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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
bd7c161d-bb54-49ec-bd4d-ba33003463e5 | from-newspaper-to-microblogging-what-does-it | null | null | https://aclanthology.org/W13-1611 | https://aclanthology.org/W13-1611.pdf | From newspaper to microblogging: What does it take to find opinions? | null | ['Nina Kr{\\"u}ger', 'Manfred Stede', 'Jonathan Sonntag', 'Stefan Stieglitz', 'Wladimir Sidorenko'] | 2013-06-01 | null | null | null | ws-2013-6 | ['twitter-sentiment-analysis'] | ['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] | [-7.496123313903809, 3.5282883644104004] |
5a0abd5f-c133-4fac-b58b-25e235d94c72 | cedas-a-compressed-decentralized-stochastic | 2301.05872 | null | https://arxiv.org/abs/2301.05872v1 | https://arxiv.org/pdf/2301.05872v1.pdf | CEDAS: A Compressed Decentralized Stochastic Gradient Method with Improved Convergence | In this paper, we consider solving the distributed optimization problem over a multi-agent network under the communication restricted setting. We study a compressed decentralized stochastic gradient method, termed ``compressed exact diffusion with adaptive stepsizes (CEDAS)", and show the method asymptotically achieves comparable convergence rate as centralized SGD for both smooth strongly convex objective functions and smooth nonconvex objective functions under unbiased compression operators. In particular, to our knowledge, CEDAS enjoys so far the shortest transient time (with respect to the graph specifics) for achieving the convergence rate of centralized SGD, which behaves as $\mathcal{O}(nC^3/(1-\lambda_2)^{2})$ under smooth strongly convex objective functions, and $\mathcal{O}(n^3C^6/(1-\lambda_2)^4)$ under smooth nonconvex objective functions, where $(1-\lambda_2)$ denotes the spectral gap of the mixing matrix, and $C>0$ is the compression-related parameter. Numerical experiments further demonstrate the effectiveness of the proposed algorithm. | ['Shi Pu', 'Kun Huang'] | 2023-01-14 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-1.04685739e-01 2.91258126e-01 -2.01784750e-03 1.28817782e-01
-9.37733769e-01 -4.37694669e-01 -4.11866456e-02 4.39349562e-01
-5.66873908e-01 8.58782411e-01 9.44587663e-02 -3.06845218e-01
-5.97036839e-01 -7.42670953e-01 -6.18467689e-01 -1.09782970e+00
-8.50188136e-01 3.44805419e-01 -2.90979415e-01 -2.31642663e-01
9.27313939e-02 8.63092393e-02 -6.95763350e-01 -8.92785788e-01
1.30449176e+00 1.22870421e+00 2.13777110e-01 4.48571026e-01
1.61548465e-01 5.57580769e-01 -5.63196540e-01 -5.61595142e-01
6.70475543e-01 -6.91702247e-01 -5.29346347e-01 3.54481310e-01
1.04669638e-01 -1.14734665e-01 -5.81959188e-01 1.82016397e+00
6.50682032e-01 4.42689955e-01 4.41581279e-01 -9.94846582e-01
-5.46316445e-01 7.99724817e-01 -1.02123904e+00 3.38279992e-01
1.52605578e-01 7.20929280e-02 8.50928187e-01 -5.52200079e-01
6.43971145e-01 1.01851630e+00 7.34935522e-01 2.70317405e-01
-1.03729653e+00 -5.85616946e-01 3.71287048e-01 -4.43544179e-01
-1.64635444e+00 -1.92244977e-01 6.29347324e-01 -2.97442317e-01
3.51482302e-01 3.80957574e-01 4.74774599e-01 2.35361263e-01
1.06054686e-01 5.44889867e-01 1.24542499e+00 -2.39320695e-01
6.35181248e-01 -2.51600612e-02 -1.94164500e-01 1.02621806e+00
5.48300445e-01 -2.02368632e-01 -1.35607973e-01 -2.76494086e-01
6.82829380e-01 -2.50411928e-01 -6.70196950e-01 2.00930595e-01
-1.03011739e+00 1.17625916e+00 3.44559848e-01 3.52083653e-01
-4.57944989e-01 3.13366681e-01 8.13005269e-02 5.88041425e-01
8.10040712e-01 1.35655180e-01 -5.80676273e-02 -1.84277847e-01
-9.27521527e-01 1.61275983e-01 1.05315089e+00 1.48553467e+00
4.68132555e-01 5.11271775e-01 2.70078424e-02 7.49780416e-01
2.32590094e-01 1.06134188e+00 6.20495575e-03 -1.41274893e+00
8.42133641e-01 2.50856578e-01 8.67454931e-02 -1.37064672e+00
-3.37613463e-01 -9.29295361e-01 -1.47258604e+00 -3.83120216e-02
4.64313775e-01 -9.18876648e-01 -5.26832119e-02 1.95615804e+00
2.88338751e-01 -1.44215703e-01 5.22064827e-02 9.74362850e-01
3.73901159e-01 7.01047242e-01 -4.33363289e-01 -1.08902681e+00
9.61689711e-01 -8.29045773e-01 -6.47848189e-01 -2.45204091e-01
5.35181284e-01 -6.51954651e-01 8.83672237e-01 -3.21451873e-02
-1.55658805e+00 2.25214690e-01 -9.13599849e-01 5.60263336e-01
2.31975988e-01 -2.37611726e-01 4.53088880e-01 7.00944066e-01
-1.30279279e+00 3.88961554e-01 -7.42530406e-01 -1.01772279e-01
3.26611161e-01 3.81002426e-01 -1.61700901e-02 -2.97419190e-01
-7.19069242e-01 1.87123045e-01 -9.69610363e-02 1.33583874e-01
-9.72816467e-01 -5.92791617e-01 -5.31221747e-01 -9.18296278e-02
7.41845071e-01 -6.28370881e-01 6.16618216e-01 -3.64189684e-01
-1.51680100e+00 4.75827396e-01 -4.38975450e-03 -4.10160899e-01
8.98899734e-01 2.31456280e-01 -7.82665089e-02 3.39723259e-01
3.08348089e-01 4.13294099e-02 6.34320736e-01 -1.15065944e+00
-5.31724036e-01 -5.06119311e-01 3.29567343e-01 4.87914592e-01
-4.57038641e-01 -2.64436807e-02 -3.29283535e-01 -7.49249995e-01
1.68457821e-01 -1.08346677e+00 -6.88719809e-01 -3.96102555e-02
-4.22901243e-01 1.70522496e-01 6.18227422e-01 -7.76311398e-01
1.29317904e+00 -2.26979756e+00 5.23657382e-01 5.80981672e-01
6.27001047e-01 -1.76681742e-01 9.58805382e-02 4.57369804e-01
5.44079542e-01 9.04086083e-02 -5.29388845e-01 -4.37653780e-01
9.39704329e-02 -7.67767355e-02 3.05953413e-01 1.09841001e+00
-8.09696436e-01 3.68082792e-01 -1.06532633e+00 -2.52587438e-01
-2.57881910e-01 3.38450074e-01 -5.18166184e-01 -1.41700685e-01
-9.54319164e-02 3.72668564e-01 -9.13917661e-01 6.21210277e-01
9.83346879e-01 -4.35954690e-01 3.54928195e-01 1.22498855e-01
-1.85010266e-02 -7.56805301e-01 -1.50893748e+00 1.64672220e+00
-2.83880562e-01 3.30251902e-01 1.25451553e+00 -1.26127982e+00
7.82565951e-01 1.63521603e-01 8.68915498e-01 -3.47059757e-01
1.55682757e-01 4.17362750e-01 -3.93308371e-01 -2.92569488e-01
1.25382081e-01 -3.24318558e-01 2.54591517e-02 6.78943515e-01
-2.81179219e-01 -1.70763120e-01 2.35771298e-01 5.59243560e-01
1.20591462e+00 -8.07545066e-01 9.09158774e-03 -9.18769360e-01
5.05959630e-01 -2.64974982e-01 6.68920100e-01 8.73866916e-01
-4.68146920e-01 1.92809671e-01 6.55126452e-01 4.84432913e-02
-9.02905643e-01 -6.71327591e-01 9.21486318e-02 9.93789256e-01
6.06918991e-01 -3.31365585e-01 -1.00285566e+00 -4.93403971e-01
-3.33170942e-03 6.30179942e-01 -3.75368327e-01 1.67097077e-01
-3.65911990e-01 -1.21792316e+00 3.67228299e-01 1.22557886e-01
9.69698429e-01 -4.83521700e-01 -1.04110166e-02 2.42224842e-01
-1.25491619e-01 -9.84253585e-01 -1.14091325e+00 -9.33404937e-02
-7.28779435e-01 -9.03491855e-01 -1.06618404e+00 -8.43605161e-01
1.12667334e+00 5.84478259e-01 5.81874192e-01 1.49934767e-02
-5.25113754e-03 6.42369151e-01 -9.85397771e-02 -6.73161149e-02
-1.18973307e-01 -1.56587988e-01 1.80129051e-01 1.18735738e-01
-4.62867618e-01 -7.18640447e-01 -9.38246906e-01 2.75189459e-01
-8.56546283e-01 -2.73752481e-01 2.50321329e-01 5.57926178e-01
7.98214853e-01 4.63303149e-01 3.46861660e-01 -6.17780447e-01
1.24519479e+00 -5.31750262e-01 -9.27321315e-01 6.06789738e-02
-8.19550097e-01 -9.16951746e-02 9.02020574e-01 -2.45527729e-01
-7.48208523e-01 -3.84322137e-01 2.41914749e-01 -4.38621074e-01
4.00253624e-01 9.58799303e-01 2.55260646e-01 -4.32092220e-01
4.92063582e-01 4.41989869e-01 2.14720100e-01 -4.12437886e-01
5.13206065e-01 4.52513337e-01 4.32218164e-01 -7.28000879e-01
8.89038444e-01 8.30884874e-01 2.03877091e-01 -8.14971387e-01
-7.33537316e-01 -9.66070518e-02 1.41824394e-01 -1.06907353e-01
5.27050197e-01 -9.78399277e-01 -9.83104706e-01 1.76326975e-01
-4.42491978e-01 -2.65118927e-01 -5.19697905e-01 8.04091632e-01
-6.52138233e-01 5.07953107e-01 -8.31601262e-01 -1.07619226e+00
-7.34310806e-01 -1.05566800e+00 3.48051190e-01 1.59347802e-01
5.22519588e-01 -1.32125366e+00 -2.26859063e-01 3.40236396e-01
8.98689270e-01 5.35242915e-01 7.08609402e-01 -2.06020132e-01
-4.92410302e-01 -2.92046785e-01 -3.56054306e-01 3.78786445e-01
4.77959076e-03 -5.09740531e-01 4.41684611e-02 -1.10701251e+00
4.86562967e-01 5.34949787e-02 3.77745926e-01 6.00445211e-01
1.03511143e+00 -1.02145374e+00 -2.35414952e-01 7.85959840e-01
1.61810970e+00 1.35825872e-01 -7.75316656e-02 -2.11417153e-02
2.60839581e-01 6.97532743e-02 2.48855919e-01 1.03748930e+00
4.93631691e-01 2.94980079e-01 5.35052598e-01 -9.02421307e-03
2.16882303e-02 1.02737084e-01 4.12945330e-01 1.39921200e+00
-2.05701515e-01 -3.32579851e-01 -5.19542694e-01 5.88159323e-01
-1.75373256e+00 -6.88071012e-01 -4.22948599e-02 2.32191467e+00
6.72274351e-01 -8.23711231e-02 1.14093520e-01 -2.14686096e-01
1.07731462e+00 3.08089465e-01 -5.43232739e-01 -1.38084680e-01
-4.13009852e-01 4.08938266e-02 8.53160203e-01 7.54361391e-01
-6.03708267e-01 5.21776021e-01 4.94297600e+00 1.16737187e+00
-8.45191061e-01 1.80196270e-01 5.77366769e-01 -3.26485723e-01
-2.63263881e-01 4.47873734e-02 -3.65186155e-01 7.84767628e-01
7.18923092e-01 -8.51174712e-01 9.67855513e-01 7.64129996e-01
4.05462146e-01 -7.48674273e-02 -5.85325658e-01 8.49375963e-01
1.13897979e-01 -1.27258408e+00 -3.94973159e-01 4.06318337e-01
1.21260834e+00 1.25467777e-01 2.04311103e-01 -2.60249823e-01
7.22262442e-01 -6.88305616e-01 4.04451907e-01 -2.06788331e-02
8.81031811e-01 -8.61785650e-01 6.50956869e-01 4.27515477e-01
-1.34166408e+00 -2.89142877e-01 -4.98332202e-01 3.40736508e-01
4.00672585e-01 9.20101404e-01 -2.67565817e-01 7.60513186e-01
7.71667719e-01 4.98509914e-01 7.08761886e-02 1.17685902e+00
1.79204091e-01 4.30745035e-01 -7.15294540e-01 -7.51850966e-05
4.99524742e-01 -8.20324481e-01 1.21859872e+00 9.35476780e-01
7.61605859e-01 5.66539943e-01 6.44473493e-01 4.47633296e-01
-4.94559258e-01 4.45623010e-01 -3.08647931e-01 3.67116416e-03
5.59418380e-01 8.95004034e-01 -8.49614441e-01 -1.83241576e-01
-1.78444684e-01 7.80937314e-01 1.70654953e-01 6.23356640e-01
-6.71288669e-01 -6.70191526e-01 4.53190625e-01 -2.58384482e-03
3.48224103e-01 -4.76457000e-01 -7.06933588e-02 -1.26742315e+00
2.43485972e-01 -6.15228713e-01 5.89196444e-01 -4.16925326e-02
-1.24135220e+00 5.44548213e-01 -1.13659754e-01 -1.05551863e+00
1.09916754e-01 9.22141131e-03 -4.03108507e-01 4.58649397e-01
-1.05169916e+00 -5.85047960e-01 -2.43882775e-01 9.39539969e-01
1.16569869e-01 -4.23421532e-01 5.56956351e-01 4.04477537e-01
-6.70068800e-01 7.18346119e-01 1.01453400e+00 3.66264880e-02
-2.86998274e-03 -9.92071986e-01 -4.37519729e-01 7.99951196e-01
-3.91722143e-01 2.21802220e-01 9.44378614e-01 -4.66212571e-01
-1.84489691e+00 -1.04223704e+00 5.40934384e-01 6.55534029e-01
7.77215123e-01 -1.10680521e-01 -4.00780439e-01 5.50216198e-01
3.30371290e-01 3.07113647e-01 6.45592868e-01 -5.00256956e-01
1.16802692e-01 -4.08778608e-01 -1.70094693e+00 5.93005478e-01
1.15245438e+00 -2.60700524e-01 3.67685974e-01 9.80408788e-01
6.82722151e-01 -5.84308982e-01 -1.41704726e+00 1.25118837e-01
-1.37623936e-01 -6.73133075e-01 6.82760239e-01 -1.85565680e-01
-8.02428275e-02 -2.16767609e-01 -5.62690318e-01 -1.33975387e+00
-1.91671401e-01 -1.41183734e+00 -2.69044667e-01 7.09838808e-01
4.54521865e-01 -8.80461872e-01 9.72656369e-01 2.53445238e-01
-1.28619492e-01 -8.56215358e-01 -1.43693829e+00 -8.57125401e-01
2.52502769e-01 -8.70380085e-03 2.24686190e-01 9.99805689e-01
2.51858622e-01 -1.22843079e-01 -3.74544322e-01 2.64255166e-01
1.11482334e+00 2.22400755e-01 2.85363227e-01 -6.38942897e-01
-5.95474482e-01 -5.26528180e-01 -1.01263367e-01 -1.22549355e+00
-5.14776967e-02 -9.17039931e-01 -1.89246505e-01 -1.30730224e+00
2.57822961e-01 -8.51424396e-01 -5.00003174e-02 -2.02445034e-02
8.86088908e-02 -3.92541066e-02 4.05564040e-01 2.75286496e-01
-8.20804596e-01 8.91851604e-01 1.37066054e+00 -2.36651987e-01
-3.63876760e-01 2.60767728e-01 -9.61618483e-01 6.03467882e-01
4.83108193e-01 -3.43494296e-01 -5.18024027e-01 -7.91995466e-01
2.02623740e-01 7.95088649e-01 -1.43909127e-01 -8.17860425e-01
4.65756923e-01 -3.72979730e-01 -4.15466100e-01 -5.45454361e-02
3.29463571e-01 -7.54803717e-01 2.84061432e-01 5.93150318e-01
-3.67650449e-01 1.79986760e-01 -3.32000196e-01 1.11040151e+00
-7.81634599e-02 -5.55743873e-02 7.04057634e-01 -1.38382271e-01
1.26897484e-01 6.52581811e-01 -2.87513852e-01 5.24914801e-01
1.24364626e+00 4.98203151e-02 -4.50381309e-01 -1.09799600e+00
-7.58304954e-01 6.14952922e-01 2.72315145e-01 -3.44441235e-01
5.59892595e-01 -1.15809476e+00 -1.04718292e+00 -1.56002507e-01
-5.57282925e-01 2.43067116e-01 2.95303583e-01 1.33521783e+00
-6.47714734e-01 1.19445994e-01 5.83909273e-01 -4.50817078e-01
-6.44306421e-01 3.96163732e-01 4.22297180e-01 -2.00841844e-01
-6.62190378e-01 1.12221634e+00 -1.85762018e-01 1.29752839e-02
4.19138998e-01 8.84700492e-02 4.10866231e-01 -2.38736019e-01
1.79512516e-01 9.57936108e-01 -1.19058840e-01 -5.33333719e-01
-1.35075286e-01 3.53731543e-01 -3.23053822e-02 -2.64017105e-01
1.51179683e+00 -7.43067622e-01 -4.37350631e-01 -1.83240891e-01
1.38194692e+00 2.70158380e-01 -1.46332395e+00 -4.80025917e-01
-4.99951333e-01 -6.20734572e-01 1.48708403e-01 -4.62134123e-01
-1.80754113e+00 2.62868106e-01 4.62175578e-01 5.86140335e-01
9.18827593e-01 3.11339349e-02 6.95729852e-01 1.22039728e-01
6.85212076e-01 -1.32376587e+00 1.42296019e-03 3.44141304e-01
7.78530955e-01 -7.84223616e-01 2.76651949e-01 -6.31900191e-01
-5.80683172e-01 7.62605846e-01 2.30253190e-01 -2.31908992e-01
1.02396977e+00 1.33859679e-01 -3.41020226e-01 -1.87541991e-01
-3.16462398e-01 1.85263857e-01 -1.79741919e-01 3.04886997e-01
1.31283954e-01 2.42990687e-01 -7.53303528e-01 3.42379868e-01
-2.85012484e-01 -3.86467367e-01 6.27879322e-01 1.08318579e+00
-4.85621750e-01 -6.73791170e-01 -2.43021116e-01 6.37881160e-01
-6.73255086e-01 -1.90768763e-02 1.55151099e-01 5.31556368e-01
-2.72561818e-01 1.30946743e+00 -3.58040750e-01 -4.93053980e-02
1.27300452e-02 -6.06036067e-01 2.91192144e-01 -4.11145985e-02
-3.91201079e-01 4.35674548e-01 -1.74135379e-02 -3.75251323e-01
-3.43675852e-01 -5.69337904e-01 -1.25447035e+00 -9.46411669e-01
-3.44387442e-01 6.73739195e-01 5.44623435e-01 8.12930584e-01
4.97230858e-01 -4.47515026e-02 1.01636338e+00 -3.97846490e-01
-9.11484182e-01 -7.06330001e-01 -1.11345077e+00 2.47337177e-01
2.29777664e-01 -3.20682079e-01 -7.21891046e-01 -3.40098679e-01] | [6.307094573974609, 4.79974365234375] |
57fd7e0f-53d7-447d-a7f1-0e381efa2a56 | euler-characteristic-tools-for-topological | 2303.14040 | null | https://arxiv.org/abs/2303.14040v1 | https://arxiv.org/pdf/2303.14040v1.pdf | Euler Characteristic Tools For Topological Data Analysis | In this article, we study Euler characteristic techniques in topological data analysis. Pointwise computing the Euler characteristic of a family of simplicial complexes built from data gives rise to the so-called Euler characteristic profile. We show that this simple descriptor achieve state-of-the-art performance in supervised tasks at a very low computational cost. Inspired by signal analysis, we compute hybrid transforms of Euler characteristic profiles. These integral transforms mix Euler characteristic techniques with Lebesgue integration to provide highly efficient compressors of topological signals. As a consequence, they show remarkable performances in unsupervised settings. On the qualitative side, we provide numerous heuristics on the topological and geometric information captured by Euler profiles and their hybrid transforms. Finally, we prove stability results for these descriptors as well as asymptotic guarantees in random settings. | ['Vadim Lebovici', 'Olympio Hacquard'] | 2023-03-24 | null | null | null | null | ['topological-data-analysis'] | ['graphs'] | [ 9.49052498e-02 1.86394881e-02 -3.38119082e-02 -4.63150553e-02
-7.64809728e-01 -8.65483522e-01 6.61620915e-01 5.43564677e-01
-3.21431696e-01 4.13229346e-01 5.57048731e-02 1.01656102e-01
-5.83809197e-01 -7.50016391e-01 -7.57147551e-01 -9.34715271e-01
-8.45285356e-01 5.85992098e-01 4.76530679e-02 -2.83852339e-01
4.03802305e-01 7.05664158e-01 -1.42082369e+00 -7.98330531e-02
7.83619881e-01 1.22619832e+00 -2.64975816e-01 8.88245106e-01
6.43151030e-02 2.41028056e-01 -7.68079385e-02 -2.19462723e-01
2.80976683e-01 -1.83364332e-01 -7.86473632e-01 -8.66976604e-02
7.09426761e-01 7.00220279e-03 -1.49851605e-01 1.15738189e+00
2.39374936e-01 4.65923995e-02 9.97931659e-01 -1.09988260e+00
-3.46473217e-01 5.48939943e-01 -2.71073967e-01 8.04216340e-02
4.09442037e-01 -3.05635124e-01 1.33708262e+00 -9.87524867e-01
9.46411312e-01 7.85338879e-01 1.07911146e+00 -7.11417720e-02
-1.73979723e+00 1.82134788e-02 -4.75729257e-01 6.38520569e-02
-1.21441340e+00 -2.23583177e-01 1.12636399e+00 -5.77134132e-01
2.69146621e-01 4.47230995e-01 7.00820982e-01 5.77907681e-01
1.97717831e-01 7.94896305e-01 1.18164039e+00 -3.51667434e-01
1.04994208e-01 -1.72853574e-01 2.36901537e-01 1.33267581e+00
2.68443882e-01 -2.41790116e-01 -2.14156479e-01 -2.79261768e-01
8.40843201e-01 5.20024858e-02 -3.37807208e-01 -7.08919406e-01
-1.45944679e+00 7.95359612e-01 2.74945855e-01 4.80729699e-01
-1.77702188e-01 3.00026476e-01 4.96792793e-01 4.69471991e-01
5.12604952e-01 3.30370098e-01 1.16555914e-02 -4.51499894e-02
-6.64190888e-01 2.97893196e-01 8.73172879e-01 9.71915543e-01
8.65960419e-01 -3.80334526e-01 1.69815347e-01 4.09906894e-01
-2.69892454e-01 6.40046060e-01 -2.48151422e-02 -1.16792142e+00
1.29510939e-01 2.28112057e-01 -1.07368201e-01 -1.41620493e+00
-5.82287967e-01 -6.27618611e-01 -1.06045651e+00 -2.97277957e-01
8.43599260e-01 5.87002695e-01 -1.83071628e-01 1.65688097e+00
1.98614210e-01 1.02397449e-01 -2.26145327e-01 5.62949836e-01
-1.16595112e-01 4.36212361e-01 -4.80918318e-01 -2.22240254e-01
1.11067939e+00 -3.90218288e-01 -6.05738819e-01 7.64889598e-01
8.17996085e-01 -5.02524376e-01 9.63877439e-01 6.78680301e-01
-1.11954331e+00 -1.95710748e-01 -8.84471655e-01 -9.91420299e-02
-4.08538997e-01 2.76745468e-01 6.18402958e-01 1.88917443e-01
-1.01183546e+00 1.40995324e+00 -9.36008394e-01 -2.22015157e-01
2.63268054e-01 2.28082966e-02 -3.61717403e-01 2.79378891e-01
-7.55206943e-01 4.26943809e-01 -1.39387876e-01 -1.99086800e-01
-7.05702484e-01 -6.80694401e-01 -7.67918944e-01 -1.83766223e-02
2.72632718e-01 -3.22862417e-01 7.59138584e-01 -2.21335888e-01
-1.14020050e+00 8.59425962e-01 4.37350385e-02 -7.48123765e-01
6.57957613e-01 2.87679788e-02 6.83217719e-02 7.71301210e-01
1.27102584e-01 -1.30424919e-02 8.24729621e-01 -8.08041751e-01
-3.88372004e-01 -3.38623375e-01 -1.42059222e-01 -3.25842053e-01
-4.55859840e-01 -4.73785967e-01 9.83750150e-02 -7.47991025e-01
3.38742405e-01 -8.21867645e-01 -2.65094470e-02 2.98482567e-01
-2.34131292e-01 -3.43116134e-01 5.87595999e-01 -4.17480230e-01
1.09450889e+00 -2.18050718e+00 4.93258506e-01 5.57209730e-01
6.44518137e-01 -1.09398954e-01 1.07310824e-01 5.93585551e-01
2.14396760e-01 1.16108052e-01 -5.18517673e-01 -1.91297024e-01
2.34432504e-01 3.65207754e-02 -4.11499858e-01 1.09629238e+00
2.20533058e-01 6.88224733e-01 -1.20875371e+00 -5.39255142e-01
1.25142306e-01 2.21479535e-01 -7.18072712e-01 -1.85210049e-01
-1.15240462e-01 4.33555305e-01 -4.41861212e-01 5.78790307e-01
6.98869765e-01 -2.65583903e-01 9.39960033e-02 -4.64144945e-01
-7.59123340e-02 2.60884166e-01 -9.05285299e-01 1.51537812e+00
-3.91183645e-01 7.85449147e-01 3.49819630e-01 -1.60571516e+00
8.44188035e-01 -6.16748184e-02 8.69774759e-01 -2.52734184e-01
1.98697388e-01 6.68214500e-01 -6.71169877e-01 -4.39717114e-01
3.68908614e-01 -3.13691676e-01 -5.14206648e-01 3.39437366e-01
1.37587965e-01 -7.80391842e-02 4.36072081e-01 3.02342325e-01
1.31069946e+00 -1.52189538e-01 6.30616322e-02 -7.26336241e-01
7.34867752e-01 -1.76124498e-01 2.52345324e-01 4.54985082e-01
-8.93783122e-02 3.83985251e-01 9.62520361e-01 -4.02142823e-01
-1.33667779e+00 -1.37637436e+00 -3.64554524e-01 7.78430820e-01
8.17174241e-02 -6.98725998e-01 -8.27300251e-01 -4.45650220e-01
1.73347414e-01 -3.09085492e-02 -7.90253401e-01 3.51730362e-02
-6.41651869e-01 -1.12752073e-01 9.38897252e-01 4.29320216e-01
3.39092880e-01 -3.38789105e-01 -7.00803220e-01 1.62502751e-01
-2.75112063e-01 -1.26080728e+00 -6.38888478e-01 1.24695837e-01
-1.22479784e+00 -1.26227462e+00 -9.84705508e-01 -6.61191404e-01
4.95062619e-01 1.84573397e-01 7.77954280e-01 -6.74713701e-02
-2.04297855e-01 5.65680802e-01 -2.31244877e-01 2.91611314e-01
-3.05096596e-01 5.70654795e-02 2.85475969e-01 2.90187299e-01
-2.24078462e-01 -9.46500540e-01 -3.15612614e-01 2.50465631e-01
-8.31547916e-01 -3.60432923e-01 4.43480968e-01 8.01903069e-01
9.75224733e-01 1.11310437e-01 1.56081721e-01 -6.37825370e-01
5.16052365e-01 -2.67375737e-01 -7.95620859e-01 1.31972700e-01
-3.36773396e-01 6.49502575e-01 1.20834446e+00 -2.32754722e-01
-1.40650153e-01 -3.52386609e-02 3.72731596e-01 -5.17943680e-01
3.87715876e-01 6.28696859e-01 3.76342773e-01 -3.06287229e-01
4.66023415e-01 3.90534014e-01 2.34071001e-01 -7.58199394e-01
5.06289363e-01 2.93581396e-01 1.07075250e+00 -9.76242661e-01
7.91857541e-01 1.12066376e+00 6.48925483e-01 -1.19260514e+00
-7.47392118e-01 -7.19634891e-01 -6.66539133e-01 -2.64687687e-01
3.77190709e-01 -4.25378829e-01 -9.07705963e-01 3.66678089e-01
-1.04235911e+00 -4.77360748e-02 -3.85498196e-01 4.45569009e-01
-1.19761002e+00 6.18932784e-01 -6.64255381e-01 -8.01157713e-01
-6.79478943e-02 -6.37724221e-01 1.23117900e+00 -3.70429277e-01
9.26183611e-02 -1.20506513e+00 2.68126220e-01 -1.32070795e-01
2.37222254e-01 7.18580544e-01 9.27112818e-01 -4.84539747e-01
-6.14830911e-01 -3.15480441e-01 -1.51344955e-01 2.50701934e-01
-3.42473149e-01 -2.08631568e-02 -5.35061359e-01 -1.50280342e-01
-2.59422529e-02 1.10273667e-01 1.14738691e+00 2.99401164e-01
1.20212221e+00 -5.73430538e-01 -2.57246137e-01 9.15076911e-01
1.45968509e+00 -5.65449417e-01 2.81333745e-01 -1.51391566e-01
5.10650754e-01 6.62389934e-01 5.32229245e-01 4.93659914e-01
1.35238424e-01 6.04500175e-01 5.83996698e-02 5.04822373e-01
1.51342779e-01 -4.27958429e-01 4.12977636e-01 1.30673814e+00
-3.32962215e-01 5.07598877e-01 -8.11797142e-01 6.55792296e-01
-1.54628682e+00 -1.18717992e+00 -3.60444069e-01 2.30731845e+00
6.90538049e-01 1.07423663e-01 4.92758363e-01 5.51840723e-01
6.50663614e-01 9.07909125e-02 -1.46726951e-01 -2.43108705e-01
-3.32518995e-01 4.29904610e-01 9.22265470e-01 6.91537678e-01
-1.09987760e+00 4.39357579e-01 6.34367561e+00 9.09681082e-01
-9.49900925e-01 1.58456385e-01 -3.50234732e-02 2.90280253e-01
-4.58850622e-01 4.61165756e-02 -3.53178233e-01 4.20956343e-01
7.12824285e-01 -4.24990594e-01 6.13901496e-01 8.78258646e-01
-7.12346211e-02 2.71701217e-02 -1.03472936e+00 1.00261557e+00
-1.65002882e-01 -1.57186866e+00 -2.37277746e-01 6.22358382e-01
4.70718235e-01 -3.31147648e-02 1.64250270e-01 -4.53160733e-01
-1.94486842e-01 -6.30659044e-01 7.92073488e-01 6.54516995e-01
1.06420124e+00 -7.61035085e-01 4.31476682e-01 9.80440453e-02
-1.63607597e+00 -1.37769386e-01 -4.04386699e-01 1.30247891e-01
1.89340636e-01 1.03979516e+00 -4.58147943e-01 6.76925778e-01
1.71098009e-01 9.98017907e-01 -3.95289660e-01 1.01161158e+00
2.97753215e-01 4.32893425e-01 -8.20031285e-01 -7.45559335e-02
3.58558834e-01 -7.19226778e-01 8.97258878e-01 1.33897746e+00
3.68888557e-01 4.63708825e-02 -4.33503091e-02 8.39574099e-01
-1.90029755e-01 4.73189242e-02 -1.14332652e+00 -3.44331592e-01
5.31306803e-01 1.30199432e+00 -9.57034945e-01 -3.62788409e-01
1.13867000e-01 5.59696078e-01 2.71035939e-01 1.07192332e-02
-5.66553652e-01 -8.03482950e-01 4.98587251e-01 4.53036159e-01
3.94775748e-01 -7.69671142e-01 -3.87524664e-01 -1.14662755e+00
4.19631273e-01 -3.86836678e-01 3.02073628e-01 -1.51547641e-01
-1.17418301e+00 1.97565421e-01 2.28652760e-01 -1.37054574e+00
-6.03703000e-02 -8.09879661e-01 -5.37027597e-01 1.69419974e-01
-1.13945055e+00 -6.61227584e-01 -9.03856009e-02 5.36852598e-01
-1.26487702e-01 1.16383903e-01 5.83995461e-01 2.11613238e-01
-6.54366165e-02 3.72648895e-01 6.62977278e-01 3.04650068e-01
1.97641090e-01 -1.43386555e+00 4.21661466e-01 4.21753407e-01
3.02391917e-01 5.68362772e-01 8.01666200e-01 -3.82348478e-01
-1.70691669e+00 -6.83051348e-01 9.04593825e-01 -2.99188644e-01
9.79313731e-01 -3.81243557e-01 -8.25844109e-01 4.78559762e-01
-5.96732974e-01 1.25868633e-01 3.32313210e-01 -1.01116352e-01
-5.35085738e-01 -1.31088302e-01 -1.13271761e+00 2.74163187e-01
1.30150485e+00 -8.45699430e-01 -4.48748678e-01 3.49228770e-01
3.77654016e-01 -5.34487553e-02 -1.22683489e+00 1.94161832e-01
7.05897808e-01 -9.02193844e-01 1.08491099e+00 -4.20544505e-01
2.36430347e-01 -1.94655627e-01 -4.48528469e-01 -9.15898740e-01
-1.23950355e-01 -1.04423010e+00 -2.44310379e-01 6.96018040e-01
1.80423371e-02 -7.93872178e-01 4.49203968e-01 -2.51618892e-01
-1.90376509e-02 -9.58076954e-01 -1.05975437e+00 -1.31789899e+00
1.74029648e-01 -2.88740575e-01 2.20563844e-01 8.56237590e-01
5.19933999e-01 1.06479451e-01 -6.17357455e-02 -3.01143318e-01
1.29837465e+00 8.59760940e-02 5.13804376e-01 -1.65602088e+00
-1.56405002e-01 -8.13992858e-01 -8.80498946e-01 -1.15373719e+00
1.27297297e-01 -1.31665885e+00 -1.97167784e-01 -9.31434751e-01
-4.41792933e-03 -6.14727914e-01 1.80439316e-02 -2.31941298e-01
5.59884965e-01 2.24521905e-01 1.18110895e-01 3.53966773e-01
-7.75249422e-01 5.76937795e-01 1.11388886e+00 -2.28686370e-02
2.22459119e-02 -2.40295023e-01 -2.56307960e-01 8.55610907e-01
6.48964107e-01 -3.43508333e-01 4.50398214e-02 3.22615691e-02
4.82221663e-01 -3.08908261e-02 5.66496432e-01 -1.13374698e+00
2.64639974e-01 1.24523357e-01 -1.88077793e-01 -5.93377054e-01
2.27085859e-01 -4.43618953e-01 -9.03097019e-02 5.06898701e-01
-5.40852308e-01 7.80345798e-02 -3.89609754e-01 8.41764808e-01
2.55400501e-02 -1.40149012e-01 8.34999561e-01 1.04477510e-01
6.35789009e-03 2.59426594e-01 -2.84629971e-01 4.24515158e-01
6.62846982e-01 -5.21534309e-02 -1.58031225e-01 -5.12103915e-01
-7.54710019e-01 1.04399234e-01 6.75381541e-01 -3.01958650e-01
6.44581258e-01 -1.68173218e+00 -4.92605329e-01 1.27134070e-01
-5.36670685e-02 -4.59008776e-02 -1.64109901e-01 1.60562563e+00
-7.62735903e-01 5.36091268e-01 -2.22728729e-01 -7.91619718e-01
-9.95509088e-01 6.41996980e-01 1.41673535e-01 -4.89348412e-01
-8.77313435e-01 4.44848657e-01 -1.81786627e-01 -2.79580414e-01
2.36794308e-01 -7.35429823e-01 3.11763078e-01 3.36068720e-01
4.31101918e-01 8.26303303e-01 -1.04261652e-01 -6.41822457e-01
-3.54525805e-01 1.11034656e+00 4.52871680e-01 -4.27253783e-01
1.33565998e+00 -1.20831430e-01 -2.56737024e-01 6.95964098e-01
1.88748610e+00 3.18710387e-01 -1.05021644e+00 -4.59652901e-01
3.70478451e-01 -3.74133676e-01 -3.12848836e-01 -1.70246027e-02
-8.06610405e-01 1.01093280e+00 -7.56001915e-04 8.13664317e-01
1.11771822e+00 3.47604305e-01 1.02557218e+00 5.53044260e-01
5.74884176e-01 -1.26165438e+00 6.13080636e-02 5.36534011e-01
7.11282849e-01 -5.06738544e-01 -4.00900133e-02 -5.71711123e-01
-4.62869778e-02 1.34962535e+00 -7.48240769e-01 -6.77480876e-01
7.89039254e-01 4.28821854e-02 -6.33017540e-01 -2.84488529e-01
-7.13476181e-01 -4.35781837e-01 2.31762126e-01 6.26986682e-01
1.51631832e-01 3.25892299e-01 -3.68024617e-01 2.42209136e-01
-4.54503566e-01 -1.92263067e-01 6.23748660e-01 6.87611282e-01
-6.07859969e-01 -7.22527444e-01 -3.06399733e-01 4.84300107e-01
-5.27615488e-01 1.71211526e-01 -4.03010458e-01 5.99617600e-01
-2.86589026e-01 4.09676284e-01 -9.21334606e-03 -5.48943162e-01
1.33602485e-01 6.77270144e-02 9.82020795e-01 -1.74552407e-02
2.86399890e-02 5.06189577e-02 1.29185747e-02 -7.35376179e-01
-4.24039483e-01 -8.38437259e-01 -1.26241183e+00 -6.51992083e-01
-1.28324434e-01 3.70461851e-01 7.35259712e-01 8.57404888e-01
2.20890015e-01 5.63711822e-02 9.48278189e-01 -8.89231622e-01
-1.02357948e+00 -7.15534568e-01 -8.55132580e-01 4.45105255e-01
7.48158634e-01 -7.52195656e-01 -6.64953887e-01 5.68100885e-02] | [7.3792405128479, 4.258005142211914] |
1825dd70-410a-4ea0-8d7d-f922e371dde2 | multi-stage-spatio-temporal-aggregation | 2301.00531 | null | https://arxiv.org/abs/2301.00531v1 | https://arxiv.org/pdf/2301.00531v1.pdf | Multi-Stage Spatio-Temporal Aggregation Transformer for Video Person Re-identification | In recent years, the Transformer architecture has shown its superiority in the video-based person re-identification task. Inspired by video representation learning, these methods mainly focus on designing modules to extract informative spatial and temporal features. However, they are still limited in extracting local attributes and global identity information, which are critical for the person re-identification task. In this paper, we propose a novel Multi-Stage Spatial-Temporal Aggregation Transformer (MSTAT) with two novel designed proxy embedding modules to address the above issue. Specifically, MSTAT consists of three stages to encode the attribute-associated, the identity-associated, and the attribute-identity-associated information from the video clips, respectively, achieving the holistic perception of the input person. We combine the outputs of all the stages for the final identification. In practice, to save the computational cost, the Spatial-Temporal Aggregation (STA) modules are first adopted in each stage to conduct the self-attention operations along the spatial and temporal dimensions separately. We further introduce the Attribute-Aware and Identity-Aware Proxy embedding modules (AAP and IAP) to extract the informative and discriminative feature representations at different stages. All of them are realized by employing newly designed self-attention operations with specific meanings. Moreover, temporal patch shuffling is also introduced to further improve the robustness of the model. Extensive experimental results demonstrate the effectiveness of the proposed modules in extracting the informative and discriminative information from the videos, and illustrate the MSTAT can achieve state-of-the-art accuracies on various standard benchmarks. | ['Liang Lin', 'Jinrui Chen', 'Zhanglin Peng', 'Ruimao Zhang', 'Ziyi Tang'] | 2023-01-02 | null | null | null | null | ['person-re-identification'] | ['computer-vision'] | [ 4.17028479e-02 -5.87619245e-01 3.55793834e-02 -4.02712196e-01
-4.85897213e-01 -2.61003971e-01 5.16259074e-01 -6.71903715e-02
-5.19632697e-01 3.38891238e-01 3.78266752e-01 4.32149142e-01
-2.84094810e-01 -5.80977380e-01 -2.36724108e-01 -9.36205804e-01
2.31880452e-02 -4.21772450e-02 -8.29691254e-03 -5.97352199e-02
1.71723351e-01 2.55264580e-01 -1.70254540e+00 1.76157057e-01
9.12703514e-01 1.17913818e+00 1.13643721e-01 2.17936933e-01
-5.94054274e-02 4.31831032e-01 -3.55648339e-01 -5.93800783e-01
1.42382100e-01 -5.27054965e-01 -3.98728311e-01 2.11674735e-01
2.37601265e-01 -5.71289062e-01 -6.93366528e-01 1.08889401e+00
7.53838003e-01 3.37946713e-01 4.37226862e-01 -1.22925079e+00
-8.75793636e-01 3.35766256e-01 -6.53037965e-01 4.23233479e-01
5.70316017e-01 3.37279201e-01 7.39339650e-01 -1.05425107e+00
1.37613878e-01 1.37397230e+00 7.11767912e-01 4.90813375e-01
-9.52871203e-01 -8.87104928e-01 4.52975780e-01 9.15607512e-01
-1.69822752e+00 -5.61684728e-01 1.02755165e+00 -3.90944064e-01
5.84899902e-01 3.16992521e-01 8.90060782e-01 1.05297387e+00
-3.09877425e-01 1.01539707e+00 9.48381364e-01 -6.09720908e-02
-2.16412142e-01 2.49406829e-01 1.48423821e-01 4.64058340e-01
1.17112637e-01 1.48848202e-02 -5.06325364e-01 1.71221673e-01
8.55489671e-01 4.73926127e-01 -4.50255275e-01 -2.28078946e-01
-1.33319151e+00 5.18792510e-01 6.03386641e-01 3.88282716e-01
-4.82487857e-01 -1.52410910e-01 6.70915127e-01 8.94575492e-02
2.16095760e-01 -6.87007681e-02 -3.41972895e-02 -2.61305030e-02
-7.19588995e-01 4.99012619e-02 7.30201975e-02 9.61457014e-01
8.73657405e-01 -1.21841885e-01 -6.13003135e-01 8.75304461e-01
1.66886240e-01 2.32539400e-01 8.40396702e-01 -5.09963691e-01
4.83047396e-01 8.54126573e-01 1.88251436e-01 -1.31064367e+00
-2.62741029e-01 -6.39904559e-01 -1.11126614e+00 -3.65485400e-01
-5.30587370e-03 1.24811560e-01 -6.82790637e-01 1.80012214e+00
3.68502855e-01 6.97698534e-01 2.73573603e-02 1.18689287e+00
9.00125444e-01 6.28378570e-01 4.13118839e-01 -2.88972229e-01
1.56089461e+00 -1.03073323e+00 -7.66330302e-01 -1.49924830e-01
2.89379984e-01 -4.69947994e-01 9.16414797e-01 -1.29736155e-01
-8.09201121e-01 -1.14428794e+00 -1.09257710e+00 -9.81368050e-02
-3.06217223e-01 5.36321223e-01 4.88419622e-01 4.96048898e-01
-7.28833139e-01 3.75734299e-01 -4.22518045e-01 -4.69161630e-01
4.10491019e-01 4.79770690e-01 -6.78906679e-01 -8.80993232e-02
-1.32242620e+00 3.46557468e-01 4.60486591e-01 6.05166733e-01
-6.55650795e-01 -4.36671972e-01 -8.90410900e-01 2.60158986e-01
1.67206973e-01 -6.70104861e-01 6.02627873e-01 -1.10552335e+00
-1.23526740e+00 6.89635038e-01 -3.76312912e-01 -9.42812935e-02
3.89885783e-01 -1.36293530e-01 -6.50370777e-01 1.90600336e-01
2.70353764e-01 5.28436899e-01 8.94914865e-01 -9.55656886e-01
-8.30612600e-01 -5.63464403e-01 -1.72054693e-02 4.47958052e-01
-1.02572238e+00 1.28068119e-01 -8.05166304e-01 -1.02731276e+00
6.67107627e-02 -6.16733909e-01 9.76839513e-02 -1.64575860e-01
-2.06934914e-01 -3.31524312e-01 7.13347971e-01 -1.05612946e+00
1.36425579e+00 -2.45460463e+00 4.26062137e-01 4.64241877e-02
1.53658196e-01 3.67998034e-01 -1.53607205e-01 2.02923089e-01
-1.00536488e-01 -1.02965727e-01 -1.19192071e-01 -4.97435868e-01
-3.27246822e-02 -1.44918025e-01 -3.30324583e-02 3.92909646e-01
2.74565816e-01 8.92127395e-01 -9.54831004e-01 -7.17459083e-01
5.04864573e-01 6.47342384e-01 -3.98489565e-01 3.26243073e-01
4.78032172e-01 6.06820107e-01 -6.25144541e-01 6.26458049e-01
9.13485646e-01 8.11464712e-02 2.41921488e-02 -6.76918566e-01
9.03762132e-03 -1.00416668e-01 -1.25679517e+00 1.62963533e+00
-2.87527442e-01 2.26501569e-01 -1.95135344e-02 -1.03057206e+00
8.66048753e-01 2.00455949e-01 5.39938807e-01 -8.23586345e-01
1.06457360e-01 7.25891516e-02 -2.11195782e-01 -6.59933746e-01
3.45553726e-01 1.83653478e-02 -5.55126220e-02 2.27301285e-01
1.71387166e-01 9.64222550e-01 5.22759892e-02 -7.49580264e-02
5.74939311e-01 1.91207200e-01 2.60327816e-01 -1.71276238e-02
1.30733633e+00 -4.07762557e-01 1.03072107e+00 1.98540077e-01
-5.48880756e-01 4.39633816e-01 5.11847511e-02 -6.14688456e-01
-8.52072001e-01 -9.35148001e-01 8.25034231e-02 1.02922678e+00
7.13848114e-01 -4.91741389e-01 -6.64784014e-01 -6.79522693e-01
1.91470347e-02 2.61360556e-01 -7.59589016e-01 -3.52157742e-01
-6.48181319e-01 -8.43230426e-01 3.28495532e-01 8.14795554e-01
1.21063054e+00 -9.25947666e-01 -2.31685653e-01 1.99460804e-01
-5.00026047e-01 -1.06135643e+00 -9.58681166e-01 -6.77500129e-01
-5.16438067e-01 -8.84885848e-01 -1.05071807e+00 -1.02674174e+00
7.81084001e-01 6.11735046e-01 3.25947255e-01 8.93407688e-02
-6.41401187e-02 3.63698721e-01 -4.37045693e-01 2.15178400e-01
4.33741212e-01 3.88832912e-02 2.85188019e-01 1.01220071e+00
7.05241024e-01 -6.98885202e-01 -9.43974495e-01 6.16298854e-01
-6.33942425e-01 1.21182054e-01 7.35448837e-01 9.96376872e-01
5.44104397e-01 1.35908335e-01 5.85792303e-01 -2.05702648e-01
5.73884785e-01 -3.23566765e-01 -1.65143143e-02 3.17328185e-01
-2.92656720e-01 -2.26366013e-01 8.48252475e-01 -6.20322108e-01
-1.10704350e+00 6.27885833e-02 -7.53443614e-02 -5.63024819e-01
-1.31308913e-01 2.92108685e-01 -7.22073793e-01 -1.10416286e-01
4.21155952e-02 8.05212021e-01 -1.01261087e-01 -6.11519158e-01
6.64824098e-02 8.70085776e-01 7.18089104e-01 -4.13921058e-01
8.37382019e-01 4.49296534e-01 -3.30169201e-01 -4.94770348e-01
-5.63530624e-01 -5.73763788e-01 -7.16970205e-01 -2.76497751e-01
9.24891412e-01 -1.16254616e+00 -9.83231246e-01 8.07433486e-01
-9.05912995e-01 3.51984084e-01 -7.91254342e-02 5.00742793e-01
-2.27968648e-01 7.74942398e-01 -5.05443513e-01 -8.19164693e-01
-4.75957572e-01 -1.20027649e+00 1.01735985e+00 7.94151783e-01
1.71608910e-01 -5.64555466e-01 -2.80059129e-01 2.72941887e-01
2.83660591e-01 -4.65935096e-02 6.38901412e-01 -5.68178415e-01
-4.94209915e-01 -3.07344407e-01 -5.49892068e-01 1.67361960e-01
2.96098679e-01 -5.43177962e-01 -1.06984103e+00 -5.39580107e-01
-7.29281753e-02 1.46526825e-02 9.97982264e-01 2.22552773e-02
1.25566244e+00 -4.57749844e-01 -4.52219009e-01 9.75111067e-01
1.23449290e+00 2.20970050e-01 6.28318012e-01 5.55718303e-01
1.00752997e+00 6.52616978e-01 7.04781413e-01 4.71305430e-01
6.00564182e-01 9.66137946e-01 1.37410998e-01 8.57776552e-02
3.29242088e-03 -4.65557098e-01 4.02923942e-01 7.71769583e-01
-3.55721325e-01 1.32641181e-01 -3.71585697e-01 6.20000184e-01
-2.05819750e+00 -1.31158841e+00 3.12182486e-01 2.34768796e+00
5.03046632e-01 -2.06229389e-01 3.82823616e-01 2.52626270e-01
1.09144926e+00 2.75258869e-01 -7.32924700e-01 1.60914108e-01
-2.24854603e-01 -2.91874498e-01 1.22025304e-01 1.05219662e-01
-1.50647926e+00 7.05597639e-01 4.78722811e+00 1.06227577e+00
-8.34121644e-01 1.42549619e-01 5.07367671e-01 3.62388864e-02
-1.70021713e-01 -1.55520812e-01 -7.57915556e-01 8.70694876e-01
3.76102597e-01 -2.46573284e-01 4.67118800e-01 6.62663341e-01
1.03605323e-01 3.88434649e-01 -9.53479052e-01 1.52053094e+00
2.85531163e-01 -8.82944822e-01 3.91668856e-01 -1.48010343e-01
3.10451716e-01 -8.39193583e-01 1.98374823e-01 3.38430256e-01
-4.23889250e-01 -7.52177000e-01 5.96193254e-01 7.21239626e-01
8.70245099e-01 -1.00299811e+00 1.02176905e+00 2.73629534e-03
-1.91541362e+00 -5.24643421e-01 -3.98770630e-01 9.64344889e-02
1.22267641e-01 2.39776209e-01 -9.41202566e-02 9.73437965e-01
9.18603480e-01 1.22464204e+00 -8.15929890e-01 1.04940569e+00
-1.33674573e-02 9.79733765e-02 -8.20065141e-02 1.22532099e-01
2.22499128e-02 -3.93256426e-01 5.93377888e-01 1.16639507e+00
3.95220697e-01 1.89522520e-01 1.60121799e-01 7.07416236e-01
7.91110471e-02 1.87738538e-01 -1.49604395e-01 2.49178946e-01
5.93413830e-01 1.33814764e+00 -2.52879173e-01 -3.59371632e-01
-4.58772928e-01 1.47775757e+00 1.88578799e-01 4.52087462e-01
-8.79563093e-01 -7.05189347e-01 9.29434061e-01 -3.72835658e-02
4.53094393e-01 -2.84082573e-02 7.72754177e-02 -1.36056030e+00
3.11556846e-01 -8.33956897e-01 6.58490241e-01 -5.37009895e-01
-1.46379888e+00 5.22598803e-01 -6.63762167e-02 -1.49352944e+00
-2.51507442e-02 -4.13068861e-01 -6.99603081e-01 1.11840641e+00
-1.42091668e+00 -1.57265460e+00 -8.35234880e-01 8.29442859e-01
4.89481598e-01 -3.63740444e-01 6.01553679e-01 6.71442211e-01
-1.20614302e+00 1.13462996e+00 -2.77066343e-02 5.96027017e-01
7.96579599e-01 -8.65484953e-01 2.91767031e-01 1.04897273e+00
-2.55692869e-01 9.80098188e-01 2.26983398e-01 -5.02129912e-01
-1.35418427e+00 -1.16557658e+00 6.22316837e-01 2.56521255e-02
2.46556744e-01 -3.12430680e-01 -8.69498849e-01 6.75036788e-01
-9.01911110e-02 -3.72507088e-02 6.81688368e-01 -3.89531329e-02
-4.49052542e-01 -6.42910123e-01 -1.07609272e+00 5.25366724e-01
1.36682093e+00 -7.11350322e-01 -5.12022078e-01 -6.40590936e-02
5.39133072e-01 -3.10225450e-02 -8.74280393e-01 4.97281164e-01
8.20781350e-01 -1.09672487e+00 1.33092308e+00 -3.59755039e-01
3.93722244e-02 -6.20879114e-01 -4.15252373e-02 -1.02457488e+00
-9.29943800e-01 -3.85157257e-01 -8.47781375e-02 1.77689755e+00
-3.18351239e-01 -8.23833406e-01 5.75218320e-01 4.03555930e-01
1.96388569e-02 -6.09053612e-01 -9.61372077e-01 -6.45716131e-01
-6.04865193e-01 5.16522303e-02 9.82694924e-01 9.19849396e-01
-1.05283976e-01 4.03726697e-01 -6.81690872e-01 7.86075443e-02
7.22985268e-01 2.58390844e-01 7.07866073e-01 -1.12146175e+00
-2.05819011e-01 -5.23125231e-01 -8.94164562e-01 -1.24275482e+00
6.77415505e-02 -6.91773117e-01 -3.46118093e-01 -1.33981955e+00
5.77037930e-01 -2.87949115e-01 -7.51157284e-01 3.91406149e-01
-6.66716874e-01 2.16548681e-01 2.57043332e-01 5.25932610e-01
-5.53602934e-01 9.94558215e-01 9.72205698e-01 -4.41222161e-01
-1.27159685e-01 -1.45838082e-01 -8.25345159e-01 4.61694032e-01
5.31292081e-01 1.75941393e-01 -4.26508337e-01 -5.04523516e-01
-3.89767766e-01 -3.99419546e-01 7.19653010e-01 -1.31004643e+00
3.16628754e-01 8.65111724e-02 9.38438237e-01 -6.14376187e-01
5.84591687e-01 -7.79534459e-01 1.72328919e-01 4.05654043e-01
-1.37132943e-01 1.66348070e-01 8.00116807e-02 7.56562889e-01
-4.34595585e-01 1.08502261e-01 5.63975215e-01 -4.45623137e-02
-1.20517349e+00 6.75891936e-01 9.15456489e-02 -4.24445301e-01
1.20621848e+00 -4.15183067e-01 -9.85419825e-02 -2.93223768e-01
-5.86555481e-01 4.29025203e-01 4.70193565e-01 4.53567654e-01
8.73111248e-01 -1.87921119e+00 -7.43188500e-01 4.91134644e-01
3.01283598e-01 -3.68399650e-01 9.68354642e-01 9.41249788e-01
-1.63709432e-01 3.19629967e-01 -6.68884993e-01 -4.65412319e-01
-1.16957641e+00 1.01639855e+00 4.34898108e-01 -1.24914728e-01
-5.52229941e-01 7.38368034e-01 5.49715638e-01 -4.03058492e-02
1.08254738e-01 2.32544050e-01 -6.24111712e-01 2.72632629e-01
8.77979279e-01 4.84852403e-01 -4.08061832e-01 -1.02874398e+00
-6.00707293e-01 1.01181698e+00 -2.20433578e-01 1.71259716e-01
1.08020627e+00 -4.49897557e-01 -1.47118881e-01 1.54313520e-01
1.19770241e+00 -8.34585503e-02 -1.21264851e+00 -4.53150690e-01
-3.71981144e-01 -7.48756409e-01 -1.66087493e-01 -3.19782048e-01
-1.17905033e+00 1.07302368e+00 8.37733984e-01 -1.13818198e-01
1.53940141e+00 -4.06860858e-01 1.05381763e+00 -1.23722062e-01
3.48317593e-01 -9.94464457e-01 -6.51554838e-02 -6.64131269e-02
7.15055346e-01 -1.01434350e+00 -1.92291364e-02 -3.84457737e-01
-5.42223752e-01 9.16693032e-01 8.88517201e-01 9.08177048e-02
3.28111917e-01 -4.65127587e-01 -3.08964521e-01 2.67532974e-01
-1.42906025e-01 -4.06102479e-01 3.77528727e-01 7.49855578e-01
1.13126107e-01 4.65847701e-02 -2.84057111e-01 1.14983952e+00
2.24961445e-01 -1.12422362e-01 -1.63153648e-01 5.26955187e-01
-1.95922881e-01 -9.76600409e-01 -3.94926101e-01 3.41759801e-01
-3.25567946e-02 5.60981594e-02 -2.82066673e-01 4.75727469e-01
5.26953816e-01 8.86054099e-01 6.24802336e-02 -1.03143883e+00
3.16181332e-01 -4.79162931e-02 3.75042796e-01 -1.02954417e-01
-4.90192950e-01 7.80992117e-03 -1.29287183e-01 -5.40957570e-01
-5.85231662e-01 -8.24579239e-01 -8.64157200e-01 -4.32160854e-01
-4.57067750e-02 1.57970876e-01 -5.30574620e-02 7.93010175e-01
5.56773663e-01 4.20054615e-01 8.67336452e-01 -1.02824008e+00
-3.00058603e-01 -1.11395538e+00 -4.22531784e-01 7.55506933e-01
2.63336837e-01 -8.53552043e-01 -1.75762177e-01 -2.62393765e-02] | [14.691429138183594, 0.9636807441711426] |
72b2ecd4-e948-4441-b1bf-d35761d2d58a | fast-l1-minimization-algorithms-for-robust | 1007.03753 | null | http://arxiv.org/abs/1007.3753v4 | http://arxiv.org/pdf/1007.3753v4.pdf | Fast L1-Minimization Algorithms For Robust Face Recognition | L1-minimization refers to finding the minimum L1-norm solution to an
underdetermined linear system b=Ax. Under certain conditions as described in
compressive sensing theory, the minimum L1-norm solution is also the sparsest
solution. In this paper, our study addresses the speed and scalability of its
algorithms. In particular, we focus on the numerical implementation of a
sparsity-based classification framework in robust face recognition, where
sparse representation is sought to recover human identities from very
high-dimensional facial images that may be corrupted by illumination, facial
disguise, and pose variation. Although the underlying numerical problem is a
linear program, traditional algorithms are known to suffer poor scalability for
large-scale applications. We investigate a new solution based on a classical
convex optimization framework, known as Augmented Lagrangian Methods (ALM). The
new convex solvers provide a viable solution to real-world, time-critical
applications such as face recognition. We conduct extensive experiments to
validate and compare the performance of the ALM algorithms against several
popular L1-minimization solvers, including interior-point method, Homotopy,
FISTA, SESOP-PCD, approximate message passing (AMP) and TFOCS. To aid peer
evaluation, the code for all the algorithms has been made publicly available. | ['S. Shankar Sastry', 'Arvind Ganesh', 'Allen Y. Yang', 'Zihan Zhou', 'Yi Ma'] | 2010-07-21 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 3.63462687e-01 -1.21395171e-01 -9.57698934e-03 -1.49442479e-01
-6.91440642e-01 -2.33510956e-01 2.37883344e-01 -5.08312464e-01
7.91455582e-02 8.62378240e-01 1.69873700e-01 -3.95384245e-02
-4.50470865e-01 -1.97279572e-01 -7.93483496e-01 -8.41366947e-01
-1.40017018e-01 3.28979284e-01 -8.04593801e-01 -1.81359261e-01
2.78917193e-01 7.39539564e-01 -1.58826888e+00 3.30433287e-02
8.09633255e-01 1.10778975e+00 -1.83805242e-01 2.56822735e-01
5.66487499e-02 4.77523386e-01 -1.12139262e-01 -2.71801531e-01
6.39660358e-01 -4.03568238e-01 -5.57353914e-01 4.19388533e-01
7.44906783e-01 -1.84994921e-01 -8.93738940e-02 1.26775098e+00
4.52977061e-01 1.50730014e-01 4.36346948e-01 -1.58089900e+00
-3.18771541e-01 2.53465641e-02 -8.60746264e-01 -9.98548642e-02
8.10545743e-01 -1.65012583e-01 3.52189124e-01 -1.56292677e+00
7.24908650e-01 1.40196943e+00 9.44431067e-01 3.18125516e-01
-1.14064014e+00 -5.53856671e-01 -1.07516116e-02 1.80299357e-01
-1.75783598e+00 -1.08048403e+00 6.83653474e-01 -2.15075597e-01
6.90134883e-01 5.81626594e-01 5.49016654e-01 6.45929575e-01
-3.37451473e-02 6.17678702e-01 1.15867484e+00 -4.45448190e-01
2.24186972e-01 6.85772747e-02 -1.84012756e-01 9.36795354e-01
3.88206512e-01 4.00101058e-02 -9.23645437e-01 -6.89674199e-01
6.14071727e-01 -1.15030244e-01 -5.67024589e-01 -3.13986480e-01
-1.08028960e+00 8.24287534e-01 -9.54641923e-02 1.13672584e-01
-6.14204705e-01 -1.49338499e-01 -2.46354938e-02 1.72224402e-01
5.51104426e-01 -2.76236646e-02 -9.82325524e-02 1.83213670e-02
-1.38493311e+00 3.40073824e-01 1.14280760e+00 8.58616531e-01
6.69621706e-01 5.05879700e-01 1.31775737e-01 7.89743245e-01
6.32471144e-01 8.11086833e-01 2.84360081e-01 -1.35610628e+00
2.35587373e-01 2.47385681e-01 -5.16597880e-03 -1.63523626e+00
-1.67445570e-01 -4.84253407e-01 -1.14116502e+00 8.08833167e-02
2.62822539e-01 -1.97781190e-01 -4.78459746e-01 1.51151431e+00
7.72594690e-01 9.31544721e-01 -8.48351419e-03 1.14941990e+00
7.17088163e-01 8.12293589e-01 -4.63773906e-01 -9.38756824e-01
8.79368484e-01 -7.93997347e-01 -8.38325441e-01 -3.07263043e-02
4.59046990e-01 -1.16724324e+00 2.04310372e-01 6.22671783e-01
-1.26322901e+00 -1.88710690e-01 -7.48215556e-01 2.17169419e-01
1.76254615e-01 1.54094607e-01 3.88272613e-01 6.45216584e-01
-1.06570721e+00 4.88315642e-01 -6.04428768e-01 -3.58693868e-01
4.55341667e-01 5.17703414e-01 -7.21447110e-01 -5.08310199e-01
-6.39758289e-01 7.88012564e-01 -1.87057525e-01 5.60575664e-01
-8.78448308e-01 -9.42532897e-01 -9.07428265e-01 -1.82749316e-01
3.98208231e-01 -3.92068565e-01 6.43685341e-01 -1.05130899e+00
-1.40477145e+00 9.58861291e-01 -7.31130898e-01 -1.08219273e-01
4.13081199e-01 -8.67629126e-02 -1.93124175e-01 1.04273461e-01
3.24400738e-02 1.98354676e-01 1.43258166e+00 -1.21227670e+00
-7.31881782e-02 -4.85810876e-01 -4.91661489e-01 1.97390750e-01
-3.35982502e-01 1.54890016e-01 -2.49044403e-01 -6.97439253e-01
5.28350472e-01 -1.08372736e+00 -2.30356410e-01 1.95217192e-01
-2.61507064e-01 3.20805937e-01 1.10365057e+00 -9.46925461e-01
9.94993091e-01 -2.22174096e+00 4.86258000e-01 5.83148360e-01
-5.57412282e-02 3.69569182e-01 -2.60845900e-01 2.87990451e-01
-2.24808604e-01 -2.16047525e-01 -5.91072738e-01 -5.00220001e-01
-2.91725636e-01 1.75364941e-01 -1.13202795e-01 1.25361955e+00
-1.71851993e-01 5.73538363e-01 -6.49848998e-01 -4.98829037e-01
9.27421674e-02 8.26465249e-01 -7.36540616e-01 9.05683637e-02
1.60317078e-01 6.07098699e-01 -2.89990813e-01 1.09592259e+00
1.06558633e+00 -5.88410273e-02 1.11373566e-01 -4.21861738e-01
-2.10922241e-01 -5.97748280e-01 -1.98865318e+00 1.83257961e+00
-2.05200627e-01 4.09345031e-01 1.08151078e+00 -1.38189578e+00
7.15713620e-01 4.73031342e-01 9.39134598e-01 -3.17362875e-01
1.42007425e-01 3.66929233e-01 -4.81237561e-01 -4.52965736e-01
2.30893552e-01 -8.87409747e-02 6.41660452e-01 3.64364624e-01
-1.38701797e-01 -2.78642271e-02 1.10545047e-01 2.45147914e-01
8.70152771e-01 -7.59287775e-02 4.05505657e-01 -5.74563265e-01
9.39745128e-01 3.00282575e-02 7.67390668e-01 3.77960414e-01
-6.82378486e-02 6.95080757e-01 2.14625821e-01 -2.70946771e-01
-6.84227169e-01 -6.18791342e-01 -4.80633706e-01 5.96660852e-01
-1.08371302e-02 -2.90258944e-01 -6.97014868e-01 -1.20013803e-01
1.91240698e-01 2.50504822e-01 -2.16254100e-01 1.85778290e-01
-5.73892057e-01 -9.76851702e-01 1.66992322e-01 -1.70120120e-01
4.49424356e-01 -5.69824696e-01 -8.77681896e-02 9.25351083e-02
-1.75226822e-01 -1.16660082e+00 -6.04455531e-01 -4.47389930e-01
-8.55266988e-01 -1.20041049e+00 -8.03189635e-01 -8.04934382e-01
1.06855702e+00 4.05705303e-01 8.10297310e-01 3.02155018e-01
-6.52011156e-01 8.71545017e-01 -1.75724551e-01 -1.57498896e-01
1.81880388e-02 -6.01985276e-01 5.09922802e-01 7.40931273e-01
1.81973092e-02 -4.46310014e-01 -3.55251700e-01 2.84562558e-01
-7.14367390e-01 -2.21409321e-01 3.22164029e-01 9.11965907e-01
8.67704034e-01 6.31558523e-02 1.92978799e-01 -7.85252869e-01
4.56336617e-01 -7.67722428e-01 -6.86324120e-01 1.76431447e-01
-4.24402863e-01 -3.02005291e-01 3.54079843e-01 -4.29752469e-01
-9.66583967e-01 5.63636839e-01 -1.70150562e-03 -8.43471169e-01
1.82352498e-01 6.95254207e-01 -2.18441501e-01 -8.34078074e-01
4.10754234e-01 4.93914694e-01 4.41005200e-01 -3.33956271e-01
2.38465190e-01 5.18772185e-01 4.26025808e-01 -6.50635481e-01
9.93171215e-01 8.28206003e-01 4.16583806e-01 -1.48190737e+00
-6.82970703e-01 -6.53016210e-01 -3.31398904e-01 -3.80293041e-01
3.38811666e-01 -8.91990840e-01 -7.18681157e-01 4.57455218e-01
-1.08154738e+00 1.50652915e-01 -9.02666524e-02 5.37321568e-01
-5.45108497e-01 7.09841192e-01 -3.24565619e-01 -8.99505258e-01
-2.76128858e-01 -1.14331269e+00 9.99567807e-01 3.82929780e-02
-9.72339138e-02 -9.11986768e-01 5.14875874e-02 6.67806029e-01
5.38601458e-01 4.92940575e-01 3.38481843e-01 -2.25978076e-01
-4.20424879e-01 -6.41894192e-02 4.12642919e-02 2.81065345e-01
-9.07449722e-02 -8.21280107e-02 -7.51172066e-01 -7.99910426e-01
5.22605956e-01 -2.53987402e-01 3.79314154e-01 6.67279959e-01
9.74524796e-01 -8.02947402e-01 -3.00444871e-01 1.17114186e+00
1.55777144e+00 -2.26434246e-01 3.75856459e-01 -8.11166167e-02
6.72384143e-01 6.76074684e-01 4.90229249e-01 9.47884619e-01
5.01214452e-02 5.02335966e-01 5.32609880e-01 3.37744989e-02
7.59066045e-02 2.00184748e-01 3.76988798e-01 8.76463234e-01
-1.60961986e-01 1.69116229e-01 -6.78188384e-01 3.53983790e-01
-1.92109215e+00 -1.10836923e+00 -3.53534818e-01 2.25665426e+00
5.39668322e-01 -6.98843300e-01 -1.47828788e-01 4.00239348e-01
7.25895643e-01 5.84333874e-02 -4.66981739e-01 -3.02006632e-01
-2.18666732e-01 1.79339454e-01 3.70224744e-01 6.38121784e-01
-8.50609064e-01 5.82037270e-01 6.76648426e+00 7.73207486e-01
-1.09621418e+00 2.11695924e-01 4.88799810e-01 -3.99062812e-01
-4.23048250e-02 -4.85837925e-03 -7.79417574e-01 3.84517759e-01
7.52637446e-01 -2.13045433e-01 9.23376381e-01 7.57196605e-01
4.31288689e-01 -1.06487803e-01 -1.07753921e+00 1.65103126e+00
6.90214276e-01 -1.37514365e+00 -1.19419441e-01 2.40935132e-01
1.19425547e+00 -3.18705857e-01 2.15238318e-01 -1.74211174e-01
-2.34294221e-01 -1.32920420e+00 3.91705304e-01 5.02368093e-01
7.45900989e-01 -5.45829952e-01 4.37103093e-01 3.17259699e-01
-1.02817833e+00 -2.67250109e-02 -3.57965291e-01 -9.41563770e-02
3.00956935e-01 9.64431643e-01 -2.34877944e-01 3.90455484e-01
5.23344874e-01 9.12756383e-01 7.00864196e-02 1.01224911e+00
2.60313958e-01 4.85709876e-01 -5.67011297e-01 5.45188010e-01
2.58632675e-02 -6.42095327e-01 9.42745507e-01 8.51093233e-01
6.46947563e-01 6.82039738e-01 2.69406766e-01 7.02805579e-01
5.86729199e-02 4.08270359e-01 -7.10819542e-01 7.59339854e-02
3.52609158e-01 1.29410267e+00 -3.39670867e-01 1.35375988e-02
-6.99933350e-01 7.73266792e-01 -1.04357786e-02 5.21900058e-01
-6.26091599e-01 1.45275861e-01 8.40262592e-01 1.87922955e-01
1.46445081e-01 -4.05606419e-01 -1.58716917e-01 -1.18961668e+00
4.68140580e-02 -1.39900720e+00 3.01246166e-01 -4.33798611e-01
-1.09131539e+00 2.82972038e-01 -9.09077525e-02 -1.14631546e+00
2.35363599e-02 -5.39056599e-01 -5.05279064e-01 8.44119847e-01
-1.47484457e+00 -8.77745628e-01 -3.42879295e-01 1.06836975e+00
4.30170447e-01 -5.52930057e-01 6.57695591e-01 4.95339930e-01
-8.50007176e-01 4.46029305e-01 3.38192642e-01 -2.21646830e-01
4.81930673e-01 -4.69517261e-01 -6.23270452e-01 9.65816855e-01
3.67508382e-02 5.99050641e-01 8.02771568e-01 -5.71446419e-01
-2.34330106e+00 -9.35016155e-01 7.39759922e-01 9.56820473e-02
4.15602744e-01 2.04416171e-01 -9.33404267e-01 6.81688905e-01
-3.27316076e-02 5.22255838e-01 7.73453832e-01 -2.21161276e-01
1.39832459e-02 -2.70182520e-01 -1.49939442e+00 6.65927082e-02
9.37579811e-01 -2.86509037e-01 -2.49865770e-01 7.70683110e-01
-1.50046468e-01 -5.39030194e-01 -8.71751904e-01 3.34968656e-01
4.38708335e-01 -8.34911644e-01 1.15465975e+00 -3.65340352e-01
2.57004291e-01 -3.25510651e-01 -4.38513160e-01 -1.14220548e+00
-3.14253271e-01 -1.05583549e+00 -4.60318297e-01 9.91440356e-01
-2.13609990e-02 -6.52610421e-01 9.39183533e-01 6.33121312e-01
8.17412958e-02 -7.32847571e-01 -1.27461088e+00 -7.03086436e-01
-3.37843746e-01 -2.76263386e-01 3.90958369e-01 1.16456163e+00
-7.24805966e-02 -2.58784264e-01 -7.17286587e-01 4.85132605e-01
1.34130692e+00 1.43716916e-01 6.63316727e-01 -1.03566790e+00
-1.46660596e-01 -9.84088257e-02 -4.25657243e-01 -6.53105378e-01
5.23619592e-01 -1.07478893e+00 -1.23936795e-01 -1.14040971e+00
1.85100824e-01 -4.04554904e-01 5.69876730e-02 3.08198512e-01
2.39556715e-01 4.89603847e-01 1.82155520e-01 2.61893451e-01
-3.25726122e-01 5.63690424e-01 1.07928252e+00 -2.83210874e-01
-1.48600906e-01 -2.24993154e-01 -5.51465273e-01 7.44593203e-01
5.07295609e-01 -4.80019152e-01 -2.56069124e-01 -6.00001872e-01
3.12029440e-02 3.78237396e-01 2.06220478e-01 -9.33672547e-01
5.52370906e-01 -3.92396510e-01 2.64860332e-01 -2.71079361e-01
6.90976024e-01 -9.79436100e-01 5.72817087e-01 5.01253426e-01
1.16529025e-01 -1.15631238e-01 1.18089601e-01 3.53708416e-01
-3.89545679e-01 -3.96789968e-01 9.24520552e-01 -2.02305377e-01
-5.72370768e-01 5.37796319e-01 -1.75981671e-01 7.11814538e-02
1.28071594e+00 -4.13119674e-01 1.42426550e-01 -5.03404200e-01
-7.17789352e-01 6.81658313e-02 2.66739935e-01 -1.61545202e-01
9.06876564e-01 -1.40227330e+00 -1.22789323e+00 5.18493176e-01
-4.17413831e-01 -1.25638172e-01 2.16200456e-01 1.20228851e+00
-6.88270152e-01 3.65668774e-01 -6.62630200e-02 -6.62615359e-01
-1.52976286e+00 3.95590514e-01 3.49927336e-01 3.47796977e-01
-3.58616292e-01 1.00552881e+00 -2.40053892e-01 -2.93720990e-01
3.59744221e-01 4.28654164e-01 1.70110352e-02 -5.03910035e-02
6.80939019e-01 9.51382279e-01 5.52906729e-02 -1.24786508e+00
-5.30877769e-01 9.39966977e-01 3.79143000e-01 5.85872233e-02
1.50924277e+00 -7.96434134e-02 -8.22966218e-01 -2.08017081e-01
1.49867582e+00 -3.97898220e-02 -8.45307350e-01 -2.11223438e-01
-3.06187004e-01 -8.21853518e-01 3.69488806e-01 -1.09043434e-01
-1.53963006e+00 3.32587600e-01 4.91835654e-01 -2.77980804e-01
1.21125412e+00 -4.38097984e-01 6.89867914e-01 2.60814846e-01
4.41901267e-01 -9.81014907e-01 -1.51402742e-01 3.70421141e-01
1.28017986e+00 -1.31639421e+00 5.63091338e-01 -7.43597984e-01
-2.17099383e-01 9.70684528e-01 3.54497135e-01 -8.88312832e-02
1.06349397e+00 4.91116524e-01 -1.39090598e-01 -4.61092629e-02
-4.88231659e-01 2.49737933e-01 2.98062235e-01 4.34968650e-01
3.48713964e-01 -2.60093421e-01 -4.60467070e-01 6.89833835e-02
-1.06260434e-01 1.02510527e-01 3.51184577e-01 9.93992150e-01
-1.72697023e-01 -8.89892817e-01 -1.19762254e+00 4.75278556e-01
-3.58505011e-01 1.35309950e-01 -1.98261172e-01 2.76053101e-01
7.68867731e-02 1.11934268e+00 -2.58660406e-01 1.16345637e-01
6.85970709e-02 -2.75341365e-02 6.36746943e-01 -4.13121909e-01
-2.06645235e-01 5.85123105e-03 -2.68570900e-01 -8.85613024e-01
-6.56852901e-01 -9.77699757e-01 -9.80176270e-01 -6.87533855e-01
-1.62076309e-01 1.89652488e-01 8.52841139e-01 9.33105350e-01
3.83019149e-01 -1.16881453e-01 6.57494009e-01 -1.12623513e+00
-6.67384684e-01 -4.26759124e-01 -7.58673310e-01 3.40309381e-01
3.26184601e-01 -6.19558990e-01 -5.25440812e-01 1.62732854e-01] | [12.528475761413574, 0.40229612588882446] |
81591949-a7e9-4fc4-a2cb-9b4f5720dc2e | stereo-image-rain-removal-via-dual-view | 2211.10104 | null | https://arxiv.org/abs/2211.10104v2 | https://arxiv.org/pdf/2211.10104v2.pdf | Stereo Image Rain Removal via Dual-View Mutual Attention | Stereo images, containing left and right view images with disparity, are utilized in solving low-vision tasks recently, e.g., rain removal and super-resolution. Stereo image restoration methods usually obtain better performance than monocular methods by learning the disparity between dual views either implicitly or explicitly. However, existing stereo rain removal methods still cannot make full use of the complementary information between two views, and we find it is because: 1) the rain streaks have more complex distributions in directions and densities, which severely damage the complementary information and pose greater challenges; 2) the disparity estimation is not accurate enough due to the imperfect fusion mechanism for the features between two views. To overcome such limitations, we propose a new \underline{Stereo} \underline{I}mage \underline{R}ain \underline{R}emoval method (StereoIRR) via sufficient interaction between two views, which incorporates: 1) a new Dual-view Mutual Attention (DMA) mechanism which generates mutual attention maps by taking left and right views as key information for each other to facilitate cross-view feature fusion; 2) a long-range and cross-view interaction, which is constructed with basic blocks and dual-view mutual attention, can alleviate the adverse effect of rain on complementary information to help the features of stereo images to get long-range and cross-view interaction and fusion. Notably, StereoIRR outperforms other related monocular and stereo image rain removal methods on several datasets. Our codes and datasets will be released. | ['Yi Yang', 'Richang Hong', 'Yang Zhao', 'ZhongQiu Zhao', 'Zhao Zhang', 'Yanyan Wei'] | 2022-11-18 | null | null | null | null | ['disparity-estimation'] | ['computer-vision'] | [ 5.10930270e-02 -4.98655349e-01 2.42057428e-01 -4.50767666e-01
-3.43567997e-01 -2.68617213e-01 3.37400168e-01 -5.90855002e-01
-2.30544820e-01 9.53912675e-01 4.02325898e-01 -3.69826667e-02
-1.07648797e-01 -8.05841088e-01 -6.39556706e-01 -1.07368255e+00
4.57236171e-01 -2.30557963e-01 4.04496282e-01 -4.57973301e-01
2.73428202e-01 3.68736863e-01 -2.24224138e+00 2.57896662e-01
1.44596386e+00 6.16446435e-01 8.87147188e-01 5.49775064e-01
-2.37515017e-01 9.22728896e-01 -3.14310312e-01 1.09571762e-01
4.25747484e-01 -5.01363695e-01 -1.63340002e-01 4.54438291e-02
6.91032767e-01 -7.26142466e-01 -5.40990233e-01 1.31788182e+00
6.54403389e-01 7.94005468e-02 4.78089362e-01 -9.37785208e-01
-7.45274007e-01 -2.93858312e-02 -1.16893899e+00 6.07761502e-01
4.20028180e-01 3.31873745e-01 6.49101377e-01 -1.08874595e+00
3.19914132e-01 1.28211713e+00 2.88469523e-01 3.49187404e-01
-8.36681366e-01 -9.72298026e-01 5.13288200e-01 2.46801764e-01
-1.20207250e+00 -4.38513666e-01 6.33072972e-01 -2.38132700e-01
5.78497112e-01 4.54596877e-01 8.12394202e-01 5.94960272e-01
9.78284478e-02 6.18337631e-01 1.59647036e+00 -8.07338059e-02
-2.56367922e-01 2.12981045e-01 1.76445097e-01 4.09548998e-01
4.75118577e-01 5.56988716e-01 -4.31058228e-01 2.67389029e-01
1.03924620e+00 4.58028376e-01 -9.54063833e-01 -1.69230215e-02
-1.06591594e+00 7.07047522e-01 6.05750084e-01 2.96165973e-01
-2.76883602e-01 -4.12578881e-01 -3.18244129e-01 3.69773507e-01
5.09325922e-01 2.52014920e-02 -2.02922419e-01 3.52172405e-01
-7.02240705e-01 2.33758986e-01 2.39598915e-01 8.35402966e-01
1.21775234e+00 2.25991383e-01 -9.44909602e-02 1.00078011e+00
3.66646051e-01 1.12636244e+00 1.87613681e-01 -1.00497186e+00
7.32258379e-01 4.62224156e-01 2.93954432e-01 -8.94630313e-01
-3.02640498e-01 -2.32799530e-01 -1.12355697e+00 6.69469297e-01
1.19141780e-01 -1.44957244e-01 -1.11150289e+00 1.62372303e+00
3.73175293e-01 2.66016662e-01 2.32677728e-01 1.40824914e+00
1.34287596e+00 6.81275189e-01 -4.46784586e-01 -5.23321450e-01
1.14785230e+00 -7.74175346e-01 -9.00757432e-01 -4.98764634e-01
-1.84757318e-02 -9.42041874e-01 8.23587954e-01 3.48434240e-01
-1.18118656e+00 -7.94901967e-01 -1.14426076e+00 -1.60349935e-01
-2.42759600e-01 -2.17365041e-01 4.61635262e-01 3.88944924e-01
-9.22303438e-01 2.34471738e-01 -2.19495460e-01 -1.12847410e-01
1.74643591e-01 1.30261973e-01 -3.98367554e-01 -8.26456010e-01
-1.27945650e+00 7.86472976e-01 -2.96999831e-02 5.65922260e-01
-5.90278983e-01 -6.21128798e-01 -9.95502710e-01 -2.59673536e-01
1.11693509e-01 -7.33804047e-01 4.40730482e-01 -9.46668863e-01
-1.04443848e+00 7.25276411e-01 -5.57438731e-01 7.48813599e-02
3.19781482e-01 -5.18658400e-01 -5.63032210e-01 4.46299724e-02
1.18024170e-01 6.36912227e-01 9.25478518e-01 -1.79173958e+00
-8.54646742e-01 -6.98121309e-01 2.47239038e-01 8.89111698e-01
1.81632593e-01 -3.24461669e-01 -5.10616839e-01 -4.97095764e-01
4.83267307e-01 -5.04841387e-01 -4.91242334e-02 -3.16084236e-01
-2.69741625e-01 5.21330595e-01 1.00608587e+00 -6.83628500e-01
1.02403879e+00 -1.86435187e+00 2.08192199e-01 -2.18886793e-01
4.12036955e-01 5.16582191e-01 -2.93165329e-03 9.31227803e-02
-1.93976417e-01 -2.81954795e-01 -1.69151977e-01 4.01058532e-02
-7.21159637e-01 4.79065329e-01 -2.92078584e-01 4.51526701e-01
-1.69534266e-01 4.76208091e-01 -8.50153565e-01 -3.69658202e-01
7.42813885e-01 5.69117308e-01 -2.18460470e-01 4.82022792e-01
2.29057059e-01 8.26379716e-01 -1.46274343e-01 5.03453493e-01
1.50507295e+00 9.36918706e-02 -3.23085189e-01 -4.44925576e-01
-3.76960933e-01 -1.52454257e-01 -1.40656793e+00 1.19479740e+00
-5.68255067e-01 4.09299701e-01 2.52437502e-01 -3.82007062e-01
1.07297122e+00 1.23540498e-01 2.20845968e-01 -1.12877595e+00
-9.40816849e-02 7.52203912e-02 -2.11806297e-01 -7.40952253e-01
3.37545574e-01 -1.72064245e-01 6.77159846e-01 3.01960826e-01
-4.21719372e-01 -2.59090781e-01 -3.12415250e-02 1.76970914e-01
5.19942522e-01 -8.90913140e-03 6.97661713e-02 -4.53598574e-02
8.40320230e-01 -5.41297197e-01 8.34072769e-01 7.05826879e-01
-4.52212617e-02 1.12507105e+00 -1.47449985e-01 -4.68263090e-01
-8.51428807e-01 -1.11231005e+00 -1.32790476e-01 5.96520126e-01
8.39696586e-01 1.08799376e-01 -2.21965298e-01 -4.21647400e-01
-1.20048516e-03 5.36573648e-01 -6.41768992e-01 4.35555279e-02
-4.03098732e-01 -9.67169166e-01 -1.30772620e-01 4.18842286e-01
8.72128904e-01 -1.02537620e+00 -3.58901769e-01 -2.33636603e-01
-5.63433051e-01 -1.07730877e+00 -5.68842053e-01 2.38524061e-02
-8.58434856e-01 -1.21718001e+00 -8.87431502e-01 -4.80878383e-01
6.35911047e-01 1.36681867e+00 1.20066619e+00 2.21792057e-01
-2.92144656e-01 1.55303270e-01 -4.01294380e-01 -2.89488524e-01
2.25163713e-01 -6.82001829e-01 -4.18561324e-02 1.60186850e-02
4.10532504e-01 -9.03834760e-01 -1.00609529e+00 4.95976508e-01
-9.62242007e-01 4.75532383e-01 5.25647640e-01 9.66584265e-01
4.31261897e-01 -8.51264670e-02 2.39723742e-01 -8.25198650e-01
1.38452947e-01 -3.65465432e-01 -6.42210901e-01 4.92818281e-03
-4.04933214e-01 -2.35591367e-01 1.92792565e-01 1.64118394e-01
-1.56082916e+00 -2.18603268e-01 -3.20385285e-02 -6.67195499e-01
-2.03168899e-01 6.81012943e-02 -5.37927866e-01 -8.39026943e-02
3.11441839e-01 4.78553325e-01 6.85895830e-02 -3.90984535e-01
2.53630012e-01 7.29656041e-01 5.37295640e-01 -2.84497719e-02
9.83191311e-01 8.44146848e-01 -1.57345653e-01 -8.61789405e-01
-1.15499663e+00 -5.97702444e-01 -6.83341444e-01 -3.30347657e-01
1.06771004e+00 -1.49416995e+00 -5.87618887e-01 7.84077525e-01
-1.04111958e+00 -5.94707131e-02 4.20622379e-02 5.92167139e-01
-1.20741472e-01 7.17651486e-01 -4.48289633e-01 -1.04666686e+00
-3.03091615e-01 -1.15512764e+00 1.03213286e+00 8.16305816e-01
5.58419347e-01 -7.06558347e-01 -1.99202210e-01 7.00933456e-01
4.03632075e-01 -1.76400170e-01 5.09878099e-01 3.32695037e-01
-8.91801000e-01 3.53288293e-01 -8.23838055e-01 6.17467761e-01
4.82512742e-01 -1.42816484e-01 -1.22570407e+00 -3.24939966e-01
2.12355465e-01 1.68302692e-02 1.07159626e+00 6.09674454e-01
8.15466821e-01 1.43482402e-01 -1.30905464e-01 9.57182467e-01
1.62762105e+00 2.34207988e-01 1.13906205e+00 3.35316837e-01
1.02285087e+00 6.95100129e-01 8.34060788e-01 3.63677412e-01
5.74963093e-01 5.19175053e-01 7.63084888e-01 -5.85351706e-01
-3.01786572e-01 1.00974604e-01 2.72960901e-01 7.05888987e-01
-3.77355516e-01 -8.43070820e-02 -3.15087080e-01 4.80368078e-01
-1.74376845e+00 -1.23635507e+00 -4.00737852e-01 2.54812407e+00
5.38708985e-01 -1.71799526e-01 -3.81913573e-01 -1.81659296e-01
8.55139554e-01 4.14217055e-01 -6.44309342e-01 1.38317198e-01
-6.89756036e-01 -1.72555178e-01 5.62475920e-01 7.33900368e-01
-9.14172828e-01 6.35015309e-01 5.40692616e+00 5.41114807e-01
-9.88104463e-01 -7.09381178e-02 5.52534521e-01 -1.24950483e-01
-6.44000649e-01 5.68757067e-03 -9.23350573e-01 7.29452729e-01
4.40788642e-02 3.33110303e-01 6.43411636e-01 1.14595890e-01
3.46872777e-01 -7.00265348e-01 -5.10684490e-01 1.35420084e+00
3.41229826e-01 -9.05312836e-01 9.48045403e-02 8.83087292e-02
8.99032176e-01 1.81972966e-01 -4.02183682e-02 9.28039327e-02
5.18381417e-01 -1.11273706e+00 2.09370270e-01 8.23828161e-01
6.91228926e-01 -7.53045738e-01 8.79605770e-01 3.22252423e-01
-1.30738974e+00 -6.92763776e-02 -5.14441609e-01 -8.13938528e-02
2.77292669e-01 1.03278983e+00 2.50964552e-01 1.13791239e+00
1.15844178e+00 9.57062066e-01 -4.44233656e-01 9.50252831e-01
-3.50117981e-01 -6.86014891e-02 -1.99790120e-01 6.69043601e-01
-7.81989321e-02 -8.39856863e-01 7.74701118e-01 7.85063744e-01
2.83555061e-01 3.70440990e-01 3.62083204e-02 7.20328629e-01
4.15605515e-01 -3.23726654e-01 -8.50818276e-01 7.02550650e-01
4.52363729e-01 1.07156110e+00 -1.85899153e-01 -2.85672188e-01
-7.26098061e-01 1.03229725e+00 -9.68896896e-02 7.32337832e-01
-7.16891587e-01 -3.24400038e-01 8.63161743e-01 8.91854465e-02
4.18461412e-01 -3.95220937e-03 -3.05294245e-01 -1.46235693e+00
1.27847180e-01 -8.54233384e-01 2.35758752e-01 -1.42697597e+00
-1.33930254e+00 6.93405390e-01 -1.48183957e-01 -1.46138191e+00
1.44156158e-01 -2.06542030e-01 -6.20131075e-01 1.39834404e+00
-2.05336714e+00 -9.29697812e-01 -1.09290242e+00 9.28425372e-01
5.08924305e-01 2.86099091e-02 3.22447211e-01 5.27021229e-01
-5.28932929e-01 1.24776162e-01 2.61853814e-01 -2.71238238e-01
9.80690420e-01 -1.21037269e+00 -9.24852714e-02 1.00308144e+00
-1.39169842e-01 3.15594435e-01 7.63624430e-01 -6.51969492e-01
-1.14218509e+00 -9.05320287e-01 4.33394998e-01 -2.50344872e-01
5.08932620e-02 5.46228103e-02 -1.15428448e+00 4.67439234e-01
1.82269976e-01 1.79127604e-01 2.11477518e-01 4.69337776e-03
-3.56065959e-01 -3.96553576e-01 -1.04783380e+00 5.85702300e-01
1.08611834e+00 -4.14563179e-01 -5.31829238e-01 2.25517914e-01
6.30689621e-01 -6.02203906e-01 -4.74239022e-01 6.94193482e-01
4.11715209e-01 -1.86686444e+00 1.13293636e+00 1.16922364e-01
4.91548896e-01 -7.19095290e-01 -3.90598863e-01 -1.20886362e+00
-1.68161288e-01 -2.33501405e-01 2.92110201e-02 1.30374002e+00
-8.58925283e-02 -9.88273680e-01 3.94649059e-01 2.01674834e-01
-2.99440268e-02 -5.87222815e-01 -7.40820765e-01 -3.68206143e-01
-2.04948112e-01 -6.60160407e-02 5.56328595e-01 9.47143734e-01
-6.09121621e-01 3.42973530e-01 -8.13567400e-01 5.49749017e-01
8.09903145e-01 7.06737518e-01 8.39144289e-01 -1.34249544e+00
-1.99777246e-01 6.66611828e-03 -5.61892726e-02 -1.30226827e+00
-3.05287510e-01 -2.88769752e-01 6.06036149e-02 -1.69344437e+00
6.17926300e-01 -4.22348708e-01 -2.90588617e-01 6.50237948e-02
-6.08517170e-01 4.20587987e-01 2.43831024e-01 4.79642868e-01
-3.31232905e-01 8.26777220e-01 1.70470738e+00 1.56379014e-01
-2.21077338e-01 -3.43987308e-02 -7.96694160e-01 8.55608821e-01
4.27537918e-01 -6.26239255e-02 -4.30556566e-01 -6.97166800e-01
1.39944315e-01 4.48655546e-01 4.13840115e-01 -1.09494436e+00
1.89330995e-01 -3.10665667e-01 7.24614322e-01 -9.36898887e-01
6.35006905e-01 -7.31916249e-01 8.68990570e-02 1.19247874e-02
4.13220853e-01 4.49921750e-02 -1.46963643e-02 8.01516473e-01
-4.63390291e-01 -2.42217761e-02 1.06625795e+00 -4.27567601e-01
-6.86913371e-01 4.18278545e-01 -2.43020460e-01 -1.11210063e-01
7.00734615e-01 -6.22491479e-01 -6.32626414e-01 -7.24388301e-01
-6.09934628e-01 5.20306945e-01 7.32009709e-01 4.59148973e-01
7.59505570e-01 -1.10783005e+00 -7.36661971e-01 4.23735231e-01
2.12977082e-02 1.67081088e-01 1.11594498e+00 1.02954805e+00
-3.34067076e-01 1.57368734e-01 -4.56395984e-01 -6.57197893e-01
-1.55137765e+00 4.53132004e-01 5.31387091e-01 -4.00596820e-02
-8.00526202e-01 7.20661581e-01 1.11807072e+00 -3.42031032e-01
-1.04262702e-01 1.55441150e-01 -5.92912674e-01 4.86957766e-02
8.43171835e-01 3.30263615e-01 -4.61071618e-02 -8.06244850e-01
-4.36250418e-02 9.36495304e-01 -2.39743039e-01 7.89200068e-02
1.30935287e+00 -9.22528684e-01 -3.01088780e-01 3.80594850e-01
7.08055139e-01 2.08645776e-01 -1.46865726e+00 -3.87832075e-01
-1.08738399e+00 -1.12577796e+00 3.10410440e-01 -6.97395384e-01
-1.63286686e+00 1.04869807e+00 9.52743828e-01 -5.14887087e-02
1.49634588e+00 -4.38931674e-01 7.49298215e-01 -3.84257026e-02
4.27355379e-01 -7.24489152e-01 -2.26464644e-02 6.41754806e-01
6.83023691e-01 -1.46911597e+00 3.28939378e-01 -7.24317372e-01
-8.72715414e-01 8.98243785e-01 9.96244192e-01 3.53816561e-02
7.39822567e-01 1.32964864e-01 3.66504848e-01 -3.29464465e-01
-4.73598152e-01 -7.00797021e-01 6.88419119e-02 8.31436038e-01
3.43770385e-01 -3.09541285e-01 -1.31589457e-01 2.33745307e-01
1.92906156e-01 -1.55822188e-01 5.38863957e-01 7.33471692e-01
-6.91383898e-01 -8.03148329e-01 -7.24252224e-01 4.44171876e-01
-8.79427046e-02 -3.87951106e-01 4.51941192e-02 6.47643209e-01
4.29319024e-01 1.25304115e+00 6.62175640e-02 -3.25615585e-01
2.81084627e-01 -5.15866876e-01 5.51841438e-01 -3.64762962e-01
-1.73469976e-01 7.30908215e-02 -2.39626512e-01 -5.50374091e-01
-6.82263553e-01 -5.67480445e-01 -8.36242676e-01 -4.22688305e-01
-4.08757448e-01 -4.82233129e-02 1.49006113e-01 1.00597394e+00
3.03863019e-01 4.98405904e-01 7.95113027e-01 -1.17028630e+00
1.58961847e-01 -9.07641888e-01 -1.11028862e+00 3.76034975e-01
5.84019899e-01 -9.38839257e-01 -7.22082913e-01 -1.96305439e-01] | [10.82850456237793, -3.0538790225982666] |
3a6f2ef0-7f8b-4e12-9dd6-eca993942014 | deep-factorised-inverse-sketching | 1808.02313 | null | http://arxiv.org/abs/1808.02313v1 | http://arxiv.org/pdf/1808.02313v1.pdf | Deep Factorised Inverse-Sketching | Modelling human free-hand sketches has become topical recently, driven by
practical applications such as fine-grained sketch based image retrieval
(FG-SBIR). Sketches are clearly related to photo edge-maps, but a human
free-hand sketch of a photo is not simply a clean rendering of that photo's
edge map. Instead there is a fundamental process of abstraction and iconic
rendering, where overall geometry is warped and salient details are selectively
included. In this paper we study this sketching process and attempt to invert
it. We model this inversion by translating iconic free-hand sketches to
contours that resemble more geometrically realistic projections of object
boundaries, and separately factorise out the salient added details. This
factorised re-representation makes it easier to match a free-hand sketch to a
photo instance of an object. Specifically, we propose a novel unsupervised
image style transfer model based on enforcing a cyclic embedding consistency
constraint. A deep FG-SBIR model is then formulated to accommodate
complementary discriminative detail from each factorised sketch for better
matching with the corresponding photo. Our method is evaluated both
qualitatively and quantitatively to demonstrate its superiority over a number
of state-of-the-art alternatives for style transfer and FG-SBIR. | ['Yi-Zhe Song', 'Jifei Song', 'Kaiyue Pang', 'Da Li', 'Timothy M. Hospedales', 'Tao Xiang'] | 2018-08-07 | deep-factorised-inverse-sketching-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Kaiyue_Pang_Deep_Factorised_Inverse-Sketching_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Kaiyue_Pang_Deep_Factorised_Inverse-Sketching_ECCV_2018_paper.pdf | eccv-2018-9 | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 5.23513734e-01 7.03044161e-02 -3.43505340e-03 -3.19187909e-01
-4.82151508e-01 -7.81362951e-01 1.07781541e+00 -2.82525271e-01
-1.46582872e-01 5.14993668e-01 4.03318763e-01 2.20432177e-01
-2.23963857e-01 -7.45312572e-01 -6.03795350e-01 -3.66207629e-01
4.18787986e-01 5.12096643e-01 1.71696663e-01 -2.36130670e-01
4.41160023e-01 1.09882748e+00 -1.39055276e+00 4.62449819e-01
4.84141648e-01 7.12648273e-01 2.41843581e-01 6.99802697e-01
-1.30450144e-01 2.86835909e-01 -3.65581214e-01 -6.49975955e-01
5.16635597e-01 -4.84916776e-01 -6.03487849e-01 4.36738133e-01
9.80368376e-01 -4.03982878e-01 -4.83174592e-01 8.85655820e-01
3.07080299e-01 2.34577730e-01 1.02578926e+00 -1.28555298e+00
-9.68076766e-01 -1.70838147e-01 -7.97798753e-01 -5.11166453e-01
4.76483554e-01 -8.76935720e-02 9.82607901e-01 -1.10070550e+00
1.20982397e+00 1.54648829e+00 4.43648964e-01 5.48634410e-01
-1.56697297e+00 -5.38833559e-01 1.50651738e-01 -1.90672323e-01
-1.40042698e+00 -3.36202562e-01 1.22165263e+00 -3.96801621e-01
4.34256524e-01 4.40737098e-01 7.37862349e-01 8.28726768e-01
1.72024637e-01 7.26603389e-01 1.08880270e+00 -6.52711570e-01
2.08398089e-01 2.90973306e-01 -4.96009558e-01 6.76747918e-01
-9.84998271e-02 1.97504252e-01 -6.45676434e-01 -1.39083266e-01
1.58440280e+00 3.74584436e-01 -2.23014623e-01 -8.53831172e-01
-1.14880562e+00 7.17410564e-01 6.19501531e-01 2.15321392e-01
-6.13887489e-01 2.24513173e-01 1.91151276e-02 1.72502771e-01
5.32739758e-01 3.96180719e-01 8.97003561e-02 2.55896151e-01
-1.24096560e+00 5.47422171e-01 6.45471752e-01 1.04397786e+00
9.26236331e-01 -1.85481951e-01 -2.96534806e-01 9.85099077e-01
2.30217114e-01 2.89905190e-01 1.20911812e-02 -1.21525192e+00
7.61364866e-03 6.32061660e-01 1.69642791e-01 -1.06975889e+00
3.06655765e-01 -1.87363420e-02 -9.65068936e-01 8.38734627e-01
2.05827609e-01 5.21957219e-01 -9.64975834e-01 1.40790546e+00
1.82776019e-01 -1.03818446e-01 -3.32616866e-01 1.04016352e+00
5.30204058e-01 6.85252130e-01 1.04235105e-01 2.87752867e-01
1.47605681e+00 -8.32463086e-01 -5.04487932e-01 -2.62579083e-01
-3.22437704e-01 -9.31284308e-01 1.13622320e+00 3.00645620e-01
-1.26825166e+00 -5.60188770e-01 -8.93878818e-01 -5.76824427e-01
-2.96239048e-01 8.84908661e-02 2.44819999e-01 3.03713769e-01
-1.06370187e+00 7.51453161e-01 -2.80216187e-01 -4.87039119e-01
4.74432886e-01 1.57043543e-02 -7.27543175e-01 -1.82738900e-01
-7.76813745e-01 6.84889317e-01 9.03360099e-02 -1.43524021e-01
-3.85606200e-01 -9.00149107e-01 -7.65537381e-01 1.24297976e-01
1.58248231e-01 -1.06864262e+00 8.51157725e-01 -1.13151658e+00
-1.57076430e+00 1.37251806e+00 -3.34277600e-01 1.40577435e-01
8.36030006e-01 -5.57591021e-03 -1.17769703e-01 2.37061217e-01
-7.64069036e-02 1.06150651e+00 1.41003418e+00 -1.81592059e+00
-3.33068430e-01 -3.15167159e-01 9.97187421e-02 2.39198327e-01
-3.44415531e-02 3.79070826e-02 -5.59325635e-01 -1.29246759e+00
-2.77220737e-02 -8.63674819e-01 -4.39922810e-02 8.93522501e-01
-6.81199357e-02 -6.05446547e-02 1.09602225e+00 -7.53150642e-01
1.06484151e+00 -1.99260461e+00 5.27252018e-01 4.38418895e-01
3.11340570e-01 2.95880198e-01 -3.81015182e-01 8.93044591e-01
-7.07108676e-02 -8.07580575e-02 -3.88598382e-01 -5.99259555e-01
9.43148881e-02 2.98509508e-01 -4.67869699e-01 1.82732046e-01
4.49001998e-01 1.15104103e+00 -9.85538423e-01 -3.67947906e-01
5.09470880e-01 8.47564816e-01 -6.21255994e-01 3.83199304e-01
-1.56585187e-01 5.23291051e-01 -2.54674852e-01 3.72899473e-01
8.29665422e-01 -1.11366995e-01 1.87051371e-02 -6.04061484e-01
-6.67872950e-02 -2.51976818e-01 -1.19170320e+00 1.97419870e+00
-5.16251981e-01 5.69814444e-01 9.94805917e-02 -5.36273360e-01
1.08613622e+00 3.13869640e-02 5.07997982e-02 -7.44153261e-01
-2.00641885e-01 8.60579163e-02 -5.12563348e-01 -4.58036289e-02
6.73241317e-01 -4.82364029e-01 1.88489854e-01 5.58112562e-01
-8.17465186e-02 -7.79023528e-01 -2.04723909e-01 4.35889155e-01
4.91608024e-01 5.03399134e-01 3.53874654e-01 -3.17954093e-01
5.79407394e-01 -4.86916006e-01 2.57439818e-02 4.16121036e-01
2.20393330e-01 1.22464132e+00 9.82794836e-02 -5.94036877e-01
-1.34687853e+00 -1.31165028e+00 1.09593540e-01 9.21073318e-01
2.00772196e-01 -3.79735112e-01 -7.44210720e-01 -5.26463568e-01
2.04063341e-01 6.86239898e-01 -8.40455174e-01 5.82459383e-02
-5.35666883e-01 3.12753528e-01 1.50925919e-01 4.48477298e-01
3.32164019e-01 -1.40037644e+00 -5.91912031e-01 1.82826892e-01
6.92625269e-02 -8.25125635e-01 -1.08390510e+00 -4.56773221e-01
-5.91973245e-01 -7.69990623e-01 -1.30364656e+00 -8.89156759e-01
8.91977787e-01 3.89777213e-01 1.23102462e+00 2.72434026e-01
-5.97081006e-01 6.34445488e-01 -1.44434988e-01 -2.35775366e-01
-4.85847890e-01 -4.11027849e-01 -1.23414733e-01 4.32817310e-01
-1.40746593e-01 -7.34288156e-01 -8.68355870e-01 3.71607393e-01
-1.41128767e+00 4.39639539e-01 7.10082054e-01 9.12504792e-01
6.48836911e-01 -3.93563956e-01 2.10053787e-01 -7.58312821e-01
6.53346896e-01 1.14349663e-01 -2.60733217e-01 5.48574328e-01
-2.53469288e-01 2.06363335e-01 4.36641514e-01 -4.80216324e-01
-1.27395904e+00 9.91610363e-02 5.05142473e-02 -9.40117538e-01
-1.69133276e-01 -9.54084098e-02 -1.53115273e-01 -3.14826936e-01
4.62415785e-01 2.48111755e-01 1.88671604e-01 -5.15715480e-01
8.62753153e-01 5.39465547e-01 6.87701464e-01 -8.16044807e-01
1.04636490e+00 7.80542433e-01 1.20451204e-01 -1.17071760e+00
-3.79333258e-01 -4.00519162e-01 -9.09519553e-01 -3.32834303e-01
7.00225890e-01 -4.77401018e-01 -4.88849610e-01 3.37825328e-01
-1.37619209e+00 -3.07965606e-01 -4.77051049e-01 -1.08582653e-01
-8.05011570e-01 5.24510145e-01 -3.80130976e-01 -7.49605238e-01
-3.28090549e-01 -8.86623800e-01 1.69497585e+00 1.67850301e-01
-4.54323024e-01 -1.01422501e+00 1.91691443e-01 5.37661044e-03
3.25065196e-01 3.42603803e-01 9.09440696e-01 2.01884881e-01
-6.41100347e-01 -2.85421550e-01 -7.21331120e-01 3.15829247e-01
2.65065193e-01 1.00400977e-01 -1.09511495e+00 -3.90985489e-01
-4.20406669e-01 1.90723334e-02 5.62371612e-01 8.41768309e-02
1.02550137e+00 -4.42666888e-01 -3.26344013e-01 5.12526214e-01
1.44782507e+00 1.87215977e-03 9.60290492e-01 -4.64922488e-02
8.70096147e-01 8.47635269e-01 3.92037183e-01 2.01721847e-01
6.32003024e-02 9.58248198e-01 3.71202752e-02 -4.22188610e-01
-5.52853465e-01 -8.18279147e-01 -1.17581442e-01 4.97037679e-01
-4.28532571e-01 -9.75520015e-02 -4.54336345e-01 3.54521692e-01
-1.65408647e+00 -8.78267169e-01 1.84589431e-01 2.37851191e+00
5.79418659e-01 -2.87311614e-01 6.68666959e-02 5.26135080e-02
6.82910442e-01 1.91818833e-01 -3.24531317e-01 -3.66283983e-01
-3.25837708e-03 4.86610681e-01 1.04526900e-01 7.28631496e-01
-6.98993325e-01 1.02021182e+00 5.62459850e+00 1.04568207e+00
-1.03289998e+00 -1.28011554e-01 5.56752026e-01 2.56849349e-01
-5.95414817e-01 1.37255676e-02 -2.24573717e-01 2.44751483e-01
-4.17061104e-03 -1.64745048e-01 6.10881209e-01 5.01160324e-01
-1.01848982e-01 5.88562079e-02 -1.41946936e+00 1.22260022e+00
1.62444621e-01 -1.57003868e+00 7.13257849e-01 2.17693180e-01
8.36619020e-01 -7.20737278e-01 8.80824029e-02 -1.19631387e-01
6.49325550e-02 -1.14202130e+00 1.02008808e+00 8.77931356e-01
1.36907625e+00 -5.79970717e-01 8.42829123e-02 1.36593819e-01
-1.33006620e+00 2.80434579e-01 -3.16243768e-01 1.20542590e-02
2.58409619e-01 1.95084929e-01 -3.94061059e-01 7.40835309e-01
4.70836461e-01 5.86966157e-01 -3.94413024e-01 7.37813056e-01
-5.36406524e-02 -2.47190759e-01 -8.69986787e-02 2.01526985e-01
1.92686990e-01 -5.95143557e-01 4.96756434e-01 1.33546770e+00
2.39608198e-01 3.73420686e-01 -1.93014055e-01 1.23513901e+00
-1.14718944e-01 6.28013462e-02 -8.52690458e-01 -6.66204989e-02
3.69187027e-01 1.42107332e+00 -7.67197251e-01 -4.87646282e-01
-2.78566688e-01 1.68946886e+00 3.90523314e-01 4.78332043e-01
-3.29003990e-01 -3.38221759e-01 7.62759387e-01 3.86295795e-01
3.50269437e-01 -1.00688703e-01 -2.07666188e-01 -1.04778361e+00
1.09569943e-02 -6.27526939e-01 -2.65663825e-02 -1.30003071e+00
-1.50897026e+00 5.99558055e-01 -4.06886861e-02 -1.33649933e+00
-2.04161659e-01 -5.53807735e-01 -8.06367874e-01 1.17906451e+00
-1.34445953e+00 -1.66426408e+00 -4.32621807e-01 4.56503212e-01
6.72820926e-01 2.21828178e-01 8.53139460e-01 1.78277090e-01
2.61970848e-01 5.14337003e-01 -1.35545909e-01 -6.21678792e-02
9.10580039e-01 -1.04399085e+00 7.35924840e-01 5.70510089e-01
3.15845460e-01 8.15241158e-01 5.79292893e-01 -7.34487891e-01
-1.39885271e+00 -1.02107286e+00 8.07509661e-01 -5.72198331e-01
3.62235427e-01 -5.12153089e-01 -1.06339824e+00 5.02746344e-01
3.02491188e-01 9.15386677e-02 5.69988005e-02 -3.75314206e-01
-6.51785493e-01 3.09420656e-02 -1.27812350e+00 7.91803420e-01
1.30050325e+00 -8.79727423e-01 -6.60944641e-01 -2.23846622e-02
1.85265377e-01 -9.08631757e-02 -7.66740620e-01 3.92156057e-02
1.16376925e+00 -1.06751549e+00 1.44514751e+00 -5.68309605e-01
5.18734634e-01 -4.03773963e-01 -1.52309230e-02 -1.41253030e+00
-6.89519227e-01 -6.54557705e-01 2.18575343e-01 1.15190303e+00
-3.25269610e-01 -4.37250078e-01 7.54846513e-01 6.19513929e-01
1.53086305e-01 -5.58059990e-01 -6.15352869e-01 -6.66247368e-01
4.60470021e-02 3.80136855e-02 5.37092268e-01 9.91234004e-01
-1.96277305e-01 1.50638774e-01 -4.88443315e-01 -2.33723208e-01
8.43030393e-01 2.18548164e-01 1.01033771e+00 -1.39034641e+00
-2.33155191e-01 -7.14121580e-01 -4.74678218e-01 -1.09659278e+00
-4.25039371e-03 -7.91235507e-01 -1.66899219e-01 -1.54248059e+00
1.63359627e-01 -5.23995101e-01 1.13599263e-01 1.91054508e-01
-9.51463878e-02 6.94410563e-01 5.94703794e-01 2.94967592e-01
-7.95461312e-02 6.26149595e-01 1.61628664e+00 -3.09405699e-02
-8.86246115e-02 -3.72442037e-01 -4.72125322e-01 6.01147771e-01
3.53459358e-01 -1.20501779e-01 -3.71107310e-01 -1.06606387e-01
-4.67223376e-02 2.06755057e-01 7.37462699e-01 -5.50955534e-01
1.25258744e-01 -1.65444747e-01 6.36167407e-01 -3.55557740e-01
6.16845906e-01 -9.14468527e-01 7.11497426e-01 2.11371258e-01
-3.11345100e-01 -1.42235592e-01 1.31809071e-01 6.25375032e-01
-4.74925786e-02 -9.21268389e-02 8.96121204e-01 -2.29022816e-01
-5.76088548e-01 4.57760006e-01 8.28706995e-02 -2.18607366e-01
8.33519280e-01 -5.37788451e-01 1.29536048e-01 -4.89746392e-01
-5.24569571e-01 -3.77353340e-01 1.01713431e+00 6.12377286e-01
9.39099729e-01 -1.61863279e+00 -7.75150061e-01 6.13760173e-01
2.44303241e-01 -1.03331767e-01 4.41680014e-01 3.07740211e-01
-5.88417709e-01 3.00173312e-01 -5.58156610e-01 -4.32438582e-01
-1.35280704e+00 6.87326074e-01 1.30925804e-01 -6.82598129e-02
-1.01894200e+00 6.74080789e-01 1.10203171e+00 -4.49352413e-01
5.43624498e-02 -2.35121891e-01 2.28126839e-01 -1.34266183e-01
6.17726564e-01 2.89929152e-01 -1.03385501e-01 -7.19425499e-01
-8.73665512e-02 1.12193644e+00 8.29012040e-03 -3.70358318e-01
1.20086849e+00 -4.31598648e-02 -1.49712548e-01 2.38772660e-01
1.17083049e+00 2.25680232e-01 -1.53613603e+00 -3.98837894e-01
-2.87337840e-01 -1.02842867e+00 -8.86957869e-02 -8.34022343e-01
-8.80844057e-01 9.44198906e-01 2.87843227e-01 -1.91030167e-02
9.99488473e-01 1.53803543e-04 5.64957440e-01 -5.79058118e-02
5.80627680e-01 -6.42578065e-01 2.51611859e-01 3.96380611e-02
1.70634353e+00 -9.61759388e-01 7.44104236e-02 -5.24655700e-01
-6.35896862e-01 1.23584926e+00 1.24251783e-01 -6.10410094e-01
5.62207818e-01 5.25337039e-03 -9.33870375e-02 -3.20265472e-01
-2.18972012e-01 1.75850242e-01 9.10017669e-01 6.11643970e-01
1.89942911e-01 1.32523522e-01 -7.22204000e-02 1.98111355e-01
8.94199684e-02 1.70617074e-01 1.17202871e-01 8.12790513e-01
-1.23386949e-01 -1.27332652e+00 -5.23656726e-01 2.18628556e-01
1.87098935e-01 2.30141524e-02 -7.23349512e-01 8.42063725e-01
-2.30472729e-01 2.46962443e-01 2.07079798e-01 -8.12594444e-02
4.72502887e-01 1.25905871e-01 9.67184961e-01 -4.78623003e-01
-2.64374614e-01 1.05911881e-01 -2.84337878e-01 -6.03078485e-01
-3.97671491e-01 -4.36537594e-01 -7.99660921e-01 -3.85743588e-01
1.31623760e-01 -2.34614372e-01 7.22293198e-01 5.04907727e-01
3.79797220e-01 2.88060993e-01 4.18545902e-01 -1.86430299e+00
-1.98270008e-02 -6.02113366e-01 -7.46174574e-01 7.98424125e-01
2.25152776e-01 -8.27356637e-01 -2.45037630e-01 3.56825769e-01] | [11.852629661560059, 0.0787278264760971] |
71137bd3-4ddf-4311-9617-ac05d117d0f8 | reconstruction-error-based-anomaly-detection | 2305.10464 | null | https://arxiv.org/abs/2305.10464v1 | https://arxiv.org/pdf/2305.10464v1.pdf | Reconstruction Error-based Anomaly Detection with Few Outlying Examples | Reconstruction error-based neural architectures constitute a classical deep learning approach to anomaly detection which has shown great performances. It consists in training an Autoencoder to reconstruct a set of examples deemed to represent the normality and then to point out as anomalies those data that show a sufficiently large reconstruction error. Unfortunately, these architectures often become able to well reconstruct also the anomalies in the data. This phenomenon is more evident when there are anomalies in the training set. In particular when these anomalies are labeled, a setting called semi-supervised, the best way to train Autoencoders is to ignore anomalies and minimize the reconstruction error on normal data. The goal of this work is to investigate approaches to allow reconstruction error-based architectures to instruct the model to put known anomalies outside of the domain description of the normal data. Specifically, our strategy exploits a limited number of anomalous examples to increase the contrast between the reconstruction error associated with normal examples and those associated with both known and unknown anomalies, thus enhancing anomaly detection performances. The experiments show that this new procedure achieves better performances than the standard Autoencoder approach and the main deep learning techniques for semi-supervised anomaly detection. | ['Luca Ferragina', 'Fabio Fassetti', 'Fabrizio Angiulli'] | 2023-05-17 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [ 2.75226571e-02 3.17928255e-01 5.90561450e-01 -4.30596858e-01
-1.91242605e-01 -1.59326896e-01 6.22253954e-01 5.56212306e-01
-2.43364066e-01 3.59983534e-01 -1.44797921e-01 -2.46680453e-01
-2.77606696e-01 -1.03338730e+00 -7.05273688e-01 -7.14521885e-01
-1.30443931e-01 7.85164595e-01 1.54130638e-01 -4.47156161e-01
2.33418137e-01 9.16195035e-01 -1.98399436e+00 3.08698416e-01
7.54234672e-01 1.01491678e+00 -3.68479729e-01 5.43806553e-01
-5.33034325e-01 8.48142743e-01 -8.89927745e-01 2.50369273e-02
2.73817986e-01 -5.90681672e-01 -6.77624226e-01 3.55720997e-01
3.78846794e-01 -3.32323432e-01 -5.19238524e-02 1.18474889e+00
1.02558881e-02 1.69995740e-01 6.21960342e-01 -1.13045824e+00
-2.33263373e-01 3.13732207e-01 -1.42522722e-01 7.01446772e-01
3.99735481e-01 -2.55357418e-02 7.44271159e-01 -6.43410087e-01
3.23523581e-01 8.29725266e-01 6.98627889e-01 6.00569904e-01
-1.16328228e+00 -1.29449517e-01 2.27286275e-02 3.67615223e-01
-9.37518299e-01 -4.51669902e-01 9.64651108e-01 -4.59838748e-01
8.78704786e-01 3.72793943e-01 5.09415090e-01 1.17971611e+00
-1.28987029e-01 3.51053566e-01 7.75135279e-01 -6.15766704e-01
5.20799339e-01 2.98368841e-01 1.82326406e-01 4.26823735e-01
2.77769953e-01 2.87269562e-01 -4.00410108e-02 -3.00438702e-01
3.03994477e-01 3.90954733e-01 -1.48952484e-01 -1.90422684e-01
-6.27878666e-01 8.45778823e-01 2.84006655e-01 9.03936207e-01
-8.84228647e-01 -2.46486217e-01 5.92402637e-01 6.58514023e-01
6.05797350e-01 6.86903715e-01 -4.37845767e-01 9.57485884e-02
-8.68977368e-01 1.51165783e-01 7.11416543e-01 1.71956807e-01
7.58125961e-01 6.62400305e-01 1.94862857e-01 7.92856216e-01
9.44271982e-02 2.79271565e-02 8.63781273e-01 -4.28865969e-01
9.57855284e-02 1.13424599e+00 -1.08918846e-01 -1.06850159e+00
-2.83869535e-01 -5.38192987e-01 -8.06489348e-01 6.14097416e-01
7.65956223e-01 -8.90790001e-02 -8.47359717e-01 1.40567410e+00
2.47883976e-01 3.65754038e-01 4.07823920e-01 6.92829788e-01
3.32924426e-01 5.63281238e-01 -7.79166147e-02 -9.28492472e-02
7.91935384e-01 -2.36001790e-01 -5.80891073e-01 -2.72136658e-01
8.97343397e-01 -4.75088626e-01 9.32449996e-01 5.19685566e-01
-6.80154562e-01 -5.36226869e-01 -1.07924891e+00 6.34956956e-01
-6.60237670e-01 9.78016034e-02 1.70817599e-01 4.39148754e-01
-8.39217067e-01 1.13478220e+00 -8.27963769e-01 -3.82460475e-01
2.41840035e-01 1.27409726e-01 -5.16160965e-01 9.63179097e-02
-1.07409012e+00 8.72809708e-01 8.26331735e-01 1.74697310e-01
-8.30197036e-01 -3.71737808e-01 -6.73177302e-01 3.10817748e-01
2.26075664e-01 1.48259833e-01 9.31939900e-01 -1.61414242e+00
-9.11048353e-01 8.78444910e-01 9.06290784e-02 -9.79604483e-01
6.26982272e-01 -1.64849684e-01 -9.44702029e-01 1.11986525e-01
-2.41079316e-01 -7.97844231e-02 1.23431861e+00 -1.19705892e+00
-6.14661157e-01 -4.42624271e-01 -3.36480141e-01 -3.17170411e-01
-5.60760915e-01 -2.09394217e-01 2.81800836e-01 -3.92796993e-01
4.45382565e-01 -5.92302680e-01 -1.60522267e-01 -3.73698056e-01
-4.50561732e-01 -2.89903432e-01 1.05983090e+00 -6.28748834e-01
9.40100908e-01 -2.30004907e+00 -2.35032693e-01 6.75779998e-01
2.11871862e-01 5.12128294e-01 1.36448532e-01 3.95043135e-01
-6.00993156e-01 -2.47917071e-01 -6.59783781e-01 -1.31308287e-01
-2.87668109e-01 6.38753593e-01 -6.72697663e-01 5.21845222e-01
4.08878893e-01 1.83687955e-01 -6.67174637e-01 -3.73478346e-02
3.81542236e-01 1.84562743e-01 -4.79312658e-01 6.89214468e-01
-4.26973879e-01 6.28573358e-01 -3.41380656e-01 3.42871219e-01
5.02545357e-01 3.94501053e-02 -1.05325431e-01 2.98034996e-01
3.45844477e-02 7.98972249e-02 -1.23509288e+00 8.03890586e-01
-2.02015921e-01 7.25538313e-01 -3.64860773e-01 -1.63895428e+00
1.33169842e+00 5.80597579e-01 6.34763181e-01 -4.59580272e-01
-3.98343503e-02 4.03520077e-01 1.14257693e-01 -6.34506166e-01
1.01948269e-01 -1.57959625e-01 4.44723994e-01 4.05761838e-01
1.93650082e-01 3.34635764e-01 1.24581940e-01 -2.23909497e-01
1.23322916e+00 -1.69909105e-01 3.35819840e-01 -2.12156922e-02
9.54382718e-01 -7.55951852e-02 5.59451282e-01 6.71695411e-01
3.40636969e-02 5.04657507e-01 8.93651009e-01 -1.06736040e+00
-1.22325861e+00 -8.65205705e-01 -8.14287737e-02 6.56660199e-01
-4.74803686e-01 -8.07604492e-02 -7.41132259e-01 -1.00566673e+00
-7.78727382e-02 1.00082386e+00 -8.90309095e-01 -5.24391234e-01
-5.70095778e-01 -7.61625469e-01 5.45092523e-01 4.21469122e-01
4.86798704e-01 -1.37235641e+00 -7.80314028e-01 2.49547213e-01
1.03357755e-01 -7.09019542e-01 5.18149734e-01 4.50415999e-01
-1.21623826e+00 -1.27746356e+00 -2.78203785e-01 -3.63908529e-01
9.47498143e-01 -6.24658465e-01 1.06562495e+00 4.80870783e-01
-1.24867909e-01 2.19051853e-01 -5.37194133e-01 -4.73690927e-01
-1.06159878e+00 -3.84788603e-01 1.31347984e-01 5.30695856e-01
7.34721422e-01 -7.55322397e-01 -2.32452139e-01 9.22387242e-02
-1.23725414e+00 -8.87116671e-01 5.42909324e-01 7.40470290e-01
5.47086954e-01 3.68936092e-01 4.91798311e-01 -1.11115527e+00
5.16051114e-01 -7.93390512e-01 -5.65989733e-01 -5.57739921e-02
-8.42539668e-01 2.99925178e-01 1.21065140e+00 -3.15928310e-01
-8.36625397e-01 -6.40446767e-02 -8.08866203e-01 -6.66949511e-01
-1.10904026e+00 3.57956469e-01 5.67798205e-02 1.04440406e-01
1.09812903e+00 5.95208883e-01 1.56221837e-01 -7.73454130e-01
-3.75391424e-01 3.70735109e-01 4.87885892e-01 -2.06687480e-01
6.88629806e-01 4.30489510e-01 -4.28374633e-02 -1.05124462e+00
-6.92380846e-01 -5.35969734e-01 -6.56565905e-01 -2.44009763e-01
5.78684032e-01 -2.94408619e-01 -1.35550961e-01 3.49186242e-01
-9.73225415e-01 8.92655104e-02 -8.69972348e-01 2.87122786e-01
-3.86297435e-01 4.83777523e-01 -2.31255114e-01 -9.62868214e-01
-9.23572928e-02 -9.47475374e-01 5.98845005e-01 -8.92971270e-03
-2.17672154e-01 -1.07039618e+00 3.57533783e-01 -3.63209248e-01
5.10116041e-01 5.32007217e-01 1.00475264e+00 -1.89981425e+00
4.54751216e-02 -8.22306812e-01 2.87380636e-01 9.38177884e-01
-6.33710064e-03 -2.61288807e-02 -1.19374502e+00 -1.52940735e-01
4.96882111e-01 9.03435051e-02 7.25501001e-01 4.63655964e-02
1.43169188e+00 -3.66531312e-01 -2.34467387e-02 4.33777809e-01
1.49740231e+00 3.49578321e-01 7.53345251e-01 5.38229048e-01
4.06571507e-01 6.57913148e-01 1.79591402e-01 5.02540410e-01
-4.28796262e-01 4.26164865e-01 8.02206218e-01 -2.43287142e-02
4.17386740e-01 -4.09609340e-02 1.05400369e-01 4.69248801e-01
-1.65150598e-01 -6.29428849e-02 -1.06509614e+00 6.48719609e-01
-1.60600448e+00 -1.10334587e+00 -2.27276266e-01 2.27682304e+00
2.01113895e-01 4.27335232e-01 2.61891961e-01 9.77809727e-01
6.29406750e-01 6.85103387e-02 -4.05943632e-01 -6.34360790e-01
-1.37394875e-01 2.07274333e-01 -4.88768145e-02 1.93327203e-01
-1.16279876e+00 4.09593791e-01 5.98619747e+00 3.33727151e-01
-1.03069174e+00 -4.48341295e-02 3.92136693e-01 3.59490901e-01
-9.08418596e-02 -1.15236416e-01 -4.08447683e-01 5.64519465e-01
1.32229459e+00 3.12619954e-01 1.20425105e-01 1.17928159e+00
-1.06547557e-01 8.02683160e-02 -1.26880097e+00 6.01111650e-01
1.68291390e-01 -9.59891140e-01 9.11602825e-02 4.80011851e-03
3.22286069e-01 -2.02896968e-01 -1.38624802e-01 3.59013677e-01
-1.27917200e-01 -9.47065175e-01 2.14126766e-01 7.42031574e-01
1.72062993e-01 -8.75173390e-01 1.14311635e+00 4.71838653e-01
-5.14168084e-01 -4.17554885e-01 -4.60448325e-01 -8.98676813e-02
-2.45570436e-01 8.53408337e-01 -8.79820704e-01 2.54239768e-01
7.89336503e-01 5.73573411e-01 -6.04468286e-01 1.02395797e+00
-1.64477706e-01 8.33721280e-01 -4.07453388e-01 2.58297235e-01
3.77194881e-01 -3.55984837e-01 1.00769579e+00 9.52416599e-01
4.59776133e-01 -1.85158446e-01 -1.29330739e-01 8.94414365e-01
3.18608582e-01 1.09844029e-01 -1.05001783e+00 -4.45955768e-02
9.20687616e-02 7.79718339e-01 -5.70019424e-01 -3.78837168e-01
-3.55039716e-01 9.78585839e-01 3.22087288e-01 2.21059978e-01
-2.94873238e-01 -3.22457314e-01 5.96708059e-01 3.23478103e-01
2.85602331e-01 2.10118964e-01 -8.90342444e-02 -9.96478379e-01
1.29261121e-01 -8.72841358e-01 7.06693292e-01 -3.89609098e-01
-1.31355321e+00 9.93592501e-01 -5.98221421e-02 -1.45867836e+00
-8.56399000e-01 -6.41388059e-01 -1.06184125e+00 6.32796049e-01
-1.17630827e+00 -4.81786191e-01 -2.11924240e-01 6.90208018e-01
4.27644402e-01 -6.16067111e-01 1.09601057e+00 1.77527323e-01
-4.42650646e-01 3.26946348e-01 2.22770646e-01 3.69981796e-01
1.92711696e-01 -1.61617899e+00 1.04566194e-01 1.22744274e+00
4.53279078e-01 1.81916669e-01 1.08831942e+00 -4.98370498e-01
-6.44705474e-01 -9.75275278e-01 8.70477140e-01 -3.72839063e-01
5.20613074e-01 1.28892615e-01 -1.70882404e+00 8.70928347e-01
-1.37960613e-01 3.74769896e-01 5.73295355e-01 -3.69281918e-02
-1.05261892e-01 -1.68319166e-01 -1.35544562e+00 3.83011848e-01
3.81764829e-01 -3.88858169e-01 -1.03790760e+00 3.62210989e-01
1.38493061e-01 -1.69272512e-01 -5.79050481e-01 3.26177984e-01
-6.50039241e-02 -1.51271427e+00 7.24915206e-01 -8.69527221e-01
4.93049234e-01 -2.26363808e-01 -1.23446924e-04 -1.60103738e+00
7.93278068e-02 -8.97633210e-02 -5.40994585e-01 9.89088118e-01
2.38773689e-01 -9.06450748e-01 8.71765792e-01 2.21137524e-01
-2.21387818e-01 -5.64289033e-01 -8.94160092e-01 -6.46727204e-01
-1.41556174e-01 -5.67582607e-01 6.91940427e-01 1.02393341e+00
-3.73561800e-01 -3.43517870e-01 -1.72386885e-01 4.92341071e-01
5.70220709e-01 -6.23610355e-02 4.98283207e-01 -1.66383982e+00
-2.05990911e-01 -3.24373662e-01 -8.73363078e-01 -2.12059274e-01
1.83839113e-01 -6.51570201e-01 4.40935567e-02 -9.37968910e-01
-6.97793663e-01 -2.84738362e-01 -5.33399045e-01 4.03433025e-01
5.26955314e-02 -8.03824887e-03 -3.84953707e-01 2.19370559e-01
-2.28945062e-01 3.14660311e-01 4.20084536e-01 1.46771371e-01
-2.36048058e-01 4.90014523e-01 -1.63045123e-01 1.13601100e+00
9.66867387e-01 -6.48350537e-01 -1.64823577e-01 -2.29981914e-02
2.12419316e-01 -5.83874397e-02 4.46244955e-01 -1.42772830e+00
6.33336306e-02 2.00136632e-01 7.60274887e-01 -6.07936144e-01
-3.65257785e-02 -1.34787261e+00 -2.35487684e-03 5.66036165e-01
-4.71901536e-01 2.95910418e-01 1.50247052e-01 7.53324032e-01
-5.57015419e-01 -6.25147343e-01 8.81717622e-01 -2.99024433e-01
-9.01138663e-01 4.86228615e-02 -7.47030258e-01 -8.62503052e-02
1.05737042e+00 -1.85864165e-01 2.46517122e-01 -3.71822685e-01
-1.19703364e+00 -2.30150089e-01 1.87396973e-01 1.55863449e-01
9.08809900e-01 -1.09951484e+00 -6.21449947e-01 7.63770342e-01
3.71109694e-01 -1.16994508e-01 2.34558403e-01 7.80699909e-01
-7.00590432e-01 1.45904139e-01 -4.45007533e-01 -6.81145906e-01
-9.57781315e-01 6.40474558e-01 7.87038505e-01 -2.63813287e-01
-1.10057151e+00 4.90621656e-01 -3.26986253e-01 -4.28400517e-01
2.35734552e-01 -5.71754128e-02 -7.14662313e-01 -1.08800054e-01
6.31390274e-01 4.32555497e-01 5.96001029e-01 -6.03931487e-01
-1.74959823e-01 3.46972756e-02 -1.54167205e-01 3.26278567e-01
1.39091241e+00 2.75903225e-01 -1.29889965e-01 6.00906909e-01
1.01063943e+00 -1.33781195e-01 -1.02683711e+00 -2.41868868e-01
5.78161359e-01 -4.11007434e-01 3.61031294e-02 -5.90411186e-01
-1.15719271e+00 8.80264521e-01 9.65832412e-01 8.66786122e-01
1.36576235e+00 -1.59157932e-01 4.50659364e-01 6.19259000e-01
-2.11928025e-01 -1.16551077e+00 7.23323002e-02 4.30694103e-01
8.18200767e-01 -1.30902958e+00 -3.81500632e-01 2.37109825e-01
-5.60416996e-01 1.59603155e+00 6.62868917e-01 -7.48608589e-01
6.89513624e-01 1.34553611e-01 1.03097580e-01 -4.73471403e-01
-5.26482880e-01 -2.19584033e-01 3.92785579e-01 5.99184096e-01
9.02194306e-02 -1.93115294e-01 -7.69828334e-02 2.53961444e-01
-6.66518733e-02 -4.19950932e-01 5.78410327e-01 7.34413981e-01
-5.25056124e-01 -8.38783622e-01 -6.33074462e-01 8.47932518e-01
-7.38670111e-01 2.34192744e-01 -4.85741526e-01 8.10449481e-01
1.95649028e-01 8.51919293e-01 5.70522010e-01 -3.65650654e-01
6.05538249e-01 7.47964025e-01 -1.70762867e-01 -4.75237936e-01
-5.99430501e-01 -2.84069121e-01 -7.99114555e-02 -6.48716331e-01
-1.77940726e-01 -5.82085669e-01 -1.16447246e+00 -9.93126482e-02
-1.10876516e-01 3.36219519e-01 7.20883846e-01 1.40942252e+00
5.36623597e-02 7.23805726e-01 7.30051935e-01 -4.61260557e-01
-7.50845492e-01 -9.77715671e-01 -7.59192646e-01 9.12275791e-01
8.34578395e-01 -5.22577643e-01 -8.61779273e-01 -1.20388381e-01] | [7.584839820861816, 2.3150157928466797] |
fd010dc9-5356-4fa2-a284-57d7d8d25827 | leverage-lexical-knowledge-for-chinese-named | null | null | https://aclanthology.org/D19-1396 | https://aclanthology.org/D19-1396.pdf | Leverage Lexical Knowledge for Chinese Named Entity Recognition via Collaborative Graph Network | The lack of word boundaries information has been seen as one of the main obstacles to develop a high performance Chinese named entity recognition (NER) system. Fortunately, the automatically constructed lexicon contains rich word boundaries information and word semantic information. However, integrating lexical knowledge in Chinese NER tasks still faces challenges when it comes to self-matched lexical words as well as the nearest contextual lexical words. We present a Collaborative Graph Network to solve these challenges. Experiments on various datasets show that our model not only outperforms the state-of-the-art (SOTA) results, but also achieves a speed that is six to fifteen times faster than that of the SOTA model. | ['Dianbo Sui', 'Yubo Chen', 'Jun Zhao', 'Shengping Liu', 'Kang Liu'] | 2019-11-01 | null | null | null | ijcnlp-2019-11 | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [-5.23590863e-01 -3.22957039e-01 -3.47408265e-01 -2.16974273e-01
-7.09591210e-01 -5.82971931e-01 1.65637165e-01 1.72267482e-01
-1.10041320e+00 8.16863298e-01 4.77118403e-01 -3.68128628e-01
1.89489007e-01 -8.06695402e-01 2.26648748e-02 -2.24422395e-01
1.71713889e-01 4.64958727e-01 2.44474113e-01 -6.02019906e-01
2.46399850e-01 2.61039972e-01 -8.96011829e-01 -7.54252076e-03
1.37140071e+00 5.81170559e-01 2.88514048e-01 2.48653088e-02
-1.04927886e+00 4.53678280e-01 -6.35321736e-01 -6.61502719e-01
1.77234486e-02 -3.41356725e-01 -8.83883536e-01 -5.59869170e-01
-1.02411425e-02 3.90934020e-01 -8.61369520e-02 1.34166026e+00
5.68282485e-01 3.31689030e-01 3.77686858e-01 -6.67165875e-01
-1.28814399e+00 1.05625665e+00 -2.15892062e-01 2.59137809e-01
3.07146698e-01 -6.53938115e-01 1.18468940e+00 -1.23682415e+00
9.01068687e-01 7.81651676e-01 8.21584821e-01 7.55832016e-01
-5.47261834e-01 -9.00933504e-01 4.15090084e-01 2.25029007e-01
-2.00101852e+00 -2.61118054e-01 4.46483552e-01 9.39810723e-02
1.61995935e+00 -7.41777644e-02 3.49384964e-01 8.70744467e-01
-4.05259021e-02 5.08256674e-01 7.98528492e-01 -6.62187397e-01
1.60139635e-01 -9.64785516e-02 5.12201548e-01 4.96613801e-01
5.80656946e-01 -4.47919726e-01 -2.64344245e-01 -3.74259725e-02
5.99482238e-01 -1.25150979e-01 -1.47891492e-01 1.33823678e-01
-1.00016499e+00 7.12627411e-01 3.22519541e-01 1.00717783e+00
-3.80714029e-01 -2.21224472e-01 3.00551474e-01 4.26392481e-02
5.61703920e-01 7.13747263e-01 -9.31899011e-01 -1.23218969e-01
-6.39267981e-01 -4.78929192e-01 1.13905752e+00 1.35123992e+00
6.96099758e-01 2.20898464e-01 3.53487343e-01 9.32194889e-01
9.21039283e-02 4.80133653e-01 7.52658069e-01 -1.04305990e-01
6.15792155e-01 7.21646965e-01 4.53784410e-03 -1.04007995e+00
-5.77996492e-01 -3.68173808e-01 -7.83172250e-01 -8.33809197e-01
3.48013341e-01 -6.24790847e-01 -9.64860499e-01 1.62702262e+00
2.71999508e-01 2.76302665e-01 1.75654739e-01 6.78531706e-01
9.54095125e-01 7.27318466e-01 4.62155402e-01 -3.06812614e-01
1.55027902e+00 -8.91709626e-01 -1.12301195e+00 -5.62971413e-01
8.64068687e-01 -8.09523940e-01 8.20950031e-01 -1.19802989e-01
-4.68142480e-01 -3.79205644e-01 -7.97663927e-01 -7.74438381e-02
-1.03880310e+00 1.97925061e-01 8.54077697e-01 9.71444726e-01
-8.65950942e-01 2.88300663e-01 -6.01999640e-01 -6.12393498e-01
1.01539977e-01 2.12053224e-01 -6.21350408e-01 -1.11602522e-01
-1.85556531e+00 1.21284962e+00 8.99068177e-01 1.59996286e-01
-6.70499876e-02 -7.13017046e-01 -1.06954837e+00 6.21990189e-02
7.33298182e-01 -2.74377584e-01 6.57353103e-01 -3.81820679e-01
-1.30461621e+00 6.16245568e-01 -3.56418461e-01 -9.32059586e-02
-1.54556051e-01 -3.67762506e-01 -1.13589513e+00 -2.67046273e-01
5.01300216e-01 3.46695244e-01 -1.29374862e-02 -6.53361201e-01
-6.13215446e-01 -2.58972615e-01 -3.65441471e-01 2.73527265e-01
-6.65926814e-01 5.45117259e-01 -7.86202908e-01 -7.20219851e-01
3.21856812e-02 -6.49755955e-01 -6.52631938e-01 -8.04436326e-01
-3.25497746e-01 -7.23868847e-01 2.78638124e-01 -7.93355703e-01
1.93534017e+00 -1.97869277e+00 -1.50998622e-01 1.40922800e-01
1.94329917e-02 5.53903103e-01 -1.52963221e-01 7.08497822e-01
7.94207230e-02 7.61141300e-01 7.90515542e-02 -1.00502588e-01
-1.51022971e-01 2.25525275e-01 -3.23415920e-02 8.30564126e-02
1.38755307e-01 1.11527848e+00 -1.15151215e+00 -5.88079214e-01
-6.16715141e-02 3.64639610e-01 -1.08820178e-01 -8.82341787e-02
9.52307060e-02 6.11696020e-02 -7.53657639e-01 5.99174082e-01
4.86691236e-01 -2.89182186e-01 6.42551184e-01 7.82556385e-02
-3.76028180e-01 6.61855698e-01 -1.25355768e+00 1.68761218e+00
-4.30452108e-01 2.49287367e-01 -3.86877596e-01 -5.48079312e-01
1.12425590e+00 4.31277186e-01 8.49737003e-02 -7.95861721e-01
2.48391464e-01 3.86676252e-01 -1.51782528e-01 -2.61971384e-01
7.63056636e-01 -1.90904751e-01 -5.76645553e-01 6.68286458e-02
1.83204353e-01 2.36675352e-01 3.63411069e-01 6.06623478e-02
8.74823689e-01 -7.79657289e-02 8.51567030e-01 -4.95728165e-01
5.40418208e-01 3.72228831e-01 1.27008450e+00 5.49425662e-01
-3.18393439e-01 3.58838350e-01 2.36161307e-01 -4.25191730e-01
-9.10553098e-01 -7.93644071e-01 6.70182928e-02 1.20086026e+00
3.21349919e-01 -8.04611027e-01 -8.46286774e-01 -1.00047135e+00
-3.16497654e-01 8.04100454e-01 -4.44143474e-01 2.43830204e-01
-8.83236706e-01 -5.63158214e-01 1.01561260e+00 9.22087252e-01
5.45612693e-01 -1.01680255e+00 2.22280115e-01 4.32025641e-01
-3.98697346e-01 -1.48268580e+00 -9.95288849e-01 1.91379920e-01
-5.58505237e-01 -8.76422524e-01 -5.07779598e-01 -1.29806244e+00
5.39821088e-01 3.06729198e-01 1.10796225e+00 1.23921327e-01
-3.05425171e-02 -2.10943788e-01 -7.20538378e-01 -4.72703755e-01
-9.57924128e-02 6.21075988e-01 1.44373417e-01 -4.54009980e-01
1.15890014e+00 -7.74313286e-02 -1.82936519e-01 3.98214638e-01
-8.40985417e-01 -3.52122843e-01 4.53405619e-01 7.83095598e-01
6.14581704e-01 2.12689564e-01 1.04613829e+00 -1.08520281e+00
7.06940591e-01 -5.56198359e-01 -5.81017792e-01 7.55336761e-01
-8.32601488e-01 7.73659348e-02 8.80357683e-01 -4.93130624e-01
-1.35523224e+00 1.25371262e-01 -3.65620434e-01 1.58940718e-01
-2.87022501e-01 9.50679898e-01 -4.29130465e-01 1.49653435e-01
5.28215468e-01 1.04797661e-01 -7.78242171e-01 -7.78143525e-01
6.71423495e-01 8.15235674e-01 2.97613382e-01 -4.44400817e-01
4.81909573e-01 1.12438343e-01 -5.37703872e-01 -1.06266379e+00
-1.12697375e+00 -9.80122268e-01 -1.03315938e+00 1.95677474e-01
1.19444084e+00 -1.01904416e+00 -1.99916840e-01 2.69380450e-01
-1.27889574e+00 1.62323311e-01 1.39477953e-01 5.45463324e-01
2.70207733e-01 4.74964112e-01 -9.35826540e-01 -4.42884684e-01
-4.84423190e-01 -5.61731577e-01 3.89071852e-01 5.00615835e-01
-8.20312276e-02 -1.36762989e+00 1.88305855e-01 2.20301390e-01
6.13304198e-01 -2.15805292e-01 8.55496109e-01 -1.20307529e+00
-3.82525921e-02 -3.31235588e-01 -2.17151389e-01 1.87470198e-01
4.33989823e-01 -4.19518173e-01 -4.58605200e-01 1.95850536e-01
-2.33924896e-01 1.61601454e-01 7.17442930e-01 -9.90206227e-02
4.47164863e-01 -1.66582689e-02 -3.98448795e-01 4.29640681e-01
1.57355034e+00 3.05541128e-01 5.07670105e-01 3.28568369e-01
9.59994197e-01 3.54518592e-01 5.78433394e-01 4.19528224e-02
6.80014968e-01 3.31759751e-01 -1.24197267e-01 6.94686128e-03
-5.82898967e-02 -5.53654492e-01 1.46487892e-01 1.72105670e+00
2.49660457e-03 -3.21918279e-01 -1.38961244e+00 9.14653003e-01
-1.74363971e+00 -6.11625731e-01 -2.93910414e-01 1.79019916e+00
1.09388149e+00 -1.07736848e-01 -2.85718352e-01 -5.77398241e-01
1.17810440e+00 7.60178566e-02 -3.46222937e-01 -3.30663234e-01
-5.34216523e-01 5.20839691e-01 7.11995065e-01 2.76644528e-01
-1.22754776e+00 1.96318364e+00 6.67052317e+00 1.13153839e+00
-7.81631529e-01 2.60845244e-01 1.81552291e-01 7.16752827e-01
-2.10989535e-01 2.34350681e-01 -1.25609314e+00 4.33007851e-02
9.39709306e-01 -5.98426640e-01 3.55557829e-01 9.85839844e-01
-2.99924970e-01 3.14336687e-01 -5.45774460e-01 7.87250817e-01
3.73836309e-01 -1.30280089e+00 1.49800614e-01 -7.92619362e-02
9.86079395e-01 3.31255794e-01 -5.74615955e-01 4.43762302e-01
6.06206894e-01 -8.86538506e-01 3.88506025e-01 1.26874506e-01
7.85258114e-01 -9.02559340e-01 1.07954741e+00 4.07502651e-01
-1.79709995e+00 3.27568561e-01 -7.61324286e-01 -2.15144396e-01
3.55945975e-01 5.71678042e-01 -5.00997901e-01 8.57071936e-01
5.58248281e-01 6.46321774e-01 -4.38804924e-01 1.04566169e+00
-8.33842218e-01 6.48869097e-01 -2.38689914e-01 -6.81108654e-01
4.21228141e-01 -2.68706888e-01 1.55353785e-01 1.64804971e+00
3.17064553e-01 3.89579386e-01 1.88830242e-01 4.99829292e-01
-4.92871046e-01 9.89265978e-01 -6.43373311e-01 -3.66506547e-01
9.04066980e-01 1.18680096e+00 -1.07795477e+00 -3.64697963e-01
-6.80494130e-01 1.04853821e+00 8.58207703e-01 2.57572860e-01
-4.66569960e-01 -1.05296493e+00 8.50903332e-01 -5.85409284e-01
5.71080327e-01 -5.31065166e-01 -2.76811093e-01 -1.55914831e+00
-1.64357379e-01 -5.24448991e-01 6.17527962e-01 -4.41633344e-01
-1.80449402e+00 8.66799057e-01 -4.64828014e-01 -8.35464716e-01
3.10633127e-02 -8.16520393e-01 -4.88831937e-01 8.66687059e-01
-1.54431939e+00 -8.80529106e-01 1.25507787e-01 5.54429471e-01
4.31475580e-01 -4.74205911e-02 1.19354069e+00 5.19384265e-01
-8.51041198e-01 7.40780473e-01 1.08637035e-01 1.00215077e+00
6.95001960e-01 -1.10241044e+00 8.18547308e-01 1.12413633e+00
4.76482928e-01 1.07104814e+00 1.59650013e-01 -1.18015468e+00
-1.18548679e+00 -9.60801303e-01 1.80120564e+00 -4.99364763e-01
1.04832470e+00 -4.20170993e-01 -9.84930873e-01 5.88538170e-01
3.72038543e-01 -6.12319149e-02 1.08266628e+00 4.81897682e-01
-4.08888131e-01 2.31140509e-01 -7.14324892e-01 7.31398046e-01
1.28654444e+00 -5.82514822e-01 -1.06645477e+00 -5.92891239e-02
1.07830644e+00 -2.54018307e-01 -9.63256061e-01 6.45553693e-02
2.06910849e-01 -3.15788120e-01 6.51803851e-01 -8.98960769e-01
-3.32217097e-01 -3.63069117e-01 -5.47670797e-02 -1.43702984e+00
-3.94578755e-01 -5.20656765e-01 4.67527986e-01 1.56866264e+00
9.99597967e-01 -6.77314460e-01 5.67065835e-01 5.68648756e-01
-9.36974660e-02 -3.38345706e-01 -8.93337727e-01 -1.09989321e+00
4.07439798e-01 -6.79982007e-01 7.82560170e-01 1.52102089e+00
4.46033210e-01 7.60094106e-01 -3.08807611e-01 1.65767774e-01
2.66652912e-01 -1.21676110e-01 -2.82382648e-02 -1.20555544e+00
6.13602519e-01 -4.21238422e-01 -4.04957354e-01 -1.07033408e+00
5.10542572e-01 -1.03072155e+00 9.47605819e-02 -1.74573398e+00
1.42559841e-01 -5.91551423e-01 -5.92515886e-01 6.04007900e-01
-5.47708213e-01 1.50334528e-02 7.48945996e-02 -2.89795771e-02
-9.38353181e-01 2.75617570e-01 8.81467938e-01 3.69288981e-01
-3.18783045e-01 -4.48148966e-01 -9.66738820e-01 6.91893935e-01
8.94886911e-01 -7.25212991e-01 1.16446190e-01 -7.51889646e-01
6.98176026e-01 -1.95000574e-01 -6.58498406e-01 -5.22997200e-01
8.88967276e-01 -3.57290983e-01 2.29437083e-01 -6.50745332e-01
-2.37520263e-01 -6.01179361e-01 -9.91861224e-02 9.76494700e-02
-1.26676917e-01 4.22946304e-01 1.69483840e-01 5.34539580e-01
-3.34473908e-01 -3.09512079e-01 2.80988067e-01 -3.06285948e-01
-1.33002996e+00 1.58246592e-01 -4.70885694e-01 6.78634405e-01
8.01125467e-01 7.18656322e-03 -3.50301772e-01 -1.58356596e-02
-5.89683890e-01 4.72807705e-01 2.58654088e-01 8.31233740e-01
5.83529711e-01 -1.34953880e+00 -5.65863967e-01 7.56029785e-03
2.62132883e-01 -2.61872023e-01 7.51907676e-02 4.24935192e-01
-5.28105080e-01 7.97495186e-01 4.70612496e-02 1.63343966e-01
-8.53776395e-01 8.03444088e-01 8.18566605e-02 -3.92457426e-01
-4.93054211e-01 6.96511865e-01 -1.73889279e-01 -6.32831395e-01
1.18317708e-01 -3.15452181e-02 -6.67688191e-01 1.37404591e-01
5.90293586e-01 4.65863198e-01 5.39389737e-02 -8.17930043e-01
-7.07360685e-01 6.34665072e-01 -1.18545443e-01 1.79177001e-01
1.25598025e+00 -3.22067559e-01 -3.31609845e-01 3.11560392e-01
9.41906214e-01 4.17650998e-01 -1.93913817e-01 -6.27486706e-01
8.84410560e-01 -1.41607493e-01 -8.73557627e-02 -6.90876126e-01
-9.01868999e-01 5.79500794e-01 -1.29176915e-01 6.57714456e-02
7.94598818e-01 -3.75397243e-02 1.16388333e+00 7.63531864e-01
8.02072704e-01 -1.58090830e+00 -4.49457318e-01 1.12191498e+00
1.58145860e-01 -1.17001188e+00 -3.10421556e-01 -7.54948616e-01
-8.54782224e-01 1.11126554e+00 6.72523797e-01 -1.54148206e-01
1.11074722e+00 2.86477506e-01 4.81633246e-01 6.33289921e-04
-5.87925494e-01 -8.61128271e-01 3.79853338e-01 5.85670352e-01
5.96992135e-01 2.23518565e-01 -8.21435511e-01 1.11250901e+00
7.38298334e-03 -3.30441654e-01 2.87658304e-01 8.02022338e-01
-5.17717063e-01 -1.47781765e+00 4.56792302e-02 2.08955541e-01
-8.75581443e-01 -8.16510320e-01 -5.90348542e-01 9.77560878e-01
4.45111394e-02 1.28454268e+00 -2.24973664e-01 -4.54465926e-01
4.73849088e-01 3.50442171e-01 -2.17056319e-01 -9.99264121e-01
-7.80171454e-01 4.94640470e-02 3.57960820e-01 -4.63823080e-01
-2.14397177e-01 -4.09104258e-01 -1.73707426e+00 -2.15376407e-01
-9.24040616e-01 6.88518703e-01 7.13069916e-01 1.05918539e+00
5.36210954e-01 1.59646913e-01 4.52391505e-01 -5.37325479e-02
-1.96678683e-01 -9.38899815e-01 -5.67865968e-01 3.00729901e-01
-4.84478712e-01 -3.63545328e-01 -1.96789280e-01 -4.16064531e-01] | [9.796140670776367, 9.735673904418945] |
34774ded-98b3-4bb0-9785-330f0328ed49 | using-domain-knowledge-for-low-resource-named | 2203.14738 | null | https://arxiv.org/abs/2203.14738v1 | https://arxiv.org/pdf/2203.14738v1.pdf | Using Domain Knowledge for Low Resource Named Entity Recognition | In recent years, named entity recognition has always been a popular research in the field of natural language processing, while traditional deep learning methods require a large amount of labeled data for model training, which makes them not suitable for areas where labeling resources are scarce. In addition, the existing cross-domain knowledge transfer methods need to adjust the entity labels for different fields, so as to increase the training cost. To solve these problems, enlightened by a processing method of Chinese named entity recognition, we propose to use domain knowledge to improve the performance of named entity recognition in areas with low resources. The domain knowledge mainly applied by us is domain dictionary and domain labeled data. We use dictionary information for each word to strengthen its word embedding and domain labeled data to reinforce the recognition effect. The proposed model avoids large-scale data adjustments in different domains while handling named entities recognition with low resources. Experiments demonstrate the effectiveness of our method, which has achieved impressive results on the data set in the field of scientific and technological equipment, and the F1 score has been significantly improved compared with many other baseline methods. | ['Yuan Shi'] | 2022-03-28 | null | null | null | null | ['low-resource-named-entity-recognition', 'chinese-named-entity-recognition'] | ['natural-language-processing', 'natural-language-processing'] | [-2.25089602e-02 -3.99055451e-01 -2.11928830e-01 -5.19606054e-01
-2.20687121e-01 -5.14306068e-01 3.56697202e-01 9.59808901e-02
-1.09020030e+00 7.24646330e-01 3.29670399e-01 -1.86484531e-01
1.87502027e-01 -1.03827178e+00 -3.31924647e-01 -5.19884527e-01
5.01804113e-01 3.92140716e-01 2.50597417e-01 -2.26279497e-01
4.07992214e-01 5.38516343e-01 -8.44545841e-01 1.13108344e-01
1.15256679e+00 8.44049156e-01 3.61924291e-01 -3.06573242e-01
-1.09505165e+00 4.80388135e-01 -7.01037407e-01 -5.19503772e-01
7.05713928e-02 -1.56194001e-01 -8.55645180e-01 -4.70832456e-03
-1.09601088e-01 -1.32029548e-01 -3.19035053e-01 1.23926604e+00
8.32661688e-01 2.06636861e-01 5.99974453e-01 -7.49996781e-01
-1.23655140e+00 6.93897009e-01 -6.84308946e-01 1.47755206e-01
-1.95112914e-01 -2.36976743e-01 5.95171809e-01 -1.06506109e+00
5.18019259e-01 1.08156681e+00 5.96498311e-01 7.20124245e-01
-7.08275735e-01 -1.17198408e+00 3.54490042e-01 4.38399166e-01
-1.56860542e+00 -8.45795274e-02 7.28921115e-01 -2.28118405e-01
7.44013727e-01 -4.25111055e-01 1.13653213e-01 8.45149636e-01
-2.18302518e-01 7.81988680e-01 7.68260062e-01 -6.03739202e-01
1.38704389e-01 4.45108086e-01 3.12001079e-01 2.70076454e-01
3.69584113e-01 -2.39760399e-01 -1.71152785e-01 2.50740618e-01
7.83283055e-01 2.29327574e-01 -1.25364318e-01 -2.69977331e-01
-1.34456766e+00 8.28431070e-01 4.65356916e-01 7.81559229e-01
-2.96175897e-01 -4.17060971e-01 6.92599595e-01 2.06286639e-01
4.89225060e-01 5.08366227e-01 -8.74636114e-01 3.10400575e-02
-4.83171076e-01 -3.75384629e-01 4.96326119e-01 1.17230093e+00
8.94462943e-01 1.28732830e-01 -8.66568759e-02 1.05290079e+00
1.48570687e-01 4.74753171e-01 8.73751521e-01 -1.64435521e-01
6.86078012e-01 1.04025567e+00 2.10970804e-01 -1.15542853e+00
-4.12222147e-01 -3.36906880e-01 -1.05416226e+00 -3.35757881e-01
2.06949323e-01 -4.88625944e-01 -9.83829558e-01 1.62085450e+00
4.02571470e-01 1.91852689e-01 3.12129319e-01 7.47374833e-01
8.81405473e-01 9.63871777e-01 6.05425417e-01 -1.44522175e-01
1.45161819e+00 -1.00978243e+00 -1.07786429e+00 -4.07014042e-01
8.97295117e-01 -8.32949579e-01 1.04673922e+00 1.74207997e-03
-4.39912975e-01 -7.20382571e-01 -8.15551877e-01 -1.10358156e-01
-8.50050151e-01 4.79017794e-01 5.78530431e-01 7.31056511e-01
-4.76249039e-01 2.14179710e-01 -5.62602758e-01 -4.62693244e-01
5.40912092e-01 3.55852157e-01 -5.31482518e-01 -2.90403157e-01
-1.64636910e+00 1.04548383e+00 1.09815824e+00 2.43350402e-01
-3.20501357e-01 -6.10160291e-01 -8.35681558e-01 2.83543617e-01
3.56419176e-01 -9.83295515e-02 1.00787139e+00 -1.00617409e+00
-1.30142879e+00 8.13378096e-01 -4.39071581e-02 -2.16132831e-02
1.29357398e-01 -3.66223902e-01 -1.03296340e+00 -2.50384480e-01
1.12233236e-01 5.15573740e-01 3.56961697e-01 -7.86736488e-01
-7.04188406e-01 -3.91159415e-01 -2.19900459e-01 2.46968344e-01
-1.20084882e+00 2.26016596e-01 -5.81361651e-01 -8.84896457e-01
-1.88385412e-01 -5.67537189e-01 -3.29975575e-01 -3.58688593e-01
9.76758003e-02 -5.73877037e-01 7.77756929e-01 -6.01075888e-01
1.41709518e+00 -2.28414845e+00 -1.55325532e-01 6.08495716e-03
-1.72323570e-01 8.60425293e-01 -3.42653483e-01 2.39462584e-01
-1.61370233e-01 2.76581615e-01 -2.72209942e-01 2.01656863e-01
-9.83741656e-02 1.08230449e-01 -1.20628886e-01 1.43606916e-01
4.63828057e-01 6.86250567e-01 -8.25737357e-01 -7.13676572e-01
7.68188611e-02 3.25997591e-01 -3.10815156e-01 3.79932225e-01
-2.80807558e-02 3.89015377e-01 -8.89280975e-01 2.62349308e-01
1.03597915e+00 -3.12430054e-01 2.73342043e-01 -3.15613747e-01
-2.24224776e-01 1.57378092e-01 -1.47070777e+00 1.81434906e+00
-6.85312152e-01 1.73129544e-01 -2.66076535e-01 -1.20133495e+00
1.33826458e+00 3.94531310e-01 2.72818416e-01 -8.46427023e-01
2.73465067e-01 3.12227041e-01 -1.79919064e-01 -6.24515593e-01
3.68997306e-01 -3.24840516e-01 -2.25186244e-01 2.01691583e-01
1.10261761e-01 2.51235843e-01 1.14682131e-01 3.66470590e-02
7.99620450e-01 -2.73083337e-02 4.19525832e-01 -1.34499639e-01
8.40303898e-01 2.55143464e-01 1.03710794e+00 2.10518807e-01
-2.42040396e-01 1.60580814e-01 -9.08092335e-02 -5.78163385e-01
-9.91159379e-01 -5.69754779e-01 -1.80696428e-01 1.30014884e+00
4.50578600e-01 4.69754729e-03 -5.02838850e-01 -9.66881156e-01
-1.51569441e-01 5.98939300e-01 -4.05366987e-01 -3.05530638e-01
-7.73525834e-01 -7.15428472e-01 5.56737185e-01 8.83475065e-01
9.83332098e-01 -1.53212583e+00 1.44038513e-01 4.36374217e-01
-1.14522614e-01 -1.15367174e+00 -6.24118745e-01 1.12296037e-01
-8.63524854e-01 -6.94167674e-01 -1.16149116e+00 -1.45928848e+00
8.38176131e-01 1.40382662e-01 9.59980726e-01 -1.60650730e-01
3.96741927e-02 -9.29825827e-02 -6.77462041e-01 -5.61295748e-01
-4.86200191e-02 4.18286562e-01 2.11023554e-01 2.60177068e-02
1.12432623e+00 -2.56850123e-01 -3.07092994e-01 5.02657354e-01
-1.20110643e+00 -2.93606192e-01 9.38398540e-01 1.11137187e+00
3.57973844e-01 1.77550137e-01 1.06375313e+00 -1.15162385e+00
5.15688837e-01 -5.84406674e-01 -5.91714501e-01 5.58574438e-01
-5.65210521e-01 1.55201256e-01 8.40123594e-01 -7.07669735e-01
-1.59708321e+00 7.01695494e-03 -1.82150334e-01 -1.13404311e-01
-3.77942860e-01 7.46937394e-01 -6.97139323e-01 -4.15535122e-02
5.66203296e-01 3.11297923e-01 -3.89623016e-01 -7.35548556e-01
4.15036201e-01 1.05327296e+00 2.19808146e-01 -4.74602431e-01
6.19576812e-01 5.05168326e-02 -4.96394753e-01 -5.56141496e-01
-9.68750775e-01 -6.78126693e-01 -8.55006337e-01 3.52453619e-01
9.96272385e-01 -1.02867711e+00 -3.41955513e-01 6.16240203e-01
-1.44346416e+00 9.31069553e-02 -8.06034878e-02 7.96061873e-01
1.80880666e-01 5.11887312e-01 -5.17749190e-01 -4.75290298e-01
-3.83740842e-01 -7.74517059e-01 5.23383021e-01 7.54624724e-01
1.71757892e-01 -1.11510634e+00 -4.61371467e-02 2.28290353e-02
5.38119018e-01 -2.98410773e-01 9.92734611e-01 -1.24290156e+00
-1.73317507e-01 -8.79833251e-02 -5.10241032e-01 5.24848282e-01
5.14650345e-01 -5.53745627e-01 -6.03420198e-01 -1.48215339e-01
-1.07250981e-01 -3.37126702e-01 5.56323409e-01 -2.59702265e-01
1.29930186e+00 -1.11399397e-01 -4.56178576e-01 2.99955159e-01
1.36100233e+00 5.73016286e-01 6.10498011e-01 6.14771008e-01
7.95010209e-01 6.30136073e-01 8.36626232e-01 3.92236918e-01
2.01727107e-01 3.69446486e-01 -1.51385114e-01 -3.52443755e-01
5.92288896e-02 -2.09456488e-01 4.95944619e-02 1.25104570e+00
2.74676293e-01 -2.32487381e-01 -1.09020770e+00 9.41281378e-01
-1.53603482e+00 -6.87481880e-01 9.64569971e-02 1.84780562e+00
1.31125927e+00 -2.82118991e-02 -3.52730632e-01 -2.40452558e-01
1.09904325e+00 -1.49773527e-02 -7.27002919e-01 -1.19810723e-01
-1.33002013e-01 3.88131768e-01 5.04416108e-01 -6.91675171e-02
-1.27393675e+00 1.26068449e+00 5.45052719e+00 1.13457775e+00
-1.22225380e+00 1.31634966e-01 3.68884236e-01 5.52747488e-01
-1.29993722e-01 -2.37497926e-01 -1.08219349e+00 6.51641548e-01
6.92197204e-01 -2.91586310e-01 -1.01965524e-01 1.15743577e+00
-7.97500983e-02 4.58278954e-01 -7.46846676e-01 9.94248331e-01
-2.11297199e-02 -1.15214181e+00 1.64613172e-01 -2.86393434e-01
7.29920745e-01 -1.09956056e-01 -2.39321843e-01 6.74050689e-01
3.21887076e-01 -7.15468407e-01 6.08257055e-02 2.30263561e-01
7.70525098e-01 -8.37984979e-01 1.06188273e+00 4.53304201e-01
-1.14962447e+00 3.01967300e-02 -8.32545459e-01 1.99525505e-01
9.88298878e-02 6.93667352e-01 -6.90588415e-01 5.57953417e-01
6.81062579e-01 6.42434061e-01 -3.03173602e-01 1.03146386e+00
-2.51277149e-01 6.07105017e-01 -2.54169166e-01 -3.14476341e-01
2.06013441e-01 -2.76676029e-01 -8.70360136e-02 1.46777046e+00
3.96886408e-01 2.89188653e-01 2.67344445e-01 4.91002619e-01
-5.09723127e-01 6.80649281e-01 -4.43943501e-01 -4.24057215e-01
6.92589760e-01 1.17981482e+00 -5.18671572e-01 -4.88244623e-01
-5.77537954e-01 9.50005114e-01 4.80970621e-01 2.05339760e-01
-7.15934873e-01 -1.00312710e+00 3.65461111e-01 -2.36964464e-01
5.71013212e-01 -3.59088004e-01 -4.06334668e-01 -1.36681688e+00
-7.82800764e-02 -8.01197767e-01 5.95178127e-01 -3.95832419e-01
-1.73933768e+00 5.57138920e-01 -3.08205456e-01 -1.43051302e+00
3.01009476e-01 -8.94126713e-01 -4.16545779e-01 1.01777589e+00
-1.80253088e+00 -9.63420630e-01 -2.31611341e-01 6.53667033e-01
4.96512115e-01 -4.92054552e-01 8.99325132e-01 9.96986985e-01
-7.59860635e-01 8.56179297e-01 4.07085747e-01 7.59990096e-01
1.23940325e+00 -8.43381286e-01 4.14938658e-01 8.67450058e-01
5.98013699e-02 9.62762833e-01 1.09317459e-01 -7.45379865e-01
-1.04817104e+00 -1.17726040e+00 1.13527417e+00 -9.02666748e-02
6.35686040e-01 -1.35929346e-01 -1.33147752e+00 5.70358813e-01
2.99193710e-01 1.17556013e-01 1.14905500e+00 2.98911482e-01
-3.88280869e-01 -2.61561483e-01 -1.02633333e+00 4.23727900e-01
9.10993516e-01 -2.53761917e-01 -9.78921056e-01 3.58355105e-01
8.65820050e-01 -1.77134618e-01 -8.39585960e-01 4.91701424e-01
1.11548148e-01 -7.69894943e-02 6.59904301e-01 -1.08839846e+00
1.52117789e-01 -6.12862349e-01 8.29623863e-02 -1.33113337e+00
-5.83445013e-01 2.41812654e-02 3.56942266e-01 1.81466675e+00
5.18329203e-01 -6.63518488e-01 6.62628174e-01 6.75762951e-01
8.68233852e-03 -2.29182690e-01 -6.53535485e-01 -8.14750850e-01
3.25288653e-01 -1.29847480e-02 7.58312583e-01 1.61209619e+00
-4.86558191e-02 6.00085258e-01 -2.81681627e-01 1.16557345e-01
4.31624576e-02 1.82832882e-01 5.03799200e-01 -1.15399945e+00
2.12939367e-01 -4.24333028e-02 -3.22856456e-01 -1.26816690e+00
3.49566877e-01 -9.07804191e-01 9.61026549e-02 -1.49116635e+00
4.95862216e-02 -9.68244076e-01 -7.70733953e-01 8.36878121e-01
-4.98165101e-01 -5.25916032e-02 7.48824254e-02 1.31542206e-01
-5.53554177e-01 7.52851248e-01 1.22437727e+00 -3.05223703e-01
-1.64340779e-01 -2.84688324e-01 -8.23345840e-01 6.19236887e-01
8.09320748e-01 -5.30384839e-01 -1.53331265e-01 -1.04061759e+00
-9.44485422e-03 -4.79760051e-01 -4.39088732e-01 -9.18274164e-01
4.59173054e-01 -3.50516886e-01 6.90540195e-01 -3.93149942e-01
-2.05804348e-01 -1.13403475e+00 -2.35776737e-01 2.61973947e-01
-2.77551681e-01 9.19422507e-02 4.23066080e-01 4.96380806e-01
-4.55250442e-01 -4.55856055e-01 8.67769301e-01 -1.26497909e-01
-1.50561595e+00 3.73830467e-01 -1.31658480e-01 2.10560426e-01
1.10073626e+00 1.23093329e-01 -1.77083507e-01 1.45832643e-01
-5.34618199e-01 3.84098142e-01 9.07247588e-02 6.17342472e-01
4.31033134e-01 -1.61076260e+00 -6.33824587e-01 7.29578286e-02
3.48725140e-01 8.44996721e-02 4.94931430e-01 1.95123225e-01
-5.75911939e-01 2.28839502e-01 -4.96475339e-01 -1.83255404e-01
-8.72645259e-01 9.32877660e-01 2.03627739e-02 -5.00328124e-01
-3.06039959e-01 9.24399137e-01 3.75671387e-01 -9.27487195e-01
1.91350371e-01 1.48838535e-01 -7.59192467e-01 1.73614681e-01
6.17451549e-01 6.87171444e-02 1.73555419e-01 -4.83734250e-01
-4.76779789e-01 6.84160709e-01 -5.34927666e-01 2.50360250e-01
1.28625023e+00 -1.32762000e-01 -9.24265608e-02 2.21776992e-01
1.32623625e+00 1.54964074e-01 -5.91285229e-01 -7.77251780e-01
2.59497792e-01 -3.32049340e-01 -1.44116417e-01 -9.22277510e-01
-1.15664697e+00 1.20445967e+00 7.42293835e-01 -2.30655864e-01
1.29778636e+00 -3.36047381e-01 1.12379611e+00 7.62260675e-01
3.69972199e-01 -1.32362235e+00 -1.24786653e-01 8.82852674e-01
3.90542716e-01 -1.44241083e+00 -9.48973671e-02 -4.46373433e-01
-8.63999069e-01 1.04108703e+00 9.46671844e-01 5.60449734e-02
7.63260424e-01 5.97149804e-02 4.77232009e-01 2.80605912e-01
1.35643948e-02 -9.84516218e-02 1.83539182e-01 6.51327252e-01
5.43887198e-01 -9.76046994e-02 -7.83944964e-01 9.53207433e-01
3.64313066e-01 1.35575205e-01 1.51542053e-01 9.58923459e-01
-4.81402338e-01 -1.59953451e+00 -1.89867184e-01 1.84252426e-01
-6.25179648e-01 -2.12097853e-01 -1.92634031e-01 6.49308205e-01
3.28628391e-01 8.75395119e-01 4.21935059e-02 -2.29173720e-01
5.34377694e-01 1.70389026e-01 -1.14900351e-01 -8.17841530e-01
-5.24891496e-01 -2.82644123e-01 -2.11933821e-01 -1.86129082e-02
-5.57812274e-01 -1.21133387e-01 -1.52882981e+00 -6.48935214e-02
-6.58790231e-01 5.68839490e-01 5.46069205e-01 9.68702912e-01
6.57857478e-01 3.87634039e-01 7.48956680e-01 -1.89071864e-01
-5.40593386e-01 -1.06728864e+00 -6.15391254e-01 5.96766591e-01
-2.39764944e-01 -6.71128392e-01 6.76422566e-02 2.12550789e-01] | [9.810770034790039, 9.648488998413086] |
00e513b3-571b-4fbf-ab49-66c285d33225 | a-survey-on-non-autoregressive-generation-for | 2204.09269 | null | https://arxiv.org/abs/2204.09269v2 | https://arxiv.org/pdf/2204.09269v2.pdf | A Survey on Non-Autoregressive Generation for Neural Machine Translation and Beyond | Non-autoregressive (NAR) generation, which is first proposed in neural machine translation (NMT) to speed up inference, has attracted much attention in both machine learning and natural language processing communities. While NAR generation can significantly accelerate inference speed for machine translation, the speedup comes at the cost of sacrificed translation accuracy compared to its counterpart, autoregressive (AR) generation. In recent years, many new models and algorithms have been designed/proposed to bridge the accuracy gap between NAR generation and AR generation. In this paper, we conduct a systematic survey with comparisons and discussions of various non-autoregressive translation (NAT) models from different aspects. Specifically, we categorize the efforts of NAT into several groups, including data manipulation, modeling methods, training criterion, decoding algorithms, and the benefit from pre-trained models. Furthermore, we briefly review other applications of NAR models beyond machine translation, such as grammatical error correction, text summarization, text style transfer, dialogue, semantic parsing, automatic speech recognition, and so on. In addition, we also discuss potential directions for future exploration, including releasing the dependency of KD, reasonable training objectives, pre-training for NAR, and wider applications, etc. We hope this survey can help researchers capture the latest progress in NAR generation, inspire the design of advanced NAR models and algorithms, and enable industry practitioners to choose appropriate solutions for their applications. The web page of this survey is at \url{https://github.com/LitterBrother-Xiao/Overview-of-Non-autoregressive-Applications}. | ['Tie-Yan Liu', 'Tao Qin', 'Min Zhang', 'Juntao Li', 'Junliang Guo', 'Lijun Wu', 'Yisheng Xiao'] | 2022-04-20 | null | null | null | null | ['text-style-transfoer'] | ['natural-language-processing'] | [ 5.20750582e-01 3.41844797e-01 -4.84833241e-01 -4.21897978e-01
-1.27653778e+00 -4.40824211e-01 6.12314165e-01 -1.82459161e-01
-2.91659962e-02 8.21521759e-01 5.31152904e-01 -7.39318311e-01
2.43152022e-01 -5.82667112e-01 -7.40297973e-01 -5.53326190e-01
5.70010185e-01 7.84068763e-01 -4.51425374e-01 -4.38519567e-01
2.96747357e-01 5.34852184e-02 -9.39040840e-01 2.32662946e-01
1.23530555e+00 5.41750371e-01 3.00715953e-01 7.25921273e-01
-3.02980572e-01 6.18857622e-01 -7.29360521e-01 -6.78909421e-01
-6.41036630e-02 -9.13509846e-01 -7.61309743e-01 -2.36377791e-01
8.78985077e-02 -2.63579816e-01 -3.79144847e-01 9.45766985e-01
8.10780466e-01 9.70712006e-02 8.11884582e-01 -9.46349800e-01
-1.28996098e+00 1.11752117e+00 -2.83591360e-01 1.90243661e-01
2.83472806e-01 -5.97064989e-03 7.68062592e-01 -1.07380748e+00
4.28971142e-01 1.37059963e+00 5.18623233e-01 1.05720949e+00
-6.64293766e-01 -5.90430677e-01 -5.56868240e-02 1.34471744e-01
-9.05422926e-01 -8.52022588e-01 6.88683093e-01 -9.14016441e-02
1.14923787e+00 5.19630075e-01 2.44291097e-01 1.42123747e+00
3.24202299e-01 1.22126031e+00 8.39293659e-01 -7.79762626e-01
8.59707892e-02 1.38073683e-01 7.37700984e-02 5.26506066e-01
-1.88794341e-02 -2.10223630e-01 -5.74200034e-01 -1.03146642e-01
7.57133722e-01 -3.89244378e-01 -6.25787675e-02 2.63407379e-01
-1.23307204e+00 9.51349437e-01 -1.38460606e-01 2.82996863e-01
-3.95660639e-01 8.21392685e-02 4.26161379e-01 5.09765327e-01
8.68349254e-01 5.29610515e-01 -7.30116189e-01 -6.06319427e-01
-7.57498324e-01 1.43916309e-01 7.28884339e-01 1.27021933e+00
3.13955188e-01 6.13173306e-01 -2.86235392e-01 1.32984495e+00
2.13247910e-01 8.15727174e-01 7.45576560e-01 -9.59830999e-01
8.95569503e-01 2.65670300e-01 -3.05405259e-01 -4.84043062e-01
-7.41553307e-02 -3.01513553e-01 -1.23807037e+00 -7.95466900e-01
1.10051230e-01 -5.81050634e-01 -7.90262640e-01 1.53721297e+00
9.53047723e-02 -2.34589055e-01 3.99811655e-01 8.36422920e-01
9.17738438e-01 9.68811810e-01 -1.47119850e-01 -5.79511046e-01
1.29846060e+00 -1.11724818e+00 -9.59533334e-01 -3.77684534e-01
8.49400163e-01 -1.10968506e+00 1.10949290e+00 8.01062286e-02
-1.41581929e+00 -2.59800375e-01 -5.75964808e-01 -4.68730420e-01
-1.99123889e-01 5.30747712e-01 8.06175292e-01 5.71332812e-01
-1.01081669e+00 5.67078412e-01 -1.00628662e+00 -5.47195137e-01
1.34231642e-01 3.88037235e-01 2.29886115e-01 4.87676933e-02
-1.54515064e+00 8.96862864e-01 -6.01679832e-02 3.34033072e-01
-4.18140590e-01 -3.93824935e-01 -7.39760339e-01 -2.01719031e-01
-7.45420170e-04 -1.14517283e+00 1.55471158e+00 -9.87300634e-01
-2.21152782e+00 5.21936476e-01 -6.21569395e-01 -5.84811747e-01
1.93809554e-01 -4.30437237e-01 -2.62871683e-01 -1.37308568e-01
-9.88433957e-02 5.89666963e-01 6.62035942e-01 -6.32379293e-01
-2.24010661e-01 -4.01552439e-01 -1.77376643e-01 4.42606658e-01
-3.81090701e-01 5.52056074e-01 -3.99099171e-01 -8.95820856e-01
1.42727587e-02 -1.10275388e+00 -2.58336872e-01 -6.41207397e-01
-4.77153182e-01 -5.35025954e-01 4.97474343e-01 -9.53658223e-01
1.33545983e+00 -1.82230330e+00 4.75939959e-01 -4.51388001e-01
-2.70476043e-01 3.50575864e-01 -2.86590964e-01 6.58781409e-01
1.64778635e-01 2.45375097e-01 -3.46458286e-01 -4.51794744e-01
-2.14213550e-01 2.10041478e-01 -6.74329460e-01 1.24428853e-01
4.35067385e-01 1.32296026e+00 -7.71968246e-01 -2.36974135e-01
3.35505269e-02 5.37981033e-01 -3.44535977e-01 1.82659000e-01
-2.63038248e-01 6.09827220e-01 -7.01138914e-01 6.92209303e-01
2.12832063e-01 -1.53396294e-01 4.87327054e-02 2.17213467e-01
-2.24160179e-02 8.06011736e-01 -4.77537245e-01 1.53085303e+00
-5.75564384e-01 7.28711247e-01 -1.22169435e-01 -9.69627976e-01
1.15275049e+00 4.92062181e-01 9.06337798e-02 -5.76077223e-01
2.42314026e-01 4.58858192e-01 1.27308564e-02 -3.56614381e-01
9.70652759e-01 1.51435927e-01 -8.39058384e-02 5.50316274e-01
-9.78291035e-02 -2.51653790e-01 1.92413494e-01 3.07307877e-02
6.65272236e-01 1.88933402e-01 2.31300637e-01 -2.81996280e-02
3.94301564e-01 2.32034311e-01 5.62060237e-01 6.57380760e-01
1.02623105e-01 5.90068579e-01 3.19210708e-01 -2.00230107e-01
-1.23778725e+00 -6.99296534e-01 3.92551795e-02 1.27131557e+00
-2.11039513e-01 -4.04573828e-01 -1.03797436e+00 -4.10924375e-01
-5.16787767e-01 9.89421725e-01 -2.04669356e-01 -2.59589404e-01
-1.14380610e+00 -1.09753644e+00 8.23288798e-01 6.52358234e-01
4.43836808e-01 -1.09111094e+00 2.43333094e-02 5.30829988e-02
-7.68423200e-01 -9.42528129e-01 -5.47093928e-01 -1.91468030e-01
-1.49162138e+00 -3.26593131e-01 -8.85694802e-01 -9.20317233e-01
6.28434598e-01 2.70313859e-01 1.04512358e+00 -4.25160602e-02
3.37696403e-01 1.87766016e-01 -5.79116702e-01 -6.39066100e-01
-9.09707010e-01 4.81233597e-01 3.21567237e-01 -5.17849982e-01
4.43636864e-01 -4.70054567e-01 -2.82028973e-01 2.08113611e-01
-6.57059252e-01 2.72699356e-01 9.11859095e-01 9.23465014e-01
4.42090929e-01 -6.53721631e-01 7.20971584e-01 -1.05235958e+00
1.07949638e+00 -4.10347998e-01 -2.72376776e-01 3.60314369e-01
-7.61607647e-01 7.74179026e-02 7.67828286e-01 -4.15376872e-01
-1.11306834e+00 -3.58191878e-01 -4.54494447e-01 -1.45180449e-01
1.63303390e-01 6.68974996e-01 -1.85434539e-02 3.69747370e-01
6.70023203e-01 6.42255962e-01 1.04135863e-01 -5.52180171e-01
5.58729708e-01 1.09729254e+00 1.61190003e-01 -5.95287979e-01
6.34387136e-01 -1.99489519e-01 -3.00553173e-01 -9.49303091e-01
-6.70255184e-01 -1.25203133e-01 -4.38965231e-01 2.37359941e-01
5.63537538e-01 -8.56104612e-01 -4.86394353e-02 3.86614203e-01
-1.49220800e+00 -4.29145873e-01 -1.86899155e-01 6.59153759e-01
-8.55656087e-01 4.13215786e-01 -1.21339965e+00 -8.94219756e-01
-1.09322608e+00 -1.19794476e+00 1.18927479e+00 3.16604584e-01
-4.52887297e-01 -1.11211169e+00 -1.70409203e-01 8.74284685e-01
5.90532303e-01 -5.96651793e-01 1.24429286e+00 -7.49529302e-01
-4.90709901e-01 -5.29060662e-02 7.27342293e-02 4.02570605e-01
-1.36865806e-02 6.01057969e-02 -6.04794025e-01 -2.87967771e-02
-1.39965182e-02 -1.95668936e-01 5.73244750e-01 7.26447523e-01
8.89375865e-01 -7.02038825e-01 -1.63466468e-01 4.47828233e-01
6.78076088e-01 2.66139418e-01 8.05231035e-01 1.28974482e-01
6.30855262e-01 5.36751688e-01 7.69340098e-01 3.10721975e-02
4.61796373e-01 7.04341948e-01 -1.56282336e-01 -3.98995653e-02
-1.71072692e-01 -3.09417993e-01 8.77081335e-01 1.83969676e+00
-4.71745431e-01 -5.62671483e-01 -6.37978256e-01 1.30276248e-01
-1.99102342e+00 -6.57934427e-01 -3.57903749e-01 2.12111259e+00
1.03302538e+00 -2.69145042e-01 -1.49163660e-02 -2.48914495e-01
7.93133616e-01 -2.76605804e-02 -5.28992772e-01 -9.28177953e-01
-3.03499818e-01 1.50952086e-01 4.05565023e-01 6.21701717e-01
-7.27508545e-01 1.54481232e+00 6.48731995e+00 8.98618221e-01
-1.09668934e+00 2.90661275e-01 8.52901697e-01 2.14170307e-01
-3.92304540e-01 5.01984842e-02 -1.11952126e+00 2.96120584e-01
1.41556108e+00 -4.37426299e-01 6.48738205e-01 8.47290039e-01
5.79452634e-01 3.78207237e-01 -8.67725015e-01 7.91496575e-01
3.02799642e-01 -1.32380867e+00 4.15802002e-01 5.92090786e-02
6.79969192e-01 1.88222155e-01 1.39878884e-01 6.96798623e-01
2.74793118e-01 -7.59279549e-01 3.25132161e-01 3.50291759e-01
7.62746692e-01 -4.94624019e-01 7.57983208e-01 4.16498393e-01
-6.79395199e-01 2.80021820e-02 -6.61269009e-01 -2.69566864e-01
2.24522501e-01 4.62742537e-01 -8.10285091e-01 7.37752974e-01
2.11021140e-01 7.16980100e-01 -2.11219072e-01 5.29702246e-01
-4.09130991e-01 1.06970513e+00 -1.44275092e-02 -3.45743865e-01
-7.91352149e-03 -5.85081875e-01 6.77119792e-01 1.12681460e+00
5.49234807e-01 1.05418451e-01 -2.44415805e-01 5.96039593e-01
-2.58790880e-01 4.81371909e-01 -4.76508468e-01 -4.60072160e-01
5.29656947e-01 9.17677581e-01 -3.12389016e-01 -5.79938769e-01
-2.37769738e-01 1.12863600e+00 1.36922807e-01 5.15941322e-01
-8.19959044e-01 -2.08694056e-01 5.78700721e-01 -6.81905672e-02
-2.46193752e-01 -5.13208151e-01 -6.16818726e-01 -1.35361445e+00
1.30135417e-01 -1.23459804e+00 1.46008000e-01 -8.64505291e-01
-1.02492094e+00 7.53550708e-01 -8.93373340e-02 -1.18611419e+00
-8.55665505e-01 -3.56849045e-01 -4.01687562e-01 9.67533350e-01
-1.35607433e+00 -1.05983996e+00 3.26657027e-01 2.32452340e-02
1.41642535e+00 -3.74854028e-01 1.02652800e+00 2.31989980e-01
-8.37149739e-01 8.90753984e-01 4.70916182e-01 2.10195839e-01
7.42897153e-01 -7.97796190e-01 8.78498793e-01 8.89567912e-01
6.93973824e-02 8.73468101e-01 6.35974646e-01 -7.08755493e-01
-1.88631141e+00 -1.22324860e+00 1.48571265e+00 -5.58144331e-01
6.31779909e-01 -3.18422943e-01 -9.08989787e-01 8.42613280e-01
2.81550556e-01 -7.88217723e-01 5.15594006e-01 2.10900098e-01
4.31308746e-02 7.03079179e-02 -6.04157627e-01 9.29742992e-01
9.83268797e-01 -2.96148568e-01 -4.52887774e-01 7.01979041e-01
1.05398595e+00 -5.86059332e-01 -8.79658520e-01 3.60908866e-01
3.85433495e-01 -3.19430262e-01 6.40566826e-01 -8.11190903e-01
7.94448078e-01 1.70298144e-01 -7.24857002e-02 -1.45226514e+00
-3.44780266e-01 -1.06542468e+00 -3.32481146e-01 1.43532431e+00
8.21600080e-01 -7.26309121e-01 6.63355231e-01 6.05499804e-01
-5.40139914e-01 -9.47576761e-01 -7.39616036e-01 -6.54107094e-01
5.68341613e-01 -3.70862186e-01 4.53541845e-01 8.25981677e-01
3.72876525e-02 9.81680632e-01 -6.62469268e-01 -1.51843548e-01
1.79860055e-01 2.75920387e-02 8.84374440e-01 -8.40040863e-01
-2.75684744e-01 -4.50072676e-01 1.37587845e-01 -1.67380238e+00
5.75615130e-02 -9.60409105e-01 -1.23709133e-02 -1.79901981e+00
1.46885589e-01 -3.12713087e-01 3.63375306e-01 4.35457855e-01
-3.36498469e-01 1.32463425e-01 2.21531950e-02 6.10086977e-01
-2.18616560e-01 8.21436286e-01 1.33861864e+00 -2.63662878e-02
-4.27061737e-01 3.48337263e-01 -9.12838519e-01 4.61917222e-01
1.18503034e+00 -4.13103253e-01 -3.46830010e-01 -8.88874769e-01
2.51194179e-01 3.38378578e-01 -2.73775458e-01 -2.92424798e-01
1.37968719e-01 -2.08968773e-01 1.10465035e-01 -4.10238683e-01
4.55432445e-01 -8.33661407e-02 -5.99051677e-02 1.31329328e-01
-5.92136919e-01 5.19750118e-01 -2.13412363e-02 2.33581692e-01
-1.73600480e-01 -3.19083244e-01 3.85150939e-01 -2.19431877e-01
-1.89353034e-01 2.17152581e-01 -7.83354580e-01 6.42182231e-02
3.50685984e-01 -3.82724814e-02 -4.33739394e-01 -6.80023015e-01
-2.93059736e-01 1.56034380e-01 1.76596031e-01 8.10862124e-01
6.18888259e-01 -1.05945396e+00 -1.13125479e+00 -1.96293071e-02
-1.72388956e-01 -1.85169966e-03 1.26616567e-01 1.16075051e+00
-2.43066415e-01 7.72435963e-01 2.24209353e-01 -4.11044240e-01
-1.27256191e+00 2.60340452e-01 -4.50516753e-02 -3.10109735e-01
-4.62728530e-01 6.13467753e-01 5.87488562e-02 -6.94939017e-01
-6.27336800e-02 -2.03708068e-01 -1.07892737e-01 -3.88089001e-01
4.45247918e-01 4.62231606e-01 1.26988575e-01 -4.83110428e-01
-5.73486416e-03 4.11523968e-01 -3.52502376e-01 -3.05347629e-02
1.18377519e+00 -4.00641233e-01 -3.48488778e-01 5.96458197e-01
7.49284923e-01 -4.44601215e-02 -5.17090917e-01 -3.24671626e-01
-1.90746367e-01 7.65525037e-03 -2.01981112e-01 -7.45566964e-01
-7.50367045e-01 1.12079012e+00 1.28642142e-01 -1.12271756e-01
1.05712616e+00 2.61848103e-02 1.18620634e+00 5.70984840e-01
1.55238941e-01 -1.13183427e+00 -1.63178086e-01 1.02384329e+00
1.03064787e+00 -9.98377383e-01 -1.48992360e-01 -5.69833577e-01
-8.18704307e-01 1.26224637e+00 3.75830173e-01 1.00458466e-01
1.40640602e-01 1.93679839e-01 2.50442028e-01 3.14560115e-01
-1.01007485e+00 2.21960008e-01 1.11692324e-01 6.16483688e-01
1.01319945e+00 1.77794456e-01 -7.23730743e-01 5.22575498e-01
-7.08857894e-01 -5.26259877e-02 5.84908187e-01 5.50378680e-01
-3.14667791e-01 -1.51861095e+00 -2.87521422e-01 5.49603522e-01
-4.77805376e-01 -6.35200918e-01 -6.49407744e-01 3.36505473e-01
-6.07100248e-01 1.15493286e+00 -5.26161306e-02 -3.55460554e-01
2.45093346e-01 2.37844497e-01 4.41386133e-01 -6.26822889e-01
-5.35169303e-01 1.47624135e-01 4.62540299e-01 4.99641383e-03
-1.83399066e-01 -6.95344448e-01 -1.01127648e+00 -5.05574286e-01
-4.41889197e-01 4.47703630e-01 9.82702076e-01 9.56673265e-01
7.74523199e-01 3.36529583e-01 5.56183636e-01 -4.85700876e-01
-7.52821267e-01 -1.44649017e+00 4.02322002e-02 -2.80927211e-01
-1.16220176e-01 -1.65654883e-01 -2.24862158e-01 1.67690098e-01] | [11.754042625427246, 9.269105911254883] |
570462f6-b9c7-4517-81be-6e0c0e7aa84c | provable-convergence-of-tensor-decomposition | 2303.06815 | null | https://arxiv.org/abs/2303.06815v1 | https://arxiv.org/pdf/2303.06815v1.pdf | Provable Convergence of Tensor Decomposition-Based Neural Network Training | Advanced tensor decomposition, such as tensor train (TT), has been widely studied for tensor decomposition-based neural network (NN) training, which is one of the most common model compression methods. However, training NN with tensor decomposition always suffers significant accuracy loss and convergence issues. In this paper, a holistic framework is proposed for tensor decomposition-based NN training by formulating TT decomposition-based NN training as a nonconvex optimization problem. This problem can be solved by the proposed tensor block coordinate descent (tenBCD) method, which is a gradient-free algorithm. The global convergence of tenBCD to a critical point at a rate of O(1/k) is established with the Kurdyka {\L}ojasiewicz (K{\L}) property, where k is the number of iterations. The theoretical results can be extended to the popular residual neural networks (ResNets). The effectiveness and efficiency of our proposed framework are verified through an image classification dataset, where our proposed method can converge efficiently in training and prevent overfitting. | ['Bo Shen', 'Chenyang Li'] | 2023-03-13 | null | null | null | null | ['model-compression'] | ['methodology'] | [-8.65146071e-02 -2.82574356e-01 -3.05216938e-01 -6.57200664e-02
-2.85537928e-01 -6.59207255e-02 -1.51976615e-01 -3.54377478e-01
-4.93940353e-01 5.80770433e-01 -1.40283618e-03 -6.91909850e-01
-5.41287482e-01 -4.41173881e-01 -7.01099396e-01 -1.12932217e+00
-1.17404899e-02 4.07842100e-02 -7.64525384e-02 -1.88251287e-01
-5.13465926e-02 5.25864124e-01 -1.31938267e+00 -1.55263385e-02
9.12511706e-01 1.32873058e+00 -4.96000610e-02 4.45548862e-01
-1.20604616e-02 1.23268688e+00 -1.85530066e-01 -6.18782461e-01
1.83629319e-01 -1.81409214e-02 -9.28243279e-01 1.22935243e-01
4.29038644e-01 -5.47912836e-01 -5.99798083e-01 1.18538833e+00
2.39774033e-01 2.26622090e-01 3.18131328e-01 -1.03732693e+00
-5.38338542e-01 4.70604777e-01 -6.04017138e-01 2.55247861e-01
-5.21659076e-01 -3.65337938e-01 1.00100112e+00 -1.18499315e+00
3.55542719e-01 1.03167391e+00 7.98220456e-01 4.36575294e-01
-9.48218822e-01 -7.09119141e-01 -1.03944736e-02 4.70253140e-01
-1.35709167e+00 -1.34027392e-01 1.02030897e+00 -2.75665402e-01
6.01112068e-01 5.45791686e-01 6.97677732e-01 4.56424922e-01
-4.70242351e-02 1.11217332e+00 7.53558695e-01 -3.28927368e-01
2.45053787e-03 -3.02543759e-01 3.15418690e-01 8.03768992e-01
5.02612948e-01 -1.47721127e-01 -2.50685811e-01 1.18815914e-01
8.48029852e-01 2.92879820e-01 -4.35297161e-01 -9.99178812e-02
-1.19489217e+00 8.44092488e-01 6.94792449e-01 3.21896315e-01
-3.14079791e-01 4.89951521e-01 7.23650873e-01 2.42905408e-01
6.49734855e-01 -1.36090383e-01 -3.88445914e-01 -2.46885419e-01
-9.39125121e-01 2.84795016e-01 5.25226951e-01 7.70532966e-01
5.81960797e-01 6.38350606e-01 -1.50786119e-03 1.07007396e+00
3.72633398e-01 3.15666169e-01 5.49944639e-01 -1.10264874e+00
8.04937124e-01 5.20010531e-01 -2.89315760e-01 -1.37670135e+00
-2.21694112e-01 -9.00332153e-01 -1.48303843e+00 -1.75313070e-01
2.03619823e-01 -2.06625804e-01 -5.72573364e-01 1.43225181e+00
5.43911099e-01 3.11726183e-01 1.10695645e-01 9.42024112e-01
7.56601810e-01 6.47793889e-01 -3.78364772e-01 -3.70826721e-01
1.06784797e+00 -9.93565261e-01 -8.47182572e-01 3.34398508e-01
1.04515600e+00 -6.22077405e-01 7.56762207e-01 5.92626631e-01
-8.83138001e-01 -3.05766433e-01 -1.02486348e+00 -2.81471640e-01
2.19793528e-01 5.90649068e-01 8.99353862e-01 7.16072798e-01
-9.87625837e-01 6.78983927e-01 -9.63509858e-01 2.06552789e-01
5.44401944e-01 5.30220032e-01 -2.86120176e-01 -4.74296302e-01
-9.17203665e-01 4.62667465e-01 5.19055247e-01 8.51097643e-01
-6.85129941e-01 -6.68906689e-01 -5.26893616e-01 -9.49595794e-02
3.61605197e-01 -4.30584013e-01 8.74172091e-01 -6.28997922e-01
-1.42121184e+00 3.41869354e-01 -3.31745595e-02 -3.67004901e-01
4.11386281e-01 -2.91291237e-01 -1.42267480e-01 2.82927155e-01
-2.87180513e-01 4.88930717e-02 8.46794844e-01 -1.01652825e+00
-2.50916272e-01 -4.78167921e-01 1.33849755e-01 1.34228766e-01
-9.66361463e-01 -1.70137305e-02 -3.40963870e-01 -9.59172368e-01
7.44499266e-01 -8.52669299e-01 -2.45869666e-01 4.17750739e-02
-4.70790535e-01 -3.80731851e-01 1.01517284e+00 -9.64926004e-01
1.33251333e+00 -1.96819854e+00 2.99563766e-01 2.71780521e-01
7.29267478e-01 3.91927570e-01 9.76057127e-02 1.72083884e-01
-1.65766612e-01 3.93201523e-02 -1.17598288e-02 -4.83886957e-01
-2.77896613e-01 4.43684697e-01 -1.92093164e-01 5.50548911e-01
-2.26877272e-01 5.49691081e-01 -6.28612876e-01 -4.38620627e-01
-7.61485621e-02 6.94175601e-01 -7.69841492e-01 -9.73841697e-02
1.68395251e-01 1.51805878e-01 -5.15949488e-01 6.14283979e-01
8.10293436e-01 -2.83172816e-01 6.13278300e-02 -6.77939653e-01
-1.80214435e-01 -3.23184431e-02 -1.10729051e+00 1.47488940e+00
-3.00355852e-01 5.40437400e-01 1.94127932e-01 -1.49813974e+00
9.25598979e-01 4.26842570e-01 8.20010900e-01 -1.85391426e-01
2.81792641e-01 4.31901425e-01 -1.36182532e-01 -5.42166114e-01
5.55988848e-01 -7.40542933e-02 5.91490626e-01 3.96946758e-01
-7.54779205e-02 4.44884568e-01 4.25275743e-01 6.83600754e-02
9.12554026e-01 -1.99103743e-01 -3.53151470e-01 -1.87204644e-01
6.25246525e-01 -9.65087563e-02 8.37924123e-01 2.85081625e-01
8.90143961e-02 3.70569229e-01 5.49345255e-01 -7.19880521e-01
-1.29630601e+00 -3.44536275e-01 -3.90432805e-01 6.50038123e-01
-5.94388656e-02 -4.83541369e-01 -7.28964329e-01 -4.13820475e-01
-3.16134065e-01 2.97636807e-01 -4.50258464e-01 -2.57623881e-01
-7.47180104e-01 -9.84777391e-01 6.39895558e-01 3.84567469e-01
1.01182961e+00 -4.53709483e-01 -1.91319093e-01 5.16827181e-02
-3.79046887e-01 -1.18699467e+00 -4.49242443e-01 -4.69828546e-02
-1.56936669e+00 -8.39725614e-01 -1.12232494e+00 -7.79432654e-01
9.23173666e-01 5.02308547e-01 6.14418387e-01 4.82405484e-01
1.67660460e-01 4.05526869e-02 -4.76946443e-01 6.37825280e-02
-1.05541743e-01 7.38443956e-02 3.60107780e-01 3.31436604e-01
-9.38326046e-02 -7.03467131e-01 -6.58300936e-01 4.54785466e-01
-1.22014618e+00 3.18803817e-01 5.90528786e-01 1.09484649e+00
6.81304991e-01 4.70207930e-01 1.53867826e-01 -6.39024973e-01
6.40136421e-01 -1.07327566e-01 -5.76657772e-01 2.37352490e-01
-7.76194394e-01 5.66064157e-02 7.80434787e-01 -4.04992282e-01
-6.45682991e-01 -2.07599238e-01 -9.31426361e-02 -1.11206901e+00
5.44698894e-01 1.16080928e+00 -2.20381711e-02 -6.10465467e-01
3.03347349e-01 6.26797438e-01 5.11283204e-02 -8.05447459e-01
4.28970382e-02 3.40620220e-01 3.42139482e-01 -7.82840073e-01
9.00927782e-01 3.21095973e-01 4.68808562e-01 -8.31819057e-01
-9.29515302e-01 -2.90126622e-01 -4.31303829e-01 -4.02888060e-01
4.88047451e-01 -8.27654302e-01 -1.18208373e+00 4.65521932e-01
-1.21539366e+00 1.06417015e-02 5.49778249e-03 8.60869467e-01
-7.37772956e-02 6.74714983e-01 -8.34527910e-01 -9.07250524e-01
-4.95084912e-01 -1.18133700e+00 2.94261605e-01 -1.22564755e-01
6.79490924e-01 -1.06530178e+00 -2.00541496e-01 7.75459230e-01
3.16963643e-01 3.01556855e-01 8.30349267e-01 -1.53483301e-01
-6.05445981e-01 -3.81018192e-01 -5.33693075e-01 1.01340806e+00
-2.35279813e-01 -6.68079779e-03 -5.23194194e-01 -4.09168810e-01
4.41417187e-01 -3.14459771e-01 8.12186062e-01 2.10502759e-01
1.49660087e+00 -9.34060693e-01 1.34816006e-01 1.04395688e+00
1.50239253e+00 -6.65793642e-02 5.51752567e-01 3.03575248e-01
1.39546335e+00 1.06265403e-01 1.33298740e-01 3.76145333e-01
4.14398551e-01 4.87857759e-01 6.41772568e-01 -4.80912700e-02
5.69008738e-02 1.07451648e-01 3.09956282e-01 1.79553294e+00
-6.99781895e-01 2.53206789e-01 -8.72183561e-01 2.79531211e-01
-1.90181339e+00 -8.27278137e-01 -4.87550974e-01 2.10072613e+00
8.67467225e-01 -6.96672872e-02 -3.19829769e-02 7.93275833e-01
5.78444362e-01 9.62244347e-02 -5.13703287e-01 -2.70351261e-01
-1.07050026e-02 1.55992974e-02 7.43229449e-01 1.27751008e-01
-9.25504982e-01 5.23425698e-01 5.32370090e+00 1.13499987e+00
-1.31688988e+00 3.25144798e-01 7.16469944e-01 9.07054991e-02
-2.83924520e-01 -4.95576225e-02 -6.59983397e-01 2.99193919e-01
5.55554986e-01 4.59945127e-02 6.47951365e-01 9.53742683e-01
3.62353563e-01 4.16987389e-01 -6.38600528e-01 1.28313613e+00
6.69804588e-02 -1.52815950e+00 1.67432576e-01 2.24242851e-01
8.38160276e-01 -1.01588115e-01 2.65947610e-01 2.07117721e-01
-8.56012776e-02 -8.80335927e-01 5.11304736e-01 2.72163033e-01
7.77949929e-01 -9.36491072e-01 7.16517568e-01 3.27826947e-01
-1.31537402e+00 -2.10755154e-01 -7.59654999e-01 6.06212281e-02
-1.57398894e-01 1.19704354e+00 -3.37255061e-01 1.01919007e+00
7.31491625e-01 1.09736991e+00 -2.78223574e-01 1.16019547e+00
1.25740319e-01 9.39945102e-01 -4.96523887e-01 -1.40495181e-01
3.86998624e-01 -6.39172733e-01 6.01467907e-01 6.38895512e-01
4.29604024e-01 1.26141742e-01 -5.65895112e-03 4.61616457e-01
-3.48152518e-01 3.21458071e-01 -1.53291807e-01 1.64489634e-02
9.38983336e-02 1.19225991e+00 -5.72423398e-01 -8.13960359e-02
-1.81335509e-01 4.86047298e-01 2.94655889e-01 5.41603386e-01
-6.92369759e-01 -3.83113712e-01 3.80593061e-01 -2.99103886e-01
3.84000838e-01 -5.35714984e-01 -5.23965716e-01 -1.47612810e+00
5.56129098e-01 -6.91293061e-01 2.50335723e-01 -5.32964230e-01
-8.55720520e-01 6.61295295e-01 -8.36613104e-02 -1.54364336e+00
3.80850554e-01 -7.83798933e-01 -3.53747398e-01 4.98917967e-01
-1.46988618e+00 -1.10842764e+00 -2.98058271e-01 7.38227963e-01
1.53180405e-01 -3.24294090e-01 5.36007881e-01 8.99118662e-01
-1.18662417e+00 8.14359903e-01 7.51285553e-01 4.51593548e-01
-1.87213644e-01 -9.25677717e-01 -3.58435005e-01 1.02826631e+00
-1.58982843e-01 5.66479146e-01 4.74363685e-01 -3.12363952e-01
-1.71897292e+00 -1.29429185e+00 8.21023941e-01 3.80319744e-01
6.63723826e-01 6.48685992e-02 -8.38685751e-01 5.19096136e-01
-4.15967315e-01 2.90372699e-01 5.68281651e-01 1.20686337e-01
-4.14711565e-01 -7.58877635e-01 -8.26195538e-01 4.96955931e-01
8.05189073e-01 -2.61671752e-01 2.98556257e-02 6.84940755e-01
8.18677843e-01 -5.51914752e-01 -1.23830128e+00 4.59684432e-01
5.17493427e-01 -6.86748087e-01 1.03773630e+00 -5.88695288e-01
7.71525502e-01 -2.53421456e-01 -3.20074886e-01 -8.83243680e-01
-1.08113877e-01 -6.27432883e-01 -5.60914457e-01 9.34774637e-01
1.23545751e-01 -5.32828271e-01 1.00176775e+00 3.99159431e-01
-3.20207626e-01 -1.27683401e+00 -1.35750115e+00 -9.30847943e-01
1.43975569e-02 -6.72946692e-01 3.62050444e-01 1.01146042e+00
-2.12536559e-01 1.33624405e-01 -6.66661322e-01 -9.13315639e-03
8.81602585e-01 -2.68806100e-01 5.19654453e-01 -1.12667704e+00
-1.76990509e-01 -3.09787482e-01 -4.16602522e-01 -1.26880479e+00
-3.98396999e-02 -1.14255726e+00 -4.12485093e-01 -1.45369124e+00
2.04378795e-02 -8.46726298e-01 -3.25906545e-01 4.64197129e-01
1.25540957e-01 2.96199530e-01 2.22351819e-01 7.63662577e-01
-3.96826297e-01 9.98418450e-01 1.76791894e+00 -1.64258957e-01
2.73163952e-02 1.02328196e-01 -4.11332548e-01 4.75618213e-01
5.84423482e-01 -4.66584533e-01 -5.73004127e-01 -8.75261307e-01
5.21217406e-01 2.28678986e-01 2.49872670e-01 -1.15575647e+00
3.74043435e-01 -1.85757298e-02 2.30070483e-02 -6.38259470e-01
4.66165811e-01 -9.16498959e-01 9.54379663e-02 5.54865777e-01
-3.18647176e-01 2.95873910e-01 -8.39914605e-02 4.75688607e-01
-3.14608604e-01 -3.70646834e-01 5.55243671e-01 1.88159078e-01
-6.24820925e-02 8.32352757e-01 9.81315412e-03 -1.65874586e-01
6.42646849e-01 -1.95292115e-01 -2.27312356e-01 -1.41880572e-01
-5.19606411e-01 1.17114365e-01 -3.22930485e-01 -1.60789073e-01
9.88895118e-01 -1.74037576e+00 -7.54119158e-01 3.75541858e-02
-3.45245004e-01 4.62331504e-01 4.18852299e-01 1.39621377e+00
-9.35292304e-01 4.62858230e-01 1.60194695e-01 -7.20201910e-01
-1.12030077e+00 2.25304440e-01 4.08840358e-01 -5.16959608e-01
-7.56100059e-01 9.21861708e-01 -2.16468126e-01 -3.55012715e-01
4.53158110e-01 -3.06607932e-01 -2.59582281e-01 -3.59810442e-01
3.45967323e-01 6.52574658e-01 3.61608565e-01 -6.52036309e-01
-5.70782833e-02 4.76505905e-01 -3.63057315e-01 1.42271355e-01
1.49468338e+00 1.76333368e-01 -7.19389975e-01 2.00110614e-01
1.68564713e+00 -5.68482637e-01 -9.65795398e-01 -4.06095862e-01
-2.43063718e-01 -3.27179641e-01 6.50322080e-01 -3.99571396e-02
-1.80130696e+00 9.01341915e-01 4.74396259e-01 1.01593353e-01
1.16600132e+00 -6.14823818e-01 1.27373981e+00 7.20241129e-01
1.57319739e-01 -9.84405220e-01 9.67285261e-02 7.91469514e-01
8.28796804e-01 -8.15484524e-01 2.66749382e-01 -2.61037260e-01
-1.43603265e-01 1.54240131e+00 4.56088096e-01 -2.29988918e-01
9.64167655e-01 -2.65814066e-01 -2.95352280e-01 -1.34148896e-01
-6.29214525e-01 3.11201215e-01 2.55237222e-01 1.14715360e-02
3.18424374e-01 -6.91811144e-02 -5.98046541e-01 3.56400996e-01
-3.33916783e-01 1.85743317e-01 4.22137082e-01 6.71671212e-01
-1.00696228e-01 -8.72516334e-01 -4.70054567e-01 7.46321797e-01
-6.60561144e-01 -1.18979044e-01 4.01059031e-01 4.24024612e-01
-1.89994052e-02 6.80057228e-01 -2.49603242e-01 -7.76545465e-01
1.54355960e-02 -2.44376644e-01 3.32800269e-01 -8.29481632e-02
-2.60320038e-01 5.88902738e-03 -1.72553167e-01 -3.10945898e-01
-5.54264188e-01 -5.05432069e-01 -1.01275504e+00 -6.99746311e-01
-4.79674369e-01 2.95660973e-01 8.09808791e-01 1.09680951e+00
-1.81287136e-02 5.26829481e-01 9.56866086e-01 -6.50557041e-01
-6.33778751e-01 -7.93213248e-01 -6.23641431e-01 8.96043777e-02
3.27682376e-01 -5.83445430e-01 -4.86824602e-01 -1.88294332e-02] | [8.204304695129395, 3.4071826934814453] |
7b58b649-6bfd-47ac-bbd6-2b5eca74d023 | zero-pixel-directional-boundary-by-vector-1 | 2203.08795 | null | https://arxiv.org/abs/2203.08795v2 | https://arxiv.org/pdf/2203.08795v2.pdf | Zero Pixel Directional Boundary by Vector Transform | Boundaries are among the primary visual cues used by human and computer vision systems. One of the key problems in boundary detection is the label representation, which typically leads to class imbalance and, as a consequence, to thick boundaries that require non-differential post-processing steps to be thinned. In this paper, we re-interpret boundaries as 1-D surfaces and formulate a one-to-one vector transform function that allows for training of boundary prediction completely avoiding the class imbalance issue. Specifically, we define the boundary representation at any point as the unit vector pointing to the closest boundary surface. Our problem formulation leads to the estimation of direction as well as richer contextual information of the boundary, and, if desired, the availability of zero-pixel thin boundaries also at training time. Our method uses no hyper-parameter in the training loss and a fixed stable hyper-parameter at inference. We provide theoretical justification/discussions of the vector transform representation. We evaluate the proposed loss method using a standard architecture and show the excellent performance over other losses and representations on several datasets. Code is available at https://github.com/edomel/BoundaryVT. | ['Luc van Gool', 'Ender Konukoglu', 'Yun Liu', 'Ajad Chhatkuli', 'Edoardo Mello Rella'] | 2022-03-16 | zero-pixel-directional-boundary-by-vector | https://openreview.net/forum?id=nxcABL7jbQh | https://openreview.net/pdf?id=nxcABL7jbQh | iclr-2022-4 | ['boundary-detection'] | ['computer-vision'] | [ 5.48468709e-01 3.09279829e-01 -8.57579336e-02 -4.99893785e-01
-7.65144825e-01 -3.38893920e-01 2.27935448e-01 4.77212191e-01
-3.61262172e-01 6.55566096e-01 -8.86811018e-02 -5.20920055e-03
4.52902950e-02 -7.17867076e-01 -8.38728428e-01 -7.93952703e-01
2.22639561e-01 4.29253399e-01 1.66170329e-01 9.40470770e-02
3.46074820e-01 4.62733239e-01 -1.47849894e+00 1.79748878e-01
9.08703446e-01 1.39626825e+00 -5.38537353e-02 4.77364123e-01
-1.33140042e-01 1.32946417e-01 -4.49652046e-01 -4.56105888e-01
4.85411614e-01 -2.14271575e-01 -5.89702487e-01 1.67697340e-01
9.62792695e-01 -1.49732515e-01 3.75656299e-02 1.12842441e+00
5.27423263e-01 5.32886237e-02 1.07751966e+00 -1.11719573e+00
-5.13790071e-01 1.17548317e-01 -9.70498264e-01 1.09636702e-01
8.59061442e-03 -1.83137655e-01 1.25184619e+00 -9.70918179e-01
6.92395091e-01 8.96931648e-01 8.64620864e-01 5.66889763e-01
-1.35387230e+00 -4.04938549e-01 8.75691846e-02 2.58765578e-01
-1.64751518e+00 -4.41797704e-01 1.05615532e+00 -7.27574825e-01
5.68296909e-01 4.21282440e-01 4.02747661e-01 7.74068832e-01
3.26672383e-03 6.88617110e-01 6.78494453e-01 -7.54171789e-01
1.56066671e-01 1.78891614e-01 1.85548246e-01 7.48425722e-01
3.29577625e-01 -3.26557457e-01 -2.28646949e-01 5.99678257e-04
7.17522144e-01 -2.79009253e-01 -5.96347272e-01 -6.02026641e-01
-6.27533197e-01 6.95812047e-01 4.81886387e-01 7.64133930e-02
-2.97407478e-01 1.59106880e-01 3.22854012e-01 9.35294293e-03
7.36986041e-01 1.60089210e-01 -3.48518267e-02 3.35972518e-01
-1.13407004e+00 1.99066684e-01 3.47262591e-01 5.79167426e-01
8.51951003e-01 -3.30015332e-01 -3.20672393e-01 1.12393713e+00
2.01563522e-01 2.08957180e-01 3.71756032e-02 -1.03081143e+00
4.96896595e-01 6.12253308e-01 2.02505402e-02 -1.02891803e+00
-2.64180839e-01 -5.02954960e-01 -9.79712486e-01 5.32738268e-01
7.05929101e-01 8.65561664e-02 -1.06862009e+00 1.74195468e+00
5.96871674e-01 1.93866178e-01 -3.56948256e-01 9.28376138e-01
5.00671744e-01 3.86807561e-01 -1.87251598e-01 5.17601380e-03
1.21558988e+00 -6.81268811e-01 -5.15139043e-01 -2.16501251e-01
4.66862351e-01 -6.41813874e-01 1.05927098e+00 4.92061377e-01
-1.02755141e+00 -2.74824619e-01 -1.10023117e+00 -3.76417905e-01
-4.50580493e-02 2.79182047e-01 3.00575495e-01 3.92034352e-01
-1.00924456e+00 6.30860925e-01 -7.07506776e-01 -1.58029407e-01
5.33293486e-01 2.96633482e-01 -2.89557427e-01 2.73607131e-02
-1.04929161e+00 5.92695057e-01 -7.07404539e-02 2.35949263e-01
-2.11768359e-01 -8.75173390e-01 -8.93573463e-01 -4.78962027e-02
1.28557324e-01 -5.30602396e-01 9.65116203e-01 -1.07817388e+00
-9.03158307e-01 1.24622440e+00 -1.87901691e-01 -4.21826005e-01
8.43213737e-01 -1.12905011e-01 1.84058070e-01 2.86677599e-01
5.02239689e-02 8.22371066e-01 9.39507127e-01 -1.30361438e+00
-4.32441920e-01 -4.51801330e-01 -1.98425740e-01 2.65606463e-01
-2.34932423e-01 -4.31290329e-01 -6.18133783e-01 -7.05146611e-01
2.42545396e-01 -6.69528782e-01 2.43524797e-02 6.85849965e-01
-6.12129331e-01 -2.25726455e-01 3.61511230e-01 -9.11008000e-01
1.32504606e+00 -2.20956683e+00 3.25154997e-02 4.23710823e-01
3.50776136e-01 9.12926421e-02 2.49517947e-01 7.21714227e-03
-8.77231807e-02 1.73553243e-01 -6.48495793e-01 -5.15024602e-01
-3.70236821e-02 -5.66653423e-02 -5.11363931e-02 7.85417855e-01
2.58576661e-01 4.35055465e-01 -3.44361097e-01 -7.24555969e-01
2.18687192e-01 7.03600943e-01 -6.50863767e-01 -7.04680979e-02
-1.54112205e-01 2.32696429e-01 -3.18514764e-01 4.97208625e-01
8.84334981e-01 -1.90732539e-01 -5.13401777e-02 -4.37837720e-01
-9.28772390e-02 1.95963174e-01 -1.25262320e+00 1.49607694e+00
-3.04129243e-01 7.01075375e-01 3.03113341e-01 -1.17075372e+00
1.04575706e+00 1.32071674e-01 4.16080117e-01 -7.63559043e-01
9.27115455e-02 1.89287633e-01 -2.64531493e-01 -2.70876557e-01
2.37468034e-01 -1.72782242e-01 1.43609643e-01 6.31247684e-02
-3.93046319e-01 6.26044124e-02 8.61248374e-02 -1.39800504e-01
8.18855882e-01 6.85955882e-02 4.56756838e-02 -1.56148151e-01
5.57111800e-01 -2.51947522e-01 9.55337286e-01 3.82121027e-01
-2.22756863e-01 1.14499652e+00 7.32202709e-01 -1.94811910e-01
-1.21802557e+00 -9.37224209e-01 -5.91150761e-01 5.19953430e-01
2.11775616e-01 -2.33634724e-03 -8.91823292e-01 -4.16946799e-01
2.62674391e-01 6.17659211e-01 -8.48409057e-01 -2.69898381e-02
-6.49316907e-01 -4.83529478e-01 4.92489755e-01 4.62116212e-01
3.12558889e-01 -7.09011734e-01 -5.28841376e-01 -3.71965878e-02
-1.40163690e-01 -6.82320356e-01 -7.08207488e-01 1.54342547e-01
-8.24758291e-01 -9.92610335e-01 -9.85760570e-01 -8.94461334e-01
9.98552382e-01 -1.35594919e-01 7.90058911e-01 1.74785390e-01
-6.76833332e-01 3.70609052e-02 2.44219396e-02 -6.57836422e-02
-7.42461607e-02 -1.31246582e-01 -2.28091255e-01 1.58928141e-01
8.12343881e-02 -5.23722708e-01 -8.73727739e-01 2.27109700e-01
-6.15198314e-01 1.46191493e-01 2.53203928e-01 8.55361044e-01
9.64053154e-01 -1.70113072e-01 4.34797496e-01 -8.73738468e-01
2.62692332e-01 -1.33610234e-01 -7.75457621e-01 3.09350312e-01
-6.41039193e-01 1.40623063e-01 4.21167552e-01 -1.13444321e-01
-9.61073339e-01 -9.39249806e-03 -1.99753597e-01 -4.64524508e-01
-1.94420516e-02 2.78811026e-02 -3.13632667e-01 -6.44592717e-02
6.00761116e-01 -1.48589775e-01 -7.64165446e-02 -6.51373208e-01
2.48948455e-01 6.21416032e-01 3.69489729e-01 -5.25187373e-01
4.95885879e-01 6.17558360e-01 8.26527476e-02 -1.02169335e+00
-8.46403360e-01 -5.60258269e-01 -4.73193437e-01 -2.77723908e-01
7.60713875e-01 -5.27202010e-01 -5.99958539e-01 6.28756940e-01
-1.14111960e+00 -4.83306974e-01 -3.29531670e-01 1.87322021e-01
-5.01323879e-01 4.46660042e-01 -5.95831037e-01 -7.10671306e-01
-6.39406681e-01 -7.71292925e-01 9.26800609e-01 2.86678404e-01
-2.06611961e-01 -9.73757207e-01 1.08746953e-01 4.53330874e-01
1.63163930e-01 3.70360166e-01 1.15629387e+00 -2.08219662e-01
-4.25140709e-01 -1.39673511e-02 -4.33727562e-01 6.62026942e-01
-5.29587530e-02 1.29924536e-01 -1.16132426e+00 -7.16952458e-02
-1.19798139e-01 -2.46468872e-01 1.16769469e+00 7.78911769e-01
1.23365629e+00 -5.21859564e-02 -3.89620662e-01 9.05301690e-01
1.57390845e+00 2.00012121e-02 5.33592105e-01 2.48646259e-01
7.57403672e-01 7.16011167e-01 4.96928424e-01 4.78908181e-01
2.49354675e-01 7.61190534e-01 4.25934970e-01 -2.88090140e-01
-4.05881494e-01 -5.79725578e-02 -1.76625535e-01 3.25584292e-01
2.00608864e-01 -3.33853751e-01 -9.98963356e-01 6.14868939e-01
-1.52643371e+00 -7.50706971e-01 -2.85440594e-01 2.52648401e+00
9.71328497e-01 3.85023683e-01 -2.21821874e-01 3.46437126e-01
1.03085911e+00 8.18002746e-02 -7.26480484e-01 -4.03890640e-01
2.51655858e-02 2.15541050e-01 6.88924313e-01 9.24667120e-01
-1.18596184e+00 6.44354463e-01 5.23683214e+00 7.42351830e-01
-1.27339578e+00 -1.41538933e-01 9.13415253e-01 -2.35646237e-02
-4.23382044e-01 -2.56772786e-01 -9.20049787e-01 3.40068221e-01
1.53418094e-01 -5.51975928e-02 1.77298352e-01 5.15636742e-01
1.41208917e-01 -2.46949092e-01 -1.16620886e+00 9.69799638e-01
3.58046591e-02 -1.21591997e+00 -8.50319788e-02 -5.51232696e-02
4.64012980e-01 -2.05851078e-01 8.02453309e-02 -2.00750947e-01
-4.20084864e-01 -9.34212327e-01 8.45368743e-01 5.41023731e-01
1.14000893e+00 -5.27626038e-01 4.58543867e-01 4.03716192e-02
-1.06507266e+00 1.29011914e-01 -4.47250783e-01 2.70648450e-02
2.39816736e-02 1.01381242e+00 -8.79450619e-01 2.43839800e-01
5.17093003e-01 4.69483674e-01 -3.31363112e-01 1.40268052e+00
-2.17840463e-01 5.52924395e-01 -6.60890043e-01 4.00265038e-01
-7.47139826e-02 -3.20797712e-01 5.04291773e-01 1.06014562e+00
2.82588392e-01 -1.82717353e-01 -5.05142137e-02 8.47080052e-01
-1.74330592e-01 1.75175399e-01 -4.19093937e-01 4.66624379e-01
6.53071284e-01 1.02840662e+00 -7.14184344e-01 -1.13996789e-02
-2.76582062e-01 9.75636959e-01 5.77868462e-01 3.40046674e-01
-7.38677442e-01 -6.53587043e-01 8.05906117e-01 4.84857231e-01
3.88372123e-01 5.87961711e-02 -7.11372674e-01 -7.31638849e-01
5.02012134e-01 -3.37348014e-01 3.56107086e-01 -5.33834636e-01
-1.24608541e+00 4.02593166e-01 -1.50124803e-01 -9.68802989e-01
-5.43331578e-02 -5.26682138e-01 -6.89157367e-01 9.79197323e-01
-1.70140207e+00 -9.41746593e-01 -2.79949278e-01 2.64122546e-01
3.35374504e-01 5.44712722e-01 4.51033205e-01 5.65347552e-01
-7.19878078e-01 9.55398679e-01 1.21542893e-01 2.63541996e-01
7.51758158e-01 -1.26419663e+00 3.02896470e-01 6.95202529e-01
-2.31231660e-01 2.33617410e-01 7.58987427e-01 -5.40424407e-01
-6.90255702e-01 -9.87927973e-01 9.63410556e-01 -2.47613657e-02
2.68474668e-01 -4.21956599e-01 -1.21058190e+00 4.74606693e-01
-1.07353278e-01 2.62899827e-02 3.96360338e-01 -1.73376083e-01
-1.98766068e-01 -2.39168838e-01 -1.21861839e+00 3.59407991e-01
8.56476605e-01 -2.66267121e-01 -4.40597892e-01 1.92856509e-02
3.73454899e-01 -3.00660223e-01 -6.02275193e-01 4.01051581e-01
4.74480271e-01 -9.75679815e-01 9.26356375e-01 -6.67647645e-02
2.69952297e-01 -2.50342786e-01 -8.12860802e-02 -9.79287028e-01
-1.79189742e-01 -2.95160592e-01 2.79219657e-01 1.43317306e+00
5.98942757e-01 -5.31579375e-01 1.02828312e+00 6.64897561e-01
-1.42095670e-01 -1.13477814e+00 -1.12844872e+00 -4.34013098e-01
1.79952279e-01 -3.90595794e-01 7.45142922e-02 7.56373763e-01
-3.36417913e-01 1.06256351e-01 -6.95697069e-02 1.35051772e-01
8.55350971e-01 1.82953075e-01 3.51408958e-01 -1.45168245e+00
-6.42915964e-02 -5.37569880e-01 -4.62394446e-01 -1.12764382e+00
4.01647575e-02 -9.68666375e-01 1.60876349e-01 -1.75859475e+00
-6.06655627e-02 -8.87443721e-01 -1.64384723e-01 5.01177192e-01
2.49336846e-02 3.94127846e-01 -1.39395565e-01 7.69180879e-02
-1.04042865e-01 5.57419717e-01 1.31300831e+00 -2.31105283e-01
-1.46081001e-01 8.02769419e-03 -5.43770671e-01 8.19841027e-01
9.59287465e-01 -4.30516571e-01 -3.87660563e-01 -4.10724878e-01
3.57537031e-01 -1.35110036e-01 4.42003906e-01 -8.20736229e-01
4.04682010e-02 6.36916375e-03 3.96309972e-01 -4.06939566e-01
5.41095674e-01 -7.03069925e-01 -1.10580094e-01 1.97091401e-01
-3.42920572e-01 -4.33508247e-01 1.32526547e-01 3.62759650e-01
-2.86209714e-02 -5.09018302e-01 1.02675664e+00 1.99007973e-01
-3.90658498e-01 1.52295128e-01 9.02196914e-02 4.15189505e-01
1.03142524e+00 -5.61360180e-01 -2.49155417e-01 -1.30389079e-01
-6.94136500e-01 3.25382769e-01 8.37914407e-01 6.77390695e-02
6.18897974e-01 -1.15695071e+00 -6.66524887e-01 3.23260248e-01
-7.66074359e-02 4.31784749e-01 3.19708198e-01 8.67705643e-01
-7.76681602e-01 1.71595857e-01 -1.35417745e-01 -6.29314303e-01
-1.26758492e+00 2.13535577e-01 5.80761433e-01 -9.29441079e-02
-1.03665626e+00 1.20311487e+00 5.45831025e-01 -5.10667749e-02
5.29673934e-01 -4.43220526e-01 -1.23365074e-01 3.13117713e-01
3.60824674e-01 3.61493319e-01 3.32716793e-01 -4.61026043e-01
-4.60780472e-01 1.00039172e+00 -1.49020612e-01 -4.16485630e-02
1.09644389e+00 -1.44907668e-01 8.35013837e-02 4.88582253e-01
1.21578121e+00 -8.95834789e-02 -1.59141660e+00 -1.11783780e-01
-7.53741935e-02 -3.41690540e-01 2.68120080e-01 -6.50631249e-01
-1.14650857e+00 1.14510787e+00 7.71010280e-01 -2.61340849e-03
1.05928075e+00 -4.60676476e-02 7.88583934e-01 -9.77809131e-02
2.54575498e-02 -1.20361435e+00 -2.62914211e-01 1.23979799e-01
9.39999640e-01 -1.01899970e+00 -3.80982980e-02 -6.64052546e-01
-4.59857106e-01 8.24487567e-01 5.70609331e-01 -1.60947114e-01
6.24612033e-01 2.94876367e-01 3.26827735e-01 -4.44771722e-02
-5.86010337e-01 -5.69357984e-02 3.76250088e-01 5.14622331e-01
5.33487320e-01 7.21567199e-02 -5.18318474e-01 2.75273383e-01
9.01551098e-02 -1.33705184e-01 3.30840051e-01 6.02938950e-01
-4.44348007e-01 -9.96320665e-01 -4.73952860e-01 5.80382884e-01
-5.55879176e-01 -4.71231341e-02 -2.46001884e-01 5.98622143e-01
2.15242147e-01 5.26478827e-01 5.50444305e-01 7.34496536e-03
3.20567280e-01 1.73027262e-01 5.64197958e-01 -4.19587761e-01
-2.45619655e-01 -3.91411781e-02 2.20526494e-02 -4.58529681e-01
8.02799091e-02 -6.08865440e-01 -1.54444611e+00 1.01131108e-02
-4.34473097e-01 9.36369374e-02 6.95450723e-01 5.23281872e-01
2.65403837e-01 3.92272502e-01 5.61996400e-01 -8.88182342e-01
-5.62718630e-01 -7.75519192e-01 -6.15382195e-01 6.18001640e-01
4.53880101e-01 -9.04828846e-01 -5.80199778e-01 6.06751107e-02] | [9.57798957824707, 0.4595753252506256] |
372100ef-2f49-4394-8b7e-4ac63433c920 | what-happened-3-seconds-ago-inferring-the | 2304.13651 | null | https://arxiv.org/abs/2304.13651v1 | https://arxiv.org/pdf/2304.13651v1.pdf | What Happened 3 Seconds Ago? Inferring the Past with Thermal Imaging | Inferring past human motion from RGB images is challenging due to the inherent uncertainty of the prediction problem. Thermal images, on the other hand, encode traces of past human-object interactions left in the environment via thermal radiation measurement. Based on this observation, we collect the first RGB-Thermal dataset for human motion analysis, dubbed Thermal-IM. Then we develop a three-stage neural network model for accurate past human pose estimation. Comprehensive experiments show that thermal cues significantly reduce the ambiguities of this task, and the proposed model achieves remarkable performance. The dataset is available at https://github.com/ZitianTang/Thermal-IM. | ['Hang Zhao', 'Wei-Chiu Ma', 'Wenjie Ye', 'Zitian Tang'] | 2023-04-26 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Tang_What_Happened_3_Seconds_Ago_Inferring_the_Past_With_Thermal_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Tang_What_Happened_3_Seconds_Ago_Inferring_the_Past_With_Thermal_CVPR_2023_paper.pdf | cvpr-2023-1 | ['human-object-interaction-detection'] | ['computer-vision'] | [ 1.14018451e-02 -3.02676648e-01 -3.95595208e-02 -4.38436151e-01
-3.78502876e-01 -1.44102052e-01 3.46049875e-01 -8.08373928e-01
-6.57501400e-01 4.70845640e-01 3.14877808e-01 4.44662124e-02
3.12353104e-01 -3.96792978e-01 -6.70832932e-01 -7.32962489e-01
1.92492753e-01 1.07787661e-01 -7.53746331e-02 2.51585562e-02
-1.18608221e-01 1.99518576e-01 -1.28804910e+00 1.32796213e-01
3.09805244e-01 1.04317284e+00 2.40115806e-01 9.07773972e-01
7.51936138e-01 8.71852815e-01 -3.21154535e-01 -8.85833800e-02
2.55504161e-01 -3.43825579e-01 -7.20015109e-01 -8.25853795e-02
5.90852126e-02 -9.26800728e-01 -1.22600245e+00 7.37930238e-01
4.96412367e-01 4.86529619e-01 2.80436546e-01 -1.36993742e+00
-5.22003353e-01 1.69387102e-01 -3.78816187e-01 1.55234858e-01
6.90390110e-01 4.36624855e-01 6.86225533e-01 -8.38078976e-01
4.87078875e-01 1.04717398e+00 4.94814813e-01 7.34781921e-01
-5.08031309e-01 -2.99980730e-01 5.36613651e-02 6.36607707e-01
-1.40923774e+00 -4.48767394e-01 1.10271072e+00 -3.65451157e-01
7.35787213e-01 4.08468038e-01 9.99810278e-01 1.70178020e+00
2.39062414e-01 1.10346830e+00 9.05877650e-01 -1.72977909e-01
7.63626397e-02 -3.07911217e-01 5.10829464e-02 7.54088700e-01
2.28939414e-01 3.75102133e-01 -7.69102573e-01 5.22963926e-02
8.27217162e-01 2.37649277e-01 -5.41398227e-01 -1.37519479e-01
-1.34789789e+00 1.74977347e-01 7.18588531e-01 -8.40583295e-02
-3.59786868e-01 6.54926658e-01 2.05735996e-01 -3.99471223e-01
2.80503422e-01 -1.16164600e-02 -1.72330603e-01 -4.53824341e-01
-6.33962154e-01 1.59472361e-01 3.18901390e-01 8.99139404e-01
5.57056963e-01 -1.89715624e-02 2.62789071e-01 2.44460374e-01
3.78714740e-01 9.04340565e-01 2.88719982e-01 -1.08474028e+00
4.76617277e-01 2.77071595e-01 4.22199070e-01 -1.15153074e+00
-6.07855618e-01 -6.83598071e-02 -9.27220404e-01 -2.94074386e-01
4.01488930e-01 -2.29184076e-01 -9.68670726e-01 1.63191354e+00
4.90860790e-01 2.40954891e-01 -2.39160344e-01 1.51488423e+00
6.65469468e-01 7.30004847e-01 -8.42585415e-02 -1.02275878e-01
1.37028706e+00 -8.30700874e-01 -9.16401625e-01 -6.62061095e-01
4.48828161e-01 -3.68476361e-01 8.20200086e-01 3.38807464e-01
-5.70431650e-01 -6.38227642e-01 -1.13388717e+00 -2.89769858e-01
-1.12536162e-01 4.43714648e-01 8.59300375e-01 4.54944760e-01
-7.57808268e-01 4.02487159e-01 -1.44821763e+00 -3.34520191e-01
2.56638229e-01 1.12296835e-01 -2.41539970e-01 -1.46724895e-01
-1.33683228e+00 7.28094637e-01 3.41680586e-01 1.09287500e+00
-7.58230031e-01 -5.23494631e-02 -9.71596539e-01 -5.46978712e-01
6.57071471e-01 -9.31765795e-01 1.39721966e+00 -4.05570030e-01
-1.40024984e+00 6.33574665e-01 -4.32303995e-01 -1.32877260e-01
7.63433456e-01 -8.66738617e-01 -4.00884926e-01 2.67368793e-01
-1.41778320e-01 5.28371096e-01 7.13511586e-01 -1.10606432e+00
-2.82254457e-01 -5.37767053e-01 -7.48353302e-02 2.94281304e-01
-1.29451424e-01 -2.82840759e-01 -9.92317557e-01 -5.42540014e-01
3.71733487e-01 -1.49682987e+00 -4.27310318e-01 -1.32868528e-01
-5.08102536e-01 7.91905448e-02 6.32595360e-01 -8.27795565e-01
1.27828133e+00 -1.77242410e+00 2.27117702e-01 3.37456949e-02
1.48115277e-01 6.66488409e-02 3.69815946e-01 2.48912349e-01
4.57420349e-02 -2.03047872e-01 -4.60448563e-02 -4.77619797e-01
5.20398887e-03 3.46876651e-01 -3.57863456e-01 7.67845631e-01
-8.99876729e-02 1.28463733e+00 -6.99555576e-01 -4.82498497e-01
4.48129207e-01 4.64927495e-01 -1.29158333e-01 3.83804440e-01
-1.13550693e-01 8.02416086e-01 -6.83339417e-01 7.39814281e-01
5.96032262e-01 -3.09648037e-01 1.20763719e-01 -5.12461305e-01
1.14356622e-01 1.98307186e-02 -8.76934111e-01 1.96374834e+00
3.70818935e-02 6.78155124e-01 -4.53103125e-01 -5.98579645e-01
4.10708457e-01 3.02296519e-01 7.51436055e-01 -8.24095309e-01
4.37667042e-01 -7.51580819e-02 -4.34347063e-01 -8.27858388e-01
9.56604660e-01 6.16762899e-02 -4.14151937e-01 5.13866901e-01
-3.20149034e-01 3.82475227e-01 -3.38135481e-01 1.75874531e-01
9.03462529e-01 6.49286509e-01 1.83015317e-01 4.14681852e-01
2.62047201e-01 7.30561242e-02 9.54338253e-01 6.65922165e-01
-8.56130660e-01 7.96769798e-01 5.98425455e-02 -7.30777025e-01
-1.01695943e+00 -1.16208708e+00 3.99464220e-01 9.04815257e-01
5.63924551e-01 -4.09951925e-01 -7.35332072e-01 -3.61710489e-01
-2.53856391e-01 3.97541136e-01 -7.18676269e-01 -3.39929968e-01
-1.16063941e+00 -7.15833127e-01 4.55007493e-01 8.91711056e-01
6.92655861e-01 -9.14593577e-01 -1.23253322e+00 -1.27354026e-01
-9.31722164e-01 -1.42758012e+00 -5.11873007e-01 -2.33563349e-01
-6.79374754e-01 -1.07750785e+00 -7.65395999e-01 -1.43012553e-01
5.42720854e-01 4.07384366e-01 7.75404572e-01 1.93733312e-02
-6.58665776e-01 8.16388786e-01 -3.00819993e-01 -1.11860752e-01
2.88425207e-01 -3.46914947e-01 4.60570842e-01 -1.00501299e-01
4.06822264e-01 -1.49407670e-01 -1.02602530e+00 3.56703967e-01
-4.79005158e-01 2.48779699e-01 4.29211169e-01 6.69052601e-01
