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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ae1893c0-3787-4b35-984d-8b681b49236f | sequential-local-learning-for-latent | 1703.04082 | null | http://arxiv.org/abs/1703.04082v2 | http://arxiv.org/pdf/1703.04082v2.pdf | Sequential Local Learning for Latent Graphical Models | Learning parameters of latent graphical models (GM) is inherently much harder
than that of no-latent ones since the latent variables make the corresponding
log-likelihood non-concave. Nevertheless, expectation-maximization schemes are
popularly used in practice, but they are typically stuck in local optima. In
the recent years, the method of moments have provided a refreshing angle for
resolving the non-convex issue, but it is applicable to a quite limited class
of latent GMs. In this paper, we aim for enhancing its power via enlarging such
a class of latent GMs. To this end, we introduce two novel concepts, coined
marginalization and conditioning, which can reduce the problem of learning a
larger GM to that of a smaller one. More importantly, they lead to a sequential
learning framework that repeatedly increases the learning portion of given
latent GM, and thus covers a significantly broader and more complicated class
of loopy latent GMs which include convolutional and random regular models. | ['Sejun Park', 'Eunho Yang', 'Jinwoo Shin'] | 2017-03-12 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 1.73684031e-01 2.85867691e-01 -3.51872742e-01 -2.19481558e-01
-8.12169969e-01 -3.50668997e-01 5.63534021e-01 -2.20496237e-01
-2.06145421e-01 7.35459983e-01 5.67432791e-02 -4.11384016e-01
-4.31235209e-02 -7.99792886e-01 -7.02174485e-01 -1.11378145e+00
-1.00821711e-01 4.22511607e-01 3.12946066e-02 2.57501364e-01
3.35009582e-02 3.94563228e-01 -1.23522222e+00 -1.09771214e-01
1.04746091e+00 5.19093454e-01 3.02712202e-01 3.07785720e-01
-1.76213291e-02 6.93282843e-01 -8.35804269e-02 -3.31872374e-01
-1.59825101e-01 -3.96132022e-01 -4.89702493e-01 2.83514529e-01
2.82682896e-01 -1.98610857e-01 -1.16643682e-01 1.09046566e+00
2.77828068e-01 -1.90981515e-02 8.76041234e-01 -1.44048762e+00
-4.56342101e-01 7.28800595e-01 -8.09081972e-01 -3.42473716e-01
-1.06628284e-01 -2.26563677e-01 1.13100743e+00 -7.96878695e-01
2.65331209e-01 1.40627849e+00 7.48862028e-01 3.14659595e-01
-1.38439643e+00 -6.19529724e-01 5.75971663e-01 -1.28504574e-01
-1.44692516e+00 -2.77259856e-01 6.36443913e-01 -4.92020160e-01
5.53862691e-01 -2.77515370e-02 4.89262640e-01 1.19519866e+00
1.10317640e-01 1.08110392e+00 1.19476736e+00 -4.72527951e-01
2.50032812e-01 9.24394876e-02 -1.48435846e-01 8.86055291e-01
2.22187117e-01 -1.64499968e-01 -2.99028724e-01 -3.56063843e-01
9.78655338e-01 1.47880569e-01 -2.27790982e-01 -8.31459522e-01
-1.02702677e+00 1.17176545e+00 5.62268086e-02 3.50732237e-01
-1.03388652e-01 3.75820100e-01 -6.61620423e-02 1.04690067e-01
6.22432649e-01 -9.48067456e-02 -2.80081779e-01 -6.13792539e-02
-1.29744244e+00 2.37880185e-01 9.48317170e-01 8.50236595e-01
1.07105243e+00 9.90235060e-03 1.10597722e-02 5.84747672e-01
7.62049973e-01 3.29664052e-01 1.50018215e-01 -5.65145254e-01
3.94452631e-01 5.38010418e-01 1.80622816e-01 -9.73730564e-01
-4.80805069e-01 -6.99077010e-01 -1.14861429e+00 8.23083371e-02
6.51764393e-01 -2.27456450e-01 -9.26051438e-01 2.19636011e+00
3.94812971e-01 2.19275206e-01 -2.10293725e-01 5.11976123e-01
1.51161388e-01 7.19622850e-01 -4.77704080e-03 -4.84393209e-01
8.95816624e-01 -1.10560811e+00 -8.04454029e-01 -4.87645030e-01
5.28335869e-01 -5.74613631e-01 9.55320716e-01 4.54695582e-01
-1.06580174e+00 -3.62701029e-01 -9.44806397e-01 -5.11354841e-02
-3.18919331e-01 1.77860171e-01 1.05787408e+00 7.38414764e-01
-1.30928171e+00 6.83546066e-01 -1.27611375e+00 -1.19682193e-01
1.58762112e-01 4.00902599e-01 -1.00022011e-01 -6.76549450e-02
-1.20214510e+00 7.45990813e-01 4.27434802e-01 3.92577201e-01
-1.03057587e+00 -4.65251863e-01 -8.48997176e-01 9.55729708e-02
6.99075520e-01 -5.63861012e-01 9.94080842e-01 -7.63988495e-01
-1.61318195e+00 8.18958282e-01 -2.25757465e-01 -1.15091644e-01
8.47072721e-01 -3.65376115e-01 -1.36588931e-01 -3.73316795e-01
3.82762179e-02 4.44825262e-01 1.32052565e+00 -1.09074485e+00
-2.82166153e-01 -2.98531085e-01 1.18843146e-01 6.51040897e-02
-3.87011528e-01 -2.01799050e-01 -6.49586201e-01 -5.79412520e-01
3.87106180e-01 -9.63195801e-01 -4.92906064e-01 -2.09006011e-01
-4.64409143e-01 -3.35897058e-01 4.78327006e-01 -4.27081347e-01
1.37879384e+00 -2.02708077e+00 5.01230478e-01 1.86544076e-01
4.05053735e-01 1.36052296e-02 4.07823734e-02 7.09391177e-01
-2.79644001e-02 9.10136253e-02 -4.28742379e-01 -7.97575772e-01
2.41089135e-01 2.75840670e-01 -3.47449899e-01 5.99645793e-01
1.53063074e-01 1.10125852e+00 -8.29895794e-01 -5.14747918e-01
7.50936642e-02 5.95134735e-01 -6.13969922e-01 9.64468196e-02
-3.53221208e-01 5.72265089e-01 -5.08929193e-01 5.35660803e-01
7.55414605e-01 -6.34419024e-01 2.84140795e-01 1.65093869e-01
-3.04479569e-01 3.13493796e-03 -1.51131260e+00 1.91359913e+00
-2.74460226e-01 3.25859725e-01 8.16043392e-02 -1.01530027e+00
7.66574919e-01 2.12343201e-01 5.28295696e-01 -7.88628682e-02
-7.46529996e-02 2.56508797e-01 -4.67214823e-01 -2.79749662e-01
3.47518563e-01 -3.63641888e-01 -5.00292704e-03 6.03025258e-01
1.05169907e-01 1.22580104e-01 2.10665047e-01 1.98025897e-01
6.22710943e-01 4.52478558e-01 4.00753379e-01 -1.58285990e-01
3.36785555e-01 -6.32914186e-01 5.10205269e-01 1.01526570e+00
1.83704108e-01 5.19942164e-01 7.77273476e-01 5.25024012e-02
-9.20341432e-01 -1.24043751e+00 -3.00432324e-01 9.50477242e-01
-9.89019349e-02 -4.85895306e-01 -7.71777213e-01 -5.83223760e-01
-1.92473546e-01 4.25603271e-01 -5.47784865e-01 -1.66939825e-01
-5.28339386e-01 -1.19094443e+00 4.71287996e-01 4.29073423e-01
3.72104168e-01 -7.84207284e-01 -2.23392889e-01 2.42974028e-01
-2.12036133e-01 -8.65232646e-01 -2.85821199e-01 4.45458710e-01
-1.10291612e+00 -6.82831764e-01 -1.07660747e+00 -5.84255219e-01
7.66188741e-01 -2.63265916e-03 1.05831301e+00 -2.44549438e-01
1.01916708e-01 1.37459144e-01 -1.22510552e-01 -1.08997285e-01
-3.04943115e-01 3.46170992e-01 -1.03240982e-02 3.17579150e-01
2.23013401e-01 -9.26122963e-01 -3.12072366e-01 1.73813969e-01
-1.18143928e+00 3.40322435e-01 8.99716437e-01 7.17541218e-01
5.51528692e-01 1.71084240e-01 5.24693072e-01 -9.13574398e-01
4.54140186e-01 -6.15973711e-01 -8.50668609e-01 4.34799194e-01
-7.03819752e-01 3.15020263e-01 4.85439271e-01 -6.25580668e-01
-1.09893167e+00 1.08796358e-01 -3.61390896e-02 -1.91386610e-01
6.65042475e-02 9.73073602e-01 -4.78332043e-01 9.82402191e-02
3.12491417e-01 1.11700229e-01 -2.25705534e-01 -7.51261890e-01
6.33164108e-01 3.15994680e-01 2.49172121e-01 -4.65995103e-01
1.00227892e+00 5.79955220e-01 1.92721784e-01 -5.40319204e-01
-8.82891655e-01 -4.08428162e-01 -7.45661259e-01 8.21617618e-02
6.71675265e-01 -9.33601022e-01 -4.29164976e-01 6.48405552e-01
-9.41104114e-01 -4.64599758e-01 -1.43100530e-01 4.81459558e-01
-5.96245766e-01 6.60016239e-01 -4.75863218e-01 -9.37396526e-01
2.04911366e-01 -1.03443027e+00 1.07134295e+00 1.61135077e-01
5.67732304e-02 -1.41241205e+00 4.19743806e-01 -3.05098128e-02
2.89513886e-01 1.67355493e-01 1.02081192e+00 -2.13928759e-01
-7.05849588e-01 -2.50568748e-01 -1.47031814e-01 2.55322188e-01
1.74594089e-01 -6.99007064e-02 -1.00675881e+00 -6.26445413e-01
2.61767328e-01 -2.97891617e-01 1.15730369e+00 5.56247830e-01
8.45116854e-01 -2.08745584e-01 -5.08650005e-01 7.39384770e-01
1.43717468e+00 -1.60755724e-01 6.33333802e-01 3.28689069e-02
7.29826629e-01 4.59959239e-01 3.06188047e-01 4.70966011e-01
3.23917091e-01 6.46402597e-01 4.94129717e-01 -3.11245561e-01
3.71520996e-01 -4.44326967e-01 5.27741849e-01 9.43485618e-01
-1.07552215e-01 -4.03730094e-01 -7.83544421e-01 4.06230211e-01
-2.21273875e+00 -6.58273280e-01 -9.50360969e-02 2.25890470e+00
1.06880593e+00 -4.60675992e-02 -1.20153554e-01 -1.65254489e-01
6.24077201e-01 3.25138301e-01 -5.52124858e-01 2.64032662e-01
-1.43339530e-01 -9.22457427e-02 3.84010285e-01 6.42822623e-01
-1.21318924e+00 1.02023208e+00 7.01995277e+00 1.12116504e+00
-8.99922550e-01 1.12769499e-01 3.91383588e-01 1.71535671e-01
-4.35389340e-01 3.50408256e-01 -1.15839624e+00 3.37659925e-01
8.37876379e-01 1.75251916e-01 3.94791901e-01 9.43623364e-01
2.22637251e-01 -2.37126827e-01 -1.19198442e+00 8.66616249e-01
-9.49800387e-02 -8.75485957e-01 -9.95613113e-02 3.73475373e-01
1.09438121e+00 -5.20580597e-02 3.15459460e-01 4.11120176e-01
3.49408120e-01 -1.26340973e+00 4.81368542e-01 6.52935505e-01
6.32018685e-01 -6.95393205e-01 4.85476315e-01 7.56259203e-01
-1.08298624e+00 1.29155189e-01 -6.80441916e-01 -2.86275558e-02
4.39365596e-01 8.72408807e-01 -4.03695583e-01 5.09377360e-01
2.51496494e-01 6.62043989e-01 -5.44568062e-01 1.00487554e+00
-5.08997977e-01 5.33802450e-01 -3.82707655e-01 2.03437611e-01
3.85781527e-01 -4.73319978e-01 3.54253113e-01 1.09236264e+00
3.02754790e-01 -4.52269822e-01 2.06666604e-01 1.17009509e+00
8.25201906e-03 6.70635253e-02 -4.32125509e-01 -3.76431584e-01
1.05860969e-02 1.33123279e+00 -9.21025872e-01 -2.78018236e-01
-5.76763630e-01 8.97097230e-01 5.70330262e-01 6.58622801e-01
-1.04626191e+00 5.06297350e-02 2.92587489e-01 6.15243278e-02
3.07816505e-01 -4.89639133e-01 3.52943912e-02 -1.60823858e+00
4.88300622e-03 -6.95381641e-01 2.44845673e-01 -4.59253639e-01
-1.27564836e+00 1.70137420e-01 3.14518929e-01 -9.87228036e-01
-5.07455528e-01 -5.56176007e-01 -2.69288510e-01 9.86455441e-01
-1.77258265e+00 -1.24750566e+00 -1.53480008e-01 6.37285948e-01
2.36462563e-01 1.66917488e-01 9.07091141e-01 3.33173156e-01
-3.51935923e-01 4.98965293e-01 5.83706021e-01 -2.31107175e-01
5.89320719e-01 -1.36970949e+00 3.48533005e-01 9.28386569e-01
2.61726558e-01 8.70778024e-01 4.86738443e-01 -7.33390510e-01
-1.18591511e+00 -9.33607996e-01 1.01119053e+00 -2.88479179e-01
8.25909913e-01 -6.05954766e-01 -8.86331320e-01 1.01765072e+00
-1.73153102e-01 -3.60359669e-01 4.68860388e-01 3.32757741e-01
-2.23846614e-01 3.34937632e-01 -6.42297864e-01 7.96113670e-01
8.67946088e-01 -6.84583664e-01 -1.70107305e-01 3.75844896e-01
5.56991816e-01 -1.73453122e-01 -6.98046148e-01 4.95972633e-01
4.69951272e-01 -9.04693425e-01 9.48135853e-01 -4.55977708e-01
1.74810633e-01 -2.36142620e-01 -2.35267170e-02 -1.01036346e+00
-2.52229959e-01 -9.34635699e-01 -4.19701099e-01 1.25883937e+00
2.68969804e-01 -6.82375848e-01 9.81208205e-01 4.08962578e-01
1.80925615e-02 -7.83711255e-01 -8.54879320e-01 -6.88270986e-01
1.96989954e-01 -5.02380371e-01 2.37397999e-01 9.89636600e-01
-1.89098269e-01 1.87777802e-01 -8.35750997e-01 1.23786420e-01
8.35379899e-01 2.38688394e-01 6.91383004e-01 -1.21374202e+00
-6.81010306e-01 -3.57036740e-01 -7.79279396e-02 -1.67163861e+00
4.20214012e-02 -9.42495346e-01 8.67565274e-02 -1.39838564e+00
4.73105043e-01 -4.67742831e-01 -3.13811451e-01 4.90010500e-01
-3.02882135e-01 1.83502272e-01 -1.98300004e-01 3.79137456e-01
-5.76409519e-01 5.39851069e-01 1.23981488e+00 2.11191811e-02
-2.47383043e-01 4.72191215e-01 -5.54328203e-01 1.00038421e+00
6.01516664e-01 -5.96186161e-01 -5.76533139e-01 -2.64451981e-01
8.44684243e-01 5.58899753e-02 4.69035655e-01 -6.60050690e-01
2.35348642e-01 -1.08017847e-01 1.24039702e-01 -7.65102148e-01
3.06666613e-01 -5.51193297e-01 3.56692851e-01 1.27355471e-01
-2.78058380e-01 -2.20594063e-01 -1.51326701e-01 7.46690154e-01
-2.12579772e-01 -4.84793574e-01 6.83661282e-01 -3.37402314e-01
-3.67098898e-01 4.83845323e-01 -4.38861758e-01 1.14624631e-02
6.61505699e-01 -1.28200725e-01 1.77329749e-01 -5.18378317e-01
-1.02008200e+00 5.95146604e-02 4.63080883e-01 6.62604123e-02
2.75533348e-01 -1.28121805e+00 -4.64500248e-01 1.96778402e-01
-1.65595815e-01 1.61989182e-01 2.39790067e-01 1.26646984e+00
-1.28908694e-01 4.81554955e-01 6.89817891e-02 -6.59035444e-01
-8.51819336e-01 5.72703779e-01 1.67907670e-01 -8.36399615e-01
-6.03855133e-01 8.03996623e-01 6.53425515e-01 -4.73173797e-01
5.18550336e-01 -3.87193233e-01 -2.49511138e-01 3.44866961e-01
3.28778893e-01 4.02026892e-01 -2.74091423e-01 -4.36129332e-01
-1.40496120e-02 5.39863825e-01 -6.60575181e-02 -2.03149319e-01
1.32523406e+00 -3.90417337e-01 -3.83841068e-01 8.24120760e-01
1.21586049e+00 1.09230295e-01 -1.69735551e+00 -4.44249272e-01
2.66366005e-01 -2.39176080e-01 -1.78400800e-01 -4.04089302e-01
-8.34286153e-01 1.11603153e+00 3.93408865e-01 4.48267043e-01
9.95985925e-01 9.64231715e-02 3.60540330e-01 2.47282773e-01
2.84735173e-01 -8.94913077e-01 6.86821267e-02 5.85265040e-01
4.91269171e-01 -1.04090416e+00 1.33713141e-01 -2.47190371e-01
-2.29309723e-01 1.11482430e+00 1.22549921e-01 1.00575015e-01
6.04558468e-01 7.93834031e-02 -2.65658706e-01 -1.36039525e-01
-4.75262940e-01 -1.80766284e-01 2.04290673e-01 3.62058342e-01
4.72973436e-01 4.18373160e-02 -3.33917826e-01 4.68961775e-01
-8.78499821e-02 4.03058864e-02 2.29534611e-01 7.43842721e-01
-3.76740694e-01 -1.36367190e+00 -2.28524759e-01 6.58373460e-02
-4.61449236e-01 -3.00803900e-01 3.88778374e-02 7.92556584e-01
9.85719338e-02 7.48020053e-01 -3.17696363e-01 8.05578008e-02
-3.17036152e-01 2.09636688e-01 4.72140312e-01 -5.72316110e-01
1.06137814e-02 5.50029576e-01 -3.55631471e-01 -4.93679136e-01
-5.73728144e-01 -6.83739424e-01 -8.92407119e-01 -2.13587031e-01
-6.48956776e-01 1.52616650e-01 7.08717465e-01 1.16445458e+00
-1.62784979e-01 3.39013398e-01 4.77134436e-01 -9.28914487e-01
-7.59337246e-01 -1.05638754e+00 -7.74541557e-01 -3.66595797e-02
2.49750316e-01 -7.37130702e-01 -4.65709716e-01 -7.24143237e-02] | [7.159030914306641, 3.964144229888916] |
cc265cf1-63dc-432c-acdc-d9bf85996223 | learning-representations-of-bi-level | 2302.02601 | null | https://arxiv.org/abs/2302.02601v3 | https://arxiv.org/pdf/2302.02601v3.pdf | Learning Representations of Bi-level Knowledge Graphs for Reasoning beyond Link Prediction | Knowledge graphs represent known facts using triplets. While existing knowledge graph embedding methods only consider the connections between entities, we propose considering the relationships between triplets. For example, let us consider two triplets $T_1$ and $T_2$ where $T_1$ is (Academy_Awards, Nominates, Avatar) and $T_2$ is (Avatar, Wins, Academy_Awards). Given these two base-level triplets, we see that $T_1$ is a prerequisite for $T_2$. In this paper, we define a higher-level triplet to represent a relationship between triplets, e.g., $\langle T_1$, PrerequisiteFor, $T_2\rangle$ where PrerequisiteFor is a higher-level relation. We define a bi-level knowledge graph that consists of the base-level and the higher-level triplets. We also propose a data augmentation strategy based on the random walks on the bi-level knowledge graph to augment plausible triplets. Our model called BiVE learns embeddings by taking into account the structures of the base-level and the higher-level triplets, with additional consideration of the augmented triplets. We propose two new tasks: triplet prediction and conditional link prediction. Given a triplet $T_1$ and a higher-level relation, the triplet prediction predicts a triplet that is likely to be connected to $T_1$ by the higher-level relation, e.g., $\langle T_1$, PrerequisiteFor, ?$\rangle$. The conditional link prediction predicts a missing entity in a triplet conditioned on another triplet, e.g., $\langle T_1$, PrerequisiteFor, (Avatar, Wins, ?)$\rangle$. Experimental results show that BiVE significantly outperforms all other methods in the two new tasks and the typical base-level link prediction in real-world bi-level knowledge graphs. | ['Joyce Jiyoung Whang', 'Chanyoung Chung'] | 2023-02-06 | null | null | null | null | ['knowledge-graph-embedding'] | ['graphs'] | [-2.87644833e-01 5.00832915e-01 -3.92342925e-01 -3.32445920e-01
-3.77704352e-01 -4.45982635e-01 3.45020592e-01 5.50128341e-01
-1.22671001e-01 1.00250256e+00 -1.59760807e-02 -5.28083205e-01
-6.93050086e-01 -1.48512101e+00 -1.13297009e+00 -1.99402645e-01
-4.85479146e-01 6.77703917e-01 2.58500844e-01 -3.81476521e-01
-1.52556062e-01 8.28134641e-02 -1.49734032e+00 2.10405178e-02
8.15862179e-01 1.15023947e+00 -2.34804511e-01 7.23837242e-02
-3.76410156e-01 4.24449593e-01 -4.10172939e-01 -1.03876102e+00
2.30312288e-01 -2.65713155e-01 -9.46234107e-01 -4.19564903e-01
5.74655950e-01 3.82216647e-02 -2.78012574e-01 1.06115329e+00
-7.73528218e-02 2.98829556e-01 6.52069032e-01 -1.81414163e+00
-8.29684794e-01 9.80786145e-01 -6.31991386e-01 6.36665598e-02
5.75421214e-01 -2.07985014e-01 1.72582686e+00 -9.25352097e-01
7.21995890e-01 1.17567086e+00 6.74699605e-01 1.10276587e-01
-8.94242644e-01 -1.03531587e+00 5.85525870e-01 6.04060590e-01
-1.57204127e+00 2.73640335e-01 8.03178370e-01 -4.69473928e-01
8.09075892e-01 3.90080884e-02 7.74501204e-01 6.87230349e-01
5.10279201e-02 4.58894461e-01 8.85727108e-01 -3.05252910e-01
1.18700536e-02 7.88467824e-02 6.86233938e-01 1.02655423e+00
5.79225242e-01 2.89194137e-02 -5.78165650e-01 -1.68120429e-01
7.36482978e-01 -2.34902665e-01 -2.47608736e-01 -2.09686518e-01
-1.16822469e+00 8.29818964e-01 7.90053248e-01 4.10677791e-01
-3.75290692e-01 1.44847780e-01 -6.11542650e-02 1.61530644e-01
-6.79032132e-02 2.59911120e-01 -6.72565997e-01 1.80034995e-01
-5.08762002e-01 8.34917501e-02 6.84848607e-01 1.36406982e+00
1.48266482e+00 -4.16667946e-02 1.23418838e-01 4.53588903e-01
4.29079473e-01 -4.41886187e-02 -6.19931240e-03 -6.52972817e-01
6.02702439e-01 1.08986247e+00 2.60429293e-01 -1.18862224e+00
-4.18811649e-01 -3.81302804e-01 -7.81835973e-01 -1.97407212e-02
3.77056241e-01 -4.07376513e-03 -9.65763509e-01 2.14855051e+00
3.97695690e-01 7.19437659e-01 1.02900870e-01 6.19209588e-01
1.28911221e+00 6.32862151e-01 1.27456918e-01 -3.07893902e-01
1.54405379e+00 -8.61364126e-01 -5.14660060e-01 -1.22149751e-01
6.13716841e-01 -5.12890995e-01 1.01103747e+00 -6.52010441e-02
-8.69403064e-01 -5.72404563e-01 -9.37762260e-01 1.21509194e-01
-1.00307429e+00 -6.67146668e-02 8.16600263e-01 5.76585293e-01
-8.88653874e-01 4.29459602e-01 -4.06771123e-01 -3.90280634e-01
-1.18647702e-03 5.13372302e-01 -7.64309168e-01 -2.95643210e-01
-1.95012140e+00 9.95602727e-01 6.70829177e-01 7.00840801e-02
-4.10588861e-01 -8.38056445e-01 -1.20481157e+00 2.53507257e-01
7.51939297e-01 -7.82387495e-01 3.35989326e-01 -2.86421180e-01
-6.02664590e-01 8.42448771e-01 -1.12255789e-01 -2.35353410e-01
1.59786701e-01 2.28551090e-01 -9.44591820e-01 -2.92538553e-01
3.91940624e-01 5.20711780e-01 3.68315279e-01 -1.38444173e+00
-7.66101182e-01 -4.55100805e-01 6.69014454e-01 2.35941499e-01
-3.20849538e-01 -2.58029997e-01 -5.23333251e-01 -3.26085418e-01
1.80264950e-01 -6.54731095e-01 1.35860831e-01 -1.84585392e-01
-8.66374195e-01 -6.39289916e-01 8.06890726e-01 -4.38999504e-01
1.50208819e+00 -1.68195307e+00 -3.01646423e-02 6.72142327e-01
3.71618092e-01 3.62417668e-01 -7.03012347e-02 4.76073921e-01
-4.18458521e-01 5.21937072e-01 -9.21539590e-02 5.34083098e-02
1.74526200e-01 5.30888319e-01 1.96222320e-01 -2.23006502e-01
9.81036872e-02 8.31291795e-01 -9.31189716e-01 -4.15017486e-01
2.12778412e-02 3.49606335e-01 -3.47440988e-01 -5.15265167e-02
-2.28415027e-01 6.80462047e-02 -6.52895570e-01 6.39555454e-01
5.89624166e-01 -2.81279713e-01 2.62517661e-01 -8.33339751e-01
-8.57326314e-02 2.92186111e-01 -1.56012702e+00 1.24718809e+00
-2.24039376e-01 -3.65345529e-03 -7.48624727e-02 -1.11066020e+00
1.16007543e+00 1.05230384e-01 4.32391971e-01 -1.93390116e-01
-1.98637489e-02 1.32263646e-01 1.51856989e-01 -3.01487267e-01
4.85402584e-01 -1.81170136e-01 -2.16560036e-01 3.41924638e-01
3.58078815e-02 9.09015387e-02 5.04844368e-01 6.17969692e-01
1.14675689e+00 2.98848361e-01 3.05310458e-01 1.16432421e-02
5.55007219e-01 -2.17377141e-01 8.63953054e-01 5.71749091e-01
-2.35800162e-01 4.82367277e-02 7.75040388e-01 -4.44029421e-01
-6.54148281e-01 -1.15128410e+00 1.59627020e-01 8.75366628e-01
6.14849031e-01 -7.85185575e-01 -1.80857509e-01 -1.06668663e+00
2.23922357e-01 8.41158092e-01 -8.54411662e-01 -2.40103155e-01
-4.95007157e-01 -3.77552688e-01 4.05059755e-01 6.51263535e-01
6.62676275e-01 -9.32168961e-01 5.70832863e-02 1.82486475e-01
-4.78443652e-01 -1.02738142e+00 -4.93024737e-01 -1.01217702e-02
-4.38849062e-01 -1.35103035e+00 4.23137425e-03 -8.12069118e-01
7.61824548e-01 -2.25562662e-01 1.31932092e+00 4.11288440e-01
-1.07258298e-01 3.31745803e-01 -3.39291513e-01 -7.38791302e-02
2.84421086e-01 -2.55077958e-01 2.33250573e-01 9.77969766e-02
4.10535276e-01 -7.12619662e-01 -4.50884312e-01 5.14416635e-01
-8.41381550e-01 -2.05973700e-01 5.36981642e-01 1.01509404e+00
8.74134123e-01 4.27535594e-01 6.96658075e-01 -9.34827685e-01
4.50971484e-01 -5.86660624e-01 -2.49267206e-01 6.44507766e-01
-7.47299969e-01 1.45477476e-03 6.53251827e-01 -4.17736351e-01
-5.84391296e-01 -1.40074596e-01 -1.36102095e-01 -4.90744561e-01
-7.32299611e-02 1.25453830e+00 -5.51602781e-01 2.86235154e-01
5.48115730e-01 -9.26984549e-02 -5.10021091e-01 -2.11441338e-01
5.10038555e-01 1.22394107e-01 5.33881366e-01 -8.57252240e-01
1.13531768e+00 1.37113318e-01 1.21405395e-02 -3.19305062e-01
-7.88832188e-01 -8.20624307e-02 -5.95746338e-01 -1.83723509e-01
6.58331990e-01 -6.47660792e-01 -9.09887195e-01 -3.49978320e-02
-7.86389291e-01 1.36023059e-01 -3.54723960e-01 6.92666769e-01
-1.84007615e-01 2.52069712e-01 -8.35989490e-02 -5.71183383e-01
-2.15272307e-01 -9.24748778e-01 4.20381933e-01 3.64331335e-01
-1.81912720e-01 -1.13056147e+00 -3.27772379e-01 4.84345526e-01
-1.87274441e-02 4.35306907e-01 1.42359555e+00 -8.42195034e-01
-7.93628633e-01 -1.96626902e-01 -4.21554595e-01 1.08332850e-01
3.48985136e-01 1.20201387e-01 -2.13414043e-01 -1.85248494e-01
-8.14610064e-01 -1.62629128e-01 6.58023119e-01 8.98505300e-02
7.56970406e-01 -4.25204486e-01 -6.87627673e-01 2.60839015e-01
1.33063722e+00 2.36055210e-01 6.99989974e-01 3.00110221e-01
7.22352087e-01 4.19738501e-01 6.46052420e-01 8.51443410e-02
9.43176270e-01 6.09984338e-01 5.27325273e-01 1.42835304e-01
-1.14499591e-01 -5.22879660e-01 1.32737264e-01 5.57804525e-01
-4.29083288e-01 -1.16706148e-01 -9.42903757e-01 8.15790296e-01
-1.81778049e+00 -8.42689574e-01 -4.06622261e-01 2.28388906e+00
9.11441386e-01 3.55622381e-01 -5.96541772e-03 -3.32659744e-02
9.55763996e-01 7.85434525e-03 -5.97742975e-01 -2.15806723e-01
-7.69403353e-02 3.88506055e-01 1.23994991e-01 5.87609231e-01
-6.94755673e-01 1.09536231e+00 4.33773184e+00 8.63399744e-01
-5.09470880e-01 -2.04851955e-01 2.73693979e-01 3.89478922e-01
-8.18723202e-01 3.92788798e-01 -1.10585070e+00 5.25873303e-01
4.20317620e-01 -5.82854688e-01 2.33744368e-01 5.37002921e-01
-3.96485090e-01 -9.82294232e-03 -1.36373234e+00 7.35949218e-01
9.09277499e-02 -1.43021357e+00 3.66035700e-01 -1.19994044e-01
6.57063842e-01 -5.59400260e-01 6.63821772e-03 8.85611832e-01
6.52840912e-01 -9.78591681e-01 3.62211734e-01 3.25616568e-01
9.35335398e-01 -8.59015286e-01 7.16570079e-01 2.83189982e-01
-1.93161082e+00 1.88740835e-01 -8.62587318e-02 1.11162134e-01
3.02626729e-01 6.04668319e-01 -4.41322833e-01 1.39989889e+00
8.27701390e-01 6.70323431e-01 -3.18290681e-01 7.76464641e-01
-5.99120259e-01 2.21096754e-01 -3.96913975e-01 1.06799938e-01
1.79428965e-01 -4.83422458e-01 4.39933181e-01 7.27647603e-01
6.02439165e-01 5.22024989e-01 4.06674176e-01 8.36384535e-01
-6.03452504e-01 -2.98302565e-02 -5.39838910e-01 -2.26865839e-02
8.75635743e-01 1.17434204e+00 -3.78588259e-01 -3.88489813e-01
-3.84744912e-01 3.86042953e-01 6.00716949e-01 3.78656387e-01
-1.00522363e+00 -7.07502365e-01 7.55765080e-01 1.93538859e-01
1.79338694e-01 7.49088973e-02 1.85307786e-02 -1.11881733e+00
2.50318915e-01 -3.64029676e-01 9.10702467e-01 -9.16353285e-01
-1.55997968e+00 6.57754183e-01 -1.02665285e-02 -1.03646517e+00
5.60490154e-02 -4.61015433e-01 -7.93184638e-01 9.67697620e-01
-1.61364186e+00 -1.52025759e+00 -2.12708890e-01 1.07419407e+00
-1.43978029e-01 -3.10133002e-03 9.72714543e-01 2.27489442e-01
-5.27775228e-01 9.71535385e-01 -4.04292375e-01 3.64099711e-01
5.06215990e-01 -1.09274781e+00 -6.59032539e-02 6.20321274e-01
3.26273084e-01 9.40065682e-01 5.53574443e-01 -9.06068087e-01
-9.95903432e-01 -1.08044446e+00 1.28270769e+00 -3.22095871e-01
9.49635863e-01 -3.41587998e-02 -1.07287169e+00 1.29981971e+00
2.42336579e-02 1.32592484e-01 8.96783769e-01 5.91191769e-01
-8.28453064e-01 -2.99638897e-01 -1.35688627e+00 5.34594417e-01
1.20343721e+00 -3.65617484e-01 -9.72569227e-01 3.24415535e-01
5.76321602e-01 -3.19224089e-01 -1.41708386e+00 8.07670534e-01
5.71333826e-01 -7.94281065e-01 1.10414159e+00 -7.71655560e-01
3.96371901e-01 -4.55748022e-01 -4.58297804e-02 -1.33655202e+00
-3.71661991e-01 -4.01644796e-01 -2.79005349e-01 1.36094773e+00
8.89156222e-01 -7.64305711e-01 1.00660574e+00 9.33447123e-01
-3.57316256e-01 -8.40928555e-01 -1.22899783e+00 -1.00177538e+00
1.82992890e-01 -1.94668725e-01 8.80030572e-01 1.48818254e+00
3.33845615e-01 2.71471530e-01 -5.18092334e-01 4.85024840e-01
6.61278367e-01 4.78072196e-01 5.47279596e-01 -1.56418681e+00
-2.05265850e-01 -2.71288842e-01 -5.81778467e-01 -9.97229755e-01
2.32740715e-01 -9.51964080e-01 -4.13103968e-01 -2.17870808e+00
1.14650220e-01 -9.19428468e-01 -6.46900177e-01 9.75122631e-01
-3.19349676e-01 1.43489793e-01 7.67462142e-03 -6.16254471e-02
-3.07993829e-01 4.37337130e-01 1.28465021e+00 -2.55318820e-01
-6.60346597e-02 -3.36321741e-01 -8.60916793e-01 7.95794129e-01
5.86944520e-01 -1.84422120e-01 -4.00347114e-01 -3.23923469e-01
4.22086775e-01 1.30236894e-01 2.35421464e-01 -6.20660603e-01
4.17668760e-01 -2.71364957e-01 1.51221916e-01 -5.18314183e-01
7.31899619e-01 -8.46470714e-01 3.83668423e-01 1.94536179e-01
-1.60897970e-01 -1.10164136e-01 8.87844618e-03 7.50050783e-01
-4.01295394e-01 -1.73400640e-02 3.39767784e-01 4.90477383e-02
-8.19155693e-01 6.44346237e-01 3.23250473e-01 1.82867181e-02
1.32681990e+00 -4.80819553e-01 -7.32385814e-01 -3.11001033e-01
-1.39009929e+00 6.53421700e-01 3.77758369e-02 5.77029705e-01
7.93242872e-01 -1.65967596e+00 -6.38981462e-01 1.12720735e-01
4.19913739e-01 5.80296628e-02 2.68378526e-01 7.21281826e-01
1.45182416e-01 -5.71359731e-02 -3.84407789e-01 -1.07385190e-02
-1.10470104e+00 5.30163050e-01 8.99831951e-02 -5.05936265e-01
-9.17050615e-02 1.23717105e+00 -9.07303318e-02 -5.22448123e-01
1.48465455e-01 -2.99119115e-01 -4.68061924e-01 9.70038921e-02
-4.51645255e-02 2.67165810e-01 -1.23506024e-01 -9.74138558e-01
-6.03023708e-01 8.29869151e-01 -1.86754495e-01 1.41836330e-01
1.06693292e+00 1.98969916e-01 -3.49660993e-01 2.89794147e-01
8.91793370e-01 2.71211993e-02 -5.83966851e-01 -3.81158710e-01
-7.98136443e-02 -5.67863584e-01 -4.34707791e-01 -9.47758436e-01
-1.25401175e+00 4.80576694e-01 4.96968590e-02 2.08430663e-01
8.61940384e-01 3.18076342e-01 6.83694005e-01 2.97879308e-01
8.54458928e-01 -9.24993396e-01 -1.72551703e-02 5.68691969e-01
7.28952169e-01 -1.09789348e+00 3.99679951e-02 -8.60619366e-01
-4.87510264e-01 7.88676918e-01 1.06450474e+00 1.16014041e-01
8.73588383e-01 -2.82613128e-01 -3.43263835e-01 -3.79997700e-01
-6.00745320e-01 -3.46995592e-01 3.32625270e-01 6.76163077e-01
2.72595674e-01 2.38766432e-01 -5.56095541e-01 9.41343307e-01
-3.20656687e-01 -2.01542869e-01 4.39067245e-01 6.97807789e-01
-3.54122013e-01 -1.48025537e+00 -2.23126248e-01 8.00612748e-01
1.67076997e-02 -2.64548063e-01 -3.59947592e-01 1.05708671e+00
7.60568559e-01 1.00280917e+00 -1.85093116e-02 -7.03666687e-01
5.29002428e-01 3.37760419e-01 3.38153750e-01 -6.53606772e-01
-6.21017277e-01 -3.11949402e-01 4.11679953e-01 -3.20962787e-01
-3.58389467e-01 -4.08271909e-01 -1.76424766e+00 -4.87999350e-01
-5.87239146e-01 5.79289496e-01 2.85286695e-01 7.87448406e-01
3.77927989e-01 3.91226113e-01 2.10088581e-01 -6.19505160e-02
4.29839157e-02 -7.00731635e-01 -8.91951680e-01 6.37773931e-01
-1.81453332e-01 -1.21317136e+00 -2.91940272e-01 -5.74032106e-02] | [8.774564743041992, 7.837462425231934] |
0f2b50b3-7a55-41fd-9b12-cde452def348 | predicting-ground-level-scene-layout-from | 1612.02709 | null | http://arxiv.org/abs/1612.02709v1 | http://arxiv.org/pdf/1612.02709v1.pdf | Predicting Ground-Level Scene Layout from Aerial Imagery | We introduce a novel strategy for learning to extract semantically meaningful
features from aerial imagery. Instead of manually labeling the aerial imagery,
we propose to predict (noisy) semantic features automatically extracted from
co-located ground imagery. Our network architecture takes an aerial image as
input, extracts features using a convolutional neural network, and then applies
an adaptive transformation to map these features into the ground-level
perspective. We use an end-to-end learning approach to minimize the difference
between the semantic segmentation extracted directly from the ground image and
the semantic segmentation predicted solely based on the aerial image. We show
that a model learned using this strategy, with no additional training, is
already capable of rough semantic labeling of aerial imagery. Furthermore, we
demonstrate that by finetuning this model we can achieve more accurate semantic
segmentation than two baseline initialization strategies. We use our network to
address the task of estimating the geolocation and geoorientation of a ground
image. Finally, we show how features extracted from an aerial image can be used
to hallucinate a plausible ground-level panorama. | ['Zachary Bessinger', 'Menghua Zhai', 'Scott Workman', 'Nathan Jacobs'] | 2016-12-08 | predicting-ground-level-scene-layout-from-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Zhai_Predicting_Ground-Level_Scene_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhai_Predicting_Ground-Level_Scene_CVPR_2017_paper.pdf | cvpr-2017-7 | ['cross-view-image-to-image-translation'] | ['computer-vision'] | [ 6.74173534e-01 5.95186949e-01 1.79711923e-01 -6.51662290e-01
-7.62790203e-01 -1.03144419e+00 4.39456999e-01 1.52368829e-01
-4.08839017e-01 2.34680459e-01 6.60612732e-02 -1.73245013e-01
4.11084332e-02 -1.13746929e+00 -9.88372028e-01 -2.75667995e-01
-2.39502221e-01 2.91767538e-01 8.47833753e-02 -1.42232165e-01
1.08175911e-01 5.71073115e-01 -1.55093527e+00 1.05712883e-01
6.90690815e-01 1.04444885e+00 3.87151390e-01 8.10421944e-01
2.79114842e-01 5.07721543e-01 -5.06691277e-01 8.24096128e-02
7.22688198e-01 -4.41893458e-01 -1.37491834e+00 8.97940874e-01
7.97455668e-01 -5.29277503e-01 -1.98007911e-01 1.10519445e+00
-1.71119839e-01 2.65770853e-01 4.56035823e-01 -9.02162492e-01
-2.93614149e-01 5.17392874e-01 -3.07125270e-01 -9.89161506e-02
2.86101043e-01 1.08112477e-01 1.04489565e+00 -6.24603570e-01
4.59873170e-01 9.45544422e-01 8.23794544e-01 -1.62997559e-01
-1.09124422e+00 -5.89310713e-02 5.19223511e-01 -4.70823050e-01
-1.55125070e+00 -1.33016229e-01 4.43462342e-01 -4.41136330e-01
6.85272932e-01 5.24147265e-02 8.56742501e-01 3.13131392e-01
-1.88830793e-01 5.54979920e-01 8.10974121e-01 -4.37766314e-01
2.12560013e-01 -7.30147138e-02 -1.51455387e-01 1.22586524e+00
-1.19825974e-01 -1.79878294e-01 -2.45335065e-02 -1.01538107e-01
8.53823304e-01 1.68286875e-01 -3.82130057e-01 -3.22276562e-01
-1.36809874e+00 6.81809783e-01 1.00197256e+00 4.98858579e-02
-5.89485288e-01 3.04254204e-01 -3.50773424e-01 -4.66034561e-02
5.08349538e-01 9.26815271e-01 -6.37459636e-01 4.19895023e-01
-1.44127917e+00 1.61239550e-01 5.99762380e-01 8.10239851e-01
1.34175599e+00 -3.31956707e-02 3.65749031e-01 2.74615437e-01
2.02588961e-01 6.46286428e-01 3.16467404e-01 -1.24952924e+00
9.40762088e-02 7.71713972e-01 4.25138623e-01 -1.14073968e+00
-5.74680269e-01 -4.05729830e-01 -2.77298659e-01 1.22062668e-01
4.21567887e-01 -5.83670318e-01 -1.29540265e+00 1.59939623e+00
2.21207187e-01 2.16885686e-01 1.19670331e-01 9.32094097e-01
3.30319375e-01 6.78429961e-01 9.12635028e-03 5.09128869e-01
1.02721643e+00 -8.58139813e-01 1.48103803e-01 -8.70477498e-01
7.28844047e-01 -4.34588760e-01 9.30807889e-01 9.63526815e-02
-9.02082682e-01 -5.65246046e-01 -1.22096479e+00 2.29138453e-02
-6.07548714e-01 6.99096680e-01 5.50450325e-01 1.68061793e-01
-1.34428012e+00 9.58496094e-01 -1.05632663e+00 -6.82728171e-01
3.66054595e-01 4.96364892e-01 -4.83878553e-01 1.33900493e-02
-8.12540829e-01 7.72400618e-01 6.83726192e-01 9.36000273e-02
-1.00151920e+00 -2.25636020e-01 -1.23756611e+00 1.29548237e-01
3.82943183e-01 -7.21775115e-01 1.16935587e+00 -1.60417771e+00
-1.08509922e+00 9.61006999e-01 3.15617248e-02 -5.55211186e-01
8.24816376e-02 -1.28766418e-01 2.10718393e-01 4.97637630e-01
5.09196103e-01 1.32350767e+00 9.44914043e-01 -1.30603516e+00
-9.08144057e-01 -4.73836541e-01 5.72952211e-01 6.00460470e-01
-5.00254110e-02 -4.55842733e-01 -2.06866100e-01 -4.78615880e-01
5.86555898e-01 -1.13958895e+00 -6.43948376e-01 5.06072305e-02
-5.03726363e-01 4.42330569e-01 5.77506959e-01 -7.68376172e-01
5.32470345e-01 -2.11524463e+00 1.13320924e-01 4.20149922e-01
5.16572676e-04 -1.65695883e-02 -3.59074503e-01 1.38892189e-01
-1.35362476e-01 2.50045210e-01 -7.94708908e-01 -1.37335852e-01
-2.10730091e-01 3.31413560e-02 -6.54827535e-01 4.81384575e-01
3.72285962e-01 8.64901841e-01 -1.06720650e+00 -9.86101553e-02
3.03874761e-01 1.82305172e-01 -5.61231315e-01 3.22336018e-01
-5.38148284e-01 3.12914103e-01 -4.07986432e-01 6.15129590e-01
5.74348032e-01 -2.88276970e-01 2.56274194e-01 -2.02656582e-01
-8.74139592e-02 1.23465508e-01 -9.44414675e-01 1.91809404e+00
-5.07881045e-01 5.30443370e-01 -7.26643130e-02 -1.03586841e+00
7.63009369e-01 -1.28338411e-01 3.96585315e-01 -1.71362180e-02
2.13161826e-01 -7.08705187e-02 -5.44994593e-01 -2.53164679e-01
7.59635806e-01 -1.16174854e-03 -3.78875017e-01 4.29860443e-01
4.35905665e-01 -6.79217815e-01 -2.66053319e-01 1.16961174e-01
8.74261022e-01 3.87942404e-01 4.20246273e-01 -2.31738612e-01
1.50918454e-01 5.78263581e-01 7.32055753e-02 7.02817261e-01
1.56638816e-01 7.99693644e-01 3.23183119e-01 -8.01542461e-01
-8.10135722e-01 -7.53638446e-01 3.01382810e-01 9.31885064e-01
4.65373576e-01 -5.32852292e-01 -1.13878095e+00 -9.08303618e-01
-2.26455688e-01 6.80543065e-01 -7.15209365e-01 -2.95924023e-02
-1.85484633e-01 -7.26357102e-01 5.03041625e-01 5.33602893e-01
8.49513412e-01 -7.47590721e-01 -9.42986965e-01 4.78551388e-02
-4.72418070e-01 -1.30805767e+00 -1.65503621e-01 3.03630471e-01
-8.54685962e-01 -1.02283382e+00 -2.99913406e-01 -8.40180397e-01
1.01550162e+00 6.38419747e-01 1.01858640e+00 2.46472880e-01
-2.14602917e-01 6.58565760e-01 -2.32157081e-01 -6.99016005e-02
3.22907902e-02 3.52306724e-01 -3.02404255e-01 8.90789777e-02
2.73638129e-01 -4.50733483e-01 -4.77436930e-01 8.09968635e-02
-1.11114454e+00 1.39923275e-01 5.10288894e-01 4.96381134e-01
7.56132603e-01 2.66973197e-01 -1.50269747e-01 -5.86318016e-01
-1.44986361e-02 -4.99372631e-01 -7.48556316e-01 1.46235108e-01
-3.41850184e-02 1.35539949e-01 6.50969207e-01 1.31749675e-01
-6.18710577e-01 9.37962890e-01 4.23732796e-04 -2.32879192e-01
-7.51961052e-01 6.68504119e-01 -7.05771372e-02 -2.65472412e-01
7.83336401e-01 1.52818605e-01 -2.88354248e-01 -6.72763959e-02
6.14729822e-01 5.82302690e-01 7.37434685e-01 -4.07449007e-01
9.60065007e-01 7.79660761e-01 -1.01139009e-01 -1.07873583e+00
-1.53930497e+00 -3.46557677e-01 -1.23745406e+00 6.15545437e-02
1.17784917e+00 -1.26605034e+00 -6.32576123e-02 2.72610635e-01
-1.00423682e+00 -7.91810811e-01 -4.63253528e-01 4.86758769e-01
-7.63500333e-01 1.98095128e-01 -6.07352518e-02 -3.66207063e-01
9.15903896e-02 -1.05547261e+00 1.49239767e+00 1.95393384e-01
-2.05780774e-01 -9.56387937e-01 -1.98588580e-01 8.39455947e-02
2.07341611e-02 4.76754814e-01 5.90263665e-01 -5.19340754e-01
-9.29212749e-01 -2.61048198e-01 -3.76493275e-01 2.21575856e-01
2.52084941e-01 -1.16473705e-01 -1.13977885e+00 -6.06478713e-02
-5.04833348e-02 -4.73122209e-01 9.82924581e-01 5.04119813e-01
1.12405431e+00 -4.11715657e-01 -2.99043953e-01 1.12951231e+00
1.65727699e+00 -3.15634608e-01 4.30534899e-01 4.61356699e-01
7.26188958e-01 6.03319824e-01 8.15337777e-01 3.02464247e-01
6.05122268e-01 2.77831763e-01 8.03151608e-01 -4.00715351e-01
2.59618521e-01 -5.54927647e-01 1.64386749e-01 -1.80061609e-01
-3.66416797e-02 -1.70922518e-01 -1.00200164e+00 7.86008358e-01
-1.69266582e+00 -6.56536162e-01 3.34834754e-01 2.05634952e+00
3.37696761e-01 -1.58546269e-01 -2.33610123e-02 -2.98460543e-01
5.37661254e-01 3.52562994e-01 -5.07306933e-01 6.74896091e-02
-2.23373473e-02 8.60425383e-02 1.18684566e+00 8.22174370e-01
-1.75790918e+00 1.59658551e+00 6.95805407e+00 8.97142366e-02
-1.16380370e+00 -3.68927062e-01 6.65815592e-01 2.65738189e-01
-7.91601837e-02 3.60529572e-01 -5.40112495e-01 -9.00661349e-02
8.22237849e-01 1.32989600e-01 7.14223921e-01 1.12346256e+00
-9.50212628e-02 -2.82682210e-01 -8.99189115e-01 7.50958264e-01
3.28903347e-01 -1.25538373e+00 2.18144625e-01 1.96898803e-01
8.42510998e-01 2.23980382e-01 -3.51931453e-02 -9.24739540e-02
8.36172402e-01 -1.04966140e+00 8.36819410e-01 4.26350534e-01
7.04420388e-01 -5.69759071e-01 6.72674358e-01 3.68140519e-01
-1.25355732e+00 -1.18935853e-01 -4.90482837e-01 -2.10960537e-01
-2.14080829e-02 1.68142170e-01 -1.12580335e+00 4.45370734e-01
4.55348492e-01 9.10902143e-01 -9.42558706e-01 8.25304568e-01
-5.31075478e-01 4.79483336e-01 -5.33708990e-01 5.79987645e-01
8.61705065e-01 -2.14106977e-01 3.02897185e-01 8.28837395e-01
7.37687588e-01 3.53670657e-01 7.92857051e-01 9.50624704e-01
-1.15510710e-01 -1.97844222e-01 -1.06798768e+00 -2.58907676e-01
2.10528091e-01 1.41879392e+00 -1.16114378e+00 -4.99159694e-01
-1.06903411e-01 1.40037513e+00 1.64732143e-01 4.53689367e-01
-6.86268508e-01 -4.86122340e-01 5.31339765e-01 1.24421887e-01
3.99660528e-01 -4.20806825e-01 -5.83933778e-02 -1.25966930e+00
-3.24682921e-01 -5.61463892e-01 5.42898625e-02 -1.31776226e+00
-4.99282777e-01 8.55373442e-01 2.07218602e-02 -1.18580973e+00
-3.17030489e-01 -6.09366953e-01 -4.75654215e-01 7.55824327e-01
-1.33832765e+00 -1.46370208e+00 -7.51294017e-01 3.22851568e-01
5.40161133e-01 1.69553682e-01 1.03171706e+00 -5.29985249e-01
-5.99336326e-02 -1.57090634e-01 -2.50012279e-01 2.89817035e-01
3.39145690e-01 -1.46420074e+00 7.43578076e-01 1.15728867e+00
3.29075336e-01 3.13078761e-01 4.62485552e-01 -4.97638047e-01
-8.47987890e-01 -1.76778138e+00 6.20919585e-01 -5.22627890e-01
5.35008907e-01 -7.33565092e-02 -5.57034492e-01 1.40152752e+00
1.17243011e-03 5.13656363e-02 5.35476208e-01 -2.37197474e-01
-7.87512586e-02 2.01720834e-01 -1.06724453e+00 5.70362568e-01
9.46842611e-01 -4.87941176e-01 -6.93331301e-01 5.90059698e-01
7.12721109e-01 -4.18783188e-01 -5.74550152e-01 1.59126863e-01
1.74987614e-01 -8.06384683e-01 1.06061316e+00 -5.41222453e-01
4.24387395e-01 -5.98216176e-01 -5.12890875e-01 -1.72863221e+00
-3.37450713e-01 -2.69310802e-01 7.45520532e-01 5.66270709e-01
4.52347457e-01 -3.83679360e-01 7.72828758e-01 4.95007426e-01
-1.61567152e-01 -8.80118757e-02 -1.84086993e-01 -4.28932339e-01
-1.32260054e-01 -2.29428217e-01 7.81380117e-01 7.77016759e-01
-2.12551475e-01 2.97924757e-01 -1.49889916e-01 9.39554870e-01
4.27381635e-01 4.94763225e-01 8.33203375e-01 -1.28858483e+00
-1.16683438e-01 1.27615228e-01 -6.20852053e-01 -1.31524563e+00
3.64611179e-01 -8.38377953e-01 3.95600259e-01 -1.69784141e+00
-2.57027268e-01 -2.64115542e-01 2.15478227e-01 9.27465618e-01
-5.64682744e-02 6.56497180e-01 2.06593841e-01 8.20336789e-02
-6.32899582e-01 2.42098436e-01 8.11068416e-01 -2.03360543e-01
-2.27949306e-01 -1.31281063e-01 -7.87919581e-01 1.25941956e+00
1.00654542e+00 -5.25932908e-01 -3.79921496e-01 -7.33363450e-01
1.35128349e-01 -3.77745368e-02 7.44560838e-01 -1.23613572e+00
4.38468494e-02 -2.44010925e-01 7.53353477e-01 -4.78864521e-01
3.58843148e-01 -8.14981580e-01 -1.48635268e-01 9.91887003e-02
-3.93612325e-01 -1.04006469e-01 1.61271021e-01 4.93703425e-01
-2.95152932e-01 -4.07951146e-01 6.31018877e-01 -6.99132144e-01
-7.32158303e-01 2.91285694e-01 -7.20498145e-01 -1.23792224e-01
8.88073325e-01 -2.11282700e-01 1.39207793e-02 -4.06056195e-01
-9.84442651e-01 1.08140096e-01 1.07639217e+00 -4.80056815e-02
4.26435709e-01 -9.56527829e-01 -2.65591651e-01 4.41510797e-01
7.12505579e-02 3.25003475e-01 -6.97473064e-02 3.52044225e-01
-9.23843443e-01 3.72789770e-01 -2.40762204e-01 -5.97709179e-01
-8.84649575e-01 3.09877038e-01 6.23006344e-01 -2.39706729e-02
-5.20568967e-01 7.16102183e-01 2.75321484e-01 -8.00917447e-01
-1.57202750e-01 -6.66760147e-01 4.12834734e-02 -3.70760970e-02
3.63390863e-01 -2.42632955e-01 8.32379013e-02 -9.78268027e-01
-1.56432644e-01 7.31260598e-01 4.46130872e-01 -4.45215225e-01
1.44796288e+00 -3.30036312e-01 -1.75030008e-02 1.62320137e-01
1.02566338e+00 -6.84202760e-02 -1.60336864e+00 -1.72500759e-01
-1.21565245e-01 -5.29217899e-01 2.83497363e-01 -7.45880902e-01
-9.58349824e-01 9.22002137e-01 3.57166141e-01 2.95626491e-01
1.15137172e+00 -1.24399938e-01 6.70095563e-01 8.54338646e-01
4.45117086e-01 -8.13526809e-01 -6.59996867e-02 5.51506460e-01
5.11926949e-01 -1.27110887e+00 -9.98831391e-02 -3.05142730e-01
-6.83720708e-01 1.30116391e+00 3.33115339e-01 -5.18610179e-01
6.27725542e-01 -1.40911723e-02 1.36922941e-01 -4.23435777e-01
-1.22996561e-01 -7.84857273e-01 2.35985726e-01 6.46463692e-01
-2.22096853e-02 1.85551047e-01 7.56579757e-01 3.51377904e-01
-5.64585328e-01 -7.30121061e-02 7.43196309e-01 9.35938716e-01
-9.78026628e-01 -6.15268946e-01 -4.67818558e-01 2.59509295e-01
-2.18801066e-01 -2.98191786e-01 -8.94665539e-01 6.52925551e-01
3.40382427e-01 7.81768680e-01 2.38916218e-01 -4.64399874e-01
1.53549343e-01 3.68621275e-02 4.40812856e-01 -1.03471577e+00
-1.96821719e-01 -5.48450910e-02 -3.38678509e-02 -7.06596673e-01
-6.27573788e-01 -3.61239731e-01 -1.13414705e+00 2.40015745e-01
-1.12771288e-01 1.57557219e-01 6.93734467e-01 1.05968821e+00
2.76262790e-01 2.78361708e-01 6.78662598e-01 -1.40969825e+00
-1.25054851e-01 -6.59600437e-01 -5.81767261e-01 6.94194362e-02
4.24361885e-01 -3.62387955e-01 -4.70928848e-01 6.54004812e-01] | [9.405879974365234, -0.16569675505161285] |
fd56a1ad-5858-4477-9abb-2647391b842b | positive-unlabeled-learning-for-binary-and | 2302.08050 | null | https://arxiv.org/abs/2302.08050v1 | https://arxiv.org/pdf/2302.08050v1.pdf | Positive-unlabeled learning for binary and multi-class cell detection in histopathology images with incomplete annotations | Cell detection in histopathology images is of great interest to clinical practice and research, and convolutional neural networks (CNNs) have achieved remarkable cell detection results. Typically, to train CNN-based cell detection models, every positive instance in the training images needs to be annotated, and instances that are not labeled as positive are considered negative samples. However, manual cell annotation is complicated due to the large number and diversity of cells, and it can be difficult to ensure the annotation of every positive instance. In many cases, only incomplete annotations are available, where some of the positive instances are annotated and the others are not, and the classification loss term for negative samples in typical network training becomes incorrect. In this work, to address this problem of incomplete annotations, we propose to reformulate the training of the detection network as a positive-unlabeled learning problem. Since the instances in unannotated regions can be either positive or negative, they have unknown labels. Using the samples with unknown labels and the positively labeled samples, we first derive an approximation of the classification loss term corresponding to negative samples for binary cell detection, and based on this approximation we further extend the proposed framework to multi-class cell detection. For evaluation, experiments were performed on four publicly available datasets. The experimental results show that our method improves the performance of cell detection in histopathology images given incomplete annotations for network training. | ['Chuyang Ye', 'Zhiwen Liu', 'Yaou Liu', 'Fengqian Pang', 'Zipei Zhao'] | 2023-02-16 | null | null | null | null | ['cell-detection'] | ['computer-vision'] | [ 3.38561624e-01 7.45252073e-02 -3.99027467e-01 -2.44490609e-01
-6.72533333e-01 -4.95289207e-01 6.70510307e-02 7.17106223e-01
-7.77433753e-01 1.14036930e+00 -5.23315012e-01 -3.21835607e-01
3.62622589e-01 -9.51052487e-01 -4.37582701e-01 -1.25054729e+00
8.72982666e-02 5.68862379e-01 3.82313848e-01 2.79184312e-01
-1.16031341e-01 5.68340361e-01 -1.07906616e+00 3.96831542e-01
7.53584027e-01 1.28484774e+00 2.78808773e-02 6.23695791e-01
-2.10255012e-01 9.02378559e-01 -5.96170902e-01 -1.97275758e-01
1.11494973e-01 -3.18963438e-01 -8.21775317e-01 -2.56694853e-03
1.64846465e-01 -3.51349562e-01 1.01360483e-02 1.31303775e+00
4.25125450e-01 -3.00359637e-01 8.16950798e-01 -1.19869328e+00
-2.32794702e-01 1.02306575e-01 -8.08844268e-01 2.81741530e-01
-3.16242367e-01 2.84082498e-02 8.37234616e-01 -7.55609870e-01
6.78866744e-01 6.44040525e-01 6.80673599e-01 7.73437560e-01
-1.10722017e+00 -8.58284056e-01 1.13043729e-02 -4.77803759e-02
-1.67493439e+00 -1.51621178e-01 4.29559857e-01 -5.99373698e-01
3.38910580e-01 2.71345556e-01 8.87982905e-01 5.77788055e-01
8.42335001e-02 1.03704262e+00 8.17801952e-01 -1.61621287e-01
3.69834572e-01 3.87966007e-01 3.16161036e-01 5.69445550e-01
3.67339641e-01 -4.36970331e-02 1.98038191e-01 -1.23300873e-01
6.92911506e-01 3.53686661e-01 -3.38825703e-01 -2.39312828e-01
-1.10743868e+00 7.70921946e-01 7.14203000e-01 3.20088893e-01
-1.04463838e-01 -8.15252140e-02 6.59081876e-01 -6.17886009e-03
5.73301852e-01 1.33130759e-01 -3.88149291e-01 3.61092061e-01
-8.24443281e-01 -1.59173124e-02 6.63567126e-01 5.78318715e-01
8.00194681e-01 -4.44000959e-01 -3.60826164e-01 9.93501067e-01
-5.58365881e-02 1.89726368e-01 6.48803651e-01 -3.99456024e-01
2.06843484e-02 1.01934350e+00 1.59891665e-01 -8.77756715e-01
-4.68563497e-01 -7.98737347e-01 -1.42145646e+00 9.87461507e-02
9.22261119e-01 -9.43801105e-02 -1.01984715e+00 1.52793574e+00
4.29494500e-01 1.72852233e-01 1.44532174e-01 1.06713462e+00
8.44008565e-01 4.54781860e-01 1.25995442e-01 -5.81532717e-01
1.28406727e+00 -7.88496792e-01 -8.13344896e-01 -1.33845508e-01
1.20519269e+00 -5.15568376e-01 6.72220826e-01 1.92175865e-01
-7.59326756e-01 -1.66412458e-01 -8.93859506e-01 8.46840069e-02
-3.76770973e-01 5.95807493e-01 6.20363772e-01 1.82245046e-01
-7.63550460e-01 1.84304461e-01 -8.17371011e-01 -3.07391852e-01
9.50848222e-01 5.35831332e-01 -3.86810631e-01 -2.46165007e-01
-9.38839197e-01 4.88161504e-01 5.08033335e-01 3.92948389e-01
-8.58917415e-01 -6.39264822e-01 -6.05439782e-01 3.48613262e-02
4.74487305e-01 -1.87399819e-01 1.25653768e+00 -1.09549057e+00
-4.98127609e-01 1.09311080e+00 -2.83418238e-01 -2.81631857e-01
7.05067694e-01 6.28372848e-01 -1.68418020e-01 9.79257300e-02
1.11676171e-01 8.00526023e-01 1.44377887e-01 -1.15317321e+00
-1.03759801e+00 -3.16744566e-01 4.58882935e-02 -1.50356844e-01
-5.09211302e-01 -3.40524614e-01 -3.86039287e-01 -5.60497344e-01
1.97968483e-01 -8.32352877e-01 -4.27385837e-01 5.10434031e-01
-5.76809764e-01 -1.64946064e-01 9.44018722e-01 -3.41866881e-01
1.05051446e+00 -2.36764550e+00 -3.99839252e-01 2.73311406e-01
4.29570735e-01 3.77204478e-01 1.19695731e-01 -3.10657918e-01
7.40559399e-02 1.78854734e-01 -3.21740240e-01 -1.97620079e-01
-4.67630327e-01 3.26710969e-01 5.03343903e-02 5.06399691e-01
3.40949357e-01 6.88500702e-01 -1.11878991e+00 -9.71043050e-01
-5.17793149e-02 4.98834193e-01 -4.41001803e-01 1.59641013e-01
-2.33928658e-04 5.09972811e-01 -4.27522540e-01 9.88606811e-01
7.16431201e-01 -6.14694953e-01 1.75905749e-01 -2.41086647e-01
2.87518650e-01 -3.63207340e-01 -1.04455268e+00 7.16962397e-01
-1.48647785e-01 6.17011070e-01 1.70613706e-01 -1.03234446e+00
6.98321164e-01 4.21319991e-01 4.57531452e-01 -3.82638246e-01
2.86989480e-01 5.34926236e-01 1.51320666e-01 -4.48387325e-01
2.38326162e-01 -5.76324105e-01 3.36465329e-01 1.16154864e-01
-1.95053190e-01 1.70259938e-01 4.07449573e-01 5.60676157e-02
1.03189468e+00 -5.94699621e-01 2.60354817e-01 1.03734925e-01
7.76948690e-01 1.27865478e-01 1.01995254e+00 4.43404377e-01
-5.36358893e-01 6.37955368e-01 9.43100095e-01 -5.60852826e-01
-8.06608438e-01 -5.77617228e-01 -4.43695426e-01 6.40480518e-01
1.25131294e-01 8.58289227e-02 -6.42716706e-01 -1.03622878e+00
-1.35888368e-01 -9.56506208e-02 -8.09369385e-01 -8.52884650e-02
-4.01215285e-01 -1.28078818e+00 5.59305251e-01 6.49780810e-01
3.15371037e-01 -1.02355897e+00 -3.16651791e-01 2.38995746e-01
-2.37352520e-01 -9.72616374e-01 -2.44284332e-01 3.64416659e-01
-8.06967854e-01 -1.57443857e+00 -9.56172764e-01 -1.21361017e+00
1.41368902e+00 2.21739575e-01 7.81006753e-01 7.27252662e-01
-5.68431735e-01 -3.28379124e-01 -1.60978496e-01 -5.97182095e-01
-4.61272538e-01 1.94199625e-02 -2.23480761e-01 3.86034548e-01
4.44492102e-01 -6.26735017e-03 -8.54775846e-01 3.21701080e-01
-9.50699747e-01 -6.94760233e-02 6.57898247e-01 1.39398849e+00
1.07825994e+00 1.38381869e-01 8.46912324e-01 -1.23253167e+00
1.88967988e-01 -3.44757527e-01 -3.97246867e-01 2.36928672e-01
-2.02618808e-01 -4.90518212e-01 7.13907301e-01 -6.76600456e-01
-5.60895145e-01 4.40263778e-01 -1.60388440e-01 -3.57592911e-01
-1.02905899e-01 6.32234097e-01 6.64739013e-02 -3.11439186e-01
4.22289521e-01 6.49667755e-02 1.57506734e-01 -5.34266569e-02
-5.23824990e-01 6.84107602e-01 4.02257860e-01 -1.12951361e-01
2.63949245e-01 7.65734017e-01 1.99092671e-01 -6.71715975e-01
-1.05942643e+00 -6.20150506e-01 -4.26401019e-01 -2.77098119e-01
7.35591412e-01 -7.55209148e-01 -6.26005650e-01 6.03953898e-01
-9.82212842e-01 -2.41986126e-01 -2.99916625e-01 5.35359204e-01
-7.40173385e-02 2.45723918e-01 -8.16536248e-01 -8.67439330e-01
-3.17338496e-01 -1.28857362e+00 9.48922276e-01 3.83859187e-01
-1.20966919e-01 -1.10680103e+00 -1.13149010e-01 1.98821630e-02
2.31733158e-01 3.18282515e-01 1.24207389e+00 -9.21579838e-01
-4.03687537e-01 -1.03502262e+00 -3.27353269e-01 3.80106837e-01
1.36418119e-01 1.54353112e-01 -9.66821134e-01 -4.96345133e-01
-2.92835951e-01 -6.06679559e-01 9.22228515e-01 4.30737168e-01
1.42344522e+00 -2.36779213e-01 -9.15221930e-01 4.82908487e-01
1.38645339e+00 1.62974268e-01 5.39952278e-01 1.37715284e-02
4.83409196e-01 6.44945264e-01 7.67994642e-01 2.21945941e-01
6.78250417e-02 2.99038172e-01 5.81798553e-01 -7.40241468e-01
1.58884957e-01 6.22488223e-02 -2.64695555e-01 3.73527318e-01
1.08448192e-01 -4.35333699e-01 -8.12597752e-01 8.65985990e-01
-1.74385583e+00 -7.12800682e-01 -3.72213781e-01 2.32056189e+00
1.03291535e+00 5.98644353e-02 -6.11241832e-02 4.36883122e-01
1.00751495e+00 -3.39847356e-01 -7.19871819e-01 2.88348109e-01
-1.01877421e-01 7.30237216e-02 2.69404918e-01 2.35547304e-01
-1.31342924e+00 4.97527063e-01 5.93665218e+00 1.02378654e+00
-1.37007535e+00 8.44776183e-02 1.41389120e+00 -1.78574950e-01
1.91094816e-01 -2.95684606e-01 -8.15451145e-01 3.81941259e-01
1.88547075e-01 5.09418659e-02 -3.00305039e-01 7.08935499e-01
1.10663213e-01 -1.92693681e-01 -1.20550299e+00 8.22125375e-01
-1.60103187e-01 -1.19271517e+00 -4.85025235e-02 2.40671039e-01
7.37678170e-01 -3.03111434e-01 -8.36007968e-02 4.81624007e-01
-1.11110210e-02 -1.00903237e+00 3.24189663e-01 1.50489226e-01
9.76381838e-01 -7.33416855e-01 1.43422043e+00 7.01969326e-01
-9.95699227e-01 -8.69675130e-02 -6.55881405e-01 1.56074511e-02
-1.49088860e-01 8.07349682e-01 -1.01887786e+00 2.81675290e-02
3.41109455e-01 6.89780951e-01 -4.32947159e-01 1.41350734e+00
1.03414126e-01 4.74601120e-01 -3.36967736e-01 -2.32577845e-01
1.44168779e-01 1.55812368e-01 2.20353692e-03 1.13174891e+00
4.02384579e-01 1.38731793e-01 4.40860033e-01 6.79244995e-01
-3.74959797e-01 2.74870247e-01 -2.35799029e-01 2.83613335e-02
3.11657816e-01 1.65493917e+00 -1.17600405e+00 -4.64241862e-01
-3.46168011e-01 5.68211854e-01 4.80188847e-01 3.93461406e-01
-6.61940336e-01 -2.99544394e-01 2.77274042e-01 1.63593382e-01
-7.95910284e-02 5.82381308e-01 -3.79767537e-01 -8.96264613e-01
-3.51124592e-02 -2.86590517e-01 6.43478096e-01 -3.55509669e-01
-1.49939072e+00 3.02779436e-01 -5.35310090e-01 -1.57717824e+00
1.58694386e-01 -6.66401565e-01 -5.18267393e-01 7.27228165e-01
-1.65248954e+00 -9.86112297e-01 -5.31846404e-01 1.49161845e-01
9.32372883e-02 1.44216076e-01 7.23755956e-01 5.72547972e-01
-7.19745696e-01 9.43227530e-01 -3.43802758e-02 5.20031154e-01
5.81265390e-01 -1.10273957e+00 -5.59321284e-01 4.22357142e-01
-4.55898374e-01 3.58685017e-01 2.13340670e-01 -4.91397381e-01
-7.90814936e-01 -1.40480793e+00 8.85653496e-01 2.57361773e-02
4.05287534e-01 -2.41591603e-01 -1.16593075e+00 5.02902806e-01
-3.92532527e-01 8.17814350e-01 9.84506667e-01 -3.44434291e-01
9.85573232e-02 -1.45793587e-01 -1.37837815e+00 4.19371545e-01
4.03328151e-01 -2.76935756e-01 1.68800950e-01 6.67412162e-01
2.31865123e-01 -4.51662123e-01 -7.64996707e-01 6.70332074e-01
5.58672249e-01 -6.10202193e-01 7.08723724e-01 -4.89500940e-01
3.12014610e-01 -4.85208988e-01 1.98882803e-01 -1.04025650e+00
-1.11548482e-02 2.93180883e-01 7.68702999e-02 9.18114424e-01
5.95950484e-01 -6.07134938e-01 1.19709456e+00 3.87731522e-01
-1.62029400e-01 -1.23957276e+00 -1.11731887e+00 -5.82111239e-01
2.17680737e-01 9.31076258e-02 3.69551778e-01 1.17375529e+00
1.05304979e-01 1.68007955e-01 -2.95676775e-02 1.26839042e-01
3.85090262e-01 -2.25901715e-02 5.11282504e-01 -1.33620548e+00
-1.50888667e-01 -4.07809854e-01 -6.98601723e-01 -8.52187574e-01
2.94388719e-02 -1.00614452e+00 1.46807209e-01 -1.43768382e+00
7.48328388e-01 -8.28449607e-01 -4.76654768e-01 7.93477058e-01
-3.79048347e-01 8.49103391e-01 -2.34303668e-01 3.66350770e-01
-6.33503795e-01 1.78059429e-01 1.43217039e+00 -5.26445866e-01
-2.57959198e-02 9.75239575e-02 -4.35298651e-01 7.41449535e-01
6.02080643e-01 -4.60070968e-01 -7.21399784e-02 2.48856340e-02
2.16770262e-01 1.05976135e-01 5.28856933e-01 -9.57408369e-01
3.05586785e-01 -1.52123302e-01 7.32261181e-01 -7.87550449e-01
2.41913423e-01 -8.99347782e-01 -2.75856592e-02 8.09276938e-01
-3.69080305e-01 -7.50084996e-01 -8.13942254e-02 5.48713207e-01
-5.15452981e-01 -3.70616049e-01 1.06288064e+00 -1.36798888e-01
-1.79888561e-01 6.19696736e-01 -2.40834117e-01 -1.18955292e-01
1.25257993e+00 -5.24821818e-01 -4.94021326e-01 2.71173986e-03
-8.50903809e-01 5.77989519e-01 5.06863654e-01 -2.56975591e-01
4.97439295e-01 -1.41169059e+00 -4.94963378e-01 1.99541628e-01
5.56255400e-01 5.40687084e-01 4.25526261e-01 1.22089732e+00
-6.71729624e-01 1.90771088e-01 1.20581120e-01 -8.18113983e-01
-1.32333064e+00 5.49727857e-01 6.32916451e-01 -4.58936602e-01
-9.02578980e-02 9.27867055e-01 6.77226126e-01 -4.14036840e-01
3.67273986e-01 -4.17929024e-01 -5.00397623e-01 7.15533346e-02
6.62166595e-01 1.01716921e-01 1.74669221e-01 -5.40195584e-01
-1.78660274e-01 3.04123491e-01 -4.84777033e-01 5.08838177e-01
9.49162722e-01 3.60923380e-01 -4.04953659e-01 4.68447149e-01
1.21911359e+00 -2.98927635e-01 -1.01712322e+00 -2.38558948e-01
-3.21968138e-01 -2.79645801e-01 3.00330631e-02 -7.37014353e-01
-1.54353189e+00 1.06334126e+00 6.96053147e-01 1.42550617e-01
1.09791172e+00 -7.93771669e-02 7.16700971e-01 3.46656531e-01
3.08898211e-01 -9.61523592e-01 -8.49599242e-02 3.54013950e-01
1.62207723e-01 -1.37748396e+00 -2.40326479e-01 -7.55385816e-01
-2.52456039e-01 1.07875204e+00 8.52458894e-01 2.65609652e-01
5.36893010e-01 3.39295775e-01 1.24707729e-01 -9.14475843e-02
-6.48507893e-01 4.73801680e-02 -2.20415056e-01 3.59326422e-01
5.20248890e-01 2.57917017e-01 -5.05030930e-01 8.10887814e-01
2.84516275e-01 2.67378747e-01 5.46351314e-01 9.71189499e-01
-4.01501447e-01 -7.70128429e-01 -1.73834592e-01 9.52247918e-01
-8.06255579e-01 8.98199156e-02 -5.10407865e-01 7.30215013e-01
1.60888761e-01 6.59388661e-01 3.11709166e-01 -1.71666116e-01
1.09249912e-01 -1.50983512e-01 9.96520743e-02 -6.15194142e-01
-4.42495197e-01 1.47038117e-01 -1.95190519e-01 1.65609434e-01
-2.59238333e-01 -3.42919618e-01 -1.65459204e+00 -5.78548685e-02
-7.12231636e-01 1.92418650e-01 1.62142113e-01 8.21653605e-01
-2.60559618e-02 5.09552062e-01 6.15124941e-01 -3.65590334e-01
-3.37671101e-01 -1.01486433e+00 -7.58244634e-01 3.23179960e-01
5.77282786e-01 -6.04052663e-01 -4.78806615e-01 1.32030934e-01] | [14.900774002075195, -3.0699961185455322] |
8b482749-060e-454b-9f04-859bce827b00 | paraphrase-generation-as-unsupervised-machine | 2109.02950 | null | https://arxiv.org/abs/2109.02950v2 | https://arxiv.org/pdf/2109.02950v2.pdf | Paraphrase Generation as Unsupervised Machine Translation | In this paper, we propose a new paradigm for paraphrase generation by treating the task as unsupervised machine translation (UMT) based on the assumption that there must be pairs of sentences expressing the same meaning in a large-scale unlabeled monolingual corpus. The proposed paradigm first splits a large unlabeled corpus into multiple clusters, and trains multiple UMT models using pairs of these clusters. Then based on the paraphrase pairs produced by these UMT models, a unified surrogate model can be trained to serve as the final \sts model to generate paraphrases, which can be directly used for test in the unsupervised setup, or be finetuned on labeled datasets in the supervised setup. The proposed method offers merits over machine-translation-based paraphrase generation methods, as it avoids reliance on bilingual sentence pairs. It also allows human intervene with the model so that more diverse paraphrases can be generated using different filtering criteria. Extensive experiments on existing paraphrase dataset for both the supervised and unsupervised setups demonstrate the effectiveness the proposed paradigm. | ['Fei Wu', 'Xiaofei Sun', 'Jiwei Li', 'Nanyun Peng', 'Yuxian Meng', 'Yufei Tian', 'Chun Fan'] | 2021-09-07 | null | https://aclanthology.org/2022.coling-1.555 | https://aclanthology.org/2022.coling-1.555.pdf | coling-2022-10 | ['paraphrase-generation', 'unsupervised-machine-translation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing', 'natural-language-processing'] | [ 4.16303605e-01 6.01404021e-03 -3.27606052e-01 -6.27356768e-01
-9.45417166e-01 -8.34177256e-01 6.72004998e-01 -1.18788883e-01
-1.33883715e-01 1.01589954e+00 3.17052692e-01 -4.83477116e-01
3.34087372e-01 -6.34437442e-01 -7.00571120e-01 -6.31770730e-01
8.92423093e-01 7.90714145e-01 -2.10596919e-01 -2.70540804e-01
3.64840299e-01 7.71847516e-02 -1.12372470e+00 5.84592402e-01
1.42729759e+00 3.44986469e-01 5.50706565e-01 1.72490314e-01
-3.96663725e-01 5.90235889e-01 -7.51689434e-01 -5.40873110e-01
3.95173013e-01 -1.09573150e+00 -8.95973086e-01 1.30411670e-01
2.38772139e-01 1.19655736e-01 -5.22436295e-03 1.18002868e+00
4.02833045e-01 -5.12160175e-02 6.54393613e-01 -1.06079185e+00
-9.58352864e-01 8.52746904e-01 -4.38851416e-01 4.51907236e-03
7.31520176e-01 -1.01385407e-01 9.14465725e-01 -1.09713328e+00
7.18100369e-01 1.16524816e+00 2.60703772e-01 6.77879453e-01
-1.34370434e+00 -8.13602686e-01 -2.42767349e-01 3.05446684e-01
-1.17138970e+00 -4.84532684e-01 9.25825596e-01 -1.44342735e-01
8.47712100e-01 2.77362853e-01 6.23088896e-01 1.23479331e+00
2.13271305e-01 7.76985168e-01 1.46555591e+00 -8.37121069e-01
1.43522620e-01 5.05377054e-01 3.49637233e-02 4.25290257e-01
7.90691823e-02 -2.76467264e-01 -3.99470657e-01 -2.79744714e-01
5.64855635e-01 -1.44908711e-01 -2.35009611e-01 -3.37617844e-01
-1.52633929e+00 9.82417583e-01 2.26987630e-01 4.69885200e-01
-1.55596539e-01 -5.99019468e-01 4.09225762e-01 8.55020583e-01
3.92716378e-01 6.79156303e-01 -2.32263491e-01 2.28392407e-01
-1.03562772e+00 9.08020884e-03 7.43741870e-01 1.24947608e+00
8.19467485e-01 -2.55495399e-01 -2.15212330e-01 1.26238179e+00
1.37064040e-01 5.39060414e-01 1.00738680e+00 -6.30749345e-01
1.01793110e+00 7.41259158e-01 1.99806362e-01 -5.47711909e-01
3.08482081e-01 -1.19419657e-01 -7.08087981e-01 -3.82636964e-01
9.02572051e-02 -1.09943129e-01 -8.49448323e-01 1.84214866e+00
1.30805999e-01 7.28198141e-03 5.26373506e-01 8.68461311e-01
6.68455303e-01 8.24978352e-01 -3.10569406e-01 -4.31512833e-01
9.70387161e-01 -1.20986938e+00 -5.76187253e-01 -2.97812790e-01
6.42894268e-01 -1.21340024e+00 1.21393740e+00 8.72821659e-02
-1.10277617e+00 -7.24223316e-01 -1.17140746e+00 2.06428878e-02
-5.76239675e-02 2.79815018e-01 2.44524404e-01 4.12328899e-01
-1.09160697e+00 3.83157253e-01 -4.18426365e-01 -7.55572021e-01
-7.20539913e-02 3.14736068e-01 -2.13010728e-01 -2.02373907e-01
-1.21708214e+00 1.10579491e+00 5.81199467e-01 2.20534950e-01
-6.35963023e-01 -5.43035381e-02 -8.11525166e-01 -1.11964524e-01
-1.49740860e-01 -1.02636325e+00 1.21921182e+00 -1.44186521e+00
-1.63925016e+00 1.11153567e+00 -5.34408271e-01 -2.51462549e-01
