665823e-01 -4.44846034e-01 -1.17269075e+00
2.20627397e-01 -5.81827164e-01 3.16118076e-02 -4.86058682e-01
-1.02087128e+00 2.91760534e-01 4.69102710e-01 -9.96690750e-01
-6.98103666e-01 -7.67512977e-01 -8.24814796e-01 8.83511782e-01
-1.00723433e+00 -6.59975052e-01 2.79802352e-01 1.01149097e-01
-1.68421417e-01 1.42686181e-02 8.40256989e-01 -2.17460662e-01
-3.94338012e-01 -5.27516454e-02 5.17360032e-01 3.08691002e-02
2.67960608e-01 -1.15572000e+00 2.65526801e-01 8.46328974e-01
-3.30025166e-01 6.42389536e-01 1.21467769e+00 -9.72899973e-01
-1.14752841e+00 -2.86928475e-01 7.92792678e-01 -3.84711802e-01
8.75052154e-01 -2.27506608e-01 -9.92657900e-01 4.47011441e-01
-3.30115467e-01 3.03992271e-01 2.80031979e-01 -1.37669414e-01
1.79063737e-01 3.86579007e-01 -1.29747033e+00 4.47006077e-01
4.57338452e-01 -3.73820633e-01 -2.49661908e-01 2.89652407e-01
2.72744596e-02 -3.25606614e-01 -1.24238062e+00 4.85016227e-01
6.36533856e-01 -7.16736495e-01 1.06430268e+00 -3.03364307e-01
4.27621007e-01 -4.07523215e-01 -1.81276143e-01 -1.12611401e+00
-3.79191786e-02 -9.35304046e-01 -3.06307048e-01 8.90952110e-01
2.83687294e-01 -6.77206159e-01 7.26160049e-01 4.95758206e-01
2.90774405e-01 -8.43450963e-01 -1.33360755e+00 -4.72562611e-01
3.74052793e-01 -2.92853236e-01 3.52449924e-01 4.76181686e-01
-2.36316338e-01 -4.19069439e-01 -1.41862065e-01 4.13609654e-01
1.49459577e+00 2.22383097e-01 1.87659711e-01 -1.36600411e+00
-4.20438111e-01 -2.77063549e-01 -3.46872330e-01 -6.75896585e-01
1.11656792e-01 -6.93889856e-01 8.73542204e-02 -7.62889564e-01
1.78605258e-01 -4.08706218e-01 4.24710900e-01 -2.49458537e-01
-2.38474924e-02 -5.12009598e-02 -3.96451056e-01 4.18178499e-01
1.30848503e-02 4.33284909e-01 1.31068361e+00 3.26579899e-01
1.18622571e-01 1.84563816e-01 1.20337009e-01 1.10653484e+00
6.15319967e-01 -6.58126533e-01 1.03293881e-01 4.28773388e-02
5.81171930e-01 4.39077824e-01 6.25948787e-01 -7.61243522e-01
7.22312778e-02 -2.12368965e-01 3.64946216e-01 -6.29145086e-01
2.87860602e-01 -5.18356681e-01 5.00344574e-01 4.92754489e-01
-2.97939539e-01 -3.82085651e-01 -3.10329586e-01 5.90462744e-01
1.91172838e-01 -9.20058131e-01 1.17042994e+00 3.44938040e-02
2.39746869e-01 1.01504728e-01 -5.64215362e-01 -2.16768719e-02
6.33919299e-01 1.73159942e-01 8.88698399e-02 -2.50112802e-01
-1.18978894e+00 -2.22356692e-01 5.43147147e-01 -5.56627154e-01
7.00056732e-01 -1.32058716e+00 -8.52846742e-01 6.20580375e-01
-2.98279732e-01 -4.08616252e-02 2.51517415e-01 1.03562462e+00
-9.42019403e-01 -3.08186524e-02 -1.73765104e-02 -8.62313926e-01
-7.64778554e-01 1.87044486e-01 7.17413783e-01 -1.94021568e-01
-5.15372217e-01 6.03965938e-01 -3.43159527e-01 -3.64438504e-01
-4.37074006e-01 -3.30614597e-01 -7.16052502e-02 -1.31718600e-02
2.40195408e-01 8.24847877e-01 -1.55455813e-01 -6.46895707e-01
-3.06354076e-01 1.06380677e+00 3.93408269e-01 -3.66657704e-01
1.25844753e+00 -2.51689315e-01 -2.94970781e-01 2.46133298e-01
1.13795364e+00 1.90042496e-01 -1.50712502e+00 -1.66073591e-01
-2.86856521e-04 -4.76509035e-01 1.77281275e-01 -2.38510609e-01
-8.03777277e-01 7.45794892e-01 4.00616437e-01 3.53232503e-01
6.43557310e-01 3.10591847e-01 4.16850269e-01 3.18591148e-02
1.18529834e-01 -9.23134208e-01 -2.37079397e-01 1.71605662e-01
7.84314096e-01 -9.00865316e-01 3.50056350e-01 -4.90887433e-01
-2.44001761e-01 1.40190887e+00 -1.09268598e-01 -3.69670212e-01
1.18701470e+00 3.12895834e-01 -3.71388912e-01 -3.61662835e-01
-3.33966672e-01 2.61478782e-01 5.69325864e-01 3.41195136e-01
1.85686886e-01 1.65214568e-01 -3.38851780e-01 4.61561345e-02
-1.43492132e-01 -1.86074480e-01 4.87446457e-01 6.70613289e-01
-4.37655985e-01 -9.49645281e-01 -4.94411767e-01 3.42048496e-01
-6.93555832e-01 1.47559673e-01 2.44081646e-01 1.21752739e-01
-1.30062208e-01 4.30384040e-01 2.44951963e-01 1.36582926e-01
-4.12787460e-02 3.17111015e-01 7.41792679e-01 -4.40933891e-02
4.45296735e-01 4.10660148e-01 6.93827346e-02 -3.62811118e-01
-2.48878822e-01 -1.35885477e+00 -9.80278432e-01 -3.50968719e-01
-2.56807148e-01 3.10710296e-02 8.96871984e-01 1.20346904e+00
1.18710868e-01 -2.62902714e-02 6.56370580e-01 -9.79116440e-01
-8.90198171e-01 -7.59994864e-01 -6.79078996e-01 1.66584790e-01
3.25421929e-01 -7.14909315e-01 -6.83079958e-01 5.82437590e-02] | [6.807473659515381, 3.765434503555298] |
44b21945-438d-4dee-a12a-bb1d1b890aec | wide-context-semantic-image-extrapolation | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Wang_Wide-Context_Semantic_Image_Extrapolation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_Wide-Context_Semantic_Image_Extrapolation_CVPR_2019_paper.pdf | Wide-Context Semantic Image Extrapolation | This paper studies the fundamental problem of extrapolating visual context using deep generative models, i.e., extending image borders with plausible structure and details. This seemingly easy task actually faces many crucial technical challenges and has its unique properties. The two major issues are size expansion and one-side constraints. We propose a semantic regeneration network with several special contributions and use multiple spatial related losses to address these issues. Our results contain consistent structures and high-quality textures. Extensive experiments are conducted on various possible alternatives and related methods. We also explore the potential of our method for various interesting applications that can benefit research in a variety of fields.
| [' Jiaya Jia', ' Xiaoyong Shen', ' Xin Tao', 'Yi Wang'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['seeing-beyond-the-visible', 'image-outpainting'] | ['computer-vision', 'computer-vision'] | [ 3.85381222e-01 1.17808543e-01 -1.78201690e-01 -3.56970251e-01
-3.79014701e-01 -3.66250873e-01 6.42780423e-01 -6.86377823e-01
1.88165065e-02 1.01434779e+00 2.46305808e-01 -1.50689408e-01
-6.48288289e-04 -8.43149006e-01 -6.44519985e-01 -7.31601357e-01
1.97787985e-01 7.83063248e-02 5.85980356e-01 -3.74683201e-01
5.88405073e-01 7.06814528e-01 -1.47527277e+00 3.33941847e-01
7.57808864e-01 1.08947921e+00 6.32469475e-01 4.82350975e-01
-2.09828287e-01 7.02745199e-01 -3.39612573e-01 -4.73469704e-01
3.08914423e-01 -5.42636514e-01 -9.52865541e-01 3.80461931e-01
7.30489910e-01 -2.24821791e-01 -2.49879941e-01 1.26091516e+00
4.37118053e-01 2.79448569e-01 4.45428997e-01 -1.19538164e+00
-1.20936620e+00 2.98321843e-01 -9.44949329e-01 2.62586981e-01
5.37642874e-02 -2.89158411e-02 7.74732053e-01 -9.74691927e-01
7.57469594e-01 1.34855402e+00 7.53237605e-01 6.35264695e-01
-1.24476063e+00 -6.07921183e-01 4.04704362e-01 3.47735047e-01
-1.45887077e+00 -4.66097206e-01 9.67454553e-01 -1.18863791e-01
7.31822073e-01 1.20480441e-01 2.74220198e-01 1.31012082e+00
2.69775480e-01 6.48642182e-01 1.45457566e+00 -5.76296926e-01
1.80968732e-01 1.39257237e-01 -2.25813448e-01 6.60949469e-01
1.35987684e-01 2.43276939e-01 -5.09435713e-01 5.23121469e-02
1.41307032e+00 -2.94386391e-02 -3.06184053e-01 -3.31882000e-01
-1.08764410e+00 9.35009181e-01 6.68948114e-01 2.76331216e-01
-1.14580601e-01 3.08339685e-01 -1.61547214e-01 -9.76143926e-02
4.45097804e-01 2.56841183e-01 -7.05427602e-02 3.88346881e-01
-9.39674437e-01 1.56665996e-01 4.33095425e-01 1.28261554e+00
8.00392628e-01 2.13089988e-01 1.05386533e-01 9.58851516e-01
2.52883937e-02 3.01654071e-01 9.45682079e-02 -1.28390765e+00
1.16183467e-01 -1.59343928e-01 5.78451063e-03 -1.10269380e+00
-1.35584190e-01 -5.40020108e-01 -1.11496294e+00 3.94263059e-01
1.76366314e-01 1.50128864e-02 -9.65881944e-01 1.77862573e+00
2.21968070e-01 4.80606079e-01 -2.59664506e-01 9.97785449e-01
8.84423673e-01 6.08080387e-01 2.00341135e-01 1.54187605e-01
1.18795300e+00 -1.31612682e+00 -5.93095422e-01 -5.60907304e-01
-3.47928494e-01 -1.09889781e+00 1.18432474e+00 2.60631800e-01
-1.18177545e+00 -7.31911719e-01 -9.22057509e-01 -4.12444293e-01
-2.04696238e-01 1.91086277e-01 1.08017290e+00 3.34298074e-01
-1.53780413e+00 6.91884577e-01 -4.67263520e-01 -6.54837549e-01
4.84883606e-01 1.82555586e-01 -1.83148339e-01 -1.29023358e-01
-9.43139851e-01 5.49973071e-01 2.00335726e-01 1.74998760e-01
-5.74535966e-01 -3.32052082e-01 -8.71700764e-01 1.07670940e-01
5.17864585e-01 -1.08425260e+00 9.96397316e-01 -9.26913381e-01
-1.35364592e+00 8.19857240e-01 -3.74768943e-01 -2.02220231e-01
5.52303255e-01 -2.01380029e-01 -2.90562242e-01 4.47361991e-02
2.06240341e-01 1.04742885e+00 8.55243981e-01 -1.51124346e+00
-7.81280041e-01 -2.88724154e-01 9.66507662e-03 3.10705334e-01
-2.13844210e-01 -2.45329946e-01 -6.29599154e-01 -1.23704422e+00
1.57813922e-01 -9.59977090e-01 -5.08822262e-01 2.11589932e-01
-3.79554927e-01 2.84209475e-02 6.61808491e-01 -6.26489818e-01
1.13949442e+00 -1.97011685e+00 1.69186786e-01 3.55535336e-02
2.92815268e-01 2.34136898e-02 -2.78870016e-01 3.01161200e-01
1.21725589e-01 2.81619817e-01 -2.06294477e-01 -3.51831466e-01
-2.82515645e-01 2.34772637e-01 -8.43054891e-01 -3.58549622e-03
3.04857105e-01 1.28531313e+00 -5.54181278e-01 -4.50317323e-01
2.36603349e-01 6.62814915e-01 -5.00863910e-01 -4.12602611e-02
-2.09860310e-01 6.50141418e-01 -3.72511357e-01 6.37403548e-01
8.46037269e-01 -5.48647702e-01 1.40704274e-01 -3.16159487e-01
2.38368902e-02 -5.91172390e-02 -1.00825870e+00 1.78187263e+00
-5.10070741e-01 8.14184725e-01 3.25872377e-02 -1.02266347e+00
9.12358046e-01 -2.93242186e-01 -4.32633385e-02 -6.03524208e-01
-8.32712576e-02 2.92621665e-02 -3.98502171e-01 -3.80100816e-01
7.41063535e-01 -4.66021270e-01 1.74194008e-01 1.98273852e-01
3.87799889e-02 -7.49480799e-02 -2.51519948e-01 7.56089985e-02
5.37970245e-01 4.25738603e-01 5.28001785e-01 -5.89484990e-01
2.63149440e-01 -1.99573010e-01 5.43984711e-01 8.13378394e-01
-2.95880646e-01 8.33838463e-01 2.64613003e-01 -6.06243968e-01
-1.16566074e+00 -1.22169971e+00 -1.27790287e-01 8.98101330e-01
6.50721610e-01 3.84973511e-02 -5.70785642e-01 -5.42960703e-01
-2.83370733e-01 5.76457202e-01 -9.12878036e-01 1.06998980e-01
-5.77703655e-01 -6.04789793e-01 1.64255485e-01 1.12512445e+00
7.84780920e-01 -1.17507315e+00 -2.26719826e-01 -1.76443458e-02
-3.75098437e-01 -1.34999430e+00 -4.78851676e-01 -1.34071201e-01
-9.87227380e-01 -7.15673149e-01 -9.55374181e-01 -1.18454671e+00
6.57910466e-01 7.41053641e-01 1.31059062e+00 1.78863302e-01
-3.60670000e-01 1.94539011e-01 -7.69530144e-03 -1.11126035e-01
-5.05954809e-02 3.52911018e-02 -2.89361149e-01 -1.96749747e-01
1.19141020e-01 -7.61009693e-01 -7.37924039e-01 4.86968458e-01
-1.02531493e+00 2.62712181e-01 7.31365681e-01 1.01404727e+00
1.03716016e+00 1.60747126e-01 7.54050136e-01 -9.12632883e-01
5.44108927e-01 -3.40095252e-01 -4.76084471e-01 4.40767229e-01
-4.37343597e-01 1.48348242e-01 5.44631183e-01 -4.28171039e-01
-1.46642423e+00 -1.22488804e-01 -2.36633509e-01 -3.41207743e-01
-2.71371454e-01 -7.85333812e-02 -3.78685862e-01 -2.45839134e-01
5.14456093e-01 3.60000372e-01 -2.11896837e-01 -4.50644642e-01
6.20095551e-01 2.28448987e-01 7.90480733e-01 -6.97091520e-01
6.03997767e-01 8.37385774e-01 1.93941265e-01 -1.07166421e+00
-1.07716334e+00 -1.06546491e-01 -4.93690103e-01 6.73147216e-02
7.44851887e-01 -8.15879464e-01 -4.77763176e-01 3.41150701e-01
-1.13603473e+00 -3.68333220e-01 -3.17623526e-01 1.28765374e-01
-6.84465289e-01 6.46182179e-01 -6.59665942e-01 -5.08506000e-01
-1.74982041e-01 -1.05196989e+00 1.13929105e+00 5.28947532e-01
-8.52057487e-02 -1.25630057e+00 -1.80653006e-01 2.17076391e-01
6.78490818e-01 1.64192379e-01 7.39466250e-01 1.13636062e-01
-1.07423651e+00 5.68603635e-01 -6.40468836e-01 2.39866987e-01
8.43179226e-02 -8.65730643e-02 -1.01501393e+00 -2.29556873e-01
8.84198844e-02 -2.12581247e-01 1.24712026e+00 7.23635852e-01
1.53914571e+00 -4.90957759e-02 -5.86056828e-01 9.21418190e-01
1.64286077e+00 1.95245296e-01 1.05351913e+00 2.54287958e-01
5.90308964e-01 5.78374445e-01 3.71486694e-01 1.27602547e-01
2.90919870e-01 6.04201019e-01 2.37913832e-01 -3.65236133e-01
-6.83933496e-01 -3.71224523e-01 -2.22675204e-01 4.64630902e-01
-2.54981279e-01 -4.13511097e-01 -6.13039911e-01 5.81384003e-01
-1.78409410e+00 -1.10310912e+00 3.26123647e-02 1.88962340e+00
5.56378901e-01 -1.87325990e-03 -1.73286498e-01 -3.57638448e-01
9.98181105e-01 1.28614321e-01 -6.11880600e-01 -1.43326133e-01
-5.29526472e-01 4.92774457e-01 1.81826428e-01 6.25026584e-01
-1.13262451e+00 1.20325017e+00 7.53441906e+00 9.94964361e-01
-1.05760825e+00 -6.70673475e-02 1.07860613e+00 1.72004193e-01
-5.28353214e-01 1.85043529e-01 -1.01000357e+00 3.24181229e-01
1.36110425e-01 1.17169887e-01 5.95922589e-01 8.29970956e-01
-1.96241662e-01 -2.78490096e-01 -7.58922398e-01 1.05727243e+00
2.80918747e-01 -1.80227828e+00 2.84429014e-01 1.36051685e-01
1.05067480e+00 -1.35608092e-01 4.32858765e-01 -8.40544030e-02
1.97696477e-01 -1.12420654e+00 8.14798057e-01 6.73285544e-01
1.15542448e+00 -6.83473647e-01 4.70333815e-01 2.73341453e-03
-1.19544291e+00 5.16008288e-02 -5.49442649e-01 -4.07968946e-02
2.66546905e-01 5.04273176e-01 -1.81938335e-01 4.04895484e-01
8.99605930e-01 7.18509734e-01 -5.61525404e-01 9.63147044e-01
-3.60026985e-01 3.86336505e-01 -2.22449243e-01 1.44237533e-01
1.65497974e-01 -1.42762467e-01 2.81857014e-01 9.71069276e-01
5.08874297e-01 1.89123884e-01 -8.15474540e-02 1.22335684e+00
-2.61445083e-02 -1.16009712e-01 -8.38313997e-01 4.01151091e-01
4.18526113e-01 1.09259248e+00 -1.23910654e+00 -3.80941898e-01
-4.10742700e-01 1.28852594e+00 5.31670749e-01 6.64433777e-01
-8.15487862e-01 -3.69071841e-01 5.14443696e-01 1.95929214e-01
5.65802753e-01 -2.31413260e-01 -6.14253700e-01 -1.33832943e+00
9.09120589e-02 -4.17326152e-01 2.92517185e-01 -1.01534116e+00
-1.57640111e+00 8.47783983e-01 -2.33236365e-02 -1.07490027e+00
-4.03888300e-02 -6.46661341e-01 -5.77234328e-01 8.15778613e-01
-1.73631907e+00 -1.40001333e+00 -5.26142776e-01 5.23437858e-01
7.26898551e-01 1.16157420e-02 5.29031157e-01 1.20391682e-01
-3.62083435e-01 5.90423465e-01 -5.79221770e-02 -1.95165157e-01
6.59811795e-01 -1.01369572e+00 7.22606003e-01 8.54552150e-01
3.93847406e-01 7.38934577e-01 5.21618485e-01 -5.62938273e-01
-6.58207655e-01 -1.04356325e+00 8.85509253e-01 -3.95223796e-01
3.74135286e-01 -4.24381316e-01 -9.64832067e-01 9.14931774e-01
5.13546288e-01 1.09969201e-02 5.16797185e-01 -5.08200005e-02
-1.47197545e-01 2.13774741e-01 -1.02997959e+00 8.52742374e-01
1.53575456e+00 -3.79033446e-01 -4.87676799e-01 -4.88653816e-02
6.81109607e-01 -2.76229978e-01 -3.92341018e-01 4.51892763e-01
4.67239976e-01 -1.33616245e+00 1.17917812e+00 -5.93963683e-01
6.92195475e-01 -1.92574680e-01 -2.93793768e-01 -1.11666894e+00
-6.64709210e-01 -7.27792680e-01 1.04957961e-01 1.19981027e+00
1.07164338e-01 -6.70965433e-01 8.59210491e-01 5.05261242e-01
5.88264130e-02 -9.56153452e-01 -6.59805775e-01 -8.82378995e-01
2.87230492e-01 -3.27553511e-01 6.35396898e-01 8.66461515e-01
-4.93930757e-01 6.46347046e-01 -6.24878049e-01 -3.92581671e-02
8.32741499e-01 5.90834022e-01 6.49068892e-01 -9.30084169e-01
-1.66209698e-01 -4.31877613e-01 -2.85863131e-01 -1.44237316e+00
1.26693010e-01 -6.66852355e-01 -1.21496774e-01 -1.51444781e+00
3.37458700e-01 -4.99075353e-01 -1.12070307e-01 2.74320841e-01
-1.78833723e-01 4.52401161e-01 7.35083148e-02 3.43418010e-02
-4.34627384e-01 6.46876574e-01 1.54647720e+00 1.00818120e-01
4.57888991e-02 -1.20239265e-01 -1.01289344e+00 9.19559777e-01
8.92872036e-01 -4.36452888e-02 -6.98311269e-01 -5.25507033e-01
9.89082456e-02 -1.56985879e-01 7.85721421e-01 -7.82605886e-01
6.89844862e-02 -5.20131946e-01 7.09980369e-01 -5.08260667e-01
3.00164312e-01 -7.16346085e-01 1.90778419e-01 3.12568955e-02
-3.02642643e-01 1.19437918e-01 3.62477928e-01 7.46877730e-01
-2.19288111e-01 5.02377702e-03 1.00360775e+00 -3.99190605e-01
-1.36398911e+00 2.01984018e-01 6.93522915e-02 9.69655365e-02
1.17893076e+00 -4.37166482e-01 -4.08861369e-01 -5.42818129e-01
-9.57714796e-01 -8.63878131e-02 7.07143903e-01 5.37518144e-01
8.54781747e-01 -1.63169360e+00 -5.38929999e-01 2.82089800e-01
-4.68042009e-02 -1.28256753e-01 4.65636373e-01 4.88314837e-01
-4.34578538e-01 4.50214535e-01 -4.81513262e-01 -5.78103960e-01
-8.82340729e-01 7.69975305e-01 1.06094502e-01 -7.13608935e-02
-7.94821918e-01 9.92758453e-01 9.03924227e-01 1.45500749e-02
3.15913484e-02 -2.69705027e-01 1.43255414e-02 -3.36036921e-01
5.62041759e-01 3.10475320e-01 -2.80235589e-01 -5.84546447e-01
-1.96678519e-01 8.27230811e-01 -7.64801726e-02 -7.51174018e-02
1.35320902e+00 -6.80227697e-01 -5.50948568e-02 5.31419255e-02
9.66779113e-01 -7.24312514e-02 -1.63734877e+00 -3.78789961e-01
-3.04912031e-01 -7.00901628e-01 -5.44201024e-02 -7.62452900e-01
-1.29778492e+00 1.06148052e+00 5.38207233e-01 2.54442245e-02
1.50384557e+00 3.42695475e-01 9.54581678e-01 -3.33312154e-02
5.41100323e-01 -9.49331105e-01 3.04266632e-01 2.84051478e-01
1.16415608e+00 -1.31015396e+00 8.67641903e-03 -7.65576303e-01
-6.33945346e-01 9.42158639e-01 8.21965158e-01 -3.02506626e-01
6.79488659e-01 3.52024376e-01 7.57197216e-02 -1.96145892e-01
-5.91749191e-01 -2.90396273e-01 3.98729444e-01 9.02747214e-01
3.65132540e-01 -1.45982325e-01 -3.16884160e-01 4.84731585e-01
-1.13076724e-01 -4.23334017e-02 4.44213688e-01 6.01392090e-01
-3.62263739e-01 -1.03030765e+00 -3.56329829e-01 9.55752432e-02
-3.50557357e-01 -3.52354437e-01 -3.35166276e-01 8.43849897e-01
1.04430176e-01 7.34006166e-01 -1.73854902e-02 -2.74447352e-01
-1.52413491e-02 -2.53008306e-01 7.57951081e-01 -4.41975921e-01
1.63479019e-02 3.57899040e-01 -2.96440929e-01 -6.27305150e-01
-6.09474242e-01 -5.44144154e-01 -9.96424317e-01 -2.43863657e-01
-2.92070657e-01 -4.55974907e-01 3.88507813e-01 6.15286767e-01
6.19137943e-01 4.22808111e-01 4.23314422e-01 -8.91414106e-01
-2.78575063e-01 -5.62574923e-01 -5.75198114e-01 3.68285567e-01
2.61366546e-01 -8.36697459e-01 -1.28028095e-01 2.48723403e-01] | [11.327943801879883, -0.8418619632720947] |
a2a9869f-42cc-4706-b295-f6e5f622036b | ssl4eo-l-datasets-and-foundation-models-for | 2306.09424 | null | https://arxiv.org/abs/2306.09424v1 | https://arxiv.org/pdf/2306.09424v1.pdf | SSL4EO-L: Datasets and Foundation Models for Landsat Imagery | The Landsat program is the longest-running Earth observation program in history, with 50+ years of data acquisition by 8 satellites. The multispectral imagery captured by sensors onboard these satellites is critical for a wide range of scientific fields. Despite the increasing popularity of deep learning and remote sensing, the majority of researchers still use decision trees and random forests for Landsat image analysis due to the prevalence of small labeled datasets and lack of foundation models. In this paper, we introduce SSL4EO-L, the first ever dataset designed for Self-Supervised Learning for Earth Observation for the Landsat family of satellites (including 3 sensors and 2 product levels) and the largest Landsat dataset in history (5M image patches). Additionally, we modernize and re-release the L7 Irish and L8 Biome cloud detection datasets, and introduce the first ML benchmark datasets for Landsats 4-5 TM and Landsat 7 ETM+ SR. Finally, we pre-train the first foundation models for Landsat imagery using SSL4EO-L and evaluate their performance on multiple semantic segmentation tasks. All datasets and model weights are available via the TorchGeo (https://github.com/microsoft/torchgeo) library, making reproducibility and experimentation easy, and enabling scientific advancements in the burgeoning field of remote sensing for a myriad of downstream applications. | ['Arindam Banerjee', 'Caleb Robinson', 'Shradha Sehgal', 'Nassim Ait Ali Braham', 'Yi-Chia Chang', 'Yi Wang', 'Isaac A. Corley', 'Nils Lehmann', 'Adam J. Stewart'] | 2023-06-15 | null | null | null | null | ['cloud-detection'] | ['computer-vision'] | [ 3.15506130e-01 -4.86252993e-01 -3.74401748e-01 -4.78047252e-01
-7.14746952e-01 -7.29717970e-01 3.69225621e-01 -1.95948809e-01
-4.79315072e-01 5.71394205e-01 -3.57182711e-01 -8.00738454e-01
-9.91381854e-02 -1.12511098e+00 -6.25321627e-01 -7.48301685e-01
-5.58695734e-01 4.34647292e-01 1.30717143e-01 -1.42795131e-01
-3.54981363e-01 6.32418215e-01 -1.53713226e+00 2.08215266e-01
9.96865630e-01 1.30469048e+00 6.66510105e-01 6.04498267e-01
4.37592231e-02 2.59281397e-01 2.25385249e-01 1.88699305e-01
5.62576056e-01 -1.71880409e-01 -8.67213547e-01 9.46546718e-02
7.55192578e-01 -4.32090521e-01 1.88873895e-02 1.21891248e+00
3.83603096e-01 -2.38517046e-01 3.48745912e-01 -1.09717143e+00
-2.60737866e-01 4.83529270e-01 -6.20050311e-01 2.33568579e-01
-6.09730244e-01 2.60430127e-01 1.30423224e+00 -7.48759747e-01
4.09486264e-01 9.66902733e-01 1.14394796e+00 9.28301550e-03
-1.26032698e+00 -7.46157706e-01 -5.81671968e-02 -4.03898442e-03
-1.49921012e+00 -4.56361562e-01 5.71491010e-02 -7.53594220e-01
1.07596743e+00 5.27039409e-01 9.46156085e-01 4.74175096e-01
1.65721655e-01 8.77688110e-01 1.54180551e+00 -1.02051914e-01
3.04468088e-02 -3.24030817e-01 1.40704587e-01 5.59909999e-01
3.27360421e-01 2.23747283e-01 9.32978243e-02 -2.57662926e-02
7.47259438e-01 3.92454624e-01 -1.12867862e-01 -6.36358112e-02
-1.00469840e+00 9.00465310e-01 8.75164092e-01 1.46004364e-01
-5.31564772e-01 1.61446258e-01 9.10124630e-02 2.18725547e-01
8.22569549e-01 2.06304461e-01 -7.53877461e-01 4.45645243e-01
-1.43709815e+00 2.61712551e-01 2.98298776e-01 7.06402600e-01
1.03956425e+00 5.28145395e-02 5.40888190e-01 1.01254058e+00
5.08191168e-01 9.25857425e-01 1.81242347e-01 -9.11693454e-01
1.75032154e-01 6.59924269e-01 1.52315319e-01 -6.74874544e-01
-5.76060057e-01 -7.64510810e-01 -9.28014815e-01 2.47363374e-01
-7.82122612e-02 -1.01113081e-01 -1.33015776e+00 1.11422193e+00
2.35225514e-01 -1.24496356e-01 -2.06364706e-01 9.64858174e-01
7.37273157e-01 8.23858380e-01 2.13288814e-01 1.82730928e-01
1.30567849e+00 -7.16025949e-01 -2.11063921e-01 -9.46736574e-01
7.81604469e-01 -3.91308725e-01 9.39927340e-01 5.95588610e-02
-6.77676052e-02 -4.83739883e-01 -8.48696828e-01 2.46741474e-01
-7.62777805e-01 2.67133743e-01 9.53445852e-01 3.53086472e-01
-1.10911274e+00 5.07201314e-01 -1.01626337e+00 -7.18772829e-01
1.06723893e+00 -3.65973338e-02 -1.91260859e-01 -1.78568408e-01
-1.27897108e+00 7.99340308e-01 5.54417193e-01 5.52671611e-01
-9.60638523e-01 -6.37597084e-01 -6.45588160e-01 -1.60363823e-01
1.43002853e-01 -3.45041543e-01 9.79494870e-01 -1.06782424e+00
-6.77003682e-01 1.49982488e+00 2.97886312e-01 -6.75121486e-01
2.96693563e-01 -3.89709860e-01 -5.05404294e-01 -1.12057745e-01
6.02357149e-01 9.80161667e-01 4.41687852e-01 -1.26305366e+00
-1.07639802e+00 -7.98221052e-01 -4.15891409e-01 5.35692722e-02
-8.20591748e-02 -3.02973259e-02 2.03546491e-02 -4.86251503e-01
5.09805799e-01 -1.10312831e+00 -5.17923236e-01 9.14163366e-02
-2.00106755e-01 1.87080860e-01 7.83539236e-01 -8.53516400e-01
9.48166132e-01 -2.18935418e+00 -3.15463454e-01 -4.45236638e-02
1.92620102e-02 3.88305604e-01 -2.90991396e-01 2.20879272e-01
-2.22493932e-01 5.55872977e-01 -9.36794460e-01 -4.12115492e-02
-1.82113349e-01 5.09057581e-01 -5.30117750e-01 6.27692521e-01
7.22604245e-02 9.60064650e-01 -7.30629146e-01 -3.53787780e-01
2.85518259e-01 1.05376929e-01 -1.46411628e-01 1.40470890e-02
-4.24890786e-01 3.02748650e-01 -4.31062251e-01 1.08023298e+00
1.03962016e+00 -2.94594556e-01 1.13510348e-01 9.97962952e-02
-7.03068078e-01 3.34539026e-01 -8.90726268e-01 1.46898210e+00
-3.12628299e-01 6.57206178e-01 3.65373731e-01 -8.43561113e-01
7.35183477e-01 -1.04417522e-02 5.02709210e-01 -6.47034347e-01
-7.07840398e-02 5.43373287e-01 -2.20635071e-01 -5.54511309e-01
3.71587515e-01 -3.45322073e-01 7.48243369e-03 1.86791345e-01
-1.44688278e-01 -4.33355719e-01 -6.34216070e-02 -2.96957701e-01
4.74195421e-01 2.08786830e-01 3.02641541e-01 -4.57331836e-01
-1.41543359e-01 8.25622678e-01 6.31251514e-01 7.84499884e-01
-3.04510593e-01 5.92627048e-01 -1.92558449e-02 -8.31509590e-01
-1.05378830e+00 -7.41147697e-01 -8.53988707e-01 1.31939042e+00
-2.61759788e-01 2.06555188e-01 -3.50399643e-01 -3.37474316e-01
4.82840806e-01 5.59641719e-01 -4.85190660e-01 5.49891889e-01
5.02711907e-02 -1.55107403e+00 8.93827319e-01 3.02099705e-01
1.16798961e+00 -9.44212139e-01 -7.30364323e-01 1.11452833e-01
-3.16365927e-01 -1.03212941e+00 3.32737684e-01 4.66659755e-01
-1.45785761e+00 -9.53371167e-01 -5.34002602e-01 -5.50257742e-01
2.09477410e-01 5.90085506e-01 1.15837872e+00 -1.38785511e-01
-3.77574474e-01 -1.33484360e-02 -3.87136191e-01 -3.89015585e-01
-7.27276132e-02 4.27202791e-01 -1.54465899e-01 -1.25952959e-01
4.33110654e-01 -6.47415757e-01 -5.28100729e-01 2.27630451e-01
-9.76213694e-01 3.27145070e-01 7.16830015e-01 5.23724198e-01
8.19591045e-01 3.20101589e-01 4.72468324e-02 -7.79764235e-01
-3.37676316e-01 -7.37206161e-01 -8.47632945e-01 1.58675369e-02
-5.65253496e-01 -5.02202511e-01 6.12045117e-02 5.24566770e-01
-7.37613618e-01 4.16054308e-01 -2.56974906e-01 -2.94997077e-02
-5.61220288e-01 1.14935362e+00 -5.58394864e-02 1.95108298e-02
7.57426023e-01 3.44226331e-01 -1.67427003e-01 -7.38591850e-01
2.53160954e-01 1.13049293e+00 4.80791003e-01 2.49944534e-02
6.43573225e-01 8.28728914e-01 -2.81981170e-01 -1.25289357e+00
-1.11348975e+00 -7.33288229e-01 -7.88342118e-01 -1.96340248e-01
9.45927322e-01 -1.39105415e+00 -4.83049732e-03 1.09820032e+00
-6.97651803e-01 -6.99353099e-01 7.55118355e-02 3.02725554e-01
-1.49171978e-01 2.27630988e-01 -1.74132362e-01 -8.54029834e-01
-5.59663773e-01 -7.95761526e-01 1.02771401e+00 5.48549965e-02
2.39298701e-01 -7.32270122e-01 1.25939801e-01 4.84925538e-01
5.36445379e-01 3.99441093e-01 6.68760061e-01 -1.35622218e-01
-6.54795170e-01 -2.18670979e-01 -6.32974029e-01 7.59078860e-01
2.82175034e-01 1.16409846e-01 -1.10855424e+00 -3.13684076e-01
-4.93640490e-02 -4.58560914e-01 1.71615613e+00 7.04033911e-01
9.36237276e-01 -7.45304897e-02 -3.99941355e-01 1.13344169e+00
1.78800356e+00 3.49369342e-03 6.55759394e-01 9.10511374e-01
7.56390214e-01 5.29062629e-01 5.51113546e-01 3.10133457e-01
4.49032545e-01 1.15563959e-01 9.89507616e-01 -5.16381979e-01
2.20974654e-01 3.65086123e-02 1.01751134e-01 3.51269037e-01
-2.98487067e-01 3.27248797e-02 -1.64548779e+00 9.45936859e-01
-1.64611518e+00 -9.51806545e-01 -5.23862243e-01 2.07289672e+00
6.75642967e-01 -1.01013027e-01 -1.44448578e-01 -1.73746452e-01
6.49460077e-01 6.61277175e-01 -7.23162055e-01 4.78956223e-01
-5.40053248e-01 1.98011249e-01 1.33229434e+00 3.23590845e-01
-1.88354194e+00 1.37750709e+00 6.28386450e+00 6.82218015e-01
-1.47722363e+00 2.28502020e-01 6.20608747e-01 1.29675582e-01
-1.15954623e-01 1.37148157e-01 -9.22092140e-01 2.34457031e-01
8.09675634e-01 4.94721860e-01 2.45637432e-01 1.06323731e+00
5.39819479e-01 -4.38608289e-01 -2.46917650e-01 5.76889038e-01
-6.94365025e-01 -1.42574584e+00 -6.78780973e-02 2.02345386e-01
7.02268064e-01 1.42292631e+00 6.53455406e-02 -1.32918721e-02
5.33415139e-01 -1.11863685e+00 5.92864335e-01 2.13863716e-01
1.12701142e+00 -2.41668940e-01 6.41252160e-01 3.54295582e-01
-1.06316137e+00 -1.71517536e-01 -3.91506582e-01 -1.39731720e-01
-2.05421984e-01 8.84169579e-01 -5.01103342e-01 4.29512084e-01
1.32671261e+00 1.17264473e+00 -5.70941806e-01 1.05060041e+00
-1.41361877e-01 1.22120440e+00 -7.92759895e-01 5.83527744e-01
6.16995275e-01 -3.33646834e-01 4.55239773e-01 1.17027235e+00
3.67557406e-01 1.68913171e-01 1.95263505e-01 7.61138201e-01
8.07361305e-02 -2.44296685e-01 -5.66058576e-01 -2.38568977e-01
4.47056323e-01 1.35527706e+00 -7.07959771e-01 -8.59185755e-02
-3.85780126e-01 7.03350544e-01 -1.86824471e-01 2.64180332e-01
-6.36404932e-01 -2.09626704e-01 9.43179250e-01 2.62873679e-01
2.46076196e-01 -6.36389494e-01 -4.62864310e-01 -1.01099491e+00
-2.36445874e-01 -8.31611037e-01 4.14725244e-01 -8.84132922e-01
-1.15540469e+00 4.16606724e-01 -1.01641856e-01 -1.28360069e+00
3.76419008e-01 -7.04372406e-01 -3.66814584e-01 8.76805902e-01
-2.22514129e+00 -1.46325707e+00 -6.24922514e-01 5.33524901e-02
3.24963987e-01 6.58017173e-02 8.48988473e-01 3.01947147e-01
-6.24907672e-01 -3.84376407e-01 7.45026052e-01 2.36541808e-01
3.82318974e-01 -9.77867484e-01 9.42202926e-01 7.46096492e-01
-1.07528172e-01 1.96651101e-01 3.27326596e-01 -5.82257986e-01
-1.19732583e+00 -1.73736107e+00 8.67300093e-01 -1.69200748e-01
8.74901593e-01 -1.31784827e-01 -8.84452283e-01 1.15366268e+00
-2.53082991e-01 3.87842394e-02 6.06997609e-01 -1.19090766e-01
-1.93020225e-01 -5.00649631e-01 -9.49728251e-01 4.99973893e-02
8.37937593e-01 -5.69462895e-01 -2.77718216e-01 6.47911251e-01
3.04417789e-01 -1.69105604e-01 -8.93672705e-01 6.67927146e-01
7.75818288e-01 -1.01289523e+00 7.64650643e-01 -3.86509508e-01
6.02653027e-01 -4.59822178e-01 -7.14642584e-01 -1.21066213e+00
-6.06933177e-01 8.23266134e-02 8.05255294e-01 8.76996160e-01
4.63936657e-01 -8.66564214e-01 6.30664527e-01 1.28037110e-03
-3.53042006e-01 -2.44236812e-01 -9.46746588e-01 -9.18156147e-01
1.85023919e-01 -5.80486000e-01 5.70437610e-01 1.25583553e+00
-9.28233206e-01 -4.94194478e-02 -2.26172522e-01 6.46219730e-01
8.44874322e-01 6.33108795e-01 6.63002789e-01 -1.56567192e+00
-2.96326559e-02 -4.94645774e-01 -7.05047771e-02 -1.05048251e+00
-2.06942186e-01 -1.13883770e+00 2.13033035e-01 -1.63056219e+00
1.54777169e-01 -9.63164032e-01 -1.06233992e-01 1.11835992e+00
-2.70179263e-03 5.40102541e-01 -7.66950473e-02 8.28277051e-01
-4.98333313e-02 4.61449683e-01 6.43412709e-01 -3.30800980e-01
-9.22469869e-02 8.76702070e-02 -2.83849001e-01 7.09625483e-01
9.43115890e-01 -8.07941139e-01 3.24114650e-01 -9.75732327e-01
2.95365512e-01 -3.34759772e-01 8.88659000e-01 -1.06465912e+00
-2.78694034e-01 -4.99992639e-01 3.66526008e-01 -1.02265131e+00
-1.39723554e-01 -8.49156976e-01 5.27982593e-01 4.93111551e-01
1.60514519e-01 -5.30185103e-01 4.33261573e-01 1.13266870e-01
-2.85055995e-01 4.49173264e-02 1.00133085e+00 -4.51448560e-01
-1.37876415e+00 6.41735077e-01 -5.68347216e-01 -2.91406125e-01
6.41627192e-01 -2.18674019e-01 -3.42335790e-01 1.91042125e-01
-6.35025322e-01 5.38597703e-01 6.51999295e-01 2.02932358e-01
1.70525908e-01 -8.87006879e-01 -9.99489307e-01 3.02229494e-01
2.75872856e-01 3.82481784e-01 3.45896900e-01 7.55287766e-01
-9.54807222e-01 5.71588337e-01 -4.20633823e-01 -9.31595147e-01
-8.90379131e-01 -2.55545825e-01 8.00516427e-01 -6.66342080e-02
-4.04098123e-01 8.98840070e-01 -1.35463521e-01 -9.98358250e-01
-3.58789802e-01 -4.41448063e-01 -5.64500168e-02 4.53226477e-01
9.01290625e-02 2.44907081e-01 7.37677887e-02 -6.71707451e-01
-5.11779130e-01 4.08882618e-01 4.10128415e-01 9.95532870e-02
1.96820581e+00 -1.06895350e-01 -5.65622568e-01 6.58816159e-01
8.77153158e-01 -6.98666155e-01 -1.08036947e+00 -2.33811006e-01
1.76919088e-01 -4.47947562e-01 7.00005054e-01 -8.29003811e-01
-1.23259008e+00 9.64517236e-01 9.60074663e-01 2.52084464e-01
1.10874331e+00 -2.68068016e-02 6.96950376e-01 3.90363365e-01
3.87307644e-01 -8.80256534e-01 -8.38258862e-01 6.29191637e-01
7.91418970e-01 -1.59551620e+00 2.62693703e-01 -1.37819573e-01
-3.80442858e-01 6.98342502e-01 2.13988170e-01 1.35424107e-01
7.76408195e-01 4.95351963e-02 3.17289442e-01 -3.68263632e-01
-2.27496967e-01 -7.52640009e-01 -1.08677454e-01 4.11081254e-01
5.72738886e-01 7.09265590e-01 1.19730793e-01 4.23869580e-01
-9.85005647e-02 3.32991481e-01 5.64427264e-02 1.04001284e+00
-1.01784599e+00 -6.68396950e-01 -5.86983621e-01 8.54876220e-01
-2.29938298e-01 -5.20806015e-01 -3.03071320e-01 7.10967839e-01
1.54385909e-01 6.31020963e-01 2.00566620e-01 -2.45853961e-01
1.34372056e-01 3.99439894e-02 -8.00408870e-02 -6.35223389e-01
-3.36082369e-01 6.94279820e-02 6.32260069e-02 -3.81244391e-01
-7.61144757e-01 -8.18234801e-01 -9.87209618e-01 -3.67935598e-01
-2.22804829e-01 -1.12017013e-01 9.18214619e-01 7.94588506e-01
4.34270114e-01 -1.86760351e-01 6.28634334e-01 -1.03289580e+00
-4.33869123e-01 -1.17751193e+00 -1.14894307e+00 -2.70239651e-01
4.16705579e-01 -2.97574610e-01 -4.64362830e-01 -1.73114836e-02] | [9.459781646728516, -1.5035566091537476] |
1808cafe-c455-4c31-b64b-c08dc5f75845 | rsca-real-time-segmentation-based-context | 2105.12789 | null | https://arxiv.org/abs/2105.12789v1 | https://arxiv.org/pdf/2105.12789v1.pdf | RSCA: Real-time Segmentation-based Context-Aware Scene Text Detection | Segmentation-based scene text detection methods have been widely adopted for arbitrary-shaped text detection recently, since they make accurate pixel-level predictions on curved text instances and can facilitate real-time inference without time-consuming processing on anchors. However, current segmentation-based models are unable to learn the shapes of curved texts and often require complex label assignments or repeated feature aggregations for more accurate detection. In this paper, we propose RSCA: a Real-time Segmentation-based Context-Aware model for arbitrary-shaped scene text detection, which sets a strong baseline for scene text detection with two simple yet effective strategies: Local Context-Aware Upsampling and Dynamic Text-Spine Labeling, which model local spatial transformation and simplify label assignments separately. Based on these strategies, RSCA achieves state-of-the-art performance in both speed and accuracy, without complex label assignments or repeated feature aggregations. We conduct extensive experiments on multiple benchmarks to validate the effectiveness of our method. RSCA-640 reaches 83.9% F-measure at 48.3 FPS on CTW1500 dataset. | ['Humphrey Shi', 'Chiu Man Ho', 'Rongrong Liu', 'Yuan Lin', 'Jiachen Li'] | 2021-05-26 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 4.94588405e-01 -4.50114965e-01 -1.14377141e-01 -3.77994567e-01
-9.72405195e-01 -4.74811614e-01 5.38501441e-01 2.79086620e-01
-4.44227606e-01 -8.07868689e-02 -1.40941754e-01 -4.28810507e-01
5.98503411e-01 -5.96963108e-01 -6.76241994e-01 -4.21262085e-01
5.16237438e-01 5.67845583e-01 1.16057622e+00 4.81858253e-02
5.38268447e-01 3.77487451e-01 -1.49371934e+00 3.95029783e-01
1.18615377e+00 8.25293064e-01 3.55297267e-01 9.69475806e-01
-5.90215921e-01 6.26204073e-01 -4.24033672e-01 -4.75982398e-01
1.29381031e-01 -3.02758217e-02 -6.13129079e-01 4.17185873e-01
9.05981183e-01 -5.42304039e-01 -2.52595723e-01 9.24604714e-01
4.77159351e-01 2.15399358e-02 6.98018968e-01 -7.06322849e-01
-1.42260119e-01 4.51565832e-01 -1.24021578e+00 3.04307908e-01
3.19954187e-01 2.91466027e-01 9.30739403e-01 -1.12591612e+00
3.62614870e-01 1.22817659e+00 8.10465038e-01 2.25967839e-01
-9.96629477e-01 -5.75986803e-01 5.66100121e-01 5.66448011e-02
-1.32683802e+00 -2.62225449e-01 6.02468610e-01 -2.66465336e-01
8.56531620e-01 3.53653461e-01 6.36276126e-01 6.42945290e-01
1.26906857e-01 1.45449209e+00 9.02357697e-01 -4.76141900e-01
8.75071362e-02 -1.39326051e-01 1.45939723e-01 1.11691380e+00
2.37965122e-01 -5.39957225e-01 -5.87307990e-01 3.43182534e-02
8.87237430e-01 5.28285466e-02 -1.52153432e-01 -2.64565021e-01
-1.28288996e+00 6.16218150e-01 3.98528963e-01 -5.83234057e-03
-2.05688737e-02 3.35341841e-01 4.71046716e-01 -4.81535971e-01
5.88823020e-01 -1.79067515e-02 -2.94038117e-01 -9.13077593e-02
-1.36773205e+00 4.96608354e-02 3.67238283e-01 1.10216177e+00
5.66457689e-01 5.30564561e-02 -5.16628504e-01 9.72142875e-01
2.24091634e-01 1.03758657e+00 3.60161245e-01 -6.60491109e-01
7.22184479e-01 9.19426262e-01 -1.00155048e-01 -1.04332852e+00
-6.35343671e-01 -3.15393388e-01 -6.35340929e-01 -1.61167920e-01
5.96749604e-01 -5.97703829e-03 -1.33433259e+00 8.59379172e-01
6.43184602e-01 2.81489432e-01 -5.13525367e-01 8.96486819e-01
6.31799042e-01 8.26252520e-01 6.33555055e-02 1.98099136e-01
1.47271121e+00 -1.32350266e+00 -5.86997032e-01 -3.45600605e-01
7.87444890e-01 -1.01828575e+00 1.59298098e+00 5.48722029e-01
-1.13335788e+00 -3.67441386e-01 -8.51175785e-01 -5.33623457e-01
-2.18341798e-01 4.65537369e-01 5.47494948e-01 7.72519767e-01
-8.71387124e-01 1.81075290e-01 -1.10460854e+00 -4.15285915e-01
6.49505019e-01 2.34910861e-01 3.02367300e-01 -8.00998658e-02
-3.83828014e-01 2.95896381e-01 -1.61758922e-02 -4.27625403e-02
-5.27370274e-01 -6.25673473e-01 -5.50166726e-01 5.13827428e-02
7.03522682e-01 -4.10798341e-01 1.25646865e+00 -5.60933292e-01
-1.30928111e+00 9.31623816e-01 -5.23721278e-01 -3.42615366e-01
9.15632725e-01 -4.03526247e-01 -9.98364761e-02 4.06189144e-01
2.19380915e-01 8.09439063e-01 1.07727373e+00 -1.12669611e+00
-1.02384007e+00 -3.82560253e-01 -3.20521921e-01 3.39807928e-01
-3.74765903e-01 3.43263745e-02 -1.22417593e+00 -8.96164417e-01
4.69766378e-01 -7.94433057e-01 -1.77188903e-01 5.64909637e-01
-6.97561800e-01 -2.52413273e-01 1.23030114e+00 -4.92732018e-01
1.26216626e+00 -1.79771948e+00 -4.09243762e-01 -1.34714264e-02
1.76307008e-01 3.13722163e-01 6.57122880e-02 -7.91745782e-02
6.39208615e-01 4.46099453e-02 -3.88502747e-01 -7.80808091e-01
4.31897342e-02 -1.04232334e-01 -3.73047113e-01 5.41603863e-01
3.33771221e-02 1.01464844e+00 -5.40103197e-01 -1.12819171e+00
8.92060339e-01 4.69831169e-01 -5.68282485e-01 -1.92792267e-01
-5.95110536e-01 -5.13979495e-02 -6.16027653e-01 9.20127451e-01
8.70736837e-01 -3.91725034e-01 -1.59475114e-02 -2.27873504e-01
-1.48627043e-01 1.29000926e-02 -1.16141140e+00 1.55967188e+00
-2.71186769e-01 8.39897573e-01 5.67457872e-03 -6.73103273e-01
7.14829743e-01 -2.33893588e-01 3.20851564e-01 -7.44913995e-01
4.83101666e-01 -1.14126513e-02 -5.52684963e-01 -3.05014253e-01
8.22698832e-01 4.81050968e-01 3.27402018e-02 5.13803601e-01
-6.19352460e-01 -4.67505932e-01 2.34576121e-01 4.01408136e-01
9.37529266e-01 1.73675537e-01 -4.45889216e-03 -3.11008453e-01
5.23742855e-01 6.84086233e-02 2.96860099e-01 9.26692903e-01
-3.01715434e-01 9.40300286e-01 2.22127631e-01 -5.09536803e-01
-8.86362791e-01 -8.92184854e-01 -2.67149657e-01 1.42123771e+00
5.55108130e-01 -4.99170452e-01 -1.12259340e+00 -8.41638029e-01
-2.25261942e-01 6.06261492e-01 -3.69432747e-01 3.55357707e-01
-6.96260989e-01 -9.37958479e-01 5.40314615e-01 7.13668287e-01
7.75733650e-01 -8.46130073e-01 -8.27544689e-01 3.18287462e-02
-3.46555293e-01 -1.62277806e+00 -8.11445892e-01 6.21006824e-02
-9.63424563e-01 -1.02882791e+00 -6.98130429e-01 -7.75144100e-01
8.47097576e-01 7.23443449e-01 8.53578031e-01 3.54811937e-01
-6.25380993e-01 3.40967625e-01 -3.64617616e-01 -2.46831372e-01
-5.77132739e-02 1.08593889e-01 -4.64527696e-01 -3.26869525e-02
1.73279345e-01 9.68029946e-02 -8.18797112e-01 6.00719810e-01
-9.09779131e-01 4.10236776e-01 4.89507228e-01 4.73970443e-01
8.80008340e-01 -1.06948450e-01 6.75514862e-02 -1.06809008e+00
2.49961823e-01 2.23140225e-01 -7.46522784e-01 3.91542733e-01
-2.59020716e-01 -1.74217373e-01 5.37841320e-01 -3.84555757e-01
-1.09707677e+00 4.92891788e-01 -1.15201794e-01 -3.46239656e-01
-2.07724005e-01 -9.43419486e-02 1.18438683e-01 -1.44969419e-01
4.71835047e-01 6.28312469e-01 -5.96766293e-01 -2.80599743e-01
3.38696212e-01 7.82535970e-01 6.58677697e-01 -6.24210179e-01
6.44065261e-01 9.70124841e-01 -1.82487696e-01 -1.15760756e+00
-1.04644585e+00 -8.49281192e-01 -8.68324280e-01 -7.28184581e-02
9.90690470e-01 -9.09857154e-01 -4.66956258e-01 9.17013049e-01
-8.73538494e-01 -8.33149433e-01 1.22186676e-01 -1.26858369e-01
-3.62243384e-01 7.74268329e-01 -7.59355485e-01 -7.30786741e-01
-6.88202441e-01 -1.16051221e+00 1.83729160e+00 2.02919856e-01
1.56475171e-01 -8.24454784e-01 -5.07725358e-01 6.35793388e-01
1.79265484e-01 -7.34438524e-02 6.26201451e-01 -1.93102539e-01
-8.48228395e-01 -3.41130137e-01 -7.44942427e-01 -1.61590815e-01
-7.00893179e-02 3.48041743e-01 -1.00050020e+00 -5.86467758e-02
-5.21050453e-01 -4.32127379e-02 1.00871539e+00 5.78025162e-01
1.65774202e+00 1.29314080e-01 -5.23533821e-01 7.28250980e-01
1.13515580e+00 -1.25833169e-01 3.28808397e-01 1.29749566e-01
1.06206787e+00 1.24087296e-01 8.87763202e-01 5.59759438e-01
5.13110697e-01 7.61838734e-01 3.03392500e-01 -3.91433656e-01
-4.10069019e-01 -2.89022714e-01 1.15983665e-01 6.43848240e-01
3.61172289e-01 -4.58968908e-01 -1.09141850e+00 3.83296937e-01
-1.74821866e+00 -4.27678585e-01 -7.67543852e-01 2.00096679e+00
7.33257294e-01 5.16462982e-01 2.64579862e-01 5.37454896e-02
9.05098975e-01 3.47995684e-02 -8.12923491e-01 -9.43125039e-02
-1.01642258e-01 6.13264777e-02 6.62883818e-01 4.41722244e-01
-1.49752629e+00 1.62320364e+00 5.92534637e+00 1.26539683e+00
-1.18868709e+00 7.33549893e-02 8.28152597e-01 -2.50698011e-02
4.68983389e-02 -2.91042745e-01 -1.10421216e+00 3.79432917e-01
2.21455321e-01 2.21452609e-01 1.81382924e-01 8.49072516e-01
3.83412302e-01 -4.70407486e-01 -6.42516911e-01 1.09872711e+00
1.77273005e-01 -1.21481180e+00 1.60456643e-01 -2.30790377e-01
9.81197953e-01 2.40216598e-01 -2.65629645e-02 1.00185931e-01
2.24470213e-01 -6.20486438e-01 8.93656909e-01 5.59459515e-02
9.14290547e-01 -5.32072723e-01 3.46706033e-01 4.16187346e-01
-1.61011505e+00 1.23229049e-01 -4.54810262e-01 3.25032681e-01
1.90190494e-01 7.15071201e-01 -8.77545655e-01 1.33992676e-02
7.35995889e-01 7.02196717e-01 -1.07012546e+00 1.07758772e+00
-2.12878346e-01 8.59748960e-01 -7.31937766e-01 -4.06053573e-01
2.98585713e-01 4.82482128e-02 2.34231427e-01 1.62139559e+00
-2.36186367e-02 8.57399181e-02 6.16847813e-01 6.29684925e-01
-9.53698382e-02 3.72527152e-01 -3.75871994e-02 3.95444900e-01
4.92986739e-01 1.41660142e+00 -1.69869053e+00 -5.20548463e-01
-3.89639139e-01 1.20970714e+00 1.26813039e-01 1.77852407e-01
-1.12537551e+00 -3.56330901e-01 5.46174273e-02 4.07077372e-01
5.50245821e-01 -3.20796400e-01 -6.77028537e-01 -1.34055400e+00
-3.27798887e-03 -5.93798161e-01 3.14167321e-01 -8.70551229e-01
-7.96239555e-01 2.58683026e-01 -3.15602601e-01 -1.00967336e+00
3.70966524e-01 -6.88795686e-01 -6.81377232e-01 1.56170428e-01
-1.51362038e+00 -1.50277901e+00 -5.49582541e-01 6.90031707e-01
1.25766349e+00 4.59938914e-01 2.23810062e-01 8.15221444e-02
-8.83242786e-01 8.12468529e-01 2.64126062e-01 3.74026269e-01
6.66037977e-01 -1.34192085e+00 8.02980781e-01 1.09699404e+00
3.19420278e-01 1.12558082e-01 4.53293711e-01 -8.27905715e-01
-1.49622631e+00 -1.31152034e+00 2.93458492e-01 -4.66357082e-01
5.62113404e-01 -6.96758330e-01 -8.75906527e-01 5.57768106e-01
-1.86450616e-01 1.83182806e-01 1.20961256e-02 -1.57039776e-01
-1.95270672e-01 7.84410387e-02 -1.01593983e+00 9.65726316e-01
1.11223114e+00 -2.65222281e-01 -1.24517791e-01 6.15562856e-01
6.34579539e-01 -7.13034451e-01 -4.10753608e-01 2.24384293e-01
3.84945303e-01 -1.04997015e+00 1.05083442e+00 2.60588437e-01
2.65103817e-01 -3.85276705e-01 8.95875506e-03 -4.17297751e-01
1.35933056e-01 -4.63984877e-01 -1.36712626e-01 1.00530481e+00
2.56488949e-01 -3.88068050e-01 1.10082233e+00 3.96188021e-01
-2.55644530e-01 -8.89227509e-01 -8.34938049e-01 -3.77634436e-01
3.51244546e-02 -8.06980252e-01 3.08403879e-01 8.20412934e-01
-2.27171615e-01 2.07940176e-01 -1.06131189e-01 1.65764466e-01
7.02882171e-01 3.50822836e-01 1.02723777e+00 -9.53849852e-01
9.26462710e-02 -6.84947252e-01 -1.59806266e-01 -1.76272929e+00
-1.61078647e-01 -4.85858947e-01 2.87560314e-01 -1.63501680e+00
3.96385908e-01 -5.88791728e-01 3.36228192e-01 5.01092613e-01
-6.13945425e-01 5.02350450e-01 1.81851402e-01 9.68097076e-02
-1.17321587e+00 4.61744279e-01 1.41222870e+00 -1.54020414e-01
-1.90275595e-01 -2.21987981e-02 -3.12804192e-01 1.15936291e+00
6.18066251e-01 -3.24067354e-01 -2.55118132e-01 -5.61830401e-01
6.52687028e-02 -1.64993420e-01 2.98854202e-01 -1.11475885e+00
4.43392456e-01 -1.41246125e-01 4.05748397e-01 -1.29094100e+00
3.79580110e-01 -6.79768145e-01 -7.27357328e-01 3.80766898e-01
-1.76924080e-01 -2.60961711e-01 2.73894548e-01 7.48016179e-01
3.06672126e-01 -2.34421402e-01 8.63939881e-01 2.24721938e-01
-8.16250205e-01 3.25484872e-01 -4.48860735e-01 2.67673194e-01
9.26612556e-01 -4.82750326e-01 -3.40894729e-01 -2.45321766e-01
-3.68165493e-01 4.40884471e-01 8.11633766e-01 3.18438023e-01
5.74564636e-01 -6.11409426e-01 -6.10053599e-01 1.25299647e-01
-2.47687735e-02 5.40903211e-01 2.78872311e-01 8.77587378e-01
-9.54205036e-01 4.79421437e-01 6.36995792e-01 -1.21562088e+00
-1.45820260e+00 3.65245730e-01 3.13074321e-01 -2.19720423e-01
-1.12293887e+00 7.65850008e-01 4.71970499e-01 -3.74588013e-01
2.73745716e-01 -7.76560903e-01 1.21524058e-01 -3.25651973e-01
4.48803663e-01 4.24717426e-01 1.23481542e-01 -5.03867805e-01
-2.57797897e-01 1.19557703e+00 -2.65345633e-01 7.26535022e-02
6.94881797e-01 -3.30717891e-01 4.33271825e-01 2.33807310e-01
7.01960385e-01 1.07332245e-01 -1.53566170e+00 -3.98329347e-01
-1.50875553e-01 -6.91157520e-01 4.78541017e-01 -7.59231210e-01
-1.15512323e+00 1.14201832e+00 4.14262801e-01 1.13735415e-01
9.95525181e-01 -1.36433234e-02 1.06730962e+00 4.48264182e-01
2.95532823e-01 -1.29832566e+00 3.95886809e-01 4.80001718e-01
3.88725489e-01 -1.26373911e+00 3.13352495e-01 -1.02107215e+00
-6.02051079e-01 1.10110736e+00 7.71044910e-01 -8.03468004e-02
2.63770998e-01 5.15015423e-01 1.33875683e-01 -1.32730633e-01
-4.89780277e-01 -1.60202354e-01 3.92738730e-01 2.77901769e-01
3.35598648e-01 5.92636876e-02 1.53920297e-02 1.31508142e-01
5.04893716e-03 -4.58570629e-01 6.00422382e-01 9.03475821e-01
-8.18046153e-01 -5.09260118e-01 -6.52746379e-01 7.03601539e-01
-4.46018934e-01 -1.65926814e-01 -3.62212241e-01 7.01759040e-01
-4.55343962e-01 8.47498000e-01 2.77481258e-01 4.34808731e-02
2.92806864e-01 -2.20985159e-01 3.82481009e-01 -5.43556213e-01
-3.66361141e-01 5.91317892e-01 -2.41416946e-01 -5.99508226e-01
-2.00388655e-01 -1.02360976e+00 -1.84719181e+00 -3.80610168e-01
-9.08016503e-01 -4.10736144e-01 7.39783943e-01 9.47980940e-01
3.42819154e-01 4.80340511e-01 5.32940388e-01 -1.08552074e+00
-3.04728389e-01 -7.86846340e-01 -2.46702194e-01 2.69123971e-01
3.39813717e-02 -3.77174139e-01 -2.10861191e-01 2.98611939e-01] | [12.06397819519043, 2.2634410858154297] |
26fef950-60e3-4db6-9a2c-d8c12ae5d853 | multimodal-short-video-rumor-detection-system | 2304.08401 | null | https://arxiv.org/abs/2304.08401v3 | https://arxiv.org/pdf/2304.08401v3.pdf | Multimodal Short Video Rumor Detection System Based on Contrastive Learning | With the rise of short video platforms as prominent channels for news dissemination, major platforms in China have gradually evolved into fertile grounds for the proliferation of fake news. However, distinguishing short video rumors poses a significant challenge due to the substantial amount of information and shared features among videos, resulting in homogeneity. To address the dissemination of short video rumors effectively, our research group proposes a methodology encompassing multimodal feature fusion and the integration of external knowledge, considering the merits and drawbacks of each algorithm. The proposed detection approach entails the following steps: (1) creation of a comprehensive dataset comprising multiple features extracted from short videos; (2) development of a multimodal rumor detection model: first, we employ the Temporal Segment Networks (TSN) video coding model to extract video features, followed by the utilization of Optical Character Recognition (OCR) and Automatic Speech Recognition (ASR) to extract textual features. Subsequently, the BERT model is employed to fuse textual and video features; (3) distinction is achieved through contrast learning: we acquire external knowledge by crawling relevant sources and leverage a vector database to incorporate this knowledge into the classification output. Our research process is driven by practical considerations, and the knowledge derived from this study will hold significant value in practical scenarios, such as short video rumor identification and the management of social opinions. | ['Haizhou Wang', 'Pengchao Wang', 'Xiangyu Min', 'Siyi Wang', 'Junhao Zhao', 'Yuxing Yang'] | 2023-04-17 | null | null | null | null | ['optical-character-recognition'] | ['computer-vision'] | [ 1.81643322e-01 -4.74777162e-01 -5.72753131e-01 1.21803224e-01
-6.65798724e-01 -3.93452644e-01 7.86660910e-01 -2.03672498e-02
-2.82115161e-01 4.68878657e-01 4.90860552e-01 -2.46707022e-01
5.95383011e-02 -5.95208943e-01 -4.10694152e-01 -6.22749746e-01
1.53688937e-01 -3.06545943e-01 1.87117562e-01 -3.90604407e-01
8.25973749e-01 4.33840960e-01 -1.66179287e+00 4.40164566e-01
7.46201098e-01 1.32794917e+00 9.15878918e-03 3.37045372e-01
-2.22287640e-01 1.40609968e+00 -7.86894917e-01 -4.66102719e-01
1.48899229e-02 -5.34552932e-01 -7.04487026e-01 4.58384335e-01
-8.40855241e-02 -7.31763482e-01 -6.32457972e-01 1.11639559e+00
3.41209054e-01 5.79352342e-02 5.15449226e-01 -1.22942841e+00
-7.32351184e-01 4.84764159e-01 -5.91687322e-01 3.91789645e-01
6.82338059e-01 -6.52970895e-02 7.69165993e-01 -1.03745282e+00
6.66725099e-01 9.36243773e-01 5.36081195e-01 -6.34658849e-03
-5.20288229e-01 -5.79998255e-01 -1.62775710e-01 5.69789469e-01
-1.38347769e+00 -4.92205173e-01 1.02804816e+00 -6.73227966e-01
5.30883431e-01 3.18474472e-01 8.79839659e-01 1.39118695e+00
3.20277177e-02 1.02040887e+00 1.07350516e+00 -2.88636357e-01
-1.17832404e-02 5.77731252e-01 -8.05309881e-03 6.16826892e-01
-5.05295657e-02 -1.49819508e-01 -8.03376138e-01 -4.03035909e-01
5.84826410e-01 8.02873895e-02 -3.51295292e-01 1.42206162e-01
-1.05745363e+00 1.05134428e+00 5.77954799e-02 4.73748505e-01
-5.79787374e-01 -5.56543350e-01 6.35604680e-01 6.07945740e-01
6.07861876e-01 2.45191067e-01 1.92120433e-01 -2.91156262e-01
-1.20330536e+00 5.88411875e-02 8.50819647e-01 6.22408748e-01
5.27323961e-01 2.14657113e-01 2.22503766e-02 8.90061975e-01
2.81633645e-01 4.09758419e-01 8.65593255e-01 -7.55064249e-01
5.77662528e-01 5.96083879e-01 2.90818582e-03 -1.99927926e+00
-7.62135834e-02 -2.88075000e-01 -5.67658305e-01 -5.65457702e-01
3.39716151e-02 -1.97253138e-01 -4.43600327e-01 1.07704234e+00
2.13288113e-01 2.07550362e-01 -1.86574254e-02 9.88864958e-01
9.06915784e-01 6.94759011e-01 -3.69174004e-01 -6.09348357e-01