4.12946969e-01 9.22700297e-03 -2.77667999e-01 -2.24297345e-01
-3.53157036e-02 -1.92929596e-01 -3.47473979e-01 2.28850320e-01
-4.57800925e-02 -6.72032684e-02 1.10206701e-01 -4.10302639e-01
-1.50402009e+00 3.97656232e-01 4.72587347e-02 -5.60442090e-01
-2.32354194e-01 3.64676803e-01 -2.94921637e-01 2.48118669e-01
4.26280081e-01 1.87635571e-01 -2.91976690e-01 -3.41723889e-01
2.97441334e-01 3.76488715e-01 1.05155492e+00 -5.02543807e-01
7.29172587e-01 8.83148909e-01 6.03944808e-02 -9.07807350e-01
-9.32926834e-01 -5.38158655e-01 -1.02729321e+00 -8.58439863e-01
1.09481514e+00 -1.15768480e+00 -1.01819932e+00 9.50546443e-01
-1.13693488e+00 -4.32456344e-01 3.78742069e-01 6.90075874e-01
-6.65530026e-01 6.36083186e-01 -8.43342960e-01 -1.22973335e+00
-1.47551149e-01 -8.78989279e-01 1.01989222e+00 2.73847997e-01
-2.78106362e-01 -7.16525018e-01 1.85780544e-02 7.37870872e-01
-1.83829665e-01 5.20945191e-01 1.87679902e-02 2.79152781e-01
-9.80718374e-01 -1.93419874e-01 8.53353813e-02 -1.09316953e-01
-1.08542226e-01 -2.07541913e-01 -1.05194211e+00 -3.11745942e-01
2.34506696e-01 -3.49165499e-01 9.97821867e-01 5.35701334e-01
8.59056950e-01 -9.30834115e-02 -4.19442415e-01 7.35504031e-01
1.15409720e+00 6.94550723e-02 6.24016047e-01 5.32332778e-01
1.27060890e+00 4.42458123e-01 1.06415296e+00 7.08785892e-01
5.42367280e-01 8.19330752e-01 3.39904308e-01 1.92662090e-01
9.59144458e-02 -4.14096057e-01 4.43319619e-01 8.22770298e-01
-3.28821957e-01 -3.22449923e-01 -1.29666555e+00 2.09710240e-01
-2.20497775e+00 -7.93388367e-01 -1.76187068e-01 1.77694368e+00
3.46006870e-01 2.07249541e-02 5.07292561e-02 -8.15511122e-02
5.34211457e-01 4.35258031e-01 -7.37671673e-01 2.39237204e-01
1.66340191e-02 -5.18163085e-01 4.80543166e-01 2.73602724e-01
-1.36205935e+00 1.04625583e+00 6.07397938e+00 3.85869503e-01
-9.61677670e-01 -3.90654197e-03 3.95985663e-01 -4.24352556e-01
2.19022945e-01 -6.91218153e-02 -5.97920001e-01 4.09889907e-01
7.49977350e-01 1.10160716e-01 4.32668030e-01 6.68583512e-01
3.78386080e-01 -5.59460580e-01 -1.06725669e+00 1.22192287e+00
1.61689118e-01 -6.40615344e-01 -2.92012453e-01 9.17756110e-02
2.90532917e-01 -4.80411388e-03 3.02921951e-01 1.42975636e-02
-1.51832402e-02 -8.46179485e-01 9.61507797e-01 1.06841648e+00
6.67739391e-01 -7.52663910e-01 6.47092879e-01 5.11261523e-01
-1.36406338e+00 -1.36107430e-01 -3.09495986e-01 -2.95261830e-01
5.85383713e-01 4.32202309e-01 -5.71034670e-01 6.92520678e-01
1.04602921e+00 7.92807221e-01 -7.09536552e-01 5.87809563e-01
-4.76423681e-01 5.33012867e-01 -3.11124682e-01 1.55971408e-01
-3.23771723e-02 -2.46060044e-01 4.39585030e-01 8.08418870e-01
3.28298807e-01 5.87894022e-01 1.31861210e-01 8.53960395e-01
3.62360299e-01 -7.86222458e-01 -5.16326666e-01 -1.70443337e-02
2.62342066e-01 1.08801663e+00 -5.03444850e-01 -1.78514063e-01
-6.09070994e-02 1.36522615e+00 2.34722137e-01 5.79672039e-01
-1.21044064e+00 1.17262028e-01 6.40868008e-01 -4.37253058e-01
1.27981175e-02 -8.84052634e-01 -2.28997260e-01 -1.44640136e+00
4.09102559e-01 -4.35666710e-01 3.80869985e-01 -1.23632205e+00
-7.54825234e-01 3.33748132e-01 7.58134052e-02 -1.41659999e+00
-4.48600322e-01 -8.73915017e-01 -1.86937824e-01 5.64717531e-01
-7.17367947e-01 -1.38914037e+00 -6.51037157e-01 4.93792117e-01
7.59693533e-02 3.51954103e-01 4.12208736e-01 7.05852583e-02
-1.00827909e+00 3.46089542e-01 -2.07873002e-01 4.28430885e-01
6.35001361e-01 -9.51700568e-01 6.62832022e-01 1.29706836e+00
7.19609018e-03 7.43389070e-01 7.35803545e-01 -9.23598111e-01
-1.90553534e+00 -8.79060447e-01 4.76178586e-01 -8.74422669e-01
4.81306970e-01 -5.07693946e-01 -6.71143949e-01 9.57217753e-01
-1.76259115e-01 -4.24521379e-02 3.65637779e-01 -1.41122460e-01
-1.22697972e-01 9.77313221e-02 -5.27993143e-01 8.18487585e-01
1.35597682e+00 -8.10350776e-01 -4.49559748e-01 2.74062436e-02
8.44409287e-01 -6.79157972e-01 -8.23390961e-01 4.14191574e-01
9.07791913e-01 -1.03072274e+00 1.21170223e+00 -2.76097417e-01
2.28152722e-01 -3.55007052e-01 -1.67303696e-01 -9.39650774e-01
-2.76711375e-01 -4.11855340e-01 -3.70524973e-01 7.08803535e-01
-1.17766321e-01 -3.14296693e-01 1.14647758e+00 1.02746129e+00
1.78897589e-01 -5.59122086e-01 -9.10959601e-01 -6.49003088e-01
-4.12825584e-01 -7.68129170e-01 3.25099468e-01 6.64238214e-01
-1.07344529e-02 3.84684429e-02 -1.19620490e+00 4.41687644e-01
9.93375301e-01 -2.28908733e-02 8.07069361e-01 -6.37061059e-01
-3.41792196e-01 1.13961458e-01 -2.99657881e-01 -1.43116283e+00
-1.26430914e-02 -1.64563924e-01 4.69848901e-01 -1.28488791e+00
2.45476246e-01 4.87414375e-02 -1.85069323e-01 2.92971373e-01
-4.47787195e-01 2.48148188e-01 1.89936906e-01 5.17522156e-01
-8.57741058e-01 9.31774020e-01 1.19248688e+00 1.04970694e-01
1.30562291e-01 -6.22802414e-02 -9.29025561e-02 9.13934052e-01
8.92713130e-01 -1.72956884e-01 -2.07803935e-01 -6.69297755e-01
3.28598648e-01 3.82501572e-01 8.83686423e-01 -1.27920616e+00
5.53808749e-01 -3.73775423e-01 8.63996625e-01 -1.09463310e+00
8.74297857e-01 -7.90485442e-01 2.83151567e-01 6.41386449e-01
-1.05539478e-01 5.64535707e-02 -3.34477872e-02 9.17323887e-01
8.77386406e-02 3.12332392e-01 2.84991413e-01 -2.62985766e-01
-1.25905871e+00 4.84075069e-01 -3.19427818e-01 -1.91222727e-01
7.22445726e-01 -2.85546839e-01 -3.91474187e-01 -5.14512002e-01
-6.93126559e-01 2.17209399e-01 4.12902892e-01 5.25853693e-01
8.70496273e-01 -1.59232116e+00 -1.81642219e-01 1.88573003e-02
2.00754255e-01 1.57574937e-03 7.21796513e-01 9.53801751e-01
-5.43815613e-01 6.10689998e-01 -1.56825274e-01 -8.71754766e-01
-1.27232707e+00 6.19405031e-01 2.99762487e-01 1.21434510e-01
-6.73842907e-01 8.18209231e-01 1.03565000e-01 -2.42660463e-01
9.81744677e-02 -2.14306355e-01 1.59217238e-01 -3.31572056e-01
4.78956372e-01 6.35383606e-01 -3.52152646e-01 -1.04678297e+00
-5.97616017e-01 5.56454182e-01 2.39887863e-01 -5.00421882e-01
1.03715336e+00 -6.85867667e-01 3.24025638e-02 7.41069734e-01
1.27181661e+00 -5.59963882e-01 -1.58215749e+00 -4.16137308e-01
-1.42912883e-02 -7.17546880e-01 -1.37924105e-01 -5.21926880e-01
-7.86373973e-01 9.17686701e-01 6.04419410e-01 -5.71556449e-01
1.21967924e+00 -1.62130579e-01 1.04933846e+00 7.64502883e-01
7.39022315e-01 -1.33156621e+00 4.74621147e-01 5.26310802e-01
7.24961698e-01 -1.33178782e+00 3.26081961e-01 -2.91074038e-01
-8.05348635e-01 9.08231258e-01 8.66077304e-01 2.21549526e-01
4.10271555e-01 5.84668107e-02 2.25046188e-01 -1.45488558e-02
-7.06666291e-01 -1.67657882e-01 3.83427620e-01 4.40096498e-01
6.66215420e-02 2.65400887e-01 3.46015126e-01 5.58254182e-01
-4.14813757e-01 1.00891344e-01 3.42333049e-01 1.18993700e+00
-3.35409135e-01 -4.87005204e-01 -7.03679204e-01 -2.04917401e-01
-6.44394197e-03 3.30029249e-01 -3.99149358e-01 6.70040786e-01
-7.41273761e-02 9.74456787e-01 -6.30276203e-02 -9.57073629e-01
1.66319475e-01 -1.06510103e-01 5.65144122e-01 7.22529516e-02
1.09814078e-01 2.56832421e-01 1.74411774e-01 -1.14361835e+00
-4.40258980e-01 -7.04310536e-01 -1.12266362e+00 -3.79792005e-01
-7.46057406e-02 -2.83786952e-01 4.02299672e-01 9.82176960e-01
1.43124133e-01 4.01454985e-01 5.22622585e-01 -1.13271570e+00
-3.43099564e-01 -8.14217091e-01 -5.02257645e-01 4.99822080e-01
4.26110178e-01 -6.64393663e-01 -1.91043600e-01 2.63943851e-01] | [7.278650760650635, -0.5248172283172607] |
b48a44bd-ef2b-4eb6-af09-d22ab4c201e4 | probabilistic-3d-multi-object-tracking-for | 2001.05673 | null | https://arxiv.org/abs/2001.05673v1 | https://arxiv.org/pdf/2001.05673v1.pdf | Probabilistic 3D Multi-Object Tracking for Autonomous Driving | 3D multi-object tracking is a key module in autonomous driving applications that provides a reliable dynamic representation of the world to the planning module. In this paper, we present our on-line tracking method, which made the first place in the NuScenes Tracking Challenge, held at the AI Driving Olympics Workshop at NeurIPS 2019. Our method estimates the object states by adopting a Kalman Filter. We initialize the state covariance as well as the process and observation noise covariance with statistics from the training set. We also use the stochastic information from the Kalman Filter in the data association step by measuring the Mahalanobis distance between the predicted object states and current object detections. Our experimental results on the NuScenes validation and test set show that our method outperforms the AB3DMOT baseline method by a large margin in the Average Multi-Object Tracking Accuracy (AMOTA) metric. | ['Hsu-kuang Chiu', 'Jie Li', 'Jeannette Bohg', 'Antonio Prioletti'] | 2020-01-16 | null | null | null | null | ['3d-multi-object-tracking'] | ['computer-vision'] | [-4.16641951e-01 -3.98349196e-01 -7.52109587e-02 -1.27189040e-01
-3.97662640e-01 -7.22176909e-01 8.74913335e-01 -1.24713235e-01
-7.55698383e-01 6.75782144e-01 -3.27393085e-01 -1.52835101e-01
-2.35587135e-01 -4.69128430e-01 -7.17682898e-01 -6.42605364e-01
-2.16058731e-01 9.33803260e-01 1.00891125e+00 -2.41643161e-01
2.05565333e-01 8.88999760e-01 -1.79360962e+00 -4.65845525e-01
4.51776057e-01 1.01321840e+00 2.80975133e-01 1.01815283e+00
1.99031055e-01 4.71784830e-01 -3.90075594e-01 3.51803564e-02
6.10815346e-01 3.30413058e-02 -1.11499198e-01 -7.03179613e-02
6.42561674e-01 -1.87475681e-01 -4.33124930e-01 1.17365491e+00
4.14505124e-01 4.90267545e-01 3.69303703e-01 -1.63322842e+00
5.62899113e-01 -7.96373859e-02 -3.02058369e-01 6.24026477e-01
1.03651755e-01 5.20741403e-01 3.20144743e-01 -6.24620438e-01
7.10450172e-01 1.27202511e+00 7.25342214e-01 3.97793204e-01
-9.56081450e-01 -1.06860518e+00 2.47725025e-01 3.36096168e-01
-1.63021290e+00 -4.73333627e-01 2.20601335e-01 -8.33694816e-01
8.11507463e-01 1.93404853e-01 7.66038775e-01 6.49667203e-01
8.28945041e-01 5.80738544e-01 1.02560067e+00 -8.01076367e-02
3.49030346e-01 1.22744530e-01 3.36653173e-01 8.13106000e-01
5.37833393e-01 8.94611299e-01 -5.50567091e-01 -8.48133340e-02
3.72681350e-01 -3.25747579e-01 3.78358811e-01 -7.48254776e-01
-1.16207588e+00 6.34326875e-01 1.08029656e-01 -1.06670700e-01
-3.20594758e-01 5.10671198e-01 1.93666711e-01 1.90292224e-01
3.14648569e-01 -3.45006883e-02 -3.10439110e-01 -1.20046720e-01
-7.08552361e-01 7.59385347e-01 6.44000351e-01 1.24694264e+00
6.63483858e-01 1.77941233e-01 -1.75799966e-01 2.06985231e-02
7.98226416e-01 1.10898721e+00 1.67948112e-01 -1.04180348e+00
3.26387435e-01 3.64776611e-01 7.16144323e-01 -5.58141887e-01
-6.83824778e-01 -3.00970525e-01 -1.32649735e-01 9.08561110e-01
4.59578305e-01 -4.70748723e-01 -9.68430758e-01 1.36304975e+00
8.12216520e-01 5.52212775e-01 6.15246855e-02 7.97508657e-01
3.52851570e-01 3.19536835e-01 1.39105842e-02 -1.64129883e-01
1.15848696e+00 -7.45922327e-01 -9.28171575e-01 -5.12412310e-01
3.42693090e-01 -8.29405725e-01 -3.50272767e-02 1.76202983e-01
-7.15573430e-01 -1.02224267e+00 -1.18909562e+00 5.63796759e-01
-4.32691127e-01 2.88389027e-02 2.98058152e-01 8.83784115e-01
-8.25032294e-01 4.88973588e-01 -1.46728802e+00 -4.59856272e-01
-2.66545336e-03 4.71965134e-01 -1.65107250e-01 3.43458682e-01
-8.99490356e-01 1.76055253e+00 7.03608990e-01 3.13158967e-02
-1.10707843e+00 -5.27195871e-01 -7.77787268e-01 -5.79421580e-01
4.82517362e-01 -3.61393452e-01 1.22340429e+00 -2.69097593e-02
-1.53807414e+00 4.25526232e-01 -2.71101356e-01 -8.54609728e-01
7.25892067e-01 -6.34915113e-01 -6.38959765e-01 -4.68401790e-01
-4.38272301e-03 7.22419322e-01 5.66197753e-01 -1.11810589e+00
-1.29052019e+00 -4.86097962e-01 -3.35578799e-01 3.24339271e-01
6.22510910e-01 -2.28303090e-01 -6.42416775e-01 4.15865472e-03
2.56041199e-01 -1.52109742e+00 -4.43193763e-01 1.14038572e-01
-7.32600689e-02 -2.48976499e-01 1.14328814e+00 -1.93940416e-01
8.07942450e-01 -2.02923656e+00 1.78805757e-02 2.36304849e-01
1.99993607e-02 2.33517870e-01 2.50645369e-01 2.07817897e-01
3.82457346e-01 -6.85901940e-01 4.30450082e-01 -3.42890590e-01
4.60799187e-02 2.79159158e-01 -2.26887345e-01 8.27155292e-01
-7.11826235e-02 6.85164630e-01 -1.01847184e+00 -4.10652131e-01
6.12815261e-01 8.72590765e-02 -2.75824554e-02 -4.24579792e-02
-2.90439129e-01 5.06701887e-01 -5.90954900e-01 2.08909199e-01
6.36013746e-01 2.36636087e-01 -1.13753542e-01 -6.96925223e-02
-7.20124662e-01 9.27221775e-02 -1.77837312e+00 1.58073545e+00
4.05564308e-02 7.20974863e-01 -2.47577384e-01 -3.02017301e-01
9.37995553e-01 1.57642722e-01 6.03701532e-01 -4.88964647e-01
2.85292447e-01 9.49068293e-02 2.43835658e-01 -2.36931726e-01
8.94492328e-01 1.15140220e-02 -1.61621451e-01 5.08205183e-02
7.96366334e-02 -3.75563890e-01 4.25914466e-01 3.10261864e-02
9.17576671e-01 5.17982304e-01 2.18547732e-01 -3.85335952e-01
5.14859557e-01 4.96524841e-01 7.21877456e-01 9.19121802e-01
-5.00588417e-01 1.48690015e-01 -1.99736327e-01 -4.84424770e-01
-8.87699068e-01 -1.20734584e+00 -1.92702696e-01 4.84514654e-01
6.39582574e-01 -3.13179344e-01 -2.66740829e-01 -5.70628166e-01
3.70516151e-01 7.86017776e-01 -5.17688036e-01 -3.34329724e-01
-5.53613484e-01 -5.36858141e-01 1.79194584e-01 6.27678037e-01
3.55333209e-01 -6.15359545e-01 -1.04058838e+00 3.77711058e-01
1.35987371e-01 -1.37710834e+00 -3.37825149e-01 2.91361004e-01
-7.27358341e-01 -1.07199132e+00 -1.25048548e-01 -1.59446806e-01
2.27709219e-01 1.39619052e-01 5.83902001e-01 -2.25679874e-01
-1.76088750e-01 2.59768486e-01 5.75477183e-02 -1.18537545e+00
-6.20074809e-01 -2.97117412e-01 5.19858420e-01 -2.53256381e-01
4.44598436e-01 1.82608351e-01 -1.27924293e-01 6.18489087e-01
-8.94272923e-02 2.50083730e-02 3.35860848e-01 1.41308427e-01
6.42896831e-01 -2.94343010e-03 6.57068118e-02 -3.05611014e-01
-2.27059945e-02 -1.36826500e-01 -1.71740067e+00 -2.24887282e-02
-6.24970794e-01 1.58484519e-01 -1.30127728e-01 -4.56079990e-01
-9.06460941e-01 7.58565009e-01 5.83033562e-02 -6.39257967e-01
-6.83274716e-02 4.91655655e-02 1.29345030e-01 -3.59592527e-01
5.34374714e-01 6.49500564e-02 1.64274842e-01 -2.51229048e-01
2.30056584e-01 3.81233692e-01 7.49830067e-01 -1.64421469e-01
1.28283370e+00 7.51600087e-01 5.30189157e-01 -5.59838772e-01
-7.22206056e-01 -9.47033465e-01 -9.48991239e-01 -7.26200998e-01
9.53902483e-01 -1.14722466e+00 -9.86484647e-01 4.95604962e-01
-1.01728427e+00 -2.86350131e-01 -2.81773299e-01 9.73520279e-01
-5.62638879e-01 3.27357389e-02 5.91610298e-02 -1.22206485e+00
6.64851964e-02 -1.06263232e+00 1.10738385e+00 3.92799795e-01
-8.47288147e-02 -7.65998125e-01 5.88914335e-01 5.06593250e-02
2.55825430e-01 1.79977074e-01 -3.77191380e-02 -5.82001328e-01
-8.82550895e-01 -5.72735131e-01 6.64611608e-02 -1.02560017e-02
-1.96340889e-01 -3.34113538e-02 -8.74047935e-01 -3.86051148e-01
-2.19887987e-01 2.36497596e-01 7.44936287e-01 4.53858465e-01
3.26815754e-01 5.00746727e-01 -8.82598817e-01 7.59944990e-02
1.34037161e+00 4.95503753e-01 3.06503117e-01 5.35656989e-01
4.93663013e-01 2.08370462e-01 1.19683647e+00 3.06977302e-01
4.88863170e-01 9.67588723e-01 5.56174636e-01 3.86728704e-01
-2.69125223e-01 -2.17619905e-04 4.71988708e-01 3.92681152e-01
4.49743606e-02 -1.82486817e-01 -1.03878832e+00 4.56836313e-01
-2.11194062e+00 -1.03076160e+00 -5.17853022e-01 2.42314649e+00
1.63252413e-01 6.90496266e-01 2.55836368e-01 -2.23418415e-01
6.66696846e-01 -2.05331996e-01 -6.48823977e-01 2.29800493e-01
3.17424685e-01 -1.39691114e-01 1.22137868e+00 7.82832265e-01
-1.31958663e+00 1.00364065e+00 6.45278549e+00 4.97537941e-01
-6.62085176e-01 1.44808516e-01 -5.54945171e-01 -1.91583738e-01
6.02350831e-01 8.06666017e-02 -1.81638873e+00 4.65781808e-01
1.47295868e+00 -4.48577076e-01 3.48214351e-04 6.91305697e-01
2.69377261e-01 -5.97587049e-01 -9.27102089e-01 5.60285568e-01
-7.83356130e-02 -1.30351698e+00 -6.00511074e-01 1.53788626e-01
6.18452072e-01 8.37991416e-01 -1.86251760e-01 3.06497812e-01
8.83324623e-01 -2.38800436e-01 1.11096191e+00 7.81419218e-01
1.24410719e-01 -6.21750534e-01 6.81208432e-01 7.09650099e-01
-1.46652460e+00 -5.96025623e-02 -2.18378812e-01 5.98837808e-02
4.97377902e-01 1.80067420e-01 -9.53852892e-01 6.19744122e-01
6.07764125e-01 5.68217218e-01 -4.79111314e-01 1.74972117e+00
7.93244988e-02 2.91410029e-01 -6.43132865e-01 -1.56391472e-01
2.40952075e-01 -1.39310092e-01 1.09408605e+00 8.67387414e-01
1.50292635e-01 -1.24036685e-01 6.03101671e-01 5.23532510e-01
5.82894444e-01 -6.33558273e-01 -5.42654157e-01 4.10237163e-01
3.94941509e-01 1.12693489e+00 -7.48521090e-01 -5.20854175e-01
-2.26181075e-01 2.79929161e-01 -1.32365048e-01 -2.92350259e-03
-1.01541317e+00 -2.49342378e-02 1.01448154e+00 -2.03149736e-01
4.19377625e-01 -7.36705303e-01 2.86109522e-02 -7.51643836e-01
-1.98784262e-01 -3.55663896e-01 1.70315459e-01 -8.14605653e-01
-6.46993876e-01 5.30630648e-01 4.83136892e-01 -1.62976277e+00
-4.59150761e-01 -5.28343260e-01 -2.36392453e-01 9.41982388e-01
-1.48855877e+00 -8.88019800e-01 -3.44904751e-01 1.91406190e-01
6.13822520e-01 -2.46653393e-01 4.08976018e-01 2.23244920e-01
-5.03424048e-01 -1.33128837e-01 3.46895486e-01 -1.44684881e-01
4.55958366e-01 -1.00005853e+00 7.76445210e-01 1.09133983e+00
7.58115798e-02 3.05968255e-01 1.39378798e+00 -1.14822602e+00
-1.49266517e+00 -1.25319672e+00 5.12455165e-01 -9.33952928e-01
9.04461026e-01 -2.59152114e-01 -5.77040792e-01 9.90754187e-01
-6.35916367e-02 1.62668511e-01 -4.03347388e-02 -1.05494253e-01
3.58686030e-01 -9.25345533e-03 -7.95455098e-01 3.67394865e-01
8.76822114e-01 -4.01883274e-02 -5.55461287e-01 4.29248929e-01
5.05175471e-01 -1.13558447e+00 -6.54016554e-01 5.25283635e-01
8.90446723e-01 -4.07193393e-01 7.97005832e-01 -5.02961338e-01
-7.24617422e-01 -9.93831277e-01 -1.07474640e-01 -1.15430832e+00
-2.79539615e-01 -3.64227653e-01 -2.88687378e-01 6.76426291e-01
2.99281031e-01 -4.71878409e-01 1.07173824e+00 3.98966163e-01
-2.42675915e-01 1.10533573e-01 -1.30900347e+00 -1.44792557e+00
-3.79366189e-01 -6.75627053e-01 2.02508688e-01 9.71692204e-02
-7.35996187e-01 -1.68210007e-02 -3.45807880e-01 7.94327497e-01
1.18368518e+00 5.45722507e-02 1.21501458e+00 -1.56106031e+00
9.55085270e-03 -1.89764984e-02 -8.91279221e-01 -9.96437132e-01
1.77409738e-01 -5.30600548e-01 6.79558635e-01 -1.34848809e+00
-8.51060599e-02 -4.62529868e-01 -1.74341753e-01 1.64437701e-03
-3.29741538e-02 -9.45846923e-03 3.91065270e-01 8.75239298e-02
-8.39937508e-01 3.17991912e-01 9.53788161e-01 -6.19978420e-02
-1.41567662e-01 5.34623086e-01 2.68350244e-01 7.06297219e-01
5.65153420e-01 -9.12507474e-01 -1.90729335e-01 -3.51284802e-01
-2.04065338e-01 -7.80245289e-02 5.76845407e-01 -1.66782987e+00
6.62620544e-01 -2.98167288e-01 3.55633646e-01 -1.39784348e+00
7.70254254e-01 -1.04637396e+00 6.90751553e-01 1.00573611e+00
1.53160289e-01 2.08398670e-01 7.71233737e-01 8.77199352e-01
3.00675839e-01 -2.46661663e-01 7.76379704e-01 1.17738210e-01
-1.22450078e+00 3.42331678e-01 -6.84441328e-01 -2.53752708e-01
1.34126759e+00 -4.26016480e-01 -3.57541412e-01 6.51782304e-02
-8.07544947e-01 7.28620291e-01 1.93287760e-01 7.95301616e-01
2.35337004e-01 -1.17771590e+00 -5.51699460e-01 3.03192914e-01
1.33392632e-01 -2.75694907e-01 -9.47535560e-02 9.73674476e-01
-1.47608429e-01 5.82713842e-01 -3.79784822e-01 -9.37862098e-01
-1.50295269e+00 3.62115681e-01 5.16417265e-01 -6.36185259e-02
-6.50295556e-01 5.02344429e-01 -2.58674055e-01 -3.64053071e-01
2.50709355e-01 -2.88430035e-01 -1.76195234e-01 -1.89938143e-01
6.11947119e-01 5.69875896e-01 1.77893892e-01 -9.92737353e-01
-5.76888919e-01 6.34425640e-01 6.00896105e-02 -3.78028870e-01
9.49720562e-01 -3.44787091e-01 5.38774431e-01 8.16497624e-01
6.06682658e-01 -2.84923226e-01 -1.71050882e+00 -1.70100540e-01
4.34750080e-01 -4.45264757e-01 4.83507216e-01 -5.94310880e-01
-6.26230836e-01 4.11642939e-01 1.40878892e+00 -2.89534451e-03
1.46815270e-01 -3.99120674e-02 4.62784231e-01 4.96991098e-01
7.19798505e-01 -1.09273815e+00 -3.85588527e-01 7.70566285e-01
5.89511633e-01 -1.24949563e+00 3.34376127e-01 -2.56131083e-01
-6.15686417e-01 7.71625400e-01 8.94437611e-01 -1.50758624e-01
7.97871947e-01 6.39814079e-01 3.22671175e-01 -2.67863452e-01
-8.87330532e-01 -6.92429721e-01 4.27130282e-01 6.35773361e-01
-3.55818003e-01 -1.13379046e-01 1.01806059e-01 -2.45065168e-01
-5.24452887e-02 7.69010186e-02 2.86381453e-01 1.20750165e+00
-1.04235733e+00 -8.96910906e-01 -5.87673426e-01 2.82528549e-01
1.09416984e-01 4.36406821e-01 7.87336603e-02 1.19222581e+00
1.30771920e-01 8.60880792e-01 2.99999982e-01 -3.98576885e-01
8.16436231e-01 1.08352706e-01 5.66707373e-01 -4.93732631e-01
-4.53345746e-01 9.04651135e-02 1.72707811e-01 -7.86896288e-01
-3.71518940e-01 -1.19376636e+00 -1.36703253e+00 -1.78333312e-01
-9.22165751e-01 1.34589180e-01 1.14893794e+00 1.17256331e+00
2.63662100e-01 6.33301854e-01 2.24694997e-01 -1.14258516e+00
-5.53170681e-01 -8.17248404e-01 -3.90099525e-01 -2.06336990e-01
4.92229372e-01 -1.23330152e+00 -7.58816451e-02 -4.71448041e-02] | [6.577953338623047, -2.0883679389953613] |
522fa1fc-7b46-4d6e-91f5-b7fc1bfbea08 | slicing-aided-hyper-inference-and-fine-tuning | 2202.06934 | null | https://arxiv.org/abs/2202.06934v5 | https://arxiv.org/pdf/2202.06934v5.pdf | Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection | Detection of small objects and objects far away in the scene is a major challenge in surveillance applications. Such objects are represented by small number of pixels in the image and lack sufficient details, making them difficult to detect using conventional detectors. In this work, an open-source framework called Slicing Aided Hyper Inference (SAHI) is proposed that provides a generic slicing aided inference and fine-tuning pipeline for small object detection. The proposed technique is generic in the sense that it can be applied on top of any available object detector without any fine-tuning. Experimental evaluations, using object detection baselines on the Visdrone and xView aerial object detection datasets show that the proposed inference method can increase object detection AP by 6.8%, 5.1% and 5.3% for FCOS, VFNet and TOOD detectors, respectively. Moreover, the detection accuracy can be further increased with a slicing aided fine-tuning, resulting in a cumulative increase of 12.7%, 13.4% and 14.5% AP in the same order. Proposed technique has been integrated with Detectron2, MMDetection and YOLOv5 models and it is publicly available at https://github.com/obss/sahi.git . | ['Alptekin Temizel', 'Sinan Onur Altinuc', 'Fatih Cagatay Akyon'] | 2022-02-14 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 1.59238994e-01 9.60613936e-02 1.65253669e-01 5.61900064e-03
-3.06776077e-01 -7.26279795e-01 6.67472184e-01 1.70667961e-01
-3.08709621e-01 3.63741577e-01 -3.87128651e-01 -2.25982100e-01
6.16290830e-02 -9.85609710e-01 -7.40853429e-01 -6.75821185e-01
-2.98904534e-03 1.36068553e-01 1.12039864e+00 4.49909344e-02
-1.10448487e-01 5.22202075e-01 -1.53908217e+00 2.72999793e-01
6.26477540e-01 1.01382518e+00 4.03042674e-01 9.93249774e-01
4.08606470e-01 5.95304370e-01 -5.08745611e-01 -2.24103853e-01
6.38471186e-01 1.28543749e-01 -1.24849074e-01 1.04584858e-01
8.45375061e-01 -8.57067108e-01 -2.25463048e-01 1.07807577e+00
4.81396794e-01 -1.96944714e-01 7.10104346e-01 -1.13922119e+00
-2.71456540e-02 3.81880045e-01 -1.11667192e+00 5.07725120e-01
5.84596917e-02 4.40551937e-01 5.57800889e-01 -7.71907568e-01
3.30856562e-01 1.33301902e+00 7.61502266e-01 1.84478730e-01
-8.83698583e-01 -7.01582074e-01 9.88363773e-02 5.44971786e-03
-1.51389635e+00 -5.86135164e-02 2.16424778e-01 -6.50855780e-01
1.04174006e+00 4.64460731e-01 3.70763779e-01 5.80914736e-01
6.66054413e-02 6.64370418e-01 9.97746944e-01 -2.72900850e-01
-2.60444969e-01 4.29768711e-01 3.70586574e-01 1.00222230e+00
8.27593029e-01 3.10633153e-01 1.37958005e-01 -6.48461431e-02
5.33996344e-01 2.67702103e-01 -1.16137810e-01 -2.51265359e-04
-1.11243081e+00 7.57770598e-01 8.50602806e-01 -1.29328504e-01
-5.95537961e-01 -2.25627106e-02 4.55256850e-01 -2.65767545e-01
3.66329819e-01 7.85552412e-02 -4.01851147e-01 4.92079735e-01
-1.02366579e+00 4.57097292e-01 3.98965895e-01 1.15003455e+00
4.48154241e-01 9.68123898e-02 -5.41418970e-01 3.79109025e-01
3.99050295e-01 1.14023340e+00 -2.73418188e-01 -7.09738672e-01
3.06705177e-01 9.32153225e-01 4.43768442e-01 -1.08961058e+00
-6.12515032e-01 -6.78160071e-01 -7.20177412e-01 5.47139704e-01
3.12692791e-01 -4.21382993e-01 -1.21513188e+00 1.36636078e+00
7.44519651e-01 4.01849568e-01 -7.82210901e-02 8.58455896e-01
1.23672211e+00 9.72947538e-01 2.29780644e-01 4.84161414e-02
1.85158074e+00 -8.89886856e-01 -2.78400570e-01 -2.02748045e-01
3.35089117e-01 -9.46165562e-01 4.10604119e-01 2.92724192e-01
-8.15987825e-01 -8.76073480e-01 -1.09571707e+00 4.06746179e-01
-4.28844750e-01 6.75628364e-01 3.98671895e-01 6.80945098e-01
-7.29535341e-01 -3.89783271e-02 -7.80130982e-01 -7.36822963e-01
6.29018247e-01 1.67620227e-01 -5.19146919e-02 9.92656797e-02
-6.40161216e-01 7.77615368e-01 9.63101387e-01 1.07149638e-01
-1.38332975e+00 -7.56277144e-01 -4.12568867e-01 3.60534973e-02
7.97178805e-01 -7.54356205e-01 1.04838526e+00 -3.61637205e-01
-8.27132761e-01 7.38866210e-01 1.28208160e-01 -7.04098821e-01
6.50589645e-01 -4.15855318e-01 -3.14357817e-01 1.98366210e-01
9.13492814e-02 7.77312398e-01 8.58489394e-01 -8.84319365e-01
-1.30960000e+00 -4.98635322e-01 4.58273381e-01 1.45718589e-01
1.94490999e-02 2.52342075e-01 -4.00076538e-01 -5.19873500e-01
-2.18221173e-01 -9.45358813e-01 -1.26786008e-01 2.66572624e-01
-5.52868307e-01 -2.67888838e-03 1.19069135e+00 -7.21087396e-01
1.02407634e+00 -1.97890818e+00 -2.02787355e-01 -2.77269036e-02
3.03747982e-01 8.77898574e-01 5.77846467e-02 2.82537371e-01
4.72303629e-01 -1.81100547e-01 -1.77839145e-01 1.57491311e-01
-3.73641431e-01 -1.82249308e-01 5.03864698e-02 4.42760438e-01
3.39479983e-01 5.24482965e-01 -7.07006752e-01 -4.45409387e-01
7.20451117e-01 4.43867922e-01 -4.49800283e-01 1.61558241e-01
-2.86310732e-01 1.71624333e-01 -5.78717053e-01 8.95610392e-01
1.15068018e+00 -1.50449704e-02 -2.86137640e-01 -5.32631874e-01
-5.93569875e-01 -3.63164991e-01 -1.51447058e+00 9.43890035e-01
-7.23618120e-02 5.75677276e-01 1.63020656e-01 -7.11611032e-01
7.54490972e-01 2.25924090e-01 3.70424241e-02 -1.35157511e-01
3.19599956e-01 -9.38530564e-02 1.03754669e-01 -5.36522865e-01
4.09110874e-01 3.34371954e-01 2.99804330e-01 -2.09634900e-01
3.20804724e-03 3.25325191e-01 5.09294450e-01 2.68042773e-01
1.01482105e+00 -1.47050316e-03 6.01801872e-01 -3.27355295e-01
6.92486346e-01 4.15991336e-01 5.34092486e-01 1.11369538e+00
-2.17793122e-01 2.01662615e-01 1.25736102e-01 -4.22038615e-01
-8.54257464e-01 -1.16566467e+00 -4.20911640e-01 9.42148805e-01
2.13309035e-01 -2.51161695e-01 -8.40951562e-01 -5.98226428e-01
5.86732589e-02 4.01165426e-01 -5.19017339e-01 2.17629075e-01
-3.65131587e-01 -1.07971954e+00 5.95632493e-01 6.49459422e-01
1.06140935e+00 -9.74142134e-01 -1.24611652e+00 -1.21128578e-02
9.46693197e-02 -1.30620694e+00 8.34607184e-02 -2.78794557e-01
-8.39093745e-01 -1.37581682e+00 -6.23038650e-01 -4.52015996e-01
6.88393772e-01 7.52488434e-01 8.72855127e-01 1.21437065e-01
-8.78082752e-01 9.64896381e-02 -5.14009058e-01 -8.66794586e-01
-2.11302936e-01 -1.07328631e-01 -3.00781019e-02 6.24889582e-02
3.89830709e-01 9.94136631e-02 -8.68471444e-01 3.88932526e-01
-7.52166092e-01 1.21077653e-02 6.58946097e-01 6.26930237e-01
4.19637442e-01 1.18951082e-01 3.91621381e-01 -1.03641415e+00
-9.80134904e-02 -4.92602825e-01 -1.44037628e+00 1.90038919e-01
-3.08152139e-01 -3.24432909e-01 3.33584070e-01 -1.62654281e-01
-1.19045389e+00 2.32252076e-01 9.72746015e-02 -2.78407454e-01
-4.17313397e-01 2.82100830e-02 -5.98124415e-02 -1.83467939e-01
7.96698928e-01 -5.73610403e-02 -2.90260106e-01 -4.16220874e-01
2.36408725e-01 6.78807497e-01 4.63584393e-01 1.13076590e-01
1.06380856e+00 7.09173799e-01 3.60650979e-02 -9.64082241e-01
-7.32861340e-01 -8.01656365e-01 -4.80146527e-01 -3.26914549e-01
1.05614161e+00 -1.20513320e+00 -5.42200685e-01 4.90385443e-01
-1.11647689e+00 1.21854125e-02 2.08955765e-01 4.85263765e-01
1.86793387e-01 2.86287516e-01 -1.76553652e-01 -9.83768761e-01
-6.91777706e-01 -1.22334468e+00 1.02362919e+00 6.18842781e-01
2.36450315e-01 -5.51980138e-01 -4.58395571e-01 3.21183622e-01
3.36828232e-01 7.11130559e-01 1.31513983e-01 -3.83158237e-01
-9.15698767e-01 -4.79494423e-01 -7.60962069e-01 3.42397183e-01
-7.79134855e-02 3.36100668e-01 -1.10795796e+00 -4.26878333e-01
-4.37388241e-01 1.32177174e-01 1.08326852e+00 7.83375204e-01
6.97084546e-01 -1.05002336e-01 -7.77904093e-01 6.94563568e-01
1.58439434e+00 2.38673896e-01 2.47965261e-01 1.76175430e-01
6.06306732e-01 2.49363467e-01 9.30917799e-01 6.92685425e-01
-3.46458852e-02 6.96264684e-01 8.29181135e-01 -1.91300690e-01
-4.09636974e-01 5.50821573e-02 1.41612574e-01 -9.15262848e-02
-3.73228312e-01 -2.31591314e-01 -8.86110365e-01 5.04743934e-01
-1.81516778e+00 -1.14288878e+00 -6.77761436e-01 2.10426307e+00
1.37383044e-01 2.98877269e-01 2.79301137e-01 9.85333975e-03
8.84114742e-01 -1.00591099e-02 -5.35372257e-01 5.41840643e-02
8.15939158e-02 -1.49562404e-01 1.09817219e+00 5.45401633e-01
-1.56672764e+00 9.19306517e-01 4.65827608e+00 7.37027109e-01
-7.64767528e-01 2.37732261e-01 2.93953747e-01 -1.27699897e-02
7.75343716e-01 -2.13862248e-02 -1.45562720e+00 4.36360210e-01
6.46607757e-01 1.78647965e-01 6.23162501e-02 1.01745605e+00
3.71630281e-01 -3.41864437e-01 -5.90552688e-01 8.37247014e-01
-1.08394064e-01 -1.20138872e+00 6.96469769e-02 -2.69826084e-01
5.66688418e-01 1.68415919e-01 -1.21998996e-01 2.06066623e-01
6.59117997e-02 -4.94092941e-01 6.05232000e-01 2.26002514e-01
5.15986979e-01 -7.46366858e-01 1.07683313e+00 5.36713779e-01
-1.39429522e+00 -1.93422318e-01 -6.78575099e-01 2.19990704e-02
9.78375375e-02 3.72644216e-01 -1.16920805e+00 4.17422056e-01
1.13954580e+00 4.79450464e-01 -9.23697531e-01 1.46822393e+00
-2.25069433e-01 7.46625960e-01 -6.06127381e-01 -7.65334442e-02
3.78768504e-01 1.01465799e-01 8.76299202e-01 1.61299837e+00
2.44052172e-01 3.25645745e-01 4.19867963e-01 6.06948912e-01
1.73080891e-01 -2.50549853e-01 -5.00024915e-01 4.65706497e-01
5.39340138e-01 1.65715790e+00 -7.62372077e-01 -6.58774972e-01
-3.90992224e-01 6.17350519e-01 -2.66153276e-01 7.31047988e-02
-1.24498081e+00 -5.56873858e-01 2.92975992e-01 3.67646813e-01
6.76639736e-01 1.84553146e-01 1.81523219e-01 -8.34342480e-01
-7.88517892e-02 -6.86714590e-01 5.75056970e-01 -5.75908601e-01
-8.31353664e-01 7.21853197e-01 4.56562489e-01 -1.20734835e+00
6.96230233e-02 -9.86338317e-01 -6.68816626e-01 6.59584820e-01
-1.32007301e+00 -1.53035319e+00 -1.03599954e+00 4.05059189e-01
7.57652283e-01 -2.85728902e-01 4.29455221e-01 4.23032641e-01
-7.94701695e-01 3.80702108e-01 -2.09780224e-02 7.58005977e-02
3.05127084e-01 -1.03761923e+00 2.43714303e-01 1.38081038e+00
-7.31882825e-02 2.24848196e-01 7.99085319e-01 -4.53045070e-01
-1.23522735e+00 -1.64298236e+00 2.41140202e-01 -6.01832330e-01
3.36393505e-01 -3.14789712e-01 -6.62475705e-01 6.85432374e-01
3.44580024e-01 1.49653286e-01 1.20074928e-01 -3.49952072e-01
-2.14926556e-01 -2.97864020e-01 -1.34859145e+00 2.47629493e-01
8.05019438e-01 2.70215273e-01 -3.66271734e-01 7.09014893e-01
5.23689032e-01 -4.09547448e-01 -5.70178509e-01 7.23749638e-01
4.71678317e-01 -1.06099629e+00 1.18664002e+00 -1.49952576e-01
-1.90824807e-01 -8.87822568e-01 -1.83350906e-01 -7.70677865e-01
-5.20171404e-01 -3.07043642e-01 -2.27340788e-01 1.37904489e+00
4.12697822e-01 -6.08069599e-01 4.03678536e-01 6.70060469e-03
-1.12707175e-01 -2.20921338e-01 -5.12632251e-01 -8.50476503e-01
-5.73753893e-01 -1.91181675e-01 2.47242764e-01 2.58747488e-01
-7.35755920e-01 2.75165677e-01 -3.83337229e-01 1.03872168e+00
1.01984501e+00 -6.88791052e-02 1.01925123e+00 -1.20017064e+00
-3.97374719e-01 -1.79368898e-01 -6.26080930e-01 -7.44733751e-01
-7.10280716e-01 -6.70652628e-01 -6.37075827e-02 -1.53976786e+00
5.27651191e-01 -2.75833845e-01 -2.34752610e-01 3.99355531e-01
-3.64881217e-01 5.84439516e-01 3.68340880e-01 5.61479945e-03
-4.90701824e-01 -5.48053682e-02 6.40312195e-01 -1.89242110e-01
-2.38682870e-02 3.22394460e-01 -2.40558222e-01 9.91616070e-01
1.08872759e+00 -5.51180601e-01 -3.05972219e-01 -4.12409842e-01
-3.78184527e-01 -2.99769193e-01 9.37027216e-01 -1.41209733e+00
5.60061149e-02 1.86080307e-01 6.35197759e-01 -1.04820096e+00
3.54873359e-01 -8.44309270e-01 9.57689360e-02 9.28364694e-01
2.89796025e-01 1.06857978e-02 7.83780694e-01 6.78521335e-01
1.27955586e-01 -3.72479469e-01 1.04872048e+00 -1.29233420e-01
-9.85280573e-01 1.43091977e-01 -2.63473570e-01 -2.21202418e-01
1.61573529e+00 -2.52146602e-01 -3.72201860e-01 2.97233582e-01
-5.16767204e-01 2.20038265e-01 -6.57228380e-02 3.08444381e-01
5.06445289e-01 -7.91888535e-01 -1.12381804e+00 1.11079827e-01
2.82393992e-01 -7.30792352e-04 3.57580811e-01 9.23372328e-01
-9.25983489e-01 6.17754698e-01 -2.49654755e-01 -8.57481122e-01
-1.77025533e+00 6.50366902e-01 1.56842083e-01 -8.28459784e-02
-6.75596118e-01 8.31251323e-01 5.39141417e-01 -7.29935169e-02
2.43697852e-01 -7.08148897e-01 -2.46240288e-01 -1.23823620e-01
8.43359828e-01 7.66723871e-01 -2.27173477e-01 -5.52266955e-01
-5.77944040e-01 6.30908072e-01 -1.58226088e-01 4.85721171e-01
1.01902699e+00 1.56848297e-01 1.64034307e-01 -1.70015737e-01
5.31125665e-01 -1.68768883e-01 -1.34290731e+00 -7.99635649e-02
-2.46554837e-01 -5.70133030e-01 3.03940088e-01 -1.01665688e+00
-1.00540304e+00 7.47654438e-01 1.08793187e+00 2.77429670e-01
1.24760616e+00 3.12432684e-02 3.49580467e-01 3.84907722e-01
1.85067326e-01 -5.16894519e-01 -3.26174021e-01 2.53326111e-02
8.24422896e-01 -1.58937228e+00 2.86354005e-01 -6.26140654e-01
-3.63408327e-01 9.33561444e-01 8.82581115e-01 -4.18613940e-01
4.54003036e-01 2.92423218e-01 -1.94594473e-01 -3.84951234e-01
-4.96049345e-01 -4.16307271e-01 3.91166121e-01 5.65094829e-01
1.98845133e-01 3.04490805e-01 -1.68313146e-01 5.66157252e-02
4.16655600e-01 -8.68511721e-02 3.66809011e-01 7.35615134e-01
-9.05824184e-01 -3.56326520e-01 -7.60894895e-01 6.17614746e-01
-5.22449553e-01 -9.82543230e-02 -4.59687151e-02 1.04658413e+00
5.55864632e-01 1.18529081e+00 -1.62817001e-01 -1.79024696e-01
4.50756311e-01 -5.26954114e-01 2.01773658e-01 -6.01700485e-01
-5.31780303e-01 9.91793573e-02 1.16957530e-01 -4.43388790e-01
-3.80695701e-01 -5.33296824e-01 -9.47463632e-01 -4.99614701e-02
-7.54856408e-01 -2.43840799e-01 5.30510843e-01 4.08116072e-01
3.59008193e-01 6.97845340e-01 8.59183371e-02 -6.78156137e-01
-5.43526590e-01 -1.20230436e+00 -2.78421491e-01 -1.44176148e-02
2.74582505e-01 -9.04846907e-01 -2.20432639e-01 2.77544826e-01] | [8.600292205810547, -0.7961934804916382] |
f12d8eb9-94c5-43ca-b48e-6c01505d46e9 | deepbreath-deep-learning-of-breathing | 1708.06026 | null | http://arxiv.org/abs/1708.06026v1 | http://arxiv.org/pdf/1708.06026v1.pdf | DeepBreath: Deep Learning of Breathing Patterns for Automatic Stress Recognition using Low-Cost Thermal Imaging in Unconstrained Settings | We propose DeepBreath, a deep learning model which automatically recognises
people's psychological stress level (mental overload) from their breathing
patterns. Using a low cost thermal camera, we track a person's breathing
patterns as temperature changes around his/her nostril. The paper's technical
contribution is threefold. First of all, instead of creating hand-crafted
features to capture aspects of the breathing patterns, we transform the
uni-dimensional breathing signals into two dimensional respiration variability
spectrogram (RVS) sequences. The spectrograms easily capture the complexity of
the breathing dynamics. Second, a spatial pattern analysis based on a deep
Convolutional Neural Network (CNN) is directly applied to the spectrogram
sequences without the need of hand-crafting features. Finally, a data
augmentation technique, inspired from solutions for over-fitting problems in
deep learning, is applied to allow the CNN to learn with a small-scale dataset
from short-term measurements (e.g., up to a few hours). The model is trained
and tested with data collected from people exposed to two types of cognitive
tasks (Stroop Colour Word Test, Mental Computation test) with sessions of
different difficulty levels. Using normalised self-report as ground truth, the
CNN reaches 84.59% accuracy in discriminating between two levels of stress and
56.52% in discriminating between three levels. In addition, the CNN
outperformed powerful shallow learning methods based on a single layer neural
network. Finally, the dataset of labelled thermal images will be open to the
community. | ['Nadia Bianchi-Berthouze', 'Youngjun Cho', 'Simon J. Julier'] | 2017-08-20 | null | null | null | null | ['physiological-computing', 'mental-stress-detection'] | ['computer-vision', 'robots'] | [ 2.21783206e-01 -2.03014687e-01 2.93726057e-01 -3.64122897e-01
-2.79015213e-01 -4.09661740e-01 1.95884973e-01 8.05317909e-02
-5.63978374e-01 3.45359594e-01 1.37060106e-01 1.74223363e-01
-4.40889923e-03 -6.45602286e-01 -3.11038494e-02 -7.09041774e-01
-1.22963980e-01 -5.55802919e-02 -2.17863634e-01 -1.37919784e-01
6.57990202e-02 2.88860321e-01 -1.57747841e+00 1.98940337e-01
4.53242749e-01 1.06939912e+00 -4.87816632e-02 9.53901112e-01
2.10873082e-01 4.72059935e-01 -8.06936383e-01 -1.56499609e-01
1.97563291e-01 -5.72701335e-01 -5.04626870e-01 8.84509906e-02
4.19169277e-01 -2.59784997e-01 -2.61416703e-01 5.55636227e-01
8.28049719e-01 3.90258908e-01 2.09620386e-01 -8.98066759e-01
-4.96627212e-01 -1.74197569e-01 -1.86508060e-01 7.64092624e-01
6.61423981e-01 4.43234742e-01 5.78733861e-01 -4.75451320e-01
-1.13102391e-01 9.51598525e-01 1.06479573e+00 6.17335200e-01
-1.20432162e+00 -5.24293780e-01 -3.66125464e-01 3.15934777e-01
-1.24925399e+00 -4.55859900e-01 8.77444923e-01 -6.25396073e-01
1.24938357e+00 3.98577958e-01 1.03227508e+00 1.51084208e+00
3.00125450e-01 -4.74738106e-02 1.44465673e+00 -1.33017972e-01
3.80399615e-01 2.02155739e-01 4.37019691e-02 6.95074379e-01
9.20322910e-02 1.96377322e-01 -5.63828051e-01 -9.33703929e-02
4.89935786e-01 6.82030842e-02 -2.27262571e-01 2.35368386e-01
-1.05911160e+00 5.97566664e-01 3.46315473e-01 5.90595961e-01
-4.41181451e-01 1.70475006e-01 5.93585908e-01 1.77941367e-01
4.80014801e-01 4.75976288e-01 -3.65851641e-01 -4.07328665e-01
-9.32004988e-01 1.32053614e-01 6.91258371e-01 -5.36113307e-02
5.53490937e-01 8.65190402e-02 -1.71866223e-01 9.17598426e-01
1.27666220e-01 6.05392992e-01 9.57017004e-01 -8.90541792e-01
1.97026193e-01 5.76069534e-01 4.96801212e-02 -1.20292640e+00
-1.07193315e+00 -4.80441332e-01 -1.14515376e+00 -1.08887702e-01
4.29308325e-01 -3.07332069e-01 -5.64449310e-01 1.73256040e+00
2.32428506e-01 1.49810150e-01 -1.04305252e-01 1.12015498e+00
7.10950971e-01 3.64396363e-01 -1.09622590e-01 -3.92370522e-01
1.66977859e+00 -5.62757313e-01 -9.35781479e-01 -4.85499442e-01
5.39448082e-01 -1.56314492e-01 1.15531743e+00 6.40042186e-01
-8.70430529e-01 -9.89830017e-01 -9.88370955e-01 -8.95030648e-02
-5.96238136e-01 -1.37604937e-01 4.20110196e-01 1.11100709e+00
-1.08511317e+00 8.01762640e-01 -8.59844267e-01 -5.27419269e-01
3.47657025e-01 2.78483093e-01 -3.14819694e-01 1.38113096e-01
-1.48266733e+00 7.73510993e-01 9.99927521e-02 4.24631447e-01
-6.07612371e-01 -5.91677964e-01 -8.90520573e-01 2.79388912e-02
6.66724239e-03 -6.26982093e-01 1.04176831e+00 -9.97802317e-01
-1.70982397e+00 8.67361486e-01 -4.65615429e-02 -3.01349670e-01
2.51996249e-01 -2.64428496e-01 -6.21937156e-01 4.26859081e-01
-2.11791888e-01 1.97173700e-01 1.00674462e+00 -5.93074262e-01
1.36623770e-01 -5.52657247e-01 -2.84241349e-01 1.10576823e-01
-6.75159633e-01 8.30349177e-02 1.00227892e-01 -3.88577729e-01
-7.81372190e-02 -9.42401290e-01 1.54126370e-02 -2.14357287e-01
-2.28712738e-01 -1.30383700e-01 6.17303312e-01 -6.79322660e-01
1.16827083e+00 -2.13267541e+00 -1.01337343e-01 1.24495506e-01
3.59085977e-01 4.29885238e-01 -2.72565205e-02 3.20341140e-01
-2.40320489e-01 3.66538018e-02 -3.62999439e-01 -4.48094904e-01
1.98673978e-01 2.09745228e-01 1.09825552e-01 7.30659127e-01
1.24055259e-01 9.09500778e-01 -8.68131697e-01 -1.00410178e-01
3.84029478e-01 6.59039974e-01 -2.85592735e-01 3.79102200e-01
3.74327987e-01 4.60532933e-01 1.23575158e-01 3.30015391e-01
4.15198267e-01 7.94677623e-03 4.90383171e-02 -1.62675962e-01
2.04231441e-02 3.06295216e-01 -7.13393569e-01 1.62472475e+00
-6.22659922e-01 7.62802005e-01 -3.24940234e-01 -1.07589221e+00
1.11920166e+00 6.02108061e-01 5.38474739e-01 -1.02693176e+00
2.14940280e-01 -5.57755344e-02 -3.41388732e-02 -1.11644483e+00
9.73621905e-02 -5.79130530e-01 -2.50052750e-01 6.83600545e-01
3.06751803e-02 3.53645161e-02 -1.57700911e-01 -4.53715831e-01
1.19013941e+00 -1.46314293e-01 1.74796060e-01 -1.12700999e-01
5.24616182e-01 -5.72610438e-01 4.68928188e-01 5.66534519e-01
-7.49339044e-01 5.95994473e-01 4.69514012e-01 -1.02449572e+00
-8.49524617e-01 -7.75306761e-01 -8.69291425e-02 1.14197576e+00
-3.87530208e-01 -4.15088028e-01 -9.43811834e-01 -2.87941486e-01
-2.13271573e-01 3.42039913e-01 -9.94493186e-01 -4.88830507e-01
-4.93009597e-01 -1.04666829e+00 7.40865409e-01 7.43324578e-01
7.97307909e-01 -1.42032063e+00 -1.35908544e+00 2.76929941e-02
-4.64979947e-01 -1.07381511e+00 -2.04260826e-01 2.37638965e-01
-7.30271518e-01 -1.01787126e+00 -3.57037157e-01 -1.20942585e-01
4.06243727e-02 1.28013194e-01 9.82579291e-01 1.04959950e-01
-8.03240120e-01 5.24321020e-01 -2.24311605e-01 -4.08448547e-01
1.05905443e-01 -2.04001032e-02 3.30675811e-01 1.39796734e-01
7.91124701e-01 -8.36974680e-01 -8.63087356e-01 1.21130005e-01
-6.52690113e-01 -3.76261383e-01 2.44682923e-01 5.19502819e-01
1.69967487e-01 2.06777915e-01 6.29066885e-01 -1.95223957e-01
6.93465650e-01 -5.20263970e-01 5.61130978e-02 -2.70177931e-01
-3.39776874e-01 -3.24127644e-01 5.70469618e-01 -4.98129338e-01
-6.47211969e-01 -1.53695658e-01 -6.18571788e-03 -3.92858326e-01
-5.54173708e-01 2.11761519e-01 2.19614238e-01 3.06566536e-01
1.07922804e+00 2.49652311e-01 -1.22067249e-04 -4.12167817e-01
6.19289614e-02 6.07155919e-01 7.85350442e-01 -1.64460778e-01
6.95229471e-01 4.50864583e-01 -8.29728227e-03 -1.04141796e+00
-1.13737035e+00 -4.54824477e-01 -9.78573501e-01 -3.79391253e-01
1.36920381e+00 -7.34021664e-01 -9.72899437e-01 7.02101588e-01
-6.90856278e-01 -6.66890621e-01 -1.39673606e-01 5.16121924e-01
-6.02743149e-01 3.18070263e-01 -5.58159888e-01 -1.20333064e+00
-5.49176931e-01 -3.82523865e-01 1.00337327e+00 3.40880454e-01
-5.47991574e-01 -1.21073878e+00 4.92634475e-01 7.98999786e-01
5.40853798e-01 7.36557364e-01 3.55404347e-01 -5.53393602e-01
1.93057746e-01 -2.84402013e-01 1.82032928e-01 5.01569033e-01
2.19066069e-01 -3.50846261e-01 -1.61726820e+00 -3.86945486e-01
6.91294551e-01 -6.17899299e-01 7.75519550e-01 1.55996338e-01
1.44386804e+00 -3.50517690e-01 2.03706384e-01 7.72897124e-01
1.07376909e+00 -5.01373131e-03 6.40185416e-01 2.38444775e-01
7.58183718e-01 8.47528517e-01 -1.81689020e-02 4.65547025e-01
3.32461059e-01 6.42011166e-01 3.72774124e-01 -1.23649232e-01
1.29802432e-02 2.87569582e-01 4.95272815e-01 7.18752742e-01
-2.97560185e-01 1.22977301e-01 -9.92691457e-01 3.76844704e-01
-1.36079586e+00 -1.20149839e+00 -1.79992601e-01 2.16286182e+00
6.08321905e-01 -7.54991025e-02 4.83589798e-01 6.75168753e-01
4.50191528e-01 3.68209213e-01 -6.26517773e-01 -8.15039456e-01
2.36846328e-01 4.72789794e-01 2.13268902e-02 2.40922689e-01
-1.08923352e+00 2.65200108e-01 6.28364563e+00 4.30342145e-02
-1.29919970e+00 1.88014314e-01 5.16719699e-01 -4.55592364e-01
3.16473961e-01 -7.44347394e-01 -1.35749593e-01 6.16313636e-01
1.79236031e+00 2.95788825e-01 6.88421011e-01 4.04900700e-01
4.86863762e-01 -1.51724055e-01 -9.50484812e-01 1.21865630e+00
2.57718056e-01 -4.09197658e-01 -9.86527741e-01 1.19123034e-01
2.53826112e-01 4.19212207e-02 2.01458424e-01 3.31698149e-01
-3.71223658e-01 -1.51583815e+00 3.12150508e-01 8.41545582e-01
8.69564116e-01 -8.30421269e-01 8.54355574e-01 3.93201411e-01
-9.39593673e-01 -5.24599135e-01 -3.68332624e-01 -5.99196553e-01
-2.83886135e-01 8.22968960e-01 -6.74554348e-01 3.44346762e-01
1.01146853e+00 5.34306765e-01 -7.25502253e-01 4.74152356e-01
-3.13414037e-02 6.87850118e-01 -2.79214799e-01 1.94800273e-01
-8.49291161e-02 4.56508575e-03 3.12338527e-02 1.46364224e+00
3.30727577e-01 3.17246675e-01 -1.58934653e-01 8.76735568e-01
2.04737708e-01 -2.01780573e-01 -7.00193822e-01 -1.34508818e-01
1.54834926e-01 1.59806406e+00 -5.28652668e-01 -2.71629274e-01
-2.51197875e-01 9.50460136e-01 1.17089443e-01 2.61164755e-01
-6.35534525e-01 -4.10458565e-01 7.07251966e-01 6.74406663e-02
5.53772412e-02 -2.21280530e-01 -4.62269425e-01 -1.02512014e+00
6.94050342e-02 -7.92756021e-01 1.78740233e-01 -9.35197175e-01
-1.31737661e+00 5.15216112e-01 -1.79798380e-01 -7.54339278e-01
-2.64758527e-01 -4.36090440e-01 -8.77231002e-01 1.16901052e+00
-1.37491155e+00 -4.52280521e-01 -9.58186388e-01 7.46882856e-01
2.63264149e-01 1.62462443e-01 1.14458859e+00 2.11224332e-01
-9.82559085e-01 4.62565422e-01 -4.44275588e-01 1.40436307e-01
6.41356945e-01 -1.40933847e+00 3.40213507e-01 5.43527722e-01
-2.49302909e-01 5.60156167e-01 5.44325233e-01 -2.43716002e-01
-1.13231182e+00 -1.23950839e+00 1.12980115e+00 -7.41275191e-01
4.19272512e-01 -7.64474988e-01 -1.25720823e+00 2.39658862e-01
1.11512497e-01 4.02730018e-01 1.21865380e+00 3.17076854e-02
-3.53865355e-01 -3.05241764e-01 -1.34856570e+00 2.28420496e-02
8.27255011e-01 -9.47553813e-01 -8.44610989e-01 4.26546931e-01
3.74595314e-01 -3.14464360e-01 -1.03650260e+00 5.41008934e-02
7.51409769e-01 -1.41888607e+00 9.41533983e-01 -3.85844827e-01
2.78544128e-01 7.75104016e-03 1.09327557e-02 -1.26925743e+00
-5.10564923e-01 -6.86219931e-01 -3.22982371e-01 6.42677367e-01
-3.25966388e-01 -6.05170429e-01 4.93385732e-01 5.46691298e-01
2.26487964e-02 -6.40564859e-01 -8.30363572e-01 -5.63854158e-01
-1.34596422e-01 -5.22402883e-01 4.54629004e-01 1.12907541e+00
1.81458324e-01 2.54848629e-01 -2.64830410e-01 7.31069297e-02
4.48142529e-01 -2.69244134e-01 4.68010902e-01 -1.41933286e+00
-2.69314080e-01 -1.88857123e-01 -3.44405234e-01 -6.59595281e-02
-2.78071556e-02 -5.92621803e-01 8.02615136e-02 -1.23611820e+00
7.80312046e-02 9.18913707e-02 -5.88832319e-01 6.47860229e-01
-3.15891266e-01 6.76157236e-01 6.50171563e-02 -9.28900540e-02
-2.54441589e-01 3.52897167e-01 9.86882567e-01 2.49974981e-01
-3.14118296e-01 -1.18600003e-01 -6.18380129e-01 6.46096587e-01
1.35456347e+00 -2.88393915e-01 -5.66556454e-01 -6.27201572e-02
4.17795986e-01 6.05014712e-02 8.34319472e-01 -1.45756793e+00
-1.67905629e-01 -5.14707826e-02 7.46542513e-01 -1.66394219e-01
5.40485442e-01 -5.49229920e-01 7.56545216e-02 6.46692514e-01
-4.90659177e-01 1.71744168e-01 2.35076934e-01 4.16879624e-01
3.56373310e-01 3.78528051e-03 9.19793606e-01 -3.23377073e-01
1.71722018e-03 3.56548354e-02 -5.93979359e-01 -8.63992646e-02
6.50983095e-01 -2.75188595e-01 -3.37934554e-01 -1.57881632e-01
-8.09377909e-01 -2.28702158e-01 1.11345470e-01 4.36238021e-01
4.41452503e-01 -1.38592589e+00 -3.88694435e-01 4.22533244e-01
-1.11980528e-01 -1.69173464e-01 6.14012599e-01 1.17867136e+00
-1.48316920e-01 5.15754640e-01 -3.46813679e-01 -6.45700693e-01
-1.00954664e+00 6.84311092e-01 8.23442936e-01 -1.78501576e-01
-7.78778255e-01 6.52601480e-01 -1.98585596e-02 -2.57949233e-01
-3.06534842e-02 -4.86494303e-01 -4.05644268e-01 2.91412950e-01
9.08065140e-01 5.56730628e-01 3.74399811e-01 -4.76165086e-01
-4.54590589e-01 5.96924484e-01 4.69245434e-01 -9.65860486e-02
1.31157339e+00 -1.57461599e-01 -7.83841610e-02 9.33968425e-01
1.35419405e+00 -3.83417249e-01 -1.28566325e+00 -1.25905827e-01
-1.87303647e-01 -2.50755519e-01 2.08236739e-01 -9.16064620e-01
-9.57134366e-01 1.32805872e+00 1.07608426e+00 5.71689546e-01
1.38165891e+00 -5.37720203e-01 9.09598112e-01 5.56048453e-01
-1.92185253e-01 -1.31010449e+00 7.17745721e-01 3.15001339e-01
7.67804265e-01 -9.99645174e-01 -2.01592639e-01 3.34479451e-01
-4.97485340e-01 1.32852221e+00 4.57819760e-01 -8.24304298e-02
3.21511269e-01 -5.67434705e-04 2.97499716e-01 -5.51110268e-01
-8.37323248e-01 -1.77923173e-01 3.95111829e-01 9.55689549e-01
4.02272612e-01 4.77675535e-02 5.74632138e-02 5.79268992e-01
-6.93219006e-01 4.88849096e-02 5.19340992e-01 5.78860939e-01
-6.87537849e-01 -4.32998300e-01 -5.67363560e-01 3.43568385e-01
-5.76812088e-01 1.55988380e-01 -3.50670278e-01 4.33748692e-01
5.43372512e-01 1.21876729e+00 2.78817207e-01 -6.07541740e-01
3.31641644e-01 6.53415740e-01 4.18599069e-01 -6.73557222e-01
-8.57043087e-01 -1.64056018e-01 -1.22537769e-01 -8.92186582e-01
-5.87611675e-01 -6.99647427e-01 -8.28071594e-01 -3.13030601e-01
3.22574466e-01 -2.61083126e-01 6.82736039e-01 1.09043384e+00
4.74961475e-02 7.69238532e-01 7.99130678e-01 -8.82314205e-01
-2.49146879e-01 -1.22562945e+00 -5.45659482e-01 4.83494610e-01
7.05226123e-01 -4.95839983e-01 -4.11810219e-01 7.50546604e-02] | [13.750341415405273, 3.101442575454712] |
c82da0bf-8681-42dc-bc0f-b20de73a4888 | lesion-based-contrastive-learning-for | 2107.08274 | null | https://arxiv.org/abs/2107.08274v1 | https://arxiv.org/pdf/2107.08274v1.pdf | Lesion-based Contrastive Learning for Diabetic Retinopathy Grading from Fundus Images | Manually annotating medical images is extremely expensive, especially for large-scale datasets. Self-supervised contrastive learning has been explored to learn feature representations from unlabeled images. However, unlike natural images, the application of contrastive learning to medical images is relatively limited. In this work, we propose a self-supervised framework, namely lesion-based contrastive learning for automated diabetic retinopathy (DR) grading. Instead of taking entire images as the input in the common contrastive learning scheme, lesion patches are employed to encourage the feature extractor to learn representations that are highly discriminative for DR grading. We also investigate different data augmentation operations in defining our contrastive prediction task. Extensive experiments are conducted on the publicly-accessible dataset EyePACS, demonstrating that our proposed framework performs outstandingly on DR grading in terms of both linear evaluation and transfer capacity evaluation. | ['Xiaoying Tang', 'Junyan Lyu', 'Pujin Cheng', 'Li Lin', 'Yijin Huang'] | 2021-07-17 | null | null | null | null | ['diabetic-retinopathy-grading'] | ['medical'] | [ 4.54503953e-01 1.63000241e-01 -4.37545091e-01 -7.76935518e-01
-9.03882265e-01 -2.90239215e-01 4.35079247e-01 -1.44801503e-02
-3.89020532e-01 7.55061269e-01 1.77315220e-01 -2.87074625e-01
-2.55405512e-02 -6.12418115e-01 -4.63954687e-01 -7.27284670e-01
6.54050037e-02 1.40460998e-01 -7.08257183e-02 1.33755088e-01
2.35181972e-01 5.49242198e-01 -1.53380024e+00 2.99477100e-01
1.43163490e+00 1.08256388e+00 1.89948559e-01 7.22552598e-01
-1.76330395e-02 1.27599359e+00 -3.22958916e-01 -1.79155603e-01
4.42359567e-01 -5.32082796e-01 -9.18206275e-01 5.82926929e-01
1.01641381e+00 -4.33237940e-01 -3.98446023e-01 1.00318217e+00
6.17508411e-01 -6.16311282e-02 9.65448916e-01 -7.23466218e-01
-9.81216073e-01 6.21467866e-02 -6.97340906e-01 6.49571061e-01
-5.97093143e-02 3.73164266e-01 8.38741004e-01 -8.40288937e-01
4.58185434e-01 6.64158523e-01 2.35953435e-01 7.58022547e-01
-1.19691896e+00 -3.48144948e-01 -8.08466747e-02 2.43004635e-01
-1.12993050e+00 -5.22376001e-01 7.43291020e-01 -5.68238318e-01
4.42590863e-01 3.80987227e-01 6.06952906e-01 5.86297274e-01
-3.06865182e-02 9.59786296e-01 1.65226746e+00 -3.77727151e-01
1.34900451e-01 2.48273015e-01 3.25588793e-01 1.02281320e+00
5.93090616e-02 2.92725474e-01 -6.70535788e-02 -7.57968351e-02
8.71337533e-01 3.42384391e-02 -2.33064532e-01 -3.98504823e-01
-8.09841990e-01 8.26184809e-01 7.12558985e-01 -1.56204477e-01
-3.24936122e-01 -1.35071605e-01 3.80171686e-01 4.16489989e-01
6.59318030e-01 3.65891367e-01 -3.19331050e-01 3.23995888e-01
-8.04904521e-01 -2.12318432e-02 2.36374304e-01 6.15150571e-01
6.99490607e-01 -5.54232188e-02 -4.89161670e-01 1.06492591e+00
8.42062682e-02 3.00160289e-01 4.86217439e-01 -8.12561035e-01
1.64627418e-01 1.07441902e+00 -1.51059881e-01 -4.51565146e-01
-4.44499254e-01 -7.22200632e-01 -1.14950621e+00 6.43836200e-01
4.06523854e-01 -2.78294295e-01 -1.32261682e+00 1.18994105e+00
1.11025013e-01 2.40581065e-01 2.21965656e-01 1.05979180e+00
1.11983299e+00 2.77443796e-01 3.52018118e-01 -2.86898315e-01
1.11624229e+00 -1.13786077e+00 -2.77150750e-01 -2.82520592e-01
7.08305538e-01 -5.22705615e-01 1.21615994e+00 3.88011426e-01
-1.15486121e+00 -5.84548831e-01 -8.20110917e-01 -2.07527742e-01
-1.68659259e-02 5.94862521e-01 9.95838642e-01 4.43361878e-01
-1.07814670e+00 9.70574021e-02 -5.14952898e-01 -1.26673415e-01
1.15016592e+00 2.76784837e-01 -3.05351764e-01 -5.64073920e-01
-5.65690815e-01 8.24086428e-01 -1.24293705e-02 -3.12166638e-03
-8.79204154e-01 -8.14202547e-01 -7.29259968e-01 -3.26244049e-02
-4.32389695e-03 -8.28595340e-01 9.44854379e-01 -1.26688969e+00
-1.25218797e+00 1.49289966e+00 -9.43619460e-02 -4.19734955e-01
6.53586090e-01 -5.12579679e-02 -3.29332650e-01 4.52163279e-01
-1.42655566e-01 9.84179854e-01 8.62072766e-01 -1.28011084e+00
-8.32584143e-01 -3.56690854e-01 9.40590352e-02 3.50391895e-01
-3.38573605e-01 -1.41723156e-02 -1.23818822e-01 -4.54429716e-01
-1.35383606e-02 -7.05271661e-01 -6.45052731e-01 4.92829978e-01
-3.60212147e-01 -2.07468107e-01 4.78896290e-01 -5.39113104e-01
8.70001674e-01 -2.13330603e+00 -7.74906054e-02 1.93922132e-01
5.56607425e-01 4.49315876e-01 -1.33036777e-01 -4.24987853e-01
-1.39486939e-01 -7.40723684e-02 -2.13000402e-01 -3.80056724e-02
-5.27202725e-01 8.62045586e-02 1.61009207e-01 4.64632988e-01
7.95533121e-01 9.71993148e-01 -7.60418177e-01 -7.61632502e-01
2.06896931e-01 3.26375782e-01 -6.13928020e-01 3.63623112e-01
-3.93938944e-02 7.66670525e-01 -5.32685876e-01 8.79602671e-01
6.65606856e-01 -6.94423258e-01 -1.16071768e-01 -2.36790940e-01
-5.71762100e-02 -1.32524878e-01 -5.60254514e-01 1.40173829e+00
-3.10699791e-01 6.06208920e-01 -5.12067974e-01 -1.17763901e+00
8.72389674e-01 1.11752339e-01 4.83337462e-01 -8.49670053e-01
5.42859398e-02 1.81949794e-01 3.48571874e-02 -7.17224240e-01
1.44504100e-01 1.43180728e-01 3.34756076e-01 7.68595636e-02
9.60048884e-02 9.48324054e-02 3.12380970e-01 -4.86836582e-03
1.04733038e+00 -1.07085004e-01 3.77553821e-01 -2.28588864e-01
6.06354177e-01 1.13512166e-01 4.69724864e-01 4.94949967e-01
-4.42788303e-01 8.97247136e-01 3.84017676e-01 -5.02117276e-01
-9.21600521e-01 -1.05845404e+00 -5.45992255e-01 7.43281007e-01
1.39678389e-01 1.90059215e-01 -4.74087447e-01 -1.12984693e+00
-7.06663132e-02 1.73784152e-01 -6.95955575e-01 -1.14258893e-01
-3.36288273e-01 -1.11303842e+00 1.01179346e-01 6.89333677e-01
6.59006536e-01 -8.69235933e-01 -3.02918345e-01 -1.80628702e-01
7.53779188e-02 -7.88942158e-01 -5.45494676e-01 1.90898776e-02
-1.06377470e+00 -1.33345926e+00 -1.09797215e+00 -1.24194634e+00
1.19804239e+00 3.71928126e-01 1.04527307e+00 1.39002725e-01
-8.47815454e-01 3.46190989e-01 -1.45608425e-01 -2.75344908e-01
-1.81186005e-01 -2.48387709e-01 -4.94666815e-01 1.82278872e-01
5.35142720e-01 -2.49570817e-01 -1.24265587e+00 5.94693869e-02
-7.75975645e-01 -1.73432305e-02 1.16038847e+00 9.87583756e-01
9.13374126e-01 -3.62804174e-01 6.90650463e-01 -1.28528118e+00
2.90082514e-01 -3.14666390e-01 -4.75741833e-01 3.49756479e-01
-8.14045906e-01 -1.19924612e-01 2.71203697e-01 -4.75350469e-01
-1.16523981e+00 3.21389318e-01 4.81173694e-02 -1.60529256e-01
-2.99018949e-01 4.06090111e-01 1.89678729e-01 -4.88619715e-01
1.02149189e+00 3.97260964e-01 2.21166909e-01 -2.25304455e-01
3.64551693e-01 7.59090185e-01 5.91355205e-01 -9.01795030e-02
7.39696562e-01 3.60927075e-01 -5.88515438e-02 -6.40056968e-01
-1.30829167e+00 -6.07800305e-01 -5.42611301e-01 -1.79362774e-01
9.53216434e-01 -1.15455914e+00 -3.29633117e-01 3.56059968e-01
-5.16715288e-01 -2.75525570e-01 -4.79028732e-01 6.59425914e-01
-6.75510943e-01 2.75590628e-01 -5.17394781e-01 -5.41186333e-01
-5.12401879e-01 -1.10649526e+00 7.53381431e-01 6.48708999e-01
2.19689310e-01 -9.72493231e-01 1.48882627e-01 8.61773431e-01
3.93461734e-01 1.94697320e-01 1.18639052e+00 -3.74724299e-01
-6.11875772e-01 -1.54648572e-01 -8.20446074e-01 8.41658890e-01
3.36895704e-01 -1.42727241e-01 -1.18222034e+00 -2.35360041e-01
-4.77112502e-01 -8.09878469e-01 1.06842077e+00 5.79692543e-01
1.56702936e+00 -1.41243249e-01 -1.19257733e-01 6.53609872e-01
1.67004645e+00 3.88082601e-02 1.04740596e+00 1.11658476e-01
5.67643583e-01 4.69456434e-01 6.75785184e-01 1.39414862e-01
4.63338703e-01 1.21301092e-01 3.61677706e-01 -9.47754204e-01
-5.20719111e-01 2.35756531e-01 -9.55509171e-02 3.80709082e-01
-4.65891927e-01 1.05113061e-02 -7.26179004e-01 5.38127661e-01
-1.47570717e+00 -7.34956205e-01 -1.39716998e-01 2.14809823e+00
1.15139079e+00 -2.09510606e-02 7.68418312e-02 -1.85257539e-01
5.78422189e-01 -2.26595357e-01 -6.68137789e-01 -2.51325723e-02
-1.90004393e-01 4.18497413e-01 3.94038290e-01 1.12846158e-01
-1.33474267e+00 6.52446687e-01 6.65206575e+00 4.36990082e-01
-1.19656229e+00 9.62340385e-02 1.29548299e+00 -1.32903997e-02
2.27061100e-02 -2.87006468e-01 -4.82707024e-01 3.25958163e-01
4.65725213e-01 8.45782310e-02 5.14320023e-02 8.52107882e-01
2.68344551e-01 -6.39257580e-03 -1.08653820e+00 1.02764785e+00
1.74639046e-01 -1.31003261e+00 1.14423394e-01 1.41664874e-02
9.82174695e-01 -1.76731199e-02 5.34227371e-01 2.50721842e-01
2.42514223e-01 -1.17871094e+00 -2.73738176e-01 8.18266988e-01
1.09448934e+00 -6.87964857e-01 7.14589477e-01 -1.98481351e-01
-5.99304020e-01 -1.58832699e-01 -5.43919504e-01 2.66071469e-01
-3.16191137e-01 5.97630620e-01 -7.88514018e-01 1.60533562e-01
5.43011844e-01 7.89184749e-01 -9.91474271e-01 1.51152313e+00
-8.29770193e-02 7.32024431e-01 3.31642389e-01 2.89080292e-01
1.84055150e-01 -3.02596867e-01 5.97963333e-02 1.01569164e+00
-2.48773713e-02 1.90808058e-01 3.13673258e-01 6.62639022e-01
-2.06064343e-01 3.97578150e-01 -3.68756413e-01 5.67222498e-02
-4.71799336e-02 1.43451679e+00 -2.58601546e-01 -3.61846447e-01
-8.01498532e-01 9.01632428e-01 4.32491064e-01 4.16934729e-01
-4.31529850e-01 -1.43383205e-01 4.00597245e-01 1.44393802e-01
1.19320564e-01 2.06627861e-01 -3.38613421e-01 -1.14121199e+00
-1.11704610e-01 -7.46198118e-01 5.02430737e-01 -7.44113207e-01
-1.58181524e+00 5.48575342e-01 -5.05341232e-01 -1.55649269e+00
-3.80227575e-03 -5.06848037e-01 -7.04767287e-01 7.79781997e-01
-2.31010556e+00 -1.28382337e+00 -7.73357868e-01 7.15294719e-01
4.31273073e-01 -5.52731395e-01 7.47992396e-01 3.91605705e-01
-5.99498272e-01 6.77111208e-01 7.31307119e-02 1.53782174e-01
9.63334322e-01 -1.49147451e+00 -2.60228693e-01 6.17331445e-01
-1.49007857e-01 1.79076552e-01 3.15490127e-01 -4.31984037e-01
-9.45036590e-01 -1.53085041e+00 4.57361281e-01 -1.38940334e-01
3.70932668e-01 2.31772617e-01 -8.62454832e-01 5.19466877e-01
2.10358813e-01 7.22289503e-01 1.01969814e+00 -1.07064962e-01
-3.45861405e-01 -2.96374828e-01 -1.45814025e+00 5.60543358e-01
9.77906287e-01 -2.66243845e-01 -3.36171955e-01 7.28889406e-01
2.17469737e-01 -2.17072651e-01 -1.11544275e+00 5.37485003e-01
2.73075193e-01 -5.82099915e-01 9.03207064e-01 -9.66906011e-01
8.24914455e-01 -9.22288522e-02 6.48658872e-02 -1.18040264e+00
-2.90656179e-01 -1.93930328e-01 -7.45273083e-02 9.36101377e-01
5.37761331e-01 -5.81069469e-01 1.00757444e+00 3.50693345e-01
-1.78059414e-01 -7.80756533e-01 -4.33868825e-01 -3.58901531e-01
1.99139744e-01 3.40489149e-01 -8.98582190e-02 1.08695197e+00
-3.11315298e-01 3.14900398e-01 -2.53316104e-01 3.42473924e-01
8.79864156e-01 1.75311416e-01 5.21464586e-01 -1.29957926e+00
-4.66030270e-01 -2.49959111e-01 -7.26749301e-01 -8.85875762e-01
1.13586988e-02 -1.16032946e+00 -1.08606126e-02 -1.55518329e+00
7.32890606e-01 -7.61722684e-01 -4.97960985e-01 5.42239964e-01
-5.63374937e-01 7.33455598e-01 -3.84065598e-01 1.66889951e-01
-5.91188490e-01 3.07547569e-01 1.71736312e+00 -5.51514030e-01
-2.48019204e-01 1.69909984e-01 -9.39908206e-01 6.02047622e-01
9.37620103e-01 -6.82392269e-02 -6.20389938e-01 -3.51262361e-01
-1.74155071e-01 -8.70627686e-02 4.64814842e-01 -8.54222357e-01
2.18176600e-02 -2.06701189e-01 7.04845488e-01 -3.60029936e-01
4.69766408e-02 -3.86988431e-01 -5.81374764e-01 3.82501543e-01
-6.43735707e-01 -4.74005312e-01 -9.36923772e-02 4.87068534e-01
-3.13402712e-01 -1.79280803e-01 1.17012525e+00 -1.45401791e-01
-7.25833535e-01 7.44650602e-01 -1.55529797e-01 2.12636292e-01
1.01031673e+00 -1.89320907e-01 -6.56764328e-01 -4.72931340e-02
-9.81183052e-01 2.79207289e-01 3.04582536e-01 4.66559120e-02
9.17862058e-01 -1.10472453e+00 -1.15295315e+00 9.47203264e-02
6.69571042e-01 5.39939925e-02 4.13033158e-01 9.50534403e-01
-5.49569607e-01 1.93280280e-01 -4.29495782e-01 -6.92400157e-01
-1.42453647e+00 5.54483294e-01 3.78686845e-01 -2.01332390e-01
-8.05035949e-01 6.85000718e-01 5.04042983e-01 -2.46138856e-01
2.55538404e-01 -2.36654148e-01 -6.15206003e-01 -1.85614511e-01
7.68471062e-01 1.64887488e-01 1.39970288e-01 -2.50948489e-01
1.07989140e-01 5.57081580e-01 -6.73577189e-01 5.55591643e-01
1.44329059e+00 -1.29811764e-01 -7.54342508e-03 -7.58345798e-02
1.16980290e+00 -2.69338071e-01 -1.33545148e+00 -5.45707345e-01
7.50476494e-03 -4.66795802e-01 5.30869722e-01 -1.04340553e+00
-1.48103666e+00 6.83451355e-01 1.43912816e+00 -1.99456230e-01
1.31970954e+00 4.33030240e-02 5.36830068e-01 3.74419928e-01
-1.25445602e-02 -8.27672601e-01 3.27493250e-01 -1.56114727e-01
6.18671358e-01 -1.79733026e+00 4.16198149e-02 -6.92378879e-01
-8.70657861e-01 9.60994601e-01 8.05479527e-01 -4.49683785e-01
4.92468268e-01 8.65988620e-03 4.31030333e-01 -1.71022981e-01
-5.76218665e-01 -5.54240644e-01 6.02454543e-01 8.61706555e-01
5.30163467e-01 -5.58244027e-02 -5.54373145e-01 1.79701656e-01
2.69312680e-01 2.89939642e-01 7.42534339e-01 9.41278875e-01
-4.29384559e-01 -1.02788496e+00 2.22735420e-01 1.09160352e+00
-5.04978061e-01 -1.98486254e-01 -3.41674387e-01 5.48582554e-01
-9.67987701e-02 7.67629564e-01 2.26874769e-01 -4.52136099e-02
1.14883244e-01 -1.45950750e-01 7.02502072e-01 -8.85046482e-01
-3.80179554e-01 -6.12220950e-02 9.15023908e-02 -3.88037413e-01
-6.37889624e-01 -5.45308352e-01 -1.10764551e+00 2.35950246e-01
-1.34535462e-01 -3.84110302e-01 3.32262099e-01 8.40489089e-01
2.28094891e-01 5.40578663e-01 1.06835091e+00 -3.53944719e-01
-6.00125372e-01 -8.13201725e-01 -5.49529374e-01 3.92579645e-01
5.08854747e-01 -5.09123564e-01 -1.81884170e-01 3.30128014e-01] | [15.791619300842285, -3.9559130668640137] |
e1d2b2b8-2fa3-40d1-aa88-965db2d2e199 | em-paste-em-guided-cut-paste-with-dall-e | 2212.07629 | null | https://arxiv.org/abs/2212.07629v1 | https://arxiv.org/pdf/2212.07629v1.pdf | EM-Paste: EM-guided Cut-Paste with DALL-E Augmentation for Image-level Weakly Supervised Instance Segmentation | We propose EM-PASTE: an Expectation Maximization(EM) guided Cut-Paste compositional dataset augmentation approach for weakly-supervised instance segmentation using only image-level supervision. The proposed method consists of three main components. The first component generates high-quality foreground object masks. To this end, an EM-like approach is proposed that iteratively refines an initial set of object mask proposals generated by a generic region proposal method. Next, in the second component, high-quality context-aware background images are generated using a text-to-image compositional synthesis method like DALL-E. Finally, the third component creates a large-scale pseudo-labeled instance segmentation training dataset by compositing the foreground object masks onto the original and generated background images. The proposed approach achieves state-of-the-art weakly-supervised instance segmentation results on both the PASCAL VOC 2012 and MS COCO datasets by using only image-level, weak label information. In particular, it outperforms the best baseline by +7.4 and +2.8 mAP0.50 on PASCAL and COCO, respectively. Further, the method provides a new solution to the long-tail weakly-supervised instance segmentation problem (when many classes may only have few training samples), by selectively augmenting under-represented classes. | ['Vibhav Vineet', 'Laurent Itti', 'Brian Nlong Zhao', 'Jiashu Xu', 'Yunhao Ge'] | 2022-12-15 | null | null | null | null | ['weakly-supervised-instance-segmentation'] | ['computer-vision'] | [ 8.61890733e-01 3.52990717e-01 -3.92184883e-01 -4.12314415e-01
-1.18146253e+00 -5.84902048e-01 7.11605251e-01 -7.72582293e-02
-6.79500818e-01 6.59139514e-01 -4.45313007e-01 -9.24752727e-02
5.66758871e-01 -5.75120449e-01 -1.05838251e+00 -9.39001739e-01
4.92311746e-01 9.18216050e-01 6.92622662e-01 1.87699452e-01
9.36120525e-02 2.42851600e-01 -1.61091375e+00 7.11767316e-01
1.02140629e+00 8.55746686e-01 4.17056918e-01 7.27957845e-01
-4.52692389e-01 8.33377182e-01 -6.36326790e-01 -5.03807902e-01
2.70521700e-01 -5.34217119e-01 -9.77279782e-01 7.78330445e-01
8.37827325e-01 -4.56883647e-02 2.68854052e-01 1.06323850e+00
1.34567410e-01 1.45016104e-01 6.19223833e-01 -1.07714999e+00
-6.83510974e-02 5.07818699e-01 -1.01804173e+00 -5.94428405e-02
-1.93059877e-01 3.28251213e-01 6.84001565e-01 -1.08215749e+00
7.81061769e-01 1.01378262e+00 3.49568665e-01 6.48502171e-01
-1.49584138e+00 -4.58872408e-01 4.42147702e-01 -1.11909531e-01
-1.23736334e+00 -1.22455440e-01 8.42090905e-01 -3.36603791e-01
5.43415844e-01 3.40775073e-01 6.06881261e-01 8.32348645e-01
-4.56146181e-01 1.41202056e+00 1.48168886e+00 -4.93995309e-01
3.03944916e-01 4.53338593e-01 5.06393649e-02 6.48597121e-01
6.24316521e-02 -3.18063110e-01 -2.24743888e-01 -3.36969551e-03
5.39329708e-01 -2.66207248e-01 -1.37670636e-01 -3.09306502e-01
-1.25031114e+00 5.10038257e-01 2.09704116e-01 2.15478539e-01
-3.22188556e-01 4.08700667e-02 2.55599082e-01 -2.42598668e-01
8.11653674e-01 2.01234639e-01 -6.14382982e-01 1.42707735e-01
-1.60457814e+00 2.77695656e-01 7.24489331e-01 1.09386146e+00
9.49318409e-01 -3.52125913e-02 -3.29440653e-01 9.58250046e-01
2.22054794e-01 4.71541464e-01 1.57915071e-01 -1.03275275e+00
5.44499338e-01 5.84601164e-01 8.83410200e-02 -4.45808351e-01
-5.55411913e-02 -8.27157438e-01 -5.81102371e-01 1.00504227e-01
6.09357536e-01 -2.57950090e-02 -1.40030146e+00 1.52439320e+00
9.15254533e-01 5.28811753e-01 2.60428097e-02 7.48802066e-01
7.84212887e-01 7.72311985e-01 1.99004054e-01 -8.67003649e-02
1.30098784e+00 -1.66248035e+00 -6.86055481e-01 -5.53659618e-01
4.27945584e-01 -7.78791904e-01 1.14931560e+00 4.00083631e-01
-1.27645421e+00 -6.86619818e-01 -9.21397805e-01 8.45122114e-02
-2.76699424e-01 4.77706939e-01 3.45097572e-01 6.48849964e-01
-8.08157504e-01 2.18983456e-01 -7.43935525e-01 1.15078561e-01
8.62632692e-01 1.48114622e-01 -1.16864748e-01 -2.20496401e-01
-4.85534668e-01 3.54795426e-01 7.42256701e-01 4.73299176e-02
-1.11474049e+00 -8.69073391e-01 -8.62395942e-01 -3.47781628e-01
7.59245694e-01 -4.89450514e-01 1.17151070e+00 -1.42585242e+00
-1.43165636e+00 1.34740770e+00 -3.57501328e-01 -4.27416176e-01
8.25235367e-01 -2.58971363e-01 1.53124873e-02 3.23358893e-01
3.17156702e-01 1.15692127e+00 1.13816786e+00 -1.94184172e+00
-1.02009916e+00 -2.57155985e-01 -2.62634814e-01 1.56244680e-01
1.09429762e-01 -8.50076526e-02 -1.18891883e+00 -9.50193405e-01
4.76271547e-02 -9.21069443e-01 -4.79433119e-01 -1.57581136e-01
-5.09122193e-01 3.50184478e-02 9.85131860e-01 -6.33402050e-01
8.44428360e-01 -2.01670623e+00 2.33242601e-01 1.20093003e-01
5.14911450e-02 4.13108170e-01 -2.40606248e-01 -2.40000218e-01
1.21135220e-01 1.10462625e-02 -9.85684097e-01 -9.16396737e-01
-1.51044175e-01 2.67132103e-01 -3.13716196e-02 3.92734796e-01
5.42087018e-01 1.02551317e+00 -9.58497226e-01 -9.28739667e-01
3.87382507e-01 3.21916938e-01 -4.33510005e-01 3.47990960e-01
-8.41816127e-01 7.15213656e-01 8.47060233e-02 8.19528639e-01
9.01459694e-01 -1.60715252e-01 9.21107978e-02 -1.39489949e-01
-6.16027750e-02 -1.77695349e-01 -1.35685420e+00 1.77287984e+00
-3.46256018e-01 4.99713659e-01 3.35464507e-01 -1.01860237e+00
6.84713542e-01 2.72724703e-02 3.45606059e-01 -3.97859246e-01
6.38403222e-02 2.69805789e-01 -2.65063643e-01 -2.25209236e-01
5.12116849e-01 -7.68252015e-02 1.68974891e-01 4.06609863e-01
2.12964520e-01 -6.02314115e-01 5.53043902e-01 2.89526850e-01
6.81618273e-01 6.34521008e-01 -1.40031755e-01 -1.37357041e-01
7.78813839e-01 2.42700875e-01 5.89076817e-01 7.59549856e-01
-2.00847596e-01 9.53771114e-01 4.11025107e-01 -2.03691330e-02
-1.00970960e+00 -8.20504129e-01 -2.29560167e-01 1.10109961e+00
2.47880384e-01 -5.76885901e-02 -1.46808279e+00 -1.07333517e+00
-2.97793835e-01 8.06992710e-01 -6.16913199e-01 2.08997697e-01
-5.52161455e-01 -1.00765872e+00 4.88325864e-01 4.83022720e-01
7.59955227e-01 -1.24010003e+00 -2.63883233e-01 1.45669878e-01
-4.66981202e-01 -1.57183766e+00 -6.68428481e-01 3.21123272e-01
-1.02156961e+00 -9.39384639e-01 -9.52444971e-01 -1.14611137e+00
1.06587386e+00 6.07081875e-02 1.27365899e+00 1.53959826e-01
-3.07071090e-01 1.73398331e-01 -2.64088035e-01 -1.96018904e-01
-4.75373328e-01 -4.97241765e-02 -4.60584074e-01 5.27558804e-01
1.37504652e-01 -1.18826397e-01 -4.25601065e-01 4.49414253e-01
-1.14010406e+00 4.63290513e-01 7.52819419e-01 8.59194577e-01
1.32531738e+00 -6.57491460e-02 2.41906375e-01 -1.41451716e+00
-7.33853877e-02 -1.46198034e-01 -7.03434348e-01 2.20857084e-01
-2.79642850e-01 -1.13664195e-01 4.17988747e-01 -7.53859758e-01
-1.40518582e+00 5.97769260e-01 -1.49784297e-01 -3.53492826e-01
-5.53709149e-01 4.99207750e-02 -5.14587820e-01 9.69706327e-02
4.61616278e-01 4.79867697e-01 -3.13604504e-01 -4.22025412e-01
7.36482024e-01 6.54753983e-01 9.63455796e-01 -8.88091564e-01
9.05735314e-01 7.74214923e-01 -3.64343256e-01 -8.69868875e-01
-1.07685733e+00 -6.94043696e-01 -9.90039766e-01 -2.84861982e-01
1.17793226e+00 -9.09436882e-01 -3.69270355e-03 7.93724239e-01
-1.07584202e+00 -1.05835474e+00 -5.65781891e-01 9.44274440e-02
-6.47162676e-01 3.26320827e-01 -7.44456112e-01 -8.37813497e-01
-2.46269956e-01 -1.24356079e+00 1.51146150e+00 2.09587544e-01
9.34990868e-02 -6.63988113e-01 -2.33905733e-01 1.11312449e+00
-2.02048179e-02 3.59539062e-01 4.42519844e-01 -5.50320804e-01
-7.48348475e-01 -9.42130536e-02 -4.06167895e-01 6.69552803e-01
-5.24758920e-02 4.68761194e-04 -1.21198356e+00 -1.16139144e-01
-9.81400684e-02 -3.93163949e-01 1.01066005e+00 3.66985559e-01
1.46334648e+00 -6.45219609e-02 -3.54876697e-01 7.43187964e-01
1.18137527e+00 1.33211194e-02 5.04504561e-01 2.30617538e-01
9.89688814e-01 7.98814654e-01 1.05644310e+00 3.81429978e-02
2.18548879e-01 5.65176964e-01 4.07424927e-01 -6.48611009e-01
-5.25795579e-01 -1.94958955e-01 1.11945651e-01 4.02296811e-01
1.61777332e-01 -1.69429600e-01 -7.01200664e-01 7.58771598e-01
-1.82478869e+00 -6.88258588e-01 -4.74984884e-01 2.06850982e+00
1.16459894e+00 4.37691391e-01 2.92462558e-01 7.95662552e-02
8.40904951e-01 5.03670052e-02 -6.04450405e-01 1.15582682e-01
-2.79405981e-01 3.68357867e-01 4.81683075e-01 6.00319862e-01
-1.33791757e+00 1.46115291e+00 4.84904623e+00 1.12460852e+00
-7.86442578e-01 3.79011333e-01 1.21035528e+00 -4.30562608e-02
-8.27195123e-02 -8.85996968e-02 -8.54505658e-01 5.81894219e-01
5.24339855e-01 5.20384192e-01 1.00750968e-01 9.41451311e-01
1.15433902e-01 -3.83968472e-01 -8.58197153e-01 8.41390967e-01
2.13644832e-01 -1.38255548e+00 2.40692850e-02 -5.43293878e-02
1.31791079e+00 -7.60350078e-02 3.53310145e-02 2.06565097e-01
9.51266512e-02 -7.04345286e-01 1.04775858e+00 1.19129725e-01
7.98674822e-01 -7.96195269e-01 7.64088452e-01 4.94421452e-01
-1.24689031e+00 2.89551198e-01 2.12511253e-02 4.03710693e-01
1.89529687e-01 8.96325588e-01 -6.95121050e-01 3.89337629e-01
7.23688126e-01 4.44071561e-01 -5.98715067e-01 8.34137797e-01
-4.86950994e-01 9.73714650e-01 -3.55617464e-01 3.41426909e-01
3.79599512e-01 -3.51427883e-01 3.59573722e-01 1.62852705e+00
-4.76382941e-01 -7.80289173e-02 4.75024134e-01 1.13388133e+00
-3.06916475e-01 8.02899618e-03 -7.98595771e-02 2.20241487e-01
1.46595642e-01 1.59681249e+00 -1.39001858e+00 -8.01455319e-01
-3.81300300e-01 1.39378071e+00 1.11059994e-01 4.51232374e-01
-1.03591335e+00 -1.16646186e-01 1.61354378e-01 9.76978019e-02
4.28402990e-01 7.36082047e-02 -4.07622695e-01 -9.86182809e-01
1.21115744e-01 -1.14926457e+00 3.00176948e-01 -5.73507547e-01
-9.19491231e-01 5.47789574e-01 5.46449237e-03 -8.84620070e-01
6.09884113e-02 -4.73946750e-01 -5.45317531e-01 7.06920981e-01
-1.64677155e+00 -1.41116214e+00 -4.23536658e-01 3.39552402e-01
1.17576087e+00 2.11137354e-01 2.88919181e-01 4.72428709e-01
-7.83500314e-01 4.24571484e-01 -1.14910610e-01 1.97150409e-01
6.12514079e-01 -1.71501744e+00 4.46513474e-01 1.10640121e+00
3.49729717e-01 1.77629203e-01 5.13214767e-01 -7.88558602e-01
-7.84495533e-01 -1.55902541e+00 3.62117648e-01 -5.99508464e-01
2.83638716e-01 -5.79127491e-01 -8.88315380e-01 4.87810910e-01
1.42840251e-01 4.01888967e-01 3.88958633e-01 -5.33678651e-01
-2.26603393e-02 1.32887647e-01 -1.29411995e+00 6.50697351e-01
9.20150280e-01 -2.96494633e-01 -4.14364278e-01 5.21381795e-01
8.09454441e-01 -7.27498472e-01 -6.53982103e-01 4.87556458e-01
1.49159387e-01 -6.24930680e-01 9.03139651e-01 -2.69475728e-01
4.20743346e-01 -7.13217735e-01 4.64209961e-03 -8.80539656e-01
2.01471671e-01 -7.58487821e-01 -2.14258716e-01 1.51556933e+00
6.60669446e-01 -1.05833709e-01 1.26332462e+00 2.98953235e-01
-2.33251929e-01 -8.36363077e-01 -6.21648788e-01 -5.36493182e-01
-6.49737567e-02 -6.58582866e-01 2.93705702e-01 8.93363595e-01
-7.58918405e-01 1.62307858e-01 -1.59190342e-01 1.27432600e-01
1.00093424e+00 2.60942012e-01 1.15803790e+00 -8.04532349e-01
-5.31540751e-01 -3.75498027e-01 1.13015890e-01 -1.24743986e+00
2.38062158e-01 -9.54245508e-01 4.51875031e-01 -1.29551756e+00
4.60012347e-01 -4.48060185e-01 -8.97653177e-02 4.45019484e-01
-6.66562974e-01 7.30134428e-01 1.49205163e-01 7.63895288e-02
-8.51003230e-01 3.29669803e-01 1.43156826e+00 -3.79845262e-01
-2.92628169e-01 1.57503381e-01 -3.65260720e-01 8.61114681e-01
6.99137151e-01 -6.28636420e-01 -2.33487695e-01 -1.44068584e-01
-4.01016533e-01 -3.13308120e-01 4.09864932e-01 -8.68609309e-01
-1.14450738e-01 -1.88747376e-01 3.80663544e-01 -9.26170707e-01
2.46622786e-01 -7.61698544e-01 -1.18281186e-01 4.58658427e-01
-3.02574277e-01 -3.90976012e-01 3.57814252e-01 7.13844597e-01
-2.57240295e-01 -3.31224293e-01 1.16680610e+00 -2.54187644e-01
-6.54500186e-01 3.07878405e-01 -2.22674593e-01 4.71758217e-01
1.13314521e+00 -2.81380743e-01 -5.81260696e-02 7.84648880e-02
-6.97056651e-01 3.41474801e-01 5.51714301e-01 2.93422699e-01
3.40816468e-01 -9.26098049e-01 -7.70835638e-01 1.22152574e-01
1.17901392e-01 7.56307244e-01 8.74147862e-02 9.50819016e-01
-5.74493825e-01 -7.76989758e-02 3.62675250e-01 -1.07718432e+00
-1.43054461e+00 4.32087779e-01 2.83701122e-01 -4.07129228e-01
-5.79594672e-01 1.13061237e+00 4.52384144e-01 -6.44530058e-01
3.27304482e-01 -2.61561811e-01 1.16600268e-01 -5.17551368e-03
5.46125054e-01 1.59469083e-01 3.33670191e-02 -7.47629344e-01
-1.78063676e-01 4.85315293e-01 -7.06585869e-02 -4.76609170e-01
1.20902288e+00 4.58311513e-02 -1.44570857e-01 2.96454191e-01
9.74440575e-01 3.00938562e-02 -1.63492882e+00 -2.16968238e-01
2.13319644e-01 -3.80646467e-01 3.53087932e-02 -9.34173763e-01
-1.32343125e+00 7.90291309e-01 5.00834048e-01 -2.81680763e-01
1.08218145e+00 1.66879296e-01 7.82281876e-01 1.78716984e-02
1.09512918e-01 -1.29751825e+00 2.58835673e-01 1.47337899e-01
4.86437857e-01 -1.36420143e+00 -1.77681953e-01 -8.65341306e-01
-7.17689633e-01 6.59826100e-01 8.72553647e-01 8.31509978e-02
1.33716851e-01 3.98410916e-01 1.97914764e-01 -2.10055849e-03
-3.91936928e-01 -5.31335413e-01 5.37150443e-01 6.31919146e-01
3.42910767e-01 -1.02689303e-02 -2.46709526e-01 4.61087942e-01
1.82719335e-01 -2.85083473e-01 2.77787566e-01 8.48776340e-01
-3.26400667e-01 -1.20349002e+00 -5.96912265e-01 4.09725547e-01
-5.81117332e-01 -1.83836713e-01 -6.02035284e-01 7.62275279e-01
4.72992659e-01 9.48375762e-01 8.44856538e-03 -3.38616595e-02
1.71347111e-01 1.56119063e-01 5.08428752e-01 -8.46711636e-01
-6.98260605e-01 5.15404820e-01 2.66275592e-02 -5.66256464e-01
-5.79300761e-01 -7.66924679e-01 -1.40602064e+00 2.79088467e-01
-5.98986804e-01 2.74026245e-02 7.42753983e-01 9.78900433e-01
6.95269480e-02 7.77537107e-01 2.85811156e-01 -1.24353993e+00
7.16974139e-02 -8.48544419e-01 -2.30964020e-01 6.77314639e-01
7.30482712e-02 -3.37930948e-01 -3.84593368e-01 4.76839811e-01] | [9.54545783996582, 0.5424870848655701] |
bac36c79-ba09-4fc5-bd42-0ee4dc4a887c | from-words-to-code-harnessing-data-for | 2305.01598 | null | https://arxiv.org/abs/2305.01598v2 | https://arxiv.org/pdf/2305.01598v2.pdf | From Words to Code: Harnessing Data for Program Synthesis from Natural Language | Creating programs to correctly manipulate data is a difficult task, as the underlying programming languages and APIs can be challenging to learn for many users who are not skilled programmers. Large language models (LLMs) demonstrate remarkable potential for generating code from natural language, but in the data manipulation domain, apart from the natural language (NL) description of the intended task, we also have the dataset on which the task is to be performed, or the "data context". Existing approaches have utilized data context in a limited way by simply adding relevant information from the input data into the prompts sent to the LLM. In this work, we utilize the available input data to execute the candidate programs generated by the LLMs and gather their outputs. We introduce semantic reranking, a technique to rerank the programs generated by LLMs based on three signals coming the program outputs: (a) semantic filtering and well-formedness based score tuning: do programs even generate well-formed outputs, (b) semantic interleaving: how do the outputs from different candidates compare to each other, and (c) output-based score tuning: how do the outputs compare to outputs predicted for the same task. We provide theoretical justification for semantic interleaving. We also introduce temperature mixing, where we combine samples generated by LLMs using both high and low temperatures. We extensively evaluate our approach in three domains, namely databases (SQL), data science (Pandas) and business intelligence (Excel's Power Query M) on a variety of new and existing benchmarks. We observe substantial gains across domains, with improvements of up to 45% in top-1 accuracy and 34% in top-3 accuracy. | ['Ashish Tiwari', 'Mukul Singh', 'Sherry Shi', 'Mohammad Raza', 'Vu Le', 'Sumit Gulwani', 'Avrilia Floratou', 'Venkatesh Emani', 'Shaleen Deep', 'Jordan Henkel', 'Joyce Cahoon', 'Anirudh Khatry'] | 2023-05-02 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 3.05879861e-01 -4.32623103e-02 -3.60161364e-01 -6.47265434e-01
-8.42817664e-01 -9.85947490e-01 6.34310484e-01 6.65474057e-01
-2.31871739e-01 2.30701834e-01 1.22905552e-01 -4.69281137e-01
5.99048194e-03 -1.03086877e+00 -1.10139656e+00 -1.70936212e-01
9.63395238e-02 4.11097229e-01 3.85175318e-01 -1.42776310e-01
5.60018301e-01 1.50570884e-01 -2.10095620e+00 9.01558697e-01
1.29460514e+00 8.37337136e-01 3.14104378e-01 7.18931973e-01
-7.01762497e-01 8.97440732e-01 -6.91158295e-01 -2.47742921e-01
1.28633589e-01 -2.09821999e-01 -9.28528190e-01 -4.98011559e-01
3.52637112e-01 1.47818282e-01 5.10736227e-01 1.07142317e+00
1.11723796e-01 -2.15925068e-01 2.73716033e-01 -1.28805101e+00
-3.47505838e-01 6.71045542e-01 -2.52411127e-01 -3.44092101e-01
8.26310396e-01 3.21367383e-01 1.12137794e+00 -8.12833071e-01
7.47698426e-01 1.12538052e+00 4.63621140e-01 5.33861160e-01
-1.65625620e+00 -2.71798015e-01 1.84543561e-02 -2.60153562e-01
-1.01007891e+00 -4.23480570e-01 3.35660398e-01 -9.17371392e-01
1.29548490e+00 5.91894031e-01 2.29954302e-01 7.15783298e-01
3.33212577e-02 4.59752828e-01 1.15036750e+00 -4.90008384e-01
5.08724928e-01 5.00320911e-01 5.17201602e-01 6.55727088e-01
3.23297292e-01 8.56653526e-02 -6.37511015e-01 -6.54944062e-01
2.28535444e-01 -4.01041247e-02 1.28026120e-02 -4.81023312e-01
-1.30518878e+00 6.61386073e-01 1.97805002e-01 2.17149392e-01
-1.53682783e-01 2.66139835e-01 4.71246570e-01 5.64181924e-01
-4.61706594e-02 1.15827930e+00 -7.76938915e-01 -2.39405051e-01
-7.83742785e-01 5.44748962e-01 1.24937737e+00 1.28913033e+00
1.16404390e+00 -2.56693363e-01 -4.06817466e-01 5.63183010e-01
8.80718976e-02 3.20835084e-01 6.36243820e-01 -7.13779271e-01
7.77179062e-01 1.22547758e+00 1.99209809e-01 -7.16702223e-01
-1.95472091e-01 1.16914749e-01 -2.47544616e-01 3.18911612e-01
5.76999128e-01 3.34125198e-02 -8.59777987e-01 1.71842563e+00
5.09376973e-02 -5.42678595e-01 2.38048732e-01 7.13262320e-01
7.91852057e-01 6.77809119e-01 2.26471260e-01 1.32139295e-01
1.38717163e+00 -8.09363902e-01 -1.10528305e-01 -4.48504746e-01
9.64555740e-01 -6.97461307e-01 1.60180032e+00 4.58766252e-01
-9.91071105e-01 -7.10723221e-01 -9.14261162e-01 -1.49311811e-01
-6.30936563e-01 5.99170588e-02 7.40249753e-01 5.14292181e-01
-1.05110514e+00 7.21287251e-01 -6.43208444e-01 -3.54427636e-01
8.74220952e-02 4.46697533e-01 -2.03661293e-01 6.78709671e-02
-7.99029768e-01 6.77180529e-01 5.40308058e-01 -6.14113688e-01
-7.99885154e-01 -9.63847339e-01 -7.57836878e-01 1.59431085e-01
4.49309677e-01 -6.57763600e-01 1.21355820e+00 -1.04943037e+00
-1.15404248e+00 1.03139126e+00 -3.85361701e-01 -2.95971900e-01
4.21382844e-01 -2.04594329e-01 -1.59371331e-01 -3.83554995e-01
1.47184998e-01 4.45799798e-01 4.98808831e-01 -1.28168106e+00
-7.28117347e-01 -4.72533226e-01 3.53955507e-01 -1.68039858e-01
-2.11106211e-01 2.70166606e-01 -3.62057328e-01 -5.12184024e-01
-4.30486277e-02 -8.79213333e-01 -2.70275533e-01 -1.11486398e-01
-3.66812587e-01 -2.62279212e-01 3.55424911e-01 -4.64194655e-01
1.40680707e+00 -2.09206223e+00 1.68478340e-01 3.94264281e-01
2.96193540e-01 7.75341541e-02 -1.37224272e-01 4.72975016e-01
-9.23063755e-02 4.30695117e-01 -2.71155089e-01 3.42285097e-01
3.60560894e-01 6.47195578e-02 -4.89893436e-01 -2.09952310e-01
4.64019507e-01 8.26359332e-01 -8.99959683e-01 -1.85550392e-01
-5.13518304e-02 -2.33286306e-01 -9.56210971e-01 6.50884390e-01
-9.49952722e-01 6.29199073e-02 -4.95926797e-01 4.69192177e-01
4.07648176e-01 -4.12702829e-01 3.17419887e-01 -8.09211433e-02
-2.94820935e-01 7.02116311e-01 -1.23621511e+00 1.59112728e+00
-6.49732232e-01 2.04279453e-01 -8.19110423e-02 -8.40172827e-01
1.19236946e+00 3.47946375e-03 1.51296377e-01 -6.70391738e-01
-6.01242781e-01 5.35412490e-01 -7.60809556e-02 -8.08516264e-01
6.49984360e-01 2.67799050e-02 -6.58022523e-01 6.06077909e-01
-6.25188053e-02 -3.84252697e-01 4.99754786e-01 6.07362539e-02
1.35554743e+00 2.95594484e-01 3.01464975e-01 -4.94606256e-01
4.33941782e-01 5.69743991e-01 4.57204252e-01 9.89367902e-01
3.66587132e-01 4.36650395e-01 1.14108181e+00 -5.75728178e-01
-1.09242499e+00 -7.98202395e-01 1.68475777e-01 1.64534509e+00
2.50457563e-02 -7.84563959e-01 -7.60379612e-01 -6.38814330e-01
3.00147623e-01 9.52939391e-01 -3.60137194e-01 -3.03421319e-01
-5.20725727e-01 -5.76333940e-01 5.75561225e-01 4.88942385e-01
1.44703880e-01 -9.88399804e-01 -8.23126793e-01 1.28965184e-01
1.64209083e-02 -8.16823900e-01 -3.62911195e-01 4.42053258e-01
-8.88739944e-01 -1.10579157e+00 -5.13102077e-02 -6.84559464e-01
8.04524779e-01 -1.77421421e-01 1.65203536e+00 2.61711925e-01
-1.49740845e-01 1.05112970e-01 -2.45304361e-01 -4.62219059e-01
-8.88941467e-01 2.51728147e-01 -2.69723564e-01 -4.04404730e-01
5.46218753e-01 -2.50279725e-01 -2.38151342e-01 2.75243819e-01
-9.73570168e-01 2.33321279e-01 5.40104210e-01 7.81950653e-01
4.11248296e-01 -7.68332481e-02 2.77603656e-01 -1.32157910e+00
6.85653150e-01 -4.78873700e-01 -9.88752544e-01 5.53209007e-01
-8.24601829e-01 6.94139719e-01 1.00546467e+00 -3.15700948e-01
-8.98501694e-01 2.33052045e-01 -4.48936932e-02 1.31910993e-02
-2.89731592e-01 7.09251463e-01 -2.82571912e-01 2.05468312e-01
1.10110867e+00 1.68360710e-01 -7.32363388e-02 -4.57896948e-01
2.49751613e-01 4.43204850e-01 3.79111469e-01 -1.40028572e+00
7.23472774e-01 -9.90950316e-03 -3.49650800e-01 -2.38849074e-01
-2.92967021e-01 -2.04537302e-01 -3.29316825e-01 2.26971209e-01
6.33969307e-01 -6.09278977e-01 -7.75864303e-01 2.99120694e-01
-1.24284303e+00 -4.40594733e-01 -3.14180583e-01 5.61484061e-02
-5.19905031e-01 -7.58196712e-02 -2.37903759e-01 -5.15853107e-01
-3.15384209e-01 -1.71627867e+00 1.17099214e+00 7.09767491e-02
-6.29227519e-01 -6.42744184e-01 -1.66884929e-01 1.92203045e-01
5.54999709e-01 3.00237626e-01 1.68278062e+00 -9.00738299e-01
-7.43828475e-01 -1.50812835e-01 -1.89649656e-01 2.12381348e-01
-2.67768744e-03 3.23135138e-01 -9.33679521e-01 1.97020750e-02
-2.01953858e-01 -2.91269243e-01 4.98406410e-01 -1.72802508e-01
1.40574968e+00 -4.70812649e-01 -4.11893219e-01 5.00538409e-01
1.56220245e+00 6.60340041e-02 5.58411121e-01 2.44466856e-01
6.41656578e-01 9.28304255e-01 5.09927869e-01 3.22273731e-01
2.08741322e-01 5.69489777e-01 2.04177275e-01 1.02618270e-01
1.77931085e-01 -5.06052196e-01 6.83615625e-01 5.00392616e-01
1.54878691e-01 1.90789893e-01 -1.31940329e+00 4.62910950e-01
-1.79685962e+00 -6.52640820e-01 -3.24501455e-01 2.62387872e+00
1.27030063e+00 1.90293759e-01 1.61516696e-01 -8.11729580e-02
4.36036557e-01 -8.49023312e-02 -6.15068257e-01 -6.52948678e-01
1.79986849e-01 4.75357950e-01 3.93541962e-01 2.94429004e-01
-7.48501658e-01 7.33767807e-01 6.11431932e+00 6.74762964e-01
-1.27108657e+00 -1.81769311e-01 5.14505386e-01 1.03569649e-01
-8.28037977e-01 4.00434583e-01 -7.96993554e-01 5.79720497e-01
1.06020749e+00 -4.98288721e-01 6.87359393e-01 1.17273057e+00
-2.81477086e-02 -2.72513181e-01 -1.86551178e+00 5.58533311e-01
-3.95815998e-01 -1.36535299e+00 7.63817206e-02 -2.55825996e-01
7.01321363e-01 -1.18401431e-01 -2.33831659e-01 4.59040910e-01
6.14001036e-01 -1.09730446e+00 9.22239304e-01 6.59783244e-01
7.93251336e-01 -3.57604295e-01 3.84963214e-01 4.00334656e-01
-1.09229350e+00 -1.32169619e-01 -2.45050556e-04 -1.19032919e-01
-6.45665705e-01 7.18962431e-01 -9.50839520e-01 4.41914588e-01
7.18034744e-01 3.20007861e-01 -7.79293418e-01 5.41781247e-01
-9.09869745e-02 4.61338937e-01 -1.84567362e-01 -3.07561845e-01
-2.22489104e-01 2.36698017e-02 2.73393542e-01 1.24134386e+00
3.33802849e-01 -2.37551868e-01 2.87511200e-01 1.51252198e+00
-6.77222162e-02 5.40905632e-02 -6.03347719e-01 -3.80783439e-01
4.56757039e-01 9.48109388e-01 -3.08731586e-01 -6.59784675e-01
-4.82767671e-01 5.36143363e-01 2.13263884e-01 3.12019616e-01
-5.58439016e-01 -7.25447893e-01 8.71278226e-01 3.20551008e-01
-1.42279252e-01 -7.87320212e-02 -7.18279243e-01 -1.10603964e+00
4.65743482e-01 -1.33996069e+00 4.14006263e-01 -7.36718714e-01
-1.15228331e+00 3.53605449e-01 -7.40524605e-02 -9.02299881e-01
-3.05358827e-01 -7.64461815e-01 -4.17299092e-01 1.15531385e+00
-1.13595700e+00 -6.67122543e-01 -4.72167075e-01 4.41300049e-02
3.08769196e-01 -9.58550274e-02 9.53850210e-01 2.91354865e-01
-3.22114944e-01 5.39970636e-01 -5.23000769e-02 5.26147634e-02
7.18452036e-01 -1.44546115e+00 5.63102186e-01 6.10736907e-01
-1.61350489e-01 1.16300452e+00 6.60945237e-01 -5.33020496e-01
-1.96477842e+00 -1.20075238e+00 9.91784751e-01 -7.50723004e-01
6.83916986e-01 -6.37807965e-01 -1.05880976e+00 5.32792747e-01
-2.15324804e-01 -1.23289518e-01 5.39669573e-01 6.10943288e-02
-6.64291978e-01 -3.41456950e-01 -1.17268217e+00 4.32472408e-01
8.52553666e-01 -6.36108875e-01 -5.89884698e-01 3.39972347e-01
1.00255036e+00 -5.54741859e-01 -1.01452768e+00 2.79593527e-01
5.95152497e-01 -9.71611142e-01 7.53095984e-01 -9.68675077e-01
9.84710217e-01 -5.05439818e-01 -3.37868184e-01 -1.09071600e+00
1.31182045e-01 -5.37469625e-01 1.27859205e-01 1.28555465e+00
7.19559550e-01 -7.12027073e-01 5.10832191e-01 1.30460167e+00
-5.20691238e-02 -6.54399335e-01 -6.96284920e-02 -6.29305124e-01
2.86705475e-02 -6.31275713e-01 1.18397152e+00 9.20185506e-01
2.39740863e-01 1.88666910e-01 2.06903711e-01 8.15217718e-02
1.47689536e-01 6.44642413e-01 1.04813325e+00 -1.19371581e+00
-6.01036370e-01 -6.59929693e-01 -9.44359824e-02 -8.90156388e-01
-8.09614211e-02 -1.34268081e+00 1.70238212e-01 -1.23216355e+00
3.44680935e-01 -7.98519969e-01 9.85294282e-02 7.06739187e-01
-2.91290343e-01 -5.50491095e-01 1.81836158e-01 2.36436993e-01
-4.99437869e-01 -1.62811026e-01 6.46280527e-01 -1.55566677e-01
-3.52248549e-01 -9.38059762e-02 -8.89684200e-01 5.01749992e-01
5.00420988e-01 -5.61294556e-01 -1.98775530e-01 -6.91369057e-01
7.16613948e-01 2.09048435e-01 3.95177633e-01 -9.49585438e-01
1.46487936e-01 -5.80750644e-01 -4.59011346e-02 -5.69913387e-02
-3.21735054e-01 -7.58401573e-01 3.69664073e-01 5.58089197e-01
-9.28453445e-01 2.63511509e-01 2.10063636e-01 2.15306550e-01
-1.93418384e-01 -5.05331576e-01 5.27818978e-01 -3.85172784e-01
-8.14649761e-01 -1.33926079e-01 -1.98272258e-01 2.87614822e-01
9.08194661e-01 7.55666271e-02 -6.79128826e-01 2.14996651e-01
-5.46337664e-01 2.49186456e-01 9.03705478e-01 5.65737367e-01
3.01610559e-01 -1.00205588e+00 -3.99794102e-01 5.23626626e-01
6.28058076e-01 1.79590464e-01 -4.15603220e-01 6.86711431e-01
-6.07422531e-01 4.00681615e-01 3.32163414e-03 -6.45922244e-01
-1.11670828e+00 5.67497730e-01 1.30080089e-01 -3.91382337e-01
-2.43974663e-02 6.55498147e-01 2.13471591e-01 -9.40812886e-01
8.33766311e-02 -8.43669653e-01 3.30549836e-01 -1.24901921e-01
3.56330007e-01 1.89934298e-01 4.20468658e-01 -5.90733178e-02
-4.45360571e-01 2.45831773e-01 4.06517051e-02 1.50393337e-01
1.38557065e+00 7.21101940e-01 -6.80924177e-01 5.34411907e-01
1.14582348e+00 1.86897498e-02 -7.36838102e-01 -2.52276033e-01
6.40893519e-01 -4.85167950e-01 -5.70532501e-01 -8.90196204e-01
-7.04030693e-01 8.89010251e-01 2.16717914e-01 4.02250081e-01
9.67067003e-01 1.34043783e-01 3.83685380e-01 4.70830977e-01
6.47591114e-01 -1.07618546e+00 1.47583723e-01 4.87229198e-01
8.42903376e-01 -1.17390609e+00 -2.41752267e-01 -3.32950383e-01
-5.40831327e-01 1.16677618e+00 8.72228026e-01 5.63545339e-02
2.27694884e-01 5.35731614e-01 -1.84369415e-01 -2.46630430e-01
-1.03278923e+00 -1.76908299e-02 1.80038422e-01 4.05250400e-01
7.33286500e-01 1.72051832e-01 -2.76550263e-01 9.28333640e-01
-2.76824534e-01 2.05039114e-01 4.05078053e-01 1.16248620e+00
-4.27947104e-01 -1.33965039e+00 -5.64748347e-01 1.00059438e+00
-2.44113177e-01 -1.70357361e-01 -5.13390303e-01 4.02707607e-01
8.11416730e-02 7.52904415e-01 -6.35984764e-02 -5.90452492e-01
5.51662445e-01 3.36962670e-01 1.14423707e-01 -1.05436337e+00
-9.78618264e-01 -4.32035446e-01 3.28234047e-01 -6.67901039e-01
5.36389090e-02 -5.49296498e-01 -1.51605439e+00 -2.50990361e-01
-3.79339941e-02 2.01611236e-01 7.01421380e-01 6.56608880e-01
8.58655334e-01 4.77600813e-01 4.56251323e-01 -4.57023680e-01
-7.97092974e-01 -4.81419861e-01 5.88016063e-02 7.67331481e-01
2.62103796e-01 -2.16335088e-01 -1.58512980e-01 3.72859627e-01] | [8.143013000488281, 7.598193168640137] |
53d7c6da-5bef-45aa-a32d-01acd5327cf1 | reverse-perspective-network-for-perspective | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Yang_Reverse_Perspective_Network_for_Perspective-Aware_Object_Counting_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Yang_Reverse_Perspective_Network_for_Perspective-Aware_Object_Counting_CVPR_2020_paper.pdf | Reverse Perspective Network for Perspective-Aware Object Counting | One of the critical challenges of object counting is the dramatic scale variations, which is introduced by arbitrary perspectives. We propose a reverse perspective network to solve the scale variations of input images, instead of generating perspective maps to smooth final outputs. The reverse perspective network explicitly evaluates the perspective distortions, and efficiently corrects the distortions by uniformly warping the input images. Then the proposed network delivers images with similar instance scales to the regressor. Thus the regression network doesn't need multi-scale receptive fields to match the various scales. Besides, to further solve the scale problem of more congested areas, we enhance the corresponding regions of ground-truth with the evaluation errors. Then we force the regressor to learn from the augmented ground-truth via an adversarial process. Furthermore, to verify the proposed model, we collected a vehicle counting dataset based on Unmanned Aerial Vehicles (UAVs). The proposed dataset has fierce scale variations. Extensive experimental results on four benchmark datasets show the improvements of our method against the state-of-the-arts.
| [' Nicu Sebe', ' Qingming Huang', ' Li Su', ' Zhe Wu', ' Guorong Li', 'Yifan Yang'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['object-counting'] | ['computer-vision'] | [ 2.01422349e-01 -2.17546389e-01 2.39086792e-01 -4.36048001e-01
-1.34605139e-01 -7.36049175e-01 4.05367583e-01 -6.66249812e-01
-5.12608290e-01 4.61593479e-01 -2.76916653e-01 4.71391296e-03
2.79773921e-01 -9.90557373e-01 -9.75904763e-01 -5.34247756e-01
2.53699094e-01 1.09223835e-01 4.73363727e-01 -2.57538468e-01
3.71545047e-01 5.77839851e-01 -1.12708294e+00 1.30129606e-01
7.61085153e-01 1.02742338e+00 2.32940391e-01 6.69755697e-01
5.16318195e-02 9.85261142e-01 -7.39670455e-01 -4.11506653e-01
9.25920188e-01 -4.32842188e-02 -1.96860403e-01 3.05171996e-01
1.00914216e+00 -8.66987526e-01 -8.34895670e-01 1.67890668e+00
2.29264885e-01 -2.16917992e-02 3.03225815e-01 -1.39124656e+00
-1.14753878e+00 -1.54679194e-02 -1.12797618e+00 3.27792495e-01
5.89107014e-02 2.38404244e-01 3.55838358e-01 -1.01281071e+00
4.95597333e-01 1.50639343e+00 6.17098749e-01 3.38193029e-01
-7.50020146e-01 -9.69960332e-01 5.23085892e-01 -7.50721246e-02
-1.29117918e+00 -2.20063731e-01 8.08699548e-01 -3.44937295e-01
3.40955704e-01 4.06500064e-02 6.60201907e-01 7.84981608e-01
2.58209169e-01 5.75438976e-01 1.37134278e+00 2.16548517e-02
4.93442975e-02 -8.07101801e-02 -2.50479370e-01 1.01226664e+00
4.29015309e-01 3.16803098e-01 -7.80597702e-02 1.12028122e-01
1.29053438e+00 5.74231923e-01 -2.65048504e-01 -4.00483549e-01
-1.46418202e+00 5.24839759e-01 9.81245518e-01 -1.93039522e-01
-1.49232328e-01 2.66302526e-01 2.47251660e-01 2.19946221e-01
4.85190809e-01 1.75354794e-01 -6.32546842e-01 6.42422676e-01
-9.10950482e-01 2.91836649e-01 3.21893364e-01 1.49479389e+00
5.08732677e-01 3.57700288e-01 -1.73017606e-01 3.90984088e-01
1.57804564e-01 1.08801889e+00 1.28699109e-01 -1.13194776e+00
1.03518105e+00 8.08182776e-01 4.80615944e-01 -1.37937260e+00
-3.69652003e-01 -5.42558134e-01 -1.02475631e+00 5.39688408e-01
6.55635059e-01 -1.84124067e-01 -1.07491553e+00 1.61477721e+00
4.44978386e-01 2.34436825e-01 -5.33412769e-02 1.11859655e+00
6.36370182e-01 7.75096238e-01 -1.39933333e-01 -1.88342586e-01
1.34927273e+00 -1.25004900e+00 -5.11128247e-01 -5.58676660e-01
-1.75354742e-02 -8.22448730e-01 1.03677201e+00 3.31408888e-01
-1.15006042e+00 -7.18859851e-01 -1.16260695e+00 -1.60735875e-01
-3.95409435e-01 5.62757909e-01 3.47372591e-01 1.65738128e-02
-6.72821045e-01 5.88548601e-01 -7.26571262e-01 -3.71365249e-02
5.65418005e-01 1.80544421e-01 -2.48850942e-01 -1.03077903e-01
-1.02526903e+00 8.35464478e-01 2.73702919e-01 3.11242551e-01
-8.45533967e-01 -7.62875199e-01 -6.03867412e-01 -1.41380191e-01
4.16834772e-01 -8.17898154e-01 9.10788774e-01 -1.11673117e+00
-1.33803201e+00 8.48110855e-01 1.21116027e-01 -8.55630934e-02
1.06024146e+00 -1.69010505e-01 -2.69587129e-01 4.92454693e-02
4.01303917e-01 7.15221286e-01 1.16696179e+00 -1.25938213e+00
-8.29839766e-01 -6.78409219e-01 4.39461172e-01 2.44938225e-01
-1.51640385e-01 1.65431011e-05 -4.74177390e-01 -7.13116109e-01
2.11598590e-01 -1.12446785e+00 -1.81071967e-01 4.33362931e-01
-2.04058632e-01 3.37899923e-01 8.11278641e-01 -8.32089186e-01
7.74775982e-01 -2.17112756e+00 1.00625865e-01 -2.45283484e-01
3.46947283e-01 2.49897260e-02 -2.42782220e-01 -1.77408755e-01
8.19579959e-02 -1.73222423e-01 -2.10240021e-01 1.15594752e-02
-3.51665467e-01 1.97181419e-01 -6.49362504e-01 6.21931553e-01
1.34125262e-01 9.09646690e-01 -1.09385133e+00 -4.79744971e-01
2.12397829e-01 3.91887367e-01 -5.14375091e-01 2.56818980e-01
-1.31576419e-01 6.17673218e-01 -4.51682925e-01 7.63430774e-01
1.20931935e+00 -1.82906255e-01 -4.38079834e-01 -3.39110076e-01
-2.51971662e-01 -4.12471950e-01 -1.04630613e+00 1.65525472e+00
-4.08861399e-01 3.14335287e-01 1.92340475e-03 -5.47231615e-01
1.00172198e+00 -3.54527324e-01 2.01396257e-01 -5.37987709e-01
4.01428014e-01 1.33664832e-01 -1.91450894e-01 -4.50096130e-01
4.53875840e-01 2.98783369e-02 -5.31108528e-02 -2.34709397e-01
4.51372191e-02 -4.02237147e-01 4.40488243e-03 1.24321543e-02
6.86994493e-01 1.82510436e-01 2.24754497e-01 -5.78910187e-02
6.00795805e-01 -6.31463304e-02 8.38233232e-01 3.58748734e-01
-3.43323469e-01 7.97511995e-01 4.19071525e-01 -9.59610999e-01
-1.26796210e+00 -9.66470420e-01 6.86542839e-02 8.63306880e-01
5.97435951e-01 3.06154370e-01 -8.49899650e-01 -9.25764143e-01
-9.84502137e-02 3.47014219e-01 -8.17700267e-01 3.20584588e-02
-7.02184856e-01 -4.88937527e-01 5.67270815e-01 8.20929348e-01
1.21706641e+00 -8.08146000e-01 -7.14432418e-01 -4.67005223e-02
-1.68328732e-01 -1.50033820e+00 -9.19884026e-01 -3.32691133e-01
-9.25854206e-01 -1.16187835e+00 -8.80693674e-01 -7.37595439e-01
1.03600228e+00 8.69170070e-01 8.42142045e-01 -4.80418429e-02
-8.70350450e-02 -1.79410771e-01 2.46398188e-02 -5.71231008e-01
-6.03570463e-03 -2.15149865e-01 1.11053698e-01 -1.12464987e-01
1.62973687e-01 -5.37091255e-01 -8.91335845e-01 6.09872639e-01
-8.95498574e-01 4.62481044e-02 6.16755366e-01 7.61104226e-01
6.99982643e-01 1.50460348e-01 1.98352888e-01 -7.37617791e-01
1.78009748e-01 -4.37701456e-02 -1.35459256e+00 2.47235239e-01
-3.43038797e-01 -1.09510727e-01 9.75075543e-01 -6.45496070e-01
-8.81447673e-01 2.59116143e-01 5.21111786e-01 -9.61301863e-01
1.09757908e-01 -2.39488766e-01 -2.16145352e-01 -4.39734727e-01
5.31059682e-01 1.87792689e-01 -3.76185209e-01 9.98362079e-02
5.98741412e-01 1.99122414e-01 7.45619714e-01 -1.96633682e-01
1.33671522e+00 8.45055759e-01 6.69267103e-02 -4.31041300e-01
-1.02905273e+00 -9.13231000e-02 -6.58871710e-01 -2.75679499e-01
1.06720686e+00 -1.12216234e+00 -5.50761819e-01 5.73139191e-01
-1.57193589e+00 4.72071804e-02 -2.00937957e-01 3.91063482e-01
-4.87145334e-01 3.02951336e-01 -5.89173734e-01 -4.20159310e-01
-2.96133250e-01 -1.12967467e+00 1.20173860e+00 6.50202692e-01
5.21492183e-01 -5.53772390e-01 -2.28941768e-01 2.70844787e-01
3.35192978e-01 4.80335087e-01 6.03107691e-01 -1.34104043e-01
-8.76778603e-01 -2.62612522e-01 -7.26982474e-01 3.78960550e-01
-1.01278320e-01 9.15876627e-02 -9.61707056e-01 -4.51129198e-01
2.79443115e-01 -7.82330781e-02 7.65711784e-01 2.87610620e-01
1.21625006e+00 -3.22727770e-01 6.73854277e-02 1.07880843e+00
1.35052896e+00 1.91048428e-01 3.59324843e-01 3.86824638e-01
8.58086288e-01 4.30574179e-01 9.33153450e-01 2.11472675e-01
4.20136154e-01 3.29971105e-01 8.39639544e-01 -3.35459948e-01
-1.09653853e-01 -8.42431724e-01 2.77593642e-01 8.31611514e-01
-3.61420244e-01 1.65704191e-02 -5.35108030e-01 2.76997030e-01
-1.61363900e+00 -9.63478208e-01 -1.01866938e-01 2.15955901e+00
1.68193415e-01 1.15509674e-01 -1.00634664e-01 -2.40881652e-01
8.25737417e-01 5.18056273e-01 -8.81996751e-01 -1.24834501e-03
5.31769320e-02 -3.32627356e-01 1.23711443e+00 4.30030644e-01
-1.23033953e+00 1.05074251e+00 5.88700438e+00 4.90961462e-01
-1.24063063e+00 1.19976677e-01 5.77832878e-01 -6.67270720e-02
1.95560772e-02 6.48860028e-03 -7.07428575e-01 5.11877179e-01
5.66419326e-02 -2.89179850e-02 9.32207882e-01 1.18249416e+00
1.03197530e-01 2.81881690e-01 -8.03600669e-01 1.03260648e+00
2.75438011e-01 -8.96526694e-01 7.85961673e-02 -7.49272928e-02
1.10797489e+00 2.44956627e-01 3.52018565e-01 3.46594185e-01
3.90531301e-01 -7.15927422e-01 8.88454378e-01 6.67176008e-01
1.06590843e+00 -6.33141041e-01 6.86712861e-01 5.33985019e-01
-1.37629545e+00 -3.41613770e-01 -1.10716498e+00 -1.06376268e-01
-1.83084771e-01 3.88180077e-01 -3.69192660e-01 2.44407371e-01
5.61198115e-01 6.81696355e-01 -7.70354807e-01 7.00891256e-01
-3.35075438e-01 -1.59136325e-01 -1.68063864e-01 2.24039912e-01
7.52377659e-02 -4.93961513e-01 4.26552355e-01 9.02835071e-01
4.85914469e-01 2.01231793e-01 2.75097579e-01 1.16377020e+00
-3.89881939e-01 -1.21836320e-01 -9.70374346e-01 2.43920311e-01
5.66093802e-01 1.31092393e+00 -7.12180376e-01 -4.46708292e-01
-5.01469672e-01 1.08771038e+00 2.51533121e-01 5.38554490e-01
-1.24768233e+00 -3.61177504e-01 2.13953018e-01 4.31402586e-02
6.54944859e-04 -1.83612317e-01 -2.47222558e-01 -1.59685123e+00
2.44290665e-01 -7.45629668e-01 -1.96122890e-03 -1.13782644e+00
-1.24030435e+00 6.35059774e-01 -1.97092414e-01 -1.70548475e+00
4.45281327e-01 -6.02799833e-01 -6.83252454e-01 7.26912200e-01
-1.69283485e+00 -1.37580848e+00 -8.82961333e-01 6.25145555e-01
8.29826057e-01 -2.27073312e-01 3.38944554e-01 3.97606224e-01
-2.47049063e-01 3.92358243e-01 -1.82860032e-01 1.98269218e-01
8.09878707e-01 -1.10335135e+00 7.38644898e-01 9.92669880e-01
-1.68109134e-01 4.82625067e-01 3.34167778e-01 -5.38167894e-01
-1.22755063e+00 -1.57249963e+00 4.41770017e-01 -6.24683857e-01
6.78568780e-01 -1.72452644e-01 -6.62987053e-01 9.22785223e-01
-6.13098852e-02 7.78774261e-01 -1.94487393e-01 -7.56238103e-01
-4.68654275e-01 -3.02640229e-01 -1.22603035e+00 2.77543992e-01
1.20623529e+00 -4.12231147e-01 -6.01825058e-01 3.54860425e-01
9.98449981e-01 -8.03308427e-01 -4.99145150e-01 6.04242384e-01
6.18065357e-01 -1.02473009e+00 1.14925110e+00 -4.29873884e-01
8.08317959e-01 -7.02060759e-01 -3.25621694e-01 -1.42401123e+00
-4.41034079e-01 -3.08051659e-03 1.53943986e-01 1.08402812e+00
-1.62455514e-02 -6.87174916e-01 7.04840124e-01 3.25305581e-01
1.54406726e-01 -5.61986446e-01 -6.87058628e-01 -6.28904998e-01
9.62545127e-02 2.71534324e-01 8.49575281e-01 9.97992396e-01
-6.63237929e-01 2.13741884e-01 -6.13383174e-01 5.98668039e-01
7.64819741e-01 1.43001243e-01 1.02330625e+00 -8.86252999e-01
-2.41050482e-01 -5.59564494e-02 -6.32631123e-01 -1.30832171e+00
-1.33223563e-01 -5.89599013e-01 1.16305519e-02 -1.07444203e+00
1.74335539e-01 -2.24326327e-01 -1.94721818e-01 -8.60535279e-02
-4.15731281e-01 2.41256133e-01 4.80922997e-01 2.95195520e-01
-4.81163591e-01 4.45799291e-01 1.89776516e+00 -3.12695056e-01
5.50055690e-02 6.29074723e-02 -4.69897717e-01 1.33718646e+00
8.14204693e-01 -5.54691017e-01 -5.62506557e-01 -1.05828929e+00
1.96676314e-01 3.00599873e-01 4.80183214e-01 -1.06828403e+00
2.41364017e-01 -5.19736409e-01 8.92146409e-01 -6.21170878e-01
2.09882617e-01 -1.15363836e+00 -2.34139621e-01 4.58880812e-01
-2.02129543e-01 4.12783563e-01 -5.56200161e-04 8.04174900e-01
-7.36428350e-02 1.78692177e-01 1.05644393e+00 -3.25573891e-01
-2.37592801e-01 7.00384915e-01 4.79169011e-01 2.01777130e-01
1.11482918e+00 -8.80685821e-03 -6.17581904e-01 -5.30101992e-02
-2.42547780e-01 2.34749913e-01 8.57966304e-01 4.10860896e-01
6.72179520e-01 -1.45084608e+00 -6.50780141e-01 4.81144935e-01
6.50435463e-02 5.83517849e-01 6.64610490e-02 6.37847960e-01
-9.08884645e-01 5.13296314e-02 -5.41929662e-01 -5.24535835e-01
-1.00256169e+00 9.72463012e-01 4.65772480e-01 -2.03547165e-01
-4.67395931e-01 8.65231335e-01 6.82590365e-01 -7.94994295e-01
4.92965057e-02 -7.00881541e-01 -1.51469842e-01 -2.28183582e-01
5.91128588e-01 4.70389456e-01 -2.19907999e-01 -5.55475771e-01
-1.78947553e-01 1.00632572e+00 3.65196168e-02 1.80960134e-01
1.15557158e+00 -8.11941028e-02 2.84519698e-02 2.52129108e-01
1.04072642e+00 1.41343310e-01 -1.67400575e+00 -2.56202221e-01
-6.25016987e-01 -9.93170023e-01 -6.79404736e-02 -4.85590219e-01
-1.51920390e+00 1.10103953e+00 7.05878079e-01 -1.28970772e-01
1.02482736e+00 -7.00426161e-01 8.51784766e-01 4.56173629e-01
6.52935684e-01 -9.91906345e-01 8.06671008e-02 4.24678415e-01
1.07887411e+00 -1.31742740e+00 1.55349031e-01 -3.94820631e-01
-6.78619683e-01 1.19961071e+00 9.95866299e-01 -9.11285520e-01
2.43453354e-01 3.04182112e-01 1.23745166e-01 -1.19077181e-02
-3.52470964e-01 2.97458619e-01 -1.06028847e-01 4.33227599e-01
-1.05396979e-01 1.43462434e-01 -6.80199713e-02 2.69134670e-01
-2.22184122e-01 2.19149351e-01 5.48210979e-01 6.28742874e-01
-3.81163806e-01 -4.10257220e-01 -6.52643800e-01 2.56994963e-01
-1.81216612e-01 4.47174609e-02 -4.32066977e-01 8.17576885e-01
4.50551450e-01 5.89255989e-01 -7.04599246e-02 -4.34166253e-01
7.75142610e-01 -4.24876809e-01 4.34297740e-01 -1.97835669e-01
-1.57582834e-01 -1.23228125e-01 -5.87774158e-01 -5.96015275e-01
-2.54606783e-01 -2.48685837e-01 -1.13735008e+00 -5.68543196e-01
-5.24532557e-01 -3.71227324e-01 5.83034575e-01 5.03572285e-01
-1.09969508e-02 8.79967570e-01 1.01207483e+00 -1.07301140e+00
-9.97605383e-01 -8.74121785e-01 -3.88670266e-01 4.31469530e-01
3.37945431e-01 -6.57476604e-01 -4.52962697e-01 1.01544395e-01] | [8.697012901306152, -2.084717273712158] |
01ceca53-d518-45e4-80bd-b4d97706eec0 | tiny-object-detection-in-aerial-images | null | null | https://github.com/jwwangchn/AI-TOD | https://drive.google.com/file/d/1IiTp7gilwDCGr8QR_H9Covz8aVK7LXiI/view | Tiny Object Detection in Aerial Images | Object detection in Earth Vision has achieved great progress in recent years. However, tiny object detection in aerial images remains a very challenging problem since the tiny objects contain a small number of pixels and are easily confused with the background. To advance tiny object detection research in aerial images, we present a new dataset for Tiny Object Detection in Aerial Images (AI-TOD). Specifically, AI-TOD comes with 700,621 object instances for eight categories across 28,036 aerial images. Compared to existing object detection datasets in aerial images, the mean size of objects in AI-TOD is about 12.8 pixels, which is much smaller than others. To build a benchmark for tiny object detection in aerial images, we evaluate the state-of-the-art object detectors on our AI-TOD dataset. Experimental results show that direct application of these approaches on AI-TOD produces suboptimal object detection results, thus new specialized detectors for tiny object detection need to be designed. Therefore, we propose a multiple center points based learning network (M-CenterNet) to improve the localization performance of tiny object detection, and experimental results show the significant performance gain over the competitors. | ['Gui-Song Xia', 'Ruixiang Zhang', 'Haowen Guo', 'Wen Yang', 'Jinwang Wang'] | 2021-01-10 | null | null | null | international-conference-on-pattern-2 | ['object-detection-in-aerial-images'] | ['computer-vision'] | [-2.34604105e-01 -4.95967895e-01 2.64890254e-01 -1.49472490e-01
-5.75985275e-02 -3.78527194e-01 4.36775200e-02 -1.36262283e-01
-4.21176821e-01 2.21208856e-01 -3.15079004e-01 -3.66851166e-02
3.08319420e-01 -1.03222799e+00 -5.95096469e-01 -5.54294288e-01
-2.85983115e-01 -7.27581605e-02 1.00091493e+00 -2.33640566e-01
-2.95898676e-01 2.24325046e-01 -1.50331056e+00 2.26456404e-01
6.96434736e-01 1.56184626e+00 4.16500121e-01 7.08458245e-01
-6.09955192e-02 8.03452790e-01 -1.08075261e+00 -1.47035331e-01
9.05669689e-01 -1.74082875e-01 -1.87057495e-01 1.60009235e-01
1.00309300e+00 -8.35615635e-01 -5.13165236e-01 1.41750956e+00
8.92789066e-01 -3.98531497e-01 4.62624729e-01 -1.25467992e+00
-1.02087796e+00 9.47149336e-01 -7.64290094e-01 8.48531783e-01
-1.74751088e-01 4.48084980e-01 5.92672467e-01 -1.16045249e+00
7.74675189e-03 1.36087573e+00 1.02818048e+00 3.28054398e-01
-6.24248445e-01 -8.74191165e-01 3.09450746e-01 -2.14666910e-02
-1.94556773e+00 -3.92259359e-02 1.95708461e-02 -1.87394232e-01
5.89961886e-01 3.98801744e-01 9.06567514e-01 5.04460275e-01
8.70425403e-02 9.47915792e-01 7.06320822e-01 -1.50000319e-01
5.83176091e-02 2.19380587e-01 -8.12642043e-04 9.44478989e-01
7.75218904e-01 -1.70526609e-01 8.32817331e-02 9.09665152e-02
7.01454937e-01 4.27136064e-01 -3.55304539e-01 -2.02116877e-01
-1.53474617e+00 5.04713356e-01 1.07370508e+00 2.19135821e-01
-1.76171467e-01 2.27776840e-01 4.84017938e-01 2.79125571e-01
3.45558852e-01 9.02875289e-02 -5.34700692e-01 5.63185334e-01
-6.49970114e-01 8.93000662e-02 5.25476396e-01 1.21800208e+00
2.70613819e-01 2.64898896e-01 -4.77744371e-01 5.29067397e-01
1.60815179e-01 1.04590750e+00 6.29241705e-01 -2.21009463e-01
7.11283386e-01 1.08906877e+00 1.91035911e-01 -1.26500154e+00
-4.04565692e-01 -2.63310373e-01 -8.24219942e-01 1.65270463e-01
3.80634993e-01 -2.98182070e-01 -8.71304035e-01 1.10973513e+00
6.66741252e-01 1.44576982e-01 8.66314769e-02 1.09957695e+00
1.54964364e+00 8.83374214e-01 -4.23239283e-02 2.03421250e-01
1.40865850e+00 -1.02638137e+00 -2.66004086e-01 -3.82937074e-01
5.77091813e-01 -7.69249082e-01 1.10356319e+00 -4.92847748e-02
-4.71788228e-01 -1.02454627e+00 -1.15911114e+00 3.92483532e-01
-6.20617807e-01 8.61501813e-01 6.33444011e-01 6.76925600e-01
-5.18406034e-01 2.02891603e-01 -5.04488468e-01 -6.76888645e-01
8.40311289e-01 3.73618543e-01 -7.70923914e-03 -1.83745041e-01
-9.06711102e-01 5.58468163e-01 7.96230018e-01 1.09898940e-01
-1.14183164e+00 -5.07351816e-01 -6.50332093e-01 4.95121069e-02
7.45509207e-01 -4.55453575e-01 1.05127060e+00 -4.79272991e-01
-6.61589503e-01 6.82861328e-01 6.49214983e-01 -5.10888577e-01
6.26356542e-01 -1.28238276e-01 -5.30983388e-01 1.43207563e-02
3.47629070e-01 7.27363229e-01 1.10211337e+00 -6.49402559e-01
-1.17255604e+00 -3.21415484e-01 4.39496219e-01 1.42096635e-02
-9.26118433e-01 3.54826629e-01 -3.80852491e-01 -7.64148235e-01
2.14035258e-01 -9.33068871e-01 -1.01863392e-01 6.30507469e-01
-4.43258852e-01 -5.68868935e-01 1.38859081e+00 1.37624085e-01
1.24220109e+00 -1.88295257e+00 -2.94644147e-01 -4.97778177e-01
3.81125718e-01 6.10076368e-01 -1.88128039e-01 -7.23669007e-02
4.42809165e-02 -2.12611351e-02 -8.64342302e-02 2.32333779e-01
-1.19090721e-01 -3.17675859e-01 -3.86402965e-01 4.43200737e-01
-8.84867087e-03 9.24111664e-01 -7.02033401e-01 -7.58406401e-01
4.50670332e-01 7.00216517e-02 4.09951285e-02 1.34981766e-01
3.28416489e-02 -4.29601729e-01 -8.98014426e-01 1.67572701e+00
1.01028526e+00 -1.80401698e-01 -7.10878193e-01 -3.87953341e-01
-2.86302716e-01 -4.98827487e-01 -1.50887275e+00 1.21969664e+00
1.27069876e-01 5.62922835e-01 3.63460416e-03 -6.44227147e-01
9.32847977e-01 2.82583404e-02 2.74483472e-01 -6.14381805e-02
5.31296134e-01 2.13277459e-01 1.36449441e-01 -7.10730672e-01
2.29288697e-01 1.35113865e-01 -7.10053667e-02 1.62738234e-01
-6.19152710e-02 -4.07112390e-01 5.45764148e-01 2.20197588e-01
1.03838551e+00 -3.02176207e-01 3.79239976e-01 -2.79476106e-01
3.50947827e-01 2.69494534e-01 5.51405013e-01 8.88704121e-01
-5.63878179e-01 5.30812502e-01 -1.43057242e-01 -1.10940731e+00
-5.25948405e-01 -6.40055001e-01 -5.73470414e-01 9.78581488e-01
5.95493555e-01 -6.01990461e-01 -9.25052762e-01 -9.32523489e-01
2.51807421e-01 -1.57296106e-01 -6.90299392e-01 -2.16245294e-01
-3.66022646e-01 -1.43191361e+00 6.84578121e-01 8.23891938e-01
1.16050351e+00 -1.13126707e+00 -1.15297771e+00 1.49978414e-01
8.92411247e-02 -1.22807729e+00 -5.17814755e-01 1.31124213e-01
-8.37899327e-01 -1.19104791e+00 -1.04049861e+00 -8.12790871e-01
6.73574865e-01 1.14172578e+00 9.24670577e-01 2.20879152e-01
-1.08411551e+00 1.07001603e-01 -4.24053043e-01 -1.09617591e+00
2.46299446e-01 -2.67184585e-01 5.96947014e-01 -2.87347645e-01
6.24802053e-01 -1.01810871e-02 -7.47721851e-01 7.99579680e-01
-9.15386260e-01 -5.79715371e-01 1.00252402e+00 5.48148155e-01
4.44325596e-01 4.11545515e-01 8.54379460e-02 -1.85763136e-01
2.59682685e-01 -2.74644732e-01 -9.72349703e-01 3.95086080e-01
-1.15856364e-01 -6.27107203e-01 7.53407538e-01 -9.42220092e-01
-3.62894744e-01 1.53847054e-01 3.55932623e-01 -5.29278457e-01
-2.57996798e-01 1.95175663e-01 -2.06081107e-01 -6.42862737e-01
8.90728712e-01 3.19942832e-01 -8.16612065e-01 -3.96118701e-01
4.87103686e-02 1.01469398e+00 3.22754860e-01 -9.33318734e-02
8.80298734e-01 4.29068059e-01 -2.73806453e-01 -1.11760521e+00
-1.03870058e+00 -5.97805679e-01 -9.68438983e-01 -1.33831874e-01
6.26436293e-01 -1.34112036e+00 -5.51575303e-01 6.35448277e-01
-9.40759182e-01 -1.77450389e-01 -5.03685713e-01 5.75173318e-01
1.38347358e-01 2.78604865e-01 -4.88838255e-01 -4.88186419e-01
-6.43105507e-01 -9.63766456e-01 1.12932587e+00 5.28898120e-01
5.21991372e-01 -1.86287731e-01 -3.34270686e-01 -2.32644938e-02
3.90161276e-01 1.84986994e-01 1.70948356e-01 -3.09938759e-01
-9.29044425e-01 -9.08348918e-01 -6.94369256e-01 3.36941123e-01
1.76183254e-01 7.08277822e-02 -7.36024678e-01 -4.08281773e-01
-1.80253386e-01 -6.83519304e-01 1.29094839e+00 4.65446442e-01
1.41277969e+00 -3.89306135e-02 -7.26665735e-01 7.94288874e-01
1.45130908e+00 6.15423080e-03 2.65226364e-01 4.26967382e-01
8.42797816e-01 1.49356693e-01 9.07830298e-01 5.99891067e-01
2.08919751e-03 2.68536389e-01 7.20975637e-01 -1.93054080e-01
-3.28096181e-01 -2.90276464e-02 3.80532861e-01 4.42814827e-01
-3.32918793e-01 -3.43695462e-01 -6.84927702e-01 5.40551484e-01
-1.64182198e+00 -9.26422000e-01 -3.21245372e-01 1.83272469e+00
4.11206871e-01 4.51599896e-01 1.62260950e-01 -8.73238256e-04
7.90048242e-01 2.89530128e-01 -9.04907882e-01 6.82459593e-01
-2.19067618e-01 -4.02960926e-01 8.82204890e-01 -6.82815254e-01
-1.90890455e+00 1.14507055e+00 6.70007944e+00 1.05375683e+00
-1.06491423e+00 -6.57883584e-02 3.09498310e-01 -2.53337026e-02
9.33129728e-01 -3.80213082e-01 -1.50815964e+00 5.42040348e-01
2.69553155e-01 -3.73492278e-02 -2.63054729e-01 1.64422894e+00
-1.49283752e-01 9.65547469e-03 -7.42884278e-01 1.20791471e+00
2.67469883e-01 -1.01684582e+00 3.30125272e-01 -3.44547123e-01
8.64644468e-01 2.40960032e-01 7.00069740e-02 3.79321367e-01
1.79848492e-01 -7.43951619e-01 8.46658230e-01 -1.70717791e-01
9.42973912e-01 -5.60473382e-01 1.07384384e+00 6.43501222e-01
-1.96547282e+00 -6.48467481e-01 -1.75415707e+00 -5.29622883e-02
-3.31975669e-01 4.96280402e-01 -5.55091262e-01 -4.04870473e-02
1.52939558e+00 8.90526831e-01 -1.05762553e+00 1.64590108e+00
-1.02907337e-01 5.18413723e-01 -5.32932520e-01 -4.22756076e-01
4.96193260e-01 -4.47318926e-02 5.29258490e-01 1.11031830e+00
5.92686892e-01 5.30188382e-01 6.84672415e-01 9.61404145e-01
-4.88581389e-01 1.94771022e-01 -8.28920901e-01 -1.36718512e-01
4.87195373e-01 1.76984203e+00 -1.20820689e+00 -6.16584957e-01
-6.08285904e-01 8.32381070e-01 -7.63707832e-02 -2.47467890e-01
-1.01318312e+00 -8.18572104e-01 4.70806271e-01 -1.65258776e-02
7.37571776e-01 -7.71928532e-03 3.31931829e-01 -1.19426417e+00
1.67153254e-01 -8.01500976e-01 4.01357502e-01 -8.81357729e-01
-1.22405612e+00 7.03447700e-01 -1.20038763e-01 -1.56605208e+00
8.78626943e-01 -1.06360650e+00 -9.65928376e-01 2.27760792e-01
-1.16008294e+00 -1.50716257e+00 -9.63998139e-01 5.90397000e-01
9.58812118e-01 -3.97532940e-01 6.12767160e-01 2.53767282e-01
-9.14389312e-01 4.31122243e-01 6.85682669e-02 5.93935966e-01
6.41413391e-01 -1.13970399e+00 6.49546742e-01 1.02684593e+00
2.92330444e-01 3.39507937e-01 3.62185687e-01 -5.16474009e-01
-1.45501554e+00 -1.58682060e+00 3.22422758e-02 -8.84238839e-01
7.14001656e-01 -2.37753496e-01 -6.06959879e-01 7.07682312e-01
-2.04469234e-01 9.08113956e-01 2.60781109e-01 -6.15861773e-01
-6.22209795e-02 -4.68123853e-01 -9.90510285e-01 3.27933788e-01
1.16667783e+00 1.07747518e-01 -5.84319293e-01 8.31335843e-01
1.12439990e+00 -2.85184473e-01 -6.00750983e-01 6.49576187e-01
2.30917245e-01 -9.52642500e-01 1.43970573e+00 -3.57051790e-01
-5.51030561e-02 -8.73430848e-01 -3.52657914e-01 -9.76283073e-01
-3.50881279e-01 -1.45538867e-01 -6.92842007e-02 1.09083915e+00
5.44304401e-02 -2.53519893e-01 5.65498829e-01 1.12896487e-01
-1.92766592e-01 -6.16056859e-01 -4.94860202e-01 -1.14648008e+00
-2.78794646e-01 -8.96040052e-02 8.56249630e-01 6.13233447e-01
-3.57133597e-01 2.35453755e-01 -4.96467739e-01 4.38912094e-01
8.32585096e-01 5.31388700e-01 8.51603806e-01 -1.42512286e+00
1.88881740e-01 -1.60384148e-01 -7.70357609e-01 -1.42014873e+00
-5.95073521e-01 -4.91525650e-01 2.52920747e-01 -1.50461900e+00
3.42341363e-01 -5.53673863e-01 -2.46489823e-01 5.81157684e-01
-4.09890890e-01 9.40994918e-01 1.08446784e-01 3.72954071e-01
-1.11787343e+00 6.80667102e-01 1.21664369e+00 -7.97174275e-01
2.55217925e-02 1.77501351e-01 -4.33447391e-01 1.12142467e+00
6.51314914e-01 -6.52014613e-01 3.34070399e-02 -5.88408649e-01
-2.59723037e-01 -5.41775763e-01 3.16385478e-01 -1.68955660e+00
3.70825708e-01 1.20648807e-02 9.72291887e-01 -1.05027366e+00
8.09091404e-02 -8.50599945e-01 -6.03221774e-01 9.36107993e-01
2.16074556e-01 -1.79168105e-01 3.58531386e-01 6.93673134e-01
-7.46597424e-02 -3.22112292e-01 8.25332940e-01 -6.36403799e-01
-1.34694076e+00 7.28285909e-01 -1.84636101e-01 -9.10427496e-02
1.57550466e+00 -2.56677687e-01 -3.07669699e-01 4.72689062e-01
-8.72804448e-02 2.70214617e-01 1.27375305e-01 4.48224336e-01
9.67028022e-01 -1.39186549e+00 -8.36479604e-01 1.72093689e-01
6.02110028e-01 2.57935375e-01 -8.15624222e-02 7.10573494e-01
-7.04585195e-01 5.21166086e-01 -2.81118631e-01 -6.91247702e-01
-1.66987121e+00 8.24319422e-01 4.37881142e-01 5.11993349e-01
-9.50969815e-01 1.47172058e+00 3.39373022e-01 -5.10051787e-01
4.39087719e-01 -8.78853142e-01 -2.69924581e-01 -2.32310444e-01
1.05463767e+00 6.00494564e-01 -1.03288189e-01 -5.08845627e-01
-3.72977525e-01 9.39098716e-01 1.06169902e-01 1.05639124e+00
1.26662970e+00 1.05917910e-02 -1.43261164e-01 2.58163452e-01
5.17035067e-01 -2.89751440e-01 -1.05233157e+00 -3.09804380e-01
-4.21383500e-01 -7.62033641e-01 1.15444787e-01 -4.23586905e-01
-1.06253171e+00 9.95325983e-01 1.12352300e+00 4.16037768e-01
7.85457253e-01 1.67566150e-01 8.94788921e-01 1.09200275e+00
6.80678189e-01 -1.00850010e+00 3.09094638e-01 2.46148199e-01
9.56698239e-01 -1.83747935e+00 4.07230169e-01 -5.48107624e-01
-2.13976189e-01 1.21803355e+00 1.11544001e+00 -2.04283670e-01
6.06241524e-01 3.73850942e-01 1.81065395e-01 -3.23486865e-01
-3.04326832e-01 -4.85671341e-01 2.14081019e-01 5.28452039e-01
8.13774988e-02 1.04727119e-01 1.04685985e-01 5.74819863e-01
1.32531613e-01 -1.34833038e-01 3.23734194e-01 9.32483315e-01
-1.26623797e+00 -3.07077765e-01 -9.52182651e-01 6.54660881e-01
-3.48617226e-01 -2.47795239e-01 -6.38687849e-01 9.22827721e-01
7.12895989e-01 8.42304289e-01 -6.57367110e-02 -4.71915096e-01
5.48397124e-01 -6.22252822e-01 1.47811040e-01 -8.08688998e-01
-1.67151287e-01 -1.86146423e-01 -5.04733860e-01 -4.24916357e-01
-2.92175204e-01 -1.36328936e-01 -1.07958674e+00 -2.11416259e-01
-1.12780070e+00 -6.23290911e-02 3.73647541e-01 4.96328920e-01
1.86269060e-02 6.03399754e-01 3.33876520e-01 -9.09162283e-01
-1.01939571e+00 -1.27441013e+00 -8.73216093e-01 -1.25964001e-01
-1.77460499e-02 -6.20717049e-01 -3.27539086e-01 1.85039118e-01] | [8.726428985595703, -0.7538167834281921] |
bf1b5c72-9854-48be-87b8-7c10885682b5 | why-the-firefly-algorithm-works | 1806.01632 | null | http://arxiv.org/abs/1806.01632v1 | http://arxiv.org/pdf/1806.01632v1.pdf | Why the Firefly Algorithm Works? | Firefly algorithm is a nature-inspired optimization algorithm and there have
been significant developments since its appearance about ten years ago. This
chapter summarizes the latest developments about the firefly algorithm and its
variants as well as their diverse applications. Future research directions are
also highlighted. | ['Xin-She Yang', 'Xingshi He'] | 2018-04-22 | null | null | null | null | ['nature-inspired-optimization-algorithm'] | ['computer-code'] | [-1.23575151e-01 -7.57882357e-01 -1.14424415e-01 -1.14543848e-01
3.62256289e-01 -5.11314094e-01 4.28960234e-01 2.36466676e-02
-6.67273462e-01 1.04003966e+00 -2.05861837e-01 1.96920529e-01
-4.54324871e-01 -8.50515127e-01 1.53944403e-01 -1.07909596e+00
-5.25533080e-01 2.60401338e-01 -8.75349622e-03 -8.37846875e-01
9.61090326e-01 5.31142831e-01 -2.23694253e+00 -3.99364501e-01
1.08647585e+00 6.72673285e-01 3.34881544e-02 1.17711079e+00
-2.47084796e-01 3.24891597e-01 -1.30145442e+00 -6.20379597e-02
1.81950014e-02 -9.31984425e-01 -2.10026264e-01 -3.97608221e-01
-7.03600645e-01 5.77176154e-01 1.03014387e-01 8.86876225e-01
1.04369175e+00 5.22852242e-01 3.13012391e-01 -1.68338692e+00
-6.73652232e-01 3.14403907e-03 -6.06544912e-01 6.72184408e-01
6.13606930e-01 2.13292852e-01 7.64332235e-01 -6.60356104e-01
5.65994203e-01 9.19239819e-01 8.72994959e-01 6.40122473e-01
-4.13292468e-01 -5.13744473e-01 9.49151069e-02 6.60472989e-01
-1.39124095e+00 9.28069055e-02 9.52314317e-01 1.94083855e-01
1.31895769e+00 1.02299047e+00 1.38519514e+00 3.36241394e-01
6.67455316e-01 9.89418685e-01 1.07052672e+00 -9.35388148e-01
7.83540368e-01 -2.89206370e-03 -1.82869643e-01 3.68803144e-01
7.61009514e-01 3.58873963e-01 -4.88896638e-01 -3.65315109e-01
2.27940336e-01 -3.34161043e-01 -1.20004229e-01 -1.99755669e-01
-7.63851047e-01 1.21036065e+00 4.17100310e-01 7.98763990e-01
-5.79626918e-01 -2.75927298e-02 2.18174294e-01 1.33806884e-01
3.45628947e-01 1.02352834e+00 -1.15519375e-01 -5.78498781e-01
-8.64779413e-01 5.02783895e-01 7.15182185e-01 6.44774556e-01
-7.86818936e-02 3.74930531e-01 2.16098845e-01 4.97466236e-01
3.39129776e-01 2.52477974e-01 7.18654096e-01 -6.01694942e-01
-2.52406746e-01 4.61181492e-01 4.07220334e-01 -9.22858655e-01
-7.12988138e-01 -5.25619626e-01 -4.92730260e-01 6.03579283e-01
-1.17534921e-01 -3.41730505e-01 -6.09718740e-01 8.14489722e-01
6.06297970e-01 5.65725081e-02 -4.54757214e-02 6.50336087e-01
9.57263887e-01 7.65975654e-01 -1.33187801e-01 -7.06963956e-01
1.06848097e+00 -1.35784388e+00 -1.16315782e+00 -3.68332081e-02
-3.87597568e-02 -1.19810545e+00 6.20392323e-01 5.73977709e-01
-1.06130731e+00 -5.23241460e-02 -1.32077467e+00 6.32942140e-01
-9.13328409e-01 -6.12104654e-01 9.65531290e-01 1.73387134e+00
-8.54078948e-01 5.22973001e-01 -7.98031867e-01 -7.37609148e-01
2.19507560e-01 4.72734809e-01 5.38220942e-01 3.82866085e-01
-1.10362613e+00 1.01664543e+00 4.51717556e-01 4.04551685e-01
7.92894512e-02 -2.41451830e-01 -3.38238597e-01 -3.93709749e-01
1.24984682e-01 -7.15131819e-01 7.41427600e-01 -6.22139752e-01
-1.89318144e+00 6.36346281e-01 -3.73568147e-01 -2.78009772e-01
5.25402963e-01 2.70080686e-01 -9.37163293e-01 -3.04829627e-01
-3.27383727e-01 -2.76452959e-01 2.94790536e-01 -9.97809291e-01
-1.26461458e+00 -2.52165377e-01 -4.97329384e-02 3.83296341e-01
-4.64952618e-01 6.57044172e-01 -2.67785847e-01 -9.74385560e-01
-3.28847244e-02 -8.45341742e-01 -4.60541785e-01 -3.71638499e-02
-1.08164459e-01 -4.03959006e-01 4.33390468e-01 2.08904579e-01
1.61113811e+00 -1.71872807e+00 9.46465656e-02 2.09508643e-01
-2.06842691e-01 3.89526308e-01 -1.39641106e-01 1.08678150e+00
-1.14512675e-01 2.41912007e-01 -2.60196552e-02 -6.16988726e-02
-6.11525923e-02 2.79732376e-01 3.28973919e-01 9.21711028e-01
-5.07290065e-01 9.35674310e-01 -1.09735608e+00 -5.95134310e-02
3.28090250e-01 5.03215373e-01 -1.65949747e-01 -8.82970616e-02
2.36888945e-01 2.61666447e-01 -5.44546545e-01 1.30394518e+00
8.09751749e-01 1.59409732e-01 -3.45596105e-01 2.42075533e-01
-9.52873349e-01 -2.25776419e-01 -1.05447602e+00 1.23122275e+00
-9.74046588e-02 7.89883137e-01 7.15495944e-02 -8.45969141e-01
6.98730171e-01 1.07587144e-01 9.63692725e-01 -6.42684817e-01
4.69558895e-01 2.45791718e-01 4.62463349e-02 -5.64801276e-01
5.72291553e-01 -2.56192446e-01 4.27727640e-01 7.63381600e-01
-6.12757206e-01 -3.53223532e-01 7.82344460e-01 -4.29042935e-01
8.54195178e-01 -2.07309157e-01 7.76618540e-01 -2.19485685e-01
1.06414282e+00 6.04802191e-01 6.60509109e-01 7.82867134e-01
-6.25719488e-01 3.65511656e-01 -4.13284361e-01 -1.00429749e+00
-4.45667863e-01 -6.90215230e-01 -5.07927015e-02 1.21366644e+00
6.06533766e-01 -6.89630210e-01 -3.94573063e-01 -7.82979131e-02
1.70084685e-01 1.05266941e+00 -1.08628511e+00 -1.00286998e-01
-6.94760382e-01 -1.52494955e+00 2.72222281e-01 1.64471179e-01
5.48133552e-01 -1.38932514e+00 -1.12742519e+00 3.53497058e-01
9.13347155e-02 -3.26976627e-01 2.02490404e-01 -5.19716293e-02
-1.04891074e+00 -1.15157104e+00 -8.52203488e-01 -7.17283607e-01
6.79045975e-01 5.10887921e-01 1.03923893e+00 4.14007694e-01
-8.90160620e-01 4.59676743e-01 -6.60894513e-01 -1.03590441e+00
-1.17102444e-01 -2.83453137e-01 1.80793762e-01 -4.59094375e-01
6.06204569e-01 -3.66929203e-01 -6.16114676e-01 1.78787157e-01
-4.11434323e-01 -5.45625389e-01 2.91266501e-01 7.86770284e-01
5.03958464e-01 1.16242921e+00 -2.02798352e-01 -5.04863441e-01
1.01595831e+00 -1.39091998e-01 -7.21348703e-01 3.52152824e-01
-9.23343718e-01 -3.73819560e-01 4.07183796e-01 -4.99447733e-02
-9.12615895e-01 -4.80809927e-01 -3.67409676e-01 5.13903499e-01
1.09334663e-01 3.65479887e-01 2.19411016e-01 -9.27868664e-01
6.41078889e-01 5.12008309e-01 2.83958074e-02 -6.00347400e-01
7.77202994e-02 6.57523394e-01 5.85009977e-02 -3.33751529e-01
1.05966091e+00 7.88879931e-01 -5.51437922e-02 -1.07206559e+00
-1.75429955e-01 -5.02009571e-01 -1.84586033e-01 -5.39929152e-01
2.80203015e-01 1.13465801e-01 -1.10106146e+00 6.73574030e-01
-8.35830033e-01 4.92839634e-01 -4.89286929e-01 2.48767257e-01
-4.66077805e-01 -8.97467975e-03 2.39769779e-02 -1.25506616e+00
-7.73676276e-01 -9.58406925e-01 1.21579938e-01 1.10477304e+00
5.15549928e-02 -1.22645676e+00 6.23259246e-01 -1.01178065e-01
9.78082895e-01 5.69799840e-01 2.71827132e-01 -3.06897610e-01
2.29846105e-01 -8.10338318e-01 5.51866055e-01 -4.18917984e-01
5.58421493e-01 6.61656559e-01 -4.38308209e-01 -7.38384366e-01
5.28879702e-01 4.10903513e-01 1.45143971e-01 7.98604608e-01
5.48544586e-01 -1.21177435e-02 -9.03337777e-01 7.58177519e-01
1.74392295e+00 1.01545632e+00 5.27216434e-01 1.59716511e+00
-2.16419950e-01 3.59304577e-01 1.01860106e+00 7.64265001e-01
1.77767649e-02 4.40754205e-01 6.61352158e-01 1.76439375e-01
1.83235854e-01 3.50355357e-01 -4.98950258e-02 6.94659412e-01
-8.64755273e-01 -6.07727826e-01 -7.73739159e-01 6.18973747e-02
-1.38754106e+00 -1.15831530e+00 -1.54610172e-01 1.94426668e+00
2.59298205e-01 2.94217337e-02 5.59077799e-01 3.58431578e-01
1.04626346e+00 1.64997175e-01 -9.19513345e-01 -6.93853140e-01
-5.36912560e-01 1.48171023e-01 2.61810750e-01 2.03304827e-01
-1.11330616e+00 7.06911862e-01 8.39091969e+00 5.28023303e-01
-1.23922062e+00 -1.67164486e-02 -1.68282185e-02 -2.28638098e-01
-9.09306183e-02 -1.36354089e-01 -5.02684712e-01 7.67155230e-01
4.86517459e-01 -1.20211792e+00 4.90300059e-01 6.85545981e-01
3.38871002e-01 -4.22364682e-01 -1.16076797e-01 1.23918223e+00
4.04348105e-01 -1.36892533e+00 -3.06475282e-01 -9.05412138e-02
8.79641235e-01 -3.24037038e-02 2.73166120e-01 -3.00834686e-01
1.08991213e-01 -1.02911282e+00 4.19659436e-01 1.69693351e-01
9.50664189e-03 -1.21652532e+00 8.56288195e-01 2.75044534e-02
-1.16135764e+00 -3.94902915e-01 -5.80793440e-01 -5.66739500e-01
2.91553557e-01 4.10405606e-01 -9.92032960e-02 6.65251672e-01
1.04724360e+00 6.86645627e-01 -4.70748007e-01 2.16667461e+00
-2.64360845e-01 4.07552600e-01 -6.59432352e-01 -9.62903619e-01
4.45993781e-01 -3.77412558e-01 1.22354376e+00 7.31849611e-01
2.93781132e-01 3.45064998e-01 -3.66625786e-01 5.07476091e-01
7.48522103e-01 2.74097860e-01 5.43051660e-02 -4.15222585e-01
8.41114938e-01 1.20566416e+00 -1.23995042e+00 9.41150486e-02
-2.61115789e-01 9.55348194e-01 -5.63433349e-01 1.26895249e-01
-9.94791448e-01 -1.02292275e+00 9.03970242e-01 -3.89131367e-01
1.50592044e-01 -1.35183588e-01 -5.67586720e-01 -6.95661604e-01
-3.97279799e-01 -6.63006961e-01 6.99680388e-01 -5.54647326e-01
-9.48474824e-01 7.83164144e-01 -4.79492366e-01 -1.63506854e+00
1.25042617e-01 -4.65151191e-01 -9.56312656e-01 6.74466550e-01
-1.32964432e+00 -7.15792060e-01 -3.70984793e-01 4.62307855e-02
7.03702152e-01 -5.11129797e-01 9.40298378e-01 -2.45660186e-01
-8.95060778e-01 5.20256102e-01 9.56087828e-01 -5.23296118e-01
3.43315274e-01 -1.09158587e+00 4.78405565e-01 7.25320041e-01
-2.19590068e-01 7.77682602e-01 1.57967556e+00 -3.23540002e-01
-1.82876754e+00 -3.08290362e-01 8.40538025e-01 5.27356565e-02
5.66765726e-01 3.21883172e-01 -1.07940666e-01 5.21429926e-02
5.28287470e-01 -4.33596641e-01 8.72789741e-01 -7.90348127e-02
6.90460086e-01 9.82201546e-02 -1.51332784e+00 8.13500702e-01
7.81643629e-01 6.05537117e-01 -4.26063865e-01 6.51602328e-01
-4.79543842e-02 -4.90225822e-01 -4.02074754e-01 6.37788251e-02
7.49487162e-01 -1.44617665e+00 1.14294362e+00 -3.78849268e-01
-4.18725461e-01 -4.53746647e-01 2.88111299e-01 -1.27073050e+00
-8.06456983e-01 -1.09655845e+00 9.61433053e-02 8.85997355e-01
-1.71176508e-01 -1.23456502e+00 1.08455527e+00 4.64848846e-01
2.96004504e-01 -8.70911777e-01 -1.03771305e+00 -1.35153711e+00
5.26058599e-02 2.94556487e-02 9.83061194e-01 7.73595333e-01
1.47994637e-01 -3.85961652e-01 -6.18266426e-02 -2.68272549e-01
6.23410046e-01 4.83153343e-01 4.76413101e-01 -1.25110865e+00
4.92265746e-02 -1.08558547e+00 -6.65231049e-01 -4.18491930e-01
-4.70019758e-01 -1.53530538e-01 -1.85990617e-01 -1.63493645e+00
-1.75320342e-01 -4.18884456e-01 -7.48340905e-01 1.61826000e-01
-3.51949364e-01 7.02108443e-01 7.86921605e-02 2.56403685e-01
-4.06994849e-01 6.70289934e-01 1.16245258e+00 -1.48666963e-01
-5.92256665e-01 3.14270705e-01 -7.38016546e-01 8.99700940e-01
1.26756394e+00 -5.04965723e-01 -1.01819642e-01 -1.26367703e-01
4.73465115e-01 -6.83124125e-01 -4.09212112e-01 -1.23551428e+00
6.19123816e-01 -5.26612282e-01 3.55836064e-01 -7.52582788e-01
4.05408502e-01 -8.05867136e-01 4.96467799e-01 1.01954103e+00
3.06410104e-01 7.22415924e-01 3.39029908e-01 4.71399307e-01
-1.05742164e-01 -5.20230830e-01 8.83060396e-01 -1.67126775e-01
-1.03651404e+00 -3.35144490e-01 -6.91924334e-01 -1.82061329e-01
1.58414316e+00 -1.05395913e+00 -1.12001352e-01 -1.48227140e-02
-5.74313581e-01 1.52775377e-01 8.16969454e-01 5.16690195e-01
6.32031381e-01 -1.06682527e+00 -4.34531808e-01 2.06379861e-01
-3.06762960e-02 -9.05561566e-01 -3.22762094e-02 6.38344586e-01
-1.17205536e+00 6.18362844e-01 -7.99792469e-01 -1.78011820e-01
-1.79480577e+00 4.85684425e-01 3.74254793e-01 2.77413934e-01
-5.14194608e-01 1.44537330e+00 -6.88643873e-01 2.64999196e-02
6.01580553e-02 6.63848341e-01 -6.83869302e-01 -2.89950043e-01
8.87924850e-01 1.14971709e+00 -7.60648847e-02 -6.41044617e-01
-1.11433399e+00 1.08011723e+00 1.67109460e-01 4.46979068e-02
1.68222106e+00 -2.71401048e-01 -7.29524255e-01 3.89118731e-01
5.02544820e-01 8.17466155e-02 -2.42367655e-01 6.73163652e-01
-2.71131307e-01 -8.99581611e-01 9.98937264e-02 -1.11211371e+00
-1.13550198e+00 3.52351218e-01 8.74280274e-01 3.74107122e-01
1.58745658e+00 -6.65823519e-01 6.88261867e-01 3.70135717e-02
6.54033959e-01 -1.28085732e+00 -4.00666684e-01 4.26675558e-01
7.82963872e-01 -7.40765750e-01 7.36811042e-01 -4.41436827e-01
-1.43662393e-01 1.22382355e+00 2.39368975e-01 -2.19111368e-01
8.76364470e-01 1.90119743e-01 1.56731263e-01 -1.92846894e-01
-4.30508018e-01 -2.15631686e-02 3.27520110e-02 1.14591217e+00
6.59647703e-01 -2.00404331e-01 -1.21181285e+00 3.22971404e-01
-4.96482670e-01 7.21609360e-03 2.12414339e-01 1.73437512e+00
-6.72710061e-01 -1.52679503e+00 -1.05432928e+00 8.31933022e-02
-2.20370010e-01 1.58717811e-01 -5.11862338e-01 7.55536973e-01
1.45615071e-01 1.39729178e+00 -2.82237530e-01 -3.63218099e-01
2.33567253e-01 -6.19472086e-01 6.67398512e-01 5.07027516e-03
-7.64286280e-01 -2.30742425e-01 -4.69750524e-01 -7.34144688e-01
-7.13156044e-01 -6.25651598e-01 -1.16706383e+00 -8.47861052e-01
-5.78680575e-01 1.06124091e+00 1.00390863e+00 5.58804452e-01
2.57200301e-01 6.35347724e-01 6.28943324e-01 -1.13091946e+00
6.52962923e-03 -3.92169237e-01 -8.58690202e-01 -4.50115353e-01
-5.72715476e-02 -1.00280523e+00 -6.17342114e-01 -4.64466989e-01] | [5.687021732330322, 3.6041648387908936] |
95147f82-b49e-4d6f-8275-1a20cb6443f7 | divided-attention-unsupervised-multi-object | 2304.01430 | null | https://arxiv.org/abs/2304.01430v2 | https://arxiv.org/pdf/2304.01430v2.pdf | Divided Attention: Unsupervised Multi-Object Discovery with Contextually Separated Slots | We introduce a method to segment the visual field into independently moving regions, trained with no ground truth or supervision. It consists of an adversarial conditional encoder-decoder architecture based on Slot Attention, modified to use the image as context to decode optical flow without attempting to reconstruct the image itself. In the resulting multi-modal representation, one modality (flow) feeds the encoder to produce separate latent codes (slots), whereas the other modality (image) conditions the decoder to generate the first (flow) from the slots. This design frees the representation from having to encode complex nuisance variability in the image due to, for instance, illumination and reflectance properties of the scene. Since customary autoencoding based on minimizing the reconstruction error does not preclude the entire flow from being encoded into a single slot, we modify the loss to an adversarial criterion based on Contextual Information Separation. The resulting min-max optimization fosters the separation of objects and their assignment to different attention slots, leading to Divided Attention, or DivA. DivA outperforms recent unsupervised multi-object motion segmentation methods while tripling run-time speed up to 104FPS and reducing the performance gap from supervised methods to 12% or less. DivA can handle different numbers of objects and different image sizes at training and test time, is invariant to permutation of object labels, and does not require explicit regularization. | ['Stefano Soatto', 'Yanchao Yang', 'Francesco Locatello', 'Zhengyang Hu', 'Dong Lao'] | 2023-04-04 | null | null | null | null | ['object-discovery', 'multi-object-discovery', 'motion-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 7.11838186e-01 2.28866145e-01 -2.56115258e-01 -1.80068359e-01
-6.13602757e-01 -8.27825904e-01 5.73710859e-01 -2.28360191e-01
-4.78179395e-01 7.18294561e-01 -3.12725641e-03 -4.14079815e-01
2.89881110e-01 -6.29951417e-01 -9.15842772e-01 -9.56302822e-01
2.47707441e-01 3.31580698e-01 4.34079230e-01 3.30725729e-01
2.84340262e-01 4.38518941e-01 -1.32309783e+00 2.92770267e-01
7.09046900e-01 8.31515014e-01 4.18941945e-01 1.02396405e+00
-7.41350576e-02 1.00379741e+00 -7.77243793e-01 -3.06435049e-01
3.72925580e-01 -8.45830858e-01 -1.10512626e+00 4.92403984e-01
6.33365035e-01 -3.61537218e-01 -3.29752713e-01 9.89305735e-01
7.92931616e-02 1.70366466e-01 8.71455312e-01 -1.11926782e+00
-6.06126845e-01 4.78761569e-02 -4.99436468e-01 2.90439785e-01
-1.53311165e-02 3.42747837e-01 9.93203044e-01 -4.09991920e-01
7.84842968e-01 1.12048948e+00 2.61719078e-01 6.96835399e-01
-1.77777529e+00 -1.76054806e-01 1.85187414e-01 -1.95305049e-02
-1.01443672e+00 -6.92507088e-01 5.11707485e-01 -7.27235198e-01
7.62908280e-01 2.53285706e-01 4.13484246e-01 9.83175695e-01
2.06304088e-01 7.11936951e-01 8.73553514e-01 -3.09682727e-01
2.83413619e-01 7.95252472e-02 -2.66031861e-01 7.75012612e-01
2.23250255e-01 -3.25303487e-02 -2.33677775e-01 2.45993882e-01
1.00002158e+00 -3.61517221e-01 -4.59294289e-01 -5.27342439e-01
-1.23194373e+00 7.79684663e-01 3.29068720e-01 6.95532411e-02
-1.57167897e-01 4.09478247e-01 3.31053734e-01 8.48719925e-02
7.86586031e-02 4.37057227e-01 -2.42342651e-01 9.28702876e-02
-1.14310646e+00 1.38593242e-01 5.88215828e-01 7.75058389e-01
1.09569120e+00 2.85552323e-01 -2.62846798e-01 3.83756369e-01
3.30668688e-01 4.83835995e-01 4.77912664e-01 -1.45454872e+00
4.69043493e-01 2.89609462e-01 1.48997590e-01 -9.67263222e-01
-1.54792517e-01 -3.35356623e-01 -5.59060156e-01 4.51901257e-01
7.89536417e-01 -2.84295648e-01 -1.24963534e+00 1.85776520e+00
1.51936218e-01 2.39114761e-01 5.79424985e-02 1.10672843e+00
2.38947436e-01 7.92725265e-01 8.36347714e-02 -2.73723267e-02
1.15544641e+00 -1.06092143e+00 -3.95951003e-01 -7.07009792e-01
5.66005349e-01 -6.73716426e-01 7.64384985e-01 1.74089685e-01
-1.20374930e+00 -5.18059015e-01 -1.14779222e+00 -2.47892469e-01
-6.39572293e-02 -1.07834212e-01 5.75760365e-01 5.76340675e-01
-9.69661653e-01 5.83536208e-01 -9.71725523e-01 9.27326456e-02
6.32778466e-01 4.07605380e-01 -4.43040818e-01 -1.16935402e-01
-9.05977488e-01 7.02218533e-01 4.49741066e-01 -1.92257743e-02
-9.73468006e-01 -5.92728496e-01 -1.03888333e+00 1.41578233e-02
1.95259020e-01 -7.89249063e-01 8.22819471e-01 -1.53027666e+00
-1.48409188e+00 8.68865967e-01 -3.90923411e-01 -6.02489710e-01
5.20417273e-01 1.51406512e-01 -1.06771320e-01 5.77363491e-01
3.87955338e-01 1.11324728e+00 1.34357655e+00 -1.24972844e+00
-5.21516860e-01 8.45284164e-02 2.15444356e-01 1.69609293e-01
1.94416434e-01 -2.70687878e-01 -6.52205348e-01 -6.79007828e-01
-1.37531888e-02 -1.03051448e+00 -1.57865450e-01 1.81208029e-01
-4.71583843e-01 4.79390234e-01 8.92178178e-01 -5.41786730e-01
7.85299301e-01 -2.25822401e+00 4.77505684e-01 -3.33757773e-02
1.62334174e-01 2.55511880e-01 -3.08894247e-01 -9.31419730e-02
-6.37113228e-02 7.38410950e-02 -7.62350082e-01 -5.92179954e-01
-3.55001569e-01 6.89499736e-01 -3.20370972e-01 8.21020842e-01
5.20311117e-01 9.58967090e-01 -9.28290725e-01 -4.93302435e-01
3.76808763e-01 4.90425974e-01 -9.36735213e-01 1.85810924e-01
-2.50372142e-01 8.51825416e-01 -2.03081369e-01 2.63726175e-01
5.52461028e-01 -4.20953363e-01 5.38234040e-02 5.64044202e-03
8.03749412e-02 3.09826672e-01 -1.11184287e+00 1.90216434e+00
-4.86425132e-01 1.05970049e+00 2.22535565e-01 -1.44488084e+00
5.23676217e-01 1.37567848e-01 6.30940318e-01 -5.38477004e-01
1.16315506e-01 1.74288422e-01 1.10344417e-01 -3.95477265e-01
3.72883677e-01 -1.26767397e-01 -1.30264089e-02 3.97397250e-01
3.45258206e-01 -1.10749356e-01 3.71144772e-01 2.02592403e-01
9.84844744e-01 3.56401086e-01 -3.41845542e-01 -1.73768267e-01
4.22162533e-01 3.71743701e-02 6.97052956e-01 6.70544684e-01
-2.87997425e-01 9.50615048e-01 6.77194953e-01 -9.22251567e-02
-1.09435964e+00 -1.09726405e+00 -1.85251340e-01 8.25545967e-01
3.69516462e-01 5.38620874e-02 -7.58805037e-01 -8.17471683e-01
-6.93331510e-02 6.42586291e-01 -6.84290707e-01 -1.95874587e-01
-6.35329247e-01 -5.62341213e-01 4.43514287e-01 2.68748522e-01
4.03366446e-01 -1.06103098e+00 -1.06551909e+00 2.32494369e-01
-5.59515238e-01 -1.36358345e+00 -7.72367954e-01 3.97056699e-01
-7.42256880e-01 -1.15776980e+00 -8.30796540e-01 -6.69460416e-01
9.42658663e-01 3.82320695e-02 1.01889777e+00 -2.34913856e-01
-5.36763668e-01 3.17267090e-01 -1.73540190e-01 1.32796228e-01
-5.01700401e-01 -9.07818377e-02 -4.02867079e-01 4.48566765e-01
-2.32493713e-01 -4.49236691e-01 -7.54033685e-01 2.68159658e-01
-1.19663715e+00 1.21360682e-01 4.86031264e-01 9.05076444e-01
4.85527515e-01 -1.25474453e-01 2.53577918e-01 -8.96597981e-01
-1.98089629e-01 -4.34255064e-01 -5.77108085e-01 -9.68404412e-02
-2.40857810e-01 4.05003875e-01 5.83735049e-01 -5.18248320e-01
-8.39643896e-01 3.56089681e-01 1.88605681e-01 -6.40524387e-01
-3.06016654e-01 1.99476746e-03 -1.43695489e-01 -5.39141074e-02
5.64527392e-01 3.19001526e-01 1.21039875e-01 -2.24299937e-01
5.51359296e-01 2.86473751e-01 7.91541517e-01 -3.42633367e-01
6.10095859e-01 7.87093639e-01 3.60436216e-02 -7.72239804e-01
-6.05279803e-01 -2.41689131e-01 -7.52022564e-01 -1.91546053e-01
1.38055217e+00 -7.18886733e-01 -3.86874467e-01 3.66088361e-01
-1.32897234e+00 -5.51348805e-01 -4.60981756e-01 4.65701491e-01
-7.97837138e-01 3.05378228e-01 -6.02605045e-01 -5.14634788e-01
1.44107983e-01 -1.47085118e+00 8.57279122e-01 1.14918679e-01
-1.63539261e-01 -1.13483369e+00 -3.90589088e-01 5.33889890e-01
2.31950030e-01 3.82834703e-01 8.08384538e-01 -2.91648477e-01
-9.85003769e-01 -2.59178057e-02 -2.48070404e-01 5.38929343e-01
1.85694724e-01 -7.75931031e-02 -1.01475120e+00 -2.93315738e-01
-4.16125320e-02 -3.53210568e-01 1.12472820e+00 4.87519294e-01
9.68172014e-01 -3.64953846e-01 -1.68621600e-01 8.03510785e-01
1.48648345e+00 2.73676366e-01 8.84574890e-01 2.62521327e-01
9.30952489e-01 7.12927759e-01 1.45012364e-01 1.23346667e-03
2.66928822e-02 5.94030261e-01 6.56762898e-01 -1.63766056e-01
-4.63459879e-01 -8.50115065e-03 3.82184476e-01 2.28729546e-01
2.01210231e-01 -4.99213696e-01 -6.64384544e-01 7.21434116e-01
-1.62200272e+00 -9.96672153e-01 -1.56262703e-02 2.33926964e+00
6.96290255e-01 2.55364031e-01 -1.16680548e-01 5.52328900e-02
6.92179561e-01 4.10058200e-01 -5.63418865e-01 -4.43586379e-01
-1.12203881e-01 1.32138878e-01 8.84997308e-01 8.92974496e-01
-1.18453908e+00 9.58734930e-01 6.00746202e+00 5.86015522e-01
-1.42798781e+00 5.88561222e-02 7.58682966e-01 -1.52365074e-01
-3.82037282e-01 2.16110811e-01 -5.34083426e-01 6.46574855e-01
8.45390558e-01 3.30864727e-01 5.29840231e-01 3.07281077e-01
1.25230595e-01 -3.70945692e-01 -1.07264566e+00 7.39009619e-01
1.10103086e-01 -1.29980230e+00 5.81127070e-02 1.84622318e-01
6.72868609e-01 1.80902481e-02 4.96895574e-02 -1.37683332e-01
1.87496915e-01 -1.11413503e+00 9.70146000e-01 4.52055305e-01
9.13190842e-01 -4.49737370e-01 2.46737987e-01 2.92917728e-01
-8.71639788e-01 -2.24572904e-02 -1.97090477e-01 1.32277340e-01
3.44799876e-01 2.64146715e-01 -4.83278304e-01 3.48214209e-01
3.05268586e-01 6.16529346e-01 -2.89713979e-01 7.75988758e-01
-2.14253273e-02 6.05614424e-01 -1.54697865e-01 4.86267686e-01
5.30274749e-01 -3.05707395e-01 8.20271194e-01 1.24225557e+00
-1.12948231e-01 -2.49065995e-01 1.66701362e-01 1.01319551e+00
5.33990152e-02 -2.36964732e-01 -3.84796023e-01 -4.68916958e-03
5.93825504e-02 8.42451394e-01 -9.73880231e-01 -2.47960031e-01
-5.11740565e-01 1.51196229e+00 1.11577578e-01 6.61062062e-01
-9.43331599e-01 -4.81762499e-01 6.66477025e-01 3.10429126e-01
7.88388908e-01 -2.50931501e-01 -3.86098593e-01 -1.11207950e+00
-3.94185968e-02 -4.82119232e-01 1.16639044e-02 -6.31053865e-01
-6.71012819e-01 4.79815215e-01 -1.47143424e-01 -1.19118643e+00
-3.40414464e-01 -5.30728459e-01 -4.72055107e-01 1.11092973e+00
-1.63527596e+00 -8.30845475e-01 5.95491044e-02 5.35504937e-01
5.53421974e-01 5.84876612e-02 6.36919141e-01 4.49638695e-01
-5.82446516e-01 4.86450315e-01 -5.19557483e-03 4.16571289e-01
7.11515486e-01 -1.24406874e+00 2.03207925e-01 1.20542693e+00
2.80131251e-01 2.79136688e-01 7.40752518e-01 -3.35352391e-01
-1.01001799e+00 -1.23629534e+00 6.59988999e-01 -3.80433023e-01
4.77889419e-01 -4.05540645e-01 -8.62030447e-01 6.31260753e-01
1.80020019e-01 4.88330394e-01 3.95693630e-01 -5.57445467e-01
-3.94853145e-01 6.36453032e-02 -9.79824007e-01 3.46613020e-01
6.88282549e-01 -5.43620706e-01 -3.05376798e-01 -1.70123880e-03
6.77344978e-01 -4.07527745e-01 -4.72365946e-01 1.20733641e-01
3.21408778e-01 -1.03201675e+00 9.27276254e-01 -6.39333904e-01
7.56909549e-01 -3.36606175e-01 -5.83699308e-02 -9.89151955e-01
-2.77637273e-01 -5.96413672e-01 -1.48772821e-01 9.31668580e-01
5.34911752e-01 -5.88353992e-01 8.90482545e-01 6.00713611e-01
-2.35426143e-01 -6.24882460e-01 -1.04986095e+00 -5.51766276e-01
8.44700113e-02 -3.10273498e-01 1.18564256e-02 9.33097541e-01
-5.50356328e-01 3.86256367e-01 -4.33195680e-01 2.21501932e-01
5.81386447e-01 2.30432272e-01 5.10897994e-01 -8.29321921e-01
-7.46441185e-01 -5.77775717e-01 -4.98630941e-01 -1.50868726e+00
2.25841910e-01 -9.24803138e-01 1.92642793e-01 -1.38076127e+00
-2.10722879e-01 -5.25297761e-01 -6.42492548e-02 5.56482494e-01
-2.90979911e-02 5.33794999e-01 3.84093285e-01 3.89478415e-01
-4.24348623e-01 3.36548179e-01 1.41423678e+00 -3.47758472e-01
-1.97905093e-01 -3.33847217e-02 -6.09787345e-01 5.93684137e-01
5.69469929e-01 -4.77523983e-01 -5.56397915e-01 -7.32004881e-01
-2.27738976e-01 2.95995712e-01 5.96794903e-01 -9.41382229e-01
1.88537836e-02 -8.78888667e-02 4.47213054e-01 7.85778165e-02
3.15050244e-01 -8.24778616e-01 1.10418154e-02 4.25293744e-01
-4.23528522e-01 -3.92097563e-01 2.87779123e-01 6.94793701e-01
-1.59818277e-01 -4.49046731e-01 1.12620127e+00 -1.45025149e-01
-6.17441893e-01 1.92739129e-01 -5.76917946e-01 3.44859630e-01
9.72568095e-01 -4.79369938e-01 -2.01193467e-01 -2.22243235e-01
-9.11202788e-01 2.64959615e-02 4.70189571e-01 3.71361792e-01
2.39421695e-01 -9.90590692e-01 -5.12081683e-01 3.61242026e-01
-2.75690317e-01 3.05048585e-01 2.74817884e-01 7.20542490e-01
-7.38764107e-01 4.69570667e-01 -2.48611778e-01 -7.86430717e-01
-8.22395623e-01 5.11116982e-01 6.26367807e-01 -2.55553704e-02
-6.06163979e-01 8.71953726e-01 4.87631619e-01 9.50713381e-02
7.18618557e-02 -1.73101604e-01 4.91853915e-02 2.53606923e-02
3.31013322e-01 7.54725561e-02 -1.56226367e-01 -8.85422826e-01
-1.80445433e-01 4.93660271e-01 8.62788782e-02 -3.39494079e-01
8.55873227e-01 -2.91307539e-01 1.35275245e-01 4.10903543e-01
1.50068700e+00 3.41877192e-02 -1.95737672e+00 1.00953937e-01
-3.31999570e-01 -5.83102047e-01 1.67662531e-01 -6.14217818e-01
-1.26091921e+00 1.02109289e+00 4.01067436e-01 1.85291141e-01
1.04359722e+00 -7.48749226e-02 8.07187021e-01 -1.05286658e-01
9.47314203e-02 -7.62745142e-01 1.47562578e-01 3.96985590e-01
3.97832423e-01 -1.14048719e+00 -3.41931224e-01 -2.75845855e-01
-7.81943798e-01 9.14448500e-01 4.47966725e-01 -3.01181495e-01
3.89314413e-01 1.92099839e-01 9.93590802e-02 2.06722289e-01
-4.60890800e-01 -1.91593200e-01 3.64627838e-01 6.58411443e-01
1.44629166e-01 -2.47307509e-01 6.14140220e-02 -3.57387736e-02
2.46220261e-01 -3.57806861e-01 4.86955673e-01 6.82291687e-01
-3.65796238e-01 -1.02864373e+00 -2.42943823e-01 3.10640574e-01
-6.84795260e-01 1.74981747e-02 5.43463603e-02 5.62851667e-01
3.49936783e-01 8.44794691e-01 6.33041561e-01 3.01478244e-02
-2.19887923e-02 -6.52377447e-03 4.30927962e-01 -5.65437794e-01
-8.80681947e-02 2.07132101e-01 -1.64729938e-01 -6.20880783e-01
-5.98519981e-01 -7.08967328e-01 -1.39099693e+00 -8.25275183e-02
-1.96560726e-01 4.54577357e-02 5.08654952e-01 1.09898961e+00
3.21735233e-01 7.17621922e-01 5.29697239e-01 -1.10581791e+00
-2.29476262e-02 -5.26132762e-01 -2.90897876e-01 4.97330874e-01
8.57487261e-01 -6.17910445e-01 -4.93932575e-01 5.51302254e-01] | [9.108579635620117, -0.3200002908706665] |
a9b5f469-0a13-4a03-bbb8-07bb25518d8d | muld-the-multitask-long-document-benchmark | 2202.07362 | null | https://arxiv.org/abs/2202.07362v1 | https://arxiv.org/pdf/2202.07362v1.pdf | MuLD: The Multitask Long Document Benchmark | The impressive progress in NLP techniques has been driven by the development of multi-task benchmarks such as GLUE and SuperGLUE. While these benchmarks focus on tasks for one or two input sentences, there has been exciting work in designing efficient techniques for processing much longer inputs. In this paper, we present MuLD: a new long document benchmark consisting of only documents over 10,000 tokens. By modifying existing NLP tasks, we create a diverse benchmark which requires models to successfully model long-term dependencies in the text. We evaluate how existing models perform, and find that our benchmark is much more challenging than their `short document' equivalents. Furthermore, by evaluating both regular and efficient transformers, we show that models with increased context length are better able to solve the tasks presented, suggesting that future improvements in these models are vital for solving similar long document problems. We release the data and code for baselines to encourage further research on efficient NLP models. | ['Noura Al Moubayed', 'G Thomas Hudson'] | 2022-02-15 | null | https://aclanthology.org/2022.lrec-1.392 | https://aclanthology.org/2022.lrec-1.392.pdf | lrec-2022-6 | ['summarization'] | ['natural-language-processing'] | [ 4.96311672e-02 1.20100178e-01 -3.87102842e-01 -5.26731431e-01
-1.40441740e+00 -1.06728804e+00 7.90668666e-01 3.75288367e-01
-5.13873219e-01 9.02166367e-01 6.90053523e-01 -7.21967518e-01
-3.46989892e-02 -6.03549898e-01 -7.13679075e-01 -3.76486629e-01
-3.27033162e-01 8.12401831e-01 3.54803592e-01 -2.21988440e-01
3.74874204e-01 1.83037639e-01 -1.03432047e+00 8.54965866e-01
4.47113663e-01 3.36792439e-01 2.57343084e-01 1.05068684e+00
-5.39683163e-01 8.05210531e-01 -7.96158493e-01 -5.76853573e-01
1.50839999e-01 -9.88714918e-02 -1.42290819e+00 -6.52788043e-01
5.69689214e-01 -2.75935363e-02 1.64049491e-01 6.33356094e-01
6.35423958e-01 1.84859633e-01 6.21355712e-01 -1.09072208e+00
-6.53540909e-01 1.26105654e+00 -3.97562265e-01 5.60454190e-01
6.34244263e-01 -1.69554517e-01 1.81407833e+00 -8.15107167e-01
6.29511297e-01 1.41485298e+00 6.83666408e-01 4.74850416e-01
-1.29769635e+00 -3.22898000e-01 3.61114174e-01 -4.51604910e-02
-7.98399627e-01 -5.19713342e-01 3.13665986e-01 -1.53583372e-02
1.99939489e+00 2.94975847e-01 6.98553994e-02 1.17856157e+00
4.04772133e-01 1.13284373e+00 9.25041676e-01 -6.87421978e-01
-3.36084247e-01 -1.64489642e-01 5.58015347e-01 5.48332274e-01
2.00399920e-01 -5.23484759e-02 -3.37066621e-01 -1.71438053e-01
2.06244275e-01 -4.58660692e-01 9.20200627e-03 3.03873748e-01
-1.55800736e+00 7.10596740e-01 -6.59664497e-02 6.27909601e-01
1.17195547e-01 2.52936393e-01 5.44207335e-01 4.50622827e-01
5.54493368e-01 8.08440149e-01 -9.53114033e-01 -5.41950285e-01
-9.54056799e-01 6.66042268e-01 1.53579104e+00 1.07725537e+00
4.32442993e-01 -5.06147146e-01 -5.50796568e-01 9.28093493e-01
7.54152685e-02 3.84367853e-01 2.88121313e-01 -1.06227791e+00
1.20238352e+00 1.21058613e-01 1.12433672e-01 -8.29639137e-01
-4.72082287e-01 -1.97012231e-01 -3.31727177e-01 -2.53743201e-01
6.82898521e-01 -2.11542711e-01 -8.46680582e-01 1.65322149e+00
-2.09587172e-01 -2.17595354e-01 3.14516991e-01 2.79804856e-01
6.18644238e-01 1.29000676e+00 2.49021947e-01 -7.50550404e-02
1.39556921e+00 -1.30921841e+00 -5.38804531e-01 -6.94968164e-01
1.23580825e+00 -1.25321758e+00 1.30632102e+00 4.71521407e-01
-1.45466077e+00 -1.61470056e-01 -8.75884533e-01 -6.28720522e-01
-6.23520613e-01 -2.28410706e-01 9.38574553e-01 4.22491431e-01
-1.17854536e+00 5.52501023e-01 -6.79192364e-01 -3.75579804e-01
7.02425838e-03 2.65679270e-01 -1.33797735e-01 -2.01425716e-01
-1.30161130e+00 1.14015138e+00 4.45518613e-01 1.67848670e-03
-5.51020205e-01 -7.67326057e-01 -7.42132664e-01 1.19785048e-01
2.16312692e-01 -5.40261269e-01 1.79032993e+00 -3.71876270e-01
-1.20841205e+00 8.71751487e-01 -3.96715015e-01 -4.00423408e-01
2.03233644e-01 -3.85492772e-01 -2.50859588e-01 -2.10989669e-01
1.88733652e-01 7.26095200e-01 2.07110301e-01 -8.33256960e-01
-9.13798511e-01 -5.41409478e-04 3.83165270e-01 2.59027034e-01
-1.85502097e-01 7.69537926e-01 -4.95891184e-01 -7.04166114e-01
-4.80835527e-01 -7.92900622e-01 -2.21620917e-01 -6.15002275e-01
-5.69794178e-01 -7.40573823e-01 3.96311849e-01 -4.64197069e-01
1.19458568e+00 -1.83976460e+00 6.98454455e-02 6.47614449e-02
-1.26407683e-01 1.77587673e-01 -6.78766191e-01 7.16148138e-01
-2.33055484e-02 6.41316831e-01 -1.17656797e-01 -6.39343739e-01
2.51571894e-01 6.11061156e-01 -6.87992632e-01 -1.72667444e-01
2.32071564e-01 1.08292067e+00 -8.49386871e-01 -7.56514072e-01
-2.94719309e-01 6.92273155e-02 -5.75147867e-01 -4.09009866e-02
-7.18765020e-01 -1.74666122e-01 -4.19020325e-01 4.86628294e-01
2.73100406e-01 -2.14095831e-01 6.93048462e-02 2.45227814e-01
-1.41773582e-01 1.00535142e+00 -8.66087735e-01 1.89828575e+00
-7.16378629e-01 6.32427275e-01 1.89516768e-01 -7.88053751e-01
4.48062181e-01 3.80616158e-01 2.95441836e-01 -7.04681635e-01
-2.32634127e-01 2.96187013e-01 7.48547986e-02 -5.26874006e-01
8.93734276e-01 -1.01933502e-01 -3.40121597e-01 9.34742928e-01
-9.55893379e-03 -3.90426666e-01 9.26189005e-01 5.23561418e-01
1.30143225e+00 -3.95467989e-02 -8.54984745e-02 -3.33164185e-01
2.69484431e-01 1.82615161e-01 2.16872498e-01 1.03785145e+00
1.17889307e-01 3.90377671e-01 8.56276512e-01 -3.17341298e-01
-1.10211504e+00 -9.22604322e-01 -2.48910114e-01 1.62179947e+00
-4.76709515e-01 -8.98457706e-01 -4.41754192e-01 -8.55874598e-01
4.76762541e-02 7.79413223e-01 -3.43763262e-01 4.46962208e-01
-1.02867651e+00 -1.11682212e+00 9.80089724e-01 9.23304558e-01
-1.31401224e-02 -1.21760821e+00 -4.08744998e-02 5.02408564e-01
-4.30299908e-01 -1.08686817e+00 -5.22255599e-01 6.47559226e-01
-8.32860768e-01 -8.34732234e-01 -6.72235727e-01 -1.16580379e+00
3.76162678e-01 -9.69476700e-02 1.58744609e+00 -4.05497700e-02
-7.49969259e-02 2.04772994e-01 -4.71845776e-01 -4.83769089e-01
-5.13965905e-01 7.05474615e-01 -3.94194961e-01 -9.60372269e-01
5.45693696e-01 -4.52093691e-01 -8.48587155e-02 2.68058181e-02
-7.82553494e-01 -6.21506460e-02 7.40029037e-01 7.42420495e-01
3.74562889e-01 -1.79421213e-02 4.02413458e-01 -1.17773664e+00
1.25830960e+00 -4.99636650e-01 -3.72809321e-01 3.80481571e-01
-5.55838883e-01 5.40126026e-01 7.93398321e-01 -1.87232971e-01
-1.12738502e+00 -3.58696133e-01 -5.83875835e-01 5.30658901e-01
-3.16876024e-02 7.41886079e-01 1.27075240e-01 2.89704293e-01
6.39989555e-01 -8.23565647e-02 -4.67674106e-01 -5.45007169e-01
5.79998910e-01 4.64743763e-01 3.61197323e-01 -1.40185690e+00
5.40836990e-01 2.67241970e-02 -9.34299752e-02 -5.10274947e-01
-1.28049767e+00 -5.74016154e-01 -3.29170793e-01 5.44911206e-01
6.50232613e-01 -6.76023901e-01 -4.67500925e-01 1.37386665e-01
-1.66654110e+00 -7.44580090e-01 -2.01443672e-01 2.82172024e-01
-4.06332254e-01 5.27246475e-01 -1.12948191e+00 -2.94189006e-01
-5.16141474e-01 -9.92105544e-01 1.22701681e+00 -3.43383223e-01
-5.27479589e-01 -1.42586279e+00 6.77503407e-01 1.76334858e-01
3.39487404e-01 -3.48729283e-01 1.26631272e+00 -1.00920391e+00
-5.13755620e-01 -3.79359201e-02 -3.41026515e-01 5.38246572e-01
-1.81596234e-01 1.13239460e-01 -6.60577834e-01 -7.45507702e-02
-3.06918532e-01 -6.70923471e-01 1.06132138e+00 2.62043506e-01
1.03400862e+00 -4.19742733e-01 -5.47976851e-01 4.35852289e-01
1.26147687e+00 -1.64600089e-02 5.22562802e-01 4.24420357e-01
4.94240910e-01 6.87832534e-01 5.12555063e-01 -9.49923173e-02
6.32346690e-01 3.62957835e-01 -3.44029516e-02 8.48910138e-02
-3.46653275e-02 -1.55400172e-01 5.83639979e-01 9.98779953e-01
-1.07214220e-01 -6.75571620e-01 -1.18555152e+00 7.05945671e-01
-1.77787113e+00 -9.83470440e-01 -3.53004694e-01 1.59714603e+00
1.30218315e+00 3.64250273e-01 -1.45701066e-01 9.37077589e-03
1.86873138e-01 5.68574607e-01 1.29670560e-01 -1.01733863e+00
-1.66565642e-01 6.64712965e-01 3.99154812e-01 9.56656456e-01
-1.09340942e+00 1.15675354e+00 8.01173973e+00 7.06875265e-01
-6.80877209e-01 -8.99665579e-02 3.61265332e-01 -3.67615610e-01
-6.88370287e-01 6.95209727e-02 -1.43720877e+00 3.26426506e-01
1.35543978e+00 -2.33934283e-01 3.59879583e-01 6.16881907e-01
8.87402147e-03 -1.64228424e-01 -1.52293932e+00 3.93875748e-01
1.57931119e-01 -1.27415287e+00 2.52988860e-02 -1.20492898e-01
6.89924061e-01 3.63966554e-01 -5.76403551e-02 6.04166210e-01
9.42459583e-01 -1.04262745e+00 4.05977875e-01 2.76474673e-02
4.73810285e-01 -6.70598686e-01 6.84820712e-01 3.60834509e-01
-1.11851072e+00 3.64793874e-02 -5.83323538e-01 -2.30611086e-01
4.35978562e-01 7.27925599e-01 -8.72320950e-01 4.39712286e-01
5.49263239e-01 8.12558353e-01 -6.53847933e-01 8.21399629e-01
-5.08716106e-01 6.33702517e-01 -4.85866636e-01 -3.16371441e-01
6.74605608e-01 4.37383614e-02 4.29101616e-01 1.89285755e+00
1.79088280e-01 -1.06287457e-01 2.81464547e-01 4.63220745e-01
-2.23471522e-01 2.61340588e-01 -7.27308571e-01 -2.17928216e-01
1.96345210e-01 1.23424804e+00 -5.45656145e-01 -3.74188423e-01
-6.61311924e-01 6.16927624e-01 6.90542221e-01 3.92154783e-01
-7.58223116e-01 -6.25663996e-01 4.32569832e-01 -3.36663127e-01
1.77277073e-01 -5.63023806e-01 -2.70391554e-01 -1.20090115e+00
2.64999211e-01 -8.77065957e-01 6.45462453e-01 -6.81379974e-01
-1.39857137e+00 4.57528025e-01 1.51779637e-01 -3.09872568e-01
-5.30490577e-01 -8.17576170e-01 -6.12366378e-01 9.49705601e-01
-1.72423279e+00 -9.36570942e-01 4.01504397e-01 5.05366743e-01
5.74962556e-01 2.67538190e-01 1.06913137e+00 3.83238167e-01
-4.02862549e-01 4.65897948e-01 1.36040926e-01 2.71306843e-01
1.01945639e+00 -1.60934734e+00 8.12889099e-01 9.07441318e-01
5.07536829e-01 6.89215958e-01 6.07153118e-01 -5.23625910e-01
-1.28447902e+00 -9.07413483e-01 1.81318533e+00 -9.39612091e-01
1.01969850e+00 -5.77781320e-01 -6.06530309e-01 1.31663930e+00
5.30049145e-01 -2.80290335e-01 6.68622732e-01 7.09041893e-01
-4.23021585e-01 -4.14857566e-02 -5.79791069e-01 4.77681637e-01
1.11267948e+00 -4.56493884e-01 -1.08457458e+00 6.63726747e-01
9.25445855e-01 -4.71030086e-01 -7.83403218e-01 1.89388558e-01
4.61819649e-01 -5.23689687e-01 9.60106373e-01 -8.74288559e-01
7.15932369e-01 1.73236623e-01 -2.97797024e-02 -1.36259460e+00
-2.53620148e-01 -4.70464736e-01 -3.69746201e-02 1.38644063e+00
1.09494507e+00 -5.73145390e-01 7.83015311e-01 5.14702618e-01
-3.97778660e-01 -7.88319767e-01 -5.81131697e-01 -9.44577694e-01
6.94577515e-01 -7.78254509e-01 2.99308449e-01 6.85276687e-01
3.29985201e-01 6.54343903e-01 -1.51742443e-01 -1.95084170e-01
2.82620668e-01 2.36432612e-01 5.08684099e-01 -9.59060669e-01
-5.29411793e-01 -4.81502920e-01 3.41539711e-01 -1.39361823e+00
5.43937027e-01 -1.35207331e+00 3.63469571e-01 -1.84971905e+00
3.15614730e-01 -6.12845600e-01 -7.94685781e-02 8.69155586e-01
-1.68769225e-01 2.80207470e-02 6.37672916e-02 1.55496998e-02
-7.51137495e-01 1.72166619e-02 1.23758376e+00 -1.94729015e-01
-5.00938892e-02 -1.70568023e-02 -9.17331994e-01 4.35458511e-01
7.99473584e-01 -6.84110641e-01 -3.65146726e-01 -1.24422491e+00
8.57015431e-01 -2.20351592e-01 -3.25633213e-02 -6.42645061e-01
4.56432968e-01 -8.54576007e-02 1.17042206e-01 -7.42405355e-01
2.13919997e-01 -5.22532403e-01 -6.25956357e-01 7.36985281e-02
-6.69517696e-01 4.92888182e-01 3.69398475e-01 2.59414613e-01
-3.52397084e-01 -5.09440243e-01 2.90683627e-01 -4.33488756e-01
-3.34190339e-01 2.37781882e-01 -4.80065733e-01 6.21875346e-01
7.98797846e-01 2.30533853e-01 -6.94263160e-01 -1.17218204e-01
-5.92330337e-01 5.51122606e-01 4.85805683e-02 3.41974705e-01
2.94715941e-01 -8.24722290e-01 -8.98618519e-01 -1.48699060e-01
-1.79336235e-01 2.84857154e-01 -3.15467864e-01 6.18894815e-01
-6.08766615e-01 1.02872789e+00 2.35819340e-01 -2.03439668e-01
-1.24674976e+00 4.85596806e-01 -2.32569873e-04 -1.08263457e+00
-4.53920007e-01 9.75651264e-01 -2.24268362e-01 -5.37540495e-01
4.55135018e-01 -7.81248271e-01 6.67896122e-02 1.65192142e-01
6.34600401e-01 2.12937668e-01 1.84943095e-01 4.55714576e-02
-3.73480558e-01 3.80435467e-01 -5.55129945e-01 -3.86553288e-01
1.50671387e+00 1.79973707e-01 -4.43248391e-01 2.94167131e-01
1.30588853e+00 4.03312206e-01 -6.90223992e-01 -2.03498766e-01
4.89078134e-01 -4.91567701e-02 -3.13768417e-01 -9.59164202e-01
-5.55691898e-01 1.01719129e+00 -4.33659703e-01 3.39014858e-01
7.49997139e-01 2.42816046e-01 1.04263508e+00 1.01491117e+00
3.06644469e-01 -1.04484046e+00 1.01891093e-01 1.36104119e+00
7.73924828e-01 -9.06166494e-01 6.86050355e-02 -2.57224292e-01
-2.80220807e-01 1.10930598e+00 3.53248268e-01 -1.51676042e-02
6.15515649e-01 8.59117210e-01 -1.24220096e-01 -2.38884568e-01
-1.25646794e+00 1.87046379e-02 -2.46046465e-02 3.83121818e-01
8.12313974e-01 -1.35945901e-01 -3.59168172e-01 5.24607003e-01
-5.92294991e-01 -1.56668440e-01 3.81828219e-01 1.07331848e+00
-4.37495947e-01 -1.82906115e+00 -1.52084783e-01 5.02188861e-01
-8.78509223e-01 -7.28778541e-01 -4.43758398e-01 8.98983419e-01
-2.76268572e-01 8.62647593e-01 8.15465450e-02 2.14381799e-01
2.41485640e-01 3.73814315e-01 8.52672517e-01 -1.13610661e+00
-7.70947754e-01 -3.32122922e-01 8.36974144e-01 -5.61161697e-01
-3.83952022e-01 -5.86664021e-01 -1.28804684e+00 -4.81792182e-01
4.14103270e-02 3.93541485e-01 5.86849272e-01 9.66425419e-01
1.67452857e-01 2.14356109e-01 1.42245963e-01 -5.90424359e-01
-6.55448854e-01 -1.05828416e+00 -4.44386572e-01 7.54049942e-02
2.02501103e-01 -7.43049383e-03 -3.74405354e-01 6.76352605e-02] | [10.957523345947266, 8.704994201660156] |
9d97ce82-69d3-4129-8553-5a2c424d2978 | talking-face-generation-by-conditional | 1804.04786 | null | https://arxiv.org/abs/1804.04786v3 | https://arxiv.org/pdf/1804.04786v3.pdf | Talking Face Generation by Conditional Recurrent Adversarial Network | Given an arbitrary face image and an arbitrary speech clip, the proposed work attempts to generating the talking face video with accurate lip synchronization while maintaining smooth transition of both lip and facial movement over the entire video clip. Existing works either do not consider temporal dependency on face images across different video frames thus easily yielding noticeable/abrupt facial and lip movement or are only limited to the generation of talking face video for a specific person thus lacking generalization capacity. We propose a novel conditional video generation network where the audio input is treated as a condition for the recurrent adversarial network such that temporal dependency is incorporated to realize smooth transition for the lip and facial movement. In addition, we deploy a multi-task adversarial training scheme in the context of video generation to improve both photo-realism and the accuracy for lip synchronization. Finally, based on the phoneme distribution information extracted from the audio clip, we develop a sample selection method that effectively reduces the size of the training dataset without sacrificing the quality of the generated video. Extensive experiments on both controlled and uncontrolled datasets demonstrate the superiority of the proposed approach in terms of visual quality, lip sync accuracy, and smooth transition of lip and facial movement, as compared to the state-of-the-art. | ['Xiaolong Wang', 'Yang Song', 'Hairong Qi', 'Dawei Li', 'Jingwen Zhu'] | 2018-04-13 | null | null | null | null | ['talking-face-generation', 'lip-sync-1'] | ['computer-vision', 'computer-vision'] | [ 3.03311437e-01 -1.19364701e-01 -5.87411113e-02 -9.82216597e-02
-8.63466501e-01 -4.75871921e-01 4.48262393e-01 -4.45633054e-01
4.05650884e-02 4.87057030e-01 1.12277143e-01 2.97868140e-02
2.41329610e-01 -4.37581927e-01 -8.38655293e-01 -8.64884019e-01
3.21687222e-01 -2.30059266e-01 7.16639161e-02 1.01770252e-01
3.01061431e-03 3.92298907e-01 -1.58290303e+00 1.37386158e-01
7.91889668e-01 1.15455413e+00 -6.79484531e-02 5.98346412e-01
2.68583417e-01 5.98222613e-01 -6.34999990e-01 -4.69276100e-01
2.89656460e-01 -5.72953045e-01 -3.42486024e-01 4.73472804e-01
5.50705969e-01 -4.25671756e-01 -4.18106824e-01 9.88210261e-01
8.72805834e-01 1.79565862e-01 4.26529974e-01 -1.43913031e+00
-3.32577467e-01 1.97164372e-01 -5.63664019e-01 -1.50889039e-01
5.24757981e-01 3.53524268e-01 4.64825749e-01 -7.93528497e-01
5.81116915e-01 1.20574212e+00 5.23981869e-01 8.02292168e-01
-1.16275132e+00 -1.02363658e+00 6.68182597e-02 3.12252402e-01
-1.63395882e+00 -1.11914337e+00 1.09420121e+00 -3.29406023e-01
2.25389674e-01 1.25030592e-01 4.61750776e-01 1.18010187e+00
-5.79410931e-03 4.64529783e-01 6.39846385e-01 -3.88850093e-01
9.35158040e-03 2.44047195e-02 -6.20141208e-01 4.93684947e-01
-3.10471773e-01 1.38896391e-01 -6.15738332e-01 -6.31041545e-03
7.22258627e-01 -2.68650979e-01 -5.45179248e-01 -2.77482420e-01
-1.01167190e+00 4.96462196e-01 -2.92874854e-02 6.82572871e-02
-4.14449960e-01 4.67966050e-02 4.46132690e-01 1.91703923e-02
4.80875611e-01 -2.03562886e-01 2.44123504e-01 3.54575254e-02
-1.20485151e+00 1.42383739e-01 4.08882052e-01 8.96513999e-01
4.59437132e-01 5.13778746e-01 -2.80965179e-01 7.20411777e-01
1.25942960e-01 6.60126925e-01 5.12983084e-01 -1.01231992e+00
5.08444250e-01 2.19465777e-01 2.31927276e-01 -1.06065798e+00
1.40372813e-01 7.11519569e-02 -7.12359786e-01 2.50108302e-01
2.97125965e-01 -5.10340333e-01 -7.32608199e-01 2.11801600e+00
5.78196168e-01 5.85569561e-01 2.29812294e-01 6.38906360e-01
6.28230274e-01 7.56941974e-01 3.55742015e-02 -7.39473462e-01
9.12768304e-01 -7.53155887e-01 -1.16011453e+00 2.63422225e-02
3.33469622e-02 -1.00265360e+00 8.92826200e-01 1.49713248e-01
-1.31310308e+00 -8.08889925e-01 -8.43219638e-01 2.10102081e-01
1.71305567e-01 3.88225228e-01 1.03429124e-01 6.30128920e-01
-9.94536936e-01 1.54810429e-01 -6.04391038e-01 -2.51690485e-03
1.91636309e-01 4.02727306e-01 -4.05624747e-01 -2.96357777e-02
-1.06865335e+00 3.50552320e-01 3.20042260e-02 1.04779162e-01
-9.57181871e-01 -6.83260024e-01 -9.24278378e-01 -3.24308649e-02
2.95837760e-01 -5.14163554e-01 1.01537430e+00 -1.45172560e+00
-2.03359818e+00 5.17260253e-01 -3.71453404e-01 -2.93750912e-01
7.71288931e-01 4.47886996e-03 -5.52879214e-01 5.09532094e-01
-1.36460945e-01 9.74343717e-01 1.44300723e+00 -1.18660021e+00
-3.92216533e-01 -4.43263948e-02 -1.70165002e-01 4.29214120e-01
-3.73936921e-01 3.11870892e-02 -6.42993987e-01 -7.75505364e-01
-2.13771626e-01 -1.05056703e+00 3.14607054e-01 1.11629009e-01
-2.88920552e-01 1.59692448e-02 1.32507670e+00 -7.40672767e-01
1.00889969e+00 -2.44458199e+00 8.85061845e-02 -1.88313231e-01
-3.23696196e-01 3.47372234e-01 -2.31778651e-01 1.97415188e-01
-1.70872152e-01 1.97639782e-02 -2.45084744e-02 -5.57594478e-01
-3.35198700e-01 -1.34898409e-01 -3.74971509e-01 6.64771914e-01
1.66269854e-01 5.43542147e-01 -5.75621605e-01 -6.87895119e-01
3.77784818e-01 9.97891843e-01 -6.50974751e-01 3.88359785e-01
-1.50608405e-01 5.48999250e-01 -1.74741268e-01 5.47006190e-01
7.30005205e-01 3.68456513e-01 1.10062659e-01 -2.51365483e-01
8.98035392e-02 -1.00938216e-01 -1.17595673e+00 1.47208667e+00
-6.21719778e-01 6.16606057e-01 2.99108088e-01 -6.71145439e-01
8.90967488e-01 8.70646119e-01 6.83153749e-01 -3.70398223e-01
2.27023751e-01 -8.31252560e-02 -1.26149401e-01 -6.55750453e-01
2.17787281e-01 -1.72693729e-01 1.75586373e-01 1.58630461e-01
-4.42707054e-02 -2.47695982e-01 3.31705250e-02 -1.52018309e-01
4.88969386e-01 1.60853922e-01 -7.30023310e-02 1.17509991e-01
7.03789353e-01 -7.01633036e-01 5.88424385e-01 -2.60220300e-02
-2.44775742e-01 6.20620430e-01 3.36435705e-01 1.44709662e-01
-9.73286390e-01 -9.47331548e-01 8.19967017e-02 7.33928204e-01
2.35581249e-01 -1.68066412e-01 -1.17084968e+00 -3.17352116e-01
-4.56749439e-01 5.13280094e-01 -2.99949139e-01 -2.47150749e-01
-6.45712972e-01 -2.94810712e-01 6.75997496e-01 2.17565715e-01
6.09645963e-01 -1.18325460e+00 -2.90590167e-01 1.99606847e-02
-5.81466019e-01 -1.49801791e+00 -9.77341950e-01 -7.57450759e-01
-5.37217081e-01 -9.15288031e-01 -8.08346450e-01 -9.36503708e-01
7.53798485e-01 6.79647028e-02 2.64930695e-01 -2.45307311e-01
-8.44790116e-02 2.72314966e-01 -8.21476057e-02 -6.90125600e-02
-7.63935626e-01 -2.81065255e-01 2.68017679e-01 6.97304070e-01
-3.07453543e-01 -5.96594691e-01 -6.46467447e-01 3.86842310e-01
-9.64926779e-01 3.22044082e-02 1.45354867e-01 7.70538449e-01
2.78276503e-01 2.99866527e-01 7.93472946e-01 -1.14394516e-01
4.90828872e-01 -3.32044154e-01 -4.57064927e-01 1.49741665e-01
-2.34378144e-01 -4.03949499e-01 8.34982872e-01 -9.21135128e-01
-1.23824024e+00 1.52744606e-01 -1.65210694e-01 -8.32890570e-01
-6.07369915e-02 -1.89512685e-01 -4.92583871e-01 -1.28978536e-01
3.21811169e-01 4.04174060e-01 3.78887653e-01 -5.34870327e-02
2.41639480e-01 7.73062408e-01 8.00762117e-01 -2.63495445e-01
6.76798105e-01 5.01736522e-01 -2.94363387e-02 -1.03151453e+00
-1.93526000e-01 -3.12181432e-02 -3.25897515e-01 -5.80339909e-01
7.70480037e-01 -9.55474615e-01 -1.08504784e+00 8.47929895e-01
-9.73995924e-01 -2.62233526e-01 -2.56211543e-03 4.33567345e-01
-7.43127406e-01 5.62522709e-01 -4.27046150e-01 -8.98934305e-01
-3.84801209e-01 -1.44218087e+00 1.04080808e+00 2.56422371e-01
-5.01766577e-02 -8.32172155e-01 -2.89187342e-01 4.14967030e-01
2.64834732e-01 3.49765897e-01 6.32482469e-01 -3.41839381e-02
-5.83313346e-01 -3.68060410e-01 1.67472959e-01 4.71243471e-01
4.70015496e-01 4.17521745e-01 -9.51822162e-01 -3.95531744e-01
2.55763549e-02 -3.50138128e-01 4.08572465e-01 6.29856467e-01
8.73096764e-01 -6.96032405e-01 -2.74609268e-01 5.25051415e-01
1.20198357e+00 3.35758954e-01 6.73155248e-01 -2.49998078e-01
5.10416329e-01 7.56359696e-01 6.33017957e-01 4.31544632e-01
1.34425834e-01 1.02460980e+00 3.51263583e-01 -1.99418794e-02
-3.67285252e-01 -3.98762077e-01 7.08653867e-01 5.03589511e-01
1.90576464e-01 -4.97175574e-01 -4.47776049e-01 6.70443296e-01
-1.51527381e+00 -1.10908139e+00 5.19446373e-01 2.36418223e+00
7.85844386e-01 -1.80003256e-01 1.78043410e-01 4.03128564e-01
1.25901759e+00 1.65620863e-01 -3.99150759e-01 -1.03188671e-01
5.82863875e-02 3.99476802e-03 1.37465626e-01 7.08399951e-01
-8.20994377e-01 9.22313392e-01 5.87123108e+00 9.55657423e-01
-1.71366620e+00 2.99940426e-02 5.83038390e-01 -3.36361974e-01
-1.45308420e-01 -2.71905065e-01 -6.14280224e-01 6.79114103e-01
7.88805783e-01 -2.11585224e-01 4.34754968e-01 5.65918624e-01
7.31540620e-01 4.65205722e-02 -8.18581045e-01 1.00486720e+00
2.96305239e-01 -1.08097100e+00 1.92116350e-01 -1.57589778e-01
6.57570779e-01 -6.45780504e-01 4.16453153e-01 -1.61723614e-01
-2.99801111e-01 -8.74428213e-01 8.90858233e-01 5.11466682e-01
1.28079402e+00 -9.05403078e-01 3.00670028e-01 2.30635986e-01
-1.23368287e+00 -6.62004277e-02 1.43861279e-01 3.97212505e-01
3.51506919e-01 -4.46511284e-02 -8.14406574e-01 4.35026199e-01
3.72752339e-01 4.06763643e-01 -2.47902140e-01 7.35522449e-01
-1.25037581e-01 5.05006194e-01 -2.79890925e-01 4.11380678e-01
-6.34667352e-02 4.40244265e-02 7.93052793e-01 8.35426211e-01
4.28590238e-01 -2.23621223e-02 1.00375883e-01 5.26769221e-01
-2.52856851e-01 2.89938241e-01 -7.61870027e-01 1.59677684e-01
6.36907041e-01 1.02714920e+00 -3.41696173e-01 -8.53763670e-02
-1.74714774e-01 9.80232298e-01 -2.62168378e-01 4.67700571e-01
-1.05844843e+00 -2.93136537e-01 7.64333606e-01 2.72365421e-01
3.78438920e-01 1.40878400e-02 7.32696131e-02 -8.63378406e-01
2.73359895e-01 -8.84063721e-01 -1.91657487e-02 -7.42825031e-01
-6.04270399e-01 7.23887682e-01 -1.68123350e-01 -1.31403196e+00
-7.02809036e-01 9.88261681e-03 -7.38842726e-01 6.90371871e-01
-1.33293867e+00 -1.30375087e+00 -2.85435349e-01 9.91623998e-01
7.82451868e-01 -2.23579809e-01 5.31390965e-01 4.20552939e-01
-6.83237553e-01 9.54792380e-01 -9.05543491e-02 1.00575618e-01
9.92749393e-01 -4.05652314e-01 1.00737654e-01 9.98662531e-01
-2.90977955e-01 2.60630757e-01 7.00365186e-01 -4.80487764e-01
-1.12999749e+00 -1.18977952e+00 6.39377952e-01 1.15947649e-01
2.22636878e-01 -4.16378736e-01 -6.19715035e-01 3.58055383e-01
3.24670583e-01 1.19297758e-01 4.01578128e-01 -6.67096496e-01
-2.50413995e-02 -4.51685488e-01 -1.36396337e+00 7.77589858e-01
5.86300969e-01 -5.74070096e-01 -1.85475107e-02 8.01192150e-02
7.02580512e-01 -4.31498528e-01 -7.11120129e-01 5.58369637e-01
6.43854499e-01 -8.79261494e-01 8.40615571e-01 -8.35425481e-02
2.94118464e-01 -3.29960555e-01 -2.61024426e-04 -1.00462961e+00
2.27889523e-01 -1.17376161e+00 5.13449870e-02 1.74754214e+00
1.31833315e-01 -3.19388777e-01 6.23766363e-01 3.53925109e-01
8.39073956e-02 -4.16614115e-01 -9.85166430e-01 -5.66799223e-01
-2.44536757e-01 -3.30830246e-01 3.83655995e-01 6.35186076e-01
-4.03783955e-02 2.04483345e-01 -7.93981254e-01 3.57291907e-01
4.96858537e-01 -1.46105260e-01 8.75976324e-01 -5.80778182e-01
1.07532345e-01 -9.55220610e-02 -3.77461225e-01 -7.39929438e-01
5.27143061e-01 -4.00193244e-01 1.66858375e-01 -9.20562804e-01
-1.49235949e-01 -1.84279367e-01 1.69032231e-01 2.17084631e-01
-1.21558882e-01 4.07122076e-01 3.27166975e-01 1.90534547e-01
-8.53494778e-02 6.40254676e-01 1.27413237e+00 2.48814896e-02
-3.73381078e-01 3.16519052e-01 -2.58754849e-01 5.88573873e-01
5.51652730e-01 -2.28800252e-01 -6.91088617e-01 -1.76210478e-01
-4.82277006e-01 7.13858128e-01 4.62887377e-01 -1.20329154e+00
2.27509618e-01 -7.43755400e-02 2.05871582e-01 -2.18741819e-01
7.30565369e-01 -8.33076835e-01 2.71557033e-01 2.22238421e-01
-3.09070796e-01 -5.71760833e-02 3.33854347e-01 4.82734025e-01
-3.33193094e-01 7.46116936e-02 1.28254259e+00 2.64127105e-01
-2.42064074e-01 4.63782221e-01 -2.38400578e-01 -4.80511673e-02
1.32925355e+00 -2.56960958e-01 8.03254619e-02 -8.16565037e-01
-6.34584069e-01 -1.59763962e-01 5.13946891e-01 5.90237439e-01
6.31879330e-01 -1.30537307e+00 -6.38124764e-01 4.72771943e-01
-3.01380187e-01 -2.44381189e-01 5.30453444e-01 6.91769660e-01
-2.35063106e-01 2.25472584e-01 -2.78499573e-01 -6.64115071e-01
-1.63143301e+00 6.73313916e-01 4.22326148e-01 2.21390024e-01
-4.94246125e-01 4.06152874e-01 2.98128575e-01 2.13756263e-01
4.49189723e-01 2.92297788e-02 -7.98500851e-02 4.38495390e-02
4.14728463e-01 2.12070078e-01 -1.75199285e-01 -8.64828527e-01
-1.79520175e-01 7.14045763e-01 2.07522497e-01 -3.54225338e-01
8.98759842e-01 -4.56282258e-01 2.60220677e-01 1.88821927e-01
1.17429638e+00 3.67086440e-01 -1.71009600e+00 1.00990184e-01
-7.18644977e-01 -5.03403366e-01 -7.80141726e-02 -3.75615299e-01
-1.23623121e+00 7.96064019e-01 7.49848068e-01 -1.37438387e-01
1.41984165e+00 -3.92893672e-01 8.72974932e-01 -2.24126011e-01
8.26496445e-03 -9.03249383e-01 3.66443902e-01 -6.36378899e-02
8.22916150e-01 -9.72012758e-01 -3.45322222e-01 -4.85137075e-01
-8.16349387e-01 1.00552511e+00 5.02852738e-01 1.30865157e-01
4.48152453e-01 4.23447698e-01 1.59839824e-01 4.92288738e-01
-6.54208660e-01 1.77685261e-01 1.53624400e-01 6.73921466e-01
2.12896839e-01 -2.95071423e-01 -2.80220546e-02 1.28819436e-01
-1.29126176e-01 2.63172060e-01 4.89900827e-01 4.94964421e-01
-1.13710672e-01 -8.78562391e-01 -3.86390209e-01 -2.12940916e-01
-7.82435894e-01 -1.91478301e-02 -7.38176480e-02 6.80657148e-01
1.61482334e-01 1.05900073e+00 3.96061949e-02 -1.32771015e-01
1.21449754e-01 3.01387817e-01 4.50940520e-01 -1.78075314e-01
-2.39309028e-01 3.84490728e-01 -1.68802887e-01 -3.73064309e-01
-4.56648439e-01 -6.67103171e-01 -9.43261206e-01 -4.61289406e-01
-3.65785062e-01 5.60440756e-02 5.88527679e-01 8.13508391e-01
4.46025789e-01 3.67348790e-01 1.05692577e+00 -9.65971768e-01
-3.42841506e-01 -1.08165264e+00 -3.77123326e-01 3.82523090e-01
6.22135639e-01 -5.56230962e-01 -3.86645406e-01 5.37454009e-01] | [13.213528633117676, -0.3822091221809387] |