4.88880366e-01 1.39585838e-01 -2.95855790e-01 1.52607396e-01
6.22217774e-01 6.49335444e-01 7.86336482e-01 -1.18118298e+00
-4.79663104e-01 -1.90578550e-01 3.87785025e-02 5.68419814e-01
-2.00242892e-01 2.16028675e-01 -2.33853534e-01 -6.37330115e-01
3.20402771e-01 -1.23547637e+00 -2.52136946e-01 -7.08692133e-01
-5.23034632e-01 -1.71129763e-01 6.69302285e-01 -6.57464743e-01
9.65744138e-01 -1.76199198e+00 4.95148361e-01 8.90503004e-02
-2.93057978e-01 2.41362497e-01 -4.31556910e-01 8.30409944e-01
-3.26443583e-01 -1.99278742e-02 -3.64911318e-01 -2.10613251e-01
-3.53287846e-01 2.61325300e-01 -4.69318807e-01 -5.93974739e-02
7.54987001e-02 9.91173208e-01 -1.10731339e+00 -7.86130786e-01
4.76732627e-02 -4.64222670e-01 -2.40013987e-01 5.05094409e-01
-2.97691189e-02 7.77348816e-01 -5.29730797e-01 3.75676066e-01
5.45202315e-01 -1.49862602e-01 4.19114739e-01 1.63039535e-01
1.22359097e-01 3.80715698e-01 -6.43420875e-01 1.89722538e+00
-6.86894596e-01 3.79857481e-01 -3.15849125e-01 -1.29198778e+00
1.15748453e+00 4.90661234e-01 4.96276990e-02 -4.39421088e-01
5.51241756e-05 5.23986518e-01 9.64288227e-03 -6.08287513e-01
2.86626577e-01 -4.76932287e-01 -3.79039824e-01 7.77852595e-01
1.39903799e-01 -3.68165940e-01 4.88158166e-01 2.62122601e-01
7.29300857e-01 1.78049296e-01 3.35075915e-01 -4.03979182e-01
8.99675190e-01 2.86096096e-01 5.58588088e-01 7.51085222e-01
5.65433018e-02 6.50255740e-01 1.28289893e-01 -2.52879053e-01
-1.35439086e+00 -1.33276594e+00 8.38293973e-03 5.91501892e-01
2.76510507e-01 -1.44533589e-01 -7.66637385e-01 -8.04264009e-01
-4.31659400e-01 8.39726150e-01 -3.98263693e-01 -2.16629982e-01
-7.10903168e-01 -6.21705711e-01 4.27262872e-01 2.43508413e-01
6.22807622e-01 -1.23613334e+00 1.61325887e-01 2.62905031e-01
-8.40618789e-01 -9.38199103e-01 -5.95712841e-01 -5.14176823e-02
-1.00050509e+00 -7.55805612e-01 -8.69708955e-01 -1.50245202e+00
9.96867001e-01 6.52353823e-01 9.96948123e-01 -1.36582732e-01
4.25158799e-01 -1.87947363e-01 -5.58078527e-01 -9.25481170e-02
-1.22377133e+00 1.27200618e-01 1.78890020e-01 7.96785876e-02
5.51637590e-01 -8.05828691e-01 -3.34606022e-01 2.99734473e-01
-7.32964277e-01 5.19670248e-01 7.26266623e-01 1.19927502e+00
4.83591825e-01 -4.99067187e-01 1.17652607e+00 -9.49027002e-01
1.07939541e+00 -6.17795706e-01 -1.30243376e-01 7.64968574e-01
-4.99637991e-01 1.91009164e-01 1.22331679e+00 -5.61916888e-01
-1.29065084e+00 -9.89088640e-02 1.98332265e-01 -4.01003927e-01
-5.79636171e-02 6.20639324e-01 -1.73214853e-01 1.04551084e-01
6.85390711e-01 7.06504703e-01 -3.62392366e-02 -3.91580999e-01
4.78901625e-01 1.26356864e+00 3.85127723e-01 -7.11468220e-01
9.23346102e-01 1.09748857e-03 -4.81008470e-01 -4.58620310e-01
-7.49343276e-01 -3.63439620e-01 -8.05357277e-01 9.93534997e-02
7.32997179e-01 -1.02623510e+00 3.34532470e-01 1.85759485e-01
-1.29641211e+00 2.78608641e-03 -1.15740083e-01 3.84635061e-01
-5.91611981e-01 6.82246208e-01 -6.73402607e-01 -3.17303777e-01
-5.57732522e-01 -1.19754922e+00 8.83199036e-01 4.04490262e-01
-4.49067146e-01 -8.66511643e-01 3.05950373e-01 7.25206614e-01
1.25370910e-02 -9.63725150e-02 1.28164399e+00 -8.50329638e-01
-3.85908246e-01 -2.34192312e-01 -1.21381663e-01 5.85595250e-01
4.65237707e-01 -3.27891886e-01 -5.50900519e-01 -4.10658717e-01
3.26258808e-01 -6.59708083e-01 4.15712476e-01 -4.89532165e-02
5.56393623e-01 -3.37312073e-01 -2.22290784e-01 3.15667272e-01
1.24625695e+00 2.93893874e-01 4.35403079e-01 1.87249348e-01
5.27288914e-01 6.09817386e-01 7.47653961e-01 -2.32421875e-01
1.59060881e-01 5.95156312e-01 -4.33714986e-01 -1.32259473e-01
3.43603864e-02 -5.98308444e-01 5.56779444e-01 1.68703330e+00
2.68962741e-01 -1.04851253e-01 -5.78210771e-01 6.65802300e-01
-2.03169632e+00 -1.10630548e+00 -8.61923993e-02 2.22226787e+00
1.19427514e+00 -5.24322316e-02 -1.48926303e-01 -1.86784819e-01
1.06935775e+00 -3.56572606e-02 -3.67002189e-01 -8.44233751e-01
-9.89313647e-02 4.44934338e-01 -9.14874524e-02 3.18241477e-01
-5.20555019e-01 1.12280285e+00 6.10880518e+00 8.06212962e-01
-9.90135849e-01 1.30071238e-01 4.44285244e-01 6.97598606e-02
-4.60544795e-01 3.96725863e-01 -3.98180753e-01 6.77751303e-01
7.65456438e-01 -6.85159504e-01 4.75289196e-01 6.33594990e-01
6.18035436e-01 -4.42468710e-02 -1.33268595e+00 8.51062894e-01
3.13098967e-01 -1.09113455e+00 5.48932612e-01 -2.82858998e-01
1.15138805e+00 -2.27311850e-02 -2.37529248e-01 4.88180906e-01
3.79112542e-01 -6.52060449e-01 5.38903713e-01 5.19323386e-02
8.10267329e-01 -6.21714234e-01 7.00844824e-01 7.84429610e-01
-1.04856312e+00 4.06702049e-02 -7.58594453e-01 -5.32206483e-02
1.00174531e-01 2.20880777e-01 -1.10022497e+00 9.66350794e-01
1.39555305e-01 6.39670312e-01 -7.59622514e-01 7.79749990e-01
-6.13600552e-01 6.17779911e-01 -7.34126046e-02 -2.07665682e-01
1.45420536e-01 -8.70314121e-01 4.05128896e-01 1.12654436e+00
5.04494786e-01 -1.35274246e-01 2.89374977e-01 8.95906150e-01
-5.83248101e-02 5.62430501e-01 -9.30443168e-01 9.22088698e-02
6.28363311e-01 1.16486967e+00 -5.17463565e-01 -7.13435531e-01
-4.33258355e-01 1.47834373e+00 5.54893494e-01 5.70105433e-01
-7.46492147e-01 -4.43642527e-01 -2.96719968e-02 -3.47791523e-01
-2.89368723e-02 1.41965479e-01 -3.29226255e-01 -1.86589122e+00
3.00667852e-01 -1.18155658e+00 1.25696406e-01 -1.05994081e+00
-1.47962034e+00 8.74616921e-01 1.58595830e-01 -1.69236243e+00
-6.29132688e-01 -5.05292192e-02 -1.11545348e+00 1.14497077e+00
-9.27938044e-01 -1.38792503e+00 1.43927783e-02 6.06344402e-01
9.62458074e-01 -3.53750408e-01 9.28415418e-01 1.46258008e-02
-6.95810080e-01 5.91262460e-01 3.22749168e-01 3.37003171e-02
9.32854772e-01 -1.08896101e+00 4.69429821e-01 1.09252214e+00
4.20151561e-01 9.26899314e-01 6.27324581e-01 -7.28265166e-01
-7.82187700e-01 -1.16674471e+00 1.41745055e+00 -3.98700863e-01
5.95310986e-01 -5.09253502e-01 -6.65646970e-01 6.44922078e-01
6.13600850e-01 -7.67675817e-01 8.71734202e-01 -5.96274622e-02
-2.69211292e-01 4.27648164e-02 -1.02266812e+00 9.17136848e-01
1.01262987e+00 -6.56105638e-01 -1.35042417e+00 6.59507513e-01
7.04659462e-01 -1.21783152e-01 -5.65579951e-01 2.11801648e-01
3.18753302e-01 -7.16822445e-01 5.86686015e-01 -8.06029320e-01
8.58004272e-01 -2.04885885e-01 8.38645697e-02 -1.58864033e+00
-2.77108580e-01 -5.50188601e-01 4.48343396e-01 1.37655902e+00
7.66604722e-01 -6.74261153e-01 6.25809073e-01 2.79318690e-01
-1.53859809e-01 -5.93708932e-01 -8.94569814e-01 -8.80089581e-01
2.16709033e-01 3.53994489e-01 5.74126720e-01 1.17313433e+00
4.74401683e-01 1.05167377e+00 -3.80455524e-01 -2.29749918e-01
5.54622531e-01 8.20972502e-01 9.42218423e-01 -7.24189699e-01
-5.15274167e-01 -7.29067028e-02 1.37868553e-01 -1.20865154e+00
5.24388254e-01 -1.43928933e+00 9.69941840e-02 -1.49908984e+00
6.81898057e-01 -2.90508986e-01 -2.00306922e-01 3.04800898e-01
-4.55350816e-01 1.64769545e-01 1.76073402e-01 8.09246182e-01
-2.81572551e-01 5.73364019e-01 1.33157957e+00 -1.40185252e-01
-4.01657611e-01 1.91734925e-01 -6.31387651e-01 4.74954844e-01
9.94607329e-01 -6.09764457e-01 -6.84509397e-01 -5.28154671e-01
-2.62913495e-01 4.97088730e-01 -5.10916673e-02 -6.94492102e-01
-3.98624949e-02 -4.35371906e-01 1.88616157e-01 -3.70449215e-01
1.18270097e-02 -4.64900672e-01 2.23586887e-01 2.54864722e-01
-5.95561624e-01 3.63447368e-01 -1.75077006e-01 3.92822325e-01
-4.25171942e-01 -5.94069958e-01 8.32207441e-01 -2.50515342e-01
-1.86685354e-01 -1.82297707e-01 -2.91909784e-01 4.10115868e-02
9.50322866e-01 -4.79151964e-01 -9.63318869e-02 -3.78478378e-01
-6.11489654e-01 2.61850953e-01 7.97263861e-01 6.22419119e-01
5.43761909e-01 -1.61470056e+00 -9.68325317e-01 2.12348685e-01
4.09898460e-01 -2.84958065e-01 -8.15524608e-02 5.73950052e-01
-4.18237895e-01 4.59747016e-01 -3.43513489e-01 -5.60620189e-01
-1.07570875e+00 6.33698821e-01 6.64805761e-04 -5.67370534e-01
-2.84362137e-01 4.83359396e-01 3.24024767e-01 -7.20674336e-01
-4.45186287e-01 -6.67723864e-02 -1.19711980e-01 -2.98263967e-01
1.38620347e-01 -2.21522063e-01 -3.24907973e-02 -7.44814038e-01
-6.36955490e-03 4.05057579e-01 -3.64127189e-01 -4.19939071e-01
9.15698230e-01 -3.30417424e-01 -3.43601853e-01 4.33132738e-01
1.21930742e+00 8.45108330e-02 -3.87974858e-01 -3.65141302e-01
-1.62410662e-02 -3.06564331e-01 -6.85128987e-01 -6.31870329e-01
-5.81846952e-01 5.98728240e-01 1.16085649e-01 2.27187872e-02
1.10914350e+00 -7.77150095e-02 1.08956027e+00 5.27997375e-01
6.87612414e-01 -9.77041304e-01 9.18240659e-03 2.76596963e-01
9.44159746e-01 -1.05696058e+00 -3.64335269e-01 -3.11913282e-01
-7.43962109e-01 9.34931755e-01 6.28307223e-01 -3.22954357e-01
-7.10448474e-02 -3.18608195e-01 3.90955508e-01 3.21508586e-01
-7.04329848e-01 1.61708504e-01 2.24366728e-02 4.69973505e-01
4.24747467e-01 -6.49678186e-02 -9.79289412e-01 3.56143802e-01
-4.79253829e-01 4.99658138e-02 6.25569642e-01 8.70906472e-01
-3.06265354e-01 -1.81749451e+00 -2.44338721e-01 4.73109126e-01
-4.83328365e-02 -2.90851027e-01 -7.85874188e-01 4.36172545e-01
4.40526195e-02 1.19281781e+00 -3.48397136e-01 -5.12427151e-01
1.95337936e-01 2.80844092e-01 6.30920708e-01 -1.16351056e+00
-7.73004293e-01 -2.18237601e-02 2.13464275e-01 2.20568310e-02
-4.57341760e-01 -5.88593304e-01 -7.98416853e-01 -3.67362052e-02
-4.86800343e-01 7.90991724e-01 1.87439755e-01 1.18453312e+00
3.11497122e-01 -2.41986156e-01 1.17742038e+00 -6.41502082e-01
-9.20684099e-01 -1.30915117e+00 -1.40926346e-01 8.56315851e-01
-2.31097624e-01 -1.75802991e-01 -3.15670729e-01 2.84603506e-01] | [11.663334846496582, 9.431385040283203] |
b870fd70-5655-4f29-813c-278c03296456 | causes-of-catastrophic-forgetting-in-class | 2209.08010 | null | https://arxiv.org/abs/2209.08010v1 | https://arxiv.org/pdf/2209.08010v1.pdf | Causes of Catastrophic Forgetting in Class-Incremental Semantic Segmentation | Class-incremental learning for semantic segmentation (CiSS) is presently a highly researched field which aims at updating a semantic segmentation model by sequentially learning new semantic classes. A major challenge in CiSS is overcoming the effects of catastrophic forgetting, which describes the sudden drop of accuracy on previously learned classes after the model is trained on a new set of classes. Despite latest advances in mitigating catastrophic forgetting, the underlying causes of forgetting specifically in CiSS are not well understood. Therefore, in a set of experiments and representational analyses, we demonstrate that the semantic shift of the background class and a bias towards new classes are the major causes of forgetting in CiSS. Furthermore, we show that both causes mostly manifest themselves in deeper classification layers of the network, while the early layers of the model are not affected. Finally, we demonstrate how both causes are effectively mitigated utilizing the information contained in the background, with the help of knowledge distillation and an unbiased cross-entropy loss. | ['Jürgen Beyerer', 'Tobias Kalb'] | 2022-09-16 | null | null | null | null | ['class-incremental-semantic-segmentation'] | ['computer-vision'] | [ 7.14708686e-01 4.94656920e-01 1.93332620e-02 -3.71116340e-01
-3.45404744e-01 -4.05616403e-01 5.55549145e-01 3.90361249e-01
-6.32904649e-01 8.90174389e-01 1.09960176e-01 1.37475625e-01
1.04726866e-01 -7.21387029e-01 -1.11775672e+00 -8.05175304e-01
3.19602787e-01 4.40300912e-01 7.30597079e-01 -5.54129593e-02
1.60729021e-01 1.72981262e-01 -1.68194675e+00 3.23532075e-01
1.04394686e+00 8.02289426e-01 2.01939195e-01 4.11582440e-01
-3.30187261e-01 6.26261055e-01 -5.87234855e-01 -4.65739787e-01
-2.39683618e-03 -5.91289282e-01 -9.39344347e-01 -1.95730641e-01
5.49720526e-01 -1.78799480e-01 -2.83936232e-01 1.24459410e+00
7.78626949e-02 2.16122672e-01 6.10315263e-01 -8.74003768e-01
-5.00526726e-01 8.13017070e-01 -3.59082401e-01 2.76270300e-01
-2.44394690e-01 9.72355157e-02 7.69906461e-01 -8.76874030e-01
7.92890191e-01 1.13779342e+00 1.00118244e+00 8.62142920e-01
-1.22303510e+00 -4.04939890e-01 6.04560673e-01 1.95957452e-01
-1.17405558e+00 -3.73857260e-01 6.72262549e-01 -4.04688150e-01
6.98121488e-01 7.41257444e-02 9.21982110e-01 1.19993162e+00
3.12972367e-01 1.14159918e+00 1.03333950e+00 -3.74743819e-01
6.61227942e-01 4.41099703e-01 4.68467444e-01 6.06737077e-01
5.81256866e-01 1.68156680e-02 -8.79576862e-01 1.18656009e-01
2.82799214e-01 2.20498182e-02 -1.49549186e-01 -6.90134585e-01
-7.22466290e-01 6.28190756e-01 5.14908552e-01 4.12142068e-01
-1.60270885e-01 1.71709761e-01 2.84132242e-01 9.38541591e-02
7.38849401e-01 4.37590092e-01 -7.64536738e-01 6.66664075e-03
-1.17966914e+00 1.35115758e-01 4.53196526e-01 6.73617244e-01
8.57756376e-01 -4.84257285e-03 -1.75971702e-01 5.50799549e-01
5.36012538e-02 2.36703306e-01 6.98311925e-01 -7.43839860e-01
3.14265974e-02 7.28667319e-01 -1.36138573e-01 -6.48709893e-01
-2.78114915e-01 -9.23069596e-01 -4.68227774e-01 1.95744097e-01
5.24932027e-01 8.69168863e-02 -1.15954959e+00 2.10480428e+00
2.29516774e-01 7.12883174e-02 2.75425427e-02 4.97714549e-01
3.45386058e-01 8.68931413e-02 5.66099882e-01 -8.55222493e-02
8.70090663e-01 -8.83904576e-01 -5.69315493e-01 -6.39600933e-01
3.32375437e-01 -2.99652189e-01 1.25139451e+00 1.70819893e-01
-9.42160964e-01 -5.18786013e-01 -1.17789817e+00 -1.17723919e-01
-8.54325891e-01 -2.88456410e-01 7.16906965e-01 6.25578403e-01
-1.07765543e+00 1.07390952e+00 -9.10471499e-01 -5.22513866e-01
9.89508390e-01 1.71103403e-01 6.05719388e-02 -6.23720000e-03
-1.16590428e+00 1.01438689e+00 5.85955799e-01 -1.76422045e-01
-1.04398131e+00 -1.17068720e+00 -5.81184030e-01 3.01200479e-01
2.82818079e-01 -8.10001671e-01 1.11452937e+00 -1.32549953e+00
-1.26048589e+00 8.83561730e-01 -2.43472859e-01 -6.60007179e-01
6.83900118e-01 -5.17465293e-01 -8.70671049e-02 -1.31011769e-01
2.25005485e-02 9.56691504e-01 1.13069487e+00 -1.40396214e+00
-6.26846850e-01 -5.87819874e-01 -3.28151464e-01 2.15679854e-01
-4.28161979e-01 -8.51044595e-01 -1.42190188e-01 -6.46234274e-01
3.87682945e-01 -8.16464484e-01 -1.26150832e-01 -7.38233030e-02
-1.94173515e-01 4.25930917e-02 7.75225997e-01 -7.44717181e-01
9.26284015e-01 -2.23788357e+00 3.05311710e-01 -1.13907605e-01
2.46003836e-01 3.93750697e-01 -4.82612699e-02 -7.65981078e-02
-5.32327555e-02 2.41452605e-02 -6.52509212e-01 -4.69178766e-01
-7.85231516e-02 3.37003738e-01 -7.22247481e-01 1.73783213e-01
4.29899126e-01 9.65513468e-01 -1.11688387e+00 2.61821505e-02
1.31931901e-01 5.89088440e-01 -6.41344011e-01 -2.93016315e-01
-5.72895467e-01 4.60608304e-01 -1.28879608e-03 3.95584762e-01
7.65408158e-01 -5.77476770e-02 8.69477466e-02 -7.08645359e-02
5.15064523e-02 1.75624460e-01 -6.74719334e-01 1.91781223e+00
-1.40485302e-01 4.26965147e-01 -3.41587037e-01 -9.56636369e-01
3.98756891e-01 -1.51930049e-01 7.40037337e-02 -7.41685271e-01
-1.58046875e-02 3.12199831e-01 -2.33312875e-01 -8.16521347e-02
6.19601965e-01 -3.10000837e-01 7.49931112e-02 2.38200068e-01
4.71090466e-01 -1.06342450e-01 8.51359144e-02 3.89797628e-01
1.10697007e+00 3.08433563e-01 -1.27212971e-01 -4.01358873e-01
7.03762844e-02 1.06523342e-01 8.04092407e-01 1.11144102e+00
-4.25700992e-01 5.90813339e-01 4.62967277e-01 -4.97305989e-01
-8.73572409e-01 -1.34489381e+00 -1.78163424e-02 9.81688321e-01
1.20250456e-01 -1.10697657e-01 -1.02626109e+00 -9.68193531e-01
2.08918646e-01 1.09961665e+00 -8.58589947e-01 -1.06532025e+00
-3.63475561e-01 -1.07169008e+00 3.32927704e-01 4.77782249e-01
7.46638119e-01 -9.51456785e-01 -8.37017894e-01 2.58145571e-01
-1.19669877e-01 -8.94559801e-01 -1.62827447e-01 3.22922111e-01
-1.41579223e+00 -1.11489391e+00 -7.77227759e-01 -4.94261861e-01
8.73204648e-01 8.88927430e-02 1.11742091e+00 -3.20036635e-02
-5.14361322e-01 4.28106487e-01 4.45696227e-02 -4.73111719e-01
-4.68286932e-01 5.96341014e-01 -1.19543515e-01 -5.55410013e-02
2.90514529e-01 -6.11177921e-01 -6.10675097e-01 -2.47077331e-01
-8.57752323e-01 2.22928807e-01 5.11150718e-01 6.29637241e-01
4.91184831e-01 2.00396061e-01 6.05774403e-01 -1.31271899e+00
2.54628062e-01 -3.81257623e-01 -3.82017910e-01 2.51443177e-01
-9.30055261e-01 3.22856665e-01 1.35035634e-01 -3.46222162e-01
-1.50323546e+00 6.15361892e-03 -1.08629338e-01 -1.39587373e-01
-1.25781238e-01 1.02658339e-01 2.96726655e-02 -7.75774717e-02
4.23002928e-01 3.09556484e-01 -3.04382563e-01 -5.98598123e-01
4.14205253e-01 -5.32042757e-02 4.39764112e-01 -3.87708426e-01
5.13744473e-01 7.65499413e-01 -3.32447350e-01 -7.10855126e-01
-1.46806443e+00 -1.93363264e-01 -7.85094500e-01 -1.51885316e-01
8.11031163e-01 -8.27231884e-01 -1.52185932e-01 8.89133930e-01
-1.02571344e+00 -4.36761588e-01 -1.05180812e+00 2.73626000e-02
-3.56830448e-01 -1.01946797e-02 -4.70997959e-01 -6.17396176e-01
-1.31740287e-01 -7.09139705e-01 8.70137572e-01 5.36476552e-01
-2.96604574e-01 -1.06057942e+00 4.52378653e-02 3.32210809e-01
7.79622018e-01 1.64001044e-02 1.38223362e+00 -4.55507010e-01
-5.54373741e-01 2.95532681e-02 -1.29405454e-01 4.69207197e-01
4.88990583e-02 -2.97648281e-01 -1.36932921e+00 -3.68440717e-01
6.00275509e-02 -1.75606236e-02 1.72283542e+00 3.02239209e-01
1.19926524e+00 -7.62969404e-02 -4.43089038e-01 4.71984982e-01
1.33212221e+00 1.63600907e-01 5.34220099e-01 1.64140224e-01
5.17313778e-01 5.74699700e-01 1.29502967e-01 1.30841359e-01
3.66507888e-01 2.02546656e-01 2.89722413e-01 -3.77338342e-02
-7.42595136e-01 -6.05481267e-01 2.33308047e-01 7.15612411e-01
3.21114957e-01 1.32390648e-01 -8.64031374e-01 8.61326754e-01
-1.85459530e+00 -7.28584647e-01 3.29735905e-01 2.46249676e+00
1.07706022e+00 4.97608066e-01 -4.64187533e-01 1.56113118e-01
6.58435762e-01 4.80036363e-02 -1.14289486e+00 -1.10457465e-01
-4.40013915e-01 3.13417345e-01 4.66925174e-01 6.89102411e-01
-1.03585804e+00 1.38553524e+00 6.90246820e+00 6.60858929e-01
-1.02706563e+00 2.62169003e-01 8.46492946e-01 -1.58474907e-01
-4.45995420e-01 1.24434888e-01 -8.61944914e-01 5.00121117e-01
8.08642149e-01 5.65712638e-02 3.21809351e-01 6.95436239e-01
-2.84669429e-01 -5.11086822e-01 -8.44149172e-01 5.72758257e-01
1.51263118e-01 -1.33398652e+00 3.32689673e-01 -4.52185482e-01
8.61342013e-01 1.36235014e-01 3.58515859e-01 5.62553525e-01
4.22573000e-01 -6.21927857e-01 1.00303388e+00 6.57630622e-01
5.60314119e-01 -5.14329731e-01 4.89231467e-01 1.28631726e-01
-5.48614681e-01 -2.08146974e-01 -3.09035957e-01 6.08298331e-02
-1.13590740e-01 1.05307722e+00 -6.01420403e-01 2.33204179e-02
8.02456915e-01 5.56070089e-01 -9.59744573e-01 8.98013771e-01
-3.27297598e-01 7.73683906e-01 -7.09991679e-02 4.55140382e-01
-4.28244248e-02 1.27924696e-01 5.94777346e-01 9.79546964e-01
1.00266077e-01 -1.57203406e-01 -4.04645741e-01 1.02505505e+00
-1.38026625e-01 -4.53350067e-01 -2.54227072e-01 -1.40387997e-01
1.01911582e-01 7.98612654e-01 -1.01765263e+00 -4.13164914e-01
8.95630866e-02 1.37687802e+00 4.86404240e-01 4.59937483e-01
-6.13308430e-01 -3.37855339e-01 5.99851668e-01 8.55125114e-02
5.01933694e-01 1.35919079e-02 -6.88219070e-01 -1.30404067e+00
-6.08439930e-02 -3.18313718e-01 3.22513133e-01 -6.32801652e-01
-9.14641559e-01 2.84879625e-01 -2.23343626e-01 -1.01068705e-01
3.45171094e-01 -3.78127515e-01 -4.12629783e-01 4.05958265e-01
-1.72099459e+00 -8.10932934e-01 -2.44539410e-01 3.26506734e-01
6.10255837e-01 -7.13063497e-03 7.04176664e-01 4.13939983e-01
-5.60337842e-01 4.87029731e-01 1.37135595e-01 -3.55673879e-01
5.09888887e-01 -1.29437780e+00 4.65426892e-01 8.53455663e-01
1.25964701e-01 4.86382514e-01 8.58767986e-01 -7.78682530e-01
-7.44678319e-01 -1.21706843e+00 1.16008663e+00 -5.95475972e-01
2.15947196e-01 -5.21321118e-01 -1.21829808e+00 5.27539372e-01
-1.05022781e-01 -2.01570362e-01 3.87875140e-01 1.09083243e-01
-5.90824187e-01 -8.40427428e-02 -1.27203894e+00 4.81969327e-01
1.33230591e+00 -5.00299931e-01 -7.18096495e-01 2.00410694e-01
9.23459172e-01 -1.95067257e-01 -9.18663144e-02 4.88968045e-01
3.41093302e-01 -1.02980506e+00 7.65997171e-01 -6.87618732e-01
6.96029887e-02 -9.57540348e-02 -1.93383191e-02 -1.39342594e+00
-8.34425539e-02 -3.69630814e-01 -8.50917250e-02 1.15215886e+00
4.46555436e-01 -6.63915992e-01 1.03555620e+00 4.03155059e-01
-1.29860133e-01 -2.83867836e-01 -1.15271199e+00 -6.82039559e-01
3.01989138e-01 -8.79127085e-02 4.01763767e-01 8.57604802e-01
-3.95347506e-01 3.24010164e-01 1.06969483e-01 -1.54602990e-01
7.87924826e-01 -1.25321478e-01 1.03098877e-01 -1.31027925e+00
4.02441174e-02 -3.26956183e-01 -1.87330857e-01 -5.40682971e-01
2.36034561e-02 -8.48444045e-01 1.83768094e-01 -1.35009551e+00
3.67104709e-01 -4.74046856e-01 -6.71810210e-01 5.72036803e-01
-2.46614501e-01 7.03451335e-02 1.81470871e-01 6.09964281e-02
-7.95821428e-01 9.18062031e-01 9.34592605e-01 -1.92413211e-01
-9.46227834e-02 -4.67679799e-02 -8.53417873e-01 9.28807020e-01
7.65768588e-01 -7.38853693e-01 -5.23351610e-01 -5.37700951e-01
5.52842319e-01 -6.63101494e-01 4.87397373e-01 -1.28695750e+00
8.69635046e-02 2.97522843e-01 4.65501547e-01 -4.14557964e-01
2.18645245e-01 -6.08243227e-01 -1.74420532e-02 8.78209770e-01
-3.54648501e-01 -4.12553102e-01 5.16753256e-01 8.74606073e-01
2.52569765e-02 -1.36002004e-01 1.17539811e+00 -2.87531823e-01
-9.66513991e-01 8.02082270e-02 -3.73495549e-01 3.40782404e-01
9.50453043e-01 -1.42760903e-01 -3.56241345e-01 9.00251940e-02
-1.15316451e+00 -1.36255860e-01 4.23542261e-01 6.60112739e-01
4.26281661e-01 -9.80447412e-01 -3.02508533e-01 3.80973727e-01
-1.46673799e-01 9.38661397e-02 4.87076044e-01 5.42882919e-01
-1.63389876e-01 2.64104664e-01 -1.73403814e-01 -4.02091742e-01
-8.41616988e-01 2.29207188e-01 4.66401815e-01 -1.34336516e-01
-5.26971579e-01 1.28398693e+00 3.74296784e-01 -4.34338212e-01
4.01386499e-01 -3.99962574e-01 6.43834844e-02 3.04388553e-01
2.97239751e-01 3.66436690e-01 3.17898452e-01 -1.69687867e-01
-3.48416984e-01 3.21447074e-01 -4.58196998e-01 4.89075668e-02
1.49403560e+00 -3.13148111e-01 -8.26400444e-02 7.61591256e-01
7.24087834e-01 -3.93751830e-01 -1.71904874e+00 -3.48584563e-01
1.91926539e-01 -1.86663061e-01 2.86681224e-02 -1.15917242e+00
-1.12633860e+00 1.12040675e+00 9.38527465e-01 -3.16102207e-01
9.68500674e-01 1.38154522e-01 8.44478130e-01 1.82636410e-01
3.71123046e-01 -1.54353297e+00 2.04820096e-01 6.83960259e-01
6.34759009e-01 -1.03935051e+00 -1.73863217e-01 -2.95098007e-01
-4.90023583e-01 7.20410764e-01 5.74073255e-01 7.59950429e-02
6.71264887e-01 -7.37588946e-03 -1.62734419e-01 -1.60093024e-01
-6.28944218e-01 -1.08373642e-01 -8.75174925e-02 5.29351115e-01
2.81055551e-02 -4.17570509e-02 2.58339401e-02 6.23916924e-01
-1.35933459e-01 3.62741500e-02 4.39156801e-01 9.00993288e-01
-6.18766606e-01 -8.30225527e-01 1.98270947e-01 3.54506880e-01
-2.44697616e-01 -2.28109717e-01 -4.08459902e-01 4.52470779e-01
5.27715445e-01 3.68964374e-01 9.34920087e-02 -7.53217191e-02
2.61429012e-01 6.00699246e-01 6.24089241e-01 -6.22522533e-01
-4.38789010e-01 -6.74149752e-01 -4.68328297e-01 -5.23217797e-01
-6.42437041e-02 -9.42125797e-01 -1.15110743e+00 -2.63797622e-02
-3.38460088e-01 -3.55402455e-02 6.31424785e-01 1.01490355e+00
5.49438655e-01 8.29873562e-01 5.52416891e-02 -4.11255002e-01
-5.10914743e-01 -5.40590763e-01 -5.91264069e-01 5.13606250e-01
3.86493266e-01 -8.14619362e-01 -6.50167108e-01 8.17995742e-02] | [9.37452220916748, 2.419447660446167] |
8873c68f-3a2a-47d1-be36-52768b2da034 | using-simulation-and-domain-adaptation-to | 1709.07857 | null | http://arxiv.org/abs/1709.07857v2 | http://arxiv.org/pdf/1709.07857v2.pdf | Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping | Instrumenting and collecting annotated visual grasping datasets to train
modern machine learning algorithms can be extremely time-consuming and
expensive. An appealing alternative is to use off-the-shelf simulators to
render synthetic data for which ground-truth annotations are generated
automatically. Unfortunately, models trained purely on simulated data often
fail to generalize to the real world. We study how randomized simulated
environments and domain adaptation methods can be extended to train a grasping
system to grasp novel objects from raw monocular RGB images. We extensively
evaluate our approaches with a total of more than 25,000 physical test grasps,
studying a range of simulation conditions and domain adaptation methods,
including a novel extension of pixel-level domain adaptation that we term the
GraspGAN. We show that, by using synthetic data and domain adaptation, we are
able to reduce the number of real-world samples needed to achieve a given level
of performance by up to 50 times, using only randomly generated simulated
objects. We also show that by using only unlabeled real-world data and our
GraspGAN methodology, we obtain real-world grasping performance without any
real-world labels that is similar to that achieved with 939,777 labeled
real-world samples. | ['Laura Downs', 'Yunfei Bai', 'Matthew Kelcey', 'Vincent Vanhoucke', 'Mrinal Kalakrishnan', 'Alex Irpan', 'Sergey Levine', 'Paul Wohlhart', 'Peter Pastor', 'Kurt Konolige', 'Konstantinos Bousmalis', 'Julian Ibarz'] | 2017-09-22 | null | null | null | null | ['industrial-robots'] | ['robots'] | [ 4.86306965e-01 1.42951692e-02 2.91985035e-01 -4.47026938e-01
-7.60884404e-01 -9.83786702e-01 4.21908081e-01 -1.01210497e-01
-5.30580044e-01 9.15142715e-01 -5.49345076e-01 -6.21158350e-03
3.63103971e-02 -8.00294816e-01 -1.40418696e+00 -6.76856101e-01
-3.19503248e-01 9.58703756e-01 3.09235454e-01 -4.67920899e-02
1.53463185e-01 7.70492554e-01 -1.91749525e+00 2.19331548e-01
6.73982382e-01 1.02911937e+00 6.36053026e-01 9.34733808e-01
2.40508597e-02 2.59066850e-01 -8.44656944e-01 1.94592904e-02
6.97605431e-01 -2.05647245e-01 -6.48430586e-01 2.00887084e-01
2.74514467e-01 -7.36973345e-01 3.11881267e-02 9.29210007e-01
4.99712259e-01 -7.99164400e-02 5.96386850e-01 -1.35456657e+00
-3.99639040e-01 4.32787329e-01 -1.27295122e-01 -5.67288458e-01
4.61504012e-01 5.91345191e-01 2.40192741e-01 -5.41529894e-01
9.93707836e-01 1.18446898e+00 4.25696224e-01 8.28353107e-01
-1.41562474e+00 -5.73062778e-01 -1.23050272e-01 -1.48227409e-01
-7.29895413e-01 -2.81808153e-02 7.38309205e-01 -2.86672264e-01
6.34572029e-01 -1.50910780e-01 8.17860067e-01 1.56672144e+00
4.01982293e-02 7.67887533e-01 1.51011109e+00 -5.36353946e-01
6.83103442e-01 -1.05520234e-01 -3.90547067e-01 7.51557767e-01
4.03677732e-01 4.09299135e-01 -1.04560316e-01 -6.30453452e-02
1.02106094e+00 -1.61283255e-01 -1.24451540e-01 -1.25380182e+00
-1.62788630e+00 5.08690774e-01 6.20237947e-01 3.54067646e-02
-5.53679705e-01 4.85196471e-01 4.37431931e-01 2.98593760e-01
-6.96326792e-02 8.85271728e-01 -6.90535188e-01 -1.15172617e-01
-3.64849895e-01 5.52797318e-01 9.77859259e-01 1.41604388e+00
8.75528693e-01 1.84657890e-02 2.57860988e-01 4.73161936e-01
-9.40331817e-02 8.81958663e-01 2.48854294e-01 -1.63429987e+00
1.42150238e-01 4.01358277e-01 7.01152503e-01 -3.65009129e-01
-3.04865390e-01 -1.57368239e-02 -2.79993147e-01 9.47710037e-01
8.74546826e-01 -1.64710417e-01 -1.45581234e+00 1.58206332e+00
2.57814765e-01 -3.77388597e-01 1.33429274e-01 9.36373532e-01
3.05342287e-01 3.80862653e-01 9.11339670e-02 1.21776283e-01
7.90501714e-01 -7.95630872e-01 -7.30402023e-02 -1.83461621e-01
4.26328778e-01 -5.96082807e-01 1.60561454e+00 8.19281161e-01
-9.82880652e-01 -5.27013421e-01 -1.17126369e+00 3.70903134e-01
-5.29348671e-01 1.43973947e-01 8.85238767e-01 5.78670025e-01
-7.92917192e-01 9.09209609e-01 -1.07850349e+00 -5.89123905e-01
5.29077172e-01 5.22362828e-01 -5.73406041e-01 -4.22069073e-01
-5.08145392e-01 9.99960244e-01 6.18197501e-01 -1.54265016e-01
-1.60079229e+00 -6.21847689e-01 -5.36349773e-01 -3.20586443e-01
4.75514144e-01 -4.98788118e-01 1.33387744e+00 -8.26077402e-01
-1.62354004e+00 9.24342036e-01 3.23104620e-01 -2.18206108e-01
7.52252758e-01 -2.74956465e-01 3.46899211e-01 3.44363689e-01
-5.25472052e-02 8.98429394e-01 7.48901904e-01 -2.16714907e+00
-2.47896433e-01 -2.52564728e-01 3.39183301e-01 -1.00916512e-01
4.63842042e-02 -2.67834872e-01 1.27895717e-02 -2.70545006e-01
1.31447390e-01 -1.06703413e+00 -2.11464211e-01 5.73606730e-01
-1.52349010e-01 2.00624257e-01 7.67901659e-01 -3.86992514e-01
-3.12064856e-01 -1.75683177e+00 2.65189558e-01 6.64607286e-02
-9.21129957e-02 3.09736967e-01 -3.15774858e-01 4.90490109e-01
2.25261331e-01 -2.94450551e-01 -5.19647956e-01 -1.41214103e-01
2.28042766e-01 6.67960107e-01 -3.59384418e-01 3.52090865e-01
9.39372182e-02 9.06478524e-01 -1.40739369e+00 -3.29849571e-01
6.81290329e-01 1.63805857e-01 -3.91847491e-01 5.20058811e-01
-8.38856280e-01 9.05635238e-01 -3.12992513e-01 8.14902425e-01
6.76236272e-01 8.01170766e-02 1.28065318e-01 -1.45373549e-02
2.79186010e-01 -2.88569659e-01 -9.74106967e-01 2.14334893e+00
-6.21006191e-01 2.67177969e-01 2.69521385e-01 -1.06381488e+00
1.08068740e+00 -7.18799159e-02 5.85905373e-01 -3.67926151e-01
2.26390362e-01 5.19144714e-01 -1.59282848e-01 -6.44916832e-01
8.10597390e-02 -2.22828180e-01 -1.62377819e-01 4.22223687e-01
3.74790788e-01 -9.02318239e-01 4.14859541e-02 -2.54954368e-01
1.35650706e+00 8.99984777e-01 -1.78768814e-01 -1.70123801e-01
-1.16159953e-01 5.30447960e-01 1.52272627e-01 8.38942170e-01
-2.53883183e-01 6.70556843e-01 2.96538860e-01 -4.87106591e-01
-1.66818690e+00 -1.62688231e+00 2.09608868e-01 8.37376714e-01
4.09751385e-01 3.53123844e-01 -9.03129518e-01 -5.13117552e-01
2.72987038e-01 7.23070681e-01 -7.27712929e-01 -1.76536530e-01
-7.06619084e-01 -4.32106465e-01 4.39052284e-01 7.54887521e-01
6.31664515e-01 -1.54983592e+00 -1.42191577e+00 2.10473001e-01
2.02398151e-01 -1.23249614e+00 6.07269943e-01 4.97307986e-01
-8.65931928e-01 -1.12400544e+00 -1.04189551e+00 -6.48402274e-01
8.19131136e-01 3.78560228e-03 1.16683233e+00 -5.40870950e-02
-5.49131691e-01 6.49768233e-01 -6.79597557e-01 -6.36880815e-01
-7.39235938e-01 -7.61912242e-02 -1.80824567e-03 -6.62258208e-01
5.25894240e-02 -5.81516802e-01 -5.10377467e-01 2.54536986e-01
-8.95287037e-01 7.45089492e-03 8.06173742e-01 9.72401679e-01
3.82572085e-01 -4.70102072e-01 4.81446117e-01 -5.52465796e-01
3.01598698e-01 -3.37086618e-02 -8.05578589e-01 4.35201049e-01
-2.61771351e-01 2.32888460e-01 7.15032816e-01 -7.79530764e-01
-9.54839230e-01 4.15155560e-01 2.32106164e-01 -7.02188194e-01
-5.89950502e-01 -1.07580714e-01 -1.15874283e-01 -3.96297276e-01
9.03533936e-01 7.06854984e-02 1.12728886e-01 -2.94178814e-01
5.99341571e-01 4.30202782e-01 7.86130846e-01 -1.22620249e+00
8.02933693e-01 6.05656207e-01 5.89174554e-02 -6.80047870e-01
-3.98387879e-01 -3.06709274e-03 -8.97725224e-01 -4.08668667e-01
7.42727578e-01 -3.94162029e-01 -9.55844820e-01 6.90506339e-01
-1.16192985e+00 -1.10188484e+00 -5.82088709e-01 5.24601579e-01
-1.30194139e+00 2.36332148e-01 -2.87345499e-01 -8.40419173e-01
3.13786454e-02 -1.25525057e+00 1.50198781e+00 1.54069429e-02
9.07100886e-02 -3.93101543e-01 -2.41443411e-01 -1.19992353e-01
3.86861145e-01 9.70021784e-01 9.18983817e-01 -3.03402394e-01
-6.96013629e-01 -1.88158467e-01 -3.32853049e-01 4.90056396e-01
2.34588623e-01 -1.27874374e-01 -9.13560390e-01 -5.62861860e-01
-1.52738780e-01 -8.47399056e-01 3.57587487e-01 7.11326227e-02
1.33993483e+00 2.08207116e-01 -3.57925087e-01 3.37741315e-01
1.52863550e+00 4.17832524e-01 5.50638199e-01 3.41692239e-01
5.72404563e-01 6.27490640e-01 1.00436747e+00 2.54241794e-01
-1.30737036e-01 4.81902689e-01 9.09027100e-01 4.77561057e-02
-4.93831784e-02 -3.12894136e-01 1.63468160e-02 7.55872130e-02
-3.65650028e-01 -1.01503722e-01 -1.12436378e+00 7.25709915e-01
-1.50644791e+00 -5.11312544e-01 3.53194803e-01 2.35121632e+00
6.63051307e-01 1.55993566e-01 3.48193906e-02 1.02693014e-01
5.14995515e-01 -4.56623077e-01 -8.83052051e-01 -1.78125560e-01
1.62763987e-02 7.29658127e-01 7.73921311e-01 1.45620778e-01
-9.29614425e-01 1.03937101e+00 6.44722986e+00 1.82947427e-01
-1.01696551e+00 -7.68992379e-02 2.22669244e-02 5.13937278e-03
-7.05801398e-02 -1.78929027e-02 -1.43712014e-02 2.87165344e-01
6.80084825e-01 1.69527635e-01 9.17845726e-01 1.27678883e+00
-2.74665952e-01 -4.72566992e-01 -1.38342047e+00 9.11544263e-01
2.54217498e-02 -9.30323541e-01 -2.37166569e-01 -5.59886694e-02
7.77015746e-01 3.60834710e-02 -2.63420045e-01 3.03854436e-01
8.90547156e-01 -9.25367355e-01 7.95910358e-01 3.91987592e-01
7.63931096e-01 -4.16122198e-01 5.18742979e-01 3.75400126e-01
-6.40475214e-01 -1.95070863e-01 -5.05894065e-01 1.42867669e-01
1.59003824e-01 1.26752242e-01 -1.03917933e+00 5.27999282e-01
8.68422389e-01 1.71226054e-01 -2.18849346e-01 1.01059318e+00
-3.04756284e-01 1.71287626e-01 -6.18662596e-01 -3.71523499e-01
2.03872502e-01 1.25833288e-01 3.28344047e-01 7.08305955e-01
2.56927937e-01 -6.52176067e-02 1.19947329e-01 1.01834512e+00
1.08857580e-01 -5.27314186e-01 -9.27489460e-01 7.12576658e-02
3.37178826e-01 7.47293413e-01 -8.84858727e-01 -4.07728523e-01
2.05714345e-01 1.12665915e+00 2.80617028e-01 3.55888098e-01
-7.38317788e-01 -4.25100386e-01 3.52330774e-01 -1.33330584e-01
3.42180282e-01 -6.71571851e-01 -3.41523170e-01 -9.27901924e-01
1.56229004e-01 -8.20278585e-01 -3.75667632e-01 -1.23508537e+00
-1.20644987e+00 5.45221150e-01 4.81245786e-01 -1.19611466e+00
-4.68785465e-01 -1.28033221e+00 7.87353814e-02 6.51069880e-01
-1.19645154e+00 -1.17012370e+00 -1.03765547e+00 3.00249577e-01
3.97337526e-01 3.46147381e-02 1.06487882e+00 -7.99335167e-02
4.57675576e-01 1.77422598e-01 1.25466794e-01 -6.84066266e-02
6.47090733e-01 -1.31972063e+00 3.80342335e-01 3.90170813e-01
-1.62886009e-01 3.24664205e-01 1.07111573e+00 -4.84114587e-01
-1.55965447e+00 -8.42517912e-01 -2.57796794e-01 -4.97344494e-01
4.71209735e-01 -6.08896196e-01 -7.72153318e-01 7.80642152e-01
-7.89608657e-02 2.15865642e-01 -1.25411540e-01 -4.74377990e-01
-2.23691508e-01 4.06099558e-02 -1.70238304e+00 3.85365397e-01
1.41561174e+00 -2.21808106e-01 -5.95322371e-01 4.21889871e-01
5.83456576e-01 -6.34621024e-01 -8.74261498e-01 7.81791389e-01
8.79453540e-01 -8.75724733e-01 1.03022790e+00 -7.11314917e-01
6.40132785e-01 -1.95324779e-01 -5.14550269e-01 -1.50658119e+00
3.36451441e-01 -2.31124625e-01 9.19602439e-02 8.08906257e-01
5.43539301e-02 -6.91666961e-01 1.07620966e+00 5.15167892e-01
-1.20162226e-01 -5.10839045e-01 -6.60001993e-01 -1.25499129e+00
3.33812356e-01 -3.28609705e-01 7.04810500e-01 6.23388350e-01
-3.25474322e-01 -4.20687765e-01 -4.17574942e-02 -4.51884046e-03
1.02992773e+00 3.68183225e-01 1.29252100e+00 -1.11984074e+00
-3.02794069e-01 7.69540593e-02 -5.58657229e-01 -9.00490284e-01
2.73371547e-01 -4.93704587e-01 6.14897311e-01 -1.64241171e+00
-1.77625809e-02 -9.72896218e-01 1.15728728e-01 5.31788468e-01
3.04179460e-01 1.59185648e-01 3.35468471e-01 3.64674069e-02
-1.13951944e-01 5.29024839e-01 1.55459154e+00 -6.09472394e-02
1.56060770e-01 -2.73620725e-01 6.54450580e-02 6.09104931e-01
7.94887125e-01 -3.03031445e-01 -3.68312031e-01 -6.56271398e-01
-2.64242798e-01 -6.39066100e-02 7.54201531e-01 -1.39487171e+00
-2.80285060e-01 -2.78980136e-01 5.71768999e-01 -3.01721662e-01
6.49698794e-01 -1.10934448e+00 1.03892639e-01 6.96506262e-01
-1.61103413e-01 -2.58592010e-01 3.54099602e-01 5.26676714e-01
3.15828860e-01 -4.92597520e-01 6.92815006e-01 -6.79941714e-01
-8.24456632e-01 -1.18803158e-01 -1.09659679e-01 -2.68224001e-01
1.53078139e+00 -2.24127531e-01 -3.94365847e-01 -2.55374670e-01
-7.66264975e-01 -7.85782859e-02 1.14187646e+00 3.14973176e-01
5.73215663e-01 -1.14843595e+00 -4.26500916e-01 1.83822781e-01
2.67625868e-01 3.03263247e-01 -1.06755339e-01 -7.58438930e-02
-1.09575570e+00 1.43732592e-01 -8.20293784e-01 -9.46038067e-01
-8.48154247e-01 8.97146702e-01 1.85601115e-01 1.18753739e-01
-5.87308347e-01 6.57469034e-01 -1.68113515e-01 -8.71949852e-01
2.28772402e-01 -6.29906774e-01 7.52543092e-01 -6.89952195e-01
-7.91908950e-02 1.70684546e-01 3.36195119e-02 -5.12513556e-02
-2.41888613e-01 5.26927471e-01 4.43446606e-01 -2.81150132e-01
1.46383178e+00 5.14708638e-01 2.27652639e-01 4.92764384e-01
9.47066247e-01 -5.83878696e-01 -1.92909300e+00 1.98534504e-01
-1.74674734e-01 -6.69893742e-01 -5.72034180e-01 -1.13849115e+00
-8.15733433e-01 9.63978469e-01 8.37977707e-01 -2.83286069e-02
1.04654908e+00 1.76470727e-01 5.39644122e-01 8.07171404e-01
1.49551761e+00 -9.45781410e-01 3.09315443e-01 1.04147673e-01
1.10643613e+00 -1.39100063e+00 -2.32155144e-01 -5.08546114e-01
-4.54660326e-01 1.02279186e+00 6.88583732e-01 -6.74605191e-01
1.69495299e-01 5.15186012e-01 9.77680907e-02 -5.65836690e-02
-2.50520527e-01 1.04755908e-02 -4.93111074e-01 1.23917377e+00
-3.62204403e-01 2.27827594e-01 2.09680125e-01 -2.17066053e-02
-2.36699939e-01 3.24288517e-01 5.68096340e-01 1.50304508e+00
-4.40477312e-01 -1.18139851e+00 -4.10337299e-01 3.30335945e-01
1.57962833e-02 5.40645063e-01 -3.16281766e-01 1.14551497e+00
-4.95598745e-03 6.22967243e-01 -4.74139713e-02 -3.69039088e-01
4.89616334e-01 4.13785279e-02 1.36724794e+00 -5.99965572e-01
-1.42317578e-01 -5.69496751e-01 -8.53806436e-02 -6.74925983e-01
-6.81644857e-01 -5.75439572e-01 -1.33659530e+00 -7.15777799e-02
-1.23693205e-01 -2.62646973e-01 1.19547820e+00 8.41054201e-01
9.15157944e-02 5.37172437e-01 3.50685775e-01 -1.68397796e+00
-7.53383696e-01 -1.00290942e+00 -3.48018795e-01 4.98939395e-01
1.97237283e-01 -1.16295862e+00 -2.55612969e-01 2.45719582e-01] | [5.8810715675354, -0.8258413076400757] |
b2d3c932-8042-42cd-be12-bbcf33433479 | frame-mining-a-free-lunch-for-learning | 2210.07442 | null | https://arxiv.org/abs/2210.07442v1 | https://arxiv.org/pdf/2210.07442v1.pdf | Frame Mining: a Free Lunch for Learning Robotic Manipulation from 3D Point Clouds | We study how choices of input point cloud coordinate frames impact learning of manipulation skills from 3D point clouds. There exist a variety of coordinate frame choices to normalize captured robot-object-interaction point clouds. We find that different frames have a profound effect on agent learning performance, and the trend is similar across 3D backbone networks. In particular, the end-effector frame and the target-part frame achieve higher training efficiency than the commonly used world frame and robot-base frame in many tasks, intuitively because they provide helpful alignments among point clouds across time steps and thus can simplify visual module learning. Moreover, the well-performing frames vary across tasks, and some tasks may benefit from multiple frame candidates. We thus propose FrameMiners to adaptively select candidate frames and fuse their merits in a task-agnostic manner. Experimentally, FrameMiners achieves on-par or significantly higher performance than the best single-frame version on five fully physical manipulation tasks adapted from ManiSkill and OCRTOC. Without changing existing camera placements or adding extra cameras, point cloud frame mining can serve as a free lunch to improve 3D manipulation learning. | ['Hao Su', 'Yangyan Li', 'Zhan Ling', 'Xuanlin Li', 'Minghua Liu'] | 2022-10-14 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [-2.14029938e-01 -3.81587356e-01 -4.59697664e-01 -2.69109488e-01
-4.91208673e-01 -7.42068172e-01 6.19089961e-01 -5.17936312e-02
-6.71355128e-01 3.37172598e-01 -1.67838082e-01 -1.60746932e-01
-1.85090140e-01 -3.99265379e-01 -1.11270761e+00 -5.59194386e-01
-1.69133693e-01 7.19582081e-01 6.09573722e-01 -4.15199906e-01
3.82939667e-01 7.50764072e-01 -1.54889846e+00 3.24542783e-02
4.98075694e-01 5.74757397e-01 7.48893142e-01 6.53848350e-01
-1.25274524e-01 7.26250529e-01 -7.47278452e-01 -1.11266457e-01
6.71940684e-01 8.70419517e-02 -6.60864770e-01 3.17365080e-01
5.43887615e-01 -5.26234150e-01 -3.42726529e-01 8.53583455e-01
4.86062586e-01 5.14704645e-01 4.75417346e-01 -1.48129499e+00
-1.17540367e-01 4.84235018e-01 -8.28796387e-01 3.03593427e-01
3.02957296e-01 6.79936767e-01 6.49355233e-01 -8.34779859e-01
9.15238440e-01 1.59391975e+00 5.90066016e-01 4.32201028e-01
-1.02005863e+00 -6.69338703e-01 5.16284466e-01 2.67046094e-01
-9.69997764e-01 -3.71476293e-01 8.06502938e-01 -4.25217211e-01
1.26289332e+00 -1.11410499e-01 9.86819863e-01 1.19756317e+00
5.16505241e-01 7.60834157e-01 6.86856925e-01 -2.80817211e-01
1.11683875e-01 -4.40907329e-01 1.34315258e-02 8.65592837e-01
2.51289040e-01 6.40634373e-02 -6.90355837e-01 5.06315986e-03
1.19399917e+00 3.38911623e-01 -2.75072098e-01 -1.28106618e+00
-1.64907718e+00 5.82049251e-01 5.92479169e-01 -4.12350819e-02
-3.16348940e-01 5.03224373e-01 2.61329472e-01 4.47642684e-01
4.54139374e-02 7.84860194e-01 -5.13643086e-01 -3.17732036e-01
-2.29972944e-01 6.36952877e-01 5.78243077e-01 1.38508654e+00
8.09176922e-01 1.18978575e-01 3.95524651e-02 5.03701210e-01
3.56123820e-02 4.53437805e-01 1.42638311e-01 -1.70761943e+00
7.23954499e-01 4.11701798e-01 1.32439882e-01 -9.59936678e-01
-6.59240961e-01 -1.27455488e-01 -2.69762009e-01 7.27101147e-01
7.78988659e-01 -1.12027384e-01 -1.11961603e+00 1.59909368e+00
4.88640934e-01 -2.50288788e-02 -1.98367253e-01 1.14544702e+00
5.42718232e-01 3.43573093e-01 8.91636685e-02 3.28211844e-01
1.20010257e+00 -1.01522911e+00 -3.78315628e-01 -5.37533283e-01
6.88288748e-01 -7.45467246e-01 1.18917572e+00 2.59791315e-01
-1.24010515e+00 -6.70866966e-01 -9.47846591e-01 -3.53799164e-01
-2.33880162e-01 -2.86646187e-02 8.85260820e-01 1.27062842e-01
-7.28141010e-01 7.52816498e-01 -1.26033545e+00 -3.30635905e-01
2.83760458e-01 5.46117425e-01 -6.53134704e-01 -1.53425649e-01
-4.04757500e-01 1.30856526e+00 4.17764843e-01 -9.42635760e-02
-1.09374022e+00 -8.66013110e-01 -8.94672096e-01 -2.05707207e-01
5.57240725e-01 -1.10387981e+00 1.53693080e+00 -6.38857245e-01
-1.56306398e+00 7.97193885e-01 4.64168116e-02 -3.83763939e-01
5.76365888e-01 -4.11050856e-01 4.44947958e-01 2.08476543e-01
4.83436137e-03 1.30428410e+00 1.03093028e+00 -1.44830966e+00
-8.41139734e-01 -3.97319764e-01 5.89533925e-01 6.81157291e-01
2.13465691e-01 -3.95689398e-01 -8.23842704e-01 -4.64861929e-01
2.56620765e-01 -1.26727629e+00 -2.77192801e-01 2.63959855e-01
-5.08838333e-02 -3.74225974e-01 1.03387702e+00 -8.83415788e-02
2.29943827e-01 -1.92786098e+00 5.65982044e-01 -5.65101281e-02
3.60384941e-01 -1.28705323e-01 -2.12411210e-01 1.37537166e-01
-3.62382382e-02 -2.40829095e-01 2.74740726e-01 -2.91856319e-01
-9.87798274e-02 4.99087095e-01 -9.28466991e-02 6.01949632e-01
1.92186207e-01 8.02430689e-01 -1.06761694e+00 -2.87787437e-01
6.69866681e-01 2.87529260e-01 -6.48240685e-01 1.15268134e-01
-4.60597813e-01 5.44562042e-01 -4.60418135e-01 6.63964212e-01
3.37069958e-01 -3.71104240e-01 -1.50462806e-01 -1.57000244e-01
3.23128402e-02 2.26294905e-01 -9.47407007e-01 2.30654144e+00
-4.10037756e-01 7.25776911e-01 1.45477921e-01 -6.27517760e-01
7.13417947e-01 -2.52418630e-02 5.93531609e-01 -3.80204856e-01
1.97067916e-01 -2.00781450e-01 2.77192771e-01 -5.32181144e-01
7.01731205e-01 2.63922870e-01 1.85052395e-01 1.94030255e-01
2.40035132e-01 -6.95464492e-01 2.89172381e-01 -3.00732125e-02
1.26259089e+00 6.70151532e-01 2.20497519e-01 -8.19058493e-02
-8.72564912e-02 6.56259716e-01 3.69979292e-01 8.00902009e-01
-3.38995486e-01 7.16138363e-01 3.98563713e-01 -6.80300891e-01
-1.21293211e+00 -1.07994664e+00 1.86281085e-01 1.26284242e+00
7.09856033e-01 -3.29807132e-01 -4.20795083e-01 -5.48993707e-01
2.82496810e-01 5.77170432e-01 -3.54862988e-01 -9.65962186e-02
-8.53220165e-01 -1.42991051e-01 6.40005022e-02 5.74218512e-01
2.34651327e-01 -1.01799214e+00 -1.16651738e+00 5.35122156e-02
-5.15492074e-02 -1.12424719e+00 -3.50573480e-01 7.03264654e-01
-9.50207174e-01 -1.28332710e+00 -6.86128557e-01 -7.93455303e-01
7.06362903e-01 1.09000790e+00 1.31425822e+00 -1.08544920e-02
5.43891639e-02 6.48759067e-01 -5.04178584e-01 -5.79021990e-01
-1.92438632e-01 3.21344376e-01 2.77471900e-01 -9.58511949e-01
2.97808558e-01 -4.89603639e-01 -4.58751559e-01 3.25548917e-01
-3.90115142e-01 1.77800938e-01 5.79974115e-01 6.43303812e-01
5.06877363e-01 -2.54024923e-01 -5.15483469e-02 -6.02587759e-01
3.29132020e-01 -2.02616736e-01 -6.62321806e-01 -5.83756864e-02
-8.32824707e-02 5.02454340e-02 3.76156539e-01 -5.92883408e-01
-7.84666121e-01 3.69638234e-01 2.93126255e-01 -1.20645690e+00
-1.56978711e-01 4.27742489e-02 1.06803164e-01 -2.71976829e-01
8.24297369e-01 -4.51062232e-01 2.02444866e-01 -3.46997112e-01
4.23202038e-01 -1.02470301e-01 6.17529035e-01 -9.26174641e-01
9.73113298e-01 4.26100224e-01 -1.73038960e-01 -5.06589174e-01
-4.49088424e-01 -5.12552023e-01 -8.10493886e-01 -3.06302100e-01
1.04306734e+00 -1.08560586e+00 -9.02306020e-01 2.90791601e-01
-1.45393205e+00 -6.30439043e-01 -1.71336472e-01 6.44870520e-01
-8.19302619e-01 7.15983286e-02 -4.56047326e-01 -2.95427740e-01
1.17249168e-01 -1.77365792e+00 1.38953495e+00 2.12569863e-01
-2.85564870e-01 -6.07953906e-01 -1.78521708e-01 3.40080969e-02
-1.84987500e-01 2.59526342e-01 9.04871523e-01 -1.83913171e-01
-8.19773376e-01 8.58229101e-02 1.09871954e-01 -2.96044081e-01
2.17169210e-01 -6.09334037e-02 -7.24469781e-01 -4.75145489e-01
-1.04361691e-01 -3.74168903e-01 6.98250830e-01 5.42366564e-01
1.12024939e+00 2.03256264e-01 -4.77575541e-01 8.49784911e-01
9.74717319e-01 2.68051475e-01 2.15184227e-01 7.35661030e-01
9.69152987e-01 4.85514134e-01 7.29094505e-01 1.81005791e-01
3.73244762e-01 8.83513331e-01 7.26629794e-01 8.89871567e-02
-5.74974641e-02 -1.19167052e-01 5.55331528e-01 4.92915750e-01
-1.29747763e-01 -1.98565453e-01 -1.02182794e+00 2.99027801e-01
-1.89468694e+00 -9.81828809e-01 7.79415146e-02 1.97518289e+00
6.65125549e-01 5.18575847e-01 2.55734026e-01 -2.31652617e-01
5.75712204e-01 2.38262758e-01 -7.95324683e-01 2.26490006e-01
6.70513511e-02 3.94410640e-02 9.01933253e-01 4.29174632e-01
-1.04368186e+00 1.05170739e+00 6.23847294e+00 5.84232450e-01
-9.70911145e-01 -5.64224124e-02 7.51086026e-02 -4.58815843e-01
1.25089988e-01 2.54068412e-02 -7.86576390e-01 1.28050283e-01
2.68357694e-01 -1.15870081e-01 4.63595450e-01 1.18340850e+00
1.74556717e-01 -1.40075639e-01 -1.49061191e+00 1.21755409e+00
-1.74686611e-01 -1.45178258e+00 -1.13699764e-01 -5.80910705e-02
5.84865928e-01 3.42084467e-01 -2.33960594e-03 1.98743179e-01
7.58463323e-01 -7.36223459e-01 1.01325858e+00 2.03575894e-01
3.43384981e-01 -6.63440824e-01 3.08155298e-01 3.91750485e-01
-1.16700101e+00 -1.58974662e-01 -5.64054072e-01 -2.12261543e-01
8.89188051e-02 9.39726457e-02 -9.73217070e-01 3.20452631e-01
1.07311213e+00 7.58468807e-01 -6.11803412e-01 1.07932448e+00
-7.32326806e-02 -1.09268222e-02 -5.15861154e-01 2.67063212e-02
3.82270366e-01 2.90454701e-02 7.82896459e-01 8.37449253e-01
1.59330949e-01 5.32255843e-02 5.42621970e-01 5.39821327e-01
1.08189844e-01 -5.96278846e-01 -9.18380022e-01 3.82265419e-01
7.46830285e-01 1.18105197e+00 -1.08602297e+00 -2.13816747e-01
-3.78844082e-01 5.93534291e-01 3.68017703e-01 4.12689418e-01
-9.10552561e-01 -1.51779860e-01 1.05818427e+00 2.90363152e-02
4.35631305e-01 -8.90387654e-01 -2.20245987e-01 -1.03068829e+00
5.60631603e-02 -1.09748006e+00 1.20203368e-01 -1.06320775e+00
-1.04988420e+00 2.05602288e-01 4.37793493e-01 -1.64388943e+00
-3.72863024e-01 -8.14931154e-01 -3.67282510e-01 4.23399568e-01
-1.25839615e+00 -9.60924327e-01 -6.47871792e-01 6.81722820e-01
9.34158325e-01 -1.30556002e-01 3.56000841e-01 -1.13399960e-01
-2.08760396e-01 2.88511962e-01 -2.02203140e-01 2.96783280e-02
7.58843184e-01 -1.28917873e+00 7.13658154e-01 5.77592492e-01
3.28747809e-01 6.53999805e-01 7.00107515e-01 -6.18119419e-01
-1.92902148e+00 -8.04252803e-01 -4.22212519e-02 -1.09651864e+00
3.54903638e-01 -3.90997618e-01 -7.20972300e-01 8.53771508e-01
2.05403164e-01 -1.33291095e-01 -4.24129702e-02 5.48654497e-02
-2.43275970e-01 -4.20885440e-03 -7.61452317e-01 9.90017831e-01
1.49687326e+00 -2.56046891e-01 -7.59900749e-01 6.47088885e-01
9.47315216e-01 -1.17866862e+00 -7.33350873e-01 3.68159205e-01
4.38412607e-01 -9.24174607e-01 1.25739837e+00 -5.69500625e-01
4.07215565e-01 -5.65126538e-01 3.10357716e-02 -1.41134346e+00
-5.16117394e-01 -6.14965975e-01 -1.47068202e-01 6.07847810e-01
1.15128390e-01 -1.75706744e-01 1.10028434e+00 5.44333398e-01
-4.32883054e-01 -4.19261694e-01 -7.71289289e-01 -7.80977964e-01
-8.59096646e-02 -4.63732064e-01 4.94195938e-01 7.13656902e-01
-2.34431341e-01 3.56035650e-01 -1.12250790e-01 2.08529904e-01
5.22299767e-01 6.65945709e-02 1.50995302e+00 -1.00650346e+00
-2.85606891e-01 -6.46520555e-01 -3.90839756e-01 -1.40267611e+00
3.51558298e-01 -7.37886727e-01 2.08156601e-01 -1.20405066e+00
-1.88044712e-01 -6.67211115e-01 1.97318867e-01 7.65045702e-01
-1.81253821e-01 -8.33282173e-02 7.30864882e-01 5.04733026e-01
-6.20265901e-01 4.21018332e-01 1.57068920e+00 -2.23629713e-01
-4.82262015e-01 -3.52499820e-02 -2.09314570e-01 9.20274675e-01
6.47329152e-01 -3.41052026e-01 -6.15684152e-01 -1.16088152e+00
-9.60953385e-02 1.67381540e-01 5.95589042e-01 -1.20767009e+00
3.12473685e-01 -4.32696611e-01 7.91582406e-01 -7.07220912e-01
7.05756426e-01 -9.86438096e-01 2.96556085e-01 4.37921107e-01
-2.36921191e-01 8.26622367e-01 2.00328454e-01 6.15320206e-01
2.32222542e-01 -2.29311734e-01 5.29463649e-01 -5.40110409e-01
-1.10598052e+00 2.04014927e-01 -3.13489228e-01 -1.25518069e-01
1.14853454e+00 -4.67627347e-01 -5.23407996e-01 -2.08259463e-01
-3.73294443e-01 3.51330519e-01 9.35420215e-01 9.06729639e-01
4.63544369e-01 -1.08657575e+00 -1.17164724e-01 1.34667516e-01
3.86280082e-02 7.75150955e-01 -1.29809499e-01 6.58289433e-01
-6.61530912e-01 1.23103052e-01 -6.44237876e-01 -1.35725689e+00
-1.06944716e+00 3.45831454e-01 2.57740676e-01 1.21425465e-01
-9.80849624e-01 9.39081430e-01 2.69770473e-01 -4.96790469e-01
5.08318603e-01 -7.65254200e-01 3.36806923e-02 -2.52494037e-01
1.90757364e-01 2.92013705e-01 8.32110271e-02 -3.18737626e-01
-2.40122899e-01 7.01620162e-01 -2.23960623e-01 1.20075598e-01
1.33688271e+00 8.38636905e-02 2.67923832e-01 4.91693169e-01
9.06817257e-01 -4.38038558e-01 -2.03849888e+00 4.47896235e-02
-2.20034286e-01 -7.18133867e-01 -1.67298898e-01 -1.76514834e-01
-1.14212847e+00 6.02211654e-01 5.43887913e-01 -3.79685313e-02
5.63563466e-01 2.01062858e-02 4.59861100e-01 1.05794621e+00
7.92332351e-01 -9.51830447e-01 2.96612263e-01 8.65627766e-01
8.41639638e-01 -1.30700183e+00 2.76735276e-01 -3.43361408e-01
-6.84567869e-01 1.23575366e+00 1.09356081e+00 -4.09505367e-01
2.56277323e-01 4.04407024e-01 7.73487911e-02 -3.42716068e-01
-7.89531469e-01 -2.53413975e-01 4.50597377e-03 1.02619278e+00
2.85335518e-02 -3.09759945e-01 3.90449047e-01 -3.99651490e-02
-4.73939359e-01 -3.09286416e-01 4.07758802e-01 1.34530032e+00
-6.02911294e-01 -9.32624757e-01 -5.27614355e-01 3.16041648e-01
7.22989440e-02 4.19328928e-01 -2.37341195e-01 1.30032361e+00
-6.61769211e-02 6.62057877e-01 2.73264259e-01 -4.42428470e-01
5.03890514e-01 -4.09917906e-02 9.68912303e-01 -7.34414935e-01
-6.51066005e-01 -1.96463987e-02 -1.44824296e-01 -7.91963518e-01
-7.14652896e-01 -7.09316552e-01 -1.53740287e+00 -5.41133940e-01
-4.66666728e-01 -1.94136634e-01 5.26358843e-01 9.58596528e-01
4.35608208e-01 5.43169022e-01 2.87476271e-01 -1.98227561e+00
-4.34543520e-01 -6.55236125e-01 -3.26163732e-02 4.45361614e-01
4.77491677e-01 -1.36396277e+00 -1.76619291e-02 1.55677751e-01] | [4.821953773498535, 0.4014095664024353] |
b0e30e51-07c6-4032-bbf8-98567fe43a48 | adaensemble-learning-adaptively-sparse | 2301.08353 | null | https://arxiv.org/abs/2301.08353v1 | https://arxiv.org/pdf/2301.08353v1.pdf | AdaEnsemble: Learning Adaptively Sparse Structured Ensemble Network for Click-Through Rate Prediction | Learning feature interactions is crucial to success for large-scale CTR prediction in recommender systems and Ads ranking. Researchers and practitioners extensively proposed various neural network architectures for searching and modeling feature interactions. However, we observe that different datasets favor different neural network architectures and feature interaction types, suggesting that different feature interaction learning methods may have their own unique advantages. Inspired by this observation, we propose AdaEnsemble: a Sparsely-Gated Mixture-of-Experts (SparseMoE) architecture that can leverage the strengths of heterogeneous feature interaction experts and adaptively learns the routing to a sparse combination of experts for each example, allowing us to build a dynamic hierarchy of the feature interactions of different types and orders. To further improve the prediction accuracy and inference efficiency, we incorporate the dynamic early exiting mechanism for feature interaction depth selection. The AdaEnsemble can adaptively choose the feature interaction depth and find the corresponding SparseMoE stacking layer to exit and compute prediction from. Therefore, our proposed architecture inherits the advantages of the exponential combinations of sparsely gated experts within SparseMoE layers and further dynamically selects the optimal feature interaction depth without executing deeper layers. We implement the proposed AdaEnsemble and evaluate its performance on real-world datasets. Extensive experiment results demonstrate the efficiency and effectiveness of AdaEnsemble over state-of-the-art models. | ['Liubo Li', 'YaChen Yan'] | 2023-01-06 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [-3.63244891e-01 -5.64713895e-01 -5.61024010e-01 -7.95240223e-01
-3.80474061e-01 -4.54803884e-01 2.72376686e-01 -2.55672246e-01
-1.44325584e-01 2.87392259e-01 2.05530375e-01 -1.70464218e-01
-5.85528374e-01 -7.69126713e-01 -6.96033239e-01 -4.64330107e-01
-4.69150424e-01 8.43320847e-01 3.99514228e-01 -4.24452364e-01
2.11544797e-01 4.63937193e-01 -1.78751504e+00 8.26088309e-01
1.06083870e+00 1.38520622e+00 1.77325666e-01 3.67451280e-01
-2.31357887e-01 6.44409418e-01 -7.45833144e-02 -3.76228064e-01
4.52103704e-01 3.11168343e-01 -5.06213009e-01 -3.59199226e-01
6.01526201e-01 -4.96350825e-01 -3.73679996e-01 5.91165721e-01
3.31739157e-01 3.72572601e-01 5.87562203e-01 -1.23263454e+00
-7.81337976e-01 1.10805130e+00 -5.56369066e-01 3.68271440e-01
1.26021402e-02 5.07474467e-02 1.53086936e+00 -1.32182467e+00
2.78944135e-01 1.25565183e+00 4.80941147e-01 2.36274898e-01
-9.22958791e-01 -1.05730367e+00 9.79786575e-01 4.01497990e-01
-1.32632816e+00 -2.94926256e-01 6.71045601e-01 -1.86482489e-01
8.68047059e-01 4.07468438e-01 6.38963163e-01 9.58816230e-01
8.46994072e-02 1.25921953e+00 5.25094986e-01 9.67675075e-02
6.76657259e-02 2.35061169e-01 8.42092693e-01 8.30818892e-01
1.14995725e-01 5.60743697e-02 -6.71689153e-01 -5.34227669e-01
7.47658968e-01 5.77630579e-01 -5.01714572e-02 -6.36110678e-02
-7.20018148e-01 1.04963565e+00 7.87447035e-01 7.32408911e-02
-3.59228313e-01 1.14012569e-01 2.42946208e-01 4.61974710e-01
7.84738511e-02 4.38751429e-01 -8.82404327e-01 2.97098994e-01
-5.86321652e-01 2.67479420e-01 8.43179405e-01 7.99765348e-01
7.29240358e-01 -2.10197955e-01 -3.49661320e-01 1.07675254e+00
4.97286439e-01 1.70163408e-01 3.70301008e-01 -7.08833814e-01
3.69454145e-01 1.05551159e+00 6.07790612e-02 -1.12866950e+00
-3.37033749e-01 -7.45700181e-01 -8.14636111e-01 -2.94927448e-01
8.31607133e-02 -1.96985617e-01 -7.02457786e-01 1.46940482e+00
4.36752647e-01 4.80488837e-01 -3.93874437e-01 9.72783983e-01
8.82665575e-01 6.53610528e-01 1.94526871e-03 2.05698118e-01
1.37985313e+00 -1.25482428e+00 -2.40290835e-02 -2.49136835e-01
6.05596006e-01 -4.42602396e-01 1.12371886e+00 6.54301643e-01
-7.04652488e-01 -7.24887729e-01 -9.75077569e-01 9.45592746e-02
-3.17556947e-01 3.01072359e-01 1.42468953e+00 2.53318369e-01
-8.41185272e-01 5.70497334e-01 -6.99248910e-01 2.25643516e-01
4.45093304e-01 8.40244591e-01 6.88005760e-02 -1.26125261e-01
-1.42014062e+00 2.92315662e-01 2.12651759e-01 3.87095779e-01
-8.18173885e-01 -9.26508248e-01 -4.00439054e-01 6.00857377e-01
5.45323014e-01 -7.42855251e-01 1.23587978e+00 -1.00033140e+00
-1.38437033e+00 -1.85358405e-01 -1.30819216e-01 -4.28987116e-01
-1.36580691e-01 -5.87209344e-01 -5.86347222e-01 -3.48638207e-01
-3.16546410e-01 5.67961991e-01 9.09725130e-01 -1.16154754e+00
-1.04056573e+00 -3.05428326e-01 3.46421927e-01 2.67183334e-01
-6.94403529e-01 -1.31333083e-01 -6.50937200e-01 -2.88386971e-01
-6.07738420e-02 -1.01034522e+00 -5.83920956e-01 -2.28427425e-01
-2.52495170e-01 -5.33444226e-01 5.75617135e-01 1.81978792e-02
1.63230824e+00 -1.98366296e+00 -9.51037630e-02 7.07329750e-01
5.81778049e-01 9.86813381e-02 -4.53461498e-01 2.85268039e-01
1.62983298e-01 -1.92402646e-01 5.54647267e-01 -9.64137912e-02
-2.23318860e-02 1.31087273e-01 -4.38859016e-01 -1.02660425e-01
-1.01724930e-01 9.84037817e-01 -7.75921047e-01 -2.58744806e-01
9.46608037e-02 6.53230190e-01 -1.07511902e+00 9.35542583e-02
-3.65209013e-01 1.04468860e-01 -1.17415285e+00 7.90553987e-01
6.19385779e-01 -9.32048559e-01 3.09745520e-01 -5.03076017e-01
6.93369210e-02 3.52674007e-01 -1.44800258e+00 1.07864606e+00
-6.92907274e-01 1.13375314e-01 -3.67320254e-02 -6.89456284e-01
7.48155951e-01 -2.27257520e-01 4.91696149e-01 -6.40865028e-01
3.55687827e-01 3.41556013e-01 2.66732156e-01 -5.09057604e-02
6.20973587e-01 5.89078963e-01 -7.60179460e-02 3.31381589e-01
-1.75893139e-02 7.75446236e-01 7.20647350e-02 3.89652282e-01
1.01079690e+00 -4.01311249e-01 -9.19334814e-02 -1.39533266e-01
6.33404255e-01 -3.13149780e-01 3.84411454e-01 1.19465637e+00
3.02288920e-01 -9.92904678e-02 6.71406835e-02 -9.05718029e-01
-3.11654478e-01 -7.49693692e-01 -6.53944016e-02 2.07482719e+00
1.41297430e-01 -6.16425931e-01 -1.28793076e-01 -9.00425255e-01
2.87536502e-01 4.86858487e-01 -6.87299669e-01 -1.61677256e-01
-7.53079355e-01 -7.53256977e-01 -1.36599824e-01 6.32315159e-01
2.21322447e-01 -1.00303400e+00 -2.64368206e-01 2.18340591e-01
1.68723583e-01 -6.82697892e-01 -6.68421268e-01 3.87497842e-01
-7.94514000e-01 -9.57740545e-01 -1.82212904e-01 -6.94385171e-01
5.34808397e-01 5.90371847e-01 1.40618777e+00 5.03031611e-01
1.31355941e-01 1.71365142e-01 -6.26321316e-01 -7.05007911e-02
2.20043197e-01 5.43014705e-01 2.62199417e-02 2.61517435e-01
4.83632833e-01 -8.63079369e-01 -1.12878430e+00 6.41066372e-01
-7.92482913e-01 -1.65614903e-01 8.06725025e-01 8.59819114e-01
5.38590908e-01 4.34126072e-02 5.31026244e-01 -1.23710227e+00
6.69054687e-01 -8.38082850e-01 -6.89282835e-01 3.46560717e-01
-8.18586409e-01 2.18285292e-01 1.03122687e+00 -6.79624498e-01
-8.71285379e-01 1.08907141e-01 -1.50008276e-01 -4.40527469e-01
1.74792930e-01 7.85233498e-01 5.29181096e-04 -2.19228297e-01
5.01644135e-01 -2.54238583e-02 -4.90958512e-01 -5.72517574e-01
5.25482297e-01 5.28416038e-01 -1.51216413e-03 -7.58330047e-01
5.75325727e-01 2.84245878e-01 -3.86968791e-01 -2.77054220e-01
-1.20267045e+00 -4.74945575e-01 -1.44033343e-01 8.39472841e-03
2.65095115e-01 -1.04144621e+00 -1.12528586e+00 2.93208867e-01
-7.06206679e-01 -1.23905696e-01 1.39509132e-02 2.07908913e-01
1.99577436e-01 5.50783165e-02 -7.34128475e-01 -5.26311696e-01
-6.64516807e-01 -1.23275304e+00 9.60050583e-01 5.34632385e-01
-2.44647264e-02 -6.75351083e-01 -1.08101957e-01 7.67874569e-02
6.08745933e-01 -4.83639389e-01 1.00954604e+00 -1.09285438e+00
-9.28256094e-01 -3.34238708e-01 -2.07078278e-01 -1.57853410e-01
-1.50173739e-01 -1.22314371e-01 -6.66718721e-01 -2.25125477e-01
-4.83992517e-01 -3.04747552e-01 1.03083074e+00 6.35034502e-01
1.55069673e+00 -3.78363848e-01 -7.30750680e-01 5.82265615e-01
1.22369730e+00 1.98536292e-01 3.04899722e-01 1.50816441e-01
8.03963006e-01 1.55354321e-01 6.34832561e-01 6.57683790e-01
5.42793334e-01 7.54461944e-01 4.62525159e-01 -4.08640727e-02
4.80164140e-01 -1.73631117e-01 3.84085476e-01 8.62586081e-01
5.40599339e-02 -4.45067316e-01 -2.62565881e-01 2.03266263e-01
-1.97299707e+00 -7.91112602e-01 8.07264075e-02 2.09584260e+00
5.98864615e-01 2.38286644e-01 2.68661827e-01 -3.59819978e-01
4.34242368e-01 1.98483720e-01 -8.68884921e-01 -2.86062688e-01
2.08205178e-01 2.20791012e-01 3.04567933e-01 3.47105175e-01
-1.01353884e+00 9.73331928e-01 5.67201996e+00 1.13274920e+00
-1.12422836e+00 -8.39502588e-02 7.25233316e-01 -3.26006889e-01
-3.78516614e-01 -1.26409486e-01 -1.44214976e+00 4.34611499e-01
8.56645107e-01 2.45889291e-01 7.27442265e-01 1.11598277e+00
-1.73770398e-01 2.81750590e-01 -1.14849901e+00 7.25196183e-01
-2.38478616e-01 -1.42476416e+00 4.22661453e-01 -3.36773950e-03
6.33432806e-01 3.58252198e-01 3.25328618e-01 7.91863680e-01
8.51421058e-01 -8.27062666e-01 2.96490580e-01 5.02784431e-01
2.70532399e-01 -7.86147118e-01 6.14884198e-01 1.95677057e-01
-1.69187307e+00 -8.69565904e-01 -3.63091797e-01 9.74846929e-02
2.02240720e-01 6.04870737e-01 -5.65183282e-01 4.32063907e-01
8.54938447e-01 8.41965020e-01 -5.97250581e-01 9.70600545e-01
1.56794548e-01 6.91396475e-01 -4.97388750e-01 -2.90956736e-01
3.20378929e-01 -1.33667558e-01 2.46218771e-01 9.34824884e-01
3.38181436e-01 1.86662212e-01 6.24015272e-01 4.06896383e-01