1.29110456e+00 -8.04606020e-01 -9.74138618e-01 9.34277922e-02
5.84207237e-01 -9.12481248e-01 5.66498339e-01 3.35680783e-01
-7.93694317e-01 -3.60409856e-01 -1.06444943e+00 1.80464864e-01
-4.37640637e-01 2.21807867e-01 2.56127536e-01 6.27040446e-01
-6.50826514e-01 8.32534134e-02 -2.83013195e-01 -2.98190117e-01
5.08101940e-01 -7.47805014e-02 -2.20781058e-01 -8.24040622e-02
-1.42542839e+00 8.34151983e-01 3.81171614e-01 3.93082798e-01
-8.89875352e-01 -1.44371772e-02 -7.16778994e-01 -3.52073431e-01
6.06294513e-01 -3.68695885e-01 7.75114238e-01 -1.49724579e+00
-1.47349584e+00 6.04241848e-01 7.41824955e-02 -2.90807724e-01
6.69872224e-01 -1.75874144e-01 -7.41181254e-01 5.97300112e-01
1.79685578e-01 -8.79549459e-02 1.52041256e+00 -1.16792297e+00
-7.07279921e-01 -3.92934293e-01 -8.91608447e-02 2.90514022e-01
-7.56282330e-01 5.63599646e-01 -4.10692930e-01 -7.08391130e-01
2.59714991e-01 -6.80483878e-01 3.21509063e-01 -6.00045919e-01
-3.55611831e-01 1.26140356e-01 1.13526285e+00 -1.20549858e+00
1.51292884e+00 -2.24003482e+00 1.76208302e-01 2.37560734e-01
3.13373417e-01 3.68812919e-01 1.82979435e-01 5.71075857e-01
3.79696101e-01 -1.18485272e-01 2.29450427e-02 -1.01327943e-02
-3.08076352e-01 -2.23070830e-01 -4.23945546e-01 6.43018901e-01
2.94813402e-02 5.66950202e-01 -9.34660912e-01 -6.31258368e-01
5.63844740e-02 3.06066632e-01 -1.58433437e-01 1.67226627e-01
2.64458090e-01 1.73026174e-01 -7.37431824e-01 1.01842952e+00
5.57790518e-01 -9.90954936e-02 5.44767343e-02 -2.37714976e-01
-2.84304082e-01 1.11706428e-01 -8.77659321e-01 8.35531414e-01
9.44980159e-02 7.86127508e-01 3.07883292e-01 -1.06667221e+00
9.26351845e-01 5.21034181e-01 5.57534456e-01 -3.91165018e-01
5.28319299e-01 1.99578181e-01 -5.14906108e-01 -1.15124857e+00
7.04420984e-01 5.56765869e-02 8.09183195e-02 4.24766243e-01
-3.99979465e-02 1.07254118e-01 -1.47425264e-01 3.24788600e-01
9.08357203e-01 -1.14808157e-01 3.51531953e-01 4.55324113e-01
7.32721090e-01 1.50928006e-01 2.53326863e-01 6.17685258e-01
-4.48841631e-01 2.94494748e-01 4.99910384e-01 -3.11625034e-01
-6.54491663e-01 -5.24229288e-01 3.69816348e-02 8.60521019e-01
1.81996167e-01 -3.06810796e-01 -5.28392375e-01 -7.61612058e-01
1.55656692e-02 3.60438585e-01 -4.62080240e-01 -8.69801566e-02
-2.56929040e-01 -8.56602728e-01 7.34146714e-01 1.17819555e-01
6.93962753e-01 -9.90511298e-01 -4.28073227e-01 1.54098704e-01
-6.33317411e-01 -1.22754741e+00 -2.51713485e-01 -5.17698944e-01
-6.00905955e-01 -1.14849234e+00 -6.65202379e-01 -6.08350277e-01
4.44218010e-01 9.55949068e-01 3.27200115e-01 3.63233060e-01
3.70939873e-04 4.19297338e-01 -6.77749276e-01 -6.56647980e-03
-5.58736205e-01 -2.05708146e-01 1.22420430e-01 4.78279978e-01
3.61553788e-01 -2.77792037e-01 -2.17454314e-01 4.18487817e-01
-1.01819170e+00 -7.96451196e-02 6.53891623e-01 8.11101317e-01
-7.50536099e-02 7.50838146e-02 5.92022896e-01 -6.29183114e-01
1.04441631e+00 -9.88823712e-01 -3.01461250e-01 1.49775110e-02
-2.66362250e-01 -8.35685074e-01 4.47020292e-01 -4.14355248e-01
-1.23246443e+00 -5.26906073e-01 1.22088723e-01 -5.66288054e-01
-4.42959927e-02 1.11297572e+00 1.38306960e-01 -2.23694414e-01
4.31529731e-01 6.80021524e-01 4.11998212e-01 -1.79365486e-01
2.35310540e-01 1.48667693e+00 3.27507943e-01 -3.03995777e-02
6.60180688e-01 5.60313702e-01 -4.40869898e-01 -1.48134124e+00
-7.78404355e-01 -6.60598516e-01 -4.43343312e-01 -5.68620741e-01
6.85284615e-01 -1.07496643e+00 -4.15551484e-01 8.84050190e-01
-9.80381548e-01 5.42525232e-01 4.01944041e-01 7.53410280e-01
-2.38364026e-01 8.71193290e-01 -1.06361997e+00 -8.44614387e-01
-1.34377062e-01 -1.17246270e+00 3.74082536e-01 -3.18053104e-02
-1.58708766e-02 -6.66656256e-01 -2.78779179e-01 1.26084447e+00
4.87067074e-01 2.27482468e-02 5.18904150e-01 -8.30414116e-01
-5.82502365e-01 -6.01667166e-01 -4.54794824e-01 6.81989074e-01
1.99830070e-01 8.15823004e-02 -9.45618033e-01 -3.02817345e-01
5.77646434e-01 -3.35611433e-01 7.05468178e-01 1.83775291e-01
8.04427326e-01 -4.52505410e-01 -2.00494453e-01 2.07260594e-01
9.27557945e-01 1.25013918e-01 5.36453009e-01 7.39566088e-01
7.84785628e-01 7.13704705e-01 6.29774809e-01 6.94484472e-01
5.78636289e-01 4.76976573e-01 4.15201217e-01 2.74802268e-01
2.05273435e-01 -1.52474105e-01 7.89523482e-01 1.37862706e+00
-2.28884563e-01 -3.31748314e-02 -7.14330435e-01 5.43839216e-01
-1.72913826e+00 -1.25171649e+00 3.17295529e-02 2.04431152e+00
3.41873229e-01 3.38860266e-02 1.65114924e-01 1.48034632e-01
9.09117639e-01 4.41407621e-01 -2.81652398e-02 -1.06517449e-01
-3.29876512e-01 -8.33920360e-01 3.44781160e-01 9.67216343e-02
-1.09406042e+00 6.77778006e-01 5.99327230e+00 8.16643298e-01
-1.37798679e+00 2.88167000e-01 3.18730444e-01 -3.42520811e-02
-1.07638426e-01 -2.80545019e-02 -3.31026107e-01 6.85337961e-01
7.92106509e-01 -1.42308310e-01 4.28756684e-01 5.13998389e-01
3.71923029e-01 -1.57508273e-02 -2.65210271e-01 9.72988367e-01
7.99312234e-01 -1.42741406e+00 2.27443174e-01 -5.10827415e-02
6.65425062e-01 1.19812831e-01 -8.01818073e-02 1.85754210e-01
-5.11232734e-01 -5.87134182e-01 7.13618338e-01 4.14811850e-01
2.05798239e-01 -6.29171848e-01 9.85035121e-01 4.95337129e-01
-6.65589631e-01 -4.18513656e-01 -1.87268317e-01 -1.61139324e-01
2.45577991e-01 4.52695340e-01 -7.74568141e-01 7.84075201e-01
4.00282979e-01 8.82416070e-01 -2.99785078e-01 9.23285425e-01
-1.01044640e-01 7.25250721e-01 1.82482809e-01 -3.38301174e-02
2.21059814e-01 -4.33831632e-01 9.09007430e-01 1.14834857e+00
3.01517606e-01 -1.09093644e-01 2.57501721e-01 3.97638083e-01
-2.29499415e-01 5.40300190e-01 -7.07039595e-01 -3.83346200e-01
5.37597656e-01 1.09731293e+00 -5.80924153e-01 -4.38097328e-01
-8.57424855e-01 8.86360884e-01 1.19765505e-01 3.07428062e-01
-8.61617386e-01 -2.28871047e-01 1.39699295e-01 4.55812626e-02
2.97361284e-01 -2.76894495e-02 6.68086410e-02 -1.50845158e+00
7.06271455e-02 -1.16892838e+00 3.03234190e-01 -6.14230216e-01
-1.09776568e+00 5.97786129e-01 -6.63433969e-02 -1.33397603e+00
-4.99501601e-02 -1.79774284e-01 -3.50345254e-01 4.39967006e-01
-1.47445548e+00 -1.15796864e+00 -3.32763553e-01 7.45127141e-01
5.47330797e-01 -5.22083759e-01 3.87059510e-01 4.48386788e-01
-8.13203335e-01 2.54889280e-01 2.58818150e-01 2.88104296e-01
6.01419210e-01 -3.61023247e-01 -3.02449495e-01 7.87302375e-01
-1.58355191e-01 5.33563852e-01 5.82543254e-01 -8.86758804e-01
-1.72858739e+00 -8.03487897e-01 8.27604592e-01 -1.83448538e-01
1.13316405e+00 1.80524752e-01 -7.65974164e-01 6.51496112e-01
3.32203060e-02 -2.92818487e-01 7.99430370e-01 -3.28131497e-01
-2.78408378e-01 2.02306733e-01 -1.04257250e+00 4.34820831e-01
5.34725308e-01 -9.10288393e-01 -7.46232212e-01 2.01443151e-01
3.33363354e-01 -1.95597112e-01 -8.05785358e-01 -5.79948984e-02
7.19249964e-01 -8.78220677e-01 6.60660923e-01 -5.11075675e-01
6.85973525e-01 -2.01382980e-01 -3.49607736e-01 -1.00107265e+00
-4.27126847e-02 -6.36545718e-01 -2.83582002e-01 1.26633763e+00
2.43784308e-01 -7.21532404e-01 5.21412611e-01 1.28013983e-01
1.21377498e-01 -4.35822338e-01 -8.07644844e-01 -3.58398050e-01
-5.39126456e-01 -4.77712482e-01 2.04887241e-01 1.29882514e+00
3.89725626e-01 6.44201562e-02 -9.92548406e-01 1.03222698e-01
5.01292527e-01 1.66597545e-01 7.42321730e-01 -8.81780803e-01
1.85759831e-02 -3.43814641e-01 -5.66327751e-01 -1.03193104e+00
1.97275117e-01 -5.62840521e-01 -2.56148368e-01 -1.01540160e+00
4.62495506e-01 7.03699887e-02 -2.87571222e-01 -7.24460706e-02
1.31057888e-01 4.12419677e-01 2.59206593e-01 7.71515846e-01
-5.06298065e-01 5.90214670e-01 1.23994601e+00 -1.28428057e-01
-1.79355517e-01 1.50998473e-01 -7.24650323e-01 8.71337771e-01
4.09293801e-01 -3.36543381e-01 -1.36862472e-01 -6.95393384e-02
2.85240561e-01 6.50573969e-01 3.85025084e-01 -7.00070620e-01
2.27795109e-01 -2.02378660e-01 2.31927142e-01 -6.47931695e-01
4.09709215e-01 -6.86562002e-01 -1.64359212e-01 2.84166366e-01
-1.38380855e-01 3.88504490e-02 -1.24954969e-01 6.99120879e-01
-6.06034696e-01 -2.90330887e-01 3.27387691e-01 -5.99277020e-02
-6.77064836e-01 -1.05020836e-01 -9.49293375e-01 -2.65527904e-01
1.14608419e+00 -5.65352619e-01 -4.58623230e-01 -7.70092845e-01
-4.67401803e-01 -5.33870459e-02 4.39651877e-01 6.07045770e-01
9.04624641e-01 -1.06037247e+00 -7.43535340e-01 3.37540299e-01
-2.57820450e-02 -7.87601471e-01 3.52172226e-01 1.39484334e+00
-4.49194819e-01 3.03341240e-01 -2.07017019e-01 -3.86059910e-01
-1.35802615e+00 4.43868488e-01 -1.37924254e-01 2.73891419e-01
-4.56723154e-01 4.39886421e-01 -3.06156874e-01 3.94275077e-02
1.11291528e-01 3.51688504e-01 -6.64260447e-01 7.33367920e-01
7.36566961e-01 6.43586457e-01 -6.44831639e-03 -1.43183374e+00
-2.13295043e-01 5.61257973e-02 -2.20304683e-01 6.28912821e-02
1.28655171e+00 -5.91344476e-01 -4.20589894e-01 2.85678327e-01
1.17886031e+00 1.35966703e-01 -7.48419285e-01 -2.65741378e-01
-9.86003131e-02 -8.38333368e-01 4.12389517e-01 -3.94926190e-01
-1.05850697e+00 3.62630516e-01 1.40711591e-01 5.28868198e-01
9.22980726e-01 -2.30672196e-01 7.91005671e-01 1.06795616e-01
2.29129821e-01 -1.12606215e+00 1.98210463e-01 4.28555667e-01
6.30867660e-01 -1.15450454e+00 1.58413187e-01 -3.76709282e-01
-8.32154989e-01 1.17191541e+00 1.23919584e-01 1.86442554e-01
5.68931818e-01 -1.43835425e-01 2.60417432e-01 -3.41835827e-01
-4.75104302e-01 1.61100388e-01 2.53015429e-01 1.89615563e-01
7.60272965e-02 -1.16248922e-02 -6.51411116e-01 6.00632966e-01
2.37657744e-02 2.28657797e-01 9.30126786e-01 1.06964290e+00
-5.55196285e-01 -5.79911947e-01 -6.08146071e-01 5.50571859e-01
-7.47060120e-01 -1.88823827e-02 -3.22812080e-01 6.14790261e-01
-7.78323859e-02 1.24998689e+00 -3.75506490e-01 -6.71137512e-01
-2.15863828e-02 -1.29289061e-01 1.01529159e-01 -1.63069874e-01
-4.90536064e-01 2.30116084e-01 3.31550777e-01 -4.10725415e-01
-6.87181354e-01 -8.28471065e-01 -5.87090790e-01 -4.70929533e-01
-7.07962215e-01 2.46483073e-01 7.72101521e-01 1.35234594e+00
2.30973437e-01 5.38192987e-02 1.04173839e+00 -8.22375596e-01
-8.43031526e-01 -9.25834179e-01 -5.48654795e-01 5.66666186e-01
4.60641772e-01 -6.72251821e-01 -8.16835642e-01 -7.14917704e-02] | [8.155094146728516, 10.282129287719727] |
11178401-0693-44c0-b0b1-4971e3d42012 | distilling-relation-embeddings-from | null | null | https://aclanthology.org/2021.emnlp-main.712 | https://aclanthology.org/2021.emnlp-main.712.pdf | Distilling Relation Embeddings from Pretrained Language Models | Pre-trained language models have been found to capture a surprisingly rich amount of lexical knowledge, ranging from commonsense properties of everyday concepts to detailed factual knowledge about named entities. Among others, this makes it possible to distill high-quality word vectors from pre-trained language models. However, it is currently unclear to what extent it is possible to distill relation embeddings, i.e. vectors that characterize the relationship between two words. Such relation embeddings are appealing because they can, in principle, encode relational knowledge in a more fine-grained way than is possible with knowledge graphs. To obtain relation embeddings from a pre-trained language model, we encode word pairs using a (manually or automatically generated) prompt, and we fine-tune the language model such that relationally similar word pairs yield similar output vectors. We find that the resulting relation embeddings are highly competitive on analogy (unsupervised) and relation classification (supervised) benchmarks, even without any task-specific fine-tuning. Source code to reproduce our experimental results and the model checkpoints are available in the following repository: https://github.com/asahi417/relbert | ['Steven Schockaert', 'Jose Camacho-Collados', 'Asahi Ushio'] | null | null | null | null | emnlp-2021-11 | ['relation-classification'] | ['natural-language-processing'] | [-1.70399100e-01 2.60180295e-01 -4.74898070e-01 -3.67900699e-01
-4.25776452e-01 -8.27641249e-01 9.84425366e-01 7.74009526e-01
-4.55674440e-01 6.30485356e-01 5.86607158e-01 -5.50465584e-01
-1.61124021e-01 -1.17331791e+00 -4.43389088e-01 -2.71822691e-01
-2.67686937e-02 5.63321769e-01 1.19037829e-01 -5.07847607e-01
2.36665487e-01 4.89124835e-01 -1.27078307e+00 1.20474584e-01
6.02663159e-01 7.33688951e-01 2.89092958e-02 4.99975353e-01
-3.67469817e-01 8.24743509e-01 -2.67322123e-01 -7.77313709e-01
-7.09999800e-02 -2.99300760e-01 -1.41142964e+00 -4.46151376e-01
4.81592454e-02 1.55506298e-01 -4.18946236e-01 8.47481191e-01
7.89859816e-02 3.98773640e-01 9.25190091e-01 -9.39019263e-01
-1.43014181e+00 8.29662263e-01 -4.61598895e-02 3.88157547e-01
6.40200853e-01 -2.89000779e-01 1.79444873e+00 -9.26852047e-01
7.89015472e-01 1.20816493e+00 5.08903682e-01 2.65553534e-01
-1.35502326e+00 -4.45703357e-01 -5.93331046e-02 4.68526185e-01
-1.67500961e+00 -3.36447299e-01 7.54234910e-01 -4.24131751e-01
1.57538724e+00 5.35629690e-02 5.17464161e-01 1.21694851e+00
1.34283230e-01 3.01575810e-01 8.52128923e-01 -7.47086942e-01
-2.49705114e-03 3.36230308e-01 4.26781744e-01 5.88207483e-01
4.96026874e-01 -3.70975360e-02 -4.73922253e-01 -3.01579535e-01
7.26979077e-01 -2.11488325e-02 -3.81331265e-01 -5.36850631e-01
-1.31711841e+00 1.12101197e+00 7.20843494e-01 9.03670669e-01
-3.51484954e-01 1.89699098e-01 4.53979999e-01 4.69063461e-01
3.27459067e-01 9.51020598e-01 -6.39535546e-01 -7.94344991e-02
-2.34160691e-01 5.22626601e-02 9.47117269e-01 8.86669278e-01
9.99343395e-01 -2.31911913e-01 5.03381751e-02 9.46214378e-01
1.37055457e-01 2.13773757e-01 7.05969751e-01 -6.53770149e-01
2.51701206e-01 5.91499388e-01 -1.90609097e-01 -1.22778761e+00
-2.31669441e-01 -1.11473799e-01 -5.44164360e-01 -3.64974618e-01
2.59948909e-01 1.77423969e-01 -4.29822683e-01 1.79806256e+00
7.12490976e-02 -4.66981158e-02 3.54762286e-01 7.08637834e-01
1.01464510e+00 5.88310361e-01 2.57283866e-01 1.79721322e-02
1.60734868e+00 -5.69463491e-01 -5.78634202e-01 -2.47040927e-01
1.11609304e+00 -6.80373549e-01 1.28902197e+00 -1.14978753e-01
-7.25013435e-01 -4.14067358e-01 -8.87473762e-01 -4.95614946e-01
-9.76383626e-01 -3.80337507e-01 1.14249611e+00 4.20868933e-01
-8.73253703e-01 5.33417940e-01 -6.31349266e-01 -8.04187655e-01
3.53985995e-01 4.82502095e-02 -7.65316308e-01 -5.02453595e-02
-1.66336071e+00 1.29957306e+00 6.03266537e-01 -1.77777573e-01
-3.42813015e-01 -5.74728549e-01 -1.23037410e+00 1.34649957e-02
4.33578104e-01 -6.82501078e-01 9.70072031e-01 -5.55397987e-01
-1.08292627e+00 1.14065003e+00 -2.35413551e-01 -4.08926100e-01
-3.09088796e-01 -5.12143858e-02 -5.08736074e-01 -1.30965307e-01
1.11025207e-01 4.38070953e-01 2.30983943e-01 -1.07508612e+00
-1.55572727e-01 -9.23584178e-02 4.03209835e-01 1.17814019e-01
-6.19056582e-01 4.66864407e-02 -1.24714367e-01 -5.86391628e-01
-1.56955123e-01 -6.89563155e-01 -1.06943818e-02 -7.72718061e-03
-3.92497331e-01 -7.16843605e-01 2.30671555e-01 -3.22324455e-01
1.21325064e+00 -2.01056957e+00 1.60497040e-01 2.10535705e-01
2.84483105e-01 3.18481952e-01 -3.22765291e-01 8.58646452e-01
-3.44092637e-01 3.84345442e-01 -1.84987038e-01 6.40188679e-02
2.10038081e-01 6.89362764e-01 -4.76695359e-01 2.28415728e-01
3.30398828e-01 1.32745993e+00 -1.24196136e+00 -3.32740039e-01
1.68481365e-01 3.81759644e-01 -3.96617055e-01 2.65733838e-01
-2.55646497e-01 -3.31409834e-02 -4.87696171e-01 2.77130038e-01
8.23922008e-02 -4.36577916e-01 4.82833654e-01 -2.91211963e-01
3.84373754e-01 9.32271779e-01 -9.42984045e-01 1.71479201e+00
-9.52875555e-01 7.25339711e-01 -7.15966463e-01 -1.22485149e+00
1.08402658e+00 4.18754578e-01 7.61158690e-02 -5.83173931e-01
2.12860152e-01 1.25606582e-01 2.40581557e-01 -4.92423207e-01
6.56422019e-01 -5.30901074e-01 -2.67208248e-01 6.01989508e-01
3.42940599e-01 -3.29265743e-01 1.40514985e-01 3.38917643e-01
1.18657184e+00 -5.92583567e-02 9.39614236e-01 -2.19735801e-01
4.75266963e-01 -7.75933778e-03 2.10938215e-01 3.59193832e-01
1.12012744e-01 2.90859789e-01 5.16075611e-01 -3.12650174e-01
-9.48260248e-01 -1.30327356e+00 -3.57523859e-01 1.09346259e+00
5.43574896e-03 -7.64024436e-01 -7.40059391e-02 -5.27407587e-01
1.56745210e-01 9.32836652e-01 -7.92268217e-01 -3.24004799e-01
-3.90391171e-01 -2.62524456e-01 5.41155577e-01 8.01607370e-01
-1.88636780e-02 -1.26396120e+00 -9.27288160e-02 1.07788034e-01
1.79826226e-02 -1.27917671e+00 -1.35040581e-01 4.12895620e-01
-6.61341965e-01 -1.11184156e+00 -1.54366121e-01 -8.75391543e-01
4.89883542e-01 8.19249451e-02 1.53206038e+00 2.52807170e-01
-2.11158440e-01 4.07041043e-01 -6.89904451e-01 -1.17699727e-01
-4.29833263e-01 1.02201127e-01 1.66230053e-01 -4.01882291e-01
9.05293047e-01 -8.31450403e-01 -1.64335400e-01 -1.68435931e-01
-8.74200046e-01 -3.40672493e-01 3.42150092e-01 9.01260853e-01
5.36187887e-01 -6.65385574e-02 4.20017272e-01 -1.00200880e+00
1.02622640e+00 -6.30742311e-01 -1.15647644e-01 3.38864684e-01
-5.47635078e-01 4.18936938e-01 7.46042311e-01 -4.46246594e-01
-5.95812082e-01 -4.13614988e-01 -2.10195675e-01 -2.08889589e-01
-3.01748842e-01 6.30612314e-01 -7.39922225e-02 1.30888283e-01
9.32324350e-01 1.41638175e-01 -4.19617444e-01 -3.67911667e-01
1.09099495e+00 5.90987682e-01 3.98353279e-01 -9.80968058e-01
8.43778312e-01 1.78068295e-01 -1.41251758e-01 -8.74948323e-01
-1.09056687e+00 -5.52196860e-01 -7.44172454e-01 5.79769790e-01
9.98160303e-01 -6.45971656e-01 -4.50624496e-01 -4.44331855e-01
-1.44057822e+00 -2.86473691e-01 -5.76999605e-01 5.49845397e-01
-3.69516671e-01 1.96928173e-01 -5.15171170e-01 -3.60650599e-01
-1.55482411e-01 -4.96784061e-01 6.26268625e-01 1.73805133e-02
-9.15813863e-01 -1.50405538e+00 2.64782965e-01 1.23524450e-01
2.35776335e-01 6.65096641e-02 1.41147327e+00 -1.17844760e+00
-2.73213357e-01 -1.41155243e-01 -2.82497793e-01 3.76058340e-01
4.60537642e-01 -1.14218324e-01 -8.21665049e-01 2.10854053e-01
-3.59827340e-01 -5.25048494e-01 7.70640194e-01 -2.22894058e-01
8.06384921e-01 -2.48200685e-01 -3.83587718e-01 3.22422445e-01
1.38067031e+00 -1.31017745e-01 5.39883614e-01 2.50108689e-01
8.96504998e-01 5.14725447e-01 3.16416711e-01 8.50219354e-02
6.60846114e-01 6.84439003e-01 -4.39412445e-02 3.81351084e-01
-1.36816725e-01 -4.97642219e-01 3.14838323e-03 1.07078946e+00
-2.19066203e-01 -1.87879324e-01 -1.16520786e+00 9.18091714e-01
-1.61311448e+00 -1.05401790e+00 -1.24491185e-01 1.99371564e+00
1.29272592e+00 1.96081083e-02 -1.98969364e-01 1.37990788e-01
2.91966230e-01 2.84044802e-01 -9.48919654e-02 -8.36288989e-01
-6.74845651e-02 7.72225678e-01 1.99906081e-01 7.88813293e-01
-8.00362587e-01 1.29881954e+00 5.66545200e+00 6.68945193e-01
-7.20910430e-01 3.51867348e-01 4.10632677e-02 3.08195591e-01
-8.31554294e-01 2.09479868e-01 -4.90085930e-01 2.04899721e-02
1.02541578e+00 -4.78832453e-01 3.36712480e-01 5.99099755e-01
-3.49068373e-01 1.60614341e-01 -1.50059819e+00 9.90038812e-01
-2.34347209e-02 -1.50860202e+00 2.19154939e-01 -1.22727789e-01
4.94573981e-01 -2.79207830e-03 -2.95198619e-01 4.75277513e-01
6.00986898e-01 -1.21908677e+00 2.15160713e-01 3.96002382e-01
6.00670993e-01 -5.26475728e-01 6.54731393e-01 1.23149671e-01
-1.33910358e+00 2.50144929e-01 -5.87220371e-01 -3.15797925e-01
6.59021288e-02 6.01884186e-01 -7.31390297e-01 7.03351617e-01
3.76725495e-01 8.00652921e-01 -6.28306031e-01 4.35912132e-01
-8.35468411e-01 5.34651339e-01 -1.63862869e-01 -3.55778068e-01
5.30077256e-02 1.08314259e-02 1.92456171e-01 1.30134165e+00
4.53695767e-02 2.91869193e-01 -1.05379730e-01 1.02334583e+00
-3.12788755e-01 2.34105423e-01 -9.38205302e-01 -4.79463279e-01
7.55119264e-01 1.26827407e+00 -4.43560660e-01 -3.65968943e-01
-6.87299609e-01 8.69045138e-01 8.79930973e-01 3.28008652e-01
-5.35843790e-01 -5.37670076e-01 9.84475672e-01 -4.16473299e-03
3.01897615e-01 -5.11854768e-01 -1.58930272e-01 -1.32187140e+00
-4.50352393e-02 -5.00876248e-01 3.66013378e-01 -8.27473342e-01
-1.67672336e+00 7.24518239e-01 -8.84732082e-02 -6.74116433e-01
-4.92983222e-01 -8.25150371e-01 -4.85681176e-01 9.73950446e-01
-1.34706569e+00 -9.93564665e-01 1.61565691e-02 4.40898150e-01
1.14477359e-01 7.66716013e-03 1.45234919e+00 7.34034032e-02
-1.56437665e-01 5.63601255e-01 -2.84248203e-01 4.78623688e-01
5.57149529e-01 -1.32718754e+00 4.88002151e-01 4.41586882e-01
9.73021567e-01 1.16337013e+00 6.73860371e-01 -3.96731287e-01
-1.29287398e+00 -8.80004227e-01 1.53186822e+00 -8.82891834e-01
1.30050647e+00 -5.49044907e-01 -1.39526784e+00 8.38404417e-01
3.16097379e-01 4.27177846e-01 1.16019368e+00 7.76611567e-01
-1.09117317e+00 1.22752979e-01 -6.98193908e-01 6.27203763e-01
1.28540123e+00 -1.14094269e+00 -1.29263210e+00 4.08809334e-01
9.55286443e-01 -1.02489581e-02 -1.22710764e+00 4.06550951e-02
3.05166990e-01 -5.45040309e-01 1.12656617e+00 -1.04329908e+00
5.93984187e-01 -1.73296943e-01 -4.28045005e-01 -1.37715089e+00
-4.09487903e-01 -3.01106572e-01 -3.09224129e-01 1.28628159e+00
7.62541473e-01 -9.33943212e-01 3.73776108e-01 4.81661141e-01
2.27893680e-01 -9.94991362e-01 -6.60524666e-01 -1.16783607e+00
5.59718609e-01 -5.83263695e-01 5.66775262e-01 1.44444036e+00
5.30877173e-01 8.96067381e-01 1.93066210e-01 7.81833231e-02
1.98901892e-01 3.98259997e-01 4.70618784e-01 -1.25282061e+00
-4.40933228e-01 -6.28050625e-01 -7.94969618e-01 -9.41492140e-01
6.78178191e-01 -1.52598476e+00 -2.73562431e-01 -1.83043540e+00
1.27537429e-01 -6.08391941e-01 -4.01675999e-01 7.18358457e-01
-2.12974682e-01 1.57643795e-01 -1.13383427e-01 -1.09862639e-02
-3.28041583e-01 5.42548478e-01 1.05180669e+00 9.04821530e-02
5.30069433e-02 -5.01042128e-01 -9.97163832e-01 5.40549219e-01
8.22205365e-01 -5.46989679e-01 -5.84950686e-01 -4.39403921e-01
5.20483255e-01 -2.52260864e-01 5.76148808e-01 -4.18945432e-01
2.13372126e-01 -2.96232760e-01 9.31029916e-02 1.41550288e-01
5.20711482e-01 -6.82136059e-01 -1.28829107e-01 1.06992066e-01
-5.42788744e-01 1.58017799e-02 1.89053893e-01 4.69072133e-01
-3.37721676e-01 -3.59633982e-01 4.54309821e-01 1.04802800e-02
-9.06844616e-01 1.55869573e-01 -1.26543090e-01 4.80307400e-01
6.95471644e-01 2.24910788e-02 -3.45924050e-01 -3.89478832e-01
-4.63094115e-01 -2.02515259e-01 4.44082826e-01 6.50182724e-01
5.10320425e-01 -1.55021298e+00 -5.25894403e-01 -1.30304754e-01
5.68901360e-01 -1.70141235e-01 -2.22418055e-01 4.91302699e-01
-1.76742002e-01 5.35489678e-01 2.44282056e-02 -4.51063178e-02
-8.71399462e-01 8.74551833e-01 1.44079164e-01 -1.60628334e-01
-6.97473943e-01 9.99506533e-01 6.97206184e-02 -6.10365510e-01
-1.92125589e-01 -4.99394059e-01 -1.96843863e-01 1.65010314e-03
3.31588089e-01 -1.41041204e-01 -9.22057591e-03 -7.37522185e-01
-6.85407162e-01 6.34006977e-01 -5.47601320e-02 -2.26977165e-03
1.34403253e+00 1.38091296e-01 -2.54226357e-01 6.82271779e-01
1.53836584e+00 1.38854831e-01 -3.19855928e-01 -6.24069035e-01
3.29961866e-01 -3.86342227e-01 -1.28275633e-01 -3.79889935e-01
-6.56582773e-01 9.53799784e-01 -2.46287391e-01 4.95423555e-01
7.66266286e-01 5.53312600e-01 8.12801957e-01 7.47147322e-01
3.24449062e-01 -5.76790214e-01 5.91607615e-02 6.99332833e-01
8.44401896e-01 -1.09788215e+00 1.51610419e-01 -4.80179340e-01
-6.71756446e-01 1.08605886e+00 2.68387526e-01 -4.08527195e-01
8.45008731e-01 1.55823857e-01 -1.24098465e-01 -3.48765910e-01
-8.95116270e-01 -5.00657558e-01 4.33530599e-01 9.48425591e-01
9.17111397e-01 2.94655114e-01 -3.74962687e-01 7.63899267e-01
-5.56809843e-01 -2.93890089e-01 3.63573968e-01 6.72313035e-01
-2.98723847e-01 -1.40239215e+00 1.42424271e-01 4.50639397e-01
-1.62928417e-01 -5.33442616e-01 -5.13074040e-01 7.80206442e-01
-3.24101113e-02 9.18731928e-01 1.31991833e-01 -3.77654970e-01
3.08498174e-01 1.85178742e-01 6.11139536e-01 -1.09979296e+00
-3.29772383e-01 -6.44601882e-01 4.80997741e-01 -4.65366125e-01
-2.85047889e-01 -3.96420002e-01 -1.44821417e+00 -4.40749139e-01
-2.01676816e-01 2.59782344e-01 2.14738786e-01 1.16820252e+00
1.97788671e-01 3.96601617e-01 1.86354905e-01 -3.65894467e-01
-2.86184132e-01 -9.01535869e-01 -5.16241848e-01 6.08867347e-01
-4.18789685e-02 -7.08386600e-01 -2.61681467e-01 -3.70341912e-02] | [10.054581642150879, 8.73697280883789] |
f90b9e29-e599-43ee-897a-d8539823ebaf | lamv-learning-to-align-and-match-videos-with | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Baraldi_LAMV_Learning_to_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Baraldi_LAMV_Learning_to_CVPR_2018_paper.pdf | LAMV: Learning to Align and Match Videos With Kernelized Temporal Layers | This paper considers a learnable approach for comparing and aligning videos. Our architecture builds upon and revisits temporal match kernels within neural networks: we propose a new temporal layer that finds temporal alignments by maximizing the scores between two sequences of vectors, according to a time-sensitive similarity metric parametrized in the Fourier domain. We learn this layer with a temporal proposal strategy, in which we minimize a triplet loss that takes into account both the localization accuracy and the recognition rate. We evaluate our approach on video alignment, copy detection and event retrieval. Our approach outperforms the state on the art on temporal video alignment and video copy detection datasets in comparable setups. It also attains the best reported results for particular event search, while precisely aligning videos. | ['Hervé Jégou', 'Rita Cucchiara', 'Matthijs Douze', 'Lorenzo Baraldi'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['video-alignment'] | ['computer-vision'] | [ 2.19010621e-01 -5.48916280e-01 -4.18369710e-01 -2.68217534e-01
-1.08669353e+00 -5.41255474e-01 8.68771136e-01 2.66205758e-01
-7.85913825e-01 1.37194246e-01 2.04840615e-01 3.65243793e-01
-4.50232327e-01 -2.59245157e-01 -1.06408155e+00 -6.14527881e-01
-6.64636731e-01 2.08726674e-01 4.93657291e-01 2.70695478e-01
3.33832502e-01 5.35430372e-01 -1.31650019e+00 4.49015111e-01
1.70087278e-01 1.20904469e+00 1.75417244e-01 9.43749189e-01
4.63587344e-01 9.25062239e-01 -3.84972155e-01 -4.28136736e-01
4.13438201e-01 -2.55621165e-01 -6.96886480e-01 -1.47566587e-01
1.17110562e+00 -3.97094637e-01 -8.68077993e-01 7.41069019e-01
4.18214351e-01 3.78261417e-01 6.48392558e-01 -1.24646342e+00
-3.83564442e-01 2.79841483e-01 -2.62685567e-01 7.33856261e-01
6.12537622e-01 5.94985075e-02 1.05861843e+00 -8.16152275e-01
7.25924730e-01 8.40160191e-01 1.09095836e+00 1.23142228e-01
-1.19564009e+00 -3.70574176e-01 2.46528909e-02 8.43013227e-01
-1.54156697e+00 -6.85804367e-01 4.09631610e-01 -5.66454649e-01
1.35488570e+00 1.02462463e-01 7.83009946e-01 1.06241715e+00
4.25524861e-01 8.15224886e-01 3.47884685e-01 -3.27720731e-01
3.64294909e-02 -4.01097357e-01 -1.61206111e-01 6.72872186e-01
-4.16711599e-01 3.04775596e-01 -1.01206720e+00 -6.57200888e-02
7.02281058e-01 -1.83591049e-03 -2.16461137e-01 -6.72745168e-01
-1.67983317e+00 5.13875246e-01 1.82531253e-01 5.45912743e-01
-6.20683670e-01 5.52936375e-01 6.32853866e-01 4.54602867e-01
3.14431906e-01 2.24165931e-01 -1.23631455e-01 -2.16810361e-01
-1.57741439e+00 2.71134585e-01 6.20042503e-01 9.05857325e-01
2.59597003e-01 -1.82896286e-01 -6.96494520e-01 4.92677420e-01
1.09727316e-01 3.02600563e-01 4.67001557e-01 -1.19833803e+00
4.41048235e-01 -5.93083203e-02 1.00250676e-01 -1.10152543e+00
-3.19316030e-01 -2.00265914e-01 -3.88510436e-01 -2.18900815e-01
3.22627753e-01 3.35654676e-01 -5.35465479e-01 1.93346882e+00
3.12910005e-02 1.08189642e+00 -2.05781624e-01 9.16283965e-01
4.26565289e-01 5.76389372e-01 -4.81881807e-03 -3.97406995e-01
1.19692767e+00 -1.01788020e+00 -7.70264149e-01 1.97882101e-01
4.71493423e-01 -9.76134956e-01 4.57848758e-01 2.91380137e-01
-1.48723066e+00 -6.32421017e-01 -1.25408340e+00 -1.34895053e-02
-1.39158294e-01 4.76932079e-01 2.56799966e-01 2.47162372e-01
-1.46341205e+00 1.02356374e+00 -1.14214265e+00 -7.07096756e-01
2.79789278e-03 4.70860898e-01 -5.52021742e-01 3.42761129e-01
-1.06845355e+00 8.56005669e-01 4.67330009e-01 -8.68591294e-03
-9.63000476e-01 -7.31295347e-01 -8.45091939e-01 2.35192180e-02
1.54235750e-01 -6.11145914e-01 1.24726415e+00 -7.98575461e-01
-1.28445268e+00 1.05567348e+00 -1.91060230e-01 -1.08738017e+00
6.25986457e-01 -3.68880868e-01 -5.22191644e-01 5.45993328e-01
1.09377913e-01 7.29704797e-01 1.12412071e+00 -3.06678683e-01
-5.54071248e-01 5.12945540e-02 -3.28072533e-02 8.02608728e-02
-4.23601121e-01 3.16510707e-01 -8.91615808e-01 -8.90739977e-01
-1.34892881e-01 -9.16700959e-01 5.32357022e-02 3.23424786e-01
2.01984239e-03 -2.73319960e-01 7.10976899e-01 -8.67943943e-01
1.39012218e+00 -2.34485769e+00 5.12958050e-01 1.20322928e-01
-4.18228768e-02 9.51691046e-02 -4.08049881e-01 4.84930933e-01
-2.63511151e-01 -4.73180145e-01 -7.59221055e-03 -5.30497432e-01
1.43740237e-01 -1.87811106e-01 -4.54623878e-01 7.53678977e-01
2.21459687e-01 7.88577139e-01 -7.71022141e-01 -6.61336899e-01
3.66455942e-01 5.17225981e-01 -5.43471813e-01 4.29866344e-01
-3.80804427e-02 3.31055075e-02 -1.08227663e-01 3.96145821e-01
3.45194638e-01 -3.22024941e-01 2.50511825e-01 -7.38729775e-01
-2.12480724e-01 1.96797132e-01 -1.13770807e+00 2.25467205e+00
-1.67088449e-01 1.16044879e+00 -4.09894675e-01 -1.17363596e+00
5.40756404e-01 6.24012887e-01 1.05281818e+00 -7.08725214e-01
-7.04231933e-02 1.75662041e-02 -3.57679278e-01 -6.83098137e-01
5.52583218e-01 3.40211421e-01 1.33014098e-01 5.37680745e-01
5.79015017e-01 4.11505997e-01 4.97839451e-01 2.28926495e-01
1.29739070e+00 5.94196498e-01 3.92880619e-01 -9.80077013e-02
6.13684297e-01 -2.62522191e-01 9.71228182e-02 7.99942374e-01
-3.65561157e-01 6.52679324e-01 3.12251449e-01 -6.05335414e-01
-1.22000992e+00 -1.35260439e+00 1.50446193e-02 1.17365682e+00
1.08261414e-01 -6.19901955e-01 -8.02838504e-01 -5.21736979e-01
-1.35084718e-01 2.09725186e-01 -6.31414592e-01 -2.33636171e-01
-1.05449021e+00 -4.26121414e-01 6.50905073e-01 6.38116062e-01
3.34609509e-01 -9.11210537e-01 -6.95373178e-01 2.81640291e-01
-4.97651219e-01 -1.46741891e+00 -1.00911951e+00 2.65303962e-02
-7.93335676e-01 -1.09666836e+00 -6.72182977e-01 -9.44057524e-01
6.22500479e-03 1.89832762e-01 1.12779701e+00 -1.51501656e-01
-4.86849666e-01 8.93136799e-01 -6.87126145e-02 1.42584935e-01
-5.90116009e-02 1.05644628e-01 1.93324909e-01 2.00837806e-01
4.60849196e-01 -5.95219851e-01 -7.24350870e-01 2.99862564e-01
-8.04778397e-01 -4.40695941e-01 2.88181394e-01 6.04304373e-01
5.70555329e-01 -4.47534651e-01 -1.27119601e-01 2.52783597e-01
1.81936011e-01 -3.48195553e-01 -7.40168154e-01 4.93094027e-01
-2.35448360e-01 1.51057929e-01 2.09570572e-01 -7.59359241e-01
-4.68721718e-01 1.61141098e-01 2.84539521e-01 -1.01442730e+00
5.07092364e-02 7.42956176e-02 4.29685622e-01 -3.82777005e-01
5.08087039e-01 3.55643511e-01 -1.94165185e-01 -2.15482488e-01
3.03944200e-01 1.76189661e-01 9.03148174e-01 -3.65502477e-01
6.77395523e-01 6.22515082e-01 7.17510208e-02 -5.72614610e-01
-5.29359519e-01 -7.55636573e-01 -9.62030411e-01 -4.63198751e-01
1.05738139e+00 -9.34651911e-01 -8.15812767e-01 3.64643455e-01
-1.36140609e+00 -1.13962680e-01 -2.05719262e-01 1.05313289e+00
-1.14466429e+00 6.22098386e-01 -8.12214077e-01 -3.55393589e-01
-2.37710774e-01 -8.65008831e-01 1.30226946e+00 -1.77534521e-01
-4.43956465e-01 -8.20941627e-01 5.04871964e-01 -2.10011184e-01
3.91472608e-01 1.57366455e-01 5.07800043e-01 -6.81600988e-01
-1.09711504e+00 -2.53805757e-01 -1.94227681e-01 1.16923675e-01
-2.19971716e-01 8.57736766e-02 -6.96209133e-01 -4.32942212e-01
-6.02544658e-02 -2.52543271e-01 9.99310315e-01 6.33257389e-01
8.56369853e-01 -1.66638240e-01 -4.14076388e-01 8.49001288e-01
1.22292018e+00 2.32042238e-01 5.44611990e-01 6.10823512e-01
4.32568759e-01 5.26570916e-01 7.77526975e-01 4.72663015e-01
-1.07537163e-02 1.40833688e+00 2.15746060e-01 2.41314292e-01
-1.67680562e-01 1.11285068e-01 5.64815104e-01 6.03260338e-01
-1.59577161e-01 -3.96744728e-01 -6.56793594e-01 7.06841290e-01
-2.27895713e+00 -1.47959971e+00 4.78570312e-01 2.31683803e+00
5.19542217e-01 -3.18325832e-02 2.67405272e-01 -1.12665981e-01
8.10077608e-01 5.36183059e-01 -2.89216399e-01 -3.99633534e-02
-1.10436238e-01 2.60718524e-01 3.79436493e-01 3.84684205e-01
-1.65328288e+00 7.38317370e-01 7.13678551e+00 7.27008343e-01
-1.11788356e+00 2.26960197e-01 1.67154986e-02 -6.04417264e-01
4.31341141e-01 -3.35807562e-01 -5.02013564e-01 4.19905186e-01
1.15682554e+00 -1.82079062e-01 4.69966888e-01 4.63381946e-01
1.38866901e-01 1.93075910e-01 -1.74613237e+00 1.15561533e+00
5.06716490e-01 -1.61708045e+00 -8.43072608e-02 -2.07963526e-01
4.42740887e-01 2.33785495e-01 1.79784581e-01 -6.04404844e-02
-2.63151944e-01 -8.70155454e-01 1.01749551e+00 9.20858979e-01
5.84369659e-01 -5.11750460e-01 5.58640718e-01 -2.18556568e-01
-1.57986343e+00 5.60445413e-02 -4.67772111e-02 3.72887373e-01
3.57900023e-01 2.98861377e-02 -6.27895534e-01 3.41161370e-01
8.18144560e-01 1.27508080e+00 -3.81013453e-01 1.49515641e+00
2.37965241e-01 2.71912932e-01 -3.63141030e-01 3.95699620e-01
3.11826020e-01 -2.15497032e-01 7.44636476e-01 1.60124290e+00
5.87335885e-01 -1.77657217e-01 2.93739825e-01 3.99844527e-01
2.73635872e-02 3.41568440e-02 -5.80815911e-01 6.15572594e-02
3.69456798e-01 9.60731387e-01 -4.92175192e-01 -3.81218046e-01
-3.38585645e-01 1.33297181e+00 3.45701367e-01 2.84093827e-01
-1.16927862e+00 -2.18378454e-01 6.60188317e-01 -6.55730665e-02
6.19026601e-01 -3.19731742e-01 5.17974257e-01 -1.16918433e+00
4.93299872e-01 -6.98013484e-01 7.11247742e-01 -6.80900335e-01
-1.01730037e+00 5.06652713e-01 1.57644570e-01 -1.58646441e+00
-6.44059777e-01 -5.85904062e-01 -4.90220040e-01 3.72626334e-01
-1.18907619e+00 -9.78887856e-01 -1.29499570e-01 7.07882166e-01
4.97816473e-01 -2.18938038e-01 5.70833862e-01 7.03652740e-01
-3.85850877e-01 7.58096695e-01 6.21612221e-02 1.25280783e-01
1.04683352e+00 -9.99338388e-01 5.77008784e-01 9.46255505e-01
5.36081731e-01 5.79560757e-01 6.28921330e-01 -2.60626435e-01
-1.28339565e+00 -9.45024192e-01 1.12204957e+00 -5.20889878e-01
9.45478499e-01 -2.71717221e-01 -8.77027810e-01 8.84483874e-01
4.54350680e-01 7.47902766e-02 5.07690370e-01 -2.97996521e-01
-6.79241836e-01 -2.13221475e-01 -8.46770644e-01 4.21195835e-01
1.22558367e+00 -1.04765236e+00 -5.42568684e-01 6.57116711e-01
5.78461230e-01 -5.12283325e-01 -1.10287476e+00 4.50697213e-01
8.78232121e-01 -1.04902875e+00 1.31415975e+00 -6.61080241e-01
2.04277799e-01 -3.28359932e-01 -3.00660789e-01 -6.21966064e-01
-5.87149560e-01 -8.12868118e-01 -4.28637683e-01 7.60873497e-01
-1.31083364e-02 8.31225701e-03 7.10075021e-01 3.59337218e-02
5.33151589e-02 -4.83063668e-01 -1.20828295e+00 -1.11734033e+00
-6.16360486e-01 -4.83558446e-01 2.09095046e-01 8.58646214e-01
-7.32462630e-02 -8.05952922e-02 -6.37528062e-01 3.22415829e-01
6.80121839e-01 1.60886779e-01 3.87044311e-01 -6.50520325e-01
-5.34387589e-01 -5.98779380e-01 -9.46834922e-01 -9.78418767e-01
3.23025346e-01 -7.38704145e-01 4.04609740e-03 -8.54584277e-01
2.85947889e-01 2.41498515e-01 -6.29438818e-01 2.40531012e-01
2.50298619e-01 5.87531030e-01 2.87934899e-01 2.62084693e-01
-1.14392149e+00 3.41188461e-01 3.68020833e-01 -1.97294459e-01
3.35971378e-02 -2.34769180e-01 4.51480061e-01 4.56579179e-01
4.42340910e-01 -4.38569933e-01 -8.78435895e-02 -5.49722075e-01
1.81036629e-03 2.76209682e-01 7.42619932e-01 -1.39346075e+00
5.83519280e-01 9.01523754e-02 3.38025302e-01 -7.91568816e-01
5.98568022e-01 -7.54795730e-01 2.94403315e-01 5.32807887e-01
-6.54653013e-01 5.39327979e-01 2.10441545e-01 6.67293608e-01
-3.92853707e-01 1.05280019e-02 6.81163192e-01 2.60804385e-01
-8.47493768e-01 4.71584380e-01 -2.60928094e-01 -2.06133619e-01
1.10073054e+00 -2.47420803e-01 -7.89255798e-02 -3.05480242e-01
-7.68243432e-01 3.94932218e-02 3.38214874e-01 6.35723174e-01
5.26310086e-01 -1.64310956e+00 -6.66644394e-01 5.67693077e-02
2.38646448e-01 -1.02794898e+00 2.35443071e-01 1.21368527e+00
-4.72946018e-01 7.46658921e-01 -2.95323789e-01 -9.73741889e-01
-1.51338196e+00 7.31522918e-01 5.95465779e-01 -3.26585591e-01
-4.39788371e-01 7.24710822e-01 -5.13004027e-02 4.58098128e-02
6.49881005e-01 -1.75409466e-01 -2.14504391e-01 1.94054902e-01
6.91185832e-01 5.38423061e-01 2.04749629e-01 -7.05687582e-01
-5.21870196e-01 9.80856299e-01 -1.29896566e-01 -1.85468599e-01
9.84560728e-01 -2.02842951e-02 -2.38469820e-02 3.15976530e-01
1.55755794e+00 -3.14223468e-01 -1.27555561e+00 -4.53621864e-01
3.39235783e-01 -4.45012420e-01 -1.57138646e-01 -2.70423055e-01
-8.18181336e-01 6.07538581e-01 9.91997480e-01 -1.10860370e-01
1.15293646e+00 7.69917965e-02 8.45022440e-01 5.35573244e-01
1.56774387e-01 -9.18979406e-01 4.72990572e-01 4.97153640e-01
6.60478234e-01 -1.05606604e+00 6.17392287e-02 -2.25874279e-02
-5.84523976e-02 1.26790082e+00 2.68762499e-01 -3.95798177e-01
4.75991219e-01 1.14613898e-01 -1.99612752e-01 -1.35523379e-01
-9.11873579e-01 -5.29769771e-02 7.62022913e-01 4.32650089e-01
4.47612464e-01 -2.87189215e-01 -2.68510580e-01 -1.59034446e-01
1.80281550e-01 -8.16095322e-02 -1.84351169e-02 6.25247955e-01
-1.25471517e-01 -1.00792909e+00 -2.41172031e-01 1.16818659e-01
-5.12894988e-01 -2.02781528e-01 -1.27238840e-01 7.66799927e-01
-1.59156919e-02 4.71478581e-01 4.64216053e-01 -3.25939983e-01
3.02694410e-01 7.55794207e-03 8.53145957e-01 -7.26133212e-02
-6.66620195e-01 1.45752341e-01 -5.68621941e-02 -1.25689781e+00
-8.77733946e-01 -1.03187144e+00 -7.03278482e-01 -7.74975717e-02
-5.04345000e-02 4.37777117e-02 4.48933512e-01 9.26897705e-01
3.46703708e-01 2.19063193e-01 5.44980884e-01 -1.04209554e+00
-4.43727106e-01 -7.09027588e-01 -2.25180864e-01 3.72670442e-01
5.82940936e-01 -4.76915538e-01 -1.19666494e-01 3.58475178e-01] | [9.162869453430176, 0.576154351234436] |
5c3963af-78ed-49bf-a631-b350aa9fbda4 | extracting-topics-with-simultaneous-word-co | null | null | https://aclanthology.org/2021.findings-emnlp.2 | https://aclanthology.org/2021.findings-emnlp.2.pdf | Extracting Topics with Simultaneous Word Co-occurrence and Semantic Correlation Graphs: Neural Topic Modeling for Short Texts | Short text nowadays has become a more fashionable form of text data, e.g., Twitter posts, news titles, and product reviews. Extracting semantic topics from short texts plays a significant role in a wide spectrum of NLP applications, and neural topic modeling is now a major tool to achieve it. Motivated by learning more coherent and semantic topics, in this paper we develop a novel neural topic model named Dual Word Graph Topic Model (DWGTM), which extracts topics from simultaneous word co-occurrence and semantic correlation graphs. To be specific, we learn word features from the global word co-occurrence graph, so as to ingest rich word co-occurrence information; we then generate text features with word features, and feed them into an encoder network to get topic proportions per-text; finally, we reconstruct texts and word co-occurrence graph with topical distributions and word features, respectively. Besides, to capture semantics of words, we also apply word features to reconstruct a word semantic correlation graph computed by pre-trained word embeddings. Upon those ideas, we formulate DWGTM in an auto-encoding paradigm and efficiently train it with the spirit of neural variational inference. Empirical results validate that DWGTM can generate more semantically coherent topics than baseline topic models. | ['Jihong Ouyang', 'Xiaotang Zhou', 'Ximing Li', 'Yiming Wang'] | null | null | null | null | findings-emnlp-2021-11 | ['topic-models'] | ['natural-language-processing'] | [-1.68892086e-01 3.09645534e-01 -6.76777065e-01 -2.76540518e-01
-3.95723224e-01 -1.30764246e-01 9.12292898e-01 1.92540437e-01
-8.89122337e-02 5.61313510e-01 8.04095030e-01 -1.13446020e-01
-2.16246858e-01 -1.24277747e+00 -6.69296384e-01 -7.11500347e-01
1.46311998e-01 4.77253050e-01 -1.34584621e-01 -1.48262993e-01
1.70851171e-01 -3.83059710e-01 -1.18990397e+00 -1.47497237e-01
9.63716805e-01 7.85684288e-01 5.53410470e-01 1.06635004e-01
-7.08170950e-01 2.02399015e-01 -4.57471043e-01 -4.52431619e-01
-3.24416816e-01 -3.40158910e-01 -6.38730168e-01 4.91609097e-01
-1.03035077e-01 -4.06380706e-02 -5.26089311e-01 1.10235751e+00
-3.03477328e-02 5.46407819e-01 9.85555768e-01 -1.23338211e+00
-1.00375104e+00 8.98241282e-01 -7.68754601e-01 -1.30719975e-01
-1.66456196e-02 -1.65455550e-01 1.70456898e+00 -8.84155869e-01
6.04606986e-01 1.53661072e+00 2.85034597e-01 1.76627576e-01
-1.29764390e+00 -5.35607040e-01 5.88523448e-01 1.24208105e-03
-1.25231934e+00 2.99611300e-01 1.24313176e+00 -4.46467876e-01
7.84302294e-01 -1.07119687e-01 8.79422247e-01 1.49523830e+00
5.67400813e-01 9.75376785e-01 5.22390485e-01 -1.12239480e-01
2.00938672e-01 1.27882794e-01 4.41001952e-01 4.67471749e-01
4.05201584e-01 -5.02196372e-01 -7.34500408e-01 -2.46374562e-01
6.50104284e-01 5.76522946e-01 -3.43580484e-01 -4.05425906e-01
-1.02161670e+00 1.63205969e+00 4.15757418e-01 4.94119227e-01
-6.34027123e-01 3.33791167e-01 4.52484041e-01 -1.32307023e-01
1.18702638e+00 2.72430599e-01 -2.66077459e-01 2.10334167e-01
-9.29443240e-01 4.10571247e-01 5.96740127e-01 8.71752501e-01
1.12282181e+00 1.33969029e-02 -2.46164963e-01 9.84651685e-01
9.50241268e-01 4.34346706e-01 9.96210277e-01 -2.42717773e-01
3.48641336e-01 6.08147681e-01 -3.01757067e-01 -1.45552421e+00
-2.02556968e-01 -4.23128545e-01 -9.02972698e-01 -7.42050469e-01
-3.09260190e-01 -2.07050517e-01 -9.24130917e-01 1.67600822e+00
2.06275538e-01 3.85640472e-01 -7.53853545e-02 7.13760436e-01
8.21275353e-01 1.32027662e+00 3.64552945e-01 -2.79589295e-01
1.70086825e+00 -8.65136802e-01 -1.04496396e+00 -2.22354800e-01
2.50178367e-01 -3.70340884e-01 1.09157944e+00 1.92980185e-01
-5.24153590e-01 -3.95944625e-01 -8.05890679e-01 -1.88441202e-01
-5.45218766e-01 -1.56446397e-01 9.24908757e-01 1.94725692e-01
-8.48791182e-01 2.54549652e-01 -8.89503300e-01 -4.42232400e-01
6.16268516e-01 -3.39576453e-02 -4.99553978e-03 -1.29449472e-01
-1.50815189e+00 5.04846036e-01 7.43035257e-01 -3.20552826e-01
-7.35957682e-01 -7.38295436e-01 -1.39253652e+00 3.11812520e-01
6.09520376e-01 -8.99780750e-01 9.10831273e-01 -4.10979182e-01
-1.34800982e+00 3.45374048e-01 -4.64802802e-01 -4.58840966e-01
-3.01513255e-01 -2.84655958e-01 -4.07222778e-01 -2.60217488e-02
4.56671834e-01 5.32471597e-01 1.11240923e+00 -1.14480472e+00
-5.11073053e-01 -3.06632102e-01 -2.55713999e-01 1.49860889e-01
-9.28035438e-01 -4.51189190e-01 -5.18012941e-01 -8.56053054e-01
1.01878997e-02 -4.95494723e-01 -3.01790446e-01 -3.95280868e-01
-6.92702353e-01 -1.03084362e+00 1.09175467e+00 -6.60310149e-01
1.24969602e+00 -2.18553114e+00 3.86910349e-01 1.54289156e-01
7.45511889e-01 -1.37968153e-01 -1.11582212e-01 5.36530137e-01
2.26533696e-01 8.18652213e-02 -3.67648929e-01 -5.96920431e-01
3.15156728e-01 3.15853655e-01 -8.54661703e-01 1.39709458e-01
3.35328996e-01 1.15509188e+00 -1.02338850e+00 -4.58189279e-01
2.26327226e-01 6.77270830e-01 -5.95537543e-01 9.41304862e-02
-9.04293656e-01 -1.06639288e-01 -1.03399777e+00 8.80290046e-02
3.65560085e-01 -5.62388957e-01 1.31702840e-01 -1.13762105e-02
2.66233176e-01 3.39816302e-01 -6.59238756e-01 1.98051894e+00
-7.72473216e-01 7.45976627e-01 -4.71841067e-01 -1.50540614e+00
1.17255509e+00 3.56072962e-01 5.00996709e-01 -3.18843931e-01
3.08380455e-01 -2.25456163e-01 -5.46640813e-01 -3.11920404e-01
9.39952433e-01 -3.66224408e-01 -2.65073389e-01 4.86624241e-01
5.95280826e-01 -1.96900278e-01 1.19851328e-01 5.55363297e-01
5.14100015e-01 -5.55693246e-02 2.56538540e-01 -3.31192434e-01
4.79486063e-02 -9.61699933e-02 2.98162520e-01 2.10833058e-01
2.95899272e-01 5.14251769e-01 7.45639205e-01 -1.71846524e-01
-8.23970854e-01 -1.13502610e+00 -1.18140675e-01 1.13460803e+00
1.63830549e-01 -7.97755241e-01 -4.60744083e-01 -6.84691310e-01
1.31604090e-01 1.00101614e+00 -7.67388761e-01 -3.12275350e-01
-1.67881042e-01 -9.08088744e-01 -4.33423668e-02 4.31816876e-01
2.60687768e-01 -1.02093220e+00 3.76587315e-03 4.79758263e-01
-2.50531703e-01 -8.46685827e-01 -7.17714548e-01 -8.47071782e-03
-7.67496169e-01 -7.19391823e-01 -1.01257038e+00 -8.18913221e-01
3.64625305e-01 4.13256079e-01 1.10925674e+00 -4.05283898e-01
-2.85677016e-01 3.04867744e-01 -3.98829103e-01 -4.70211327e-01
1.47165492e-01 3.40593487e-01 -7.48651326e-02 1.80001333e-01
7.00230300e-01 -6.60694182e-01 -4.11608130e-01 -1.83790222e-01
-1.19913065e+00 7.20304325e-02 3.84957910e-01 9.67097223e-01
5.48248053e-01 2.45201886e-01 7.37450302e-01 -1.09681654e+00
1.00820923e+00 -1.11363661e+00 -5.20740867e-01 -4.71021868e-02
-7.38557816e-01 2.05668822e-01 3.82797301e-01 -3.93871218e-01
-1.14152098e+00 -5.66210806e-01 -5.60126640e-02 -5.27841687e-01
1.37059942e-01 1.07605791e+00 -2.94966161e-01 1.04173005e+00
1.63946554e-01 5.03526986e-01 -3.24700065e-02 -3.85431349e-01
9.89626646e-01 6.25922859e-01 1.11277893e-01 -4.04915631e-01
7.00770795e-01 4.50504631e-01 -3.76719356e-01 -1.11722279e+00
-1.06523764e+00 -6.82886183e-01 -1.15166962e-01 1.50427774e-01
1.15176201e+00 -1.04182017e+00 -2.53854603e-01 1.30000785e-01
-1.28493524e+00 3.31044905e-02 -3.87914032e-01 6.29735529e-01
-2.88820893e-01 2.68762410e-01 -4.04843092e-01 -4.92956430e-01
-3.43910754e-01 -8.67271602e-01 1.56275690e+00 3.00296485e-01
-2.96734482e-01 -1.72636425e+00 3.11675280e-01 -2.20546369e-02
3.61833215e-01 1.25794008e-01 1.14581919e+00 -7.85807312e-01
-4.29688752e-01 -1.09235127e-03 -3.98691326e-01 2.06083983e-01
4.19888258e-01 -3.38710636e-01 -8.06985438e-01 -1.28307417e-01
-3.52802947e-02 -2.23740637e-02 1.31351900e+00 7.61601329e-01
1.15086341e+00 -4.22658652e-01 -7.68562853e-01 3.63154560e-01
1.32654500e+00 -2.01844737e-01 3.50908548e-01 1.00600775e-02
1.06645787e+00 7.11875916e-01 3.07640493e-01 5.60180902e-01
6.32762253e-01 3.91539752e-01 2.99558491e-01 2.03014851e-01
1.80355430e-01 -7.78842509e-01 2.26074845e-01 1.26648939e+00
3.02256525e-01 -4.79716867e-01 -7.08983898e-01 7.84556925e-01
-1.79987478e+00 -6.56406105e-01 -2.62041762e-02 1.51839697e+00
7.34650373e-01 -4.77336235e-02 -3.20887826e-02 -2.44533867e-01
8.10570300e-01 9.06675220e-01 -3.21191430e-01 -3.67337314e-04
5.99614754e-02 1.85202628e-01 6.98769167e-02 3.55132252e-01
-9.45540667e-01 1.26519895e+00 4.65234852e+00 1.10168684e+00
-9.75591719e-01 3.15725803e-01 5.48366368e-01 1.82759598e-01
-1.08084965e+00 -9.79319289e-02 -8.25291455e-01 6.54575109e-01
7.38080263e-01 -6.86441779e-01 -1.02334484e-01 1.17600441e+00
7.88986906e-02 2.88003504e-01 -7.67705500e-01 9.03270900e-01
4.41846132e-01 -1.36869705e+00 5.20523429e-01 3.52343410e-01
9.27049279e-01 -1.58165365e-01 2.07443669e-01 6.20545924e-01
6.83937848e-01 -9.79576647e-01 2.57496506e-01 3.30960631e-01
5.45673013e-01 -8.10646892e-01 6.82801604e-01 2.00595707e-01
-1.21840286e+00 2.81245142e-01 -8.32464337e-01 1.64435804e-01
5.12700677e-01 1.24127543e+00 -7.68055797e-01 6.80882871e-01
3.48258466e-01 1.34535325e+00 2.28011366e-02 6.58496439e-01
-4.08443213e-01 9.58521605e-01 -9.88356695e-02 -5.43322682e-01
5.00641525e-01 -3.91365170e-01 4.80976582e-01 1.03359437e+00
4.32843775e-01 -1.13609262e-01 1.88891247e-01 1.17449510e+00
-4.35661614e-01 3.41188967e-01 -6.26785040e-01 -5.33544421e-01
2.80577421e-01 1.24106753e+00 -8.53593946e-01 -4.90588993e-01
-4.60262507e-01 1.03550148e+00 2.53687292e-01 6.26155078e-01
-6.63145900e-01 -5.15137255e-01 8.57734263e-01 -7.54047260e-02
2.81956106e-01 -2.45167941e-01 -1.56704076e-02 -1.42468762e+00
-1.17573343e-01 -1.77550435e-01 1.96906909e-01 -6.33372307e-01
-1.66364324e+00 4.79623318e-01 2.66561687e-01 -8.74353349e-01
-3.02625090e-01 -4.59958851e-01 -9.56890762e-01 9.62974012e-01
-1.53892004e+00 -1.19377947e+00 -1.83778748e-01 4.05750602e-01
1.06101072e+00 -2.61376023e-01 5.67072809e-01 -1.05481669e-01
-3.96075904e-01 9.35439914e-02 1.76882640e-01 2.16959394e-03
3.25174093e-01 -1.36721826e+00 6.22893810e-01 4.05032516e-01
6.60673797e-01 8.05131018e-01 5.74937224e-01 -8.47788155e-01
-1.28608012e+00 -1.61690879e+00 9.20492768e-01 -2.89682835e-01
9.35211897e-01 -5.61862588e-01 -9.90956426e-01 9.12081122e-01
4.83626008e-01 -3.08178961e-01 7.24681437e-01 5.94082117e-01
-2.49143228e-01 2.89925009e-01 -3.76133174e-01 6.72081232e-01
6.37290299e-01 -6.56732500e-01 -1.05905199e+00 7.03911960e-01
1.67620051e+00 8.90243426e-03 -6.57713652e-01 -1.87011510e-01
5.14237396e-02 -2.40099058e-01 7.67342508e-01 -5.46614051e-01
8.25210810e-01 7.94706494e-02 -1.05984785e-01 -1.91859496e+00
-2.55201250e-01 -4.65366155e-01 -2.58536994e-01 1.42632461e+00
4.33461308e-01 -8.77445281e-01 8.24505627e-01 -4.80379760e-02
-1.97124496e-01 -7.94616938e-01 -5.86165547e-01 -5.06025553e-01
1.88202828e-01 -5.98683298e-01 6.50029957e-01 1.03578818e+00
2.67841399e-01 7.97890961e-01 -3.73671651e-01 -3.23434658e-02
4.98925060e-01 3.02391887e-01 5.63277483e-01 -1.33747351e+00
-3.25620621e-01 -5.88829517e-01 -3.23158592e-01 -1.37900174e+00
5.40593207e-01 -9.84595001e-01 1.29983082e-01 -1.93143904e+00
3.88557017e-01 -6.09599575e-02 -1.35101259e-01 2.33833134e-01
-5.61056435e-01 -1.13553077e-01 -5.84169216e-02 3.89079303e-02
-6.17204785e-01 1.35067141e+00 1.19009125e+00 -2.48947933e-01
-6.38879463e-02 -7.43976086e-02 -9.17518020e-01 5.48969448e-01
5.79382241e-01 -4.21661615e-01 -7.22696543e-01 -2.33778000e-01
3.52592647e-01 -1.02207422e-01 2.33502403e-01 -3.18942010e-01
2.73327772e-02 -2.04753399e-01 6.45488203e-02 -6.31671548e-01
3.04625154e-01 -6.05037391e-01 -4.83754724e-01 1.22371346e-01
-4.16072965e-01 -3.44946742e-01 -1.09190561e-01 1.05519402e+00
-5.42484820e-01 -5.22209294e-02 5.38901091e-02 2.53275055e-02
-6.43371463e-01 7.03130186e-01 -4.48004305e-01 1.47744521e-01
7.93827832e-01 1.18463904e-01 -1.61876112e-01 -4.94571179e-01
-5.46708763e-01 4.83615011e-01 -7.93014318e-02 8.25133801e-01
6.57343984e-01 -1.45312083e+00 -6.08380497e-01 1.74497530e-01
2.01336160e-01 4.08085316e-01 4.19301867e-01 3.95408213e-01