0998518b-71ae-4b95-a1d0-efe877b9fb1e | towards-legally-enforceable-hate-speech | 2305.13677 | null | https://arxiv.org/abs/2305.13677v1 | https://arxiv.org/pdf/2305.13677v1.pdf | Towards Legally Enforceable Hate Speech Detection for Public Forums | Hate speech is a serious issue on public forums, and proper enforcement of hate speech laws is key for protecting groups of people against harmful and discriminatory language. However, determining what constitutes hate speech is a complex task that is highly open to subjective interpretations. Existing works do not align their systems with enforceable definitions of hate speech, which can make their outputs inconsistent with the goals of regulators. Our work introduces a new task for enforceable hate speech detection centred around legal definitions, and a dataset annotated on violations of eleven possible definitions by legal experts. Given the challenge of identifying clear, legally enforceable instances of hate speech, we augment the dataset with expert-generated samples and an automatically mined challenge set. We experiment with grounding the model decision in these definitions using zero-shot and few-shot prompting. We then report results on several large language models (LLMs). With this task definition, automatic hate speech detection can be more closely aligned to enforceable laws, and hence assist in more rigorous enforcement of legal protections against harmful speech in public forums. | ['Samuel Dahan', 'Xiaodan Zhu', 'Rohan Bhambhoria', 'Chu Fei Luo'] | 2023-05-23 | null | null | null | null | ['hate-speech-detection'] | ['natural-language-processing'] | [ 3.46906334e-01 -3.88734695e-03 -3.26951951e-01 -3.35421443e-01
-6.88816190e-01 -1.11245000e+00 8.93340230e-01 2.19074965e-01
-2.36724854e-01 6.17505074e-01 6.63121998e-01 -5.46607673e-01
3.41694872e-03 -3.53204906e-01 -8.56491998e-02 -4.27963465e-01
3.09113413e-01 4.76651080e-02 1.47422567e-01 -2.71653384e-01
4.29199696e-01 3.19523662e-01 -1.04024172e+00 6.05610609e-01
9.55479980e-01 2.10068405e-01 -4.71160352e-01 8.30095708e-01
-8.13746080e-02 1.35522735e+00 -1.37885499e+00 -9.05053675e-01
8.57446790e-02 -3.31484765e-01 -8.23847890e-01 1.02801457e-01
9.80203152e-01 -4.16430593e-01 -2.43346199e-01 1.39839637e+00
3.67614537e-01 6.63950518e-02 6.05055809e-01 -1.35238302e+00
-1.36716270e+00 5.24942338e-01 -3.79363060e-01 4.45961714e-01
3.73820961e-01 4.90535915e-01 1.06203759e+00 -6.99200273e-01
7.64166594e-01 1.52077138e+00 4.04659212e-01 9.38453317e-01
-1.21041679e+00 -8.45663667e-01 1.61500767e-01 2.83819214e-02
-1.15800381e+00 -8.40131521e-01 6.82369649e-01 -1.19285548e+00
8.71714830e-01 5.12234807e-01 2.38609329e-01 1.66126025e+00
-2.88056493e-01 6.31988883e-01 1.01806569e+00 -1.96806207e-01
3.10411304e-01 3.57294738e-01 5.23180604e-01 5.68331540e-01
4.49843436e-01 -2.05888823e-01 -4.99530375e-01 -9.32934403e-01
-7.43918791e-02 -6.44284934e-02 -3.62647951e-01 4.07812297e-01
-5.22737026e-01 1.26110601e+00 -4.75877821e-02 5.97418904e-01
6.40327930e-02 -2.18359277e-01 5.87288499e-01 6.68342710e-02
7.75368690e-01 1.02298951e+00 4.94722091e-02 -1.91280603e-01
-9.90450084e-01 4.90909308e-01 8.81587863e-01 5.14847636e-01
4.48923141e-01 -2.67177701e-01 -3.82044077e-01 9.84726012e-01
1.44919619e-01 9.03396070e-01 1.43969944e-02 -8.69266748e-01
6.04563594e-01 4.72747028e-01 3.69017988e-01 -1.34038675e+00
-8.57702792e-02 -1.41452840e-02 -3.57740015e-01 1.13372818e-01
4.80399549e-01 -5.00600815e-01 -8.59589934e-01 1.55197275e+00
2.29891673e-01 -1.07226908e-01 -3.75518471e-01 7.16751754e-01
4.61510658e-01 7.46320605e-01 5.14226496e-01 -2.04229563e-01
1.33294380e+00 -6.19224966e-01 -1.23576617e+00 -4.52032447e-01
8.17297459e-01 -7.33108580e-01 9.26699877e-01 2.58789629e-01
-3.97798926e-01 1.80525452e-01 -7.74132133e-01 -2.84164578e-01
-6.85837269e-01 -4.31711316e-01 3.60257298e-01 1.07001019e+00
-5.59545517e-01 2.18354568e-01 -2.47526973e-01 -2.74149477e-01
7.31737196e-01 -2.25975841e-01 -4.17212576e-01 1.06639676e-02
-1.52026594e+00 1.34099591e+00 -1.19928993e-01 -5.75546585e-02
-7.84482121e-01 -5.07498145e-01 -1.04800761e+00 -2.13205159e-01
6.48782492e-01 1.14827409e-01 1.31060576e+00 -9.40990329e-01
-7.22281396e-01 1.16187656e+00 -2.01047927e-01 -3.56414437e-01
1.83720753e-01 -3.53523672e-01 -6.72846317e-01 -1.06637493e-01
4.21318978e-01 8.47448185e-02 1.02028227e+00 -1.17449141e+00
-3.18321675e-01 -4.01879221e-01 3.62680316e-01 -3.57561022e-01
-7.18602180e-01 9.06343102e-01 4.30775881e-01 -7.41627216e-01
-1.01087892e+00 -9.57944334e-01 2.43546646e-02 -1.20067000e-01
-8.46391141e-01 -4.83294010e-01 1.17392886e+00 -1.04694104e+00
1.93389487e+00 -2.16680527e+00 -2.48325691e-01 1.35979578e-01
5.44124961e-01 9.77580249e-01 -7.36774430e-02 5.00101030e-01
1.57196790e-01 8.28956664e-01 -3.19723755e-01 -4.00024563e-01
2.74795473e-01 3.49460810e-01 -6.83606803e-01 7.01528609e-01
3.10108691e-01 5.10840178e-01 -1.27598345e+00 -3.57959241e-01
2.99757626e-02 4.01185095e-01 -3.85131091e-01 3.92395228e-01
-2.99229503e-01 2.91300416e-02 -4.47898388e-01 5.64669967e-01
4.31122571e-01 3.91655713e-02 1.05901934e-01 4.04351592e-01
-3.00812364e-01 4.97680664e-01 -6.07216477e-01 8.61625075e-01
-1.27565220e-01 1.13858497e+00 3.71012032e-01 -3.09891760e-01
8.83541703e-01 4.23055589e-01 -8.05246532e-02 -5.28957665e-01
1.10300086e-01 6.67846873e-02 1.93894119e-03 -1.04271591e+00
4.16757524e-01 -1.05840705e-01 -2.24710941e-01 6.52942538e-01
-1.50000781e-01 4.40936387e-02 2.77796865e-01 3.46808583e-01
1.41267002e+00 -5.05119801e-01 4.18809533e-01 -7.10261166e-02
3.93482983e-01 -3.93937714e-02 6.15408003e-01 8.53364110e-01
-8.52914035e-01 2.02242672e-01 8.95051658e-01 -7.05340087e-01
-1.07535291e+00 -7.05306172e-01 8.04719329e-02 1.48667407e+00
-3.19716692e-01 -7.63053954e-01 -9.11058962e-01 -1.05497861e+00
5.09506417e-03 9.27606106e-01 -8.57473433e-01 -9.12169926e-03
-4.49727774e-01 -4.40163255e-01 9.19492722e-01 -5.37964217e-02
1.76831231e-01 -1.00650263e+00 -6.10272288e-01 -8.19050148e-03
2.39609908e-02 -1.19300425e+00 -6.92374051e-01 -1.49720863e-01
4.75540906e-01 -1.45872056e+00 -4.99001384e-01 -4.10729319e-01
5.78689873e-01 2.38501310e-01 8.04989696e-01 5.59531927e-01
-3.48813027e-01 6.09107278e-02 -5.20925343e-01 -7.75743186e-01
-9.75237727e-01 9.21409056e-02 6.10783175e-02 1.39433682e-01
8.40097189e-01 -6.15398698e-02 -1.17041118e-01 8.99497792e-02
-1.25446677e+00 -2.32420981e-01 -1.64591566e-01 4.33391601e-01
-4.48558509e-01 -2.83122540e-01 3.49360764e-01 -1.44932759e+00
1.20065141e+00 -7.42515385e-01 -3.94633263e-01 3.27774197e-01
-1.03280917e-01 -1.53633296e-01 7.17904687e-01 -4.97601032e-01
-1.06974423e+00 -1.55352324e-01 -4.90388349e-02 -3.41824234e-01
-5.20877481e-01 1.06432393e-01 1.07621431e-01 1.97934121e-01
1.11664164e+00 -2.85309583e-01 -2.41454467e-01 -4.70997900e-01
5.11453807e-01 1.10059333e+00 3.84995669e-01 -6.16898954e-01
1.30096555e+00 1.56009093e-01 -6.41849339e-01 -1.20510399e+00
-1.55129635e+00 -6.78940773e-01 -5.60682297e-01 -2.81534225e-01
1.11669648e+00 -4.80824441e-01 -3.70065063e-01 3.97915930e-01
-1.79673493e+00 -2.32097521e-01 5.28880417e-01 -1.77904353e-01
2.55035430e-01 5.05764484e-01 -6.79610491e-01 -1.49828506e+00
-3.57708931e-01 -9.88042176e-01 1.04363275e+00 -8.64503905e-03
-9.13551688e-01 -1.09816062e+00 4.79771167e-01 7.79580474e-01
5.10576308e-01 5.12886763e-01 1.01709819e+00 -1.09974599e+00
6.07390516e-02 -1.27279297e-01 -5.89747839e-02 6.56191707e-01
2.76941419e-01 4.69891965e-01 -1.27109206e+00 2.02495698e-02
-1.26392663e-01 -6.65398240e-01 5.73174000e-01 -3.56520444e-01
7.25189626e-01 -1.10182524e+00 -1.23283789e-01 -1.22221731e-01
1.01996052e+00 1.30165368e-01 3.43991578e-01 2.10716918e-01
6.47002518e-01 9.38570857e-01 2.60155529e-01 6.77871108e-01
6.27650991e-02 5.72157741e-01 1.87204719e-01 5.20047871e-03
2.34685868e-01 -2.28272751e-01 5.60032785e-01 4.24839944e-01
2.00534523e-01 -3.68139982e-01 -1.36655235e+00 6.95883870e-01
-1.80274439e+00 -1.60047925e+00 -6.04240522e-02 1.79913342e+00
9.96576786e-01 4.91877161e-02 3.57482761e-01 2.32420978e-03
1.14351201e+00 7.08016336e-01 -2.90466458e-01 -9.44863737e-01
-4.00089324e-02 3.85102555e-02 3.58085990e-01 9.58646655e-01
-1.42879987e+00 1.01382411e+00 6.33940744e+00 5.64653397e-01
-9.76039648e-01 3.05835664e-01 6.13474607e-01 -2.66442895e-01
-3.69801491e-01 -1.60415620e-01 -7.18455017e-01 8.58699977e-01
8.95596027e-01 -3.35898668e-01 6.15754545e-01 7.56804883e-01
4.37406719e-01 2.11811945e-01 -1.03556752e+00 7.66406238e-01
5.38399696e-01 -1.28610003e+00 -1.03370853e-01 1.38389841e-01
6.77106559e-01 -1.44308820e-01 1.89870238e-01 2.93558657e-01
6.19529545e-01 -1.26806772e+00 8.23803782e-01 9.28433239e-02
4.17228758e-01 -4.56748039e-01 5.64722776e-01 6.79423809e-01
-4.78961527e-01 -2.12239623e-01 -1.80580258e-01 -3.59315485e-01
7.77579024e-02 4.42686707e-01 -8.80609572e-01 -4.05387938e-01
1.92077562e-01 5.43445647e-01 -6.63976490e-01 4.80935752e-01
-7.63845682e-01 8.00014853e-01 1.22260479e-02 -4.35484886e-01
5.15817165e-01 2.96740711e-01 5.21019518e-01 1.71241248e+00
-3.28870744e-01 1.98780730e-01 2.90845752e-01 1.03920722e+00
-1.61778107e-01 -1.23767383e-01 -1.22476602e+00 -5.62557817e-01
8.08349013e-01 1.17413723e+00 -1.09189153e-01 -3.13245565e-01
-4.70887989e-01 5.89815736e-01 6.10080481e-01 3.01563561e-01
-9.84133601e-01 -4.14161623e-01 1.14074624e+00 1.44517481e-01
-2.34691799e-01 -2.88541615e-01 6.76564202e-02 -1.11986744e+00
-1.03678457e-01 -1.30003428e+00 3.98749292e-01 -5.41260958e-01
-1.64466822e+00 3.14159542e-01 -1.74206451e-01 -5.73785007e-01
-2.09070385e-01 -6.49190843e-01 -7.44869411e-01 7.64567435e-01
-1.20275235e+00 -9.39093411e-01 1.84409708e-01 1.83723927e-01
5.85301518e-01 1.20565228e-01 8.39388549e-01 2.78760552e-01
-8.33164811e-01 3.47145230e-01 -4.05452698e-01 8.21085632e-01
8.96830857e-01 -1.01214337e+00 4.68580693e-01 1.04679179e+00
1.62333429e-01 1.09182608e+00 9.82659042e-01 -8.78897607e-01
-8.94168615e-01 -1.22151148e+00 1.31939185e+00 -1.23566556e+00
1.37040710e+00 -8.83389294e-01 -1.30503345e+00 5.26241601e-01
5.38979053e-01 -2.70343572e-01 1.35340774e+00 3.14534605e-01
-1.04015017e+00 6.59401894e-01 -1.11147630e+00 5.15836477e-01
1.00369000e+00 -1.20403218e+00 -9.50631738e-01 8.26848507e-01
7.51103640e-01 3.79789621e-02 -4.65800941e-01 -3.59949291e-01
3.32077563e-01 -5.97204626e-01 3.07838023e-01 -1.31354070e+00
4.59567934e-01 -2.92876840e-01 1.45567909e-01 -1.15214336e+00
-3.60006243e-01 -9.02704656e-01 5.98380938e-02 1.47236931e+00
4.47005689e-01 -1.62618130e-01 3.30367833e-01 1.29953897e+00
2.67199099e-01 -4.10388649e-01 -8.68923366e-01 -7.42405415e-01
1.37118444e-01 -4.39183831e-01 2.48857275e-01 1.66579878e+00
3.17932993e-01 7.02108741e-01 -8.33293676e-01 3.53576660e-01
5.51309407e-01 -4.13061231e-01 8.04851413e-01 -9.72615123e-01
8.24388415e-02 -3.87973487e-01 -2.22462833e-01 -2.98941076e-01
6.89206541e-01 -6.66795850e-01 4.75759692e-02 -1.29483271e+00
3.81029099e-01 -1.77242216e-02 1.75003678e-01 8.79405499e-01
-4.38606054e-01 5.56738302e-02 4.72892582e-01 -2.73409802e-02
-1.01829326e+00 1.78501159e-01 7.14611948e-01 -5.01852512e-01
4.47180681e-02 -4.52256739e-01 -7.83611178e-01 9.42789137e-01
6.83971107e-01 -6.91906750e-01 -2.85259008e-01 -5.38858473e-01
3.56776416e-01 -5.45174003e-01 3.25180531e-01 -5.26271939e-01
2.35670000e-01 -7.15754569e-01 -2.11901814e-01 6.24086708e-02
7.72163570e-02 -3.72376859e-01 -3.17890674e-01 3.43181878e-01
-7.98218191e-01 -9.64065641e-02 -1.03713684e-01 3.34114373e-01
2.01500785e-02 -4.08011466e-01 9.00703490e-01 -1.60514966e-01
-4.92318392e-01 1.78605631e-01 -8.33798826e-01 5.53904712e-01
9.29543495e-01 1.07592642e-01 -1.25311255e+00 -4.94739532e-01
-5.39141476e-01 2.22444147e-01 5.77549458e-01 8.09666038e-01
3.92913550e-01 -9.84624147e-01 -9.06929195e-01 -1.68064252e-01
3.48794818e-01 -6.41481698e-01 -1.34694755e-01 2.30895147e-01
-1.25492126e-01 3.98687243e-01 1.51295185e-01 6.61575347e-02
-1.53101742e+00 6.87567949e-01 2.70224154e-01 -5.62042147e-02
-9.50181782e-02 6.58404052e-01 1.75382018e-01 -2.46868148e-01
3.12360734e-01 1.69139087e-01 -1.97344750e-01 -4.69019301e-02
1.20275414e+00 2.13843510e-01 -4.46187854e-01 -1.00814724e+00
-5.10961473e-01 -3.16801593e-02 -1.83070630e-01 1.28759429e-01
1.21933770e+00 2.69074500e-01 -3.78330410e-01 3.32469910e-01
1.30728149e+00 6.04376912e-01 -7.25263596e-01 -1.05557017e-01
5.20242870e-01 -9.63462770e-01 -3.89381319e-01 -8.70566428e-01
-4.68515635e-01 7.70889819e-01 -1.98273472e-02 7.94375241e-01
3.66002649e-01 9.67522934e-02 8.02211404e-01 5.29342532e-01
1.07872270e-01 -1.23713040e+00 1.80418298e-01 7.55127609e-01
9.88214791e-01 -1.40123022e+00 -1.79728583e-01 -4.72069323e-01
-6.42914474e-01 9.66302633e-01 8.31858516e-01 2.29582012e-01
2.44546175e-01 2.24267602e-01 4.18183804e-01 -5.59255838e-01
-9.25297201e-01 -1.77229308e-02 2.81973809e-01 7.64161229e-01
7.22035706e-01 2.56952196e-01 -2.74360836e-01 4.32393789e-01
-4.53458205e-02 -4.61974502e-01 6.33062720e-01 8.41369152e-01
-8.55005443e-01 -8.57354522e-01 -6.13111258e-01 3.34455639e-01
-7.05096483e-01 -2.39335835e-01 -1.46377766e+00 5.33940673e-01
5.38202822e-01 1.38302636e+00 -2.75625497e-01 -3.45072448e-01
1.71306625e-01 3.63185853e-02 -1.51483789e-01 -1.15081406e+00
-8.59037757e-01 -5.05262673e-01 8.18676233e-01 -2.97129363e-01
-2.40653709e-01 -3.41901898e-01 -6.62486374e-01 -5.40458322e-01
-2.15943933e-01 2.63321668e-01 4.78861570e-01 1.18748331e+00
3.28344792e-01 -2.35228255e-01 5.39228857e-01 -1.93450749e-01
-5.36305845e-01 -7.50772119e-01 -4.20171946e-01 1.20774424e+00
7.43231595e-01 -5.23811638e-01 -5.17334819e-01 4.58032414e-02] | [8.73484992980957, 10.49686336517334] |
bd081b39-bd4f-4f38-950e-80989f7330ca | unicode-analogies-an-anti-objectivist-visual | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Spratley_Unicode_Analogies_An_Anti-Objectivist_Visual_Reasoning_Challenge_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Spratley_Unicode_Analogies_An_Anti-Objectivist_Visual_Reasoning_Challenge_CVPR_2023_paper.pdf | Unicode Analogies: An Anti-Objectivist Visual Reasoning Challenge | Analogical reasoning enables agents to extract relevant information from scenes, and efficiently navigate them in familiar ways. While progressive-matrix problems (PMPs) are becoming popular for the development and evaluation of analogical reasoning in computer vision, we argue that the dominant methodology in this area struggles to expose the lack of meaningful generalisation in solvers, and reinforces an objectivist stance on perception -- that objects can only be seen one way -- which we believe to be counter-productive. In this paper, we introduce the Unicode Analogies challenge, consisting of polysemic, character-based PMPs to benchmark fluid conceptualisation ability in vision systems. Writing systems have evolved characters at multiple levels of abstraction, from iconic through to symbolic representations, producing both visually interrelated yet exceptionally diverse images when compared to those exhibited by existing PMP datasets. Our framework has been designed to challenge models by presenting tasks much harder to complete without robust feature extraction, while remaining largely solvable by human participants. We therefore argue that Unicode Analogies elegantly captures and tests for a facet of human visual reasoning that is severely lacking in current-generation AI. | ['Tim Miller', 'Krista A. Ehinger', 'Steven Spratley'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [ 4.51439798e-01 6.97967485e-02 4.77281630e-01 3.13722827e-02
-8.62787887e-02 -8.08424056e-01 1.15862215e+00 1.90376580e-01
-4.48179930e-01 5.67127943e-01 1.94334880e-01 -6.17236674e-01
-6.60982311e-01 -5.84930420e-01 -5.41990519e-01 -1.16759464e-01
2.04905331e-01 8.19653511e-01 9.63177830e-02 -8.64284635e-01
6.86888695e-01 6.08155251e-01 -1.77568841e+00 5.98363280e-01
8.34172130e-01 6.38231397e-01 2.92850614e-01 6.88473105e-01
3.86736728e-02 1.29876804e+00 -5.44997334e-01 -6.70077264e-01
4.00987148e-01 -6.92710161e-01 -1.08400679e+00 -1.72429934e-01
9.90507603e-01 -1.46788388e-01 -4.14734721e-01 1.03075850e+00
8.14968795e-02 -1.05903327e-01 8.65810633e-01 -1.49269950e+00
-1.35469723e+00 3.52608740e-01 -3.13006043e-01 4.57980841e-01
8.31698418e-01 7.55885422e-01 1.16076946e+00 -6.37602746e-01
6.83968008e-01 1.64265585e+00 7.39210248e-01 4.80752587e-01
-1.48631811e+00 -2.80552119e-01 -1.22234272e-02 7.92891383e-01
-9.19463873e-01 -2.35345036e-01 3.72321844e-01 -5.65162718e-01
1.31590736e+00 5.88581741e-01 1.53577614e+00 1.10356283e+00
9.06587541e-02 6.06909275e-01 1.67452061e+00 -5.13429403e-01
1.07857063e-01 -5.88737503e-02 -5.95378801e-02 5.02743363e-01
3.36978346e-01 4.35357124e-01 -5.76056778e-01 1.05301611e-01
1.02235162e+00 -2.15648904e-01 -3.36762667e-01 -3.36128861e-01
-1.49596345e+00 4.84804422e-01 6.47790551e-01 4.30486172e-01
-4.59465355e-01 7.60458261e-02 1.58322141e-01 6.39539301e-01
-6.80443466e-01 1.56427932e+00 -5.52905239e-02 -9.06632170e-02
-4.98971730e-01 9.10104275e-01 7.50724912e-01 8.86701822e-01
3.65786076e-01 9.61795077e-02 -9.67065096e-02 5.26566088e-01
1.93035275e-01 5.22105932e-01 4.17796522e-01 -1.59321940e+00
6.15228973e-02 8.09577465e-01 4.18055765e-02 -1.33021414e+00
-3.94355506e-01 -2.03670651e-01 -3.68580610e-01 8.68932664e-01
7.16798544e-01 4.03475255e-01 -6.85452640e-01 1.81926751e+00
-1.35959595e-01 -3.01889449e-01 1.86303169e-01 1.03548026e+00
5.59346139e-01 5.73692143e-01 2.38547549e-01 -1.19397417e-02
1.42327964e+00 -7.90736556e-01 -3.07994276e-01 -7.02049911e-01
1.74077153e-01 -6.91333532e-01 1.76503682e+00 5.79387724e-01
-1.60087788e+00 -4.24226999e-01 -1.35907638e+00 -2.88872927e-01
-4.59039330e-01 -7.10435629e-01 9.34850335e-01 4.73712593e-01
-1.35989034e+00 7.28318334e-01 -2.10865647e-01 -6.18703187e-01
5.43734908e-01 1.06100231e-01 -3.92042935e-01 -1.32764429e-01
-9.38347101e-01 1.74884009e+00 4.18862134e-01 -1.57673687e-01
-5.87967932e-01 -7.31746554e-01 -6.89219832e-01 -8.38137865e-02
3.75256747e-01 -1.16162765e+00 1.43745112e+00 -1.15905893e+00
-1.16277254e+00 1.12185788e+00 2.72639245e-01 -5.52716851e-01
5.95812201e-01 7.24067613e-02 -2.92031169e-01 1.97861999e-01
1.32032081e-01 1.01294994e+00 5.40925086e-01 -1.42680275e+00
-4.94315088e-01 -3.50076914e-01 6.12620354e-01 4.36102867e-01
-8.12666770e-03 4.66772318e-02 6.89168125e-02 -6.21956825e-01
2.36390114e-01 -8.76967669e-01 -2.41722912e-02 4.74777013e-01
4.80484888e-02 -5.93166500e-02 4.64869916e-01 -5.33974946e-01
6.68855190e-01 -1.84611189e+00 5.31195462e-01 1.63924228e-03
5.31638920e-01 1.25892967e-01 -1.66234285e-01 5.97026765e-01
-1.72784775e-01 6.90534115e-02 -2.94361591e-01 1.88041359e-01
5.28536320e-01 3.84422064e-01 -6.09732747e-01 7.47402618e-03
3.13623875e-01 1.38757920e+00 -1.15424538e+00 -5.23735404e-01
1.59236029e-01 1.71679378e-01 -5.12338638e-01 -2.15669274e-01
-2.60197431e-01 1.06590092e-01 1.71332255e-01 4.30496484e-01
4.16263670e-01 -2.23236233e-01 2.11455926e-01 1.12313535e-02
-2.42744479e-02 9.80559662e-02 -9.20351923e-01 1.72325730e+00
-1.75870419e-01 1.12297893e+00 -3.89254540e-01 -6.23174071e-01
5.13755858e-01 -8.45930055e-02 -3.10187340e-01 -1.35742521e+00
1.38606858e-02 1.47325411e-01 8.11416686e-01 -5.76108992e-01
6.13341808e-01 -3.72498810e-01 1.48017317e-01 2.68125176e-01
-1.34224892e-01 -8.83951008e-01 5.26924133e-01 1.79736510e-01
1.06176305e+00 3.08907717e-01 3.44456732e-01 -4.13347244e-01
2.43786305e-01 5.49332857e-01 1.48765206e-01 9.20545578e-01
-1.96179971e-01 4.67673689e-01 7.13436484e-01 -5.49653232e-01
-1.34867907e+00 -1.64369190e+00 -3.80210131e-02 8.68713498e-01
1.51621208e-01 -3.74351263e-01 -5.94145656e-01 9.58770066e-02
-1.32809967e-01 1.10754848e+00 -8.02020848e-01 -1.21307969e-01
-5.08395731e-01 -4.60448951e-01 6.48310959e-01 6.45720661e-01
3.68213207e-01 -1.54575598e+00 -1.39348829e+00 -7.95063600e-02
1.23713575e-01 -8.88085961e-01 3.83474618e-01 -8.81767124e-02
-5.48166156e-01 -1.15638638e+00 -5.75419486e-01 -7.57747948e-01
3.47407103e-01 1.50567412e-01 1.58140504e+00 2.19852328e-01
-6.68570638e-01 5.70119441e-01 -1.93224356e-01 -5.93444288e-01
-6.92688644e-01 -7.50358284e-01 -1.78231165e-01 -7.30126798e-01
3.62691432e-01 -7.31176317e-01 -5.41435242e-01 1.69578806e-01
-8.73870254e-01 4.61101741e-01 8.86516094e-01 7.76376188e-01
1.15500629e-01 -3.31149727e-01 4.09200490e-01 -4.73129869e-01
1.00887656e+00 -1.98963508e-01 -3.92581284e-01 3.65275204e-01
-5.19185185e-01 -1.11745551e-01 6.41743720e-01 -4.81293648e-01
-8.33541512e-01 -5.72576284e-01 4.25514460e-01 -2.76527345e-01
-2.48451784e-01 3.45268488e-01 4.47819121e-02 -1.24972515e-01
1.23693156e+00 3.08293074e-01 7.31513724e-02 2.00976908e-01
7.72137523e-01 3.38334590e-02 9.01632845e-01 -8.38993073e-01
8.96991432e-01 3.33082318e-01 2.44999900e-01 -8.03088725e-01
-3.54059935e-01 2.89921075e-01 -5.30327857e-01 -3.62899937e-02
8.94209385e-01 -6.03137970e-01 -1.03757644e+00 1.80343941e-01
-1.19292581e+00 -4.21043277e-01 -6.73753500e-01 3.51862580e-01
-9.37918782e-01 2.55473763e-01 -5.95890105e-01 -4.63344961e-01
1.70943230e-01 -1.08185625e+00 3.39799017e-01 2.29030773e-01
-1.00470698e+00 -6.24795735e-01 2.40872744e-02 3.54347736e-01
5.92149079e-01 2.60917842e-01 1.60733902e+00 -3.70579630e-01
-5.90862930e-01 1.25421301e-01 -6.28132224e-01 -8.36827010e-02
-3.65942299e-01 -8.08903426e-02 -9.60231125e-01 2.86335219e-02
-1.63249229e-03 -6.76705480e-01 4.47118878e-01 -1.49216488e-01
7.07324207e-01 -2.04548463e-01 -8.69732350e-02 3.51619542e-01
1.25650465e+00 5.05884171e-01 9.89941835e-01 6.33694887e-01
3.66228253e-01 8.76931727e-01 2.65101820e-01 6.70047775e-02
5.02085209e-01 6.50969446e-01 3.69669706e-01 1.51616976e-01
-2.90446788e-01 -1.34364352e-01 -1.13682352e-01 2.11978242e-01
-3.02466810e-01 -6.88477233e-02 -1.31031942e+00 4.70196486e-01
-1.75512409e+00 -1.33050394e+00 -2.87029326e-01 1.83562839e+00
8.88173401e-01 2.13705227e-01 1.53377444e-01 2.26265535e-01
2.67624617e-01 -1.52004987e-01 -4.54671055e-01 -6.65289283e-01
-3.26762021e-01 2.81604528e-01 -3.07342261e-01 3.18570524e-01
-5.11516273e-01 1.00856960e+00 6.92119980e+00 4.49103147e-01
-5.81126451e-01 -3.41442078e-01 1.27568752e-01 -1.42558619e-01
-4.73845392e-01 2.47993663e-01 1.46332562e-01 8.99236426e-02
5.91595113e-01 -2.50016093e-01 8.72464478e-01 4.90981072e-01
-3.65366012e-01 -1.03259631e-01 -1.79672229e+00 1.16448331e+00
3.10307741e-01 -1.37899339e+00 4.84901458e-01 -1.80338651e-01
3.58503908e-01 -3.67519498e-01 5.13812780e-01 3.55540603e-01
4.24029887e-01 -1.77119875e+00 1.07407892e+00 5.54481030e-01
3.73427838e-01 -3.90920848e-01 1.49931073e-01 1.30135924e-01
-6.37894273e-01 -3.73921275e-01 -4.47921246e-01 -8.56973350e-01
-1.58378601e-01 -5.03843904e-01 -6.19814575e-01 8.86645317e-02
8.69648457e-01 3.28860402e-01 -9.54676628e-01 1.14461899e+00
-2.53954440e-01 -2.72731572e-01 1.35931429e-02 -1.39378339e-01
2.72954613e-01 -6.75303489e-02 5.19942164e-01 8.57919931e-01
1.20733470e-01 1.77786320e-01 -2.46468857e-01 1.35776889e+00
5.12387455e-01 -1.13476805e-01 -8.95697653e-01 -1.43012494e-01
6.19157851e-01 9.44832146e-01 -7.06157982e-01 -1.67544007e-01
-4.52435195e-01 1.02898908e+00 4.42245603e-01 2.46453106e-01
-7.82005668e-01 -2.72893190e-01 5.21983385e-01 1.33467570e-01
-6.08656630e-02 -2.86383957e-01 -5.30148089e-01 -7.66442657e-01
8.45196024e-02 -1.56254864e+00 1.43778488e-01 -1.50081217e+00
-1.45522833e+00 5.20305932e-01 1.43832117e-01 -8.48272324e-01
-4.21049714e-01 -1.00792134e+00 -6.25918210e-01 9.32630122e-01
-9.99259830e-01 -1.28404784e+00 -6.65723383e-01 5.82472742e-01
3.58387113e-01 -1.76312149e-01 9.77933705e-01 -3.18827629e-01
-5.46111204e-02 3.86030853e-01 -4.97992963e-01 -1.68800458e-01
3.35708082e-01 -1.44805491e+00 7.98257530e-01 5.69764256e-01
4.60107356e-01 8.02585065e-01 9.17157590e-01 -2.55545497e-01
-1.33124709e+00 -4.93661873e-02 4.55861926e-01 -9.44606900e-01
8.85310292e-01 -3.52624133e-02 -9.08616424e-01 6.08242571e-01
6.57336235e-01 -3.91867280e-01 4.78183866e-01 -1.64291739e-01
-8.35453987e-01 3.84599358e-01 -9.18630779e-01 1.39415228e+00
1.44133091e+00 -6.23207808e-01 -1.54719758e+00 2.61332273e-01
5.45923471e-01 -1.79414332e-01 -5.92460096e-01 6.94464371e-02
7.35347629e-01 -1.34522307e+00 1.33504152e+00 -9.28944111e-01
9.18093085e-01 -4.43117708e-01 -5.41166306e-01 -1.32630980e+00
-6.39384627e-01 -5.69720507e-01 2.39553541e-01 8.05982172e-01
3.85686517e-01 -6.83641374e-01 4.37333196e-01 6.77900553e-01
-1.48982719e-01 -5.05659580e-01 -7.29861975e-01 -8.26572716e-01
3.80111635e-01 -4.11176115e-01 4.01079774e-01 1.08030438e+00
3.53466123e-01 5.39118469e-01 2.94802845e-01 -1.74588740e-01
5.76642573e-01 8.55204538e-02 7.16211140e-01 -1.49586046e+00
-6.44623876e-01 -1.21682334e+00 -6.92025065e-01 -5.41884959e-01
3.58084738e-02 -1.04721463e+00 -3.66361827e-01 -1.66809821e+00
3.01669836e-01 -1.43263087e-01 4.00656015e-02 2.89406657e-01
2.00722337e-01 3.26754749e-01 8.57166111e-01 2.34065533e-01
-3.72835398e-01 1.68479726e-01 1.52478111e+00 -1.66607931e-01
1.72926828e-01 -8.46054733e-01 -1.21733487e+00 1.15886617e+00
5.17821848e-01 9.19752344e-02 -7.75833726e-01 -7.18327403e-01
7.91283607e-01 -9.11425278e-02 1.15964735e+00 -1.34192717e+00
3.27574819e-01 -4.31250095e-01 1.02116096e+00 -3.90393957e-02
6.84613168e-01 -8.67622674e-01 1.63179159e-01 6.92085922e-01
-3.12372118e-01 7.32509673e-01 5.33439755e-01 3.46517861e-01
1.99508727e-01 -2.04679370e-01 6.06359601e-01 -6.21331215e-01
-1.16911268e+00 -3.15966696e-01 -2.94625282e-01 4.74168003e-01
1.19392908e+00 -7.88952172e-01 -8.81414056e-01 -2.87960231e-01
-5.68890035e-01 1.37928158e-01 1.06879389e+00 2.31588289e-01
7.92185843e-01 -1.20203412e+00 -7.90594220e-01 -5.86880259e-02
3.44179869e-01 -5.23728251e-01 2.40839109e-01 5.82035720e-01
-9.80504155e-01 1.86608851e-01 -1.16021478e+00 -3.73472214e-01
-8.66687715e-01 9.51191485e-01 4.50734645e-01 2.95479238e-01
-8.02349091e-01 9.85656619e-01 3.70006353e-01 -1.22125506e-01
-7.80320242e-02 -2.50908643e-01 -2.51094192e-01 -6.54456019e-02
5.33039868e-01 3.71661365e-01 -4.41978484e-01 -3.73255074e-01
-2.24700004e-01 6.77159011e-01 -1.97724208e-01 -4.50623482e-01
1.05223775e+00 1.75187051e-01 -3.69355112e-01 4.55128998e-01
4.86360341e-01 -3.90587568e-01 -1.24529374e+00 3.70930135e-02
-4.27654013e-02 -7.04801083e-01 -4.25875425e-01 -1.23896575e+00
-5.04936464e-02 9.73623276e-01 3.15690994e-01 3.05736363e-01
8.89121830e-01 -2.39800848e-02 3.06739151e-01 8.84201109e-01
2.46579990e-01 -9.41278636e-01 4.95412529e-01 5.31059742e-01
1.58324027e+00 -9.58649635e-01 1.52477816e-01 -7.24311359e-03
-8.46863151e-01 1.01370704e+00 9.68949974e-01 -9.21057016e-02
-2.03765884e-01 1.60988316e-01 -9.39139351e-03 -4.20136958e-01
-8.91959488e-01 -2.93847144e-01 2.33854577e-01 1.10632229e+00
2.20998675e-01 -1.58735305e-01 6.56762868e-02 1.54631078e-01
-8.56544197e-01 -3.99368078e-01 6.61489785e-01 1.00192618e+00
-2.93481499e-01 -7.20708072e-01 -4.84740943e-01 1.89834952e-01
1.77626461e-01 -3.57945174e-01 -8.71341586e-01 1.26737821e+00
8.03122446e-02 7.44178116e-01 2.81828344e-01 -1.68220416e-01
4.66732442e-01 8.65955874e-02 1.32826459e+00 -2.52757370e-01
-4.92633849e-01 -6.64689124e-01 -5.53807318e-02 -5.84928811e-01
-5.74353933e-02 -5.98509610e-01 -1.08533812e+00 -6.80899084e-01
4.46026832e-01 -3.15193504e-01 3.62194031e-01 7.86874592e-01
3.21489386e-02 6.01109862e-01 -3.49237025e-01 -1.06266952e+00
-6.03107214e-01 -6.54811025e-01 -2.68235028e-01 7.15330064e-01
8.38941187e-02 -7.26040900e-01 -1.10255279e-01 -1.41966313e-01] | [10.632253646850586, 2.2695798873901367] |
cfc8c030-8e16-4ec2-bac6-f0477a1f1032 | a-multilevel-framework-for-ai-governance | 2307.03198 | null | https://arxiv.org/abs/2307.03198v1 | https://arxiv.org/pdf/2307.03198v1.pdf | A multilevel framework for AI governance | To realize the potential benefits and mitigate potential risks of AI, it is necessary to develop a framework of governance that conforms to ethics and fundamental human values. Although several organizations have issued guidelines and ethical frameworks for trustworthy AI, without a mediating governance structure, these ethical principles will not translate into practice. In this paper, we propose a multilevel governance approach that involves three groups of interdependent stakeholders: governments, corporations, and citizens. We examine their interrelationships through dimensions of trust, such as competence, integrity, and benevolence. The levels of governance combined with the dimensions of trust in AI provide practical insights that can be used to further enhance user experiences and inform public policy related to AI. | ['John S. Seberger', 'Prabu David', 'Hyesun Choung'] | 2023-07-04 | null | null | null | null | ['ethics'] | ['miscellaneous'] | [-2.09848717e-01 3.75379145e-01 -3.05547893e-01 -3.53573382e-01
-5.06194048e-02 -5.15939951e-01 4.91221905e-01 3.06393355e-01
-2.18585134e-01 6.89212680e-01 6.49495065e-01 -2.58882552e-01
-1.80295482e-01 -7.07243502e-01 -1.87092036e-01 -4.43939209e-01
3.98602039e-01 -4.14889395e-01 -3.52542907e-01 -4.19110745e-01
3.79360884e-01 2.50888765e-01 -1.09443510e+00 2.50324048e-02
1.14585340e+00 9.45766926e-01 -8.22539985e-01 6.52190745e-02
5.25080085e-01 1.38805997e+00 -3.23770672e-01 -1.05399156e+00
3.64868760e-01 -4.10987914e-01 -7.36469746e-01 -1.72241583e-01
8.00635815e-02 -9.74052727e-01 3.35128367e-01 1.01576555e+00
7.49748349e-02 -6.50603592e-01 4.68502432e-01 -1.51865494e+00
-1.23017478e+00 7.54574418e-01 7.20304577e-03 -4.97271627e-01
3.35982949e-01 5.12427986e-01 1.09799778e+00 -2.36230373e-01
3.68675202e-01 7.21787989e-01 6.02621794e-01 4.82319027e-01
-9.67771709e-01 -8.99675608e-01 2.46613268e-02 3.60240578e-03
-1.08182395e+00 -5.89995265e-01 4.11495447e-01 -4.25512552e-01
6.70036912e-01 3.41735095e-01 1.40690589e+00 9.27758038e-01
6.57152295e-01 1.98043540e-01 1.14751017e+00 -5.08500695e-01
2.05313474e-01 5.68840444e-01 1.74887732e-01 6.72168983e-03
1.43913043e+00 9.53885075e-03 -3.55925351e-01 -2.39257172e-01
6.34131253e-01 -9.26759616e-02 4.13563196e-03 -1.83568463e-01
-1.27405000e+00 7.87864983e-01 3.56078565e-01 7.71336496e-01
-9.37785804e-01 5.33374488e-01 2.21091986e-01 1.53421596e-01
-1.26345120e-02 8.86464298e-01 8.27959627e-02 -5.02504885e-01
-2.50796914e-01 -1.33254722e-01 9.76370394e-01 5.09354711e-01
2.44320586e-01 2.30095416e-01 2.37813279e-01 -2.86594480e-02
1.03875244e+00 4.66041446e-01 -4.63999689e-01 -1.63084078e+00
-2.85430670e-01 8.74857306e-01 4.93017495e-01 -1.42069685e+00
6.67407885e-02 -2.90040642e-01 -3.15031052e-01 3.33096951e-01
1.98440999e-01 -2.25266248e-01 -1.97180659e-01 1.44009411e+00
3.32996041e-01 -4.76353556e-01 6.12531185e-01 9.26207185e-01
5.80706835e-01 3.90570432e-01 4.18332100e-01 -1.12208150e-01
1.12969804e+00 -5.26952744e-01 -1.21367526e+00 -2.58112669e-01
6.42834842e-01 -3.43999237e-01 1.07605839e+00 4.41781789e-01
-1.27877426e+00 1.85328797e-01 -1.09281409e+00 1.16538368e-01
5.37272580e-02 -2.31550947e-01 9.98837650e-01 1.16844153e+00
-9.75962877e-01 9.51043293e-02 -5.52067101e-01 -4.56767172e-01
3.88917357e-01 9.12014693e-02 -3.32962573e-01 2.69376844e-01
-1.34136462e+00 1.28896320e+00 2.98749935e-03 6.88069761e-01
-7.74208307e-01 -1.01534903e-01 -6.36692107e-01 -8.23572129e-02
1.39300629e-01 -7.68351495e-01 1.17127287e+00 -1.51240861e+00
-1.34207320e+00 3.72802645e-01 7.22393513e-01 -2.71603823e-01
3.36465269e-01 -5.70934653e-01 -2.94600099e-01 3.10844719e-01
1.42277360e-01 1.57744661e-01 -5.85550070e-02 -1.64513230e+00
-4.28640336e-01 -3.23851615e-01 3.05267602e-01 2.49485493e-01
-8.75123680e-01 3.91987771e-01 4.09612268e-01 2.21427411e-01
-2.30273873e-01 -7.51235247e-01 -3.83139640e-01 6.66599423e-02
8.38609934e-02 1.69477001e-01 3.37410212e-01 -4.91235226e-01
1.25158024e+00 -2.08102679e+00 -5.81977308e-01 4.20634627e-01
4.11449343e-01 2.02682614e-01 1.21543445e-01 9.21294332e-01
7.57451475e-01 5.70639551e-01 2.22981974e-01 5.79159558e-01
2.48205557e-01 1.29832894e-01 -5.05107008e-02 4.68420208e-01
6.33353367e-02 8.28123689e-01 -1.03668785e+00 -2.95790911e-01
-2.99517345e-02 6.62808061e-01 -3.83626997e-01 2.23533496e-01
3.76697183e-01 3.05898517e-01 -9.21979427e-01 7.43392646e-01
2.98542231e-01 -2.99821019e-01 7.13229120e-01 8.43722001e-02
-3.57812524e-01 4.85059768e-01 -7.05048025e-01 7.88634717e-01
-1.20526142e-01 3.03725660e-01 4.28564757e-01 -2.71706641e-01
9.42915559e-01 8.51249397e-01 3.15417558e-01 -7.64949441e-01
4.95041102e-01 3.59568775e-01 5.91908813e-01 -5.48597872e-01
4.69259351e-01 -1.50235027e-01 -1.84809104e-01 8.98942769e-01
-4.55403656e-01 -4.44791973e-01 -3.17093790e-01 3.58080387e-01
8.34645450e-01 2.72296220e-01 5.66136956e-01 -3.27603370e-01
3.86181697e-02 1.68330342e-01 8.30475092e-01 4.84967791e-02
-7.35314190e-01 -1.88540980e-01 7.32054651e-01 -5.82286000e-01
-7.72975564e-01 -4.67485994e-01 -3.39539871e-02 2.54248023e-01
2.58864611e-01 -3.25304389e-01 -8.53315353e-01 -4.89473313e-01
-7.67155662e-02 1.20117354e+00 -5.41690230e-01 -3.67304295e-01
3.36438492e-02 -6.23130389e-02 6.17202103e-01 3.89226288e-01
7.01393843e-01 -9.14347768e-01 -1.58031821e+00 -2.03674473e-02
1.26144867e-02 -9.72415984e-01 1.35162145e-01 -3.26318622e-01
-5.14410973e-01 -1.09116375e+00 1.09563246e-01 -7.03680739e-02
7.81213880e-01 3.48022997e-01 9.33098793e-01 7.49690175e-01
2.63707936e-01 6.57067060e-01 -4.42855358e-01 -6.31197453e-01
-6.05822682e-01 -5.65228403e-01 3.04342322e-02 -2.42999569e-01
3.84336561e-01 -2.82608092e-01 -7.36712515e-01 2.48498946e-01
-7.83930480e-01 2.03909591e-01 6.67696357e-01 2.90385514e-01
9.90764722e-02 -2.61480570e-01 9.32822347e-01 -6.77779317e-01
9.26994205e-01 -5.68996966e-01 -2.31910944e-01 4.62359458e-01
-1.24530482e+00 -7.06467628e-01 3.01477611e-01 -4.36345674e-02
-1.19421768e+00 -4.72208470e-01 1.93336934e-01 3.80846709e-01
-8.70120078e-02 7.08274364e-01 -3.54076982e-01 -2.90471613e-01
5.22787631e-01 -4.85426039e-01 3.96877199e-01 4.45147932e-01
3.36526901e-01 8.25217187e-01 -2.22782671e-01 -5.76414645e-01
6.98020041e-01 3.15233439e-01 -3.09791565e-01 -5.44667542e-01
-5.59934497e-01 3.44339877e-01 -3.93256396e-01 -8.86842012e-01
1.02029979e+00 -9.07080412e-01 -9.65399742e-01 -2.14736201e-02
-8.95491838e-01 -3.38870317e-01 -3.49152803e-01 9.32187378e-01
-3.34983170e-01 4.37948376e-01 -3.77503365e-01 -1.18000090e+00
-6.48407578e-01 -9.68879163e-01 9.16610807e-02 4.65553492e-01
-6.96137011e-01 -8.46083879e-01 -1.11556426e-01 9.51234281e-01
7.80850470e-01 6.56256437e-01 5.88485301e-01 -3.89160395e-01
-8.13814700e-01 -4.46018755e-01 4.44307253e-02 8.21128249e-01
4.15718138e-01 5.54402471e-01 -4.26041484e-01 6.36970103e-02
3.98711413e-01 -7.62369931e-01 -2.80159205e-01 6.72793016e-02
-1.06790170e-01 -9.41840172e-01 5.12524135e-02 1.84569154e-02
1.29650366e+00 7.70787179e-01 9.76213574e-01 6.24790132e-01
4.27813679e-01 1.14325058e+00 1.13808060e+00 5.10475099e-01
9.62095976e-01 -9.17267650e-02 5.59620857e-01 2.96048615e-02
7.31075108e-01 -3.92145887e-02 4.34754103e-01 9.28842247e-01
-5.52956998e-01 1.25726938e-01 -1.33988690e+00 5.49214542e-01
-1.80517840e+00 -8.01535249e-01 -4.64505792e-01 1.90043187e+00
7.43807912e-01 9.06734914e-02 -5.51787889e-05 -1.60025328e-01
1.04838669e-01 -1.35599062e-01 -1.93642020e-01 -9.77777302e-01
3.00889254e-01 -5.76073349e-01 2.17432395e-01 4.43849713e-02
-3.18639129e-01 1.00318408e+00 7.22746277e+00 -3.96971166e-01
-7.04082012e-01 6.74668252e-02 9.27486718e-01 1.47762880e-01
-1.18643045e+00 6.01668000e-01 -2.06669331e-01 -1.89695358e-01
9.42945242e-01 -6.23308003e-01 2.03568771e-01 7.28442848e-01
3.03234071e-01 -2.37404034e-01 -7.80394971e-01 -3.64855714e-02
-8.86467695e-02 -9.12956655e-01 -4.57739592e-01 3.26937914e-01
7.40459621e-01 -5.85031211e-01 1.46443024e-01 -3.45812798e-01
7.17818379e-01 -1.14476764e+00 9.05242801e-01 5.00433385e-01
1.18764095e-01 -7.32057273e-01 9.96033669e-01 1.31638095e-01
-5.52729905e-01 -1.22147866e-01 -2.75112808e-01 -6.14963412e-01
9.12680998e-02 8.36555958e-02 -3.42542470e-01 3.69304806e-01
6.54307663e-01 4.12646443e-01 -6.25551119e-02 4.81483668e-01
-5.58407545e-01 5.88477790e-01 9.37143415e-02 -1.82245091e-01
2.26323083e-01 -6.88804924e-01 -8.68228078e-03 5.43941975e-01
7.37810954e-02 6.33558571e-01 -6.36652827e-01 8.95323575e-01
3.57689708e-01 3.09276521e-01 -1.02624929e+00 -7.21757889e-01
8.69146883e-01 1.47613180e+00 -5.52058518e-01 -5.01472428e-02
-8.63346994e-01 1.46216482e-01 -1.83481112e-01 2.92996138e-01
-6.68337345e-01 4.24467809e-02 8.94148231e-01 2.25580245e-01
-2.67259032e-01 -3.44313771e-01 -7.83517420e-01 -8.62526357e-01
-1.15238912e-01 -1.42870975e+00 -4.42530550e-02 -2.96428710e-01
-9.84570801e-01 5.77803075e-01 -1.18244074e-01 -9.64274228e-01
-7.04077333e-02 -1.33736655e-01 -6.12516403e-01 3.33571851e-01
-1.32673073e+00 -1.81477809e+00 -3.24753761e-01 -2.26146113e-02
-8.58891904e-01 1.68845028e-01 9.17802215e-01 -1.23441160e-01
-3.78934115e-01 3.84031147e-01 -2.24696711e-01 -9.65446308e-02
6.38716638e-01 -6.38738930e-01 4.79084253e-02 9.50271845e-01
-4.19456720e-01 1.19542623e+00 3.80177557e-01 -9.27756488e-01
-1.76065838e+00 -4.99353498e-01 1.00593138e+00 -2.86692888e-01
7.69371331e-01 2.13168804e-02 -7.37483144e-01 8.84269834e-01
9.23042834e-01 -4.41247940e-01 1.42188168e+00 1.18100487e-01
-2.13432387e-01 -4.65000838e-01 -1.40699148e+00 9.62079883e-01
8.27747822e-01 -3.82011682e-01 -3.85281891e-01 -1.80032790e-01
7.04121530e-01 2.84915149e-01 -1.43532658e+00 2.30534405e-01
9.41975534e-01 -1.22707486e+00 4.77489203e-01 -2.05702171e-01
4.01185751e-01 -1.82005793e-01 -2.69452602e-01 -6.93339407e-01
-3.04781675e-01 -7.65808702e-01 4.30815309e-01 1.41012299e+00
3.19409341e-01 -1.14842725e+00 5.14736474e-01 1.83368087e+00
-1.86044946e-02 -6.00227594e-01 -6.10006392e-01 -3.83143276e-01
1.03458874e-01 -3.59498918e-01 1.00640762e+00 1.33549893e+00
5.99433124e-01 3.22586554e-03 -4.60712045e-01 1.22055188e-01
6.67948782e-01 -2.38972142e-01 9.70532835e-01 -1.32611132e+00
3.82288337e-01 -1.25949755e-01 -2.61306673e-01 8.39306340e-02
-1.99258581e-01 -1.24218874e-01 -1.88523635e-01 -2.22163272e+00
1.13652445e-01 -4.58790362e-01 -1.94366664e-01 6.16003156e-01
8.69035199e-02 -2.98040211e-02 4.64333266e-01 2.94405878e-01
-5.00783801e-01 5.32401264e-01 1.30003476e+00 5.42089701e-01
-1.30016133e-01 -5.96894205e-01 -1.87194228e+00 7.51724422e-01
1.00046968e+00 -3.01482171e-01 -4.30366129e-01 -3.80700439e-01
6.98552907e-01 2.26376146e-01 1.57742232e-01 -7.65982270e-01
3.06974262e-01 -1.03699660e+00 -5.05470559e-02 1.41737357e-01
1.11870147e-01 -1.49042428e+00 9.43107069e-01 8.00690234e-01
-2.16960669e-01 6.71536326e-02 -2.16102272e-01 -2.58652866e-01
-2.32693061e-01 -1.57791734e-01 5.12672663e-01 1.17871389e-01
1.10466518e-01 -2.90555865e-01 -6.70081735e-01 -5.06471336e-01
1.68118930e+00 -5.72223365e-01 -6.58650160e-01 -7.21294641e-01
2.99704485e-02 3.21826339e-01 1.14577615e+00 -3.93561423e-02
7.59396374e-01 -1.25873816e+00 -6.77576602e-01 -2.13704500e-02
-4.59399037e-02 -3.44181627e-01 -2.48174276e-02 8.27757299e-01
-6.30228579e-01 3.37081224e-01 -7.94650435e-01 1.04161493e-01
-9.89596605e-01 1.06544040e-01 1.52356133e-01 1.81446329e-01
-1.35057285e-01 2.78962910e-01 -7.13643909e-04 5.01254909e-02
8.41000006e-02 -3.12294867e-02 -3.75574559e-01 6.37197569e-02
4.92626339e-01 2.85521835e-01 -7.93608248e-01 -8.64509463e-01
-4.64696199e-01 2.65813679e-01 1.32668272e-01 -4.51988548e-01
1.38207948e+00 -2.06460789e-01 -7.60362923e-01 2.33514756e-01
2.86733478e-01 3.31592828e-01 -1.05376124e+00 5.95954180e-01
2.06066504e-01 -1.14684975e+00 -2.74787657e-02 -8.84314716e-01
-8.68066072e-01 6.55989408e-01 2.65986830e-01 4.01382118e-01
1.13005757e+00 -4.28880513e-01 4.74742770e-01 1.56339109e-01
4.92088050e-01 -1.44839418e+00 1.76465511e-01 -1.19812332e-01
9.01676476e-01 -8.15035582e-01 1.00751802e-01 -5.02821684e-01
-1.53667867e+00 9.05929983e-01 9.57542956e-01 2.15885997e-01
5.25127351e-01 2.87629932e-01 6.76340878e-01 -1.28588930e-01
-9.61673379e-01 8.30039196e-03 -1.64857522e-01 7.29954123e-01
9.05111849e-01 2.82568753e-01 -9.80615854e-01 7.95643926e-01
2.03578353e-01 3.80217493e-01 8.14930260e-01 9.36246395e-01
-3.19313258e-01 -1.32647753e+00 -2.56590396e-01 1.86501846e-01
-7.50585973e-01 1.13129010e-02 -9.89162803e-01 1.02712500e+00
3.19886394e-02 1.30199373e+00 -3.39681566e-01 -5.72687089e-01
3.83172393e-01 -4.19708192e-01 -7.04677477e-02 -2.29378074e-01
-1.33369207e+00 1.50620744e-01 7.67198920e-01 -4.46365714e-01
-5.92474163e-01 -7.75171697e-01 -1.31140625e+00 -7.29703665e-01
-3.13043833e-01 4.29242522e-01 5.05745053e-01 5.78677297e-01
5.09561300e-01 1.31597482e-02 5.63722193e-01 1.31740406e-01
-5.77107728e-01 -6.06134415e-01 -3.76356840e-01 1.38140067e-01
-7.61475191e-02 -5.08511290e-02 -2.18671873e-01 -2.35844567e-01] | [9.04535961151123, 6.325340270996094] |
f3345328-baaa-4544-bbf8-d64299ada656 | learning-to-grow-artificial-hippocampi-in | 2303.08250 | null | https://arxiv.org/abs/2303.08250v2 | https://arxiv.org/pdf/2303.08250v2.pdf | Learning to Grow Artificial Hippocampi in Vision Transformers for Resilient Lifelong Learning | Lifelong learning without catastrophic forgetting (i.e., resiliency) possessed by human intelligence is entangled with sophisticated memory mechanisms in the brain, especially the long-term memory (LM) maintained by Hippocampi. With the dominance of Transformers in deep learning, it is a pressing need to explore what would be, and how to implement, Artificial Hippocampi (ArtiHippo) in Transformers. This paper presents a method of learning to grow ArtiHippo in Vision Transformers (ViTs) for resilient lifelong learning. We study four aspects: (i) Where to place ArtiHippo in ViTs to enable plasticity while preserving the core function of ViTs at streaming tasks? (ii) What representational scheme to use to realize ArtiHippo to ensure expressivity and adaptivity for tackling tasks of different nature in lifelong learning? (iii) How to learn to grow ArtiHippo to exploit task synergies and to overcome catastrophic forgetting? (iv) How to harness the best of our proposed ArtiHippo and prompting-based approaches? In experiments, the proposed method is tested on the challenging Visual Domain Decathlon (VDD) benchmark and the recently proposed 5-Dataset benchmark. It obtains consistently better performance than the prior art with sensible ArtiHippo learned continually. | ['Tianfu Wu', 'Michelle Dai', 'Chinmay Savadikar'] | 2023-03-14 | null | null | null | null | ['architecture-search'] | ['methodology'] | [-1.02839112e-01 6.07138798e-02 4.96694714e-01 1.12193719e-01
1.01207308e-01 -2.32205749e-01 6.45756543e-01 -2.52525717e-01
-3.39021951e-01 8.03973198e-01 1.84988976e-02 9.77076776e-03
-5.14439285e-01 -6.48946285e-01 -9.88761246e-01 -9.31880176e-01
-3.60514432e-01 2.84499645e-01 6.20248437e-01 -4.63050097e-01
3.95818114e-01 4.64228183e-01 -1.93800068e+00 4.58129436e-01
1.03686941e+00 9.61221278e-01 6.63683116e-01 6.69060946e-01
2.83177383e-02 1.18331504e+00 -3.37752879e-01 -3.80604595e-01
1.75581649e-01 -1.82984889e-01 -8.58998537e-01 -7.75674358e-02
6.01893008e-01 -1.20162435e-01 -4.20175791e-01 6.43612444e-01
6.13405943e-01 8.33440647e-02 7.32438684e-01 -1.05200684e+00
-1.09054399e+00 4.97761965e-01 -3.74909073e-01 8.55050802e-01
-2.28643477e-01 6.01763368e-01 4.63407218e-01 -1.20898032e+00
4.74247664e-01 1.14516604e+00 9.07690465e-01 6.40413642e-01
-8.13069463e-01 -5.96016705e-01 1.26475230e-01 5.77747762e-01
-1.10097682e+00 -5.43672740e-01 4.18924123e-01 -3.77515942e-01
1.30738652e+00 -2.22117111e-01 1.09554791e+00 1.36851764e+00
8.66767764e-01 7.80008793e-01 1.31949496e+00 -1.20752975e-01
3.04382890e-01 -4.96533886e-02 3.58889937e-01 9.46902633e-01
1.34734124e-01 2.61376381e-01 -1.34300470e+00 2.72430480e-01
8.75602722e-01 9.26369727e-02 -2.56564170e-01 -1.36181951e-01
-1.31867981e+00 2.39634648e-01 4.48775142e-01 3.80390018e-01
-5.71551800e-01 3.29707295e-01 1.43640950e-01 8.55460346e-01
2.78243989e-01 5.84265351e-01 -6.29613459e-01 -6.21658117e-02
-9.06037509e-01 -1.00642323e-01 4.09991980e-01 7.23658025e-01
5.93490720e-01 5.66315472e-01 -3.24072301e-01 8.00180018e-01
-1.70243550e-02 4.19355184e-01 1.02733314e+00 -8.67725313e-01
1.04163364e-01 4.81463790e-01 -1.86019644e-01 -5.67685783e-01
-4.90704238e-01 -9.57562268e-01 -9.82279301e-01 2.99977541e-01
3.68284285e-01 9.69416797e-02 -1.11692464e+00 1.75513220e+00
-2.95124147e-02 5.90142488e-01 2.45197505e-01 5.55695593e-01
6.64381027e-01 7.36589313e-01 -4.59185382e-03 -1.98122233e-01
1.10318530e+00 -1.02648020e+00 -4.07599449e-01 -4.91507292e-01
4.19183433e-01 -4.31592315e-01 1.53077817e+00 6.79422140e-01
-1.10013521e+00 -5.68834305e-01 -1.34454548e+00 2.35375557e-02
-4.36754316e-01 -2.60800391e-01 5.53241313e-01 2.72084981e-01
-1.46966159e+00 8.11746836e-01 -7.30488718e-01 -4.87176120e-01
5.53835571e-01 2.12399006e-01 -2.89268583e-01 1.50924504e-01
-1.29739857e+00 1.01222610e+00 4.64871615e-01 -7.13237822e-02
-1.62156320e+00 -9.84093547e-01 -2.15654910e-01 6.61049485e-02
1.10188812e-01 -1.28166974e+00 8.60985398e-01 -1.17513120e+00
-1.48475015e+00 7.89461136e-01 2.41000831e-01 -8.84784877e-01
2.42474943e-01 -3.30746740e-01 -1.89484417e-01 1.93922952e-01
-4.45271671e-01 8.44233334e-01 1.37660968e+00 -1.01608109e+00
-4.49460238e-01 -5.16479135e-01 -4.33539860e-02 1.82203531e-01
-1.00106776e+00 -5.15303910e-01 -7.41997212e-02 -9.15363610e-01
9.24607813e-02 -9.72562850e-01 2.96454549e-01 1.91418171e-01
2.28211939e-01 -2.35383242e-01 9.32309151e-01 -5.67041278e-01
9.54091012e-01 -2.21498942e+00 2.77006805e-01 -4.65864599e-01
4.25196677e-01 5.65432310e-01 -4.07121629e-01 2.77282804e-01
7.60217160e-02 -3.26615930e-01 -2.38167152e-01 -2.47435376e-01
-3.21214199e-01 5.24135709e-01 -6.90785825e-01 1.39204979e-01
1.62283793e-01 1.23744273e+00 -8.87037396e-01 -1.69486299e-01
-2.11266205e-01 5.15293121e-01 -2.79707789e-01 1.95938602e-01
-2.88277596e-01 3.08009654e-01 -4.25265804e-02 7.89068460e-01
3.71886849e-01 -2.81842738e-01 -3.48321021e-01 2.32538935e-02
-2.53240969e-02 -1.96609288e-01 -6.24346852e-01 1.62480974e+00
-3.97065043e-01 5.04263043e-01 -1.45618886e-01 -7.96997428e-01
1.04629302e+00 2.04733774e-01 5.33058308e-02 -1.17832756e+00
-2.45256558e-01 2.19137043e-01 -2.44824082e-01 -4.92964953e-01
2.55010992e-01 -2.59660423e-01 2.31200889e-01 4.07579094e-01
5.50261319e-01 1.44962475e-01 -1.03053935e-01 2.86866307e-01
1.42683744e+00 6.18544267e-03 -1.15377396e-01 -6.16327465e-01
4.32263047e-01 -3.68321449e-01 6.26102209e-01 7.27026045e-01
-4.93765503e-01 5.23964465e-01 2.64085621e-01 -9.34761882e-01
-9.54222918e-01 -1.40081024e+00 2.48567984e-01 1.33533287e+00
-1.69842586e-01 6.87037110e-02 -5.61167121e-01 -4.14194137e-01
-2.03668207e-01 6.14251316e-01 -7.14238465e-01 -6.85785711e-01
-4.76163685e-01 -7.91331351e-01 7.38246083e-01 5.15954077e-01
1.00198603e+00 -1.31797433e+00 -8.95429254e-01 1.52175412e-01
3.03259157e-02 -7.10573971e-01 -4.15004253e-01 5.42877555e-01
-1.24996710e+00 -8.01937461e-01 -9.20723021e-01 -9.33831811e-01
4.45855409e-01 5.86097717e-01 1.38907647e+00 -4.61867545e-03
-1.82488095e-02 6.63783610e-01 -2.55799711e-01 -4.47408259e-01
-1.59086809e-02 1.24136768e-01 2.93762624e-01 -7.83327967e-02
-8.54514465e-02 -1.17875612e+00 -7.77452886e-01 2.32994139e-01
-1.02696860e+00 1.88174859e-01 1.01396620e+00 1.05477571e+00
7.65784800e-01 2.22608835e-01 1.05914783e+00 -8.04681420e-01
5.52626252e-01 -5.81751049e-01 -2.59952188e-01 5.07986724e-01
-1.06761575e+00 3.69757146e-01 7.22409010e-01 -7.10268259e-01
-1.12278223e+00 -1.54696286e-01 5.95602430e-02 -7.53019392e-01
3.73539895e-01 2.52877355e-01 -8.88773054e-02 -2.72991806e-01
7.59561241e-01 7.11986721e-01 2.36291755e-02 -3.55389059e-01
5.24968684e-01 6.55769706e-02 8.92815709e-01 -5.84630668e-01
4.79165286e-01 3.93176109e-01 -1.33916602e-01 -8.39094520e-01
-8.93260419e-01 5.80409206e-02 -5.56480348e-01 -3.66658032e-01
5.33487082e-01 -1.00856864e+00 -4.41986501e-01 1.03793097e+00
-9.45462167e-01 -7.16463029e-01 -5.63176095e-01 1.71850994e-02
-7.73176253e-01 -4.04422507e-02 -6.50427818e-01 -4.67945218e-01
-8.55385721e-01 -5.41424692e-01 5.70658267e-01 4.79963928e-01
2.01232895e-01 -9.23655033e-01 2.05342680e-01 3.09249341e-01
7.37328649e-01 -1.55474588e-01 1.13680184e+00 -7.76145756e-02
-7.19098568e-01 5.31744599e-01 -1.85254887e-01 4.09752667e-01
-3.68373483e-01 -3.93780082e-01 -1.18784964e+00 -5.63898861e-01
1.62031233e-01 -6.10759258e-01 1.63084388e+00 6.54447600e-02
1.06174040e+00 -6.49143338e-01 1.12772323e-01 9.11007762e-01
1.28133655e+00 -6.97694421e-02 9.36395228e-01 5.03105819e-01
5.46674073e-01 5.80561697e-01 3.85482073e-01 4.21813220e-01
4.93021220e-01 1.02809228e-01 6.99412882e-01 4.68679428e-01
-7.39125907e-01 -3.37652624e-01 9.43816423e-01 1.23022711e+00
-3.44074592e-02 -8.22079703e-02 -9.79568660e-01 7.93151259e-01
-1.94442511e+00 -9.19697940e-01 1.66415751e-01 1.92586780e+00
9.73651707e-01 2.64376879e-01 -3.09889019e-01 1.32294878e-01
2.68359363e-01 1.71110451e-01 -9.91703570e-01 -3.59655827e-01
-5.23703754e-01 1.16185144e-01 2.38254815e-01 4.11106199e-02
-6.65304720e-01 9.63202655e-01 5.82335663e+00 8.13337564e-01
-1.21554136e+00 5.14388800e-01 6.02311611e-01 -2.75463104e-01
-2.46006057e-01 -1.00645453e-01 -8.44869494e-01 4.30027008e-01
1.27928913e+00 1.39196229e-03 6.80033565e-01 4.75645363e-01
-8.30736980e-02 -3.69682088e-02 -8.27447057e-01 9.73657131e-01
7.82753453e-02 -1.37430727e+00 3.30701709e-01 -3.14595938e-01
9.17804658e-01 1.54870927e-01 7.57465780e-01 6.72084808e-01
-3.12484279e-02 -1.03120291e+00 8.19244087e-01 1.27467990e+00
5.35025120e-01 -7.75452971e-01 3.63580585e-01 5.46401024e-01
-9.42538261e-01 -5.97549498e-01 -6.18748546e-01 2.78582033e-02
-5.31735659e-01 9.11581039e-01 -6.23633981e-01 1.08977042e-01
1.08282292e+00 8.55153799e-01 -1.05244005e+00 1.08217835e+00
-2.64918327e-01 7.05433071e-01 6.02303147e-02 2.98484325e-01
1.81832418e-01 1.91050336e-01 7.04882801e-01 1.02864277e+00
6.00002766e-01 -1.55724347e-01 -4.03402090e-01 7.19861031e-01
-1.68370530e-01 -4.02152985e-01 -7.29509592e-01 -1.54420674e-01
4.57137764e-01 1.15105629e+00 -6.20531082e-01 -1.30862594e-01
4.40865271e-02 1.10384691e+00 6.91493332e-01 2.86821127e-01
-4.24425870e-01 2.22188700e-02 4.47521985e-01 3.85997951e-01
3.69089484e-01 -4.99873787e-01 -5.27945697e-01 -9.25735533e-01
-2.95411702e-02 -8.10892344e-01 5.50108969e-01 -1.15028000e+00
-1.53786826e+00 8.29354644e-01 -4.50963557e-01 -8.95394802e-01
2.40367755e-01 -2.99579293e-01 -7.56876230e-01 4.51790690e-01
-1.82607150e+00 -1.42820215e+00 -3.79077792e-01 1.11988902e+00
7.28691638e-01 -5.08940041e-01 6.96701467e-01 2.31248856e-01
-4.16654468e-01 6.50254726e-01 1.69830844e-01 -7.09412098e-01
8.58896136e-01 -1.22734988e+00 1.55703798e-01 8.42422187e-01
1.84693888e-01 5.32525241e-01 6.15988910e-01 -6.53590024e-01
-1.71821547e+00 -1.55611515e+00 8.05272162e-01 -4.42021161e-01
6.08329773e-01 -2.89009869e-01 -1.33097410e+00 4.33621794e-01
2.81153947e-01 6.67933300e-02 1.32576868e-01 -1.31196186e-01
-6.51655674e-01 -5.66032469e-01 -9.69290912e-01 4.34693485e-01
1.24013031e+00 -3.98647070e-01 -8.27740073e-01 2.37577826e-01
9.84077513e-01 1.47641286e-01 -4.76132005e-01 2.25115061e-01
5.64105034e-01 -1.19530725e+00 1.01452708e+00 -1.12494662e-01
2.24480942e-01 -3.13203871e-01 1.61253393e-01 -1.46641219e+00
-4.98086721e-01 -6.72713101e-01 -7.62253582e-01 1.14132166e+00
8.29882175e-02 -8.11337173e-01 5.86261094e-01 -8.58255476e-02
-4.69435245e-01 -1.08498418e+00 -1.10403693e+00 -1.18512332e+00
3.73526752e-01 -2.15849243e-02 2.86610395e-01 6.90397084e-01
-5.22772729e-01 3.73256803e-01 -5.55364549e-01 -3.48603278e-02
6.06486559e-01 -2.52752274e-01 2.44905353e-01 -1.26847780e+00
-3.95035535e-01 -2.40175322e-01 -1.42002299e-01 -6.68224752e-01
-3.53312343e-02 -9.96169746e-01 -1.43231630e-01 -1.30207217e+00
2.58720785e-01 -3.76219332e-01 -7.55037725e-01 7.58026600e-01
-3.12533081e-02 3.06189209e-01 2.08968058e-01 5.58286250e-01
-8.72920990e-01 9.18443680e-01 1.42919397e+00 -1.84430197e-01
-1.37531549e-01 -3.28392714e-01 -7.80082703e-01 6.29579246e-01
7.67141223e-01 -4.77611154e-01 -8.89252841e-01 -7.12013781e-01
7.27958143e-01 -4.87767048e-02 6.00700617e-01 -1.50503516e+00
6.33216500e-01 9.75896120e-02 4.37390566e-01 -4.53530878e-01
2.73262471e-01 -4.08740878e-01 9.24229771e-02 7.25384176e-01
-1.05710700e-01 3.14769387e-01 2.89010733e-01 8.50999475e-01
6.90197647e-02 7.19937980e-02 1.12325466e+00 -2.96218693e-01
-1.12497675e+00 4.63139802e-01 -5.77212811e-01 1.04068495e-01
9.81060207e-01 -2.03600213e-01 -6.73906803e-01 5.77887967e-02
-6.67573869e-01 3.73236805e-01 3.01398069e-01 5.97285092e-01
1.20206594e+00 -1.22169232e+00 -6.89581394e-01 2.55017787e-01
1.20278470e-01 -4.41275209e-01 4.48115230e-01 1.01306200e+00
-1.70531780e-01 3.01475942e-01 -8.02294433e-01 -3.42519343e-01
-9.09597814e-01 7.06080377e-01 4.99627054e-01 -4.48909998e-01
-8.09911549e-01 1.33445835e+00 3.67108673e-01 -4.68758978e-02
3.75294000e-01 8.04715455e-02 -2.27991819e-01 2.45607018e-01
6.03503883e-01 4.30736661e-01 2.85346448e-01 -7.53655657e-02
-2.90693760e-01 2.86156714e-01 -3.37628871e-01 1.51336581e-01
1.68958008e+00 -3.80901188e-01 -1.96660891e-01 6.76932633e-01
5.57301283e-01 -6.27269149e-01 -1.77583671e+00 -2.78503239e-01
5.01051433e-02 -1.16526876e-02 1.73375592e-01 -1.13212430e+00
-1.11750603e+00 1.21393025e+00 9.91448283e-01 -3.14748853e-01
1.34155333e+00 -2.61894673e-01 1.21342802e+00 6.24500453e-01
7.86700070e-01 -1.21141422e+00 9.52931166e-01 1.11343992e+00
1.24330723e+00 -7.70862162e-01 -3.67241323e-01 4.38535064e-01
-7.15294480e-01 1.16866457e+00 6.80198729e-01 -3.64132315e-01
7.19948947e-01 1.28279746e-01 -1.74050644e-01 -3.42913240e-01
-1.54103255e+00 -8.70947465e-02 7.94364512e-02 7.99257874e-01
-9.78985205e-02 -3.45145017e-01 -7.03411177e-02 7.48077095e-01
-2.20448487e-02 1.40438527e-01 5.51943004e-01 9.00036752e-01
-9.94369805e-01 -7.33319342e-01 -1.95295617e-01 5.32674193e-01
-1.30167514e-01 -2.04310745e-01 -3.19139004e-01 6.75840378e-02
5.22649229e-01 8.33693922e-01 -3.23835313e-01 -4.95678186e-01
4.74869274e-02 2.63682604e-01 6.94218516e-01 -4.59702969e-01
-7.57058203e-01 -5.83310187e-01 -3.00737411e-01 -5.62155664e-01
-5.85935898e-02 -4.34926838e-01 -1.15882313e+00 -3.21212649e-01
9.39871371e-02 -2.81060427e-01 3.45133245e-01 8.50332499e-01
6.61796689e-01 7.01100707e-01 3.91742021e-01 -4.84067917e-01
-6.11916721e-01 -7.50790656e-01 -7.17566133e-01 7.10642114e-02
2.27984443e-01 -7.97398865e-01 -2.18872383e-01 2.42701545e-01] | [9.862603187561035, 3.435033082962036] |
6b52460d-f7c9-4185-b05e-ad4399b10316 | discriminative-multiple-canonical-correlation | 2103.00361 | null | https://arxiv.org/abs/2103.00361v1 | https://arxiv.org/pdf/2103.00361v1.pdf | Discriminative Multiple Canonical Correlation Analysis for Information Fusion | In this paper, we propose the Discriminative Multiple Canonical Correlation Analysis (DMCCA) for multimodal information analysis and fusion. DMCCA is capable of extracting more discriminative characteristics from multimodal information representations. Specifically, it finds the projected directions which simultaneously maximize the within-class correlation and minimize the between-class correlation, leading to better utilization of the multimodal information. In the process, we analytically demonstrate that the optimally projected dimension by DMCCA can be quite accurately predicted, leading to both superior performance and substantial reduction in computational cost. We further verify that Canonical Correlation Analysis (CCA), Multiple Canonical Correlation Analysis (MCCA) and Discriminative Canonical Correlation Analysis (DCCA) are special cases of DMCCA, thus establishing a unified framework for Canonical Correlation Analysis. We implement a prototype of DMCCA to demonstrate its performance in handwritten digit recognition and human emotion recognition. Extensive experiments show that DMCCA outperforms the traditional methods of serial fusion, CCA, MCCA and DCCA. | ['Ling Guan', 'Enqing Chen', 'Lin Qi', 'Lei Gao'] | 2021-02-28 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [-7.36074373e-02 -7.65610993e-01 1.03691496e-01 -2.59050131e-01
-9.72327888e-01 -5.99404812e-01 4.74488407e-01 -6.88201413e-02
-6.73559904e-02 3.16852868e-01 2.61364311e-01 2.48503238e-02
-2.99442559e-01 -2.43371084e-01 4.93512908e-03 -9.98949051e-01
-2.50666022e-01 -6.85960278e-02 -3.85347277e-01 -1.22937284e-01
3.08987528e-01 6.13503873e-01 -1.37088883e+00 2.42387533e-01
9.23544466e-01 1.08754385e+00 2.30066460e-02 7.35001087e-01
1.13887109e-01 3.31277698e-01 -3.80457729e-01 -4.37255859e-01
-9.24457014e-02 -5.41731060e-01 -3.13901693e-01 2.29464337e-01
-2.46266603e-01 7.58668706e-02 6.33535981e-02 9.06624973e-01
5.69890797e-01 2.97751039e-01 8.47950935e-01 -1.44923842e+00
-3.38934511e-01 2.42567882e-01 -1.10669041e+00 -2.95215577e-01
6.44353688e-01 -4.23487097e-01 9.43989098e-01 -1.22552240e+00
1.74244419e-01 1.68373108e+00 4.12682563e-01 2.79606014e-01
-1.31752872e+00 -6.89206600e-01 -1.05435923e-02 1.21439122e-01
-1.58210742e+00 -2.12310210e-01 9.76885140e-01 -3.51900190e-01
5.64633310e-01 5.47881067e-01 5.19899964e-01 9.05857980e-01
2.92834312e-01 1.18746555e+00 1.30307829e+00 -6.58983409e-01
1.96485341e-01 -1.79195538e-01 2.95839459e-01 5.59207976e-01
-1.48384199e-01 -6.16565347e-02 -6.61277235e-01 -5.35061657e-01
4.63514835e-01 -2.04934701e-02 -2.13680908e-01 -4.40507174e-01
-1.35483110e+00 6.96138203e-01 5.18456399e-02 3.68525267e-01
-3.99027914e-01 3.56700905e-02 3.18781555e-01 8.58021453e-02
-1.85204461e-01 9.92694721e-02 -1.02022342e-01 -2.94408828e-01
-4.90456730e-01 1.70571208e-01 5.53884089e-01 8.81360054e-01
6.58864081e-01 -6.17617555e-02 1.18880354e-01 1.04742277e+00
7.61319101e-01 1.04089820e+00 5.15172362e-01 -9.34159040e-01
3.42376143e-01 8.23842227e-01 -1.43805489e-01 -1.42250192e+00
-4.88870114e-01 -8.94132257e-02 -1.10417998e+00 -9.66340080e-02
6.84925169e-02 -1.48855969e-01 -3.07989299e-01 1.66077363e+00
-7.04656765e-02 -1.71580836e-01 5.44279695e-01 9.78353798e-01
4.97567415e-01 4.20630306e-01 -9.21500206e-04 -4.53207135e-01
1.46815598e+00 -4.39145327e-01 -9.77683008e-01 4.28863205e-02
4.18831080e-01 -1.21868002e+00 7.29134560e-01 7.05335200e-01
-7.03730285e-01 -5.38634002e-01 -1.19897485e+00 3.21093082e-01
-1.20288849e-01 6.82345688e-01 9.46005106e-01 8.70445907e-01
-6.16333485e-01 1.66543752e-01 -8.85503948e-01 -2.46855468e-02
-1.23623826e-01 4.01589841e-01 -6.56157732e-01 -1.46143839e-01
-8.49196255e-01 7.25092411e-01 9.99417007e-02 3.47180843e-01
-5.00592291e-01 -1.08691812e-01 -8.14218938e-01 -7.73345530e-02
-8.53356495e-02 -2.55957216e-01 7.02830553e-01 -8.47965181e-01
-1.33835852e+00 4.29244041e-01 -7.17008591e-01 2.46790290e-01
-1.17615856e-01 -3.16667408e-01 -7.73094416e-01 2.50145137e-01
-1.93802282e-01 4.87371773e-01 7.43716061e-01 -1.51752865e+00
-1.86027959e-01 -6.51938021e-01 -4.51112896e-01 1.15066804e-01
-3.38227183e-01 1.46584511e-01 -4.89156008e-01 -4.77007538e-01
7.60757923e-01 -1.02013910e+00 -3.34843665e-01 -6.48516178e-01
-3.87375742e-01 -1.61385521e-01 9.61029708e-01 -6.43121243e-01
1.45766068e+00 -2.29136467e+00 6.63380623e-01 7.61859119e-01
-3.05290166e-02 -1.20552100e-01 -1.97200224e-01 5.84054947e-01
-2.52826959e-01 -1.03710733e-01 -2.05196336e-01 -3.73492271e-01
-8.12841207e-02 3.08239490e-01 -1.77835837e-01 4.60390717e-01
4.26645786e-01 6.47391260e-01 -6.42221332e-01 -5.82579494e-01
2.46959627e-01 5.99120498e-01 -4.53314692e-01 9.03372765e-02
5.01558125e-01 2.83107251e-01 -4.11395460e-01 1.14896953e+00
1.08150935e+00 -4.16828832e-03 7.00326264e-01 -6.62144423e-01
1.70796420e-02 -6.61307573e-01 -1.40405047e+00 1.54740655e+00
-2.48311326e-01 6.71407878e-01 1.81803908e-02 -7.19276905e-01
1.41238356e+00 9.38531384e-02 6.50650084e-01 -5.26699305e-01
3.40206712e-01 1.29407093e-01 -5.30116595e-02 -4.52131480e-01
6.43058062e-01 -9.67938080e-02 -4.28032875e-01 3.17007989e-01
6.90761954e-02 2.31682882e-01 3.22238952e-02 4.37589079e-01
4.10786629e-01 -2.85003573e-01 5.54019272e-01 -1.44848689e-01
9.09768283e-01 -4.40200657e-01 6.40574694e-01 1.58111319e-01
-2.16937199e-01 4.59275037e-01 7.01729596e-01 2.13831410e-01
-6.65399253e-01 -1.16207159e+00 -1.74086578e-02 6.04387820e-01
2.94583559e-01 -7.64446676e-01 -5.41824520e-01 -3.93150210e-01
-3.69414277e-02 3.96286398e-01 -4.47727650e-01 -8.86160433e-02
-3.11284870e-01 -8.82005870e-01 5.03449023e-01 6.48935735e-01
3.97169709e-01 -1.82093814e-01 -3.56060266e-01 -1.24172710e-01
-2.87631571e-01 -8.98342609e-01 -3.48814398e-01 4.00617011e-02
-8.23191106e-01 -1.34425485e+00 -4.90307897e-01 -5.60380697e-01
7.88006604e-01 6.38632476e-01 5.06014228e-01 -2.48103321e-01
-2.09903672e-01 8.82894993e-01 -5.65073192e-01 9.74513870e-03
-1.68717921e-01 -6.09443963e-01 2.18553826e-01 2.58529395e-01
7.54872620e-01 -5.89797020e-01 -3.76399904e-01 6.62983954e-01
-8.51199150e-01 -1.19072787e-01 6.92910194e-01 1.21997678e+00
4.90527391e-01 -1.19146101e-01 4.46009666e-01 2.53885090e-02
1.02057707e+00 -2.26530984e-01 -4.62625414e-01 4.72600371e-01
-4.91363525e-01 7.84950927e-02 3.36834192e-01 -2.73286581e-01
-1.12263703e+00 3.64008904e-01 1.60263851e-01 -5.62208235e-01
1.66564749e-03 8.76664400e-01 -4.33526486e-01 -6.44479617e-02
3.48480433e-01 4.21517700e-01 3.71558428e-01 -4.87093121e-01
4.61122870e-01 7.10065365e-01 5.12528002e-01 -7.41618812e-01
4.30067450e-01 4.10692930e-01 3.85598570e-01 -7.41128623e-01
-1.83392167e-01 -6.82201564e-01 -1.03543627e+00 -6.23169482e-01
7.78110325e-01 -9.51932669e-01 -1.38264120e+00 2.93319732e-01
-1.05333173e+00 7.50160694e-01 5.97785294e-01 9.64248240e-01
-3.25142324e-01 9.52624142e-01 -3.17115217e-01 -1.34012067e+00
-6.72694370e-02 -1.30618894e+00 1.01483071e+00 3.98543864e-01
-2.12636054e-01 -9.68126595e-01 1.40850609e-02 1.77945629e-01
-7.67958059e-04 2.98319578e-01 8.98931980e-01 -5.09327590e-01
-2.33985838e-02 -3.67660671e-01 -2.28846073e-01 4.56699699e-01
7.45303780e-02 2.59591788e-01 -8.71469796e-01 -3.67073506e-01
-1.72840670e-01 -1.15397774e-01 7.05330372e-01 2.33958527e-01
7.19481707e-01 1.65614188e-01 -3.20732832e-01 3.28591228e-01
1.28695905e+00 7.61460304e-01 6.35441780e-01 -6.40012622e-02
4.56544548e-01 7.23218918e-01 8.82184327e-01 7.96125591e-01
3.32464904e-01 6.72796011e-01 2.86244124e-01 1.11164629e-01
4.44419146e-01 -8.67027864e-02 5.46391070e-01 1.53919184e+00
-2.15390265e-01 1.78090975e-01 -7.84199059e-01 1.86621904e-01
-2.09541440e+00 -8.50511849e-01 -2.57045001e-01 2.05338550e+00
3.56821299e-01 -6.60317838e-01 1.14987649e-01 3.01780611e-01
7.42660999e-01 -7.49228001e-02 -2.05155447e-01 -5.27849972e-01
-6.20980799e-01 -9.66875330e-02 5.34042083e-02 3.88043404e-01
-1.20585167e+00 5.61636567e-01 7.28043222e+00 7.78877437e-01
-7.23832428e-01 -3.62114877e-01 3.73618275e-01 2.28670478e-01
-1.39600769e-01 8.04489031e-02 -6.15527451e-01 2.11685359e-01
5.54695427e-01 -2.27850750e-01 4.44143146e-01 9.67566431e-01
9.58357826e-02 -3.01343620e-01 -8.74645531e-01 1.57525289e+00
2.99027413e-01 -7.26625741e-01 7.98584707e-03 8.40120688e-02
6.45859957e-01 -6.14576340e-01 1.04661502e-01 2.82747131e-02
1.73072815e-01 -8.18932533e-01 2.20863238e-01 7.38995373e-01
2.33331084e-01 -1.21083641e+00 8.66286933e-01 2.28482317e-02
-1.34273338e+00 -1.21129118e-01 -4.08929259e-01 1.95486441e-01
-2.13680160e-03 4.78070706e-01 -3.64495009e-01 9.68737662e-01
2.78959006e-01 9.31258976e-01 -6.49939716e-01 8.11408997e-01
5.92708252e-02 1.48574993e-01 -3.76702435e-02 -1.27207056e-01
1.72375605e-01 -6.92165375e-01 5.70723116e-01 1.46321249e+00
5.72893381e-01 1.62775129e-01 7.97722861e-02 4.91612315e-01
4.42361236e-01 4.10436869e-01 -3.17989320e-01 -2.29562357e-01
5.45485198e-01 1.29109049e+00 -5.16153991e-01 -1.84913978e-01
-3.17711979e-01 7.89075136e-01 -1.09288476e-01 3.66582632e-01
-5.67292154e-01 -2.93431431e-01 7.38809407e-01 -1.01415575e+00
2.41934001e-01 -6.71078980e-01 -5.08845747e-01 -1.46748924e+00
-1.04113661e-01 -1.02406454e+00 6.52066827e-01 -6.74068153e-01
-1.32221901e+00 4.12902117e-01 7.52379373e-02 -1.74002111e+00
-2.65370280e-01 -8.75138044e-01 -2.68688291e-01 9.13597882e-01
-9.91809666e-01 -9.94996786e-01 -3.26631218e-01 9.26325977e-01
9.59045067e-02 -6.27919376e-01 1.15361822e+00 3.05072576e-01
-7.49284267e-01 6.55011952e-01 3.29828650e-01 5.14950566e-02
7.41166770e-01 -1.05216920e+00 -5.73723614e-01 8.25116634e-01
-1.86639011e-01 9.67018604e-01 5.46507776e-01 -3.24764043e-01
-2.09185767e+00 -4.68898684e-01 4.74642128e-01 -1.08695971e-02
5.99696577e-01 -2.36861017e-02 -6.00211501e-01 2.80938536e-01
1.30583838e-01 -6.35914624e-01 1.39909697e+00 2.97235161e-01
-6.69559300e-01 -2.15875924e-01 -9.09904480e-01 6.34038031e-01
3.16709608e-01 -6.63640440e-01 -3.33313495e-01 -9.79689136e-02
1.65998444e-01 1.42654972e-02 -1.39133322e+00 2.67225832e-01
1.06003630e+00 -1.03844535e+00 1.01276982e+00 -5.08308530e-01
3.98981780e-01 -6.59750283e-01 -9.58353817e-01 -1.33373392e+00
-2.63237804e-01 -4.11117077e-01 -3.17852534e-02 1.36174583e+00
1.77371308e-01 -4.13630366e-01 3.14830542e-01 5.93665123e-01
6.83084801e-02 -6.20467067e-01 -9.55016375e-01 -7.53759623e-01
-1.01432703e-01 -7.55098522e-01 5.38686037e-01 9.89113808e-01
7.31206238e-01 2.13346764e-01 -7.86316872e-01 2.44139925e-01
7.81439006e-01 3.41160268e-01 7.47382462e-01 -9.77250040e-01
-2.45838925e-01 -3.61439079e-01 -7.34359443e-01 -1.10997260e+00
4.32446413e-02 -5.78288972e-01 -1.70434952e-01 -1.05211961e+00
5.27502954e-01 -6.53935671e-02 -4.63217854e-01 1.60911158e-01
-1.67013243e-01 -3.44009697e-03 4.29224759e-01 3.12059611e-01
-5.62177360e-01 6.18008614e-01 1.14500415e+00 -1.28921092e-01
-2.43899465e-01 -2.14244172e-01 -7.06016362e-01 5.20946801e-01
5.71696520e-01 -1.11034326e-02 -1.77861139e-01 5.23595102e-02
5.48021197e-02 4.34133530e-01 1.55861497e-01 -7.39433289e-01
2.00803295e-01 -1.46235853e-01 6.73835337e-01 -1.07416904e+00
6.86105251e-01 -9.31363881e-01 1.90075278e-01 3.92991066e-01
-2.58372277e-01 3.70764881e-01 2.48634413e-01 5.80091238e-01
-7.36296713e-01 7.26022199e-02 5.83326638e-01 2.84546018e-01
-8.37121785e-01 -3.80577564e-01 -7.10071564e-01 -9.19740498e-01
9.59154606e-01 -1.25220418e-02 -2.67113000e-01 -4.43612099e-01
-9.20167029e-01 2.41510198e-01 7.30619952e-02 3.25624138e-01
1.19831026e+00 -1.94026029e+00 -3.31209093e-01 2.54206568e-01
1.52778849e-01 -7.76565254e-01 4.47128415e-01 1.34667623e+00
-9.67085585e-02 6.14552498e-01 -2.84951657e-01 -7.32788861e-01
-1.87082434e+00 5.06315291e-01 6.35852590e-02 -2.12198153e-01
-4.31772321e-02 3.81793737e-01 1.89939421e-02 -1.79249570e-01
7.29096010e-02 -3.02036200e-02 -3.60840648e-01 2.91480541e-01
6.66993916e-01 5.53086221e-01 -2.63668776e-01 -9.42048967e-01
-6.14697397e-01 9.14279640e-01 1.94354936e-01 -2.56343842e-01
9.83514309e-01 -3.18437248e-01 -4.66980785e-01 3.77504468e-01
1.48536217e+00 6.86150119e-02 -7.13016808e-01 -2.89577246e-02
1.00749187e-01 -7.95400321e-01 -4.19995375e-02 -7.75140941e-01
-9.47731495e-01 1.05257785e+00 8.09135735e-01 -1.81506965e-02
1.63886225e+00 -2.68869221e-01 2.75094837e-01 4.19902235e-01
3.09839755e-01 -1.05383337e+00 3.72222453e-01 2.75783300e-01
1.03368556e+00 -1.07364380e+00 2.30430201e-01 -5.23724318e-01
-1.21860635e+00 1.68943584e+00 4.41084027e-01 -6.90453127e-02
7.13095427e-01 2.38404319e-01 3.73537987e-02 -8.83987173e-02
-8.64988506e-01 -4.34641093e-02 5.84263563e-01 6.31991446e-01
5.55722892e-01 4.40652966e-01 -4.69530821e-01 1.02420962e+00
6.40986348e-03 -5.03094018e-01 3.14001948e-01 7.86467731e-01
-2.90502608e-01 -1.24134910e+00 -9.89088714e-01 -3.66225876e-02
2.84229349e-02 2.48413682e-01 -7.06661999e-01 6.95416927e-01
-2.53786951e-01 1.30917990e+00 -7.51846507e-02 -1.08055270e+00
2.81087190e-01 4.95966040e-02 4.98913527e-01 2.54691422e-01
-2.05244839e-01 8.12550485e-01 8.44626129e-02 -5.95009387e-01
-7.05393493e-01 -9.61085320e-01 -1.07582390e+00 -2.93313891e-01
-4.79865879e-01 3.16597432e-01 8.48057270e-01 8.70043755e-01
3.90763551e-01 2.76324242e-01 9.41267252e-01 -7.38465965e-01
-2.39624336e-01 -8.90382946e-01 -7.39701271e-01 2.83178002e-01
2.09096093e-02 -8.62935603e-01 -2.80935436e-01 3.75792384e-02] | [13.163491249084473, 5.084508895874023] |
0ceef3fe-9e9c-4fc1-b379-4fd9a5864de5 | transdocs-optical-character-recognition-with | 2304.07637 | null | https://arxiv.org/abs/2304.07637v1 | https://arxiv.org/pdf/2304.07637v1.pdf | TransDocs: Optical Character Recognition with word to word translation | While OCR has been used in various applications, its output is not always accurate, leading to misfit words. This research work focuses on improving the optical character recognition (OCR) with ML techniques with integration of OCR with long short-term memory (LSTM) based sequence to sequence deep learning models to perform document translation. This work is based on ANKI dataset for English to Spanish translation. In this work, I have shown comparative study for pre-trained OCR while using deep learning model using LSTM-based seq2seq architecture with attention for machine translation. End-to-end performance of the model has been expressed in BLEU-4 score. This research paper is aimed at researchers and practitioners interested in OCR and its applications in document translation. | ['Phani Krishna Uppala', 'Abhishek Bamotra'] | 2023-04-15 | null | null | null | null | ['optical-character-recognition', 'word-translation'] | ['computer-vision', 'natural-language-processing'] | [ 6.56242430e-01 -3.94537359e-01 -2.66624123e-01 -3.02961826e-01
-1.12981689e+00 -6.08845770e-01 6.37526512e-01 -2.09585816e-01
-5.53274453e-01 1.09045696e+00 4.15644944e-01 -5.96301973e-01
5.95133603e-01 -2.94660181e-01 -8.51613224e-01 -3.94891053e-01
5.37005007e-01 5.24342418e-01 -3.49571198e-01 -1.85338572e-01
7.01341748e-01 6.46049976e-01 -5.68694651e-01 9.73219156e-01
8.07127714e-01 5.02748430e-01 4.06203121e-01 1.43207455e+00
-2.84497291e-01 8.05802763e-01 -9.52714443e-01 -4.74052280e-01
1.13863014e-01 -7.10509837e-01 -8.36086988e-01 -2.84981638e-01
8.30002487e-01 -2.45756015e-01 -2.84455240e-01 9.17785764e-01
9.30524409e-01 1.09789371e-01 8.76678050e-01 -4.52832907e-01
-1.58688033e+00 2.41973311e-01 -3.68529558e-01 5.49849451e-01
3.18293810e-01 7.13013560e-02 6.75779700e-01 -1.08952022e+00
5.55753887e-01 1.26277399e+00 5.61640263e-01 7.03519404e-01
-7.45999157e-01 -2.94426799e-01 -5.84195495e-01 2.36403838e-01
-9.91017699e-01 -2.69493967e-01 1.64651379e-01 -9.49249789e-02
1.91103518e+00 2.54296184e-01 2.40994453e-01 1.30741405e+00
9.52781737e-01 9.37874854e-01 1.18454468e+00 -7.97874033e-01
-2.27296621e-01 2.93520510e-01 1.84160899e-02 3.54345143e-01
-1.32763520e-01 1.28704756e-01 -6.31790340e-01 5.07462859e-01
7.81033695e-01 -1.88571904e-02 2.49448344e-01 8.06548953e-01
-1.19466460e+00 6.61979377e-01 2.43074715e-01 6.27036989e-01
-3.11832875e-01 3.23501050e-01 5.62906563e-01 7.54003346e-01
6.93991363e-01 5.99158466e-01 -6.16949499e-01 -4.70912606e-01
-1.14765251e+00 -1.38305992e-01 5.22594213e-01 1.05383039e+00
3.40495110e-01 4.31085676e-01 -6.60813451e-01 1.17290449e+00
2.83099145e-01 9.00789797e-01 1.00507617e+00 -5.01040637e-01
6.90329790e-01 2.81905770e-01 -1.50015159e-02 -6.71977639e-01
6.11471385e-03 -5.06821990e-01 -6.41051173e-01 5.28559238e-02
8.14458728e-02 -3.40457201e-01 -1.39931166e+00 9.77392018e-01
-5.75922191e-01 -1.35235891e-01 4.92023677e-01 8.78331780e-01
6.55322611e-01 1.39172411e+00 -1.92145720e-01 8.36126320e-03
9.54485655e-01 -1.54531431e+00 -9.29625869e-01 -2.73126423e-01
8.92635465e-01 -1.37833250e+00 1.02372193e+00 2.90697277e-01
-9.72752273e-01 -5.80790162e-01 -1.03295660e+00 -5.21692216e-01
-6.56925142e-01 4.95244980e-01 6.92787543e-02 7.50614405e-01
-1.39715254e+00 6.11491084e-01 -5.64990759e-01 -7.79445887e-01
1.27560019e-01 5.06043136e-01 -2.69356638e-01 -9.99757946e-02
-1.03356171e+00 1.30417717e+00 4.22320992e-01 2.52750874e-01
-9.32314754e-01 -2.80049890e-01 -4.25066710e-01 -1.50283784e-01
-3.05445582e-01 -3.34989041e-01 1.38289857e+00 -1.53186059e+00
-2.07026982e+00 7.28231490e-01 -5.06764889e-01 -7.49435782e-01
3.94818604e-01 -5.20434797e-01 -4.97864753e-01 1.74467545e-02
-2.07172230e-01 8.70830774e-01 6.58675969e-01 -7.28601158e-01
-3.38996470e-01 -3.96074831e-01 -4.01627302e-01 5.37705660e-01
-3.19422573e-01 5.99892735e-01 -3.50682884e-02 -8.90513062e-01
-3.55417907e-01 -1.04503620e+00 2.59866178e-01 -5.02809048e-01
-3.35685551e-01 -2.60480233e-02 9.69593227e-01 -1.31327569e+00
1.04215002e+00 -1.61444879e+00 -5.08809015e-02 -4.69788045e-01
-6.67760611e-01 9.18120742e-01 -5.39211392e-01 7.60537505e-01
1.73196897e-01 3.84127766e-01 -1.76719934e-01 -3.56142789e-01
-4.32434618e-01 1.04577057e-01 -4.47351187e-01 2.07062006e-01
4.16088432e-01 1.40626597e+00 -5.45525789e-01 -3.64948362e-01
1.73957050e-01 6.44621670e-01 8.34019631e-02 3.11130017e-01
-4.09358472e-01 2.64624417e-01 -1.28286988e-01 7.04339147e-01
4.83427197e-01 2.72876143e-01 -4.63405341e-01 3.01291168e-01
-2.16254845e-01 2.72604376e-01 -2.02456266e-01 2.03719258e+00
-6.59134507e-01 1.47022295e+00 -6.90408826e-01 -6.75549269e-01
1.22907555e+00 5.60410976e-01 -2.74433315e-01 -1.08335161e+00
1.33911699e-01 6.72182679e-01 -3.22672836e-02 -6.37061894e-01
8.11560392e-01 -1.44945130e-01 5.62371910e-01 2.81311333e-01
7.04006404e-02 1.24534920e-01 -4.65041521e-04 -1.98704466e-01
7.90208340e-01 4.79987383e-01 -2.31321543e-01 -1.66510999e-01
5.54356992e-01 3.49035412e-01 -1.26756296e-01 6.03689432e-01
-8.30531046e-02 9.31779206e-01 -5.05092144e-02 -3.91915500e-01
-1.67832649e+00 -6.39819860e-01 2.25336283e-01 9.25340414e-01
-6.16516411e-01 3.44531804e-01 -8.36007476e-01 -1.80329427e-01
-6.06528342e-01 9.57990885e-01 -5.40711820e-01 -9.25158709e-03
-8.91561329e-01 -6.50526762e-01 1.00831795e+00 5.20014584e-01
5.27613342e-01 -1.43947339e+00 -2.89953411e-01 4.15243030e-01
-3.47490492e-03 -9.90909278e-01 -7.34742045e-01 9.44743827e-02
-1.30439246e+00 -2.46219367e-01 -1.62532032e+00 -1.12238932e+00
1.62119627e-01 1.98532686e-01 8.87947321e-01 -3.42234075e-01
-2.22057909e-01 -1.13196746e-01 -6.78375125e-01 -6.90343380e-01
-7.08203316e-01 5.06611168e-01 -3.66820067e-01 -2.19355777e-01
9.31334257e-01 1.65294651e-02 -4.57886875e-01 -1.73021972e-01
-9.14992034e-01 2.97867060e-02 1.12802339e+00 7.21234858e-01
3.34420353e-01 -9.37891066e-01 5.20014346e-01 -5.93332648e-01
8.84450555e-01 -1.30094558e-01 -2.21149132e-01 5.54855943e-01
-5.37624300e-01 1.89078420e-01 6.71517849e-01 -2.96078920e-01
-1.02686536e+00 -3.98654670e-01 -8.87928829e-02 -2.42209420e-01
-1.66338727e-01 5.97883403e-01 3.55468124e-01 5.27228452e-02
6.01885259e-01 7.14052796e-01 -3.88914570e-02 -4.48623210e-01
-2.74703521e-02 1.36456978e+00 2.85387695e-01 -8.23236778e-02
1.42139077e-01 -1.25075564e-01 -2.60993212e-01 -1.14006186e+00
-7.36283481e-01 -2.65263468e-01 -7.43554890e-01 -2.52413601e-01
1.20033121e+00 -7.42497802e-01 -1.84452966e-01 8.58570099e-01
-1.60586786e+00 -5.14231563e-01 4.25716579e-01 5.91232300e-01
-3.72412354e-01 1.26926154e-01 -1.02278793e+00 -8.36337745e-01
-9.35403109e-01 -1.20319116e+00 1.06004643e+00 3.25512528e-01
7.82041997e-02 -1.12562096e+00 4.59110051e-01 5.81498623e-01
8.27236116e-01 -1.73372608e-02 7.17698574e-01 -7.79017568e-01
-5.51571906e-01 -4.03378338e-01 -3.84671152e-01 7.09783852e-01
-1.81129109e-02 1.72528803e-01 -9.25313473e-01 -2.37210825e-01
-2.88241088e-01 -4.03746367e-01 9.47429121e-01 4.62881476e-01
4.95423913e-01 -4.21500921e-01 2.62557507e-01 3.94716263e-01
1.79934442e+00 5.91071188e-01 1.28452015e+00 6.28287435e-01
8.73012066e-01 2.99628526e-01 5.45171440e-01 -2.15490505e-01
-1.92899764e-01 4.83148068e-01 -1.21797182e-01 5.95376492e-02
-4.42182839e-01 -2.59152114e-01 1.07040775e+00 9.91402388e-01
5.36290631e-02 -7.67162561e-01 -8.70623767e-01 4.41745818e-01
-1.59201372e+00 -8.45889330e-01 -3.51487458e-01 2.00956082e+00
8.79711151e-01 -1.47157490e-01 -1.84573501e-01 -3.51773590e-01
8.32807779e-01 4.08714637e-02 -3.30971748e-01 -1.61493075e+00
-3.02295953e-01 4.10648406e-01 8.69616866e-01 8.76649320e-01
-6.13011539e-01 1.49511266e+00 6.24571896e+00 9.81636703e-01
-1.61592138e+00 2.69641608e-01 8.16169977e-01 -1.14934936e-01
-1.64950877e-01 -1.48346335e-01 -9.24532175e-01 3.45135361e-01
1.67990947e+00 1.91109300e-01 4.86506045e-01 2.90210128e-01
4.65683281e-01 -4.18527275e-02 -8.52426410e-01 8.36706161e-01
5.08317471e-01 -1.34171212e+00 5.32397747e-01 -8.14124569e-03
1.13246095e+00 5.21329999e-01 4.35580939e-01 2.77662814e-01
-1.50104553e-01 -1.62708580e+00 4.91295010e-01 5.95925808e-01
1.07832575e+00 -8.04060042e-01 1.05064416e+00 5.64603508e-02
-3.58376473e-01 1.99747711e-01 -7.74191499e-01 -1.92485172e-02
-1.10859632e-01 1.05487496e-01 -1.27883410e+00 2.21496090e-01
1.49603963e-01 6.56140029e-01 -3.70859921e-01 9.18507159e-01
8.58921278e-03 6.97505653e-01 1.49731964e-01 -6.39292002e-01
8.79680872e-01 -3.50991577e-01 4.33032781e-01 1.92011487e+00
6.03306949e-01 -2.60266542e-01 -3.98041368e-01 6.84844553e-01
-2.73124099e-01 4.84281510e-01 -7.88396120e-01 -7.25335181e-01
-4.28475067e-02 8.29455018e-01 -4.61129546e-01 -5.60665250e-01
-3.73567849e-01 1.69049656e+00 1.23374917e-01 5.96776783e-01
-5.43645501e-01 -6.56257093e-01 3.29690874e-01 -1.48808673e-01
1.42514825e-01 -5.03134966e-01 -6.94939733e-01 -8.57268333e-01
-1.11065388e-01 -1.06712568e+00 -8.86482000e-02 -1.11439836e+00
-7.83077180e-01 8.75352204e-01 -5.98308980e-01 -9.85776365e-01
-1.15051888e-01 -9.66402173e-01 -5.80475926e-01 1.31128645e+00
-1.52288222e+00 -1.37518024e+00 2.47081950e-01 5.27898408e-02
1.40698075e+00 -6.57040119e-01 7.31732368e-01 2.50951409e-01
-5.97524822e-01 8.51914704e-01 8.43764961e-01 2.42265984e-01
1.08778918e+00 -1.13226902e+00 5.56407809e-01 1.01718998e+00
3.49411041e-01 6.39162421e-01 7.67646492e-01 -8.96946013e-01
-1.48720872e+00 -1.18530869e+00 1.44720995e+00 -4.56430525e-01
3.24961096e-01 -3.65585722e-02 -6.29773080e-01 7.69750774e-01
9.88376677e-01 -6.17167294e-01 5.21418095e-01 -5.78094780e-01
-8.39226991e-02 7.07888678e-02 -1.07501566e+00 6.58472896e-01
3.17646593e-01 -8.33095074e-01 -5.10926008e-01 4.57238644e-01
8.97270441e-01 -2.50220776e-01 -6.66847110e-01 1.27390370e-01
5.36532283e-01 -6.00678265e-01 4.71185148e-01 -8.73817682e-01
9.84346747e-01 -4.84646074e-02 -2.71868378e-01 -1.24392152e+00
1.92751251e-02 -6.24534190e-01 2.32739836e-01 9.79799330e-01
1.04278517e+00 -3.09517622e-01 6.37076616e-01 2.38627166e-01
-4.51705784e-01 -6.54585123e-01 -7.07341313e-01 -9.04590964e-01
5.84953368e-01 -1.65312707e-01 -1.38874739e-01 5.23893595e-01
-4.61102635e-01 5.57338119e-01 -8.43946338e-01 -1.40760228e-01
-3.09249125e-02 -2.69333333e-01 3.36209983e-01 -3.12397093e-01
-2.30399311e-01 -3.03257644e-01 -2.64478356e-01 -9.94211555e-01
-8.73111784e-02 -9.41294849e-01 3.19786854e-02 -1.80724049e+00
5.21984279e-01 3.14663619e-01 -2.42345169e-01 1.23869292e-01
-8.21982548e-02 5.80865920e-01 1.85000762e-01 2.12014660e-01
-2.87223369e-01 4.17079180e-01 1.28101695e+00 -2.78338581e-01
-1.38945907e-01 -2.32217759e-01 -1.36664987e-01 -6.92337081e-02
1.17560410e+00 -5.76466441e-01 -2.35573146e-02 -1.09430552e+00
1.93598866e-01 -7.88161252e-03 -4.07818481e-02 -9.18442369e-01
-2.41305269e-02 2.79568527e-02 6.08148575e-01 -7.88848996e-01
2.89151102e-01 -3.51995587e-01 -3.20642710e-01 6.57067418e-01
-9.24365222e-01 7.36581147e-01 3.33434224e-01 3.32728922e-01
-3.35113376e-01 -6.16335809e-01 7.82479405e-01 -4.77446437e-01
-4.59602654e-01 1.11013107e-01 -8.12346876e-01 -1.98540047e-01
6.69266164e-01 -4.40538734e-01 -2.67699569e-01 -3.84677947e-01
-3.99175197e-01 -2.46924430e-01 4.66252416e-01 6.64493322e-01
7.83735633e-01 -1.17358279e+00 -1.21645093e+00 -2.03258336e-01
-3.11673060e-02 -6.73032224e-01 -1.73732787e-01 7.25397587e-01
-1.43132436e+00 1.28042579e+00 -4.47859704e-01 -3.38046193e-01
-1.52498209e+00 5.06474614e-01 3.58046472e-01 -2.30076060e-01
-2.36731857e-01 9.56204951e-01 -4.88227367e-01 -3.59320104e-01
2.09703624e-01 -1.39980808e-01 -2.51087546e-01 -2.84574896e-01
6.76143289e-01 4.38015491e-01 1.95572659e-01 -5.00266910e-01
-1.39645590e-02 5.25355220e-01 -4.21431422e-01 -6.76485419e-01
1.10426724e+00 -2.15108156e-01 -2.51503140e-01 8.01688850e-01
1.71493244e+00 -2.61118323e-01 -6.36610568e-01 -8.43051970e-02
2.52398163e-01 -3.40819597e-01 -5.27919829e-02 -1.31746852e+00
-7.11976469e-01 1.61045980e+00 1.04719460e+00 -6.61438406e-01
7.29723632e-01 -7.36737072e-01 1.26569891e+00 8.52740049e-01
-8.38871300e-02 -1.43136847e+00 2.93579638e-01 9.89912093e-01
9.25951719e-01 -1.37526965e+00 -5.56235969e-01 5.03308117e-01
-8.03844690e-01 1.73628283e+00 3.92121226e-01 -3.55237335e-01
-1.83723807e-01 1.40962541e-01 5.07728159e-01 3.46012056e-01
-8.06235433e-01 2.11518213e-01 2.59993643e-01 5.90917587e-01
1.12288451e+00 4.24416363e-02 -5.39246023e-01 -3.13696831e-01
9.67776030e-02 1.79107353e-01 7.29568064e-01 7.91967988e-01
-6.64684713e-01 -1.26148915e+00 -3.76908332e-01 3.57966393e-01
-1.10447001e+00 -8.12244117e-01 -9.28073764e-01 1.50863141e-01
-5.30424297e-01 1.03374481e+00 4.08406183e-02 -3.61011952e-01
-2.53902406e-01 2.91357458e-01 5.14946222e-01 -6.43270671e-01
-8.23367357e-01 1.98710337e-02 2.12613806e-01 -9.08398852e-02
-1.28211871e-01 -4.47908431e-01 -9.15399373e-01 -2.92421967e-01
-2.17366070e-01 -1.19600177e-01 1.10352743e+00 9.85515058e-01
5.00319660e-01 3.39831024e-01 3.54705364e-01 -3.94229680e-01
-4.23365295e-01 -1.62617886e+00 -1.48162663e-01 -7.39132911e-02
4.96300966e-01 4.13548172e-01 1.86772704e-01 6.06693886e-02] | [14.20512580871582, 7.257715225219727] |
0e4a67ed-a57c-419c-a373-5e02cba1ae0e | semeval-2017-task-3-community-question-1 | 1912.00730 | null | https://arxiv.org/abs/1912.00730v1 | https://arxiv.org/pdf/1912.00730v1.pdf | SemEval-2017 Task 3: Community Question Answering | We describe SemEval-2017 Task 3 on Community Question Answering. This year, we reran the four subtasks from SemEval-2016:(A) Question-Comment Similarity,(B) Question-Question Similarity,(C) Question-External Comment Similarity, and (D) Rerank the correct answers for a new question in Arabic, providing all the data from 2015 and 2016 for training, and fresh data for testing. Additionally, we added a new subtask E in order to enable experimentation with Multi-domain Question Duplicate Detection in a larger-scale scenario, using StackExchange subforums. A total of 23 teams participated in the task, and submitted a total of 85 runs (36 primary and 49 contrastive) for subtasks A-D. Unfortunately, no teams participated in subtask E. A variety of approaches and features were used by the participating systems to address the different subtasks. The best systems achieved an official score (MAP) of 88.43, 47.22, 15.46, and 61.16 in subtasks A, B, C, and D, respectively. These scores are better than the baselines, especially for subtasks A-C. | ['Hamdy Mubarak', 'Lluís Màrquez', 'Doris Hoogeveen', 'Preslav Nakov', 'Timothy Baldwin', 'Karin Verspoor', 'Alessandro Moschitti'] | 2019-12-02 | semeval-2017-task-3-community-question | https://aclanthology.org/S17-2003 | https://aclanthology.org/S17-2003.pdf | semeval-2017-8 | ['question-similarity'] | ['natural-language-processing'] | [-4.00097109e-02 -1.32267401e-01 5.94286025e-01 -3.15925360e-01
-1.45785558e+00 -9.13121700e-01 6.34477615e-01 5.77968121e-01
-5.86500883e-01 8.25616777e-01 3.03084642e-01 -3.35985452e-01
9.61261168e-02 -2.73171186e-01 -5.17301202e-01 -2.26333514e-01
1.69183224e-01 4.76492673e-01 8.23596418e-01 -5.30152261e-01
5.09975910e-01 -3.46877545e-01 -1.34938037e+00 1.03795075e+00
1.47634029e+00 7.86794662e-01 1.78884715e-01 9.96597826e-01
-3.29686642e-01 8.46786559e-01 -1.04278970e+00 -1.02152860e+00
-1.28297120e-01 -6.09869123e-01 -1.34687841e+00 -1.94947973e-01
8.80774438e-01 -1.49431810e-01 6.80252397e-03 7.26556659e-01
6.28456414e-01 6.77659735e-02 5.80773950e-01 -1.08155406e+00
-1.04728305e+00 4.30338711e-01 -3.37596536e-01 2.85160333e-01
9.76484895e-01 7.06590489e-02 1.32814074e+00 -1.38054311e+00
5.73654950e-01 1.08807492e+00 7.22262323e-01 3.91259789e-01
-7.30758369e-01 -6.27885640e-01 5.05096391e-02 5.77643096e-01
-1.14120877e+00 -3.82379740e-01 -2.86241304e-02 -3.94632965e-01
1.24266827e+00 4.95507687e-01 7.27568790e-02 9.28570449e-01
-1.68629602e-01 7.33185768e-01 1.28143764e+00 -4.79872584e-01
1.57456592e-01 1.80889070e-01 6.12133265e-01 2.80670673e-01
1.37927175e-01 -6.59535050e-01 -4.19346273e-01 -5.26729524e-01
-2.30913147e-01 -4.74494845e-01 -4.79040176e-01 3.81577730e-01
-1.20577848e+00 7.93513060e-01 4.55646366e-01 4.03425813e-01
-3.12870562e-01 -3.99633080e-01 4.36237484e-01 8.06229770e-01
2.31397197e-01 7.51180232e-01 -7.55165219e-01 -1.68941803e-02
-5.16588211e-01 8.80211651e-01 1.31221676e+00 9.80836630e-01
6.17432714e-01 -6.21972620e-01 -6.70907259e-01 1.03216600e+00
1.43096581e-01 4.98195857e-01 5.15896380e-01 -1.12719977e+00
1.19849229e+00 6.42495513e-01 4.52913910e-01 -8.26901376e-01
-4.54707503e-01 -2.07989842e-01 -5.29777050e-01 -5.36400557e-01
8.33218992e-01 -2.34404013e-01 -5.88138044e-01 1.37019587e+00
1.84700817e-01 -2.47652262e-01 1.78674161e-01 8.08768034e-01
1.41004813e+00 6.23065829e-01 -2.46774387e-02 1.05066836e-01
1.79828584e+00 -1.49250484e+00 -7.86319554e-01 -3.02904844e-01
7.80961037e-01 -1.38146329e+00 1.40915525e+00 1.79932445e-01
-1.09988403e+00 -4.16110009e-01 -7.16271460e-01 -1.91401586e-01
-3.15456539e-01 1.67681813e-01 -8.69610906e-02 5.25443137e-01
-1.19988060e+00 2.20708296e-01 -9.81895104e-02 -7.03743756e-01
-2.01745182e-01 -9.39802378e-02 -2.54618704e-01 -2.66080827e-01
-1.59428644e+00 1.04078293e+00 1.90318584e-01 -1.35701075e-01
-4.48350877e-01 -5.06238163e-01 -5.16665518e-01 -4.74429317e-02
3.21013182e-01 -6.20706439e-01 1.68215585e+00 -6.42101049e-01
-1.25802541e+00 1.07954991e+00 -1.38001919e-01 -2.91533411e-01
3.33150744e-01 -3.71224701e-01 -5.15507698e-01 2.70863622e-01
4.64964628e-01 3.59070569e-01 5.68860173e-01 -1.02089179e+00
-7.12113082e-01 -4.03955609e-01 3.85937572e-01 3.03257793e-01
-2.11910143e-01 6.79319561e-01 -4.68968421e-01 -6.83574617e-01
1.06058456e-01 -8.55490923e-01 2.20341623e-01 -5.00614405e-01
-2.89821595e-01 -6.45842910e-01 4.22094613e-01 -1.48035622e+00
1.40083373e+00 -1.87199950e+00 -1.00575708e-01 -2.63285011e-01
2.20945820e-01 4.75732356e-01 -5.77079475e-01 8.00509691e-01
-1.16418295e-01 1.80191636e-01 -4.49493468e-01 -3.64091754e-01
-1.91779132e-03 -9.09853950e-02 -1.65113524e-01 -7.78295994e-02
3.85828316e-01 9.06111538e-01 -8.76195192e-01 -2.58827060e-01
-7.02217937e-01 -2.38666669e-01 -5.13574839e-01 4.09718603e-01
-4.43415523e-01 2.96966374e-01 -3.04614305e-01 6.32848620e-01
6.86683297e-01 -5.05158603e-01 -6.08122833e-02 2.46499494e-01
6.52974844e-02 7.97732890e-01 -1.09803391e+00 1.41511285e+00
-2.27411568e-01 5.28292298e-01 2.51588494e-01 -3.87239546e-01
8.44794154e-01 5.59434414e-01 -1.41412139e-01 -8.56213987e-01
-2.92616129e-01 6.62628353e-01 9.66183618e-02 -9.90911901e-01
7.64839888e-01 3.67617130e-01 -1.03863664e-01 8.54203641e-01
4.54051979e-02 -1.31387353e-01 6.29667222e-01 5.81292748e-01
1.45622182e+00 -3.18272948e-01 -2.16920907e-03 -2.10081175e-01
9.36137199e-01 -1.01301856e-02 2.35218965e-02 8.93400848e-01
-3.49999577e-01 9.99812424e-01 6.95503116e-01 -1.80545732e-01
-7.99328864e-01 -8.15309286e-01 1.74831972e-01 1.44178140e+00
-8.79841074e-02 -7.44030178e-01 -7.97367930e-01 -9.16921198e-01
-2.37349346e-02 5.54486036e-01 -4.93246973e-01 1.89954087e-01
-8.20276499e-01 -6.27048194e-01 8.38746488e-01 3.72513950e-01
6.93811417e-01 -9.70206261e-01 -1.34344339e-01 2.17367351e-01
-1.02305949e+00 -1.13673377e+00 -8.69783044e-01 -1.04134262e-01
-5.91136098e-01 -1.40271997e+00 -1.00992835e+00 -1.06831419e+00
1.59058303e-01 4.28014129e-01 1.55744839e+00 4.40179020e-01
2.34072939e-01 3.33202243e-01 -8.52906764e-01 -1.08157575e-01
-3.44890863e-01 4.41566736e-01 -4.54074651e-01 -3.43633890e-01
2.71283805e-01 -6.65945932e-02 -4.15284783e-01 6.62459493e-01
-6.51951790e-01 -5.87376297e-01 3.43612492e-01 9.38714206e-01
1.75451357e-02 -7.55909204e-01 1.03261566e+00 -6.69832289e-01
1.11426854e+00 -7.32683480e-01 -2.00924173e-01 7.24510252e-01
-2.62918025e-01 -2.09848672e-01 5.21504521e-01 -1.23003110e-01
-1.03144991e+00 -6.27841771e-01 -3.44439626e-01 3.07319313e-01
6.54227138e-02 6.75258577e-01 5.33653125e-02 1.71051115e-01
1.09474421e+00 -8.77927840e-02 -1.76616520e-01 -7.09977031e-01
2.32551202e-01 1.06265640e+00 4.34846789e-01 -5.82155168e-01
5.86627185e-01 -2.48723105e-01 -8.84577155e-01 -4.97074425e-01
-1.07392979e+00 -7.42488205e-01 -3.18121642e-01 8.72309282e-02
8.77921939e-01 -1.07158089e+00 -5.65715253e-01 8.24023187e-01
-1.66272998e+00 -1.45344242e-01 2.98349075e-02 1.59032419e-01
1.36488989e-01 6.34327173e-01 -7.64005303e-01 -3.87501627e-01
-5.57117999e-01 -8.76812220e-01 7.94053316e-01 2.53574848e-01
-3.69186908e-01 -6.72233760e-01 2.09077731e-01 1.02993953e+00
5.64884663e-01 -1.75810963e-01 8.06158304e-01 -1.24840391e+00
-2.44763196e-01 -9.48800519e-02 -4.42999661e-01 3.91403884e-01
-1.54229581e-01 -5.18104360e-02 -9.66515124e-01 -3.36164862e-01
5.41340448e-02 -8.88895035e-01 8.05688500e-01 -3.98654461e-01
7.41485775e-01 -3.99392188e-01 1.71202272e-01 -2.37890378e-01
7.69628942e-01 -2.52603978e-01 5.21218300e-01 5.03052890e-01
3.04077268e-01 8.94130409e-01 4.71975088e-01 2.01808885e-01
1.18357933e+00 7.92497516e-01 2.53844947e-01 5.18867910e-01
-1.11252807e-01 1.11076489e-01 4.91536915e-01 1.34757853e+00
2.21870348e-01 -4.20794308e-01 -1.14358675e+00 7.93917835e-01
-1.76200104e+00 -5.99117100e-01 -9.10797119e-01 1.99725807e+00
1.04397273e+00 -1.96353808e-01 2.62789100e-01 -6.76011741e-02
8.79732966e-01 -2.40977891e-02 -2.57013381e-01 -4.04441625e-01
-3.00407261e-01 2.91448236e-01 -5.95948957e-02 3.61718237e-01
-9.68862951e-01 6.15914702e-01 6.09754372e+00 4.82330978e-01
-4.15141255e-01 5.49674511e-01 2.88483113e-01 3.11091483e-01
-3.68971169e-01 7.80104995e-02 -7.92126238e-01 5.15091002e-01
1.12095225e+00 -1.87174492e-02 1.53284162e-01 4.06553358e-01
-4.84802306e-01 -3.37711841e-01 -8.57882977e-01 5.44633865e-01
5.10115027e-01 -9.69994605e-01 -5.86363412e-02 -6.08935237e-01
9.80086267e-01 2.38402203e-01 -2.73951530e-01 7.59336650e-01
3.20184082e-01 -9.49334443e-01 5.66159427e-01 2.28260487e-01
2.39919558e-01 -3.44545454e-01 1.13262022e+00 5.00991166e-01
-9.29443061e-01 -1.61688998e-01 -3.11904937e-01 -2.09334075e-01
4.77854423e-02 3.81868839e-01 -5.46801150e-01 8.45838010e-01
1.06299973e+00 2.44676769e-01 -1.17410111e+00 1.23968852e+00
-7.10032165e-01 6.90348089e-01 -2.34997720e-01 -3.07495534e-01
3.03974777e-01 1.65509224e-01 5.33129156e-01 1.20916247e+00
3.84511277e-02 -1.22350723e-01 -6.40561581e-02 5.12539029e-01
-5.35546064e-01 2.19955981e-01 2.30819210e-01 1.96374163e-01
7.42036700e-01 1.25652564e+00 -2.75367141e-01 -3.43635470e-01
-5.13715625e-01 1.06403220e+00 5.68950355e-01 4.39557135e-01
-7.05982447e-01 -9.03646648e-01 2.85963982e-01 -8.29193927e-03
3.55822265e-01 -1.16926387e-01 -1.11267090e-01 -1.24376178e+00
6.07596874e-01 -1.47214806e+00 8.63609612e-01 -7.94391513e-01
-1.74484491e+00 7.66006649e-01 -2.75485188e-01 -8.06610584e-01
-2.50467509e-01 -4.07952636e-01 -7.59168386e-01 1.27428126e+00
-1.53902543e+00 -7.75918126e-01 -6.36194706e-01 6.53991282e-01
4.28224444e-01 -1.47096574e-01 9.50535357e-01 6.08482718e-01
-3.59704643e-01 8.81739259e-01 2.91093796e-01 2.19568834e-01
1.50710833e+00 -1.40847588e+00 7.24227786e-01 7.08470643e-01
-2.49637198e-02 6.54971600e-01 4.88703609e-01 -5.28808355e-01
-1.02939487e+00 -9.50603366e-01 1.79472387e+00 -8.91547680e-01
9.46834147e-01 -2.46381670e-01 -1.39565134e+00 4.41660732e-01
5.65612912e-01 -3.04666460e-01 6.36462152e-01 1.80424079e-01
-5.55326521e-01 1.71742409e-01 -9.95455384e-01 3.48109663e-01
5.25996804e-01 -7.95588374e-01 -1.01064014e+00 6.65037036e-01
9.16694999e-01 -6.93870068e-01 -9.37039256e-01 1.97651640e-01
2.49310866e-01 -8.76081944e-01 7.40003049e-01 -5.64863741e-01
5.82736135e-01 -3.09474081e-01 -1.35593861e-01 -1.05274248e+00
-1.57156244e-01 -5.40794671e-01 -1.20882906e-01 1.35339892e+00
7.50650764e-01 -7.16866016e-01 3.14507365e-01 3.57415825e-01
-3.08669358e-01 -6.36203587e-01 -9.89227355e-01 -7.20266044e-01
4.83076245e-01 4.08226550e-02 6.11512959e-01 9.72739339e-01
-1.17720403e-01 7.05224097e-01 9.72894430e-02 4.90322337e-02
3.75608280e-02 8.88531879e-02 6.83060110e-01 -9.94063556e-01
-2.16945410e-01 -3.61657113e-01 2.57334322e-01 -1.17316258e+00
-1.18805766e-01 -1.01833022e+00 -3.76670994e-02 -1.82615316e+00
2.69469351e-01 -9.34994221e-02 1.21223249e-01 3.68736476e-01
-8.30442786e-01 3.12842607e-01 3.43669444e-01 5.31116605e-01
-1.01319313e+00 4.96958464e-01 1.09515226e+00 3.08032930e-02
1.05416715e-01 8.13672468e-02 -9.40963328e-01 4.34158564e-01
8.32938612e-01 -5.83808482e-01 1.28494471e-01 -8.04025471e-01
4.36721772e-01 -1.75982192e-01 4.54696953e-01 -6.49208188e-01
4.72415596e-01 4.14400399e-01 2.99464203e-02 -5.53288758e-01
-6.03512488e-02 -2.92888343e-01 -4.91042703e-01 3.50289226e-01
-2.92519122e-01 5.11669397e-01 -6.90330658e-03 2.61191636e-01
-4.68325824e-01 -7.69651055e-01 5.16738713e-01 -8.60240161e-02
-4.36000019e-01 -2.66489923e-01 -4.36910421e-01 9.87524271e-01
7.30993629e-01 8.08619335e-02 -8.71209919e-01 -4.89603341e-01
-6.35823011e-01 7.49891877e-01 1.70972079e-01 6.45875216e-01
3.56829137e-01 -1.24207878e+00 -1.35806882e+00 -2.99436837e-01
5.43151259e-01 -1.21956870e-01 3.01735014e-01 1.01241195e+00
-4.79765028e-01 4.77918148e-01 8.57374370e-02 -4.54624444e-01
-1.27599919e+00 -1.31584287e-01 2.14430824e-01 -8.32484543e-01
-8.12267885e-02 1.09941077e+00 -4.05187368e-01 -1.04341817e+00
2.52595246e-01 -1.41620934e-01 -4.62822378e-01 4.43264514e-01
7.60548711e-01 6.44236982e-01 5.84253609e-01 -4.44322258e-01
-4.78002757e-01 3.80227625e-01 -3.80385846e-01 -1.99389588e-02
9.36538160e-01 -2.49307454e-01 -5.48414350e-01 1.60002381e-01
1.00665343e+00 -4.80887145e-02 -6.37242198e-01 -5.65166116e-01
5.42243898e-01 -1.90605015e-01 -7.24979222e-01 -1.34876859e+00
-4.68786836e-01 7.82202244e-01 1.83151022e-01 5.45959115e-01
8.78830492e-01 1.31346315e-01 9.69067216e-01 6.70554817e-01
1.51730880e-01 -1.02467871e+00 5.24373233e-01 1.43733251e+00
1.34953725e+00 -1.23376858e+00 -2.62615293e-01 -1.67336643e-01
-7.38907158e-01 9.81866598e-01 7.66600013e-01 1.81515560e-01
2.14148745e-01 -2.35202223e-01 1.17475018e-01 -1.57800615e-01
-9.03664947e-01 -6.25144169e-02 5.31589866e-01 3.81676883e-01
7.57779241e-01 -1.61049426e-01 -5.13112962e-01 1.04234076e+00
-1.94163352e-01 -3.14932823e-01 5.01391649e-01 1.03616774e+00
-5.06848633e-01 -1.04046845e+00 -4.97285157e-01 3.87660861e-01
-6.05950713e-01 -3.30954820e-01 -7.28748679e-01 7.31717169e-01
-1.06977642e-01 1.49481320e+00 -1.14084005e-01 -2.85739630e-01
7.15647399e-01 2.25129128e-01 3.03173006e-01 -7.65871108e-01
-1.46050143e+00 -5.67258477e-01 5.55310309e-01 -3.18823487e-01
-2.39105225e-01 -7.61314154e-01 -1.01398778e+00 -4.41405386e-01
-6.16502762e-01 4.37807918e-01 4.51957792e-01 1.00873208e+00
4.71167713e-01 2.21221521e-01 4.25902814e-01 -5.56600988e-02
-9.26534116e-01 -1.51964664e+00 -1.56331986e-01 4.58066493e-01
8.82466361e-02 -1.76764011e-01 -5.03392100e-01 -5.22266403e-02] | [11.372658729553223, 8.028656959533691] |
dc6a827d-d61d-4217-b515-4d625def0cbc | clustering-tweets-usingwikipedia-concepts | null | null | https://aclanthology.org/L14-1640 | https://aclanthology.org/L14-1640.pdf | Clustering tweets usingWikipedia concepts | Two challenging issues are notable in tweet clustering. Firstly, the sparse data problem is serious since no tweet can be longer than 140 characters. Secondly, synonymy and polysemy are rather common because users intend to present a unique meaning with a great number of manners in tweets. Enlightened by the recent research which indicates Wikipedia is promising in representing text, we exploit Wikipedia concepts in representing tweets with concept vectors. We address the polysemy issue with a Bayesian model, and the synonymy issue by exploiting the Wikipedia redirections. To further alleviate the sparse data problem, we further make use of three types of out-links in Wikipedia. Evaluation on a twitter dataset shows that the concept model outperforms the traditional VSM model in tweet clustering. | ['Yunqing Xia', 'Weizhi Wang', 'Raymond Lau', 'Guoyu Tang', 'Fang Zheng'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['text-clustering'] | ['natural-language-processing'] | [-3.78752261e-01 -2.76999712e-01 -4.11897838e-01 -2.60111570e-01
8.28445926e-02 -4.64821309e-01 9.17926073e-01 6.58559561e-01
-5.95841110e-01 7.61234343e-01 6.51219249e-01 -9.13441554e-02
-3.17435443e-01 -9.04019713e-01 -1.35775641e-01 -2.27677673e-01
5.49562946e-02 4.35589254e-01 2.11644366e-01 -6.52515888e-01
5.55118084e-01 -1.88478231e-01 -1.69412017e+00 1.82327241e-01
1.01645756e+00 4.44148213e-01 2.33754054e-01 5.62946610e-02
-1.00263464e+00 5.32632351e-01 -7.45947242e-01 -6.62284791e-01
-6.78683147e-02 6.51785880e-02 -8.35784018e-01 1.68923214e-02
3.10595036e-01 1.62622795e-01 -2.17840105e-01 1.14134192e+00
8.68370309e-02 2.45179489e-01 8.96881521e-01 -1.61074305e+00
-6.18513405e-01 1.19314134e+00 -9.38065231e-01 3.92964035e-02
6.09536290e-01 -7.40210176e-01 1.25495875e+00 -8.41414154e-01
6.24914229e-01 1.19612408e+00 7.51301348e-01 1.99598402e-01
-1.00255299e+00 -7.89817870e-01 3.52236658e-01 -9.95513722e-02
-1.98471200e+00 1.49572730e-01 5.32858074e-01 -6.43416524e-01
7.98842251e-01 6.09762013e-01 6.68174803e-01 1.11400855e+00
-1.76785022e-01 4.54389185e-01 7.14285195e-01 -2.66525060e-01
-1.51257843e-01 5.13804018e-01 5.85939169e-01 2.03036174e-01
6.69874251e-01 -7.53721118e-01 -5.25840759e-01 -4.31711853e-01
1.29346162e-01 4.70989734e-01 -1.52000010e-01 -7.00571463e-02
-1.17181611e+00 1.19548428e+00 1.39172316e-01 5.10818362e-01
-1.35217503e-01 -6.71353415e-02 5.22547066e-01 1.76227957e-01
5.58237612e-01 7.66038358e-01 -2.07255080e-01 -2.36046925e-01
-1.14265347e+00 1.88937441e-01 1.02236092e+00 1.26076984e+00
1.08879912e+00 -2.34359011e-01 2.22846061e-01 1.06086433e+00
4.04810339e-01 3.00003678e-01 1.06731844e+00 -6.50817394e-01
4.66798425e-01 7.01813102e-01 -3.99679393e-02 -1.72377276e+00
-4.65128362e-01 -3.33212167e-01 -7.76649356e-01 -7.08139360e-01
2.92678084e-03 -2.16843307e-01 -4.29494590e-01 1.49915421e+00
1.34035736e-01 4.78717864e-01 -1.44567624e-01 3.44620615e-01
1.16193187e+00 6.50902629e-01 3.06731105e-01 -3.06875437e-01
1.41712809e+00 -5.18104136e-01 -8.79254699e-01 -9.20280516e-02
7.42531776e-01 -8.85769784e-01 9.47160482e-01 2.71493971e-01
-4.05850351e-01 -3.39061290e-01 -9.68763411e-01 2.74080902e-01
-8.81714463e-01 -2.76648939e-01 8.73664916e-01 7.96920776e-01
-6.70593560e-01 3.78510416e-01 -2.97972172e-01 -7.76281476e-01
9.13635641e-02 -1.39696840e-02 -3.89834225e-01 1.92936510e-01
-1.63442934e+00 7.50171900e-01 8.72846007e-01 -5.08173823e-01
5.65701760e-02 -6.86590016e-01 -6.89125121e-01 -1.35688007e-01
2.91344672e-01 -6.78930342e-01 5.49671531e-01 -8.28057170e-01
-8.68255794e-01 6.18364930e-01 -1.27615005e-01 -2.31306180e-01
1.24931186e-01 2.74679121e-02 -9.24532294e-01 -4.63946201e-02
4.70203221e-01 4.52266902e-01 6.38119519e-01 -1.30849588e+00
-7.74979591e-01 5.55439964e-02 3.17570716e-02 3.30456376e-01
-1.14457667e+00 -2.50638798e-02 -6.31979764e-01 -8.34346235e-01
2.93704301e-01 -8.91826153e-01 -6.14228100e-02 -6.55523241e-01
-7.23116517e-01 -4.58741695e-01 8.66072178e-01 -8.87077302e-02
2.07811332e+00 -2.04346514e+00 -1.74144253e-01 6.57396853e-01
7.04282224e-01 -3.62486318e-02 1.56904086e-01 9.87088621e-01
4.83049601e-02 6.64793730e-01 4.63211462e-02 -1.02070920e-01
9.82001275e-02 3.94025683e-01 -3.72940689e-01 1.29417047e-01
-4.37600702e-01 4.61543471e-01 -1.22311437e+00 -7.27067411e-01
-1.95471928e-01 3.35635275e-01 -4.81813163e-01 -2.52191961e-01
4.31305356e-02 -7.80017003e-02 -7.43283749e-01 2.72712380e-01
6.58234537e-01 -4.75500792e-01 4.40285563e-01 -1.68551251e-01
-2.59733468e-01 3.06136370e-01 -1.34321499e+00 1.53964758e+00
-2.12358102e-01 6.80081189e-01 -3.48447055e-01 -8.23306322e-01
7.33036280e-01 2.06727728e-01 7.92269647e-01 -9.06114653e-02
1.31585315e-01 -1.21162705e-01 -2.59174675e-01 -5.30278265e-01
1.27204442e+00 -1.07067615e-01 -2.72820652e-01 6.05269194e-01
-8.44651461e-02 -1.38269484e-01 6.50116503e-01 1.12318647e+00
6.85324013e-01 -4.70374286e-01 4.10337448e-01 -5.27686000e-01
2.10433349e-01 2.70158257e-02 3.52556229e-01 7.23434150e-01
1.74794212e-01 5.62081695e-01 3.79441708e-01 8.03540498e-02
-6.69815779e-01 -9.19632912e-01 -2.88830221e-01 1.12654150e+00
2.67847836e-01 -1.49675822e+00 -3.63915652e-01 -5.02623081e-01
3.63473892e-01 7.15359032e-01 -6.08401358e-01 1.26216531e-01
8.20635725e-03 -9.30231750e-01 4.26582485e-01 2.35995427e-01
1.58351198e-01 -3.21014255e-01 1.26993775e-01 2.93058276e-01
-5.86222768e-01 -1.01610124e+00 -3.13912511e-01 -2.85375148e-01
-5.15024245e-01 -8.76364231e-01 -4.23436403e-01 -5.46989143e-01
6.08949661e-01 8.43564272e-01 1.21540260e+00 3.82046521e-01
-1.19674928e-01 7.27844536e-01 -8.26921523e-01 -4.71973211e-01
-3.36412825e-02 1.10823080e-01 3.85015696e-01 -2.58080177e-02
9.77644205e-01 -5.75248241e-01 -3.63496512e-01 1.79207206e-01
-1.04769707e+00 -2.37808466e-01 -1.44928694e-01 6.24079227e-01
-6.92453310e-02 6.36084259e-01 5.72732925e-01 -1.35589290e+00
1.02386189e+00 -1.19829929e+00 1.08787172e-01 3.38696688e-02
-8.58638108e-01 -3.47091079e-01 1.83040380e-01 -4.01087224e-01
-8.64577830e-01 -3.37794334e-01 9.04829279e-02 2.21775562e-01
1.01061366e-01 1.12617135e+00 3.28931570e-01 2.84008473e-01
7.11251616e-01 1.02866236e-02 -3.96315679e-02 -3.89134198e-01
6.38321042e-01 9.91125822e-01 7.23241549e-03 -5.53589463e-01
8.47834706e-01 6.21566594e-01 -2.94069171e-01 -1.41553938e+00
-6.62825763e-01 -1.23337746e+00 -5.79025745e-01 -1.89971760e-01
5.93700945e-01 -1.25063050e+00 -5.74209809e-01 5.27466163e-02
-9.29755747e-01 6.02126002e-01 7.07661211e-02 6.46417260e-01
7.77632147e-02 9.77849126e-01 -4.08666074e-01 -4.30962831e-01
1.03113614e-01 -7.24805593e-01 3.59015316e-01 1.09773092e-02
-8.43630373e-01 -1.26038325e+00 1.27116190e-02 1.30635664e-01
4.06498671e-01 1.43937543e-01 8.44082654e-01 -1.09155893e+00
1.44821275e-02 -3.54123801e-01 -2.31472835e-01 -5.42263240e-02
2.78117537e-01 3.30293775e-01 -5.10478437e-01 -2.25562036e-01
-2.26895913e-01 -5.38919941e-02 8.71679008e-01 8.99341479e-02
1.01834130e+00 -4.24599409e-01 -7.44745851e-01 6.92343414e-02
1.36431241e+00 -2.61500567e-01 4.14043874e-01 4.35804367e-01
9.30182219e-01 8.49494517e-01 4.03619796e-01 1.08740699e+00
9.13601816e-01 4.61310953e-01 1.65822938e-01 3.10184121e-01
9.51577723e-02 -4.90013331e-01 -7.54807964e-02 1.45040774e+00
1.46516904e-01 -4.25578326e-01 -9.67035592e-01 4.74499553e-01
-1.82678926e+00 -1.18907773e+00 -7.42387354e-01 2.03372478e+00
1.00769722e+00 -4.48806919e-02 2.62545675e-01 7.82913640e-02
8.15335035e-01 1.68038234e-01 3.11994910e-01 -1.62691280e-01
-2.51087070e-01 -4.31923062e-01 5.65624774e-01 4.87846673e-01
-9.07956243e-01 8.78351688e-01 6.59278488e+00 1.14644742e+00
-8.13001037e-01 1.18946061e-01 2.59711239e-02 7.99461380e-02
-7.91599751e-01 9.24977735e-02 -1.05068958e+00 9.99600470e-01
8.21411312e-01 -5.62619567e-01 -8.32522660e-02 5.97195029e-01
-3.00551765e-02 -1.68707103e-01 -7.50204086e-01 1.17534530e+00
3.94119084e-01 -1.29806376e+00 4.31429774e-01 -2.60787368e-01
9.20563698e-01 6.84425905e-02 7.28511736e-02 1.99152425e-01
4.52199280e-01 -7.04375029e-01 3.90621483e-01 3.55841011e-01
5.11229575e-01 -7.72977114e-01 4.63686526e-01 2.88688600e-01
-1.40434921e+00 9.64721516e-02 -6.65998816e-01 -8.45046043e-02
1.40300557e-01 8.22135389e-01 -8.23934972e-01 5.95704019e-01
5.53549290e-01 1.24470639e+00 -6.86221063e-01 1.16060638e+00
-1.28676444e-01 5.01814365e-01 -3.54832381e-01 -3.93015832e-01
5.61629057e-01 -3.23377550e-01 5.62501431e-01 1.83654237e+00
3.45573962e-01 -7.06400573e-02 2.56844699e-01 6.11109495e-01
-8.35042745e-02 5.14924169e-01 -6.57133460e-01 -2.18071803e-01
1.01673937e+00 1.34389257e+00 -8.97055864e-01 -4.40027714e-01
-7.50011086e-01 8.14577878e-01 7.02424049e-02 5.51527977e-01
-5.69793880e-01 -6.53746903e-01 5.58169246e-01 3.29657972e-01
-8.98905247e-02 -2.42442191e-01 -5.50366528e-02 -1.23847783e+00
-3.27460319e-01 -4.46515709e-01 3.93096626e-01 -5.47080576e-01
-1.67100418e+00 4.36117083e-01 2.74841070e-01 -1.42804825e+00
-5.94435297e-02 -3.82321000e-01 -6.44949257e-01 4.38508630e-01
-1.39756322e+00 -9.03405845e-01 -2.15367556e-01 7.29377210e-01
2.13138849e-01 -3.13169003e-01 6.05493963e-01 5.95629871e-01
-3.31277043e-01 4.78234559e-01 3.92434418e-01 1.22265860e-01
9.17018414e-01 -1.32418728e+00 1.48000360e-01 3.46598297e-01
1.86914563e-01 1.39836597e+00 8.88401508e-01 -9.14751947e-01
-1.07018960e+00 -1.08773184e+00 1.39698076e+00 -7.69568145e-01
1.19840121e+00 -1.62590533e-01 -7.98311949e-01 5.98277807e-01
3.38180304e-01 -7.36429274e-01 1.38452387e+00 5.48418105e-01
-6.80989623e-01 2.10453644e-01 -7.21276045e-01 6.59565747e-01
6.09431446e-01 -6.21716142e-01 -9.12987411e-01 5.87469161e-01
8.47101688e-01 3.12900454e-01 -8.36358011e-01 -2.05394477e-01
3.77881616e-01 -5.75124145e-01 9.98322189e-01 -4.06636953e-01
1.47343025e-01 -3.90697688e-01 -2.07893521e-01 -1.48828006e+00
-3.74025464e-01 -6.53009892e-01 1.69827089e-01 1.73031986e+00
6.42184436e-01 -7.00825572e-01 7.11900115e-01 7.98853099e-01
2.36321971e-01 -2.43552495e-02 -6.89659357e-01 -9.31451976e-01
4.03835401e-02 -4.60689276e-01 5.96400738e-01 1.75091839e+00
8.99741471e-01 5.65385699e-01 -5.54396212e-01 -4.18599218e-01
4.19322789e-01 -2.21363842e-01 6.27625465e-01 -1.82174492e+00
2.76690245e-01 -6.13485813e-01 -4.55639601e-01 -1.00666261e+00
3.17118734e-01 -1.22436774e+00 -3.05822879e-01 -1.34325004e+00
6.13086283e-01 -5.37881076e-01 -1.57173842e-01 5.23692649e-03
-2.30770767e-01 1.89427629e-01 2.03173980e-01 4.36908543e-01
-9.19088066e-01 2.99519926e-01 6.27025008e-01 -1.89754099e-01
-4.26248759e-02 -1.76200002e-01 -1.10619318e+00 8.74196172e-01
6.31251931e-01 -6.37814641e-01 -6.54075861e-01 -3.93054247e-01
1.09228694e+00 -5.54697752e-01 -1.44379646e-01 -6.24553919e-01
6.32788539e-01 -3.91289406e-02 -6.35105073e-02 -6.57754779e-01
4.77838784e-01 -1.07084894e+00 9.64364186e-02 1.47871852e-01
-2.66814500e-01 2.74447203e-01 -2.10342795e-01 1.01574910e+00
-5.33982635e-01 -5.02366006e-01 3.03532004e-01 -2.24422887e-01
-8.92601848e-01 2.73177236e-01 -8.92382026e-01 3.67825419e-01
7.42684066e-01 -4.02800769e-01 -2.23859161e-01 -5.99736392e-01
-4.20900077e-01 4.24546063e-01 3.86096150e-01 6.59465253e-01
6.35572314e-01 -1.45130134e+00 -7.49031901e-01 -7.43570700e-02
5.96453547e-01 -5.77198923e-01 1.17304884e-01 6.46783888e-01
-2.52555311e-01 3.39386821e-01 8.57149139e-02 -3.68589699e-01
-1.14397550e+00 5.61475635e-01 -3.89490366e-01 3.45787078e-01
-4.76246059e-01 9.49687183e-01 3.86267751e-02 -3.62640917e-01
2.64789313e-01 1.25027061e-01 -9.26032960e-01 8.95029902e-01
6.25308871e-01 6.36864483e-01 -2.74988204e-01 -1.04416668e+00
-2.99205720e-01 5.81243455e-01 -2.36230522e-01 -2.62130741e-02
1.03384733e+00 -6.93725467e-01 -6.41169548e-01 5.74663877e-01
1.45459294e+00 3.15483540e-01 -3.37290913e-01 -3.67105514e-01
4.69592184e-01 -5.83868921e-01 -5.06260507e-02 -3.27249676e-01
-7.33186781e-01 5.08219123e-01 -6.51173443e-02 7.82856941e-01
4.52306956e-01 -1.74496099e-01 7.63020098e-01 5.05882382e-01
1.17546327e-01 -1.48705184e+00 -4.23422270e-03 9.18970287e-01
5.36513388e-01 -1.15943563e+00 3.70833188e-01 -5.95865309e-01
-1.05744469e+00 1.06170809e+00 5.43010414e-01 2.35459417e-01
1.35890281e+00 -1.28167598e-02 4.06311303e-02 -4.49745893e-01
-5.09653687e-01 -2.26416767e-01 3.40480357e-01 5.28442502e-01
7.69262254e-01 2.41063476e-01 -9.78515744e-01 6.89760327e-01
-3.96226436e-01 -7.00343072e-01 1.02849913e+00 7.16399670e-01
-6.68213367e-01 -1.10550272e+00 -1.60840467e-01 7.14834988e-01
-6.30371511e-01 -4.79305774e-01 -4.70477015e-01 7.42759228e-01
-2.12054960e-02 1.33617568e+00 2.29373813e-01 -6.58277810e-01
-2.45181575e-01 -7.19781592e-02 -3.11213881e-01 -9.31603968e-01
-6.24684155e-01 -1.02077629e-02 2.46053413e-01 -2.87608922e-01
-6.79587007e-01 -5.18324554e-01 -1.32879293e+00 -7.52458930e-01
-5.20428240e-01 7.20596910e-01 7.13946760e-01 7.17009842e-01
3.46319973e-01 2.21287921e-01 6.17374182e-01 -3.91717345e-01
-1.92216620e-01 -9.62421775e-01 -1.04883313e+00 7.66182721e-01
3.28069367e-02 -7.00579047e-01 -5.32376826e-01 -1.94516614e-01] | [10.468135833740234, 8.197687149047852] |
3d63de81-99e3-4d34-8e7d-fc75281a583a | modeling-input-uncertainty-in-neural-network | null | null | https://aclanthology.org/D18-1542 | https://aclanthology.org/D18-1542.pdf | Modeling Input Uncertainty in Neural Network Dependency Parsing | Recently introduced neural network parsers allow for new approaches to circumvent data sparsity issues by modeling character level information and by exploiting raw data in a semi-supervised setting. Data sparsity is especially prevailing when transferring to non-standard domains. In this setting, lexical normalization has often been used in the past to circumvent data sparsity. In this paper, we investigate whether these new neural approaches provide similar functionality as lexical normalization, or whether they are complementary. We provide experimental results which show that a separate normalization component improves performance of a neural network parser even if it has access to character level information as well as external word embeddings. Further improvements are obtained by a straightforward but novel approach in which the top-N best candidates provided by the normalization component are available to the parser. | ['Rob van der Goot', 'Gertjan van Noord'] | 2018-10-01 | null | null | null | emnlp-2018-10 | ['lexical-normalization'] | ['natural-language-processing'] | [ 4.08085316e-01 3.31933618e-01 -4.46794897e-01 -5.31145930e-01
-3.58706176e-01 -4.83581454e-01 4.10405606e-01 5.70480525e-01
-1.12810218e+00 7.73809075e-01 4.93768603e-01 -3.01229119e-01
7.67628551e-02 -1.00518584e+00 -5.58585286e-01 -5.85859060e-01
1.78892568e-01 3.46446037e-01 1.63146377e-01 -2.23838329e-01
7.41692036e-02 5.36330819e-01 -1.53732550e+00 5.56546897e-02
6.51471198e-01 4.62695062e-01 1.09720357e-01 4.64593977e-01
-7.76152551e-01 3.98617834e-01 -4.05273885e-01 -5.33537805e-01
4.87309813e-01 -1.72045052e-01 -7.33644366e-01 -1.29097536e-01
4.22503710e-01 -1.55496687e-01 -1.11407153e-01 1.25656009e+00
3.50689232e-01 1.06605746e-01 3.81661415e-01 -6.55173659e-01
-4.47045326e-01 1.07968581e+00 -2.92292863e-01 2.35551983e-01
7.85889030e-02 -2.75552034e-01 1.20237064e+00 -7.87281573e-01
7.51954019e-01 1.08459139e+00 7.50987887e-01 6.36641860e-01
-1.42261887e+00 -4.23865467e-01 3.18005651e-01 1.07449017e-01
-1.18442631e+00 -5.09343922e-01 7.59307861e-01 -1.59157112e-01
1.21152997e+00 -8.57494101e-02 2.18454078e-01 1.14845073e+00
-3.19656968e-01 6.77367091e-01 7.86428988e-01 -1.00556052e+00
2.05496907e-01 1.98651209e-01 7.05684304e-01 4.33782220e-01
5.02849460e-01 -2.10774124e-01 -5.25295556e-01 -5.80629185e-02
5.69697738e-01 -2.31008634e-01 -2.53910404e-02 -4.29586560e-01
-1.00345790e+00 1.20027959e+00 2.12426230e-01 7.54734635e-01
-4.59307730e-01 -9.14359614e-02 6.07403576e-01 4.19938296e-01
4.77110773e-01 6.46329165e-01 -7.31383443e-01 -2.06592605e-01
-9.45489407e-01 -5.21908700e-02 1.02819347e+00 8.51336241e-01
8.34087312e-01 2.45274037e-01 3.84253748e-02 9.20983076e-01
9.31944922e-02 1.82727039e-01 7.69319117e-01 -6.49788082e-01
6.82629168e-01 5.67507982e-01 -2.10840747e-01 -7.40680218e-01
-6.68003261e-01 -4.31555182e-01 -1.02514613e+00 -1.16563179e-01
6.50261998e-01 -2.56670088e-01 -1.00348759e+00 2.28676820e+00
1.20504156e-01 -9.17948857e-02 2.46470854e-01 5.28587282e-01
5.79200149e-01 4.29453909e-01 3.55158627e-01 -3.29453230e-01
1.15092611e+00 -7.60067463e-01 -8.58657122e-01 -3.00054640e-01
1.02815819e+00 -7.51909733e-01 1.13976860e+00 3.77664000e-01
-9.65217471e-01 -4.67890590e-01 -1.00174677e+00 -1.18181169e-01
-7.58699238e-01 1.73570171e-01 5.12863874e-01 1.03322971e+00
-9.61913466e-01 6.91841602e-01 -8.61998379e-01 -7.40829945e-01
2.75814652e-01 6.34095013e-01 -6.26441777e-01 -7.93454051e-02
-1.30415106e+00 9.02731061e-01 7.03155935e-01 -1.00165591e-01
-1.52135029e-01 -3.29422712e-01 -1.04253006e+00 2.55845726e-01
3.98216903e-01 -3.64963084e-01 1.13239503e+00 -1.03919470e+00
-1.50295281e+00 7.74938107e-01 -2.31319800e-01 -7.80865848e-01
3.10904384e-01 -3.15705717e-01 -2.07393825e-01 -1.79638460e-01
-4.87647858e-03 6.51905477e-01 6.38753414e-01 -7.46516049e-01
-5.42072475e-01 -2.86587745e-01 6.50773346e-02 -6.39456958e-02
-1.08938301e+00 1.25260249e-01 -3.97999614e-01 -7.31715024e-01
1.01863548e-01 -6.78240955e-01 -4.29380327e-01 -4.08274084e-01
-3.83803487e-01 -3.58090758e-01 3.69428694e-01 -5.29993296e-01
1.43206251e+00 -1.94015419e+00 2.31465638e-01 3.56095403e-01
7.53526464e-02 7.33849049e-01 -5.16461432e-01 4.27811921e-01
-2.91498929e-01 3.09069246e-01 -3.28408480e-01 -5.22855043e-01
4.41193096e-02 5.90583265e-01 -1.75779350e-02 2.61228055e-01
5.28646946e-01 5.98002017e-01 -6.05315149e-01 -3.45191568e-01
1.05893739e-01 3.95882100e-01 -6.76708758e-01 3.73300239e-02
-5.84076568e-02 9.21929106e-02 -1.53637692e-01 2.65427113e-01
6.72882497e-01 1.25978917e-01 5.27451813e-01 -5.54762185e-02
-1.62075743e-01 5.09688616e-01 -1.43615854e+00 1.68927586e+00
-5.42932689e-01 4.34270144e-01 1.02048524e-01 -1.44264174e+00
9.67523575e-01 3.06932092e-01 1.74841598e-01 -7.36035526e-01
2.82841593e-01 1.92276254e-01 9.61901173e-02 -3.45980376e-01
5.77601612e-01 -3.17180991e-01 -1.94488749e-01 3.56204599e-01
5.16640782e-01 5.17126381e-01 7.05734730e-01 -5.43175489e-02
1.19016361e+00 7.12773670e-03 8.02176774e-01 -3.09661895e-01
7.77611434e-01 -5.58878630e-02 7.59841442e-01 9.71154392e-01
2.05561042e-01 4.13343102e-01 5.14735997e-01 -5.47011018e-01
-1.31328666e+00 -7.54996240e-01 -1.78738981e-01 1.27910137e+00
-4.66813296e-01 -6.54539824e-01 -8.36007774e-01 -7.94321001e-01
-2.31683284e-01 5.56602657e-01 -6.72472775e-01 -1.59615397e-01
-1.02773535e+00 -9.16303158e-01 6.45776510e-01 8.41961563e-01
2.19444279e-02 -9.94285405e-01 -3.99254233e-01 5.68058908e-01
7.36158490e-02 -1.27018833e+00 8.49468261e-03 1.18522084e+00
-1.10132074e+00 -7.93458045e-01 -5.64910173e-01 -8.47214937e-01
6.68367624e-01 -7.90900812e-02 1.03554916e+00 4.48888242e-02
2.71644723e-02 -1.40326455e-01 -5.68782985e-01 -2.49526501e-01
-5.80832124e-01 9.11422312e-01 3.00051242e-01 -1.63525254e-01
7.95471311e-01 -6.78637326e-01 2.21221045e-01 -6.14794865e-02
-1.11408794e+00 -2.46051341e-01 7.42442906e-01 8.98348451e-01
3.44855249e-01 -1.87904522e-01 6.56306386e-01 -1.29020953e+00
7.54411221e-01 -3.53889406e-01 -6.77709758e-01 -3.16782631e-02
-6.02658093e-01 7.04210401e-01 1.15909731e+00 -4.85681027e-01
-9.47114229e-01 4.48147327e-01 -7.06781209e-01 -1.41737275e-02
-6.86856031e-01 6.83901310e-01 -5.01500845e-01 9.99523252e-02
6.44360304e-01 -1.12486385e-01 -2.07378060e-01 -9.45783734e-01
5.56756854e-01 6.77920520e-01 5.20304918e-01 -6.38034582e-01
1.00975549e+00 1.26555458e-01 -1.02913402e-01 -7.92667985e-01
-9.14854765e-01 -5.35247326e-01 -1.15004265e+00 5.15003502e-01
8.19786668e-01 -6.17458165e-01 -2.15558633e-01 1.81119800e-01
-1.43273556e+00 -9.60373655e-02 -5.64201415e-01 5.69392025e-01
-1.69422105e-01 4.98797566e-01 -5.27830243e-01 -4.88695353e-01
-1.41380861e-01 -9.20963705e-01 4.59475726e-01 2.78903574e-01
-1.42571822e-01 -1.04427373e+00 2.22458139e-01 -8.35732147e-02
5.00248253e-01 -2.51263529e-01 1.14081538e+00 -1.38531697e+00
-7.31220702e-03 -4.01238531e-01 -2.92904168e-01 6.96110666e-01
1.61759272e-01 -3.26680839e-01 -1.03997517e+00 -7.41924101e-04
-1.33426458e-01 -5.22134379e-02 9.77796555e-01 2.44048446e-01
8.30678582e-01 -1.28665090e-01 6.10660687e-02 5.93979478e-01
1.45713460e+00 -2.12480873e-01 3.41246367e-01 6.41242206e-01
7.31952727e-01 5.61796010e-01 2.22545817e-01 3.14602733e-01
1.69075444e-01 5.97761154e-01 3.89436394e-01 -1.48569003e-01
-1.08838983e-01 -9.44320783e-02 2.01914653e-01 1.18621600e+00
7.79523700e-02 -2.92741269e-01 -8.65022361e-01 6.80175900e-01
-1.70327234e+00 -5.83728790e-01 -2.64297873e-01 2.26152635e+00
1.05860114e+00 4.69943255e-01 -4.17530164e-03 3.43442202e-01
7.10394382e-01 -2.95758881e-02 -2.51993686e-01 -8.15367699e-01
-4.72240686e-01 6.99854314e-01 8.07920277e-01 5.05154908e-01
-1.29982567e+00 1.07060623e+00 6.33883286e+00 6.99060142e-01
-9.02598143e-01 2.76487350e-01 1.31380931e-01 1.32077271e-02
-1.28637135e-01 -5.13750613e-02 -1.20227146e+00 1.10820420e-01
1.29237521e+00 4.31659222e-02 1.42872915e-01 8.92909706e-01
1.15025761e-02 4.20652777e-02 -1.12145102e+00 6.29248977e-01
8.95149708e-02 -1.18776739e+00 6.98332787e-02 1.03501163e-01
3.44649076e-01 1.00174814e-01 -3.51944625e-01 3.28239799e-01
3.66550237e-01 -8.40896189e-01 3.59786272e-01 1.11657523e-01
4.15361851e-01 -8.73888791e-01 1.19224727e+00 5.25428712e-01
-1.13753235e+00 1.30464910e-02 -5.36171079e-01 -3.85774851e-01
1.84245303e-01 6.49252236e-01 -7.56283939e-01 4.74435449e-01
2.54089922e-01 5.59359312e-01 -6.62617087e-01 9.10764992e-01
-4.61763948e-01 8.15281212e-01 -5.82432330e-01 -1.30188745e-02
4.42949206e-01 3.68113853e-02 4.10208941e-01 1.62225986e+00
1.36184022e-01 -4.73266989e-01 1.54888360e-02 5.34484982e-01
-3.93161923e-01 5.95098615e-01 -5.54156363e-01 -2.63186498e-03
3.09349120e-01 1.28237009e+00 -9.12609577e-01 -2.91186422e-01
-6.98331118e-01 7.58331776e-01 7.07128346e-01 5.88374138e-02
-4.51980829e-01 -5.13444722e-01 5.87028205e-01 -2.65353592e-04
6.07069016e-01 -3.71577442e-01 -3.64655107e-01 -1.20800853e+00
6.24224395e-02 -6.32808208e-01 3.96619827e-01 -1.06252007e-01
-1.25779927e+00 6.40859365e-01 -6.68066042e-03 -7.90952086e-01
-3.35175723e-01 -9.47001994e-01 -2.38489509e-01 7.46674299e-01
-1.90865803e+00 -8.49657178e-01 1.15854479e-01 4.50097233e-01
4.55872923e-01 -2.52552688e-01 1.27628291e+00 5.79870760e-01
-6.93041384e-01 7.97214985e-01 2.14572474e-01 4.56002623e-01
8.04413199e-01 -1.48681164e+00 4.81370330e-01 1.16053689e+00
5.46228588e-01 8.15989852e-01 6.90830886e-01 -3.92634481e-01
-1.06584251e+00 -9.25990880e-01 1.24055326e+00 -4.86204252e-02
7.23967791e-01 -6.06735051e-01 -1.13507044e+00 5.71596384e-01
3.15564752e-01 1.23518489e-01 9.52889979e-01 4.93402690e-01
-5.09516060e-01 9.04299393e-02 -8.57547283e-01 3.39661986e-01
8.04859638e-01 -3.29061896e-01 -9.53318357e-01 -3.29147018e-02
6.97341084e-01 -7.87952542e-02 -7.15564251e-01 1.53273180e-01
2.61643112e-01 -5.96285462e-01 7.61372328e-01 -9.07164752e-01
2.09737092e-01 -6.34369114e-03 -3.25235456e-01 -1.25723553e+00
-2.06956476e-01 -4.65638608e-01 -3.50053124e-02 1.46564662e+00
5.37087262e-01 -5.62260270e-01 9.52164829e-01 2.86735147e-01
-4.27966565e-02 -2.74547696e-01 -1.04089725e+00 -8.86461139e-01
3.78256142e-01 -6.77450418e-01 4.66691881e-01 9.73099053e-01
-1.72703132e-01 5.01920998e-01 -4.17025536e-01 4.79235165e-02
4.06012625e-01 -2.96475023e-01 4.85592097e-01 -1.65818632e+00
-9.68932956e-02 -4.42124009e-01 -4.99988198e-01 -9.51901674e-01
6.28025353e-01 -1.14214551e+00 -1.57828648e-02 -1.25578976e+00
-1.65331420e-02 -2.79728413e-01 -5.66557169e-01 7.23024964e-01
8.26257914e-02 3.03058863e-01 1.55327037e-01 -1.07113212e-01
-3.50705445e-01 1.75261304e-01 5.48013985e-01 1.26997590e-01
-3.35166484e-01 -1.67035416e-01 -7.69941330e-01 1.02525294e+00
1.00526142e+00 -8.80845726e-01 -1.10428482e-01 -7.95317888e-01
4.48217094e-01 -4.89417017e-01 -5.54474145e-02 -9.59214449e-01
3.16093981e-01 2.49741152e-01 1.16054147e-01 -3.54872584e-01
1.57634374e-02 -8.97977233e-01 -4.13236797e-01 1.79052263e-01
-4.45284605e-01 1.51158512e-01 2.54065067e-01 4.06563520e-01
-3.93686086e-01 -9.15944755e-01 7.71616101e-01 -2.27944225e-01
-6.36952579e-01 -3.27008665e-02 -6.74840510e-01 8.23745951e-02
4.96242315e-01 -1.92369863e-01 5.04616871e-02 2.70855799e-02
-1.01899838e+00 -2.29024664e-01 3.69776011e-01 3.83300036e-01
9.08000395e-02 -1.21668160e+00 -4.95365858e-01 3.99417877e-01
1.25795931e-01 -1.41523212e-01 -2.44437769e-01 7.18828201e-01
-1.70376137e-01 5.35231769e-01 -3.02675486e-01 -2.19477698e-01
-1.30353010e+00 5.01828611e-01 -1.13783488e-02 -6.39181316e-01
-5.71032763e-01 7.07511961e-01 -1.19061343e-01 -6.01597607e-01
5.12754560e-01 -5.55468857e-01 -4.66712743e-01 5.55868864e-01
4.98242080e-01 -1.36571499e-02 5.28092206e-01 -7.39470541e-01
-3.37984025e-01 5.21896303e-01 -3.17599654e-01 8.56905207e-02
1.83297443e+00 1.44932987e-02 -5.13538830e-02 3.38746279e-01
1.16457784e+00 1.58123344e-01 -6.67491078e-01 -7.24040389e-01
5.68390369e-01 -5.03169857e-02 9.29926559e-02 -4.16926116e-01
-1.08453941e+00 1.08190155e+00 4.99489427e-01 3.37955207e-01
1.18046057e+00 -9.40797254e-02 6.42017841e-01 8.14773440e-01
-2.58913822e-02 -1.33014011e+00 -3.59954059e-01 7.95246422e-01
1.57743633e-01 -1.14092839e+00 -1.22432344e-01 -3.09184939e-01
-1.60120293e-01 1.47246695e+00 4.00770515e-01 -1.94062710e-01
5.54006100e-01 6.77622974e-01 -1.07899681e-02 2.55919337e-01
-5.74748516e-01 -5.69665730e-01 -4.57466692e-02 6.03235304e-01
6.72196209e-01 -1.99236482e-01 -6.83109403e-01 8.66253793e-01
-9.17292610e-02 -1.12700008e-01 6.31524920e-01 1.04759324e+00
-5.34839511e-01 -1.97675765e+00 -2.81913817e-01 4.57168788e-01
-6.29597783e-01 -4.89912242e-01 -2.03664109e-01 8.74860883e-01
1.92240492e-01 6.98097467e-01 -4.89375032e-02 -1.45411596e-01
4.63859558e-01 6.12563312e-01 2.05679163e-01 -9.73847628e-01
-8.18349361e-01 1.18577503e-01 2.21126109e-01 -4.20093745e-01
-5.56813478e-01 -6.52239561e-01 -1.15581548e+00 1.26558900e-01
-4.55127656e-01 1.76233664e-01 8.11498940e-01 1.08540642e+00
1.07058212e-01 5.21605968e-01 1.93145081e-01 -6.32609546e-01
-6.80007637e-01 -1.00759959e+00 -3.16934586e-01 2.48012066e-01
2.14675680e-01 -5.75603783e-01 -2.79225707e-01 1.22395240e-01] | [10.296049118041992, 9.6600923538208] |
7ee067dd-ae28-4cf8-9b93-7820bf9d0fbd | hardware-accelerated-mars-sample-localization | 2206.02622 | null | https://arxiv.org/abs/2206.02622v2 | https://arxiv.org/pdf/2206.02622v2.pdf | Hardware-accelerated Mars Sample Localization via deep transfer learning from photorealistic simulations | The goal of the Mars Sample Return campaign is to collect soil samples from the surface of Mars and return them to Earth for further study. The samples will be acquired and stored in metal tubes by the Perseverance rover and deposited on the Martian surface. As part of this campaign, it is expected that the Sample Fetch Rover will be in charge of localizing and gathering up to 35 sample tubes over 150 Martian sols. Autonomous capabilities are critical for the success of the overall campaign and for the Sample Fetch Rover in particular. This work proposes a novel system architecture for the autonomous detection and pose estimation of the sample tubes. For the detection stage, a Deep Neural Network and transfer learning from a synthetic dataset are proposed. The dataset is created from photorealistic 3D simulations of Martian scenarios. Additionally, the sample tubes poses are estimated using Computer Vision techniques such as contour detection and line fitting on the detected area. Finally, laboratory tests of the Sample Localization procedure are performed using the ExoMars Testing Rover on a Mars-like testbed. These tests validate the proposed approach in different hardware architectures, providing promising results related to the sample detection and pose estimation. | ['Levin Gerdes', 'Gonzalo Jesús Paz-Delgado', 'Carlos Jesús Pérez-del-Pulgar', 'Raúl Castilla-Arquillo'] | 2022-06-06 | null | null | null | null | ['contour-detection'] | ['computer-vision'] | [ 1.40639812e-01 3.22055668e-01 1.43905610e-01 -3.92230600e-01
-1.24632023e-01 -2.73028910e-01 5.25896490e-01 2.58024663e-01
-5.61218143e-01 4.60388273e-01 -9.35018659e-01 -3.02614599e-01
1.27320485e-02 -1.07790160e+00 -5.71637750e-01 -3.38528693e-01
-5.05145252e-01 1.22973144e+00 1.06125496e-01 -5.16344011e-01
3.15759599e-01 1.37654829e+00 -1.45439208e+00 -1.96571007e-01
5.76980233e-01 9.95395660e-01 5.47812760e-01 6.62507236e-01
5.77165723e-01 2.71025479e-01 -2.61521190e-01 3.42814863e-01
4.51955765e-01 9.02759805e-02 -4.76716995e-01 5.89924492e-02
1.39534459e-01 -7.55332887e-01 7.29872882e-02 5.17362177e-01
1.92670122e-01 2.17137977e-01 9.54817951e-01 -9.35521841e-01
8.52684021e-01 3.10971707e-01 -6.82340741e-01 -2.10945562e-01
2.29934469e-01 -9.27398056e-02 9.37884390e-01 -1.00382257e+00
6.38535500e-01 1.07675731e+00 6.47404552e-01 -1.92870110e-01
-7.98628509e-01 -5.05206764e-01 -5.36090851e-01 2.88702816e-01
-1.31441069e+00 -6.34366930e-01 5.77566981e-01 -5.15772223e-01
6.59438312e-01 2.98164964e-01 8.23968112e-01 4.72962677e-01
3.60116959e-01 8.00084412e-01 5.89898348e-01 -6.56762421e-01
5.74791253e-01 2.40804240e-01 -7.07029179e-02 6.73026741e-01
7.46830285e-01 3.13132882e-01 -1.16760112e-01 -1.03031151e-01
6.30219698e-01 -2.67918915e-01 -5.24884239e-02 -6.79984391e-01
-8.82391512e-01 9.48188901e-01 7.00519681e-01 1.41679615e-01
-6.70400202e-01 1.94073841e-02 4.70508456e-01 5.96492970e-03
1.04470961e-01 6.16166413e-01 -1.21817425e-01 1.54299483e-01
-9.67097104e-01 4.30706024e-01 1.01762557e+00 9.02443767e-01
7.75559425e-01 2.42602408e-01 6.04262531e-01 3.70241374e-01
8.69216025e-01 1.14320397e+00 3.12837251e-02 -3.25528145e-01
2.19883069e-01 5.96533060e-01 5.26488006e-01 -9.94896829e-01
-8.89986694e-01 -3.31243396e-01 -2.15132579e-01 4.86988574e-01
-2.89065577e-02 -4.14406389e-01 -7.27764308e-01 5.58158994e-01
7.93321848e-01 -3.25595260e-01 2.11220533e-01 1.02140749e+00
4.24236417e-01 5.87266386e-01 -4.16211694e-01 4.79545712e-01
1.46348500e+00 -4.55290228e-01 -1.48220956e-01 -6.66194677e-01
7.33540118e-01 -7.51800656e-01 3.44101727e-01 6.23552382e-01
-4.56255257e-01 -3.64050269e-01 -1.79534650e+00 2.38048956e-01
-4.26306784e-01 9.86522019e-01 6.99824035e-01 7.21891522e-01
-4.20821220e-01 5.61444461e-01 -1.23339987e+00 -9.59584534e-01
1.66456297e-01 2.73914874e-01 -2.11896136e-01 2.12429017e-01
-7.45424151e-01 1.12533617e+00 4.69126284e-01 6.90258145e-01
-1.35366309e+00 -8.34117681e-02 -1.04346287e+00 -1.85578123e-01
-1.73700392e-01 -3.02945405e-01 1.33558381e+00 -3.15810472e-01
-1.36089909e+00 1.00807750e+00 2.70565599e-01 -1.01699460e+00
8.16318870e-01 -5.65522432e-01 -2.47917145e-01 2.61915892e-01
1.32922709e-01 4.63561237e-01 7.53704607e-01 -1.08961618e+00
-9.43286717e-01 -4.40649033e-01 -3.33225787e-01 1.75903574e-01
2.71113336e-01 -3.20739269e-01 2.21801311e-01 1.68670014e-01
4.20256346e-01 -1.02581537e+00 -9.58127528e-02 1.22801304e-01
-4.36119497e-01 4.56634402e-01 1.31526363e+00 -6.18928730e-01
2.52725095e-01 -1.96443760e+00 -2.45973006e-01 5.48487842e-01
-2.95961440e-01 -2.59895567e-02 2.19443291e-01 6.03556097e-01
-5.67629840e-03 -7.74769843e-01 -3.47332329e-01 -1.75899312e-01
-1.47249684e-01 -8.72177929e-02 -1.42359182e-01 1.11307609e+00
-5.14773838e-02 2.03833237e-01 -5.99244475e-01 -9.65500902e-03
5.00717878e-01 4.60166717e-03 -6.62729964e-02 -2.23117284e-02
-4.20542628e-01 1.19548202e-01 -6.43523037e-01 9.94508505e-01
1.17154360e+00 5.02289057e-01 2.34978154e-01 -1.17589630e-01
-3.55218768e-01 1.11532465e-01 -1.25844622e+00 1.14318907e+00
-5.82693458e-01 7.33178973e-01 7.85801947e-01 -8.94272745e-01
1.58868253e+00 -1.18629456e-01 3.44258130e-01 -5.78265905e-01
4.01260376e-01 5.74394226e-01 -3.98277268e-02 -6.20357275e-01
1.38387573e+00 -1.87230423e-01 -1.82888880e-02 2.71862209e-01
-5.04258335e-01 -7.98484504e-01 -5.99364452e-02 -2.54386552e-02
6.42799735e-01 3.10592383e-01 2.69978195e-01 -5.01837969e-01
5.34410119e-01 5.75033844e-01 7.17774406e-02 2.73855776e-01
-3.44641432e-02 7.49572590e-02 1.51006386e-01 -4.68708038e-01
-1.20944846e+00 -8.15852940e-01 -6.17082238e-01 4.33321685e-01
3.38980287e-01 2.63447672e-01 -4.58008587e-01 -3.20488483e-01
4.44681734e-01 8.91577899e-01 -4.68164891e-01 -9.45143625e-02
-3.23077649e-01 -4.92990643e-01 4.20777202e-01 3.21731895e-01
4.02046293e-01 -9.86160934e-01 -1.59329760e+00 3.38101804e-01
1.99057668e-01 -1.03605103e+00 6.03618383e-01 5.34218013e-01
-9.10763979e-01 -1.44801974e+00 -1.14963137e-01 -3.75211567e-01
6.77210629e-01 3.06866318e-01 5.07371306e-01 -2.19561547e-01
-6.68617189e-01 1.63890213e-01 -4.18229789e-01 -9.93986011e-01
-4.32568997e-01 4.60470393e-02 -1.21145993e-02 -3.78175580e-04
2.21331432e-01 7.37238973e-02 -3.44061226e-01 5.63038230e-01
-4.15480554e-01 -2.14578077e-01 5.78233719e-01 4.20947909e-01
3.05971086e-01 -9.13276300e-02 1.98298216e-01 -6.85512662e-01
2.00282224e-02 -5.41964710e-01 -1.11918676e+00 -1.41757905e-01
-4.16968130e-02 -1.59977973e-01 2.47941419e-01 1.34791791e-01
-8.42123687e-01 3.35551590e-01 8.09798688e-02 6.83740005e-02
-2.45995313e-01 4.03459042e-01 -6.04188419e-04 -4.41974014e-01
1.08011425e+00 -4.90903519e-02 1.85775444e-01 -1.75618231e-01
-9.94658172e-02 9.79282558e-01 4.30645615e-01 -5.00060678e-01
9.41586435e-01 9.70215917e-01 4.07890707e-01 -1.73313475e+00
-2.80626118e-01 -6.20255768e-01 -5.43822229e-01 -5.53147018e-01
4.93262976e-01 -9.92186368e-01 -5.37831724e-01 3.78231674e-01
-8.80273581e-01 -3.50431174e-01 -9.08848122e-02 9.48043287e-01
-5.89369655e-01 1.44075662e-01 -8.08037221e-02 -1.17085707e+00
-6.28670514e-01 -1.10253930e+00 1.24085784e+00 2.30166644e-01
-2.04315722e-01 -6.33686304e-01 -6.51534945e-02 1.76253319e-01
2.27314889e-01 4.89670366e-01 1.50815070e-01 -3.62939209e-01
-6.00488126e-01 -8.58645380e-01 -6.14903308e-02 -2.02018514e-01
-1.10524721e-01 2.91109681e-01 -9.86223519e-01 -5.32069564e-01
2.83568740e-01 -2.70777285e-01 7.64818192e-01 3.80128354e-01
1.90276340e-01 5.25780439e-01 -8.55395257e-01 5.05544960e-01
1.30523324e+00 2.95713246e-01 6.60933137e-01 8.95994306e-01
2.74366200e-01 5.24245143e-01 1.50000000e+00 8.01711977e-01
1.50662765e-01 4.82458144e-01 1.08575904e+00 1.12242065e-01
4.77247775e-01 4.17596586e-02 4.02015656e-01 -3.61639336e-02
1.14193959e-02 1.39467508e-01 -9.98705447e-01 4.91639078e-01
-1.45165360e+00 -5.92708588e-01 -5.46850860e-01 2.39292932e+00
-3.24392319e-01 1.21936761e-01 -9.32822376e-02 1.31560430e-01
5.55619836e-01 -1.23840891e-01 -3.80613625e-01 -5.25504053e-01
4.47494090e-01 8.57152324e-03 1.01286972e+00 5.99983096e-01
-1.28828979e+00 7.27419913e-01 5.37864161e+00 1.46393150e-01
-1.45710754e+00 -4.83345658e-01 1.64576024e-01 3.34191650e-01
1.56064168e-01 2.10631341e-01 -1.16942728e+00 -6.77290261e-02
9.87888992e-01 1.16620034e-01 6.09395169e-02 1.21844864e+00
3.49773079e-01 -9.31568921e-01 -9.58117425e-01 4.54177916e-01
-1.09933391e-01 -9.25192714e-01 -4.94276047e-01 6.29173815e-02
1.03290953e-01 4.65275496e-01 -4.74952430e-01 1.94237426e-01
2.87637394e-02 -5.02767026e-01 1.18334544e+00 7.45654464e-01
5.16172230e-01 -9.09956634e-01 6.98093534e-01 5.35422623e-01
-1.24425125e+00 -5.66365123e-02 -5.57296753e-01 -4.82521206e-02
9.36660841e-02 6.05953872e-01 -2.10078263e+00 7.05310583e-01
3.95513475e-01 2.90012360e-01 -5.70935667e-01 1.37384641e+00
-2.57729441e-01 3.04253936e-01 -9.89205062e-01 -5.14017940e-01
3.26751590e-01 -5.77658594e-01 6.74981117e-01 9.04532373e-01
6.31516695e-01 -4.92549062e-01 -2.32753959e-02 7.95235872e-01
2.84996897e-01 5.92934527e-02 -7.78865635e-01 2.09489346e-01
3.94541860e-01 1.63164413e+00 -9.08540905e-01 -8.58902037e-02
2.62302577e-01 4.16201621e-01 -7.30225220e-02 -1.09147243e-01
-7.36372530e-01 -7.56176531e-01 1.72246799e-01 6.78569138e-01
3.29613805e-01 -4.41209912e-01 -5.35342276e-01 -5.50872266e-01
7.49563426e-02 -2.62061536e-01 1.95074119e-02 -8.50079179e-01
-2.59285361e-01 2.22904041e-01 1.22188292e-02 -1.37951493e+00
-2.90937543e-01 -7.49811828e-01 -7.62480557e-01 6.87439740e-01
-1.17730260e+00 -1.12202227e+00 -6.28022373e-01 -2.46784031e-01
1.75196365e-01 -2.93119818e-01 7.75457621e-01 -5.41980416e-02
-3.37584615e-01 1.17808767e-01 4.20317739e-01 -3.41070384e-01
4.56809819e-01 -1.05572152e+00 5.11145830e-01 7.80944049e-01
-3.28841060e-01 8.01834688e-02 8.09653223e-01 -1.10445786e+00
-1.81245804e+00 -1.07077348e+00 2.73742288e-01 1.12406991e-01
5.91561139e-01 -6.25697732e-01 -5.49716055e-01 8.41981769e-01
-2.79284447e-01 -2.56160408e-01 -4.14148010e-02 -2.13325635e-01
5.01451671e-01 -2.50961810e-01 -1.66758657e+00 1.36144325e-01
3.02424077e-02 -2.03893885e-01 -3.18578511e-01 5.95578551e-01
-2.00921282e-01 -6.26104534e-01 -4.28341776e-01 4.96599585e-01
6.37058020e-01 -7.89784014e-01 6.08817220e-01 1.78183109e-01
2.56842822e-01 -4.64565068e-01 -1.53393582e-01 -1.09597778e+00
1.60031289e-01 -2.06163540e-01 1.64793044e-01 6.16495013e-01
4.08566684e-01 -5.05614877e-01 1.21360123e+00 2.51996741e-02
-2.64809012e-01 -1.28174037e-01 -1.03016329e+00 -4.66064125e-01
-3.19636822e-01 -3.60367119e-01 2.19000265e-01 6.00630403e-01
-2.20339611e-01 1.43371359e-01 6.27168864e-02 8.10944378e-01
7.85887659e-01 1.56357870e-01 1.16881216e+00 -1.59806776e+00
-2.22845599e-02 7.18256980e-02 -6.18525565e-01 -4.59472150e-01
-1.95010722e-01 -8.15909088e-01 3.08566719e-01 -1.54138041e+00
-6.58109844e-01 -5.18194795e-01 9.64302957e-01 1.83715880e-01
7.04756498e-01 -3.97307992e-01 -1.47472635e-01 1.74378008e-01
3.50440964e-02 6.01228356e-01 9.10084665e-01 -1.58803910e-01
-2.16301128e-01 2.27983609e-01 2.49569103e-01 8.23259234e-01
6.40289962e-01 -2.89666921e-01 6.78777546e-02 -7.25414902e-02
2.29044497e-01 3.71742100e-01 4.95563090e-01 -1.66907096e+00
2.04421520e-01 3.64341885e-02 5.78008294e-01 -1.35335565e+00
5.56298852e-01 -1.09990895e+00 1.30096257e-01 9.54394162e-01
4.85791713e-01 -6.89825833e-01 4.69253719e-01 2.85931587e-01
7.18832463e-02 -8.05536032e-01 1.21855116e+00 8.21557865e-02
-7.53299713e-01 -2.05434367e-01 -4.41608697e-01 -8.77936304e-01
1.68013072e+00 -2.64732033e-01 1.05140684e-02 8.01440850e-02
-6.17958367e-01 5.06702125e-01 5.21650434e-01 -3.93740349e-02
6.41238868e-01 -6.88575625e-01 -9.14719999e-01 6.46344423e-01
3.13826740e-01 4.24323022e-01 1.57189518e-01 6.20319963e-01
-1.54743755e+00 2.39123419e-01 -3.16411138e-01 -9.08335805e-01
-8.91493797e-01 1.09869547e-01 7.05267489e-01 7.42259175e-02
-7.17988968e-01 5.20053625e-01 -3.58914703e-01 -6.91126585e-01
-2.82446831e-01 -3.88563007e-01 -1.13516189e-01 3.43717098e-01
5.33639908e-01 5.20209491e-01 5.18406034e-01 -5.52450836e-01
-2.97398061e-01 2.41652176e-01 -2.52658188e-01 -7.70267919e-02
1.57741320e+00 2.32471749e-01 1.16782188e-01 1.37128338e-01
7.32838035e-01 -7.34736770e-02 -9.69843090e-01 1.01855978e-01
-3.03139891e-02 -3.02572131e-01 1.34535640e-01 -4.14585948e-01
-7.46594667e-01 6.67397916e-01 8.16643536e-01 -4.41277586e-02
5.76628923e-01 -2.85935432e-01 2.08862156e-01 1.09997129e+00
8.30159247e-01 -1.02867746e+00 -4.72534746e-01 6.41479433e-01
1.17744780e+00 -9.85794067e-01 5.69092929e-01 -4.41183969e-02
-3.21260482e-01 1.56444776e+00 4.36997533e-01 -4.30958569e-01
4.74876463e-01 3.96623135e-01 -1.54249668e-01 -4.68958735e-01
4.86834683e-02 2.69267470e-01 -3.85483921e-01 5.48934519e-01
1.11124024e-01 5.67267716e-01 -1.42455876e-01 -9.39659402e-02
-5.34964442e-01 -1.77676499e-01 9.99854863e-01 1.45647156e+00
-1.15336657e+00 -6.13150179e-01 -1.21177602e+00 4.86256480e-01
3.19350094e-01 5.28712094e-01 -2.73906291e-01 1.02635622e+00
-2.47960657e-01 5.06912470e-01 2.17674538e-01 1.48843983e-02
5.78405261e-01 -2.87350982e-01 2.94312477e-01 -4.32104737e-01
-2.60892481e-01 -9.58067924e-02 5.88839233e-01 -1.94378912e-01
1.00291334e-01 -8.60282004e-01 -1.60473263e+00 3.20153646e-02
-4.94600803e-01 2.10498050e-01 1.58045828e+00 5.91604352e-01
-5.31857498e-02 2.01979160e-01 8.64977241e-01 -1.47111261e+00
-6.70976996e-01 -1.35240805e+00 -1.28308618e+00 -4.34125960e-01
1.08475558e-01 -8.10491920e-01 -3.15339535e-01 -3.49878520e-01] | [7.719748497009277, -1.7949564456939697] |
ef7bcfe5-d967-4daa-ae53-38ab4553c55d | machine-learning-on-big-data-from-twitter-to | 2005.08817 | null | https://arxiv.org/abs/2005.08817v3 | https://arxiv.org/pdf/2005.08817v3.pdf | Public discourse and sentiment during the COVID-19 pandemic: using Latent Dirichlet Allocation for topic modeling on Twitter | The study aims to understand Twitter users' discourse and psychological reactions to COVID-19. We use machine learning techniques to analyze about 1.9 million Tweets (written in English) related to coronavirus collected from January 23 to March 7, 2020. A total of salient 11 topics are identified and then categorized into ten themes, including "updates about confirmed cases," "COVID-19 related death," "cases outside China (worldwide)," "COVID-19 outbreak in South Korea," "early signs of the outbreak in New York," "Diamond Princess cruise," "economic impact," "Preventive measures," "authorities," and "supply chain." Results do not reveal treatments and symptoms related messages as prevalent topics on Twitter. Sentiment analysis shows that fear for the unknown nature of the coronavirus is dominant in all topics. Implications and limitations of the study are also discussed. | ['Sijia Li', 'Chengda Zheng', 'Chen Chen', 'Jia Xue', 'Tingshao Zhu', 'Junxiang Chen'] | 2020-05-18 | null | null | null | null | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-2.88269848e-01 1.32838368e-01 -6.41768038e-01 -3.51417661e-02
-4.59965527e-01 -6.04314685e-01 7.38264859e-01 1.20150054e+00
-5.51605403e-01 9.24783349e-01 7.13526070e-01 -4.06683117e-01
8.28958377e-02 -6.09243512e-01 -4.02370214e-01 -6.17656052e-01
-3.07433665e-01 1.77982062e-01 -6.30746007e-01 -6.26558006e-01
3.76073807e-01 4.31641608e-01 -8.52134228e-01 1.59051120e-01
6.85933709e-01 3.82392555e-01 9.61980447e-02 2.50809878e-01
-1.05362125e-01 7.79201269e-01 -7.98437178e-01 -1.89917639e-01
-5.24066746e-01 -1.91981986e-01 -6.56613231e-01 -2.71437049e-01
-4.99527514e-01 -4.72700238e-01 9.77882296e-02 8.76188099e-01
2.71528929e-01 -2.61452705e-01 5.62188685e-01 -1.43604779e+00
-1.66732609e-01 4.30196226e-01 -6.02679789e-01 3.94970864e-01
5.97350180e-01 1.12202890e-01 4.96743470e-01 -6.20520115e-01
1.40036142e+00 1.10309887e+00 9.63224828e-01 5.00414073e-01
-8.91053200e-01 -9.26465631e-01 5.46947978e-02 -3.16705912e-01
-1.29137766e+00 -1.03052400e-01 3.20756912e-01 -1.15158629e+00
1.03575456e+00 4.20088351e-01 7.99927831e-01 1.14067245e+00
6.25672579e-01 1.05692461e-01 1.06479299e+00 3.76437396e-01
1.14729077e-01 6.21157169e-01 2.07683995e-01 1.19037747e-01
5.90620160e-01 -3.38384151e-01 -1.06339745e-01 -1.15757072e+00
-2.22370595e-01 3.01674336e-01 -3.84293020e-01 5.88688850e-01
-1.27206409e+00 1.49980485e+00 7.91109800e-02 2.05992997e-01
-8.84214044e-01 -3.97380799e-01 8.11756134e-01 1.53216526e-01
1.19615674e+00 1.51095763e-01 -7.00126052e-01 -1.45096734e-01
-3.46290588e-01 4.11454350e-01 8.85562181e-01 3.62739235e-01
7.93635428e-01 -3.06094646e-01 2.59604007e-01 4.64600503e-01
4.86267656e-01 1.17678773e+00 -1.38427109e-01 -4.62581635e-01
3.01913172e-01 4.31896180e-01 5.51813304e-01 -1.61754167e+00
-1.22783804e+00 -4.09295022e-01 -7.93408573e-01 -5.83471119e-01
-3.16042602e-01 -1.12396026e+00 -3.00139964e-01 1.70148647e+00
2.51587331e-01 2.38208994e-02 3.90028134e-02 8.13476980e-01
9.99460340e-01 1.04522204e+00 7.20734239e-01 -7.36695290e-01
1.78674912e+00 -1.00396974e-02 -1.15963221e+00 -1.59291565e-01
8.24406981e-01 -8.26741159e-01 2.51983047e-01 -5.38613535e-02
-7.22307324e-01 2.17419520e-01 -4.61572349e-01 5.33847630e-01
-8.01978767e-01 -3.58386993e-01 5.36675632e-01 6.97616756e-01
-8.80704522e-01 8.47821012e-02 -2.99886048e-01 -1.24261081e+00
3.54995489e-01 -1.01628408e-01 -2.93737352e-02 2.99532145e-01
-1.32924271e+00 1.03211403e+00 1.07025988e-01 -2.35096201e-01
-5.83523393e-01 -7.05031037e-01 -6.58711672e-01 -4.66402382e-01
-1.37250826e-01 -6.59308553e-01 6.42837703e-01 -4.92279142e-01
-6.16195261e-01 1.15714383e+00 -5.71675062e-01 -3.22061330e-01
-6.13157563e-02 -2.61141658e-01 -1.01473534e+00 3.44485372e-01
4.31024253e-01 4.53372866e-01 2.55440474e-01 -8.57225478e-01
-5.88708282e-01 -2.88247406e-01 -1.80271074e-01 -3.01960856e-01
-2.31788397e-01 1.09197509e+00 4.05013919e-01 -8.11934292e-01
-7.21162379e-01 -1.00136077e+00 -3.54043484e-01 -5.75814366e-01
-4.03294981e-01 -6.30580723e-01 9.73504603e-01 -8.09187055e-01
1.08737266e+00 -1.96103847e+00 -6.65408194e-01 -3.62322177e-03
1.48663580e-01 7.52134100e-02 1.25443608e-01 1.09932137e+00
1.72244087e-01 7.14612424e-01 2.32957393e-01 3.66892636e-01
-3.65060449e-01 1.37832895e-01 -5.62105894e-01 8.96120369e-01
5.46646059e-01 5.90650618e-01 -1.24400282e+00 -7.47056380e-02
-7.65667334e-02 6.91535771e-01 -5.18411994e-01 -2.28028558e-02
-1.51592627e-01 4.86241370e-01 -8.58426511e-01 3.49129319e-01
1.19017780e+00 -6.69434071e-01 2.69356072e-01 1.20099317e-02
-5.69080293e-01 5.21826804e-01 -5.25452532e-02 5.67943990e-01
1.60752892e-01 8.09258401e-01 4.84938145e-01 -3.68127495e-01
7.10802913e-01 6.10561669e-01 3.96238536e-01 -1.85751170e-01
6.33993387e-01 -3.61859798e-01 -5.04764497e-01 -8.55123937e-01
5.17520487e-01 -2.76608676e-01 -3.01294744e-01 7.38534093e-01
-8.44789207e-01 3.04715544e-01 -8.39382708e-02 4.39350367e-01
7.41879582e-01 -6.44562185e-01 2.82294720e-01 -6.22234702e-01
1.87640131e-01 6.62671328e-01 7.09341466e-01 2.70596147e-01
-3.98763299e-01 -1.38804942e-01 7.77784884e-01 -2.79745549e-01
-3.89272809e-01 -7.57139981e-01 -1.18853308e-01 9.14443314e-01
9.48641524e-02 -5.79089165e-01 -6.99711740e-01 -3.22436355e-02
-1.57932758e-01 9.77230668e-01 -8.72750640e-01 4.82063204e-01
-3.59872013e-01 -1.22718823e+00 2.02392295e-01 -1.05323628e-01
4.96753782e-01 -8.78657877e-01 -6.82763278e-01 1.39460102e-01
-8.52479398e-01 -1.20839226e+00 -1.41212091e-01 -1.63169250e-01
-5.11764824e-01 -1.25279939e+00 -5.66067576e-01 -4.76221293e-01
7.23447382e-01 2.20282406e-01 6.41661584e-01 8.12723767e-03
-3.19963068e-01 4.27076191e-01 -1.96992934e-01 -1.00394070e+00
-5.14792264e-01 -2.69176334e-01 4.26028401e-01 -2.80718327e-01
6.67312384e-01 2.38859639e-01 -7.24467814e-01 -7.75138587e-02
-5.83671868e-01 -2.35585675e-01 5.21717034e-02 1.44279869e-02
-2.44078919e-01 -1.84588879e-01 1.05985975e+00 -1.02429569e+00
7.86331594e-01 -1.61519921e+00 -3.71511243e-02 -4.31450367e-01
-4.45782065e-01 -1.13635039e+00 1.52144715e-01 -1.40773460e-01
-9.17804003e-01 -7.35299051e-01 -1.03753902e-01 3.65855515e-01
-4.98378843e-01 8.73825550e-01 5.49784720e-01 6.70596242e-01
6.22322679e-01 -2.00772792e-01 3.03270578e-01 -2.33064592e-01
-1.83530077e-01 8.97887051e-01 -8.46999064e-02 2.00156435e-01
7.04006255e-01 1.14179659e+00 -5.91376841e-01 -1.45814288e+00
-6.50435030e-01 -7.65278697e-01 3.61634642e-01 -3.88032705e-01
1.41932321e+00 -1.35564256e+00 -1.25594234e+00 7.36139834e-01
-1.54136443e+00 1.20430268e-01 5.20740092e-01 7.57694662e-01
3.43298644e-01 1.40098915e-01 -1.08154309e+00 -8.24701846e-01
-5.60696244e-01 -4.37749416e-01 3.93875599e-01 2.46096060e-01
-9.87600744e-01 -1.36214888e+00 5.07200956e-01 4.95292008e-01
8.72477412e-01 7.68077016e-01 1.01473331e+00 -8.59475493e-01
9.74997133e-02 -5.83107024e-02 -4.55127954e-01 -8.89375135e-02
5.80024123e-01 8.02899450e-02 -6.08978271e-01 -5.38643837e-01
1.66023031e-01 -3.88695180e-01 1.27232959e-02 2.74257243e-01
9.50988084e-02 -9.92372215e-01 -1.02045918e+00 -2.99083740e-01
1.28375053e+00 4.55280066e-01 3.08521390e-01 3.63796830e-01
4.21615466e-02 1.02146316e+00 4.88667786e-01 1.26060081e+00
6.50387645e-01 5.47550283e-02 3.49889219e-01 -9.83094871e-02
6.94042921e-01 -1.09413311e-01 5.81815660e-01 8.80779862e-01
9.15821791e-02 -4.31228727e-01 -1.09697294e+00 7.70698845e-01
-9.97609556e-01 -9.99064088e-01 -4.97718513e-01 1.54423392e+00
2.96670735e-01 -1.49934471e-01 1.09210908e-01 -6.36520565e-01
9.41188395e-01 2.73541242e-01 -1.69329196e-01 -6.64390326e-01
-1.86513498e-01 -3.48419607e-01 3.19029748e-01 3.75426799e-01
-1.09016538e+00 4.37241077e-01 6.89383268e+00 2.04364479e-01
-1.36594248e+00 1.87275082e-01 5.58565974e-01 3.63457888e-01
-6.22986138e-01 2.32767612e-01 -6.07956409e-01 4.76719320e-01
1.06371319e+00 -3.59604925e-01 -2.23984778e-01 6.68196142e-01
9.20708179e-01 -2.13183120e-01 -1.33930326e-01 6.59627020e-01
2.95596778e-01 -1.80343997e+00 -1.53231397e-01 6.50568828e-02
6.57764316e-01 6.62179172e-01 -7.56060854e-02 -7.85456784e-03
-1.71161816e-01 -5.49076915e-01 2.53640294e-01 3.16500068e-01
5.61804891e-01 -9.05871153e-01 8.26803148e-01 4.10749108e-01
-6.87013984e-01 4.56607819e-01 -2.34183416e-01 -2.21028268e-01
6.63194060e-01 7.82168925e-01 -1.18976653e+00 7.61609152e-02
8.24985445e-01 6.88364565e-01 -9.90713611e-02 4.20147419e-01
3.78327165e-03 9.09206569e-01 -1.09323017e-01 -5.65595388e-01
3.23821723e-01 -8.22466463e-02 8.79930675e-01 1.68313992e+00
6.94004297e-02 5.24010539e-01 -1.41735703e-01 4.10985470e-01
6.94839582e-02 2.20768183e-01 -7.79949784e-01 -6.78223610e-01
2.09765673e-01 1.03930283e+00 -9.02901351e-01 -4.27127153e-01
-3.09521079e-01 4.64135766e-01 -4.15794611e-01 6.09732687e-01
-7.78878629e-01 -6.20456636e-01 8.61005723e-01 6.53829336e-01
-1.52668143e-02 2.54521340e-01 1.06065832e-01 -7.10830033e-01
-7.45833516e-01 -6.69749618e-01 4.32594061e-01 -8.14621806e-01
-1.23551571e+00 4.26698148e-01 5.26149310e-02 -8.97304714e-01
-1.45985931e-01 -8.44699591e-02 -9.43558335e-01 4.59233880e-01
-1.24721313e+00 -5.91988683e-01 7.40723461e-02 2.89319843e-01
4.57299314e-02 7.34714493e-02 1.16502202e+00 7.45303482e-02
-3.64045650e-01 -6.06478117e-02 1.10767670e-01 3.82973552e-02
5.76620281e-01 -1.03315219e-01 3.47627588e-02 -4.06863727e-02
-1.41408074e+00 8.26930761e-01 9.63949025e-01 -1.24932170e+00
-1.13636506e+00 -1.28644013e+00 2.05479050e+00 -1.15961947e-01
1.09078658e+00 -2.59351194e-01 -5.23115277e-01 7.27546215e-01
9.78068352e-01 -1.03378093e+00 1.20350635e+00 3.36735286e-02
-1.47574276e-01 4.62762147e-01 -1.53587461e+00 8.54259968e-01
1.69098973e-01 -5.13787150e-01 -7.76556194e-01 1.03703582e+00
7.39584267e-01 1.70662418e-01 -9.84672725e-01 1.32669941e-01
3.16646129e-01 -4.78746116e-01 5.16667545e-01 -9.06946540e-01
2.08569795e-01 2.15018764e-01 2.42890313e-01 -1.11588776e+00
-3.53276670e-01 -1.24995053e+00 7.71439016e-01 8.92213404e-01
5.01095116e-01 -1.25629520e+00 4.80224192e-01 4.44861144e-01
1.27618164e-01 -4.42701310e-01 -6.96548939e-01 -2.21042018e-02
-9.27558169e-02 -3.15173328e-01 1.36282414e-01 1.74772060e+00
5.02567768e-01 3.74439359e-01 -3.35527182e-01 1.87323228e-01
3.74246031e-01 -3.07005011e-02 5.27359962e-01 -1.15507722e+00
2.89499313e-01 -1.49991527e-01 1.70183599e-01 -3.93432856e-01
-1.07675545e-01 -5.54417431e-01 -5.65390944e-01 -1.57981575e+00
6.26675725e-01 -2.59388864e-01 3.91995683e-02 2.51746655e-01
1.32158548e-01 3.29092406e-02 2.30106376e-02 3.32688764e-02
-2.71972746e-01 1.03207141e-01 8.25437009e-01 -9.77814272e-02
-6.86778948e-02 -1.98428378e-01 -9.17349815e-01 8.68605196e-01
1.10493565e+00 -6.41086817e-01 -1.68785781e-01 2.53713899e-03
1.10851359e+00 1.62569508e-01 3.39900315e-01 -2.07987390e-02
7.27125108e-02 -4.52367187e-01 1.45445392e-01 -1.19320929e+00
2.59765327e-01 -5.41288078e-01 5.00879347e-01 1.24555504e+00
1.37475982e-01 4.53216463e-01 5.59339702e-01 5.01019835e-01
5.92454225e-02 1.65611371e-01 4.35258180e-01 2.42448583e-01
1.55643940e-01 -3.57632264e-02 -1.53271294e+00 5.08450568e-01
1.15727925e+00 2.76529729e-01 -1.05229771e+00 -7.74214566e-01
-5.72913587e-01 5.17833710e-01 3.06899518e-01 5.48999310e-01
6.04308784e-01 -9.70441282e-01 -1.19062078e+00 -3.39512229e-01
2.05866676e-02 -7.30025589e-01 7.17256963e-01 1.36141956e+00
-5.90493143e-01 4.91761744e-01 -5.82208904e-03 -2.54501641e-01
-9.84404802e-01 8.05887640e-01 -3.39289010e-01 1.66211337e-01
-2.91295707e-01 3.58542472e-01 3.14181626e-01 -1.14104941e-01
9.38474387e-02 -2.02270858e-02 -2.62353212e-01 7.44986415e-01
6.55183852e-01 6.84278786e-01 -4.73540574e-01 -1.03202462e+00
-1.02497458e+00 2.51860350e-01 -8.86959136e-02 3.88041586e-01
1.22039723e+00 -4.22843695e-01 -7.62993693e-01 5.09091258e-01
1.59710884e+00 2.10389689e-01 4.90664020e-02 9.78298560e-02
1.25395343e-01 3.67432721e-02 -4.81844515e-01 -7.49608397e-01
-6.20346844e-01 3.61953944e-01 4.49449241e-01 4.53603715e-01
7.60592818e-01 2.26489738e-01 1.14537239e+00 1.66269168e-01
3.34207900e-02 -1.04884791e+00 -4.89812553e-01 5.07669330e-01
9.91639495e-01 -1.04143083e+00 5.93798980e-02 -4.14812565e-01
-6.55961871e-01 7.21295595e-01 -2.73549378e-01 2.19219610e-01
1.44311130e+00 1.40068680e-01 4.90915716e-01 -6.30669236e-01
-9.87425804e-01 4.48397905e-01 -4.09733802e-01 5.48547506e-01
3.15722376e-01 3.73430014e-01 -9.63687181e-01 4.38444465e-01
1.72815263e-01 -5.99751733e-02 7.99716890e-01 9.68427479e-01
-4.47631180e-01 -5.25623858e-01 -6.15390480e-01 5.63330948e-01
-9.65196669e-01 -4.90644425e-02 -5.48592567e-01 8.90316725e-01
2.44292498e-01 1.38531458e+00 3.56862128e-01 -2.14152500e-01
-9.05512124e-02 -1.42788425e-01 -5.90600133e-01 -1.73847482e-01
-8.45534623e-01 3.32580298e-01 7.66912997e-01 -2.25690216e-01
-8.42663527e-01 -8.09458137e-01 -1.54030108e+00 -8.44543934e-01
-1.43376768e-01 7.07562149e-01 7.84671724e-01 8.58886719e-01
5.64629197e-01 -1.23698324e-01 8.17646325e-01 -2.02033758e-01
1.01671509e-01 -8.19444537e-01 -2.99494565e-01 3.12135488e-01
6.01120234e-01 -4.26389575e-02 -7.58925974e-01 -1.59312636e-01] | [8.452508926391602, 9.733599662780762] |
ec9f4aa8-250d-489b-80ea-b5e39e8669ee | recurrent-batch-normalization | 1603.09025 | null | http://arxiv.org/abs/1603.09025v5 | http://arxiv.org/pdf/1603.09025v5.pdf | Recurrent Batch Normalization | We propose a reparameterization of LSTM that brings the benefits of batch
normalization to recurrent neural networks. Whereas previous works only apply
batch normalization to the input-to-hidden transformation of RNNs, we
demonstrate that it is both possible and beneficial to batch-normalize the
hidden-to-hidden transition, thereby reducing internal covariate shift between
time steps. We evaluate our proposal on various sequential problems such as
sequence classification, language modeling and question answering. Our
empirical results show that our batch-normalized LSTM consistently leads to
faster convergence and improved generalization. | ['Çağlar Gülçehre', 'César Laurent', 'Tim Cooijmans', 'Nicolas Ballas', 'Aaron Courville'] | 2016-03-30 | null | null | null | null | ['sequential-image-classification'] | ['computer-vision'] | [ 2.36529320e-01 1.03413537e-01 -2.41079614e-01 -7.04692483e-01
-5.71048081e-01 -3.15913379e-01 5.73809922e-01 -2.06591845e-01
-8.09267581e-01 6.69339061e-01 3.60640764e-01 -9.09524977e-01
2.77304709e-01 -5.05649745e-01 -6.25069082e-01 -5.86968422e-01
6.36485070e-02 1.32778749e-01 -5.23737743e-02 -1.95223734e-01
-3.46137397e-02 7.48172402e-01 -1.10711884e+00 2.79395342e-01
3.29033524e-01 7.62180507e-01 -1.13544650e-01 9.18588638e-01
-2.22108364e-01 1.25490010e+00 -5.91905296e-01 -1.51822850e-01
1.82361871e-01 -5.56080937e-01 -9.82659996e-01 -1.22353911e-01
4.17992383e-01 -2.20825076e-01 -5.17145097e-01 7.85274386e-01
4.02552187e-01 8.77572179e-01 4.69893306e-01 -9.90225792e-01
-7.77390361e-01 9.90980685e-01 -2.58404464e-01 7.40609765e-01
-1.86039761e-01 -2.37308025e-01 1.07408750e+00 -8.23387742e-01
4.88549769e-01 1.39200461e+00 6.74825668e-01 6.14094079e-01
-1.51634490e+00 -6.47305131e-01 4.39203501e-01 1.09550788e-03
-8.57636034e-01 -7.00121343e-01 4.94505316e-01 -1.93126932e-01
1.55993772e+00 9.46666822e-02 2.36289814e-01 1.23113048e+00
2.48948336e-01 8.50777626e-01 6.92762256e-01 -6.01980150e-01
5.97197115e-02 -1.65177852e-01 5.96036613e-01 3.63507897e-01
-2.60746270e-01 -1.32262915e-01 -3.47110808e-01 1.14921462e-02
8.74114037e-01 3.54908615e-01 7.10794181e-02 1.12455025e-01
-1.10195446e+00 8.60176504e-01 3.28235954e-01 6.00580275e-01
-5.30875564e-01 3.86127174e-01 5.36728501e-01 7.73144722e-01
6.53131366e-01 2.55923808e-01 -6.96891963e-01 -2.43772879e-01
-1.09092736e+00 -5.86909428e-02 7.51509368e-01 7.20419347e-01
7.54107833e-01 6.18776858e-01 -4.14076656e-01 9.95231926e-01
-1.42445594e-01 2.69116849e-01 9.80379760e-01 -1.10941470e+00
3.66850078e-01 2.18725473e-01 -2.46505186e-01 -5.87089002e-01
-6.60661697e-01 -6.16029739e-01 -1.07685542e+00 -2.23881140e-01
2.47433826e-01 -2.94241905e-01 -1.30495048e+00 2.08343124e+00
-2.23981217e-01 2.31968254e-01 -1.99769647e-03 2.96458334e-01
5.87665141e-01 7.51751244e-01 2.61772573e-01 -3.02150160e-01
8.62965465e-01 -1.18712556e+00 -1.00450599e+00 -3.11224729e-01
1.05535376e+00 -5.13799965e-01 1.21061230e+00 -1.11150481e-01
-1.28817201e+00 -4.08857822e-01 -5.19058883e-01 -3.23466301e-01
-3.82358491e-01 1.33518532e-01 6.35203779e-01 4.66365755e-01
-1.25580001e+00 6.60788000e-01 -1.19262111e+00 -5.70807874e-01
7.01766983e-02 6.21163547e-01 -2.85570323e-01 5.45773149e-01
-1.29567158e+00 1.16992819e+00 6.13890529e-01 2.31992885e-01
-4.21661675e-01 -6.46119118e-01 -8.13225210e-01 1.41148776e-01
1.61414385e-01 -6.75410688e-01 1.73725092e+00 -9.09064889e-01
-1.91854203e+00 5.51298320e-01 -7.99930453e-01 -1.05591547e+00
1.00876503e-01 -1.05262637e-01 -2.16943637e-01 -2.62501419e-01
-3.54560912e-01 6.05393946e-01 6.59907639e-01 -5.29612482e-01
-4.26161528e-01 -3.59985642e-02 -1.22110970e-01 1.94084570e-01
-7.31618762e-01 1.16526254e-01 -1.61764652e-01 -8.70092690e-01
2.89167553e-01 -8.19103837e-01 -4.42143410e-01 -7.79904246e-01
-3.45012486e-01 -2.73253173e-01 7.56713033e-01 -7.82093465e-01
1.32545388e+00 -1.77125573e+00 1.60077304e-01 1.99703678e-01
8.69530588e-02 2.23975062e-01 -1.99262783e-01 2.71318108e-01
-5.70821226e-01 1.15700960e-01 -1.60688743e-01 -9.76243794e-01
-1.93365887e-01 6.69906616e-01 -7.98545539e-01 4.76779610e-01
2.00035423e-03 1.30450523e+00 -5.53967655e-01 -1.61753505e-01
1.40901521e-01 4.79949534e-01 -4.47027713e-01 1.24035180e-01
-3.59759852e-02 1.71492592e-01 1.18720666e-01 2.27205113e-01
1.04115970e-01 -2.91289002e-01 2.24455763e-02 1.89672187e-01
-3.78834568e-02 9.71073270e-01 -6.16511881e-01 1.50419724e+00
-5.82540810e-01 1.05452895e+00 -2.67465293e-01 -1.10643399e+00
9.02841032e-01 4.71346945e-01 1.98348895e-01 -6.86354518e-01
2.31140614e-01 -2.15315983e-01 -7.75718037e-03 -1.74574330e-01
7.70357549e-01 -4.22854930e-01 1.73175067e-01 6.66000366e-01
2.88510859e-01 3.71519417e-01 1.28869906e-01 -2.19156202e-02
9.37947810e-01 1.02886215e-01 2.17115536e-01 -8.69478583e-02
3.16201508e-01 -5.06101251e-01 4.39179331e-01 9.10097659e-01
3.15040872e-02 4.76742744e-01 4.59381431e-01 -2.11877733e-01
-1.26331961e+00 -8.56694877e-01 2.33899951e-01 1.86385453e+00
-8.88152599e-01 -4.79527190e-02 -8.55236828e-01 -1.74747318e-01
-2.88048059e-01 1.01368868e+00 -9.15262341e-01 -1.62712127e-01
-9.34205174e-01 -8.95781636e-01 9.48827446e-01 8.74569774e-01
1.04953840e-01 -1.29013610e+00 -3.25295269e-01 3.46313000e-01
-6.21636324e-02 -1.02207530e+00 -6.16951406e-01 6.69237435e-01
-1.44283783e+00 -2.47830421e-01 -7.33249962e-01 -9.35123742e-01
5.93100250e-01 9.90784690e-02 8.41335952e-01 -1.69977710e-01
2.42362455e-01 1.82553262e-01 3.49823646e-02 -8.52851719e-02
-4.82928067e-01 8.90561640e-01 1.78656176e-01 -2.55384650e-02
3.60968113e-01 -8.10628235e-01 -9.21603441e-02 1.52225286e-01
-1.04361284e+00 5.64110698e-03 4.17224079e-01 7.16274559e-01
3.93008649e-01 -5.87369800e-01 4.70900983e-01 -9.86657202e-01
9.57936883e-01 -2.38665000e-01 -3.75159115e-01 3.86166573e-01
-5.15768886e-01 3.55996311e-01 6.69037282e-01 -8.37482810e-01
-9.22134519e-01 -1.79674044e-01 -3.85199964e-01 -7.55343854e-01
1.90657675e-02 4.92525756e-01 2.76691228e-01 1.61305696e-01
5.21666527e-01 2.44517773e-01 1.81197319e-02 -5.62370121e-01
6.01522803e-01 1.55894622e-01 7.24499822e-01 -2.79941261e-01
6.42203331e-01 2.91959763e-01 -1.88266635e-01 -8.72122228e-01
-6.26824915e-01 -4.73469585e-01 -7.98427045e-01 8.96266028e-02
7.31304944e-01 -6.83407426e-01 -9.02131140e-01 3.61224025e-01
-1.34447837e+00 -6.04202926e-01 -5.18197477e-01 4.93302375e-01
-4.77939755e-01 2.43129373e-01 -1.10290122e+00 -8.94058883e-01
-6.09017670e-01 -7.11834610e-01 3.89039129e-01 8.37118626e-02
-4.74843293e-01 -1.45690596e+00 3.17389041e-01 -2.57230818e-01
7.16377854e-01 -2.29638368e-01 9.21148300e-01 -1.15365946e+00
-1.45070270e-01 -1.45954505e-01 1.15442768e-01 6.09726608e-01
1.60373822e-01 -5.90754040e-02 -1.19655967e+00 -3.42714548e-01
3.64689142e-01 -6.88396990e-02 1.29905689e+00 6.53940320e-01
1.10226083e+00 -4.43247169e-01 -2.60888692e-02 7.34948456e-01
7.50613809e-01 1.90894380e-01 8.15263927e-01 3.91603768e-01
7.69594193e-01 4.80614990e-01 -4.79556732e-02 -5.08896820e-02
3.19138527e-01 2.96654671e-01 -1.16573729e-01 -2.71100819e-01
7.45491982e-02 -2.26789743e-01 6.37895703e-01 1.42524624e+00
-1.02700539e-01 -2.11698294e-01 -8.39187622e-01 5.80550194e-01
-1.91746962e+00 -1.10459340e+00 1.39655560e-01 2.01180887e+00
8.39389324e-01 3.69701713e-01 2.45120078e-01 -3.66376899e-02
7.94290900e-01 2.94331253e-01 -4.57970589e-01 -6.88836753e-01
-2.45559707e-01 3.80943835e-01 7.35442340e-01 7.99070656e-01
-9.79936898e-01 1.23360634e+00 8.13182259e+00 6.00442827e-01
-1.26508534e+00 3.60766143e-01 7.43349612e-01 -4.75111634e-01
-3.53793383e-01 -8.97561461e-02 -1.05171502e+00 -2.48698387e-02
1.55534112e+00 -1.38912275e-01 4.25276190e-01 6.62409484e-01
2.72398621e-01 3.68711710e-01 -1.26746452e+00 6.35814369e-01
-1.77571084e-02 -1.22626495e+00 1.88536882e-01 -5.96463494e-02
6.51703477e-01 2.39625111e-01 1.87736034e-01 5.74886978e-01
4.92788106e-01 -1.11093962e+00 4.20700520e-01 5.43891072e-01
3.41395855e-01 -7.03708768e-01 4.69218880e-01 2.28623509e-01
-1.01042354e+00 -9.62934922e-03 -4.97610152e-01 -3.64624113e-01
2.04324275e-01 4.31807756e-01 -1.07151353e+00 8.96406844e-02
4.50083196e-01 6.88944280e-01 -4.97629613e-01 5.59630096e-01
-1.07207045e-01 9.62678134e-01 -3.22453022e-01 6.21361993e-02
4.27259117e-01 -2.59516388e-01 2.82629520e-01 1.44864142e+00
1.14562050e-01 -4.41882685e-02 -1.19122311e-01 6.76998079e-01
-3.60294968e-01 1.07133903e-01 -5.68457782e-01 -2.63420790e-01
2.79421151e-01 1.02918231e+00 -7.71872222e-01 -4.73659337e-01
-2.37478256e-01 8.17058563e-01 6.50058746e-01 9.51136887e-01
-6.92821562e-01 -4.83021498e-01 6.80657923e-01 -1.75714046e-01
4.81813163e-01 -5.01804352e-01 -4.51032907e-01 -1.13364971e+00
-1.42710999e-01 -5.92412293e-01 4.41225559e-01 -5.08618653e-01
-9.45910096e-01 7.86920905e-01 -4.18095700e-02 -5.33629239e-01
-8.14714313e-01 -4.45578009e-01 -7.52055824e-01 9.70521092e-01
-1.45643783e+00 -1.01878560e+00 3.10033619e-01 4.00483340e-01
4.72316206e-01 -1.56180590e-01 7.80128300e-01 3.20852965e-01
-9.49101508e-01 9.55218852e-01 2.81637102e-01 2.83031493e-01
5.05468071e-01 -1.00850320e+00 1.16815066e+00 9.54954207e-01
1.43876895e-01 1.15396881e+00 7.40710378e-01 -4.71976489e-01
-8.05310309e-01 -1.15020239e+00 1.17131650e+00 -3.06496084e-01
9.58083808e-01 -5.53096294e-01 -1.27930284e+00 1.49299908e+00
2.65389621e-01 -3.86898994e-01 6.88763201e-01 3.57574880e-01
-3.34855497e-01 7.18833804e-02 -5.01997530e-01 9.22838271e-01
7.52873242e-01 -1.06022680e+00 -8.99447918e-01 1.20962098e-01
9.87909377e-01 -3.57625633e-01 -8.60492647e-01 2.46428877e-01
4.22560900e-01 -5.89011669e-01 9.23811615e-01 -1.13558388e+00
3.98895964e-02 2.20452279e-01 -1.33169532e-01 -1.04960489e+00
-3.45169485e-01 -7.64258265e-01 -1.75479665e-01 1.11874402e+00
7.12520599e-01 -9.76263702e-01 7.92152464e-01 7.43898213e-01
-1.41693905e-01 -5.58363914e-01 -1.00740814e+00 -7.85338998e-01
3.92217070e-01 -6.34205282e-01 3.58039707e-01 9.18489158e-01
-1.56812325e-01 3.36756945e-01 -4.75078434e-01 -1.33906797e-01
1.15297846e-01 -2.71890938e-01 4.97153968e-01 -1.02289069e+00
-3.05302858e-01 -7.41477549e-01 3.79567482e-02 -1.33609569e+00
5.60729325e-01 -1.04020369e+00 1.47710696e-01 -1.55322659e+00
-6.11288752e-03 1.40900642e-01 -7.99386561e-01 8.90421987e-01
-6.06084540e-02 1.82557449e-01 9.91039798e-02 1.44466802e-01
-5.24762332e-01 4.59018648e-01 7.59317040e-01 1.87915012e-01
-4.97352540e-01 1.87371150e-01 -2.56085306e-01 7.07349777e-01
9.15711582e-01 -6.80975378e-01 -2.49774128e-01 -4.08338726e-01
2.22672090e-01 -1.09917879e-01 1.72086030e-01 -5.49872935e-01
3.68280470e-01 -6.48867413e-02 1.38769194e-01 -7.65878797e-01
4.14856762e-01 -3.28932613e-01 -1.78622961e-01 4.64041948e-01
-9.21422839e-01 4.05410558e-01 4.22861785e-01 2.25040361e-01
-1.95789933e-02 -2.95175880e-01 6.51374161e-01 -2.51115002e-02
-1.77853346e-01 1.06698856e-01 -8.44673693e-01 6.67088851e-02
3.41789752e-01 -4.33052965e-02 -2.52403654e-02 -8.58252883e-01
-7.89426386e-01 1.10489570e-01 2.25135580e-01 4.33470726e-01
3.32891136e-01 -1.21596909e+00 -3.46756905e-01 3.77240717e-01
-5.83908617e-01 -3.10683548e-01 -1.87634870e-01 9.55937564e-01
-1.11132093e-01 7.63887703e-01 3.18789072e-02 -3.78406912e-01
-1.31380272e+00 3.12971741e-01 5.75493693e-01 -3.78957540e-01
-6.49883687e-01 9.92789209e-01 1.20091759e-01 -5.90277255e-01
7.52615988e-01 -9.47581291e-01 -1.78981856e-01 2.23711088e-01
3.79520506e-01 5.59681833e-01 2.86147296e-01 -2.96007901e-01
-1.45074323e-01 2.02769324e-01 -5.75064719e-01 -4.03760910e-01
1.35619926e+00 -9.03850645e-02 -2.57451296e-01 1.17184365e+00
1.38297939e+00 -4.77255672e-01 -1.03127956e+00 -5.93649566e-01
3.32634509e-01 2.52195269e-01 1.21386901e-01 -1.44425318e-01
-9.82200086e-01 1.08523810e+00 2.38077804e-01 2.67294914e-01
9.16759074e-01 -3.77265245e-01 9.01962221e-01 1.07168269e+00
-2.40515307e-01 -1.12668324e+00 -2.49826580e-01 1.24461365e+00
4.78046358e-01 -6.88374341e-01 -1.67545721e-01 2.72197694e-01
-4.12320822e-01 1.00526428e+00 1.30712256e-01 -1.42800584e-01
5.59783041e-01 4.03171450e-01 1.84730694e-01 2.65655190e-01
-1.13818324e+00 -1.27948495e-02 2.41391733e-01 1.08004630e-01
7.24482954e-01 -3.30297530e-01 8.57822523e-02 1.27279252e-01
-4.26084995e-01 1.33547047e-02 4.60745543e-01 9.29491103e-01
-3.95947516e-01 -1.17533004e+00 -3.28466356e-01 6.22236311e-01
-6.28293455e-01 -5.04007161e-01 -5.21333702e-02 5.87309062e-01
-6.46181226e-01 6.93442464e-01 4.47512567e-01 -3.17404717e-01
2.75635570e-01 6.99629426e-01 2.97265708e-01 -6.05925977e-01
-9.56968367e-01 1.61699846e-01 -7.85896406e-02 -2.92425811e-01
-3.93138856e-01 -5.42481840e-01 -1.52379549e+00 -2.78144628e-01
-2.75468141e-01 1.30756423e-01 7.56506264e-01 1.27500057e+00
2.28257492e-01 9.30435777e-01 4.11340296e-01 -8.43502998e-01
-6.34539306e-01 -1.27398849e+00 -3.10222834e-01 1.76159889e-01
7.42599607e-01 -2.06661314e-01 -4.81568545e-01 1.52835950e-01] | [10.858165740966797, 6.468289375305176] |
a2f85bb3-3299-4418-8d18-3f98f9cc421a | a-phoneme-informed-neural-network-model-for | 2304.05917 | null | https://arxiv.org/abs/2304.05917v1 | https://arxiv.org/pdf/2304.05917v1.pdf | A Phoneme-Informed Neural Network Model for Note-Level Singing Transcription | Note-level automatic music transcription is one of the most representative music information retrieval (MIR) tasks and has been studied for various instruments to understand music. However, due to the lack of high-quality labeled data, transcription of many instruments is still a challenging task. In particular, in the case of singing, it is difficult to find accurate notes due to its expressiveness in pitch, timbre, and dynamics. In this paper, we propose a method of finding note onsets of singing voice more accurately by leveraging the linguistic characteristics of singing, which are not seen in other instruments. The proposed model uses mel-scaled spectrogram and phonetic posteriorgram (PPG), a frame-wise likelihood of phoneme, as an input of the onset detection network while PPG is generated by the pre-trained network with singing and speech data. To verify how linguistic features affect onset detection, we compare the evaluation results through the dataset with different languages and divide onset types for detailed analysis. Our approach substantially improves the performance of singing transcription and therefore emphasizes the importance of linguistic features in singing analysis. | ['Juhan Nam', 'Li Su', 'Sangeon Yong'] | 2023-04-12 | null | null | null | null | ['music-transcription', 'music-information-retrieval'] | ['music', 'music'] | [ 2.30420113e-01 -7.29239583e-01 -8.97861421e-02 1.00104168e-01
-1.11931396e+00 -9.08593833e-01 1.23242401e-01 7.15730712e-02
-1.18500464e-01 3.59500289e-01 4.67658728e-01 1.71692058e-01
-2.44727820e-01 -2.15109035e-01 -2.17201516e-01 -7.36301661e-01
-5.16661443e-03 -6.23206869e-02 -1.48717269e-01 -1.12488389e-01
3.28367382e-01 2.79842496e-01 -1.80850577e+00 3.84295583e-01
6.45073414e-01 9.76549983e-01 3.68597537e-01 9.20314312e-01
4.76776902e-03 4.28279191e-01 -8.29750121e-01 1.27725840e-01
2.77904361e-01 -9.81490731e-01 -4.22096789e-01 -9.69856605e-02
3.99687320e-01 1.97105501e-02 9.69223529e-02 9.50668991e-01
8.34841609e-01 3.74081939e-01 7.39596486e-01 -7.42592514e-01
-2.15823874e-01 9.34276700e-01 -2.38476813e-01 1.62812427e-01
3.48611087e-01 -1.57968048e-02 1.36797047e+00 -8.88969839e-01
3.59822214e-01 9.60579216e-01 5.59382856e-01 4.04255867e-01
-8.17731023e-01 -8.04056704e-01 -3.88945401e-01 3.86037618e-01
-1.41843998e+00 -6.10092521e-01 1.21179938e+00 -5.08615732e-01
4.80212152e-01 4.11155105e-01 6.35601401e-01 6.75464928e-01
-1.59435987e-01 6.73016250e-01 8.51365745e-01 -7.44045496e-01
-4.08971645e-02 -2.49973074e-01 -7.61873201e-02 1.06255300e-01
-6.65588021e-01 -4.16082237e-03 -9.57014322e-01 8.24735537e-02
6.65728569e-01 -3.26030523e-01 -4.67751652e-01 3.23449820e-01
-1.33727753e+00 3.76349270e-01 9.80129465e-02 7.27262914e-01
-4.02527660e-01 6.16644844e-02 4.87108588e-01 1.91346496e-01
1.30261287e-01 7.54173517e-01 -2.07498014e-01 -5.83648562e-01
-1.39589489e+00 3.12759370e-01 5.99667609e-01 2.98205197e-01
1.61340147e-01 3.94319117e-01 -4.59707588e-01 1.26517367e+00
-5.31649292e-02 3.14219862e-01 7.71875322e-01 -1.00678742e+00
3.44068170e-01 2.08959773e-01 1.32261962e-01 -9.07706857e-01
-1.98428065e-01 -6.91915393e-01 -7.21447229e-01 -8.82062539e-02
5.99682212e-01 9.17104445e-03 -4.06812817e-01 1.81370032e+00
3.89814153e-02 2.73173809e-01 -1.28347138e-02 1.12481129e+00
7.24417150e-01 9.97296333e-01 -2.77241230e-01 -6.28016472e-01
1.29975343e+00 -1.02007496e+00 -9.60022926e-01 1.28978908e-01
1.09431937e-01 -1.30214882e+00 1.26870656e+00 7.07004607e-01
-1.06018972e+00 -1.08184564e+00 -9.96842325e-01 -6.48458526e-02
1.59064740e-01 8.20058703e-01 1.61128357e-01 2.36129209e-01
-4.30901915e-01 9.10440564e-01 -4.12071466e-01 -8.28821659e-02
-1.19813323e-01 7.72842020e-02 -8.75642244e-03 5.19568861e-01
-1.16090369e+00 3.43084663e-01 3.66794795e-01 9.75606665e-02
-9.14084077e-01 -5.30120432e-01 -3.67150337e-01 1.92404509e-01
1.37201548e-01 -1.06962413e-01 1.55101943e+00 -8.83816361e-01
-1.66533732e+00 6.26859128e-01 -2.04912290e-01 -1.89517722e-01
1.34518757e-01 -2.51961678e-01 -6.52010918e-01 9.02077109e-02
-1.17352106e-01 2.36640185e-01 1.14212024e+00 -8.72165024e-01
-5.32805800e-01 -2.52157360e-01 -4.15122539e-01 4.73874599e-01
-3.21336001e-01 3.26075286e-01 -2.03749731e-01 -9.97096658e-01
3.10370892e-01 -9.30150330e-01 3.58807802e-01 -5.06183565e-01
-5.44516027e-01 -2.83973783e-01 4.92599905e-01 -9.57944632e-01
1.90396619e+00 -2.62566948e+00 1.13723971e-01 -7.18110055e-03
-4.13695276e-01 3.42537612e-01 -7.43343011e-02 6.12174809e-01
-4.63228785e-02 -1.32603586e-01 -2.09775612e-01 -2.15422377e-01
9.39718410e-02 -1.54706001e-01 -6.10460460e-01 1.13263138e-01
-6.46181628e-02 5.77445269e-01 -8.56144011e-01 -5.33586323e-01
8.36177543e-03 4.00783300e-01 -2.51642495e-01 4.82663482e-01
-2.16531292e-01 8.27794552e-01 -1.10298824e-02 5.67755461e-01
8.58377814e-02 4.25554365e-01 -3.57942954e-05 -4.26038563e-01
-3.86437356e-01 7.76110590e-01 -1.41397369e+00 1.79490161e+00
-5.18422961e-01 5.51829696e-01 5.01286983e-02 -5.94081283e-01
1.13151145e+00 7.85326540e-01 2.40111753e-01 -2.34089479e-01
4.90488224e-02 5.08631587e-01 4.81898069e-01 -7.14565456e-01
6.52656972e-01 -4.19023812e-01 9.57797805e-04 4.77825552e-01
-3.53513584e-02 -3.46671224e-01 2.04458907e-01 -3.68379056e-01
7.43245423e-01 1.22945324e-01 3.02869260e-01 1.82936966e-01
5.46022952e-01 -2.76897550e-01 6.69505596e-01 3.96831274e-01
6.63247518e-03 9.95732486e-01 6.69149905e-02 1.03967413e-01
-8.76782238e-01 -8.35439384e-01 -1.32281348e-01 1.22035873e+00
-2.24802777e-01 -7.72499144e-01 -7.61856616e-01 -4.73309215e-03
-2.31413782e-01 5.16419649e-01 -1.06450371e-01 8.10662508e-02
-7.27558732e-01 -3.67866248e-01 9.86745000e-01 3.66707742e-01
1.87845677e-01 -1.58736491e+00 -2.55457997e-01 4.32562262e-01
-4.73685414e-01 -7.84878552e-01 -8.96593511e-01 1.36493191e-01
-7.49996305e-01 -8.33280146e-01 -7.81531096e-01 -9.62956607e-01
-1.03625849e-01 2.93286946e-02 6.93999350e-01 -1.23199917e-01
-2.17864618e-01 -1.71750069e-01 -4.93234515e-01 -3.85538578e-01
-6.35353923e-01 2.72453398e-01 3.15999687e-01 2.04295754e-01
1.11316375e-01 -7.88491905e-01 -5.34509242e-01 2.77617455e-01
-8.38829815e-01 -1.33657217e-01 3.04895818e-01 6.26824081e-01
7.84536481e-01 4.03319955e-01 9.15915012e-01 -4.38090533e-01
1.01972425e+00 -2.27108169e-02 -2.80891061e-01 -6.83581680e-02
-2.64999837e-01 -3.90537918e-01 9.57707763e-01 -7.19473541e-01
-6.69323027e-01 1.85291786e-02 -3.59260470e-01 -4.83548015e-01
-2.80273080e-01 5.54211736e-01 -2.12367892e-01 3.46054435e-01
6.68044865e-01 2.62452811e-01 -1.82974145e-01 -9.21988547e-01
9.73708555e-02 1.19853759e+00 8.63213360e-01 -4.75046307e-01
7.42133439e-01 -1.28168359e-01 -1.54923573e-01 -1.05477440e+00
-9.75416362e-01 -6.90763295e-01 -5.30525565e-01 -3.62428427e-01
6.59761429e-01 -6.36370897e-01 -6.19969845e-01 4.71136242e-01
-1.12054837e+00 8.62584859e-02 -4.06147093e-01 8.01885784e-01
-5.61945617e-01 3.61643344e-01 -8.48303437e-01 -1.18963087e+00
-7.07453907e-01 -9.51620281e-01 8.24051797e-01 1.41657919e-01
-4.93065566e-01 -3.67904723e-01 3.06050003e-01 3.43277514e-01
2.94938713e-01 8.74345899e-02 1.15022767e+00 -5.08143604e-01
-2.27071121e-01 -1.13959521e-01 2.96555638e-01 7.14476585e-01
5.11114419e-01 1.26019090e-01 -1.40329790e+00 -2.28104200e-02
2.04845980e-01 -2.42999077e-01 6.23926818e-01 4.31192279e-01
1.04809082e+00 -3.59571338e-01 2.71944404e-01 4.22655493e-01
9.78819966e-01 4.05988991e-01 3.31505984e-01 -6.27337024e-02
5.59260368e-01 7.21881211e-01 9.65918779e-01 4.70524788e-01
-2.75418252e-01 9.69440877e-01 4.51540910e-02 2.74649709e-01
-4.21580434e-01 -5.55021405e-01 6.43142045e-01 1.75948465e+00
-2.07939550e-01 -5.82488626e-02 -5.49388528e-01 6.50050044e-01
-1.64748693e+00 -9.94215310e-01 -4.04405519e-02 2.45752382e+00
1.18294466e+00 -1.52848646e-01 4.28614020e-01 7.79690623e-01
8.69462609e-01 2.64113277e-01 -4.73803490e-01 -3.60567391e-01
-1.47485986e-01 4.52797443e-01 -1.82588175e-01 3.55482936e-01
-8.85496974e-01 8.16153109e-01 5.81237745e+00 1.27310836e+00
-1.22921896e+00 -4.03148830e-02 6.35764450e-02 -2.24390313e-01
-8.04926921e-03 -1.08642645e-01 -5.63716888e-01 4.75107640e-01
7.11982071e-01 7.67418146e-02 8.92980516e-01 4.81300384e-01
6.42285407e-01 1.88534141e-01 -1.18074334e+00 1.27821410e+00
9.78764519e-02 -9.26203012e-01 -1.39368236e-01 -4.03270692e-01
5.50301254e-01 -3.60763460e-01 7.23704416e-03 1.32108793e-01
-6.62166357e-01 -1.11310089e+00 9.47441518e-01 3.50945830e-01
9.06228065e-01 -7.97854781e-01 3.93358231e-01 5.31807899e-01
-1.32001781e+00 -4.32825424e-02 -1.06528178e-01 -2.11416543e-01
1.25803500e-01 4.11467761e-01 -1.11157787e+00 3.93618196e-01
3.72333020e-01 6.76684082e-01 -1.92852199e-01 1.26682079e+00
-4.69197661e-01 1.30234635e+00 -3.31162184e-01 1.41830102e-01
-2.44432628e-01 -2.13608697e-01 8.16711426e-01 1.22651029e+00
5.61308801e-01 -4.22122255e-02 8.59230906e-02 7.94799685e-01
-8.20683539e-02 7.49422252e-01 -2.90667087e-01 -5.35710096e-01
6.14886284e-01 1.30409443e+00 -4.16945994e-01 7.51954168e-02
1.46145359e-01 6.37056172e-01 -1.15124002e-01 2.66304433e-01
-4.18816775e-01 -3.92218292e-01 4.23943698e-01 8.74042511e-02
2.20202114e-02 -3.21024209e-01 8.81933048e-03 -7.90481687e-01
-1.32284937e-02 -1.10715210e+00 2.02797547e-01 -8.60602498e-01
-1.22608924e+00 6.16486073e-01 -4.40885514e-01 -1.61408544e+00
-6.64213359e-01 -3.29244137e-01 -7.82175660e-01 1.04846406e+00
-1.25486636e+00 -7.91849375e-01 -6.53177649e-02 3.59347165e-01
8.66417885e-01 -1.04837731e-01 9.85045850e-01 5.91622770e-01
-4.45222586e-01 5.40133893e-01 2.45900471e-02 2.64277577e-01
1.05824506e+00 -1.21248841e+00 5.26994653e-02 5.32186270e-01
8.55023980e-01 4.27421302e-01 6.88683987e-01 -2.97554374e-01
-1.12624764e+00 -7.86448598e-01 1.03637326e+00 -4.20441218e-02
4.94844586e-01 -2.39752755e-01 -9.11568820e-01 8.96808282e-02
1.54883340e-01 -4.80544448e-01 8.32558930e-01 1.73905417e-01
-4.35365215e-02 -2.99288839e-01 -4.48179930e-01 4.66283590e-01
7.53715575e-01 -9.31556225e-01 -7.73625314e-01 1.22580290e-01
5.35639167e-01 -3.33986253e-01 -8.45141172e-01 3.93591940e-01
7.90309608e-01 -8.24922681e-01 7.10857511e-01 -3.32191646e-01
2.87162334e-01 -7.41169155e-01 -1.13465734e-01 -1.41161251e+00
-1.71642751e-01 -9.41317499e-01 4.59776632e-02 1.75295210e+00