-1.04447313e-01 2.62924641e-01 1.31864414e-01 -1.34721890e-01
9.19963539e-01 1.56718814e+00 -4.92996275e-01 -3.37843031e-01
-6.12230420e-01 6.32746160e-01 5.52720129e-01 3.53146255e-01
-1.01036870e+00 -7.28721246e-02 8.43409300e-01 3.23688179e-01
8.20674837e-01 1.57814965e-01 -3.77418585e-02 -1.06429291e+00
-2.38713369e-01 -9.68629301e-01 9.26051855e-01 -3.97075325e-01
-1.78750944e+00 8.99238408e-01 -8.75858366e-02 -1.28763890e+00
2.38840580e-02 -7.23129451e-01 -8.39695394e-01 5.06072402e-01
-1.29523981e+00 -1.10141373e+00 -2.18576536e-01 7.34892428e-01
7.22449541e-01 -3.45349610e-01 5.83264112e-01 4.36960280e-01
-6.26242876e-01 1.04870200e+00 1.38428822e-01 -1.59074336e-01
5.00405550e-01 -8.85283768e-01 2.63872057e-01 1.60416707e-01
3.12520862e-01 1.11106157e+00 2.81852931e-01 -5.28593183e-01
-1.68864131e+00 -1.03643787e+00 4.49209660e-01 -2.28046909e-01
6.70675635e-01 -3.00203830e-01 -7.75177002e-01 6.65810227e-01
-1.11692131e-01 9.15199444e-02 8.55945349e-01 9.62597013e-01
-5.50735056e-01 -5.00177801e-01 -7.50746608e-01 6.02722824e-01
1.16881788e+00 -1.62979007e-01 -1.33346081e-01 3.12948436e-01
8.54434371e-01 -1.81844175e-01 -8.17544222e-01 4.42692995e-01
1.00370586e+00 -9.63257730e-01 1.18179560e+00 -7.89168358e-01
4.23728526e-01 -2.01741472e-01 -1.69927835e-01 -1.07880819e+00
-8.41449976e-01 -3.15882057e-01 -6.57600582e-01 8.12217116e-01
8.07852149e-01 -8.24513495e-01 9.86744940e-01 7.30897903e-01
-1.18268169e-01 -1.43384671e+00 -4.12888557e-01 -1.37151107e-01
-2.79080421e-01 -2.01268017e-01 7.61940420e-01 8.28373790e-01
-3.42949957e-01 7.53227234e-01 -6.03351474e-01 2.70651162e-01
9.46597233e-02 6.82665765e-01 6.83974683e-01 -1.80751848e+00
-8.81787717e-01 -3.60045314e-01 1.12370878e-01 -1.75002134e+00
1.18499631e-02 -1.04410672e+00 -2.73558348e-01 -1.09369218e+00
3.92543644e-01 -9.16564226e-01 -8.93034697e-01 6.17396832e-01
-3.06796581e-01 -6.84242398e-02 -4.70355786e-02 2.42906392e-01
-1.09286726e+00 5.13108730e-01 1.16919637e+00 -7.39842057e-02
-6.21842682e-01 2.00591817e-01 -1.13956153e+00 6.67663157e-01
4.21782553e-01 -5.44518232e-01 -7.35088944e-01 -4.71035510e-01
6.14574432e-01 -9.36643258e-02 9.06639546e-02 -7.75305390e-01
3.62475246e-01 -2.13768974e-01 7.32559681e-01 -8.53220642e-01
2.65000403e-01 -9.98858511e-01 2.07807451e-01 1.16832219e-01
-5.28035104e-01 9.12111849e-02 -5.90051934e-02 7.41779625e-01
1.13350041e-01 2.81808549e-03 2.54199743e-01 -1.51217356e-01
-7.68044353e-01 8.49345982e-01 -2.10842595e-01 -1.83521941e-01
5.50124943e-01 -5.10721058e-02 -2.45457083e-01 -1.85757309e-01
-6.42288804e-01 6.70393825e-01 -1.39935911e-01 6.22155130e-01
5.49629748e-01 -1.33967197e+00 -3.64216000e-01 2.33055681e-01
7.73722157e-02 -9.03204381e-02 5.81258893e-01 7.67614782e-01
1.72079176e-01 2.81434417e-01 1.33259252e-01 -5.18662333e-01
-1.10072970e+00 4.66365784e-01 1.42219514e-01 -7.30715930e-01
-4.27455544e-01 1.24538493e+00 4.03166145e-01 -6.20717227e-01
2.70629704e-01 -2.51860976e-01 -6.31788790e-01 -6.45360276e-02
7.32748389e-01 2.70805836e-01 -2.97855996e-02 -2.32615203e-01
-2.50714093e-01 2.93523967e-01 -9.63711977e-01 4.50929314e-01
1.42062068e+00 2.08149571e-02 1.63374126e-01 8.00417271e-04
1.04659581e+00 1.14500038e-01 -9.80960786e-01 -3.72613013e-01
-2.03104854e-01 -5.08717477e-01 1.61467507e-01 -9.12046850e-01
-1.49017382e+00 3.02560002e-01 6.66580498e-01 1.50366798e-01
1.19389522e+00 3.02375909e-02 1.19123960e+00 9.13728237e-01
3.68185997e-01 -8.65895808e-01 7.37501308e-02 7.97271609e-01
6.46980464e-01 -1.00686216e+00 -5.11998460e-02 -5.58627784e-01
-6.99815810e-01 8.42038155e-01 1.00523508e+00 -5.16799927e-01
1.18830454e+00 3.24343562e-01 -1.53492436e-01 -5.47719240e-01
-1.26755106e+00 7.46274590e-02 6.52085543e-01 4.66730352e-03
3.93284589e-01 6.73073828e-02 -2.52435803e-01 1.21478975e+00
4.96805534e-02 1.08020090e-01 -1.76510245e-01 6.93952382e-01
-5.61679780e-01 -1.26650190e+00 1.42318323e-01 1.28997767e+00
-1.37183100e-01 -5.11478364e-01 -2.91042328e-01 4.87431228e-01
1.00674115e-01 6.64472520e-01 7.30208727e-03 -9.64457810e-01
3.03661942e-01 -7.66379312e-02 1.68018699e-01 -3.93454969e-01
-1.11443412e+00 1.79298893e-01 2.62003150e-02 -7.65227199e-01
1.02692377e-02 -2.53938287e-01 -1.26306760e+00 -1.54327676e-01
-6.89848065e-01 4.35716957e-01 2.46447966e-01 8.82701159e-01
9.88520861e-01 5.13125181e-01 9.87810194e-01 -8.34402323e-01
-7.59211898e-01 -8.40320468e-01 -4.85355198e-01 -1.29841283e-01
1.18357956e-01 -1.02321839e+00 -2.42415383e-01 -5.65929174e-01] | [10.144776344299316, 5.484459400177002] |
40743fa8-3301-48ea-a999-602d9f62d158 | multi-timeline-summarization-mtls-improving | null | null | https://aclanthology.org/2021.acl-long.32 | https://aclanthology.org/2021.acl-long.32.pdf | Multi-TimeLine Summarization (MTLS): Improving Timeline Summarization by Generating Multiple Summaries | In this paper, we address a novel task, Multiple TimeLine Summarization (MTLS), which extends the flexibility and versatility of Time-Line Summarization (TLS). Given any collection of time-stamped news articles, MTLS automatically discovers important yet different stories and generates a corresponding time-line for each story.To achieve this, we propose a novel unsupervised summarization framework based on two-stage affinity propagation. We also introduce a quantitative evaluation measure for MTLS based on previousTLS evaluation methods. Experimental results show that our MTLS framework demonstrates high effectiveness and MTLS task can give bet-ter results than TLS. | ['Masatoshi Yoshikawa', 'Kazunari Sugiyama', 'Antoine Doucet', 'Adam Jatowt', 'Yi Yu'] | 2021-08-01 | null | null | null | acl-2021-5 | ['timeline-summarization'] | ['natural-language-processing'] | [ 2.96462744e-01 9.47682261e-02 -6.58264279e-01 -3.74529302e-01
-1.26728368e+00 -5.22481382e-01 6.84974313e-01 6.27805412e-01
-3.08137536e-01 1.04651034e+00 1.02574646e+00 1.38942540e-01
-1.29173055e-01 -5.32111287e-01 -6.58051968e-01 -3.15159947e-01
1.03930958e-01 6.22269154e-01 4.78463233e-01 -1.78463906e-01
7.07185984e-01 1.61697403e-01 -1.02391541e+00 4.90888804e-01
1.38182807e+00 4.02163893e-01 2.55529154e-02 7.19774842e-01
-2.96300650e-01 8.50021005e-01 -1.11033952e+00 -3.90773654e-01
-2.18532130e-01 -7.65555441e-01 -1.01417160e+00 -5.87630123e-02
2.71166950e-01 -1.97080866e-01 -2.83569187e-01 6.90671563e-01
4.71289963e-01 2.64971375e-01 7.63951421e-01 -1.11900699e+00
-3.01709414e-01 1.27670491e+00 -8.91891122e-01 6.10163927e-01
6.50439560e-01 -3.23461890e-01 1.26918650e+00 -5.48421264e-01
7.24350631e-01 1.14184940e+00 8.03355753e-01 1.85088634e-01
-9.97779965e-01 -2.90538341e-01 3.00464958e-01 2.05998495e-01
-8.87756288e-01 -3.97771984e-01 9.11807358e-01 -1.86281487e-01
1.02501237e+00 5.25986731e-01 7.45762944e-01 9.14328635e-01
5.27808905e-01 1.41904175e+00 6.45189226e-01 -4.03095305e-01
2.01717377e-01 -2.36198947e-01 5.84338427e-01 2.89138854e-01
2.63387591e-01 -8.69765520e-01 -7.65046120e-01 -3.12752515e-01
2.96987623e-01 -1.29728660e-01 -2.08146840e-01 2.83772975e-01
-1.61386156e+00 6.53710604e-01 -8.97745192e-02 5.16357839e-01
-5.03097534e-01 7.38334805e-02 8.58478904e-01 2.92586535e-01
1.06891072e+00 8.77270460e-01 -1.21456617e-02 -3.03584129e-01
-1.55960286e+00 3.36956471e-01 8.55170369e-01 9.71757591e-01
3.12544405e-01 -1.97025925e-01 -9.48250413e-01 7.03561246e-01
-3.14831793e-01 3.08266044e-01 7.69550741e-01 -7.21588254e-01
5.66183388e-01 6.51347458e-01 4.82092276e-02 -1.01247787e+00
-4.44050461e-01 -5.37097454e-01 -8.81774008e-01 -8.52342069e-01
-3.08943689e-01 -1.20177001e-01 -5.62669694e-01 1.40372765e+00
1.00672469e-01 2.57722586e-01 1.26140565e-01 4.91251409e-01
9.52555954e-01 1.25409532e+00 -3.35445791e-01 -9.62583721e-01
9.66312706e-01 -1.57731771e+00 -1.07258356e+00 -3.59205492e-02
3.84919345e-01 -6.31264031e-01 9.33277667e-01 3.44608128e-01
-1.42177355e+00 -1.51640892e-01 -1.14818132e+00 -2.02784725e-02
3.31479348e-02 1.15489185e-01 6.20662749e-01 4.63498607e-02
-1.03099692e+00 5.53593993e-01 -8.13213229e-01 -7.66595840e-01
1.91570312e-01 -3.92889008e-02 -5.33521213e-02 2.76806384e-01
-1.15770185e+00 7.95007825e-01 7.77717173e-01 -3.55332017e-01
-3.91917437e-01 -6.09973431e-01 -6.28961325e-01 2.03110389e-02
5.13204694e-01 -8.98652196e-01 1.68878543e+00 -5.36290526e-01
-1.52010882e+00 4.44680005e-01 -4.89851266e-01 -8.21279168e-01
4.74729538e-01 -4.59702611e-01 -4.91612107e-01 4.52920318e-01
6.33709252e-01 2.07786962e-01 5.95016360e-01 -9.12239909e-01
-7.14054525e-01 5.70125170e-02 -2.72062212e-01 4.27252412e-01
-4.46896404e-01 1.84456453e-01 -6.43241644e-01 -1.03480911e+00
-3.62827443e-02 -3.45641017e-01 -2.55378127e-01 -9.02768850e-01
-9.72849369e-01 -5.53828001e-01 5.26083767e-01 -6.72356009e-01
2.10195374e+00 -1.73458517e+00 4.57424492e-01 -1.67851627e-01
3.20165843e-01 4.73933853e-02 -5.11647537e-02 1.03776503e+00
3.78705978e-01 6.52602389e-02 -4.83570725e-01 -3.89227122e-01
3.54430564e-02 -7.79469460e-02 -7.36013055e-01 1.13928549e-01
3.09367534e-02 1.07327271e+00 -1.16303301e+00 -8.41001570e-01
-1.79507300e-01 -2.70722747e-01 -3.62819314e-01 -1.89712516e-03
-4.39072162e-01 2.92830050e-01 -6.60428166e-01 5.07557571e-01
2.00097784e-01 -2.35952973e-01 -1.56797245e-01 -2.66960710e-01
-4.24067438e-01 2.82092750e-01 -5.07829130e-01 1.81411111e+00
-1.01927631e-01 6.61945641e-01 -8.27788651e-01 -7.74759293e-01
8.25293541e-01 1.41096771e-01 5.22600770e-01 -3.80838424e-01
1.25279725e-01 1.74486160e-01 -6.53199375e-01 -4.79594111e-01
1.12131584e+00 1.38481483e-01 -6.89047217e-01 8.40200007e-01
8.90630856e-02 -2.48914629e-01 6.92280531e-01 7.99105525e-01
1.15961242e+00 -1.81964621e-01 7.26960242e-01 -2.16157988e-01
3.02725881e-01 2.78114527e-01 5.80135643e-01 1.00531983e+00
-2.58335769e-02 6.68050408e-01 6.47223949e-01 -1.22784287e-01
-1.18374503e+00 -9.29529250e-01 2.17526108e-01 1.00759184e+00
2.06041738e-01 -8.71010602e-01 -7.11966038e-01 -7.86180198e-01
-1.96025699e-01 1.09228504e+00 -5.64199865e-01 -1.75731242e-01
-6.70730710e-01 -8.12516391e-01 7.34826624e-01 4.65953052e-01
5.62899411e-01 -9.49688137e-01 -5.26470840e-01 4.19433832e-01
-6.85565710e-01 -8.69279087e-01 -9.38383520e-01 -3.10035735e-01
-8.51427913e-01 -5.66759109e-01 -7.49299109e-01 -8.06866586e-01
4.45155263e-01 4.61309433e-01 9.81421649e-01 -5.05886972e-01
8.09519514e-02 1.70261919e-01 -6.39884830e-01 -6.15268171e-01
-4.01146114e-01 6.47607803e-01 -3.51719582e-03 -4.28127162e-02
9.54840779e-02 -7.56035209e-01 -3.57603639e-01 -2.31706738e-01
-1.04156446e+00 5.29791594e-01 6.54773891e-01 6.41172826e-01
6.38766825e-01 1.86010972e-01 1.13491952e+00 -1.10615814e+00
1.31977069e+00 -5.35873294e-01 -1.14271641e-01 6.17700160e-01
-5.78076065e-01 1.37719005e-01 5.84807456e-01 -3.90395969e-01
-1.11437380e+00 -5.17082095e-01 3.40884142e-02 8.24607760e-02
3.36063832e-01 1.13137674e+00 3.24430317e-01 6.70768857e-01
4.91317987e-01 6.41309202e-01 -3.21905166e-01 -4.49752212e-01
4.78998661e-01 6.43568575e-01 9.03872371e-01 -2.89018363e-01
6.83760345e-01 3.82707477e-01 -6.59000456e-01 -8.91168118e-01
-1.29934251e+00 -7.32641876e-01 -4.11872506e-01 -4.55659777e-01
6.20990396e-01 -7.68310189e-01 -8.65896270e-02 3.58383209e-01
-1.34069359e+00 1.43585671e-02 -5.46757638e-01 4.66168821e-01
-5.30811012e-01 6.30768716e-01 -7.05372393e-01 -6.66171134e-01
-9.04649496e-01 -4.02475864e-01 1.12362754e+00 3.69973153e-01
-7.06793308e-01 -9.01209712e-01 5.19412100e-01 1.41785756e-01
2.54155904e-01 6.01014316e-01 7.10918784e-01 -1.02274764e+00
-1.24976285e-01 -3.85242790e-01 -9.85156093e-03 -1.00043781e-01
1.71263162e-02 1.26676753e-01 -4.75041479e-01 -2.07371607e-01
-1.48964664e-02 -2.46409521e-01 1.34187949e+00 6.51217103e-01
8.85277927e-01 -8.12863350e-01 -5.07508337e-01 1.74682438e-01
1.10869610e+00 2.17018276e-01 4.85099673e-01 5.01855195e-01
6.92797244e-01 3.84590626e-01 7.81156600e-01 6.75599873e-01
7.10563421e-01 3.50181103e-01 -2.86354005e-01 -1.34530365e-01
9.73137841e-02 -3.75415355e-01 5.80487728e-01 1.77014685e+00
-2.06306837e-02 -6.74076736e-01 -5.52564025e-01 7.36590624e-01
-2.42095685e+00 -1.38878477e+00 -1.80409417e-01 1.90498734e+00
1.08619058e+00 3.44049424e-01 4.34463322e-01 7.24366158e-02
8.92430782e-01 6.19216740e-01 -6.45241678e-01 -4.33186710e-01
-3.71749699e-01 -2.12868541e-01 2.76595950e-01 3.83355409e-01
-9.61773634e-01 1.01537955e+00 6.90127420e+00 1.11413443e+00
-8.08516204e-01 9.95081440e-02 3.55325460e-01 -2.37356171e-01
-6.25964165e-01 -2.23371878e-01 -5.61045587e-01 4.39437538e-01
8.44362855e-01 -1.21719134e+00 -3.12923118e-02 5.07267177e-01
4.08123910e-01 -1.62547320e-01 -9.09452736e-01 6.29423916e-01
4.52395767e-01 -1.62345541e+00 4.59887952e-01 -6.21918738e-01
9.73090112e-01 -9.54748765e-02 -3.88696671e-01 4.01982665e-01
2.14841023e-01 -4.25369143e-01 7.86239982e-01 5.27169824e-01
6.74229205e-01 -9.39524174e-01 8.02878201e-01 5.04114091e-01
-1.13287926e+00 2.53913134e-01 -1.35388568e-01 5.63738272e-02
5.89735329e-01 8.46119642e-01 -9.62688386e-01 1.17776370e+00
2.90284157e-01 1.16736293e+00 -6.30202711e-01 1.28945184e+00
-2.75205165e-01 9.18812454e-01 -1.56205714e-01 -3.26103657e-01
3.51427913e-01 -2.57018320e-02 1.08096647e+00 1.81375217e+00
3.19179237e-01 1.73153147e-01 3.87863368e-01 4.84909743e-01
-1.91116020e-01 3.74184608e-01 -4.09201592e-01 -3.73708248e-01
6.88797176e-01 1.12717021e+00 -9.88641560e-01 -7.68705130e-01
9.55548733e-02 1.30002308e+00 2.37552375e-01 2.00386420e-01
-9.55472350e-01 -8.10953796e-01 -2.49890968e-01 -1.56239152e-01
-5.87832965e-02 9.53340717e-03 -1.54487967e-01 -1.38814437e+00
6.52095228e-02 -4.74768579e-01 5.64404190e-01 -7.05162525e-01
-1.35783875e+00 7.80558944e-01 3.36728007e-01 -1.31420517e+00
-2.55045265e-01 5.32932639e-01 -1.08595896e+00 2.13557377e-01
-1.23180437e+00 -1.03714371e+00 -7.36600980e-02 1.39526516e-01
1.20739520e+00 -1.24801092e-01 4.94219542e-01 -1.87309533e-02
-7.19403267e-01 4.90588069e-01 4.04410958e-01 -3.08766991e-01
9.01129901e-01 -1.38187623e+00 7.44027555e-01 1.07443619e+00
-2.87472922e-02 4.13513809e-01 1.07175612e+00 -8.42551708e-01
-1.17203546e+00 -1.31212509e+00 1.42444193e+00 -1.12991720e-01
7.15591371e-01 -2.09035538e-02 -7.65607119e-01 7.01154590e-01
6.96775675e-01 -1.21573222e+00 6.65709078e-01 3.41412947e-02
-1.90429285e-01 1.55420657e-02 -7.84989178e-01 7.46750414e-01
8.58848691e-01 -2.29447052e-01 -1.27128756e+00 7.55583704e-01
1.29991937e+00 -2.42623433e-01 -6.37220502e-01 3.60033900e-01
3.94921154e-01 -7.87159026e-01 5.15250742e-01 -3.95028800e-01
8.02948892e-01 -3.39642376e-01 2.72538841e-01 -1.53392649e+00
-5.16365469e-01 -9.46387053e-01 -3.12304080e-01 1.53921032e+00
4.51466560e-01 -6.23482704e-01 3.30950141e-01 -3.51813361e-02
-4.82476592e-01 -7.08369374e-01 -5.58777928e-01 -8.25165987e-01
-3.17350149e-01 -2.03133360e-01 6.04584396e-01 1.08222091e+00
5.91963470e-01 8.76522899e-01 -5.61988056e-01 -3.58492993e-02
5.64193249e-01 4.36787546e-01 6.55488670e-01 -1.12313092e+00
-1.38859421e-01 -8.00190866e-01 -1.14494711e-02 -1.04209304e+00
5.80980964e-02 -8.56454015e-01 1.38941735e-01 -2.20171380e+00
7.99075544e-01 1.96832314e-01 -3.70166153e-01 3.26060683e-01
-3.65050524e-01 1.13434233e-01 -7.22514242e-02 5.09747446e-01
-1.55435610e+00 8.16545546e-01 1.11161506e+00 -2.88626939e-01
-4.41245973e-01 -7.60622397e-02 -7.55798936e-01 6.14355862e-01
8.56481910e-01 -4.36856538e-01 -6.20639920e-01 -3.67564112e-01
1.14029922e-01 3.75013500e-01 -2.89345413e-01 -9.00027037e-01
6.29553676e-01 -2.52603859e-01 -3.37906517e-02 -1.37875497e+00
-1.79522052e-01 8.48187655e-02 9.30429175e-02 3.17022055e-01
-7.28727102e-01 2.14404985e-01 1.58819750e-01 7.98238516e-01
-4.94685262e-01 -4.82803397e-02 1.71298325e-01 1.02223158e-01
-6.55397952e-01 1.19704828e-01 -5.76894403e-01 1.70175895e-01
9.49500382e-01 -2.12540984e-01 -4.37864900e-01 -5.22659779e-01
-2.68019497e-01 6.03927255e-01 2.84285545e-01 4.38485742e-01
5.78676581e-01 -1.41380310e+00 -1.17140496e+00 -4.26554263e-01
3.61744881e-01 7.00170472e-02 2.10258693e-01 9.43252504e-01
-4.85480070e-01 5.07681549e-01 9.29777510e-03 -3.71951729e-01
-1.04626560e+00 3.51850986e-01 -3.04152697e-01 -5.78477025e-01
-8.70326698e-01 7.85319090e-01 8.39537233e-02 -1.36577021e-02
1.30131051e-01 -3.10072035e-01 -5.02778590e-01 3.53536248e-01
7.86503732e-01 5.21472335e-01 -7.92769119e-02 -3.52547646e-01
-1.67252153e-01 3.48666519e-01 -4.98050869e-01 -2.46107325e-01
1.49856579e+00 -2.18460739e-01 -3.14392149e-01 1.04501343e+00
8.89044464e-01 2.98301697e-01 -7.59985030e-01 -2.51985162e-01
3.60919863e-01 -1.24674151e-02 -1.24427564e-01 -7.63518512e-01
-4.88196880e-01 5.16723022e-02 -4.45867211e-01 4.84149009e-01
1.27979410e+00 1.46636814e-01 1.33258677e+00 3.41805816e-01
2.78708220e-01 -1.20836246e+00 2.32208505e-01 6.37918472e-01
1.03726172e+00 -8.51591706e-01 2.45596468e-01 -3.44822943e-01
-8.31032932e-01 9.07648563e-01 3.14724445e-01 -1.29958943e-01
-7.64124393e-02 6.69442164e-03 -3.39577079e-01 -3.24890643e-01
-1.03018260e+00 5.49435541e-02 4.15837139e-01 7.52691049e-05
3.66729140e-01 4.82247323e-02 -9.07686591e-01 7.97621548e-01
-3.62874568e-01 -9.36523825e-02 7.55906522e-01 9.66670156e-01
-7.92253852e-01 -6.93046451e-01 -1.29337221e-01 6.63623631e-01
-3.29106331e-01 2.01884117e-02 -7.87324011e-01 5.44221640e-01
-5.50570548e-01 9.23768222e-01 -5.93032539e-02 -5.74042499e-01
3.40182871e-01 -6.51468476e-03 3.18918765e-01 -6.35336697e-01
-6.59764171e-01 3.08038116e-01 3.57575476e-01 -2.24534854e-01
-5.53953826e-01 -6.57109737e-01 -1.24948478e+00 -4.19073790e-01
-4.21946734e-01 7.44349360e-01 2.04894215e-01 9.49655890e-01
4.16256070e-01 8.79165292e-01 7.47327983e-01 -9.48893547e-01
-2.72352576e-01 -1.04459119e+00 -4.46427315e-01 2.45841503e-01
3.48052919e-01 -1.30844742e-01 -1.09172903e-01 8.18187967e-02] | [12.560020446777344, 9.485445022583008] |
4c60af3d-ba6f-453f-ac34-06c5e6a48920 | learning-sparse-auto-encoders-for-green-ai | 2209.04448 | null | https://arxiv.org/abs/2209.04448v1 | https://arxiv.org/pdf/2209.04448v1.pdf | Learning sparse auto-encoders for green AI image coding | Recently, convolutional auto-encoders (CAE) were introduced for image coding. They achieved performance improvements over the state-of-the-art JPEG2000 method. However, these performances were obtained using massive CAEs featuring a large number of parameters and whose training required heavy computational power.\\ In this paper, we address the problem of lossy image compression using a CAE with a small memory footprint and low computational power usage. In order to overcome the computational cost issue, the majority of the literature uses Lagrangian proximal regularization methods, which are time consuming themselves.\\ In this work, we propose a constrained approach and a new structured sparse learning method. We design an algorithm and test it on three constraints: the classical $\ell_1$ constraint, the $\ell_{1,\infty}$ and the new $\ell_{1,1}$ constraint. Experimental results show that the $\ell_{1,1}$ constraint provides the best structured sparsity, resulting in a high reduction of memory and computational cost, with similar rate-distortion performance as with dense networks. | ['Michel Barlaud', 'Marc Antonini', 'Frédéric Guyard', 'Cyprien Gille'] | 2022-09-09 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [ 1.79910645e-01 -1.13048486e-01 -2.29958519e-01 -1.18788801e-01
-5.16609788e-01 2.72385150e-01 -1.13434449e-01 -2.35590767e-02
-6.48955643e-01 7.47821033e-01 -3.27971689e-02 -1.53582990e-01
-2.77483881e-01 -8.05478036e-01 -7.34587848e-01 -7.88914502e-01
-2.08196193e-01 -8.27185586e-02 2.55456328e-01 -3.58334519e-02
1.25611991e-01 6.01114519e-02 -1.60113418e+00 5.53474352e-02
9.61392879e-01 1.39443302e+00 4.84941304e-01 5.42532504e-02
-5.33905774e-02 1.13186002e+00 -2.61590421e-01 -4.89296436e-01
4.26171213e-01 -5.73688686e-01 -3.57941210e-01 1.08754359e-01
2.02718750e-01 -4.12753582e-01 -5.26519835e-01 1.31800282e+00
3.65018398e-01 6.09815195e-02 2.94611216e-01 -8.42779458e-01
-3.03450882e-01 5.18918753e-01 -6.63881660e-01 1.25738397e-01
-7.44494200e-02 -2.98312813e-01 8.98497224e-01 -9.56809223e-01
4.63400066e-01 7.34613895e-01 7.91303813e-01 1.92085162e-01
-1.07197642e+00 -8.77412498e-01 9.86222029e-02 3.02663028e-01
-1.81879401e+00 -5.75027585e-01 9.20642138e-01 -2.17977971e-01
9.11112785e-01 8.96586701e-02 5.63253105e-01 5.54527044e-01
3.50860469e-02 5.99755347e-01 9.44990396e-01 -6.42336488e-01
4.14023757e-01 1.03369944e-01 -3.40990573e-01 9.93690491e-01
3.71829063e-01 -3.85714173e-02 -5.46238601e-01 1.91903505e-02
8.46039951e-01 4.27181739e-03 -4.55912352e-01 -3.60597700e-01
-6.89401567e-01 1.11889613e+00 2.68672556e-01 5.73768556e-01
-3.74518603e-01 2.07455084e-01 2.90703118e-01 2.45359644e-01
5.17364264e-01 -4.48638014e-02 -2.09510669e-01 -2.22541213e-01
-1.31676388e+00 -6.47703037e-02 7.74671793e-01 9.13421869e-01
7.80594409e-01 4.59659189e-01 2.42189839e-01 1.21212041e+00
4.43952233e-01 3.22483331e-01 3.73798490e-01 -1.15253091e+00
7.49649465e-01 4.50587362e-01 -2.23962128e-01 -1.37722266e+00
-2.93486476e-01 -5.70499361e-01 -1.42441630e+00 1.96997017e-01
1.19045608e-01 -2.81676855e-02 -5.83482862e-01 1.68737662e+00
-1.88094303e-01 1.62245587e-01 2.25116201e-02 8.46351385e-01
4.92542177e-01 8.44037771e-01 -5.60728610e-02 -6.98397100e-01
9.23804224e-01 -8.69312346e-01 -8.34843636e-01 -2.10265473e-01
6.54840887e-01 -1.02657437e+00 7.70770967e-01 5.15260339e-01
-1.46410787e+00 -5.00916541e-01 -1.19991529e+00 7.62446970e-02
1.38707191e-01 3.94888490e-01 6.92295551e-01 6.26179397e-01
-9.02179897e-01 6.71609044e-01 -7.92064905e-01 8.28693956e-02
5.23267627e-01 4.10959303e-01 -2.57747173e-01 -3.22730362e-01
-9.87718642e-01 5.36179304e-01 5.25304615e-01 -1.19382955e-01
-6.10485971e-01 -5.48317194e-01 -8.41024697e-01 3.80959481e-01
4.33474779e-01 -2.52520144e-01 7.19357967e-01 -7.94473231e-01
-1.42201185e+00 5.96247733e-01 -1.12412199e-01 -6.81849539e-01
3.23197544e-01 -2.80159026e-01 -2.98197269e-01 3.84434223e-01
-1.53175890e-01 4.64855552e-01 8.28675568e-01 -1.17680979e+00
-3.85363817e-01 -2.25607485e-01 -1.09785832e-01 1.00404816e-02
-7.62999713e-01 -6.31300211e-02 -5.83695769e-01 -9.39582169e-01
4.64605808e-01 -8.23056817e-01 -2.08483294e-01 1.90776601e-01
-7.62169063e-02 1.18109703e-01 6.96918130e-01 -7.20651567e-01
1.72035253e+00 -2.37324023e+00 2.11000130e-01 2.93780893e-01
1.47108614e-01 6.69907749e-01 2.50105411e-01 3.64352703e-01
6.09179549e-02 -5.67114092e-02 -5.67097127e-01 -3.71976852e-01
-2.83587962e-01 3.24811697e-01 1.45457700e-01 4.67518181e-01
-2.11546674e-01 3.07820410e-01 -5.89900792e-01 -6.80703163e-01
1.92764878e-01 7.51417577e-01 -9.46687043e-01 1.95233390e-01
-5.73947132e-02 5.17293736e-02 -2.75987625e-01 5.25443554e-01
8.65715444e-01 -4.58297729e-01 2.07002729e-01 -4.07492518e-01
-3.13369274e-01 -1.45648822e-01 -1.53219640e+00 1.77080333e+00
-4.38859880e-01 4.57362205e-01 6.38859510e-01 -1.50605917e+00
1.10046256e+00 3.75185072e-01 7.66362250e-01 -8.71375620e-01
3.63557369e-01 4.21820164e-01 -1.83876842e-01 -4.37829942e-01
3.08092386e-01 -2.61294931e-01 4.11307961e-01 1.48934498e-01
2.28652302e-02 -3.31189223e-02 5.17964840e-01 1.49358347e-01
1.10055912e+00 -1.89200565e-02 2.79142052e-01 -3.44234169e-01
7.26663888e-01 -3.15544397e-01 8.55106950e-01 5.20512521e-01
8.43253359e-02 7.09762990e-01 5.69471240e-01 -3.49655181e-01
-1.27627230e+00 -5.93173087e-01 -2.12522835e-01 6.97248101e-01
1.38212606e-01 -7.08267629e-01 -7.62083232e-01 -3.15503627e-02
-3.55972350e-01 5.54176152e-01 7.65561312e-03 -1.63064629e-01
-8.27350855e-01 -8.90366256e-01 5.01126647e-01 3.02139819e-01
1.19527578e+00 -7.89839387e-01 -7.54423738e-01 2.59528011e-01
-3.89379531e-01 -1.17122042e+00 -3.01596552e-01 2.44724810e-01
-1.01526511e+00 -6.90173507e-01 -8.29269886e-01 -9.27663565e-01
7.61208057e-01 2.23512053e-01 9.00440812e-01 2.87270069e-01
-2.85041481e-01 -8.63239840e-02 -6.06269181e-01 -3.89109664e-02
-1.06799461e-01 -1.35648474e-01 -9.18194652e-02 1.26187608e-01
1.25532538e-01 -8.40174377e-01 -7.09918976e-01 2.23629504e-01
-1.27634335e+00 4.83044311e-02 7.62499273e-01 9.32768762e-01
8.71225834e-01 4.63581830e-01 3.40755165e-01 -7.60000408e-01
4.17006850e-01 -3.31374586e-01 -7.26000488e-01 5.41638061e-02
-9.30571079e-01 1.58160597e-01 1.06253743e+00 -1.15236901e-01
-1.02830589e+00 1.78706586e-01 -5.50648749e-01 -7.82022834e-01
2.49347821e-01 8.37070107e-01 2.52001788e-02 -2.71394551e-01
4.07093436e-01 6.48258686e-01 1.05249658e-01 -7.28242457e-01
-6.38985559e-02 4.02221292e-01 3.76818269e-01 -4.43761051e-01
6.80576384e-01 5.47799349e-01 1.44995987e-01 -8.52095485e-01
-6.88920498e-01 -2.78080493e-01 -3.60067874e-01 6.13616966e-02
5.56493104e-01 -1.18427348e+00 -3.20608646e-01 3.61167789e-01
-8.92905533e-01 -2.03686804e-02 -3.48029017e-01 8.11195135e-01
-5.92172325e-01 8.35732460e-01 -5.37671268e-01 -5.24509549e-01
-3.62647831e-01 -1.20366764e+00 4.07815158e-01 -8.51384401e-02
9.02251899e-02 -6.50080502e-01 -1.98712066e-01 3.81161392e-01
7.53627539e-01 1.31450608e-01 9.67823625e-01 7.38433329e-03
-6.36660397e-01 -1.22396633e-01 -5.81118405e-01 7.55345821e-01
-1.13976434e-01 -3.63265514e-01 -4.33973551e-01 -6.45147383e-01
3.60099435e-01 -4.10839885e-01 1.05418825e+00 3.88750881e-01
1.44194055e+00 -5.21598160e-01 -9.49437672e-04 9.98824656e-01
1.83206546e+00 4.71680850e-01 9.86158967e-01 2.52938300e-01
4.13442254e-01 1.28646731e-01 2.43626952e-01 7.95484066e-01
2.76296064e-02 5.55676222e-01 6.00313306e-01 -1.90079622e-02
-1.49899393e-01 -7.63447583e-02 1.99593827e-01 1.18889737e+00
-3.25602412e-01 -2.84889311e-01 -5.06650031e-01 5.24423420e-01
-1.72090709e+00 -8.41316104e-01 1.24318272e-01 2.25538039e+00
9.55404162e-01 2.79005021e-01 -3.87718499e-01 5.76806068e-01
4.78369206e-01 5.34658611e-01 -3.95054668e-02 -2.77208537e-01
-1.25133112e-01 5.49542248e-01 5.63366592e-01 3.94810051e-01
-9.77410555e-01 6.42710328e-01 5.49098396e+00 1.27047837e+00
-1.05660105e+00 4.08436954e-02 4.79339272e-01 -1.98827028e-01
-1.16902500e-01 1.18957877e-01 -7.17550337e-01 7.41923392e-01
7.95928776e-01 8.57241359e-03 4.17979896e-01 9.08812165e-01
1.46112069e-01 -2.15152338e-01 -6.68796480e-01 1.28733182e+00
3.19063634e-01 -1.31501114e+00 -2.65503339e-02 3.10124792e-02
7.21760333e-01 -1.76541001e-01 -1.83272064e-02 -8.41950253e-03
-1.68174908e-01 -9.57393825e-01 5.55577338e-01 2.45741934e-01
9.55184519e-01 -7.31527150e-01 7.19785452e-01 4.20846373e-01
-1.45949268e+00 -1.96605846e-01 -5.86071193e-01 -1.25882626e-01
3.45291466e-01 1.01243353e+00 1.10011972e-01 5.17154038e-01
7.78942466e-01 7.51795828e-01 -8.60303715e-02 1.09012640e+00
-9.02323499e-02 6.43139243e-01 -3.79075706e-01 2.76731789e-01
3.16277891e-01 -4.28983480e-01 5.03298461e-01 1.19867551e+00
6.83835626e-01 3.92833263e-01 3.37424636e-01 6.84632301e-01
-2.85717398e-01 2.73458719e-01 -4.33802485e-01 1.46243960e-01
2.53550887e-01 7.73624599e-01 -6.95625663e-01 -1.86152473e-01
-6.64313555e-01 7.99293935e-01 2.01204196e-01 2.18213841e-01
-7.35060155e-01 -5.91635644e-01 2.15911970e-01 3.30404907e-01
6.16280794e-01 -5.03431380e-01 -1.51709959e-01 -1.20250368e+00
1.96533293e-01 -6.81166947e-01 2.37613454e-01 -3.48972052e-01
-8.01002741e-01 4.82118845e-01 7.73819920e-04 -1.50091028e+00
2.54919450e-03 -3.63267452e-01 -1.17443033e-01 5.79619288e-01
-1.81631434e+00 -5.89003563e-01 -3.92564505e-01 8.28073323e-01
5.53455949e-01 -3.96067530e-01 7.58489788e-01 9.19637322e-01
-4.53378320e-01 6.86383963e-01 5.13014674e-01 6.50004148e-02
2.79347539e-01 -5.11489928e-01 -5.82923770e-01 7.98696339e-01
-9.48950425e-02 4.03568268e-01 3.99623871e-01 -4.47401404e-01
-1.22580922e+00 -8.96940351e-01 1.08296192e+00 7.12488651e-01
2.87674785e-01 -1.57814901e-02 -9.95632887e-01 4.73704010e-01
1.62635118e-01 2.39608213e-01 6.50754452e-01 -3.51102918e-01
-2.60523140e-01 -2.82701313e-01 -1.32875919e+00 2.64543504e-01
9.36244071e-01 -2.52048314e-01 -3.05434257e-01 1.23644747e-01
5.05645454e-01 -3.18046361e-01 -1.10009432e+00 5.18033564e-01
4.56646532e-01 -1.34778142e+00 1.14327741e+00 1.10480994e-01
7.88376808e-01 -1.17292888e-01 -4.84574497e-01 -9.68810380e-01
-5.00327289e-01 -4.01062876e-01 -3.13766748e-01 8.96223247e-01
1.66634306e-01 -2.89608628e-01 8.39486897e-01 3.15846413e-01
-2.04595700e-01 -9.45011675e-01 -1.30860651e+00 -8.07726264e-01
-1.29437611e-01 -3.52552861e-01 5.14315143e-02 6.58255458e-01
-1.21097885e-01 -2.54493672e-02 -9.08787668e-01 -2.19813854e-01
8.84043396e-01 -9.77099538e-02 9.55775753e-02 -1.08528101e+00
-3.56348217e-01 -2.49154449e-01 -4.33845878e-01 -1.24642873e+00
-1.26985401e-01 -7.41994202e-01 -1.36451364e-01 -1.40641427e+00
1.33066848e-01 -6.67485535e-01 -2.96523452e-01 4.22021836e-01
2.68321216e-01 3.56691897e-01 2.79950857e-01 4.31219190e-01
-5.05431235e-01 8.11026633e-01 9.78504777e-01 -2.15670615e-01
-1.98346362e-01 -3.07354867e-01 -3.75453353e-01 9.59411561e-01
7.65465140e-01 -5.17565548e-01 -5.55336058e-01 -8.45266640e-01
3.72183621e-01 1.99093059e-01 1.06282018e-01 -1.36259222e+00
3.93991977e-01 4.53762934e-02 1.62775069e-01 -4.20779407e-01
6.59183860e-01 -9.97970819e-01 1.37624532e-01 6.41132951e-01
-1.43605411e-01 -2.53163815e-01 -5.13043115e-03 4.56067145e-01
-6.98681056e-01 -6.44636571e-01 1.24382043e+00 -4.85613227e-01
-5.96340299e-01 1.33934572e-01 -2.29385436e-01 -3.27773318e-02
1.00311041e+00 -1.88283518e-01 1.51252210e-01 -3.33566368e-01
-6.59171283e-01 -5.77037260e-02 1.78730011e-01 -5.26203811e-02
8.45458329e-01 -1.26954031e+00 -7.80833304e-01 4.04417306e-01
-2.31880546e-01 8.56285542e-02 2.37167224e-01 7.65275836e-01
-8.56313229e-01 5.32891035e-01 -2.20623791e-01 -4.14094210e-01
-1.06912994e+00 3.50713760e-01 9.06898454e-02 -5.33323169e-01
-7.19087541e-01 1.03585744e+00 -1.30126894e-01 2.06691444e-01
5.28486252e-01 1.21378995e-01 -1.40728101e-01 -7.09873885e-02
4.16670173e-01 6.19732857e-01 7.75494054e-02 -6.92869604e-01
-1.12965353e-01 8.37356567e-01 4.32795994e-02 2.64873475e-01
1.50532055e+00 -1.50104687e-01 -3.42276961e-01 -1.42919913e-01
1.46244729e+00 -3.08798909e-01 -1.16839945e+00 -4.34000909e-01
-3.68014961e-01 -9.04852271e-01 4.39013630e-01 -2.23895267e-01
-1.59177613e+00 7.95584977e-01 8.02051485e-01 1.23657845e-01
1.37335789e+00 -3.24597776e-01 1.02104652e+00 2.82035083e-01
5.92241764e-01 -1.35163474e+00 1.43103167e-01 4.60019588e-01
7.44089186e-01 -1.10995746e+00 3.96025598e-01 -4.89529967e-01
-2.28924423e-01 9.99877393e-01 4.57531303e-01 -2.30032563e-01
1.02365673e+00 3.18134010e-01 -4.45218801e-01 -8.75940993e-02
-3.50817323e-01 1.24459051e-01 -8.24087262e-02 1.47375733e-01
4.99460727e-01 -3.19684416e-01 -1.01983166e+00 3.29316556e-01
-5.08704036e-02 4.60369945e-01 2.27438271e-01 1.12842953e+00
-5.70315957e-01 -1.11291790e+00 -2.33869255e-01 5.23694396e-01
-7.37154901e-01 -2.20277622e-01 4.57235962e-01 5.82765341e-01
5.22591949e-01 9.31337595e-01 -1.81903884e-01 -2.10287914e-01
1.37315392e-01 -3.10874488e-02 3.05031061e-01 -2.52713561e-01
-1.12870790e-01 2.80817211e-01 -4.90378365e-02 -5.31472743e-01
-7.40111828e-01 -3.37880433e-01 -1.23037589e+00 -3.17531198e-01
-1.66714981e-01 1.51512340e-01 4.77580637e-01 7.34628260e-01
1.52220145e-01 3.90482157e-01 7.28353322e-01 -6.52950764e-01
-5.78608572e-01 -7.49207675e-01 -8.39162588e-01 3.57106864e-01
9.93997827e-02 -5.61612308e-01 -5.10991812e-01 2.48969406e-01] | [11.35383415222168, -1.7352826595306396] |
98b7a31b-5f42-4ca9-8c85-481d5e6190c8 | prior-information-based-decomposition-and | 2303.01776 | null | https://arxiv.org/abs/2303.01776v1 | https://arxiv.org/pdf/2303.01776v1.pdf | Prior Information based Decomposition and Reconstruction Learning for Micro-Expression Recognition | Micro-expression recognition (MER) draws intensive research interest as micro-expressions (MEs) can infer genuine emotions. Prior information can guide the model to learn discriminative ME features effectively. However, most works focus on researching the general models with a stronger representation ability to adaptively aggregate ME movement information in a holistic way, which may ignore the prior information and properties of MEs. To solve this issue, driven by the prior information that the category of ME can be inferred by the relationship between the actions of facial different components, this work designs a novel model that can conform to this prior information and learn ME movement features in an interpretable way. Specifically, this paper proposes a Decomposition and Reconstruction-based Graph Representation Learning (DeRe-GRL) model to effectively learn high-level ME features. DeRe-GRL includes two modules: Action Decomposition Module (ADM) and Relation Reconstruction Module (RRM), where ADM learns action features of facial key components and RRM explores the relationship between these action features. Based on facial key components, ADM divides the geometric movement features extracted by the graph model-based backbone into several sub-features, and learns the map matrix to map these sub-features into multiple action features; then, RRM learns weights to weight all action features to build the relationship between action features. The experimental results demonstrate the effectiveness of the proposed modules, and the proposed method achieves competitive performance. | ['Guoying Zhao', 'Yue Xie', 'Jingjie Yan', 'Guanming Lu', 'Haoyu Chen', 'Jinsheng Wei'] | 2023-03-03 | null | null | null | null | ['micro-expression-recognition'] | ['computer-vision'] | [ 8.99796039e-02 1.17672369e-01 -4.24055785e-01 -5.82089782e-01
-9.48568359e-02 8.04501399e-02 5.29541016e-01 -4.01241720e-01
2.60988146e-01 -7.15042725e-02 5.57485044e-01 4.22341824e-01
-2.85444885e-01 -8.34412694e-01 -3.43347847e-01 -8.08765233e-01
-1.82868928e-01 -8.18598494e-02 -1.88988566e-01 -6.04570508e-01
1.73302323e-01 5.54279685e-01 -1.49065936e+00 5.86888373e-01
5.00593781e-01 1.33802629e+00 -8.54570493e-02 2.48351544e-01
-4.06631649e-01 1.09635592e+00 -1.35883614e-01 -2.69349933e-01
8.27925876e-02 -7.86416829e-01 -6.57980680e-01 5.19751728e-01
2.34983876e-01 -4.19799685e-01 -5.00980735e-01 9.11010742e-01
2.11581141e-01 1.47144571e-01 9.18976367e-01 -1.64515662e+00
-6.67888880e-01 2.53771871e-01 -1.04302037e+00 -1.00249678e-01
5.47972083e-01 2.19070576e-02 1.08467484e+00 -1.08049595e+00
7.20122516e-01 1.80011094e+00 4.31393802e-01 4.14505601e-01
-9.05657172e-01 -8.20323110e-01 5.96740842e-01 5.14507473e-01
-1.47301793e+00 -3.96788806e-01 1.29646361e+00 -3.18683565e-01
6.28103733e-01 2.11614445e-01 8.74880433e-01 9.90990460e-01
2.72234619e-01 1.05239153e+00 1.04499280e+00 -2.02524647e-01
-1.97405398e-01 3.90971899e-02 2.19756275e-01 1.16792583e+00
-2.63133228e-01 -3.95662725e-01 -7.25340307e-01 -1.16700441e-01
8.73782814e-01 2.61149794e-01 -2.17249259e-01 -2.60817796e-01
-6.93556666e-01 8.37533057e-01 4.59510684e-01 5.21263480e-01
-4.20522124e-01 4.71809469e-02 2.95300901e-01 3.91175121e-01
4.35764313e-01 -2.36913204e-01 -3.33811522e-01 6.31825253e-02
-4.85027850e-01 -1.24103628e-01 5.67978263e-01 8.50645661e-01
1.26851344e+00 8.83427113e-02 -7.55043849e-02 9.36924875e-01
6.50560498e-01 2.75402486e-01 6.16137803e-01 -7.17402339e-01
2.52301872e-01 1.21646190e+00 -5.02208531e-01 -2.00121093e+00
-5.07713735e-01 -5.89053743e-02 -1.07912838e+00 3.69799100e-02
-2.07126409e-01 8.26599356e-03 -5.81301808e-01 2.00151396e+00
5.35672307e-01 3.56588721e-01 -2.55952597e-01 7.72791386e-01
1.15653300e+00 9.09415007e-01 7.60315955e-02 -5.54158986e-01
1.13534975e+00 -7.45804131e-01 -8.75741839e-01 1.52269537e-02
6.58335268e-01 -5.66568494e-01 8.43351543e-01 4.23478216e-01
-5.45601606e-01 -7.49758661e-01 -1.02989089e+00 -5.69923893e-02
-1.97849438e-01 4.30323213e-01 9.95445132e-01 1.50088161e-01
-7.88882077e-01 3.85449052e-01 -6.57847285e-01 -2.89369941e-01
4.74902183e-01 6.40795648e-01 -5.50323725e-01 3.21383737e-02
-1.07605588e+00 5.70274174e-01 1.65131286e-01 5.62546611e-01
-6.23844802e-01 -4.45426136e-01 -1.04372144e+00 -1.55173123e-01
3.69533896e-01 -5.90070486e-01 6.63825214e-01 -1.56318390e+00
-1.81543529e+00 7.91521072e-01 -2.01182947e-01 4.01484221e-01
2.00483307e-01 3.17283161e-02 -8.05351198e-01 3.05156440e-01
-2.47126669e-01 5.46545565e-01 1.23365378e+00 -1.21810782e+00
-4.54980016e-01 -6.26532018e-01 1.12661153e-01 1.78894386e-01
-6.20250523e-01 -1.10983830e-02 -2.87825495e-01 -5.34738183e-01
4.06380296e-01 -6.45683408e-01 -6.38737008e-02 1.75649479e-01
-1.45820379e-01 -6.39186382e-01 1.02815652e+00 -5.67526281e-01
1.46893287e+00 -2.20287871e+00 5.48142433e-01 5.39623559e-01
4.59551305e-01 -5.59621714e-02 -5.15841603e-01 3.90391499e-01
-3.34498525e-01 -1.14530750e-01 1.46087175e-02 -1.77601889e-01
3.57648246e-02 1.94393754e-01 -1.59909636e-01 4.84787852e-01
2.59404033e-01 9.05267715e-01 -6.14282966e-01 -5.84043682e-01
1.82679713e-01 5.19164622e-01 -3.63916397e-01 4.37738866e-01
6.58265688e-03 3.60566676e-01 -9.59560454e-01 6.94144487e-01
8.75076175e-01 -1.42649770e-01 3.05282146e-01 -6.73032641e-01
3.12037736e-01 -2.77658045e-01 -1.52794182e+00 1.69391215e+00
-4.79317993e-01 1.53111309e-01 2.91324943e-01 -1.25279772e+00
1.28265548e+00 -5.91493249e-02 8.60534012e-01 -4.60377693e-01
4.23807919e-01 -9.84828472e-02 -2.57240295e-01 -8.04472685e-01
-4.87649255e-02 -1.15046002e-01 1.23805366e-01 3.18212092e-01
2.40609080e-01 2.93877065e-01 -3.26691866e-01 1.74824700e-01
7.42371023e-01 4.35518116e-01 4.79184479e-01 -1.15514100e-01
1.03892970e+00 -4.84107643e-01 8.52634311e-01 -1.93477392e-01
9.54801142e-02 -1.56194512e-02 5.40916562e-01 -6.15488708e-01
-1.62833959e-01 -8.22822332e-01 1.83617081e-02 1.38407099e+00
4.89423066e-01 -8.51398826e-01 -6.51552379e-01 -7.93655992e-01
-5.41671552e-02 2.18167379e-01 -1.10652912e+00 -5.13581514e-01
-4.22850072e-01 -7.48001635e-01 6.49757609e-02 4.25081640e-01
7.42823303e-01 -1.09204698e+00 -7.72695802e-03 2.26127002e-02
-1.85257107e-01 -7.63545930e-01 -3.97589147e-01 -5.12874544e-01
-7.08374023e-01 -1.15616786e+00 -1.51349276e-01 -7.75650561e-01
9.32326615e-01 3.52103442e-01 6.96618974e-01 3.89833778e-01
-4.72263604e-01 7.06349611e-01 -4.65399951e-01 -4.97512659e-03
-6.87184408e-02 -3.63906473e-01 8.98717940e-02 1.00615311e+00
6.57638013e-01 -1.05904961e+00 -5.65129042e-01 3.63735259e-01
-8.45385611e-01 1.98404476e-01 9.71135974e-01 6.16176486e-01
7.93012440e-01 -2.62003213e-01 4.39151019e-01 -6.99307144e-01
4.91825044e-01 -7.65770614e-01 3.71426456e-02 3.66659850e-01
-4.70072567e-01 -5.32564893e-02 4.76067543e-01 -6.09230161e-01
-1.14788270e+00 1.18754804e-01 -5.70514835e-02 -6.92282021e-01
-1.01351338e-02 5.26100218e-01 -9.23282444e-01 -3.41759473e-01
2.25319833e-01 5.60556352e-01 2.82584608e-01 -4.14258540e-01
4.93471295e-01 5.84013224e-01 2.41987199e-01 -6.69909775e-01
6.40689313e-01 3.87995660e-01 4.34118688e-01 -8.84398639e-01
-8.16740572e-01 -2.66832292e-01 -8.17737758e-01 -4.82833266e-01
9.04923797e-01 -8.34257126e-01 -8.17303598e-01 3.59406263e-01
-8.60787511e-01 4.03892137e-02 7.59517476e-02 3.38191241e-01
-7.78229237e-01 6.28018022e-01 -7.06316948e-01 -6.01883948e-01
-1.67621419e-01 -9.64121759e-01 1.13947582e+00 3.50042105e-01
-1.33849293e-01 -9.13331568e-01 2.41877399e-02 1.59455255e-01
-8.37731548e-03 6.72258496e-01 8.21037769e-01 -2.08464876e-01
-5.17989576e-01 -9.95977782e-03 -1.55771732e-01 2.80746311e-01
3.10340226e-01 2.56746858e-01 -8.22994947e-01 -3.80148292e-02
1.47908419e-01 -4.17202920e-01 8.06260526e-01 2.29895413e-01
1.42814326e+00 -5.29230893e-01 -3.50502819e-01 9.04462874e-01
1.22916520e+00 -4.32149433e-02 7.82501757e-01 3.66184823e-02
9.56370592e-01 7.20687449e-01 7.97609270e-01 6.54422522e-01
5.18440545e-01 6.17564559e-01 4.98285532e-01 -2.95103434e-02
5.25126569e-02 -4.77184772e-01 7.82753587e-01 1.18478823e+00
-5.31214416e-01 3.14447820e-01 -1.24469355e-01 3.86636816e-02
-2.06986332e+00 -1.09495354e+00 6.54892400e-02 1.38250494e+00
5.77414215e-01 -3.72293234e-01 1.59151971e-01 -5.41265607e-02
4.71450388e-01 4.57289785e-01 -4.44317549e-01 -4.78637278e-01
-3.21950018e-02 4.83615436e-02 -3.21372986e-01 2.20310777e-01
-9.20356631e-01 8.76379967e-01 5.16487741e+00 1.15070915e+00
-1.10638881e+00 -4.56117988e-02 5.37161410e-01 3.39845449e-01
-5.00713766e-01 3.45838964e-02 -7.59403527e-01 1.82426929e-01
3.57679337e-01 -1.52457893e-01 3.30844045e-01 1.03478456e+00
9.35140923e-02 3.00054699e-01 -1.06852448e+00 1.48155487e+00
3.77066553e-01 -1.11800981e+00 6.35813534e-01 1.50238633e-01
4.25152421e-01 -7.56036758e-01 1.06206812e-01 4.47287291e-01
-2.76804138e-02 -1.10554290e+00 3.18953067e-01 1.13131940e+00
7.49085903e-01 -8.86254132e-01 5.46907306e-01 3.57350349e-01
-1.81315982e+00 -9.09475163e-02 -6.83475137e-01 -3.01070035e-01
-2.38074049e-01 2.31145084e-01 -3.55363339e-01 9.02100563e-01
4.34168816e-01 1.42840099e+00 -6.88218951e-01 2.10558534e-01
-3.66780609e-01 4.25606877e-01 -1.43378288e-01 -1.61827624e-01
1.11982286e-01 -7.23236084e-01 2.69286454e-01 1.11365402e+00
1.83530807e-01 5.62799275e-01 4.15211409e-01 8.20774794e-01
8.55331793e-02 7.12182045e-01 -4.64395434e-01 3.67030911e-02
2.01950185e-02 1.85914636e+00 -3.42621267e-01 4.64015128e-03
-5.28865218e-01 1.05517685e+00 5.28270543e-01 2.15743124e-01
-7.32807338e-01 -2.47471720e-01 7.52575517e-01 5.59994876e-02
1.83522314e-01 -3.23612243e-02 3.10860634e-01 -1.26555657e+00
-1.54174278e-02 -8.40195894e-01 6.00139081e-01 -7.70947516e-01
-1.43238640e+00 5.09880900e-01 4.11341004e-02 -1.23464954e+00
-2.22353354e-01 -6.77175760e-01 -8.17700028e-01 6.17198348e-01
-1.22629869e+00 -1.91264319e+00 -5.93696594e-01 1.15801406e+00
3.95621777e-01 -2.68098205e-01 8.91337335e-01 -3.03473277e-03
-7.76294172e-01 9.09241259e-01 -4.22144890e-01 3.13085467e-01
3.95431846e-01 -7.38318861e-01 -5.41338742e-01 3.36545408e-01
3.53986233e-01 7.54309952e-01 2.51996547e-01 -4.17353302e-01
-1.78005421e+00 -9.44078922e-01 3.57305497e-01 -2.72896707e-01
5.84044278e-01 -2.53974497e-01 -7.48174846e-01 8.58175814e-01
-1.19318627e-01 2.51667351e-01 9.26719904e-01 1.43493444e-01
-4.61744815e-01 -4.48446810e-01 -8.89151156e-01 4.33184773e-01
1.43417442e+00 -5.05112767e-01 -6.66512072e-01 7.72768855e-02
3.92693311e-01 1.33459382e-02 -1.15216768e+00 5.73651791e-01
5.22196889e-01 -1.00449383e+00 7.40154922e-01 -7.74628758e-01
5.88850737e-01 -4.01733607e-01 -2.00241327e-01 -1.04369605e+00
-6.69680655e-01 -5.63243687e-01 -3.92371863e-01 1.47347629e+00
-3.17440629e-02 -4.09202576e-01 5.17642796e-01 2.36463308e-01
5.61290532e-02 -1.30697572e+00 -7.91523337e-01 -3.26841027e-01
-3.24716032e-01 -4.19548512e-01 8.41754735e-01 1.30726612e+00
1.55604586e-01 6.23579562e-01 -5.06148100e-01 3.94128338e-02
2.33356610e-01 5.31183600e-01 1.03587329e+00 -1.15459251e+00
-3.19304556e-01 -6.78898752e-01 -9.00183201e-01 -1.19144607e+00
4.77685750e-01 -1.08525634e+00 -3.83273661e-01 -1.39003241e+00
4.86681491e-01 -1.05199836e-01 -3.77336085e-01 5.30666292e-01
-1.49857596e-01 -9.78132188e-02 1.80924702e-02 1.96370110e-01
-7.04499602e-01 1.09772933e+00 1.51161718e+00 -2.61919022e-01
-1.53269634e-01 -2.96374321e-01 -8.08626831e-01 1.09642386e+00
2.95438588e-01 -3.23096253e-02 -7.17600405e-01 7.25139538e-03
2.44891748e-01 -5.79141155e-02 2.04700962e-01 -7.34245002e-01
6.45404011e-02 -6.51028931e-01 6.92887127e-01 -3.21858823e-01
3.04540724e-01 -8.90300333e-01 1.27006501e-01 -4.49886173e-02
-1.48087546e-01 -3.09421003e-01 -5.96566387e-02 7.11765766e-01
-3.59308064e-01 1.74453482e-01 4.83722001e-01 -7.24994615e-02
-1.15406871e+00 1.05546451e+00 -1.98116265e-02 -1.60726994e-01
1.09310174e+00 -5.20534337e-01 2.27496698e-01 -4.92436916e-01
-8.72369170e-01 1.50877193e-01 4.30989452e-02 6.55597329e-01
9.16045904e-01 -1.82611644e+00 -6.01177812e-01 3.14222187e-01
3.31450611e-01 -2.24738464e-01 4.96563882e-01 8.89126182e-01
-1.45287171e-01 -3.14757377e-01 -4.76777077e-01 -6.05059564e-01
-1.27445054e+00 5.30202329e-01 3.38966101e-01 -1.31931350e-01
-6.11854255e-01 9.37011719e-01 5.19447684e-01 -2.59344071e-01
-1.26586212e-02 1.11926692e-02 -8.21081161e-01 2.39223272e-01
7.92527437e-01 2.63546348e-01 -5.27967036e-01 -1.16662121e+00
-3.24418724e-01 1.16867006e+00 -2.34267246e-02 2.90908515e-01
1.53794622e+00 -2.43754730e-01 -5.84256291e-01 3.93304527e-01
1.51000762e+00 1.36720628e-01 -1.11837363e+00 -2.86958396e-01
-2.85822093e-01 -5.01339734e-01 1.68085210e-02 -2.12482616e-01
-1.39149916e+00 9.03786898e-01 4.65341479e-01 -2.94720620e-01
1.66215885e+00 5.15292212e-02 6.38407648e-01 2.33714983e-01
3.47826511e-01 -1.07385361e+00 6.56012833e-01 1.52827978e-01
1.16959119e+00 -8.93005729e-01 1.74071327e-01 -7.59189129e-01
-6.73624992e-01 1.62402296e+00 9.92012858e-01 -3.15538377e-01
1.14001656e+00 -2.22058058e-01 -2.41320267e-01 -5.24260521e-01
-5.25194526e-01 -4.95899245e-02 6.01886809e-01 4.95258719e-01
1.84675992e-01 -2.05823798e-02 -4.36992764e-01 1.30575299e+00
-8.17188993e-02 3.03311050e-02 -1.05776116e-02 6.25599384e-01
-6.15038872e-01 -1.01660156e+00 8.68532434e-02 3.78790617e-01
-1.21971255e-03 3.06331038e-01 -5.59785545e-01 8.40499043e-01
5.71011364e-01 7.10345149e-01 -2.89042061e-03 -9.99986470e-01
2.45598271e-01 -1.00251600e-01 5.61821818e-01 -3.22871000e-01
-2.36733891e-02 8.63098204e-02 -8.90552923e-02 -1.02022219e+00
-8.53545606e-01 -3.94032866e-01 -1.50026011e+00 -3.32554579e-01
-9.73285139e-02 -3.81124429e-02 1.55896306e-01 1.11452341e+00
3.55713159e-01 3.67953628e-01 1.20141828e+00 -6.91595495e-01
-4.35877711e-01 -9.57763970e-01 -8.85260284e-01 7.99807250e-01
-8.98846537e-02 -9.93327260e-01 -4.27251637e-01 4.88265678e-02] | [13.681722640991211, 1.5798745155334473] |
5270d004-2aff-49fa-835d-d45d345dfcea | alternative-telescopic-displacement-an | 2306.16950 | null | https://arxiv.org/abs/2306.16950v1 | https://arxiv.org/pdf/2306.16950v1.pdf | Alternative Telescopic Displacement: An Efficient Multimodal Alignment Method | Feature alignment is the primary means of fusing multimodal data. We propose a feature alignment method that fully fuses multimodal information, which alternately shifts and expands feature information from different modalities to have a consistent representation in a feature space. The proposed method can robustly capture high-level interactions between features of different modalities, thus significantly improving the performance of multimodal learning. We also show that the proposed method outperforms other popular multimodal schemes on multiple tasks. Experimental evaluation of ETT and MIT-BIH-Arrhythmia, datasets shows that the proposed method achieves state of the art performance. | ['Xiaojun Zhang', 'Zong Lu', 'Zihong Luo Chengzhi Liu', 'Yitao Xu', 'Jiahao Qin'] | 2023-06-29 | null | null | null | null | ['arrhythmia-detection', 'cross-modal-retrieval', 'time-series-forecasting'] | ['medical', 'miscellaneous', 'time-series'] | [ 1.07432753e-01 -5.63720345e-01 -2.80161828e-01 -5.13118207e-01
-1.44878566e+00 -5.70038080e-01 7.79902220e-01 2.33786449e-01
-2.95787573e-01 6.02808535e-01 6.11211598e-01 3.96247923e-01
-1.76983252e-01 -1.44096509e-01 -1.64277643e-01 -8.74099255e-01
9.31162667e-03 2.08466962e-01 -2.19106779e-01 5.78806736e-02
4.47021276e-02 1.83252618e-01 -1.57702935e+00 7.41007209e-01
5.57801068e-01 1.16113806e+00 -5.61388060e-02 4.92094278e-01
6.32336065e-02 3.93164396e-01 -1.24611370e-01 -3.77269953e-01
2.79259235e-02 -4.95157570e-01 -9.30196404e-01 2.72269368e-01
6.57741129e-01 4.91620749e-02 -4.37734067e-01 8.03567946e-01
7.54457414e-01 1.48698285e-01 5.69960296e-01 -1.52617514e+00
-2.94438064e-01 6.50948107e-01 -8.19397867e-01 -4.85402048e-02
8.74929547e-01 -1.78062007e-01 1.06006205e+00 -1.22380376e+00
5.02310932e-01 1.38002217e+00 4.02475268e-01 4.21772361e-01
-1.19535351e+00 -6.58454001e-01 -4.35171910e-02 2.75763810e-01
-1.64161062e+00 -6.38928473e-01 7.70936787e-01 -1.94977239e-01
7.55961657e-01 5.00682771e-01 5.25970817e-01 1.07388735e+00
1.51887193e-01 1.00609255e+00 1.26745605e+00 -3.30072284e-01
-2.41851032e-01 -7.63272569e-02 2.76942700e-01 8.05398703e-01
-2.88292378e-01 -2.15259850e-01 -1.45542824e+00 -7.04526782e-01
2.79862493e-01 1.30180210e-01 -4.12407964e-01 -2.56471306e-01
-1.91292644e+00 4.71206576e-01 1.61575153e-01 4.03779387e-01
-3.55103791e-01 6.14135945e-03 4.48813677e-01 3.93228531e-01
-1.55093342e-01 -2.66433749e-02 -2.08158031e-01 -2.72786736e-01
-6.41712546e-01 -1.29915169e-02 4.33465242e-01 9.35095549e-01
6.70523286e-01 -3.05852562e-01 -4.22510326e-01 9.48348880e-01
7.36025214e-01 7.94555247e-01 5.65832853e-01 -9.30321813e-01
6.62327051e-01 8.10848951e-01 -1.35859042e-01 -9.09730971e-01
-6.26386285e-01 3.92530039e-02 -9.23436403e-01 -4.84510750e-01
1.19787529e-01 -1.97590888e-01 -6.52276099e-01 1.91547942e+00
3.92876446e-01 1.24876037e-01 3.92152518e-01 8.80823076e-01
1.30085444e+00 4.45320398e-01 7.83719495e-02 -1.14186682e-01
1.51879370e+00 -7.20589876e-01 -1.04762959e+00 -9.04477295e-03
3.43356043e-01 -9.55756247e-01 4.73590732e-01 3.15924525e-01
-9.22846556e-01 -5.25563478e-01 -9.18457568e-01 6.72755390e-02
-1.86443552e-01 2.28260860e-01 7.97128737e-01 5.75984299e-01
-7.73405492e-01 2.36165702e-01 -7.35773921e-01 -5.89764893e-01
2.65836924e-01 6.64782166e-01 -1.16866565e+00 -1.03013054e-01
-1.04846597e+00 7.48172998e-01 5.47047734e-01 2.40926415e-01
-5.99137425e-01 -3.86853039e-01 -1.17357564e+00 -1.32586375e-01
8.28518644e-02 -8.44774008e-01 8.97777677e-01 -6.02620900e-01
-1.29477596e+00 6.48486674e-01 -5.61799645e-01 1.60318285e-01
1.17692299e-01 -2.21299365e-01 -6.48322344e-01 2.94820726e-01
-2.17199713e-01 1.00074077e+00 7.96724260e-01 -1.34780574e+00
-7.68039286e-01 -5.02527297e-01 -5.90322971e-01 7.51821756e-01
-6.55220151e-01 2.99553312e-02 -5.64394176e-01 -3.04974645e-01
5.15212595e-01 -1.07138193e+00 8.08766186e-02 -4.31519866e-01
-5.74398398e-01 -2.22041100e-01 1.03986645e+00 -3.26669037e-01
1.11450744e+00 -2.12431526e+00 7.52726614e-01 4.90853339e-01
3.59054208e-01 -2.24877715e-01 -3.27892959e-01 7.55883634e-01
-2.61033094e-03 -1.21650010e-01 -2.42595166e-01 -6.96495593e-01
5.77506460e-02 2.16668472e-01 -9.55007896e-02 5.38444281e-01
4.21179011e-02 1.01539111e+00 -6.36456311e-01 -8.00195754e-01
2.80316412e-01 6.66338980e-01 -9.51306075e-02 2.52884924e-01
5.50026536e-01 7.58119166e-01 -2.82798320e-01 1.11602366e+00
5.65056562e-01 -2.23962829e-01 3.52776349e-01 -7.13475764e-01
1.70230597e-01 -3.65402758e-01 -1.24618030e+00 2.32259846e+00
4.49049957e-02 6.43467009e-01 -3.39622825e-01 -6.54660344e-01
8.14110816e-01 5.29954910e-01 7.13891208e-01 -5.91598928e-01
3.59323263e-01 1.35127902e-02 -6.96053356e-02 -6.07654989e-01
4.13234591e-01 -4.70781848e-02 -5.56865692e-01 3.63977224e-01
6.45530879e-01 1.77015126e-01 7.01507404e-02 1.50899097e-01
8.18900347e-01 -2.10895702e-01 4.50382888e-01 9.27125812e-02
7.13897169e-01 -5.07476449e-01 5.25710166e-01 6.97740614e-01
-2.00619072e-01 5.79794824e-01 1.42994076e-01 -4.84444341e-03
-4.49352860e-01 -1.11548102e+00 -3.21147501e-01 1.19342816e+00
3.35718095e-01 -7.75358617e-01 -2.84265250e-01 -8.57562184e-01
-8.52123648e-03 1.74253404e-01 -8.00975978e-01 -1.69737190e-01
-1.62476018e-01 -8.52631509e-01 6.67261243e-01 5.01187086e-01
5.31640172e-01 -5.52451730e-01 -3.39828193e-01 -1.12096161e-01
-8.45988989e-01 -1.18213129e+00 -5.00093520e-01 1.15975421e-02
-7.72278607e-01 -1.01499701e+00 -6.83772624e-01 -6.52628958e-01
6.13537610e-01 4.09467906e-01 7.71924913e-01 2.23990902e-02
-6.48448542e-02 9.52758789e-01 -4.63005126e-01 4.63698655e-02
-2.90059987e-02 -4.57356274e-02 1.61443591e-01 6.74081743e-01
3.39222878e-01 -2.65326053e-01 -3.80772918e-01 4.45150048e-01
-9.09552872e-01 5.11780940e-02 7.44196534e-01 1.17384815e+00
5.91427088e-01 -3.31466943e-01 6.43158734e-01 -4.36863661e-01
5.38866520e-01 -4.57002521e-01 8.88605043e-02 5.36391079e-01
-2.18486398e-01 8.07052776e-02 -8.13796297e-02 -5.53403795e-01
-1.06805110e+00 4.57272232e-01 2.66256213e-01 -3.64441365e-01
-3.55974972e-01 8.03231537e-01 -4.54524785e-01 -4.41879332e-01
1.82437241e-01 4.48638052e-01 -6.01608679e-02 -4.30460632e-01
5.83864450e-01 6.81698740e-01 7.34467864e-01 -6.51186705e-01
6.38212800e-01 5.25446117e-01 2.69373804e-01 -6.47140920e-01
-2.48579219e-01 -5.79690695e-01 -1.08153403e+00 -3.50121915e-01
6.09238684e-01 -1.09592021e+00 -7.86228061e-01 5.56999087e-01
-8.83494914e-01 5.06181836e-01 1.51408464e-01 7.03025460e-01
-4.49144632e-01 7.02580392e-01 -4.20313746e-01 -5.63037097e-01
-3.36740047e-01 -1.18939734e+00 1.31223774e+00 5.49647629e-01
-3.08689862e-01 -1.06094038e+00 4.70599234e-01 4.84737664e-01
1.09594740e-01 1.87495425e-01 6.56453371e-01 -8.92586768e-01
-1.73756763e-01 -3.46987486e-01 -2.31302410e-01 -1.46570772e-01
4.20239955e-01 -6.42142966e-02 -1.06994784e+00 -3.56383622e-01
-4.54145849e-01 -6.08786225e-01 9.54376221e-01 -2.89281886e-02
6.29294157e-01 1.21748842e-01 -4.90128756e-01 2.98090219e-01
1.09694648e+00 4.97368462e-02 3.83158714e-01 -4.13280800e-02
8.93684030e-01 5.46476066e-01 6.76898301e-01 6.40401661e-01
6.92079604e-01 6.67026281e-01 2.05787957e-01 -2.90879190e-01
-1.76356696e-02 1.07814863e-01 4.48917598e-01 1.10762739e+00
-6.10702205e-03 3.34062567e-03 -8.73455763e-01 4.55674589e-01
-2.40290666e+00 -9.98560011e-01 -4.38531861e-02 2.02552795e+00
8.54988217e-01 -6.03056788e-01 1.62985802e-01 -1.02651887e-01
5.81842899e-01 -4.37168926e-02 -1.93207055e-01 7.26275742e-02
-5.57675183e-01 -3.04384768e-01 -5.86588755e-02 2.77697384e-01
-1.37019825e+00 5.35330474e-01 7.44637632e+00 5.60738862e-01
-8.72271419e-01 1.78942263e-01 6.87473118e-02 -1.00314520e-01
-3.14282387e-01 -1.31610200e-01 -6.02924585e-01 2.52481818e-01
6.49964631e-01 -1.33224651e-01 2.19914630e-01 1.75677150e-01
-2.42422715e-01 -2.85396010e-01 -1.50065160e+00 1.57381546e+00
4.98523772e-01 -1.03346848e+00 3.56694549e-01 3.24342921e-02
6.71860218e-01 -8.70990753e-02 2.28539929e-01 1.67558372e-01
-1.71767563e-01 -1.07624125e+00 5.77506244e-01 9.33559954e-01
5.23163676e-01 -8.35915625e-01 1.01513529e+00 2.06873901e-02
-1.44740880e+00 -5.48287667e-02 1.23195782e-01 4.31026578e-01
1.62112623e-01 1.85182869e-01 -6.62292063e-01 1.11282659e+00
5.36571264e-01 8.51894438e-01 -1.09393966e+00 1.21634805e+00
2.21773595e-01 1.20221794e-01 -2.25964919e-01 3.69533002e-01
-9.96987075e-02 1.63734630e-01 6.08802021e-01 1.48207903e+00
4.01556611e-01 -4.06331643e-02 4.23090041e-01 2.54606545e-01
-1.84607282e-01 2.54479527e-01 -7.00304389e-01 -1.68635994e-01
6.02305770e-01 1.60287714e+00 -2.95841992e-01 -2.47232050e-01
-8.01014423e-01 8.76260877e-01 3.07384133e-01 1.89004049e-01
-7.92765379e-01 -1.77353919e-01 3.75731558e-01 -8.87118220e-01
-2.09892038e-02 -1.88602716e-01 -6.96984082e-02 -1.36063075e+00
-1.52872384e-01 -9.74097550e-01 9.32345152e-01 -6.92743540e-01
-1.55295801e+00 6.38711452e-01 1.73663139e-01 -1.63418472e+00
-3.20967704e-01 -3.28116387e-01 -2.80265242e-01 8.60876501e-01
-1.20762992e+00 -1.76149619e+00 -4.87471014e-01 1.18362784e+00
2.30133221e-01 -5.29116988e-01 1.07138216e+00 4.04763848e-01
-5.83936632e-01 7.52195358e-01 -1.80042922e-01 -9.20265075e-03
1.21072304e+00 -1.10008526e+00 -5.63221335e-01 6.18792713e-01
4.00175005e-01 6.81536734e-01 2.79758722e-01 -5.91153264e-01
-2.01367188e+00 -6.34890318e-01 7.33914852e-01 -4.56213266e-01
4.14345503e-01 -6.91510458e-03 -7.84064770e-01 5.74295104e-01
8.39272976e-01 -2.39500657e-01 1.57957792e+00 2.69044697e-01
-6.65584505e-01 -1.78870872e-01 -1.11009622e+00 3.83568943e-01
5.91822088e-01 -7.20650911e-01 -7.99533963e-01 -7.30739161e-03
2.03250557e-01 -3.90455157e-01 -1.23830426e+00 7.96803176e-01
9.83028412e-01 -7.41572678e-01 8.67651582e-01 -6.97613299e-01
-3.53689189e-03 -4.46892411e-01 -8.32970798e-01 -1.30059946e+00
-2.05943435e-01 -6.90178990e-01 -4.24526811e-01 1.48951268e+00
4.17420566e-01 -3.98966670e-01 1.60215959e-01 7.90892124e-01
1.40121743e-01 -4.16158944e-01 -1.34051800e+00 -3.70385557e-01
-4.41190720e-01 -3.31458598e-01 5.08031249e-01 1.25215292e+00
6.80706561e-01 3.99399668e-01 -6.14009976e-01 2.10403189e-01
7.56053507e-01 3.06017250e-01 7.28005350e-01 -1.04495370e+00
1.14446897e-02 -4.52139676e-01 -6.30289555e-01 -4.96968359e-01
3.10589820e-01 -1.04555106e+00 -1.93918958e-01 -1.15784824e+00
9.64614630e-01 6.98165074e-02 -8.68281782e-01 1.06894529e+00
-3.01266044e-01 6.76325500e-01 3.45250666e-01 2.10852921e-01
-1.02109122e+00 7.07594275e-01 1.02140701e+00 -2.74739683e-01
-2.26329803e-01 -3.59774351e-01 -7.79911458e-01 5.61218917e-01
4.70972270e-01 -2.54784673e-01 -1.03837684e-01 -2.31734857e-01
7.20943883e-02 6.88832402e-02 3.31362903e-01 -8.31645191e-01
5.00925601e-01 -1.61881715e-01 8.14411700e-01 -8.56131971e-01
6.13125741e-01 -1.06936979e+00 2.67217427e-01 4.71932217e-02
-4.62456435e-01 1.69762149e-01 3.79140109e-01 5.91013551e-01
-4.83648807e-01 1.88611224e-01 4.35376614e-01 4.31603223e-01
-7.25690186e-01 1.61932826e-01 -2.53608048e-01 -3.71135324e-01
8.64408076e-01 1.24914363e-01 -4.37773764e-01 -3.61263037e-01
-8.59281540e-01 5.62053859e-01 1.31231546e-01 6.82465494e-01
9.80272233e-01 -2.04677272e+00 -6.80736423e-01 1.10723451e-01
5.34487963e-01 -6.79402471e-01 5.02203166e-01 1.36152732e+00
2.87672490e-01 3.32119614e-01 -5.07392824e-01 -1.07503104e+00
-1.82422805e+00 1.84642136e-01 1.93539977e-01 1.30273178e-01
-2.90891975e-01 4.98946816e-01 -1.21049874e-01 -4.46295559e-01
2.73214847e-01 2.95729816e-01 -4.81270730e-01 5.63217044e-01
6.92789912e-01 3.39817822e-01 -4.63815480e-02 -1.26705348e+00
-7.79546738e-01 7.69686580e-01 -8.23708102e-02 -4.51812744e-01
9.91093695e-01 -6.01112008e-01 -3.26404572e-01 8.04651976e-01
1.17523193e+00 6.65116459e-02 -7.12183356e-01 -6.38441443e-01
-1.23882703e-01 -5.78019738e-01 1.62404180e-02 -9.88812268e-01
-9.70621347e-01 7.54780233e-01 9.31010008e-01 -1.73733547e-01
1.50507462e+00 2.50215083e-01 5.19383311e-01 5.24111211e-01
1.97412133e-01 -8.51765871e-01 1.68345034e-01 2.47476667e-01
8.77260447e-01 -1.44354224e+00 1.06005386e-01 -2.73725331e-01
-1.05788434e+00 1.35534465e+00 5.83324969e-01 5.85981846e-01
4.34401810e-01 1.30910665e-01 2.27449134e-01 -2.51537710e-01
-8.60807300e-01 -4.53785449e-01 1.13661587e+00 4.30225968e-01
4.24331903e-01 1.06442859e-02 -2.21192136e-01 9.04320300e-01
3.04953486e-01 -4.34395254e-01 2.69160513e-02 1.12563860e+00
-1.16679586e-01 -1.37851918e+00 -6.38498843e-01 2.13399291e-01
-1.84504554e-01 1.00296631e-01 -7.58809924e-01 6.52548552e-01
-1.01523502e-02 1.14009011e+00 -2.61947811e-01 -8.44616055e-01
1.75550893e-01 4.78791744e-01 9.23967361e-01 -1.94433346e-01
-5.76215386e-01 7.33994603e-01 1.29456162e-01 -7.05042362e-01
-9.87876356e-01 -1.04032803e+00 -1.20419717e+00 9.92944743e-03
-1.90278113e-01 6.22424260e-02 5.18606722e-01 1.13753426e+00
6.08005643e-01 2.60580719e-01 8.32310617e-01 -1.05135942e+00
-1.11312158e-01 -9.54914629e-01 -4.76534158e-01 5.56627512e-01
3.68217200e-01 -8.33958209e-01 -5.78524312e-03 1.30521536e-01] | [13.138978004455566, 4.9800310134887695] |
2a32ef0b-279e-4006-baa5-3cd3df96fffa | rgbd-salient-object-detection-via-deep-fusion | 1607.03333 | null | http://arxiv.org/abs/1607.03333v1 | http://arxiv.org/pdf/1607.03333v1.pdf | RGBD Salient Object Detection via Deep Fusion | Numerous efforts have been made to design different low level saliency cues
for the RGBD saliency detection, such as color or depth contrast features,
background and color compactness priors. However, how these saliency cues
interact with each other and how to incorporate these low level saliency cues
effectively to generate a master saliency map remain a challenging problem. In
this paper, we design a new convolutional neural network (CNN) to fuse
different low level saliency cues into hierarchical features for automatically
detecting salient objects in RGBD images. In contrast to the existing works
that directly feed raw image pixels to the CNN, the proposed method takes
advantage of the knowledge in traditional saliency detection by adopting
various meaningful and well-designed saliency feature vectors as input. This
can guide the training of CNN towards detecting salient object more effectively
due to the reduced learning ambiguity. We then integrate a Laplacian
propagation framework with the learned CNN to extract a spatially consistent
saliency map by exploiting the intrinsic structure of the input image.