1.34696886e-01 6.72349572e-01 3.95745575e-01 -6.47141993e-01
-5.84652543e-01 5.48152626e-01 -4.09368753e-01 -3.08063537e-01
-8.99314702e-01 9.42146361e-01 7.64988840e-01 -2.92408496e-01
-6.30815551e-02 -4.24592525e-01 -4.49097484e-01 4.09271449e-01
4.49922681e-01 -1.15199238e-01 -3.64300489e-01 -3.91118377e-01
1.53997079e-01 4.44061160e-01 -1.85267076e-01 -1.91910520e-01
1.49532831e+00 -1.72154397e-01 -6.00864664e-02 7.79973149e-01
1.60153592e+00 -2.14392677e-01 -1.13620496e+00 -6.65544152e-01
1.47756040e-02 -2.58153975e-01 3.22412342e-01 6.59369379e-02
-1.12488949e+00 1.13569319e+00 -1.00267157e-01 5.60175478e-01
6.37284756e-01 6.10549867e-01 1.20941913e+00 1.51393652e-01
7.32013509e-02 -8.01818252e-01 3.66988897e-01 4.33998823e-01
6.98650241e-01 -1.10872161e+00 -2.84764636e-02 -4.35868889e-01
-7.63731599e-01 9.12003517e-01 4.13658261e-01 -3.45382452e-01
1.03481185e+00 -3.02824110e-01 -4.31822360e-01 -6.08971596e-01
-6.64402843e-01 -1.28775656e-01 6.40664101e-01 5.52285492e-01
4.86879230e-01 3.00918460e-01 -4.10942495e-01 1.01482797e+00
-4.39646810e-01 -4.58540857e-01 2.03818545e-01 3.34079593e-01
-6.54647827e-01 -8.56653571e-01 2.84532718e-02 8.15750122e-01
-2.50726968e-01 -2.79218465e-01 1.39471772e-03 6.04549646e-01
-2.17129081e-01 6.06904924e-01 3.30042511e-01 -3.25091511e-01
-2.53701746e-01 2.00424954e-01 -2.12170511e-01 -1.08112466e+00
1.75432488e-01 2.69576430e-01 -2.08770409e-01 -2.98882425e-01
-2.25549147e-01 -5.13097644e-01 -1.15149033e+00 1.02921665e-01
-4.99423355e-01 5.95811248e-01 8.61780643e-01 1.31256640e+00
2.28503585e-01 1.02460599e+00 5.39872885e-01 -6.85520828e-01
-3.06215912e-01 -1.25943935e+00 -8.64690363e-01 3.26301843e-01
8.69474411e-02 -8.82277727e-01 -2.24945933e-01 1.38913438e-01] | [10.38088321685791, 6.942087173461914] |
a4e0adae-eaaa-4497-b402-9fc26a20e10c | affordance-detection-with-dynamic-tree | 2211.05200 | null | https://arxiv.org/abs/2211.05200v1 | https://arxiv.org/pdf/2211.05200v1.pdf | Affordance detection with Dynamic-Tree Capsule Networks | Affordance detection from visual input is a fundamental step in autonomous robotic manipulation. Existing solutions to the problem of affordance detection rely on convolutional neural networks. However, these networks do not consider the spatial arrangement of the input data and miss parts-to-whole relationships. Therefore, they fall short when confronted with novel, previously unseen object instances or new viewpoints. One solution to overcome such limitations can be to resort to capsule networks. In this paper, we introduce the first affordance detection network based on dynamic tree-structured capsules for sparse 3D point clouds. We show that our capsule-based network outperforms current state-of-the-art models on viewpoint invariance and parts-segmentation of new object instances through a novel dataset we only used for evaluation and it is publicly available from github.com/gipfelen/DTCG-Net. In the experimental evaluation we will show that our algorithm is superior to current affordance detection methods when faced with grasping previously unseen objects thanks to our Capsule Network enforcing a parts-to-whole representation. | ['Matteo Saveriano', 'Jakob Mittelberger', 'Chris Engelhardt', 'David Peer', 'Simon Haller-Seeber', 'Antonio Rodríguez-Sánchez'] | 2022-11-09 | null | null | null | null | ['affordance-detection'] | ['computer-vision'] | [-6.69275522e-02 1.71728302e-02 -5.56891523e-02 -1.74776614e-01
-3.07083223e-02 -7.68818438e-01 4.07133847e-01 8.03205296e-02
-3.86905432e-01 2.76123226e-01 2.36024503e-02 1.06604006e-02
-3.89996558e-01 -3.39397162e-01 -9.95767832e-01 -2.75724679e-01
-4.80170101e-01 5.79523325e-01 4.57610518e-01 -2.90761203e-01
1.82636887e-01 9.63486552e-01 -1.55758834e+00 2.08796069e-01
5.74152887e-01 8.58678401e-01 6.08563304e-01 4.52432603e-01
3.48604083e-01 1.55727848e-01 -1.90911293e-01 1.61106139e-01
7.23160088e-01 9.49259475e-02 -6.34717107e-01 2.39055142e-01
6.65306270e-01 -5.38249373e-01 -5.42355001e-01 1.02126384e+00
1.32919386e-01 1.58112645e-02 4.70180988e-01 -1.33047497e+00
-7.67397165e-01 4.80749935e-01 -1.48841754e-01 1.82353720e-01
5.51816463e-01 1.81678563e-01 7.67773032e-01 -1.10779417e+00
1.25871015e+00 1.40547585e+00 4.47345614e-01 7.27108955e-01
-1.27589059e+00 -1.11766823e-01 6.29291892e-01 6.25629872e-02
-1.03355992e+00 -2.79184252e-01 8.48409951e-01 -3.81651878e-01
1.32700014e+00 3.02589804e-01 1.14641690e+00 1.03328395e+00
3.03994179e-01 1.15679133e+00 8.07444811e-01 -3.06678921e-01
1.18909799e-01 -3.26732844e-01 -2.01038197e-01 7.43118584e-01
5.26343107e-01 3.25820535e-01 -3.15684050e-01 -1.43118796e-03
1.39242089e+00 4.87819791e-01 -4.80288118e-01 -1.46938968e+00
-1.79591680e+00 4.79841232e-01 1.19038343e+00 4.84354198e-01
-2.49254078e-01 3.16853940e-01 1.18480407e-01 2.97744334e-01
1.48368403e-01 7.45766222e-01 -6.14140093e-01 2.97450125e-01
-4.93733972e-01 7.75827408e-01 9.01455343e-01 1.46039474e+00
2.33772948e-01 -2.82951146e-01 1.26754552e-01 2.33554587e-01
4.44302320e-01 1.70082942e-01 2.67689884e-01 -1.16393101e+00
3.48336309e-01 7.02807069e-01 2.38839090e-01 -9.51372921e-01
-7.21727371e-01 -3.23034465e-01 -3.74651074e-01 6.23994350e-01
5.76841652e-01 2.61553705e-01 -1.25098431e+00 1.40276027e+00
3.98667574e-01 -4.79772121e-01 -1.70730948e-01 1.34193909e+00
9.82734144e-01 -2.28500858e-01 -2.85049170e-01 3.33696872e-01
1.25696909e+00 -1.14659989e+00 -5.05234778e-01 -2.35242620e-01
2.61400044e-01 -6.21723711e-01 8.76779795e-01 4.65380162e-01
-8.82519186e-01 -3.79006535e-01 -1.14159656e+00 -4.06101942e-01
-5.52592516e-01 1.50724366e-01 9.98851120e-01 6.96226805e-02
-9.03985858e-01 8.88228476e-01 -1.19730341e+00 -5.88685632e-01
6.02477610e-01 5.65670013e-01 -7.42661715e-01 -1.79669276e-01
-3.48177135e-01 9.58620012e-01 3.89983684e-01 3.35601449e-01
-1.16105187e+00 -4.00219291e-01 -9.73701537e-01 8.01771656e-02
8.09428871e-01 -8.78567815e-01 1.26626682e+00 -6.06866956e-01
-1.13790536e+00 8.95559788e-01 7.39507675e-02 -2.19266593e-01
9.03226614e-01 -4.24591780e-01 2.13654369e-01 2.20651567e-01
-8.73279385e-03 9.77840185e-01 7.63300538e-01 -1.52056730e+00
-2.15179592e-01 -5.70644796e-01 5.24572194e-01 1.32130608e-01
2.29590550e-01 -2.72013724e-01 -1.98076084e-01 -6.85659707e-01
9.59128916e-01 -1.02509212e+00 -3.40725213e-01 8.12720001e-01
-6.73902333e-01 -3.59743267e-01 1.01085055e+00 -2.40668207e-01
1.58724338e-01 -2.03541636e+00 6.81386113e-01 -1.43725589e-01
4.46789414e-01 -1.76360868e-02 -2.23122135e-01 4.03312474e-01
-1.47506803e-01 -7.19384402e-02 -7.03641996e-02 -3.21768194e-01
2.20638961e-01 2.85704881e-01 -5.22153601e-02 7.15158463e-01
2.51212358e-01 8.61969352e-01 -1.08661318e+00 -2.07909122e-01
3.84762585e-01 4.61167872e-01 -7.03984320e-01 2.60804087e-01
-5.32471001e-01 6.94792449e-01 -3.85880440e-01 1.13142228e+00
6.89632773e-01 -4.67907488e-02 5.02611659e-02 -2.81918973e-01
-1.71532214e-01 1.18886814e-01 -1.06975150e+00 2.51851606e+00
4.14198190e-02 4.07859027e-01 2.63868123e-01 -8.02038848e-01
6.39664352e-01 1.73692673e-01 5.02955079e-01 -1.02213681e-01
2.83423454e-01 4.57511038e-01 3.75706196e-01 -6.03269339e-01
2.55091965e-01 1.64596811e-01 1.01860389e-02 1.62381679e-02
3.48242581e-01 -4.93464053e-01 3.35907698e-01 3.98355164e-02
9.21774626e-01 7.88335264e-01 2.43452311e-01 -3.93468738e-01
6.86071664e-02 3.09203923e-01 3.18976283e-01 5.93419135e-01
-4.19324547e-01 8.15741539e-01 4.07889217e-01 -9.17594135e-01
-1.08060575e+00 -1.18347156e+00 -2.93702483e-01 6.14390671e-01
5.23866355e-01 -1.72737017e-01 -4.90725487e-01 -5.25350213e-01
5.15816391e-01 1.08236499e-01 -7.61621535e-01 2.01234013e-01
-5.78250945e-01 -1.46193057e-01 -2.27427050e-01 6.33009374e-01
3.37183177e-01 -1.33023310e+00 -1.28020275e+00 1.85669303e-01
1.25170544e-01 -1.28709352e+00 -1.73057616e-01 3.21730644e-01
-1.16915095e+00 -1.40119290e+00 -8.62262487e-01 -8.72685790e-01
9.53017116e-01 4.95458961e-01 8.61235321e-01 2.24482521e-01
-6.65296197e-01 4.90356833e-01 -3.68765622e-01 -4.69022870e-01
-5.80752268e-02 1.49185434e-01 2.21865281e-01 -6.23670995e-01
1.73999906e-01 -6.08465075e-01 -8.36300313e-01 1.71803206e-01
-8.85229468e-01 -4.22470970e-03 5.42860866e-01 6.09727681e-01
5.62798023e-01 -5.28801799e-01 3.13192904e-02 -3.20663661e-01
3.81167114e-01 -1.99398905e-01 -6.71581447e-01 9.35713500e-02
2.52338666e-02 8.24917778e-02 1.97502404e-01 -7.20730662e-01
-4.41279680e-01 5.47508717e-01 7.80863464e-02 -7.88792610e-01
-4.28306073e-01 3.13290179e-01 -3.52339074e-02 -3.64508241e-01
5.87422490e-01 -2.79946178e-01 8.14636201e-02 -7.58589268e-01
4.94155198e-01 1.35496140e-01 4.24231350e-01 -5.04279554e-01
6.80409431e-01 8.81226659e-01 6.71297014e-02 -3.82357121e-01
-6.08845830e-01 -5.25765836e-01 -1.48422563e+00 -1.71963170e-01
8.55189860e-01 -8.35838437e-01 -8.94650459e-01 3.07994813e-01
-1.30643451e+00 -3.08954746e-01 -3.77186120e-01 5.37450135e-01
-8.56678903e-01 2.90441722e-01 -5.05848110e-01 -5.48014104e-01
-9.75969806e-02 -1.21719122e+00 1.22496450e+00 -1.65739674e-02
-2.16502428e-01 -6.32068276e-01 -3.39045644e-01 -1.52063817e-01
2.87671924e-01 9.44551706e-01 6.23398185e-01 -4.39827472e-01
-1.11701846e+00 -5.06571293e-01 -1.51996240e-01 -8.34166035e-02
2.87719905e-01 -2.70302683e-01 -7.04898000e-01 -6.83787525e-01
1.22544296e-01 -3.95586163e-01 9.56399262e-01 2.70611554e-01
1.11463785e+00 -1.51464984e-01 -5.61118305e-01 6.72867596e-01
1.53269720e+00 -2.23212555e-01 1.95534244e-01 5.29306412e-01
8.34513664e-01 5.89961767e-01 5.34557402e-01 2.91150481e-01
2.46262997e-01 6.18065536e-01 1.24140584e+00 4.33405600e-02
-1.56976357e-01 -1.01806276e-01 -4.48348820e-02 3.04750800e-01
-3.83470625e-01 1.80343585e-03 -8.29574168e-01 7.00398564e-01
-1.87923777e+00 -4.99851644e-01 -9.89123881e-02 2.06355500e+00
4.63819563e-01 1.87720075e-01 -3.99825163e-02 -1.40018463e-01
1.68722928e-01 -1.24732308e-01 -8.74874651e-01 5.63290901e-02
8.46097693e-02 -2.32821092e-01 2.63386756e-01 2.42524087e-01
-1.28935516e+00 9.43273187e-01 5.67868137e+00 -4.91091013e-02
-9.78935659e-01 9.09982920e-02 -2.42318258e-01 -1.95179507e-01
-6.47347197e-02 -1.52206467e-02 -4.03923064e-01 -2.07251593e-01
-1.67680666e-01 2.83776492e-01 5.28473377e-01 1.09861302e+00
-1.56701639e-01 -2.34244704e-01 -1.78206658e+00 8.12515616e-01
3.19846064e-01 -1.03739345e+00 -6.94392771e-02 8.16573352e-02
4.89132464e-01 4.45230037e-01 -1.59330592e-01 2.15440225e-02
1.65387437e-01 -9.50271368e-01 1.03945827e+00 6.03170156e-01
3.76491666e-01 -1.00797802e-01 4.51825231e-01 3.70102733e-01
-1.07662892e+00 -2.20061228e-01 -6.69829607e-01 -1.55162334e-01
5.79872914e-02 1.83953509e-01 -6.58479929e-01 4.50674534e-01
1.02664602e+00 7.83344030e-01 -4.90941495e-01 1.47636545e+00
-2.97813207e-01 -5.47209859e-01 -6.04924381e-01 -4.16900933e-04
2.04310745e-01 2.56294012e-01 9.41334546e-01 6.80680573e-01
2.77468443e-01 -1.16597958e-01 4.68355536e-01 1.46092439e+00
1.00754469e-03 -2.28803858e-01 -8.93354535e-01 9.99085605e-02
7.64525905e-02 1.19929457e+00 -1.05178416e+00 -2.40084589e-01
-3.53150278e-01 1.00582898e+00 6.77778900e-01 4.07767653e-01
-3.43196332e-01 -1.63456306e-01 6.07250750e-01 1.90987304e-01
6.44305110e-01 -8.15006673e-01 -1.09285876e-01 -1.46620667e+00
4.24049199e-01 -7.01531291e-01 1.10128164e-01 -9.46713328e-01
-1.15545034e+00 5.61314583e-01 2.25337610e-01 -1.49228954e+00
1.58902690e-01 -1.24123895e+00 -2.21622527e-01 4.80158240e-01
-1.61716795e+00 -1.34707713e+00 -6.77811444e-01 5.12027144e-01
7.32002139e-01 2.16036797e-01 9.15928066e-01 -2.35806599e-01
2.47659057e-01 -7.51849338e-02 -2.21092716e-01 2.10670009e-01
5.43166816e-01 -1.24777746e+00 3.05910945e-01 7.00855196e-01
2.22765967e-01 9.39084768e-01 7.52195358e-01 -5.91682673e-01
-1.74985945e+00 -5.19423068e-01 5.79425871e-01 -8.65341842e-01
5.06745040e-01 -7.91707158e-01 -8.93699765e-01 1.04197645e+00
1.54701203e-01 5.44159234e-01 4.79865633e-02 -1.02145493e-01
-2.77265251e-01 3.30543905e-01 -1.22967756e+00 5.74591577e-01
1.53671622e+00 -1.05448343e-01 -9.96779382e-01 7.28730381e-01
9.13734615e-01 -1.12995267e+00 -9.41257358e-01 6.99025869e-01
7.39889145e-01 -9.97465253e-01 1.12287748e+00 -7.26431787e-01
4.18954134e-01 -4.74153519e-01 -1.26499087e-01 -1.24465358e+00
-3.37982714e-01 -2.74536848e-01 -2.16854393e-01 1.10241413e-01
3.71390507e-02 -3.42505515e-01 5.58892131e-01 4.49654728e-01
-3.97410482e-01 -7.32427061e-01 -1.05782032e+00 -1.04286766e+00
7.48370886e-02 -1.32710025e-01 6.02396011e-01 8.57879400e-01
2.47949451e-01 -1.66164398e-01 1.05761953e-01 1.65471092e-01
7.14106500e-01 5.60161173e-01 8.38507295e-01 -1.45973217e+00
-1.20761536e-01 -4.46680099e-01 -6.31056190e-01 -1.10782325e+00
-1.13874778e-01 -9.16023374e-01 2.99193054e-01 -1.76843941e+00
1.14383079e-01 -3.17969829e-01 -7.47005045e-02 7.81577885e-01
2.46847674e-01 1.17401853e-01 5.57327986e-01 4.48462695e-01
-5.78969121e-01 5.76692581e-01 1.96255565e+00 -2.33301744e-01
-2.52530098e-01 -1.73422426e-01 -1.60910517e-01 7.15762854e-01
6.52467787e-01 -4.77796048e-01 6.87445607e-03 -7.45798528e-01
9.40566212e-02 -1.64732367e-01 8.91056061e-01 -1.06800723e+00
1.48831010e-01 8.46388340e-02 5.78616202e-01 -6.85680091e-01
4.06183869e-01 -1.12176967e+00 -1.13250718e-01 9.15828049e-01
3.03186360e-04 1.67475432e-01 3.35014880e-01 6.49063349e-01
9.89775136e-02 -2.99960256e-01 2.47522190e-01 -7.74027407e-01
-7.96091199e-01 4.48775381e-01 -2.33574301e-01 -4.07884210e-01
9.84348714e-01 -2.09869921e-01 -2.12175846e-01 1.57694146e-02
-1.31510770e+00 7.48669207e-02 7.78316081e-01 9.49689984e-01
8.94290686e-01 -1.26223671e+00 -4.75456387e-01 3.33651692e-01
4.22923893e-01 4.90905911e-01 -3.80045315e-03 8.85157645e-01
-8.30140889e-01 4.60838854e-01 -5.45574188e-01 -9.20581341e-01
-1.09982681e+00 9.56845164e-01 4.62662429e-01 1.20331846e-01
-1.01437199e+00 8.53047371e-01 5.62953055e-02 -8.63689184e-01
4.75920200e-01 -1.20771515e+00 4.71266638e-03 -5.15841722e-01
-7.59196058e-02 -1.90995261e-01 -8.88311788e-02 -4.55867559e-01
-4.26061928e-01 5.61714351e-01 9.71451327e-02 2.55890131e-01
1.55040038e+00 5.67240342e-02 -4.42513406e-01 5.47532082e-01
8.91993344e-01 -3.84324253e-01 -1.59678710e+00 -2.04607770e-01
6.22143969e-03 -7.39349425e-01 -3.98709238e-01 -7.24584877e-01
-8.13441455e-01 9.37989414e-01 6.05311453e-01 1.33771315e-01
4.78448838e-01 3.30115110e-01 3.86506468e-01 8.76226366e-01
7.27068365e-01 -7.43633747e-01 3.96946043e-01 5.56875825e-01
1.70142341e+00 -1.58108795e+00 3.59920114e-01 -7.10338771e-01
-3.01821288e-02 1.36462152e+00 8.30702305e-01 -6.96886241e-01
7.21339047e-01 7.24658221e-02 -1.22260936e-02 -6.49770319e-01
-3.89494866e-01 -2.57382214e-01 5.08108854e-01 7.56894767e-01
2.36086279e-01 1.39558122e-01 -2.38847602e-02 7.63447881e-02
-2.68467575e-01 -6.99367598e-02 5.53607583e-01 1.32923412e+00
-3.53465229e-01 -8.94869149e-01 -2.76708335e-01 1.80381283e-01
-1.76209882e-01 1.40953377e-01 -5.01620352e-01 1.20299304e+00
1.17246017e-01 5.05338967e-01 1.40395254e-01 1.83311198e-02
5.70687175e-01 -2.22603768e-01 1.12325191e+00 -8.39648008e-01
-3.01980942e-01 2.34421313e-01 -2.11096436e-01 -9.18386757e-01
-9.05763984e-01 -8.39978456e-01 -1.27157176e+00 2.96443343e-01
-3.35929543e-01 -5.56951523e-01 7.64404893e-01 8.72837126e-01
3.46298516e-01 7.08975554e-01 -1.14378199e-01 -1.74108946e+00
-5.65985262e-01 -1.08418870e+00 -3.81087512e-01 5.78142285e-01
6.63413167e-01 -1.07949555e+00 -2.42329985e-01 -6.29802942e-02] | [5.294111251831055, -0.26682034134864807] |
ddb99ed6-d1c5-41af-b6fe-2c6cdb295549 | subspace-clustering-with-active-learning | 1911.03299 | null | https://arxiv.org/abs/1911.03299v2 | https://arxiv.org/pdf/1911.03299v2.pdf | Subspace Clustering with Active Learning | Subspace clustering is a growing field of unsupervised learning that has gained much popularity in the computer vision community. Applications can be found in areas such as motion segmentation and face clustering. It assumes that data originate from a union of subspaces, and clusters the data depending on the corresponding subspace. In practice, it is reasonable to assume that a limited amount of labels can be obtained, potentially at a cost. Therefore, algorithms that can effectively and efficiently incorporate this information to improve the clustering model are desirable. In this paper, we propose an active learning framework for subspace clustering that sequentially queries informative points and updates the subspace model. The query stage of the proposed framework relies on results from the perturbation theory of principal component analysis, to identify influential and potentially misclassified points. A constrained subspace clustering algorithm is proposed that monotonically decreases the objective function subject to the constraints imposed by the labelled data. We show that our proposed framework is suitable for subspace clustering algorithms including iterative methods and spectral methods. Experiments on synthetic data sets, motion segmentation data sets, and Yale Faces data sets demonstrate the advantage of our proposed active strategy over state-of-the-art. | ['Nicos G. Pavlidis', 'Hankui Peng'] | 2019-11-08 | null | null | null | null | ['motion-segmentation', 'face-clustering'] | ['computer-vision', 'computer-vision'] | [ 2.31744215e-01 -1.50321811e-01 -2.53447980e-01 -3.89658034e-01
-7.44436204e-01 -6.56491995e-01 3.60164285e-01 -1.10631078e-01
-4.27602828e-01 3.71772021e-01 2.14193370e-02 1.02201141e-01
-3.10750991e-01 -4.56843138e-01 -3.51990312e-01 -1.25511634e+00
-9.85882804e-03 6.64427400e-01 2.53332257e-01 4.58827227e-01
4.24497992e-01 7.23743141e-01 -1.57737398e+00 -1.34238675e-01
1.13410020e+00 5.63453078e-01 1.17554471e-01 9.66271535e-02
-3.21046919e-01 2.41833761e-01 -2.14061499e-01 1.20394334e-01
3.81536037e-01 -3.76710206e-01 -5.95737338e-01 7.57380188e-01
9.51548517e-02 3.89616415e-02 4.88244966e-02 1.21192229e+00
3.58333319e-01 5.89435101e-01 9.31532860e-01 -1.36430538e+00
1.17476046e-01 3.13332766e-01 -8.35942209e-01 -4.55826633e-02
7.08254054e-02 -1.00711033e-01 6.07434928e-01 -1.14550912e+00
6.56625807e-01 1.31636143e+00 2.26143047e-01 5.22634149e-01
-1.36869311e+00 -6.85882270e-01 2.24185929e-01 3.99218470e-01
-1.59941769e+00 -7.50577748e-01 1.26061881e+00 -6.34478390e-01
2.93836892e-01 3.21223021e-01 4.28127378e-01 6.37814403e-01
-5.51827073e-01 1.04089987e+00 8.04028809e-01 -5.13039351e-01
7.43480861e-01 1.90200552e-01 3.09936523e-01 4.67880040e-01
1.39024943e-01 -3.00654709e-01 -3.44689995e-01 -5.95176220e-01
5.80878079e-01 1.54101148e-01 -3.69385481e-01 -1.31929362e+00
-9.06373441e-01 8.92874241e-01 -4.14479850e-03 2.26502508e-01
-3.50717843e-01 -2.56189317e-01 1.12811029e-01 -2.22327337e-01
5.65719783e-01 -8.50186646e-02 -4.80550639e-02 6.84369579e-02
-1.34771812e+00 -9.95153412e-02 6.08675301e-01 8.57172310e-01
7.69769847e-01 -5.33261187e-02 3.33784431e-01 1.06355786e+00
7.48491824e-01 3.84331316e-01 1.76004902e-01 -1.23603511e+00
6.69488832e-02 8.09480667e-01 3.25579762e-01 -1.04588568e+00
-3.99438813e-02 -3.03821824e-02 -6.38688862e-01 2.09023237e-01
4.38763767e-01 -1.12163497e-03 -6.72187865e-01 1.51409948e+00
7.88902581e-01 5.26734233e-01 -2.82725114e-02 9.96499658e-01
4.66743946e-01 6.24698043e-01 -1.92844868e-01 -1.08927727e+00
4.87348318e-01 -5.84307790e-01 -7.14739144e-01 2.90874153e-01
3.57005298e-01 -7.74841845e-01 5.96981108e-01 4.17605639e-01
-8.55234385e-01 -4.93445903e-01 -8.31818342e-01 4.64318126e-01
-9.09260567e-03 2.74556309e-01 3.06184113e-01 7.72491992e-01
-9.73208427e-01 3.84424657e-01 -1.22362494e+00 -7.47155666e-01
3.09367359e-01 6.95879757e-01 -1.31850958e-01 -1.41352192e-02
-4.84802455e-01 1.90252021e-01 5.51344156e-01 2.51347631e-01
-7.92504191e-01 -1.96635053e-01 -5.75266063e-01 -4.89094220e-02
6.52967691e-01 -2.14217275e-01 8.26696098e-01 -1.06486499e+00
-1.34374213e+00 6.65811360e-01 -4.75721300e-01 -6.19565845e-02
4.18163657e-01 -6.82610646e-02 -2.14630008e-01 3.95566404e-01
-4.20928076e-02 4.79549199e-01 1.09740663e+00 -1.44804728e+00
-3.67737770e-01 -7.77390182e-01 -4.38807517e-01 4.17849511e-01
-5.84925830e-01 2.08726928e-01 -9.17090774e-01 -3.94773632e-01
6.92001224e-01 -1.23476017e+00 -4.40302938e-01 -1.16968397e-02
-2.98842669e-01 -3.70960206e-01 1.38828981e+00 -3.52008373e-01
1.08604133e+00 -2.41913104e+00 5.09733737e-01 6.27192259e-01
9.55661833e-02 1.29922748e-01 1.97524115e-01 3.42193127e-01
-1.08383045e-01 -1.28424034e-01 -7.29829252e-01 -2.71606594e-01
-2.64726132e-01 7.64896646e-02 -1.66696936e-01 8.39427173e-01
-1.87994272e-01 1.73467264e-01 -7.52508640e-01 -7.72206664e-01
3.20687830e-01 3.08047146e-01 -4.37422961e-01 2.52653241e-01
-1.79148410e-02 5.90893924e-01 -4.73846674e-01 6.87748730e-01
8.03965807e-01 -1.07284717e-01 3.36450487e-01 -1.56057745e-01
-1.85999334e-01 -4.62051928e-01 -1.76559794e+00 1.62401438e+00
3.65774035e-01 3.19637597e-01 4.63148773e-01 -1.35101449e+00
8.52597117e-01 3.54204893e-01 1.13100731e+00 2.69890100e-01
4.47658859e-02 1.52515441e-01 1.96815021e-02 -3.54451835e-01
1.75100699e-01 2.41712034e-01 2.73907989e-01 4.70085472e-01
-3.09008658e-02 2.54306793e-01 2.50808418e-01 2.72710651e-01
6.23665333e-01 2.14477882e-01 4.06520307e-01 -4.11633849e-01
9.21305418e-01 1.59652233e-01 8.74735057e-01 3.83251369e-01
-3.60562384e-01 2.71541595e-01 1.27494916e-01 4.43969704e-02
-8.43883157e-01 -9.84237254e-01 -1.71833709e-01 9.23114359e-01
2.66438752e-01 -1.65668622e-01 -1.09133828e+00 -6.68107450e-01
-2.33833551e-01 4.93140668e-01 -2.86334455e-01 1.19733453e-01
-5.20369589e-01 -9.33574021e-01 8.16166475e-02 2.82503456e-01
5.33677876e-01 -7.84857631e-01 -3.50291640e-01 1.20773770e-01
-1.32753506e-01 -6.99196875e-01 -4.03460443e-01 -1.16352320e-01
-1.32520211e+00 -1.16880834e+00 -7.36376762e-01 -9.34906483e-01
1.13974059e+00 7.30280519e-01 4.58373994e-01 -1.51298478e-01
-6.83891326e-02 8.05852771e-01 -4.54797149e-01 4.69058491e-02
-2.67832428e-01 -1.29190415e-01 4.15135443e-01 5.65703094e-01
6.33945942e-01 -4.62813526e-01 -3.69568408e-01 5.74683964e-01
-8.54424953e-01 -2.38937005e-01 3.56425256e-01 7.00566769e-01
7.63766646e-01 4.12774742e-01 3.05319637e-01 -9.64497864e-01
2.09882244e-01 -4.84239995e-01 -7.65168071e-01 2.42385089e-01
-4.87914711e-01 -4.48224135e-02 3.63441318e-01 -4.53672618e-01
-1.27962732e+00 8.50578308e-01 4.43087250e-01 -8.25352490e-01
-4.49421704e-01 3.02679688e-01 -6.73714519e-01 -1.30995601e-01
4.67031896e-01 3.24824244e-01 3.81077901e-02 -5.57409048e-01
5.35434365e-01 9.37595725e-01 4.94824529e-01 -6.32796764e-01
1.02998126e+00 7.75470674e-01 -1.00865848e-01 -1.21793723e+00
-5.95990084e-02 -1.24307013e+00 -1.12501907e+00 -5.96740425e-01
6.64219677e-01 -7.24238873e-01 -5.09265184e-01 3.57334375e-01
-8.72769654e-01 2.20421091e-01 -1.45847397e-03 5.91154814e-01
-5.70826054e-01 7.81087458e-01 -1.16976872e-01 -1.29583931e+00
-6.44733682e-02 -1.21614158e+00 8.35928857e-01 1.50598764e-01
-1.06142983e-01 -9.21479642e-01 5.87040968e-02 4.37228113e-01
-1.45117968e-01 2.23384827e-01 7.33945787e-01 -7.83223629e-01
-7.54380465e-01 4.21356782e-02 2.69464314e-01 1.47552773e-01
3.87186378e-01 2.40380839e-01 -9.21976805e-01 -5.87693870e-01
1.96771741e-01 -1.33452907e-01 8.48984420e-01 4.73136753e-01
1.02018964e+00 -1.87085599e-01 -6.78060532e-01 4.18015271e-01
1.29652905e+00 7.74857521e-01 3.25302333e-01 -1.85288504e-01
8.10745060e-01 9.27418649e-01 7.49741912e-01 4.42709029e-01
-2.15995535e-01 5.90860844e-01 9.79476720e-02 -3.11050471e-02
5.36993563e-01 1.43788293e-01 3.32769245e-01 1.08290124e+00
6.98596388e-02 2.13632844e-02 -1.00929725e+00 5.55259109e-01
-2.19295001e+00 -1.05664825e+00 -2.05332693e-02 2.46473765e+00
5.70926607e-01 -1.30837187e-01 4.02033925e-01 5.54203928e-01
1.16151690e+00 -4.28847745e-02 -8.91299963e-01 9.48276520e-02
1.30575940e-01 -3.23667824e-01 1.08674988e-01 3.67537022e-01
-1.34511280e+00 9.21383083e-01 5.34014845e+00 8.02823961e-01
-9.21442509e-01 -1.06929578e-01 4.01972204e-01 2.61821616e-02
-4.87661175e-02 2.05303043e-01 -5.94391823e-01 3.95831227e-01
4.09414113e-01 -2.50830084e-01 4.54856068e-01 1.06430948e+00
5.14567673e-01 -1.14197113e-01 -1.13103688e+00 1.27109647e+00
2.68010587e-01 -7.92427182e-01 -1.89922899e-02 1.84012875e-01
8.04184616e-01 -4.90029097e-01 7.93129206e-02 -1.59145445e-01
4.70518693e-02 -5.86152852e-01 2.82688588e-01 4.47142333e-01
4.44402993e-01 -8.96027565e-01 3.49905849e-01 7.62726367e-01
-1.15353084e+00 -8.13946500e-02 -3.86277050e-01 2.66527772e-01
-9.81851816e-02 4.03794765e-01 -1.06363952e+00 3.91627967e-01
5.31866133e-01 8.55173767e-01 -4.47651416e-01 1.38608682e+00
1.55451834e-01 8.63556564e-01 -4.84579384e-01 9.01739597e-02
-9.08396393e-02 -9.44550037e-01 8.06247950e-01 9.13143218e-01
2.81794518e-01 4.51737791e-01 3.47004235e-01 7.72596121e-01
1.90720215e-01 5.65479219e-01 -6.37673736e-01 -2.02773884e-01
8.42276514e-01 1.26719880e+00 -1.34683466e+00 -3.36212873e-01
-3.74257803e-01 9.96549070e-01 8.51945952e-02 6.13193631e-01
-4.22181010e-01 6.79318085e-02 2.28654444e-01 -3.45237665e-02
3.16624522e-01 -5.07254362e-01 -1.46291643e-01 -1.11266160e+00
-2.20631823e-01 -7.17328310e-01 4.98907208e-01 -3.82929504e-01
-1.06448531e+00 2.75185145e-02 2.87068248e-01 -1.45989692e+00
-3.97561431e-01 -2.21354797e-01 -4.74194735e-01 5.69997072e-01
-6.39465630e-01 -7.01459169e-01 -2.58022994e-01 8.84136319e-01
6.08071268e-01 -3.31447899e-01 5.85769773e-01 1.25383027e-02
-8.12458813e-01 2.48934805e-01 7.04229653e-01 1.53970942e-01
6.35500908e-01 -1.02789438e+00 -2.66040981e-01 1.14751363e+00
3.63149196e-01 9.88077402e-01 6.96370065e-01 -7.02585220e-01
-1.36091018e+00 -9.91936445e-01 2.79522359e-01 -9.04243290e-02
2.92164683e-01 -3.11797500e-01 -9.56950068e-01 4.04650450e-01
-7.33734742e-02 -2.55231589e-01 1.07759869e+00 1.26066497e-02
6.75717592e-02 -2.51021653e-01 -1.22875977e+00 5.12847364e-01
8.67523074e-01 -2.90708989e-01 -5.34269929e-01 3.00140232e-01
1.91807196e-01 4.22867388e-02 -5.19871473e-01 5.18289804e-01
3.04712266e-01 -8.44078183e-01 8.31677318e-01 -3.98786634e-01
-3.46123397e-01 -5.95455050e-01 -2.21481800e-01 -1.14353597e+00
-4.53050911e-01 -5.06009758e-01 -1.82251468e-01 1.42213154e+00
5.10101244e-02 -2.75195062e-01 1.21341825e+00 7.03444421e-01
1.24679416e-01 -3.66735786e-01 -1.03429794e+00 -5.96353710e-01
-2.46680871e-01 -2.27537483e-01 3.83902341e-02 1.19895923e+00
3.95082682e-02 2.91090965e-01 -1.94129333e-01 3.30126703e-01
1.27842045e+00 2.22714335e-01 7.72921324e-01 -1.59648800e+00
-1.78487629e-01 -1.95779204e-01 -4.42544460e-01 -1.01384437e+00
3.40185016e-01 -6.58106863e-01 1.25217229e-01 -1.30852568e+00
3.83663177e-01 -3.44592184e-01 -1.75178364e-01 2.71431267e-01
-2.41011471e-01 1.91776603e-02 1.36763379e-01 8.05652559e-01
-6.76651597e-01 5.94350219e-01 6.50880516e-01 -1.51306123e-01
-6.16963029e-01 3.54231857e-02 -2.60727495e-01 8.83422494e-01
5.77597439e-01 -2.37204209e-01 -7.98650563e-01 4.13238108e-02
-4.55298156e-01 1.15074404e-01 -6.21512495e-02 -8.78164351e-01
5.88700771e-01 -2.77507335e-01 4.35647398e-01 -8.65101278e-01
4.56170470e-01 -1.19492888e+00 3.76553953e-01 1.91081285e-01
-2.27720886e-01 -5.40499806e-01 -1.13440678e-01 8.81082833e-01
-1.78024888e-01 -2.34028444e-01 1.03908408e+00 -5.36189787e-02
-7.94842720e-01 3.09114695e-01 -4.12235349e-01 -2.87614584e-01
1.31959057e+00 -5.54741740e-01 3.46931517e-01 -2.30899036e-01
-9.38383520e-01 1.45650446e-01 6.98129594e-01 2.57154256e-01
7.06892729e-01 -1.39244068e+00 -4.64422047e-01 2.09205762e-01
9.08208489e-02 -1.32231629e-02 7.40900859e-02 7.80966640e-01
-2.90889174e-01 3.91327053e-01 8.42661932e-02 -1.03801358e+00
-1.57559097e+00 8.07667375e-01 -3.10631981e-03 4.74882185e-01
-1.70436352e-01 4.68257606e-01 1.94626257e-01 -2.68970072e-01
4.39107329e-01 2.07197368e-01 -5.86782157e-01 2.59637892e-01
2.52386957e-01 8.44968259e-01 -1.86811492e-01 -1.13799322e+00
-5.31561434e-01 7.85937548e-01 4.21186052e-02 -3.69126976e-01
1.09507692e+00 -2.58884579e-01 -3.13972950e-01 6.37389541e-01
1.07819521e+00 2.34678295e-02 -1.11345351e+00 -4.01078790e-01
3.07051927e-01 -5.60598195e-01 3.87827456e-02 -2.82716691e-01
-9.75683570e-01 6.86039746e-01 8.62739027e-01 3.84221338e-02
1.38532627e+00 -3.61589119e-02 2.76893795e-01 3.49255711e-01
5.77459574e-01 -1.35830629e+00 -4.44961935e-02 -1.05790831e-01
4.95351732e-01 -1.23025858e+00 4.23136987e-02 -8.52486134e-01
-4.13139760e-01 9.55283582e-01 6.18156791e-01 4.61711409e-03
6.93541050e-01 -5.09496666e-02 3.80902030e-02 9.79746059e-02
-3.95456016e-01 -1.91113427e-01 2.62436479e-01 4.15412098e-01
2.95526147e-01 3.26639265e-02 -5.00206172e-01 2.57262170e-01
3.89433950e-01 -1.70243844e-01 2.77380526e-01 8.50847781e-01
-7.39672959e-01 -1.13424313e+00 -1.01952839e+00 2.09803864e-01
-7.25559443e-02 3.74484509e-01 -7.36128390e-01 5.38102090e-01
1.96816906e-01 1.22448707e+00 -9.43809468e-03 -1.98405221e-01
-9.54369903e-02 2.97192544e-01 2.72177786e-01 -6.28206909e-01
1.94481358e-01 8.57941210e-01 -3.28496873e-01 -4.43798631e-01
-1.02945554e+00 -1.13633394e+00 -1.34875977e+00 1.06182709e-01
-3.80551934e-01 6.35380566e-01 4.16904479e-01 6.96484566e-01
2.14753941e-01 -1.93311170e-01 9.80887711e-01 -6.26943886e-01
-3.18763554e-01 -8.74359787e-01 -7.35168517e-01 4.31514293e-01
-7.57885203e-02 -8.52599204e-01 -5.89388430e-01 4.79989886e-01] | [7.72127103805542, 4.447030544281006] |
16f29791-d532-40e0-9244-31b3d02f30f6 | risclip-referring-image-segmentation | 2306.08498 | null | https://arxiv.org/abs/2306.08498v1 | https://arxiv.org/pdf/2306.08498v1.pdf | RISCLIP: Referring Image Segmentation Framework using CLIP | Recent advances in computer vision and natural language processing have naturally led to active research in multi-modal tasks, including Referring Image Segmentation (RIS). Recent approaches have advanced the frontier of RIS by impressive margins, but they require an additional pretraining stage on external visual grounding datasets to achieve the state-of-the-art performances. We attempt to break free from this requirement by effectively adapting Contrastive Language-Image Pretraining (CLIP) to RIS. We propose a novel framework that residually adapts frozen CLIP features to RIS with Fusion Adapters and Backbone Adapters. Freezing CLIP preserves the backbone's rich, general image-text alignment knowledge, whilst Fusion Adapters introduce multi-modal communication and Backbone Adapters inject new knowledge useful in solving RIS. Our method reaches a new state of the art on three major RIS benchmarks. We attain such performance without additional pretraining and thereby absolve the necessity of extra training and data preparation. Source code and model weights will be available upon publication. | ['Jaesik Park', 'Minguk Kang', 'Seoyeon Kim'] | 2023-06-14 | null | null | null | null | ['visual-grounding'] | ['computer-vision'] | [ 7.25866914e-01 2.00475469e-01 -1.58582017e-01 -4.67376530e-01
-1.05299723e+00 -5.80595851e-01 7.37806320e-01 1.85279161e-01
-7.60319948e-01 2.28110313e-01 1.19619161e-01 -2.97323018e-01
1.72753409e-01 -4.17463392e-01 -8.72380674e-01 -5.21564901e-01
2.20439777e-01 6.57032371e-01 4.99931306e-01 -4.04882401e-01
1.30173951e-01 3.01522464e-01 -1.49326050e+00 6.00695014e-01
8.47616792e-01 9.46886063e-01 2.49938965e-01 7.89703846e-01
-4.40810889e-01 7.23798633e-01 -2.31157571e-01 -5.93825698e-01
3.15121502e-01 -2.04478636e-01 -1.18858731e+00 2.42252827e-01
6.33376598e-01 -5.73855713e-02 -6.16548993e-02 9.11804795e-01
4.22948718e-01 1.05401061e-01 6.14901125e-01 -1.20168674e+00
-5.90948761e-01 6.80155277e-01 -8.95503461e-01 1.47244632e-01
1.14698403e-01 2.04566613e-01 1.11512017e+00 -8.05044770e-01
6.19085550e-01 1.13129127e+00 7.15708435e-01 5.86411059e-01
-1.41916442e+00 -2.56237775e-01 3.47902209e-01 6.06243126e-02
-1.22041643e+00 -4.84519869e-01 7.14078069e-01 -4.46060508e-01
1.05658245e+00 3.17492723e-01 3.81869555e-01 9.89019156e-01
-1.91471323e-01 1.16777718e+00 1.08569622e+00 -5.45963585e-01
-1.19130544e-01 1.13630153e-01 2.56181240e-01 1.01664352e+00
-1.35733694e-01 -4.23002876e-02 -3.77318949e-01 2.11833179e-01
7.21627891e-01 -2.14093715e-01 -1.60070568e-01 -5.21054327e-01
-1.25949001e+00 7.97578335e-01 6.26394868e-01 3.12524736e-01
-1.05870897e-02 -1.73927993e-02 3.53067249e-01 2.41413280e-01
2.96041071e-01 3.10761660e-01 -5.32018542e-01 1.81586877e-01
-1.18030381e+00 2.79024150e-02 5.94669521e-01 8.97124648e-01
8.77617836e-01 -1.09439544e-01 -1.89828098e-01 8.65624487e-01
5.80132157e-02 5.49318910e-01 4.98118043e-01 -1.03276598e+00
6.29751444e-01 8.44555855e-01 -3.35793287e-01 -6.89611018e-01
-6.55259013e-01 -4.29898024e-01 -9.68434632e-01 1.67165503e-01
4.91444677e-01 4.84146662e-02 -1.30513299e+00 1.55186594e+00
2.69217372e-01 3.06557063e-02 8.86596590e-02 7.70358086e-01
7.77791858e-01 5.82500994e-01 3.67951170e-02 6.83880970e-02
1.35285532e+00 -1.36122262e+00 -2.53373593e-01 -6.47349358e-01
6.27022862e-01 -8.17997575e-01 1.45799100e+00 2.95319527e-01
-1.16874099e+00 -5.41984856e-01 -9.00059819e-01 -2.96092868e-01
-3.26786399e-01 -1.74436886e-02 8.39216828e-01 4.01219547e-01
-1.14418018e+00 3.85756850e-01 -9.77057099e-01 -3.05583805e-01
6.53966904e-01 6.15555644e-01 -5.73900223e-01 -1.52001664e-01
-7.87690103e-01 7.67672837e-01 4.37790841e-01 1.17355518e-01
-5.99405527e-01 -7.54922688e-01 -1.03069544e+00 -1.67684823e-01
6.77696168e-01 -8.32962036e-01 1.27357054e+00 -1.45650148e+00
-1.58502424e+00 1.15658665e+00 -1.78466644e-02 -4.55172747e-01
5.62070489e-01 -2.55552411e-01 -1.10453635e-01 3.85457724e-01
2.25575287e-02 1.20173967e+00 1.08969855e+00 -1.45941889e+00
-5.24439037e-01 -2.49682501e-01 1.96963146e-01 2.50666767e-01
-4.62872714e-01 1.50333136e-01 -1.03893363e+00 -5.48822224e-01
2.67658383e-02 -1.06050789e+00 -4.31491762e-01 -7.52939656e-02
-2.89007068e-01 -8.01387883e-04 5.71220577e-01 -3.67268205e-01
9.55740392e-01 -1.99091291e+00 5.38292348e-01 1.05459414e-01
1.95806354e-01 4.31537032e-01 -5.64174950e-01 1.38027836e-02
6.73144981e-02 3.30379466e-03 -6.73656404e-01 -6.73495829e-01
-2.84663811e-02 3.62315923e-01 -2.72499055e-01 3.00713986e-01
3.70357394e-01 1.11871135e+00 -6.73959851e-01 -7.67645240e-01
3.33316207e-01 5.17482996e-01 -8.49317968e-01 1.10063925e-01
-4.05142635e-01 4.38360095e-01 -1.89311832e-01 4.41652983e-01
4.41599756e-01 -5.98742425e-01 -5.87514639e-02 -3.90515596e-01
-3.63982841e-03 -4.94625792e-02 -9.31342721e-01 2.04535484e+00
-4.70077217e-01 4.71800178e-01 1.39441431e-01 -1.24072778e+00
5.40334761e-01 -9.14411247e-02 4.77379501e-01 -8.29855561e-01
2.32853636e-01 1.12589169e-02 -5.23327440e-02 -3.80779028e-01
4.53830838e-01 -8.61777551e-03 -1.27081186e-01 2.08913952e-01
1.51252747e-01 -2.78104454e-01 3.08544040e-01 3.27183276e-01
8.63731742e-01 3.54293436e-01 -3.25490460e-02 -1.75433531e-01
8.60700786e-01 2.14284226e-01 3.42104286e-01 6.76394761e-01
-1.09607548e-01 7.25238621e-01 2.46272117e-01 -3.60208541e-01
-8.78407180e-01 -1.26196551e+00 1.37843207e-01 1.39387155e+00
1.22543544e-01 -4.02151853e-01 -9.18118596e-01 -8.55866015e-01
-2.61224121e-01 3.23236108e-01 -5.12723267e-01 5.67683019e-02
-7.73688912e-01 -7.88941860e-01 6.22064710e-01 6.87149107e-01
5.58993161e-01 -1.03151023e+00 -5.80381393e-01 3.28742573e-03
-4.14077908e-01 -1.50335073e+00 -4.21701908e-01 4.07254815e-01
-8.99073660e-01 -8.91861320e-01 -8.64660740e-01 -9.14085329e-01
7.91802704e-01 2.05213711e-01 1.21879339e+00 1.23094395e-01
-5.04888833e-01 4.76646930e-01 -1.64614126e-01 -1.53664976e-01
-3.43743026e-01 4.59542423e-01 -3.63376111e-01 -1.21952621e-02
5.54769523e-02 -3.28295588e-01 -4.22744423e-01 1.69112071e-01
-1.13165224e+00 3.95087779e-01 7.73700178e-01 8.08214486e-01
7.15135276e-01 -3.70147407e-01 1.40137941e-01 -9.60830331e-01
3.14276107e-02 -4.34715301e-02 -6.25977159e-01 2.12585598e-01
-4.36510831e-01 4.44943219e-01 6.14663839e-01 -1.71603188e-01
-1.09729075e+00 3.71945202e-01 -2.45405972e-01 -3.90045494e-01
-3.22468013e-01 4.34019744e-01 -3.32428999e-02 -2.53583133e-01
6.32417500e-01 -7.49485707e-03 -1.38673410e-01 -4.20155913e-01
7.60403812e-01 5.13654530e-01 9.63122010e-01 -8.00301790e-01
8.03588569e-01 7.30316401e-01 -2.32639499e-02 -7.44646609e-01
-1.32048988e+00 -6.88949525e-01 -8.80876958e-01 1.05023928e-01
1.01365471e+00 -9.34251547e-01 -5.29025793e-01 4.29098785e-01
-9.49918747e-01 -6.22273088e-01 -2.07283974e-01 1.00569315e-01
-7.00673401e-01 4.87720847e-01 -6.43960357e-01 -1.70456693e-01
-4.31042939e-01 -1.19468904e+00 1.28423738e+00 1.76595658e-01
-1.42784175e-02 -9.06156003e-01 -2.84078456e-02 8.00632775e-01
2.87622780e-01 -4.21999721e-03 8.30850780e-01 -3.26915622e-01
-6.25464559e-01 1.22750953e-01 -5.33830225e-01 3.10937345e-01
-1.66402489e-01 -1.49374023e-01 -1.10817063e+00 -2.89091468e-01
-2.24244565e-01 -5.06737649e-01 1.28841841e+00 3.30602169e-01
1.09656775e+00 -5.98496944e-03 -2.23061591e-01 9.83481050e-01
1.51724505e+00 -2.81443805e-01 5.82962871e-01 5.00519097e-01
1.03295398e+00 6.26763046e-01 3.31791967e-01 -1.55630251e-02
7.20673978e-01 8.76290083e-01 4.35769081e-01 -4.92624551e-01
-3.30847681e-01 -1.22908149e-02 4.63350773e-01 7.61584282e-01
-1.01516955e-01 -1.03801250e-01 -1.16104782e+00 6.09241724e-01
-1.88190699e+00 -7.80651629e-01 -8.85180547e-04 1.84446728e+00
1.02261817e+00 2.93359995e-01 1.60245657e-01 6.70658052e-02
3.23321372e-01 2.07075849e-01 -4.29708332e-01 -3.46724212e-01
-7.27579519e-02 3.45863372e-01 6.84778452e-01 6.72856033e-01
-1.46490812e+00 1.49168313e+00 5.98555946e+00 7.55266964e-01
-1.15670049e+00 8.18383768e-02 5.72288632e-01 9.26514044e-02
-3.47762614e-01 -6.28039660e-03 -9.15482104e-01 -1.22974440e-01
8.47083271e-01 2.86981761e-01 4.92655575e-01 5.97944498e-01
-1.35124460e-01 -2.04367489e-01 -1.01221955e+00 9.87017095e-01
3.67378265e-01 -1.47318041e+00 2.39925444e-01 -2.05019534e-01
8.23982358e-01 4.70026702e-01 1.34106666e-01 1.83159977e-01
3.34889740e-01 -9.75784063e-01 6.47504807e-01 3.85945499e-01
7.02798784e-01 -6.51524186e-01 5.68880081e-01 1.88401148e-01
-1.27908123e+00 -4.10831608e-02 -5.01003042e-02 1.01332283e-02
1.43072188e-01 3.80336165e-01 -4.97054338e-01 7.38925815e-01
5.91926515e-01 6.34180069e-01 -9.77700055e-01 6.93418801e-01
-1.01438634e-01 3.66598696e-01 -3.50092649e-01 4.84727919e-01
5.79903722e-01 -1.10995069e-01 3.41466218e-01 1.39374721e+00
-2.43447319e-01 -2.31141821e-01 5.88819981e-01 5.15270829e-01
-1.04704291e-01 1.50264233e-01 -3.65705758e-01 -1.56246766e-01
-9.00614262e-02 1.36296332e+00 -1.18854880e+00 -4.11805749e-01
-5.43030620e-01 1.29610085e+00 4.60548013e-01 2.22262710e-01
-8.48709285e-01 -1.75921127e-01 4.37340736e-01 7.00943032e-03
5.10088623e-01 -3.41336042e-01 -4.78934675e-01 -1.24825454e+00
-1.44018427e-01 -9.95031416e-01 4.88839060e-01 -5.24034858e-01
-1.08436930e+00 7.05472350e-01 -3.93739045e-02 -7.82056689e-01
-2.42334545e-01 -7.70246208e-01 -2.11915389e-01 3.40521008e-01
-1.84074080e+00 -1.61858404e+00 -2.43730307e-01 1.03673255e+00
7.12837934e-01 -7.72521198e-02 7.74125457e-01 3.49233896e-01
-5.78166723e-01 7.18960047e-01 -1.50702268e-01 2.57103235e-01
7.29029655e-01 -1.34731126e+00 3.95286053e-01 9.19371188e-01
5.53607047e-01 3.20423633e-01 4.04022723e-01 -2.20802262e-01
-1.27362418e+00 -1.02654624e+00 7.37664044e-01 -5.82741737e-01
7.94699430e-01 -3.59221339e-01 -6.98945463e-01 8.29674542e-01
3.20871979e-01 2.13025302e-01 5.00685871e-01 2.14867622e-01
-7.15389729e-01 -8.20688456e-02 -7.16582775e-01 8.05251539e-01
1.10189748e+00 -5.89674473e-01 -6.39999747e-01 2.89817750e-01
6.75990462e-01 -4.92589206e-01 -6.40639722e-01 3.60202760e-01
3.12131107e-01 -9.26758289e-01 1.24959099e+00 -4.49691534e-01
4.52327520e-01 -3.87204230e-01 -8.30889866e-02 -8.36424410e-01
-9.05873999e-02 -6.60016894e-01 8.31201151e-02 1.19336593e+00
3.78950924e-01 -2.46902928e-01 7.63393164e-01 4.73429263e-01
-4.83290255e-01 -6.09094620e-01 -7.55306959e-01 -5.94291329e-01
1.56004310e-01 -6.08753204e-01 1.01215608e-01 7.86870480e-01
-1.30965695e-01 6.16606057e-01 -2.40138650e-01 9.30931419e-02
6.69873297e-01 2.44623780e-01 8.78888667e-01 -1.11441326e+00
-4.50958401e-01 -6.60252988e-01 -2.15364024e-01 -1.12842035e+00
3.76658440e-01 -1.22592211e+00 1.67822391e-01 -1.61914289e+00
3.17681015e-01 -3.67803574e-01 -2.38520414e-01 8.95290136e-01
-2.93163687e-01 7.35030472e-01 5.15300632e-01 9.53588784e-02
-8.60773921e-01 3.39060932e-01 1.19868100e+00 -4.32752967e-01
-3.09681177e-01 -1.89459369e-01 -7.80874074e-01 1.05936408e+00
7.09551454e-01 -2.50145614e-01 -5.49386501e-01 -7.35757828e-01
2.35446945e-01 -3.01287800e-01 5.70033491e-01 -9.61326420e-01
2.48286232e-01 -4.85079503e-03 2.21825972e-01 -3.08799088e-01
2.12582976e-01 -8.83413434e-01 -1.51112109e-01 2.58321851e-01
-4.00295049e-01 5.83133847e-02 4.60854292e-01 4.27044541e-01
-2.07040206e-01 -1.47763357e-01 9.39051688e-01 -6.43451065e-02
-9.49490845e-01 2.40187570e-01 -1.36686191e-01 3.67085934e-01
7.53701448e-01 -1.63419828e-01 -3.88421059e-01 -3.63437720e-02
-7.41690040e-01 3.55646610e-01 5.75899541e-01 5.11823416e-01
3.71896178e-01 -8.10895026e-01 -4.74064320e-01 3.27579111e-01
1.06813051e-01 1.56157985e-01 2.18808666e-01 1.05486083e+00
-4.70385998e-01 3.18133563e-01 -1.51608840e-01 -9.29237485e-01
-1.31836510e+00 6.20514691e-01 1.94213107e-01 -6.24151230e-01
-8.54166985e-01 9.42941308e-01 3.75666410e-01 -4.16355044e-01
2.44571462e-01 -3.25984478e-01 -1.25862524e-01 4.88077886e-02
3.96019787e-01 2.71643326e-02 1.26870126e-01 -8.03602517e-01
-4.04500335e-01 9.17290330e-01 -4.21282947e-01 -1.79435104e-01
1.24398685e+00 -2.57566124e-01 5.92682585e-02 3.57845545e-01
1.14424872e+00 -1.18095390e-01 -1.37090182e+00 -2.78438538e-01
2.50620484e-01 -1.96142316e-01 2.49236554e-01 -8.03032339e-01
-1.23997355e+00 1.03948689e+00 4.83163744e-01 -2.28467643e-01
1.28254414e+00 1.66512251e-01 9.68664348e-01 4.54175651e-01
1.98612124e-01 -1.10973990e+00 1.87029779e-01 6.93366349e-01
6.34531677e-01 -1.43111897e+00 -5.53238913e-02 -4.23906237e-01
-8.60205173e-01 9.46240842e-01 4.86204416e-01 -1.31502926e-01
4.68438476e-01 4.72918332e-01 3.47352862e-01 -1.57131106e-01
-3.86745155e-01 -7.12829590e-01 5.53917170e-01 7.41770506e-01
3.74959826e-01 -2.46353075e-01 -6.99342415e-02 3.21501166e-01
3.72037361e-03 -3.88779081e-02 1.72798708e-01 7.03724742e-01
-3.71718436e-01 -1.26109755e+00 -1.26099765e-01 2.00472593e-01
-3.80834788e-01 -4.14456606e-01 -2.19442040e-01 7.75169730e-01
7.54468422e-03 6.93289995e-01 2.14725748e-01 -6.73126578e-02
2.28526592e-01 1.33908801e-02 7.66332090e-01 -5.50363183e-01
-7.40917444e-01 2.71382660e-01 -7.69580752e-02 -7.69497871e-01
-7.92069793e-01 -6.02527440e-01 -1.63454580e+00 -1.07033655e-01
-2.82710731e-01 -1.20615013e-01 4.83441770e-01 1.25809741e+00
3.57742548e-01 6.19443119e-01 1.98412940e-01 -1.15064788e+00
-3.48174810e-01 -4.98405576e-01 -3.53143066e-02 4.30157870e-01
4.08980519e-01 -4.08017337e-01 -5.43775223e-02 4.91493404e-01] | [10.264892578125, 1.3371589183807373] |
384d8dd6-1f02-4c5b-89b9-a7046c1f3941 | densepoint-learning-densely-contextual | 1909.03669 | null | https://arxiv.org/abs/1909.03669v1 | https://arxiv.org/pdf/1909.03669v1.pdf | DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing | Point cloud processing is very challenging, as the diverse shapes formed by irregular points are often indistinguishable. A thorough grasp of the elusive shape requires sufficiently contextual semantic information, yet few works devote to this. Here we propose DensePoint, a general architecture to learn densely contextual representation for point cloud processing. Technically, it extends regular grid CNN to irregular point configuration by generalizing a convolution operator, which holds the permutation invariance of points, and achieves efficient inductive learning of local patterns. Architecturally, it finds inspiration from dense connection mode, to repeatedly aggregate multi-level and multi-scale semantics in a deep hierarchy. As a result, densely contextual information along with rich semantics, can be acquired by DensePoint in an organic manner, making it highly effective. Extensive experiments on challenging benchmarks across four tasks, as well as thorough model analysis, verify DensePoint achieves the state of the arts. | ['Jiwen Lu', 'Yongcheng Liu', 'Shiming Xiang', 'Gaofeng Meng', 'Chunhong Pan', 'Bin Fan'] | 2019-09-09 | densepoint-learning-densely-contextual-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Liu_DensePoint_Learning_Densely_Contextual_Representation_for_Efficient_Point_Cloud_Processing_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Liu_DensePoint_Learning_Densely_Contextual_Representation_for_Efficient_Point_Cloud_Processing_ICCV_2019_paper.pdf | iccv-2019-10 | ['3d-part-segmentation'] | ['computer-vision'] | [-1.23635024e-01 -2.67248720e-01 -5.58620207e-02 -3.66086721e-01
-4.63326365e-01 -6.96382999e-01 7.40316391e-01 4.14826840e-01
1.15192175e-01 2.43915126e-01 8.68881717e-02 -1.89783767e-01
-2.39126787e-01 -1.16312933e+00 -1.12778294e+00 -5.96496284e-01
-2.45677993e-01 4.50162649e-01 2.41116062e-01 -3.75007719e-01
4.02215153e-01 9.00164306e-01 -1.53069043e+00 5.69080830e-01
5.13019323e-01 1.13527238e+00 3.12104315e-01 9.89520699e-02
-4.41764146e-01 2.40154088e-01 -1.93454221e-01 -2.10481972e-01
4.19440866e-01 3.56852233e-01 -7.36773551e-01 -3.77913415e-02
7.88237691e-01 -1.40134722e-01 -1.84157118e-01 8.92433107e-01
1.30242497e-01 1.63360331e-02 3.75093520e-01 -1.02269793e+00
-1.10630178e+00 3.33344042e-01 -5.42971611e-01 1.45354509e-01
1.26737922e-01 4.23281729e-01 1.33674002e+00 -1.33171833e+00
3.57782334e-01 1.29227662e+00 9.23447073e-01 7.35970289e-02
-1.20720148e+00 -6.74792886e-01 4.12534773e-01 -7.83365145e-02
-1.44134355e+00 -1.75046753e-02 9.00023639e-01 -3.67974788e-01
1.03483975e+00 2.44797051e-01 9.55766857e-01 1.10258889e+00
9.17721689e-02 7.82017648e-01 8.50703359e-01 1.03047915e-01
3.12350273e-01 -6.37644470e-01 1.35960743e-01 5.94595850e-01
3.92867088e-01 8.57408941e-02 -4.19250190e-01 -1.56279817e-01
1.07637525e+00 8.54027569e-01 1.40380701e-02 -4.84795988e-01
-1.39892566e+00 4.71639305e-01 1.16374254e+00 2.81932861e-01
-3.96030933e-01 5.19753873e-01 9.91566703e-02 2.22817063e-01
4.68704730e-01 4.67001230e-01 -6.00200713e-01 1.98193759e-01
-7.75973737e-01 4.78412122e-01 4.44107473e-01 1.38272023e+00
1.17852378e+00 -2.21202150e-02 -9.30981711e-02 6.71052873e-01
3.01237732e-01 7.02743113e-01 4.12260406e-02 -8.22643757e-01
4.90091830e-01 1.09799600e+00 -2.70297557e-01 -1.28447425e+00
-3.87616009e-01 -6.83792353e-01 -1.15064919e+00 3.40286016e-01
1.07795820e-01 3.98589015e-01 -1.06850910e+00 1.41844571e+00
1.97971597e-01 8.21691453e-01 -3.13464850e-01 8.17703784e-01
9.78818893e-01 4.85995084e-01 1.02191836e-01 5.54769397e-01
1.42410111e+00 -7.45607674e-01 -4.39281948e-02 -2.24546731e-01
1.34634152e-01 -4.26179022e-01 1.38502777e+00 2.38442779e-01
-1.11231482e+00 -6.37851179e-01 -1.08237696e+00 -2.85436481e-01
-5.11619031e-01 -2.24659070e-01 1.01886058e+00 5.95821962e-02
-1.14286923e+00 8.31445873e-01 -1.07787502e+00 -2.35254616e-01
9.32531118e-01 3.91926676e-01 -3.10986549e-01 -1.88366026e-01
-6.84822083e-01 3.20428014e-01 2.12838680e-01 1.78466365e-01
-5.58043599e-01 -1.27861285e+00 -9.13178682e-01 3.39446068e-01
1.85560048e-01 -1.14728928e+00 9.67477262e-01 -5.56914926e-01
-1.19323051e+00 9.03055429e-01 -1.51323453e-01 -3.04558903e-01
1.74156845e-01 -2.16673970e-01 -6.83107153e-02 1.04852304e-01
1.61925882e-01 8.45321119e-01 9.41413641e-01 -1.44512010e+00
-5.66768587e-01 -6.41334534e-01 1.34777650e-01 -1.29744606e-02
-4.87752184e-02 -3.06894660e-01 -6.75539792e-01 -7.20153630e-01
4.85717207e-01 -8.37813318e-01 -3.70592266e-01 8.73973295e-02
-3.10917288e-01 -6.56408668e-01 7.50554144e-01 1.67533174e-01
6.64423466e-01 -2.38033056e+00 -5.97008057e-02 5.78688145e-01
6.84091687e-01 -1.96587533e-01 -1.47431090e-01 4.63138759e-01
-2.16234595e-01 2.92017639e-01 -5.14701009e-01 -4.98751551e-01
3.03935587e-01 3.51711929e-01 -8.48158836e-01 3.83242697e-01
7.51969695e-01 1.48854327e+00 -1.04026747e+00 -1.00930311e-01
4.19340491e-01 4.21013176e-01 -7.19561696e-01 -1.65926620e-01
-4.10034865e-01 3.00159097e-01 -7.14449227e-01 1.12882519e+00
8.62060726e-01 -7.62046099e-01 -3.06624293e-01 -2.92670131e-01
-2.58852690e-02 1.22408360e-01 -8.36194515e-01 2.24970722e+00
-3.61484736e-01 2.89285421e-01 -1.07753590e-01 -6.54336751e-01
1.02048898e+00 -1.98070094e-01 6.19550109e-01 -6.16287410e-01
1.88545883e-02 1.90411597e-01 -3.83181334e-01 1.48895204e-01
5.26216328e-01 -1.08221054e-01 -1.56347767e-01 1.09648898e-01
-1.13351159e-02 -4.00741458e-01 -3.99919003e-01 1.42363980e-01
1.27286685e+00 1.19106464e-01 -2.21551433e-02 -4.46777701e-01
9.66508985e-02 -1.08033484e-02 3.86021554e-01 7.14827061e-01
1.62623927e-01 9.62375104e-01 1.53403103e-01 -8.57961059e-01
-8.87525260e-01 -1.65035856e+00 -2.97018468e-01 8.49718571e-01
5.78311622e-01 -5.66451788e-01 -2.87170440e-01 -3.25674266e-01
5.34522176e-01 1.16056316e-01 -6.08048499e-01 7.30775371e-02
-8.27425003e-01 -4.34114188e-01 2.50092238e-01 7.58034527e-01
3.66484731e-01 -1.24427795e+00 -1.37801886e-01 5.55621050e-02
3.36616993e-01 -1.18906558e+00 -1.18196435e-01 2.25689337e-02
-1.13108563e+00 -9.17057753e-01 -2.30749965e-01 -8.74481738e-01
5.62552035e-01 6.21141851e-01 1.71704483e+00 6.62289321e-01
-1.75424278e-01 3.53504121e-01 -3.19166005e-01 -4.75092977e-01
3.40850413e-01 3.05291027e-01 -6.70200661e-02 -5.33333533e-02