3.10853153e-01 7.14187622e-02 3.23127925e-01 -3.39944512e-01
-1.61467806e-01 -3.27895433e-01 -7.00660586e-01 -8.48205149e-01
-2.79484272e-01 -7.33528852e-01 5.47875941e-01 8.59620929e-01
1.63805082e-01 5.77322721e-01 -6.62529647e-01 2.36818707e-03
2.71717668e-01 6.56027615e-01 6.80535257e-01 -1.08759487e+00
-7.08230078e-01 -2.93792218e-01 -7.41251186e-02 -8.84382367e-01
-7.90376514e-02 -1.09053552e+00 4.08876181e-01 -1.06153512e+00
-1.40602231e-01 -3.36280316e-01 -6.53357625e-01 3.33521277e-01
-9.51803848e-02 4.54706818e-01 5.95911801e-01 5.83150208e-01
-1.94028020e-01 4.55510885e-01 1.33208811e+00 -8.86257216e-02
-5.69399953e-01 4.30027425e-01 -1.87320724e-01 9.18097496e-01
8.45978737e-01 -5.31914890e-01 -1.94241926e-01 -6.40401989e-02
1.68686152e-01 4.56404060e-01 1.87695660e-02 -1.17270863e+00
3.28137964e-01 9.01670158e-02 7.52868503e-02 -8.78740132e-01
5.78029215e-01 -4.23779756e-01 1.79040477e-01 2.56990660e-02
-7.01610267e-01 9.76556167e-03 2.35764720e-02 1.67862445e-01
-7.22493052e-01 -5.99401057e-01 4.99510467e-01 -8.46686494e-03
-3.61120880e-01 3.30924690e-02 -2.64409304e-01 1.52943879e-01
1.35610640e-01 -7.96698853e-02 2.07675233e-01 -4.94521528e-01
-6.67802930e-01 -4.31904316e-01 1.39052972e-01 4.99345541e-01
4.10796046e-01 -1.38128662e+00 -8.25681329e-01 2.01716840e-01
1.56210929e-01 -1.67952016e-01 3.76416326e-01 7.49942839e-01
-2.55020648e-01 1.92309469e-01 7.96432421e-02 -5.09688377e-01
-1.44562316e+00 3.20018679e-01 2.17736036e-01 5.71092777e-03
-3.55102003e-01 6.52575374e-01 2.52110884e-02 -1.24243177e-01
6.04414880e-01 -4.99203473e-01 -5.45800209e-01 2.48814687e-01
5.54155469e-01 3.60399604e-01 1.45425899e-02 -7.31492341e-01
-5.27199022e-02 7.65532255e-01 2.83614069e-01 -3.53985757e-01
9.83774781e-01 -5.20506948e-02 -1.50200233e-01 1.16382563e+00
8.63348126e-01 7.24076986e-01 -8.64012778e-01 -4.25322875e-02
2.39970088e-02 -3.67673367e-01 6.53768033e-02 -8.48522425e-01
-6.45853817e-01 1.15072393e+00 5.56926489e-01 2.80193031e-01
1.16837835e+00 -3.49725246e-01 1.08083880e+00 1.30659312e-01
6.78408369e-02 -1.29614818e+00 5.23528941e-02 3.83886635e-01
9.57036078e-01 -8.94992292e-01 -4.22002584e-01 -2.87327647e-01
-5.15009999e-01 1.21838319e+00 2.53515810e-01 -4.32777628e-02
3.85507852e-01 5.52974902e-02 3.88808519e-01 2.37860426e-01
-3.36507499e-01 -2.47368664e-01 5.92176557e-01 1.50823891e-01
7.58131385e-01 1.95437804e-01 -4.79480922e-01 9.20589745e-01
-8.38898718e-01 -7.91150182e-02 1.41105697e-01 2.92632103e-01
-5.06255388e-01 -1.38749039e+00 -6.49888754e-01 1.19765006e-01
-9.18647289e-01 -3.45243603e-01 -4.54878211e-01 1.73057020e-01
3.21522444e-01 1.20619643e+00 -2.68645406e-01 -4.34379876e-01
2.54145652e-01 4.15598005e-01 5.07012904e-01 -7.54330873e-01
-9.28742647e-01 6.43502235e-01 -5.33528738e-02 -8.10296237e-02
-3.62282634e-01 -5.31005263e-01 -1.33889389e+00 4.51118290e-01
-4.25740808e-01 4.71007407e-01 7.84844100e-01 7.89069116e-01
1.81135267e-01 6.81878388e-01 9.28948700e-01 -8.67477357e-01
-6.33236885e-01 -1.36121142e+00 -1.06050348e+00 6.11363351e-01
3.76702070e-01 -2.48847052e-01 -4.34367567e-01 1.55464143e-01] | [15.725540161132812, 5.546482563018799] |
8293cabc-f3a0-40e7-9e07-98544a76d61f | deep-learning-based-framework-for-iranian | 2201.06825 | null | https://arxiv.org/abs/2201.06825v1 | https://arxiv.org/pdf/2201.06825v1.pdf | Deep Learning Based Framework for Iranian License Plate Detection and Recognition | License plate recognition systems have a very important role in many applications such as toll management, parking control, and traffic management. In this paper, a framework of deep convolutional neural networks is proposed for Iranian license plate recognition. The first CNN is the YOLOv3 network that detects the Iranian license plate in the input image while the second CNN is a Faster R-CNN that recognizes and classifies the characters in the detected license plate. A dataset of Iranian license plates consisting of ill-conditioned images also developed in this paper. The YOLOv3 network achieved 99.6% mAP, 98.26% recall, 98.08% accuracy, and average detection speed is only 23ms. Also, the Faster R-CNN network trained and tested on the developed dataset and achieved 98.97% recall, 99.9% precision, and 98.8% accuracy. The proposed system can recognize the license plate in challenging situations like unwanted data on the license plate. Comparing this system with other Iranian license plate recognition systems shows that it is Faster, more accurate and also this system can work in an open environment. | ['Roozbeh Rajabi', 'Mojtaba Shahidi Zandi'] | 2022-01-18 | null | null | null | null | ['license-plate-recognition', 'license-plate-detection'] | ['computer-vision', 'computer-vision'] | [-2.83473492e-01 -6.50718510e-01 2.25062743e-01 -1.12562038e-01
-3.69765222e-01 -5.81664145e-01 3.29984754e-01 -8.34395468e-01
-6.17076159e-01 6.02066636e-01 -4.44831669e-01 -2.90199101e-01
3.33527833e-01 -9.95442152e-01 -5.95045626e-01 -6.27849817e-01
3.88453960e-01 6.77453399e-01 7.35363960e-01 -2.83545464e-01
8.07392657e-01 9.01425004e-01 -1.34140027e+00 2.11988419e-01
5.81777573e-01 9.21374440e-01 7.05364496e-02 7.78531492e-01
9.35325250e-02 9.51710463e-01 -6.47727668e-01 -2.13060290e-01
7.00189948e-01 1.39961213e-01 -2.87938684e-01 2.32827798e-01
4.47977275e-01 -5.97544611e-01 -7.26379275e-01 1.00086617e+00
2.88047373e-01 3.35773021e-01 8.76617968e-01 -1.09044766e+00
-7.23400593e-01 -3.28415602e-01 -6.80351138e-01 4.55809593e-01
-2.92936623e-01 5.22464961e-02 1.56186581e-01 -1.24661613e+00
4.28681552e-01 7.40896761e-01 8.79215300e-01 4.10644561e-01
-1.96160063e-01 -1.17388141e+00 -7.16968834e-01 3.12370569e-01
-1.79131567e+00 -3.97482932e-01 1.21753797e-01 -6.31175458e-01
1.07629001e+00 1.29405856e-01 1.71059102e-01 1.84092019e-02
4.61796641e-01 8.64509881e-01 1.01863086e+00 -4.07293111e-01
-1.58088416e-01 4.69654173e-01 5.10843694e-01 5.92940271e-01
2.94880718e-01 -7.15161189e-02 3.78633767e-01 6.41617954e-01
1.08397365e+00 2.98492372e-01 2.20633686e-01 5.55197954e-01
-7.23402679e-01 7.14627028e-01 1.80482715e-01 3.82747233e-01
-2.31860593e-01 -2.59350389e-01 2.83829838e-01 7.91598558e-02
5.27370907e-02 1.70260981e-01 -8.24155286e-02 -4.24517393e-02
-1.13373077e+00 8.33728388e-02 4.96640384e-01 1.12428784e+00
6.14313722e-01 6.76122308e-01 3.07948440e-01 1.11327958e+00
4.00105089e-01 9.56560194e-01 4.88368303e-01 -3.49694967e-01
6.98565841e-01 6.35434270e-01 2.26274565e-01 -1.16907680e+00
-4.18999076e-01 -2.14197308e-01 -7.12816417e-01 6.28714085e-01
2.58847237e-01 -2.72758394e-01 -1.36111677e+00 5.14532566e-01
-4.17070508e-01 9.18791816e-02 2.90976435e-01 1.08789074e+00
8.41736555e-01 1.35929406e+00 -3.27740133e-01 5.83352327e-01
1.26963341e+00 -1.26350653e+00 -5.57354450e-01 -3.55780035e-01
1.35898516e-01 -1.13945901e+00 4.91909623e-01 2.97689766e-01
-8.16294789e-01 -7.09370732e-01 -1.28135836e+00 1.46475732e-01
-6.97086930e-01 1.02040327e+00 3.50682586e-02 7.37025857e-01
-1.05984807e+00 -1.53618781e-02 -3.05868059e-01 -4.75875646e-01
2.55881935e-01 8.69494438e-01 -5.11637390e-01 -4.30791318e-01
-1.05780137e+00 1.24810576e+00 2.20524251e-01 3.16084743e-01
-5.62468290e-01 1.21791728e-01 -5.21061957e-01 2.79900096e-02
-7.43861794e-02 3.00462872e-01 8.98471236e-01 -1.20865631e+00
-1.34639895e+00 1.05898488e+00 2.48852998e-01 -1.80546969e-01
3.54208916e-01 1.10598788e-01 -1.15403771e+00 -3.93831870e-03
1.11849599e-01 5.65262854e-01 4.30513620e-01 -5.70947349e-01
-1.11326838e+00 -5.55731773e-01 -2.55421370e-01 3.06005746e-01
3.86348307e-01 3.19640428e-01 -8.25995266e-01 -1.07911676e-01
1.37925325e-02 -1.09893930e+00 2.29313180e-01 -4.25721943e-01
-2.96106219e-01 -1.19892590e-01 1.08570266e+00 -7.99636841e-01
5.25795877e-01 -2.32252526e+00 -9.89835858e-01 4.71466333e-01
-1.24722049e-01 1.12829876e+00 1.36755481e-01 2.66294569e-01
1.22760888e-02 7.00440705e-02 1.09344833e-01 2.47096196e-01
-2.19400182e-01 -2.27072626e-01 -6.58524185e-02 7.43014812e-01
3.87165964e-01 6.35702789e-01 8.05476010e-02 -1.91178977e-01
4.66266841e-01 5.41583836e-01 -1.01995897e-02 -5.29605336e-02
7.07983136e-01 -2.23065555e-01 -2.59017885e-01 9.68198657e-01
1.27628386e+00 1.39749721e-01 -3.72869283e-01 -1.53166428e-02
-5.78724205e-01 -5.08461535e-01 -1.18565023e+00 3.93788666e-01
-1.39154375e-01 1.67999029e+00 5.47092361e-03 -9.66527343e-01
1.55996871e+00 2.26545498e-01 -1.60017431e-01 -1.16082978e+00
5.40891469e-01 4.64880258e-01 4.94384505e-02 -7.99231350e-01
7.88978994e-01 -6.01112507e-02 6.07727915e-02 -1.99677069e-02
-1.69739246e-01 3.38107735e-01 1.27691478e-01 -2.77033955e-01
5.68415642e-01 -3.33756804e-01 -7.39312172e-02 -2.11753219e-01
9.97926533e-01 4.02020633e-01 3.89212012e-01 6.01043642e-01
-4.25266922e-01 6.54517651e-01 -5.73814549e-02 -7.13263810e-01
-1.33999300e+00 -6.69026911e-01 -9.09269229e-02 6.24613643e-01
3.28122735e-01 7.39263952e-01 -6.47208035e-01 -7.41860224e-03
-1.40483618e-01 3.66955549e-01 -3.10915291e-01 6.04049154e-02
-7.73175538e-01 -4.69079584e-01 1.07001805e+00 6.91962302e-01
1.30507231e+00 -1.07400715e+00 -1.81879759e-01 -5.98519063e-03
2.59650916e-01 -1.16907680e+00 -3.74115199e-01 -3.81420702e-01
-5.60553074e-01 -1.15190458e+00 -8.62372756e-01 -1.55764747e+00
7.34496236e-01 5.22464633e-01 7.38601506e-01 5.62959760e-02
-1.65460661e-01 -1.93809807e-01 5.18851504e-02 -5.59817970e-01
-2.76297361e-01 -2.57373154e-01 -2.99490206e-02 7.48308152e-02
8.83840442e-01 1.24214835e-01 -3.59232515e-01 6.58574700e-01
-7.61813819e-01 -2.89969027e-01 9.64374721e-01 4.59393620e-01
1.76174656e-01 3.38166177e-01 2.81395406e-01 -7.04395890e-01
5.48587203e-01 -4.18468505e-01 -1.14562857e+00 1.09702989e-01
-5.39546788e-01 -5.65414131e-01 7.46066451e-01 1.21812411e-01
-8.10403049e-01 2.44581506e-01 -4.33997035e-01 -4.00733769e-01
-4.62465435e-01 1.50642321e-01 5.48286624e-02 -2.88999289e-01
3.91011029e-01 7.33313739e-01 1.87743455e-01 -1.78822502e-01
-3.98042828e-01 1.17553222e+00 7.30603337e-01 3.05968225e-01
7.40979433e-01 2.36391515e-01 -1.70084447e-01 -1.22498941e+00
2.15060160e-01 -9.76515532e-01 -5.00428796e-01 -6.39858305e-01
9.73078310e-01 -1.00779641e+00 -9.92133141e-01 1.20897794e+00
-1.02912247e+00 2.76801437e-01 5.14791071e-01 6.52628779e-01
4.98162583e-02 2.96926079e-03 -6.68494046e-01 -1.05627739e+00
-4.67372745e-01 -1.25499928e+00 6.14533126e-01 6.94766045e-01
4.25919950e-01 -6.89878285e-01 -8.78249034e-02 6.26999199e-01
7.74916053e-01 -1.35459229e-01 2.58011460e-01 -1.13917947e+00
-6.80769980e-01 -1.02467060e+00 -8.20259750e-01 4.91529703e-01
-1.36279881e-01 4.14186776e-01 -9.24722016e-01 2.13550001e-01
-4.03314531e-01 -1.80864826e-01 8.23256314e-01 5.06785274e-01
3.71775717e-01 -1.21991448e-01 -1.40374392e-01 3.80039126e-01
1.74029458e+00 1.09413815e+00 1.41665685e+00 7.68153131e-01
5.61788619e-01 2.88601100e-01 5.35214961e-01 9.57552120e-02
1.93727866e-01 3.63146991e-01 1.68763444e-01 -3.10854107e-01
1.52942136e-01 1.34716645e-01 4.27869767e-01 7.39476144e-01
-3.38586152e-01 -2.99272448e-01 -1.40840220e+00 2.18993902e-01
-1.34257269e+00 -1.22803974e+00 -6.18782878e-01 1.86400235e+00
3.87400873e-02 1.83265254e-01 2.59270847e-01 2.33778223e-01
1.29512811e+00 -4.11758363e-01 -3.56926858e-01 -5.67457497e-01
-1.93644315e-01 -4.70281765e-02 1.16964197e+00 5.42724729e-01
-1.34192538e+00 1.17206192e+00 5.87825441e+00 5.68221688e-01
-1.67138159e+00 -1.80120870e-01 7.30372787e-01 4.93272632e-01
5.88343740e-01 -6.58363998e-01 -9.62344944e-01 4.36878026e-01
9.77250814e-01 5.11383228e-02 1.64664820e-01 9.53887463e-01
1.29838377e-01 -1.35177836e-01 -2.79685676e-01 1.13485622e+00
3.39506984e-01 -1.53781152e+00 -1.43888667e-01 1.28251776e-01
4.00007427e-01 4.30739731e-01 9.58491862e-02 6.90175593e-01
3.04275770e-02 -1.23931909e+00 4.32518989e-01 7.00558960e-01
9.32696700e-01 -1.32050002e+00 1.45166528e+00 5.06831050e-01
-8.30435693e-01 1.89268012e-02 -7.70192444e-01 -1.44794494e-01
-4.31196243e-01 -1.14702445e-03 -1.41203368e+00 -1.25816450e-01
4.70600218e-01 6.16308689e-01 -3.78771394e-01 1.30501938e+00
3.15538108e-01 5.95052838e-01 -1.34448990e-01 -3.40839207e-01
8.61516893e-01 -4.87908393e-01 1.36009559e-01 1.64234221e+00
3.79565001e-01 2.57231176e-01 -1.62849262e-01 6.00146174e-01
-8.89531076e-02 1.77789763e-01 -7.37821877e-01 7.96761215e-02
3.76580149e-01 1.11863530e+00 -9.51972723e-01 -4.33903903e-01
-3.88886333e-01 7.70078778e-01 -3.99162769e-01 2.01677561e-01
-1.02735007e+00 -9.79998291e-01 2.76473969e-01 1.53482243e-01
3.98595542e-01 -1.86063573e-01 -1.08311236e-01 -1.01769090e+00
-2.50319630e-01 -6.51559591e-01 7.12773651e-02 -1.06655955e+00
-7.37106264e-01 7.73040950e-01 -6.05411887e-01 -1.57482243e+00
5.33849522e-02 -1.33879912e+00 -8.25334191e-01 9.99367893e-01
-1.33997226e+00 -8.71865273e-01 -4.25426543e-01 8.02816391e-01
1.00994980e+00 -9.39170122e-01 4.83228028e-01 6.77411318e-01
-1.01172662e+00 4.85783577e-01 7.96524107e-01 9.52428162e-01
5.28645158e-01 -7.55045414e-01 3.29157889e-01 9.59387004e-01
-4.44893599e-01 4.89678651e-01 1.82863116e-01 -4.84693140e-01
-1.29257584e+00 -1.23848403e+00 7.19885588e-01 2.93049868e-02
1.65378183e-01 -8.31217691e-02 -7.06367552e-01 6.84877694e-01
2.88971901e-01 -1.29349232e-01 5.99448979e-01 -5.89964926e-01
-8.87522250e-02 -3.97983104e-01 -1.42375684e+00 2.73436815e-01
-1.52287036e-01 -1.53699234e-01 -2.88546175e-01 3.20244968e-01
-2.37292111e-01 -4.19583976e-01 -3.32916826e-01 -1.93816125e-01
8.28704238e-01 -8.27042580e-01 7.39642262e-01 -2.18867898e-01
3.67796630e-01 -7.15295196e-01 -2.34349057e-01 -6.31577909e-01
-6.27701819e-01 3.59467030e-01 9.24054563e-01 1.03532851e+00
6.53761446e-01 -6.52319551e-01 7.78422534e-01 8.39922726e-01
-3.52253944e-01 -2.46737510e-01 -7.67245233e-01 -6.51424170e-01
-2.87539531e-02 -1.37398556e-01 1.60282508e-01 8.20447505e-01
-6.40549362e-01 3.46395262e-02 -5.88663220e-01 3.96804720e-01
2.22802579e-01 -3.59486133e-01 6.50976598e-01 -1.06496799e+00
5.21955609e-01 -3.41608673e-01 -1.13021111e+00 -7.07443714e-01
-1.23126276e-01 -4.24500138e-01 2.05568105e-01 -1.43075359e+00
6.72199875e-02 -2.58806646e-01 -3.39306474e-01 3.22662294e-01
4.04890090e-01 8.77002716e-01 2.32040912e-01 7.47875810e-01
-5.32043397e-01 -1.07574426e-01 8.85582983e-01 -5.76632619e-01
7.31060505e-02 3.88466179e-01 -1.66302353e-01 5.73484957e-01
1.16573954e+00 -3.09977800e-01 -1.64679974e-01 -4.02602077e-01
-9.09776390e-02 3.09376605e-02 -6.72850609e-02 -1.28110778e+00
7.45254636e-01 8.93217921e-02 9.23837364e-01 -1.04099143e+00
2.59778202e-01 -7.29526579e-01 3.99827622e-02 4.49486017e-01
2.79508203e-01 2.20396474e-01 6.05410218e-01 7.67108202e-02
-5.80099106e-01 -3.78895909e-01 1.09544313e+00 -3.06880921e-01
-1.51954329e+00 3.93640473e-02 -1.09786427e+00 -3.03571403e-01
1.22805321e+00 -9.88552094e-01 -3.64199460e-01 -3.17742705e-01
-2.50738889e-01 -6.75384328e-02 2.48208046e-01 4.97524768e-01
9.65277970e-01 -1.28144574e+00 -9.33082640e-01 3.00645918e-01
-4.33681011e-02 -4.24112707e-01 1.83483958e-01 5.07609189e-01
-1.53682303e+00 9.64379251e-01 -9.38822746e-01 -4.75951195e-01
-1.30080307e+00 -1.11457072e-02 6.52435720e-01 2.29508251e-01
-3.08297843e-01 6.44001007e-01 -1.41686797e-01 -5.20225525e-01
-1.61771759e-01 1.42158553e-01 -7.17167377e-01 -1.61919653e-01
5.96605003e-01 7.32661784e-01 2.74848491e-01 -1.17768466e+00
-7.71607816e-01 9.72067416e-01 -2.87034214e-01 -8.15028548e-02
1.35893607e+00 2.23223731e-01 -5.92744648e-02 1.36286557e-01
1.17346406e+00 -1.68940630e-02 -8.89559150e-01 2.05833450e-01
-8.64539295e-02 -5.08843601e-01 5.81401438e-02 -9.97994304e-01
-1.26798177e+00 9.40833390e-01 8.49542975e-01 -1.29495859e-01
9.51450109e-01 -7.64029801e-01 5.85320771e-01 6.38620436e-01
1.51012793e-01 -1.38702202e+00 -5.10598123e-01 9.99231398e-01
7.24908113e-01 -1.28951478e+00 -1.69791907e-01 1.30986556e-01
-1.02698052e+00 1.72454667e+00 8.69607031e-01 -7.02234507e-01
3.89346838e-01 3.81557912e-01 3.49766523e-01 -9.39171240e-02
-1.89929754e-01 6.38221577e-02 3.00337315e-01 5.19588172e-01
3.61536235e-01 1.07011624e-01 -3.04929796e-03 4.82487947e-01
6.25312477e-02 1.86502263e-01 9.57269371e-01 8.20941985e-01
-8.28375757e-01 -3.01322848e-01 -8.84568274e-01 3.68566036e-01
-5.74126005e-01 -1.34393796e-01 -4.94926274e-01 1.40963650e+00
1.87673897e-01 9.37883675e-01 5.54303467e-01 -6.69128239e-01
4.82593596e-01 8.18091705e-02 -1.95602283e-01 -1.75228611e-01
-3.13266039e-01 -4.74174414e-03 3.39165912e-04 2.33295895e-02
-1.22663759e-01 -2.83989638e-01 -1.35054088e+00 -7.46619523e-01
-4.42350805e-01 1.17166445e-01 1.03817081e+00 8.89392257e-01
1.97754875e-01 2.07468107e-01 7.60044873e-01 -6.09664857e-01
-1.21734962e-01 -1.03449869e+00 -9.13743556e-01 -8.75677988e-02
2.81372577e-01 -3.71120840e-01 -4.66679074e-02 1.19391032e-01] | [9.828861236572266, -4.953187942504883] |
51b0316d-f158-4d17-bcc9-6d6fb1626749 | medical-diffusion-on-a-budget-textual | 2303.13430 | null | https://arxiv.org/abs/2303.13430v1 | https://arxiv.org/pdf/2303.13430v1.pdf | Medical diffusion on a budget: textual inversion for medical image generation | Diffusion-based models for text-to-image generation have gained immense popularity due to recent advancements in efficiency, accessibility, and quality. Although it is becoming increasingly feasible to perform inference with these systems using consumer-grade GPUs, training them from scratch still requires access to large datasets and significant computational resources. In the case of medical image generation, the availability of large, publicly accessible datasets that include text reports is limited due to legal and ethical concerns. While training a diffusion model on a private dataset may address this issue, it is not always feasible for institutions lacking the necessary computational resources. This work demonstrates that pre-trained Stable Diffusion models, originally trained on natural images, can be adapted to various medical imaging modalities by training text embeddings with textual inversion. In this study, we conducted experiments using medical datasets comprising only 100 samples from three medical modalities. Embeddings were trained in a matter of hours, while still retaining diagnostic relevance in image generation. Experiments were designed to achieve several objectives. Firstly, we fine-tuned the training and inference processes of textual inversion, revealing that larger embeddings and more examples are required. Secondly, we validated our approach by demonstrating a 2\% increase in the diagnostic accuracy (AUC) for detecting prostate cancer on MRI, which is a challenging multi-modal imaging modality, from 0.78 to 0.80. Thirdly, we performed simulations by interpolating between healthy and diseased states, combining multiple pathologies, and inpainting to show embedding flexibility and control of disease appearance. Finally, the embeddings trained in this study are small (less than 1 MB), which facilitates easy sharing of medical data with reduced privacy concerns. | ['Henkjan Huisman', 'Richard P. G. ten Broek', 'Anindo Saha', 'Bram de Wilde'] | 2023-03-23 | null | null | null | null | ['medical-image-generation'] | ['medical'] | [ 5.02911866e-01 3.44156325e-01 -1.02887284e-02 -1.44420043e-01
-9.14044619e-01 -4.78551686e-01 5.16895235e-01 3.21785033e-01
-6.83015943e-01 8.21849048e-01 2.67236471e-01 -4.30607736e-01
-8.94248411e-02 -7.78646708e-01 -4.35798675e-01 -7.84688592e-01
-2.74640232e-01 4.58740234e-01 1.29197631e-02 2.47885212e-01
-1.27575114e-01 6.02493703e-01 -1.08973193e+00 5.35407104e-02
9.62962747e-01 8.30104291e-01 2.66726255e-01 7.90363073e-01
3.55525792e-01 5.84823430e-01 -5.29329956e-01 -6.39585853e-01
2.43323892e-01 -4.25354183e-01 -5.56621492e-01 2.51286566e-01
1.81977957e-01 -7.71392524e-01 -3.94803375e-01 8.05405796e-01
8.43492568e-01 -2.44458899e-01 5.79119742e-01 -9.12158608e-01
-1.00621355e+00 1.10155553e-01 -5.62765360e-01 1.99386463e-01
9.38755646e-02 4.85018313e-01 6.14198506e-01 -5.13880730e-01
1.06895125e+00 6.83543205e-01 6.56135857e-01 8.82777452e-01
-1.35079324e+00 -4.39190000e-01 -5.26548982e-01 -1.25544414e-01
-1.07160223e+00 -3.27234358e-01 4.54008073e-01 -4.55082685e-01
7.52194822e-01 3.89372766e-01 8.26723576e-01 1.43495142e+00
6.50084555e-01 6.46202028e-01 1.15518165e+00 -2.57505953e-01
2.88007468e-01 2.83202291e-01 -3.32277179e-01 7.52898455e-01
5.07373750e-01 2.39232853e-02 -3.24613780e-01 -5.57016969e-01
9.99522984e-01 1.42753288e-01 -2.75970310e-01 -3.12369704e-01
-1.48379457e+00 1.14309168e+00 3.86091948e-01 3.22038084e-01
-4.88914341e-01 1.06715582e-01 5.31468987e-01 3.47152390e-02
5.45852780e-01 4.17123199e-01 1.19054198e-01 -1.56458005e-01
-9.26624656e-01 -5.34135178e-02 6.12092793e-01 5.49684942e-01
5.69782183e-02 -1.18424408e-01 -1.09367266e-01 7.98345029e-01
5.99566698e-02 6.29397810e-01 7.59914041e-01 -8.86595547e-01
2.92763680e-01 2.74910510e-01 -1.80690456e-02 -1.14371622e+00
-4.14554566e-01 -4.02951002e-01 -1.27541530e+00 -1.81551892e-02
4.24605995e-01 -2.55611360e-01 -9.45878386e-01 1.56329882e+00
3.82603049e-01 -3.82896923e-02 1.05320506e-01 8.61784399e-01
5.02979100e-01 4.18488204e-01 1.19945228e-01 -1.35941476e-01
1.66706812e+00 -7.05573201e-01 -6.22817874e-01 -2.06763685e-01
8.10098588e-01 -7.47294486e-01 9.55513775e-01 3.04609627e-01
-1.04585159e+00 -6.25852272e-02 -8.92033160e-01 -1.58060640e-01
-5.48034869e-02 1.93451717e-01 9.56080437e-01 9.19784307e-01
-1.05650151e+00 5.50540328e-01 -1.22891974e+00 -5.77777386e-01
7.82468855e-01 3.63955081e-01 -5.82462490e-01 -5.08718967e-01
-1.06854177e+00 8.23878706e-01 -1.38847828e-01 1.11767657e-01
-8.58254254e-01 -7.79895425e-01 -7.31166661e-01 -1.68952659e-01
-8.85993838e-02 -1.03167832e+00 8.25594366e-01 -5.89101434e-01
-1.23626101e+00 9.06300366e-01 5.37830070e-02 -5.51168323e-01
8.51857185e-01 1.77715629e-01 -3.06537807e-01 4.90120143e-01
2.07274288e-01 8.73356462e-01 8.40227485e-01 -9.09967601e-01
-1.27482161e-01 -4.14742887e-01 -7.88653269e-02 6.73355162e-02
-6.83939099e-01 -1.64087668e-01 -4.35955346e-01 -6.41852736e-01
-9.65065360e-02 -1.16547465e+00 -5.88815093e-01 4.47374791e-01
-2.02666312e-01 4.20571238e-01 5.83594918e-01 -8.28729451e-01
8.92884016e-01 -2.24081349e+00 -1.09593235e-01 1.61072537e-01
4.53050882e-01 7.94936270e-02 -3.76718678e-02 2.34197617e-01
2.68192708e-01 2.39923671e-01 -4.94211972e-01 -3.39190006e-01
-1.98350698e-01 3.60842854e-01 -4.84839045e-02 5.63307166e-01
2.80849218e-01 1.00822699e+00 -8.88045549e-01 -7.59307504e-01
1.15976937e-01 6.72975957e-01 -7.98495650e-01 -3.93334553e-02
2.29071125e-01 6.04493499e-01 -4.12685305e-01 6.96214855e-01
5.32616615e-01 -5.92550516e-01 3.86058301e-01 -1.36729494e-01
3.36168587e-01 -1.67747036e-01 -7.90654600e-01 1.73406577e+00
-6.03282690e-01 5.15720904e-01 1.11222744e-01 -6.76659226e-01
4.96883988e-01 2.97112703e-01 8.22300017e-01 -7.61496961e-01
3.81453931e-02 2.77000487e-01 1.95054799e-01 -5.61972439e-01
5.23747861e-01 -3.66076887e-01 -5.85121438e-02 7.51704216e-01
-1.37309849e-01 -4.06709641e-01 2.60793298e-01 2.41430327e-01
1.46856987e+00 -3.27564359e-01 -9.45892110e-02 9.25162807e-03
-1.26894400e-01 1.29373074e-01 2.68393189e-01 5.71850896e-01
-2.20173672e-01 7.43355632e-01 4.45466936e-01 -3.75932872e-01
-1.25124192e+00 -1.15575814e+00 -5.64218044e-01 3.60543698e-01
-1.57356724e-01 -3.47807050e-01 -5.99186301e-01 -5.60632408e-01
-1.33548990e-01 4.92425829e-01 -6.97476804e-01 3.23533751e-02
-3.45990300e-01 -1.38001537e+00 7.72871733e-01 4.47630465e-01
2.98944294e-01 -7.73322523e-01 -8.59188378e-01 3.00107598e-01
-1.32382944e-01 -1.03051734e+00 -4.48973328e-01 -5.89574315e-02
-1.15838826e+00 -9.85976875e-01 -1.23798442e+00 -5.73394060e-01
1.20100367e+00 -3.63344103e-02 7.71405339e-01 2.65676901e-02
-9.72723544e-01 4.67925608e-01 -6.74520712e-03 -1.89108863e-01
-4.91839349e-01 6.19901456e-02 -1.10808104e-01 -2.37855509e-01
-1.50212809e-01 -3.58932137e-01 -9.89392996e-01 5.84464520e-02
-1.33537114e+00 9.28701088e-02 9.70779955e-01 1.20472062e+00
6.44778311e-01 -1.06014907e-01 3.91905725e-01 -1.05680823e+00
9.33952332e-01 -5.64531744e-01 -2.04505891e-01 1.95396170e-02
-6.74873471e-01 3.62031050e-02 4.74855870e-01 -4.82386410e-01
-9.11885083e-01 3.94955575e-02 -2.04834208e-01 -3.13658506e-01
1.62607655e-01 4.58801270e-01 5.82345665e-01 -3.88453226e-03
8.91475260e-01 2.25421518e-01 4.84059632e-01 -2.26812251e-02
3.33695114e-01 6.32439554e-01 2.58129209e-01 -4.88184720e-01
5.87411821e-01 7.47321069e-01 -3.68773490e-02 -8.42603803e-01
-3.91902357e-01 2.26473641e-02 -2.89707541e-01 4.45882343e-02
8.15697074e-01 -7.41453767e-01 -5.04688621e-01 2.19036072e-01
-7.66716838e-01 -2.83020675e-01 -5.21525145e-01 5.81026316e-01
-3.40437502e-01 5.31258047e-01 -1.00198889e+00 -3.83874536e-01
-5.68849206e-01 -1.35016143e+00 1.01686811e+00 -1.70352273e-02
-4.25978363e-01 -1.25929022e+00 -5.10743447e-03 4.76611137e-01
6.78334653e-01 5.60679078e-01 9.91685688e-01 -2.38175124e-01
-5.01574457e-01 -3.76268059e-01 -3.22679102e-01 2.39420682e-01
3.88194621e-01 -1.66355029e-01 -7.72388101e-01 -4.80432153e-01
1.74963757e-01 -4.52078909e-01 5.41227102e-01 4.45701659e-01
1.24923956e+00 -2.38984168e-01 -4.70968992e-01 3.80409688e-01
1.24498510e+00 1.02532744e-01 7.44675457e-01 2.36771896e-01
3.68131965e-01 3.40410978e-01 3.50377560e-01 4.14525002e-01
3.07541341e-01 3.31457675e-01 1.84857413e-01 -3.96323174e-01
-1.83622211e-01 -1.69363543e-02 1.02436423e-01 9.06211197e-01
4.11594212e-02 -2.44451705e-02 -9.93609726e-01 7.71445751e-01
-1.38042569e+00 -6.97698891e-01 -1.69526152e-02 2.22673225e+00
1.06047547e+00 7.93524832e-02 -2.28721336e-01 -1.66668475e-01
3.47540647e-01 -2.76402272e-02 -6.99880242e-01 -2.39151731e-01
1.22614168e-01 3.18431050e-01 6.13857746e-01 2.22595349e-01
-8.25577736e-01 2.87699431e-01 6.48996973e+00 6.74981534e-01
-1.38324583e+00 4.48160142e-01 1.04544115e+00 -4.21374828e-01
-5.06533027e-01 -2.73873776e-01 -2.87510693e-01 4.69740957e-01
1.09574592e+00 -2.15680420e-01 2.08594263e-01 7.00575888e-01
1.37620971e-01 -3.13245416e-01 -9.13358867e-01 9.93354619e-01
1.15224212e-01 -1.50003278e+00 -1.24408849e-01 4.58492696e-01
7.63885796e-01 4.19991352e-02 3.36141318e-01 -3.39076146e-02
1.77623078e-01 -1.17985165e+00 2.45795101e-01 3.29436302e-01
1.10456038e+00 -4.95406747e-01 8.48172545e-01 1.98346138e-01
-5.82963943e-01 1.48372874e-01 -2.96168059e-01 4.15573269e-01
1.56917557e-01 9.06569719e-01 -1.04346502e+00 4.13740724e-01
5.21253526e-01 3.56940001e-01 -5.41337192e-01 7.28762925e-01
7.32263178e-02 4.08164918e-01 -4.09013003e-01 2.20772251e-02
1.42425999e-01 -4.43967618e-02 9.66152847e-02 1.08076394e+00
5.72954178e-01 -4.50068386e-03 -1.77891389e-01 7.68933356e-01
-1.39573187e-01 3.45121250e-02 -7.84951210e-01 -2.55597174e-01
7.43840039e-02 1.47809088e+00 -8.97845745e-01 -1.47749647e-01
-3.33091587e-01 1.17663515e+00 3.26210856e-02 -5.01772389e-02
-1.03242171e+00 -2.62138516e-01 3.35589439e-01 2.96751946e-01
2.96099633e-02 -2.28039324e-01 -3.39844823e-01 -1.09702575e+00
9.69173678e-04 -9.13883567e-01 2.65865415e-01 -5.60271502e-01
-1.38695419e+00 6.06536210e-01 -2.25988284e-01 -1.09935975e+00
-4.03079063e-01 -4.95149970e-01 -1.25083685e-01 7.43750513e-01
-1.28085077e+00 -1.05239606e+00 -2.53702641e-01 3.32882345e-01
1.30704924e-01 2.47358382e-01 1.19787705e+00 4.43791687e-01
-4.59189802e-01 5.56376100e-01 3.39408189e-01 1.92236334e-01
6.77327037e-01 -8.57746065e-01 1.83216453e-01 5.56789696e-01
-5.72411232e-02 9.50431645e-01 3.65047276e-01 -6.09896183e-01
-1.58211315e+00 -1.03102243e+00 6.87364340e-01 -3.06012541e-01
7.62216032e-01 -2.52308041e-01 -6.30023956e-01 5.46951175e-01
1.69125900e-01 2.23873675e-01 9.81809020e-01 -2.06216812e-01
1.53971806e-01 4.48258668e-02 -1.45830464e+00 8.27927887e-01
7.97917664e-01 -5.59371650e-01 -4.67415042e-02 6.42498255e-01
2.36955300e-01 -6.80629015e-01 -1.40319443e+00 5.00413701e-02
4.83608931e-01 -6.53074861e-01 9.16666567e-01 -1.76305115e-01
8.14633667e-01 -2.09709741e-02 8.09586421e-02 -1.24918723e+00
-3.67615335e-02 -4.10391450e-01 1.14895746e-01 9.31494057e-01
4.39604044e-01 -7.49209881e-01 9.41899896e-01 1.00307894e+00
2.26663634e-01 -9.74581242e-01 -1.10684144e+00 -5.28077722e-01
1.45982951e-01 -3.75437826e-01 2.34891489e-01 1.02031004e+00
-6.43491149e-02 -8.64354521e-02 -2.28022844e-01 -8.74001235e-02
5.67023814e-01 -2.43903100e-02 5.06130755e-01 -8.04317296e-01
-4.30012971e-01 -5.53831980e-02 -4.26551551e-01 -6.50825918e-01
-1.90700620e-01 -1.14371407e+00 -2.59371936e-01 -1.67185616e+00
4.36435401e-01 -7.78393149e-01 -7.10066035e-02 5.82444370e-01
-6.59325942e-02 6.98991239e-01 6.61172420e-02 4.05520350e-01
-5.81566058e-02 4.39535737e-01 1.66584527e+00 -4.03353035e-01
1.04228526e-01 -4.26866889e-01 -7.77315378e-01 4.30997044e-01
6.95165634e-01 -6.44333780e-01 -5.62444985e-01 -5.41820645e-01
5.50640821e-02 2.32876435e-01 4.59483176e-01 -9.60114002e-01
1.35474578e-01 1.31399423e-01 5.91647804e-01 -6.92728981e-02
4.76580799e-01 -6.79737270e-01 4.29418743e-01 8.84537280e-01
-1.23274855e-01 -1.01497546e-01 3.03631753e-01 5.63786805e-01
-7.44489953e-02 -3.17584634e-01 6.48278594e-01 -2.09119469e-01
-2.58241653e-01 2.85558701e-01 -3.37933064e-01 2.15621814e-02
1.15747213e+00 -1.43291861e-01 -2.01066539e-01 -2.98756063e-01
-7.67870128e-01 -1.60400927e-01 6.20830536e-01 1.13198191e-01
6.55099988e-01 -1.23778296e+00 -6.46386147e-01 2.10783780e-01
1.21195801e-02 -5.21951430e-02 5.15447319e-01 1.09407091e+00
-8.77667844e-01 2.75790513e-01 -2.31831849e-01 -7.70204365e-01
-1.06967628e+00 3.65612090e-01 6.26086146e-02 -6.64618433e-01
-8.66078377e-01 5.76754212e-01 -4.43199603e-03 -3.59386265e-01
-2.25431845e-01 -4.14024472e-01 3.18966329e-01 -5.98240038e-03
4.72380489e-01 1.19477920e-01 1.52342096e-01 -2.54416466e-01
-2.71420360e-01 1.87001362e-01 -3.25255930e-01 -2.36289680e-01
1.46055496e+00 7.29162544e-02 -1.56311656e-03 7.86572881e-03
1.06214046e+00 1.74243748e-01 -1.22202432e+00 1.93087891e-01
-3.71531636e-01 -5.26318669e-01 8.11889544e-02 -6.25053465e-01
-1.23710001e+00 8.84124637e-01 8.41496110e-01 2.14979693e-01
1.10470200e+00 -1.57757968e-01 1.13131332e+00 9.67466906e-02
4.68736172e-01 -8.08790505e-01 1.56297535e-01 -1.80406421e-02
7.19188392e-01 -1.20165670e+00 2.40021423e-01 -1.45584822e-01
-8.46473515e-01 9.70955372e-01 1.96889043e-01 1.05204657e-01
4.51298177e-01 4.64745015e-01 9.53368992e-02 -2.65868723e-01
-5.86754680e-01 3.64350408e-01 -1.30291566e-01 6.93961978e-01
4.80931401e-01 1.08796783e-01 -4.29124266e-01 2.39733458e-01
-2.23822162e-01 2.72389352e-01 7.14071274e-01 1.10268509e+00
2.49924898e-01 -1.26387763e+00 -3.73391479e-01 8.39226604e-01
-7.83673882e-01 -9.97192487e-02 -6.85613230e-03 7.89018631e-01
-8.77656192e-02 6.43516660e-01 -5.26304431e-02 -3.64363343e-02
7.55776390e-02 -1.88019633e-01 5.60052812e-01 -5.37724793e-01
-4.36867714e-01 -1.15458690e-01 5.29417396e-02 -3.68669063e-01
-2.83675641e-01 -7.85358906e-01 -1.06043327e+00 -5.23148119e-01
-1.66237161e-01 -1.44793987e-01 7.66565979e-01 5.03709137e-01
6.60832882e-01 7.14991808e-01 2.85516202e-01 -7.73885369e-01
-5.18933177e-01 -7.13994086e-01 -6.18221283e-01 5.38350821e-01
-1.84233710e-02 -3.95419657e-01 -1.93977475e-01 1.59075484e-01] | [14.31087875366211, -1.9556688070297241] |
056e2253-462f-421a-a96f-b16e5d6d57f5 | hierarchical-neural-implicit-pose-network-for | 2112.00958 | null | https://arxiv.org/abs/2112.00958v1 | https://arxiv.org/pdf/2112.00958v1.pdf | Hierarchical Neural Implicit Pose Network for Animation and Motion Retargeting | We present HIPNet, a neural implicit pose network trained on multiple subjects across many poses. HIPNet can disentangle subject-specific details from pose-specific details, effectively enabling us to retarget motion from one subject to another or to animate between keyframes through latent space interpolation. To this end, we employ a hierarchical skeleton-based representation to learn a signed distance function on a canonical unposed space. This joint-based decomposition enables us to represent subtle details that are local to the space around the body joint. Unlike previous neural implicit method that requires ground-truth SDF for training, our model we only need a posed skeleton and the point cloud for training, and we have no dependency on a traditional parametric model or traditional skinning approaches. We achieve state-of-the-art results on various single-subject and multi-subject benchmarks. | ['Sameh Khamis', 'Sanja Fidler', 'Maria Shugrina', 'Kangxue Yin', 'Sourav Biswas'] | 2021-12-02 | null | null | null | null | ['motion-retargeting'] | ['computer-vision'] | [-1.22215366e-02 2.18389779e-01 -2.93193191e-01 -2.93594331e-01
-7.76823103e-01 -3.66093010e-01 5.73098302e-01 -3.25627148e-01
-3.15219849e-01 7.66876757e-01 4.16793078e-01 3.64136100e-01
2.30825320e-01 -4.04070467e-01 -7.56676018e-01 -6.01731122e-01
-3.19453925e-01 5.14589190e-01 3.01192820e-01 -2.14126915e-01
-3.16538036e-01 4.61925477e-01 -8.56113970e-01 -1.25099262e-02
4.03099030e-01 4.56650883e-01 -5.24454176e-01 6.15624428e-01
6.59531176e-01 2.56351173e-01 -5.18889964e-01 -1.80812731e-01
4.81965780e-01 -3.16048503e-01 -6.40649855e-01 1.19493390e-02
1.06815088e+00 -3.91666472e-01 -6.15851760e-01 7.30892658e-01
5.34939826e-01 3.98787260e-01 5.84657371e-01 -9.47208464e-01
-2.90412456e-01 6.42479658e-02 -8.29287112e-01 -1.47172138e-01
5.58621705e-01 4.38865930e-01 8.25738609e-01 -9.07697201e-01
1.05492604e+00 1.37536895e+00 1.08301628e+00 8.10036719e-01
-1.85569131e+00 -4.41725016e-01 1.94834799e-01 -9.66781080e-02
-1.36122930e+00 -4.73368555e-01 1.01798213e+00 -5.62202036e-01
4.11386132e-01 3.04785818e-01 1.09068620e+00 1.38089061e+00
4.79400814e-01 6.00358784e-01 1.02464449e+00 -6.24335967e-02
8.45759548e-03 -6.37122452e-01 -4.63577211e-02 6.74585164e-01
5.62939607e-02 2.12312087e-01 -7.88272917e-01 -1.42471880e-01
1.42162728e+00 -1.54780596e-01 -4.07052457e-01 -1.18341732e+00
-1.44954133e+00 7.72410810e-01 6.48485363e-01 -1.10423185e-01
-4.64452267e-01 6.47069395e-01 3.07291240e-01 -2.37237617e-01
4.20693725e-01 4.47420865e-01 -4.53778267e-01 -5.41089065e-02
-1.26941097e+00 7.94842064e-01 6.93333089e-01 5.83040774e-01
6.05507135e-01 1.73608944e-01 -2.22180605e-01 2.90720910e-01
3.71787310e-01 2.79963225e-01 1.78045809e-01 -1.45278525e+00
1.88801900e-01 2.00442672e-01 -8.37996975e-03 -1.01427221e+00
-4.85180289e-01 -6.56690240e-01 -7.54207730e-01 4.98935223e-01
7.21318841e-01 -1.37972817e-01 -1.25980377e+00 1.96829259e+00
6.37865841e-01 3.97125155e-01 -5.94072580e-01 1.14426720e+00
6.50671005e-01 1.77785948e-01 1.71085894e-01 1.21588923e-01
1.35583735e+00 -9.66073513e-01 -5.43384433e-01 -2.84669071e-01
2.48210624e-01 -4.47212338e-01 1.08975935e+00 3.54168266e-01
-1.34543800e+00 -5.00372112e-01 -1.12705743e+00 -4.56646860e-01
-6.28599301e-02 -1.17569476e-01 5.60089827e-01 1.84963286e-01
-1.01203060e+00 9.19834673e-01 -1.33204854e+00 -2.06137285e-01
3.12170208e-01 5.48612714e-01 -7.35040009e-01 2.37705648e-01
-1.08981848e+00 8.74424398e-01 -1.55488299e-02 2.38877460e-01
-1.07228804e+00 -1.08695459e+00 -1.12764156e+00 -4.08459693e-01
2.78728783e-01 -1.36499774e+00 1.14544606e+00 -7.42441893e-01
-1.62392735e+00 9.19222593e-01 -8.76270682e-02 -3.47114772e-01
8.69292498e-01 -6.97667480e-01 6.21590205e-02 3.46926600e-01
6.79087862e-02 9.06059504e-01 1.01550198e+00 -1.22389269e+00
3.63546669e-01 -3.66090685e-01 -1.74614042e-02 3.58863562e-01
2.39580408e-01 -1.02962106e-01 -6.79975152e-01 -1.25799882e+00
3.20355415e-01 -1.16909528e+00 -4.17467803e-01 7.19488978e-01
-6.03458822e-01 1.95655078e-01 8.84425163e-01 -1.07193148e+00
7.73942530e-01 -1.86429644e+00 7.61653662e-01 3.17870170e-01
6.46487236e-01 -7.10371733e-02 -5.60256653e-02 -1.95814624e-01
-3.75883937e-01 -1.38923705e-01 -3.78444254e-01 -7.28356421e-01
-4.94410768e-02 2.12044910e-01 2.01431140e-01 9.88639712e-01
8.71262178e-02 1.12137699e+00 -9.03773904e-01 -5.26247263e-01
1.89700305e-01 8.88565660e-01 -7.53160775e-01 1.26759276e-01
-1.69618919e-01 1.06347203e+00 -3.14534009e-01 5.37594378e-01
3.80194694e-01 -1.26685083e-01 2.40441621e-03 -5.95801234e-01
2.14757666e-01 2.42909312e-01 -1.11369884e+00 2.49258757e+00
-1.96797922e-01 3.88414770e-01 3.36893499e-01 -6.63599610e-01
4.41389918e-01 4.82935816e-01 8.53972793e-01 -2.84214556e-01
1.28477111e-01 -6.59033358e-02 -1.85410112e-01 1.26505233e-02
1.48409352e-01 -2.76379108e-01 -6.70736209e-02 1.70508176e-01
8.45803022e-02 -2.25536823e-02 -1.74240977e-01 7.34921619e-02
9.78949547e-01 9.27322924e-01 1.72652826e-01 -3.76402587e-01
2.25082040e-01 -2.53377527e-01 6.61610901e-01 6.87635243e-02
-2.02065080e-01 1.08246112e+00 3.42972517e-01 -6.92134023e-01
-1.03243995e+00 -1.49704242e+00 -2.12531909e-01 6.66835964e-01
-8.98406357e-02 -4.58029240e-01 -7.95741618e-01 -5.87540090e-01
6.78163394e-02 3.07935387e-01 -1.01663101e+00 -2.07071692e-01
-1.07929766e+00 -4.12328809e-01 4.87523079e-01 6.77934706e-01
3.05668056e-01 -7.16632068e-01 -6.01203501e-01 2.55816907e-01
-2.68200874e-01 -1.11149669e+00 -8.09899807e-01 -9.60949361e-02
-1.04348242e+00 -1.12019277e+00 -1.16879630e+00 -2.90786713e-01
7.62045801e-01 -4.86747742e-01 1.27183235e+00 -1.71949357e-01
-4.24815983e-01 4.57365274e-01 1.50349587e-01 1.50446340e-01
1.00193331e-02 7.36066699e-02 2.33392358e-01 -1.48294181e-01
-1.14659689e-01 -1.00004220e+00 -1.00643480e+00 2.65068412e-01
-4.55899894e-01 1.47065118e-01 2.83021331e-01 6.43582642e-01
6.72825038e-01 -6.34210169e-01 -5.89706451e-02 -5.64788759e-01
2.13662252e-01 -1.15598775e-01 -1.69620886e-01 -3.92041802e-02
-2.18330720e-03 8.11700430e-03 7.66353905e-02 -6.64777398e-01
-6.26873016e-01 4.87678915e-01 2.02572867e-02 -7.42869198e-01
-6.72358721e-02 1.98722854e-01 -1.63408920e-01 -3.40117604e-01
8.15423131e-01 -8.64454061e-02 1.14320531e-01 -7.76119769e-01
7.30338395e-01 -4.51964438e-01 1.09575808e+00 -1.02348864e+00
1.18157732e+00 5.71197271e-01 3.90286714e-01 -6.55679941e-01
-6.30895197e-01 -2.44075492e-01 -1.42833138e+00 -2.03525916e-01
1.35709739e+00 -1.00066388e+00 -5.67940652e-01 3.24573010e-01
-1.25750113e+00 -6.84000731e-01 -5.34648657e-01 4.43722486e-01
-7.40083933e-01 5.02752721e-01 -8.09196353e-01 -1.69191033e-01
-3.49145740e-01 -1.04031038e+00 1.36366141e+00 -2.01259017e-01
-1.00325143e+00 -1.20456207e+00 5.29321969e-01 1.63111508e-01
1.82590839e-02 1.15160751e+00 4.69973087e-01 -1.03485629e-01
-6.50693595e-01 -2.81736195e-01 1.92155823e-01 5.23721389e-02
7.37513155e-02 9.93254580e-05 -8.89989614e-01 -5.30347526e-01
-1.81899741e-01 -1.99423641e-01 5.58560073e-01 6.58184052e-01
1.08334744e+00 -4.33803946e-01 -2.58666962e-01 1.23438656e+00
9.86605227e-01 -5.36350846e-01 6.76127374e-01 3.69094014e-01
1.23834181e+00 4.86887366e-01 2.27079272e-01 1.43013701e-01
3.91972125e-01 1.12147737e+00 2.03613013e-01 -4.37338591e-01
-4.54166293e-01 -3.99671555e-01 3.61880392e-01 5.58601320e-01
-5.92798114e-01 5.29213548e-01 -7.54974186e-01 3.68051797e-01
-1.63881338e+00 -6.45926476e-01 -1.05435722e-01 2.24433637e+00
1.09779227e+00 6.52289540e-02 4.21932250e-01 -1.38552830e-01
2.40458935e-01 3.27888459e-01 -6.21765077e-01 2.09927604e-01
1.72159985e-01 5.33034563e-01 4.35174257e-01 6.53903306e-01
-1.23605263e+00 8.99681687e-01 7.16009283e+00 3.66010517e-01
-1.13458657e+00 1.77506447e-01 3.57461601e-01 -4.32853401e-01
-1.94713697e-01 -2.98782028e-02 -4.23537761e-01 1.30904987e-01
5.52674949e-01 -4.15205397e-02 2.54195780e-01 6.72969878e-01
2.22510278e-01 1.19182520e-01 -1.37609708e+00 8.27691853e-01
-1.14652157e-01 -1.15020108e+00 -1.71711612e-02 1.37884706e-01
6.92355692e-01 -2.46131808e-01 6.87352419e-02 3.48720290e-02
2.56501257e-01 -1.14777017e+00 8.15623105e-01 6.97611392e-01
1.01276672e+00 -3.48876685e-01 9.26649719e-02 5.23483530e-02
-1.36998439e+00 6.83191895e-01 9.42763127e-03 1.77157015e-01
4.92865413e-01 3.22798014e-01 -3.65860254e-01 5.10676801e-01
7.10993350e-01 8.02822232e-01 -5.43535471e-01 9.31887031e-01
-4.78680730e-01 3.32545847e-01 -4.65929329e-01 8.54681015e-01
-1.47347867e-01 -9.17507038e-02 9.41654742e-01 9.40457404e-01
5.69947511e-02 1.09164245e-01 3.51571798e-01 9.37818170e-01
1.56141728e-01 -1.35869130e-01 -3.79227400e-01 3.55704039e-01
-1.40635699e-01 1.19912541e+00 -4.85114396e-01 -2.40196213e-01
-7.92109668e-02 1.17320693e+00 1.26209989e-01 7.78761506e-01
-9.22545612e-01 -5.91528937e-02 8.50553989e-01 5.19472003e-01
3.89858074e-02 -8.16865265e-01 -3.75837833e-01 -1.48056805e+00
8.12069029e-02 -9.24445689e-01 2.14641079e-01 -7.79744387e-01
-9.92946208e-01 2.91719854e-01 3.62850279e-01 -1.04453993e+00
-4.16284949e-01 -4.72214580e-01 -7.07359374e-01 1.14468932e+00
-9.12329018e-01 -1.40629637e+00 -2.44413570e-01 7.83134222e-01
2.69185752e-01 3.21796387e-01 7.79316306e-01 2.94444561e-01
-4.35583383e-01 5.83825588e-01 -6.37061834e-01 4.20460790e-01
9.80968297e-01 -1.26855648e+00 7.33924925e-01 7.64807642e-01
3.05177104e-02 1.15155494e+00 9.12209034e-01 -9.18095767e-01
-1.25725317e+00 -7.91883171e-01 3.55883986e-01 -1.01709247e+00
4.97632027e-01 -4.27371562e-01 -1.00258172e+00 1.07220101e+00
-1.03364423e-01 5.75892627e-01 4.80154842e-01 2.78716564e-01
-4.01639283e-01 6.89895749e-02 -9.59176064e-01 8.78737450e-01
1.20533538e+00 -5.29437542e-01 -6.40517116e-01 1.76514775e-01
7.17647910e-01 -1.06756544e+00 -1.11477327e+00 4.69818771e-01
9.33741748e-01 -5.15163481e-01 1.51134229e+00 -8.83734405e-01
4.12945658e-01 -3.95199686e-01 1.21791974e-01 -1.20223248e+00
-5.43502390e-01 -8.87545645e-01 -7.13528752e-01 6.54877543e-01
5.31071834e-02 -2.55073905e-01 1.29721141e+00 1.00734687e+00
-8.13140441e-03 -7.84075141e-01 -1.03380823e+00 -6.61105454e-01
3.44327211e-01 -2.50554472e-01 4.06121433e-01 1.02862144e+00
-3.58893633e-01 4.98423316e-02 -5.73810339e-01 1.10074975e-01
1.16536331e+00 -3.22066277e-01 1.05024827e+00 -1.08205724e+00
-5.78338385e-01 -1.60964683e-01 -4.89020497e-01 -1.21133101e+00
1.71164215e-01 -6.36411786e-01 8.34038481e-02 -1.30016911e+00
3.26102152e-02 -1.28119916e-01 -1.00554869e-01 7.67197609e-01
-1.78639188e-01 6.89166844e-01 1.68643087e-01 2.43273139e-01
-2.67683268e-01 7.85994053e-01 1.74500072e+00 -4.56452789e-03
-2.40975976e-01 -1.08098090e-01 -3.07154298e-01 9.63936448e-01
5.08612990e-01 -4.96409595e-01 -3.85089904e-01 -2.57391423e-01
4.01696051e-03 6.83440343e-02 1.04130542e+00 -1.07519233e+00
-3.40182073e-02 -2.06514463e-01 8.47590983e-01 -4.27624345e-01
7.45535433e-01 -7.23273814e-01 4.74527627e-01 5.35940707e-01
-2.27753654e-01 -8.00675899e-02 2.51422644e-01 4.69257146e-01
1.28082275e-01 3.95422250e-01 7.52427399e-01 -2.01927140e-01
-4.39032406e-01 8.07971239e-01 4.93414775e-02 1.39336124e-01
7.64182389e-01 -3.68677706e-01 1.62779704e-01 -3.50301504e-01
-1.41924834e+00 7.73988441e-02 8.32059979e-01 2.93918073e-01
4.53574568e-01 -1.67104208e+00 -5.29366553e-01 1.41653985e-01
-1.99313343e-01 3.76305908e-01 1.50710464e-01 1.03477323e+00
-7.55399823e-01 7.65602365e-02 -3.60506356e-01 -7.99686313e-01
-1.19654989e+00 1.87724516e-01 7.08625019e-01 -2.25040346e-01
-1.03856730e+00 6.84865773e-01 4.47791845e-01 -4.71202433e-01
1.29983112e-01 -2.79995561e-01 1.91448405e-01 -2.30731145e-01
1.31085858e-01 3.88065487e-01 -2.43238851e-01 -9.08481658e-01
-4.08409685e-01 9.80515778e-01 2.73539841e-01 -4.65207547e-01
1.16459095e+00 1.83227271e-01 -1.34035759e-02 3.91896695e-01
1.35298419e+00 7.13186488e-02 -1.86450517e+00 -1.24246761e-01
-4.14949059e-01 -4.09951240e-01 -5.64309303e-03 -6.45483017e-01
-1.14281726e+00 9.71873641e-01 4.39709753e-01 -5.45536041e-01
6.91205084e-01 -2.12259695e-01 8.30289125e-01 5.74728474e-02
2.69066364e-01 -8.73727322e-01 2.97919571e-01 2.60153562e-01
1.27982938e+00 -8.98011506e-01 6.44439936e-01 -5.41098058e-01
-2.80719131e-01 1.11424887e+00 6.92471623e-01 -5.31244993e-01
7.01655984e-01 1.02108754e-01 1.32163674e-01 -3.28605056e-01
-3.01202774e-01 2.01414540e-01 1.12897706e+00 7.86479712e-01
6.13195837e-01 5.53611256e-02 -5.18339649e-02 2.05003440e-01
-4.07697290e-01 3.53399217e-02 -3.22290026e-02 8.92736375e-01
-3.24962400e-02 -1.40667331e+00 -4.86644775e-01 -2.84955986e-02
-4.30977702e-01 2.14267910e-01 -3.00480992e-01 1.08731556e+00
1.11021623e-01 -1.37281306e-02 -7.43793324e-02 -1.07579008e-01
4.32192594e-01 1.21222533e-01 8.78297567e-01 -7.93342531e-01
-3.25893432e-01 3.58625561e-01 7.59683996e-02 -1.13433576e+00
-4.20740873e-01 -8.81431818e-01 -1.16267192e+00 -2.23106951e-01
2.79579461e-01 -3.04458916e-01 4.38305497e-01 6.91901743e-01
1.01597108e-01 6.12363160e-01 -8.96259174e-02 -1.52425957e+00
-3.65283549e-01 -6.92035377e-01 -3.77342939e-01 5.70808649e-01
6.11263454e-01 -8.39017332e-01 -7.78739899e-02 3.25463653e-01] | [7.02618932723999, -1.157141923904419] |
9334b422-0263-484a-91e9-c52df7826666 | predicting-stock-price-movement-after | 2206.12528 | null | https://arxiv.org/abs/2206.12528v2 | https://arxiv.org/pdf/2206.12528v2.pdf | Predicting Stock Price Movement after Disclosure of Corporate Annual Reports: A Case Study of 2021 China CSI 300 Stocks | In the current stock market, computer science and technology are more and more widely used to analyse stocks. Not same as most related machine learning stock price prediction work, this work study the predicting the tendency of the stock price on the second day right after the disclosure of the companies' annual reports. We use a variety of different models, including decision tree, logistic regression, random forest, neural network, prototypical networks. We use two sets of financial indicators (key and expanded) to conduct experiments, these financial indicators are obtained from the EastMoney website disclosed by companies, and finally we find that these models are not well behaved to predict the tendency. In addition, we also filter stocks with ROE greater than 0.15 and net cash ratio greater than 0.9. We conclude that according to the financial indicators based on the just-released annual report of the company, the predictability of the stock price movement on the second day after disclosure is weak, with maximum accuracy about 59.6% and maximum precision about 0.56 on our test set by the random forest classifier, and the stock filtering does not improve the performance. And random forests perform best in general among all these models which conforms to some work's findings. | ['Yue Wang', 'Fengyu Han'] | 2022-06-25 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-9.12327349e-01 -2.00387031e-01 -6.91502094e-01 -2.31836382e-02
2.04288825e-01 -7.92214513e-01 6.71493769e-01 -1.34536490e-01
-4.93008554e-01 1.23942494e+00 2.47941747e-01 -7.02285945e-01
9.47151780e-02 -1.23536956e+00 -3.61688763e-01 -5.40006697e-01
-1.51370376e-01 1.15276895e-01 5.41568577e-01 -4.01402771e-01
1.00049031e+00 4.65388060e-01 -1.40335536e+00 -5.93336560e-02
5.68083107e-01 1.68437052e+00 -3.54407698e-01 5.34385443e-02
-4.30419922e-01 1.11863649e+00 -5.85769832e-01 -7.35129058e-01
8.31802070e-01 -4.21299860e-02 -2.10080937e-01 -4.47061718e-01
-2.68115073e-01 -5.23300350e-01 -3.44334380e-03 1.14527345e+00
-1.26689747e-01 -5.75309575e-01 6.75990760e-01 -1.30305910e+00
-7.08152056e-01 9.95838702e-01 -7.62479126e-01 7.14535892e-01
-1.30932694e-02 -1.49136648e-01 1.09775722e+00 -6.93902314e-01
4.57713157e-01 5.85590601e-01 8.22993398e-01 -5.50098494e-02
-7.64314055e-01 -1.34038091e+00 2.77459919e-01 6.65042028e-02
-1.07015288e+00 6.93299770e-02 5.99256694e-01 -7.53391087e-01
7.35342205e-01 2.84090072e-01 1.22244692e+00 3.79615366e-01
8.95775020e-01 1.76949769e-01 1.79486215e+00 -2.50954121e-01
1.11765176e-01 6.30947411e-01 3.63335282e-01 3.39385122e-02
1.15326381e+00 2.89217800e-01 -4.74615693e-01 -3.31820041e-01
6.91348672e-01 2.83176810e-01 -1.64833531e-01 4.88145381e-01
-9.99425769e-01 1.11421108e+00 1.65153325e-01 6.88133001e-01
-7.07970381e-01 -4.30394471e-01 1.53510869e-01 7.56049931e-01
6.66049540e-01 4.10256892e-01 -9.39740777e-01 -8.21989626e-02
-1.14946711e+00 5.25974691e-01 1.09354949e+00 4.89796817e-01
5.63317239e-01 3.96823019e-01 2.78259367e-01 1.41954515e-04
3.49236488e-01 5.36072969e-01 1.10944712e+00 -3.99634928e-01
4.02081728e-01 5.88007689e-01 4.20120895e-01 -1.11857617e+00
-4.77147251e-01 -7.94585466e-01 -8.36814821e-01 5.92773974e-01
5.57982326e-01 -3.35791051e-01 -4.52615619e-01 1.00904977e+00
-4.11741793e-01 1.59629863e-02 2.61024088e-01 5.86147606e-01
4.49483991e-01 7.42754757e-01 -1.57756060e-01 -8.82379413e-01
1.18324471e+00 -6.03679419e-01 -9.20763373e-01 9.96878445e-02
2.04031169e-01 -7.24100769e-01 3.43287259e-01 7.11618721e-01
-9.16092992e-01 -2.62110621e-01 -1.15614271e+00 9.04794335e-01
-7.45005310e-01 -3.42813551e-01 7.20142484e-01 6.20527744e-01
-7.95730472e-01 9.34213817e-01 -3.83765906e-01 5.16318321e-01
-2.01964173e-02 1.69028208e-01 -1.77648310e-02 7.16865718e-01
-1.42828739e+00 1.12412941e+00 5.42439997e-01 -1.10119678e-01
2.30187401e-01 -4.23593640e-01 -2.02189595e-01 -1.34651484e-02
8.36657509e-02 -1.31932497e-01 1.06422210e+00 -9.04883921e-01
-1.21245217e+00 5.31770647e-01 4.34350312e-01 -1.07126904e+00
6.69953823e-01 -1.53202564e-01 -8.03804457e-01 -3.57752681e-01
1.50152713e-01 -1.55854896e-01 3.04848462e-01 -5.97768724e-01
-9.57199156e-01 -5.42407870e-01 -2.62831926e-01 -3.26173395e-01
-2.05460712e-01 3.01886797e-01 4.05809015e-01 -1.09639692e+00
4.26427960e-01 -7.23485649e-01 -1.21710107e-01 -9.92059827e-01
-2.51832843e-01 -1.43878892e-01 3.55769724e-01 -8.55651259e-01
1.68461204e+00 -1.52116084e+00 -9.64579403e-01 5.24159610e-01
-1.11034013e-01 -4.07496393e-02 9.58575130e-01 4.43607211e-01
-4.43143517e-01 5.16341269e-01 4.76747304e-02 5.42934954e-01
-8.11969768e-03 -3.19706231e-01 -8.97503555e-01 3.29525650e-01
-9.31813642e-02 7.83433259e-01 -1.39346644e-01 -1.58790618e-01
-4.32329595e-01 -3.23451966e-01 -2.40242334e-05 1.66637506e-02
7.66957179e-02 -2.31163904e-01 -3.00272733e-01 8.11567724e-01
8.24147165e-01 -1.47752821e-01 -2.34668002e-01 2.07246363e-01
-8.90727878e-01 4.71848816e-01 -1.34297860e+00 3.20037246e-01
3.43187362e-01 4.00424778e-01 -5.47994614e-01 -6.64384604e-01
1.47680771e+00 3.08880150e-01 2.95697570e-01 -5.36178648e-01
-1.27670169e-01 5.95830798e-01 2.15678364e-01 -2.63416348e-03
3.61823946e-01 -6.31148338e-01 -3.18108201e-02 6.74163640e-01
-5.44988394e-01 3.09322536e-01 2.01735958e-01 -2.24273339e-01
7.98738003e-01 -1.95556447e-01 8.70626748e-01 -4.97708559e-01
4.83219475e-01 1.09788567e-01 9.06140506e-01 3.20785582e-01
-7.31148571e-02 1.01989076e-01 8.80282104e-01 -6.50417268e-01
-7.35371232e-01 -6.52519464e-01 -3.04674208e-01 5.51985562e-01
-3.03382188e-01 1.36410132e-01 -3.36765856e-01 -4.42996860e-01
6.40017331e-01 7.42867351e-01 -5.15044570e-01 3.53968203e-01
-8.32780227e-02 -1.15215206e+00 3.16883549e-02 2.76635766e-01
8.44250739e-01 -1.19715869e+00 -6.75768733e-01 3.21100354e-01
5.58695018e-01 -7.26009786e-01 8.67297575e-02 2.41571561e-01
-1.14125490e+00 -1.06648290e+00 -9.01429653e-01 -2.38906652e-01
1.33265823e-01 -7.43846968e-02 1.15797377e+00 5.46389744e-02
5.98405063e-01 -4.14432973e-01 -2.69425094e-01 -1.06990314e+00
-7.35517368e-02 1.25403807e-01 1.39956638e-01 2.01730393e-02
4.95538354e-01 -5.90832293e-01 -4.08398747e-01 2.06140414e-01
-5.17891824e-01 -3.35327089e-01 7.67250240e-01 4.30363476e-01
1.79846093e-01 6.51239455e-01 9.94519711e-01 -8.69153976e-01
6.80881441e-01 -8.51355612e-01 -1.13916886e+00 -5.36354817e-02
-1.63967443e+00 -6.43614680e-02 2.87884265e-01 -2.88533092e-01
-8.47681642e-01 -4.01908129e-01 1.87756956e-01 -8.02189112e-02
2.61350363e-01 9.57909226e-01 3.22077036e-01 1.81929216e-01
1.38584808e-01 5.17428577e-01 6.87530041e-02 -5.96112967e-01
-5.32540083e-01 5.83472490e-01 3.50416511e-01 2.95416832e-01
1.00407565e+00 2.06632450e-01 -3.24872583e-02 -1.83509797e-01
-7.40420341e-01 -1.73632219e-01 -5.80631673e-01 -2.78596193e-01
5.04599214e-01 -8.83554518e-01 -6.78385675e-01 9.00838912e-01
-7.89174378e-01 2.10339859e-01 1.29241899e-01 8.86589944e-01
-1.84171766e-01 -8.80078152e-02 -7.90337503e-01 -1.54489076e+00
-6.81075513e-01 -4.56193924e-01 -2.51415223e-02 3.23056102e-01
-2.59845614e-01 -7.77735114e-01 2.88278520e-01 -3.96563634e-02
6.12138689e-01 5.87208152e-01 6.95728064e-01 -1.21112549e+00
-6.14454329e-01 -5.53039074e-01 -1.84856668e-01 3.12486559e-01
1.52903035e-01 2.73618758e-01 -6.00979090e-01 3.55755575e-02
4.72345144e-01 1.59775048e-01 1.05559313e+00 3.91027331e-01
3.00217807e-01 -7.60871410e-01 -2.34799702e-02 1.78434640e-01
1.61691451e+00 8.28624368e-01 6.73994124e-01 1.25436342e+00
-9.67181772e-02 7.03046799e-01 7.75751352e-01 6.85861409e-01
3.45678926e-01 1.91608831e-01 2.25162163e-01 4.37552959e-01
8.07617664e-01 -1.78003863e-01 6.40957773e-01 9.17876482e-01
-5.74694395e-01 4.49528903e-01 -9.09416080e-01 1.72582656e-01
-1.59090149e+00 -1.30039799e+00 -3.35719436e-01 2.22522807e+00
8.77204597e-01 1.01614916e+00 7.20790565e-01 5.40618241e-01
5.81593871e-01 2.77631283e-01 -3.20315957e-01 -1.02173567e-01
-3.88817847e-01 -1.03629887e-01 1.28696930e+00 9.94548872e-02
-8.48820090e-01 4.98835623e-01 6.80279636e+00 3.05392265e-01
-1.37732494e+00 -2.18994781e-01 9.27983165e-01 1.14630073e-01
-3.94415677e-01 8.13050866e-02 -1.29782593e+00 1.09874904e+00
1.14815640e+00 -7.47085094e-01 -1.61691159e-01 9.75665569e-01
1.59192368e-01 -3.20274740e-01 -2.75428742e-01 5.60991287e-01
-4.02737677e-01 -1.61310029e+00 -2.13474467e-01 5.88174999e-01
6.04489028e-01 -1.97988689e-01 8.00020918e-02 2.62517899e-01
2.48252437e-01 -6.86461568e-01 1.06704473e+00 1.15038073e+00
1.22264482e-01 -9.07825410e-01 1.44264376e+00 6.35962248e-01
-9.98781443e-01 -2.12932765e-01 -3.41701120e-01 -7.31965184e-01
-7.79964402e-02 9.61903989e-01 -4.76061821e-01 3.85118127e-01
9.62564349e-01 5.95824301e-01 -4.61968839e-01 9.03026760e-01
1.62153747e-02 6.97312295e-01 -1.95379704e-01 -4.71275121e-01
2.48540565e-01 -5.50541162e-01 5.90140261e-02 7.60639846e-01
6.61922872e-01 3.41228545e-01 -4.10602719e-01 6.29218221e-01
3.92452478e-01 3.27139884e-01 -6.79283500e-01 1.05193943e-01
2.75511652e-01 7.23592222e-01 -6.59959137e-01 -4.72696662e-01
-9.07731354e-01 8.94864723e-02 -2.63257086e-01 -1.25735113e-02
-3.98800194e-01 -3.12371701e-01 3.57044697e-01 6.81289732e-01
3.65770638e-01 -5.67196263e-03 -8.05985332e-01 -1.12639582e+00
2.20772803e-01 -4.89040643e-01 3.35751921e-01 -6.29431129e-01
-1.37301981e+00 5.48282206e-01 6.48920313e-02 -1.51080573e+00
-5.28813481e-01 -9.06898260e-01 -9.76287484e-01 9.40202177e-01
-1.72635162e+00 -1.12340979e-01 2.57725507e-01 3.04451436e-01
-1.56071424e-01 -9.73292470e-01 5.00395775e-01 -1.12654671e-01
-5.40919423e-01 1.02458233e-02 1.65448651e-01 4.40345675e-01
3.79323512e-01 -1.46043479e+00 5.07543266e-01 6.42496884e-01
-1.31739497e-01 3.95192981e-01 7.13422000e-01 -1.25822294e+00
-6.04272544e-01 -5.45965970e-01 1.14353979e+00 -2.81786863e-02
1.37522304e+00 3.12900126e-01 -9.68258262e-01 7.29146123e-01
4.63525057e-01 -3.56039613e-01 6.76736414e-01 -2.01185152e-01
-2.39108935e-01 -3.34225446e-01 -1.08032548e+00 1.99717149e-01
2.33946443e-01 1.98249727e-01 -9.45814192e-01 -1.41854212e-01
4.70348954e-01 1.63717076e-01 -1.27181935e+00 3.88186514e-01
9.34176683e-01 -1.51252961e+00 4.90466744e-01 -4.60673153e-01
2.70342022e-01 2.56674681e-02 -7.37196254e-03 -1.09001470e+00
-5.02192259e-01 -4.25929010e-01 -2.54504569e-02 1.29311776e+00
8.67021143e-01 -1.52252769e+00 1.09945929e+00 7.03237355e-01
5.34066021e-01 -8.29958975e-01 -8.63943994e-01 -9.85896885e-01
3.50929707e-01 -1.22434177e-01 1.02703941e+00 1.10810125e+00
1.43742729e-02 -1.06763914e-02 -2.85996974e-01 -1.97172523e-01
5.69526732e-01 6.00168765e-01 6.81278348e-01 -1.74826455e+00
-1.66694790e-01 -7.85103500e-01 -3.61816049e-01 -4.53656316e-01
4.60424423e-02 -2.93152779e-01 -7.64352858e-01 -1.08576608e+00
-8.38513374e-02 -3.99778485e-01 -7.31738210e-01 1.72714099e-01
9.14485753e-02 -1.09281056e-01 2.72111952e-01 8.83662343e-01
2.19972163e-01 9.94636267e-02 1.02618015e+00 3.30799036e-02
-2.19678313e-01 7.86167562e-01 -9.75492299e-01 9.75148380e-01
1.08803678e+00 -3.41131449e-01 2.80191272e-01 6.14193201e-01
6.35164440e-01 3.05787146e-01 -1.02047376e-01 -7.46889293e-01
1.11340813e-01 -5.94945192e-01 7.15305388e-01 -1.13093007e+00
-7.78454095e-02 -8.20178092e-01 4.60122734e-01 9.79776621e-01
1.21613324e-01 6.98327899e-01 -7.55074173e-02 1.94517344e-01
-6.39008403e-01 -6.75883353e-01 4.14889634e-01 -4.65311766e-01
-4.21729982e-01 1.37193784e-01 -4.02472436e-01 -2.62271762e-01
1.16834915e+00 -4.16692495e-01 -3.69568229e-01 -5.91632843e-01
-4.84890968e-01 -7.62725547e-02 2.79622525e-01 1.39454633e-01
1.58093005e-01 -1.45725548e+00 -8.80393624e-01 1.25753302e-02
-2.65281826e-01 -6.95964217e-01 -5.19148409e-01 8.19412529e-01
-6.20448172e-01 8.30793858e-01 -4.95068967e-01 2.94144630e-01
-6.72591448e-01 5.66776574e-01 2.81750798e-01 -6.79198861e-01
-2.94673234e-01 4.04924929e-01 -2.35083073e-01 4.10587341e-01
-7.40016252e-02 -6.20720923e-01 -7.81906784e-01 8.30227911e-01
7.67760754e-01 6.37273073e-01 1.27647191e-01 -5.56691587e-01
-2.34501213e-01 5.90705276e-01 -1.55138567e-01 -4.48364705e-01
1.68069649e+00 2.55895048e-01 -3.72557670e-01 1.01319408e+00
7.88088858e-01 4.02706563e-01 -8.34513664e-01 -1.98655993e-01
8.05638790e-01 -4.77014214e-01 -9.67175290e-02 -5.84076524e-01
-1.36283696e+00 7.66557604e-02 2.43637592e-01 1.19601095e+00
1.06187236e+00 -3.82265031e-01 6.27852380e-01 2.61861682e-01
5.41493952e-01 -1.11727786e+00 -6.58343494e-01 3.19830894e-01
1.12512422e+00 -1.14895856e+00 5.12581468e-01 -2.57347256e-01
-7.31686652e-01 1.14127421e+00 2.86919832e-01 -5.65092623e-01
1.41682315e+00 4.61298555e-01 2.67718315e-01 5.87078333e-02
-1.05040693e+00 1.57809585e-01 8.18600580e-02 1.73529088e-01
3.91168773e-01 1.60727054e-01 -7.96159744e-01 1.33705354e+00
-1.08621347e+00 6.96291998e-02 8.20294976e-01 8.52212489e-01
-8.90958130e-01 -7.66466379e-01 -7.59745836e-01 1.00866556e+00
-1.23066235e+00 -2.17927977e-01 -5.29780984e-01 1.23564970e+00
-1.36302993e-01 5.64945400e-01 5.33310711e-01 -4.05846894e-01
4.64296192e-01 3.56676251e-01 -4.22074974e-01 -2.53750950e-01
-7.76881993e-01 1.46847755e-01 1.44721046e-02 -1.34094268e-01
-4.63210404e-01 -1.20623052e+00 -1.04147816e+00 -6.73122823e-01
-3.45498145e-01 4.46276754e-01 5.28562725e-01 7.59224832e-01
-2.70035028e-01 -8.22595432e-02 1.15277636e+00 -4.17862982e-01
-8.19015682e-01 -1.19994724e+00 -1.42295957e+00 -2.49812707e-01
3.18036020e-01 -7.14696169e-01 -9.25606310e-01 -7.74201900e-02] | [4.5296244621276855, 4.216752529144287] |
df535c8b-f345-4ff7-97af-abb4649609e8 | predictive-maneuver-planning-with-deep | 2306.09055 | null | https://arxiv.org/abs/2306.09055v1 | https://arxiv.org/pdf/2306.09055v1.pdf | Predictive Maneuver Planning with Deep Reinforcement Learning (PMP-DRL) for comfortable and safe autonomous driving | This paper presents a Predictive Maneuver Planning with Deep Reinforcement Learning (PMP-DRL) model for maneuver planning. Traditional rule-based maneuver planning approaches often have to improve their abilities to handle the variabilities of real-world driving scenarios. By learning from its experience, a Reinforcement Learning (RL)-based driving agent can adapt to changing driving conditions and improve its performance over time. Our proposed approach combines a predictive model and an RL agent to plan for comfortable and safe maneuvers. The predictive model is trained using historical driving data to predict the future positions of other surrounding vehicles. The surrounding vehicles' past and predicted future positions are embedded in context-aware grid maps. At the same time, the RL agent learns to make maneuvers based on this spatio-temporal context information. Performance evaluation of PMP-DRL has been carried out using simulated environments generated from publicly available NGSIM US101 and I80 datasets. The training sequence shows the continuous improvement in the driving experiences. It shows that proposed PMP-DRL can learn the trade-off between safety and comfortability. The decisions generated by the recent imitation learning-based model are compared with the proposed PMP-DRL for unseen scenarios. The results clearly show that PMP-DRL can handle complex real-world scenarios and make better comfortable and safe maneuver decisions than rule-based and imitative models. | ['Narasimhan Sundararajan', 'Suresh Sundaram', 'Vishruth Veerendranath', 'Jayabrata Chowdhury'] | 2023-06-15 | null | null | null | null | ['imitation-learning'] | ['methodology'] | [-4.19931233e-01 3.07043105e-01 -1.54784948e-01 -3.92866105e-01
-4.44333196e-01 -4.07946557e-01 7.87120879e-01 -1.30500853e-01
-4.62959766e-01 1.12492251e+00 1.75000921e-01 -5.19016504e-01
-1.69198990e-01 -9.18039978e-01 -7.17509151e-01 -7.39611745e-01
-4.88251656e-01 7.22928941e-01 6.67113006e-01 -8.15250397e-01
1.95949703e-01 8.55690598e-01 -1.66780865e+00 -2.69129008e-01
1.00606346e+00 4.94245738e-01 7.00208962e-01 9.13792908e-01
3.51709276e-01 8.36369574e-01 -2.20538601e-01 4.35506344e-01
5.00644267e-01 1.06513992e-01 -2.99994171e-01 -1.05316259e-01
-3.98437679e-01 -3.81888270e-01 -9.12674367e-01 4.61207449e-01
3.13649446e-01 8.64550054e-01 5.86923897e-01 -1.48117006e+00
1.20262012e-01 1.33088931e-01 -1.14503801e-01 2.25466311e-01
8.82684067e-02 1.06490755e+00 2.75054693e-01 -1.61822349e-01
5.32471061e-01 1.28480721e+00 2.70989686e-01 5.07605076e-01
-7.26510406e-01 -5.09206712e-01 3.96101534e-01 8.49992931e-01
-1.15920091e+00 -8.08763281e-02 7.02816963e-01 -2.58370250e-01
1.29520118e+00 1.23245828e-02 8.42417300e-01 9.15757179e-01
1.05563104e+00 6.62720382e-01 9.33494747e-01 2.50245512e-01
5.54221213e-01 1.66774932e-02 -5.17851353e-01 4.64569092e-01
-9.44932550e-03 1.14837241e+00 -6.49369955e-02 3.58950943e-01
1.95430174e-01 -8.84556249e-02 6.64945543e-02 -3.37070704e-01
-1.07759714e+00 6.64076447e-01 6.25218332e-01 -8.66289139e-02
-7.09781468e-01 6.26329929e-02 5.14192045e-01 4.62655723e-01
-8.24978426e-02 6.32971346e-01 -4.73964036e-01 -4.63015974e-01
-6.49348617e-01 7.10760593e-01 6.73302829e-01 9.86089230e-01
7.63772428e-01 7.22092330e-01 -2.54301459e-01 2.64735430e-01
2.84509301e-01 9.75786090e-01 4.39883173e-01 -1.13304532e+00
3.09423715e-01 2.52109796e-01 7.08863735e-01 -1.02143896e+00
-7.68665135e-01 -1.95718214e-01 -2.90728539e-01 8.39855194e-01
-1.56678468e-01 -6.08509481e-01 -9.32090163e-01 1.35816288e+00
4.23776329e-01 4.59215164e-01 5.85768044e-01 8.16111565e-01
2.55903006e-01 1.18016946e+00 1.86574608e-01 -8.38239416e-02
5.87817252e-01 -1.07041752e+00 -8.07973444e-01 -3.89301091e-01
5.65264463e-01 -2.53326923e-01 6.72211349e-01 3.35539013e-01
-6.23033404e-01 -1.01042366e+00 -1.36824143e+00 6.96557045e-01
-5.08236110e-01 -1.65936798e-01 1.18494995e-01 1.17124334e-01
-1.14105976e+00 6.13217652e-01 -8.12632978e-01 -4.13025945e-01
7.99385607e-02 3.31462801e-01 -5.82794398e-02 1.83546662e-01
-1.45374513e+00 1.48412681e+00 7.71804988e-01 3.66541604e-03
-1.70717251e+00 -4.13464934e-01 -9.80934799e-01 -3.44217807e-01
5.87571859e-01 -4.85413596e-02 1.40634692e+00 -4.25014108e-01
-1.94064498e+00 -3.15681584e-02 -1.35647338e-02 -9.44561183e-01
5.24650753e-01 -1.20141774e-01 -8.48718703e-01 -1.72118708e-01
-1.73422154e-02 9.34957504e-01 7.35339284e-01 -1.45269310e+00
-1.15392542e+00 3.53516430e-01 -3.90452170e-03 7.18958676e-01
5.65317392e-01 -9.07495439e-01 -3.29183564e-02 -2.89241150e-02
-5.26465654e-01 -1.27063525e+00 -5.65311253e-01 -4.49148595e-01
6.12555332e-02 -3.65936399e-01 1.30791855e+00 -3.83501470e-01
9.29190576e-01 -2.02476048e+00 5.01872115e-02 2.13842139e-01
-3.41128886e-01 6.56609237e-01 -2.07819462e-01 8.64832640e-01
4.83459264e-01 -5.06884515e-01 2.75211722e-01 7.91464895e-02
8.44733417e-02 1.03867590e+00 -6.32194936e-01 2.07082391e-01
-1.95974689e-02 9.54450727e-01 -1.05547619e+00 -1.98931038e-01
7.98823714e-01 2.45396435e-01 -2.53131121e-01 4.98466641e-01
-6.91066265e-01 8.65921378e-01 -7.03886986e-01 2.50997573e-01
5.01628041e-01 5.67921758e-01 1.22316554e-02 2.37528980e-01
-5.33795059e-01 -5.08272573e-02 -8.13207150e-01 1.20418274e+00
-7.00977147e-01 8.78508866e-01 -1.93413466e-01 -7.64590263e-01
1.26588166e+00 1.47616103e-01 1.65375561e-01 -1.30015695e+00
2.15630919e-01 1.38555825e-01 1.70895278e-01 -9.22123432e-01
8.01504910e-01 -5.26043400e-02 -1.75401077e-01 -3.47157568e-02
-3.70864481e-01 -6.02916479e-01 1.34237716e-02 -1.06300265e-01
9.63047266e-01 4.76213247e-01 1.91874623e-01 -1.14442319e-01
8.27484310e-01 4.00759190e-01 7.54004002e-01 6.63108706e-01
-6.95016384e-01 -3.81659240e-01 1.51299268e-01 -8.74411643e-01
-1.00847256e+00 -1.07144439e+00 2.48906672e-01 8.37261677e-01
6.34773254e-01 1.12893336e-01 -3.49518508e-01 -5.88150978e-01
1.65680200e-01 1.59654379e+00 -6.74989343e-01 -4.09949094e-01
-7.88403809e-01 -1.84281975e-01 1.93097498e-02 4.87793237e-01
7.06517577e-01 -1.34807372e+00 -1.16941059e+00 5.02749622e-01
2.79791921e-01 -1.04124427e+00 -2.40140200e-01 2.53866725e-02
-4.59462047e-01 -9.19405580e-01 1.87503099e-01 -5.49212575e-01
2.90793359e-01 1.19140267e-01 5.77212155e-01 -6.81932792e-02
2.53481835e-01 3.74214619e-01 -3.12727571e-01 -6.32089019e-01
-8.58675659e-01 -9.38923210e-02 2.81512290e-01 -1.90553918e-01
1.58749849e-01 -5.37147462e-01 -6.30516231e-01 6.71870649e-01
-4.39076245e-01 2.24257067e-01 6.90482140e-01 5.94207764e-01
7.62683213e-01 2.80368984e-01 8.09633255e-01 -2.60043919e-01
6.24623299e-01 -5.90689123e-01 -8.43063712e-01 -5.43808863e-02
-7.94356048e-01 3.51952881e-01 8.77442837e-01 -2.61209249e-01
-1.27424884e+00 -2.67941039e-02 -2.92766988e-01 -3.90018642e-01
-4.35267687e-01 1.88628078e-01 -4.57276814e-02 6.98388517e-02
4.42348361e-01 4.82984036e-01 1.63108289e-01 1.47357166e-01
3.39317232e-01 5.38115025e-01 6.56920195e-01 -3.60130668e-01
1.10618055e+00 2.98548102e-01 2.46439293e-01 -4.96286690e-01
-4.04404789e-01 2.80501507e-03 -5.55230558e-01 -7.80706584e-01
9.26579177e-01 -8.33133876e-01 -8.60880673e-01 2.31435627e-01
-5.88567853e-01 -1.03070831e+00 -5.09743929e-01 4.44401234e-01
-1.09486616e+00 -1.72012061e-01 -9.50822160e-02 -9.03533518e-01
7.01523433e-03 -1.33156586e+00 6.24142647e-01 5.56569874e-01
6.54760469e-03 -1.23179090e+00 4.25735205e-01 -6.49293438e-02
7.54195869e-01 6.41033232e-01 5.91330647e-01 -6.24858081e-01
-7.24261403e-01 -1.99584544e-01 4.38938856e-01 -9.69982520e-02
-2.39849985e-02 -2.50907481e-01 -5.48326850e-01 -5.28706849e-01
-2.42478073e-01 -1.23426743e-01 4.08394963e-01 2.34743953e-01
8.49444509e-01 -4.75617975e-01 -6.00999117e-01 3.01469475e-01
1.37863457e+00 1.09191549e+00 7.34246671e-01 6.54004037e-01
2.90396899e-01 4.33695376e-01 1.44069386e+00 3.27829570e-01
8.63363922e-01 6.60322309e-01 9.07193065e-01 1.49095550e-01
1.42104685e-01 -7.41121948e-01 4.86163169e-01 5.10528624e-01
7.81705379e-02 -2.07355529e-01 -8.78955781e-01 6.05581820e-01
-2.19607902e+00 -1.08957064e+00 2.28324205e-01 2.00483465e+00
2.12056950e-01 5.67893982e-01 1.24908365e-01 -1.71865538e-01
1.65944532e-01 2.73963839e-01 -1.09069324e+00 -9.75950956e-01
8.01402181e-02 -4.01808619e-01 8.98675978e-01 8.92673314e-01
-9.07107413e-01 1.07737386e+00 6.07021809e+00 7.87690818e-01
-1.29289913e+00 -1.19310044e-01 4.87835944e-01 -4.85857762e-02
-1.54060170e-01 -3.82596925e-02 -6.74036086e-01 4.11827236e-01
1.40350056e+00 -5.33503413e-01 6.75203145e-01 8.77898574e-01
9.55225706e-01 -4.19586509e-01 -8.58754933e-01 5.51119924e-01
-3.17188114e-01 -1.35293448e+00 -1.85885027e-01 -1.48881316e-01
8.17051589e-01 4.04318601e-01 7.61893094e-02 1.01185572e+00
7.13006079e-01 -1.10067523e+00 6.64779305e-01 9.54440773e-01
2.46827736e-01 -1.27422667e+00 9.57091928e-01 7.43025661e-01
-1.25387168e+00 -5.58130324e-01 -2.41667822e-01 -2.25441650e-01
4.77977782e-01 -3.31937134e-01 -1.41807413e+00 6.53141439e-01
3.51939529e-01 8.93401742e-01 -3.77485156e-01 7.89992630e-01
-3.91943187e-01 6.28478587e-01 -4.28204723e-02 -5.65074742e-01
8.50220144e-01 -2.08761275e-01 9.67270732e-01 9.03010190e-01
2.54400998e-01 4.80904281e-02 4.88052309e-01 5.18333256e-01
7.73121059e-01 -3.96573365e-01 -9.88926828e-01 4.55470622e-01
5.34716368e-01 1.03579664e+00 -1.80775911e-01 -3.33565652e-01
5.68150431e-02 3.12906802e-01 1.43361822e-01 4.95096743e-01
-1.14603603e+00 -1.05295099e-01 9.41630721e-01 1.72987059e-01
3.41111153e-01 -5.34434319e-01 5.17809466e-02 -2.45480940e-01
-5.33546269e-01 -5.61131239e-01 -2.74842214e-02 -9.61634696e-01
-6.93581700e-01 8.51196289e-01 3.48788053e-01 -1.60640490e+00
-6.32135749e-01 -3.02320302e-01 -9.02163923e-01 6.86527491e-01
-1.90967715e+00 -8.59884679e-01 -2.01840371e-01 4.30308640e-01
9.59937215e-01 -6.50115907e-01 4.56620514e-01 -3.77726674e-01
-3.42382520e-01 8.73076171e-02 1.65127873e-01 -4.21279252e-01
3.07097942e-01 -1.07952476e+00 3.77437621e-01 7.41597295e-01
-6.37513638e-01 -2.99117267e-01 1.28166938e+00 -8.21435213e-01
-1.53874862e+00 -1.55047917e+00 3.76024902e-01 -1.04832619e-01
5.54295242e-01 1.60113394e-01 -8.20495963e-01 6.18876040e-01
5.98153293e-01 -2.87376255e-01 -5.71560450e-02 -5.84410191e-01
4.23834920e-01 -4.75950718e-01 -1.24342477e+00 9.15648997e-01
6.93580568e-01 2.88352761e-02 -5.03008723e-01 2.03395203e-01
7.57810116e-01 -6.57619894e-01 -4.44960386e-01 5.76370835e-01
2.47707084e-01 -7.82760322e-01 4.17813748e-01 -5.20203590e-01
-2.30998650e-01 -6.70797169e-01 -7.56183490e-02 -1.92016804e+00
-3.26169312e-01 -8.28467250e-01 8.41871127e-02 3.75182003e-01
3.83886755e-01 -8.27042043e-01 4.69384074e-01 5.18330872e-01
-6.38994575e-01 -1.04722536e+00 -1.31260502e+00 -8.37468088e-01
1.81329712e-01 -5.42600036e-01 8.68118107e-01 2.04010203e-01
-6.45734742e-02 -6.12625331e-02 -6.83281362e-01 5.19177914e-01
2.97876328e-01 -1.92614291e-02 1.00577784e+00 -6.02149546e-01
-1.20895468e-01 -6.79952577e-02 -5.64297497e-01 -9.63904023e-01
4.63702470e-01 -6.53059185e-01 5.60272992e-01 -1.70359862e+00
-5.12462020e-01 -4.61936712e-01 -2.84937806e-02 4.91980016e-01
3.02654188e-02 -6.07930541e-01 1.60520449e-01 -1.25581965e-01
-7.31528819e-01 1.00973153e+00 1.59343350e+00 -1.53072357e-01
-5.76294839e-01 1.92424595e-01 -2.57560816e-02 4.50720668e-01
1.25394177e+00 -1.91867247e-01 -9.02434170e-01 -9.74920020e-02
2.46192869e-02 6.21621072e-01 4.67684083e-02 -1.50667715e+00
3.57816607e-01 -8.11804116e-01 3.75907533e-02 -9.90945995e-01
2.99283415e-01 -9.68171239e-01 3.21022213e-01 9.95640457e-01
-2.64159948e-01 3.59629691e-01 7.01302230e-01 1.00103188e+00
-7.59255737e-02 4.09524798e-01 7.88380802e-01 1.75167993e-01
-1.51761448e+00 3.60849351e-01 -1.10212493e+00 -2.12518498e-01
1.75977945e+00 -3.24286610e-01 -2.29047596e-01 -6.70136094e-01
-7.39402294e-01 1.19495511e+00 1.32479519e-01 9.23036993e-01
8.88930857e-01 -1.26741672e+00 -6.36122823e-01 3.69645983e-01
-7.47536197e-02 -2.14643046e-01 5.03068209e-01 6.30659521e-01
-4.94893938e-01 5.67532063e-01 -6.34154916e-01 -4.71935153e-01
-7.84506142e-01 8.03553760e-01 8.32483649e-01 -3.71964306e-01
-8.41182590e-01 9.85561535e-02 -7.50900507e-02 -6.20252967e-01
-3.62233259e-02 -2.32693449e-01 -4.27043468e-01 -5.41009665e-01
4.08586413e-01 4.57948774e-01 -1.87561840e-01 -8.34610879e-01
-1.63944781e-01 2.01446563e-01 1.31030962e-01 -2.44095474e-01
1.25321174e+00 -3.11853170e-01 7.48898983e-01 5.11758149e-01
6.82989299e-01 -3.61854970e-01 -2.13213277e+00 6.55137450e-02
-1.26769006e-01 -3.53531390e-01 2.61205360e-02 -1.01998174e+00
-8.45938444e-01 4.92789000e-01 7.94295788e-01 -1.93477511e-01
1.00890779e+00 -3.39183182e-01 9.07264054e-01 4.72561687e-01
7.37434328e-01 -1.48999405e+00 8.40388834e-02 8.84165585e-01
1.17780221e+00 -1.23712587e+00 -3.73894989e-01 2.56849170e-01
-1.40329397e+00 9.59814906e-01 9.34936762e-01 -3.97778958e-01
7.58082390e-01 3.56967747e-01 2.29462907e-01 7.29434714e-02
-1.24891424e+00 -1.92379281e-01 1.14465371e-01 9.36820984e-01
-6.14173412e-01 5.42200983e-01 6.16015568e-02 -2.51196139e-02
-3.10009420e-01 -1.99682653e-01 5.48224568e-01 8.99595916e-01
-8.97621393e-01 -8.86275947e-01 -2.19850525e-01 3.55886877e-01
6.68550968e-01 7.71484673e-01 2.45207265e-01 1.04130530e+00
2.36862734e-01 1.05608118e+00 1.79702863e-01 -8.24005604e-01
5.08875132e-01 -2.21245170e-01 -1.27032511e-02 -1.89411104e-01
-3.12045723e-01 -3.72642696e-01 2.31579006e-01 -8.06788385e-01
-2.40145456e-02 -8.56185377e-01 -1.81747353e+00 -3.77418518e-01
5.43252945e-01 1.21449552e-01 4.69697744e-01 1.16138875e+00
4.65019196e-01 8.55514586e-01 9.93401229e-01 -1.10521984e+00
-6.17328882e-01 -6.52903795e-01 -3.25462639e-01 8.36829618e-02
6.37637794e-01 -8.95276785e-01 -1.04331151e-01 -6.02200687e-01] | [5.115784168243408, 1.33548903465271] |
450cc8e8-c322-4785-8b98-0ea9bd76f22c | efficient-gaussian-process-model-on-class | 2210.06120 | null | https://arxiv.org/abs/2210.06120v1 | https://arxiv.org/pdf/2210.06120v1.pdf | Efficient Gaussian Process Model on Class-Imbalanced Datasets for Generalized Zero-Shot Learning | Zero-Shot Learning (ZSL) models aim to classify object classes that are not seen during the training process. However, the problem of class imbalance is rarely discussed, despite its presence in several ZSL datasets. In this paper, we propose a Neural Network model that learns a latent feature embedding and a Gaussian Process (GP) regression model that predicts latent feature prototypes of unseen classes. A calibrated classifier is then constructed for ZSL and Generalized ZSL tasks. Our Neural Network model is trained efficiently with a simple training strategy that mitigates the impact of class-imbalanced training data. The model has an average training time of 5 minutes and can achieve state-of-the-art (SOTA) performance on imbalanced ZSL benchmark datasets like AWA2, AWA1 and APY, while having relatively good performance on the SUN and CUB datasets. | ['Russell Tsuchida', 'Lars Petersson', 'Nick Barnes', 'Changkun Ye'] | 2022-10-11 | null | null | null | null | ['generalized-zero-shot-learning', 'generalized-zero-shot-learning'] | ['computer-vision', 'methodology'] | [ 1.09923715e-02 5.93507141e-02 -6.53150737e-01 -6.92921460e-01
-8.48739028e-01 9.43762884e-02 5.16084194e-01 3.37352604e-01
-1.72901317e-01 7.56431162e-01 -3.04877073e-01 -2.89468747e-02
-2.98494458e-01 -1.13911891e+00 -6.64765000e-01 -7.33591974e-01
1.57605857e-01 8.88872802e-01 1.52192980e-01 9.83023345e-02
8.15282390e-02 3.54839802e-01 -2.06001067e+00 3.67836267e-01
8.33370805e-01 1.08633363e+00 -4.68795955e-01 6.33937597e-01
-4.12986845e-01 1.03406107e+00 -7.31505990e-01 -3.98886412e-01
1.22784734e-01 7.75615200e-02 -6.45722568e-01 -5.11446595e-02
6.10005975e-01 -1.31663635e-01 -3.21340740e-01 6.80564940e-01
8.07298481e-01 6.19590133e-02 7.51713395e-01 -1.99140882e+00
-5.06379068e-01 1.82941198e-01 -6.74967289e-01 3.02612513e-01
-2.46907994e-01 1.39780462e-01 9.84381139e-01 -9.56928849e-01
4.92917389e-01 1.19552982e+00 9.13356543e-01 5.76394200e-01
-1.21316457e+00 -8.14390242e-01 -3.94103527e-02 5.80034196e-01
-1.28680205e+00 -4.62896854e-01 4.76129383e-01 -4.54477996e-01
1.21707046e+00 -5.95706627e-02 4.41143245e-01 1.13026023e+00
3.32484663e-01 1.09047031e+00 7.26338029e-01 -4.44192469e-01
6.72274709e-01 4.61822264e-02 5.96062481e-01 2.60011464e-01
5.34601390e-01 -1.63504526e-01 -1.06234360e+00 -1.53232798e-01
1.12215646e-01 2.11086184e-01 2.97545344e-02 -8.31615210e-01
-8.16797197e-01 8.79372239e-01 2.13455722e-01 9.43926349e-02
-2.57356346e-01 -3.51384107e-04 5.62803566e-01 2.43685409e-01
1.14910233e+00 1.69277877e-01 -7.71365643e-01 -2.89078951e-01
-1.03922224e+00 1.51384592e-01 8.98943782e-01 8.01746309e-01
7.35446870e-01 7.05387294e-02 -3.72376263e-01 9.94727492e-01
-1.16658404e-01 3.43108714e-01 6.42904520e-01 -6.87734783e-01
3.27627897e-01 8.72989714e-01 -6.81650862e-02 -8.22855651e-01
-3.67904097e-01 -5.51244378e-01 -8.84797990e-01 3.59871864e-01
6.80639446e-02 1.61350876e-01 -1.00245404e+00 1.33871341e+00
4.20022011e-01 4.89076823e-01 3.52066696e-01 2.00269192e-01
9.24712598e-01 7.83570409e-01 1.09104879e-01 -1.85866550e-01
7.26545095e-01 -1.14551258e+00 -7.29907215e-01 -4.07251954e-01
6.83981359e-01 -2.36932993e-01 9.02024746e-01 5.06305635e-01
-7.95450032e-01 -4.67750996e-01 -1.25512087e+00 -8.22908282e-02
-6.32294178e-01 3.86125073e-02 6.89240456e-01 7.04451799e-01
-7.90683985e-01 9.13044870e-01 -9.42210913e-01 -2.74503410e-01
9.86152232e-01 2.75925428e-01 -3.30556780e-01 -3.12071145e-01
-9.00088251e-01 7.82452703e-01 4.81371552e-01 -3.28201115e-01
-8.30986202e-01 -1.18574846e+00 -8.52120578e-01 3.19671899e-01
2.14791223e-01 -3.03805619e-01 1.26804936e+00 -7.75078416e-01
-1.40499234e+00 1.05788922e+00 -7.95805380e-02 -6.84886873e-01
5.17692089e-01 -3.42215627e-01 -4.15419489e-01 -1.38111666e-01
3.13738696e-02 5.81776023e-01 8.53724837e-01 -9.32068586e-01
-6.83653235e-01 -5.33353090e-01 -3.55147958e-01 2.79502422e-02
-3.96274120e-01 -2.49774128e-01 -2.28883013e-01 -2.44244769e-01
1.51316822e-01 -5.44271767e-01 1.26832323e-02 2.75607586e-01
-2.75055200e-01 -6.64644897e-01 8.30796599e-01 -2.13419765e-01
1.00868154e+00 -2.18910766e+00 -3.04992020e-01 -1.75219342e-01
2.16105804e-01 4.50279117e-01 -2.78581411e-01 3.33462328e-01
-3.93663168e-01 -5.88078611e-02 -6.83722124e-02 -5.70178151e-01
1.23527221e-01 3.35580885e-01 -3.38832378e-01 6.92151070e-01
3.08346897e-01 8.60649824e-01 -8.76423478e-01 -1.05244167e-01
1.21490426e-01 3.62942308e-01 -4.52703267e-01 4.14575815e-01
-1.66756973e-01 -1.62835106e-01 2.50430316e-01 8.80646646e-01
8.29984665e-01 -4.15523231e-01 -2.00680926e-01 -1.17335990e-01
7.58532509e-02 5.41276783e-02 -1.12291431e+00 1.42720044e+00
-3.50580275e-01 7.66639948e-01 -6.70683742e-01 -1.16007543e+00
1.05100298e+00 3.55097681e-01 3.91922176e-01 -6.27977610e-01
8.06696862e-02 7.79165924e-02 -2.34591380e-01 -4.33285415e-01
2.90031098e-02 -1.23290092e-01 -4.70193895e-03 4.35967535e-01
6.21411800e-01 -1.77338207e-03 3.18838865e-01 -1.21635206e-01
1.07160580e+00 -9.66313779e-02 4.27050799e-01 -4.49343808e-02
6.18958212e-02 -1.41017348e-01 9.35198009e-01 9.97890055e-01
-3.59892011e-01 5.13539612e-01 6.72196031e-01 -9.52971697e-01
-9.53384221e-01 -1.23910201e+00 -2.94823825e-01 1.13176072e+00
9.47389677e-02 -4.21763659e-01 -4.04534519e-01 -6.72688782e-01
9.62065309e-02 1.04963350e+00 -7.28299499e-01 -6.68067336e-01
9.29813609e-02 -1.18506575e+00 3.60701174e-01 4.86926913e-01
4.43311244e-01 -1.09407961e+00 -6.39763236e-01 2.09660098e-01
8.83278325e-02 -7.69994974e-01 4.83602732e-01 5.11886239e-01
-8.01322937e-01 -1.24084663e+00 -1.83287770e-01 -5.65219581e-01
6.03189468e-01 2.41227020e-02 1.47609544e+00 -2.93347418e-01
-7.48366714e-01 -1.74884826e-05 1.76282562e-02 -1.00015867e+00
5.84532320e-02 8.63678232e-02 2.73446053e-01 4.43465114e-02
1.05630159e+00 -4.49822605e-01 -4.14410204e-01 9.22266841e-02
-6.94072783e-01 5.58440052e-02 2.85401434e-01 1.06949139e+00
5.71643770e-01 2.46978238e-01 8.25222671e-01 -9.24638867e-01
3.44001234e-01 -5.94712615e-01 -5.24309278e-01 3.02813172e-01
-8.39939713e-01 -2.93337196e-01 2.88514853e-01 -5.55021703e-01
-9.52579021e-01 -3.23664993e-01 -1.56290252e-02 -5.85084498e-01
-4.24519420e-01 1.67889729e-01 -1.98179796e-01 9.53663364e-02
7.25825489e-01 -3.69862132e-02 -9.47323292e-02 -5.77266216e-01
1.06654957e-01 8.31018746e-01 3.78479391e-01 -2.97727168e-01
6.57217264e-01 5.47638476e-01 -1.40605405e-01 -5.80275536e-01
-1.55228853e+00 -6.39880836e-01 -6.52336717e-01 3.58832232e-03
5.69353044e-01 -1.12687469e+00 -5.58133066e-01 8.90795827e-01
-9.43156600e-01 -3.51136357e-01 -7.59206831e-01 2.62102336e-01
-7.33602047e-01 -3.20452601e-01 -7.36028194e-01 -7.25730836e-01
-5.98968208e-01 -7.51132131e-01 9.60008144e-01 3.39891970e-01
-2.18575448e-01 -7.13863432e-01 4.74793524e-01 1.46082893e-01
5.47699571e-01 1.46314994e-01 1.09626317e+00 -1.12241089e+00
-5.25208294e-01 -5.67776620e-01 -2.86105722e-01 5.97240388e-01
1.12372562e-01 1.16853319e-01 -1.38708913e+00 -4.75346267e-01
-8.08647498e-02 -6.49383008e-01 8.17573905e-01 3.59799474e-01
1.52998567e+00 -1.70795694e-02 -2.73902386e-01 6.92251086e-01
1.55237484e+00 9.70459729e-02 5.35260141e-01 3.23078603e-01
3.82153481e-01 4.10460055e-01 6.68193460e-01 3.88871640e-01
1.84001356e-01 2.88376957e-01 4.26272601e-01 -1.22179329e-01
4.25734706e-02 -8.54903832e-02 -1.13920487e-01 7.12081552e-01
4.89176005e-01 -2.69472092e-01 -1.19598019e+00 7.25393713e-01
-1.97279060e+00 -8.66551340e-01 8.68588686e-02 2.24696255e+00
8.50043654e-01 4.10293132e-01 -3.72742534e-01 5.22765219e-01
6.04423046e-01 2.54485786e-01 -7.89519727e-01 -3.28090131e-01
-9.27189440e-02 4.43494469e-01 3.29920441e-01 1.16098471e-01
-1.37717545e+00 7.97433674e-01 6.85854912e+00 1.16294956e+00
-1.10690701e+00 2.09760994e-01 1.09779549e+00 -5.42285085e-01
3.57197791e-01 -4.71879095e-01 -1.09599876e+00 4.13442850e-01
1.17800367e+00 -5.80494761e-01 -5.77734597e-03 1.33333504e+00
-2.59355843e-01 -1.15691960e-01 -1.21950781e+00 1.17748427e+00
3.76749367e-01 -1.39867461e+00 -1.53190523e-01 -2.93201715e-01
9.23466980e-01 3.69125009e-01 1.12170987e-01 1.00805938e+00
2.82079458e-01 -1.12565243e+00 2.34293252e-01 4.80961382e-01
7.86258638e-01 -1.15508509e+00 9.67520714e-01 5.98670602e-01
-4.53911871e-01 -2.22939745e-01 -7.79654682e-01 -2.12881967e-01
-2.76825488e-01 9.96780038e-01 -8.16490769e-01 1.68713331e-01
9.81665492e-01 9.28557575e-01 -7.05833793e-01 1.57903206e+00
-2.02201605e-01 8.17173898e-01 -1.53759360e-01 1.99514106e-01
-7.65535012e-02 1.67134419e-01 2.09403679e-01 7.36067951e-01
2.93946594e-01 -3.88670951e-01 4.22968976e-02 5.02817512e-01
-2.39445791e-01 1.94067940e-01 -6.32456362e-01 3.68827991e-02
3.44790012e-01 1.11180985e+00 -5.91300011e-01 -5.40098071e-01
-2.95702577e-01 8.78638983e-01 4.81753379e-01 2.72858828e-01
-5.77922344e-01 -3.96775246e-01 1.01274455e+00 -1.13093518e-01
2.09288254e-01 4.52750444e-01 -4.16528821e-01 -1.16149795e+00
-1.91447377e-01 -6.18706703e-01 3.63222152e-01 -6.69515073e-01
-1.84695363e+00 4.72019583e-01 -2.67792523e-01 -1.26917028e+00
-1.08146295e-01 -6.43232644e-01 -7.73247004e-01 6.41442835e-01
-1.69660640e+00 -8.69503856e-01 -5.67648292e-01 9.09709409e-02
7.10307956e-01 -3.33243638e-01 1.08251750e+00 3.95264119e-01
-8.71222913e-01 3.89893532e-01 4.99294192e-01 -9.96692255e-02
9.55974162e-01 -1.26956332e+00 6.11028016e-01 3.29398543e-01
-1.46432584e-02 7.50521496e-02 7.02612162e-01 -5.23943424e-01
-7.79934049e-01 -1.47726095e+00 1.18210208e+00 -5.18780828e-01
5.18316925e-01 -3.29013526e-01 -1.14498198e+00 5.67836404e-01
-1.45444036e-01 5.16160011e-01 1.17755473e+00 2.39029258e-01
-4.62476343e-01 -3.61610204e-01 -1.19522941e+00 1.78574309e-01
7.27365315e-01 -5.57659805e-01 -6.31400108e-01 5.10905802e-01
7.23443568e-01 -3.90243530e-01 -6.11855626e-01 6.79134667e-01
4.31303442e-01 -9.34121132e-01 1.10044217e+00 -9.42066252e-01
5.44240415e-01 -4.30523045e-02 4.97530252e-02 -1.51495922e+00
-2.14184448e-01 1.54891819e-01 -5.27324855e-01 1.20219803e+00
1.40248552e-01 -4.32550281e-01 1.11874473e+00 5.18858612e-01
3.90049279e-01 -1.01368153e+00 -1.12737823e+00 -9.20298100e-01
-1.35444002e-02 -4.69294369e-01 6.14851058e-01 1.00957274e+00
-3.80482823e-01 1.96382508e-01 -4.37413871e-01 -6.69814125e-02
1.02381611e+00 1.54627874e-01 8.42829049e-01 -1.87599111e+00
-4.83354032e-02 -1.19281590e-01 -6.69959784e-01 -2.87405074e-01
4.61764455e-01 -7.65139699e-01 1.84494595e-03 -1.39095092e+00
3.29814821e-01 -4.22665745e-01 -5.99667072e-01 6.05952621e-01
-2.36668333e-01 4.69863236e-01 -9.63052586e-02 -1.11233734e-01
-1.00219178e+00 7.43408382e-01 5.24125040e-01 -2.83196360e-01
-1.81984599e-03 3.06978106e-01 -3.58628482e-01 7.35068560e-01
9.65256631e-01 -7.79154718e-01 -4.21957403e-01 -1.25222266e-01
2.00998023e-01 -2.81860918e-01 1.49166957e-01 -1.27455032e+00
2.73442417e-01 -9.59795117e-02 6.85786963e-01 -1.09514308e+00
4.13349867e-01 -6.48802876e-01 -8.56247842e-02 4.39406216e-01
-3.32412630e-01 -4.19303447e-01 2.00827479e-01 5.85882962e-01
-2.23465845e-01 -3.45498651e-01 9.45765197e-01 1.24490418e-01
-5.42919576e-01 6.37418270e-01 -2.06425875e-01 1.11239664e-01
1.14619696e+00 -2.25909669e-02 -5.41185617e-01 -2.18759641e-01
-6.30003393e-01 4.52213943e-01 4.53560501e-01 7.11004496e-01
5.12449205e-01 -1.37425876e+00 -5.24208665e-01 5.11620164e-01
5.38088918e-01 2.62195855e-01 3.82661521e-01 3.77643049e-01
-3.38074476e-01 2.62716144e-01 -3.51071179e-01 -7.96734750e-01
-1.16404939e+00 5.79544306e-01 3.45788658e-01 -4.73882288e-01
-4.09164071e-01 8.98697853e-01 -1.26016229e-01 -8.24726939e-01
6.17862642e-01 9.89337787e-02 -2.25828588e-01 3.13085884e-01
1.01734269e+00 5.46252966e-01 3.70727748e-01 -2.48280838e-01
-1.74158901e-01 1.65052474e-01 -1.07400455e-01 5.19964695e-01
1.70984900e+00 1.89274937e-01 -1.96536258e-01 1.09686506e+00
1.28406739e+00 -6.66748762e-01 -1.24645209e+00 -4.49473292e-01
3.19963694e-01 -4.59976822e-01 1.55280426e-01 -6.33980870e-01
-7.96846151e-01 1.25553703e+00 9.82834578e-01 -1.02188103e-01
8.01205158e-01 -1.67238176e-01 5.15565515e-01 4.99186724e-01
2.77704448e-01 -1.21597695e+00 2.76963353e-01 6.18900537e-01
4.79000062e-01 -1.41902280e+00 -2.33625546e-01 -2.68497109e-01
-2.00935453e-01 1.10277200e+00 9.57851529e-01 -1.52143613e-01
7.55397201e-01 3.46847445e-01 9.16366279e-02 -2.33366862e-01
-1.30707347e+00 3.99098583e-02 3.47357132e-02 5.57155132e-01
5.47405779e-02 -9.09919813e-02 1.40639618e-01 6.93301678e-01
1.86485983e-02 3.58820289e-01 3.25861573e-01 1.06501245e+00
-5.07918894e-01 -7.79898465e-01 -4.32500094e-02 8.12226117e-01
-3.82289261e-01 -4.00703549e-02 1.93002596e-01 3.86694908e-01
2.85944372e-01 6.69072568e-01 6.77056789e-01 -5.23509644e-03
2.96701133e-01 5.61077774e-01 1.59063652e-01 -8.03003073e-01
-2.10735634e-01 -4.73455817e-01 -1.74538627e-01 -7.70061791e-01
-1.73534244e-01 -2.45154947e-01 -7.12934673e-01 -2.01415226e-01
-3.03139687e-01 -6.69703111e-02 6.58608496e-01 9.42572415e-01
2.57894605e-01 7.27165639e-01 5.30500591e-01 -9.07453597e-01
-5.43691456e-01 -9.75652218e-01 -7.16465175e-01 3.96327943e-01
2.61368185e-01 -8.58060539e-01 -6.60582662e-01 -1.53252169e-01] | [9.762779235839844, 3.239572525024414] |
93665ccc-9b5d-4697-a2b8-c584175718ba | using-the-fast-fourier-transform-in-binding | 1708.07045 | null | http://arxiv.org/abs/1708.07045v1 | http://arxiv.org/pdf/1708.07045v1.pdf | Using the Fast Fourier Transform in Binding Free Energy Calculations | According to implicit ligand theory, the standard binding free energy is an
exponential average of the binding potential of mean force (BPMF), an
exponential average of the interaction energy between the ligand apo ensemble
and a rigid receptor. Here, we use the Fast Fourier Transform (FFT) to
efficiently estimate BPMFs by calculating interaction energies as rigid ligand
configurations from the apo ensemble are discretely translated across rigid
receptor conformations. Results for standard binding free energies between T4
lysozyme and 141 small organic molecules are in good agreement with previous
alchemical calculations based on (1) a flexible complex (R ~ 0.9 for 24
systems) and (2) flexible ligand with multiple rigid receptor configurations (R
~ 0.8 for 141 systems). While the FFT is routinely used for molecular docking,
to our knowledge this is the first time that the algorithm has been used for
rigorous binding free energy calculations. | ['Huan-Xiang Zhou', 'Trung Hai Nguyen', 'David D. L. Minh'] | 2017-07-27 | null | null | null | null | ['molecular-docking'] | ['medical'] | [ 5.22428036e-01 -4.27733734e-02 1.29698604e-01 -3.21930110e-01
-7.02030122e-01 -6.44465923e-01 5.09148300e-01 1.61898121e-01
-8.58143210e-01 1.44822001e+00 -1.28936067e-01 -6.04250968e-01
-1.71368986e-01 -4.34131891e-01 -8.07200909e-01 -1.34878433e+00
-4.67211664e-01 7.67120719e-01 2.26721331e-01 -3.48146319e-01
4.81731087e-01 1.05033541e+00 -1.14045608e+00 2.40175113e-01
9.13541257e-01 3.42546463e-01 4.64334078e-02 4.67171729e-01
1.71872064e-01 3.07730973e-01 -3.15961838e-01 -1.68378145e-01
3.79888117e-02 -4.49609667e-01 -9.17836428e-01 -5.71310520e-01
1.94007412e-01 -7.96852261e-02 -3.83565910e-02 4.97386575e-01
8.74706268e-01 4.23036754e-01 1.14460731e+00 -9.76178050e-02
-3.87983829e-01 1.53830394e-01 -4.34574991e-01 -8.44603702e-02
7.11811423e-01 9.62607861e-02 6.45778894e-01 -1.31234133e+00
8.47962797e-01 1.06897414e+00 7.21977890e-01 5.53819537e-01
-1.76166439e+00 -7.84857452e-01 -3.09777975e-01 -2.13740051e-01
-1.72311842e+00 -4.96596605e-01 1.52101601e-02 -5.01233876e-01
1.77618039e+00 4.94838357e-01 7.71748304e-01 6.58636570e-01
8.36604893e-01 -3.47872525e-01 1.17466247e+00 -6.34164989e-01
3.50348353e-01 -2.79134512e-01 2.52315402e-01 3.65943104e-01
4.45422173e-01 2.55508363e-01 -2.69699961e-01 -1.05890369e+00
4.56702083e-01 4.76124957e-02 -4.20208901e-01 -4.51553524e-01
-9.03253496e-01 8.52596521e-01 3.63075137e-01 1.26562029e-01
-4.72545385e-01 9.95353833e-02 1.25966102e-01 4.88542318e-02
-5.96393198e-02 4.97708440e-01 -6.42204702e-01 7.27048963e-02
-4.40187693e-01 8.55046213e-01 1.03419244e+00 2.89328754e-01
7.21037269e-01 -3.43010902e-01 5.11000931e-01 4.97798383e-01
6.59064829e-01 5.88483274e-01 1.11054160e-01 -6.74153507e-01
-1.29215240e-01 2.05350518e-01 8.36010635e-01 -3.39025199e-01
-4.17386442e-01 3.67915064e-01 -2.88760006e-01 6.00604534e-01
8.39393139e-01 -4.74182516e-02 -7.32081413e-01 1.43105614e+00
2.63722926e-01 -4.27417338e-01 3.17069083e-01 6.68044269e-01
7.84748852e-01 6.49987340e-01 4.49319720e-01 -1.01622319e+00
1.10273528e+00 -4.11137104e-01 -6.17674589e-01 5.34307837e-01
8.04951429e-01 -9.71250892e-01 4.06580031e-01 4.38792616e-01
-9.81869340e-01 -7.07372874e-02 -9.40640271e-01 -1.75477203e-03
-3.35003823e-01 -3.68642211e-01 5.71422219e-01 5.28507411e-01
-7.23825455e-01 9.11186337e-01 -8.49562407e-01 -3.34643155e-01
-3.07991326e-01 1.18725383e+00 -7.16417849e-01 3.58077735e-02
-1.22806311e+00 1.18511784e+00 5.47622800e-01 4.06102510e-03
-4.23587829e-01 -5.35358012e-01 -4.32509601e-01 -5.83375156e-01
-3.75759304e-02 -4.24943477e-01 9.53882992e-01 -7.33541667e-01
-1.75162899e+00 8.36770952e-01 -4.42428976e-01 -1.83209985e-01
2.31964812e-01 -1.03471437e-02 -1.98421732e-01 -1.70522153e-01
-3.31234246e-01 3.69373500e-01 9.80370194e-02 -1.08742213e+00
3.72058034e-01 -4.58221048e-01 -1.01587668e-01 4.30183083e-01
8.06123674e-01 2.84218132e-01 4.99866009e-01 -2.48483151e-01
2.50115752e-01 -1.15784132e+00 -6.07395411e-01 -5.65748811e-01
-1.41321018e-01 -2.27251593e-02 1.96205288e-01 -4.00814682e-01
9.25064862e-01 -1.63843441e+00 3.17737222e-01 9.24083352e-01
1.86119124e-01 2.76064366e-01 1.74116775e-01 1.12137818e+00
-7.59430170e-01 -8.23380053e-02 -1.63817376e-01 6.65123820e-01
-2.88731992e-01 1.92425862e-01 -4.63550627e-01 8.48254383e-01
-3.32295746e-01 7.77928531e-01 -7.04111218e-01 -2.18759966e-03
-1.08456604e-01 8.29740405e-01 -6.61169469e-01 -1.60113513e-01
-5.04799411e-02 5.85237384e-01 -5.43245256e-01 2.05747932e-01
9.69728947e-01 1.10396137e-02 8.17248821e-01 -3.74006301e-01
-7.54668593e-01 4.44294751e-01 -7.36404181e-01 1.22489583e+00
5.48823178e-01 -1.85220778e-01 -1.91482782e-01 -2.06430316e-01
9.69465435e-01 6.20337129e-01 5.93371570e-01 -4.54425335e-01
1.30880428e-02 7.13774979e-01 7.80866325e-01 6.78464919e-02
4.03928384e-02 -8.14731538e-01 3.71518523e-01 3.69164497e-01
-1.86878800e-01 1.90878004e-01 1.05669670e-01 -3.64513621e-02
8.76106918e-01 4.44809049e-01 6.51548326e-01 -5.59156835e-01
7.42134809e-01 9.60809737e-02 1.70596817e-03 4.73387063e-01
2.03183055e-01 3.81550610e-01 3.07779193e-01 -9.13835645e-01
-9.65775311e-01 -8.73748958e-01 -7.29848087e-01 1.10048604e+00
-1.99935175e-02 -7.23260641e-01 -9.17010188e-01 5.75764887e-02
3.47267479e-01 3.16507876e-01 -5.20190299e-01 9.93890781e-03
-6.11883044e-01 -1.39402127e+00 4.40791667e-01 -9.79375169e-02
-4.10620421e-01 -1.15868938e+00 -3.13628465e-01 6.10238731e-01
2.98472792e-01 -2.13321701e-01 -2.88656473e-01 8.99572790e-01
-6.58203483e-01 -1.18461585e+00 -5.59420824e-01 -1.43753186e-01
6.01478517e-01 3.35032120e-02 6.70974970e-01 1.93248391e-01
-3.26637208e-01 -8.77536610e-02 5.73089868e-02 -2.18927920e-01
-3.36907297e-01 -3.40030491e-01 5.20770967e-01 -5.60586214e-01
6.43131077e-01 -6.43204808e-01 -7.27372468e-01 4.94166106e-01
-5.64263940e-01 -2.25359634e-01 3.99027616e-01 8.64010453e-01
1.10336185e+00 -5.80465794e-01 1.99319527e-01 -9.14311349e-01
3.83651644e-01 -2.27283224e-01 -5.92790067e-01 4.64642495e-02
-6.33516490e-01 1.76297218e-01 3.75150889e-01 -4.70602185e-01
-1.03391123e+00 3.57789874e-01 -4.29169983e-01 4.03341442e-01
-1.90822542e-01 3.98545086e-01 -5.76491877e-02 -8.43034446e-01
1.05175209e+00 1.54606938e-01 -9.72264819e-03 -5.36679745e-01
1.14284225e-01 4.05268073e-01 1.53832272e-01 -8.95706415e-01
4.82209235e-01 2.79890567e-01 3.43231380e-01 -9.14498448e-01
-4.24558848e-01 -2.05339819e-01 -8.87968957e-01 3.11870396e-01
9.31168020e-01 -5.11206388e-01 -1.47831500e+00 -9.49688032e-02
-1.16244400e+00 -3.11574280e-01 1.69170529e-01 8.50798905e-01
-7.95212209e-01 6.51243925e-01 -4.16128844e-01 -7.04227388e-01
-3.47068310e-01 -1.34740412e+00 7.66482711e-01 -8.05789828e-02
-5.62960088e-01 -9.15585041e-01 7.74649739e-01 1.31395802e-01
1.75870687e-01 7.86352158e-01 8.72948349e-01 -6.28928065e-01
-2.98862338e-01 -2.53832549e-01 2.38867491e-01 -4.35473174e-01
1.69285625e-01 2.54283756e-01 -7.83647239e-01 -5.97119987e-01
-1.72547147e-01 -3.98423165e-01 7.59723783e-01 4.82273638e-01
4.62597311e-01 -3.32416654e-01 -7.78667450e-01 3.84518117e-01
1.36637104e+00 8.06903660e-01 9.31186080e-01 3.98328304e-01
5.55213094e-01 3.73985499e-01 6.34860516e-01 2.56618977e-01
-2.74056047e-01 7.57767856e-01 4.93280478e-02 -2.69254506e-01
7.11911142e-01 1.62750483e-01 5.54867208e-01 3.12692255e-01
-1.08806646e+00 1.15387375e-02 -9.36677217e-01 -5.19223988e-01
-1.69157505e+00 -1.41623926e+00 -5.00124633e-01 2.53639436e+00
1.40448463e+00 5.32531738e-02 1.76188275e-01 -1.75257012e-01
1.93311676e-01 -2.95131326e-01 -7.13405371e-01 -6.46110594e-01
5.62193990e-02 6.39688492e-01 6.56735718e-01 1.10382032e+00
-8.01187694e-01 6.47283375e-01 8.10461521e+00 5.92954457e-01
-1.13530016e+00 -1.54030383e-01 1.06395185e-01 -1.70810092e-02
-1.74358174e-01 4.36020583e-01 -8.13853502e-01 2.38449305e-01
1.16945100e+00 -2.16570869e-01 6.15634859e-01 3.39907587e-01
1.84913799e-01 -2.32700154e-01 -1.11084259e+00 5.49263477e-01
-6.76215649e-01 -1.13057590e+00 1.42897800e-01 5.33439577e-01
4.49389875e-01 -2.75059696e-02 -1.49175525e-01 -1.14598133e-01
1.46306092e-02 -1.55867732e+00 2.34038025e-01 7.12180495e-01
1.12499118e+00 -8.77052188e-01 5.73045969e-01 3.23067605e-01
-1.08901262e+00 4.80556905e-01 -7.25660264e-01 -2.06168041e-01
-2.65900698e-02 7.13805333e-02 -7.07446635e-01 2.69140303e-01
3.22715074e-01 2.21258000e-01 5.29033951e-02 4.72527266e-01
4.57130224e-01 6.59676567e-02 -4.16390181e-01 1.33016750e-01
2.10200429e-01 -8.93074512e-01 2.38569796e-01 1.12446582e+00
-1.92628801e-01 9.45287585e-01 -5.39660901e-02 5.39844871e-01
4.07433361e-01 4.47006732e-01 -4.79243755e-01 -2.46510312e-01
1.74643710e-01 9.56968367e-01 -6.73104584e-01 5.60461394e-02
-3.54916960e-01 5.30643404e-01 1.69611812e-01 6.15497708e-01
-6.19429886e-01 -5.24615943e-01 9.30741966e-01 2.69565105e-01
8.67109224e-02 -1.58866495e-01 6.99932933e-01 -7.88591683e-01
-6.96404636e-01 -5.83466887e-01 2.98081785e-02 -6.35565281e-01
-1.01013136e+00 3.05717498e-01 1.13581851e-01 -4.25288796e-01
-7.94526860e-02 -1.03476274e+00 -3.08681130e-01 1.42860711e+00
-8.00342679e-01 -4.44868535e-01 3.87305737e-01 4.68670964e-01
-3.55666399e-01 1.76903546e-01 1.45896530e+00 5.02354279e-02
-1.47366911e-01 3.63873571e-01 8.02292287e-01 -6.16880536e-01
1.00169909e+00 -1.23615444e+00 2.04084858e-01 -2.17064038e-01
-6.87430918e-01 1.70301890e+00 1.15629280e+00 -9.61696863e-01
-1.72601724e+00 -5.73854983e-01 6.56603694e-01 -6.06303036e-01
3.41285884e-01 -2.23177329e-01 -1.22810113e+00 7.50089824e-01
7.35806078e-02 -1.16781011e-01 1.34048939e+00 -1.96930617e-01
-7.78495297e-02 4.81483847e-01 -1.23503721e+00 4.15133178e-01
8.35684001e-01 -3.32309544e-01 -4.96629089e-01 6.11754894e-01
3.68793070e-01 -7.81231701e-01 -1.38073659e+00 4.14563000e-01
1.14545894e+00 -9.96447861e-01 1.31371200e+00 -7.33215094e-01
-6.35057569e-01 -4.50006485e-01 -3.66150588e-01 -4.65129524e-01
-5.98029435e-01 -8.04649472e-01 2.26390779e-01 2.71430790e-01
3.59507889e-01 -9.58369672e-01 3.28889310e-01 7.51389980e-01
3.45371403e-02 -1.00636721e+00 -1.05153835e+00 -4.94264483e-01
2.81482190e-01 4.42952454e-01 3.56681615e-01 8.32256496e-01
3.62363935e-01 4.02525574e-01 -1.05737492e-01 -1.39868110e-01
5.74330449e-01 -4.61126044e-02 4.75394219e-01 -1.57056308e+00
-9.94213447e-02 1.62637249e-01 -1.34854436e-01 -7.16514707e-01
1.84691101e-01 -9.27610278e-01 -3.41013193e-01 -1.09678686e+00
5.90875745e-01 -3.56087148e-01 -2.10609838e-01 5.53491175e-01
3.73613924e-01 1.23134911e-01 -4.48412776e-01 4.28698570e-01
-1.60471424e-01 4.38193053e-01 1.11020303e+00 3.25921476e-01
-4.19366091e-01 -3.29579949e-01 -4.60762501e-01 6.87543035e-01
3.71526867e-01 -8.77653658e-01 -1.09411851e-01 5.96646726e-01
4.67312336e-01 -2.78135762e-02 -7.71087185e-02 -3.14409107e-01
-3.19339186e-01 -6.06723845e-01 6.19010806e-01 -5.58781803e-01
3.72670442e-01 -8.80574465e-01 1.25402761e+00 9.66174901e-01
8.96516815e-02 -4.32583056e-02 2.96086401e-01 4.11527634e-01
3.50314051e-01 -2.50624001e-01 8.71119082e-01 -2.62754917e-01
-3.14680301e-02 1.90977409e-01 -7.94516861e-01 -5.28308809e-01
8.18804502e-01 -7.55696058e-01 -2.99290866e-01 2.57090032e-01
-1.10945308e+00 -5.00412822e-01 1.20187807e+00 -6.93157792e-01
5.15237868e-01 -1.25098681e+00 -1.65171638e-01 1.15933977e-01
-1.14207625e-01 -3.28145444e-01 -5.08620664e-02 1.16960371e+00
-1.20217550e+00 7.36543119e-01 -1.99659586e-01 -3.31564039e-01
-1.52188706e+00 5.81934094e-01 8.46891880e-01 -7.72935227e-02
-2.83024073e-01 2.90645778e-01 5.52320957e-01 -4.06800419e-01
-2.34870404e-01 1.25997603e-01 -3.87118161e-02 -2.10595846e-01
7.19547391e-01 3.93803388e-01 1.00051999e-01 -1.11000240e+00
-7.38139749e-01 1.03601837e+00 -4.51939464e-01 1.94463760e-01
1.38999474e+00 3.17464828e-01 -5.91077149e-01 1.10222332e-01
1.02563798e+00 4.70808357e-01 -1.04992473e+00 1.42369494e-01
-1.94802120e-01 -2.40659282e-01 -4.12832350e-01 -7.27159560e-01
1.07425794e-01 3.58518809e-01 6.92337453e-01 -3.09018612e-01
4.71983075e-01 -1.16690546e-01 1.58152357e-01 8.49745929e-01
4.84747380e-01 -5.24235308e-01 -2.51558483e-01 6.38363719e-01
1.10675073e+00 -5.70610523e-01 6.50950551e-01 -4.65667725e-01
-2.57179707e-01 1.35446405e+00 2.02911884e-01 -4.71884646e-02
3.46322864e-01 3.07871848e-01 -1.28755674e-01 -4.04974669e-01
-6.36079907e-01 2.40226552e-01 3.05758357e-01 4.31460857e-01
1.16741991e+00 -1.61519423e-01 -9.13039744e-01 4.15695876e-01
8.61141384e-02 -3.85351442e-02 3.08565944e-01 1.07037008e+00
-7.06460178e-01 -1.54852855e+00 -6.67583644e-01 -3.14911827e-02
-4.96899188e-01 -6.08448163e-02 -1.03182495e+00 9.16227877e-01
-4.56393585e-02 5.41945696e-01 -2.64902532e-01 2.46796399e-01
4.15900201e-01 3.81235033e-01 1.11517441e+00 -4.17367548e-01
-6.07749701e-01 3.37627023e-01 -9.34108347e-02 -5.37871480e-01
-6.85410082e-01 -4.67441827e-01 -2.04931474e+00 -6.56264901e-01
-8.10841978e-01 7.04382956e-01 4.57045287e-01 8.92450511e-01
2.80675143e-01 -2.25361258e-01 2.16532618e-01 -1.52493930e+00
-4.49834466e-01 -7.12103248e-01 -9.79912281e-01 3.78971361e-02
2.76303768e-01 -1.02839935e+00 -3.68854046e-01 -7.54185915e-02] | [4.8104472160339355, 5.323380470275879] |
39f59b9d-0942-4f7f-bf58-b95055d493ce | edgeserve-an-execution-layer-for | 2303.08028 | null | https://arxiv.org/abs/2303.08028v1 | https://arxiv.org/pdf/2303.08028v1.pdf | EdgeServe: An Execution Layer for Decentralized Prediction | The relevant features for a machine learning task may be aggregated from data sources collected on different nodes in a network. This problem, which we call decentralized prediction, creates a number of interesting systems challenges in managing data routing, placing computation, and time-synchronization. This paper presents EdgeServe, a machine learning system that can serve decentralized predictions. EdgeServe relies on a low-latency message broker to route data through a network to nodes that can serve predictions. EdgeServe relies on a series of novel optimizations that can tradeoff computation, communication, and accuracy. We evaluate EdgeServe on three decentralized prediction tasks: (1) multi-camera object tracking, (2) network intrusion detection, and (3) human activity recognition. | ['Sanjay Krishnan', 'Ted Shaowang'] | 2023-03-02 | null | null | null | null | ['human-activity-recognition', 'network-intrusion-detection', 'human-activity-recognition'] | ['computer-vision', 'miscellaneous', 'time-series'] | [-2.76801318e-01 -1.36117965e-01 -6.61842525e-01 -7.68946826e-01
-3.25303167e-01 -1.40781835e-01 6.00519061e-01 6.30152166e-01
-1.33113891e-01 6.17982447e-01 3.09659932e-02 -6.66284887e-03
-2.42963225e-01 -9.39865351e-01 -3.01102012e-01 -4.18550551e-01
-5.02716720e-01 6.89279318e-01 5.47939420e-01 2.17471734e-01
7.09860846e-02 1.03290367e+00 -1.45633566e+00 5.69031000e-01
-6.52388260e-02 1.69536448e+00 -2.12572977e-01 9.30525243e-01
-1.07608326e-01 1.27678537e+00 -5.50145507e-01 -2.39445567e-01
1.71391457e-01 2.77778301e-02 -7.77677953e-01 -1.80238113e-01
2.46559694e-01 -3.19556445e-01 -1.45183042e-01 4.02886540e-01
7.91295022e-02 -1.59769118e-01 1.81009576e-01 -2.34607887e+00
5.57014607e-02 6.84739292e-01 -4.07722414e-01 5.19679010e-01
1.38017952e-01 -9.27588642e-02 8.85565162e-01 -4.38218802e-01
7.03151226e-01 9.70316470e-01 9.52414453e-01 3.15483958e-01
-8.88465226e-01 -7.38566518e-01 1.36417672e-01 4.82683271e-01
-1.18778861e+00 -8.10140073e-01 5.15743077e-01 -4.43882167e-01
1.14906251e+00 7.66283870e-01 5.39329350e-01 3.80863518e-01
6.54808402e-01 5.44977844e-01 6.40807450e-01 3.34060788e-02
5.32591999e-01 1.38632223e-01 3.27674508e-01 5.90311646e-01
4.14659604e-02 9.11298692e-02 -1.35828424e+00 -7.89821982e-01
2.13056728e-01 5.02644598e-01 1.04472473e-01 7.70908408e-03
-1.19114423e+00 6.67375207e-01 5.34818113e-01 -2.66041368e-01
-7.91566193e-01 3.94338369e-01 6.14301622e-01 5.20852029e-01
7.31333017e-01 1.82099015e-01 -1.08328366e+00 -3.98024656e-02
-7.11000204e-01 -1.39633730e-01 1.52129364e+00 1.06478584e+00
1.13641644e+00 -2.34787300e-01 8.21776409e-03 2.78922409e-01
6.81774616e-01 3.69413704e-01 1.65505111e-01 -1.03715253e+00
2.30933309e-01 7.73185968e-01 -7.40049407e-02 -1.33821774e+00
-7.62809038e-01 2.47405767e-02 -1.01097286e+00 -9.02675986e-02
-2.37021849e-01 -6.22049868e-01 -3.82296257e-02 1.28936136e+00
1.05474412e+00 4.83052850e-01 -1.14296012e-01 9.32642460e-01
6.28149807e-01 8.04060042e-01 3.30369979e-01 -3.99777532e-01
1.08627748e+00 -1.21020591e+00 -2.64675379e-01 -2.12994348e-02
7.53898263e-01 -6.94375098e-01 -2.44405754e-02 2.40101576e-01
-8.05983186e-01 -1.40924737e-01 -4.86236840e-01 6.61945269e-02
-5.51437020e-01 -1.39328063e-01 1.04067743e+00 2.92535365e-01
-1.28162634e+00 7.27537155e-01 -1.20151913e+00 -5.98784029e-01
4.30851698e-01 5.74196398e-01 -4.12920147e-01 4.10578966e-01
-4.73500848e-01 5.70532739e-01 1.59732342e-01 -2.50925183e-01
-8.33852053e-01 -8.32478046e-01 -2.96648532e-01 2.78776437e-01
2.45828137e-01 -8.61099660e-01 1.34537566e+00 -7.84946620e-01
-1.30645096e+00 3.80996168e-01 -2.57004648e-01 -4.70353752e-01
1.41997024e-01 -1.51545228e-02 -5.33567011e-01 -3.81694511e-02
-2.74587292e-02 4.20078725e-01 5.94772577e-01 -5.10014296e-01
-1.28045511e+00 -4.44136560e-01 -6.15130842e-01 -2.24002711e-02
-7.01849401e-01 4.12992299e-01 -3.40643317e-01 -2.89347947e-01
-1.68881133e-01 -9.54092622e-01 -6.00896597e-01 5.32334924e-01
-4.58734959e-01 -6.15621924e-01 1.40771568e+00 -1.56361416e-01
9.77068245e-01 -1.89469540e+00 -3.68224323e-01 5.30815542e-01
7.61004865e-01 -8.21073875e-02 -8.83148089e-02 6.11914515e-01
4.18730021e-01 6.76415041e-02 7.12523043e-01 -4.59423095e-01
-2.09287569e-01 2.05769405e-01 -2.84419566e-01 4.99910921e-01
-3.16529237e-02 6.18613958e-01 -5.39058745e-01 -7.13065326e-01
-2.06628330e-02 2.00388342e-01 -6.17333353e-01 5.67181036e-02
-4.78473216e-01 1.20992474e-01 -8.73738766e-01 6.30700886e-01
4.41909730e-01 -9.97521996e-01 4.10367548e-01 -4.56341766e-02
-1.14913203e-01 2.87720442e-01 -1.05266309e+00 1.00370300e+00
-2.78158426e-01 7.78176904e-01 4.89133626e-01 -7.96505272e-01
8.12704802e-01 3.11603606e-01 1.27124059e+00 -2.52408892e-01
-1.29598334e-01 -2.19562948e-01 -6.96041346e-01 -1.86742038e-01
4.70983982e-01 5.66313624e-01 -1.75849926e-02 1.04162955e+00
-1.86804593e-01 6.01873279e-01 6.96867928e-02 5.05738735e-01
1.88095391e+00 -6.69271350e-01 3.67572784e-01 -1.01200692e-01
-3.35169630e-03 5.16143501e-01 9.60752904e-01 7.34141588e-01
-2.41161376e-01 -3.02302986e-01 5.32922029e-01 -1.12243652e+00
-8.57742548e-01 -8.03979516e-01 3.32942635e-01 1.47957718e+00
1.92662418e-01 -9.02610421e-01 -1.31516844e-01 -7.30892956e-01
3.99065018e-01 1.32228723e-02 -2.41363317e-01 1.99435592e-01
-3.39587368e-02 -3.91210049e-01 2.12267101e-01 4.37714845e-01
2.35405549e-01 -9.86889660e-01 -8.04170787e-01 4.73424613e-01
-4.04654332e-02 -1.18252742e+00 -2.91538417e-01 -3.15191341e-03
-9.32762623e-01 -1.47223115e+00 5.36346793e-01 -3.97532970e-01
4.91565049e-01 5.83405495e-01 1.37471688e+00 4.94174391e-01
-4.44212705e-01 7.83637345e-01 -2.52689600e-01 -7.81764925e-01
-2.28718802e-01 3.52683067e-01 1.88718781e-01 1.02408335e-01
6.24536097e-01 -5.19645333e-01 -5.39021134e-01 6.98570907e-01
-5.72153747e-01 1.81735143e-01 3.11559439e-01 1.25116065e-01
6.26653373e-01 5.48560619e-02 6.69753194e-01 -1.01411426e+00
4.43269134e-01 -1.13316357e+00 -9.13863003e-01 5.06849170e-01
-7.90924311e-01 -3.98599237e-01 8.51784229e-01 -1.16542555e-01
-7.28295565e-01 7.25458860e-01 4.40875560e-01 -5.13654649e-01
-3.56573969e-01 3.41038674e-01 2.37953395e-01 -1.88458472e-01
7.81423926e-01 -1.33325696e-01 2.80356139e-01 -3.69702846e-01
3.34520757e-01 9.18254793e-01 -5.07766865e-02 -2.31699213e-01
4.59897220e-01 5.84843099e-01 3.15862298e-01 -7.82114565e-01
-5.95528960e-01 -7.17746377e-01 -2.78585970e-01 -3.36831152e-01
3.01566601e-01 -1.04083502e+00 -1.41546941e+00 2.91660637e-01
-1.23021495e+00 -4.04905379e-01 -3.19315195e-01 3.01639438e-01
-3.43904793e-01 -1.35184199e-01 -9.79833543e-01 -4.58999962e-01
-8.04697573e-01 -6.31860614e-01 7.54560888e-01 2.46466219e-01
-2.46148109e-01 -7.97324538e-01 4.77921106e-02 1.43220395e-01
1.02175152e+00 1.24555051e-01 3.12983125e-01 -1.17309070e+00
-1.27580428e+00 -4.61850077e-01 -5.04911244e-01 -2.01085031e-01
3.60924490e-02 2.28628457e-01 -7.84791529e-01 -2.91629463e-01
-4.26353306e-01 -3.93536538e-01 3.61454368e-01 6.30271882e-02
1.42908859e+00 -8.47577631e-01 -1.04634666e+00 5.56180418e-01
1.15267634e+00 -1.58607036e-01 -9.70871150e-02 -7.17341807e-03
4.43197727e-01 3.79677087e-01 4.86056060e-01 1.26688266e+00
6.08300209e-01 4.83432114e-01 3.89231026e-01 -4.74087037e-02
1.52926207e-01 6.80566430e-02 7.00960383e-02 7.50313580e-01
4.09792513e-01 -2.21089587e-01 -1.06781805e+00 3.46858293e-01
-2.26389241e+00 -8.60209048e-01 -1.98192924e-01 1.57645953e+00
5.20798147e-01 -4.65243965e-01 2.40675613e-01 -4.00165647e-01
5.69526017e-01 2.02250481e-02 -8.71315837e-01 -3.29095304e-01
4.29224074e-01 -2.71463931e-01 6.00674570e-01 -1.13632634e-01
-1.10539949e+00 7.73882031e-01 7.03766823e+00 5.00255287e-01
-1.37355518e+00 2.66938478e-01 8.38577509e-01 -2.71136224e-01
1.31213993e-01 6.34326115e-02 -1.08187318e+00 4.63563949e-01
1.38003492e+00 -6.21411562e-01 3.84812564e-01 1.49990487e+00
2.75746793e-01 5.14580049e-02 -1.31214917e+00 8.71654153e-01
-1.38288900e-01 -2.00625300e+00 -3.54476094e-01 4.22184095e-02
6.45726800e-01 9.34273541e-01 -4.84172225e-01 -1.29564196e-01
8.45262706e-01 -6.23134851e-01 1.21436626e-01 9.21777546e-01
3.83296400e-01 -5.57892978e-01 3.80260497e-01 5.61949551e-01
-1.38972044e+00 -2.57676661e-01 -3.63692015e-01 -1.34606063e-01
7.58878365e-02 1.10520732e+00 -9.94625032e-01 1.47929415e-01
9.53827977e-01 1.01341164e+00 -4.79797989e-01 1.40088272e+00
3.62852037e-01 5.10140121e-01 -9.40946937e-01 -2.85889268e-01
-4.01642591e-01 3.01230639e-01 3.72089058e-01 1.01265132e+00
1.16924703e-01 3.69779587e-01 7.05221772e-01 4.42301005e-01
-5.34217715e-01 2.55788546e-02 -5.07830024e-01 2.21982002e-01
1.02596748e+00 1.67707264e+00 -7.00519860e-01 -5.35512805e-01
-6.09793246e-01 5.06029367e-01 5.41394651e-01 8.46472606e-02
-4.78951603e-01 -1.44153506e-01 1.21050143e+00 8.21447596e-02
3.06835510e-02 -9.13057253e-02 -2.40061268e-01 -9.25583184e-01
-1.00891717e-01 -5.74258924e-01 6.97133601e-01 -5.55498660e-01
-1.67934251e+00 5.84336758e-01 -3.96458119e-01 -1.04133010e+00
-3.20612967e-01 -1.41167745e-01 -5.78863502e-01 3.18643868e-01
-1.41925299e+00 -8.76510382e-01 -5.87013006e-01 9.94764209e-01
9.77410302e-02 -3.42465669e-01 8.66525710e-01 3.96600962e-01
-7.72883892e-01 2.69760460e-01 -6.76054088e-03 -1.00478359e-01
6.06394291e-01 -4.87705320e-01 4.85104203e-01 4.42055076e-01
1.60938650e-01 4.66778362e-03 1.68443039e-01 -7.09914505e-01
-1.70868850e+00 -1.57675958e+00 1.19156468e+00 -4.66374785e-01
7.25108027e-01 4.86723259e-02 -4.23754454e-01 1.06385112e+00
1.20036252e-01 6.90858603e-01 1.00621426e+00 3.36589247e-01
-2.71812022e-01 -7.81808376e-01 -1.14349890e+00 1.89951673e-01
7.35616088e-01 -1.44114524e-01 3.13959867e-01 8.70124519e-01
5.16684115e-01 -2.02401549e-01 -1.28345048e+00 2.48665791e-02
3.05642843e-01 -9.06864166e-01 6.48299336e-01 -1.08795822e+00
1.22176096e-01 1.12008139e-01 2.94038579e-02 -1.11881340e+00
-3.10981750e-01 -9.12903488e-01 -5.04520893e-01 1.07417738e+00
4.63424265e-01 -7.56454945e-01 1.40691316e+00 1.39516068e+00
2.82478303e-01 -6.78622544e-01 -9.24721658e-01 -3.59481961e-01
-8.21984231e-01 -3.97332430e-01 8.67220998e-01 9.70227242e-01
3.08481783e-01 2.93003500e-01 -2.80831903e-01 2.63752222e-01
8.04603159e-01 3.59814316e-01 1.20392478e+00 -1.76040697e+00
4.95997816e-02 -1.08924165e-01 -5.97874284e-01 -1.02833295e+00
8.21770877e-02 -6.45236433e-01 -2.21997276e-01 -1.28709102e+00
1.51198760e-01 -1.11087322e+00 -1.66484043e-01 1.10745871e+00
4.09061760e-01 -1.85244381e-01 5.54043174e-01 8.13431084e-01
-1.41513145e+00 2.37569764e-01 4.13235366e-01 1.28475606e-01
-1.24269292e-01 4.67991322e-01 -3.53897393e-01 7.50654459e-01
1.09485686e+00 -8.42020988e-01 -1.82397783e-01 -4.67232436e-01
3.72958601e-01 6.28592432e-01 2.68807203e-01 -1.08995450e+00
1.20806670e+00 -6.34454846e-01 5.73495507e-01 -6.41358912e-01
3.88139874e-01 -1.36684918e+00 3.42297316e-01 4.25766259e-01
-4.97645617e-01 4.78263110e-01 8.05123057e-03 8.60122204e-01
-2.68419921e-01 6.54996574e-01 5.34238398e-01 7.30289221e-02
-8.02791357e-01 8.92658532e-01 -4.97083575e-01 -9.66996029e-02
1.54370487e+00 3.09823662e-01 -8.65194678e-01 -3.86356264e-01
-7.69734979e-01 7.35692918e-01 3.50084126e-01 4.44939554e-01
7.74970055e-01 -1.25652659e+00 -5.50953269e-01 2.85235792e-01
-7.67799765e-02 -2.75655419e-01 -1.53981566e-01 8.51936698e-01
-3.59200746e-01 3.10513943e-01 -1.43142626e-01 -7.65548825e-01
-1.50899792e+00 3.37214768e-01 2.29925305e-01 -3.77249300e-01
-4.89534885e-01 6.16194963e-01 -5.61438382e-01 -3.90263259e-01
3.64738256e-01 1.99880704e-01 1.73389390e-01 2.39562597e-02
5.99615693e-01 6.84760332e-01 1.81079224e-01 -2.58591741e-01
-5.43684363e-01 -1.42848164e-01 1.56300869e-02 4.10366386e-01
1.49573302e+00 -3.31540972e-01 -6.35467768e-01 -1.02172326e-02
9.96568680e-01 -6.04830444e-01 -1.09811902e+00 -5.95257461e-01
3.83707404e-01 -6.11353517e-01 2.04801247e-01 -5.91896713e-01
-1.57018280e+00 1.97220996e-01 2.12073147e-01 5.84646642e-01
1.20406878e+00 6.84243962e-02 9.34281766e-01 7.59134233e-01
5.52707195e-01 -1.14494705e+00 3.72013054e-03 4.12567705e-01
4.01471227e-01 -1.23221445e+00 1.78734943e-01 -4.42468196e-01
-5.38947403e-01 1.16077185e+00 9.05250490e-01 1.99494377e-01
1.45627189e+00 6.49144769e-01 7.00512454e-02 -4.79197741e-01
-2.02608895e+00 2.85969704e-01 -2.11770445e-01 3.45956177e-01
6.19227961e-02 1.63652807e-01 1.50503352e-01 4.52464342e-01
3.11630834e-02 3.70776653e-01 3.49611223e-01 1.10882640e+00
-6.22403979e-01 -1.05492485e+00 -2.23884687e-01 1.03032279e+00
-2.46519938e-01 2.13351637e-01 -6.11864209e-01 2.42773801e-01
-3.18900228e-01 1.14203727e+00 5.84765017e-01 -5.39673269e-01
1.02617173e-02 -1.83664814e-01 -5.79441786e-01 -5.31221688e-01
-8.73514712e-01 -3.32140148e-01 2.31618047e-01 -1.23812652e+00
-3.26748013e-01 -3.09751570e-01 -1.19024932e+00 -9.72274661e-01
-1.61268458e-01 3.85774761e-01 1.09369695e+00 7.24259555e-01
1.23934317e+00 1.76754504e-01 1.08394372e+00 -6.52950466e-01
-3.60786438e-01 -4.86510694e-01 -5.40213525e-01 1.94346756e-01
2.82270074e-01 2.77459379e-02 -6.31197318e-02 -7.45078549e-02] | [6.079092502593994, 6.212115287780762] |
6fd35c29-d5df-4cdb-bc9d-120d1a54b2e5 | deep-reinforcement-learning-based-resource-1 | 2305.06249 | null | https://arxiv.org/abs/2305.06249v1 | https://arxiv.org/pdf/2305.06249v1.pdf | Deep Reinforcement Learning Based Resource Allocation for Cloud Native Wireless Network | Cloud native technology has revolutionized 5G beyond and 6G communication networks, offering unprecedented levels of operational automation, flexibility, and adaptability. However, the vast array of cloud native services and applications presents a new challenge in resource allocation for dynamic cloud computing environments. To tackle this challenge, we investigate a cloud native wireless architecture that employs container-based virtualization to enable flexible service deployment. We then study two representative use cases: network slicing and Multi-Access Edge Computing. To optimize resource allocation in these scenarios, we leverage deep reinforcement learning techniques and introduce two model-free algorithms capable of monitoring the network state and dynamically training allocation policies. We validate the effectiveness of our algorithms in a testbed developed using Free5gc. Our findings demonstrate significant improvements in network efficiency, underscoring the potential of our proposed techniques in unlocking the full potential of cloud native wireless networks. | ['Jingjing Zhang', 'Yue Gao', 'Jiasheng Wu', 'Lin Wang'] | 2023-05-10 | null | null | null | null | ['edge-computing'] | ['time-series'] | [-4.82149452e-01 -1.72791153e-01 -5.58998525e-01 -1.44945279e-01
1.66150033e-01 -7.95495331e-01 7.95489922e-02 -7.61843085e-01
2.82240510e-02 9.77407217e-01 -2.10460857e-01 -1.21333051e+00
-3.67140383e-01 -8.51168752e-01 -1.01263866e-01 -4.99344468e-01
-8.91825378e-01 6.09048486e-01 -1.40597641e-01 7.05507398e-03
1.53627722e-02 8.53002071e-01 -1.19277608e+00 -3.43448102e-01
6.91911995e-01 1.34095371e+00 1.10532872e-01 7.25758731e-01
-1.48142591e-01 5.80098510e-01 -8.41321826e-01 -5.84819205e-02
7.05222011e-01 2.76422352e-01 -6.01070881e-01 1.65857479e-01
-1.02697343e-01 -5.63148737e-01 -5.02371848e-01 5.28453469e-01
6.91259205e-01 -1.39587954e-01 -1.62256628e-01 -1.67094147e+00
-2.85428554e-01 5.20480692e-01 -2.38264546e-01 6.26928508e-01
-1.08413786e-01 3.61601114e-01 1.03009903e+00 -9.43283662e-02
6.32997692e-01 6.01622641e-01 2.85627127e-01 4.82972503e-01
-9.84287381e-01 -9.63991344e-01 4.78472233e-01 2.12065607e-01
-1.12108445e+00 -9.33099270e-01 3.72274160e-01 -3.11076790e-02
1.13587606e+00 3.76544327e-01 9.12561715e-01 5.07424712e-01
1.01675510e-01 5.36123775e-02 7.21171856e-01 -3.46015900e-01
4.91185009e-01 -2.82463506e-02 -5.11508644e-01 3.71891379e-01
1.83414653e-01 2.58252561e-01 5.57491854e-02 -1.34112433e-01
9.16508794e-01 -1.21601291e-01 -2.51461059e-01 -3.14070612e-01
-7.75115430e-01 2.12273911e-01 4.01722163e-01 1.41845986e-01
-7.73455262e-01 7.10162818e-01 2.95548260e-01 5.22450805e-01
3.08072865e-01 5.79940617e-01 -6.57813787e-01 -5.27320564e-01
-1.19632328e+00 -9.91022065e-02 1.00964272e+00 1.28919232e+00
4.48047936e-01 6.32247210e-01 2.00193338e-02 2.57381320e-01
1.23732783e-01 5.17150342e-01 1.79695562e-01 -1.45666003e+00
1.70445800e-01 -1.88986026e-03 1.45511061e-01 -4.73664284e-01
-6.54886067e-01 -1.25214148e+00 -5.92514038e-01 1.65619969e-01
-2.60266513e-01 -5.76070547e-01 -5.83999217e-01 1.76796663e+00
2.17098415e-01 7.39691138e-01 2.47876849e-02 7.06799865e-01
-4.23306748e-02 2.99656779e-01 -6.39742091e-02 -3.63350749e-01
6.96336567e-01 -1.25910127e+00 -5.48668861e-01 9.32570100e-02
2.91712910e-01 -4.56641674e-01 7.03236818e-01 -1.52271450e-01
-1.08829439e+00 -1.89609423e-01 -1.16366208e+00 8.02694261e-01
-2.04863146e-01 -4.99308676e-01 9.88708496e-01 1.35013258e+00
-1.49849927e+00 3.86842340e-01 -7.76625693e-01 -4.50709850e-01
4.25694972e-01 7.58080542e-01 1.11361183e-01 -1.28893584e-01
-8.97276282e-01 2.99352169e-01 1.60143122e-01 -1.69244751e-01
-7.30880022e-01 -1.01115119e+00 -1.48841068e-01 7.57597744e-01
5.26023209e-01 -9.14274037e-01 1.33851027e+00 -6.34487391e-01
-1.64072549e+00 2.16389179e-01 1.91515595e-01 -5.48450351e-01
3.71747524e-01 4.19545136e-02 -1.21006072e+00 3.48803908e-01
-5.03785983e-02 6.98176166e-03 6.55574024e-01 -9.49712336e-01
-7.52130628e-01 -6.79834560e-02 4.54238206e-01 -6.01042323e-02
-5.96982062e-01 1.86895877e-01 -3.27524602e-01 -1.86718687e-01
-1.30314946e-01 -8.94844651e-01 -5.78886986e-01 -3.34437221e-01
-3.67144719e-02 5.75571120e-01 1.04276896e+00 1.31928576e-02
1.28455651e+00 -1.90450811e+00 -3.66769761e-01 5.35935521e-01
7.39278734e-01 1.59777194e-01 -1.40842333e-01 2.41153777e-01
1.79431930e-01 6.69613838e-01 6.76286340e-01 -8.37792754e-02
-1.43220015e-02 4.08556789e-01 6.44935179e-04 -8.25895220e-02
-3.65185440e-01 6.90716088e-01 -7.92807639e-01 -1.30852178e-01
1.43759295e-01 1.46771327e-01 -8.97174776e-01 2.81173408e-01
-2.88683027e-01 5.12585640e-01 -3.91086698e-01 9.36192393e-01
8.32518458e-01 -6.23373389e-01 1.02401721e+00 -1.21382110e-01
-1.49353355e-01 1.65938176e-02 -8.33299220e-01 1.11643231e+00
-1.10194707e+00 3.90498310e-01 4.88791585e-01 -8.26415658e-01
4.66167957e-01 4.65363890e-01 8.05470347e-01 -8.92946661e-01
3.26831117e-02 3.28211069e-01 2.73614973e-01 -3.39405656e-01
3.09874356e-01 1.24276038e-02 1.96798876e-01 4.89873916e-01
3.21108289e-02 3.77494156e-01 3.32322046e-02 7.72181079e-02
1.30406177e+00 -1.81850284e-01 -2.30660499e-03 -2.62837619e-01
-6.05985485e-02 -5.79873919e-01 7.52851665e-01 9.36963439e-01
-8.46498489e-01 -2.59524018e-01 5.61354876e-01 -5.06843984e-01
-8.03934336e-01 -1.38817418e+00 2.16159627e-01 1.09929824e+00
2.74539869e-02 -2.55537033e-01 -4.75334823e-01 -3.41590911e-01
-7.46043101e-02 5.93978345e-01 1.71619039e-02 -1.28979117e-01
-2.50925273e-01 -6.90413892e-01 2.73552328e-01 2.20204249e-01
6.54949129e-01 -4.14782286e-01 -8.06658506e-01 6.31496251e-01
1.71987116e-01 -1.96874011e+00 -2.92108297e-01 8.72916430e-02
-7.42537856e-01 -7.19225168e-01 -1.80695280e-02 -2.99544722e-01
4.82389443e-02 4.84872490e-01 1.49456549e+00 3.13711554e-01
-1.98721185e-01 5.92991829e-01 -4.70755063e-02 1.82247072e-01
-2.60264099e-01 6.10215187e-01 4.52270210e-01 -9.47618857e-02
-2.19497263e-01 -1.54558659e+00 -7.69775033e-01 1.09973349e-01
-2.25159734e-01 -1.82504669e-01 5.05046606e-01 3.41972232e-01
2.26553127e-01 3.28166664e-01 1.02444935e+00 -9.46665049e-01
5.43311357e-01 -8.45822036e-01 -8.27340007e-01 1.80332482e-01
-1.05464518e+00 -2.99710244e-01 7.92119861e-01 7.49465451e-02
-6.85176253e-01 -7.60460496e-01 7.34735429e-02 -6.33901954e-01
3.32262009e-01 5.26186049e-01 -3.45930576e-01 -4.27880257e-01
2.19207242e-01 -2.12362349e-01 -1.14686608e-01 2.74589788e-02
3.35587651e-01 7.52205312e-01 2.39782915e-01 -8.75480711e-01
8.33324134e-01 4.06443119e-01 1.73877329e-01 -5.79976976e-01
-4.02870744e-01 1.05196852e-02 -4.03553620e-02 -4.16298509e-01
2.78067380e-01 -7.93370187e-01 -1.02477467e+00 -2.60987937e-01
-5.00100851e-01 -6.08379662e-01 -8.82203802e-02 1.66922256e-01
-5.09454548e-01 -1.09753430e-01 -6.67961001e-01 -6.80338025e-01
-5.82104921e-01 -1.19816244e+00 2.61547416e-01 4.81444299e-01
3.28838378e-01 -8.95598173e-01 -3.55622917e-01 2.53753662e-01
1.48947418e+00 2.15966552e-01 1.02923071e+00 -2.46889085e-01
-1.18701553e+00 4.98197600e-02 -5.82266510e-01 -3.38412896e-02
2.28445977e-01 2.57159412e-01 -7.30425417e-01 -6.57366872e-01
-6.33029342e-01 2.06264034e-01 3.49236205e-02 5.17267823e-01
1.56605804e+00 -3.08182627e-01 -3.25370997e-01 1.28945935e+00
1.77141392e+00 3.35751265e-01 5.17587900e-01 5.32132626e-01
3.67077291e-01 -7.89431632e-02 3.28283459e-01 8.93819749e-01
4.49439704e-01 6.79711580e-01 1.10359514e+00 -9.98820364e-02
7.18509033e-02 2.27174908e-01 -1.20784998e-01 5.68295360e-01
-3.21722388e-01 -5.24317086e-01 -7.47435987e-01 1.09861664e-01
-1.59469199e+00 -1.08694875e+00 6.88293636e-01 1.98763394e+00
-4.38406095e-02 6.73056960e-01 1.83009952e-01 -2.03902289e-01
4.78605777e-01 1.38425469e-01 -6.76509202e-01 -5.53667247e-01
1.15093142e-01 4.92605120e-01 7.09963083e-01 1.65607169e-01
-6.66912913e-01 1.17530537e+00 6.98868513e+00 3.09580505e-01
-1.52112520e+00 2.92361856e-01 5.51126480e-01 -3.61174285e-01
-1.51057199e-01 1.84628487e-01 -5.30599710e-03 7.17517257e-01
1.61028636e+00 -4.51120734e-01 9.29431081e-01 9.35461104e-01
8.12346876e-01 4.30101395e-01 -5.52226305e-01 7.72812784e-01
-8.00345719e-01 -1.94335377e+00 -1.05327561e-01 4.61256534e-01
6.58155143e-01 4.91390735e-01 1.50272354e-01 5.16908169e-01
2.59809762e-01 -7.99879789e-01 4.98306543e-01 2.82502472e-01
1.13931441e+00 -1.00161862e+00 6.38329566e-01 -2.70640552e-01
-1.13890207e+00 -6.43812895e-01 1.31013870e-01 -3.50556880e-01
3.07581007e-01 6.32972121e-01 -7.10597813e-01 4.79831904e-01
6.67945802e-01 2.52993375e-01 -1.33592650e-01 1.14863276e+00
3.52197170e-01 6.70860350e-01 -1.22650005e-01 4.61352229e-01
-4.46371883e-02 -9.95708779e-02 4.02226657e-01 8.46833766e-01
3.77386779e-01 -1.74313053e-01 6.73158586e-01 7.01822400e-01
-5.01828194e-01 -1.69235453e-01 -1.74279228e-01 -1.26096219e-01
1.29874122e+00 1.44788122e+00 -6.69258535e-01 -2.57876098e-01
-4.66272593e-01 7.68320680e-01 1.59022324e-02 8.32042933e-01
-9.22964633e-01 -5.22270322e-01 1.57070196e+00 2.48797372e-01
1.77267879e-01 -7.00726390e-01 -1.66747734e-01 -1.18326569e+00
-2.15691552e-01 -7.35225201e-01 1.99017450e-01 -5.18496573e-01
-6.63688540e-01 8.07615459e-01 -5.57622969e-01 -9.19151783e-01
-1.82745636e-01 -3.41499716e-01 -6.79924428e-01 5.16722322e-01
-1.84769309e+00 -1.05388212e+00 -5.08789957e-01 3.76412958e-01
1.83199942e-01 -5.73173463e-01 9.58745897e-01 7.77048945e-01
-7.79913306e-01 8.73721242e-01 2.85965890e-01 -3.08204979e-01
3.06244552e-01 -1.05906951e+00 4.32383239e-01 1.01281989e+00
-2.50484556e-01 3.86345476e-01 5.31678021e-01 -3.13248523e-02
-1.52477503e+00 -8.80996108e-01 -1.68063447e-01 3.69503915e-01
7.69941211e-01 -3.50025058e-01 -1.45357743e-01 7.72532940e-01
1.80460319e-01 6.35888159e-01 9.35233772e-01 2.16076314e-01
-1.22176223e-01 -5.41805506e-01 -1.31740630e+00 8.62162590e-01
1.13103235e+00 -3.27757865e-01 9.29869235e-01 2.81946093e-01
1.22393656e+00 -3.71704698e-01 -9.39007223e-01 2.34904125e-01
6.30996168e-01 -9.68881845e-01 7.59790361e-01 -8.85525942e-01
-3.33895594e-01 -8.96744281e-02 -7.53265977e-01 -1.23868918e+00
-5.40211380e-01 -1.13285351e+00 -6.96873367e-01 9.60251808e-01
2.01227114e-01 -9.06497300e-01 1.14421678e+00 4.73626703e-01
-2.80795604e-01 -8.56531441e-01 -9.87945199e-01 -1.00999773e+00
-2.39364013e-01 -3.49436492e-01 1.39025760e+00 8.41989577e-01
-2.42141023e-01 8.93349424e-02 -1.64998055e-01 4.93607402e-01
5.41631460e-01 4.31511670e-01 6.37550652e-01 -1.05722380e+00
-6.10470653e-01 -5.41956365e-01 -5.58644533e-01 -6.87321842e-01
3.72093022e-01 -6.37990594e-01 -7.66994298e-01 -1.04860008e+00
-2.78064400e-01 -1.11456609e+00 -5.45402467e-01 2.46630818e-01
3.52538228e-01 1.24921545e-01 3.55556905e-01 7.15751126e-02
-7.16856003e-01 2.77834505e-01 8.83723676e-01 1.92457929e-01
-1.52625293e-01 3.77557725e-01 -7.75498211e-01 1.82284221e-01
1.29082334e+00 6.28077462e-02 -7.96686113e-01 -5.93182564e-01
-5.52563630e-02 3.99458259e-01 1.07617855e-01 -1.34279728e+00
1.26885008e-02 -6.25095844e-01 1.31172523e-01 2.80772746e-01
4.19845842e-02 -1.25738394e+00 1.27231330e-03 3.42075467e-01
3.28505933e-01 2.67924845e-01 5.86730093e-02 5.20561337e-01
3.36339772e-01 5.90315104e-01 8.42645109e-01 4.10728939e-02
-1.06427121e+00 9.65976119e-01 -7.69100666e-01 1.38468519e-01
9.30082321e-01 -2.08650127e-01 -3.36880773e-01 -7.15161681e-01
-1.07680023e+00 4.60473508e-01 3.73996317e-01 2.57906109e-01
3.87302697e-01 -9.77708042e-01 -2.24978596e-01 1.12869702e-01
-1.53405726e-01 -8.20651591e-01 3.12350929e-01 5.76535404e-01
-7.42104828e-01 2.59186715e-01 -5.97609341e-01 -3.40070963e-01
-8.34283292e-01 6.18127108e-01 9.41235006e-01 -3.38631421e-01
-2.68662065e-01 2.84837216e-01 -5.45448005e-01 -2.31836632e-01
4.40490842e-02 2.00876251e-01 3.75781506e-01 -6.43298328e-01
9.23953056e-02 4.72144127e-01 2.61661530e-01 7.41590261e-02
-3.67186964e-01 -2.12274000e-01 -5.55084348e-02 -5.17115444e-02
1.26662076e+00 -6.14505291e-01 -2.66857982e-01 -3.41505170e-01
8.44026387e-01 1.63060918e-01 -8.43735456e-01 3.71483564e-02
6.93572313e-02 -7.25761831e-01 5.38298488e-01 -8.69294345e-01
-1.55577493e+00 3.79009008e-01 9.98547554e-01 3.95718694e-01
1.55394006e+00 -3.72732967e-01 7.77310729e-01 1.65195420e-01
9.31965947e-01 -7.89504945e-01 -1.01654686e-01 3.11684102e-01
-1.31199673e-01 -9.00550127e-01 -3.15886706e-01 -4.38177347e-01
1.05667114e-01 1.17717910e+00 9.84374106e-01 2.05220103e-01
1.03323603e+00 4.77321774e-01 1.21144325e-01 -2.08681360e-01
-1.02344644e+00 -3.49975586e-01 -8.11335802e-01 6.92349732e-01
2.69839168e-01 6.94119632e-01 2.59694010e-01 2.83846766e-01
-8.41603279e-02 -7.22424537e-02 9.67928529e-01 8.88774216e-01
-3.65942508e-01 -1.36595035e+00 1.36363506e-01 7.84859002e-01
-5.84289312e-01 -2.99377710e-01 5.74356019e-01 6.56253159e-01
-1.09925754e-01 1.00319195e+00 1.75262079e-01 -5.93815506e-01
-1.02969602e-01 -1.91137776e-01 2.62955785e-01 -3.85900617e-01
-1.46796912e-01 -1.95301071e-01 2.67603964e-01 -8.24879348e-01
2.51499880e-02 -1.97577298e-01 -1.00908399e+00 -7.85941660e-01
-1.25798225e-01 2.76650935e-01 7.85498023e-01 8.66183043e-01
1.07052994e+00 1.16402900e+00 1.10671926e+00 -5.43098152e-01
-4.27930653e-01 -2.76157111e-01 -7.95627415e-01 -4.78228420e-01
4.12511826e-01 -6.57343388e-01 -2.03653961e-01 -5.91787636e-01] | [5.914285182952881, 1.6793313026428223] |
f550d301-e9c7-436b-9fa9-5730f81cc7e2 | cuslink-single-linkage-agglomerative | 2306.16354 | null | https://arxiv.org/abs/2306.16354v1 | https://arxiv.org/pdf/2306.16354v1.pdf | cuSLINK: Single-linkage Agglomerative Clustering on the GPU | In this paper, we propose cuSLINK, a novel and state-of-the-art reformulation of the SLINK algorithm on the GPU which requires only $O(Nk)$ space and uses a parameter $k$ to trade off space and time. We also propose a set of novel and reusable building blocks that compose cuSLINK. These building blocks include highly optimized computational patterns for $k$-NN graph construction, spanning trees, and dendrogram cluster extraction. We show how we used our primitives to implement cuSLINK end-to-end on the GPU, further enabling a wide range of real-world data mining and machine learning applications that were once intractable. In addition to being a primary computational bottleneck in the popular HDBSCAN algorithm, the impact of our end-to-end cuSLINK algorithm spans a large range of important applications, including cluster analysis in social and computer networks, natural language processing, and computer vision. Users can obtain cuSLINK at https://docs.rapids.ai/api/cuml/latest/api/#agglomerative-clustering | ['Tim Oates', 'Brad Rees', 'John Zedlewski', 'Edward Raff', 'Joe Eaton', 'Mahesh Doijade', 'Alex Fender', 'Divye Gala', 'Corey J. Nolet'] | 2023-06-28 | null | null | null | null | ['graph-construction', 'clustering'] | ['graphs', 'methodology'] | [-3.75541389e-01 -2.63558090e-01 6.22974057e-03 -3.36980015e-01
-6.76537275e-01 -7.88103878e-01 2.50218242e-01 7.66533136e-01
-4.70584661e-01 1.93944097e-01 -2.27519661e-01 -7.10217416e-01
-1.71970710e-01 -1.10064173e+00 -4.65613127e-01 -4.36216265e-01
-6.68867350e-01 7.68988490e-01 4.21041608e-01 2.48654671e-02
3.88009667e-01 6.00168228e-01 -1.55756617e+00 7.47311190e-02
7.71233916e-01 6.28576100e-01 -7.32369721e-02 1.06234801e+00
-7.31854215e-02 4.39741880e-01 -3.76695335e-01 -4.27320570e-01
2.39523783e-01 -1.41394943e-01 -7.37365425e-01 -2.93990910e-01
8.75407681e-02 1.73883215e-01 -2.21638843e-01 8.68823946e-01
6.31694078e-01 5.64473346e-02 4.16792601e-01 -1.38560259e+00
-6.85168803e-02 8.77705932e-01 -9.73320723e-01 2.43850455e-01
3.28697652e-01 -7.47765228e-02 8.46012354e-01 -6.95328414e-01
6.18622243e-01 1.18392205e+00 8.03020060e-01 -2.06743136e-01
-1.20460653e+00 -8.00471187e-01 -1.15011036e-01 2.53888935e-01
-1.71379876e+00 -3.49849105e-01 4.06067908e-01 -3.71109098e-01
8.29206526e-01 6.90811038e-01 6.73972785e-01 1.05181806e-01
-1.53181300e-01 8.76649022e-01 1.04209483e+00 -4.79913056e-01
3.81347179e-01 -2.86323011e-01 5.49727380e-01 1.03520262e+00
3.50017995e-01 -3.92196715e-01 -3.50672632e-01 -8.20478380e-01
5.73359847e-01 -5.00000315e-03 -9.12359282e-02 -3.87032151e-01
-1.11813021e+00 9.45482731e-01 3.49930495e-01 6.37094900e-02
6.61931410e-02 3.69553864e-01 5.81405640e-01 1.44403294e-01
3.97874981e-01 2.49900714e-01 -2.95937479e-01 -4.00885344e-01
-9.44842398e-01 1.45897701e-01 1.12129736e+00 9.66637194e-01
8.97876441e-01 -5.39358854e-01 3.62662107e-01 7.38259375e-01
-9.42752659e-02 3.58553529e-01 2.79116575e-02 -1.11600721e+00
3.23818065e-02 6.21741474e-01 -2.69628644e-01 -1.26184332e+00
-6.24464869e-01 -4.26894188e-01 -8.62602115e-01 -2.85908841e-02
3.49858552e-01 -9.52867121e-02 -4.11608368e-01 1.42428851e+00
8.77474546e-01 1.94456071e-01 -4.68844146e-01 6.62254572e-01
4.38577682e-01 6.53645635e-01 -2.19344959e-01 -2.17605054e-01
1.43930256e+00 -1.10647786e+00 -1.56105042e-01 6.67289719e-02
1.11871326e+00 -9.73008931e-01 1.01361477e+00 4.68071491e-01
-1.24126434e+00 -9.98693034e-02 -7.85312831e-01 -2.55530089e-01
-4.73649383e-01 -1.97628587e-01 1.15530658e+00 4.83517021e-01
-1.38728857e+00 7.69398093e-01 -1.34210813e+00 -6.95395887e-01
3.81950408e-01 4.38225985e-01 -5.92476614e-02 -1.97369799e-01
-3.99878472e-01 3.01765203e-01 2.26452306e-01 -3.03503603e-01
-1.34134650e-01 -5.56813478e-01 -5.53362787e-01 1.15322493e-01
6.73166156e-01 -6.22341633e-01 1.05555713e+00 -5.33568859e-01
-1.10453665e+00 7.09318042e-01 -3.46625566e-01 -2.74205446e-01
2.88304687e-01 -7.76287261e-03 1.71846189e-02 1.84219837e-01
8.69372264e-02 3.59447181e-01 3.13248098e-01 -9.12248850e-01
-6.34878099e-01 -5.62280953e-01 -4.51937348e-01 2.25002900e-01
-2.53390253e-01 7.57071003e-02 -1.05279231e+00 -5.33886373e-01
-7.36323521e-02 -1.16432118e+00 -5.10550618e-01 -3.11420169e-02
-4.31459576e-01 -4.96702880e-01 6.97682083e-01 -2.85867542e-01
1.48217809e+00 -2.15998960e+00 1.12087503e-02 7.98524737e-01
5.09361327e-01 1.38521522e-01 1.05380394e-01 8.75894547e-01
1.30348995e-01 9.15098265e-02 -3.29609692e-01 -2.31059358e-01
3.94386835e-02 2.39857025e-02 1.11176990e-01 6.93313420e-01
-4.15712029e-01 5.50522149e-01 -1.10504591e+00 -6.03551090e-01
1.40293092e-01 1.78272203e-01 -4.67886955e-01 -1.47610828e-01
-1.95998363e-02 -3.00566882e-01 -2.41314635e-01 5.19097149e-01
7.98441231e-01 -5.24168730e-01 4.98317361e-01 1.76715165e-01
-3.03604335e-01 1.74720392e-01 -1.26621950e+00 1.77148712e+00
-1.13504983e-01 4.41196173e-01 5.10806501e-01 -1.00975001e+00
4.48286951e-01 -2.84183145e-01 5.26978731e-01 -1.82685196e-01
3.22163135e-01 2.21786663e-01 -5.22720702e-02 -1.93587437e-01
6.18893743e-01 5.41890502e-01 -2.26289362e-01 1.01764631e+00
-6.11018598e-01 -2.09256098e-01 6.49176061e-01 6.50163293e-01
1.62960935e+00 -3.45954359e-01 3.51332515e-01 -5.84878445e-01
7.15580881e-02 4.23033237e-01 2.58818835e-01 5.38541019e-01
1.72906481e-02 2.51594633e-01 5.31252325e-01 -2.89355516e-01
-9.49047983e-01 -9.23076272e-01 -6.85845017e-02 1.34241009e+00
-4.62291241e-02 -8.56341779e-01 -8.19371521e-01 -3.67523670e-01
4.37862203e-02 3.93515348e-01 -2.71562040e-01 2.43967816e-01
-5.68051100e-01 -8.14526260e-01 4.81992364e-01 3.40956450e-01
9.88649428e-02 -5.59631705e-01 -6.56232476e-01 2.31932908e-01
1.45274296e-01 -7.50058234e-01 -7.01965630e-01 8.94019604e-02
-7.82289088e-01 -1.29325533e+00 -1.46474779e-01 -8.00176322e-01
8.81696463e-01 6.98330343e-01 1.04948997e+00 5.18990397e-01
-7.26512194e-01 1.36857197e-01 -4.81494635e-01 -2.74131030e-01
-1.56186849e-01 3.79071772e-01 -7.38924295e-02 -4.25435424e-01
4.77777094e-01 -8.07893276e-01 -8.29838276e-01 3.25799018e-01
-8.09234738e-01 1.74016535e-01 2.30786204e-01 3.97754639e-01
7.00212419e-01 2.80488729e-01 7.12467432e-02 -9.94698882e-01
7.45225787e-01 -6.21433079e-01 -8.41150284e-01 6.79910630e-02
-4.87827331e-01 -1.25619471e-01 6.85678601e-01 -1.24691814e-01
-9.63372886e-02 2.03467265e-01 -2.70220608e-01 -2.58063793e-01
5.62105812e-02 7.58541584e-01 2.30020568e-01 1.06150404e-01
5.21517515e-01 -3.72301266e-02 2.61949182e-01 -3.24397057e-01
6.32551551e-01 7.64910042e-01 4.68988389e-01 -6.54609025e-01
5.30858219e-01 8.45950365e-01 4.54694368e-02 -9.11791682e-01
-4.00394589e-01 -8.87696803e-01 -3.90659630e-01 1.19776517e-01
4.30370510e-01 -6.13553703e-01 -1.17059875e+00 4.26555127e-01
-7.60669231e-01 -7.58526266e-01 -1.13473423e-02 8.93208534e-02
-2.92416722e-01 6.42882466e-01 -7.85803974e-01 -4.93196696e-01
-6.90463364e-01 -9.56927657e-01 8.49699259e-01 -7.17639402e-02
-4.36699420e-01 -7.22099245e-01 1.69417426e-01 3.33015360e-02
1.27675623e-01 3.70732248e-01 9.78501678e-01 -5.61284721e-01
-4.59820062e-01 -1.90500706e-01 -4.99893099e-01 -2.66710073e-01
-2.17322960e-01 3.94792318e-01 -4.44734901e-01 -6.56336188e-01
-4.56198066e-01 -2.00065393e-02 6.64087653e-01 2.17734218e-01
1.46450007e+00 -2.62447983e-01 -7.66839385e-01 8.10003996e-01
1.48348749e+00 1.42071798e-01 3.23003352e-01 -6.31590001e-03
6.32761955e-01 4.63866651e-01 5.30353010e-01 5.98869562e-01
6.13818228e-01 4.66038316e-01 1.51301891e-01 -2.64900148e-01
1.73028573e-01 1.91174150e-01 9.58673656e-02 1.34486234e+00
2.20783114e-01 -2.09991619e-01 -1.37281203e+00 7.28524089e-01
-2.21910477e+00 -7.48006225e-01 -4.59012121e-01 2.20680714e+00
6.72767401e-01 4.96099815e-02 5.18884897e-01 2.91689813e-01
6.94518149e-01 -2.56530344e-01 -5.18117368e-01 -6.89086735e-01
2.91005909e-01 6.28061354e-01 7.89745927e-01 5.90169311e-01
-9.05723810e-01 1.12660098e+00 5.59343290e+00 1.33775878e+00
-1.04848087e+00 3.07915546e-02 5.98268628e-01 -3.88980508e-01
-1.28707260e-01 6.60912916e-02 -5.25304437e-01 3.34346831e-01
9.27659929e-01 -4.29093540e-01 6.90321565e-01 9.70298231e-01
2.21969709e-01 -4.31826770e-01 -9.14260387e-01 9.95852888e-01
-2.16472790e-01 -1.44538116e+00 -4.11094069e-01 3.67970377e-01
3.98100972e-01 4.73196954e-01 -2.81588793e-01 -1.29727289e-01
9.41986501e-01 -7.93791771e-01 5.04380465e-01 -2.10182115e-01
9.65379536e-01 -1.13548934e+00 3.45216274e-01 2.81098902e-01
-1.47929883e+00 2.28506476e-01 -2.21554428e-01 -2.71036357e-01
1.01536930e-01 1.04157543e+00 -9.09248650e-01 4.76530761e-01
9.05365944e-01 4.92620856e-01 -4.75090742e-01 1.11419022e+00
1.18647203e-01 6.02687776e-01 -9.34659243e-01 -3.86637568e-01
1.81648016e-01 -3.95808548e-01 3.82713556e-01 1.47747505e+00
3.92959923e-01 5.53110898e-01 3.61562401e-01 2.83563584e-01
-9.29507241e-02 2.55440533e-01 -5.13715327e-01 -1.02278911e-01
9.63742733e-01 1.42793858e+00 -1.46517873e+00 -5.29527605e-01
-1.59487844e-01 7.27908015e-01 6.66753411e-01 2.10632756e-03
-8.63920629e-01 -9.24397349e-01 7.44867146e-01 3.81267637e-01
4.08661395e-01 -8.25072587e-01 -5.20322978e-01 -9.87743378e-01
-1.85636267e-01 -8.02062988e-01 6.59572542e-01 -4.26467419e-01
-1.19875336e+00 5.65800071e-01 -4.17329557e-02 -7.42058575e-01
-3.66687775e-02 -4.76961285e-01 -5.77396631e-01 3.59176338e-01
-9.10369754e-01 -6.41881168e-01 -2.67603844e-01 7.41689622e-01
1.60016030e-01 4.23121661e-01 7.57608950e-01 2.81854749e-01
-6.47157133e-01 4.89859372e-01 4.75587487e-01 7.41074309e-02
6.02767169e-01 -1.02748036e+00 7.67047286e-01 9.19167280e-01
-5.84430695e-02 8.50325346e-01 6.48596644e-01 -6.32249594e-01
-1.65736616e+00 -9.67925727e-01 5.63575089e-01 -5.71556902e-03
8.96833777e-01 -7.57755041e-01 -6.69525027e-01 5.90629399e-01
1.68488517e-01 -2.28939354e-02 9.83802736e-01 5.06367862e-01
-3.32000822e-01 -7.56643713e-02 -9.44034576e-01 9.66464996e-01
1.30770350e+00 -1.33430123e-01 1.42379507e-01 3.54211360e-01
5.35841227e-01 -4.06740814e-01 -8.51341486e-01 6.70301169e-02
4.88766581e-01 -8.66644204e-01 9.34286237e-01 -4.73115332e-02
8.78953636e-02 -4.92940992e-01 4.46576886e-02 -1.05469561e+00
-4.51620013e-01 -9.07622099e-01 -3.36279944e-02 9.27224457e-01
3.12576354e-01 -8.10134947e-01 9.49160635e-01 2.91135997e-01
-7.65548646e-02 -1.01401663e+00 -7.06584573e-01 -5.38373709e-01
-1.74985245e-01 -7.12218404e-01 6.64280355e-01 1.20855737e+00
5.15658855e-01 2.59858400e-01 1.91785634e-01 1.02376059e-01
8.75950515e-01 5.91101408e-01 1.13212454e+00 -1.03543615e+00
-3.92855138e-01 -6.10377610e-01 -1.92209691e-01 -1.05037785e+00
-2.18146041e-01 -1.07315457e+00 -2.38482237e-01 -1.57275963e+00
1.87776849e-01 -1.14964712e+00 2.70618349e-01 5.28783202e-01
-1.38043776e-01 4.02952015e-01 2.44690552e-01 3.54740441e-01
-8.04163337e-01 7.43489116e-02 8.34447742e-01 3.18684369e-01
-2.94344366e-01 -1.49848685e-01 -6.63227141e-01 8.19221139e-01
8.87496114e-01 -6.09455585e-01 -1.51735082e-01 -6.59402490e-01
4.23669100e-01 3.21342493e-03 -9.50857138e-05 -8.50279093e-01
5.34394324e-01 -4.73766252e-02 -1.09591722e-01 -7.78386116e-01
3.13071489e-01 -5.43549836e-01 2.13778093e-01 6.99976623e-01
1.47170424e-01 6.02835894e-01 2.22709447e-01 3.67823958e-01
1.23508915e-01 1.72186568e-01 7.14989603e-01 -1.16245553e-01
-2.75653094e-01 4.90521759e-01 -4.31656420e-01 1.13718577e-01
1.26259112e+00 -1.88130602e-01 -6.96778893e-01 -1.68618336e-01
-5.51802218e-01 6.60040498e-01 1.03693771e+00 -5.23202382e-02
3.02599430e-01 -9.26726639e-01 -5.95036447e-01 -8.95767659e-02
-1.70880511e-01 3.98187637e-01 4.46191803e-02 9.17045057e-01
-1.24717200e+00 1.54138610e-01 1.17843680e-01 -6.19743645e-01
-1.66852140e+00 7.19330251e-01 -3.39505702e-01 -3.84111613e-01
-6.14435673e-01 9.58253324e-01 -1.91983193e-01 -2.64179260e-01
4.83447313e-02 -1.07669793e-01 5.78149199e-01 -1.49459675e-01
4.58768368e-01 7.73495913e-01 7.21354038e-02 -2.30839580e-01
-5.06077528e-01 4.97286201e-01 -6.86068833e-02 -1.56677142e-01
1.51234186e+00 -7.73915350e-02 -6.93169951e-01 2.48853058e-01
1.17947078e+00 1.68157399e-01 -5.50385118e-01 -1.35608688e-01
8.19363818e-02 -3.28965783e-01 -1.22168303e-01 -3.07606339e-01
-9.33008730e-01 6.42626226e-01 1.28228769e-01 5.71430922e-01
1.14871979e+00 1.54654518e-01 1.09816551e+00 2.12467209e-01
6.26614928e-01 -1.00042379e+00 -3.81847292e-01 3.54222983e-01
3.17799598e-01 -7.47717500e-01 4.71117228e-01 -8.79066825e-01
-2.25040182e-01 8.64542603e-01 1.99232981e-01 -3.58682543e-01
8.50561738e-01 6.54848397e-01 -1.16025008e-01 -4.40289766e-01
-7.32480884e-01 -1.25589341e-01 -4.01450574e-01 2.67488271e-01
3.28603268e-01 3.50261331e-01 -6.77282929e-01 1.32875189e-01
-6.18235230e-01 -7.25945607e-02 6.26530170e-01 1.34538531e+00
-4.56024379e-01 -1.27396369e+00 -4.28840250e-01 6.06747866e-01
-4.71271455e-01 -2.27669373e-01 -3.44131321e-01 7.14896441e-01
-1.01933315e-01 8.38457346e-01 3.79739076e-01 -2.64745533e-01
-1.34022996e-01 -2.55321562e-01 2.74339080e-01 -6.11470580e-01
-6.53506577e-01 1.70458198e-01 2.41025880e-01 -6.89600348e-01
-7.17164725e-02 -7.40489125e-01 -1.52640986e+00 -1.11651826e+00
-1.94651306e-01 4.02709335e-01 9.29113150e-01 4.28428650e-01
9.36608970e-01 7.82400668e-02 5.22611499e-01 -7.90455818e-01
1.36624485e-01 -6.34750247e-01 -5.51712811e-01 1.29214674e-01
-1.78074345e-01 -2.32527718e-01 -1.82070881e-01 -1.88037649e-01] | [7.086864948272705, 5.284994125366211] |
1eb11e1d-bf3e-4965-abbb-4fc9f0fd6c42 | generating-annotated-high-fidelity-images | 2006.12150 | null | https://arxiv.org/abs/2006.12150v3 | https://arxiv.org/pdf/2006.12150v3.pdf | Generating Annotated High-Fidelity Images Containing Multiple Coherent Objects | Recent developments related to generative models have made it possible to generate diverse high-fidelity images. In particular, layout-to-image generation models have gained significant attention due to their capability to generate realistic complex images containing distinct objects. These models are generally conditioned on either semantic layouts or textual descriptions. However, unlike natural images, providing auxiliary information can be extremely hard in domains such as biomedical imaging and remote sensing. In this work, we propose a multi-object generation framework that can synthesize images with multiple objects without explicitly requiring their contextual information during the generation process. Based on a vector-quantized variational autoencoder (VQ-VAE) backbone, our model learns to preserve spatial coherency within an image as well as semantic coherency between the objects and the background through two powerful autoregressive priors: PixelSNAIL and LayoutPixelSNAIL. While the PixelSNAIL learns the distribution of the latent encodings of the VQ-VAE, the LayoutPixelSNAIL is used to specifically learn the semantic distribution of the objects. An implicit advantage of our approach is that the generated samples are accompanied by object-level annotations. We demonstrate how coherency and fidelity are preserved with our method through experiments on the Multi-MNIST and CLEVR datasets; thereby outperforming state-of-the-art multi-object generative methods. The efficacy of our approach is demonstrated through application on medical imaging datasets, where we show that augmenting the training set with generated samples using our approach improves the performance of existing models. | ['Bryan G. Cardenas', 'Devanshu Arya', 'Deepak K. Gupta'] | 2020-06-22 | null | null | null | null | ['layout-to-image-generation'] | ['computer-vision'] | [ 4.53433365e-01 2.72249013e-01 2.99741596e-01 -2.25642309e-01
-8.21072876e-01 -3.89444172e-01 8.54131162e-01 -1.70731485e-01
-2.43464392e-02 8.36175978e-01 2.05584154e-01 2.68311799e-01
-1.75525412e-01 -1.01770532e+00 -1.15440321e+00 -1.02226591e+00
4.11780059e-01 6.85066164e-01 1.97797664e-03 -5.22306524e-02
-9.01770964e-02 4.24662858e-01 -1.68556118e+00 4.69185621e-01
8.69969904e-01 7.56987810e-01 6.88371420e-01 6.34323597e-01
4.35124300e-02 7.76364863e-01 -6.26785159e-01 -6.88784570e-02
1.28512233e-01 -6.23593092e-01 -5.47270834e-01 5.51652372e-01
5.03354311e-01 -3.75263333e-01 -3.08669776e-01 8.24259698e-01
3.51540148e-01 1.70814171e-01 1.05600178e+00 -1.14589632e+00
-9.99510348e-01 4.06029314e-01 -5.20676434e-01 -1.39142305e-01
-1.01981601e-02 2.33672440e-01 9.93495166e-01 -8.65392148e-01
9.34217393e-01 1.16735613e+00 4.29066479e-01 4.72221732e-01
-1.75705898e+00 -2.79019892e-01 8.07420537e-02 8.61828178e-02
-1.46233630e+00 -3.50937277e-01 9.61543560e-01 -6.11580431e-01
5.92897892e-01 5.93406446e-02 6.22121453e-01 1.36031699e+00
2.79983312e-01 8.77109528e-01 1.17647195e+00 -4.39953625e-01
3.96994203e-01 2.60376811e-01 -4.12975729e-01 5.36113083e-01
1.91981792e-01 5.87122664e-02 -4.95870709e-01 -7.80026838e-02
1.16278601e+00 7.79939145e-02 -3.28199506e-01 -4.87865567e-01
-1.13423502e+00 9.16974306e-01 5.88725865e-01 3.73760313e-01
-7.25249887e-01 3.93630892e-01 -2.50458509e-01 -4.27329361e-01
5.41255355e-01 3.80061418e-01 7.87456054e-03 4.71489012e-01
-1.20640349e+00 4.59509254e-01 4.00648564e-01 1.10719335e+00
9.37695444e-01 2.44406611e-01 -5.80366135e-01 8.38977695e-01
4.28868324e-01 5.75917661e-01 2.75651932e-01 -1.08345401e+00
1.23459399e-01 2.47987315e-01 2.75690317e-01 -1.05739713e+00
-6.17015548e-03 -6.58251941e-01 -1.07539916e+00 1.98109016e-01
9.48624983e-02 1.01697579e-01 -1.16223145e+00 1.79846191e+00
2.47857541e-01 4.64745820e-01 1.01692908e-01 6.03005588e-01
7.86071181e-01 8.43912542e-01 1.21770673e-01 7.96913728e-03
1.16802704e+00 -8.29149306e-01 -7.40355372e-01 -2.95546800e-01
5.67660928e-02 -4.95165646e-01 9.29781437e-01 2.67858028e-01
-1.11144686e+00 -7.09493101e-01 -8.96187782e-01 -5.24505489e-02
-9.60498676e-02 1.38916463e-01 3.28605413e-01 4.04500782e-01
-1.17329872e+00 3.47925454e-01 -8.35995257e-01 4.46936376e-02
8.13108146e-01 -1.12833187e-01 -1.78459048e-01 -1.93756849e-01
-8.72334003e-01 6.57504499e-01 5.00210822e-01 6.12661894e-03
-1.33193541e+00 -9.31473076e-01 -9.63007510e-01 1.80253521e-01
1.00506216e-01 -1.18336797e+00 8.73324513e-01 -9.89305198e-01
-1.20834446e+00 6.14225805e-01 -1.03772871e-01 -2.56908834e-01
5.08444488e-01 1.65127844e-01 4.91954871e-02 4.00451332e-01
2.50796705e-01 1.12385392e+00 9.78030562e-01 -1.92092800e+00
-3.64680618e-01 -6.72862232e-02 -1.30262613e-01 1.69115901e-01
-6.01858124e-02 -4.53646094e-01 -4.21332955e-01 -8.91916335e-01
-5.68925813e-02 -9.05666947e-01 -3.91092628e-01 1.10510968e-01
-5.69341600e-01 1.47000581e-01 7.13072658e-01 -6.90997899e-01
7.09268689e-01 -2.14716244e+00 3.97732019e-01 1.39062643e-01
1.98336959e-01 -3.43911648e-02 -3.61418813e-01 3.37664336e-01
5.86856827e-02 9.39781370e-04 -5.43096721e-01 -6.37508273e-01
8.32052007e-02 5.36784947e-01 -3.93653870e-01 3.10715914e-01
4.92061079e-01 1.16886294e+00 -1.02518535e+00 -5.38150012e-01
3.94909382e-01 1.08872795e+00 -7.66153693e-01 2.49617234e-01
-5.79140425e-01 8.31329823e-01 -3.72078419e-01 2.78463900e-01
5.65659702e-01 -6.54155195e-01 9.56899449e-02 -3.04770589e-01
3.01188350e-01 -1.61376953e-01 -1.07847071e+00 1.83399081e+00
-5.27375400e-01 3.90854925e-01 -5.44029698e-02 -9.42971230e-01
5.73457420e-01 3.81855130e-01 5.25124848e-01 -5.32053530e-01
-9.79741663e-02 -3.47746573e-02 -2.84605652e-01 -3.43201935e-01
4.81294036e-01 -3.69418353e-01 1.13502055e-01 3.15100133e-01
2.10336447e-01 -5.21792233e-01 1.68741882e-01 4.23236519e-01
6.82127535e-01 5.23032367e-01 1.05123907e-01 -1.65096357e-01
2.26803705e-01 -1.82357132e-01 3.90792757e-01 8.98126960e-01
4.26643223e-01 1.11426294e+00 2.32263297e-01 -2.22048201e-02
-1.33805025e+00 -1.39604986e+00 -1.56117275e-01 4.49976057e-01
-1.04524776e-01 -1.50320083e-01 -8.55383396e-01 -4.41722184e-01
-1.90110594e-01 9.69992816e-01 -8.15459013e-01 3.45763974e-02
-3.64915639e-01 -7.90061474e-01 2.93673992e-01 5.46347916e-01
3.66872042e-01 -1.23486543e+00 -6.84091330e-01 3.78643513e-01
-5.40456116e-01 -1.31408179e+00 -2.59946764e-01 -2.68862605e-01
-6.91696584e-01 -6.71515048e-01 -8.58747125e-01 -5.15533030e-01
9.96101141e-01 5.85913807e-02 1.32573307e+00 -2.62726024e-02
-6.46471083e-01 5.47344029e-01 -3.06539565e-01 -3.41569841e-01
-6.64052725e-01 -2.56446928e-01 -3.14819723e-01 3.39085728e-01
-4.21823055e-01 -7.75248826e-01 -6.05536044e-01 -3.83385569e-02
-1.43683600e+00 6.09442055e-01 7.09059060e-01 1.01762378e+00
9.92704093e-01 1.57897860e-01 4.19576406e-01 -9.75742221e-01
1.73129514e-01 -5.75167060e-01 -4.99867946e-01 2.62745678e-01
-3.38306040e-01 1.72631100e-01 3.92634958e-01 -4.35265481e-01
-1.24210310e+00 1.25415772e-01 -3.68045792e-02 -5.96487880e-01
-3.06384653e-01 4.81694847e-01 -2.80825615e-01 2.85293579e-01
6.05176806e-01 6.38838232e-01 -3.91262360e-02 -1.95646107e-01
6.19810045e-01 2.58108616e-01 6.49121583e-01 -6.92405760e-01
7.92990148e-01 7.40166485e-01 1.34720579e-01 -1.08267450e+00
-7.27983356e-01 -6.55258745e-02 -5.85275173e-01 -1.76994443e-01
1.08809257e+00 -1.02794528e+00 -2.19687164e-01 4.24480855e-01
-1.20160973e+00 -4.40388411e-01 -5.42527378e-01 1.55404285e-01
-8.79449189e-01 2.76958972e-01 -4.42543536e-01 -6.85965478e-01
-6.93255663e-02 -1.32017207e+00 1.44129848e+00 5.22327311e-02
-7.53322318e-02 -1.22974634e+00 -1.02218643e-01 4.29839790e-01
3.24789554e-01 5.84241152e-01 9.95793760e-01 -7.55559281e-02
-1.12599492e+00 2.32932419e-01 -1.53189123e-01 4.09978002e-01
1.94374159e-01 -1.51870430e-01 -1.05350184e+00 -2.02698156e-01
-1.63688242e-01 -2.52222449e-01 7.57520676e-01 7.48542726e-01
1.30804217e+00 -3.96359622e-01 -3.49294960e-01 5.31216502e-01
1.57434499e+00 -4.34912592e-02 8.96204710e-01 -7.46635869e-02
9.18444753e-01 6.72088027e-01 2.26941928e-01 5.03367126e-01
3.47121835e-01 8.65632296e-01 4.99915719e-01 -3.39789152e-01
-5.51535726e-01 -4.06812072e-01 5.95427416e-02 3.64294559e-01
6.62472546e-02 -5.09359837e-01 -7.32381463e-01 7.27981746e-01
-1.81501257e+00 -1.08374977e+00 -1.50888190e-01 1.85602689e+00
8.85801136e-01 -4.10541058e-01 -2.47328088e-01 -1.07727498e-01
3.65500510e-01 2.06272647e-01 -3.68457079e-01 3.62949967e-01
-3.60464007e-01 3.32919091e-01 5.17533049e-02 6.06281757e-01
-9.29773092e-01 8.04189444e-01 6.02564573e+00 8.45981658e-01
-8.06219161e-01 2.39357948e-01 8.52325618e-01 -8.27183947e-02
-7.51877487e-01 -1.83338121e-01 -6.81544900e-01 4.91990447e-01
6.91738844e-01 2.76952505e-01 3.52296621e-01 5.45695841e-01
2.10615471e-01 -1.92236155e-01 -1.11171830e+00 9.49497700e-01
3.03148717e-01 -1.69988358e+00 4.23853308e-01 2.36051872e-01
1.13253045e+00 -2.71003455e-01 3.80223185e-01 -1.22991353e-01
4.28801596e-01 -1.20225751e+00 1.05517495e+00 8.38956833e-01
7.36256361e-01 -6.12316310e-01 3.92687976e-01 4.21913534e-01
-8.39324772e-01 2.79424638e-01 -3.05524945e-01 4.13912863e-01
4.09206212e-01 8.26715052e-01 -9.77312386e-01 7.62540936e-01
5.14865875e-01 7.06831038e-01 -4.91369545e-01 6.53412640e-01
-4.42793339e-01 4.06793833e-01 -1.46748066e-01 3.65455687e-01
2.50143617e-01 -3.23663116e-01 4.33770776e-01 1.02074480e+00
4.36033249e-01 1.79236591e-01 2.11635292e-01 1.63052583e+00
5.21784164e-02 -1.22827791e-01 -5.25955439e-01 -6.72874972e-02
2.06137732e-01 1.20697570e+00 -8.42900872e-01 -4.38118517e-01
-6.66030645e-02 1.12298191e+00 1.56097114e-01 6.34727955e-01
-8.20480347e-01 1.10841788e-01 3.57935190e-01 2.75113463e-01
6.98906302e-01 -1.21561147e-01 -3.24414164e-01 -1.06598485e+00
-2.76543386e-02 -8.51889133e-01 -5.74570000e-02 -1.14243889e+00
-1.21112275e+00 6.29031897e-01 2.51500905e-01 -1.01520467e+00
-4.17552173e-01 -4.22559053e-01 -1.82526201e-01 1.10388064e+00
-1.36025262e+00 -1.71543300e+00 -4.00332749e-01 4.51474845e-01
4.83785480e-01 -8.85217488e-02 8.50434422e-01 1.72442421e-01
-2.09863007e-01 1.07178584e-01 1.25300288e-01 3.16700004e-02
3.44867587e-01 -1.18508899e+00 2.00226948e-01 8.41493070e-01
6.34105086e-01 5.11771679e-01 8.97521973e-01 -7.44349599e-01
-9.23545897e-01 -1.31939673e+00 5.54401278e-01 -5.91648340e-01
1.65949404e-01 -4.59635168e-01 -7.91733503e-01 7.51859784e-01
3.16469491e-01 1.76640928e-01 6.19239926e-01 -3.04651231e-01
-2.13481784e-01 5.11582829e-02 -9.76897776e-01 5.32634437e-01
8.65685642e-01 -4.60473001e-01 -2.58935422e-01 4.35769171e-01
4.50102925e-01 -4.51398075e-01 -7.27201581e-01 1.86751172e-01
3.29105616e-01 -1.10342193e+00 1.22149575e+00 -2.41278604e-01
8.26510966e-01 -4.87511933e-01 -2.98156261e-01 -1.49103427e+00
-4.92869914e-01 -9.01386738e-02 4.22825385e-03 1.12417126e+00
2.61007845e-01 -2.54505575e-01 6.53190136e-01 4.43905205e-01
-2.48113498e-01 -5.90047598e-01 -7.06438959e-01 -5.17354727e-01
-2.29571499e-02 -2.87134945e-01 5.32866418e-01 7.02675939e-01
-8.59076977e-01 2.61398554e-01 -4.99944985e-01 3.12271804e-01
8.24622035e-01 9.00223702e-02 7.66003966e-01 -1.05056584e+00
-7.64473557e-01 -2.15852097e-01 -2.85487145e-01 -9.00812268e-01
3.38978827e-01 -8.70938778e-01 3.58054459e-01 -1.80852199e+00
4.14858907e-01 -5.40232420e-01 -3.31516862e-02 5.13008475e-01
-2.46317700e-01 5.07336557e-01 2.02582747e-01 9.38700140e-02
-3.31828743e-01 8.52933884e-01 1.45935965e+00 -3.36795688e-01
1.49844983e-03 -2.81761110e-01 -6.57779992e-01 4.60261613e-01
2.89013922e-01 -4.71879810e-01 -8.05396259e-01 -5.36275566e-01
1.84903800e-01 1.36918137e-02 8.77513528e-01 -9.21928644e-01
-4.78608608e-02 -1.50712430e-01 7.28586078e-01 -5.26379168e-01
6.74692214e-01 -6.74758732e-01 8.46667349e-01 2.17987657e-01
-2.02729315e-01 -3.81538570e-01 5.36542907e-02 7.30294406e-01
-1.25698432e-01 -1.12698339e-01 8.36881757e-01 -3.14020425e-01
-3.98678988e-01 4.37226981e-01 -2.40286782e-01 -6.02916144e-02
9.74674404e-01 -1.02392673e-01 -1.01613946e-01 -4.52459782e-01
-7.60624528e-01 -1.43675938e-01 5.91107965e-01 2.99662590e-01
8.72625828e-01 -1.38712060e+00 -9.38428164e-01 4.17081892e-01
1.80630878e-01 3.70428115e-01 6.78243101e-01 5.77231288e-01
-4.25673455e-01 2.33318701e-01 -1.61411136e-01 -9.30324197e-01
-9.82503057e-01 4.52702552e-01 2.70511150e-01 -2.49218762e-01
-7.75826931e-01 7.97399044e-01 8.67043972e-01 -2.13734850e-01
-1.85946733e-01 -2.58098632e-01 3.62232849e-02 -1.36570796e-01
4.95368719e-01 -4.01828513e-02 -1.46107972e-01 -8.36796403e-01
-1.67887975e-02 3.72274697e-01 -5.74028306e-03 -4.94207531e-01
1.55287707e+00 2.09565014e-02 -4.59539406e-02 3.96390855e-01
9.15669441e-01 -1.52037606e-01 -1.65602899e+00 -2.26668060e-01
-3.67051572e-01 -4.92407411e-01 2.33921930e-01 -7.39880025e-01
-1.06429279e+00 7.67264187e-01 3.42651755e-01 -1.73554361e-01
1.03361940e+00 2.46817067e-01 4.32172894e-01 -8.69489536e-02
4.73436356e-01 -7.87656724e-01 4.56634730e-01 1.01722479e-01
1.09099257e+00 -1.03892434e+00 -9.08082630e-03 -4.33131337e-01
-7.16832101e-01 6.00712419e-01 2.70421952e-01 -1.63203642e-01
5.12790263e-01 1.01557098e-01 -9.87641662e-02 -1.89609915e-01
-6.63505554e-01 -3.79915982e-02 5.42419255e-01 8.71457994e-01
2.42929891e-01 6.10462576e-02 2.39285365e-01 3.29628080e-01
-9.91733819e-02 -9.72954556e-02 6.14758432e-01 7.81389952e-01
-7.77688026e-02 -1.06520295e+00 -4.05445695e-01 3.23627084e-01
-2.48637900e-01 -2.41286710e-01 2.86382716e-02 5.89080811e-01
3.04051489e-01 7.32903540e-01 1.98409542e-01 1.03270866e-01
3.42827588e-02 -1.17269516e-01 8.10157537e-01 -8.67878854e-01
-2.20602721e-01 3.96497816e-01 -2.45451704e-01 -4.07353431e-01
-6.79549992e-01 -7.85201311e-01 -1.13393509e+00 1.96719661e-01
5.02471114e-03 7.71609787e-03 6.18756056e-01 9.05743182e-01
4.04326558e-01 9.98806000e-01 2.55232632e-01 -1.17861938e+00
-3.20668817e-01 -8.87884021e-01 -7.80841231e-01 5.80895424e-01
3.71916950e-01 -7.25507855e-01 -3.36679332e-02 5.75246871e-01] | [11.320099830627441, -0.4158773124217987] |
813b8586-e735-4d05-9cc4-15d8582a5086 | multi-objective-matrix-normalization-for-fine | 2003.13272 | null | https://arxiv.org/abs/2003.13272v2 | https://arxiv.org/pdf/2003.13272v2.pdf | Multi-Objective Matrix Normalization for Fine-grained Visual Recognition | Bilinear pooling achieves great success in fine-grained visual recognition (FGVC). Recent methods have shown that the matrix power normalization can stabilize the second-order information in bilinear features, but some problems, e.g., redundant information and over-fitting, remain to be resolved. In this paper, we propose an efficient Multi-Objective Matrix Normalization (MOMN) method that can simultaneously normalize a bilinear representation in terms of square-root, low-rank, and sparsity. These three regularizers can not only stabilize the second-order information, but also compact the bilinear features and promote model generalization. In MOMN, a core challenge is how to jointly optimize three non-smooth regularizers of different convex properties. To this end, MOMN first formulates them into an augmented Lagrange formula with approximated regularizer constraints. Then, auxiliary variables are introduced to relax different constraints, which allow each regularizer to be solved alternately. Finally, several updating strategies based on gradient descent are designed to obtain consistent convergence and efficient implementation. Consequently, MOMN is implemented with only matrix multiplication, which is well-compatible with GPU acceleration, and the normalized bilinear features are stabilized and discriminative. Experiments on five public benchmarks for FGVC demonstrate that the proposed MOMN is superior to existing normalization-based methods in terms of both accuracy and efficiency. The code is available: https://github.com/mboboGO/MOMN. | ['Yongdong Zhang', 'Zheng-Jun Zha', 'Hantao Yao', 'Hongtao Xie', 'Shaobo Min'] | 2020-03-30 | null | null | null | null | ['fine-grained-visual-recognition'] | ['computer-vision'] | [-2.30269283e-01 -7.46502995e-01 -2.85565913e-01 -4.82812136e-01
-8.06343079e-01 -2.80931324e-01 2.07900524e-01 -1.12555049e-01
-3.82539719e-01 5.65721095e-01 3.03553045e-01 1.71945505e-02
-9.98893529e-02 -5.92221737e-01 -5.51976323e-01 -9.94828641e-01
2.95069456e-01 -2.10993826e-01 1.62929704e-04 -1.19391479e-01
1.15747295e-01 4.25497800e-01 -1.25172377e+00 3.08306158e-01
1.03885436e+00 1.26448691e+00 1.26175210e-01 2.56214708e-01
1.00866407e-01 6.66594446e-01 -7.24897757e-02 -2.18128145e-01
2.69374371e-01 -3.18651497e-02 -3.93387586e-01 3.40848528e-02
6.15976751e-01 -3.10145348e-01 -4.52508003e-01 1.17036903e+00
5.47961354e-01 2.50037879e-01 4.52625543e-01 -1.29077756e+00
-7.87763596e-01 2.36419737e-01 -8.24082553e-01 1.77433174e-02
-7.24653378e-02 6.23768829e-02 1.18032467e+00 -1.48339510e+00
1.89895734e-01 1.37840438e+00 7.52625167e-01 1.91519812e-01
-1.28522480e+00 -8.49162877e-01 3.32782537e-01 3.01849157e-01
-1.79999745e+00 -2.90710688e-01 5.69687605e-01 -5.11659801e-01
5.15188932e-01 6.99814916e-01 5.16785383e-01 6.10839427e-01
5.73207922e-02 7.58001029e-01 1.04564786e+00 1.98057964e-02
-3.30573350e-01 -7.78760156e-03 2.49170244e-01 8.68741989e-01
4.37421530e-01 -1.79589167e-01 -5.20070493e-01 -1.03987679e-01
8.21833432e-01 1.70567900e-01 -3.68613869e-01 -1.92080677e-01
-1.34132576e+00 8.86946976e-01 7.38175929e-01 2.53235340e-01
-3.34327251e-01 5.78085221e-02 3.48426282e-01 -1.43360421e-01
5.07459998e-01 1.57666475e-01 -3.47104162e-01 1.50969565e-01
-7.48918295e-01 2.80576110e-01 4.40129936e-01 7.71634400e-01
1.06104851e+00 2.74590533e-02 -5.71992218e-01 1.01660693e+00
6.13638997e-01 5.25249541e-01 4.54306036e-01 -6.34362340e-01
6.64862752e-01 7.38709986e-01 -1.29331648e-01 -1.62796378e+00
-4.32071358e-01 -5.05971193e-01 -1.36939287e+00 -1.73765093e-01
1.32346794e-01 8.67194682e-02 -6.26334965e-01 1.71476269e+00
3.64799768e-01 3.38993400e-01 -3.16184610e-01 1.14180040e+00
1.13072836e+00 9.21331346e-01 -5.26852161e-02 -2.44262382e-01
1.37851143e+00 -1.18231201e+00 -6.84015274e-01 -5.81162013e-02
3.29841495e-01 -1.13443840e+00 1.19716430e+00 2.00559065e-01
-8.12798738e-01 -5.21878362e-01 -1.11262381e+00 -6.11956418e-01
-1.68321263e-02 6.49884582e-01 8.01373363e-01 3.86416972e-01
-7.07289755e-01 3.47812921e-01 -9.10247803e-01 1.87900335e-01
5.12410343e-01 4.38142419e-01 -3.81494761e-01 -1.45995826e-01
-1.00779164e+00 5.12675762e-01 3.63652855e-01 6.04936242e-01
-4.47427303e-01 -9.18291390e-01 -8.13236058e-01 6.49601743e-02
2.29219362e-01 -5.19080937e-01 8.37518752e-01 -5.13550997e-01
-1.50702357e+00 6.93748713e-01 -4.08973157e-01 5.72627485e-02
4.85639483e-01 -2.06345707e-01 -2.83500314e-01 -3.37480426e-01
1.59465998e-01 3.10441583e-01 6.65969014e-01 -9.83211339e-01
-4.95248348e-01 -2.39306852e-01 -4.30157334e-02 2.51135439e-01
-7.04805672e-01 -1.79354399e-02 -1.03512502e+00 -1.02636015e+00
4.32796806e-01 -7.37035096e-01 -3.05686086e-01 -1.32183833e-02
-5.16830027e-01 -1.76055908e-01 5.51537752e-01 -8.30354869e-01
1.58535993e+00 -2.28330898e+00 2.34285429e-01 5.24905980e-01
4.98550951e-01 2.48828918e-01 -1.61183491e-01 -7.17760921e-02
5.87151013e-02 1.05426274e-01 -1.85387045e-01 -5.60714185e-01
1.02328043e-02 1.62196010e-01 -1.78681836e-01 7.27401078e-01
5.44003844e-02 9.85470414e-01 -5.53211033e-01 -5.31802237e-01
1.98924839e-01 7.17541218e-01 -8.19567680e-01 1.23356216e-01
2.41218299e-01 3.92758608e-01 -6.06279373e-01 7.04812050e-01
1.23536789e+00 -4.01381910e-01 1.50315079e-03 -9.38262522e-01
-4.46181774e-01 1.06689528e-01 -1.55953312e+00 1.30891562e+00
-3.16021293e-01 2.80891329e-01 1.20833315e-01 -9.11442816e-01
8.10665429e-01 -1.78234994e-01 4.87409204e-01 -5.83389997e-01
3.15956861e-01 2.56005704e-01 -3.11249316e-01 -2.11565226e-01
4.28445995e-01 1.84721693e-01 8.28754976e-02 4.65005189e-02
-1.64987952e-01 1.39092758e-01 2.16889590e-01 -1.23962976e-01
3.98681700e-01 -1.18303701e-01 3.32567841e-01 -4.49017346e-01
9.35118496e-01 -3.00480157e-01 1.15471768e+00 4.14749354e-01
6.97829351e-02 6.15247607e-01 3.30409467e-01 -3.51616621e-01
-7.28366911e-01 -9.08920825e-01 -3.50126147e-01 1.16657698e+00
4.21389848e-01 -7.44369984e-01 -5.58704615e-01 -2.03420863e-01
1.15194134e-01 -2.69276593e-02 -5.07717490e-01 -1.12763807e-01
-7.92772889e-01 -1.20474029e+00 3.63259256e-01 3.93997192e-01
6.76688969e-01 -5.02579272e-01 3.69467326e-02 1.81428969e-01
-3.56890172e-01 -1.05236471e+00 -1.11007261e+00 -1.68680578e-01
-7.76099384e-01 -8.18355799e-01 -6.59583688e-01 -8.67414892e-01
8.94124269e-01 6.20996356e-01 7.61123776e-01 2.51170635e-01
-2.42688760e-01 -8.99676755e-02 -7.09681958e-02 -2.43107267e-02
3.51543039e-01 1.12733923e-01 2.07814410e-01 4.32812750e-01
8.86835903e-02 -4.29826468e-01 -6.56108022e-01 5.35479903e-01
-9.14088547e-01 2.62804568e-01 7.55300105e-01 1.21347272e+00
1.15674543e+00 -1.90538242e-01 1.37965027e-02 -6.76077962e-01
6.47235394e-01 -3.29710454e-01 -7.26161480e-01 4.48401034e-01
-5.08545995e-01 4.99097407e-02 7.04705656e-01 -5.45240223e-01
-9.33849990e-01 5.21695353e-02 -1.82287425e-01 -5.94666064e-01
5.30888498e-01 8.36064696e-01 -5.91894746e-01 -6.84569657e-01
3.71795923e-01 3.53294730e-01 -1.16085380e-01 -5.98271310e-01
4.26728189e-01 3.65174562e-01 5.07311881e-01 -7.13257849e-01
9.92665112e-01 3.17905754e-01 8.10533985e-02 -6.98801339e-01
-1.02196217e+00 -5.39823949e-01 -3.58422965e-01 1.52191117e-01
5.68300247e-01 -1.07597101e+00 -1.00326872e+00 7.18452096e-01
-9.34211850e-01 -2.06017289e-02 1.75664335e-01 6.52719676e-01
-1.87100336e-01 5.03048420e-01 -6.75248444e-01 -3.09398443e-01
-4.27735537e-01 -1.27985013e+00 7.10497379e-01 5.16955137e-01
2.98146605e-01 -7.58971930e-01 -3.24084908e-02 4.15240288e-01
4.28125620e-01 7.61412382e-02 5.04302084e-01 -1.87318940e-02
-7.10089624e-01 -5.06515354e-02 -6.90835953e-01 6.44995689e-01
2.56350100e-01 6.03626184e-02 -7.81622052e-01 -5.27383268e-01
6.52692318e-02 -2.21304938e-01 9.72686529e-01 3.82538050e-01
1.48689866e+00 -6.48886025e-01 -1.23341650e-01 1.37553596e+00
1.31391096e+00 -2.36799315e-01 4.55003709e-01 2.65021235e-01
1.18664384e+00 7.35884383e-02 7.04881132e-01 6.24713540e-01
6.85726225e-01 7.34212637e-01 1.70101658e-01 -3.13105732e-01
3.69665325e-02 -1.35574579e-01 2.72111207e-01 1.25688004e+00
-2.44264945e-01 3.05627733e-01 -6.15112066e-01 1.37632191e-01
-1.99855375e+00 -6.27899051e-01 -2.78554887e-01 2.00698805e+00
9.81885731e-01 -2.79664487e-01 -5.30958548e-02 -1.04471050e-01
7.61800170e-01 3.15700829e-01 -6.39565468e-01 -1.86990220e-02
-5.05332112e-01 6.17758296e-02 6.65941954e-01 6.14184558e-01
-1.52245963e+00 9.19162154e-01 5.01046705e+00 1.15967464e+00
-1.40525949e+00 2.61470675e-01 7.07916141e-01 -9.15482044e-02
-3.29226255e-01 -1.55579776e-01 -9.70716059e-01 5.64986110e-01
6.66476339e-02 -3.95718098e-01 6.65818632e-01 8.21498334e-01
2.00731441e-01 3.25824678e-01 -7.02953875e-01 1.42660654e+00
6.17690273e-02 -1.42828512e+00 1.85012370e-01 -3.63879465e-02
8.53551149e-01 -9.05611515e-02 3.11467946e-01 2.90876567e-01
-1.03790022e-01 -1.11984944e+00 6.79130673e-01 5.60568750e-01
8.80077124e-01 -7.11062968e-01 6.32768273e-01 7.68670365e-02
-1.72380733e+00 9.90758091e-02 -7.07058012e-01 7.14482740e-02
-7.18066171e-02 8.77071857e-01 -7.95009434e-02 6.85457349e-01
7.63401270e-01 9.80293989e-01 -5.60569644e-01 1.20928347e+00
-2.45924518e-01 3.43565136e-01 -4.42172378e-01 1.37246470e-03
3.16489011e-01 -5.46074450e-01 3.50711405e-01 1.25206912e+00
3.48070264e-01 3.36321741e-01 4.89935160e-01 7.46241927e-01
-1.63336188e-01 6.77949488e-01 1.89680934e-01 1.73458055e-01
3.12533855e-01 1.54084337e+00 -2.84155279e-01 5.16059529e-03
-5.48514664e-01 8.26142251e-01 5.31085849e-01 4.14131433e-01
-9.22009826e-01 -3.70125324e-01 1.02665377e+00 -2.18360826e-01
1.65371180e-01 -3.38543832e-01 -5.29888332e-01 -1.73695397e+00
4.26129967e-01 -9.89393651e-01 4.27572340e-01 -2.15904936e-01
-1.41495717e+00 5.77998519e-01 -4.03354585e-01 -1.32979572e+00
4.61905479e-01 -7.24985003e-01 -4.14255857e-01 1.12446928e+00
-1.60875738e+00 -1.33802879e+00 -7.09520936e-01 8.60940576e-01
-3.02660950e-02 7.63839707e-02 5.59090018e-01 8.80025446e-01
-1.08856654e+00 1.03612041e+00 2.85922587e-01 1.63205728e-01
8.35658789e-01 -8.83054852e-01 -3.04518007e-02 1.04285991e+00
-3.23008224e-02 8.93526256e-01 2.74587095e-01 -4.40780103e-01
-1.53934324e+00 -1.43890727e+00 7.54847348e-01 7.05691203e-02
6.31626606e-01 -4.42949176e-01 -1.17938399e+00 5.31406343e-01
-4.73977745e-01 5.92187047e-01 6.41964316e-01 -3.91673781e-02
-5.57001650e-01 -5.96884191e-01 -8.08236659e-01 6.00478292e-01
8.97221506e-01 -5.58936954e-01 -1.57897800e-01 4.91728365e-01
4.97976333e-01 -8.46027851e-01 -9.40759003e-01 5.85445166e-01
6.18738770e-01 -7.02742100e-01 1.19788086e+00 -6.00225091e-01
1.94017529e-01 -7.35978663e-01 -3.70404243e-01 -1.00876236e+00
-7.68433690e-01 -6.03611887e-01 -1.31698504e-01 1.33085859e+00
2.16406286e-01 -8.88399005e-01 4.91506070e-01 4.42006528e-01
-8.23503807e-02 -1.23833096e+00 -9.40134346e-01 -6.93049550e-01
-9.26975235e-02 -3.71858925e-01 5.57064414e-01 9.17871952e-01
-3.94823253e-01 1.33423433e-01 -8.46199274e-01 3.22654039e-01
6.01362765e-01 2.66869456e-01 8.40734601e-01 -9.83230829e-01
-8.92610382e-03 -5.07317722e-01 -4.63667214e-01 -1.34288824e+00
6.80974275e-02 -1.07873404e+00 3.65024731e-02 -1.38166595e+00
4.74388331e-01 -6.49286330e-01 -6.00426435e-01 6.65400684e-01
-5.50543070e-01 4.88649040e-01 2.86000013e-01 3.97878200e-01
-5.47263622e-01 8.33857000e-01 1.38358784e+00 -1.97570056e-01
-2.22575456e-01 -1.71719119e-01 -1.00496888e+00 7.45766938e-01
7.60642767e-01 -2.35217318e-01 5.89511823e-03 -4.97746706e-01
1.31260112e-01 -4.41786051e-01 2.54753321e-01 -8.08927894e-01
3.66538346e-01 -3.97153050e-01 5.34624994e-01 -5.19110084e-01
2.39164740e-01 -4.46297735e-01 8.41574222e-02 3.33049238e-01
7.59432167e-02 -2.28821505e-02 2.57595748e-01 1.70024872e-01
-5.23321152e-01 2.16502354e-01 1.01318467e+00 3.47247124e-01
-6.11213386e-01 9.92082179e-01 7.53825605e-02 5.26822656e-02
8.52083802e-01 8.07924122e-02 -2.23284766e-01 -3.15945819e-02
-4.27299649e-01 4.69203889e-01 2.78825164e-01 3.68508697e-01
6.81868434e-01 -1.94300950e+00 -8.79363716e-01 2.75242686e-01
9.56909324e-04 3.23542878e-02 5.56259394e-01 1.18881583e+00
-5.28515458e-01 4.15060848e-01 -1.74568102e-01 -5.72198272e-01
-1.30029547e+00 3.18971545e-01 2.52065063e-01 -4.80370522e-01
-2.79726774e-01 1.09807098e+00 4.51895475e-01 -4.93892938e-01
1.83175936e-01 -4.35278594e-01 -2.29849651e-01 2.47642994e-01
6.20209396e-01 4.51300532e-01 4.91027385e-02 -9.32541966e-01
-6.02983594e-01 9.65016723e-01 -1.40436634e-01 4.40348178e-01
1.28903341e+00 -1.29160941e-01 -6.47764981e-01 1.46498144e-01
1.60084748e+00 3.22748840e-01 -1.16109586e+00 -4.91976529e-01
-3.55829000e-01 -8.03424776e-01 1.98311076e-01 -1.57630980e-01
-1.32303321e+00 7.34972715e-01 5.23536861e-01 -2.30061337e-01
1.36805165e+00 -3.60733092e-01 7.68265843e-01 3.48400861e-01
1.74735151e-02 -7.91080236e-01 5.84942941e-03 7.92229235e-01
1.20728505e+00 -1.16815412e+00 3.22882205e-01 -8.73924017e-01
-3.66519928e-01 1.09466970e+00 7.80452490e-01 -2.54543155e-01
7.77687490e-01 5.24811968e-02 4.00127396e-02 2.57556021e-01
-2.42358044e-01 3.07384552e-03 8.85907650e-01 1.55441659e-02
4.52929825e-01 2.48747140e-01 -6.12928808e-01 1.06880629e+00
-2.83793926e-01 -4.01191488e-02 -4.63563465e-02 3.70116860e-01
-1.00831222e-02 -1.06715345e+00 -4.86020386e-01 5.51535845e-01
-4.22503054e-01 -4.34689313e-01 1.13517590e-01 4.81473953e-01
4.21492040e-01 7.38993108e-01 -1.49984121e-01 -5.83111167e-01
3.52280080e-01 -7.00836599e-01 2.84530789e-01 -4.82507169e-01
-3.90489280e-01 1.91360325e-01 -2.91223198e-01 -8.43473256e-01
-1.83284089e-01 -6.29532397e-01 -1.03443074e+00 -4.53855842e-01
-3.25772703e-01 5.73086143e-02 3.40015650e-01 7.34014153e-01
3.50712180e-01 4.03338462e-01 8.08652043e-01 -8.99279892e-01
-5.49100161e-01 -7.02787757e-01 -5.16134262e-01 2.92918175e-01
3.43668103e-01 -7.83511102e-01 -3.23414207e-01 -1.75487891e-01] | [13.39547061920166, 0.5360827445983887] |
568334c6-53b8-4ed6-9a6a-9293bb7a44ce | privacy-guarantees-for-de-identifying-text | 2008.03101 | null | https://arxiv.org/abs/2008.03101v2 | https://arxiv.org/pdf/2008.03101v2.pdf | Privacy Guarantees for De-identifying Text Transformations | Machine Learning approaches to Natural Language Processing tasks benefit from a comprehensive collection of real-life user data. At the same time, there is a clear need for protecting the privacy of the users whose data is collected and processed. For text collections, such as, e.g., transcripts of voice interactions or patient records, replacing sensitive parts with benign alternatives can provide de-identification. However, how much privacy is actually guaranteed by such text transformations, and are the resulting texts still useful for machine learning? In this paper, we derive formal privacy guarantees for general text transformation-based de-identification methods on the basis of Differential Privacy. We also measure the effect that different ways of masking private information in dialog transcripts have on a subsequent machine learning task. To this end, we formulate different masking strategies and compare their privacy-utility trade-offs. In particular, we compare a simple redact approach with more sophisticated word-by-word replacement using deep learning models on multiple natural language understanding tasks like named entity recognition, intent detection, and dialog act classification. We find that only word-by-word replacement is robust against performance drops in various tasks. | ['Dietrich Klakow', 'David Ifeoluwa Adelani', 'Ali Davody', 'Thomas Kleinbauer'] | 2020-08-07 | null | null | null | null | ['dialog-act-classification'] | ['natural-language-processing'] | [ 4.35507476e-01 3.33297461e-01 1.87209230e-02 -7.32232451e-01
-9.25101042e-01 -9.26898539e-01 5.56746125e-01 4.78183329e-01
-8.05387318e-01 6.37434065e-01 6.60278141e-01 -4.54240888e-01
2.82650024e-01 -4.92475510e-01 -3.30616862e-01 -6.20268643e-01
3.53332609e-01 3.06027323e-01 -2.36104026e-01 -1.19170301e-01
2.57986505e-02 4.91311103e-01 -7.22425640e-01 5.36528051e-01
6.22508466e-01 8.36688340e-01 -5.33873200e-01 4.10724908e-01
-5.39624132e-02 3.26321214e-01 -5.98869920e-01 -1.03715062e+00
6.30967021e-01 -2.39932761e-01 -8.20179462e-01 3.19917053e-02
7.76763409e-02 -5.00735521e-01 -4.53826606e-01 1.08713698e+00
5.13617873e-01 9.59203020e-02 3.71706784e-01 -1.14816308e+00
-4.18716818e-01 5.48789382e-01 -4.56157982e-01 -1.68462187e-01
5.15649736e-01 2.82144755e-01 1.10334849e+00 -4.31358844e-01
4.61251706e-01 1.20402992e+00 4.19483691e-01 9.18051124e-01
-1.58645368e+00 -5.44254899e-01 -6.50957599e-02 -4.29845810e-01
-1.25696707e+00 -7.86006868e-01 3.79447937e-01 -4.43551093e-01
6.19597733e-01 6.14506602e-01 -2.17629895e-01 1.20582533e+00
1.65072560e-01 6.99450672e-01 8.22116792e-01 -3.12944800e-01
2.96998262e-01 5.66885114e-01 4.62478906e-01 5.01749754e-01
2.87968367e-01 -1.48093715e-01 -4.93895113e-01 -8.86996984e-01
-2.30928436e-01 1.87080652e-01 -4.59886879e-01 -4.00136292e-01
-8.46281648e-01 9.05120850e-01 -1.55068517e-01 1.85205758e-01
-4.10930328e-02 -3.50569963e-01 7.32334673e-01 4.44003522e-01
5.92368543e-01 5.50092041e-01 -6.13744557e-01 1.81836560e-01
-5.38725257e-01 2.70513713e-01 1.02061975e+00 7.63205469e-01
5.87158978e-01 -4.46290314e-01 -5.41240931e-01 7.63399839e-01
-4.52593975e-02 7.31815547e-02 6.62783384e-01 -5.07255077e-01
5.20029008e-01 4.13549483e-01 2.77473152e-01 -7.04470694e-01
-2.55047977e-01 5.99674024e-02 -8.13620210e-01 -8.51288736e-02
5.73629558e-01 -6.53355956e-01 -5.08756340e-01 2.01846623e+00
2.02937067e-01 -5.19374371e-01 1.58318579e-01 4.88352984e-01
1.59401760e-01 3.57685208e-01 1.64560691e-01 -2.54449666e-01
1.69221771e+00 -4.44642782e-01 -8.23306561e-01 -2.95808792e-01
9.02902246e-01 -4.24976617e-01 8.92470956e-01 6.73666075e-02
-7.01047063e-01 1.16766848e-01 -7.11126149e-01 -3.73819768e-01
-3.64808708e-01 -2.13051528e-01 4.67465699e-01 1.27858734e+00
-7.84411967e-01 4.07282323e-01 -7.47184634e-01 -3.31436038e-01
4.69753832e-01 5.50996542e-01 -7.26742148e-01 1.45420596e-01
-1.26693547e+00 6.51706159e-01 1.66635409e-01 -2.44054824e-01
-3.24807197e-01 -6.30290329e-01 -8.70050311e-01 4.10121650e-01
4.58290964e-01 -5.28353453e-01 1.46685898e+00 -6.74669206e-01
-1.16423213e+00 1.09295225e+00 -2.48662293e-01 -7.82173097e-01
8.54393780e-01 -1.84599712e-01 -1.83837712e-01 -3.96529555e-01