Extensive quantitative and qualitative experimental evaluations on three
datasets demonstrate that the proposed method consistently outperforms
state-of-the-art methods. | ['Shengfeng He', 'Jiandong Tian', 'Yandong Tang', 'Qingxiong Yang', 'Liangqiong Qu', 'Jiawei Zhang'] | 2016-07-12 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 4.05681223e-01 8.79768282e-03 -2.37507209e-01 -3.32340777e-01
-4.54777628e-01 -1.10517412e-01 3.40518683e-01 1.90676004e-01
-3.22967887e-01 5.24749815e-01 2.77848154e-01 2.21116859e-02
7.27714673e-02 -6.10346735e-01 -5.94089925e-01 -6.01261199e-01
1.80894718e-01 -4.64905351e-01 9.99242544e-01 -2.45749295e-01
6.41424119e-01 2.27283299e-01 -1.82600296e+00 1.60761997e-02
1.00414300e+00 1.12287354e+00 6.95702970e-01 1.58153012e-01
-8.92693251e-02 8.30066085e-01 -2.05248579e-01 1.87917233e-01
2.84500778e-01 -4.23438281e-01 -6.33434057e-01 1.21510223e-01
1.39915630e-01 -2.18378469e-01 -5.08920178e-02 1.32861376e+00
4.21000153e-01 8.25148728e-03 4.05383170e-01 -1.22500086e+00
-8.19986761e-01 4.24764246e-01 -9.50315416e-01 4.43911165e-01
3.98712218e-01 1.14701562e-01 8.33176374e-01 -1.08815157e+00
4.14303899e-01 1.12296236e+00 4.02507991e-01 2.45134398e-01
-8.79698873e-01 -5.01853108e-01 3.20875168e-01 1.69674262e-01
-1.10285795e+00 -5.19629754e-02 1.50178933e+00 -1.88683659e-01
2.49229997e-01 1.38708293e-01 7.49257803e-01 7.19740629e-01
-1.11075059e-01 1.22869456e+00 1.27492976e+00 -4.36594188e-01
1.21454351e-01 3.88785422e-01 1.16747409e-01 8.82041633e-01
3.49712253e-01 -1.44334182e-01 -8.09339523e-01 1.73033774e-01
9.85263586e-01 2.77397543e-01 -2.66267955e-01 -7.90784359e-01
-1.36815190e+00 6.39017522e-01 1.19680357e+00 4.34719980e-01
-5.47545314e-01 2.70978026e-02 -1.72959398e-02 -4.26869512e-01
3.15610111e-01 3.85030717e-01 -1.87888667e-01 4.25069124e-01
-1.04760242e+00 1.23717412e-01 2.04679713e-01 9.22533035e-01
1.27431583e+00 1.39196649e-01 -3.49966586e-01 4.90307927e-01
5.56697607e-01 5.39846003e-01 4.45116699e-01 -8.50029409e-01
2.34580874e-01 1.07698154e+00 3.04063499e-01 -1.45163143e+00
-4.98563975e-01 -6.02404654e-01 -5.82977235e-01 4.01996911e-01
9.76845771e-02 7.73733705e-02 -1.05373454e+00 1.52313745e+00
4.76857513e-01 3.27193439e-01 3.24057713e-02 1.44655049e+00
1.04476881e+00 4.28472370e-01 8.83767381e-02 2.71659046e-01
1.17676973e+00 -1.01568151e+00 -5.50696850e-01 -5.72675347e-01
3.66475619e-02 -8.45497906e-01 1.22914815e+00 -1.86076224e-01
-1.07211316e+00 -6.12163186e-01 -1.22268772e+00 -2.73044258e-01
-5.19991577e-01 3.09238732e-01 8.20034802e-01 3.09684575e-01
-1.00665045e+00 1.59872994e-01 -6.81099832e-01 -2.89117306e-01
6.55284643e-01 2.59509683e-01 -3.83902155e-02 8.26927722e-02
-1.29281938e+00 9.74654794e-01 5.35975873e-01 1.65429443e-01
-9.42727208e-01 -5.54081619e-01 -9.74659264e-01 -3.65475826e-02
3.75847667e-01 -5.83804131e-01 9.71100271e-01 -1.19675624e+00
-1.27664459e+00 7.70945132e-01 -3.45798522e-01 -1.90960661e-01
1.11062862e-01 -2.14732185e-01 1.34468749e-01 3.98788780e-01
3.75440091e-01 1.09172463e+00 9.08905864e-01 -1.52707386e+00
-9.61961567e-01 -1.84513211e-01 2.84864336e-01 4.72139865e-01
-2.76165664e-01 -5.31872474e-02 -5.41428566e-01 -5.65458357e-01
5.38172126e-01 -5.59703767e-01 -3.02840114e-01 8.58427864e-03
-5.57237566e-01 -1.93233848e-01 1.01849103e+00 -3.43024164e-01
9.08206701e-01 -2.05209446e+00 3.45402628e-01 -1.21457204e-02
2.81471848e-01 1.07310563e-01 5.79630621e-02 -1.54616416e-01
2.15834722e-01 -1.72042727e-01 -4.15784687e-01 -1.88052967e-01
-4.52695377e-02 -2.39902332e-01 -2.25428447e-01 3.82754624e-01
7.08786726e-01 1.13409936e+00 -1.25412583e+00 -7.09491909e-01
5.85851490e-01 4.90040123e-01 -3.22177023e-01 2.45284215e-01
-1.13627277e-01 4.26181823e-01 -8.96163940e-01 9.84205306e-01
7.25459158e-01 -2.65053004e-01 -4.68830615e-01 -2.90019542e-01
-3.36875677e-01 2.03222618e-01 -1.03792572e+00 1.88641965e+00
-6.73970431e-02 5.34118772e-01 -4.93574850e-02 -8.90324295e-01
1.10214102e+00 -2.55921185e-01 3.27990651e-01 -7.08621740e-01
3.19760054e-01 3.51511568e-01 -3.25488508e-01 -2.98986673e-01
5.86908340e-01 3.65106501e-02 -9.57494900e-02 3.04547161e-01
-2.58713812e-02 -1.39722288e-01 -1.96955070e-01 1.59844786e-01
5.74229777e-01 2.15187743e-01 3.43474671e-02 -3.28985095e-01
6.55514300e-01 1.47001415e-01 5.72804570e-01 5.97876430e-01
-4.33298796e-01 8.87342632e-01 1.39478192e-01 -3.08893234e-01
-6.95258558e-01 -1.01626754e+00 4.24095429e-02 1.09369791e+00
1.00380456e+00 6.93860129e-02 -7.43938982e-01 -5.19515097e-01
-1.45206958e-01 3.51348579e-01 -7.77569294e-01 -2.13941321e-01
-3.77208740e-01 -5.55383921e-01 -2.03722082e-02 3.95435393e-01
1.07179642e+00 -1.37068021e+00 -1.26345551e+00 1.04011983e-01
-1.20036408e-01 -9.69711542e-01 -3.73733014e-01 2.69756347e-01
-8.02824557e-01 -9.34725821e-01 -9.60107505e-01 -1.21090126e+00
8.24813485e-01 9.87268209e-01 8.25260580e-01 9.69004780e-02
-3.32929641e-01 2.09003940e-01 -4.34966356e-01 -6.02431834e-01
2.37609580e-01 3.84493977e-01 -3.03689927e-01 8.53973776e-02
4.30163205e-01 -4.08052146e-01 -9.23325777e-01 1.85918197e-01
-1.06627250e+00 5.99941432e-01 1.03758335e+00 6.14449203e-01
5.43349266e-01 -2.14786336e-01 5.29803276e-01 -4.80827451e-01
4.35516864e-01 -4.79937106e-01 -4.74543512e-01 1.65644765e-01
-2.61741787e-01 8.10677633e-02 1.00113571e-01 -1.28334433e-01
-9.75425124e-01 4.38067108e-01 3.03960800e-01 -4.89746332e-01
-1.92875668e-01 3.56138229e-01 -1.37691200e-01 -2.91177005e-01
3.12497795e-01 5.30123591e-01 -2.23298118e-01 -2.80672193e-01
5.15853882e-01 5.26459098e-01 4.87803549e-01 -1.78663954e-01
9.65507448e-01 5.70806205e-01 -7.58238509e-02 -5.70130885e-01
-1.30839026e+00 -5.12140930e-01 -9.19714212e-01 -2.88618594e-01
1.05612826e+00 -1.00256109e+00 -3.73041362e-01 5.61311364e-01
-1.03369749e+00 -1.33717164e-01 -4.49116295e-03 2.01217160e-01
-3.96027029e-01 1.45950004e-01 -1.49391770e-01 -6.85390890e-01
-2.99547762e-01 -1.24592090e+00 1.47409415e+00 8.58324647e-01
1.92676604e-01 -8.55773509e-01 -3.73021632e-01 -6.03663735e-03
6.00357294e-01 4.19519961e-01 5.93309343e-01 7.16432929e-02
-1.05700958e+00 2.81353034e-02 -7.73645639e-01 2.47953646e-02
5.10917842e-01 -2.57481843e-01 -9.49746490e-01 6.34471029e-02
8.91670585e-02 -2.37884253e-01 1.05680501e+00 5.12250841e-01
1.04440475e+00 -9.86938849e-02 -4.20977116e-01 5.10435462e-01
1.46189201e+00 -1.38670817e-01 3.05952430e-01 5.56432903e-01
9.02856946e-01 6.55922115e-01 8.57348800e-01 2.74522752e-01
5.42859674e-01 4.66717988e-01 6.78584158e-01 -6.81152642e-01
-1.51792645e-01 -3.91993523e-01 2.33771637e-01 4.66817468e-01
8.05776343e-02 5.13231516e-01 -7.73476183e-01 7.91388512e-01
-1.94869852e+00 -7.83892393e-01 2.59443633e-02 1.84133708e+00
1.04714024e+00 3.64997625e-01 8.87566358e-02 9.56794396e-02
8.34207058e-01 2.68527865e-01 -7.89176881e-01 1.00064829e-01
-3.78506392e-01 -1.44807592e-01 5.27058303e-01 2.22930014e-01
-1.35739279e+00 1.21368885e+00 6.13650084e+00 5.60034871e-01
-1.54626751e+00 -4.56934050e-02 5.85184157e-01 6.67622462e-02
-5.31255543e-01 4.19318117e-03 -6.96730494e-01 4.10707474e-01
1.00697406e-01 -2.86405869e-02 1.92542553e-01 9.55104887e-01
2.06559405e-01 -5.69658995e-01 -6.29940271e-01 9.23996449e-01
1.56944200e-01 -1.30521798e+00 7.45843025e-03 -3.44913602e-01
1.06268513e+00 -4.59575467e-02 4.78679210e-01 -1.14412546e-01
2.10974127e-01 -8.28001797e-01 9.98098552e-01 6.06319189e-01
1.85433388e-01 -5.76050580e-01 6.38988614e-01 9.20471922e-02
-1.25598860e+00 -6.18587136e-02 -5.46072185e-01 -1.70598671e-01
-4.79385853e-02 6.60889328e-01 -8.40237260e-01 3.06169599e-01
9.13618922e-01 1.11010981e+00 -1.08017397e+00 1.26839399e+00
-4.90402550e-01 3.62182915e-01 -1.17850915e-01 -3.03183258e-01
5.84829509e-01 -2.20009647e-02 5.80088079e-01 9.93091106e-01
7.27091208e-02 -2.69917082e-02 2.52482861e-01 1.29150188e+00
1.72085553e-01 -3.67795140e-03 -5.42658389e-01 3.47890317e-01
3.70060086e-01 1.49954510e+00 -1.04885566e+00 -2.54264027e-01
-3.95571887e-01 8.80352199e-01 2.76990920e-01 4.88450706e-01
-8.11543465e-01 -5.86911440e-01 4.45474446e-01 5.13923988e-02
4.60503310e-01 -2.02590406e-01 -6.62518740e-01 -9.77775633e-01
1.76695511e-02 -3.65820676e-01 -4.89437208e-02 -1.18458843e+00
-9.23455834e-01 3.92426968e-01 -9.59718749e-02 -1.30591357e+00
2.66820908e-01 -4.58195329e-01 -7.48465717e-01 1.00733554e+00
-2.24667001e+00 -1.39316511e+00 -6.19162202e-01 6.49306595e-01
4.91080791e-01 1.14424840e-01 2.67185450e-01 -1.18018806e-01
-4.40408468e-01 6.80636540e-02 -2.53768772e-01 1.18971393e-01
4.87710357e-01 -1.34594274e+00 -2.04810947e-02 1.06871939e+00
-6.60318807e-02 7.19579637e-01 6.47979140e-01 -4.97722477e-01
-1.37254417e+00 -1.11033010e+00 3.96920860e-01 -2.87086606e-01
4.40118343e-01 -3.57100010e-01 -8.55688334e-01 2.09023938e-01
3.90041858e-01 4.49052528e-02 1.42409757e-01 -2.94505477e-01
-5.50424904e-02 -2.18258798e-01 -9.44252253e-01 6.80351794e-01
8.58501256e-01 -4.71509665e-01 -9.11622345e-01 -5.10347486e-02
1.13649559e+00 -3.41620475e-01 -2.68109709e-01 4.29941922e-01
2.50039011e-01 -1.13795626e+00 1.15730298e+00 -2.51621783e-01
5.35760820e-01 -8.34813952e-01 8.94333869e-02 -1.08641100e+00
-2.88234264e-01 -3.12200725e-01 2.26609945e-01 1.20761836e+00
2.48997748e-01 -3.15071344e-01 6.52000666e-01 4.78104770e-01
-2.80962348e-01 -7.75454044e-01 -6.80510342e-01 -1.25656858e-01
-3.87693614e-01 -5.09260856e-02 5.71664453e-01 6.72255993e-01
-1.05614118e-01 2.52586395e-01 -2.28964120e-01 3.73002738e-01
8.30443203e-01 7.20124304e-01 4.48125273e-01 -1.23321676e+00
4.17336196e-01 -6.40981853e-01 -4.62060958e-01 -9.74082351e-01
-4.84541245e-02 -7.52700329e-01 3.97118151e-01 -1.69167769e+00
3.28669339e-01 -5.81673741e-01 -8.02732646e-01 6.69543087e-01
-6.62536979e-01 4.43903297e-01 1.21243052e-01 4.25349846e-02
-1.03087997e+00 8.47431421e-01 1.53262532e+00 -2.08432376e-01
-3.21856260e-01 -2.08783746e-01 -1.13385737e+00 8.29938889e-01
8.31330061e-01 -3.07551146e-01 -3.31358373e-01 -4.27009463e-01
4.80496846e-02 -2.94030577e-01 6.95992053e-01 -1.12816691e+00
4.49626118e-01 -2.89519817e-01 6.80702984e-01 -8.52116942e-01
1.43282384e-01 -7.02824235e-01 -5.04061222e-01 3.22524309e-01
-3.27555776e-01 -3.45419019e-01 2.76839852e-01 4.72223669e-01
-3.68444324e-01 -1.03381582e-01 7.40004718e-01 -2.28526309e-01
-1.15782714e+00 8.91808346e-02 -6.46156743e-02 6.37646243e-02
1.06599879e+00 -3.59113425e-01 -1.16437651e-01 -1.16142489e-01
-2.25786224e-01 2.57056653e-01 5.84300399e-01 6.95721745e-01
9.42288637e-01 -1.40492368e+00 -3.55806231e-01 2.77271777e-01
1.45800725e-01 1.92257822e-01 2.12117098e-02 8.88665080e-01
-2.15345547e-01 4.58595037e-01 -6.67399108e-01 -8.75439823e-01
-8.46351445e-01 6.15852475e-01 1.73294976e-01 2.91406423e-01
-2.88971245e-01 8.26570988e-01 3.70484024e-01 -1.84629112e-01
2.65392870e-01 -6.14429116e-01 -4.16535199e-01 -2.02783756e-02
3.58587444e-01 7.99997710e-03 -3.32318246e-01 -7.58792460e-01
-5.24999261e-01 9.54484403e-01 1.66454047e-01 7.81582147e-02
1.48581707e+00 -4.97813225e-01 -1.94403321e-01 5.50379932e-01
1.00850463e+00 -2.07545549e-01 -1.63352859e+00 -4.06559080e-01
1.39114678e-01 -5.93294322e-01 3.30940396e-01 -5.51601410e-01
-1.17450070e+00 1.05297816e+00 7.54334927e-01 1.14957817e-01
1.35633290e+00 7.65634403e-02 6.52985632e-01 -1.12375766e-02
5.06756783e-01 -9.10685062e-01 5.20787597e-01 2.94218659e-01
8.02755833e-01 -1.63510561e+00 1.60335321e-02 -4.57962751e-01
-7.36201286e-01 8.55216444e-01 7.33161330e-01 -3.89118135e-01
5.89053690e-01 -2.19558075e-01 1.81564987e-01 -2.48805031e-01
-1.18496023e-01 -7.74343967e-01 6.20949626e-01 5.93455017e-01
1.33140221e-01 -1.97416753e-01 -5.91509007e-02 6.70437574e-01
5.10258153e-02 -5.25277629e-02 4.01710540e-01 9.65033054e-01
-1.02341461e+00 -6.90513313e-01 -3.45813811e-01 2.35172078e-01
-2.26518780e-01 -1.83475748e-01 -4.58101630e-01 4.62404549e-01
1.92549169e-01 8.40136886e-01 -1.56265825e-01 -3.02079499e-01
1.81635916e-01 -2.88655311e-01 1.78726181e-01 -6.81298733e-01
-3.07292551e-01 6.55221418e-02 -6.93942666e-01 -4.62003917e-01
-9.62410271e-01 -6.47035122e-01 -1.40896225e+00 3.36382568e-01
-3.84663105e-01 -2.43486967e-02 5.53491235e-01 9.00102198e-01
3.60958368e-01 6.38487339e-01 7.90691137e-01 -1.29936206e+00
1.60810910e-02 -7.87928224e-01 -4.16378528e-01 4.15088117e-01
5.39883673e-01 -1.06313694e+00 -2.66742617e-01 5.04375398e-02] | [9.784577369689941, -0.555191159248352] |
6fdbbfdd-3590-4848-8dfb-b50139e3334d | adversarial-robustness-and-feature-impact | 2303.13649 | null | https://arxiv.org/abs/2303.13649v1 | https://arxiv.org/pdf/2303.13649v1.pdf | Adversarial Robustness and Feature Impact Analysis for Driver Drowsiness Detection | Drowsy driving is a major cause of road accidents, but drivers are dismissive of the impact that fatigue can have on their reaction times. To detect drowsiness before any impairment occurs, a promising strategy is using Machine Learning (ML) to monitor Heart Rate Variability (HRV) signals. This work presents multiple experiments with different HRV time windows and ML models, a feature impact analysis using Shapley Additive Explanations (SHAP), and an adversarial robustness analysis to assess their reliability when processing faulty input data and perturbed HRV signals. The most reliable model was Extreme Gradient Boosting (XGB) and the optimal time window had between 120 and 150 seconds. Furthermore, SHAP enabled the selection of the 18 most impactful features and the training of new smaller models that achieved a performance as good as the initial ones. Despite the susceptibility of all models to adversarial attacks, adversarial training enabled them to preserve significantly higher results, especially XGB. Therefore, ML models can significantly benefit from realistic adversarial training to provide a more robust driver drowsiness detection. | ['André Lourenço', 'Isabel Praça', 'Eva Maia', 'Lourenço Rodrigues', 'João Vitorino'] | 2023-03-23 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [-1.49292117e-02 1.40322044e-01 1.31092444e-01 -3.41498137e-01
-2.41466984e-01 -3.69926989e-01 1.94416881e-01 1.61977068e-01
-5.37806213e-01 8.20414543e-01 -7.36843497e-02 -4.56689537e-01
-2.80265480e-01 -5.68009734e-01 -4.10358012e-01 -7.77817190e-01
-2.23012567e-01 -2.76898444e-01 1.36359006e-01 -6.91948295e-01
2.66860723e-01 8.21901739e-01 -1.96514976e+00 -6.54075220e-02
9.69585657e-01 9.11108971e-01 -3.68185043e-01 9.76561844e-01
4.75904495e-01 5.91644406e-01 -1.03536069e+00 -3.27596664e-01
3.84024501e-01 -3.29669148e-01 -4.75284969e-03 -6.03103697e-01
2.39154324e-01 -1.49390593e-01 -1.78805202e-01 6.03223741e-01
1.00332582e+00 3.57823402e-01 1.79379582e-01 -1.92195451e+00
1.27246771e-02 1.09335415e-01 3.19072306e-02 6.21777356e-01
3.53614300e-01 6.91408098e-01 1.76636890e-01 -3.15028340e-01
3.02735597e-01 9.71847236e-01 7.63502240e-01 6.01754010e-01
-1.19643426e+00 -9.95799363e-01 -1.76969528e-01 6.95195735e-01
-1.29178429e+00 -5.32096922e-01 1.04020548e+00 -3.16879034e-01
8.98120821e-01 6.60520315e-01 8.24422061e-01 1.21992588e+00
7.72846460e-01 -2.19214350e-01 1.51059020e+00 -1.44568026e-01
2.04608515e-01 5.37295938e-01 4.60606784e-01 3.68181407e-01
4.75971520e-01 8.11952114e-01 -5.03086090e-01 -2.10595250e-01
-2.39048928e-01 -2.41512910e-01 -3.24138671e-01 2.28200391e-01
-8.58304560e-01 6.68387055e-01 3.38774949e-01 9.43864137e-02
-4.37696934e-01 -9.29243024e-03 7.14097321e-01 5.42062998e-01
1.57165542e-01 6.15032196e-01 -3.18947852e-01 -2.89132535e-01
-6.72075033e-01 2.64905185e-01 6.48445845e-01 3.37509036e-01
5.38992405e-01 3.22248161e-01 -3.57178301e-01 3.66612166e-01
-1.65033340e-01 7.83536375e-01 4.28998828e-01 -8.67810786e-01
5.33378273e-02 4.97512609e-01 3.93808186e-02 -1.26253510e+00
-7.35627949e-01 -7.41561055e-01 -5.99303365e-01 7.61873722e-01
1.78284198e-01 -4.14373159e-01 -6.48052573e-01 1.53188384e+00
3.32705602e-02 1.51160985e-01 1.43161669e-01 9.16142285e-01
6.96624041e-01 4.03221697e-01 1.56282395e-01 -4.17579651e-01
1.22467065e+00 -2.38252133e-01 -9.94025588e-01 -3.14845473e-01
3.28273475e-01 -4.90430206e-01 8.98143053e-01 5.32292306e-01
-7.32182801e-01 -1.12179065e+00 -1.44334579e+00 2.87524253e-01
-5.35992444e-01 -5.03905237e-01 3.14725727e-01 1.31624138e+00
-8.78771901e-01 6.88206434e-01 -3.72101754e-01 5.06086797e-02
-5.68615682e-02 3.26646119e-01 -2.67327935e-01 -5.09277843e-02
-1.64345801e+00 1.53490746e+00 9.56701711e-02 3.46439719e-01
-6.84903443e-01 -7.83084810e-01 -6.85195267e-01 -2.87814438e-01
7.95966610e-02 -4.68865961e-01 4.49706525e-01 -7.92324066e-01
-1.25277495e+00 5.63055158e-01 -1.94663763e-01 -1.04656148e+00
8.05286467e-01 -9.14497748e-02 -9.01402593e-01 -8.97436123e-03
-5.09984493e-01 3.59459579e-01 1.26346743e+00 -1.15370858e+00
-1.41922876e-01 -2.76220530e-01 -2.76859432e-01 -3.55823576e-01
1.01656914e-01 -1.23194732e-01 6.15110517e-01 -1.72106415e-01
-4.16311592e-01 -1.16736555e+00 6.73707649e-02 -5.73235631e-01
-2.36479566e-01 5.19956686e-02 6.36152446e-01 -9.20433640e-01
1.28342807e+00 -2.46677852e+00 -4.54954028e-01 3.81731689e-01
2.31387049e-01 4.36583996e-01 9.17039886e-02 1.66934326e-01
-3.90522361e-01 9.98023301e-02 2.63575613e-01 -2.02686209e-02
-3.43665704e-02 2.57164031e-01 -3.30346525e-01 6.58474028e-01
2.12126926e-01 7.59460747e-01 -5.32664061e-01 -1.46015570e-01
5.32044232e-01 5.33151686e-01 -1.21787645e-01 3.34495604e-01
4.56613868e-01 3.23398590e-01 3.15811545e-01 3.86634290e-01
6.13811553e-01 9.33563292e-01 -5.18141329e-01 -2.67131358e-01
-1.46060511e-01 -2.61478275e-01 -7.65865088e-01 4.71030802e-01
-4.19847518e-01 1.12764108e+00 -2.81411022e-01 -6.97148323e-01
1.50601685e+00 2.68430680e-01 4.07762706e-01 -1.11292970e+00
3.38687837e-01 -1.50362300e-02 5.26675463e-01 -8.76482964e-01
4.11089480e-01 -2.29882985e-01 -4.78225872e-02 -1.11022741e-02
-4.39217538e-01 -6.88000321e-02 -6.54580593e-02 -1.09140560e-01
9.92581069e-01 -2.98204780e-01 1.25455216e-01 -4.01074849e-02
5.40177524e-01 -1.59654111e-01 7.04151511e-01 5.67188621e-01
-8.54346097e-01 2.41298422e-01 6.18499994e-01 -6.16541684e-01
-6.22745633e-01 -9.80429173e-01 -6.59536943e-02 6.92095757e-01
4.63100001e-02 -4.02120613e-02 -4.85544026e-01 -2.86081403e-01
2.04027936e-01 1.50035119e+00 -7.14118958e-01 -1.20073235e+00
-4.48924005e-01 -6.28693342e-01 8.00363600e-01 3.16749573e-01
3.48122925e-01 -1.03779137e+00 -1.05343986e+00 -7.59529248e-02
-2.30236635e-01 -9.23054814e-01 -9.50274616e-02 2.49539137e-01
-6.60772204e-01 -1.06411552e+00 -1.16819277e-01 -1.95482541e-02
3.33541721e-01 2.41177365e-01 7.70161927e-01 2.29273602e-01
-6.01863563e-01 2.51502067e-01 -2.67927557e-01 -1.12667167e+00
-9.17487442e-01 -3.98745567e-01 1.42840102e-01 -1.61498319e-02
3.85339200e-01 -3.81441653e-01 -5.34664631e-01 4.31153089e-01
-4.90142822e-01 -5.29220164e-01 4.96116340e-01 4.71189022e-01
1.16495334e-01 2.21808001e-01 1.05325580e+00 -5.01880109e-01
8.55384409e-01 -2.50293851e-01 -3.53587180e-01 -2.14271650e-01
-1.12342083e+00 -1.30225956e-01 6.60018086e-01 -4.46068704e-01
-7.42341101e-01 -2.22087964e-01 -1.39601827e-01 -4.16065365e-01
-4.44145948e-01 2.20435467e-02 -1.06618509e-01 -2.52908170e-01
1.20869851e+00 -4.81587164e-02 5.40682733e-01 1.66391298e-01
1.82963818e-01 4.39301431e-01 4.65817928e-01 2.53011733e-01
8.72553527e-01 4.75230925e-02 2.54547536e-01 -9.76237774e-01
-2.90781260e-01 -3.45133007e-01 -4.87189710e-01 -8.39409232e-01
7.47681022e-01 -7.99636722e-01 -8.66911829e-01 2.40551949e-01
-8.41223896e-01 -1.31625712e-01 -2.92541355e-01 5.66922724e-01
-3.22897643e-01 -4.48370876e-04 1.19015142e-01 -1.11970317e+00
-2.34498471e-01 -8.28620315e-01 2.69934773e-01 2.44980127e-01
-4.61235255e-01 -6.23437583e-01 1.11018792e-01 7.00555742e-01
6.62510514e-01 9.81342375e-01 8.25673938e-01 -6.31283522e-01
1.88440740e-01 -7.34200776e-01 2.44356617e-01 7.57285774e-01
-1.25011384e-01 2.63934046e-01 -1.42459619e+00 -4.23048735e-01
1.72274247e-01 2.33720560e-02 7.48735964e-01 3.43128383e-01
5.90601385e-01 -5.04396223e-02 -1.15587682e-01 3.56436014e-01
1.15748262e+00 4.43053275e-01 1.07707202e+00 3.42248350e-01
1.97499186e-01 7.32901871e-01 7.76597500e-01 1.94471076e-01
1.12748042e-01 5.39828956e-01 7.44052529e-01 -4.50669438e-01
-2.96961404e-02 1.41507596e-01 7.74943352e-01 1.09953448e-01
-4.92850333e-01 1.10750377e-01 -5.24774611e-01 1.14104837e-01
-1.33538747e+00 -1.33803117e+00 -4.50694650e-01 2.56271744e+00
2.69115329e-01 7.19593108e-01 5.18170238e-01 7.44502008e-01
6.24929905e-01 5.49484864e-02 -7.16200948e-01 -1.21113157e+00
-3.71652320e-02 6.32896125e-02 6.69647515e-01 3.17442566e-01
-7.15383828e-01 1.96100324e-01 6.54117680e+00 -5.92519082e-02
-1.17090106e+00 3.06988764e-03 5.62994480e-01 -5.10792732e-01
1.46304116e-01 -2.74712145e-01 -6.65672660e-01 8.11834931e-01
1.70959210e+00 -3.41369689e-01 3.13327372e-01 7.96022117e-01
8.81732702e-01 -2.32423291e-01 -4.21312213e-01 7.51276910e-01
3.07309717e-01 -5.82186818e-01 -7.34130919e-01 1.06715478e-01
3.36104959e-01 -2.43989944e-01 -1.07069463e-01 6.51271462e-01
-4.67287958e-01 -1.17492235e+00 5.90973318e-01 9.35048223e-01
4.17632341e-01 -1.23747802e+00 1.34792233e+00 3.40705246e-01
-6.51940703e-01 -5.27355075e-01 -3.93859565e-01 -2.50968397e-01
-6.33760318e-02 7.19064832e-01 -9.33295786e-01 3.54795396e-01
4.40134794e-01 3.85559015e-02 -1.00218594e+00 8.78756344e-01
-2.65273839e-01 7.59020090e-01 -3.91735323e-02 -1.01900697e-01
-4.06106770e-01 1.93279728e-01 7.44465232e-01 1.08391738e+00
1.46533206e-01 -1.53661191e-01 -1.81293651e-01 6.03620946e-01
5.71737170e-01 -1.81706965e-01 -7.81329870e-01 3.52766365e-01
4.93265182e-01 1.24286449e+00 -3.63230079e-01 -6.93729753e-03
-6.17014095e-02 5.82613647e-01 -2.28307322e-01 2.63328224e-01
-1.29473603e+00 -6.92325175e-01 8.94177198e-01 4.00265276e-01
-2.69442886e-01 -1.06297024e-01 -4.39899087e-01 -3.50598395e-01
2.56999936e-02 -6.76432788e-01 2.27401629e-01 -7.32061446e-01
-6.91547036e-01 6.95314944e-01 1.18292540e-01 -1.25740492e+00
-3.33849579e-01 -2.34037921e-01 -7.88224101e-01 1.14406085e+00
-1.53223836e+00 -5.26803732e-01 -7.62362957e-01 5.90228677e-01
1.41411453e-01 -8.28865692e-02 6.35501623e-01 1.43659011e-01
-7.02299774e-01 6.94521129e-01 -5.82848966e-01 -4.45379406e-01
9.13978636e-01 -1.04645634e+00 1.90814510e-02 1.09468329e+00
-4.74071264e-01 3.30985785e-01 1.35617530e+00 -4.64707077e-01
-1.23346913e+00 -1.12245607e+00 7.76316524e-01 -5.18829942e-01
1.30534053e-01 -1.37532398e-01 -8.55750263e-01 1.16712175e-01
1.77119449e-01 -2.74173710e-02 6.69607699e-01 -1.93599597e-01
7.63798226e-03 -5.89495122e-01 -1.34135556e+00 3.85563463e-01
5.33764720e-01 -1.93343520e-01 -7.56844163e-01 -1.47345349e-01
5.35794914e-01 -2.05632299e-01 -8.10085952e-01 3.72945815e-01
7.69471228e-01 -1.41584802e+00 9.44636524e-01 -2.89861202e-01
-1.57326430e-01 -2.52110809e-01 4.53294247e-01 -1.65654767e+00
-2.91700363e-01 -6.97547853e-01 -7.32337311e-03 1.03495848e+00
4.07146960e-01 -1.16102481e+00 2.06372261e-01 8.25157940e-01
-2.32112423e-01 -4.87768680e-01 -7.89179802e-01 -9.33061004e-01
-2.84160882e-01 -7.03350008e-01 4.88833189e-01 5.89734614e-01
1.49552058e-02 6.04063980e-02 -4.18766737e-01 2.50286520e-01
4.18306738e-01 -2.37884313e-01 8.97810221e-01 -1.33725095e+00
1.21928386e-01 -3.08193177e-01 -9.36601877e-01 4.12668526e-01
1.66299894e-01 -6.46739304e-01 3.52087557e-01 -1.01807463e+00
-5.46555579e-01 -3.69515293e-03 -5.13976932e-01 4.52432007e-01
-5.13359129e-01 3.07157695e-01 2.62280881e-01 -3.15875947e-01
2.07485303e-01 1.06724992e-01 8.80717099e-01 1.15437798e-01
-6.14157021e-01 4.66425925e-01 -6.49168789e-01 2.90280312e-01
1.20425165e+00 -6.39045119e-01 -6.79450274e-01 5.90873480e-01
1.86782569e-01 8.35156739e-02 7.70523906e-01 -1.29143870e+00
-4.90663648e-02 -1.72268506e-02 6.91316545e-01 -3.66420537e-01
3.22413534e-01 -7.28126109e-01 3.80805045e-01 1.03140402e+00
-2.63200611e-01 2.41402835e-01 6.79289520e-01 3.60408366e-01
4.61479723e-02 -7.38965021e-03 9.39128399e-01 3.94917816e-01
-5.88300943e-01 -2.51288176e-01 -7.05125570e-01 -3.35978627e-01
1.34943807e+00 -4.65823919e-01 -4.98113751e-01 -2.72972077e-01
-8.29764903e-01 -3.77902389e-02 2.29862541e-01 6.54938877e-01
6.65399432e-01 -8.16472054e-01 -7.03529239e-01 5.78827739e-01
2.02508032e-01 -8.29204440e-01 4.42211479e-01 1.16917479e+00
-1.76410392e-01 4.12767023e-01 -6.01707578e-01 -2.89788187e-01
-1.65820408e+00 9.11299586e-01 4.57893074e-01 2.51136363e-01
-5.30009627e-01 4.25843805e-01 -7.81411052e-01 2.23697513e-01
1.33619919e-01 -3.57306004e-01 -4.10090983e-01 1.84759572e-01
6.79280579e-01 1.07549620e+00 5.07640362e-01 -6.40112162e-01
-5.25701582e-01 2.76636630e-01 4.58627284e-01 1.04381360e-01
6.99634969e-01 -1.31184563e-01 1.44384235e-01 5.21330833e-01
9.19555306e-01 8.14406797e-02 -1.21071923e+00 6.84586525e-01
-3.36979717e-01 -3.59699309e-01 3.12639982e-01 -1.09360135e+00
-8.95010471e-01 8.56696665e-01 1.27683961e+00 5.38903713e-01
1.34846377e+00 -7.12509453e-01 5.56372643e-01 1.67379305e-01
1.09402485e-01 -1.01288247e+00 -2.75778443e-01 -7.27504417e-02
9.17585552e-01 -1.15798414e+00 5.80907911e-02 -1.27229869e-01
-9.22316074e-01 1.12758291e+00 4.08452153e-01 -1.45476609e-01
5.74900627e-01 1.36290401e-01 3.80237222e-01 -1.35517325e-02
-8.74971509e-01 -2.64342338e-01 3.95359606e-01 9.48072374e-01
-8.11531208e-03 2.06060529e-01 -5.65452635e-01 4.47982371e-01
-5.45564950e-01 2.94798762e-02 7.20633328e-01 5.74065924e-01
-3.93543661e-01 -5.69957852e-01 -6.25770152e-01 5.33536136e-01
-5.95074408e-02 3.22632015e-01 -3.91013294e-01 9.60779846e-01
5.52999377e-01 1.46002030e+00 -8.86413008e-02 -9.79184449e-01
8.77182066e-01 6.00546420e-01 1.03849858e-01 -1.83575880e-02
-8.80579293e-01 -4.98843342e-01 2.62147278e-01 -7.45721340e-01
-2.26961315e-01 -5.62084258e-01 -1.03215110e+00 -6.28647268e-01
-2.73926705e-01 2.43876018e-02 1.00540924e+00 9.00965571e-01
4.16798681e-01 9.39098120e-01 1.14644372e+00 -5.61173558e-01
-4.72228080e-01 -9.84129846e-01 -5.26524365e-01 3.83491665e-01
6.89643562e-01 -7.36885846e-01 -9.07953918e-01 -6.93274364e-02] | [13.499878883361816, 2.8742592334747314] |
e2f4c887-b001-4928-8691-08b09a3a4173 | max-pooling-loss-training-of-long-short-term | 1705.02411 | null | http://arxiv.org/abs/1705.02411v1 | http://arxiv.org/pdf/1705.02411v1.pdf | Max-Pooling Loss Training of Long Short-Term Memory Networks for Small-Footprint Keyword Spotting | We propose a max-pooling based loss function for training Long Short-Term
Memory (LSTM) networks for small-footprint keyword spotting (KWS), with low
CPU, memory, and latency requirements. The max-pooling loss training can be
further guided by initializing with a cross-entropy loss trained network. A
posterior smoothing based evaluation approach is employed to measure keyword
spotting performance. Our experimental results show that LSTM models trained
using cross-entropy loss or max-pooling loss outperform a cross-entropy loss
trained baseline feed-forward Deep Neural Network (DNN). In addition,
max-pooling loss trained LSTM with randomly initialized network performs better
compared to cross-entropy loss trained LSTM. Finally, the max-pooling loss
trained LSTM initialized with a cross-entropy pre-trained network shows the
best performance, which yields $67.6\%$ relative reduction compared to baseline
feed-forward DNN in Area Under the Curve (AUC) measure. | ['Geng-Shen Fu', 'Ming Sun', 'Anirudh Raju', 'Nikko Strom', 'George Tucker', 'Arindam Mandal', 'Spyros Matsoukas', 'Shiv Vitaladevuni', 'Sankaran Panchapagesan'] | 2017-05-05 | null | null | null | null | ['small-footprint-keyword-spotting'] | ['speech'] | [ 1.45306855e-01 -2.51059681e-02 -4.11802560e-01 -4.88822281e-01
-1.01821589e+00 -2.77937222e-02 2.99729556e-01 -1.03119828e-01
-1.07073641e+00 7.15874314e-01 1.12856537e-01 -6.17425025e-01
1.44317016e-01 -5.34374177e-01 -8.68757069e-01 -5.47912657e-01
-1.59566984e-01 -8.15818235e-02 2.98053846e-02 1.25942126e-01
2.68750377e-02 3.82503510e-01 -8.63157094e-01 4.44330215e-01
6.83726728e-01 1.60940874e+00 3.44509006e-01 6.50509953e-01
-6.25133961e-02 8.07247460e-01 -5.45295954e-01 -4.14902270e-01
1.75151601e-01 1.88794080e-02 -9.40533280e-01 -1.02485883e+00
4.73910689e-01 -4.82158571e-01 -7.31800854e-01 9.14763212e-01
8.05965781e-01 3.65570068e-01 2.62211233e-01 -8.75925779e-01
-5.06466687e-01 8.27261984e-01 -2.82161552e-02 3.22140247e-01
1.92384012e-02 3.99512872e-02 1.10235274e+00 -1.19421673e+00
2.13410527e-01 1.01887524e+00 1.05627120e+00 6.29617333e-01
-1.01048505e+00 -1.05180407e+00 -6.29269481e-02 1.18667804e-01
-1.37966752e+00 -3.26895535e-01 3.43414307e-01 2.05011308e-01
1.97177732e+00 3.25032800e-01 1.98191091e-01 1.12991273e+00
5.26386738e-01 1.11417663e+00 5.56818604e-01 -4.19717550e-01
-1.50813147e-01 5.31168640e-01 -1.01102283e-02 7.75605798e-01
-1.37124836e-01 2.10404530e-01 -7.77095199e-01 -8.78276080e-02
1.20736800e-01 -4.28046882e-02 -1.80238456e-01 3.14453214e-01
-1.01674497e+00 5.38870096e-01 5.78569889e-01 5.85416734e-01
-9.78034437e-02 5.56826532e-01 8.25799644e-01 6.32140636e-01
8.52656722e-01 7.05221236e-01 -1.01801157e+00 -4.80088204e-01
-1.61230958e+00 -2.48143673e-01 7.73408055e-01 7.72978902e-01
6.29864156e-01 2.53739923e-01 -6.51363194e-01 1.17153454e+00
2.05608699e-02 2.28992850e-01 1.04355681e+00 -3.73333216e-01
7.95064747e-01 3.02435756e-01 -4.41064477e-01 -6.94172382e-01
-2.27984339e-01 -5.28041601e-01 -9.78862941e-01 -1.98822483e-01
-2.82039344e-01 -4.63039011e-01 -1.05527604e+00 1.69738293e+00
-6.08552217e-01 1.90571353e-01 1.98981568e-01 4.38596338e-01
8.07851613e-01 9.99731660e-01 1.17116950e-01 -1.73211142e-01
9.24570024e-01 -1.33430815e+00 -7.93065786e-01 -2.39170134e-01
1.14284015e+00 -7.05680490e-01 1.27378225e+00 4.16009575e-02
-9.84187365e-01 -2.95031607e-01 -1.00949252e+00 -3.53286356e-01
-9.49222744e-01 4.93910760e-01 6.56828433e-02 5.22438705e-01
-1.24747789e+00 1.03753257e+00 -8.22580040e-01 -2.33622476e-01
2.89465249e-01 6.32001162e-01 -2.15205118e-01 4.98179883e-01
-1.76258814e+00 1.02699006e+00 9.21489537e-01 -4.60001901e-02
-7.97996700e-01 -1.17932272e+00 -7.19001293e-01 4.15701061e-01
9.04772524e-03 -2.31260151e-01 1.31208432e+00 -6.89822674e-01
-1.75777340e+00 5.76176226e-01 -3.54568250e-02 -1.19641232e+00
5.10860860e-01 -3.90351713e-01 -3.39850694e-01 -4.06467706e-01
-4.23361510e-01 9.04544532e-01 4.90994096e-01 -4.95669216e-01
-4.02920067e-01 1.54888883e-01 -4.32105243e-01 3.20048600e-01
-1.08039880e+00 3.15255016e-01 -3.27044904e-01 -9.76986408e-01
-4.25144106e-01 -5.91467619e-01 -9.20856022e-04 -1.53936923e-01
-7.59348691e-01 -4.35289234e-01 1.22885597e+00 -1.08118117e+00
1.93156433e+00 -2.00973749e+00 -4.77585077e-01 1.35491446e-01
-1.04364544e-01 8.29358160e-01 -1.50402501e-01 1.70900077e-01
2.37207785e-02 5.54255068e-01 4.36873920e-02 -9.06546354e-01
2.10806385e-01 4.82334644e-02 -5.31009674e-01 1.22778781e-01
7.52321556e-02 1.08860958e+00 -5.93294859e-01 -3.72656167e-01
1.57311872e-01 7.20726252e-01 -2.21327320e-01 2.97195673e-01
-2.65908718e-01 -3.86526942e-01 4.55141515e-02 4.50697988e-01
4.52071339e-01 -3.64711992e-02 -3.02932650e-01 2.40369849e-02
1.19344406e-02 6.37063503e-01 -4.72412556e-01 1.88027430e+00
-8.58712852e-01 1.14800179e+00 -4.98932928e-01 -7.03777671e-01
1.09507632e+00 4.21722680e-01 2.83673294e-02 -9.31139708e-01
1.58194155e-01 4.81913537e-01 -5.05920649e-01 -1.24174282e-01
9.12518740e-01 7.42097422e-02 -6.82715997e-02 4.18562531e-01
4.29641366e-01 3.94816905e-01 -1.94437221e-01 2.14884570e-03
1.09724724e+00 -1.78424567e-01 -2.38582730e-01 -1.00303248e-01
4.16714787e-01 -6.74407482e-01 4.02041793e-01 7.78110802e-01
5.41178230e-03 4.04822081e-01 2.70340949e-01 -4.02299792e-01
-1.20165646e+00 -5.73883116e-01 -1.51887566e-01 1.41048598e+00
-3.81148070e-01 -4.14253920e-01 -7.14216948e-01 -7.00089276e-01
-7.51132518e-02 7.57386625e-01 -4.19969320e-01 -5.11813521e-01
-5.64486980e-01 -6.20947182e-01 1.27533913e+00 6.12395704e-01
8.99614871e-01 -1.20006883e+00 -2.52039909e-01 1.94345355e-01
-1.63565934e-01 -9.13778841e-01 -7.84677505e-01 6.21848702e-01
-8.79714966e-01 -2.08423764e-01 -1.21333301e+00 -9.77141738e-01
2.78498262e-01 -4.69596207e-01 8.46590638e-01 -2.29264006e-01
-2.00738370e-01 -1.96976617e-01 -2.48954054e-02 -2.88493693e-01
-4.24366668e-02 8.25499058e-01 3.54604810e-01 -4.50438738e-01
7.34064817e-01 -3.82134348e-01 -7.06999600e-01 2.18932912e-01
-4.69965786e-01 -1.16708644e-01 7.70045400e-01 1.12030339e+00
5.42209268e-01 -5.78234941e-02 3.38501155e-01 -3.04802746e-01
1.00495422e+00 -2.16441646e-01 -4.30015206e-01 7.33182371e-01
-1.12595963e+00 4.70699608e-01 7.10604250e-01 -6.50423110e-01
-9.28943813e-01 -5.46826780e-01 -2.43249983e-01 -7.44351625e-01
3.62766236e-01 4.66282427e-01 1.84298977e-01 -4.62236673e-01
3.94962966e-01 4.15975928e-01 -1.37634113e-01 -9.44905519e-01
5.24018183e-02 7.61430681e-01 2.12717131e-01 -5.75771220e-02
2.14634195e-01 -3.13444167e-01 -5.11543334e-01 -5.65952778e-01
-6.79191053e-01 -1.85713977e-01 -1.99632168e-01 1.43794864e-01
5.91211259e-01 -7.17639863e-01 -8.02577198e-01 6.47499144e-01
-1.27438760e+00 -5.47987580e-01 -3.05947721e-01 6.06649339e-01
-1.86840609e-01 -7.36887231e-02 -8.53098631e-01 -8.16135347e-01
-1.20361936e+00 -9.01225686e-01 7.73291171e-01 1.03453353e-01
-1.50841698e-01 -1.38134992e+00 7.80768925e-03 -1.72840610e-01
1.03431261e+00 -6.21127002e-02 7.11314082e-01 -1.13920045e+00
-1.29944339e-01 -5.73180616e-01 -4.89631057e-01 1.04856122e+00
-1.36919752e-01 -2.62250066e-01 -1.07304239e+00 -5.12600124e-01
-2.59201139e-01 -5.58080316e-01 1.42095387e+00 4.97268021e-01
1.67579281e+00 -7.05821395e-01 -4.27001029e-01 9.30404961e-01
1.49109316e+00 1.86032444e-01 4.96820569e-01 4.81562793e-01
6.32123113e-01 2.43555196e-02 9.99922827e-02 3.23377222e-01
2.01875400e-02 4.85364765e-01 -3.19475055e-01 -2.64144629e-01
-1.49051934e-01 -6.26953602e-01 6.01561487e-01 8.45833361e-01
5.72452486e-01 -4.80524719e-01 -9.83612418e-01 3.37104887e-01
-1.59741473e+00 -7.08209395e-01 7.58669972e-01 2.10768032e+00
1.29192805e+00 4.71247137e-01 -1.65925995e-01 1.46262929e-01
4.90458757e-01 3.96142513e-01 -7.47924209e-01 -1.05792546e+00
-1.61554068e-01 3.82819593e-01 1.16021848e+00 6.44793868e-01
-1.15840197e+00 1.22683322e+00 6.31038189e+00 1.62562251e+00
-1.56128907e+00 1.66536555e-01 1.08672833e+00 -3.68902355e-01
-2.09373593e-01 -3.34164739e-01 -1.20395207e+00 7.47158885e-01
1.61145830e+00 -2.69927442e-01 2.61746436e-01 8.01004469e-01
1.82381287e-01 -2.21416615e-02 -1.10524845e+00 1.01512802e+00
3.86026800e-02 -1.30810261e+00 4.46975790e-02 4.91339574e-03
5.58223486e-01 4.66163635e-01 5.57470143e-01 6.41582966e-01
1.51761219e-01 -1.24966276e+00 4.17281002e-01 6.21725440e-01
1.35991871e+00 -1.05767941e+00 9.42443371e-01 2.97531992e-01
-1.11600482e+00 1.20647242e-02 -4.69417334e-01 2.34520689e-01
3.53144342e-03 7.93725729e-01 -1.15286410e+00 4.61790189e-02
7.88956404e-01 4.78313595e-01 -2.19579577e-01 1.00326300e+00
2.18053490e-01 6.80365801e-01 -7.05224633e-01 -3.17964166e-01
6.37846172e-01 1.61807463e-01 3.91822696e-01 1.90407658e+00
2.98836470e-01 -5.07562637e-01 -1.93780482e-01 7.32988119e-01
-8.56359124e-01 1.95117116e-01 -4.75585341e-01 -2.96147048e-01
3.18860769e-01 9.02851045e-01 -1.41430497e-01 -3.99539679e-01
-8.75972062e-02 1.15738928e+00 3.36055368e-01 5.03784180e-01
-7.30666935e-01 -1.18522358e+00 7.16105223e-01 -2.13844478e-01
2.81921685e-01 2.24613607e-01 -4.19078678e-01 -1.00036955e+00
3.20800185e-01 -2.16956258e-01 2.19661325e-01 -4.10072446e-01
-8.68291795e-01 8.72816443e-01 -1.42754138e-01 -9.72057164e-01
-1.73305929e-01 -4.58951265e-01 -5.89991808e-01 1.17369056e+00
-1.88216984e+00 -9.21737850e-01 2.54927754e-01 1.00088812e-01
4.63916779e-01 -4.89087909e-01 9.29846883e-01 5.92200220e-01
-8.51058781e-01 1.68111968e+00 4.88554090e-01 3.71163189e-01
5.40510237e-01 -1.06505525e+00 5.58788538e-01 5.93503892e-01
-1.22860134e-01 4.83302116e-01 3.52944106e-01 -4.40604031e-01
-8.22614431e-01 -1.37186158e+00 1.37451291e+00 1.92465827e-01
5.99431634e-01 -5.78450799e-01 -9.29258049e-01 6.63318455e-01
3.69891077e-01 2.50260472e-01 6.79872811e-01 -6.38464689e-02
-3.42684865e-01 -3.73861760e-01 -1.34623611e+00 1.61146924e-01
5.93676507e-01 -9.90252733e-01 -3.26348126e-01 3.69137675e-01
1.21551609e+00 -3.77454519e-01 -9.13794279e-01 6.93481266e-01
7.05350697e-01 -4.94452596e-01 8.76746356e-01 -6.49114072e-01
1.88083768e-01 6.17453039e-01 6.32748157e-02 -1.16451430e+00
1.99326858e-01 -7.89605558e-01 -1.89135537e-01 1.12092650e+00
1.05650890e+00 -6.13020837e-01 1.11482191e+00 8.43329668e-01
-4.64878790e-02 -1.36761403e+00 -1.10659134e+00 -1.02203226e+00
3.15247297e-01 -6.37661755e-01 2.90421873e-01 6.49662375e-01
1.06902249e-01 -2.12313250e-01 -6.32193804e-01 -3.77857625e-01
2.24936187e-01 -6.79515064e-01 5.88997826e-03 -6.21877253e-01
1.17456138e-01 -6.85455501e-01 -3.61416847e-01 -1.37235260e+00
5.16666055e-01 -9.95599568e-01 8.42894763e-02 -1.35149837e+00
1.93843350e-01 -4.39736009e-01 -1.00781667e+00 9.76455390e-01
1.67194024e-01 1.00655288e-01 -7.14240372e-02 -1.16640531e-01
-6.85687840e-01 8.75105262e-01 6.09153390e-01 -1.89141408e-01
-3.61854106e-01 7.26006366e-03 7.70321265e-02 4.90499586e-01
9.04848933e-01 -5.94885767e-01 -2.10457236e-01 -5.61098158e-01
1.25819132e-01 -2.64984041e-01 -1.16109088e-01 -1.12213421e+00
6.07604265e-01 2.18807146e-01 3.03982735e-01 -6.63774729e-01
4.53452617e-01 -4.91442293e-01 -4.30023879e-01 5.60539126e-01
-8.85158539e-01 -8.59807990e-03 5.17892897e-01 2.18624994e-01
-3.61313462e-01 -1.44711792e-01 7.91333139e-01 -1.54897599e-02
-6.11417055e-01 4.17319655e-01 -3.16845238e-01 5.83940521e-02
7.32483268e-01 -2.07616851e-01 -1.96642727e-02 -2.52005041e-01
-6.40537202e-01 3.23094219e-01 -1.95892617e-01 4.93580252e-01
9.96072054e-01 -1.48363316e+00 -4.12876040e-01 3.39220583e-01
-3.10278907e-02 -3.10619950e-01 -6.53851107e-02 6.49368107e-01
-4.94311601e-01 1.05902112e+00 2.69857347e-01 -2.10208401e-01
-1.40635467e+00 -4.88457233e-02 8.26954365e-01 -5.98458409e-01
-3.03787202e-01 1.68767762e+00 -3.83429706e-01 -6.22058749e-01
1.12741768e+00 -8.53245616e-01 2.16498494e-01 -9.34978649e-02
6.13848507e-01 5.49157619e-01 5.25355220e-01 -1.78809494e-01
-4.25257504e-01 1.00420572e-01 -3.70172799e-01 -8.37366953e-02
1.11501729e+00 8.88392329e-02 -8.61991867e-02 4.75443184e-01
2.10892391e+00 -8.64321828e-01 -9.08270419e-01 -8.99492502e-01
2.49971732e-01 -1.52733251e-01 6.62825108e-01 -1.02190447e+00
-1.04933977e+00 9.52063680e-01 8.81070793e-01 -2.85318285e-01
1.03217828e+00 -3.30672085e-01 1.49387121e+00 8.36819708e-01
-6.22444786e-02 -1.53826141e+00 8.97371992e-02 9.36286628e-01
6.37327552e-01 -1.17084873e+00 -2.79442042e-01 6.72668278e-01
-3.18181664e-01 1.19709659e+00 5.61140120e-01 -2.98623554e-02
1.09328306e+00 5.49253106e-01 -3.16921204e-01 1.69411063e-01
-1.12009227e+00 3.11791211e-01 5.92786014e-01 -8.54251683e-02
3.99944216e-01 -1.06187072e-02 -8.42867568e-02 3.80196750e-01
-3.73248130e-01 2.25337088e-01 -2.48236001e-01 6.25782371e-01
-4.10102814e-01 -7.22047865e-01 3.30709159e-01 8.40588808e-01
-8.50204170e-01 -7.34153271e-01 -2.89841980e-01 1.25623241e-01
-1.61075249e-01 3.96321744e-01 3.15044284e-01 -9.37564075e-01
1.88588321e-01 3.98170561e-01 -4.20323834e-02 -2.64347464e-01
-8.13886166e-01 -3.91267866e-01 4.53175195e-02 -4.46357340e-01
1.40636384e-01 2.18046382e-02 -9.44167495e-01 -6.65091574e-01
-7.68595219e-01 2.36069426e-01 1.01678538e+00 9.37635958e-01
4.01556045e-01 3.71134907e-01 6.36455178e-01 -5.67403615e-01
-6.75554037e-01 -1.38006914e+00 -3.24506581e-01 -6.29111379e-02
6.93288743e-01 -1.46413788e-01 -6.46608174e-01 -3.86124432e-01] | [14.19853687286377, 6.459183216094971] |
1e1e3f6b-7248-47ea-842d-b4705723c371 | multimodal-end-to-end-group-emotion | 2111.05890 | null | https://arxiv.org/abs/2111.05890v1 | https://arxiv.org/pdf/2111.05890v1.pdf | Multimodal End-to-End Group Emotion Recognition using Cross-Modal Attention | Classifying group-level emotions is a challenging task due to complexity of video, in which not only visual, but also audio information should be taken into consideration. Existing works on multimodal emotion recognition are using bulky approach, where pretrained neural networks are used as a feature extractors and then extracted features are being fused. However, this approach does not consider attributes of multimodal data and feature extractors cannot be fine-tuned for specific task which can be disadvantageous for overall model accuracy. To this end, our impact is twofold: (i) we train model end-to-end, which allows early layers of neural network to be adapted with taking into account later, fusion layers, of two modalities; (ii) all layers of our model was fine-tuned for downstream task of emotion recognition, so there were no need to train neural networks from scratch. Our model achieves best validation accuracy of 60.37% which is approximately 8.5% higher, than VGAF dataset baseline and is competitive with existing works, audio and video modalities. | ['Lev Evtodienko'] | 2021-11-10 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 2.74958253e-01 -3.23077254e-02 1.56197757e-01 -4.76396084e-01
-6.30345523e-01 -5.36666572e-01 4.06381309e-01 2.60772742e-02
-7.93274522e-01 5.88223934e-01 2.80509919e-01 2.54293501e-01
1.33488998e-01 -4.34819549e-01 -6.52145267e-01 -6.13707483e-01
-1.51825622e-01 1.27958953e-02 6.49890769e-03 -1.29138932e-01
-5.07527813e-02 4.46945578e-01 -1.80075061e+00 6.46596193e-01
4.62891281e-01 1.66079271e+00 -1.93087295e-01 1.07137609e+00
1.76525656e-02 8.60774159e-01 -4.09666926e-01 -5.08260250e-01
2.37523019e-02 -2.87903726e-01 -8.09452236e-01 5.21548651e-02
4.57462251e-01 -2.58631796e-01 -1.99035063e-01 7.04008698e-01
7.89943755e-01 1.70218185e-01 7.68789589e-01 -1.37762976e+00
-2.59376585e-01 6.26667976e-01 -4.62264299e-01 -1.95019126e-01
2.76655674e-01 -3.47350016e-02 9.52406466e-01 -9.95213270e-01
4.91868436e-01 1.00657368e+00 6.62668228e-01 7.07175672e-01
-8.77954543e-01 -6.69823587e-01 3.65272969e-01 3.07188481e-01
-1.18952453e+00 -5.97409070e-01 7.36959815e-01 -3.68733317e-01
1.16886616e+00 3.54299694e-01 7.08039045e-01 1.29999220e+00
-9.04586688e-02 8.27680469e-01 1.00769961e+00 -3.29074502e-01
1.06832370e-01 5.09752154e-01 1.08340129e-01 4.25073266e-01
-4.70942169e-01 -1.82120115e-01 -7.30431974e-01 5.13463235e-03
3.85007262e-01 -6.24572672e-02 -2.31465116e-01 -2.25531593e-01
-1.04496276e+00 5.36340594e-01 4.88614440e-01 4.87452626e-01
-7.20905364e-01 7.66126364e-02 7.40585625e-01 6.48330092e-01
2.69965857e-01 1.98489383e-01 -6.23853683e-01 -4.63393509e-01
-9.87791657e-01 -1.75653800e-01 6.11743629e-01 5.31172812e-01
6.84578001e-01 8.74119550e-02 -1.47967353e-01 9.89981532e-01
3.48853201e-01 1.88364759e-01 5.74374199e-01 -7.81200230e-01
4.07072455e-01 7.24240839e-01 -4.33460772e-01 -9.34554636e-01
-5.79391122e-01 -2.32539579e-01 -9.75915253e-01 2.01112166e-01
2.43276939e-01 -4.81848538e-01 -1.07614052e+00 1.82185173e+00
1.29582226e-01 1.04636617e-01 3.15327227e-01 1.01932549e+00
1.20231450e+00 6.89390361e-01 2.71341711e-01 -1.46342248e-01
1.43851197e+00 -1.04102480e+00 -5.97058415e-01 -1.60863712e-01
6.13225460e-01 -7.90759563e-01 9.23146129e-01 7.33442962e-01
-1.10081625e+00 -5.49641252e-01 -9.15934801e-01 3.37636122e-03
-6.96591258e-01 3.22176397e-01 6.49409115e-01 6.44941986e-01
-1.23711562e+00 3.30897659e-01 -5.25487185e-01 -5.46128273e-01
4.15510565e-01 6.75620258e-01 -7.96427310e-01 2.38804460e-01
-1.26095259e+00 7.01216638e-01 5.54023147e-01 3.92165422e-01
-8.35496724e-01 -3.11603576e-01 -8.95761967e-01 1.98386997e-01
3.22555095e-01 -6.06615424e-01 9.55458701e-01 -1.56179082e+00
-1.75691009e+00 6.49323702e-01 -1.38644606e-01 -2.16720834e-01
4.28671896e-01 -2.37222180e-01 -5.65794230e-01 3.12690228e-01
-5.77214003e-01 8.52713466e-01 9.36228096e-01 -1.21616638e+00
-6.74064398e-01 -2.84278393e-01 1.07509486e-01 2.98424840e-01
-8.38044882e-01 1.62148908e-01 -5.61010003e-01 -2.83577174e-01
-1.87016338e-01 -8.60139251e-01 -5.34517094e-02 -4.20055926e-01
-2.83905387e-01 -1.39065757e-01 7.97762454e-01 -6.45699799e-01
1.41678238e+00 -2.19403934e+00 2.03714028e-01 3.74493599e-01
-6.11816533e-02 2.72701949e-01 -3.88450325e-01 5.37989020e-01
-1.81481570e-01 1.80291727e-01 8.46435726e-02 -5.03125072e-01
1.91890165e-01 -2.32143149e-01 -6.34628907e-02 1.43146008e-01
5.12962520e-01 6.92423880e-01 -4.30318862e-01 -4.26143378e-01
2.71036029e-01 7.68498838e-01 -6.81417584e-01 3.47745806e-01
1.01342231e-01 3.54863644e-01 -2.54446328e-01 6.82574332e-01
6.34769857e-01 1.77697122e-01 -3.46793309e-02 -5.19349098e-01
6.47614822e-02 -1.17851691e-02 -1.26928592e+00 1.80355883e+00
-6.50364041e-01 5.57538867e-01 1.92456096e-01 -1.08364213e+00
7.27432907e-01 7.24788129e-01 5.09439111e-01 -5.07332444e-01
5.02756298e-01 3.57497409e-02 -1.00293860e-01 -8.23228359e-01
4.41377044e-01 -4.41255122e-02 -3.38328272e-01 2.21585259e-01
5.50698221e-01 2.03158259e-01 8.99489820e-02 1.36868834e-01
8.69735003e-01 1.99624270e-01 1.63060445e-02 3.10486853e-01
6.92850649e-01 -2.60219961e-01 5.09659111e-01 4.79610622e-01
7.15606436e-02 8.06671023e-01 6.91420496e-01 -2.62841552e-01
-7.08794892e-01 -5.76092064e-01 9.03049260e-02 1.47037780e+00
-2.21246049e-01 -5.19588649e-01 -7.65895307e-01 -8.59851360e-01
-3.51836443e-01 2.12658539e-01 -8.53962779e-01 -3.04435402e-01
-2.95676112e-01 -5.89495540e-01 6.70499980e-01 6.21510804e-01
3.58552337e-01 -1.17856491e+00 -5.66020668e-01 1.04422309e-01
-1.49876803e-01 -1.26443470e+00 -2.06139117e-01 2.57556617e-01
-7.37030149e-01 -8.27528000e-01 -6.52731657e-01 -5.23860693e-01
3.86073381e-01 -1.82152703e-01 9.96390045e-01 4.46290970e-02
-1.57052297e-02 5.86146355e-01 -5.78745663e-01 -5.06630540e-01
-2.42561046e-02 2.96146095e-01 -3.06012630e-01 5.94055772e-01
3.87009770e-01 -5.96359551e-01 -5.39728463e-01 1.88125789e-01
-1.05902052e+00 -1.49596974e-01 7.84295917e-01 8.99399400e-01
3.44963014e-01 -1.69679910e-01 6.47510350e-01 -6.07076466e-01
3.80680710e-01 -3.48455727e-01 8.36632326e-02 2.52465755e-01
-1.58927158e-01 -3.72526854e-01 5.53266406e-01 -5.86158633e-01
-9.99167383e-01 3.93891841e-01 -4.06482249e-01 -3.87591481e-01
-5.70080996e-01 6.51050091e-01 -4.17615116e-01 -1.95018537e-02
3.22023660e-01 -8.55403692e-02 -7.77973980e-02 -2.90746123e-01
2.36990720e-01 1.05603981e+00 3.00651819e-01 -3.12947214e-01
2.65006632e-01 1.65540889e-01 -1.59513056e-01 -7.99530149e-01
-4.22013938e-01 -3.49087626e-01 -7.51959801e-01 -4.26286459e-01
8.88186216e-01 -1.18175721e+00 -9.35044587e-01 5.59961498e-01
-8.90106618e-01 -2.13558763e-01 2.01944679e-01 7.45890021e-01
-4.69391912e-01 1.17593691e-01 -7.26283848e-01 -1.03045607e+00
-3.33504081e-01 -1.03908849e+00 1.12137687e+00 3.99744809e-01
-2.78626204e-01 -8.65394235e-01 -3.29796553e-01 4.85791087e-01
6.18319392e-01 4.89264011e-01 6.93824112e-01 -9.06348228e-01
-8.66088346e-02 -4.66519505e-01 -2.32672259e-01 6.01332664e-01
-2.23973706e-01 2.29520485e-01 -1.57272613e+00 -1.93461150e-01
-2.31845424e-01 -9.68898833e-01 1.03255630e+00 9.92491022e-02
1.11823523e+00 -1.56013817e-01 -9.74868611e-02 4.55316901e-01
1.19578755e+00 1.04290189e-03 6.70035779e-01 2.62482315e-01
7.48220086e-01 7.03748167e-01 6.44116223e-01 5.96579015e-01
6.13854110e-01 6.80553317e-01 6.03405416e-01 -4.43521738e-01
1.49177434e-02 2.37331226e-01 5.97587883e-01 7.24879622e-01
-4.23456043e-01 -2.98630267e-01 -7.51970112e-01 4.96565312e-01
-1.99680209e+00 -8.32646370e-01 1.03638038e-01 2.07124233e+00
6.48096263e-01 -1.04384519e-01 4.45196092e-01 2.59693682e-01
4.91080105e-01 -1.08699143e-01 -2.20431522e-01 -7.25394428e-01
-1.19131140e-01 2.00694054e-01 8.49851668e-02 1.98058680e-01
-1.29870486e+00 8.72757196e-01 5.25782537e+00 6.83180869e-01
-1.63251054e+00 1.13884583e-01 5.80579877e-01 -4.54865456e-01
9.41680744e-02 -2.78589696e-01 -4.89451170e-01 2.76236892e-01
1.16219544e+00 5.35246074e-01 2.72842705e-01 4.71702933e-01
-1.19505245e-02 3.06817461e-02 -1.17901874e+00 1.03181267e+00
1.67770028e-01 -5.45226455e-01 -5.08398525e-02 -1.03828125e-01
3.37680966e-01 -6.55416176e-02 -6.52054772e-02 7.55038142e-01
-1.59227788e-01 -1.25680518e+00 8.06662500e-01 6.72583878e-01
4.57497150e-01 -1.06693423e+00 1.10621333e+00 1.88351408e-01
-1.05737913e+00 -9.65231061e-02 -2.61472434e-01 3.33583057e-02
2.18203112e-01 3.57517242e-01 -5.67347050e-01 6.21107578e-01
9.55697000e-01 5.41024446e-01 -5.54340184e-01 9.19070780e-01
7.59118572e-02 6.49116457e-01 -3.94374460e-01 4.62453626e-02
3.49882275e-01 2.87145287e-01 1.12740509e-01 1.61116183e+00
4.86230314e-01 -1.61139056e-01 1.70586139e-01 1.62168533e-01
-1.25233114e-01 3.65212291e-01 -4.39448416e-01 -1.80704758e-01
9.95598212e-02 1.80234218e+00 -5.52510202e-01 -2.43560493e-01
-5.28531611e-01 9.84598637e-01 3.90758127e-01 4.00781721e-01
-9.92801547e-01 -5.58689356e-01 4.47839588e-01 -2.99785465e-01
5.87413013e-01 1.67453438e-01 3.08589917e-02 -1.22394609e+00
9.03153606e-03 -1.00560141e+00 5.57276249e-01 -6.78897083e-01
-1.14522314e+00 9.41731930e-01 -3.90768290e-01 -1.23789394e+00
-4.51998472e-01 -8.30690026e-01 -4.66238946e-01 6.74370050e-01
-1.39650679e+00 -1.50821388e+00 -4.09880221e-01 8.49944472e-01
1.55163378e-01 -1.21782795e-01 1.04372132e+00 6.34866178e-01
-6.12605929e-01 9.33530390e-01 -4.56371129e-01 1.77130312e-01
1.00838566e+00 -1.01588976e+00 -4.50120151e-01 6.29680216e-01
3.75622436e-02 3.46311301e-01 3.66289437e-01 -1.56743839e-01
-1.46834064e+00 -8.93005967e-01 9.33060706e-01 -3.35677803e-01
6.36925399e-01 -6.66600704e-01 -9.31421757e-01 3.65373045e-01
5.75360179e-01 1.44018963e-01 1.04582226e+00 3.48996252e-01
-4.04920161e-01 -3.16605777e-01 -1.01535392e+00 2.76702911e-01
8.57419133e-01 -5.89610577e-01 -2.05225885e-01 -1.63650662e-01
3.83306056e-01 -3.62616867e-01 -1.25496686e+00 6.76272273e-01
9.49987054e-01 -1.15437627e+00 7.62130141e-01 -7.53309608e-01
4.83694226e-01 -1.49923876e-01 -2.29352966e-01 -1.29080176e+00
-7.01338872e-02 -3.11865896e-01 -1.83882356e-01 1.78465056e+00
7.53916919e-01 -4.43483919e-01 5.20882666e-01 6.08881593e-01
-7.71222413e-02 -8.87042463e-01 -7.89035916e-01 -3.14070880e-01
-2.41679013e-01 -5.63158214e-01 5.31070173e-01 1.05980277e+00
1.72151327e-01 6.51832521e-01 -7.01604366e-01 7.68600926e-02
-3.54528166e-02 -5.41363135e-02 7.73125768e-01 -1.06000018e+00
-2.86846966e-01 -6.33989215e-01 -5.35776258e-01 -4.77204174e-01
8.08260962e-02 -6.62096441e-01 -1.27483428e-01 -1.39896476e+00
1.16076790e-01 -1.23366691e-01 -7.63534546e-01 7.33651876e-01
-9.81967002e-02 7.40624726e-01 3.74798596e-01 -1.85412332e-01
-7.35094368e-01 6.53469503e-01 9.24300134e-01 -6.27808198e-02
-2.60206819e-01 -2.15396315e-01 -7.66857684e-01 7.47505426e-01
7.12032735e-01 -2.34135568e-01 -3.24957579e-01 -4.33223516e-01
4.19047832e-01 1.48066925e-02 3.84517640e-01 -1.01108360e+00
1.84211642e-01 1.15026325e-01 7.53490508e-01 -3.68937969e-01
6.23756051e-01 -1.17933607e+00 8.89583007e-02 -8.00878778e-02
-3.45243841e-01 -4.68727611e-02 4.07641679e-01 1.70958966e-01
-6.41456842e-01 -2.00090826e-01 4.85583812e-01 1.65313691e-01
-8.67703855e-01 3.21229786e-01 -4.51152802e-01 -4.72085387e-01
9.93613541e-01 -3.12443614e-01 -4.84327488e-02 -5.36765635e-01
-9.67051744e-01 1.73586816e-01 2.53668636e-01 5.35003781e-01
3.74198109e-01 -1.46193838e+00 -5.72650492e-01 6.07129978e-03
1.54996231e-01 -2.16783658e-01 6.35305941e-01 1.28106570e+00
-5.03781140e-02 5.45843244e-02 -2.75024116e-01 -5.63447595e-01
-1.51145995e+00 5.05933404e-01 1.64610505e-01 -1.78318217e-01
-7.44041055e-02 8.98892045e-01 5.85666159e-03 -5.09303093e-01
5.32607019e-01 -1.63575187e-01 -7.16182232e-01 6.53087497e-01
5.85531056e-01 8.87913909e-03 2.77807921e-01 -9.22489882e-01
-4.85598624e-01 8.65558863e-01 -8.80605355e-02 -6.98224232e-02
1.43469727e+00 -2.53469199e-01 -6.40367642e-02 5.30154884e-01
1.39007652e+00 -4.74467129e-02 -1.01226246e+00 -5.13485000e-02
-2.07109600e-01 6.35529682e-02 1.94790319e-01 -1.05435991e+00
-1.26124215e+00 1.20769691e+00 7.42463589e-01 2.85857409e-01
1.76917291e+00 -1.47270799e-01 6.28597975e-01 3.34270835e-01
8.93432647e-02 -1.32217765e+00 3.94122638e-02 6.44619286e-01
8.15275788e-01 -1.31812906e+00 -3.63158017e-01 -1.73930794e-01
-9.71383095e-01 1.29376507e+00 5.73714197e-01 -2.40239352e-02
4.12778646e-01 2.10834280e-01 3.64925653e-01 -9.14315656e-02
-9.64008272e-01 -2.86012828e-01 6.74161494e-01 2.71419555e-01
8.46748114e-01 -1.99978769e-01 -8.30183476e-02 1.09800172e+00
-9.03981552e-02 1.95449486e-01 1.98148087e-01 8.84469509e-01
-2.16088176e-01 -1.01216185e+00 -1.82774559e-01 3.70608389e-01
-8.22414875e-01 -3.08188125e-02 -5.00340462e-01 7.00635791e-01
3.14695418e-01 9.50475514e-01 -1.00652806e-01 -7.31391251e-01
4.65157688e-01 3.34321409e-01 4.95955437e-01 -3.23616505e-01
-9.69665527e-01 2.25894362e-01 3.62599969e-01 -6.94092333e-01
-6.91757441e-01 -5.96190214e-01 -1.11425102e+00 -3.42412060e-03
-3.29790473e-01 1.90010130e-01 7.48949051e-01 9.35277283e-01
5.92344761e-01 6.15675330e-01 6.47302091e-01 -1.16342139e+00
-1.34604335e-01 -1.19074738e+00 -4.08234328e-01 4.59040761e-01
2.83482164e-01 -5.12827575e-01 -2.35393956e-01 2.00736582e-01] | [13.266549110412598, 5.1063127517700195] |
46a73860-86fb-4d10-84a4-23a00fe18a4c | fast-and-flexible-protein-design-using-deep | null | null | https://www.cell.com/cell-systems/fulltext/S2405-4712(20)30327-6 | https://www.cell.com/action/showPdf?pii=S2405-4712%2820%2930327-6 | Fast and Flexible Protein Design Using Deep Graph Neural Networks | Protein structure and function is determined by the arrangement of the linear sequence of amino acids in 3D space. We show that a deep graph neural network, ProteinSolver, can precisely design sequences that fold into a predetermined shape by phrasing this challenge as a constraint satisfaction problem (CSP), akin to Sudoku puzzles. We trained ProteinSolver on over 70,000,000 real protein sequences corresponding to over 80,000 structures. We show that our method rapidly designs new protein sequences and benchmark them in silico using energy-based scores, molecular dynamics, and structure prediction methods. As a proofof-principle validation, we use ProteinSolver to generate sequences that match the structure of serum albumin, then synthesize the top-scoring design and validate it in vitro using circular dichroism. ProteinSolver is freely available at http://design.proteinsolver.org and https://gitlab.com/ostrokach/proteinsolver. | ['Philip M. Kim', 'Albert Perez-Riba', 'Carles Corbi-Verge', 'David Becerra', 'Alexey Strokach'] | 2020-09-23 | null | null | null | null | ['protein-design'] | ['medical'] | [-9.78067592e-02 1.66688189e-01 -1.51120439e-01 -3.23591471e-01
-2.66862094e-01 -1.03290355e+00 -1.41633630e-01 5.02319150e-02
-1.15583457e-01 1.31104493e+00 1.01902373e-01 -7.46751428e-01
6.59155175e-02 -4.97545958e-01 -1.33930731e+00 -6.71338320e-01
-1.95227191e-01 8.15604448e-01 -1.14416152e-01 -2.76340485e-01
1.76882938e-01 7.20968783e-01 -7.80296326e-01 3.01366270e-01
1.05235600e+00 2.65986294e-01 3.86714697e-01 6.59422219e-01
1.48545817e-01 1.77444682e-01 -2.83103228e-01 -2.96852946e-01
3.22291374e-01 -8.30675900e-01 -8.84084642e-01 -1.37189090e-01
8.28521922e-02 2.34029591e-01 -1.31520316e-01 6.39809370e-01
4.51297909e-01 2.58857775e-02 4.74394560e-01 -6.36043072e-01
-1.18239164e+00 9.80947018e-02 -2.06204116e-01 -1.03406772e-01
6.07366204e-01 6.79466248e-01 1.23624682e+00 -1.06526232e+00
1.27458751e+00 7.46072412e-01 6.48001254e-01 7.63071954e-01
-1.66946840e+00 -2.60116190e-01 -2.76581526e-01 1.68774456e-01
-1.15634036e+00 -2.13663176e-01 5.13536632e-01 -3.94821942e-01
1.57815981e+00 1.15694486e-01 1.00890934e+00 8.59910965e-01
5.83804786e-01 2.47160345e-01 6.57720625e-01 -8.20686445e-02
4.88371968e-01 -7.57633388e-01 8.98160338e-02 1.07845259e+00
3.29300642e-01 4.13013343e-03 -3.76861006e-01 -6.46799862e-01
4.67943758e-01 -1.29999623e-01 -4.27163988e-01 -9.64394450e-01
-1.08836102e+00 8.34168553e-01 6.15804911e-01 -5.07077985e-02
-4.82393563e-01 1.37630060e-01 2.83040386e-02 1.35807633e-01
-3.93420815e-01 1.03274000e+00 -8.71739209e-01 1.75950378e-01
-2.19477862e-01 5.82669139e-01 1.15514719e+00 8.67762625e-01
7.38889337e-01 -9.69308093e-02 4.70445037e-01 5.59233904e-01
1.53869912e-01 2.97352761e-01 5.50584607e-02 -1.00853193e+00
-1.00335985e-01 4.52248305e-01 4.20832485e-01 -6.69063866e-01
-7.09490061e-01 8.26437771e-02 -5.25836229e-01 3.00573319e-01
4.12955791e-01 -3.47356915e-01 -9.30045605e-01 1.68912745e+00
2.66297847e-01 -1.93432122e-01 2.15012819e-01 1.27628350e+00
6.68562770e-01 7.68771827e-01 -1.02431895e-02 -2.82414317e-01
1.33753765e+00 -8.77360702e-01 -9.84772295e-02 1.62509039e-01
6.11943901e-01 -4.76295382e-01 7.12305129e-01 5.55387378e-01
-1.18124080e+00 -1.98268428e-01 -1.42779267e+00 -1.06577337e-01
-2.08303377e-01 -1.94761425e-01 8.28391552e-01 2.24445298e-01
-1.00203478e+00 9.00074601e-01 -7.84531355e-01 -3.27348024e-01
2.49950022e-01 5.64561069e-01 -6.09638035e-01 1.60127103e-01
-1.08163273e+00 1.01853168e+00 6.36769950e-01 -1.34294078e-01
-8.72377038e-01 -6.32265151e-01 -5.38946688e-01 3.86798345e-02
2.54987121e-01 -8.77573550e-01 1.04641104e+00 -6.50507212e-01
-1.34833002e+00 8.99872899e-01 -2.29425415e-01 -4.52436000e-01
3.08647230e-02 1.09882489e-01 -5.09474147e-03 -2.04976033e-02
-3.95983189e-01 5.32080054e-01 -2.14201715e-02 -9.61435854e-01
1.75916418e-01 -1.08057916e-01 -5.37201166e-02 9.63198692e-02
8.12137842e-01 3.38371284e-02 2.48972494e-02 -3.90858114e-01
1.88098416e-01 -1.07217777e+00 -7.79307425e-01 8.10170770e-02
-4.92064774e-01 -1.07705362e-01 7.42149651e-02 -5.65895438e-01
5.72472811e-01 -1.53606761e+00 7.79693544e-01 4.12552118e-01
4.50248957e-01 3.64650160e-01 -3.40940624e-01 9.86468196e-01
-5.49031913e-01 1.50066495e-01 -4.57705468e-01 6.94827199e-01
-1.03025399e-01 2.16929048e-01 1.77042142e-01 5.37251592e-01
3.62132430e-01 1.34841907e+00 -7.25404263e-01 2.83564001e-01
-4.41141017e-02 3.95020097e-01 -8.39946747e-01 1.65328726e-01
-9.69082773e-01 4.56457049e-01 -4.46104288e-01 6.17335916e-01
6.25711799e-01 -7.86336064e-01 1.25674105e+00 -1.18382499e-01
3.41031477e-02 2.82636076e-01 -4.14363593e-01 1.73179805e+00
2.80958980e-01 2.58489430e-01 -1.03982218e-01 -8.94661546e-01
1.14268458e+00 9.77061242e-02 5.85550249e-01 -3.50232512e-01
3.45945843e-02 2.04564855e-01 5.33825397e-01 -4.43956763e-01
-4.96116504e-02 -9.25183073e-02 1.38014942e-01 5.02818108e-01
1.21600963e-02 5.63759841e-02 3.99102479e-01 1.88461721e-01
1.26032853e+00 5.55267811e-01 4.94538993e-01 -3.80460739e-01
2.01251641e-01 6.40514314e-01 7.12044299e-01 2.65832812e-01
2.87106968e-02 5.71841180e-01 8.28577101e-01 -9.64193046e-01
-1.89264226e+00 -1.06520665e+00 1.54068381e-01 9.00643945e-01
-3.65766250e-02 -4.87409890e-01 -8.55059445e-01 -4.10988152e-01
3.50650102e-01 2.16465876e-01 -2.33086288e-01 -1.77674949e-01
-8.18824530e-01 -8.70711565e-01 3.54597062e-01 8.42589960e-02
-3.19177240e-01 -1.35470927e+00 -3.14159840e-01 5.03830671e-01
1.34998024e-01 -4.09006894e-01 -6.64122939e-01 5.39013386e-01
-5.62466383e-01 -1.46548617e+00 -7.30149209e-01 -9.29878056e-01
7.26897597e-01 4.40870896e-02 1.16259837e+00 3.49687815e-01
-5.39719284e-01 -5.88328242e-01 -1.79257885e-01 -8.67194012e-02
-4.41434056e-01 -7.51429573e-02 2.63544172e-01 -6.01366103e-01
6.84090436e-01 -8.90485466e-01 -8.45634222e-01 3.94517601e-01
-6.70103371e-01 2.98661947e-01 5.37836432e-01 9.21576262e-01
8.68120551e-01 -6.47996664e-01 6.62163079e-01 -9.01700258e-01
7.71580815e-01 -4.44612235e-01 -9.66394067e-01 4.35558319e-01
-7.14797616e-01 5.55393159e-01 8.22820127e-01 -3.41280520e-01
-3.00480127e-01 5.91859281e-01 -2.54792005e-01 -1.52193695e-01
-7.66709074e-02 7.34134138e-01 -1.66448593e-01 -1.39091238e-01
1.03141534e+00 4.05170739e-01 3.94660801e-01 -4.02191430e-01
4.56507653e-01 8.84216800e-02 3.09305191e-01 -8.93944263e-01
4.52423096e-01 -1.00888357e-01 5.13163507e-02 -4.99561757e-01
-3.73446047e-01 5.90422712e-02 -5.76886535e-01 2.22710475e-01
8.63198042e-01 -5.50356746e-01 -1.52721131e+00 1.66202322e-01
-1.14256430e+00 -5.22007883e-01 1.78828418e-01 1.84478879e-01
-8.91280055e-01 5.53803623e-01 -5.06751418e-01 -1.91238046e-01
-4.19577479e-01 -1.29210937e+00 5.44724703e-01 4.50366959e-02
-4.17799145e-01 -4.23295587e-01 3.16605181e-01 4.50096160e-01
1.25830591e-01 6.09181583e-01 1.35696554e+00 -5.35450220e-01
-9.75440383e-01 3.32174987e-01 -2.16155648e-02 -2.07547814e-01
-1.15269393e-01 1.32743061e-01 3.30322608e-02 -2.62207776e-01
-6.42726302e-01 -5.25774181e-01 6.82300866e-01 3.39274138e-01
9.13000047e-01 -5.10023534e-01 -1.31750762e-01 7.66845942e-01
1.55992031e+00 6.56386197e-01 7.57731378e-01 2.76923746e-01
6.24810100e-01 3.57248276e-01 1.96437106e-01 3.33316207e-01
2.50920281e-02 5.84739089e-01 3.08512568e-01 3.30788456e-02
2.10831687e-01 -2.98424751e-01 1.68779328e-01 4.10833597e-01
-3.07982862e-01 -3.36493075e-01 -1.18583286e+00 1.04042597e-01
-1.98986912e+00 -9.11997378e-01 -1.26166463e-01 1.70254147e+00
1.16797221e+00 -1.35642245e-01 3.16183507e-01 -5.77653885e-01
5.96706748e-01 -2.34656930e-01 -1.16508138e+00 -6.21221542e-01
-3.34427148e-01 5.66034079e-01 6.84030592e-01 6.22025549e-01
-6.20249152e-01 1.04984319e+00 6.29240561e+00 2.67296344e-01
-9.57317948e-01 -3.64302367e-01 6.16336107e-01 -9.82269123e-02
-4.49153900e-01 2.33169705e-01 -4.05540228e-01 3.08881223e-01
9.95901465e-01 -3.84701312e-01 9.09192681e-01 7.81490505e-01
5.36672354e-01 2.65385270e-01 -9.44613218e-01 5.33825815e-01
-5.03174663e-01 -2.21244121e+00 -2.22085387e-01 2.38926321e-01
7.47208655e-01 3.18293989e-01 -3.46175075e-01 -1.88756466e-01
8.30179930e-01 -1.48512578e+00 3.65187526e-01 6.60717607e-01
7.28588641e-01 -6.77465260e-01 3.17012757e-01 1.22010186e-01
-6.00232422e-01 3.75045359e-01 -6.65344596e-01 1.81101128e-01
1.01184823e-01 4.56546694e-01 -1.01641631e+00 1.51214600e-01
3.08583796e-01 3.76247048e-01 3.50110158e-02 7.76554585e-01
-2.32465401e-01 3.18450183e-01 -1.07911550e-01 -4.71333474e-01
1.14338934e-01 -7.55607247e-01 3.23832959e-01 8.07692885e-01
-9.25650671e-02 7.73117542e-01 2.32036710e-01 1.11630726e+00
-3.64721417e-01 1.28444694e-02 -3.80461663e-01 -5.85250437e-01
2.75245160e-01 7.89239585e-01 -4.15171772e-01 -3.64541169e-03
-1.90508574e-01 9.09488499e-01 6.15783632e-01 6.62303150e-01
-7.98845828e-01 -5.06778121e-01 1.14522266e+00 -1.41266033e-01
4.56651241e-01 -3.97589415e-01 1.02058068e-01 -1.07917988e+00
-2.11946126e-02 -1.37516284e+00 3.12419981e-02 -1.12301731e+00
-1.14393067e+00 3.57824832e-01 -6.95648968e-01 -4.92338091e-01
2.81384215e-02 -9.23258662e-01 -3.34251970e-01 9.93129551e-01
-8.10407698e-01 -7.55881190e-01 1.11797236e-01 -1.15852706e-01
2.49495089e-01 -1.40492022e-01 8.89338970e-01 -2.27662548e-01
-5.50004959e-01 3.90516281e-01 5.20332694e-01 -1.91383407e-01
3.74303371e-01 -1.13704920e+00 1.02507639e+00 2.70826429e-01
-3.69761735e-01 1.03907013e+00 9.95343089e-01 -7.97586262e-01
-1.73705220e+00 -9.27055001e-01 8.48643899e-01 -4.18172240e-01
5.20105362e-01 -4.96817619e-01 -1.10411465e+00 6.40746236e-01
1.58557266e-01 -3.16884905e-01 8.63818526e-01 -1.65152252e-01
-4.12994206e-01 6.18099034e-01 -1.18741357e+00 7.31748402e-01
1.44622052e+00 -2.45455086e-01 -5.28248191e-01 8.15538585e-01
1.10569704e+00 -6.12939596e-01 -1.07562542e+00 1.22300893e-01
6.03657544e-01 -9.03094053e-01 1.04600143e+00 -1.58209896e+00
6.12777054e-01 -4.85061049e-01 -1.26638085e-01 -1.20192814e+00
-7.55705476e-01 -7.95878530e-01 -1.36135802e-01 -4.39057648e-02
1.07718062e+00 -7.14494348e-01 1.27630043e+00 4.99626070e-01
-2.42926031e-01 -1.26505947e+00 -6.14074171e-01 -6.15298986e-01
3.16263348e-01 4.05166119e-01 8.50034773e-01 9.45368886e-01
4.43894267e-01 3.73612970e-01 -3.25813293e-01 -1.15086496e-01
2.25115374e-01 4.06257600e-01 6.10765398e-01 -9.26182389e-01
-6.20144427e-01 -1.61853701e-01 -1.72682643e-01 -9.77987230e-01
1.55434966e-01 -1.13331687e+00 -5.40228002e-02 -1.57029033e+00
3.11704457e-01 -1.87438622e-01 -9.56557691e-02 7.19872415e-01
2.10608438e-01 -1.22504599e-01 -5.82427420e-02 9.35472101e-02
-4.40854728e-01 4.80638236e-01 1.28298306e+00 -5.58084697e-02
-1.75187290e-01 -4.14493114e-01 -8.51509690e-01 8.60968232e-02
1.22429955e+00 -3.81668180e-01 -1.95951033e-02 -1.47653865e-02
5.69802761e-01 3.06411922e-01 1.26009658e-02 -6.07615888e-01
-1.97282240e-01 -8.22966337e-01 4.38784242e-01 -4.23382491e-01
2.79303312e-01 -5.82612395e-01 7.81416476e-01 8.40340734e-01
-3.44793290e-01 3.07850093e-01 1.30904213e-01 3.50720823e-01
4.43270326e-01 -1.42438769e-01 8.68037641e-01 -3.52071792e-01
-4.05568391e-01 3.81605059e-01 -6.04655802e-01 -1.75573543e-01
1.10382628e+00 -1.40102908e-01 -6.04972541e-01 -9.24299210e-02
-1.05390060e+00 3.03121179e-01 1.07384002e+00 3.22512127e-02
8.63803566e-01 -8.90454412e-01 -4.88149256e-01 2.31916085e-01