5.98398447e-01 -1.30814505e+00 -8.06481719e-01 9.85287204e-02
-8.94823372e-01 -1.43501893e-01 7.80859768e-01 5.40555656e-01
1.09885728e+00 -1.70062587e-01 3.77981633e-01 -7.95503139e-01
3.95397067e-01 -7.50633240e-01 -3.72592032e-01 -2.12245256e-01
-2.63528973e-01 -1.25053123e-01 6.88242972e-01 1.71763211e-01
-3.75229836e-01 -1.17642567e-01 -3.06884795e-01 -9.75711286e-01
-5.23185670e-01 3.50117534e-01 -1.25972450e-01 -1.56386375e-01
5.42298794e-01 1.64426595e-01 -1.63330808e-01 -7.21379340e-01
5.84316015e-01 1.08822308e-01 7.42805123e-01 -1.08141053e+00
1.11343527e+00 1.16039932e+00 1.70686230e-01 -7.66579986e-01
-8.26738358e-01 -5.80909193e-01 -7.84219384e-01 2.54228145e-01
7.73428082e-01 -1.11097682e+00 -8.23627412e-01 3.61538529e-01
-1.18897271e+00 -3.18338662e-01 -6.67901099e-01 2.27951948e-02
-5.15792131e-01 1.47155121e-01 -4.17672187e-01 -1.21065408e-01
-2.30697423e-01 -9.42397118e-01 1.61075723e+00 -5.11119813e-02
-9.41390991e-02 -9.13829327e-01 -4.79309261e-03 -1.38208836e-01
2.45369509e-01 4.34611380e-01 8.95671546e-01 -4.07680690e-01
-1.34554303e+00 -4.89711016e-02 -4.69824404e-01 1.03912801e-01
5.25527708e-02 -2.35562194e-02 -8.10473740e-01 -3.83791149e-01
-1.65797666e-01 -1.87826693e-01 1.02134109e+00 6.29093945e-02
1.96618295e+00 -1.46335913e-02 -4.21496481e-01 1.19181836e+00
1.52381849e+00 -5.09225726e-01 6.82902634e-01 2.90975153e-01
1.07482219e+00 1.15340605e-01 2.21771762e-01 4.04860765e-01
5.42803288e-01 3.40630382e-01 1.00515914e+00 -3.36989552e-01
-1.02617554e-01 -3.99682283e-01 -1.70987681e-01 8.62989247e-01
-1.72834083e-01 1.73674867e-01 -1.09461892e+00 5.60993254e-01
-1.79557025e+00 -1.00308740e+00 -1.62061512e-01 1.71358383e+00
4.82240826e-01 1.51279584e-01 -1.79675564e-01 -1.07839584e-01
4.02450383e-01 4.47348207e-01 -6.29780948e-01 -3.71467788e-03
-2.75588959e-01 8.15897644e-01 4.84183013e-01 5.87741993e-02
-1.11575615e+00 1.08141947e+00 6.33928776e+00 9.32026386e-01
-1.15301096e+00 -2.48329900e-02 3.78062606e-01 -1.60522804e-01
-7.32752323e-01 -2.72365697e-02 -6.21497571e-01 4.85165238e-01
2.92143196e-01 2.02492505e-01 3.18932682e-01 9.66897845e-01
-1.41637370e-01 5.83093941e-01 -1.16396058e+00 1.31399155e+00
-2.20000356e-01 -1.89318764e+00 4.03196484e-01 3.85095254e-02
8.20796490e-01 7.03744173e-01 3.08716983e-01 3.05815846e-01
5.33395410e-01 -1.19085991e+00 8.07148814e-01 5.88292599e-01
7.09044576e-01 -7.24070191e-01 3.75321299e-01 2.53634214e-01
-1.46180379e+00 5.05555561e-03 -6.81662381e-01 -2.79453397e-01
-1.70838878e-01 7.09773779e-01 -3.41861606e-01 6.62403166e-01
1.15346920e+00 1.34290051e+00 -6.10641479e-01 9.76588488e-01
-2.13388085e-01 5.09376407e-01 -5.60085177e-01 2.35931650e-01
4.75422978e-01 -3.07846367e-01 4.94011283e-01 1.23641109e+00
2.91076362e-01 8.02856758e-02 4.30442303e-01 1.22849989e+00
-3.77626687e-01 -1.05179965e-01 -8.33014429e-01 3.39857608e-01
6.07360005e-01 1.38158798e+00 -6.83054268e-01 -1.70041710e-01
-5.61980665e-01 6.64133668e-01 6.76716983e-01 2.66367316e-01
-5.28832018e-01 -5.90780973e-02 1.28114700e+00 2.32514501e-01
4.81179535e-01 -6.57455266e-01 -9.82892692e-01 -1.04265380e+00
3.93511921e-01 -5.46710432e-01 8.33500177e-02 -8.23351502e-01
-1.81863511e+00 4.84570354e-01 -1.14057191e-01 -1.45058763e+00
2.99612314e-01 -7.68105984e-01 -9.41235065e-01 8.64501238e-01
-1.82322550e+00 -1.52666891e+00 -7.13961661e-01 8.31143856e-01
5.23942709e-01 -1.22866809e-01 5.60420156e-01 2.99558431e-01
-2.08496571e-01 4.21398550e-01 -1.67543367e-01 2.16524452e-01
2.01095879e-01 -1.38962293e+00 9.44374323e-01 5.80493510e-01
4.25071597e-01 1.06780422e+00 5.52738383e-02 -5.74070513e-01
-1.76893389e+00 -1.50454473e+00 3.00771832e-01 -9.14974689e-01
8.53356957e-01 -5.09079039e-01 -1.21822810e+00 5.81634760e-01
3.42760538e-03 4.84779000e-01 4.83135939e-01 3.48946095e-01
-6.41737640e-01 -1.45660833e-01 -7.58265913e-01 6.98016286e-01
1.68971539e+00 -6.56998694e-01 -8.48073900e-01 3.53801489e-01
1.15078020e+00 -4.98860776e-01 -9.30016935e-01 7.63022184e-01
1.21003158e-01 -9.25529063e-01 1.51972365e+00 -7.16828108e-01
6.53452754e-01 -4.76224363e-01 -2.96878010e-01 -1.16603911e+00
-8.88094962e-01 -4.29161251e-01 -2.14642167e-01 8.49009156e-01
3.21428269e-01 -5.91731489e-01 1.04267097e+00 2.64217317e-01
-7.49120831e-01 -1.15132403e+00 -8.18177283e-01 -7.83002198e-01
3.51488471e-01 -7.95973718e-01 1.48906934e+00 1.11074257e+00
-5.00289857e-01 1.07041366e-01 2.66472250e-01 5.76564074e-01
6.55762911e-01 6.59388602e-01 8.48354042e-01 -1.51449549e+00
1.22493133e-02 -8.23129714e-01 -7.40944862e-01 -1.49119687e+00
2.43028209e-01 -1.41674435e+00 -2.77650684e-01 -1.49808526e+00
-9.59602296e-02 -9.62514997e-01 -5.21330178e-01 6.51872337e-01
-2.02356547e-01 5.66722631e-01 2.14943916e-01 6.96063280e-01
-6.84591830e-01 6.93260431e-01 1.37302458e+00 -3.20585459e-01
-1.11069679e-02 -1.78395271e-01 -8.96690607e-01 9.19489086e-01
7.97837317e-01 -2.01137140e-01 -2.93840289e-01 -9.85069335e-01
5.91049314e-01 -6.39523864e-01 9.03491437e-01 -1.15215719e+00
3.14992279e-01 -2.01380014e-01 5.65501332e-01 -9.89238501e-01
4.04870540e-01 -1.02214909e+00 2.59198397e-02 -1.64168283e-01
8.51993784e-02 4.15513039e-01 3.46958578e-01 9.30983186e-01
-3.22047770e-01 3.82697374e-01 4.88443524e-01 -3.52129579e-01
-1.00476158e+00 1.16478038e+00 5.44617593e-01 4.44860421e-02
8.61011267e-01 -4.11384761e-01 -2.79970258e-01 2.92488009e-01
-6.58580780e-01 3.42611581e-01 8.19731891e-01 6.02855623e-01
8.63363385e-01 -1.66640067e+00 -5.72230637e-01 5.32953858e-01
2.38144562e-01 8.23044240e-01 2.01888502e-01 5.46976924e-01
-5.93389392e-01 1.31960928e-01 -1.34974882e-01 -1.21355498e+00
-6.14987910e-01 5.98560274e-01 4.40290682e-02 5.39877824e-02
-1.19222808e+00 8.22092295e-01 4.85689491e-01 -5.63857973e-01
-6.32991567e-02 -8.22151780e-01 1.40066206e-01 -3.53618681e-01
3.53081167e-01 1.16158994e-02 4.53801602e-01 -3.06674361e-01
-3.67054284e-01 9.28944647e-01 1.51804969e-01 4.01999146e-01
1.54135597e+00 1.67029291e-01 -4.19045091e-01 4.52895433e-01
1.18148136e+00 1.42457873e-01 -1.37283182e+00 -4.31175321e-01
-3.43132727e-02 -7.15329945e-01 -2.01478571e-01 -3.90615493e-01
-1.10065627e+00 9.43946242e-01 2.75660846e-02 4.18638170e-01
9.30756450e-01 2.74959743e-01 9.06513512e-01 6.26158357e-01
6.48379445e-01 -5.68943739e-01 9.38162208e-02 8.71129215e-01
1.11196208e+00 -1.21233988e+00 -1.15887500e-01 -5.67279279e-01
-2.35848680e-01 9.10181820e-01 5.88447988e-01 -9.20691669e-01
1.12902820e+00 2.21826896e-01 -2.47716710e-01 -7.53197730e-01
-7.96693087e-01 -3.04308057e-01 3.45875293e-01 5.02530336e-01
-4.78001870e-03 2.42108509e-01 4.17869836e-01 5.88910878e-01
-4.88305002e-01 -2.04793677e-01 -7.99725205e-02 8.14987719e-01
-3.61636132e-01 -8.80194902e-01 -1.77412316e-01 5.88169456e-01
-9.61688384e-02 -3.00706804e-01 -4.21911597e-01 7.93810666e-01
3.14231306e-01 3.85126173e-01 5.69094121e-01 -3.95667166e-01
5.91648519e-01 -3.09710205e-01 2.28587434e-01 -9.18008029e-01
-7.07393765e-01 -4.21754271e-01 -6.42413378e-01 -9.67234671e-01
-1.73130348e-01 -5.38471937e-01 -1.42822981e+00 -5.74913025e-01
1.95748493e-01 -2.16201603e-01 5.68417907e-01 8.30460966e-01
8.69075119e-01 5.30795217e-01 6.23042583e-01 -1.22995555e+00
-4.41828430e-01 -5.85281253e-01 -4.82230574e-01 7.27403939e-01
3.88212234e-01 -7.72106230e-01 -3.06170642e-01 -3.22136641e-01] | [7.956679821014404, -3.601789712905884] |
0d9de8da-1873-4479-a4d5-cfac003f48af | accurate-and-interpretable-evaluation-of | 1908.07319 | null | https://arxiv.org/abs/1908.07319v1 | https://arxiv.org/pdf/1908.07319v1.pdf | Accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networks | Purpose: Manual feedback from senior surgeons observing less experienced trainees is a laborious task that is very expensive, time-consuming and prone to subjectivity. With the number of surgical procedures increasing annually, there is an unprecedented need to provide an accurate, objective and automatic evaluation of trainees' surgical skills in order to improve surgical practice. Methods: In this paper, we designed a convolutional neural network (CNN) to classify surgical skills by extracting latent patterns in the trainees' motions performed during robotic surgery. The method is validated on the JIGSAWS dataset for two surgical skills evaluation tasks: classification and regression. Results: Our results show that deep neural networks constitute robust machine learning models that are able to reach new competitive state-of-the-art performance on the JIGSAWS dataset. While we leveraged from CNNs' efficiency, we were able to minimize its black-box effect using the class activation map technique. Conclusions: This characteristic allowed our method to automatically pinpoint which parts of the surgery influenced the skill evaluation the most, thus allowing us to explain a surgical skill classification and provide surgeons with a novel personalized feedback technique. We believe this type of interpretable machine learning model could integrate within "Operation Room 2.0" and support novice surgeons in improving their skills to eventually become experts. | ['Pierre-Alain Muller', 'Jonathan Weber', 'Hassan Ismail Fawaz', 'Germain Forestier', 'Lhassane Idoumghar'] | 2019-08-20 | null | null | null | null | ['skills-evaluation', 'surgical-skills-evaluation'] | ['computer-vision', 'medical'] | [ 3.07755861e-02 5.43934703e-01 -3.85975510e-01 -4.45025802e-01
-4.21474695e-01 -6.49000168e-01 2.15257946e-02 2.99402535e-01
-7.52772927e-01 3.43945771e-01 3.90131831e-01 -7.22030640e-01
-4.47074145e-01 -3.23643327e-01 -5.91384470e-01 -4.97650027e-01
-8.37703049e-02 2.61150390e-01 -1.47979885e-01 -4.46552962e-01
3.11042964e-01 7.64312088e-01 -1.23191309e+00 5.93399644e-01
6.19151831e-01 9.43485618e-01 4.37923163e-01 7.16855109e-01
3.18668962e-01 1.22381020e+00 -3.54795486e-01 4.48934697e-02
4.38228339e-01 -4.94132750e-02 -9.78582740e-01 -3.09561133e-01
4.76677477e-01 -3.62361580e-01 -3.52229625e-01 8.23560238e-01
4.57479566e-01 9.17806625e-02 2.49346703e-01 -3.30164343e-01
-3.38841796e-01 5.36538005e-01 1.48519531e-01 3.12904447e-01
1.43814191e-01 3.54747146e-01 5.03004134e-01 -6.25914693e-01
7.60598958e-01 6.87467575e-01 8.04603755e-01 8.55359852e-01
-1.09497511e+00 -5.42450130e-01 -9.88890454e-02 -4.39267978e-02
-7.52527952e-01 -1.92344874e-01 6.86548293e-01 -8.99200320e-01
6.75695539e-01 4.38968062e-01 9.79786396e-01 1.26302564e+00
7.74208128e-01 7.32993782e-01 1.07990384e+00 -3.28650922e-01
-1.46629885e-01 4.70768839e-01 1.12142162e-02 1.17925715e+00
2.27083459e-01 5.55151105e-01 -5.38010240e-01 3.82047087e-01
1.01323438e+00 5.04989624e-01 -5.25768936e-01 -5.58739245e-01
-1.51337624e+00 6.88169599e-01 9.59132552e-01 4.99230713e-01
-5.20795047e-01 3.49553168e-01 4.26582158e-01 3.91805857e-01
7.33083040e-02 1.23987949e+00 -5.36220610e-01 -4.20335025e-01
-6.52866960e-01 -1.36945814e-01 7.71834016e-01 5.04991114e-01
2.88502693e-01 -2.32332334e-01 -3.73732299e-01 4.65076596e-01
-8.50237310e-02 -4.54643629e-02 8.09422255e-01 -9.83049691e-01
8.62356350e-02 8.97605777e-01 -3.49104553e-02 -1.12383592e+00
-1.00589323e+00 -9.49245274e-01 -6.64694607e-01 6.98942423e-01
3.31850499e-01 -1.67136043e-01 -9.91157591e-01 1.36711967e+00
-2.60295659e-01 -4.87893313e-01 -1.38184100e-01 1.05780613e+00
9.61535394e-01 -1.70562312e-01 1.18979000e-01 1.61145091e-01
1.25557315e+00 -9.91847634e-01 -4.57091987e-01 -4.25222039e-01
1.07034492e+00 -5.61006248e-01 1.15471303e+00 6.62446201e-01
-9.00635362e-01 -6.99588120e-01 -9.82371569e-01 9.83691774e-03
-1.25326246e-01 7.63482571e-01 1.12227440e+00 4.46686149e-01
-1.05028439e+00 1.07395256e+00 -1.39302826e+00 -1.92534581e-01
6.08827114e-01 8.76679897e-01 -7.95630157e-01 3.27756435e-01
-6.85717762e-01 1.33959818e+00 4.21068549e-01 4.56627816e-01
-9.77288067e-01 -8.96317601e-01 -7.77067304e-01 1.40770227e-01
3.49745005e-01 -7.53883243e-01 1.45541596e+00 -1.13883209e+00
-1.63607025e+00 1.22781610e+00 2.29376495e-01 -3.84410471e-01
4.59605783e-01 -3.73421937e-01 -1.33976698e-01 5.73046952e-02
-3.15367430e-01 5.14814734e-01 2.78794855e-01 -1.07462788e+00
-2.91702092e-01 -3.31847757e-01 2.41945222e-01 7.66391829e-02
-5.19281268e-01 -4.65180665e-01 -9.90143642e-02 -5.89828432e-01
2.79949695e-01 -1.34906590e+00 -5.84578216e-01 3.67614508e-01
-2.63059616e-01 1.54315919e-01 3.38695973e-01 -6.23992562e-01
1.12185693e+00 -2.26484895e+00 3.83740962e-01 1.30119607e-01
6.85268402e-01 4.57693666e-01 2.97874898e-01 2.76167303e-01
-2.88649827e-01 -1.01331577e-01 2.34231994e-01 -9.16250199e-02
-5.25876939e-01 2.79409081e-01 6.42583296e-02 4.63577658e-01
1.25537381e-01 9.18655515e-01 -1.19231915e+00 -3.28524411e-01
3.94920170e-01 1.56103685e-01 -8.47978294e-01 5.04326522e-01
2.99410373e-01 1.00079763e+00 -2.60536015e-01 5.45438468e-01
-1.00776538e-01 -3.35869312e-01 3.15816164e-01 -2.50011057e-01
-6.17578439e-02 2.38582119e-01 -5.09276032e-01 2.28194380e+00
-1.00157332e+00 9.40560162e-01 1.40916770e-02 -8.81034315e-01
1.00088465e+00 4.02913988e-01 5.77739000e-01 -5.45645654e-01
5.15113235e-01 4.11699861e-01 5.14685214e-01 -9.09313202e-01
2.47868374e-01 -3.33498687e-01 -9.12667364e-02 -4.59706560e-02
3.87651801e-01 -1.56863928e-01 -1.55078351e-01 1.62060803e-03
1.23477697e+00 1.45124421e-01 4.40115541e-01 -7.19192505e-01
2.72881180e-01 3.62022936e-01 2.53419012e-01 7.70700455e-01
-3.41587275e-01 4.42025840e-01 4.08138335e-01 -1.06245971e+00
-5.21014452e-01 -1.01224029e+00 1.50171474e-01 8.31626475e-01
-1.40981033e-01 1.01018548e-01 -3.28005612e-01 -9.10936058e-01
-1.54646952e-02 4.44590718e-01 -1.36994648e+00 -6.78988576e-01
-5.32947004e-01 4.17088792e-02 5.82433417e-02 7.00888336e-01
-4.88074422e-02 -1.27065086e+00 -8.94785583e-01 2.96827018e-01
1.80521682e-02 -8.59099329e-01 -2.93693006e-01 6.34501338e-01
-1.17261398e+00 -1.03645289e+00 -5.86426020e-01 -8.99143457e-01
1.14235890e+00 1.90728195e-02 1.04586196e+00 5.12962490e-02
-7.76803613e-01 3.29554379e-01 -3.49717408e-01 -7.82547474e-01
-6.67443812e-01 3.93120766e-01 9.42465849e-03 -3.95314693e-01
2.32291147e-01 -3.39896679e-01 -1.25731897e+00 1.17829166e-01
-7.15253174e-01 2.80442327e-01 1.21427512e+00 9.53762710e-01
9.59873646e-02 -7.04489410e-01 3.39134131e-03 -1.14933980e+00
5.29988229e-01 -2.79898733e-01 -3.22472960e-01 -5.98197691e-02
-7.92149186e-01 3.03222954e-01 7.84302831e-01 -4.69721138e-01
-7.80259252e-01 3.84591639e-01 -3.23847570e-02 -5.13573825e-01
-1.48135900e-01 6.51894867e-01 7.91366339e-01 -4.08785373e-01
1.29185486e+00 -8.47922340e-02 4.04729366e-01 -3.79430115e-01
-1.82215303e-01 3.29469860e-01 7.70609379e-01 -2.76767612e-01
4.62185711e-01 4.97333199e-01 7.96863213e-02 -1.34555578e-01
-1.20067608e+00 -8.05103838e-01 -7.97634065e-01 -6.50544584e-01
9.02149022e-01 -7.75058746e-01 -1.15303934e+00 -1.69455498e-01
-9.22301471e-01 -4.71255988e-01 -3.73378247e-01 9.38456059e-01
-4.32134718e-01 4.30138875e-03 -7.55763531e-01 -3.61044198e-01
-4.70149040e-01 -1.42742777e+00 8.63675296e-01 2.74778515e-01
-6.15203857e-01 -1.29189920e+00 3.17798406e-01 3.01971048e-01
6.86202168e-01 5.73307276e-01 6.81955099e-01 -5.42180419e-01
-4.30273563e-01 -6.37234390e-01 1.36887595e-01 5.34716189e-01
2.37400115e-01 -2.70778388e-01 -8.14673722e-01 -4.63758856e-01
-1.34020746e-01 -2.08169982e-01 6.45438492e-01 3.52042466e-01
1.62651610e+00 -1.67339176e-01 -2.62733042e-01 9.31975663e-01
1.34051406e+00 4.40942533e-02 4.81008172e-01 4.23027724e-01
5.22564352e-01 6.26726747e-01 5.05831718e-01 6.85933903e-02
-5.88672310e-02 5.43392837e-01 5.50769925e-01 -5.12783825e-01
-1.53732881e-01 -7.64048249e-02 9.17266011e-02 1.01984966e+00
-6.38682663e-01 3.59798282e-01 -1.16247261e+00 4.58840251e-01
-1.68318522e+00 -6.94119751e-01 3.12413666e-02 2.12784719e+00
7.01583147e-01 2.07125023e-01 -4.66032535e-01 -1.42761230e-01
-9.52561200e-02 -3.26792002e-01 -3.78216624e-01 -7.08965778e-01
5.84975839e-01 5.24962783e-01 9.64971900e-01 1.85803622e-01
-9.21305954e-01 5.81499398e-01 6.11959934e+00 1.51925445e-01
-1.61351812e+00 -1.31219506e-01 4.44401175e-01 -2.28192195e-01
1.41063035e-01 -4.17867601e-01 -2.07618922e-01 1.54660434e-01
7.82774031e-01 1.70728087e-01 7.54775107e-02 1.12301934e+00
3.92489016e-01 -7.97744468e-02 -1.31433833e+00 8.24388981e-01
-7.21627986e-03 -1.65974236e+00 -3.04340094e-01 -6.91411123e-02
7.55066037e-01 -1.79965049e-02 2.22075120e-01 4.39154655e-01
2.62803555e-01 -1.26928437e+00 2.79897839e-01 8.36969256e-01
9.07856703e-01 -2.69752741e-01 1.08049536e+00 2.85924077e-01
-5.86794913e-01 -4.54384685e-01 1.13308378e-01 -1.68095171e-01
-4.11054581e-01 1.34841159e-01 -1.48550665e+00 2.75560856e-01
6.73533320e-01 4.72087383e-01 -4.04023141e-01 9.21839952e-01
-2.84419119e-01 3.36765498e-01 1.59380168e-01 -2.34210536e-01
4.10649925e-01 4.75613892e-01 2.95872658e-01 1.26207638e+00
2.76209831e-01 6.02307431e-02 -3.15747000e-02 5.49713135e-01
-4.54126671e-02 -7.68842583e-04 -5.37177742e-01 -7.76439086e-02
-2.68707514e-01 1.45791602e+00 -7.25724339e-01 -4.58695069e-02
-5.79568259e-02 8.92492414e-01 4.69400316e-01 -2.38608308e-02
-3.86447757e-01 -3.43228400e-01 4.79875326e-01 2.42394373e-01
-3.03278148e-01 -3.52800369e-01 -6.30224586e-01 -1.02931130e+00
-5.24088331e-02 -7.46885955e-01 1.62207484e-01 -7.09681630e-01
-6.12966895e-01 7.38683283e-01 -3.82981449e-01 -1.68934858e+00
-3.33519399e-01 -1.33449149e+00 -4.08861846e-01 7.52894938e-01
-1.24433160e+00 -7.60869503e-01 -1.03716564e+00 4.47957098e-01
4.04486686e-01 -1.65854111e-01 1.16246200e+00 7.07853809e-02
-1.79510489e-01 4.79127854e-01 -9.92514715e-02 4.59409386e-01
8.70997906e-01 -1.33245933e+00 -3.08290273e-01 4.55601364e-01
-1.40604302e-01 1.07319188e+00 8.38776112e-01 -1.64715663e-01
-1.32324636e+00 -5.51993549e-01 4.02022928e-01 -6.70109332e-01
5.89365423e-01 -8.65437314e-02 -4.70559567e-01 8.42828393e-01
-1.29031777e-01 1.32740401e-02 1.10550809e+00 3.82104844e-01
-1.13055706e-02 -2.87449837e-01 -7.80384123e-01 7.41194606e-01
9.90412116e-01 -3.47668082e-01 -8.14335465e-01 5.51927149e-01
3.97963077e-01 -9.14959133e-01 -1.05216026e+00 7.51812458e-01
8.56760621e-01 -1.02499139e+00 7.43512332e-01 -8.37718725e-01
1.00252819e+00 2.17391923e-01 5.50056040e-01 -1.52112341e+00
-3.71000439e-01 -3.82184327e-01 1.74353734e-01 -6.02540374e-02
6.32370949e-01 -4.71494883e-01 1.12298405e+00 6.60278797e-01
-7.95194685e-01 -1.11475933e+00 -6.99733496e-01 -4.10133481e-01
-1.13619186e-01 -1.77671626e-01 -2.42519706e-01 8.74847054e-01
4.66230720e-01 -1.32734299e-01 -1.60887033e-01 -6.22991174e-02
7.50972703e-02 -1.47393150e-02 7.09232569e-01 -1.33679080e+00
-5.25088251e-01 -7.72635758e-01 -7.26567090e-01 -7.26114213e-01
-3.02067846e-01 -1.10570264e+00 2.01515287e-01 -1.70062172e+00
1.04551584e-01 -3.57107401e-01 -6.35067642e-01 6.33328140e-01
-7.70959035e-02 -4.37965849e-03 6.19763955e-02 3.11504215e-01
-2.27003172e-01 -2.95367781e-02 1.66775250e+00 1.02183685e-01
-2.90832430e-01 3.92342597e-01 -8.30523193e-01 5.24361789e-01
7.60142565e-01 -4.82565910e-01 -1.94738492e-01 -5.15605688e-01
3.07213813e-01 2.15589538e-01 4.30596322e-01 -1.14613366e+00
4.46969181e-01 9.33354199e-02 5.24756312e-01 -8.89207423e-03
1.88549206e-01 -1.09125543e+00 -1.36930779e-01 1.19502831e+00
-5.13232172e-01 -1.59762517e-01 5.87483048e-01 1.87873468e-01
-3.93787533e-01 6.91094063e-03 7.47688949e-01 -5.01135409e-01
-7.26392090e-01 2.32897684e-01 -5.01764953e-01 -4.42548335e-01
9.13211823e-01 -1.69579715e-01 -6.01145029e-02 -2.06332579e-01
-1.18963039e+00 -1.22084931e-01 1.02785908e-01 6.27651274e-01
6.61888421e-01 -9.22576606e-01 -5.17524064e-01 1.99255645e-01
3.26369226e-01 6.55135810e-02 7.10300148e-01 1.19632864e+00
-1.20645678e+00 8.09758544e-01 -5.94187260e-01 -6.79436028e-01
-1.09367800e+00 5.00638247e-01 6.22005105e-01 -6.15494609e-01
-5.95801473e-01 1.12922132e+00 1.81269929e-01 -6.76991820e-01
4.27293390e-01 -6.62845910e-01 -3.05837005e-01 -3.10183227e-01
1.99386597e-01 -1.89295769e-01 2.98715711e-01 1.05546154e-01
-3.12367558e-01 2.59484202e-01 -2.16490537e-01 3.30519706e-01
1.26774704e+00 5.19315183e-01 2.61305600e-01 4.46677983e-01
1.22426546e+00 -8.33172426e-02 -1.17201769e+00 4.36794721e-02
6.32648775e-03 -4.25793827e-01 2.38213211e-01 -1.28716791e+00
-1.08820939e+00 1.00701296e+00 9.06466186e-01 -2.03372747e-01
1.01757789e+00 -1.11179568e-01 4.11297828e-01 5.58409393e-01
4.36171979e-01 -1.04055464e+00 2.04059675e-01 3.04720819e-01
1.04105818e+00 -1.61971724e+00 -1.48573399e-01 -1.62598535e-01
-7.19924688e-01 1.56623077e+00 7.10509956e-01 -2.38319471e-01
6.25644028e-01 1.77704275e-01 6.32530570e-01 -6.19657695e-01
-4.53175098e-01 9.25968960e-02 7.60639250e-01 8.24985206e-02
7.36662686e-01 3.60987306e-01 -2.12365210e-01 4.08926547e-01
-4.23504502e-01 4.30575013e-01 5.64619005e-01 1.04779375e+00
-3.41322154e-01 -7.43860245e-01 1.62137479e-01 6.86278164e-01
-5.86470246e-01 1.30582601e-02 -1.20232582e-01 9.25947547e-01
8.23764727e-02 5.64811409e-01 -1.80517331e-01 -5.79726636e-01
7.75711536e-01 -1.44218147e-01 5.28192520e-01 -9.96982038e-01
-1.24584305e+00 -4.85080451e-01 3.71659696e-02 -8.72696936e-01
-1.56764016e-01 -2.72547990e-01 -1.01129615e+00 -7.73622915e-02
-7.09361061e-02 2.02118438e-02 1.05801511e+00 7.28378296e-01
2.60444731e-01 1.36498511e+00 4.22241747e-01 -9.23720479e-01
-5.34414768e-01 -1.11910081e+00 -2.53100723e-01 4.68151271e-01
4.43722963e-01 -7.45988190e-01 -3.19129884e-01 -5.53839952e-02] | [14.084404945373535, -3.363438844680786] |
49f09090-764c-49a3-b039-ed9049e5de65 | enhancing-llm-with-evolutionary-fine-tuning | 2307.02839 | null | https://arxiv.org/abs/2307.02839v1 | https://arxiv.org/pdf/2307.02839v1.pdf | Enhancing LLM with Evolutionary Fine Tuning for News Summary Generation | News summary generation is an important task in the field of intelligence analysis, which can provide accurate and comprehensive information to help people better understand and respond to complex real-world events. However, traditional news summary generation methods face some challenges, which are limited by the model itself and the amount of training data, as well as the influence of text noise, making it difficult to generate reliable information accurately. In this paper, we propose a new paradigm for news summary generation using LLM with powerful natural language understanding and generative capabilities. We use LLM to extract multiple structured event patterns from the events contained in news paragraphs, evolve the event pattern population with genetic algorithm, and select the most adaptive event pattern to input into the LLM to generate news summaries. A News Summary Generator (NSG) is designed to select and evolve the event pattern populations and generate news summaries. The experimental results show that the news summary generator is able to generate accurate and reliable news summaries with some generalization ability. | ['Xiaolin Chen', 'Le Xiao'] | 2023-07-06 | null | null | null | null | ['natural-language-understanding'] | ['natural-language-processing'] | [ 4.15014774e-01 -5.46755530e-02 -1.54228851e-01 -1.85898572e-01
-5.16888976e-01 -2.75295794e-01 8.75184715e-01 4.96423304e-01
-5.17972596e-02 1.23434937e+00 7.51969576e-01 2.24951237e-01
-1.24300011e-01 -1.22532833e+00 -3.43086153e-01 -5.16254365e-01
-1.61967184e-02 6.93408906e-01 2.17930689e-01 -3.29654843e-01
6.95629418e-01 1.65829018e-01 -1.81652272e+00 4.12159055e-01
1.50489497e+00 6.03346825e-01 5.31702220e-01 8.35297346e-01
-7.11151898e-01 9.42010224e-01 -1.69131839e+00 -1.49188399e-01
-4.03784007e-01 -1.26895225e+00 -3.67510647e-01 2.21897829e-02
-5.02544224e-01 6.00061864e-02 3.46865840e-02 9.11078811e-01
7.81287909e-01 6.16948783e-01 8.47588360e-01 -1.16188800e+00
-5.85843444e-01 1.16788077e+00 -3.33064049e-01 3.49532783e-01
5.74588180e-01 -1.69577189e-02 4.56143826e-01 -3.04492921e-01
8.59922886e-01 1.27873647e+00 3.62870902e-01 7.14505732e-01
-7.10848689e-01 -3.84656698e-01 1.83082625e-01 9.27518532e-02
-9.82384503e-01 -3.22858721e-01 8.46003771e-01 -4.33170274e-02
7.14690089e-01 6.15430713e-01 1.02945244e+00 1.10643291e+00
6.87246621e-01 9.02254820e-01 6.22382164e-01 -3.04918289e-01
6.24675930e-01 1.54892966e-01 9.97787435e-03 3.13138038e-01
5.59897423e-01 -1.65636510e-01 -7.29006231e-01 -3.98030430e-01
5.19835770e-01 -4.50942814e-02 -4.67360258e-01 4.82388139e-01
-1.16119063e+00 1.05158651e+00 1.43793551e-02 4.08085674e-01
-7.96353877e-01 -3.49778622e-01 3.98482174e-01 2.03802764e-01
5.48475564e-01 1.01605916e+00 -1.59731641e-01 -3.68079007e-01
-8.44706297e-01 6.27483785e-01 1.01439548e+00 7.09787965e-01
2.62963444e-01 3.60140741e-01 -6.62555218e-01 6.98921978e-01
-1.32760793e-01 7.84223199e-01 1.10426414e+00 -2.45430544e-01
4.67361182e-01 8.23415577e-01 2.10706651e-01 -1.40137744e+00
-4.23664868e-01 -6.61114573e-01 -9.40828204e-01 -5.20785861e-02
-2.61956185e-01 -6.48958802e-01 -7.27569401e-01 1.58059287e+00
1.62190333e-01 1.46293834e-01 6.12531364e-01 5.18501699e-01
1.26572454e+00 1.45291328e+00 -1.88052639e-01 -7.13467121e-01
1.17337048e+00 -8.91476214e-01 -1.05495846e+00 -4.72555012e-01
1.11184493e-01 -5.77607930e-01 8.47205341e-01 8.15508142e-02
-1.10553575e+00 -4.58229780e-01 -1.02757096e+00 5.26705265e-01
-2.76367217e-01 2.42416024e-01 5.10828435e-01 1.92144260e-01
-4.93822962e-01 2.37365171e-01 -4.27010328e-01 -4.00179029e-01
8.24590325e-02 -7.54534751e-02 1.81634590e-01 1.53412551e-01
-1.44465077e+00 7.23540068e-01 1.08167017e+00 -1.50640756e-01
-4.76267606e-01 -4.14261162e-01 -8.65169287e-01 3.04154485e-01
3.45954806e-01 -9.97876406e-01 1.22021985e+00 -5.71582854e-01
-1.56010902e+00 4.65297699e-03 -2.72774160e-01 -5.35422564e-01
3.20838720e-01 1.38581604e-01 -5.66751420e-01 -9.04095620e-02
2.06554741e-01 2.47383490e-01 5.25779486e-01 -1.03412914e+00
-8.69131923e-01 -9.55293234e-03 -6.47361219e-01 5.87091386e-01
-1.41512126e-01 -3.75469252e-02 -7.23897070e-02 -9.81845319e-01
-2.63289064e-01 -3.96459281e-01 -3.95082951e-01 -9.88665044e-01
-5.27984560e-01 -1.00795068e-01 7.05951273e-01 -5.10640681e-01
1.49876010e+00 -1.86027586e+00 1.44439459e-01 1.34233922e-01
-2.49455944e-01 3.86402041e-01 -7.45647587e-03 7.81896651e-01
4.05678570e-01 1.35960862e-01 -1.09776206e-01 -1.56306624e-02
-6.34009913e-02 1.07731096e-01 -6.15104377e-01 -5.20087123e-01
-2.06608959e-02 7.70564616e-01 -9.07896876e-01 -4.31365103e-01
-1.42434970e-01 2.71969661e-03 -1.45694330e-01 5.64939201e-01
-8.80127728e-01 3.82802457e-01 -9.58513856e-01 3.05631105e-02
1.42767951e-01 -2.00299561e-01 -2.39756063e-01 3.15613151e-01
-1.62526324e-01 2.36149997e-01 -1.30388725e+00 1.24083388e+00
-1.91907763e-01 4.15910602e-01 -6.29970133e-01 -6.06143951e-01
1.46364331e+00 2.91783810e-01 -6.18258901e-02 -5.22718072e-01
3.68785620e-01 1.13884009e-01 -1.86336383e-01 -7.32171953e-01
8.78997505e-01 -6.27833754e-02 -4.38935727e-01 9.96062160e-01
-3.47945005e-01 -4.38449740e-01 6.43191278e-01 9.92760956e-02
7.38337040e-01 -4.56131607e-01 5.72432041e-01 2.02068180e-01
4.70230192e-01 4.25658703e-01 7.77809560e-01 9.07109857e-01
6.15848958e-01 6.52603149e-01 5.50195515e-01 -5.46137631e-01
-8.61594558e-01 -6.05253935e-01 3.73905659e-01 5.63183248e-01
1.49364024e-01 -4.02625948e-01 -6.35569215e-01 -3.92119378e-01
-3.32885206e-01 1.26920521e+00 -4.52061474e-01 -5.65695226e-01
-4.09649819e-01 -1.37365782e+00 4.54860449e-01 2.20488951e-01
8.71557117e-01 -1.64037943e+00 -6.21737659e-01 7.41264880e-01
-5.50567925e-01 -5.23166060e-01 -3.89438629e-01 -3.53941321e-01
-5.46505511e-01 -6.11013114e-01 -7.16489375e-01 -7.84355938e-01
5.73647380e-01 -2.87573170e-02 9.63661909e-01 1.30212158e-02
-9.93179977e-02 -2.79950082e-01 -6.60499275e-01 -9.76746202e-01
-1.06033826e+00 3.50958228e-01 -2.57097960e-01 -8.67564157e-02
-5.55370599e-02 -3.15556049e-01 -1.65404066e-01 -6.91980124e-02
-1.14456201e+00 4.85392153e-01 6.46858633e-01 8.18393707e-01
4.26905870e-01 8.65572572e-01 1.21444452e+00 -8.40141594e-01
1.55534732e+00 -5.91100216e-01 -2.62857586e-01 4.66977954e-01
-4.75208282e-01 1.48452461e-01 9.99379933e-01 -5.73617220e-01
-1.51449370e+00 -6.40667796e-01 -8.06607753e-02 4.29144561e-01
-2.08741501e-01 9.69422698e-01 -1.37674555e-01 5.18006563e-01
7.28166521e-01 9.35934126e-01 -6.32408783e-02 -8.42860192e-02
7.59489536e-02 6.74836278e-01 5.29733360e-01 -3.81435990e-01
3.96104008e-01 -3.53490328e-03 -3.54026824e-01 -6.72351778e-01
-8.13737154e-01 5.56314625e-02 2.34329939e-01 -2.49097943e-01
7.48386145e-01 -4.45638776e-01 -1.55786663e-01 6.42957866e-01
-1.15526497e+00 1.02307513e-01 -4.55647618e-01 5.33309877e-01
-3.73100549e-01 -1.04137354e-01 -4.64884400e-01 -7.64343262e-01
-7.63811409e-01 -8.04902434e-01 7.57690966e-01 9.83535469e-01
-4.49862450e-01 -8.71592760e-01 1.63927466e-01 -1.24449342e-01
6.11851811e-01 7.22077250e-01 1.07284784e+00 -1.13104033e+00
-3.12818944e-01 -3.59108120e-01 4.42782968e-01 -1.51963696e-01
2.93714017e-01 4.83458564e-02 -2.44847283e-01 1.02347046e-01
2.25237384e-01 4.18777280e-02 6.36025190e-01 4.86347347e-01
6.15683913e-01 -8.91816616e-01 -5.48660457e-01 2.73883134e-01
1.02448797e+00 8.90031159e-01 5.21276593e-01 4.22947854e-01
1.40600890e-01 5.72125256e-01 7.35809147e-01 7.46791601e-01
2.64336407e-01 2.27337539e-01 -2.33605579e-01 2.45729581e-01
6.89656511e-02 -5.72523594e-01 1.94439873e-01 9.15086150e-01
1.26565471e-01 -1.03083587e+00 -6.51198387e-01 4.09211129e-01
-2.04295635e+00 -1.50745893e+00 6.46042749e-02 1.86542428e+00
9.94536877e-01 3.55970025e-01 -2.98804650e-03 1.38176322e-01
9.58947659e-01 3.51901382e-01 -5.70901930e-01 -3.44885737e-01
-5.00237465e-01 -1.97906122e-01 -2.43840516e-01 2.33091429e-01
-5.54225981e-01 7.92499006e-01 6.06319237e+00 8.60165060e-01
-1.08267558e+00 -4.57779080e-01 6.52584493e-01 -1.21995993e-01
-7.32029200e-01 -3.55001181e-01 -8.32524121e-01 9.11495149e-01
7.83123553e-01 -1.14964414e+00 1.10510960e-01 6.27471030e-01
3.94349784e-01 -2.60439932e-01 -4.73189145e-01 6.77561104e-01
5.57295442e-01 -1.74803317e+00 7.40453541e-01 -4.15791988e-01
1.20567358e+00 -6.29882634e-01 -2.32556835e-01 6.98420256e-02
4.58841473e-01 -9.13743317e-01 5.16815722e-01 8.85930181e-01
1.78918213e-01 -1.15006256e+00 1.15755022e+00 9.01996136e-01
-1.02232921e+00 -7.13993013e-02 -4.34618205e-01 -7.41174445e-02
5.30432165e-01 7.59188056e-01 -1.13823831e+00 8.01226556e-01
2.77441919e-01 3.27116996e-01 -4.41303879e-01 1.24349785e+00
-3.82480264e-01 5.32170773e-01 -1.58249855e-01 -8.81268978e-01
2.69510411e-02 -2.10094720e-01 9.69635963e-01 9.38606262e-01
6.93872154e-01 2.50100583e-01 3.14393580e-01 7.73297071e-01
1.77363694e-01 2.75139064e-01 -4.92858976e-01 -2.70183086e-01
7.54261851e-01 8.05807173e-01 -9.41491485e-01 -5.82658708e-01
2.62067646e-01 7.79347658e-01 -1.57600701e-01 2.97839016e-01
-6.81738377e-01 -7.09864795e-01 4.87596728e-02 -2.22085640e-01
-3.43595296e-02 1.72610030e-01 -3.85181457e-01 -1.15609014e+00
-1.45314261e-01 -1.10542154e+00 4.84194309e-01 -9.32883918e-01
-1.08671200e+00 1.08983004e+00 1.50152802e-01 -9.32347953e-01
-9.29126501e-01 2.55942583e-01 -1.33550823e+00 7.85122156e-01
-8.75632823e-01 -6.17907703e-01 -3.97482008e-01 1.41593084e-01
9.22307134e-01 -7.77540147e-01 7.10633099e-01 -2.61757433e-01
-5.85032701e-01 2.84721851e-01 1.79864451e-01 -1.49242729e-01
3.25045943e-01 -9.96049762e-01 7.20976770e-01 9.93013799e-01
-7.58505762e-02 4.45979774e-01 1.12590289e+00 -1.10535181e+00
-1.02493167e+00 -1.16421211e+00 1.01661360e+00 1.43576875e-01
2.43598029e-01 -5.25229611e-02 -8.46638143e-01 2.78027385e-01
2.92897195e-01 -9.11831200e-01 6.50431156e-01 -3.65181267e-01
4.52838629e-01 6.24931641e-02 -1.12828338e+00 9.68679786e-01
7.36990929e-01 3.84945631e-01 -1.02830529e+00 2.37982512e-01
1.01027822e+00 -5.69217265e-01 -3.57115209e-01 2.85808206e-01
1.16594881e-01 -7.51039028e-01 4.73351538e-01 -4.45241392e-01
4.98530239e-01 -5.17503977e-01 5.06773353e-01 -1.93740487e+00
-2.56616354e-01 -7.86934078e-01 3.09699364e-02 1.64628541e+00
6.44903243e-01 -9.35791194e-01 5.27055383e-01 2.14714810e-01
-1.46644205e-01 -6.61556423e-01 -3.15050215e-01 -4.75041538e-01
-5.45922101e-01 1.56563446e-01 1.33539939e+00 6.58231437e-01
-4.37058462e-03 6.47655129e-01 -3.86452138e-01 -1.37417018e-03
1.68827936e-01 5.21645546e-01 7.71583378e-01 -1.29027617e+00
-2.17094243e-01 -6.51342869e-01 -1.99543461e-01 -7.70259321e-01
-6.60281107e-02 -4.55901384e-01 2.94721514e-01 -2.16484094e+00
2.06947714e-01 -1.88621596e-01 3.42214942e-01 7.89123327e-02
-7.26855159e-01 -3.88883293e-01 5.64708114e-02 1.58508688e-01
-2.99589932e-01 8.31027269e-01 1.25990450e+00 -5.38979061e-02
-5.92742324e-01 4.83089745e-01 -9.69908893e-01 5.33736765e-01
1.05440521e+00 -5.07418811e-01 -8.75399292e-01 -3.83688770e-02
3.56551111e-01 4.65267450e-01 -2.14243710e-01 -1.03607857e+00
3.48034710e-01 -4.86046880e-01 5.66038489e-01 -7.97844529e-01
-8.65898877e-02 -2.49122325e-02 5.22458792e-01 5.09875536e-01
-5.82406282e-01 4.35358286e-01 1.52439177e-01 4.74393696e-01
-5.31251192e-01 -5.71794629e-01 2.59572208e-01 -3.14689487e-01
-5.64990759e-01 1.01357460e-01 -5.64891338e-01 2.42123917e-01
1.09814513e+00 -6.85181543e-02 -7.30723917e-01 -7.25715697e-01
-2.40299672e-01 4.51728046e-01 3.94646347e-01 4.07905996e-01
6.98776543e-01 -1.28998804e+00 -1.16615844e+00 2.31571078e-01
-6.33706599e-02 2.35733107e-01 2.79403508e-01 5.72356628e-03
-6.89608097e-01 1.59678385e-01 -2.36371636e-01 -1.06837042e-01
-9.17703688e-01 3.98899943e-01 -1.22150062e-02 -4.66327190e-01
-7.08743811e-01 6.56088591e-01 -2.68302768e-01 -6.81392550e-02
1.06566645e-01 -1.25057995e-01 -8.46605122e-01 2.68287003e-01
1.14178467e+00 2.29085028e-01 -1.86451793e-01 -1.51567474e-01
9.20386240e-02 2.86976993e-01 -1.73457842e-02 -3.60847890e-01
1.33578086e+00 -2.84456424e-02 -1.74925312e-01 4.57232267e-01
5.02478719e-01 2.45433807e-01 -6.38176441e-01 3.69948819e-02
-8.47666785e-02 -1.08958304e-01 -4.02599871e-01 -9.65961218e-01
-7.44933784e-01 1.96708381e-01 -1.46216914e-01 7.64385283e-01
1.19970191e+00 -7.91757852e-02 1.19036078e+00 2.81095475e-01
1.57256365e-01 -9.19914305e-01 8.46665800e-02 3.91807407e-01
1.12984979e+00 -7.35162079e-01 -2.13728081e-02 -2.03398451e-01
-1.05548656e+00 1.03408420e+00 5.70491254e-01 7.75340945e-02
3.53683508e-03 3.52189362e-01 1.02272414e-01 -2.38169823e-03
-9.97880280e-01 5.13534360e-02 2.31251791e-01 5.25970221e-01
9.83985141e-03 -1.83032393e-01 -8.12134981e-01 8.65903199e-01
-7.68342793e-01 -1.48205906e-01 7.16901183e-01 9.85255003e-01
-9.10655022e-01 -9.30100799e-01 -5.80053926e-01 7.46144414e-01
-3.62152368e-01 3.41942571e-02 -5.04023731e-01 3.89429063e-01
-4.44446914e-02 1.14366996e+00 1.42656893e-01 -2.78838873e-01
3.64573181e-01 -5.06660417e-02 -1.62357703e-01 -7.87762225e-01
-5.67537010e-01 -1.88427180e-01 3.41395169e-01 2.84889191e-01
-2.26888165e-01 -7.00555623e-01 -1.47743893e+00 -3.60596091e-01
-2.01915413e-01 9.77651894e-01 4.33243781e-01 8.99143159e-01
6.19883478e-01 8.52683783e-01 6.15448534e-01 -3.66659760e-01
-3.05843323e-01 -1.06427729e+00 -2.57914424e-01 2.66053349e-01
3.22717917e-03 -3.32830459e-01 -3.43547910e-01 9.51763093e-02] | [12.464227676391602, 9.375334739685059] |
ba2dd3d6-dc0c-4953-9f96-585edb2b7526 | bed-a-real-time-object-detection-system-for | 2202.07503 | null | https://arxiv.org/abs/2202.07503v4 | https://arxiv.org/pdf/2202.07503v4.pdf | BED: A Real-Time Object Detection System for Edge Devices | Deploying deep neural networks~(DNNs) on edge devices provides efficient and effective solutions for the real-world tasks. Edge devices have been used for collecting a large volume of data efficiently in different domains. DNNs have been an effective tool for data processing and analysis. However, designing DNNs on edge devices is challenging due to the limited computational resources and memory. To tackle this challenge, we demonstrate Object Detection System for Edge Devices~(BED) on the MAX78000 DNN accelerator. It integrates on-device DNN inference with a camera and an LCD display for image acquisition and detection exhibition, respectively. BED is a concise, effective and detailed solution, including model training, quantization, synthesis and deployment. The entire repository is open-sourced on Github, including a Graphical User Interface~(GUI) for on-chip debugging. Experiment results indicate that BED can produce accurate detection with a 300-KB tiny DNN model, which takes only 91.9 ms of inference time and 1.845 mJ of energy. The real-time detection is available at YouTube. | ['Xuanting Cai', 'Xia Hu', 'Erman Okman', 'Gorkem Ulkar', 'Afshin Niktash', 'Alfredo Costilla Reyes', 'Daochen Zha', 'Yi-Wei Chen', 'Zhimeng Jiang', 'Zaid Pervaiz Bhat', 'Guanchu Wang'] | 2022-02-14 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [-3.12809259e-01 -5.26580930e-01 -3.17861676e-01 -3.61129552e-01
-2.69329417e-02 -3.53602737e-01 -1.15651023e-02 -6.25701696e-02
-7.30322897e-01 3.19228351e-01 -3.09844077e-01 -8.17752659e-01
4.06517118e-01 -9.27092373e-01 -4.82114702e-01 -6.25106275e-01
2.95388013e-01 2.48143062e-01 3.89006495e-01 1.37478337e-01
-1.14109986e-01 6.69974744e-01 -1.53586900e+00 -6.37731403e-02
3.19178849e-01 1.50751936e+00 6.47877336e-01 9.04712141e-01
1.17320800e-03 5.55237353e-01 -6.97704792e-01 -3.85644734e-01
2.22612694e-01 5.35500757e-02 -8.35250542e-02 -7.88068771e-01
2.27442384e-01 -9.95745420e-01 -7.99570501e-01 1.59893036e+00
9.90296185e-01 -3.47365648e-01 2.41825789e-01 -1.51318872e+00
-2.35906094e-01 6.20323002e-01 -1.89207777e-01 7.36131847e-01
-2.20112607e-01 3.92795205e-01 4.13758814e-01 -6.59326971e-01
3.74781430e-01 1.04613483e+00 6.34741604e-01 8.54729295e-01
-3.66290152e-01 -1.18856037e+00 -6.18273199e-01 4.01653469e-01
-1.32361054e+00 -9.35687006e-01 3.84198010e-01 1.56923681e-01
1.62126005e+00 1.56633943e-01 8.83335173e-01 1.17513275e+00
5.26651084e-01 8.94209683e-01 2.30319336e-01 -2.50533015e-01
5.35129964e-01 9.01721045e-02 4.69103366e-01 8.26567531e-01
7.60253608e-01 -4.38915752e-02 -6.08916700e-01 1.47134617e-01
1.04381812e+00 2.68490314e-01 -7.62433931e-02 6.48442209e-01
-8.25764954e-01 4.76386011e-01 5.78277588e-01 3.88677865e-02
-3.21511298e-01 7.93176293e-01 1.00886190e+00 -6.30968586e-02
2.40001068e-01 -3.58615428e-01 -5.09196579e-01 -9.31709588e-01
-4.76005286e-01 -1.29228041e-01 6.15647256e-01 1.52995193e+00
1.09546088e-01 5.27432382e-01 1.87103987e-01 5.46978831e-01
4.63313878e-01 7.98714221e-01 6.05227172e-01 -7.51332581e-01
5.25908053e-01 6.12416387e-01 -2.41735935e-01 -6.12144411e-01
-6.65030241e-01 -3.42163354e-01 -1.24693060e+00 2.09023267e-01
4.29259539e-02 -7.08625257e-01 -8.87078047e-01 1.31010175e+00
3.43387246e-01 2.79201269e-01 3.62105332e-02 9.21107948e-01
1.39189756e+00 9.01350617e-01 1.94440652e-02 2.44989827e-01
1.65650749e+00 -9.18154359e-01 -1.00907612e+00 -2.29314819e-01
7.88275242e-01 -5.14309466e-01 8.80212963e-01 5.24498582e-01
-1.14055145e+00 -6.58650696e-01 -1.36477637e+00 -6.57591462e-01
-4.23912346e-01 5.92630506e-01 8.43123257e-01 8.90206277e-01
-1.17835486e+00 4.16487485e-01 -1.55035126e+00 -4.88243192e-01
7.67960727e-01 7.27633893e-01 8.33189785e-02 1.21722734e-02
-8.61056507e-01 4.22309607e-01 5.99663377e-01 2.89910883e-01
-7.84285605e-01 -7.52187490e-01 -6.19813323e-01 3.91869873e-01
4.53729369e-03 -8.11046839e-01 1.62723088e+00 -4.31078434e-01
-1.76375341e+00 4.56742615e-01 4.94792499e-02 -5.78937769e-01
2.18118399e-01 -1.47408664e-01 -7.66140699e-01 -3.48271728e-02
-2.75344372e-01 8.08481157e-01 4.87592906e-01 -1.18355349e-01
-7.02324986e-01 -3.52787644e-01 -2.83795208e-01 2.98226047e-02
-8.29707444e-01 3.56160998e-01 -8.34156215e-01 -1.89624146e-01
-1.57093778e-01 -5.86114407e-01 1.08553655e-01 5.16656697e-01
-4.09527749e-01 -4.27032679e-01 1.28057098e+00 -2.80714780e-01
1.47765994e+00 -2.30411577e+00 -5.89966476e-01 -2.68003136e-01
2.89819539e-01 7.40901411e-01 6.47132039e-01 -1.80566847e-01
2.41448566e-01 -5.35249375e-02 3.34967375e-01 -4.98641580e-01
1.80442438e-01 1.53747976e-01 2.85579637e-02 2.91025519e-01
-3.43090236e-01 9.37664688e-01 -5.74684262e-01 -2.83519357e-01
4.16295707e-01 6.32740736e-01 -3.85055691e-01 -6.66803941e-02
-1.41755277e-02 -2.90666372e-01 -3.11016649e-01 9.67910290e-01
8.11267793e-01 -1.96457297e-01 5.09947129e-02 -5.35635471e-01
-1.09240808e-01 3.71478558e-01 -1.09132099e+00 1.88546777e+00
-1.77183479e-01 1.29895937e+00 3.84137392e-01 -6.44561350e-01
6.99002564e-01 2.59379208e-01 -1.55219927e-01 -8.47754657e-01
8.58944237e-01 2.68424600e-01 -4.65798825e-02 -4.35305119e-01
6.66652143e-01 1.53364599e-01 -9.24257115e-02 2.50116229e-01
2.91886747e-01 1.98916212e-01 -5.13454862e-02 3.03301662e-01
1.38218009e+00 -4.53342617e-01 -9.38282236e-02 -3.17975879e-01
-2.44968742e-01 1.16618918e-02 5.99440753e-01 6.13334000e-01
-5.09049535e-01 -1.24420114e-01 2.78079569e-01 -5.68794668e-01
-1.18160832e+00 -8.35963428e-01 -4.87152427e-01 7.12332070e-01
2.08358288e-01 -7.87693441e-01 -9.18472707e-01 7.32171582e-03
-3.70711952e-01 6.11192107e-01 2.08583146e-01 -2.85056561e-01
-3.74433041e-01 -9.05691206e-01 9.08225775e-01 1.14449763e+00
1.04228950e+00 -9.29932475e-01 -1.29034698e+00 2.02366531e-01
3.38037878e-01 -1.16859496e+00 -3.05961575e-02 5.38495481e-01
-1.11515880e+00 -4.42580670e-01 -6.00883141e-02 -8.76931310e-01
5.50464153e-01 1.52115092e-01 1.05964863e+00 2.27307677e-01
-7.18193233e-01 -1.63184643e-01 -4.92470041e-02 -7.37312853e-01
-3.36467810e-02 8.51411894e-02 5.60173869e-01 -9.36504900e-01
8.07110608e-01 -4.69828874e-01 -8.65649521e-01 -1.99360322e-04
-7.82908201e-01 2.39945740e-01 4.76896822e-01 4.39778239e-01
2.88403720e-01 4.13720459e-01 2.28248119e-01 -4.71533537e-01
4.42108840e-01 -4.37037617e-01 -1.16770518e+00 -1.96711496e-01
-3.77824306e-01 -2.16581136e-01 9.03597534e-01 -1.45220339e-01
-8.10488701e-01 3.85672711e-02 -7.33983994e-01 -4.99465466e-01
-1.11516319e-01 1.12595551e-01 -5.10218501e-01 1.01973027e-01
5.22287369e-01 -1.15115158e-01 -4.09106880e-01 -3.85365456e-01
1.17785223e-02 1.11309481e+00 6.71156466e-01 -1.86268792e-01
3.62221114e-02 3.02995026e-01 -1.48279592e-01 -9.15065110e-01
2.02361750e-03 -1.95806280e-01 -2.26899326e-01 -2.44438916e-01
9.15642560e-01 -1.29487979e+00 -1.21313846e+00 8.48331869e-01
-1.06692338e+00 -5.65880895e-01 1.58796489e-01 5.75591505e-01
1.98567703e-01 -1.85495883e-01 -1.04053533e+00 -5.55373788e-01
-1.03676319e+00 -1.38296115e+00 9.28671896e-01 8.39400291e-01
1.21659659e-01 -8.21343839e-01 -5.12012482e-01 -1.97667375e-01
6.33866489e-01 -2.57532090e-01 6.61845028e-01 -2.90689141e-01
-8.20515752e-01 -4.58394021e-01 -5.62952757e-01 2.70787209e-01
-1.31276429e-01 2.92578667e-01 -1.06667852e+00 -2.33365282e-01
1.46551937e-01 -2.99045116e-01 7.22850323e-01 7.49955893e-01
1.65750301e+00 3.40997800e-02 -8.53967011e-01 1.02094579e+00
1.75768912e+00 6.61939681e-01 5.41234255e-01 -2.37040315e-02
7.62415588e-01 -7.50937343e-01 1.95268556e-01 7.15905607e-01
-2.73573734e-02 5.74305296e-01 7.59853125e-01 5.57107944e-03
9.01597962e-02 2.06295475e-01 3.99854600e-01 1.10367882e+00
2.99620509e-01 -9.01494384e-01 -9.06176269e-01 2.00052321e-01
-1.53875375e+00 -7.47301102e-01 -5.35623610e-01 1.81852686e+00
5.08634567e-01 6.83836460e-01 -3.52544263e-02 -3.76316831e-02
5.68097174e-01 -1.49415553e-01 -1.09920633e+00 -5.95059276e-01
3.27951580e-01 3.30575824e-01 9.86919641e-01 1.95280150e-01
-8.00791383e-01 7.09586740e-01 5.55963707e+00 9.82318521e-01
-1.40398681e+00 2.57562131e-01 6.65842414e-01 -4.92909551e-01
1.81931823e-01 -6.92865252e-01 -1.59305692e+00 8.04542184e-01
1.72850752e+00 -1.52513161e-01 9.43278894e-02 1.39939404e+00
2.96447296e-02 -1.82172611e-01 -1.18484759e+00 1.52650440e+00
-3.14782441e-01 -1.78865457e+00 -3.23175192e-01 9.21900794e-02
9.34049711e-02 6.34159982e-01 -1.90418601e-01 3.86601627e-01
3.93934041e-01 -6.59030735e-01 6.45281971e-01 6.27306700e-02
1.17831457e+00 -1.20360970e+00 8.41424227e-01 4.88191873e-01
-1.19799089e+00 -8.97744447e-02 -1.04119194e+00 -4.03816253e-01
2.45126918e-01 8.03622246e-01 -7.92425692e-01 -2.17631236e-02
1.18575895e+00 5.96084237e-01 -6.91732392e-02 9.04146314e-01
1.81703046e-01 6.10135376e-01 -8.64814878e-01 -6.06835246e-01
-1.28240198e-01 -9.66759101e-02 8.82958621e-02 1.19771099e+00
5.50542235e-01 3.76351565e-01 -3.12779486e-01 7.67590463e-01
-6.32480383e-01 -6.66509986e-01 -4.63504761e-01 4.36410010e-02
9.15106952e-01 1.64781034e+00 -1.19200468e+00 -6.14761949e-01
-3.00970197e-01 1.22282875e+00 -6.67823628e-02 -2.47928157e-01
-1.28204477e+00 -7.74597704e-01 9.42803204e-01 -2.67728239e-01
7.49392360e-02 -3.62961560e-01 -3.30604583e-01 -9.71929789e-01
7.05590919e-02 -4.00723279e-01 -5.90050630e-02 -9.80484545e-01
-7.74792016e-01 8.08986187e-01 -2.79525787e-01 -9.07942593e-01
1.21752456e-01 -1.26651847e+00 -5.41342199e-01 5.41472018e-01
-9.24231768e-01 -3.70226383e-01 -7.69490719e-01 4.13501054e-01
7.30400145e-01 3.74396220e-02 9.22237396e-01 9.22106504e-01
-1.45924532e+00 7.26227999e-01 4.36614007e-01 3.43141317e-01
2.17036709e-01 -1.03166664e+00 9.60581839e-01 9.11898613e-01
-2.51068830e-01 4.62670624e-01 3.94913226e-01 -6.68998599e-01
-2.10348630e+00 -9.87006426e-01 1.97380647e-01 -1.44572034e-01
4.45741266e-01 -9.73453939e-01 -4.57784265e-01 8.51888001e-01
2.97546536e-01 4.93597627e-01 5.86266398e-01 -5.05519569e-01
4.06760752e-01 -3.92198384e-01 -1.16691840e+00 6.36387527e-01
1.24149525e+00 -5.11960149e-01 3.11708570e-01 5.77002645e-01
9.11808014e-01 -9.96741652e-01 -4.73574430e-01 3.66192497e-02
3.56315374e-01 -8.86359930e-01 7.17573404e-01 -1.08560465e-01
2.89748132e-01 -3.08606714e-01 -9.06458870e-02 -8.25720191e-01
-1.00987144e-01 -4.87528205e-01 -3.79425973e-01 1.16871190e+00
2.51894772e-01 -4.46335912e-01 1.05929875e+00 9.57875311e-01
-6.78899586e-01 -6.87234342e-01 -1.06169403e+00 -5.50505877e-01
-7.22013831e-01 -9.76072431e-01 4.32354033e-01 3.75672460e-01
9.15862769e-02 5.12504220e-01 -1.43691152e-02 4.00147527e-01
4.21661049e-01 -6.38099372e-01 4.98113394e-01 -9.50779200e-01
-1.54743716e-01 -3.10037971e-01 -7.30588913e-01 -1.45449913e+00
-2.82716662e-01 -5.57663143e-01 -6.72819912e-02 -1.41157568e+00
4.09417879e-03 -3.92278373e-01 9.21516269e-02 3.60766202e-01
2.41524577e-01 2.11937681e-01 -1.00196496e-01 4.74489434e-03
-7.82952487e-01 3.48252296e-01 5.20562053e-01 1.62579924e-01
9.74770337e-02 -1.05228968e-01 -3.71555686e-01 8.51178706e-01
8.20117891e-01 -4.99976575e-01 -6.48291826e-01 -1.01723552e+00
2.52033502e-01 -1.44348651e-01 2.97196656e-01 -1.51933873e+00
7.78178573e-01 6.51140571e-01 7.53859878e-01 -8.70214462e-01
6.19063854e-01 -1.11128891e+00 1.87670514e-01 7.87117124e-01
4.14844036e-01 5.89309812e-01 8.69387925e-01 3.37450057e-01
2.13826820e-01 -4.08525586e-01 4.59692895e-01 -1.73992757e-02
-1.02226365e+00 3.97574157e-01 -4.07062113e-01 -2.79738605e-01
9.00202096e-01 -2.25310385e-01 -7.68640280e-01 -1.29655913e-01
-2.39129364e-01 -3.51669937e-02 3.96198899e-01 -2.12539807e-02
6.96159005e-01 -1.32299376e+00 -3.54025811e-02 5.45225024e-01
-3.62934411e-01 5.56932032e-01 3.45301777e-01 3.78197908e-01
-1.22542477e+00 5.95319688e-01 -3.49882752e-01 -6.94436431e-01
-1.21979678e+00 3.26895595e-01 4.30876285e-01 2.74255514e-01
-9.48131382e-01 1.34718549e+00 -1.84190214e-01 1.94955125e-01
5.86107850e-01 -1.05311859e+00 4.00113255e-01 -4.15805519e-01
9.51956332e-01 5.37978649e-01 4.42930371e-01 2.08201725e-02
-5.29647410e-01 -1.98637098e-02 -9.35961753e-02 1.64024144e-01
1.36147189e+00 1.00641623e-01 1.23386770e-01 2.26797432e-01
1.31366670e+00 -5.84448397e-01 -1.35583031e+00 2.57843524e-01
-4.47809935e-01 -1.12064309e-01 8.07229400e-01 -5.94375730e-01
-1.29773116e+00 9.53189433e-01 1.15384901e+00 2.80658305e-02
1.36168826e+00 -3.11424553e-01 1.27675331e+00 5.34863591e-01
3.71442020e-01 -1.11483431e+00 -2.31102332e-01 3.12314332e-01
1.42117105e-02 -1.11472535e+00 1.26201976e-02 -1.25824615e-01
-4.31185924e-02 1.34978461e+00 9.78041589e-01 7.62125626e-02
8.89527440e-01 1.04449117e+00 -2.29980633e-01 -4.72975731e-01
-9.10172999e-01 5.19424260e-01 -5.15079021e-01 2.32113108e-01
1.77903250e-01 1.00764416e-01 2.09066600e-01 6.67167842e-01
-2.19493955e-01 3.58147591e-01 4.80839968e-01 1.06755960e+00
-3.46436292e-01 -7.26331174e-01 2.34322972e-03 6.06430411e-01
-4.72440809e-01 -4.02391344e-01 2.39822760e-01 5.03333151e-01
5.26265576e-02 6.76080942e-01 7.88311362e-01 -6.25740945e-01
9.38961655e-02 -2.98857749e-01 1.33351967e-01 -3.76756638e-01
-4.36142772e-01 -2.01369554e-01 8.39206576e-02 -5.34790337e-01
2.20633954e-01 -9.70053747e-02 -1.56801212e+00 -9.81467843e-01
-5.67917705e-01 -4.46979046e-01 1.45988715e+00 5.95436335e-01
7.43638396e-01 9.85482693e-01 2.61864960e-02 -9.34664190e-01
-1.26846462e-01 -8.66992176e-01 -5.43396115e-01 -3.56614560e-01
1.41692430e-01 -3.30630809e-01 -1.46539167e-01 -2.05402792e-01] | [8.425315856933594, 2.8930511474609375] |
66059e6c-ce70-40bb-a32d-12413c4b2d8c | active-anomaly-detection-via-ensembles-1 | 1901.08930 | null | http://arxiv.org/abs/1901.08930v1 | http://arxiv.org/pdf/1901.08930v1.pdf | Active Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability | Anomaly detection (AD) task corresponds to identifying the true anomalies
from a given set of data instances. AD algorithms score the data instances and
produce a ranked list of candidate anomalies, which are then analyzed by a
human to discover the true anomalies. However, this process can be laborious
for the human analyst when the number of false-positives is very high.