-2.10021455e-02 2.20543042e-01 8.19511294e-01 -9.43673909e-01
-5.67548633e-01 -8.02007496e-01 -1.04205841e-02 1.24191277e-01
-6.82337284e-01 2.94450700e-01 -1.21667877e-01 -6.72739089e-01
-3.37805003e-01 -1.12247741e+00 -3.78791869e-01 1.46398023e-01
-6.25495434e-01 4.21169177e-02 7.42535889e-01 -8.94302487e-01
1.06634784e+00 -2.64353991e+00 -2.33484998e-01 2.62112916e-01
3.25830519e-01 3.84192526e-01 2.30846442e-02 3.57183784e-01
4.50426452e-02 4.23137963e-01 -4.83919173e-01 -7.62782454e-01
1.46763459e-01 7.22199166e-03 -6.00649655e-01 6.08905554e-01
-4.83653545e-02 6.71814740e-01 -4.38310027e-01 -1.40812114e-01
4.17824201e-02 3.24961364e-01 -7.60355353e-01 1.58967480e-01
-3.02090079e-01 4.16853219e-01 -4.20124710e-01 1.89579234e-01
6.14123642e-01 6.79158419e-02 3.18438619e-01 1.41543910e-01
3.78466427e-01 5.52438676e-01 -6.22785628e-01 1.35265756e+00
-3.84077519e-01 5.57470798e-01 5.30156434e-01 -5.16045153e-01
6.32773519e-01 4.95974571e-01 3.82830173e-01 -3.76366258e-01
2.55535394e-01 -1.07864052e-01 5.17958626e-02 -2.46553198e-01
5.44121325e-01 -4.60504979e-01 -4.46478844e-01 7.93193460e-01
-3.49533290e-01 3.55749846e-01 -3.48474056e-01 1.07056864e-01
1.27528489e+00 -7.71173418e-01 4.90321845e-01 -1.08173914e-01
3.63710493e-01 -3.28328758e-01 6.17048085e-01 6.29334807e-01
-4.90450352e-01 5.30070662e-01 6.61934674e-01 -2.29669929e-01
-7.71288753e-01 -6.25909448e-01 -7.69269913e-02 1.20514154e+00
-3.34333152e-01 -3.51016760e-01 -8.99447799e-01 -9.89034593e-01
2.70947248e-01 1.09293056e+00 -4.19152886e-01 -4.98479098e-01
-3.49703103e-01 -7.43678927e-01 9.27879095e-01 1.05848379e-01
3.15612763e-01 -8.32888782e-01 -4.43671972e-01 1.75776500e-02
-1.52973369e-01 -1.27892125e+00 -1.11988521e+00 1.51008159e-01
-5.91903687e-01 -6.61220610e-01 -3.75348121e-01 -3.88837785e-01
6.23576999e-01 1.91734195e-01 5.63091278e-01 -1.25680402e-01
-1.17110893e-01 3.62699389e-01 -1.46388352e-01 -3.97843450e-01
-7.93411672e-01 2.15818107e-01 5.10811806e-02 5.83787560e-01
6.08521402e-01 -3.12194943e-01 -2.57528603e-01 2.83231512e-02
-1.11601472e+00 -4.03777719e-01 2.96428740e-01 7.35862613e-01
1.80120245e-02 -2.68026013e-02 1.78686082e-01 -1.42196155e+00
1.16151083e+00 -3.01482797e-01 -4.12094235e-01 4.80870366e-01
-4.85767484e-01 4.71188903e-01 7.21443772e-01 -2.39574164e-01
-1.27850044e+00 3.61413300e-01 -3.55199039e-01 -1.60298377e-01
-3.24771196e-01 5.45774437e-02 -6.97595060e-01 2.16853604e-01
5.90248942e-01 1.92600682e-01 1.61509782e-01 -5.93876660e-01
4.58197415e-01 1.12146914e+00 3.55234355e-01 -3.83330166e-01
5.39109468e-01 4.50142235e-01 -4.16613936e-01 -9.40014660e-01
-4.99761373e-01 -4.58727330e-01 -3.68853927e-01 4.91628557e-01
9.96493518e-01 -5.93619764e-01 -7.93050587e-01 5.25404572e-01
-1.17944515e+00 -3.31640728e-02 -2.66726375e-01 1.51971728e-01
-3.02621901e-01 7.17870653e-01 -5.54179251e-01 -9.69050705e-01
-5.65717876e-01 -9.39163983e-01 1.02249408e+00 -1.99728906e-01
-7.09728479e-01 -7.10937738e-01 5.36763668e-02 6.18507385e-01
1.06174253e-01 6.59982068e-03 1.14870501e+00 -1.47557044e+00
-1.73306078e-01 -5.95636845e-01 7.03532100e-02 3.86202723e-01
4.90051061e-01 -4.83262986e-01 -1.21847057e+00 -4.44551259e-01
6.55757248e-01 -1.21020347e-01 7.91986704e-01 4.16832864e-02
1.17712271e+00 -1.09998500e+00 -2.50811249e-01 5.81564665e-01
8.59235704e-01 2.84381747e-01 5.54426789e-01 -1.11201130e-01
6.31933570e-01 1.02923667e+00 2.02646554e-01 6.16679370e-01
-6.08654646e-03 5.34777522e-01 -2.42143148e-03 1.00483395e-01
7.14222729e-01 -3.13713998e-01 3.08496565e-01 9.96572673e-02
8.41788054e-01 -4.45708781e-01 -7.04706252e-01 4.74445790e-01
-1.61574817e+00 -7.62783051e-01 3.84489119e-01 2.45821238e+00
1.07376337e+00 4.32426110e-02 1.93714097e-01 -6.32504970e-02
8.19942892e-01 3.23295802e-01 -5.31324208e-01 -5.64970791e-01
7.19764754e-02 7.12302402e-02 9.02005911e-01 6.89926326e-01
-1.16131175e+00 7.15913177e-01 5.21802139e+00 5.09033442e-01
-9.40165460e-01 9.33042690e-02 1.06122696e+00 -2.24443093e-01
-3.38866770e-01 -1.06947526e-01 -6.20826006e-01 4.62209791e-01
9.30596948e-01 -4.36371416e-01 5.20390689e-01 8.52385581e-01
1.48544786e-02 2.60883480e-01 -1.50932872e+00 9.89547849e-01
-1.81769624e-01 -1.00577116e+00 -7.37157762e-02 4.40829098e-01
1.78591833e-01 -2.67953098e-01 9.92513523e-02 -3.09220031e-02
5.80175221e-01 -8.08471620e-01 3.37021172e-01 8.97858590e-02
6.57881379e-01 -5.79897404e-01 5.30557036e-01 4.80607331e-01
-5.04939020e-01 -2.48261467e-01 -1.17057756e-01 2.66662985e-01
4.29434814e-02 6.03035331e-01 -1.00635612e+00 2.04249039e-01
2.08452195e-01 -8.39315951e-02 -3.07216704e-01 4.32212442e-01
6.53683916e-02 4.25078630e-01 -3.79131258e-01 -5.15338555e-02
-5.87406978e-02 -7.63447732e-02 5.25151730e-01 1.26422763e+00
3.03063020e-02 2.42302075e-01 -1.65117726e-01 6.48690104e-01
-6.73477054e-01 2.38433480e-01 -9.31773305e-01 -4.55276817e-01
5.40124118e-01 9.36853349e-01 -3.14702779e-01 3.24287452e-02
-5.57997823e-01 1.28432882e+00 1.69255435e-01 1.54180467e-01
-3.07957232e-01 -1.66101247e-01 1.22163868e+00 1.80298135e-01
-4.49906997e-02 -1.10773325e-01 -5.11029422e-01 -1.25452757e+00
2.76935250e-01 -1.14760089e+00 5.95008910e-01 -8.32456723e-02
-1.46714222e+00 2.84904748e-01 -1.54852867e-01 -6.32409692e-01
-4.24211383e-01 -3.67175847e-01 -4.14338976e-01 8.40935588e-01
-7.21448541e-01 -6.76684678e-01 3.79497737e-01 6.20229721e-01
2.91536003e-01 -2.26362497e-01 1.00127578e+00 1.33074865e-01
-5.33750117e-01 1.13107681e+00 3.21383744e-01 7.24774480e-01
7.69638717e-01 -9.03120756e-01 8.68814290e-01 8.47599447e-01
2.66217172e-01 8.60934973e-01 6.12345278e-01 -6.06589496e-01
-1.32710421e+00 -1.09462309e+00 1.24557221e+00 -6.20793581e-01
3.74383271e-01 -9.28454816e-01 -1.05516398e+00 1.00964975e+00
1.10656343e-01 -3.44459593e-01 8.89893830e-01 1.24449424e-01
-5.63131988e-01 -2.62323003e-02 -1.66188502e+00 9.26018417e-01
7.68460751e-01 -9.29975271e-01 -3.85500699e-01 3.85166615e-01
1.02485311e+00 -1.15935244e-01 -4.51883972e-01 4.18279171e-02
5.24103224e-01 -8.08843970e-01 7.46650696e-01 -1.03666890e+00
-1.49364024e-01 1.92858711e-01 -2.89541185e-01 -9.74991381e-01
7.62286335e-02 -1.11957848e+00 2.85328537e-01 1.41121697e+00
6.18077219e-01 -9.71931398e-01 9.69765842e-01 1.81349266e+00
5.42847574e-01 -1.11197159e-01 -1.08729470e+00 -5.33389270e-01
2.17506275e-01 -2.94964105e-01 7.52783656e-01 1.08323979e+00
2.40716964e-01 5.37161708e-01 -7.15655744e-01 1.13585554e-01
4.70040649e-01 -2.97003657e-01 7.96964824e-01 -9.09128964e-01
-2.40480095e-01 -6.30873889e-02 -2.44749010e-01 -7.93247402e-01
4.39719617e-01 -7.65448630e-01 2.99920086e-02 -7.55138457e-01
1.24368340e-01 -1.50123566e-01 6.47548586e-02 6.53610349e-01
-2.57821470e-01 -4.51368928e-01 3.34007621e-01 1.42447338e-01
-6.51911199e-02 5.31038225e-01 4.75290477e-01 -2.14110196e-01
-4.03636903e-01 4.86698747e-01 -1.08803689e+00 4.45289910e-01
1.05934191e+00 -7.56848156e-01 -3.36060226e-01 -2.80420095e-01
-1.12304404e-01 1.44197002e-01 1.61222905e-01 -5.09510517e-01
1.60072133e-01 -2.59804782e-02 -2.38618791e-01 -9.44872387e-03
2.51982629e-01 -1.12107098e+00 -7.64469653e-02 5.13485610e-01
-9.68877077e-01 -7.64812455e-02 1.22138374e-01 7.02981889e-01
9.51770023e-02 -2.93851674e-01 8.07934046e-01 -1.48228332e-01
-9.02877152e-02 2.61955172e-01 -5.33248603e-01 2.59110987e-01
8.47980380e-01 -2.53925435e-02 -2.21435353e-01 -7.30477095e-01
-6.74815595e-01 1.03141867e-01 5.49291134e-01 4.53986526e-01
2.19417110e-01 -7.65675247e-01 -6.68778360e-01 5.16321540e-01
1.09984696e-01 -3.86624962e-01 -2.18396243e-02 3.73955935e-01
4.75090630e-02 4.58346277e-01 1.21821448e-01 -9.44020301e-02
-1.69827449e+00 6.95882857e-01 1.87231183e-01 -3.11985403e-01
-4.90015954e-01 7.27907777e-01 7.33934879e-01 -5.81533909e-01
4.33864802e-01 -3.32822800e-01 4.41122085e-01 -7.96999931e-02
5.38224041e-01 2.91935727e-02 2.93440521e-01 -4.72903311e-01
-4.10964251e-01 -3.94574612e-01 -4.77339178e-01 -3.92848402e-01
1.00156939e+00 -2.03402713e-01 -5.76243065e-02 7.36942375e-03
1.43381584e+00 4.00235951e-01 -9.90363359e-01 -3.94763261e-01
2.42229179e-01 -5.31967342e-01 -3.93852770e-01 -5.24949431e-01
-1.01923060e+00 7.55418718e-01 5.24727046e-01 3.63854527e-01
1.02042902e+00 -1.58463955e-01 9.35387373e-01 7.10418463e-01
2.64895946e-01 -7.92392254e-01 -5.83308280e-01 3.31833422e-01
6.28902018e-01 -1.17224729e+00 -1.49818674e-01 -3.40200484e-01
-9.24776912e-01 4.56148386e-01 1.92099378e-01 4.09636378e-01
7.46069729e-01 3.09286654e-01 8.78787339e-02 2.18839154e-01
-6.56656325e-01 2.97756165e-01 -7.06449002e-02 5.88976264e-01
2.94030070e-01 1.39150053e-01 -2.86645025e-01 7.74059713e-01
-3.58768225e-01 -3.42269510e-01 3.89147043e-01 1.03345478e+00
-3.35787311e-02 -1.32697392e+00 -2.78423369e-01 6.10431254e-01
-8.94812346e-01 -2.82079428e-01 -1.07404685e+00 4.02221024e-01
-3.91009361e-01 1.02492905e+00 -9.53457355e-02 -4.95614141e-01
2.15513214e-01 5.31733096e-01 -1.55178413e-01 -7.96407163e-01
-1.01152825e+00 -3.51815403e-01 4.15389359e-01 -2.96839684e-01
1.62230656e-01 -9.41433489e-01 -1.05473423e+00 -5.21812677e-01
-1.87095985e-01 1.58035904e-01 4.95917380e-01 8.84727955e-01
6.19945347e-01 -1.52518466e-01 6.73430085e-01 -8.51935521e-03
-1.15438461e+00 -5.51370800e-01 -5.34498870e-01 6.28486037e-01
5.61202466e-01 7.04987943e-02 -4.31250483e-01 -3.87564264e-02] | [6.031612873077393, 7.009664058685303] |
5b679bc6-e5af-493f-b1cb-4377b1b4da06 | lisa-localized-image-stylization-with-audio | 2211.11381 | null | https://arxiv.org/abs/2211.11381v1 | https://arxiv.org/pdf/2211.11381v1.pdf | LISA: Localized Image Stylization with Audio via Implicit Neural Representation | We present a novel framework, Localized Image Stylization with Audio (LISA) which performs audio-driven localized image stylization. Sound often provides information about the specific context of the scene and is closely related to a certain part of the scene or object. However, existing image stylization works have focused on stylizing the entire image using an image or text input. Stylizing a particular part of the image based on audio input is natural but challenging. In this work, we propose a framework that a user provides an audio input to localize the sound source in the input image and another for locally stylizing the target object or scene. LISA first produces a delicate localization map with an audio-visual localization network by leveraging CLIP embedding space. We then utilize implicit neural representation (INR) along with the predicted localization map to stylize the target object or scene based on sound information. The proposed INR can manipulate the localized pixel values to be semantically consistent with the provided audio input. Through a series of experiments, we show that the proposed framework outperforms the other audio-guided stylization methods. Moreover, LISA constructs concise localization maps and naturally manipulates the target object or scene in accordance with the given audio input. | ['Sangpil Kim', 'Jinkyu Kim', 'Sang Ho Yoon', 'Wonmin Byeon', 'Chanyoung Kim', 'Seung Hyun Lee'] | 2022-11-21 | null | null | null | null | ['image-stylization', 'visual-localization'] | ['computer-vision', 'computer-vision'] | [ 6.28820896e-01 -8.21770579e-02 1.10735856e-01 -5.65694161e-02
-8.24209630e-01 -5.48702180e-01 3.51208687e-01 -5.80482818e-02
-1.30982295e-01 3.66681904e-01 4.14437801e-01 1.24290220e-01
2.19781399e-01 -6.87615097e-01 -1.11459959e+00 -6.60212696e-01
3.48356575e-01 1.00185335e-01 2.73202151e-01 1.86263010e-01
5.16210735e-01 5.35562575e-01 -1.55827534e+00 3.78188223e-01
6.32835925e-01 8.88404667e-01 7.54664600e-01 7.54484951e-01
-4.59216744e-01 5.04590631e-01 -9.19424891e-01 1.51735425e-01
9.30236932e-03 -5.62789500e-01 -7.41249681e-01 3.64022076e-01
6.56249464e-01 -1.75917864e-01 -6.74869400e-03 1.28120935e+00
6.22187018e-01 1.52849570e-01 5.81280410e-01 -1.42471027e+00
-1.06612945e+00 8.56464148e-01 -7.75007129e-01 1.73797682e-02
6.14452600e-01 2.05673635e-01 8.63866687e-01 -1.03922558e+00
5.54482102e-01 1.63068783e+00 3.61235350e-01 4.79411900e-01
-1.36299288e+00 -7.38169909e-01 3.72617304e-01 -4.98006959e-03
-1.63737071e+00 -2.72173643e-01 1.24599898e+00 -4.17710245e-01
1.27335683e-01 2.59648919e-01 5.38301110e-01 7.25345433e-01
2.08485708e-01 8.25059295e-01 9.41997945e-01 -5.12562633e-01
2.43093535e-01 3.79878849e-01 -2.42939159e-01 6.07345283e-01
-3.73774439e-01 -1.79028302e-01 -8.68822634e-01 -2.92181149e-02
1.23023677e+00 -3.36291566e-02 -5.17130911e-01 -3.90236497e-01
-1.60113704e+00 4.48659569e-01 6.82709873e-01 1.89061984e-01
-2.62538880e-01 6.44026041e-01 3.65415424e-01 -6.42435402e-02
3.37637186e-01 6.56008422e-01 -7.70545825e-02 -5.83356805e-02
-1.02408421e+00 4.28861342e-02 1.46774426e-01 9.28658128e-01
9.94552791e-01 4.24976468e-01 -4.18968976e-01 6.17800951e-01
1.38694689e-01 5.82860112e-01 4.26366687e-01 -9.60627317e-01
3.63916188e-01 3.58318359e-01 1.77710176e-01 -1.18608379e+00
3.17898542e-02 -2.57210732e-01 -6.27988935e-01 4.08130318e-01
2.17297241e-01 5.07252999e-02 -7.34179676e-01 1.72908068e+00
2.94931203e-01 6.37402594e-01 -9.34836343e-02 9.79894638e-01
8.69018018e-01 1.01149106e+00 6.65017292e-02 1.58468485e-01
1.40026009e+00 -1.04649961e+00 -9.46339548e-01 2.20069643e-02
1.29581422e-01 -8.53413463e-01 1.94158065e+00 2.41109133e-01
-8.60065341e-01 -9.94078219e-01 -8.02039564e-01 -6.97029084e-02
-3.02658945e-01 4.32687134e-01 2.40878612e-01 4.17121977e-01
-1.29983222e+00 5.89936264e-02 -4.14066672e-01 -4.50367033e-01
2.22933173e-01 6.45089149e-02 -1.73865780e-01 2.66994625e-01
-9.48738337e-01 1.17605142e-01 2.38231167e-01 -3.81854475e-02
-1.16412604e+00 -9.25122321e-01 -1.03841209e+00 2.16521248e-01
2.87385583e-01 -7.22740293e-01 1.05489159e+00 -1.25969052e+00
-1.70575559e+00 5.29532790e-01 -3.73898178e-01 -2.91279197e-01
2.01446161e-01 -3.54434460e-01 -1.30857348e-01 4.64342266e-01
3.98187160e-01 1.16878414e+00 1.46612728e+00 -1.66886258e+00
-6.38151348e-01 6.40758453e-03 1.24277949e-01 3.64332169e-01
-1.47243604e-01 5.51929176e-02 -7.35416293e-01 -1.18294537e+00
2.51485668e-02 -4.85096693e-01 2.05782447e-02 1.84269339e-01
-7.86239684e-01 7.27410689e-02 1.29863834e+00 -3.95058513e-01
1.04346979e+00 -2.25134301e+00 9.05274376e-02 1.43437833e-01
5.07825501e-02 -1.29339263e-01 -3.94332916e-01 4.15095121e-01
-3.25737029e-01 2.27235660e-01 -1.95594784e-02 -3.72452050e-01
-1.40704870e-01 -1.83167338e-01 -7.92649984e-01 1.23779707e-01
1.07327595e-01 8.83496940e-01 -1.05120277e+00 -6.54976666e-01
4.55614120e-01 5.67593515e-01 -7.50307620e-01 2.43209183e-01
-2.88269609e-01 8.68812978e-01 -3.70907843e-01 6.96331143e-01
5.32348633e-01 7.07722306e-02 -4.57389563e-01 -5.52444935e-01
6.34942576e-02 -1.99860454e-01 -1.34508491e+00 2.01775241e+00
-7.36114025e-01 7.09924757e-01 1.38471071e-02 -6.18002653e-01
9.30539250e-01 2.22037315e-01 3.77937168e-01 -2.87658542e-01
-8.05086270e-02 -2.26806417e-01 -6.80599213e-01 -5.68120599e-01
5.15799165e-01 7.17542693e-02 -8.27246979e-02 7.11493909e-01
-7.21352398e-02 -4.83257860e-01 -3.67988676e-01 3.46176028e-01
6.33661866e-01 2.47272611e-01 7.04295933e-02 -1.64025009e-01
6.55130208e-01 -3.43555480e-01 1.11273333e-01 8.14630628e-01
-1.07133491e-02 7.40408540e-01 3.72219235e-01 -1.63677186e-01
-7.61621356e-01 -1.30232906e+00 -4.94552255e-02 1.24955726e+00
5.07499754e-01 -4.24584448e-01 -1.11489654e+00 -5.75189114e-01
-1.78847700e-01 6.97041690e-01 -7.31462896e-01 -1.22382343e-01
-2.91545808e-01 1.69485450e-01 5.06571829e-01 3.57406229e-01
5.90387881e-01 -1.56410336e+00 -5.11657178e-01 1.07789841e-02
-2.42074490e-01 -1.05132174e+00 -1.19310200e+00 -5.87392189e-02
-5.11914074e-01 -5.89770734e-01 -9.68510449e-01 -9.78687525e-01
8.75544786e-01 4.74803120e-01 7.48949468e-01 -3.77493948e-01
-2.54246175e-01 6.96793437e-01 -2.82013446e-01 -2.40908340e-01
-1.10573299e-01 -1.63099885e-01 1.96957588e-01 5.23829758e-01
-1.22820824e-01 -6.91851974e-01 -4.76784170e-01 2.78612703e-01
-1.17679381e+00 2.81784743e-01 3.20967197e-01 7.22874105e-01
9.14635003e-01 2.58072168e-01 5.52677214e-01 -7.28756070e-01
8.76083136e-01 -9.50334147e-02 -2.86652446e-01 7.75223598e-02
1.59185216e-01 1.58671349e-01 5.41326284e-01 -8.16756785e-01
-1.02042162e+00 3.97303253e-01 2.05771953e-01 -7.90112197e-01
-3.24065030e-01 3.27101290e-01 -5.50933719e-01 -6.13193586e-02
7.63180792e-01 4.20311183e-01 -2.68318385e-01 -5.04534602e-01
7.76380122e-01 6.84819520e-01 9.49160814e-01 -8.54619384e-01
9.86590564e-01 6.40408337e-01 -2.46807903e-01 -8.07748079e-01
-7.10826874e-01 -1.92706630e-01 -7.69517720e-01 -4.72258627e-01
1.02022052e+00 -7.98101664e-01 -6.92348063e-01 2.02018857e-01
-1.29941833e+00 -4.33297217e-01 -5.96499205e-01 2.71651685e-01
-6.82615578e-01 2.39078760e-01 -1.31757841e-01 -7.88281262e-01
-1.68797493e-01 -1.62874579e+00 1.75978732e+00 2.44595215e-01
-3.08767259e-01 -7.39336133e-01 -2.42842019e-01 -3.76804434e-02
1.27800643e-01 1.42544210e-01 8.73645246e-01 1.70591604e-02
-7.94981003e-01 -3.78438607e-02 -4.65091527e-01 1.22745745e-02
6.67305648e-01 5.12883067e-02 -1.22161818e+00 2.88063250e-02
-3.15890372e-01 -8.18948448e-02 6.97212040e-01 5.92902899e-01
1.81990767e+00 -2.53829032e-01 -2.02090859e-01 7.27061272e-01
1.11857641e+00 3.29816371e-01 4.39583778e-01 2.31868267e-01
9.23990667e-01 5.64369917e-01 9.09045398e-01 4.01354641e-01
-5.99021241e-02 8.00347984e-01 4.36313063e-01 -4.53161269e-01
-3.65995198e-01 -8.49650621e-01 4.63169813e-01 3.23947698e-01
4.30446267e-01 -2.80966423e-02 -4.81684536e-01 6.00213289e-01
-1.55067801e+00 -7.22040236e-01 3.85102242e-01 1.84931517e+00
8.78989875e-01 -1.25073999e-01 -1.42633123e-02 1.90653145e-01
9.62560833e-01 2.24286661e-01 -5.90995193e-01 -3.37755263e-01
-1.99280344e-02 2.30111361e-01 1.68645322e-01 6.12156093e-01
-1.00688660e+00 1.39394510e+00 6.49510193e+00 1.17189217e+00
-1.57002878e+00 -6.13094717e-02 4.14327502e-01 1.04050666e-01
-5.03255248e-01 -1.80526510e-01 -5.27597010e-01 4.10676807e-01
3.84113818e-01 -2.96593368e-01 5.94507754e-01 8.65772724e-01
7.02598333e-01 -1.88406378e-01 -1.11325908e+00 1.33310199e+00
2.12935343e-01 -1.39665926e+00 8.43056321e-01 -3.66215557e-01
5.79192579e-01 -8.76709223e-01 6.94752812e-01 -1.57125682e-01
-5.85488044e-02 -1.04764581e+00 1.06616545e+00 7.04457223e-01
1.24077892e+00 -9.15503442e-01 1.96553007e-01 1.18091606e-01
-1.48876417e+00 2.15738013e-01 -2.29484126e-01 9.49039608e-02
9.95063111e-02 2.08419710e-01 -1.00264192e+00 1.36377037e-01
1.06237853e+00 7.09326386e-01 -6.93754077e-01 7.63615727e-01
-4.61202681e-01 6.69319272e-01 -1.10726235e-02 1.26671478e-01
2.59700805e-01 -2.43965864e-01 6.82135284e-01 1.17568338e+00
3.37281585e-01 -3.36967617e-01 1.63697436e-01 1.34635329e+00
-5.43759950e-02 4.25469965e-01 -8.54034126e-01 1.55781899e-02
5.87407351e-01 1.17901742e+00 -7.95145988e-01 -3.64684880e-01
2.79904772e-02 1.30581331e+00 -1.26345187e-01 7.43664503e-01
-9.55985606e-01 -7.70827353e-01 5.74886501e-01 2.63549417e-01
1.67905018e-01 -2.25491356e-02 -3.72662723e-01 -7.87794948e-01
-8.20463672e-02 -7.09796369e-01 -3.91712599e-02 -1.67060781e+00
-7.73935616e-01 5.94046950e-01 -9.92637798e-02 -1.48297215e+00
1.60670280e-01 -2.73250848e-01 -9.40513134e-01 1.04301918e+00
-1.20095336e+00 -1.42769980e+00 -5.19022048e-01 8.47692847e-01
7.35291660e-01 -1.85112849e-01 6.67188108e-01 7.10590035e-02
-3.35774064e-01 6.91762567e-01 -2.29475081e-01 3.17762978e-02
1.04547870e+00 -1.25562596e+00 2.05741197e-01 6.68925524e-01
5.81869423e-01 6.71977162e-01 9.49949920e-01 -5.41191161e-01
-1.10014594e+00 -1.41246843e+00 3.57761621e-01 -2.50803232e-01
5.83148301e-01 -4.53962028e-01 -7.76529610e-01 8.00860405e-01
3.24806482e-01 -8.83073285e-02 3.59393835e-01 -3.75031352e-01
-2.23073632e-01 -2.65025377e-01 -1.04257274e+00 8.38158786e-01
7.69722819e-01 -9.95205522e-01 -4.06370580e-01 2.31176049e-01
1.22841132e+00 -3.36347282e-01 -3.59728456e-01 -6.27402216e-02
2.81823844e-01 -6.43754780e-01 1.11686540e+00 -3.76072973e-01
3.61790091e-01 -9.80368495e-01 -1.49360061e-01 -1.39722872e+00
-1.58021107e-01 -6.96688533e-01 3.81407827e-01 1.55560064e+00
5.41383736e-02 -1.78666145e-01 6.45971656e-01 4.89568990e-03
-1.57138512e-01 -2.83950567e-01 -6.62152231e-01 -4.46219862e-01
-3.42955172e-01 -7.24749923e-01 8.35144520e-01 7.85830379e-01
-6.06224015e-02 2.08824649e-01 -4.96138602e-01 5.91146886e-01
5.83598256e-01 2.33946100e-01 1.19899106e+00 -7.49023914e-01
-9.67866108e-02 -7.97308460e-02 -6.05700791e-01 -1.34660947e+00
3.05882543e-01 -7.37363815e-01 1.28016129e-01 -1.37416172e+00
-3.70538473e-04 -1.36787698e-01 -2.19490990e-01 4.85702217e-01
-1.27165765e-01 5.71652114e-01 1.19242340e-01 9.25724879e-02
-4.07880247e-01 7.44711995e-01 1.61683428e+00 -3.85457247e-01
-5.20238042e-01 -8.93770456e-02 -7.94204712e-01 7.04827666e-01
5.96265137e-01 -1.59757212e-01 -5.92269957e-01 -4.51340646e-01
-1.09138221e-01 2.57571459e-01 6.10332072e-01 -1.01957130e+00
2.31809825e-01 -1.03347033e-01 3.67602199e-01 -9.38039720e-01
5.29282689e-01 -8.83099735e-01 8.19599926e-02 2.16364145e-01
-6.76289856e-01 -1.61385253e-01 3.32248747e-01 7.56133914e-01
-5.21913290e-01 -9.72884297e-02 7.88091063e-01 1.29995823e-01
-7.88607836e-01 2.12345213e-01 -3.86412323e-01 -2.94212043e-01
9.76861238e-01 -4.01362538e-01 2.10324138e-01 -7.99142540e-01
-9.37839270e-01 -1.50206462e-01 3.71830285e-01 4.51116681e-01
8.98550034e-01 -1.75778711e+00 -2.84266204e-01 3.86820942e-01
1.81471318e-01 4.94136894e-03 3.51825565e-01 4.21771973e-01
-5.20025611e-01 2.77727842e-01 -3.35814804e-01 -9.89235401e-01
-1.41468060e+00 8.64355445e-01 2.55093455e-01 3.58254939e-01
-7.85452008e-01 9.07779813e-01 1.09440482e+00 -1.75517425e-01
4.77269113e-01 -7.51316488e-01 -2.15304166e-01 -1.20088883e-01
6.49724901e-01 -6.24940507e-02 -5.62280536e-01 -7.04865277e-01
7.06079230e-03 1.01244140e+00 2.67348439e-01 -4.22084451e-01
8.76353383e-01 -4.53628719e-01 -1.90044229e-03 7.55403101e-01
1.12686777e+00 4.12676275e-01 -1.40860987e+00 -5.67573786e-01
-5.09355783e-01 -6.50239885e-01 8.00805017e-02 -5.05421400e-01
-1.22442281e+00 1.23484230e+00 6.24686182e-01 -1.20336339e-01
1.35338962e+00 1.36956751e-01 5.10310590e-01 1.70853406e-01
3.52931708e-01 -7.39968061e-01 7.87488401e-01 1.93275645e-01
1.26093721e+00 -6.36642039e-01 -3.15214306e-01 -4.89681005e-01
-9.05595660e-01 1.02386951e+00 5.93508959e-01 -1.34652317e-01
6.02420390e-01 2.22933218e-01 3.66449147e-01 -1.51804686e-01
-9.21247974e-02 -1.16783269e-01 3.81868273e-01 1.03573787e+00
1.62244245e-01 -6.81152046e-02 5.76831698e-01 6.89006627e-01
-3.79297346e-01 -2.84158856e-01 3.93167228e-01 4.10529584e-01
-6.50293291e-01 -6.69625759e-01 -9.91504431e-01 -1.09945178e-01
-1.33457854e-01 -2.69564122e-01 -5.55430770e-01 5.31407595e-01
1.76651627e-01 7.07366168e-01 1.75459757e-01 -4.51134890e-01
2.22249895e-01 -4.80591059e-02 2.30946481e-01 -8.20689678e-01
-2.49187544e-01 5.76212943e-01 -7.05940843e-01 -6.56959593e-01
-2.86779702e-01 -3.94607097e-01 -1.41897571e+00 9.69964787e-02
-7.57955089e-02 1.94728732e-01 4.73338813e-01 5.25812447e-01
2.29012877e-01 9.46292400e-01 6.19601607e-01 -1.42725229e+00
1.79506510e-01 -8.51301908e-01 -1.02376831e+00 3.04955274e-01
4.23089296e-01 -6.88465238e-01 -2.10724592e-01 6.56523287e-01] | [14.810810089111328, 4.696695327758789] |
80571a77-99cd-4fd1-bf33-390546b383da | multi-scale-aggregation-using-feature-pyramid | 2004.03194 | null | https://arxiv.org/abs/2004.03194v4 | https://arxiv.org/pdf/2004.03194v4.pdf | Improving Multi-Scale Aggregation Using Feature Pyramid Module for Robust Speaker Verification of Variable-Duration Utterances | Currently, the most widely used approach for speaker verification is the deep speaker embedding learning. In this approach, we obtain a speaker embedding vector by pooling single-scale features that are extracted from the last layer of a speaker feature extractor. Multi-scale aggregation (MSA), which utilizes multi-scale features from different layers of the feature extractor, has recently been introduced and shows superior performance for variable-duration utterances. To increase the robustness dealing with utterances of arbitrary duration, this paper improves the MSA by using a feature pyramid module. The module enhances speaker-discriminative information of features from multiple layers via a top-down pathway and lateral connections. We extract speaker embeddings using the enhanced features that contain rich speaker information with different time scales. Experiments on the VoxCeleb dataset show that the proposed module improves previous MSA methods with a smaller number of parameters. It also achieves better performance than state-of-the-art approaches for both short and long utterances. | ['Youngmoon Jung', 'Seong Min Kye', 'Yeunju Choi', 'Hoirin Kim', 'Myunghun Jung'] | 2020-04-07 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [-8.30875188e-02 -6.27017915e-02 4.54489850e-02 -7.61728942e-01
-1.09808481e+00 -3.49478245e-01 5.08477807e-01 2.59467959e-01
-6.12569392e-01 9.42623168e-02 4.75339949e-01 1.23286471e-01
1.52179137e-01 -3.41823041e-01 -3.07058901e-01 -8.17416966e-01
-2.16154486e-01 -1.85783401e-01 2.02598616e-01 -2.37426028e-01
3.25776339e-02 6.09322488e-01 -1.67794740e+00 3.90755147e-01
5.12009621e-01 1.07021058e+00 -4.06116806e-02 6.80796862e-01
-2.69003749e-01 7.56716952e-02 -7.77852178e-01 -2.43581638e-01
-3.55270058e-02 -2.03006327e-01 -5.02994597e-01 5.32188900e-02
4.04777288e-01 -2.19640046e-01 -3.27049881e-01 6.73638463e-01
9.20382142e-01 -5.05183227e-02 3.88470978e-01 -1.13685477e+00
-4.15708274e-01 9.12378252e-01 -3.78668368e-01 3.70475739e-01
4.02656168e-01 -2.50818998e-01 1.17313695e+00 -1.22401536e+00
2.16584712e-01 1.45551860e+00 6.41690791e-01 5.62721074e-01
-1.25966799e+00 -7.51616240e-01 2.63224274e-01 4.69712019e-01
-1.64806783e+00 -8.04143250e-01 1.09329891e+00 -3.44113350e-01
1.11203110e+00 4.06630844e-01 3.80066544e-01 1.04131508e+00
1.32831365e-01 1.12851131e+00 8.97872508e-01 -3.97964150e-01
5.41821904e-02 5.28359175e-01 4.01267648e-01 6.71460867e-01
-4.59240526e-01 1.13056764e-01 -9.87834632e-01 -1.64982200e-01
2.03385562e-01 2.08734088e-02 -2.12990791e-01 -7.55667537e-02
-1.21688318e+00 1.04387259e+00 4.79528010e-01 7.76181340e-01
-3.88931364e-01 -1.57586038e-01 5.80471635e-01 4.82035905e-01
4.11663622e-01 2.29552537e-02 -4.96950418e-01 -1.85297185e-03
-1.27225399e+00 7.81629086e-02 6.99409604e-01 6.24236763e-01
7.10170448e-01 3.15488726e-01 -3.37926269e-01 9.97329950e-01
6.89798295e-01 3.88054222e-01 9.19094920e-01 -2.09549934e-01
4.21835780e-01 5.47267079e-01 -3.08994144e-01 -5.89881301e-01
-6.60010755e-01 -4.61193234e-01 -6.10897005e-01 1.27995729e-01
1.88603759e-01 -1.09736755e-01 -7.94225276e-01 1.77964985e+00
4.28806752e-01 2.72643507e-01 3.76343966e-01 7.07204878e-01
1.28440428e+00 8.13270509e-01 -8.58715326e-02 -6.14780523e-02
1.77286065e+00 -8.91264439e-01 -8.92998040e-01 1.05954006e-01
3.14103097e-01 -7.27518201e-01 8.91912878e-01 1.32892936e-01
-7.53554642e-01 -7.09564686e-01 -1.26002002e+00 -5.39322942e-02
-7.87868857e-01 3.69578511e-01 3.32534462e-01 1.08899415e+00
-1.21527946e+00 2.59182096e-01 -7.26897180e-01 -2.80469239e-01
1.76826537e-01 6.52664363e-01 -5.56530774e-01 4.60497320e-01
-1.42041457e+00 6.54795349e-01 5.09599037e-02 1.84727013e-01
-7.45277584e-01 -7.17944860e-01 -1.24500787e+00 4.00157064e-01
-2.70799071e-01 -1.80900678e-01 9.86606419e-01 -5.74952364e-01
-2.17081332e+00 4.58346397e-01 -6.19685173e-01 -4.34537649e-01
1.01544939e-01 -4.95702922e-02 -5.77579737e-01 1.65499851e-01
-3.66545826e-01 7.43522942e-01 1.33629596e+00 -6.58686101e-01
-4.61554348e-01 -6.06335819e-01 -1.55186296e-01 -1.03557564e-01
-7.84474671e-01 4.10057306e-01 -1.67591441e-02 -5.67903757e-01
2.97795415e-01 -7.57067442e-01 1.38217330e-01 -3.34465981e-01
-3.27041805e-01 -5.97664475e-01 1.15473783e+00 -9.85748291e-01
1.23657119e+00 -2.53661108e+00 2.50474244e-01 6.47897795e-02
-2.04823930e-02 1.94275200e-01 -3.93859953e-01 4.94866669e-01
1.84600372e-02 -5.73980510e-02 -1.15694113e-01 -8.88973415e-01
3.56018752e-01 -2.21548527e-01 -2.17624366e-01 4.68494117e-01
4.26281720e-01 7.53857076e-01 -3.63448948e-01 -5.54266334e-01
2.33997688e-01 1.00056815e+00 -5.84782243e-01 1.68894514e-01
3.26708615e-01 3.33854795e-01 -1.61103725e-01 5.20292938e-01
8.00446570e-01 3.14679384e-01 -1.69938773e-01 2.08003726e-02
-3.13624799e-01 5.77662826e-01 -1.32158554e+00 1.84513009e+00
-6.13461375e-01 8.68536294e-01 3.10655236e-01 -6.95475698e-01
1.10509396e+00 8.72393370e-01 2.55455077e-01 -1.94547385e-01
1.17409669e-01 1.77331820e-01 4.22387607e-02 -5.27009547e-01
4.56614792e-01 -2.89137810e-01 -3.06761146e-01 2.80800909e-01
5.52977264e-01 -4.37532179e-02 5.22669591e-03 8.67109224e-02
8.65992546e-01 -3.92970830e-01 1.67417020e-01 -2.04635009e-01
1.22562683e+00 -1.01460969e+00 5.46122611e-01 2.71504402e-01
-4.09719676e-01 4.77037430e-01 3.52105170e-01 -3.69489123e-03
-6.69117033e-01 -1.00365162e+00 -4.66644973e-01 1.35568082e+00
-4.13593262e-01 -5.42251408e-01 -7.77033806e-01 -7.27560401e-01
2.42047548e-01 3.68595809e-01 -7.28492379e-01 -7.32720224e-03
-5.28080344e-01 -4.99999374e-01 8.31525743e-01 6.35401845e-01
4.15646166e-01 -1.01964414e+00 -3.27903390e-01 4.17381048e-01
-1.30953297e-01 -1.13163781e+00 -7.41159320e-01 1.44307613e-01
-6.57120049e-01 -3.08862060e-01 -8.32870424e-01 -9.69834089e-01
3.47042471e-01 -1.08174816e-01 2.92214781e-01 -3.08780164e-01
-1.94954813e-01 2.05079779e-01 -3.09584230e-01 -4.84376669e-01
-4.38020736e-01 3.68807852e-01 3.29214782e-01 6.43413544e-01
6.29957855e-01 -4.46952075e-01 -1.44068301e-01 1.28532246e-01
-7.66399980e-01 -5.70448935e-01 5.90685606e-01 9.91354764e-01
3.39739174e-02 -2.21203417e-01 1.28178477e+00 -6.30492494e-02
6.26900494e-01 -5.57847582e-02 -5.38740635e-01 3.98964472e-02
-2.02792034e-01 4.55579698e-01 4.58818823e-01 -4.28194195e-01
-1.03550684e+00 9.52003747e-02 -4.83886003e-01 -1.96746096e-01
-3.45361024e-01 2.47029439e-01 -4.71595645e-01 -8.20252392e-03
2.52572894e-01 5.97475290e-01 1.34233072e-01 -7.18053699e-01
3.63992393e-01 1.25096512e+00 -5.45905307e-02 -4.43682559e-02
8.34133983e-01 3.06629479e-01 -4.63388473e-01 -1.34265137e+00
-3.26361984e-01 -7.32293725e-01 -8.85641932e-01 -3.09847351e-02
7.91864336e-01 -9.71580684e-01 -8.94313991e-01 6.33600891e-01
-1.29632521e+00 3.16643357e-01 -1.34051681e-01 8.38919222e-01
-2.26652473e-01 3.12226206e-01 -8.56519580e-01 -9.67473805e-01
-5.38140357e-01 -1.37726748e+00 1.29845810e+00 3.29781145e-01
-1.47630498e-01 -7.62420118e-01 4.63601984e-02 1.11472532e-01
7.45514691e-01 -2.91205019e-01 5.31022251e-01 -9.46848512e-01
-2.88922042e-01 -2.79989868e-01 7.24360347e-02 6.50156319e-01
2.26119399e-01 -4.50981669e-02 -1.57627785e+00 -5.04194260e-01
8.00355449e-02 -1.44298803e-02 1.15480244e+00 2.70478278e-01
8.09741974e-01 -2.41868198e-01 -1.43105060e-01 4.38475251e-01
1.02529943e+00 -3.43587063e-02 2.65949786e-01 1.35450095e-01
3.50232452e-01 8.07701528e-01 1.42499939e-01 4.51458484e-01
4.20526862e-01 7.38607764e-01 3.99050601e-02 1.15171811e-02
-6.38467968e-02 1.09149121e-01 1.00387907e+00 1.13595009e+00
2.91605711e-01 2.87938923e-01 -4.86994535e-01 7.30934262e-01
-1.46035171e+00 -1.19168890e+00 4.23193187e-01 2.01594377e+00
7.61909604e-01 6.47597713e-03 4.43575174e-01 6.08857214e-01
6.35929286e-01 4.91634905e-01 -2.87977725e-01 -7.78607607e-01
-1.13942675e-01 1.89079538e-01 -8.92199576e-02 7.28159845e-01
-1.18406892e+00 8.96947503e-01 6.21371841e+00 5.22924244e-01
-1.65694010e+00 2.78926343e-01 -5.07357903e-02 -2.61684150e-01
-1.71935841e-01 -4.81434911e-01 -1.28182900e+00 3.78643841e-01
1.34352851e+00 -1.96176559e-01 3.75212692e-02 8.27023029e-01
1.01300098e-01 4.72800940e-01 -1.32819951e+00 9.94434595e-01
4.99881774e-01 -1.03126550e+00 -2.88701177e-01 3.22283469e-02
3.26038778e-01 5.36205098e-02 3.35267425e-01 6.28139138e-01
-4.61153299e-01 -7.53798723e-01 7.22834945e-01 1.69633344e-01
4.35712337e-01 -8.86467099e-01 1.02940512e+00 9.50005054e-02
-1.62773263e+00 -4.03861403e-01 -1.74492612e-01 2.59465784e-01
2.69148648e-01 4.58841592e-01 -1.15939903e+00 4.18200374e-01
5.93240321e-01 5.31008661e-01 -6.02774441e-01 9.13868308e-01
-2.06642896e-01 5.73886395e-01 -5.22673190e-01 -2.28489771e-01
1.79756209e-01 1.64242342e-01 7.33353376e-01 1.72440875e+00
2.54566967e-01 -4.39434588e-01 -2.73693025e-01 5.94873905e-01
-4.40347567e-02 4.15077239e-01 -4.43886429e-01 1.37378206e-03
4.68600959e-01 1.39705145e+00 -7.07607344e-02 -3.85126621e-01
-4.73305106e-01 8.91741872e-01 1.39708012e-01 3.62914175e-01
-7.65712917e-01 -7.69318044e-01 9.17332768e-01 -1.34117782e-01
8.34690213e-01 -2.22593397e-01 -2.08217055e-02 -1.08093667e+00
1.00746699e-01 -6.79912210e-01 3.15412045e-01 -1.78564675e-02
-1.02728772e+00 1.04713464e+00 -2.17026383e-01 -1.15807819e+00
-4.26087677e-01 -5.15889883e-01 -6.92614079e-01 9.58328426e-01
-1.76963651e+00 -1.28422618e+00 3.02617736e-02 6.81922615e-01
7.31571972e-01 -4.94465888e-01 1.12234116e+00 3.93241674e-01
-7.10007250e-01 1.11300719e+00 -5.78296511e-03 1.73070565e-01
6.42565608e-01 -1.14926815e+00 3.26113850e-01 7.20884562e-01
3.11784327e-01 7.73148298e-01 5.14088631e-01 5.65925166e-02
-1.23749733e+00 -7.82110631e-01 1.32047057e+00 -1.98166460e-01
5.02414942e-01 -8.11427116e-01 -9.74019766e-01 6.66257322e-01
4.93258864e-01 -9.25700739e-02 1.08184707e+00 2.43371248e-01
-6.35429561e-01 -5.38363814e-01 -1.30754566e+00 1.49921194e-01
3.87287647e-01 -9.81192470e-01 -9.88894761e-01 -1.99615911e-01
8.18536639e-01 1.29271010e-02 -9.99020994e-01 2.06120744e-01
7.46669471e-01 -7.38421261e-01 8.08701932e-01 -3.47337961e-01
-2.78588206e-01 -3.37673873e-01 -3.35422099e-01 -1.47514474e+00
-1.83982044e-01 -6.08107150e-01 -1.87048718e-01 1.48839641e+00
6.62771404e-01 -8.98397624e-01 4.76209968e-01 1.46230429e-01
-9.83874053e-02 -6.23824835e-01 -1.44003093e+00 -9.51487303e-01
-8.03428590e-02 -2.70407677e-01 9.62894440e-01 7.20065236e-01
4.72954005e-01 5.74942172e-01 -1.48793086e-01 4.10637021e-01
6.40109897e-01 1.60792008e-01 5.84843516e-01 -1.34244847e+00
-4.81948704e-02 -4.55458492e-01 -8.81522357e-01 -9.67817307e-01
4.97340083e-01 -1.05134308e+00 1.00915603e-01 -1.25731707e+00
-9.13930982e-02 8.81241485e-02 -6.43968284e-01 4.05418307e-01
-1.28110349e-01 1.49321616e-01 2.56015390e-01 -3.31015557e-01
-2.12900355e-01 8.88342321e-01 7.02310264e-01 -3.50768089e-01
-5.74587703e-01 1.19705722e-02 -3.95468801e-01 5.24116874e-01
8.40716898e-01 -4.36134815e-01 6.18742481e-02 -1.09868824e-01
-6.54371858e-01 -2.50982106e-01 -5.61889587e-03 -1.01980019e+00
2.27697581e-01 5.74883640e-01 3.41077447e-01 -9.21862185e-01
8.47304165e-01 -6.85369551e-01 -3.76903206e-01 5.72204590e-01
-4.17370498e-01 -8.93110186e-02 3.94221097e-01 2.84496993e-01
-6.90622270e-01 -3.32432240e-02 8.56533408e-01 4.94539440e-01
-4.83975083e-01 1.52282044e-01 -4.80423987e-01 -7.27134585e-01
8.90777290e-01 7.14271218e-02 5.17422259e-02 -2.34919578e-01
-9.89830554e-01 2.47641001e-02 -1.64840549e-01 8.29522550e-01
8.22202682e-01 -1.41326261e+00 -1.06312382e+00 7.43073881e-01
2.34631270e-01 -6.23851120e-01 4.72859055e-01 1.01005697e+00
1.51144028e-01 7.26785004e-01 -4.04874720e-02 -8.84861290e-01
-1.71385443e+00 3.97689462e-01 2.26525158e-01 -8.52900371e-02
-4.51849580e-01 1.11193657e+00 5.88702075e-02 -6.30778432e-01
4.30217117e-01 -6.13725901e-01 -4.71108317e-01 6.31423354e-01
1.02008665e+00 2.31457219e-01 2.43008003e-01 -1.16948855e+00
-8.49759758e-01 6.84837699e-01 -3.52209747e-01 -4.98245239e-01
1.51121140e+00 -1.54390320e-01 2.89022587e-02 6.70770347e-01
1.56985044e+00 3.98584872e-01 -1.06876707e+00 -5.10070026e-01
-5.98272607e-02 -2.24993318e-01 1.09931670e-01 -3.49493980e-01
-9.85016167e-01 1.24379778e+00 8.76907766e-01 2.79666960e-01
8.86646509e-01 1.41163185e-01 7.81398773e-01 1.61648721e-01
3.38425077e-02 -9.83674824e-01 1.11353457e-01 4.62629795e-01
1.31331348e+00 -1.21894574e+00 -4.11330611e-01 -1.38009861e-01
-6.10705972e-01 1.25932181e+00 3.17697406e-01 1.80886835e-01
1.02091503e+00 3.20151061e-01 4.18608755e-01 1.41394466e-01
-6.36698902e-01 -1.25740886e-01 4.56888586e-01 3.17508548e-01
6.30483210e-01 1.62218079e-01 -2.37803206e-01 8.13458920e-01
-4.28015321e-01 -5.56139588e-01 2.45502934e-01 6.31556928e-01
-4.79972929e-01 -1.31404889e+00 -5.34056842e-01 2.24730782e-02
-4.32481408e-01 -1.20021977e-01 -2.23221779e-01 3.96772712e-01
-1.06134057e-01 1.23073030e+00 -2.77077835e-02 -5.41812956e-01
3.55206966e-01 6.90588593e-01 2.60371655e-01 -4.64598358e-01
-9.44555938e-01 2.24688113e-01 -1.29740939e-01 -4.81364042e-01
-3.21961701e-01 -9.14941788e-01 -1.33621705e+00 3.56374606e-02
-6.35261536e-01 2.73500949e-01 1.30531263e+00 7.89920390e-01
5.72462380e-01 6.88316286e-01 1.03849375e+00 -1.02038801e+00
-8.22183251e-01 -1.51376796e+00 -8.07837129e-01 1.24452226e-01
9.86153543e-01 -5.46222448e-01 -6.39172316e-01 -6.98055476e-02] | [14.395940780639648, 6.046393871307373] |
09d679a2-6f3e-49b7-9c01-2c7a300bd1b0 | finding-counterfactual-explanations-through | 2204.03429 | null | https://arxiv.org/abs/2204.03429v1 | https://arxiv.org/pdf/2204.03429v1.pdf | Finding Counterfactual Explanations through Constraint Relaxations | Interactive constraint systems often suffer from infeasibility (no solution) due to conflicting user constraints. A common approach to recover infeasibility is to eliminate the constraints that cause the conflicts in the system. This approach allows the system to provide an explanation as: "if the user is willing to drop out some of their constraints, there exists a solution". However, one can criticise this form of explanation as not being very informative. A counterfactual explanation is a type of explanation that can provide a basis for the user to recover feasibility by helping them understand which changes can be applied to their existing constraints rather than removing them. This approach has been extensively studied in the machine learning field, but requires a more thorough investigation in the context of constraint satisfaction. We propose an iterative method based on conflict detection and maximal relaxations in over-constrained constraint satisfaction problems to help compute a counterfactual explanation. | ["Barry O'Sullivan", 'Begum Genc', 'Sharmi Dev Gupta'] | 2022-04-07 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 6.09265089e-01 7.89055467e-01 -4.37951773e-01 -4.66781110e-01
-2.73661494e-01 -8.60663354e-01 3.10300916e-01 2.54654974e-01
-2.29500383e-01 1.14242542e+00 1.28149241e-01 -8.13664198e-01
-4.36922610e-01 -6.97545469e-01 -4.56871957e-01 -3.79728168e-01
-8.81324187e-02 6.92052126e-01 1.12180397e-01 -2.10673735e-01
5.65050960e-01 3.92321080e-01 -1.47346675e+00 3.16849500e-01
1.03837156e+00 3.28495950e-01 -1.70230404e-01 2.62862116e-01
-1.37829810e-01 2.30230734e-01 -6.14566863e-01 -2.30678007e-01
3.26829553e-01 -6.12880647e-01 -1.20572901e+00 3.43239039e-01
-4.97297570e-02 -1.75729141e-01 6.33189499e-01 1.23468053e+00
-2.02012360e-01 3.15288752e-01 4.41095412e-01 -1.63462341e+00
-1.28787428e-01 7.13056862e-01 -4.48535115e-01 -1.05373658e-01
9.47299838e-01 -1.04712471e-02 1.24430120e+00 -3.56670380e-01
7.95541644e-01 1.21303606e+00 1.47723388e-02 5.88021994e-01
-1.50328386e+00 -5.49743891e-01 6.69225514e-01 2.19488204e-01
-1.23430705e+00 -2.34708846e-01 6.18629098e-01 -1.36931434e-01
1.00377560e+00 1.07172990e+00 7.23638535e-01 4.99344796e-01
1.09166227e-01 4.74867463e-01 1.03120577e+00 -7.18727887e-01
6.44319177e-01 4.66037303e-01 -2.09475923e-02 4.38022703e-01
6.52257144e-01 9.06802788e-02 -3.03333521e-01 -5.91169059e-01
4.29813683e-01 -3.58179450e-01 -4.48099256e-01 -4.89508659e-01
-6.83688402e-01 1.02391791e+00 4.14189361e-02 2.96167314e-01
-3.02957773e-01 -2.42416009e-01 3.45049798e-01 4.15743619e-01
2.09108829e-01 9.59419847e-01 -6.97313607e-01 1.19432122e-01
-6.89635038e-01 7.34146774e-01 1.08807635e+00 7.45310545e-01
6.02006376e-01 -1.70869634e-01 9.15977284e-02 1.79027870e-01
3.31078291e-01 -1.08169161e-01 -1.00850515e-01 -1.20472646e+00
3.35916638e-01 8.27592731e-01 6.14255607e-01 -8.81772637e-01
-1.16720632e-01 -5.91800921e-02 -2.83160716e-01 6.84970617e-01
4.39547867e-01 -6.74504712e-02 -6.20881796e-01 1.64500606e+00
2.58879602e-01 1.28764540e-01 7.30509907e-02 1.28028905e+00
4.38798256e-02 4.85744983e-01 -1.01007633e-01 -9.36812520e-01
9.45544720e-01 -3.77324790e-01 -8.81246030e-01 -2.83619344e-01
7.17900515e-01 -5.23019135e-01 7.74929166e-01 8.38224828e-01
-1.29231310e+00 2.03526884e-01 -1.18189597e+00 4.41582561e-01
-1.74036011e-01 -6.42029822e-01 1.01560736e+00 7.03810632e-01
-7.65405536e-01 5.26005924e-01 -4.38364506e-01 -1.03621580e-01
-6.07429706e-02 6.28523290e-01 -4.07204717e-01 -4.09883529e-01
-1.16257501e+00 1.07728302e+00 6.70559049e-01 2.31258199e-01
-2.56795168e-01 -3.77886683e-01 -9.21314716e-01 4.42597657e-01
1.21100116e+00 -5.56039870e-01 1.22932386e+00 -1.31289053e+00
-1.09918702e+00 5.85378468e-01 -3.53834957e-01 -2.48383462e-01
5.79641044e-01 1.69370398e-01 -2.24672869e-01 -3.43047947e-01
1.40531525e-01 2.06125483e-01 4.92555588e-01 -1.47206020e+00
-6.58839941e-01 -3.91425997e-01 6.32256627e-01 2.29096577e-01
4.58900243e-01 2.64667004e-01 -4.26502436e-01 -2.83997506e-01
5.71792364e-01 -1.20440948e+00 -6.43232524e-01 -2.94024616e-01
-7.12969244e-01 -2.91479230e-01 5.10469794e-01 -2.48395965e-01
1.40843725e+00 -1.68117011e+00 3.28150392e-01 7.35602915e-01
-8.43074098e-02 1.33762807e-01 8.38244706e-02 4.41517264e-01
-5.43169320e-01 8.22375536e-01 -5.97354591e-01 1.16653234e-01
1.79962575e-01 4.76476550e-01 -3.33760679e-01 3.03760827e-01
1.10538825e-01 2.92019904e-01 -7.53793418e-01 -2.02932715e-01
1.82193711e-01 -2.31608406e-01 -9.62168574e-01 1.20463043e-01
-6.73146248e-01 2.30274498e-01 -4.33145493e-01 4.02066797e-01
8.50773931e-01 2.38744393e-01 1.02886379e+00 2.15266719e-01
-2.98040003e-01 2.11292520e-01 -1.72091150e+00 1.12615669e+00
-1.90049618e-01 3.90492797e-01 5.88791072e-02 -1.22996163e+00
4.26710606e-01 4.11749542e-01 4.51785743e-01 -4.86401916e-01
1.25393972e-01 2.04708710e-01 1.82030976e-01 -6.94440126e-01
2.33649239e-01 -5.05802095e-01 -2.54991502e-01 6.73610747e-01
-6.98569775e-01 -1.55968800e-01 3.67316455e-01 3.42130214e-01
7.95515478e-01 8.14663023e-02 7.44990110e-01 -2.10905090e-01
6.90616727e-01 2.59759218e-01 1.11224127e+00 7.35198617e-01
1.18404694e-01 1.85865507e-01 9.94712889e-01 -4.90206957e-01
-6.66451156e-01 -5.32583714e-01 3.91289964e-03 7.26732612e-01
1.70034468e-01 -4.52663928e-01 -4.36673671e-01 -8.54018331e-01
9.29035991e-02 1.17409539e+00 -5.43096244e-01 -1.81359231e-01
-4.90573138e-01 -4.31667835e-01 -1.31138757e-01 1.40174046e-01
1.14675492e-01 -9.06983852e-01 -9.04285133e-01 2.00578213e-01
-5.41065991e-01 -5.41315615e-01 -2.62837887e-01 9.24083367e-02
-8.38359594e-01 -1.41065109e+00 9.01979208e-02 -2.00731218e-01
1.02272952e+00 1.67877316e-01 8.71970534e-01 8.66596580e-01
-3.82523867e-03 -2.52317693e-02 -3.06416005e-01 -4.52911675e-01
-3.55219066e-01 -3.72554570e-01 1.67348742e-01 -3.22238266e-01
2.13818461e-01 -3.31393182e-01 -5.12891971e-02 3.88975620e-01
-1.05464745e+00 -1.61204033e-03 1.85367540e-01 5.69083214e-01
5.10370970e-01 5.60140312e-01 5.49179435e-01 -1.47249663e+00
8.45290303e-01 -5.19298017e-01 -7.03296006e-01 4.52062994e-01
-9.97719228e-01 3.26989532e-01 6.24534965e-01 -2.82635599e-01
-1.21677768e+00 2.04293579e-01 2.70976782e-01 2.89937463e-02
-3.62562746e-01 9.18603778e-01 -5.21489739e-01 1.56636864e-01
3.57581198e-01 -3.23870391e-01 -5.75020015e-01 -1.51129603e-01
3.04157794e-01 2.50985384e-01 3.86950105e-01 -8.87271404e-01
7.03885674e-01 2.20943362e-01 4.13618088e-02 -2.44009674e-01
-6.43276870e-01 -4.72416818e-01 -5.63979685e-01 -2.05759823e-01
4.84907418e-01 -8.11911076e-02 -1.07078624e+00 -5.42599142e-01
-1.19241464e+00 4.78775017e-02 -7.11930394e-02 2.75540203e-01
-4.73977298e-01 4.09508079e-01 2.88578242e-01 -1.34840858e+00
4.22896922e-01 -1.12909877e+00 2.28891626e-01 1.43334433e-01
-8.18547904e-01 -8.15723479e-01 -1.17371529e-02 3.14862013e-01
2.75074355e-02 6.19825482e-01 1.17280734e+00 -7.25435257e-01
-5.57702601e-01 -3.44217956e-01 1.37766048e-01 -2.54673183e-01
2.02762395e-01 2.81930059e-01 -5.26184738e-01 -3.61828893e-01
-1.86303875e-03 2.63356090e-01 3.85935485e-01 4.75281864e-01
8.15942705e-01 -9.78962421e-01 -5.28105736e-01 -4.33426201e-02
1.68793881e+00 5.19468844e-01 5.67986190e-01 2.87415653e-01
3.54085900e-02 9.85028625e-01 9.42506671e-01 5.48930883e-01
1.21347934e-01 1.04748547e+00 5.84768713e-01 -1.76510233e-02
7.13386118e-01 2.29877636e-01 -1.07739478e-01 -1.48085102e-01
-2.46097907e-01 -3.46530110e-01 -8.42936397e-01 4.71524179e-01
-2.29084229e+00 -1.08402574e+00 -5.65371275e-01 2.40422583e+00
6.39639795e-01 4.99581277e-01 4.47477400e-02 4.46448058e-01
5.95825553e-01 -2.48143569e-01 -5.49223125e-01 -1.27897763e+00
2.61838764e-01 -9.68739018e-02 1.52566046e-01 1.08542287e+00
-5.03789544e-01 7.79596627e-01 6.49960518e+00 5.74354120e-02
-6.66190147e-01 -2.67750740e-01 2.12269053e-01 -8.20708275e-02
-7.76922882e-01 7.63711810e-01 -2.31912762e-01 2.27376819e-01
5.90411365e-01 -6.08932912e-01 4.47742224e-01 5.73199153e-01
5.80799997e-01 -7.97891080e-01 -1.46926343e+00 1.15553014e-01
-5.29204272e-02 -1.25774670e+00 3.32840718e-02 1.40570149e-01
5.89468002e-01 -8.94064367e-01 -3.84479403e-01 1.23841308e-01
3.79164308e-01 -9.72193599e-01 7.17616975e-01 3.89706910e-01
5.65149724e-01 -1.13264906e+00 8.02458465e-01 7.63278186e-01
-8.89966369e-01 -1.48035064e-01 -1.07428879e-01 -7.27488995e-01
2.51329482e-01 4.18414026e-01 -9.43456948e-01 7.12828577e-01
2.75771141e-01 -9.82663110e-02 3.45924348e-02 1.00883043e+00
-5.91314614e-01 2.06189230e-01 -2.68090963e-01 2.17354074e-01
1.85786597e-02 -4.44164783e-01 6.19422019e-01 8.26792479e-01
-1.64157748e-01 7.52247930e-01 2.65045434e-01 1.15180027e+00
4.08909231e-01 -1.23407289e-01 -6.35473251e-01 2.78394103e-01
5.04657388e-01 7.14377463e-01 -8.54027689e-01 -3.53919894e-01
-3.22831869e-01 9.03245270e-01 1.26277106e-02 6.11219525e-01
-6.55425787e-01 -8.75126645e-02 6.09634578e-01 6.97845817e-02
-2.39266992e-01 3.69023755e-02 -4.74298626e-01 -9.90177035e-01
1.74658895e-01 -9.79632139e-01 6.08853459e-01 -6.64750755e-01
-6.51509702e-01 2.02184200e-01 4.21465725e-01 -7.84369826e-01
-5.23623526e-01 -9.87614989e-02 -9.04936910e-01 9.19078290e-01
-1.25318491e+00 -4.45496291e-01 1.43053338e-01 3.75309527e-01
5.96580267e-01 4.47032005e-01 8.30831230e-01 -1.63128048e-01
-6.07527018e-01 1.38316959e-01 -7.40285099e-01 -4.76382077e-01
3.79965991e-01 -1.26045167e+00 -2.54125476e-01 1.20962894e+00
-1.19557619e-01 9.67456758e-01 1.40004313e+00 -8.05112302e-01
-1.27750289e+00 -5.55224657e-01 1.36671352e+00 -1.54038936e-01
1.50934115e-01 9.04139429e-02 -1.17550087e+00 9.65524852e-01
1.48447618e-01 -5.91487050e-01 6.25046551e-01 4.23268825e-01
-1.59672052e-01 3.11230808e-01 -1.38122225e+00 7.58451343e-01
7.61206508e-01 -1.59374610e-01 -7.79905736e-01 3.50953251e-01
5.29304028e-01 -1.52049437e-01 -3.67221802e-01 3.70273381e-01
5.29298067e-01 -1.00114536e+00 6.37952089e-01 -1.01360750e+00
1.82448685e-01 -4.90526080e-01 -5.66739626e-02 -1.24650824e+00
-3.98548156e-01 -7.25948095e-01 2.10603982e-01 9.75117445e-01
7.04945743e-01 -5.77087462e-01 7.88214862e-01 1.73110592e+00
-1.06296204e-01 -6.29861355e-01 -9.71479058e-01 -6.72661662e-01
-8.19106847e-02 -7.01360881e-01 7.05269754e-01 1.47031665e+00
9.41846132e-01 5.68613745e-02 -3.68433595e-01 4.05776083e-01
3.43769401e-01 3.56595635e-01 5.54755807e-01 -1.47938919e+00
-1.78202689e-01 -5.10940194e-01 2.13551193e-01 -2.54365444e-01
3.98788691e-01 -7.36535370e-01 1.89216211e-01 -1.57693148e+00
2.07795843e-01 -4.58879411e-01 5.19838706e-02 7.84516454e-01
9.55360457e-02 -2.54490018e-01 2.08434910e-01 1.01338536e-01
-5.26521623e-01 -9.12400112e-02 8.45226407e-01 1.13019913e-01
-5.18480480e-01 1.67094424e-01 -1.06343329e+00 8.59083712e-01
8.26715589e-01 -6.65014565e-01 -3.25407147e-01 -3.85806113e-02
8.27223539e-01 6.55662119e-01 8.89712125e-02 -3.25838149e-01
2.31916368e-01 -1.03421521e+00 -2.46934071e-01 -9.75914747e-02
-1.30118713e-01 -1.28770173e+00 7.60517597e-01 7.73938179e-01
-4.53054816e-01 -1.38088942e-01 3.14880133e-01 2.94944882e-01
-8.13706405e-03 -7.35467792e-01 4.16431814e-01 -2.00949654e-01
-6.03829980e-01 -2.07215145e-01 -9.05000627e-01 -4.64102864e-01
1.13212228e+00 -4.27572012e-01 -4.36947159e-02 -4.84344900e-01
-1.02844894e+00 5.48902094e-01 5.29071689e-01 2.69540727e-01
6.49113953e-01 -1.05890965e+00 -5.30542910e-01 3.43611687e-02
4.27903496e-02 -9.51181948e-02 -6.67947307e-02 6.24241650e-01
-1.32918552e-01 6.65026128e-01 -1.52199551e-01 -1.04733941e-03
-1.35024977e+00 6.79447412e-01 2.93065906e-01 -2.47922003e-01
-2.64425755e-01 5.31672537e-01 -5.00727482e-02 -1.77005887e-01
1.65086687e-01 -4.77580488e-01 -2.47630283e-01 -1.82705447e-02
3.77813667e-01 2.49073610e-01 6.87582344e-02 -1.90773278e-01
-7.56675303e-01 1.28545269e-01 -3.52804177e-02 -4.09967095e-01
1.17690408e+00 -2.12553129e-01 -8.58440921e-02 1.28170803e-01
4.82302278e-01 1.18545458e-01 -8.96606624e-01 -7.40035623e-02
3.60287249e-01 -8.60290051e-01 -1.18950166e-01 -1.08835614e+00
-6.79738581e-01 2.65363634e-01 -1.35632694e-01 6.91007018e-01
1.26365793e+00 -7.90469348e-02 5.39128855e-02 4.94352818e-01
4.51195478e-01 -1.18098700e+00 -3.30868870e-01 2.81525075e-01
1.19152367e+00 -1.20316780e+00 3.82624865e-01 -8.45744729e-01
-6.36331499e-01 1.01988077e+00 6.90021813e-01 -2.10512392e-02
2.20962316e-01 2.36025363e-01 -3.49165082e-01 -3.03138316e-01
-8.55416179e-01 -8.79231319e-02 1.74858555e-01 3.18946868e-01
3.86668742e-01 2.41529346e-01 -1.10954201e+00 7.15674579e-01
-2.65658088e-02 -4.17993553e-02 1.14260435e+00 1.07425117e+00
-4.71066058e-01 -1.26975310e+00 -6.20113611e-01 2.29394510e-01
-1.90414384e-01 1.17930301e-01 -8.66124749e-01 8.20950687e-01
2.76698440e-01 1.40036845e+00 -2.23015875e-01 6.41242415e-02
6.92449570e-01 8.58985782e-02 4.19119120e-01 -9.93363261e-01
-4.37614530e-01 1.47085086e-01 5.58978081e-01 -7.04418302e-01
-5.51457345e-01 -7.05451488e-01 -1.66810691e+00 -4.19657558e-01
-6.77620292e-01 4.68151271e-01 4.40052718e-01 1.37656581e+00
-1.96194619e-01 4.35521334e-01 5.62031507e-01 -4.46838707e-01
-1.13602869e-01 -4.48918521e-01 -4.68149066e-01 3.17884862e-01
1.46949083e-01 -7.98756301e-01 -4.87501472e-01 6.97090477e-02] | [8.622931480407715, 5.985284805297852] |
356c47fe-9583-427f-82a9-9610317644c2 | a-review-on-the-use-of-deep-learning-in | 1812.10360 | null | http://arxiv.org/abs/1812.10360v1 | http://arxiv.org/pdf/1812.10360v1.pdf | A Review on The Use of Deep Learning in Android Malware Detection | Android is the predominant mobile operating system for the past few years.
The prevalence of devices that can be powered by Android magnetized not merely
application developers but also malware developers with criminal intention to
design and spread malicious applications that can affect the normal work of
Android phones and tablets, steal personal information and credential data, or
even worse lock the phone and ask for ransom. Researchers persistently devise
countermeasures strategies to fight back malware. One of these strategies
applied in the past five years is the use of deep learning methods in Android
malware detection. This necessitates a review to inspect the accomplished work
in order to know where the endeavors have been established, identify unresolved
problems, and motivate future research directions. In this work, an extensive
survey of static analysis, dynamic analysis, and hybrid analysis that utilized
deep learning methods are reviewed with an elaborated discussion on their key
concepts, contributions, and limitations. | ['Abdelmonim Naway', 'Yuancheng LI'] | 2018-12-26 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 2.61280835e-01 -1.73906863e-01 -9.76247013e-01 3.38818759e-01
-1.19642708e-02 -7.85053909e-01 6.82431877e-01 -6.07658744e-01
-1.20799309e-02 5.48302472e-01 -3.62639725e-01 -1.27409565e+00
1.09348632e-01 -3.96631896e-01 -5.09854138e-01 -5.23207843e-01
2.32506376e-02 -1.99059799e-01 1.61117792e-01 -1.81061998e-01
6.84296966e-01 2.89870918e-01 -1.50782657e+00 2.95103341e-01
7.15575933e-01 7.98296034e-01 -6.00981303e-02 8.13349605e-01
1.58454686e-01 1.05686939e+00 -1.21858680e+00 -5.38722098e-01
-1.63751721e-01 -1.77806150e-02 -6.86090648e-01 -3.67620766e-01
3.67451226e-03 -5.78249872e-01 -2.60586739e-01 1.29103065e+00
1.61658138e-01 -5.02029240e-01 3.41127843e-01 -1.51982868e+00
-7.73274064e-01 3.23449105e-01 -7.01616228e-01 6.58809483e-01
5.62947094e-01 1.28716111e-01 1.93082020e-01 -2.57113129e-01
2.09612936e-01 8.15368235e-01 1.05928898e+00 8.15279901e-01
-3.72461081e-01 -8.23952198e-01 -5.03988676e-02 1.60972074e-01
-9.51450586e-01 -5.72717190e-01 8.60782623e-01 -7.93925881e-01
1.34987485e+00 4.86392885e-01 7.12019384e-01 1.85158002e+00
1.12508392e+00 4.70449418e-01 9.56854343e-01 -1.85277611e-01
-1.63769983e-02 3.03270251e-01 4.60599244e-01 7.21944213e-01
7.75900781e-01 1.35279864e-01 3.39180231e-02 -8.91002893e-01
-1.02848813e-01 2.83203512e-01 1.83263570e-01 -3.43974791e-02
-6.12617552e-01 9.37408447e-01 -2.22574979e-01 7.01939523e-01
-1.44583687e-01 8.73351917e-02 8.07262421e-01 -2.59268619e-02
3.57261360e-01 4.52361405e-01 -7.99450099e-01 -9.12653744e-01
-7.02329636e-01 -2.00976908e-01 9.31163609e-01 1.62752718e-01
2.77985334e-01 3.83116692e-01 7.60332048e-01 4.14551526e-01
6.71223044e-01 5.44283986e-01 9.89276052e-01 -8.55837703e-01
1.09440170e-01 4.72405672e-01 -2.86604851e-01 -1.28163147e+00
-4.85603549e-02 2.24133115e-02 -4.88080651e-01 1.50422931e-01
2.69556399e-02 -4.22083229e-01 -5.75061738e-01 9.62756097e-01
3.26259099e-02 3.05390060e-01 -2.33343408e-01 -2.95880020e-01
6.25278771e-01 4.75996733e-01 -2.05308929e-01 -2.56539166e-01
1.36092389e+00 -7.66832113e-01 -7.82151937e-01 -4.55871046e-01
6.17669761e-01 -7.03607798e-01 1.06358027e+00 3.71618360e-01
-7.73720801e-01 -4.45474058e-01 -1.46793449e+00 3.45611125e-01
-7.94003367e-01 -1.03509240e-01 7.11344421e-01 1.81309736e+00
-8.63820255e-01 3.94092739e-01 -1.52603352e+00 -2.62428045e-01
8.81893516e-01 7.28346467e-01 3.03534001e-01 5.20993412e-01
-1.07625806e+00 7.71288693e-01 -8.88367221e-02 -1.85048327e-01
-1.06978059e+00 -2.80949026e-01 -6.80762589e-01 -2.80322433e-01
4.24712062e-01 -1.33058563e-01 1.42411411e+00 -7.97050416e-01
-1.57655966e+00 9.23626423e-01 -6.75366493e-03 -5.47682285e-01
-8.40962008e-02 -2.38628015e-01 -8.25433969e-01 -4.14372832e-01
1.21600688e-01 -1.71453193e-01 1.48597264e+00 -9.05095160e-01
-6.15802526e-01 -4.93493527e-01 2.86215693e-01 -4.46197689e-01
-7.44101346e-01 1.90736398e-01 -6.50703609e-02 -2.24225253e-01
-8.24489594e-01 -1.36164558e+00 2.43159920e-01 -1.17057085e+00
-5.08591831e-01 -1.50411218e-01 1.80070317e+00 -8.40274394e-01
1.69704998e+00 -2.27478004e+00 -2.45377645e-01 -1.46839753e-01
5.72273076e-01 1.00907087e+00 5.76596856e-01 1.79344594e-01
5.02720289e-02 6.06414199e-01 7.26137683e-02 1.46537289e-01
-3.33972901e-01 5.51570915e-02 -5.26527464e-01 6.44923270e-01
-4.32201952e-01 1.10993040e+00 -6.75433278e-01 -3.70884202e-02
1.93279788e-01 6.99879944e-01 -1.58227012e-01 -2.78273821e-01
8.02135654e-03 4.07318771e-01 -6.74015403e-01 1.17289567e+00
5.68949759e-01 -4.40079302e-01 5.36707759e-01 3.00572366e-01
-7.20500946e-03 7.21445680e-01 -1.24585196e-01 6.11141026e-01
-5.06158471e-01 1.05408943e+00 1.21438004e-01 -9.66043711e-01
1.55232951e-01 -1.51221445e-02 6.40956998e-01 -3.65140736e-01
5.26843131e-01 3.96692604e-01 3.31065804e-01 -8.55254650e-01
3.71740997e-01 5.76409698e-01 -2.36354038e-01 5.19413233e-01
-6.80945575e-01 4.63795900e-01 -5.19071519e-01 -1.46055043e-01
1.43039799e+00 -9.46452692e-02 5.72888434e-01 1.19002044e-01
1.00198197e+00 -1.46004379e-01 5.98862171e-02 5.79793751e-01
-8.21095169e-01 -7.47132659e-01 9.60033417e-01 -3.49194944e-01
-4.07941908e-01 -7.34108210e-01 7.25380555e-02 1.42312527e+00
-2.13200180e-03 -4.51364070e-01 -1.13022530e+00 -1.47834086e+00
-6.20018616e-02 3.01468223e-01 -6.28489196e-01 -5.65222561e-01
-7.89021373e-01 -7.77726054e-01 1.17832386e+00 2.24307179e-01
6.47638083e-01 -1.11144245e+00 -1.03942311e+00 -2.52178103e-01
-6.66207895e-02 -9.30917799e-01 -9.94887799e-02 1.87670868e-02
-8.60073447e-01 -1.52131784e+00 -3.70239645e-01 -7.54491627e-01
2.94042528e-01 4.28486496e-01 7.17461467e-01 4.40161914e-01
-1.06180675e-01 4.45815116e-01 -1.83158249e-01 -4.20091063e-01
-9.45854664e-01 5.61551929e-01 4.17268008e-01 -4.32074457e-01
7.83721924e-01 -2.39606753e-01 -1.21299652e-02 2.39600882e-01
-4.80494529e-01 -9.45214450e-01 4.69661951e-01 5.43173730e-01
-1.56078354e-01 4.86702174e-01 6.17881954e-01 -9.21001017e-01
1.01578677e+00 -8.69314253e-01 -4.80599403e-01 -1.32297158e-01
-9.30462897e-01 -5.70066512e-01 5.12515008e-01 -7.68077374e-01
-7.75734425e-01 -2.42958501e-01 -5.08081734e-01 -5.02069555e-02
-1.86870173e-02 1.38941407e-01 -2.77187586e-01 -5.49322605e-01
6.92219257e-01 2.42837414e-01 4.13637459e-01 -1.43775657e-01
-8.06780383e-02 1.29675329e+00 1.50823534e-01 9.22884345e-02
5.27267873e-01 6.30239904e-01 -4.27599877e-01 -1.36080766e+00
-3.76921356e-01 -1.17617868e-01 -1.39070883e-01 -1.19934082e-01
8.68569672e-01 -3.93457919e-01 -1.07881021e+00 9.73148346e-01
-1.09774435e+00 -3.19356412e-01 4.74747866e-01 -1.37722895e-01
-7.99086690e-02 8.36242020e-01 -7.68477917e-01 -7.44751453e-01
-2.35686138e-01 -1.56242800e+00 7.46001184e-01 3.38717736e-02
-5.98615289e-01 -1.21444762e+00 1.32625833e-01 8.73441756e-01
5.73776305e-01 9.01877359e-02 7.47811496e-01 -7.07265079e-01
-1.16411388e-01 -4.05885041e-01 1.30739689e-01 5.71437061e-01
6.61571860e-01 4.58424062e-01 -1.06032979e+00 -4.31807637e-01
7.95358896e-01 2.17184454e-01 4.17989969e-01 5.62375665e-01
1.36201346e+00 -6.64335847e-01 -1.13989675e+00 5.71570754e-01
7.53622115e-01 9.82170701e-01 8.37484598e-01 6.02143347e-01
1.07852566e+00 3.02237213e-01 4.26744550e-01 2.33615786e-02
1.98158368e-01 4.69978899e-01 7.72157073e-01 4.65080231e-01
2.53590405e-01 1.06171213e-01 9.75584805e-01 7.26707399e-01
-1.80458292e-01 4.00848407e-03 -9.50810134e-01 1.17631562e-01
-1.32913768e+00 -9.63122308e-01 -2.78374970e-01 1.95802939e+00
3.99619550e-01 5.72414756e-01 3.79359573e-01 1.96349636e-01
8.30693185e-01 4.32105988e-01 -7.52216935e-01 -6.26352906e-01
5.52105963e-01 6.96012676e-02 6.40610099e-01 6.07879877e-01
-1.34250927e+00 9.32916045e-01 7.06329346e+00 6.85122311e-01
-1.51983798e+00 8.40948999e-01 4.92943972e-01 1.84862658e-01
2.58552104e-01 -1.52479082e-01 -8.85713160e-01 1.06784213e+00
1.37472332e+00 2.52723843e-01 5.82085788e-01 1.40959048e+00
-1.17114171e-01 4.64508384e-02 -5.21331191e-01 9.56676245e-01
3.05776387e-01 -1.29004681e+00 -6.71110213e-01 1.04461920e+00
6.39730692e-01 2.44894981e-01 8.26051295e-01 5.53619027e-01
-2.43439421e-01 -9.53176320e-01 8.62822831e-02 -2.12523490e-02
7.54233539e-01 -6.93699658e-01 8.30309808e-01 2.62074500e-01
-8.17696512e-01 -6.80867076e-01 6.07271194e-02 -6.00385725e-01
-1.81587905e-01 2.62584656e-01 -9.44329381e-01 -3.01080376e-01
9.16461468e-01 9.24699962e-01 -8.39457989e-01 2.03778729e-01
-8.39811862e-02 9.53649700e-01 2.72781461e-01 -4.12158132e-01
1.43417686e-01 2.72400290e-01 5.82366765e-01 7.92582870e-01
7.99838752e-02 -8.16086829e-01 -2.76256859e-01 4.30813760e-01
8.67013335e-02 -5.39358556e-01 -1.24938405e+00 -8.19832623e-01
3.97101372e-01 1.18291807e+00 -7.28565872e-01 -2.56579936e-01
-5.81200540e-01 9.06116068e-01 -2.98565835e-01 4.00771461e-02
-1.28825283e+00 -5.43902695e-01 1.02261949e+00 5.82524180e-01
-1.53798629e-02 -4.43823487e-01 -4.52904582e-01 -9.28351879e-01
-8.14212635e-02 -1.41370595e+00 -1.09204397e-01 -1.74669802e-01
-9.27718461e-01 3.85142207e-01 -1.37290835e-01 -9.24449265e-01
-6.23413026e-01 -9.29082930e-01 -7.54407883e-01 5.28755747e-02
-8.55213404e-01 -8.77593517e-01 6.88096602e-03 2.61678159e-01
5.14282703e-01 -8.25611770e-01 4.75049466e-01 4.37155277e-01
-8.10048997e-01 5.57436049e-01 1.40044734e-01 -5.88529333e-02
1.87348485e-01 -7.57075608e-01 6.42477572e-01 6.54308379e-01
-3.17495972e-01 1.19704378e+00 2.82394409e-01 -1.17456031e+00
-1.50038838e+00 -7.28931904e-01 5.65522850e-01 -1.21035802e+00
9.70803976e-01 -4.43128824e-01 -7.48176575e-01 9.12183702e-01
2.66900837e-01 -5.84259808e-01 8.75871897e-01 -3.69199459e-03
-1.26949742e-01 8.16288218e-02 -1.04878068e+00 5.79974353e-01
5.71763933e-01 -6.79524720e-01 -3.06946307e-01 3.77463788e-01
6.80662334e-01 -2.73759931e-01 -2.97417700e-01 2.37018585e-01
8.73064458e-01 -1.31314683e+00 9.93788362e-01 -4.38371867e-01
1.28856301e-01 2.16036156e-01 2.14728266e-02 -4.61505175e-01
7.96918496e-02 -1.15251839e+00 -1.11666167e+00 8.21665227e-01
1.28421366e-01 -1.05494702e+00 9.87918258e-01 -1.14719346e-01
-9.56016108e-02 -1.10017240e+00 -8.42289984e-01 -8.92144382e-01
5.71432374e-02 -6.37993634e-01 6.24475896e-01 1.12146664e+00
8.17878991e-02 4.40171450e-01 -4.73508090e-01 -1.52814195e-01
2.86402971e-01 -7.48724580e-01 7.29158819e-01 -1.06350875e+00
-2.84740478e-01 -7.54356980e-01 -3.00976098e-01 -9.05817091e-01
5.62543035e-01 -5.01577020e-01 -4.77697045e-01 -7.89203048e-01
1.16458811e-01 -8.15561265e-02 3.95915136e-02 4.43539500e-01
2.05456778e-01 2.63395190e-01 -2.85420865e-01 3.68792981e-01
-4.78363216e-01 -2.27009341e-01 5.30455887e-01 -2.41931275e-01
-4.64037120e-01 7.45453298e-01 -1.13739681e+00 1.36616802e+00
1.04743397e+00 -3.24723601e-01 -5.88403404e-01 2.28096955e-02
7.71910787e-01 -3.96595329e-01 3.51521879e-01 -9.10195231e-01
-3.08437645e-01 -3.98227349e-02 1.98197484e-01 -3.83046120e-01
1.71112105e-01 -7.81389296e-01 -1.97185040e-01 1.17000067e+00
4.03919727e-01 3.09443504e-01 2.26376966e-01 6.35120869e-01
3.67064863e-01 -2.70234525e-01 8.00828338e-01 6.74228417e-03
-5.46772480e-01 -8.80347267e-02 -1.13299179e+00 -2.30144784e-01
1.54409492e+00 -5.12743950e-01 -5.51731050e-01 -3.48435611e-01
-3.79987270e-01 -4.78080362e-01 6.26745164e-01 9.10790741e-01
5.11231601e-01 -9.41712439e-01 3.09074074e-01 3.99894953e-01
-3.93343091e-01 -1.04193008e+00 5.02796061e-02 1.10861194e+00
-5.77283680e-01 7.62064338e-01 -2.18950793e-01 -4.51721758e-01
-1.67396200e+00 9.81121480e-01 3.80990565e-01 -3.98776501e-01
-7.61311455e-03 5.27078271e-01 -2.98189878e-01 -3.26699764e-01
1.85260460e-01 -1.71240345e-01 -5.03678918e-01 -1.96237773e-01
6.41993701e-01 1.07027912e+00 6.04842491e-02 -8.99861455e-01
-1.04406059e+00 4.05947030e-01 -2.18565613e-01 4.32456464e-01
7.77088523e-01 -1.29042864e-01 -6.08696342e-01 3.71428102e-01
1.28744817e+00 7.17427611e-01 -6.88363194e-01 8.28270793e-01
-6.45617172e-02 -3.96699160e-01 -2.46207505e-01 -6.82703912e-01
-8.92056763e-01 7.99786627e-01 8.18508148e-01 1.18965495e+00
7.89145827e-01 1.00965835e-01 1.27374423e+00 2.49290928e-01
3.14728528e-01 -7.61592686e-01 5.52883148e-01 6.85830235e-01
1.74689084e-01 -1.23184812e+00 1.39384447e-02 -2.94870347e-01
-3.15805197e-01 8.48398864e-01 6.81253672e-01 1.50042459e-01
1.08272803e+00 3.27729523e-01 -2.63800293e-01 -3.81302118e-01
-9.23232511e-02 5.45117438e-01 -2.35547498e-02 1.29087484e+00
6.09103501e-01 1.27802119e-01 -2.55937457e-01 6.28607571e-01
-5.20081371e-02 -1.18365817e-01 6.29038274e-01 1.18012094e+00
-4.24746722e-01 -1.25901079e+00 -4.88032550e-01 8.08720410e-01
-1.35804248e+00 4.38504480e-02 -1.01217198e+00 6.83769166e-01
6.70961559e-01 1.26748812e+00 -3.28571200e-01 -1.14931369e+00
-3.20762396e-01 -9.02549103e-02 1.42115042e-01 -5.30831397e-01
-4.97947305e-01 -2.92563260e-01 -2.29490381e-02 -3.63980323e-01
-1.08050130e-01 -4.51972395e-01 -9.62686777e-01 -6.32467210e-01
-4.04814065e-01 -2.38727033e-01 9.48717892e-01 1.15112817e+00
7.14280307e-01 6.52734518e-01 4.94632214e-01 -7.68943727e-01
-5.24697781e-01 -9.04990673e-01 -1.31488934e-01 -4.55624402e-01
7.56700456e-01 -8.44939113e-01 -5.68102241e-01 -8.04505348e-02] | [14.427237510681152, 9.683520317077637] |
44cad76e-f45d-4e67-80ec-dba21a804c40 | explaining-clinical-decision-support-systems | 2010.05759 | null | https://arxiv.org/abs/2010.05759v3 | https://arxiv.org/pdf/2010.05759v3.pdf | Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization | Clinical decision support using deep neural networks has become a topic of steadily growing interest. While recent work has repeatedly demonstrated that deep learning offers major advantages for medical image classification over traditional methods, clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend. In recent years, this has been addressed by a variety of approaches that have successfully contributed to providing deeper insight. Most notably, additive feature attribution methods are able to propagate decisions back into the input space by creating a saliency map which allows the practitioner to "see what the network sees." However, the quality of the generated maps can become poor and the images noisy if only limited data is available - a typical scenario in clinical contexts. We propose a novel decision explanation scheme based on CycleGAN activation maximization which generates high-quality visualizations of classifier decisions even in smaller data sets. We conducted a user study in which we evaluated our method on the LIDC dataset for lung lesion malignancy classification, the BreastMNIST dataset for ultrasound image breast cancer detection, as well as two subsets of the CIFAR-10 dataset for RBG image object recognition. Within this user study, our method clearly outperformed existing approaches on the medical imaging datasets and ranked second in the natural image setting. With our approach we make a significant contribution towards a better understanding of clinical decision support systems based on deep neural networks and thus aim to foster overall clinical acceptance. | ['Horst-Michael Groß', 'Michael Sühling', 'Alexander Mühlberg', 'Stephen Ahmad', 'Oliver Taubmann', 'Alexander Katzmann'] | 2020-10-09 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 6.40066504e-01 5.84614277e-01 -1.94089159e-01 -5.16012609e-01
-5.36940455e-01 -2.74123460e-01 4.83846277e-01 3.64332736e-01
-2.93411136e-01 3.57052565e-01 2.55269349e-01 -8.79881144e-01
-2.81331271e-01 -5.15409887e-01 -2.66171694e-01 -7.40314722e-01
-3.00408676e-02 3.85501564e-01 -1.80929840e-01 -1.23749197e-01
2.30546579e-01 4.70127881e-01 -1.34896088e+00 6.41707003e-01
8.13055873e-01 9.98816073e-01 1.67203143e-01 6.92788303e-01
1.26402378e-01 1.20073497e+00 -5.08159459e-01 -3.97742271e-01
-6.51860014e-02 -5.37160456e-01 -9.48144972e-01 3.48679163e-02
3.03885162e-01 -3.54322225e-01 -9.22177359e-02 8.97020340e-01
6.87892556e-01 -2.29365021e-01 6.55665576e-01 -1.08615053e+00
-6.65287375e-01 5.48112929e-01 -4.73711193e-01 3.97203326e-01
-2.11836770e-02 3.90941828e-01 7.80962050e-01 -5.73934495e-01
6.24080241e-01 1.03575516e+00 5.68747699e-01 6.88445449e-01
-1.31764579e+00 -6.34327948e-01 -9.00678486e-02 3.53687882e-01
-9.08344209e-01 -2.87123740e-01 6.00326300e-01 -5.09000957e-01
6.73597991e-01 4.80391145e-01 7.80037880e-01 1.17698491e+00
4.48127747e-01 8.28680992e-01 1.22047091e+00 -4.64445561e-01
3.01639706e-01 4.76466984e-01 2.31852233e-01 6.93080127e-01
3.43839467e-01 2.43452445e-01 -3.68384153e-01 -3.42303514e-02
7.45727420e-01 2.84204870e-01 -5.30526876e-01 -3.59662473e-01
-1.38618851e+00 1.07851028e+00 9.46112871e-01 4.92099404e-01
-5.30314744e-01 6.21958710e-02 2.47885540e-01 2.16578141e-01
5.35401464e-01 6.68227196e-01 2.47046687e-02 -4.85999063e-02
-9.78279650e-01 1.44185126e-01 7.36513913e-01 1.58924431e-01
1.56105876e-01 -1.41478911e-01 -4.56562191e-01 5.55292189e-01
1.87676623e-01 1.64908946e-01 7.52768099e-01 -9.26189184e-01
2.99482383e-02 6.00329340e-01 -6.26204088e-02 -1.27796793e+00
-6.99333429e-01 -9.22854304e-01 -1.02982962e+00 6.87077165e-01
3.97709817e-01 1.87701229e-02 -1.11190820e+00 1.43834257e+00
-4.11761776e-02 -1.46121010e-01 1.97960243e-01 1.04116595e+00
1.10192299e+00 8.81063640e-02 1.39465958e-01 1.82489365e-01
1.44388509e+00 -8.00338149e-01 -8.01563978e-01 -2.19428807e-01
8.81701350e-01 -3.07002902e-01 1.09352362e+00 5.14895022e-01
-8.28576207e-01 -4.05528396e-01 -1.30077064e+00 1.24897316e-01
-1.76447257e-01 1.74079835e-01 7.34377801e-01 7.13014185e-01
-1.20396209e+00 7.75269568e-01 -1.07482386e+00 -4.83720392e-01
1.05600893e+00 3.33357483e-01 -2.10477456e-01 -1.76051512e-01
-9.90505874e-01 1.25536871e+00 3.30460489e-01 2.04057902e-01
-9.04490411e-01 -8.43282223e-01 -6.99756563e-01 1.98176205e-01
2.71073103e-01 -7.82029092e-01 1.41659105e+00 -1.22430480e+00
-1.12044477e+00 1.03755653e+00 1.31264746e-01 -6.90155029e-01
7.72774696e-01 1.51669383e-01 -3.16632897e-01 2.30768263e-01
2.37215799e-03 1.03966033e+00 8.15428555e-01 -9.28442359e-01
-5.65440178e-01 -4.11895633e-01 5.74173257e-02 2.66281664e-01
-3.57160360e-01 -2.74314582e-01 -2.06004977e-02 -6.09940469e-01
3.98087725e-02 -8.21978271e-01 -5.51751256e-01 2.30965495e-01
-6.42880559e-01 -8.68636067e-04 8.62968862e-01 -5.81494033e-01
1.03692639e+00 -2.14677286e+00 -1.06484115e-01 1.42158210e-01
8.56239974e-01 2.32578114e-01 3.48238379e-01 -7.81243816e-02
-4.25640672e-01 3.53273660e-01 -2.73923814e-01 -1.31009564e-01
-3.01855743e-01 2.84955517e-04 -5.02427183e-02 4.52341169e-01
4.19854671e-01 1.06554115e+00 -8.75006080e-01 -4.04349029e-01
3.37841600e-01 5.71328700e-01 -4.21287209e-01 6.14824630e-02
5.99391349e-02 5.32718897e-01 -1.93466917e-01 5.10061383e-01
3.71820211e-01 -9.94480610e-01 1.63966551e-01 -1.52819440e-01
1.77982911e-01 2.41776347e-01 -7.33412981e-01 1.56115592e+00
-1.66321665e-01 9.07573640e-01 -2.79935986e-01 -1.04084647e+00
4.95527089e-01 3.57489854e-01 3.62889528e-01 -5.98156750e-01
1.71082452e-01 -9.46541224e-03 6.06840968e-01 -6.08557940e-01
1.11563303e-01 -4.18928325e-01 2.65245199e-01 5.46463013e-01
-2.08294228e-01 1.51314717e-02 -2.04933867e-01 3.62960637e-01
1.30309892e+00 -2.55334586e-01 5.12557745e-01 -2.00522467e-01
-2.46029883e-03 3.82556558e-01 1.57617390e-01 8.02536666e-01
-3.19660872e-01 7.88019598e-01 6.63876235e-01 -4.96895939e-01
-5.61870158e-01 -8.48875880e-01 -2.88684905e-01 7.28627324e-01
-1.10020101e-01 -8.05239975e-02 -7.02092707e-01 -9.29273605e-01
-5.15144430e-02 9.33887422e-01 -1.19081569e+00 -2.69302487e-01
-1.18179128e-01 -7.51563728e-01 3.28581393e-01 6.11553848e-01
4.84115332e-01 -1.37797225e+00 -1.29665244e+00 6.67721480e-02
-7.68110016e-03 -8.12579930e-01 3.73733579e-03 4.10228163e-01
-8.68468106e-01 -1.25758719e+00 -9.95957911e-01 -6.24422967e-01
9.01111126e-01 1.97225511e-01 1.09850776e+00 2.87916213e-01
-8.06422472e-01 2.72465289e-01 -1.99929491e-01 -7.40801573e-01
-7.22788930e-01 -1.92022622e-02 -3.38280976e-01 -1.69975922e-01
3.95296395e-01 -1.35383502e-01 -9.22243714e-01 1.32312551e-01
-9.34751332e-01 6.93236828e-01 7.71761775e-01 1.01218450e+00
2.93223232e-01 -2.20574528e-01 5.69654047e-01 -1.17417324e+00
8.07862997e-01 -5.60634017e-01 -9.45700184e-02 2.47407728e-03
-8.90308142e-01 -3.45312171e-02 1.70362413e-01 -2.29588136e-01
-9.97696638e-01 -2.11983562e-01 -1.92927718e-02 -3.55473548e-01
-2.74799198e-01 7.48730242e-01 3.99443030e-01 -1.79117471e-02
1.05621326e+00 -1.05791681e-01 4.16250974e-01 -1.04765683e-01
7.90162683e-02 6.14727080e-01 5.02247751e-01 1.51693866e-01
2.82231867e-01 5.53361654e-01 -4.58023213e-02 -5.37557960e-01
-7.86911428e-01 -6.51628673e-02 -3.49049360e-01 -2.77938724e-01
9.74274337e-01 -6.23906612e-01 -6.65124893e-01 9.78272036e-02
-8.20490897e-01 -2.97725350e-01 -3.83417964e-01 3.87277931e-01
-2.69468218e-01 8.50185528e-02 -4.43239182e-01 -4.84328330e-01
-3.58684480e-01 -1.42496812e+00 6.22116387e-01 2.53713489e-01
-6.01687312e-01 -1.17535961e+00 -2.59958446e-01 1.78837568e-01
7.30624676e-01 5.23777783e-01 1.13810873e+00 -9.15601134e-01
-2.59649187e-01 -1.75158098e-01 -4.27902311e-01 2.48763770e-01
4.16613847e-01 -2.14030564e-01 -1.25442970e+00 -2.58949071e-01
2.83685029e-02 -2.70518690e-01 9.64804888e-01 5.82715392e-01
1.48275626e+00 -7.61945099e-02 -6.32239759e-01 4.44195896e-01
1.03740323e+00 2.79421896e-01 3.82919580e-01 2.94570535e-01
5.94915628e-01 7.73197949e-01 4.33034390e-01 1.04367495e-01
3.10023516e-01 4.60764736e-01 7.36043334e-01 -9.13459659e-01
-1.77738190e-01 6.27610385e-02 -1.60480008e-01 1.39907390e-01
4.75387909e-02 -1.40704140e-01 -1.23443985e+00 4.72752780e-01
-1.79277980e+00 -7.16243267e-01 8.82220492e-02 1.98146832e+00
4.88413662e-01 2.21999079e-01 -1.67026535e-01 2.70334631e-01
4.62605566e-01 -1.95247412e-01 -8.47697496e-01 -2.27240831e-01
-2.21905038e-02 8.73399228e-02 2.29342937e-01 5.64019196e-02
-1.09942162e+00 4.18791234e-01 6.32581186e+00 4.71655488e-01
-1.59093904e+00 1.86963156e-01 1.36777866e+00 -2.39593610e-01
-2.48172730e-01 -3.95919919e-01 -2.41004258e-01 3.66113156e-01
8.51282001e-01 3.86340767e-02 -2.30079368e-01 8.13228071e-01
2.92537093e-01 -3.05287093e-01 -1.48524058e+00 1.12775600e+00
-1.10838888e-02 -1.78526413e+00 -1.51689559e-01 2.50858039e-01
4.09487367e-01 -2.26872414e-03 5.34434617e-01 6.31273091e-02
2.88745672e-01 -1.62138557e+00 3.26337069e-01 3.20965439e-01
1.00514770e+00 -5.61172009e-01 9.35355008e-01 2.76506394e-01
-2.39616126e-01 -1.34008408e-01 7.27297142e-02 3.74803133e-02
-2.65461922e-01 4.57061142e-01 -1.58892691e+00 1.62010565e-01
8.06231558e-01 7.13780463e-01 -7.81108379e-01 9.94331658e-01
-1.57338634e-01 9.08009350e-01 -3.81365009e-02 5.42507507e-02
3.36312920e-01 4.14697617e-01 3.32423955e-01 1.12801015e+00
1.71366394e-01 1.72526032e-01 -5.00704125e-02 9.10098374e-01
5.80913275e-02 1.02724992e-01 -6.28632247e-01 -2.13132516e-01
-9.65766609e-02 1.47709191e+00 -1.10534155e+00 -4.33152050e-01
-1.12621225e-01 8.81763220e-01 1.01817213e-01 2.59247929e-01
-5.95908880e-01 -1.32367924e-01 3.61504972e-01 3.27207446e-01
1.60934061e-01 4.56520617e-01 -6.80177987e-01 -8.63710165e-01
-2.94953287e-01 -1.09918129e+00 4.92757976e-01 -8.66579890e-01
-7.76713014e-01 7.70841002e-01 -2.04524368e-01 -1.17813003e+00
-6.04780495e-01 -6.47770822e-01 -5.82835615e-01 7.81917393e-01
-1.48396409e+00 -9.74655986e-01 -5.89695215e-01 2.62177080e-01
5.14388859e-01 -1.44889340e-01 1.02090836e+00 7.55418092e-02
-3.97072464e-01 4.98873264e-01 -7.53831267e-02 -3.54493670e-02
6.69475615e-01 -1.32359791e+00 6.73400983e-02 5.37407935e-01
7.07820207e-02 4.53165084e-01 7.60838211e-01 -4.34970230e-01
-8.37958336e-01 -8.53419721e-01 4.11732346e-01 -3.44441950e-01
2.38004893e-01 -1.21270539e-02 -1.03543639e+00 5.44580102e-01
3.24084342e-01 5.46147674e-02 9.57917750e-01 -4.12540026e-02
1.04004048e-01 1.53962195e-01 -1.37267387e+00 5.61757743e-01
6.62986040e-01 -1.57116026e-01 -3.74759376e-01 2.85061777e-01
4.65840489e-01 -4.12350059e-01 -6.32241547e-01 5.57253599e-01
5.44549048e-01 -1.10841501e+00 6.89708531e-01 -8.22791338e-01
8.41724217e-01 -5.48463836e-02 1.18943252e-01 -1.54200232e+00
-4.50239569e-01 -3.23870122e-01 1.04684480e-01 6.67179346e-01
5.98270059e-01 -5.60180545e-01 8.90993536e-01 6.54104531e-01
-2.51467917e-02 -1.18678153e+00 -6.23844564e-01 3.96914482e-02
-8.90135393e-02 -5.40853560e-01 3.05080742e-01 1.05844092e+00
2.34081149e-02 5.48692867e-02 6.04035519e-03 -6.63958564e-02
4.61211473e-01 8.31754878e-02 2.94445813e-01 -1.18743634e+00
-3.47497076e-01 -7.81120121e-01 -5.61024547e-01 -5.38759649e-01
-2.61078447e-01 -1.08021545e+00 -1.24480523e-01 -1.71780252e+00
4.27148074e-01 -5.03959298e-01 -7.05076575e-01 6.98845327e-01
-2.78691083e-01 3.82454723e-01 1.39677942e-01 2.24010527e-01
-2.66982466e-01 -1.85482632e-02 1.36698389e+00 -2.90815771e-01
3.26357633e-02 1.92608148e-01 -1.33464718e+00 8.17265213e-01
6.50132060e-01 -4.33616012e-01 -3.92718256e-01 -2.18285009e-01
1.05422311e-01 7.34513327e-02 6.11824334e-01 -9.08001721e-01
1.40158325e-01 1.41430557e-01 8.65244329e-01 -5.57088852e-01
1.69943407e-01 -6.67952001e-01 1.37091978e-02 8.67014349e-01
-7.07031429e-01 -1.00328371e-01 2.63422757e-01 4.69916344e-01
-9.54465494e-02 -6.86457604e-02 7.49984086e-01 -1.54391274e-01
-6.77093744e-01 -2.66555697e-02 -4.23380435e-01 -1.85269713e-01
9.96376991e-01 -2.52868652e-01 -2.40969613e-01 -7.51053095e-01
-9.18537915e-01 1.71337962e-01 3.00212324e-01 3.64403963e-01
8.57761204e-01 -1.08119750e+00 -8.53130877e-01 2.05050185e-01
1.05560541e-01 5.29693961e-02 2.94510245e-01 1.16221869e+00
-5.21722198e-01 5.55818856e-01 -2.01224029e-01 -1.02320111e+00
-1.35427773e+00 2.80192494e-01 5.31644523e-01 -4.76005554e-01
-7.57248223e-01 8.97133589e-01 5.05367696e-01 -1.16589792e-01
4.39499110e-01 -5.45742691e-01 -4.68503714e-01 1.29648149e-01
7.05027759e-01 3.50207984e-02 1.90854430e-01 -2.31910646e-01
-3.60405177e-01 -2.69095227e-02 -5.34681439e-01 3.28299738e-02
1.37441790e+00 1.76922157e-01 2.47711867e-01 3.43982458e-01
9.27746713e-01 -4.86555755e-01 -1.02518845e+00 -1.61649063e-01
-1.70119144e-02 -2.93155491e-01 3.78570765e-01 -1.41882014e+00
-1.05096769e+00 1.24097908e+00 1.20351458e+00 3.46891522e-01
1.19116092e+00 4.89795394e-02 1.52478576e-01 3.09083432e-01
-9.58646610e-02 -5.92261851e-01 2.72251666e-01 -9.73624662e-02
9.24356759e-01 -1.84154725e+00 -5.73403873e-02 -2.25749075e-01
-9.70441937e-01 1.23520648e+00 4.30533558e-01 1.89445719e-01
5.36921859e-01 2.05813184e-01 4.02299076e-01 -5.37845552e-01
-7.45002508e-01 2.98574958e-02 3.29990923e-01 4.90223348e-01
5.95363140e-01 1.88830212e-01 1.61801115e-01 6.11617684e-01
-1.69913411e-01 2.87656069e-01 5.69999099e-01 9.57893133e-01
-2.40719631e-01 -6.43473744e-01 -3.16445529e-01 8.46529722e-01
-8.32612336e-01 -1.38007358e-01 -2.04328239e-01 7.99827158e-01
-8.04851055e-02 7.68268943e-01 1.28393129e-01 -2.63882637e-01
1.10150659e-02 -7.47988895e-02 2.42615744e-01 -7.12465942e-01
-6.72946751e-01 -6.07939214e-02 7.23704621e-02 -5.15067816e-01
-2.25699544e-01 -5.08755803e-01 -1.25921965e+00 5.01224175e-02
-1.92197382e-01 -7.37953186e-03 9.25064504e-01 8.95227075e-01
5.02926528e-01 1.10257256e+00 3.04199964e-01 -7.10806251e-01
-5.35183191e-01 -1.04193163e+00 -2.98761338e-01 3.86733174e-01
4.47702855e-01 -5.15648961e-01 -1.25360593e-01 -3.24122943e-02] | [15.153916358947754, -2.4432404041290283] |
f8726e0c-de4a-4c23-9212-65f87e4d8876 | open-vocabulary-panoptic-segmentation-with | 2208.08984 | null | https://arxiv.org/abs/2208.08984v2 | https://arxiv.org/pdf/2208.08984v2.pdf | Open-Vocabulary Universal Image Segmentation with MaskCLIP | In this paper, we tackle an emerging computer vision task, open-vocabulary universal image segmentation, that aims to perform semantic/instance/panoptic segmentation (background semantic labeling + foreground instance segmentation) for arbitrary categories of text-based descriptions in inference time. We first build a baseline method by directly adopting pre-trained CLIP models without finetuning or distillation. We then develop MaskCLIP, a Transformer-based approach with a MaskCLIP Visual Encoder, which is an encoder-only module that seamlessly integrates mask tokens with a pre-trained ViT CLIP model for semantic/instance segmentation and class prediction. MaskCLIP learns to efficiently and effectively utilize pre-trained partial/dense CLIP features within the MaskCLIP Visual Encoder that avoids the time-consuming student-teacher training process. MaskCLIP outperforms previous methods for semantic/instance/panoptic segmentation on ADE20K and PASCAL datasets. We show qualitative illustrations for MaskCLIP with online custom categories. Project website: https://maskclip.github.io. | ['Zhuowen Tu', 'Jieke Wang', 'Zheng Ding'] | 2022-08-18 | null | null | null | null | ['panoptic-segmentation', 'open-vocabulary-panoptic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 0.6849126 0.38103032 -0.37401512 -0.57474494 -1.1974416 -0.9425775
0.68249744 -0.16593638 -0.36226752 0.31229874 -0.08621425 -0.4184441
0.55603737 -0.43082455 -1.074418 -0.59250873 0.58757156 0.8169365
0.4551439 0.35570163 0.03935736 0.03572635 -1.6358391 0.65794384
0.7317679 1.2381592 0.5144206 1.0509583 -0.47858182 1.0673678
-0.5125517 -0.62400126 0.23918742 -0.2563307 -1.1788415 0.40173325
0.97580564 -0.18611403 -0.1843136 1.1189494 0.20004587 -0.146896
0.6026663 -1.3680437 -0.5710269 0.7224347 -0.55996937 0.20010146
-0.05898473 0.4223446 1.072501 -0.80532485 0.8784287 1.230081
0.59686047 0.638446 -1.3084356 -0.6816704 0.25981674 0.30528113
-1.5788293 -0.33116773 0.5098248 -0.7343822 1.0080397 0.28982309
0.7137399 1.018044 -0.07934266 1.364089 0.974717 -0.29657903
0.04654308 0.3632563 0.31097057 0.7250262 -0.3118542 -0.3988405
-0.11425311 0.37395263 0.70556206 -0.03812379 -0.04964883 -0.2658241
-1.2201886 0.7173692 0.19565423 0.06560261 0.05693668 0.5192803
0.6563037 0.0606487 0.6118341 0.11217836 -0.85290784 -0.03952424
-1.5137395 0.20311773 0.81849486 1.4151376 1.1287293 -0.0621075
-0.45650658 0.90906835 0.2638313 0.4980976 0.3869398 -1.3398618
0.22227219 0.48877224 -0.32730624 -0.25151238 0.03497051 -0.2667676
-0.4361299 -0.11990065 0.41469646 0.02611117 -1.6049246 1.3021337
0.5037448 0.5726566 0.02045324 0.6706506 1.117752 0.84835464
0.33818534 0.22688219 1.4779483 -1.697924 -0.6118686 -0.4349395
0.48266023 -0.68530047 1.2334815 0.4037731 -0.9681712 -0.62418085
-0.82415855 -0.6395827 -0.68504417 0.21041638 0.42997906 0.36260456
-1.2096436 0.2527637 -0.84686136 -0.17480592 0.7824307 0.1980666
-0.12724885 -0.14672449 -0.7320006 0.6354575 0.6931224 -0.25513572
-1.5114074 -1.1538136 -1.1687942 -0.07058553 0.7169575 -0.4313032
1.7703105 -1.5304677 -1.4021766 1.4041587 -0.23055327 -0.5801838
0.48398322 -0.20121755 0.03140102 0.40601298 0.4260738 1.5016208
1.1247528 -1.2440946 -0.74778366 -0.0579719 0.15736224 0.08656989
0.24265516 0.0722444 -1.1777289 -0.61865526 -0.36835694 -0.71219134
-0.36657742 0.07197773 -0.7240092 -0.4075107 1.0885645 -0.9229131
0.7896587 -2.1000092 0.14646977 -0.23683289 0.28029782 0.33544123
-0.17407244 -0.04188148 0.12216537 0.10222673 -0.6928154 -0.66106725
0.1806962 0.5654929 -0.3011317 0.26736644 0.39294124 1.1715331
-0.7606356 -1.0820638 0.6697757 0.61997974 -0.7429454 0.23744099
-1.0620939 0.40058178 -0.03907265 0.9688708 0.73038596 -0.30466995
-0.13737646 -0.13052012 -0.24918692 0.12258349 -0.86365974 2.0569735
-0.4968343 0.97624856 0.2755453 -1.4506624 0.5030501 0.1591467
0.30834 -0.6317838 0.21599458 0.17188075 -0.74690217 -0.3890222
0.4241198 0.21673025 -0.24620526 0.00835666 0.76783043 -0.5802161
0.3466269 0.38202265 0.8404734 0.7124649 0.10711174 -0.44036105
0.5175654 0.43761954 0.56591254 0.7068274 -0.24045326 0.95610344
0.57733023 -0.19732825 -1.1295439 -0.9588661 -0.37198377 1.3841596
0.13084698 -0.37977096 -1.2896062 -0.7912049 -0.11325158 0.8129044
-0.6563546 0.4490536 -0.32606634 -0.38658527 0.70249474 0.6096218
0.50481963 -1.0201845 -0.4293629 0.13049367 -0.19132832 -1.6003165
-0.62444204 0.5382057 -0.3600254 -1.0219736 -0.813207 -1.0025207
0.52813524 -0.01762279 1.3329086 -0.08985849 -0.6523326 0.6637254
-0.24734622 -0.3944149 -0.35124892 0.1120199 -0.77685726 -0.02944141
0.4472027 -0.22113733 -0.56569 0.18721771 -1.0322185 0.70747715
0.22739531 0.5622801 1.1529605 -0.26935765 0.19726562 -1.0772959
-0.34909794 -0.44116855 -0.8492139 0.33472505 -0.2135306 -0.2916049
0.4709589 -0.4048283 -0.9752719 0.38169995 -0.3681488 -0.78768116
-0.49563774 0.10015488 -0.39361203 0.19230206 0.12416392 0.49336985
-0.3515129 -0.58498955 0.82764715 0.8508647 0.96997905 -0.56977814
0.5344445 0.44257036 -0.60505664 -0.9106691 -1.2371207 -0.66774243
-0.93574244 -0.19299711 1.4782295 -1.2853584 -0.36140034 0.49214852
-1.0589312 -1.1635295 -0.5427831 -0.25673926 -0.8001756 0.15302782
-0.6293175 -0.27795875 -0.29082566 -1.4749335 1.525304 0.34801924
0.07766066 -1.0632356 -0.26687324 0.8839149 0.16631758 0.1946419
0.355569 -0.7817137 -0.9009917 0.18524653 -0.6743461 0.71931446
-0.3056251 0.1787908 -1.3058671 0.03182865 -0.45281893 -0.56450796
1.1609874 0.3893126 1.7207953 -0.43375912 -0.44162786 1.0195938
1.5561289 0.09531302 0.61407936 0.2246767 1.1843523 0.3644486
0.44748178 0.04839675 0.78621566 0.48669893 0.3632715 -0.3254862
-0.54364777 -0.3713612 0.333608 0.49480876 0.40784112 -0.2428224
-1.082209 0.9825391 -1.6195806 -0.6698374 0.10084041 1.4670134
1.150221 0.07134621 -0.1537546 -0.14906114 0.73109555 0.2051453
-0.6996456 -0.39680353 0.04692177 0.5750526 0.75041777 0.73731387
-1.5093646 1.6682305 5.242383 1.3173234 -1.0233048 0.538318
0.9110607 0.09858131 -0.2531629 0.10660499 -1.0153059 0.4172823
0.9939636 0.09109887 0.29168212 1.0867866 -0.26912543 -0.1520828
-1.1414981 0.7285156 0.20218895 -1.5105869 0.05016865 -0.2655379
0.74269485 0.4821319 -0.13137762 0.60801417 0.5316231 -1.03802
1.0862746 0.24707812 1.1042973 -0.39836222 0.28413263 0.09842532
-1.3121287 0.2082042 -0.28456128 0.30904907 0.04250314 0.35918978
-0.7734539 0.20898898 0.890491 1.0060471 -0.68276316 0.8054387
-0.33717346 0.9994391 -0.37029642 0.49833864 0.5644614 -0.07011668
0.28135866 1.8743896 -0.18261783 -0.17646296 0.5835564 0.9799863
-0.07257534 -0.16434175 -0.21833047 -0.08233452 0.26855645 1.4174376
-1.1158969 -0.99754745 -0.5560593 1.2383796 0.04765913 0.30362886
-1.1615196 -0.2775074 0.6192083 0.07630878 0.88621825 0.04941745
-0.09538433 -1.3296435 -0.46896368 -0.8126171 0.34497064 -0.90638435
-0.8369252 0.3408109 0.16269971 -0.69345826 0.02253198 -0.6663396
-0.51712227 0.34463295 -1.7044222 -1.6608821 -0.26240286 0.7560309
1.3079056 0.2681466 0.44743326 0.38983575 -0.7884956 0.60601664
-0.02472428 0.37341386 0.44981626 -1.6683444 0.40134233 0.84394985
0.17586096 -0.03322093 0.6000585 -0.5125253 -1.0150704 -1.6801745
0.5741004 -0.57489914 0.7209323 -0.72452265 -0.7639579 1.3042144
0.57143795 0.35230687 0.50254583 -0.43129948 -0.49726224 0.09083816
-1.0710572 0.45545498 0.96819943 -0.5307099 -0.5913925 0.4776779
1.3714372 -0.669187 -0.8801295 0.25991282 0.2878443 -0.50385183
1.0406774 -0.10852356 0.581093 -0.30590913 -0.34897506 -0.76086664
0.13929906 -0.5321635 0.14614771 1.4180474 0.46440047 -0.2702074
0.64619416 0.40023047 -0.57418096 -0.502711 -0.921722 -0.33450726
0.23794213 -0.8072171 0.2952776 0.9105831 -0.4262024 0.11989754
-0.09627111 0.0478695 0.7801025 0.02265928 0.87325513 -0.8063933
-0.19366558 -0.33525836 -0.28536484 -1.1531125 0.40313715 -1.3142725
0.22387631 -1.6154047 0.390426 -0.43211612 -0.1665518 0.93499255
-0.0249305 0.75932986 0.26488706 -0.0405809 -1.2262285 0.3088532
1.3237323 -0.5911022 0.04947476 -0.2639012 -0.6499238 0.7750083
0.7356963 -0.47594225 -0.26612866 -0.56074756 -0.23230931 -0.01032131
0.7214298 -1.0155642 0.02935229 -0.14393398 0.2254301 -0.76591843
0.29229894 -0.5554881 -0.05506336 0.11502884 -0.48629525 -0.45185074
0.4102158 0.36689222 -0.26032442 -0.27466953 0.87766814 -0.3972965
-1.0433459 0.35770223 -0.64310503 0.3743792 1.1667593 -0.21990271
-0.2682476 0.05707284 -1.0295297 0.55668974 0.48353067 0.48244044
0.31992573 -0.83226675 -0.5187068 0.13671304 0.13761686 0.59437984
0.32869285 0.6711143 -0.7874059 0.37140065 0.10911403 -1.0794909
-1.1607558 0.5752862 0.47811285 -0.28277266 -0.9355945 1.1475344
0.633154 -0.69000584 0.3895706 -0.5304408 -0.03886846 0.1080083
0.5007424 -0.01080632 -0.15115212 -0.74108046 -0.23709297 0.620145
-0.16667019 -0.06968895 1.1207398 -0.2604471 -0.1377336 0.3755056
1.3887688 -0.4827781 -1.6925899 -0.30658367 -0.01825118 -0.03140023
0.30962315 -0.89717376 -1.3067291 1.0768509 0.48998216 -0.22708663
1.1160302 0.4006236 0.88239855 0.02435497 0.04828985 -1.2250779
-0.05974117 0.52960604 0.47612444 -1.267338 -0.34277177 -0.53378415
-0.8588165 0.7688779 0.8497967 -0.14363882 0.7369769 0.4943228
0.26710057 -0.09925032 -0.8412144 -0.64311004 0.25306147 0.59706765
0.23672956 0.2294768 0.13701893 0.62349194 -0.24947618 -0.04801064
0.40372735 0.69731814 -0.4729261 -0.86782265 -0.10394621 0.3863367
-0.73666465 -0.30043808 -0.11683561 0.68839544 0.6141876 0.6840193
0.59090227 -0.1069429 -0.1845013 0.4068857 0.44113335 -0.760426
-0.59113264 0.34165722 -0.1327019 -0.79457885 -0.38679835 -0.5634047
-1.2747263 0.23437196 -0.10652904 0.00628636 0.8113739 0.9656207
0.18243949 0.79480696 0.01749939 -1.0911323 0.18897115 -0.87097365
-0.22922821 0.2630703 0.39207998 -0.36227685 -0.16490078 0.6849719 ] | [9.687036514282227, 0.7538371086120605] |
634ff0e7-351c-474f-868b-6e9f8624664a | non-intrusive-load-monitoring-with-an | 1912.00759 | null | https://arxiv.org/abs/1912.00759v3 | https://arxiv.org/pdf/1912.00759v3.pdf | Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network | Energy disaggregation, known in the literature as Non-Intrusive Load Monitoring (NILM), is the task of inferring the power demand of the individual appliances given the aggregate power demand recorded by a single smart meter which monitors multiple appliances. In this paper, we propose a deep neural network that combines a regression subnetwork with a classification subnetwork for solving the NILM problem. Specifically, we improve the generalization capability of the overall architecture by including an encoder-decoder with a tailored attention mechanism in the regression subnetwork. The attention mechanism is inspired by the temporal attention that has been successfully applied in neural machine translation, text summarization, and speech recognition. The experiments conducted on two publicly available datasets--REDD and UK-DALE--show that our proposed deep neural network outperforms the state-of-the-art in all the considered experimental conditions. We also show that modeling attention translates into the network's ability to correctly detect the turning on or off an appliance and to locate signal sections with high power consumption, which are of extreme interest in the field of energy disaggregation. | ['Veronica Piccialli', 'Antonio M. Sudoso'] | 2019-11-15 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 2.98978716e-01 3.58622223e-01 7.33174384e-02 -4.04188752e-01
-8.10683548e-01 -4.35730547e-01 7.83308208e-01 1.18567288e-01
-8.03708211e-02 5.77825308e-01 3.62368852e-01 -1.42873675e-01
-5.43627404e-02 -5.81906021e-01 -8.87201726e-01 -9.45121884e-01
1.07488111e-01 4.75978911e-01 -4.03349519e-01 6.37329891e-02
-1.29484057e-01 3.65078211e-01 -1.32231975e+00 1.06875904e-01
8.80819380e-01 1.35349965e+00 2.04185978e-01 5.31672835e-01
3.58718455e-01 1.26136339e+00 -8.69040787e-01 -1.37407541e-01
-1.00287780e-01 -5.77147067e-01 -6.91372812e-01 4.11295258e-02
1.93766912e-03 -5.06788790e-01 -3.51185083e-01 1.04560304e+00
7.01421738e-01 8.09400529e-02 6.15629137e-01 -1.28628242e+00
-5.51953971e-01 1.01974452e+00 -4.29577410e-01 4.46197927e-01
4.18376684e-01 9.27551538e-02 1.07511222e+00 -4.46503833e-02
-5.75008728e-02 6.63225770e-01 4.59471583e-01 1.58570066e-01
-1.33675826e+00 -4.96380538e-01 1.21278808e-01 7.96321869e-01
-1.25847268e+00 -5.54108858e-01 1.16977823e+00 -2.53777832e-01
1.62801242e+00 5.53152740e-01 3.95681024e-01 1.52278817e+00
3.20933551e-01 9.69150007e-01 7.88624287e-01 -2.55814672e-01
5.43401301e-01 -4.29215021e-02 1.42440975e-01 1.62685782e-01
1.37739629e-01 -2.99455255e-01 -2.80996114e-01 1.76387340e-01
-2.70313844e-02 -2.06655279e-01 -3.58079672e-01 1.88072041e-01
-9.82667685e-01 6.96110606e-01 4.06716436e-01 7.67326474e-01
-8.28395188e-01 2.92576134e-01 6.88208044e-01 1.55947953e-01
7.11247683e-01 4.23702568e-01 -4.70432013e-01 -7.12165460e-02
-1.15160775e+00 -3.63649398e-01 1.09005785e+00 7.28579581e-01
2.16249853e-01 4.66647387e-01 -3.35855484e-01 3.27962995e-01
-1.65468659e-02 4.04463679e-01 9.31525111e-01 -6.49744570e-01
5.19421697e-01 5.44119477e-01 -2.19246373e-02 -6.05531931e-01
-9.56032097e-01 -5.72665155e-01 -1.30332088e+00 -1.48648664e-01
-2.04688936e-01 -4.55849290e-01 -3.84992629e-01 1.74510717e+00
-8.20636097e-03 3.75273198e-01 1.03769243e-01 5.96393049e-01
6.08492196e-01 9.29625392e-01 -3.25800516e-02 -5.56050360e-01
1.36978722e+00 -9.86962020e-01 -1.08989871e+00 -1.58005938e-01
3.08860183e-01 -2.45258972e-01 6.80616617e-01 4.60308313e-01
-1.04362249e+00 -3.76806378e-01 -1.37845278e+00 -1.68680251e-01
-5.99744141e-01 3.94383341e-01 5.79569302e-02 3.01555574e-01
-8.15954924e-01 7.59992957e-01 -8.92801344e-01 -5.43063819e-01
4.21218812e-01 5.66592872e-01 9.28521680e-04 5.56440055e-01
-1.09390855e+00 1.19505250e+00 4.50335115e-01 3.79416585e-01
-7.53005624e-01 -5.73315918e-01 -7.05024481e-01 5.90648890e-01
2.36700118e-01 -6.30904257e-01 1.37005222e+00 -1.02039266e+00
-1.94234467e+00 3.04582000e-01 2.25799438e-02 -9.94260252e-01
7.04600215e-01 -2.55144477e-01 -6.57774627e-01 2.15848237e-02
-6.57292902e-02 2.25939855e-01 8.56391549e-01 -8.83360624e-01
-6.83201075e-01 -4.41014439e-01 -1.83964640e-01 -1.34854600e-01
-3.78635913e-01 -4.41996783e-01 1.87657520e-01 -5.72230279e-01
-5.62221289e-01 -6.22829258e-01 1.85971260e-01 -9.24818754e-01
-1.00198102e+00 -4.15436625e-01 9.79097962e-01 -1.25455761e+00
1.18176579e+00 -1.98586464e+00 3.03270936e-01 -3.91250756e-03
-1.15564704e-01 2.36072212e-01 9.13903564e-02 5.36651015e-01
-4.32840705e-01 -3.80303532e-01 -1.81795448e-01 -7.81870723e-01
6.03834391e-01 2.21087039e-01 -3.54576677e-01 6.80567324e-01
6.00766912e-02 1.28519011e+00 -6.81608438e-01 5.74542955e-02
7.21749365e-01 5.99047303e-01 1.39125347e-01 4.25756365e-01
-3.71949494e-01 5.18383563e-01 -2.26921633e-01 1.33160189e-01
2.91760594e-01 -3.21159512e-01 3.00031841e-01 -4.66934264e-01
-1.44024685e-01 6.61984921e-01 -7.61525810e-01 1.71559322e+00
-7.72929013e-01 9.49856877e-01 7.19042942e-02 -1.36424637e+00
6.21160507e-01 5.95523477e-01 7.99140453e-01 -1.04661930e+00
5.27831674e-01 -9.49802324e-02 -2.58946419e-01 -6.62898660e-01
3.13716799e-01 3.34103942e-01 -2.77100772e-01 5.62525094e-01
1.27893284e-01 1.58986658e-01 2.35596493e-01 -3.57293665e-01
1.24368191e+00 -2.99727440e-01 4.71606612e-01 -4.49373364e-01
7.05684781e-01 -4.62934256e-01 3.45711499e-01 4.32628751e-01
2.15042323e-01 1.35135829e-01 5.74208140e-01 -1.89262152e-01
-8.83754373e-01 -6.07407510e-01 1.11647628e-01 9.78921115e-01
-2.69689977e-01 -1.13313563e-01 -1.06742847e+00 -6.15483701e-01
-1.98053539e-01 1.58430445e+00 -6.35486186e-01 -3.10335696e-01
-5.76141715e-01 -1.08560801e+00 3.58281583e-01 6.32231414e-01
6.30116880e-01 -1.04499340e+00 -9.79969859e-01 5.04138052e-01
-3.66845995e-01 -1.30174327e+00 -4.16694313e-01 8.32097113e-01
-2.61744916e-01 -9.56150532e-01 -4.05082464e-01 -6.35034442e-01
3.15363228e-01 -3.46265465e-01 1.13411546e+00 -6.89502478e-01
-1.20585077e-01 2.81650901e-01 -2.03694969e-01 -3.82523954e-01
-6.13473117e-01 7.64384449e-01 -5.25953919e-02 4.52147961e-01
5.00839531e-01 -1.21002126e+00 -4.25549030e-01 4.88435701e-02
-9.21064734e-01 1.06648795e-01 6.17698729e-01 4.15320128e-01
2.81051397e-01 4.64404762e-01 8.92576754e-01 -7.32508004e-01
6.68872654e-01 -7.13667989e-01 -8.95981252e-01 1.51730329e-01
-7.23669827e-01 2.78851956e-01 1.18003476e+00 -3.69277477e-01
-6.94544911e-01 1.77258864e-01 -3.40656191e-01 -2.35388681e-01
-3.51103842e-01 -2.66893208e-02 -6.44971430e-01 5.05252481e-01
7.26445054e-04 4.96276170e-01 -5.61602354e-01 -7.91726947e-01
2.60073125e-01 8.21432054e-01 8.59458029e-01 7.58520439e-02
5.25895953e-01 2.45020926e-01 4.47055772e-02 -7.92884588e-01
-5.29105008e-01 -1.52806669e-01 -5.23773909e-01 1.09794475e-01
1.18221807e+00 -8.55101287e-01 -1.17786586e+00 5.57071626e-01
-1.39720356e+00 -4.91099447e-01 -6.72530353e-01 1.19967893e-01
-6.31822169e-01 1.66806191e-01 -5.02747476e-01 -6.79028213e-01
-8.63144815e-01 -1.20421708e+00 1.26790333e+00 2.09894851e-01
-4.09823805e-01 -1.07391226e+00 -8.21303353e-02 2.14587733e-01
5.67898393e-01 4.34558630e-01 1.21799779e+00 -1.02466583e+00
-2.75126904e-01 -2.93854505e-01 8.71039778e-02 5.13387799e-01
2.90435165e-01 -5.10987937e-01 -1.38714576e+00 -3.53379756e-01
3.66927445e-01 1.74452096e-01 6.78339899e-01 4.79119152e-01
1.25138628e+00 -7.25831747e-01 -1.16029292e-01 4.15736020e-01
1.47997367e+00 2.14644790e-01 5.02317607e-01 -8.08222964e-02
4.85915095e-01 2.05643505e-01 -3.89142960e-01 5.14539480e-01
6.15313649e-01 7.89535046e-01 6.00229204e-01 -2.16448188e-01
7.26416633e-02 -1.60943642e-01 5.32641470e-01 9.53203440e-01
3.15809935e-01 -6.92123652e-01 -2.82253712e-01 5.53681612e-01
-2.12058187e+00 -8.51214945e-01 2.40186490e-02 1.89509106e+00
4.62861419e-01 -1.79432612e-02 1.83524981e-01 5.52659631e-01
4.60439801e-01 4.01064068e-01 -9.52302516e-01 -5.11354566e-01
-7.70807117e-02 -1.45227788e-02 7.20053911e-01 5.44164181e-01
-1.05212927e+00 1.16212793e-01 5.45520067e+00 6.95009291e-01
-1.02835846e+00 5.37284732e-01 5.91840506e-01 -2.73157299e-01
1.97825879e-01 -8.84472549e-01 -4.35253918e-01 9.60879743e-01
1.49377549e+00 -2.07942843e-01 7.88880110e-01 5.24928451e-01
6.80844963e-01 -1.19793497e-01 -1.77000368e+00 1.00505650e+00
2.20829546e-01 -8.15926909e-01 -2.75970280e-01 7.68761188e-02
7.17231631e-01 3.19221884e-01 -8.04472417e-02 1.06308803e-01
3.34298909e-01 -8.92901123e-01 7.29874849e-01 7.10169256e-01
3.83531332e-01 -6.88429713e-01 9.51689303e-01 5.67915618e-01
-1.16479445e+00 -4.78896469e-01 1.34836957e-01 1.80091530e-01
2.59260267e-01 7.90595353e-01 -7.42741287e-01 7.57621288e-01
6.15440190e-01 6.68050826e-01 -5.49462259e-01 4.99712616e-01
-3.78200233e-01 6.29378855e-01 -5.25000155e-01 3.11754178e-02
4.72993888e-02 -1.30509064e-01 4.65753973e-01 1.26689875e+00
2.04524308e-01 -3.06934416e-01 -1.53649986e-01 9.62265372e-01
-3.45573604e-01 -2.78546691e-01 -3.20068568e-01 1.74301907e-01
1.52934700e-01 1.27219522e+00 -3.95486921e-01 -3.10783535e-01
-3.64966899e-01 1.49616957e+00 1.04445955e-02 3.12825948e-01
-1.07266951e+00 -3.22805047e-01 5.80887198e-01 -2.07950577e-01
6.08261406e-01 1.24019362e-01 -3.08902055e-01 -1.10582101e+00
1.29640803e-01 -5.13056517e-01 1.91048563e-01 -8.44412565e-01
-1.31606424e+00 6.85311377e-01 -5.12537248e-02 -7.37454295e-01
-8.59672189e-01 -2.37172231e-01 -8.47852588e-01 5.63012779e-01
-1.59626615e+00 -1.10823178e+00 -3.03568333e-01 5.03552735e-01
8.49323928e-01 1.00377120e-01 7.88790345e-01 5.39115608e-01
-1.11222327e+00 5.29493153e-01 5.29433012e-01 8.71459916e-02
-3.46703529e-02 -1.60800517e+00 6.42565012e-01 8.08318555e-01
1.26503389e-02 -4.01965499e-01 9.81520295e-01 -1.95483968e-01
-1.48083436e+00 -1.40507376e+00 8.49627376e-01 -1.61835581e-01
7.18195975e-01 -6.84391677e-01 -6.42636955e-01 9.22550142e-01
9.28657711e-01 -4.21643674e-01 4.21034247e-01 -4.59846646e-01
1.81125715e-01 -5.41308343e-01 -1.20997500e+00 1.62625089e-02
5.00136971e-01 -5.11309147e-01 -6.01920366e-01 5.25682271e-01
5.02814293e-01 -7.69504011e-02 -9.14187312e-01 4.35972176e-02
1.79014534e-01 -7.52895415e-01 6.20373547e-01 -1.18309483e-01
5.00170663e-02 -1.44065350e-01 -2.69810200e-01 -1.64245772e+00
-2.94696122e-01 -8.56719732e-01 -8.11269403e-01 1.62677836e+00
2.06394777e-01 -8.62327814e-01 2.71736056e-01 4.21212614e-01
-5.64119555e-02 -4.71260488e-01 -1.36685872e+00 -5.51604092e-01
-1.91227973e-01 -2.84706086e-01 1.00365603e+00 5.28095305e-01
1.89135261e-02 8.22069168e-01 -3.78215343e-01 4.89360303e-01
4.16690022e-01 4.62377891e-02 2.42960304e-01 -1.08712590e+00
-4.16209400e-01 -6.32556260e-01 -2.67973334e-01 -1.01607144e+00
5.43571115e-01 -6.87039375e-01 2.85209753e-02 -1.57976377e+00
-1.12831868e-01 5.60499966e-01 -2.89896548e-01 5.21566391e-01
4.52582508e-01 1.63507417e-01 1.68588400e-01 -2.07066119e-01
-5.59125900e-01 6.29859388e-01 2.86933035e-01 -6.16389990e-01
-7.59677365e-02 1.67581320e-01 -6.49749458e-01 5.36662936e-01
9.17037487e-01 -2.14480773e-01 -2.21974894e-01 -5.31773865e-01
6.15009777e-02 -8.58602449e-02 3.46993238e-01 -1.09353173e+00
3.35429072e-01 6.40677512e-01 3.46689343e-01 -7.24610209e-01
3.57530802e-01 -1.40288210e+00 4.03553128e-01 5.26615739e-01
-3.57976615e-01 2.75820881e-01 8.14624801e-02 5.19325316e-01
1.39899075e-01 -4.93064411e-02 6.62221134e-01 1.63390890e-01
-3.99765849e-01 -1.69306546e-01 -6.22375488e-01 -3.19441646e-01
1.05706549e+00 4.53020394e-01 -4.14303064e-01 -5.06962121e-01
-6.05259299e-01 5.11140645e-01 -7.75596639e-03 5.84825397e-01
-1.73518792e-01 -1.32698929e+00 -6.61256433e-01 2.63740182e-01
-3.36313069e-01 -1.63873419e-01 4.00300398e-02 9.58755016e-01
8.57099146e-02 7.84222662e-01 2.44383201e-01 -4.61898178e-01
-7.87016928e-01 7.33026147e-01 7.82050014e-01 -5.74573755e-01
-7.57244527e-01 -1.00867003e-01 -8.69878456e-02 1.74762428e-01
4.63169575e-01 -1.06174350e+00 -2.87197262e-01 3.60400200e-01
4.20168251e-01 8.46710920e-01 6.50567174e-01 -6.36664808e-01
-3.23138595e-01 3.59669745e-01 2.20026717e-01 3.91605884e-01
1.60021222e+00 -2.96754748e-01 -1.17287748e-01 7.88526416e-01
1.49795187e+00 -4.92165089e-01 -1.12753963e+00 -3.65507230e-02
5.68274744e-02 4.60770488e-01 4.46198404e-01 -1.09488654e+00
-1.31373525e+00 6.97119057e-01 7.47897089e-01 9.39846098e-01
1.62099147e+00 -1.96092073e-02 9.93108690e-01 2.30778024e-01
1.19502485e-01 -1.24494576e+00 -5.89435458e-01 1.23363130e-01
8.05869758e-01 -1.02591419e+00 -3.17202240e-01 2.81505466e-01
-1.91878334e-01 9.58737671e-01 1.26613170e-01 -1.64132267e-02
5.72016060e-01 5.37489355e-01 -3.90953451e-01 1.13432677e-02
-7.90101290e-01 -8.40492100e-02 1.19482420e-01 3.48123640e-01
-9.37683135e-02 1.83117211e-01 -4.27797495e-04 1.02510118e+00
-2.91047841e-01 1.51012048e-01 4.18507278e-01 4.84530389e-01
1.97508223e-02 -4.56473917e-01 -1.65091500e-01 4.80572462e-01
-4.84939724e-01 1.80600613e-01 -3.27353120e-01 4.56911087e-01
1.36418596e-01 1.34240639e+00 3.47905278e-01 -2.98259228e-01
6.55066907e-01 3.10658425e-01 2.55766630e-01 -4.28785793e-02
-8.83053005e-01 -1.21282026e-01 -6.19755797e-02 -5.60732126e-01
-3.21619481e-01 -6.34129226e-01 -9.06328857e-01 -3.60182881e-01
-2.15366974e-01 3.34757566e-02 7.09888041e-01 1.26224303e+00
4.14078563e-01 1.25830412e+00 1.00562847e+00 -1.08632004e+00
-7.09026337e-01 -1.35129619e+00 -7.75253952e-01 2.78226048e-01
8.70551944e-01 -8.64702240e-02 -2.84454674e-01 -5.92954382e-02] | [16.06941032409668, 7.582823276519775] |
b390b974-dc1f-4a0f-8cdc-145efd0a89b4 | storir-stochastic-room-impulse-response | 2008.07231 | null | https://arxiv.org/abs/2008.07231v1 | https://arxiv.org/pdf/2008.07231v1.pdf | StoRIR: Stochastic Room Impulse Response Generation for Audio Data Augmentation | In this paper we introduce StoRIR - a stochastic room impulse response generation method dedicated to audio data augmentation in machine learning applications. This technique, in contrary to geometrical methods like image-source or ray tracing, does not require prior definition of room geometry, absorption coefficients or microphone and source placement and is dependent solely on the acoustic parameters of the room. The method is intuitive, easy to implement and allows to generate RIRs of very complicated enclosures. We show that StoRIR, when used for audio data augmentation in a speech enhancement task, allows deep learning models to achieve better results on a wide range of metrics than when using the conventional image-source method, effectively improving many of them by more than 5 %. We publish a Python implementation of StoRIR online | ['Michał Romaniuk', 'Mateusz Matuszewski', 'Piotr Masztalski', 'Karol Piaskowski'] | 2020-08-17 | null | null | null | null | ['room-impulse-response'] | ['audio'] | [ 2.33326927e-01 1.44405738e-01 9.81885433e-01 -4.56388183e-02
-1.06004405e+00 -4.52710927e-01 4.79761064e-01 2.33957380e-01
-4.08780128e-01 5.48150778e-01 4.37790811e-01 -4.94373202e-01
-6.64582849e-02 -8.06992531e-01 -6.62435412e-01 -8.55621934e-01
6.85478980e-03 1.71951637e-01 3.71502675e-02 -3.76277357e-01
-2.52826005e-01 5.72578132e-01 -1.45937288e+00 1.46652922e-01
3.65838796e-01 9.50016618e-01 1.87741056e-01 1.01540780e+00
-4.82109860e-02 8.92694294e-01 -7.49144197e-01 3.07542142e-02
2.75629573e-02 -4.40304518e-01 -5.41619480e-01 -2.98434287e-01
2.29562372e-01 -2.43254587e-01 -4.00379628e-01 3.99962068e-01
1.06416225e+00 5.03139257e-01 7.51911879e-01 -6.09382272e-01
-6.07104182e-01 7.36082613e-01 -1.63334217e-02 6.05881140e-02
4.72513974e-01 -3.39933664e-01 6.37144208e-01 -1.00550187e+00
3.79570462e-02 1.11724377e+00 1.06542933e+00 6.39636278e-01
-1.40041101e+00 -4.51794595e-01 -3.68974507e-01 -2.00971410e-01
-1.26110864e+00 -5.70523858e-01 1.01158953e+00 -2.75723726e-01
6.89576089e-01 7.47627914e-01 2.81988084e-01 9.60836947e-01
-4.46390152e-01 3.45870495e-01 1.17425680e+00 -8.86669695e-01
3.89903575e-01 2.53968269e-01 -1.13970213e-01 6.01173341e-01
-5.61283171e-01 3.38990688e-02 -2.34395400e-01 -1.79098353e-01
1.03415120e+00 -4.40067202e-01 -4.28061485e-01 -5.58780171e-02
-1.31660616e+00 7.22201705e-01 5.66745162e-01 5.32741547e-01
-9.52533334e-02 4.45316702e-01 2.33873278e-01 1.86676204e-01
5.99138021e-01 5.14868319e-01 -1.37042239e-01 1.21788308e-02
-6.87998652e-01 1.35492340e-01 6.17946446e-01 5.52833617e-01
2.47520968e-01 4.53067005e-01 -2.39385039e-01 1.26584101e+00
3.44023466e-01 7.65549481e-01 2.15754792e-01 -9.11728203e-01
1.59901321e-01 -1.32658049e-01 2.09449247e-01 -5.50597250e-01
-5.56142867e-01 -4.11740065e-01 -1.00322986e+00 3.96562040e-01
3.35726708e-01 -2.82677948e-01 -8.54393184e-01 1.42911351e+00
3.50609511e-01 2.28290513e-01 1.48650274e-01 8.32376897e-01
1.45384097e+00 1.01840854e+00 -2.82767892e-01 -2.37733009e-03
1.08518088e+00 -8.72919202e-01 -7.03756869e-01 2.68211272e-02
4.14549291e-01 -1.02699685e+00 1.35391188e+00 6.03137672e-01
-1.05381107e+00 -6.29074991e-01 -1.01239836e+00 5.61508350e-02
-1.89782634e-01 7.51522323e-03 5.51946759e-01 1.10969639e+00
-1.46742380e+00 3.65519166e-01 -3.85361254e-01 3.04334283e-01
-1.00313626e-01 1.81231886e-01 -1.77874491e-01 1.37257516e-01
-9.37659085e-01 4.60312784e-01 -4.41282541e-01 8.45144317e-02
-8.86662483e-01 -9.65500355e-01 -1.05789733e+00 7.53847696e-03
-4.68248464e-02 -5.01564324e-01 1.51739979e+00 -5.23690820e-01
-1.91872525e+00 4.95052576e-01 3.96588072e-02 -3.63763392e-01
4.93195415e-01 -4.07313317e-01 -4.64417696e-01 3.59065756e-02
-6.30584478e-01 3.44759554e-01 9.32200134e-01 -1.55301559e+00
2.95433283e-01 8.84927139e-02 -6.27694502e-02 7.60170668e-02
-1.28968865e-01 1.92235440e-01 -8.38084221e-02 -7.88520336e-01
7.38966465e-02 -6.96454644e-01 -5.28084576e-01 -1.74622580e-01
-4.00221527e-01 2.96777070e-01 6.29372776e-01 -7.45134413e-01
6.74898446e-01 -2.00007153e+00 -8.96841437e-02 2.67296672e-01
3.14581618e-02 2.37670854e-01 -1.08355954e-01 5.77315688e-01
-4.21167165e-01 6.06920524e-03 -4.41881955e-01 -7.37289250e-01
-7.67221376e-02 -3.01505893e-01 -5.43650508e-01 5.62091589e-01
-2.54064858e-01 4.61622655e-01 -4.88096535e-01 -1.40884161e-01
6.88141227e-01 1.23683989e+00 -6.55976415e-01 2.58031607e-01
6.64458424e-02 9.17116761e-01 -1.29459411e-01 5.27598001e-02
7.16972172e-01 2.71177292e-01 -5.12332916e-01 7.33301640e-02
-2.53878713e-01 4.80531782e-01 -1.28960931e+00 1.49337745e+00
-1.41762471e+00 7.91386604e-01 4.61425990e-01 -7.87660956e-01
1.07376862e+00 6.50518179e-01 5.14624298e-01 -5.79376876e-01
1.52064234e-01 1.61310241e-01 -4.64456707e-01 -3.39888066e-01
2.51838893e-01 -2.43707344e-01 1.52184382e-01 5.46423435e-01
-8.18335712e-02 -5.78797519e-01 -5.75140059e-01 -1.29949152e-01
1.13662004e+00 -1.67704970e-01 -2.68210977e-01 -9.90080461e-02
6.47625029e-01 -4.65593964e-01 -1.43764094e-01 6.11958921e-01
5.05326629e-01 1.15736222e+00 -2.20009252e-01 -1.52097210e-01
-1.04676998e+00 -1.36876345e+00 -3.71929139e-01 1.13189709e+00
-2.39430949e-01 -1.08243532e-01 -1.10701776e+00 5.22091761e-02
-5.72821319e-01 8.08513343e-01 -3.59923005e-01 -4.74781077e-03
-6.21495366e-01 -6.40471816e-01 7.22761095e-01 4.51405734e-01
3.83466244e-01 -1.25757360e+00 -2.49015465e-01 1.78560764e-01
-3.55687499e-01 -1.19251144e+00 -3.31609637e-01 1.69218644e-01
-7.38843858e-01 -5.27541041e-01 -1.04353797e+00 -7.09903419e-01
4.89049166e-01 1.34727776e-01 1.13175917e+00 4.43375409e-02
-6.31326199e-01 9.30610836e-01 -4.10697132e-01 -5.07899344e-01
-8.47092271e-01 -4.63935316e-01 -5.44103719e-02 -1.87848322e-02
-3.84731025e-01 -7.14396238e-01 -6.04643285e-01 2.87724823e-01
-8.04464638e-01 -1.60174876e-01 1.26889288e-01 4.54138309e-01
3.50820363e-01 1.45159513e-02 4.87271577e-01 -4.31019545e-01
6.64227128e-01 1.17797278e-01 -5.66968858e-01 -1.04220420e-01
-1.38027117e-01 1.60000697e-02 7.04737365e-01 -3.36954504e-01
-1.05659056e+00 1.30872633e-02 -9.14050221e-01 -1.73757181e-01
-4.30912554e-01 1.25980616e-01 -1.42594129e-01 -2.22184598e-01
9.09198642e-01 9.31098908e-02 -2.09434167e-01 -7.26795554e-01
3.02046984e-01 5.23863077e-01 5.94501376e-01 -4.08305168e-01
1.04482234e+00 4.44539428e-01 4.43290994e-02 -1.49278688e+00
-4.16295558e-01 -6.20457768e-01 -3.73288959e-01 -3.34703624e-01
9.56634104e-01 -7.99139082e-01 -7.07433462e-01 6.13571763e-01
-1.19551373e+00 -8.34621727e-01 -4.64354068e-01 6.92127705e-01
-5.94470739e-01 1.45122483e-01 -5.56933165e-01 -1.30888033e+00
-4.24591064e-01 -9.28009629e-01 1.02457762e+00 -7.25712478e-02
1.73922256e-01 -1.21470773e+00 2.34921023e-01 3.66229445e-01
7.37159491e-01 2.46375382e-01 6.45521164e-01 -1.99556097e-01
-3.65899354e-01 -1.46161139e-01 3.10421605e-02 6.29965067e-01
6.57093450e-02 -1.73940375e-01 -1.78388524e+00 -5.00482600e-03
3.36358100e-01 -2.20603690e-01 9.43607509e-01 6.56817138e-01
1.55802405e+00 -3.07540298e-01 1.49301857e-01 5.58866739e-01
1.29112709e+00 -3.78882103e-02 9.14781868e-01 4.95764725e-02
6.19359553e-01 6.89439178e-01 1.28490835e-01 5.52100062e-01
-1.00140803e-01 7.58215368e-01 6.22774720e-01 -6.90365314e-01
-2.72960663e-01 -1.20987184e-01 3.40886176e-01 5.60899496e-01
-3.21289301e-01 -1.59615159e-01 -7.96122313e-01 3.62618178e-01
-1.14508593e+00 -9.42986786e-01 -4.56893414e-01 2.44520092e+00
6.76566243e-01 -3.92585486e-01 1.12287834e-01 7.42672086e-01
4.76978064e-01 9.95945856e-02 2.21046746e-01 -7.70360589e-01
-2.00911681e-03 7.85424173e-01 4.12779689e-01 1.05444062e+00
-9.82839465e-01 4.78876352e-01 7.18319464e+00 4.61127430e-01
-1.02960980e+00 3.80139649e-01 5.86569011e-01 1.11712098e-01
-5.83181322e-01 -6.37994051e-01 -2.84967124e-01 -1.69469237e-01
1.01582432e+00 4.35887665e-01 6.86561942e-01 6.49301231e-01
5.23599207e-01 1.03589438e-01 -9.77896273e-01 1.08491385e+00
5.92755713e-02 -1.27072001e+00 -4.71822500e-01 -4.01573852e-02
2.51746148e-01 -1.07278123e-01 3.88147414e-01 1.32845700e-01
1.46453202e-01 -1.45812106e+00 6.21176243e-01 2.92524397e-01
8.33186388e-01 -9.30165529e-01 3.73191595e-01 2.10607842e-01
-1.04636383e+00 6.95230067e-02 -2.43381619e-01 7.81445131e-02
2.63706893e-01 7.63370216e-01 -1.01689041e+00 2.07708791e-01
8.48799348e-01 -3.82665426e-01 -1.73426434e-01 1.21141100e+00
-9.80454907e-02 1.10236621e+00 -5.47800124e-01 2.04343840e-01
1.28755003e-01 -1.78933620e-01 6.30685747e-01 1.42826617e+00
5.42898715e-01 -6.13036677e-02 -2.59355456e-01 8.95235956e-01
-7.12702721e-02 3.89380574e-01 -6.52962208e-01 5.12652755e-01
1.14324629e-01 1.32688606e+00 -5.09309351e-01 2.49546051e-01
-1.48493975e-01 8.96382868e-01 -3.44518036e-01 4.77235794e-01
-1.11313355e+00 -4.61593240e-01 2.66033858e-01 4.47996140e-01
1.55837014e-01 -4.71156090e-01 -2.07492694e-01 -5.21635294e-01
-3.26419957e-02 -5.37683904e-01 -2.21648052e-01 -1.07912266e+00
-6.97922707e-01 8.95583034e-01 -6.77614566e-03 -9.89628017e-01
-1.59030840e-01 -7.69748151e-01 -9.55579579e-01 1.02764666e+00
-1.36436713e+00 -1.03715050e+00 -4.42707330e-01 8.52529883e-01
4.01052922e-01 7.21487962e-03 1.31436300e+00 4.13757801e-01
-7.77055323e-02 4.87869859e-01 2.66862988e-01 1.42614171e-01
4.26675886e-01 -1.34619021e+00 4.22410905e-01 5.95935225e-01
3.90729427e-01 2.79282689e-01 1.22621429e+00 1.84699759e-01
-1.05392694e+00 -9.98390555e-01 4.16829169e-01 -2.49130741e-01
4.28018868e-01 -7.14253068e-01 -8.39543462e-01 1.88258231e-01
3.05963993e-01 8.97042304e-02 9.14211988e-01 1.90184250e-01
-3.80096823e-01 -1.82084844e-01 -1.23626208e+00 4.30636346e-01
6.64421082e-01 -4.46779430e-01 -2.83254892e-01 5.59922278e-01
9.28049922e-01 -3.73967499e-01 -7.62643874e-01 2.04726502e-01
8.23719054e-02 -1.03757656e+00 1.58893406e+00 -4.86271381e-02
2.86336392e-01 -5.68132326e-02 -1.93950638e-01 -1.43274331e+00
3.91541887e-03 -8.85722518e-01 3.40671837e-02 1.32251918e+00
5.59971035e-01 -5.35075247e-01 4.95849371e-01 3.60284835e-01
-4.60872084e-01 -2.65196711e-01 -1.11399603e+00 -6.55237734e-01
1.32138267e-01 -1.06151807e+00 5.60141444e-01 5.25703907e-01
-3.17321479e-01 2.08165824e-01 -4.12971586e-01 5.18461466e-01
6.05718136e-01 -2.17735440e-01 7.32632518e-01 -1.09973562e+00
-5.31847119e-01 -2.10748091e-01 -4.52517420e-02 -9.47372913e-01
1.95068400e-02 -5.71176291e-01 3.26315761e-01 -1.89980936e+00
-4.87514406e-01 -8.95451784e-01 -2.27955982e-01 3.07864934e-01
2.33391285e-01 6.65911376e-01 -2.08518073e-01 -3.50686938e-01
-1.46946348e-02 6.60299659e-01 1.23011386e+00 -4.46200371e-02
-4.68723208e-01 5.38467944e-01 -4.02927130e-01 7.77651668e-01
8.94456267e-01 -2.21503109e-01 -4.27566528e-01 -3.65276456e-01
2.24165753e-01 -7.29164556e-02 8.61875474e-01 -1.49259341e+00
-1.44523591e-01 3.71099025e-01 3.25008363e-01 -4.04109776e-01
8.10626268e-01 -8.99781466e-01 -4.62935530e-02 1.07837327e-01
-4.70994025e-01 -2.24841446e-01 6.44707680e-01 3.00980479e-01
-3.92786339e-02 -3.31956714e-01 8.87557089e-01 1.69797316e-02
-1.69367507e-01 -1.16442489e-02 -7.28613734e-01 -8.70086178e-02
4.18922514e-01 -4.79866117e-02 3.93995233e-02 -7.73399591e-01
-9.00810778e-01 -4.74423379e-01 1.24462441e-01 3.18082213e-01
7.91631639e-01 -1.18754172e+00 -8.34370375e-01 8.94029513e-02
-1.73573464e-01 3.46797407e-01 4.12286758e-01 6.63331747e-01
-7.23996043e-01 2.64104247e-01 4.09039825e-01 -5.47843218e-01
-1.53906476e+00 5.28486252e-01 8.08378935e-01 -1.48768455e-01
-9.85642374e-01 1.06346452e+00 4.74406719e-01 -7.36695230e-01
3.77117842e-01 -3.58631611e-01 -2.44549423e-01 -3.29750508e-01
7.37590015e-01 2.83409417e-01 4.40220445e-01 -5.04874945e-01
-1.28846765e-01 4.14653420e-01 6.69925928e-01 -6.40228152e-01
1.52484894e+00 -7.90882111e-02 1.53791346e-02 6.48465991e-01
1.00011623e+00 6.71181917e-01 -1.10682333e+00 -1.08440399e-01
-5.84037602e-01 -2.02999622e-01 5.38883805e-01 -9.11367953e-01
-1.02389562e+00 1.18858123e+00 7.95215905e-01 2.79075563e-01
1.18543673e+00 7.53771365e-02 6.35156929e-01 4.56688374e-01
2.31221065e-01 -8.46890926e-01 4.43215609e-01 4.76083249e-01
1.31942356e+00 -8.95297885e-01 -3.52399290e-01 -5.10686755e-01
-3.79462957e-01 1.01071751e+00 1.46789551e-01 9.65355486e-02
8.65964711e-01 8.49674106e-01 3.88109207e-01 1.42813638e-01
-1.90894127e-01 -1.15589343e-01 3.94912511e-01 1.01567650e+00
8.31434548e-01 -1.57113671e-01 5.84771037e-01 1.78003430e-01
-4.53519732e-01 -4.40132499e-01 5.50066173e-01 4.24926966e-01
-5.89385390e-01 -1.05007231e+00 -1.08511412e+00 -1.71658605e-01
-6.93381906e-01 -3.33818734e-01 -1.94629058e-01 4.58071053e-01
-1.37922883e-01 1.17809665e+00 5.67141622e-02 -1.75784588e-01
2.96458334e-01 -5.97530268e-02 6.94118440e-01 -5.21465719e-01
-5.91701746e-01 3.23971063e-01 1.60884514e-01 -8.60527158e-02
-4.77136165e-01 -4.05195385e-01 -1.44372976e+00 -2.32238665e-01
-3.38954717e-01 2.34191149e-01 1.12203097e+00 6.64452970e-01
-1.74744844e-01 1.07125986e+00 7.73378313e-01 -1.09573352e+00
-2.49925405e-01 -1.02899218e+00 -5.20797908e-01 -9.54758450e-02
6.87256157e-01 -4.06287014e-01 -4.86352652e-01 -5.66392168e-02] | [15.219470024108887, 5.756626129150391] |
c7687aa8-8b8a-4523-8aba-e103487e7d48 | transfer-learning-in-deep-learning-models-for | 2301.10663 | null | https://arxiv.org/abs/2301.10663v2 | https://arxiv.org/pdf/2301.10663v2.pdf | Transfer Learning in Deep Learning Models for Building Load Forecasting: Case of Limited Data | Precise load forecasting in buildings could increase the bill savings potential and facilitate optimized strategies for power generation planning. With the rapid evolution of computer science, data-driven techniques, in particular the Deep Learning models, have become a promising solution for the load forecasting problem. These models have showed accurate forecasting results; however, they need abundance amount of historical data to maintain the performance. Considering the new buildings and buildings with low resolution measuring equipment, it is difficult to get enough historical data from them, leading to poor forecasting performance. In order to adapt Deep Learning models for buildings with limited and scarce data, this paper proposes a Building-to-Building Transfer Learning framework to overcome the problem and enhance the performance of Deep Learning models. The transfer learning approach was applied to a new technique known as Transformer model due to its efficacy in capturing data trends. The performance of the algorithm was tested on a large commercial building with limited data. The result showed that the proposed approach improved the forecasting accuracy by 56.8% compared to the case of conventional deep learning where training from scratch is used. The paper also compared the proposed Transformer model to other sequential deep learning models such as Long-short Term Memory (LSTM) and Recurrent Neural Network (RNN). The accuracy of the transformer model outperformed other models by reducing the root mean square error to 0.009, compared to LSTM with 0.011 and RNN with 0.051. | ['Huangjie Gong', 'Samy Faddel', 'Moustafa Shomer', 'Menna Nawar'] | 2023-01-25 | null | null | null | null | ['load-forecasting'] | ['miscellaneous'] | [-3.68507564e-01 -3.22596520e-01 5.98505624e-02 -1.84035093e-01
-3.80369574e-01 -1.72434784e-02 5.02424002e-01 -1.84548467e-01
4.98891734e-02 9.96347904e-01 1.91623300e-01 -4.12696451e-01
-2.48533338e-01 -1.32173526e+00 -4.14956421e-01 -1.04379463e+00
-1.67409897e-01 2.87121952e-01 -1.44172996e-01 -4.80855227e-01
2.18142048e-01 5.55717409e-01 -1.49637938e+00 1.58261448e-01
9.26197946e-01 1.17478418e+00 7.52294183e-01 2.93985277e-01
-2.47207746e-01 1.21714687e+00 -8.07762027e-01 3.91394794e-01
4.83259000e-02 -1.64077044e-01 -5.36588550e-01 -3.23821485e-01
-4.88928139e-01 -6.29356563e-01 -3.43086004e-01 4.79611099e-01
9.33331788e-01 2.69901633e-01 4.29457724e-01 -1.03798819e+00
-6.18325412e-01 4.99725193e-01 -5.54344594e-01 3.97530198e-01
7.17478245e-03 -4.28853005e-01 4.63999301e-01 -6.37519300e-01
-1.68585703e-01 6.70727551e-01 8.97688210e-01 1.48468763e-01
-8.40955436e-01 -7.25746334e-01 -1.11681968e-01 5.11279047e-01
-1.31632912e+00 -8.57151151e-02 1.05280077e+00 -5.07201433e-01
1.37168407e+00 5.13525717e-02 9.40986156e-01 6.05970562e-01
3.83122176e-01 5.65388083e-01 1.07439744e+00 -5.34632087e-01
2.23980516e-01 1.07361712e-01 -1.48757383e-01 3.46365124e-01
-4.56463918e-02 2.67440438e-01 -4.13768413e-03 2.14571983e-01
6.41774595e-01 3.65776926e-01 5.08342832e-02 1.94546998e-01
-7.29820371e-01 8.84950399e-01 7.64546633e-01 1.00228071e+00
-6.25663757e-01 2.55573820e-02 5.48932791e-01 2.28489324e-01
7.24724889e-01 -9.45836753e-02 -6.34834349e-01 -9.58751664e-02
-1.17746091e+00 9.25860703e-02 6.73830211e-01 6.67440474e-01
4.24297810e-01 9.99704242e-01 9.50411856e-02 9.75627303e-01
1.40330523e-01 5.15486717e-01 9.88258958e-01 -1.48118064e-01
4.56831843e-01 6.94617152e-01 7.78647661e-02 -1.19820869e+00
-8.14567149e-01 -7.98675537e-01 -1.34045291e+00 6.43240288e-02
-3.11760791e-02 -3.59812647e-01 -8.92690718e-01 1.36131442e+00
-3.47799957e-02 9.33184102e-02 9.78742689e-02 4.06257808e-01
6.79138184e-01 1.46692276e+00 9.01623964e-02 -1.81311667e-01
7.26411760e-01 -8.36042881e-01 -7.65945911e-01 -2.63458733e-02
7.17454135e-01 -7.33219147e-01 7.97774494e-01 3.63319159e-01
-7.18815982e-01 -8.56597483e-01 -1.19138300e+00 3.04507524e-01
-7.59919047e-01 3.91522408e-01 4.32376355e-01 5.96066594e-01
-1.09123027e+00 7.73120821e-01 -7.41420567e-01 -5.81863344e-01
3.04621756e-01 3.57913345e-01 3.74020010e-01 1.25049368e-01
-1.36226821e+00 1.11927152e+00 9.33019936e-01 5.59494376e-01
-8.84315670e-01 -6.14692509e-01 -4.37477767e-01 3.49279344e-01
-7.51355290e-02 -3.89734328e-01 1.15919709e+00 -7.31312275e-01
-1.60078788e+00 -7.52290264e-02 1.32529348e-01 -6.86705410e-01
1.58229947e-01 -2.23641366e-01 -8.36469948e-01 -4.70539123e-01
-2.47394413e-01 8.68267715e-02 5.33337951e-01 -9.31254447e-01
-8.82770717e-01 -2.42859259e-01 -1.63603127e-01 -6.45305291e-02
-6.10168993e-01 -3.99474740e-01 4.68768477e-01 -6.76201642e-01
-7.03279004e-02 -6.99122667e-01 -1.65458277e-01 -9.27433014e-01
1.10763803e-01 -3.60878050e-01 1.19330072e+00 -1.31020021e+00
1.41300285e+00 -1.73730886e+00 -3.88385981e-01 2.67066509e-01
-3.64313632e-01 3.86406511e-01 3.36094588e-01 7.58719444e-01
-3.11058402e-01 -2.00036526e-01 -4.20240462e-02 2.55476296e-01
-1.38670444e-01 4.47270364e-01 -1.66143999e-01 1.09785512e-01
-2.31506616e-01 9.81128335e-01 -6.59925520e-01 -7.01117963e-02
7.26806343e-01 5.76006174e-01 8.25557187e-02 2.25256741e-01
-8.30794051e-02 3.35733920e-01 -3.84007990e-01 4.71376091e-01
4.83056396e-01 -1.72962070e-01 -1.52075021e-02 -1.69948563e-01
-3.82979304e-01 8.66508335e-02 -9.27145064e-01 1.38615656e+00
-1.07860315e+00 7.02329755e-01 -5.31006515e-01 -1.54230285e+00
1.47995412e+00 6.53904021e-01 7.88381100e-01 -9.96495843e-01
1.33505538e-01 3.83114547e-01 -7.24142268e-02 -4.99369651e-01
2.97484010e-01 -4.18937713e-01 1.71039879e-01 2.04883963e-01
-2.23372966e-01 1.48371030e-02 8.86331797e-02 -3.78136039e-01
7.58879006e-01 1.86703473e-01 1.39796406e-01 -4.25485313e-01
7.08919048e-01 -1.69709727e-01 4.89991397e-01 -5.52707212e-03
3.17268729e-01 1.40318722e-02 -2.85178393e-01 -1.04722643e+00
-1.21642840e+00 -5.10425627e-01 -1.31874010e-01 1.00438893e+00
-5.23100674e-01 -1.65494923e-02 -5.38048148e-01 -1.08277075e-01
-1.24077372e-01 1.19126582e+00 -2.97841966e-01 -8.13924000e-02
-5.96719682e-01 -1.09570432e+00 1.79006502e-01 8.09619904e-01
1.02715373e+00 -1.24443376e+00 -6.39906704e-01 7.42196381e-01
-1.43825114e-01 -6.86239600e-01 3.03626984e-01 2.86805093e-01
-1.15927124e+00 -6.01670921e-01 -9.21211958e-01 -8.11304450e-01
4.66568887e-01 -1.90295532e-01 1.12273920e+00 -4.77457084e-02
-4.89197634e-02 -1.84871815e-02 -3.15541089e-01 -6.73000634e-01
-3.01511884e-01 5.56402385e-01 -1.21645547e-01 -4.41394895e-01
4.10936505e-01 -7.95141399e-01 -4.77518678e-01 1.65428430e-01
-7.08021164e-01 1.00813173e-01 5.86912930e-01 8.84775102e-01
1.08923636e-01 1.02805090e+00 1.21002150e+00 -4.31757689e-01
7.08389640e-01 -5.98739505e-01 -8.39063585e-01 2.40095124e-01
-1.14168024e+00 -1.77994892e-01 9.39709008e-01 -6.59624785e-02
-1.33890080e+00 -1.50427848e-01 -3.35020393e-01 1.24335252e-02
1.05872899e-01 8.19884002e-01 9.26757790e-03 1.61859110e-01
2.14085028e-01 4.43919629e-01 -3.37348014e-01 -6.84112310e-01
-1.59727618e-01 7.67816782e-01 8.93365741e-02 -3.92509878e-01
7.40269244e-01 -1.17173538e-01 4.04573753e-02 -8.47762167e-01
-6.32200062e-01 -5.30830175e-02 -7.28175342e-01 -5.23006320e-01
6.19555235e-01 -9.76711094e-01 -5.47122180e-01 8.23818028e-01
-9.00648475e-01 -4.29502547e-01 -3.03406715e-01 4.01267886e-01
-3.80871087e-01 -1.23221822e-01 -6.76079631e-01 -1.04318738e+00
-7.19628334e-01 -8.06544423e-01 3.84693861e-01 2.53460377e-01
3.66761871e-02 -1.37635422e+00 -3.48233841e-02 9.64425728e-02
1.07611167e+00 6.01906836e-01 1.21027625e+00 -4.65522408e-01
-1.72099784e-01 -5.25326252e-01 -1.55623844e-02 7.51487017e-01
4.20395702e-01 -2.39377856e-01 -1.01144373e+00 -4.13278282e-01
3.16839010e-01 4.49814461e-03 3.02721262e-01 5.39467394e-01
1.33040929e+00 -2.74538457e-01 -2.06241325e-01 6.28869459e-02
1.75580239e+00 9.45609868e-01 8.36352468e-01 6.73815429e-01
5.83918095e-01 3.16812813e-01 2.17611298e-01 6.80382907e-01
3.46105874e-01 2.91057199e-01 3.06529671e-01 -2.79744506e-01
2.25500777e-01 -2.67250985e-01 1.33903980e-01 1.42483926e+00
-3.84382129e-01 -6.32598475e-02 -1.05237556e+00 6.90887988e-01
-1.81845903e+00 -1.05231404e+00 -1.61921035e-03 1.94546545e+00
4.19223607e-01 2.86696821e-01 9.81744379e-03 7.67333210e-01
5.12937009e-01 1.94448635e-01 -4.53775376e-01 -6.06864870e-01
8.75434726e-02 3.13860565e-01 5.67376792e-01 1.71627611e-01
-7.81708837e-01 4.04999793e-01 6.09759617e+00 9.03060317e-01
-1.23329639e+00 1.01085462e-01 8.10982704e-01 4.04812358e-02
3.47815529e-02 -4.38744158e-01 -6.70074105e-01 7.88848400e-01
1.47961509e+00 -4.03423697e-01 4.43957269e-01 1.05856574e+00
5.37936389e-01 -9.09372941e-02 -8.13015223e-01 9.15935397e-01
-1.43514618e-01 -1.20916188e+00 -1.22307956e-01 8.43887329e-02
1.01598561e+00 9.37363356e-02 -1.80662796e-01 6.37318492e-01
1.77253589e-01 -1.01137280e+00 1.87663972e-01 8.10794532e-01
2.45421201e-01 -1.16449583e+00 1.36346769e+00 6.06288970e-01
-1.44753408e+00 -4.79600012e-01 -4.63200778e-01 -6.59295857e-01
2.48687908e-01 8.30791771e-01 -9.22217131e-01 9.05275047e-01
1.01609111e+00 8.28082442e-01 -2.54103571e-01 7.53414810e-01
9.66694281e-02 7.78363526e-01 -5.64770758e-01 -8.16309005e-02
3.15410256e-01 -3.75827521e-01 -2.34374627e-01 6.95208251e-01
8.95090520e-01 2.41065472e-02 1.54719532e-01 4.33320016e-01
2.01161578e-01 1.77492380e-01 -6.48554921e-01 6.98303804e-02
2.22633705e-01 1.02122843e+00 -4.64690596e-01 -4.17548031e-01
-4.70942408e-01 5.41345179e-01 -1.92450225e-01 2.95771062e-01
-8.31985295e-01 -3.82230192e-01 -9.44837555e-02 3.65070581e-01
4.39368099e-01 -1.40116394e-01 -3.43844503e-01 -4.77087170e-01
-1.22044943e-01 -4.04697865e-01 3.34795892e-01 -8.95101368e-01
-1.31337035e+00 7.17640579e-01 1.44782186e-01 -1.05798662e+00
-6.36869669e-01 -3.80457222e-01 -7.58798480e-01 1.06274509e+00
-1.59943366e+00 -1.24387598e+00 -4.35059667e-01 5.04978299e-01
7.76680410e-01 -5.39581358e-01 9.11866486e-01 7.57599592e-01
-6.39182210e-01 4.49673422e-02 7.31642604e-01 -1.56414993e-02
-4.07548398e-02 -1.07726455e+00 2.15482250e-01 6.67866409e-01
-3.04395616e-01 8.17713048e-03 4.56406325e-01 -5.26246727e-01
-8.61057937e-01 -1.15872610e+00 9.28510010e-01 1.71800286e-01
4.76178497e-01 -4.92791384e-02 -9.43479955e-01 7.64633298e-01
5.22294641e-01 -1.98808685e-01 5.80678880e-01 -6.53393865e-02
5.89744925e-01 -7.19731390e-01 -1.26611185e+00 1.15920287e-02
3.71656716e-01 -2.58956641e-01 -4.96906638e-01 1.89077035e-01
3.45952630e-01 -1.31361842e-01 -1.29927862e+00 5.63516855e-01
4.70753491e-01 -8.48370850e-01 7.72672415e-01 3.12065538e-02
2.23705590e-01 -2.15404019e-01 -2.36306027e-01 -1.44729400e+00
-7.38406956e-01 -7.77527466e-02 -3.78725737e-01 1.48583221e+00
2.81746000e-01 -8.35290432e-01 8.26876104e-01 6.74123466e-01
-2.52081633e-01 -8.97572577e-01 -8.44978213e-01 -7.22328782e-01
1.36715114e-01 -2.89303064e-01 1.12557662e+00 7.48452187e-01
-3.82305443e-01 3.08285415e-01 -5.55715144e-01 -3.39781418e-02
2.41934299e-01 2.06069246e-01 4.22868669e-01 -1.36358011e+00
1.75813749e-01 -1.52829707e-01 -3.27556074e-01 -7.19280541e-01
-2.90507283e-02 -6.63890481e-01 -2.97762126e-01 -2.01518130e+00
-2.01142281e-01 -5.78462422e-01 -7.98072338e-01 5.54641724e-01
3.03787291e-01 -1.55277461e-01 1.01606503e-01 1.70958653e-01
2.45411858e-01 9.17600513e-01 8.29900622e-01 -2.78912723e-01
-2.84798384e-01 4.50411618e-01 -1.50649995e-01 7.52654433e-01
1.46663272e+00 -1.47538736e-01 -6.06850743e-01 -4.90971327e-01
9.67417806e-02 -3.87864262e-02 5.19652339e-03 -1.51270545e+00
1.69183135e-01 2.77648419e-01 1.03643334e+00 -1.25078118e+00
4.66268659e-02 -1.35115063e+00 6.78098261e-01 8.46480429e-01
1.49401098e-01 4.27868754e-01 5.42181909e-01 1.65388823e-01
-2.68504083e-01 -2.67218053e-01 4.10385102e-01 -1.36431456e-01
-1.12317765e+00 9.32101086e-02 -4.16917861e-01 -6.91726506e-01
1.20036221e+00 -1.92573369e-01 4.29918543e-02 -2.88413703e-01
-7.27107882e-01 2.09905714e-01 -1.52971968e-01 4.23478901e-01
4.45683986e-01 -1.69006014e+00 -5.54416299e-01 2.20535815e-01
-4.37050432e-01 3.25375982e-02 3.65362883e-01 4.38046783e-01
-6.36302292e-01 7.55268574e-01 -3.70237052e-01 -4.76066351e-01
-7.75775909e-01 5.97100198e-01 5.58214605e-01 -5.20905972e-01
-6.75060749e-01 2.37536162e-01 -3.04411173e-01 -4.61289734e-01
1.54958859e-01 -5.94741225e-01 -4.69510615e-01 1.93379417e-01
3.37228447e-01 9.14774835e-01 5.73583245e-01 -4.52921778e-01
-1.28794193e-01 5.94022095e-01 1.36336744e-01 3.51747125e-01
1.81703269e+00 -1.68828711e-01 -1.43162981e-01 8.95034730e-01
1.10487509e+00 -5.84355891e-01 -7.81289458e-01 -1.89770609e-01
2.94509172e-01 -2.47143731e-01 5.42316616e-01 -1.07304811e+00
-1.44521308e+00 7.96151638e-01 1.11902404e+00 6.02672756e-01
1.55319631e+00 -6.09149694e-01 1.26551270e+00 3.43308955e-01
5.52878857e-01 -1.30824888e+00 -3.84477615e-01 2.69029289e-01
8.69491816e-01 -1.13722813e+00 1.07643552e-01 3.25606018e-01
-5.00654317e-02 1.33411241e+00 3.90957564e-01 -2.04593334e-02
1.02387834e+00 3.21729600e-01 -1.62375689e-01 2.80136149e-02
-5.75069129e-01 1.07246093e-01 6.28229454e-02 5.28300762e-01
5.63333452e-01 1.95284784e-01 -3.95635478e-02 5.21149695e-01
-4.59659457e-01 6.17514193e-01 9.03075561e-02 1.02710176e+00
-4.48081791e-01 -1.00252700e+00 -4.17657882e-01 7.67338753e-01
-4.38092560e-01 1.11418515e-02 5.81747830e-01 8.40725780e-01
1.51396900e-01 1.08072948e+00 1.23671547e-01 -3.50218117e-01
5.44876695e-01 1.03911392e-01 1.74643338e-01 -2.23128006e-01
-5.40243328e-01 2.66578961e-02 -2.34415650e-01 -5.03514633e-02
-5.73284209e-01 -3.14436585e-01 -1.12852871e+00 -7.31325030e-01
-3.93596917e-01 2.57094920e-01 8.87047708e-01 9.96420443e-01
1.20576441e-01 1.08328879e+00 1.07409251e+00 -9.21944499e-01
-3.86258155e-01 -1.30156493e+00 -8.01727057e-01 -1.69668961e-02
8.42072144e-02 -5.39268732e-01 -1.36821851e-01 4.85544791e-04] | [6.240879058837891, 2.8063790798187256] |
a468b4d8-63ca-445c-ab27-c2ed87f12ec9 | inference-of-binary-regime-models-with-jump | 1910.10606 | null | https://arxiv.org/abs/1910.10606v4 | https://arxiv.org/pdf/1910.10606v4.pdf | Inference of Binary Regime Models with Jump Discontinuities | Identifying the instances of jumps in a discrete-time-series sample of a jump diffusion model is a challenging task. We have developed a novel statistical technique for jump detection and volatility estimation in a return time series data using a threshold method. The consistency of the volatility estimator has been obtained. Since we have derived the threshold and the volatility estimator simultaneously by solving an implicit equation, we have obtained unprecedented accuracy across a wide range of parameter values. Using this method, the increments attributed to jumps have been removed from a large collection of historical data of Indian sectorial indices. Subsequently, we have tested the presence of regime-switching dynamics in the volatility coefficient using a new discriminating statistic. The statistic has been shown to be sensitive to the transition kernel of the regime-switching model. We perform the testing using the Bootstrap method and find a clear indication of presence of multiple regimes of volatility in the data. A link to all Python codes is given in the conclusion. The methodology is suitable for analyzing high-frequency data and may be applied for algorithmic trading. | ['Sharan Rajani', 'Anindya Goswami', 'Milan Kumar Das'] | 2019-10-23 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-2.80926209e-02 -3.42462569e-01 6.69139549e-02 -1.76272273e-01
-6.05416894e-01 -9.61818337e-01 1.08616459e+00 2.00337902e-01
-2.43111938e-01 8.36837411e-01 -2.21001625e-01 -6.76384330e-01
-4.37165588e-01 -7.90302455e-01 -4.54426169e-01 -7.83951461e-01
-4.45699483e-01 7.03139901e-01 5.40970087e-01 -1.45100608e-01
6.63498163e-01 4.36658949e-01 -1.49163234e+00 5.24886996e-02
4.49607223e-01 1.36893678e+00 -2.85416991e-01 6.38613880e-01
-3.41637135e-01 5.61700463e-01 -6.97762251e-01 -1.88469470e-01
6.25652909e-01 -4.66249555e-01 -2.03812689e-01 -4.07625616e-01
-5.85554428e-02 -1.88583031e-01 3.76696140e-01 1.12844932e+00
2.01333225e-01 -1.24280937e-01 9.04733300e-01 -1.11532474e+00
5.75553589e-02 6.53618336e-01 -7.30322421e-01 9.09195244e-01
9.01852846e-02 2.36213319e-02 8.45547199e-01 -5.11747539e-01
6.27707362e-01 9.35812652e-01 7.02556252e-01 -3.03620726e-01
-1.49289322e+00 -6.48021400e-01 -3.98787796e-01 -3.46487552e-01
-9.79139626e-01 -4.50203456e-02 7.19188511e-01 -7.79911816e-01
9.33345079e-01 3.19622070e-01 8.61088514e-01 1.01998925e+00
6.17629588e-01 -3.66590768e-02 1.69546461e+00 -2.27058053e-01
4.12175149e-01 1.86494291e-01 1.24123074e-01 8.85117650e-02
4.09276158e-01 5.50859213e-01 -3.13026570e-02 -7.46042907e-01
1.05974281e+00 -6.32317066e-02 4.84819502e-01 -2.67328024e-02
-8.57333839e-01 1.28024364e+00 -1.76878855e-01 6.07363403e-01
-5.72239459e-01 4.71590310e-02 6.86175048e-01 8.71396422e-01
8.37486506e-01 1.89085931e-01 -5.66796958e-01 -4.25645262e-01
-1.23299372e+00 3.43425632e-01 1.18801486e+00 3.11590374e-01
2.65948981e-01 2.27134779e-01 -2.23346651e-01 3.25829685e-01
3.60683613e-02 6.06334925e-01 6.21034741e-01 -9.17888880e-01
2.03071609e-01 3.67606491e-01 2.80422688e-01 -6.67319238e-01
-1.64472759e-01 -2.91370958e-01 -5.75334311e-01 5.12108982e-01
7.00193703e-01 -3.04659843e-01 -7.32872069e-01 1.40944457e+00
5.22916973e-01 1.09127425e-01 -8.69053006e-02 2.57130384e-01
-1.25384852e-01 6.69679940e-01 -2.73206085e-01 -7.65582919e-01
1.19189298e+00 -1.07599087e-01 -6.25908911e-01 5.98430157e-01
4.03868547e-03 -8.06981146e-01 7.04317093e-01 5.59720039e-01
-8.93977582e-01 -2.55109131e-01 -6.07998073e-01 8.60751092e-01
-3.87717217e-01 -6.26155734e-01 6.61697745e-01 6.20320857e-01
-8.27431262e-01 9.17878926e-01 -6.80819690e-01 1.24742948e-02
-9.61387157e-02 3.41983557e-01 1.80198193e-01 8.87651205e-01
-1.05452847e+00 5.22653520e-01 3.50256145e-01 -8.10291171e-02
-4.23578084e-01 -4.77852196e-01 -1.82293653e-01 7.80313611e-02
6.39097095e-02 -1.61218166e-01 1.21732485e+00 -1.17895985e+00
-1.36750877e+00 7.44600058e-01 3.98721427e-01 -8.85862231e-01
1.24976277e+00 3.81850481e-01 -6.24351263e-01 1.89305425e-01
2.30215535e-01 -3.46087813e-01 1.42497003e+00 -6.51552856e-01
-7.24614680e-01 -2.40660921e-01 -6.47290587e-01 -6.15154386e-01
2.18871772e-01 2.70860583e-01 4.52807546e-01 -1.01312888e+00
1.28048398e-02 -8.97748351e-01 1.36144444e-01 -8.79517317e-01
8.17686841e-02 -9.89912152e-02 6.63721681e-01 -8.48482370e-01
1.36823094e+00 -2.20513511e+00 -6.51654422e-01 9.66119528e-01
-2.41778433e-01 -2.02517927e-01 6.10199928e-01 7.88733900e-01
-2.11710006e-01 1.53213829e-01 -5.67658246e-01 3.13909799e-01
1.05200976e-01 3.96039300e-02 -1.02934480e+00 5.28743863e-01
-4.73979190e-02 3.85199785e-01 -3.48650068e-01 -2.66702235e-01
-2.87302826e-02 1.44968763e-01 -2.67559946e-01 1.10970043e-01
-2.34245956e-01 5.18349230e-01 -1.45431653e-01 5.69489598e-01
7.21103668e-01 -5.03966101e-02 -2.95814574e-02 6.05680108e-01
-7.49566555e-01 1.41221553e-01 -1.46791232e+00 6.33696616e-01
-7.08382130e-02 5.76812863e-01 -5.31821288e-02 -8.76457095e-01
1.04745030e+00 3.20516914e-01 5.70558012e-01 -7.46009767e-01
1.00632593e-01 7.44763255e-01 2.48146757e-01 -1.76708937e-01
3.21594238e-01 -4.22115922e-01 -1.36381745e-01 5.95163345e-01
-1.62655115e-01 -1.46123305e-01 3.57570231e-01 -2.36401469e-01
1.09360933e+00 -8.74566957e-02 3.14745128e-01 -6.89964652e-01
3.01766217e-01 9.61932838e-02 2.41668224e-01 8.72409225e-01
2.31744233e-03 5.28780706e-02 1.10328186e+00 -3.04365486e-01
-1.07117462e+00 -1.08238685e+00 -6.94160402e-01 7.47198343e-01
-5.30581534e-01 2.57749289e-01 -4.61337507e-01 -1.36179477e-01
5.72548687e-01 6.75261080e-01 -9.68597651e-01 3.44311863e-01
-4.52620655e-01 -8.46288919e-01 2.73697436e-01 9.38401818e-02
2.87237585e-01 -1.29172063e+00 -9.06175017e-01 4.67220098e-01
4.53948975e-01 -6.11332953e-01 -9.14269462e-02 5.03358960e-01
-1.24123859e+00 -9.93894517e-01 -4.27290112e-01 -2.62300074e-01
1.30640119e-01 -4.84677255e-01 8.80625725e-01 -3.01744699e-01
-3.92587274e-01 4.01158303e-01 1.26321428e-02 -5.56949973e-01
-6.85709238e-01 -3.70872974e-01 -4.44751978e-02 2.15679035e-01
8.20657313e-02 -5.95013440e-01 -6.04276776e-01 1.81915537e-01
-8.98668408e-01 -9.78235006e-01 1.58499464e-01 6.11052036e-01
4.90135550e-01 2.59143710e-01 6.58093393e-01 -8.67967367e-01
1.10740674e+00 -6.98679090e-01 -1.51368678e+00 -1.49534345e-01
-1.00195467e+00 1.78785950e-01 4.64333713e-01 -7.00099826e-01
-9.38941777e-01 -2.53019094e-01 2.06032157e-01 -3.46983373e-01
4.59235022e-03 5.97321868e-01 8.23245108e-01 1.54724553e-01
7.01149106e-02 4.44292389e-02 1.93168953e-01 -6.09346867e-01
-5.83351962e-02 5.09874940e-01 3.85496706e-01 -3.94837081e-01
4.99050975e-01 6.46902502e-01 1.05234705e-01 -8.32029104e-01
-1.65798604e-01 -6.11442208e-01 -2.47699425e-01 -2.04098135e-01
5.20689547e-01 -6.95187211e-01 -7.14089870e-01 6.71594501e-01
-7.77272940e-01 -3.03545803e-01 -5.39859653e-01 6.30982637e-01
-4.89190131e-01 1.93665639e-01 -7.87734568e-01 -1.25521135e+00
-2.38334030e-01 -5.95905006e-01 4.49788064e-01 1.15444824e-01
-3.76322627e-01 -1.35381842e+00 9.53520417e-01 -4.10041928e-01
5.90148866e-01 8.40143740e-01 9.04388428e-01 -1.29099107e+00
-1.86274216e-01 -3.78326416e-01 1.62605122e-01 2.48770595e-01
9.94836241e-02 8.00410211e-01 -7.32165813e-01 -2.95689672e-01
6.63427711e-01 3.86386424e-01 8.06447744e-01 7.18325555e-01
5.08151591e-01 -3.08435172e-01 1.13997780e-01 4.08366948e-01
1.38464677e+00 3.85545492e-01 1.82160124e-01 7.24324107e-01
-2.67174780e-01 5.92623830e-01 6.58232927e-01 7.66377449e-01
-5.16539156e-01 2.83450216e-01 1.89552158e-01 2.29380369e-01
7.08581507e-01 -7.28933187e-03 5.70948422e-01 4.82395679e-01
-1.47922620e-01 3.90909433e-01 -1.02300632e+00 5.11165917e-01
-1.57522345e+00 -1.47354281e+00 -6.90325022e-01 2.47078919e+00
7.30232656e-01 7.42679954e-01 8.51352394e-01 -6.53624767e-03
8.75253797e-01 7.31947497e-02 -4.87997025e-01 -9.08192754e-01
4.23672982e-02 4.60384011e-01 8.51496041e-01 4.55215305e-01
-1.07705784e+00 5.26266575e-01 7.47201014e+00 4.81343716e-01
-1.32893443e+00 -1.54035538e-02 3.61925989e-01 9.14621651e-02
-2.81898826e-01 2.39543766e-01 -7.21091509e-01 8.88950169e-01
1.40094697e+00 -4.37881470e-01 2.80404747e-01 7.20813394e-01
2.97114551e-01 -4.87006873e-01 -5.37364960e-01 6.27680898e-01
-5.91996431e-01 -1.03252256e+00 -2.43144199e-01 5.27843058e-01
7.18115389e-01 1.20737173e-01 3.18362802e-01 2.15585977e-01
2.03410074e-01 -5.94579875e-01 7.36273468e-01 7.53482878e-01
4.19719577e-01 -9.77974474e-01 6.42377913e-01 3.40816110e-01
-1.03730834e+00 -4.15877134e-01 -2.03430980e-01 -2.16381013e-01
-2.13645641e-02 9.88029540e-01 -7.77828097e-01 1.45373479e-01
7.06871867e-01 2.27030471e-01 -6.06641352e-01 1.01112521e+00
3.50282520e-01 8.72092664e-01 -7.60662735e-01 9.69518349e-02
2.93933421e-01 -9.61488426e-01 5.95774949e-01 1.06035507e+00
6.85336947e-01 -4.68574554e-01 -4.51093674e-01 9.11027968e-01
5.43656409e-01 9.49831605e-02 -7.73404837e-01 -3.90978694e-01
2.72027344e-01 8.73705387e-01 -1.22938561e+00 -3.99700105e-01
-3.62941235e-01 5.19841671e-01 -2.61901021e-01 1.56283781e-01
-7.50621915e-01 -3.42871338e-01 2.24585816e-01 2.36437961e-01
8.83017778e-01 -1.47406101e-01 -2.96840608e-01 -9.91597354e-01
1.70039967e-01 -8.61314833e-01 7.20287919e-01 -1.19881846e-01
-1.26973987e+00 3.76548380e-01 3.31828356e-01 -1.18725920e+00
-9.13393319e-01 -4.61821526e-01 -1.11974823e+00 8.77562940e-01
-1.11438942e+00 -1.68559194e-01 4.26714957e-01 7.79762864e-01
7.25969598e-02 -3.54747474e-01 4.37073767e-01 -1.74464256e-01
-2.74986535e-01 -1.52550906e-01 7.61817396e-01 -6.38381764e-02
5.23026049e-01 -1.46653795e+00 4.75072235e-01 7.85827696e-01
5.29634468e-02 7.46143043e-01 1.14914036e+00 -1.12946272e+00
-7.56487668e-01 -4.33578044e-01 6.11399233e-01 -4.10879195e-01
1.47641826e+00 -3.20185095e-01 -1.08549297e+00 6.40700638e-01
3.22070390e-01 -2.65380114e-01 5.76870441e-01 -2.94823140e-01
-1.57977015e-01 -1.32187262e-01 -1.18715715e+00 -2.06735786e-02
2.98813909e-01 -5.01633883e-01 -9.21359837e-01 3.39723155e-02
2.37276450e-01 2.17173085e-01 -1.12817848e+00 1.97941050e-01
6.92382514e-01 -1.50911546e+00 5.66496551e-01 -5.04746377e-01
-1.05757333e-01 6.07508570e-02 2.48223394e-02 -9.28068459e-01
7.82253891e-02 -1.14474964e+00 -2.69882679e-01 1.44774866e+00
3.35699975e-01 -1.43855834e+00 5.68869375e-02 4.46500927e-01
6.76545799e-01 -6.05668053e-02 -1.33492267e+00 -1.21128547e+00
1.39803976e-01 -2.82029510e-02 4.75153446e-01 9.06023622e-01
-2.21205533e-01 -8.11228082e-02 -5.33572249e-02 -9.94804949e-02
9.13255692e-01 6.48067474e-01 5.39584935e-01 -1.66652286e+00
-5.63400209e-01 -6.51937842e-01 -9.87919047e-02 -4.13633548e-02
-8.21228400e-02 -4.44811344e-01 -3.74055564e-01 -6.92373157e-01
-1.60797015e-01 -1.40978470e-01 -4.74162936e-01 -3.06709915e-01
3.06832373e-01 -1.32730022e-01 -1.97847545e-01 7.21073627e-01
2.39712551e-01 4.37651128e-02 5.37382662e-01 3.84635597e-01
-4.32740569e-01 5.83061576e-01 -8.27737674e-02 6.01845205e-01
1.04647100e+00 -9.15048242e-01 -1.13496408e-01 7.54349530e-01
4.99616593e-01 4.57205147e-01 4.92644817e-01 -7.10159302e-01
-1.23398483e-01 -1.41849920e-01 2.00345203e-01 -8.54363680e-01
-1.30058929e-01 -7.56633639e-01 5.61835349e-01 8.66762280e-01
-1.52198151e-01 7.36213803e-01 1.30036399e-01 7.76261985e-01
-2.96368271e-01 -2.59153873e-01 7.92166650e-01 -2.02808440e-01
-4.30866659e-01 -3.97578441e-02 -5.30260563e-01 1.77662805e-01
1.17499673e+00 6.71689026e-03 6.21161759e-02 -2.81046331e-01
-7.98666179e-01 -1.72193229e-01 5.63406408e-01 2.05083918e-02
4.82649021e-02 -9.66925681e-01 -4.78296667e-01 4.35671866e-01
-2.94462055e-01 -8.90097797e-01 -9.77040008e-02 1.21497774e+00
-6.70837998e-01 2.23490238e-01 -3.11187565e-01 -5.63430548e-01
-1.07687247e+00 7.77656794e-01 3.20770264e-01 -3.92221659e-01
-5.55212259e-01 1.34599254e-01 -5.44069707e-01 5.13955168e-02
3.35212499e-02 -6.12208545e-01 -7.92589337e-02 7.82484293e-01
4.28746194e-01 4.91534591e-01 -1.31778240e-01 -3.37004006e-01
-2.94701338e-01 4.60564435e-01 2.07575783e-01 -7.02852190e-01
1.52785206e+00 -4.37068025e-04 -4.30703193e-01 1.25426519e+00
1.05789709e+00 2.74653524e-01 -1.44877374e+00 2.18122110e-01
7.34734297e-01 -2.40325496e-01 -3.93145829e-01 -3.66974831e-01
-5.11315644e-01 5.86451173e-01 8.14971387e-01 1.31555152e+00
6.60797894e-01 -2.47831479e-01 3.00225824e-01 1.20484065e-02
1.46705076e-01 -1.50529230e+00 -3.88795286e-01 4.60849524e-01
7.30904400e-01 -1.20639598e+00 -6.23878650e-02 2.00626716e-01
-6.00767136e-01 1.18875313e+00 -3.41829002e-01 -6.68945312e-01
1.03765070e+00 3.81364584e-01 1.63920447e-01 -2.61251599e-01
-8.67326379e-01 -1.23496972e-01 -1.50342837e-01 2.44340137e-01
1.13358423e-01 2.53807187e-01 -6.95679486e-01 2.45177090e-01
-3.80444467e-01 -1.70819655e-01 5.27532279e-01 8.84432435e-01
-4.34437007e-01 -9.59220350e-01 -4.92413521e-01 4.62936699e-01
-9.73734975e-01 -6.42583966e-02 -5.84315777e-01 1.23342788e+00
-5.77367544e-01 6.78181171e-01 3.28094125e-01 2.41877705e-01
1.37416437e-01 5.89388192e-01 1.04103163e-01 -3.29170674e-02
-1.01379347e+00 4.97459888e-01 -9.56033617e-02 -3.30972522e-01
-3.21309716e-01 -1.15242410e+00 -9.72809732e-01 -4.19699639e-01
-8.00959244e-02 4.84058291e-01 7.67986953e-01 6.61781549e-01
9.59545821e-02 8.24815482e-02 9.40479457e-01 -5.14216602e-01
-1.25440824e+00 -9.37702835e-01 -1.23216927e+00 3.51155579e-01
4.86705899e-01 -5.78339696e-01 -1.06076419e+00 -2.63850331e-01] | [4.8256330490112305, 4.126916885375977] |
df86ba90-63e0-4ccf-a80d-6aedb9b086c9 | simfle-simple-facial-landmark-encoding-for | 2303.07648 | null | https://arxiv.org/abs/2303.07648v1 | https://arxiv.org/pdf/2303.07648v1.pdf | SimFLE: Simple Facial Landmark Encoding for Self-Supervised Facial Expression Recognition in the Wild | One of the key issues in facial expression recognition in the wild (FER-W) is that curating large-scale labeled facial images is challenging due to the inherent complexity and ambiguity of facial images. Therefore, in this paper, we propose a self-supervised simple facial landmark encoding (SimFLE) method that can learn effective encoding of facial landmarks, which are important features for improving the performance of FER-W, without expensive labels. Specifically, we introduce novel FaceMAE module for this purpose. FaceMAE reconstructs masked facial images with elaborately designed semantic masking. Unlike previous random masking, semantic masking is conducted based on channel information processed in the backbone, so rich semantics of channels can be explored. Additionally, the semantic masking process is fully trainable, enabling FaceMAE to guide the backbone to learn spatial details and contextual properties of fine-grained facial landmarks. Experimental results on several FER-W benchmarks prove that the proposed SimFLE is superior in facial landmark localization and noticeably improved performance compared to the supervised baseline and other self-supervised methods. | ['Seongsik Park', 'Jiyong Moon'] | 2023-03-14 | null | null | null | null | ['face-alignment', 'facial-expression-recognition'] | ['computer-vision', 'computer-vision'] | [ 3.64165604e-01 1.55107841e-01 -1.44792199e-01 -9.35125351e-01
-8.78863573e-01 -2.70844489e-01 3.37219417e-01 -5.16430855e-01
-3.52465361e-01 4.66116160e-01 2.72392780e-01 2.23015636e-01
1.11082293e-01 -4.07045275e-01 -8.29532444e-01 -7.99343765e-01
-2.39852637e-01 -3.14789079e-02 -1.74191758e-01 -3.16578269e-01
-1.15646772e-01 6.26554728e-01 -1.86252761e+00 5.31091988e-01
3.36292118e-01 1.64784765e+00 6.80549517e-02 2.59241909e-02
-3.21606338e-01 7.58287549e-01 -2.49702469e-01 -4.15883064e-01
1.64021432e-01 -4.22450006e-01 -6.99056149e-01 4.12804663e-01
7.92566121e-01 -1.79926187e-01 -2.87227090e-02 1.12667978e+00
4.70806211e-01 -1.12893537e-01 4.86293733e-01 -1.40019727e+00
-3.60762149e-01 3.11597347e-01 -7.16350377e-01 -1.14260130e-01
2.60129452e-01 -8.72677416e-02 8.41520250e-01 -1.53264809e+00
5.67487240e-01 1.46998858e+00 8.34452689e-01 9.17001069e-01
-1.22286701e+00 -1.18485332e+00 2.64124513e-01 5.77894188e-02
-1.92228997e+00 -1.13458359e+00 9.10075784e-01 -1.73812553e-01
2.66493022e-01 1.59644589e-01 2.34714419e-01 9.45335209e-01
-3.49286735e-01 6.52141094e-01 1.40225744e+00 -3.59841526e-01
2.22501665e-01 -9.72134992e-02 -3.16570461e-01 1.46460748e+00
-4.05769527e-01 6.53232858e-02 -1.09974873e+00 -1.12468116e-01
6.28372550e-01 1.05693467e-01 -1.24760330e-01 -1.34851918e-01
-7.53614664e-01 5.75142741e-01 5.65831184e-01 4.64467593e-02
-1.76107645e-01 3.40971768e-01 1.33948147e-01 2.36456692e-01
6.31345809e-01 -1.10838771e-01 -2.46680021e-01 1.51319489e-01
-1.18534374e+00 -7.80153871e-02 1.78800166e-01 7.93463826e-01
1.17927325e+00 8.17847028e-02 -2.53424257e-01 8.50422502e-01
4.68813360e-01 6.14075124e-01 1.86239198e-01 -1.00896394e+00
2.85151929e-01 5.76476932e-01 -1.41098693e-01 -1.10465550e+00
-4.83335495e-01 -3.21737155e-02 -1.02648520e+00 3.37367475e-01
1.82827845e-01 2.27945969e-02 -1.16396368e+00 2.12614083e+00
3.15011948e-01 5.46654999e-01 -1.32438049e-01 9.50298250e-01
1.04513717e+00 2.86896974e-01 3.56732428e-01 -3.97374369e-02
1.31215775e+00 -9.05958116e-01 -8.84722352e-01 -2.70141155e-01
3.58318567e-01 -6.27612352e-01 8.48591089e-01 -2.21799151e-03
-9.42869008e-01 -7.11418211e-01 -6.96240842e-01 5.19599020e-02
-3.81178975e-01 3.18970144e-01 1.00384259e+00 6.66133165e-01
-1.26860785e+00 3.81802857e-01 -6.24682188e-01 4.50430997e-02
1.29838419e+00 5.68069518e-01 -9.47322726e-01 -2.23760679e-01
-1.04954708e+00 3.80001366e-01 -4.20893878e-02 6.32556677e-01
-1.10905349e+00 -6.81575537e-01 -1.30227816e+00 6.29030680e-03
1.37582511e-01 -3.48303514e-03 9.44779634e-01 -1.36530006e+00
-1.54655445e+00 1.22235501e+00 -7.71385849e-01 -3.44499424e-02
3.02417725e-01 1.03060290e-01 -4.36192065e-01 3.38650942e-01
2.14565665e-01 1.15136266e+00 1.26859546e+00 -1.40031075e+00
-4.30935174e-01 -6.16950929e-01 -3.36684138e-01 -1.05656564e-01
-4.24745560e-01 2.29784518e-01 -6.41154885e-01 -6.93761647e-01
1.97485581e-01 -6.90596879e-01 -1.48999304e-01 5.42493999e-01
-2.34766528e-01 -9.25384313e-02 7.71349907e-01 -6.20689273e-01
9.41179872e-01 -2.53134418e+00 -9.10594836e-02 5.62091887e-01
2.89099783e-01 1.65523022e-01 -5.25968254e-01 -4.36483733e-02
-2.89268911e-01 -6.02397881e-02 -4.00632501e-01 -8.66666019e-01
-3.37807685e-02 2.36124814e-01 -3.31381947e-01 6.46677554e-01
5.76642811e-01 1.12384272e+00 -7.23760366e-01 -6.62765205e-01
-1.58141240e-01 7.11388588e-01 -5.44995308e-01 2.24351183e-01
-6.32553250e-02 7.60356307e-01 -3.26869845e-01 1.27959120e+00
1.10065019e+00 -2.74751484e-01 -5.62874191e-02 -3.92673671e-01
2.42836982e-01 -3.29903752e-01 -9.70778584e-01 2.06077337e+00
-5.28911591e-01 3.93667400e-01 4.37462032e-01 -7.46121049e-01
9.90577221e-01 2.51246452e-01 5.04123151e-01 -8.77965927e-01
1.11135736e-01 1.45885184e-01 -6.39818311e-01 -2.95550227e-01
-1.04509339e-01 -1.70544818e-01 1.19606473e-01 4.09511834e-01
3.88070107e-01 3.44808608e-01 -2.49066859e-01 4.93238587e-03
9.20663238e-01 2.16243267e-01 -1.83502331e-01 -1.35419339e-01
6.59079432e-01 -5.24609745e-01 6.96791410e-01 2.41716206e-01
-2.49235094e-01 6.39040172e-01 2.60785937e-01 -4.06300634e-01
-2.74440795e-01 -1.03498268e+00 -2.38571942e-01 1.47757685e+00
7.52827376e-02 -5.46691537e-01 -1.06923676e+00 -8.53468001e-01
7.82450065e-02 -1.85533196e-01 -1.04140377e+00 -1.22196183e-01
-3.71824563e-01 -5.14427722e-01 7.70426273e-01 5.71067333e-01
7.82876432e-01 -1.00143135e+00 -1.79268315e-01 -1.43776119e-01
-2.80918986e-01 -1.35762990e+00 -6.39394104e-01 1.36374876e-01
-4.42438573e-01 -9.70229745e-01 -5.94643474e-01 -1.02771378e+00
1.20985126e+00 3.59310210e-01 8.36040854e-01 3.69294256e-01
-6.65018201e-01 1.43793941e-01 -1.96991920e-01 -2.75154859e-01
9.91873294e-02 -3.29100311e-01 -1.43682316e-01 8.30402970e-01
4.99708265e-01 -4.26841319e-01 -7.04466522e-01 6.02019370e-01
-9.16057825e-01 -2.11829811e-01 5.81702828e-01 1.03027427e+00
8.21085572e-01 1.76880732e-02 4.82557446e-01 -1.02545977e+00
8.42263326e-02 -2.82687813e-01 -5.06785214e-01 2.52408773e-01
-2.30594620e-01 -5.07829152e-02 2.92636991e-01 -1.92880511e-01
-1.12265730e+00 5.44999003e-01 -3.47892880e-01 -4.89588380e-01
-2.02393666e-01 1.18076861e-01 -6.06637001e-01 -7.04488575e-01
3.90363485e-01 2.07655951e-01 3.92716110e-01 -4.41258013e-01
3.72015089e-01 5.71950972e-01 5.49324930e-01 -6.11204922e-01
7.74972022e-01 1.05647337e+00 3.12913239e-01 -6.07478738e-01
-1.10707164e+00 -3.72215122e-01 -7.64556468e-01 -2.25672513e-01
8.01023960e-01 -1.19138515e+00 -7.46355772e-01 5.05126834e-01
-1.02103209e+00 -4.31700110e-01 -1.26942247e-01 -7.91720524e-02
-5.78687489e-01 2.82084774e-02 -4.21061218e-01 -6.65489793e-01
-1.29117919e-02 -1.22308290e+00 1.75804353e+00 2.53858548e-02
-1.11501858e-01 -7.99922109e-01 -3.18261385e-01 3.81544054e-01
5.12639284e-01 2.71861881e-01 4.66595769e-01 6.99459016e-02
-6.27593994e-01 7.79930223e-03 -4.45813775e-01 5.04207253e-01
2.95896292e-01 -4.23293710e-01 -1.60774088e+00 -1.33116186e-01
-2.96461791e-01 -6.93470478e-01 1.09039509e+00 -1.43273419e-03
1.75053811e+00 -2.31201798e-01 -2.94938058e-01 1.14877880e+00
1.23025191e+00 -3.44228446e-01 6.90928102e-01 -8.45057741e-02
6.09019458e-01 8.62603128e-01 6.62592828e-01 4.70829546e-01
3.66431475e-01 7.68004119e-01 5.16962349e-01 -7.24361002e-01
-3.44061643e-01 -5.46160936e-01 3.16034138e-01 2.10184857e-01
-6.42577484e-02 4.88618255e-01 -6.03782773e-01 2.01014295e-01
-1.67443037e+00 -8.82276833e-01 5.85054755e-01 1.71670091e+00
1.04761457e+00 -5.28178930e-01 -1.47169843e-01 1.17792077e-01
5.67130744e-01 3.85962129e-01 -2.81282991e-01 1.89112835e-02
-4.13368762e-01 7.32812703e-01 3.87373269e-01 5.58687091e-01
-1.30468917e+00 1.43000841e+00 6.26761150e+00 1.09940624e+00
-1.21798992e+00 4.31166291e-01 1.00681973e+00 1.01034649e-01
-6.71941116e-02 -4.08824950e-01 -9.89559472e-01 4.21268523e-01
5.27336299e-01 5.12452483e-01 4.79686409e-01 7.64082730e-01
7.92157203e-02 9.87683982e-02 -1.03639448e+00 1.36724174e+00
3.70491832e-01 -1.31889606e+00 -3.68616660e-03 -1.12736501e-01
6.49551094e-01 -3.92692953e-01 4.38775808e-01 2.22345460e-02
4.69171628e-02 -1.57007277e+00 6.82934999e-01 5.10272384e-01
1.38489723e+00 -8.17308784e-01 5.02079785e-01 -2.10289374e-01
-1.28974628e+00 3.83579955e-02 -3.93689334e-01 2.33058721e-01
-2.02618167e-02 4.00818855e-01 -4.15402263e-01 6.43118471e-02
8.78209770e-01 6.77051723e-01 -7.27293253e-01 4.17664617e-01
-4.24302340e-01 5.05241632e-01 -1.96197227e-01 5.78318596e-01
1.86752915e-01 4.63237539e-02 -1.61915645e-01 1.27163208e+00
1.03863135e-01 1.61257293e-02 7.33498856e-02 6.66569889e-01
-3.93199116e-01 9.60422605e-02 -5.05011261e-01 2.12328196e-01
3.39928329e-01 1.46897721e+00 -5.49661756e-01 2.05230787e-01
-4.64063942e-01 1.35761285e+00 4.74973619e-01 5.58806956e-01
-8.54131162e-01 -5.06212115e-02 9.92235541e-01 1.99255705e-01
3.86155993e-01 1.90805551e-02 1.07010223e-01 -8.01765382e-01
1.27209619e-01 -7.97865331e-01 2.24422470e-01 -4.73569393e-01
-1.23497152e+00 8.18280697e-01 -4.44601953e-01 -8.81815851e-01
1.02258474e-01 -8.05864155e-01 -3.29397321e-01 7.13689208e-01
-2.16753721e+00 -1.61788690e+00 -6.12127185e-01 1.21794236e+00
1.92504913e-01 -3.97498339e-01 1.16192567e+00 4.99190569e-01
-4.22240555e-01 1.14377689e+00 -3.57031584e-01 5.15963733e-01
8.66753340e-01 -8.00031364e-01 1.15178443e-01 4.98208255e-01
4.57947940e-01 6.49695992e-01 2.28795800e-02 -3.74073356e-01
-1.44553256e+00 -1.60605645e+00 7.54054964e-01 -2.31114790e-01
4.57306743e-01 -8.14303637e-01 -6.23606622e-01 5.76046169e-01
-1.64257273e-01 9.87138152e-01 1.10280871e+00 -1.11633919e-01
-8.59071434e-01 -4.75133598e-01 -1.25915527e+00 3.96534681e-01
1.52486873e+00 -8.66869271e-01 1.02519937e-01 3.70966077e-01
3.32409829e-01 -2.42138907e-01 -6.10714257e-01 5.76637566e-01
5.63221753e-01 -9.21091080e-01 1.01011431e+00 -6.12395227e-01
2.29565322e-01 -3.38976294e-01 -3.73219848e-01 -1.12616575e+00
2.39407271e-02 -8.57976377e-01 1.64185837e-01 1.32331920e+00
2.97349930e-01 -5.37034690e-01 1.02671123e+00 3.51306617e-01
2.79399455e-01 -6.98303640e-01 -1.19355118e+00 -5.59277833e-01
-4.44333404e-01 -2.63413578e-01 9.60846782e-01 9.36517239e-01
-3.49359900e-01 -6.15666769e-02 -5.00784516e-01 2.42433637e-01
8.95249486e-01 2.05894366e-01 7.76107848e-01 -1.01617777e+00
5.71483150e-02 -2.49280423e-01 -6.87681675e-01 -1.03762567e+00
8.73439074e-01 -1.04366767e+00 1.30879536e-01 -8.41031134e-01
8.35784003e-02 -6.29182398e-01 -3.69630396e-01 9.26973641e-01
-1.65350214e-01 9.98654008e-01 -1.00865453e-01 -1.95671059e-03
-9.34596837e-01 7.54925907e-01 1.14321113e+00 -1.39835566e-01
2.78385848e-01 -2.71013826e-01 -7.39218175e-01 8.69151711e-01
4.70623940e-01 -3.10728580e-01 -2.72891104e-01 -5.34112155e-01
-1.24413677e-01 -2.91035324e-01 5.27247369e-01 -7.32636034e-01
3.46385270e-01 -4.46183793e-02 6.08924747e-01 -1.33024871e-01
5.84531784e-01 -1.00518739e+00 -1.49893045e-01 -1.34735420e-01
-3.57863247e-01 -1.49125993e-01 2.33810112e-01 5.34939647e-01
-7.40721643e-01 2.63188213e-01 9.30655479e-01 -9.22558904e-02
-1.12891698e+00 7.80283928e-01 7.43955597e-02 -7.78702646e-02
9.46350396e-01 -1.63232058e-01 8.39859918e-02 -3.68843883e-01
-8.48157883e-01 3.77547182e-02 2.37090752e-01 3.42101932e-01
9.49001312e-01 -1.55406475e+00 -5.20854235e-01 8.37805331e-01
4.50897485e-01 -1.49380732e-02 3.30663830e-01 6.69317305e-01
-3.46272260e-01 1.10035181e-01 -2.74642617e-01 -5.95063806e-01
-1.35133648e+00 2.86949784e-01 2.95598269e-01 2.79632956e-01
-2.68882543e-01 1.35396028e+00 4.49033946e-01 -3.65653604e-01
5.98224819e-01 1.57374352e-01 -4.54339832e-02 8.00948963e-02
9.63682175e-01 -1.26714081e-01 1.41847879e-01 -1.19013417e+00
-4.28524822e-01 9.34770405e-01 1.06971979e-01 6.25474155e-02
1.33945549e+00 -2.15326458e-01 -4.89491820e-01 -1.03329532e-01
1.51685023e+00 1.81878820e-01 -1.45144999e+00 -5.31881511e-01
5.48803760e-03 -7.76270688e-01 2.18527079e-01 -4.82728541e-01
-1.53792644e+00 1.01580381e+00 8.51143420e-01 -7.91722178e-01
1.54327559e+00 1.19880147e-01 6.22527242e-01 1.64568856e-01
6.28026009e-01 -9.08465922e-01 2.70002067e-01 1.34345010e-01
8.17309141e-01 -1.44138682e+00 -4.05771971e-01 -8.90861690e-01
-6.17112219e-01 8.55422080e-01 5.88962674e-01 2.00613439e-01
1.00279880e+00 5.07947445e-01 4.55614686e-01 -4.36648756e-01
-3.05316478e-01 -5.20431161e-01 2.83458441e-01 6.55250788e-01
2.57664025e-01 -2.35552207e-01 3.76539171e-01 6.26442254e-01
6.29420101e-04 1.17496498e-01 -5.01738012e-01 8.02835763e-01
-2.32786447e-01 -1.12476766e+00 -3.18833172e-01 -4.05682670e-03
-5.23935616e-01 -1.21157847e-01 -3.02381396e-01 4.79992360e-01
3.80765676e-01 7.78469920e-01 5.16152419e-02 -3.01069081e-01
5.58991060e-02 1.86242640e-01 5.81748664e-01 -6.55440509e-01
-2.37247482e-01 3.46880965e-03 -2.69800365e-01 -1.18658912e+00
-6.96991801e-01 -2.69293010e-01 -1.30646050e+00 1.02950752e-01
-4.42585573e-02 1.35843277e-01 6.78005099e-01 8.03873420e-01
5.16083658e-01 1.07428432e-01 9.23367977e-01 -8.96777451e-01
-2.96222150e-01 -6.03090227e-01 -7.16029882e-01 6.72095299e-01
5.71683586e-01 -1.09854949e+00 -2.74676621e-01 2.07203865e-01] | [13.573103904724121, 1.1483415365219116] |
6ef8c5b6-10ac-42d6-a843-2b6c415cba78 | word-embedding-algorithms-as-generalized-low | 1911.02639 | null | https://arxiv.org/abs/1911.02639v1 | https://arxiv.org/pdf/1911.02639v1.pdf | Word Embedding Algorithms as Generalized Low Rank Models and their Canonical Form | Word embedding algorithms produce very reliable feature representations of words that are used by neural network models across a constantly growing multitude of NLP tasks. As such, it is imperative for NLP practitioners to understand how their word representations are produced, and why they are so impactful. The present work presents the Simple Embedder framework, generalizing the state-of-the-art existing word embedding algorithms (including Word2vec (SGNS) and GloVe) under the umbrella of generalized low rank models. We derive that both of these algorithms attempt to produce embedding inner products that approximate pointwise mutual information (PMI) statistics in the corpus. Once cast as Simple Embedders, comparison of these models reveals that these successful embedders all resemble a straightforward maximum likelihood estimate (MLE) of the PMI parametrized by the inner product (between embeddings). This MLE induces our proposed novel word embedding model, Hilbert-MLE, as the canonical representative of the Simple Embedder framework. We empirically compare these algorithms with evaluations on 17 different datasets. Hilbert-MLE consistently observes second-best performance on every extrinsic evaluation (news classification, sentiment analysis, POS-tagging, and supersense tagging), while the first-best model depends varying on the task. Moreover, Hilbert-MLE consistently observes the least variance in results with respect to the random initialization of the weights in bidirectional LSTMs. Our empirical results demonstrate that Hilbert-MLE is a very consistent word embedding algorithm that can be reliably integrated into existing NLP systems to obtain high-quality results. | ['Kian Kenyon-Dean'] | 2019-11-06 | null | null | null | null | ['news-classification'] | ['natural-language-processing'] | [ 9.85523388e-02 2.87889302e-01 -4.48640496e-01 -2.05817431e-01
-5.48762918e-01 -6.44619226e-01 1.09460878e+00 3.83735090e-01
-8.33831608e-01 3.60912591e-01 5.36521673e-01 -5.86869836e-01
-1.75086334e-01 -8.16109002e-01 -5.14896989e-01 -6.84796631e-01
-6.70625493e-02 3.50544333e-01 -1.46820098e-01 -3.11452687e-01
1.08065218e-01 4.23780918e-01 -1.09811401e+00 -8.79423544e-02
4.62473571e-01 8.06109011e-01 -5.52697554e-02 5.48297763e-01
-3.71881336e-01 3.28561276e-01 -4.82620180e-01 -8.79595697e-01
1.68640509e-01 5.44535704e-02 -6.73532546e-01 -6.32342756e-01
2.77996987e-01 3.73291261e-02 -5.37528276e-01 1.08719134e+00
3.95320505e-01 2.15736434e-01 9.12672758e-01 -1.11072755e+00
-1.33762622e+00 8.95333886e-01 -1.55167744e-01 1.60058185e-01
1.08736359e-01 -6.31355718e-02 1.82707500e+00 -1.26140285e+00
7.05537140e-01 1.18703508e+00 7.38829672e-01 3.97015363e-01
-1.50614345e+00 -4.41925824e-01 -8.57912824e-02 2.23191023e-01
-1.29320610e+00 -7.92026818e-02 6.70751810e-01 -3.07696521e-01
1.42975414e+00 2.50887603e-01 4.93291110e-01 1.40828383e+00
4.09892261e-01 7.49273539e-01 1.01672304e+00 -7.26801276e-01
1.32618397e-01 5.67297995e-01 6.98053598e-01 6.23878896e-01
5.75390875e-01 1.05494425e-01 -6.77005768e-01 -3.75535578e-01
2.44892269e-01 -1.23516791e-01 -3.39965492e-01 -4.40239877e-01
-1.34017539e+00 1.06215072e+00 2.89375901e-01 8.46791208e-01
-3.75836074e-01 4.29744750e-01 3.73452604e-01 3.30099493e-01
5.81531286e-01 6.00706875e-01 -5.20576596e-01 -2.54284352e-01
-7.45868862e-01 1.47552803e-01 9.38125491e-01 5.53294420e-01
6.23867691e-01 2.50607401e-01 -2.91875869e-01 9.37935650e-01
6.07868254e-01 4.17923272e-01 8.56542587e-01 -3.90888989e-01
2.86388874e-01 4.24752355e-01 -4.47706878e-02 -1.36077034e+00
-2.47773513e-01 -6.59375608e-01 -5.76895177e-01 -9.43002626e-02
1.30617008e-01 -4.06678505e-02 -6.80984437e-01 1.89758337e+00
-3.73212211e-02 4.45196815e-02 1.67858928e-01 4.47632164e-01
7.38092601e-01 9.45127666e-01 1.80198029e-01 1.28856391e-01
1.51123106e+00 -8.40771496e-01 -8.81369472e-01 -3.70297968e-01
8.95519495e-01 -6.92569733e-01 1.08997786e+00 1.00881763e-01
-6.45471632e-01 -3.94494474e-01 -1.33452284e+00 -1.47793800e-01
-9.69555676e-01 6.00248724e-02 6.03033662e-01 7.79858708e-01
-1.13585103e+00 8.42601895e-01 -6.49327457e-01 -3.73102695e-01
5.82381226e-02 2.94252306e-01 -7.36901939e-01 1.42234623e-01
-1.57434118e+00 1.38040590e+00 5.77221215e-01 1.33768618e-01
-3.82339537e-01 -6.83473229e-01 -1.09600425e+00 1.00653321e-01
-2.99492538e-01 -4.65431869e-01 8.67008507e-01 -5.45965433e-01
-1.33828437e+00 8.49619448e-01 -1.75558582e-01 -6.50974989e-01
-1.08009100e-01 -3.06878656e-01 -5.38272798e-01 -3.76377970e-01
-3.17465276e-01 7.55900264e-01 6.99765086e-01 -1.15654349e+00
-2.00772375e-01 -1.45999983e-01 -4.32688855e-02 -1.36592891e-02
-1.05802202e+00 -1.58132434e-01 3.76376882e-02 -6.65072083e-01
-1.45811006e-01 -7.28096843e-01 -2.91639678e-02 -4.51040715e-02
-3.88477087e-01 -5.55256486e-01 5.10897577e-01 -6.47847533e-01
1.46882522e+00 -2.21843863e+00 2.24514619e-01 1.53187945e-01
3.03094596e-01 5.05828381e-01 -5.74596047e-01 8.24876547e-01
-3.98396969e-01 4.04277653e-01 -2.08232388e-01 -5.73075056e-01
6.92771137e-01 5.17262399e-01 -4.75745320e-01 6.92854822e-01
3.01182330e-01 1.09822798e+00 -9.11913097e-01 -3.12177360e-01
1.83664143e-01 8.51518810e-01 -3.46369714e-01 7.62746111e-02
1.28179058e-01 -4.06324297e-01 -9.49745327e-02 2.05304354e-01
3.11784565e-01 -4.29528803e-02 1.58817559e-01 -3.95802945e-01
-1.38294790e-02 5.31770289e-01 -9.15433764e-01 1.54269779e+00
-6.25401616e-01 1.00978839e+00 -4.57466304e-01 -1.05325782e+00
1.10961282e+00 5.06610096e-01 2.33205095e-01 -3.54788005e-01
2.97682434e-01 3.16336453e-01 2.56790400e-01 -3.04946005e-01
7.53374577e-01 -1.91673115e-01 -3.41534987e-02 5.46961367e-01
5.96710861e-01 2.09700048e-01 2.70057142e-01 2.56455868e-01
1.21045017e+00 -1.47714436e-01 5.24338841e-01 -3.28362912e-01
3.54130507e-01 -3.65500748e-01 2.14684531e-01 6.65545523e-01
-2.12985933e-01 4.01723921e-01 4.76481140e-01 -3.27367514e-01
-1.13031578e+00 -1.12831056e+00 -4.45895791e-01 8.98099780e-01
-3.93114150e-01 -4.80601817e-01 -4.58020151e-01 -5.37180960e-01
-6.22353107e-02 1.23379052e+00 -8.31333458e-01 -3.18476856e-01
-3.37917030e-01 -8.65312219e-01 8.23857725e-01 3.79378766e-01
-9.60871577e-02 -1.10133040e+00 -2.01102421e-01 1.54822201e-01
1.01092733e-01 -1.12793624e+00 -1.97164789e-01 5.00142038e-01
-6.61594987e-01 -7.22847104e-01 -6.63519263e-01 -8.93382668e-01
4.27442074e-01 -7.72626102e-02 1.18596804e+00 -1.90155685e-01
-1.28528148e-01 4.18047100e-01 -6.18874013e-01 -2.99104750e-01
-3.29231709e-01 1.40894443e-01 2.45137900e-01 8.68304446e-02
9.79378521e-01 -7.69656956e-01 -3.01707178e-01 -2.81882167e-01
-1.10888076e+00 -4.77327883e-01 6.87545419e-01 1.08184087e+00
3.09091926e-01 -2.53483444e-01 3.14311117e-01 -7.85903692e-01
1.14416289e+00 -5.61731637e-01 -1.75973058e-01 1.81579411e-01
-9.42718327e-01 3.90915722e-01 4.52297539e-01 -5.57448745e-01
-5.16203344e-01 -4.67677474e-01 -4.74371344e-01 -2.31479123e-01
-5.02896979e-02 6.86250806e-01 1.61271095e-01 7.04704300e-02
6.24293923e-01 3.95163238e-01 -2.75152206e-01 -5.70081413e-01
9.55482781e-01 7.09383309e-01 1.97714105e-01 -3.93455058e-01
9.78443205e-01 2.35480711e-01 -2.44354829e-01 -9.81119454e-01
-6.76858068e-01 -4.37754095e-01 -6.10510290e-01 2.53353715e-01
9.32232797e-01 -5.24569690e-01 -3.83265167e-01 1.08370788e-01
-1.57789898e+00 1.02184452e-01 -4.73212749e-01 7.22601771e-01
-1.22590415e-01 4.93678182e-01 -5.97911179e-01 -8.27412963e-01
-4.84300375e-01 -9.33995545e-01 8.82067859e-01 -2.14634076e-01
-6.70687616e-01 -1.62831008e+00 5.72136641e-01 -7.04073086e-02
5.35344839e-01 6.85788020e-02 1.42212713e+00 -1.23349547e+00
1.21020637e-01 -5.77678919e-01 -1.89480290e-01 7.03801811e-01
-7.22728553e-04 -6.02582209e-02 -1.15841627e+00 -1.82715312e-01
-1.20634288e-01 -2.16400400e-02 9.07656014e-01 2.32865870e-01
5.94179451e-01 -5.16129792e-01 -1.70233384e-01 4.93859380e-01
1.57227170e+00 -1.06305160e-01 5.92814863e-01 4.87815440e-01
5.75276315e-01 5.54608166e-01 7.92158097e-02 9.48900580e-02
1.96868852e-01 6.43749774e-01 4.22464550e-01 4.76871207e-02
-7.51282135e-03 -4.34214920e-01 7.51999974e-01 1.51761496e+00
1.27626300e-01 -3.64710987e-01 -7.61703491e-01 8.59010518e-01
-1.53158450e+00 -7.76743054e-01 -1.21870801e-01 2.06905937e+00
8.39244425e-01 1.71105579e-01 -2.99828738e-01 2.94766068e-01
3.96063894e-01 7.74618387e-01 -3.66368368e-02 -9.19167340e-01
-1.06509782e-01 6.22044265e-01 5.70531070e-01 6.10128164e-01
-9.15281475e-01 8.56984854e-01 6.42986584e+00 9.57325995e-01
-9.08166170e-01 4.52087879e-01 1.14903212e-01 1.63888544e-01
-7.24741042e-01 -1.85087826e-02 -8.59220207e-01 3.14683229e-01
1.24506342e+00 -2.20427081e-01 2.86166877e-01 8.18776011e-01
-1.52609363e-01 4.92091745e-01 -1.31872594e+00 9.59366441e-01
2.59416372e-01 -1.20835245e+00 1.81558356e-01 1.62751362e-01
5.47068655e-01 2.59630919e-01 2.38514766e-01 4.93835360e-01
3.58537108e-01 -1.05787563e+00 4.79250282e-01 2.06562728e-01
5.05933702e-01 -5.42823076e-01 9.61877704e-01 1.17176928e-01
-9.09214497e-01 9.36654508e-02 -5.57855606e-01 -2.92220842e-02
3.67440492e-01 8.91951501e-01 -7.37141430e-01 3.59241128e-01
1.98611751e-01 7.00098872e-01 -3.89706224e-01 4.36459243e-01
-4.31735486e-01 7.40193069e-01 -2.20097035e-01 -5.57029963e-01
5.98806083e-01 -2.65087366e-01 8.18917096e-01 1.56526387e+00
3.77978295e-01 -4.35408980e-01 -3.63512874e-01 9.65032279e-01
-2.77898818e-01 4.55181450e-01 -9.36405778e-01 -7.38954544e-01
4.48742181e-01 1.40786862e+00 -3.13514769e-01 -1.72511563e-01
-4.75848645e-01 7.77423263e-01 5.78769505e-01 3.87299001e-01
-7.50818908e-01 -6.15846872e-01 1.06886101e+00 -2.94252366e-01
3.35004121e-01 -4.57175523e-01 -2.05153033e-01 -1.10420167e+00
1.25047117e-01 -6.73594773e-01 -3.04684136e-03 -4.66340631e-01
-1.52789378e+00 8.82717431e-01 -7.48464763e-02 -9.55374777e-01
-3.33436191e-01 -1.29241192e+00 -4.83579636e-01 7.81118631e-01
-1.66352403e+00 -8.35340917e-01 3.78936052e-01 4.49513644e-02
3.39646280e-01 -2.98712134e-01 1.30279481e+00 3.36712927e-01
-6.17225349e-01 6.80205226e-01 2.93376476e-01 3.82488281e-01
3.90319079e-01 -1.36077774e+00 5.83809853e-01 7.08036423e-01
1.07439160e+00 1.01940215e+00 9.04594779e-01 -1.88737094e-01
-1.46766329e+00 -9.15845037e-01 1.58120382e+00 -5.71121752e-01
1.18653858e+00 -4.37542766e-01 -7.67459810e-01 7.30166852e-01
5.03532112e-01 6.90485910e-03 8.82109046e-01 4.26644981e-01
-8.13685954e-01 8.86148438e-02 -7.04632103e-01 7.35103428e-01
6.33390307e-01 -7.71460533e-01 -1.08070397e+00 4.60142404e-01
9.80242014e-01 1.59179136e-01 -9.31068242e-01 2.65287131e-01
6.17625535e-01 -6.83207810e-01 1.08098853e+00 -8.48041415e-01
4.77389634e-01 6.93212375e-02 -5.22369504e-01 -1.52589917e+00
-4.20264363e-01 -3.04175049e-01 -3.59971136e-01 1.28667617e+00
7.69718945e-01 -9.44281220e-01 3.83231610e-01 4.24794972e-01
1.06326975e-01 -1.14117944e+00 -1.03956831e+00 -9.73740697e-01
3.29880387e-01 -7.99777210e-01 3.00124973e-01 1.12808645e+00
2.10850969e-01 4.40807432e-01 -2.37589374e-01 -7.98830166e-02
5.45606434e-01 -3.63911361e-01 4.43230510e-01 -1.20477080e+00
-4.61600363e-01 -6.55720115e-01 -8.07676613e-01 -1.04997802e+00
5.28193474e-01 -1.40542209e+00 -1.29756108e-01 -1.66143048e+00
2.54402123e-02 -1.40464857e-01 -6.41391695e-01 5.55776119e-01
1.54005904e-02 1.81235507e-01 1.28014594e-01 -9.67167988e-02
-5.53569011e-02 7.79796064e-01 6.23756647e-01 -3.05792421e-01
8.33067819e-02 -4.89421070e-01 -5.69950759e-01 7.79013872e-01
6.35664582e-01 -7.67750502e-01 -1.62312001e-01 -6.09719992e-01
7.02402353e-01 -7.18070745e-01 1.97129592e-01 -6.85162425e-01
8.07976350e-02 3.00297260e-01 -3.81604172e-02 -1.65647507e-01
5.65562963e-01 -7.87130952e-01 -2.38423645e-01 3.26793075e-01
-5.15297949e-01 2.70043582e-01 8.78939554e-02 5.39345741e-01
-2.94998676e-01 -6.67009711e-01 3.77540469e-01 2.06212416e-01
-5.97645342e-01 1.06826186e-01 -3.17213535e-01 1.92795560e-01
6.34114027e-01 -3.30498666e-02 -2.21579388e-01 -2.72884607e-01
-5.00930071e-01 -2.97924310e-01 -6.08687438e-02 6.68294191e-01
5.44101417e-01 -1.55610991e+00 -6.75475657e-01 1.34496495e-01
1.35397971e-01 -7.95308530e-01 -2.05813989e-01 7.11448431e-01
-3.04226011e-01 6.77890122e-01 2.34794185e-01 -1.76350683e-01
-9.93868947e-01 4.36292887e-01 3.60690989e-02 -5.96662879e-01
-5.54943383e-01 9.62158978e-01 -7.92744532e-02 -6.70547128e-01
2.52871722e-01 -4.06183600e-01 -3.23687851e-01 2.74467647e-01
4.65123862e-01 1.56431571e-01 5.19910604e-02 -7.79489756e-01
-2.09546506e-01 4.07294929e-01 -5.70461433e-03 -3.06545615e-01
1.59480727e+00 1.84461609e-01 -3.06769997e-01 8.32318604e-01
1.65972459e+00 -8.39356706e-02 -4.03244674e-01 -3.96926373e-01
1.44821763e-01 -2.28082329e-01 3.18963468e-01 -4.25210685e-01
-7.78790116e-01 1.22490621e+00 5.94610989e-01 4.00759667e-01
4.22206104e-01 -2.17589185e-01 9.50374424e-01 5.72796822e-01
2.07267091e-01 -9.61910546e-01 -1.96489453e-01 5.38048148e-01
7.15885580e-01 -7.86821425e-01 2.43328195e-02 -7.08324686e-02
-3.62554461e-01 1.03821957e+00 2.36816388e-02 -3.87126774e-01
9.09685969e-01 1.62492752e-01 2.85894517e-03 -2.38480479e-01
-8.16912055e-01 -6.24066517e-02 4.87912744e-01 5.65217793e-01
6.17131233e-01 1.05869286e-02 -7.10153520e-01 5.99604309e-01
-3.22640449e-01 -5.81135094e-01 2.13926569e-01 4.50354069e-01
-2.84485161e-01 -1.33555710e+00 -1.91690326e-02 2.71476448e-01
-4.96067524e-01 -5.16506493e-01 -3.10117036e-01 8.86873066e-01
-1.53354211e-02 7.31263518e-01 -2.41070278e-02 -5.02800941e-01
1.88476533e-01 6.20503843e-01 2.22042263e-01 -5.98754108e-01
-6.13431096e-01 -5.58995187e-01 1.48928791e-01 -3.44651610e-01
-3.21310639e-01 -3.08344722e-01 -9.50972915e-01 -6.06235042e-02
-4.68550295e-01 1.69883966e-01 1.08789694e+00 1.07725918e+00
2.04500943e-01 4.44640726e-01 3.48813862e-01 -7.64517963e-01
-9.17854548e-01 -1.22564757e+00 -6.89015925e-01 5.59110284e-01
7.75631890e-02 -5.61208308e-01 -7.10986197e-01 -1.93384469e-01] | [10.544248580932617, 8.664650917053223] |
7a605e3f-3539-42a9-9d3e-e2f2c4275dcd | learning-sequential-and-structural | null | null | https://aclanthology.org/2021.findings-acl.251 | https://aclanthology.org/2021.findings-acl.251.pdf | Learning Sequential and Structural Information for Source Code Summarization | null | ['Jee-Hyong Lee', 'CheolWon Na', 'JinYeong Bak', 'YunSeok Choi'] | null | null | null | null | findings-acl-2021-8 | ['code-summarization'] | ['computer-code'] | [-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] | [-7.334739685058594, 3.7157199382781982] |
6f7469e0-a8bc-4ec7-a44b-cbfcaca5a17e | 190412181 | 1904.12181 | null | http://arxiv.org/abs/1904.12181v1 | http://arxiv.org/pdf/1904.12181v1.pdf | Non-Local Context Encoder: Robust Biomedical Image Segmentation against Adversarial Attacks | Recent progress in biomedical image segmentation based on deep convolutional
neural networks (CNNs) has drawn much attention. However, its vulnerability
towards adversarial samples cannot be overlooked. This paper is the first one
that discovers that all the CNN-based state-of-the-art biomedical image
segmentation models are sensitive to adversarial perturbations. This limits the
deployment of these methods in safety-critical biomedical fields. In this
paper, we discover that global spatial dependencies and global contextual
information in a biomedical image can be exploited to defend against
adversarial attacks. To this end, non-local context encoder (NLCE) is proposed
to model short- and long range spatial dependencies and encode global contexts
for strengthening feature activations by channel-wise attention. The NLCE
modules enhance the robustness and accuracy of the non-local context encoding
network (NLCEN), which learns robust enhanced pyramid feature representations
with NLCE modules, and then integrates the information across different levels.