1.12827577e-01 -3.04582268e-01 -4.04228978e-02 3.89189303e-01
-1.43196106e+00 7.37528622e-01 -3.90720278e-01 -2.15375245e-01
-1.21969330e+00 8.15129936e-01 7.87037730e-01 1.14704400e-01
-4.07779783e-01 7.47875810e-01 4.31802310e-02 -8.42195511e-01
-2.55611658e-01 -6.44907430e-02 3.46147031e-01 -8.18582296e-01
1.67598650e-01 -2.15579942e-01 -7.94220567e-02 -3.61546397e-01
-3.76280278e-01 3.70766282e-01 -8.96447748e-02 4.30938154e-01
1.59786999e+00 4.49590921e-01 -3.86983484e-01 -5.66282988e-01
1.10095811e+00 -3.50309521e-01 -1.13137043e+00 -1.69882849e-02
2.09177136e-01 -1.53963491e-01 -7.81013727e-01 -1.01071942e+00
-6.36400282e-01 1.09621294e-01 3.58232617e-01 -3.50067109e-01
6.57934725e-01 2.11317942e-01 9.25152600e-01 1.02989864e+00
5.78397989e-01 -6.62204266e-01 1.87919065e-02 6.79922640e-01
8.72622013e-01 -9.54581916e-01 5.09172678e-02 -1.74923897e-01
-5.24597883e-01 9.02333498e-01 4.74491984e-01 -4.00252908e-01
3.50390315e-01 1.14223555e-01 -1.55571988e-02 -5.80424488e-01
-1.12347937e+00 4.36170213e-03 -6.66762069e-02 6.83507621e-01
6.44063771e-01 2.72774577e-01 -7.27420568e-01 4.64005381e-01
-2.84212619e-01 1.76662147e-01 5.50929844e-01 9.70711827e-01
-7.08076477e-01 -1.53078151e+00 -1.18829787e-01 3.02085787e-01
-3.53747070e-01 -1.88522756e-01 -1.06771564e+00 3.37288320e-01
-1.41514614e-01 6.36351347e-01 -3.86536956e-01 -3.16794425e-01
3.69386166e-01 2.53908455e-01 5.87080479e-01 -5.14580667e-01
-3.48175645e-01 -2.77104259e-01 3.18773627e-01 -5.38680732e-01
-3.87787074e-02 -4.18570638e-01 -1.77901673e+00 -6.00208640e-01
-1.30610406e-01 6.20261550e-01 5.81759810e-01 4.42014843e-01
1.01525509e+00 1.57932058e-01 3.83484870e-01 -6.22434974e-01
-2.88174629e-01 -5.32859564e-01 -2.90854037e-01 2.20989689e-01
1.94288522e-01 -2.34206498e-01 -3.12684029e-02 1.05684429e-01] | [4.730828762054443, 5.606196403503418] |
06569eb3-00b5-4998-8d64-22e003535bf4 | relational-boosted-bandits | 2012.09220 | null | https://arxiv.org/abs/2012.09220v1 | https://arxiv.org/pdf/2012.09220v1.pdf | Relational Boosted Bandits | Contextual bandits algorithms have become essential in real-world user interaction problems in recent years. However, these algorithms rely on context as attribute value representation, which makes them unfeasible for real-world domains like social networks are inherently relational. We propose Relational Boosted Bandits(RB2), acontextual bandits algorithm for relational domains based on (relational) boosted trees. RB2 enables us to learn interpretable and explainable models due to the more descriptive nature of the relational representation. We empirically demonstrate the effectiveness and interpretability of RB2 on tasks such as link prediction, relational classification, and recommendations. | ['Balaraman Ravindran', 'Sriraam Natarajan', 'Ashutosh Kakadiya'] | 2020-12-16 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 5.06969579e-02 6.24503374e-01 -1.34399462e+00 -6.81800008e-01
-2.99691200e-01 -2.44728982e-01 4.93348360e-01 1.31284997e-01
2.55799741e-01 1.04931736e+00 4.90900487e-01 -8.03148270e-01
-7.82732308e-01 -1.11186516e+00 -8.71184707e-01 -2.25926638e-01
-2.37535685e-01 7.99399495e-01 4.87211496e-02 -3.54107171e-01
-3.37106228e-01 3.90362442e-01 -1.18499303e+00 7.08902478e-01
7.50495076e-01 1.20285082e+00 -4.20906305e-01 3.77352327e-01
-2.21443802e-01 1.17562342e+00 -9.07488018e-02 -9.21046078e-01
1.30053937e-01 -1.97892934e-01 -1.04142523e+00 -3.70803446e-01
2.51737446e-01 -3.72436196e-01 -5.46654344e-01 6.15864873e-01
-2.24506468e-01 4.82799351e-01 6.21541023e-01 -1.26784742e+00
-9.99821663e-01 1.28153706e+00 -4.12685484e-01 2.73659110e-01
4.27434355e-01 -5.35392165e-01 2.09330535e+00 -4.22539413e-01
3.96725774e-01 1.58942413e+00 7.69042611e-01 3.65525007e-01
-1.88066435e+00 -4.90942687e-01 6.59579635e-01 5.01441479e-01
-8.49393725e-01 -2.90268421e-01 9.07775700e-01 -1.17951617e-01
8.48584831e-01 1.03626847e+00 9.97707784e-01 1.13128257e+00
-6.55284524e-01 1.08695686e+00 1.10642469e+00 -2.91569352e-01
1.70036241e-01 1.68474391e-01 6.78990126e-01 4.26533371e-01
3.83994132e-01 2.36940205e-01 -7.75936007e-01 -3.92719090e-01
9.02973533e-01 4.40784216e-01 -6.60993308e-02 -4.79908347e-01
-6.86719298e-01 1.06992269e+00 1.06276739e+00 -1.21644989e-01
-6.11019611e-01 4.84344512e-01 2.24008799e-01 4.25512016e-01
9.93415117e-01 3.83784115e-01 -3.74607563e-01 -8.63316506e-02
-3.05684388e-01 2.18485594e-01 7.61296034e-01 1.02164960e+00
6.51226521e-01 -3.34550798e-01 -6.00968413e-02 1.20343935e+00
3.58366072e-01 1.59488365e-01 9.91051197e-02 -7.30733156e-01
5.62207222e-01 6.17865145e-01 4.50059026e-02 -1.02001250e+00
-1.75743490e-01 -4.16631162e-01 -7.21782148e-01 -5.68480372e-01
1.71955645e-01 2.78559685e-01 -7.17772305e-01 1.43541217e+00
6.20475411e-01 -1.40562981e-01 -1.14336327e-01 8.80508125e-01
8.71669650e-01 5.83223343e-01 1.01302683e-01 -1.36144340e-01
1.04621577e+00 -1.05739880e+00 -8.43688369e-01 -3.48967701e-01
5.72921753e-01 5.41102141e-03 1.13186264e+00 3.75888884e-01
-8.14558566e-01 -1.82456881e-01 -8.38243604e-01 -2.20172688e-01
-1.98337406e-01 -7.55424142e-01 1.64095592e+00 8.54321480e-01
-5.71916699e-01 5.69088042e-01 -7.80317962e-01 -3.52062225e-01
8.77476215e-01 4.61341828e-01 -3.49456072e-01 -1.54219389e-01
-1.33812439e+00 7.35001445e-01 2.44564012e-01 1.15252107e-01
-7.64921606e-02 -6.51887953e-01 -4.68638659e-01 1.89345330e-01
6.24674082e-01 -4.91208494e-01 1.21746993e+00 -9.47641551e-01
-1.33432722e+00 5.56110084e-01 -1.71031445e-01 -7.63801336e-01
5.43641508e-01 -2.50456274e-01 -2.31053174e-01 -2.16155127e-01
-3.68839651e-01 1.34000465e-01 3.79790097e-01 -1.08935082e+00
-4.96571839e-01 -4.74337339e-01 6.54357553e-01 2.93546915e-01
-3.19135785e-01 -3.26165020e-01 -2.53470331e-01 -6.55896425e-01
3.07561249e-01 -9.31436658e-01 -9.72961262e-02 -1.45907596e-01
-5.21421790e-01 -5.12836218e-01 5.26910305e-01 -4.95951831e-01
1.32903719e+00 -1.99732685e+00 8.92135799e-02 6.62999451e-01
4.13454711e-01 -1.14767671e-01 2.06428021e-01 3.78851086e-01
-1.97524816e-01 3.81058544e-01 3.77552837e-01 -4.64645885e-02
1.40660703e-01 6.47595167e-01 -6.90874398e-01 1.19055502e-01
-2.42911965e-01 1.07083416e+00 -8.00440609e-01 -5.02945721e-01
6.84299767e-02 2.76978463e-02 -6.75614178e-01 1.07199118e-01
-6.79950535e-01 7.48735219e-02 -7.68376231e-01 6.81958258e-01
1.69997126e-01 -5.39297760e-01 5.51430464e-01 -1.16176702e-01
5.48904657e-01 7.03648090e-01 -6.42010450e-01 9.84324694e-01
-4.92434919e-01 4.97499585e-01 -3.13117534e-01 -1.16577458e+00
8.73333216e-01 1.25148386e-01 4.35171008e-01 -5.61955929e-01
-3.82914782e-01 -1.44229338e-01 -2.06659168e-01 -2.20316201e-01
3.35798234e-01 -2.73838937e-01 2.61847377e-01 5.01795113e-01
-5.26603103e-01 4.53259647e-02 -1.33926094e-01 3.96989942e-01
8.41471434e-01 9.14366469e-02 4.25795019e-01 -1.42265528e-01
-7.40816146e-02 2.22217906e-02 4.34420288e-01 8.78545105e-01
1.75457448e-01 5.58304824e-02 6.94404602e-01 -1.12820625e+00
-8.11815023e-01 -9.23655033e-01 -3.04275393e-01 1.66820955e+00
2.59585202e-01 -3.75411451e-01 -1.21362828e-01 -8.39489222e-01
5.54682851e-01 8.35880995e-01 -8.32432508e-01 -1.49826065e-01
-4.35972869e-01 -7.96029568e-01 7.58626610e-02 7.83921421e-01
1.13657005e-01 -7.84200549e-01 -7.42317364e-02 1.80155605e-01
-4.18742418e-01 -1.09067428e+00 -8.84080380e-02 2.31094897e-01
-1.16664433e+00 -9.07671690e-01 2.86779404e-01 -1.01062730e-01
3.11472535e-01 3.85533035e-01 1.36022627e+00 3.25447559e-01
4.24531437e-02 1.95496589e-01 -5.73505044e-01 -3.96036118e-01
-4.68110964e-02 1.83616847e-01 -6.07837103e-02 1.92476586e-02
5.06248176e-01 -8.60840321e-01 -4.94449973e-01 5.16799569e-01
-6.28226399e-01 3.84319901e-01 3.43211770e-01 9.55560327e-01
5.83707094e-01 -1.16632968e-01 5.83689094e-01 -1.63149691e+00
6.94883227e-01 -6.97566032e-01 -4.69048530e-01 5.22257328e-01
-8.44002962e-01 -2.21673902e-02 2.90155202e-01 -4.49844688e-01
-1.01620865e+00 -4.10692662e-01 6.10954352e-02 3.29876617e-02
2.38956377e-01 8.43649268e-01 1.95478141e-01 1.83061898e-01
9.38759923e-01 -3.22163522e-01 -1.83784157e-01 -4.45310444e-01
5.96392810e-01 8.26224327e-01 -1.98303629e-02 -9.97092247e-01
3.85638118e-01 5.98739624e-01 1.86010838e-01 -5.97238421e-01
-1.27033937e+00 -2.38094583e-01 -7.52558708e-02 -2.39752512e-02
4.47741330e-01 -6.76752925e-01 -1.11771250e+00 -5.61018109e-01
-9.14480686e-01 -3.38626355e-01 -3.69450450e-01 3.96677196e-01
-7.14972198e-01 -1.32254183e-01 -7.22681165e-01 -1.19156301e+00
-3.20648849e-01 -5.25459707e-01 3.85230899e-01 -1.84020519e-01
-6.40893519e-01 -8.98864150e-01 -1.68207750e-01 9.73584116e-01
1.91857740e-01 1.13583483e-01 1.43398023e+00 -8.85335028e-01
-9.09519315e-01 -2.04790980e-01 -4.97624874e-01 -8.01887736e-02
2.49352567e-02 -1.29091725e-01 -1.03693724e+00 1.22343674e-01
-6.91567481e-01 -4.17461008e-01 8.18363607e-01 3.38661432e-01
1.39537144e+00 -8.29466403e-01 -5.67299902e-01 4.19339359e-01
9.64263141e-01 2.62224078e-01 5.01262903e-01 2.26879865e-01
6.89877152e-01 7.47223914e-01 6.40980303e-01 3.19784343e-01
5.58974981e-01 8.49656463e-01 6.88149929e-01 -1.55787289e-01
6.46245480e-02 -6.33116961e-01 -2.74450243e-01 3.27909529e-01
-5.05218923e-01 -6.84107095e-02 -8.51311743e-01 4.29820180e-01
-2.57264090e+00 -1.01179790e+00 1.57476775e-02 1.88228250e+00
1.08056843e+00 3.74070704e-01 4.16499555e-01 -4.05418277e-02
7.29002118e-01 6.17736466e-02 -7.60256231e-01 -5.60734153e-01
2.58399732e-02 -4.25235629e-02 3.12267542e-01 4.93330538e-01
-9.94512498e-01 1.04149902e+00 6.79020643e+00 6.87818766e-01
-6.31835818e-01 4.10354882e-03 1.13500190e+00 -3.67035359e-01
-6.35188341e-01 -2.25486234e-04 -5.51419497e-01 1.72577947e-01
7.10005164e-01 9.91657749e-02 9.54952598e-01 1.12209773e+00
-2.19393030e-01 1.38697982e-01 -1.61195183e+00 9.86651480e-01
-6.67922914e-01 -1.61098516e+00 1.19219884e-01 2.17357710e-01
6.52555704e-01 -2.95248255e-03 2.11624414e-01 4.17307496e-01
1.03287840e+00 -1.13174677e+00 5.45197964e-01 3.39442492e-01
5.72302818e-01 -7.30063736e-01 4.26384598e-01 1.79300576e-01
-6.77571952e-01 -4.06484783e-01 -4.07594323e-01 -2.42639840e-01
-3.14245641e-01 6.88148022e-01 -9.53983486e-01 4.83594656e-01
1.00266862e+00 7.63264894e-01 9.45812166e-02 5.35067677e-01
-1.60739630e-01 7.36382663e-01 -4.26142842e-01 -3.52191687e-01
5.74887246e-02 -4.55304295e-01 2.48183995e-01 8.15833569e-01
-8.56938213e-02 5.98578215e-01 1.68022364e-01 7.27442145e-01
-4.85598207e-01 1.65580601e-01 -5.53312421e-01 -1.74783364e-01
7.27484167e-01 8.83044839e-01 -4.22333449e-01 -2.51397938e-01
-4.56607580e-01 1.66349024e-01 6.89376056e-01 4.87823337e-01
-6.95398092e-01 2.69594103e-01 8.83499265e-01 3.22698414e-01
2.84476042e-01 2.96553910e-01 -6.62235379e-01 -9.41133022e-01
-1.09117739e-01 -9.22630191e-01 8.40533018e-01 -8.00180972e-01
-1.61708784e+00 5.16714454e-01 2.38082543e-01 -5.01387358e-01
-2.82779157e-01 -3.66914928e-01 5.04788570e-03 5.07183015e-01
-1.45239496e+00 -1.53561902e+00 -4.65674065e-02 4.97783601e-01
2.09726647e-01 8.99300426e-02 8.47394586e-01 -5.19995838e-02
-4.76932466e-01 4.84515786e-01 9.51101556e-02 5.52544184e-02
3.54103923e-01 -1.31279302e+00 3.89079392e-01 9.69013572e-02
5.02032161e-01 9.57675874e-01 8.82314682e-01 -6.12691581e-01
-1.35246754e+00 -8.41292262e-01 5.61368465e-01 -5.49090147e-01
8.55416179e-01 -5.03114522e-01 -6.92813873e-01 1.17010403e+00
-3.63881856e-01 2.10387722e-01 1.02329969e+00 1.45452785e+00
-8.26698780e-01 -7.56096363e-01 -1.05962348e+00 7.16293931e-01
1.52159989e+00 -7.30023742e-01 -5.50675750e-01 6.02206588e-01
1.05379128e+00 -2.49585330e-01 -1.09508896e+00 4.17162538e-01
9.05030131e-01 -1.00727153e+00 1.19354951e+00 -1.11809599e+00
4.98696387e-01 2.70001173e-01 -3.43655527e-01 -1.12413871e+00
-4.83185053e-01 -6.45689607e-01 -7.24434316e-01 8.48720968e-01
6.27133906e-01 -9.52833176e-01 1.21069586e+00 1.17589998e+00
3.01126897e-01 -1.02145290e+00 -7.66076565e-01 -6.91770196e-01
-3.43721271e-01 -5.60065389e-01 1.22955930e+00 1.18637574e+00
5.02105355e-01 4.69206423e-01 -4.66989666e-01 -1.71375692e-01
4.69484806e-01 6.84471965e-01 7.13776231e-01 -1.67725611e+00
-7.52399206e-01 -4.33719635e-01 -1.99118882e-01 -1.22048497e+00
9.86802280e-02 -6.88814640e-01 -3.83875966e-01 -1.46099663e+00
2.74060965e-01 -1.10199475e+00 -5.71885586e-01 7.68702567e-01
-7.76641890e-02 2.34252796e-01 -3.39549668e-02 3.99031579e-01
-5.27309418e-01 4.13663089e-01 1.08146596e+00 -2.92856395e-01
-2.97723234e-01 4.73507464e-01 -1.06618953e+00 3.70853543e-01
5.62831998e-01 -3.39401126e-01 -7.98455000e-01 -2.68634498e-01
9.71254230e-01 3.54377866e-01 2.17309803e-01 -9.20223296e-02
-5.25642671e-02 -5.91744602e-01 1.71782583e-01 -4.36581731e-01
8.23080361e-01 -1.06571269e+00 3.90045494e-01 4.99444306e-02
-7.30013847e-01 -2.67502904e-01 -1.11206323e-01 9.42751288e-01
-7.01361001e-02 2.70360589e-01 3.50912720e-01 8.26003253e-02
-1.86208040e-01 4.37580466e-01 1.93998456e-01 -1.91635489e-01
8.67588460e-01 -3.31707895e-01 -4.29075241e-01 -8.45643163e-01
-8.83577943e-01 1.71903968e-02 1.64578315e-02 1.24613859e-01
5.60045123e-01 -1.45065475e+00 -3.99760574e-01 -5.31463847e-02
2.91737258e-01 6.25644252e-02 -2.40431335e-02 6.35166109e-01
-3.60342264e-01 7.06344247e-01 -2.32553259e-02 -4.60957170e-01
-1.34932148e+00 7.52465367e-01 1.08272679e-01 -3.32179070e-01
-4.71629947e-01 1.01877666e+00 3.07415515e-01 -4.12967324e-01
2.39743978e-01 -3.60313833e-01 -1.89513355e-01 5.53194154e-03
1.61667466e-01 3.91570151e-01 -1.56170316e-03 -2.72357427e-02
-1.07784837e-01 -1.30928040e-01 -3.45695704e-01 1.74618348e-01
1.73439193e+00 1.09195597e-02 -3.99368048e-01 5.15421629e-01
5.57420671e-01 -2.22120658e-01 -1.03004873e+00 -6.97514772e-01
2.81590790e-01 -7.27075458e-01 1.79652631e-01 -8.32743049e-01
-7.15348125e-01 4.59977865e-01 -1.89057723e-01 1.01868880e+00
7.07021534e-01 2.16525689e-01 5.19742370e-01 7.62727976e-01
3.98399711e-01 -8.90200853e-01 -3.28051686e-01 6.95023611e-02
8.42121363e-01 -1.18602598e+00 4.39742863e-01 -8.96882534e-01
-5.76271772e-01 8.14924479e-01 3.50931108e-01 1.91817820e-01
7.02346087e-01 -3.82404327e-01 -3.75914544e-01 7.84354806e-02
-1.11518288e+00 2.28941292e-02 5.11743724e-01 5.37119091e-01
4.81466234e-01 4.59614336e-01 5.55380341e-03 5.59736788e-01
-2.35464662e-01 -2.51791596e-01 -1.29236747e-02 5.01272619e-01
-1.91548914e-01 -1.08852756e+00 -2.34005123e-01 8.83551121e-01
-3.37396443e-01 -2.96614200e-01 -5.41210413e-01 7.15708375e-01
-5.23036718e-01 1.04224062e+00 -2.34670565e-02 -4.17144150e-01
1.76358134e-01 -1.24852479e-01 4.80507433e-01 -5.18198609e-01
-3.18142533e-01 -1.86083332e-01 7.52508759e-01 -5.59347510e-01
-3.82276118e-01 -4.11095500e-01 -9.84471440e-01 -8.29305470e-01
-5.17416060e-01 4.32886869e-01 5.52978992e-01 8.78172517e-01
2.16576457e-01 3.30274761e-01 4.58793193e-01 -1.89413294e-01
-5.56622326e-01 -8.03040385e-01 -5.04137397e-01 6.87197566e-01
4.61000621e-01 -7.35155821e-01 1.59952790e-01 -1.80931151e-01] | [8.888039588928223, 7.651078224182129] |
e0ce67f9-ab58-4ba8-b2ae-668e5ba51a40 | evolutionary-hierarchical-dirichlet-process | null | null | https://aclanthology.org/P13-2099 | https://aclanthology.org/P13-2099.pdf | Evolutionary Hierarchical Dirichlet Process for Timeline Summarization | null | ['Jiwei Li', 'Sujian Li'] | 2013-08-01 | null | null | null | acl-2013-8 | ['timeline-summarization'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01
7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01
7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00
-1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01
3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01
9.28529128e-02 -8.59008506e-02 -2.09758401e-01 9.22211111e-01
5.37373662e-01 4.49454933e-02 1.08379531e+00 -1.42548037e+00
-5.91619551e-01 6.10313773e-01 -4.01565880e-02 1.21627934e-01
1.03678203e+00 -5.39447069e-01 1.19919395e+00 -1.13026452e+00
7.20913768e-01 1.26888943e+00 8.66221786e-01 5.44149756e-01
-1.22286928e+00 -1.94712028e-01 -3.26822817e-01 -9.51717794e-02
-1.46558487e+00 -3.25250506e-01 4.25783843e-02 -2.08119690e-01
1.66093647e+00 1.26596653e+00 1.20609856e+00 1.01401424e+00
1.26658809e+00 8.34431887e-01 1.04267764e+00 -5.13792276e-01
3.35295945e-01 3.66983831e-01 1.54683650e-01 6.33519173e-01
8.40953708e-01 5.26628852e-01 -7.06372619e-01 -9.13127720e-01
9.33553874e-01 -2.94925272e-01 1.71355158e-01 -5.05680561e-01
-9.05919552e-01 6.91228509e-01 1.78732842e-01 3.83959889e-01
-1.39880210e-01 9.89067405e-02 1.26390755e-01 5.30987144e-01
-2.58292928e-02 6.47037446e-01 -9.11868811e-01 -1.33165747e-01
-8.71728659e-01 5.10332465e-01 1.25398111e+00 1.52653182e+00
1.24482810e-01 2.94908643e-01 -9.34252143e-02 3.17179203e-01
8.92314315e-01 1.01808000e+00 4.28362608e-01 -1.36146402e+00
-6.87414408e-02 1.72361732e-01 5.01781464e-01 -8.52631688e-01
-6.33224547e-01 -9.64177120e-03 -8.93263519e-01 4.49267089e-01
3.49161088e-01 4.57367361e-01 -8.02827001e-01 5.07305264e-01
4.33481112e-02 -2.34125629e-01 4.53833073e-01 5.55570945e-02
4.99930978e-01 3.76208365e-01 -1.34477139e-01 -5.73289394e-01
1.06082785e+00 -1.36716676e+00 -1.35299087e+00 2.33215362e-01
9.05734658e-01 -1.07320261e+00 4.35900748e-01 5.33875942e-01
-1.55548143e+00 -1.37560293e-01 -1.08699942e+00 1.78573877e-01
-7.27255583e-01 -3.14239264e-01 8.57801437e-01 1.43120694e+00
-1.60129595e+00 9.73287821e-01 -4.91727620e-01 6.59165755e-02
1.20568443e-02 8.24621081e-01 -2.64718989e-03 4.62812334e-01
-1.33193445e+00 1.08501506e+00 2.22979754e-01 -1.21242590e-01
-1.65216476e-01 -2.13068098e-01 -8.23704481e-01 -5.63443303e-01
-4.78693932e-01 -5.29636025e-01 1.44139910e+00 -2.59346128e-01
-1.65295815e+00 9.71794367e-01 -1.42069459e-01 -1.97814897e-01
6.14786744e-01 -1.28011424e-02 -8.31891418e-01 2.42498964e-01
-1.89849049e-01 5.76383233e-01 9.28263724e-01 -1.35132408e+00
-7.59897232e-01 -1.67359829e-01 -1.23336017e-01 2.66287565e-01
-1.25510961e-01 1.89734384e-01 2.11616129e-01 -1.12999000e-01
3.27147305e-01 -7.20919967e-01 -2.53068686e-01 -5.32041907e-01
-1.46512717e-01 -7.10518599e-01 7.70373225e-01 -4.51523662e-01
1.83705616e+00 -1.67618537e+00 -2.06720144e-01 3.98590982e-01
3.57815564e-01 -1.24705513e-03 2.40583986e-01 1.08380008e+00
-2.76906848e-01 7.32199550e-01 4.11965609e-01 -1.10722095e-01
2.24991128e-01 4.85861301e-01 -4.16602850e-01 3.05609167e-01
-1.29282743e-01 1.15307164e+00 -1.16605783e+00 -5.23096442e-01
4.11106765e-01 9.42391157e-02 -4.58732933e-01 4.79237735e-01
2.99364805e-01 1.47170946e-01 -3.56553018e-01 1.39399457e+00
1.15709066e+00 -1.31984919e-01 1.45911396e-01 5.30878425e-01
-3.79135728e-01 3.55090618e-01 -6.96863770e-01 1.05554795e+00
7.08333924e-02 5.00173986e-01 1.02364108e-01 -7.94621468e-01
3.33247900e-01 8.61540735e-01 4.37155962e-01 -9.67555881e-01
-2.26398129e-02 6.92409754e-01 1.12803578e-01 -6.07703328e-01
7.58228302e-01 3.94563079e-02 -3.64872098e-01 6.40070081e-01
-2.37588286e-01 -6.59476995e-01 9.64643434e-02 3.08100313e-01
6.22585893e-01 -5.18246442e-02 5.62923312e-01 -1.05613089e+00
7.86340594e-01 -1.82965681e-01 -1.81299388e-01 1.03415680e+00
-3.09923887e-01 3.19085121e-01 2.99841821e-01 -6.65102363e-01
-6.45341039e-01 -1.12307119e+00 -4.89381433e-01 1.30636716e+00
3.24267983e-01 -4.39044595e-01 -9.54439282e-01 -2.49762803e-01
1.77620783e-01 6.89606130e-01 -5.90509653e-01 3.84124845e-01
-5.03739953e-01 -8.32535863e-01 7.39044368e-01 3.45434904e-01
-5.07752821e-02 -1.33414865e+00 -6.58416986e-01 1.25490099e-01
-2.20292807e-01 -6.63697243e-01 -6.23428151e-02 4.48765576e-01
-1.35989368e+00 -5.18594682e-01 -6.66252747e-02 -8.20914626e-01
5.87345481e-01 2.46782884e-01 1.27047324e+00 5.39230824e-01
-2.31483161e-01 4.26904231e-01 -1.21292919e-01 -4.95818377e-01
-4.59671497e-01 -8.00336525e-02 5.28869390e-01 -5.87835789e-01
5.19427478e-01 -2.50617653e-01 -7.29350567e-01 5.37953973e-01
-6.88540697e-01 1.62748516e-01 1.79803044e-01 1.04410267e+00
1.35816500e-01 -9.34035778e-02 1.22507080e-01 -6.38007045e-01
8.72274399e-01 -1.69219792e-01 -3.78732830e-01 5.77745810e-02
-6.77108407e-01 -3.74140263e-01 3.21430594e-01 -3.25342178e-01
-1.01981449e+00 -4.87835288e-01 -9.82677937e-02 2.45538145e-01
1.11353043e-02 -1.46784872e-01 6.47139177e-02 -5.24923325e-01
8.02199244e-01 9.25758183e-02 1.99174434e-02 -6.80815242e-03
3.01039815e-01 7.09525108e-01 -6.82967342e-03 -6.68678164e-01
8.44880998e-01 4.91470337e-01 7.98524171e-02 -9.57177758e-01
-1.52186140e-01 -2.60129690e-01 -9.51962709e-01 -6.54426932e-01
6.56643391e-01 -6.78531289e-01 -9.10833478e-01 3.91110867e-01
-9.38691139e-01 -3.38627815e-01 -3.91645581e-01 4.25431967e-01
-1.01278400e+00 2.75717527e-02 -3.90154392e-01 -1.27895141e+00
-5.10977268e-01 -1.02017939e+00 9.43384409e-01 5.30070923e-02
-5.10597289e-01 -1.26927447e+00 5.87685481e-02 2.71537274e-01
1.81734428e-01 -1.73075795e-01 6.90226793e-01 -2.38256708e-01
-4.24233019e-01 -1.53791070e-01 2.34436691e-02 -1.39755070e-01
1.70832314e-02 4.95917559e-01 -9.81751978e-01 -5.31145096e-01
6.65065646e-02 -1.92070693e-01 -1.08835101e-01 6.52520418e-01
5.91872573e-01 -2.29931593e-01 -8.56000841e-01 5.40386558e-01
1.38545322e+00 3.85070026e-01 5.32770038e-01 7.28214979e-01
1.41836226e-01 5.53460240e-01 9.17806149e-01 4.63203549e-01
1.30579369e-02 3.28798652e-01 2.40537539e-01 1.49327129e-01
1.11720070e-01 -1.54819340e-01 3.77893507e-01 1.16112018e+00
-8.18235934e-01 -2.69281328e-01 -5.07867396e-01 4.42987174e-01
-1.72482407e+00 -1.40330648e+00 -4.32368398e-01 6.90478683e-01
6.25676990e-01 1.56016424e-01 -1.48347050e-01 3.35214496e-01
4.99015123e-01 -2.03574806e-01 -1.19133167e-01 -1.06291151e+00
-1.43546045e-01 3.15233678e-01 7.37729073e-01 1.00061214e+00
-7.20721722e-01 1.03317809e+00 1.29781246e+01 1.02230716e+00
2.21112028e-01 1.03134915e-01 5.16071796e-01 3.48020852e-01
-4.36954498e-01 -4.56139445e-02 -1.04416132e+00 2.72933897e-02
1.38140702e+00 -4.30666685e-01 6.85999811e-01 5.44219851e-01
3.44648361e-01 -4.23268199e-01 -1.26188684e+00 5.26221812e-01
9.73738134e-02 -1.40886843e+00 -2.83300440e-04 6.85225725e-01
7.73699820e-01 -5.08050561e-01 6.22419357e-01 3.24184299e-01
6.09259963e-01 -1.14389277e+00 8.60300779e-01 2.53660440e-01
1.03040910e+00 -6.05088234e-01 5.67372203e-01 1.68872893e-01
-1.14389896e+00 -2.20873043e-01 -8.77727985e-01 -1.00755692e+00
3.93533185e-02 -1.81779593e-01 -4.29956943e-01 3.48861217e-01
9.58353162e-01 2.99398601e-01 -3.93658698e-01 9.95779395e-01
-4.78694476e-02 1.04875881e-02 -2.71853864e-01 -4.48467314e-01
4.83122796e-01 -3.54241252e-01 4.66730654e-01 1.00164843e+00
2.48499006e-01 3.51035744e-01 -9.84472036e-02 4.01770771e-01
5.45058846e-01 3.29446048e-02 -1.19659424e+00 -1.78908288e-01
2.83276141e-01 9.16795909e-01 -4.83487815e-01 -4.22520459e-01
-2.00212970e-01 8.62069130e-01 -3.55488248e-02 5.01107454e-01
-6.11489356e-01 -4.35615242e-01 9.72222984e-01 -1.27327025e-01
-1.14700586e-01 -3.48497719e-01 -6.23769283e-01 -7.30352640e-01
-5.89872956e-01 -4.54965204e-01 5.93606755e-02 -5.53365827e-01
-1.39813089e+00 5.79277515e-01 -2.27688253e-02 -1.40553558e+00
-6.99901402e-01 -1.27676582e+00 -4.76714373e-01 4.92853165e-01
-1.11898029e+00 -1.10984349e+00 2.50124663e-01 4.52870727e-01
1.64141744e-01 -5.34416080e-01 1.39563632e+00 3.57715860e-02
1.00637585e-01 9.24474537e-01 6.69434488e-01 -7.37814724e-01
5.56605101e-01 -1.27867436e+00 5.68737745e-01 -1.37897313e-01
-4.31265175e-01 9.05828118e-01 6.28349900e-01 -5.39804697e-01
-1.41196322e+00 -3.66917729e-01 1.08350635e+00 -9.83769417e-01
6.55218959e-01 -3.86345625e-01 4.23767231e-02 7.88592756e-01
7.15902448e-01 -6.18741751e-01 8.21781039e-01 -1.83753878e-01
1.80774391e-01 5.75296998e-01 -1.39248300e+00 6.12354755e-01
1.66275799e+00 -4.63594139e-01 -6.25784039e-01 7.60327101e-01
8.13696027e-01 -6.94087505e-01 -1.30082703e+00 3.34633321e-01
8.65424156e-01 -8.75409484e-01 1.61978090e+00 -1.32660246e+00
-4.43697497e-02 2.81152606e-01 -2.61993498e-01 -9.32519078e-01
-5.96193194e-01 -1.23518765e+00 -5.33532679e-01 -5.83747849e-02
5.96577883e-01 -1.13057327e+00 3.42365682e-01 8.74560475e-01
-2.82833427e-01 -6.42737269e-01 -1.06996536e+00 -1.32016802e+00
-3.35779637e-02 -1.45572275e-01 4.90409225e-01 7.63798356e-01
6.83744550e-01 1.09839931e-01 -6.36873543e-02 -1.00294888e-01
5.33176839e-01 9.55312885e-03 4.41501856e-01 -1.34294486e+00
3.86843324e-01 -5.75816095e-01 -3.07655483e-01 -9.45992947e-01
-8.85957032e-02 -8.12076271e-01 -6.53862000e-01 -1.28511906e+00
-8.32044985e-03 -1.91056758e-01 -1.11109078e-01 -1.65725678e-01
3.67937148e-01 2.13746816e-01 1.20859891e-02 1.03788137e-01
-3.71160030e-01 6.18435517e-02 1.29639816e+00 6.91750320e-05
-1.62315920e-01 4.85058486e-01 -4.81304944e-01 7.84440815e-01
8.58408585e-02 -2.99253196e-01 -6.78878546e-01 6.11881316e-02
6.69384480e-01 4.61409837e-02 3.24159935e-02 -7.42885649e-01
5.37211418e-01 -3.75702560e-01 4.78586555e-01 -1.32223868e+00
1.30741090e-01 -9.61415648e-01 6.95283338e-02 9.40189242e-01
2.74610907e-01 1.20424610e-02 8.66204947e-02 5.39500564e-02
-1.44871444e-01 -5.70943117e-01 9.21121240e-01 -4.07591403e-01
-4.92852688e-01 -5.20386267e-03 -1.03226590e+00 8.97834301e-02
9.92593169e-01 -7.84614205e-01 -3.59281451e-01 -4.20183957e-01
-8.29068601e-01 -1.95836127e-02 6.50830388e-01 3.03609259e-02
7.30431557e-01 -1.51530886e+00 -2.30721906e-01 7.18729138e-01
-3.22939813e-01 -3.74400020e-01 -1.70157343e-01 6.58265352e-01
-1.32361674e+00 1.02442718e+00 -5.41665435e-01 -4.55340147e-01
-1.14228773e+00 4.78126436e-01 4.28307921e-01 -2.41845414e-01
-2.15481281e-01 1.11954463e+00 2.71224789e-02 -8.23025763e-01
1.85185194e-01 -9.21545625e-02 -7.54407048e-01 3.55081353e-03
6.88606799e-01 1.05194807e+00 -2.91290224e-01 -6.04341030e-01
-4.56784427e-01 6.65885091e-01 2.32151806e-01 -2.87484169e-01
9.17833567e-01 -2.19243199e-01 -9.89108324e-01 4.28274393e-01
8.38715494e-01 -1.36269778e-01 -3.69319022e-02 4.16855574e-01
1.25943512e-01 -8.02164078e-01 -4.32406247e-01 -3.55811834e-01
-1.85641110e-01 5.54822803e-01 5.34874737e-01 8.99602413e-01
8.57008278e-01 -3.02566767e-01 8.18335712e-01 9.66778398e-01
5.72402716e-01 -1.68019545e+00 -2.13140488e-01 6.89524531e-01
9.12339568e-01 -9.28350806e-01 5.44190466e-01 -7.27165341e-01
-4.14997995e-01 1.32979155e+00 4.68304873e-01 -1.55325383e-01
1.27306652e+00 5.74917436e-01 1.14069022e-02 -3.70670199e-01
-9.44949508e-01 1.12705544e-01 3.75366658e-01 1.11147714e+00
5.06513238e-01 5.07374525e-01 -9.66500878e-01 3.21953118e-01
-7.28706717e-01 -2.34555230e-01 4.90474731e-01 1.41972518e+00
-6.43810987e-01 -1.20391917e+00 -7.23931909e-01 4.61561680e-01
-5.49773455e-01 -1.16372630e-01 -5.28106689e-01 8.46754074e-01
-5.76629937e-02 1.49448860e+00 -1.97535474e-03 -5.03491640e-01
4.11356747e-01 1.41089618e-01 7.21762300e-01 -1.23501487e-01
-9.04846430e-01 4.15413082e-01 3.84890139e-01 -1.23056793e+00
-8.58632207e-01 -1.05834293e+00 -1.40667629e+00 -1.19437599e+00
-5.12782812e-01 1.89310342e-01 3.83317530e-01 3.90289724e-01
-2.06836104e-01 2.85260603e-02 9.80917513e-01 -1.07949340e+00
-3.90341938e-01 -9.39418614e-01 -1.00026262e+00 -7.84516707e-02
2.89751232e-01 -8.00943017e-01 -7.83523321e-01 2.83909619e-01] | [-7.35828971862793, 3.7603485584259033] |
7637d6cf-6c08-4f3a-81b9-9bf403be52fc | video-based-frame-level-facial-analysis-of | null | null | https://openaccess.thecvf.com/content/CVPR2022W/ABAW/html/Savchenko_Video-Based_Frame-Level_Facial_Analysis_of_Affective_Behavior_on_Mobile_Devices_CVPRW_2022_paper.html | https://openaccess.thecvf.com/content/CVPR2022W/ABAW/papers/Savchenko_Video-Based_Frame-Level_Facial_Analysis_of_Affective_Behavior_on_Mobile_Devices_CVPRW_2022_paper.pdf | Video-Based Frame-Level Facial Analysis of Affective Behavior on Mobile Devices Using EfficientNets | In this paper, we consider the problem of real-time video-based facial emotion analytics, namely, facial expression recognition, prediction of valence and arousal and detection of action unit points. We propose the novel frame-level emotion recognition algorithm by extracting facial features with the single EfficientNet model pre-trained on AffectNet. The predictions for sequential frames are smoothed using mean or median filters. It is demonstrated that our approach may be implemented even for video analytics on mobile devices. Experimental results for the large scale Aff-Wild2 database from the third Affective Behavior Analysis in-the-wild Competition demonstrate that our simple model is significantly better when compared to the VggFace baseline. In particular, our method is characterized by 0.1-0.5 higher performance measures for test sets in the uni-task Expression Classification, Valence-Arousal Estimation, Action Unit Detection and Multi-Task Learning. Our team took the 3rd place in the multi-task learning challenge and 4th places in Valence-Arousal and Expression challenges. Due to simplicity, the proposed approach may be considered as a new baseline for all four sub-challenges. | ['Savchenko A.V.'] | 2022-06-10 | null | null | null | cvpr-workshop-2022-6 | ['facial-expression-recognition', 'action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 2.28242591e-01 -1.87755432e-02 7.62001649e-02 -7.72823095e-01
-7.66865969e-01 -2.45775297e-01 4.51530188e-01 3.13562937e-02
-5.83633840e-01 5.19147277e-01 9.56002697e-02 6.07451379e-01
3.70827913e-01 -9.49034914e-02 -3.72689396e-01 -7.81389654e-01
-4.32289034e-01 -1.19334422e-01 -3.81213218e-01 -3.76383275e-01
-1.26423463e-01 4.82707769e-01 -2.02691650e+00 8.81676018e-01
8.09136257e-02 1.85810792e+00 -6.73967898e-01 8.51637721e-01
2.77225167e-01 1.04433048e+00 -4.33799148e-01 -6.24217510e-01
1.13265917e-01 -3.67550164e-01 -6.31787777e-01 7.26863220e-02
6.71481490e-01 -2.33671665e-01 2.44137928e-01 8.46482635e-01
7.46696472e-01 1.94758788e-01 4.21567261e-01 -1.74746704e+00
2.72507608e-01 -1.35118052e-01 -7.33202100e-01 2.01277837e-01
5.70296407e-01 -8.86789337e-02 9.82283592e-01 -1.10634232e+00
6.41401291e-01 1.20381129e+00 7.43290663e-01 8.05198312e-01
-9.06746507e-01 -6.43989921e-01 1.79276153e-01 4.57656205e-01
-1.32336891e+00 -8.49410892e-01 7.24540353e-01 -3.27053577e-01
1.28252292e+00 2.53699690e-01 8.47964287e-01 1.56390524e+00
2.08053425e-01 9.87437010e-01 1.33722556e+00 -1.68357596e-01
2.03038409e-01 5.73512986e-02 1.80271920e-02 9.03328836e-01
-6.96125805e-01 -2.38433540e-01 -8.62253666e-01 -3.00485194e-01
1.77329615e-01 -3.47121716e-01 8.37303028e-02 3.27844247e-02
-5.87087035e-01 7.23657548e-01 6.64932746e-03 3.01833779e-01
-8.69974792e-01 3.48268986e-01 1.09036899e+00 5.14307618e-01
1.06112158e+00 8.18709508e-02 -4.53124791e-01 -7.24371791e-01
-1.01962006e+00 4.03112292e-01 5.80411434e-01 4.73122954e-01
5.65576971e-01 1.79779351e-01 -3.19295049e-01 8.88696134e-01
8.24424773e-02 9.97115485e-03 4.53168035e-01 -9.51981127e-01
-1.74399287e-01 3.77723157e-01 -1.37523338e-01 -1.03390110e+00
-7.96568692e-01 2.99876809e-01 -7.69241393e-01 4.74489659e-01
2.01658070e-01 -5.43877840e-01 -5.92580080e-01 1.89004493e+00
3.74996036e-01 5.27285814e-01 -1.77833378e-01 9.14832592e-01
8.35003674e-01 6.85061812e-01 4.07851994e-01 -7.44012237e-01
1.55995870e+00 -8.44976604e-01 -9.25348520e-01 1.00621082e-01
7.25333452e-01 -4.97389585e-01 7.94237912e-01 9.00490999e-01
-1.19617605e+00 -6.33767188e-01 -8.68908763e-01 1.01860493e-01
-3.79825830e-01 1.46883875e-01 9.82220948e-01 6.88265502e-01
-1.28221858e+00 6.61976039e-01 -7.71253526e-01 -3.49588841e-01
7.73168087e-01 5.41551590e-01 -5.83418131e-01 5.54262996e-01
-9.30913866e-01 7.34161019e-01 -5.67483194e-02 1.84047922e-01
-8.23094189e-01 -5.59922814e-01 -7.99715579e-01 6.39849063e-03
-7.20645413e-02 -2.75311619e-01 1.27810001e+00 -2.10097885e+00
-1.78324378e+00 1.39212716e+00 -3.10456187e-01 -3.50222558e-01
4.14977700e-01 -3.81014109e-01 -5.76258719e-01 3.66065830e-01
-3.31112951e-01 8.36312711e-01 1.06985831e+00 -6.10873938e-01
-6.31425142e-01 -6.92601323e-01 -7.97885731e-02 1.31297976e-01
-3.97024632e-01 5.29802263e-01 -1.58870593e-01 -4.11053002e-01
-5.29231966e-01 -8.16768706e-01 -4.38373350e-02 -1.93816368e-02
2.46359020e-01 -5.38725793e-01 8.37344766e-01 -4.88864243e-01
1.09372234e+00 -2.28214931e+00 9.30017009e-02 3.43888104e-01
2.70002842e-01 1.42634183e-01 -2.26854339e-01 -1.68003514e-01
-4.54116851e-01 -1.75878108e-01 3.90866131e-01 -7.05940664e-01
-5.14238998e-02 -1.88450545e-01 5.63942790e-02 6.92435324e-01
2.98613250e-01 9.24149632e-01 -5.49280345e-01 -4.15985644e-01
1.43520450e-02 6.33123457e-01 -5.13570249e-01 1.63183227e-01
-1.64154079e-02 3.20962727e-01 -1.49407178e-01 9.96451974e-01
4.82328564e-01 1.91908091e-01 2.98657361e-02 -3.12552989e-01
-1.63622405e-02 -2.80117303e-01 -9.37209070e-01 1.80947804e+00
-5.35129070e-01 1.01956952e+00 3.73746961e-01 -9.55052257e-01
9.51738179e-01 7.24383593e-01 9.16093349e-01 -7.85548270e-01
6.08645260e-01 -1.10854730e-02 -1.98527887e-01 -6.89080596e-01
4.01127845e-01 -2.76757956e-01 -1.87654316e-01 8.53365660e-02
5.86203635e-01 4.28324223e-01 5.58112785e-02 -1.44222766e-01
1.05428410e+00 1.75395891e-01 4.34120625e-01 -3.09765786e-01
5.82696140e-01 -5.20672321e-01 5.25467455e-01 2.06743568e-01
-7.52990723e-01 3.12774509e-01 9.47643042e-01 -7.40644336e-01
-5.57478905e-01 -6.00199759e-01 1.40101954e-01 1.87336969e+00
-3.57798249e-01 -8.40618014e-01 -9.03657556e-01 -7.12379158e-01
-3.10867161e-01 1.25515804e-01 -1.08371210e+00 -5.08800372e-02
-2.79459655e-01 -6.76745772e-01 7.40153372e-01 4.61071789e-01
1.91484585e-01 -1.34368789e+00 -8.30305398e-01 2.28090789e-02
-1.37670442e-01 -1.28528810e+00 1.07008377e-02 2.44992331e-01
-4.48647022e-01 -7.84918725e-01 -4.90957022e-01 -4.91919309e-01
1.19929105e-01 -3.96247566e-01 9.98610616e-01 -1.28188789e-01
-4.25075471e-01 6.20240569e-01 -4.02910382e-01 -7.55471170e-01
1.08144507e-02 -3.08133036e-01 1.93011299e-01 8.66271615e-01
8.31829965e-01 -6.52370632e-01 -5.94013035e-01 -6.92455992e-02
-5.35216749e-01 -2.98877925e-01 9.42255855e-02 5.86068630e-01
6.27461076e-01 -6.84724629e-01 6.97341263e-01 -4.82793152e-01
6.91560149e-01 -4.28408116e-01 -2.57225692e-01 5.70996068e-02
-2.67400593e-01 -4.17907923e-01 4.12204951e-01 -3.50522757e-01
-9.65888917e-01 3.84546667e-01 -4.51349169e-01 -7.54190505e-01
-3.67063880e-01 5.36966659e-02 -1.31718367e-01 -1.75352320e-01
5.78020871e-01 -2.23532900e-01 2.06069976e-01 4.31722887e-02
1.45719424e-02 6.13923848e-01 2.79985249e-01 -1.92321435e-01
-2.12251589e-01 5.58939755e-01 2.47373968e-01 -9.76445019e-01
-7.11169362e-01 -5.43130577e-01 -6.43945456e-01 -8.21635008e-01
1.18160260e+00 -1.40955019e+00 -1.24575186e+00 5.93697727e-01
-1.13470781e+00 -2.49512434e-01 -1.32888397e-02 1.87075958e-01
-9.98259306e-01 1.26803711e-01 -6.31799757e-01 -1.04509592e+00
-7.30497837e-01 -1.00646472e+00 1.40839565e+00 1.45457536e-01
-6.70679331e-01 -9.02863503e-01 2.31393412e-01 1.34859949e-01
2.22224474e-01 8.13824415e-01 3.12168896e-01 -5.52253246e-01
3.19280475e-01 -1.38992041e-01 2.24165305e-01 5.80450356e-01
-3.11109900e-01 2.74562240e-01 -1.61787713e+00 -6.78895935e-02
-2.34531183e-02 -1.04278600e+00 9.31915283e-01 5.49800038e-01
1.54701352e+00 -9.19128582e-02 1.70527935e-01 7.52669454e-01
1.15578890e+00 5.71962036e-02 8.47430289e-01 9.37130079e-02
4.07994300e-01 6.44717336e-01 8.63737941e-01 9.63973999e-01
1.15030140e-01 8.79832745e-01 5.68026662e-01 -7.84852728e-02
4.81725037e-01 3.25764358e-01 7.34793007e-01 2.46553808e-01
-5.87537646e-01 1.70724429e-02 -5.27826130e-01 2.72878677e-01
-2.02050781e+00 -1.40229678e+00 -1.37501821e-01 1.69174874e+00
4.96944726e-01 -1.33575097e-01 7.50465035e-01 6.06435239e-02
2.70892113e-01 3.17002445e-01 -1.74940124e-01 -1.23346269e+00
-2.17073679e-01 6.87680781e-01 -1.55601770e-01 2.99891233e-01
-1.47022939e+00 9.16577995e-01 5.82164955e+00 8.19221437e-01
-1.47064853e+00 3.76864403e-01 1.03216338e+00 -8.47285450e-01
4.80078876e-01 -7.33751714e-01 -6.73995554e-01 2.70171553e-01
1.40152621e+00 6.33438900e-02 1.74192220e-01 1.10148239e+00
4.54683870e-01 -2.24230573e-01 -1.15553164e+00 1.68397832e+00
4.04165655e-01 -1.05766201e+00 -2.97307253e-01 -8.93645808e-02
4.58008140e-01 -1.82895139e-02 -7.33209848e-02 5.07815242e-01
-7.15496540e-01 -1.13381827e+00 6.44870281e-01 6.14262581e-01
8.22675705e-01 -1.14132369e+00 6.44464135e-01 -1.27535284e-01
-1.00638998e+00 -1.21519916e-01 -1.12691142e-01 -3.56599987e-01
9.46058258e-02 2.58399367e-01 -3.52889359e-01 3.88432071e-02
8.16891611e-01 8.49676609e-01 -4.61493492e-01 4.31675047e-01
2.50769109e-01 4.71850425e-01 -1.58647999e-01 -2.17593163e-01
3.08974355e-01 -2.31369324e-02 2.58776248e-01 1.64395046e+00
2.72944748e-01 2.17256725e-01 -1.23101123e-01 1.07480563e-01
-2.29450226e-01 4.47079450e-01 -6.18588328e-01 -2.42607407e-02
-3.63619149e-01 2.10162973e+00 -5.03604114e-01 -3.98702919e-01
-4.42320615e-01 1.26227295e+00 3.35396886e-01 4.22779620e-02
-1.18574297e+00 -1.75837353e-01 1.16732645e+00 -7.49267414e-02
1.62290215e-01 3.74151111e-01 -6.84123635e-02 -1.05072308e+00
-7.83879962e-03 -9.78124857e-01 4.28632587e-01 -7.99081624e-01
-9.17701781e-01 8.55518699e-01 -2.65476882e-01 -9.35398996e-01
-5.62701583e-01 -9.52334046e-01 -6.87198222e-01 4.25884068e-01
-1.07163775e+00 -1.06085300e+00 -5.58471501e-01 9.57938433e-01
6.37930155e-01 -2.69767731e-01 1.13624549e+00 4.91329610e-01
-5.53171396e-01 8.28830123e-01 -5.16831279e-01 -5.87474741e-02
7.42517292e-01 -1.01702046e+00 -2.85972327e-01 4.47191685e-01
2.07876712e-01 -2.01997355e-01 6.23832226e-01 5.35826907e-02
-1.21675372e+00 -1.01107311e+00 8.33917141e-01 -4.65090185e-01
5.86910605e-01 -7.51684546e-01 -6.77303731e-01 6.31092727e-01
4.23645854e-01 1.55137092e-01 1.13776493e+00 3.15541983e-01
-2.69983739e-01 -3.12597156e-01 -1.19321001e+00 2.69803524e-01
8.15739989e-01 -4.79665220e-01 -1.06892392e-01 3.80059451e-01
-6.90886518e-03 -2.78037429e-01 -9.93236482e-01 5.31088114e-01
1.03481936e+00 -1.24912226e+00 5.16906321e-01 -8.44310522e-01
7.31402934e-01 4.04432833e-01 -2.86047518e-01 -1.20093441e+00
-1.22835815e-01 -8.80382955e-01 -3.29433978e-01 1.14190698e+00
1.41369343e-01 5.87173253e-02 7.63507903e-01 3.97668004e-01
7.62360394e-02 -1.03376544e+00 -1.28904843e+00 -2.25316986e-01
-4.37560827e-01 -6.84416056e-01 2.54339129e-02 7.59757817e-01
2.85369813e-01 5.90092123e-01 -6.65176272e-01 -3.86900097e-01
3.19358557e-01 -1.46804705e-01 7.11865604e-01 -1.22289872e+00
-9.42590311e-02 -5.67598104e-01 -9.41423655e-01 -2.67494738e-01
6.48380518e-01 -5.86088061e-01 -1.61947712e-01 -6.47374690e-01
1.15662754e-01 5.06928861e-01 -5.28244138e-01 6.01323903e-01
1.07273437e-01 5.95673621e-01 2.54518718e-01 -4.80240017e-01
-1.06341827e+00 5.78395009e-01 6.82715058e-01 2.31496453e-01
-1.51917577e-01 -8.09288844e-02 -4.35379684e-01 9.53778744e-01
7.51034200e-01 -9.51995477e-02 -2.32474372e-01 1.89846754e-01
3.08956981e-01 2.66914140e-03 5.09295702e-01 -1.03384125e+00
-1.71042114e-01 1.05552524e-01 6.29905522e-01 -2.42947876e-01
9.19646382e-01 -6.50561810e-01 -1.67675316e-01 8.62057954e-02
-2.64721215e-01 2.61752337e-01 6.00454152e-01 2.04370275e-01
-3.06790382e-01 2.75405705e-01 9.65020657e-01 -3.40970047e-02
-1.20545053e+00 4.26869452e-01 -5.73019922e-01 -2.50703335e-01
1.37379622e+00 -2.27844819e-01 9.34513807e-02 -5.91052949e-01
-1.31688070e+00 -4.65835482e-02 -1.12695873e-01 5.99025905e-01
5.36871731e-01 -1.41298687e+00 -7.65196383e-01 2.66128004e-01
2.56874144e-01 -8.26651633e-01 5.85923731e-01 1.30321050e+00
-9.05208513e-02 -1.71134770e-02 -7.77782261e-01 -8.01160216e-01
-2.13014412e+00 1.64272889e-01 5.07446051e-01 -1.72328472e-01
4.57760431e-02 1.01249385e+00 1.10183157e-01 1.32356957e-01
2.34445527e-01 5.45299277e-02 -3.65750700e-01 8.25523198e-01
8.73925090e-01 3.39871377e-01 2.78404266e-01 -9.66745138e-01
-5.15813172e-01 2.53239423e-01 1.31248176e-01 -4.54801954e-02
1.54904819e+00 -5.23491018e-02 -2.60265052e-01 6.88177168e-01
1.64376080e+00 -3.82840395e-01 -1.13779604e+00 3.68214071e-01
-2.78714318e-02 -8.54558945e-02 1.41022503e-01 -6.37117028e-01
-1.08629882e+00 1.00819230e+00 1.11359310e+00 -3.87132764e-02
1.75257099e+00 -7.00809211e-02 2.82028615e-01 2.45889142e-01
1.40478358e-01 -1.56143022e+00 2.03829333e-01 4.82160330e-01
9.34485018e-01 -1.29584432e+00 -2.77490973e-01 -3.43084335e-04
-9.27879810e-01 1.21562600e+00 9.51257646e-01 -1.23449564e-01
7.28190243e-01 3.44377577e-01 2.00886205e-01 -5.07776856e-01
-1.30893791e+00 -2.95724660e-01 2.52228469e-01 3.55897248e-01
7.85469890e-01 1.13283214e-03 -2.48684406e-01 7.47252762e-01
5.50310239e-02 3.11381102e-01 2.68516034e-01 6.04838133e-01
-2.99669862e-01 -6.60115182e-01 -9.81828105e-03 4.43440467e-01
-1.00987518e+00 1.60415545e-01 -6.19684756e-01 6.57871842e-01
2.22520679e-01 7.62093902e-01 3.36095572e-01 -6.22466862e-01
4.12892729e-01 6.90344334e-01 6.47078872e-01 -1.45561755e-01
-1.06129837e+00 3.10990065e-01 3.29324186e-01 -1.47352719e+00
-9.18637753e-01 -8.03198755e-01 -1.02469110e+00 -1.77555427e-01
3.15221936e-01 -9.43002105e-02 6.96091115e-01 7.75746107e-01
6.43892527e-01 3.11991066e-01 7.90415049e-01 -1.09840572e+00
-1.30829141e-01 -1.06148732e+00 -7.81036675e-01 6.87863111e-01
3.12638789e-01 -6.45985126e-01 -1.68479815e-01 9.22340751e-02] | [13.578600883483887, 2.0753636360168457] |
012cadd0-f152-4b06-bae1-deb88e4f0352 | three-dimensional-blind-image-deconvolution | 1904.09974 | null | http://arxiv.org/abs/1904.09974v1 | http://arxiv.org/pdf/1904.09974v1.pdf | Three dimensional blind image deconvolution for fluorescence microscopy using generative adversarial networks | Due to image blurring image deconvolution is often used for studying
biological structures in fluorescence microscopy. Fluorescence microscopy image
volumes inherently suffer from intensity inhomogeneity, blur, and are corrupted
by various types of noise which exacerbate image quality at deeper tissue
depth. Therefore, quantitative analysis of fluorescence microscopy in deeper
tissue still remains a challenge. This paper presents a three dimensional blind
image deconvolution method for fluorescence microscopy using 3-way spatially
constrained cycle-consistent adversarial networks. The restored volumes of the
proposed deconvolution method and other well-known deconvolution methods,
denoising methods, and an inhomogeneity correction method are visually and
numerically evaluated. Experimental results indicate that the proposed method
can restore and improve the quality of blurred and noisy deep depth microscopy
image visually and quantitatively. | ['Shuo Han', 'Edward J. Delp', 'Paul Salama', 'Soonam Lee', 'Kenneth W. Dunn'] | 2019-04-19 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [ 4.03108269e-01 -6.45393848e-01 7.73830116e-01 -1.78439140e-01
-4.70677733e-01 -8.27966869e-01 -9.16981697e-03 -4.34740067e-01
-8.00283670e-01 1.28226626e+00 1.05816759e-02 -4.88236845e-02
-5.38988505e-03 -2.27702763e-02 -3.87423933e-01 -1.39730072e+00
3.21723878e-01 1.98918320e-02 -3.73949222e-02 3.41600955e-01
3.73938322e-01 8.79296422e-01 -8.63884449e-01 4.84231375e-02
8.47746372e-01 7.46730030e-01 3.48774046e-01 9.07701313e-01
-1.07112475e-01 7.35812783e-01 -7.17872918e-01 2.04382706e-02
1.18966132e-01 -4.88357246e-01 -8.13890457e-01 2.09961936e-01
2.44425982e-01 -7.64701664e-01 -2.97883898e-01 1.47848654e+00
7.37361848e-01 2.38645151e-01 7.38584638e-01 -8.17507982e-01
-8.47317100e-01 8.62616077e-02 -8.98604453e-01 7.63044655e-01
-1.46456689e-01 4.37806249e-01 -2.81952471e-01 -9.25746858e-01
6.43781602e-01 9.56938446e-01 7.51239121e-01 8.44309092e-01
-1.66414177e+00 -4.98230696e-01 -3.18480432e-01 3.25045967e-03
-1.13480115e+00 -3.95028532e-01 6.82137728e-01 -6.42568171e-01
4.42640156e-01 1.63759649e-01 2.49255344e-01 6.43670917e-01
6.34606063e-01 4.68866713e-02 1.80770338e+00 -9.67036784e-02
1.30827323e-01 -2.34340638e-01 1.29707113e-01 5.14673173e-01
3.01299423e-01 1.86182797e-01 -9.17079449e-02 -3.22635978e-01
1.28798378e+00 2.17839286e-01 -1.00178742e+00 1.95230007e-01
-1.24590588e+00 1.53616086e-01 2.50313729e-01 3.56341511e-01
-2.58072346e-01 8.95342976e-02 1.22701392e-01 3.73970084e-02
4.70929444e-01 2.44825125e-01 -1.64128527e-01 1.72696948e-01
-1.11187196e+00 1.88148469e-02 3.64386082e-01 2.75892079e-01
6.64203048e-01 1.10592514e-01 -2.28939489e-01 4.52585608e-01
1.93503022e-01 5.73020816e-01 5.87028980e-01 -1.46843565e+00
-2.08588392e-01 2.94395953e-01 4.17312473e-01 -7.20296443e-01
-3.77923608e-01 -1.28296791e-02 -1.28977060e+00 8.50132227e-01
9.02382076e-01 -2.35773146e-01 -9.92689908e-01 1.39529598e+00
3.07397574e-01 3.03200811e-01 3.02517395e-02 1.40999293e+00
7.81062305e-01 2.89673984e-01 -8.92130807e-02 -5.82294464e-01
1.01267111e+00 -4.92095232e-01 -1.20802212e+00 1.33959770e-01
-1.62789837e-01 -9.11152244e-01 6.91921771e-01 2.48960450e-01
-1.23381054e+00 -2.68006086e-01 -7.13897109e-01 -2.35787109e-01
1.13439433e-01 -2.62431409e-02 3.12775254e-01 3.90920639e-01
-1.28153121e+00 7.54492521e-01 -1.02701771e+00 5.54367602e-02
8.81879866e-01 3.14417273e-01 -6.05964005e-01 -2.56988674e-01
-7.06924498e-01 6.84685528e-01 -1.46781176e-01 5.16407371e-01
-1.02291977e+00 -1.01785910e+00 -4.77672398e-01 -2.17669263e-01
-5.54813027e-01 -7.87111044e-01 8.85892928e-01 -5.71972311e-01
-1.48714232e+00 1.08241892e+00 -6.27915919e-01 -1.42857403e-01
6.27135813e-01 1.57748327e-01 1.65906414e-01 6.16682172e-01
-6.86276183e-02 3.36938322e-01 6.01404250e-01 -1.48301053e+00
5.06553799e-02 -8.66030574e-01 -4.72193480e-01 1.40240893e-01
2.23529592e-01 7.02019855e-02 1.84947431e-01 -4.99870718e-01
1.14398815e-01 -5.09340942e-01 -2.06197158e-01 5.24764061e-01
-3.47776294e-01 5.97544551e-01 1.01371241e+00 -9.03806329e-01
5.84316969e-01 -2.08164597e+00 1.24249272e-01 -1.57748774e-01
5.36609650e-01 1.25819042e-01 -8.03510994e-02 -3.40698399e-02
-1.02991000e-01 7.03739643e-04 -3.84537756e-01 -3.37984800e-01
-5.42471528e-01 2.72628784e-01 9.13522542e-02 1.21015680e+00
-1.43873990e-01 9.01407003e-01 -7.95100212e-01 -5.25639296e-01
3.10813010e-01 1.10394394e+00 3.78723908e-03 3.24638069e-01
2.74441719e-01 1.47081947e+00 -1.51239917e-01 6.49843872e-01
1.35299182e+00 -4.18719947e-01 -1.38182238e-01 -6.48690999e-01
-2.30630606e-01 -7.81952083e-01 -5.52329063e-01 1.43427968e+00
6.88947141e-02 8.54210973e-01 6.32756233e-01 -8.43404651e-01
4.84170705e-01 3.63144219e-01 3.49651456e-01 -1.12606615e-01
4.01921183e-01 2.96616614e-01 -1.46508113e-01 -8.34007442e-01
-7.90227298e-03 -7.82719135e-01 7.76055992e-01 2.04243481e-01
-2.72535294e-01 -2.24020526e-01 -1.18337378e-01 -8.91347378e-02
1.11211538e+00 -2.16210168e-02 -2.92413890e-01 -4.25578862e-01
8.40335131e-01 -3.62075865e-01 4.68077123e-01 3.40383887e-01
-8.47942293e-01 1.05373693e+00 3.93709421e-01 -3.63814652e-01
-1.10632670e+00 -9.18538749e-01 -3.98274928e-01 1.83811188e-01
3.41356099e-01 9.73927319e-01 -1.02456915e+00 -1.91649616e-01
-3.25027183e-02 1.69869736e-02 -6.96740210e-01 2.55243331e-01
-3.31951767e-01 -9.30923998e-01 7.63106763e-01 1.70660540e-01
6.60344005e-01 -8.67714822e-01 -3.30948323e-01 6.02227869e-03
-5.21764100e-01 -1.37161291e+00 -6.52024627e-01 -3.33646089e-02
-9.81790841e-01 -1.32326984e+00 -1.52586544e+00 -1.00766850e+00
1.26058900e+00 5.16611040e-01 6.72492862e-01 9.43045989e-02
-6.00800812e-01 1.72087416e-01 2.25978568e-01 1.27729937e-01
-6.53407276e-01 -9.69432831e-01 -1.82765294e-02 -7.29989668e-04
-9.02442075e-03 -7.47797191e-01 -1.18662274e+00 3.83465350e-01
-1.23327172e+00 -4.16485339e-01 3.80591571e-01 1.11016893e+00
8.72207761e-01 3.42490703e-01 2.31059164e-01 -7.00987756e-01
6.04078531e-01 6.10819571e-02 -8.09871137e-01 3.22781615e-02
-3.71108681e-01 -2.21636936e-01 6.60699725e-01 -3.81719261e-01
-1.39052296e+00 -6.07232675e-02 2.68112659e-01 -7.67547846e-01
-5.00762403e-01 2.50342041e-01 8.86147618e-02 -7.86876678e-01
6.95270121e-01 5.08783042e-01 7.82899380e-01 -2.27288932e-01
-1.74872249e-01 4.52180505e-01 8.81663084e-01 -1.30058601e-01
6.96019948e-01 1.26831961e+00 3.50971043e-01 -8.57777417e-01
-4.36880022e-01 -5.38075864e-01 -3.83412808e-01 -3.58930975e-01
9.18736815e-01 -7.46208966e-01 -1.20086741e+00 1.21584725e+00
-1.40677500e+00 -4.69573885e-01 1.89229831e-01 4.28339422e-01
-3.51026565e-01 1.09039772e+00 -1.25263047e+00 -7.82758772e-01
-5.41003823e-01 -1.27652061e+00 7.09299266e-01 5.11148214e-01
3.11363518e-01 -1.29162920e+00 -6.45140260e-02 6.12356842e-01
6.60379410e-01 6.06327116e-01 7.45565772e-01 4.98457789e-01
-4.94655937e-01 2.54645228e-01 -5.31992793e-01 6.28287494e-01
4.47321296e-01 -4.18870598e-02 -1.07412207e+00 -4.34896886e-01
6.38843715e-01 -8.31963420e-02 8.72468293e-01 1.10397375e+00
1.28416824e+00 -2.35499129e-01 -2.41777837e-01 9.57761288e-01
1.86862361e+00 3.29692140e-02 9.32862580e-01 5.00460304e-02
5.96173763e-01 6.90477967e-01 9.45511758e-02 1.97518095e-01
-3.04378629e-01 1.42684177e-01 5.71573019e-01 -4.87897635e-01
-1.69097155e-01 5.80334544e-01 -2.36296691e-02 2.72891790e-01
-3.88370425e-01 -2.90414751e-01 -4.50261325e-01 6.60578489e-01
-1.43786144e+00 -1.01150775e+00 -4.99889493e-01 2.01450157e+00
1.18997395e+00 -6.11835897e-01 -3.74700665e-01 5.05018048e-02
1.06741667e+00 -5.00241816e-01 -8.11475098e-01 2.02454478e-01
-5.53220809e-01 1.10902444e-01 7.65026808e-01 6.87595069e-01
-7.65923500e-01 3.04505348e-01 6.75907230e+00 4.75899190e-01
-1.11315739e+00 -4.30273637e-03 8.84503484e-01 4.48081344e-02
-2.47218594e-01 -3.76965404e-01 -4.23617303e-01 8.45134854e-01
3.95835876e-01 7.84381405e-02 6.87909007e-01 -1.52770340e-01
8.33659351e-01 -5.16327679e-01 -7.94054866e-01 1.27471983e+00
-2.72925079e-01 -1.36998165e+00 -1.25915200e-01 2.53072202e-01
8.94151032e-01 -4.98437211e-02 3.71183544e-01 -8.43513668e-01
-1.84729695e-02 -1.17140126e+00 1.47476435e-01 8.68722260e-01
9.22906876e-01 -5.35237253e-01 1.12129712e+00 1.86696485e-01
-3.63467336e-01 2.33027086e-01 -5.82180083e-01 1.48394108e-01
3.51239711e-01 1.10913074e+00 -2.86486775e-01 8.92632753e-02
8.42356145e-01 5.91075003e-01 -7.43871629e-02 1.02483845e+00
6.96420595e-02 2.70028532e-01 1.46171749e-01 4.16547596e-01
-1.71139799e-02 -4.22195882e-01 7.06672907e-01 8.99769545e-01
3.51414442e-01 3.82126421e-01 -6.18571579e-01 1.26638615e+00
-2.70736553e-02 -5.10435581e-01 -3.33861291e-01 -1.14855571e-02
2.97111422e-01 1.46697080e+00 -8.52768362e-01 -5.20258062e-02
-2.08147317e-01 1.25575924e+00 -1.62377238e-01 9.34006035e-01
-6.68502390e-01 -1.99864924e-01 9.78439212e-01 2.07133383e-01
4.64111976e-02 -1.26942053e-01 -1.69660419e-01 -1.16471016e+00
-1.81658968e-01 -4.18284625e-01 -9.85213444e-02 -1.09602880e+00
-1.54915559e+00 3.80600274e-01 -5.34973860e-01 -9.45666313e-01
5.72707832e-01 -7.61429250e-01 -5.29351652e-01 1.33361089e+00
-2.10560131e+00 -4.84276831e-01 -6.52687967e-01 8.18946898e-01
5.28143905e-02 1.86042398e-01 6.78510845e-01 3.18032980e-01
-4.76236135e-01 1.50472999e-01 6.81840003e-01 6.46099895e-02
8.76221955e-01 -1.38093078e+00 -3.46754879e-01 8.63717735e-01
-8.10260415e-01 7.11898625e-01 8.49719644e-01 -3.74917984e-01
-1.06137431e+00 -9.59270298e-01 3.89444172e-01 -1.27961030e-02
5.68593323e-01 3.43569458e-01 -1.25230539e+00 2.59078771e-01
5.69424450e-01 5.90325654e-01 7.17548013e-01 -1.16949427e+00
1.38799608e-01 3.20595875e-02 -1.96438849e+00 4.02636677e-01
4.17736650e-01 -4.56096053e-01 -1.92060202e-01 3.54487628e-01
3.14691991e-01 -6.33970857e-01 -9.98288333e-01 9.16138440e-02
4.77182478e-01 -1.04770267e+00 9.71998394e-01 -5.12214638e-02
5.21258116e-01 -7.04670727e-01 3.08400810e-01 -1.40690148e+00
-2.04261065e-01 -9.32547569e-01 1.77083731e-01 9.76094484e-01
-1.73487589e-01 -8.79888117e-01 9.34024453e-01 5.05156636e-01
-5.05032167e-02 -1.31167725e-01 -1.10003233e+00 -5.91326237e-01
2.32570112e-01 6.01187706e-01 3.86294015e-02 8.77254188e-01
-1.05954356e-01 3.42361704e-02 6.51486665e-02 2.19482273e-01
1.43919301e+00 -1.02108300e-01 2.32113928e-01 -1.00561297e+00
1.61217868e-01 -3.75408769e-01 -5.57678401e-01 -9.10585344e-01
1.24007925e-01 -5.84118545e-01 2.58315980e-01 -1.28600395e+00
5.58224797e-01 2.30545506e-01 -2.47684196e-01 9.02150720e-02
-2.38394365e-01 5.92491686e-01 -4.37581629e-01 5.19710720e-01
3.97056974e-02 2.27462545e-01 2.00460148e+00 -2.16152877e-01
1.86393812e-01 -3.58502060e-01 -5.61750352e-01 5.26120603e-01
7.66511142e-01 -2.60070413e-01 -2.49067172e-01 -4.23335582e-01
-2.58628279e-01 1.78744793e-01 8.81002724e-01 -8.04332137e-01
3.32097113e-01 -6.07697964e-02 8.06308091e-01 -3.65209818e-01
1.66326314e-01 -8.98409426e-01 3.10452372e-01 5.57192326e-01
-1.62052542e-01 -2.45029882e-01 2.14932442e-01 7.07904160e-01
-3.43455493e-01 -1.14238240e-01 1.44798720e+00 -4.91479307e-01
-1.94970846e-01 1.25862807e-01 -6.92925870e-01 1.21485991e-02
8.22741389e-01 -6.23275042e-01 -8.30095410e-01 -2.23457113e-01
-9.48837876e-01 -1.31459802e-01 6.97631657e-01 -5.71030915e-01
1.02546275e+00 -9.69776332e-01 -6.68582201e-01 6.57050833e-02
-4.78318483e-01 2.03961030e-01 8.20579827e-01 1.36170793e+00
-1.11712754e+00 6.53002188e-02 -5.10564566e-01 -6.11878753e-01
-1.39078546e+00 3.09032291e-01 8.36639106e-01 -7.20580071e-02
-3.55015397e-01 1.10944462e+00 4.89468873e-01 -4.54614162e-02
-6.68496415e-02 -3.36756200e-01 -1.91261753e-01 -6.86962664e-01
7.81464756e-01 7.11896837e-01 -2.37610355e-01 -7.12737024e-01
-3.47213119e-01 8.85132730e-01 4.38663624e-02 7.21165985e-02
1.12809110e+00 -7.86700785e-01 -7.29059219e-01 2.94462275e-02
1.29986739e+00 -3.51076305e-01 -1.76715827e+00 5.98775297e-02
-6.66786075e-01 -4.93554652e-01 3.96974087e-01 -9.50563073e-01
-1.49500394e+00 1.02011800e+00 8.44070971e-01 3.28318253e-02
1.23240221e+00 -2.95599699e-01 8.47193003e-01 -1.54239163e-01
5.96677549e-02 -6.13608837e-01 -1.73810720e-02 6.78570271e-02
6.79744363e-01 -1.24943900e+00 -9.07069594e-02 -5.34827888e-01
-2.22846314e-01 1.18396735e+00 4.13099557e-01 -6.57821894e-02
5.60901403e-01 7.09403336e-01 5.11662960e-01 -4.18922976e-02
-3.04785967e-01 1.59344435e-01 -2.13085577e-01 1.02554381e+00
4.38178867e-01 -5.21128297e-01 -3.41264307e-01 2.48690233e-01
7.04898536e-01 3.66060406e-01 8.33634853e-01 6.31980240e-01
-2.49146581e-01 -5.31329751e-01 -6.61549211e-01 3.05623915e-02
-1.08993161e+00 1.27098961e-02 -1.84408948e-01 2.84752011e-01
-9.71087739e-02 9.29189205e-01 -1.11662105e-01 3.81296158e-01
8.31163898e-02 -3.19781423e-01 8.51439834e-01 -5.02152480e-02
-4.22244370e-01 3.85871172e-01 -8.49316120e-01 -3.53701353e-01
-9.10861611e-01 -3.82054776e-01 -1.60973179e+00 -5.28080583e-01
-2.83523440e-01 4.17053476e-02 6.20517492e-01 6.95152819e-01
1.81476027e-01 5.31057835e-01 5.62765777e-01 -1.02326035e+00
-2.74476677e-01 -9.34229732e-01 -1.04850876e+00 4.37849402e-01
8.97163749e-01 -4.02731836e-01 -1.07773280e+00 5.92549086e-01] | [12.62283992767334, -2.665886402130127] |
ac30f5b1-0a3c-48f8-bf21-ccc1a52f0f3d | semi-supervised-soil-moisture-prediction | 2012.03506 | null | https://arxiv.org/abs/2012.03506v2 | https://arxiv.org/pdf/2012.03506v2.pdf | Dynamic Structure Learning through Graph Neural Network for Forecasting Soil Moisture in Precision Agriculture | Soil moisture is an important component of precision agriculture as it directly impacts the growth and quality of vegetation. Forecasting soil moisture is essential to schedule the irrigation and optimize the use of water. Physics based soil moisture models need rich features and heavy computation which is not scalable. In recent literature, conventional machine learning models have been applied for this problem. These models are fast and simple, but they often fail to capture the spatio-temporal correlation that soil moisture exhibits over a region. In this work, we propose a novel graph neural network based solution that learns temporal graph structures and forecast soil moisture in an end-to-end framework. Our solution is able to handle the problem of missing ground truth soil moisture which is common in practice. We show the merit of our algorithm on real-world soil moisture data. | ['Sambaran Bandyopadhyay', 'Anoushka Vyas'] | 2020-12-07 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [ 2.70013362e-01 -8.02743137e-02 -3.09668362e-01 -2.14339137e-01
3.35345715e-01 -4.32055175e-01 1.82385251e-01 7.86364734e-01
1.07213557e-02 8.50510299e-01 -3.15455645e-01 -9.76531863e-01
-3.89830112e-01 -1.74162531e+00 -5.89395165e-01 -6.94901943e-01
-5.34762621e-01 1.05181627e-01 5.65880656e-01 -5.96714675e-01
-5.64439688e-04 5.61842561e-01 -1.40780938e+00 -1.85888156e-01
9.41839635e-01 5.49153328e-01 8.20693672e-01 9.15704012e-01
-1.46499604e-01 2.70850182e-01 1.27110139e-01 2.75679797e-01
1.41687663e-02 -2.25938708e-01 -6.72185600e-01 2.59333879e-01
3.42921257e-01 -3.55620891e-01 -1.69772431e-01 9.76704478e-01
2.76100099e-01 -4.78161732e-03 5.77675462e-01 -9.69883442e-01
-2.27540676e-02 4.56576437e-01 -9.77030516e-01 1.16028627e-02
-9.81091857e-02 -2.94243038e-01 7.91376829e-01 -4.76418763e-01
4.69136298e-01 9.49524045e-01 8.75371516e-01 -3.76370549e-02
-1.09270263e+00 -1.70502588e-01 4.93722677e-01 2.59430885e-01
-9.66088355e-01 -1.57284155e-01 6.23278677e-01 -3.20121914e-01
7.79990137e-01 1.93158850e-01 1.10122287e+00 3.61980617e-01
6.51232660e-01 4.80333328e-01 8.49834681e-01 -4.84063178e-01
3.90484244e-01 -3.95001233e-01 1.55998528e-01 7.66161263e-01
6.35408521e-01 1.47085100e-01 -2.83622235e-01 -8.29021409e-02
7.88893700e-01 2.57918715e-01 -3.03595930e-01 -6.03359818e-01
-8.15675080e-01 9.34679866e-01 8.34254742e-01 2.87373457e-02
-5.43836117e-01 1.57087654e-01 1.69763476e-01 1.36603832e-01
7.67105699e-01 6.33948948e-03 -8.35837781e-01 5.39341271e-01
-1.14967847e+00 3.56792510e-01 1.00750291e+00 6.89035714e-01
9.35665727e-01 1.99698210e-01 4.10077214e-01 5.28343618e-01
5.72964191e-01 9.53772008e-01 -3.32664438e-02 -7.56326258e-01
1.20439969e-01 5.04079878e-01 1.35621130e-01 -1.54627311e+00
-8.75400245e-01 -3.24479222e-01 -1.29000115e+00 2.65679479e-01
3.02476197e-01 -3.61502171e-01 -8.87072444e-01 1.47033882e+00
5.39134979e-01 3.24484736e-01 2.31926255e-02 7.16077387e-01
6.29777968e-01 9.98193979e-01 1.72400072e-01 -3.84149432e-01
8.98601532e-01 -6.77843153e-01 -9.03845012e-01 -3.58273268e-01
6.12346172e-01 -6.09398425e-01 6.09252930e-01 -3.67422402e-02
-7.26453483e-01 -2.52295882e-02 -9.29863214e-01 5.03485441e-01
-7.89712250e-01 5.67902327e-02 1.11107695e+00 3.25679004e-01
-1.25012112e+00 9.61870551e-01 -1.26328790e+00 -8.06494415e-01
-1.01747870e-01 1.37359053e-01 -2.34875038e-01 -1.28315100e-02
-1.15700471e+00 9.97424126e-01 4.55828190e-01 7.28254199e-01
-3.35055083e-01 -4.29954439e-01 -9.13327873e-01 1.61419675e-01
5.75781107e-01 -3.80838931e-01 1.01905608e+00 -4.65189010e-01
-1.50290728e+00 4.32269365e-01 -4.19516087e-01 -3.76101822e-01
3.15888762e-01 -3.14298011e-02 -2.41980091e-01 -1.46038428e-01
3.83274071e-02 1.72545731e-01 6.33225977e-01 -1.24641883e+00
-6.92296088e-01 -4.64229643e-01 -2.71988899e-01 5.15730679e-02
-1.50311813e-01 -6.85271859e-01 6.89644963e-02 -2.54302233e-01
7.95732319e-01 -9.91662443e-01 -6.84110999e-01 2.00003058e-01
-2.23972321e-01 3.00066382e-01 9.62902963e-01 -6.64929509e-01
9.75629270e-01 -1.54895818e+00 -6.10816777e-02 4.94002104e-01
2.16457725e-01 3.76851410e-01 -1.92889765e-01 7.68841028e-01
7.59323239e-02 -1.26803160e-01 -6.43922567e-01 3.87535840e-01
-3.50256115e-01 5.08175850e-01 -2.48317316e-01 5.85649490e-01
2.31605336e-01 5.26009977e-01 -1.05058181e+00 -2.48757601e-01
3.90877157e-01 2.59396523e-01 -2.79970795e-01 6.63406327e-02
-3.80350888e-01 -8.37065093e-03 -5.08202434e-01 6.07645094e-01
1.20618999e+00 -3.34015936e-01 8.05265546e-01 8.07020720e-03
-3.96063060e-01 -1.61254838e-01 -1.33244014e+00 1.14997041e+00
-3.33663046e-01 5.58495820e-01 2.75658637e-01 -1.36881328e+00
1.00431848e+00 1.22264035e-01 4.20023531e-01 -4.63781655e-01
-2.70482600e-01 2.51865327e-01 -2.02693269e-01 -4.48017657e-01
6.27670169e-01 2.10337237e-01 5.15046954e-01 1.26112297e-01
-2.84763187e-01 -3.74199718e-01 2.53628373e-01 1.19253457e-01
7.85489261e-01 4.40257013e-01 5.00059545e-01 -5.86062789e-01
2.05015004e-01 2.29874223e-01 4.64575827e-01 5.92176855e-01
-3.87221426e-02 2.37349495e-01 4.86389250e-01 -5.51322341e-01
-9.47482109e-01 -8.16593826e-01 -9.42395404e-02 9.93950307e-01
2.02195466e-01 -1.52710199e-01 -3.32511276e-01 -1.80221796e-01
2.62006432e-01 4.04970109e-01 -4.71296549e-01 1.47213817e-01
-2.40370229e-01 -1.05532169e+00 -1.51312768e-01 3.95283848e-01
4.28989917e-01 -8.48045111e-01 -6.07298791e-01 7.29825675e-01
-3.04842979e-01 -1.06632185e+00 3.83191079e-01 4.60636675e-01
-1.40896285e+00 -1.20277524e+00 -5.34767628e-01 -4.81111139e-01
6.01611853e-01 8.17476809e-01 1.25784671e+00 2.18464449e-01
-2.95239240e-01 9.43398476e-02 -2.76474297e-01 -5.31091630e-01
-2.75895864e-01 5.19341528e-01 -2.26341888e-01 -3.61114860e-01
1.16278060e-01 -7.28750885e-01 -5.57926893e-01 1.37616619e-01
-8.36687565e-01 2.41255626e-01 3.66274238e-01 7.00981975e-01
5.62228024e-01 3.65459323e-01 4.38085318e-01 -9.83630657e-01
3.51100981e-01 -8.89847100e-01 -1.18991888e+00 4.88625228e-01
-7.96177149e-01 -9.27356109e-02 2.58712858e-01 -7.92039260e-02
-6.79688990e-01 5.63331008e-01 4.18975741e-01 3.37419033e-01
-9.41516086e-02 1.37462056e+00 2.73072004e-01 -1.81990385e-01
4.26838368e-01 9.19106454e-02 1.37984201e-01 -3.79793674e-01
5.93199171e-02 2.21849576e-01 4.60249722e-01 -1.76362768e-01
8.29336405e-01 2.21018627e-01 7.86104500e-01 -1.33517206e+00
-6.77174807e-01 -5.04108906e-01 -7.76735008e-01 -3.67517948e-01
3.43512356e-01 -9.32697177e-01 -7.18314111e-01 7.26178348e-01
-1.10450912e+00 -6.26018047e-01 3.23845923e-01 5.20758331e-01
-3.09123456e-01 3.62181962e-01 -2.73242712e-01 -9.04788971e-01
-3.64038736e-01 -4.57972646e-01 7.80302644e-01 3.04024965e-01
2.82229751e-01 -1.42319536e+00 2.99131989e-01 -4.55623120e-01
7.23995924e-01 6.87088311e-01 8.67171228e-01 2.82444239e-01
-5.22405505e-01 -1.16906174e-01 -5.62371492e-01 -4.60337371e-01
5.35699308e-01 6.44407153e-01 -6.30929708e-01 -3.43947291e-01
-4.68719900e-01 5.57954907e-02 1.15739095e+00 1.01739323e+00
8.11752975e-01 -1.38133034e-01 -3.92142653e-01 4.57841724e-01
1.93614471e+00 -2.88845122e-01 4.20539230e-01 2.91576147e-01
5.78049600e-01 8.49188924e-01 6.29040718e-01 4.82545376e-01
4.64748353e-01 1.76443517e-01 8.69881034e-01 -5.46148896e-01
3.19616795e-01 4.27073054e-02 7.93309882e-02 8.36182117e-01
-2.52708375e-01 -4.88656640e-01 -1.42134142e+00 7.75999486e-01
-2.41211629e+00 -1.00965738e+00 -6.12702072e-01 1.96076310e+00
6.41600788e-01 -1.33942515e-01 -1.87718734e-01 2.32688755e-01
7.55023539e-01 4.04208928e-01 -4.92279381e-01 -3.20562333e-01
-9.49342772e-02 1.22739345e-01 1.18787336e+00 6.39875352e-01
-1.22709095e+00 1.31545448e+00 6.81467390e+00 1.56163305e-01
-1.38956702e+00 -4.80067283e-01 1.06089205e-01 4.03107733e-01
-2.76208401e-01 1.55857056e-01 -6.12990320e-01 -1.37218148e-01
8.21801782e-01 4.99638617e-02 3.04486632e-01 8.00498366e-01
6.92562759e-01 -7.53629863e-01 -4.71754074e-01 2.38629118e-01
-5.92235565e-01 -1.36757517e+00 -3.28279018e-01 -1.02484070e-01
7.37393141e-01 1.57062650e-01 -3.55247587e-01 -2.05697581e-01
6.80854380e-01 -7.98187256e-01 -5.03158476e-03 5.61095238e-01
4.21326905e-01 -5.08670688e-01 7.27463126e-01 2.90763110e-01
-1.58082402e+00 2.26933077e-01 -6.71189964e-01 -3.98925215e-01
2.40527764e-02 1.04998899e+00 -8.77176106e-01 7.41778612e-01
7.21748352e-01 1.09904110e+00 -5.05047262e-01 1.25084937e+00
-1.78729236e-01 8.74897659e-01 -6.18184090e-01 -1.73728794e-01
4.72381830e-01 -3.88753921e-01 2.96201348e-01 9.54319179e-01
5.07022619e-01 1.35311857e-01 4.72748190e-01 4.21525002e-01
3.45239073e-01 7.61713237e-02 -1.06897032e+00 -7.68971071e-02
3.03423613e-01 1.18920195e+00 -9.21991765e-01 1.72189981e-01
-2.68286616e-01 5.94034970e-01 1.48540333e-01 5.37633181e-01
-2.96306998e-01 -4.36489612e-01 4.29529786e-01 1.63625985e-01
3.29763710e-01 -7.21370935e-01 -2.13062212e-01 -9.09480870e-01
-3.81467119e-02 -4.58140492e-01 1.36550395e-02 -6.41175747e-01
-9.73419964e-01 2.39050258e-02 -1.71066999e-01 -9.15038884e-01
-2.46923178e-01 -7.18610823e-01 -7.00482130e-01 9.15099919e-01
-2.13822150e+00 -1.15922737e+00 -8.19016635e-01 3.70871067e-01
1.39657259e-01 -2.19782274e-02 1.26579142e+00 -8.24362338e-02
-5.36135256e-01 -3.30891758e-01 5.56830585e-01 -3.01997453e-01
4.99926537e-01 -1.32529187e+00 5.73878944e-01 9.89045620e-01
-3.75898480e-01 6.17597960e-02 9.17432904e-01 -9.68755782e-01
-1.62439990e+00 -1.33849561e+00 1.19717228e+00 5.11172533e-01
7.89282858e-01 1.39972135e-01 -1.11423028e+00 4.15815920e-01
-2.09201649e-02 2.20322460e-01 2.80896068e-01 3.98520082e-01
1.18676096e-01 -2.45050207e-01 -8.77581596e-01 5.28260171e-01
6.26322508e-01 -2.47515216e-01 2.33395457e-01 4.86627609e-01
5.02005041e-01 -3.63288581e-01 -6.75817668e-01 6.08469546e-01
5.46365798e-01 -7.12904572e-01 6.76383853e-01 -5.06281853e-01
5.85303247e-01 -1.91843897e-01 -2.45827273e-01 -1.40490031e+00
-6.21153176e-01 -3.12674016e-01 -2.26701915e-01 7.66370893e-01