Therefore, in many real-world AD applications including computer security and
fraud prevention, the anomaly detector must be configurable by the human
analyst to minimize the effort on false positives.
In this paper, we study the problem of active learning to automatically tune
ensemble of anomaly detectors to maximize the number of true anomalies
discovered. We make four main contributions towards this goal. First, we
present an important insight that explains the practical successes of AD
ensembles and how ensembles are naturally suited for active learning. Second,
we present several algorithms for active learning with tree-based AD ensembles.
These algorithms help us to improve the diversity of discovered anomalies,
generate rule sets for improved interpretability of anomalous instances, and
adapt to streaming data settings in a principled manner. Third, we present a
novel algorithm called GLocalized Anomaly Detection (GLAD) for active learning
with generic AD ensembles. GLAD allows end-users to retain the use of simple
and understandable global anomaly detectors by automatically learning their
local relevance to specific data instances using label feedback. Fourth, we
present extensive experiments to evaluate our insights and algorithms. Our
results show that in addition to discovering significantly more anomalies than
state-of-the-art unsupervised baselines, our active learning algorithms under
the streaming-data setup are competitive with the batch setup. | ['Md. Rakibul Islam', 'Nitthilan Kannappan Jayakodi', 'Janardhan Rao Doppa', 'Shubhomoy Das'] | 2019-01-23 | null | null | null | null | ['computer-security'] | ['miscellaneous'] | [ 2.96492249e-01 6.70432225e-02 -1.30857127e-02 -5.48369765e-01
-9.19311047e-01 -5.91905832e-01 3.56349528e-01 6.16510630e-01
-1.29984006e-01 2.63155431e-01 -1.54511020e-01 -4.55313683e-01
-3.23878765e-01 -7.99978971e-01 -3.90049875e-01 -6.49713874e-01
-6.93226814e-01 7.09499061e-01 2.86918700e-01 -5.33282794e-02
4.10787910e-01 6.44527733e-01 -1.73978686e+00 4.75013763e-01
1.12711549e+00 1.05664778e+00 -5.48129797e-01 7.04326808e-01
-3.19293231e-01 9.22843635e-01 -6.64668083e-01 -1.35287121e-01
4.31513101e-01 -5.01267016e-01 -7.48561919e-01 3.59932572e-01
3.64456326e-01 -2.77148485e-01 4.36201751e-01 7.56027997e-01
1.68062001e-01 2.75112510e-01 5.40495515e-01 -1.59060049e+00
-3.33735012e-02 4.36167657e-01 -4.71132725e-01 6.97756410e-01
2.64295220e-01 2.54084259e-01 1.25298488e+00 -9.15969551e-01
1.47299021e-01 9.74581242e-01 4.63276893e-01 5.75288773e-01
-1.33286965e+00 -5.90972722e-01 6.75550640e-01 3.99096727e-01
-9.59972560e-01 -6.96979702e-01 8.79006386e-01 -4.17346030e-01
9.58592892e-01 6.21145487e-01 6.36252582e-01 1.10093439e+00
-1.49334311e-01 9.40706551e-01 4.77291971e-01 -4.75666791e-01
8.17769229e-01 -3.57437506e-02 3.99663955e-01 6.14646316e-01
4.80422556e-01 -3.85488593e-03 -5.41657031e-01 -9.69612300e-01
2.13959619e-01 4.33020651e-01 6.84357584e-02 -6.23225451e-01
-6.51186585e-01 1.03756487e+00 -2.02854834e-02 4.49830294e-03
-5.80747366e-01 -3.94247651e-01 4.02068734e-01 5.17365038e-01
8.78831685e-01 8.61715555e-01 -5.88992774e-01 -4.84979227e-02
-7.73618698e-01 2.66155601e-01 9.41846430e-01 4.25553232e-01
6.69010639e-01 3.27847227e-02 4.66815159e-02 8.31985176e-01
4.88819867e-01 1.52177155e-01 2.55275309e-01 -7.58262277e-01
3.10521185e-01 1.15049303e+00 -9.65036303e-02 -8.45139623e-01
-1.99712262e-01 -2.93668777e-01 -5.42229056e-01 4.82247353e-01
2.95205235e-01 -1.10862166e-01 -7.87873864e-01 1.27293491e+00
3.37961018e-01 3.31854224e-01 -9.82128382e-02 3.32507133e-01
1.82575762e-01 4.34397936e-01 5.25045954e-02 -6.77856922e-01
6.84830070e-01 -6.96813941e-01 -5.02691329e-01 -3.97156090e-01
8.79157603e-01 -4.01715875e-01 9.37353611e-01 6.59998655e-01
-7.53987432e-01 -5.87464720e-02 -8.53922427e-01 7.64513075e-01
-3.10536772e-01 -5.08377969e-01 6.24605656e-01 4.88655627e-01
-6.25350416e-01 5.02268791e-01 -1.49769998e+00 -3.87687653e-01
6.07842863e-01 2.60569394e-01 8.37394744e-02 2.58099973e-01
-8.23002636e-01 5.73402107e-01 5.65617740e-01 -6.29932210e-02
-8.02345097e-01 -6.57580316e-01 -8.22090149e-01 -5.90711124e-02
6.64023936e-01 -2.02858061e-01 1.43239963e+00 -1.24086189e+00
-9.00355399e-01 6.11887395e-01 -3.42121840e-01 -8.54881704e-01
4.95828539e-01 -4.11661208e-01 -7.65481710e-01 4.19053398e-02
-5.66359684e-02 -4.53505144e-02 7.64315188e-01 -1.11481750e+00
-9.33277905e-01 -4.11348939e-01 -2.12038457e-01 7.75840133e-03
-3.74543905e-01 1.76870115e-02 7.28763118e-02 -5.02084851e-01
2.55935609e-01 -6.88409686e-01 -4.80702758e-01 -1.11757457e-01
-2.51936048e-01 -3.69396389e-01 1.27426958e+00 -1.33641794e-01
1.45865154e+00 -2.36888719e+00 -3.92802715e-01 8.32657933e-01
3.91333669e-01 3.72435957e-01 1.11339673e-01 6.03763998e-01
-3.67953569e-01 6.24324307e-02 -6.17015660e-01 -2.21358910e-01
-2.75288880e-01 5.00694156e-01 -9.09632742e-01 2.18960345e-01
6.40915632e-01 5.60264587e-01 -1.07098007e+00 -3.33241552e-01
1.40486404e-01 -1.92904726e-01 -6.86849236e-01 5.86109459e-01
-3.02391499e-01 5.27987659e-01 -4.42874730e-01 7.72214055e-01
2.39412159e-01 -3.73632550e-01 -1.54778287e-02 5.90996206e-01
7.97854885e-02 3.77640218e-01 -1.42993629e+00 9.89876211e-01
-1.43737748e-01 3.12417299e-01 -3.44299078e-01 -1.38234484e+00
1.04471052e+00 2.75476247e-01 9.17939544e-01 -4.92580265e-01
-6.21202290e-01 4.06983107e-01 -4.14964147e-02 -5.37663341e-01
2.87476312e-02 3.15964073e-01 7.66972899e-02 9.13534224e-01
-1.70520201e-01 4.85990226e-01 3.51352721e-01 3.39104265e-01
1.56648755e+00 -3.59240949e-01 6.63060308e-01 7.67381117e-02
4.71610546e-01 1.03304915e-01 8.48062396e-01 1.03921020e+00
-1.48971006e-01 4.40856874e-01 7.39962637e-01 -8.27886045e-01
-7.68360972e-01 -1.23512542e+00 -1.12381257e-01 1.27419031e+00
-2.23424658e-01 -6.58520997e-01 -3.20703954e-01 -1.16424143e+00
-7.59278461e-02 9.31373715e-01 -4.96858865e-01 -3.24259818e-01
-6.08093381e-01 -1.14947855e+00 2.55293518e-01 4.57333148e-01
2.27407813e-01 -1.22421372e+00 -6.95405185e-01 1.84569255e-01
-3.82634550e-02 -6.21876717e-01 1.59509201e-02 4.41868126e-01
-1.19874322e+00 -1.35585701e+00 3.09767902e-01 -2.61849731e-01
8.74872029e-01 2.71436255e-02 1.30904400e+00 3.05901617e-01
-3.66249353e-01 5.20476520e-01 -7.63400316e-01 -9.42829609e-01
-4.70784068e-01 4.65529691e-03 1.91601604e-01 3.50470454e-01
7.97948003e-01 -6.58936918e-01 -3.32594216e-01 2.91649848e-01
-1.00555527e+00 -6.27180874e-01 3.91783297e-01 6.78989351e-01
6.20938301e-01 2.08794788e-01 6.77962005e-01 -1.36839545e+00
6.27076566e-01 -7.44023561e-01 -6.58425152e-01 2.83982575e-01
-9.75463390e-01 9.32006463e-02 5.01004279e-01 -3.92845303e-01
-7.56083131e-01 3.03904712e-01 8.80102441e-03 -2.37390205e-01
-3.55255574e-01 2.15116382e-01 3.21895331e-02 2.54205197e-01
1.17662466e+00 7.80260786e-02 -5.64806834e-02 -3.50137383e-01
-1.22246057e-01 5.63815951e-01 2.11104155e-01 -4.63881582e-01
7.67866075e-01 4.05494422e-01 -2.86709189e-01 -7.65635252e-01
-1.06980371e+00 -7.20777631e-01 -7.81442881e-01 -5.17896488e-02
2.37060681e-01 -6.07804418e-01 -2.31924951e-01 3.93670768e-01
-8.09556305e-01 -2.05413684e-01 -6.60187125e-01 7.39911124e-02
-3.63406777e-01 2.93736398e-01 -9.66954306e-02 -1.30603695e+00
-4.95702177e-01 -7.86816180e-01 8.29678833e-01 -4.98618744e-02
-7.06175506e-01 -1.13283312e+00 4.44969803e-01 -2.89266137e-03
3.21019948e-01 4.87898082e-01 7.59600282e-01 -1.71252620e+00
-4.16082144e-01 -3.87675226e-01 3.84399295e-01 2.65961647e-01
3.56713086e-01 2.09406644e-01 -1.16000819e+00 -3.15791428e-01
-1.24329396e-01 -1.34833708e-01 8.71599913e-01 9.13324729e-02
1.55423892e+00 -6.34794533e-01 -2.58159310e-01 4.13691133e-01
9.17007327e-01 4.64379996e-01 4.11722064e-01 3.38122994e-01
4.37706798e-01 4.52671945e-01 6.62896872e-01 6.22839034e-01
-5.18830307e-02 3.73591810e-01 4.24217016e-01 -1.74284160e-01
6.33074522e-01 4.91532385e-02 4.87912774e-01 5.00607073e-01
-1.45647218e-02 -1.18274584e-01 -1.24023378e+00 5.63704133e-01
-2.11015534e+00 -1.21569312e+00 -6.51883334e-02 2.62339377e+00
6.58672571e-01 4.98970687e-01 6.74281538e-01 6.33140385e-01
5.43863177e-01 -4.26544398e-02 -7.13125527e-01 -5.11556566e-01
1.12016715e-01 3.03023398e-01 7.26347566e-02 3.05525213e-01
-1.30934381e+00 4.17025656e-01 6.43800306e+00 2.51387864e-01
-9.60034430e-01 -1.70027807e-01 6.70148313e-01 -3.51425678e-01
-3.68047133e-02 9.31772292e-02 -6.97774827e-01 4.77904707e-01
1.14390969e+00 -1.49489924e-01 1.93126798e-01 1.00006163e+00
1.29568502e-01 7.09991679e-02 -1.45096624e+00 7.73571730e-01
-4.66037579e-02 -1.01863551e+00 -4.69040349e-02 1.65940836e-01
6.72579646e-01 1.26851752e-01 -1.63611621e-01 2.43355513e-01
3.99473518e-01 -7.46942818e-01 2.62606114e-01 4.99425560e-01
7.70363882e-02 -8.89213443e-01 6.01363957e-01 5.25794625e-01
-8.83614540e-01 -6.50791407e-01 -5.63970730e-02 -1.52059153e-01
3.54425609e-03 1.02361965e+00 -1.05415547e+00 2.45598376e-01
7.66901672e-01 6.77439451e-01 -7.83842325e-01 1.15543640e+00
1.57141928e-02 1.08656704e+00 -4.84927833e-01 2.34466970e-01
3.54791582e-02 7.66216293e-02 8.50081325e-01 9.45619583e-01
1.05509870e-01 -1.44030349e-02 4.19565171e-01 8.07101548e-01
3.30191344e-01 2.08079651e-01 -8.68087888e-01 -2.03363523e-01
5.83902240e-01 1.13832617e+00 -7.13838577e-01 -2.17320725e-01
-2.98620790e-01 6.81098402e-01 3.50917846e-01 3.01165372e-01
-3.11899871e-01 -3.26643527e-01 6.24259114e-01 2.52408117e-01
8.70152414e-02 1.92346256e-02 -4.33275759e-01 -1.21500707e+00
2.01991647e-01 -1.13020289e+00 1.13820624e+00 4.00844887e-02
-1.69067645e+00 3.69085580e-01 -5.08099236e-02 -1.53111589e+00
-7.24230111e-01 -4.81809020e-01 -1.19373143e+00 3.30646843e-01
-8.75952780e-01 -5.49212277e-01 -2.58247524e-01 5.34042239e-01
6.82988405e-01 -4.73377228e-01 1.09259975e+00 2.81295665e-02
-8.14268887e-01 6.36404276e-01 5.24163879e-02 3.62239242e-01
6.69058263e-01 -1.47372544e+00 7.91467667e-01 1.21150494e+00
6.27907455e-01 5.70799828e-01 5.77309251e-01 -7.81291187e-01
-8.33509624e-01 -1.24365211e+00 6.67618096e-01 -8.89342785e-01
6.70526266e-01 -4.46852565e-01 -1.41027927e+00 8.63052905e-01
-4.36478913e-01 3.34702075e-01 9.69558775e-01 6.63647413e-01
-3.20569783e-01 -2.97806084e-01 -1.17184913e+00 3.56603026e-01
9.67308402e-01 -2.79281944e-01 -6.88152075e-01 4.11751837e-01
3.18599254e-01 -1.43859863e-01 -4.09430295e-01 5.42848229e-01
1.31552562e-01 -1.05789328e+00 7.38742352e-01 -1.01979506e+00
1.56622037e-01 -1.08785532e-01 1.27713919e-01 -1.29058993e+00
-2.13179871e-01 -5.73044360e-01 -8.20676029e-01 1.13428915e+00
6.86808825e-01 -9.91917908e-01 5.72364926e-01 7.09328532e-01
-2.64373012e-02 -7.72017777e-01 -6.82907999e-01 -6.60521209e-01
-4.15645897e-01 -7.96312749e-01 5.79937875e-01 1.11537528e+00
1.20474592e-01 -1.86657684e-03 -7.34218284e-02 4.03019369e-01
7.79633045e-01 3.92555408e-02 6.61261320e-01 -1.90359247e+00
-3.64714235e-01 -2.24656761e-01 -6.22853458e-01 -4.11250651e-01
-1.24662727e-01 -6.68303311e-01 -1.65213808e-01 -8.63583505e-01
1.00217359e-02 -5.67024887e-01 -5.95822513e-01 8.25383604e-01
-3.76906902e-01 1.04256310e-02 -3.93979549e-01 4.00450319e-01
-9.88202393e-01 8.79040584e-02 3.21303040e-01 7.41337985e-02
-7.10743129e-01 5.25311887e-01 -7.05039859e-01 8.91465127e-01
8.25442374e-01 -6.10754728e-01 -5.38037002e-01 -2.51355134e-02
3.88283908e-01 -3.83088678e-01 4.38550234e-01 -7.63890147e-01
2.62314349e-01 -3.85988325e-01 4.89318788e-01 -5.90645909e-01
-1.90859839e-01 -7.69215107e-01 -2.25089744e-01 3.70904684e-01
-5.56405544e-01 2.04028562e-01 2.66123358e-02 7.94225395e-01
-2.03071684e-01 -9.76491868e-02 8.47805321e-01 -9.31496173e-02
-7.83153355e-01 3.57705534e-01 -4.09320056e-01 3.65830570e-01
1.22143924e+00 -1.56369284e-01 -1.13030583e-01 -4.41200465e-01
-7.23823130e-01 4.13046300e-01 2.64214844e-01 4.39274669e-01
5.79544365e-01 -1.14858425e+00 -7.19012141e-01 7.13670075e-01
6.73575938e-01 3.52506280e-01 -1.36577174e-01 8.15004110e-01
-2.44682938e-01 -2.66155088e-03 6.14953972e-02 -8.64319205e-01
-1.27258515e+00 3.77680093e-01 3.60426128e-01 -2.95585364e-01
-8.58159900e-01 6.64200723e-01 -7.61436718e-03 -4.38800097e-01
4.28722173e-01 1.13858856e-01 -1.50510788e-01 1.03004597e-01
9.20392632e-01 5.18351257e-01 5.36030829e-01 -3.32892500e-02
-4.25495535e-01 -3.45755108e-02 -5.97169459e-01 1.12542607e-01
1.55854619e+00 1.70908734e-01 -2.53736489e-02 7.55968332e-01
6.48293853e-01 -5.58752473e-03 -1.01569366e+00 -4.55079585e-01
6.26468003e-01 -4.65075999e-01 -2.29585811e-01 -6.67289317e-01
-9.28027391e-01 5.70420563e-01 6.53617918e-01 7.29036927e-01
1.39491832e+00 8.27234611e-02 4.31930125e-01 7.42030203e-01
6.90537766e-02 -1.11206686e+00 2.80446976e-01 4.80116725e-01
5.48975945e-01 -1.48996568e+00 -1.66079570e-02 -7.97033608e-02
-7.51280189e-01 1.19803441e+00 7.70857930e-01 -1.32078081e-01
5.40705085e-01 2.30666861e-01 3.12869728e-01 -4.03555959e-01
-1.10412788e+00 -5.05812913e-02 4.55181390e-01 4.43149716e-01
2.42815688e-01 -1.33842140e-01 1.74163431e-01 2.84319013e-01
4.74363565e-03 -5.46675563e-01 3.88116658e-01 1.13661385e+00
-7.11923659e-01 -1.13660169e+00 -4.19430643e-01 8.70160401e-01
-4.86126661e-01 1.88405499e-01 -8.16615522e-01 3.64643663e-01
-5.24646696e-03 1.09663844e+00 5.76055884e-01 -1.97139874e-01
4.21786219e-01 6.04995131e-01 -1.26478329e-01 -8.90587091e-01
-3.36811990e-01 -1.81577504e-01 2.22449973e-02 -7.27969885e-01
-1.60656929e-01 -9.07591462e-01 -1.16387570e+00 -4.42689843e-03
-2.88395494e-01 3.40234131e-01 1.63575262e-01 1.25465310e+00
4.27488536e-01 2.38510087e-01 1.08029497e+00 -1.80335000e-01
-5.95525384e-01 -7.74317145e-01 -4.59794104e-01 6.59000933e-01
5.06941020e-01 -5.08615077e-01 -8.35683048e-01 -6.73903432e-03] | [7.57821798324585, 2.5090701580047607] |
4a459d07-45ed-4a2a-b188-f01f0ff51982 | deep-recurrent-neural-network-for-protein | 1701.08318 | null | http://arxiv.org/abs/1701.08318v1 | http://arxiv.org/pdf/1701.08318v1.pdf | Deep Recurrent Neural Network for Protein Function Prediction from Sequence | As high-throughput biological sequencing becomes faster and cheaper, the need
to extract useful information from sequencing becomes ever more paramount,
often limited by low-throughput experimental characterizations. For proteins,
accurate prediction of their functions directly from their primary amino-acid
sequences has been a long standing challenge. Here, machine learning using
artificial recurrent neural networks (RNN) was applied towards classification
of protein function directly from primary sequence without sequence alignment,
heuristic scoring or feature engineering. The RNN models containing
long-short-term-memory (LSTM) units trained on public, annotated datasets from
UniProt achieved high performance for in-class prediction of four important
protein functions tested, particularly compared to other machine learning
algorithms using sequence-derived protein features. RNN models were used also
for out-of-class predictions of phylogenetically distinct protein families with
similar functions, including proteins of the CRISPR-associated nuclease,
ferritin-like iron storage and cytochrome P450 families. Applying the trained
RNN models on the partially unannotated UniRef100 database predicted not only
candidates validated by existing annotations but also currently unannotated
sequences. Some RNN predictions for the ferritin-like iron sequestering
function were experimentally validated, even though their sequences differ
significantly from known, characterized proteins and from each other and cannot
be easily predicted using popular bioinformatics methods. As sequencing and
experimental characterization data increases rapidly, the machine-learning
approach based on RNN could be useful for discovery and prediction of
homologues for a wide range of protein functions. | ['Xueliang Liu'] | 2017-01-28 | null | null | null | null | ['protein-function-prediction'] | ['medical'] | [ 7.84013271e-01 -1.72838867e-02 8.35020617e-02 -3.91324818e-01
-6.17347658e-01 -5.83756983e-01 1.25079960e-01 4.73620981e-01
-6.75116420e-01 1.48520756e+00 -8.15377310e-02 -6.80365026e-01
-1.08678877e-01 -3.84145141e-01 -7.62731612e-01 -9.67073917e-01
-2.84613073e-01 6.88064754e-01 2.46605694e-01 -2.77949095e-01
2.55541593e-01 6.48609102e-01 -1.76763296e+00 8.82643878e-01
9.03357029e-01 6.36310160e-01 1.00014901e+00 7.56409287e-01
-3.52815688e-01 8.50479245e-01 -4.78936344e-01 3.59560251e-02
-9.57186371e-02 -6.46908760e-01 -9.12827969e-01 -5.30039549e-01
-4.03624684e-01 1.45301819e-01 4.65348959e-01 6.49893641e-01
5.24239957e-01 -3.38986292e-02 4.62239265e-01 -4.44929063e-01
-7.17715502e-01 1.07476488e-01 -1.48949903e-02 -1.65981919e-01
5.16474724e-01 2.42200494e-01 1.04479504e+00 -9.31751251e-01
1.02714562e+00 1.01501727e+00 7.99672246e-01 5.91810822e-01
-1.45733464e+00 -1.54459015e-01 -5.40140331e-01 5.78281581e-01
-9.49435413e-01 4.37948555e-02 4.44403552e-02 -5.35215795e-01
1.71474540e+00 1.08585209e-01 4.70176131e-01 9.73879993e-01
3.05464774e-01 3.98526162e-01 8.70839953e-01 -2.92237401e-01
1.32232413e-01 -2.36336991e-01 -1.20903671e-01 6.36517882e-01
-1.56433054e-03 -1.38934001e-01 -2.99013704e-01 -5.16421676e-01
1.41576782e-01 2.26670310e-01 -4.55711722e-01 -4.07205343e-01
-1.36150134e+00 7.70110309e-01 2.09579870e-01 4.14302975e-01
-7.82020986e-01 -2.30067655e-01 8.87153208e-01 3.60489547e-01
3.24123949e-01 7.10498452e-01 -1.42413151e+00 -2.40524173e-01
-5.38773775e-01 -3.74193400e-01 7.80650318e-01 1.42769560e-01
9.52998519e-01 -4.41190340e-02 5.77422321e-01 9.95851099e-01
1.88819543e-02 2.79365420e-01 1.16909564e+00 -5.18090725e-01
-3.22170615e-01 8.47921073e-01 3.43969941e-01 -6.04625106e-01
-6.96610987e-01 2.00697795e-01 -6.15958989e-01 6.20060898e-02
6.87219679e-01 -5.84817305e-02 -5.58733106e-01 1.49594724e+00
3.68313193e-01 -8.97305757e-02 4.91669565e-01 9.86673832e-01
5.25395811e-01 8.74297380e-01 4.05790918e-02 -5.30570805e-01
1.39693415e+00 -4.96263146e-01 -3.24652463e-01 4.51201528e-01
8.58296752e-01 -6.60038769e-01 6.75590932e-01 5.34345806e-01
-5.03948152e-01 -4.32633102e-01 -9.73889410e-01 -5.53218499e-02
-7.06731021e-01 2.14594945e-01 5.09974420e-01 2.61588603e-01
-8.70332599e-01 1.11185920e+00 -1.02112091e+00 -6.38986111e-01
-4.07610647e-02 6.70415103e-01 -6.80017829e-01 1.74172834e-01
-1.16759181e+00 1.15791285e+00 9.03069258e-01 3.22503746e-02
-9.17540491e-01 -4.40471262e-01 -4.76661682e-01 9.96690169e-02
4.71624397e-02 -4.76981312e-01 9.60550010e-01 -1.13636827e+00
-1.54365039e+00 7.82908678e-01 -3.42083216e-01 -9.12029445e-01
-3.04267526e-01 -2.36361310e-01 -2.80833840e-01 2.86049932e-01
-2.32266597e-02 3.41623634e-01 2.58730650e-01 -4.88226324e-01
-4.91272628e-01 -3.70328695e-01 -3.53964090e-01 -2.00076699e-01
5.27935214e-02 3.22664976e-01 7.75263429e-01 -2.03027129e-01
-9.21362415e-02 -5.87065995e-01 -4.56512302e-01 -1.25514995e-03
-2.53051557e-02 -4.03225958e-01 7.19412565e-01 -9.01942015e-01
4.63584155e-01 -1.62846231e+00 2.27610841e-01 -2.38822833e-01
2.20499635e-02 7.01547146e-01 -3.34080040e-01 8.04036915e-01
-5.86050332e-01 1.77669525e-02 -7.13673905e-02 9.62220967e-01
-5.51746964e-01 2.10639074e-01 -7.35071003e-02 5.82175434e-01
5.12809157e-01 7.06849396e-01 -1.06500268e+00 1.32834643e-01
2.43564799e-01 8.12549829e-01 -1.68606088e-01 2.85774440e-01
-5.66035211e-01 4.72894579e-01 -4.09772098e-01 5.08152783e-01
3.05673152e-01 -5.56418240e-01 7.18893409e-01 -1.84341133e-01
-2.44119897e-01 4.01495069e-01 -3.66682917e-01 1.57412958e+00
-4.36810642e-01 3.80176932e-01 -2.45224997e-01 -1.44453812e+00
1.11620235e+00 4.96447861e-01 5.74202299e-01 -4.93671358e-01
-1.15994103e-01 4.48535413e-01 5.68213522e-01 -5.98441482e-01
3.81935947e-02 -2.61582404e-01 5.79399586e-01 2.66976625e-01
2.36732259e-01 7.78695583e-01 2.56027967e-01 -2.45343804e-01
1.44107938e+00 7.69670427e-01 7.31557667e-01 -3.90885681e-01
8.50826263e-01 3.07757139e-01 8.25733662e-01 3.69884580e-01
-2.52258847e-03 4.24714833e-01 4.41936195e-01 -8.79961729e-01
-1.47639906e+00 -4.05188769e-01 -2.98107415e-01 1.43877852e+00
-4.85686541e-01 -3.34568322e-01 -5.43165386e-01 -4.65207458e-01
-1.96154445e-01 3.49998713e-01 -3.53298396e-01 4.00477238e-02
-3.68625104e-01 -1.04857111e+00 7.15743959e-01 3.05819642e-02
-1.88473031e-01 -1.34619343e+00 -9.95543659e-01 8.60136151e-01
-9.66410413e-02 -7.09863186e-01 4.02282417e-01 1.00513697e+00
-6.73040390e-01 -1.60687792e+00 -9.87514079e-01 -5.55001557e-01
2.84552425e-01 8.10512081e-02 8.40682864e-01 5.90036362e-02
-5.98682880e-01 -4.18728620e-01 -4.67798799e-01 -3.73655707e-01
-7.70050645e-01 -1.36257380e-01 3.86074781e-01 -3.21935266e-01
6.65805161e-01 -6.47898674e-01 -4.64312315e-01 5.94060600e-01
-7.54440546e-01 5.85526153e-02 6.97692037e-01 1.46018183e+00
7.07005739e-01 -3.90946269e-01 1.21337593e+00 -8.28784287e-01
3.54921401e-01 -5.65766871e-01 -5.43423653e-01 5.17549515e-01
-3.30583900e-01 4.22199249e-01 1.20763826e+00 -3.96184772e-01
-9.84426379e-01 4.67920691e-01 -5.19105256e-01 1.18200876e-01
-3.49210620e-01 6.99319422e-01 -1.85490981e-01 1.91590503e-01
9.06801045e-01 6.00246608e-01 3.57161433e-01 -5.99229217e-01
5.68171851e-02 6.89870715e-01 2.86359102e-01 -4.81216818e-01
-1.79520957e-02 1.39531717e-01 2.76643187e-02 -1.11263084e+00
-5.88370144e-01 -5.45208156e-01 -6.32525325e-01 3.18285853e-01
9.20212686e-01 -5.63264847e-01 -1.30546665e+00 2.01800928e-01
-1.22740102e+00 -9.00438204e-02 2.25787200e-02 6.17436707e-01
-6.96243227e-01 5.85978448e-01 -7.40083992e-01 -7.51174152e-01
-6.27849340e-01 -1.17337561e+00 8.49305689e-01 -4.69596833e-02
-2.89437145e-01 -6.41848981e-01 1.82031915e-01 3.76992375e-01
3.00937265e-01 1.71316534e-01 1.32225800e+00 -1.29938471e+00
-1.92278683e-01 5.68289123e-02 -1.23985469e-01 5.12975872e-01
3.90068769e-01 9.34679713e-03 -6.10983551e-01 -3.74204405e-02
-1.07044034e-01 -6.79829597e-01 8.61580491e-01 3.21864896e-02
8.52343082e-01 -2.81185597e-01 -2.34401807e-01 1.87427521e-01
1.47720957e+00 5.04593611e-01 5.86887181e-01 5.28712869e-01
4.83748138e-01 6.68693602e-01 7.96843171e-01 3.02336544e-01
-5.42079806e-01 4.63757604e-01 5.59171498e-01 1.90760374e-01
3.97294551e-01 4.63071950e-02 4.33302134e-01 2.62534142e-01
-3.63683999e-01 -2.06580862e-01 -1.07112026e+00 3.29193473e-01
-1.93624496e+00 -1.20765245e+00 -3.80718321e-01 2.28926992e+00
8.95232439e-01 -2.63463378e-01 1.06025431e-02 -3.51042449e-02
8.37345183e-01 -6.16972208e-01 -1.02202809e+00 -5.45114338e-01
-4.45999533e-01 4.24019873e-01 3.95699501e-01 6.51002899e-02
-7.61791050e-01 6.61897242e-01 6.10840893e+00 5.74726880e-01
-1.12873995e+00 -1.19462267e-01 5.72682738e-01 2.29209792e-02
1.67805031e-01 1.24547079e-01 -7.18579113e-01 3.45073640e-01
1.51374519e+00 -4.17146534e-02 4.76244718e-01 1.01328242e+00
5.44065952e-01 -5.40023856e-02 -1.02102268e+00 5.44454217e-01
-2.83699542e-01 -1.76796341e+00 -2.57598430e-01 1.22437097e-01
3.01244497e-01 6.97440207e-01 -6.84364915e-01 -8.69012345e-03
5.84429979e-01 -1.00946414e+00 -1.42651781e-01 6.32278323e-01
6.96663678e-01 -8.76272678e-01 1.04680729e+00 5.44688880e-01
-6.79490030e-01 -2.25681253e-02 -1.01728189e+00 -1.80788770e-01
4.48933728e-02 7.65986919e-01 -1.30774724e+00 4.20654178e-01
6.01567090e-01 7.60499537e-01 -2.40123570e-01 6.05606854e-01
-7.89837241e-02 6.62509382e-01 -2.30777696e-01 -4.68791753e-01
6.61991090e-02 -2.92551845e-01 2.04263732e-01 1.07234836e+00
1.19687460e-01 1.75685793e-01 1.17628045e-01 6.03753328e-01
7.53643364e-02 4.23562199e-01 -3.18901420e-01 -5.51291525e-01
-7.85372853e-02 1.41216695e+00 -6.92986429e-01 -4.28085983e-01
-4.33098435e-01 9.88554299e-01 4.97794509e-01 2.13768199e-01
-4.80641007e-01 -5.67950189e-01 9.77523088e-01 7.53002688e-02
4.46126282e-01 9.96178612e-02 5.89880109e-01 -8.88999045e-01
-4.53217268e-01 -1.07897353e+00 2.76446790e-01 -9.33063924e-01
-1.38767457e+00 5.83701074e-01 -5.52783847e-01 -8.64825606e-01
-5.84241450e-01 -9.47042346e-01 -2.18963176e-02 1.03747547e+00
-1.08691633e+00 -9.34368730e-01 5.69545686e-01 -7.22177252e-02
5.76119065e-01 -3.69057208e-01 1.32801974e+00 -3.45235057e-02
-3.82713079e-01 -1.68388277e-01 8.75200570e-01 -2.80233711e-01
7.22938716e-01 -1.06824577e+00 1.96404025e-01 2.12749332e-01
-2.88192034e-01 8.68491709e-01 8.16701591e-01 -6.55334055e-01
-1.25577259e+00 -1.10198295e+00 1.05464339e+00 -1.41120271e-03
8.61504197e-01 -3.45113605e-01 -1.34725952e+00 4.53963637e-01
1.45517707e-01 -2.58315325e-01 1.16015244e+00 -2.96424534e-02
-1.66944206e-01 3.24580103e-01 -1.23564315e+00 7.66767487e-02
5.31874895e-01 -4.48391676e-01 -6.30711198e-01 7.73647428e-01
6.80496871e-01 2.10724026e-01 -1.00091028e+00 4.36429411e-01
7.15683460e-01 -9.45683837e-01 1.03947461e+00 -1.23616111e+00
5.88480353e-01 -5.15138805e-01 -2.66685396e-01 -1.02202880e+00
-3.39652598e-01 -2.71978587e-01 2.70513177e-01 7.74700165e-01
5.40354908e-01 -6.20336533e-01 6.97829604e-01 4.11164276e-02
-2.13392854e-01 -8.89507711e-01 -8.38441312e-01 -5.96509159e-01
-2.68736571e-01 1.58726111e-01 2.68414736e-01 9.01344895e-01
4.08370584e-01 7.77208865e-01 -4.92582530e-01 -2.68799186e-01
-1.20996036e-01 2.04771891e-01 3.75108927e-01 -1.47115135e+00
-5.24328053e-01 -6.68677390e-02 -6.96480274e-01 -6.95401013e-01
3.74315262e-01 -9.01540041e-01 -3.51682380e-02 -1.49355412e+00
3.01493704e-01 1.91255242e-01 -4.40874904e-01 8.53864372e-01
1.52401030e-01 5.75213432e-02 -4.89485383e-01 -5.70630319e-02
-4.50062603e-01 2.80697435e-01 4.78502333e-01 -2.56540962e-02
5.17617874e-02 -4.83710691e-02 -2.86714166e-01 7.92708635e-01
8.50808203e-01 -5.10737956e-01 -1.47896349e-01 2.47363642e-01
5.07236302e-01 1.82445049e-01 2.89413556e-02 -6.92286670e-01
-1.93646684e-01 -2.89488912e-01 5.43667436e-01 -7.47801304e-01
2.02897906e-01 -7.66917706e-01 4.59088892e-01 1.13789749e+00
-3.92780095e-01 1.56749204e-01 6.38517216e-02 4.39737469e-01
2.34041009e-02 -3.73867780e-01 8.06028605e-01 -5.51995456e-01
-7.44916439e-01 -3.04714203e-01 -1.09542787e+00 -5.59010088e-01
8.54345441e-01 -3.72596115e-01 -1.70591041e-01 1.43446326e-02
-9.61555660e-01 -3.57427776e-01 4.81389403e-01 3.02569151e-01
5.67652941e-01 -6.81348860e-01 -7.34906495e-01 -1.48912948e-02
2.91364521e-01 -5.60070515e-01 1.58423617e-01 6.61499143e-01
-8.96693587e-01 8.60606372e-01 -5.13619602e-01 -7.34387338e-01
-1.57722831e+00 1.12375247e+00 3.65153760e-01 -3.38248849e-01
-3.16014588e-01 5.82591355e-01 2.09855378e-01 -8.83340657e-01
-3.48842442e-01 -2.04128120e-02 -3.20195138e-01 -2.11402491e-01
6.81337774e-01 1.80002466e-01 2.96057701e-01 -7.45850503e-01
-3.90996188e-01 -4.94289473e-02 -1.56283453e-01 7.50939667e-01
1.83141148e+00 1.05668999e-01 -6.10802293e-01 5.34377158e-01
1.35880876e+00 -7.04632938e-01 -6.11543953e-01 -4.90903147e-02
5.38204908e-01 -2.28371918e-02 -5.94655752e-01 -1.14294600e+00
-4.59474623e-01 7.76078522e-01 7.38528907e-01 -2.87169009e-01
9.34520304e-01 -1.56036749e-01 5.51228762e-01 1.03434658e+00
5.52935123e-01 -9.09379065e-01 -2.43086621e-01 6.91483855e-01
4.98868734e-01 -1.04178822e+00 -2.27786765e-01 1.14447027e-01
-1.69673994e-01 1.29447210e+00 3.67801905e-01 3.29868704e-01
-7.20766652e-03 -2.51981765e-02 -1.64400697e-01 -4.45235334e-02
-1.35742712e+00 4.31646816e-02 -1.84231937e-01 7.13248372e-01
1.11666894e+00 -1.29013732e-01 -8.14332962e-01 3.21613550e-01
1.57508075e-01 2.03509517e-02 5.82284451e-01 7.83533573e-01
-8.19537818e-01 -1.52182734e+00 -2.05766559e-01 6.85950816e-01
-8.70824695e-01 -3.14636201e-01 -5.83720744e-01 3.34348857e-01
1.48207517e-02 7.40387440e-01 -3.28917265e-01 -1.66330218e-01
-8.91802907e-02 5.56320429e-01 1.23809300e-01 -5.30426621e-01
-6.37378871e-01 1.69797540e-01 3.37523341e-01 -4.40286338e-01
-4.14357275e-01 -6.67638600e-01 -1.70669675e+00 2.13433072e-01
-7.77302265e-01 6.10550404e-01 9.36407983e-01 8.94382358e-01
7.09886849e-01 3.11666399e-01 4.09837961e-01 -7.31998980e-01
-5.13371110e-01 -1.06754458e+00 -4.55971479e-01 2.88350910e-01
5.01083620e-02 -2.57553160e-01 7.15830252e-02 3.20314765e-01] | [4.733170509338379, 5.578851699829102] |
a97f9595-d2b4-4713-986a-d18fb27c4099 | combining-discrete-and-continuous-features | null | null | https://aclanthology.org/D15-1153 | https://aclanthology.org/D15-1153.pdf | Combining Discrete and Continuous Features for Deterministic Transition-based Dependency Parsing | null | ['Meishan Zhang', 'Yue Zhang'] | 2015-09-01 | null | null | null | emnlp-2015-9 | ['transition-based-dependency-parsing'] | ['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] | [-7.230048179626465, 3.805534601211548] |
a064128a-b055-4660-aee7-b2920e706fbf | towards-unsupervised-object-detection-from | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Towards_Unsupervised_Object_Detection_From_LiDAR_Point_Clouds_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Towards_Unsupervised_Object_Detection_From_LiDAR_Point_Clouds_CVPR_2023_paper.pdf | Towards Unsupervised Object Detection From LiDAR Point Clouds | In this paper, we study the problem of unsupervised object detection from 3D point clouds in self-driving scenes. We present a simple yet effective method that exploits (i) point clustering in near-range areas where the point clouds are dense, (ii) temporal consistency to filter out noisy unsupervised detections, (iii) translation equivariance of CNNs to extend the auto-labels to long range, and (iv) self-supervision for improving on its own. Our approach, OYSTER (Object Discovery via Spatio-Temporal Refinement), does not impose constraints on data collection (such as repeated traversals of the same location), is able to detect objects in a zero-shot manner without supervised finetuning (even in sparse, distant regions), and continues to self-improve given more rounds of iterative self-training. To better measure model performance in self-driving scenarios, we propose a new planning-centric perception metric based on distance-to-collision. We demonstrate that our unsupervised object detector significantly outperforms unsupervised baselines on PandaSet and Argoverse 2 Sensor dataset, showing promise that self-supervision combined with object priors can enable object discovery in the wild. For more information, visit the project website: https://waabi.ai/research/oyster. | ['Raquel Urtasun', 'Mengye Ren', 'Bin Yang', 'Sergio Casas', 'Yuwen Xiong', 'Anqi Joyce Yang', 'Lunjun Zhang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['object-discovery'] | ['computer-vision'] | [ 1.37712330e-01 1.52546942e-01 9.34074540e-03 -4.07335699e-01
-6.83895290e-01 -6.57035828e-01 7.08870709e-01 3.77270103e-01
-6.41216815e-01 2.34729603e-01 -1.85796648e-01 7.35083073e-02
-2.31657743e-01 -5.92988312e-01 -1.12928796e+00 -4.39211398e-01
-3.92979413e-01 1.01099384e+00 9.09852266e-01 -7.98697323e-02
2.25064948e-01 9.12783146e-01 -1.85466003e+00 -2.54780084e-01
6.07839167e-01 8.36075187e-01 5.36961973e-01 7.75807559e-01
2.25941285e-01 3.07727307e-01 -4.82551567e-02 1.94179103e-01
6.41703188e-01 3.46101344e-01 -4.44378585e-01 2.74802655e-01
6.70137942e-01 -3.96177232e-01 -2.51495183e-01 8.76440585e-01
1.99004889e-01 2.04255134e-01 5.23001313e-01 -1.31576991e+00
-1.34294018e-01 2.38433525e-01 -6.39281154e-01 4.14780289e-01
-2.85368383e-01 7.56468177e-01 9.16602075e-01 -8.54265988e-01
5.58622599e-01 1.28323746e+00 8.26649129e-01 5.07527255e-02
-1.33802021e+00 -7.44479716e-01 2.71429867e-01 -4.68024388e-02
-1.62319255e+00 -4.37500268e-01 4.95426655e-01 -5.94101787e-01
1.18370962e+00 -7.83022195e-02 5.39006412e-01 8.64008963e-01
-8.73294026e-02 5.56329727e-01 7.33199120e-01 3.67341610e-03
4.54809427e-01 -1.18150108e-01 8.44172612e-02 6.28356814e-01
4.58409220e-01 5.53538680e-01 -5.49136162e-01 -9.93632823e-02
7.10326910e-01 1.47113264e-01 3.17396522e-01 -9.02675211e-01
-1.51927209e+00 7.12854087e-01 7.08605170e-01 9.09562260e-02
-4.18820471e-01 4.05575514e-01 5.77549078e-02 -6.96375072e-02
4.06452239e-01 5.28444648e-01 -5.08248091e-01 -1.43675758e-02
-9.35214758e-01 4.27758604e-01 4.39050555e-01 1.41344714e+00
1.14069557e+00 -1.45728678e-01 1.19820029e-01 3.87255698e-01
1.71300262e-01 7.92205811e-01 -9.41140726e-02 -1.22329438e+00
1.85625926e-01 4.71669763e-01 4.29345042e-01 -7.65367448e-01
-7.70784914e-01 -7.10371137e-01 -5.04507840e-01 5.08163154e-01
8.14440176e-02 4.55259271e-02 -1.18112361e+00 1.56049645e+00
5.21173775e-01 2.21964046e-01 -6.54681697e-02 1.05522692e+00
3.70998800e-01 3.64569962e-01 -6.68431818e-02 1.64203912e-01
1.02330995e+00 -7.17308640e-01 -7.97474459e-02 -7.23968863e-01
5.29652417e-01 -3.53777528e-01 6.26311064e-01 2.71919727e-01
-8.54175210e-01 -7.81406641e-01 -1.08590496e+00 -3.06276083e-02
-2.57825494e-01 -9.61268768e-02 7.03201890e-01 9.03528035e-02
-1.05103755e+00 6.03873432e-01 -1.29469597e+00 -7.23645210e-01
7.20003664e-01 4.57110792e-01 -3.43539089e-01 -5.46000898e-02
-4.07505870e-01 6.94635928e-01 6.14342391e-01 -2.82019824e-01
-1.15595269e+00 -7.51265585e-01 -7.77105510e-01 -2.04526395e-01
7.28002727e-01 -6.25545263e-01 1.20885158e+00 -5.55672884e-01
-8.91916931e-01 8.30074072e-01 -1.46189258e-01 -7.63289154e-01
4.91917074e-01 -4.28497314e-01 -3.02453339e-02 1.02720827e-01
6.70179009e-01 1.33165836e+00 6.05978191e-01 -1.52976453e+00
-1.19968784e+00 -6.52357638e-01 -1.66058868e-01 2.64844865e-01
2.43827358e-01 -3.15109938e-01 -7.67171323e-01 -1.57689974e-01
7.56955802e-01 -1.23429334e+00 -5.05296409e-01 1.80311263e-01
-4.17845279e-01 -3.79057884e-01 8.32473457e-01 5.36554679e-02
2.21138149e-01 -2.26865315e+00 -9.07267928e-02 3.50092918e-01
3.46638381e-01 -1.77035391e-01 -1.68131202e-01 3.18997234e-01
2.98194587e-01 -6.39218837e-02 -4.01586741e-01 -6.07946157e-01
-6.01150766e-02 5.95864296e-01 -2.49827906e-01 7.70143569e-01
4.68069434e-01 8.34077060e-01 -1.09968078e+00 -4.43504632e-01
4.11331862e-01 1.87349662e-01 -5.40703475e-01 2.16614567e-02
-4.38225955e-01 6.23560488e-01 -4.14122850e-01 7.38399267e-01
8.70948911e-01 -2.91583151e-01 -3.19716781e-01 -8.65914449e-02
-6.57564640e-01 2.51422614e-01 -1.31553769e+00 1.84282112e+00
-2.60402765e-02 5.75900197e-01 1.75025880e-01 -6.72081351e-01
8.39950681e-01 -2.89421558e-01 6.86339200e-01 -6.62311494e-01
-5.93139492e-02 7.80597329e-02 -1.24918491e-01 -3.08731884e-01
5.23851097e-01 1.67211935e-01 1.28632173e-01 1.88579708e-01
2.04468563e-01 -4.02597487e-01 2.85864800e-01 3.91094983e-01
1.53193462e+00 1.99805140e-01 -2.28249803e-02 -2.62589991e-01
-2.51458496e-01 5.71667671e-01 5.49845695e-01 1.24002671e+00
-1.38454631e-01 7.49573767e-01 -1.32417768e-01 -3.39932323e-01
-1.17459106e+00 -1.36220360e+00 -2.86945373e-01 1.00000131e+00
5.39532542e-01 -2.08320096e-01 -1.51232526e-01 -5.78232169e-01
4.96872753e-01 6.87565625e-01 -5.56914806e-01 2.04018392e-02
-4.25724536e-01 -3.49121571e-01 4.13987428e-01 5.91335893e-01
3.25914890e-01 -7.59523690e-01 -1.03499722e+00 3.40325028e-01
7.11178929e-02 -1.37032807e+00 -1.04623452e-01 8.82894337e-01
-9.72842336e-01 -9.69035625e-01 -2.34516844e-01 -4.53326464e-01
6.06865644e-01 8.88032556e-01 1.02508330e+00 -2.58547496e-02
-2.43490472e-01 6.48068964e-01 -4.02132511e-01 -7.47734368e-01
-7.75349066e-02 6.72889650e-02 3.80095184e-01 -3.92224938e-01
5.90739369e-01 -7.67801225e-01 -4.36962992e-01 4.93733883e-01
-5.20943403e-01 -1.13641039e-01 8.95522237e-01 2.31224269e-01
8.53727221e-01 5.82937486e-02 1.85780413e-02 -4.99120772e-01
-2.86466539e-01 -5.73538005e-01 -8.92146826e-01 -4.35939610e-01
-5.50054908e-01 5.25970683e-02 -1.20982990e-01 -2.69424558e-01
-5.33978224e-01 5.92208385e-01 2.09291831e-01 -7.93253183e-01
-5.09957492e-01 -2.99651064e-02 1.64744645e-01 -2.23978534e-01
9.78621304e-01 1.28131926e-01 -4.78881970e-03 -5.07204950e-01
4.06006545e-01 2.63539344e-01 8.19302201e-01 -4.41845119e-01
1.26599932e+00 1.15699613e+00 1.75241791e-02 -8.25484991e-01
-8.86363864e-01 -1.08966982e+00 -9.20124054e-01 -1.31211042e-01
9.33623552e-01 -1.28377509e+00 -6.35934412e-01 4.37008552e-02
-1.04340816e+00 -5.40541291e-01 -4.97576058e-01 6.35661721e-01
-6.40363276e-01 1.58804461e-01 -1.14051238e-01 -1.03567278e+00
1.38641670e-01 -7.29600370e-01 1.31215608e+00 7.40463659e-02
-3.31105776e-02 -3.25860351e-01 1.54542953e-01 1.12072229e-01
4.55398150e-02 2.56066591e-01 4.09423336e-02 -5.98899603e-01
-1.10377824e+00 -6.59229383e-02 -3.98017287e-01 1.72822103e-01
-1.18707098e-01 -1.60265028e-01 -9.24191952e-01 -3.63560587e-01
-1.82263136e-01 -2.82199800e-01 1.12234938e+00 2.56043434e-01
9.44403589e-01 -2.70598312e-03 -7.50635982e-01 7.10581779e-01
1.23881733e+00 -1.36124909e-01 2.32845619e-01 4.36928093e-01
7.41555750e-01 5.83193183e-01 8.79915178e-01 4.99586850e-01
7.06684113e-01 5.30258596e-01 1.10052097e+00 -2.05719039e-01
2.08560769e-02 -2.46838316e-01 -2.77327187e-02 1.86945602e-01
-1.01709925e-01 -1.16064675e-01 -1.17968166e+00 1.00277317e+00
-2.10405707e+00 -8.14488769e-01 -2.74527341e-01 2.03723598e+00
2.86802739e-01 6.03419542e-01 2.24090174e-01 -2.65867472e-01
6.11882746e-01 -1.07542567e-01 -8.50475311e-01 4.21899021e-01
-1.78377032e-01 -7.50820711e-02 1.26928067e+00 4.38860476e-01
-1.26367974e+00 1.07608593e+00 5.49466705e+00 2.92117566e-01
-7.34608531e-01 3.67446154e-01 7.85370767e-02 -2.57225335e-01
1.30636156e-01 3.01312834e-01 -9.37968552e-01 2.72151500e-01
5.62401831e-01 4.16134059e-01 1.72006860e-01 1.09034789e+00
1.64264202e-01 -4.03459430e-01 -1.23766387e+00 8.05676877e-01
-1.99890658e-01 -1.40459144e+00 -4.50584471e-01 3.76967728e-01
5.33464134e-01 1.07370710e+00 -1.90389574e-01 2.35636979e-01
7.47284710e-01 -6.27455056e-01 1.14277172e+00 3.02448452e-01
3.28726858e-01 -5.36859453e-01 5.21329224e-01 7.97591567e-01
-1.13677382e+00 -1.50698408e-01 -5.50204396e-01 -1.90677822e-01
6.35219216e-02 7.17958093e-01 -1.19905758e+00 3.52540165e-01
1.24206781e+00 7.61341989e-01 -6.77657723e-01 1.20565033e+00
-1.95412920e-03 5.28841794e-01 -1.16673028e+00 1.28239110e-01
4.70751375e-01 7.16931000e-02 1.06419373e+00 1.30304623e+00
2.42335752e-01 2.12891802e-01 5.19280910e-01 1.09960759e+00
3.63496304e-01 -5.21236897e-01 -5.92424691e-01 3.59967470e-01
5.87727427e-01 1.14442217e+00 -9.12873030e-01 -3.19106102e-01
-9.82694328e-02 6.11513317e-01 3.74996930e-01 1.48168191e-01
-6.56287432e-01 7.42598474e-02 7.85516620e-01 5.66550612e-01
8.29698384e-01 -7.39368677e-01 -3.54880840e-01 -7.20698416e-01
3.10531538e-02 -2.37307623e-01 1.50359765e-01 -9.03672278e-01
-1.08605754e+00 1.80908978e-01 1.45749331e-01 -1.32607508e+00
-4.31526862e-02 -2.99857855e-01 -4.02279824e-01 3.79831851e-01
-1.62063456e+00 -1.12531483e+00 -5.76580286e-01 4.45892453e-01
4.14187044e-01 1.29120946e-01 1.76717311e-01 4.08258177e-02
-6.55340105e-02 8.94758757e-03 -8.58296603e-02 -7.00364634e-02
5.73898792e-01 -1.27432501e+00 5.36803186e-01 9.48641658e-01
3.56162786e-01 4.01999205e-01 9.58173692e-01 -9.96526778e-01
-1.35853958e+00 -1.35735023e+00 3.29083532e-01 -8.10050547e-01
5.72632432e-01 -5.69616616e-01 -8.86346281e-01 7.87371933e-01
-3.12085420e-01 2.51189500e-01 3.22721675e-02 2.47496665e-01
-3.26113433e-01 -1.89870626e-01 -1.03861487e+00 2.01754674e-01
1.38648689e+00 -3.88391912e-02 -5.07216692e-01 7.64134109e-01
9.14803505e-01 -4.32655096e-01 -4.62965250e-01 7.43578374e-01
1.35900095e-01 -1.03470218e+00 1.01046157e+00 -1.05278552e-01
-1.07049249e-01 -7.55331933e-01 -2.79907465e-01 -8.45914006e-01
-6.69991672e-01 -4.40274894e-01 -4.97266389e-02 9.35384631e-01
3.00568670e-01 -4.71918523e-01 9.07849729e-01 1.41102910e-01
-5.75669706e-01 -1.90347761e-01 -1.10098588e+00 -1.17710245e+00
-4.02085871e-01 -8.28412116e-01 4.04373318e-01 6.85751677e-01
-6.18097305e-01 2.12507486e-01 6.77886829e-02 9.91432786e-01
1.11560798e+00 -2.26174314e-02 1.20831800e+00 -1.49542534e+00
-1.84199780e-01 -2.48861253e-01 -6.53741419e-01 -1.29457068e+00
-1.89850181e-01 -7.60844052e-01 6.70571864e-01 -1.49823534e+00
-5.19735962e-02 -7.87652791e-01 -7.40092844e-02 6.35941267e-01
2.37645075e-01 3.16859782e-01 1.41515777e-01 6.29201770e-01
-1.19197643e+00 4.22752440e-01 6.36135340e-01 -8.40926617e-02
-2.56842345e-01 -3.74593884e-02 -3.26608568e-01 7.56313324e-01
7.49122739e-01 -7.93698370e-01 -2.94642933e-02 -5.67118049e-01
8.55633840e-02 -3.18149060e-01 8.84480119e-01 -1.63067138e+00
5.30008197e-01 -1.17869504e-01 4.45587605e-01 -1.16613734e+00
5.91564059e-01 -9.38194990e-01 2.09647696e-02 4.90569830e-01
1.64809008e-03 -6.90000877e-02 3.26823413e-01 9.02494609e-01
3.03388655e-01 -9.17004794e-02 7.52618551e-01 -2.66171247e-01
-1.03081715e+00 4.08520550e-01 -2.58944988e-01 -1.03521317e-01
9.99891102e-01 -4.90654290e-01 -9.01290774e-02 -4.94912267e-02
-6.75694883e-01 6.78829372e-01 8.64130497e-01 5.56975543e-01
3.97396803e-01 -8.80391896e-01 -8.12472522e-01 1.39875948e-01
6.51549578e-01 7.34456718e-01 1.25016034e-01 8.46008599e-01
-3.09155941e-01 2.19460800e-01 1.01124331e-01 -1.56682897e+00
-8.02547872e-01 5.15905619e-01 9.36442837e-02 2.17993617e-01
-9.63393331e-01 9.44941461e-01 3.08424681e-01 -5.10379374e-01
3.16202581e-01 -3.56570482e-01 2.81885952e-01 -3.04346561e-01
2.55392641e-01 2.39607379e-01 9.94807631e-02 -5.14385879e-01
-5.99851072e-01 5.15945792e-01 2.24644318e-03 -2.06856549e-01
1.41391790e+00 -2.61321455e-01 1.81612059e-01 5.07142365e-01
8.14376056e-01 -1.30433604e-01 -1.95094860e+00 -5.05441010e-01
1.79607853e-01 -4.80906278e-01 2.85302609e-01 -5.68367124e-01
-7.02000141e-01 4.50339288e-01 8.99941325e-01 1.07275024e-01
4.79415894e-01 7.26921082e-01 4.31248814e-01 7.81837940e-01
6.04006708e-01 -1.14603937e+00 8.86491537e-02 6.46830082e-01
6.95401192e-01 -1.57542443e+00 9.97301191e-02 -2.28862926e-01
-4.49247986e-01 7.92019010e-01 7.12470233e-01 -4.55727488e-01
4.40205753e-01 4.70628321e-01 -1.61459357e-01 -5.00244319e-01
-7.22515345e-01 -8.75000477e-01 3.10008954e-02 8.81767631e-01
-5.24698436e-01 -4.29595970e-02 5.54693401e-01 6.27536774e-02
-2.17487678e-01 -2.17990592e-01 2.74688423e-01 1.07734537e+00
-1.06477261e+00 -2.75393426e-01 -5.04574597e-01 4.56832200e-01
2.34072998e-01 1.45497218e-01 -1.92715883e-01 9.30326521e-01