Experiments on both lung and skin lesion segmentation datasets have
demonstrated that NLCEN outperforms any other state-of-the-art biomedical image
segmentation methods against adversarial attacks. In addition, NLCE modules can
be applied to improve the robustness of other CNN-based biomedical image
segmentation methods. | ['Guanbin Li?', 'Huiyou Chang', 'Haofeng Li', 'Yizhou Yu', 'Sibei Yang', 'Xiang He'] | 2019-04-27 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 6.66616440e-01 1.65838411e-03 3.26874182e-02 -1.91167474e-01
-6.25076473e-01 -6.65648162e-01 7.74687901e-02 3.72548588e-02
-6.10318542e-01 5.67818999e-01 -7.97280148e-02 -4.67258513e-01
6.83248639e-02 -7.59278238e-01 -8.82642269e-01 -1.15741265e+00
-1.80159539e-01 -3.57837886e-01 5.15233576e-01 -2.76704073e-01
8.61888975e-02 8.85820866e-01 -6.22236609e-01 8.24779451e-01
7.16381669e-01 1.01646435e+00 -2.63168782e-01 8.40926588e-01
2.65925467e-01 7.27911413e-01 -8.06464791e-01 -4.40369308e-01
6.66730925e-02 -4.18816388e-01 -1.05540836e+00 -5.65388441e-01
3.37222993e-01 -1.51778266e-01 -5.11815131e-01 1.38747382e+00
9.22669947e-01 -3.68647099e-01 4.50302273e-01 -9.13789392e-01
-9.88923788e-01 4.90141600e-01 -4.17974412e-01 6.14793658e-01
-9.08058733e-02 4.85564470e-01 3.73027891e-01 -2.40569443e-01
5.15699625e-01 9.98770595e-01 9.31748629e-01 1.06493473e+00
-9.01918173e-01 -8.75166416e-01 4.11648909e-03 2.35499501e-01
-1.15037489e+00 2.38460734e-01 8.47595096e-01 -1.21605240e-01
9.02709186e-01 5.11557519e-01 4.04578090e-01 1.54702055e+00
9.88636494e-01 7.86780477e-01 9.73347723e-01 -6.64627776e-02
5.87594062e-02 -4.02517498e-01 -1.02969199e-01 6.95479929e-01
8.91652554e-02 3.57896417e-01 -1.02020159e-01 -2.67779171e-01
7.43347526e-01 -2.97865551e-02 -3.21409076e-01 2.82721430e-01
-1.09483731e+00 7.88825929e-01 1.10102689e+00 8.73844802e-01
-5.39616644e-02 2.99538702e-01 8.51801395e-01 1.25417173e-01
1.50547355e-01 5.43651164e-01 -4.32694942e-01 4.80608284e-01
-7.75974751e-01 -6.69655278e-02 4.52176839e-01 6.38769925e-01
1.71601504e-01 1.08990431e-01 -4.02033120e-01 3.40187520e-01
-1.49659645e-02 3.40799510e-01 5.33467114e-01 -5.69781542e-01
2.61050373e-01 4.63218302e-01 -6.68947875e-01 -1.13482940e+00
-6.80831015e-01 -6.45440876e-01 -1.46107674e+00 1.18292511e-01
2.27012202e-01 -3.26788992e-01 -1.09076059e+00 1.76226699e+00
2.42549613e-01 7.65076935e-01 1.73318550e-01 7.49317884e-01
9.69588697e-01 2.22920388e-01 4.17716026e-01 8.42720866e-02
1.43537271e+00 -7.75303602e-01 -8.61268938e-01 -1.72309265e-01
5.15692532e-01 -7.41934121e-01 4.92235899e-01 2.83875376e-01
-7.97324419e-01 -6.68160677e-01 -1.25888932e+00 -1.58844180e-02
-7.14047730e-01 -4.46862340e-01 4.89835441e-01 1.01123095e+00
-8.51402342e-01 6.95953071e-01 -1.11693311e+00 1.46203205e-01
1.15890884e+00 6.49061918e-01 -4.48564768e-01 -1.15414836e-01
-1.76511168e+00 6.37445390e-01 2.85996586e-01 4.33715582e-01
-8.64121914e-01 -8.76471102e-01 -8.43219340e-01 -2.41540864e-01
-1.18885428e-01 -7.01502860e-01 6.70612156e-01 -1.13526905e+00
-1.15987062e+00 8.07678223e-01 3.58901978e-01 -6.52190566e-01
4.11957651e-01 1.60273518e-02 -4.81836170e-01 7.95288265e-01
-2.15119813e-02 7.12600291e-01 8.15922499e-01 -1.01755393e+00
-2.16925129e-01 -3.58824879e-01 -2.58467168e-01 -3.07575524e-01
-2.62865722e-01 1.64231941e-01 -2.07401440e-01 -1.18681371e+00
-2.24332049e-01 -8.54092836e-01 -6.08113706e-01 1.77519664e-01
-6.57000184e-01 1.06679812e-01 1.11163497e+00 -6.46348357e-01
1.03624797e+00 -2.21441412e+00 -8.52089077e-02 3.94552827e-01
1.26605912e-03 8.03736091e-01 -3.46720338e-01 5.73543496e-02
-3.23742896e-01 5.27845740e-01 -5.90073109e-01 1.78113699e-01
-4.69153106e-01 4.16081548e-01 -2.44686633e-01 7.60898948e-01
4.79201466e-01 1.48124945e+00 -8.42812419e-01 -6.18943810e-01
1.87294900e-01 8.43503058e-01 -5.94370544e-01 -7.96247274e-03
1.39891833e-01 1.05099881e+00 -6.72048509e-01 8.69939625e-01
9.05738950e-01 -1.36413360e-02 -4.28015701e-02 -4.69775081e-01
5.27292669e-01 -5.12159765e-01 -4.09355164e-01 1.77739048e+00
-1.04068831e-01 3.57676983e-01 9.38196108e-02 -1.37070620e+00
4.32590425e-01 6.10670209e-01 5.12487888e-01 -5.17609358e-01
5.30099511e-01 1.24394894e-01 1.55057430e-01 -7.39183187e-01
-2.60152310e-01 -1.46689534e-01 -3.62310737e-01 -6.59193620e-02
1.31371036e-01 7.31351003e-02 -3.56254637e-01 1.38267308e-01
1.18995392e+00 -3.07678938e-01 1.08374245e-01 -3.96356046e-01
9.54631031e-01 -3.42936665e-01 7.09442973e-01 9.37381804e-01
-6.31258726e-01 6.82801306e-01 4.85605925e-01 -5.27723134e-01
-6.41493738e-01 -1.11407053e+00 -5.04437625e-01 7.51279056e-01
2.55966336e-01 2.86930293e-01 -1.29044282e+00 -1.02397764e+00
-8.23433474e-02 2.00883895e-01 -1.15861237e+00 -6.46690965e-01
-8.82749081e-01 -8.62413168e-01 1.55204630e+00 8.57886016e-01
7.94766188e-01 -1.36477828e+00 -6.19771183e-01 2.06089541e-01
-7.15805292e-02 -1.25661874e+00 -7.20294595e-01 1.88034296e-01
-8.55009198e-01 -1.32368338e+00 -6.65319443e-01 -9.90416527e-01
8.29957247e-01 -4.18222696e-01 6.51646554e-01 4.19425309e-01
-1.04370284e+00 2.70617306e-01 -3.46498072e-01 -5.81587553e-01
-5.00870824e-01 2.92181950e-02 -2.66490877e-01 -6.41061664e-02
3.22468877e-01 -6.28692687e-01 -9.34114397e-01 2.36880869e-01
-1.41853607e+00 -5.06878018e-01 6.19621098e-01 1.24920321e+00
9.05056357e-01 8.85863137e-03 8.24421883e-01 -1.22330821e+00
4.18863803e-01 -3.68302077e-01 -8.50401521e-02 2.07255751e-01
-8.33980292e-02 -2.82920420e-01 9.24978137e-01 -6.15714073e-01
-7.77226150e-01 1.15178660e-01 -6.52871132e-01 -4.70067799e-01
-4.29143637e-01 3.78417045e-01 -2.68514365e-01 -6.54757619e-01
7.99146652e-01 2.05488116e-01 -4.65784445e-02 2.73834132e-02
2.97298878e-01 5.21949351e-01 9.61988449e-01 -3.87277246e-01
6.10787451e-01 8.01282048e-01 3.10868591e-01 -7.51624167e-01
-7.59382367e-01 -2.47363999e-01 -6.46027923e-01 6.35171495e-03
1.47202015e+00 -5.47043085e-01 -7.68669426e-01 9.71688330e-01
-1.04033136e+00 -3.45534533e-01 -1.50355384e-01 6.25158846e-02
-4.95647907e-01 6.47400320e-01 -1.11903667e+00 -1.53347239e-01
-7.64142275e-01 -1.51247203e+00 9.03708100e-01 2.97194391e-01
5.15855290e-02 -1.09290743e+00 -2.78639704e-01 4.37722445e-01
6.01101279e-01 1.02460194e+00 9.14096415e-01 -8.76644909e-01
-4.21967179e-01 -4.86530215e-01 1.54251698e-02 6.67916477e-01
1.70039535e-01 -2.05489025e-01 -1.08514452e+00 -5.63880622e-01
1.64796934e-01 -3.51474434e-01 1.00053704e+00 6.74659073e-01
1.72451723e+00 -3.35979849e-01 -5.52822948e-01 1.10319495e+00
1.27500653e+00 2.31691420e-01 1.17539501e+00 1.72332928e-01
7.85580695e-01 1.79934129e-01 9.68200937e-02 -2.01413795e-01
-3.19912970e-01 5.86455315e-02 8.04545045e-01 -7.14779794e-01
-1.26025662e-01 2.30217993e-01 -1.09987848e-01 4.94798928e-01
2.26522371e-01 -1.43497989e-01 -7.93721318e-01 5.57271063e-01
-1.53715360e+00 -8.34815919e-01 8.89048874e-02 1.38971937e+00
1.13838708e+00 1.57632321e-01 -5.29739499e-01 9.00198445e-02
7.24831581e-01 9.95933488e-02 -7.81096935e-01 -6.08071804e-01
-1.58106551e-01 8.34796906e-01 6.71018183e-01 2.53735811e-01
-1.69769824e+00 9.44758475e-01 6.06138897e+00 1.04258537e+00
-1.35075808e+00 2.03136533e-01 9.50480521e-01 2.38164499e-01
8.82031769e-02 -7.18597412e-01 -2.40677387e-01 3.95168900e-01
7.43948817e-01 4.03615296e-01 3.12289726e-02 7.18630791e-01
-3.76128465e-01 4.18499023e-01 -8.00229907e-01 5.70021033e-01
3.00329849e-02 -1.59788811e+00 6.69433698e-02 -1.43891677e-01
9.42459762e-01 2.49875560e-02 5.29509842e-01 -2.43592281e-02
2.86838622e-03 -1.48287356e+00 2.00744733e-01 4.56132263e-01
1.02866471e+00 -1.23694956e+00 1.20711744e+00 8.28359649e-02
-1.11935282e+00 -1.66095912e-01 -3.98620456e-01 7.02466488e-01
-9.06413943e-02 3.19414079e-01 -4.25427616e-01 7.30599344e-01
9.03134942e-01 4.54775840e-01 -6.46860957e-01 7.17391431e-01
-4.51705575e-01 7.72289872e-01 -1.07983807e-02 2.26279750e-01
5.22248626e-01 5.45625985e-01 4.82465267e-01 1.75211442e+00
-1.76627830e-01 2.10997030e-01 2.44166255e-02 7.93385029e-01
-2.41931781e-01 -9.49867964e-02 -4.82281923e-01 2.22665906e-01
1.73781738e-01 1.18131816e+00 -8.51201057e-01 -8.56249928e-02
-1.70547202e-01 1.17884350e+00 4.76789800e-03 4.61964071e-01
-9.13044751e-01 -7.62639642e-01 7.41191685e-01 -3.63778234e-01
3.70238394e-01 2.15293512e-01 -6.24518514e-01 -7.70208538e-01
-3.88853580e-01 -1.12311351e+00 6.69501424e-01 -2.72192419e-01
-1.62457585e+00 7.64039993e-01 -3.45223308e-01 -9.42369580e-01
2.93242067e-01 -9.81583774e-01 -8.79410982e-01 8.10072422e-01
-1.66399729e+00 -1.59038997e+00 2.41932925e-02 1.18512774e+00
1.63786098e-01 -1.70182496e-01 1.04526114e+00 1.30873114e-01
-5.28329730e-01 1.27167928e+00 -1.66513637e-01 8.74868870e-01
6.29296422e-01 -9.77606952e-01 4.11307395e-01 1.06739748e+00
-2.40543157e-01 8.40949059e-01 2.51515925e-01 -6.97783649e-01
-9.62638021e-01 -1.59604979e+00 3.91910851e-01 -2.75925606e-01
5.27989805e-01 -1.39790818e-01 -1.11832261e+00 5.87683499e-01
3.61401498e-01 8.12327206e-01 1.04350829e+00 -6.80314779e-01
-4.91568774e-01 8.80146325e-02 -1.61810327e+00 7.05673039e-01
7.63882101e-01 -7.58089721e-01 -5.27005136e-01 2.43243322e-01
9.44217920e-01 -7.11646795e-01 -1.10863626e+00 7.85601974e-01
3.69352877e-01 -5.97742915e-01 1.42549717e+00 -8.17011476e-01
3.90959948e-01 -2.42534190e-01 1.18058883e-01 -9.57421839e-01
-3.06446105e-01 -6.41501844e-01 2.46647939e-01 6.26125336e-01
2.27840960e-01 -7.74590671e-01 6.71762466e-01 1.45343006e-01
-3.74390960e-01 -9.63805556e-01 -1.56246197e+00 -4.57752407e-01
8.27712119e-01 -4.60743427e-01 5.52060187e-01 1.01139653e+00
-7.55122527e-02 -3.57280493e-01 -1.58676460e-01 5.84631383e-01
5.54253340e-01 -3.20536256e-01 1.05405383e-01 -6.36814892e-01
6.28000274e-02 -3.70122224e-01 -9.07170177e-01 -4.10779297e-01
2.72418320e-01 -1.06039464e+00 5.96603788e-02 -1.08454740e+00
-1.11080252e-01 -7.93731585e-02 -9.56835628e-01 8.09881687e-01
-4.96309996e-01 7.71729529e-01 -2.07548905e-02 -2.24225581e-01
-3.37388933e-01 1.50275407e-02 1.55783844e+00 -5.26404321e-01
2.62358010e-01 -1.20196983e-01 -7.32715011e-01 6.61680877e-01
1.12664056e+00 -6.77924097e-01 -2.15451896e-01 -1.09621406e-01
-2.77124971e-01 -2.00364590e-01 5.69630742e-01 -1.07563734e+00
4.87373322e-01 2.54701078e-02 7.67678618e-01 -3.36419106e-01
-2.63585299e-01 -9.01158810e-01 -1.58496991e-01 1.03380430e+00
-4.14529502e-01 -4.32889462e-01 6.30994081e-01 6.12482131e-01
-2.08285019e-01 -5.65096214e-02 1.16081583e+00 -2.49321327e-01
-3.09983879e-01 5.97305238e-01 -6.36422396e-01 1.14188008e-01
1.14018571e+00 -2.51842469e-01 -2.88969725e-01 2.43951946e-01
-9.29044783e-01 5.10208420e-02 -1.16872214e-01 2.14780316e-01
8.24122548e-01 -1.26312399e+00 -6.84488714e-01 4.72603828e-01
-6.29700571e-02 7.92272165e-02 8.22295785e-01 8.77598047e-01
-8.40660036e-01 3.67809206e-01 -3.46910328e-01 -6.41344428e-01
-1.35971177e+00 9.78806555e-01 7.02546418e-01 -5.13023973e-01
-6.44693255e-01 1.27007318e+00 4.39549088e-01 -3.53536725e-01
2.64468670e-01 -3.37670445e-01 -1.78075954e-01 -4.73711669e-01
6.82171285e-01 -2.04284713e-02 5.01574092e-02 -5.95861971e-01
-6.11820996e-01 6.02674484e-01 -3.00910980e-01 5.22151351e-01
1.10879636e+00 3.23664695e-01 -2.47842446e-01 -3.92319620e-01
1.46042180e+00 -2.73782879e-01 -1.22846127e+00 -2.47227331e-03
-2.78985530e-01 -4.39604595e-02 3.25899608e-02 -1.04912961e+00
-1.59421456e+00 1.25024462e+00 9.28037345e-01 -9.99801233e-02
1.52033603e+00 -2.69705027e-01 1.28094685e+00 1.68643743e-01
1.27179325e-01 -8.81935954e-01 3.91371965e-01 2.53335178e-01
9.16776001e-01 -8.46864164e-01 -1.70265615e-01 -5.23581386e-01
-5.30523121e-01 1.19874692e+00 6.01163387e-01 -4.61514086e-01
9.24308181e-01 6.71345055e-01 3.96264136e-01 -1.54908806e-01
-9.89074111e-02 1.64423466e-01 3.64615440e-01 9.55791175e-01
1.14750221e-01 -1.04831927e-01 -1.25163332e-01 9.96777654e-01
2.49663472e-01 -6.17585257e-02 1.31935984e-01 1.00922918e+00
-1.22836597e-01 -1.13752556e+00 -3.36463839e-01 5.56704067e-02
-1.39786077e+00 -3.24984819e-01 -2.92185277e-01 7.06937790e-01
6.58921897e-01 7.43496180e-01 -3.47430974e-01 -4.04016137e-01
2.82185197e-01 -2.26718053e-01 3.30745012e-01 -2.22706467e-01
-1.31203163e+00 9.20164883e-02 -5.36673903e-01 -7.77227700e-01
-2.42125794e-01 -1.90196186e-01 -1.39089239e+00 1.28971234e-01
-2.71080822e-01 -1.45263106e-01 2.14986324e-01 6.92787230e-01
2.07930371e-01 1.13984799e+00 4.42663908e-01 -5.24271250e-01
-3.95163327e-01 -5.88184774e-01 -3.27188343e-01 5.10176122e-01
5.42703331e-01 -6.78458810e-02 -2.71844774e-01 9.11161005e-02] | [5.61004638671875, 7.806966304779053] |
8454c4c3-b6af-4f13-9ff5-c7987318bfb5 | emphasis-an-emotional-phoneme-based-acoustic | 1806.09276 | null | http://arxiv.org/abs/1806.09276v2 | http://arxiv.org/pdf/1806.09276v2.pdf | EMPHASIS: An Emotional Phoneme-based Acoustic Model for Speech Synthesis System | We present EMPHASIS, an emotional phoneme-based acoustic model for speech
synthesis system. EMPHASIS includes a phoneme duration prediction model and an
acoustic parameter prediction model. It uses a CBHG-based regression network to
model the dependencies between linguistic features and acoustic features. We
modify the input and output layer structures of the network to improve the
performance. For the linguistic features, we apply a feature grouping strategy
to enhance emotional and prosodic features. The acoustic parameters are
designed to be suitable for the regression task and waveform reconstruction.
EMPHASIS can synthesize speech in real-time and generate expressive
interrogative and exclamatory speech with high audio quality. EMPHASIS is
designed to be a multi-lingual model and can synthesize Mandarin-English speech
for now. In the experiment of emotional speech synthesis, it achieves better
subjective results than other real-time speech synthesis systems. | [] | 2018-06-26 | null | null | null | null | ['parameter-prediction', 'emotional-speech-synthesis'] | ['miscellaneous', 'speech'] | [-3.02710235e-01 3.57159823e-01 -1.10654399e-01 -5.94823718e-01
-5.11070549e-01 -5.22966869e-02 5.67226186e-02 -4.09232706e-01
2.24262383e-02 6.19901955e-01 6.27663672e-01 -2.35355422e-01
3.70589405e-01 -5.47708929e-01 4.79623713e-02 -6.44779027e-01
-1.46780768e-02 1.60065498e-02 1.29862502e-02 -5.18539846e-01
-7.98224136e-02 4.68878329e-01 -1.64657056e+00 5.28100014e-01
5.99796712e-01 9.51917946e-01 5.69612741e-01 1.18226612e+00
-1.63566768e-01 7.47850657e-01 -9.14488852e-01 9.51584727e-02
-3.50245684e-01 -6.98694348e-01 -4.65250641e-01 -4.15097773e-02
-5.51650763e-01 1.57614186e-01 -1.11971483e-01 8.92568648e-01
1.14557421e+00 5.02520382e-01 7.67891526e-01 -1.09258699e+00
-8.23002160e-01 1.07178366e+00 2.41778746e-01 -4.17626686e-02
4.28870797e-01 -5.09161688e-02 1.00739932e+00 -1.03980851e+00
3.20425242e-01 1.48979628e+00 5.82615614e-01 9.04704332e-01
-8.55899930e-01 -7.31858909e-01 -5.36475815e-02 1.60900220e-01
-1.28995693e+00 -8.28742504e-01 1.04444623e+00 -1.29207566e-01
1.44473755e+00 6.53344572e-01 5.71980298e-01 9.60187793e-01
2.85556316e-01 6.97999477e-01 7.46115267e-01 -7.42501497e-01
1.08088419e-01 4.15147662e-01 3.50683555e-02 4.68802661e-01
-1.02605689e+00 4.60296422e-01 -3.95040959e-01 1.40251875e-01
4.50632334e-01 -8.26220214e-01 -4.14828122e-01 7.84399211e-01
-8.51835907e-01 1.03256488e+00 -1.39389351e-01 6.60162747e-01
-3.68473202e-01 -3.95660847e-02 7.75256395e-01 5.67590654e-01
3.73371333e-01 5.08483887e-01 -3.85683656e-01 -2.26996213e-01
-7.61069953e-01 -3.63794677e-02 8.67605448e-01 1.00464308e+00
2.38961563e-01 9.90638494e-01 -2.46783569e-01 1.66400361e+00
5.17293155e-01 7.09084809e-01 1.00850856e+00 -9.44151521e-01
5.50778359e-02 -1.08001620e-01 -2.21914545e-01 -6.73942327e-01
-5.75306654e-01 3.69653739e-02 -7.13293076e-01 1.33319005e-01
-4.73581940e-01 -5.80428898e-01 -7.17955947e-01 1.73498464e+00
1.33388191e-02 -1.57786354e-01 6.01543069e-01 7.40116835e-01
1.53384805e+00 1.76341343e+00 2.09243909e-01 -9.00041342e-01
1.27353024e+00 -1.15838039e+00 -1.72608316e+00 1.24501064e-01
4.54907924e-01 -1.16198504e+00 1.49415815e+00 4.46216077e-01
-1.36768806e+00 -9.91519511e-01 -1.10255384e+00 -1.18254043e-01
-3.17587167e-01 4.79716688e-01 1.11296445e-01 9.22847807e-01
-9.01089609e-01 2.66421109e-01 -2.58927315e-01 -5.33368364e-02
-6.17843866e-01 4.73404199e-01 -1.18421696e-01 9.64428186e-01
-1.59744918e+00 9.12963927e-01 5.73763907e-01 2.72914842e-02
-1.85143083e-01 -3.47417921e-01 -1.14142418e+00 3.41580808e-01
-3.33249271e-01 -1.08743779e-01 1.45537519e+00 -1.03446162e+00
-2.55005193e+00 3.55053157e-01 -1.84261873e-01 -9.64277834e-02
-3.74218404e-01 2.86401987e-01 -1.26953804e+00 8.17950964e-02
-6.28291428e-01 8.90874326e-01 8.47650886e-01 -1.20963562e+00
-7.11724102e-01 4.83233005e-01 -7.31213808e-01 2.42283061e-01
-2.89825052e-01 5.95612347e-01 -7.78023824e-02 -1.06188023e+00
-2.96969682e-01 -7.52685368e-01 7.27698356e-02 -5.95155656e-01
-3.64714831e-01 -5.46122551e-01 1.13607788e+00 -8.57334793e-01
1.74051440e+00 -2.25421214e+00 4.98898439e-02 1.26301676e-01
-5.47271788e-01 4.86063451e-01 -3.76292206e-02 3.32861274e-01
-2.99221337e-01 1.09844990e-01 9.47641209e-02 -5.46208858e-01
2.62861282e-01 3.14441979e-01 -4.11025912e-01 -8.96806791e-02
3.36914092e-01 8.08074415e-01 -3.88447016e-01 -6.61482275e-01
4.79649961e-01 5.26693404e-01 -6.07288182e-01 6.67489946e-01
-2.50596732e-01 2.18032211e-01 -1.33835405e-01 4.19900090e-01
2.48991489e-01 7.43050039e-01 -9.47864130e-02 -3.15130085e-01
-3.68126720e-01 5.49962997e-01 -1.04490376e+00 1.20692527e+00
-9.26257730e-01 6.89845383e-01 2.95193464e-01 -7.00660050e-01
1.69816220e+00 1.09806561e+00 4.51228619e-02 -5.75220585e-01
5.80499947e-01 3.09982926e-01 2.54416019e-01 -9.42065120e-01
8.70857120e-01 -6.26806676e-01 -3.13836873e-01 1.52475253e-01
1.91697329e-01 -9.89898443e-01 -2.81463563e-01 -4.68633115e-01
2.62525499e-01 -2.19527215e-01 1.65718988e-01 -3.49762619e-01
9.54234421e-01 -6.39781177e-01 7.69063234e-01 -2.13326752e-01
-7.95617104e-02 4.93449241e-01 2.57965833e-01 1.26363248e-01
-9.83293474e-01 -7.50074804e-01 2.97497064e-02 1.35925853e+00
-1.95024148e-01 -4.95528996e-01 -6.94540441e-01 -6.08806238e-02
-5.98713160e-01 1.33719635e+00 -5.72629049e-02 -3.56405914e-01
-6.59200191e-01 -3.70715439e-01 7.20148206e-01 4.30745751e-01
-9.40260217e-02 -1.80918014e+00 7.33910501e-02 4.62854028e-01
-4.08316940e-01 -9.01371002e-01 -8.30222845e-01 5.76765716e-01
-2.18725368e-01 -1.13499656e-01 -3.62466097e-01 -1.58546114e+00
5.03190681e-02 -7.05459177e-01 7.08830774e-01 -8.69283080e-03
1.63651645e-01 -1.61068156e-01 -5.63398540e-01 -7.27480948e-01
-1.17729878e+00 -2.57669352e-02 2.89146811e-01 -6.13400415e-02
7.17962161e-02 -6.68710113e-01 8.78764316e-02 2.78389543e-01
-6.53705239e-01 -8.05825517e-02 -2.57332735e-02 8.28105450e-01
4.56776053e-01 1.13430522e-01 1.41010654e+00 -3.76514643e-01
1.36472499e+00 -7.22088739e-02 -1.25332624e-01 4.14094813e-02
-1.86433986e-01 -4.98701558e-02 1.13340843e+00 -8.32736194e-01
-1.45026457e+00 -1.17104918e-01 -1.04030347e+00 -2.95762837e-01
-1.08305790e-01 5.14257371e-01 -6.27074063e-01 2.99232125e-01
4.61640418e-01 1.22772884e-02 -4.17428762e-02 -3.23033869e-01
2.56347269e-01 1.65219450e+00 6.00633442e-01 -5.42441368e-01
3.13321292e-01 -6.13747597e-01 -5.55976391e-01 -1.33716393e+00
-1.93965465e-01 -1.51910096e-01 -2.68392682e-01 -4.20714587e-01
8.82755220e-01 -6.94629312e-01 -8.78798664e-01 2.79139936e-01
-1.26770985e+00 -4.14595366e-01 -5.06224394e-01 9.35175300e-01
-9.22251582e-01 2.06735402e-01 -1.05887151e+00 -1.05698800e+00
-5.88650346e-01 -1.29832840e+00 8.75137568e-01 3.77820194e-01
-5.48550189e-01 -8.54406953e-01 7.28881136e-02 -6.99334145e-02
4.45107967e-01 -2.80300528e-01 1.13393509e+00 -6.56513333e-01
3.02812904e-01 2.31845632e-01 3.45147282e-01 9.85391259e-01
1.99411288e-01 5.60906470e-01 -1.16459095e+00 3.29639286e-01
2.06194118e-01 -4.60339725e-01 3.78236145e-01 4.77893502e-01
1.00875545e+00 -3.44355047e-01 1.50156349e-01 5.37339389e-01
6.95871174e-01 1.02152431e+00 7.42862523e-01 -5.71627080e-01
2.27514237e-01 8.60483348e-01 8.43069851e-01 3.32607448e-01
2.29489461e-01 5.26816130e-01 -1.26222625e-01 -2.42683098e-01
-3.10427517e-01 -1.13335103e-01 7.09170163e-01 2.17792535e+00
2.47208729e-01 -6.39699459e-01 -3.89202803e-01 4.22766805e-01
-1.60998642e+00 -9.78057265e-01 -1.46125600e-01 1.70753920e+00
1.20852304e+00 1.59489095e-01 6.39472976e-02 3.17271352e-01
7.47578204e-01 2.40076035e-01 1.17186561e-01 -1.74603093e+00
-3.16330999e-01 2.90452808e-01 -2.16435611e-01 9.66576099e-01
-7.45904922e-01 1.27622473e+00 7.12406778e+00 1.29440534e+00
-1.36950326e+00 -7.98760653e-02 5.41074514e-01 1.56261384e-01
-4.98267382e-01 -5.28653979e-01 -9.36098158e-01 4.91566360e-01
1.58261192e+00 -1.93107113e-01 4.39083248e-01 6.76405668e-01
7.80681670e-01 2.08282560e-01 -9.94281888e-01 9.24022138e-01
1.14535838e-01 -1.05618024e+00 -1.91925585e-01 -4.57396746e-01
4.35671985e-01 -4.17616546e-01 -5.75917494e-03 6.95644379e-01
-6.99898601e-02 -1.26064229e+00 8.47148240e-01 5.01917064e-01
9.32504952e-01 -1.26342928e+00 6.25548422e-01 5.28904498e-01
-1.30295491e+00 2.39641201e-02 -3.46837103e-01 -1.16475774e-02
6.10793293e-01 1.73651710e-01 -8.60223711e-01 7.68930614e-02
2.43753180e-01 2.97563732e-01 1.46992594e-01 6.09360218e-01
-2.66090602e-01 8.33705366e-01 -2.69055963e-01 -5.44875503e-01
1.75179258e-01 -1.57378033e-01 5.02908289e-01 1.76069438e+00
5.74857414e-01 3.31583411e-01 2.02359185e-01 6.45977974e-01
1.54751137e-01 6.95563674e-01 -2.96968728e-01 -3.05182040e-01
6.71925843e-01 1.13538587e+00 -2.63730854e-01 -2.27510378e-01
4.04669810e-03 6.10487998e-01 -2.43916735e-01 2.51718760e-01
-1.02008641e+00 -8.97035837e-01 4.90180075e-01 -5.27321696e-01
3.15187365e-01 -1.62241146e-01 -1.62831828e-01 -5.85714757e-01
-4.31589186e-01 -9.71348643e-01 -2.62749463e-01 -1.28000247e+00
-1.06991768e+00 1.07418776e+00 -1.92494333e-01 -1.01190102e+00
-7.35376120e-01 -4.70793962e-01 -9.41014171e-01 1.19667721e+00
-1.33190703e+00 -1.06415725e+00 3.58299851e-01 5.05581439e-01
1.00705945e+00 -5.64991295e-01 1.30156291e+00 2.65560776e-01
-5.24897993e-01 5.07417560e-01 -2.94520050e-01 -1.21590689e-01
6.65739655e-01 -1.01925707e+00 1.15677103e-01 3.79935563e-01
-2.59453982e-01 1.10128626e-01 8.80467534e-01 -3.56436282e-01
-7.17036664e-01 -1.00494146e+00 1.42338371e+00 2.82831609e-01
5.20166516e-01 -4.79028791e-01 -9.46369469e-01 3.06755304e-01
7.26420105e-01 -3.38522315e-01 9.66874599e-01 -1.22865722e-01
2.06050813e-01 -3.79225537e-02 -1.08033442e+00 6.91464901e-01
4.50557321e-01 -6.06378138e-01 -9.03357148e-01 1.04351826e-01
1.41194487e+00 -2.00744048e-01 -1.20893145e+00 4.15291220e-01
4.07425225e-01 -6.58022225e-01 6.50297582e-01 -2.81892300e-01
1.57024264e-01 -2.57181257e-01 -3.10488611e-01 -1.73560297e+00
-3.46637428e-01 -1.10339344e+00 1.75781876e-01 1.67348850e+00
7.43011773e-01 -4.33789790e-01 1.39120564e-01 1.09051853e-01
-7.26108789e-01 -6.06483221e-01 -7.57609427e-01 -7.01714396e-01
-7.83091336e-02 -5.59622526e-01 6.86992228e-01 7.40733624e-01
5.96074820e-01 9.65193927e-01 -6.34397089e-01 1.21186711e-01
-4.13568854e-01 -7.71855116e-02 2.10769370e-01 -8.23813915e-01
-2.99529463e-01 -5.29498458e-01 4.39789630e-02 -8.68189871e-01
7.20225036e-01 -6.48136854e-01 5.32813311e-01 -1.06813562e+00
-7.91151643e-01 -4.83194619e-01 -3.69664580e-02 3.61698538e-01
1.28940731e-01 -7.95344189e-02 2.46744707e-01 -4.68620598e-01
2.68232703e-01 1.06170940e+00 1.17788649e+00 4.86347526e-02
-7.70142138e-01 3.48097026e-01 -2.01807484e-01 6.96103454e-01
9.63213086e-01 -2.44150892e-01 -6.39210999e-01 3.38639647e-01
-1.41342267e-01 5.96262336e-01 -4.72306728e-01 -7.33889401e-01
1.46367878e-01 -2.67611951e-01 2.97800708e-03 -8.73807132e-01
8.58224988e-01 -7.93235481e-01 7.74907768e-02 1.58246800e-01
-6.62970364e-01 -2.09295824e-02 2.32116699e-01 -1.46372452e-01
-6.02386057e-01 -6.12493932e-01 1.09932315e+00 2.13389888e-01
-3.91090602e-01 -5.48234805e-02 -1.04368281e+00 -2.22596690e-01
7.69203305e-01 -2.39800304e-01 -6.04876876e-02 -7.41797030e-01
-1.07256210e+00 3.49973030e-02 -3.19889277e-01 6.76145852e-01
8.43826115e-01 -1.57009208e+00 -6.48054421e-01 6.48724735e-01
-1.79636434e-01 -5.13634145e-01 1.02026686e-01 5.08227944e-01
-4.50087160e-01 2.96249449e-01 -1.55559450e-01 -1.83190212e-01
-1.65529597e+00 4.73611832e-01 5.22454500e-01 1.10683963e-01
-1.98600307e-01 9.22143519e-01 -4.66008903e-03 -7.72076190e-01
5.95835388e-01 -6.70921385e-01 -7.20142543e-01 1.27573296e-01
3.57086897e-01 1.60857663e-01 -2.51439493e-02 -9.00774479e-01
-2.43986510e-02 3.11976254e-01 4.43167120e-01 -4.51475084e-01
1.17238724e+00 -4.04680759e-01 -1.21021271e-01 8.42636645e-01
1.34505033e+00 4.98032629e-01 -6.33903027e-01 1.55684352e-01
-3.30061138e-01 2.67874569e-01 3.42289478e-01 -8.75341237e-01
-8.55134547e-01 8.83671641e-01 3.06914717e-01 4.08213645e-01
1.27457213e+00 -3.02042186e-01 1.14296973e+00 3.17364305e-01
-3.28193456e-01 -1.89244175e+00 4.29767621e-04 9.77799773e-01
1.26692104e+00 -8.11104953e-01 -5.55328727e-01 -5.75485528e-01
-1.00180984e+00 1.38115823e+00 5.68064570e-01 9.93880071e-03
1.07033646e+00 9.07494068e-01 2.92309105e-01 2.80190796e-01
-1.11918330e+00 1.02125771e-01 4.08672303e-01 7.10006475e-01
6.85670853e-01 3.17350566e-01 -5.09345949e-01 1.17008710e+00
-8.72804880e-01 -3.74347478e-01 5.07343352e-01 2.58318901e-01
-9.47988927e-01 -1.21714079e+00 -4.73523319e-01 -1.54654235e-01
-4.45440412e-01 -1.18949480e-01 -2.75590986e-01 4.42372739e-01
1.00126386e-01 1.39850962e+00 2.31808305e-01 -6.93372071e-01
5.13093889e-01 4.46236670e-01 4.20393348e-02 -6.94171727e-01
-1.09332705e+00 8.65232408e-01 7.39458323e-01 -1.22550786e-01
-3.35710347e-01 -1.80067360e-01 -1.91577339e+00 -6.49321675e-02
-6.82901680e-01 4.72595483e-01 9.14929867e-01 8.17482114e-01
-1.87331543e-03 9.60219264e-01 1.25301492e+00 -7.05698192e-01
-2.94973135e-01 -1.18663502e+00 -9.05123174e-01 -2.02598438e-01
2.46001929e-01 -1.25330701e-01 -2.93922305e-01 2.56321728e-01] | [14.71563720703125, 6.474836349487305] |
0b909fbd-a533-4f8f-a683-0a865f1272cd | data-copying-in-generative-models-a-formal | 2302.13181 | null | https://arxiv.org/abs/2302.13181v2 | https://arxiv.org/pdf/2302.13181v2.pdf | Data-Copying in Generative Models: A Formal Framework | There has been some recent interest in detecting and addressing memorization of training data by deep neural networks. A formal framework for memorization in generative models, called "data-copying," was proposed by Meehan et. al. (2020). We build upon their work to show that their framework may fail to detect certain kinds of blatant memorization. Motivated by this and the theory of non-parametric methods, we provide an alternative definition of data-copying that applies more locally. We provide a method to detect data-copying, and provably show that it works with high probability when enough data is available. We also provide lower bounds that characterize the sample requirement for reliable detection. | ['Kamalika Chaudhuri', 'Sanjoy Dasgupta', 'Robi Bhattacharjee'] | 2023-02-25 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 2.82278121e-01 3.90933365e-01 -1.40069917e-01 -3.43477964e-01
-6.40883148e-01 -5.21424413e-01 8.58788192e-01 -7.76943797e-03
-5.65954447e-01 9.25378203e-01 2.67178286e-03 -4.52628076e-01
-1.69165686e-01 -1.14279306e+00 -1.12857234e+00 -6.14003778e-01
-1.78109497e-01 3.59718174e-01 -3.45702805e-02 5.64971082e-02
4.67165530e-01 4.64612097e-01 -1.70866919e+00 1.13826618e-01
6.90815985e-01 6.06666327e-01 1.00419223e-01 9.64906275e-01
1.58867896e-01 3.32655430e-01 -9.42366183e-01 -9.88363773e-02
2.73837298e-01 -6.35844707e-01 -1.01587152e+00 -3.19237262e-02
6.12005115e-01 -3.23109239e-01 -2.42335334e-01 1.03753650e+00
4.70265329e-01 1.18107587e-01 7.90163279e-01 -1.34806740e+00
-9.04398680e-01 8.20001662e-01 -3.55628908e-01 4.91595715e-01
2.50576049e-01 -2.01560333e-01 5.05012989e-01 -8.64816189e-01
4.94020581e-01 1.11151958e+00 7.42198825e-01 8.66532445e-01
-1.32996917e+00 -6.96592987e-01 -1.76308617e-01 -1.21021301e-01
-1.40234625e+00 -4.21889126e-01 6.39087200e-01 -3.23749721e-01
8.47443998e-01 5.17898440e-01 8.21609795e-01 1.22452211e+00
3.61180454e-01 1.00279570e+00 1.13849747e+00 -9.09491479e-01
2.78332740e-01 -5.00997109e-03 3.98052096e-01 8.87602031e-01
6.21073365e-01 3.58803183e-01 -7.43077040e-01 -2.01208875e-01
9.63993847e-01 -2.20573276e-01 -6.94673508e-02 -1.70325413e-01
-6.83299005e-01 1.00865412e+00 1.32547796e-01 3.99800450e-01
5.79312146e-02 3.85009080e-01 1.76702186e-01 4.78173435e-01
6.90276027e-01 2.95006961e-01 1.52229732e-02 -6.21532090e-02
-1.21392286e+00 3.05028826e-01 7.65973687e-01 1.27385247e+00
7.34731793e-01 3.55852008e-01 2.47514680e-01 4.56631362e-01
4.17106636e-02 3.78269166e-01 9.53911066e-01 -8.01564276e-01
2.56044120e-01 1.32351309e-01 6.68811649e-02 -5.74269772e-01
-5.02797961e-01 -5.86908102e-01 -9.84327972e-01 -3.80477705e-03
1.24938913e-01 -1.44219929e-02 -8.80527496e-01 2.16068077e+00
-1.58521339e-01 6.46738857e-02 -1.38099849e-01 2.68102586e-01
3.91544133e-01 4.37197715e-01 -2.49206543e-01 -2.43810192e-01
5.37669659e-01 -4.40272838e-01 -3.43284398e-01 -1.64225660e-02
8.22971582e-01 -3.65601063e-01 9.27024603e-01 4.42462653e-01
-1.38011551e+00 -6.52158737e-01 -1.27413189e+00 1.20536231e-01
-3.02713364e-01 -2.39121825e-01 6.23595893e-01 1.19047570e+00
-1.42851973e+00 5.58342159e-01 -8.56877029e-01 -6.55675650e-01
3.71905506e-01 3.58665049e-01 -2.04841241e-01 3.32121849e-01
-1.01674902e+00 7.06071317e-01 5.75959265e-01 1.67647060e-02
-1.12257743e+00 -3.25980395e-01 -8.53575051e-01 -8.51211697e-02
1.62876584e-02 -7.94149280e-01 1.29008019e+00 -8.68275523e-01
-1.00235224e+00 9.86885786e-01 -3.24072957e-01 -8.62540424e-01
6.46427095e-01 -4.05678421e-01 -2.88326889e-01 -1.88642532e-01
1.13368079e-01 5.83804786e-01 8.56080234e-01 -1.36196136e+00
-4.49033499e-01 -3.06343347e-01 -6.82953149e-02 -2.22954333e-01
-4.19328153e-01 -1.00610293e-01 8.45573619e-02 -5.34803212e-01
1.24655291e-01 -7.13772953e-01 7.67181739e-02 -4.30444449e-01
-8.65378082e-01 -3.94538313e-01 5.38430393e-01 -1.88474745e-01
1.00972986e+00 -1.96383560e+00 -3.06011468e-01 4.57441092e-01
4.43794161e-01 1.27783582e-01 -2.48940423e-01 4.21506882e-01
-1.14898391e-01 5.20701170e-01 -2.94020712e-01 -6.29743695e-01
1.40673995e-01 4.63809520e-01 -7.93921053e-01 6.92928731e-01
1.33922771e-01 9.30954754e-01 -6.62331104e-01 -2.93642104e-01
-1.72088459e-01 1.82285368e-01 -5.07817090e-01 9.60679054e-02
1.57792747e-01 -8.85857269e-02 -1.50989592e-01 5.81523418e-01
8.06923866e-01 -2.41048902e-01 -2.44220646e-05 5.22673309e-01
-5.41467294e-02 2.59328604e-01 -1.05911553e+00 1.30774713e+00
-2.42710307e-01 8.98807883e-01 -1.63982898e-01 -1.26373851e+00
9.83425319e-01 -1.57585051e-02 -8.44892785e-02 -3.72780889e-01
1.48922831e-01 7.44628906e-02 -3.61001402e-01 -3.26728135e-01
9.26851809e-01 -2.12286949e-01 -2.26621941e-01 8.68788064e-01
1.09605856e-01 1.79980956e-02 1.52487159e-01 2.60477722e-01
1.10729873e+00 5.68683930e-02 3.17224830e-01 -6.54923439e-01
-2.50153989e-02 -2.00139269e-01 3.40576649e-01 1.65507364e+00
-7.13480562e-02 4.46524680e-01 1.67571574e-01 -3.54837865e-01
-1.12617183e+00 -1.45708370e+00 -9.10918564e-02 1.32182980e+00
-4.99133356e-02 -2.30445862e-01 -1.08852446e+00 -2.64021665e-01
9.35107395e-02 5.68385541e-01 -1.08114076e+00 -3.37799340e-01
-4.63088870e-01 -6.89740181e-01 1.01523018e+00 8.80882382e-01
4.48920399e-01 -1.04150474e+00 -7.68822849e-01 7.61601031e-02
9.92098637e-03 -5.76515436e-01 -8.58946741e-02 4.98116970e-01
-1.22975492e+00 -8.52328718e-01 -6.50790155e-01 -7.32848465e-01
8.90823901e-01 1.85844824e-01 1.22533262e+00 4.04577762e-01
-3.31562400e-01 5.41887701e-01 3.43328179e-03 -5.25265694e-01
-9.10501122e-01 3.37193459e-01 3.93428087e-01 -6.05406642e-01
4.45771545e-01 -5.10516882e-01 -2.85894156e-01 9.81115997e-02
-1.25011766e+00 -1.49952635e-01 5.88518500e-01 7.72151351e-01
1.24773279e-01 -2.23529022e-02 6.98751092e-01 -9.32465255e-01
9.95826542e-01 -5.29389322e-01 -4.82116163e-01 2.18884289e-01
-8.90954673e-01 3.25758666e-01 4.09323096e-01 -4.19994116e-01
-7.94310749e-01 -2.33358055e-01 -1.30781621e-01 -1.31513551e-01
-3.84182274e-01 4.67555881e-01 9.14129540e-02 -1.39148057e-01
9.04470265e-01 6.34078264e-01 -4.78168242e-02 -5.80491722e-01
3.05891514e-01 5.54248810e-01 7.32487798e-01 -9.13763940e-01
9.01075006e-01 4.79042947e-01 1.04871867e-02 -8.34358871e-01
-7.17207134e-01 -1.60159916e-01 -5.39133668e-01 -1.07175492e-01
2.41250530e-01 -6.38833344e-01 -5.01018882e-01 5.19424736e-01
-1.16366732e+00 -4.96215492e-01 -3.76615256e-01 1.58990830e-01
-8.75050128e-01 5.06233215e-01 -5.42334795e-01 -1.12776482e+00
-3.05130154e-01 -6.15004182e-01 7.12005913e-01 1.65697616e-02
-3.54019105e-01 -8.79019439e-01 3.00618798e-01 -1.53124675e-01
5.83755493e-01 -1.73055921e-02 8.94423187e-01 -7.70797849e-01
-4.66038793e-01 -1.81409955e-01 -1.07289009e-01 3.59560519e-01
-1.46138489e-01 8.31212699e-02 -1.13701117e+00 -4.63627100e-01
8.35428834e-02 -4.40339833e-01 1.27131724e+00 3.92499387e-01
1.41514611e+00 -5.18850446e-01 -3.88135254e-01 4.07869071e-01
1.28334355e+00 -4.52740192e-02 6.59596801e-01 3.77868742e-01
2.29159132e-01 4.47593629e-01 8.10226128e-02 6.22354627e-01
8.65981877e-02 1.20052367e-01 2.40199804e-01 3.17423567e-02
2.17419416e-01 -4.29151505e-01 2.19769806e-01 7.94287086e-01
-2.68145949e-01 -5.82369149e-01 -7.24653065e-01 7.02297926e-01
-1.70923555e+00 -1.12557828e+00 -4.84432280e-02 2.25002694e+00
1.08185720e+00 4.61729318e-01 3.66212696e-01 4.22412843e-01
9.45613623e-01 -1.76429942e-01 -3.34545910e-01 -6.63681984e-01
-3.57008278e-01 5.83231449e-01 5.80123484e-01 6.29675150e-01
-7.16719806e-01 6.30875766e-01 8.68601322e+00 8.24329376e-01
-6.32553637e-01 1.81366086e-01 6.19698346e-01 8.99734907e-03
-5.21415234e-01 -1.87424645e-01 -9.72861528e-01 3.70163083e-01
9.38489914e-01 -3.13656628e-01 2.13361830e-01 9.62759137e-01
-2.29551882e-01 -4.06943321e-01 -1.22148597e+00 6.56684756e-01
3.82441103e-01 -1.31430364e+00 4.46199812e-02 1.36264071e-01
8.32918406e-01 -1.96907297e-01 3.14261228e-01 3.31862301e-01
4.70135868e-01 -1.00941622e+00 7.20313549e-01 6.09074950e-01
6.98696196e-01 -9.26392019e-01 4.34013277e-01 7.49863088e-01
-7.87131906e-01 1.62745014e-01 -7.60054111e-01 -2.25711480e-01
-1.70814067e-01 8.75565708e-01 -9.22054470e-01 2.02431425e-01
2.37593427e-01 -2.06994191e-02 -7.00923741e-01 1.09023917e+00
-2.85906762e-01 9.12639797e-01 -6.64553940e-01 -2.28680089e-01
-1.33825108e-01 3.23676348e-01 3.01525086e-01 1.32022583e+00
6.39316738e-01 -4.42791700e-01 -2.62038052e-01 1.12299144e+00
-2.99182743e-01 -3.56415272e-01 -7.99751461e-01 1.47619331e-02
6.10204816e-01 6.27690613e-01 -7.12092996e-01 -4.23237145e-01
1.83777913e-01 9.06024933e-01 2.71560818e-01 2.88589150e-01
-5.43489993e-01 -3.74118567e-01 1.41750827e-01 1.84861049e-01
-7.40571991e-02 -4.63014841e-01 -3.02938968e-01 -9.11385059e-01
-2.56314781e-02 -2.97699243e-01 3.32446426e-01 -5.93998313e-01
-1.30556822e+00 3.03079396e-01 3.98214966e-01 -6.35115623e-01
-7.58411407e-01 -5.94295502e-01 -7.99140930e-01 8.11792016e-01
-8.35369527e-01 -9.22322214e-01 -1.93253115e-01 4.96015370e-01
2.91460484e-01 -1.69585466e-01 8.28234851e-01 -1.69985101e-01
-3.37588429e-01 9.56209421e-01 1.77478984e-01 6.20932542e-02
4.76842701e-01 -1.41151166e+00 6.74516261e-01 1.10922790e+00
2.43688494e-01 1.06912482e+00 1.05297494e+00 -5.56496680e-01
-1.20579898e+00 -9.05966818e-01 1.00387192e+00 -5.08068383e-01
3.00503820e-01 -6.39554083e-01 -7.94720292e-01 1.08384514e+00
1.24004774e-01 -4.76985157e-01 7.14862883e-01 3.45069319e-01
-4.06738698e-01 2.51434445e-01 -1.10381973e+00 3.23845059e-01
1.16249728e+00 -6.62331879e-01 -9.42182183e-01 1.88855037e-01
4.13010895e-01 -3.19141686e-01 -4.55545813e-01 2.64101565e-01
5.09176612e-01 -1.18504333e+00 7.00987101e-01 -5.55327713e-01
1.82648167e-01 2.21568719e-01 5.74175902e-02 -1.19139576e+00
-1.78068548e-01 -8.28297853e-01 -3.14721584e-01 9.70603764e-01
1.94576532e-01 -7.70761549e-01 9.94973242e-01 4.76786941e-01
-1.14225537e-01 -4.87329870e-01 -1.08786464e+00 -1.48759019e+00
4.60717112e-01 -5.79776764e-01 4.63325381e-01 8.41610909e-01
1.28239766e-01 3.41754667e-02 -5.04726231e-01 -2.11118668e-01
6.49897516e-01 1.07534237e-01 8.00194800e-01 -1.26731026e+00
-3.43623310e-01 -5.35140097e-01 -5.37027359e-01 -1.47624767e+00
2.20986307e-01 -7.61968672e-01 2.88278818e-01 -1.24943948e+00
4.15042400e-01 -3.75790566e-01 -3.91832858e-01 4.72745448e-01
6.88391253e-02 3.17451000e-01 -3.46485168e-01 2.51624733e-01
-5.15115082e-01 3.64905149e-01 7.05588043e-01 1.62826777e-01
2.69910544e-02 -1.37394238e-02 -7.65700638e-01 5.14448881e-01
1.31578016e+00 -6.07712328e-01 -3.56629670e-01 -2.67360806e-01
4.73508388e-01 -4.20956790e-01 5.87587893e-01 -1.17829609e+00
1.27559856e-01 3.51660512e-02 6.96837306e-01 -9.05379474e-01
1.78066626e-01 -1.28633916e-01 -5.07061481e-02 7.87474573e-01
-7.11533248e-01 3.18572104e-01 1.55201122e-01 6.92685246e-01
2.44525984e-01 -6.75886154e-01 7.01021671e-01 -6.50762022e-02
-4.80802208e-01 6.87367693e-02 -7.65031934e-01 2.44582862e-01
7.79773712e-01 -2.52477854e-01 -5.80920756e-01 -5.88522255e-01
-7.33346999e-01 -2.66962647e-01 3.96204084e-01 2.12676674e-01
7.47935116e-01 -1.17727661e+00 -4.88564134e-01 5.08812129e-01
-1.11389458e-02 -1.93076044e-01 -7.02807307e-02 3.53381664e-01
-2.29624078e-01 3.94972205e-01 -1.23346552e-01 -5.11104107e-01
-1.02232814e+00 5.37831128e-01 2.39034981e-01 1.78227186e-01
-3.44457477e-01 1.14158499e+00 1.43972009e-01 -9.66045484e-02
1.89430952e-01 -4.48910207e-01 5.24758697e-01 -3.00270468e-01
5.25244355e-01 4.07692641e-01 -3.09878066e-02 -2.21550792e-01
-1.75127119e-01 3.59596871e-02 -2.55467594e-01 -3.93404514e-01
1.19582427e+00 -6.42412603e-02 5.22535071e-02 6.38827682e-01
1.03245389e+00 -5.26583157e-02 -9.18699980e-01 -3.76641393e-01
2.09781118e-02 -3.28031391e-01 -3.06354582e-01 -3.98414642e-01
-4.91947740e-01 1.00408876e+00 6.22380614e-01 9.78287041e-01
9.30025637e-01 1.89965636e-01 5.83157837e-01 8.24921906e-01
4.65829909e-01 -1.34558189e+00 8.61850381e-02 5.08283377e-01
8.65328550e-01 -8.21765721e-01 -7.18364343e-02 -8.71139318e-02
1.75194457e-01 1.20938361e+00 5.74216306e-01 -2.82553464e-01
5.05398333e-01 4.74030942e-01 -4.11778986e-01 -1.65831029e-01
-6.21771574e-01 -4.19265032e-02 -1.55966962e-02 8.36188018e-01
2.57155985e-01 1.02321357e-01 -2.05921143e-01 4.21928048e-01
-4.99994814e-01 2.22134784e-01 6.72644317e-01 1.22925150e+00
-9.98730123e-01 -9.54468012e-01 -4.01000112e-01 5.97057343e-01
-2.49318212e-01 -1.36427641e-01 -5.00257134e-01 9.64210331e-01
9.96758267e-02 8.44127119e-01 1.91231981e-01 -4.82888162e-01
-2.16613308e-01 3.02805334e-01 8.68731022e-01 -6.62817776e-01
-1.72448754e-01 -7.85862133e-02 -1.13597654e-01 -2.22157151e-01
-6.02726579e-01 -3.20854872e-01 -9.47490573e-01 -8.63336205e-01
-5.05246460e-01 1.62992835e-01 4.99528408e-01 1.10329866e+00
1.82396248e-01 1.23140298e-01 5.35426736e-01 -5.90364337e-01
-7.98639834e-01 -1.08325994e+00 -8.19429278e-01 1.34356860e-02
3.65975320e-01 -4.08601195e-01 -6.21086061e-01 4.77823131e-02] | [8.456254005432129, 3.538506507873535] |
3e21ea09-c326-4bdc-bb09-40321ef5b151 | explicitly-antisymmetrized-neural-network | 2112.03491 | null | https://arxiv.org/abs/2112.03491v1 | https://arxiv.org/pdf/2112.03491v1.pdf | Explicitly antisymmetrized neural network layers for variational Monte Carlo simulation | The combination of neural networks and quantum Monte Carlo methods has arisen as a path forward for highly accurate electronic structure calculations. Previous proposals have combined equivariant neural network layers with an antisymmetric layer to satisfy the antisymmetry requirements of the electronic wavefunction. However, to date it is unclear if one can represent antisymmetric functions of physical interest, and it is difficult to measure the expressiveness of the antisymmetric layer. This work attempts to address this problem by introducing explicitly antisymmetrized universal neural network layers as a diagnostic tool. We first introduce a generic antisymmetric (GA) layer, which we use to replace the entire antisymmetric layer of the highly accurate ansatz known as the FermiNet. We demonstrate that the resulting FermiNet-GA architecture can yield effectively the exact ground state energy for small systems. We then consider a factorized antisymmetric (FA) layer which more directly generalizes the FermiNet by replacing products of determinants with products of antisymmetrized neural networks. Interestingly, the resulting FermiNet-FA architecture does not outperform the FermiNet. This suggests that the sum of products of antisymmetries is a key limiting aspect of the FermiNet architecture. To explore this further, we investigate a slight modification of the FermiNet called the full determinant mode, which replaces each product of determinants with a single combined determinant. The full single-determinant FermiNet closes a large part of the gap between the standard single-determinant FermiNet and FermiNet-GA. Surprisingly, on the nitrogen molecule at a dissociating bond length of 4.0 Bohr, the full single-determinant FermiNet can significantly outperform the standard 64-determinant FermiNet, yielding an energy within 0.4 kcal/mol of the best available computational benchmark. | ['Lin Lin', 'Gil Goldshlager', 'Jeffmin Lin'] | 2021-12-07 | null | null | null | null | ['variational-monte-carlo'] | ['miscellaneous'] | [ 4.36870784e-01 -2.38841511e-02 9.19224322e-02 -1.37767419e-01
-2.28676602e-01 -4.92049605e-01 7.69082427e-01 -4.03173119e-01
-4.26263779e-01 1.14863324e+00 -8.02546963e-02 -5.22629678e-01
-1.61937118e-01 -8.80240858e-01 -7.61240840e-01 -1.25524759e+00
1.68138593e-01 4.31159049e-01 3.12472526e-02 -7.83330917e-01
1.39017269e-01 7.08606184e-01 -1.38109303e+00 1.44723088e-01
9.80805397e-01 8.43518734e-01 -3.82451057e-01 4.33897346e-01
3.89205933e-01 2.60490954e-01 -4.53498289e-02 -2.92915136e-01
5.43745816e-01 -6.04689479e-01 -8.84981096e-01 -6.44319296e-01
6.33672297e-01 1.44444451e-01 -3.91297162e-01 1.29750323e+00
3.14120382e-01 4.10879314e-01 8.44530702e-01 -8.44394028e-01
-6.54444516e-01 6.42546654e-01 7.60885179e-02 -7.35478923e-02
-1.35434136e-01 1.55485258e-01 1.28319895e+00 -9.06648755e-01
5.25203466e-01 9.39747274e-01 9.78273213e-01 5.52527785e-01
-1.66031194e+00 -5.74298024e-01 -5.96333802e-01 2.10941937e-02
-1.63324201e+00 -4.95281875e-01 8.79056990e-01 -3.04375857e-01
1.62409496e+00 1.57439336e-01 7.46185720e-01 9.22263145e-01
8.17601025e-01 -2.52597958e-01 1.07703197e+00 -7.48952746e-01
2.07183138e-01 -2.58299261e-01 3.59369248e-01 8.12456369e-01
8.67787063e-01 4.54205751e-01 -2.70165324e-01 -3.46179247e-01
7.36721098e-01 -7.87242129e-02 -1.91729560e-01 -6.90180361e-01
-1.16909015e+00 8.28475893e-01 5.87955773e-01 4.02128011e-01
-3.88400793e-01 4.01831806e-01 3.98992479e-01 2.62013793e-01
-1.50916437e-02 1.09864461e+00 -2.95581669e-01 2.26840958e-01
-8.91564012e-01 5.25431693e-01 1.01018882e+00 1.69803306e-01
1.13288641e+00 6.46385968e-01 2.13423371e-01 3.34021837e-01
-2.03801282e-02 3.06544781e-01 5.55549040e-02 -1.11221075e+00
-2.17410922e-01 5.05025566e-01 9.18174684e-02 -5.85730553e-01
-6.55742645e-01 -2.94003874e-01 -1.08865595e+00 5.20926654e-01
5.59223056e-01 -3.98977876e-01 -6.62547290e-01 1.79410291e+00
1.34026378e-01 -3.88671279e-01 1.17944308e-01 7.56871700e-01
3.93017113e-01 5.59087276e-01 -1.85224414e-01 -4.03597541e-02
1.07253432e+00 -6.41373277e-01 -2.89659053e-01 1.06607810e-01
8.29857826e-01 -4.91981477e-01 7.14705706e-01 3.21047574e-01
-1.19218707e+00 -3.31797838e-01 -1.34821606e+00 -6.34413287e-02
-5.46307862e-01 -9.71045196e-02 1.22682893e+00 7.62801886e-01
-1.24261940e+00 1.29855967e+00 -9.39525485e-01 -1.24526881e-01
-3.17083061e-01 9.06754553e-01 -7.76907086e-01 3.58447820e-01
-1.62687922e+00 1.11070538e+00 6.59490168e-01 -5.78834396e-03
-4.73186880e-01 -6.29900217e-01 -8.13170969e-01 5.59277982e-02
1.28414333e-01 -7.82837808e-01 1.19078350e+00 -1.13499272e+00
-1.49394548e+00 4.94224012e-01 -1.64687529e-01 -3.09349895e-01
-1.62075475e-01 5.32313585e-01 -5.13359249e-01 -1.25681996e-01
-1.37040988e-01 5.62038720e-01 4.34136450e-01 -8.22030008e-01
1.57344371e-01 -2.61936903e-01 3.99477184e-01 -1.23101585e-01
-3.34604420e-02 -2.17384517e-01 4.16661263e-01 -2.66730398e-01
1.47958234e-01 -1.28762138e+00 -3.33095968e-01 -4.52230126e-01
-3.95628303e-01 -1.24657221e-01 1.98896036e-01 -1.59520507e-02
1.02176785e+00 -1.73035967e+00 4.66380417e-02 6.13440692e-01
4.36144024e-01 2.15564474e-01 3.20600979e-02 6.56899571e-01
-7.66517043e-01 -8.69111195e-02 -2.45009661e-01 1.41277969e-01
1.22528210e-01 3.77418637e-01 1.34717301e-01 7.05488920e-01
2.59905338e-01 8.92014563e-01 -5.43498516e-01 1.15838617e-01
-9.40673947e-02 8.00565779e-01 -1.21977174e+00 -5.73975921e-01
-4.89484407e-02 2.61173040e-01 -3.41535270e-01 3.21978867e-01
7.50896931e-01 -3.04302692e-01 3.04795474e-01 -5.10773480e-01
-6.09085977e-01 7.33698547e-01 -1.18586838e+00 1.49931300e+00
-1.60542764e-02 4.04682964e-01 1.82034001e-01 -1.18365693e+00
7.13298976e-01 2.48246849e-01 5.38724244e-01 -5.70040822e-01
1.47057101e-01 5.29290020e-01 9.46532071e-01 2.08534285e-01
7.39654303e-01 -8.47006083e-01 -1.46963611e-01 3.08532685e-01
5.09950280e-01 1.12079717e-02 3.29132825e-01 9.91267785e-02
1.09914958e+00 5.29325530e-02 4.50621158e-01 -9.69855070e-01
6.81666672e-01 8.77748244e-03 3.39119822e-01 8.81185770e-01
-2.14008689e-01 5.34088492e-01 7.00340748e-01 -9.36208606e-01
-1.32353437e+00 -1.04659057e+00 -5.66319048e-01 9.07851756e-01
-1.21069439e-01 -6.87588990e-01 -9.64663804e-01 -1.63877368e-01
-9.54174250e-02 6.07394993e-01 -6.74323797e-01 -6.27862573e-01
-5.06549776e-01 -1.17143631e+00 7.79816747e-01 2.07128629e-01
5.69210052e-01 -9.64168549e-01 -3.56226385e-01 1.16171896e-01
3.49861026e-01 -5.45665145e-01 -1.47558287e-01 9.01724339e-01
-5.02692521e-01 -9.21644330e-01 -2.42735192e-01 -2.60068983e-01
3.41001451e-01 -1.26902580e-01 1.05332625e+00 5.61442673e-02
4.82114777e-03 -8.59234780e-02 1.93227738e-01 1.78437546e-01
-7.27607727e-01 2.31027454e-01 5.82878292e-01 -2.00122014e-01
4.75508720e-01 -7.80429065e-01 -6.49088025e-01 5.76322712e-02
-8.93295407e-01 7.05629354e-03 3.92215222e-01 9.44275975e-01
3.68724763e-01 -1.12520307e-01 3.03725898e-01 -5.91014445e-01
4.25560057e-01 -2.71530241e-01 -5.74024141e-01 -8.26586336e-02
-7.42496669e-01 8.61664414e-01 1.12672901e+00 -5.18787839e-02
-9.05600190e-01 1.65451080e-01 -4.74691927e-01 6.23243488e-02
3.00629474e-02 4.38892692e-01 -1.60772651e-01 -8.76063406e-01
9.25188124e-01 2.40304187e-04 2.15488710e-02 -2.55805433e-01
1.34800866e-01 3.12424183e-01 5.20696282e-01 -8.45466197e-01
5.95263779e-01 2.52998888e-01 7.84092844e-01 -6.79652452e-01
-5.73710024e-01 4.07288447e-02 -7.39004433e-01 9.19773728e-02
9.44519937e-01 -4.59734440e-01 -1.13448691e+00 3.44260752e-01
-1.38616049e+00 -2.10868642e-02 -3.55969161e-01 5.72411180e-01
-5.53874969e-01 3.61970007e-01 -6.61750495e-01 -5.04455328e-01
-2.94448495e-01 -1.46147215e+00 5.97130477e-01 -7.59694278e-02
-4.01055932e-01 -1.15115535e+00 3.11794609e-01 2.77248886e-03
7.09447861e-01 1.88372076e-01 1.23487246e+00 -8.93125176e-01
-3.07083517e-01 -4.02216166e-01 -1.06688149e-01 4.95272040e-01
-1.50087029e-01 -7.36736506e-02 -7.96483397e-01 -2.12107643e-01
8.44239816e-02 -9.35206637e-02 1.16908646e+00 2.51570225e-01
6.82263315e-01 -1.88034713e-01 -1.60706285e-02 5.90904057e-01
1.41567659e+00 3.17882389e-01 5.98385334e-01 2.63948172e-01
7.95296490e-01 6.88417405e-02 -5.41952312e-01 1.65819570e-01
1.19983666e-01 5.98726451e-01 3.67903739e-01 1.31820902e-01
1.07557043e-01 4.67299111e-02 6.03921771e-01 9.21627641e-01
-8.17070425e-01 4.86132465e-02 -1.09931624e+00 -1.01781435e-01
-1.43413854e+00 -1.14770412e+00 -3.91700149e-01 2.36530590e+00
7.25822866e-01 2.40253225e-01 -1.89859524e-01 2.02205583e-01
3.53431731e-01 3.20659161e-01 -5.23732305e-01 -8.83224607e-01
-2.69516259e-01 8.12572658e-01 7.47512519e-01 8.40350389e-01
-1.04855204e+00 8.84815335e-01 7.19789219e+00 7.48100162e-01
-1.21650159e+00 1.92898914e-01 7.37501085e-02 8.87255743e-02
-3.92054796e-01 4.47662324e-01 -8.44786704e-01 2.75662661e-01
1.43449855e+00 2.41196193e-02 7.42863238e-01 7.31639922e-01
3.93320173e-02 3.54938880e-02 -1.14594984e+00 6.88166559e-01
-4.07305174e-02 -1.67294443e+00 4.23402309e-01 2.18945280e-01
8.97134364e-01 2.70663410e-01 -1.41922221e-01 3.40886712e-01
1.25051573e-01 -1.26417875e+00 4.68851030e-01 5.64471066e-01
9.22275662e-01 -8.35429370e-01 4.58541006e-01 -3.11897825e-02
-1.00383675e+00 3.09040308e-01 -4.07488883e-01 -7.08283544e-01
-8.03011805e-02 2.67604232e-01 -1.49508089e-01 3.87867332e-01
2.60589600e-01 4.04562354e-01 -2.39476159e-01 4.85861927e-01
3.05372238e-01 3.99464488e-01 -4.21459705e-01 -2.06296612e-03
6.84557259e-01 -7.55795360e-01 4.67594236e-01 8.72933149e-01
3.08188468e-01 -2.74784141e-03 -1.82549372e-01 1.21317589e+00
-1.69606447e-01 -2.19747305e-01 -7.70541489e-01 -1.70054078e-01
-1.25976697e-01 1.17586350e+00 -4.93731260e-01 -2.37039566e-01
-4.60853815e-01 7.67886460e-01 3.25538129e-01 5.12366354e-01
-5.25195122e-01 -6.63175941e-01 8.94751549e-01 5.88263497e-02
1.93982810e-01 -3.37012082e-01 -7.76072964e-02 -1.37487614e+00
-2.34816417e-01 -7.23971784e-01 -1.28832176e-01 -6.07224226e-01
-9.72072005e-01 5.62441289e-01 -1.05467150e-02 -5.12216091e-01
-4.18929666e-01 -1.29095614e+00 -7.82547057e-01 1.05406582e+00
-1.06540501e+00 -7.01570511e-01 1.62751079e-01 5.74371636e-01
-4.58793312e-01 -1.05303273e-01 1.21042502e+00 2.72641122e-01
-5.58623731e-01 4.90524650e-01 5.71564317e-01 -1.52815104e-01
4.64718729e-01 -1.27484536e+00 4.55705076e-01 7.57875621e-01
-1.16620466e-01 1.13877010e+00 8.74481142e-01 -5.16248763e-01
-1.75378764e+00 -7.47380972e-01 9.03030813e-01 -4.57529753e-01
7.95135438e-01 -3.08090895e-01 -9.78456259e-01 6.22378469e-01
1.83847368e-01 2.03592718e-01 5.54490685e-01 3.83976847e-02
-4.05371159e-01 -4.10773717e-02 -1.04623115e+00 6.62111878e-01
1.06644642e+00 -8.06502581e-01 -4.66832876e-01 3.79738480e-01
4.39882904e-01 1.08734153e-01 -8.11366439e-01 5.22926152e-01
7.61293054e-01 -1.35406423e+00 8.73070002e-01 -8.39390218e-01
2.75859773e-01 -2.31143177e-01 -3.20082128e-01 -1.18161929e+00
-5.09774387e-01 -1.03566062e+00 2.81487763e-01 3.93382549e-01
5.38194180e-01 -1.03767645e+00 8.20027828e-01 8.52286518e-01
-5.40734828e-01 -6.66130006e-01 -1.34148717e+00 -6.98689699e-01
7.92008162e-01 -3.47449780e-01 3.55637074e-01 8.61409783e-01
1.23195961e-01 4.57976550e-01 -1.94247603e-01 -2.66663969e-01
3.48100185e-01 -1.85242787e-01 2.10908130e-01 -1.37221479e+00
-2.66509235e-01 -6.05471730e-01 -4.58815515e-01 -7.07640588e-01
3.97718430e-01 -1.50310433e+00 -2.72084028e-01 -9.54202235e-01
2.17246115e-01 -1.62332013e-01 -5.37797868e-01 4.76185411e-01
4.05921012e-01 4.50792134e-01 -2.01801166e-01 4.87388717e-03
-3.21649462e-01 4.80529606e-01 1.02852309e+00 2.02099755e-01
-5.67658432e-02 -4.49830413e-01 -6.74614191e-01 1.04772842e+00
8.03412974e-01 -3.80753726e-01 8.52171332e-02 1.56224638e-01
7.29693890e-01 -2.24899188e-01 4.90158468e-01 -1.20823836e+00
2.94722080e-01 2.82680970e-02 3.69421721e-01 -7.78315514e-02
5.77404082e-01 -4.88669723e-01 4.28080946e-01 5.47308862e-01
-5.12258299e-02 2.66931504e-01 2.58717164e-02 -1.27257444e-02
-5.48916683e-02 -3.90154809e-01 9.21556234e-01 -3.87196511e-01
-2.56692499e-01 1.33812472e-01 -5.67641377e-01 -2.24968433e-01
4.14076746e-01 -3.70605439e-01 -2.02923462e-01 6.03971891e-02
-5.63793540e-01 -5.14848590e-01 7.44012654e-01 -3.29207063e-01
1.09507501e-01 -1.41466391e+00 -1.84175134e-01 6.24412358e-01
-1.94262728e-01 -5.52375972e-01 2.60232300e-01 1.11396432e+00
-7.55149364e-01 7.49686122e-01 -5.31586111e-01 -1.19735360e-01
-5.36834776e-01 2.56772995e-01 1.02245867e+00 -2.77603120e-01
-2.59802997e-01 4.45764184e-01 3.73694777e-01 -6.97795570e-01
-4.89314556e-01 -3.06259513e-01 2.44130239e-01 -2.62597620e-01
1.57042548e-01 4.20269817e-01 3.66978645e-01 -1.04903972e+00
-2.85454065e-01 5.18033743e-01 9.75033119e-02 9.99092460e-02
1.21503222e+00 5.45758367e-01 -5.75091958e-01 1.98178411e-01
1.21116853e+00 1.21309198e-01 -8.76218021e-01 9.20618400e-02
-3.30511868e-01 3.60509366e-01 1.42406628e-01 -4.60546821e-01
-6.51445091e-01 8.66625130e-01 2.32935324e-01 1.67589545e-01
5.16900063e-01 -2.57613480e-01 6.93498313e-01 9.78996754e-01
1.09294578e-01 -6.96377158e-01 -4.74471778e-01 9.82395589e-01
7.26153314e-01 -1.06001198e+00 -1.77860096e-01 1.08098760e-01
-1.81466222e-01 1.41381299e+00 4.87068295e-01 -6.96446419e-01
6.72714591e-01 1.15391366e-01 -5.49010873e-01 -3.67050827e-01
-5.60515165e-01 6.01244010e-02 5.27963877e-01 4.95680124e-02
5.82934320e-01 2.66410232e-01 -4.43611950e-01 7.30655491e-01
-5.73118567e-01 -3.36222410e-01 6.29488587e-01 6.33461475e-01
-4.62714881e-01 -1.37762535e+00 -2.04575017e-01 3.40237737e-01
-5.37692845e-01 -5.30310631e-01 -5.13709784e-01 9.90142882e-01
3.25311631e-01 4.38171118e-01 6.72538429e-02 -3.82350743e-01
1.05184399e-01 6.59124494e-01 6.96364164e-01 -3.54688913e-01
-6.86781883e-01 -4.18237656e-01 3.29457782e-02 -4.72132742e-01
-4.95530099e-01 -3.81354451e-01 -1.29191720e+00 -7.85584807e-01
-4.04115021e-01 4.93405581e-01 5.35697460e-01 1.07663405e+00
3.45844358e-01 2.05939353e-01 8.99667963e-02 -1.40876818e+00
-8.07580769e-01 -7.54535496e-01 -7.28097320e-01 1.66140631e-01
6.00709379e-01 -8.58955204e-01 -6.09820306e-01 -6.39108002e-01] | [5.399946212768555, 5.117935657501221] |
a8db19f1-7983-4a36-9e1f-f4a366a2c78b | hear-to-segment-unmixing-the-audio-to-guide | 2305.07223 | null | https://arxiv.org/abs/2305.07223v1 | https://arxiv.org/pdf/2305.07223v1.pdf | Hear to Segment: Unmixing the Audio to Guide the Semantic Segmentation | In this paper, we focus on a recently proposed novel task called Audio-Visual Segmentation (AVS), where the fine-grained correspondence between audio stream and image pixels is required to be established. However, learning such correspondence faces two key challenges: (1) audio signals inherently exhibit a high degree of information density, as sounds produced by multiple objects are entangled within the same audio stream; (2) the frequency of audio signals from objects with the same category tends to be similar, which hampers the distinction of target object and consequently leads to ambiguous segmentation results. Toward this end, we propose an Audio Unmixing and Semantic Segmentation Network (AUSS), which encourages unmixing complicated audio signals and distinguishing similar sounds. Technically, our AUSS unmixs the audio signals into a set of audio queries, and interacts them with visual features by masked attention mechanisms. To encourage these audio queries to capture distinctive features embedded within the audio, two self-supervised losses are also introduced as additional supervision at both class and mask levels. Extensive experimental results on the AVSBench benchmark show that our AUSS sets a new state-of-the-art in both single-source and multi-source subsets, demonstrating the effectiveness of our AUSS in bridging the gap between audio and vision modalities. | ['Yabiao Wang', 'Mingmin Chi', 'Jiangning Zhang', 'Zhenye Gan', 'Yuxi Li', 'Yuhang Ling'] | 2023-05-12 | null | null | null | null | ['scene-understanding'] | ['computer-vision'] | [ 5.23731530e-01 -3.22209120e-01 5.64555451e-02 -2.22793147e-01
-1.00664890e+00 -4.79356170e-01 2.12879539e-01 1.00678839e-01
-2.03327358e-01 2.31505767e-01 7.87395164e-02 2.29142651e-01
2.93866061e-02 -3.46244037e-01 -7.84338892e-01 -7.90301442e-01
-2.51079258e-02 -5.55899106e-02 4.61889446e-01 1.29056603e-01
-4.22123000e-02 1.25643313e-01 -1.89690161e+00 4.75190312e-01
9.58202660e-01 1.60505545e+00 3.30992669e-01 5.40272415e-01
-1.76331997e-01 5.65201700e-01 -6.60072446e-01 3.27833705e-02
2.94049561e-01 -4.74093556e-01 -7.19477296e-01 2.98894852e-01
6.25424981e-01 -9.95332971e-02 -1.69163078e-01 1.26015258e+00
4.05459672e-01 1.30013496e-01 5.76373339e-01 -1.62157249e+00
-5.30651629e-01 8.36218596e-01 -8.17862749e-01 2.68612534e-01
2.86026418e-01 2.80111402e-01 1.29207826e+00 -9.03369367e-01
-3.31468433e-02 1.35607982e+00 5.08275986e-01 1.58029541e-01
-1.25251353e+00 -1.08174229e+00 4.71831411e-01 3.46285850e-01
-1.66595685e+00 -4.25284415e-01 1.09530139e+00 -5.54072618e-01
2.40217254e-01 4.43184584e-01 8.35942030e-01 9.79392409e-01
-4.10357088e-01 1.08866274e+00 1.02306318e+00 -1.47308961e-01
1.76477879e-01 4.88231517e-02 3.77791449e-02 3.01950961e-01
-2.43523687e-01 -2.08045146e-03 -8.97364497e-01 7.56015107e-02
6.84974253e-01 -2.04722732e-01 -5.22492588e-01 -4.15053181e-02
-1.08576882e+00 7.11002052e-01 5.04337668e-01 3.07911098e-01
-1.42923072e-01 1.35691404e-01 3.21717322e-01 1.70302585e-01
2.75105119e-01 3.01649988e-01 -1.83595568e-01 9.40119103e-02
-9.35881853e-01 3.55306976e-02 2.86347955e-01 7.36423492e-01
7.69551575e-01 1.08735645e-02 -3.51052135e-01 9.40161705e-01
4.95724857e-01 4.24497157e-01 3.90143365e-01 -1.05586243e+00
5.32192707e-01 3.40534329e-01 -1.05527163e-01 -1.20286298e+00
-1.84585497e-01 -5.13906837e-01 -1.00164592e+00 -6.32427856e-02
4.10211444e-01 1.12863459e-01 -8.05086315e-01 1.91374934e+00
4.14675117e-01 7.30820239e-01 -3.56194228e-01 1.18094015e+00
1.02277231e+00 8.71267736e-01 3.64119820e-02 -2.62818217e-01
1.40392852e+00 -1.03216016e+00 -7.59003043e-01 -2.24550515e-01
-2.42048427e-01 -8.34815443e-01 1.24658227e+00 4.47146505e-01
-1.00163817e+00 -1.08805656e+00 -9.73538578e-01 9.94671509e-02
-1.10278670e-02 -9.50294137e-02 4.62466091e-01 2.60438681e-01
-7.31654286e-01 4.04739916e-01 -7.20047951e-01 1.44305125e-01
7.22725987e-01 5.30738309e-02 4.89130989e-02 8.43805522e-02
-1.29120851e+00 -7.77796954e-02 3.60707641e-01 2.36198276e-01
-1.04476857e+00 -8.41988325e-01 -8.69257092e-01 1.06458612e-01
5.99419117e-01 -4.81213033e-01 1.06426954e+00 -1.22586668e+00
-1.35655296e+00 6.71524763e-01 -3.08994614e-02 -3.20753694e-01
4.43566144e-01 -2.51600593e-01 -5.61132967e-01 5.18836856e-01
4.11858857e-01 9.48621571e-01 1.49642444e+00 -1.41431808e+00
-8.65772545e-01 -2.93560505e-01 -2.15795293e-01 3.63197058e-01
-4.36171561e-01 -4.41553211e-03 -7.65794873e-01 -1.03344011e+00
1.75597027e-01 -5.86494803e-01 2.14694619e-01 1.03443682e-01
-7.07283199e-01 -2.22276837e-01 6.72010362e-01 -3.92492056e-01
1.14764941e+00 -2.65253854e+00 1.24647669e-01 1.46300346e-01
2.86631703e-01 1.14018656e-01 -4.22511399e-01 -5.34834266e-02
-1.28503516e-02 -7.03347027e-02 -3.73387009e-01 -2.89238721e-01
-6.28621727e-02 3.25773656e-03 -6.42964125e-01 1.99716300e-01
4.13134277e-01 5.76991320e-01 -1.00987697e+00 -7.43325114e-01
9.54221748e-03 4.47146118e-01 -5.15461147e-01 3.79977912e-01
-1.28868014e-01 7.63788044e-01 -3.46111745e-01 8.55309069e-01
6.23139799e-01 -2.14205489e-01 -4.63094294e-01 -4.18868721e-01
1.18298098e-01 2.70915926e-01 -1.49613237e+00 1.81233275e+00
-1.91585764e-01 5.11223972e-01 4.15948898e-01 -1.02038217e+00
7.53980160e-01 2.07026929e-01 6.48754179e-01 -4.69353378e-01
1.02643818e-01 9.79561880e-02 -1.01289429e-01 -6.12091303e-01
1.49664551e-01 -6.92703351e-02 -2.15349887e-02 1.55715451e-01
2.16313735e-01 -3.92090827e-01 1.21570565e-01 6.96359575e-02
6.61474526e-01 -2.98207223e-01 -1.48774415e-01 1.03707172e-01
5.93680501e-01 -4.18959588e-01 7.38049507e-01 8.34443510e-01
-3.83359313e-01 7.79906750e-01 3.27877253e-01 1.61506280e-01
-5.03065109e-01 -1.36398470e+00 -3.76711488e-01 1.25621104e+00
6.02590919e-01 -3.74841601e-01 -7.02212036e-01 -5.47401011e-01
-5.42830396e-03 1.92280486e-01 -4.13754821e-01 -1.73258141e-01
-1.55769169e-01 -4.89140898e-01 7.26421416e-01 5.57691574e-01
5.16033888e-01 -9.80159879e-01 -4.12401021e-01 2.10831210e-01
-5.90385854e-01 -1.27448201e+00 -9.47152615e-01 1.18233785e-01
-4.11454648e-01 -1.05494833e+00 -6.66990161e-01 -9.06101227e-01
3.26060593e-01 4.54156101e-01 9.71997976e-01 -1.20261572e-01
-4.02133554e-01 3.65127206e-01 -3.67632717e-01 -3.95720929e-01
-1.96677700e-01 -1.30081058e-01 -1.06883071e-01 7.42354691e-01
1.67765722e-01 -8.15939426e-01 -6.52741790e-01 3.95845920e-01
-1.02194095e+00 -2.07996145e-02 5.52578509e-01 8.25360060e-01
8.67608011e-01 4.25658435e-01 6.63822412e-01 -4.10663754e-01
3.20132703e-01 -4.45733726e-01 -4.48588550e-01 -1.09048508e-01
-1.41975343e-01 -4.11084741e-01 7.50786960e-01 -8.13622296e-01
-7.66982853e-01 2.50314355e-01 -1.89366713e-02 -7.20432281e-01
-4.10626322e-01 3.43576163e-01 -4.29881632e-01 9.30371135e-02
2.88840473e-01 2.78379977e-01 4.69448324e-03 -5.74227452e-01
3.81720841e-01 9.35561299e-01 9.28102076e-01 -4.37083960e-01
8.44142318e-01 6.61928356e-01 -2.48948187e-01 -9.80639994e-01
-9.72995102e-01 -7.66669571e-01 -2.88549632e-01 -3.42579186e-01
8.95615816e-01 -1.13764501e+00 -5.82118154e-01 8.10288608e-01
-9.93556738e-01 -1.43956691e-01 -4.56177294e-01 4.46164310e-01
-3.14373940e-01 4.24335599e-01 -5.89959681e-01 -8.05122614e-01
-2.12567579e-02 -1.29942441e+00 1.33884799e+00 3.39396507e-01
-2.42660806e-01 -3.57610613e-01 -2.27345392e-01 6.63473725e-01
-5.94274187e-03 -4.26411908e-03 7.08953857e-01 -5.52960992e-01
-6.35373771e-01 9.06673670e-02 -3.32709134e-01 4.70845908e-01
3.05602908e-01 6.36302382e-02 -1.35893977e+00 -1.67561173e-01
3.76967154e-02 -6.09997749e-01 1.14097381e+00 4.00516480e-01
1.53702569e+00 -1.58800676e-01 -7.01415772e-03 6.51900649e-01
9.47540939e-01 1.49902835e-01 2.19709709e-01 -9.70471427e-02
9.04521525e-01 7.45926023e-01 7.02672482e-01 3.96283686e-01
2.62664974e-01 7.50519872e-01 6.81086004e-01 -3.50727051e-01
-2.39135623e-01 -3.68592680e-01 3.72602969e-01 9.77006674e-01
4.05806035e-01 9.63100716e-02 -5.18417418e-01 6.64855480e-01
-1.64191282e+00 -6.67214394e-01 -1.46521151e-01 2.02480435e+00
1.27958429e+00 8.56414884e-02 3.48542541e-01 5.86079776e-01
9.24564183e-01 3.77260208e-01 -7.68352211e-01 3.72571707e-01
-1.42292172e-01 2.80031681e-01 -2.15835907e-02 3.03265214e-01
-1.41722727e+00 6.16625249e-01 5.29366875e+00 1.32626164e+00
-1.30448163e+00 1.12190917e-01 6.66390300e-01 -2.62030751e-01
-1.27867877e-01 -2.29294315e-01 -5.18997133e-01 8.34930062e-01
4.40197319e-01 1.55977085e-01 5.67867935e-01 4.76225466e-01
1.46021202e-01 -3.44056599e-02 -1.08288765e+00 1.19986200e+00
-5.98408468e-02 -9.60237563e-01 5.10167405e-02 -3.24850619e-01
5.90526521e-01 -1.86870009e-01 3.55237216e-01 5.63075058e-02
-4.18193191e-02 -1.04243755e+00 1.18320107e+00 1.93221867e-01
7.11247921e-01 -7.14907289e-01 3.49417120e-01 9.82560366e-02
-1.62758684e+00 -8.22557658e-02 -8.91437456e-02 2.61135489e-01
5.69084333e-03 6.51800811e-01 -5.51433742e-01 5.02492607e-01
1.12714493e+00 7.98928142e-01 -4.29197341e-01 1.12568438e+00
-3.29783469e-01 7.24019468e-01 -4.11773324e-01 3.18132341e-01
1.23779178e-01 -1.43315539e-01 8.25102270e-01 1.17718208e+00
-5.93910634e-04 -1.66193157e-01 4.12932247e-01 1.16446137e+00
-1.30163938e-01 5.95717728e-02 5.00408327e-03 -1.40274748e-01
7.32902527e-01 1.15091205e+00 -8.48062813e-01 -1.62072942e-01
-2.37883389e-01 7.85720527e-01 -1.24151893e-01 4.24815714e-01
-9.49588895e-01 -4.53231573e-01 8.60988259e-01 -1.58657487e-02
4.22405064e-01 1.96023807e-01 -2.60573924e-01 -9.12942708e-01
2.50097781e-01 -8.80984843e-01 4.47238833e-01 -7.19167411e-01
-1.52156353e+00 4.72206622e-01 -3.56082112e-01 -1.52198792e+00
1.72422379e-01 -1.86591208e-01 -4.72010791e-01 7.04706609e-01
-1.71946359e+00 -9.89401221e-01 -4.66519833e-01 8.22515190e-01
6.81119144e-01 7.89654255e-02 3.73106182e-01 5.18343151e-01
-5.16041219e-01 6.20387673e-01 -1.27215371e-01 3.29523176e-01
8.40524614e-01 -1.17878819e+00 -6.32458553e-02 7.54350543e-01
5.91240227e-01 1.80838391e-01 5.37845075e-01 -4.04722542e-01
-1.11606038e+00 -1.38981068e+00 2.49714747e-01 -4.53508981e-02
8.25904250e-01 -4.33227718e-01 -1.24440503e+00 1.46513030e-01
-1.01140320e-01 2.96483755e-01 8.10315847e-01 -2.03298450e-01
-5.58962882e-01 -3.96887243e-01 -6.00639522e-01 2.97122538e-01
9.46158409e-01 -9.52392280e-01 -4.89104420e-01 1.29065737e-01
9.48247015e-01 -2.44827673e-01 -6.69581175e-01 3.78805071e-01
3.49217504e-01 -9.54281330e-01 1.16213536e+00 -5.01051307e-01
5.08104801e-01 -6.28730476e-01 -1.31093532e-01 -1.27486408e+00
-6.55753389e-02 -7.80845940e-01 -8.38050898e-03 1.64937675e+00
8.33658874e-02 -2.78176695e-01 3.34219754e-01 -1.07334740e-01
-1.13362044e-01 -5.91587782e-01 -1.02335882e+00 -9.11134481e-01
-2.64623940e-01 -8.15415740e-01 5.86214364e-01 1.04202914e+00
-2.24759176e-01 4.15815413e-01 -2.71095753e-01 5.11749506e-01
6.87698662e-01 3.19101840e-01 4.70363945e-01 -1.36651909e+00
-5.23027539e-01 -6.21421993e-01 -3.40447366e-01 -1.36001265e+00
1.60668805e-01 -7.52094686e-01 3.80597144e-01 -1.03978610e+00
2.10684892e-02 -4.41005886e-01 -4.46859390e-01 3.55739594e-01
-3.80754590e-01 5.98118365e-01 3.12279791e-01 3.74015719e-01
-8.71623933e-01 8.08363318e-01 1.18986320e+00 -5.47924519e-01
-3.49054575e-01 1.67084247e-01 -8.18920910e-01 9.02595699e-01
3.12716246e-01 -4.52064067e-01 -4.29368347e-01 -1.98090151e-01
-5.38396351e-02 4.19789255e-02 5.81407547e-01 -9.77450430e-01
2.86802799e-01 -9.43836197e-02 1.81394219e-01 -6.48258150e-01
4.17890340e-01 -8.67065489e-01 -1.07329100e-01 1.78858206e-01
-4.39555258e-01 -7.03132093e-01 8.62688646e-02 9.12450969e-01
-7.47033656e-01 4.31021750e-02 9.82638657e-01 1.93813890e-01
-4.82471168e-01 4.09442067e-01 -2.34611630e-01 4.74747986e-01
7.51976669e-01 -1.85157478e-01 1.83953010e-02 -3.70520294e-01
-7.17469633e-01 4.04635996e-01 6.56851009e-02 5.41547298e-01
5.57444274e-01 -1.42645037e+00 -6.04246140e-01 3.28655005e-01
1.50420263e-01 5.22848487e-01 4.51986313e-01 9.70180154e-01
1.27689019e-01 -7.26694567e-03 -1.35352425e-02 -1.12722671e+00
-1.21187878e+00 4.50735778e-01 1.23170704e-01 2.36269638e-01
-5.09319603e-01 1.21613407e+00 6.73606634e-01 1.34101465e-01
7.94567108e-01 -5.10198772e-01 -4.15785164e-01 4.96882111e-01
5.76242149e-01 2.86397517e-01 -1.11058258e-01 -7.14537501e-01
-1.90118685e-01 6.32422626e-01 9.80526581e-02 -1.75726768e-02
1.18626153e+00 -3.54794562e-01 -8.74605868e-03 9.08637345e-01
1.29463553e+00 3.57269235e-02 -1.63208473e+00 -5.85863590e-01
-4.00395900e-01 -5.91595232e-01 1.39110312e-01 -5.26202083e-01
-1.32433736e+00 1.07929730e+00 5.85129023e-01 6.08052135e-01
1.54724967e+00 3.09489191e-01 1.03853226e+00 -1.28518507e-01
-1.28534749e-01 -1.21356988e+00 5.14929831e-01 2.42441162e-01
8.01172733e-01 -1.21847212e+00 -5.43434083e-01 -7.88614869e-01
-6.89346552e-01 8.75816107e-01 5.62657237e-01 2.84588826e-03
6.15521550e-01 2.99373657e-01 1.65004835e-01 -3.50200012e-02
-3.33068997e-01 -4.01837856e-01 5.83749890e-01 6.41576827e-01
-2.85374150e-02 -6.06012158e-02 3.56736481e-01 1.12909770e+00
-2.46386737e-01 -2.85888910e-01 7.88409188e-02 5.23493528e-01
-3.75520885e-01 -5.82569897e-01 -6.50977552e-01 2.64972985e-01
-4.74726200e-01 -1.97935849e-01 -5.27530313e-01 3.93686473e-01
3.30106556e-01 1.20509362e+00 3.12986583e-01 -4.27557409e-01
2.35542223e-01 -5.65379038e-02 2.20909089e-01 -5.47817945e-01
-5.09214401e-01 7.17026293e-01 -4.29477662e-01 -5.96280158e-01
-4.69460964e-01 -6.27314568e-01 -1.28494382e+00 4.32623982e-01
-3.14164817e-01 1.63683966e-01 2.63764948e-01 8.68603528e-01
2.76193082e-01 9.15019453e-01 1.02812278e+00 -9.83985841e-01
-6.94471121e-01 -7.52863288e-01 -9.79262650e-01 6.11128986e-01
6.62545085e-01 -7.40590632e-01 -6.82057321e-01 3.08810115e-01] | [14.794636726379395, 4.8265380859375] |
0f2c732e-19da-4b10-9a76-d546a47bbcea | exploring-textual-and-speech-information-in | 1810.07455 | null | http://arxiv.org/abs/1810.07455v1 | http://arxiv.org/pdf/1810.07455v1.pdf | Exploring Textual and Speech information in Dialogue Act Classification with Speaker Domain Adaptation | In spite of the recent success of Dialogue Act (DA) classification, the
majority of prior works focus on text-based classification with oracle
transcriptions, i.e. human transcriptions, instead of Automatic Speech
Recognition (ASR)'s transcriptions. In spoken dialog systems, however, the
agent would only have access to noisy ASR transcriptions, which may further
suffer performance degradation due to domain shift. In this paper, we explore
the effectiveness of using both acoustic and textual signals, either oracle or
ASR transcriptions, and investigate speaker domain adaptation for DA
classification. Our multimodal model proves to be superior to the unimodal
models, particularly when the oracle transcriptions are not available. We also
propose an effective method for speaker domain adaptation, which achieves
competitive results. | ['Quan Hung Tran', 'Laurent Besacier', 'Gholamreza Haffari', 'Xuanli He', 'William Havard', 'Ingrid Zukerman'] | 2018-10-17 | exploring-textual-and-speech-information-in-2 | https://aclanthology.org/U18-1007 | https://aclanthology.org/U18-1007.pdf | alta-2018-12 | ['dialogue-act-classification'] | ['natural-language-processing'] | [ 1.83626652e-01 7.80757889e-02 2.41299242e-01 -6.62770629e-01
-1.22322130e+00 -8.83959055e-01 8.75750899e-01 -1.71039715e-01
-4.11202461e-01 9.86126244e-01 4.69792455e-01 -3.88800830e-01
3.03685337e-01 -2.04634666e-01 -9.87990797e-02 -8.33778262e-01
5.51203549e-01 8.49645317e-01 -5.38753392e-03 -5.75526357e-01
1.75451003e-02 3.00908983e-01 -1.18811131e+00 2.49320135e-01
9.27004695e-01 7.79657304e-01 1.93359300e-01 9.17331934e-01
-3.64759266e-01 7.23650694e-01 -1.28060746e+00 -3.63751054e-01
-8.67972746e-02 -7.66408563e-01 -1.06476462e+00 4.27755862e-01
-4.16914113e-02 -5.90897322e-01 -5.70263326e-01 8.76711011e-01
6.45745695e-01 2.23444730e-01 9.36143637e-01 -1.08680630e+00
-4.71628845e-01 5.91837883e-01 -4.16184999e-02 -1.03105485e-01
8.58272374e-01 6.08382635e-02 7.66134441e-01 -9.30655003e-01
4.01739419e-01 1.30438292e+00 1.61760956e-01 1.05133140e+00
-1.37156808e+00 -3.19941878e-01 -4.20422181e-02 1.29875287e-01
-1.30421364e+00 -1.07871091e+00 7.67185926e-01 -2.21312463e-01
9.91836071e-01 2.27198362e-01 8.25271010e-02 1.69665062e+00
-1.94882855e-01 1.03901219e+00 1.06884611e+00 -7.29738295e-01
4.52882022e-01 4.78622496e-01 1.08261421e-01 1.13709442e-01
-5.32189548e-01 -1.77355483e-01 -7.58914471e-01 -3.30351144e-01
4.85816091e-01 -3.70768070e-01 -4.46544200e-01 -1.46439299e-01
-1.15223181e+00 9.05117333e-01 -3.66590410e-01 3.48564833e-01
-5.62139034e-01 -6.32387459e-01 5.33865392e-01 7.30145574e-01
4.07721698e-01 4.32299852e-01 -3.81457448e-01 -5.72224736e-01
-6.22416377e-01 -8.63253325e-02 1.14936733e+00 1.05287504e+00
3.54483575e-01 2.63226479e-01 -2.30561078e-01 1.50207973e+00
3.76621306e-01 7.11014330e-01 6.34431601e-01 -9.52551782e-01
6.03723586e-01 2.87444353e-01 3.25874120e-01 -3.57945144e-01
-2.41886213e-01 4.94568050e-03 -6.72305942e-01 -1.99741766e-01
7.82785237e-01 -3.67498994e-01 -9.09064054e-01 1.65405929e+00
1.05245300e-01 -4.37693030e-01 8.50802600e-01 7.81139016e-01
7.53897130e-01 9.31571305e-01 -1.43566355e-01 -4.01973277e-01
1.04414725e+00 -9.31314766e-01 -1.25189555e+00 -1.71960562e-01
5.80156147e-01 -8.24489117e-01 1.13330948e+00 5.76798916e-01
-1.05591738e+00 -2.85586476e-01 -9.82455671e-01 1.97480857e-01
-2.91891545e-01 1.67284280e-01 1.82634406e-03 1.10873556e+00
-1.08900368e+00 -1.54009849e-01 -7.06887662e-01 -6.62790537e-01
-3.66931856e-01 4.51786995e-01 -3.23212534e-01 -1.15070030e-01
-1.39778781e+00 1.17785811e+00 -7.64592066e-02 2.27246881e-01
-1.06321466e+00 2.04262078e-01 -9.24733162e-01 -3.88805270e-02
3.74168456e-01 -1.35429636e-01 1.78348958e+00 -9.29284751e-01
-2.27810121e+00 5.34384072e-01 -4.88367707e-01 -3.76237541e-01
3.87027800e-01 -8.29768460e-03 -5.24553597e-01 2.04838559e-01
-3.19853127e-01 2.95352131e-01 8.81583035e-01 -1.41615927e+00
-5.41861832e-01 -4.86131132e-01 1.56819280e-02 6.45106494e-01
-5.15351117e-01 2.05773368e-01 -2.24179208e-01 -2.86272436e-01
3.46933603e-02 -7.29021966e-01 -5.59895821e-02 -7.68969774e-01
-4.45813209e-01 -3.98649544e-01 7.10727692e-01 -9.42496717e-01
1.19480085e+00 -2.27870274e+00 2.74724960e-01 2.56706774e-01
-8.73893574e-02 2.39983216e-01 -1.50447816e-01 7.81477451e-01
1.91908702e-01 -2.49212101e-01 -3.63868266e-01 -6.36695445e-01
3.38515103e-01 5.98809063e-01 -5.05867720e-01 2.72736371e-01
8.39980245e-02 4.10705566e-01 -5.36708057e-01 -3.27801019e-01
2.73562968e-01 2.18862638e-01 -1.24630921e-01 6.33232534e-01
7.51005635e-02 6.79507434e-01 -3.21759701e-01 5.21808565e-01
4.48982030e-01 2.14223042e-01 5.23988068e-01 2.86981434e-01
-2.54842006e-02 6.44725919e-01 -9.94062483e-01 1.66239452e+00
-5.18264055e-01 4.96149391e-01 7.53792048e-01 -1.09820032e+00
1.15883994e+00 9.99212980e-01 1.48194313e-01 -6.85216308e-01
-9.20533910e-02 3.01332086e-01 1.42643407e-01 -3.11049402e-01
5.70068657e-01 -1.68751538e-01 -2.69061893e-01 3.88680279e-01
1.94233298e-01 -2.34812617e-01 -5.19401878e-02 2.68567502e-01
1.06575620e+00 -2.48283625e-01 3.19349945e-01 1.47320688e-01
8.53209496e-01 2.84620095e-02 2.34858781e-01 8.64943802e-01
-4.62603420e-01 6.02977097e-01 4.30205137e-01 1.99931473e-01
-9.78126109e-01 -1.09696972e+00 -1.73451379e-01 1.50671780e+00
-1.36539051e-02 -2.45911598e-01 -1.03403175e+00 -7.26651609e-01
-4.19990838e-01 9.94047403e-01 -8.11360106e-02 -1.36312306e-01
-5.93858838e-01 -5.40768981e-01 1.02004373e+00 4.67055589e-01
4.04997528e-01 -7.97034502e-01 3.76900584e-02 4.38102275e-01
-5.33580899e-01 -1.09987438e+00 -6.46360040e-01 4.21204865e-01
-5.47862709e-01 -4.42122638e-01 -9.40556645e-01 -6.41026974e-01
2.51711011e-01 1.65914714e-01 7.74129868e-01 -4.27745372e-01
5.95140755e-01 9.63850856e-01 -6.28403902e-01 -1.79609165e-01
-1.19448745e+00 1.88757032e-01 3.21496904e-01 2.92632848e-01
5.62088013e-01 -2.43559092e-01 -1.16993524e-01 7.92925358e-01
-7.52349019e-01 -2.79424369e-01 5.10606408e-01 1.35047138e+00
-1.67938858e-01 -2.41675973e-01 1.14564371e+00 -6.48505688e-01
1.12909496e+00 -1.77804783e-01 -2.18988255e-01 4.25500602e-01
-4.16293114e-01 -5.60256876e-02 6.14732206e-01 -3.36008638e-01
-1.63349366e+00 1.26094371e-01 -3.83262187e-01 -1.85375437e-01
-7.06592560e-01 4.98618484e-01 -5.72931528e-01 3.02789986e-01
7.45962560e-01 6.60774589e-01 3.05104494e-01 -5.28517127e-01
1.72227636e-01 1.56889236e+00 5.19466817e-01 -6.38292491e-01
2.97299713e-01 -1.98678404e-01 -7.97246099e-01 -1.30724192e+00
-1.38055950e-01 -9.10557866e-01 -7.03463376e-01 -2.81690985e-01
8.31187248e-01 -8.57753694e-01 -5.02048671e-01 5.84482133e-01
-1.35581338e+00 -2.07485884e-01 1.21517427e-01 6.34264648e-01
-6.87812448e-01 4.68712687e-01 -8.19634199e-01 -1.43187034e+00
3.19247320e-02 -1.27375507e+00 1.03501081e+00 8.19651596e-03
-3.47986341e-01 -9.93941784e-01 -7.65914321e-02 6.09522521e-01
3.53179872e-01 -4.94112551e-01 7.52019823e-01 -1.52912295e+00
-9.46119055e-02 -3.97706330e-01 1.82431877e-01 6.44314408e-01
3.25054616e-01 -3.59017402e-01 -1.45889437e+00 -3.05338681e-01
4.55880344e-01 -5.80645144e-01 2.85924703e-01 1.30368963e-01
4.40428823e-01 -4.60026592e-01 -7.96667486e-02 -2.24601209e-01
6.34935319e-01 8.90589237e-01 6.38416052e-01 -1.06665641e-01
2.52037644e-01 8.60799015e-01 6.19718015e-01 4.46548551e-01
4.14067358e-01 9.98682618e-01 -3.43078673e-02 8.43406692e-02
1.37595668e-01 -1.39996275e-01 7.67319322e-01 1.26311016e+00
1.28558949e-01 -5.92611134e-01 -1.20515585e+00 4.45225328e-01
-1.84367228e+00 -7.32885838e-01 8.37676898e-02 2.29668498e+00
1.09380114e+00 -2.13613674e-01 3.26195002e-01 1.32650152e-01
9.18519795e-01 -6.10475801e-03 -4.04159129e-01 -4.17348683e-01
-2.10067019e-01 -1.38935940e-02 1.82785466e-01 7.38585889e-01
-9.32251155e-01 7.46597290e-01 6.88466358e+00 8.02557170e-01
-8.58824492e-01 2.72566766e-01 3.27024430e-01 1.46176934e-01
7.44960904e-02 -3.73629749e-01 -6.31627440e-01 3.45136642e-01
1.38831532e+00 3.23884971e-02 6.14427090e-01 7.45115519e-01
1.40228048e-01 -1.68064058e-01 -1.39824212e+00 1.03275609e+00
2.58614182e-01 -5.29161096e-01 -6.89607635e-02 8.01969692e-02
2.80707747e-01 -2.55820096e-01 -1.15971580e-01 6.19540274e-01
3.10512334e-01 -1.11612058e+00 4.69290614e-01 1.33224249e-01
5.29608190e-01 -6.20805025e-01 1.10298491e+00 6.94766283e-01
-7.00172663e-01 -2.71736067e-02 -1.74564973e-01 1.92046434e-01
9.05539617e-02 -2.00280055e-01 -1.40638912e+00 7.28094101e-01
1.99811578e-01 2.44173154e-01 -1.72707468e-01 5.55377603e-01
5.58923967e-02 9.72270787e-01 -3.65387678e-01 -2.31437251e-01
9.50779989e-02 -2.52489626e-01 6.76435351e-01 1.37913644e+00
1.25035346e-01 2.45962709e-01 3.69138002e-01 3.78354520e-01
5.54552823e-02 1.99142948e-01 -7.15286791e-01 -1.89762011e-01
7.05649495e-01 7.35379517e-01 -2.28405848e-01 -3.73668462e-01
-4.29762244e-01 1.20505333e+00 -1.08716249e-01 6.59969568e-01
-3.87599200e-01 -3.81400198e-01 3.57975483e-01 -3.81503642e-01
-1.44311532e-01 -4.36516017e-01 -2.94149280e-01 -1.13838351e+00
-1.71769187e-01 -1.32706308e+00 2.63329089e-01 -4.63436455e-01
-1.47882473e+00 7.09315598e-01 6.46566274e-03 -1.17829502e+00
-8.81236076e-01 -5.56509018e-01 -3.07468742e-01 9.50340807e-01
-9.10407186e-01 -9.97336209e-01 2.11550906e-01 7.15117753e-01
1.05435574e+00 -5.51050007e-01 1.22243190e+00 3.82348269e-01
-4.39741194e-01 7.18315542e-01 5.30171871e-01 2.75384188e-01
1.16831100e+00 -1.54423654e+00 -9.91227478e-03 2.59454817e-01
3.25933434e-02 6.62797570e-01 9.17131066e-01 -2.50735044e-01
-1.34046555e+00 -4.80703861e-01 7.98310101e-01 -6.32532716e-01
4.89540428e-01 -6.69517398e-01 -1.21856475e+00 5.85964501e-01
6.64094687e-01 -9.73921657e-01 8.76112521e-01 3.06513071e-01
-8.76906440e-02 -4.42575999e-02 -1.10511196e+00 5.77642620e-01
4.92184430e-01 -8.44702065e-01 -9.71952677e-01 1.15617424e-01
4.75524902e-01 -3.73726964e-01 -8.63625407e-01 1.05168164e-01
4.04077411e-01 -6.57387018e-01 7.43570507e-01 -4.71553534e-01
-1.69073269e-01 -1.71721414e-01 -6.02906168e-01 -1.42126334e+00
3.66398275e-01 -5.59866965e-01 2.46659935e-01 1.56263447e+00
6.95463121e-01 -8.50898981e-01 3.60648930e-01 8.67422700e-01
-2.92507499e-01 3.20270136e-02 -1.27364767e+00 -8.03295910e-01
8.92988816e-02 -3.38041514e-01 3.83298218e-01 1.04770792e+00
4.36525881e-01 9.44328070e-01 -6.61096871e-01 1.85480282e-01
1.14726767e-01 -3.14952880e-01 8.85184705e-01 -8.98787975e-01
-2.77204871e-01 -2.87054569e-01 9.81303211e-03 -1.47544146e+00
2.02290490e-01 -4.39993292e-01 6.81902468e-01 -1.24701524e+00
-2.48709008e-01 9.52602476e-02 -4.75219116e-02 3.48224580e-01
1.35427758e-01 -1.85753033e-01 1.10079311e-01 2.19548166e-01
-5.32564282e-01 1.08278155e+00 9.10378218e-01 -3.20337415e-01
-5.41367710e-01 2.96817988e-01 -1.41063273e-01 5.30234575e-01
7.25247800e-01 -2.49406904e-01 -4.61690187e-01 -1.02901578e-01
-6.62057102e-01 7.46892095e-01 -4.22898643e-02 -6.16275251e-01
4.65294123e-01 -6.52399287e-02 1.98767409e-01 -5.69659352e-01
8.78005862e-01 -7.13549018e-01 -2.44883537e-01 3.44009139e-02
-7.39754260e-01 -2.30659783e-01 8.63369033e-02 6.36634946e-01
-6.54063702e-01 -5.10302007e-01 6.11962557e-01 7.60446489e-02
-2.31553659e-01 -3.17054123e-01 -1.09997165e+00 -2.75127083e-01
5.59943020e-01 -2.37150788e-01 -3.43660742e-01 -1.04966354e+00
-9.24804688e-01 2.35918030e-01 3.02236289e-01 2.83928782e-01
5.61338663e-01 -1.13035762e+00 -6.96772099e-01 1.06544048e-01
4.16690260e-02 -1.72698438e-01 2.57208318e-01 7.84053266e-01
-1.92396846e-02 7.06533134e-01 1.03768438e-01 -6.65693760e-01
-1.32634687e+00 2.31611580e-01 2.88487852e-01 -2.80891005e-02
-6.02268241e-02 4.34655786e-01 4.60113287e-01 -7.80717790e-01
6.28010333e-01 1.82696566e-01 -3.71676505e-01 1.10211194e-01
3.62418473e-01 1.86367929e-01 1.06169455e-01 -6.65191948e-01
-2.90368825e-01 -8.27910006e-02 -1.54794529e-01 -9.61155593e-01
8.05501759e-01 -7.03866720e-01 1.68679297e-01 8.57772529e-01
6.20088637e-01 1.14438437e-01 -9.29761171e-01 -4.20265257e-01
2.91095465e-01 -3.95321608e-01 -2.29445413e-01 -9.88107681e-01
-2.79461861e-01 1.02304685e+00 4.60237741e-01 7.73233950e-01
9.92842197e-01 -1.18170753e-02 5.99415362e-01 8.41337562e-01
3.82624328e-01 -1.40287828e+00 1.63263693e-01 8.94369125e-01
1.10084057e+00 -1.21697760e+00 -6.16477847e-01 -1.13806777e-01
-1.30126464e+00 1.07572806e+00 6.06233418e-01 6.05858505e-01
2.29004055e-01 -3.79697867e-02 4.83446836e-01 3.03468496e-01
-8.32173049e-01 -1.90202549e-01 9.30704474e-02 9.56193447e-01
4.47332978e-01 -3.73913161e-02 -1.02607384e-01 7.57048190e-01
-1.71233281e-01 -5.39220273e-01 7.03271747e-01 8.52129340e-01
-4.36528981e-01 -1.35781169e+00 -7.47169912e-01 7.47985691e-02
-2.82133371e-01 3.38758864e-02 -9.31938589e-01 4.92083788e-01
-6.83422089e-01 1.72483778e+00 -2.33921051e-01 -2.93594360e-01
4.81678963e-01 8.24335158e-01 6.41408935e-02 -7.62155056e-01
-5.46792388e-01 4.65910614e-01 6.80979609e-01 -1.88517701e-02
-4.09231961e-01 -7.46704102e-01 -1.04458356e+00 -1.92794442e-01
-4.87810254e-01 5.70533156e-01 7.34898567e-01 1.01981366e+00
-6.86464608e-02 2.02752888e-01 8.78505468e-01 -5.36173344e-01
-1.10572374e+00 -1.49766028e+00 -7.22571731e-01 2.41183892e-01
4.19128209e-01 -4.37754929e-01 -3.13122779e-01 1.75877094e-01] | [14.192205429077148, 6.800258636474609] |
507ebd80-db02-4f64-99bc-68ca47146b53 | rotation-invariance-and-extensive-data | 2109.00823 | null | https://arxiv.org/abs/2109.00823v2 | https://arxiv.org/pdf/2109.00823v2.pdf | Rotation Invariance and Extensive Data Augmentation: a strategy for the Mitosis Domain Generalization (MIDOG) Challenge | Automated detection of mitotic figures in histopathology images is a challenging task: here, we present the different steps that describe the strategy we applied to participate in the MIDOG 2021 competition. The purpose of the competition was to evaluate the generalization of solutions to images acquired with unseen target scanners (hidden for the participants) under the constraint of using training data from a limited set of four independent source scanners. Given this goal and constraints, we joined the challenge by proposing a straight-forward solution based on a combination of state-of-the-art deep learning methods with the aim of yielding robustness to possible scanner-related distributional shifts at inference time. Our solution combines methods that were previously shown to be efficient for mitosis detection: hard negative mining, extensive data augmentation, rotation-invariant convolutional networks. We trained five models with different splits of the provided dataset. The subsequent classifiers produced F1-scores with a mean and standard deviation of 0.747+/-0.032 on the test splits. The resulting ensemble constitutes our candidate algorithm: its automated evaluation on the preliminary test set of the challenge returned a F1-score of 0.6828. | ['Viktor H. Koelzer', 'Maxime W. Lafarge'] | 2021-09-02 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [ 5.20321727e-01 3.94434333e-01 1.50388494e-01 -4.59016085e-01
-1.33539939e+00 -4.13240463e-01 5.77381253e-01 3.93858641e-01
-8.14293683e-01 9.41150427e-01 -1.63035035e-01 -2.28393093e-01
-2.59454399e-01 -3.83891821e-01 -7.38875747e-01 -1.03856170e+00
-4.52663973e-02 8.96271646e-01 3.36821586e-01 1.33461639e-01
5.23258328e-01 8.01006436e-01 -1.49887562e+00 6.66320920e-01
5.48113227e-01 8.54057133e-01 1.24508418e-01 9.58678305e-01
2.01006979e-01 4.95850116e-01 -6.93126321e-01 -3.78748924e-01
1.77959666e-01 -2.61764973e-01 -1.01477313e+00 8.51390511e-02
4.37084883e-01 6.65768087e-02 -6.33153543e-02 8.00070047e-01
8.16441894e-01 -3.13215613e-01 8.32134426e-01 -9.64304209e-01
-1.56443119e-01 4.25753653e-01 -6.54930651e-01 6.10035717e-01
-8.42838734e-03 3.53463233e-01 5.02078116e-01 -6.10065103e-01
1.07111144e+00 6.11652076e-01 7.79584527e-01 6.72142267e-01
-1.54666424e+00 -4.91675377e-01 -3.05698007e-01 1.93245634e-01
-1.50336969e+00 -6.69745862e-01 1.71582550e-01 -5.13531387e-01
8.23824883e-01 3.34746659e-01 3.71391714e-01 1.13365388e+00
3.13157171e-01 4.63512957e-01 1.39606202e+00 -5.15743673e-01
4.68446612e-01 3.98038000e-01 1.34788672e-04 5.89938283e-01
2.91022509e-01 -9.18823481e-02 -4.00166690e-01 -1.33184478e-01
3.95123959e-01 -5.96646786e-01 -2.06710815e-01 -4.40671891e-01
-1.26261926e+00 7.34842956e-01 2.85886407e-01 6.74089909e-01
-7.08535388e-02 -2.74053901e-01 5.58549881e-01 2.11052373e-01
3.13331753e-01 3.74601364e-01 -4.12418634e-01 4.39818203e-01
-1.14285111e+00 2.35140041e-01 5.69897532e-01 4.16767329e-01
4.25434798e-01 -4.38755870e-01 -4.33698684e-01 4.07927543e-01
1.27889514e-01 1.63767487e-01 8.10586095e-01 -6.93666279e-01
1.05755314e-01 5.74009597e-01 -3.01623136e-01 -5.27635276e-01
-9.22627628e-01 -7.94200361e-01 -9.77011263e-01 2.05177128e-01
9.94314015e-01 -9.90311503e-02 -1.31112719e+00 1.64085495e+00
2.82240063e-01 -6.58799484e-02 -1.31939277e-01 5.94353616e-01
6.89112246e-01 -3.77428485e-03 1.66190248e-02 -3.20739955e-01
1.28166842e+00 -5.40936291e-01 -4.92566049e-01 1.07596084e-01
1.06835270e+00 -7.99172342e-01 7.46929526e-01 5.81055641e-01
-8.99493814e-01 -4.26566660e-01 -1.12766123e+00 1.56768128e-01
-5.81579208e-01 3.66953433e-01 2.38113865e-01 9.07595754e-01
-1.39101362e+00 4.88174379e-01 -8.16517889e-01 -5.85132539e-01
7.11316466e-01 7.87992954e-01 -5.20839691e-01 7.68991699e-03
-6.45422637e-01 1.04635561e+00 4.12518114e-01 1.24914408e-01
-7.74720967e-01 -7.74905205e-01 -4.75575298e-01 -3.82444412e-01
2.21060976e-01 -7.34712124e-01 1.11787331e+00 -9.57454443e-01
-1.10394812e+00 1.71881473e+00 -1.29037216e-01 -6.13903761e-01
9.33501542e-01 4.27827686e-01 -1.41672045e-01 2.76479244e-01
1.87780052e-01 9.54947948e-01 4.37026054e-01 -1.02365112e+00
-7.50516176e-01 -6.22930706e-01 -6.64862990e-01 -2.48501658e-01
-1.47532210e-01 -2.31399208e-01 -3.05336237e-01 -3.67466450e-01
-1.06489815e-01 -1.02831018e+00 -3.68996650e-01 -2.46689096e-01
-5.51186442e-01 -2.04919688e-02 3.59103471e-01 -6.89737320e-01
8.58245909e-01 -1.93549192e+00 -5.77480346e-03 3.66373956e-01
1.92523330e-01 1.02869481e-01 -1.77830428e-01 -2.08027847e-02
-4.31800604e-01 9.98248681e-02 -3.19336385e-01 -3.42088759e-01
-9.05145183e-02 -2.28592694e-01 1.19944111e-01 9.85100389e-01
2.82355011e-01 8.55134726e-01 -6.45496130e-01 -6.36506975e-01
1.19118020e-01 3.23649764e-01 -1.55364528e-01 2.40126196e-02
-4.21730243e-02 4.93764669e-01 -2.46276334e-02 7.17497885e-01
7.94727087e-01 -6.88927323e-02 2.47855589e-01 -5.46201169e-02
1.49069382e-02 -9.14183185e-02 -1.08324838e+00 1.78301144e+00
8.94495547e-02 5.85497737e-01 1.73663154e-01 -1.07454860e+00
8.02483499e-01 7.46565685e-02 5.28479874e-01 -7.13393033e-01
2.95650542e-01 6.18368328e-01 3.65528703e-01 -5.71249604e-01
7.98859969e-02 -2.71394670e-01 2.54281964e-02 2.73041874e-01
3.93742621e-01 4.92415391e-02 5.27288795e-01 -4.58183922e-02
1.38092375e+00 -9.34820622e-02 2.89755642e-01 -5.62907219e-01
8.37336302e-01 2.30090115e-02 3.24496061e-01 6.84659362e-01
-6.31521106e-01 9.29060280e-01 7.32455015e-01 -6.67970598e-01
-1.09292436e+00 -7.44214118e-01 -3.92509550e-01 7.39365101e-01
-5.19517839e-01 1.01392254e-01 -9.10028100e-01 -9.95091677e-01
-2.85046309e-01 3.78217012e-01 -1.23444724e+00 2.11255029e-02
-4.93406385e-01 -1.28048241e+00 6.57316029e-01 2.29694545e-01
9.97089148e-02 -9.58900988e-01 -7.29324102e-01 -1.27060816e-01
6.16791546e-02 -1.01208615e+00 -8.61312356e-03 6.96292579e-01
-6.57708466e-01 -1.33246243e+00 -9.81140137e-01 -7.96576917e-01
7.24705696e-01 -2.00241476e-01 1.01689470e+00 1.01366878e-01
-8.02932918e-01 8.38132873e-02 -8.61029103e-02 -4.57786679e-01
-5.46045840e-01 5.46506703e-01 -2.21874431e-01 7.56469220e-02
5.87048888e-01 -1.97758675e-01 -5.71971774e-01 2.30311394e-01
-1.18099880e+00 -2.24069163e-01 1.02311456e+00 9.03531134e-01
7.55844176e-01 -1.46324873e-01 4.19213831e-01 -1.07001209e+00
2.16043219e-01 -2.23462343e-01 -5.64853430e-01 2.93039322e-01
-4.16228682e-01 -7.74773955e-03 3.14548641e-01 -3.37751031e-01
-6.87558413e-01 5.72568953e-01 -2.07129151e-01 -1.21000014e-01
-5.51799357e-01 2.31832653e-01 -1.15485445e-01 -2.61965871e-01
8.71756554e-01 2.12693095e-01 1.40061915e-01 -1.80428922e-01
6.59248754e-02 4.33902681e-01 7.30264306e-01 -1.68307960e-01
4.97023821e-01 6.37739182e-01 4.40592408e-01 -7.11790562e-01
-5.69159985e-01 -5.51594436e-01 -8.02246153e-01 -1.37192443e-01
7.83496201e-01 -6.58344686e-01 -4.08774406e-01 7.15776384e-01
-9.22465265e-01 -3.81221622e-01 -3.58276427e-01 3.44396830e-01
-5.92306912e-01 3.28413367e-01 -3.65357935e-01 -5.28534591e-01
-3.98746550e-01 -1.21753824e+00 1.09077024e+00 1.81080744e-01
-4.53616083e-01 -8.26268375e-01 3.69027436e-01 6.74398482e-01
3.43327075e-01 5.28164148e-01 7.68230081e-01 -1.28203464e+00
-2.84652740e-01 -3.36198509e-01 -8.30772370e-02 1.32021427e-01
-1.30963132e-01 5.17590642e-02 -1.40210426e+00 -3.55446070e-01
8.31644759e-02 -5.12171686e-01 1.15574598e+00 5.86589932e-01
1.25680447e+00 2.76838869e-01 -5.28622150e-01 5.73923945e-01
1.41628528e+00 -1.00147977e-01 7.99429119e-01 7.13230491e-01
5.88264549e-03 9.14087713e-01 2.43059605e-01 8.87314528e-02
-7.68454671e-02 5.23186684e-01 4.92056191e-01 -2.00091913e-01
-1.21847399e-01 2.17648521e-01 -3.11067086e-02 4.14129123e-02
1.53541207e-01 -1.11467600e-01 -1.21142328e+00 7.72598684e-01
-1.55528021e+00 -5.90885997e-01 -4.25714344e-01 2.24449706e+00
5.62996089e-01 4.75083649e-01 3.39327008e-01 2.41814926e-01
8.46461713e-01 -3.88347685e-01 -4.70751911e-01 -1.90380380e-01
-2.97989875e-01 4.62621808e-01 6.80981100e-01 3.02999020e-01
-1.23333454e+00 4.09020036e-01 6.59868002e+00 9.99072731e-01
-1.00910902e+00 -1.17192343e-01 1.15024471e+00 -2.71663785e-01
1.15617827e-01 -3.77149642e-01 -7.71413386e-01 4.09297913e-01
1.31757939e+00 5.97849302e-02 -1.91594958e-01 3.55882198e-01
-6.28556460e-02 -4.30010855e-01 -1.07520592e+00 6.28463447e-01
3.68394852e-01 -1.45524693e+00 -1.88598305e-01 4.99132544e-01
7.09741473e-01 2.28362963e-01 2.74792671e-01 2.27020860e-01
6.66468311e-03 -1.32046902e+00 3.29410374e-01 5.42604744e-01
8.55289578e-01 -5.93941808e-01 1.35531318e+00 3.80651206e-01
-5.40651917e-01 2.84908433e-02 -1.47584885e-01 1.78131327e-01
-3.30125213e-01 7.88766444e-01 -1.25583589e+00 6.84772253e-01
4.67082947e-01 1.27851516e-01 -1.02012002e+00 1.29594064e+00
1.89920887e-01 2.11690038e-01 -4.27124709e-01 1.08600259e-01
1.16253726e-01 3.70130748e-01 2.57744938e-01 1.39423311e+00
3.38635057e-01 -4.33445424e-01 -1.54215068e-01 5.57193279e-01
-3.92413996e-02 1.84467882e-01 -3.73113841e-01 1.46194741e-01
7.88250044e-02 1.56350946e+00 -1.34738779e+00 -7.25927502e-02
-7.87039194e-03 5.19151151e-01 3.24868321e-01 -1.29408196e-01
-5.67218065e-01 -2.98802972e-01 -7.09603131e-02 3.19551438e-01
3.99995714e-01 4.94678706e-01 -4.85222876e-01 -7.14094698e-01
-3.36135216e-02 -9.55186605e-01 7.92886794e-01 -4.16148752e-01
-1.27566135e+00 7.54386365e-01 -1.71987459e-01 -8.49917948e-01
-1.63811833e-01 -9.18923259e-01 -5.05418956e-01 9.79055762e-01
-1.43577099e+00 -1.31377530e+00 -1.85354471e-01 2.61763692e-01
3.11293632e-01 -4.03550625e-01 9.72420275e-01 2.47344777e-01
-4.88815099e-01 7.15023220e-01 7.19246566e-02 -5.02796918e-02
1.00825763e+00 -1.41140366e+00 -2.51868106e-02 6.44418418e-01
-4.51976843e-02 3.61709863e-01 7.72948444e-01 -3.44134510e-01
-9.15152252e-01 -9.35468137e-01 1.21563256e+00 -5.42015314e-01
6.77295804e-01 -2.36974090e-01 -7.95192242e-01 5.21430612e-01
2.19367370e-01 1.48314446e-01 1.10022712e+00 -9.16443691e-02
-7.92446360e-03 4.32229564e-02 -1.37527716e+00 6.71087354e-02
4.94836748e-01 -2.23612547e-01 -4.17701423e-01 3.96527827e-01
-4.29946445e-02 -5.18441617e-01 -5.11035502e-01 5.37438035e-01
4.05271232e-01 -1.12841332e+00 6.26446009e-01 -5.69329858e-01
3.51378024e-01 -2.90555120e-01 -6.03844263e-02 -9.69449699e-01
-2.67249435e-01 -3.27205390e-01 2.71115482e-01 1.01806951e+00
5.15883446e-01 -1.78036362e-01 1.23957932e+00 1.98582128e-01
-1.35071844e-01 -9.27461922e-01 -1.44295037e+00 -4.41001058e-01
4.45731431e-01 -1.97508439e-01 8.46977904e-02 5.74572861e-01
-1.87029719e-01 2.05265358e-01 1.45891398e-01 -8.86651278e-02
5.90956211e-01 -2.15528816e-01 7.76862621e-01 -1.26600862e+00
-2.28876904e-01 -7.36958861e-01 -7.24325299e-01 -1.19060233e-01
1.76360115e-01 -1.06034768e+00 -7.09310323e-02 -1.14922035e+00
6.03164673e-01 4.58554514e-02 -4.73837316e-01 3.34334999e-01
-7.16473209e-03 6.01354361e-01 -5.97303361e-02 5.99304102e-02
-6.53171360e-01 -1.13466002e-01 8.33938003e-01 -2.13088930e-01
6.05241805e-02 1.38329878e-01 -9.63868856e-01 5.83918154e-01
7.68241346e-01 -5.23905635e-01 -3.89583632e-02 -1.48932338e-02
2.34071866e-01 -4.41672027e-01 4.05855209e-01 -1.17873871e+00
2.47702092e-01 1.89339489e-01 8.60203266e-01 -8.15714419e-01
-8.48010462e-03 -6.58176780e-01 3.43216181e-01 7.88566291e-01
-4.99984145e-01 -2.36434326e-01 4.14758682e-01 3.77666295e-01
4.63053100e-02 -4.13333565e-01 9.81582046e-01 -2.51798779e-01
-3.57990295e-01 -7.97756612e-02 -4.37031001e-01 -1.35845736e-01
1.39768910e+00 -3.37588221e-01 -4.15273517e-01 1.11589372e-01
-1.03356624e+00 2.05325872e-01 5.65041661e-01 -4.44455557e-02
1.46170512e-01 -8.29872191e-01 -9.90623415e-01 8.43465403e-02
1.56081259e-01 -1.09369829e-02 4.24421251e-01 1.13810682e+00
-5.57504117e-01 6.11512125e-01 -4.42798048e-01 -8.89329374e-01
-1.59388483e+00 5.45383751e-01 6.28727674e-01 -9.04454589e-01
-1.97111204e-01 1.04829657e+00 4.87481803e-03 -6.16369128e-01
1.38235047e-01 -2.02835366e-01 -1.77695766e-01 2.91231841e-01
5.73054433e-01 3.42839420e-01 8.10358584e-01 -4.91513193e-01
-5.24243116e-01 3.16857159e-01 -3.45852971e-01 -1.27394602e-01
1.54530668e+00 1.86195791e-01 8.66759196e-03 2.96821713e-01
1.18176031e+00 -1.69119924e-01 -8.23204935e-01 8.99850950e-02
2.46887550e-01 -3.26174125e-02 6.06217682e-02 -1.10281205e+00
-9.50092971e-01 8.14348340e-01 1.02457476e+00 1.45587042e-01
1.01452184e+00 -6.47325665e-02 2.46826664e-01 2.00551823e-01
1.16633199e-01 -1.12377822e+00 -2.63586313e-01 2.20320493e-01
5.71099401e-01 -1.36074090e+00 1.89064145e-01 -3.37905824e-01
-2.56310314e-01 1.29723561e+00 4.05230850e-01 1.67018101e-01
1.85219944e-01 3.15860689e-01 8.81060362e-02 -2.77113438e-01
-8.57009053e-01 -1.87857062e-01 2.00734198e-01 9.05961812e-01
4.80913997e-01 -1.98295802e-01 -5.55924416e-01 5.06181300e-01
-6.46020025e-02 3.21179241e-01 6.11936629e-01 9.75904107e-01
-3.51373613e-01 -1.07135260e+00 -5.19589007e-01 5.26194036e-01
-7.02353656e-01 4.13811147e-01 -6.75060928e-01 1.00807083e+00
4.21672344e-01 5.33055365e-01 -2.68108435e-02 -6.60601780e-02
2.78900743e-01 2.72660464e-01 6.07275367e-01 -3.88273269e-01
-7.33999670e-01 1.94347724e-01 -8.00427422e-02 -3.00269455e-01
-5.20992398e-01 -9.05388772e-01 -9.44707215e-01 -8.95182863e-02
-1.12642892e-01 5.21022710e-04 6.25522017e-01 9.23325956e-01
2.13817999e-01 7.28712499e-01 3.86745185e-01 -7.92400599e-01
-6.77663624e-01 -1.10500979e+00 -5.88363707e-01 4.11375403e-01
3.10467005e-01 -3.59331340e-01 -4.87565547e-01 3.43676567e-01] | [15.097122192382812, -3.044065475463867] |
cf3883ac-d5e4-408d-8813-e3c26ee71f6b | mammoganesis-controlled-generation-of-high | 2010.05177 | null | https://arxiv.org/abs/2010.05177v1 | https://arxiv.org/pdf/2010.05177v1.pdf | MammoGANesis: Controlled Generation of High-Resolution Mammograms for Radiology Education | During their formative years, radiology trainees are required to interpret hundreds of mammograms per month, with the objective of becoming apt at discerning the subtle patterns differentiating benign from malignant lesions. Unfortunately, medico-legal and technical hurdles make it difficult to access and query medical images for training. In this paper we train a generative adversarial network (GAN) to synthesize 512 x 512 high-resolution mammograms. The resulting model leads to the unsupervised separation of high-level features (e.g. the standard mammography views and the nature of the breast lesions), with stochastic variation in the generated images (e.g. breast adipose tissue, calcification), enabling user-controlled global and local attribute-editing of the synthesized images. We demonstrate the model's ability to generate anatomically and medically relevant mammograms by achieving an average AUC of 0.54 in a double-blind study on four expert mammography radiologists to distinguish between generated and real images, ascribing to the high visual quality of the synthesized and edited mammograms, and to their potential use in advancing and facilitating medical education. | ['Ghina Berjawi', 'Elie Najem', 'Ghida Saheb', 'Cyril Zakka'] | 2020-10-11 | null | null | null | null | ['radiologist-binary-classification', 'medical-image-generation'] | ['medical', 'medical'] | [ 6.44453168e-01 6.35202169e-01 1.17733650e-01 -5.63854039e-01
-1.30055821e+00 -5.63388228e-01 3.58942509e-01 2.15406105e-01
-2.73719847e-01 6.46573186e-01 2.42518321e-01 -6.55372500e-01
-2.20459439e-02 -6.17536783e-01 -9.01114821e-01 -6.71379983e-01
-2.88320392e-01 5.11964858e-01 -1.38252624e-03 7.72735551e-02
-1.24220490e-01 7.07075834e-01 -1.18114185e+00 5.50783992e-01
8.78314912e-01 8.49977851e-01 2.41446942e-01 1.28729427e+00
2.92329907e-01 9.34895873e-01 -5.78462601e-01 -6.93739295e-01
2.82666117e-01 -8.48947883e-01 -4.83982712e-01 3.40844810e-01
5.59842646e-01 -6.79044008e-01 -3.13595325e-01 9.12703097e-01
7.19714999e-01 -2.44680911e-01 7.27552414e-01 -5.62260509e-01
-7.42415488e-01 4.30248201e-01 -6.04916573e-01 4.68468547e-01
1.33752912e-01 2.41774127e-01 3.95990521e-01 -4.65815872e-01
6.22421920e-01 7.99528658e-01 6.55808270e-01 7.79300034e-01
-1.23754418e+00 -4.25988793e-01 -4.65306908e-01 -2.44953245e-01
-1.04953384e+00 -5.58292210e-01 2.83883005e-01 -6.06033444e-01
1.53683096e-01 7.52321005e-01 7.38416910e-01 1.13388002e+00
9.34804559e-01 4.36222434e-01 1.36668909e+00 -4.30795521e-01
1.38448551e-01 3.53202075e-01 -5.03037989e-01 9.07790661e-01
3.29244643e-01 2.93829411e-01 -4.37178761e-02 -1.82025194e-01
1.23802626e+00 -1.32533461e-01 -2.88902074e-01 -3.04496378e-01
-1.22663665e+00 6.84673429e-01 5.05921423e-01 2.45997116e-01
-6.06775939e-01 5.49327433e-02 8.10107440e-02 -6.05262034e-02
-4.03641583e-03 4.41611171e-01 3.78204256e-01 2.87347764e-01
-8.26327503e-01 -1.68132797e-01 4.78344738e-01 5.08306503e-01
-1.63991861e-02 2.29535848e-01 -3.95825565e-01 6.16708279e-01
1.13289170e-01 6.86726749e-01 1.00957334e+00 -8.84997368e-01
-2.30758730e-02 2.22386822e-01 1.08719625e-01 -9.88066733e-01
-1.70689538e-01 -5.65652728e-01 -1.08173525e+00 3.10398519e-01
3.15130532e-01 -5.93049638e-02 -1.44042754e+00 1.48069775e+00
3.73657733e-01 -1.79899171e-01 2.33795509e-01 9.11919296e-01
9.98311341e-01 2.24249765e-01 3.70123804e-01 -7.75563270e-02
1.54773247e+00 -4.44387883e-01 -5.26687443e-01 -3.88907313e-01
2.91246414e-01 -6.95526302e-01 9.59101021e-01 1.33774027e-01
-1.54955840e+00 -4.18149263e-01 -1.00425076e+00 2.31781140e-01
2.91620791e-01 1.68181151e-01 5.09316683e-01 8.57312679e-01
-9.34466660e-01 3.86726290e-01 -1.24142885e+00 3.92620191e-02
8.09347093e-01 3.04245621e-01 -5.02571881e-01 -2.94586509e-01
-8.88858199e-01 9.14588869e-01 2.61944234e-01 5.85243255e-02
-1.08368897e+00 -1.03032053e+00 -7.81833708e-01 -2.07080409e-01
3.50277424e-02 -7.53202021e-01 1.41148269e+00 -1.20504892e+00
-9.99704659e-01 1.25940192e+00 1.01730242e-01 -5.41705489e-01
1.00395703e+00 4.07730311e-01 -4.96635735e-01 6.26502335e-01
2.29369462e-01 8.16629946e-01 8.71476650e-01 -1.30651200e+00
-6.12095535e-01 -2.47698694e-01 -3.92825544e-01 3.30244899e-01
1.45460024e-01 -2.31920749e-01 -2.37871423e-01 -8.60997438e-01
2.04615057e-01 -7.22115278e-01 -5.70203662e-01 1.73420250e-01
-1.68319955e-01 8.53970766e-01 2.84887850e-01 -1.13240182e+00
6.18035078e-01 -2.29882264e+00 -3.58578354e-01 3.34658444e-01
4.79909897e-01 -5.61006106e-02 1.55291498e-01 -4.07542139e-01
-1.71813548e-01 2.52194166e-01 -2.96460152e-01 1.99683324e-01
-3.37633520e-01 1.48703113e-01 4.64980192e-02 5.68607211e-01
2.62487054e-01 1.38932168e+00 -9.87139225e-01 -7.34359205e-01
1.73073828e-01 5.84147632e-01 -2.43537217e-01 2.93741196e-01
2.55409300e-01 8.74785841e-01 -4.69905019e-01 6.92326486e-01
3.54022861e-01 -4.63802755e-01 2.43307590e-01 -1.17507637e-01
5.32973051e-01 -6.82437494e-02 -6.95892930e-01 1.18705988e+00
-4.34735566e-01 5.35633326e-01 1.93032861e-01 -5.25237441e-01
5.39365232e-01 5.28467536e-01 5.57168484e-01 -9.61502731e-01
1.94811016e-01 2.61408031e-01 4.26317692e-01 -5.10502934e-01
2.13498473e-01 -6.78983629e-01 1.14647016e-01 4.75136667e-01
-2.71019042e-01 -4.10591394e-01 -5.00401482e-02 1.35694325e-01
1.02990687e+00 -4.37278926e-01 2.96711087e-01 -2.08296224e-01
2.75612529e-03 3.62057202e-02 -2.53121760e-02 9.21147227e-01
-1.53154150e-01 9.92903888e-01 3.34910929e-01 -2.74397641e-01
-1.15535665e+00 -1.33452606e+00 -2.97540843e-01 4.75726813e-01
-2.64225006e-01 5.36933720e-01 -6.69860661e-01 -5.83032191e-01
-1.33363202e-01 7.68368721e-01 -9.14336085e-01 -3.72048855e-01
-5.96356392e-01 -6.18915856e-01 4.93280172e-01 6.34911478e-01
1.46050274e-01 -1.03597486e+00 -1.15109456e+00 3.08901310e-01
-2.61669010e-01 -8.41189206e-01 -3.74277234e-01 8.44019204e-02
-9.35172796e-01 -8.80821049e-01 -1.07272053e+00 -8.37399304e-01
1.14038420e+00 -1.23966701e-01 1.27925277e+00 -6.19552098e-02
-9.20893788e-01 5.27073562e-01 -1.00816876e-01 -5.73225975e-01
-1.14144349e+00 -4.75469142e-01 -4.29764777e-01 -7.95752853e-02
-2.05836654e-01 -3.36644679e-01 -8.76412868e-01 2.84899950e-01
-1.27686262e+00 2.06656724e-01 1.29119349e+00 1.02700162e+00
1.00873530e+00 -4.63955030e-02 2.73838490e-01 -1.23087287e+00
4.80163634e-01 -4.68043655e-01 -2.71220416e-01 3.82207483e-01
-2.85252273e-01 -9.65827554e-02 2.78054863e-01 -6.16309047e-01
-1.06212485e+00 -1.86027884e-02 -7.16698170e-02 -2.39796177e-01
-1.66056857e-01 4.33614820e-01 4.00424421e-01 -2.64708221e-01
1.05039668e+00 3.56337368e-01 1.22239456e-01 1.39161780e-01
-1.17459018e-02 3.75765473e-01 1.12618434e+00 -2.65702933e-01
6.95222735e-01 4.14239645e-01 4.35452238e-02 -4.64695692e-01
-7.06221938e-01 9.94694680e-02 -3.40168446e-01 -1.78167030e-01
1.03554344e+00 -8.69529486e-01 -1.57611996e-01 3.11337616e-02
-5.10238767e-01 -3.64237696e-01 -7.95933545e-01 6.06761873e-01
-4.50160235e-01 1.51260510e-01 -6.89041734e-01 -5.01698136e-01
-2.27204844e-01 -1.35512197e+00 1.02306831e+00 3.16145271e-01
-3.46833289e-01 -9.61169004e-01 -3.06758493e-01 4.52119827e-01
4.23889995e-01 8.81113529e-01 1.14940202e+00 -4.88379300e-01
-4.79708761e-01 -5.72111368e-01 -6.39103279e-02 2.83782065e-01
3.52279812e-01 -2.64679193e-01 -9.31744576e-01 -2.46175855e-01
2.73664296e-01 -3.58855277e-01 4.35896248e-01 9.80545402e-01
1.22842419e+00 -2.99403012e-01 -1.43089220e-01 5.49264193e-01
1.16504347e+00 4.58919495e-01 7.69736409e-01 -1.96369290e-02
3.85396481e-01 4.76249069e-01 4.17918652e-01 1.19857721e-01
-1.38957426e-01 1.10648409e-01 3.29990655e-01 -6.50279045e-01
-2.42158666e-01 -3.95560741e-01 -1.90837711e-01 3.57203633e-01
9.92726237e-02 -1.32911906e-01 -1.00733125e+00 5.62439263e-01
-9.56445932e-01 -6.55185759e-01 1.83300063e-01 2.08904624e+00
1.01079893e+00 1.37067840e-01 -2.95424700e-01 -1.50783181e-01
7.37100124e-01 -2.31307477e-01 -6.70524299e-01 -2.00505614e-01
6.99286954e-03 5.60230434e-01 7.32636988e-01 4.05102551e-01
-1.01397264e+00 2.23495230e-01 7.14697933e+00 4.88774389e-01
-1.30761254e+00 2.24432219e-02 1.51412380e+00 2.68832091e-02
-4.52549070e-01 -6.38565242e-01 -2.25482896e-01 2.41369888e-01
1.08528054e+00 -1.87464833e-01 -1.65015962e-02 5.97179115e-01
-7.64515623e-02 -2.65993446e-01 -1.06191552e+00 6.53571725e-01
7.38511533e-02 -1.28459001e+00 1.34128839e-01 3.25440168e-02
9.23322439e-01 -5.06994069e-01 5.37852108e-01 -1.65248662e-01
5.01520693e-01 -1.78741992e+00 6.41875923e-01 6.37240767e-01
1.48635328e+00 -5.15496790e-01 8.28490317e-01 1.07215315e-01
-4.79753613e-01 1.47830918e-01 2.22269725e-02 6.43429458e-01
-2.72297502e-01 3.77277166e-01 -1.40063393e+00 3.36455613e-01
4.41663206e-01 -1.06993234e-02 -7.32679963e-01 8.41085255e-01
8.15292224e-02 5.93513548e-01 -6.70190528e-02 2.55797744e-01
1.20583981e-01 2.98736304e-01 3.10733676e-01 1.08321214e+00
4.35668886e-01 5.45081437e-01 -2.28322953e-01 7.17740297e-01
-7.42554739e-02 3.39395143e-02 -2.49523416e-01 -2.19154000e-01
2.07605377e-01 1.21555579e+00 -1.00782633e+00 -3.14613312e-01
-2.73392685e-02 7.74878800e-01 -4.02181059e-01 2.25768805e-01
-7.57078409e-01 -5.71555831e-02 -2.11280808e-01 5.55512071e-01
2.22099736e-01 3.53615403e-01 -4.34416652e-01 -7.59891272e-01
7.63226375e-02 -1.39368987e+00 2.22701728e-01 -8.21833432e-01
-1.05007100e+00 7.70507395e-01 -2.28691548e-01 -1.08545613e+00
-4.96396333e-01 -3.93956304e-01 -3.96596760e-01 1.13704360e+00
-1.22257602e+00 -1.01584351e+00 -4.96682346e-01 2.74302214e-01
1.64834425e-01 -5.61002195e-02 9.93021488e-01 1.01902999e-01
1.37048781e-01 8.07514668e-01 1.57745495e-01 3.10031116e-01
6.41069651e-01 -1.33990228e+00 2.34797657e-01 5.73153734e-01
-1.56818822e-01 3.38036329e-01 6.91329598e-01 -7.06783414e-01
-1.21507370e+00 -1.15909827e+00 4.10389155e-01 -4.01498228e-01
2.71628022e-01 8.86938721e-02 -7.51522779e-01 6.22217834e-01
-1.28672600e-01 2.64724344e-01 9.29444373e-01 -8.33889663e-01
5.83135523e-02 -1.54415919e-02 -1.69052243e+00 6.20088041e-01
4.50396061e-01 -9.93060619e-02 -4.06958550e-01 3.36741269e-01
2.49028102e-01 -9.76067662e-01 -1.03598583e+00 4.34506685e-01
6.18070602e-01 -9.03567076e-01 1.01473403e+00 -6.46031797e-01
1.09046328e+00 2.04402313e-01 -2.78017446e-02 -1.06205261e+00
-1.74261972e-01 -3.57193410e-01 3.20379496e-01 6.37221515e-01
4.67889756e-01 -4.68399942e-01 8.80981326e-01 7.14547694e-01
-1.54214408e-02 -8.32100034e-01 -8.75550628e-01 -3.83066684e-02
6.83809668e-02 6.30959645e-02 1.95364743e-01 7.66876161e-01
-6.33366883e-01 -1.98819831e-01 -4.80386093e-02 2.13011146e-01
5.60518861e-01 -1.11071020e-01 3.64414513e-01 -9.26660538e-01
-7.39164054e-01 -4.60646361e-01 -6.46158099e-01 -6.07276976e-01
-4.50461566e-01 -8.08405399e-01 1.26423150e-01 -1.36841691e+00
3.86870563e-01 -5.31175673e-01 -2.02355430e-01 2.43103877e-01
-3.83282721e-01 5.99393070e-01 -2.16501981e-01 9.12970826e-02
-5.39119996e-04 -1.08203076e-01 1.81091094e+00 -2.03515410e-01
8.47762525e-02 1.88064411e-01 -1.12936282e+00 4.72013742e-01
5.58565617e-01 -4.61689085e-01 -3.10425460e-01 -3.23444784e-01
-1.14662051e-01 6.70536041e-01 6.70531929e-01 -1.04720724e+00
-1.68162957e-01 -1.14742607e-01 1.16819799e+00 -3.12114507e-01
1.26209259e-02 -6.96887672e-01 5.94105124e-01 9.38637078e-01
-5.96266687e-01 -1.00145958e-01 4.31321144e-01 4.93111163e-01
-1.87776089e-01 -2.35480011e-01 1.08346665e+00 -6.00834072e-01
-1.79753706e-01 6.04344681e-02 -3.69424075e-01 1.83738008e-01
1.17718518e+00 -3.71735632e-01 1.92422494e-01 -6.05444551e-01
-9.97987092e-01 -5.78416437e-02 4.20286864e-01 -5.95670566e-02
6.90393150e-01 -1.14541543e+00 -1.08514273e+00 4.42527324e-01
-1.30099252e-01 2.10879579e-01 6.37643695e-01 8.49125683e-01
-1.03740847e+00 1.69140741e-01 -4.07749206e-01 -8.11284244e-01
-1.31634963e+00 5.70789762e-02 6.88979924e-01 -5.67623496e-01
-6.63929760e-01 9.26156640e-01 3.04718792e-01 -6.00263961e-02
1.55323744e-01 -1.76107109e-01 1.83206961e-01 -4.34063017e-01
5.81727564e-01 -1.61472455e-01 1.97899207e-01 -3.76340032e-01
2.66111270e-02 1.48763567e-01 -3.53743285e-01 -1.88358799e-01
1.13189662e+00 1.45730183e-01 3.97251010e-01 1.62543342e-01
8.69392574e-01 8.23924392e-02 -1.21561956e+00 2.90836580e-02
-4.72745270e-01 -5.84442437e-01 2.00041570e-02 -1.19794679e+00
-9.98862147e-01 7.43565857e-01 1.10277414e+00 1.74090788e-01
1.23401785e+00 4.58679199e-02 4.61485982e-01 -1.02358200e-01
8.25260133e-02 -4.47198093e-01 -2.59389263e-03 -3.96980345e-01
7.63613701e-01 -1.28576934e+00 1.03984945e-01 -2.62082011e-01
-9.87284839e-01 9.81787622e-01 2.22017273e-01 2.52620950e-02
1.11526728e-01 5.23750663e-01 5.03201962e-01 -1.21603690e-01
-2.93116540e-01 5.14882803e-01 5.31874418e-01 7.82828450e-01
5.43167055e-01 3.64562154e-01 -1.12372220e-01 3.53273273e-01
-4.14179385e-01 -1.28090605e-01 5.80872536e-01 1.13476038e+00
-3.73399168e-01 -7.38144517e-01 -6.56027317e-01 9.39046025e-01
-9.64418471e-01 2.05858089e-02 -2.70172447e-01 8.46341670e-01
-4.65770485e-03 4.77131724e-01 9.68987867e-02 1.03630885e-01
1.93521023e-01 -1.03475183e-01 6.27882481e-01 -7.16013551e-01
-5.57968020e-01 1.93320766e-01 -1.94386780e-01 -2.19745770e-01
-1.03571698e-01 -5.99639952e-01 -1.02222669e+00 4.07418720e-02
-1.15257846e-02 4.62088548e-02 6.49835706e-01 4.94266331e-01
4.86048721e-02 9.50239301e-01 5.11707127e-01 -4.96767610e-01
-8.65795076e-01 -7.96991706e-01 -5.33060133e-01 6.32990360e-01
4.72666800e-01 -7.33208507e-02 -8.24453756e-02 4.51435447e-01] | [14.395525932312012, -1.9228919744491577] |
a187299f-da02-474f-89c9-6c517a17562d | no-pain-big-gain-classify-dynamic-point-cloud | 2203.11113 | null | https://arxiv.org/abs/2203.11113v2 | https://arxiv.org/pdf/2203.11113v2.pdf | No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time Surfaces | Scene flow is a powerful tool for capturing the motion field of 3D point clouds. However, it is difficult to directly apply flow-based models to dynamic point cloud classification since the unstructured points make it hard or even impossible to efficiently and effectively trace point-wise correspondences. To capture 3D motions without explicitly tracking correspondences, we propose a kinematics-inspired neural network (Kinet) by generalizing the kinematic concept of ST-surfaces to the feature space. By unrolling the normal solver of ST-surfaces in the feature space, Kinet implicitly encodes feature-level dynamics and gains advantages from the use of mature backbones for static point cloud processing. With only minor changes in network structures and low computing overhead, it is painless to jointly train and deploy our framework with a given static model. Experiments on NvGesture, SHREC'17, MSRAction-3D, and NTU-RGBD demonstrate its efficacy in performance, efficiency in both the number of parameters and computational complexity, as well as its versatility to various static backbones. Noticeably, Kinet achieves the accuracy of 93.27% on MSRAction-3D with only 3.20M parameters and 10.35G FLOPS. | ['Andrew Markham', 'Niki Trigoni', 'Bing Wang', 'Qingyong Hu', 'Kaichen Zhou', 'Jia-Xing Zhong'] | 2022-03-21 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhong_No_Pain_Big_Gain_Classify_Dynamic_Point_Cloud_Sequences_With_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhong_No_Pain_Big_Gain_Classify_Dynamic_Point_Cloud_Sequences_With_CVPR_2022_paper.pdf | cvpr-2022-1 | ['point-cloud-classification'] | ['computer-vision'] | [-3.99339795e-01 -5.73184013e-01 1.33804837e-02 -4.72085774e-02
-2.57844459e-02 -7.59571016e-01 3.73559237e-01 -2.18451276e-01
-4.16535974e-01 3.03694218e-01 -4.85065937e-01 -7.19769359e-01
-1.52434960e-01 -8.32773328e-01 -7.03237653e-01 -3.45089108e-01
-3.59403998e-01 6.43734634e-01 4.24170136e-01 -3.19836199e-01
1.36374250e-01 1.32179010e+00 -1.55511189e+00 -2.42282942e-01
7.54504502e-01 1.06756830e+00 1.49116203e-01 8.32058191e-01
-4.77588952e-01 4.93168026e-01 -2.31513590e-01 -1.28169963e-02
6.89857781e-01 2.95309544e-01 -7.28407025e-01 -1.37065547e-02
9.50465918e-01 -5.28069675e-01 -5.50882339e-01 7.83172905e-01
2.27285534e-01 1.25430346e-01 2.92352617e-01 -1.48676527e+00
-1.77066028e-01 -3.59687805e-01 -6.56288445e-01 1.63973734e-01
9.75204930e-02 3.34184945e-01 5.94653070e-01 -9.84305203e-01
7.93275416e-01 1.15705979e+00 9.23867881e-01 3.14700156e-01
-8.62540603e-01 -6.76710129e-01 2.86496669e-01 -9.32375342e-03
-1.33993423e+00 -2.74325103e-01 7.89017856e-01 -5.42155921e-01
1.26972795e+00 2.72700131e-01 1.09569740e+00 6.91077948e-01
2.18265593e-01 5.88729680e-01 4.01368439e-01 5.15550710e-02
1.52336031e-01 -5.11684060e-01 1.38416126e-01 7.63643086e-01
3.46413612e-01 1.55562580e-01 -4.11478519e-01 -1.63577616e-01
1.43334389e+00 1.62254393e-01 -2.40603447e-01 -1.01807237e+00
-1.21529937e+00 6.64461911e-01 6.40752375e-01 -1.20014280e-01
-1.73139453e-01 6.06652737e-01 2.71682501e-01 2.36039028e-01
4.83213902e-01 2.03797534e-01 -6.64674520e-01 -5.30958772e-01
-7.72421420e-01 4.45933819e-01 8.53388846e-01 1.40281105e+00
8.87830555e-01 3.83759797e-01 5.40223241e-01 2.57292956e-01
3.16236168e-01 9.03937399e-01 -2.67647263e-02 -1.39197564e+00
5.27696490e-01 7.60106862e-01 1.93437472e-01 -1.41035342e+00
-6.57195628e-01 -4.39787596e-01 -9.72725093e-01 3.89614195e-01
2.03628913e-01 -1.55427992e-01 -9.24113452e-01 1.28591847e+00
7.54797339e-01 6.45898163e-01 -3.01843792e-01 9.54130709e-01
6.45159245e-01 6.58801317e-01 -5.21835208e-01 8.06322247e-02
8.26383054e-01 -8.86972904e-01 -1.22457743e-01 -1.25523955e-01
7.56863236e-01 -6.44922197e-01 9.88960803e-01 3.33906375e-02
-1.03389418e+00 -4.63849306e-01 -9.61182892e-01 -2.30313584e-01
-9.94811133e-02 -2.36815423e-01 1.00240028e+00 1.07648820e-01
-1.22406363e+00 8.18500340e-01 -1.47170234e+00 -2.59599924e-01
4.67496097e-01 7.25678086e-01 -2.21889958e-01 4.89682890e-02
-5.68135798e-01 5.72645485e-01 5.93957789e-02 3.34090322e-01
-4.50841427e-01 -1.26093102e+00 -7.36729026e-01 4.18707207e-02
2.41393358e-01 -1.04418802e+00 1.23702443e+00 -3.83221775e-01
-1.52572060e+00 3.98433238e-01 -2.93074429e-01 -1.22401759e-01
7.67775834e-01 -4.11111712e-01 -7.56437483e-04 2.59284407e-01
7.42437989e-02 6.98834896e-01 5.11119902e-01 -1.04893041e+00
-6.14212692e-01 -2.66866028e-01 4.59106229e-02 3.50485384e-01
-1.75329447e-01 -3.81287485e-01 -8.87550592e-01 -1.79168627e-01
4.71634597e-01 -1.21579242e+00 -3.58021766e-01 7.49960482e-01
-2.15120330e-01 -1.04607895e-01 1.39800572e+00 -1.67433172e-01
6.26955569e-01 -2.09129333e+00 -5.27707934e-02 2.38251850e-01
4.63413656e-01 4.40630794e-01 -1.07648000e-01 1.38381332e-01
1.79752946e-01 1.38986679e-02 -1.66958392e-01 -3.00933003e-01
-1.22072637e-01 4.97043520e-01 -2.59145141e-01 6.12866998e-01
1.70058638e-01 1.00597644e+00 -9.91939902e-01 -2.45739162e-01
5.50169408e-01 6.86624229e-01 -8.36468220e-01 -3.33814994e-02
-2.46063635e-01 3.53421837e-01 -6.44068301e-01 6.42116010e-01
1.01967430e+00 -5.77075660e-01 -1.03782669e-01 -1.74937069e-01
-4.41020340e-01 2.46368304e-01 -1.40663612e+00 2.04503822e+00
-4.19180930e-01 7.81457841e-01 2.35544547e-01 -4.87396836e-01
6.94063365e-01 -7.69899786e-02 8.24142218e-01 -4.67098296e-01
2.21790373e-02 1.62253410e-01 -1.63998693e-01 -2.24551246e-01
7.83448875e-01 2.73288667e-01 8.38605314e-02 3.39316875e-01
-2.65180349e-01 -3.69527280e-01 -1.12814112e-02 1.44144833e-01
1.09517205e+00 4.19429153e-01 -2.04259604e-01 -1.64276257e-01
2.45600343e-01 3.73797148e-01 5.91937602e-01 5.38679957e-01
1.46109760e-02 4.90937412e-01 2.09747180e-01 -8.11011434e-01
-1.03111446e+00 -1.15770721e+00 -5.69620505e-02 4.93833482e-01
5.90162873e-01 -5.07594943e-01 -2.28095427e-01 -4.49545473e-01
4.26328093e-01 2.51536787e-01 -1.63061693e-01 8.84809196e-02
-1.04223752e+00 -3.00658673e-01 3.06176245e-01 6.37256265e-01
6.17595196e-01 -5.93283236e-01 -9.92561042e-01 1.78978190e-01
1.02047332e-01 -1.23862028e+00 -3.73997182e-01 -3.56066786e-02
-1.38034689e+00 -1.10756350e+00 -3.11442137e-01 -6.16434515e-01
5.44082403e-01 8.02113414e-01 1.18768942e+00 3.84065092e-01
-1.23166308e-01 4.12840009e-01 -5.82411364e-02 -2.01534361e-01
-6.47536654e-04 3.35694104e-01 9.98966545e-02 -4.90287781e-01
1.83186203e-01 -8.97805870e-01 -6.35214150e-01 4.33921874e-01
-5.76621294e-01 1.91163838e-01 3.60803939e-02 5.10732114e-01
6.32034421e-01 -2.40336180e-01 -2.28072032e-01 -3.99944037e-01
1.31192105e-02 -1.73490822e-01 -9.43173885e-01 -2.36569405e-01
-3.73319179e-01 -2.13794619e-01 5.54991186e-01 -3.99762660e-01
-6.92093551e-01 2.87952721e-01 7.61354491e-02 -1.26968396e+00
4.18001832e-03 2.24080339e-01 2.07592741e-01 -5.04591703e-01
4.38753873e-01 4.05301861e-02 1.52757689e-01 -5.13230324e-01
3.73596847e-01 2.76867300e-01 6.49069488e-01 -6.11024320e-01
1.28111517e+00 8.75870407e-01 3.38942617e-01 -1.06443000e+00
-4.66324121e-01 -6.15537465e-01 -9.73060966e-01 -1.82349369e-01
6.80467963e-01 -8.60855520e-01 -1.24971831e+00 5.69448233e-01
-1.39634776e+00 -3.61608684e-01 -2.23724633e-01 3.26751560e-01
-5.18289030e-01 4.18420821e-01 -6.67664349e-01 -3.07238013e-01
-4.38105047e-01 -1.13707578e+00 1.19246471e+00 6.31465465e-02
-1.49423569e-01 -9.82140243e-01 -1.16419597e-02 -9.30455625e-02
4.61592138e-01 4.56446797e-01 7.11269319e-01 1.77347749e-01
-1.23247969e+00 -1.34509593e-01 -3.12195629e-01 -1.00881241e-01
2.75676787e-01 4.00903016e-01 -7.52501309e-01 -4.28713709e-01
-1.03074359e-02 7.40305660e-03 3.51616353e-01 3.40367675e-01
1.22746968e+00 -5.73046878e-02 -4.42361563e-01 1.36924684e+00
1.44252169e+00 1.56139642e-01 3.18686903e-01 4.44041550e-01
1.27362549e+00 1.42230600e-01 4.04680848e-01 3.49739164e-01
4.61534202e-01 7.31402814e-01 7.85627544e-01 -2.41086766e-01
4.44350615e-02 -2.60353565e-01 -1.11895474e-02 1.20013428e+00
-2.12442487e-01 -6.07277565e-02 -1.17106545e+00 3.81805658e-01
-1.85091090e+00 -6.92559481e-01 -4.06173259e-01 1.89741397e+00
3.22275817e-01 9.21114683e-02 -1.94211707e-01 -1.42305553e-01
3.43901694e-01 2.03116819e-01 -7.74460375e-01 -2.04216346e-01
1.64370701e-01 2.44313970e-01 6.40193224e-01 5.03528714e-01
-9.70929325e-01 1.22063720e+00 5.61776495e+00 3.55405539e-01
-1.54370713e+00 -2.01144755e-01 1.11878134e-01 -5.07461488e-01
-6.61502331e-02 4.98612896e-02 -8.12008500e-01 2.73164660e-01
7.58596957e-01 -1.02901369e-01 3.46320063e-01 9.33797717e-01
3.15057725e-01 1.88027412e-01 -1.14376199e+00 1.15782690e+00
-3.25059414e-01 -1.79019654e+00 1.12679303e-01 2.70446151e-01
5.02546012e-01 6.80740774e-01 -1.32304668e-01 5.81299774e-02
3.07614356e-01 -8.41707051e-01 7.15679944e-01 2.43267387e-01
8.76226544e-01 -8.45737338e-01 4.19504434e-01 5.21078646e-01
-1.42616141e+00 3.44496727e-01 -5.33073783e-01 -3.97937655e-01
3.98564607e-01 2.24001557e-01 -8.10814023e-01 7.34164357e-01
9.41411912e-01 1.07617688e+00 -1.81846544e-01 1.08219957e+00
2.00066850e-01 2.13822246e-01 -9.29434597e-01 1.78450361e-01
4.49598700e-01 -3.52852881e-01 6.93069577e-01 8.23094606e-01
5.37638605e-01 5.79217114e-02 4.15858597e-01 7.09353328e-01
-1.96514484e-02 -3.13991636e-01 -7.46625125e-01 3.38147730e-01
5.59334934e-01 1.23017490e+00 -8.12556744e-01 -1.16180405e-01
-3.91288102e-01 8.19253027e-01 3.24628234e-01 3.03141803e-01
-8.18516612e-01 -2.02494949e-01 1.33481085e+00 1.27607971e-01
4.73554641e-01 -9.02291596e-01 -3.06749582e-01 -1.28490174e+00
2.23063499e-01 -4.75426733e-01 -2.01858148e-01 -7.64168203e-01
-1.02837837e+00 6.40140235e-01 -7.64131360e-03 -1.55527556e+00
-2.01754972e-01 -7.10668743e-01 -5.17830908e-01 7.59926081e-01
-1.62645006e+00 -9.53927100e-01 -7.31513143e-01 7.45382071e-01
3.29028040e-01 2.46717885e-01 5.03416479e-01 2.60948718e-01
-5.04826725e-01 2.32233360e-01 2.80455891e-02 6.58139512e-02
1.41516402e-01 -1.08527350e+00 1.04302430e+00 7.22179711e-01
1.26936004e-01 6.29036844e-01 2.89192140e-01 -7.14490533e-01
-1.91370189e+00 -1.21863723e+00 4.80367422e-01 -6.24107242e-01
6.69702351e-01 -4.48424131e-01 -1.08552408e+00 6.99473858e-01
-2.61549175e-01 4.44219887e-01 8.72495696e-02 -7.13283643e-02
-9.96797457e-02 -1.57989562e-01 -8.39581311e-01 7.54520416e-01
1.67436481e+00 -2.99643606e-01 -2.29130864e-01 3.29517812e-01
1.00037324e+00 -1.20366514e+00 -8.97727013e-01 4.87592995e-01
3.68453085e-01 -6.81595862e-01 1.21030617e+00 -4.81960386e-01
1.73351482e-01 -6.61964893e-01 -6.34637699e-02 -9.40324545e-01
-3.56289625e-01 -8.55100214e-01 -3.36483508e-01 6.66707873e-01
-7.03298999e-03 -5.64620376e-01 1.34546840e+00 5.87114751e-01
-4.57100660e-01 -8.31242502e-01 -1.10440624e+00 -9.01712000e-01
8.63892362e-02 -7.69061625e-01 8.84200633e-01 1.17476666e+00
-6.20487094e-01 6.84572011e-02 -3.38101611e-02 5.91579616e-01
5.40417314e-01 3.71987313e-01 1.31828225e+00 -1.38510716e+00
-8.36215466e-02 -2.79193521e-01 -6.10079110e-01 -1.62245655e+00
1.40555933e-01 -8.52488637e-01 -1.65185779e-01 -1.47204041e+00
-5.05506992e-01 -8.58265400e-01 1.04392096e-01 4.86930043e-01
2.14685187e-01 4.41899747e-02 4.33190823e-01 4.93297994e-01
-3.23709160e-01 6.62910342e-01 1.48839462e+00 1.15684584e-01
-5.68043530e-01 -8.96234885e-02 -1.88799560e-01 9.51970398e-01
6.32999539e-01 -2.67478198e-01 -6.54344261e-01 -1.01487505e+00
5.42092957e-02 1.63293079e-01 5.67234576e-01 -1.06740558e+00
4.11685407e-01 -3.56373101e-01 3.89495760e-01 -1.16977835e+00
5.87234259e-01 -9.93906379e-01 4.52574909e-01 4.07884389e-01
4.33643281e-01 6.97076082e-01 5.11110544e-01 4.90540713e-01
2.69470215e-02 2.28371158e-01 4.19009924e-01 -1.19061798e-01
-8.72729599e-01 8.45056415e-01 -4.08019014e-02 -3.40779349e-02
9.71397221e-01 -6.18407071e-01 -2.65345514e-01 -5.13615422e-02
-2.34785005e-01 4.26685810e-01 9.67687011e-01 4.36162472e-01
7.58548975e-01 -1.19227636e+00 -1.96517318e-01 4.44965005e-01
-1.98085517e-01 9.53420937e-01 2.84182638e-01 5.17966807e-01
-1.31006873e+00 4.28041101e-01 -5.92542067e-02 -1.45403409e+00
-9.59998608e-01 1.41247064e-01 5.77473760e-01 4.51777354e-02
-1.15925038e+00 6.33438051e-01 8.93088803e-02 -6.21079147e-01
2.51722652e-02 -6.58671737e-01 2.67662138e-01 -3.79285067e-01
1.11225761e-01 5.73797584e-01 2.94630498e-01 -5.25621295e-01
-4.81723726e-01 1.01591706e+00 -1.58859994e-02 2.79418141e-01
1.43536270e+00 1.31029874e-01 -1.05023637e-01 2.69707382e-01
1.29796374e+00 -3.14403206e-01 -1.63949955e+00 1.46810770e-01
-1.80519193e-01 -6.45909905e-01 1.53398275e-01 -1.62494272e-01
-1.40375364e+00 8.81434500e-01 4.20869887e-01 -1.56265020e-01
7.89214194e-01 -3.38626564e-01 9.61007476e-01 6.70841396e-01
7.09915698e-01 -5.43879211e-01 -1.42541498e-01 9.55950677e-01
6.13651037e-01 -7.66117215e-01 2.69658361e-02 -7.66999424e-01
4.65501882e-02 1.26678860e+00 8.48174572e-01 -3.86825800e-01
7.02296197e-01 3.79073590e-01 9.47804302e-02 -4.16204154e-01
-7.46578574e-01 3.48087370e-01 4.23337892e-02 5.31240761e-01
2.31098179e-02 -2.70219982e-01 5.23854136e-01 -4.45676953e-01
-5.18259883e-01 2.05402419e-01 6.08228266e-01 1.21396422e+00
-9.67674106e-02 -8.74093711e-01 -1.42995462e-01 4.21386600e-01
2.68937722e-02 1.48038983e-01 5.57871461e-02 1.29041266e+00
-1.94756389e-01 5.61062336e-01 6.75808012e-01 -4.46983516e-01
5.11736274e-01 -4.07485127e-01 2.18860760e-01 -4.73718733e-01
-5.09329438e-01 2.19993535e-02 -1.37271151e-01 -1.04082274e+00
-4.81747568e-01 -6.26359463e-01 -1.37960017e+00 -8.87164414e-01
-1.21128157e-01 -1.69029590e-02 6.88036203e-01 6.80988431e-01
1.02471161e+00 2.68119156e-01 5.72154820e-01 -1.23454130e+00
-3.66955787e-01 -4.12716180e-01 -2.12236330e-01 8.63808393e-02
5.33119977e-01 -7.71119237e-01 -2.60642856e-01 -2.49867693e-01] | [8.519189834594727, -2.082984685897827] |
12a870ec-a9f3-4089-b207-72ee9f0d3f33 | weakly-supervised-representation-learning-for-1 | 2302.04064 | null | https://arxiv.org/abs/2302.04064v1 | https://arxiv.org/pdf/2302.04064v1.pdf | Weakly-supervised Representation Learning for Video Alignment and Analysis | Many tasks in video analysis and understanding boil down to the need for frame-based feature learning, aiming to encapsulate the relevant visual content so as to enable simpler and easier subsequent processing. While supervised strategies for this learning task can be envisioned, self and weakly-supervised alternatives are preferred due to the difficulties in getting labeled data. This paper introduces LRProp -- a novel weakly-supervised representation learning approach, with an emphasis on the application of temporal alignment between pairs of videos of the same action category. The proposed approach uses a transformer encoder for extracting frame-level features, and employs the DTW algorithm within the training iterations in order to identify the alignment path between video pairs. Through a process referred to as ``pair-wise position propagation'', the probability distributions of these correspondences per location are matched with the similarity of the frame-level features via KL-divergence minimization. The proposed algorithm uses also a regularized SoftDTW loss for better tuning the learned features. Our novel representation learning paradigm consistently outperforms the state of the art on temporal alignment tasks, establishing a new performance bar over several downstream video analysis applications. | ['Ehud Rivlin', 'Michael Elad', 'George Leifman', 'Guy Bar-Shalom'] | 2023-02-08 | null | null | null | null | ['video-alignment'] | ['computer-vision'] | [ 3.47060144e-01 -1.02326192e-01 -2.75092214e-01 -5.63835263e-01
-7.50897527e-01 -3.22803319e-01 8.04355204e-01 5.01141012e-01
-6.13330483e-01 5.37268877e-01 3.63248199e-01 1.39225438e-01
-2.95248330e-01 -4.02556986e-01 -7.51209080e-01 -8.39994788e-01
-4.14374024e-01 1.39304683e-01 3.40284914e-01 1.83435138e-02
4.12531495e-01 3.11775208e-01 -1.67059970e+00 4.17930841e-01
3.48958880e-01 1.15744638e+00 3.68069619e-01 5.23569405e-01
-8.20662752e-02 1.03865361e+00 -1.31617635e-01 -3.43769729e-01
4.62525010e-01 -5.81160426e-01 -8.58739197e-01 3.35342675e-01
4.89803255e-01 -1.26743376e-01 -3.31613511e-01 8.04044604e-01
3.30375731e-01 4.46845263e-01 7.42416382e-01 -1.17492199e+00
5.58328182e-02 1.71245843e-01 -6.26264632e-01 5.47900617e-01
6.78111970e-01 -4.35875170e-02 1.33969629e+00 -9.14473832e-01
7.62194395e-01 8.83185565e-01 6.71874046e-01 2.33940065e-01
-1.09454572e+00 -1.56740427e-01 1.48819342e-01 6.60293400e-01
-1.29005766e+00 -5.04844308e-01 8.91593814e-01 -6.75930500e-01
8.88360083e-01 -3.68285179e-02 6.26817942e-01 8.52334023e-01
1.60896406e-01 7.61752725e-01 7.74984658e-01 -6.32966220e-01
1.60694897e-01 1.13394752e-01 -1.00518063e-01 8.58660460e-01
-1.77898943e-01 9.91109237e-02 -8.80706310e-01 1.35482818e-01
5.75700104e-01 -4.49770242e-02 -2.46998161e-01 -9.05144453e-01
-1.29953134e+00 7.20508993e-01 3.50144267e-01 4.70971465e-01
-4.57913578e-01 3.94642577e-02 6.82978511e-01 2.67952591e-01
4.70858723e-01 2.00026125e-01 -2.70731151e-01 -3.24268401e-01
-1.07485831e+00 1.81810364e-01 4.01630878e-01 7.51586139e-01
8.59340608e-01 -1.35695711e-01 -3.10083210e-01 6.03332639e-01
4.58106071e-01 -1.17216982e-01 4.81748074e-01 -8.22951913e-01
6.59574747e-01 4.24954593e-01 1.74122844e-02 -1.24820745e+00
-5.50500415e-02 -2.48319790e-01 -5.20104051e-01 3.91076773e-01
6.85060620e-01 1.46033168e-01 -4.94105011e-01 1.62542832e+00
2.58856475e-01 4.60940242e-01 -2.28824820e-02 8.41512561e-01
3.42010826e-01 6.92162871e-01 1.32830903e-01 -4.45785135e-01
1.15399814e+00 -8.76019597e-01 -5.52963495e-01 -7.67173842e-02
5.84167600e-01 -8.19892704e-01 7.01911449e-01 2.13011846e-01
-1.14214027e+00 -7.52761424e-01 -8.81521940e-01 -9.89900157e-03
-2.24877879e-01 1.87524706e-01 3.00583214e-01 2.86892056e-01
-9.78334010e-01 8.78385901e-01 -7.26278365e-01 -5.58270514e-01
5.46152413e-01 2.75897175e-01 -7.01027036e-01 9.51158777e-02
-7.67901361e-01 9.98722076e-01 4.87378210e-01 2.21556887e-01
-6.99511766e-01 -5.39431691e-01 -1.10207295e+00 -1.02678984e-01
2.73410290e-01 -5.64840615e-01 9.23029304e-01 -1.32583725e+00
-1.49239397e+00 1.03691256e+00 -2.90501773e-01 -7.63585687e-01
6.72695756e-01 -4.78713483e-01 -4.59858440e-02 4.50227827e-01
1.58205628e-01 5.59465110e-01 1.37766623e+00 -9.77689505e-01
-9.27245617e-01 -1.32651016e-01 6.21243119e-02 4.16387260e-01
-2.08150953e-01 1.07566379e-01 -3.54068756e-01 -8.41404617e-01
-4.79305349e-02 -6.53774738e-01 -1.89789638e-01 2.69341320e-01
1.80057392e-01 -3.14817339e-01 9.02910411e-01 -6.91694319e-01
1.12232995e+00 -2.29045010e+00 5.66136360e-01 2.27413297e-01
-1.63081642e-02 3.80695820e-01 -5.86574003e-02 3.89722735e-01
-2.76578009e-01 -5.29270530e-01 -2.26278335e-01 -4.52794969e-01
-2.34212711e-01 7.23768547e-02 -2.50363827e-01 7.21109092e-01
5.35997987e-01 5.14372051e-01 -1.18774974e+00 -6.61669135e-01
6.55447304e-01 3.90106320e-01 -5.08988440e-01 5.50466239e-01
-1.40395179e-01 5.31352282e-01 -1.62099868e-01 2.55838275e-01
1.71764433e-01 7.01231137e-02 1.41588328e-02 -5.83983123e-01
-1.79709956e-01 8.19348320e-02 -1.08124256e+00 2.00744939e+00
-3.86231810e-01 8.74187529e-01 -2.97364295e-01 -1.60202861e+00
9.91229296e-01 2.70091295e-01 7.93679893e-01 -4.82499570e-01
9.71020460e-02 2.16187034e-02 -1.34318739e-01 -7.08029926e-01
2.59433806e-01 -3.33022289e-02 1.44836724e-01 1.99524388e-01
4.15727019e-01 1.63620859e-01 3.84430379e-01 6.00717142e-02
8.90186667e-01 8.32002699e-01 6.18618131e-01 -1.37291998e-01
8.97178233e-01 -1.92408547e-01 5.37421167e-01 3.96669745e-01
-2.42270574e-01 5.86953819e-01 3.54291886e-01 -6.59738481e-01
-1.04183531e+00 -9.95835662e-01 7.92717785e-02 1.00260198e+00
8.58542770e-02 -5.53442597e-01 -5.95261037e-01 -8.50722671e-01
-2.09891632e-01 3.56346190e-01 -6.54154241e-01 -1.62041441e-01
-6.22282863e-01 -3.43302131e-01 1.06885843e-01 4.60428059e-01
4.46572483e-01 -1.00402486e+00 -9.04390991e-01 3.31379056e-01
-1.02163024e-01 -1.07386827e+00 -4.06769186e-01 2.60129511e-01
-9.75307584e-01 -1.15146112e+00 -7.27781773e-01 -8.65817368e-01
7.08320737e-01 1.53213352e-01 9.05050695e-01 -1.41524568e-01
-4.94008452e-01 4.51351970e-01 -6.37409687e-01 2.11203039e-01
-1.28665343e-01 -2.21571892e-01 1.23489080e-02 4.92850512e-01
3.93882811e-01 -5.16310155e-01 -6.33714378e-01 2.68622756e-01
-6.22038722e-01 -8.51882026e-02 4.17218119e-01 9.20531809e-01
7.05508590e-01 -5.30306697e-02 1.55125707e-01 -4.92891669e-01
6.33299127e-02 -3.77977252e-01 -4.30486292e-01 2.78707802e-01
-2.14857534e-01 2.24641249e-01 5.86137176e-01 -3.29924077e-01
-9.81028259e-01 3.11848700e-01 5.99839073e-03 -6.53160214e-01
-1.39515072e-01 3.30219388e-01 -9.27984715e-03 -4.57221009e-02
4.56260771e-01 2.79345930e-01 8.04851130e-02 -2.75661141e-01
3.98555845e-01 2.86693037e-01 5.83335638e-01 -5.13065159e-01
7.89963841e-01 5.01869678e-01 1.79499879e-01 -9.22879815e-01
-9.63659048e-01 -7.31457472e-01 -9.80575144e-01 -5.12553096e-01
9.94087756e-01 -7.33821332e-01 -3.72608066e-01 2.68488914e-01
-1.11221230e+00 -7.00185224e-02 -5.46931148e-01 8.00871849e-01
-1.13345635e+00 6.60850942e-01 -3.01904708e-01 -6.46236837e-01
-4.40273434e-02 -1.02490366e+00 9.32463586e-01 8.43986571e-02
-3.19835335e-01 -9.90338981e-01 3.18874568e-01 2.15445697e-01
6.01954795e-02 2.59895056e-01 7.05033123e-01 -6.46743536e-01
-3.66776794e-01 -2.79781997e-01 -1.59161881e-01 5.88024914e-01
3.21403265e-01 7.02603385e-02 -8.62289190e-01 -3.46749544e-01
-1.60506070e-01 -2.31875882e-01 7.59547591e-01 4.93776560e-01
1.03786469e+00 -1.17091820e-01 -1.88227072e-01 5.49030781e-01
1.27681077e+00 3.97943743e-02 6.54064178e-01 4.17716444e-01
6.38882995e-01 9.03851032e-01 9.92734730e-01 5.37258685e-01
6.88281581e-02 1.03736770e+00 4.08562720e-01 6.83009475e-02
-8.00213888e-02 -2.05003351e-01 5.20720184e-01 5.87516963e-01
-3.77454251e-01 -1.16431294e-03 -5.62864184e-01 4.78269666e-01
-2.12314081e+00 -1.35877538e+00 1.88462660e-01 2.47873664e+00
4.00268376e-01 2.57463723e-01 3.83144915e-01 3.21550071e-01
7.71832108e-01 4.73879039e-01 -5.49945533e-02 -3.46875042e-01
1.03801906e-01 1.56639859e-01 1.83502659e-01 4.46998775e-01
-1.39540648e+00 6.85626209e-01 5.34456015e+00 7.30929554e-01
-8.69059801e-01 1.65018644e-02 4.06046003e-01 8.54092091e-02
1.69897035e-01 1.31765932e-01 -5.57397604e-01 5.25241196e-01
7.36599326e-01 -1.32840142e-01 1.45269677e-01 6.49119616e-01
4.77023691e-01 -2.23580912e-01 -1.39392209e+00 1.13369322e+00
3.50625426e-01 -1.34385705e+00 5.24967350e-02 -1.53243557e-01
3.75217706e-01 -2.72053182e-01 -9.61795077e-02 2.55065644e-03
-3.33651692e-01 -6.28537714e-01 8.47928524e-01 7.06602335e-01
4.71977979e-01 -7.55364656e-01 7.58231580e-01 6.60571828e-03
-1.54438746e+00 -1.94470957e-01 -2.92128772e-01 -9.31132883e-02
2.85230309e-01 3.03482980e-01 -6.46100581e-01 6.58656895e-01
7.17297673e-01 1.30098104e+00 -4.59787816e-01 1.25135553e+00
-4.33759801e-02 3.70114654e-01 -1.26396671e-01 1.94848627e-01
3.77544522e-01 -2.59111464e-01 6.71695650e-01 1.36615658e+00
2.68684745e-01 -2.99261838e-01 1.93068892e-01 3.35511923e-01
3.18706244e-01 1.74502954e-01 -7.92093754e-01 1.15242839e-01
3.80025692e-02 1.12056279e+00 -8.51275861e-01 -1.13201395e-01
-5.34563839e-01 1.19705117e+00 4.65336114e-01 4.46306691e-02
-7.44523585e-01 -2.06419110e-01 5.77504754e-01 2.63925612e-01
5.67914069e-01 -1.86230168e-01 2.26089448e-01 -1.01932263e+00
1.06409363e-01 -6.01291478e-01 6.29601061e-01 -5.37736416e-01
-1.20710945e+00 5.95282376e-01 1.66500196e-01 -1.76234901e+00
-6.57299221e-01 -5.55919409e-01 -7.27560878e-01 5.26811242e-01
-1.51194227e+00 -9.94598269e-01 -1.68757454e-01 7.33848572e-01
9.60868478e-01 -3.49849641e-01 6.68259263e-01 3.20864022e-01
-3.60082686e-01 4.14640337e-01 -2.79357228e-02 9.89874378e-02
7.29645789e-01 -1.13629127e+00 -2.64175236e-02 9.77770090e-01
3.90672058e-01 2.67993480e-01 8.31992865e-01 -2.51526892e-01
-1.09454811e+00 -1.02904212e+00 1.02053165e+00 -1.56955600e-01
7.23696232e-01 -1.71475396e-01 -7.64040828e-01 4.41350907e-01
2.66754389e-01 3.54299366e-01 6.86915457e-01 -2.08431706e-01
-3.74253720e-01 -3.66354883e-01 -9.34732378e-01 3.71540129e-01
1.07026172e+00 -6.01005793e-01 -7.93446243e-01 2.03157991e-01
1.82023942e-01 -5.24307899e-02 -7.25370646e-01 2.46335983e-01
5.85549176e-01 -1.10707772e+00 1.00066113e+00 -6.53010070e-01
5.19868553e-01 -6.15272284e-01 -2.08443627e-01 -1.02017784e+00
-4.00763929e-01 -6.73790693e-01 -1.72863036e-01 1.24160266e+00
4.69055735e-02 -1.52360082e-01 6.97689116e-01 7.38617554e-02
1.67104118e-02 -6.82825327e-01 -1.12849188e+00 -6.21738672e-01
-5.25273860e-01 -3.47238928e-01 -6.09841347e-02 6.93116426e-01
1.72464803e-01 1.71074480e-01 -5.38719714e-01 4.07597609e-02
7.08572268e-01 -3.99943106e-02 6.11619532e-01 -1.03149116e+00
-4.38416004e-01 -4.88020122e-01 -1.15841126e+00 -9.08814013e-01
3.57096612e-01 -7.74891496e-01 1.80289045e-01 -1.16710901e+00
1.26116008e-01 -1.64012522e-01 -3.95161539e-01 1.60727814e-01
6.92636371e-02 4.05326664e-01 2.68696427e-01 9.82950479e-02
-8.11075509e-01 4.97979552e-01 8.21898222e-01 -5.91857210e-02
-2.17982847e-02 1.46313488e-01 -8.37959256e-03 8.23139548e-01
5.28279305e-01 -3.26576531e-01 -4.93239880e-01 -3.15751582e-01
-3.05871554e-02 4.94705886e-02 4.83389437e-01 -1.20982718e+00
2.44774193e-01 3.74673717e-02 3.70896965e-01 -5.37392199e-01
3.57256919e-01 -1.06165504e+00 7.31047802e-03 3.76095295e-01
-5.63893557e-01 1.99136630e-01 -1.28899246e-01 7.92083323e-01
-5.13859332e-01 -4.70740110e-01 8.35425377e-01 -7.37002045e-02
-1.01821506e+00 3.23121250e-01 -2.96555430e-01 -1.61282008e-03
1.45886767e+00 -5.98643124e-01 2.48209149e-01 -3.90159518e-01
-8.71365547e-01 -5.30937426e-02 3.38600785e-01 4.71171945e-01
7.50154078e-01 -1.20889544e+00 -6.28399491e-01 2.24706262e-01
2.44307294e-01 -2.01481134e-01 1.48881048e-01 9.24389005e-01
-4.32895005e-01 1.94359362e-01 -4.70090806e-01 -7.63374090e-01
-1.31431890e+00 4.71129566e-01 2.51065552e-01 -3.17346126e-01
-8.20891261e-01 8.04136992e-01 2.87593044e-02 1.23986363e-01
4.19107616e-01 -1.50833074e-02 -4.26916957e-01 3.52218121e-01
5.76221526e-01 3.95174325e-01 9.89633724e-02 -1.07663715e+00
-4.30957139e-01 7.47093737e-01 -1.92929283e-01 4.89119813e-02
1.38333035e+00 -2.89033443e-01 1.05099313e-01 5.19842446e-01
1.42162132e+00 -3.13916862e-01 -1.65637958e+00 -3.61894965e-01
3.28045487e-01 -8.03148925e-01 -6.36208579e-02 -1.74872950e-01
-9.78954434e-01 8.18770111e-01 6.75329328e-01 -8.57244153e-03
1.13154066e+00 -9.60893650e-03 3.75223339e-01 1.20915957e-01
3.65529239e-01 -1.04569650e+00 3.20988834e-01 2.60614455e-01
6.23944044e-01 -1.16940439e+00 3.60166952e-02 -3.03466827e-01
-6.14086866e-01 1.31798482e+00 3.77158463e-01 -3.88909996e-01
5.83500385e-01 -4.27288301e-02 -1.89468071e-01 -7.83201586e-03
-5.59771717e-01 -3.30744952e-01 4.38855559e-01 8.37189794e-01
5.77186525e-01 -4.49561149e-01 -4.22470480e-01 -2.53272858e-02
2.53277361e-01 2.96146981e-03 1.33006766e-01 9.11838472e-01
-2.89884657e-01 -1.17405605e+00 -5.76854087e-02 4.94417012e-01
-3.42067719e-01 1.85226768e-01 1.26119584e-01 5.44243872e-01
1.77507639e-01 7.24379957e-01 3.09072196e-01 -2.89769948e-01
1.99489966e-01 -6.81285514e-04 7.18068838e-01 -5.97360432e-01
-4.51525897e-01 7.18941796e-04 9.39194709e-02 -8.18799853e-01
-1.07154822e+00 -9.95860577e-01 -8.52727950e-01 1.34720623e-01
-1.57080114e-01 1.71550483e-01 3.26980770e-01 1.22521138e+00
-5.49489353e-03 2.42037013e-01 8.83044064e-01 -1.28581011e+00
-3.23464841e-01 -5.20371795e-01 -4.07253027e-01 7.26130307e-01
3.41577560e-01 -8.63489628e-01 -2.54772991e-01 4.25090283e-01] | [8.496333122253418, 0.6457517743110657] |
ff966ba0-4b26-494a-a46b-55a087edad9e | unipoll-a-unified-social-media-poll | 2306.06851 | null | https://arxiv.org/abs/2306.06851v1 | https://arxiv.org/pdf/2306.06851v1.pdf | UniPoll: A Unified Social Media Poll Generation Framework via Multi-Objective Optimization | Social media platforms are essential outlets for expressing opinions, providing a valuable resource for capturing public viewpoints via text analytics. However, for many users, passive browsing is their preferred mode of interaction, leading to their perspectives being overlooked by text analytics methods. Meanwhile, social media polls have emerged as a practical feature for gathering public opinions, allowing post authors to pose questions with pre-defined answer options for readers to vote on. To broaden the benefits of polls for posts without them, this article explores the automatic generation of a poll from a social media post by leveraging cutting-edge natural language generation (NLG) techniques. However, existing NLG techniques, primarily developed for general-domain texts, may be ineffective when applied to noisy social media data, which often feature implicit context-question-answer relations. To tackle these challenges, we enrich a post context with its comments and propose a novel unified poll generation framework called UniPoll. It employs prompt tuning with multi-objective optimization to bolster the connection exploration between contexts (posts and comments) and polls (questions and answers). Experimental comparisons on a large-scale Chinese Weibo dataset show that UniPoll significantly outperforms T5, the state-of-the-art NLG model, which generates question and answer separately. Comprehensive qualitative and quantitative analyses further underscore the superiority of UniPoll through various evaluation lenses. | ['Jing Li', 'Yanlin Song', 'Rong Xiang', 'Yixia Li'] | 2023-06-12 | null | null | null | null | ['poll-generation', 'question-generation', 'answer-generation'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-8.26947466e-02 2.59955496e-01 -3.76194566e-01 -2.92054772e-01
-1.03957427e+00 -7.14962423e-01 8.36139917e-01 4.66406941e-01
-3.71534050e-01 8.31703722e-01 8.28261018e-01 -5.34794867e-01
1.69147193e-01 -9.77690995e-01 -1.27363965e-01 -4.28315103e-01
6.45558774e-01 4.65549767e-01 2.82175187e-02 -7.00551331e-01
4.70920801e-01 -3.82781446e-01 -1.50250459e+00 5.14192104e-01
1.48646903e+00 1.04050517e+00 1.92081019e-01 3.83665621e-01
-9.72773612e-01 7.92123973e-01 -8.05305064e-01 -8.24551582e-01
-1.13969639e-01 -3.00872415e-01 -7.11121023e-01 -1.36419579e-01
1.80050537e-01 -1.06907196e-01 1.16799966e-01 9.55051661e-01
6.60111845e-01 7.97910914e-02 2.45525986e-01 -1.04652631e+00
-9.04877603e-01 9.43546891e-01 -2.43590325e-01 -1.02943517e-01
9.03037012e-01 -5.55585735e-02 1.63166142e+00 -8.49643469e-01
5.93055785e-01 1.22899103e+00 3.87824088e-01 2.75072187e-01
-9.31906521e-01 -5.18212974e-01 3.31165582e-01 -3.12231295e-02
-8.64853919e-01 -2.41986781e-01 9.05577064e-01 -3.14889967e-01
4.93496627e-01 6.61619961e-01 7.22122490e-01 1.22074223e+00
6.58188984e-02 1.21513963e+00 1.38856304e+00 -2.20283538e-01
3.60749841e-01 2.46174216e-01 2.24444032e-01 2.15204597e-01
6.41907081e-02 -6.81507587e-01 -7.90659308e-01 -5.81493378e-01
-1.28031909e-01 3.69225107e-02 -2.25721151e-01 4.75800872e-01
-1.03645277e+00 1.11220813e+00 2.93243200e-01 1.25462607e-01
-6.08863533e-01 -4.82044399e-01 2.53956139e-01 2.11749360e-01
1.09007490e+00 6.94803119e-01 -4.51095253e-01 -3.60713780e-01
-7.62196362e-01 7.87341535e-01 1.36165917e+00 8.92350972e-01
8.81089568e-01 -4.82784688e-01 -6.03751838e-01 9.28434968e-01
4.83912617e-01 7.95252681e-01 6.29985094e-01 -5.91949463e-01
6.89281583e-01 1.32795656e+00 3.96803170e-01 -1.41346157e+00
-2.19423220e-01 -3.17603946e-01 -6.17957473e-01 -7.60839105e-01
4.27442580e-01 -6.73345685e-01 -2.46029720e-01 1.27751184e+00
6.45525694e-01 -5.01979053e-01 -1.53117001e-01 8.26095223e-01
1.16277111e+00 7.69459665e-01 -1.09579526e-01 -2.56744027e-01
1.55904913e+00 -7.80936778e-01 -9.47495162e-01 -4.12684590e-01
4.89496231e-01 -1.01378453e+00 1.44774365e+00 3.22227538e-01
-7.58905947e-01 -3.28249991e-01 -5.07072687e-01 -1.83173656e-01
-5.37796378e-01 2.54910570e-02 5.79929352e-01 5.19171834e-01
-6.39385402e-01 1.62913918e-01 -1.48724273e-01 -2.46740773e-01
2.89069623e-01 -4.79498543e-02 2.62355715e-01 1.05375461e-01
-1.55302751e+00 3.72882336e-01 -1.62739232e-01 2.91667044e-01
4.35184576e-02 -6.42305911e-01 -5.49521089e-01 -2.10923180e-01
6.57262564e-01 -6.63977444e-01 1.39306569e+00 -5.81275642e-01
-1.66211724e+00 4.56244111e-01 -6.63902342e-01 -8.30823779e-02
5.59377015e-01 -4.16029543e-01 -5.47237694e-01 1.90130789e-02
4.87458587e-01 3.11389744e-01 7.33333886e-01 -1.10213053e+00
-7.45382309e-01 -1.21405870e-01 2.98117340e-01 1.29066005e-01
-5.73814988e-01 3.62327285e-02 -2.30657503e-01 -4.93108898e-01
1.33513315e-02 -8.57051194e-01 -3.39962155e-01 -5.97280085e-01
-7.32878983e-01 -6.93418860e-01 8.65775883e-01 -5.58317184e-01
1.64719927e+00 -1.60078633e+00 -3.61009240e-01 1.15730450e-01
3.39650363e-01 2.17001036e-01 1.04901075e-01 9.31249142e-01
6.27093315e-01 4.20083821e-01 2.05477685e-01 -4.76832330e-01
4.05252844e-01 1.07625589e-01 -7.44788647e-01 -9.07843858e-02
-7.56879663e-03 1.23105121e+00 -1.27398968e+00 -6.93369687e-01
-3.59657675e-01 9.68556553e-02 -6.82421803e-01 2.84909576e-01
-6.99127972e-01 4.50586945e-01 -1.04903591e+00 7.19951153e-01
5.11673391e-01 -6.40899837e-01 1.67302325e-01 2.15727647e-04
-3.07803750e-01 4.72823888e-01 -7.59089172e-01 1.13621461e+00
-6.59251451e-01 4.91893947e-01 8.63262862e-02 -3.38149160e-01
1.12797379e+00 2.01425225e-01 3.89118701e-01 -7.26629317e-01
2.01560035e-01 3.59099448e-01 -4.24105883e-01 -8.41033220e-01
1.01908457e+00 7.92815723e-03 -5.05993903e-01 6.42484844e-01
-5.40735722e-01 -3.67109984e-01 6.33732259e-01 4.98753816e-01
9.56937730e-01 -1.90334976e-01 9.36339945e-02 -2.51989901e-01
5.10245979e-01 9.05325189e-02 3.13421011e-01 8.12225401e-01
2.59882770e-03 6.21998429e-01 7.36731768e-01 -1.46395981e-01
-4.72472817e-01 -5.69466889e-01 1.87076360e-01 1.19924939e+00
1.71525732e-01 -7.49696553e-01 -6.36493742e-01 -9.04799461e-01
1.56330038e-02 6.96322024e-01 -5.12869477e-01 5.62240243e-01
-5.38553655e-01 -9.11814630e-01 2.23106518e-02 3.00163254e-02
5.67346275e-01 -1.07771087e+00 -1.87693343e-01 4.18421894e-01
-1.12081027e+00 -1.14788580e+00 -6.73773885e-01 -4.66762185e-01
-4.76081014e-01 -1.12904930e+00 -7.70713091e-01 -4.06114966e-01
7.01224983e-01 3.76536846e-01 1.31261516e+00 1.91410676e-01
4.16633487e-01 1.89025581e-01 -7.99151421e-01 -4.65623170e-01
-3.39456797e-01 5.97894549e-01 -2.65159696e-01 4.62116063e-01
5.04166186e-01 -6.21214807e-01 -1.05497229e+00 6.02049589e-01
-1.07084751e+00 2.46786736e-02 4.03112769e-01 6.69327796e-01
1.37319818e-01 -5.10178268e-01 1.17739463e+00 -1.32292950e+00
1.42649412e+00 -8.73964906e-01 -4.02826101e-01 1.46433175e-01
-6.94014072e-01 -1.09358378e-01 8.05354834e-01 -3.82514775e-01
-1.21763551e+00 -6.99837089e-01 -2.36075372e-01 4.16485578e-01
2.04916432e-01 1.02779126e+00 -1.07581697e-01 4.50521678e-01
5.90232968e-01 1.71179455e-02 -1.11795533e-02 -3.86479288e-01
3.85861129e-01 9.82930601e-01 -1.09441906e-01 -5.53764284e-01
6.11121774e-01 4.90359306e-01 -6.24382496e-01 -9.00257230e-01
-1.49428773e+00 -8.61110866e-01 -2.69427132e-02 -3.39388132e-01
6.60218358e-01 -6.32370532e-01 -9.97022867e-01 2.94034451e-01
-1.19287574e+00 -1.34490535e-01 -1.08486399e-01 -4.46454994e-02
2.60200109e-02 4.48788881e-01 -4.02509391e-01 -9.44768131e-01
-5.60185671e-01 -8.71094406e-01 1.15761971e+00 4.09875125e-01
-7.85032511e-01 -1.17071760e+00 1.46950796e-01 1.10973394e+00
7.31152773e-01 2.85757005e-01 7.12584555e-01 -8.44977319e-01
-4.49836910e-01 -5.12307465e-01 -3.53080302e-01 5.75760230e-02
1.03353649e-01 3.78277488e-02 -9.53239262e-01 1.61649659e-01
-8.39947760e-02 -3.32249820e-01 6.09792590e-01 2.40254290e-02
1.03213525e+00 -1.00526500e+00 -2.05749527e-01 -1.34627685e-01
9.73222017e-01 -3.81394088e-01 3.07956636e-01 3.44637483e-01
4.94609416e-01 8.66320848e-01 6.02735639e-01 7.86372781e-01
1.11651254e+00 3.27863187e-01 4.16565448e-01 3.67079303e-02
3.47673416e-01 -5.72358310e-01 3.46608400e-01 1.29618514e+00
2.07535446e-01 -7.15361059e-01 -6.38562858e-01 5.25117934e-01
-2.05221820e+00 -8.89952183e-01 -5.03316343e-01 1.77397931e+00
9.71841693e-01 4.90109958e-02 6.88795298e-02 2.13497225e-02
5.79643369e-01 7.37357736e-01 -1.80534303e-01 -1.28720805e-01
-3.42646599e-01 1.08730167e-01 7.39106685e-02 5.23311734e-01
-6.98897183e-01 6.67294264e-01 5.23032570e+00 8.90801966e-01
-9.27280784e-01 2.68568024e-02 6.27833545e-01 1.52076274e-01
-9.87592280e-01 1.68238521e-01 -1.07097459e+00 8.17734659e-01
6.71541035e-01 -2.43020087e-01 1.27958924e-01 6.95944786e-01
7.44607031e-01 -2.61829615e-01 -7.02167809e-01 5.96179962e-01
2.60348599e-02 -1.31406260e+00 7.64857829e-02 1.51438400e-01
1.11993659e+00 -8.58915821e-02 2.00815629e-02 4.48170364e-01
4.03183162e-01 -5.57719469e-01 6.10664248e-01 3.53473842e-01
2.75185883e-01 -2.68629342e-01 7.92885542e-01 5.87053061e-01
-9.47739422e-01 -9.25121233e-02 -9.06417668e-02 -2.69106448e-01
6.64783180e-01 1.11252427e+00 -9.34205115e-01 4.91955757e-01
3.62594008e-01 4.72776026e-01 -5.73730171e-01 6.26080275e-01
-5.09065449e-01 8.56849194e-01 -2.03339130e-01 -8.62513006e-01
4.38470691e-01 -3.00952107e-01 4.77653056e-01 1.16180861e+00
4.11548853e-01 -1.86051596e-02 3.85253519e-01 6.31563008e-01
-4.02649403e-01 5.18542409e-01 -2.11037815e-01 -2.61488527e-01
5.90158939e-01 1.66705585e+00 -5.53209305e-01 -3.14061224e-01
-3.16141754e-01 4.55525011e-01 1.32813320e-01 5.18555641e-01
-6.31825745e-01 -3.46146852e-01 2.42630392e-01 4.49691474e-01
-1.04220033e-01 -4.64625619e-02 -2.94811636e-01 -1.26787531e+00
2.62574941e-01 -1.17517006e+00 2.74706572e-01 -4.82586265e-01
-1.50172234e+00 5.00461400e-01 -1.28489926e-01 -1.14367950e+00
-3.99015784e-01 -3.23571801e-01 -8.85593414e-01 7.65451133e-01
-1.68251336e+00 -1.07056689e+00 -5.29063284e-01 2.33231574e-01
6.46786153e-01 1.96084172e-01 5.29991388e-01 3.30416471e-01
-4.38074499e-01 3.85612905e-01 -9.24655050e-02 -2.07713544e-02
6.97782755e-01 -1.28080499e+00 3.55297565e-01 4.79219824e-01
-1.39966205e-01 7.72617519e-01 7.68880308e-01 -7.07579911e-01
-1.58854425e+00 -8.67099345e-01 1.64078391e+00 -4.98068899e-01
9.81852770e-01 -4.25360590e-01 -6.40111864e-01 8.06428790e-02
3.49379003e-01 -4.80985999e-01 1.06706095e+00 4.25700992e-01
4.98047918e-02 -4.38164137e-02 -7.94907689e-01 9.98688161e-01
6.05041265e-01 -5.75880110e-01 -5.13072491e-01 5.12612939e-01
7.43199766e-01 -2.38623112e-01 -5.81408441e-01 8.65203440e-02
5.87820232e-01 -9.39961076e-01 4.24403965e-01 -1.64753184e-01
7.73644090e-01 -1.76328123e-01 4.44969758e-02 -1.42978275e+00
3.37691516e-01 -1.29009295e+00 -6.08250201e-02 1.48983145e+00
8.09190273e-01 -9.54760313e-01 8.61722648e-01 8.61491621e-01
2.84906346e-02 -8.85839343e-01 -5.24557769e-01 -8.14691558e-02
-2.25661859e-01 -5.30488253e-01 8.88331890e-01 7.41364479e-01
3.03900510e-01 5.67100167e-01 -1.48876965e-01 -3.06294709e-01
1.31742388e-01 5.25531411e-01 1.04462337e+00 -1.24644208e+00
-8.70467201e-02 -6.41755819e-01 4.71506119e-01 -1.57150245e+00
5.86218871e-02 -5.65563917e-01 5.32346070e-02 -1.70582354e+00
-3.68372314e-02 -5.51545620e-01 2.98711449e-01 -8.38304460e-02
-5.61176717e-01 1.75020307e-01 1.66672006e-01 2.45580032e-01
-8.32789123e-01 6.97768152e-01 1.65941441e+00 -1.13054127e-01
-3.10915500e-01 3.66997868e-01 -1.36029577e+00 7.36448765e-01
6.42512143e-01 -3.09011996e-01 -3.19964319e-01 -2.83159077e-01
1.30951941e+00 3.62379290e-02 -8.08797777e-02 -2.68547177e-01
3.59053314e-01 -2.87658781e-01 -2.51499802e-01 -7.33010113e-01
2.08975703e-01 -2.57588953e-01 -3.51459146e-01 -1.53882086e-01
-5.28036118e-01 1.83272570e-01 -4.04066235e-01 7.01126337e-01
-5.26017666e-01 -1.80355161e-01 -3.15903942e-03 -1.61150709e-01
-7.87367523e-02 2.29380965e-01 -4.79842424e-01 5.95771670e-01
2.74585783e-01 1.49308350e-02 -6.79391086e-01 -8.98902595e-01
-3.20156306e-01 7.00179994e-01 2.69493103e-01 5.10740459e-01
3.90382499e-01 -1.15409672e+00 -9.51943398e-01 -1.07067570e-01
1.60384521e-01 2.55651683e-01 2.22119063e-01 9.77121472e-01
-1.34332091e-01 3.52745175e-01 7.14097500e-01 -2.93523878e-01
-8.01447093e-01 -6.80009322e-03 -3.33596975e-01 -7.20880091e-01
-4.08356041e-02 6.80414915e-01 -1.13704409e-02 -6.74813271e-01
-1.72673076e-01 -5.60417473e-01 -4.50233638e-01 8.53683233e-01
5.18917918e-01 4.72010016e-01 1.37295544e-01 -4.09957975e-01
1.13308080e-01 8.35674256e-02 1.01540349e-01 -1.75018251e-01
1.10225928e+00 -4.57270950e-01 -3.59432608e-01 3.94496024e-01
1.04047287e+00 6.67159975e-01 -7.59627759e-01 -3.21251780e-01
1.67512029e-01 -4.42116708e-01 -2.27432594e-01 -6.00282252e-01
-7.50090003e-01 4.88893807e-01 -4.11697268e-01 1.08427000e+00
7.37018287e-01 3.22193913e-02 1.32969785e+00 5.01563191e-01
7.15079159e-02 -1.37819648e+00 3.09898913e-01 7.68897653e-01
9.02841449e-01 -1.45867252e+00 -5.88659383e-02 -3.53665590e-01
-6.83319449e-01 9.91227925e-01 4.02872056e-01 2.02019483e-01
8.12203288e-01 -1.25805750e-01 4.00769353e-01 -2.56148130e-01
-8.45007122e-01 -1.50973231e-01 3.54212433e-01 2.38984637e-02
6.11066997e-01 3.33411880e-02 -7.42148995e-01 8.35556626e-01
-7.01893747e-01 -1.91273004e-01 4.53675866e-01 7.98635364e-01
-3.93631518e-01 -1.17007971e+00 -1.97177947e-01 7.48923421e-01
-7.25404799e-01 -1.87466815e-01 -5.93369842e-01 3.00784349e-01
-9.65087637e-02 1.57140744e+00 -2.33490512e-01 -2.94789821e-01
1.71396017e-01 -5.06541505e-02 -3.97714019e-01 -6.27532840e-01
-1.06586242e+00 -8.18241686e-02 5.37598073e-01 -2.05739036e-01
-5.23872554e-01 -6.48959935e-01 -7.59498298e-01 -3.43162149e-01
-6.16463661e-01 6.15341544e-01 6.89061224e-01 1.14647090e+00
6.21916354e-01 1.25465199e-01 9.80701923e-01 -5.88454545e-01
-6.92275345e-01 -1.07580364e+00 -1.27941072e-01 4.19024616e-01
2.92104661e-01 -2.32647553e-01 -4.88877565e-01 -2.55124807e-01] | [11.848621368408203, 8.263736724853516] |
9eae454e-a79b-4a91-8e8b-bab9bb4c25a3 | joint-extraction-of-entities-and-relations-2 | 1908.08672 | null | https://arxiv.org/abs/1908.08672v2 | https://arxiv.org/pdf/1908.08672v2.pdf | Jointly Modeling Hierarchical and Horizontal Features for Relational Triple Extraction | Recent works on relational triple extraction have shown the superiority of jointly extracting entities and relations over the pipelined extraction manner. However, most existing joint models fail to balance the modeling of entity features and the joint decoding strategy, and thus the interactions between the entity level and triple level are not fully investigated. In this work, we first introduce the hierarchical dependency and horizontal commonality between the two levels, and then propose an entity-enhanced dual tagging framework that enables the triple extraction (TE) task to utilize such interactions with self-learned entity features through an auxiliary entity extraction (EE) task, without breaking the joint decoding of relational triples. Specifically, we align the EE and TE tasks in a position-wise manner by formulating them as two sequence labeling problems with identical encoder-decoder structure. Moreover, the two tasks are organized in a carefully designed parameter sharing setting so that the learned entity features could be naturally shared via multi-task learning. Empirical experiments on the NYT benchmark demonstrate the effectiveness of the proposed framework compared to the state-of-the-art methods. | ['Yi Chang', 'Mark Steedman', 'Sujian Li', 'Yuan Tian', 'Yantao Jia', 'Zhepei Wei', 'Mohammad Javad Hosseini'] | 2019-08-23 | null | null | null | null | ['entity-extraction'] | ['natural-language-processing'] | [-3.45591158e-02 4.70280856e-01 -3.39545101e-01 -3.54899764e-01
-9.62987006e-01 -5.28019369e-01 5.69292188e-01 1.71968609e-01
-3.70767027e-01 7.69326866e-01 2.64138699e-01 -2.80800402e-01
-3.74028496e-02 -6.49322391e-01 -9.47183371e-01 -5.46207309e-01
-7.03707710e-02 3.05520773e-01 1.89137936e-01 -4.87543605e-02
-2.76671946e-01 -2.32403353e-01 -1.18423212e+00 2.02906236e-01
1.11778450e+00 8.74297678e-01 1.18689381e-01 2.41856202e-01
-1.64008826e-01 7.69619823e-01 -1.57921270e-01 -9.29600358e-01
2.00999081e-01 -8.86690021e-02 -8.17851067e-01 -3.02316733e-02
-6.10233955e-02 -2.22564250e-01 -5.01473308e-01 8.16575468e-01
4.31345016e-01 -3.06204587e-01 3.92302334e-01 -1.41851687e+00
-7.32067704e-01 1.07437849e+00 -8.59385550e-01 -2.20687941e-01
2.11799905e-01 -3.56728703e-01 1.51102519e+00 -1.11390603e+00
5.19127905e-01 1.01985669e+00 6.56971157e-01 2.17022315e-01
-1.01329291e+00 -7.42432654e-01 3.08624625e-01 2.27689147e-01
-1.57213259e+00 -5.25562286e-01 5.94339252e-01 -3.65870833e-01
1.10564685e+00 -1.30650163e-01 3.62376302e-01 9.23844576e-01
-5.21622226e-02 1.02808821e+00 7.86718309e-01 -3.48374516e-01
-2.85698473e-01 1.69130832e-01 2.62475163e-01 9.20920253e-01
4.47451919e-01 -1.51206404e-01 -5.72295249e-01 -1.27402991e-01
3.22986454e-01 -1.89908192e-01 -1.14645049e-01 -5.68722785e-01
-1.40345132e+00 5.26307642e-01 2.47541681e-01 4.58969682e-01
-3.17426234e-01 1.85584649e-01 6.70017123e-01 5.48285097e-02
5.71012020e-01 8.84252489e-02 -9.26706672e-01 6.93292841e-02
-6.92699373e-01 1.00839101e-02 8.69952083e-01 1.51061058e+00
9.32466447e-01 -4.16776866e-01 -3.95399094e-01 7.11530805e-01
4.23341632e-01 1.18938200e-01 1.30185351e-01 -2.90169239e-01
9.15341139e-01 6.55669153e-01 3.02195791e-02 -8.37523162e-01
-4.01884198e-01 -7.09190607e-01 -6.55933738e-01 -5.96998513e-01
2.94932097e-01 -5.27506888e-01 -6.21463954e-01 2.11870599e+00
7.31273890e-01 3.66830736e-01 3.66434544e-01 4.49813843e-01
8.65986228e-01 4.50345337e-01 2.92343885e-01 -1.63385585e-01
1.82064986e+00 -1.21689260e+00 -1.00407910e+00 -1.90079927e-01
1.06929338e+00 -5.83271801e-01 4.77718264e-01 -1.25556976e-01
-1.08815038e+00 -5.07955492e-01 -1.11438286e+00 -5.57684302e-01
-3.36565495e-01 6.27122045e-01 9.23774481e-01 4.38043594e-01
-5.55925786e-01 3.34636599e-01 -1.02648473e+00 -1.79692119e-01
2.87800878e-01 3.69057000e-01 -5.30900836e-01 2.15466812e-01
-1.57932389e+00 1.00506079e+00 7.64943957e-01 6.20633364e-01
-5.02666354e-01 -8.14420223e-01 -1.19657099e+00 3.25865626e-01
7.97875226e-01 -9.28225935e-01 1.22082078e+00 -3.16438884e-01
-1.27566648e+00 7.47008443e-01 -3.55048805e-01 -3.11236352e-01
3.59737277e-01 -4.65492815e-01 -3.05174917e-01 -2.15911508e-01
2.89039105e-01 3.74848604e-01 2.84374356e-01 -1.25799084e+00
-9.49824870e-01 -3.32024068e-01 2.78261542e-01 3.05635601e-01
-4.93608564e-01 1.14289358e-01 -1.00665295e+00 -4.70291078e-01
6.16551638e-02 -7.83719957e-01 -7.98770692e-03 -3.03804398e-01
-8.24064612e-01 -5.37409484e-01 5.84332407e-01 -8.28706741e-01
1.52112532e+00 -2.15320826e+00 4.12508875e-01 4.78828326e-02
4.30446595e-01 1.18660130e-01 -7.01822042e-02 5.90576112e-01
-1.65273190e-01 1.76528022e-01 -1.58925474e-01 -7.42125452e-01
3.36776972e-01 1.34068877e-01 -2.54657655e-03 2.41870940e-01
4.26689684e-01 1.13521087e+00 -8.89776468e-01 -7.89045870e-01
-3.43647242e-01 3.94151807e-01 -5.47550440e-01 3.50999415e-01
-7.97571018e-02 3.19204748e-01 -8.10646236e-01 6.16426826e-01
6.88788414e-01 -5.08042932e-01 8.21546733e-01 -6.35512114e-01
-1.95884526e-01 6.93931282e-01 -1.19073796e+00 1.77053249e+00
-4.36729610e-01 3.78198586e-02 2.86680404e-02 -9.18196380e-01
6.84876382e-01 5.93820095e-01 5.86598873e-01 -5.05092859e-01
-2.70258598e-02 3.98605049e-01 -3.35127139e-03 -7.19698429e-01
6.17531776e-01 -1.10562474e-01 -5.01278818e-01 2.89081067e-01
3.93473625e-01 5.95548987e-01 3.03068429e-01 3.64123762e-01
9.74294603e-01 6.16145372e-01 4.70624983e-01 1.22178718e-01
4.74583268e-01 -2.72859365e-01 1.05515110e+00 4.56434727e-01
2.39267841e-01 1.54462844e-01 8.36003065e-01 6.59286790e-03
-8.85990679e-01 -6.86861634e-01 -4.47598211e-02 1.00894189e+00
2.77042896e-01 -8.53616476e-01 -5.95101655e-01 -1.06354642e+00
-3.64834815e-02 4.94587243e-01 -6.61622345e-01 -1.15218639e-01
-7.83932447e-01 -9.49035347e-01 7.58663177e-01 7.60980070e-01
5.28463721e-01 -4.81480420e-01 -1.79170966e-01 2.72047430e-01
-3.63780022e-01 -1.73039067e+00 -4.49799776e-01 5.65500736e-01
-2.49504104e-01 -8.63226116e-01 -3.77865106e-01 -8.59993577e-01
3.79835129e-01 -2.37087347e-02 1.01752365e+00 1.04360506e-01
2.31266573e-01 -3.53416838e-02 -4.91585374e-01 4.24236767e-02
-5.04271947e-02 8.06042492e-01 -1.63728908e-01 1.12223305e-01
3.54019105e-01 -7.02290356e-01 -2.61792451e-01 4.22252715e-03
-7.45993137e-01 5.20316482e-01 8.80229175e-01 9.81904685e-01
3.90103847e-01 -3.16960514e-02 8.45093906e-01 -1.33386993e+00
2.75079697e-01 -7.79444635e-01 -3.77317995e-01 8.83974016e-01
-5.34065068e-01 3.88820499e-01 4.05642569e-01 -7.50693381e-02
-1.35941446e+00 1.85745165e-01 -1.41310588e-01 -5.09873629e-02
2.22984433e-01 9.14260745e-01 -8.40133607e-01 3.14929098e-01
-1.86849162e-01 1.33891180e-01 -6.48055911e-01 -5.37373006e-01
5.97108662e-01 5.92398763e-01 3.70031357e-01 -9.20932591e-01
1.07587349e+00 6.51906282e-02 -2.32520163e-01 -1.33185193e-01
-1.12546885e+00 -2.76303828e-01 -9.73631084e-01 4.04006958e-01
1.01723301e+00 -1.19194245e+00 -6.93642974e-01 4.12114471e-01
-1.39755893e+00 8.04057047e-02 -1.00560203e-01 4.24623221e-01
-3.18726093e-01 4.70535636e-01 -7.53011167e-01 -7.73869872e-01
-2.21789926e-01 -1.28582883e+00 1.37528861e+00 1.25838771e-01
1.84179500e-01 -8.89795721e-01 -2.32228842e-02 3.94465387e-01
-2.78063398e-02 -1.08091757e-02 1.25080597e+00 -9.84976411e-01
-7.89853692e-01 -1.43463509e-02 -6.54182971e-01 -5.97783029e-02
1.64606139e-01 -8.31191838e-02 -9.29699063e-01 -3.94969396e-02
-3.65277708e-01 -4.75665629e-01 8.69184077e-01 -2.74213135e-01
7.71294415e-01 -1.72260225e-01 -7.37265229e-01 6.73704624e-01
1.34205437e+00 -1.11968547e-01 5.33535182e-01 2.47419715e-01
1.05213141e+00 6.27749145e-01 6.15263045e-01 3.65019262e-01