3.86493713e-01 -5.02094209e-01 7.93786287e-01 2.98352450e-01
4.17312056e-01 -4.55462635e-01 -3.45221847e-01 -5.63365221e-01
4.58336584e-02 -8.56741592e-02 4.85436201e-01 1.14367974e+00
-3.13176185e-01 2.82635745e-02 -4.02650446e-01 7.39385724e-01
8.00962090e-01 2.90284485e-01 5.56747019e-01 -1.75239027e+00
1.41841531e-01 -3.01829427e-01 -2.75506198e-01 -4.60135370e-01
1.94071367e-01 -5.12493849e-01 8.59612375e-02 -1.87976611e+00
-4.53116484e-02 -4.81353283e-01 -8.27617794e-02 7.54415154e-01
-4.23773527e-01 -2.07406417e-01 -2.24391222e-01 -1.67528003e-01
1.95658103e-01 3.79773140e-01 1.04560947e+00 -2.38708019e-01
-4.33003455e-01 2.63607979e-01 4.21467051e-02 7.12800562e-01
1.19938171e+00 -5.22765458e-01 -5.12256563e-01 -5.57037890e-01
4.95065719e-01 3.72728109e-01 4.84982044e-01 -7.39394009e-01
6.47120774e-02 -8.25045407e-01 3.58287930e-01 -1.02398682e+00
-1.66663244e-01 -1.01034582e+00 -3.46772820e-02 6.86455369e-01
6.59348592e-02 5.80317527e-02 4.55555320e-01 7.85508931e-01
-3.09854001e-02 -1.54134542e-01 6.05085075e-01 -5.90462238e-02
-8.78971934e-01 5.71128368e-01 -6.96476996e-01 -5.65472066e-01
7.14537978e-01 1.78444833e-01 -2.02345923e-01 -3.79069448e-01
-6.33192241e-01 5.65948844e-01 3.01138163e-01 2.80901313e-01
5.01803637e-01 -1.03178942e+00 -9.68059301e-01 -8.49332009e-03
-2.62631103e-02 -5.52468523e-02 -3.37740802e-03 4.21897322e-01
-1.07120121e+00 2.28698090e-01 -2.86309272e-01 -7.87035346e-01
-1.13266337e+00 2.36246020e-01 4.28529173e-01 -4.30667430e-01
-5.46316385e-01 3.42853725e-01 -4.40116465e-01 -6.15576446e-01
3.00384611e-02 -3.91029030e-01 -4.15462434e-01 1.63004011e-01
9.93842706e-02 1.97334245e-01 4.10086438e-02 -1.55587539e-01
-2.75017083e-01 6.28858984e-01 3.48617613e-01 2.66900752e-02
1.80343831e+00 -2.34185100e-01 -3.93896222e-01 6.03291571e-01
7.32908189e-01 -4.66029674e-01 -1.10548794e+00 -5.21362007e-01
2.57603288e-01 -2.68162280e-01 5.89706063e-01 -3.18144768e-01
-1.34350240e+00 8.52207065e-01 9.03346777e-01 5.43801546e-01
1.09087431e+00 -7.93544829e-01 5.76002777e-01 9.70888019e-01
1.70475185e-01 -1.16280723e+00 -3.69572371e-01 8.26408029e-01
6.44796908e-01 -1.57680881e+00 3.48818809e-01 -6.12449706e-01
-8.68478715e-02 1.46774125e+00 4.25062537e-01 9.64064337e-03
1.05962014e+00 3.68020386e-01 2.56496631e-02 -1.36172056e-01
-5.51434636e-01 -4.67982143e-01 -1.37950733e-01 7.13091850e-01
5.38136542e-01 4.57339734e-01 -2.92993069e-01 -2.77841449e-01
1.34743452e-01 2.82294899e-01 5.35058260e-01 1.31807017e+00
-8.36506546e-01 -9.71730292e-01 -1.89774111e-01 4.39317882e-01
3.90061103e-02 -1.68230727e-01 -4.75296900e-02 4.70812589e-01
-3.84770542e-01 9.25204933e-01 -1.23365000e-01 3.85543816e-02
7.69986212e-02 -1.18748710e-01 3.17701131e-01 -4.10337985e-01
1.37246232e-02 -6.66942298e-02 -8.91222805e-02 -5.01730323e-01
-8.06586981e-01 -4.94103283e-01 -1.09913266e+00 -8.71491790e-01
-5.83293259e-01 -1.51531070e-01 1.10722792e+00 8.67581367e-01
2.54520088e-01 5.92240512e-01 8.15333664e-01 -1.03733492e+00
-2.92735696e-01 -7.65685558e-01 -1.02717710e+00 -2.44891435e-01
5.88774025e-01 -5.86215973e-01 1.70825552e-02 4.22705822e-02] | [9.380097389221191, -1.5409661531448364] |
3b164b15-c1dc-4165-a12a-d432f5539f49 | table-detection-for-visually-rich-document | 2305.19181 | null | https://arxiv.org/abs/2305.19181v1 | https://arxiv.org/pdf/2305.19181v1.pdf | Table Detection for Visually Rich Document Images | Table Detection (TD) is a fundamental task towards visually rich document understanding. Current studies usually formulate the TD problem as an object detection problem, then leverage Intersection over Union (IoU) based metrics to evaluate the model performance and IoU-based loss functions to optimize the model. TD applications usually require the prediction results to cover all the table contents and avoid information loss. However, IoU and IoU-based loss functions cannot directly reflect the degree of information loss for the prediction results. Therefore, we propose to decouple IoU into a ground truth coverage term and a prediction coverage term, in which the former can be used to measure the information loss of the prediction results. Besides, tables in the documents are usually large, sparsely distributed, and have no overlaps because they are designed to summarize essential information to make it easy to read and interpret for human readers. Therefore, in this study, we use SparseR-CNN as the base model, and further improve the model by using Gaussian Noise Augmented Image Size region proposals and many-to-one label assignments. To demonstrate the effectiveness of proposed method and compare with state-of-the-art methods fairly, we conduct experiments and use IoU-based evaluation metrics to evaluate the model performance. The experimental results show that the proposed method can consistently outperform state-of-the-art methods under different IoU-based metric on a variety of datasets. We conduct further experiments to show the superiority of the proposed decoupled IoU for the TD applications by replacing the IoU-based loss functions and evaluation metrics with proposed decoupled IoU counterparts. The experimental results show that our proposed decoupled IoU loss can encourage the model to alleviate information loss. | ['Ala Abu Alkheir', 'Burak Kantarci', 'Murat Simsek', 'Bin Xiao'] | 2023-05-30 | null | null | null | null | ['table-detection'] | ['miscellaneous'] | [-7.84197822e-02 -1.71376541e-01 -2.85720229e-01 -2.53307223e-01
-8.04868102e-01 -3.60658646e-01 3.92658085e-01 3.09906095e-01
-1.09005466e-01 5.81296325e-01 3.60015512e-01 -1.17467448e-01
3.19552273e-02 -8.73337328e-01 -8.72143030e-01 -5.97993731e-01
2.56509721e-01 2.99460977e-01 4.56588835e-01 1.61920875e-01
2.80484974e-01 1.72126457e-01 -1.34434986e+00 5.66319883e-01
1.04307294e+00 1.42064667e+00 4.53722239e-01 1.29560009e-01
-3.22948247e-01 8.69412780e-01 -5.30422628e-01 -3.62061679e-01
3.80953282e-01 -2.29722679e-01 -5.03503919e-01 1.58707738e-01
2.63974458e-01 -5.37063479e-01 -4.81524497e-01 1.25975943e+00
5.13766646e-01 2.50473440e-01 7.80369163e-01 -1.02860904e+00
-9.70146060e-01 5.05462229e-01 -1.09848678e+00 1.14337564e-01
2.42627174e-01 -9.32839140e-02 1.17234159e+00 -1.00898373e+00
6.78147793e-01 1.63508797e+00 5.42669833e-01 1.76545009e-01
-1.01099169e+00 -7.51057565e-01 5.98003030e-01 3.27317715e-01
-1.47393942e+00 -2.48874202e-01 7.20384717e-01 -2.94786423e-01
4.79760647e-01 3.00962657e-01 2.22254351e-01 6.07715189e-01
-7.06560016e-02 1.47106326e+00 8.52442801e-01 -3.68853986e-01
-3.04175727e-02 5.37786782e-01 2.06734166e-01 8.24947059e-01
5.76449990e-01 -4.29446936e-01 -2.80999839e-01 2.67312258e-01
6.42697275e-01 2.19895840e-01 -5.28067887e-01 -3.73668373e-01
-1.18388951e+00 6.05037153e-01 7.55284965e-01 8.25479347e-03
-5.02762608e-02 -1.75961271e-01 5.16023159e-01 -1.53164148e-01
4.62703645e-01 -1.10469684e-01 -2.39646599e-01 2.30681837e-01
-8.48710954e-01 2.18356401e-01 4.26072061e-01 1.16831326e+00
6.15833580e-01 -3.72916460e-01 -9.66814995e-01 1.14566875e+00
4.30810928e-01 3.39321703e-01 3.35929513e-01 -4.91908014e-01
9.28345263e-01 9.11931634e-01 2.22898155e-01 -1.43576956e+00
-1.44108817e-01 -6.23714030e-01 -1.03169560e+00 -1.11768000e-01
3.86103123e-01 2.54662991e-01 -8.57887328e-01 1.44426584e+00
3.07227015e-01 -2.50232100e-01 -1.74819991e-01 1.12692845e+00
1.03859925e+00 1.00213504e+00 -1.95265770e-01 -3.90635401e-01
1.35415077e+00 -1.22900915e+00 -9.33259845e-01 -5.70571646e-02
7.90585160e-01 -8.37697327e-01 1.55291033e+00 4.12669897e-01
-9.38682318e-01 -5.42207122e-01 -1.13173854e+00 -2.71782488e-01
-2.44839832e-01 6.85026765e-01 3.29319924e-01 3.38773161e-01
-4.59450305e-01 2.35851794e-01 -6.04095042e-01 -2.39382029e-01
8.71576846e-01 -7.27248192e-02 -9.07867402e-02 -4.91116256e-01
-1.04729092e+00 6.28038824e-01 5.94134629e-01 1.62226960e-01
-8.94809306e-01 -5.26941538e-01 -6.86790705e-01 3.16679209e-01
5.96270263e-01 -3.16247463e-01 1.02259004e+00 -3.61134678e-01
-7.66991138e-01 7.82941401e-01 -1.66541472e-01 -1.90263852e-01
6.67176604e-01 -2.71814078e-01 -2.11854100e-01 -2.72411690e-03
2.73860961e-01 5.02851844e-01 2.02087313e-01 -1.58678663e+00
-8.29057455e-01 -2.96654761e-01 1.10828631e-01 4.25460994e-01
-5.87582767e-01 -1.94504410e-01 -1.15996897e+00 -9.28936362e-01
3.02994758e-01 -3.54850799e-01 2.36983001e-01 4.44466025e-01
-8.80051434e-01 -1.61746770e-01 9.39526558e-01 -8.06538522e-01
1.64657605e+00 -2.02779198e+00 -2.29767561e-01 9.11736935e-02
3.10777664e-01 7.36454502e-02 3.17358151e-02 6.50239810e-02
3.54906768e-01 1.98125601e-01 -2.35624105e-01 -4.31787670e-01
2.22118143e-02 -5.94418831e-02 -1.71030149e-01 2.63443917e-01
-9.63732079e-02 7.19818950e-01 -7.20634878e-01 -8.16817462e-01
6.01150952e-02 3.42178047e-01 -2.62433946e-01 2.06490368e-01
-3.16933662e-01 1.05000235e-01 -6.54102325e-01 7.55680203e-01
1.08772397e+00 -5.16121328e-01 -1.61492750e-01 -5.68980098e-01
1.20396670e-02 2.21769854e-01 -1.54199648e+00 1.39172101e+00
-2.44701013e-01 4.42452937e-01 -1.91266295e-02 -9.73230720e-01
1.01006544e+00 -2.06360489e-01 1.40929058e-01 -9.24865186e-01
3.49199444e-01 5.24547435e-02 -2.90304005e-01 -3.08876574e-01
2.86090374e-01 1.45644382e-01 2.85399348e-01 3.93240362e-01
-3.33127439e-01 4.96774375e-01 3.31059545e-01 3.95400912e-01
8.60791683e-01 6.52632043e-02 2.70871878e-01 -1.63432404e-01
7.65561461e-01 -7.57720992e-02 8.28732848e-01 6.92565024e-01
-2.06315830e-01 7.57847190e-01 7.37372935e-01 -3.31245691e-01
-8.35196316e-01 -8.32679331e-01 -1.49329737e-01 8.30473602e-01
7.55585968e-01 -2.25378856e-01 -7.66070247e-01 -9.94562328e-01
7.92827271e-03 7.33672738e-01 -6.18989766e-01 -7.30644912e-02
-3.51522177e-01 -9.34436083e-01 1.80006266e-01 6.66987121e-01
9.05840933e-01 -8.70500326e-01 -5.13282791e-02 6.52384907e-02
-3.95579815e-01 -9.87447083e-01 -7.13702619e-01 -1.56682491e-01
-6.83074415e-01 -1.04188025e+00 -8.36697400e-01 -8.19037735e-01
7.31854200e-01 6.76555514e-01 1.01153016e+00 1.10949665e-01
-1.70437127e-01 8.97245575e-03 -4.26698208e-01 -5.01133859e-01
5.94614893e-02 2.25902889e-02 -1.64529637e-01 1.68446600e-01
2.95056075e-01 -2.09352478e-01 -9.66224372e-01 5.99628210e-01
-9.75251555e-01 3.50817353e-01 6.91268682e-01 7.95849204e-01
1.02043772e+00 2.72842109e-01 4.07105386e-01 -9.44313526e-01
6.60367846e-01 -3.75744104e-01 -5.57634532e-01 6.12076998e-01
-8.93368363e-01 7.37427399e-02 6.44690871e-01 -3.02864999e-01
-1.15822887e+00 -3.10879022e-01 3.32239121e-01 -5.38885355e-01
2.94052422e-01 5.97238362e-01 -7.79825687e-01 1.90502480e-01
2.14976326e-01 4.11262214e-01 -3.00639629e-01 -6.73876107e-01
2.94020981e-01 7.22323656e-01 4.14844304e-01 -4.38476741e-01
5.75432539e-01 5.05098820e-01 -1.87478453e-01 -1.86760992e-01
-1.14864349e+00 -5.49385846e-01 -2.17594728e-01 -2.75481522e-01
6.33140147e-01 -9.60630178e-01 -7.70010769e-01 2.83263147e-01
-1.26730657e+00 2.53428131e-01 1.12164237e-01 3.22623014e-01
-1.89649209e-01 4.39296395e-01 -5.46989501e-01 -8.84667218e-01
-4.58222181e-01 -1.22868896e+00 1.05141950e+00 3.66282046e-01
4.17838752e-01 -7.48638511e-01 -2.13466510e-01 3.45018715e-01
-2.65148841e-02 5.63981906e-02 1.09351778e+00 -6.68588936e-01
-9.57893074e-01 -3.90770853e-01 -1.03190923e+00 2.77710378e-01
1.20125845e-01 -3.15107077e-01 -1.01407754e+00 -2.39164859e-01
-1.45828739e-01 -3.31629694e-01 1.22831881e+00 4.01977718e-01
1.61431026e+00 -4.86633509e-01 -6.09387279e-01 4.95243222e-01
1.52710783e+00 3.28050584e-01 7.94624567e-01 6.50966227e-01
1.02332163e+00 5.40697694e-01 1.05798328e+00 5.19809783e-01
5.30057251e-01 6.79779768e-01 4.81643140e-01 -9.01249200e-02
-2.88042039e-01 -4.76826668e-01 1.40855104e-01 8.27116132e-01
2.86012739e-01 -6.87991500e-01 -7.68522918e-01 4.27053094e-01
-2.05095792e+00 -7.13318467e-01 -7.95507208e-02 2.33700013e+00
8.14708710e-01 5.70185483e-01 -6.77049309e-02 2.22050846e-01
1.01472354e+00 1.75385416e-01 -5.25201857e-01 7.97010809e-02
-2.54454315e-01 -5.30537903e-01 3.86779338e-01 2.30231330e-01
-1.20317268e+00 7.11847425e-01 5.31266499e+00 1.47096264e+00
-9.00003254e-01 2.66835894e-02 1.26583099e+00 -1.18450217e-01
-4.84240264e-01 -2.23729402e-01 -1.04560316e+00 5.10350704e-01
1.76240817e-01 -7.75000975e-02 1.03864968e-01 8.70154679e-01
4.28069711e-01 1.22087654e-02 -1.08253670e+00 1.14245641e+00
7.46620595e-02 -1.38295317e+00 5.40904999e-01 -1.34424165e-01
6.57749474e-01 -5.41434824e-01 1.55454546e-01 1.99690685e-01
-2.39569396e-02 -1.00303030e+00 8.35924089e-01 5.32343268e-01
8.73706102e-01 -7.68211305e-01 8.15629303e-01 3.78476292e-01
-1.50931299e+00 -3.25966105e-02 -6.98793709e-01 1.57761753e-01
-1.04329765e-01 7.75384486e-01 -5.06572723e-01 5.76782286e-01
9.35492814e-01 9.79615510e-01 -6.44803941e-01 1.21818411e+00
9.37995464e-02 4.81241643e-01 -2.51806259e-01 -2.30655715e-01
2.63269335e-01 -1.98253259e-01 4.79918092e-01 1.19290340e+00
1.85095951e-01 -1.01701915e-01 3.14937174e-01 1.07243395e+00
-5.23888886e-01 4.67177629e-01 -2.77579427e-01 2.46350914e-01
6.54012382e-01 1.22322357e+00 -9.33229148e-01 -3.15011889e-01
-6.69568300e-01 6.80980027e-01 3.94300282e-01 2.50534594e-01
-8.03766608e-01 -6.09642267e-01 3.27882051e-01 2.34519497e-01
5.37276983e-01 2.95774370e-01 -5.99705935e-01 -1.24722099e+00
6.74731731e-01 -7.23665714e-01 4.00845706e-01 -9.06223059e-01
-1.21811390e+00 5.63140273e-01 -1.29426315e-01 -1.69640434e+00
4.73972708e-01 -4.49469358e-01 -6.15403533e-01 8.12670887e-01
-1.65801454e+00 -1.22335124e+00 -6.84432089e-01 2.82010019e-01
5.91215134e-01 -7.80367628e-02 2.30820432e-01 3.97622615e-01
-1.08759582e+00 9.97416258e-01 5.64185262e-01 1.52941972e-01
8.05852592e-01 -1.09584105e+00 -1.34501280e-02 9.63097811e-01
-1.48601070e-01 5.40021658e-01 4.79583859e-01 -6.78195000e-01
-9.99831080e-01 -1.31058884e+00 3.30360323e-01 -2.40542144e-01
2.33898893e-01 -4.11112547e-01 -1.07602644e+00 4.30985749e-01
-1.71676651e-01 1.64575621e-01 2.68426210e-01 -1.61052510e-01
-5.13702571e-01 -5.83274364e-01 -1.21405470e+00 5.51288009e-01
9.90635574e-01 -1.10760383e-01 -3.27744871e-01 3.73421282e-01
9.88439381e-01 -3.32134128e-01 -6.51461005e-01 4.63212788e-01
6.67916298e-01 -1.00315273e+00 9.81581211e-01 -1.56205490e-01
6.81462765e-01 -5.79012513e-01 -2.46189833e-01 -7.73290515e-01
-4.22855675e-01 5.00752367e-02 -3.06617528e-01 1.63711560e+00
4.71615851e-01 -2.17251778e-01 9.57726002e-01 3.61260623e-01
-4.55528647e-02 -1.19104934e+00 -6.11676693e-01 -7.48490632e-01
-1.11406423e-01 -3.19250643e-01 6.95640802e-01 6.40476406e-01
-3.26979756e-01 2.80280858e-01 -4.14738536e-01 1.26906276e-01
6.10604286e-01 1.95426941e-01 6.26791060e-01 -1.10179555e+00
-2.83929403e-03 -5.94927430e-01 -2.21742108e-01 -1.55666494e+00
-3.58392239e-01 -8.12610567e-01 1.25795200e-01 -1.95161784e+00
7.97604680e-01 -5.05461216e-01 -6.22070134e-01 3.13107640e-01
-5.24100602e-01 6.63173795e-02 1.80725724e-01 6.39821887e-01
-9.91236269e-01 9.26723182e-01 1.55065131e+00 -5.26756883e-01
-1.95363387e-01 -2.50641078e-01 -8.48957956e-01 6.01308167e-01
3.74692112e-01 -4.37990338e-01 -4.64565247e-01 -4.24969405e-01
1.79839045e-01 -1.57950997e-01 1.84187189e-01 -1.00406063e+00
2.00817347e-01 -1.79122031e-01 7.59156704e-01 -9.89244878e-01
-8.37219507e-02 -7.89210081e-01 -4.49089855e-01 1.91864058e-01
-3.76596540e-01 -3.67015004e-01 2.15129703e-01 8.94368470e-01
-4.23943937e-01 -2.03986466e-02 6.83904111e-01 -2.18071826e-02
-6.15171790e-01 5.45748413e-01 2.87288100e-01 3.42171676e-02
8.97649229e-01 -3.85159850e-01 -4.65582550e-01 -3.82549345e-01
-1.84374213e-01 5.76950788e-01 3.72491032e-01 2.68559456e-01
7.90296257e-01 -1.59504926e+00 -7.51180172e-01 -5.22067472e-02
5.62461019e-01 2.74263352e-01 2.32217535e-01 7.01481760e-01
-6.54683173e-01 4.46117640e-01 7.42539356e-04 -5.71670115e-01
-1.13558841e+00 7.14685202e-01 9.96449590e-02 -6.46864831e-01
-5.79872727e-01 9.57292676e-01 8.59928966e-01 -3.78111154e-01
7.26208091e-01 -4.57510263e-01 -3.73974591e-01 -1.52189866e-01
7.57899523e-01 4.13769335e-01 8.98148715e-02 -2.25379273e-01
-3.73995602e-01 5.19366503e-01 -5.60801029e-01 2.62131363e-01
9.75298882e-01 -4.31607515e-01 6.51750043e-02 3.61324698e-01
1.18020046e+00 -1.63364738e-01 -1.19566309e+00 -4.86686826e-01
-3.58618170e-01 -6.44559503e-01 2.21782371e-01 -9.06525373e-01
-1.16773331e+00 9.86363173e-01 5.61921477e-01 5.30027188e-02
1.39791441e+00 -1.49878815e-01 1.01749420e+00 3.91978294e-01
1.10638849e-01 -1.02485204e+00 1.87164262e-01 1.74480334e-01
1.07025099e+00 -1.37810981e+00 2.83155084e-01 -6.84405506e-01
-7.05527067e-01 8.75181139e-01 9.47274029e-01 1.88631207e-01
5.42578816e-01 -5.93237393e-02 -9.85299125e-02 -2.96532968e-03
-6.55179620e-01 -9.44050550e-02 6.80747926e-01 3.93510967e-01
4.07294512e-01 -1.59332737e-01 -4.75837260e-01 1.00581968e+00
1.98660642e-01 -1.01622239e-01 2.67420322e-01 7.38567829e-01
-6.83943748e-01 -7.39846766e-01 -5.41916311e-01 9.54885125e-01
-2.89625823e-01 -9.88634899e-02 -2.33100072e-01 7.50251830e-01
1.09185696e-01 7.90180027e-01 -3.81434746e-02 -5.21076202e-01
3.95001054e-01 -3.10020268e-01 1.56630516e-01 -3.52501035e-01
-6.75783083e-02 2.43568107e-01 -1.58485338e-01 -4.38075900e-01
-1.03616446e-01 -4.49281931e-01 -1.21494150e+00 -3.89985949e-01
-6.98645711e-01 -1.11985706e-01 1.16706081e-01 8.52083087e-01
2.77867287e-01 7.13306069e-01 6.75992429e-01 -2.99026191e-01
-4.64206547e-01 -1.09745634e+00 -6.98679268e-01 4.29059207e-01
1.07418895e-01 -9.64288533e-01 -1.51511163e-01 2.50165231e-05] | [11.486440658569336, 2.305534601211548] |
dea3bb65-eed4-4a73-9770-7a8002b82aa7 | pythia-a-suite-for-analyzing-large-language | 2304.01373 | null | https://arxiv.org/abs/2304.01373v2 | https://arxiv.org/pdf/2304.01373v2.pdf | Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling | How do large language models (LLMs) develop and evolve over the course of training? How do these patterns change as models scale? To answer these questions, we introduce \textit{Pythia}, a suite of 16 LLMs all trained on public data seen in the exact same order and ranging in size from 70M to 12B parameters. We provide public access to 154 checkpoints for each one of the 16 models, alongside tools to download and reconstruct their exact training dataloaders for further study. We intend \textit{Pythia} to facilitate research in many areas, and we present several case studies including novel results in memorization, term frequency effects on few-shot performance, and reducing gender bias. We demonstrate that this highly controlled setup can be used to yield novel insights toward LLMs and their training dynamics. Trained models, analysis code, training code, and training data can be found at \url{https://github.com/EleutherAI/pythia}. | ['Oskar van der Wal', 'Lintang Sutawika', 'Aviya Skowron', 'Edward Raff', 'USVSN Sai Prashanth', 'Shivanshu Purohit', 'Mohammad Aflah Khan', 'Eric Hallahan', "Kyle O'Brien", 'Herbie Bradley', 'Quentin Anthony', 'Hailey Schoelkopf', 'Stella Biderman'] | 2023-04-03 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [-1.08689934e-01 -2.57384181e-01 -3.97861421e-01 -4.35543776e-01
-8.84861708e-01 -7.20521271e-01 6.93723261e-01 8.43022093e-02
-5.33954799e-01 6.10684812e-01 2.88065635e-02 -6.66076958e-01
6.87341094e-02 -2.96904624e-01 -8.57383907e-01 -4.67508763e-01
-2.09320247e-01 6.05766177e-01 1.31258428e-01 -1.67988092e-01
4.30846363e-01 1.87737525e-01 -1.31905794e+00 3.23606133e-01
6.82760656e-01 4.25104290e-01 3.25801224e-01 9.42475140e-01
1.45362616e-01 7.28306651e-01 -6.17395341e-01 -5.77282131e-01
8.44776258e-02 -2.10327253e-01 -7.79592931e-01 -3.77426177e-01
5.35353482e-01 -1.61204964e-01 -5.88185608e-01 6.11340702e-01
8.32030952e-01 2.90438622e-01 5.02090871e-01 -1.20817721e+00
-4.79305625e-01 8.52698326e-01 -4.67358977e-01 8.33634317e-01
-6.96905181e-02 4.70330328e-01 7.34943271e-01 -8.27741265e-01
5.88791013e-01 1.12522125e+00 7.15695322e-01 8.39382529e-01
-1.25268674e+00 -1.05983841e+00 -1.60442516e-01 -1.17908165e-01
-1.34289575e+00 -1.08209336e+00 1.16181195e-01 -3.99420559e-01
1.00796378e+00 3.42340589e-01 3.14552635e-01 1.55582428e+00
1.72909811e-01 8.07077110e-01 1.16588175e+00 -7.32043207e-01
-1.76737346e-02 2.53706396e-01 6.54202580e-01 8.19555759e-01
3.05895478e-01 5.11145927e-02 -9.68298733e-01 -3.64618659e-01
4.96060878e-01 -2.25576594e-01 3.68933678e-02 7.13012218e-02
-1.02623975e+00 7.53596783e-01 -6.75581023e-02 3.36673468e-01
1.34831935e-01 4.31399912e-01 3.61296594e-01 3.61843079e-01
6.12152040e-01 5.27947783e-01 -4.73503768e-01 -6.88757718e-01
-1.03686428e+00 5.13640225e-01 8.08420062e-01 9.60383832e-01
5.90280950e-01 -2.72701271e-02 -3.89005542e-01 1.06158423e+00
1.61588844e-02 5.79675198e-01 6.62241876e-01 -1.25179660e+00
5.49798787e-01 3.99702080e-02 -2.74493005e-02 -2.83887863e-01
-4.50009823e-01 -4.57321674e-01 -4.09902811e-01 -2.99738735e-01
5.43978155e-01 -3.79021943e-01 -7.84670353e-01 1.83572602e+00
4.45951847e-03 3.77159804e-01 -4.00970668e-01 2.22900748e-01
7.36950457e-01 5.93157291e-01 1.70871034e-01 2.03352515e-02
1.28997386e+00 -9.56309438e-01 -3.39170694e-01 -4.17691618e-01
1.01645398e+00 -8.54180455e-01 1.41564584e+00 1.35430038e-01
-1.40514052e+00 -3.98568779e-01 -7.28006065e-01 -1.24179706e-01
-2.58326769e-01 1.45556316e-01 6.78038061e-01 9.04321313e-01
-1.27848697e+00 6.94445074e-01 -1.14024639e+00 -7.28416920e-01
4.76491928e-01 2.82805830e-01 1.45844042e-01 -6.76044002e-02
-1.14126670e+00 9.26681578e-01 7.35149439e-03 -3.55558246e-01
-1.06729329e+00 -7.51157165e-01 -4.84076202e-01 -2.40274575e-02
2.18315959e-01 -6.56805992e-01 1.65663338e+00 -4.86208528e-01
-1.17418694e+00 9.70919251e-01 -3.94168139e-01 -5.10673165e-01
3.24885547e-01 -2.08686098e-01 -1.47362828e-01 -3.20407093e-01
-7.81150013e-02 6.01587415e-01 4.09862727e-01 -1.17089009e+00
-4.53977227e-01 -2.76813060e-01 -2.15592280e-01 2.81786956e-02
-6.44967258e-01 3.47885132e-01 -6.39394403e-01 -6.18784368e-01
-5.60382724e-01 -1.18955696e+00 1.89640578e-02 -7.89944351e-01
-4.32758719e-01 -1.98594853e-01 1.36144102e-01 -7.41651535e-01
1.75433278e+00 -2.00907564e+00 -1.43949538e-01 -4.33453284e-02
-1.46978283e-02 3.92107069e-01 -4.57863152e-01 8.03756952e-01
3.05781037e-01 5.84162951e-01 -1.38638362e-01 -1.01697195e+00
1.68633938e-01 1.48697779e-01 -3.03863198e-01 2.37932950e-01
-2.42891103e-01 9.54768777e-01 -5.88019609e-01 -5.16621470e-01
-2.44915068e-01 2.55464852e-01 -4.49275672e-01 2.65649915e-01
-1.63094059e-01 4.21793669e-01 -2.22750634e-01 5.47887504e-01
4.75183725e-01 -3.73262376e-01 4.73630764e-02 5.86089194e-01
-1.58761263e-01 5.27134180e-01 -6.25351608e-01 1.59336412e+00
-5.49466193e-01 7.79357910e-01 1.87136069e-01 -6.80633664e-01
5.82255781e-01 4.22992222e-02 1.15100339e-01 -7.62931228e-01
1.91038802e-01 1.51835144e-01 1.06224924e-01 -4.25773293e-01
8.20931017e-01 1.60101041e-01 -6.00700118e-02 9.42288458e-01
1.77959248e-01 5.55396639e-02 5.16746759e-01 5.18179536e-01
1.22250617e+00 -2.72788405e-01 -1.45192474e-01 -2.41038591e-01
-1.72282740e-01 -1.63038120e-01 3.01708668e-01 1.34018075e+00
-1.40362188e-01 3.25709194e-01 4.36011583e-01 -2.26687685e-01
-1.29964876e+00 -1.06663287e+00 -4.66921777e-01 1.86401486e+00
-4.41743016e-01 -7.27248013e-01 -8.13496530e-01 -1.74000874e-01
5.97478785e-02 8.51632357e-01 -6.01010561e-01 -1.76386684e-01
-4.84355897e-01 -8.43284428e-01 1.08437884e+00 2.89011687e-01
2.89151132e-01 -1.14265501e+00 -2.42405847e-01 -3.11561346e-01
-2.08316684e-01 -7.25739837e-01 -5.91828823e-01 2.75091112e-01
-1.02402818e+00 -6.35960519e-01 -6.60717010e-01 -6.60367548e-01
4.71712947e-01 1.55759603e-01 1.56764519e+00 6.63185954e-01
-2.52915293e-01 3.71756524e-01 -2.31678247e-01 -7.50686586e-01
-7.60293126e-01 8.56547117e-01 1.45011202e-01 -6.61680222e-01
3.23562086e-01 -6.00551963e-01 -3.93719465e-01 1.46702692e-01
-7.60147333e-01 2.01719269e-01 5.41363597e-01 5.87801039e-01
1.32846937e-01 -3.71056259e-01 4.84806359e-01 -1.30604672e+00
9.91431355e-01 -7.45813310e-01 -5.15539169e-01 3.93180400e-01
-8.72327328e-01 -9.60784480e-02 2.35169545e-01 -3.29149574e-01
-9.76397753e-01 -4.29240137e-01 -1.67972758e-01 -2.80863672e-01
-1.97367743e-01 5.13146102e-01 5.68920434e-01 2.11602196e-01
8.57167244e-01 3.38468641e-01 -4.54508439e-02 -8.24205160e-01
1.69148996e-01 7.28268325e-01 2.26824388e-01 -8.77469122e-01
6.87560022e-01 8.57462958e-02 -4.04234886e-01 -7.98417091e-01
-7.33651817e-01 -4.01284575e-01 -4.33930397e-01 1.13716070e-02
3.85018140e-01 -1.09210384e+00 -5.21804094e-01 6.13572121e-01
-7.67048359e-01 -1.37953079e+00 3.05054430e-02 2.42564231e-01
-3.21818590e-01 7.42570497e-03 -1.08630395e+00 -9.39890265e-01
-4.35119987e-01 -8.50120783e-01 9.32104051e-01 4.68160212e-01
-3.34531426e-01 -1.19533908e+00 4.55053687e-01 6.35761976e-01
6.55748129e-01 -4.90875304e-01 1.11090541e+00 -8.43845606e-01
-3.69040310e-01 -1.26232654e-01 1.79129824e-01 6.43644631e-02
-3.25732261e-01 2.17451736e-01 -1.01883674e+00 -7.44348168e-01
-3.03826004e-01 -8.16440403e-01 1.03899205e+00 4.47210461e-01
1.24529624e+00 -3.00377637e-01 -5.07425785e-01 5.72521627e-01
9.79817450e-01 -1.96920499e-01 3.77644539e-01 2.54915744e-01
5.97560406e-01 4.18097556e-01 3.17948222e-01 6.38479352e-01
4.47602034e-01 6.79257572e-01 -1.15304030e-01 1.64928973e-01
8.78005475e-02 -3.48060727e-01 5.98403692e-01 1.08079851e+00
-1.76423460e-01 -4.41328704e-01 -1.21535957e+00 4.21427041e-01
-1.61668086e+00 -1.13481295e+00 6.09894805e-02 2.36868358e+00
1.08022165e+00 1.71235427e-01 4.10666227e-01 -4.95983243e-01
5.14076531e-01 2.60291606e-01 -6.39205277e-01 -5.73464096e-01
1.22023098e-01 3.61633271e-01 6.50729775e-01 6.00491047e-01
-6.57035708e-01 1.07279623e+00 6.88293362e+00 1.14152169e+00
-1.12154913e+00 4.46896166e-01 1.08304656e+00 -7.18041182e-01
-4.28081870e-01 1.38873070e-01 -1.17043495e+00 5.33727050e-01
1.70894790e+00 -4.00055796e-01 8.48027527e-01 5.57072699e-01
3.68028760e-01 -3.90964031e-01 -9.33534145e-01 6.55357182e-01
-6.90345094e-02 -1.29210329e+00 -2.30461314e-01 1.93749011e-01
9.11543489e-01 6.67567253e-01 3.78790230e-01 8.26870322e-01
6.39330328e-01 -1.10945988e+00 4.89766032e-01 6.34833694e-01
7.68368185e-01 -7.19260275e-01 1.71774104e-01 7.83161640e-01
-7.06688643e-01 -6.03650883e-02 -5.12417555e-01 -1.06432401e-01
-1.36904553e-01 3.24153006e-01 -9.03982103e-01 2.23443210e-01
6.47012115e-01 3.66288394e-01 -1.30870318e+00 9.21883047e-01
2.14464992e-01 1.43089783e+00 -2.05638513e-01 -7.09766746e-02
-2.64531195e-01 1.14944443e-01 2.20269293e-01 1.34077752e+00
2.84235597e-01 -5.36354855e-02 -2.18136441e-02 6.97381616e-01
-3.53490740e-01 1.03138760e-02 -4.32049572e-01 -2.95517445e-01
9.22574818e-01 1.30570495e+00 -6.90733254e-01 -4.07758892e-01
-3.03857028e-01 7.22114980e-01 6.34726167e-01 4.84670490e-01
-9.19420242e-01 -2.50676781e-01 6.06249034e-01 3.36260408e-01
-8.05684105e-02 -4.10876006e-01 -3.13733071e-01 -1.22095048e+00
-1.81662470e-01 -1.06514239e+00 4.05603260e-01 -7.73265481e-01
-1.26239705e+00 4.02174562e-01 2.07360104e-01 -4.69328433e-01
-3.08324724e-01 -3.39106858e-01 -9.16537821e-01 8.12061012e-01
-1.08768845e+00 -1.02364445e+00 -2.44934484e-01 3.26894611e-01
5.94386697e-01 -2.42765397e-01 8.64690244e-01 3.58948201e-01
-9.30556774e-01 1.06402004e+00 6.01908326e-01 1.59964692e-02
1.05553377e+00 -9.70360577e-01 8.23560953e-01 5.55474758e-01
3.66863668e-01 9.81264472e-01 7.70907223e-01 -5.58630586e-01
-1.27958930e+00 -8.51473927e-01 9.72770214e-01 -1.04049420e+00
5.71759820e-01 -8.21100771e-01 -8.16233695e-01 1.09617877e+00
4.49261099e-01 -3.13458085e-01 8.43627989e-01 6.34640813e-01
-5.33821508e-02 1.34885937e-01 -4.94891644e-01 6.91123903e-01
1.10244596e+00 -5.98345816e-01 7.31019601e-02 5.39380848e-01
6.49002016e-01 -5.30514300e-01 -7.81634986e-01 6.60624504e-02
5.77470362e-01 -1.12096334e+00 6.73861265e-01 -7.56351292e-01
4.49155837e-01 4.78917211e-01 9.71297696e-02 -1.12414372e+00
-2.40640700e-01 -6.34128571e-01 -9.77010354e-02 1.44722807e+00
7.20126450e-01 -5.84162474e-01 6.35540187e-01 6.17786109e-01
-6.08672248e-03 -9.88272727e-01 -7.40346313e-01 -8.22345018e-01
6.97182000e-01 -4.93913114e-01 3.61446112e-01 9.01517272e-01
-4.43514436e-02 2.99502730e-01 -4.31894809e-01 -1.12786077e-01
3.26697469e-01 -1.29739523e-01 1.02211380e+00 -8.19458604e-01
-5.44958055e-01 -3.88273835e-01 4.25948441e-01 -9.50616658e-01
3.22574914e-01 -1.25864589e+00 -1.43187612e-01 -1.25518131e+00
5.73071897e-01 -8.87107253e-01 -2.47600928e-01 6.57241046e-01
-2.29101494e-01 2.38648877e-01 4.42000628e-01 5.62263727e-01
-8.92165899e-01 3.68047416e-01 7.53947318e-01 3.71517748e-01
-1.04806423e-01 1.90696284e-01 -6.79321349e-01 3.47575694e-01
9.83514607e-01 -6.79910243e-01 -4.50000316e-01 -7.19651282e-01
1.10574223e-01 3.88035625e-02 8.98247287e-02 -1.19350398e+00
2.68509150e-01 -1.08420335e-01 3.71011049e-01 -2.05002263e-01
3.30321401e-01 -7.07443878e-02 8.60021114e-02 3.71256679e-01
-7.78106809e-01 6.11887693e-01 5.27008116e-01 1.55298382e-01
2.29648575e-01 -4.26111221e-01 6.27137840e-01 -2.52103925e-01
-3.64068061e-01 3.90723526e-01 -5.11102498e-01 5.12385607e-01
7.46878326e-01 4.78505671e-01 -5.78481853e-01 -5.64554036e-01
-5.70691884e-01 3.87185633e-01 6.94758832e-01 4.95733678e-01
-5.37990499e-03 -1.09075367e+00 -7.11457014e-01 7.08631650e-02
1.09749801e-01 -2.28658989e-01 3.88843298e-01 9.94810224e-01
-3.10367107e-01 6.00175560e-01 -5.06273890e-03 -3.29874605e-01
-1.36541092e+00 1.61793068e-01 3.40044945e-01 -4.86361176e-01
4.19376455e-02 1.08518386e+00 -3.25316675e-02 -5.77069521e-01
2.56798953e-01 -1.36700822e-02 2.12933749e-01 2.17083748e-02
4.80836123e-01 4.49877620e-01 -9.07727629e-02 -3.06380183e-01
-2.14397252e-01 5.32115772e-02 -2.93973088e-01 -2.59616941e-01
1.30173409e+00 -2.06779838e-01 1.46617042e-02 8.87039423e-01
9.88870203e-01 1.67209387e-01 -1.21417987e+00 -2.14110121e-01
1.84393171e-02 -3.38941455e-01 -3.91278952e-01 -8.80009353e-01
-8.25593352e-01 8.32965553e-01 5.20439863e-01 9.49103385e-02
7.57444263e-01 1.46308199e-01 7.29106545e-01 3.58783662e-01
3.13267648e-01 -1.07258224e+00 1.56448647e-01 6.94587409e-01
5.10906160e-01 -1.26789618e+00 1.39776403e-02 3.18950593e-01
-6.48705363e-01 6.17846429e-01 7.97446012e-01 1.78187281e-01
5.66938818e-01 3.47182781e-01 1.84079692e-01 -1.07771672e-01
-1.40694511e+00 1.49372593e-01 -2.28937268e-01 2.66910017e-01
8.54370475e-01 -1.50304362e-02 -2.67621845e-01 6.53809309e-01
-4.65268612e-01 3.82671952e-02 4.32904273e-01 8.95548046e-01
-3.18613857e-01 -1.53895748e+00 -2.52857566e-01 8.46071482e-01
-6.66684926e-01 -5.07351220e-01 -2.20984086e-01 6.88145339e-01
-9.58409682e-02 8.23290944e-01 2.81405598e-01 -4.82871324e-01
6.10384569e-02 5.84105015e-01 4.27940071e-01 -8.15278709e-01
-7.66958594e-01 -1.79472893e-01 3.41260344e-01 -2.25579664e-01
1.48358509e-01 -8.53038669e-01 -1.08700812e+00 -1.01169729e+00
-2.18109101e-01 1.53882235e-01 6.30632758e-01 6.57837987e-01
6.37408137e-01 2.14037582e-01 4.00326997e-01 -8.27711403e-01
-7.75224209e-01 -1.31867015e+00 -4.58463252e-01 3.96341830e-01
-2.70484146e-02 -2.27468953e-01 -4.02437121e-01 9.72500909e-03] | [10.646623611450195, 8.310639381408691] |
da969a55-f3ac-4724-9153-4c01ecbef544 | abcnet-v2-adaptive-bezier-curve-network-for | 2105.03620 | null | https://arxiv.org/abs/2105.03620v3 | https://arxiv.org/pdf/2105.03620v3.pdf | ABCNet v2: Adaptive Bezier-Curve Network for Real-time End-to-end Text Spotting | End-to-end text-spotting, which aims to integrate detection and recognition in a unified framework, has attracted increasing attention due to its simplicity of the two complimentary tasks. It remains an open problem especially when processing arbitrarily-shaped text instances. Previous methods can be roughly categorized into two groups: character-based and segmentation-based, which often require character-level annotations and/or complex post-processing due to the unstructured output. Here, we tackle end-to-end text spotting by presenting Adaptive Bezier Curve Network v2 (ABCNet v2). Our main contributions are four-fold: 1) For the first time, we adaptively fit arbitrarily-shaped text by a parameterized Bezier curve, which, compared with segmentation-based methods, can not only provide structured output but also controllable representation. 2) We design a novel BezierAlign layer for extracting accurate convolution features of a text instance of arbitrary shapes, significantly improving the precision of recognition over previous methods. 3) Different from previous methods, which often suffer from complex post-processing and sensitive hyper-parameters, our ABCNet v2 maintains a simple pipeline with the only post-processing non-maximum suppression (NMS). 4) As the performance of text recognition closely depends on feature alignment, ABCNet v2 further adopts a simple yet effective coordinate convolution to encode the position of the convolutional filters, which leads to a considerable improvement with negligible computation overhead. Comprehensive experiments conducted on various bilingual (English and Chinese) benchmark datasets demonstrate that ABCNet v2 can achieve state-of-the-art performance while maintaining very high efficiency. | ['Hao Chen', 'Chongyu Liu', 'Peng Chen', 'Tong He', 'Lianwen Jin', 'Chunhua Shen', 'Yuliang Liu'] | 2021-05-08 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 4.21264261e-01 -5.42643249e-01 1.47010639e-01 -2.41218165e-01
-6.40538871e-01 -6.30686820e-01 6.64292157e-01 1.71513334e-01
-6.60748482e-01 2.03746930e-01 -1.12114184e-01 -4.92838621e-01
1.83079496e-01 -7.19374835e-01 -6.33661628e-01 -5.26775360e-01
5.16796172e-01 3.08617324e-01 3.23601902e-01 -2.31570065e-01
4.83921707e-01 5.30208886e-01 -1.35888052e+00 3.83019626e-01
1.23659527e+00 1.19063401e+00 3.61532897e-01 5.70181549e-01
-5.65069377e-01 1.30411014e-01 -5.85538328e-01 -6.21252477e-01
3.14629734e-01 -2.12778986e-01 -4.62960243e-01 1.28275931e-01
4.83687967e-01 -1.99980944e-01 -3.47624987e-01 1.10738146e+00
6.21322215e-01 -5.00927307e-02 4.74721700e-01 -6.14486158e-01
-5.79186201e-01 6.99778438e-01 -5.05782485e-01 -1.27259478e-01
1.33387923e-01 2.49277383e-01 1.07080960e+00 -1.13475728e+00
2.35073566e-01 1.00653934e+00 9.13080990e-01 3.96932989e-01
-1.24055529e+00 -5.27145684e-01 2.69846857e-01 1.04527520e-02
-1.57737052e+00 -2.65688598e-01 6.34548664e-01 -3.53539199e-01
8.97562683e-01 5.63832581e-01 5.24493396e-01 7.97504187e-01
-1.72865584e-01 1.12772429e+00 7.86579847e-01 -5.31372547e-01
5.26649272e-03 2.45377719e-02 1.72128856e-01 8.09438169e-01
4.64138016e-02 -4.06491429e-01 -2.46954635e-01 2.09094122e-01
7.66969800e-01 1.17307119e-01 -4.71346647e-01 -6.35846332e-02
-1.57558393e+00 5.02635419e-01 4.58997995e-01 4.50845152e-01
-1.13888979e-01 -1.79850832e-02 5.41113317e-01 1.96717054e-01
1.47756740e-01 2.82550633e-01 -2.81537056e-01 -1.90224364e-01
-1.27867234e+00 4.05198298e-02 6.78808630e-01 1.04384530e+00
6.15703046e-01 1.44368291e-01 -5.31547785e-01 1.01939595e+00
-5.39726131e-02 5.96609354e-01 6.18754089e-01 2.59861737e-01
9.56361353e-01 9.32732999e-01 -1.14148460e-01 -1.01745462e+00
-4.13020462e-01 -3.84422094e-01 -1.11960208e+00 9.29007027e-03
5.47125280e-01 -1.07317992e-01 -9.90291297e-01 1.14776802e+00
8.12813407e-04 -1.60909086e-01 -1.92334756e-01 9.67893481e-01
8.29053700e-01 6.62283778e-01 -2.36388251e-01 1.27407208e-01
1.56696355e+00 -1.10109961e+00 -5.54128110e-01 -9.46825594e-02
6.22241259e-01 -1.09758663e+00 1.34168029e+00 4.23598260e-01
-7.29939878e-01 -5.19908845e-01 -1.18366385e+00 -3.35672349e-01
-5.35198629e-01 8.45111430e-01 3.16791266e-01 8.59985232e-01
-8.71608436e-01 4.99828875e-01 -7.40990520e-01 -2.88953960e-01
4.06928211e-01 5.46570361e-01 -1.88733518e-01 1.95502803e-01
-9.39033329e-01 4.10953164e-01 3.33912551e-01 3.06184858e-01
-1.18022203e-01 -5.03763616e-01 -6.78647280e-01 3.31957579e-01
5.50714970e-01 -3.62656653e-01 1.02986407e+00 -1.03965902e+00
-1.82842994e+00 5.89711964e-01 -1.52545363e-01 -1.82822451e-01
9.88458574e-01 -2.39144802e-01 -3.55953753e-01 9.82161835e-02
-2.19466418e-01 4.56415027e-01 1.09662449e+00 -6.35991156e-01
-5.59169412e-01 -2.73174226e-01 -4.45088744e-01 3.02930295e-01
-8.46531868e-01 1.69773564e-01 -1.19128084e+00 -1.16254842e+00
2.86068588e-01 -6.78923488e-01 -6.49028271e-02 2.62878746e-01
-6.01671755e-01 -2.57187963e-01 1.05007315e+00 -5.83983004e-01
1.50181198e+00 -2.14680696e+00 -4.16532569e-02 4.00592647e-02
2.13654593e-01 6.01129830e-01 -6.29349947e-02 2.74951994e-01
1.21966369e-01 1.76236197e-01 -3.03074777e-01 -4.02272820e-01
2.21203789e-01 -3.91715050e-01 -5.31434715e-01 5.14657080e-01
3.81217927e-01 1.18756199e+00 -4.54105943e-01 -5.08952737e-01
4.86832917e-01 3.77147853e-01 -3.13729376e-01 -1.28393441e-01
-4.22676116e-01 6.71923086e-02 -5.09645164e-01 6.23002470e-01
7.90720880e-01 -2.34967947e-01 9.70370546e-02 -2.82927483e-01
-4.88518149e-01 1.74161956e-01 -1.23929703e+00 1.45343292e+00
-2.39711076e-01 8.00594032e-01 1.70810670e-01 -9.29823637e-01
1.19335544e+00 9.96068418e-02 1.66702271e-01 -6.61668897e-01
2.89804310e-01 3.29417437e-01 -2.91832238e-01 -3.49436611e-01
7.49694824e-01 2.61549652e-01 -1.75481617e-01 2.84779787e-01
-3.62383306e-01 -6.74913526e-02 7.16283470e-02 -7.39250705e-02
7.67294228e-01 2.01052278e-01 7.38082230e-02 -2.64319450e-01
8.16926062e-01 -8.76780674e-02 3.90352756e-01 7.21949697e-01
9.79539827e-02 9.33122456e-01 5.66526532e-01 -5.08320451e-01
-1.07537866e+00 -4.35110778e-01 -2.98327863e-01 9.14251089e-01
2.37155452e-01 -5.57308555e-01 -8.71229827e-01 -6.82046056e-01
-6.18997291e-02 3.62790376e-01 -3.53159726e-01 4.36367214e-01
-8.95896971e-01 -8.23782563e-01 8.68405759e-01 6.38393044e-01
7.96065688e-01 -8.12271893e-01 -5.22448540e-01 1.97489932e-01
-1.04192635e-02 -1.19018531e+00 -9.27759588e-01 1.58273771e-01
-8.99922669e-01 -7.21485972e-01 -1.17235279e+00 -9.65746880e-01
8.13903272e-01 3.08955938e-01 6.07396781e-01 2.53140181e-01
-3.40959698e-01 -1.18127197e-01 -4.43497300e-01 -7.58751929e-02
-8.72563571e-03 2.86199212e-01 -3.43083233e-01 3.70350361e-01
4.62934524e-01 -2.60416955e-01 -5.63666463e-01 6.41515732e-01
-1.02627981e+00 4.17896509e-01 8.23321700e-01 9.86515820e-01
6.07927680e-01 -4.33241464e-02 1.10541388e-01 -7.08127141e-01
5.31659007e-01 2.11700946e-01 -9.06542003e-01 4.33129817e-01
-5.27760386e-01 3.20331864e-02 1.16873538e+00 -5.42402804e-01
-7.57964969e-01 3.52596343e-01 -1.86835513e-01 -3.59169275e-01
-2.38202244e-01 2.88274527e-01 -2.93119788e-01 -1.93965033e-01
5.14615536e-01 6.41311765e-01 -1.00452423e-01 -6.43289983e-01
3.19232404e-01 9.89278316e-01 5.87913275e-01 -6.30235076e-01
8.65191281e-01 4.30690646e-01 -2.25558460e-01 -1.09267163e+00
-3.02396357e-01 -4.49845821e-01 -8.54183078e-01 2.41494272e-02
6.41703665e-01 -6.62375808e-01 -9.65416014e-01 8.96205068e-01
-1.10703790e+00 -2.93251872e-01 2.00278729e-01 3.09320569e-01
-2.20003694e-01 8.37136269e-01 -6.16008162e-01 -6.60915017e-01
-8.30604196e-01 -1.38280201e+00 1.22634733e+00 1.69511259e-01
1.27790824e-01 -6.48798645e-01 -5.92396677e-01 1.72277197e-01
4.81108218e-01 -1.24237165e-02 1.00778818e+00 -7.20945895e-01
-6.25709474e-01 -2.73864061e-01 -6.30319417e-01 7.09412023e-02
-1.52904212e-01 4.01576683e-02 -8.46563578e-01 -3.16120684e-01
-3.07021618e-01 -7.56557509e-02 1.04378879e+00 2.14762725e-02
1.24767971e+00 -1.73297167e-01 -2.82774955e-01 9.50744152e-01
1.41693282e+00 -3.82662788e-02 7.16066718e-01 3.64062399e-01
8.30439091e-01 3.69227618e-01 4.93879884e-01 5.13191819e-01
-7.04985186e-02 9.01715040e-01 2.04804510e-01 -2.84847707e-01
-6.36594743e-02 -1.37914434e-01 3.59716117e-01 7.75725245e-01
2.18585446e-01 -3.78629059e-01 -8.76306117e-01 2.98736840e-01
-1.89843786e+00 -6.93803251e-01 -5.14260292e-01 2.19166827e+00
7.21038938e-01 2.19401553e-01 1.52576208e-01 2.64504939e-01
1.03941727e+00 1.22460850e-01 -6.32310927e-01 -1.70180067e-01
-3.50381762e-01 3.72759908e-01 5.95067084e-01 1.62308678e-01
-1.28016126e+00 1.20163441e+00 5.09699583e+00 1.28668869e+00
-1.56933963e+00 -2.90784985e-01 4.73017603e-01 1.38825236e-03
1.22008426e-02 -1.29586160e-01 -1.12185383e+00 5.33771932e-01
3.48541200e-01 -1.38963312e-02 3.62986237e-01 7.05451727e-01
3.59679423e-02 3.17738861e-01 -9.39641297e-01 1.14599133e+00
5.60543053e-02 -1.33822286e+00 2.43663818e-01 -2.90451735e-01
5.09725988e-01 -1.26054823e-01 1.41767323e-01 2.19103932e-01
-1.29403695e-01 -9.62539911e-01 1.02545249e+00 2.68930703e-01
1.34703064e+00 -7.05398023e-01 4.40885991e-01 2.39791825e-01
-1.39462435e+00 -4.86886799e-02 -3.98638994e-01 1.98732853e-01
-5.57169244e-02 7.30786264e-01 -5.67239702e-01 5.79179406e-01
4.46085602e-01 6.53337240e-01 -5.01948059e-01 1.14638710e+00
-1.95900470e-01 5.33715189e-01 -4.54951108e-01 -6.06364012e-01
4.76579845e-01 -3.36924225e-01 4.80708957e-01 1.64624357e+00
3.22960854e-01 -7.02001154e-02 1.31769598e-01 1.05588603e+00
-2.41377592e-01 6.21983230e-01 -4.64798287e-02 -1.35358438e-01
2.71552414e-01 1.34501827e+00 -1.14802301e+00 -3.58599454e-01
-4.83534127e-01 1.27572620e+00 2.17044696e-01 2.04526871e-01
-6.82751775e-01 -1.10457599e+00 2.24714458e-01 -6.16905726e-02
8.26290786e-01 -3.00886840e-01 -6.63970292e-01 -1.30234909e+00
4.92742240e-01 -1.00067794e+00 1.44821569e-01 -2.95990258e-01
-8.87842417e-01 7.96908438e-01 -6.84854209e-01 -1.25774705e+00
3.08772802e-01 -9.94245172e-01 -4.68402445e-01 1.05257750e+00
-1.47807610e+00 -1.28155375e+00 -4.83996809e-01 4.31076467e-01
8.18397105e-01 -1.29858315e-01 7.01449871e-01 2.62755960e-01
-8.76630425e-01 1.10938656e+00 4.43937212e-01 6.24186635e-01
7.38946319e-01 -1.10267806e+00 9.01705205e-01 9.33943450e-01
1.21955723e-01 5.90385139e-01 1.63671508e-01 -6.17424667e-01
-1.67394352e+00 -1.17070508e+00 7.98257709e-01 -2.15500921e-01
6.00168884e-01 -8.57149899e-01 -1.06368995e+00 2.20114633e-01
-5.09167053e-02 -1.06251426e-01 2.51388818e-01 -2.32856125e-01
-4.78706002e-01 -6.20679520e-02 -8.17710578e-01 9.02203679e-01
8.78977299e-01 -3.40713084e-01 -4.10487264e-01 2.35234082e-01
5.37344098e-01 -5.50061643e-01 -4.89532143e-01 2.07696378e-01
5.75368881e-01 -9.42891419e-01 6.31298542e-01 -1.53203964e-01
3.37225974e-01 -4.01156187e-01 7.22227395e-02 -7.07870364e-01
-2.44703338e-01 -1.02795565e+00 2.46931553e-01 1.16735363e+00
3.96920264e-01 -6.06014132e-01 7.95255065e-01 3.20888788e-01
-1.82909921e-01 -8.60875130e-01 -7.52730846e-01 -6.84783816e-01
1.30652577e-01 -5.41525066e-01 6.96861029e-01 7.92728543e-01
2.82136239e-02 2.25538552e-01 -2.46696427e-01 -7.39783328e-03
1.70166016e-01 3.63529116e-01 7.68277943e-01 -1.19884849e+00
-2.32002988e-01 -1.12435663e+00 -4.24949288e-01 -1.81675041e+00
-6.88494891e-02 -9.52028334e-01 2.32879549e-01 -1.14768493e+00
-1.80797838e-03 -5.75537562e-01 8.48605335e-02 5.98818839e-01
-3.09701353e-01 2.91815370e-01 2.06444561e-01 3.41288865e-01
-4.65322971e-01 6.10039175e-01 1.14038205e+00 -2.47359931e-01
-2.59831190e-01 7.39732664e-03 -4.55668658e-01 5.08133054e-01
5.24033308e-01 -6.96744919e-02 9.81798917e-02 -6.89130783e-01
2.25468621e-01 -1.66808352e-01 1.71312332e-01 -9.12824810e-01
5.07000387e-01 1.10653915e-01 3.59092295e-01 -9.37899053e-01
-8.82944465e-03 -7.41892576e-01 -3.15750271e-01 3.13093543e-01
-2.59773344e-01 1.78203881e-02 4.55180794e-01 4.43347752e-01
-1.21531166e-01 -5.28500259e-01 8.61783385e-01 1.83154672e-01
-5.91124713e-01 1.65542141e-01 -3.65343809e-01 -2.17774644e-01
7.40352154e-01 -4.88304406e-01 -2.49912560e-01 -1.19674310e-01
-1.92115650e-01 8.98251906e-02 6.05186880e-01 3.25640351e-01
5.67094803e-01 -1.01473069e+00 -7.27183223e-01 4.82117087e-01
1.36944640e-03 1.04475833e-01 1.25070438e-01 9.41979527e-01
-7.98359632e-01 8.03188145e-01 1.71014220e-01 -7.20671237e-01
-1.29730773e+00 5.07018745e-01 2.53195763e-01 -2.25842580e-01
-9.44047213e-01 5.96863151e-01 1.63549751e-01 -3.49689484e-01
4.77644533e-01 -3.68226916e-01 -3.14111598e-02 -5.76202273e-02
6.43812537e-01 2.39262834e-01 3.48300070e-01 -4.86358076e-01
-1.37680486e-01 9.32445824e-01 -4.00385022e-01 8.10192972e-02
1.16860366e+00 1.32327482e-01 -7.88874403e-02 5.38886636e-02
9.53283191e-01 1.90098986e-01 -1.19861758e+00 -3.36367369e-01
4.88243587e-02 -3.64876449e-01 1.97991133e-02 -7.93428659e-01
-9.73637462e-01 1.14933908e+00 3.59619796e-01 4.39855009e-02
1.13817906e+00 -6.38060927e-01 1.02104664e+00 6.05452716e-01
3.02477665e-02 -1.19880891e+00 -1.45477345e-02 7.85224736e-01
7.76431680e-01 -9.58590746e-01 -2.25919038e-02 -5.43303251e-01
-4.21367645e-01 1.59760666e+00 5.84477782e-01 2.22945631e-01
2.44942814e-01 4.75688368e-01 8.20923895e-02 1.35423720e-01
-3.52179855e-01 -2.03452900e-01 4.02745992e-01 1.16865598e-01
5.15549123e-01 -1.64773896e-01 -4.02442127e-01 7.10293293e-01
-1.02963015e-01 -2.19809160e-01 6.29706755e-02 7.14849532e-01
-4.19932783e-01 -1.10507512e+00 -5.37865579e-01 4.90086257e-01
-5.49046159e-01 -4.63502556e-01 -5.02578497e-01 6.55550838e-01
-2.84197301e-01 6.39293849e-01 9.13833603e-02 -4.40901488e-01
4.09335434e-01 9.10971463e-02 1.40395656e-01 -3.88091028e-01
-9.18192387e-01 4.07821059e-01 -3.56827855e-01 -1.74919665e-01
1.87453941e-01 -4.37671065e-01 -1.36756825e+00 -2.43337721e-01
-7.27196693e-01 -1.27088606e-01 8.72417510e-01 8.36731076e-01
5.13346732e-01 3.38339597e-01 5.57536364e-01 -7.66968906e-01
-7.92567074e-01 -9.16005850e-01 -2.77823031e-01 4.03192818e-01
1.55394897e-01 -1.39312595e-01 -9.77463424e-02 1.28229916e-01] | [12.035871505737305, 2.2355618476867676] |
15ff7eec-4272-4631-88e7-e49f119816a1 | teacher-student-network-for-3d-point-cloud | 2210.17258 | null | https://arxiv.org/abs/2210.17258v2 | https://arxiv.org/pdf/2210.17258v2.pdf | Teacher-Student Network for 3D Point Cloud Anomaly Detection with Few Normal Samples | Anomaly detection, which is a critical and popular topic in computer vision, aims to detect anomalous samples that are different from the normal (i.e., non-anomalous) ones. The current mainstream methods focus on anomaly detection for images, whereas little attention has been paid to 3D point cloud. In this paper, drawing inspiration from the knowledge transfer ability of teacher-student architecture and the impressive feature extraction capability of recent neural networks, we design a teacher-student structured model for 3D anomaly detection. Specifically, we use feature space alignment, dimension zoom, and max pooling to extract the features of the point cloud and then minimize a multi-scale loss between the feature vectors produced by the teacher and the student networks. Moreover, our method only requires very few normal samples to train the student network due to the teacher-student distillation mechanism. Once trained, the teacher-student network pair can be leveraged jointly to fulfill 3D point cloud anomaly detection based on the calculated anomaly score. For evaluation, we compare our method against the reconstruction-based method on the ShapeNet-Part dataset. The experimental results and ablation studies quantitatively and qualitatively confirm that our model can achieve higher performance compared with the state of the arts in 3D anomaly detection with very few training samples. | ['Chao Zhang', 'Jun Yu', 'Chunzhi Gu', 'Jianjian Qin'] | 2022-10-31 | null | null | null | null | ['3d-anomaly-detection'] | ['methodology'] | [-3.60702202e-02 -1.21040586e-02 3.09140533e-01 -2.61108130e-01
-4.84459162e-01 -2.19577551e-01 5.54809809e-01 4.16150540e-01
-1.75779030e-01 -2.00205043e-01 -4.11527067e-01 -2.54885048e-01
6.46485314e-02 -5.13207734e-01 -6.55209303e-01 -8.31778288e-01
-1.19534083e-01 3.51747304e-01 4.64815766e-01 -1.30307779e-01
3.99705887e-01 1.01371706e+00 -1.64123368e+00 -2.59699915e-02
1.21493602e+00 1.44970560e+00 -2.50921875e-01 2.91541219e-01
-3.11887860e-01 3.25367153e-01 -5.74391425e-01 -1.87203407e-01
4.66566652e-01 -1.28014788e-01 -2.46076211e-01 2.97989279e-01
1.00930977e+00 -5.72450042e-01 -4.09716457e-01 1.28136539e+00
4.73170966e-01 2.98543274e-01 7.74088442e-01 -1.49510860e+00
-7.25513160e-01 -2.50516891e-01 -8.27572942e-01 5.95449984e-01
1.10655010e-01 2.03949332e-01 8.43184471e-01 -1.24602687e+00
1.54482454e-01 9.59718227e-01 6.45507216e-01 4.91817802e-01
-1.08996534e+00 -7.10859537e-01 5.64949155e-01 4.42032278e-01
-1.14647007e+00 -9.29287523e-02 1.14441061e+00 -4.37394679e-01
9.97549117e-01 1.22552060e-01 8.60850632e-01 9.28371072e-01
1.20341390e-01 1.11619675e+00 3.33901465e-01 -9.32447910e-02
1.69330433e-01 -1.08702235e-01 1.42615577e-02 6.64332449e-01
2.73554951e-01 -4.29600626e-02 -3.28291059e-01 -2.26520061e-01
6.67656839e-01 5.29915929e-01 -2.39134476e-01 -8.42044830e-01
-8.17390621e-01 7.75863171e-01 6.89667463e-01 3.43746431e-02
-6.64508760e-01 -8.03465098e-02 4.13716555e-01 6.34337425e-01
7.31998146e-01 4.01144683e-01 -3.59452605e-01 5.44886775e-02
-7.99395800e-01 2.57407427e-01 3.85964841e-01 8.18999052e-01
5.84857941e-01 3.46235096e-01 -5.60642034e-02 6.87016428e-01
3.41256976e-01 5.41483104e-01 4.97664094e-01 -4.61050838e-01
3.12925994e-01 1.12699759e+00 -3.81030798e-01 -1.16667402e+00
-2.00509772e-01 -3.74130219e-01 -8.59253764e-01 5.78926861e-01
3.64834458e-01 2.88263619e-01 -1.17721593e+00 1.23165751e+00
6.90391839e-01 7.93290555e-01 -5.18856794e-02 8.38502705e-01
6.51707828e-01 3.30985725e-01 -4.04335976e-01 1.80598766e-01
8.09453070e-01 -6.94396734e-01 -1.69582278e-01 -1.88782826e-01
7.22691834e-01 -6.08563900e-01 9.62958992e-01 4.59511906e-01
-9.80301499e-01 -3.00021261e-01 -9.76777673e-01 2.11414278e-01
-3.41429234e-01 -1.85677230e-01 3.84801179e-01 2.02996343e-01
-6.47638261e-01 6.45440638e-01 -1.13708532e+00 -2.58306623e-01
9.79299963e-01 2.36105278e-01 -4.23697084e-01 -9.66649130e-02
-4.48141336e-01 7.19291389e-01 1.54445335e-01 6.73260763e-02
-8.07975948e-01 -9.59223211e-01 -8.24585259e-01 -7.31790811e-03
3.19579065e-01 -2.88817763e-01 1.14384329e+00 -6.68781281e-01
-1.07152545e+00 7.60019720e-01 2.34868020e-01 -4.99012560e-01
4.09435660e-01 -3.88062477e-01 -1.88834414e-01 3.12247992e-01
-4.34805937e-02 4.38104600e-01 1.15057158e+00 -9.84304726e-01
-8.82728815e-01 -6.70851707e-01 -3.42492551e-01 1.95417896e-01
-4.40078378e-01 -3.34687829e-01 -2.95279682e-01 -6.20800138e-01
8.39032292e-01 -6.99080944e-01 -2.14065343e-01 3.41462553e-01
-3.77846867e-01 -5.52338123e-01 1.30143726e+00 -3.67112100e-01
7.25087225e-01 -2.52889562e+00 -2.06566095e-01 6.32835925e-01
3.87609035e-01 4.36880827e-01 -2.38946587e-01 -2.80755740e-02
-4.16724145e-01 -2.51916856e-01 -3.60351205e-01 -3.31704050e-01
-1.19117320e-01 2.58406788e-01 -6.98335648e-01 8.01211655e-01
6.24173999e-01 7.24616587e-01 -8.81784022e-01 -1.40152022e-01
4.33045119e-01 2.13787377e-01 -8.31540942e-01 3.77076775e-01
-1.76856458e-01 5.15789449e-01 -6.62532330e-01 9.26491082e-01
8.33064675e-01 2.56244503e-02 -7.21366823e-01 1.43658355e-01
1.14833668e-01 1.59910187e-01 -9.72280681e-01 1.78266084e+00
-8.85036364e-02 5.12091815e-01 -1.73278853e-01 -1.25406694e+00
1.16963947e+00 2.04028279e-01 7.13794649e-01 -7.41523921e-01
-1.03050666e-02 4.94614154e-01 2.03475356e-01 -5.38124621e-01
8.35027322e-02 2.34310597e-01 2.91505039e-01 3.68205100e-01
7.50913396e-02 -2.96919703e-01 -1.87036261e-01 1.45625427e-01
1.25462377e+00 -7.36453803e-03 -1.16828315e-01 1.00347862e-01
6.41761243e-01 -1.62394524e-01 5.69750428e-01 6.46542907e-01
-4.94281560e-01 7.47110069e-01 6.10435545e-01 -7.26995647e-01
-1.02454531e+00 -1.34446132e+00 -1.76904529e-01 7.61778891e-01
1.88773081e-01 -3.83790731e-02 -4.81632948e-01 -1.22366238e+00
3.22490513e-01 6.93542778e-01 -5.12634337e-01 -6.61725640e-01
-7.28949726e-01 -4.09503162e-01 4.12011206e-01 6.07420087e-01
4.06263471e-01 -1.08849883e+00 -6.54421806e-01 -1.08796097e-01
3.49000514e-01 -1.04828703e+00 -3.36165994e-01 -1.61248911e-02
-1.21093869e+00 -1.23160481e+00 -4.01386827e-01 -6.96184278e-01
1.04431736e+00 3.88995230e-01 9.44535375e-01 3.75831425e-01
-4.03276652e-01 7.08912849e-01 -2.38459438e-01 -7.81304181e-01
-2.15359032e-02 -1.06418401e-01 2.71482915e-01 1.33543342e-01
8.35003614e-01 -1.19695318e+00 -6.62235796e-01 2.04616249e-01
-9.14876282e-01 -4.70283747e-01 7.52856612e-01 8.42885852e-01
7.47915924e-01 -1.37849480e-01 3.95347387e-01 -4.89084244e-01
3.25725466e-01 -4.46378440e-01 -6.40511572e-01 -2.40910098e-01
-6.82088673e-01 -5.60910515e-02 4.79158670e-01 -5.55557728e-01
-6.11051202e-01 3.11078951e-02 -2.90908813e-01 -1.18388450e+00
-4.42538351e-01 2.25587022e-02 -1.17048189e-01 -2.79906213e-01
5.56445897e-01 4.63862509e-01 3.06329310e-01 -6.58188581e-01
7.60002285e-02 2.52732068e-01 6.92775428e-01 -4.08393383e-01
1.24538600e+00 6.62202001e-01 8.02432075e-02 -8.85618687e-01
-8.30802023e-01 -5.95432103e-01 -7.98462391e-01 -1.09308854e-01
6.15615010e-01 -7.45631099e-01 -5.40338457e-01 6.69547617e-01
-1.04950988e+00 2.60852017e-02 -7.75582254e-01 4.94471729e-01
-4.31843311e-01 4.13433462e-01 -2.43562847e-01 -7.25520015e-01
-4.68158215e-01 -1.00902581e+00 9.28367138e-01 1.91063881e-01
1.45415112e-01 -8.08835983e-01 -2.59881876e-02 -1.70294374e-01
3.84835601e-01 4.17204380e-01 1.02209842e+00 -1.36138940e+00
-6.46732509e-01 -7.16741920e-01 -3.47520113e-01 5.05372167e-01
7.63447359e-02 -8.91595706e-02 -1.02883506e+00 -3.26353729e-01
2.29422197e-01 -1.51916891e-01 9.10591662e-01 1.65988415e-01
1.61297894e+00 -2.46399298e-01 -2.08116814e-01 8.10881615e-01
8.81615102e-01 -8.04134160e-02 4.14751112e-01 3.67334813e-01
1.07774150e+00 4.52727437e-01 5.90479136e-01 3.37812692e-01
1.68698594e-01 3.96266699e-01 1.09489441e+00 2.72834394e-02
1.63430259e-01 -1.77026704e-01 3.45780134e-01 6.90158486e-01
1.01112209e-01 3.36989731e-01 -1.02999580e+00 7.04515457e-01
-1.85268104e+00 -8.07985663e-01 -3.46367992e-02 2.23979855e+00
2.26521954e-01 2.72399664e-01 1.66567378e-02 2.29762942e-01
4.04716045e-01 7.29351863e-02 -9.43858206e-01 -2.61134237e-01
5.15926890e-02 1.36098593e-01 -3.91797945e-02 -6.58776611e-02
-1.20407557e+00 7.05005527e-01 4.95496464e+00 6.19415343e-01
-1.27536690e+00 -2.35754341e-01 2.98205167e-01 -4.29947793e-01
-9.79580209e-02 -3.38910520e-01 -4.16434824e-01 3.79538298e-01
4.80656236e-01 9.64969620e-02 -4.62717079e-02 1.09050214e+00
-1.34333074e-01 2.41093814e-01 -1.38651574e+00 1.04158771e+00
2.10487187e-01 -9.78437901e-01 1.95156455e-01 2.20057622e-01
6.70038760e-01 3.60806018e-01 2.53615737e-01 4.06184852e-01
-1.69141009e-01 -8.79136205e-01 3.72015566e-01 3.85862738e-01
2.35447958e-01 -9.70629632e-01 8.35152924e-01 4.90681916e-01
-9.50050116e-01 -2.01941609e-01 -5.57302356e-01 1.18857771e-01
-2.38810167e-01 7.49142349e-01 -8.72503936e-01 4.47762728e-01
1.05181623e+00 9.65407610e-01 -7.33123720e-01 1.39612103e+00
-1.82614937e-01 5.40151536e-01 -6.68620706e-01 2.92037368e-01
4.57832754e-01 -4.53615695e-01 1.12617004e+00 7.91995764e-01
5.07195294e-01 -8.53856429e-02 3.62475365e-01 8.96106601e-01
3.17194015e-02 2.18985719e-03 -9.05696332e-01 2.35401750e-01
3.14525753e-01 1.16931009e+00 -5.12044251e-01 -4.91224825e-02
-5.31896114e-01 1.02181530e+00 3.51414770e-01 2.44295880e-01
-3.51296574e-01 -4.01207387e-01 1.12093568e+00 4.72471491e-02
6.29742980e-01 -5.32834977e-02 -3.91814291e-01 -1.11300087e+00
4.41350758e-01 -7.16464102e-01 4.65781093e-01 -3.39967102e-01
-1.64544129e+00 4.59806979e-01 -1.46550730e-01 -1.54709291e+00
-3.18668932e-01 -7.55054116e-01 -1.36395764e+00 7.40933180e-01
-1.47530186e+00 -9.61814880e-01 -4.57893491e-01 7.10456789e-01
3.65579039e-01 -4.41807419e-01 6.33910656e-01 2.51578033e-01
-7.61651456e-01 7.64385045e-01 -1.59868151e-01 4.11908954e-01
4.29207027e-01 -1.52759123e+00 7.85533905e-01 1.07898891e+00
2.81168193e-01 2.97448277e-01 4.36594695e-01 -5.57839930e-01
-1.19869757e+00 -1.19313431e+00 3.32247406e-01 -6.56455338e-01
5.82534194e-01 -3.11712950e-01 -1.57374907e+00 5.98232567e-01
-2.86124468e-01 6.78003013e-01 5.05449712e-01 -2.21544337e-02
-4.85993385e-01 -8.00463185e-02 -1.27616942e+00 5.71350694e-01
9.79137719e-01 -2.58234113e-01 -7.34495521e-01 5.76152951e-02
6.07945502e-01 -6.86310530e-01 -5.66975057e-01 6.17609084e-01
1.63205132e-01 -1.01663101e+00 1.01913977e+00 -8.45363677e-01
2.27258071e-01 -3.41280341e-01 -1.82388145e-02 -1.44358981e+00
-8.30858350e-02 -1.60723463e-01 -6.47375882e-01 9.09485102e-01
2.12589413e-01 -7.78866768e-01 1.07689905e+00 4.16844875e-01
-6.14864945e-01 -1.30957425e+00 -1.21583891e+00 -8.38609457e-01
1.18800968e-01 -6.02981865e-01 6.99073255e-01 9.31688309e-01
-4.81480151e-01 3.01391706e-02 1.66025713e-01 6.24031246e-01
7.08678603e-01 2.70348012e-01 9.43070471e-01 -1.69586229e+00
1.97473064e-01 -6.75793767e-01 -9.55251992e-01 -1.04393589e+00
5.39469458e-02 -9.62991118e-01 4.62453961e-02 -1.18724692e+00
-2.97006607e-01 -4.51411337e-01 -4.40860003e-01 5.23926735e-01
-2.35206157e-01 2.85713315e-01 3.60436328e-02 2.20523223e-01
-5.52677035e-01 9.99494314e-01 1.15755439e+00 -1.00836083e-01
-4.50776100e-01 4.55525279e-01 -4.77420539e-01 1.06473255e+00
6.77379191e-01 -4.82191712e-01 -2.83493787e-01 -2.99010575e-01
8.06891769e-02 -6.72407091e-01 5.62548816e-01 -1.10916722e+00
2.81080157e-01 1.09929591e-01 6.66887999e-01 -9.76448536e-01
1.88385248e-01 -1.10589576e+00 -8.32332015e-01 3.61632079e-01
1.28757566e-01 3.02611858e-01 3.02702844e-01 8.25186729e-01
-2.28208885e-01 -1.63036197e-01 6.73498750e-01 2.20304132e-01
-6.52225375e-01 9.90573883e-01 -4.61994112e-02 1.15911201e-01
1.07718229e+00 -3.68399024e-01 -9.20856819e-02 -1.53644845e-01
-4.65511262e-01 5.41561961e-01 3.39942694e-01 6.12826347e-01
1.08739829e+00 -1.59184241e+00 -7.13721335e-01 7.39845455e-01
4.70790863e-01 8.58389676e-01 2.63625324e-01 1.08397353e+00
-3.43347371e-01 2.77061295e-03 -2.03160301e-01 -1.23795068e+00
-9.99624789e-01 4.32612300e-01 4.38731283e-01 3.90482582e-02
-1.30433643e+00 8.62373531e-01 2.76372463e-01 -7.82404244e-01
5.66496372e-01 -3.62020046e-01 -6.38096631e-02 -1.64095119e-01
5.81426442e-01 3.75761926e-01 2.88004875e-01 -2.80380249e-01
-4.23450798e-01 4.83152300e-01 -4.96963799e-01 4.32962954e-01
1.35732484e+00 3.16958636e-01 4.57269289e-02 3.80946726e-01
1.04684615e+00 -1.48972273e-01 -1.40154505e+00 -5.51337183e-01
1.72486141e-01 -5.89861989e-01 6.32802397e-02 -2.25523040e-01
-1.33350134e+00 1.08862925e+00 8.94049108e-01 3.49092722e-01
1.21197534e+00 7.22683892e-02 9.25095081e-01 6.69462562e-01
-2.09673300e-01 -9.12274599e-01 5.31368375e-01 6.57332540e-01
8.12480986e-01 -1.50897264e+00 -1.03710689e-01 -1.62656635e-01
-3.35401356e-01 9.78542030e-01 1.21797943e+00 -5.80726922e-01
7.69654036e-01 -1.75360829e-01 2.15960652e-01 -5.06337523e-01
-5.72036982e-01 -3.60748172e-02 6.31804168e-01 7.01130807e-01
-3.46406251e-02 -3.69877577e-01 5.21473825e-01 3.83352757e-01
-1.81201771e-01 -6.43471897e-01 2.54558980e-01 8.29043269e-01
-6.76490366e-01 -6.70378923e-01 -2.56189674e-01 8.52719665e-01
-2.94608116e-01 9.40993130e-02 -3.51780325e-01 6.24088705e-01
1.07376995e-02 4.17782187e-01 7.34846532e-01 -2.38986403e-01
7.21573591e-01 1.69500843e-01 3.31674308e-01 -6.21455967e-01
-4.61267054e-01 -2.75077462e-01 -7.15207815e-01 -8.90294135e-01
1.67576611e-01 -6.23198152e-01 -1.34455502e+00 -1.88272074e-01
-3.73931587e-01 7.59969279e-02 5.69886804e-01 1.07737434e+00
5.91528475e-01 5.50641835e-01 9.01819170e-01 -9.76866961e-01
-8.14600766e-01 -8.81628811e-01 -4.42245901e-01 6.07139349e-01
7.61369407e-01 -7.44599938e-01 -6.90019786e-01 -5.88616431e-01] | [7.633332252502441, 2.166276693344116] |
2b7e088b-ce3a-4c4d-89ee-85253b238fab | evaluating-pre-trained-language-models-on | null | null | https://aclanthology.org/2022.sdp-1.22 | https://aclanthology.org/2022.sdp-1.22.pdf | Evaluating Pre-Trained Language Models on Multi-Document Summarization for Literature Reviews | Systematic literature reviews in the biomedical space are often expensive to conduct. Automation through machine learning and large language models could improve the accuracy and research outcomes from such reviews. In this study, we evaluate a pre-trained LongT5 model on the MSLR22: Multi-Document Summarization for Literature Reviews Shared Task datasets. We weren’t able to make any improvements on the dataset benchmark, but we do establish some evidence that current summarization metrics are insufficient in measuring summarization accuracy. A multi-document summarization web tool was also built to demonstrate the viability of summarization models for future investigators: https://ben-yu.github.io/summarizer | ['Benjamin Yu'] | null | null | null | null | sdp-coling-2022-10 | ['document-summarization'] | ['natural-language-processing'] | [ 7.72917792e-02 1.77119106e-01 -6.92016423e-01 -2.60415226e-01
-1.69420266e+00 -4.72208142e-01 5.24410963e-01 8.15796971e-01
-3.86303216e-01 1.16907132e+00 1.07790899e+00 -6.37126982e-01
-2.85786480e-01 -1.00967944e-01 -6.08554006e-01 -4.64332709e-03
2.35038653e-01 4.41709578e-01 -3.12471837e-01 2.14033216e-01
7.90820479e-01 1.24503210e-01 -6.82749808e-01 6.55924082e-01
9.24259186e-01 4.00693994e-03 4.18385491e-02 1.01111722e+00
-2.09544510e-01 7.95285702e-01 -9.28028584e-01 -2.30399400e-01
-3.39380771e-01 -5.30608892e-01 -9.77332056e-01 -4.94404197e-01
6.11692667e-01 -1.65947899e-01 -1.29423868e-02 5.62933266e-01
1.02597904e+00 -2.42540270e-01 7.59743571e-01 -7.31864452e-01
-8.40146959e-01 1.02354586e+00 -6.33972406e-01 6.16695106e-01
4.91173506e-01 1.59375116e-01 1.11254096e+00 -6.82197094e-01
1.03450811e+00 1.11832929e+00 7.61557937e-01 4.64447856e-01
-1.05468178e+00 -7.69557118e-01 -1.95702910e-01 -6.05184436e-02
-8.65566671e-01 -8.86181295e-01 4.57864195e-01 -4.67484295e-01
1.33564270e+00 3.89353454e-01 4.58021492e-01 1.27557874e+00
8.63577485e-01 2.92376608e-01 9.28380489e-01 -2.41046935e-01
2.79245734e-01 -8.31844136e-02 6.76599860e-01 3.85166436e-01
1.06349599e+00 -6.33907616e-01 -6.05844319e-01 -6.87124193e-01
3.73931378e-02 -2.59533226e-01 -3.41634117e-02 4.46674168e-01
-1.22226083e+00 9.00627732e-01 1.14706075e-02 4.04214859e-01
-4.68185037e-01 2.99983323e-01 9.71542478e-01 2.43490428e-01
9.92620885e-01 8.73348951e-01 -4.81532037e-01 -3.09983462e-01
-1.67862070e+00 3.24158311e-01 8.97107899e-01 8.10245156e-01
-8.09424818e-02 -2.08083391e-01 -4.52503949e-01 8.45173120e-01
2.27540135e-01 3.98921102e-01 5.83908856e-01 -1.20434952e+00
6.21991992e-01 3.96190852e-01 -1.84364289e-01 -8.86883438e-01
-8.03823113e-01 -4.31716561e-01 -7.61663973e-01 -5.69067001e-01
-1.56842992e-02 -5.76847017e-01 -6.60445809e-01 1.17403734e+00
-2.87491947e-01 -1.01691738e-01 2.14240044e-01 3.30723703e-01
1.81333125e+00 5.83714724e-01 4.53832269e-01 -5.69580078e-01
1.46581388e+00 -7.57907808e-01 -1.16630077e+00 -2.18525723e-01
1.20575142e+00 -1.01836371e+00 5.76286435e-01 2.63355881e-01
-1.44350708e+00 -1.46452770e-01 -9.80376542e-01 -4.01492774e-01
-3.24321359e-01 4.09579039e-01 5.58483124e-01 5.43532312e-01
-1.18073320e+00 4.98091280e-01 -8.09958398e-01 -6.86898589e-01
8.02268624e-01 -1.49121629e-02 -4.06601876e-01 1.26768351e-01
-9.22901154e-01 1.13640738e+00 1.19687185e-01 -3.30252782e-03
-6.29584849e-01 -1.10015976e+00 -6.98059320e-01 2.66840565e-03
-4.46399711e-02 -1.17974937e+00 1.22348869e+00 4.73335162e-02
-1.03602529e+00 9.02951300e-01 -4.52417731e-01 -5.69935262e-01
1.40744388e-01 -1.37460589e-01 -3.03866327e-01 3.71753871e-01
5.01549184e-01 4.11817729e-01 -6.08683117e-02 -5.33182144e-01
-2.24954903e-01 -4.70446736e-01 -5.37735462e-01 8.58025998e-02
-8.66933838e-02 5.17082274e-01 -2.08396260e-02 -6.19802773e-01
-5.32223821e-01 -7.24496841e-01 -5.20940423e-01 -6.10543609e-01
-6.60123289e-01 -3.77785385e-01 1.26717821e-01 -9.99678016e-01
1.60382974e+00 -1.42624915e+00 -2.78904259e-01 -3.50144655e-01
2.91276217e-01 3.08018297e-01 -4.03590262e-01 6.02716386e-01
-1.55974567e-01 9.49731410e-01 -1.59413457e-01 -4.19973403e-01
-3.61994565e-01 -3.43455255e-01 -2.24629223e-01 4.71065491e-01
3.65187615e-01 1.30779696e+00 -9.00544882e-01 -7.03407645e-01
-2.24137753e-01 2.53118753e-01 -4.65119272e-01 -2.99441874e-01
-5.28810471e-02 1.35957792e-01 -6.71349585e-01 4.61599767e-01
3.62131149e-01 -4.01704431e-01 2.41804466e-01 5.26484326e-02
-3.27486873e-01 9.73992407e-01 -5.44550598e-01 1.97249925e+00
-2.58077651e-01 6.69294298e-01 -2.14908525e-01 -1.04373598e+00
6.78974867e-01 4.89151508e-01 5.36025584e-01 -3.69920373e-01
2.85972692e-02 4.02539581e-01 1.13675162e-01 -5.61142385e-01
4.83935803e-01 -1.78958790e-03 -1.99003503e-01 5.88542759e-01
-5.63294142e-02 -4.43450511e-01 5.01248837e-01 5.04502177e-01
1.47790706e+00 -1.58096641e-01 7.03219652e-01 -5.62862813e-01
1.72836259e-01 3.92222971e-01 3.51751029e-01 1.04346263e+00
6.74777031e-02 9.52304125e-01 7.73593307e-01 -1.27034634e-01
-9.12577450e-01 -6.24550462e-01 -4.31284040e-01 5.54759860e-01
-9.19336736e-01 -1.14618254e+00 -5.83181262e-01 -5.45860291e-01
-2.15192422e-01 1.08121765e+00 -4.12110716e-01 -1.23241551e-01
-1.65789738e-01 -1.10937691e+00 9.57827449e-01 1.34150520e-01
-4.82945889e-02 -1.08820939e+00 -6.95437014e-01 3.73276293e-01