4.72283751e-01 8.88513327e-01 6.18836761e-01 -3.02375853e-01
4.51446414e-01 -3.74463558e-01 3.35845947e-01 -9.03414130e-01
-1.32454261e-01 2.11664334e-01 -1.22265510e-01 -9.59884584e-01
-4.73975033e-01 -1.00150418e+00 -1.33403957e+00 -4.32737544e-02
-3.06400239e-01 -1.47694245e-01 9.65220451e-01 9.47342753e-01
7.21969128e-01 3.90320063e-01 4.26982433e-01 -1.47625268e+00
-3.70476305e-01 -8.94507647e-01 -3.62754196e-01 1.34933114e-01
5.82805753e-01 -9.51341391e-01 -5.17103255e-01 -9.75180566e-02] | [7.754405975341797, -2.6245453357696533] |
fd7a5d84-17e7-4402-93f0-4b02c7c8d8f2 | extra-explanation-ranking-datasets-for | 2102.10315 | null | https://arxiv.org/abs/2102.10315v3 | https://arxiv.org/pdf/2102.10315v3.pdf | EXTRA: Explanation Ranking Datasets for Explainable Recommendation | Recently, research on explainable recommender systems has drawn much attention from both academia and industry, resulting in a variety of explainable models. As a consequence, their evaluation approaches vary from model to model, which makes it quite difficult to compare the explainability of different models. To achieve a standard way of evaluating recommendation explanations, we provide three benchmark datasets for EXplanaTion RAnking (denoted as EXTRA), on which explainability can be measured by ranking-oriented metrics. Constructing such datasets, however, poses great challenges. First, user-item-explanation triplet interactions are rare in existing recommender systems, so how to find alternatives becomes a challenge. Our solution is to identify nearly identical sentences from user reviews. This idea then leads to the second challenge, i.e., how to efficiently categorize the sentences in a dataset into different groups, since it has quadratic runtime complexity to estimate the similarity between any two sentences. To mitigate this issue, we provide a more efficient method based on Locality Sensitive Hashing (LSH) that can detect near-duplicates in sub-linear time for a given query. Moreover, we make our code publicly available to allow researchers in the community to create their own datasets. | ['Li Chen', 'Yongfeng Zhang', 'Lei LI'] | 2021-02-20 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 3.85533534e-02 7.03598708e-02 -2.61931449e-01 -6.40576720e-01
-8.34847271e-01 -6.55716002e-01 3.02537531e-01 2.23865017e-01
2.73552239e-01 5.18100202e-01 5.09113431e-01 -3.82546335e-01
-3.14277947e-01 -6.14143312e-01 -5.63875616e-01 -4.26616162e-01
1.55439332e-01 4.22774106e-01 2.46217892e-01 -2.01613843e-01
6.52489424e-01 5.94335683e-02 -1.81410873e+00 5.47627509e-01
1.00658154e+00 6.53537810e-01 3.76526207e-01 4.95353341e-01
-2.82687545e-01 3.78646374e-01 -3.89584005e-01 -7.95967877e-01
4.73288298e-02 -6.33495271e-01 -9.42724645e-01 -1.66766703e-01
4.44465607e-01 -2.37821698e-01 -1.38881251e-01 9.28681314e-01
3.14491093e-01 1.10368416e-01 6.47105336e-01 -1.33501709e+00
-1.31764889e+00 1.09565306e+00 -8.84177759e-02 -4.57357131e-02
4.04734403e-01 -5.29662907e-01 1.59250402e+00 -1.12735915e+00
3.23984742e-01 1.20009649e+00 4.44304049e-01 5.38648665e-01
-1.08108377e+00 -4.53954399e-01 1.97411001e-01 4.55950826e-01
-1.23478270e+00 -1.44973472e-01 3.90790790e-01 -1.11762360e-01
7.04593778e-01 9.11860406e-01 4.52797115e-01 8.85463655e-01
-3.41356918e-02 6.70378089e-01 7.72100627e-01 -3.02945942e-01
2.92993784e-01 4.29587543e-01 5.40228367e-01 3.40129018e-01
4.98845369e-01 -2.25634485e-01 -5.75897872e-01 -3.21787864e-01
3.83748740e-01 7.03665555e-01 -4.09927338e-01 -5.03457367e-01
-9.88840163e-01 1.07408190e+00 4.51770842e-01 2.04332247e-01
-1.13642968e-01 -1.18232640e-02 7.47879893e-02 4.01012212e-01
3.35302442e-01 6.49892628e-01 -5.18263161e-01 -2.51905844e-02
-3.93263996e-01 3.57421488e-01 8.59693468e-01 9.18989480e-01
9.49464321e-01 -6.33304596e-01 -4.15126532e-02 8.33949208e-01
5.65018713e-01 3.21819693e-01 4.52114493e-01 -8.11772823e-01
2.74660468e-01 6.17981851e-01 7.37700462e-02 -1.35141385e+00
-1.90331176e-01 -3.25816333e-01 -7.41341591e-01 -4.76007909e-01
2.13345855e-01 2.94753700e-01 -1.58184290e-01 1.58365333e+00
3.04518878e-01 6.92361966e-02 4.52897660e-02 1.31919098e+00
8.69005144e-01 5.05177975e-01 -4.24906254e-01 -1.87524542e-01
1.40704036e+00 -1.36951184e+00 -4.63054538e-01 -2.59358227e-01
8.18741977e-01 -9.16133225e-01 1.38725853e+00 2.45821491e-01
-8.41764688e-01 -4.21098262e-01 -9.50940251e-01 -1.77254185e-01
-3.10590118e-01 -2.36154214e-01 9.56293643e-01 5.22075832e-01
-8.71976852e-01 5.36611021e-01 -5.92221200e-01 -5.37299871e-01
-1.89869732e-01 4.11168665e-01 -1.97605729e-01 -2.28054792e-01
-1.19860494e+00 5.49043894e-01 -1.16716824e-01 -7.55134970e-02
-3.21519017e-01 -4.91133362e-01 -5.40416121e-01 4.15419370e-01
3.46964419e-01 -7.52482951e-01 1.14636540e+00 -7.83530831e-01
-1.07337165e+00 2.24793926e-01 -4.72166419e-01 -3.51325899e-01
-9.89775211e-02 -2.99661100e-01 -4.13666546e-01 -3.32918674e-01
1.15185387e-01 2.47481167e-01 5.80578923e-01 -1.17053533e+00
-5.65089762e-01 -4.20128703e-01 4.60154444e-01 2.00796753e-01
-6.52539790e-01 -2.47908831e-02 -7.31808484e-01 -5.58417022e-01
3.36827368e-01 -1.26732063e+00 -3.44976395e-01 -2.31248751e-01
-6.54483914e-01 -3.97936255e-01 4.29127574e-01 -1.69103950e-01
1.46356332e+00 -2.12812805e+00 6.55269846e-02 1.36494055e-01
4.24025744e-01 -1.29511179e-02 -2.09985614e-01 6.28412664e-01
4.22385260e-02 3.49607646e-01 -2.53514238e-02 -4.44716841e-01
2.92912900e-01 2.67652899e-01 -7.05663323e-01 1.27693489e-01
-2.41520926e-01 6.87689126e-01 -9.24653292e-01 -2.35355929e-01
-5.95094636e-02 5.03086567e-01 -9.83043969e-01 3.02884787e-01
-9.03037563e-03 2.45143831e-01 -6.73098147e-01 2.84695178e-01
5.93121529e-01 -6.14322782e-01 1.62203699e-01 1.88252870e-02
5.40369563e-02 6.36076212e-01 -1.18188524e+00 1.34835374e+00
-4.57958192e-01 4.69269574e-01 -6.12699270e-01 -7.45574951e-01
9.51737881e-01 1.46068111e-01 2.20875531e-01 -3.68554354e-01
-2.21123725e-01 4.28042114e-01 -2.04583094e-01 -2.84552783e-01
9.06897664e-01 1.84990570e-01 -3.78147960e-02 8.73581886e-01
-6.37253523e-01 3.22591305e-01 3.09781265e-02 4.46731895e-01
1.31947935e+00 -4.84194100e-01 1.15432985e-01 -1.81801971e-02
5.39103985e-01 -1.43743128e-01 4.56786931e-01 9.88282561e-01
1.48516610e-01 9.93886769e-01 2.87481725e-01 -7.93023646e-01
-7.42879808e-01 -8.45649183e-01 -2.11679470e-02 1.04317296e+00
6.63674951e-01 -8.13029885e-01 -6.85842216e-01 -8.50584269e-01
2.41022836e-02 5.23584306e-01 -5.29025197e-01 -1.67886466e-01
-2.91954845e-01 -3.91383111e-01 -2.57093489e-01 3.65409791e-01
-3.91439311e-02 -8.60160291e-01 -3.17234129e-01 -3.94029021e-02
-4.31618273e-01 -7.49383211e-01 -9.21983004e-01 1.21703334e-01
-8.24866533e-01 -1.00909138e+00 -4.58099931e-01 -5.12814641e-01
8.58140051e-01 1.13886142e+00 1.35941279e+00 7.74748862e-01
6.02972247e-02 2.33992726e-01 -7.35778570e-01 -1.26885131e-01
-4.86099645e-02 2.06600904e-01 1.58509061e-01 -7.83008933e-02
5.92469692e-01 -3.78266603e-01 -9.22829747e-01 8.07974100e-01
-9.70691621e-01 -6.51773140e-02 5.58257282e-01 7.54984438e-01
8.53501678e-01 -1.61698088e-01 4.44846451e-01 -1.11285567e+00
7.10550964e-01 -6.88120425e-01 -2.69733071e-01 4.73048538e-01
-8.95389557e-01 3.93304020e-01 8.35216045e-01 -1.69333443e-01
-5.02237856e-01 4.16047722e-02 -4.44317656e-03 -1.05669200e-01
5.90065680e-02 6.09452605e-01 -9.23710689e-02 1.20203122e-01
5.56774676e-01 5.57123125e-02 -2.32290819e-01 -5.83906412e-01
5.63823998e-01 8.94138217e-01 3.60406339e-01 -1.51767045e-01
8.58189404e-01 2.89584488e-01 -4.25013006e-01 -5.49785674e-01
-1.24514985e+00 -6.49882734e-01 -2.46244356e-01 2.07006067e-01
5.20363331e-01 -6.64115131e-01 -5.70139349e-01 -4.54051852e-01
-1.30131340e+00 2.88466424e-01 -7.72362649e-02 6.23022079e-01
-2.84675449e-01 6.39683306e-01 -3.32115382e-01 -5.96329689e-01
-3.41817558e-01 -1.30374873e+00 1.03342688e+00 9.59978998e-02
-5.36131918e-01 -7.31488347e-01 2.89096504e-01 6.57564342e-01
4.95770007e-01 -4.38335776e-01 8.88638556e-01 -9.36971009e-01
-1.04103875e+00 -2.27705911e-01 -2.75713682e-01 -4.99838106e-02
1.07391886e-01 -9.56417397e-02 -6.23063207e-01 -2.00251535e-01
-4.59046811e-02 -2.03039628e-02 8.08192790e-01 2.24032447e-01
1.40713716e+00 -4.89055753e-01 -3.82479668e-01 4.09019083e-01
1.03896129e+00 -2.96593696e-01 5.17399192e-01 2.05197468e-01
6.43198192e-01 5.94551563e-01 8.90858591e-01 5.54792345e-01
7.90407598e-01 1.10539186e+00 4.88996327e-01 1.10666744e-01
5.41760512e-02 -3.01830083e-01 3.62180382e-01 1.17892337e+00
7.61818141e-02 -5.65037549e-01 -3.26308697e-01 3.61512661e-01
-2.21480536e+00 -9.83245790e-01 -4.79778469e-01 2.48162460e+00
3.74301553e-01 -3.11766624e-01 -4.85394932e-02 7.04956725e-02
7.75559008e-01 -1.44176632e-01 -4.80759919e-01 -5.55199623e-01
4.45870832e-02 -3.23280692e-01 1.83288395e-01 4.93103921e-01
-7.21058667e-01 6.30143166e-01 5.85851717e+00 5.15404165e-01
-8.93441677e-01 -6.89242706e-02 4.42301869e-01 4.77408692e-02
-1.02985704e+00 3.55549902e-01 -9.00352418e-01 6.48863673e-01
9.24420595e-01 -3.91668797e-01 6.12694025e-01 9.99888122e-01
1.14851579e-01 3.55165988e-01 -1.22038424e+00 9.90134776e-01
3.13204318e-01 -1.16385126e+00 2.13533998e-01 1.57407001e-01
7.08647847e-01 -5.15803806e-02 2.55674571e-01 2.76224166e-01
1.33459821e-01 -8.09951186e-01 3.51767182e-01 3.52140099e-01
1.09629706e-01 -7.45886505e-01 7.73543000e-01 2.99492508e-01
-1.22225380e+00 -1.21116124e-01 -1.06341171e+00 -1.32951692e-01
6.92618787e-02 7.44870901e-01 -6.71107352e-01 4.57923442e-01
8.00737143e-01 7.27395654e-01 -4.19455379e-01 1.11767161e+00
-2.91405648e-01 3.95972610e-01 -3.47369760e-02 -3.20014447e-01
-1.21029653e-01 -1.19881526e-01 2.48943597e-01 6.80893362e-01
8.77578735e-01 9.00669619e-02 -3.54298353e-02 7.21840918e-01
-1.87724769e-01 4.91961360e-01 -6.33956969e-01 1.16091974e-01
8.14715207e-01 1.39423549e+00 -7.10526347e-01 -7.13087097e-02
-6.51974857e-01 1.19374132e+00 5.22390842e-01 -1.30324557e-01
-9.72233713e-01 -2.41135523e-01 9.46018636e-01 9.72600654e-02
4.53257024e-01 5.10335639e-02 -1.22318365e-01 -1.39280951e+00
1.81097358e-01 -1.00328135e+00 5.06298184e-01 -5.77779353e-01
-1.47144890e+00 8.13177288e-01 -3.93758446e-01 -1.36960137e+00
-1.99573085e-01 -1.82641998e-01 -4.72869545e-01 4.41668957e-01
-1.38396811e+00 -5.89600146e-01 -5.23039639e-01 3.69039357e-01
5.96868515e-01 1.04688987e-01 9.32767987e-01 4.71879035e-01
-5.35856128e-01 8.55920315e-01 2.80248284e-01 -4.93734062e-01
9.67204332e-01 -1.29907739e+00 5.93077540e-01 5.18605649e-01
8.38425994e-01 1.03311419e+00 9.41007733e-01 -2.91681767e-01
-1.67122376e+00 -9.75270092e-01 1.57537436e+00 -9.03388143e-01
4.99512047e-01 -3.06435555e-01 -1.07096732e+00 5.28167605e-01
-4.52541448e-02 4.34397683e-02 9.84306037e-01 6.03665471e-01
-4.49836224e-01 -1.89787969e-01 -6.58130825e-01 7.77451217e-01
1.16509664e+00 -5.62834918e-01 -4.12385583e-01 5.71442842e-01
9.58790898e-01 -1.14747018e-01 -6.42826855e-01 1.77507907e-01
6.15350723e-01 -1.31198943e+00 9.45500195e-01 -5.37644207e-01
6.14151061e-01 -5.26304483e-01 -1.89668700e-01 -1.23521328e+00
-3.37154061e-01 -6.24862015e-01 -2.51129150e-01 1.19745159e+00
7.96836317e-01 -7.04957485e-01 8.61995816e-01 1.04291523e+00
-1.07040323e-01 -9.55971062e-01 -5.12652874e-01 -7.91674614e-01
-3.39112163e-01 -2.82218367e-01 1.14155734e+00 6.35600626e-01
3.57743293e-01 6.50844693e-01 -6.94758534e-01 3.99824709e-01
3.74581695e-01 8.26776087e-01 9.90688443e-01 -1.30924261e+00
-4.46344137e-01 -1.90443158e-01 -3.41718137e-01 -1.64338064e+00
9.52132717e-02 -9.57049787e-01 1.36181757e-01 -1.46466398e+00
8.08553040e-01 -5.50788701e-01 -3.45796108e-01 3.53890359e-01
-4.61677432e-01 3.57662767e-01 1.03844759e-04 6.79722667e-01
-1.08255267e+00 6.38001382e-01 1.01391530e+00 9.42696631e-02
-4.58060466e-02 1.39067918e-01 -1.30944598e+00 6.00813985e-01
5.94119012e-01 -7.51720607e-01 -6.06828392e-01 -7.30980992e-01
5.15661001e-01 5.30583225e-02 2.17977196e-01 -7.42566109e-01
3.08741927e-01 -3.94888408e-03 -2.72653520e-01 -6.16049588e-01
1.07390039e-01 -9.00881529e-01 2.44801164e-01 1.97901040e-01
-6.66103482e-01 4.30141032e-01 -4.47545707e-01 7.87237167e-01
-3.79704118e-01 -3.16701025e-01 1.96600124e-01 2.25426182e-01
-3.76136571e-01 4.81905937e-01 -5.52621037e-02 -5.76507375e-02
7.32355952e-01 -7.66254440e-02 -5.14783025e-01 -7.27944016e-01
-3.15772235e-01 6.71547949e-02 6.69023216e-01 6.42626047e-01
6.53997362e-01 -1.46077716e+00 -5.23435831e-01 6.36158744e-03
4.18586373e-01 -4.41443533e-01 3.65503460e-01 7.40356266e-01
-8.96739587e-02 7.38669872e-01 3.09354901e-01 -3.71040910e-01
-1.48188460e+00 7.42356002e-01 -1.73531532e-01 -1.70681864e-01
-4.77234900e-01 7.70076334e-01 3.79164189e-01 -6.65443361e-01
2.35886261e-01 -1.34820908e-01 -2.18841866e-01 -3.69797677e-01
7.21358180e-01 2.48393536e-01 1.47199435e-02 -4.57898319e-01
-3.52073669e-01 5.48355460e-01 -3.00722361e-01 3.91769528e-01
1.24453783e+00 -5.50330281e-01 -9.79884267e-02 2.44610682e-01
1.24069226e+00 2.31020972e-01 -6.61228061e-01 -1.95307359e-01
6.60361582e-03 -6.70549989e-01 -3.20194185e-01 -3.98121268e-01
-9.80259597e-01 8.00772846e-01 3.54254752e-01 8.35752368e-01
8.71382236e-01 3.17048520e-01 1.12934732e+00 6.70142472e-01
3.54516953e-01 -6.19185567e-01 9.28183496e-02 5.45687258e-01
7.98674524e-01 -1.25671411e+00 -5.62366545e-02 -6.65338337e-01
-7.23055303e-01 1.00088024e+00 3.74643236e-01 2.46218309e-01
5.87699175e-01 -2.69688308e-01 7.80698359e-02 -2.48584941e-01
-9.08362329e-01 -2.78784148e-03 4.76904452e-01 1.08201303e-01
8.30851436e-01 5.53179383e-02 -6.16131246e-01 9.95558143e-01
-3.45038921e-01 -2.98562139e-01 3.73275906e-01 3.38259965e-01
-5.39137244e-01 -1.56244504e+00 -1.02056816e-01 4.73147035e-01
-3.34940702e-01 -2.59498805e-01 -6.06317878e-01 2.65095353e-01
-2.61626333e-01 1.27800691e+00 -2.01596037e-01 -6.04917705e-01
2.04999000e-01 -4.28602219e-01 -1.94924250e-01 -7.11260080e-01
-3.92949134e-01 -4.92532700e-01 -8.54865909e-02 -7.38626122e-01
-1.63708359e-01 -5.38539231e-01 -1.28078485e+00 -5.56669235e-01
-7.39194274e-01 7.72443831e-01 5.81383049e-01 9.60607409e-01
8.00359726e-01 -3.69536225e-03 1.05123389e+00 -4.68412995e-01
-8.48823309e-01 -5.07920623e-01 -5.55888653e-01 5.61900079e-01
1.28815338e-01 -5.99973261e-01 -7.46964097e-01 -3.31377804e-01] | [10.00525951385498, 5.74029541015625] |
88c7c2ef-a9a4-460a-bcce-07329a387a53 | a-neural-multi-task-learning-framework-to | 1812.06081 | null | http://arxiv.org/abs/1812.06081v1 | http://arxiv.org/pdf/1812.06081v1.pdf | A Neural Multi-Task Learning Framework to Jointly Model Medical Named Entity Recognition and Normalization | State-of-the-art studies have demonstrated the superiority of joint modelling
over pipeline implementation for medical named entity recognition and
normalization due to the mutual benefits between the two processes. To exploit
these benefits in a more sophisticated way, we propose a novel deep neural
multi-task learning framework with explicit feedback strategies to jointly
model recognition and normalization. On one hand, our method benefits from the
general representations of both tasks provided by multi-task learning. On the
other hand, our method successfully converts hierarchical tasks into a parallel
multi-task setting while maintaining the mutual supports between tasks. Both of
these aspects improve the model performance. Experimental results demonstrate
that our method performs significantly better than state-of-the-art approaches
on two publicly available medical literature datasets. | ['Ting Liu', 'Fei Wang', 'Sicheng Zhao', 'Sendong Zhao'] | 2018-12-14 | null | null | null | null | ['medical-named-entity-recognition'] | ['natural-language-processing'] | [ 1.13474257e-01 9.51814428e-02 -3.01147223e-01 -5.73018193e-01
-9.79182184e-01 9.27924886e-02 5.11815846e-01 3.65489990e-01
-7.31091142e-01 6.95143342e-01 4.36293632e-01 6.46822453e-02
-2.10391968e-01 -4.49167371e-01 -5.44619143e-01 -5.08740067e-01
2.14020178e-01 3.80602211e-01 1.50011286e-01 4.08226997e-02
-1.09921210e-01 3.34057987e-01 -1.26632977e+00 5.17326295e-01
7.51127958e-01 6.28162026e-01 8.93579389e-04 3.16131592e-01
-1.64360404e-01 8.14119339e-01 -2.61676461e-01 -6.75739229e-01
6.90800697e-02 2.12145567e-01 -8.92039001e-01 -2.32241169e-01
1.48864210e-01 -2.33306229e-01 -7.65693113e-02 8.99779439e-01
8.26823711e-01 -2.08312855e-03 3.82787496e-01 -9.84439075e-01
-6.14067435e-01 5.43275535e-01 -7.08631575e-01 -2.70696450e-02
-8.44856799e-02 -6.00186661e-02 1.03435755e+00 -8.24542761e-01
4.56065089e-01 1.06219101e+00 7.95253336e-01 5.24947941e-01
-1.16669774e+00 -9.55607414e-01 2.11216956e-01 -5.44349849e-02
-1.26415372e+00 -6.74194455e-01 3.39680970e-01 -4.28616762e-01
1.26454496e+00 -4.82653193e-02 2.26309616e-02 9.80753303e-01
3.43967348e-01 9.05971408e-01 8.31282675e-01 -1.95832506e-01
-8.28745887e-02 1.12069741e-01 2.58873165e-01 6.73808038e-01
4.27274853e-01 -2.73935080e-01 -4.20732707e-01 -3.73622984e-01
6.91906273e-01 1.65818691e-01 -2.73090769e-02 -2.38065809e-01
-1.36941099e+00 5.23952544e-01 1.52154207e-01 7.52482355e-01
-7.08645642e-01 5.23236059e-02 7.77888298e-01 -6.92882761e-02
6.38192415e-01 3.09238166e-01 -7.93113351e-01 -1.42967150e-01
-1.13453209e+00 3.64578664e-02 7.66548157e-01 8.43311906e-01
5.28438210e-01 -2.64234781e-01 -5.75099707e-01 9.32202697e-01
3.97770017e-01 3.85555290e-02 6.04668438e-01 -5.20825744e-01
3.59817147e-01 6.97308898e-01 -3.34422410e-01 -7.20263541e-01
-8.87032509e-01 -7.65648603e-01 -1.26988328e+00 -1.52543351e-01
3.49084616e-01 -6.43136203e-02 -9.49261725e-01 1.97637129e+00
3.94578040e-01 3.64097089e-01 1.28629133e-01 3.60247999e-01
1.07828808e+00 -2.07028203e-02 6.43029809e-01 -2.22928245e-02
1.80360973e+00 -1.11312687e+00 -1.19756567e+00 -2.88371384e-01
9.36870635e-01 -7.55536258e-01 5.26992083e-01 1.73298448e-01
-9.59090352e-01 -5.21298766e-01 -8.54081333e-01 -2.55656511e-01
-3.66295785e-01 3.89814436e-01 1.01425445e+00 6.92920148e-01
-8.53583336e-01 4.21644717e-01 -1.29128766e+00 -1.44265696e-01
6.98529601e-01 4.20188159e-01 -5.34450531e-01 1.36479706e-01
-1.18195224e+00 9.66869414e-01 5.20505250e-01 -2.00306699e-02
-5.82491696e-01 -1.01843548e+00 -9.14022207e-01 2.58134037e-01
3.77192199e-01 -1.09781730e+00 1.32324767e+00 -4.25065994e-01
-1.29827988e+00 1.01109207e+00 -3.93492222e-01 -3.84508312e-01
6.27581060e-01 -4.65699822e-01 -3.04403186e-01 -7.85434172e-02
1.52728781e-01 4.51347291e-01 3.52152586e-01 -7.05209613e-01
-6.72040522e-01 -5.14046788e-01 -1.41902119e-01 -1.08166877e-03
-8.50106776e-01 4.12305802e-01 -7.92603314e-01 -6.51751101e-01
-2.46629879e-01 -6.35088980e-01 -6.44452274e-01 -7.66275972e-02
-4.90164608e-01 -4.65438426e-01 3.45359892e-01 -6.39949858e-01
1.50804448e+00 -2.13078451e+00 1.03693224e-01 -1.80645883e-01
5.63148379e-01 5.80370069e-01 -6.36451393e-02 2.03907549e-01
-3.01107287e-01 3.24783355e-01 -1.24367855e-01 -1.03562522e+00
-2.00078756e-01 1.44353986e-01 2.81912863e-01 5.04651427e-01
2.49967590e-01 1.12154591e+00 -7.48959899e-01 -6.49813831e-01
-6.96046799e-02 7.21679509e-01 -4.61815983e-01 2.45101228e-01
3.19055885e-01 5.66116631e-01 -6.56740904e-01 4.74308401e-01
6.33774161e-01 -6.41068101e-01 2.76657104e-01 -2.69385099e-01
6.87207729e-02 3.75660211e-01 -1.26190352e+00 2.15797877e+00
-4.41230923e-01 5.82422614e-02 3.01694214e-01 -9.84659553e-01
5.58027446e-01 8.50782990e-01 6.14807963e-01 -4.73192424e-01
1.95245948e-02 1.32231757e-01 -4.47850004e-02 -4.14532393e-01
3.80118877e-01 -3.54757607e-01 1.09681459e-02 3.78354967e-01
3.48220378e-01 4.28364068e-01 9.12337378e-02 1.45188436e-01
1.02965355e+00 2.25115135e-01 9.30428803e-01 -7.83519521e-02
5.60940444e-01 -6.33181512e-01 1.12915194e+00 8.61028016e-01
-3.22131991e-01 3.67641419e-01 3.01588774e-01 -4.12662894e-01
-6.65850818e-01 -6.79612398e-01 -2.42294788e-01 1.41576314e+00
-5.06892145e-01 -5.66262126e-01 -6.11860037e-01 -7.28572786e-01
3.31630185e-02 5.19920290e-01 -9.27803695e-01 3.80502008e-02
-7.71825194e-01 -1.19611037e+00 1.07589746e+00 8.29250097e-01
3.19163352e-01 -8.51225376e-01 -5.64417899e-01 3.86111677e-01
-1.20516896e-01 -1.41885149e+00 -2.90255666e-01 2.95927048e-01
-1.02824759e+00 -8.76476765e-01 -8.41003478e-01 -5.78913629e-01
4.09263432e-01 4.56485488e-02 1.15194345e+00 5.74624129e-02
-3.56910110e-01 -1.64868182e-03 -5.89192733e-02 -5.87540984e-01
-4.09961641e-01 5.85937738e-01 -7.96322152e-02 -1.96708110e-03
3.54965299e-01 -7.18996763e-01 -2.60410309e-01 1.20102346e-01
-1.17839670e+00 4.28075135e-01 1.00709713e+00 8.92080069e-01
5.94253123e-01 -3.08327466e-01 9.26349163e-01 -1.22623181e+00
5.39411604e-01 -5.42920709e-01 -1.68605477e-01 6.33266926e-01
-7.63321400e-01 2.85383612e-01 3.41433406e-01 -2.78154075e-01
-1.31602240e+00 1.94929034e-01 -1.83211878e-01 -1.25860527e-01
-3.96051735e-01 6.12908900e-01 -3.02493393e-01 2.63354063e-01
2.73929030e-01 2.98384554e-03 -9.28204134e-02 -8.07304978e-01
5.56014419e-01 7.34375417e-01 5.30027270e-01 -4.87213939e-01
5.35484791e-01 5.76691508e-01 -5.37854619e-03 -5.16170859e-01
-9.69030797e-01 -7.81917930e-01 -9.38226283e-01 3.51594180e-01
1.18188941e+00 -1.22344542e+00 -7.71104097e-01 5.24702787e-01
-1.36061597e+00 1.64404541e-01 9.45712551e-02 4.64694053e-01
-1.76822543e-01 4.82796580e-01 -8.77846003e-01 -7.25487888e-01
-8.07241797e-01 -1.20851040e+00 1.18488610e+00 1.72583804e-01
-1.23834647e-01 -1.13578296e+00 8.79945382e-02 4.37910676e-01
5.86366773e-01 9.08708796e-02 8.15412700e-01 -1.26773131e+00
-1.72646448e-01 -2.26447105e-01 -5.18967509e-01 1.33668184e-01
3.18880379e-01 -3.21645439e-01 -1.24991941e+00 -2.89821148e-01
7.60648996e-02 -2.97774017e-01 1.12558174e+00 3.26559991e-01
1.16491640e+00 1.48523137e-01 -6.48625255e-01 7.33072281e-01
1.13153362e+00 -3.14393312e-01 5.39293051e-01 5.06884038e-01
8.18797410e-01 5.25330484e-01 3.33253205e-01 4.74042982e-01
7.92703211e-01 6.69415712e-01 1.69696689e-01 -6.60794675e-01
-6.08013980e-02 1.40283585e-01 -1.26903787e-01 8.70911479e-01
3.90942208e-02 1.35840908e-01 -1.15299761e+00 5.85443139e-01
-2.15158081e+00 -6.19221151e-01 -2.86470503e-02 1.88733149e+00
1.14460123e+00 1.00523271e-01 -9.94166881e-02 -2.10138157e-01
5.01108646e-01 6.33623078e-02 -4.22973394e-01 -6.02045767e-02
-1.33774206e-01 2.10135221e-01 4.49012667e-01 -1.51277304e-01
-1.49431217e+00 7.35408068e-01 6.17335606e+00 9.26681817e-01
-8.39752793e-01 4.76021469e-01 6.26948357e-01 -2.03034922e-01
3.65135111e-02 -4.02091265e-01 -1.10270762e+00 6.30105808e-02
1.12755477e+00 -1.58263460e-01 -1.93698779e-01 5.93131363e-01
-1.74159594e-02 3.00308675e-01 -1.35060298e+00 9.44408894e-01
-1.37919607e-02 -1.17453289e+00 -9.26346928e-02 2.96246946e-01
4.90028441e-01 2.73314714e-01 -1.18394636e-01 5.22063911e-01
4.16924596e-01 -9.95444000e-01 4.49290842e-01 5.56367993e-01
5.32117605e-01 -4.44063812e-01 1.04047120e+00 4.82309610e-01
-1.21063793e+00 1.03063174e-01 -6.57621697e-02 3.57600302e-02
4.17803437e-01 9.05976057e-01 -7.63480067e-01 1.25865793e+00
5.09165287e-01 7.74375677e-01 -7.72208512e-01 1.11879039e+00
-1.50679663e-01 4.54930037e-01 -1.31146222e-01 4.60875243e-01
1.08043037e-01 2.20546097e-01 3.01431507e-01 1.75553071e+00
-5.49009703e-02 -1.31592795e-01 3.09472293e-01 7.63570189e-01
-4.61753786e-01 4.70274210e-01 -4.12015587e-01 -4.51154411e-02
2.70417213e-01 1.59694815e+00 -5.82890809e-01 -4.67902392e-01
-7.56153703e-01 6.46154523e-01 6.33663654e-01 3.18470076e-02
-7.78174222e-01 -3.07558566e-01 7.18258083e-01 -3.01934600e-01
3.33318591e-01 -5.56763150e-02 -6.17401898e-01 -1.34121263e+00
1.73983574e-01 -8.07062209e-01 6.40984297e-01 -3.63182984e-02
-1.53017640e+00 4.88273382e-01 -1.87719941e-01 -8.78768742e-01
-8.52789581e-02 -3.93794507e-01 -3.33545923e-01 9.83839035e-01
-1.90388262e+00 -1.72470522e+00 1.60079584e-01 3.06902528e-01
2.04428568e-01 -6.54462501e-02 1.37253940e+00 6.84543252e-01
-9.28161442e-01 8.39118063e-01 3.67526487e-02 4.76391226e-01
1.17159331e+00 -1.21388268e+00 6.00408494e-01 7.46996462e-01
-2.07401395e-01 9.58478630e-01 1.98999941e-02 -6.71093345e-01
-1.00292206e+00 -1.10292220e+00 1.17258179e+00 -4.02639151e-01
5.84106624e-01 -4.09772158e-01 -1.21499789e+00 7.57920802e-01
2.12331027e-01 -8.70351586e-03 1.39509881e+00 7.02514648e-01
-5.47107637e-01 1.49936348e-01 -9.98602986e-01 1.23421118e-01
7.55878389e-01 -4.78335142e-01 -8.22531521e-01 1.78649038e-01
7.89780498e-01 -4.23559785e-01 -1.30040586e+00 6.60345554e-01
6.42185569e-01 -5.63614905e-01 1.08159149e+00 -9.53234553e-01
4.18623388e-01 -3.46555673e-02 -1.01978362e-01 -9.72191334e-01
-4.50126141e-01 -5.27444780e-01 -1.17981143e-01 1.60657179e+00
5.76896429e-01 -6.26709878e-01 4.39513415e-01 8.90696764e-01
-2.36741319e-01 -1.01121747e+00 -1.16264439e+00 -5.30113876e-01
3.80817235e-01 -4.91877168e-01 5.77127635e-01 1.14160216e+00
-8.59804004e-02 4.69563276e-01 -6.12027228e-01 1.96376368e-01
5.99197090e-01 -8.40339884e-02 5.90971172e-01 -1.47303236e+00
-4.70144957e-01 -4.39772546e-01 -2.58798063e-01 -7.45348811e-01
3.36940348e-01 -1.13365519e+00 -1.03565075e-01 -1.72839522e+00
7.62113988e-01 -3.15947503e-01 -8.96231472e-01 1.02740049e+00
-7.47219503e-01 6.98729381e-02 2.35573873e-01 2.27744952e-01
-1.03324521e+00 4.55760181e-01 9.40481424e-01 2.42407084e-01
-1.95310310e-01 -4.06833552e-02 -1.18232894e+00 8.53187084e-01
4.57685202e-01 -7.32916296e-01 3.00755739e-01 -7.77531683e-01
1.28158689e-01 -6.26202151e-02 -7.92974606e-02 -6.64758325e-01
5.47090471e-01 2.30879575e-01 3.44843417e-01 -4.31158662e-01
9.36482698e-02 -5.43082356e-01 -4.51576076e-02 4.33839619e-01
-3.95935923e-01 -4.57979403e-02 5.46051502e-01 5.81575096e-01
-1.68326214e-01 1.04735926e-01 6.70598745e-01 -5.28611103e-03
-3.37731689e-01 1.78162500e-01 -1.60441086e-01 -8.13904852e-02
8.15615833e-01 2.61591822e-01 -2.23123237e-01 6.66186959e-02
-8.22679520e-01 5.44646263e-01 -1.79514010e-02 5.49110591e-01
2.59316146e-01 -1.08185375e+00 -9.89437580e-01 1.43867284e-01
1.14198975e-01 -1.60829313e-02 3.24978411e-01 1.31048810e+00
2.41570905e-01 6.62550092e-01 -1.22747511e-01 -6.12849057e-01
-1.47029912e+00 4.06783193e-01 2.53872693e-01 -1.19148517e+00
-4.46382135e-01 6.48014069e-01 4.75854218e-01 -8.24439168e-01
3.49197328e-01 -3.33615750e-01 -3.15874755e-01 2.54182249e-01
7.89691627e-01 3.58112246e-01 5.26418626e-01 -4.73738611e-01
-6.03626072e-01 1.91618949e-01 -6.27068460e-01 1.97697729e-01
1.81428516e+00 3.74449119e-02 -3.00034612e-01 2.29775757e-01
1.04507649e+00 -1.58124998e-01 -6.72178686e-01 -6.82668090e-01
3.39953631e-01 -5.77875786e-02 1.81142166e-01 -1.03676069e+00
-1.02126980e+00 8.50523472e-01 4.64099199e-01 -3.52117717e-01
1.24986458e+00 -3.59798260e-02 6.98516130e-01 4.03662503e-01
5.93307503e-02 -8.06328118e-01 -1.78764611e-01 4.21419650e-01
4.18512553e-01 -1.31152451e+00 1.60314068e-01 -5.52923501e-01
-5.68986297e-01 1.02667928e+00 4.48199749e-01 3.38357747e-01
7.21076190e-01 5.10977209e-01 1.24316134e-01 -2.37415746e-01
-8.60310912e-01 -1.69880077e-01 4.81357604e-01 2.80708760e-01
1.09757316e+00 -5.15131615e-02 -4.10908520e-01 1.16731274e+00
3.60783696e-01 4.64498252e-01 1.11905456e-01 9.49425817e-01
4.18309234e-02 -1.36275709e+00 -6.63421229e-02 4.58071589e-01
-1.25173390e+00 -3.22712570e-01 1.30985186e-01 6.41251445e-01
-2.54752301e-03 8.70444298e-01 -3.13965768e-01 -7.56545067e-02
5.79032660e-01 3.20849419e-01 1.05959699e-01 -8.49899173e-01
-1.23319793e+00 3.84204447e-01 1.13535598e-01 -7.48611271e-01
-5.82232714e-01 -5.75470448e-01 -1.32237697e+00 2.23399237e-01
-3.83383870e-01 -1.01285920e-01 6.90970302e-01 1.12228692e+00
8.67345870e-01 1.12814045e+00 -6.62934687e-03 -5.80924153e-01
-7.68149376e-01 -1.29964185e+00 -3.69979799e-01 5.18331587e-01
1.65496364e-01 -5.67761660e-01 1.63713232e-01 -7.82216415e-02] | [8.669329643249512, 8.877745628356934] |
c229571f-d7de-4b38-bdf7-e5c66a7f5a7f | iris-segmentation-techniques-to-recognize-the | 2005.02450 | null | https://arxiv.org/abs/2005.02450v1 | https://arxiv.org/pdf/2005.02450v1.pdf | Iris segmentation techniques to recognize the behavior of a vigilant driver | In this paper, we clarify how to recognize different levels of vigilance for vehicle drivers. In order to avoid the classical problems of crisp logic, we preferred to employ a fuzzy logic-based system that depends on two variables to make the final decision. Two iris segmentation techniques are well illustrated. A new technique for pupil position detection is also provided here with the possibility to correct the pupil detected position when dealing with some noisy cases. | ['Abdullatif Baba'] | 2020-05-05 | null | null | null | null | ['iris-segmentation'] | ['medical'] | [-2.15846766e-02 1.30397841e-01 -8.02360401e-02 -6.64401114e-01
2.11739108e-01 -3.89735162e-01 2.38794789e-01 3.22337717e-01
-9.20498908e-01 9.41102803e-01 -4.65652108e-01 -8.22953999e-01
-6.86279356e-01 -7.02388167e-01 -1.23306364e-02 -6.88747168e-01
5.16801953e-01 3.74590814e-01 1.89153254e-01 -3.49626929e-01
6.93082631e-01 7.61809051e-01 -2.04953218e+00 -4.43269014e-02
1.24364090e+00 7.03393281e-01 -1.28083974e-01 6.94877863e-01
-2.17381537e-01 5.96274734e-01 -8.32022130e-01 -4.65572238e-01
2.16880232e-01 -3.89864951e-01 -5.84133089e-01 5.05434871e-01
3.06991488e-01 7.83944577e-02 3.95407885e-01 1.46435559e+00
9.47702006e-02 2.55343139e-01 8.99155557e-01 -9.58161354e-01
-2.34262317e-01 3.18713397e-01 -3.16718459e-01 6.44736469e-01
5.10534823e-01 3.98434341e-01 7.48794824e-02 -2.49554992e-01
2.62251705e-01 9.36304033e-01 1.10891297e-01 4.17463005e-01
-1.13445079e+00 -4.28524941e-01 -1.11250654e-02 6.68202519e-01
-1.52043664e+00 -8.33091289e-02 5.02607167e-01 -5.41134298e-01
6.55181944e-01 3.36850315e-01 8.26504409e-01 2.77579308e-01
2.20538706e-01 2.45792791e-02 1.87217128e+00 -6.60854995e-01
-1.70061924e-02 7.76280046e-01 7.72595525e-01 7.59957135e-01
8.64754140e-01 2.80180693e-01 1.34718403e-01 2.50172287e-01
5.15957654e-01 -1.42490998e-01 -1.57417774e-01 1.55306831e-01
-6.31355524e-01 7.11864889e-01 6.11610301e-02 7.89905667e-01
-2.32810184e-01 -4.74381268e-01 1.11064166e-01 2.40314126e-01
-1.12647101e-01 7.92021453e-01 1.38834506e-01 -1.04420081e-01
-7.85138011e-01 8.73629376e-02 7.87598193e-01 5.07576883e-01
5.26732147e-01 -1.52488658e-02 6.84014941e-03 1.01449832e-01
3.29139918e-01 4.70258772e-01 4.48409021e-01 -7.36155391e-01
-1.17956907e-01 1.02090502e+00 1.97441146e-01 -9.45520878e-01
-4.31819171e-01 -1.93083003e-01 -3.41463119e-01 1.12356305e+00
7.69061387e-01 -1.45896718e-01 -1.11462712e+00 9.04239714e-01
1.13271646e-01 -2.08360627e-02 2.93159217e-01 8.16817999e-01
7.94452071e-01 1.91167176e-01 -7.75476033e-03 -4.83069599e-01
1.33468509e+00 -4.14530009e-01 -1.32451713e+00 3.71673614e-01
2.58334577e-01 -8.36543560e-01 6.42706692e-01 1.22676992e+00
-1.06660724e+00 -7.15235412e-01 -9.51749861e-01 2.07550973e-01
-8.67827475e-01 1.34235054e-01 4.98887777e-01 1.21544325e+00
-9.45359170e-01 2.62565494e-01 -3.66231054e-01 -3.27729166e-01
-5.92670813e-02 6.81037426e-01 -1.58293456e-01 5.34176290e-01
-1.03369963e+00 1.51098597e+00 6.12189710e-01 5.58531582e-01
2.07313269e-01 7.95085430e-02 -6.74261451e-01 -2.77722776e-01
4.30068254e-01 -3.26198518e-01 9.74754095e-01 -8.36876690e-01
-1.45441914e+00 1.21416879e+00 -4.42727417e-01 -6.35228097e-01
5.75523376e-01 4.01690900e-01 -9.86298144e-01 3.81933898e-01
-3.19189817e-01 2.90910304e-01 9.58883286e-01 -1.01359737e+00
-9.87332225e-01 -2.50796407e-01 3.04637343e-01 -2.64873169e-02
3.70377034e-01 1.00008622e-01 -1.24638021e-01 -1.44902850e-02
-5.73160127e-02 -5.96809387e-01 -2.82544196e-01 -5.59652984e-01
-1.16659515e-01 -6.29719079e-01 2.70344317e-01 -1.71704978e-01
1.32906795e+00 -2.09160256e+00 -3.03202897e-01 7.82786846e-01
2.82895595e-01 6.90361619e-01 6.33078635e-01 -3.76689464e-01
-5.35196699e-02 1.37301669e-01 -3.54536846e-02 -2.88422350e-02
-1.11417584e-02 4.42220658e-01 5.17881922e-02 3.20725977e-01
2.72223949e-01 3.07561666e-01 -6.46359146e-01 -9.31142688e-01
7.15578675e-01 4.14483577e-01 -8.66868440e-03 -1.71275347e-01
2.62356699e-01 3.16486508e-01 -3.47229749e-01 6.82624161e-01
8.27051282e-01 3.00621748e-01 -1.37129113e-01 -6.34084567e-02
-6.61696434e-01 -7.20185265e-02 -1.58917975e+00 7.30864882e-01
-6.16400056e-02 5.26467919e-01 9.51278433e-02 -8.56091559e-01
1.23530567e+00 6.04966462e-01 1.41046017e-01 -6.41567647e-01
7.99579859e-01 4.77508992e-01 3.15861225e-01 -9.64395106e-01
5.06252766e-01 -3.12037975e-01 3.69387090e-01 4.16714437e-02
-2.48696551e-01 -3.02660167e-01 7.38647103e-01 -3.96978080e-01
6.51313066e-02 -1.49666637e-01 4.78595078e-01 -2.32952029e-01
1.03288567e+00 2.67239064e-01 4.14095163e-01 4.62512702e-01
-6.00875437e-01 7.34371021e-02 7.55288482e-01 -5.60037613e-01
-1.93175852e-01 -4.85360026e-01 -8.32386613e-01 1.73958521e-02
2.71099210e-01 2.63884991e-01 -8.65222454e-01 -4.46390688e-01
-6.76557124e-02 7.36205876e-01 -5.46951056e-01 1.43170446e-01
-3.53770584e-01 -7.76659548e-01 8.52703750e-02 -5.66704683e-02
3.09976816e-01 -9.47459161e-01 -1.11832821e+00 -2.00200845e-02
1.65826321e-01 -6.89791143e-01 2.28901386e-01 1.82141706e-01
-8.48812938e-01 -1.28908515e+00 -2.65105665e-01 -5.89863598e-01
8.91062677e-01 1.37381777e-01 7.07671642e-01 5.84945619e-01
-3.38035434e-01 1.46482825e-01 -2.94954449e-01 -7.69287646e-01
-6.28962219e-01 -2.04777122e-01 1.16150454e-01 2.16090083e-01
1.25174284e+00 2.71533281e-02 -2.34234363e-01 2.92766452e-01
-7.11978674e-01 -7.52834558e-01 5.24854839e-01 3.13943267e-01
5.88476360e-01 5.19552886e-01 3.57059449e-01 -8.61274660e-01
7.90817022e-01 1.24341123e-01 -1.05174851e+00 2.64056057e-01
-1.14896739e+00 -1.16548063e-02 3.14719409e-01 -2.86062837e-01
-9.68179524e-01 1.92149971e-02 -9.48853567e-02 -1.36269584e-01
-7.78804302e-01 1.33375153e-01 2.21330728e-02 -6.60154581e-01
8.96986365e-01 -2.00886875e-01 3.30285817e-01 -3.74560863e-01
-1.13420062e-01 8.70982885e-01 7.25998640e-01 -2.50044204e-02
5.88663399e-01 1.14498585e-01 4.47638363e-01 -6.91456616e-01
-3.06597888e-01 -4.77618068e-01 -8.26537848e-01 -4.50877756e-01
1.12676823e+00 -3.55898112e-01 -1.28762829e+00 2.32647970e-01
-9.84940171e-01 2.95945078e-01 -2.60906368e-01 9.11975563e-01
-3.55699837e-01 2.62137800e-01 -4.32030596e-02 -1.22536266e+00
1.06742367e-01 -1.53248346e+00 1.14945732e-01 8.30500007e-01
3.77496071e-05 -1.04147744e+00 -1.33925915e-01 3.00503135e-01
1.65037047e-02 2.74674416e-01 5.40058911e-01 -5.68783879e-01
-2.13350713e-01 -4.96916980e-01 7.15875924e-02 4.06155497e-01
1.07745796e-01 5.09160697e-01 -9.25152123e-01 2.00239420e-01
3.59529525e-01 2.72967100e-01 7.50740111e-01 5.21183848e-01
8.77165198e-01 1.87342703e-01 -3.01845908e-01 6.01456761e-01
1.56167853e+00 8.78007293e-01 7.39614367e-01 4.82732147e-01
1.28728360e-01 7.09034324e-01 1.19010746e+00 4.29570265e-02
4.39203456e-02 4.03165311e-01 4.06848937e-01 -4.40817684e-01
2.33341157e-01 2.84382135e-01 -2.93768108e-01 -1.49260491e-01
-6.59645200e-01 -2.45764237e-02 -7.11690426e-01 3.29809964e-01
-1.45883405e+00 -1.11070561e+00 -6.97871625e-01 2.34262490e+00
6.50234699e-01 7.13316798e-01 3.84415954e-01 7.24853575e-01
8.15518081e-01 -4.12272960e-01 1.24239624e-01 -1.18349290e+00
-2.21512869e-01 3.78942996e-01 6.12483919e-01 1.15611088e+00
-1.01622224e+00 5.61745703e-01 7.20555305e+00 3.77281219e-01
-1.30867755e+00 -1.90521568e-01 3.48957092e-01 -8.52951929e-02
-1.84054952e-02 -9.76190343e-02 -1.08821321e+00 6.24371231e-01
8.11977148e-01 -1.02745086e-01 9.91029292e-02 2.60292381e-01
4.45862174e-01 -9.43601489e-01 -3.94959241e-01 1.03695381e+00
1.38833195e-01 -8.96280766e-01 -2.13847235e-01 1.55188441e-01
4.02057350e-01 -9.23206568e-01 1.04982570e-01 -9.96175930e-02
-3.87350887e-01 -1.17208743e+00 2.25595579e-01 1.14735889e+00
4.93133008e-01 -1.09679818e+00 1.11663580e+00 2.54046638e-02
-5.99707484e-01 -9.38044786e-02 -4.05788422e-01 -3.81942779e-01
1.57628447e-01 3.96414697e-01 -1.09992087e+00 4.05998230e-01
3.79043847e-01 1.74076557e-01 -7.99913943e-01 1.50373173e+00
-4.05226707e-01 2.17236161e-01 -4.06292439e-01 -3.41977358e-01
2.47880876e-01 -6.31703794e-01 6.24193609e-01 1.07135928e+00
7.77664855e-02 2.36352786e-01 -3.81968081e-01 1.07578194e+00
8.04881811e-01 2.04506531e-01 -5.91150105e-01 2.77368039e-01
1.77412093e-01 1.30316675e+00 -9.35299158e-01 -4.06078905e-01
-3.56837094e-01 5.29141128e-01 -4.13678259e-01 1.27472654e-01
-5.71908474e-01 -7.50791907e-01 3.91958088e-01 3.46274674e-01
2.09025051e-02 1.67498642e-04 -5.45663834e-01 -6.54019952e-01
-2.02887520e-01 -5.64386070e-01 2.88982302e-01 -4.51446265e-01
-4.37532574e-01 7.46419609e-01 3.43524486e-01 -1.20019412e+00
-3.07069182e-01 -1.09654164e+00 -6.45297408e-01 1.40365934e+00
-1.59767103e+00 -5.83254755e-01 -1.62661057e-02 6.99564457e-01
-1.25511214e-02 -3.10766369e-01 4.65088248e-01 6.46055564e-02
-7.26459265e-01 4.45839196e-01 -5.08039951e-01 -6.29495755e-02
8.15945864e-01 -1.61175346e+00 -4.38135833e-01 1.18738508e+00
-3.05728436e-01 6.67957783e-01 1.12980783e+00 -4.53969240e-01
-5.26387155e-01 -2.27109149e-01 1.39427376e+00 -3.80868047e-01
3.52416784e-01 4.17475194e-01 -8.97959650e-01 3.25030565e-01
4.24085379e-01 -3.76017869e-01 5.33383489e-01 4.06158390e-04
2.98415661e-01 -3.78908008e-01 -1.40439916e+00 2.97765225e-01
2.23302945e-01 -2.58566529e-01 -1.20015287e+00 3.47627793e-03
1.01088576e-01 -4.15285707e-01 -8.97981286e-01 2.01903924e-01
1.99498862e-01 -1.39123583e+00 5.15477955e-01 -2.17519388e-01
-5.59675634e-01 -7.92388022e-01 6.64803565e-01 -9.60021257e-01
-1.36946604e-01 -7.65830994e-01 5.06163955e-01 7.12290347e-01
5.47750890e-01 -1.09252179e+00 3.53423715e-01 6.92085505e-01
2.41824351e-02 -4.62650388e-01 -8.41892481e-01 -3.18261057e-01
-4.37201738e-01 -1.46058083e-01 5.91899395e-01 6.66306794e-01
3.92483413e-01 -3.81038375e-02 1.25458866e-01 2.54824966e-01
5.16968191e-01 1.91940486e-01 1.92977846e-01 -1.66463184e+00
-6.28123954e-02 -9.09838259e-01 -1.01303506e+00 -1.58677220e-01
2.00964455e-02 -2.75237024e-01 -1.95874557e-01 -1.17820537e+00
-5.57017863e-01 -6.11592755e-02 -4.50366586e-01 2.48064131e-01
-1.54140994e-01 4.92288619e-01 -3.04387473e-02 -3.25830430e-01
-5.00652827e-02 -5.50345302e-01 1.25467050e+00 1.84850365e-01
-5.82694709e-01 6.95321023e-01 -9.03376758e-01 6.60283089e-01
9.05932367e-01 -2.11646557e-01 -5.38792551e-01 8.29735398e-02
3.91293615e-01 -3.50878239e-02 3.54374826e-01 -1.16791952e+00
3.51440132e-01 -3.63208562e-01 3.02107394e-01 -7.83521533e-01
1.27886251e-01 -1.15880311e+00 6.20888732e-02 5.87287009e-01
-3.81789617e-02 1.24902613e-01 1.85894340e-01 1.28100976e-01
-4.88042593e-01 -8.63007665e-01 1.00028849e+00 -5.24909347e-02
-7.80957341e-01 -3.34574938e-01 -8.48170757e-01 -6.75888181e-01
1.31877422e+00 -9.58146393e-01 -2.70760357e-01 -2.50741150e-02
-1.28200912e+00 2.79554904e-01 5.21982849e-01 -2.41357684e-02
4.51560259e-01 -6.49432361e-01 -3.16299170e-01 4.39763218e-01
2.42493272e-01 -3.87949795e-01 2.96908855e-01 1.44678295e+00
-9.07189131e-01 8.22103560e-01 -7.64617980e-01 -3.35256845e-01
-1.54705691e+00 1.00322771e+00 8.34473193e-01 2.39649877e-01
-1.79246500e-01 4.67928708e-01 -6.59353852e-01 3.91468525e-01
2.63288885e-01 -6.78773701e-01 -1.24000931e+00 2.94343919e-01
7.83284605e-01 6.58641398e-01 2.85492957e-01 -8.04142416e-01
-3.13991100e-01 8.86540890e-01 2.91738242e-01 -6.35349303e-02
4.68282670e-01 -2.89509386e-01 -3.12147796e-01 5.08888185e-01
3.91079515e-01 2.47715145e-01 -8.59989226e-01 1.31662279e-01
-1.07867427e-01 -6.27678335e-01 2.46433169e-01 -1.00800776e+00
-6.84934616e-01 9.20718849e-01 9.42486644e-01 6.58251643e-01
1.55139339e+00 -5.56136370e-01 -6.11657538e-02 4.63169575e-01
1.94872931e-01 -1.38672578e+00 -1.03353930e+00 4.27414551e-02
3.78136218e-01 -1.36885130e+00 9.42471176e-02 -5.04162729e-01
-7.72913456e-01 1.61939847e+00 4.82723266e-01 1.21631734e-02
8.04167986e-01 1.73151657e-01 4.00655687e-01 -1.53387710e-01
-4.02467847e-01 -9.96515453e-01 4.63850796e-01 7.90241838e-01
3.61594766e-01 2.85161942e-01 -1.26763773e+00 1.48996279e-01
-8.98865387e-02 2.98751861e-01 9.51513469e-01 6.74244940e-01
-8.81289423e-01 -1.31509411e+00 -9.73748803e-01 4.37655121e-01
-5.02865970e-01 3.30922306e-01 -5.00912488e-01 1.06740654e+00
8.67787838e-01 1.44327748e+00 2.55338609e-01 -2.96776533e-01
5.76739192e-01 1.88401237e-01 5.45084178e-01 -3.37249488e-01
-7.27585852e-01 2.71106064e-01 -3.06856576e-02 -3.17843169e-01
-8.99398446e-01 -6.31938696e-01 -1.29181755e+00 -3.21197778e-01
-2.24995643e-01 5.22296011e-01 8.16027224e-01 1.00297761e+00
-3.80191594e-01 3.75268817e-01 3.26195866e-01 -2.50379801e-01
-1.53784052e-01 -8.63524258e-01 -1.03754544e+00 1.09534703e-01
6.62835896e-01 -9.51564014e-01 -6.34887636e-01 -6.66129291e-02] | [13.396288871765137, 3.0665299892425537] |
4a61257b-89f7-41e1-b009-1e7f926dc454 | correspondence-insertion-for-as-projective-as | 1608.07997 | null | http://arxiv.org/abs/1608.07997v1 | http://arxiv.org/pdf/1608.07997v1.pdf | Correspondence Insertion for As-Projective-As-Possible Image Stitching | Spatially varying warps are increasingly popular for image alignment. In
particular, as-projective-as-possible (APAP) warps have been proven effective
for accurate panoramic stitching, especially in cases with significant depth
parallax that defeat standard homographic warps. However, estimating spatially
varying warps requires a sufficient number of feature matches. In image regions
where feature detection or matching fail, the warp loses guidance and is unable
to accurately model the true underlying warp, thus resulting in poor
registration. In this paper, we propose a correspondence insertion method for
APAP warps, with a focus on panoramic stitching. Our method automatically
identifies misaligned regions, and inserts appropriate point correspondences to
increase the flexibility of the warp and improve alignment. Unlike other warp
varieties, the underlying projective regularization of APAP warps reduces
overfitting and geometric distortion, despite increases to the warp complexity.