1.17979789e+00 8.89503241e-01 2.62215376e-01 -1.40969634e-01
1.04472339e-01 -6.31135285e-01 1.67875066e-01 1.20244706e+00
1.56620458e-01 -3.49554807e-01 -8.04105818e-01 5.29158592e-01
-2.17148232e+00 -6.51411355e-01 -1.62132248e-01 1.87630188e+00
1.18264532e+00 -3.70270796e-02 -6.20010905e-02 -1.91153362e-02
7.00259268e-01 1.47313088e-01 -2.45828316e-01 1.10260978e-01
-6.49201078e-03 -7.32607543e-02 5.77079117e-01 3.08294952e-01
-1.31157637e+00 1.00131047e+00 5.34743357e+00 9.21700537e-01
-7.05361664e-01 3.34771723e-01 1.59974352e-01 3.14681023e-01
-5.95324039e-01 5.45364499e-01 -1.25581694e+00 2.91417569e-01
8.44773114e-01 -1.72448441e-01 -3.06653008e-02 4.63939577e-01
-2.59790778e-01 2.74686813e-01 -1.29154122e+00 5.66143632e-01
-2.58674592e-01 -1.01492286e+00 -2.15841681e-01 3.28558274e-02
4.22185004e-01 -9.44087207e-02 -2.05445617e-01 7.46688485e-01
4.32085901e-01 -6.61323428e-01 8.59960914e-01 4.79688019e-01
6.99982285e-01 -4.74404246e-01 7.26325870e-01 3.28422040e-01
-1.66717839e+00 -1.93651598e-02 -9.20826867e-02 3.62239689e-01
5.19020796e-01 7.82812774e-01 -5.81893504e-01 1.46715701e+00
5.06219864e-01 9.17354345e-01 -4.43900526e-01 7.24129140e-01
-4.00617957e-01 3.60509753e-01 -2.60704994e-01 2.62121767e-01
7.11789355e-02 -1.51434958e-01 3.40390652e-01 1.41998470e+00
3.30859870e-01 -9.35584214e-03 3.41430813e-01 7.37998486e-01
-4.64285254e-01 1.34685114e-01 -4.07399595e-01 -1.75777346e-01
8.02250922e-01 1.50045764e+00 -3.20187390e-01 -3.25765610e-01
-9.81911898e-01 8.59363437e-01 8.78648460e-01 2.54838169e-01
-1.16393983e+00 -4.37224090e-01 4.71410751e-01 -2.91127115e-01
7.53875613e-01 -2.27773979e-01 -2.46487841e-01 -1.60431194e+00
3.55537117e-01 -7.28529274e-01 3.51348072e-01 -3.87494594e-01
-1.24721193e+00 6.79645598e-01 1.02361813e-02 -1.13081837e+00
-1.27821162e-01 -2.76151180e-01 -2.45152488e-01 8.40959489e-01
-1.94739747e+00 -1.71851492e+00 9.96208489e-02 2.80957907e-01
7.36648077e-03 3.33497137e-01 6.83730423e-01 9.09234166e-01
-1.15125859e+00 1.01774597e+00 -1.67670637e-01 4.04599428e-01
6.71441078e-01 -1.30690551e+00 2.98094779e-01 9.80138004e-01
-7.15923905e-02 7.51176834e-01 3.78083706e-01 -7.31021106e-01
-1.48028767e+00 -1.18287373e+00 1.49342978e+00 -2.47133866e-01
8.20879281e-01 -8.66404593e-01 -9.89184976e-01 1.05997062e+00
3.84977818e-01 1.66782320e-01 7.84548104e-01 5.18802404e-01
-6.32507265e-01 -4.45896164e-02 -7.54491985e-01 4.24660057e-01
1.32395756e+00 -6.58392608e-01 -5.73302925e-01 1.68254793e-01
1.08575225e+00 -5.73840857e-01 -1.30094552e+00 6.61472321e-01
6.29195690e-01 -4.76390868e-01 8.38008463e-01 -6.99575722e-01
4.78147149e-01 -4.07915771e-01 -2.76549399e-01 -1.04209554e+00
-3.34101677e-01 -5.30276835e-01 -5.18100262e-01 2.01181269e+00
8.07642341e-01 -5.90856493e-01 3.66240114e-01 4.29222554e-01
-3.13783050e-01 -9.78181660e-01 -7.77885318e-01 -6.02504015e-01
-1.54883102e-01 -1.47695214e-01 7.60342598e-01 9.22371507e-01
2.36147404e-01 9.14075553e-01 -7.25329757e-01 5.57525337e-01
3.67760152e-01 3.76977116e-01 6.45544708e-01 -1.11075771e+00
-4.77507770e-01 3.06587256e-02 7.94020817e-02 -1.31866682e+00
3.80803347e-01 -1.13887072e+00 1.38329834e-01 -1.56621242e+00
5.06561935e-01 -7.50796437e-01 -3.24984878e-01 8.28327954e-01
-6.65183127e-01 -2.75462687e-01 2.42128819e-02 1.95419461e-01
-8.93219709e-01 9.93824363e-01 1.03339887e+00 1.36498526e-01
1.44085914e-01 -3.54446977e-01 -1.09688401e+00 3.50478977e-01
2.98391223e-01 -6.28345668e-01 -3.70305121e-01 -8.33302617e-01
5.35040736e-01 3.22850347e-01 1.07164763e-01 -5.16670048e-01
5.64096510e-01 1.53117180e-01 -1.87220017e-03 -5.70748210e-01
2.21627399e-01 -9.15100217e-01 6.97295964e-02 -5.18854745e-02
-3.27580363e-01 -9.54916328e-02 -3.81666310e-02 7.11708844e-01
-3.68028998e-01 -1.78080022e-01 1.93132907e-01 2.32893690e-01
-4.74150479e-01 4.95520800e-01 2.16743186e-01 3.27336490e-02
1.09065711e+00 1.65302113e-01 -3.44882488e-01 6.78465739e-02
-6.41133428e-01 6.03122771e-01 1.31727353e-01 4.10624832e-01
6.09251857e-02 -1.38143528e+00 -7.68832803e-01 1.20280154e-01
3.03068519e-01 1.45994663e-01 2.86217809e-01 1.23206592e+00
2.21286833e-01 4.34991747e-01 1.66734055e-01 -2.89592326e-01
-9.57934260e-01 6.98846996e-01 2.29690775e-01 -1.07916820e+00
-4.45315450e-01 6.61678851e-01 3.69525373e-01 -6.79895163e-01
1.55152082e-01 -1.21853866e-01 -2.97730953e-01 2.22636893e-01
9.79696214e-02 -1.68993101e-02 1.75588593e-01 -5.46405911e-01
-4.62721080e-01 3.51191461e-01 -3.75241458e-01 8.45237300e-02
1.43689716e+00 -4.28992659e-01 -2.43409842e-01 3.50368559e-01
1.30488420e+00 1.32050872e-01 -9.82801080e-01 -6.37542605e-01
4.80919689e-01 -4.50981259e-02 -2.27399185e-01 -4.95412648e-01
-1.13488519e+00 8.11512172e-01 -7.11589307e-02 1.33325651e-01
9.91818905e-01 1.52067423e-01 1.08062303e+00 2.74549007e-01
4.01146114e-01 -7.64593363e-01 -3.95342976e-01 5.13757110e-01
3.19555551e-01 -1.00396490e+00 -1.92358509e-01 -1.03722632e+00
-5.38867652e-01 8.24335575e-01 7.08917379e-01 1.43403202e-01
6.96317017e-01 6.24939680e-01 -4.17029291e-01 -2.27998421e-01
-1.10331082e+00 -5.20562530e-01 2.83779562e-01 2.91154683e-01
8.27926159e-01 -6.07035533e-02 -6.60241008e-01 1.21112108e+00
1.88742325e-01 1.29183143e-01 3.64881866e-02 9.63360310e-01
3.99591401e-02 -1.58212757e+00 3.57958525e-01 2.67126948e-01
-6.09040022e-01 -4.16470259e-01 5.72205335e-02 8.42204094e-01
3.82873029e-01 7.11215019e-01 -2.13100836e-01 -6.21558726e-01
3.90536606e-01 2.55066484e-01 4.07090008e-01 -7.75531411e-01
-6.92694068e-01 1.38557643e-01 5.47474980e-01 -2.54008979e-01
-5.44871628e-01 -7.57881820e-01 -1.19400787e+00 -9.62417480e-03
-7.14181423e-01 3.96303087e-01 2.90460497e-01 1.31900191e+00
6.38391972e-01 8.30209434e-01 6.87348485e-01 -4.02375400e-01
-5.42884529e-01 -9.64198828e-01 -3.96342903e-01 1.78341717e-01
1.47679836e-01 -9.10534739e-01 7.91741833e-02 -1.53498277e-01] | [9.285296440124512, 8.724596977233887] |
04dfb5b4-870c-4a41-be96-413c4931f84e | playing-with-embeddings-evaluating-embeddings | null | null | https://aclanthology.org/W17-5305 | https://aclanthology.org/W17-5305.pdf | Playing with Embeddings : Evaluating embeddings for Robot Language Learning through MUD Games | Acquiring language provides a ubiquitous mode of communication, across humans and robots. To this effect, distributional representations of words based on co-occurrence statistics, have provided significant advancements ranging across machine translation to comprehension. In this paper, we study the suitability of using general purpose word-embeddings for language learning in robots. We propose using text-based games as a proxy to evaluating word embedding on real robots. Based in a risk-reward setting, we review the effectiveness of the embeddings in navigating tasks in fantasy games, as an approximation to their performance on more complex scenarios, like language assisted robot navigation. | ['Anmol Gulati', 'Kumar Krishna Agrawal'] | 2017-09-01 | null | null | null | ws-2017-9 | ['text-based-games'] | ['playing-games'] | [-1.67492971e-01 5.44879556e-01 -1.40477931e-02 -2.52231002e-01
-5.89584112e-01 -4.07226503e-01 8.09605777e-01 2.90313035e-01
-1.13432026e+00 4.83935505e-01 3.48704875e-01 -5.73911428e-01
-1.87851667e-01 -7.58098781e-01 -6.17547989e-01 -2.26024687e-01
-5.43866277e-01 5.77841222e-01 -2.43832543e-01 -7.28780985e-01
3.13997000e-01 2.38133714e-01 -1.47947705e+00 -3.22621614e-01
5.87330222e-01 6.04918003e-01 5.36262095e-01 6.80848658e-01
-2.03813255e-01 7.13955760e-01 -4.45602357e-01 -4.85656559e-01
2.54654735e-01 3.39881554e-02 -7.94525027e-01 -5.67539394e-01
-8.78981650e-02 -4.14042234e-01 -4.17745769e-01 1.07311714e+00
5.68907082e-01 3.86151254e-01 1.13042772e+00 -1.27602136e+00
-1.05529213e+00 8.91055226e-01 -3.66590284e-02 -7.40306005e-02
7.67418504e-01 2.59733461e-02 1.42002094e+00 -8.71778369e-01
6.68538928e-01 1.71644926e+00 7.72306740e-01 5.48927009e-01
-9.47868288e-01 -2.65836090e-01 -3.46450619e-02 1.29653722e-01
-1.22991002e+00 -2.37227678e-01 4.01785314e-01 -6.30557418e-01
1.37728345e+00 -5.65686107e-01 4.44070905e-01 1.50445473e+00
7.49288440e-01 6.86922133e-01 6.61345422e-01 -4.41644073e-01
3.97531599e-01 1.97765663e-01 -2.68159479e-01 6.59131706e-01
3.96106005e-01 2.74564236e-01 -3.76360834e-01 -2.98494548e-01
5.23048818e-01 -2.91855156e-01 3.82669386e-03 -7.78622448e-01
-1.17900634e+00 1.30429041e+00 5.52308023e-01 1.85151845e-01
-6.55308843e-01 6.77603841e-01 5.95050991e-01 5.23194909e-01
3.19376677e-01 8.95711005e-01 -6.80767417e-01 -6.50922596e-01
7.87173435e-02 4.21413392e-01 1.02344477e+00 9.73665118e-01
6.57005548e-01 -8.85198638e-02 4.18002158e-02 1.05896282e+00
6.99775815e-01 4.54728216e-01 8.68357122e-01 -1.05203104e+00
1.75099269e-01 2.74666809e-02 1.64281607e-01 -1.24326229e+00
-7.00348794e-01 4.25514840e-02 -1.04993910e-01 2.65767097e-01
1.82390362e-01 -2.27051988e-01 -5.25982082e-01 2.01380467e+00
6.95478618e-02 -3.10065985e-01 4.57953930e-01 8.10950994e-01
4.17307287e-01 6.56329989e-01 1.98738217e-01 3.74836713e-01
1.40683675e+00 -1.00715721e+00 -4.83598113e-01 -4.09292340e-01
9.74029422e-01 -3.57415855e-01 1.56721091e+00 6.49265885e-01
-4.32779133e-01 -3.31308633e-01 -1.14085519e+00 -4.41832691e-01
-6.33827925e-01 -3.77826899e-01 7.99426675e-01 5.40139675e-01
-1.25389338e+00 6.08988047e-01 -4.97452408e-01 -9.13123906e-01
3.57475057e-02 2.07449302e-01 -5.50792277e-01 -1.30414292e-01
-1.45377302e+00 1.51538765e+00 2.78713435e-01 -4.39006031e-01
-1.04241073e+00 -3.73687208e-01 -1.41586030e+00 -2.06165776e-01
7.40965009e-02 -5.03929794e-01 1.55458260e+00 -4.21413720e-01
-1.85557115e+00 8.53386223e-01 5.69826722e-01 -7.00906754e-01
2.75825500e-01 -6.46066725e-01 8.75894651e-02 -1.07047491e-01
3.48430812e-01 1.17263198e+00 5.91739178e-01 -9.96709764e-01
-4.56635863e-01 -8.26583877e-02 6.02070153e-01 4.54950839e-01
-4.32387859e-01 -3.00330192e-01 8.05553049e-02 -4.18021113e-01
-3.39296848e-01 -9.80243087e-01 -6.23755276e-01 1.65463090e-01
-1.45467352e-02 -5.30340791e-01 1.71297222e-01 -5.21330237e-01
4.29191589e-01 -2.11042309e+00 2.98055887e-01 -1.06663495e-01
1.38530508e-01 -2.42919549e-01 -5.03315628e-01 7.49547958e-01
2.93921381e-01 7.41579309e-02 -1.50863171e-01 -3.86506855e-01
5.56141198e-01 4.75144774e-01 -3.40910316e-01 5.17847478e-01
3.49560320e-01 9.05179560e-01 -1.31878448e+00 -1.87653452e-01
2.38404393e-01 4.00791258e-01 -7.93667674e-01 2.37729147e-01
-2.58570910e-01 -1.02950282e-01 -3.68658185e-01 2.00708747e-01
5.51072806e-02 3.84940058e-01 7.44699687e-02 4.96462107e-01
-8.12137276e-02 5.84991455e-01 -5.01973808e-01 2.38988805e+00
-1.11865175e+00 7.98665345e-01 1.28148407e-01 -8.42833459e-01
9.58705485e-01 -6.14410564e-02 1.64069444e-01 -7.39529014e-01
4.02152359e-01 1.17570728e-01 2.63177961e-01 -5.94760060e-01
9.62527394e-01 -2.47398496e-01 -6.39203072e-01 6.48160994e-01
4.18040901e-01 -6.20074809e-01 -6.57053888e-02 1.41320065e-01
1.33408797e+00 3.69953871e-01 5.80573499e-01 -5.25328338e-01
3.67079601e-02 1.06293142e-01 -1.70163631e-01 8.89140785e-01
-4.02587533e-01 3.87201458e-01 4.46880758e-01 3.21017094e-02
-1.08117056e+00 -1.33581424e+00 -7.11185485e-02 1.50122988e+00
7.89714977e-02 -3.52901340e-01 -5.47605455e-01 -3.46388519e-01
1.77171797e-01 1.30110240e+00 -5.94144523e-01 -5.90354383e-01
-4.76319194e-02 -3.08157563e-01 8.91660631e-01 3.10989618e-01
-1.42871708e-01 -1.30701232e+00 -1.04113472e+00 3.81091863e-01
2.48078093e-01 -9.81261909e-01 -2.47446805e-01 7.62899816e-01
-4.39555824e-01 -6.26021802e-01 -6.75721943e-01 -1.04164290e+00
7.25062266e-02 2.44464189e-01 1.19205260e+00 -2.53182143e-01
-3.25059146e-01 1.10789800e+00 -7.63905585e-01 -6.53738618e-01
-4.29226369e-01 1.55263385e-02 7.17830896e-01 -7.78996885e-01
4.13890094e-01 -6.92175150e-01 -2.51159251e-01 -1.26949981e-01
-7.65830159e-01 -6.05826676e-01 8.22242141e-01 1.05351126e+00
-1.73393995e-01 -4.60863918e-01 4.67635930e-01 -4.24990952e-01
1.43480480e+00 -8.84864151e-01 -2.57620543e-01 -2.19029114e-01
-5.97514153e-01 3.15793037e-01 4.06368077e-01 -6.52997255e-01
-4.95989114e-01 -2.44416252e-01 -3.17997515e-01 -6.33777678e-02
-9.05942917e-02 8.31659019e-01 8.09325054e-02 7.27241710e-02
9.34535146e-01 -3.12764868e-02 3.70537728e-01 -1.74231395e-01
1.10107791e+00 8.91149104e-01 3.06515098e-01 -9.47313249e-01
4.69455004e-01 4.34994735e-02 -4.44075435e-01 -1.23729134e+00
-4.41272035e-02 -2.72050411e-01 -3.93011838e-01 5.51587939e-02
9.95017350e-01 -8.08866918e-01 -7.23831892e-01 1.17748808e-02
-1.45186365e+00 -5.71168005e-01 -3.96485806e-01 7.72967398e-01
-1.07862151e+00 2.83821791e-01 -4.74096209e-01 -1.04481816e+00
1.15934230e-01 -1.14109170e+00 1.13683176e+00 1.49288569e-02
-4.79092836e-01 -1.15492213e+00 3.96217406e-01 -3.04814368e-01
4.80680019e-01 -2.31493101e-01 9.76695538e-01 -9.83899891e-01
-9.94588807e-02 -1.56636965e-02 -1.11913756e-01 3.62704009e-01
4.73163575e-02 -5.71498275e-01 -9.56202686e-01 -7.97590241e-02
-6.31116480e-02 -8.76022220e-01 5.56817174e-01 1.71654910e-01
5.04455745e-01 4.56590876e-02 -1.07745692e-01 7.41549209e-02
1.08657861e+00 2.62917101e-01 5.50670385e-01 6.42664850e-01
4.09549624e-01 1.07590246e+00 7.74648249e-01 5.05246341e-01
7.11982012e-01 4.53557938e-01 5.27996778e-01 5.03649175e-01
3.33293021e-01 -6.51748180e-01 8.15872610e-01 1.04097342e+00
6.24234021e-01 -3.76896381e-01 -1.22966063e+00 7.36998498e-01
-1.55852306e+00 -3.68977100e-01 3.85730714e-01 1.76558125e+00
5.50401270e-01 1.81148812e-01 -2.21285909e-01 -4.08769816e-01
3.15154463e-01 7.56635442e-02 -4.49321330e-01 -9.41184819e-01
2.67649084e-01 1.84721574e-01 6.25896394e-01 6.24670923e-01
-9.02225733e-01 1.05060410e+00 6.85176897e+00 7.61223257e-01
-8.19217324e-01 3.09333503e-01 2.35028580e-01 4.75949913e-01
-3.90888631e-01 -3.08003962e-01 -5.69713823e-02 3.85706103e-03
1.12907624e+00 -2.68398464e-01 5.65657556e-01 1.19370544e+00
-1.41547211e-02 -2.00940207e-01 -1.49975991e+00 1.13106477e+00
2.81889349e-01 -5.85653305e-01 6.48492128e-02 7.56078139e-02
5.33909798e-02 3.40612382e-01 4.59589064e-01 8.37500572e-01
7.45235801e-01 -1.43241680e+00 9.78156865e-01 1.72162503e-01
5.57024598e-01 -6.46626353e-01 6.78063989e-01 4.33318257e-01
-8.37473035e-01 -1.11654706e-01 -8.41641426e-01 -5.74137807e-01
2.52027929e-01 4.69589010e-02 -1.27064776e+00 3.97026807e-01
6.02685750e-01 4.30655688e-01 -7.46693984e-02 5.31217277e-01
-3.25227052e-01 6.86238334e-02 -3.19453120e-01 -7.28476107e-01
4.98441160e-01 -4.14223284e-01 5.36334753e-01 1.11465371e+00
4.84446853e-01 -2.50724196e-01 9.54928845e-02 7.68343270e-01
5.78066111e-02 3.00620347e-01 -1.51648033e+00 -3.86729240e-01
4.76723611e-01 9.60180640e-01 -5.04453003e-01 1.89745829e-01
-5.31144798e-01 1.13385010e+00 6.75049782e-01 9.61914845e-03
-7.29352176e-01 -7.07243323e-01 1.38028765e+00 -5.91258228e-01
7.73107633e-02 -8.22495043e-01 -5.57576120e-02 -6.79204047e-01
-1.39343321e-01 -6.40381157e-01 -2.62634128e-01 -8.56953442e-01
-1.40233719e+00 6.02782488e-01 1.40033320e-01 -1.15319681e+00
-5.00057161e-01 -1.34370685e+00 -2.67047226e-01 5.19734502e-01
-1.28032625e+00 -6.11893415e-01 2.47961774e-01 -9.36922133e-02
6.30081058e-01 -3.35637718e-01 1.18294191e+00 5.75961173e-02
8.98938105e-02 5.15501499e-01 1.13423638e-01 -5.63711375e-02
6.48492217e-01 -1.30460835e+00 7.67996669e-01 2.51136124e-01
5.66770673e-01 7.10894585e-01 9.76409554e-01 -3.39814991e-01
-1.76321888e+00 -7.76054323e-01 4.22497571e-01 -6.75856352e-01
1.01594210e+00 -6.32221758e-01 -4.08910275e-01 5.81757963e-01
3.08795154e-01 -6.07680202e-01 6.00091100e-01 3.68748665e-01
-4.11045969e-01 4.32678163e-01 -1.16688406e+00 1.03835058e+00
1.28206754e+00 -7.83232987e-01 -1.02476895e+00 2.12127432e-01
1.36758399e+00 -1.27090186e-01 -6.23122692e-01 1.03881031e-01
5.97262621e-01 -4.83018517e-01 9.43099260e-01 -5.83560228e-01
5.56096017e-01 2.14310557e-01 -6.72624290e-01 -1.87473571e+00
-8.85652602e-02 -5.61254621e-01 4.59640950e-01 8.33267689e-01
6.53997898e-01 -8.33170176e-01 5.03098190e-01 3.74744713e-01
-2.38377467e-01 -5.80299795e-01 -1.19699013e+00 -8.70549381e-01
5.88226914e-01 -9.06371593e-01 1.37372717e-01 5.96628189e-01
3.88369471e-01 5.75340688e-01 -2.04729378e-01 -3.66229541e-03
2.37144321e-01 -7.00730085e-01 7.87022889e-01 -1.17791116e+00
-2.73893833e-01 -5.22579312e-01 -8.31281960e-01 -1.51870632e+00
6.28686190e-01 -9.87997115e-01 7.31546938e-01 -1.44324136e+00
-1.95733204e-01 -4.50080305e-01 -6.54102191e-02 -1.83376740e-03
2.24706724e-01 -2.77913106e-03 2.48638242e-01 -2.94901907e-01
-5.85270703e-01 1.07983255e+00 6.96853876e-01 -3.72253925e-01
1.93102896e-01 -7.56824791e-01 -6.66025162e-01 8.48550439e-01
8.16186786e-01 -4.63028133e-01 -6.17215455e-01 -7.54564226e-01
4.59347069e-01 -2.54818022e-01 -1.14202693e-01 -8.91566575e-01
7.66603276e-02 6.84258342e-02 -3.88778925e-01 4.88714427e-02
6.74510062e-01 -7.95737565e-01 -5.75441837e-01 6.46070659e-01
-5.77406347e-01 5.25024414e-01 2.13198677e-01 9.42359626e-01
4.00330462e-02 -6.59064293e-01 2.87566632e-01 -8.81557912e-02
-1.04476941e+00 -8.21354464e-02 -1.01103652e+00 2.81890064e-01
9.05599177e-01 -3.73444371e-02 8.70669484e-02 -8.40496898e-01
-2.76850373e-01 4.03058499e-01 5.93140364e-01 9.85606968e-01
8.78854215e-01 -1.28691018e+00 -5.02017260e-01 -7.57368281e-02
4.15200859e-01 -2.80765027e-01 -2.32402101e-01 4.45993692e-01
-8.66501868e-01 4.32328135e-01 -3.58133942e-01 -4.24304068e-01
-6.10754728e-01 7.44176626e-01 1.54902609e-02 1.02154978e-01
-4.85077709e-01 1.12941360e+00 3.47477794e-01 -1.18725705e+00
4.73789543e-01 -5.53092420e-01 -3.54392260e-01 -1.07707568e-02
2.07530111e-01 6.62918240e-02 -1.85204476e-01 -3.96560371e-01
-3.98100078e-01 2.53695995e-01 1.61074072e-01 -6.24662280e-01
1.10443580e+00 -3.92056763e-01 -2.01988202e-02 1.11603332e+00
1.31397450e+00 -2.83663925e-02 -9.91112113e-01 1.19371846e-01
4.88226682e-01 -1.06199525e-01 -2.74392128e-01 -3.63737524e-01
-2.84755588e-01 8.77511740e-01 5.89235306e-01 1.62679538e-01
1.76075608e-01 1.99776217e-01 6.57781661e-01 1.09795856e+00
1.12741947e+00 -1.07796228e+00 3.27786773e-01 1.15319407e+00
8.65633428e-01 -1.28672206e+00 -2.81131446e-01 1.07629243e-02
-7.61572242e-01 1.07273901e+00 4.32424217e-01 -4.77509737e-01
8.89388800e-01 1.04169138e-01 3.03601772e-01 -8.96764994e-02
-7.63338268e-01 -3.44857872e-01 -3.22527319e-01 1.29423559e+00
3.03078681e-01 1.88183516e-01 -3.08529526e-01 6.49805605e-01
-6.90865517e-01 -5.47188401e-01 8.19412112e-01 9.86777425e-01
-6.06144130e-01 -1.01878273e+00 -9.46303979e-02 4.10595149e-01
-8.69610459e-02 -3.21066350e-01 -4.46447849e-01 8.53401124e-01
-2.20014900e-01 1.23024738e+00 1.56148314e-01 -7.07875073e-01
4.18745875e-01 7.12545123e-03 4.46237504e-01 -9.02391136e-01
-1.86634228e-01 -8.68359327e-01 4.41826463e-01 -5.93656957e-01
-9.88780856e-02 -5.21008551e-01 -1.35258090e+00 8.40717256e-02
-3.08747496e-02 2.80102104e-01 9.92319643e-01 7.41540909e-01
2.71471292e-01 3.19652855e-01 2.70414591e-01 -1.43818998e+00
-1.15007877e+00 -1.20616531e+00 -5.47584176e-01 2.15649173e-01
2.05538094e-01 -1.12630498e+00 -3.84192616e-01 -5.97686887e-01] | [4.475819110870361, 0.8890737891197205] |
14849a66-2f8c-4f3e-b2c3-f2eaffc1c13e | partial-attack-supervision-and-regional | 2111.04336 | null | https://arxiv.org/abs/2111.04336v1 | https://arxiv.org/pdf/2111.04336v1.pdf | Partial Attack Supervision and Regional Weighted Inference for Masked Face Presentation Attack Detection | Wearing a mask has proven to be one of the most effective ways to prevent the transmission of SARS-CoV-2 coronavirus. However, wearing a mask poses challenges for different face recognition tasks and raises concerns about the performance of masked face presentation detection (PAD). The main issues facing the mask face PAD are the wrongly classified bona fide masked faces and the wrongly classified partial attacks (covered by real masks). This work addresses these issues by proposing a method that considers partial attack labels to supervise the PAD model training, as well as regional weighted inference to further improve the PAD performance by varying the focus on different facial areas. Our proposed method is not directly linked to specific network architecture and thus can be directly incorporated into any common or custom-designed network. In our work, two neural networks (DeepPixBis and MixFaceNet) are selected as backbones. The experiments are demonstrated on the collaborative real mask attack (CRMA) database. Our proposed method outperforms established PAD methods in the CRMA database by reducing the mentioned shortcomings when facing masked faces. Moreover, we present a detailed step-wise ablation study pointing out the individual and joint benefits of the proposed concepts on the overall PAD performance. | ['Naser Damer', 'Arjan Kuijper', 'Fadi Boutros', 'Meiling Fang'] | 2021-11-08 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 3.41051370e-01 1.19058698e-01 3.01582903e-01 -4.74962741e-01
-4.20241892e-01 -4.85860676e-01 6.50140882e-01 -4.04225141e-01
-3.12588900e-01 4.87909019e-01 -2.09349886e-01 -3.94577652e-01
-1.46515325e-01 -5.04255414e-01 -5.35793662e-01 -1.03400528e+00
-3.29700440e-01 3.82479578e-01 1.59091458e-01 -2.44174242e-01
-2.16630772e-01 9.92335975e-01 -1.74725997e+00 6.91513598e-01
6.18699491e-01 9.25755322e-01 2.81838793e-02 2.66467094e-01
1.65838271e-01 2.57332355e-01 -9.37027156e-01 -5.58484316e-01
4.80721623e-01 -1.82607949e-01 -5.57520501e-02 -7.35390484e-02
8.01591754e-01 -4.93472964e-01 2.66558826e-01 8.16795051e-01
7.02702582e-01 -4.23179388e-01 6.47837937e-01 -1.21134114e+00
-7.60941282e-02 3.24096441e-01 -7.00510859e-01 3.40225160e-01
-1.47182301e-01 1.96225137e-01 3.23421061e-01 -9.65182662e-01
3.16150308e-01 1.38835454e+00 8.24540496e-01 7.24304616e-01
-9.47525501e-01 -1.11462712e+00 3.19332153e-01 1.37868091e-01
-1.59675372e+00 -6.29322231e-01 6.49203300e-01 -6.56173229e-01
5.12237310e-01 3.97565544e-01 2.44646132e-01 1.05236542e+00
1.94183644e-02 1.52151495e-01 1.33887935e+00 -1.56108707e-01
2.45088572e-03 5.39450884e-01 3.87407169e-02 7.44520485e-01
4.02454466e-01 2.59311587e-01 -1.13231555e-01 -5.07046223e-01
2.82057136e-01 -1.31010070e-01 -3.43500078e-01 -5.32866605e-02
-4.54686105e-01 8.90997171e-01 2.68599838e-01 5.24435461e-01
-4.48250592e-01 -2.18544394e-01 2.42126495e-01 2.86140554e-02
8.09324086e-01 -5.91427870e-02 -3.22171539e-01 7.85363615e-01
-1.24958909e+00 6.88303038e-02 4.64730918e-01 2.50025898e-01
5.31915188e-01 1.85339198e-01 -4.84657466e-01 8.41841519e-01
5.25817513e-01 5.15066743e-01 -1.02701269e-01 -3.37478638e-01
2.25955859e-01 3.55248481e-01 8.11090246e-02 -1.32591057e+00
-6.31837785e-01 -6.86772525e-01 -8.28335404e-01 5.68017185e-01
3.26611429e-01 -4.06176418e-01 -1.08302212e+00 1.87744713e+00
5.35817862e-01 4.75078255e-01 -3.07106733e-01 7.72187412e-01
7.22380877e-01 4.52980906e-01 1.12669528e-01 -2.72379458e-01
1.77878082e+00 -5.34085572e-01 -9.30929184e-01 -2.35844493e-01
3.44051540e-01 -8.95801187e-01 3.47970456e-01 3.93326908e-01
-7.03865469e-01 -6.23795569e-01 -1.34786320e+00 6.29514217e-01
-4.81375188e-01 4.42730427e-01 2.51509219e-01 1.50068688e+00
-1.29166961e+00 3.41077536e-01 -5.26186407e-01 -2.97321320e-01
8.25723410e-01 7.94903040e-01 -3.87145758e-01 -1.30012650e-02
-1.18858397e+00 1.02012372e+00 -5.71809337e-02 7.27890015e-01
-9.30344105e-01 -7.35562682e-01 -5.21023154e-01 -9.64027345e-02
2.62832016e-01 -3.91585201e-01 6.46041155e-01 -9.01120305e-01
-1.21864402e+00 1.07594621e+00 -2.71717161e-01 -5.70146263e-01
6.25242531e-01 -1.69141382e-01 -6.76410079e-01 2.19159916e-01
-2.92031944e-01 6.00332916e-01 1.31251121e+00 -1.41214955e+00
-3.42157513e-01 -5.24278164e-01 -1.49387747e-01 -3.38559777e-01
-2.74966985e-01 4.19552922e-01 -1.26022622e-01 -6.80191398e-01
-3.76559466e-01 -8.25539052e-01 2.30190717e-02 7.59281814e-02
-1.90623567e-01 -2.29614466e-01 1.12485754e+00 -8.43161106e-01
1.07015491e+00 -2.31912708e+00 -4.80943799e-01 4.61145312e-01
1.61179617e-01 9.02701974e-01 -2.68033534e-01 1.65422976e-01
-1.41403124e-01 2.89152693e-02 -2.76167393e-01 -5.25260389e-01
-3.06731194e-01 8.37168396e-02 -3.00358444e-01 8.42155457e-01
5.45573294e-01 4.61897373e-01 -2.83919394e-01 -3.48088771e-01
1.75564155e-01 1.07735634e+00 -5.66317558e-01 2.63664991e-01
-1.43057346e-01 5.45992970e-01 -1.47248015e-01 4.61620122e-01
1.56093812e+00 1.19589105e-01 4.07644749e-01 -4.71277297e-01
1.26282364e-01 -7.60696977e-02 -1.21205127e+00 8.72271419e-01
-2.73074061e-01 2.31559530e-01 6.15932584e-01 -8.55513930e-01
9.69702303e-01 5.25601745e-01 3.67958218e-01 -2.28545010e-01
3.96697551e-01 4.84642237e-02 2.15352029e-01 -4.85433936e-01
-4.75800894e-02 -2.11105123e-01 4.83323008e-01 3.35826457e-01
-1.37981474e-01 6.66995883e-01 -2.01660708e-01 -1.45416915e-01
6.08282030e-01 -3.84740494e-02 1.06272504e-01 -5.01466691e-01
8.61120164e-01 -2.37383500e-01 6.64532840e-01 6.21392846e-01
-4.66209710e-01 5.93008935e-01 4.40496236e-01 -3.13738555e-01
-3.52846593e-01 -9.15317178e-01 -3.46204013e-01 8.74808431e-01
-1.72271267e-01 -2.66884845e-02 -1.13394642e+00 -9.21487093e-01
-4.43250611e-02 3.61967266e-01 -8.27835202e-01 1.09791450e-01
-7.11862326e-01 -1.11806107e+00 7.81198561e-01 1.73697188e-01
4.61409271e-01 -8.37739170e-01 -5.48957109e-01 -1.22850165e-01
1.19159408e-01 -1.22611117e+00 -4.58337873e-01 -2.09089696e-01
-3.98375362e-01 -1.39018655e+00 -6.45295322e-01 -7.60990798e-01
7.60890603e-01 4.55021918e-01 5.35082340e-01 4.83358949e-01
-4.37367082e-01 1.34933717e-03 -2.38348097e-02 -6.09825671e-01
-4.67166632e-01 -2.55557388e-01 2.31501460e-01 8.20653796e-01
4.03351456e-01 -4.34908003e-01 -9.95286286e-01 7.19072640e-01
-7.95757115e-01 -3.89757574e-01 4.99247611e-01 6.76953971e-01
1.66282535e-01 1.31728694e-01 8.77092183e-01 -1.27863944e+00
5.03638566e-01 -4.78189617e-01 -8.35662246e-01 2.16053218e-01
-4.19812471e-01 -4.88333374e-01 4.06451583e-01 -3.74593765e-01
-1.30883384e+00 1.33553609e-01 -3.52437466e-01 -4.30360198e-01
-2.72499919e-01 -1.85230508e-01 -4.86438811e-01 -3.25403184e-01
5.57973146e-01 -1.07305638e-01 4.66732532e-01 -5.63389719e-01
1.63842514e-01 8.13442171e-01 1.85728312e-01 -1.04516335e-01
9.75221038e-01 9.71711099e-01 -1.06460415e-01 -7.55791903e-01
-5.55151820e-01 -2.01920673e-01 -4.44319069e-01 -3.50678235e-01
9.49683487e-01 -9.80632007e-01 -8.27662170e-01 5.11840105e-01
-1.20382714e+00 1.45072728e-01 4.70980942e-01 1.96649715e-01
5.56536764e-02 3.62654716e-01 -4.55527008e-01 -1.01523292e+00
-5.54758310e-01 -1.26401401e+00 9.22546744e-01 6.74673682e-03
-2.64644355e-01 -7.51373172e-01 -1.51879683e-01 6.43313944e-01
7.92466879e-01 3.43676150e-01 9.07233298e-01 -8.56076002e-01
-4.43003714e-01 -2.10502803e-01 -2.33963236e-01 7.24451482e-01
3.67126405e-01 -1.58778820e-02 -1.68312240e+00 -5.60410559e-01
4.50554430e-01 2.20854715e-01 8.15895915e-01 4.36607927e-01
8.76158297e-01 -3.49459082e-01 -5.47716558e-01 4.44070697e-01
1.25078785e+00 3.67192835e-01 6.76612914e-01 -2.31643245e-01
3.28703642e-01 1.09871697e+00 2.97399938e-01 2.22661525e-01
-1.64193854e-01 7.66488254e-01 5.81511021e-01 -4.23685580e-01
-3.37418914e-01 1.01133235e-01 4.00749087e-01 2.27428555e-01
1.13422267e-01 -2.48800352e-01 -4.77421105e-01 4.13484275e-01
-1.34437895e+00 -1.01640141e+00 -1.86388865e-01 2.05781937e+00
3.55356395e-01 -1.18469916e-01 1.15851583e-02 6.76159114e-02
1.00264847e+00 3.61786425e-01 -1.21038206e-01 -3.86698335e-01
-3.14309478e-01 5.79932868e-01 1.40334114e-01 6.34215951e-01
-1.29082906e+00 5.21949112e-01 6.10443544e+00 9.20517087e-01
-1.18227351e+00 5.91039956e-01 6.40703738e-01 -1.56981379e-01
1.19644225e-01 -3.57295662e-01 -1.04369020e+00 5.48871756e-01
7.42753506e-01 5.86567461e-01 1.88233033e-01 5.43491721e-01
3.68207008e-01 1.66809708e-01 -8.04283321e-01 6.73512876e-01
3.94152135e-01 -1.34757781e+00 7.33804852e-02 9.43140313e-02
4.40463811e-01 -1.24070019e-01 2.64468879e-01 -1.28417060e-01
-2.79433966e-01 -1.12179101e+00 2.31173947e-01 2.49804124e-01
7.74816096e-01 -6.35258555e-01 8.90770793e-01 2.38435641e-02
-9.55432177e-01 9.20171440e-02 -1.54127225e-01 2.74486661e-01
3.12387105e-02 5.51281631e-01 -1.07432008e+00 4.00363922e-01
6.11019611e-01 7.95331374e-02 -3.97077292e-01 7.87092507e-01
-9.08498913e-02 5.86646318e-01 -4.34893161e-01 2.33651906e-01
-2.40820758e-02 2.46468112e-02 4.75796670e-01 1.35151434e+00
8.67664665e-02 -4.83109839e-02 -2.41977289e-01 8.35923612e-01
1.27481520e-01 1.19947508e-01 -5.99598646e-01 3.53117824e-01
2.52455056e-01 1.51133966e+00 -6.47471488e-01 -1.14474937e-01
-5.07267594e-01 5.85532784e-01 -1.05402306e-01 2.49398962e-01
-8.28093648e-01 5.49596287e-02 1.05587471e+00 5.77718675e-01
6.36918843e-01 2.59909868e-01 -7.88194388e-02 -5.39766312e-01
1.41899109e-01 -1.02554619e+00 6.04035497e-01 -4.33915496e-01
-1.43164444e+00 1.12634289e+00 3.85065638e-02 -1.19166458e+00
2.08573639e-01 -8.19185972e-01 -7.19929099e-01 1.16472971e+00
-1.74549818e+00 -1.12229741e+00 -2.18124211e-01 6.14501476e-01
1.70771137e-01 -3.52873027e-01 7.84259081e-01 8.43008339e-01
-6.64580941e-01 9.17767882e-01 -2.73156196e-01 -5.68866171e-03
6.28960133e-01 -3.57100636e-01 1.59712851e-01 9.80976701e-01
-1.61480099e-01 7.71699250e-01 5.43879330e-01 -6.43059790e-01
-8.77196431e-01 -1.21237981e+00 6.77371442e-01 -3.77538890e-01
1.89460263e-01 -7.20432162e-01 -9.95442450e-01 3.81107390e-01
4.07891512e-01 -3.26688662e-02 7.19128728e-01 -3.01527888e-01
-4.85847980e-01 -4.51970249e-01 -1.66636908e+00 4.20822442e-01
8.62587690e-01 -3.82277876e-01 -2.72705555e-01 4.35901135e-01
4.37988043e-01 9.49127823e-02 -3.49209160e-01 7.84834743e-01
4.25996423e-01 -1.21155453e+00 8.25628161e-01 -4.17165995e-01
-2.90270835e-01 -5.96703291e-01 -2.65728645e-02 -9.94752407e-01
-7.39116967e-02 -6.70779645e-01 1.79803833e-01 1.24172199e+00
2.88715512e-01 -1.01349664e+00 7.97326505e-01 8.53727236e-02
1.51430264e-01 -7.55445182e-01 -1.16166747e+00 -6.27405822e-01
-2.11553305e-01 -2.18389526e-01 7.02766597e-01 8.52814913e-01
-6.20456815e-01 4.57511172e-02 -6.62794530e-01 8.17941427e-01
6.97728574e-01 -2.31737182e-01 5.58197200e-01 -1.22333968e+00
-1.60154760e-01 -2.77351886e-01 -3.03070098e-01 -4.08583403e-01
-8.25540069e-03 -7.34146476e-01 -1.09995708e-01 -1.02455759e+00
-1.00251116e-01 -5.13446569e-01 -3.67662519e-01 4.06550527e-01
3.46416570e-02 5.97049892e-01 2.50723004e-01 -4.90218289e-02
2.58302420e-01 1.31735101e-01 9.52915251e-01 -1.09442726e-01
1.12773128e-01 3.01557660e-01 -7.31588185e-01 7.04944372e-01
9.11452949e-01 -6.44633830e-01 -4.29777563e-01 -2.61667073e-01
-2.15912089e-01 -3.35589290e-01 4.52114135e-01 -7.98634708e-01
9.45136547e-02 2.18551680e-01 3.73800755e-01 -7.08214998e-01
5.73634505e-01 -1.07678092e+00 3.52113813e-01 6.93530321e-01
2.41303980e-01 3.69445863e-03 5.17175794e-01 3.87224287e-01
6.64505884e-02 1.35062141e-02 1.12673128e+00 2.21050560e-01
-8.43118206e-02 7.50433207e-02 -4.88311946e-01 -4.25720841e-01
1.22477865e+00 -3.60716015e-01 -4.99622822e-01 -1.63661614e-01
-6.74515665e-01 8.39042068e-02 -6.72877431e-02 5.36215425e-01
4.55547810e-01 -8.25249791e-01 -9.50955391e-01 8.56055677e-01
-9.34471637e-02 -5.01317203e-01 6.67799890e-01 9.64091003e-01
-3.99230748e-01 4.32649463e-01 -1.55426681e-01 -5.81174791e-01
-1.77784014e+00 6.89258814e-01 7.60510921e-01 -1.81963481e-02
-3.24233830e-01 7.66443908e-01 5.59220374e-01 -2.46822998e-01
4.16010320e-01 1.23393685e-01 -4.86959755e-01 2.96916485e-01
8.16886604e-01 2.34683409e-01 4.70197707e-01 -7.14684248e-01
-6.71558797e-01 6.43288791e-01 -3.67082447e-01 3.51064801e-01
1.00880659e+00 1.51400015e-01 -3.45965296e-01 -2.99683928e-01
1.01646173e+00 3.01485628e-01 -1.04272163e+00 -1.20561518e-01
-3.04253489e-01 -3.85275453e-01 -1.79493472e-01 -9.06553507e-01
-1.45526159e+00 9.21765566e-01 1.33802986e+00 -1.06898993e-01
1.25325799e+00 -1.55502006e-01 4.23433453e-01 -6.54909089e-02
2.62298256e-01 -6.43345833e-01 -3.41966897e-01 -1.89675614e-01
8.68343711e-01 -9.99821424e-01 -9.08442438e-02 -8.32504213e-01
-3.91331315e-01 7.61923313e-01 6.17559493e-01 -4.17160019e-02
1.01649797e+00 4.56007093e-01 5.31885147e-01 -3.76957566e-01
-5.14621437e-01 2.88441521e-03 3.22179258e-01 7.82152653e-01
1.85995430e-01 -4.81896512e-02 -5.18531501e-01 5.62509537e-01
2.32589871e-01 -2.11351797e-01 1.49379581e-01 6.30335927e-01
-2.28259906e-01 -1.06765378e+00 -6.54688776e-01 4.25723016e-01
-7.49882162e-01 -1.94527376e-02 -3.86120677e-01 7.20080197e-01
6.98832214e-01 1.13781905e+00 4.34116200e-02 -3.28674018e-01
2.38588169e-01 2.41744444e-01 3.54132563e-01 -4.18695271e-01
-1.05166578e+00 3.14433463e-02 5.47137223e-02 -3.97563577e-01
-6.50024176e-01 -4.32881832e-01 -7.75409997e-01 -1.75897673e-01
-3.07886273e-01 3.58864665e-02 6.49154305e-01 8.30028117e-01
6.56731546e-01 3.73173535e-01 7.92948008e-01 -4.77658004e-01
-4.09999818e-01 -9.61436570e-01 -3.54519457e-01 2.11151853e-01
4.74059492e-01 -7.93063283e-01 -5.20152926e-01 -1.79305390e-01] | [13.191229820251465, 0.8266248106956482] |
c4e0e589-84e7-404c-9a2f-b36a0ac4daba | cico-domain-aware-sign-language-retrieval-via | 2303.12793 | null | https://arxiv.org/abs/2303.12793v1 | https://arxiv.org/pdf/2303.12793v1.pdf | CiCo: Domain-Aware Sign Language Retrieval via Cross-Lingual Contrastive Learning | This work focuses on sign language retrieval-a recently proposed task for sign language understanding. Sign language retrieval consists of two sub-tasks: text-to-sign-video (T2V) retrieval and sign-video-to-text (V2T) retrieval. Different from traditional video-text retrieval, sign language videos, not only contain visual signals but also carry abundant semantic meanings by themselves due to the fact that sign languages are also natural languages. Considering this character, we formulate sign language retrieval as a cross-lingual retrieval problem as well as a video-text retrieval task. Concretely, we take into account the linguistic properties of both sign languages and natural languages, and simultaneously identify the fine-grained cross-lingual (i.e., sign-to-word) mappings while contrasting the texts and the sign videos in a joint embedding space. This process is termed as cross-lingual contrastive learning. Another challenge is raised by the data scarcity issue-sign language datasets are orders of magnitude smaller in scale than that of speech recognition. We alleviate this issue by adopting a domain-agnostic sign encoder pre-trained on large-scale sign videos into the target domain via pseudo-labeling. Our framework, termed as domain-aware sign language retrieval via Cross-lingual Contrastive learning or CiCo for short, outperforms the pioneering method by large margins on various datasets, e.g., +22.4 T2V and +28.0 V2T R@1 improvements on How2Sign dataset, and +13.7 T2V and +17.1 V2T R@1 improvements on PHOENIX-2014T dataset. Code and models are available at: https://github.com/FangyunWei/SLRT. | ['Wenqiang Zhang', 'Dong Chen', 'Jianmin Bao', 'Fangyun Wei', 'Yiting Cheng'] | 2023-03-22 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Bao_CiCo_Domain-Aware_Sign_Language_Retrieval_via_Cross-Lingual_Contrastive_Learning_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Bao_CiCo_Domain-Aware_Sign_Language_Retrieval_via_Cross-Lingual_Contrastive_Learning_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-text-retrieval'] | ['computer-vision'] | [ 4.26509753e-02 -6.25218272e-01 -5.53234458e-01 -3.22040081e-01
-1.27024877e+00 -7.56528914e-01 9.00211990e-01 -7.89789617e-01
-6.04651034e-01 4.06692803e-01 7.77716219e-01 3.97638511e-03
-1.06596455e-01 -2.28871256e-01 -6.49203420e-01 -7.60768592e-01
1.40763342e-01 1.64343253e-01 3.27057280e-02 -7.59004802e-02
9.55540910e-02 2.32100695e-01 -1.64148319e+00 3.22352558e-01
5.77385783e-01 9.35168087e-01 -1.03918221e-02 5.79632044e-01
-2.94948190e-01 8.59933078e-01 -3.54429096e-01 -1.96903303e-01
2.18700960e-01 -6.11631870e-01 -4.39209610e-01 4.18373570e-02
9.42479074e-01 -5.29705524e-01 -7.55236804e-01 9.62447822e-01
7.39025772e-01 -1.32680805e-02 8.70416582e-01 -1.45980716e+00
-9.12133515e-01 1.01663634e-01 -5.37860096e-01 -1.25703394e-01
2.92670012e-01 3.48266780e-01 1.24241829e+00 -1.06058848e+00
1.05260420e+00 1.29721808e+00 1.53030649e-01 8.55780780e-01
-6.92936420e-01 -9.50887680e-01 1.99883267e-01 3.16785127e-01
-1.27391326e+00 -4.08528775e-01 7.31870890e-01 -5.69558799e-01
6.90698743e-01 1.50652364e-01 6.76912069e-01 1.24713194e+00
-3.04163277e-01 1.45129085e+00 1.17739654e+00 -3.45634013e-01
-1.80574313e-01 -2.83035159e-01 7.59894177e-02 7.11793721e-01
6.77945241e-02 2.67161518e-01 -7.46216834e-01 1.96225181e-01
6.57846332e-01 2.76981965e-02 -5.06850064e-01 -2.90299237e-01
-1.19875979e+00 6.73349380e-01 3.35360199e-01 4.46295172e-01
-2.69009948e-01 5.44525027e-01 6.68102443e-01 5.50031066e-01
8.41065869e-02 -2.01624539e-02 -4.07186627e-01 -2.59523243e-01
-7.05347419e-01 8.42835307e-02 4.05837059e-01 1.11000979e+00
3.51052314e-01 1.25717536e-01 -2.12875351e-01 9.26771283e-01
6.70701385e-01 1.12926340e+00 7.69275606e-01 -6.97220564e-01
5.38452148e-01 3.50915313e-01 -1.14511982e-01 -4.68448788e-01
3.38079035e-02 -2.86096409e-02 -5.34899294e-01 1.84414908e-01
4.61091995e-01 5.35032488e-02 -1.32124126e+00 1.81621671e+00
-2.79726535e-02 1.14291638e-01 7.04171807e-02 1.31157470e+00
1.12974167e+00 5.07842422e-01 2.82181889e-01 1.03346705e-01
1.56047642e+00 -1.10462427e+00 -7.99030602e-01 -8.35234895e-02
6.05879009e-01 -8.23164523e-01 1.22331095e+00 2.90952772e-02
-7.91891932e-01 -2.36826539e-01 -6.96162879e-01 -3.31314355e-01
-4.01456147e-01 4.44673687e-01 3.01058054e-01 3.72056007e-01
-8.83292198e-01 -2.24249899e-01 -6.01778626e-01 -5.10762632e-01
3.77832264e-01 -1.07027637e-02 -5.60895443e-01 -4.96053129e-01
-1.14669347e+00 8.84444237e-01 7.15480223e-02 6.96226433e-02
-7.07765996e-01 -5.05921304e-01 -9.22803104e-01 -3.84483606e-01
3.58008236e-01 -4.97201055e-01 1.06688643e+00 -1.01082063e+00
-1.31671023e+00 1.27337492e+00 -4.20444608e-01 1.23567823e-02
8.20905626e-01 -3.10593694e-01 -6.27614260e-01 3.37344408e-01
8.85449201e-02 6.54949605e-01 1.12751830e+00 -1.13243818e+00
-6.34139836e-01 -1.62649900e-01 -1.80075675e-01 2.15072632e-01
-4.53142375e-02 3.65044743e-01 -9.05092478e-01 -9.59194303e-01
1.80630177e-01 -9.76942778e-01 3.98378521e-01 5.72456419e-01
8.56474712e-02 -3.92857254e-01 9.61559117e-01 -8.53530586e-01
9.88357008e-01 -2.17910624e+00 1.61600992e-01 1.44924149e-01
2.10732043e-01 4.38549191e-01 -7.23475993e-01 3.62678230e-01
7.17877001e-02 -5.90234250e-02 -1.65814161e-01 -1.64751559e-01
3.62338215e-01 3.99056137e-01 -2.81521827e-01 5.76245964e-01
2.22518742e-01 1.35859060e+00 -9.83717918e-01 -6.13494515e-01
3.81321430e-01 6.76335275e-01 -3.12756419e-01 -3.42214033e-02
-6.37765899e-02 3.49715739e-01 -5.97377896e-01 1.14359558e+00
4.59961593e-01 -1.37606159e-01 7.15421289e-02 -2.75145262e-01
-8.03254638e-03 1.44106388e-01 -7.65164673e-01 1.76658010e+00
-5.39936543e-01 1.03160906e+00 3.55941579e-02 -8.66866708e-01
6.75716341e-01 3.97958219e-01 6.46845222e-01 -1.02339518e+00
2.16307029e-01 7.58388698e-01 -1.55584216e-01 -7.88253009e-01
1.79165289e-01 -3.48673433e-01 -1.44150525e-01 4.71792996e-01
1.46752760e-01 -9.59228054e-02 2.76431352e-01 6.87237009e-02
9.11385894e-01 2.23006621e-01 1.23026334e-01 1.97304308e-01
5.24590790e-01 -1.62620261e-01 2.66995758e-01 4.74724710e-01
-4.65067178e-01 5.68145692e-01 2.45721012e-01 4.59993705e-02
-7.41637707e-01 -1.18474782e+00 -8.12833607e-02 9.57549453e-01
2.21166924e-01 -3.11807871e-01 -1.85879856e-01 -8.10334504e-01
2.71686137e-01 3.13309878e-01 -3.82949710e-01 -4.82907072e-02
-7.71467745e-01 -1.97107688e-01 8.29030931e-01 5.91587245e-01
4.82763439e-01 -1.31270981e+00 -5.02799153e-01 -2.81396836e-01
-2.21932933e-01 -1.27845001e+00 -1.06980753e+00 -2.85018891e-01
-4.79097277e-01 -1.20982170e+00 -1.38208401e+00 -1.16776502e+00
5.94931960e-01 2.58727461e-01 7.87582219e-01 5.26598692e-02
-3.11874330e-01 8.74536872e-01 -7.57986188e-01 -1.49449095e-01
-2.17653394e-01 -4.26121563e-01 -1.97548997e-02 1.70896694e-01
6.92372382e-01 -1.71333000e-01 -5.67254722e-01 2.43583322e-01
-1.15790725e+00 -1.72830582e-01 7.43922532e-01 1.22009754e+00
5.30407310e-01 -5.92065275e-01 2.54853815e-01 -1.15813591e-01
5.20480931e-01 -1.20980956e-01 -5.42879760e-01 5.40275991e-01
-2.97485292e-01 1.97533548e-01 1.97432175e-01 -7.63192058e-01
-6.70270622e-01 -1.47024959e-01 5.62707558e-02 -8.12923431e-01
3.05726733e-02 5.52960396e-01 -1.78232417e-01 -1.50488332e-01
9.84645858e-02 6.13188684e-01 1.04056254e-01 -3.72154266e-01
7.12877750e-01 8.12050700e-01 4.65672851e-01 -3.74414206e-01
9.57343876e-01 5.48698545e-01 -2.29850218e-01 -8.91399920e-01
-3.27248663e-01 -7.62511969e-01 -4.69885677e-01 -4.95057851e-01
8.34047079e-01 -9.68180180e-01 -5.52782357e-01 7.05762088e-01
-9.84322071e-01 -2.69120216e-01 -3.19159478e-01 8.38135779e-01
-5.85489333e-01 5.73806465e-01 -4.41788077e-01 -6.59440398e-01
-3.32486153e-01 -1.00648046e+00 1.37544537e+00 -1.12023547e-01
-8.19960423e-03 -6.78623974e-01 -6.43522665e-02 4.57629532e-01
3.34381104e-01 -2.25836076e-02 7.92400599e-01 -3.64017636e-01
-7.44041622e-01 -3.89466166e-01 -6.55441105e-01 6.22180104e-01
2.73297518e-01 -2.57695854e-01 -7.36795425e-01 -3.40855509e-01
-4.56481516e-01 -5.61082244e-01 1.13664627e+00 3.89194608e-01
6.57751620e-01 -1.07752629e-01 -4.46299464e-02 6.46769643e-01
1.33591032e+00 2.55473137e-01 6.00568354e-01 1.32441014e-01
7.02225208e-01 4.89060074e-01 6.30908191e-01 1.46353513e-01
3.89093429e-01 8.75516713e-01 -1.05425000e-01 1.91762540e-02
-8.50184560e-01 -6.16049826e-01 6.35941088e-01 1.08329964e+00
-1.42801302e-02 -2.23519012e-01 -8.57174098e-01 8.03916276e-01
-1.80405569e+00 -8.94722819e-01 1.98133308e-02 2.02547145e+00
6.66940510e-01 -4.53984112e-01 -3.10277217e-03 -1.06520489e-01
5.06839395e-01 3.85316730e-01 -6.88318729e-01 1.26388088e-01
-5.00397027e-01 2.26033285e-01 4.50289994e-01 4.83077228e-01
-1.09561229e+00 1.26272571e+00 5.05190182e+00 9.50418890e-01
-1.57248533e+00 1.65855050e-01 -1.88414842e-01 -3.53062212e-01
-2.60931730e-01 -1.08280070e-01 -4.02444035e-01 5.55808067e-01
3.30686837e-01 -1.90006703e-01 2.93690175e-01 4.79194403e-01
9.26602855e-02 1.93788588e-01 -9.54455376e-01 1.47092628e+00
5.29446006e-01 -9.66187775e-01 2.91197002e-01 -7.61143938e-02
6.51841760e-01 4.64553118e-01 3.45074162e-02 3.52464795e-01
5.37726246e-02 -7.90857315e-01 8.56277943e-01 4.62457418e-01
1.32423162e+00 -9.34266523e-02 4.48195040e-01 -7.40733445e-02
-1.53587377e+00 9.24281701e-02 2.33178034e-01 4.30947721e-01
5.60308754e-01 2.05414519e-02 -4.96737249e-02 3.71984869e-01
6.77304566e-01 1.13286626e+00 -1.68290079e-01 1.03497159e+00
-5.61236143e-01 4.27818149e-01 -3.93930256e-01 -2.05591530e-01
3.37520003e-01 -1.79572687e-01 7.37799108e-01 1.25083876e+00
3.99958491e-01 -2.24501546e-02 1.57204289e-02 5.47540426e-01
-2.44866326e-01 2.06806004e-01 -6.43153071e-01 -3.51938874e-01
1.73393071e-01 5.52016079e-01 -1.27414897e-01 -5.23154676e-01
-5.55582345e-01 1.22129285e+00 -2.48146906e-01 8.08762729e-01
-6.61447823e-01 -3.92812580e-01 8.52516413e-01 -1.74345896e-01
5.21178722e-01 -1.98231518e-01 2.18774974e-01 -1.59857762e+00
5.19408941e-01 -9.71319914e-01 4.06614900e-01 -9.42195833e-01
-1.51677942e+00 4.38142896e-01 -1.07813664e-01 -1.84403121e+00
-3.02329689e-01 -9.60968256e-01 -8.12305585e-02 8.13740313e-01
-2.12568212e+00 -1.60119402e+00 -3.09081197e-01 9.04957771e-01
5.22808135e-01 -3.20323199e-01 5.69677830e-01 6.66670322e-01
4.60023433e-02 8.59532952e-01 2.79130131e-01 5.10735750e-01
9.61762309e-01 -8.10214400e-01 3.57408971e-02 7.20364451e-01
3.61400783e-01 3.60363096e-01 9.00532976e-02 -5.28993726e-01
-1.59798717e+00 -9.53763485e-01 1.26573825e+00 -4.36523616e-01
1.00490451e+00 -1.74176097e-01 -6.10680223e-01 4.84282613e-01
-5.97396567e-02 2.55245209e-01 2.78419971e-01 -4.84602302e-01
-9.08565342e-01 1.04624078e-01 -8.05431187e-01 8.12775016e-01
1.49506807e+00 -1.25437248e+00 -6.59524083e-01 3.93899053e-01
4.04755175e-01 -2.91627884e-01 -6.45410120e-01 3.89024228e-01
1.17373300e+00 -3.67430240e-01 9.20893490e-01 -7.36704409e-01
3.89891833e-01 -3.21706474e-01 -5.68614781e-01 -8.28792393e-01
3.06208760e-01 -4.81514871e-01 2.87034716e-02 8.87768567e-01
7.08942860e-02 -8.12528312e-01 4.59722847e-01 3.35296988e-01
4.37504165e-02 -4.69990373e-01 -1.23713136e+00 -1.16924226e+00
1.68424442e-01 -6.22948110e-01 1.93233505e-01 9.71859455e-01
-2.10626781e-01 7.41938949e-02 -6.40455842e-01 -1.93885207e-01
6.71733439e-01 2.92730391e-01 7.64348567e-01 -7.59678602e-01
-4.47081961e-02 -8.36714983e-01 -5.86410344e-01 -1.54569733e+00
4.05816317e-01 -1.17217195e+00 1.65237546e-01 -1.49431658e+00
-6.00562571e-03 -5.96448220e-02 -4.77459460e-01 6.33965552e-01
1.24911383e-01 3.36968839e-01 6.07613683e-01 4.49280620e-01
-4.92911309e-01 6.90222859e-01 1.51494288e+00 -4.20109272e-01
3.73239256e-02 -3.55659723e-01 -1.87685177e-01 4.61404771e-01
4.66847956e-01 -3.01628351e-01 -1.56426311e-01 -6.46331608e-01
-3.86239022e-01 -1.29672671e-02 5.59810638e-01 -4.67817366e-01
1.87598914e-01 3.52672907e-03 -2.82433063e-01 -5.52755475e-01
5.59246123e-01 -9.18502033e-01 -2.98275381e-01 5.14835775e-01
-3.56176317e-01 -1.69990584e-01 5.61835542e-02 5.04873157e-01
-7.43627727e-01 1.74443088e-02 4.29232091e-01 1.44159675e-01
-1.22495413e+00 3.97420049e-01 -3.48240852e-01 3.13171566e-01
6.68213487e-01 -3.34897071e-01 -5.94455421e-01 -7.40562320e-01
-4.91540581e-01 4.18293357e-01 1.79228261e-01 9.38154697e-01
8.87698114e-01 -1.61527026e+00 -8.31540227e-01 4.12873626e-01
7.29248047e-01 -5.41475892e-01 4.01099324e-01 9.66825187e-01
-3.18971157e-01 6.23405635e-01 -1.31791040e-01 -5.72177112e-01
-1.65870583e+00 -1.42041652e-03 2.70422339e-01 3.59106809e-02
-7.66040444e-01 8.39526355e-01 2.17074528e-01 -4.17392313e-01
4.81545508e-01 -5.06510437e-01 1.53226763e-01 1.78462088e-01
3.74613553e-01 3.57928127e-02 -4.38584238e-01 -1.07372975e+00
-5.45250416e-01 1.29888880e+00 2.04868644e-01 -2.63294637e-01
1.00339258e+00 1.30570903e-01 -4.12610034e-03 2.85584420e-01
1.70746052e+00 -1.54691711e-01 -9.48594928e-01 -6.79461122e-01
1.02755375e-01 -6.27415061e-01 -2.48589646e-03 -1.03136563e+00
-1.21293843e+00 9.47095156e-01 8.30183923e-01 -6.35464787e-01
1.30988455e+00 3.71658683e-01 9.45652068e-01 3.94630164e-01
2.93951482e-01 -1.07222641e+00 1.30831465e-01 5.61680973e-01
1.30120242e+00 -1.51961267e+00 -1.48909286e-01 -9.73306224e-02
-7.91123152e-01 8.04544270e-01 1.67606160e-01 -1.95921019e-01
7.93120503e-01 -1.35743067e-01 6.07926548e-01 -2.63524532e-01
-5.82861781e-01 -6.99464798e-01 8.07161748e-01 5.27106524e-01
2.99116105e-01 2.26144150e-01 -4.78117317e-01 2.74283648e-01
5.86586110e-02 3.39317292e-01 -5.30094840e-02 1.01930463e+00
9.28049907e-02 -1.30199039e+00 -1.68846548e-01 3.70638371e-01
4.97148708e-02 -1.25202373e-01 -4.45661634e-01 1.03422356e+00
-7.70788565e-02 6.99094713e-01 -1.28072351e-01 -3.72635007e-01
4.81798202e-01 1.66930839e-01 6.43149674e-01 -9.00588483e-02
-2.46455058e-01 3.41071784e-01 7.61812031e-02 -5.18586755e-01
-7.74424791e-01 -7.06090569e-01 -1.27511275e+00 1.04524359e-01
-2.27459550e-01 -3.19897383e-01 7.20613956e-01 1.01247501e+00
1.13402843e-01 2.13694423e-01 2.10083231e-01 -5.52797258e-01
-5.58085263e-01 -8.21662724e-01 -7.03498483e-01 7.83932745e-01
5.89356124e-01 -7.66853213e-01 -5.08474886e-01 3.69664878e-02] | [9.211539268493652, -6.519320964813232] |
b86f6561-6d22-47a7-baa3-d8448e6d68c4 | entropic-one-class-classifiers | 1407.7556 | null | http://arxiv.org/abs/1407.7556v3 | http://arxiv.org/pdf/1407.7556v3.pdf | Entropic one-class classifiers | The one-class classification problem is a well-known research endeavor in
pattern recognition. The problem is also known under different names, such as
outlier and novelty/anomaly detection. The core of the problem consists in
modeling and recognizing patterns belonging only to a so-called target class.
All other patterns are termed non-target, and therefore they should be
recognized as such. In this paper, we propose a novel one-class classification
system that is based on an interplay of different techniques. Primarily, we
follow a dissimilarity representation based approach; we embed the input data
into the dissimilarity space by means of an appropriate parametric
dissimilarity measure. This step allows us to process virtually any type of
data. The dissimilarity vectors are then represented through a weighted
Euclidean graphs, which we use to (i) determine the entropy of the data
distribution in the dissimilarity space, and at the same time (ii) derive
effective decision regions that are modeled as clusters of vertices. Since the
dissimilarity measure for the input data is parametric, we optimize its
parameters by means of a global optimization scheme, which considers both
mesoscopic and structural characteristics of the data represented through the
graphs. The proposed one-class classifier is designed to provide both hard
(Boolean) and soft decisions about the recognition of test patterns, allowing
an accurate description of the classification process. We evaluate the
performance of the system on different benchmarking datasets, containing either
feature-based or structured patterns. Experimental results demonstrate the
effectiveness of the proposed technique. | ['Witold Pedrycz', 'Alireza Sadeghian', 'Lorenzo Livi'] | 2014-07-28 | null | null | null | null | ['one-class-classifier'] | ['methodology'] | [ 2.34463945e-01 -8.48281458e-02 5.37444726e-02 -3.70896012e-01
-1.60720050e-01 -4.10377920e-01 8.48593235e-01 7.19597042e-01
-2.72319317e-01 3.78702819e-01 -3.96791071e-01 -1.73855841e-01
-4.84502137e-01 -9.70586419e-01 -4.20376241e-01 -1.14034128e+00
-1.79942131e-01 6.72505558e-01 3.01749915e-01 -7.05833063e-02
5.75031579e-01 9.40604985e-01 -2.00354242e+00 2.37595305e-01
1.02387977e+00 1.22739041e+00 -3.63974750e-01 5.11020482e-01
-1.92694739e-01 3.13817859e-01 -6.23563945e-01 9.11281854e-02
2.92420834e-01 -3.56589317e-01 -5.25963962e-01 2.26056337e-01
-2.91425996e-02 5.65626562e-01 2.37004384e-01 1.13696671e+00
2.23243818e-01 4.80238229e-01 1.17484367e+00 -1.23661149e+00
-2.07378298e-01 3.29174176e-02 -3.26776236e-01 2.40320206e-01
4.28621948e-01 6.89697266e-02 8.78066838e-01 -9.00214553e-01
6.47814453e-01 1.00135720e+00 1.50019780e-01 1.61369503e-01
-1.43512619e+00 -1.46212190e-01 -8.55456516e-02 4.08540428e-01
-1.66446877e+00 -3.01920801e-01 9.61790740e-01 -8.12466979e-01
5.38675904e-01 5.14694154e-01 5.87293744e-01 7.51835346e-01
4.17134970e-01 1.87324256e-01 1.23005116e+00 -4.70658332e-01
8.10154676e-01 3.53793949e-01 3.66723388e-01 3.76792818e-01
4.77326870e-01 -1.05884813e-01 -1.13016337e-01 -4.21612829e-01
4.07508723e-02 2.78510958e-01 -2.89882183e-01 -7.65001476e-01
-7.86373317e-01 7.06097186e-01 3.40513021e-01 7.82199621e-01
-4.74455297e-01 -5.56972623e-01 2.00420618e-01 4.85884368e-01
4.46631759e-01 3.86304557e-01 1.36350170e-01 1.57761991e-01
-8.90044749e-01 2.05046460e-01 1.18995690e+00 3.73832375e-01
8.87437761e-01 -1.37313753e-01 -1.22622333e-01 5.84152997e-01
2.61534363e-01 3.65229249e-01 6.70306146e-01 -5.52328862e-03
3.05073768e-01 1.12621820e+00 2.88393088e-02 -1.56933761e+00
-4.93656814e-01 -5.39697170e-01 -8.60190451e-01 6.15453541e-01
4.83657748e-01 2.06172466e-01 -6.82110608e-01 1.33448231e+00
6.08661890e-01 1.01752289e-01 1.91163689e-01 6.07847810e-01
3.14517498e-01 6.45524681e-01 -3.30234796e-01 -3.29634696e-01
9.46172953e-01 -4.03407425e-01 -4.55830276e-01 3.09820324e-01
5.13316929e-01 -4.42736596e-01 8.84232879e-01 5.10334730e-01
-6.60683751e-01 -4.46479082e-01 -1.09103894e+00 5.22922516e-01
-8.69822145e-01 5.32166706e-03 -1.74136981e-01 6.09869242e-01
-6.95681930e-01 8.06677520e-01 -7.97793090e-01 -4.62190151e-01
2.79221535e-02 4.34740067e-01 -3.83423358e-01 1.38718680e-01
-6.73433781e-01 7.73170590e-01 6.21348798e-01 1.60265356e-01
-4.72324938e-01 -1.11824036e-01 -6.19944453e-01 -3.56649794e-02
2.33773127e-01 -2.47390509e-01 3.34176600e-01 -1.05498195e+00
-1.25730705e+00 9.33622718e-01 -1.18201524e-01 -1.92487851e-01
5.93756557e-01 5.51464796e-01 -8.05203974e-01 -3.36525077e-03
-7.87954554e-02 -2.27132663e-01 1.08636272e+00 -1.34600258e+00
-4.83788520e-01 -5.74016750e-01 -4.68626499e-01 1.86153837e-02
-3.99759859e-01 -2.19483092e-01 -1.66445579e-02 -4.17533278e-01
3.74520123e-01 -7.70855904e-01 3.53237391e-02 -2.94201910e-01
-6.26582503e-01 -2.21501276e-01 1.00816524e+00 -3.81559163e-01
1.25486946e+00 -2.25256801e+00 3.84900570e-01 1.05998123e+00
3.29151660e-01 1.31491601e-01 2.93400288e-01 6.53966606e-01
-2.36285672e-01 4.68624942e-02 -6.79379523e-01 -2.27267832e-01
-1.21889398e-01 1.79470345e-01 -2.12879151e-01 8.03218365e-01
3.04575831e-01 2.99030602e-01 -7.00397670e-01 -2.65776485e-01
3.67000997e-01 2.25414902e-01 -2.65238374e-01 2.84292847e-01
-1.39658079e-01 5.90739012e-01 -5.66604972e-01 6.80340767e-01
7.45876670e-01 9.25795585e-02 2.12253183e-01 -8.86854902e-02
-3.66276771e-01 -2.42513970e-01 -1.62330937e+00 8.96381915e-01
-2.21966386e-01 2.59094149e-01 -2.29809508e-01 -1.42997801e+00
1.40240288e+00 -4.02970687e-02 5.11406124e-01 -3.35367560e-01
2.82678872e-01 5.85451484e-01 3.51189047e-01 -5.25716305e-01
2.53125101e-01 9.77257118e-02 7.82039091e-02 3.74603748e-01
-1.51730105e-01 2.21403167e-01 2.55293638e-01 -3.94272417e-01
1.06439579e+00 -4.35363978e-01 5.28899729e-01 -5.57917595e-01
1.01853192e+00 -1.73518956e-01 3.41444701e-01 5.80665290e-01
9.75784510e-02 4.16688621e-01 7.12835133e-01 -5.55728793e-01
-8.61739516e-01 -1.02197635e+00 -5.29450417e-01 2.88317084e-01
2.61136383e-01 2.50112955e-02 -5.21174192e-01 -6.23508990e-01
2.91407883e-01 5.16062737e-01 -8.00701976e-01 -3.65505338e-01
-3.44960809e-01 -9.19030070e-01 1.57861486e-01 -1.35118023e-01
1.90626949e-01 -1.06597841e+00 -6.89988017e-01 1.37240455e-01
3.13257605e-01 -6.41963243e-01 2.37878591e-01 3.47929537e-01
-9.71007586e-01 -1.27237189e+00 -2.50735253e-01 -4.46633101e-01
8.87515068e-01 -1.41057834e-01 7.34825671e-01 1.68802232e-01
-4.37041730e-01 2.66626954e-01 -4.17603195e-01 -8.06545243e-02
-5.32324493e-01 -3.04952506e-02 2.82339513e-01 1.05957282e+00
3.10795605e-01 -7.45900095e-01 -4.70137298e-01 3.80783290e-01
-1.17617178e+00 -6.63642049e-01 5.75552106e-01 8.06073964e-01
7.82027662e-01 3.40920687e-01 4.35226023e-01 -8.19904506e-01
6.96286738e-01 -8.18260610e-01 -6.28317058e-01 3.47704023e-01
-6.72743201e-01 1.73763230e-01 1.04336047e+00 -4.41124618e-01
-5.11843324e-01 -7.46640339e-02 1.30360126e-01 -4.82537657e-01
-3.66960943e-01 4.50465739e-01 -4.45711195e-01 -4.72436637e-01
6.44253075e-01 3.54476839e-01 3.96319740e-02 -5.31689048e-01
5.04076108e-02 8.93638194e-01 2.95209378e-01 -5.45613647e-01
8.72144103e-01 5.57255924e-01 3.12830538e-01 -1.21712554e+00
-7.16462312e-03 -5.97502530e-01 -7.32167542e-01 -3.16192210e-01
7.28959978e-01 -1.38817057e-01 -7.06733882e-01 4.06053334e-01
-7.53337502e-01 1.30790457e-01 -5.27965844e-01 3.74054790e-01
-3.53680074e-01 4.47987080e-01 -1.50327841e-02 -1.02072704e+00
-1.63134456e-01 -1.17818189e+00 8.27916026e-01 7.66895190e-02
-1.04083322e-01 -1.09710634e+00 3.21574271e-01 -2.61977881e-01
1.57964602e-01 8.80131900e-01 1.17955649e+00 -1.33073032e+00
-3.82097036e-01 -6.41332865e-01 2.13527292e-01 3.81902188e-01
2.89080292e-01 3.80976051e-01 -8.72769356e-01 -3.69696081e-01
3.86990160e-01 1.61393970e-01 6.18528426e-01 3.59792337e-02
1.07639551e+00 -2.13435844e-01 -4.26127732e-01 6.02644145e-01
1.52699780e+00 4.58526254e-01 4.72208798e-01 3.40805441e-01
5.21988988e-01 7.21519649e-01 6.29829884e-01 5.13115942e-01
-7.27788880e-02 8.66445720e-01 5.54397047e-01 1.46238789e-01
4.90776539e-01 8.87208655e-02 1.51142478e-01 8.01406205e-01
-1.15789231e-02 -2.77869463e-01 -1.12900126e+00 2.79477984e-01
-1.90774333e+00 -9.66439426e-01 -1.59474164e-01 2.65678287e+00
2.01897189e-01 2.45795846e-01 1.62957430e-01 5.61470509e-01
9.46888566e-01 3.21673527e-02 -4.94891405e-01 -6.74962401e-01
-1.27273798e-01 2.36751974e-01 1.01082928e-01 4.50612366e-01
-1.03224802e+00 2.15536654e-01 4.88205290e+00 7.83048332e-01
-1.41614783e+00 -3.87715727e-01 5.72599471e-01 1.93497941e-01
-1.25784233e-01 -6.94505349e-02 -5.13172150e-01 7.11134732e-01
6.71037793e-01 -2.95524508e-01 2.78009117e-01 8.43068123e-01
1.21153601e-01 -1.78045228e-01 -1.20911658e+00 9.07950103e-01
1.85385242e-01 -8.57767642e-01 2.00781003e-01 2.62799233e-01
6.66096985e-01 -3.46716225e-01 6.97617754e-02 -6.90001324e-02
-4.39710826e-01 -8.78875852e-01 5.76124310e-01 9.82067645e-01
4.73612696e-01 -8.10675144e-01 7.33333290e-01 4.82876331e-01
-1.19237459e+00 -3.31722945e-01 -3.83651108e-01 -1.04199853e-02
-3.56929183e-01 8.84643137e-01 -5.73745906e-01 7.91950524e-01
3.54528636e-01 7.76425421e-01 -7.08132148e-01 1.39434695e+00
2.80201524e-01 2.25497395e-01 -5.09549141e-01 -2.26971030e-01
4.30388339e-02 -7.76995838e-01 8.95267367e-01 9.47798550e-01
4.97244149e-01 -1.94252476e-01 2.88025409e-01 9.07645762e-01
6.68288469e-02 5.05207717e-01 -1.03387094e+00 1.68839678e-01
4.01141465e-01 1.16129649e+00 -1.09267688e+00 -2.05303326e-01
-2.70708185e-02 6.78827703e-01 2.83825010e-01 2.13384211e-01
-3.81286621e-01 -5.62409341e-01 6.72708631e-01 2.09289324e-02
2.22661659e-01 -3.76025820e-03 -2.35533088e-01 -1.29593623e+00
4.09250051e-01 -7.44725585e-01 4.08463717e-01 -4.50289622e-02
-1.28737545e+00 8.06788266e-01 -3.08732055e-02 -1.63026321e+00
-3.40662718e-01 -7.87321329e-01 -1.03172934e+00 7.89626658e-01
-1.32479692e+00 -5.83958685e-01 -4.54909444e-01 6.81681275e-01
-4.95135523e-02 -1.21674150e-01 7.46489286e-01 2.60120690e-01
-7.56386220e-01 4.09901500e-01 7.04979479e-01 -2.04864681e-01
4.39219773e-01 -1.39282811e+00 -6.55200258e-02 8.34216237e-01
7.63765201e-02 5.11851311e-01 7.83584476e-01 -5.89325845e-01
-1.20160806e+00 -1.06968522e+00 6.52966797e-01 -2.84698069e-01
6.45033360e-01 -3.96705776e-01 -1.05388272e+00 2.28121400e-01
-3.82213861e-01 3.18970442e-01 5.66906393e-01 -2.86240101e-01
-1.41662359e-01 -3.34917724e-01 -1.46786332e+00 3.51755470e-01
6.08188629e-01 -4.25565958e-01 -6.22924209e-01 3.04498702e-01
9.24928114e-02 2.81669348e-02 -9.60361063e-01 4.56771642e-01
3.33396077e-01 -1.11559260e+00 6.94894314e-01 -5.30265927e-01
3.85456197e-02 -5.72210252e-01 -8.38304237e-02 -1.30312383e+00
-1.16922468e-01 -2.97115028e-01 -4.34788652e-02 1.13952696e+00
2.26958483e-01 -1.14213908e+00 5.72184265e-01 3.03754956e-01
7.42520764e-02 -9.28918660e-01 -1.08247733e+00 -8.73868585e-01
-2.05118611e-01 -1.54314861e-01 5.64741373e-01 1.01928139e+00
2.92863715e-02 -1.48894697e-01 5.06057553e-02 2.73175269e-01
6.44675612e-01 4.98503715e-01 5.33930957e-01 -1.55462611e+00
-2.81099468e-01 -6.51597381e-01 -1.09747779e+00 -1.60464495e-01
6.68481067e-02 -1.14511168e+00 -1.03078336e-01 -9.64847684e-01
-3.35750550e-01 -5.08263946e-01 -3.77530724e-01 4.99193035e-02
1.13104895e-01 1.58977248e-02 -1.33580565e-01 4.40730959e-01
-2.58341253e-01 5.49132645e-01 6.04139566e-01 -1.10017480e-02
-4.18975979e-01 2.77648002e-01 -1.09641142e-01 5.54650903e-01
7.55134583e-01 -4.83963996e-01 -2.01896727e-01 3.09509724e-01
-7.74395913e-02 -8.79417509e-02 3.46332401e-01 -1.41640806e+00
2.19796002e-01 -2.33106464e-01 2.60360658e-01 -2.85707325e-01
1.68500632e-01 -1.13348484e+00 3.06947529e-01 5.86683452e-01
-1.64819285e-01 1.02655686e-01 -1.40237629e-01 7.26873219e-01
-4.10241038e-01 -3.82654399e-01 1.02142525e+00 3.06150794e-01
-3.29014599e-01 1.47334382e-01 -2.34567404e-01 -3.98803465e-02
1.46700442e+00 -3.82831544e-01 -1.34849504e-01 -2.04183664e-02
-9.97676432e-01 -3.77806462e-02 7.64010191e-01 1.19366415e-01
5.88803470e-01 -1.25415540e+00 -4.29005921e-01 4.63194400e-01
5.49704492e-01 -2.70513952e-01 7.94913024e-02 8.67179036e-01
-5.45078516e-01 8.91064778e-02 -3.30532014e-01 -8.59470546e-01
-1.05981612e+00 6.78740501e-01 5.27042091e-01 -2.24606335e-01
-4.30748850e-01 1.29493281e-01 -1.74823105e-01 -3.97912294e-01
-2.70286892e-02 -3.52459252e-01 -5.27499855e-01 2.53806174e-01
3.11219692e-01 5.75972974e-01 4.10427749e-01 -7.62560606e-01
-4.16894615e-01 8.25388610e-01 2.74447054e-01 1.21356592e-01
1.22190166e+00 2.38289908e-01 -4.28721488e-01 7.98808038e-01
1.38705385e+00 5.00347428e-02 -6.17978692e-01 -2.55614109e-02
4.21359748e-01 -5.39665461e-01 -4.36974078e-01 -3.27176511e-01
-7.53953695e-01 8.42149258e-01 8.27172875e-01 8.38925421e-01
1.08547127e+00 -2.66969204e-01 1.21817127e-01 5.35467327e-01
1.78575292e-01 -9.34812307e-01 -1.69174597e-01 2.82885104e-01
7.67497420e-01 -9.70315456e-01 -3.20050001e-01 -2.67754912e-01
-2.66768336e-01 1.43075061e+00 3.26704293e-01 -4.03596669e-01
7.63636649e-01 -4.20889296e-02 -2.49809414e-01 -3.33409905e-01
-5.63700676e-01 -1.86229944e-01 5.57214797e-01 3.16820085e-01
-5.08575961e-02 1.69223115e-01 -6.16657436e-01 2.72411704e-01
3.02252769e-02 -3.54788750e-01 3.64542335e-01 7.87998319e-01
-5.30497909e-01 -1.12846029e+00 -5.11866391e-01 7.00910509e-01
4.24666971e-04 3.85211796e-01 -5.28533757e-01 7.62294650e-01
2.81963825e-01 9.12717342e-01 1.05207600e-01 -5.74646711e-01
5.79180241e-01 3.25887710e-01 -6.55795038e-02 -4.63885784e-01
-5.25020719e-01 -5.04537225e-01 -2.58372128e-01 -4.80675936e-01
-4.80013601e-02 -7.67069459e-01 -1.02490807e+00 -9.98606086e-02
-1.83421284e-01 4.78829145e-01 7.73449898e-01 9.29915667e-01
2.66669691e-01 2.88920879e-01 1.19506526e+00 -7.49526262e-01
-6.92403018e-01 -6.98044002e-01 -7.85422683e-01 7.11278260e-01
3.55316013e-01 -8.55075300e-01 -8.46461654e-01 -4.69642013e-01] | [8.004076957702637, 3.9954519271850586] |
474e5537-5c4a-40e0-a83f-31f0719d5a2d | imle-net-an-interpretable-multi-level-multi | 2204.05116 | null | https://arxiv.org/abs/2204.05116v1 | https://arxiv.org/pdf/2204.05116v1.pdf | IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG Classification | Early detection of cardiovascular diseases is crucial for effective treatment and an electrocardiogram (ECG) is pivotal for diagnosis. The accuracy of Deep Learning based methods for ECG signal classification has progressed in recent years to reach cardiologist-level performance. In clinical settings, a cardiologist makes a diagnosis based on the standard 12-channel ECG recording. Automatic analysis of ECG recordings from a multiple-channel perspective has not been given enough attention, so it is essential to analyze an ECG recording from a multiple-channel perspective. We propose a model that leverages the multiple-channel information available in the standard 12-channel ECG recordings and learns patterns at the beat, rhythm, and channel level. The experimental results show that our model achieved a macro-averaged ROC-AUC score of 0.9216, mean accuracy of 88.85\%, and a maximum F1 score of 0.8057 on the PTB-XL dataset. The attention visualization results from the interpretable model are compared against the cardiologist's guidelines to validate the correctness and usability. | ['U. Deva Priyakumar', 'Raju. S. Bapi', 'Shanmukh Alle', 'Vivek Talwar', 'Likith Reddy'] | 2022-04-06 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 1.10987544e-01 -1.01628400e-01 7.72473030e-03 -4.04377520e-01
-8.33615839e-01 -5.41342676e-01 -3.62122923e-01 6.05695188e-01
-2.99223475e-02 6.19328856e-01 -9.78746340e-02 -8.09747458e-01
-3.85527790e-01 -3.01605701e-01 -1.57100514e-01 -5.45936704e-01
-6.25081718e-01 8.28873813e-02 -2.72506118e-01 2.82565743e-01
5.76072149e-02 5.45719683e-01 -8.56902659e-01 5.00230610e-01
9.24051046e-01 1.31401396e+00 -1.09016798e-01 1.06511378e+00
3.54610056e-01 3.85790020e-01 -1.02992344e+00 -1.53054953e-01
1.64362013e-01 -9.84572947e-01 -2.97453076e-01 -2.58721024e-01
2.22030714e-01 -8.86862576e-02 1.07718632e-03 7.40692973e-01
1.10438693e+00 -6.83999896e-01 4.45705652e-01 -7.99289763e-01
-1.17808938e-01 4.86000955e-01 -5.60284436e-01 5.41622460e-01
-2.00350769e-02 2.73546308e-01 7.64453292e-01 -5.14147758e-01
3.60624880e-01 4.84367192e-01 1.07832956e+00 1.79481376e-02
-1.24952281e+00 -4.71406877e-01 -2.92035818e-01 2.84467131e-01
-1.46488869e+00 7.15117306e-02 8.84106159e-01 -5.71898341e-01
6.76939428e-01 5.93705952e-01 1.21600771e+00 6.11548245e-01
7.89341569e-01 3.15302044e-01 1.14361525e+00 -4.13420707e-01
1.17878079e-01 4.07125875e-02 1.74481615e-01 4.67353284e-01
5.04082978e-01 -6.73443824e-02 -3.25847864e-01 -1.66909471e-01
9.16052222e-01 1.48481071e-01 -4.50277448e-01 -1.57453626e-01
-1.27704370e+00 4.99950826e-01 3.81130248e-01 3.84771407e-01
-5.32564104e-01 -9.36367363e-02 6.87507987e-01 5.11882842e-01
1.55466780e-01 8.30013514e-01 -5.16787648e-01 -4.97033864e-01
-1.02382231e+00 -4.22609784e-03 5.21422744e-01 4.56765115e-01
7.20570087e-02 3.21241587e-01 -3.98037493e-01 4.74675477e-01
2.03383401e-01 6.12036109e-01 3.22599888e-01 -8.71428430e-01
1.16301432e-01 7.75210321e-01 7.55434409e-02 -1.20126402e+00
-7.08795309e-01 -1.11595023e+00 -1.33610868e+00 2.85007626e-01
4.96315479e-01 -3.17124695e-01 -7.17829049e-01 1.19944894e+00
-3.50294150e-02 9.05572027e-02 -1.59892336e-01 1.13068831e+00
8.56305540e-01 1.64912477e-01 1.33387610e-01 -5.98700106e-01
1.26511848e+00 -2.57518530e-01 -8.65618527e-01 1.77005321e-01
8.31959546e-01 -1.91344678e-01 1.06161296e+00 8.21481884e-01
-8.79587233e-01 -7.38438606e-01 -1.40877235e+00 4.44439620e-01
1.67980567e-01 5.78842998e-01 1.75383985e-01 8.90698850e-01
-6.36109591e-01 8.27960312e-01 -9.54616487e-01 -1.11307733e-01
7.18455076e-01 1.76277414e-01 -1.77347556e-01 4.91464019e-01
-1.22989953e+00 8.02715361e-01 1.53536633e-01 3.45677465e-01
-7.02724576e-01 -7.13938832e-01 -5.29638410e-01 1.62997827e-01
-1.52887926e-01 -6.09857559e-01 7.27680862e-01 -6.75211251e-01
-1.05602682e+00 7.31657565e-01 1.41608864e-02 -6.63445175e-01
6.68026805e-01 -2.35322013e-01 -6.37020946e-01 4.73197103e-01
-2.64450490e-01 -6.51832223e-02 7.45405972e-01 -9.67253327e-01
-4.29148912e-01 -5.95081270e-01 -1.13253906e-01 -2.84041198e-05
-1.34997800e-01 -1.74445614e-01 -1.31743401e-01 -6.74760044e-01
2.37618104e-01 -7.71881461e-01 -2.71248639e-01 -6.72765309e-03
-5.40536523e-01 4.10180181e-01 5.78913510e-01 -9.37635481e-01
1.86447871e+00 -2.22149420e+00 -5.24943583e-02 5.21370530e-01
8.81659746e-01 5.47174692e-01 4.91930097e-01 2.03511551e-01
-3.49227160e-01 2.40926281e-01 -3.67571115e-01 2.08034694e-01
-4.24452841e-01 -2.03500688e-01 3.78468819e-03 5.43992579e-01
2.92636212e-02 1.13112485e+00 -6.41546071e-01 -3.96274090e-01
2.77103752e-01 5.62257290e-01 -1.32785454e-01 1.88718855e-01
4.73896891e-01 8.86899292e-01 -2.40710542e-01 5.37427127e-01
3.55493695e-01 -6.42470717e-01 7.93549299e-01 -4.56802189e-01
1.40346736e-01 -2.22714245e-02 -1.04079461e+00 1.81316566e+00
-4.28601429e-02 7.81690359e-01 -2.76654392e-01 -1.05954325e+00
1.13725269e+00 5.88055670e-01 5.75488210e-01 -6.71354473e-01
8.50416794e-02 1.51125789e-01 7.42310166e-01 -7.58528531e-01
-5.48046947e-01 -1.08707048e-01 9.80279669e-02 4.96878892e-01
-4.10555333e-01 2.10319787e-01 -1.21128030e-01 6.21422678e-02
1.21194255e+00 8.11059773e-02 7.51119852e-01 -3.43515188e-01
2.32160673e-01 -1.64396867e-01 5.98228693e-01 8.82125854e-01
-2.01225206e-01 8.12079370e-01 7.26641238e-01 -9.91653383e-01
-7.35863805e-01 -1.04142308e+00 -4.23077017e-01 1.77042454e-01
-1.60540864e-01 -6.75707102e-01 -6.50528669e-01 -7.32776523e-01
1.08056881e-01 2.34296411e-01 -7.43684769e-01 -3.01910907e-01
-5.43432415e-01 -7.10915685e-01 8.46637666e-01 9.12582457e-01
2.50834852e-01 -9.77727294e-01 -1.66293395e+00 4.21680450e-01
-1.29809633e-01 -6.89887226e-01 1.88439205e-01 2.28491351e-01
-1.19676709e+00 -1.15372384e+00 -8.32000852e-01 -2.98229575e-01
2.76029170e-01 -5.76138735e-01 1.16155767e+00 1.19244397e-01
-1.00730073e+00 9.31694210e-02 -2.09013179e-01 -8.46469223e-01
-2.43086845e-01 -6.28932938e-02 -8.31465200e-02 1.31739274e-01
1.41225189e-01 -6.91253722e-01 -1.15177119e+00 1.34798452e-01
-2.96042562e-01 -8.20611790e-02 7.40602732e-01 8.27472746e-01
7.51174927e-01 -2.58396119e-01 9.94215131e-01 -8.36818278e-01
6.93270266e-01 -1.54387891e-01 -1.97915971e-01 1.94023311e-01
-1.11480117e+00 -5.23815572e-01 5.48195481e-01 -1.24319032e-01
-4.56202805e-01 5.79061992e-02 5.93276881e-02 -3.83910537e-01
-3.55227262e-01 7.84637749e-01 -1.03209645e-01 3.12213153e-01
9.13876474e-01 7.64602572e-02 -1.33335479e-02 -4.37068641e-01
-2.15676352e-01 7.24783003e-01 5.72409034e-01 -2.14565709e-01
2.65894234e-01 2.13119254e-01 2.08628759e-01 -7.48887181e-01
-5.56488276e-01 -2.42865250e-01 -7.85447717e-01 -3.64533454e-01
8.58860791e-01 -6.57456577e-01 -8.53630304e-01 2.04997286e-01
-1.02992189e+00 -1.54683858e-01 -3.59282106e-01 5.42563021e-01
-3.02696377e-01 5.30716002e-01 -4.01989877e-01 -8.78661811e-01
-8.31207395e-01 -8.55106890e-01 7.12088585e-01 -8.68313238e-02
-7.46203721e-01 -9.35691357e-01 2.56329421e-02 -1.90427616e-01
3.34199667e-01 8.98283720e-01 1.09037662e+00 -6.80672765e-01
-1.21626087e-01 -3.88164699e-01 -1.31983116e-01 2.60130197e-01
2.62410998e-01 -1.84839353e-01 -1.07809544e+00 -1.73511416e-01
1.36319578e-01 1.38713539e-01 3.96062344e-01 8.30603182e-01
1.46994650e+00 1.64461136e-01 -2.79297620e-01 6.84505284e-01
1.17127705e+00 6.75293267e-01 7.36882031e-01 9.58297923e-02
6.99187636e-01 1.79678485e-01 3.22720617e-01 4.82080251e-01
5.20853102e-02 4.80214268e-01 2.24420995e-01 -5.81683636e-01
-2.89699268e-02 1.38156280e-01 -2.82950670e-01 6.27411902e-01
-3.64038795e-01 1.20271310e-01 -1.31838357e+00 3.03503960e-01
-1.87432694e+00 -7.54632413e-01 -5.80103338e-01 2.31556416e+00
6.25754833e-01 2.66250432e-01 2.69743979e-01 7.03373253e-01
5.20962954e-01 -3.38510722e-01 -8.78260374e-01 -3.89996618e-01
-7.00987503e-02 2.35566065e-01 -4.39361371e-02 -2.66468562e-02
-9.89128590e-01 -6.92315847e-02 6.75477028e+00 1.68799788e-01
-1.57695246e+00 -1.52496696e-01 9.25128102e-01 3.41112353e-02
1.81074157e-01 -4.00853276e-01 -1.54293597e-01 4.64661866e-01
1.13386977e+00 -2.42249697e-01 -1.83951810e-01 5.46496868e-01
4.33317780e-01 1.37651518e-01 -1.25694799e+00 1.61553514e+00
-1.49934039e-01 -1.36266923e+00 -3.45451772e-01 4.90712151e-02
1.66965276e-01 -3.72223377e-01 8.49477649e-02 6.34137541e-02
-7.82013059e-01 -1.32251418e+00 3.97167504e-01 8.05514097e-01
1.41712809e+00 -6.27641976e-01 9.14424598e-01 1.63435787e-01
-1.16490817e+00 -1.24875478e-01 1.91328377e-01 -2.33270124e-01
4.02579904e-02 6.94460988e-01 -9.32032168e-01 7.32375085e-01
8.31257463e-01 1.09469306e+00 -7.00758040e-01 1.22080028e+00
-1.19819127e-01 1.00974119e+00 4.64475863e-02 3.43856335e-01
-3.04753691e-01 -6.76688179e-03 5.04978299e-01 1.27932215e+00
3.27487022e-01 1.93065062e-01 1.22844331e-01 6.73424482e-01
3.00918162e-01 4.27031070e-01 -4.59787697e-01 -2.14656554e-02
2.12318152e-01 1.28540349e+00 -7.73627460e-01 -5.76889753e-01
-1.55937165e-01 5.74875355e-01 -2.07653776e-01 3.42830330e-01
-8.67718697e-01 -9.05970335e-01 2.14561641e-01 3.58869106e-01
-1.26511872e-01 2.36031011e-01 -1.15276480e+00 -8.33960056e-01
2.55985975e-01 -1.13310981e+00 5.71089625e-01 -5.22895157e-01
-1.01135123e+00 8.11282456e-01 -2.54242212e-01 -1.70409644e+00
-2.93202907e-01 -4.26303029e-01 -6.87062144e-01 1.08688962e+00
-9.24922347e-01 -7.47344673e-01 -4.39164132e-01 1.92825779e-01
1.46147490e-01 -1.15864404e-01 1.33041048e+00 3.64050001e-01
-5.03881574e-01 7.11202025e-01 -3.09253395e-01 3.92088264e-01
5.75477421e-01 -1.63657987e+00 2.01354071e-01 6.05597138e-01
1.13747448e-01 7.42817104e-01 5.98730981e-01 -4.59674686e-01
-1.11562979e+00 -9.23643172e-01 7.33603716e-01 -4.82770413e-01
8.89628679e-02 5.24630509e-02 -1.05489087e+00 2.96352327e-01
-2.24714018e-02 1.87695205e-01 1.08095837e+00 1.16724432e-01
-1.55154904e-02 -4.93413240e-01 -8.67211699e-01 3.49910706e-01
5.62773228e-01 -4.36493576e-01 -5.61038375e-01 -2.23841608e-01
-1.06206968e-01 -5.71595728e-01 -1.36504638e+00 6.71226442e-01
1.20966387e+00 -8.97589266e-01 7.89902866e-01 -7.43515968e-01
2.58500248e-01 -3.76843065e-01 3.61249298e-01 -1.28388619e+00
-2.82365501e-01 -6.45303309e-01 -2.45236605e-01 4.35281545e-01
5.67846835e-01 -5.69127917e-01 4.97918665e-01 3.41556728e-01
-1.64442256e-01 -1.20394957e+00 -5.76682925e-01 -3.98497194e-01
-2.37076268e-01 -4.97052997e-01 2.63646781e-01 9.81398702e-01
3.00445348e-01 4.24195319e-01 -4.74731505e-01 9.37060360e-03
5.95576882e-01 4.90043998e-01 6.30465448e-01 -1.67423308e+00
-4.50245470e-01 -3.95275950e-01 -7.49505758e-01 -4.45343763e-01
-6.43001974e-01 -8.88353586e-01 -4.16297555e-01 -1.74512661e+00
2.17565238e-01 -2.93790251e-01 -1.00390816e+00 4.36110318e-01
-3.55320007e-01 5.53519368e-01 6.47993833e-02 8.69473591e-02
-3.75817209e-01 -1.04790896e-01 1.20243442e+00 -9.36342478e-02
-5.25170684e-01 7.68048409e-03 -7.91358292e-01 8.23022008e-01
9.18771029e-01 -4.22780752e-01 -5.08503258e-01 -3.80930193e-02
3.08313757e-01 4.89942104e-01 3.00539792e-01 -1.22595787e+00
-2.60031611e-01 3.79243225e-01 1.03429615e+00 -4.65471029e-01
1.39597014e-01 -5.99143207e-01 4.81030673e-01 7.73779631e-01
-4.59376007e-01 2.70113319e-01 3.32542419e-01 5.48180401e-01
-2.14066446e-01 4.69263822e-01 6.65680945e-01 4.19100821e-02
-1.79555520e-01 1.66851699e-01 -2.96094596e-01 2.03858137e-01
8.33253801e-01 -4.50837463e-01 6.72516450e-02 -2.84244746e-01
-1.31489575e+00 -1.20200172e-01 -5.15742004e-02 2.34291982e-02
8.36455703e-01 -1.14399540e+00 -8.62756729e-01 3.62242371e-01
2.87895918e-01 -3.01715165e-01 4.89872456e-01 1.39822757e+00
-7.06861973e-01 2.17849448e-01 -5.03341794e-01 -1.17485321e+00
-1.41740572e+00 3.85279618e-02 6.12299025e-01 -1.19049102e-01
-1.25252914e+00 3.74468505e-01 1.72811281e-02 3.21664304e-01
3.04863900e-01 -4.15649831e-01 -4.11150545e-01 -1.68436673e-02
7.53264606e-01 4.62018222e-01 4.32659149e-01 -1.81890965e-01
-5.57816327e-01 6.50568306e-01 2.26607561e-01 6.07095174e-02
1.10275531e+00 -2.90690898e-03 2.34147355e-01 8.34004819e-01
7.99385786e-01 -2.50776917e-01 -7.32153654e-01 2.65011847e-01
-1.00019276e-01 -3.96951705e-01 3.29717994e-02 -1.35619831e+00
-1.01880002e+00 1.49701118e+00 1.28208971e+00 4.10503000e-01
1.21917725e+00 -5.39075553e-01 5.55676639e-01 2.81968564e-01
1.86748758e-01 -7.87882090e-01 -7.67362341e-02 -1.77938253e-01
8.08946371e-01 -9.91656721e-01 1.40977308e-01 -1.90917570e-02
-9.53603327e-01 1.34021711e+00 2.41788507e-01 1.16849065e-01
7.75707901e-01 2.95087576e-01 7.47489691e-01 -3.75977844e-01
-4.50416148e-01 3.75948697e-01 4.60880607e-01 5.75377822e-01
7.26188421e-01 2.97533095e-01 -4.37947422e-01 9.08146083e-01
1.06170438e-01 2.25571960e-01 2.37869054e-01 9.70663309e-01
-2.64741480e-01 -7.75834203e-01 -1.62151217e-01 7.47531414e-01
-8.43916833e-01 1.48968637e-01 -2.05201074e-01 4.53491986e-01
-3.46992873e-02 9.23713386e-01 -1.63491473e-01 -3.60739261e-01
3.51831526e-01 4.92898107e-01 4.19369578e-01 -5.41263998e-01
-7.58341968e-01 3.75271946e-01 -3.83525118e-02 -4.90746796e-01
-1.47145212e-01 -3.57301325e-01 -1.29946434e+00 3.85741055e-01
3.00132893e-02 2.33394355e-01 6.27506554e-01 6.84736609e-01
7.70108521e-01 1.06357658e+00 4.23426658e-01 -3.64668310e-01
-3.33444118e-01 -1.09044802e+00 -8.48807216e-01 3.44328493e-01
4.91500646e-01 -1.93339065e-01 -5.68361916e-02 4.02013183e-01] | [14.315068244934082, 3.290292263031006] |
6681b52f-c218-42e2-b44b-0fd58f7ffaeb | sogar-self-supervised-spatiotemporal | 2305.06310 | null | https://arxiv.org/abs/2305.06310v2 | https://arxiv.org/pdf/2305.06310v2.pdf | SoGAR: Self-supervised Spatiotemporal Attention-based Social Group Activity Recognition | This paper introduces a novel approach to Social Group Activity Recognition (SoGAR) using Self-supervised Transformers network that can effectively utilize unlabeled video data. To extract spatio-temporal information, we created local and global views with varying frame rates. Our self-supervised objective ensures that features extracted from contrasting views of the same video were consistent across spatio-temporal domains. Our proposed approach is efficient in using transformer-based encoders to alleviate the weakly supervised setting of group activity recognition. By leveraging the benefits of transformer models, our approach can model long-term relationships along spatio-temporal dimensions. Our proposed SoGAR method achieved state-of-the-art results on three group activity recognition benchmarks, namely JRDB-PAR, NBA, and Volleyball datasets, surpassing the current numbers in terms of F1-score, MCA, and MPCA metrics. | ['Khoa Luu', 'Page Daniel Dobbs', 'Xin Li', 'Han-Seok Seo', 'Alexander H Nelson', 'Pha Nguyen', 'Naga VS Raviteja Chappa'] | 2023-04-27 | null | null | null | null | ['group-activity-recognition'] | ['computer-vision'] | [ 9.64405984e-02 -3.22164744e-01 -7.36231804e-01 -3.45047593e-01
-7.10880339e-01 -5.96137762e-01 8.05812418e-01 -1.58934250e-01
-4.17560190e-01 6.04502857e-01 6.91567659e-01 3.86547074e-02
-4.42244798e-01 -6.41361356e-01 -6.93915844e-01 -5.68548262e-01
-5.73579013e-01 -7.77518675e-02 2.66660899e-01 1.61848422e-02
1.52426869e-01 2.54404515e-01 -1.62549484e+00 6.79104805e-01
3.52488488e-01 1.25838649e+00 -2.49873817e-01 7.79289007e-01
2.48928115e-01 1.72754705e+00 -3.42499435e-01 -4.33743298e-01
1.54471770e-01 -6.03749871e-01 -7.75836349e-01 4.65766788e-01
6.44633234e-01 -3.16897810e-01 -8.20451915e-01 3.27431321e-01
2.01716125e-01 1.67632565e-01 3.92577380e-01 -1.39088011e+00
-5.01951694e-01 4.37462628e-01 -5.63949347e-01 7.08749771e-01
6.48775995e-01 1.06849544e-01 1.29048586e+00 -8.50437582e-01
7.16150224e-01 8.52900445e-01 6.49635851e-01 2.56268919e-01
-1.01125455e+00 -8.00785959e-01 5.25021970e-01 3.32568139e-01
-1.30533326e+00 -5.58458686e-01 6.81394398e-01 -3.82634342e-01
9.89238918e-01 1.20739572e-01 9.80479598e-01 1.40041685e+00
-4.31275181e-02 1.05364668e+00 1.02556026e+00 -6.70539141e-02
1.60127163e-01 -5.17443240e-01 8.10566321e-02 1.00569880e+00
-1.57351151e-01 -1.25747383e-01 -1.53638911e+00 -4.30559479e-02
8.65244448e-01 1.35169148e-01 9.61945131e-02 -7.63933361e-01
-1.68624723e+00 5.29773414e-01 -6.24697767e-02 3.95406455e-01
-2.70255804e-01 3.13012630e-01 5.51463127e-01 5.11678100e-01
7.35385656e-01 1.50029078e-01 -1.71248257e-01 -7.80173361e-01
-9.62033272e-01 -6.69809654e-02 4.92212653e-01 8.07064056e-01
3.78160298e-01 -3.56421471e-02 -3.73634517e-01 6.98893726e-01
2.17381254e-01 4.20034140e-01 3.78872782e-01 -1.24122691e+00
7.78479040e-01 6.06307209e-01 -9.63295922e-02 -1.07409525e+00
-5.49175255e-02 -5.39763391e-01 -7.16255248e-01 -1.15528435e-01
5.24128318e-01 1.28368363e-01 -6.95063531e-01 1.65116191e+00
6.77570626e-02 5.80932677e-01 2.62466986e-02 6.39974415e-01
6.33186817e-01 5.19740522e-01 5.33198118e-02 -3.70701313e-01
9.31073725e-01 -1.19153285e+00 -7.40585864e-01 -1.89678997e-01
6.44069850e-01 -2.19941393e-01 9.35343027e-01 3.74148846e-01
-1.14519382e+00 -5.66924989e-01 -1.07759774e+00 1.60182178e-01
-6.28420413e-02 1.52012154e-01 7.47045577e-01 6.34176850e-01
-9.87265646e-01 4.96822000e-01 -1.18713546e+00 -3.88425916e-01
9.35799658e-01 2.46432170e-01 -7.84923911e-01 7.39380019e-04
-7.17995405e-01 2.91954041e-01 -8.38714652e-03 -1.30150750e-01
-1.19209504e+00 -5.45171261e-01 -8.18440378e-01 -1.32403851e-01
5.22769868e-01 -3.54122788e-01 9.51836646e-01 -9.56687689e-01
-1.32241428e+00 9.36015368e-01 -3.05159837e-01 -8.07342112e-01
6.03388846e-01 -4.49723512e-01 -6.60053194e-01 3.37023884e-01
2.21044242e-01 2.01416597e-01 6.51431799e-01 -6.49897099e-01
-6.64449811e-01 -3.83783132e-01 1.37967959e-01 4.54199314e-01
-6.56700850e-01 2.06755221e-01 -5.94022095e-01 -9.50696766e-01
1.07370473e-01 -7.48534739e-01 1.62427947e-01 1.35874122e-01
1.19955331e-01 -1.55187622e-01 9.75242972e-01 -5.45785844e-01
1.29529989e+00 -2.35991764e+00 1.62237212e-01 1.31590545e-01
3.25241774e-01 9.05713961e-02 -1.85113847e-01 4.23944920e-01
-1.03866130e-01 -1.69015713e-02 2.22746894e-01 -4.62499827e-01
-2.67735064e-01 3.32905978e-01 -1.87713370e-01 6.73099875e-01
-4.01440961e-03 9.34287488e-01 -1.20119977e+00 -6.55245781e-01
2.82517284e-01 1.85004085e-01 -6.08329058e-01 1.70679227e-01
1.44125208e-01 4.32419509e-01 -2.41114572e-01 7.05687940e-01
1.51846126e-01 -5.98415077e-01 4.04777169e-01 -4.40590866e-02
3.15488614e-02 4.87738043e-01 -1.04536116e+00 2.00249648e+00
-2.31163368e-01 8.73207986e-01 -3.79867345e-01 -1.25110197e+00
5.26425719e-01 4.22324359e-01 1.18389416e+00 -9.19459522e-01
-3.01957577e-01 -2.04955488e-01 -4.29756373e-01 -2.69198865e-01
2.12442815e-01 3.27967882e-01 -2.76552707e-01 7.13308156e-01
5.14870048e-01 7.26090491e-01 4.47690606e-01 3.54671866e-01
1.47495234e+00 4.14622396e-01 3.50883782e-01 7.61522576e-02
4.15253550e-01 -2.94991314e-01 6.66655540e-01 9.37934637e-01
-3.23176563e-01 4.99977410e-01 6.89460039e-01 -6.33374572e-01
-6.63180590e-01 -1.33451307e+00 4.16599542e-01 1.52174151e+00
9.88448113e-02 -8.27675223e-01 -2.59342641e-01 -1.09440482e+00
-4.43076730e-01 -5.14832288e-02 -7.16924250e-01 -8.71935636e-02
-7.70224988e-01 -5.68728089e-01 6.91107094e-01 7.57307410e-01
8.16065550e-01 -7.48008370e-01 -3.91346455e-01 1.75872505e-01
-4.44605440e-01 -1.59782374e+00 -6.23510540e-01 -6.16195537e-02
-8.85689020e-01 -1.25238812e+00 -5.78625321e-01 -6.56507671e-01
4.44321185e-01 5.15286505e-01 1.16129684e+00 -3.76655668e-01
1.63685575e-01 4.06012088e-01 -4.42131311e-01 1.75889716e-01
-1.48062548e-02 3.12444028e-02 5.78996167e-02 4.11645383e-01
2.71672696e-01 -7.71821082e-01 -6.05393469e-01 7.77888119e-01
-5.09316683e-01 1.78624123e-01 2.30687663e-01 7.39630342e-01
5.64555109e-01 -1.35624811e-01 5.10082603e-01 -5.97989976e-01
2.53547490e-01 -3.92946541e-01 -2.80330688e-01 3.35168898e-01
-2.96021849e-01 -2.43871212e-01 3.92382443e-01 -5.17322958e-01
-9.63431418e-01 -8.31231177e-02 3.66754353e-01 -5.64872265e-01
1.25290290e-01 4.48679745e-01 2.63425410e-02 4.56114262e-02
5.25041342e-01 3.35202038e-01 -8.26478284e-03 -2.55668461e-01
1.44074664e-01 4.50035214e-01 6.11661673e-01 -4.05836701e-01
6.24895930e-01 8.03570926e-01 -1.74129486e-01 -5.12332916e-01
-1.15965188e+00 -9.14759696e-01 -6.63604915e-01 -4.89095956e-01
9.52533543e-01 -1.44699502e+00 -6.29775047e-01 7.74380445e-01
-6.16218507e-01 -4.38710421e-01 -4.85648543e-01 5.49157619e-01
-6.86742783e-01 4.84209448e-01 -6.59582198e-01 -6.90622866e-01
-5.59634902e-03 -6.02568269e-01 1.03379774e+00 -1.40850201e-01
-3.09064597e-01 -9.09120977e-01 1.72837168e-01 9.85107601e-01
5.24028577e-02 2.81167030e-01 2.51393735e-01 -4.52015907e-01
-7.69814551e-01 2.18027155e-03 -4.21896577e-02 4.79669452e-01
3.98621202e-01 -2.14614868e-01 -8.45506430e-01 -3.26890528e-01
-4.51365471e-01 -4.42499369e-01 7.51623154e-01 1.56320482e-01
1.44820404e+00 -3.64947766e-01 -1.13851003e-01 5.75751781e-01
8.41317594e-01 1.34617254e-01 6.77330256e-01 1.39868990e-01
9.00797784e-01 1.30094856e-01 8.01409960e-01 6.32316530e-01
4.83628958e-01 9.45787489e-01 2.05766544e-01 -7.22690448e-02
-1.29688486e-01 -5.62672079e-01 7.31430233e-01 8.41126859e-01
-5.32666683e-01 -3.49529177e-01 -8.07328820e-01 8.57030869e-01
-2.27032042e+00 -1.62178612e+00 2.71948874e-01 2.04351640e+00
3.62724811e-01 2.08574519e-01 6.26559079e-01 3.19581240e-01
3.50656688e-01 8.21531117e-01 -3.41370702e-01 1.73757613e-01
-3.18322837e-01 5.26615009e-02 5.89154422e-01 -5.29459380e-02
-1.52930987e+00 6.12251520e-01 7.08512068e+00 7.16781080e-01
-7.60028124e-01 2.09794655e-01 6.40294254e-01 -6.49925828e-01
1.95776463e-01 -2.85690218e-01 -4.56017673e-01 5.88815212e-01
1.06438029e+00 4.48591597e-02 4.58976835e-01 6.02085292e-01
3.64284962e-01 1.20128477e-02 -1.22924280e+00 1.15619659e+00
3.28210801e-01 -1.77504492e+00 -2.02126250e-01 2.98246175e-01
8.38024735e-01 2.55004585e-01 8.21122602e-02 2.52949744e-01
4.80463058e-01 -9.66829836e-01 7.58644044e-01 4.87053424e-01
7.98127353e-01 -5.57073534e-01 4.81078237e-01 2.22750619e-01
-1.41101348e+00 -1.69790506e-01 4.78650779e-01 -1.81845903e-01
1.73214033e-01 3.05051535e-01 -4.58820462e-01 5.67334890e-01
9.92041588e-01 1.63044560e+00 -7.05362201e-01 6.10883534e-01
1.04653865e-01 8.81222367e-01 -2.32930750e-01 3.17354590e-01
2.82788783e-01 -1.23065256e-01 3.70595336e-01 9.78419065e-01
1.79192886e-01 -9.16207284e-02 2.18144715e-01 2.38839760e-01
-2.87191242e-01 -2.52678275e-01 -6.99407935e-01 -3.10424328e-01
2.77890533e-01 6.90354764e-01 -5.89030862e-01 -3.41899246e-01
-7.91552126e-01 9.36631739e-01 3.61792564e-01 1.96193740e-01
-1.10818028e+00 2.81567156e-01 7.22232461e-01 3.78084779e-01
4.59668726e-01 -3.85490507e-01 4.36526313e-02 -1.57723999e+00
4.14575398e-01 -1.17696297e+00 8.46475780e-01 -7.52692640e-01
-1.09317338e+00 1.17256053e-01 1.98981285e-01 -1.58274305e+00
-3.04863483e-01 -2.20394716e-01 -2.60237992e-01 7.63294697e-02
-1.12039518e+00 -1.31142604e+00 -4.04607505e-01 7.94826448e-01
6.59129441e-01 -5.11817515e-01 6.20163500e-01 5.59319496e-01
-5.57557523e-01 5.17268181e-01 -2.81284135e-02 6.25518382e-01
6.78667009e-01 -1.03336442e+00 4.19933200e-01 1.08700264e+00
6.39094353e-01 1.79586634e-01 1.36614427e-01 -3.40774179e-01
-1.29762340e+00 -1.03094256e+00 8.29326808e-01 -6.46106005e-01
7.57056832e-01 -6.11346662e-01 -5.33155322e-01 9.19910252e-01
3.55190318e-03 6.21088982e-01 9.59969938e-01 3.65443021e-01
-5.63446760e-01 -2.79610723e-01 -8.30464602e-01 4.86227721e-01
1.79326200e+00 -9.97682929e-01 -4.14338291e-01 3.05512458e-01
3.17470372e-01 -2.89506525e-01 -1.08175457e+00 4.18855399e-01
8.38387549e-01 -1.15487874e+00 1.17863953e+00 -8.33303988e-01
5.12335598e-01 -2.11535498e-01 -1.69028595e-01 -9.78337884e-01
-3.30458522e-01 -7.24677086e-01 -7.33187616e-01 9.08443451e-01
7.13196546e-02 -2.89880067e-01 1.28562546e+00 8.14152062e-02
1.23290733e-01 -5.99206090e-01 -1.03829360e+00 -9.41170514e-01
-6.04167521e-01 -4.68430161e-01 4.16184872e-01 1.05319583e+00
2.47071445e-01 1.99187860e-01 -7.91750491e-01 -7.89732859e-02
6.69183671e-01 -7.18173310e-02 8.13821614e-01 -9.28713381e-01
-4.69081968e-01 -1.30645856e-01 -7.75776267e-01 -1.18635893e+00
2.33697712e-01 -5.84685743e-01 -4.86496538e-01 -1.35536933e+00
3.05575281e-01 -7.49753714e-02 -6.09450936e-01 5.24839580e-01
1.88826859e-01 6.97183609e-01 1.54570043e-01 2.27661237e-01
-1.39154875e+00 3.52474302e-01 1.05767584e+00 -1.34443372e-01
-6.72438294e-02 -2.00786889e-01 -4.72092807e-01 6.64693654e-01
5.83246529e-01 -2.19753250e-01 -6.69290960e-01 -4.64175403e-01
2.21310616e-01 3.26737702e-01 4.77478802e-01 -1.29723096e+00
1.93938389e-01 -3.56761277e-01 3.17802131e-01 -6.10281408e-01
5.66921175e-01 -6.35412455e-01 1.77508160e-01 1.02953963e-01
-5.87180078e-01 8.68258774e-02 -2.59177804e-01 9.74518359e-01
-5.51504910e-01 6.71713173e-01 4.99391645e-01 2.04054192e-02
-8.33047628e-01 5.13253808e-01 -6.53851748e-01 2.38121614e-01
1.18585598e+00 -5.66744387e-01 -4.18164939e-01 -7.19554543e-01
-7.85091043e-01 2.72333443e-01 3.21353167e-01 7.03565240e-01
4.78940785e-01 -1.53990138e+00 -5.28883755e-01 2.59286791e-01
5.69810688e-01 -3.12008888e-01 3.48082095e-01 9.19196069e-01
-3.67183954e-01 2.78111339e-01 -3.97464484e-01 -7.66870856e-01
-1.55572748e+00 1.30996317e-01 4.03012544e-01 -7.85825729e-01
-5.26561916e-01 5.68672359e-01 3.16211693e-02 -1.24628313e-01
4.13038641e-01 -2.15219259e-01 -2.53047436e-01 1.65562347e-01
5.15747428e-01 4.31802392e-01 1.01166099e-01 -7.80073345e-01
-5.30340731e-01 3.39517832e-01 2.78678127e-02 -1.60962284e-01
1.51360846e+00 -1.79715112e-01 3.54203641e-01 5.35015464e-01
1.31072104e+00 -1.44519089e-02 -1.51789057e+00 -5.38122356e-01
-1.99539610e-03 -6.42795622e-01 9.33871269e-02 -6.38519347e-01
-1.08753014e+00 4.02218908e-01 6.07351720e-01 8.43466260e-03
1.01732981e+00 9.39673111e-02 5.79720914e-01 3.48188877e-01
5.03042400e-01 -1.24588537e+00 7.20075846e-01 4.66732204e-01
4.23027068e-01 -1.19578123e+00 1.13606170e-01 -4.76792514e-01
-7.74299204e-01 5.10465622e-01 3.95553142e-01 -1.03862613e-01
5.67342222e-01 1.38929829e-01 -1.53349146e-01 -3.79063010e-01
-1.06424987e+00 -8.52777287e-02 4.27967072e-01 6.06060088e-01
4.14637655e-01 -1.09171219e-01 1.24632008e-01 2.43283972e-01
1.99169025e-01 3.36706102e-01 2.88387388e-01 1.18079257e+00
7.18906000e-02 -8.76722693e-01 1.07548898e-02 6.04787052e-01
-6.51520789e-01 2.79588878e-01 -1.85623989e-01 6.52697027e-01
-6.54541031e-02 8.87816429e-01 3.57091457e-01 -4.85590816e-01
1.84134260e-01 -3.93957086e-03 5.37474096e-01 -4.88381773e-01
-4.79418665e-01 -1.53442482e-02 6.68174684e-01 -1.10186124e+00
-1.25587893e+00 -9.44737077e-01 -6.55610263e-01 -2.55645484e-01
2.31566921e-01 -3.94095816e-02 3.37726474e-02 9.53864694e-01
4.64178681e-01 4.43728477e-01 8.26524734e-01 -4.29817706e-01
2.89899167e-02 -7.64085948e-01 -4.80744541e-01 6.84800088e-01
4.15174365e-01 -6.55305147e-01 -2.60311157e-01 3.30785662e-01] | [8.38140869140625, 0.67973393201828] |
5d574ba5-19ae-45c5-b295-df7be8470097 | hw-tscs-participation-at-wmt-2020-automatic | null | null | https://aclanthology.org/2020.wmt-1.85 | https://aclanthology.org/2020.wmt-1.85.pdf | HW-TSC’s Participation at WMT 2020 Automatic Post Editing Shared Task | The paper presents the submission by HW-TSC in the WMT 2020 Automatic Post Editing Shared Task. We participate in the English-German and English-Chinese language pairs. Our system is built based on the Transformer pre-trained on WMT 2019 and WMT 2020 News Translation corpora, and fine-tuned on the APE corpus. Bottleneck Adapter Layers are integrated into the model to prevent over-fitting. We further collect external translations as the augmented MT candidates to improve the performance. The experiment demonstrates that pre-trained NMT models are effective when fine-tuning with the APE corpus of a limited size, and the performance can be further improved with external MT augmentation. Our system achieves competitive results on both directions in the final evaluation. | ['Yimeng Chen', 'Shiliang Sun', 'Shimin Tao', 'Ying Qin', 'Lizhi Lei', 'Zongyao Li', 'Jiaxin Guo', 'Hengchao Shang', 'Daimeng Wei', 'Minghan Wang', 'Hao Yang'] | null | null | null | null | wmt-emnlp-2020-11 | ['automatic-post-editing', 'automatic-post-editing'] | ['computer-vision', 'natural-language-processing'] | [ 1.44411206e-01 1.33858800e-01 -3.71344954e-01 -4.28171724e-01
-1.24467623e+00 -4.93307203e-01 6.37740552e-01 -4.33748335e-01
-9.97522175e-01 9.10137951e-01 4.26367700e-01 -6.58577859e-01
4.89678621e-01 -3.95353079e-01 -7.33947277e-01 -5.01890155e-03
4.23000544e-01 9.38637376e-01 2.52326399e-01 -6.43336713e-01
-1.89762320e-02 -4.17745531e-01 -5.30286789e-01 6.81959629e-01
1.40916348e+00 4.40620333e-01 6.05930805e-01 5.78375936e-01
-1.37983292e-01 4.94015545e-01 -5.73423505e-01 -1.05582023e+00
4.45073783e-01 -3.21128398e-01 -9.68278766e-01 -4.87100393e-01
2.42030248e-01 -1.67692155e-01 -2.30948806e-01 8.09908330e-01
8.32297385e-01 -1.20686583e-01 4.96212095e-01 -8.01945627e-01
-9.35996294e-01 1.41416502e+00 -3.60227674e-01 2.37154052e-01
4.32415195e-02 -2.33451333e-02 8.84276569e-01 -1.62919998e+00
7.33740151e-01 1.06832278e+00 6.19579017e-01 8.23479652e-01
-1.03323889e+00 -7.49436200e-01 9.39514637e-02 3.62033278e-01
-1.25008678e+00 -8.22618127e-01 1.10304371e-01 2.42251940e-02
1.78377092e+00 3.16132903e-01 2.22080991e-01 1.47995114e+00
2.76596904e-01 9.34704304e-01 9.74067509e-01 -6.24383271e-01
-4.31749910e-01 -2.98173241e-02 -1.28002077e-01 2.83226013e-01
-2.25196838e-01 -7.83856511e-02 -5.73276401e-01 1.59254476e-01
3.48410100e-01 -5.85738719e-01 -5.26821055e-02 5.76230884e-01
-1.86606300e+00 4.89344090e-01 -5.08264750e-02 3.81540865e-01
-2.59325325e-01 -1.25716433e-01 6.73985302e-01 7.77697504e-01
8.76776636e-01 6.90434277e-01 -9.89367545e-01 -5.85759342e-01
-1.18095362e+00 -5.66120073e-02 5.37887275e-01 1.53750992e+00
4.22179282e-01 -1.83219388e-01 -7.04143047e-01 1.42773306e+00
-1.02160670e-01 8.97558808e-01 9.46951032e-01 -6.58256352e-01
1.37986255e+00 1.68515682e-01 2.51950175e-02 -1.96716234e-01
-3.40418555e-02 -7.05249310e-01 -8.30681622e-01 -5.80199599e-01
1.61094777e-02 -4.21659380e-01 -1.17450857e+00 1.74015892e+00
-8.74273255e-02 -3.71810764e-01 1.28643796e-01 5.52386642e-01
6.37462139e-01 8.89515519e-01 -1.47355363e-01 -3.56243491e-01
1.10797441e+00 -1.80967510e+00 -1.10188556e+00 -6.21530056e-01
1.03054130e+00 -1.44795465e+00 1.39125299e+00 -1.10374250e-01
-1.44228196e+00 -6.66491330e-01 -1.01855814e+00 -4.46264118e-01
-2.82882780e-01 7.00249374e-01 9.52482969e-02 2.97869921e-01
-1.02415144e+00 5.48581183e-01 -1.05110621e+00 -4.08967048e-01
6.26839101e-02 2.59294391e-01 -2.79108107e-01 -8.69409591e-02
-1.61815643e+00 1.51252973e+00 4.88686413e-01 2.27594972e-01
-6.14372075e-01 -5.18687427e-01 -5.82165778e-01 -1.74200416e-01
6.71017095e-02 -7.31190264e-01 1.70270216e+00 -7.26219177e-01
-1.88150632e+00 9.03545976e-01 -5.05481780e-01 -4.91479158e-01
1.01468098e+00 -5.38873076e-01 -7.23965228e-01 -5.10986447e-01
5.01727343e-01 7.52277195e-01 4.23334509e-01 -2.72102147e-01
-7.54694462e-01 6.09814115e-02 -3.90674978e-01 5.00988424e-01
-4.75162715e-01 4.98417050e-01 -7.99033523e-01 -1.03953123e+00
-1.12599537e-01 -1.14697301e+00 -3.53882223e-01 -9.32324052e-01
-5.79833388e-01 -1.44349769e-01 4.27392364e-01 -1.09211648e+00
1.64355135e+00 -1.67515862e+00 1.60243317e-01 -2.17156589e-01
-4.15537924e-01 3.94971848e-01 -5.74400723e-01 6.88977420e-01
2.37225488e-01 2.18017608e-01 -2.23963305e-01 -7.01363325e-01
-1.47561459e-02 1.51255101e-01 -3.03344876e-01 -6.37449697e-02
2.22925156e-01 1.31194592e+00 -7.14217484e-01 -5.05544543e-01
-1.86473459e-01 -1.04730017e-01 -2.84315467e-01 2.53390908e-01
-1.95296347e-01 5.34748077e-01 -1.19721688e-01 2.97872931e-01
5.77848017e-01 3.83710787e-02 1.83085218e-01 8.97334069e-02
-2.31235251e-01 1.29461813e+00 -4.88368630e-01 2.05788016e+00
-5.86741924e-01 5.15174150e-01 -2.97307819e-01 -3.93569887e-01
7.06167519e-01 6.04977787e-01 -6.63552135e-02 -8.56612384e-01
2.43625380e-02 7.19194233e-01 2.91330874e-01 -2.11065292e-01
9.40231740e-01 6.02962524e-02 -9.27691758e-02 6.23863220e-01
1.79834843e-01 -9.89871994e-02 5.18631458e-01 1.30471036e-01
8.97515357e-01 3.27330023e-01 7.68734142e-02 -3.49425226e-01
3.35297495e-01 2.31357068e-01 7.52379954e-01 5.67688644e-01
2.24127620e-01 7.20742881e-01 -1.69412866e-01 -1.56149358e-01
-1.32738340e+00 -8.23614717e-01 -9.30339321e-02 1.35826087e+00
-3.52786303e-01 -8.47942233e-01 -9.80218291e-01 -7.52198875e-01
-6.00405395e-01 8.92635167e-01 -4.94041651e-01 -1.12652801e-01
-1.19640386e+00 -9.17480469e-01 8.67305934e-01 5.69476724e-01
6.94449961e-01 -9.07132685e-01 2.85787404e-01 5.64369142e-01
-1.07035029e+00 -1.35645759e+00 -1.19908965e+00 7.13618696e-02
-8.83035183e-01 -3.63278627e-01 -1.08092916e+00 -1.11254859e+00
3.72871906e-01 5.14207818e-02 1.22638631e+00 -1.99582770e-01
6.07926905e-01 -3.37047696e-01 -3.21244687e-01 -3.88765037e-01
-8.69716227e-01 1.03685629e+00 2.54833221e-01 -3.83031130e-01
3.80082875e-01 -5.43227553e-01 -1.48726195e-01 5.72560668e-01
-2.45919153e-01 7.17880070e-01 7.00892985e-01 1.02778685e+00
5.45606196e-01 -7.57829785e-01 4.86356437e-01 -9.91158724e-01
6.64615512e-01 -1.15272522e-01 -2.68196553e-01 6.19512618e-01
-7.84978092e-01 8.38598609e-02 6.79515302e-01 -7.13609517e-01
-1.34167325e+00 -3.80641103e-01 -4.12939370e-01 -1.65737215e-02
5.21741331e-01 8.06388140e-01 -3.98798227e-01 3.28000218e-01
5.65533459e-01 1.96347192e-01 -4.81845945e-01 -8.66112649e-01
4.32771683e-01 9.22042251e-01 6.54734254e-01 -7.39390194e-01
8.41319323e-01 -4.80550855e-01 -6.22159958e-01 -1.92492217e-01
-8.18500161e-01 -3.99390534e-02 -7.49554157e-01 2.56936073e-01
6.40990436e-01 -1.12665391e+00 2.77354032e-01 4.45822775e-01
-1.45282733e+00 -6.01333201e-01 -1.31582431e-02 7.57004380e-01
-4.81258392e-01 1.63856551e-01 -1.35775518e+00 -6.28766641e-02
-8.93433034e-01 -1.19013119e+00 8.49048615e-01 -3.07444572e-01
-4.00901347e-01 -7.12011218e-01 4.17405963e-01 4.52409983e-01
6.53900623e-01 -7.29511380e-01 8.71851683e-01 -7.48706400e-01
-2.33435929e-01 -7.22507909e-02 7.12699294e-02 5.24857700e-01
-4.36505564e-02 1.11598372e-02 -7.83425868e-01 -3.44242811e-01
-2.15977341e-01 -2.63581991e-01 7.84863949e-01 1.90102905e-01
6.19287312e-01 -3.88083398e-01 -3.29161346e-01 6.84163392e-01
7.06237972e-01 -1.33071929e-01 7.09104240e-01 6.26892209e-01
7.19960868e-01 2.85303116e-01 8.07607472e-01 -1.10421479e-01
6.84901118e-01 8.68644714e-01 -1.99371547e-01 -1.28012508e-01
-2.75275350e-01 -6.11093998e-01 7.72247791e-01 2.12318158e+00
-3.22894990e-01 -4.18864995e-01 -9.22478497e-01 5.65218449e-01
-2.08124781e+00 -7.12698162e-01 -3.39697301e-01 2.02537179e+00
1.43813550e+00 2.68795162e-01 -1.55812845e-01 -4.81361389e-01
8.58098209e-01 -1.80416822e-01 -1.55980706e-01 -6.15028858e-01
-6.31623328e-01 4.21878815e-01 5.78541875e-01 7.41645694e-01
-8.60613763e-01 1.64085996e+00 6.92543840e+00 1.16115415e+00
-8.72259259e-01 7.88390696e-01 4.30900961e-01 -1.69905707e-01
-3.03266168e-01 1.93037391e-01 -9.64910328e-01 5.89929998e-01
1.35560906e+00 -5.53883612e-01 4.83715355e-01 4.92472738e-01
1.04027294e-01 6.16693318e-01 -1.04863775e+00 5.66438854e-01
-2.24060208e-01 -1.18809855e+00 8.83942768e-02 6.17208220e-02
9.49810028e-01 8.05871964e-01 8.02579299e-02 9.09649491e-01
4.47570562e-01 -8.64121199e-01 9.24573123e-01 1.62650764e-01
1.21728170e+00 -5.72602272e-01 8.67728770e-01 5.58534324e-01
-8.99982274e-01 3.87997538e-01 -6.49333775e-01 -1.64255977e-01
3.08918387e-01 4.74309325e-01 -9.63334560e-01 6.25651598e-01
3.41485977e-01 7.48729289e-01 -6.43483043e-01 6.50886893e-01
-6.91129863e-01 1.04880321e+00 -2.73314089e-01 8.24878663e-02
1.31530926e-01 -1.34926975e-01 5.55569172e-01 1.60506189e+00
6.48407221e-01 -3.36736590e-01 -5.32715581e-02 6.11589313e-01
-4.90854979e-01 5.09716570e-01 -1.69126645e-01 -1.47494748e-01
6.83652699e-01 1.15295756e+00 -6.87349439e-02 -6.83145761e-01
-4.69250441e-01 1.44804561e+00 7.52751529e-01 4.19151098e-01
-9.01171207e-01 -3.04292589e-01 5.10716677e-01 -1.34532303e-01
1.01795815e-01 -1.99322671e-01 -2.32159212e-01 -1.65719509e+00
2.74274677e-01 -1.12035429e+00 1.16689667e-01 -7.51927912e-01
-1.28936350e+00 1.19360936e+00 -2.72989661e-01 -1.37672877e+00
-5.32214224e-01 -4.73018646e-01 -5.08278191e-01 1.29784155e+00
-1.15273273e+00 -1.43027401e+00 5.22868097e-01 4.18300569e-01
7.88594782e-01 -2.99733251e-01 9.80194867e-01 6.45967901e-01
-7.16758072e-01 1.28871953e+00 4.08137083e-01 3.56043279e-01
1.39237440e+00 -1.02650011e+00 1.31836843e+00 1.21474814e+00
1.50995836e-01 8.32346439e-01 4.46863383e-01 -8.89575541e-01
-9.27705169e-01 -1.28104985e+00 1.83271086e+00 -7.79550374e-01
6.56669080e-01 -5.98865569e-01 -7.72293687e-01 1.02813983e+00
7.96035886e-01 -3.78843665e-01 4.64392304e-01 4.59613353e-01
-2.01170564e-01 2.53001869e-01 -5.45751035e-01 8.99785697e-01
1.29524589e+00 -6.57027245e-01 -9.17067170e-01 3.94514650e-01
1.18579888e+00 -8.14525306e-01 -1.00572467e+00 5.69924116e-01
3.40700388e-01 -3.89920734e-03 5.49560130e-01 -8.43978584e-01
5.77340662e-01 -1.53145760e-01 -2.60422498e-01 -1.86020982e+00
-5.42490125e-01 -9.71028507e-01 8.82456005e-02 1.33729613e+00
1.33563411e+00 -5.45350492e-01 3.42320889e-01 1.83302283e-01
-5.82692146e-01 -4.89588171e-01 -1.13628650e+00 -9.99248207e-01
5.62725306e-01 -4.74380434e-01 8.14380527e-01 1.12620294e+00
3.69089991e-01 1.07132947e+00 -6.27593100e-01 -2.44896889e-01
1.66105241e-01 -2.32873335e-01 7.73653805e-01 -6.40677273e-01
-4.15847927e-01 -4.18700665e-01 2.40441725e-01 -1.37513125e+00
1.85055826e-02 -1.26505077e+00 5.27845696e-02 -1.35751629e+00
4.23813164e-01 -2.53850818e-01 -2.30055615e-01 6.90994740e-01
-6.36249125e-01 2.56191075e-01 2.25117803e-01 5.68441808e-01
-4.34829503e-01 7.59308159e-01 1.48122966e+00 -2.26426676e-01
-3.94652374e-02 1.22570813e-01 -3.25326502e-01 2.48381361e-01
8.43120873e-01 -6.30310774e-01 -7.22799227e-02 -1.30579603e+00
2.90538132e-01 -1.53855130e-01 -4.38997269e-01 -6.31763399e-01
2.59912193e-01 -8.39910731e-02 2.49512419e-01 -7.56336808e-01
1.16802856e-01 -3.05271149e-01 -4.28226963e-03 1.44477144e-01
-6.26745641e-01 7.86242664e-01 3.01912159e-01 -1.67648166e-01
-1.90090328e-01 -3.64399999e-02 4.01379019e-01 -9.35882032e-02
-6.76898882e-02 3.84023458e-01 -4.53347564e-01 3.65692437e-01
1.96585700e-01 1.12916522e-01 -5.41276753e-01 -3.15392345e-01
-5.65779448e-01 4.15576488e-01 4.05961603e-01 7.22026646e-01
1.22677378e-01 -1.71437156e+00 -1.27218735e+00 1.61371782e-01
2.15281293e-01 -3.83173794e-01 -1.37582168e-01 1.07335031e+00
-2.34556898e-01 5.11920035e-01 -1.73271224e-01 -3.28271240e-01
-1.00374603e+00 2.68835455e-01 3.16289008e-01 -6.95339441e-01
-3.12723547e-01 7.94601262e-01 -1.37211755e-01 -1.13704276e+00
5.91431782e-02 -3.30098271e-01 3.30717355e-01 -5.33609390e-01
6.31065488e-01 4.32568491e-01 3.98337424e-01 -5.89476943e-01
-2.96653897e-01 2.31088206e-01 -5.92883825e-01 -8.55758190e-01
1.15833092e+00 -4.92095143e-01 -3.29271466e-01 4.10850435e-01
1.02709162e+00 4.47312355e-01 -5.43330312e-01 -7.36618638e-01
3.33074719e-01 -5.51705174e-02 -2.18923718e-01 -1.37560105e+00
-7.04212010e-01 9.93864179e-01 2.54993498e-01 -5.81446171e-01
9.06832039e-01 -3.85343432e-01 1.16036892e+00 6.30637646e-01
3.69220912e-01 -1.61734354e+00 -4.27522272e-01 1.33092237e+00
8.85332525e-01 -1.18603218e+00 -4.17905688e-01 -2.75227189e-01
-6.92930400e-01 9.48388755e-01 7.95775652e-01 3.95516157e-01
1.92619994e-01 4.31307793e-01 4.36684549e-01 4.74189848e-01
-1.17105770e+00 9.66713578e-02 6.16198897e-01 3.91772181e-01
9.65793788e-01 3.55444252e-01 -8.50736320e-01 9.02337551e-01
-6.07158244e-01 -1.11847222e-01 2.66199619e-01 6.61839545e-01
-2.12049335e-01 -1.64682949e+00 7.82407001e-02 2.75626898e-01
-6.58213258e-01 -9.63040829e-01 -5.79315603e-01 6.26905501e-01
-1.76064130e-02 9.94025707e-01 -2.80869097e-01 -6.63301885e-01
4.62684393e-01 2.99244434e-01 5.70248783e-01 -7.30357409e-01
-8.63539219e-01 3.56581450e-01 5.71045995e-01 -3.73905867e-01
1.04296893e-01 -4.56103772e-01 -9.35481906e-01 -4.46491539e-01
-4.41716433e-01 3.48256350e-01 5.24246931e-01 9.65756238e-01
5.14257252e-01 3.34196895e-01 4.74146485e-01 -5.48114598e-01
-6.47467971e-01 -1.87772608e+00 1.56891748e-01 7.56546333e-02
-8.48400593e-02 -3.35244276e-03 -2.73367483e-02 -1.18067212e-01] | [11.667025566101074, 10.290751457214355] |
6507b4db-221d-4a36-8331-d2f2f057a1e2 | improving-factual-consistency-in | 2211.06196 | null | https://arxiv.org/abs/2211.06196v1 | https://arxiv.org/pdf/2211.06196v1.pdf | Improving Factual Consistency in Summarization with Compression-Based Post-Editing | State-of-the-art summarization models still struggle to be factually consistent with the input text. A model-agnostic way to address this problem is post-editing the generated summaries. However, existing approaches typically fail to remove entity errors if a suitable input entity replacement is not available or may insert erroneous content. In our work, we focus on removing extrinsic entity errors, or entities not in the source, to improve consistency while retaining the summary's essential information and form. We propose to use sentence-compression data to train the post-editing model to take a summary with extrinsic entity errors marked with special tokens and output a compressed, well-formed summary with those errors removed. We show that this model improves factual consistency while maintaining ROUGE, improving entity precision by up to 30% on XSum, and that this model can be applied on top of another post-editor, improving entity precision by up to a total of 38%. We perform an extensive comparison of post-editing approaches that demonstrate trade-offs between factual consistency, informativeness, and grammaticality, and we analyze settings where post-editors show the largest improvements. | ['Caiming Xiong', 'Chien-Sheng Wu', 'Jesse Vig', 'Prafulla Kumar Choubey', 'Alexander R. Fabbri'] | 2022-11-11 | null | null | null | null | ['sentence-compression'] | ['natural-language-processing'] | [ 4.27873939e-01 4.73337829e-01 -2.74868459e-01 -1.98835298e-01
-1.34347761e+00 -7.28208780e-01 4.20182198e-01 1.06731665e+00
-4.44725275e-01 1.00182831e+00 5.36477387e-01 -2.58885473e-01
-4.62635607e-03 -6.36218548e-01 -9.27911222e-01 7.86869526e-02
1.19152263e-01 5.28611839e-01 2.95917809e-01 -1.35676086e-01
7.51378357e-01 9.08557419e-03 -1.41823077e+00 4.22902465e-01
1.68486845e+00 3.10246766e-01 1.59098744e-01 1.04192758e+00
-2.52452165e-01 5.34031391e-01 -1.02526510e+00 -7.38208354e-01
1.39702722e-01 -5.91270268e-01 -9.24655020e-01 -1.40332520e-01
6.68903112e-01 -3.26308459e-01 -1.93954796e-01 1.02531743e+00
4.34433043e-01 -3.90123352e-02 5.69616079e-01 -8.98316860e-01
-6.01780534e-01 1.12262642e+00 -4.19214785e-01 1.91068605e-01
5.79473078e-01 -1.09776780e-01 1.13890123e+00 -7.06738651e-01
9.16098475e-01 9.01318550e-01 7.08804131e-01 3.05589467e-01
-1.14168024e+00 -1.24363959e-01 6.54468909e-02 9.63172764e-02
-1.00825143e+00 -7.15073884e-01 2.74414062e-01 -6.85649505e-03
1.25026393e+00 7.14634597e-01 3.16412359e-01 6.28046811e-01
2.43652239e-01 6.90300763e-01 5.00876546e-01 -6.73788786e-01
2.00415671e-01 4.73042689e-02 4.60252553e-01 5.52956641e-01
8.69870603e-01 -5.78985870e-01 -6.05055630e-01 -3.10760081e-01
-8.35258961e-02 -2.74989814e-01 -3.50566328e-01 4.16443124e-02
-1.14871490e+00 5.47014177e-01 -2.04924517e-03 3.35924745e-01
-5.72168112e-01 1.04792237e-01 6.89721107e-01 3.13831657e-01
5.80539048e-01 1.03883171e+00 -4.32111442e-01 -4.63639468e-01
-1.48771977e+00 5.60877800e-01 9.55372512e-01 1.19627297e+00
6.48651540e-01 -1.89945072e-01 -6.08837366e-01 8.75316203e-01
-2.76894629e-01 5.32317400e-01 4.54243839e-01 -1.06748712e+00
8.97370398e-01 6.22090161e-01 1.97336912e-01 -8.87332618e-01
-1.99367449e-01 -6.67317688e-01 -6.05035007e-01 -3.35325927e-01
2.98729865e-03 -1.12748720e-01 -7.24459231e-01 1.49589205e+00
6.90027624e-02 -2.70924568e-01 1.26849160e-01 2.71803379e-01
5.97177565e-01 6.87501252e-01 -1.46700099e-01 -5.48712671e-01
1.16036272e+00 -1.02408814e+00 -7.61166036e-01 -3.37090224e-01
1.09811497e+00 -9.10597503e-01 9.42590773e-01 1.43618166e-01
-1.55629051e+00 -2.02817723e-01 -1.13564873e+00 -2.64830410e-01
3.14283483e-02 3.35127473e-01 1.94565542e-02 4.49693471e-01
-9.57197547e-01 1.14673257e+00 -9.58053887e-01 -4.63224947e-01
3.16819459e-01 -1.27163520e-02 -3.80305320e-01 -7.63948932e-02
-8.16602409e-01 9.24132645e-01 4.84654218e-01 -3.99567306e-01
-1.98358327e-01 -1.09250355e+00 -8.34102809e-01 4.61310118e-01
5.90446115e-01 -1.10558248e+00 1.55870259e+00 -5.41513860e-01
-9.78442490e-01 3.08888406e-01 -6.76624119e-01 -7.08893418e-01
6.32956684e-01 -5.21324515e-01 -2.65330821e-01 2.44021788e-01
3.73639703e-01 1.50813773e-01 3.72428477e-01 -1.16588175e+00
-8.85259509e-01 -2.70534217e-01 -9.32626352e-02 3.29074532e-01
-4.97881949e-01 -1.45781236e-02 -6.35464907e-01 -8.72024775e-01
7.07786810e-03 -8.25570524e-01 -6.50985464e-02 -3.99299681e-01
-8.47323418e-01 -5.51368892e-02 4.20587152e-01 -1.18820572e+00
1.96237195e+00 -1.72656310e+00 1.58652365e-02 3.58044240e-03
1.65974230e-01 4.12199259e-01 -1.98713750e-01 7.73002088e-01
3.54840279e-01 6.22273684e-01 -7.75711238e-01 -7.05346644e-01
6.24559447e-02 -2.45723724e-02 -4.14721102e-01 1.09048765e-02
1.47234201e-01 9.02920842e-01 -1.05252397e+00 -5.60712814e-01
-2.69882649e-01 3.78956869e-02 -7.33794689e-01 1.10221200e-01
-3.99532884e-01 -1.67170048e-01 -8.87458995e-02 3.34086835e-01
4.78804111e-01 -8.90936553e-02 2.37231404e-01 4.64015082e-02
-4.22445983e-02 8.99555743e-01 -1.26853681e+00 1.60306716e+00
-4.94624555e-01 7.03404427e-01 -1.82357073e-01 -5.20784318e-01
6.72452748e-01 1.09607510e-01 1.79188326e-01 -5.35351396e-01
-3.58092815e-01 4.87191856e-01 -2.97672093e-01 -4.97765779e-01
1.48402023e+00 2.09011778e-01 -1.21043161e-01 7.21845090e-01
-1.86154038e-01 3.20360959e-02 7.74299502e-01 7.68930793e-01
1.42200470e+00 -1.10742636e-01 3.59029621e-01 5.19600064e-02
2.84343332e-01 1.78220838e-01 6.14937663e-01 1.00526452e+00
3.76351029e-01 7.57738054e-01 7.46186197e-01 8.52861628e-02
-1.39562964e+00 -6.13862514e-01 1.59235850e-01 7.52075672e-01
-5.49825579e-02 -1.10676086e+00 -9.30896044e-01 -8.10152590e-01
7.01957047e-02 1.46709538e+00 -3.83673608e-01 -3.97226214e-01
-8.30896318e-01 -3.44492614e-01 7.71715522e-01 5.98714828e-01
3.13093960e-01 -7.77446628e-01 -6.41402006e-01 3.77971709e-01
-5.43466210e-01 -8.44127536e-01 -5.54255068e-01 -2.71833036e-02
-9.95169282e-01 -8.73624861e-01 -4.86751288e-01 -3.11001807e-01
8.17224264e-01 9.31017846e-02 1.14926994e+00 3.36802125e-01
2.09835723e-01 -2.78685838e-02 -3.91196847e-01 -3.11950713e-01
-1.03359318e+00 6.51876390e-01 -1.65795147e-01 -6.14115715e-01
-1.46553561e-01 -3.55507523e-01 -2.34940886e-01 -2.01431334e-01
-1.17769253e+00 3.44525874e-02 5.21222293e-01 7.81951129e-01
4.89224046e-01 -7.25322515e-02 8.01927805e-01 -1.42633986e+00
7.71637082e-01 -3.53407651e-01 -1.60968184e-01 6.67321742e-01
-8.89133275e-01 3.83623123e-01 9.78227198e-01 9.21971872e-02
-1.04633248e+00 -3.51044744e-01 -2.28936151e-01 -1.06072038e-01
1.18332259e-01 8.17105949e-01 -1.80925839e-02 5.59443057e-01
6.62793636e-01 2.82465756e-01 -2.55581558e-01 -6.41855299e-01
4.70245093e-01 7.49485493e-01 8.14034164e-01 -3.44637662e-01
6.23857021e-01 -2.57911067e-02 -3.14087361e-01 -5.86331129e-01
-7.51990855e-01 -2.48048559e-01 -5.85283399e-01 2.47444287e-01
1.45766065e-01 -7.22954035e-01 4.79171909e-02 2.10627928e-01
-1.22002327e+00 -3.80262509e-02 -5.06871462e-01 1.10754833e-01
-3.51603866e-01 9.63296950e-01 -4.91474301e-01 -4.99224067e-01
-7.99631953e-01 -6.23841822e-01 9.75210786e-01 9.64731127e-02
-5.61940670e-01 -6.97725594e-01 8.51488635e-02 1.67913496e-01
4.38117534e-01 8.08793232e-02 1.18942201e+00 -9.47857261e-01
-3.69666189e-01 -4.04743284e-01 4.80706953e-02 1.48528889e-01
1.53882846e-01 3.09380323e-01 -4.63145792e-01 -2.50120044e-01
-3.58678132e-01 1.52465105e-01 1.17027712e+00 2.24676624e-01
9.27848756e-01 -8.43192816e-01 -3.38410676e-01 3.20271730e-01
1.20950019e+00 -1.74989253e-01 6.60684764e-01 3.68375659e-01
4.87931401e-01 3.87530088e-01 7.33353257e-01 5.90928376e-01
6.10799909e-01 5.72869122e-01 7.87604153e-02 4.13253665e-01
-2.48453304e-01 -6.38073444e-01 1.59061030e-01 1.09497178e+00
2.99406737e-01 -7.74530411e-01 -5.46147525e-01 9.53156352e-01
-1.88294268e+00 -1.08429480e+00 -2.50720322e-01 2.52958155e+00
1.15896773e+00 3.71779084e-01 4.78569344e-02 2.59304732e-01
7.18081713e-01 5.83762303e-02 -4.17333573e-01 -5.43689787e-01
-1.66643932e-01 6.69189766e-02 5.90858757e-01 6.87713802e-01
-8.43864799e-01 9.13738728e-01 6.29544497e+00 6.85790241e-01
-9.62732077e-01 -1.94622248e-01 3.38706225e-01 -3.27285379e-01
-8.35877657e-01 3.59539270e-01 -9.45917964e-01 7.25062490e-01
1.15655291e+00 -7.52418280e-01 7.63666183e-02 7.08243370e-01
1.66519657e-01 -3.39124054e-01 -1.10985625e+00 4.68782336e-01
2.73686051e-01 -1.57935834e+00 2.29959369e-01 -1.49155974e-01
7.68891275e-01 -3.66368234e-01 -4.73871559e-01 3.02426308e-01
1.31717473e-01 -6.41105294e-01 8.36753011e-01 6.82838202e-01
7.15819359e-01 -8.27022851e-01 7.35136509e-01 4.27289844e-01
-7.07037628e-01 1.52887091e-01 -3.72803003e-01 1.32163435e-01
4.53997552e-01 9.15649235e-01 -1.02437282e+00 1.00337994e+00
2.72817343e-01 6.56016886e-01 -8.55318904e-01 1.22051632e+00
-2.16589555e-01 6.44601762e-01 -4.18221474e-01 -1.49097860e-01
-1.46267056e-01 2.12075159e-01 8.88792098e-01 1.58884561e+00
6.91777110e-01 -7.47773573e-02 -1.17135279e-01 6.10452831e-01
-5.46902120e-01 1.86247617e-01 -3.54570329e-01 -1.76924765e-01
1.03072572e+00 9.77608383e-01 -4.21636194e-01 -9.21054661e-01
5.16939461e-02 1.19370246e+00 4.01486397e-01 -3.31777856e-02
-7.14202940e-01 -8.97592962e-01 3.38185936e-01 1.77261218e-01
5.92736483e-01 -1.84899658e-01 -7.29997754e-01 -1.27577972e+00
6.03040695e-01 -9.95092750e-01 3.50738257e-01 -6.26696706e-01
-7.19363511e-01 5.28085113e-01 -7.92844892e-02 -9.39239264e-01
-4.65548068e-01 2.55941182e-01 -6.51893497e-01 7.47146785e-01
-1.42463124e+00 -6.46227062e-01 -1.00721590e-01 -2.88505524e-01
6.44184530e-01 1.72020480e-01 5.76719105e-01 3.33076149e-01
-5.98707497e-01 9.37888682e-01 3.88645947e-01 -1.24760985e-01
8.78133059e-01 -1.51076877e+00 8.60918820e-01 1.39929163e+00
1.07619643e-01 9.43773627e-01 1.06596744e+00 -1.09343731e+00
-1.40669072e+00 -1.29984081e+00 1.69466686e+00 -3.46470267e-01
2.71966219e-01 -4.95396592e-02 -1.21582317e+00 6.98729038e-01
4.34763491e-01 -4.77108121e-01 3.44956279e-01 8.22856501e-02
-3.41416419e-01 -5.52221248e-03 -1.00201654e+00 6.90543771e-01
9.98199344e-01 -2.88091272e-01 -9.46427822e-01 3.29847485e-01
8.75905931e-01 -5.98938942e-01 -8.14922810e-01 2.97878832e-01
2.49395102e-01 -6.81703925e-01 5.22576213e-01 -6.90503716e-01
8.99099171e-01 -3.28504562e-01 6.07925728e-02 -1.66516554e+00
-2.29448780e-01 -7.05950320e-01 -4.94472951e-01 1.64696801e+00
6.67784452e-01 -3.84845614e-01 4.55309033e-01 6.59437835e-01
-5.74737668e-01 -6.04634821e-01 -6.98274016e-01 -8.44266772e-01
1.28850807e-02 -2.13024259e-01 6.74089432e-01 6.31421268e-01
3.48338276e-01 4.22194451e-01 -2.91178674e-01 -5.06875142e-02
2.56108463e-01 2.09688589e-01 9.84971821e-01 -9.12623763e-01
-2.60770738e-01 -5.03120720e-01 1.69264257e-01 -1.17906320e+00
-5.14915213e-02 -9.18026865e-01 1.10571384e-01 -2.03443551e+00
3.67716759e-01 -3.99557620e-01 1.60280272e-01 5.53806424e-01
-7.36743808e-01 -1.81032971e-01 3.54311675e-01 2.67629534e-01
-7.62478232e-01 4.93683875e-01 6.29299581e-01 4.08686697e-02
-3.63867134e-01 -1.00272313e-01 -1.17239499e+00 3.92038822e-01
6.27888262e-01 -7.29866326e-01 2.50292127e-03 -5.48667431e-01
3.66280794e-01 1.87882334e-01 -8.60959664e-03 -1.03797996e+00
4.39918131e-01 1.28434837e-01 9.23434496e-02 -7.81677902e-01
-1.60942912e-01 -3.53018492e-01 1.26177371e-01 4.05334800e-01
-7.85710514e-01 3.71249259e-01 2.20189407e-01 4.99723256e-01
-1.29061624e-01 -6.57410681e-01 4.67440575e-01 4.95824926e-02
-2.84394860e-01 -1.57519102e-01 -3.24361593e-01 3.97753119e-01
6.47419691e-01 -5.83373867e-02 -7.75374651e-01 -3.57465118e-01
-1.82924956e-01 2.11601228e-01 9.39021349e-01 1.99940950e-01
2.80062050e-01 -9.07245874e-01 -9.75655198e-01 -4.83747385e-02
9.29949880e-02 -6.99265227e-02 1.66290045e-01 7.52918124e-01
-6.18803918e-01 5.43274820e-01 2.59585157e-02 -2.82252371e-01
-1.52490008e+00 3.13583255e-01 -1.23638935e-01 -6.05938077e-01
-6.92424834e-01 3.91806632e-01 -5.57478249e-01 -3.63038152e-01
6.66455925e-02 -6.13572001e-01 9.05956328e-02 9.18197620e-04
6.09825373e-01 6.47935569e-01 6.92835748e-01 -2.12833419e-01
-2.14595005e-01 1.69346794e-01 -3.92847359e-01 -1.40672922e-01
1.37850332e+00 -3.01110893e-01 -1.25470683e-01 1.86046124e-01
1.04069257e+00 6.51508451e-01 -9.02502954e-01 -2.23127738e-01
2.74850279e-01 -4.86368120e-01 -1.13345228e-01 -1.06395674e+00
-6.67631626e-01 5.28953373e-01 -2.32071057e-01 3.43510538e-01
1.06107736e+00 -1.81721121e-01 1.24566209e+00 5.54410636e-01
7.49163255e-02 -1.14366102e+00 -3.07332933e-01 5.90588689e-01
8.04978728e-01 -8.57668638e-01 3.51232499e-01 -4.81339008e-01
-6.91703141e-01 1.07861185e+00 3.61835569e-01 5.58806397e-02
-4.44134884e-02 9.75696966e-02 -4.03636664e-01 1.39587820e-01
-1.01924622e+00 2.36882910e-01 2.71797478e-01 2.34699100e-01
3.74709308e-01 -1.35919964e-02 -7.02365518e-01 6.87932611e-01
-5.46543837e-01 -9.89934057e-02 1.03977299e+00 1.18454099e+00
-6.92216098e-01 -1.11944604e+00 -1.14250101e-01 9.45261836e-01
-7.05747902e-01 -3.18962455e-01 -4.65330452e-01 5.18870711e-01
-4.09747124e-01 9.84527290e-01 1.24696612e-01 -1.85560703e-01
5.92486441e-01 2.36368895e-01 3.58572692e-01 -7.23280132e-01
-8.61551225e-01 -1.15124799e-01 6.08897030e-01 -2.58433372e-01
1.01685904e-01 -9.18969810e-01 -1.35563970e+00 -6.59112334e-01
-3.90941083e-01 3.07115674e-01 6.51909351e-01 8.57394159e-01
1.05219924e+00 4.95296448e-01 5.12951136e-01 -1.96196750e-01
-7.96777964e-01 -1.03226626e+00 -1.19470745e-01 4.18937892e-01
3.64501923e-01 -1.03515118e-01 -3.89236957e-01 2.02411532e-01] | [12.360733032226562, 9.41561508178711] |
1bf9bc79-4742-4709-9aa2-b0ad1c2e15c7 | a-two-phase-paradigm-for-joint-entity | 2208.08659 | null | https://arxiv.org/abs/2208.08659v1 | https://arxiv.org/pdf/2208.08659v1.pdf | A Two-Phase Paradigm for Joint Entity-Relation Extraction | An exhaustive study has been conducted to investigate span-based models for the joint entity and relation extraction task. However, these models sample a large number of negative entities and negative relations during the model training, which are essential but result in grossly imbalanced data distributions and in turn cause suboptimal model performance. In order to address the above issues, we propose a two-phase paradigm for the span-based joint entity and relation extraction, which involves classifying the entities and relations in the first phase, and predicting the types of these entities and relations in the second phase. The two-phase paradigm enables our model to significantly reduce the data distribution gap, including the gap between negative entities and other entities, as well as the gap between negative relations and other relations. In addition, we make the first attempt at combining entity type and entity distance as global features, which has proven effective, especially for the relation extraction. Experimental results on several datasets demonstrate that the spanbased joint extraction model augmented with the two-phase paradigm and the global features consistently outperforms previous state-of-the-art span-based models for the joint extraction task, establishing a new standard benchmark. Qualitative and quantitative analyses further validate the effectiveness the proposed paradigm and the global features. | ['Huijun Liu', 'Yuke Ji', 'Jun Ma', 'Shasha Li', 'Jie Yu', 'Hao Xu', 'Bin Ji'] | 2022-08-18 | null | null | null | null | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [-1.91250801e-01 2.39197269e-01 -4.96230781e-01 -1.76678851e-01
-5.18552780e-01 -3.36423337e-01 6.17693663e-01 6.26090527e-01
-4.52461511e-01 1.00224221e+00 -1.22290842e-01 -1.38788551e-01
-4.79934514e-01 -1.17822802e+00 -2.14493901e-01 -7.01601923e-01
-3.11639100e-01 7.94476211e-01 3.19158942e-01 -1.06437072e-01
1.36986032e-01 4.79278684e-01 -1.46299136e+00 -4.00842465e-02
1.09017015e+00 1.13769269e+00 -2.28797570e-01 1.17637552e-01
-4.16555047e-01 5.08938611e-01 -5.85932851e-01 -7.06208289e-01
1.24855168e-01 -4.00922149e-02 -9.48754728e-01 -4.99549299e-01
-7.26956278e-02 -3.36781889e-02 -1.85719118e-01 9.62356567e-01
5.92902660e-01 4.67887223e-02 8.44684899e-01 -1.57027209e+00
-3.28923047e-01 6.46522880e-01 -8.27015758e-01 2.21856907e-01
3.45801026e-01 -3.57383162e-01 1.38632798e+00 -1.16574931e+00
6.21629775e-01 1.05669713e+00 7.28865981e-01 9.31178704e-02
-8.78878295e-01 -9.22813892e-01 2.05194131e-01 4.00033027e-01
-1.79827750e+00 -2.28427187e-01 7.69841969e-01 -4.69883800e-01
1.12619460e+00 3.70643049e-01 6.43166840e-01 4.74060118e-01
4.07376699e-02 6.57832623e-01 7.99877942e-01 -5.99937916e-01
-3.34462486e-02 4.33294594e-01 5.88634372e-01 3.24797362e-01
7.83975780e-01 -3.72533463e-02 -3.79920572e-01 -2.33451813e-01
1.60892442e-01 -1.58497587e-01 -1.17150597e-01 -2.15706006e-01
-9.37944770e-01 6.02849126e-01 3.25900882e-01 6.30265892e-01
-5.25689900e-01 -4.68536556e-01 4.59425002e-01 3.31759155e-02
6.81559682e-01 6.13936782e-01 -7.67304838e-01 9.14057270e-02
-8.06188524e-01 4.91154701e-01 9.82379377e-01 1.05133080e+00
7.62757182e-01 -6.69646800e-01 -4.65510607e-01 8.92726779e-01
3.93373638e-01 2.50512302e-01 2.01484531e-01 -1.00041293e-01
9.68439937e-01 1.25947213e+00 1.02984995e-01 -1.48513615e+00
-5.36011398e-01 -6.68315589e-01 -1.03956449e+00 -3.13159406e-01
2.08523497e-01 -1.49953261e-01 -7.51605928e-01 1.74460673e+00
7.19003439e-01 7.28311064e-03 1.49609506e-01 4.64929909e-01
1.21723163e+00 3.33411187e-01 3.82525772e-01 -6.02765620e-01
1.55807614e+00 -8.02972317e-01 -1.05040801e+00 -3.92104350e-02
1.14358807e+00 -7.39636183e-01 4.47181612e-01 3.42490114e-02
-8.95453393e-01 -2.71122217e-01 -9.50566053e-01 6.94613159e-02
-8.65956604e-01 3.96251500e-01 9.27074075e-01 5.43927908e-01
-4.26373750e-01 4.61328745e-01 -4.30896610e-01 -2.81624466e-01
4.36991751e-01 6.42307103e-01 -5.50985873e-01 1.33888707e-01
-1.78546691e+00 1.21141934e+00 7.44581759e-01 5.33097446e-01
1.98789254e-01 -6.95541978e-01 -9.01958346e-01 3.63365918e-01
4.84186858e-01 -5.68386436e-01 5.07274210e-01 -9.62295681e-02
-5.85557520e-01 6.43686950e-01 -3.70704651e-01 -3.36817205e-01
2.19267756e-01 -2.71087468e-01 -6.77015185e-01 -1.92958727e-01
1.69330135e-01 2.91623712e-01 8.50286931e-02 -1.21641874e+00
-9.56449270e-01 -4.63316590e-01 -4.01692912e-02 3.89417499e-01
-6.34151220e-01 -1.49326371e-02 -4.17003840e-01 -4.61521447e-01
1.57727569e-01 -6.58490121e-01 -2.94744205e-02 -6.53741300e-01
-7.09348440e-01 -8.95995438e-01 8.94926727e-01 -6.11175716e-01
2.07985377e+00 -1.88670850e+00 2.87287659e-03 5.25960147e-01
3.60006720e-01 5.31535029e-01 1.21547438e-01 5.91225803e-01
-3.23177010e-01 3.68809313e-01 3.68020013e-02 -3.27069730e-01
-1.45790160e-01 -7.54694939e-02 -6.84758555e-03 1.04020700e-01
4.40891892e-01 8.04088354e-01 -7.82461584e-01 -9.67361450e-01
-1.43502265e-01 1.51544422e-01 -1.35459781e-01 2.21035570e-01
2.62488246e-01 6.31103199e-03 -5.51953495e-01 8.41194272e-01
9.65792775e-01 -1.58032566e-01 3.20385605e-01 -5.67321062e-01
1.49509713e-01 5.20496786e-01 -1.37190151e+00 9.72218812e-01
-1.24034859e-01 1.65334359e-01 -3.18605185e-01 -1.07193387e+00
9.99000907e-01 5.01475632e-01 8.30852091e-01 -5.80340207e-01
1.86228260e-01 4.00777966e-01 1.63519606e-01 -5.20914435e-01
6.98781908e-01 -1.32805407e-01 -1.54748350e-01 4.20965217e-02
1.92606747e-01 2.67665416e-01 7.68134236e-01 3.15590322e-01
1.00702274e+00 -6.34718835e-02 6.57631934e-01 -6.63154125e-02
7.26939499e-01 -7.73304626e-02 9.25892115e-01 3.16483885e-01
-6.68126196e-02 1.17675751e-01 6.95646286e-01 -2.98808962e-01
-5.85462511e-01 -6.76048934e-01 -8.99474397e-02 4.94923264e-01
3.92188847e-01 -7.52804816e-01 -3.56393844e-01 -1.23187459e+00
8.81636590e-02 4.24948335e-01 -6.29386365e-01 -2.32073262e-01
-3.97126615e-01 -1.22214711e+00 5.11894286e-01 5.53204954e-01
4.50128287e-01 -7.82502949e-01 7.13418936e-03 1.51017204e-01
-4.64805245e-01 -1.22405100e+00 2.28339769e-02 3.58798742e-01
-6.42555952e-01 -1.31452608e+00 -3.19286555e-01 -7.60808170e-01
6.54041767e-01 -9.76794809e-02 1.19015551e+00 2.45831698e-01
-1.13828123e-01 -2.95329303e-01 -5.13948381e-01 -4.21826810e-01
1.04440242e-01 6.11746550e-01 2.47387053e-03 -1.06377617e-01
8.46223116e-01 -5.82086384e-01 -4.51614738e-01 3.39908540e-01
-5.39927065e-01 -7.60908574e-02 8.39597046e-01 8.14302087e-01
3.35971832e-01 4.55315977e-01 1.06714880e+00 -1.23205507e+00
6.46319032e-01 -7.77761519e-01 -1.82663634e-01 6.57477736e-01
-1.08430731e+00 -1.55324772e-01 3.69662315e-01 -3.16331297e-01
-1.04641414e+00 -1.74909666e-01 -8.36955532e-02 1.57972455e-01
9.47892740e-02 8.03704679e-01 -4.74052131e-01 1.83264211e-01
4.42165762e-01 -1.29562348e-01 -4.83858526e-01 -3.54019314e-01
1.43534705e-01 8.23696673e-01 1.30912792e-02 -4.36096281e-01
1.04408360e+00 9.88821983e-02 2.59268939e-01 -5.37558436e-01
-8.55709612e-01 -8.04629207e-01 -8.74331534e-01 2.50797421e-01
5.57179809e-01 -9.67958450e-01 -5.68759263e-01 5.56058943e-01
-1.17620158e+00 3.52001607e-01 -2.81180114e-01 5.21485150e-01
7.32332990e-02 3.14019173e-01 -4.47384685e-01 -1.02076507e+00
-5.35987973e-01 -6.41665041e-01 8.75579298e-01 4.69306082e-01
-4.13150221e-01 -9.49526131e-01 -3.27930376e-02 1.10418007e-01
1.69169948e-01 3.64738643e-01 1.18102884e+00 -1.28232872e+00
-5.07056832e-01 -4.73503739e-01 -5.42472482e-01 6.51159734e-02
3.12023371e-01 1.37287183e-02 -6.20547116e-01 -2.97755450e-02
-3.57569546e-01 -2.02282235e-01 6.01377785e-01 -1.09493390e-01
5.17812073e-01 -1.10869937e-01 -9.20762539e-01 3.18714976e-01
1.35481012e+00 2.22732857e-01 6.09735906e-01 2.97309339e-01
8.05929542e-01 8.17633986e-01 1.34973562e+00 3.55076194e-01
6.78138018e-01 6.65652692e-01 2.00469464e-01 -2.57577747e-01
6.44551888e-02 -1.43567413e-01 -3.30939412e-01 8.35895538e-01
-2.71017313e-01 -3.00730169e-01 -8.40729177e-01 7.75985122e-01
-1.86660171e+00 -8.37423384e-01 -4.06714797e-01 2.05093980e+00
1.11038005e+00 1.66814521e-01 6.55364841e-02 5.17345726e-01
6.18450165e-01 4.10261005e-02 -4.74421680e-02 -6.11719601e-02
-1.20281965e-01 2.00828999e-01 3.19950461e-01 1.43533990e-01
-1.17476773e+00 8.68222296e-01 5.98624039e+00 1.13036823e+00
-8.29834700e-01 -1.69868499e-01 4.54945356e-01 2.33211368e-01
-3.43749136e-01 2.15453550e-01 -1.25034976e+00 3.52345169e-01
6.55976713e-01 -3.52705419e-01 -1.52731463e-01 6.62938237e-01
-1.79848298e-01 -4.31086689e-01 -1.13224924e+00 7.52722204e-01
-2.53648072e-01 -1.00780714e+00 -9.59275886e-02 2.81533778e-01
5.91321528e-01 -4.39558536e-01 -3.74868095e-01 6.43003285e-01
-3.74432057e-02 -8.82151783e-01 2.37302035e-01 4.71705884e-01
6.39345467e-01 -8.72420549e-01 1.36549819e+00 3.71226043e-01
-1.61081541e+00 -6.05339818e-02 -1.16613545e-01 5.60456626e-02
9.83021781e-02 1.13058221e+00 -8.26044738e-01 1.25118899e+00
7.14075029e-01 5.65806806e-01 -6.15017295e-01 1.10736191e+00
-9.32551399e-02 3.14889282e-01 -5.61066151e-01 -1.17977686e-01
-2.39154980e-01 -1.40842348e-01 3.44456285e-01 1.30507529e+00
2.69631475e-01 8.35518762e-02 -1.94999706e-02 6.84645951e-01
-1.53095096e-01 5.28275907e-01 -5.50598264e-01 -1.63217664e-01
8.97375762e-01 1.50496984e+00 -6.30906522e-01 -4.68370825e-01
-3.84022743e-01 2.47358188e-01 6.50146365e-01 2.79103458e-01
-6.64904714e-01 -8.13065052e-01 2.45255187e-01 -8.75632018e-02
1.65482685e-01 -7.17434734e-02 -5.84974229e-01 -1.06189466e+00
3.97762984e-01 -5.53251386e-01 5.30209005e-01 -5.65293357e-02
-1.27271283e+00 5.26626110e-01 2.28780150e-01 -1.30163968e+00
-3.27085704e-01 -3.00640434e-01 -4.58941668e-01 1.04203963e+00
-1.60838020e+00 -1.32414722e+00 -3.34674418e-01 2.49564230e-01
-1.15800403e-01 -6.74774721e-02 9.33317363e-01 7.03030765e-01
-8.20960402e-01 8.82455170e-01 -2.92200595e-01 3.45146716e-01
6.53007627e-01 -1.30465317e+00 7.04404637e-02 6.28966033e-01
1.14105120e-02 9.05515969e-01 5.68962216e-01 -7.08182454e-01
-8.06747556e-01 -8.27745497e-01 1.46380591e+00 -3.02244484e-01
4.62968528e-01 -2.71691322e-01 -8.82027030e-01 4.76103097e-01
-8.72717276e-02 9.87675413e-02 9.14171755e-01 6.71501696e-01
-2.43378326e-01 -3.32651168e-01 -1.22737026e+00 4.20309126e-01
9.96992171e-01 -1.41544312e-01 -6.29238129e-01 1.24300368e-01
5.84370852e-01 -1.80844665e-01 -1.26603317e+00 1.14513719e+00
5.80739677e-01 -8.14125896e-01 8.13828647e-01 -4.47127193e-01
3.38611722e-01 -2.58035630e-01 1.31306097e-01 -1.15072703e+00
-2.18031436e-01 -2.60551810e-01 -7.45279253e-01 1.95162714e+00
8.58879745e-01 -7.56553769e-01 7.11302578e-01 5.25233746e-01
4.64867622e-01 -1.41841364e+00 -7.22820520e-01 -7.63710499e-01
-8.31197426e-02 6.85930625e-02 7.88007200e-01 9.91384804e-01
1.71043560e-01 7.07094252e-01 -1.68648019e-01 8.76402482e-02
2.15681016e-01 3.69986236e-01 7.32370794e-01 -1.50244188e+00
5.70034012e-02 -3.21102947e-01 -4.26946193e-01 -7.97496736e-01
2.54027434e-02 -6.32142365e-01 -1.73817039e-01 -1.69809937e+00
3.18323821e-01 -1.05724192e+00 -4.36983109e-01 4.40276802e-01
-7.03031123e-01 -8.15290064e-02 -1.69434384e-01 2.21993417e-01
-4.32753623e-01 6.72554433e-01 9.81438279e-01 1.73440561e-01
-3.15705657e-01 2.04595685e-01 -8.39517891e-01 5.75428128e-01
5.46518505e-01 -5.78539550e-01 -4.63196099e-01 1.51081458e-01
4.27813619e-01 -3.33780080e-01 -1.09235667e-01 -6.54973328e-01
3.39893639e-01 -1.87215194e-01 3.38717788e-01 -9.34327602e-01
3.43025953e-01 -1.09915388e+00 -1.35187671e-01 1.62938669e-01
8.19738284e-02 -8.51079002e-02 -5.21542551e-03 5.28510928e-01
-6.27184510e-01 -1.44744664e-01 2.45100036e-01 2.90223837e-01
-3.73592794e-01 3.60900372e-01 4.38659161e-01 1.62141159e-01
1.32771504e+00 -1.48828104e-01 -5.37890196e-01 8.27305168e-02
-7.00233817e-01 5.35439849e-01 -8.80695786e-03 3.56212050e-01
3.13852757e-01 -1.48290133e+00 -5.90847552e-01 3.52687836e-02
2.25361720e-01 3.30529302e-01 2.02479973e-01 1.22396648e+00
-1.02573641e-01 3.61375242e-01 6.91788718e-02 -2.15804324e-01
-1.53909588e+00 5.73560536e-01 9.90712419e-02 -1.30952179e+00
-8.57130140e-02 7.57885575e-01 -7.74520338e-02 -4.53452259e-01
1.14620559e-01 -9.83245820e-02 -7.62205958e-01 5.80147922e-01
1.39230385e-01 4.57793683e-01 1.99853137e-01 -5.98504245e-01
-6.97774351e-01 5.38679659e-01 -2.92614967e-01 1.06002256e-01
1.22392833e+00 -1.39664069e-01 -4.77925450e-01 3.04282337e-01
1.02869964e+00 3.19618076e-01 -3.29045117e-01 -3.25323910e-01
5.11052012e-01 -4.39117491e-01 -4.46995527e-01 -6.79705441e-01
-9.07147288e-01 7.08331406e-01 9.03738365e-02 6.82392478e-01
1.22134197e+00 -1.17930584e-01 9.17038560e-01 7.36953840e-02
3.75400662e-01 -1.04193342e+00 -4.64716285e-01 4.40443605e-01
5.24496257e-01 -1.21817553e+00 4.21259731e-01 -1.28015924e+00
-3.21393043e-01 7.99452603e-01 8.90304208e-01 1.37443826e-01
9.26585376e-01 4.24716383e-01 -3.26976478e-01 -2.54652768e-01
-8.37577820e-01 -2.82910556e-01 5.65545321e-01 4.78634745e-01
5.90412378e-01 1.09439708e-01 -9.55920577e-01 8.88942897e-01
-1.00996867e-01 -5.82891703e-03 -1.52745217e-01 8.96044075e-01
-1.86479032e-01 -1.48868704e+00 -1.68199409e-02 5.93629718e-01
-5.57061791e-01 -1.56292647e-01 -4.73356485e-01 1.02819908e+00
4.57364798e-01 1.01141429e+00 -2.48008311e-01 -6.81039035e-01
5.41625977e-01 2.17771158e-01 2.84770906e-01 -6.72359705e-01
-6.85011625e-01 -2.83679038e-01 5.53274214e-01 -1.85253322e-01
-4.82682616e-01 -3.82220060e-01 -1.28999043e+00 -7.53521472e-02
-1.18410468e+00 4.79990572e-01 3.96857589e-01 1.20529890e+00
5.53851068e-01 6.09301090e-01 6.05809569e-01 -2.68732369e-01
-5.06064713e-01 -1.41837287e+00 -6.43555701e-01 4.78568912e-01
1.09582543e-02 -9.31008875e-01 -3.55772257e-01 -4.41254228e-01] | [9.192660331726074, 8.591378211975098] |
18b1950f-7db6-490e-90d7-180a724e5aa0 | mitigating-spurious-correlations-for-self | 2212.04282 | null | https://arxiv.org/abs/2212.04282v1 | https://arxiv.org/pdf/2212.04282v1.pdf | Mitigating Spurious Correlations for Self-supervised Recommendation | Recent years have witnessed the great success of self-supervised learning (SSL) in recommendation systems. However, SSL recommender models are likely to suffer from spurious correlations, leading to poor generalization. To mitigate spurious correlations, existing work usually pursues ID-based SSL recommendation or utilizes feature engineering to identify spurious features. Nevertheless, ID-based SSL approaches sacrifice the positive impact of invariant features, while feature engineering methods require high-cost human labeling. To address the problems, we aim to automatically mitigate the effect of spurious correlations. This objective requires to 1) automatically mask spurious features without supervision, and 2) block the negative effect transmission from spurious features to other features during SSL. To handle the two challenges, we propose an invariant feature learning framework, which first divides user-item interactions into multiple environments with distribution shifts and then learns a feature mask mechanism to capture invariant features across environments. Based on the mask mechanism, we can remove the spurious features for robust predictions and block the negative effect transmission via mask-guided feature augmentation. Extensive experiments on two datasets demonstrate the effectiveness of the proposed framework in mitigating spurious correlations and improving the generalization abilities of SSL models. | ['Fuli Feng', 'Yang Zhang', 'Wenjie Wang', 'Yiyan Xu', 'Xinyu Lin'] | 2022-12-08 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [ 3.50393057e-01 -2.35928208e-01 -2.77256578e-01 -6.07659459e-01
-2.79844642e-01 -3.82135570e-01 4.31080163e-01 1.79380029e-02
5.50825819e-02 4.99826491e-01 2.80796230e-01 -9.79681090e-02
-5.23476005e-01 -7.06967533e-01 -7.00590670e-01 -5.77463269e-01
-5.92285432e-02 -1.52442873e-01 1.67347565e-01 -3.18231463e-01
4.43075716e-01 1.97027460e-01 -1.78372872e+00 3.19943160e-01
1.36009729e+00 9.21935499e-01 7.84467682e-02 9.46467463e-03
-2.59011686e-01 4.60343599e-01 -3.76313150e-01 -1.60477087e-02
4.40137893e-01 -4.77480888e-01 -3.64179969e-01 1.02503344e-01
4.16701525e-01 -1.35128513e-01 -8.32692906e-02 1.12080503e+00
3.02036256e-01 3.07702422e-01 4.05124098e-01 -1.48631954e+00
-9.34764683e-01 7.10053325e-01 -5.47586441e-01 -8.73743668e-02
4.75023359e-01 8.76724273e-02 1.27422762e+00 -1.12244737e+00
1.73806712e-01 1.20187163e+00 8.66415560e-01 4.89090025e-01
-1.28789055e+00 -9.55357432e-01 6.40134335e-01 -1.50287030e-02
-1.45530355e+00 -3.78052443e-01 9.81853366e-01 -4.15771157e-01
6.30179286e-01 3.72973114e-01 5.01883864e-01 1.04353535e+00
-1.45287842e-01 9.08355713e-01 1.08475757e+00 -3.67724687e-01
1.17984332e-01 6.27721846e-01 5.48243821e-01 6.43383801e-01
3.83634210e-01 1.31871611e-01 -6.94612265e-01 -3.16080451e-01
5.54718614e-01 4.70685422e-01 -2.52327859e-01 -4.67417777e-01
-9.78727698e-01 7.31228471e-01 3.37474614e-01 2.39036426e-01
-1.32518202e-01 -4.35328871e-01 2.33753219e-01 4.42405641e-01
4.07421201e-01 7.13014126e-01 -7.34982073e-01 2.04921469e-01
-6.92810714e-01 2.49414012e-01 4.51595128e-01 9.95900989e-01
9.03028667e-01 -1.25221908e-01 -2.39505365e-01 1.09502709e+00
3.74247879e-01 4.33789015e-01 6.51259661e-01 -3.66524160e-01
2.62916297e-01 9.39440250e-01 2.08745167e-01 -1.38996637e+00
-4.76424187e-01 -8.56693566e-01 -7.41532207e-01 -1.25351980e-01
-3.44706886e-02 3.48936860e-03 -6.58867359e-01 1.94699585e+00
4.56479669e-01 3.20296943e-01 -2.40242794e-01 8.58886600e-01
6.46458745e-01 3.92867267e-01 -1.20556794e-01 -3.04917097e-01
7.34179795e-01 -9.98972893e-01 -6.76033616e-01 -6.95844144e-02
9.10128593e-01 -7.00766802e-01 1.40208673e+00 3.87954265e-01
-6.54734850e-01 -7.92456269e-01 -1.24400449e+00 3.87628227e-01
-2.17825472e-01 1.37345076e-01 8.14707160e-01 8.54951560e-01
-4.86526400e-01 7.97818601e-01 -6.09123647e-01 -1.54080003e-01
3.92891169e-01 7.32274115e-01 -9.12098736e-02 1.24035381e-01
-1.34481525e+00 3.96732181e-01 1.16779961e-01 7.34002665e-02
-2.76658952e-01 -9.05806363e-01 -8.04297686e-01 1.85380831e-01
4.92223293e-01 -2.94632912e-01 8.57925236e-01 -1.11848438e+00
-1.34835112e+00 -2.53966339e-02 2.24370183e-03 -5.68797998e-02
2.52813399e-01 -5.37334383e-01 -7.85105646e-01 -5.65951467e-01
1.59367621e-01 1.66681632e-01 1.03992951e+00 -1.32693231e+00
-8.16145897e-01 -3.25692356e-01 -1.20365053e-01 1.82062402e-01
-9.83246207e-01 -3.06867719e-01 -8.18361789e-02 -8.23597252e-01
2.57846892e-01 -9.72007394e-01 -2.74000853e-01 -2.72289783e-01
-2.75159627e-01 -3.44753742e-01 8.99240732e-01 3.22340312e-03
1.45877516e+00 -2.25956631e+00 -3.08547527e-01 4.99910593e-01
1.18746504e-01 4.96216148e-01 -4.42492843e-01 2.78174102e-01
-6.26781508e-02 -3.26380343e-03 1.25114679e-01 -2.05705762e-01
-1.19641691e-01 1.36919245e-01 -4.45986390e-01 1.98612332e-01
2.06254184e-01 5.50910711e-01 -9.19938684e-01 -2.41030574e-01
2.11035952e-01 3.13886702e-01 -9.84791696e-01 3.34191114e-01
-1.92785755e-01 4.22604650e-01 -6.14943862e-01 4.56902742e-01
8.60717475e-01 -3.00264806e-01 1.19096212e-01 -4.85349804e-01
-2.12186664e-01 4.83034551e-01 -1.60293460e+00 1.47078347e+00
-5.02537429e-01 -1.18218258e-01 -2.67356873e-01 -9.15118396e-01
1.15384483e+00 -4.07248326e-02 6.88057065e-01 -5.68311214e-01
-1.66988298e-02 1.68767989e-01 -2.35754345e-02 -3.66278887e-01
3.85422319e-01 4.85041551e-03 -2.84370948e-02 6.00958049e-01
7.93288574e-02 4.90513980e-01 -2.46382087e-01 8.66091922e-02
9.74555016e-01 1.55660510e-01 1.39835879e-01 -2.04328582e-01
6.65056467e-01 -2.76459098e-01 8.60264659e-01 9.65835690e-01
1.37425149e-02 2.59028614e-01 1.63680166e-02 -3.81436914e-01
-4.48738873e-01 -8.70395601e-01 -3.56575996e-02 1.29558456e+00
4.74636018e-01 -7.59564996e-01 -4.01521116e-01 -1.15979040e+00
2.18494177e-01 6.37339234e-01 -5.81308544e-01 -7.74291217e-01
-2.85063863e-01 -7.63919592e-01 1.10131435e-01 4.88051385e-01
3.82908434e-01 -8.69495392e-01 4.38722409e-02 2.10398704e-01
2.07956702e-01 -6.84445381e-01 -7.40783155e-01 2.62605369e-01
-6.63164735e-01 -8.35129261e-01 -7.11373240e-02 -7.38025069e-01
8.81753087e-01 9.59916770e-01 6.44278586e-01 4.19583231e-01
-1.14486568e-01 3.26105915e-02 -6.72395468e-01 -2.17222229e-01
-1.47398233e-01 1.35921523e-01 5.67843556e-01 3.56099308e-01
6.09530330e-01 -7.65633225e-01 -5.54317355e-01 7.37822413e-01
-7.82478631e-01 -2.25748960e-02 6.07445836e-01 1.18932807e+00
3.28981757e-01 3.21443647e-01 9.59386230e-01 -1.22609806e+00
6.50203288e-01 -5.34626365e-01 -3.89451057e-01 2.10272461e-01
-1.15097547e+00 1.23077728e-01 1.15385199e+00 -7.04425752e-01
-1.11556661e+00 1.19720683e-01 1.06808968e-01 -3.45831573e-01
-1.30276278e-01 5.51721036e-01 -4.26872522e-01 -2.20783293e-01
6.70433342e-01 3.79847646e-01 -1.69421077e-01 -6.73069477e-01
2.59296656e-01 8.55010331e-01 8.03151429e-02 -5.75370312e-01
1.05365968e+00 2.95790166e-01 -2.86870390e-01 -6.82030320e-01
-1.42701530e+00 -5.20425618e-01 -6.86527550e-01 4.61276732e-02
1.43894479e-01 -7.63051271e-01 -5.11590302e-01 1.15951449e-01
-5.74151933e-01 1.62361875e-01 -2.07681164e-01 5.27901292e-01
-2.34271958e-01 3.19384217e-01 -1.29645631e-01 -7.59429395e-01
-3.07363629e-01 -9.16224718e-01 7.09947050e-01 3.48392665e-01
-3.05994660e-01 -6.94826365e-01 7.45512694e-02 3.07435125e-01
5.09891152e-01 -4.00224149e-01 8.48562002e-01 -9.34552133e-01
-3.39082807e-01 -3.74970973e-01 -2.45117769e-01 4.06799555e-01
7.53694534e-01 -2.34809726e-01 -8.62033427e-01 -4.25031424e-01
-4.53331694e-02 -1.94737270e-01 6.73868716e-01 -2.14972738e-02
1.29079700e+00 -2.83626437e-01 -3.55230808e-01 5.85353553e-01
1.02038562e+00 1.55878887e-01 6.64592534e-02 1.19320400e-01
9.32614326e-01 4.40729380e-01 8.82529855e-01 7.69508183e-01
1.90879986e-01 7.07675695e-01 6.93429783e-02 -1.71395794e-01
-2.43992787e-02 -6.55919373e-01 3.34504128e-01 9.51831520e-01
2.77374417e-01 1.84941486e-01 -2.85073131e-01 2.81729966e-01
-2.11350298e+00 -7.25102305e-01 3.19024846e-02 2.29247427e+00
7.45968938e-01 2.57915288e-01 1.88699931e-01 1.95577562e-01
6.45344019e-01 6.63473010e-02 -6.59203231e-01 -1.39969423e-01
3.89083400e-02 -5.43153174e-02 1.36167273e-01 1.91354975e-01
-1.16868103e+00 9.02356863e-01 5.52760172e+00 6.56803608e-01
-1.10244179e+00 -1.66596547e-01 5.67945987e-02 -1.33059874e-01
-3.87556225e-01 -4.90352027e-02 -1.01108038e+00 5.83964646e-01
3.83664876e-01 -9.13685039e-02 5.26496887e-01 1.12627435e+00
3.26421469e-01 3.99798483e-01 -1.10090196e+00 8.21834028e-01
1.41032726e-01 -9.64328766e-01 3.53051692e-01 -7.95484111e-02
8.71256053e-01 -3.64644766e-01 1.90451801e-01 6.50584161e-01
2.53291547e-01 -7.50233889e-01 4.47652042e-01 3.82547826e-01
4.28518325e-01 -9.39047277e-01 5.59272885e-01 3.24936479e-01
-1.14387453e+00 -3.08062732e-01 -4.14093733e-01 -2.86253780e-01
-2.53292322e-01 6.79499388e-01 -7.43299067e-01 4.45390284e-01
5.00404418e-01 1.06857419e+00 -6.48441374e-01 9.73115027e-01
-1.15826085e-01 7.25448012e-01 -1.31646797e-01 -1.40688866e-01
3.18020470e-02 -2.72112757e-01 4.83625203e-01 1.03801584e+00
2.27307513e-01 -5.78161068e-02 5.61437309e-01 7.32930362e-01
1.64978616e-02 4.43663985e-01 -6.08108222e-01 1.60170905e-02
6.22250199e-01 1.18425691e+00 -4.39000010e-01 -2.88402811e-02
-7.24106789e-01 1.00176466e+00 3.89950633e-01 1.37883887e-01
-7.35839486e-01 -3.82838577e-01 7.65982866e-01 2.62532949e-01
3.88928801e-01 -9.66417715e-02 -4.80023831e-01 -1.26888919e+00
6.44412637e-03 -9.98850822e-01 2.70037621e-01 -2.24788219e-01
-1.67526901e+00 2.71661282e-01 -3.53476226e-01 -1.59233320e+00
2.42366359e-01 -1.56063765e-01 -4.85804170e-01 6.11958027e-01
-1.27622712e+00 -1.26682377e+00 -2.99514323e-01 6.25797331e-01
5.33969164e-01 -2.74851859e-01 8.20014060e-01 4.89100486e-01
-7.94652224e-01 1.06104422e+00 2.40870222e-01 -2.66562611e-01
9.84500527e-01 -9.88196671e-01 1.25393733e-01 5.92733085e-01
2.21139833e-01 1.23249924e+00 6.04696691e-01 -7.53802598e-01
-1.43330348e+00 -1.34887850e+00 6.12129569e-01 -3.07338148e-01
5.20243466e-01 -4.46699172e-01 -1.04420233e+00 5.16318560e-01
-5.13124347e-01 1.17371716e-01 9.25641716e-01 6.61303580e-01
-6.38019264e-01 -3.16533923e-01 -9.87183630e-01 6.32561922e-01
1.33378291e+00 -3.51915359e-01 -4.97782081e-01 2.75388062e-01
6.80616558e-01 1.44076183e-01 -6.82261467e-01 5.60969472e-01
7.89154410e-01 -7.29460657e-01 8.78784418e-01 -9.16439712e-01
2.35410243e-01 -4.88845170e-01 -1.31874695e-01 -1.48221266e+00
-8.04295123e-01 -6.13521457e-01 -4.47621569e-02 1.47913623e+00
3.83287966e-01 -4.90364999e-01 8.03263068e-01 6.71986401e-01
-2.37548262e-01 -6.58653855e-01 -4.07207757e-01 -1.00382185e+00
-3.18086267e-01 -3.19573581e-01 8.87609303e-01 1.26164234e+00
2.05528289e-01 4.77417439e-01 -8.25940430e-01 3.06443095e-01
5.17314255e-01 4.91730392e-01 9.98444378e-01 -1.42921817e+00
-4.54786777e-01 -2.34619081e-01 -7.05054253e-02 -1.21428382e+00
1.33662418e-01 -8.87132704e-01 1.31309092e-01 -9.30643439e-01
3.00517380e-01 -9.64890420e-01 -7.81476915e-01 5.68901956e-01
-4.77954388e-01 1.24535300e-01 -1.96735188e-02 1.94203272e-01
-9.84558880e-01 6.63225353e-01 1.12430274e+00 1.55552089e-01
-5.92328668e-01 2.82124579e-01 -1.07336211e+00 8.54455769e-01
6.31620884e-01 -5.24565101e-01 -7.69914925e-01 -1.26718909e-01
3.61595541e-01 -4.35344636e-01 -9.88462288e-03 -8.19798589e-01
1.15868397e-01 -5.26203215e-01 4.72846448e-01 -5.14677465e-01
4.85675186e-02 -9.81288850e-01 -1.15210228e-01 2.03606844e-01
-5.14087319e-01 -4.77781087e-01 -1.25383735e-01 6.72200263e-01
7.87890516e-03 -7.59342015e-02 6.35135651e-01 1.28799438e-01
-4.58263934e-01 2.49582276e-01 -1.00389294e-01 -2.21482813e-01
7.76920617e-01 -1.03290237e-01 -4.86361682e-02 -1.11287676e-01
-5.05007446e-01 2.84559995e-01 4.22950596e-01 7.80504405e-01
5.01616120e-01 -1.37680507e+00 -2.38443345e-01 8.35103095e-01
3.67050618e-01 -3.35291088e-01 3.19307417e-01 7.21698165e-01
4.17492241e-01 3.42941344e-01 3.07973218e-03 -3.94237936e-01
-1.27714086e+00 7.10896194e-01 2.93618930e-03 -1.14698999e-01
-4.74009454e-01 8.68499100e-01 2.19098613e-01 -6.57474756e-01
2.78644621e-01 2.25079656e-02 -3.47323120e-01 -8.25350732e-03
6.43669784e-01 2.20575899e-01 1.86985746e-01 -3.46098512e-01
-3.68059546e-01 4.77219284e-01 -6.01393819e-01 4.47514921e-01
1.50688899e+00 -3.55609566e-01 2.97192067e-01 3.35760534e-01
1.05990636e+00 2.20514759e-01 -1.04275894e+00 -5.23862600e-01
5.59469685e-02 -8.18961084e-01 2.19204545e-01 -8.29458117e-01
-9.06741381e-01 5.32259762e-01 5.63313186e-01 3.42049211e-01
1.09311604e+00 -3.80719990e-01 1.05699122e+00 4.16875064e-01
2.94319570e-01 -1.15998447e+00 1.82374835e-01 4.77261066e-01
5.01190364e-01 -1.34622896e+00 7.07989633e-02 -6.69771552e-01
-4.85230446e-01 7.37062335e-01 7.88527489e-01 -2.15655476e-01
9.26345468e-01 1.04162484e-01 -1.42911404e-01 -6.42930344e-02
-7.23146439e-01 -2.80101337e-02 5.24212301e-01 4.62542117e-01
4.76865351e-01 -1.60862356e-02 -3.78784835e-01 1.20536745e+00
-1.10938326e-01 5.46358377e-02 1.39800221e-01 9.69055533e-01
-4.61412519e-01 -1.32923365e+00 -1.83384791e-01 8.09537947e-01
-1.08031496e-01 -9.70488861e-02 -4.32742178e-01 3.68667424e-01
3.31599593e-01 1.01950181e+00 -3.07174444e-01 -9.60193574e-01
5.43201804e-01 -2.60882918e-02 1.33785427e-01 -7.47608542e-01
-7.21806526e-01 1.55428767e-01 -1.09475888e-01 -4.72723275e-01
-2.93549299e-01 -6.32885635e-01 -1.07554400e+00 1.82988849e-02
-8.61536860e-01 3.45727772e-01 4.06017900e-01 1.07108867e+00
5.77923238e-01 4.45748299e-01 1.32803071e+00 -5.23712456e-01
-9.19884384e-01 -8.72292519e-01 -7.58885443e-01 8.55833411e-01
2.95209914e-01 -1.12664258e+00 -6.01690114e-01 -2.27809221e-01] | [10.103570938110352, 5.51362943649292] |
9c8446b0-efd5-495a-8478-b0bd1e5e4199 | deep-learning-based-multi-label-image | 2301.04212 | null | https://arxiv.org/abs/2301.04212v1 | https://arxiv.org/pdf/2301.04212v1.pdf | Deep Learning based Multi-Label Image Classification of Protest Activities | With the rise of internet technology amidst increasing rates of urbanization, sharing information has never been easier thanks to globally-adopted platforms for digital communication. The resulting output of massive amounts of user-generated data can be used to enhance our understanding of significant societal issues particularly for urbanizing areas. In order to better analyze protest behavior, we enhanced the GSR dataset and manually labeled all the images. We used deep learning techniques to analyze social media data to detect social unrest through image classification, which performed good in predict multi-attributes, then also used map visualization to display protest behaviors across the country. | ['Jialu Wang', 'Kosaku Sato', 'Yingzhou Lu'] | 2023-01-10 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [-2.12136477e-01 -3.44613381e-02 2.01505497e-02 -1.54027328e-01
-5.77419877e-01 -4.52815354e-01 8.41869831e-01 7.20245004e-01
-4.11632836e-01 7.62679219e-01 6.50950074e-01 -5.73275030e-01
4.72251624e-02 -1.27370155e+00 -4.49505091e-01 -4.77910578e-01
-2.37336218e-01 3.09197068e-01 5.70153212e-03 -7.01542556e-01
3.32947820e-01 5.30491292e-01 -1.56812704e+00 3.14981997e-01
1.22655368e+00 5.71703017e-01 1.00506075e-01 2.80071139e-01
-3.26812178e-01 6.92313910e-01 -4.01793331e-01 -2.39515558e-01
1.29357457e-01 -1.98060960e-01 -5.83816350e-01 -2.56556440e-02
2.42569551e-01 -1.13842443e-01 -4.38559800e-01 1.11079407e+00
2.45177478e-01 -1.29404413e-02 5.26516557e-01 -8.42922688e-01
-9.10109460e-01 7.89347887e-01 -8.77584398e-01 4.55268234e-01
3.90340865e-01 2.05067825e-02 6.81853652e-01 -4.88151103e-01
9.83101368e-01 1.56832242e+00 2.72910357e-01 -3.82666618e-01
-1.23332751e+00 -8.25921357e-01 -1.04481198e-01 4.10730362e-01
-1.38227236e+00 -1.24877222e-01 8.02235723e-01 -8.57351840e-01
6.56069577e-01 2.28542998e-01 9.79202867e-01 1.10669971e+00
1.59710363e-01 4.91090953e-01 1.37714970e+00 -8.96597281e-02
5.02155349e-02 9.24683064e-02 -1.16416790e-01 5.96043110e-01
3.71366382e-01 -3.80735606e-01 -2.43149981e-01 8.22251588e-02
2.04141110e-01 2.45976821e-01 1.94034994e-01 2.25804582e-01
-1.21692359e+00 1.17278314e+00 1.11474872e+00 4.97888148e-01
-2.54915267e-01 -1.46886840e-01 2.59180963e-01 4.19033438e-01
1.10764825e+00 3.94065082e-01 1.17509263e-02 -1.04682438e-01
-8.56305301e-01 1.31650716e-01 2.23135173e-01 -8.96983296e-02
1.09166157e+00 -4.63163815e-02 3.87548745e-01 9.27191615e-01
1.88560396e-01 7.85592616e-01 4.55405086e-01 -7.97557771e-01
7.04465210e-01 1.32616103e+00 2.95174140e-02 -1.91911328e+00
-7.96283662e-01 -1.69172600e-01 -1.04839396e+00 1.97063476e-01
3.90335411e-01 -1.27859667e-01 -8.17394853e-01 1.38333452e+00
2.37386152e-01 -2.23294005e-01 -2.81343132e-01 7.94120312e-01
4.24956292e-01 8.45463336e-01 3.28753263e-01 1.88396856e-01
1.03203094e+00 -7.83021599e-02 -6.43129885e-01 -9.39992140e-04
9.49055135e-01 -4.83256668e-01 1.08190405e+00 1.13441974e-01
-3.44477803e-01 -3.25243771e-01 -9.68243062e-01 7.84861073e-02
-1.16875970e+00 -1.18021592e-01 5.23574948e-01 3.88399869e-01
-9.59493935e-01 5.35721421e-01 -6.93080008e-01 -9.72999930e-01
1.25878632e+00 1.18455198e-03 -6.09054208e-01 1.13924995e-01
-1.16517639e+00 9.08302009e-01 4.63029802e-01 -2.59576648e-01
-3.84571642e-01 -4.38080639e-01 -8.40674520e-01 -5.66094220e-02
-6.17949180e-02 -7.84801543e-02 3.01874131e-01 -1.08775866e+00
-8.01948428e-01 1.20894158e+00 3.42712790e-01 -3.59749734e-01
6.90710008e-01 -1.30489632e-01 -8.27425003e-01 -1.48208797e-01
3.60879540e-01 7.21085012e-01 3.66170734e-01 -1.11085045e+00
-6.48003876e-01 -7.96679974e-01 -1.18553571e-01 -1.99226234e-02
-7.87431836e-01 5.54650724e-02 3.98374498e-01 -5.35008192e-01
2.86763340e-01 -9.08549130e-01 -3.02083671e-01 -2.94555902e-01
-4.84184951e-01 -1.22653961e-01 1.43518853e+00 -9.99144197e-01
1.22931552e+00 -2.25980902e+00 -3.37566040e-03 2.38772437e-01
3.04378211e-01 1.40126705e-01 3.22857201e-02 7.72023976e-01
8.72047544e-02 7.19414234e-01 -2.75229245e-01 -1.68974012e-01
-2.04664871e-01 7.64482170e-02 -3.74845475e-01 6.21892929e-01
5.62450513e-02 6.21556759e-01 -9.99482930e-01 -4.38970715e-01
6.70776486e-01 4.90305632e-01 -2.46998027e-01 -2.65925735e-01
-2.01356895e-02 6.18738770e-01 -6.30066276e-01 6.56687140e-01
4.82863098e-01 -4.84714746e-01 1.43489555e-01 1.83089823e-01
-6.32843137e-01 -1.68843061e-01 -6.00972712e-01 1.35852027e+00
-4.20995653e-01 1.30492651e+00 -2.11500779e-01 -8.59737039e-01
9.95895863e-01 -2.60545224e-01 4.99520689e-01 -1.10181844e+00
4.48338807e-01 4.14016955e-02 -8.65453556e-02 -6.76481485e-01
3.74478966e-01 3.84584427e-01 -3.93510997e-01 4.55509245e-01
-4.71409261e-01 1.94719866e-01 5.73857091e-02 4.63873953e-01
8.10413122e-01 -4.58433568e-01 3.25314492e-01 -5.95229745e-01
3.99124354e-01 2.96521515e-01 1.63625628e-01 2.57910937e-01
-1.74063891e-01 4.60372657e-01 4.41391349e-01 -1.18587184e+00
-1.23325443e+00 -1.06057024e+00 -2.16949105e-01 8.57473314e-01
-2.57860329e-02 -1.51272565e-01 -3.69364381e-01 -4.04452294e-01
3.55793923e-01 6.08739674e-01 -5.91638625e-01 2.97555000e-01
-4.95978147e-01 -1.16540432e+00 7.18609169e-02 -3.64112481e-02
9.21535611e-01 -1.11119306e+00 -5.04823446e-01 1.43639609e-01
-2.32493758e-01 -9.12208200e-01 3.94905895e-01 -3.52459878e-01
-4.42447215e-01 -1.30859005e+00 -4.54159886e-01 -4.94476020e-01
6.25026941e-01 2.40207553e-01 8.87626052e-01 8.57835412e-02
-5.96391559e-01 -2.73499954e-02 -3.43233675e-01 -4.05945450e-01
-4.97685462e-01 2.53942370e-01 -6.31036013e-02 1.63193613e-01
3.03823352e-01 -1.04616201e+00 -7.98208296e-01 -7.32728690e-02
-6.81140959e-01 3.05405587e-01 3.18058759e-01 -1.85391400e-03
-9.74890962e-02 1.98462270e-02 6.64981604e-01 -6.45607293e-01
5.70658624e-01 -1.14387083e+00 -6.08473003e-01 -1.86143443e-01
-2.24530354e-01 -2.29906544e-01 5.19608915e-01 -8.30449015e-02
-7.99344182e-01 -1.28673837e-01 -3.52580920e-02 2.23796725e-01
-3.55274022e-01 6.73395574e-01 2.42988080e-01 3.49437803e-01
6.97000563e-01 -2.91983515e-01 -7.78152868e-02 -3.83482128e-01
4.67282504e-01 1.01987338e+00 1.43924773e-01 3.20048258e-02
8.25056970e-01 9.07180369e-01 -1.19244643e-01 -1.36713850e+00
-7.83972859e-01 -1.10708430e-01 -5.75020254e-01 -6.03966355e-01
1.15183234e+00 -1.04234731e+00 -7.14960814e-01 5.71585238e-01
-8.28503728e-01 -2.39240855e-01 8.18495899e-02 2.59221792e-01
7.94022605e-02 1.78469002e-01 -2.43045881e-01 -7.53720343e-01
-6.78833872e-02 -7.70433784e-01 5.76358080e-01 2.67934650e-01
-2.13263974e-01 -1.11556113e+00 3.62040550e-01 5.61730802e-01
6.53341711e-01 9.22657371e-01 1.07913816e+00 -5.27898967e-01
-5.62287390e-01 -1.03318244e-01 -6.13138497e-01 -5.41439652e-03
2.86805183e-01 1.23748202e-02 -8.59762847e-01 -7.91857466e-02
-7.87427187e-01 -3.87467504e-01 1.01813531e+00 3.14020216e-01
1.28329027e+00 -5.68054378e-01 -5.17233968e-01 2.44731113e-01
1.45119739e+00 6.67107403e-02 8.97860885e-01 7.70235956e-01
8.43994200e-01 6.68659091e-01 6.41193613e-02 5.93892515e-01
6.17498338e-01 2.87136108e-01 6.54453635e-01 -3.82886708e-01
1.12396412e-01 -2.12976411e-01 -9.70677380e-03 3.69283110e-01
-7.96905681e-02 -3.70947689e-01 -1.53694868e+00 6.67287588e-01
-1.98720753e+00 -1.14505839e+00 -2.86960036e-01 1.87304127e+00
1.84013769e-01 -9.30573642e-02 1.67968199e-01 3.77578512e-02
6.43236339e-01 5.75653315e-01 -4.01947081e-01 -2.89308250e-01
-5.02681792e-01 -3.11548978e-01 6.62390649e-01 4.32997435e-01
-1.21286106e+00 1.01830184e+00 6.67502832e+00 4.82112527e-01
-1.29652798e+00 8.64086598e-02 1.12091470e+00 4.16203961e-02
-3.60125035e-01 -2.34779850e-01 -4.61216778e-01 7.19278514e-01
8.39028060e-01 -7.77094662e-02 4.05385524e-01 6.34066522e-01
6.39155924e-01 -2.27572128e-01 -1.50138170e-01 8.95564198e-01
-1.13246746e-01 -1.64375472e+00 4.38631810e-02 3.62347782e-01
9.46564317e-01 6.62127197e-01 1.28427550e-01 8.99155661e-02
7.44078219e-01 -1.01139808e+00 2.10996166e-01 4.50327039e-01
5.58143497e-01 -7.68594682e-01 3.85901183e-01 2.33659655e-01
-9.68104601e-01 -4.25147414e-01 -1.70693532e-01 -4.98139083e-01
6.96727857e-02 7.44935930e-01 -7.55157292e-01 2.02990100e-01
8.74226332e-01 1.07472205e+00 -1.00493026e+00 6.62305832e-01
-4.23087850e-02 5.79234540e-01 -3.97080153e-01 9.66799930e-02
3.46989900e-01 -3.74897867e-01 5.61072409e-01 1.08184791e+00
6.10431075e-01 -1.53051555e-01 3.51123124e-01 6.66608691e-01
2.16335449e-02 3.38392824e-01 -1.02649975e+00 -1.95226237e-01
4.06592965e-01 1.43336558e+00 -9.30066526e-01 -2.35593304e-01
-2.92171091e-01 6.48718536e-01 5.50720334e-01 2.82547444e-01
-6.55680418e-01 -3.65325399e-02 7.25003004e-01 6.92952335e-01
-2.77779520e-01 -6.61153972e-01 -5.74818440e-02 -1.09731889e+00
-6.23194098e-01 -4.02971953e-01 5.50332725e-01 -7.47827291e-01
-1.07502306e+00 2.90678352e-01 -1.27305910e-01 -8.44017506e-01
1.87496036e-01 -2.63069391e-01 -8.00147772e-01 3.19174737e-01
-1.31936479e+00 -1.29376924e+00 -4.53905910e-01 3.94384742e-01
4.60473567e-01 -2.84679294e-01 6.25471413e-01 3.51586789e-01
-7.28464305e-01 -3.22101295e-01 4.28929925e-01 2.15845942e-01
4.52753127e-01 -9.55375373e-01 3.33650708e-01 6.53852522e-01
1.18944369e-01 -2.64305547e-02 6.42738342e-01 -7.52864838e-01
-7.71230817e-01 -1.27913272e+00 8.47962916e-01 -2.68003345e-01
1.04385996e+00 -3.95729870e-01 -5.69530845e-01 6.81485057e-01
5.08127809e-01 -3.57175082e-01 7.73035109e-01 3.46066564e-01
-2.11621165e-01 -3.49513710e-01 -1.13912368e+00 6.65869176e-01
8.50888550e-01 -1.86057970e-01 -1.99995056e-01 5.32148004e-01
4.71721053e-01 3.54489774e-01 -5.57148337e-01 2.14288235e-01
2.62683660e-01 -1.00022089e+00 8.21923435e-01 -4.57862049e-01
6.39804780e-01 -8.58076438e-02 -2.71469533e-01 -1.49791718e+00
-5.00172555e-01 -1.72143325e-01 3.71681035e-01 1.15971816e+00
4.46233630e-01 -7.80405283e-01 6.15928411e-01 4.65497196e-01
7.09505379e-02 -4.43100818e-02 -9.76189494e-01 -2.58806437e-01
7.42419139e-02 -8.80060121e-02 5.65562844e-01 1.29554117e+00
-3.39250043e-02 8.70069563e-02 -2.94012070e-01 2.18658939e-01
4.68149126e-01 1.30360052e-01 8.66245031e-01 -1.45160329e+00
6.03892863e-01 -5.48972368e-01 -7.24607229e-01 -2.98337964e-03
2.41671689e-02 -8.79415452e-01 -8.11223269e-01 -1.69084024e+00
3.46593529e-01 -4.44384545e-01 -2.92774111e-01 5.95626771e-01
2.47689202e-01 9.17380631e-01 6.46478310e-02 2.49851584e-01
-5.62585652e-01 4.16245610e-01 9.87545848e-01 -3.63316864e-01
-3.62945288e-01 -5.69540322e-01 -6.67096615e-01 7.89961457e-01
9.99262094e-01 -2.61828572e-01 -2.92053092e-02 -3.81198257e-01
1.00055325e+00 -4.45010096e-01 6.04519606e-01 -1.50632906e+00
-4.35974389e-01 -2.04282209e-01 6.86606944e-01 -9.22267735e-01
1.51981423e-02 -8.25684786e-01 3.42338771e-01 5.71152449e-01
-1.52192533e-01 5.22895493e-02 6.26127645e-02 4.27138597e-01
-1.72220439e-01 7.62275696e-01 8.72481287e-01 -2.57170536e-02
-6.82705045e-01 1.55556023e-01 -8.49824548e-01 -3.54479879e-01
1.31900775e+00 6.44566491e-02 -5.28637409e-01 -5.64190149e-01
-8.45718503e-01 3.55913520e-01 6.93103552e-01 7.33685970e-01
2.42002368e-01 -1.28635812e+00 -9.95981872e-01 2.71263063e-01
-2.45859679e-02 -2.67244071e-01 4.53604847e-01 5.75338483e-01
-8.45398068e-01 1.07750855e-01 -5.31157374e-01 -5.89278519e-01
-1.26311171e+00 3.01175117e-01 -1.60607025e-02 -7.44645149e-02
-8.57558787e-01 1.45103946e-01 -1.08331032e-01 -3.50166231e-01
-3.51224661e-01 -2.69683953e-02 -7.61912465e-01 7.77869046e-01
5.47979891e-01 5.83942890e-01 -3.08653384e-01 -9.14711714e-01
-2.23178983e-01 3.31553191e-01 7.45225251e-02 -9.76847578e-03
1.86344910e+00 -5.69443226e-01 -5.67126498e-02 5.40956080e-01
1.35856676e+00 -9.48820934e-02 -8.83873641e-01 2.68917769e-01
9.63279083e-02 -7.31330276e-01 1.90260887e-01 -7.96763718e-01
-1.10178578e+00 7.62042940e-01 8.96459877e-01 7.21998036e-01
8.36999953e-01 1.70519680e-01 6.27188146e-01 3.32205951e-01
3.09946895e-01 -1.09679186e+00 9.59557891e-02 2.01503932e-01
8.68959069e-01 -1.68587911e+00 2.44256884e-01 -8.54911506e-02
-4.72008765e-01 9.27900493e-01 2.14330718e-01 -3.93348813e-01
8.37744832e-01 -7.60298893e-02 8.46339688e-02 -2.89429873e-01
-1.30402535e-01 -2.27788463e-01 -2.51808427e-02 4.78264421e-01
1.70781389e-01 3.40476662e-01 -2.67777205e-01 -1.51285991e-01
-3.42193842e-01 -3.74194413e-01 4.56079572e-01 4.63874400e-01
-9.01936591e-01 -8.69337499e-01 -4.88041699e-01 7.14555681e-01
-3.16701889e-01 1.18045114e-01 -4.82694507e-01 1.02028155e+00
5.94294369e-02 8.34377050e-01 4.17422831e-01 -3.69907320e-01
-1.77858502e-01 -9.80394855e-02 -1.53113261e-01 -1.22771472e-01
-3.10805172e-01 -2.70359397e-01 1.33462220e-01 -5.56715488e-01
-3.81125599e-01 -8.79673004e-01 -8.98009062e-01 -7.73507237e-01
3.00570726e-01 -3.22856486e-01 8.88036072e-01 8.58391166e-01
5.91043472e-01 1.57166854e-01 9.62733567e-01 -7.67274559e-01
3.54972720e-01 -9.93206024e-01 -3.97899270e-01 7.92202652e-01
1.49047330e-01 -3.64889950e-01 -3.97470742e-01 -1.95026755e-01] | [9.466228485107422, -1.255569338798523] |
186e4c95-f824-446a-b921-cc3a25cbb166 | temporal-needle-a-view-and-appearance | 1612.04854 | null | http://arxiv.org/abs/1612.04854v1 | http://arxiv.org/pdf/1612.04854v1.pdf | Temporal-Needle: A view and appearance invariant video descriptor | The ability to detect similar actions across videos can be very useful for
real-world applications in many fields. However, this task is still challenging
for existing systems, since videos that present the same action, can be taken
from significantly different viewing directions, performed by different actors
and backgrounds and under various video qualities. Video descriptors play a
significant role in these systems. In this work we propose the
"temporal-needle" descriptor which captures the dynamic behavior, while being
invariant to viewpoint and appearance. The descriptor is computed using multi
temporal scales of the video and by computing self-similarity for every patch
through time in every temporal scale. The descriptor is computed for every
pixel in the video. However, to find similar actions across videos, we consider
only a small subset of the descriptors - the statistical significant
descriptors. This allow us to find good correspondences across videos more
efficiently. Using the descriptor, we were able to detect the same behavior
across videos in a variety of scenarios. We demonstrate the use of the
descriptor in tasks such as temporal and spatial alignment, action detection
and even show its potential in unsupervised video clustering into categories.