-1.70727625e-01 -9.97882783e-01 -4.84814167e-01 1.14579059e-01
-1.15445709e+00 -1.05316591e+00 -1.00185490e+00 -4.82393980e-01
2.19977468e-01 -3.16585004e-02 1.25991118e+00 -1.64547533e-01
-4.99252439e-01 4.04210061e-01 -2.37976730e-01 -1.12065530e+00
-6.13830209e-01 4.45439577e-01 -1.55493557e-01 -1.27072108e+00
5.56909442e-01 -3.10442746e-01 -7.59703219e-01 -5.40950775e-01
-7.06328511e-01 -1.61560416e-01 7.25117207e-01 5.63956320e-01
3.86163384e-01 -4.87729847e-01 1.48476076e+00 -1.16640341e+00
1.34348607e+00 -6.31817818e-01 3.90809923e-02 1.68433785e-01
-7.31299698e-01 -3.51914391e-02 2.05432877e-01 -1.23373948e-01
-6.23709023e-01 -4.87985581e-01 -2.68756121e-01 3.50866467e-01
4.66016121e-02 1.25104666e+00 4.81847644e-01 4.35777396e-01
7.72441924e-01 -2.03477129e-01 1.13892093e-01 -5.06115317e-01
3.89756113e-01 8.53562057e-01 7.79252872e-02 -1.91746965e-01
1.87575985e-02 4.14167643e-02 -4.85730693e-02 -9.39473450e-01
-8.71198475e-01 -5.91543436e-01 -1.13545209e-01 3.45277935e-01
8.64633203e-01 -1.05123985e+00 -4.91921633e-01 -2.13371634e-01
-1.35757053e+00 -1.46590248e-01 -3.10728550e-01 6.04960144e-01
-3.28686476e-01 6.06129408e-01 -7.79513478e-01 -4.08477277e-01
-1.29245532e+00 -1.12598968e+00 9.89472747e-01 1.89674288e-01
-1.05410039e+00 -1.01659787e+00 4.42484021e-01 5.16045749e-01
3.73121470e-01 1.71755791e-01 7.85956502e-01 -1.09981799e+00
1.78290859e-01 -2.85787225e-01 -1.14753097e-01 8.33761990e-02
2.91163385e-01 3.58082294e-01 -5.99736631e-01 6.39607161e-02
-7.58537427e-02 -2.54659683e-01 1.01863003e+00 1.16773856e+00
1.13131714e+00 -4.78833586e-01 -5.27060807e-01 -5.50862886e-02
9.39088225e-01 5.56682097e-03 5.14887452e-01 2.94151962e-01
5.19553185e-01 4.58718717e-01 3.61239374e-01 3.21061790e-01
5.45798302e-01 1.85573310e-01 -6.47857666e-01 -7.30536208e-02
-3.77439380e-01 3.28759961e-02 2.99315929e-01 1.05138922e+00
3.48422855e-01 -2.36243054e-01 -1.09965289e+00 6.43024445e-01
-1.70741928e+00 -9.21445012e-01 -3.73398811e-01 1.75692618e+00
1.24710774e+00 4.30571645e-01 -5.64802513e-02 -6.36061430e-01
2.53201038e-01 1.49419844e-01 -3.16856414e-01 -1.05193853e+00
-1.83645442e-01 3.51386428e-01 4.04042661e-01 2.89856821e-01
-7.15152919e-01 6.98422253e-01 7.06549120e+00 6.96173191e-01
-7.99274266e-01 2.60799170e-01 8.96449745e-01 -4.44916576e-01
-5.23661137e-01 6.86792210e-02 -8.03190827e-01 4.32089478e-01
1.61007583e+00 -8.18142653e-01 -3.73413861e-01 2.96183348e-01
8.07717383e-01 -2.45256707e-01 -1.11322904e+00 6.31077528e-01
1.48692340e-01 -1.96865165e+00 3.40728641e-01 -1.45515949e-02
7.97833562e-01 3.22760075e-01 -1.66402996e-01 3.10868949e-01
2.11961299e-01 -1.11285591e+00 5.13363928e-02 6.72404945e-01
8.56975138e-01 -4.52253670e-01 8.98518980e-01 1.46411583e-01
-3.50345105e-01 3.64541143e-01 -5.02841234e-01 1.83879524e-01
2.55670965e-01 1.04974580e+00 -1.14392674e+00 8.00571680e-01
3.27932060e-01 1.06465352e+00 -9.96965826e-01 1.29983449e+00
1.27495276e-02 1.03345382e+00 -6.27672765e-03 -2.60277122e-01
1.04534000e-01 1.66634098e-02 8.13771009e-01 1.70177054e+00
3.57430905e-01 8.02716911e-02 -2.26206005e-01 6.89528406e-01
-4.48116452e-01 4.03639585e-01 -8.80580902e-01 -5.90893984e-01
6.84899688e-02 1.22766185e+00 -6.24029338e-01 -4.54602957e-01
-3.79254937e-01 2.97319323e-01 1.16469324e-01 2.63183266e-01
-4.24116611e-01 -4.14154649e-01 1.37973934e-01 -1.32366017e-01
-1.67566031e-01 5.74902482e-02 -8.20499301e-01 -1.13833404e+00
-2.04508990e-01 -1.04203594e+00 7.52411902e-01 -8.89293432e-01
-1.07883084e+00 3.17973793e-01 -6.69727055e-03 -7.27512538e-01
-1.67833537e-01 -1.35049239e-01 -5.89259744e-01 9.05332565e-01
-1.14373171e+00 -9.27299798e-01 4.97235894e-01 -3.44078958e-01
8.54459226e-01 -2.24750593e-01 8.09558988e-01 -1.27646029e-02
-7.59402514e-01 3.40785831e-01 2.35892370e-01 -3.36459368e-01
1.25423932e+00 -1.20067728e+00 5.05049109e-01 5.19017994e-01
-2.65656978e-01 1.23243678e+00 9.46140885e-01 -1.24241519e+00
-1.22057009e+00 -9.60693598e-01 1.33822918e+00 -1.00886571e+00
8.00370932e-01 9.74151641e-02 -7.78167069e-01 7.68156648e-01
7.07772493e-01 -8.53371263e-01 1.07365513e+00 4.59875852e-01
4.57610600e-02 1.21453509e-01 -8.23750913e-01 7.96470702e-01
6.50741160e-01 -3.16981047e-01 -1.01830602e+00 7.39821076e-01
7.86504745e-01 -1.29336327e-01 -1.19027662e+00 4.78994638e-01
4.57955629e-01 -8.02159607e-02 9.55283523e-01 -8.67838025e-01
1.14596283e+00 1.21356249e-01 5.09686172e-01 -1.43349671e+00
-3.08952600e-01 -4.96012300e-01 8.87084901e-02 1.39657724e+00
9.65199649e-01 -5.51454961e-01 5.69477439e-01 5.66204488e-01
-3.77535313e-01 -7.52750218e-01 -7.97652841e-01 -3.02931160e-01
7.22346365e-01 -3.14323217e-01 9.11734030e-02 7.20302880e-01
5.67658901e-01 9.64924634e-01 1.44027591e-01 -3.55956167e-01
4.92786646e-01 -4.23522443e-02 5.38354754e-01 -9.39607263e-01
2.04998687e-01 -8.42732251e-01 1.61466226e-01 -1.43714204e-01
4.09102559e-01 -1.26260459e+00 -2.73736149e-01 -2.49185991e+00
9.50284660e-01 1.19672820e-01 -2.82991767e-01 4.92653817e-01
-5.01450658e-01 -2.71849558e-02 -1.84875250e-01 1.88959539e-01
-9.17395890e-01 2.31412157e-01 1.12375343e+00 -3.05106282e-01
-1.29785493e-01 -4.62396815e-02 -1.50078928e+00 2.94674784e-01
9.89617884e-01 -8.24276686e-01 -2.88754612e-01 -3.36612999e-01
3.04647744e-01 2.42860541e-01 -2.94440007e-03 -6.53623939e-01
4.10803109e-01 7.68186748e-02 4.04030651e-01 -7.54078209e-01
-3.55615675e-01 1.06724184e-02 1.39296085e-01 6.00654125e-01
-1.05661654e+00 2.89735764e-01 7.17117548e-01 2.96648324e-01
1.29750237e-01 -5.53577662e-01 1.94180489e-01 -3.43655139e-01
2.10454375e-01 -7.40244910e-02 -7.87480295e-01 2.06921920e-01
3.69893730e-01 3.80270094e-01 -9.11859512e-01 -3.20026904e-01
-4.02135164e-01 5.48108995e-01 1.31353125e-01 3.66603613e-01
4.84662175e-01 -6.96660876e-01 -1.33543789e+00 -7.24365473e-01
1.08496912e-01 -2.21742153e-01 3.40012640e-01 1.05856955e+00
-6.02949321e-01 1.15175366e+00 3.61526683e-02 -1.38150260e-01
-1.45659232e+00 4.79324073e-01 -1.11642659e-01 -8.00768495e-01
-6.90445781e-01 2.97329068e-01 -8.76399949e-02 -3.48908693e-01
5.01139797e-02 -5.88531256e-01 -5.57842195e-01 4.16192830e-01
7.85523653e-01 5.34945607e-01 4.28981096e-01 -2.20346525e-02
-5.45378864e-01 1.82919845e-01 -3.82927626e-01 -2.87804991e-01
1.67631090e+00 -1.67934313e-01 -3.54190439e-01 6.76137030e-01
1.28499508e+00 1.53219342e-01 2.59023011e-02 2.44646713e-01
3.53364527e-01 4.34050292e-01 2.96566278e-01 -1.14720416e+00
-3.63585174e-01 5.90153992e-01 9.15937200e-02 3.70688178e-02
7.31307209e-01 -5.18594906e-02 5.18291712e-01 6.01121128e-01
-3.49009991e-01 -1.01005805e+00 -1.34931579e-01 3.65123838e-01
1.21752524e+00 -1.15952468e+00 8.58009636e-01 6.30637482e-02
-8.14072430e-01 1.11574674e+00 5.69313839e-02 8.23084638e-02
4.10911411e-01 2.43097588e-01 -3.41148786e-02 -7.84140706e-01
-1.21731174e+00 1.66319653e-01 5.22434950e-01 2.05576897e-01
9.97420251e-01 2.39654891e-02 -1.26703227e+00 8.22931767e-01
-2.38838851e-01 4.23714846e-01 1.07191980e+00 8.22915852e-01
-2.75404588e-03 -9.58158314e-01 -1.03325218e-01 1.23064506e+00
-1.22079968e+00 -4.12854582e-01 -5.75526416e-01 4.88783568e-01
-6.44247711e-01 1.31396925e+00 -3.75313669e-01 1.99410200e-01
2.95275033e-01 7.29884580e-02 1.91614792e-01 -1.12339377e+00
-8.30106437e-01 2.21744716e-01 6.68272197e-01 -2.37459421e-01
-5.02517581e-01 -9.58343089e-01 -1.02126348e+00 -1.71280786e-01
-6.50105327e-02 5.91165304e-01 8.11562181e-01 6.05908215e-01
8.12021196e-01 9.01562095e-01 1.54488042e-01 -2.74688900e-01
-4.60819334e-01 -1.19931030e+00 -1.92839757e-01 -6.48080781e-02
2.74571329e-01 4.02618833e-02 -2.85814792e-01 1.30710304e-01] | [12.36037540435791, 9.604561805725098] |
a8e9ee19-005f-4874-a407-a26b42caca13 | measuring-faithful-and-plausible-visual | 2305.15015 | null | https://arxiv.org/abs/2305.15015v1 | https://arxiv.org/pdf/2305.15015v1.pdf | Measuring Faithful and Plausible Visual Grounding in VQA | Metrics for Visual Grounding (VG) in Visual Question Answering (VQA) systems primarily aim to measure a system's reliance on relevant parts of the image when inferring an answer to the given question. Lack of VG has been a common problem among state-of-the-art VQA systems and can manifest in over-reliance on irrelevant image parts or a disregard for the visual modality entirely. Although inference capabilities of VQA models are often illustrated by a few qualitative illustrations, most systems are not quantitatively assessed for their VG properties. We believe, an easily calculated criterion for meaningfully measuring a system's VG can help remedy this shortcoming, as well as add another valuable dimension to model evaluations and analysis. To this end, we propose a new VG metric that captures if a model a) identifies question-relevant objects in the scene, and b) actually relies on the information contained in the relevant objects when producing its answer, i.e., if its visual grounding is both "faithful" and "plausible". Our metric, called "Faithful and Plausible Visual Grounding" (FPVG), is straightforward to determine for most VQA model designs. We give a detailed description of FPVG and evaluate several reference systems spanning various VQA architectures. Code to support the metric calculations on the GQA data set is available on GitHub. | ['Tanja Schultz', 'Felix Putze', 'Daniel Reich'] | 2023-05-24 | null | null | null | null | ['visual-grounding'] | ['computer-vision'] | [-4.69224714e-03 1.75214857e-01 1.37511846e-02 -4.50256407e-01
-7.26876318e-01 -8.27153921e-01 7.83099592e-01 3.68239433e-01
1.39909685e-01 4.08544332e-01 1.95692927e-01 -5.46754897e-01
-2.48802930e-01 -7.71354914e-01 -5.17599463e-01 -3.02044272e-01
5.78563869e-01 3.23431104e-01 3.90463918e-01 -4.79260713e-01
3.86996001e-01 5.06417811e-01 -1.82703781e+00 6.48604810e-01
7.96616614e-01 9.76852536e-01 2.28347629e-01 7.86855102e-01
-4.45308328e-01 1.42215931e+00 -8.30511987e-01 -6.08896494e-01
-5.74937426e-02 -7.13385820e-01 -1.26962566e+00 1.07987046e-01
9.15612638e-01 -3.16718578e-01 -2.33029187e-01 1.11705291e+00
1.01760648e-01 -1.53766423e-01 7.36952007e-01 -1.70662963e+00
-9.24012780e-01 1.24002524e-01 -6.73412904e-02 4.52165008e-01
7.99801469e-01 6.37842774e-01 1.42395103e+00 -8.89991045e-01
6.96477413e-01 1.45531309e+00 1.81954965e-01 4.65733051e-01
-1.22908401e+00 -1.40958980e-01 7.89280385e-02 5.38009226e-01
-1.36118150e+00 -3.68291557e-01 9.08337653e-01 -5.87204218e-01
8.40052068e-01 7.39093542e-01 7.07772911e-01 7.43341446e-01
2.10672989e-01 7.10143268e-01 1.28080285e+00 -4.89188761e-01
3.14970851e-01 3.11166406e-01 3.86823177e-01 8.24062407e-01
2.38694251e-01 -1.13101788e-01 -6.76841557e-01 -1.07818633e-01
4.92126197e-01 -4.17782575e-01 -3.25961560e-01 -5.42815804e-01
-1.05289400e+00 8.51542711e-01 7.27792621e-01 2.41864130e-01
-4.09144223e-01 1.54816151e-01 -5.84198255e-03 5.30908644e-01
-2.68469099e-02 5.87530196e-01 1.24113344e-01 -6.55504167e-02
-8.07842255e-01 4.36445445e-01 6.22710288e-01 8.08972776e-01
9.63475525e-01 2.04662476e-02 -6.23320520e-01 4.14177865e-01
4.93644208e-01 4.78063285e-01 5.94458692e-02 -1.32274854e+00
1.86003849e-01 1.22055805e+00 2.60609537e-01 -1.21889997e+00
-2.28597239e-01 -5.39093800e-02 -3.55723202e-01 5.16970754e-01
6.62125051e-01 5.23700893e-01 -8.84638369e-01 1.68324113e+00
3.44027847e-01 -5.65963387e-01 1.22807501e-02 1.18247020e+00
1.47370422e+00 4.38195437e-01 2.85025060e-01 1.15797110e-01
1.66264856e+00 -7.08307445e-01 -7.51565695e-01 -4.23931718e-01
4.25908536e-01 -8.16282332e-01 1.79285848e+00 1.96111396e-01
-1.13928318e+00 -7.04909205e-01 -1.05414999e+00 -5.04117429e-01
-4.87587810e-01 -1.90319702e-01 4.19179052e-01 7.38984466e-01
-1.34824336e+00 9.24513116e-02 -7.65977874e-02 -3.96302760e-01
2.01951161e-01 -5.37320338e-02 -3.62834066e-01 -1.93454221e-01
-1.00018096e+00 1.18330586e+00 5.01917228e-02 -5.42069711e-02
-1.06235051e+00 -4.08451259e-01 -7.62637436e-01 2.11748764e-01
3.56005341e-01 -1.02775013e+00 1.27745247e+00 -1.11909974e+00
-9.29929674e-01 1.04913306e+00 -3.39619190e-01 -2.13490635e-01
3.86299163e-01 1.82508543e-01 -5.10412991e-01 6.91732526e-01
1.25141412e-01 8.62764776e-01 8.53226900e-01 -1.74785626e+00
-3.18066508e-01 -3.91108900e-01 8.54098439e-01 1.99848637e-01
1.90838352e-01 -5.26247099e-02 -4.48898196e-01 -2.14489281e-01
1.95985794e-01 -5.71168125e-01 1.48573264e-01 4.31461394e-01
-3.24274182e-01 -2.59482563e-01 7.92603731e-01 -6.68789268e-01
1.27445006e+00 -1.84878385e+00 -1.91004336e-01 2.65352011e-01
5.04549503e-01 2.95029074e-01 -2.44055390e-01 6.23113453e-01
1.12075232e-01 1.55246958e-01 -1.86294124e-01 1.30060196e-01
1.86927691e-01 4.54105645e-01 -3.94046485e-01 2.14418143e-01
4.52606440e-01 1.25548267e+00 -9.82428014e-01 -8.69428217e-01
4.93704319e-01 4.08165276e-01 -3.80817443e-01 3.53352278e-01
-4.31341380e-01 2.18413815e-01 -3.59234184e-01 7.20248759e-01
5.06379366e-01 -6.20231688e-01 5.84023856e-02 -4.76092219e-01
6.63550347e-02 2.32345775e-01 -9.86984789e-01 1.21562910e+00
-1.14636712e-01 8.51232708e-01 -1.59070626e-01 -5.23277462e-01
9.70498919e-01 2.20940098e-01 -4.40506190e-02 -1.24193585e+00
1.54519994e-02 -1.80124249e-02 1.17984526e-01 -7.45721161e-01
7.15693116e-01 -3.51547860e-02 2.07455009e-01 3.99028420e-01
1.03643306e-01 -5.67968488e-01 3.20057273e-01 6.43614709e-01
8.64757597e-01 1.48045853e-01 4.98053640e-01 -3.28438312e-01
6.31047189e-01 4.34134781e-01 8.15479606e-02 9.82240975e-01
-5.34398019e-01 7.57418096e-01 7.94393420e-01 -3.78298581e-01
-1.01222551e+00 -1.27089894e+00 5.81034645e-02 9.27961409e-01
4.64448005e-01 -5.23828328e-01 -8.97147775e-01 -6.36342347e-01
-1.48693085e-01 9.65660930e-01 -7.79373825e-01 -1.80197015e-01
-2.22420007e-01 6.67494461e-02 5.17725468e-01 4.18271393e-01
3.69795054e-01 -1.27081919e+00 -9.17628765e-01 -3.30419898e-01
-4.73516554e-01 -8.42333078e-01 -1.23300441e-01 -3.58963549e-01
-7.05674767e-01 -1.37014246e+00 -4.11781371e-01 -4.39005464e-01
7.12395847e-01 6.89928174e-01 1.75894034e+00 6.43817246e-01
-1.09916944e-02 7.52858698e-01 -4.27938879e-01 -3.03712696e-01
-5.94410181e-01 -4.01664078e-01 -4.75695610e-01 -9.62376744e-02
4.12586570e-01 1.31098405e-01 -8.44942570e-01 3.93736601e-01
-1.06871235e+00 1.70665726e-01 3.18840742e-01 5.34351647e-01
7.31136382e-01 -4.84238654e-01 4.40647990e-01 -8.00563633e-01
8.54308009e-01 -2.80721068e-01 -3.46441597e-01 6.63364649e-01
-5.94880819e-01 1.87697440e-01 2.82402426e-01 -3.20231915e-03
-9.57848608e-01 -3.17861319e-01 -1.92248777e-01 -3.93624216e-01
-9.79785249e-02 4.26582009e-01 -2.79090017e-01 -1.15864240e-01
9.29820001e-01 1.30091049e-02 -8.92176330e-02 -1.39130056e-01
7.40010679e-01 4.16828007e-01 6.12146080e-01 -5.30381322e-01
6.38507307e-01 6.17647469e-01 -6.96498454e-02 -7.93097675e-01
-8.26843500e-01 -6.19964957e-01 -2.92592257e-01 -6.37920976e-01
8.41691911e-01 -4.84166324e-01 -9.87800002e-01 -1.55666411e-01
-1.16581738e+00 -1.26981169e-01 -4.81742650e-01 1.11759342e-02
-5.67694664e-01 5.40038466e-01 -6.74545541e-02 -9.43946660e-01
-2.94305593e-01 -1.18069744e+00 9.40302372e-01 2.37399668e-01
-5.30939341e-01 -8.83341730e-01 -4.53347415e-02 7.87294209e-01
3.47957402e-01 1.15562178e-01 1.16049445e+00 -2.86027879e-01
-5.38347006e-01 -3.16979475e-02 -7.60570109e-01 3.16481113e-01
-7.29983374e-02 2.10090995e-01 -1.21654832e+00 -1.08058080e-01
1.12357162e-01 -3.00714314e-01 6.11031532e-01 2.51364648e-01
8.45584393e-01 -3.67882043e-01 1.46116734e-01 8.44125673e-02
1.49989462e+00 5.70277348e-02 8.24580431e-01 1.02782547e-01
7.25478113e-01 8.87405753e-01 8.01934779e-01 1.30426586e-01
6.41334176e-01 7.08642483e-01 9.02211964e-01 -1.89049184e-01
-6.02770448e-01 -3.95917743e-01 8.92632827e-02 5.30440271e-01
-7.00538009e-02 -3.90996963e-01 -1.12148821e+00 6.54216826e-01
-1.70123363e+00 -9.32694256e-01 -5.79182923e-01 2.06727982e+00
5.44381499e-01 -2.11809147e-02 6.53894395e-02 1.97431654e-01
4.98300791e-01 2.32083902e-01 -3.03099990e-01 -6.48019612e-01
-3.53422582e-01 4.98117618e-02 -1.59435615e-01 6.68374896e-01
-5.13267398e-01 7.81231046e-01 6.92296124e+00 6.00465178e-01
-7.52518237e-01 7.88925812e-02 5.10054708e-01 2.30355784e-01
-8.58372450e-01 3.63790452e-01 -2.60328323e-01 1.90475240e-01
7.52388239e-01 -1.49877980e-01 1.86840475e-01 7.47981310e-01
1.69403434e-01 -3.96796733e-01 -1.20145833e+00 9.74958241e-01
1.57553166e-01 -1.45379949e+00 6.56888068e-01 -1.95089027e-01
3.79419684e-01 -4.03344065e-01 1.09803401e-01 5.59426583e-02
1.20354265e-01 -1.25543678e+00 1.22613561e+00 6.67079687e-01
7.18786418e-01 -5.67289829e-01 6.23647749e-01 9.98705477e-02
-1.07891536e+00 1.70699432e-01 -3.96907836e-01 -2.31409937e-01
1.07783064e-01 1.51906669e-01 -7.53578961e-01 4.88671929e-01
6.60794139e-01 5.34227304e-02 -1.14986479e+00 8.69014502e-01
-5.15335679e-01 4.94034022e-01 1.30540088e-01 -8.57986286e-02
2.90801764e-01 -4.16096766e-03 5.47789335e-01 9.23596740e-01
5.04055507e-02 8.13631564e-02 -3.25138509e-01 1.24941862e+00
1.81209445e-01 1.21629685e-01 -5.72359741e-01 -9.79767591e-02
3.74959648e-01 1.06994557e+00 -6.20454431e-01 -3.83940876e-01
-4.97403800e-01 7.41842151e-01 2.13210970e-01 5.25574625e-01
-6.98456049e-01 -1.08793683e-01 4.63957161e-01 3.00585717e-01
2.79572941e-02 2.35726498e-03 -3.65978748e-01 -9.10127699e-01
1.25365570e-01 -1.15823650e+00 6.20347381e-01 -1.65138865e+00
-1.13635445e+00 7.71595597e-01 -1.35497088e-02 -1.14536917e+00
-3.08764458e-01 -7.54846811e-01 -4.33715284e-01 9.61303353e-01
-1.38673961e+00 -1.40079200e+00 -8.43037665e-01 8.45104396e-01
2.48082876e-01 2.68521577e-01 7.47041583e-01 -9.47631672e-02
1.10728843e-02 3.72624576e-01 -6.39384747e-01 -2.28582978e-01
4.59441960e-01 -1.34929514e+00 2.89470941e-01 1.01645100e+00
5.88151753e-01 6.40738606e-01 1.04623556e+00 -3.65099907e-01
-1.29127932e+00 -6.17666066e-01 1.02880442e+00 -9.68314528e-01
4.06189591e-01 -3.89784612e-02 -1.21194839e+00 4.68133599e-01
2.79930323e-01 -1.65875748e-01 3.90825927e-01 -7.97990039e-02
-8.04008842e-01 -2.23267823e-02 -1.14344537e+00 7.35633850e-01
8.00880253e-01 -9.74865794e-01 -8.92003953e-01 6.82644472e-02
6.24410093e-01 -1.58952639e-01 -7.03165591e-01 3.06387424e-01
4.52109098e-01 -1.44071925e+00 1.09353721e+00 -5.93360424e-01
4.30166900e-01 -6.82400167e-01 -3.05571347e-01 -8.94727528e-01
-4.06036913e-01 -2.07044527e-01 -1.97750151e-01 9.82245386e-01
2.55333960e-01 -3.80805790e-01 5.79518378e-01 6.59877419e-01
-7.37652108e-02 -5.28170586e-01 -9.06368792e-01 -5.16975641e-01
-1.35396689e-01 -6.62036240e-01 7.50105977e-01 8.14012408e-01
-1.34391025e-01 4.47738975e-01 -1.01246446e-01 1.75050944e-01
3.95000428e-01 2.85921365e-01 8.72254252e-01 -1.07842410e+00
-9.21684504e-02 -5.63743174e-01 -5.48046947e-01 -7.96736419e-01
-3.59360009e-01 -5.90740860e-01 -1.39561728e-01 -2.13354135e+00
2.79682577e-01 -7.28009418e-02 -1.58889323e-01 3.56321394e-01
-2.70514667e-01 5.01832128e-01 3.50208044e-01 3.83883983e-01
-7.61590183e-01 2.34115466e-01 1.65467906e+00 -2.72265106e-01
2.18850553e-01 -4.87781763e-01 -8.59229207e-01 6.00494266e-01
6.38523459e-01 -1.11353397e-01 -7.24850833e-01 -4.03476596e-01
6.24836445e-01 1.11342452e-01 1.02687836e+00 -8.57628047e-01
7.31096342e-02 -3.09848040e-01 2.13451520e-01 -4.68868017e-01
4.75664824e-01 -7.04378664e-01 2.45313659e-01 4.33220744e-01
-1.79591313e-01 1.77356064e-01 1.09891579e-01 2.10670650e-01
-5.27159154e-01 -3.64011407e-01 6.34011388e-01 -1.00822143e-01
-1.23253250e+00 -1.09745905e-01 -1.80797622e-01 2.43601769e-01
7.06012666e-01 -5.72406888e-01 -8.92380774e-01 -6.61940098e-01
-4.17606592e-01 1.12683252e-01 8.18023264e-01 4.20354635e-01
8.22429299e-01 -1.32761395e+00 -6.62941456e-01 -1.71267390e-02
7.73727417e-01 -5.54444969e-01 5.42546093e-01 5.87776005e-01
-8.12925458e-01 5.31246841e-01 -3.98800045e-01 -6.90543652e-01
-1.30543947e+00 7.41780281e-01 3.48222405e-01 -6.98588695e-03
-3.44849557e-01 6.31444037e-01 5.23190498e-01 7.63823688e-02
-8.35108161e-02 -4.18137461e-01 -4.31725413e-01 9.19641703e-02
5.01493633e-01 2.35683471e-01 4.87532727e-02 -1.06762755e+00
-5.37337184e-01 4.16226715e-01 2.40619928e-01 -1.99230865e-01
5.56699038e-01 -2.76758909e-01 -6.46907762e-02 4.77291197e-01
7.52818286e-01 -1.43051073e-01 -1.07736003e+00 -1.24581121e-01
-2.62465924e-01 -6.35694146e-01 -4.00214419e-02 -9.04571176e-01
-7.38705397e-01 1.18175304e+00 5.48858285e-01 6.59321129e-01
1.08583939e+00 3.42899114e-01 1.42789647e-01 2.82068670e-01
3.08613300e-01 -8.59672368e-01 4.31768060e-01 1.76676601e-01
1.35330522e+00 -1.26682818e+00 1.67116486e-02 -3.83000225e-01
-9.81135070e-01 8.02182436e-01 5.91293752e-01 1.14505261e-01
1.40853167e-01 -3.78062457e-01 4.61534798e-01 -8.14907074e-01
-7.16389477e-01 -5.07742226e-01 8.19837511e-01 7.43190944e-01
4.26553726e-01 9.72585455e-02 -1.52321711e-01 3.28335673e-01
-3.31183732e-01 -1.71320304e-01 4.79329556e-01 7.27759957e-01
-5.93638837e-01 -6.30098701e-01 -4.23039734e-01 2.85502195e-01
-1.30637348e-01 -4.96179871e-02 -8.27095687e-01 9.62097824e-01
6.08493276e-02 1.39422524e+00 7.67750964e-02 -2.57902712e-01
3.80840123e-01 -1.21937528e-01 7.30530858e-01 -4.02466774e-01
-6.76482797e-01 -5.38227975e-01 2.80425370e-01 -8.16199601e-01
-4.92315829e-01 -1.55766830e-01 -1.10890770e+00 -5.02014518e-01
-1.37449697e-01 -3.72363739e-02 4.43179369e-01 9.23829675e-01
2.62967288e-01 3.87042046e-01 -2.56391075e-02 -2.81504244e-01
-2.05307245e-01 -7.47660041e-01 -2.96472549e-01 9.22485590e-01
2.65187800e-01 -6.96852088e-01 -3.57936323e-01 8.80559757e-02] | [10.92969036102295, 1.8096896409988403] |
c9ac957a-6df8-4e36-b102-e77eb72b6230 | self-eval-self-supervised-fine-grained | 2208.08094 | null | https://arxiv.org/abs/2208.08094v5 | https://arxiv.org/pdf/2208.08094v5.pdf | SelF-Eval: Self-supervised Fine-grained Dialogue Evaluation | This paper introduces a novel Self-supervised Fine-grained Dialogue Evaluation framework (SelF-Eval). The core idea is to model the correlation between turn quality and the entire dialogue quality. We first propose a novel automatic data construction method that can automatically assign fine-grained scores for arbitrarily dialogue data. Then we train \textbf{SelF-Eval} with a multi-level contrastive learning schema which helps to distinguish different score levels. Experimental results on multiple benchmarks show that SelF-Eval is highly consistent with human evaluations and better than the state-of-the-art models. We give a detailed analysis of the experiments in this paper. Our code is available on GitHub. | ['Ting Liu', 'Mingda Li', 'Weinan Zhang', 'Ziyu Zhuang', 'Longxuan Ma'] | 2022-08-17 | null | https://aclanthology.org/2022.coling-1.39 | https://aclanthology.org/2022.coling-1.39.pdf | coling-2022-10 | ['dialogue-evaluation'] | ['natural-language-processing'] | [-3.67018521e-01 3.16921115e-01 -1.31778613e-01 -8.97847295e-01
-1.09996414e+00 -5.67643762e-01 8.02576125e-01 2.10952356e-01
-3.92597467e-01 1.09260511e+00 7.59357989e-01 4.44031432e-02
-1.56385019e-01 -6.73358858e-01 1.24529107e-02 -1.82310581e-01
1.88179791e-01 9.74890947e-01 2.99746990e-01 -9.20916677e-01
5.17495692e-01 -1.34072363e-01 -1.22157013e+00 8.31867278e-01
1.24203539e+00 9.36237454e-01 -1.95668384e-01 1.11221588e+00
-3.58385772e-01 1.04423118e+00 -1.08067334e+00 -6.99344575e-01
-9.70140621e-02 -7.94293404e-01 -1.65842760e+00 -5.03717847e-02
1.77200764e-01 -5.21134615e-01 -1.08184330e-02 8.62832844e-01
5.39757967e-01 2.44368613e-01 6.61925614e-01 -1.18183756e+00
-2.07597032e-01 9.21849012e-01 1.28000364e-01 6.57461490e-03
8.82275641e-01 3.18199933e-01 1.34520674e+00 -6.78974152e-01
4.30494577e-01 1.57700598e+00 3.78614068e-01 5.72102845e-01
-1.16753674e+00 -2.42460728e-01 -1.66651279e-01 2.53911763e-01
-7.65710413e-01 -3.42484117e-01 7.69553423e-01 -2.69796133e-01
7.00362623e-01 4.66314584e-01 4.57492828e-01 1.03722167e+00
1.65585876e-02 9.57440913e-01 1.70109499e+00 -4.94154811e-01
9.18665528e-02 2.29829252e-01 8.23678732e-01 1.05060065e+00
-6.54240727e-01 -1.48552239e-01 -7.34982610e-01 -3.32846969e-01
5.27452290e-01 -5.45253754e-01 -4.81451750e-02 -1.67998448e-01
-1.22367525e+00 1.10919809e+00 1.25677541e-01 3.81666720e-01
-3.99474241e-03 -4.90018636e-01 7.47237861e-01 9.86690402e-01
5.26659429e-01 7.82154322e-01 -6.01558328e-01 -8.43250692e-01
-7.07320929e-01 4.88215387e-01 1.37033725e+00 5.18334568e-01
7.30585635e-01 -4.55474734e-01 -8.20360899e-01 1.39893150e+00
7.87971914e-02 -1.43856055e-03 6.62249267e-01 -1.37805426e+00
4.37454283e-01 8.54398012e-01 2.39258319e-01 -6.94541276e-01
-6.15383863e-01 -1.28734773e-02 -7.64610112e-01 1.40855625e-01
5.81912279e-01 -3.52164775e-01 -1.24732226e-01 1.62944245e+00
3.39341879e-01 -6.08869374e-01 1.36381596e-01 7.60451198e-01
1.44063020e+00 5.89261174e-01 -2.41879255e-01 -3.78280997e-01
1.20718145e+00 -1.51646686e+00 -1.04086196e+00 2.55698413e-01
6.69198692e-01 -6.50737047e-01 1.73000193e+00 6.01444960e-01
-1.19139040e+00 -7.75700212e-01 -9.04659808e-01 -5.31279817e-02
-1.93106860e-01 2.67642699e-02 6.97057664e-01 6.89347684e-01
-1.14392972e+00 6.77389801e-01 -2.99075723e-01 -1.10571221e-01
-2.16361865e-01 1.42087996e-01 -1.79230690e-01 5.27919054e-01
-1.58045089e+00 1.11175799e+00 3.50495070e-01 -1.83726043e-01
-6.98787153e-01 3.49840941e-03 -8.94345999e-01 -1.43831670e-01
4.43353057e-01 -4.08980608e-01 1.86574531e+00 -8.31658244e-01
-2.30670428e+00 1.06280458e+00 -9.64338779e-02 -3.07481110e-01
6.45014644e-01 -3.49079013e-01 -1.88193291e-01 -7.37635270e-02
1.47120267e-01 2.79561311e-01 2.52980918e-01 -1.08948052e+00
-5.34144938e-01 -1.26712561e-01 6.16130173e-01 3.87117088e-01
-2.56070554e-01 1.21626362e-01 -4.51465607e-01 -3.65075320e-01
-3.14391285e-01 -6.85039818e-01 -8.27616081e-02 -6.70945108e-01
-4.62321222e-01 -9.75646019e-01 2.23463118e-01 -3.22722018e-01
1.87105036e+00 -1.55710280e+00 3.14620167e-01 -2.70738397e-02
3.72317135e-01 3.68165821e-01 -1.75371274e-01 6.59222484e-01
3.08528632e-01 -1.10492453e-01 -9.03998762e-02 -3.35437417e-01
3.84247273e-01 -1.98095455e-03 2.20607042e-01 4.88434695e-02
-7.45468959e-02 9.58038092e-01 -1.07726622e+00 -7.10281074e-01
3.16045880e-01 -2.35170260e-01 -6.07108235e-01 8.58767152e-01
-2.78003514e-01 3.08057576e-01 -4.86278534e-01 3.04344237e-01
2.20953822e-01 -3.13292623e-01 1.81724548e-01 -1.89097404e-01
-3.76408957e-02 8.31480980e-01 -1.03091645e+00 1.83207452e+00
-5.34781575e-01 3.07659030e-01 -2.05076323e-03 -5.08904874e-01
1.04349649e+00 1.23941951e-01 1.09891981e-01 -7.11213470e-01
3.49314630e-01 -3.68793420e-02 -4.81807180e-02 -4.68460202e-01
9.72281158e-01 -7.69998319e-03 -7.14127004e-01 7.93672323e-01
4.57908571e-01 -4.48519230e-01 5.07691681e-01 4.94787574e-01
8.88089895e-01 -1.39549062e-01 5.32564580e-01 -2.81232476e-01
9.64590788e-01 -8.45502615e-02 3.35293561e-01 6.58227921e-01
-5.84688008e-01 1.72683656e-01 8.61690462e-01 -3.34324807e-01
-6.99342966e-01 -8.93599510e-01 -9.65146422e-02 1.83975708e+00
6.83791563e-02 -8.78046334e-01 -1.20782006e+00 -1.11131012e+00
-3.56369764e-01 5.65313160e-01 -7.35935450e-01 1.01424828e-01
-2.85014480e-01 -5.64580798e-01 5.71723402e-01 6.73944280e-02
8.15217674e-01 -1.26039279e+00 -7.77660236e-02 1.92456320e-01
-8.11580300e-01 -7.96631336e-01 -4.49361920e-01 6.32216930e-02
-4.80357587e-01 -1.01894248e+00 -2.62646198e-01 -5.63068807e-01
-3.05066966e-02 -7.01931566e-02 1.66518593e+00 3.71620178e-01
2.33331710e-01 1.24223799e-01 -7.55490124e-01 -6.32300228e-02
-9.07376826e-01 2.86204338e-01 -9.12721083e-02 -2.36178130e-01
4.39973980e-01 -1.81193314e-02 -4.00057256e-01 5.12226343e-01
-4.43256438e-01 1.86181948e-01 8.22428167e-02 1.24223411e+00
2.00085372e-01 -2.20809668e-01 7.73609877e-01 -1.25906003e+00
1.59258020e+00 -1.85519516e-01 -1.27753228e-01 3.25234145e-01
-7.97240257e-01 4.41374421e-01 5.03329515e-01 2.32111663e-01
-1.11699247e+00 -5.52123725e-01 -6.89823985e-01 5.10048866e-01
-3.33733648e-01 3.43496472e-01 -1.62031874e-01 2.27349073e-01
6.94794238e-01 -1.52846336e-01 -1.37478672e-02 -2.86884934e-01
5.31332970e-01 1.09968615e+00 3.96804661e-01 -8.34578097e-01
1.69939324e-01 -1.52547747e-01 -6.12368822e-01 -6.79832041e-01
-1.26241446e+00 -6.08237803e-01 -8.16718340e-01 -5.70528269e-01
6.95266664e-01 -6.14974260e-01 -9.18382645e-01 5.09252250e-01
-1.07868671e+00 -7.67005682e-01 -1.88641518e-01 5.33581451e-02
-1.00008178e+00 4.97310430e-01 -1.01065278e+00 -7.14322627e-01
-5.27931511e-01 -1.11841989e+00 9.58005071e-01 2.64575988e-01
-7.17780530e-01 -1.30816329e+00 5.94533503e-01 9.52852786e-01
2.16934904e-01 7.77175128e-02 6.28566027e-01 -9.47788656e-01
1.39726713e-01 1.41683847e-01 -9.25560445e-02 5.10396957e-01
3.84064876e-02 -5.98237924e-02 -9.05199587e-01 -9.67813889e-04
1.95009336e-02 -1.28614533e+00 7.06096590e-01 8.11077952e-02
8.83255064e-01 -4.68277931e-01 2.34610453e-01 9.20471475e-02
8.33756208e-01 -1.77614450e-01 4.75907862e-01 5.33300102e-01
3.42359185e-01 8.16310048e-01 1.23040903e+00 7.20300376e-01
9.64617312e-01 8.30514073e-01 7.68740475e-02 -1.80007190e-01
9.24597085e-02 -1.39859319e-02 3.38771373e-01 1.18394518e+00
-8.76799151e-02 -3.19295675e-01 -6.30687118e-01 2.63586789e-01
-2.08750629e+00 -1.07304335e+00 -2.56659359e-01 1.72552407e+00
1.46594083e+00 3.34899008e-01 6.75586164e-01 2.48485938e-01
6.26760721e-01 4.07663494e-01 -1.37569189e-01 -1.11871696e+00
-3.83316949e-02 2.73705810e-01 -2.06508532e-01 1.09869432e+00
-1.05078948e+00 1.22802222e+00 6.86658955e+00 8.37436914e-01
-4.19839501e-01 1.92980081e-01 7.49608874e-01 1.84957638e-01
-1.61949098e-01 -4.49449956e-01 -6.59858942e-01 2.79006958e-01
1.15414870e+00 -2.61285990e-01 2.54874915e-01 6.70493186e-01
2.81076729e-01 -7.60018453e-02 -1.08055961e+00 7.52032697e-01
6.48480579e-02 -1.04275405e+00 -5.05406111e-02 -2.88699985e-01
6.70392513e-01 -1.66142359e-01 -3.81389260e-01 6.68158114e-01
1.05856252e+00 -9.26249266e-01 3.63817900e-01 6.59035802e-01
7.30565488e-01 -9.46123123e-01 9.72763777e-01 4.86004502e-01
-7.77944803e-01 1.53456271e-01 -2.32487142e-01 -2.91300416e-01
-3.60014178e-02 4.12491232e-01 -8.85129809e-01 6.18004858e-01
5.16139984e-01 3.61226141e-01 -6.55117750e-01 4.01208252e-01
-4.11366463e-01 5.59582889e-01 3.02088231e-01 -4.05586928e-01
4.79270816e-01 -1.46637157e-01 2.68568248e-01 1.64732122e+00
-4.04830843e-01 3.09905469e-01 4.79476810e-01 4.85149592e-01
-1.65262803e-01 4.24503982e-01 -9.85349789e-02 2.13103771e-01
3.91682416e-01 1.54263091e+00 -2.89500654e-01 -4.93725568e-01
-1.59172446e-01 1.09234786e+00 7.95229316e-01 -3.12484324e-01
-5.51995993e-01 -5.67173243e-01 5.36881208e-01 -3.25771242e-01
-1.90879747e-01 -2.39975676e-02 -3.08970034e-01 -1.08006799e+00
-2.63062686e-01 -1.27452147e+00 4.83646542e-01 -3.69427383e-01
-1.50315773e+00 1.02850592e+00 -1.84343457e-01 -1.10222459e+00
-7.78136194e-01 -4.51487392e-01 -5.39202452e-01 6.82492077e-01
-1.42220652e+00 -7.46014297e-01 -6.18169069e-01 8.48755121e-01
1.00317705e+00 -3.45689625e-01 1.32500422e+00 -3.15438718e-01
-2.04891473e-01 8.71962845e-01 1.05746739e-01 3.80925924e-01
1.24152768e+00 -1.74500132e+00 2.23386392e-01 1.50484324e-01
-1.07304081e-01 3.37667406e-01 7.97750115e-01 -3.17214757e-01
-7.04847872e-01 -6.30669653e-01 1.05977106e+00 -7.82696784e-01
6.76862419e-01 -1.38044223e-01 -8.63624752e-01 2.37944387e-02
7.94695854e-01 -5.73867381e-01 1.00308120e+00 6.34423435e-01
-4.00550812e-01 1.08474875e-02 -1.19018829e+00 1.78473160e-01
7.99759626e-01 -6.03610992e-01 -1.09941077e+00 3.96571219e-01
6.59320414e-01 -5.90115309e-01 -1.18472052e+00 3.49534303e-01
4.65672016e-01 -1.65336680e+00 6.01007998e-01 -5.91781795e-01
7.07149506e-01 1.45898759e-01 1.41380811e-02 -1.78928375e+00
-3.82692963e-01 -7.71940351e-01 -7.05221817e-02 1.22054625e+00
4.23420489e-01 -2.83977360e-01 5.52233279e-01 4.20554608e-01
-8.42496529e-02 -8.31508636e-01 -5.98954201e-01 -5.15261889e-01
1.66805670e-01 -9.62781608e-02 6.38341427e-01 9.03271794e-01
6.58167839e-01 1.05000997e+00 -4.89438176e-01 -4.79839385e-01
4.86003995e-01 2.86029279e-01 1.08073211e+00 -1.39846563e+00
-3.40110153e-01 -7.21192420e-01 -1.04042500e-01 -1.21349275e+00
2.84316808e-01 -5.35039604e-01 2.34487325e-01 -1.77575266e+00
3.05008739e-01 -2.47656137e-01 -2.86376864e-01 2.51909316e-01
-6.09110951e-01 1.58910245e-01 7.61105027e-03 6.25454634e-02
-1.43264866e+00 7.54483342e-01 1.47382712e+00 -2.42152847e-02
-4.09857690e-01 1.51444256e-01 -7.45127499e-01 6.75812185e-01
9.59496200e-01 -8.74747813e-04 -3.37832928e-01 -3.58774886e-02
1.04715431e-03 4.51628566e-01 -1.08771719e-01 -8.56298685e-01
6.63161427e-02 -2.95412838e-01 -5.53887561e-02 -6.61512136e-01
2.69967765e-01 -1.26909986e-01 -6.50996506e-01 1.51927307e-01
-9.59265172e-01 -3.13705532e-03 -1.81927383e-01 1.13946997e-01
-6.67478263e-01 -2.94572949e-01 7.82944560e-01 -3.00054997e-01
-4.18838173e-01 -6.02522306e-02 -3.20071757e-01 4.88283694e-01
5.78443229e-01 2.19643354e-01 -4.13244784e-01 -7.73964345e-01
-7.32705832e-01 5.83922625e-01 2.98795372e-01 5.21784246e-01
3.93042028e-01 -1.36251128e+00 -1.01948845e+00 -1.96844772e-01
3.59719664e-01 -4.23878402e-01 2.80286223e-01 4.02653366e-01
-3.14256251e-01 6.75896227e-01 -3.47092271e-01 -4.76984352e-01
-1.60165596e+00 8.15090612e-02 3.42265069e-01 -9.77518380e-01
-6.19204789e-02 8.39358211e-01 -1.15565300e-01 -1.08925736e+00
4.19544488e-01 -1.71527416e-01 -6.18268669e-01 2.04484299e-01
6.55897021e-01 4.60835189e-01 1.39810771e-01 -6.26448810e-01
3.96263115e-02 5.40999882e-02 -1.87304914e-01 -2.73080558e-01
1.20691264e+00 -4.68777776e-01 -2.57145196e-01 9.08546448e-01
1.04914939e+00 -6.19551390e-02 -1.10416603e+00 -5.66258013e-01
-4.10614461e-02 -3.15381497e-01 -6.28648996e-02 -1.27698839e+00
-4.36684459e-01 6.69636309e-01 4.61543709e-01 7.03765631e-01
9.36478198e-01 1.07962541e-01 6.83548391e-01 7.17547417e-01
5.25944293e-01 -1.61021566e+00 5.77077627e-01 1.17747664e+00
1.12335777e+00 -1.55807209e+00 -1.38886511e-01 -2.15485886e-01
-1.15465689e+00 1.09142923e+00 8.32085848e-01 -1.52212055e-03
3.54659498e-01 -7.51569960e-03 3.82349610e-01 -3.29484344e-01
-1.08550596e+00 -3.29227656e-01 3.32812905e-01 1.94320545e-01
1.03112578e+00 3.79830718e-01 -7.79507101e-01 9.17430460e-01
-7.20007181e-01 -1.42992020e-01 5.08197904e-01 6.81596339e-01
-7.98495531e-01 -1.46249330e+00 -1.42363593e-01 4.10873830e-01
-2.77600467e-01 5.61876446e-02 -1.05906081e+00 4.71579880e-01
-2.90833056e-01 1.48355091e+00 -2.28092983e-01 -8.40883851e-01
7.23893106e-01 2.13188440e-01 4.50211108e-01 -7.68993616e-01
-1.14067590e+00 -8.99677575e-02 8.14625025e-01 -7.40825713e-01
-6.92957580e-01 -5.03657341e-01 -1.11726522e+00 -5.79435587e-01
-3.32530081e-01 6.89643621e-01 2.67760694e-01 1.00522673e+00
-8.71661901e-02 6.26112163e-01 1.15421319e+00 -4.93507445e-01
-7.59958982e-01 -1.44587159e+00 -4.55480576e-01 5.95101058e-01
3.17246109e-01 -7.04609513e-01 -2.97590107e-01 -3.21666449e-01] | [12.767324447631836, 8.131373405456543] |
2fb437b7-b6fd-4cd3-b9a1-20cd422c0c97 | personalized-federated-recommender-systems | 2212.08779 | null | https://arxiv.org/abs/2212.08779v1 | https://arxiv.org/pdf/2212.08779v1.pdf | Personalized Federated Recommender Systems with Private and Partially Federated AutoEncoders | Recommender Systems (RSs) have become increasingly important in many application domains, such as digital marketing. Conventional RSs often need to collect users' data, centralize them on the server-side, and form a global model to generate reliable recommendations. However, they suffer from two critical limitations: the personalization problem that the RSs trained traditionally may not be customized for individual users, and the privacy problem that directly sharing user data is not encouraged. We propose Personalized Federated Recommender Systems (PersonalFR), which introduces a personalized autoencoder-based recommendation model with Federated Learning (FL) to address these challenges. PersonalFR guarantees that each user can learn a personal model from the local dataset and other participating users' data without sharing local data, data embeddings, or models. PersonalFR consists of three main components, including AutoEncoder-based RSs (ARSs) that learn the user-item interactions, Partially Federated Learning (PFL) that updates the encoder locally and aggregates the decoder on the server-side, and Partial Compression (PC) that only computes and transmits active model parameters. Extensive experiments on two real-world datasets demonstrate that PersonalFR can achieve private and personalized performance comparable to that trained by centralizing all users' data. Moreover, PersonalFR requires significantly less computation and communication overhead than standard FL baselines. | ['Jie Ding', 'Vahid Tarokh', 'Ali Anwar', 'Xinran Wang', 'Enmao Diao', 'Qi Le'] | 2022-12-17 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [-3.24880093e-01 5.50397597e-02 -3.93867016e-01 -7.64534116e-01
-7.85575986e-01 -4.14675176e-01 1.56213045e-01 -1.61458671e-01
-2.36448571e-01 4.78426248e-01 5.75956523e-01 -8.14811587e-02
-2.17831701e-01 -8.92632961e-01 -8.82882535e-01 -7.02062726e-01
-1.01706184e-01 4.09652531e-01 -2.33693078e-01 -2.16925547e-01
-2.30369538e-01 1.67160183e-01 -1.53548634e+00 5.94645381e-01
8.83477926e-01 1.27708340e+00 4.16524380e-01 5.26713490e-01
1.09586120e-01 6.44191802e-01 -3.87666553e-01 -6.38252735e-01
3.64631206e-01 4.90273684e-02 -4.17499542e-01 -2.87537485e-01
1.45349056e-01 -1.06274402e+00 -7.54490972e-01 7.58942246e-01
6.87184870e-01 6.97948575e-01 2.45551720e-01 -1.01120734e+00
-1.38550222e+00 1.33094013e+00 8.99839588e-03 -2.24416733e-01
1.29801989e-01 -2.42988676e-01 1.09212995e+00 -7.20409036e-01
2.38161132e-01 9.35464025e-01 7.17121959e-01 6.87599421e-01
-9.67733979e-01 -6.10181570e-01 3.43542218e-01 1.57387897e-01
-1.24903417e+00 -5.63176632e-01 3.54240298e-01 4.34617326e-02
6.71721935e-01 5.26059389e-01 4.15547967e-01 1.33199394e+00
-4.69618887e-02 1.05357361e+00 4.51021045e-01 3.35055590e-02
5.20764053e-01 5.24047554e-01 2.10045323e-01 7.46176764e-02
2.12228879e-01 8.67884904e-02 -4.66258556e-01 -6.90057874e-01
5.99537253e-01 7.49882579e-01 -4.36812967e-01 -1.28276467e-01
-8.12073112e-01 9.09053087e-01 2.88064629e-01 8.95790905e-02
-6.67873442e-01 -6.80456161e-02 1.19580194e-01 5.13964355e-01
4.49049532e-01 7.29449838e-02 -9.33286607e-01 7.15942830e-02
-5.93800843e-01 1.36559620e-01 1.05640352e+00 1.25303531e+00
8.77866924e-01 -1.08145513e-01 8.81116092e-02 9.14470375e-01
4.23511505e-01 5.45596778e-01 1.23012805e+00 -9.68227744e-01
2.92161673e-01 4.03719276e-01 2.07073420e-01 -9.74772573e-01
1.79750994e-01 -7.01899946e-01 -7.06788778e-01 -7.99356163e-01
-3.13843220e-01 -5.67992270e-01 -1.09710850e-01 1.55325103e+00
5.20693660e-01 6.18735790e-01 2.99762338e-01 8.92102122e-01
9.86883938e-01 9.56878483e-01 -1.99104220e-01 -3.94910164e-02
8.53418171e-01 -1.26057637e+00 -5.28507710e-01 2.07959916e-02
5.89404047e-01 -4.38953012e-01 8.65527332e-01 4.23385471e-01
-1.08657014e+00 -6.45758033e-01 -9.23967242e-01 -1.27330765e-01
-3.08361948e-01 3.40495050e-01 8.15224051e-01 6.27438426e-01
-9.55758810e-01 8.28110099e-01 -7.89287150e-01 -8.68078619e-02
3.03103656e-01 7.61189878e-01 -3.67678970e-01 4.05177101e-03
-1.05376506e+00 6.98285103e-02 2.71558613e-02 -3.04414667e-02
-7.54455507e-01 -9.90047157e-01 -5.16072035e-01 5.58814108e-01
1.60917073e-01 -7.11065650e-01 1.69936025e+00 -9.32986796e-01
-1.97670579e+00 -6.91911355e-02 8.70707035e-02 -5.31560183e-01
1.05804272e-01 -4.85051811e-01 -1.04624796e+00 -3.68859798e-01
-3.96104395e-01 -2.11834870e-02 7.49330342e-01 -1.03839171e+00
-9.89564121e-01 -5.78023851e-01 2.71132253e-02 8.88425261e-02
-9.85263765e-01 -2.61044234e-01 -8.22680175e-01 -6.06423676e-01
-2.52292633e-01 -9.60770369e-01 -3.98546726e-01 -4.37376738e-01
-6.86630327e-03 -2.51047492e-01 8.19142520e-01 -7.67282963e-01
1.49059963e+00 -2.44693732e+00 -2.05124110e-01 3.05223227e-01
1.56686693e-01 3.49372566e-01 -4.12160009e-01 5.76818645e-01
2.26440817e-01 -3.37596595e-01 4.76082414e-01 -6.30846381e-01
3.41331124e-01 4.32530761e-01 -6.68357372e-01 3.17812085e-01
-8.89360964e-01 8.62899244e-01 -6.97438538e-01 1.07385777e-02
-5.65576218e-02 8.39048803e-01 -9.97298479e-01 5.68131745e-01
-2.62920976e-01 1.48429722e-01 -7.59353518e-01 3.25753391e-02
7.84859657e-01 -4.86058503e-01 4.60004270e-01 -3.47471356e-01
2.24891633e-01 3.76678109e-01 -1.48294032e+00 1.68627656e+00
-7.30712056e-01 -7.26652965e-02 2.96822697e-01 -7.54078746e-01
7.67444253e-01 3.92960966e-01 6.72527730e-01 -5.69305837e-01
2.51217782e-01 -2.22612265e-02 -5.54971159e-01 -3.61889154e-01
8.28875363e-01 7.06292868e-01 1.90536287e-02 1.10731542e+00
2.93402523e-01 1.12688792e+00 -4.51777816e-01 4.63191748e-01
9.13011551e-01 -1.66214883e-01 -1.37477741e-01 1.28946856e-01
4.09880847e-01 -6.07062042e-01 7.68730402e-01 6.97489083e-01
2.43308708e-01 3.28617722e-01 -4.52610314e-01 -5.04360557e-01
-6.15065396e-01 -8.58754933e-01 1.31424963e-01 1.84696007e+00
1.96853548e-01 -9.24994171e-01 -6.46347106e-01 -7.92188883e-01
2.65080094e-01 9.51346517e-01 -5.18574715e-01 -3.50772440e-01
-4.00299281e-01 -5.23913205e-01 -6.11557020e-03 5.41345119e-01
2.38036275e-01 -6.55115604e-01 -1.34482812e-02 4.49610293e-01
-2.35402569e-01 -8.04575741e-01 -1.12414825e+00 -1.15687996e-01
-8.18318784e-01 -7.32033730e-01 -3.37792307e-01 -5.25179803e-01
6.24715626e-01 8.66587758e-01 7.30494380e-01 -2.31331930e-01
2.90922254e-01 6.14541471e-01 -5.34095466e-01 -1.30941346e-01
-1.78249195e-01 1.39158845e-01 2.70880520e-01 6.32876635e-01
5.32800376e-01 -7.61927247e-01 -1.02791548e+00 5.61422288e-01
-8.75809431e-01 -3.53452861e-01 4.84461457e-01 6.91983700e-01
6.13941133e-01 5.25146648e-02 7.23472655e-01 -1.35476172e+00
6.61915660e-01 -1.06664026e+00 -2.01458469e-01 4.36491787e-01
-8.90754044e-01 -2.28429377e-01 1.12392986e+00 -6.14607573e-01
-1.24462557e+00 -1.61568925e-01 -2.22618043e-01 -4.63771611e-01
-1.59682184e-02 4.47444797e-01 -4.20419812e-01 2.04439372e-01
7.59509265e-01 3.81293267e-01 -2.12197769e-02 -1.10643911e+00
7.95654714e-01 1.52832460e+00 6.02445126e-01 -4.83778298e-01
4.08057183e-01 2.33050749e-01 -9.50498819e-01 -3.42642725e-01
-7.43225455e-01 -5.32139122e-01 -4.65324372e-02 1.76017582e-01
1.21435657e-01 -1.36623764e+00 -9.20259178e-01 -1.45467920e-02
-5.65177023e-01 -2.21769959e-01 -6.28518343e-01 7.43394613e-01
-4.73393410e-01 2.99224049e-01 -7.45614231e-01 -4.14847851e-01
-1.01953042e+00 -8.68792772e-01 6.66986465e-01 4.09129441e-01
1.14616565e-01 -8.99345160e-01 1.92206174e-01 5.07807910e-01
8.12001646e-01 -5.57603359e-01 3.91299844e-01 -1.13004172e+00
-5.17016053e-01 -6.34537578e-01 2.80352980e-01 6.33514762e-01
1.55017495e-01 -4.21970993e-01 -8.62843156e-01 -6.90596342e-01
-1.72667429e-01 -6.08066618e-02 3.20082635e-01 5.45326993e-02
1.57418990e+00 -1.03436017e+00 -3.72626960e-01 8.12722325e-01
1.28119707e+00 6.29245043e-02 2.60414362e-01 -3.02748471e-01
5.11898816e-01 1.23256542e-01 3.49799484e-01 8.43540668e-01
7.77299166e-01 6.19172752e-01 2.06293747e-01 4.50311422e-01
1.34770215e-01 -7.23574758e-01 7.94151962e-01 1.29226923e+00
2.96674930e-02 -3.69017810e-01 2.48479471e-01 2.45317265e-01
-2.30328035e+00 -9.53165591e-01 2.87550598e-01 2.48547864e+00
7.17318892e-01 -7.66635895e-01 2.76686251e-01 -3.48644018e-01
6.45663857e-01 -2.24673957e-01 -9.95357215e-01 -3.66645753e-01
1.55550420e-01 1.27472520e-01 6.76187098e-01 1.35037199e-01
-7.77882993e-01 6.64344013e-01 5.65744877e+00 6.85155690e-01
-1.19639218e+00 5.72920799e-01 2.47955605e-01 -4.32357550e-01
-5.91906309e-01 -2.60092884e-01 -1.02288640e+00 8.73800814e-01
1.48994446e+00 -3.56101751e-01 1.01848519e+00 1.56760573e+00
-1.29240602e-01 6.56634092e-01 -1.06268167e+00 1.18015623e+00
1.64306566e-01 -1.43715096e+00 5.30259013e-02 3.04726034e-01
7.98972249e-01 5.55185497e-01 3.45057875e-01 5.46930373e-01
1.03063285e+00 -4.48370606e-01 3.52617860e-01 8.27245951e-01
4.27862734e-01 -8.23096514e-01 5.04822433e-01 5.84177792e-01
-8.78281713e-01 -6.95678711e-01 -8.72137427e-01 3.28887612e-01
-1.32266253e-01 5.97340882e-01 -5.63382745e-01 6.54660225e-01
9.84478354e-01 8.36081982e-01 -2.17834502e-01 8.46819162e-01
2.83945382e-01 8.08673024e-01 -4.21922386e-01 5.30127175e-02
-2.80148685e-01 -4.53711420e-01 2.80113518e-02 7.72815168e-01
7.71654129e-01 2.04953209e-01 5.15567251e-02 2.51141071e-01
-4.13226694e-01 4.57493305e-01 -8.43171254e-02 -1.58323329e-02
8.97058129e-01 1.36464322e+00 3.98252577e-01 -2.89467245e-01
-6.95993781e-01 1.17622924e+00 4.16299492e-01 3.91608477e-01
-5.83320856e-01 -2.63656497e-01 1.04041302e+00 5.52730113e-02
7.93804049e-01 1.19181730e-01 4.16590780e-01 -1.62094700e+00
-1.45928741e-01 -1.01032531e+00 7.06970692e-01 -5.77774942e-01
-1.63654339e+00 5.30096591e-01 -6.36385143e-01 -1.10992658e+00
-3.45893890e-01 -7.82679021e-02 -4.45700377e-01 6.44190192e-01
-1.10231590e+00 -1.22348058e+00 -1.82840526e-01 1.29262781e+00
1.08148634e-01 -5.07002831e-01 1.15016401e+00 7.96366394e-01
-7.92445600e-01 1.47919130e+00 1.07992721e+00 -3.58576104e-02
8.46445978e-01 -8.13758671e-01 2.12682769e-01 4.40589547e-01
2.42478102e-01 1.14838898e+00 2.49448165e-01 -3.33924860e-01
-2.19473743e+00 -1.63400126e+00 8.79730046e-01 -2.79341906e-01
3.81123990e-01 -2.21564397e-01 -6.26584828e-01 1.28241539e+00
3.97621542e-02 1.37017161e-01 1.55888820e+00 3.73852551e-01
-5.07040203e-01 -7.50250816e-01 -1.21277094e+00 5.35001814e-01
9.93207276e-01 -4.70621586e-01 -2.43867964e-01 4.49488997e-01
9.81351674e-01 -1.71832249e-01 -1.44793272e+00 -2.33070195e-01
7.11744547e-01 -7.62139618e-01 9.18648720e-01 -7.79507875e-01
-2.77566239e-02 -8.18745047e-02 -5.53935409e-01 -1.45719874e+00
-7.55879700e-01 -8.88095081e-01 -9.28772569e-01 1.30476582e+00
2.68213689e-01 -9.65709448e-01 7.86087692e-01 1.16172862e+00
-2.88194150e-01 -6.30172789e-01 -5.00956237e-01 -4.74854589e-01
-3.82644624e-01 -4.69448090e-01 1.69761038e+00 8.78392160e-01
1.22680053e-01 2.39944741e-01 -7.54814148e-01 3.54825437e-01
5.77574849e-01 4.31561142e-01 1.22133815e+00 -1.14384604e+00
-9.23744559e-01 2.30879366e-01 1.58857062e-01 -1.64926291e+00
5.33633791e-02 -1.03129482e+00 -4.18268263e-01 -1.23025954e+00
-1.26908630e-01 -7.45887458e-01 -7.17462301e-01 5.51495194e-01
2.44129702e-01 -1.58152014e-01 -6.17258362e-02 4.64574665e-01
-9.37191963e-01 7.45827079e-01 9.79476273e-01 1.15974307e-01
-4.12176609e-01 5.40407419e-01 -1.55727494e+00 3.65418673e-01
5.82539201e-01 -2.13841960e-01 -7.30825365e-01 -7.08755672e-01
3.48903120e-01 1.09926403e-01 -2.08476111e-01 -7.95436502e-01
6.02279186e-01 -9.68980566e-02 3.31364334e-01 -4.46051389e-01
2.50577748e-01 -1.15407038e+00 4.96649802e-01 -1.66401714e-01
-5.43295205e-01 -1.65404946e-01 -5.29671013e-01 8.79803598e-01
1.63881302e-01 5.47629856e-02 3.28554302e-01 8.89545456e-02
-2.88126618e-01 1.03208411e+00 -3.85694094e-02 -6.02045774e-01
8.49808574e-01 1.64393336e-01 -3.23872536e-01 -6.64382517e-01
-8.92821014e-01 2.93338865e-01 3.03401083e-01 5.86146533e-01
4.67460960e-01 -1.43961453e+00 -4.86112446e-01 4.23833489e-01
4.73918580e-02 -1.64655939e-01 8.85733724e-01 3.35600436e-01
1.77533075e-01 4.48592216e-01 2.97623724e-01 4.56448980e-02
-8.72222900e-01 8.62829387e-01 9.85642746e-02 8.67848471e-02
-8.38071942e-01 7.89328218e-01 -1.82805974e-02 -8.32310021e-01
5.48380375e-01 6.87255412e-02 -1.71226531e-01 -2.14062408e-01
1.11001086e+00 5.42752504e-01 2.38615870e-01 -5.71105242e-01
2.26756528e-01 -9.64803547e-02 -5.65636158e-01 3.41459990e-01
1.71567798e+00 -5.20306647e-01 8.23926628e-02 1.33430120e-02
1.47760499e+00 2.39203379e-01 -1.16253436e+00 -1.00197542e+00
-8.63438547e-01 -6.29122257e-01 5.03004789e-01 -8.62029374e-01
-1.60412359e+00 3.02630275e-01 5.53127646e-01 5.83265051e-02
9.98829365e-01 -1.61623865e-01 1.70779896e+00 6.08268261e-01
5.06316185e-01 -1.28393698e+00 -4.53582376e-01 1.05872408e-01
6.28025472e-01 -8.18264306e-01 -2.08394974e-01 1.37195781e-01
-7.55476594e-01 8.16824913e-01 4.31753427e-01 -8.16876814e-02
1.38425648e+00 3.60692758e-03 -4.10102755e-02 4.20363873e-01
-1.22636998e+00 3.60889673e-01 1.36445463e-01 5.60714543e-01
1.08794466e-01 3.20493609e-01 1.51213235e-03 1.93450511e+00
-4.26986158e-01 3.84509027e-01 2.30596602e-01 7.49003649e-01
-2.34781519e-01 -1.36009550e+00 2.26492397e-02 8.48286033e-01
-3.67399395e-01 6.06086813e-02 2.13478804e-01 -2.27924049e-01
1.08821668e-01 1.15393102e+00 1.04650415e-01 -9.33964789e-01
1.79533005e-01 -1.96837172e-01 -1.87358055e-02 -4.97339129e-01
-9.11543131e-01 1.33178711e-01 -1.88815206e-01 -8.38606119e-01
8.18545297e-02 -5.82692266e-01 -9.79378104e-01 -8.87857258e-01
-4.95951504e-01 8.14968526e-01 8.90062988e-01 6.49533808e-01
1.39156270e+00 9.80651230e-02 1.32559729e+00 -6.15638077e-01
-1.07416570e+00 -7.15925813e-01 -9.75005627e-01 3.20551872e-01
2.10745633e-01 -4.50979024e-02 -2.56599009e-01 5.71875423e-02] | [5.9230546951293945, 6.293081760406494] |
db797666-3100-46c6-b797-5547648da1b1 | deep-representation-learning-for | null | null | https://www.sciencedirect.com/science/article/pii/S1532046419302229 | https://www.sciencedirect.com/science/article/pii/S1532046419302229/pdfft?md5=b6a2b5ddc61984744f599ba097379d32&pid=1-s2.0-S1532046419302229-main.pdf | Deep representation learning for individualized treatment effect estimation using electronic health records | Utilizing clinical observational data to estimate individualized treatment effects (ITE) is a challenging task, as confounding inevitably exists in clinical data. Most of the existing models for ITE estimation tackle this problem by creating unbiased estimators of the treatment effects. Although valuable, learning a balanced representation is sometimes directly opposed to the objective of learning an effective and discriminative model for ITE estimation. We propose a novel hybrid model bridging multi-task deep learning and K-nearest neighbors (KNN) for ITE estimation. In detail, the proposed model firstly adopts multi-task deep learning to extract both outcome-predictive and treatment-specific latent representations from Electronic Health Records (EHR), by jointly performing the outcome prediction and treatment category classification. Thereafter, we estimate counterfactual outcomes by KNN based on the learned hidden representations. We validate the proposed model on a widely used semi-simulated dataset, i.e. IHDP, and a real-world clinical dataset consisting of 736 heart failure (HF) patients. The performance of our model remains robust and reaches 1.7 and 0.23 in terms of Precision in the estimation of heterogeneous effect (PEHE) and average treatment effect (ATE), respectively, on IHDP dataset, and 0.703 and 0.796 in terms of accuracy and F1 score respectively, on HF dataset. The results demonstrate that the proposed model achieves competitive performance over state-of-the-art models. In addition, the results reveal several findings which are consistent with existing medical domain knowledge, and discover certain suggestive hypotheses that could be validated through further investigations in the clinical domain. | ['Zhengxing Huang', 'Kunlun He', 'Uzay Kaymak', 'Xudong', 'Wei Dong', 'Peipei Chen'] | 2019-12-01 | null | null | null | journal-of-biomedical-informatics-2019-12 | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 2.15343535e-01 3.63729708e-02 -8.77167046e-01 -5.44418395e-01
-1.09354186e+00 -1.10033907e-01 2.10497111e-01 2.32200980e-01
-2.66146868e-01 1.27520072e+00 6.80780530e-01 -2.78666973e-01
-6.85873508e-01 -5.78867972e-01 -6.25746191e-01 -7.90297866e-01
-3.41920525e-01 5.81243455e-01 -5.46365798e-01 4.88777131e-01
-2.40268093e-02 1.60742626e-01 -1.02440095e+00 2.98335075e-01
1.10491407e+00 8.64144325e-01 -1.59235880e-01 2.06380233e-01
4.47729558e-01 8.96154761e-01 -2.82654405e-01 -2.19651356e-01
-5.48462607e-02 -3.20922315e-01 -5.26085794e-01 -2.05413908e-01
-2.92312074e-02 -5.07110655e-01 -1.54857174e-01 5.77637792e-01
1.02103198e+00 -2.38800094e-01 1.16935146e+00 -1.14657199e+00
-8.01984847e-01 7.07112253e-01 -6.11095607e-01 -7.50265410e-03
-1.00216769e-01 2.77957797e-01 9.28415537e-01 -4.72550064e-01
4.26070273e-01 1.03431904e+00 8.84169161e-01 4.05372202e-01
-1.30726242e+00 -8.53234589e-01 -9.48253199e-02 7.12295622e-02
-1.32585633e+00 -3.94575804e-01 4.02427495e-01 -7.42048860e-01
5.48156619e-01 1.09054901e-01 3.62255067e-01 1.34226763e+00
7.00082779e-01 7.31919646e-01 1.32147384e+00 -8.59765634e-02
1.96753517e-01 1.03350142e-02 8.05667490e-02 3.48452806e-01
2.73873091e-01 4.90411788e-01 -8.08328986e-02 -7.50877023e-01
8.60663176e-01 5.25502324e-01 -4.39816475e-01 -2.90929198e-01
-1.28828382e+00 9.94907916e-01 4.28497493e-01 -1.66529864e-02
-9.67238188e-01 1.26276508e-01 6.39483690e-01 1.31867011e-03
6.46437645e-01 2.13270877e-02 -9.75677669e-01 1.13769747e-01
-8.85029733e-01 4.41704780e-01 5.11841476e-01 6.17380917e-01
6.54937327e-02 -6.82250713e-04 -6.98816061e-01 9.36264157e-01
3.06740791e-01 7.02153862e-01 6.05131090e-01 -7.76159525e-01
3.96754086e-01 5.22281945e-01 3.02602172e-01 -7.23742425e-01
-7.15974152e-01 -8.00501943e-01 -1.39705276e+00 -3.11001986e-01
2.95329750e-01 -4.78123933e-01 -8.62338722e-01 1.99700356e+00
3.82747948e-01 4.43831116e-01 1.02822706e-01 8.27314079e-01
7.94669390e-01 3.33866298e-01 7.24698663e-01 -6.42822742e-01
1.57219684e+00 -5.12959957e-01 -1.01131439e+00 4.15944830e-02
1.07678390e+00 -3.81838799e-01 7.13206887e-01 1.66355267e-01
-9.37985361e-01 -3.01059246e-01 -5.31832755e-01 1.55006632e-01
7.47712180e-02 5.63476145e-01 6.00671172e-01 4.35097754e-01
-3.64120305e-01 6.14469647e-01 -5.69903791e-01 -2.07073498e-03
8.58379781e-01 5.12350380e-01 -3.50185990e-01 -1.56340644e-01
-1.51799464e+00 6.01063728e-01 2.77516961e-01 1.28364682e-01
-1.12817776e+00 -1.37103808e+00 -6.97217226e-01 2.45273575e-01
2.01474443e-01 -1.37415862e+00 1.09181726e+00 -6.60826087e-01
-1.28847206e+00 6.09236479e-01 -4.21550237e-02 -5.76782048e-01
5.35295725e-01 -3.46067041e-01 -3.60745072e-01 -2.24429995e-01
1.35205224e-01 3.34958404e-01 4.92716759e-01 -8.69475663e-01
-4.82252181e-01 -7.09522784e-01 -4.01481837e-01 5.80458306e-02
-2.45092422e-01 -2.83922162e-02 2.12468982e-01 -9.58587945e-01
-3.16443384e-01 -9.69960928e-01 -4.77396846e-01 -1.73697114e-01
-5.23159087e-01 -2.76228398e-01 3.25979143e-01 -9.52362895e-01
1.58426046e+00 -1.99287200e+00 8.49642232e-02 -4.58643436e-02
5.41651666e-01 1.92330226e-01 1.50944620e-01 3.64947915e-01
-3.80746335e-01 1.74454182e-01 -3.57610106e-01 -1.70692936e-01
-3.90252843e-02 -1.20725065e-01 -7.90897384e-02 7.89994776e-01
1.65716431e-03 1.21858191e+00 -7.90720463e-01 -5.27386904e-01
2.43911743e-01 5.18898487e-01 -6.67816937e-01 2.57123739e-01
1.17417835e-02 5.88587821e-01 -8.08930099e-01 4.79492337e-01
7.57081747e-01 -6.42611802e-01 6.20998502e-01 -1.29610091e-01
1.64040923e-01 1.44153744e-01 -9.51769888e-01 1.37504447e+00
-5.72235942e-01 -9.92865413e-02 -4.87717301e-01 -1.21039069e+00
6.56700373e-01 8.21625829e-01 7.84116030e-01 -4.83269840e-01
1.74953550e-01 2.54034907e-01 1.48373142e-01 -6.27679169e-01
-4.42485482e-01 -5.67408323e-01 -2.58461058e-01 3.16947281e-01
-7.28063583e-02 6.97527885e-01 -5.23574650e-01 -2.99956173e-01
9.64472592e-01 -6.34447262e-02 9.73095834e-01 -4.04250383e-01
3.76647264e-01 -2.90448338e-01 9.22426760e-01 7.84773290e-01
-1.94594383e-01 5.03330767e-01 6.46769583e-01 -6.48127019e-01
-8.53890955e-01 -1.08914673e+00 -7.17805743e-01 5.28946161e-01
-5.22641957e-01 -1.13912448e-01 -3.59409750e-01 -8.07824254e-01
4.07993674e-01 5.98406911e-01 -8.20014656e-01 -4.16476011e-01
-2.20391437e-01 -1.33605671e+00 5.47942936e-01 7.79086530e-01
4.19814438e-01 -1.15575397e+00 -3.74438763e-01 3.21716517e-01
-2.58840531e-01 -7.22215116e-01 -2.24015042e-01 1.68802738e-02
-1.18955481e+00 -1.08782768e+00 -1.11870170e+00 -3.85197759e-01
2.34290510e-01 -4.04916435e-01 1.08724463e+00 -2.95566916e-01
-9.20853093e-02 -9.42908525e-02 2.96083861e-03 -4.88089621e-01
-1.57751560e-01 4.34692875e-02 1.04345031e-01 2.73472965e-02
5.22035241e-01 -5.24268866e-01 -1.14208949e+00 1.37859836e-01
-6.88507140e-01 -9.31183025e-02 9.74655032e-01 1.12028921e+00
5.99174082e-01 -1.70625210e-01 1.24483562e+00 -1.37751639e+00
5.12864947e-01 -1.02523279e+00 -2.97699720e-01 1.03781372e-01
-9.66188788e-01 -6.43621907e-02 6.22847974e-01 -4.78528261e-01
-1.12164390e+00 -7.57660493e-02 1.25356197e-01 -5.04000962e-01
-1.94757029e-01 8.15770209e-01 -4.09651957e-02 6.67464674e-01
5.62545896e-01 -2.79173087e-02 -1.71803325e-01 -6.26104534e-01
9.22175348e-02 1.00778925e+00 -2.67415605e-02 -2.99660623e-01
5.11153862e-02 3.05311531e-01 1.04304053e-01 -2.17022166e-01
-1.00223780e+00 -3.20376962e-01 -2.54561275e-01 3.04870486e-01
8.21778655e-01 -1.33539104e+00 -1.09729898e+00 4.29465801e-01
-7.89156973e-01 -3.14277768e-01 -1.12764426e-01 9.45513308e-01
-7.14859426e-01 9.16321501e-02 -6.37979507e-01 -8.43649209e-01
-6.56765044e-01 -1.08728242e+00 1.25403750e+00 -2.27397367e-01
-3.26004237e-01 -1.21861899e+00 3.04873794e-01 3.62977535e-01
2.03256726e-01 5.46140015e-01 1.36334622e+00 -6.52687788e-01
-2.01483801e-01 -2.55932808e-01 -3.47958207e-01 2.11440518e-01
4.10073161e-01 -4.38053608e-01 -9.14188206e-01 -3.48022461e-01
-4.02199179e-02 -3.13473582e-01 8.18726063e-01 1.40978336e+00
1.55613947e+00 -4.16613996e-01 -6.08026326e-01 5.41956127e-01
1.43177938e+00 1.81458220e-01 6.34828210e-01 7.00955233e-03
5.65657675e-01 3.65589976e-01 4.73969877e-01 9.87283766e-01
4.99777049e-01 7.47167826e-01 1.76679790e-01 -2.55390286e-01
1.01916805e-01 -2.16819972e-01 -7.17707351e-02 3.56460631e-01
-1.18722528e-01 -2.02566653e-01 -9.96043384e-01 5.82713664e-01
-2.15343928e+00 -7.83366442e-01 -3.38353217e-01 2.45700645e+00
1.04647303e+00 -6.21308535e-02 1.80175275e-01 -1.14187367e-01
6.36123121e-01 -1.25099093e-01 -6.35683596e-01 -2.84798414e-01
6.31671324e-02 1.33236468e-01 6.56187236e-01 1.21896639e-01
-1.32084596e+00 4.21508640e-01 6.22341824e+00 7.21277416e-01
-9.34253812e-01 3.73081803e-01 1.13516819e+00 -3.04567832e-02
-1.48606449e-01 -1.95862934e-01 -4.91794348e-01 6.67654335e-01
1.28960073e+00 -2.37708196e-01 -7.23264813e-02 6.01214230e-01
7.43398070e-01 3.35171342e-01 -1.03458881e+00 7.94636369e-01
-3.59664053e-01 -1.27861333e+00 1.20142624e-02 3.01381797e-01
8.88601422e-01 -1.37765542e-01 2.52571940e-01 5.77325702e-01
3.40469658e-01 -1.24360025e+00 9.85581726e-02 8.62828135e-01
1.04991758e+00 -6.29437327e-01 1.27452755e+00 3.99707437e-01
-6.44899905e-01 -4.27622110e-01 -2.68617898e-01 -8.18691403e-02
1.87241033e-01 9.88705933e-01 -7.72855878e-01 8.04938853e-01
6.04956329e-01 1.01473022e+00 -1.17461115e-01 1.02884591e+00
2.07685549e-02 8.49244714e-01 7.11792558e-02 2.72654414e-01
1.10431118e-02 4.26397957e-02 -8.63430463e-03 8.91400397e-01
3.64472568e-01 1.61632389e-01 4.18414213e-02 8.78503323e-01
-3.71639490e-01 3.33976418e-01 -6.35063291e-01 6.10139035e-02
3.04019988e-01 9.25550580e-01 2.87429299e-02 -4.95308101e-01
-3.48852813e-01 5.11279404e-01 1.67090431e-01 4.46274668e-01
-1.13588095e+00 1.11015461e-01 5.41624010e-01 7.85915479e-02
9.39027816e-02 6.19720459e-01 -4.26628470e-01 -1.06739473e+00
-2.13759467e-01 -1.05436599e+00 7.81967580e-01 -5.29029369e-01
-1.67930722e+00 2.08309665e-01 9.60546881e-02 -1.38230765e+00
-3.17307085e-01 -3.74027729e-01 -1.05512574e-01 1.14048433e+00
-1.45973110e+00 -1.10255659e+00 3.01832166e-02 3.20380569e-01
2.87965298e-01 2.85062827e-02 1.06717992e+00 5.93511760e-01
-8.12491238e-01 6.36790335e-01 5.51287472e-01 -2.64712580e-04
9.90967929e-01 -1.02763438e+00 -1.92865044e-01 -6.82780668e-02
-4.12167460e-01 6.83779418e-01 2.60836273e-01 -8.21943879e-01
-9.45029736e-01 -1.31053853e+00 9.88683403e-01 -4.13643062e-01
3.65569413e-01 3.52621749e-02 -7.53695846e-01 8.75768542e-01
-2.87239194e-01 2.06869394e-01 9.85249460e-01 4.72013861e-01
-1.04407810e-01 -9.25448537e-02 -1.23438644e+00 3.77048880e-01
8.17700148e-01 -2.85464138e-01 -4.06205982e-01 5.12994528e-01
5.82841277e-01 -2.62091964e-01 -1.41333103e+00 9.99043405e-01
9.06188667e-01 -6.15391433e-01 1.07911479e+00 -1.23703039e+00
8.11872244e-01 1.03701726e-01 -6.09779470e-02 -1.20850956e+00
-6.21906102e-01 -8.44970793e-02 -2.11796463e-01 8.98980439e-01
4.55983847e-01 -6.27501488e-01 6.00676775e-01 5.55500567e-01
4.95488830e-02 -1.04519129e+00 -9.02336359e-01 -4.56263423e-01
2.89177954e-01 -1.40619516e-01 6.35143161e-01 1.27000892e+00
-2.28719175e-01 3.24657679e-01 -8.18606377e-01 3.81012231e-01
7.35686302e-01 3.22350532e-01 4.30511653e-01 -1.40937531e+00
-2.91467577e-01 -6.24271519e-02 -2.24390998e-01 -5.05631745e-01
2.86911160e-01 -7.96584904e-01 -4.66036737e-01 -1.58036840e+00
7.27220118e-01 -5.12606144e-01 -9.51092124e-01 4.31168646e-01
-4.75594491e-01 -1.10366158e-01 -2.26864606e-01 2.39904389e-01
-2.31437847e-01 8.07025492e-01 1.22257507e+00 -3.41241173e-02
-2.08967075e-01 3.80965382e-01 -7.33229578e-01 6.74931943e-01
8.28323305e-01 -9.07098413e-01 -4.92345124e-01 -1.03301577e-01
-1.78524166e-01 5.96973300e-01 5.32474875e-01 -5.42154729e-01
-3.60176742e-01 -2.59819627e-01 6.63868129e-01 -3.20699632e-01
-4.06554760e-03 -8.38205695e-01 3.68357420e-01 7.95078516e-01
-6.17909729e-01 -4.67515402e-02 1.20987415e-01 8.12804580e-01
-4.44780178e-02 1.53676584e-01 5.43207049e-01 1.40525233e-02
-2.55759031e-01 5.21663189e-01 -9.63167623e-02 1.14176959e-01
9.07685578e-01 2.67400116e-01 -2.00031891e-01 -2.30532110e-01
-9.00501370e-01 2.92321801e-01 -2.01221406e-01 3.15813720e-01
5.02971411e-01 -1.52929616e+00 -1.08263612e+00 8.19072127e-02
3.28503698e-01 -3.26410770e-01 8.05469751e-01 1.23968387e+00
-1.08089224e-01 6.81707382e-01 -1.37407836e-02 -5.99072874e-01
-9.42966282e-01 8.11468840e-01 3.69286597e-01 -6.65373921e-01
-6.72152996e-01 1.82247996e-01 8.15144837e-01 -4.91122901e-01
4.98094186e-02 -3.97673279e-01 -2.43738621e-01 -6.79513663e-02
3.73390138e-01 4.09939110e-01 -8.59646574e-02 -2.10988075e-01
-4.86714214e-01 3.37893993e-01 -5.89283668e-02 3.56187284e-01
1.55020344e+00 1.75483804e-03 1.51765281e-02 4.60099727e-01
1.30535603e+00 -4.79098886e-01 -9.70921040e-01 -3.08950514e-01
-9.49621946e-02 -3.44513178e-01 2.90584773e-01 -1.11243796e+00
-9.95276451e-01 7.44724929e-01 9.73608732e-01 -3.50142539e-01
1.18483913e+00 -1.34223342e-01 5.91051161e-01 5.78485196e-03
-4.82686907e-02 -5.75931132e-01 -3.80975366e-01 -1.71430737e-01
7.78436124e-01 -1.37464058e+00 2.14948893e-01 -1.43728837e-01
-7.34296024e-01 4.69738722e-01 2.85661310e-01 -1.44137800e-01
8.85036469e-01 -3.88838276e-02 3.20570767e-02 -2.65593767e-01
-9.59825754e-01 2.50216484e-01 3.55781227e-01 3.51901650e-01
8.17663074e-01 4.51252550e-01 -6.68803394e-01 1.07589424e+00
3.63972187e-01 7.73671210e-01 1.19445376e-01 4.52792913e-01
2.23673746e-01 -8.93639803e-01 -1.31494105e-01 8.82331371e-01
-8.82573605e-01 -2.96432555e-01 1.59112185e-01 8.18184435e-01
7.53881037e-02 6.06487036e-01 -2.64392257e-01 3.89225036e-02
4.19646353e-01 2.07508937e-01 6.78033456e-02 -3.75984401e-01
-4.94596571e-01 1.22343190e-01 5.24330251e-02 -4.26192343e-01
-5.52896440e-01 -5.51658094e-01 -9.28332388e-01 -2.07847953e-01
-4.04175997e-01 6.91359937e-02 1.87590420e-01 8.75692666e-01
6.77059293e-01 9.07679081e-01 7.51420677e-01 -3.16691190e-01
-1.00093412e+00 -1.11332881e+00 -6.98130548e-01 5.64367175e-01
4.38235641e-01 -8.48960817e-01 -2.63529062e-01 -8.20333138e-02] | [7.998507976531982, 5.6124982833862305] |
537818c9-3da0-4f33-89a7-aab940cff139 | reflection-removal-using-low-rank-matrix | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Han_Reflection_Removal_Using_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Han_Reflection_Removal_Using_CVPR_2017_paper.pdf | Reflection Removal Using Low-Rank Matrix Completion | The images taken through glass often capture a target transmitted scene as well as undesired reflected scenes. In this paper, we propose a low-rank matrix completion algorithm to remove reflection artifacts automatically from multiple glass images taken at slightly different camera locations. We assume that the transmitted scenes are more dominant than the reflected scenes in typical glass images. We first warp the multiple glass images to a reference image, where the gradients are consistent in the transmission images while the gradients are varying across the reflection images. Based on this observation, we compute a gradient reliability such that the pixels belonging to the salient edges of the transmission image are assigned high reliability. Then we suppress the gradients of the reflection images and recover the gradients of the transmission images only, by solving a low-rank matrix completion problem in gradient domain. We reconstruct an original transmission image using the resulting optimal gradient map. Experimental results show that the proposed algorithm removes the reflection artifacts from glass images faithfully and outperforms the existing algorithms on typical glass images.