Comparisons with recent techniques for parallax-tolerant image stitching
demonstrate the effectiveness and simplicity of our approach. | ['Tat-Jun Chin', 'William X. Liu'] | 2016-08-29 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 5.38040400e-01 -3.14041972e-01 -9.15662721e-02 2.04982594e-01
-7.17297077e-01 -8.71690035e-01 6.58847153e-01 -2.88716912e-01
-5.83320521e-02 3.12339604e-01 3.19680870e-01 8.88768584e-02
-1.62337616e-01 -3.76691222e-01 -5.73456287e-01 -9.23638701e-01
1.91000253e-02 4.26451266e-01 4.90107000e-01 -4.25569475e-01
6.19863749e-01 6.92811787e-01 -9.67994928e-01 -2.57996351e-01
8.34788740e-01 5.21753848e-01 7.32776821e-02 6.66819274e-01
3.32091004e-01 8.44275579e-02 -4.30488974e-01 -3.75289261e-01
8.22646141e-01 -2.19016746e-01 -5.11331975e-01 2.45121524e-01
1.23130691e+00 -5.66464067e-01 -6.40951931e-01 9.59203899e-01
3.65715772e-01 1.22563154e-01 3.57692152e-01 -1.33100510e+00
7.32777268e-02 9.32319909e-02 -1.14464784e+00 1.66450307e-04
4.14004982e-01 1.53891012e-01 7.43087530e-01 -8.56163561e-01
8.56006444e-01 9.82000768e-01 1.15924156e+00 1.35404542e-01
-1.36199522e+00 -9.21618521e-01 -5.26237369e-01 -6.67254478e-02
-1.42985880e+00 -5.54592490e-01 1.09719515e+00 -3.33466768e-01
6.18898690e-01 5.27495742e-01 6.95538878e-01 7.28907645e-01
8.54489625e-01 1.74928546e-01 7.38929689e-01 -4.66344506e-01
-2.49623686e-01 -6.04814827e-01 -2.87091881e-01 4.33773100e-01
1.02133140e-01 3.75194281e-01 -5.60985029e-01 -5.00533283e-01
1.43302059e+00 2.19981432e-01 -5.55589080e-01 -8.94610167e-01
-1.71855855e+00 6.10440671e-01 4.87123042e-01 3.39415580e-01
-2.11809054e-01 3.29061955e-01 1.09276377e-01 2.39309385e-01
5.27257442e-01 9.13497150e-01 1.79591432e-01 -1.88403562e-01
-1.47546101e+00 2.57269204e-01 2.53649205e-01 9.09091771e-01
6.06104910e-01 1.87876061e-01 4.15301591e-01 8.65616202e-01
-2.24778056e-01 6.72597826e-01 3.06988627e-01 -1.09846294e+00
5.83023369e-01 1.32875040e-01 5.45669682e-02 -1.61132586e+00
-2.26928815e-01 -4.40023392e-02 -8.29066157e-01 4.31267977e-01
3.90398353e-01 1.74844325e-01 -8.00007284e-01 1.22767341e+00
3.58595312e-01 4.29873258e-01 -5.46115376e-02 9.17303085e-01
7.77038783e-02 6.78639591e-01 -7.66093791e-01 -1.47082672e-01
1.19786954e+00 -8.35455835e-01 -6.48096502e-01 -4.35030967e-01
2.59892195e-01 -1.56145155e+00 6.92249417e-01 1.81441024e-01
-1.02534437e+00 -1.10719800e-01 -1.25010455e+00 3.01412977e-02
3.70915651e-01 -5.96911848e-01 2.04895124e-01 4.12720948e-01
-1.01433873e+00 7.63874829e-01 -7.79495835e-01 -2.91233480e-01
7.28317574e-02 3.59801352e-01 -6.48073494e-01 -2.49474451e-01
-7.62196481e-01 1.02873981e+00 3.72695565e-01 -1.22909956e-01
-3.77034217e-01 -8.17517161e-01 -8.95791650e-01 -2.41372749e-01
3.21698785e-01 -4.21410501e-01 7.51563609e-01 -9.90647733e-01
-1.32541275e+00 6.57846451e-01 7.06767710e-03 -4.33753133e-01
7.57705629e-01 -2.10020617e-01 -8.45971107e-02 2.44902730e-01
-1.46156937e-01 7.81121612e-01 1.53364074e+00 -1.39296937e+00
-7.90928528e-02 -1.45272210e-01 -5.32668233e-01 4.39672381e-01
-1.14586994e-01 4.82387356e-02 -4.96837884e-01 -1.09545350e+00
7.17180371e-01 -1.18992531e+00 -4.06095982e-01 1.51662767e-01
-3.87309402e-01 7.36828566e-01 1.31355882e+00 -9.72116947e-01
1.10076320e+00 -2.27957058e+00 2.58612931e-01 5.22221923e-01
3.85574043e-01 6.70923740e-02 -3.11360747e-01 6.48109257e-01
-1.73998713e-01 -4.36451584e-01 -5.55576324e-01 -2.27471888e-01
-6.01582110e-01 2.88497388e-01 -6.69881165e-01 9.30347443e-01
-8.48612562e-02 6.38166666e-01 -7.53733575e-01 -4.59513962e-01
4.62812960e-01 5.84169924e-01 -4.60186452e-01 -7.40217045e-02
2.76126176e-01 4.77964878e-01 8.59568827e-03 6.24350846e-01
1.00091565e+00 3.25702429e-01 -1.76235005e-01 -6.20942891e-01
-4.55971628e-01 -1.82498276e-01 -1.03903449e+00 1.73052263e+00
-2.85118759e-01 1.11945653e+00 -2.74724197e-02 -4.12071764e-01
1.16558886e+00 2.39416987e-01 9.00647521e-01 -3.82074893e-01
1.53249517e-01 5.86161613e-01 -5.60190305e-02 -1.53061867e-01
1.05458713e+00 -4.26039733e-02 1.68752410e-02 2.11547419e-01
-2.36685172e-01 -1.01694453e+00 -3.35495412e-01 -6.29623309e-02
8.85003626e-01 8.88678879e-02 5.21624088e-01 -3.62237990e-01
2.04910338e-01 2.67756552e-01 5.61004162e-01 3.15824926e-01
-3.82718667e-02 1.41981208e+00 1.16488993e-01 -4.92762148e-01
-1.83008182e+00 -8.83943379e-01 -2.67847240e-01 1.14166476e-01
7.33712196e-01 -2.22585097e-01 -8.20268393e-01 -1.31471172e-01
-1.86207637e-01 4.48050737e-01 -3.00687581e-01 3.10531585e-03
-1.20004356e+00 -4.06083584e-01 5.18129110e-01 2.20992118e-01
5.07803619e-01 -4.47107852e-01 -6.74635768e-01 2.48078033e-01
-3.12739760e-01 -1.15451038e+00 -1.19417524e+00 -5.00377715e-01
-1.21766484e+00 -1.20126569e+00 -1.11001813e+00 -7.57548451e-01
9.07946944e-01 9.07706439e-01 6.18970633e-01 -2.86989938e-03
-2.56048650e-01 2.60701895e-01 -3.46525103e-01 2.43754745e-01
-4.82255101e-01 -2.53055751e-01 1.07567117e-01 -4.11342755e-02
-2.67495930e-01 -7.66969085e-01 -7.46214271e-01 1.01349783e+00
-1.08006763e+00 2.66007900e-01 1.71256021e-01 1.05684423e+00
3.12041849e-01 -6.26664534e-02 -3.79501313e-01 -1.91604555e-01
2.47365177e-01 1.72855437e-01 -6.66825294e-01 -4.50144485e-02
-3.35513979e-01 -3.79532278e-02 2.53611416e-01 -8.07143807e-01
-7.21854627e-01 6.46855384e-02 2.06544101e-01 -9.01768506e-01
5.19088030e-01 8.44412968e-02 3.01426589e-01 -9.20918405e-01
7.69223213e-01 2.85850435e-01 5.77421069e-01 -1.61392048e-01
1.05304219e-01 1.53295368e-01 9.32901680e-01 -3.16611737e-01
1.45038784e+00 6.98500454e-01 1.81450576e-01 -1.08432245e+00
-9.99330878e-02 -4.59050566e-01 -7.95255721e-01 -2.68107951e-01
6.69677377e-01 -5.21670043e-01 -2.63503969e-01 7.09910631e-01
-1.22205436e+00 -3.49533260e-02 -2.54436880e-01 5.56343853e-01
-6.78975880e-01 9.44755435e-01 -4.12851840e-01 -2.36504778e-01
-4.14772779e-01 -1.44438064e+00 9.60238457e-01 -1.53833153e-02
-4.63694155e-01 -9.45644915e-01 4.14332926e-01 2.65600115e-01
4.90773350e-01 6.43537045e-01 4.49459821e-01 -8.95966068e-02
-6.44351125e-01 -4.51376379e-01 9.79137942e-02 -1.13701085e-02
3.11320841e-01 3.32730711e-01 -5.25116086e-01 -6.98707283e-01
1.09360062e-01 2.02782929e-01 2.68882394e-01 3.78880292e-01
5.55080235e-01 -5.22217929e-01 -4.20621485e-01 8.99921238e-01
1.29654562e+00 2.93128461e-01 9.93411303e-01 5.90727031e-01
7.86651611e-01 6.11366868e-01 8.21160853e-01 1.53357401e-01
-2.66451806e-01 1.40208817e+00 5.70021987e-01 -2.68457741e-01
-6.87530786e-02 -2.96177566e-01 4.81207550e-01 7.43280530e-01
-2.15874925e-01 -1.53964609e-02 -1.01163590e+00 4.74749774e-01
-1.66480279e+00 -8.16689670e-01 -6.43603578e-02 2.58421874e+00
7.84421444e-01 -3.07599694e-01 -1.84689164e-01 1.88277364e-01
8.59911203e-01 7.44232595e-01 -3.17447633e-01 -1.77324623e-01
-2.74848133e-01 -8.86640884e-03 1.10007823e+00 9.57470536e-01
-8.38781416e-01 1.00107729e+00 6.02961445e+00 9.84344542e-01
-1.22310197e+00 -7.32035935e-02 6.14658669e-02 2.26920079e-02
-3.44015568e-01 4.15432662e-01 -3.75240475e-01 3.32692742e-01
1.87438145e-01 9.40688420e-03 3.85576189e-01 4.29886103e-01
-6.82542920e-02 -8.92842636e-02 -7.77871013e-01 1.28450644e+00
4.50457424e-01 -1.47735393e+00 -1.06678374e-01 2.01008573e-01
8.15992594e-01 -2.79044695e-02 6.68312684e-02 -6.86619282e-01
-4.22304347e-02 -7.18497276e-01 6.42663538e-01 5.46859689e-02
9.38095093e-01 -7.57757485e-01 4.67791349e-01 -3.03817797e-03
-1.12048972e+00 2.19295025e-01 -2.89054096e-01 4.61819857e-01
5.70476174e-01 3.93447220e-01 -8.03071916e-01 5.12176514e-01
2.95902461e-01 5.86754501e-01 -1.57057017e-01 1.12320888e+00
6.97355270e-02 5.86271919e-02 -5.85475206e-01 6.27169907e-01
3.26486647e-01 -5.53737164e-01 1.24829936e+00 7.43749321e-01
8.80146027e-01 2.21174002e-01 1.84988342e-02 3.20025474e-01
3.94412696e-01 -8.05364996e-02 -8.22194040e-01 2.29297936e-01
6.77741230e-01 1.01464081e+00 -8.02989423e-01 7.00488890e-05
-5.66716157e-02 1.21058667e+00 -4.70527768e-01 1.47916064e-01
-6.32776797e-01 -2.29502842e-01 9.42915797e-01 4.89720076e-01
-2.31181309e-02 -8.05654764e-01 -3.82981479e-01 -1.22025120e+00
-6.23242110e-02 -1.12490928e+00 1.01560183e-01 -7.40339816e-01
-7.93246984e-01 5.73558509e-01 1.40044704e-01 -2.03944826e+00
-2.50810385e-01 -2.66957432e-01 -6.81787848e-01 5.42429984e-01
-9.74369824e-01 -1.35260367e+00 -4.14588213e-01 5.60817003e-01
7.62781084e-01 1.67255953e-01 4.47153449e-01 1.39157418e-02
-1.66206732e-01 5.46406150e-01 2.08416432e-01 -7.71515965e-02
1.19094551e+00 -4.90821600e-01 6.51870668e-01 1.18075502e+00
1.27367765e-01 5.93771756e-01 1.25889575e+00 -7.74714053e-01
-1.44329560e+00 -5.38574815e-01 5.03405154e-01 -3.39916289e-01
7.06405580e-01 -1.23445518e-01 -1.07509398e+00 7.16114223e-01
2.66913235e-01 -2.12915316e-01 1.06772102e-01 -5.01148403e-01
-5.87843835e-01 -3.06548160e-02 -1.29907310e+00 9.32098806e-01
7.80658126e-01 -2.31491148e-01 -6.01621628e-01 1.77417144e-01
5.07869422e-01 -9.15221870e-01 -8.20842743e-01 2.92836636e-01
6.88604057e-01 -8.29559445e-01 1.46562076e+00 3.48309159e-01
5.08021593e-01 -5.10628343e-01 8.25002044e-02 -1.36016679e+00
-3.15339327e-01 -1.31730592e+00 2.55308747e-01 9.83749688e-01
-1.16825826e-01 -8.55563998e-01 7.29265094e-01 4.35262680e-01
-1.68104470e-01 -1.30151153e-01 -1.26607132e+00 -8.60004067e-01
-1.50057867e-01 6.99709877e-02 3.77392143e-01 1.45829809e+00
1.60111729e-02 -3.00668627e-01 -8.16643655e-01 3.07678610e-01
8.93048048e-01 -3.78858484e-02 1.03771532e+00 -7.81048357e-01
-1.92849964e-01 -3.68928939e-01 -7.73454964e-01 -9.46087360e-01
-2.26433262e-01 -4.23257530e-01 1.52002320e-01 -7.15808392e-01
-1.50264680e-01 -6.55288339e-01 5.15017867e-01 3.32166612e-01
-5.77078089e-02 7.35384226e-01 3.23635697e-01 8.48410428e-01
5.55992663e-01 2.89049447e-01 1.32364798e+00 6.54678792e-02
-3.49883974e-01 -2.50610650e-01 1.23022914e-01 6.22669637e-01
6.41437888e-01 -4.60745543e-01 -1.78346559e-01 -5.38619578e-01
5.04202507e-02 3.65165740e-01 4.03623939e-01 -9.45043862e-01
3.10294598e-01 -4.14490342e-01 6.29697293e-02 -5.56250334e-01
7.00598717e-01 -1.14547992e+00 8.41448545e-01 6.34331644e-01
5.40478453e-02 4.21508014e-01 1.28811494e-01 1.82083055e-01
-4.65559125e-01 -3.16621006e-01 1.02674782e+00 3.83592784e-01
-4.09030825e-01 4.22705859e-01 3.69072333e-02 -4.14978266e-01
1.14873505e+00 -8.49035621e-01 -3.37324291e-01 -4.90191609e-01
8.35463032e-02 -1.04105249e-01 1.32017028e+00 3.50520790e-01
8.31598759e-01 -1.35751057e+00 -6.07694030e-01 4.42915022e-01
9.57666114e-02 1.38844565e-01 2.58483380e-01 9.74129736e-01
-1.28609300e+00 -1.08701147e-01 -4.49824840e-01 -8.14634919e-01
-1.84930694e+00 4.03777242e-01 2.52171844e-01 6.64522648e-02
-1.13137722e+00 5.55569887e-01 1.90479577e-01 -7.24641234e-02
-2.62372822e-01 1.91914123e-02 2.03498155e-01 -9.45049673e-02
5.34090281e-01 4.43620265e-01 -1.02229640e-01 -9.28250968e-01
-1.59838244e-01 1.38544595e+00 -2.05728024e-01 -4.30222690e-01
1.25580144e+00 -9.65004340e-02 -2.24916562e-01 -1.58456251e-01
1.17365265e+00 4.16518599e-01 -1.87657428e+00 -2.63001531e-01
-3.50345165e-01 -1.26869965e+00 9.04830769e-02 4.10198309e-02
-1.09539974e+00 5.44405460e-01 3.14599395e-01 -1.29703715e-01
1.08807945e+00 -3.96261722e-01 1.19014156e+00 -1.32031053e-01
4.76491332e-01 -6.53296947e-01 6.47418797e-02 4.13689047e-01
1.19408453e+00 -8.84646773e-01 4.15808171e-01 -7.18217134e-01
-5.74111164e-01 1.54332566e+00 2.03921020e-01 -5.54719448e-01
2.44402066e-02 5.25468946e-01 9.42634940e-02 -8.02497119e-02
9.15811211e-02 6.91242635e-01 3.66419613e-01 6.05677187e-01
-1.36286706e-01 -2.72477210e-01 -2.81495780e-01 -7.30906427e-01
-5.53080499e-01 -4.74242330e-01 7.43580639e-01 1.09625399e+00
-3.50138694e-01 -1.23214698e+00 -1.15758228e+00 -2.15838909e-01
-2.94116940e-02 -1.87415823e-01 -2.62438864e-01 8.88430178e-01
-5.65724134e-01 5.12409806e-01 4.26422536e-01 -5.17778397e-01
2.65799135e-01 -3.45409125e-01 7.04715431e-01 3.55824530e-02
-6.15753055e-01 3.80189896e-01 1.52042573e-02 -7.60123789e-01
-3.75750899e-01 -5.92968524e-01 -8.09885204e-01 -6.77579701e-01
-4.16430563e-01 -1.45257071e-01 6.73390985e-01 5.92622161e-01
1.92908853e-01 -2.47815952e-01 9.35898781e-01 -1.34109509e+00
-3.28388959e-01 -4.82455701e-01 -2.22760081e-01 3.39272588e-01
6.15959346e-01 -6.31995559e-01 -5.31673074e-01 1.19394839e-01] | [9.405171394348145, -2.365860939025879] |
b85c5bdf-4284-4918-8a23-e412c7c13863 | data-generation-using-pass-phrase-dependent | 2102.02074 | null | https://arxiv.org/abs/2102.02074v1 | https://arxiv.org/pdf/2102.02074v1.pdf | Data Generation Using Pass-phrase-dependent Deep Auto-encoders for Text-Dependent Speaker Verification | In this paper, we propose a novel method that trains pass-phrase specific deep neural network (PP-DNN) based auto-encoders for creating augmented data for text-dependent speaker verification (TD-SV). Each PP-DNN auto-encoder is trained using the utterances of a particular pass-phrase available in the target enrollment set with two methods: (i) transfer learning and (ii) training from scratch. Next, feature vectors of a given utterance are fed to the PP-DNNs and the output from each PP-DNN at frame-level is considered one new set of generated data. The generated data from each PP-DNN is then used for building a TD-SV system in contrast to the conventional method that considers only the evaluation data available. The proposed approach can be considered as the transformation of data to the pass-phrase specific space using a non-linear transformation learned by each PP-DNN. The method develops several TD-SV systems with the number equal to the number of PP-DNNs separately trained for each pass-phrases for the evaluation. Finally, the scores of the different TD-SV systems are fused for decision making. Experiments are conducted on the RedDots challenge 2016 database for TD-SV using short utterances. Results show that the proposed method improves the performance for both conventional cepstral feature and deep bottleneck feature using both Gaussian mixture model - universal background model (GMM-UBM) and i-vector framework. | ['Zheng-Hua Tan', 'Md Sahidullah', 'Achintya Kumar Sarkar'] | 2021-02-03 | null | null | null | null | ['text-dependent-speaker-verification'] | ['speech'] | [ 7.37451464e-02 -1.26706421e-01 1.67568877e-01 -6.41379237e-01
-1.22642124e+00 -3.83997679e-01 6.17810905e-01 -2.03062609e-01
-4.24737424e-01 5.36922336e-01 5.33387005e-01 -4.51684207e-01
5.29299796e-01 -4.99408007e-01 -5.76030672e-01 -9.07211840e-01
2.81581938e-01 4.29456264e-01 2.43981052e-02 -1.85897782e-01
-2.35215053e-01 3.46639484e-01 -1.36917663e+00 3.16433221e-01
4.53726768e-01 1.01797140e+00 2.63844550e-01 1.19443846e+00
3.82106751e-02 5.34012198e-01 -9.74260330e-01 -4.07098979e-01
2.59588450e-01 -5.88932395e-01 -4.33168441e-01 3.51244137e-02
4.16172296e-01 -5.31768322e-01 -5.23907721e-01 9.45832431e-01
9.21806395e-01 5.80437243e-01 6.36974216e-01 -9.76252913e-01
-7.07697630e-01 6.64501429e-01 -1.81147888e-01 4.38332438e-01
2.37489969e-01 1.10338628e-01 8.03987563e-01 -1.37399781e+00
3.03084970e-01 1.49591756e+00 5.56482434e-01 8.67990017e-01
-9.57893372e-01 -7.33260572e-01 -2.53959417e-01 4.12343562e-01
-1.30116308e+00 -7.13851690e-01 7.83492029e-01 -2.92244941e-01
1.21962392e+00 1.14494361e-01 3.56531858e-01 1.27482009e+00
-6.38519078e-02 9.53986406e-01 7.21479356e-01 -7.03800559e-01
2.75246114e-01 3.53844613e-01 4.75442231e-01 3.77291411e-01
-4.72747892e-01 3.63706142e-01 -7.72231996e-01 -1.09885829e-02
3.27384681e-01 -4.36785638e-01 -2.75418550e-01 2.53411204e-01
-8.96320760e-01 9.91523683e-01 -1.32342577e-01 4.66443539e-01
-4.94015843e-01 -3.23250562e-01 6.39051020e-01 2.97200680e-01
3.84969354e-01 -3.01486641e-01 -6.23968005e-01 -2.12945431e-01
-1.16614330e+00 2.77116239e-01 8.08701336e-01 7.56505847e-01
4.86513823e-01 7.38501787e-01 -6.04580760e-01 1.02058911e+00
7.09023654e-01 7.12387025e-01 9.11768675e-01 -4.92502272e-01
6.74191415e-01 5.05093597e-02 -1.41503736e-01 -5.32328129e-01
6.86243474e-02 -6.15700305e-01 -7.32293069e-01 1.08661510e-01
1.29870102e-01 -5.25953293e-01 -1.10462379e+00 1.85898042e+00
4.23800796e-01 4.57325161e-01 6.44646406e-01 4.67466325e-01
1.13303006e+00 1.25491703e+00 4.08007316e-02 -2.64584482e-01
1.40695393e+00 -1.16162145e+00 -1.06667638e+00 1.26860082e-01
4.14450616e-01 -8.87146413e-01 6.99796975e-01 3.99362087e-01
-9.42611754e-01 -1.04143965e+00 -1.21790838e+00 9.02465284e-02
-3.54144871e-01 4.76917893e-01 -4.19184655e-01 9.91674721e-01
-1.38061988e+00 8.41157660e-02 -5.67572951e-01 -1.74341947e-01
1.50957614e-01 5.15445113e-01 -2.65423417e-01 4.12106849e-02
-1.39608657e+00 9.23160434e-01 5.99391043e-01 5.00493757e-02
-1.46180701e+00 -6.36680186e-01 -1.01932001e+00 2.06679955e-01
-3.33203524e-01 -4.54415917e-01 1.53777361e+00 -7.87684917e-01
-1.97388756e+00 4.85944808e-01 -4.63600069e-01 -8.11496317e-01
1.47116214e-01 -8.58256780e-03 -7.44332135e-01 2.62699667e-02
-2.05700979e-01 5.59267819e-01 1.26897860e+00 -1.05670285e+00
-9.04313624e-01 -3.64773035e-01 -2.92221636e-01 4.62585509e-01
-3.63374054e-01 4.21004951e-01 -1.73516169e-01 -5.54937840e-01
-1.83684453e-01 -7.66087294e-01 1.78769439e-01 -7.84130335e-01
-5.59889138e-01 -6.41538918e-01 1.33513331e+00 -1.28293037e+00
1.20036638e+00 -2.48480916e+00 -1.78613085e-02 -4.48069312e-02
-2.55088270e-01 8.98652673e-01 -3.14405322e-01 1.76170096e-01
-2.74168670e-01 -3.26048881e-01 -1.09020986e-01 -1.01086485e+00
9.21577215e-02 1.37765810e-01 -2.56288320e-01 2.72973061e-01
2.58942664e-01 4.85859990e-01 -5.49979925e-01 -4.90967810e-01
4.50596184e-01 9.23223078e-01 -3.46929073e-01 4.88067210e-01
8.82002041e-02 3.95927191e-01 1.35855516e-02 1.67855859e-01
9.76471782e-01 6.65148914e-01 -2.34952003e-01 -1.72899097e-01
-1.24379136e-02 5.45002759e-01 -1.19289947e+00 1.43052530e+00
-5.08105516e-01 7.71060824e-01 -3.30907181e-02 -9.51657832e-01
1.09259617e+00 9.53397691e-01 -1.06142997e-03 -2.40779206e-01
1.44415438e-01 2.02009663e-01 1.26123250e-01 -3.66935372e-01
4.93154377e-01 -3.62119704e-01 1.38922393e-01 3.11987311e-01
1.02832341e+00 8.58064666e-02 1.43553345e-02 -1.19696511e-02
7.76109457e-01 -9.05102417e-02 1.07398733e-01 1.27801418e-01
1.11596656e+00 -4.29313540e-01 6.43533349e-01 5.08178174e-01
-4.10721004e-01 4.91630644e-01 -1.69607386e-01 -9.37522873e-02
-1.13161516e+00 -1.11378467e+00 -1.86603814e-01 1.18901634e+00
-5.97064674e-01 -9.93771106e-02 -1.09395671e+00 -7.39479601e-01
-2.94851333e-01 1.16824150e+00 -4.11131382e-01 -1.27162755e-01
-4.62200671e-01 -4.31422651e-01 9.22856271e-01 4.67225760e-01
5.61294973e-01 -1.18541479e+00 2.62479577e-02 4.37963188e-01
-2.64424652e-01 -1.23313963e+00 -6.84089601e-01 2.15554029e-01
-6.07993126e-01 -3.61346692e-01 -9.84334767e-01 -1.16425204e+00
4.86141711e-01 5.86531013e-02 5.35405517e-01 -5.87156355e-01
2.77883053e-01 2.22017646e-01 -3.96705270e-01 -5.93732238e-01
-1.07893920e+00 -1.89963222e-01 4.11417305e-01 5.80368042e-01
8.96662712e-01 -1.50374308e-01 -2.40873873e-01 3.03300079e-02
-5.98562360e-01 -3.63635451e-01 3.94351274e-01 1.02468824e+00
3.73681605e-01 1.87065527e-01 6.71413720e-01 -2.66508758e-01
7.93628931e-01 -2.85310000e-01 -4.79664415e-01 1.03112310e-02
-1.18038304e-01 2.79352423e-02 4.50731248e-01 -5.79574406e-01
-1.53270221e+00 1.22407794e-01 -5.97621262e-01 -6.17653608e-01
-4.37802792e-01 4.97132689e-01 -3.98447633e-01 3.51602554e-01
5.85934401e-01 7.37311244e-01 -1.41003495e-02 -4.73655164e-01
2.69928187e-01 1.25558543e+00 7.35614061e-01 -1.73774406e-01
8.88490200e-01 -2.42233127e-01 -6.23999596e-01 -1.02582526e+00
-4.76994634e-01 -5.66714108e-01 -6.44622982e-01 -2.28232190e-01
1.19560814e+00 -1.05076146e+00 -4.06676114e-01 7.75202513e-01
-1.52460527e+00 4.98728044e-02 -3.29471856e-01 7.97801137e-01
-2.41898581e-01 3.82316083e-01 -6.30168319e-01 -1.23972654e+00
-7.01865613e-01 -1.44505143e+00 9.85600114e-01 4.00992811e-01
-5.19077433e-03 -9.75309551e-01 4.81594831e-01 5.21968484e-01
4.97018099e-01 -4.63633865e-01 6.42905474e-01 -1.45798373e+00
5.22049628e-02 -3.99592370e-01 1.92203209e-01 1.34371698e+00
4.55804579e-02 -8.97645056e-02 -1.55404818e+00 -3.33456069e-01
5.49730003e-01 -1.13195643e-01 5.24044275e-01 6.96235180e-01
6.77506447e-01 -4.73818004e-01 2.91974824e-02 3.34387213e-01
1.18943906e+00 5.85648596e-01 4.60723668e-01 2.84770206e-02
3.77286881e-01 3.50232005e-01 3.74134481e-01 1.88246891e-01
4.11213934e-01 7.73804486e-01 -9.86912325e-02 -3.69747877e-02
-4.29431200e-01 -1.95400104e-01 9.27267730e-01 1.40225339e+00
7.43860900e-02 -3.82421911e-01 -7.38004088e-01 6.45575643e-01
-1.45085359e+00 -1.11127830e+00 1.69924833e-03 2.33199978e+00
7.25460112e-01 2.93522459e-02 1.44558683e-01 3.63596976e-01
1.00782514e+00 -3.01861931e-02 -2.72556275e-01 -7.46486664e-01
-2.20694810e-01 4.10309255e-01 -4.33665104e-02 7.57130623e-01
-1.14318275e+00 8.10873747e-01 5.16941214e+00 9.23046827e-01
-1.06207526e+00 5.94333053e-01 5.39643586e-01 1.41945273e-01
1.79197714e-01 -3.44551116e-01 -1.31864023e+00 4.66085881e-01
1.64358711e+00 -1.52214020e-01 1.46624207e-01 9.63130295e-01
3.65051538e-01 1.71982303e-01 -1.11929917e+00 1.09305537e+00
4.50544178e-01 -9.13580954e-01 6.00138307e-02 6.31956011e-02
6.12746358e-01 1.90733790e-01 3.84026825e-01 7.35841393e-01
3.54488015e-01 -6.23841941e-01 6.85023904e-01 1.07643634e-01
6.82376444e-01 -8.93469155e-01 1.11338592e+00 3.01897973e-01
-1.09674072e+00 -4.87021916e-02 -3.95067573e-01 5.05913138e-01
2.18438491e-01 2.57476926e-01 -1.45998240e+00 5.67446411e-01
3.96508694e-01 1.84943169e-01 -1.19462334e-01 8.33557129e-01
-8.80450010e-02 1.08935547e+00 -2.94371992e-01 6.88460767e-02
2.67143965e-01 8.88950229e-02 8.44670653e-01 1.58024931e+00
5.41591644e-01 -1.86154649e-01 -2.71063745e-01 6.84737086e-01
-6.44593686e-02 9.38176662e-02 -5.60151756e-01 6.78791255e-02
5.81771433e-01 1.10511839e+00 9.13246721e-03 -6.90849543e-01
-4.33742255e-01 8.60303164e-01 -3.42748538e-02 5.44761419e-01
-7.30892241e-01 -3.01382452e-01 5.01557410e-01 -3.78459275e-01
6.75285518e-01 -5.62736727e-02 2.08651513e-01 -1.02415335e+00
-1.58554748e-01 -1.04773033e+00 2.48032629e-01 -6.00011945e-01
-1.13809979e+00 1.17978287e+00 -1.62506774e-01 -1.32449067e+00
-6.26202822e-01 -5.08896828e-01 -6.77690625e-01 1.45743072e+00
-1.48804843e+00 -1.24309993e+00 8.92681554e-02 8.79904628e-01
1.10777891e+00 -8.50842535e-01 1.20312965e+00 3.77880722e-01
-6.96285009e-01 9.82613742e-01 2.15235397e-01 4.70698535e-01
6.05672061e-01 -1.18671465e+00 4.21866894e-01 1.27069974e+00
2.48726219e-01 3.75524849e-01 6.52684450e-01 -3.78908098e-01
-1.05290413e+00 -1.17803490e+00 1.27138937e+00 -2.94989318e-01
1.21806934e-01 -5.27458191e-01 -8.46570373e-01 6.31069303e-01
5.21648407e-01 -1.03032388e-01 1.01586223e+00 -2.31086239e-01
-2.05588385e-01 -1.76677063e-01 -1.32112467e+00 1.35481730e-01
1.18948922e-01 -8.46025586e-01 -1.05237389e+00 1.03575476e-01
9.06006157e-01 -4.66300696e-01 -7.54653394e-01 6.17511310e-02
2.81233400e-01 -6.71203077e-01 8.86432648e-01 -4.78712648e-01
7.54549876e-02 -3.50994617e-01 -6.46045089e-01 -1.53160930e+00
-1.76915094e-01 -5.39844573e-01 -2.18574882e-01 1.69809568e+00
6.22025907e-01 -4.04000640e-01 4.96803313e-01 4.64028656e-01
-5.22086620e-01 -2.26883858e-01 -1.31651199e+00 -6.61214650e-01
-1.56918779e-01 -7.64366508e-01 7.35077262e-01 8.04293573e-01
-1.65699050e-01 5.78125656e-01 -2.54230529e-01 4.42349583e-01
5.92867553e-01 -7.22944677e-01 6.77020729e-01 -8.76137197e-01
-3.75714958e-01 -7.27808177e-02 -5.75316846e-01 -9.70934391e-01
2.12002993e-01 -8.56158197e-01 2.57277936e-01 -1.13256633e+00
7.37626329e-02 8.46417248e-03 -4.37836856e-01 3.35108161e-01
-2.55825371e-01 -8.87746885e-02 3.30226719e-01 -1.48672506e-01
-5.52515462e-02 6.31928742e-01 8.63702893e-01 -2.44569764e-01
-4.02995586e-01 3.81200820e-01 -1.57724857e-01 3.86390716e-01
6.95258737e-01 -5.05651474e-01 -3.76807719e-01 -1.43181667e-01
-7.94590175e-01 3.13606709e-01 -1.30961305e-02 -1.29315984e+00
2.83746630e-01 4.66242343e-01 4.33027178e-01 -1.05062711e+00
6.90227509e-01 -5.73614717e-01 -1.62530869e-01 3.98514777e-01
-3.42236578e-01 -2.26735830e-01 3.90349299e-01 4.58764315e-01
-5.60311735e-01 -4.57564741e-01 8.51252198e-01 1.91741556e-01
-4.34738934e-01 1.39011025e-01 -6.71775103e-01 -4.07187790e-01
8.59131277e-01 -1.74324885e-01 -8.21320340e-03 -5.94754100e-01
-9.33574080e-01 -2.65000224e-01 -3.96326423e-01 4.57826048e-01
7.66048908e-01 -1.59908807e+00 -1.21079445e+00 5.53661823e-01
-2.27320105e-01 -9.43993255e-02 6.26780450e-01 4.98813123e-01
-1.36523694e-01 7.90970504e-01 -1.05380543e-01 -7.34891951e-01
-1.57901132e+00 3.53404880e-01 4.24918860e-01 -3.62833828e-01
-2.07808778e-01 1.25318694e+00 2.95466542e-01 -6.40124679e-01
4.93100792e-01 -3.69684190e-01 -4.87272948e-01 7.97199924e-03
7.11014688e-01 1.57003269e-01 3.59113008e-01 -1.29169214e+00
-2.28769571e-01 1.20499119e-01 -3.08853477e-01 -7.41805494e-01
1.40130007e+00 -1.51701793e-01 2.32434586e-01 3.66632164e-01
1.50141311e+00 -1.64846465e-01 -1.02356982e+00 -5.37984312e-01
-3.92411262e-01 2.92236079e-02 4.34824049e-01 -6.77292287e-01
-1.01789737e+00 1.12563014e+00 1.02224505e+00 -9.20577645e-02
1.16193211e+00 -2.73806870e-01 8.81116807e-01 8.93405154e-02
-6.30922168e-02 -1.07529294e+00 -4.98852506e-02 6.34046078e-01
8.97088170e-01 -1.04736531e+00 -6.65814042e-01 2.51577944e-01
-7.82711446e-01 1.20209980e+00 5.42688489e-01 1.18193254e-01
8.19133639e-01 1.60508856e-01 2.86417216e-01 3.28246564e-01
-6.26246572e-01 1.37132347e-01 4.32428330e-01 8.60743821e-01
2.90045887e-01 1.27693132e-01 1.48986608e-01 8.16077709e-01
-4.65124518e-01 1.33339381e-02 4.43124890e-01 6.43059850e-01
-2.98127711e-01 -1.26707089e+00 -7.35028923e-01 3.32128763e-01
-3.80527258e-01 -2.02716738e-01 5.66119514e-02 3.36523473e-01
1.24502584e-01 1.25810528e+00 -1.30200321e-02 -6.91180468e-01
1.67534187e-01 6.50738776e-01 1.87690765e-01 -7.17479289e-01
-7.04253733e-01 2.37664059e-01 2.09444389e-01 2.01927125e-02
-2.98218846e-01 -9.42221224e-01 -1.01510036e+00 -1.18767731e-02
-5.66450298e-01 3.15314770e-01 1.13398957e+00 1.06246066e+00
2.03339115e-01 5.19347489e-01 8.86138797e-01 -7.39337027e-01
-7.71593034e-01 -1.59929788e+00 -4.87958670e-01 1.92459419e-01
7.42312968e-01 -3.29603732e-01 -5.06300926e-01 4.42113757e-01] | [14.37320327758789, 6.1202239990234375] |
be2a4f25-ba68-4e11-afab-e28731fcab4b | causal-explanation-of-convolutional-neural | null | null | https://2021.ecmlpkdd.org/wp-content/uploads/2021/07/sub_287.pdf | https://2021.ecmlpkdd.org/wp-content/uploads/2021/07/sub_287.pdf | Causal Explanation of Convolutional Neural Networks | In this paper we introduce an explanation technique for Convolutional Neural Networks (CNNs) based on the theory of causality by Halpern and Pearl [12]. The causal explanation technique (CexCNN) is based on measuring the filter importance to a CNN decision, which is measured through counterfactual reasoning. In addition, we employ extended definitions of causality, which are responsibility and blame to weight the importance of such filters and project their contribution on input images. Since CNNs form a hierarchical structure, and since causal models can be hierarchically abstracted, we employ this similarity to perform the most important contribution of this paper, which is localizing the important features in the input image that contributed the most to a CNN’s decision. In addition to its ability in localization, we will show that CexCNN can be useful as well for model compression through pruning the less important filters. We tested CexCNN on several CNNs architectures and datasets. (The code is available on https://github.com/HichemDebbi/CexCNN) | ['Hichem Debbi'] | 2021-09-13 | null | null | null | ecml-2021-9 | ['counterfactual-explanation'] | ['miscellaneous'] | [ 1.57572120e-01 5.38115144e-01 -1.33488357e-01 -4.23543483e-01
4.10050213e-01 -3.58658403e-01 8.21657777e-01 2.14973152e-01
-4.27013844e-01 7.44307101e-01 7.40056634e-01 -5.81296802e-01
-5.48398077e-01 -8.89249265e-01 -9.74794865e-01 -5.36032498e-01
-3.36066127e-01 -2.47595564e-01 1.65239885e-01 4.50780615e-03
5.41834831e-01 6.02005541e-01 -1.50302052e+00 6.92300856e-01
4.36316252e-01 1.02645695e+00 1.38200717e-02 6.63693070e-01
1.15677334e-01 1.25554800e+00 -5.85998058e-01 -6.08686805e-01
-2.38336110e-03 -6.14700437e-01 -9.47782993e-01 -6.69592023e-01
4.69964623e-01 -3.80552471e-01 -4.36102360e-01 1.00684881e+00
-2.92682517e-02 -7.53923208e-02 7.26016521e-01 -1.60808027e+00
-7.54871488e-01 1.17602742e+00 -1.57712057e-01 5.20022750e-01
-2.33116880e-01 -6.91009760e-02 1.30738032e+00 -8.63341391e-01
3.84358853e-01 1.50767934e+00 4.74906862e-01 7.32159972e-01
-1.15439618e+00 -8.74960184e-01 3.11673194e-01 6.18581176e-01
-1.00985110e+00 -1.81418315e-01 6.25019252e-01 -2.91091681e-01
8.83047462e-01 4.61009383e-01 7.54062474e-01 1.13357604e+00
5.56459665e-01 8.65739584e-01 8.06180239e-01 -4.89620119e-01
3.71809840e-01 -2.26317093e-01 2.29701236e-01 9.31083202e-01
4.80804265e-01 4.48566943e-01 -8.52671981e-01 -3.90610912e-05
8.30991030e-01 4.69973385e-02 -3.57825667e-01 -7.01279342e-02
-1.18903065e+00 1.09108377e+00 9.89235342e-01 2.41618246e-01
-3.06381375e-01 9.17291343e-01 1.86224505e-01 1.72768235e-01
1.56129032e-01 4.85947073e-01 -6.23301029e-01 4.32335585e-01
-6.30616367e-01 3.47275466e-01 6.83566570e-01 5.46609700e-01
3.88506919e-01 -1.00528941e-01 -2.95991898e-01 2.93233007e-01
4.28512275e-01 -2.89479438e-02 2.27059036e-01 -1.20195067e+00
7.48715177e-02 6.53269112e-01 -3.16513270e-01 -1.02914321e+00
-3.31199288e-01 -6.11336470e-01 -1.09019184e+00 5.30670106e-01
2.73676842e-01 8.63244943e-03 -8.16002250e-01 1.96960783e+00
-2.00225487e-01 3.75337213e-01 -3.34921591e-02 7.81905949e-01
8.36049736e-01 3.10471237e-01 1.77413002e-01 -3.55502479e-02
1.27218914e+00 -7.38736928e-01 -6.21942997e-01 -9.76349413e-02
4.28180337e-01 -4.63193595e-01 8.47596645e-01 4.80502784e-01
-8.27282548e-01 -3.69195551e-01 -1.34090519e+00 -7.35829175e-02
-5.26457965e-01 -8.72985050e-02 1.00530887e+00 4.48703766e-01
-1.08543932e+00 1.14729428e+00 -5.99864244e-01 -1.33253634e-01
8.09602201e-01 3.55698943e-01 -2.36328229e-01 6.31264076e-02
-1.45625365e+00 7.11442411e-01 5.36129057e-01 9.03556421e-02
-1.05610526e+00 -7.86292613e-01 -4.82599437e-01 5.25975227e-01
1.30081594e-01 -8.00798297e-01 1.21146214e+00 -8.79916370e-01
-8.89576077e-01 3.69345367e-01 -4.00160849e-02 -1.07197583e+00
3.38220179e-01 -2.70088255e-01 -3.76412794e-02 3.68284844e-02
-9.73604545e-02 1.05789328e+00 6.02332950e-01 -1.09433734e+00
-8.54149818e-01 -3.41653794e-01 1.88928142e-01 -2.41134673e-01
-4.95869547e-01 -1.06964000e-01 -1.95977852e-01 -7.33191907e-01
2.49757275e-01 -7.19043553e-01 -2.17654422e-01 1.77193344e-01
-6.19285166e-01 -3.22883189e-01 6.24230206e-01 -1.94796428e-01
1.15982211e+00 -2.13834071e+00 -6.34637550e-02 1.53544918e-01
7.82121778e-01 -2.67037284e-02 -5.14847971e-02 2.11752564e-01
-5.78002572e-01 7.58079588e-01 -2.71851003e-01 -1.05571322e-01
-5.86525537e-02 3.56374264e-01 -6.53461576e-01 2.96892434e-01
5.17049015e-01 8.89981329e-01 -9.39298153e-01 -4.05504614e-01
1.08218096e-01 4.24680889e-01 -5.39383173e-01 -4.48575020e-02
-2.48668283e-01 2.19613984e-02 -1.40409902e-01 1.44495472e-01
3.49756956e-01 -2.30536163e-01 1.55345678e-01 -2.63482243e-01
-2.40352347e-01 4.63106662e-01 -1.01757634e+00 1.16333508e+00
-2.20389977e-01 1.01366925e+00 -5.40731728e-01 -7.94863045e-01
7.09384024e-01 3.05136293e-01 1.01831764e-01 -6.31169379e-01
3.39923084e-01 2.97193937e-02 3.51732552e-01 -1.35055780e-01
1.95025027e-01 1.97544485e-01 1.66228250e-01 4.71310288e-01
1.54584020e-01 2.99423575e-01 2.94433951e-01 7.08528280e-01
1.36055911e+00 -3.41123074e-01 4.78448272e-01 -5.77251852e-01
2.58401394e-01 -1.03353590e-01 4.91063267e-01 9.19781268e-01
-1.02101341e-01 5.24442077e-01 1.12244356e+00 -6.95499361e-01
-8.68820906e-01 -9.76918697e-01 -7.53649399e-02 9.26553190e-01
-1.52973592e-01 -4.52258438e-01 -7.58720398e-01 -7.32726097e-01
1.23863108e-02 1.05789959e+00 -1.32486057e+00 -3.88569802e-01
-2.90705562e-01 -5.56462348e-01 6.52543664e-01 7.76754200e-01
6.26497746e-01 -1.21643317e+00 -1.11491752e+00 4.37409841e-02
-4.49294075e-02 -4.78705883e-01 -2.87891440e-02 6.27859592e-01
-1.04174948e+00 -1.18461442e+00 -2.47564748e-01 -9.50628966e-02
6.10036612e-01 1.83205292e-01 1.18489349e+00 6.12998426e-01
-3.33125263e-01 -1.18517555e-01 -2.90615559e-01 -9.27864969e-01
-3.48462254e-01 -1.59042165e-01 -5.69130629e-02 -2.26378664e-01
2.98556089e-01 -6.74281120e-01 -8.29208910e-01 7.17108371e-03
-9.10790443e-01 3.66652966e-01 5.65966427e-01 8.49784017e-01
3.66995364e-01 1.76137924e-01 8.68504494e-02 -9.05759573e-01
6.41737103e-01 -2.93823034e-01 -5.08469522e-01 -8.12045857e-02
-9.74423289e-01 5.43790519e-01 5.39955974e-01 -3.57817262e-02
-8.69314790e-01 -2.49247123e-02 1.10121176e-01 -4.72845942e-01
-1.30909860e-01 4.51490611e-01 6.22997358e-02 1.65403977e-01
8.37227285e-01 -3.53961319e-01 -3.64454567e-01 -3.18650603e-01
4.22610253e-01 9.58683714e-02 5.31419456e-01 -1.69258639e-01
3.77053827e-01 5.77802122e-01 3.82931739e-01 -2.03562692e-01
-8.35287988e-01 -3.30692790e-02 -5.56692421e-01 -2.84419656e-01
9.55343783e-01 -4.68797266e-01 -1.15141535e+00 4.79564667e-02
-1.57664180e+00 -1.86442181e-01 -2.07841307e-01 5.25850952e-01
-2.52997011e-01 -2.80558854e-01 -4.37886745e-01 -7.43313372e-01
-1.35378495e-01 -8.05007935e-01 4.93267208e-01 2.04170540e-01
-4.90200609e-01 -9.47617471e-01 -3.11279505e-01 -2.97520161e-01
1.73418269e-01 3.18436295e-01 1.30259848e+00 -5.35448849e-01
-7.01722443e-01 -1.16009954e-02 -5.36014259e-01 2.92550415e-01
-3.64231139e-01 2.21788794e-01 -1.12667441e+00 2.29028299e-01
-1.53698489e-01 -5.18619381e-02 1.64138460e+00 6.38438821e-01
1.40992725e+00 -5.07775366e-01 -3.68754506e-01 4.19892013e-01
1.40188682e+00 -4.00259160e-03 8.93774331e-01 2.97811508e-01
5.89110792e-01 7.72686362e-01 2.56442577e-01 3.97831202e-01
8.58433843e-02 3.54788452e-01 1.26350462e+00 -2.79904395e-01
-2.55704463e-01 -3.73423934e-01 5.75741157e-02 3.20329905e-01
-1.95599958e-01 -3.26359004e-01 -8.93807054e-01 5.81860304e-01
-2.06203628e+00 -1.08894575e+00 -7.14326084e-01 1.89392149e+00
6.33589447e-01 2.87844539e-01 -3.94682854e-01 3.33794981e-01
4.32522446e-01 -4.46727909e-02 -3.86989444e-01 -6.71072364e-01
-1.53487667e-01 1.93790883e-01 9.08878922e-01 4.05942351e-01
-8.55945230e-01 7.37605691e-01 5.97684097e+00 5.01255929e-01
-8.87439907e-01 1.61426872e-01 8.74810934e-01 -1.57532722e-01
-3.21041316e-01 1.43695831e-01 -6.56316102e-01 2.47128099e-01
8.86001229e-01 -1.95863277e-01 2.73563117e-01 6.64627910e-01
2.65204191e-01 -1.69740155e-01 -1.38074398e+00 5.63176751e-01
-4.05470997e-01 -1.72863090e+00 2.54643887e-01 1.10435396e-01
6.57690763e-01 -3.36096506e-03 1.85144991e-01 -2.40672097e-01
7.86478519e-01 -1.35790706e+00 1.07860196e+00 4.61654246e-01
3.78027886e-01 -8.15798402e-01 7.66590178e-01 1.38556287e-01
-7.78956532e-01 -3.94665241e-01 -7.48857141e-01 -3.85078281e-01
-3.96310627e-01 8.47015500e-01 -9.16748881e-01 1.39420182e-01
1.11122859e+00 5.48712075e-01 -6.74723804e-01 8.89343381e-01
-9.78966177e-01 8.89984846e-01 -1.74332615e-02 -1.80390030e-01
1.42224401e-01 4.31172818e-01 2.13032961e-01 1.29679084e+00
2.23367274e-01 1.38632059e-01 -7.00683177e-01 1.27737629e+00
-3.21488261e-01 -3.32114995e-01 -4.51896250e-01 6.64453581e-02
4.80867565e-01 1.00853491e+00 -9.41869974e-01 -2.70224601e-01
-6.44264147e-02 8.46449494e-01 3.11994314e-01 2.49022439e-01
-9.42634046e-01 -1.14502020e-01 7.32413292e-01 -1.95976898e-01
2.69764155e-01 1.39710039e-01 -9.03419197e-01 -6.68506265e-01
-3.66065592e-01 -4.69667912e-01 4.85822767e-01 -9.31249022e-01
-8.70710135e-01 4.84303147e-01 2.07017809e-02 -9.52622354e-01
1.31780393e-02 -7.70149291e-01 -7.26897359e-01 8.37698936e-01
-1.30416536e+00 -8.09349537e-01 -2.89798766e-01 5.75974643e-01
4.03082311e-01 2.35858876e-02 8.61752748e-01 -8.42741132e-02
-4.26706225e-01 4.19980019e-01 -2.37381205e-01 1.67473435e-01
3.37402195e-01 -1.39334834e+00 7.09403932e-01 9.36632752e-01
3.42778713e-01 1.04582858e+00 9.03873622e-01 -5.44389963e-01
-8.67983460e-01 -1.19173193e+00 1.17288494e+00 -4.20094103e-01
5.97797871e-01 -2.36543030e-01 -7.21253455e-01 5.42209744e-01
4.73866969e-01 -1.43035665e-01 4.99083847e-01 4.02205348e-01
-7.39833891e-01 7.32243294e-03 -7.23379433e-01 8.93405855e-01
1.28119802e+00 -1.17505677e-01 -5.94233871e-01 1.06563985e-01
8.64428163e-01 4.39761207e-02 -3.51107538e-01 4.09134328e-01
8.00653994e-01 -1.39114690e+00 7.75249839e-01 -7.83409476e-01
1.13197803e+00 -2.36832008e-01 -8.43592733e-02 -1.36949861e+00
-6.53778195e-01 -5.16025014e-02 -4.28395979e-02 8.38840961e-01
7.11295247e-01 -4.69144106e-01 6.73991621e-01 3.40817630e-01
-7.60480622e-03 -7.73306131e-01 -8.62936795e-01 -6.09228909e-01
1.19828517e-02 -7.75417268e-01 7.51231968e-01 8.86773884e-01
-1.77362263e-01 3.38693917e-01 -1.72651678e-01 2.82422245e-01
7.62860060e-01 -2.68842906e-01 2.30833605e-01 -1.38357568e+00
-9.14269686e-02 -7.66217291e-01 -3.74609023e-01 -4.70841974e-01
9.05400068e-02 -8.61043751e-01 -2.95208823e-02 -1.56756556e+00
2.23781362e-01 2.25263685e-02 -6.81546271e-01 8.43058109e-01
2.55322978e-02 3.15471679e-01 3.00950736e-01 2.11172208e-01
-2.91133374e-01 2.27541775e-01 9.86435473e-01 -1.21556975e-01
1.27110660e-01 -1.80687144e-01 -9.17234361e-01 9.13280308e-01
9.91834760e-01 -8.46623302e-01 -4.49040860e-01 -6.53851271e-01
6.04701996e-01 -1.67480394e-01 1.07104599e+00 -9.43817437e-01
4.35062766e-01 1.08440835e-02 6.32431567e-01 -3.27059418e-01
-1.72958940e-01 -7.66487837e-01 1.20921291e-01 9.47725892e-01
-8.43949199e-01 3.11771948e-02 1.56053424e-01 5.66575289e-01
-1.58460438e-01 -2.71508813e-01 5.90702653e-01 -2.97393888e-01
-7.80723155e-01 8.63327757e-02 -2.76570499e-01 -5.81414521e-01
5.83602190e-01 8.64188001e-02 -5.28313577e-01 -3.59416097e-01
-5.14321804e-01 7.47391284e-02 4.62302975e-02 4.09063041e-01
8.51486266e-01 -1.27401590e+00 -6.06641054e-01 3.04143727e-02
5.88927269e-02 -4.14014012e-01 1.63208768e-02 6.89012289e-01
-3.37119401e-01 6.75604582e-01 -3.64829183e-01 -2.15169609e-01
-1.24889386e+00 5.06164193e-01 2.49756530e-01 -1.25110850e-01
-2.47632042e-01 1.29233229e+00 5.38216174e-01 1.88087016e-01
4.42575544e-01 -5.17628431e-01 -4.06712890e-01 1.43266674e-02
7.20943272e-01 2.37273306e-01 1.22027650e-01 1.56380758e-02
-3.93214494e-01 -1.19507991e-01 -5.96176833e-02 -2.91900277e-01
1.31756759e+00 2.05830425e-01 -3.84554714e-01 2.56384462e-01
1.05278575e+00 -4.51380223e-01 -1.29472578e+00 7.89835528e-02
1.15769215e-01 -3.51957560e-01 4.75162983e-01 -9.46829557e-01
-1.11860466e+00 1.18055642e+00 6.76137626e-01 2.83148676e-01
1.18141735e+00 1.23923577e-01 1.94692850e-01 1.87517911e-01
6.11366108e-02 -6.81136250e-01 4.64222990e-02 6.05850279e-01
1.27423823e+00 -7.78048217e-01 -8.09117127e-03 -3.40441465e-01
-2.23627493e-01 1.21138513e+00 3.95085245e-01 -1.30679756e-01
7.03363121e-01 2.84430981e-01 -2.27138907e-01 -4.30724174e-01
-1.21564031e+00 -2.21450359e-01 1.69781432e-01 3.82014006e-01
5.43409169e-01 2.42049947e-01 -5.53524196e-01 7.01625884e-01
-3.36793929e-01 1.34879157e-01 6.11132860e-01 3.91793281e-01
-4.04864848e-01 -7.72060454e-01 -3.06460857e-01 6.06580079e-01
-4.58384782e-01 -6.87154949e-01 -7.12437630e-01 6.94951415e-01
4.25426692e-01 9.91452873e-01 2.00324506e-01 -7.54413009e-01
2.68315226e-01 -1.64458275e-01 2.57814914e-01 -4.75676596e-01
-4.49896872e-01 -3.63303155e-01 -1.31286025e-01 -8.26399446e-01
-2.80056477e-01 -5.10195851e-01 -1.44623029e+00 -5.81281364e-01
-6.76475167e-02 -3.98827046e-02 7.36128926e-01 9.54082191e-01
1.41328350e-01 9.37016964e-01 3.12539399e-01 -3.81709963e-01
-3.49546462e-01 -9.07149017e-01 -4.31268096e-01 2.51058638e-01
2.45937660e-01 -5.95935285e-01 -5.33195972e-01 2.16348723e-01] | [8.925890922546387, 5.551112174987793] |
a5a5151c-319f-4333-a6ba-a12e913f8b82 | training-a-first-order-theorem-prover-from | 2103.03798 | null | https://arxiv.org/abs/2103.03798v2 | https://arxiv.org/pdf/2103.03798v2.pdf | Training a First-Order Theorem Prover from Synthetic Data | A major challenge in applying machine learning to automated theorem proving is the scarcity of training data, which is a key ingredient in training successful deep learning models. To tackle this problem, we propose an approach that relies on training purely with synthetically generated theorems, without any human data aside from axioms. We use these theorems to train a neurally-guided saturation-based prover. Our neural prover outperforms the state-of-the-art E-prover on this synthetic data in both time and search steps, and shows significant transfer to the unseen human-written theorems from the TPTP library, where it solves 72\% of first-order problems without equality. | ['Shibl Mourad', 'Doina Precup', 'Lei Zhang', 'Laurent Orseau', 'Xavier Glorot', 'Zafarali Ahmed', 'Ankit Anand', 'Eser Aygun', 'Vlad Firoiu'] | 2021-03-05 | null | null | null | null | ['automated-theorem-proving', 'automated-theorem-proving'] | ['miscellaneous', 'reasoning'] | [ 1.90846950e-01 6.24565899e-01 -5.80996946e-02 -1.09815402e-02
-8.32921982e-01 -6.79164827e-01 5.71481049e-01 3.13977264e-02
-2.02958420e-01 1.17701960e+00 -4.71851498e-01 -1.24404371e+00
-3.00143044e-02 -1.11386132e+00 -1.39569211e+00 1.98913172e-01
-1.63318932e-01 8.88609946e-01 3.98993611e-01 -6.06670320e-01
-5.35264723e-02 -1.04061782e-01 -1.26175761e+00 6.94768310e-01
9.82819259e-01 7.83096790e-01 -2.98700213e-01 8.61345291e-01
4.23756577e-02 1.44264972e+00 -5.69073200e-01 -5.88762760e-01
1.46825761e-01 -4.75855857e-01 -1.30968440e+00 -7.63290405e-01
6.71537399e-01 -6.44254684e-01 -5.68356872e-01 1.23769045e+00
-2.40656058e-03 -3.94868791e-01 1.80522785e-01 -1.72064757e+00
-4.91198987e-01 1.26300049e+00 1.64235547e-01 2.78818727e-01
3.70976686e-01 2.56324530e-01 1.51677072e+00 -4.58543181e-01
7.74941921e-01 1.37008643e+00 8.33749771e-01 8.81654739e-01
-1.59166658e+00 -6.27869368e-01 -4.54823792e-01 5.65324783e-01
-1.17192376e+00 -5.72947025e-01 6.51243627e-01 -1.66737169e-01
1.43030620e+00 2.87444144e-01 5.58658063e-01 1.04556394e+00
1.24591544e-01 1.07883179e+00 7.90732920e-01 -5.76019943e-01
2.12470531e-01 1.85938701e-01 -1.89143624e-02 1.01899660e+00
2.88247406e-01 3.42235297e-01 -1.67897090e-01 -1.49205536e-01
4.85517651e-01 -6.60251498e-01 -1.05371341e-01 -4.15805072e-01
-1.47526789e+00 8.21498215e-01 3.71822745e-01 2.95473188e-01
1.70428634e-01 6.47527874e-01 7.85025418e-01 7.18613446e-01
4.11371067e-02 1.26561022e+00 -1.06019723e+00 -2.42080435e-01
-1.05533934e+00 7.41203010e-01 1.32088959e+00 9.43412662e-01
3.70643109e-01 3.44580673e-02 1.02229960e-01 -8.39320719e-02
-8.15721080e-02 4.97673601e-01 -9.10856724e-02 -1.15401316e+00
5.29689133e-01 5.59309781e-01 1.03183791e-01 -7.21329868e-01
-2.19475895e-01 -1.74168065e-01 -4.35611099e-01 1.93306133e-01
6.79492235e-01 -4.08866644e-01 -5.74226975e-01 1.77829719e+00
1.61077373e-03 3.29061478e-01 4.00278896e-01 5.83377123e-01
9.57011878e-01 5.94459951e-01 -3.63568038e-01 -6.65841773e-02
9.75649714e-01 -7.69565880e-01 -3.43263835e-01 -2.08746493e-01
9.13763225e-01 -4.10129391e-02 7.84432590e-01 6.96374476e-01
-1.42388010e+00 -1.18524954e-02 -1.18641400e+00 -1.05022743e-01
-4.03696477e-01 -1.60597488e-01 1.20138776e+00 1.19885787e-01
-1.16091549e+00 8.35860252e-01 -6.40682280e-01 3.46754454e-02
6.97120249e-01 6.01821005e-01 -2.63277888e-01 -2.09192172e-01
-1.61127853e+00 1.12393332e+00 9.48765457e-01 4.77849208e-02
-1.04802179e+00 -1.04876256e+00 -1.17781007e+00 3.51257950e-01
8.71662974e-01 -7.00052977e-01 1.88765550e+00 -8.99357975e-01
-1.27930009e+00 6.86165750e-01 8.07886943e-02 -9.01964009e-01
6.80624068e-01 1.15386926e-01 -1.33467510e-01 1.62295904e-02
1.63231380e-02 7.65693426e-01 3.75776947e-01 -1.09195399e+00
-7.02940881e-01 1.59072146e-01 7.94813812e-01 -5.19723296e-01
2.83282340e-01 3.29611711e-02 2.40522355e-01 1.10697180e-01
-2.11383402e-01 -6.35108292e-01 1.99029043e-01 -4.05274406e-02
-4.91085798e-01 -7.03050196e-01 7.74893939e-01 -4.42059517e-01
7.36305356e-01 -1.72579861e+00 2.83449948e-01 1.93678841e-01
5.82425237e-01 3.90419692e-01 -1.87681630e-01 1.90715849e-01
-2.64701217e-01 2.84853488e-01 1.04727492e-01 9.95417535e-02
6.02368057e-01 2.73165137e-01 -4.97156054e-01 1.73001707e-01
8.42718661e-01 1.44331419e+00 -1.62336695e+00 -8.35478365e-01
1.37082040e-02 -8.66850913e-02 -1.06848967e+00 3.33585478e-02
-1.30897820e+00 9.26322713e-02 -3.76834542e-01 6.22367263e-01
3.95166755e-01 -3.88895631e-01 4.08434123e-01 2.21510440e-01
1.62067592e-01 8.46512437e-01 -8.03092957e-01 1.70770335e+00
-3.08414847e-01 9.84507084e-01 4.14202362e-02 -1.22000527e+00
1.18393585e-01 5.05258679e-01 5.21666594e-02 -8.04473162e-01
3.84875923e-01 4.72429395e-01 4.84970421e-01 -4.59642142e-01
1.35199234e-01 -3.02131802e-01 -9.92592201e-02 3.93521965e-01
3.26325566e-01 -4.97777790e-01 6.15101039e-01 4.12617534e-01
1.62303746e+00 2.27921382e-01 -3.36059369e-02 -3.42474312e-01
4.69263881e-01 4.89953458e-01 5.46858251e-01 9.11737442e-01
4.78034606e-03 2.11731456e-02 1.32207799e+00 -8.22401464e-01
-1.34925997e+00 -5.74866474e-01 1.07184546e-02 8.58974159e-01
-3.62796485e-01 -6.79288924e-01 -7.66869068e-01 -9.55413699e-01
1.78523228e-01 1.00091231e+00 -5.82794547e-01 -3.13067645e-01
-7.07711399e-01 1.47969663e-01 1.08322752e+00 3.68654728e-01
5.29191554e-01 -1.39028311e+00 -6.35908663e-01 1.21635154e-01
-3.49443078e-01 -1.17174447e+00 4.81730551e-01 4.04712290e-01
-5.74325085e-01 -1.62752151e+00 1.48687378e-01 -7.61731744e-01
5.70752144e-01 -4.75637406e-01 1.66781878e+00 5.15210032e-01
2.51449202e-03 -2.35021427e-01 9.97450650e-02 -6.23153448e-01
-9.32409942e-01 3.51406932e-01 -2.16829237e-02 -1.00000787e+00
4.21058714e-01 -5.73945522e-01 2.58093596e-01 -2.48009637e-01
-6.52118146e-01 3.13687652e-01 4.40832883e-01 1.27838385e+00
-4.15750183e-02 4.36402619e-01 2.91326731e-01 -1.10483313e+00
5.30078232e-01 -2.56117910e-01 -1.22390819e+00 3.57874990e-01
-2.59668469e-01 4.32031214e-01 1.23072636e+00 -2.81034797e-01
-1.32127568e-01 -4.22161907e-01 4.71140519e-02 -4.34156686e-01
1.89428143e-02 6.97578728e-01 -2.86580976e-02 -3.47678848e-02
9.65837836e-01 1.54676571e-01 -4.97513376e-02 2.01225430e-01
3.32199544e-01 1.37735710e-01 7.56644547e-01 -1.20497274e+00
1.23334849e+00 -1.97571546e-01 3.27069968e-01 -2.44174615e-01
-1.14465427e+00 2.48723447e-01 -3.50113541e-01 3.89788032e-01
2.72015244e-01 -6.01127386e-01 -1.40803313e+00 3.84769216e-02
-1.58216584e+00 -1.03365135e+00 -2.51916379e-01 -1.10901892e-02
-8.38220358e-01 -3.97241712e-02 -7.62806654e-01 -7.34124959e-01
-4.69978005e-01 -1.02267385e+00 7.59845614e-01 -2.75502115e-01
-3.52071047e-01 -9.77424741e-01 -1.78989582e-02 2.70346776e-02
4.40670818e-01 4.42864835e-01 1.38661790e+00 -9.81063366e-01
-7.75120199e-01 -2.26863950e-01 -5.01614630e-01 5.22229671e-01
-3.09559196e-01 -1.24611512e-01 -8.78479123e-01 -1.09293148e-01
-2.14972883e-01 -1.08170485e+00 5.47067046e-01 -7.75927007e-02
1.18266571e+00 -6.71974480e-01 -1.93200558e-01 3.90073806e-01
1.04258883e+00 -3.92013222e-01 5.80565870e-01 4.23072964e-01
1.89401045e-01 -1.59703553e-01 2.35836744e-01 -4.03032415e-02
3.32224160e-01 9.24194902e-02 6.76464796e-01 8.27557500e-03
3.41137260e-01 -4.14892465e-01 1.52596505e-02 9.21579264e-03
-1.16542131e-02 7.38388598e-02 -1.37043822e+00 4.47722882e-01
-1.93895686e+00 -1.33381677e+00 2.29598105e-01 1.69560945e+00
1.59653461e+00 8.20908785e-01 -7.31107444e-02 5.69174469e-01
1.69510961e-01 -4.10230190e-01 -4.16567564e-01 -3.18880767e-01
1.33374006e-01 8.85246396e-01 2.59633690e-01 6.50279105e-01
-1.24086249e+00 1.23851764e+00 7.22164202e+00 5.04092276e-01
-1.15902996e+00 -1.95835143e-01 1.59568280e-01 -6.49009272e-02
-2.36504257e-01 4.85623702e-02 -2.05737695e-01 9.97566879e-02
1.30425727e+00 -3.67134005e-01 1.22928488e+00 1.07341230e+00
-2.31197849e-01 4.80347825e-03 -1.95408475e+00 4.71443295e-01
-9.16296914e-02 -1.80059588e+00 -3.08265448e-01 -2.32227787e-01
5.49609601e-01 5.45077808e-02 -1.33935735e-01 1.10309315e+00
6.29367232e-01 -1.45676565e+00 8.51055861e-01 2.51658354e-02
1.06538200e+00 -7.01205552e-01 1.05849898e+00 6.37011528e-01
-4.22842234e-01 -1.14667580e-01 -2.74259150e-01 -6.25389218e-01
-6.64364219e-01 1.71044022e-01 -1.40230727e+00 2.80433267e-01
2.40716845e-01 5.48466802e-01 -4.93838400e-01 7.03353763e-01
-6.11765683e-01 5.14769435e-01 -6.73894227e-01 -3.42679828e-01
3.34493548e-01 4.87454534e-01 3.01587701e-01 1.10003042e+00
-2.38452420e-01 2.55763292e-01 2.36589815e-02 1.69200683e+00
-3.53459656e-01 -7.57552326e-01 -7.80906260e-01 -4.14779723e-01
1.77791551e-01 1.09246945e+00 -1.98873639e-01 -4.98556554e-01
-2.23601952e-01 5.30286849e-01 6.46737754e-01 2.90745974e-01
-1.14563584e+00 -4.93735015e-01 2.41056100e-01 3.18931416e-02
3.53509992e-01 -1.80003271e-01 -1.22996144e-01 -1.25567508e+00
1.68798879e-01 -1.50837827e+00 1.08127631e-01 -6.40607059e-01
-8.78187656e-01 2.54345506e-01 1.63212940e-01 -4.55172926e-01
-5.35220444e-01 -1.07741284e+00 -6.12456024e-01 7.85734773e-01
-1.69875693e+00 -1.23794925e+00 1.10686913e-01 5.08060396e-01
1.42498901e-02 -2.08638921e-01 1.05315113e+00 2.05641195e-01
-3.16873312e-01 8.32546771e-01 -3.99153233e-01 6.49994373e-01
2.46680841e-01 -1.59866059e+00 5.84438026e-01 8.29500735e-01
-1.59747396e-02 9.14195359e-01 9.39906240e-01 -2.09644645e-01
-2.12315321e+00 -8.67989480e-01 1.18697417e+00 -8.17682087e-01
1.26557004e+00 -6.12622559e-01 -5.51153183e-01 1.08964360e+00
4.48228307e-02 3.22271615e-01 9.80372205e-02 5.85643828e-01
-5.97194374e-01 1.01449691e-01 -1.16692269e+00 6.64789379e-01
1.05240083e+00 -8.43327403e-01 -1.07388723e+00 7.64506221e-01
1.00453305e+00 -1.04855120e+00 -5.82987726e-01 3.82694632e-01
3.70809764e-01 -6.64956808e-01 6.64347649e-01 -1.41329420e+00
1.06835127e+00 -2.21582368e-01 -1.10907033e-01 -1.12739682e+00
-5.55894710e-02 -8.96768630e-01 -6.64520502e-01 6.56568825e-01
8.09361339e-01 -4.85542536e-01 8.19738805e-01 7.68135369e-01
-9.49202552e-02 -8.98672402e-01 -9.66331363e-01 -5.35139084e-01
5.94619393e-01 -6.88851714e-01 4.47596431e-01 1.07289779e+00
9.07158554e-01 6.36601686e-01 6.49855360e-02 4.50039133e-02
2.67528355e-01 4.71955761e-02 9.98151541e-01 -1.25554335e+00
-4.75536317e-01 -4.91400719e-01 -3.15847248e-01 -5.91165125e-01
8.88881326e-01 -1.19956267e+00 1.98260009e-01 -1.35851002e+00
1.62767187e-01 -3.71866524e-01 -1.86228678e-01 1.04369092e+00
2.05586046e-01 -6.14609495e-02 -8.22444037e-02 -5.31809032e-01
-1.08051264e+00 2.59849221e-01 1.11961102e+00 -5.14799237e-01
2.71116108e-01 -2.08375648e-01 -6.29978657e-01 6.56372428e-01
4.53138441e-01 -3.19929212e-01 -2.26744339e-01 -5.49510062e-01
9.28723633e-01 1.80557415e-01 6.97917640e-01 -1.17692220e+00
4.00655955e-01 -4.22846854e-01 3.82058993e-02 -2.15237126e-01
-1.78360239e-01 -8.37452233e-01 -3.97322536e-01 6.11244321e-01
-4.15747583e-01 1.37084126e-01 6.74969614e-01 -1.30131185e-01
-3.46365348e-02 -3.94349881e-02 4.65452462e-01 -3.14018786e-01
-4.62631613e-01 -2.54706526e-03 3.82753573e-02 5.64948142e-01
7.09909141e-01 3.94746810e-01 -6.46538258e-01 -2.13736579e-01
-4.11432475e-01 6.55097961e-01 3.87606770e-01 2.55204234e-02
5.14981270e-01 -1.00013483e+00 -8.77853990e-01 1.86587349e-01
-1.17600173e-01 5.33419669e-01 -6.20723426e-01 8.26767206e-01
-8.72325242e-01 9.08122957e-01 -1.33234188e-01 -3.90699357e-01
-8.47265601e-01 8.09274733e-01 7.84099996e-01 -4.68216449e-01
-6.15027726e-01 7.54060090e-01 -3.79158586e-01 -6.74459696e-01
3.18666697e-01 -1.00031137e+00 6.13961637e-01 -9.14175510e-01
5.41518271e-01 -2.14007616e-01 1.50308132e-01 3.75918210e-01
-5.37036121e-01 -3.73122096e-01 1.31831281e-02 1.14027811e-02
1.43814611e+00 1.20637441e+00 -4.23821062e-01 2.45649651e-01
7.01345444e-01 -3.05465758e-01 -5.71332574e-01 -2.89180338e-01
1.14186294e-01 -4.22217883e-02 -3.72121893e-02 -1.02369153e+00
-5.23884475e-01 7.36664593e-01 -4.44101810e-01 3.22014987e-01
6.27878547e-01 -7.41549721e-03 7.38538504e-01 1.40783083e+00
6.89840019e-01 -7.90332496e-01 -4.02298868e-02 1.19348693e+00
6.87250495e-01 -1.45960450e+00 2.24570520e-02 -1.38949782e-01
5.89204319e-02 1.25193095e+00 7.34110475e-01 -4.26969349e-01
-1.12561937e-02 5.18752456e-01 -3.92206967e-01 -2.03427657e-01
-1.23491609e+00 2.00500771e-01 1.97296347e-02 3.86926651e-01
4.85466123e-01 -1.64347887e-01 1.36604339e-01 5.46529293e-01
-6.89582467e-01 4.90158707e-01 3.88954520e-01 1.06186616e+00
-1.80268124e-01 -9.73373175e-01 -8.55037943e-02 2.81043649e-01
-5.38961947e-01 -4.86495644e-01 -5.45091212e-01 1.28546989e+00
2.98215717e-01 6.56716526e-01 -2.49727100e-01 -3.21995109e-01
1.54745102e-01 3.08367610e-01 1.14725399e+00 -7.01456904e-01
-4.26721245e-01 -7.55548596e-01 6.37675345e-01 -6.27662122e-01
1.02373317e-01 -4.10109907e-01 -1.27299547e+00 -9.68831658e-01
-3.96261901e-01 4.38331962e-01 2.98046976e-01 1.23107088e+00
-1.46463677e-01 6.90877795e-01 8.68484676e-02 -7.03281045e-01
-1.04951870e+00 -9.17005956e-01 -1.00007601e-01 2.24992886e-01
8.56483221e-01 -3.40357810e-01 -3.91656101e-01 -2.07599163e-01] | [8.925095558166504, 7.064830780029297] |
c01fefbc-c546-4ecf-9fd0-3cf6af0401f8 | covid-ct-mask-net-prediction-of-covid-19-from | null | null | https://www.medrxiv.org/content/10.1101/2020.10.11.20211052v1 | https://www.medrxiv.org/content/10.1101/2020.10.11.20211052v1.full.pdf | COVID-CT-Mask-Net: Prediction of COVID-19 from CT Scans Using Regional Features | We present COVID-CT-Mask-Net model that predicts COVID-19 from CT scans. The model works in two stages: first, it detects the instances of ground glass opacity and consolidation in CT scans, then predicts the condition from the ranked bounding box detections. To develop the solution for the three-class problem (COVID, common pneumonia and control), we used the COVIDx-CT dataset derived from the dataset of CT scans collected by China National Center for Bioinformation. We use about 5% of the training split of COVIDx-CT to train the model, and without any complicated data normalization, balancing and regularization, and training only a small fraction of the model's parameters, we achieve a 90.80% COVID sensitivity, 91.62% common pneumonia sensitivity and 92.10% normal sensitivity, and an overall accuracy of 91.66% on the test data (21182 images), bringing the ratio of test/train data to 7.06, which implies a very high capacity of the model to generalize to new data. We also establish an important result, that ranked regional predictions (bounding boxes with scores) in Mask R-CNN can be used to make accurate predictions of the image class. The full source code, models and pretrained weights are available on https://github.com/AlexTS1980/COVID-CT-Mask-Net. | ['Aram Ter-Sarkisov'] | 2020-10-14 | null | null | null | null | ['covid-19-image-segmentation'] | ['computer-vision'] | [ 2.09186047e-01 -1.46543071e-01 -1.98695555e-01 -6.17636800e-01