In this work we handled only videos taken with stationary cameras, but the
descriptor can be extended to handle moving camera as well. | ['Michal Irani', 'Michal Yarom'] | 2016-12-14 | null | null | null | null | ['unsupervised-video-clustering'] | ['computer-vision'] | [ 9.32555199e-02 -6.42010450e-01 -7.51156509e-02 -1.26219630e-01
-3.27999294e-01 -8.64189148e-01 5.60659707e-01 3.43376249e-01
-3.27611506e-01 2.30120569e-01 3.53456549e-02 4.77166504e-01
-3.83904248e-01 -4.52165663e-01 -5.28255403e-01 -7.80935228e-01
-4.61938083e-01 6.13606051e-02 1.00603390e+00 -2.17758715e-01
3.04105580e-01 7.75511563e-01 -1.85474157e+00 4.44483608e-01
6.51861366e-04 9.21513438e-01 4.13526416e-01 7.00717330e-01
2.20877782e-01 8.75480473e-01 -5.55395722e-01 -1.18738152e-01
5.20958185e-01 -5.32555223e-01 -7.12903082e-01 5.52628338e-01
9.17659223e-01 -1.79989055e-01 -3.51394147e-01 1.18998420e+00
1.06925547e-01 4.97439623e-01 4.47126269e-01 -1.27716017e+00
1.49382532e-01 -1.06577165e-02 -4.22448993e-01 5.00070691e-01
9.42394078e-01 -1.41163766e-01 8.01713645e-01 -5.59136569e-01
1.07619023e+00 1.25668800e+00 6.46890104e-01 3.19502383e-01
-1.23077917e+00 -7.94739947e-02 7.21653998e-02 7.24317849e-01
-1.27622449e+00 -3.28707665e-01 6.67522311e-01 -5.63915610e-01
7.10162103e-01 3.74486923e-01 8.82640302e-01 8.45887601e-01
1.81400850e-01 7.35563636e-01 9.60652769e-01 -3.99946749e-01
1.54817879e-01 -1.06855735e-01 -7.54528046e-02 5.53182185e-01
-8.86042640e-02 -2.04256862e-01 -3.46054286e-01 1.98648293e-02
6.72912478e-01 4.12971079e-01 -4.38038975e-01 -6.80786252e-01
-1.54608035e+00 5.43253124e-01 1.31600380e-01 9.38253343e-01
-3.41073275e-01 1.28358081e-01 6.70540094e-01 6.48212731e-01
1.18164927e-01 4.15531158e-01 -3.54557663e-01 -3.32085073e-01
-9.37839627e-01 2.90830672e-01 7.13629842e-01 8.41067135e-01
6.55790567e-01 -3.14383775e-01 5.06500565e-02 6.52759910e-01
-2.61331886e-01 3.35778385e-01 6.14805639e-01 -1.22532904e+00
2.04495564e-01 3.87850404e-01 -3.15239690e-02 -1.57894444e+00
-4.16657537e-01 4.09707606e-01 -7.16873884e-01 1.99876532e-01
6.92798436e-01 3.85595590e-01 -5.26234031e-01 1.50129068e+00
4.00336176e-01 1.95647031e-01 -1.31176248e-01 8.29981506e-01
2.93810248e-01 5.41071534e-01 -2.28885829e-01 -5.79532266e-01
1.38500214e+00 -7.80570865e-01 -6.49438083e-01 9.22044441e-02
5.55591822e-01 -1.03611755e+00 5.65900028e-01 6.88538194e-01
-1.06912875e+00 -8.52037251e-01 -7.87719190e-01 2.56858110e-01
-3.84804636e-01 2.78027542e-02 2.75584906e-02 2.21834704e-01
-1.24757266e+00 8.39482009e-01 -8.34071815e-01 -1.02275288e+00
-2.42023513e-01 3.58763158e-01 -9.43401873e-01 -1.71562955e-01
-8.07633936e-01 7.73133457e-01 4.61336643e-01 -2.82359660e-01
-7.25762963e-01 6.52997335e-03 -7.69275308e-01 -8.64411294e-02
5.71383715e-01 -1.85823351e-01 9.04044867e-01 -1.51780069e+00
-1.13550699e+00 8.62098098e-01 -3.59240472e-01 -3.67882937e-01
6.85489893e-01 -1.18636768e-02 -5.06182671e-01 7.15526342e-01
1.44461617e-01 3.56166214e-01 1.16885531e+00 -9.39182818e-01
-8.05934668e-01 -3.72373611e-01 1.70668453e-01 3.63669321e-02
-3.30307126e-01 4.23767328e-01 -7.92951047e-01 -8.09180677e-01
1.42133340e-01 -1.23313904e+00 -1.11669764e-01 8.71073455e-02
1.48537576e-01 -1.51079670e-01 1.11645293e+00 -6.24987066e-01
9.89112139e-01 -2.39156079e+00 5.87945402e-01 1.82170063e-01
-2.35445686e-02 2.45568007e-01 -1.34090304e-01 6.40644491e-01
-1.42165631e-01 -2.38617465e-01 4.99232188e-02 4.61153649e-02
-4.07781482e-01 2.47564107e-01 1.46234021e-01 7.79682994e-01
-1.79298352e-02 2.88888574e-01 -9.10009205e-01 -7.82394171e-01
4.26093817e-01 2.08127454e-01 -3.66722167e-01 2.48021379e-01
8.60954598e-02 5.07560551e-01 -3.63834202e-01 3.95391375e-01
4.68878031e-01 2.36728176e-01 2.88961053e-01 -4.46156532e-01
-1.72034144e-01 -4.47423846e-01 -1.56263947e+00 1.54406893e+00
-1.57533437e-01 1.05331588e+00 1.09917611e-01 -1.30798876e+00
8.10037553e-01 3.26901764e-01 1.03459036e+00 -7.21618056e-01
4.80560735e-02 1.38847753e-01 3.13522108e-02 -9.80562985e-01
5.09061754e-01 1.34417921e-01 1.87847316e-01 3.49046260e-01
2.07877696e-01 8.33584890e-02 8.11278224e-01 -3.59754004e-02
1.23602521e+00 -8.22688788e-02 5.51306129e-01 -1.81557670e-01
8.30491900e-01 -7.95472190e-02 4.09370720e-01 4.70682770e-01
-3.13920885e-01 6.11585319e-01 3.25388253e-01 -5.88095605e-01
-1.01818156e+00 -7.45127797e-01 -2.85592340e-02 9.99210119e-01
4.99980181e-01 -5.59237182e-01 -6.14450336e-01 -6.84825182e-01
-7.99913481e-02 -1.76348701e-01 -6.25629902e-01 -2.21718214e-02
-7.73576200e-01 -1.73431516e-01 1.60465300e-01 3.36384505e-01
4.24116880e-01 -8.70828509e-01 -8.55289698e-01 2.32607588e-01
-1.72784194e-01 -1.38233280e+00 -4.56253469e-01 -1.37401640e-01
-8.45441818e-01 -1.42008328e+00 -8.22688937e-01 -7.17184722e-01
4.38196599e-01 8.47114563e-01 9.22818840e-01 1.76131241e-02
-4.34106559e-01 9.83012915e-01 -8.55350614e-01 3.15910041e-01
-5.60269713e-01 -4.70984340e-01 3.49812061e-01 3.83001059e-01
2.32188001e-01 -4.31879163e-01 -5.72185457e-01 9.97168183e-01
-1.14285982e+00 -4.92759109e-01 2.07688019e-01 5.29125333e-01
5.02342701e-01 3.25786769e-01 -1.61855265e-01 -3.72914106e-01
2.51434922e-01 -2.36592472e-01 -4.05459613e-01 4.40113246e-01
1.32384732e-01 -1.83093905e-01 9.11403358e-01 -6.05587900e-01
-5.56964755e-01 2.19680011e-01 1.94840997e-01 -7.54995525e-01
-4.17630553e-01 2.73422357e-02 -9.28665102e-02 -2.89668769e-01
4.72314835e-01 3.12383890e-01 5.35435230e-02 -4.47569847e-01
1.84877604e-01 3.77696306e-01 3.75118554e-01 -2.32167095e-01
6.49899662e-01 7.28171229e-01 2.70832002e-01 -1.28631425e+00
-3.56671780e-01 -1.16020274e+00 -1.15792406e+00 -6.12929761e-01
1.05550075e+00 -6.33760393e-01 -5.71914256e-01 4.04472977e-01
-1.16226304e+00 -2.78489683e-02 -2.88399696e-01 7.84764349e-01
-7.79006541e-01 8.93710613e-01 -2.96454221e-01 -5.22506714e-01
2.72352487e-01 -1.11394846e+00 1.02667761e+00 4.52779233e-02
-2.19162017e-01 -1.21361125e+00 3.21525186e-01 3.96294966e-02
1.81616209e-02 3.42852086e-01 4.77882087e-01 -5.46016634e-01
-4.48604763e-01 -2.97582448e-01 1.28357694e-01 5.91428876e-01
3.86869550e-01 4.03484881e-01 -5.51160753e-01 -5.58344662e-01
1.13006935e-01 2.60572918e-02 7.40217745e-01 3.70750308e-01
1.01309729e+00 -2.80279852e-02 -3.75016421e-01 3.79563302e-01
1.49242234e+00 3.73780221e-01 5.93222916e-01 3.87344539e-01
6.03542328e-01 9.52966750e-01 9.81649816e-01 3.88366997e-01
-2.24588856e-01 1.38866103e+00 3.40726316e-01 6.56455569e-03
-4.83938567e-02 2.56871551e-01 7.74996459e-01 7.09605098e-01
-5.66842914e-01 -3.05864792e-02 -7.07613409e-01 4.72779065e-01
-2.06724787e+00 -1.61701846e+00 -2.80161530e-01 2.38994479e+00
1.17736116e-01 -1.39876038e-01 5.96540511e-01 3.04482669e-01
9.07018840e-01 1.88771889e-01 -1.54713601e-01 -2.45461389e-01
-2.16365501e-01 -6.25665709e-02 4.22955811e-01 1.45295262e-01
-1.28123844e+00 5.35400629e-01 6.43483257e+00 8.25190485e-01
-1.12062538e+00 4.95090149e-02 1.45791903e-01 4.27914299e-02
4.12842929e-01 -1.37101756e-02 -4.85744148e-01 5.68351269e-01
6.30350769e-01 -1.49498597e-01 2.24462017e-01 9.24278975e-01
4.20823663e-01 -5.24037719e-01 -1.24227536e+00 1.16791034e+00
5.64491570e-01 -9.50160742e-01 -7.50453621e-02 -5.09197637e-02
7.02644110e-01 -2.46987253e-01 -2.05877781e-01 -2.87262827e-01
-3.81926000e-01 -6.35146976e-01 6.75863326e-01 4.65961754e-01
4.11248535e-01 -5.33953369e-01 5.99690199e-01 6.98652342e-02
-1.62669146e+00 -2.28650481e-01 -4.89974707e-01 1.18878812e-01
1.27347991e-01 1.53158188e-01 -5.61022401e-01 5.81139207e-01
8.27685714e-01 1.31854963e+00 -8.03949296e-01 1.21650136e+00
3.59334528e-01 -9.06390250e-02 -2.16238946e-01 1.33925900e-01
1.87447682e-01 -4.24098462e-01 5.76721728e-01 1.43582058e+00
6.70996070e-01 -1.65123373e-01 4.35078949e-01 9.55887791e-03
4.34348315e-01 3.55505675e-01 -9.61525321e-01 2.34873071e-01
-1.40881920e-02 1.17341304e+00 -1.09851515e+00 -2.99100459e-01
-6.58100605e-01 1.40987790e+00 -5.15176058e-02 6.07316568e-02
-7.50299394e-01 -1.87363505e-01 6.35951102e-01 3.47897381e-01
4.65606511e-01 -5.48155248e-01 8.17856073e-01 -1.32068729e+00
2.02975333e-01 -9.92383540e-01 5.48179030e-01 -6.65674508e-01
-9.81219769e-01 5.47318637e-01 3.98426235e-01 -1.92738760e+00
-4.40816820e-01 -7.87078440e-01 -4.43604499e-01 1.30517766e-01
-9.54448938e-01 -8.33767056e-01 -4.90169019e-01 1.25472951e+00
8.56203258e-01 -1.88439742e-01 5.46534002e-01 4.14562106e-01
-2.32277259e-01 3.21394354e-02 5.09208500e-01 4.81469631e-02
8.95069957e-01 -1.21872294e+00 -8.48317742e-02 8.75462055e-01
5.81746161e-01 4.29993778e-01 8.80525649e-01 -2.48544604e-01
-1.35795701e+00 -8.42916846e-01 4.97615844e-01 -2.49318674e-01
7.42608130e-01 -2.31602415e-02 -8.42759609e-01 4.01218116e-01
2.03036502e-01 1.34603024e-01 5.53689480e-01 -2.16283619e-01
-1.92723200e-01 -3.04366052e-01 -8.81639063e-01 3.80398840e-01
1.00998962e+00 -5.80473781e-01 -4.69408542e-01 5.77391863e-01
7.24478364e-02 -5.36726341e-02 -9.40913677e-01 7.05099255e-02
6.23619437e-01 -1.45668101e+00 9.63170230e-01 -4.59951818e-01
1.63863093e-01 -6.07612848e-01 -2.22420752e-01 -1.07778513e+00
-2.59708643e-01 -5.01887977e-01 1.42488196e-01 1.07703471e+00
-2.77761459e-01 -1.88715309e-01 4.76660013e-01 1.48532286e-01
2.79419899e-01 -1.92769051e-01 -1.03864801e+00 -1.19116819e+00
-5.31120062e-01 -2.81851381e-01 -1.09055296e-01 8.65590274e-01
1.14587910e-01 -2.16802731e-01 -5.79678953e-01 -9.98499920e-04
4.34158415e-01 2.14474395e-01 9.35078025e-01 -1.20416331e+00
-3.90563667e-01 -3.14907908e-01 -1.38301647e+00 -9.18118179e-01
1.55980974e-01 -5.42434752e-01 -6.32339194e-02 -9.78874445e-01
2.75299132e-01 5.68936020e-02 -1.07219882e-01 4.99053150e-02
1.56835482e-01 4.51066613e-01 5.52946508e-01 4.62258190e-01
-8.62136364e-01 5.02783246e-03 9.29101527e-01 -1.14579603e-01
3.13878730e-02 8.69911686e-02 3.23297709e-01 8.75818908e-01
5.91025651e-01 -3.08933765e-01 -2.37975165e-01 -2.09576190e-01
-6.76807240e-02 1.10917471e-01 4.35105413e-01 -1.43600512e+00
2.51200050e-01 -2.05962673e-01 2.13746086e-01 -1.87350124e-01
4.54756200e-01 -1.24283338e+00 4.06840771e-01 5.69324732e-01
-1.45275474e-01 4.90719974e-01 -3.52548547e-02 7.84361541e-01
-6.98331118e-01 -4.28816199e-01 1.03797221e+00 -3.44203740e-01
-1.21049678e+00 9.02498215e-02 -4.29988801e-01 -1.85208648e-01
1.67178583e+00 -5.33252239e-01 1.86370105e-01 -4.73712355e-01
-9.48191702e-01 -1.41046584e-01 9.52333629e-01 4.78572965e-01
6.11800790e-01 -1.35327685e+00 -3.76046211e-01 1.42685428e-01
2.39670187e-01 -5.90646327e-01 5.07576764e-01 1.16858613e+00
-8.51822734e-01 1.95517868e-01 -6.49246454e-01 -9.79706645e-01
-2.02947307e+00 9.03759003e-01 2.90560067e-01 -6.97470605e-02
-5.81144512e-01 2.21132010e-01 1.31806703e-02 4.21308637e-01
1.66731924e-01 -3.73215377e-01 -4.49671775e-01 6.13466561e-01
7.51307607e-01 4.97776151e-01 -2.09852066e-02 -1.19181573e+00
-3.64272058e-01 1.34370959e+00 1.15214497e-01 3.06765046e-02
1.07621181e+00 -3.04861188e-01 -1.56879678e-01 6.08097315e-01
1.59531498e+00 1.82840496e-01 -1.11264074e+00 -9.13176313e-02
8.80194306e-02 -1.02626526e+00 -4.12123799e-01 3.58139984e-02
-1.18888295e+00 7.15038896e-01 8.16817224e-01 7.44556427e-01
1.31519830e+00 8.70973468e-02 4.33124274e-01 3.39594454e-01
5.64939797e-01 -1.12460744e+00 3.01678807e-01 2.83340812e-01
6.76031709e-01 -1.24317193e+00 6.25322536e-02 -4.01475489e-01
-7.18505383e-01 1.64905429e+00 1.18320063e-01 -2.19630316e-01
4.11470592e-01 -3.35209444e-02 -6.66987225e-02 -1.27004847e-01
-4.99014676e-01 -5.14076233e-01 1.94448590e-01 6.55897677e-01
3.39629620e-01 -2.68292993e-01 -4.10212547e-01 -5.60886323e-01
3.79470736e-01 -2.51441211e-01 6.49858534e-01 1.00024724e+00
-4.44429249e-01 -1.24983537e+00 -5.27652383e-01 1.34746075e-01
-4.92761910e-01 4.88768756e-01 -5.02446055e-01 9.48228061e-01
2.70310044e-01 8.98979187e-01 1.61697358e-01 -4.86488074e-01
5.62831521e-01 -1.76933780e-01 6.49127364e-01 -3.81374210e-01
-5.45864046e-01 2.71226823e-01 -1.75015122e-01 -1.09128404e+00
-1.26073420e+00 -1.13259768e+00 -6.77783728e-01 -2.79003143e-01
1.07428748e-02 5.16629182e-02 3.77172530e-01 7.09900141e-01
5.49494773e-02 8.13401416e-02 9.12748396e-01 -1.03867459e+00
-1.56311944e-01 -6.88642681e-01 -9.77345884e-01 1.10530150e+00
3.21681142e-01 -7.99260020e-01 -3.81254315e-01 6.67238295e-01] | [8.263177871704102, 0.1360042542219162] |
b6d38c8a-9023-468b-934b-3ca53e3d3b45 | pushing-the-limits-of-deep-cnns-for | 1603.04525 | null | http://arxiv.org/abs/1603.04525v2 | http://arxiv.org/pdf/1603.04525v2.pdf | Pushing the Limits of Deep CNNs for Pedestrian Detection | Compared to other applications in computer vision, convolutional neural
networks have under-performed on pedestrian detection. A breakthrough was made
very recently by using sophisticated deep CNN models, with a number of
hand-crafted features, or explicit occlusion handling mechanism. In this work,
we show that by re-using the convolutional feature maps (CFMs) of a deep
convolutional neural network (DCNN) model as image features to train an
ensemble of boosted decision models, we are able to achieve the best reported
accuracy without using specially designed learning algorithms. We empirically
identify and disclose important implementation details. We also show that pixel
labelling may be simply combined with a detector to boost the detection
performance. By adding complementary hand-crafted features such as optical
flow, the DCNN based detector can be further improved. We set a new record on
the Caltech pedestrian dataset, lowering the log-average miss rate from
$11.7\%$ to $8.9\%$, a relative improvement of $24\%$. We also achieve a
comparable result to the state-of-the-art approaches on the KITTI dataset. | ['Anton Van Den Hengel', 'Chunhua Shen', 'Qichang Hu', 'Peng Wang', 'Fatih Porikli'] | 2016-03-15 | null | null | null | null | ['occlusion-handling'] | ['computer-vision'] | [ 1.32892966e-01 -1.40477195e-02 1.68923050e-01 -4.14954424e-01
-5.20389080e-01 -2.83285648e-01 6.09138072e-01 1.42459050e-01
-9.45899904e-01 6.91462040e-01 -1.66225567e-01 -3.15748930e-01
3.68308961e-01 -9.65211689e-01 -7.90092528e-01 -6.28487587e-01
-2.64709353e-01 8.26706812e-02 7.21971929e-01 -8.98760259e-02
1.06685817e-01 5.86875379e-01 -1.85367978e+00 4.11943793e-01
3.67504001e-01 1.22981858e+00 6.40789270e-02 8.43368411e-01
1.49256065e-01 6.55450821e-01 -5.72854638e-01 -5.75014114e-01
6.52535856e-01 -1.00134544e-01 -4.23913091e-01 1.13127023e-01
9.08633709e-01 -4.88369673e-01 -4.95440215e-01 9.12767947e-01
7.12543190e-01 -3.54339518e-02 5.07075906e-01 -1.07728946e+00
-2.20213845e-01 1.22032285e-01 -5.21800220e-01 3.70033264e-01
1.59497008e-01 5.71006536e-01 6.50094748e-01 -8.06189537e-01
4.98759121e-01 1.26795340e+00 8.77673447e-01 3.76275182e-01
-1.10040152e+00 -5.74159145e-01 -6.47561450e-04 4.79464978e-01
-1.23408592e+00 -4.40955251e-01 5.53971589e-01 -4.37821090e-01
1.21476626e+00 7.83335231e-03 6.62967980e-01 9.71886098e-01
-4.45957072e-02 7.47890949e-01 1.11788094e+00 -6.86635017e-01
1.51734859e-01 2.38622382e-01 4.61502820e-02 1.05958283e+00
5.77338576e-01 5.85517168e-01 -2.38074753e-02 1.32495180e-01
7.20537424e-01 -5.87344542e-02 -1.53861810e-02 -4.10337210e-01
-8.38056207e-01 9.65068758e-01 9.30980802e-01 3.55763733e-02
-1.79784521e-01 3.58268410e-01 6.65420771e-01 5.99971451e-02
1.19610913e-01 1.46525905e-01 -5.34558415e-01 1.00181431e-01
-9.29312110e-01 2.40774110e-01 5.98680556e-01 7.33680248e-01
8.34649742e-01 -4.68416512e-02 -3.41715842e-01 5.32372057e-01
1.40163720e-01 3.85439277e-01 8.75589848e-02 -1.06517291e+00
3.55331957e-01 5.85829258e-01 2.31652424e-01 -9.03147340e-01
-5.63106596e-01 -6.87922060e-01 -6.88874066e-01 7.63994694e-01
9.40460086e-01 -2.71148741e-01 -1.03138924e+00 1.30552197e+00
7.87090957e-02 -2.68077925e-02 -9.65734050e-02 1.05114865e+00
6.69221044e-01 2.78982401e-01 2.02556312e-01 2.47417152e-01
1.48609614e+00 -1.02941275e+00 -1.23539068e-01 -2.25961164e-01
7.34609425e-01 -8.69784415e-01 6.10109568e-01 5.73857963e-01
-7.34859288e-01 -9.30645883e-01 -1.25171137e+00 -4.11243141e-02
-5.64400852e-01 5.52383840e-01 7.57965744e-01 1.13850129e+00
-1.02359676e+00 6.71539843e-01 -8.22423935e-01 -5.87478518e-01
7.37516880e-01 4.85985458e-01 -4.08923119e-01 -1.53637320e-01
-7.33481824e-01 1.01798630e+00 3.81626636e-01 1.50659546e-01
-9.79303181e-01 -3.51494342e-01 -7.39507794e-01 2.63101030e-02
3.40094328e-01 -6.66419148e-01 9.77189779e-01 -9.01999354e-01
-1.39340758e+00 8.97600591e-01 -2.95627136e-02 -9.40148532e-01
7.92241216e-01 -3.26405197e-01 -2.37409413e-01 1.05132073e-01
2.54793130e-02 1.07993805e+00 6.80190265e-01 -1.04057395e+00
-1.09446883e+00 -5.39916456e-02 2.63500303e-01 -4.76058632e-01
-2.99680769e-01 1.36199698e-01 -3.48466367e-01 -4.34476644e-01
-2.48987287e-01 -9.72179353e-01 -4.99274880e-01 5.54607332e-01
-3.06479156e-01 -1.59894630e-01 9.20210361e-01 -5.20127058e-01
6.73578501e-01 -1.77809405e+00 -3.35345536e-01 7.40888901e-03
1.89548552e-01 9.48535442e-01 -1.83237255e-01 -6.15795925e-02
-1.63851562e-03 -1.73050091e-01 -1.98363528e-01 -4.56850171e-01
-1.01685978e-01 1.75642118e-01 2.53844082e-01 6.69261634e-01
5.40887237e-01 7.76593268e-01 -7.77042806e-01 -3.85483652e-01
7.04464734e-01 6.77269042e-01 -6.29433155e-01 -1.05664559e-01
6.25168309e-02 7.90718421e-02 -9.63691697e-02 5.53678036e-01
8.28395903e-01 7.07130581e-02 -1.84885070e-01 -2.15267479e-01
-2.76533514e-01 1.40634716e-01 -1.41326106e+00 1.47928119e+00
-2.62295425e-01 1.09257543e+00 -7.55524710e-02 -1.31433320e+00
1.02827954e+00 8.12236816e-02 2.54137307e-01 -7.78361499e-01
4.09512192e-01 7.45093003e-02 2.34690353e-01 -4.18168545e-01
2.13716090e-01 2.48347610e-01 1.56118870e-01 -3.40786055e-02
3.87921631e-01 2.70996779e-01 3.35159689e-01 -1.06943585e-01
1.26723373e+00 2.12689027e-01 1.42852381e-01 -5.12096226e-01
9.15672898e-01 1.04727149e-01 3.89672011e-01 9.96161580e-01
-6.86511219e-01 7.73788810e-01 3.64169180e-01 -9.48409796e-01
-1.19272113e+00 -8.39005947e-01 -2.32154086e-01 9.20312345e-01
-2.23436236e-01 -4.09246624e-01 -9.14431512e-01 -7.70838559e-01
-2.34916946e-03 2.53243953e-01 -6.45297647e-01 -1.98684298e-02
-9.94442999e-01 -9.92848277e-01 7.37878501e-01 9.20040190e-01
8.72391701e-01 -8.44392300e-01 -1.11570752e+00 4.12480950e-01
2.33982354e-01 -1.40050578e+00 1.65879354e-01 4.23982441e-01
-6.17489100e-01 -1.11431456e+00 -8.01641405e-01 -7.06569374e-01
5.24522126e-01 3.00707877e-01 9.65862334e-01 2.87668198e-01
-7.98464835e-01 -2.49500629e-02 -3.39248568e-01 -4.80403244e-01
-2.88438760e-02 5.02850227e-02 -1.21387644e-02 -7.13055357e-02
5.03287315e-01 -1.84245169e-01 -9.16298091e-01 2.62391120e-01
-3.77760321e-01 -2.17452973e-01 6.29624069e-01 8.91950190e-01
1.76136449e-01 -1.76999018e-01 2.04554588e-01 -5.19865096e-01
-9.19257924e-02 2.81403422e-01 -8.36856544e-01 -2.00254291e-01
-4.00666118e-01 2.73150325e-01 5.36471605e-01 -3.50683659e-01
-9.15889204e-01 6.42698884e-01 -4.09731001e-01 -3.10529172e-01
-5.82907379e-01 -2.15784654e-01 -5.97723909e-02 -5.06880462e-01
9.96458709e-01 -3.75806652e-02 -7.57604986e-02 -5.54749966e-01
5.44345617e-01 4.37398523e-01 6.03533566e-01 -4.10205662e-01
6.66545987e-01 7.61603713e-01 2.41848290e-01 -6.80660367e-01
-7.40074575e-01 -4.60462362e-01 -8.58089149e-01 -2.23843351e-01
8.50803435e-01 -9.62006807e-01 -7.00897992e-01 5.24935901e-01
-1.14086807e+00 -1.83145136e-01 -2.36964032e-01 4.89820212e-01
-4.47476149e-01 4.41401720e-01 -5.78576505e-01 -7.93032467e-01
-2.02731296e-01 -1.22610152e+00 1.03862369e+00 3.94333124e-01
2.09171563e-01 -5.41948259e-01 -2.36625537e-01 1.79661125e-01
6.15715027e-01 4.34591740e-01 2.99027443e-01 -4.28220719e-01
-6.87008381e-01 -5.17608702e-01 -8.45441818e-01 5.50059319e-01
-1.70217827e-01 2.63352878e-02 -1.29925156e+00 -3.40646088e-01
-3.82661164e-01 4.30695293e-03 1.50330627e+00 4.90356684e-01
9.80417252e-01 9.62661058e-02 -5.05795419e-01 5.25235832e-01
1.42567027e+00 -2.96184365e-02 7.56953478e-01 7.05465615e-01
4.46431428e-01 4.63202268e-01 2.92847544e-01 4.44331616e-01
2.01111689e-01 8.43233168e-01 5.45275390e-01 -3.19626927e-01
-6.20162725e-01 2.95789130e-02 1.60688430e-01 -2.86420405e-01
-5.09441614e-01 3.47826555e-02 -8.35559726e-01 4.53376800e-01
-1.86752427e+00 -9.65273678e-01 -3.38862091e-01 2.02260423e+00
2.96549320e-01 6.52288735e-01 4.62634951e-01 4.02862281e-01
7.00631738e-01 -1.61435261e-01 -1.26965776e-01 -3.39724123e-01
-1.67317256e-01 5.59282362e-01 9.09729064e-01 4.16608453e-01
-1.63570738e+00 9.16683018e-01 6.26055336e+00 5.83311379e-01
-1.22225344e+00 1.34544954e-01 5.50093889e-01 -5.03116986e-03
7.90469766e-01 -2.96176169e-02 -1.10042202e+00 3.20225060e-01
8.89321625e-01 4.57704753e-01 -5.87496385e-02 1.00608385e+00
1.26235828e-01 -3.07076424e-01 -9.95761931e-01 9.32673156e-01
1.23551786e-01 -1.28608227e+00 -3.69973987e-01 2.13302180e-01
4.22276914e-01 2.58666389e-02 -6.65324554e-02 3.83279145e-01
2.24530101e-01 -9.43332732e-01 6.46108806e-01 3.47857207e-01
4.08506304e-01 -7.72113860e-01 1.10735571e+00 2.18658507e-01
-1.33154202e+00 -2.54281253e-01 -6.90256000e-01 -5.74465990e-01
-2.37626489e-02 5.41342020e-01 -8.45294595e-01 3.84254247e-01
9.95311439e-01 5.51168740e-01 -1.00847375e+00 1.53270686e+00
-2.73918539e-01 4.46768850e-01 -5.40091217e-01 -2.06394628e-01
3.20743144e-01 2.51060963e-01 3.09849590e-01 1.74632657e+00
5.02832159e-02 -2.11618692e-01 1.36100471e-01 6.13949716e-01
2.24457413e-01 -1.65590003e-01 -3.54022801e-01 6.07467175e-01
-9.49477628e-02 1.44419718e+00 -8.33738148e-01 -6.34159625e-01
-5.15169561e-01 9.74946022e-01 3.93298596e-01 1.00018866e-01
-7.86912084e-01 -4.47963178e-01 6.87635064e-01 2.59231269e-01
8.58669758e-01 -2.87265450e-01 -2.89183080e-01 -1.05226672e+00
3.77660170e-02 -2.65858054e-01 1.42205521e-01 -4.08977807e-01
-1.09875238e+00 6.03356183e-01 -1.61679506e-01 -1.14040661e+00
-2.24542230e-01 -1.21427214e+00 -5.62682629e-01 7.92988300e-01
-1.60443628e+00 -1.07826591e+00 -3.91713828e-01 4.09171462e-01
3.41845691e-01 -2.67570078e-01 7.35179365e-01 6.31776631e-01
-6.15623116e-01 7.26919174e-01 -2.33783096e-01 6.74790502e-01
5.65398633e-01 -1.09356439e+00 5.74312031e-01 1.14054012e+00
-3.03424206e-02 1.93437845e-01 7.80033946e-01 -7.08261058e-02
-1.00751722e+00 -1.27952504e+00 8.11797321e-01 -4.33221430e-01
2.98451185e-01 -4.98077184e-01 -5.33488572e-01 4.29158747e-01
2.09403589e-01 5.61066866e-01 3.02497536e-01 -1.14782967e-01
-4.13486838e-01 -2.87529200e-01 -1.33760929e+00 3.13685000e-01
1.20625961e+00 -1.27406031e-01 -4.14658844e-01 1.15152374e-01
3.76544118e-01 -2.14308083e-01 -4.33347225e-01 4.22048628e-01
6.87816203e-01 -1.28016996e+00 1.35664368e+00 -5.70322692e-01
1.78851843e-01 -4.26233053e-01 -2.54341632e-01 -9.16530907e-01
-4.15024281e-01 -1.07794516e-01 -1.06698312e-01 9.70914602e-01
2.03293383e-01 -5.51258743e-01 1.02359903e+00 4.67788130e-01
-1.21438563e-01 -4.01550591e-01 -1.13874984e+00 -8.58589649e-01
4.49010171e-02 -4.45881784e-01 6.35537282e-02 3.15583348e-01
-3.59221190e-01 1.24707997e-01 -3.62311661e-01 5.78052923e-02
8.15321684e-01 -3.15560788e-01 8.99388015e-01 -1.27917290e+00
-2.06355229e-01 -5.36540091e-01 -1.11412919e+00 -8.94542277e-01
-2.00393319e-01 -5.28321028e-01 2.00713705e-02 -1.30542076e+00
-6.46307133e-04 -4.03053999e-01 -3.93422067e-01 5.51221490e-01
-4.83428016e-02 7.48376191e-01 4.92181897e-01 -2.95828670e-01
-4.98008251e-01 9.01081264e-02 8.52031469e-01 -1.40029669e-01
1.06107377e-01 -1.60995312e-02 -4.48536128e-01 7.83584952e-01
8.47893298e-01 -4.21337932e-01 2.98459470e-01 -2.64588386e-01
-4.17177975e-01 -4.38581079e-01 8.35669756e-01 -1.67862749e+00
3.00580561e-01 5.27010679e-01 9.27916288e-01 -5.01277506e-01
4.66956019e-01 -6.08916700e-01 -2.80361801e-01 1.00547671e+00
-8.39579031e-02 -1.32363215e-01 4.24387604e-01 3.60829294e-01
3.05103809e-02 -2.22288564e-01 9.80642378e-01 -2.66175002e-01
-9.88577425e-01 1.59545783e-02 -5.04017889e-01 -4.68657941e-01
1.07287633e+00 -2.91264087e-01 -3.32126707e-01 6.21050708e-02
-6.55405223e-01 -1.81391314e-01 1.71878412e-01 3.68543863e-01
4.35148805e-01 -1.12336636e+00 -7.84571111e-01 3.61501902e-01
-4.02188674e-02 -3.25929195e-01 7.83398002e-03 6.18650079e-01
-6.74355567e-01 7.96267092e-01 -6.25756919e-01 -8.62845361e-01
-1.18532252e+00 7.23153293e-01 4.97060269e-01 -1.20194376e-01
-7.33327508e-01 8.21100295e-01 -2.28351891e-01 -1.00607529e-01
4.15887386e-01 -3.81116062e-01 -1.06942751e-01 -7.74901453e-03
6.59337640e-01 5.10183156e-01 3.80490154e-01 -6.01383746e-01
-5.12244105e-01 6.09091401e-01 -1.41998683e-03 1.03611471e-02
1.20553303e+00 1.77463725e-01 4.06223863e-01 -3.99985343e-01
1.25365889e+00 -4.20002550e-01 -1.58975458e+00 -2.15642840e-01
-1.34084210e-01 -5.20202637e-01 2.30629534e-01 -7.89213777e-01
-1.29251528e+00 1.18337297e+00 1.31969357e+00 -7.17793107e-02
1.02233899e+00 -1.02495626e-01 6.16836667e-01 4.84547138e-01
4.91259843e-01 -9.50151920e-01 -1.66498427e-03 3.74166876e-01
4.50709879e-01 -1.51728833e+00 -2.36645043e-02 -3.65016341e-01
-1.52368397e-01 1.35667932e+00 6.35990798e-01 -7.12655902e-01
7.39961565e-01 4.90180761e-01 -8.72263592e-03 2.10276425e-01
-4.29976493e-01 -8.71401846e-01 1.41344160e-01 9.98057187e-01
4.03850257e-01 1.56323999e-01 -2.94820607e-01 2.82583147e-01
2.31467094e-02 2.27786019e-01 3.70017499e-01 9.59122300e-01
-7.98503816e-01 -1.34417534e+00 -4.57278788e-01 4.36141551e-01
-3.43828768e-01 -5.26046120e-02 -8.26276392e-02 8.54265094e-01
6.24805629e-01 9.64602113e-01 1.07935391e-01 -3.44199359e-01
4.73492235e-01 -1.34383097e-01 6.36097193e-01 -2.11005688e-01
-6.15250051e-01 -1.65631130e-01 1.43587053e-01 -6.47893369e-01
-5.85515976e-01 -7.16439307e-01 -7.79860795e-01 -3.88742238e-01
-3.20049912e-01 -4.35615927e-01 6.52415276e-01 9.49270070e-01
2.19046623e-01 4.17089522e-01 9.83179733e-02 -1.30638838e+00
-1.13683477e-01 -8.67621779e-01 -2.91369762e-02 2.42560953e-01
3.90578568e-01 -8.97277176e-01 -1.24454521e-01 1.18567117e-01] | [8.40793228149414, -0.5640954971313477] |
002074c6-10a7-41a9-a9ab-cab15744bf3e | multimodal-feature-extraction-for-memes | 2207.03317 | null | https://arxiv.org/abs/2207.03317v1 | https://arxiv.org/pdf/2207.03317v1.pdf | Multimodal Feature Extraction for Memes Sentiment Classification | In this study, we propose feature extraction for multimodal meme classification using Deep Learning approaches. A meme is usually a photo or video with text shared by the young generation on social media platforms that expresses a culturally relevant idea. Since they are an efficient way to express emotions and feelings, a good classifier that can classify the sentiment behind the meme is important. To make the learning process more efficient, reduce the likelihood of overfitting, and improve the generalizability of the model, one needs a good approach for joint feature extraction from all modalities. In this work, we proposed to use different multimodal neural network approaches for multimodal feature extraction and use the extracted features to train a classifier to identify the sentiment in a meme. | ['Tomas Horvath', 'Tsegaye Misikir Tashu', 'Sofiane Ouaari'] | 2022-07-07 | null | null | null | null | ['meme-classification'] | ['natural-language-processing'] | [-1.20725922e-01 -2.26343080e-01 -2.03435585e-01 -4.73622203e-01
-2.27492586e-01 -3.97425860e-01 6.65563345e-01 4.79514867e-01
-4.27589267e-01 7.23300338e-01 4.63512003e-01 4.21864599e-01
2.73466200e-01 -9.29702878e-01 -5.29868126e-01 -6.03272915e-01
4.18357581e-01 -2.18283564e-01 -1.43900603e-01 -4.34228361e-01
4.49125201e-01 9.94733721e-02 -1.81537580e+00 8.97330701e-01
3.93177539e-01 1.22875679e+00 1.57278450e-03 5.62617660e-01
-5.97701311e-01 1.15687716e+00 -5.96202374e-01 -8.36358190e-01
-3.52221757e-01 -5.75489104e-01 -7.38277495e-01 7.52562955e-02
3.57722819e-01 -2.07386136e-01 -7.59925321e-02 8.41339707e-01
3.61150622e-01 1.88002288e-02 9.88026023e-01 -1.20872605e+00
-3.39808464e-01 7.73640394e-01 -4.38802838e-01 -1.87738657e-01
7.10396886e-01 -4.94866818e-01 6.05042160e-01 -8.34772408e-01
8.52837026e-01 1.01431131e+00 6.09669745e-01 5.04379153e-01
-6.31710827e-01 -4.19317156e-01 -1.36637390e-01 3.82499285e-02
-1.19971967e+00 -4.19922709e-01 1.03386831e+00 -6.69640660e-01
4.49605644e-01 2.53904879e-01 9.94934201e-01 1.41775882e+00
1.91260532e-01 9.73027050e-01 1.21816134e+00 -4.84591246e-01
1.63512498e-01 7.28583097e-01 1.06830075e-01 9.25802648e-01
-2.22539306e-01 -7.61570752e-01 -1.14566374e+00 -1.78123951e-01
1.61638752e-01 2.79391706e-01 2.61365045e-02 7.04048127e-02
-9.49520767e-01 8.73876572e-01 2.93831944e-01 5.23638606e-01
-4.39816087e-01 3.60455029e-02 5.83025157e-01 2.94792324e-01
7.11409271e-01 2.22879589e-01 -9.15686488e-02 -4.85439926e-01
-8.83240163e-01 -1.11109696e-01 8.90294313e-01 1.63164198e-01
8.18135202e-01 -5.05506754e-01 2.77109146e-01 1.14335060e+00
2.87391901e-01 3.77044559e-01 6.37312889e-01 -6.12637043e-01
2.89742053e-01 1.20444608e+00 -1.17380485e-01 -1.47146666e+00
-5.95697880e-01 1.74042940e-01 -7.19116986e-01 -1.41778365e-01
1.53866231e-01 -6.50783658e-01 -5.98083794e-01 1.52425754e+00
1.72620192e-01 -3.52667987e-01 1.10632189e-01 7.71958888e-01
1.32455528e+00 1.05393612e+00 1.14429660e-01 5.93658872e-02
1.21439159e+00 -6.08405590e-01 -8.03458035e-01 -9.56777260e-02
6.37318492e-01 -9.05191898e-01 7.27673411e-01 1.92881331e-01
-9.43311214e-01 -3.31638306e-01 -1.07180893e+00 -5.51813729e-02
-9.45559978e-01 4.87356424e-01 7.96135902e-01 6.84716642e-01
-6.94728315e-01 3.50110769e-01 -4.72984195e-01 -8.50733042e-01
2.89776266e-01 3.89166743e-01 -8.48471224e-01 6.91842586e-02
-1.04898369e+00 7.11196899e-01 4.55072582e-01 6.74663782e-02
-2.25834832e-01 -1.68704006e-04 -8.04960191e-01 -9.62912217e-02
-2.64666319e-01 -5.80305934e-01 5.60805976e-01 -1.83203101e+00
-1.49715817e+00 1.04557753e+00 -1.41860694e-01 1.71332091e-01
6.97206035e-02 -1.30000085e-01 -4.16036189e-01 2.97138631e-01
-1.83388799e-01 8.64377141e-01 1.13218355e+00 -1.06941104e+00
-4.94005322e-01 -3.23713332e-01 4.49795164e-02 3.55155878e-02
-1.25541139e+00 3.51327449e-01 -2.76416779e-01 -4.22233224e-01
1.33315980e-01 -9.46281135e-01 3.65868539e-01 -3.69615853e-01
-3.01907092e-01 -1.86389044e-01 8.95511687e-01 -7.88189173e-01
1.01842904e+00 -2.42120242e+00 2.82741338e-01 3.07731211e-01
2.36116096e-01 1.05166174e-02 -4.47699875e-02 8.78026485e-01
3.68366241e-01 5.59027866e-02 1.57483146e-01 -3.64476562e-01
-2.85230074e-02 -1.35780960e-01 1.29760623e-01 2.69799441e-01
2.79670298e-01 5.31804740e-01 -4.97998893e-01 -5.13539076e-01
-1.76178262e-01 6.58904076e-01 -2.66428024e-01 3.98798198e-01
-2.55324598e-02 1.61543101e-01 -5.37015736e-01 4.68291909e-01
3.72575939e-01 -2.06800431e-01 1.00375891e-01 -3.39075446e-01
-9.43413600e-02 -7.87672177e-02 -9.76435602e-01 1.38567924e+00
-4.94184315e-01 1.21471107e+00 -8.77972767e-02 -8.40215027e-01
1.17248285e+00 3.89163136e-01 4.71977025e-01 -3.60189945e-01
6.64551735e-01 1.30565882e-01 -5.04409909e-01 -1.11447418e+00
6.56887531e-01 -1.26414359e-01 -2.56473899e-01 5.45374691e-01
1.25793070e-01 2.65085667e-01 9.78436992e-02 1.38756156e-01
6.97153270e-01 -5.24372645e-02 1.78056166e-01 3.41121256e-02
6.06597364e-01 -1.27883360e-01 3.57151985e-01 2.77260780e-01
1.73524186e-01 4.77393538e-01 6.75709426e-01 -6.89180374e-01
-7.12112308e-01 -3.68231952e-01 9.77693349e-02 1.32615864e+00
-3.72885801e-02 -4.68379915e-01 -8.53178680e-01 -5.45575261e-01
-3.30253333e-01 -1.51504781e-02 -7.79170334e-01 -1.36745811e-01
-1.52175829e-01 -6.95265174e-01 3.91349107e-01 2.65375286e-01
7.23911226e-01 -9.52811956e-01 -4.74694610e-01 9.14928224e-03
-6.41675353e-01 -1.01033545e+00 -1.38372919e-02 -4.24063206e-03
-5.69809198e-01 -6.31047785e-01 -8.63017797e-01 -1.08761191e+00
7.40386665e-01 -2.59597916e-02 8.02587092e-01 2.19595030e-01
2.04893649e-01 5.00252008e-01 -6.88896298e-01 -3.94808143e-01
-4.09319550e-01 4.57415760e-01 8.89454316e-03 4.98076439e-01
4.49656188e-01 -3.46650332e-01 -3.48624110e-01 -6.93559088e-03
-9.90648150e-01 2.70785332e-01 2.85944432e-01 6.22712791e-01
-1.78686634e-01 -9.85357538e-02 4.90636170e-01 -4.64904159e-01
7.59390414e-01 -8.32028985e-01 1.19023964e-01 3.50279450e-01
2.83509821e-01 -1.17622949e-01 4.89565820e-01 -5.54363787e-01
-9.94879007e-01 2.36298084e-01 -1.76337525e-01 1.07060023e-01
-2.66841620e-01 9.07076776e-01 6.04634546e-02 -1.71418458e-01
4.22667265e-01 -1.09654076e-01 2.68954621e-03 -3.90972525e-01
3.97082679e-02 1.14811957e+00 -1.87630370e-01 -3.27848822e-01
2.97877103e-01 3.48202258e-01 1.23758917e-03 -1.17776275e+00
-8.25180829e-01 -3.56462300e-01 -4.00441110e-01 -7.47982204e-01
1.34469175e+00 -8.94805789e-01 -8.71508479e-01 6.55047417e-01
-1.11567545e+00 1.07194558e-01 5.07164419e-01 6.22930825e-01
-2.43276000e-01 6.77622110e-02 -6.51131153e-01 -8.34264994e-01
-1.81253612e-01 -8.17091107e-01 7.69228160e-01 5.90464771e-01
-4.07790512e-01 -1.08981943e+00 2.65803635e-01 6.20910108e-01
2.40960315e-01 3.95135820e-01 7.69316912e-01 -6.46486700e-01
-9.82125849e-02 -3.66421521e-01 -2.40486875e-01 3.61674994e-01
-1.79862324e-03 4.37004089e-01 -1.12892497e+00 2.89011467e-02
-2.09711626e-01 -8.85340035e-01 9.55345452e-01 1.37998894e-01
7.56013989e-01 -3.11048329e-01 -1.92236200e-01 2.18273506e-01
1.40187132e+00 -9.53827128e-02 5.35379171e-01 4.98002976e-01
5.78096926e-01 9.70617414e-01 4.34511542e-01 6.61826015e-01
5.20656645e-01 2.79399365e-01 2.16777354e-01 -1.35439500e-01
4.09035563e-01 -1.42095730e-01 7.83822060e-01 1.00560546e+00
-1.92375213e-01 -3.86245370e-01 -9.31307852e-01 5.07934451e-01
-2.04581761e+00 -1.01962733e+00 2.28558499e-02 1.58740866e+00
4.56744581e-01 -3.43012571e-01 4.09860522e-01 8.17144439e-02
7.35351205e-01 1.45839885e-01 1.61186278e-01 -7.91468799e-01
-3.07159513e-01 -3.25233907e-01 -1.28474370e-01 4.27697133e-03
-1.16248524e+00 7.10141540e-01 5.85847473e+00 5.48844218e-01
-1.53006899e+00 8.02821964e-02 7.31800735e-01 -9.32930876e-03
-1.66789472e-01 -4.29702580e-01 -5.53855479e-01 3.97688031e-01
8.40478361e-01 3.80079776e-01 2.72518575e-01 5.03094137e-01
3.67923453e-02 -4.79467630e-01 -9.33668971e-01 1.07674110e+00
5.86753249e-01 -1.35134947e+00 -1.65661097e-01 -2.10068822e-01
8.83690178e-01 -2.12665871e-01 1.89031601e-01 7.73020089e-02
-5.78025281e-01 -8.19669843e-01 6.18332803e-01 8.96001756e-01
1.53916001e-01 -8.50585103e-01 1.04038644e+00 3.43609333e-01
-8.03178549e-01 -1.25208661e-01 -1.85538456e-01 -3.35348070e-01
-6.24449365e-02 4.28294033e-01 -5.62041283e-01 8.31748992e-02
7.71117449e-01 6.60692751e-01 -7.71189392e-01 7.70221949e-01
1.16277665e-01 3.90687853e-01 -3.13065231e-01 -9.33228612e-01
1.65449560e-01 -9.80854332e-02 3.40848476e-01 1.38434577e+00
5.57280362e-01 -1.22184768e-01 -1.30618215e-01 3.80977690e-01
-3.22917312e-01 7.02144802e-01 -7.92671323e-01 -7.03781128e-01
-1.35021899e-02 1.50926423e+00 -9.03604269e-01 -5.90925664e-02
-5.39723873e-01 1.12876058e+00 3.26309711e-01 1.99249923e-01
-6.23627007e-01 -4.50562298e-01 2.50246376e-01 1.13390246e-02
1.13796182e-01 -1.10298075e-01 -5.52023128e-02 -1.22091377e+00
1.21282004e-01 -6.44955754e-01 1.67830393e-01 -9.00605917e-01
-1.34402537e+00 6.35806501e-01 -3.10164839e-01 -1.03958797e+00
-1.42110631e-01 -6.27365828e-01 -7.17569411e-01 3.61179888e-01
-8.77524614e-01 -1.51756847e+00 -3.00122589e-01 5.72690666e-01
9.07610729e-02 -5.42062700e-01 7.07767725e-01 3.71489346e-01
-5.60306430e-01 4.06590670e-01 -5.52766956e-02 3.45334381e-01
7.64780939e-01 -7.63362527e-01 -6.23728931e-01 3.63418788e-01
1.26912311e-01 5.95192909e-01 4.98278409e-01 -5.59886456e-01
-1.40676630e+00 -4.98507470e-01 1.14549387e+00 -2.07376093e-01
6.84628010e-01 -3.38009983e-01 -5.07142186e-01 2.61126935e-01
6.39967442e-01 -6.01341784e-01 1.24125731e+00 4.04801577e-01
-1.72988102e-02 -1.44091666e-01 -1.16677880e+00 6.15756154e-01
3.09420913e-01 -7.18572140e-01 -4.63011533e-01 2.50515968e-01
2.23294169e-01 7.86813572e-02 -1.13684726e+00 -7.42565915e-02
8.61135066e-01 -9.88855958e-01 4.02105212e-01 -5.12212396e-01
9.71691787e-01 3.75197455e-02 -2.16199920e-01 -1.21542227e+00
2.95140147e-01 -3.42912436e-01 1.08034067e-01 1.63483226e+00
5.76955378e-01 -3.57856125e-01 7.11181104e-01 5.95474482e-01
4.07736570e-01 -5.39670467e-01 -5.84442437e-01 -3.22509035e-02
-2.05782488e-01 -2.34757230e-01 4.46583837e-01 1.29579210e+00
4.38000262e-01 5.60279310e-01 -6.74813032e-01 -1.35053113e-01
1.67625904e-01 -5.38628511e-02 6.77319050e-01 -1.19971514e+00
1.73993960e-01 -3.43134105e-01 -6.95723653e-01 -3.57570022e-01
3.43118787e-01 -6.62668943e-01 -2.05627590e-01 -1.57185853e+00
6.78231835e-01 3.43396217e-02 -1.49584860e-01 3.77635121e-01
1.74470797e-01 6.34615660e-01 2.97062516e-01 -6.84214085e-02
-6.30001664e-01 3.90441149e-01 8.95549178e-01 -2.09556565e-01
-2.63870090e-01 -2.06849739e-01 -4.47460949e-01 8.64093244e-01
1.02051044e+00 -5.26539445e-01 -5.60270026e-02 -1.91383854e-01
1.28346920e+00 2.36707851e-02 2.79259682e-01 -9.61441457e-01
2.26506427e-01 -1.13037139e-01 5.93923151e-01 -6.08175933e-01
6.13530636e-01 -7.74614632e-01 3.81657854e-03 9.29213688e-02
-4.23423827e-01 3.91945653e-02 -2.22103503e-02 4.75676693e-02
-5.92181683e-01 -5.69986343e-01 4.47542578e-01 2.87496876e-02
-6.42464757e-01 -8.09776261e-02 -9.71962512e-01 -4.16160285e-01
8.04198146e-01 -1.08173117e-01 -3.46899003e-01 -7.61567414e-01
-8.52666855e-01 -1.91578984e-01 5.01934588e-01 6.04660749e-01
7.29996681e-01 -1.39542091e+00 -5.38304389e-01 -1.44290507e-01
2.43094981e-01 -9.12569225e-01 2.91784108e-01 7.47627318e-01
-4.08837974e-01 -1.25252664e-01 -5.55950940e-01 -2.49327600e-01
-1.47860134e+00 4.67687584e-02 1.05863661e-01 3.24241728e-01
2.23575104e-02 7.53383160e-01 -3.11923385e-01 -3.79765868e-01
1.07290991e-01 2.93482840e-01 -8.35013211e-01 7.84167051e-01
5.73538542e-01 4.42099333e-01 -2.12696463e-01 -9.93323982e-01
-2.54781902e-01 6.49999261e-01 2.73333013e-01 -2.66023666e-01
1.60543597e+00 -1.66760668e-01 -7.08438635e-01 9.28564608e-01
1.63330483e+00 1.98308975e-01 -5.08905411e-01 1.54151842e-01
-1.14266515e-01 -3.33774596e-01 2.49006823e-01 -5.95288515e-01
-1.19647324e+00 9.40566897e-01 7.99696326e-01 5.52403152e-01
1.02746499e+00 1.60917025e-02 8.15545201e-01 6.48847878e-01
-8.63366202e-02 -1.57898045e+00 3.81422073e-01 4.18928951e-01
7.56151795e-01 -1.51461661e+00 -1.74773987e-02 6.78742826e-02
-9.26177561e-01 1.59385288e+00 5.16830862e-01 2.10026234e-01
7.16378152e-01 1.33204922e-01 3.41573238e-01 -4.00892973e-01
-4.82935131e-01 -4.92784418e-02 5.85929990e-01 2.74505198e-01
7.64585078e-01 -7.53613263e-02 -4.75296915e-01 7.19901621e-01
-2.75808293e-02 -4.63051461e-02 6.96579695e-01 9.54567373e-01
-6.57903314e-01 -9.91267979e-01 -3.04663658e-01 2.39161924e-01
-6.06435061e-01 2.64255404e-01 -9.43243384e-01 3.84929270e-01
3.84313703e-01 1.11876214e+00 1.59637351e-02 -6.76427960e-01
-2.12772876e-01 3.00356805e-01 3.56250077e-01 -2.16237649e-01
-8.15563679e-01 -1.26001835e-01 5.05466282e-01 -3.28618407e-01
-1.03661227e+00 -4.84365791e-01 -1.03542256e+00 -4.45629478e-01
-7.36870840e-02 -6.71982914e-02 1.25407112e+00 1.17433107e+00
1.07035309e-01 -1.26087107e-02 9.15730715e-01 -7.81639218e-01
3.75379533e-01 -8.39068055e-01 -4.36989307e-01 4.51829255e-01
2.01025739e-01 -4.68526095e-01 -1.23374209e-01 1.28222108e-01] | [8.56832504272461, 10.659457206726074] |
cd2bbc05-1145-4833-a4d3-0387ca4fad36 | 3d-lmnet-latent-embedding-matching-for | 1807.07796 | null | http://arxiv.org/abs/1807.07796v2 | http://arxiv.org/pdf/1807.07796v2.pdf | 3D-LMNet: Latent Embedding Matching for Accurate and Diverse 3D Point Cloud Reconstruction from a Single Image | 3D reconstruction from single view images is an ill-posed problem. Inferring
the hidden regions from self-occluded images is both challenging and ambiguous.
We propose a two-pronged approach to address these issues. To better
incorporate the data prior and generate meaningful reconstructions, we propose
3D-LMNet, a latent embedding matching approach for 3D reconstruction. We first
train a 3D point cloud auto-encoder and then learn a mapping from the 2D image
to the corresponding learnt embedding. To tackle the issue of uncertainty in
the reconstruction, we predict multiple reconstructions that are consistent
with the input view. This is achieved by learning a probablistic latent space
with a novel view-specific diversity loss. Thorough quantitative and
qualitative analysis is performed to highlight the significance of the proposed
approach. We outperform state-of-the-art approaches on the task of single-view
3D reconstruction on both real and synthetic datasets while generating multiple
plausible reconstructions, demonstrating the generalizability and utility of
our approach. | ['Priyanka Mandikal', 'R. Venkatesh Babu', 'Mayank Agarwal', 'K L Navaneet'] | 2018-07-20 | null | null | null | null | ['3d-point-cloud-reconstruction', 'point-cloud-reconstruction', 'single-view-3d-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.81734133e-01 4.13425624e-01 -8.47923011e-02 -4.14338857e-01
-1.03473556e+00 -5.60153604e-01 8.12696338e-01 -3.98492128e-01
1.28883764e-01 4.98343915e-01 5.55331349e-01 -4.73925360e-02
-3.36736590e-02 -6.89240813e-01 -9.73273516e-01 -5.42784572e-01
4.17287886e-01 6.22970343e-01 -5.00667728e-02 1.69644982e-01
1.76207021e-01 7.84520864e-01 -1.53657901e+00 2.72740275e-01
5.98953545e-01 9.42499876e-01 3.61111283e-01 4.42939848e-01
6.17851242e-02 3.25074524e-01 -7.82477930e-02 -2.49387562e-01
4.64790940e-01 -2.73038656e-01 -5.32849014e-01 6.63153172e-01
4.57620651e-01 -5.71355641e-01 -3.11494440e-01 8.40356469e-01
3.30921680e-01 -2.52131343e-01 9.26504731e-01 -9.99179363e-01
-4.04742658e-01 -2.31676288e-02 -4.77493674e-01 -2.63917267e-01
6.70756340e-01 1.47230536e-01 1.02910244e+00 -1.24657249e+00
9.88303900e-01 1.39529574e+00 5.58107615e-01 4.64653343e-01
-1.62725019e+00 -3.83645356e-01 3.16057354e-02 -2.74478942e-01
-1.26112747e+00 -6.53125465e-01 1.07415807e+00 -6.74294710e-01
7.53409624e-01 3.07507701e-02 5.97091913e-01 1.46683073e+00
2.27438986e-01 6.10539913e-01 1.34549606e+00 -3.43476921e-01
2.39456668e-01 2.00289711e-01 -6.23962045e-01 6.88277602e-01
1.14972651e-01 3.93088490e-01 -7.28145897e-01 -1.51581600e-01
1.16336453e+00 1.69321314e-01 -3.05020690e-01 -9.31334436e-01
-1.45671237e+00 7.63884425e-01 2.75347710e-01 -2.10191488e-01
-6.21578395e-01 1.80600405e-01 -9.41601843e-02 -7.32825473e-02
7.63263702e-01 2.93367237e-01 -1.57147408e-01 1.15392886e-01
-9.46732163e-01 2.84325927e-01 5.76789677e-01 9.06709611e-01
6.95611119e-01 1.67686924e-01 1.60359949e-01 4.24956530e-01
6.18855059e-01 4.99314755e-01 -2.02065989e-01 -1.32527745e+00
4.51387167e-01 4.55547661e-01 3.26935410e-01 -9.34951127e-01
-2.56965552e-02 -4.07919258e-01 -8.48043859e-01 6.68721974e-01
1.58148393e-01 3.34831417e-01 -9.92670715e-01 1.61382782e+00
4.48654085e-01 2.08824113e-01 1.34933457e-01 9.14270341e-01
6.20399237e-01 5.20469487e-01 -4.81656432e-01 -1.32964313e-01
9.18557763e-01 -6.50100350e-01 -5.92390060e-01 -2.42618933e-01
-1.64103031e-01 -7.13808894e-01 9.29569662e-01 1.33717850e-01
-1.28319407e+00 -4.97260392e-01 -1.11490035e+00 -2.24239707e-01
1.27874136e-01 -1.22307435e-01 1.81908220e-01 1.66000366e-01
-7.62826920e-01 5.18801749e-01 -9.39456105e-01 -1.98243693e-01
3.91121030e-01 1.38707424e-03 -7.66783774e-01 -3.41869235e-01
-6.63265407e-01 8.86541247e-01 2.22356051e-01 -1.00767408e-02
-1.06953895e+00 -6.70892239e-01 -1.07764852e+00 -1.06547765e-01
3.27725232e-01 -1.23203611e+00 7.54319549e-01 -4.26687121e-01
-1.55539489e+00 1.13636410e+00 -2.06766218e-01 -1.69143334e-01
8.09578180e-01 -8.79319161e-02 5.24324458e-03 2.84238547e-01
3.12979788e-01 8.37191820e-01 1.11294329e+00 -1.94773436e+00
-1.14377558e-01 -3.50823551e-01 7.47546628e-02 4.01613176e-01
4.34705049e-01 -6.58668280e-01 -4.28142518e-01 -6.24265850e-01
7.77670801e-01 -8.33529115e-01 -2.32377812e-01 5.18516421e-01
-5.74246943e-01 3.18277776e-01 6.51199281e-01 -6.85024261e-01
5.41203022e-01 -2.17157841e+00 7.33733237e-01 -1.27619132e-02
3.67756337e-01 -3.60396653e-01 5.19453585e-02 4.85871196e-01
7.28446692e-02 -2.30200756e-02 -4.67247844e-01 -8.28920901e-01
1.88513249e-01 4.38847095e-01 -5.51602960e-01 6.37506843e-01
2.69725621e-01 1.03541625e+00 -6.35446131e-01 -3.32924902e-01
7.01615989e-01 8.41304660e-01 -6.07271314e-01 6.48844182e-01
-4.00893360e-01 9.42806125e-01 -2.69565284e-01 5.67978740e-01
6.40637219e-01 -4.77570027e-01 -4.77603674e-02 -6.32919133e-01
-3.79830562e-02 2.15130493e-01 -1.13421047e+00 2.22896981e+00
-6.17128670e-01 3.56493592e-01 1.14916051e-02 -6.89736187e-01
9.40920651e-01 3.81733149e-01 5.08152902e-01 -3.54503721e-01
-6.13056235e-02 2.53421694e-01 -5.58565259e-01 -5.55146813e-01
3.10883194e-01 -6.28609478e-01 -4.45961542e-02 5.47404587e-01
1.98396921e-01 -6.33386552e-01 -6.92503691e-01 1.11520223e-01
6.45763516e-01 7.43948400e-01 3.64998192e-01 2.93941833e-02
2.73610085e-01 -3.36251706e-01 2.92663515e-01 4.52814639e-01
1.71488330e-01 1.27636898e+00 3.96045029e-01 -5.49005389e-01
-1.41425264e+00 -1.48992586e+00 -1.12809800e-01 -9.28657874e-02
2.42819414e-01 -1.57893375e-01 -3.41518939e-01 -6.78765595e-01
8.63789991e-02 8.70459437e-01 -6.52363479e-01 -4.74055558e-02
-3.16897571e-01 -1.74182758e-01 9.73771140e-02 3.49900037e-01
2.13087171e-01 -7.77361453e-01 -7.08555639e-01 3.04241385e-02
-5.01564384e-01 -1.49044704e+00 -1.41774461e-01 2.90531404e-02
-9.91942048e-01 -8.33748877e-01 -6.20301723e-01 -4.52493310e-01
9.03940558e-01 2.43456811e-01 1.25291014e+00 -2.98058867e-01
-3.87672745e-02 5.74144900e-01 -3.36934440e-03 3.41415443e-02
-7.32389688e-01 -3.19238544e-01 8.94433931e-02 1.44317269e-01
-1.82174072e-01 -1.04791105e+00 -6.00729406e-01 2.54433930e-01
-9.79699612e-01 6.61249280e-01 6.69876695e-01 9.55513656e-01
1.09195518e+00 -1.88023835e-01 3.00553799e-01 -7.01776028e-01
2.09029615e-01 -5.12145996e-01 -5.45042515e-01 1.12394780e-01
-5.60345173e-01 3.64558697e-01 4.42001671e-01 -1.47650689e-01
-9.90678191e-01 4.00927067e-01 -2.43893772e-01 -1.07764494e+00
-3.05116326e-01 2.21375123e-01 -3.16619068e-01 1.22699641e-01
4.54554737e-01 4.44518656e-01 2.65407950e-01 -6.01591289e-01
6.00016892e-01 3.02467942e-01 5.77341199e-01 -5.18861353e-01
8.67991745e-01 9.48592126e-01 2.29086533e-01 -5.43664277e-01
-1.01361895e+00 -1.43103406e-01 -9.14150536e-01 -2.37734556e-01
1.02708888e+00 -1.20080602e+00 -4.41840470e-01 9.76460576e-02
-1.27105272e+00 2.12940760e-03 -5.24723291e-01 5.33165574e-01
-1.12090552e+00 4.64114606e-01 -2.30118901e-01 -6.43155098e-01
-6.62729219e-02 -1.34720731e+00 1.66546965e+00 -3.55685413e-01
-1.62348807e-01 -9.30981815e-01 4.70783412e-02 5.37938654e-01
-2.39319913e-02 7.38310993e-01 9.58435893e-01 -2.63028834e-02
-1.19937813e+00 -8.32182318e-02 -2.92204112e-01 2.73302913e-01
1.76889017e-01 -3.19753170e-01 -1.12173140e+00 -3.21662098e-01
2.92671442e-01 -3.86832476e-01 7.29458332e-01 4.39804763e-01
8.79697859e-01 -1.89905912e-01 -1.03018045e-01 9.20233190e-01
1.64358592e+00 -4.55935091e-01 5.81204414e-01 6.31615743e-02
7.82709241e-01 6.63348317e-01 3.84567112e-01 3.71744931e-01
5.34501016e-01 8.36072326e-01 8.70523810e-01 8.00138414e-02
-2.25700110e-01 -8.63742650e-01 7.99654797e-02 8.95434499e-01
6.08742656e-03 -3.19535404e-01 -7.16682076e-01 5.22906125e-01
-1.69166851e+00 -8.21603060e-01 2.92792886e-01 2.09602523e+00
4.54294413e-01 2.44139031e-01 -3.84298146e-01 2.70515550e-02
2.61624396e-01 4.76543903e-01 -7.15956628e-01 1.09182462e-01
-2.10213110e-01 -1.15351625e-01 1.56271383e-01 7.72974730e-01
-7.17484176e-01 7.20061660e-01 6.51823282e+00 4.89314675e-01
-1.09340155e+00 2.50842213e-03 5.53175509e-01 6.87497144e-04
-1.01888776e+00 1.63855553e-01 -5.38757563e-01 3.02969426e-01
4.27309364e-01 1.54941246e-01 3.89302433e-01 5.10978937e-01
1.33825555e-01 -6.54633343e-02 -1.32402384e+00 1.16792083e+00
3.28075022e-01 -1.80097771e+00 3.78430277e-01 3.31244767e-01
9.25673783e-01 -8.09132606e-02 4.68133166e-02 -2.41265103e-01
3.17316763e-02 -1.12482226e+00 1.00260270e+00 8.95092487e-01
1.12798858e+00 -5.69610119e-01 3.63985807e-01 5.38702369e-01
-9.67440009e-01 2.58885443e-01 -1.62554801e-01 1.66091651e-01
7.59207070e-01 5.42438626e-01 -7.35808671e-01 8.69411111e-01
6.32183313e-01 9.87914443e-01 -2.53801942e-01 4.61646318e-01
-2.52046436e-01 1.41827285e-01 -3.50172997e-01 7.52154231e-01
6.54456466e-02 -3.27693194e-01 9.31693554e-01 4.93665963e-01
5.06054997e-01 5.23020141e-02 7.40929618e-02 1.49547887e+00
1.00563020e-02 -5.09948194e-01 -1.04128253e+00 7.10464790e-02
3.03332239e-01 9.95976210e-01 -5.41284382e-01 -8.47535804e-02
-2.49367520e-01 1.23048401e+00 3.75107765e-01 3.85003150e-01
-5.76893806e-01 3.19569528e-01 5.58560491e-01 3.32080096e-01
4.24441963e-01 -4.21684504e-01 -4.31829304e-01 -1.34058559e+00
3.00241202e-01 -6.35222614e-01 -4.67304625e-02 -1.18877101e+00
-1.34268510e+00 6.92797482e-01 2.40144640e-01 -1.70320976e+00
-5.85537732e-01 -3.92136753e-01 -2.45368347e-01 9.57953334e-01
-1.53901851e+00 -1.41972101e+00 -3.95195454e-01 2.85648704e-01
6.03899360e-01 -1.02300823e-01 8.67552221e-01 1.75916832e-02
5.05158566e-02 1.43565118e-01 -2.48713940e-02 -4.26807284e-01
4.42820042e-01 -9.95115042e-01 6.03691459e-01 7.42704153e-01
2.46339619e-01 3.60475332e-01 7.29502499e-01 -5.33736050e-01
-1.52136850e+00 -1.00231779e+00 7.29091644e-01 -7.27675736e-01
1.62331045e-01 -5.33652067e-01 -7.61940718e-01 7.92689919e-01
6.73773289e-02 2.16717467e-01 3.47038656e-01 -4.29670334e-01
-4.63765472e-01 2.53927708e-01 -1.26536345e+00 5.70955813e-01
1.19002366e+00 -8.55575562e-01 -5.65560818e-01 -7.70461420e-03
9.64127958e-01 -6.66643262e-01 -9.54082131e-01 4.73581076e-01
6.75064147e-01 -1.33499658e+00 1.45039296e+00 -2.56390095e-01
8.45416903e-01 -3.21144611e-01 -6.73729002e-01 -1.29839599e+00
-1.44930243e-01 -5.09840190e-01 -2.79661059e-01 8.27539086e-01
1.42581895e-01 -4.73596066e-01 8.66264701e-01 3.58084291e-01
-1.66487277e-01 -9.60553706e-01 -1.01285696e+00 -6.28476322e-01
4.80944514e-02 -3.66972059e-01 5.15971243e-01 7.72080600e-01
-6.51249886e-01 4.10113961e-01 -6.86324596e-01 4.50430721e-01
1.09306622e+00 4.80515182e-01 9.10867333e-01 -1.03728759e+00
-3.20767075e-01 5.51999286e-02 -4.21960086e-01 -1.30989611e+00
2.64870018e-01 -9.34010088e-01 -1.51963634e-02 -1.65346789e+00
9.42555889e-02 -3.78312230e-01 2.26973474e-01 -1.27022490e-01
6.74994811e-02 3.10037673e-01 1.87453598e-01 3.43854755e-01
-2.18862057e-01 9.45161879e-01 1.45427752e+00 1.51358739e-01
1.28254339e-01 -1.03054039e-01 -3.52858484e-01 6.83191121e-01
3.94112200e-01 -5.14136255e-01 -5.44376075e-01 -6.21784627e-01
3.69849741e-01 5.64727962e-01 8.22917402e-01 -8.40664566e-01
1.41535494e-02 -1.75797120e-01 5.14410734e-01 -1.06866610e+00
1.03660846e+00 -1.09862936e+00 7.14817166e-01 1.07165307e-01
-2.56963789e-01 4.75657843e-02 -1.95697672e-03 9.45346177e-01
-2.00461358e-01 2.10986584e-02 7.34413385e-01 -3.00269097e-01
-3.06922525e-01 5.40592849e-01 6.73448294e-02 3.06880325e-02
8.06579411e-01 -5.35463691e-01 1.74452767e-01 -5.83207250e-01
-7.76302993e-01 -7.09019601e-02 1.02469611e+00 4.04487997e-01
1.09498668e+00 -1.57598197e+00 -7.58933842e-01 7.09567785e-01
3.52614611e-01 4.25550103e-01 2.76056409e-01 5.81195533e-01
-5.07919908e-01 1.60094649e-01 -3.00804734e-01 -1.09480584e+00
-8.18715811e-01 4.05123711e-01 3.79611433e-01 -1.17042065e-01
-1.12754822e+00 4.62842047e-01 3.99686545e-01 -8.22296202e-01
4.03990299e-02 -1.79407209e-01 1.37456745e-01 -2.67694324e-01
6.25040531e-02 1.06317602e-01 -2.90711403e-01 -8.62819791e-01
-4.62100655e-02 8.57451856e-01 3.18729997e-01 -5.67623734e-01
1.62891972e+00 -3.69472176e-01 1.47879556e-01 6.72866344e-01
1.35869074e+00 1.81447193e-02 -1.83529735e+00 -3.27238083e-01
-4.57089633e-01 -7.97303140e-01 -1.19270580e-02 -5.75854719e-01
-8.57918680e-01 1.03160250e+00 4.56030697e-01 -1.79824755e-01
7.58361816e-01 2.79566705e-01 6.46689177e-01 -5.56605905e-02
4.18313861e-01 -4.59836602e-01 3.45766813e-01 1.60109237e-01
1.28399611e+00 -1.46442306e+00 3.31134260e-01 -4.66495305e-01
-5.41005731e-01 1.02214432e+00 3.92927855e-01 -1.46086231e-01
7.04158187e-01 -5.32052331e-02 -6.57615298e-03 -5.50750852e-01
-7.61378527e-01 2.04457104e-01 5.23762107e-01 4.43900734e-01
-9.71054360e-02 -1.17991023e-01 3.14359039e-01 1.98968709e-01
-1.23191036e-01 -1.17776684e-01 4.08031553e-01 7.61358142e-01
5.61959557e-02 -9.84203458e-01 -2.95149207e-01 1.12105221e-01
-7.39633217e-02 2.04837337e-01 -2.38210529e-01 5.90107262e-01
-7.17180744e-02 4.83280987e-01 -5.90997189e-03 -4.50693995e-01
2.67655671e-01 -9.13146511e-02 7.40130544e-01 -5.79677105e-01
1.21196538e-01 1.98933363e-01 -9.73014086e-02 -7.41719544e-01
-6.44234121e-01 -6.44003391e-01 -8.42295706e-01 -9.97822434e-02
-1.71266109e-01 -3.24173927e-01 7.02316344e-01 7.58796275e-01
6.21833026e-01 2.91691422e-01 7.81541824e-01 -1.41634357e+00
-5.91303468e-01 -4.99673903e-01 -2.87263185e-01 6.25441194e-01
5.99950373e-01 -8.86051595e-01 -6.44882858e-01 9.55010802e-02] | [8.87582778930664, -3.1731197834014893] |
a50025b3-bf1a-4d12-9a83-da4a25f169f9 | vflow-more-expressive-generative-flows-with | 2002.09741 | null | https://arxiv.org/abs/2002.09741v2 | https://arxiv.org/pdf/2002.09741v2.pdf | VFlow: More Expressive Generative Flows with Variational Data Augmentation | Generative flows are promising tractable models for density modeling that define probabilistic distributions with invertible transformations. However, tractability imposes architectural constraints on generative flows, making them less expressive than other types of generative models. In this work, we study a previously overlooked constraint that all the intermediate representations must have the same dimensionality with the original data due to invertibility, limiting the width of the network. We tackle this constraint by augmenting the data with some extra dimensions and jointly learning a generative flow for augmented data as well as the distribution of augmented dimensions under a variational inference framework. Our approach, VFlow, is a generalization of generative flows and therefore always performs better. Combining with existing generative flows, VFlow achieves a new state-of-the-art 2.98 bits per dimension on the CIFAR-10 dataset and is more compact than previous models to reach similar modeling quality. | ['Tian Tian', 'Jun Zhu', 'Jianfei Chen', 'Biqi Chenli', 'Cheng Lu'] | 2020-02-22 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/1619-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/1619-Paper.pdf | icml-2020-1 | ['normalising-flows'] | ['methodology'] | [-4.22362983e-01 2.32550487e-01 -3.45079750e-01 -4.13344890e-01
-4.80846196e-01 -7.03033507e-01 8.18422914e-01 -7.66538262e-01
-9.51211751e-02 1.12274230e+00 4.58563983e-01 -5.87958753e-01
-3.59868199e-01 -1.04280365e+00 -6.62124932e-01 -7.96783984e-01
-1.56736732e-01 9.41744268e-01 -7.05927461e-02 3.58937204e-01
-8.20387080e-02 6.10657334e-01 -1.05660021e+00 2.96177175e-02
7.66125321e-01 8.70933473e-01 3.35608609e-02 8.54515731e-01
-3.94244999e-01 8.64735782e-01 -5.32071829e-01 -7.19285429e-01
1.36741012e-01 -4.81372535e-01 -6.02346897e-01 -1.82607844e-01
5.53238630e-01 -8.00608456e-01 -8.98574769e-01 9.49270546e-01
2.91550279e-01 1.34837061e-01 1.33504665e+00 -1.60779297e+00
-1.33578634e+00 9.99665856e-01 -3.99406403e-01 3.72150004e-01
-3.97141457e-01 -1.38859954e-02 1.02479327e+00 -6.80182934e-01
4.53486770e-01 1.48728728e+00 5.06937504e-01 8.58745098e-01
-1.63560128e+00 -7.42233038e-01 4.29148197e-01 -1.62172362e-01
-1.49227417e+00 -4.59245861e-01 5.52979112e-01 -5.91403008e-01
8.57362926e-01 -1.03029698e-01 5.56986690e-01 1.61103964e+00
9.73858014e-02 7.27317989e-01 5.05718946e-01 3.17977853e-02
7.25797355e-01 2.40233585e-01 2.05994353e-01 4.30123389e-01
5.41329622e-01 1.61193609e-01 -4.28499788e-01 -3.80037755e-01
1.10018802e+00 1.31692782e-01 -1.03517435e-01 -6.01454377e-01
-8.01804543e-01 1.22828269e+00 2.92654395e-01 1.15783945e-01
-1.87125728e-01 8.30894232e-01 3.12416889e-02 -9.56705138e-02
4.30821180e-01 7.86589645e-03 -2.80184537e-01 -3.73071194e-01
-1.12176824e+00 4.77415413e-01 1.21092141e+00 1.30251968e+00
7.80505896e-01 5.64155638e-01 -3.87200177e-01 3.77670199e-01
7.50710785e-01 8.18807185e-01 1.68266892e-02 -1.11623287e+00
6.18847787e-01 1.97921738e-01 -1.64299995e-01 -6.51101172e-01
-4.85602953e-02 -6.41734362e-01 -1.15652049e+00 -1.14396671e-02
6.14320219e-01 -5.44090092e-01 -1.11802959e+00 2.15042424e+00
-2.92722322e-02 3.99643302e-01 -3.81220616e-02 7.29850054e-01
3.38033766e-01 9.08131778e-01 2.61557311e-01 1.59972399e-01
9.01528239e-01 -4.75044817e-01 -5.49079418e-01 -4.88556027e-02
2.39191979e-01 -3.21552157e-01 8.71013641e-01 4.24566656e-01
-9.97639358e-01 -2.67117053e-01 -7.93563902e-01 -2.27130219e-01
-2.19806567e-01 8.12234804e-02 1.16226995e+00 1.22566545e+00
-1.29159951e+00 3.96205187e-01 -1.08623171e+00 8.73098522e-02
8.48233879e-01 7.85601586e-02 5.68592511e-02 -1.04163485e-02
-8.89310539e-01 4.17540103e-01 1.33923605e-01 -1.24299973e-01
-1.20161808e+00 -1.43033063e+00 -7.87811518e-01 4.40128207e-01
-1.09508961e-01 -1.02275527e+00 8.73101473e-01 -2.84293383e-01
-1.42783892e+00 1.75756916e-01 -2.84001291e-01 -8.39784920e-01
6.79815412e-01 -3.17709178e-01 -1.15495019e-01 -1.77780330e-01
-2.61247039e-01 8.25665295e-01 8.75411391e-01 -1.06442285e+00
-2.43139908e-01 -2.31720716e-01 1.44569099e-01 -3.96292031e-01
-4.49832052e-01 -7.61749983e-01 -4.08794433e-01 -6.81757808e-01
-4.06250209e-01 -7.38699794e-01 -1.81384534e-01 1.32179543e-01
-6.22756124e-01 -1.82282895e-01 8.37989151e-01 -2.83823252e-01
1.42052388e+00 -1.99188185e+00 2.92464048e-01 1.88685536e-01
5.39711535e-01 7.22265169e-02 -1.84494540e-01 2.95796692e-01
2.53719330e-01 6.25721633e-01 -2.23889202e-01 -8.91222954e-01
5.11666179e-01 5.49938679e-01 -8.55471790e-01 4.63117510e-01
2.11721689e-01 1.02656639e+00 -6.33507729e-01 -3.27421010e-01
2.73320675e-01 1.10969377e+00 -1.18838656e+00 7.30543062e-02
-3.74925375e-01 2.29870170e-01 -2.97561288e-01 2.37142295e-01
1.15755916e+00 -2.13415578e-01 3.44471796e-03 -8.30116421e-02
2.09806606e-01 2.47031078e-01 -1.13068056e+00 1.84252512e+00
-5.72405934e-01 6.90139592e-01 -3.15709770e-01 -6.86427236e-01
9.13886189e-01 2.93569677e-02 3.98920625e-01 -8.16564038e-02
1.47920683e-01 -8.86939391e-02 -6.26862198e-02 1.45168483e-01
4.08051074e-01 -2.12211654e-01 -2.78224945e-02 5.93208611e-01
6.90782487e-01 4.71645184e-02 3.63775045e-01 5.64891398e-01
7.55671382e-01 1.84651092e-01 -2.40541354e-01 -4.28851306e-01
-1.46216646e-01 -6.04079723e-01 3.82977307e-01 1.09929931e+00
5.07128797e-02 5.82597911e-01 1.11751485e+00 -2.63839543e-01
-1.25215888e+00 -1.95125902e+00 -3.59869152e-01 5.52300632e-01
-3.52043748e-01 -4.56168920e-01 -6.70144320e-01 -7.93478072e-01
2.35893771e-01 9.36763108e-01 -8.63631248e-01 -1.82381466e-01
-2.07550839e-01 -1.11247170e+00 8.28989089e-01 8.08612823e-01
3.54065031e-01 -3.49639118e-01 -1.53681546e-01 1.23598568e-01
1.25382662e-01 -9.36868370e-01 -5.46858370e-01 1.71879500e-01
-9.62831616e-01 -6.50338233e-01 -8.43851686e-01 -8.10072049e-02
4.69969571e-01 -4.12422389e-01 1.14779162e+00 -6.87105119e-01
-2.41673499e-01 1.51572442e-02 7.82434046e-02 -2.02811033e-01
-3.07145804e-01 5.05735815e-01 -4.04520594e-02 -1.06339931e-01
4.00773525e-01 -1.08815086e+00 -4.89311785e-01 -1.15786776e-01
-8.58476281e-01 -2.33441949e-01 4.36413974e-01 6.79093421e-01
4.41601247e-01 -7.61238113e-02 7.36504436e-01 -9.80487168e-01
5.71716130e-01 -8.90020907e-01 -6.29023433e-01 1.77458581e-02
-5.03310382e-01 5.20940006e-01 6.21036410e-01 -6.37040377e-01
-1.18042111e+00 -1.80167302e-01 -9.61433724e-02 -8.74966860e-01
-1.99661944e-02 2.16732398e-01 -3.44023496e-01 4.75878894e-01
5.45167446e-01 5.30401133e-02 -1.32529169e-01 -6.10214829e-01
8.71279836e-01 3.33138436e-01 4.27390486e-01 -6.65733814e-01
8.21860671e-01 5.37005424e-01 2.86030143e-01 -8.03884685e-01
-7.28668213e-01 5.29920645e-02 -6.00986302e-01 2.51170933e-01
6.74104631e-01 -9.51341569e-01 -6.86490595e-01 2.80123085e-01
-1.10430980e+00 -3.07333380e-01 -7.18533695e-01 6.05401635e-01
-6.45269454e-01 -3.43268365e-02 -5.84583282e-01 -1.04810822e+00
-8.43546838e-02 -8.20081413e-01 9.12950337e-01 1.65190369e-01
-1.42630219e-01 -1.35728502e+00 2.92347759e-01 -3.79401475e-01
7.82698512e-01 -3.35531235e-02 9.77452576e-01 -3.86990190e-01
-8.61921906e-01 2.16853306e-01 -3.66544098e-01 4.88064528e-01
-1.52337959e-03 1.81872949e-01 -1.01642215e+00 6.30381471e-03
-2.75442690e-01 2.71604322e-02 1.27022946e+00 8.30489039e-01
1.26470220e+00 -4.28815603e-01 -2.42131308e-01 1.19806325e+00
1.37717664e+00 6.97778836e-02 8.81450951e-01 -4.70357418e-01
7.20626533e-01 1.05679929e-01 -4.92790520e-01 7.74141133e-01
2.83730835e-01 1.51157007e-01 2.58392602e-01 1.56877711e-01
-2.73289502e-01 -8.43021572e-01 2.08427340e-01 4.64359820e-01
1.23300105e-01 -7.03263879e-01 -6.60895407e-01 5.30834496e-01
-1.55997527e+00 -1.12626529e+00 5.49655259e-02 1.83388150e+00
6.06586933e-01 -2.13034218e-03 2.13341326e-01 -1.35226443e-01
3.74704212e-01 3.01514834e-01 -7.28606045e-01 -3.00716043e-01
-1.10026412e-01 4.61509883e-01 6.01200581e-01 6.69245899e-01
-1.07290733e+00 6.92845345e-01 7.39348650e+00 8.34257960e-01
-7.09152818e-01 1.99846804e-01 8.45920146e-01 -4.99085784e-01
-8.45763624e-01 -7.74304941e-02 -1.36350024e+00 6.78970158e-01
1.19414985e+00 -2.28794724e-01 4.93340194e-01 9.66210008e-01
-1.69214666e-01 1.40460715e-01 -1.19532597e+00 8.42023432e-01
-3.86040509e-02 -1.65772271e+00 5.98732591e-01 4.33888286e-01
7.79323995e-01 -2.06106738e-03 6.25774086e-01 4.68807429e-01
8.05288255e-01 -1.26938856e+00 7.36926079e-01 9.39930618e-01
8.93053472e-01 -1.04017603e+00 5.01436114e-01 2.28862435e-01
-9.09730911e-01 1.91675812e-01 -7.23520160e-01 8.46426263e-02
5.26312649e-01 9.21316028e-01 -4.07938331e-01 1.77757531e-01
4.03432608e-01 7.26574421e-01 -3.64058584e-01 9.87611771e-01
-1.33431643e-01 9.67386842e-01 -5.78900218e-01 -3.34230298e-03
2.54350603e-01 -2.30513781e-01 4.63468969e-01 1.25067770e+00
5.97548068e-01 -5.15272439e-01 -2.06816494e-01 1.83322251e+00
-2.66283244e-01 -6.07334614e-01 -6.23287976e-01 -3.21969301e-01
6.37868285e-01 8.86312604e-01 -4.56181437e-01 -2.38671526e-01
-1.76420391e-01 6.55680299e-01 4.44709510e-01 8.06142509e-01
-1.15349865e+00 -1.54679000e-01 1.12551212e+00 6.37147278e-02
5.03068686e-01 -4.65317726e-01 -3.81851643e-01 -1.45317495e+00
-2.79171765e-01 -5.71439154e-02 1.99288368e-01 -3.13620448e-01
-1.73915386e+00 5.91418266e-01 2.93892890e-01 -7.98276305e-01
-5.85340083e-01 -6.30978465e-01 -4.71734792e-01 1.33475268e+00
-1.33834887e+00 -1.08521986e+00 8.12663808e-02 6.00230873e-01
1.66089714e-01 -1.94841266e-01 7.39690959e-01 4.10196632e-01
-5.43735862e-01 7.55556822e-01 1.39668420e-01 2.00357921e-02
2.10292861e-01 -1.38338435e+00 7.05844045e-01 8.84973049e-01
4.72099483e-01 7.72467494e-01 4.38845515e-01 -5.66963553e-01
-1.25884843e+00 -1.27651227e+00 5.20925701e-01 -6.36947095e-01
6.99883938e-01 -7.95156837e-01 -7.79650509e-01 1.01853669e+00
8.24114308e-03 1.68823823e-01 7.52504528e-01 1.68130189e-01
-5.35416961e-01 1.19498923e-01 -9.97336745e-01 3.46918374e-01
1.32824731e+00 -4.58718359e-01 -2.58728713e-01 -1.48361906e-01
8.00577402e-01 -1.61444619e-01 -8.04102600e-01 -1.39050990e-01
4.56066489e-01 -9.10299182e-01 1.08612847e+00 -7.89095461e-01
3.41477126e-01 -9.37570184e-02 -4.07339036e-01 -1.24685633e+00
-4.62763667e-01 -7.46231973e-01 -7.60789275e-01 1.53963995e+00
4.87913460e-01 -7.03223348e-01 1.09329736e+00 6.80860162e-01
1.23980775e-01 -6.36200547e-01 -1.05669308e+00 -9.65763211e-01
7.84865260e-01 -6.54164970e-01 8.90027702e-01 4.74575192e-01
-3.82640123e-01 2.15907574e-01 -5.20153582e-01 1.63496614e-01
1.02168989e+00 -2.31541261e-01 7.12451100e-01 -1.26042151e+00
-3.98579091e-01 -5.49601018e-01 -4.38298166e-01 -1.48851216e+00
4.58836496e-01 -1.02598500e+00 -5.02538383e-01 -1.43137729e+00
1.35765478e-01 -6.64037406e-01 5.30487075e-02 1.15097217e-01
1.75420448e-01 7.71606043e-02 1.53147042e-01 8.26451406e-02
-1.45548031e-01 9.03928399e-01 9.98048007e-01 -1.39855519e-02
-1.15688317e-01 6.95752958e-03 -9.81326044e-01 5.02895057e-01
6.55488789e-01 -2.80285865e-01 -1.06980705e+00 -6.00959182e-01
4.20522206e-02 -1.44592464e-01 4.09455299e-01 -9.09880102e-01
1.82225674e-01 -1.15407452e-01 7.88922966e-01 -7.62920797e-01
4.18396443e-01 -5.84416568e-01 4.31837082e-01 6.51719421e-02
-3.44430149e-01 -2.99849033e-01 2.23450840e-01 7.16694415e-01
-3.84805873e-02 -7.56947994e-02 6.78795695e-01 2.28664964e-01
-2.99616545e-01 8.43948960e-01 -3.94791782e-01 4.82562870e-01
9.10059631e-01 1.53511688e-01 -5.64850509e-01 -4.42259431e-01
-9.29346561e-01 -2.12830547e-02 1.47207662e-01 2.64784843e-01
5.12559295e-01 -1.42113173e+00 -6.48364365e-01 4.80931312e-01
-4.02690858e-01 8.19946751e-02 5.53657115e-01 4.44535345e-01
-2.75255263e-01 5.29745460e-01 -3.06845047e-02 -5.68664372e-01
-2.73005694e-01 5.31377912e-01 4.38827991e-01 -3.42730105e-01
-6.30525529e-01 8.90909791e-01 4.60278153e-01 -1.98362783e-01
2.50668734e-01 -4.79072958e-01 1.60958707e-01 1.12431116e-01
7.13458061e-01 4.60019439e-01 -3.43694061e-01 -1.62565678e-01
-2.13786900e-01 2.10594177e-01 5.56688569e-03 -3.21247518e-01
1.22123671e+00 -1.76891848e-01 2.45086581e-01 5.11720300e-01
1.32303321e+00 -2.61049688e-01 -1.71406114e+00 -1.01793379e-01
-5.86447954e-01 -6.57901585e-01 1.54064626e-01 -6.63582683e-01
-1.45588565e+00 1.30620325e+00 2.90847480e-01 6.10305034e-02
6.59314752e-01 2.41514787e-01 4.39530641e-01 6.51573017e-02
3.54445785e-01 -4.82935280e-01 -1.34243205e-01 5.67429960e-01
6.26597047e-01 -6.23323619e-01 -2.16656148e-01 -2.06045821e-01
-4.14078027e-01 9.17267382e-01 4.77059841e-01 -4.59409952e-01
1.23741651e+00 8.24600756e-01 -5.20042479e-01 1.23299733e-01
-9.37316358e-01 -5.22379242e-02 3.58988136e-01 1.11723721e+00
3.45253110e-01 8.56634229e-02 2.25921586e-01 7.57645607e-01
-5.36940753e-01 1.85239345e-01 4.65710193e-01 3.65668386e-01
-1.62744358e-01 -9.17085588e-01 1.03601173e-01 6.44094348e-01
-3.94470692e-01 -2.57848024e-01 4.72067855e-02 8.80601645e-01
-1.05963483e-01 6.28850639e-01 7.23140359e-01 -5.28354645e-02
5.21398485e-02 4.28780317e-02 6.01236939e-01 -3.95208478e-01
1.37116864e-01 -7.43268430e-02 -2.62568176e-01 -4.43339765e-01
-7.23177269e-02 -6.27249599e-01 -8.91304076e-01 -8.80229950e-01
-3.82314809e-02 4.10083048e-02 3.84642631e-01 5.52206576e-01
6.04909420e-01 6.96129262e-01 1.75156713e-01 -7.64480531e-01
-7.72109926e-01 -1.05574048e+00 -1.01810551e+00 2.81632580e-02
4.16187078e-01 -8.16299677e-01 -6.77710712e-01 5.52972890e-02] | [7.191006183624268, 3.774919271469116] |
922d7318-7efe-41ae-804d-4dcb776b5dcf | rade-resource-efficient-supervised-anomaly | 1909.11877 | null | https://arxiv.org/abs/1909.11877v2 | https://arxiv.org/pdf/1909.11877v2.pdf | RADE: Resource-Efficient Supervised Anomaly Detection Using Decision Tree-Based Ensemble Methods | Decision-tree-based ensemble classification methods (DTEMs) are a prevalent tool for supervised anomaly detection. However, due to the continued growth of datasets, DTEMs result in increasing drawbacks such as growing memory footprints, longer training times, and slower classification latencies at lower throughput. In this paper, we present, design, and evaluate RADE - a DTEM-based anomaly detection framework that augments standard DTEM classifiers and alleviates these drawbacks by relying on two observations: (1) we find that a small (coarse-grained) DTEM model is sufficient to classify the majority of the classification queries correctly, such that a classification is valid only if its corresponding confidence level is greater than or equal to a predetermined classification confidence threshold; (2) we find that in these fewer harder cases where our coarse-grained DTEM model results in insufficient confidence in its classification, we can improve it by forwarding the classification query to one of expert DTEM (fine-grained) models, which is explicitly trained for that particular case. We implement RADE in Python based on scikit-learn and evaluate it over different DTEM methods: RF, XGBoost, AdaBoost, GBDT and LightGBM, and over three publicly available datasets. Our evaluation over both a strong AWS EC2 instance and a Raspberry Pi 3 device indicates that RADE offers competitive and often superior anomaly detection capabilities as compared to standard DTEM methods, while significantly improving memory footprint (by up to 5.46x), training-time (by up to 17.2x), and classification latency (by up to 31.2x). | ['Yaniv Ben-Itzhak', 'Shay Vargaftik', 'Isaac Keslassy', 'Ariel Orda'] | 2019-09-26 | null | null | null | null | ['supervised-anomaly-detection'] | ['computer-vision'] | [-2.68022008e-02 -5.19075274e-01 4.78784414e-03 -3.69792819e-01
-3.46207023e-01 -4.17785466e-01 3.26788127e-01 4.69008535e-01
-5.84679782e-01 8.32519829e-01 -4.67970580e-01 -7.68615544e-01
-4.48439956e-01 -1.03262711e+00 -2.08992839e-01 -6.22339785e-01
-7.37623125e-02 5.70356309e-01 5.82560360e-01 -1.64393231e-01
3.06958020e-01 6.57105446e-01 -1.92121279e+00 3.49285126e-01
1.13308692e+00 1.50130618e+00 -7.61443079e-01 6.42755091e-01
-2.30656832e-01 6.61595941e-01 -9.46812451e-01 -1.93987563e-01
1.54753149e-01 2.47508474e-02 -7.67940521e-01 -6.79921746e-01
4.31945264e-01 -3.26343417e-01 2.84533501e-01 5.00562966e-01
3.98698837e-01 1.69512965e-02 4.54547644e-01 -1.56073654e+00
-1.73374359e-02 5.66579886e-02 -4.90242720e-01 4.99614865e-01
1.17733084e-01 8.66063759e-02 8.07835162e-01 -5.15619099e-01
6.42031431e-02 8.83739054e-01 9.95693505e-01 5.59217751e-01
-1.33664489e+00 -7.81519651e-01 1.56501845e-01 2.54087031e-01
-1.35750341e+00 -2.29789123e-01 1.84761479e-01 -3.34515452e-01
1.35743642e+00 7.55151153e-01 4.60406512e-01 1.04135907e+00
6.25789762e-01 3.53978038e-01 1.24766397e+00 -3.16391528e-01
7.68151343e-01 -3.02004814e-03 6.06523991e-01 4.67360377e-01
4.77466881e-01 2.27861941e-01 -5.00724912e-01 -7.98696280e-01
2.10151017e-01 2.24044546e-01 2.72150159e-01 7.81296045e-02
-4.91659045e-01 7.48598814e-01 -1.97507739e-02 2.76538640e-01
-6.06006920e-01 -1.48201779e-01 6.69859469e-01 5.44302404e-01
5.58712184e-01 4.25418705e-01 -7.61295319e-01 -3.80947381e-01
-9.90447342e-01 1.35642886e-01 9.89224494e-01 3.77928495e-01
4.45109844e-01 2.08555564e-01 -1.40135273e-01 7.52642632e-01
1.31127000e-01 4.26380247e-01 6.44920111e-01 -4.76183534e-01
1.46084458e-01 9.93008912e-01 -7.95048103e-03 -7.83470154e-01
-4.46142316e-01 -6.00780725e-01 -8.45437706e-01 6.51345611e-01
2.90851980e-01 -1.28275976e-01 -9.06284750e-01 1.32817614e+00
3.50447208e-01 1.12387583e-01 -8.89304951e-02 4.99674082e-01
4.04680699e-01 3.00708771e-01 5.43632090e-01 -6.51228055e-02
1.20414579e+00 -4.85097736e-01 -2.74058342e-01 -1.91101477e-01
7.87949204e-01 -4.26501006e-01 8.13936412e-01 8.33569109e-01
-4.95218873e-01 -4.69211519e-01 -1.17321742e+00 5.00587642e-01
-6.53140485e-01 -2.70820946e-01 6.33589029e-01 1.15527129e+00
-8.17482054e-01 6.79935515e-01 -1.01656747e+00 -5.30889809e-01
4.92887855e-01 5.00216186e-01 -2.00394198e-01 8.61294195e-02
-8.80895674e-01 9.09445047e-01 2.72040397e-01 -2.36926794e-01
-3.60183805e-01 -6.62027061e-01 -2.84414113e-01 8.25846270e-02
2.10306004e-01 -6.05447054e-01 8.25196147e-01 -5.88590682e-01
-1.10064590e+00 5.79152644e-01 -3.39239389e-02 -7.10539520e-01
4.91135091e-01 -3.70885283e-01 -9.60397124e-01 -1.42881379e-01
-1.02103777e-01 1.32562265e-01 5.82398772e-01 -6.74089849e-01
-1.05662715e+00 -6.56722963e-01 -2.25625128e-01 -2.38947332e-01
-4.47526962e-01 4.21190076e-02 1.76717594e-01 -6.02885902e-01
4.57768701e-02 -7.85123825e-01 -1.33048758e-01 -1.85896963e-01
-1.87758699e-01 -2.64608026e-01 1.29979360e+00 -5.61378479e-01
1.70141280e+00 -2.09782791e+00 -5.57455897e-01 6.17415607e-01
3.09329063e-01 8.60161781e-01 2.91251153e-01 2.19986752e-01
-5.41005619e-02 1.37214258e-01 -2.85147339e-01 -2.26825912e-04
-2.58468747e-01 5.64072609e-01 -3.47270221e-01 8.60296860e-02
4.46704775e-02 5.33742249e-01 -5.46027362e-01 -2.51491070e-01
3.04905683e-01 1.54326573e-01 -4.56306785e-01 1.11578785e-01
-6.63346574e-02 2.93968350e-01 -5.18236816e-01 8.82477522e-01
7.13180244e-01 -9.80249494e-02 1.58759893e-03 5.00380471e-02
-1.10258698e-01 1.03203565e-01 -1.37960052e+00 8.77538204e-01
-4.39909041e-01 2.33145759e-01 -2.62158006e-01 -1.09573674e+00
1.10289419e+00 1.41685516e-01 4.49523568e-01 -6.77435219e-01
-6.21399805e-02 4.40544009e-01 -4.75914255e-02 -3.06567878e-01
3.69212665e-02 1.81844473e-01 5.99131046e-04 6.42215788e-01
-5.99704012e-02 6.03287518e-01 1.48459390e-01 -1.52729541e-01
1.54556072e+00 -1.07892498e-01 4.81986105e-01 -2.94146091e-01
6.39332056e-01 4.36781645e-02 5.84539652e-01 1.06021547e+00
-2.33697727e-01 -8.43767747e-02 2.44544387e-01 -8.51431131e-01
-5.36250830e-01 -1.04030585e+00 -4.63650525e-01 1.31358445e+00
-2.41955563e-01 -5.66564798e-01 -5.40626526e-01 -9.59463239e-01
3.90263170e-01 9.84587908e-01 -3.84522289e-01 -2.65450150e-01
-3.66152167e-01 -1.22933221e+00 7.64002740e-01 6.45851910e-01
8.39233398e-01 -8.93312514e-01 -9.93401706e-01 5.32971025e-01
1.91051230e-01 -6.35432422e-01 1.71784833e-01 3.49285394e-01
-1.28684223e+00 -1.06608832e+00 2.74549931e-01 3.84322777e-02
3.29361409e-01 -1.77053750e-01 1.22937179e+00 4.56502765e-01
-6.26911104e-01 3.11659455e-01 -3.63360614e-01 -6.68965340e-01
-3.05452287e-01 3.09687573e-02 1.64241120e-01 4.73272726e-02
1.00362921e+00 -7.50700235e-01 -6.84995770e-01 5.72779834e-01
-7.57045925e-01 -4.31726068e-01 4.57068205e-01 8.71127784e-01
3.99872512e-01 1.73684865e-01 8.02958310e-01 -9.33018744e-01
5.00661135e-01 -5.77485144e-01 -7.48274863e-01 2.95163304e-01
-1.49602389e+00 -1.59440599e-02 5.20617127e-01 -3.19639832e-01
-8.99196088e-01 -2.64135420e-01 -3.77874374e-01 -1.29631817e-01
-4.22391802e-01 3.15715045e-01 2.28431165e-01 -1.58695072e-01
1.01823378e+00 9.79475006e-02 3.46619636e-02 -6.46347821e-01
-2.57095486e-01 9.82303977e-01 3.72993886e-01 -7.76413202e-01
3.66204292e-01 3.18236768e-01 5.87948188e-02 -6.12150371e-01
-4.28704321e-01 -3.80799145e-01 -3.80043834e-01 1.82620194e-02
5.42680621e-01 -6.07145131e-01 -9.49575424e-01 5.32137752e-01
-5.25985658e-01 3.82213891e-02 -1.61660343e-01 2.35466525e-01
1.81212977e-01 1.85775265e-01 -4.68953788e-01 -1.05874562e+00
-9.56222475e-01 -7.55306721e-01 7.70644546e-01 8.66303146e-02
-6.32101953e-01 -8.98753822e-01 1.60097890e-02 2.03370452e-01
9.33688581e-01 4.71459448e-01 9.74516809e-01 -1.15747106e+00
-1.02974899e-01 -6.69257343e-01 -8.14911798e-02 4.87546384e-01
2.39334647e-02 1.32229358e-01 -1.10402334e+00 -3.13988239e-01
-1.08905464e-01 8.97683352e-02 6.86109304e-01 -1.48820169e-02
1.39182115e+00 -2.08009809e-01 -5.88142097e-01 3.91816825e-01
1.27634478e+00 5.41522861e-01 5.59120059e-01 5.01043141e-01
3.28874767e-01 2.59355634e-01 5.11300921e-01 5.19377351e-01
6.40088543e-02 6.77047014e-01 2.74264634e-01 -1.39106408e-01
3.94559950e-01 2.18259320e-01 2.07141727e-01 1.39421791e-01
-1.69560194e-01 -1.77303344e-01 -1.00061464e+00 2.03112006e-01
-1.95368886e+00 -8.42970490e-01 -2.95430899e-01 2.59558845e+00
6.59391880e-01 4.07751262e-01 3.41648400e-01 6.37186050e-01
3.49472314e-01 -4.00828272e-01 -7.52073586e-01 -8.36818159e-01
9.07907262e-02 7.51184881e-01 2.76245952e-01 -5.39717861e-02
-1.11643493e+00 4.74759012e-01 6.09152317e+00 8.34829926e-01
-1.29278338e+00 1.33826822e-01 6.57709301e-01 -1.55615106e-01
4.02465731e-01 -2.39611983e-01 -8.18880260e-01 7.85418391e-01
1.31972647e+00 -7.91971684e-02 1.53296620e-01 1.11772084e+00
-2.98358202e-01 -3.28992158e-01 -1.13449955e+00 8.43143463e-01
-4.12530154e-01 -1.16500282e+00 -1.81471631e-01 1.76307112e-01
2.62530863e-01 8.10270309e-02 -2.50816822e-01 4.90934014e-01
4.02525485e-01 -8.95550370e-01 2.34827518e-01 3.18469137e-01
6.90596223e-01 -7.77422547e-01 1.08527172e+00 3.18747461e-01
-8.93681467e-01 -3.81295979e-01 -8.40345100e-02 -1.05568953e-01
-4.13000911e-01 9.97717083e-01 -6.62002146e-01 7.06697583e-01
1.26681685e+00 5.51467761e-02 -7.26917088e-01 9.64993536e-01
7.75718838e-02 9.57217157e-01 -7.27945209e-01 1.44981459e-01
-1.65109009e-01 1.00825325e-01 3.78824443e-01 1.17339313e+00
3.44834447e-01 1.07972331e-01 1.12379499e-01 5.20188153e-01
3.50158632e-01 5.01713157e-02 -8.47800598e-02 3.28636974e-01
5.95048249e-01 1.28372478e+00 -5.12917638e-01 -4.35015768e-01
-4.03976798e-01 7.80490041e-01 6.28592521e-02 9.31905657e-02
-6.18460834e-01 -4.14792031e-01 7.62659371e-01 1.02425404e-01
1.38879165e-01 3.36689293e-01 -6.10444129e-01 -8.33940387e-01
-1.05955498e-02 -9.71772969e-01 1.10020161e+00 -3.36288542e-01
-1.67039526e+00 7.44339645e-01 -1.32680371e-01 -1.08808708e+00
-3.74407321e-01 -8.08692575e-01 -8.09868813e-01 1.01484358e+00
-9.08553421e-01 -7.68987894e-01 -4.83812332e-01 5.67488492e-01
1.27699003e-02 -2.93541908e-01 1.37145078e+00 4.28542852e-01
-9.09730434e-01 7.70006657e-01 1.19562961e-01 -5.62359728e-02
5.88040590e-01 -1.28512931e+00 4.26876038e-01 7.25818396e-01
-1.66205958e-01 5.45669675e-01 5.30845821e-01 -6.78394794e-01
-9.36993420e-01 -1.11276865e+00 7.94682741e-01 -7.36664176e-01
3.83062631e-01 -2.06471980e-01 -1.29295480e+00 4.84804600e-01
-3.33505183e-01 3.17262381e-01 9.31432366e-01 4.81294036e-01
-4.93464977e-01 -5.84700406e-01 -1.61638248e+00 3.41309100e-01
7.68939018e-01 -1.55802161e-01 -5.56299746e-01 1.02983534e-01
-1.40377536e-01 -3.12683433e-01 -1.17983162e+00 6.47762477e-01
8.71831000e-01 -1.23549223e+00 8.62564743e-01 -6.79563284e-01
-8.35854784e-02 -3.32327604e-01 -9.53679308e-02 -1.10986269e+00
-5.15736751e-02 -2.62200564e-01 -4.59125280e-01 1.12941849e+00
2.96517402e-01 -1.31583953e+00 7.25193262e-01 8.91657174e-01
1.51855454e-01 -1.05254114e+00 -1.04321492e+00 -8.54292572e-01
-1.75314143e-01 -5.05402148e-01 9.11070347e-01 9.25800443e-01
-7.70517737e-02 8.41667652e-02 -1.76117718e-02 1.92827553e-01
5.47726631e-01 6.42386153e-02 6.96369350e-01 -1.80503213e+00
-2.81357408e-01 -3.19188952e-01 -6.56781256e-01 -3.85800332e-01
-3.51945192e-01 -5.58107495e-01 -5.56896985e-01 -1.07281017e+00
-1.90799594e-01 -7.65063167e-01 -7.77405858e-01 9.38901663e-01
-1.99336246e-01 2.63184100e-01 -4.03348923e-01 1.60228625e-01
-4.35683697e-01 -9.76426452e-02 1.97806165e-01 2.97503412e-01
-2.03366101e-01 2.64383286e-01 -4.67993349e-01 4.86332238e-01
8.63791645e-01 -5.82440257e-01 -1.23633444e-01 3.76697220e-02
8.52819681e-02 -2.00849220e-01 4.56836551e-01 -1.42364192e+00
1.96236670e-01 1.14401383e-02 8.88585150e-01 -5.51897407e-01
-9.90539491e-02 -8.24968040e-01 3.42542708e-01 6.62701488e-01
-2.07086913e-02 2.51119703e-01 5.90638757e-01 4.71912235e-01
6.56766742e-02 1.43747656e-02 6.45964205e-01 2.80470252e-01
-7.11885333e-01 1.34280562e-01 -5.49030542e-01 -1.67970538e-01
1.15567207e+00 -3.83748144e-01 -5.66805780e-01 5.85536882e-02
-7.45249271e-01 2.71343499e-01 2.00941935e-01 2.89751440e-01
2.56827742e-01 -9.88253534e-01 -5.91798604e-01 7.06783593e-01
4.06424850e-01 -2.99460024e-01 1.40600666e-01 9.01862919e-01
-3.26792538e-01 3.18351537e-01 -3.77852589e-01 -8.62363160e-01
-1.53256416e+00 3.37654352e-01 5.49732804e-01 -3.61215800e-01
-5.25226772e-01 7.10560799e-01 -3.07849407e-01 -3.03911209e-01
1.35156989e-01 -1.26936315e-02 -7.01281279e-02 -1.43859819e-01
5.65228760e-01 9.49686587e-01 8.40180993e-01 2.80182380e-02
-7.24575698e-01 3.62359077e-01 -3.24974984e-01 3.71698588e-01
1.20506036e+00 2.78657079e-01 -1.81374386e-01 1.60268515e-01
6.32475734e-01 -7.19776377e-02 -7.06882596e-01 -2.52817184e-01
3.74091834e-01 -4.78929818e-01 2.42302388e-01 -1.31461537e+00
-9.10365522e-01 5.21950781e-01 1.12103546e+00 4.96053994e-01
1.49946225e+00 -3.68116379e-01 6.39010489e-01 3.90217036e-01
4.77697581e-01 -1.13657618e+00 -3.47579449e-01 2.33856797e-01
3.91032130e-01 -1.13246191e+00 2.07170025e-01 -1.16532490e-01
-3.28049034e-01 1.18892038e+00 9.26918983e-01 1.33322567e-01
6.76671982e-01 4.17562336e-01 5.39235882e-02 -1.69577554e-01
-9.82535958e-01 2.00963616e-01 2.82643706e-01 4.54736441e-01
2.39425272e-01 7.48588145e-02 -2.95884907e-01 6.78337455e-01
-6.97861388e-02 9.38758329e-02 -1.39810830e-01 1.18326306e+00
-3.99423480e-01 -1.26349354e+00 -4.30118293e-01 1.22154510e+00
-6.35967493e-01 5.07373698e-02 -2.51754910e-01 7.39422202e-01
3.27600598e-01 1.18835926e+00 4.48275566e-01 -6.03793025e-01
5.22055686e-01 4.63244706e-01 -6.21515512e-03 -2.71704078e-01
-8.95569503e-01 -3.05625796e-01 2.65396625e-01 -8.42898786e-01
6.82964772e-02 -5.53035915e-01 -1.06673813e+00 -6.77136123e-01
-3.43439817e-01 2.51393318e-01 5.96580982e-01 1.07582641e+00
8.10435832e-01 4.73367661e-01 4.78976607e-01 -8.78736600e-02
-6.76260293e-01 -9.17688847e-01 -4.60516781e-01 1.86666667e-01
1.04784437e-01 -7.07080424e-01 -5.54731548e-01 -3.79033685e-01] | [7.580688953399658, 2.8902974128723145] |
126113dc-37f7-44d4-abaf-084816d8f257 | application-of-quantum-computers-in-foreign | 2203.15716 | null | https://arxiv.org/abs/2203.15716v1 | https://arxiv.org/pdf/2203.15716v1.pdf | Application of Quantum Computers in Foreign Exchange Reserves Management | The main purpose of this article is to evaluate possible applications of quantum computers in foreign exchange reserves management. The capabilities of quantum computers are demonstrated by means of risk measurement using the quantum Monte Carlo method and portfolio optimization using a linear equations system solver (the Harrow-Hassidim-Lloyd algorithm) and quadratic unconstrained binary optimization (the quantum approximate optimization algorithm). All demonstrations are carried out on the cloud-based IBM Quantum(TM) platform. Despite the fact that real-world applications are impossible under the current state of development of quantum computers, it is proven that in principle it will be possible to apply such computers in FX reserves management in the future. In addition, the article serves as an introduction to quantum computing for the staff of central banks and financial market supervisory authorities. | ['Martin Veselý'] | 2022-03-29 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-4.41844612e-01 -9.99883041e-02 2.11563349e-01 -4.21215072e-02
-6.60803199e-01 -5.47183692e-01 6.25288486e-01 -1.63856477e-01
-4.43192780e-01 1.15680504e+00 -3.55687827e-01 -9.61778641e-01
-3.59489210e-02 -1.03882575e+00 -1.04070380e-01 -6.05338991e-01
-1.39584258e-01 8.31351936e-01 -2.78429985e-01 -6.89424157e-01
8.17900300e-01 8.75584960e-01 -1.00704360e+00 2.96053775e-02
5.84533930e-01 1.17480075e+00 -5.78476548e-01 5.98203361e-01
4.20409918e-01 6.70143306e-01 -4.43496525e-01 -7.34270453e-01
9.38053727e-01 -3.62132251e-01 -5.72366595e-01 -5.11589587e-01
-2.69604653e-01 -5.76115727e-01 -5.02404094e-01 1.49780107e+00
5.72212994e-01 3.66338670e-01 4.31516498e-01 -4.13706452e-01
-5.32396793e-01 4.61845398e-01 1.71400636e-01 5.40555835e-01
2.11585119e-01 3.83291364e-01 1.22706950e+00 -5.05863607e-01
1.66677892e-01 7.82395005e-01 2.20904633e-01 1.26587629e-01
-1.10959458e+00 -4.68009472e-01 -9.26635742e-01 2.82648265e-01
-1.43626440e+00 -3.40741962e-01 2.09993646e-01 -1.49315700e-01
1.63223648e+00 4.47179556e-01 7.16568351e-01 1.97433289e-02
6.64061606e-01 3.30521576e-02 1.57774103e+00 -6.92195296e-01
7.84474015e-01 4.07446086e-01 -7.53464326e-02 4.88912642e-01
2.47380406e-01 1.14009082e+00 -4.32286650e-01 -5.66869557e-01
7.55782962e-01 -4.38154638e-01 -1.43023968e-01 1.43602252e-01
-1.01771355e+00 1.20925832e+00 2.37825111e-01 4.81597960e-01
-5.44427752e-01 2.50518978e-01 1.69167846e-01 4.87498671e-01
3.58064979e-01 6.31889224e-01 -2.06330732e-01 -4.67832744e-01
-1.32002807e+00 7.37586737e-01 1.26954520e+00 5.83306849e-01
3.62953842e-01 4.53582585e-01 2.67478287e-01 -4.33267027e-01
3.42359006e-01 7.59037971e-01 3.83036435e-01 -1.04083157e+00
1.98549390e-01 -3.57659727e-01 5.77437937e-01 -4.59146172e-01
-1.64764076e-01 -3.26965809e-01 -2.93705165e-01 6.43394947e-01
2.44395465e-01 -2.92132288e-01 -1.97034299e-01 7.18227983e-01
4.39902321e-02 2.50373781e-01 2.56655574e-01 9.94581819e-01
-3.23964655e-02 6.38222516e-01 -4.38562810e-01 -6.56987488e-01
1.35462821e+00 -5.90010822e-01 -7.83018172e-01 5.76668382e-01
5.50060928e-01 -1.00990677e+00 2.69000590e-01 6.95308208e-01
-1.36994863e+00 4.69932556e-02 -1.19709969e+00 4.60378975e-01
-3.85094941e-01 -3.91710699e-01 1.08424163e+00 1.48144341e+00
-8.20742786e-01 1.27257323e+00 -9.15641606e-01 4.19840336e-01
-1.42475352e-01 6.99685216e-01 3.26628760e-02 6.14487767e-01
-1.67408252e+00 1.39371276e+00 5.97636878e-01 2.05510169e-01
-5.54409146e-01 -2.45302707e-01 -3.24449718e-01 2.49871820e-01
-1.40700683e-01 -5.64635873e-01 1.25056350e+00 -4.87329394e-01
-2.07785678e+00 6.89691424e-01 4.44804758e-01 -1.04004252e+00
3.46653610e-01 2.03382179e-01 -6.60234809e-01 2.43885279e-01
-2.57273674e-01 -3.62406343e-01 3.11568499e-01 1.42339498e-01
-3.12882692e-01 -3.44148815e-01 1.23546362e-01 1.03037283e-01
2.57852495e-01 4.70231056e-01 6.52680874e-01 -4.07170594e-01
2.66608238e-01 -1.11285484e+00 -3.66076797e-01 -1.01654840e+00
-6.91945553e-02 1.44559890e-01 -2.18872041e-01 -6.78422391e-01
1.20901060e+00 -1.60806274e+00 -1.55871302e-01 5.93761027e-01
-3.91358465e-01 1.86584204e-01 6.41944587e-01 9.33263600e-01
-4.57262218e-01 8.69461074e-02 -9.19800601e-04 3.23417634e-01
5.96018791e-01 -8.98217410e-02 -7.91073084e-01 8.56764793e-01
-1.95721656e-01 8.26724410e-01 -5.33069432e-01 -1.36832952e-01
3.32928956e-01 -1.61606222e-01 -5.02895474e-01 -2.05080703e-01
-3.72030847e-02 3.97295922e-01 -5.22178292e-01 7.53470659e-01
8.49120736e-01 -1.50647447e-01 2.42961079e-01 3.77725512e-02
-4.06849384e-01 6.65574551e-01 -1.35089397e+00 1.07272160e+00
-1.16249576e-01 1.22368611e-01 -1.35430545e-01 -8.88322234e-01
4.28231329e-01 4.80940193e-01 1.46818236e-01 -7.02461720e-01
1.40917063e-01 6.25207722e-01 1.09748662e-01 -1.35929942e-01
9.75669682e-01 -1.21457148e+00 -7.33172148e-02 9.21040535e-01
-1.59874156e-01 -5.79821229e-01 2.73180932e-01 2.95156445e-02
8.97376835e-01 3.11401691e-02 2.12853670e-01 -3.73093426e-01
5.08362770e-01 1.27408579e-01 1.87621310e-01 5.37249446e-01
-5.46504676e-01 1.01998672e-01 3.56253743e-01 -6.53041184e-01
-1.17458463e+00 -1.21865904e+00 -7.77993083e-01 3.44312519e-01
-1.76121090e-02 -4.79546934e-01 -5.90313971e-01 -2.18767975e-03
1.11126587e-01 1.21303487e+00 1.41501740e-01 1.37638360e-01
-3.38852018e-01 -1.30171919e+00 3.41645271e-01 6.88220700e-03
2.50156224e-01 -9.60880280e-01 -5.52420616e-01 3.52591604e-01
2.73555785e-01 -9.37052250e-01 2.01266095e-01 2.60292143e-01
-1.09639549e+00 -7.55647182e-01 -3.84081841e-01 2.89219320e-01
5.54251187e-02 -4.06770885e-01 8.97020161e-01 -7.35295266e-02
-2.35667661e-01 8.26539621e-02 -2.25777239e-01 -7.91667849e-02
-5.91816068e-01 -4.56272513e-01 3.92750084e-01 -4.23364639e-01
6.53313816e-01 -3.72271538e-01 -6.57071829e-01 -8.28990713e-02
-5.46165168e-01 -7.31774807e-01 3.10974777e-01 1.11125576e+00
4.34511095e-01 4.07473266e-01 1.16093732e-01 -5.81566036e-01
7.80282557e-01 -7.86085241e-03 -1.44333315e+00 2.46378273e-01
-1.03100169e+00 1.33231997e-01 4.44291294e-01 3.38017464e-01
-8.45008373e-01 -3.22706938e-01 -8.36056098e-02 -9.59792733e-02
3.10992897e-01 6.16834819e-01 3.73301715e-01 -5.88650882e-01
5.27032912e-01 1.88245419e-02 3.27920914e-02 -6.03249855e-02
3.52800995e-01 6.92696393e-01 1.27846316e-01 -4.77093309e-01
8.19268227e-01 4.68286812e-01 4.93142217e-01 -6.44112587e-01
-4.53765154e-01 -1.73689276e-02 -2.18094915e-01 3.02911103e-02
1.09827924e+00 -9.10092950e-01 -1.29133415e+00 6.88467249e-02
-7.88137078e-01 1.50878310e-01 -3.59165370e-01 1.04579377e+00
-8.17463517e-01 5.97550452e-01 -1.01195467e+00 -1.36615062e+00
-4.27449942e-01 -1.32694173e+00 6.57239854e-01 2.57922113e-01
1.60701260e-01 -9.91944671e-01 1.33936167e-01 5.43038666e-01
4.18689966e-01 1.50335118e-01 5.96509755e-01 -7.42212176e-01
-1.14173186e+00 -7.65992820e-01 2.07560211e-01 4.60393906e-01
-6.64754868e-01 -2.26159999e-03 -7.45075583e-01 -4.09839332e-01
7.29472518e-01 -1.61131263e-01 4.34312105e-01 2.85304874e-01
3.05039078e-01 -7.42693990e-03 3.20982456e-01 4.95707124e-01
1.55335283e+00 4.63888049e-02 7.50338554e-01 7.67029583e-01
-3.40566248e-01 6.52443618e-02 8.56234372e-01 8.20754409e-01
-1.20030388e-01 6.94244921e-01 4.64745134e-01 8.02306771e-01
8.27265561e-01 2.68150747e-01 5.31505823e-01 8.94074261e-01
-5.53570390e-01 4.31041807e-01 -6.83262169e-01 -2.12817073e-01
-1.56357479e+00 -1.44167578e+00 -1.75534964e-01 2.51843572e+00
5.29083490e-01 3.94206136e-01 -1.83216631e-01 -1.85221896e-01
6.64985001e-01 -1.63728848e-01 -1.71815202e-01 -9.27627206e-01
-6.90585598e-02 1.03129101e+00 1.03854692e+00 4.86011684e-01
-8.85121942e-01 8.30171704e-01 6.96516275e+00 9.52135563e-01
-6.89720988e-01 3.06512922e-01 5.22621930e-01 -1.35426685e-01
-1.86994702e-01 8.53993595e-01 -7.18011856e-01 5.55600464e-01
1.72033691e+00 -7.42237568e-01 1.11502934e+00 6.86372459e-01
1.07604727e-01 -4.76125151e-01 -6.69006944e-01 7.95593321e-01
-3.66410345e-01 -1.62882578e+00 -3.70680571e-01 5.51130772e-01
7.90276170e-01 2.43215278e-01 1.05645373e-01 4.56645727e-01
-8.23305696e-02 -1.05204940e+00 8.34029377e-01 7.96911895e-01
6.88963652e-01 -1.25231159e+00 1.07983160e+00 4.38256174e-01
-5.41500270e-01 -1.15225807e-01 -8.18617463e-01 -5.66100121e-01
6.02872491e-01 6.19084179e-01 -5.84455371e-01 7.76399195e-01
3.72176349e-01 -1.67737931e-01 8.83590281e-02 7.94414461e-01
-2.85773605e-01 4.85752821e-01 -4.84606743e-01 -1.43460065e-01
3.34446847e-01 -1.25826263e+00 4.25126404e-01 4.86348778e-01
4.06777561e-01 7.09662735e-01 -2.71930844e-01 1.13462222e+00
3.96424592e-01 -1.86908200e-01 -2.89898217e-01 -5.92911482e-01
2.14833349e-01 1.02218080e+00 -6.60921872e-01 -3.90870154e-01
-2.06917614e-01 7.39113450e-01 -3.31297815e-01 -4.69280686e-03
-8.36110890e-01 -5.41252792e-01 2.68301427e-01 -7.35192746e-02
3.93707424e-01 -5.04218221e-01 -5.84608257e-01 -1.44429469e+00
-3.16076219e-01 -9.65855002e-01 1.99389726e-01 -6.19529545e-01
-9.65545774e-01 3.68277282e-01 -3.16454113e-01 -1.19096828e+00
-5.62984109e-01 -9.86731887e-01 -6.23675466e-01 1.47956705e+00
-1.24138558e+00 -3.80878389e-01 8.39638889e-01 1.62356451e-01
-4.16011423e-01 -7.89234221e-01 1.21224821e+00 2.01616101e-02
-2.46630594e-01 2.47703508e-01 8.00850868e-01 -2.57472694e-01
2.37259582e-01 -1.39170921e+00 3.79216254e-01 7.73633480e-01
2.66514570e-01 1.02622974e+00 1.12154269e+00 -7.29386985e-01
-1.57530355e+00 -5.76109588e-02 8.31740558e-01 -2.06391186e-01
1.40280211e+00 1.05414510e-01 -1.84890062e-01 5.14024138e-01
2.78920978e-01 -1.52850568e-01 9.29692924e-01 -1.98698312e-01
3.03468138e-01 1.34352580e-01 -1.22146821e+00 3.13098818e-01
-4.42583859e-02 -1.21040511e+00 -7.60634601e-01 1.22301447e+00
2.21957162e-01 -5.07952929e-01 -1.18432140e+00 5.40922657e-02
3.48226398e-01 -1.41172957e+00 9.88365054e-01 -6.73622906e-01
-6.59344494e-02 -2.07662269e-01 -3.36346209e-01 -7.43583798e-01
1.30297154e-01 -1.42464542e+00 -4.48134169e-02 3.89914930e-01
3.00429165e-01 -1.04801524e+00 7.68184185e-01 1.11281550e+00
1.26093298e-01 -3.40338647e-01 -1.73099673e+00 -8.63811672e-01
5.32921195e-01 -3.97531807e-01 6.41978383e-01 6.87446475e-01
6.78863645e-01 -1.30425185e-01 -2.57675737e-01 3.75069648e-01
7.90180027e-01 6.14571512e-01 2.97194779e-01 -6.18720651e-01
-8.73686969e-01 -3.52079690e-01 -7.88766980e-01 -5.68503022e-01
2.47270614e-01 -1.10260129e+00 -6.03140652e-01 -5.89441121e-01
-1.73299551e-01 -1.94979340e-01 -3.55186343e-01 -3.44571352e-01
3.17534387e-01 2.90543828e-02 2.77984440e-01 2.95622915e-01
-1.93634599e-01 5.97574353e-01 8.54889870e-01 2.03804448e-01
3.69920582e-01 5.66420674e-01 -2.93840952e-02 3.68644148e-01
6.47858918e-01 -5.37284374e-01 3.28724265e-01 3.35796803e-01
6.16340578e-01 7.39268303e-01 3.90173048e-01 -9.26256359e-01
2.91870207e-01 -8.02965090e-02 1.55802341e-02 -7.07449079e-01
4.55433786e-01 -4.28128242e-01 3.83305818e-01 9.23411012e-01
3.09755445e-01 4.18429762e-01 -2.05379248e-01 3.30169022e-01
-2.75534034e-01 -1.07769573e+00 7.40281165e-01 -4.69409853e-01
-1.99379280e-01 -1.05376840e-01 -2.40033716e-01 -2.79113740e-01
1.34444976e+00 3.52771282e-02 -4.53010380e-01 -3.55138600e-01
-1.04600096e+00 -1.71734765e-01 7.75572658e-01 -6.50848091e-01
3.82263094e-01 -1.09075236e+00 -5.54206491e-01 1.05029799e-01
-6.20192409e-01 -1.00221479e+00 2.08149046e-01 1.02821529e+00
-1.49552560e+00 9.78584111e-01 -1.63379222e-01 1.02757193e-01
-4.38621521e-01 6.16254032e-01 7.39715874e-01 -5.33609986e-01
-3.48223865e-01 5.85536540e-01 -5.71505547e-01 9.75918211e-03
-5.33568501e-01 1.74390405e-01 3.55041444e-01 -2.95286089e-01
7.52113044e-01 6.16741776e-01 3.91003311e-01 -6.20023608e-01
-1.67902008e-01 1.19296812e-01 1.55618146e-01 -7.18796134e-01
1.31912982e+00 2.26791069e-01 -6.05458796e-01 3.30859810e-01
7.28032708e-01 1.37439352e-02 -3.53127241e-01 4.63347286e-01
2.58113772e-01 -4.10752952e-01 4.31679070e-01 -5.89546442e-01
-6.96202159e-01 1.06258845e+00 7.13185310e-01 4.44345772e-01
6.91263318e-01 -7.89257169e-01 8.43000710e-01 7.68306851e-01
9.24420953e-01 -1.55670345e+00 -7.60924578e-01 3.84493649e-01
3.57079327e-01 -8.96122158e-01 7.93788791e-01 1.21090516e-01
-6.14319444e-01 1.68878496e+00 -5.18144250e-01 -5.18591642e-01
8.65801990e-01 9.19596106e-02 -3.53921115e-01 -2.88682699e-01
-6.95355713e-01 -2.20981985e-01 -1.75634205e-01 -2.11195752e-01
3.01513344e-01 5.39043725e-01 -9.59515214e-01 2.66121626e-01
-4.24016625e-01 9.65154916e-02 1.01881397e+00 1.00964701e+00
-4.09916967e-01 -1.49208808e+00 -7.42575765e-01 3.50116611e-01
-9.10546422e-01 -5.58726966e-01 2.28175953e-01 6.59392893e-01
-3.07886750e-01 8.04703891e-01 -2.23790407e-01 -8.75932276e-02
7.92227499e-03 2.91477829e-01 5.27107060e-01 -5.29314160e-01
-1.07679915e+00 1.72447413e-01 3.31983805e-01 -6.55005813e-01
-1.33860081e-01 -9.78245080e-01 -1.36113238e+00 -8.28081727e-01
-9.34232116e-01 7.36228526e-01 1.07557416e+00 8.73789608e-01
-1.96941551e-02 -3.14211994e-02 6.51762605e-01 -7.59844959e-01
-2.04081535e+00 -7.00161695e-01 -1.67140520e+00 -4.30989265e-01
-4.36517149e-01 -4.63183701e-01 -7.24254429e-01 -4.70603526e-01] | [5.5658183097839355, 4.895813941955566] |
2741bd3a-a778-44b2-b4c7-e6134432fdec | the-oracle-estimator-is-suboptimal-for-global | 2112.07521 | null | https://arxiv.org/abs/2112.07521v2 | https://arxiv.org/pdf/2112.07521v2.pdf | Non-linear shrinkage of the price return covariance matrix is far from optimal for portfolio optimisation | Portfolio optimization requires sophisticated covariance estimators that are able to filter out estimation noise. Non-linear shrinkage is a popular estimator based on how the Oracle eigenvalues can be computed using only data from the calibration window. Contrary to common belief, NLS is not optimal for portfolio optimization because it does not minimize the right cost function when the asset dependence structure is non-stationary. We instead derive the optimal target. Using historical data, we quantify by how much NLS can be improved. Our findings reopen the question of how to build the covariance matrix estimator for portfolio optimization in realistic conditions. | ['Damien Challet', 'Christian Bongiorno'] | 2021-12-14 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-1.21742137e-01 -8.40499699e-02 -1.33746862e-01 -3.71521056e-01
-9.06432629e-01 -1.00742626e+00 3.08196723e-01 -1.82650343e-01
-3.89846772e-01 7.57929802e-01 8.04681852e-02 -5.90785742e-01
-5.60647726e-01 -5.84523380e-01 -5.08164585e-01 -7.00678051e-01
1.34006113e-01 3.76903206e-01 -2.69965440e-01 1.84128404e-01
3.09163481e-01 3.80661994e-01 -7.68510222e-01 -3.01252782e-01
7.91925490e-01 1.10016143e+00 -2.92540789e-01 4.65785623e-01
2.10118249e-01 3.38473469e-01 -3.93118054e-01 -7.51384497e-01
9.64603662e-01 -5.14569521e-01 -2.71950420e-02 -5.73197380e-02
3.95503253e-01 -4.86244351e-01 -1.48525760e-01 1.28202343e+00
4.76007849e-01 -8.08202773e-02 8.66348028e-01 -6.81208313e-01
-2.97241211e-01 7.53686249e-01 -3.34532410e-01 5.72996914e-01
-9.00199562e-02 2.08806470e-02 1.32505953e+00 -8.53371084e-01
3.15141052e-01 1.08627474e+00 1.03555977e+00 2.81270649e-02
-1.44537532e+00 -6.27648950e-01 2.09348500e-02 -1.18127577e-01
-1.11447334e+00 -5.20836651e-01 6.59472346e-01 -5.63926041e-01
5.55971503e-01 3.79721552e-01 4.80210423e-01 1.00888908e+00
3.65753502e-01 2.52444834e-01 1.27227211e+00 -4.62652802e-01
1.79691330e-01 3.31841946e-01 3.76451612e-01 1.95883527e-01
1.01860940e+00 6.16412163e-01 -3.84464502e-01 -4.59414542e-01
8.93256307e-01 -1.41478419e-01 -3.28812569e-01 -5.38996994e-01
-8.88064384e-01 1.17466307e+00 -3.34846139e-01 1.15841076e-01
-4.77470338e-01 3.17514092e-01 1.24796875e-01 8.48300397e-01
4.37360317e-01 7.34305263e-01 -7.32793212e-01 -2.79941261e-01
-1.01075923e+00 2.70366102e-01 1.16824579e+00 6.24234080e-01
3.94535273e-01 3.40922982e-01 -2.19346195e-01 3.77786517e-01
4.58970040e-01 1.18862188e+00 2.67456621e-02 -1.28618336e+00
7.43178785e-01 8.13351348e-02 4.68297780e-01 -7.16120839e-01
-4.08947408e-01 -9.01716828e-01 -4.63978440e-01 2.09741190e-01
9.80508268e-01 -7.47350991e-01 -2.34190285e-01 1.63104618e+00
1.27797499e-01 2.37712078e-03 -2.03481130e-02 7.03488469e-01
1.31290350e-02 3.36069733e-01 -4.71635789e-01 -6.29324675e-01
9.05073225e-01 -5.83605945e-01 -9.20016944e-01 -4.32051957e-01
3.27039510e-01 -8.09765160e-01 7.17766643e-01 4.38908428e-01
-1.06406462e+00 3.49637792e-02 -1.09963322e+00 7.91265428e-01
2.45852917e-01 -1.42499030e-01 7.82677531e-01 1.26976800e+00
-7.49220908e-01 8.65841091e-01 -6.65331841e-01 2.33783707e-01
1.89492583e-01 2.46676847e-01 4.73122261e-02 4.26640093e-01
-1.02463901e+00 1.09390628e+00 3.20547551e-01 1.77008912e-01
-5.57429910e-01 -9.32597160e-01 -3.86809528e-01 2.50318885e-01
6.02413237e-01 -7.20007360e-01 1.23918164e+00 -1.00277948e+00
-1.76229620e+00 2.32302502e-01 2.58859366e-01 -6.58190548e-01
7.17105687e-01 -2.63215214e-01 -2.64374495e-01 1.06775917e-01
-1.96603596e-01 -6.42249107e-01 1.40650129e+00 -7.06367791e-01
-3.45981359e-01 -2.15506911e-01 -3.09019238e-01 -1.82019025e-02
-2.03747094e-01 -1.09423175e-02 1.80041324e-02 -9.62731481e-01
2.78467655e-01 -1.19623590e+00 -3.11436445e-01 -3.83916467e-01
-2.07122967e-01 4.75474566e-01 3.63569185e-02 -6.78469300e-01
1.49823332e+00 -1.82622766e+00 -2.53260076e-01 7.66056836e-01
-8.68653357e-02 -2.00037673e-01 6.45286143e-02 5.44318199e-01
-2.85717815e-01 -3.45856920e-02 -3.11740518e-01 8.95568430e-02
3.62950504e-01 -1.65359855e-01 -6.74195766e-01 8.67290080e-01
-3.90335619e-01 7.33588517e-01 -5.45908809e-01 6.02689572e-03
1.19960278e-01 1.66449323e-02 -6.24544144e-01 7.10633099e-02
4.29297566e-01 1.80454880e-01 -4.13999140e-01 4.17356819e-01
6.87635124e-01 -4.22949165e-01 4.50407982e-01 1.03429109e-01
-1.96903590e-02 1.59528807e-01 -1.74515307e+00 9.40369427e-01
-1.87823087e-01 7.31766343e-01 1.63893238e-01 -1.25923395e+00
7.05860496e-01 2.40378261e-01 4.09243643e-01 -3.14338207e-01
1.49292409e-01 4.42614377e-01 2.58386135e-01 -1.16182394e-01
-7.06983581e-02 -5.44378877e-01 -1.74465384e-02 7.11190522e-01
-8.34810548e-03 -3.77341062e-02 7.27967843e-02 -2.72560418e-01
1.08721960e+00 -2.71794707e-01 5.03140390e-01 -6.47662222e-01
3.07328522e-01 -4.87064600e-01 6.53333783e-01 1.28140259e+00
3.03097046e-03 5.20274282e-01 7.68948436e-01 -7.00098649e-02
-1.07238090e+00 -1.46755946e+00 -4.83749598e-01 7.83873975e-01
-4.99110967e-01 -2.67303810e-02 -5.85533798e-01 -6.13531828e-01
5.89683652e-01 8.24593008e-01 -6.28083110e-01 -3.75050791e-02
-3.69748950e-01 -1.23201907e+00 1.44014247e-02 3.33411068e-01
7.16542602e-02 -3.67296457e-01 -4.32653248e-01 5.50703585e-01
2.69676983e-01 -7.62332857e-01 -7.32411683e-01 2.24870175e-01
-1.16817272e+00 -1.20901728e+00 -9.37235117e-01 2.63191372e-01
6.03718340e-01 8.10798928e-02 1.14242935e+00 -3.67088646e-01
2.23419517e-01 7.55381584e-01 -3.97655033e-02 -6.34974003e-01
-2.56003916e-01 -6.74510896e-02 1.65334538e-01 1.87893212e-01
2.03296825e-01 -6.65590227e-01 -6.61607444e-01 2.18764603e-01
-2.79232860e-01 -8.09536159e-01 7.10972428e-01 1.03562486e+00
2.56853819e-01 -6.33654818e-02 7.50850499e-01 -1.10135615e+00
9.81473505e-01 -3.40583235e-01 -1.27775145e+00 5.94639480e-01
-1.27428281e+00 4.11517054e-01 2.18786195e-01 -7.34200358e-01
-1.24629247e+00 -2.68976182e-01 3.95287663e-01 -3.19245636e-01
6.58679605e-01 5.99536836e-01 2.40773678e-01 -1.28545746e-01
5.63663781e-01 -1.25838220e-01 -5.33177704e-02 -5.45537472e-01
2.11084485e-01 1.52670056e-01 1.88274503e-01 -6.72453761e-01
1.05121398e+00 3.37860048e-01 3.46823335e-01 -5.22960842e-01
-1.21618414e+00 -3.33088994e-01 -3.52753520e-01 -9.24971104e-02
4.91041601e-01 -7.54107356e-01 -8.64537299e-01 5.83351366e-02
-7.82473028e-01 -2.06441611e-01 -6.59723282e-01 1.04322314e+00
-6.57709539e-01 3.99545938e-01 -3.44513595e-01 -1.37170255e+00
-4.94996220e-01 -7.65881002e-01 4.51126188e-01 1.13928773e-01
-3.38657469e-01 -1.31353867e+00 3.87245595e-01 -8.63684639e-02
5.57430446e-01 2.60244280e-01 4.39508885e-01 -8.39654803e-01
-4.36945915e-01 -4.86803174e-01 -5.98371252e-02 5.85330307e-01
-1.23932488e-01 -7.78949782e-02 -6.68589473e-01 -5.09655535e-01
7.00643301e-01 2.74334222e-01 7.23491371e-01 9.27085996e-01
6.52945995e-01 -6.63577139e-01 7.05102831e-02 8.26912403e-01
1.34743297e+00 2.29195684e-01 2.47433856e-01 5.22142529e-01
1.46210536e-01 5.54598272e-01 4.35287535e-01 7.54383147e-01
-1.70374721e-01 3.85665983e-01 -1.45666599e-01 3.99117559e-01
5.37823677e-01 -1.51882723e-01 6.24175787e-01 8.83734584e-01
7.18200579e-02 1.12964466e-01 -8.37026715e-01 1.92237884e-01
-1.96418512e+00 -1.15437233e+00 -1.28588602e-01 2.58271313e+00
6.34684026e-01 4.90384787e-01 1.05490074e-01 -2.45330125e-01
4.69553888e-01 1.30293742e-01 -6.30150318e-01 -1.90977246e-01
-2.19455838e-01 2.50796437e-01 1.33713567e+00 5.74920475e-01
-9.24416542e-01 1.71269298e-01 8.38770676e+00 7.21515477e-01
-6.38245523e-01 2.39940986e-01 3.32161188e-01 -2.91933775e-01
-4.68317002e-01 4.73979741e-01 -9.60565031e-01 6.61262631e-01
1.15477109e+00 -5.88821352e-01 4.08707589e-01 9.64700043e-01
7.92230293e-02 -7.75067955e-02 -1.10534072e+00 8.88035178e-01
-2.40303904e-01 -1.17737389e+00 -5.71839213e-01 3.46602231e-01
8.58731925e-01 -2.45684698e-01 3.50853682e-01 2.98400849e-01
3.51182997e-01 -8.30017865e-01 7.13489473e-01 1.07251608e+00
3.28753173e-01 -8.54003906e-01 9.86523211e-01 3.40491802e-01
-6.87381327e-01 -3.30495328e-01 -5.73639154e-01 -3.10314610e-03
4.35260981e-01 9.87193286e-01 -6.27002954e-01 2.24433705e-01
4.25901443e-01 4.64820087e-01 -4.90119338e-01 1.16734231e+00
-2.58506358e-01 1.01745152e+00 -6.82566404e-01 1.93593219e-01
5.09008765e-02 -1.07330894e+00 8.50888431e-01 9.96566653e-01
7.89620399e-01 5.15985340e-02 -3.50435793e-01 8.43398929e-01
3.35268319e-01 5.92550859e-02 -5.54052114e-01 -2.38543078e-01
4.91718084e-01 8.65013242e-01 -5.14432490e-01 -1.71209738e-01
-7.02361166e-01 3.53553474e-01 -5.66432113e-03 5.24686098e-01
-5.78197718e-01 -2.13940024e-01 6.10472083e-01 3.49334069e-02
5.29571176e-01 -2.66805679e-01 -4.52597797e-01 -1.41746950e+00
-1.60875581e-02 -9.94316161e-01 6.23567641e-01 -1.97915673e-01
-1.52598822e+00 -9.42989439e-02 3.01468015e-01 -1.09362853e+00
-4.88408476e-01 -7.73103893e-01 -6.57797277e-01 7.76079893e-01
-1.09156179e+00 -2.74306923e-01 4.47961509e-01 1.21557198e-01
8.57962482e-03 -4.24473047e-01 5.18878877e-01 2.76758764e-02
-7.16027915e-01 7.13831365e-01 9.04185057e-01 -1.55868217e-01
7.72381127e-01 -1.55555463e+00 1.66260943e-01 9.41273510e-01
2.59442151e-01 8.33145678e-01 1.19694138e+00 -6.22243881e-01
-1.32931936e+00 -3.36396039e-01 4.01201338e-01 -7.46411681e-01
1.20430803e+00 -3.28063779e-02 -8.21434557e-01 8.62040341e-01
2.95749456e-02 -1.68599486e-01 8.45733702e-01 3.95589113e-01
-2.11936429e-01 -4.12358522e-01 -9.53280449e-01 4.26340014e-01
6.70573294e-01 -3.86120319e-01 -7.34147191e-01 3.03600460e-01
3.87099057e-01 -1.38516843e-01 -1.25021625e+00 3.93719733e-01
7.38771141e-01 -1.06171894e+00 1.18594909e+00 -5.74592412e-01
-2.99106300e-01 1.63391083e-01 -2.55036980e-01 -1.11044466e+00
-1.53376102e-01 -1.10887599e+00 -2.13642180e-01 1.25055850e+00
6.57573998e-01 -1.39731264e+00 7.29371071e-01 9.19840813e-01
4.17006850e-01 -2.75397480e-01 -1.07191193e+00 -1.28578746e+00
2.34590486e-01 -5.95198512e-01 5.00458956e-01 7.36159861e-01
-2.86256820e-01 2.25390241e-01 -4.99753386e-01 1.78410448e-02
1.28569818e+00 3.39072347e-01 6.30705833e-01 -1.46568668e+00
-7.27767944e-01 -6.10820532e-01 1.45439014e-01 -9.15182412e-01
2.38824964e-01 -6.66981339e-01 -3.16245615e-01 -7.36711919e-01
1.81229547e-01 -2.44672507e-01 -2.74107426e-01 -1.82090074e-01
-1.20621338e-01 -2.71840811e-01 2.72783786e-01 1.40326351e-01
-2.43517846e-01 3.95766228e-01 9.41711605e-01 2.17267781e-01
-8.07999969e-02 5.25560677e-01 -7.74988830e-01 9.45235252e-01
7.85609841e-01 -8.91675651e-01 -3.63494754e-01 1.91122904e-01
6.70494556e-01 4.12391454e-01 5.10121696e-03 -4.03463840e-01
1.49300899e-02 -4.48969990e-01 3.83280367e-01 -5.64433813e-01
7.85251483e-02 -9.35953856e-01 5.84556401e-01 4.41545278e-01
-2.31683046e-01 2.20620930e-01 -1.63970366e-01 8.20416451e-01
6.83597028e-02 -1.09244919e+00 7.13416576e-01 8.94473027e-03
1.99710533e-01 1.18810482e-01 -3.47174615e-01 3.24597597e-01
8.13246489e-01 6.91396892e-02 -1.32271692e-01 -7.02646494e-01
-8.21799755e-01 3.46063860e-02 4.34802711e-01 -2.40338907e-01
2.09052578e-01 -1.47329235e+00 -8.11733603e-01 3.20758880e-03
-3.50510776e-01 -7.07133234e-01 8.21898878e-02 1.05447459e+00
-3.57294858e-01 6.84387088e-01 4.73620296e-01 -2.48903349e-01
-8.32383752e-01 4.97864366e-01 4.34706926e-01 -5.42631388e-01
-4.48363990e-01 6.40145779e-01 2.17902601e-01 -1.24815069e-01
2.53748000e-02 -1.50816157e-01 1.62723929e-01 3.98167729e-01
5.05761385e-01 7.10020006e-01 -1.54286608e-01 -6.28483519e-02
-1.91908464e-01 6.44593835e-01 2.09554538e-01 -5.26033461e-01
1.31310356e+00 -3.78814042e-01 6.02153654e-04 6.91798389e-01
1.12310374e+00 3.06337535e-01 -1.50558710e+00 -6.28328472e-02
5.41545153e-01 -6.98608220e-01 3.80937383e-02 -3.43505591e-01
-1.04136372e+00 5.77301860e-01 5.33039033e-01 4.52237964e-01
8.15105915e-01 -4.85420614e-01 3.30615044e-01 6.13548160e-01
2.09372655e-01 -1.48046458e+00 -1.47407815e-01 4.47491318e-01
1.07488263e+00 -1.24779332e+00 5.78621209e-01 -1.09395817e-01
-6.99173391e-01 1.19559860e+00 -1.46341443e-01 -5.46451867e-01
1.24947679e+00 3.12184900e-01 -1.01713166e-01 -1.41682401e-01
-7.08170295e-01 -2.17717346e-02 6.39073312e-01 3.37184161e-01
3.80177870e-02 6.81182668e-02 -7.01772928e-01 8.85273814e-01
-4.99536633e-01 -6.01160347e-01 5.74102044e-01 4.64201868e-01
-5.14463723e-01 -1.16223812e+00 -6.25341356e-01 7.98539400e-01
-8.65282476e-01 3.44892847e-03 -1.31843120e-01 9.07650411e-01
-7.50002861e-01 7.71472156e-01 -7.05854595e-02 1.96200669e-01
4.56853688e-01 1.94060922e-01 5.06289959e-01 -4.13997024e-01
-4.78542000e-01 3.80374610e-01 2.68991768e-01 -4.99486268e-01
-1.93521693e-01 -1.27412665e+00 -2.68611640e-01 -3.77616882e-01
-7.65194178e-01 1.96716189e-01 3.87637794e-01 7.60291159e-01
-1.30050302e-01 1.39248818e-01 9.10446942e-01 -4.11106527e-01
-1.76780438e+00 -7.51492441e-01 -9.19610560e-01 2.83104107e-02
2.67905653e-01 -6.55175805e-01 -1.13607490e+00 -3.57955933e-01] | [5.048413276672363, 3.9874789714813232] |
5efa67f8-714b-4072-ac5f-837c1180f79a | on-the-power-of-deep-but-naive-partial-label | 2010.11600 | null | https://arxiv.org/abs/2010.11600v2 | https://arxiv.org/pdf/2010.11600v2.pdf | On the Power of Deep but Naive Partial Label Learning | Partial label learning (PLL) is a class of weakly supervised learning where each training instance consists of a data and a set of candidate labels containing a unique ground truth label. To tackle this problem, a majority of current state-of-the-art methods employs either label disambiguation or averaging strategies. So far, PLL methods without such techniques have been considered impractical. In this paper, we challenge this view by revealing the hidden power of the oldest and naivest PLL method when it is instantiated with deep neural networks. Specifically, we show that, with deep neural networks, the naive model can achieve competitive performances against the other state-of-the-art methods, suggesting it as a strong baseline for PLL. We also address the question of how and why such a naive model works well with deep neural networks. Our empirical results indicate that deep neural networks trained on partially labeled examples generalize very well even in the over-parametrized regime and without label disambiguations or regularizations. We point out that existing learning theories on PLL are vacuous in the over-parametrized regime. Hence they cannot explain why the deep naive method works. We propose an alternative theory on how deep learning generalize in PLL problems. | ['Joon Suk Huh', 'Junghoon Seo'] | 2020-10-22 | null | null | null | null | ['partial-label-learning'] | ['methodology'] | [ 2.56024778e-01 5.03363848e-01 -5.28358042e-01 -3.36773366e-01
-6.79846227e-01 -5.55802405e-01 8.77123594e-01 8.48171338e-02
-5.01123846e-01 9.02952492e-01 4.51957658e-02 -2.36219808e-01
-1.17596939e-01 -6.48715079e-01 -7.43741691e-01 -9.59885418e-01
1.56885400e-01 7.56295979e-01 2.10747540e-01 -1.33926675e-01
-4.65756319e-02 1.96176499e-01 -1.55016720e+00 2.21499428e-01
6.58173740e-01 1.05315030e+00 -3.29851985e-01 2.42330208e-01
-2.27082044e-01 9.15601492e-01 -3.93173844e-01 -5.73153555e-01
3.96079451e-01 -3.21770936e-01 -1.13029242e+00 3.12903784e-02
9.37303245e-01 -8.96616578e-02 -1.08194232e-01 1.15210426e+00
3.74579638e-01 1.06243767e-01 7.82987416e-01 -1.20665383e+00
-5.78536987e-01 7.79131591e-01 -2.43404776e-01 5.41599691e-02
-1.37098342e-01 3.85290757e-02 1.37289977e+00 -8.77207577e-01
7.84469962e-01 1.08349431e+00 9.82679904e-01 8.38138521e-01
-1.53638971e+00 -3.46897304e-01 3.60627919e-01 7.49052269e-03
-1.04980767e+00 -3.04665685e-01 7.53565311e-01 -5.84289074e-01
6.90012038e-01 -4.42837505e-03 1.82608187e-01 1.38576221e+00
-1.29894555e-01 8.43087494e-01 1.47056913e+00 -6.24938488e-01
4.22086835e-01 2.75666028e-01 6.37597501e-01 8.19434106e-01
3.50891292e-01 1.98784545e-01 -4.94911045e-01 -2.18584552e-01
4.14036632e-01 -2.33805105e-01 -2.07090035e-01 -5.54260612e-01
-1.06481409e+00 9.36864138e-01 4.61312264e-01 5.36731422e-01
4.00415214e-04 2.12190211e-01 4.99620020e-01 3.95359337e-01
8.21521878e-01 8.84949207e-01 -8.65213752e-01 5.04486978e-01
-1.07755470e+00 3.63615572e-01 9.00093973e-01 7.58346438e-01
9.29191232e-01 -1.11287668e-01 -1.60185486e-01 7.02301264e-01
1.58089772e-01 -4.98663858e-02 5.20976543e-01 -1.20983613e+00
7.44252056e-02 5.38464308e-01 9.96035635e-02 -6.42205536e-01
-8.00799251e-01 -9.48225379e-01 -7.35564947e-01 2.92680979e-01
8.63171339e-01 -1.78975716e-01 -8.64964724e-01 2.22845936e+00
9.99792069e-02 3.90714735e-01 1.05225705e-01 7.78453529e-01
6.13525927e-01 2.55873561e-01 2.23786369e-01 -4.38584626e-01
9.74718690e-01 -1.18289411e+00 -6.28572047e-01 -4.81177032e-01
1.11022031e+00 -4.76184398e-01 1.24760127e+00 5.37504494e-01
-8.46736372e-01 -4.79738176e-01 -1.05820620e+00 -1.11875519e-01
-5.07784784e-01 -3.72418016e-02 8.32672000e-01 5.41951299e-01
-1.11531472e+00 1.05356133e+00 -6.42955661e-01 -5.64787984e-01
3.74838561e-01 3.68628204e-01 -2.92634398e-01 -9.81307472e-04
-1.35578096e+00 1.12913954e+00 4.52556819e-01 -2.76066121e-02
-7.00496554e-01 -5.58923542e-01 -5.91640055e-01 -7.88272619e-02
5.53673148e-01 -6.98254168e-01 1.55054462e+00 -1.26240253e+00
-1.16905987e+00 1.33160198e+00 6.84357574e-03 -6.67842031e-01
5.29801548e-01 -2.13818908e-01 -1.24372825e-01 -1.45927429e-01
1.90333322e-01 6.61056578e-01 5.74673712e-01 -1.48946238e+00
-5.88172376e-01 -3.11964810e-01 2.62854338e-01 2.34488640e-02
-1.66843742e-01 -4.60461468e-01 2.95482166e-02 -4.59204584e-01
3.04753482e-01 -1.08731580e+00 -2.16597870e-01 -3.09222609e-01
-4.91670609e-01 -7.50470161e-01 6.07831776e-01 1.51682412e-02
1.07319605e+00 -2.02944350e+00 3.09958179e-02 -6.04956113e-02
2.63298661e-01 3.36891741e-01 3.55428308e-02 3.02453518e-01
-2.02050924e-01 3.59228402e-01 -2.51621395e-01 -5.91509819e-01
5.61994255e-01 4.82688099e-01 -4.37655836e-01 6.39107883e-01
1.44741878e-01 9.00528312e-01 -9.92888868e-01 -4.12354887e-01
-3.53361256e-02 2.18985811e-01 -4.54475850e-01 1.12386815e-01
-6.91640019e-01 5.28128266e-01 -1.97537169e-01 5.14426947e-01
4.93817747e-01 -6.10454440e-01 3.17694247e-01 -1.84924796e-01
8.19477066e-02 3.98135513e-01 -1.01539218e+00 1.70001638e+00
-1.70302778e-01 5.83122730e-01 -1.64215103e-01 -1.35037458e+00
6.11170888e-01 2.76058018e-01 2.63435245e-01 -4.60310519e-01
1.73459366e-01 5.83379149e-01 -1.66834265e-01 -5.50278604e-01
1.84233442e-01 -6.33969486e-01 -7.13847131e-02 5.35442173e-01
4.06333119e-01 1.46281913e-01 1.82893515e-01 1.11523427e-01
1.11061251e+00 2.47119337e-01 2.41986603e-01 -5.73581874e-01
3.22041214e-01 -6.49442524e-02 7.03983009e-01 1.16338313e+00
-3.42628002e-01 4.43807632e-01 6.53748453e-01 -7.99647093e-01
-9.25053537e-01 -8.61902773e-01 -4.09445018e-01 1.48960662e+00
-2.31405254e-02 -3.51529449e-01 -8.83293450e-01 -1.03220809e+00
-8.84338468e-02 6.78965569e-01 -7.87002504e-01 -1.92004651e-01
-5.30517936e-01 -9.28474188e-01 5.41208506e-01 3.30295503e-01
4.00063723e-01 -1.08225811e+00 -2.81889230e-01 1.76709667e-01
-4.73938957e-02 -1.05534887e+00 1.84280798e-01 7.34587133e-01
-8.16113710e-01 -1.10383403e+00 -4.94087517e-01 -7.79022813e-01
4.58964676e-01 -2.52043337e-01 1.52542365e+00 2.06135646e-01
2.46262163e-01 1.44902438e-01 -1.68102384e-01 -1.06947199e-01
-3.83130491e-01 5.24280250e-01 3.26115042e-01 -1.62962228e-02
5.60115516e-01 -6.68735325e-01 -4.58783984e-01 9.39510092e-02
-8.53558064e-01 -1.03001609e-01 4.85004842e-01 1.01515472e+00
5.15608132e-01 -9.63282585e-02 7.99416959e-01 -1.62396765e+00
4.90099400e-01 -5.46263337e-01 -4.50470030e-01 3.35077673e-01
-8.61649692e-01 4.97259915e-01 5.17354548e-01 -4.19569016e-01
-8.46827865e-01 4.28139269e-02 -1.43412873e-01 -7.71465003e-02
-4.34039891e-01 5.29867411e-01 -1.33528084e-01 -2.52018217e-02
1.01064241e+00 -3.39533567e-01 -4.31557447e-01 -6.46331847e-01
5.30635655e-01 4.68875974e-01 5.28436005e-01 -9.43821073e-01
3.67535830e-01 5.85703671e-01 2.68478215e-01 -2.68312186e-01
-1.92373359e+00 -2.89536208e-01 -8.89517486e-01 -1.01211339e-01
8.85794878e-01 -7.05098927e-01 -4.55722749e-01 4.18611109e-01
-1.11056840e+00 -6.56168163e-01 -4.61242974e-01 3.50958079e-01
-7.32982099e-01 1.09553978e-01 -7.53005981e-01 -6.35374486e-01
1.55300111e-01 -1.18096578e+00 9.83572900e-01 4.47626747e-02
-3.55845124e-01 -1.34339476e+00 1.47306114e-01 1.88583657e-01
3.88430864e-01 3.88655931e-01 1.02562892e+00 -1.18162036e+00
-4.12199676e-01 -2.86038704e-02 -2.47367427e-01 3.95838499e-01
-1.52580723e-01 -3.86031985e-01 -1.43729722e+00 -2.93647826e-01
1.24380127e-01 -7.37839222e-01 1.24077392e+00 3.07766467e-01
9.12887871e-01 -3.07094991e-01 -3.02759290e-01 4.96597767e-01
1.55140424e+00 -3.76921088e-01 1.11593716e-01 3.51718396e-01
7.53137767e-01 7.59524584e-01 2.16525510e-01 -9.31933075e-02
2.92747378e-01 7.01188207e-01 5.07265449e-01 4.56666388e-02
-1.29860848e-01 -1.62184536e-01 5.41455932e-02 8.00602257e-01
2.04212040e-01 -2.22544715e-01 -1.05016184e+00 4.26211625e-01
-2.18985581e+00 -6.64251506e-01 -2.34048530e-01 2.02478027e+00
1.03744066e+00 4.94349748e-01 -2.68873237e-02 2.49108568e-01
5.95932841e-01 2.07777485e-01 -5.61465442e-01 -2.49414846e-01
-2.60867715e-01 2.07889587e-01 4.71998245e-01 5.66008270e-01
-1.42833996e+00 1.09277189e+00 6.69860554e+00 9.09066737e-01
-1.05853069e+00 4.58927184e-01 7.05367565e-01 2.06777990e-01
-1.60189956e-01 8.81975219e-02 -9.40915585e-01 2.19974577e-01
1.05906713e+00 3.87520581e-01 1.24031402e-01 9.16513979e-01
-2.71276444e-01 -1.35159522e-01 -1.64806390e+00 6.72783315e-01
8.52877647e-02 -1.26427972e+00 -1.00004897e-01 5.57426326e-02
1.12750471e+00 2.20308900e-01 1.28434584e-01 5.49738705e-01
5.64888239e-01 -1.11452186e+00 6.41121686e-01 5.00259757e-01
5.69099665e-01 -3.94468069e-01 9.02306199e-01 6.00050926e-01
-6.02455854e-01 -8.62077624e-02 -4.31550622e-01 -4.12163585e-01
4.66018729e-02 8.61027479e-01 -5.27009130e-01 3.34401190e-01
3.17552805e-01 7.03491509e-01 -7.08797753e-01 7.78341591e-01
-4.15518522e-01 8.54505181e-01 -3.79140079e-01 3.70532304e-01
6.21500969e-01 1.20993316e-01 2.47343063e-01 1.23375845e+00
-7.14819729e-02 -1.79368138e-01 5.76468289e-01 6.88724577e-01
-2.91923136e-01 1.64680909e-02 -7.75082231e-01 1.52369991e-01
2.23159879e-01 1.02874899e+00 -9.10634279e-01 -3.71528625e-01
-4.34495836e-01 5.19755781e-01 8.20264995e-01 4.81199235e-01
-3.28189850e-01 2.72583514e-01 1.65129319e-01 5.36785685e-02
5.99636957e-02 3.75813581e-02 -6.00541413e-01 -1.32218087e+00
-1.39869303e-01 -6.21642113e-01 5.17019749e-01 -5.20529509e-01
-1.81315780e+00 4.84639287e-01 -7.43221343e-02 -8.03403795e-01
-2.83810049e-01 -9.48276937e-01 -3.22965533e-01 5.16975164e-01
-1.45232415e+00 -1.05748558e+00 1.12800844e-01 3.89943212e-01
3.84816229e-01 -8.13310295e-02 9.84948993e-01 1.83588177e-01
-5.03170073e-01 3.80600482e-01 1.71009600e-01 -3.25303674e-02
7.61798322e-01 -1.61808109e+00 1.43588424e-01 6.85931981e-01
4.70184147e-01 5.89628577e-01 9.85069871e-01 -3.26601624e-01
-6.83504581e-01 -1.08852577e+00 1.12606192e+00 -7.39867151e-01
7.77798951e-01 -4.42996085e-01 -1.11924744e+00 8.54568720e-01
1.93402156e-01 4.95096385e-01 7.12866724e-01 4.73516822e-01
-6.87112629e-01 4.79778685e-02 -9.09555376e-01 3.93909752e-01
9.91456091e-01 -6.74302399e-01 -7.79518068e-01 7.59156287e-01
7.43836284e-01 -2.08964363e-01 -6.16073370e-01 6.52167797e-01
3.03143531e-01 -1.20551789e+00 7.05455363e-01 -7.87296593e-01
3.60942602e-01 -4.96802218e-02 -2.43643478e-01 -1.18540478e+00
-5.39971404e-02 -4.64433700e-01 -1.65848345e-01 1.11228609e+00
5.23245990e-01 -6.29703999e-01 8.73073459e-01 5.68266928e-01
-1.56887740e-01 -9.53212440e-01 -9.17895555e-01 -8.43383431e-01
6.82173610e-01 -5.68559229e-01 2.55243242e-01 1.35107994e+00
7.05238944e-03 5.43533683e-01 -3.24188352e-01 -9.81586352e-02
7.12429225e-01 -8.37488100e-03 3.58781219e-01 -1.72582209e+00
-2.43437216e-01 -5.87785244e-01 -1.23726100e-01 -8.77181828e-01
8.49951625e-01 -1.13095343e+00 9.91409421e-02 -1.36564398e+00
1.07385576e-01 -7.17386782e-01 -5.23163557e-01 6.07016146e-01
-6.64240345e-02 4.31888998e-01 8.24648887e-02 3.63823384e-01
-9.29692864e-01 3.84426117e-02 8.61332357e-01 -2.21638307e-02
1.72377005e-01 -3.06876972e-02 -7.54271567e-01 1.03569245e+00
9.00455892e-01 -7.33802855e-01 -3.39069366e-01 -3.40080321e-01
7.39465415e-01 -4.02120501e-01 4.73009527e-01 -8.09841752e-01
2.80584604e-01 9.72093083e-03 2.42996607e-02 -1.78942502e-01
8.30313489e-02 -7.14737177e-01 -1.26834616e-01 2.64046311e-01
-8.26187968e-01 -3.94916028e-01 -1.62915990e-01 5.53527057e-01
-6.87155798e-02 -6.34415567e-01 8.36365104e-01 -4.58186597e-01
-5.46497285e-01 1.92515373e-01 -1.67813048e-01 3.65528643e-01
7.09026575e-01 1.60478130e-01 -4.38660204e-01 -9.65761021e-02
-1.09960294e+00 1.69215202e-02 4.64359075e-01 4.51678522e-02
-1.80589944e-01 -1.36657310e+00 -5.19020677e-01 -4.20045294e-02
9.55431089e-02 1.72358721e-01 -1.72145411e-01 7.98646510e-01
-8.07723776e-02 4.80947465e-01 6.85164779e-02 -5.53697467e-01
-6.38096154e-01 7.20468700e-01 4.86184925e-01 -6.35523021e-01
-3.99164289e-01 9.86029804e-01 4.00947064e-01 -6.87748432e-01
5.51341951e-01 -1.55550376e-01 -1.87371016e-01 2.56698310e-01
2.11488947e-01 1.37285873e-01 2.58261234e-01 -6.32915795e-01
-1.84166953e-01 5.00567853e-01 -8.11386630e-02 3.39795835e-02
1.14084160e+00 -1.48726115e-02 -1.57374054e-01 1.03430390e+00
1.24874651e+00 -1.74979195e-01 -1.17446589e+00 -5.98028719e-01
5.61207950e-01 6.90249950e-02 7.31991753e-02 -7.99208701e-01
-9.39187884e-01 1.07309878e+00 4.34853464e-01 5.89837730e-01
7.54947841e-01 2.18980461e-01 4.22673881e-01 6.16610050e-01
3.61122221e-01 -1.07918108e+00 4.50714566e-02 6.77814364e-01
4.85313952e-01 -1.54787230e+00 -8.81588683e-02 -1.36357620e-01
-4.03695703e-01 1.11159277e+00 6.41752422e-01 -3.53509307e-01
6.98224664e-01 1.04832053e-01 2.31289223e-01 -3.68530959e-01
-9.04546440e-01 -5.30686498e-01 3.29897463e-01 3.77721280e-01
5.03415883e-01 9.16808248e-02 -4.00706738e-01 6.79002225e-01
-1.34980723e-01 -1.65556550e-01 3.88112366e-01 7.52852142e-01
-5.57567954e-01 -1.27128172e+00 -8.07206705e-02 3.33889812e-01
-7.66801476e-01 -9.80661213e-02 -5.27737975e-01 9.04811382e-01
5.88049412e-01 7.38796473e-01 -2.50044107e-01 -2.40712747e-01
1.05124846e-01 6.08814359e-01 3.69270951e-01 -8.97291243e-01
-6.42709911e-01 -6.65791258e-02 3.09827141e-02 -3.89559001e-01
-9.09308493e-01 -5.38965046e-01 -9.69087660e-01 -4.95428368e-02
-4.84120101e-01 2.08262041e-01 4.13558036e-01 1.44207609e+00
-9.14900750e-02 2.44011745e-01 1.75056010e-01 -9.19578850e-01
-7.53614485e-01 -1.02136660e+00 -5.78614950e-01 5.63044190e-01
5.27926385e-01 -9.62123036e-01 -7.91130424e-01 -8.58477131e-03] | [9.46529483795166, 3.8872976303100586] |
85be6334-1ed0-4dc2-bcdf-cd29410761d4 | blind-graph-matching-using-graph-signals | 2306.15747 | null | https://arxiv.org/abs/2306.15747v1 | https://arxiv.org/pdf/2306.15747v1.pdf | Blind Graph Matching Using Graph Signals | Classical graph matching aims to find a node correspondence between two unlabeled graphs of known topologies. This problem has a wide range of applications, from matching identities in social networks to identifying similar biological network functions across species. However, when the underlying graphs are unknown, the use of conventional graph matching methods requires inferring the graph topologies first, a process that is highly sensitive to observation errors. In this paper, we tackle the blind graph matching problem with unknown underlying graphs directly using observations of graph signals, which are generated from graph filters applied to graph signal excitations. We propose to construct sample covariance matrices from the observed signals and match the nodes based on the selected sample eigenvectors. Our analysis shows that the blind matching outcome converges to the result obtained with known graph topologies when the signal sampling size is large and the signal noise is small. Numerical results showcase the performance improvement of the proposed algorithm compared to matching two estimated underlying graphs learned from the graph signals. | ['Hoi-To Wai', 'Anna Scaglione', 'Hang Liu'] | 2023-06-27 | null | null | null | null | ['graph-matching'] | ['graphs'] | [ 6.09966040e-01 3.24298441e-01 1.46288812e-01 -6.77449629e-02
-3.66277188e-01 -6.32655025e-01 4.92574275e-01 2.77620137e-01
7.69082755e-02 5.02185643e-01 -1.39256805e-01 -7.78374597e-02
-4.74637777e-01 -8.26694429e-01 -6.80182874e-01 -5.64780414e-01
-3.68709326e-01 5.91555297e-01 1.25562660e-02 1.78524479e-01
9.34581682e-02 4.38230872e-01 -1.24190009e+00 -4.56052989e-01
7.60734677e-01 6.47443950e-01 -1.29412943e-02 9.66876566e-01
-1.13424772e-04 3.21340919e-01 -4.19628739e-01 -4.55008686e-01
5.58196843e-01 -7.03950465e-01 -2.90866822e-01 4.63153362e-01
5.58904648e-01 3.10572386e-01 -5.26072562e-01 1.67402887e+00
4.24046844e-01 1.30512476e-01 5.79331815e-01 -1.44249642e+00
-3.67868394e-01 7.23350823e-01 -5.78615129e-01 4.65820394e-02
4.90946531e-01 -1.55824736e-01 1.03600860e+00 -3.94794226e-01
6.57401919e-01 1.30553460e+00 9.01602089e-01 1.88762456e-01
-1.80084288e+00 -8.73582959e-01 -1.17558688e-01 4.58510406e-02
-1.44900370e+00 -4.13527459e-01 1.05535495e+00 -6.38865411e-01
8.54668319e-02 2.57312417e-01 5.25810182e-01 7.73683429e-01
-2.34446779e-01 3.89272766e-03 1.08487499e+00 -4.02095050e-01
2.21902221e-01 -2.60061268e-02 9.40289795e-02 9.27883029e-01
5.64404190e-01 2.80937642e-01 -2.97494739e-01 -5.63525379e-01
5.17026126e-01 1.04864925e-01 -4.97904897e-01 -5.43544292e-01
-1.14589894e+00 7.68307149e-01 5.19208848e-01 2.75406748e-01
-4.26746845e-01 1.22746944e-01 -4.03836854e-02 6.29380584e-01
2.80194134e-01 4.24482554e-01 2.12700844e-01 5.78277290e-01
-7.14665353e-01 -2.11899817e-01 1.13914657e+00 1.10565209e+00
1.02360857e+00 1.39826283e-01 1.35393322e-01 6.29634202e-01
3.59015614e-01 1.02699757e+00 3.06543410e-02 -7.81904399e-01
2.51119286e-01 7.66975284e-01 -2.30584979e-01 -1.71021223e+00
-3.28731686e-01 -5.32753944e-01 -1.16354585e+00 -1.55545741e-01
9.95340049e-01 -2.77135611e-01 -7.03682780e-01 1.82447100e+00
2.83308446e-01 6.82582676e-01 8.22592527e-04 8.45999479e-01
7.23804057e-01 3.47836792e-01 -3.06693047e-01 -3.80948812e-01
1.02081704e+00 -2.51540720e-01 -5.97398460e-01 -3.35173637e-01
-8.83065723e-03 -7.92888045e-01 4.13124889e-01 1.50806919e-01
-6.20120466e-01 -3.36291254e-01 -1.02035928e+00 6.29433930e-01
-1.39195696e-01 5.78751862e-02 2.35926136e-01 7.87176967e-01
-9.29752290e-01 7.28792429e-01 -5.15136480e-01 -6.77444518e-01
-1.09019518e-01 5.42376399e-01 -5.33001184e-01 -8.15135762e-02
-9.90588129e-01 2.20427692e-01 2.39003375e-01 3.88883621e-01
-6.29728198e-01 -3.97111058e-01 -6.59815252e-01 6.50806054e-02
5.15681803e-01 -4.76546466e-01 6.40998363e-01 -1.19816101e+00
-1.18195915e+00 8.43286693e-01 -1.09030381e-01 -4.33791965e-01
5.22206724e-01 6.20655358e-01 -6.13528788e-01 3.12688231e-01
1.28667846e-01 -1.62987098e-01 1.28518152e+00 -1.07804596e+00
-1.92704260e-01 -6.07351780e-01 -2.56218880e-01 -1.26751989e-01
-2.37298548e-01 -5.36695123e-01 -4.50309455e-01 -4.56022859e-01
5.66389203e-01 -1.19178760e+00 -3.43209058e-01 5.37661910e-02
-6.36335373e-01 4.49355841e-01 4.53181207e-01 -7.30961084e-01
8.72645557e-01 -2.20493555e+00 1.46286994e-01 1.09349179e+00
6.12289250e-01 -3.19479704e-01 -3.27262789e-01 7.01033652e-01
-1.62552863e-01 -2.68171310e-01 -2.14473724e-01 1.76531687e-01
-2.25138724e-01 -1.93987757e-01 -1.07110245e-02 1.03067982e+00
-2.82730728e-01 4.97160435e-01 -1.09133041e+00 -3.36617410e-01
3.09582818e-02 1.17230974e-01 -1.78681463e-01 4.22436774e-01
5.58838248e-02 5.28229237e-01 -5.14219165e-01 2.75525391e-01
5.99939644e-01 -6.07256711e-01 9.26845312e-01 -5.05705535e-01
5.34335852e-01 -3.02041978e-01 -1.58593535e+00 1.24394906e+00
-2.93332994e-01 7.45863497e-01 5.98639011e-01 -1.24237621e+00
1.32037258e+00 3.49074781e-01 6.12116039e-01 -6.51113540e-02
1.30980358e-01 1.34797871e-01 4.49580789e-01 -5.08958139e-02
-1.51365399e-01 -2.07544602e-02 4.60852981e-02 3.06023151e-01
1.18708424e-01 -9.29837674e-02 2.48260111e-01 4.74901974e-01
1.42498064e+00 -7.08648682e-01 4.48290199e-01 -4.08012748e-01
7.09097564e-01 -2.70895600e-01 4.74227250e-01 7.37202346e-01
9.00488645e-02 2.31817365e-01 5.64450502e-01 6.24501891e-03
-8.21454942e-01 -1.27019012e+00 1.97969407e-01 2.65635967e-01
3.13027859e-01 -1.35480925e-01 -6.79670215e-01 -4.62132424e-01
3.04229319e-01 -1.23137124e-01 -5.74209988e-01 -2.11798742e-01
-2.97888130e-01 -5.86933792e-01 3.80975336e-01 -1.55234665e-01
2.00383157e-01 -5.38837969e-01 1.72396019e-01 2.11161181e-01
-5.79902269e-02 -1.33308601e+00 -6.97817564e-01 -2.12137625e-01
-8.82903218e-01 -1.73313141e+00 -5.22343755e-01 -9.50447559e-01
1.22883511e+00 3.59447628e-01 8.71155262e-01 2.62620807e-01
-2.57261336e-01 6.05077744e-01 -3.93859446e-02 -6.89107971e-03
-7.56807923e-01 -1.15434647e-01 1.93379447e-01 7.15910137e-01
-2.22304687e-02 -1.00911629e+00 -1.52245119e-01 4.59247440e-01
-6.12985194e-01 -1.23000607e-01 5.57412446e-01 9.28780317e-01
3.20341170e-01 2.48216495e-01 5.71155012e-01 -1.20324349e+00
7.79345691e-01 -3.45743060e-01 -1.03938198e+00 4.07585829e-01
-6.48172617e-01 3.34753394e-01 8.77011895e-01 -6.00429833e-01
-4.24831986e-01 4.21143472e-01 5.88537753e-01 -5.92061818e-01
2.80114748e-02 3.97474527e-01 -3.11670244e-01 -4.98975635e-01
6.28874481e-01 1.16585001e-01 4.63901550e-01 -3.30970883e-01
5.10605276e-01 4.15389895e-01 6.86151862e-01 -4.33652908e-01
1.25298941e+00 3.82842124e-01 5.37469506e-01 -1.28009844e+00
-2.10913181e-01 -5.70018351e-01 -2.79428571e-01 -3.54562730e-01
3.87664318e-01 -6.22108161e-01 -1.10638750e+00 2.64648765e-01
-8.76041591e-01 2.11223289e-01 -6.95287511e-02 7.00823426e-01
-3.05728823e-01 7.65144706e-01 -5.79796314e-01 -9.61125076e-01
-2.77795583e-01 -7.55628765e-01 7.13943720e-01 8.84519741e-02
-9.12770778e-02 -1.09550619e+00 2.20491946e-01 1.06510948e-02
6.61935583e-02 4.79655862e-01 7.96570480e-01 -8.24751735e-01
-6.77618444e-01 -6.27896130e-01 -2.82647878e-01 -1.64053082e-01
5.90550303e-01 -1.54762477e-01 -7.89230764e-01 -5.97254038e-01
-2.93247581e-01 3.00140947e-01 3.49426121e-01 5.51146746e-01
4.37536240e-01 -3.53538215e-01 -5.63842773e-01 4.41655993e-01
1.39328623e+00 5.94069138e-02 8.92791059e-03 -4.95483041e-01
7.36708879e-01 7.78286397e-01 7.35958442e-02 3.13333154e-01
-2.91740507e-01 5.11243582e-01 2.45361999e-01 -1.81000546e-01
-6.56950101e-02 -5.57263494e-01 1.02526844e-01 8.85474086e-01
1.84817806e-01 -2.70156831e-01 -7.14206278e-01 4.65213418e-01
-1.78257942e+00 -8.89458179e-01 -5.04988909e-01 2.66210699e+00
3.71524274e-01 7.81664401e-02 2.10084796e-01 1.23311639e-01
1.41100550e+00 -7.17622489e-02 -6.99273765e-01 2.48263225e-01
-7.27628246e-02 2.23309342e-02 8.45279932e-01 8.96614611e-01
-7.05438435e-01 3.21363151e-01 6.04177952e+00 4.20757979e-01
-7.98013866e-01 -2.82884598e-01 1.18413702e-01 4.79442358e-01
-1.22173004e-01 3.63265127e-01 -2.31713384e-01 4.37586814e-01
8.38447094e-01 -7.46552169e-01 8.86494219e-01 3.32938194e-01
1.95239708e-01 1.65659487e-01 -1.07186174e+00 1.20064116e+00
-1.65750347e-02 -9.97774482e-01 -1.97767898e-01 3.04561615e-01
5.58642209e-01 -2.31629342e-01 -3.75571668e-01 -3.69623303e-01
6.63496792e-01 -7.52116144e-01 1.17872819e-01 5.86964488e-01
6.07786775e-01 -4.33388203e-01 4.16785866e-01 2.59324700e-01
-1.54683709e+00 5.21869957e-02 -2.72319734e-01 3.55647020e-02
9.52225775e-02 1.01218235e+00 -1.26101494e+00 6.50716186e-01
4.93704751e-02 8.83833230e-01 -4.15493488e-01 1.22235084e+00
-1.49663538e-01 7.99453437e-01 -4.84979421e-01 -1.61500856e-01
-3.27021539e-01 -8.37410808e-01 9.83839154e-01 7.37541318e-01
4.18811768e-01 -1.81435958e-01 3.76096934e-01 9.81403351e-01
-2.51679540e-01 2.23766163e-01 -9.93741512e-01 -4.00649697e-01
6.75146282e-01 1.30326557e+00 -1.04923201e+00 -2.41504565e-01
-3.15095991e-01 9.35552597e-01 2.08184883e-01 5.11624157e-01
-3.09251428e-01 -5.25285661e-01 4.48217779e-01 2.05629036e-01
-6.81149634e-03 -6.73719198e-02 3.18883002e-01 -1.01765764e+00
-3.39568071e-02 -9.06514883e-01 4.00426030e-01 -3.79796356e-01
-1.65968227e+00 4.04178262e-01 -1.81193218e-01 -1.37443268e+00
-1.89853117e-01 -2.65306741e-01 -5.89360535e-01 7.52536654e-01
-6.96381211e-01 -7.71668613e-01 -5.09644032e-01 5.90991437e-01
-1.84099853e-01 -8.65939781e-02 5.91421127e-01 3.47651184e-01
-3.97887439e-01 4.05658185e-01 3.35466713e-01 4.40254003e-01
4.34861362e-01 -1.09174442e+00 4.85632867e-01 1.10926819e+00
5.20625472e-01 5.00390470e-01 9.72946465e-01 -8.58757198e-01
-1.67481482e+00 -8.49654913e-01 5.84528148e-01 1.00118093e-01
9.77968454e-01 -5.54791749e-01 -8.39835286e-01 4.43080634e-01
-1.18728191e-01 3.27764541e-01 3.21365267e-01 -1.01943091e-01
-2.14230791e-01 -2.64409840e-01 -1.23846340e+00 3.67386192e-01
1.11215842e+00 -6.38962924e-01 -4.61404659e-02 3.96399140e-01
2.09102482e-01 -8.14309642e-02 -1.03228068e+00 3.97471413e-02
5.46917975e-01 -5.41480958e-01 8.19367409e-01 -5.92031240e-01
-4.40767497e-01 -5.83096504e-01 -1.99809168e-02 -1.50350273e+00
-3.32762539e-01 -1.02108550e+00 3.21614623e-01 1.27763808e+00
4.50008929e-01 -9.38840628e-01 1.02614474e+00 3.11747640e-01
6.12155616e-01 3.27979565e-01 -7.08295941e-01 -8.49489093e-01
-7.91886449e-01 4.43834104e-02 4.51551676e-01 1.13665521e+00
4.87018656e-03 7.83743143e-01 -3.87464911e-01 7.09966838e-01
1.31914103e+00 4.01693255e-01 9.01498735e-01 -1.77506816e+00
-7.29179978e-01 -2.12086156e-01 -8.98025751e-01 -7.42142379e-01
3.96295160e-01 -1.04709542e+00 1.97416827e-01 -1.15016389e+00
-4.23008017e-02 -3.21742535e-01 2.09105909e-01 -8.74693841e-02
-1.61596879e-01 1.36924505e-01 1.92623079e-01 -1.89448465e-02
-7.04988912e-02 1.26939327e-01 1.04157960e+00 -3.83291811e-01
-1.26952022e-01 4.41252530e-01 -2.81162173e-01 5.56491137e-01
4.77922499e-01 -5.78492880e-01 -5.63794613e-01 2.20655024e-01
2.18566414e-02 6.36516035e-01 3.24028760e-01 -9.75185275e-01
3.11747998e-01 9.95838121e-02 -3.73466425e-02 6.34495914e-02
1.70285016e-01 -1.15742528e+00 9.20603037e-01 7.62913346e-01
-2.99033970e-01 -6.42944723e-02 -1.88418999e-01 1.22222579e+00
-1.26889929e-01 -1.54327437e-01 7.79870510e-01 7.60421827e-02
-2.76851356e-01 3.09603482e-01 -2.44913518e-01 1.79674804e-01
6.86931014e-01 -2.20331237e-01 -6.17638044e-02 -9.24895287e-01
-9.55368698e-01 7.98080564e-02 5.11863887e-01 9.34207514e-02
5.55049002e-01 -1.23741364e+00 -7.30540395e-01 3.21014106e-01
9.36664864e-02 -5.87747276e-01 -1.16477638e-01 8.11727166e-01
-1.41490728e-01 -8.87257755e-02 9.50188860e-02 -5.74649572e-01
-1.50864768e+00 6.19216084e-01 3.60020518e-01 -2.80746166e-03
-4.50967014e-01 3.41599613e-01 -1.93189308e-02 -5.47068954e-01
1.12336412e-01 -9.47578251e-02 -2.11945269e-02 -1.53689578e-01
1.36592746e-01 5.29339373e-01 3.31709869e-02 -7.64631867e-01
-2.44447723e-01 8.25596094e-01 4.52818424e-01 6.52242973e-02
9.32533383e-01 -1.35622114e-01 -1.99542493e-01 2.41801232e-01
1.29553437e+00 3.12332273e-01 -8.36487055e-01 -6.11848056e-01
2.78393626e-01 -6.41052902e-01 -2.45844141e-01 -6.91793784e-02
-1.33972597e+00 5.04142582e-01 6.80053413e-01 5.96673131e-01
1.03269005e+00 -6.53463081e-02 2.68286169e-01 3.95345837e-01
4.57894623e-01 -3.59338284e-01 -3.18000793e-01 -1.98907033e-01
5.28687775e-01 -1.07231522e+00 2.64360569e-02 -8.82992327e-01
2.87897009e-02 1.05608284e+00 2.59821773e-01 -5.04304886e-01
8.24173808e-01 -3.56823504e-02 -1.36490077e-01 -3.25264513e-01
-2.88635552e-01 -3.73518676e-01 5.32715917e-01 8.06381166e-01
1.08408354e-01 2.11433694e-01 -1.88079298e-01 -1.92786939e-02
-1.50202855e-01 -3.59022647e-01 6.05884790e-01 3.27779919e-01
-2.20716879e-01 -1.13295364e+00 -6.50951266e-01 8.53806794e-01
-1.67410523e-01 7.13160411e-02 -8.37289751e-01 5.58641076e-01
-5.40736496e-01 1.22488904e+00 -2.26307020e-01 -3.85848910e-01
4.26709175e-01 -2.75904715e-01 4.20343578e-01 -4.79775280e-01
-1.91434503e-01 6.13611713e-02 1.85577393e-01 -4.28836584e-01
-5.96022844e-01 -6.30574465e-01 -9.44548070e-01 -3.95349503e-01
-6.80813015e-01 5.71328044e-01 3.17583561e-01 7.31580436e-01
3.42897713e-01 1.84013873e-01 8.33079636e-01 -5.84831357e-01
-5.09490252e-01 -7.80008733e-01 -1.17359543e+00 6.44682765e-01
4.24369067e-01 -5.52490890e-01 -6.35585248e-01 -1.36295203e-02] | [6.939715385437012, 5.204104900360107] |
eb64d735-ffed-42b2-98ab-950af73bccf8 | analyzing-and-characterizing-user-intent-in | 1804.08759 | null | http://arxiv.org/abs/1804.08759v1 | http://arxiv.org/pdf/1804.08759v1.pdf | Analyzing and Characterizing User Intent in Information-seeking Conversations | Understanding and characterizing how people interact in information-seeking
conversations is crucial in developing conversational search systems. In this
paper, we introduce a new dataset designed for this purpose and use it to
analyze information-seeking conversations by user intent distribution,
co-occurrence, and flow patterns. The MSDialog dataset is a labeled dialog
dataset of question answering (QA) interactions between information seekers and
providers from an online forum on Microsoft products. The dataset contains more
than 2,000 multi-turn QA dialogs with 10,000 utterances that are annotated with
user intent on the utterance level. Annotations were done using crowdsourcing.
With MSDialog, we find some highly recurring patterns in user intent during an
information-seeking process. They could be useful for designing conversational
search systems. We will make our dataset freely available to encourage
exploration of information-seeking conversation models. | ['Yongfeng Zhang', 'W. Bruce Croft', 'Johanne R. Trippas', 'Liu Yang', 'Minghui Qiu', 'Chen Qu'] | 2018-04-23 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [-1.41127333e-01 6.25967026e-01 -3.59822273e-01 -6.54379487e-01
-9.27164614e-01 -1.01415408e+00 9.29840147e-01 2.36657947e-01
-1.97443247e-01 4.95521277e-01 9.55560327e-01 -5.66863060e-01
4.57098931e-02 -9.06515494e-02 2.13174775e-01 6.63815439e-02
1.40821457e-01 8.21566582e-01 2.53989577e-01 -7.78366268e-01
4.64855373e-01 -7.24236947e-03 -9.16513801e-01 8.98617387e-01
8.75039697e-01 8.38658929e-01 3.83791268e-01 8.74951422e-01
-7.90464938e-01 1.41009378e+00 -6.42354369e-01 -5.22272527e-01
-1.45956710e-01 -5.58070421e-01 -1.79782140e+00 1.98921502e-01
1.43453360e-01 -2.96301395e-01 -2.03738347e-01 5.64324439e-01
3.87846470e-01 2.63713241e-01 4.10427600e-01 -1.45912588e+00
-4.38884646e-01 5.68876088e-01 2.13925540e-01 5.38544774e-01
1.21547234e+00 1.12938717e-01 1.26691699e+00 -5.86691439e-01
8.02405596e-01 1.42560267e+00 3.00435454e-01 7.92614520e-01
-9.27196324e-01 -1.04058176e-01 -2.13960648e-01 2.13351339e-01
-1.08648503e+00 -7.60969341e-01 7.16957927e-01 -7.72908270e-01
8.89440536e-01 7.07189798e-01 1.34851277e-01 1.14577150e+00
-2.90587932e-01 1.15115130e+00 7.60518849e-01 -5.74097693e-01
6.02051131e-02 7.40074158e-01 9.28837180e-01 6.56732440e-01
-7.40071118e-01 -6.26112282e-01 -5.91235697e-01 -9.66646373e-01
2.72643059e-01 -1.95471764e-01 -2.47834295e-01 1.94621697e-01
-7.81170130e-01 1.15365791e+00 2.64632583e-01 7.08131850e-01
-3.18950295e-01 -4.51016873e-01 3.24225247e-01 5.28222978e-01
5.60206234e-01 7.62484312e-01 -3.60160261e-01 -6.69418573e-01
-9.44266841e-02 5.55902898e-01 1.73525083e+00 1.14351475e+00
9.78914797e-01 -8.98232639e-01 -6.38558745e-01 1.42408097e+00
4.73175734e-01 4.41543609e-02 5.19135714e-01 -1.37649834e+00
5.99146128e-01 1.02410507e+00 6.85501635e-01 -9.89350677e-01
-5.52539229e-01 6.94697618e-01 9.13713053e-02 -8.74491453e-01
6.35422707e-01 -4.31575745e-01 -9.18601826e-02 1.20741737e+00
1.75505623e-01 -8.16790819e-01 8.98557007e-02 8.01817894e-01
1.38873827e+00 3.96919847e-01 7.46217668e-02 -2.90535599e-01
1.93097842e+00 -1.05441368e+00 -1.19484377e+00 -1.82439223e-01
8.95886481e-01 -9.59636688e-01 1.43180299e+00 -1.95219234e-01
-9.57422972e-01 -1.56440631e-01 -5.19283190e-02 -3.72344971e-01
-3.40717614e-01 -2.65256166e-01 5.88294923e-01 9.29686308e-01
-8.53925884e-01 -1.94212690e-01 -3.81650090e-01 -6.91265583e-01
-5.96637167e-02 -6.04631267e-02 2.21675068e-01 1.28463611e-01
-1.36697876e+00 6.87944829e-01 -4.78652656e-01 -4.08434749e-01
-5.53929090e-01 -4.32425320e-01 -8.39754283e-01 1.33390455e-02
5.75505972e-01 -4.90209237e-02 2.27184463e+00 -5.81828058e-01
-1.46040452e+00 7.87171602e-01 -8.70805740e-01 -1.52222648e-01
5.34914583e-02 8.61674733e-03 -4.07528698e-01 7.11712092e-02
3.77896994e-01 4.48492646e-01 3.01326305e-01 -1.16590011e+00
-6.09586656e-01 -4.28917766e-01 3.46097052e-01 4.25916165e-01
-2.89760768e-01 5.15335083e-01 -4.57598090e-01 5.57816513e-02
-2.80169845e-01 -9.49802399e-01 -2.40615681e-02 -6.37831688e-01
-5.59103906e-01 -1.08172739e+00 7.91060209e-01 -8.87124836e-01
1.37387073e+00 -1.93182862e+00 -2.27231503e-01 -2.68959880e-01
4.04552072e-01 5.52226864e-02 3.80093195e-02 1.08032596e+00
5.53383708e-01 3.53341222e-01 2.43842468e-01 -5.35953879e-01
2.10736185e-01 5.36972703e-03 -3.23731691e-01 -1.30272210e-01
-1.86859891e-01 9.93461609e-01 -1.02498114e+00 -7.51339555e-01
-1.78652093e-01 -1.37381330e-01 -3.04342717e-01 7.28280485e-01
-7.23038375e-01 5.42351902e-01 -8.36230457e-01 6.98729217e-01
1.30166486e-01 -4.94798154e-01 1.15963884e-01 1.18672632e-01
-1.03175670e-01 7.69255221e-01 -2.71485031e-01 1.49372447e+00
-6.31163239e-01 1.01992154e+00 5.13866007e-01 -7.18623847e-02
7.83496141e-01 5.19415140e-01 4.46347326e-01 -7.57577717e-01
1.51514634e-01 -1.31232649e-01 -8.29299465e-02 -1.02845573e+00
9.46445107e-01 2.77511597e-01 -4.01961118e-01 1.03640354e+00
-5.86642362e-02 -1.54597700e-01 2.82461166e-01 6.12439573e-01
1.15962934e+00 -9.92602110e-01 1.33136719e-01 -2.89685220e-01
6.50498927e-01 4.95023221e-01 2.31374661e-03 9.35174406e-01
-5.63938498e-01 6.87005669e-02 6.87388837e-01 -4.21869785e-01
-4.29315478e-01 -5.81772804e-01 -7.34270290e-02 1.61386037e+00
3.25969234e-02 -6.35802150e-01 -7.64835954e-01 -7.54288614e-01
-1.59854025e-01 6.92515135e-01 -3.06004226e-01 4.06475425e-01
-2.88196653e-01 -1.48579270e-01 5.87076008e-01 3.05321831e-02
6.95137560e-01 -1.29549623e+00 -2.46194020e-01 1.59646139e-01
-1.00245023e+00 -1.30114448e+00 -9.63813543e-01 -4.20174688e-01
-2.47804120e-01 -1.18108952e+00 -7.05795467e-01 -7.99574494e-01
1.51139200e-01 4.37117130e-01 1.47253418e+00 2.96341777e-01
-1.75412223e-01 9.76294696e-01 -7.62408018e-01 -3.30351740e-01
-6.78167403e-01 4.20840055e-01 -1.96824893e-01 -4.81638033e-03
9.66827571e-01 1.64709717e-01 -5.20199835e-01 8.86460066e-01
-5.70290029e-01 -3.92605782e-01 -2.10419357e-01 5.01238286e-01
-6.48038089e-01 -4.18725014e-01 5.36359191e-01 -8.83239388e-01
1.82437408e+00 -8.50395918e-01 -2.48574317e-01 4.29773778e-01
-2.77267665e-01 -2.72501200e-01 8.59702229e-02 -1.44831240e-01
-1.48244178e+00 -1.72690883e-01 -2.08334669e-01 4.37530965e-01
-4.05196935e-01 3.34457278e-01 2.27846637e-01 2.63717454e-02
1.10853946e+00 -5.16623370e-02 8.02700073e-02 -6.49970710e-01
3.36882472e-01 1.56756246e+00 6.14421479e-02 -3.93490344e-01
-6.70685545e-02 6.50493428e-02 -1.08981562e+00 -1.38741004e+00
-6.83821321e-01 -1.26913035e+00 -2.37664446e-01 -4.40815330e-01
9.97773111e-01 -5.82778454e-01 -1.55836344e+00 3.06587517e-01
-1.27224767e+00 -6.15181565e-01 2.69340008e-01 -1.78167701e-01
-3.59622121e-01 5.16323745e-01 -1.11610222e+00 -1.51347804e+00
-2.43639380e-01 -1.18434215e+00 8.59263361e-01 4.85539883e-01
-1.14909971e+00 -1.29711771e+00 3.51256073e-01 1.15828979e+00
5.34507871e-01 -7.13008702e-01 7.46990621e-01 -1.20889604e+00
-2.02392206e-01 5.76161547e-04 -7.35066533e-02 -3.27373564e-01
6.33688509e-01 -4.11114335e-01 -9.88439023e-01 2.32470222e-02
2.06600562e-01 -8.76000404e-01 2.04798982e-01 4.28531319e-02
8.54110360e-01 -8.90845060e-01 -3.66718650e-01 -7.20374703e-01
5.95398128e-01 6.18955851e-01 2.72929579e-01 -9.06296913e-03
2.35089362e-01 1.44293869e+00 5.50565064e-01 5.65651953e-01
9.30324614e-01 8.56086552e-01 -1.42728901e-02 3.43762517e-01
4.13121194e-01 -1.63460627e-01 7.21504390e-02 5.08378983e-01
5.12798846e-01 -5.19320667e-01 -1.32186425e+00 6.77090526e-01
-1.86070013e+00 -8.98995817e-01 -1.06637336e-01 1.48924053e+00
1.16685724e+00 -3.68603885e-01 6.50862455e-01 -5.10222435e-01
5.19668877e-01 2.63541877e-01 -3.73516947e-01 -4.39983964e-01
4.80135530e-01 -4.52788621e-01 -4.60882820e-02 1.37569821e+00
-6.74336672e-01 9.43983257e-01 6.73837280e+00 2.90575713e-01
-4.10200328e-01 1.52865082e-01 8.18163157e-01 4.20830369e-01
-5.47030210e-01 2.22456851e-03 -9.42815959e-01 5.29678226e-01
1.02895308e+00 -2.85503417e-01 5.55934846e-01 8.73598516e-01
2.28975520e-01 -4.41873819e-01 -1.11597764e+00 8.36009920e-01
5.75367101e-02 -1.42736638e+00 -4.74511236e-01 -8.72128084e-02
2.37890333e-01 -1.12654231e-01 -3.29607010e-01 5.64198434e-01
7.46975660e-01 -8.19660902e-01 -2.52021492e-01 4.49901968e-01
1.29104868e-01 -8.63933563e-02 5.76859534e-01 9.96823251e-01
-8.19147289e-01 -2.56524950e-01 5.62495738e-02 -1.76731601e-01
6.17703676e-01 1.62702590e-01 -1.68263447e+00 -1.46086011e-02
7.47889519e-01 3.01522166e-01 -6.03559971e-01 4.61488813e-01
5.35019815e-01 4.07242417e-01 -1.41492948e-01 -7.40959525e-01
2.57573754e-01 -2.78132260e-01 4.70992953e-01 1.25266159e+00
-5.10156274e-01 3.99976075e-01 3.64587128e-01 9.13296103e-01
-1.10240348e-01 9.27144215e-02 -8.42417538e-01 -5.17473042e-01
7.73382902e-01 1.34187710e+00 -4.77366894e-01 -2.90130824e-01
-4.10037816e-01 9.25644457e-01 9.65320095e-02 4.25362706e-01
-1.53623104e-01 -2.76140720e-01 6.69703305e-01 6.63016513e-02
-5.49863756e-01 -1.59681037e-01 -6.62584975e-02 -9.47014034e-01
-1.84303243e-02 -1.19671345e+00 4.83031273e-01 -6.93018138e-01
-1.50159442e+00 8.91986132e-01 3.93031240e-02 -5.10528982e-01
-1.05124235e+00 -2.26994216e-01 -5.70781827e-01 9.48456824e-01
-8.47436190e-01 -5.24676502e-01 -7.14640677e-01 6.70568943e-01
1.08204496e+00 -3.11773866e-01 8.15673888e-01 2.28307098e-01
-3.00238818e-01 3.77644032e-01 -5.53724706e-01 4.07461524e-01
8.08333337e-01 -1.13526464e+00 5.13205945e-01 -1.28146932e-01
6.99006468e-02 9.38705444e-01 7.60405779e-01 -6.54769599e-01
-1.27306974e+00 -3.32656592e-01 1.45512772e+00 -1.21249962e+00
7.28041232e-01 -5.23717642e-01 -9.72407758e-01 8.49865675e-01
9.19963896e-01 -9.48487341e-01 1.15112984e+00 5.27903318e-01
1.18947916e-01 4.91850555e-01 -1.15408051e+00 7.24562466e-01
7.51134872e-01 -1.16096377e+00 -7.77488351e-01 1.04370654e+00
9.27651584e-01 -4.01780635e-01 -9.16310728e-01 -6.49253130e-02
1.39653161e-01 -8.71378481e-01 6.05761945e-01 -7.42778599e-01
8.45787972e-02 4.50490266e-01 -7.12485313e-02 -1.03057015e+00
1.01306319e-01 -1.12125361e+00 1.25897735e-01 1.39760351e+00
6.27997994e-01 -6.19692266e-01 8.30380023e-01 1.71050823e+00
2.87835542e-02 -3.61410260e-01 -7.52365053e-01 -1.78944528e-01
-1.49826199e-01 -1.74999371e-01 3.15133214e-01 1.01331413e+00
1.00770843e+00 9.81637239e-01 -2.48705477e-01 -2.88902313e-01
-1.92508046e-02 -8.53765681e-02 9.76164222e-01 -1.03855777e+00
-9.04879272e-02 -3.61998111e-01 4.86231208e-01 -1.76990330e+00
2.51818299e-01 -4.67405498e-01 2.55731553e-01 -1.33059359e+00
5.66454977e-02 -5.98852873e-01 9.25482690e-01 4.65569764e-01
-1.22680567e-01 -3.41596723e-01 5.66949211e-02 6.80552602e-01
-8.76469851e-01 5.29837251e-01 9.22969639e-01 -7.97086954e-02
-7.18481004e-01 4.11100030e-01 -7.82558084e-01 5.12060106e-01
7.61821389e-01 -1.20564438e-01 -5.06428123e-01 -1.69306979e-01
4.05764729e-02 6.76752985e-01 -5.17082438e-02 -2.52846658e-01
5.46485066e-01 -1.74761534e-01 -4.43504363e-01 -5.67704737e-01
5.72284281e-01 -6.08791530e-01 -4.38785851e-01 1.30418897e-01
-1.07607603e+00 1.83921903e-01 -1.15462348e-01 3.29775602e-01
-3.70292664e-01 -6.18399441e-01 6.18283860e-02 -4.45354968e-01
-5.40160596e-01 1.54642453e-02 -1.16136253e+00 3.23700249e-01
6.75904453e-01 2.73697674e-01 -6.12007439e-01 -1.41600811e+00
-7.11344957e-01 8.55973601e-01 2.11586043e-01 7.54690409e-01
1.48744732e-01 -1.05353165e+00 -3.91240895e-01 -1.89984605e-01
3.46783161e-01 -4.10636336e-01 1.48708969e-01 3.90013099e-01
-7.21417665e-02 9.92674887e-01 1.02528788e-01 -5.39888918e-01
-1.35731924e+00 1.29858389e-01 2.43090674e-01 -2.38791451e-01
4.33770083e-02 7.17448652e-01 -1.66868344e-01 -8.42159450e-01
5.06158412e-01 1.10649895e-02 -6.25695407e-01 3.43358904e-01
9.47125852e-01 4.71082330e-01 -1.27284616e-01 -4.55938458e-01
-2.74673790e-01 -3.32905948e-01 -2.92809755e-01 -6.38028383e-01
4.44165677e-01 -6.95501268e-01 -2.77944207e-01 7.33534217e-01
1.38256788e+00 4.30361032e-02 -6.52455926e-01 -5.33490896e-01
6.60599172e-01 -5.33280909e-01 -5.90945303e-01 -6.88396513e-01
-3.30767035e-01 4.58395272e-01 2.18004361e-01 1.37622619e+00
4.87164676e-01 7.43602931e-01 9.38475847e-01 9.36731398e-01
1.92123637e-01 -9.89263356e-01 5.92407286e-01 9.41520154e-01
1.09064817e+00 -1.81289470e+00 -5.95248222e-01 -6.68908060e-01
-1.17835629e+00 8.71202528e-01 7.97839224e-01 6.18330717e-01
7.16289282e-01 -1.78772017e-01 6.71752393e-01 -6.63290977e-01
-8.87788534e-01 -3.04562449e-01 2.86780447e-01 4.09567058e-01
8.27844262e-01 -2.89436251e-01 -4.18376327e-01 5.76543450e-01
-2.77704954e-01 -2.31304899e-01 4.75989789e-01 1.01307547e+00
-4.59588140e-01 -1.20972407e+00 -1.18155569e-01 4.51475114e-01
-2.62954146e-01 -1.43887937e-01 -1.30551028e+00 3.84407043e-01
-9.29085493e-01 1.93712425e+00 8.07120875e-02 -4.64592010e-01
2.41585001e-01 5.08373678e-01 -3.44019532e-01 -8.32157075e-01
-1.01909781e+00 -3.67438823e-01 9.92052078e-01 -6.30580962e-01
-4.35775012e-01 -5.31454563e-01 -8.07085752e-01 -2.50997066e-01
-2.19465867e-01 8.48294675e-01 5.33122122e-01 9.39816713e-01
6.41327858e-01 -2.83255816e-01 7.62260079e-01 -3.42683911e-01
-3.36235315e-01 -1.48316312e+00 -1.27881601e-01 6.08076632e-01
4.46980506e-01 -2.99910635e-01 -4.19227153e-01 4.34571505e-03] | [12.367846488952637, 7.847703456878662] |
36e7eba4-b381-4f7e-b09d-eacd479f54ed | structure-informed-shadow-removal-networks | 2301.03182 | null | https://arxiv.org/abs/2301.03182v1 | https://arxiv.org/pdf/2301.03182v1.pdf | Structure-Informed Shadow Removal Networks | Shadow removal is a fundamental task in computer vision. Despite the success, existing deep learning-based shadow removal methods still produce images with shadow remnants. These shadow remnants typically exist in homogeneous regions with low intensity values, making them untraceable in the existing image-to-image mapping paradigm. We observe from our experiments that shadows mainly degrade object colors at the image structure level (in which humans perceive object outlines filled with continuous colors). Hence, in this paper, we propose to remove shadows at the image structure level. Based on this idea, we propose a novel structure-informed shadow removal network (StructNet) to leverage the image structure information to address the shadow remnant problem. Specifically, StructNet first reconstructs the structure information of the input image without shadows and then uses the restored shadow-free structure prior to guiding the image-level shadow removal. StructNet contains two main novel modules: (1) a mask-guided shadow-free extraction (MSFE) module to extract image structural features in a non-shadow to shadow directional manner, and (2) a multi-scale feature & residual aggregation (MFRA) module to leverage the shadow-free structure information to regularize feature consistency. In addition, we also propose to extend StructNet to exploit multi-level structure information (MStructNet), to further boost the shadow removal performance with minimum computational overheads. Extensive experiments on three shadow removal benchmarks demonstrate that our method outperforms existing shadow removal methods, and our StructNet can be integrated with existing methods to boost their performances further. | ['Rynson W. H. Lau', 'Ivor W. Tsang', 'Wei Feng', 'Ke Xu', 'Zhanghan Ke', 'Lan Fu', 'Qing Guo', 'Yuhao Liu'] | 2023-01-09 | null | null | null | null | ['shadow-removal'] | ['computer-vision'] | [ 8.20743382e-01 4.30429280e-02 2.61167765e-01 -2.77987659e-01
-5.01870411e-03 -4.67690438e-01 4.24999744e-01 -4.61614460e-01
4.50468287e-02 6.05518043e-01 3.56785916e-02 -4.42352295e-01
5.19850791e-01 -8.11157048e-01 -7.89265215e-01 -1.06653810e+00
3.75583798e-01 -1.43788725e-01 9.51234639e-01 -2.12128624e-01
3.68369073e-01 7.56599963e-01 -1.57331121e+00 3.26731890e-01
1.11247528e+00 1.02224374e+00 6.45983398e-01 4.66482371e-01
-6.48937747e-02 9.02158141e-01 -7.15061605e-01 1.91891044e-01
5.47852218e-01 -2.50079960e-01 -3.20427835e-01 2.46125400e-01
5.45965135e-01 -8.27857971e-01 -4.91190523e-01 7.65705943e-01
3.53653282e-01 9.63554829e-02 2.92331129e-01 -1.25013912e+00
-6.43270969e-01 -1.80173710e-01 -7.97808409e-01 -1.69114694e-02
6.14698641e-02 3.13285410e-01 6.01695061e-01 -1.03986657e+00
5.65318406e-01 1.18929660e+00 6.55834913e-01 2.42280886e-01
-1.02327311e+00 -8.25411558e-01 2.02605486e-01 2.21363053e-01
-1.15233433e+00 -3.96699309e-01 1.17395151e+00 7.27723986e-02
5.61816931e-01 4.34312791e-01 5.45960605e-01 8.61800253e-01
5.34084201e-01 8.70621443e-01 1.74788094e+00 -3.08293670e-01
1.74724877e-01 2.31381450e-02 -4.55425074e-03 1.10122263e+00
3.18676740e-01 4.53156292e-01 -7.24064469e-01 -5.27216084e-02
7.67893195e-01 2.50415593e-01 -6.50971532e-01 -4.41775292e-01
-9.61789012e-01 5.95862627e-01 8.83694112e-01 -2.07151085e-01
-1.12913631e-01 1.74967080e-01 -9.22223702e-02 6.81190267e-02
3.72643828e-01 1.63981065e-01 -4.26018178e-01 6.80345237e-01
-8.41061354e-01 2.19509583e-02 7.10971415e-01 7.94379294e-01
1.33452928e+00 1.51056111e-01 -2.05415502e-01 5.99659801e-01
8.24411213e-02 9.42321479e-01 1.72309694e-03 -9.27823722e-01
5.00168987e-02 7.34782457e-01 6.07412085e-02 -1.19481790e+00
-3.46331686e-01 -3.09623837e-01 -8.61692786e-01 7.39703178e-01
-2.56872028e-02 1.87961861e-01 -1.35864389e+00 1.39982378e+00
4.65900064e-01 4.39239889e-01 3.97328623e-02 8.79906178e-01
1.04152024e+00 5.91372132e-01 -5.89706540e-01 -1.95218042e-01
1.26457357e+00 -1.28186285e+00 -5.41529000e-01 -4.62226510e-01
5.17696887e-02 -9.09657001e-01 1.30373502e+00 1.91863075e-01
-5.45053840e-01 -4.02499020e-01 -1.33012915e+00 -1.18308783e-01
-4.13875252e-01 4.32402678e-02 7.80993819e-01 5.77362359e-01
-9.69010711e-01 3.11282843e-01 -5.31049788e-01 -5.77591136e-02
6.47119164e-01 2.77684271e-01 -8.13986436e-02 -3.74424487e-01
-7.79658675e-01 5.62255800e-01 2.47773230e-01 2.67850965e-01
-9.77202117e-01 -7.42497206e-01 -7.90880263e-01 -2.46934332e-02
9.03207481e-01 -4.95853066e-01 7.97119975e-01 -1.10277283e+00
-1.35347569e+00 4.70091850e-01 -5.02434552e-01 -9.70303416e-02
3.82913768e-01 -1.59523681e-01 -7.43287876e-02 1.39730573e-01
-1.57434784e-03 3.50732505e-01 1.27259195e+00 -2.22500610e+00
-5.96904755e-01 -5.04580289e-02 1.32911548e-01 3.67969543e-01
-2.19133317e-01 -2.95895189e-01 -7.17500985e-01 -7.67041624e-01
2.13141993e-01 -1.07999325e+00 -4.40258086e-02 2.51920253e-01
-7.16704667e-01 2.86270231e-01 1.52583563e+00 -5.93704641e-01
9.88068879e-01 -1.93014956e+00 -3.55827838e-01 2.49210015e-01
4.54637825e-01 4.31906432e-01 -2.14055449e-01 1.78453356e-01
2.11039841e-01 -4.27899182e-01 -8.13737452e-01 -3.66096318e-01
-3.16900522e-01 5.38004994e-01 -6.26090348e-01 4.75040525e-01
-6.98490888e-02 1.17387497e+00 -7.93344021e-01 -5.30043721e-01
4.72354442e-01 4.19838011e-01 -1.13623887e-01 4.42656785e-01
-1.86374485e-01 1.08503588e-01 -4.34944510e-01 1.07077003e+00
1.26353049e+00 -1.39993444e-01 6.56898469e-02 -5.16194642e-01
-2.28850499e-01 -1.15582563e-01 -9.10013199e-01 1.19815195e+00
-4.67787683e-01 8.27811539e-01 3.47346842e-01 -3.69265407e-01
8.12665105e-01 -3.00314575e-01 4.11791205e-01 -8.78984809e-01
-5.45715727e-02 -4.42926660e-02 -1.48361683e-01 -7.13507682e-02
5.74202180e-01 1.94032922e-01 1.91321626e-01 6.44128621e-01
-6.77910149e-01 -1.76081702e-01 -4.54650968e-01 3.62912536e-01
1.38534653e+00 1.79196313e-01 4.70232219e-02 -2.58925766e-01
5.00636220e-01 -1.62434310e-01 7.41582036e-01 8.33733022e-01
-6.70527443e-02 8.69701505e-01 7.22750425e-02 -3.97588104e-01
-6.15802586e-01 -1.34120464e+00 -7.59834610e-03 1.16640496e+00
8.98966432e-01 -1.63829669e-01 -6.47065163e-01 -9.07600880e-01
2.02204123e-01 5.86919963e-01 -7.19329774e-01 -1.49723992e-01
-8.38632703e-01 -6.80668175e-01 2.52289683e-01 4.31601346e-01
9.40545976e-01 -1.41492975e+00 -1.20012546e+00 -9.66052338e-02
-9.10269916e-02 -1.05935836e+00 -7.34978020e-01 3.29908758e-01
-4.43260938e-01 -1.24951851e+00 -4.25418109e-01 -5.57491779e-01
8.40233088e-01 1.15522766e+00 9.72806215e-01 7.14127481e-01
-7.03599155e-01 3.45293015e-01 -4.39956814e-01 -5.95415771e-01
-1.83643967e-01 -3.02365035e-01 -2.28951529e-01 2.20411673e-01
-3.38598102e-01 -6.51124001e-01 -1.05215275e+00 4.30093527e-01
-1.23756015e+00 5.19244194e-01 1.07446468e+00 9.52137291e-01
3.81339014e-01 2.66460627e-01 1.43225551e-01 -1.16421211e+00
2.29001641e-01 4.97865230e-02 -6.93518758e-01 3.62140119e-01
-1.02544272e+00 -1.40793219e-01 6.03250384e-01 -1.66267306e-01
-1.64816952e+00 2.84127384e-01 4.60249275e-01 -4.04543966e-01
-9.35653225e-03 -1.37570336e-01 -5.90123892e-01 -6.40413105e-01
3.58199894e-01 6.09561384e-01 -1.79798499e-01 -2.82961875e-01
2.88281828e-01 3.61801565e-01 6.54019117e-01 -3.43404591e-01
1.42486274e+00 1.13847530e+00 7.20649511e-02 -9.05748904e-01
-1.08529007e+00 -3.91897708e-01 -6.79571331e-01 -2.68006712e-01
5.25553524e-01 -6.18452787e-01 -6.02454722e-01 8.53232980e-01
-1.07919014e+00 -9.19871151e-01 1.58132445e-02 -3.22099954e-01
-2.13901982e-01 6.25483215e-01 -3.51028293e-01 -7.52390206e-01
-4.91356701e-01 -9.76477861e-01 1.31692648e+00 4.66667473e-01
5.18214703e-01 -7.05181301e-01 -3.38582098e-01 3.11926365e-01
4.54407513e-01 3.41898322e-01 8.58327985e-01 1.45279095e-01
-1.38307142e+00 5.07510781e-01 -9.38694298e-01 4.30409223e-01
4.96566951e-01 -3.82677823e-01 -1.22640646e+00 -3.20777774e-01
-9.76243580e-04 -3.96902747e-02 1.30540037e+00 2.68125474e-01
1.24888062e+00 -2.19592899e-01 -4.86056983e-01 9.73995447e-01
1.54253519e+00 1.00892626e-01 8.69517624e-01 4.50628102e-01
1.26703846e+00 4.68603849e-01 8.24166000e-01 2.70634562e-01
2.89399981e-01 6.35762513e-01 5.74950159e-01 -9.14345145e-01
-8.90067399e-01 1.70892030e-02 3.98825824e-01 3.57954860e-01
1.31020680e-01 -2.44457975e-01 -5.65551519e-01 2.83217520e-01
-1.76847839e+00 -6.05636537e-01 -2.82897145e-01 1.89974988e+00
7.27957249e-01 2.17600202e-04 -5.39824188e-01 -2.76633352e-01
3.94415706e-01 6.46193326e-01 -9.27790403e-01 3.21699027e-03
-5.75865805e-01 2.73626179e-01 1.01620936e+00 6.02754712e-01
-9.88613963e-01 1.46329308e+00 5.44290733e+00 8.25481236e-01
-1.10824132e+00 2.80789062e-02 3.46902370e-01 2.77254641e-01
-5.19305706e-01 3.49329650e-01 -4.85916823e-01 3.94122690e-01
-7.43578300e-02 2.75573254e-01 6.55917108e-01 6.45614445e-01
1.17558137e-01 -7.28295922e-01 -5.55495322e-01 8.93568754e-01
3.12011003e-01 -1.30078745e+00 -1.63243756e-01 1.94714457e-01
9.64002132e-01 -3.60614285e-02 -1.41309563e-03 -9.61397663e-02
2.81802624e-01 -9.15980875e-01 6.28350198e-01 5.14182806e-01
8.54553401e-01 -5.73329687e-01 6.34177983e-01 2.44783703e-02
-1.57813346e+00 -1.01169288e-01 -3.65357608e-01 2.94928223e-01
6.71570972e-02 9.19877768e-01 -9.77897882e-01 6.95081413e-01
8.34346652e-01 4.71337914e-01 -8.38436842e-01 6.43479943e-01
-4.47667837e-01 5.12220025e-01 -1.97721288e-01 3.61689508e-01
-4.03445326e-02 -2.87909955e-01 5.53002179e-01 1.23153102e+00
-1.52111664e-01 2.06937343e-01 3.63562405e-01 9.41433787e-01
-4.29788902e-02 -4.43595529e-01 -4.91830617e-01 5.63847125e-01
5.24617434e-01 1.48488724e+00 -1.26263511e+00 -2.42999345e-01
-3.95924717e-01 1.78848410e+00 8.25366527e-02 6.98121965e-01
-8.92135203e-01 -4.33435470e-01 5.44929564e-01 8.49205405e-02
3.90336305e-01 -2.06032962e-01 -5.10186911e-01 -8.40947986e-01
1.72158688e-01 -7.48017728e-01 -6.10220693e-02 -9.88501728e-01
-1.00188005e+00 5.06413400e-01 -1.43880904e-01 -8.91351163e-01
4.57629025e-01 -4.23348367e-01 -8.19175065e-01 7.47704804e-01
-2.13155675e+00 -1.65644121e+00 -1.01428449e+00 8.64349008e-01
6.23164415e-01 1.13203466e-01 4.66237634e-01 -1.35698348e-01
-3.54687572e-01 4.67997551e-01 -2.53973179e-03 -1.68862697e-02
8.21750283e-01 -1.23930442e+00 3.87034237e-01 1.05623090e+00
-1.56500652e-01 4.33188707e-01 5.37828147e-01 -1.03416348e+00
-1.67331755e+00 -1.30448186e+00 1.18743271e-01 -3.76015455e-01
2.30954438e-01 -4.91890073e-01 -1.11090493e+00 2.91127533e-01
7.77040794e-02 1.58617765e-01 4.50994708e-02 -3.31665903e-01
-5.66421270e-01 -4.75780785e-01 -9.60544169e-01 6.83867276e-01
1.26879680e+00 -5.84459543e-01 -3.67993027e-01 3.22108567e-01
8.05479169e-01 -4.75314856e-01 -1.31677538e-01 7.02711344e-01
6.96494222e-01 -1.49426520e+00 1.16857803e+00 3.33498955e-01
1.48205251e-01 -6.61683261e-01 -1.74410120e-01 -1.05976284e+00
-1.53807625e-01 -7.36998975e-01 -2.99597651e-01 1.21956384e+00
-2.07482316e-02 -8.69713068e-01 9.57088232e-01 4.09467012e-01
-5.11768043e-01 -8.93081427e-01 -7.22193301e-01 -7.61455774e-01
-6.29099965e-01 -5.34160137e-02 5.80870211e-01 5.83327472e-01
-1.11334658e+00 7.32946545e-02 -4.88175243e-01 5.81976831e-01
9.44641232e-01 8.69309306e-01 1.15582740e+00 -1.14710414e+00
-2.20180213e-01 -1.79129511e-01 1.87953040e-01 -1.03186846e+00
2.42381826e-01 -5.26159346e-01 6.96665585e-01 -1.58534873e+00
5.47790706e-01 -7.95370221e-01 -3.09516698e-01 7.39691198e-01
-4.80380774e-01 5.66089928e-01 2.56177247e-01 3.33911657e-01
-5.17343998e-01 7.81283557e-01 1.40780330e+00 -4.03006040e-02
-3.91718835e-01 -9.63713974e-02 -5.60955465e-01 8.35235238e-01
5.67855000e-01 -4.27489072e-01 -4.83172506e-01 -9.53989103e-02
-4.97810423e-01 -4.60333228e-01 8.10679257e-01 -8.48478973e-01
-4.45581554e-03 -5.19067168e-01 6.24577165e-01 -9.33889866e-01
5.89136720e-01 -8.31761956e-01 -1.21711150e-01 5.54601669e-01
3.95334423e-01 -4.47054952e-01 9.07527804e-02 7.60534942e-01
2.25024655e-01 2.63443261e-01 8.50919425e-01 -3.62530053e-02
-9.64713275e-01 3.53221864e-01 -1.77195460e-01 -4.09022272e-01
1.00232053e+00 -4.78162020e-01 -6.76731646e-01 -2.38458097e-01
2.22901985e-01 -9.29310510e-04 9.35500264e-01 2.87859678e-01
1.06755722e+00 -1.00793576e+00 -3.56339484e-01 4.16601539e-01
6.93482235e-02 1.85421199e-01 2.99861401e-01 7.14263022e-01
-5.35263419e-01 2.20232204e-01 -5.80107756e-02 -4.68722433e-01
-1.60267794e+00 4.05874491e-01 1.62307233e-01 4.41361815e-02
-1.09151483e+00 8.47427249e-01 1.26104510e+00 -2.05552399e-01
1.46745145e-01 -2.07723737e-01 4.17570859e-01 -4.09100115e-01
4.05813426e-01 2.27561980e-01 8.11141133e-02 -5.29813349e-01
-3.86320710e-01 5.46564996e-01 -1.01039693e-01 8.60679224e-02
1.18958390e+00 -3.44559044e-01 -3.90836298e-01 1.78400099e-01
9.70535994e-01 4.16119397e-01 -1.83324265e+00 -3.20676655e-01
-2.93516487e-01 -8.40814352e-01 3.07196379e-01 -1.01657903e+00
-1.30566418e+00 5.10367751e-01 7.42978632e-01 -1.72878563e-01
1.57523596e+00 -4.36287038e-02 1.30121124e+00 4.63041514e-01
3.94909501e-01 -1.05090213e+00 4.80360836e-01 4.42411005e-01
9.57925797e-01 -1.15268850e+00 4.05890435e-01 -7.90792167e-01
-5.16369045e-01 1.01642108e+00 7.94059455e-01 -4.91285957e-02
5.74609876e-01 6.08112395e-01 2.43178353e-01 -3.48712474e-01
-3.66239488e-01 -4.74820167e-01 3.99497718e-01 9.00851548e-01
-3.16503614e-01 4.58586328e-02 1.71540007e-01 2.33618468e-01
1.10698640e-01 -3.88749093e-01 5.49887359e-01 1.15280521e+00
-7.13085532e-01 -1.09442675e+00 -6.99712694e-01 4.30029362e-01
2.55528778e-01 -4.22411799e-01 -8.29479337e-01 6.78433478e-01
2.90770918e-01 9.79061544e-01 -3.09376657e-01 -4.86272037e-01
7.37061948e-02 -4.79090154e-01 4.03688848e-01 -5.71794808e-01
-2.50739217e-01 8.73293951e-02 -2.15355381e-01 -1.03038871e+00
-2.18909398e-01 -4.58647519e-01 -1.44004548e+00 -2.03949362e-01
-4.42964762e-01 -4.38626349e-01 6.60447180e-01 9.19682562e-01
2.97072798e-01 6.76667631e-01 8.54598463e-01 -1.10052550e+00
9.19931009e-02 -5.44040978e-01 -6.58738911e-01 1.29599810e-01
6.33403301e-01 -9.13664877e-01 -4.34848726e-01 9.34366286e-02] | [10.846511840820312, -4.101534366607666] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.