-8.36276948e-01 -3.00024033e-01 1.07528001e-01 5.18279821e-02
-5.30623794e-01 5.97774625e-01 -5.78255355e-02 -3.95103127e-01
-2.34782901e-02 -8.29431653e-01 -6.37711406e-01 -7.81813025e-01
-2.04537615e-01 8.58132601e-01 6.45804048e-01 3.90725791e-01
-1.80344969e-01 6.56428456e-01 -1.07577944e+00 7.74163485e-01
6.35817111e-01 1.24963403e+00 6.46323919e-01 8.66932333e-01
1.68776989e-01 6.33716166e-01 -3.43828917e-01 4.89695789e-03
2.04911441e-01 -2.34317705e-01 -6.56993687e-01 -3.74991864e-01
1.50002092e-01 -6.11113667e-01 -3.14571708e-01 6.66449189e-01
6.26871169e-01 -2.26913556e-01 1.08690631e+00 -9.51018035e-01
-3.37804109e-01 2.57467091e-01 -7.23754883e-01 8.16708326e-01
-1.50341824e-01 3.63656104e-01 8.26402783e-01 -9.46192145e-01
5.59113383e-01 8.59863698e-01 7.93289304e-01 6.52974010e-01
-7.79685140e-01 -9.73380268e-01 -2.69648969e-01 -2.42609233e-02
-1.51962805e+00 2.72510827e-01 -2.65980542e-01 -7.95860052e-01
9.44832206e-01 6.16280198e-01 7.37733305e-01 9.29002225e-01
4.87201720e-01 4.95754331e-01 1.03320944e+00 2.21842423e-01
2.11914685e-02 -1.67057008e-01 6.04649447e-02 9.52095509e-01
2.70468563e-01 1.12824827e-01 1.35472611e-01 -1.81786537e-01
9.17181790e-01 5.60937583e-01 -4.02028590e-01 9.27099586e-02
-1.19588470e+00 7.87502527e-01 8.38506877e-01 2.22734228e-01
-3.08560640e-01 -2.06731632e-03 3.93165588e-01 -2.67179519e-01
2.43267342e-01 2.02338532e-01 -7.15764403e-01 4.43962455e-01
-6.58472300e-01 8.08588695e-03 1.20823324e-01 6.93542719e-01
3.86581987e-01 -2.74845183e-01 -3.91863018e-01 1.02497005e+00
2.34603822e-01 7.65679240e-01 9.38508213e-01 -5.74919522e-01
4.64556932e-01 6.53442204e-01 -3.83523494e-01 -6.36265337e-01
-9.11721587e-01 -5.36736250e-01 -1.10502529e+00 -1.83676839e-01
1.48131356e-01 -1.61662400e-01 -1.45178831e+00 1.41818655e+00
2.33192652e-01 1.81712940e-01 -3.59531343e-01 8.62483561e-01
1.02616727e+00 6.46804392e-01 1.76831648e-01 -1.76136464e-01
1.70011139e+00 -6.00669503e-01 -2.51592577e-01 -1.11830272e-01
7.21043229e-01 -4.53586817e-01 1.18347049e+00 3.32533240e-01
-7.28006303e-01 -3.68500561e-01 -9.35667694e-01 3.15042317e-01
-3.61143887e-01 2.45869815e-01 2.64489889e-01 1.58187494e-01
-8.30680609e-01 3.00180376e-01 -9.53382194e-01 -2.97082812e-01
7.83707321e-01 3.51397157e-01 -2.77321696e-01 -3.23354363e-01
-1.09599316e+00 9.79711294e-01 5.57790458e-01 1.65241007e-02
-1.29089880e+00 -8.22979212e-01 -3.96539897e-01 2.80512348e-02
2.40975007e-01 -7.91455150e-01 1.12218380e+00 -2.23627284e-01
-6.57440901e-01 1.00442636e+00 2.45862864e-02 -2.24392310e-01
6.05831683e-01 -1.90047398e-01 -1.69863865e-01 2.34087631e-01
1.87343046e-01 7.03336418e-01 3.66952062e-01 -1.03842175e+00
-7.44449496e-01 -4.34276581e-01 -4.83148813e-01 -7.00868815e-02
-6.41313195e-02 1.37626156e-01 -5.73784232e-01 -4.84908938e-01
3.27379733e-01 -1.00194919e+00 -4.84057039e-01 2.94867903e-02
-4.37616229e-01 -1.70426041e-01 6.06214881e-01 -7.78498650e-01
1.03623831e+00 -1.99040067e+00 -4.57967252e-01 3.70611966e-01
5.69854975e-01 4.34289187e-01 3.74779627e-02 -1.78579926e-01
-3.80966604e-01 3.75741303e-01 -4.30614471e-01 2.28511572e-01
-5.62423468e-01 2.81852931e-01 3.95422429e-02 4.88320708e-01
5.15622079e-01 9.12999749e-01 -6.57395899e-01 -7.65111148e-01
5.30876219e-02 3.31442952e-01 -5.89782059e-01 4.89877224e-01
-1.39221782e-02 4.64472920e-01 -5.76253295e-01 6.74923897e-01
7.34566629e-01 -6.94644988e-01 1.50237173e-01 -5.04718907e-02
1.03160501e-01 8.95055290e-03 -8.66101444e-01 9.69152629e-01
-2.06734255e-01 2.36667082e-01 -2.06768379e-01 -7.60153532e-01
6.07917428e-01 3.66993606e-01 7.18539298e-01 -4.01630431e-01
2.48903051e-01 2.84556538e-01 2.03122705e-01 -8.46551538e-01
-2.11486474e-01 -4.19836044e-01 1.73117667e-01 4.71784085e-01
-1.00508891e-01 -1.50449753e-01 -3.91577817e-02 -2.65763719e-02
1.30784321e+00 -5.21838725e-01 3.86323392e-01 -2.53191516e-02
3.11891556e-01 1.34726837e-01 6.56800389e-01 7.26139307e-01
-2.56818354e-01 1.03040874e+00 4.92393434e-01 -4.97240782e-01
-1.01790452e+00 -1.31897831e+00 -6.95956230e-01 8.99886966e-01
-3.36411923e-01 -2.56892387e-02 -6.62941754e-01 -8.29738319e-01
-4.66816463e-02 2.67413020e-01 -8.58632803e-01 -3.70236076e-02
-9.10619080e-01 -1.34372854e+00 6.42403662e-01 9.25858617e-01
3.51337522e-01 -1.12431586e+00 -6.30809307e-01 -6.02367334e-02
-2.81392127e-01 -8.84212971e-01 -3.44782799e-01 4.07573372e-01
-1.02262616e+00 -1.36669040e+00 -8.04946423e-01 -8.80218208e-01
7.18169868e-01 8.77524018e-02 9.71153319e-01 6.67688191e-01
-7.27950633e-01 -1.18643939e-01 -1.33579075e-01 -5.37109315e-01
-2.62824267e-01 -9.72429290e-02 -2.22093258e-02 -5.18647313e-01
1.21131986e-01 -3.12022060e-01 -8.01882029e-01 4.96144623e-01
-9.47858512e-01 1.17610797e-01 6.78577721e-01 8.74297798e-01
1.06006527e+00 -1.31596565e-01 4.75595832e-01 -9.10658002e-01
2.27469563e-01 -7.36887395e-01 -4.28519309e-01 2.50868469e-01
-5.82442045e-01 -4.97365057e-01 4.98225719e-01 -4.35561091e-01
-7.79858470e-01 1.63710415e-01 -4.22696412e-01 -4.34821188e-01
-1.23113133e-01 3.87059391e-01 2.26527140e-01 4.37619865e-01
6.19052649e-01 2.19857886e-01 -9.70402509e-02 -3.22548300e-01
-7.37355798e-02 7.63033271e-01 4.64540780e-01 -2.87032694e-01
5.91351867e-01 5.25419176e-01 1.32126575e-02 -3.98835927e-01
-1.02538562e+00 -7.03866601e-01 -7.03437150e-01 -1.43595979e-01
1.33640742e+00 -9.05174732e-01 -5.41094244e-01 2.85928637e-01
-8.81804764e-01 -4.07242596e-01 -1.52855873e-01 7.63978004e-01
-3.58934551e-01 6.24514222e-02 -8.04235518e-01 -3.60882550e-01
-6.74753726e-01 -1.31305242e+00 8.57465863e-01 1.35968474e-03
-1.16467744e-01 -5.35463870e-01 9.11029279e-02 3.71935308e-01
3.13949883e-01 2.44380087e-01 1.24273384e+00 -1.04179406e+00
-4.87056822e-01 -1.67596012e-01 -6.11037791e-01 4.81400788e-01
1.08805940e-01 1.25344187e-01 -9.57474053e-01 -9.64471325e-02
1.05893604e-01 -3.52502465e-01 1.10257530e+00 5.76972067e-01
1.77069092e+00 -1.15378112e-01 -5.58047771e-01 8.70604336e-01
1.49760866e+00 5.58656037e-01 5.19635439e-01 6.27348945e-02
6.06147408e-01 2.45524094e-01 5.29500425e-01 4.27714080e-01
8.97602215e-02 2.08315641e-01 6.95603490e-01 -4.33742911e-01
-8.69120061e-02 6.29775785e-03 -2.06633016e-01 8.09340954e-01
-1.76819146e-01 -2.73668706e-01 -1.47621548e+00 4.45973247e-01
-1.34732282e+00 -7.05479383e-01 -4.61584687e-01 1.70596206e+00
8.32770288e-01 9.55739468e-02 -6.65653870e-02 -1.67923659e-01
7.86657810e-01 -2.06392407e-01 -5.92896521e-01 -1.22303799e-01
1.27963215e-01 4.53834593e-01 4.88480508e-01 1.42855525e-01
-1.23843610e+00 4.46700007e-01 6.82136822e+00 7.92108059e-01
-1.28251934e+00 3.64036679e-01 1.01933777e+00 -1.81537554e-01
1.86600551e-01 -5.96077859e-01 -7.21031487e-01 4.69200015e-01
7.79868424e-01 2.93843091e-01 3.18582989e-02 7.99341917e-01
2.69093782e-01 1.18408799e-02 -9.09051716e-01 5.20603597e-01
-5.71757071e-02 -1.23191237e+00 2.02788543e-02 5.77433966e-02
5.79242527e-01 7.36971200e-01 3.26483287e-02 3.14466536e-01
3.25735867e-01 -1.32429540e+00 2.47373641e-01 2.26590574e-01
1.32683718e+00 -2.57442087e-01 1.09943831e+00 4.42152470e-01
-1.12750268e+00 -1.11135736e-01 -6.13923669e-01 4.72870022e-01
-6.99950382e-02 5.38559496e-01 -1.52894855e+00 2.19527751e-01
1.08425272e+00 3.92817020e-01 -6.60536230e-01 1.17686284e+00
-1.09924197e-01 8.14637184e-01 -4.92602110e-01 -2.57347133e-02
1.45613149e-01 1.05070077e-01 6.39563277e-02 1.55414128e+00
4.10855621e-01 4.91029412e-01 1.49244398e-01 8.01879168e-01
-5.38231954e-02 2.61004210e-01 -2.74483621e-01 4.44755346e-01
2.32569277e-01 1.20361197e+00 -8.54187846e-01 -6.51743054e-01
-2.78368026e-01 3.68691057e-01 2.42508620e-01 1.21641450e-01
-1.24587440e+00 1.12188220e-01 3.49859893e-01 2.42328271e-01
4.63121831e-01 3.14148217e-01 -4.25920933e-01 -8.86910617e-01
-2.10527301e-01 -7.55490065e-01 8.06117296e-01 -9.68165517e-01
-1.45691776e+00 6.96736932e-01 9.86578688e-02 -1.28530908e+00
1.53470606e-01 -9.17411745e-01 -7.59589493e-01 8.17789078e-01
-1.28380966e+00 -7.75379658e-01 -5.51147223e-01 5.38572967e-01
4.06637251e-01 3.76893915e-02 8.00175846e-01 3.36837351e-01
-7.19451249e-01 4.21293855e-01 2.08160475e-01 4.09544826e-01
6.13674879e-01 -1.10694623e+00 1.71235502e-01 4.28535700e-01
-6.81815863e-01 6.48590982e-01 2.55617827e-01 -7.05169976e-01
-6.45171463e-01 -1.61799657e+00 6.77564323e-01 -4.68817562e-01
5.98311305e-01 -1.12674095e-01 -1.08351851e+00 8.06924343e-01
-4.45181906e-01 3.93505692e-01 8.76452029e-01 -3.76991391e-01
-1.84379131e-01 1.25073139e-02 -1.26864135e+00 2.84439862e-01
9.50179100e-01 -9.76148397e-02 -6.14666581e-01 7.23798215e-01
7.81949937e-01 -6.01874650e-01 -1.10593307e+00 8.22813630e-01
6.37787998e-01 -7.54814923e-01 1.15493000e+00 -7.62022257e-01
5.35360336e-01 -2.22182885e-01 -2.53396332e-01 -9.29994166e-01
-5.94011366e-01 5.96961319e-01 2.10264578e-01 5.21285772e-01
5.89792490e-01 -8.36828828e-01 7.19099820e-01 9.15380046e-02
-4.34903741e-01 -1.42395961e+00 -1.01211381e+00 -6.43845201e-01
3.29910636e-01 -4.58040863e-01 6.86670482e-01 8.58367503e-01
-4.82098967e-01 -6.22031689e-02 9.79040116e-02 2.96905488e-01
1.92917690e-01 -1.32241130e-01 3.47042531e-01 -1.12522304e+00
-2.39636391e-01 -2.67271101e-01 -1.86171114e-01 -6.93896115e-01
-3.64006400e-01 -1.10622370e+00 1.71872362e-01 -1.77030849e+00
8.84734988e-01 -7.35169411e-01 -6.95808768e-01 8.09795558e-01
-2.47910500e-01 5.36259532e-01 -3.35323554e-03 5.22821546e-01
-2.62885630e-01 1.20058969e-01 1.70400071e+00 -2.17011824e-01
6.35695234e-02 2.72565603e-01 -4.59537268e-01 8.53078067e-01
1.09824228e+00 -7.96265543e-01 -2.67313421e-01 -1.78911686e-01
-2.79461984e-02 1.67768568e-01 3.58840525e-01 -9.99324620e-01
-2.52501726e-01 -5.41848183e-01 9.48680043e-01 -9.47348237e-01
2.96470314e-01 -7.88500369e-01 2.27278352e-01 1.12061584e+00
-2.12072179e-01 2.28586704e-01 8.09704512e-02 3.40234905e-01
1.08274296e-01 -3.43076885e-01 1.01922750e+00 -4.34031904e-01
-2.41409138e-01 6.74746513e-01 -6.28962576e-01 1.58435494e-01
1.04596663e+00 -8.89979154e-02 -6.53671324e-01 9.32638347e-02
-7.66204298e-01 2.16885790e-01 1.43476322e-01 5.25611639e-03
8.37086976e-01 -1.14083922e+00 -8.81824017e-01 2.90916115e-01
2.47850567e-01 4.04753119e-01 4.12774295e-01 1.13391685e+00
-9.70990419e-01 4.82077986e-01 -1.78465918e-01 -1.07749784e+00
-1.21762180e+00 4.98561263e-01 7.26850450e-01 -4.99815464e-01
-7.79910266e-01 9.97546077e-01 8.32474709e-01 -4.63821411e-01
-4.13599424e-02 -5.56471407e-01 -2.38140658e-01 -2.27635592e-01
5.12403309e-01 1.45617023e-01 2.35203579e-01 -4.91959035e-01
-6.28654599e-01 5.19798338e-01 -1.89967036e-01 3.69553745e-01
1.54427528e+00 4.59490716e-01 -1.54136419e-01 1.69966146e-01
1.29176176e+00 -3.26965958e-01 -7.94441342e-01 -8.76829848e-02
-2.81599015e-01 -2.71746069e-01 -2.68107146e-01 -1.00304759e+00
-1.20205462e+00 9.32327986e-01 1.14221025e+00 -1.76039279e-01
1.02484906e+00 4.51958925e-01 7.68620908e-01 2.14019328e-01
-6.88921586e-02 -5.96811950e-01 1.94657847e-01 6.65266931e-01
8.74639809e-01 -1.20475769e+00 2.07110360e-01 -4.01190609e-01
-6.06440604e-01 7.91221380e-01 9.43344116e-01 -5.88967241e-02
8.15633297e-01 3.37437212e-01 2.49722242e-01 -5.52012742e-01
-9.36551750e-01 1.80479303e-01 3.01968783e-01 6.85867608e-01
3.85991156e-01 3.92617285e-01 -1.92253605e-01 9.24323857e-01
-2.46334940e-01 -1.51662216e-01 4.00155574e-01 7.96471298e-01
-8.43761146e-01 -4.42404181e-01 -4.72123206e-01 1.20330048e+00
-7.07136571e-01 -1.92848533e-01 -1.05242394e-01 1.01418245e+00
4.70146239e-01 4.04027671e-01 2.31796473e-01 -6.15529358e-01
3.23903799e-01 6.67078644e-02 1.21111078e-02 -8.58578801e-01
-5.79996169e-01 1.97912067e-01 -2.29625836e-01 -4.45310920e-01
-8.95627192e-04 -6.15882635e-01 -1.78216004e+00 4.12049750e-03
-5.49418807e-01 -1.74722314e-01 3.29821408e-01 7.69131601e-01
-9.29326266e-02 8.62370610e-01 4.78665531e-01 -3.72777998e-01
-4.49273258e-01 -1.18010080e+00 -5.27933061e-01 2.24729866e-01
2.45879859e-01 -6.05192304e-01 -3.76072496e-01 3.12151220e-02] | [15.426780700683594, -1.87602961063385] |
59ad6309-eb13-444d-976d-9045ddd1605d | semantic-segmentation-of-skin-lesions-using-a | 1910.10534 | null | https://arxiv.org/abs/1910.10534v1 | https://arxiv.org/pdf/1910.10534v1.pdf | Semantic Segmentation of Skin Lesions using a Small Data Set | Early detection of melanoma is difficult for the human eye but a crucial step towards reducing its death rate. Computerized detection of these melanoma and other skin lesions is necessary. The central research question in this paper is "How to segment skin lesion images using a neural network with low available data?". This question is divided into three sub questions regarding best performing network structure, training data and training method. First theory associated with these questions is discussed. Literature states that U-net CNN structures have excellent performances on the segmentation task, more training data increases network performance and utilizing transfer learning enables networks to generalize to new data better. To validate these findings in the literature two experiments are conducted. The first experiment trains a network on data sets of different size. The second experiment proposes twelve network structures and trains them on the same data set. The experimental results support the findings in the literature. The FCN16 and FCN32 networks perform best in the accuracy, intersection over union and mean BF1 Score metric. Concluding from these results the skin lesion segmentation network is a fully convolutional structure with a skip architecture and an encoder depth of either one or two. Weights of this network should be initialized using transfer learning from the pre trained VGG16 network. Training data should be cropped to reduce complexity and augmented during training to reduce the likelihood of overfitting. | ['Beril Sirmacek', 'Max Kivits'] | 2019-10-23 | null | null | null | null | ['small-data', 'skin-lesion-segmentation'] | ['computer-vision', 'medical'] | [ 6.14253163e-01 2.22562313e-01 -4.12714332e-01 -2.54815012e-01
-2.75759220e-01 -2.03774661e-01 9.94080231e-02 -1.77904248e-01
-8.10480714e-01 8.27968180e-01 -1.29188105e-01 -5.37681997e-01
-2.47911766e-01 -7.11311817e-01 -2.18801022e-01 -7.71872222e-01
1.47379145e-01 -2.84977406e-01 4.15875018e-01 -1.33048519e-01
3.90488774e-01 5.68122268e-01 -1.21756732e+00 3.99251223e-01
9.21690881e-01 9.33258355e-01 2.71132559e-01 1.00721991e+00
2.19457299e-02 7.47843266e-01 -5.56078017e-01 -1.77710697e-01
4.13257271e-01 -5.15223265e-01 -1.16226900e+00 2.76563406e-01
3.61932486e-01 -3.35736156e-01 -6.69212192e-02 8.40910375e-01
6.22939825e-01 -2.27197707e-01 5.70975721e-01 -6.75015152e-01
-2.49944225e-01 1.84135303e-01 -5.33104897e-01 4.94056463e-01
8.33468512e-03 1.92179501e-01 3.29822958e-01 -1.27378792e-01
7.08746135e-01 7.49014318e-01 1.22438169e+00 1.02997935e+00
-8.70917380e-01 -5.31108797e-01 -3.51270378e-01 8.66892710e-02
-1.28137922e+00 4.63500470e-02 1.32340610e-01 -3.94457340e-01
1.11840248e+00 5.50497830e-01 7.01859891e-01 9.01245475e-01
3.21530968e-01 3.96255165e-01 1.25714862e+00 -6.93652272e-01
-3.58801037e-02 4.91834611e-01 2.32314900e-01 7.82077968e-01
3.53605360e-01 1.24268971e-01 -7.88194314e-02 4.11006302e-01
1.04775488e+00 -1.85573354e-01 -2.38663390e-01 4.52208847e-01
-6.15188658e-01 9.20237899e-01 7.51048625e-01 7.45482385e-01
-3.49360853e-01 1.46497533e-01 6.71379685e-01 4.14558291e-01
2.42140666e-01 4.81584638e-01 -4.67336655e-01 1.50691926e-01
-9.30283725e-01 -4.56326097e-01 4.38120753e-01 4.40352827e-01
5.07708549e-01 -7.05282688e-02 9.76565201e-03 8.31363440e-01
1.58159316e-01 -1.16072081e-01 7.91855335e-01 -6.23165548e-01
9.90964100e-03 9.10948932e-01 -5.42439461e-01 -4.69887465e-01
-6.48950636e-01 -5.54284275e-01 -9.80014741e-01 6.16161048e-01
7.18335211e-01 -8.61627698e-01 -1.67594314e+00 1.10327482e+00
6.75489902e-02 1.95320062e-02 1.44697785e-01 6.54233992e-01
9.81299222e-01 3.34079623e-01 3.07016760e-01 1.27343282e-01
1.27350783e+00 -9.70683336e-01 -5.25303900e-01 2.77710184e-02
1.01408875e+00 -8.00693929e-01 5.46648681e-01 4.62605357e-01
-7.52208114e-01 -5.97094178e-01 -1.02339506e+00 -7.06018806e-02
-6.53053701e-01 5.06880939e-01 8.14441860e-01 1.07518220e+00
-1.34127343e+00 5.75629711e-01 -7.41290808e-01 -1.18464279e+00
5.49783826e-01 7.76319146e-01 -4.72269237e-01 6.79655373e-02
-9.86165524e-01 1.15470362e+00 7.93385446e-01 1.90499708e-01
-5.16646206e-01 -4.41594094e-01 -5.64442098e-01 -4.21278656e-01
7.41565973e-02 -6.51519477e-01 1.02528811e+00 -1.65540445e+00
-1.33428419e+00 9.64495957e-01 1.64612234e-02 -8.11575711e-01
4.99587685e-01 3.40856344e-01 -4.25822914e-01 3.46391499e-01
-1.77381992e-01 1.15935743e+00 3.79221171e-01 -9.97213781e-01
-1.07172513e+00 -1.79827854e-01 8.94019678e-02 1.41303420e-01
-2.20392659e-01 -1.13060407e-01 -2.27529675e-01 -3.65275413e-01
-6.57109544e-02 -8.21902335e-01 -4.07771319e-01 8.69948193e-02
-4.47724521e-01 -1.57056868e-01 7.94790626e-01 -9.34480429e-01
1.15565383e+00 -1.82455194e+00 -3.93733621e-01 5.25832534e-01
-4.91659977e-02 6.73203647e-01 -8.16945881e-02 2.93290347e-01
-3.42479378e-01 4.13066328e-01 -2.51108850e-03 2.38063440e-01
-7.44342685e-01 3.53761643e-01 7.11407125e-01 3.23405325e-01
3.10872406e-01 8.37113321e-01 -4.56521600e-01 -7.08942056e-01
2.15485826e-01 6.17689669e-01 -1.94190949e-01 -1.06753521e-01
2.09970221e-01 2.52891839e-01 -1.45459935e-01 7.29412019e-01
5.25892258e-01 -2.42195874e-01 -9.81605276e-02 -3.37653667e-01
-1.47434711e-01 -4.78948861e-01 -9.13793087e-01 1.47601366e+00
-2.26981685e-01 1.06794977e+00 -3.26502137e-02 -8.57504547e-01
9.08349216e-01 4.81106669e-01 2.06790105e-01 -5.45765102e-01
5.68713009e-01 1.61822990e-01 4.57941145e-01 -1.03995585e+00
1.79717049e-01 -9.39017087e-02 6.56365216e-01 -1.29044369e-01
1.79375961e-01 3.40846360e-01 4.04276252e-01 -1.61431447e-01
1.05048752e+00 -2.19556466e-01 3.44689846e-01 -2.61215866e-01
6.61252558e-01 3.07249278e-01 3.76516670e-01 4.66472864e-01
-2.80453712e-01 3.50376695e-01 6.47207916e-01 -5.07074416e-01
-8.97604883e-01 -5.97791314e-01 -4.26402837e-01 8.15534770e-01
-3.18241686e-01 2.38985121e-01 -1.17630982e+00 -7.12743461e-01
-3.19109619e-01 3.42610866e-01 -1.27935016e+00 1.91511914e-01
-1.94863424e-01 -9.75650966e-01 7.75994480e-01 6.05197489e-01
9.46280539e-01 -1.03009558e+00 -8.89116108e-01 -6.14900179e-02
2.56837010e-01 -6.03417218e-01 1.13022789e-01 3.82185668e-01
-1.24878883e+00 -1.58980799e+00 -1.08245993e+00 -1.30003548e+00
1.25700533e+00 6.60284841e-03 5.08233249e-01 5.18568456e-01
-8.13353360e-01 1.11539409e-01 -2.96005487e-01 -6.18731260e-01
-4.40052778e-01 3.95748466e-01 -7.10223854e-01 -2.80713379e-01
7.63664365e-01 6.53007627e-02 -7.14178383e-01 1.30272657e-01
-1.08552134e+00 1.09947138e-01 1.17502701e+00 1.02463174e+00
4.37436104e-01 2.57085711e-01 5.03670335e-01 -9.35818255e-01
8.83427620e-01 -2.38915548e-01 -1.37936607e-01 2.59137779e-01
-4.52612281e-01 -3.70274186e-01 2.95666307e-01 -1.63942963e-01
-8.42020750e-01 1.57470047e-01 -3.11960548e-01 2.64944956e-02
-5.01232982e-01 4.65496927e-01 4.24414217e-01 -5.42961776e-01
1.03778708e+00 -1.40027165e-01 6.87729955e-01 -8.70710239e-02
-2.25008667e-01 7.23143935e-01 3.69520187e-01 1.65002078e-01
2.32338995e-01 4.44418281e-01 1.68876335e-01 -1.18476617e+00
-5.97340167e-01 -6.55531466e-01 -8.33077192e-01 -3.96394730e-01
1.22506583e+00 -4.72816199e-01 -3.65292341e-01 7.40327835e-01
-8.37854266e-01 -5.25669634e-01 -1.56979859e-01 4.55771983e-01
-9.84601974e-02 1.48291916e-01 -5.70850313e-01 -5.58720171e-01
-6.05963767e-01 -1.12848878e+00 3.28006804e-01 9.45245683e-01
-1.50767669e-01 -1.50971603e+00 -1.83842838e-01 2.46386364e-01
5.00718713e-01 6.00320101e-01 5.12381732e-01 -5.50166368e-01
3.51385842e-03 -4.18659538e-01 -4.44207162e-01 8.28054488e-01
4.18033987e-01 4.13904756e-01 -8.27848434e-01 -3.01436126e-01
-2.41786957e-01 -1.80623546e-01 1.15368545e+00 7.33376801e-01
1.10512054e+00 -2.64412109e-02 -5.28823137e-01 6.06719375e-01
2.04861212e+00 4.68285769e-01 9.34915364e-01 4.81164455e-01
2.05315769e-01 7.11518347e-01 1.22403957e-01 -2.76656330e-01
-1.20129943e-01 -2.61101842e-01 5.49829423e-01 -4.98670995e-01
-5.58753192e-01 1.51215032e-01 -1.30348682e-01 4.17510629e-01
-5.74542224e-01 -3.37035544e-02 -9.95940685e-01 7.74276376e-01
-1.18427336e+00 -7.67138779e-01 -3.45356762e-01 1.91858315e+00
5.91352463e-01 2.66807228e-01 1.93405628e-01 3.90724510e-01
7.54364669e-01 -5.56761980e-01 -3.63096774e-01 -7.24893451e-01
-6.17965497e-02 5.18258154e-01 9.08964634e-01 3.86991858e-01
-1.09625304e+00 6.48915291e-01 6.79687691e+00 6.39086127e-01
-1.61782384e+00 -1.05311751e-01 9.58941221e-01 1.73656300e-01
2.22840950e-01 -2.13856459e-01 -7.67564237e-01 3.14946622e-01
9.76995647e-01 2.97256708e-01 6.24213517e-02 2.85845637e-01
1.40599206e-01 -8.12271714e-01 -7.47375965e-01 6.54854655e-01
8.49323347e-02 -1.48414588e+00 6.39326647e-02 1.51226699e-01
8.20201397e-01 3.43001895e-02 7.81266764e-02 -2.65333448e-02
5.38920723e-02 -1.53461719e+00 -3.13171685e-01 8.00313175e-01
8.44162345e-01 -7.52171576e-01 1.34551024e+00 1.45140275e-01
-6.65507078e-01 -8.01695213e-02 -4.19816613e-01 4.92617628e-03
-2.12269858e-01 1.24108970e-01 -1.32064319e+00 4.31221485e-01
6.35567009e-01 3.87740761e-01 -1.08867240e+00 1.55096769e+00
1.50938323e-02 8.44188809e-01 -1.95505142e-01 -5.09908199e-01
8.37731302e-01 -9.60907340e-03 4.80356626e-02 1.40938604e+00
2.41409212e-01 -6.40981197e-02 -4.62663502e-01 4.30487633e-01
4.67541963e-01 1.63928524e-01 -4.79515523e-01 1.47545919e-01
4.12388742e-02 1.44714224e+00 -9.43987250e-01 -3.54187675e-02
-4.16770935e-01 7.61484504e-01 -1.76107079e-01 2.93929070e-01
-4.07744408e-01 -6.62391424e-01 1.03370763e-01 3.47595543e-01
1.45388633e-01 3.43775183e-01 -5.03643811e-01 -3.50091845e-01
-4.76635784e-01 -6.13133430e-01 5.40229261e-01 -6.81518793e-01
-1.08310616e+00 7.02536047e-01 -2.16671005e-01 -9.32186604e-01
-8.32509026e-02 -1.23605502e+00 -1.03516197e+00 1.08239985e+00
-1.48239565e+00 -1.32439363e+00 -7.48391449e-01 6.48224354e-01
4.56150115e-01 -3.39859575e-01 9.69771564e-01 5.79510741e-02
-7.59606540e-01 6.60583019e-01 -1.02093302e-01 6.47191286e-01
5.22627473e-01 -1.29964423e+00 -2.59649783e-01 6.26978457e-01
-6.59449697e-01 3.94752204e-01 2.99888045e-01 -5.37669480e-01
-6.09244049e-01 -1.09782374e+00 5.57535172e-01 6.36840984e-02
4.36356604e-01 3.64274114e-01 -6.61792338e-01 3.74929011e-01
8.23273063e-01 -1.43290862e-01 1.20171356e+00 -1.90263391e-01
2.91363329e-01 3.64035666e-02 -1.66326177e+00 4.19399798e-01
2.79908657e-01 -1.39947996e-01 -2.72282898e-01 3.21706116e-01
1.95286289e-01 -4.39631313e-01 -1.11030686e+00 2.70246089e-01
5.24452448e-01 -1.02799785e+00 4.51756448e-01 -7.34581709e-01
5.64459741e-01 1.34873567e-02 1.80089697e-01 -1.18239164e+00
-1.41571894e-01 -2.79796362e-01 5.32166421e-01 8.28116655e-01
8.19019377e-01 -6.65174484e-01 1.33838367e+00 2.64098167e-01
7.82584772e-03 -1.23424637e+00 -8.50956500e-01 -5.13807476e-01
3.25571090e-01 -9.57189407e-03 -4.73694950e-02 7.65939057e-01
-3.36365342e-01 1.47262111e-01 1.28538430e-01 -1.07693158e-01
4.61517036e-01 -7.41021872e-01 3.69401306e-01 -1.23886085e+00
1.54233709e-01 -4.79321212e-01 -7.64373600e-01 -4.05973285e-01
-3.70216191e-01 -6.72446549e-01 -4.33165342e-01 -1.98952425e+00
-3.55182104e-02 -3.01038265e-01 -1.98561400e-01 6.54385805e-01
1.58789441e-01 4.91063029e-01 -8.67155790e-02 -2.03506723e-01
1.11048311e-01 -5.85424960e-01 1.57891631e+00 -1.51147142e-01
-2.06184372e-01 1.58336237e-01 -6.58859730e-01 8.96322668e-01
1.35473228e+00 3.68407033e-02 -4.65008527e-01 -3.60085279e-01
1.56776945e-03 -1.02598019e-01 3.76669466e-01 -1.37988269e+00
4.90431607e-01 -3.75377499e-02 7.98874080e-01 -6.19761169e-01
1.41451284e-02 -9.71351981e-01 -9.45450813e-02 9.65446353e-01
-2.02371746e-01 -9.46611613e-02 4.73328412e-01 1.22125342e-01
-1.95578173e-01 -9.60411429e-01 1.13995302e+00 -4.46384132e-01
-1.02266502e+00 2.81063672e-02 -5.40313005e-01 -4.03567612e-01
1.55200601e+00 -9.95107830e-01 -1.97873160e-01 1.85329728e-02
-1.05761600e+00 1.42067432e-01 2.85438985e-01 9.75532159e-02
4.15975422e-01 -9.70025361e-01 -7.17822373e-01 1.62754744e-01
-1.75202161e-01 1.05921842e-01 2.95437813e-01 1.14634347e+00
-1.27914679e+00 6.24238849e-01 -7.71850944e-01 -4.48637426e-01
-1.56916177e+00 8.29017907e-02 1.04135513e+00 -2.10051879e-01
-1.74950510e-01 1.23579669e+00 -4.94322240e-01 -1.94299862e-01
3.51255149e-01 -4.51665848e-01 -6.31756663e-01 4.16975133e-02
4.35514867e-01 6.40546262e-01 6.34037480e-02 -3.53581905e-01
3.57644930e-02 4.60320443e-01 -4.60710198e-01 2.16959745e-01
1.07863784e+00 2.03372359e-01 -2.56985158e-01 1.21336496e-02
1.29460514e+00 -5.85018873e-01 -1.03431666e+00 2.42851317e-01
-9.76102352e-02 -9.28904265e-02 2.06667408e-01 -1.28105092e+00
-1.24102616e+00 8.29478562e-01 1.20344496e+00 3.29747617e-01
1.46263242e+00 -4.53981638e-01 4.28710312e-01 2.12693676e-01
-2.56078422e-01 -1.48005307e+00 1.46493148e-02 3.08449835e-01
5.65383613e-01 -1.16458726e+00 -5.31566292e-02 -4.40830857e-01
-5.95931530e-01 1.54733658e+00 9.02949989e-01 -4.61641401e-01
6.89637303e-01 2.91779667e-01 4.29217100e-01 -2.43775383e-01
-4.85170782e-01 -3.06967378e-01 3.09239119e-01 8.82335007e-01
6.93799913e-01 -7.21949786e-02 -8.09132695e-01 2.02279642e-01
-2.89908089e-02 4.02651012e-01 6.10382795e-01 8.77693295e-01
-5.69953978e-01 -1.09207892e+00 -2.70399779e-01 1.06692767e+00
-8.49424899e-01 1.10291354e-01 -6.78462744e-01 1.35997355e+00
7.06158280e-01 8.18939447e-01 6.24829605e-02 -3.64093959e-01
5.62862493e-02 -1.74133182e-01 4.33421314e-01 -3.94015074e-01
-1.00701225e+00 -6.53615594e-02 1.27343029e-01 -2.04725564e-01
-7.63495803e-01 -3.27219486e-01 -1.09524798e+00 -1.69377401e-01
-6.03034616e-01 1.15506807e-02 9.00148988e-01 7.65130579e-01
-1.16739340e-01 8.67337227e-01 1.51696041e-01 -1.70562223e-01
-3.81384879e-01 -1.30412078e+00 -4.70184833e-01 -1.04863197e-01
1.81130052e-01 -1.08837269e-01 -1.44218758e-01 1.80887192e-01] | [15.639915466308594, -3.009631872177124] |
6a1da082-21da-4143-bba6-7639fc26f685 | multi-branch-deep-radial-basis-function | 2109.03336 | null | https://arxiv.org/abs/2109.03336v1 | https://arxiv.org/pdf/2109.03336v1.pdf | Multi-Branch Deep Radial Basis Function Networks for Facial Emotion Recognition | Emotion recognition (ER) from facial images is one of the landmark tasks in affective computing with major developments in the last decade. Initial efforts on ER relied on handcrafted features that were used to characterize facial images and then feed to standard predictive models. Recent methodologies comprise end-to-end trainable deep learning methods that simultaneously learn both, features and predictive model. Perhaps the most successful models are based on convolutional neural networks (CNNs). While these models have excelled at this task, they still fail at capturing local patterns that could emerge in the learning process. We hypothesize these patterns could be captured by variants based on locally weighted learning. Specifically, in this paper we propose a CNN based architecture enhanced with multiple branches formed by radial basis function (RBF) units that aims at exploiting local information at the final stage of the learning process. Intuitively, these RBF units capture local patterns shared by similar instances using an intermediate representation, then the outputs of the RBFs are feed to a softmax layer that exploits this information to improve the predictive performance of the model. This feature could be particularly advantageous in ER as cultural / ethnicity differences may be identified by the local units. We evaluate the proposed method in several ER datasets and show the proposed methodology achieves state-of-the-art in some of them, even when we adopt a pre-trained VGG-Face model as backbone. We show it is the incorporation of local information what makes the proposed model competitive. | ['Hugo Jair Escalante', 'Fernanda Hernández-Luquin'] | 2021-09-07 | null | null | null | null | ['facial-emotion-recognition', 'face-model'] | ['computer-vision', 'computer-vision'] | [ 8.33904594e-02 2.97743499e-01 -9.66631696e-02 -7.50843585e-01
-2.93906182e-01 -9.61779803e-02 7.43234754e-01 -8.43272917e-03
-3.23608696e-01 5.10196447e-01 2.28207961e-01 3.64689916e-01
-2.06008688e-01 -5.99805772e-01 -6.40644848e-01 -7.33484149e-01
-1.47394225e-01 2.10803226e-01 -2.80469686e-01 -4.05405253e-01
7.30854496e-02 7.44780064e-01 -1.79668462e+00 6.98468149e-01
4.22980279e-01 1.51187193e+00 -1.38514593e-01 3.94060731e-01
-2.21580148e-01 1.06110406e+00 -1.96756393e-01 -4.69037533e-01
1.70922950e-01 -2.14529589e-01 -7.02025652e-01 -4.00333814e-02
2.15277538e-01 1.30536586e-01 -5.91400899e-02 7.18772113e-01
4.67054158e-01 2.50746846e-01 7.62290478e-01 -1.14767957e+00
-6.41914070e-01 4.47614074e-01 -4.33078796e-01 -1.80731699e-01
1.94758818e-01 -1.43582270e-01 9.67918754e-01 -1.21248865e+00
5.80080390e-01 1.10194361e+00 9.01679218e-01 6.60379350e-01
-9.11323607e-01 -4.74683166e-01 1.10753626e-01 3.14675182e-01
-1.31553102e+00 -7.19475389e-01 1.13348520e+00 -2.62767076e-01
1.00675166e+00 4.07789508e-03 5.08127630e-01 1.08287489e+00
5.75082675e-02 9.74511683e-01 1.09415650e+00 -4.20106649e-01
-5.25784167e-03 4.50810194e-01 -1.47942137e-02 1.03174567e+00
-3.78649563e-01 -1.38819635e-01 -6.80025041e-01 -2.09108018e-03
4.31672722e-01 1.64048240e-01 -1.50169224e-01 -1.80673748e-01
-3.39737773e-01 8.97017717e-01 7.22131908e-01 5.16779482e-01
-7.40604818e-01 6.39313906e-02 4.36585456e-01 2.93086499e-01
6.01677001e-01 1.97538316e-01 -5.47129393e-01 9.24671348e-03
-1.15871358e+00 -1.08464919e-01 5.77391207e-01 3.90519172e-01
9.82899785e-01 2.60612637e-01 -2.36406133e-01 1.15098357e+00
2.32620284e-01 -1.41744986e-01 7.12036133e-01 -4.42787439e-01
4.14955132e-02 8.88713181e-01 -2.21803576e-01 -1.13694119e+00
-5.82185149e-01 -3.65588695e-01 -8.35302055e-01 3.29387635e-01
1.50287628e-01 -2.78576791e-01 -1.07429183e+00 1.79074061e+00
6.24331012e-02 3.11805427e-01 9.06619728e-02 6.73375428e-01
7.92502224e-01 5.80517709e-01 2.60584950e-01 -2.80261729e-02
1.05563331e+00 -1.07609713e+00 -5.15360177e-01 -4.71976884e-02
3.45276922e-01 -6.13895833e-01 6.24350488e-01 3.16150337e-01
-1.05768609e+00 -7.44901597e-01 -8.55313003e-01 1.81300148e-01
-7.12270617e-01 4.07607764e-01 5.58535099e-01 6.30893886e-01
-1.23750186e+00 6.07999384e-01 -6.27965033e-01 -4.60871488e-01
7.14330137e-01 6.27446353e-01 -4.95875239e-01 1.87142298e-01
-1.08936989e+00 1.05371177e+00 1.85266823e-01 5.30641198e-01
-6.77332163e-01 -3.84692162e-01 -8.08698714e-01 3.14546376e-01
6.45326599e-02 -5.54268837e-01 7.61625350e-01 -1.92461240e+00
-1.82176363e+00 8.80664766e-01 -1.69640437e-01 -4.91088033e-01
3.54678541e-01 -2.26024657e-01 -4.54937994e-01 1.68782219e-01
-4.59608108e-01 7.11058259e-01 1.08442807e+00 -1.37732661e+00
-4.14793789e-01 -4.39350873e-01 -1.28609642e-01 -1.09086605e-02
-6.12909555e-01 2.17848748e-01 -2.79024869e-01 -4.29549694e-01
-2.10214525e-01 -9.45619524e-01 -2.00465798e-01 -8.72826651e-02
-1.15185171e-01 -3.43854636e-01 8.79112005e-01 -4.36311692e-01
1.01974785e+00 -2.03946352e+00 1.89188510e-01 4.90948170e-01
-6.19574822e-02 4.83206451e-01 -2.97968060e-01 3.70418668e-01
-3.31437171e-01 2.67394762e-02 6.04391210e-02 -6.00324631e-01
5.10600954e-02 7.41939768e-02 -8.91798735e-02 4.38094735e-01
6.88881755e-01 9.68575835e-01 -4.33127016e-01 -1.17622450e-01
2.17090294e-01 9.24689770e-01 -3.98696482e-01 3.25900227e-01
-6.58567473e-02 2.10606471e-01 -2.36618921e-01 6.22910738e-01
5.01473725e-01 2.02935711e-01 5.77771962e-02 -3.33601087e-01
1.87170263e-02 -2.32322440e-01 -9.96337056e-01 1.44056118e+00
-6.40078723e-01 4.54319119e-01 1.39654532e-01 -1.47208726e+00
1.33917391e+00 5.21935284e-01 6.19477153e-01 -5.67809939e-01
3.27086061e-01 1.44136205e-01 -1.79208174e-01 -6.42231286e-01
3.95150214e-01 -3.14611882e-01 1.70313329e-01 4.16647736e-03
5.53968072e-01 4.35877532e-01 -3.60066205e-01 -4.53786016e-01
6.89233899e-01 3.23322415e-01 3.44645441e-01 -4.66309302e-02
7.86294460e-01 -3.71833801e-01 5.55251539e-01 3.48378748e-01
-1.74838424e-01 6.17142320e-01 4.70672309e-01 -7.87865639e-01
-8.10839236e-01 -6.37089014e-01 -1.40755266e-01 1.29024589e+00
-3.39497119e-01 -2.10297361e-01 -6.20879114e-01 -6.76886261e-01
-2.92191863e-01 3.27291876e-01 -1.11166048e+00 -3.41158926e-01
-2.63173968e-01 -6.51956439e-01 6.54405653e-01 6.36172831e-01
4.52219695e-01 -1.47833979e+00 -7.67742932e-01 1.99125186e-01
3.76934826e-01 -9.14052129e-01 2.57028788e-01 5.81393063e-01
-6.62542105e-01 -7.27868915e-01 -7.15386748e-01 -7.55978227e-01
6.46785617e-01 -2.54892468e-01 9.99713480e-01 -2.57982854e-02
-2.79925168e-01 4.52930897e-01 -4.81286347e-01 -5.98804712e-01
9.16907340e-02 5.91962710e-02 2.26443261e-02 8.82000566e-01
6.32525384e-01 -5.41453600e-01 -5.49234152e-01 -2.47243568e-02
-7.72205770e-01 -1.70743451e-01 6.14751518e-01 1.01552808e+00
3.93244058e-01 -1.14647977e-01 7.46400714e-01 -9.56899822e-01
5.94288647e-01 -7.83828914e-01 -1.23899532e-02 2.92217553e-01
-3.31009001e-01 9.49960351e-02 8.28599751e-01 -3.46622646e-01
-1.14560235e+00 3.13187212e-01 -4.57872659e-01 -7.36057520e-01
-3.95464957e-01 7.50650644e-01 -8.78129527e-02 -1.80617034e-01
3.89462352e-01 9.46734846e-02 -2.48063207e-02 -3.20422798e-01
2.05462545e-01 6.49086654e-01 8.94085616e-02 -5.82447767e-01
3.08675587e-01 2.64174044e-01 -6.08242564e-02 -6.77718818e-01
-6.09322071e-01 -2.71608263e-01 -6.17204487e-01 -3.96178663e-01
8.27876091e-01 -7.54897773e-01 -6.63095236e-01 3.57094228e-01
-9.08230126e-01 -1.23869732e-01 -1.80157259e-01 1.38256088e-01
-5.20735979e-01 -3.40085298e-01 -4.32797611e-01 -1.14083886e+00
-4.30472821e-01 -1.04450786e+00 9.68598962e-01 6.68874800e-01
-2.12991357e-01 -1.11939621e+00 1.82215553e-02 2.33228970e-02
7.20805705e-01 4.62004840e-01 9.12383020e-01 -8.96759868e-01
7.23730475e-02 -3.00621599e-01 -1.13250747e-01 4.83577192e-01
6.06990978e-02 1.86490357e-01 -1.46227622e+00 3.72522883e-02
-1.41842380e-01 -6.67582154e-01 1.13370788e+00 3.48563731e-01
1.40312684e+00 -2.27459207e-01 -7.20970705e-02 7.06436276e-01
1.61970055e+00 1.55562907e-01 7.73947120e-01 2.32028604e-01
5.82211196e-01 8.82808566e-01 2.48640075e-01 5.58124959e-01
3.44095796e-01 5.58687091e-01 5.62333941e-01 -4.28262353e-01
1.06587052e-01 -4.74855229e-02 5.37651956e-01 5.57278395e-01
-6.42537773e-01 1.33402556e-01 -7.25474119e-01 5.12008667e-01
-2.05571604e+00 -1.03933835e+00 3.73237997e-01 1.82158339e+00
5.04767597e-01 -9.46655422e-02 1.42189875e-01 1.09991200e-01
3.98308516e-01 2.22732961e-01 -3.10807377e-01 -1.09676933e+00
-2.09280610e-01 8.28138471e-01 -3.25844027e-02 1.54065669e-01
-1.09034109e+00 1.04312801e+00 5.36049366e+00 5.73243260e-01
-1.69590235e+00 -1.26402244e-01 9.94471967e-01 -5.50941788e-02
-1.81638300e-02 -4.84492302e-01 -5.51309526e-01 1.45789146e-01
1.02297580e+00 3.45312774e-01 2.05535159e-01 1.09193015e+00
1.18961915e-01 2.37504303e-01 -9.28951561e-01 9.82230365e-01
2.72328824e-01 -1.14368343e+00 9.74575728e-02 -2.47451261e-01
7.09497452e-01 -1.03365630e-01 4.65427071e-01 5.24620235e-01
5.93437254e-02 -1.58363283e+00 4.51539755e-01 9.87159669e-01
5.18361628e-01 -1.13635015e+00 1.05959892e+00 -9.36726183e-02
-1.13875556e+00 -3.25202435e-01 -5.76008081e-01 1.11738369e-02
-3.01357418e-01 2.79106647e-01 -7.99206972e-01 5.73968172e-01
7.48956800e-01 6.61926150e-01 -5.66828430e-01 7.44265616e-01
9.23246983e-03 5.63741326e-01 -2.02825069e-01 5.09055192e-03
5.85184932e-01 -1.74773946e-01 8.09614733e-02 1.50173450e+00
3.28500986e-01 -1.24181710e-01 4.85031633e-03 7.28784442e-01
-3.20422500e-01 3.55447620e-01 -7.34138131e-01 -4.15532961e-02
-1.41478300e-01 1.75447011e+00 -4.67722476e-01 -3.64061855e-02
-4.67313379e-01 9.87001181e-01 8.25978160e-01 3.70164782e-01
-7.61617780e-01 -3.11895758e-01 6.71946406e-01 -7.22590834e-02
4.90172446e-01 7.93112963e-02 -1.39415234e-01 -9.71784949e-01
-1.70823202e-01 -8.36539686e-01 3.37633520e-01 -6.40305877e-01
-1.41598046e+00 1.12656164e+00 -5.02590597e-01 -8.77230287e-01
-3.83650184e-01 -8.06091011e-01 -8.43073308e-01 8.48018348e-01
-1.69811833e+00 -1.69794881e+00 -3.41182470e-01 9.39987898e-01
5.31594336e-01 -4.16758239e-01 1.18668449e+00 1.19715661e-01
-6.00026667e-01 8.13750267e-01 -2.74066366e-02 1.41226366e-01
7.74723589e-01 -1.18340385e+00 -5.74968278e-01 5.24589360e-01
3.60007793e-01 6.42971814e-01 4.52195257e-01 -1.54086813e-01
-1.09531748e+00 -1.05524611e+00 8.27989101e-01 -2.58763693e-02
3.95916641e-01 -2.68348634e-01 -8.15468013e-01 6.12347662e-01
4.67532814e-01 4.28203881e-01 9.30667818e-01 3.98710549e-01
-4.50896293e-01 -3.27448308e-01 -1.25101328e+00 3.87136132e-01
6.24985456e-01 -5.53668976e-01 -3.43556881e-01 -1.39914408e-01
-1.84752584e-01 4.31899838e-02 -8.31128597e-01 4.61299092e-01
8.34912241e-01 -1.08665025e+00 7.06541955e-01 -9.36532855e-01
7.91872084e-01 7.82652870e-02 -1.81621030e-01 -1.48669803e+00
-2.91955382e-01 -4.53930706e-01 -5.38370870e-02 1.39229953e+00
4.74350750e-01 -3.52739453e-01 8.56494248e-01 7.46484578e-01
-7.62490928e-02 -1.29844975e+00 -7.19369709e-01 -1.07804880e-01
1.78248566e-02 -2.88716584e-01 4.42453772e-01 1.01206458e+00
2.22934484e-02 3.11708242e-01 -6.13433659e-01 -1.00518532e-01
1.13298297e-01 -3.34583111e-02 4.81546462e-01 -1.12358618e+00
-2.52225567e-02 -6.09316468e-01 -6.26130700e-01 -3.84112418e-01
4.77483958e-01 -8.79517734e-01 -2.46488266e-02 -1.26899004e+00
1.59360114e-02 -4.56951946e-01 -7.55487978e-01 8.23591352e-01
-2.18538538e-01 5.03845513e-01 2.83185720e-01 -1.99565679e-01
-5.37349701e-01 7.97512770e-01 7.55823791e-01 -4.10898402e-02
-3.35248768e-01 -3.26090157e-02 -6.27823710e-01 8.62010002e-01
8.99638474e-01 -1.69884786e-01 -2.48922020e-01 -2.27608353e-01
1.22813620e-01 -1.88114688e-01 3.39546978e-01 -1.09302652e+00
3.87543023e-01 8.93774480e-02 9.09787655e-01 2.59231087e-02
4.93920475e-01 -1.10298204e+00 2.06694733e-02 1.71722863e-02
-4.00952488e-01 -1.30921185e-01 2.41828769e-01 2.64811397e-01
-6.12937748e-01 -2.18771696e-01 1.00869644e+00 -1.33215114e-01
-8.59515250e-01 4.30095643e-01 -3.66691321e-01 -5.45016468e-01
1.02735949e+00 -4.17624533e-01 1.89112499e-01 -4.05832142e-01
-1.01240575e+00 -1.14228517e-01 7.04127178e-02 5.53101599e-01
6.77283764e-01 -1.27458656e+00 -6.19554043e-01 2.38899589e-01
1.37945235e-01 -4.33732837e-01 1.87069044e-01 7.90818334e-01
-1.36697337e-01 2.39429086e-01 -7.34687150e-01 -3.99050593e-01
-1.29611099e+00 2.95088947e-01 6.40458167e-01 -3.47504407e-01
-1.15639850e-01 9.94860172e-01 -1.43734053e-01 -4.22912031e-01
1.80463210e-01 1.24638930e-01 -8.27002466e-01 3.77018154e-01
4.15388227e-01 -7.81705379e-02 9.67478454e-02 -9.48199034e-01
-3.22313100e-01 6.91634536e-01 -1.27207205e-01 1.59091696e-01
2.02284074e+00 8.43453184e-02 -7.01832399e-02 5.08637309e-01
1.36391449e+00 -7.05378130e-02 -1.09403002e+00 -3.06085974e-01
1.48542717e-01 -7.52356648e-02 1.03435725e-01 -9.07361925e-01
-1.56841564e+00 1.02571714e+00 7.62181401e-01 -1.52751163e-01
1.50017726e+00 -1.84611320e-01 4.61483389e-01 2.38876164e-01
1.07981153e-01 -1.13362813e+00 8.17650408e-02 5.07061958e-01
7.21006632e-01 -1.29185200e+00 -4.29801673e-01 2.98472680e-02
-8.43958199e-01 1.68900299e+00 5.84652960e-01 -4.87842232e-01
7.82305419e-01 2.06601217e-01 1.94133297e-02 -2.97045916e-01
-7.90295720e-01 -3.84750634e-01 5.23287356e-01 5.01400888e-01
7.51688302e-01 -1.07245907e-01 -1.75453484e-01 1.12487233e+00
9.13852677e-02 2.41551667e-01 8.27605203e-02 7.88590074e-01
-3.50858450e-01 -1.04542410e+00 -4.54686955e-03 4.12655830e-01
-7.40071595e-01 6.09653257e-02 -5.51367939e-01 5.98012984e-01
5.31022489e-01 7.93711841e-01 -1.10516129e-02 -6.33089423e-01
2.31401488e-01 5.22502124e-01 4.83650476e-01 -3.98663431e-01
-1.10169685e+00 -1.30992219e-01 1.07684195e-01 -5.97653568e-01
-7.19854951e-01 -4.25958127e-01 -1.08064926e+00 -5.02501689e-02
-2.09290013e-02 8.27850774e-02 7.10231125e-01 9.73158777e-01
4.05159891e-01 4.95692313e-01 7.62700617e-01 -9.70115483e-01
-7.92041346e-02 -1.05767536e+00 -4.98476863e-01 6.11460865e-01
2.78753877e-01 -6.98606968e-01 -3.31862532e-02 2.09783301e-01] | [13.563758850097656, 1.8396761417388916] |
407df860-dde2-4e2a-a03d-9fd05af90489 | regionclip-region-based-language-image | 2112.09106 | null | https://arxiv.org/abs/2112.09106v1 | https://arxiv.org/pdf/2112.09106v1.pdf | RegionCLIP: Region-based Language-Image Pretraining | Contrastive language-image pretraining (CLIP) using image-text pairs has achieved impressive results on image classification in both zero-shot and transfer learning settings. However, we show that directly applying such models to recognize image regions for object detection leads to poor performance due to a domain shift: CLIP was trained to match an image as a whole to a text description, without capturing the fine-grained alignment between image regions and text spans. To mitigate this issue, we propose a new method called RegionCLIP that significantly extends CLIP to learn region-level visual representations, thus enabling fine-grained alignment between image regions and textual concepts. Our method leverages a CLIP model to match image regions with template captions and then pretrains our model to align these region-text pairs in the feature space. When transferring our pretrained model to the open-vocabulary object detection tasks, our method significantly outperforms the state of the art by 3.8 AP50 and 2.2 AP for novel categories on COCO and LVIS datasets, respectively. Moreoever, the learned region representations support zero-shot inference for object detection, showing promising results on both COCO and LVIS datasets. Our code is available at https://github.com/microsoft/RegionCLIP. | ['Jianfeng Gao', 'Yin Li', 'Lu Yuan', 'Xiyang Dai', 'Luowei Zhou', 'Liunian Harold Li', 'Noel Codella', 'Chunyuan Li', 'Pengchuan Zhang', 'Jianwei Yang', 'Yiwu Zhong'] | 2021-12-16 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhong_RegionCLIP_Region-Based_Language-Image_Pretraining_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhong_RegionCLIP_Region-Based_Language-Image_Pretraining_CVPR_2022_paper.pdf | cvpr-2022-1 | ['open-vocabulary-object-detection'] | ['computer-vision'] | [ 3.95766526e-01 -1.07444286e-01 -5.26862979e-01 -3.85684490e-01
-1.31905448e+00 -7.33796358e-01 9.08971906e-01 6.53043911e-02
-5.44198871e-01 1.78162843e-01 4.53567393e-02 2.72090454e-02
4.56494898e-01 -4.27902460e-01 -1.23325098e+00 -4.46915030e-01
3.87172312e-01 3.11884105e-01 3.76856625e-01 1.26877442e-01
2.10384160e-01 1.42295823e-01 -1.52583957e+00 6.74198329e-01
3.91601622e-01 1.14471412e+00 2.20162228e-01 7.36013889e-01
-1.11328848e-01 6.25035167e-01 -2.62803435e-01 -3.46479893e-01
4.40731078e-01 -2.36275733e-01 -7.13041604e-01 3.14945966e-01
1.21831810e+00 -4.78884041e-01 -4.51829523e-01 1.10374022e+00
3.33818913e-01 4.16271563e-04 9.60376918e-01 -1.32608402e+00
-1.07328200e+00 4.07466739e-01 -8.72508883e-01 3.05381835e-01
1.18055783e-01 2.86717981e-01 1.21305823e+00 -1.50162435e+00
7.99862742e-01 1.17096710e+00 5.55576563e-01 4.28270727e-01
-1.36443424e+00 -8.30858409e-01 1.60964727e-01 3.56603652e-01
-1.73415041e+00 -6.06616974e-01 4.89557266e-01 -6.62464619e-01
1.09002709e+00 -7.45691508e-02 3.25482607e-01 1.22159243e+00
2.32597262e-01 1.11235368e+00 8.11849952e-01 -4.98253793e-01
-2.70922463e-02 3.44436854e-01 3.85138728e-02 6.68995619e-01
1.77451707e-02 -4.61780727e-02 -5.26836812e-01 1.29383415e-01
6.22992337e-01 2.69971550e-01 2.50351150e-02 -6.25107169e-01
-1.21777093e+00 7.79334366e-01 7.18395531e-01 2.64738292e-01
-1.53139621e-01 2.63124704e-01 4.84053850e-01 6.62798658e-02
5.33233523e-01 3.05794507e-01 -2.83204705e-01 3.03336412e-01
-1.16553378e+00 1.92957401e-01 3.97145480e-01 1.31779623e+00
8.42498660e-01 -2.26385459e-01 -6.26772463e-01 9.30928648e-01
6.88722581e-02 7.73833036e-01 4.16627675e-01 -7.38396227e-01
5.28643131e-01 4.89457697e-01 -6.85313717e-02 -7.18243659e-01
-8.63383859e-02 -5.29258311e-01 -4.88670409e-01 5.39681129e-02
4.72794354e-01 2.52757698e-01 -1.29864836e+00 1.62006557e+00
2.63006911e-02 1.60518825e-01 -4.65991609e-02 8.83428812e-01
7.96471536e-01 7.89788961e-01 1.99448556e-01 3.21216375e-01
1.77461934e+00 -1.23545587e+00 -3.72551382e-01 -6.35943592e-01
5.80054700e-01 -8.16959739e-01 1.34688532e+00 3.84263471e-02
-1.03367364e+00 -6.52995586e-01 -9.41154063e-01 -4.27018553e-01
-5.38099468e-01 2.81404763e-01 4.82183136e-02 2.07568809e-01
-8.66401672e-01 2.53990944e-02 -5.56208014e-01 -6.43841922e-01
8.42120230e-01 2.01884229e-02 -4.06360090e-01 -3.94091308e-01
-8.50006819e-01 6.87494993e-01 4.34523106e-01 -4.48810577e-01
-1.18682504e+00 -9.29132104e-01 -1.10391653e+00 2.43559465e-01
6.17696524e-01 -4.19272214e-01 1.24479902e+00 -1.14123023e+00
-7.70155370e-01 1.32788503e+00 -2.09670812e-01 -6.84824944e-01
4.56998587e-01 -1.35234103e-01 -9.68980119e-02 3.90874177e-01
4.76714194e-01 1.21473658e+00 1.21061063e+00 -1.06201231e+00
-6.99169576e-01 -2.94725657e-01 -1.94567263e-01 8.39385577e-03
-3.55987936e-01 1.71602279e-01 -9.59422350e-01 -7.73501873e-01
-1.74750209e-01 -8.81551504e-01 1.13331437e-01 5.61310589e-01
-2.96948642e-01 -3.35331410e-01 7.76614726e-01 -5.17603874e-01
7.79723167e-01 -2.28116846e+00 -1.36170104e-01 -6.23121634e-02
1.87538460e-01 1.97221860e-01 -5.26263773e-01 1.75994903e-01
-1.20780617e-02 7.35521596e-03 -2.89859325e-01 -4.47879970e-01
4.38544042e-02 -1.77348033e-02 -5.35023749e-01 5.83484590e-01
4.90889162e-01 1.31273532e+00 -7.12071300e-01 -7.72726536e-01
2.64255375e-01 3.75939697e-01 -7.03192115e-01 1.59474984e-01
-3.15044791e-01 5.07491827e-02 -6.85850233e-02 7.41503775e-01
6.14667654e-01 -5.71504951e-01 -9.54566002e-02 -3.31888199e-01
1.77933261e-01 -1.76252112e-01 -6.75498903e-01 1.84051490e+00
-4.68956232e-01 1.01537693e+00 -1.36418119e-01 -1.04906321e+00
7.16847301e-01 1.27918854e-01 3.84426743e-01 -9.21950936e-01
8.40444192e-02 -8.65676105e-02 -2.38725007e-01 -4.51776117e-01
2.78068513e-01 4.01406251e-02 -3.15466613e-01 4.01597232e-01
5.43780506e-01 -7.74398223e-02 1.01519562e-01 4.81671184e-01
7.95626938e-01 2.14683190e-01 2.97925949e-01 -2.06561834e-01
3.21335465e-01 6.53519705e-02 2.43571907e-01 1.10492659e+00
-3.04515094e-01 9.02070224e-01 3.23014319e-01 -2.97485888e-01
-1.32293499e+00 -1.35844994e+00 -3.12471867e-01 1.54291201e+00
1.76681623e-01 -3.64806920e-01 -7.28535771e-01 -7.74992704e-01
1.94362715e-01 6.40048027e-01 -8.57302070e-01 -9.37868208e-02
-3.08304012e-01 -1.79633483e-01 5.49450874e-01 7.62265146e-01
3.71559381e-01 -8.35683823e-01 -4.37990367e-01 -7.40341619e-02
-2.13020027e-01 -1.46984637e+00 -9.96300399e-01 1.32900551e-01
-3.32524657e-01 -9.33370948e-01 -9.72917080e-01 -1.11336100e+00
7.05347121e-01 5.50999165e-01 1.07370114e+00 -2.74377391e-02
-8.25229228e-01 5.45153618e-01 -3.00617933e-01 -4.00515050e-01
-2.83717364e-01 -3.00695989e-02 -1.76422089e-01 8.98132622e-02
5.27097762e-01 -1.14322864e-01 -6.88201845e-01 3.67065042e-01
-9.30101693e-01 1.60445854e-01 6.82494819e-01 8.78877223e-01
6.66377544e-01 -5.78702629e-01 3.40850025e-01 -6.85397208e-01
4.06546704e-02 -5.11508048e-01 -6.46166146e-01 5.12548447e-01
-5.13152599e-01 1.46592796e-01 3.80305320e-01 -5.17536461e-01
-8.66325200e-01 3.28533173e-01 1.48815230e-01 -9.01043117e-01
-2.11461931e-01 3.60636674e-02 1.03889920e-01 -3.42987143e-02
7.97555149e-01 4.45896894e-01 -5.39263226e-02 -3.39457393e-01
7.19750941e-01 8.46355438e-01 8.32940042e-01 -4.74266231e-01
8.77388000e-01 6.65672243e-01 -5.38902879e-01 -6.62059188e-01
-1.22971797e+00 -9.17374730e-01 -7.92350471e-01 -7.68164769e-02
1.20152855e+00 -1.29170799e+00 -2.04019487e-01 1.25634953e-01
-1.12905276e+00 -3.22302073e-01 -1.30924806e-01 2.65571594e-01
-6.93802714e-01 1.90014243e-01 -5.08942366e-01 -4.20557857e-01
-4.35127646e-01 -1.10525179e+00 1.40999556e+00 -6.00343831e-02
-3.57611552e-02 -6.71114743e-01 -2.50219166e-01 3.83705556e-01
2.68556446e-01 -4.27835435e-02 7.16959715e-01 -7.62106299e-01
-5.71848035e-01 -3.37166309e-01 -7.90472090e-01 3.05716991e-01
-1.76348016e-01 -2.27828935e-01 -1.14230978e+00 -4.21337664e-01
-3.45614016e-01 -7.45816767e-01 1.12812746e+00 2.05550268e-01
1.23303986e+00 -3.18320125e-01 -5.00392139e-01 7.24304199e-01
1.49863136e+00 -1.95902675e-01 4.60033089e-01 1.99506953e-01
6.06528103e-01 4.05167043e-01 7.26967156e-01 3.09930861e-01
1.66771770e-01 9.33833539e-01 2.11422935e-01 -2.94072807e-01
-4.31260496e-01 -4.64932561e-01 3.04275870e-01 4.63301465e-02
4.81544912e-01 -1.71948388e-01 -1.06890702e+00 8.17226052e-01
-1.81564450e+00 -9.97761965e-01 3.62711072e-01 1.99156713e+00
8.56384158e-01 2.10607126e-01 1.12786710e-01 -5.82201898e-01
7.94443130e-01 1.04030624e-01 -7.06890643e-01 -6.34364113e-02
3.88224348e-02 1.26283050e-01 7.82996833e-01 3.37763250e-01
-1.34759390e+00 1.27120614e+00 5.79156351e+00 9.55551207e-01
-1.10769153e+00 4.52776372e-01 6.70745492e-01 -4.37686592e-01
1.03091367e-01 -2.15675086e-01 -1.03138494e+00 3.07822347e-01
6.31959260e-01 -2.58425832e-01 3.30743998e-01 1.10362363e+00
-1.01673260e-01 1.89600646e-01 -1.31273282e+00 1.26398921e+00
6.20415926e-01 -1.53708005e+00 2.82071561e-01 -7.58570433e-02
8.82673740e-01 4.33033735e-01 2.82355517e-01 5.67875504e-01
-1.04207955e-02 -1.05391181e+00 9.42873955e-01 1.86746225e-01
1.01899207e+00 -3.45216095e-01 4.63164747e-01 1.27548888e-01
-1.19528961e+00 -1.29148707e-01 -5.42544067e-01 2.06792295e-01
-1.34590179e-01 3.33179571e-02 -1.08942926e+00 2.01858245e-02
8.17802727e-01 8.76280546e-01 -9.19555545e-01 8.96764517e-01
-7.44455680e-02 5.30213714e-01 -5.81020676e-02 2.46424779e-01
4.29108322e-01 2.59304911e-01 3.70291799e-01 1.42530215e+00
6.38303608e-02 -2.37591639e-01 4.01181102e-01 1.21356475e+00
-4.34392095e-01 1.47161201e-01 -6.26075745e-01 3.96984406e-02
4.66193616e-01 1.29972804e+00 -6.51045501e-01 -7.36352742e-01
-6.89110458e-01 1.17332196e+00 4.58229691e-01 4.16026473e-01
-1.03770101e+00 -3.97187561e-01 5.79378605e-01 2.17737198e-01
7.29258418e-01 3.36660957e-03 -1.53985560e-01 -1.28894258e+00
-3.92883234e-02 -7.15256989e-01 4.28425312e-01 -9.05838907e-01
-1.31382549e+00 3.72864991e-01 1.12476826e-01 -1.28493667e+00
-1.95042357e-01 -8.32006574e-01 -5.95316947e-01 5.61735809e-01
-1.44083226e+00 -1.49500811e+00 -3.17728668e-01 7.29730010e-01
1.03135991e+00 -9.09250602e-02 5.22054315e-01 2.24614516e-01
-4.02601719e-01 9.62436497e-01 2.04306647e-01 5.24188221e-01
1.11026955e+00 -1.03868473e+00 5.99825799e-01 9.19335246e-01
5.66027224e-01 5.14559209e-01 5.22007763e-01 -3.82098705e-01
-1.33028150e+00 -1.39898777e+00 5.85147977e-01 -7.28650331e-01
7.47433245e-01 -9.56122577e-01 -1.07784116e+00 9.23190176e-01
3.34436089e-01 6.60676956e-01 4.22271281e-01 -6.06805272e-02
-1.04918528e+00 2.98162960e-02 -8.78767192e-01 5.19959748e-01
9.62840378e-01 -8.86154234e-01 -6.93115354e-01 6.42224848e-01
7.98614204e-01 -2.35976994e-01 -7.81260490e-01 1.29760265e-01
6.21994853e-01 -4.89819586e-01 1.18223166e+00 -6.54776394e-01
5.42225957e-01 -1.97200134e-01 -2.86268920e-01 -8.21498096e-01
-3.96392703e-01 -9.14928094e-02 4.96173613e-02 1.09770286e+00
5.21872997e-01 -3.85527462e-01 5.86962163e-01 3.49508762e-01
3.08307540e-02 -6.30981982e-01 -7.73250341e-01 -9.27106619e-01
2.86437869e-01 -4.04874086e-01 6.62639141e-02 7.89165616e-01
-7.25846663e-02 4.97105151e-01 -3.17431003e-01 4.26332280e-03
6.90959990e-01 2.65484452e-01 8.75324130e-01 -7.68441498e-01
-3.96586359e-01 -5.91430664e-01 -4.34265554e-01 -1.04969573e+00
3.39920580e-01 -1.28004003e+00 3.52211535e-01 -1.30835688e+00
7.72593975e-01 -1.29469395e-01 -4.19176400e-01 6.69866562e-01
-2.10755989e-01 8.52562726e-01 5.60336232e-01 3.50617915e-01
-1.06996310e+00 3.41511786e-01 9.22916234e-01 -5.59266210e-01
1.41816512e-01 -4.16181386e-01 -5.46073020e-01 5.16381025e-01
6.79851711e-01 -5.55559456e-01 -1.62979901e-01 -4.46675390e-01
-1.03950135e-01 -3.26126635e-01 7.05411613e-01 -9.91304219e-01
3.13706309e-01 7.00621828e-02 7.07183838e-01 -6.51363790e-01
2.72168726e-01 -6.79520845e-01 -5.07968903e-01 4.29289728e-01
-7.45037079e-01 -2.52721637e-01 4.09699768e-01 7.94773340e-01
-4.65642698e-02 -1.08492084e-01 1.05760896e+00 -1.09235503e-01
-1.08496928e+00 3.92315060e-01 -2.15071291e-01 2.73700327e-01
1.19723701e+00 -1.25556543e-01 -5.06774545e-01 -2.39293754e-01
-5.64203620e-01 3.75322223e-01 6.18819356e-01 8.03725243e-01
6.44995332e-01 -1.24874270e+00 -7.76353300e-01 3.60250503e-01
8.94237399e-01 -1.88116953e-01 2.39401221e-01 8.56488764e-01
-1.42871454e-01 6.04282618e-01 -1.72138736e-01 -1.10169995e+00
-1.36200368e+00 1.01828814e+00 3.42280507e-01 9.45352986e-02
-8.38012874e-01 9.02265251e-01 9.38170552e-01 -3.33553016e-01
4.20837253e-01 -2.32391909e-01 2.79527217e-01 -1.03643447e-01
7.92005122e-01 -1.63410917e-01 -1.14893846e-01 -6.58033133e-01
-4.61075544e-01 7.95793951e-01 -5.01906872e-01 1.88015215e-02
9.78354514e-01 -2.07460552e-01 2.49609917e-01 2.69148976e-01
1.65045536e+00 -3.14805239e-01 -1.60113561e+00 -7.78380990e-01
-2.27700695e-02 -4.41524476e-01 7.85044581e-02 -7.84560382e-01
-7.55378425e-01 1.00339246e+00 8.09568107e-01 -4.62125421e-01
7.79790044e-01 5.74949324e-01 6.77093983e-01 4.01174843e-01
1.38009310e-01 -9.54093754e-01 5.68063080e-01 4.38371420e-01
8.46156478e-01 -1.67271674e+00 -1.72712952e-01 -1.97090805e-01
-7.60685503e-01 8.91429782e-01 7.42665052e-01 -3.34083796e-01
3.79123926e-01 1.82377174e-01 -5.25544807e-02 -7.14437738e-02
-7.95789778e-01 -3.89108717e-01 6.88138068e-01 5.61089993e-01
3.15523118e-01 -1.09444439e-01 2.92285144e-01 3.71487588e-01
2.15000480e-01 -6.41442612e-02 2.09184662e-01 7.36455500e-01
-6.85278296e-01 -6.37880445e-01 -4.52969968e-01 4.63182807e-01
-4.38776791e-01 -3.60962093e-01 -2.55751163e-01 7.40925074e-01
-2.21852716e-02 5.88447034e-01 5.35476208e-01 -5.26834279e-03
1.34039402e-01 1.22551955e-01 4.77561533e-01 -8.95470083e-01
-2.64803886e-01 9.59996209e-02 -4.73270893e-01 -6.93493068e-01
1.51819997e-02 -5.42301536e-01 -1.13390446e+00 2.49400973e-01
-1.66735083e-01 -2.17620417e-01 5.95027685e-01 8.70589614e-01
4.73465770e-01 3.72254699e-01 3.15601498e-01 -9.17083859e-01
-5.64496219e-01 -8.93534303e-01 -2.58983463e-01 7.09530532e-01
4.04130042e-01 -6.45208776e-01 -6.05471209e-02 2.86720663e-01] | [9.992239952087402, 1.6455931663513184] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.