| ['Jae-Young Sim', 'Byeong-Ju Han'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['reflection-removal'] | ['computer-vision'] | [ 0.66825014 -0.19038658 0.5519239 -0.20232275 -0.72354466 -0.10995226
-0.18761599 -0.7207479 -0.12585324 0.2822958 0.41321465 0.4610328
-0.2375103 -0.43803048 -0.6551302 -1.2409654 0.24336089 -0.17198001
0.20397963 -0.11654833 0.39437443 0.01245691 -1.2974501 0.48753113
0.7448243 0.78007966 0.65001607 0.66003156 0.37684226 0.9964706
-0.33988592 0.04942811 0.578655 -0.3198927 -0.28940296 0.8465308
0.85184956 -0.7495554 -0.7317829 1.4779369 0.04157566 0.14238974
0.12450279 -0.7287855 -0.75887203 0.31386018 -1.1271832 -0.23769298
0.61450243 -0.04188 0.66322464 -1.1837275 0.5291729 1.126192
0.5089163 0.16465196 -1.0721312 -0.28312165 0.28085282 0.07277539
-1.4889852 -0.77854735 1.0103408 -0.01737018 0.31288844 0.45454407
0.68788767 0.4656744 0.3586237 0.60054725 1.161681 -0.43159676
-0.273868 0.1369961 0.07804339 0.81157005 0.36089274 -0.0288638
-0.5939318 -0.016185 0.5646239 0.55521715 -0.95751095 -0.12765561
-1.3659085 0.29132745 0.29339162 0.3054996 -0.44732556 0.04596154
-0.39742813 0.4622939 0.32983625 0.01218176 0.15228447 0.59963083
-0.9496079 -0.03825999 0.5855443 1.0972681 1.1871083 0.14133972
0.03531416 1.0279956 0.74630195 1.1299162 0.12643348 -0.9602891
0.712378 0.3675985 0.26591522 -1.2811786 -0.11571077 -0.21092454
-0.9984832 0.0463954 0.29423308 -0.00668715 -0.7954631 1.2805305
0.27153602 0.2892216 0.31828773 1.305711 0.6870248 1.0569295
-0.949951 -0.6231272 0.82309306 -0.86524427 -0.9282748 -0.3450483
-0.04338688 -1.4493017 0.8983023 0.80180395 -1.1990088 -0.40263197
-1.1946052 -0.04458773 0.58149993 0.45424962 0.14187258 0.4980097
-0.98061484 0.38530084 -0.5649507 -0.02277368 -0.23751019 0.04904401
-0.19260709 -0.8166148 -0.7417377 0.43351305 -0.44171464 0.7404166
-0.8864666 -0.398851 -0.6081894 -0.42931515 0.48164368 -0.43548438
0.6417269 -0.9015543 -1.4110848 0.758354 -0.2591664 0.29904583
0.4238858 -0.4390525 -0.49770373 0.38250527 0.06192904 -0.2792032
1.5572244 -1.5949109 -0.4032899 -0.36688316 -0.24044049 0.5113035
-0.04102386 -0.27551055 -0.930498 -0.22808324 0.8799366 -1.2677073
-0.23151566 -0.15655167 -0.6852785 0.79519117 1.0299484 -0.6605152
0.9622642 -2.6291275 0.18642752 0.3998354 0.46068743 -0.30361012
-0.22796021 0.4508613 0.02750155 -0.8104558 -0.2702949 -0.27646333
-0.6422867 0.02246457 -0.33021483 0.9761341 -0.45693877 0.21267919
-0.9060002 -0.31206086 0.37923676 0.5718423 -0.34544787 0.31136078
0.34752792 0.57341754 -0.6371572 0.42298648 1.2044832 -0.12791637
0.14815445 -0.73952395 -0.28283864 -0.1277985 -1.524013 1.4827942
-0.5085135 0.6288208 0.5694771 -0.5026387 0.9161364 0.12316942
0.7102132 -0.548053 -0.2808738 0.13105279 -0.19923642 -0.71552724
0.8389684 -0.11625037 0.31623897 0.34074157 -0.508697 -0.6905229
0.04071777 0.47407943 0.87761676 -0.11060578 -0.29678982 -0.12967755
0.5360721 -0.41168088 0.65165854 0.7846054 0.24012817 1.1756258
-0.05808342 -0.21316195 -0.87092423 -1.1438518 0.130809 0.5167249
0.6782066 -0.45289588 -0.57922137 -0.03862978 -0.47266793 0.02838157
-0.21167095 -0.1392623 -0.5496673 -0.8021549 -0.17594992 -0.30905056
1.066568 -0.46391135 -0.17557077 0.13516909 -0.673345 -1.379492
-0.81855124 -0.4591255 -0.7971748 -1.1348709 -0.7521077 -0.87182105
1.1126944 1.300726 0.8944785 0.19984174 -0.3243639 0.5782708
-0.15908253 0.23270187 -0.09331562 -0.78142804 -0.02066143 0.783496
-0.2550994 -0.54685545 -0.79059374 0.57348025 -0.98602045 0.4360899
0.73254573 0.6609294 0.7665333 0.44720575 -0.30505073 -1.0559552
0.53330475 0.06133844 -0.79366237 0.28883055 -0.39875796 -0.14654206
0.5867853 -0.24913096 -1.3277748 0.26830885 0.4118874 -0.5987669
0.3802604 0.28255266 -0.07710699 -0.31430176 0.39988375 0.52604175
-0.19395937 -0.32232803 0.1669031 0.3922594 0.41110015 -0.3400333
1.1185184 1.0295616 0.10845295 -1.5960822 -0.99446785 -0.7300225
-0.34117252 -0.53032345 0.63059676 -1.0898671 -0.4884384 0.89004135
-0.79228514 -0.20231421 0.00724882 0.7354603 -0.23938529 0.83711034
-0.89932233 -0.7423424 -0.12730744 -1.2420182 1.1773584 0.15823373
0.29580683 -0.5647736 0.09256724 0.41364953 0.19072962 -0.3069475
0.45992792 0.7968629 -1.2927392 -0.03643896 -0.29521692 0.5625625
0.5240586 0.00849647 -0.9658297 -0.4948114 0.754228 0.05467384
0.9041922 0.67828053 0.58785737 -0.3333185 -0.05310207 0.8683613
1.6353282 -0.07245596 1.0194176 -0.06166545 1.1352286 0.7744409
0.70983464 0.18488434 -0.06602889 0.59454626 0.24757244 -0.23737313
-0.12862885 -0.07034609 0.6187413 1.1860127 -0.09496462 -0.17407174
-0.33103862 0.42932045 -1.812025 -0.88988185 -0.79120547 2.5346377
0.27323988 -0.18456705 -0.43351454 0.14058459 0.69432944 0.49026066
-0.3758177 0.14012215 -0.28454608 -0.22342533 0.6583356 0.91232747
-0.6650144 0.60495514 6.5573177 0.36677828 -1.0333089 -0.14416979
0.31690305 -0.29413053 -0.42668107 0.17234986 -0.4892986 0.3491677
0.26700556 0.11123742 0.5864804 0.3571282 0.5967375 -0.48599145
-0.70917785 1.3017852 0.28925776 -0.76656216 0.0651799 0.04331198
1.0588527 -0.08732308 0.46417716 -0.58956283 0.22753064 -0.557454
0.46300977 0.78913873 0.49319032 -0.44795504 0.29592147 -0.16292457
-1.1139336 0.26989266 -0.6360323 0.03341369 0.15830411 1.2762278
-0.38858795 0.65020126 0.9064186 1.1720784 -0.16328128 0.73538727
-0.40168142 0.32717276 -0.39027962 0.43585017 0.21574809 -1.2390774
0.72294587 0.709605 0.51209456 0.3034213 0.30138165 0.58119184
0.05390181 -0.17318927 -0.8205022 0.3936109 -0.10977765 1.3745668
-0.46331757 -0.22050333 -0.7957572 1.3792614 -0.36940417 0.9707361
-0.49385446 -0.27065155 0.5050664 0.16167511 0.10049305 -0.21090297
0.18472382 -1.5302364 0.40504628 -0.85589117 0.13050085 -1.2632687
-1.075881 0.4303753 -0.32734612 -1.6699288 0.32144955 -0.32731453
-0.49534515 0.7484549 -1.498168 -0.89886236 -0.6039764 1.1785465
0.24832997 0.34477878 0.09269206 0.4432636 -0.5600257 -0.01061777
0.71254265 -0.06107998 0.71467435 -0.7256626 -0.22814554 1.2330087
0.06307974 0.6470372 0.76184314 -0.5464288 -1.8337691 -0.8082707
0.3192277 0.15414675 0.47512385 -0.09923875 -0.81358874 0.7779261
0.21461214 -0.08573394 0.23282838 -0.29728565 -0.13951117 -0.5801143
-0.87791514 0.56636256 0.837642 -0.64061743 -0.18341942 0.57687104
0.19222921 -0.51067424 -0.45326295 0.025542 0.52822286 -1.317661
1.0986084 0.33676615 0.40207332 -0.71466583 -0.3603489 -1.3654217
-0.24578688 -0.90979576 0.51281166 0.99512386 0.3221224 -0.51818484
0.6902541 0.48333898 -0.2620957 0.01181159 -0.5811858 -0.3335922
-0.88267994 -0.09477271 0.03874884 0.8281158 -0.1695993 0.49172437
-1.1149095 0.7183712 1.3820417 0.678424 0.73767406 -0.65761155
-0.42382532 0.403946 -0.21829435 -1.4380165 -0.31107524 -0.3685577
0.48274726 -1.4566698 0.48375854 -0.26728895 -0.09062137 -0.16035095
-0.14878069 0.41721708 0.07405144 0.51582813 -0.5337605 0.45230547
1.5934203 -0.28660566 -0.267243 0.051472 -0.50582117 0.92869925
0.28370494 -0.3596303 -0.5505593 -0.91302276 0.38762206 0.3104748
0.02069868 -0.99202156 0.22208132 -0.27981558 0.3171235 -0.4014801
0.6901547 -1.0258346 0.4050819 0.21535443 -0.05118663 -0.31139633
-0.29611918 0.8968272 -0.28861538 -0.19924076 0.9110375 -0.2948155
-0.4742549 0.29929703 -0.576064 -0.34102863 0.51776934 -0.3497786
-0.2899823 -0.8553917 -0.66252494 -0.02336457 0.69512314 -0.05993146
1.3398142 -0.9790393 -0.7761943 0.4459053 0.06440604 -0.09926552
0.6600279 0.95129037 -0.6307698 -0.13836826 0.1116781 -0.7565381
-1.6851101 0.29620156 0.35520828 -0.19847468 -1.0785908 0.66035587
0.7533491 0.0697493 -0.12466592 -0.39467233 0.01884175 -0.48983845
0.7166294 0.42055392 0.11282938 -0.88728577 -0.03174856 1.2283908
-0.26700553 -0.2245261 1.4415976 -0.7640063 -0.33394036 0.37254027
1.4315069 0.70001656 -1.2106162 -0.39826512 -0.5074225 -1.1967571
0.43369466 -0.0152883 -1.6715769 0.58334094 0.6010229 0.05541226
1.5014017 -0.45875522 0.7316171 0.493532 0.37968883 -0.9262812
0.354383 0.13735966 0.7782772 -0.8995369 0.5625741 -0.84885705
-0.575486 1.0181547 0.19610398 -0.320643 0.42472443 0.05474732
0.35488918 -0.41738394 -0.4196321 -0.07324517 0.03655489 0.41900283
0.20243947 -0.27825463 0.06385998 -0.5696136 0.08899646 -0.3056045
1.1242836 0.6637554 -0.43150407 -0.7882928 -0.92658544 0.25728077
-0.2627137 -0.2151156 -0.3540077 0.28282517 -0.50184584 1.450195
-0.23412804 -0.32737252 0.4592351 -0.61282164 0.723126 -0.5956794
-0.03654141 0.69454294 -0.02868829 -0.67036414 -0.57438546 -0.45593324
-0.93004274 -0.38747847 -0.40112212 0.23617668 0.30475688 0.48342025
-0.07196974 0.31615278 1.0740035 -0.87443686 0.03075885 -0.73969555
-1.3262335 0.6487165 0.7178256 -0.10479936 -0.8098519 0.19836602] | [10.370481491088867, -2.7857789993286133] |
f2f4c8ca-2d76-41db-a63c-968b0a768a3f | real-or-fake-text-investigating-human-ability | 2212.12672 | null | https://arxiv.org/abs/2212.12672v1 | https://arxiv.org/pdf/2212.12672v1.pdf | Real or Fake Text?: Investigating Human Ability to Detect Boundaries Between Human-Written and Machine-Generated Text | As text generated by large language models proliferates, it becomes vital to understand how humans engage with such text, and whether or not they are able to detect when the text they are reading did not originate with a human writer. Prior work on human detection of generated text focuses on the case where an entire passage is either human-written or machine-generated. In this paper, we study a more realistic setting where text begins as human-written and transitions to being generated by state-of-the-art neural language models. We show that, while annotators often struggle at this task, there is substantial variance in annotator skill and that given proper incentives, annotators can improve at this task over time. Furthermore, we conduct a detailed comparison study and analyze how a variety of variables (model size, decoding strategy, fine-tuning, prompt genre, etc.) affect human detection performance. Finally, we collect error annotations from our participants and use them to show that certain textual genres influence models to make different types of errors and that certain sentence-level features correlate highly with annotator selection. We release the RoFT dataset: a collection of over 21,000 human annotations paired with error classifications to encourage future work in human detection and evaluation of generated text. | ['Chris Callison-Burch', 'Sherry Shi', 'Arun Kirubarajan', 'Daphne Ippolito', 'Liam Dugan'] | 2022-12-24 | null | null | null | null | ['human-detection'] | ['computer-vision'] | [ 2.96167403e-01 1.43018499e-01 9.85059440e-02 -2.58507848e-01
-1.02403283e+00 -8.23955655e-01 6.80593073e-01 7.33829260e-01
-8.25043678e-01 5.55854559e-01 4.84600037e-01 -4.30869192e-01
3.89816701e-01 -3.31335694e-01 -5.83634377e-01 -3.58205549e-02
5.14442861e-01 5.49555957e-01 7.46638253e-02 -1.93199903e-01
5.75587511e-01 -1.21891342e-01 -9.97953057e-01 5.30334473e-01
8.61507416e-01 3.73437494e-01 1.79970175e-01 1.26316965e+00
8.81013125e-02 1.10597324e+00 -1.17145383e+00 -6.83522046e-01
6.73879981e-02 -6.45973384e-01 -1.03964841e+00 -7.50229582e-02
6.24156773e-01 -1.13688633e-01 -2.68995643e-01 9.15029109e-01
7.19878197e-01 7.61510879e-02 9.55471933e-01 -8.54560673e-01
-7.97142744e-01 1.00036502e+00 -4.14218992e-01 7.17559099e-01
5.77691197e-01 4.50937122e-01 9.43223596e-01 -8.30540180e-01
6.94041789e-01 1.12651420e+00 8.52473795e-01 4.72537011e-01
-1.21230066e+00 -4.01301384e-01 -3.07006817e-02 -1.68732107e-01
-1.03982294e+00 -7.33367085e-01 1.55967310e-01 -9.61388826e-01
1.15187109e+00 2.79761702e-01 1.99105233e-01 1.71021521e+00
2.43275717e-01 7.35691786e-01 8.64433050e-01 -9.28764939e-01
-5.55375442e-02 3.11796248e-01 5.00738382e-01 5.37895143e-01
4.27412927e-01 -1.62065923e-01 -6.66078329e-01 -2.62206197e-01
2.09146783e-01 -6.14713430e-01 -3.60576153e-01 3.99859935e-01
-1.33634245e+00 8.16102803e-01 -1.74379110e-01 5.15027940e-01
-1.16448902e-01 -6.45999163e-02 6.24989688e-01 3.19061697e-01
5.16445577e-01 1.08432257e+00 -2.52424508e-01 -6.18248463e-01
-1.19387794e+00 4.03457642e-01 1.18398702e+00 9.41196203e-01
8.42653438e-02 -2.26902679e-01 -7.77413487e-01 9.12295580e-01
-3.43224220e-02 2.85984755e-01 9.39426363e-01 -7.45051026e-01
9.29545522e-01 4.98703301e-01 5.85769594e-01 -1.07793427e+00
-5.63140869e-01 -4.17264819e-01 -3.70402128e-01 -1.82166934e-01
1.00825095e+00 -5.43458462e-01 -4.87818748e-01 1.51313770e+00
-3.16388726e-01 -7.03019202e-01 -3.47581297e-01 8.16740334e-01
3.25716555e-01 4.38315600e-01 1.47148728e-01 -2.15915605e-01
1.48103583e+00 -1.11646390e+00 -6.49344563e-01 -6.97181940e-01
1.12573123e+00 -1.01642060e+00 1.23476923e+00 4.44306612e-01
-1.19568729e+00 -6.31146133e-01 -8.57091427e-01 -1.91229671e-01
-8.12869444e-02 4.19697493e-01 -7.10427538e-02 7.59281278e-01
-7.57611334e-01 5.48706055e-01 -4.84051317e-01 -6.40843093e-01
1.74565256e-01 -2.36754090e-01 -8.81258119e-03 3.60478073e-01
-1.22975218e+00 1.13393450e+00 3.50223005e-01 -7.42593333e-02
-6.62430763e-01 -3.00038725e-01 -6.31053746e-01 -1.20310616e-02
2.23991081e-01 -4.42554742e-01 1.81700575e+00 -1.05231786e+00
-8.15877140e-01 1.16747463e+00 -3.12915176e-01 -5.28777003e-01
9.54591036e-01 -3.10804904e-01 -3.92562509e-01 -3.78890410e-02
4.51861978e-01 1.69882774e-01 6.55892074e-01 -8.06407154e-01
-8.63448322e-01 -3.22039425e-01 -9.09581855e-02 1.87045917e-01
-4.69560564e-01 5.76546192e-01 1.68497842e-02 -7.42195487e-01
-4.28615630e-01 -8.35664868e-01 5.72321564e-02 -3.70370269e-01
-8.34249973e-01 -4.96317714e-01 4.27768044e-02 -8.24708104e-01
1.77583647e+00 -2.00363684e+00 -2.01070249e-01 1.04385823e-01
4.92566377e-01 1.14438593e-01 -4.07187082e-02 6.87525451e-01
1.78600803e-01 8.14795136e-01 9.83456001e-02 -5.45225918e-01
1.65637180e-01 -5.48118949e-01 -1.72657326e-01 4.26833421e-01
-4.38659303e-02 7.17777371e-01 -9.23723757e-01 -5.66206694e-01
-4.06910717e-01 1.63606722e-02 -3.24893832e-01 1.06826141e-01
-1.29009888e-01 2.91246653e-01 -3.32512796e-01 2.25976184e-01
-4.15952271e-03 -5.26610911e-01 -1.61451966e-01 3.37336391e-01
-2.95298934e-01 6.34215355e-01 -7.61790693e-01 1.24121118e+00
-3.14111143e-01 1.21589375e+00 -9.46842656e-02 -5.99632978e-01
5.05855799e-01 3.82459044e-01 -3.09058487e-01 -6.45068586e-01
3.13720644e-01 2.36769617e-01 3.91023219e-01 -8.02594721e-01
9.33443487e-01 2.89691299e-01 -1.53663471e-01 9.36785281e-01
-2.48784959e-01 3.40348482e-01 7.28219569e-01 4.27271754e-01
1.26515949e+00 -4.11755085e-01 2.25380823e-01 -1.61730230e-01
-1.98798310e-02 3.91749650e-01 9.99428704e-02 1.64425159e+00
-3.79435450e-01 6.53610647e-01 6.76408947e-01 -2.04204813e-01
-1.42378581e+00 -4.86772567e-01 -1.88632086e-01 1.41599798e+00
-2.53186882e-01 -5.10390937e-01 -1.05385530e+00 -5.62101424e-01
-2.01649129e-01 1.08503091e+00 -6.06506467e-01 -2.11756587e-01
-5.12808859e-01 -6.38412595e-01 9.86321509e-01 4.86431986e-01
1.35893241e-01 -1.13496935e+00 -7.48500109e-01 3.36925060e-01
-6.60935581e-01 -1.08129966e+00 -9.68035519e-01 5.22033572e-02
-3.49480182e-01 -1.05537355e+00 -5.78218699e-01 -6.85807645e-01
6.15067005e-01 2.15959623e-02 1.36589170e+00 3.56347531e-01
-3.91995549e-01 3.91735971e-01 -6.87535703e-01 -5.66050053e-01
-9.60234523e-01 4.67136264e-01 -4.81388681e-02 -2.74301678e-01
5.39011419e-01 1.85236156e-01 -3.22599143e-01 2.44476736e-01
-4.61942613e-01 4.54850681e-02 3.40496033e-01 9.01112258e-01
-2.15885326e-01 4.90898117e-02 2.83601522e-01 -1.11592114e+00
1.37675548e+00 -4.70106035e-01 -1.22805305e-01 3.25467229e-01
-5.49001336e-01 -7.55602196e-02 6.18421018e-01 -5.86144030e-01
-8.35872650e-01 -2.06507459e-01 1.11197181e-01 1.11935183e-01
-2.35838026e-01 4.86252218e-01 5.50627232e-01 4.16205913e-01
1.35744750e+00 8.60958323e-02 -2.43557721e-01 -2.93966681e-01
-4.81021814e-02 9.88559902e-01 2.98471957e-01 -4.89923984e-01
6.72109663e-01 -1.85495391e-01 -8.72933447e-01 -7.40911961e-01
-1.03049719e+00 -3.46566767e-01 -4.80078876e-01 -1.77327976e-01
9.03542280e-01 -8.89326394e-01 -6.64846838e-01 4.84019011e-01
-1.50223529e+00 -6.52574778e-01 9.19887498e-02 3.10965985e-01
-3.06430042e-01 2.97982603e-01 -8.59989762e-01 -1.18130755e+00
-2.76402324e-01 -1.08373046e+00 9.61757064e-01 8.43980238e-02
-1.08308625e+00 -8.60842407e-01 -1.65489018e-02 6.23606205e-01
2.84488142e-01 -1.39490813e-01 6.83818460e-01 -1.29903722e+00
-4.22333181e-02 -4.98545170e-01 -2.42384262e-02 1.05747260e-01
-2.77523249e-01 1.19304165e-01 -7.96316803e-01 -1.90873206e-01
-1.04320876e-01 -5.68007827e-01 6.17945254e-01 2.58232623e-01
9.46922421e-01 -6.07919276e-01 -4.32876110e-01 -9.95927677e-02
7.49650538e-01 -1.64852850e-02 2.67620474e-01 5.04407585e-01
6.79736555e-01 6.79035842e-01 3.03015202e-01 3.72597069e-01
3.26091319e-01 5.96378088e-01 -1.52080610e-01 2.10515812e-01
2.88596541e-01 -4.26335067e-01 3.69332105e-01 5.52399993e-01
8.55747834e-02 -9.88351941e-01 -1.17555141e+00 4.84904021e-01
-1.77229774e+00 -1.26100338e+00 -5.26858866e-01 2.16635299e+00
1.07170546e+00 5.83779514e-01 4.35068071e-01 1.53979346e-01
9.79806125e-01 -1.45979971e-01 -1.76907420e-01 -4.64005798e-01
-5.82696423e-02 -2.07724720e-01 5.15369594e-01 6.72800720e-01
-1.01890099e+00 8.09253275e-01 6.99180984e+00 6.65872991e-01
-8.42794001e-01 1.91108316e-01 9.39681828e-01 -2.14408040e-01
4.37640026e-02 -3.00845891e-01 -1.08711278e+00 9.30136740e-01
1.18922353e+00 -2.97805041e-01 3.14449430e-01 6.84300601e-01
6.71911597e-01 -2.75770217e-01 -1.35270727e+00 6.91306889e-01
3.05661112e-01 -8.87455165e-01 -3.57516706e-01 -2.60571279e-02
5.99702358e-01 -1.30496785e-01 -2.57975131e-01 4.90221858e-01
5.41822970e-01 -1.08865201e+00 1.27344036e+00 4.91472036e-01
4.58299637e-01 -2.33124763e-01 6.21210098e-01 8.20083439e-01
-4.98116463e-01 -2.22808152e-01 -1.68744326e-01 -3.68261278e-01
1.97117522e-01 5.61349273e-01 -1.00709486e+00 -2.60794491e-01
4.84441549e-01 9.72258151e-02 -1.06039381e+00 8.92785966e-01
-2.58446634e-01 9.97929335e-01 -4.69162576e-02 -4.11433011e-01
-3.18254763e-03 5.00744581e-01 6.66260600e-01 1.56232584e+00
1.81112677e-01 -3.90695445e-02 1.42024502e-01 9.91418362e-01
-2.56800354e-01 2.37500772e-01 -4.06160414e-01 -5.12197733e-01
7.29340076e-01 1.12930930e+00 -7.22973883e-01 -4.37532067e-01
-2.81808943e-01 1.12467802e+00 5.86609840e-01 3.63617212e-01
-6.55078232e-01 -5.06833375e-01 7.07568154e-02 3.48511279e-01
-1.61317870e-01 -1.82524905e-01 -6.44911826e-01 -1.23991084e+00
2.03912556e-01 -1.15320325e+00 1.71033338e-01 -9.34795678e-01
-1.44796169e+00 6.56555712e-01 -4.69212860e-01 -7.40598798e-01
-3.89137059e-01 -5.03380775e-01 -6.66866302e-01 1.04372299e+00
-7.85244584e-01 -4.44687694e-01 -3.61472577e-01 1.13392752e-02
8.47650647e-01 -1.62793145e-01 4.66401279e-01 2.64188558e-01
-7.85645127e-01 9.61230755e-01 2.73413002e-03 7.20058799e-01
1.10513055e+00 -1.27535439e+00 7.98411667e-01 1.00512445e+00
2.65365005e-01 7.70915151e-01 1.02854276e+00 -8.82068694e-01
-7.94942200e-01 -8.95521820e-01 1.42238581e+00 -1.10555422e+00
7.14920342e-01 -5.74618459e-01 -7.09035933e-01 7.55182385e-01
2.48993129e-01 -4.28651273e-01 5.14084399e-01 3.22979242e-01
-2.01308876e-01 6.10092223e-01 -8.20275903e-01 7.31695771e-01
1.09022427e+00 -6.54231787e-01 -6.73162580e-01 6.61994338e-01
5.46565890e-01 -5.84129274e-01 -4.44882542e-01 -1.99646190e-01
4.93059456e-01 -9.08998668e-01 4.09767181e-01 -8.24407101e-01
8.45371127e-01 1.21792100e-01 3.13641131e-01 -1.44736350e+00
-5.55218577e-01 -4.96772051e-01 6.48080930e-02 1.22158182e+00
9.75854456e-01 -3.47133249e-01 2.46266514e-01 1.02777040e+00
-1.58786505e-01 -4.45277184e-01 -4.25519109e-01 -5.53281605e-01
2.17299402e-01 -2.92872876e-01 -7.59387910e-02 9.57054853e-01
3.25721562e-01 5.78794956e-01 -3.37935776e-01 -2.04466254e-01
1.40990824e-01 -5.24624884e-01 6.94357812e-01 -1.18871331e+00
-3.69190544e-01 -7.51116812e-01 -6.67648539e-02 -1.00941646e+00
-2.36865841e-02 -7.75210142e-01 3.72937262e-01 -1.35337770e+00
3.47038597e-01 -1.67258888e-01 2.12034032e-01 3.17699939e-01
-6.27061605e-01 1.55051410e-01 2.21665800e-01 5.00290692e-01
-7.75831163e-01 -1.15737468e-01 9.47433531e-01 -4.15315814e-02
-1.67538300e-01 -1.09182559e-02 -9.53368485e-01 5.25919974e-01
6.71855628e-01 -6.24791563e-01 5.26197441e-03 -6.90451443e-01
6.22037411e-01 2.58868709e-02 2.77724326e-01 -1.01899529e+00
4.16190714e-01 3.68058942e-02 6.35001957e-01 -1.67576358e-01
-2.59718001e-01 -2.01802224e-01 -4.15088743e-01 2.79869884e-01
-1.14393020e+00 4.09649551e-01 1.01638511e-02 5.13623178e-01
1.75081968e-01 -8.08973312e-01 5.98178983e-01 -3.57671142e-01
-5.32929003e-02 -2.17350513e-01 -9.58063185e-01 8.21127117e-01
7.62435317e-01 -2.45791718e-01 -5.30607820e-01 -4.85996485e-01
-7.08494008e-01 2.15893060e-01 4.59720343e-01 5.00895202e-01
-1.47971988e-01 -8.67159665e-01 -9.88780618e-01 -1.47067398e-01
2.84749240e-01 -4.48585063e-01 -1.10737212e-01 9.01894808e-01
-5.62504649e-01 3.92559171e-01 3.14045519e-01 -4.02985811e-01
-1.06350887e+00 2.25973994e-01 3.88478816e-01 -2.77710140e-01
-3.87067527e-01 9.76712942e-01 -1.17766179e-01 1.28796035e-02
4.01811242e-01 -1.28615946e-01 -9.10915211e-02 3.18689525e-01
7.84392238e-01 4.71984208e-01 8.78237113e-02 -5.98110437e-01
5.86931519e-02 -7.79715925e-02 -3.98490369e-01 -2.83434033e-01
6.92593336e-01 -2.38503367e-01 1.80803031e-01 8.23736668e-01
8.33252907e-01 2.42216766e-01 -8.15220952e-01 -1.36338044e-02
1.69052675e-01 -3.11962485e-01 -1.47620052e-01 -1.04711509e+00
-3.63641649e-01 6.92977965e-01 2.06006691e-01 7.62377024e-01
3.45250577e-01 -1.21601954e-01 7.12825000e-01 5.02535284e-01
1.86319441e-01 -1.48172688e+00 2.53845066e-01 7.01079965e-01
7.58926809e-01 -1.48035097e+00 -2.87573487e-01 2.60708183e-02
-6.81684434e-01 1.10307848e+00 9.20851171e-01 2.02944219e-01
4.96863127e-02 9.96694118e-02 2.01689880e-02 -1.20899439e-01
-7.59496927e-01 1.78490385e-01 1.21624261e-01 1.54483303e-01
1.02777719e+00 -3.14737968e-02 -5.33035040e-01 7.07530618e-01
-7.04487503e-01 -1.84391692e-01 8.38394940e-01 7.79332280e-01
-6.12613440e-01 -7.02522218e-01 -4.62844491e-01 8.87117863e-01
-8.02928507e-01 -3.41464639e-01 -7.47991562e-01 4.99901146e-01
-1.12256743e-01 1.26920378e+00 1.19566277e-01 -1.42875165e-01
3.20863962e-01 4.25543636e-01 2.76992619e-01 -1.04837191e+00
-9.09282446e-01 -2.70603389e-01 5.85815430e-01 -7.97973350e-02
8.21890458e-02 -7.87184000e-01 -9.45180357e-01 -3.18563730e-01
-6.02396190e-01 2.48121083e-01 4.18459177e-01 1.20213044e+00
1.92143053e-01 3.20728958e-01 1.26826569e-01 -6.43374801e-01
-7.80169547e-01 -1.51841319e+00 -3.02673787e-01 6.29982293e-01
3.96870941e-01 -2.68412739e-01 -6.91507995e-01 3.98602992e-01] | [11.708534240722656, 8.938375473022461] |
2eeaf98b-13b9-442f-8451-678326c345e9 | a-new-approach-for-pedestrian-density | 1811.05006 | null | https://arxiv.org/abs/1811.05006v2 | https://arxiv.org/pdf/1811.05006v2.pdf | A new approach for pedestrian density estimation using moving sensors and computer vision | An understanding of pedestrian dynamics is indispensable for numerous urban applications including the design of transportation networks and planing for business development. Pedestrian counting often requires utilizing manual or technical means to count individuals in each location of interest. However, such methods do not scale to the size of a city and a new approach to fill this gap is here proposed. In this project, we used a large dense dataset of images of New York City along with computer vision techniques to construct a spatio-temporal map of relative person density. Due to the limitations of state of the art computer vision methods, such automatic detection of person is inherently subject to errors. We model these errors as a probabilistic process, for which we provide theoretical analysis and thorough numerical simulations. We demonstrate that, within our assumptions, our methodology can supply a reasonable estimate of person densities and provide theoretical bounds for the resulting error. | ['Roberto M. Cesar-Jr.', 'Gabriel B. A. Ferreira', 'Eric K. Tokuda', 'David Boyle', 'Yitzchak Lockerman', 'Claudio T. Silva', 'Ethan Sorrelgreen'] | 2018-11-12 | null | null | null | null | ['pedestrian-density-estimation'] | ['computer-vision'] | [-1.45080581e-01 -3.05344194e-01 7.01652616e-02 -1.72669411e-01
-5.08204877e-01 -3.15071017e-01 6.27047658e-01 1.87573627e-01
-6.86163127e-01 8.77452791e-01 -6.00249879e-02 -5.61008096e-01
1.05998717e-01 -1.17573357e+00 -4.78088737e-01 -5.59136689e-01
-8.75348300e-02 6.68120027e-01 4.86701429e-01 7.10818693e-02
1.41891226e-01 6.70071661e-01 -1.45021927e+00 -5.15798211e-01
8.44841838e-01 5.85886955e-01 2.20956672e-02 1.01126778e+00
-1.42160147e-01 2.51589864e-01 -3.71729761e-01 -4.47595686e-01
3.10485780e-01 -2.18565881e-01 -5.51636159e-01 3.11305016e-01
3.37747425e-01 -5.81639469e-01 -4.51542407e-01 9.99636710e-01
2.90811390e-01 3.52807313e-01 8.95341396e-01 -1.37871039e+00
-1.04580507e-01 1.38207898e-01 -9.08118367e-01 5.92813849e-01
2.33613029e-01 1.69389486e-01 4.92066920e-01 -7.72276223e-01
9.54703316e-02 1.11446047e+00 7.63872981e-01 2.86470354e-01
-1.05857301e+00 -5.15053749e-01 7.69276023e-02 -2.05055214e-02
-1.83903015e+00 -5.66195607e-01 4.62724626e-01 -6.10501230e-01
5.83087742e-01 6.70515001e-02 8.57089818e-01 5.85888565e-01
-3.33456129e-01 5.63789248e-01 7.14715660e-01 -6.63477361e-01
3.32623512e-01 2.34233320e-01 2.93583602e-01 8.15182090e-01
7.57355809e-01 9.28137451e-02 -5.39073162e-02 -1.90991998e-01
1.09229517e+00 3.22708599e-02 2.39935771e-01 -2.83693254e-01
-7.98390627e-01 9.30288613e-01 3.98632377e-01 1.14977986e-01
-2.27837011e-01 3.81653965e-01 1.78493243e-02 -4.63234067e-01
5.52081585e-01 -3.83839965e-01 1.60158411e-01 -2.98114806e-01
-1.00168717e+00 3.90431583e-01 6.75479352e-01 1.01210356e+00
7.07926035e-01 -2.42991388e-01 2.43640706e-01 5.72186887e-01
3.51127595e-01 8.20953548e-01 -2.80865848e-01 -1.01183248e+00
6.52960122e-01 4.26112175e-01 5.68256378e-01 -1.12065852e+00
-3.63959730e-01 5.14028855e-02 -8.81893694e-01 1.31506249e-01
1.11995316e+00 -1.77287608e-01 -6.52428627e-01 1.37495160e+00
4.29806739e-01 2.44294912e-01 -4.51651514e-01 6.68593824e-01
-3.04762134e-03 5.18829942e-01 2.76285708e-01 -1.06579289e-01
1.21081066e+00 -3.51163149e-01 -3.94850552e-01 -1.54823557e-01
4.33067977e-01 -4.76820707e-01 7.22677052e-01 -1.98363028e-02
-1.12991488e+00 -2.54390061e-01 -5.96677542e-01 1.65646017e-01
-3.89228165e-01 9.68163759e-02 6.07138336e-01 1.22547519e+00
-8.36449265e-01 1.58899963e-01 -1.09041262e+00 -9.41850603e-01
5.96346378e-01 2.31490165e-01 1.28084078e-01 -4.93301749e-02
-6.71553135e-01 9.44659114e-01 2.28627156e-02 5.97302318e-02
-2.85414517e-01 -4.41708207e-01 -8.25362265e-01 1.25064582e-01
9.69945863e-02 -7.84234881e-01 1.11584997e+00 -3.40096146e-01
-8.64403844e-01 6.35316908e-01 -6.64708912e-01 -6.60645247e-01
8.41269612e-01 -8.83004963e-02 -2.26704463e-01 1.87999174e-01
4.16606486e-01 4.44496274e-01 4.22922313e-01 -1.23432422e+00
-1.03071439e+00 -4.81240779e-01 -7.04308301e-02 -3.86759192e-02
-1.93680421e-01 1.01125322e-01 -4.69493181e-01 -2.47097299e-01
3.05689033e-02 -7.94565916e-01 -6.87463164e-01 2.27253735e-01
-1.99956834e-01 -9.69404206e-02 5.88897467e-01 -3.98466796e-01
1.19212794e+00 -1.93253267e+00 -5.97028077e-01 4.88633513e-01
3.78747940e-01 6.57367930e-02 5.05350649e-01 2.78650135e-01
6.04914129e-01 1.43533736e-01 -1.64801553e-01 -6.10685110e-01
-5.13965972e-02 1.10850371e-01 -6.27419278e-02 8.54652643e-01
-7.90785626e-02 5.98273933e-01 -1.04682124e+00 -6.88022435e-01
7.52346516e-01 5.48590779e-01 -3.32184792e-01 -3.27917427e-01
3.60762149e-01 1.08246133e-01 -6.17256463e-01 6.84031963e-01
7.74639070e-01 -2.57211834e-01 -9.60360095e-02 3.24480653e-01
-4.08090502e-01 2.05066074e-02 -1.30831742e+00 8.27767968e-01
-3.71303856e-01 6.47106946e-01 1.06551662e-01 -1.02170765e+00
5.30111313e-01 5.43161202e-03 6.65400624e-01 -2.82641739e-01
2.50436604e-01 7.59632364e-02 -2.82843232e-01 -2.12356567e-01
7.36792266e-01 -2.73969382e-01 -8.55201781e-02 4.11304533e-01
-5.98806262e-01 2.26594478e-01 4.02895361e-01 1.52301386e-01
1.09063530e+00 -4.11208928e-01 4.10469264e-01 -2.59340405e-01
3.25830728e-01 3.12832087e-01 2.15000749e-01 9.74987924e-01
-7.89962053e-01 3.82754654e-01 6.11859709e-02 -5.07340789e-01
-1.41211832e+00 -1.36875629e+00 -2.68163413e-01 5.19210875e-01
2.74726212e-01 6.89953519e-03 -9.36603367e-01 -1.88841358e-01
9.41755250e-02 5.52826166e-01 -4.30785418e-01 3.12875509e-01
-6.22552514e-01 -1.02465940e+00 4.58349377e-01 7.30608046e-01
6.45386279e-01 -3.91690880e-01 -6.83045745e-01 2.66295105e-01
-2.37159193e-01 -1.41241002e+00 -4.54492062e-01 -5.44950545e-01
-7.11027503e-01 -1.23223948e+00 -9.02175903e-01 -5.03261268e-01
8.51008952e-01 7.73888290e-01 8.72044921e-01 2.67628312e-01
-3.61994207e-01 6.11257017e-01 -3.53839286e-02 -3.26919168e-01
-2.81186372e-01 -5.52008301e-02 2.92592317e-01 -5.77477589e-02
8.82850945e-01 -4.81144726e-01 -7.64585972e-01 3.66303742e-01
-1.59698546e-01 -8.08964521e-02 2.38519564e-01 2.71543831e-01
2.10473478e-01 4.95963842e-01 3.11505556e-01 -4.67053235e-01
4.52620655e-01 -6.17668211e-01 -1.05200648e+00 -7.69998208e-02
-3.08874011e-01 -4.63679761e-01 2.53212601e-01 -2.67683744e-01
-9.28979218e-01 2.66291559e-01 1.13437995e-01 2.08886005e-02
-4.71278280e-01 8.44811052e-02 -4.08439487e-02 -3.18481750e-03
4.44650650e-01 6.54707998e-02 -1.16199911e-01 -1.94809958e-01
2.77416766e-01 5.77179015e-01 6.15166187e-01 -5.64537048e-01
9.00698006e-01 1.01641524e+00 2.10032508e-01 -1.20026505e+00
-5.81173599e-01 -8.94661963e-01 -8.80544305e-01 -5.15091717e-01
7.92764306e-01 -9.02003050e-01 -9.91228938e-01 3.05668354e-01
-1.09870756e+00 -2.16855839e-01 -2.17771754e-02 5.89002550e-01
-6.13649309e-01 3.74044240e-01 -4.25282389e-01 -1.59955454e+00
2.36169755e-01 -8.38514626e-01 9.41390395e-01 3.12266976e-01
-1.15800142e-01 -1.16964352e+00 3.56324133e-04 3.45435053e-01
2.91508436e-01 2.36326188e-01 5.14154494e-01 2.09782310e-02
-8.17104459e-01 -4.94833618e-01 -6.41025245e-01 -1.31356642e-01
9.33295339e-02 1.31554008e-01 -6.92168176e-01 -3.45037173e-04
-5.48153043e-01 3.30981493e-01 7.75730193e-01 9.91972089e-01
9.81341064e-01 -1.37598023e-01 -8.40303719e-01 1.38876125e-01
1.49630105e+00 -9.18743014e-02 6.85090959e-01 3.87466311e-01
6.62813246e-01 6.05326891e-01 3.58529717e-01 7.81406581e-01
9.04801667e-01 6.66274071e-01 6.96743876e-02 2.23170687e-02
6.29875213e-02 -2.61759788e-01 -7.69179687e-02 2.53888994e-01
-5.25565326e-01 -3.27642590e-01 -1.15549147e+00 1.05915737e+00
-1.95972526e+00 -1.42656600e+00 -5.34879029e-01 2.57721972e+00
2.15844393e-01 2.68425077e-01 7.94037223e-01 4.29404587e-01
1.03449476e+00 -4.87195849e-01 -1.97819293e-01 1.15436688e-01
2.81522870e-01 -1.34606779e-01 1.05499434e+00 7.69476771e-01
-1.10194945e+00 6.74819589e-01 7.33784342e+00 5.13520300e-01
-3.43142658e-01 5.96244894e-02 5.74956715e-01 7.70310685e-02
1.60017416e-01 -1.22125439e-01 -1.15807664e+00 6.54791534e-01
9.67256069e-01 -2.32520521e-01 3.15292060e-01 6.75030410e-01
7.45751858e-01 -6.73325181e-01 -6.72043443e-01 9.89299595e-01
-2.53667146e-01 -1.23463750e+00 -2.93715954e-01 6.11448407e-01
5.04337668e-01 -2.07489073e-01 -1.87006652e-01 1.57324597e-01
7.66462445e-01 -8.93496931e-01 5.30222416e-01 4.87658560e-01
4.54233915e-01 -1.07830799e+00 5.20167947e-01 5.79792857e-01
-1.54128850e+00 5.72706014e-02 -4.32691067e-01 -5.04196584e-01
7.04919279e-01 7.48255551e-01 -8.07504177e-01 1.25462413e-02
5.91295600e-01 2.72396117e-01 -3.95638883e-01 1.37099957e+00
2.09494025e-01 5.93922496e-01 -9.33092773e-01 -1.77949563e-01
9.43074003e-02 -2.45364279e-01 3.52917373e-01 1.10431564e+00
5.50669432e-01 3.41027766e-01 2.89007872e-01 8.70763958e-01
1.47394672e-01 -2.53983468e-01 -7.21579313e-01 2.97024727e-01
8.07603180e-01 9.10838544e-01 -1.05863059e+00 -4.49976176e-01
-6.30315542e-01 6.10468984e-01 3.54685485e-01 3.87057751e-01
-9.15653467e-01 -7.20370114e-02 7.24869370e-01 8.28414321e-01
2.13173449e-01 -8.11049461e-01 -6.55539989e-01 -9.71669257e-01
5.83833922e-03 7.20142275e-02 1.33614361e-01 -3.08472693e-01
-1.15905857e+00 -1.54812858e-02 4.54160213e-01 -9.78919148e-01
-1.79678634e-01 -5.65458357e-01 -7.03146279e-01 8.83710742e-01
-1.33206105e+00 -1.03337264e+00 -3.11290324e-01 4.19118226e-01
4.89566863e-01 2.62085460e-02 3.94134611e-01 5.89961052e-01
-5.89005053e-01 2.53113866e-01 2.19247378e-02 3.87447327e-01
4.03095596e-02 -1.11855125e+00 7.38403082e-01 1.07673693e+00
-1.13752939e-01 5.05541623e-01 9.94311929e-01 -6.08544827e-01
-8.60989094e-01 -1.01856422e+00 1.20487082e+00 -6.51782155e-01
7.69135475e-01 -2.74888545e-01 -4.38743800e-01 6.09965563e-01
-3.68861109e-01 1.93108141e-01 5.23644567e-01 3.25695187e-01
2.09171429e-01 2.32336238e-01 -1.28121603e+00 7.18297005e-01
1.04646325e+00 -2.88749218e-01 -1.10998295e-01 3.79161298e-01
1.21409267e-01 -1.04518272e-01 -5.98473310e-01 -1.44667491e-01
6.45971298e-01 -9.84730422e-01 1.20585263e+00 -1.58483922e-01
-3.36584151e-02 -3.96896452e-01 -2.19230190e-01 -8.06460619e-01
-2.21995011e-01 -1.71649948e-01 -6.19733743e-02 1.23343098e+00
2.01002464e-01 -6.15008414e-01 1.14834964e+00 1.18106472e+00
2.89882421e-01 -1.97130367e-01 -1.11477828e+00 -8.97873521e-01
1.84787214e-01 -7.58519709e-01 5.06735146e-01 4.34905112e-01
-1.27783045e-01 -5.45254126e-02 -2.44667053e-01 5.95005095e-01
1.28268623e+00 -4.07298177e-01 1.00793707e+00 -1.28474331e+00
2.42615536e-01 -5.87174416e-01 -7.15165854e-01 -1.20389891e+00
2.42109485e-02 -1.33887827e-01 9.42552611e-02 -1.63525236e+00
4.66370523e-01 -8.97719920e-01 2.26455793e-01 -1.30049407e-01
-1.19305089e-01 3.04777414e-01 -4.55374196e-02 1.39335647e-01
-3.85391861e-01 2.57298142e-01 6.92738235e-01 -2.84948703e-02
-9.77900699e-02 4.50913101e-01 -4.28438276e-01 9.83015001e-01
9.77799952e-01 -4.22271460e-01 -1.86528891e-01 -2.02659965e-01
-6.15542801e-03 -1.36410058e-01 1.00384080e+00 -1.18723524e+00
3.58210802e-01 -4.04273897e-01 4.87423152e-01 -9.42893028e-01
4.94803995e-01 -9.38421011e-01 -2.13992298e-01 3.14431816e-01
1.92515448e-01 8.92941747e-03 -9.58864205e-03 8.63926709e-01
2.01219022e-01 -2.05097258e-01 8.46144855e-01 -2.58612245e-01
-5.59445679e-01 3.99464875e-01 -7.69043386e-01 -2.99950968e-02
1.13706398e+00 -4.74406779e-01 -2.91781336e-01 -5.31792879e-01
-5.17368853e-01 2.69498229e-01 6.55002832e-01 -2.35293433e-01
4.96563852e-01 -1.37686920e+00 -5.68202138e-01 -5.27342111e-02
-6.38127699e-02 -1.48077786e-01 2.10418776e-02 7.45967627e-01
-6.59770370e-01 5.01342714e-01 1.44736126e-01 -6.22543275e-01
-1.14534199e+00 4.86991763e-01 2.33291015e-01 -1.21221550e-01
-6.71384096e-01 4.12480533e-01 -6.31863112e-03 -7.42685646e-02
1.86849907e-02 -1.93888426e-01 4.52699978e-03 -3.05148751e-01
7.72883952e-01 8.16274285e-01 -3.93644333e-01 -9.90262270e-01
-3.76916707e-01 5.64021826e-01 2.65774220e-01 -4.78165567e-01
9.29402053e-01 -7.67422974e-01 3.71397853e-01 3.14797461e-01
6.83377385e-01 -1.73165891e-02 -1.30883968e+00 -1.15808941e-01
1.67150386e-02 -8.26787949e-01 3.61890532e-02 -8.78183078e-03
-6.97348535e-01 8.23805273e-01 5.15791774e-01 5.10692418e-01
7.23829031e-01 -7.28486804e-03 9.02365804e-01 2.47115165e-01
7.84338117e-01 -1.13188362e+00 -4.06544417e-01 2.96637803e-01
1.22732762e-02 -1.62002158e+00 1.54122040e-01 -7.63102651e-01
-2.12038428e-01 8.66132975e-01 3.73824686e-01 -1.03277594e-01
9.36902702e-01 2.85604626e-01 -3.75754744e-01 -3.85851711e-02
-2.34463736e-01 -8.25708091e-01 -9.84117165e-02 9.02674139e-01
2.04340413e-01 2.99072206e-01 -1.55485094e-01 1.10028528e-01
-2.15371177e-01 1.31836176e-01 6.88564777e-01 8.18785131e-01
-8.99136841e-01 -7.72730827e-01 -6.88786328e-01 4.17126954e-01
-1.88185558e-01 3.52291279e-02 1.63590267e-01 9.11476254e-01
-5.42054847e-02 1.24340975e+00 3.26826334e-01 1.82440400e-01
2.36167818e-01 -3.02570730e-01 6.55791461e-01 -2.85988033e-01
2.47848183e-01 -1.75402954e-01 1.71038613e-01 -3.61788049e-02
-5.90578496e-01 -9.99957383e-01 -1.07512164e+00 -1.09346092e+00
-3.14085990e-01 -1.78181946e-01 6.28056705e-01 1.12746978e+00
-1.54715955e-01 2.11311523e-02 4.63710159e-01 -9.86541748e-01
-1.22573420e-01 -6.59195542e-01 -7.92297959e-01 1.99802503e-01
2.13093817e-01 -9.45935130e-01 -3.27539861e-01 2.54764020e-01] | [8.22743034362793, -0.43251267075538635] |
3e651286-8776-428c-999d-f31da33f664b | focusing-on-what-to-decode-and-what-to-train | 2307.02291 | null | https://arxiv.org/abs/2307.02291v1 | https://arxiv.org/pdf/2307.02291v1.pdf | Focusing on what to decode and what to train: Efficient Training with HOI Split Decoders and Specific Target Guided DeNoising | Recent one-stage transformer-based methods achieve notable gains in the Human-object Interaction Detection (HOI) task by leveraging the detection of DETR. However, the current methods redirect the detection target of the object decoder, and the box target is not explicitly separated from the query embeddings, which leads to long and hard training. Furthermore, matching the predicted HOI instances with the ground-truth is more challenging than object detection, simply adapting training strategies from the object detection makes the training more difficult. To clear the ambiguity between human and object detection and share the prediction burden, we propose a novel one-stage framework (SOV), which consists of a subject decoder, an object decoder, and a verb decoder. Moreover, we propose a novel Specific Target Guided (STG) DeNoising strategy, which leverages learnable object and verb label embeddings to guide the training and accelerates the training convergence. In addition, for the inference part, the label-specific information is directly fed into the decoders by initializing the query embeddings from the learnable label embeddings. Without additional features or prior language knowledge, our method (SOV-STG) achieves higher accuracy than the state-of-the-art method in one-third of training epochs. The code is available at \url{https://github.com/cjw2021/SOV-STG}. | ['Keiji Yanai', 'Yingcheng Wang', 'Junwen Chen'] | 2023-07-05 | null | null | null | null | ['human-object-interaction-detection', 'object-detection'] | ['computer-vision', 'computer-vision'] | [ 3.52688372e-01 2.79192090e-01 -2.10769743e-01 -4.31470007e-01
-9.16515052e-01 -3.01797926e-01 3.33494753e-01 -1.20325349e-01
-4.77938831e-01 2.52716064e-01 1.58050403e-01 2.07358282e-02
2.31633395e-01 -4.81608719e-01 -9.24070537e-01 -5.37899375e-01
5.73438764e-01 5.67651808e-01 4.76882279e-01 1.52774706e-01
-3.33267868e-01 -1.13314711e-01 -1.33964396e+00 4.95275170e-01
7.15011775e-01 1.30060232e+00 6.46806002e-01 3.38699967e-01
2.08412409e-01 5.69377780e-01 -2.61869103e-01 -4.57148492e-01
6.11967184e-02 -3.04587662e-01 -5.61645746e-01 3.53188775e-02
3.55507821e-01 -5.45368373e-01 -5.20091891e-01 1.05867791e+00
6.29931152e-01 -2.79039945e-02 4.63551402e-01 -1.34485650e+00
-8.06942284e-01 3.45152527e-01 -5.88793099e-01 -1.15654230e-01
8.97757635e-02 3.50445211e-01 1.17665136e+00 -1.36970043e+00
4.80876178e-01 1.28317487e+00 5.26912093e-01 8.61545980e-01
-1.19665790e+00 -9.40048277e-01 4.70010012e-01 3.27290803e-01
-1.56531858e+00 -5.22773206e-01 7.16976345e-01 -5.41169822e-01
8.22102964e-01 7.84411877e-02 6.40047252e-01 1.33552504e+00
-1.96602538e-01 1.38013256e+00 6.75400019e-01 -1.03900038e-01
-1.07010499e-01 2.74287075e-01 2.83948064e-01 7.95892239e-01
1.98176265e-01 2.04108164e-01 -6.75442874e-01 1.16237201e-01
5.95441997e-01 2.13638902e-01 -3.03643376e-01 -3.17160130e-01
-1.24479449e+00 7.10445166e-01 4.09438223e-01 2.80327238e-02
-3.10953528e-01 2.85662830e-01 4.03945476e-01 -1.35027274e-01
3.94219548e-01 -9.30110887e-02 -5.64174950e-01 1.41668454e-01
-8.73849034e-01 2.78950751e-01 5.04715085e-01 1.26664424e+00
8.04085493e-01 -3.61003995e-01 -7.10173726e-01 7.64071286e-01
8.92581344e-01 4.81901586e-01 2.43026122e-01 -7.59946048e-01
7.59617388e-01 7.52672136e-01 1.74618289e-01 -7.77703106e-01
-1.96078569e-01 -5.76554120e-01 -6.66134059e-01 -1.05231799e-01
4.84633476e-01 1.24531418e-01 -9.78404880e-01 1.85159802e+00
6.63469553e-01 1.94327801e-01 -2.71805227e-01 1.26322627e+00
9.37305987e-01 6.88320577e-01 3.29361886e-01 1.22095972e-01
1.68804312e+00 -1.31900382e+00 -6.68161511e-01 -6.73472166e-01
7.63742089e-01 -5.73948562e-01 1.26391065e+00 1.75732046e-01
-8.73162270e-01 -7.07625270e-01 -9.47433710e-01 -5.83158493e-01
-1.19407356e-01 6.41239107e-01 3.33865196e-01 8.83727968e-02
-7.14003742e-01 1.52523920e-01 -7.57489860e-01 -2.25894913e-01
6.87370181e-01 3.77354681e-01 -1.12781562e-01 -1.90013900e-01
-1.22184920e+00 6.75606608e-01 4.07545149e-01 2.71863759e-01
-1.24666131e+00 -6.05201840e-01 -9.12532985e-01 1.32524818e-01
7.25526989e-01 -5.95983922e-01 1.37480700e+00 -6.97781324e-01
-1.29188955e+00 1.11483026e+00 -4.79722470e-01 -2.14779019e-01
6.58095300e-01 -3.23984832e-01 -9.57119316e-02 -2.11955346e-02
1.84370205e-01 9.45072830e-01 1.02059710e+00 -1.18747413e+00
-8.67768645e-01 -3.67349654e-01 9.23547447e-02 1.05644092e-01
-4.64890659e-01 6.35570362e-02 -8.67868662e-01 -6.51726186e-01
1.08019799e-01 -9.03604507e-01 3.30011010e-01 5.22024274e-01
-4.95700926e-01 -6.98108196e-01 8.62932146e-01 -8.78438473e-01
1.17474186e+00 -2.61353779e+00 1.84250608e-01 -2.16398895e-01
5.55854321e-01 3.20125669e-01 -4.37252313e-01 3.51564325e-02
7.19559714e-02 -1.15001209e-01 -1.12868138e-01 -7.42807209e-01
3.36981952e-01 2.09591419e-01 -3.08438569e-01 3.78194064e-01
2.73535103e-01 1.24656403e+00 -9.71411645e-01 -6.56516314e-01
2.33015027e-02 5.32138944e-01 -6.50280893e-01 4.92715120e-01
-2.97997177e-01 5.30485213e-01 -4.71596271e-01 6.97090805e-01
4.81586337e-01 -5.84892869e-01 9.04373378e-02 -6.37057543e-01
6.28832504e-02 5.82092524e-01 -1.04434991e+00 1.64654386e+00
-3.59601051e-01 4.11796689e-01 9.77288261e-02 -1.19180906e+00
5.37999928e-01 5.35636246e-01 3.56696516e-01 -6.88771069e-01
1.70603022e-01 1.66063353e-01 -6.84491545e-02 -4.56931025e-01
-8.66580103e-03 1.81023046e-01 3.37557285e-03 2.83801496e-01
2.32330486e-01 3.93957466e-01 4.17573825e-02 1.68241873e-01
8.10023129e-01 3.50382656e-01 3.15479748e-02 1.40508994e-01
3.55807722e-01 -1.22932017e-01 1.03020811e+00 7.08823383e-01
-4.41687822e-01 5.97857535e-01 3.14702630e-01 -2.11029887e-01
-8.48734915e-01 -1.05135596e+00 -2.85270661e-02 1.38846779e+00
4.45819527e-01 -4.04436469e-01 -6.97210670e-01 -8.87066841e-01
1.18944325e-01 6.67953551e-01 -5.52538991e-01 -3.19064438e-01
-5.88531017e-01 -5.34453571e-01 1.55353233e-01 6.71928823e-01
4.37140495e-01 -9.79784489e-01 -3.97650838e-01 2.33006954e-01
-4.74847525e-01 -1.21833777e+00 -9.44849730e-01 1.46178668e-02
-6.33495569e-01 -9.30406511e-01 -6.23222291e-01 -9.30341423e-01
7.09156752e-01 3.34141731e-01 8.52301776e-01 1.53276742e-01
-3.32548290e-01 2.80733883e-01 -2.36511171e-01 -4.40575570e-01
-1.59218729e-01 1.32927611e-01 -1.19575396e-01 1.69639930e-01
5.69378197e-01 -1.59565240e-01 -7.83764303e-01 4.96606648e-01
-5.89098155e-01 4.29741144e-01 6.86450839e-01 1.00025034e+00
7.75223196e-01 -2.96301275e-01 3.69253069e-01 -7.22708106e-01
1.80144235e-02 -4.32615221e-01 -6.86490476e-01 3.51367891e-01
-6.37598455e-01 -2.08604541e-02 3.60070318e-01 -8.16098571e-01
-1.00102639e+00 3.84754092e-01 -7.46788755e-02 -5.06983697e-01
6.26974702e-02 2.05130860e-01 -5.96506596e-01 1.40587628e-01
2.94513315e-01 2.07126141e-01 -1.44394040e-01 -7.23970115e-01
3.47038805e-01 7.20877290e-01 3.12352926e-01 -3.65437180e-01
8.34459782e-01 3.22191924e-01 -6.05662048e-01 -2.90245563e-01
-1.36218846e+00 -4.93457884e-01 -4.72324252e-01 -1.54050514e-01
1.01055801e+00 -1.16027176e+00 -7.87461221e-01 4.99651253e-01
-1.48744464e+00 -6.17818058e-01 -2.46193662e-01 4.22612190e-01
-2.70177901e-01 1.28768951e-01 -6.79179192e-01 -6.82516754e-01
-3.87183249e-01 -1.24036133e+00 1.50734794e+00 1.37337700e-01
-1.00140199e-01 -5.67517102e-01 -4.42272037e-01 6.49130642e-01
1.14663586e-01 -3.80981296e-01 7.96445906e-01 -6.80772781e-01
-8.97635400e-01 -2.96802014e-01 -6.65013731e-01 4.29716408e-01
-9.11184549e-02 -5.12905836e-01 -1.07757950e+00 -4.20596212e-01
9.92226079e-02 -4.86552179e-01 8.03631723e-01 2.17280805e-01
1.17198646e+00 -3.75194192e-01 -6.61691368e-01 5.56446910e-01
1.05877304e+00 -1.81112122e-02 3.65792304e-01 8.71530920e-02
1.04309952e+00 4.99241889e-01 7.25286305e-01 3.85355502e-01
7.80353546e-01 8.85974407e-01 3.88145953e-01 -8.10286403e-02
-4.63342994e-01 -5.37339866e-01 4.61304903e-01 6.11771524e-01
3.38508904e-01 -2.70278245e-01 -7.33570337e-01 5.99046588e-01
-2.04701686e+00 -6.94546640e-01 -1.72362011e-02 2.16596413e+00
1.13158691e+00 8.72713849e-02 1.34945944e-01 3.82667407e-02
6.92478001e-01 1.05976611e-01 -8.27065647e-01 3.81298006e-01
3.88857871e-01 -2.33512819e-01 3.36513609e-01 5.14011204e-01
-1.18489707e+00 1.14156151e+00 5.16850519e+00 8.09357762e-01
-9.76418257e-01 8.45462441e-01 4.19230700e-01 -3.15944672e-01
-3.65029387e-02 -6.79283077e-03 -1.39464200e+00 6.63624644e-01
5.55670321e-01 9.32892114e-02 4.48194176e-01 9.41624701e-01
2.66918242e-01 1.09852299e-01 -1.39466298e+00 1.11128545e+00
1.72142908e-02 -8.68967295e-01 -1.10047258e-01 -7.14376420e-02
1.78761378e-01 1.29883260e-01 3.20627168e-02 4.49790776e-01
4.57640998e-02 -6.21676743e-01 1.10022402e+00 5.09413660e-01
8.35014284e-01 -2.08173573e-01 4.26874787e-01 4.15956646e-01
-1.43353426e+00 -2.37097114e-01 -3.31755191e-01 2.16652676e-01
2.42899045e-01 6.61988080e-01 -5.49531579e-01 1.70674562e-01
7.49747276e-01 8.25644910e-01 -4.77177948e-01 8.82011950e-01
-6.64322913e-01 9.37834740e-01 -4.20336872e-01 1.10897064e-01
-5.91050014e-02 6.30188137e-02 5.29313028e-01 1.06889176e+00
1.18997030e-01 8.67055953e-02 4.65698779e-01 1.24378490e+00
-1.53894231e-01 -1.05628870e-01 -1.95610419e-01 -8.84673968e-02
6.35303795e-01 1.25385058e+00 -2.91237921e-01 -4.27977443e-01
-6.85519040e-01 1.29168534e+00 5.31459332e-01 5.01262665e-01
-1.20461094e+00 -4.34680372e-01 4.92096335e-01 1.87173024e-01
4.41270858e-01 -5.77536114e-02 -2.55701751e-01 -1.24977469e+00
4.33123469e-01 -7.10590482e-01 3.78323764e-01 -6.32155120e-01
-1.25115454e+00 3.33998531e-01 -1.45431280e-01 -1.12759602e+00
4.92556207e-02 -7.32562184e-01 -2.76347637e-01 8.52248371e-01
-1.53122830e+00 -1.53040850e+00 -4.06815708e-01 3.64442319e-01
4.94165152e-01 7.30798692e-02 4.33711320e-01 7.36453116e-01
-8.60705018e-01 1.06587112e+00 -2.29467690e-01 3.09070945e-01
7.04414129e-01 -8.71191442e-01 2.27645874e-01 6.72380626e-01
5.89070246e-02 4.19764310e-01 4.05017674e-01 -6.40061677e-01
-1.39012253e+00 -1.41978621e+00 1.13309026e+00 -6.58703268e-01
5.63655019e-01 -9.38538015e-01 -9.94217396e-01 8.31914425e-01
-2.92039752e-01 2.79477298e-01 3.32182616e-01 -5.07137214e-04
-4.15672123e-01 -2.92516917e-01 -7.93623447e-01 4.99711961e-01
1.42660630e+00 -7.08735108e-01 -4.68718410e-01 6.57773197e-01
8.79500031e-01 -4.14940923e-01 -5.43665409e-01 3.08683902e-01
4.94543463e-01 -4.27365541e-01 1.02145350e+00 -2.96944827e-01
2.58685172e-01 -4.96566385e-01 -7.93110877e-02 -6.52114630e-01
-4.24772769e-01 -3.30988199e-01 -5.12403131e-01 1.27852094e+00
4.40183461e-01 -6.48870468e-01 6.65549815e-01 6.81297123e-01
-1.68459445e-01 -9.11394596e-01 -8.75916541e-01 -8.38583767e-01
-3.44501346e-01 -4.14315194e-01 3.21522325e-01 5.27045131e-01
-3.34666371e-01 5.86574495e-01 -5.05676568e-01 3.92762601e-01
8.20895672e-01 5.75076789e-02 6.09558582e-01 -1.06480217e+00
-5.05813956e-01 -1.31661847e-01 -1.22287914e-01 -1.66014373e+00
1.67334631e-01 -1.12437689e+00 4.97797430e-01 -1.62043977e+00
4.80483741e-01 -4.32286948e-01 -3.78911555e-01 1.03824210e+00
-4.72192049e-01 2.66579717e-01 3.32882583e-01 4.77583081e-01
-8.32238495e-01 6.82276309e-01 1.17362177e+00 -3.22788507e-01
-1.88942850e-02 -9.59750861e-02 -6.03563845e-01 6.48477077e-01
4.77873564e-01 -8.07593524e-01 -5.05396068e-01 -8.81419182e-01
9.50201899e-02 -2.83227324e-01 8.69705677e-01 -8.06581318e-01
3.43613893e-01 1.33823648e-01 2.28771627e-01 -6.48349285e-01
4.36323881e-01 -7.20733404e-01 -1.43099546e-01 3.99919689e-01
-4.33860540e-01 -4.02760595e-01 -2.95612197e-02 6.75235689e-01
6.67570112e-03 -3.21335226e-01 8.16805363e-01 1.44562975e-01
-7.19697416e-01 5.79778969e-01 -2.24642515e-01 1.99621826e-01
8.56318235e-01 -8.71484429e-02 -7.14265779e-02 -1.84987113e-02
-6.73419595e-01 5.21129489e-01 2.71231204e-01 5.01909733e-01
6.45189404e-01 -1.30290663e+00 -5.68380594e-01 7.40803331e-02
2.80055314e-01 3.60464275e-01 1.84860736e-01 1.19421840e+00
2.69623458e-01 2.08708301e-01 4.39522952e-01 -6.76200688e-01
-1.12947786e+00 6.47461295e-01 3.25302601e-01 -8.89214575e-02
-7.97324836e-01 1.08579040e+00 7.96933651e-01 -3.99850398e-01
6.15569413e-01 -1.82826936e-01 1.40753314e-01 -4.93752398e-02
4.89614695e-01 1.36623845e-01 -4.28390831e-01 -4.96292382e-01
-5.03058016e-01 3.68811250e-01 -3.75128269e-01 -4.08376986e-03
1.10123575e+00 -1.39309764e-01 -2.98079513e-02 3.78175616e-01
1.30671811e+00 -5.06634176e-01 -1.47997594e+00 -5.78172326e-01
5.40108196e-02 -3.04421186e-01 3.98567133e-02 -8.18721950e-01
-1.12164676e+00 1.10690105e+00 7.63152063e-01 -3.19836438e-01
9.02550220e-01 4.05224711e-01 1.16787577e+00 2.98069268e-01
2.49926120e-01 -1.13069642e+00 2.68102378e-01 4.47937548e-01
8.24064732e-01 -1.46856403e+00 -3.42703283e-01 -6.82842791e-01
-6.50001109e-01 5.57752728e-01 8.97204399e-01 1.29997417e-01
4.82915729e-01 3.66337709e-02 -8.29714760e-02 -1.81615382e-01
-6.59404874e-01 -1.60910115e-01 4.47391123e-01 3.72192770e-01
3.13487113e-01 -6.31615743e-02 -2.81877100e-01 1.15207434e+00
4.18597192e-01 2.08234981e-01 -2.80908048e-01 7.02382505e-01
-3.51826459e-01 -9.30321634e-01 -2.80834064e-02 4.77867782e-01
-5.60647435e-02 -2.28070736e-01 -9.62877348e-02 4.97318089e-01
4.37450051e-01 8.03494751e-01 -1.37605801e-01 -4.13748056e-01
4.12569106e-01 2.16110900e-01 1.99803710e-01 -8.25612128e-01
-1.73580825e-01 8.60772654e-02 -1.08373195e-01 -8.57981384e-01
-1.42777607e-01 -4.98052537e-01 -1.42003226e+00 2.28473336e-01
-5.46736658e-01 -9.19408947e-02 4.06244546e-01 1.09769201e+00
6.91532731e-01 4.70320523e-01 3.35794330e-01 -8.63254666e-01
-7.56969154e-01 -9.54419911e-01 -3.13301593e-01 5.13848245e-01
2.29037210e-01 -9.67341900e-01 -2.64638364e-01 1.01806574e-01] | [9.624897003173828, 1.3614614009857178] |
d6886628-fbcf-47a9-bb15-e9f9422024bd | dynamic-spatial-propagation-network-for-depth | 2202.09769 | null | https://arxiv.org/abs/2202.09769v1 | https://arxiv.org/pdf/2202.09769v1.pdf | Dynamic Spatial Propagation Network for Depth Completion | Image-guided depth completion aims to generate dense depth maps with sparse depth measurements and corresponding RGB images. Currently, spatial propagation networks (SPNs) are the most popular affinity-based methods in depth completion, but they still suffer from the representation limitation of the fixed affinity and the over smoothing during iterations. Our solution is to estimate independent affinity matrices in each SPN iteration, but it is over-parameterized and heavy calculation. This paper introduces an efficient model that learns the affinity among neighboring pixels with an attention-based, dynamic approach. Specifically, the Dynamic Spatial Propagation Network (DySPN) we proposed makes use of a non-linear propagation model (NLPM). It decouples the neighborhood into parts regarding to different distances and recursively generates independent attention maps to refine these parts into adaptive affinity matrices. Furthermore, we adopt a diffusion suppression (DS) operation so that the model converges at an early stage to prevent over-smoothing of dense depth. Finally, in order to decrease the computational cost required, we also introduce three variations that reduce the amount of neighbors and attentions needed while still retaining similar accuracy. In practice, our method requires less iteration to match the performance of other SPNs and yields better results overall. DySPN outperforms other state-of-the-art (SoTA) methods on KITTI Depth Completion (DC) evaluation by the time of submission and is able to yield SoTA performance in NYU Depth v2 dataset as well. | ['Hua Yang', 'Wending Zhou', 'Qi Zhong', 'Tao Cheng', 'Yuankai Lin'] | 2022-02-20 | null | null | null | null | ['depth-completion'] | ['computer-vision'] | [ 7.77468681e-02 2.33120441e-01 9.43265706e-02 -2.85068363e-01
-5.12431204e-01 5.54339541e-03 6.01331413e-01 2.48803392e-01
-8.48802030e-01 6.22735202e-01 3.69970113e-01 1.48982629e-01
-2.60561913e-01 -1.06899917e+00 -7.16944337e-01 -7.49979615e-01
8.65292549e-02 7.72732794e-01 7.27737546e-01 -2.28443250e-01
4.65247989e-01 7.03090429e-01 -1.60036910e+00 4.20420542e-02
1.17332721e+00 9.24906075e-01 4.91230994e-01 7.22925603e-01
-3.81088644e-01 8.47831488e-01 -2.31863409e-01 -2.16578662e-01
2.62811333e-01 -1.82770863e-01 -7.85455167e-01 -1.81222707e-01
6.24465942e-01 -5.08387864e-01 -3.34642380e-01 9.34605122e-01
6.38705134e-01 2.38908872e-01 5.96310675e-01 -9.13332045e-01
-5.54444611e-01 5.50079465e-01 -8.00001860e-01 3.35679129e-02
3.77871655e-02 -6.28172513e-03 6.84410751e-01 -1.10741293e+00
6.60120964e-01 1.11088085e+00 6.33483946e-01 7.04865456e-01
-1.27349138e+00 -3.75323474e-01 3.64182830e-01 3.26963544e-01
-1.40610778e+00 -9.72711071e-02 7.21836686e-01 -2.70132869e-01
1.00805831e+00 2.90784743e-02 1.06419420e+00 8.19086909e-01
8.00295174e-02 7.97734857e-01 7.27435470e-01 -3.45291883e-01
6.11100376e-01 -9.56330895e-02 1.13511905e-01 6.80440724e-01
2.40449086e-02 -1.77639961e-01 -5.98574519e-01 7.82550126e-02
1.00307834e+00 -3.46618816e-02 -4.39090461e-01 -5.93915343e-01
-1.07694459e+00 8.30808222e-01 8.40751410e-01 4.20016527e-01
-6.22087061e-01 2.77331829e-01 -2.00248305e-02 -2.66530454e-01
6.12483084e-01 1.93828925e-01 -1.59686908e-01 -8.09848383e-02
-1.12045050e+00 3.25258881e-01 5.01277030e-01 5.50236702e-01
1.21900296e+00 -2.48883203e-01 -1.76087216e-01 7.84698367e-01
2.41493776e-01 2.63888359e-01 5.27405202e-01 -9.60574865e-01
4.99353647e-01 7.59262383e-01 -8.91370401e-02 -1.00403440e+00
-5.36147416e-01 -4.33854729e-01 -9.62855279e-01 5.39259136e-01
5.39592683e-01 -1.46528268e-02 -1.27823186e+00 1.60126090e+00
4.26837385e-01 4.21478331e-01 -2.19548084e-02 1.10557115e+00
7.23065257e-01 7.01304793e-01 9.33826268e-02 1.97906494e-01
8.07437420e-01 -1.16275072e+00 -5.74146628e-01 -4.44210738e-01
4.56528753e-01 -4.24855947e-01 9.64419723e-01 4.96552408e-01
-1.24913073e+00 -5.62324405e-01 -1.05622339e+00 -4.92663026e-01
-3.38047862e-01 1.27226021e-02 8.01556408e-01 4.50006783e-01
-1.54475462e+00 7.96324968e-01 -1.02947545e+00 -2.83563018e-01
3.80376190e-01 6.86260283e-01 -2.57454544e-01 -2.25624293e-01
-8.92376184e-01 6.00793302e-01 2.67018974e-01 3.01635593e-01
-8.28153551e-01 -9.58558202e-01 -8.61071885e-01 2.46175006e-02
1.49194179e-02 -9.20334518e-01 6.78885818e-01 -9.41106319e-01
-1.68137443e+00 5.38328707e-01 -2.09630862e-01 -4.82729465e-01
6.24887645e-01 -2.17371792e-01 2.25477874e-01 2.42685869e-01
-3.04926652e-02 1.45583355e+00 8.14926505e-01 -1.17036510e+00
-7.10955143e-01 -5.64848065e-01 1.81044936e-01 5.18929601e-01
-5.17982543e-01 -6.62864029e-01 -1.03506827e+00 -5.12513816e-01
6.08257473e-01 -9.12017524e-01 -4.64646339e-01 1.29290745e-01
-2.10743994e-01 8.34210403e-03 5.68214059e-01 -4.39434469e-01
1.13061619e+00 -2.07293224e+00 7.97057986e-01 3.59905779e-01
4.66072559e-01 5.38532734e-02 -1.86279058e-01 1.98145568e-01
1.57760158e-01 -3.42502713e-01 -5.20168126e-01 -1.02979565e+00
-2.64517963e-01 3.87447149e-01 1.39079288e-01 4.73853856e-01
-5.84617704e-02 7.51745343e-01 -8.94896626e-01 -4.22997206e-01
4.85530376e-01 1.06154501e+00 -8.38796318e-01 4.07709554e-02
-3.08154732e-01 3.91431779e-01 -1.41073406e-01 4.60824668e-01
9.23716187e-01 -1.38983607e-01 -1.62564442e-01 -4.18211520e-01
-3.70090544e-01 -6.43858984e-02 -1.33004832e+00 2.22663283e+00
-6.70303643e-01 4.77372676e-01 1.20003790e-01 -5.70391953e-01
8.28575134e-01 -2.32109442e-01 4.77598429e-01 -5.05663157e-01
1.08621351e-01 2.78008193e-01 -1.15730792e-01 -2.02970892e-01
7.32167423e-01 2.18336448e-01 5.32945216e-01 1.64862588e-01
-1.60752125e-02 -2.89553016e-01 1.44354567e-01 4.16491121e-01
1.01366389e+00 9.07906145e-02 -2.81574070e-01 -3.14001948e-01
6.98249102e-01 -8.26881379e-02 5.03763497e-01 5.22136331e-01
4.39264551e-02 1.06678855e+00 2.35674173e-01 -3.54714274e-01
-8.95665646e-01 -8.56187284e-01 3.14750001e-02 6.89036846e-01
4.12184745e-01 -2.04844430e-01 -8.94676864e-01 -5.47679901e-01
-7.22349882e-02 4.48612779e-01 -9.30985749e-01 1.71873271e-01
-5.28760612e-01 -8.16732943e-01 2.54694313e-01 5.71636260e-01
6.55986309e-01 -1.00244033e+00 -3.50660861e-01 2.60268748e-01
-1.19424507e-01 -9.39160705e-01 -2.87800997e-01 1.38224974e-01
-1.09622443e+00 -8.40295434e-01 -1.23713624e+00 -6.13468647e-01
9.26921785e-01 7.66547248e-02 8.23206782e-01 -2.65846420e-02
-9.88505259e-02 2.19137087e-01 -2.88111538e-01 -1.60894189e-02
2.19017919e-02 4.11908269e-01 -3.12774450e-01 1.71150908e-01
2.82780260e-01 -8.69828641e-01 -9.84924972e-01 -2.11281446e-03
-1.02519047e+00 2.35872507e-01 7.29452968e-01 6.71380758e-01
8.10934007e-01 -3.35294716e-02 1.46168485e-01 -7.79472768e-01
4.22843367e-01 -2.45329112e-01 -7.77974606e-01 -5.18481061e-02
-6.79189801e-01 1.77574232e-01 4.96511996e-01 -3.39045167e-01
-1.10079479e+00 3.42499107e-01 -5.10466576e-01 -5.04418254e-01
-7.99224060e-03 1.81364268e-01 -3.83649841e-02 -5.41611075e-01
6.09268904e-01 2.38327131e-01 -6.47748560e-02 -4.44137365e-01
3.20543915e-01 2.01411992e-01 3.61182898e-01 -2.69326836e-01
6.44211769e-01 8.31355393e-01 6.69673309e-02 -7.53297389e-01
-6.00659609e-01 -3.24779391e-01 -6.68633997e-01 -2.48539701e-01
9.74148393e-01 -8.65532458e-01 -6.43122733e-01 7.54102230e-01
-1.17248225e+00 -6.83652222e-01 -2.51184046e-01 6.13959491e-01
-3.15283805e-01 4.95807707e-01 -8.71741712e-01 -6.63945138e-01
-4.04004306e-01 -1.08798981e+00 1.03688645e+00 3.07515085e-01
9.23805237e-02 -1.19095480e+00 2.79017121e-01 2.12692097e-01
5.14920592e-01 5.27613312e-02 6.89025342e-01 1.08827695e-01
-8.91476572e-01 1.38712242e-01 -4.29905474e-01 1.90447137e-01
-1.04016803e-01 3.87514615e-03 -1.01404870e+00 -2.17723086e-01
-2.32447162e-01 1.07605420e-02 1.11109912e+00 7.44637787e-01
1.06892693e+00 1.59873441e-01 -2.02814490e-01 9.22475338e-01
1.70366848e+00 -1.21441513e-01 1.05211496e+00 5.06119013e-01
1.04469168e+00 7.18522608e-01 4.83418494e-01 4.31770980e-01
5.99413633e-01 5.88465989e-01 7.95257449e-01 -4.43805546e-01
-3.83036435e-01 -1.62654147e-01 4.03209440e-02 5.50212622e-01
-3.02259088e-01 -2.02570587e-01 -7.88523853e-01 7.93547511e-01
-1.98598826e+00 -5.46852350e-01 -2.75233477e-01 2.26798916e+00
4.90702808e-01 2.30845436e-01 -6.81413487e-02 2.21898481e-01
3.82673293e-01 1.22486085e-01 -4.82853353e-01 -8.36682096e-02
-2.19648287e-01 4.47494358e-01 7.82310605e-01 1.07332551e+00
-6.86175525e-01 1.06771767e+00 5.27671003e+00 7.79682934e-01
-1.07303107e+00 1.34747237e-01 5.98928690e-01 -2.98085153e-01
-5.06968081e-01 -4.80001979e-02 -9.61855769e-01 3.17084163e-01
4.11218971e-01 3.62837344e-01 4.06162918e-01 7.82258689e-01
2.58685797e-01 -5.64393222e-01 -8.43603194e-01 1.10258627e+00
1.52996436e-01 -1.41600227e+00 1.02324203e-01 3.95169016e-03
8.70172083e-01 3.63459766e-01 -2.51873061e-02 -1.21892512e-01
3.74485031e-02 -7.31064320e-01 6.90484941e-01 5.50792933e-01
3.45526129e-01 -8.40880871e-01 8.74272645e-01 2.47887865e-01
-1.09652054e+00 -1.45008713e-01 -5.23227334e-01 -4.91966195e-02
3.68026406e-01 9.80476916e-01 -3.70688438e-01 4.15861458e-01
8.46245110e-01 7.83193886e-01 -5.88966250e-01 1.12137306e+00
-3.07823926e-01 2.53730807e-02 -5.52837610e-01 -3.53982039e-02
5.64097822e-01 -3.04891884e-01 2.20250353e-01 9.61719573e-01
3.83956134e-01 5.76354302e-02 -2.09769189e-01 8.57924044e-01
2.23299623e-01 2.20697165e-01 -2.02920973e-01 5.85429788e-01
2.62111723e-01 1.20824385e+00 -9.07203138e-01 -3.23339134e-01
-1.44938216e-01 1.28178108e+00 6.93199992e-01 3.31354976e-01
-7.52865553e-01 -2.59189069e-01 6.16107047e-01 4.60495532e-01
3.61368865e-01 -3.82675856e-01 -4.01838958e-01 -8.83788645e-01
-4.42436598e-02 -2.91103840e-01 1.42685115e-01 -7.36698329e-01
-9.78006363e-01 8.36791515e-01 -1.67138994e-01 -9.07818496e-01
5.22903949e-02 -3.19220334e-01 -3.84793520e-01 9.19840991e-01
-1.85543633e+00 -9.95835423e-01 -8.85706425e-01 7.66475141e-01
5.95413625e-01 4.30303693e-01 4.07549202e-01 6.63481712e-01
-5.89897811e-01 4.80106086e-01 -2.79242635e-01 -2.08927527e-01
4.71485019e-01 -1.26422429e+00 3.31971943e-01 8.17725480e-01
-1.23128928e-01 3.72985065e-01 5.06874919e-01 -8.31370294e-01
-1.10635793e+00 -1.01712584e+00 8.26509476e-01 -2.01658234e-02
4.18686450e-01 -3.25066715e-01 -1.01129818e+00 3.30931008e-01
4.98048263e-03 -5.10057341e-03 1.24133959e-01 -1.33717969e-01
1.46939248e-01 -2.16406301e-01 -1.06431043e+00 6.44957244e-01
1.14984405e+00 -2.13149801e-01 -7.18159527e-02 2.06753850e-01
5.25525630e-01 -5.98152459e-01 -7.24904656e-01 3.99169385e-01
3.47645283e-01 -1.58298957e+00 9.31693375e-01 4.62567955e-01
4.14050788e-01 -5.19819915e-01 6.98374361e-02 -1.11714673e+00
-4.49196279e-01 -3.29503834e-01 -3.54656391e-02 1.02628255e+00
4.26053494e-01 -6.76727593e-01 1.40055132e+00 7.64396071e-01
-2.34162167e-01 -8.44199955e-01 -8.57637167e-01 -4.14590448e-01
-2.13218749e-01 -4.06961888e-01 4.67700899e-01 7.06609845e-01
-2.54935861e-01 1.85027748e-01 -2.83823878e-01 3.18236947e-01
8.33471298e-01 -2.95828313e-01 7.70983875e-01 -1.06082880e+00
-2.99313962e-01 -4.16182488e-01 -4.49543774e-01 -1.49497938e+00
-3.02789547e-02 -5.30800819e-01 1.20701957e-02 -1.86102426e+00
-5.14837615e-02 -8.47226977e-01 -4.69888113e-02 3.46319020e-01
-1.17635138e-01 5.78693628e-01 -3.73152792e-02 2.05600888e-01
-2.57929802e-01 7.67440498e-01 1.18553603e+00 -6.53709099e-02
-6.43009424e-01 -2.06055343e-01 -1.93406895e-01 6.29238188e-01
6.62157893e-01 -4.49490845e-01 -7.18868494e-01 -7.83747435e-01
2.39860922e-01 -1.66113213e-01 3.26760650e-01 -1.34490693e+00
6.16886914e-01 1.96104318e-01 3.46594483e-01 -8.31505656e-01
6.94503069e-01 -7.53607929e-01 2.58621037e-01 5.17723203e-01
5.78817874e-02 -2.95017231e-02 2.15813056e-01 5.08310318e-01
-2.68364966e-01 -2.82852709e-01 8.86619985e-01 -1.29898190e-01
-8.55922997e-01 5.42758703e-01 -1.59595490e-01 -3.57293844e-01
8.48944366e-01 -5.91103971e-01 -6.34575188e-02 -3.30423117e-01
-7.73419499e-01 1.58188835e-01 7.61838317e-01 1.51571199e-01
8.25831950e-01 -9.89915609e-01 -5.77583075e-01 1.98147491e-01
-1.41343206e-01 4.79245692e-01 6.64895654e-01 9.76779044e-01
-1.02755344e+00 1.94185108e-01 -1.69826925e-01 -7.03615606e-01
-9.55232799e-01 3.17662150e-01 3.79039466e-01 -3.32496762e-01
-9.56227124e-01 1.20224893e+00 3.65391523e-01 -1.14189655e-01
5.18690586e-01 -4.18958783e-01 -3.77965748e-01 -3.97414602e-02
5.78932643e-01 4.50297564e-01 2.39265472e-01 -4.32006776e-01
-2.86586612e-01 1.04157114e+00 -2.07969382e-01 -2.26028383e-01
1.34124970e+00 -2.48887494e-01 -1.15532689e-01 6.67746440e-02
1.04623985e+00 2.25146469e-02 -1.57814860e+00 5.50941899e-02
-3.59628618e-01 -6.00617945e-01 4.82303143e-01 -4.94818658e-01
-1.47999549e+00 7.84516513e-01 7.96348870e-01 -1.48494437e-01
1.18541825e+00 -1.77620694e-01 8.95623207e-01 -2.64616366e-02
2.42981970e-01 -1.00596631e+00 6.41593039e-02 4.32530612e-01
6.44760251e-01 -1.10230875e+00 4.10593525e-02 -6.18032277e-01
-3.73129129e-01 9.28825498e-01 8.06990921e-01 -2.18641862e-01
6.48322225e-01 2.67410636e-01 9.77729931e-02 -2.76912063e-01
-3.15509140e-01 -3.01042855e-01 1.29481614e-01 6.56547427e-01
1.09031379e-01 -3.55836749e-01 -4.33509290e-01 -8.43571275e-02
-2.35487483e-02 -3.15691717e-03 4.40067172e-01 5.96074760e-01
-3.27195406e-01 -1.13116455e+00 -2.72303045e-01 3.15702781e-02
-1.21635124e-01 -2.84222603e-01 -1.24812990e-01 7.24531114e-01
2.65313745e-01 6.50004447e-01 2.19373286e-01 -3.27268422e-01
3.08708131e-01 -4.70888942e-01 6.26594245e-01 -4.82126325e-01
-5.86978078e-01 -8.24732147e-03 -2.50120312e-01 -8.18762541e-01
-5.29009402e-01 -3.67364585e-01 -1.45590425e+00 -4.66561049e-01
-2.31724620e-01 1.62007343e-02 9.03364420e-01 7.83599079e-01
4.22084153e-01 6.07687354e-01 3.20607841e-01 -1.38703740e+00
2.80502021e-01 -9.09597635e-01 -6.01802886e-01 2.23437145e-01
9.05984491e-02 -6.96087837e-01 -4.75355655e-01 -4.63551342e-01] | [8.846720695495605, -2.3612003326416016] |
f96eea17-a43c-4f90-a194-016f8af882bb | balancing-effect-of-training-dataset | 2305.15582 | null | https://arxiv.org/abs/2305.15582v1 | https://arxiv.org/pdf/2305.15582v1.pdf | Balancing Effect of Training Dataset Distribution of Multiple Styles for Multi-Style Text Transfer | Text style transfer is an exciting task within the field of natural language generation that is often plagued by the need for high-quality paired datasets. Furthermore, training a model for multi-attribute text style transfer requires datasets with sufficient support across all combinations of the considered stylistic attributes, adding to the challenges of training a style transfer model. This paper explores the impact of training data input diversity on the quality of the generated text from the multi-style transfer model. We construct a pseudo-parallel dataset by devising heuristics to adjust the style distribution in the training samples. We balance our training dataset using marginal and joint distributions to train our style transfer models. We observe that a balanced dataset produces more effective control effects over multiple styles than an imbalanced or skewed one. Through quantitative analysis, we explore the impact of multiple style distributions in training data on style-transferred output. These findings will better inform the design of style-transfer datasets. | ['Dongyeop Kang', 'David Ma', 'Debarati Das'] | 2023-05-24 | null | null | null | null | ['style-transfer', 'text-style-transfoer'] | ['computer-vision', 'natural-language-processing'] | [ 5.47194004e-01 -1.24694400e-01 -1.24411546e-01 -5.33540010e-01
-7.31706083e-01 -8.88724506e-01 7.49181509e-01 -1.02751860e-02
-4.61434782e-01 9.64516461e-01 4.70832497e-01 -2.64774173e-01
8.84776637e-02 -8.77701223e-01 -7.83268213e-01 -4.45750684e-01
5.02063274e-01 6.92168236e-01 -1.05622225e-01 -3.82600129e-01
3.32252592e-01 1.91635579e-01 -1.37315714e+00 3.78692031e-01
1.14780021e+00 5.66612244e-01 1.64371848e-01 6.79520726e-01
-4.66558218e-01 1.80974409e-01 -1.02908647e+00 -5.23926437e-01
4.01308060e-01 -7.55874574e-01 -5.01428246e-01 -7.37604201e-02
5.55124462e-01 -2.11122692e-01 9.21567678e-02 8.19476068e-01
6.77268863e-01 2.91924104e-02 1.10677516e+00 -1.38355780e+00
-7.50635862e-01 5.66035092e-01 -4.16339695e-01 -9.33628678e-02
1.91395387e-01 3.53793591e-01 8.95742893e-01 -6.36587262e-01
7.33752072e-01 1.45847571e+00 3.44681114e-01 6.65609241e-01
-1.80635810e+00 -8.28614950e-01 -3.76835056e-02 -4.07618314e-01
-8.80071580e-01 -4.19468403e-01 8.20795596e-01 -4.72995579e-01
2.40239099e-01 1.85432360e-01 3.72385949e-01 1.57105374e+00
9.73445326e-02 6.63437068e-01 1.42656338e+00 -6.55362964e-01
2.38620982e-01 5.64827740e-01 -2.66434640e-01 6.97884634e-02
3.47701311e-01 -6.10390939e-02 -7.48247564e-01 -8.87274370e-02
7.96377659e-01 -4.73492265e-01 -8.75895694e-02 -3.40029240e-01
-1.19103360e+00 8.05773079e-01 1.98557168e-01 2.03195944e-01
7.93307871e-02 -5.28760329e-02 5.68584085e-01 6.60297215e-01
5.67401886e-01 1.00669515e+00 -6.19249523e-01 -3.21550786e-01
-8.06338489e-01 5.89540422e-01 6.19360387e-01 1.21364093e+00
8.24036837e-01 3.17711309e-02 -6.70368791e-01 1.02586102e+00
1.12863015e-02 6.19171500e-01 4.10636008e-01 -6.09763384e-01
7.80622721e-01 5.45444548e-01 2.15837032e-01 -6.58338845e-01
1.61971208e-02 -3.04285794e-01 -6.07073069e-01 3.45026642e-01
8.49102676e-01 -4.57505554e-01 -7.58575976e-01 1.99989104e+00
1.63333759e-01 -5.61738312e-01 -1.04927041e-01 5.78382075e-01
2.73562491e-01 4.82271105e-01 3.40709984e-01 1.02489188e-01
1.17531443e+00 -5.44993401e-01 -5.97700417e-01 -2.57586658e-01
6.80825472e-01 -1.03775883e+00 1.90534556e+00 1.93871871e-01
-1.05331945e+00 -5.82840443e-01 -9.04329062e-01 -1.80437982e-01
-2.67692983e-01 1.60151079e-01 4.27569300e-01 7.52375543e-01
-8.36584508e-01 7.22142160e-01 -3.22230905e-01 -2.77270198e-01
5.27283013e-01 1.24099195e-01 -1.69653460e-01 -4.12028618e-02
-1.04730117e+00 9.49765801e-01 2.74714440e-01 -4.27152395e-01
-4.07780051e-01 -1.05945730e+00 -6.38448715e-01 -4.53232042e-02
-5.16368728e-03 -8.71388614e-01 1.16304183e+00 -1.39534152e+00
-1.54501843e+00 8.63455236e-01 -3.62344533e-02 4.67529409e-02
9.35189962e-01 -2.65717953e-01 -3.58371884e-02 -4.92666364e-01
2.25095153e-01 9.93044436e-01 9.23491597e-01 -1.39450157e+00
-4.03091788e-01 -3.58358681e-01 -2.90382862e-01 3.06426853e-01
-6.30231440e-01 -1.05219267e-01 -1.20333292e-01 -1.09588361e+00
-5.09240925e-01 -1.07285786e+00 -3.52606960e-02 -3.41974795e-02
-3.71986657e-01 -1.81292742e-02 5.16449630e-01 -4.16387886e-01
1.06684983e+00 -2.17717385e+00 4.60229546e-01 1.09771669e-01
-1.50702864e-01 1.07778616e-01 -4.04881924e-01 4.25664067e-01
1.89726755e-01 3.31690520e-01 -1.42339155e-01 -4.86199468e-01
4.89644445e-02 2.75126565e-02 -4.22940940e-01 -1.70366198e-01
5.28996229e-01 7.01846957e-01 -7.62506366e-01 -4.54968244e-01
-1.68002248e-01 2.14694679e-01 -7.81107903e-01 5.20017862e-01
-4.76999998e-01 6.69703543e-01 -4.33323383e-01 2.88170308e-01
5.52627325e-01 4.23333719e-02 9.93465856e-02 -4.59660925e-02
3.63390818e-02 2.19696224e-01 -9.07882690e-01 1.74168372e+00
-6.49069488e-01 6.56886697e-01 -2.18240157e-01 -1.50623709e-01
1.24785161e+00 -3.27537283e-02 -9.27198119e-03 -7.07102597e-01
1.93044186e-01 1.42183498e-01 1.43504649e-01 -1.04058966e-01
7.48455286e-01 -6.06714129e-01 -2.20582142e-01 8.00689876e-01
9.13195238e-02 -6.55138195e-01 2.31325448e-01 7.71438554e-02
6.76801383e-01 1.31526381e-01 -2.70721406e-01 -6.33595288e-01
6.12830408e-02 -2.56673902e-01 5.22112429e-01 7.00614989e-01
-3.56761850e-02 6.90620065e-01 7.75180876e-01 -1.57894135e-01
-1.50457752e+00 -1.10969412e+00 -1.73256606e-01 1.36324513e+00
-2.65960962e-01 -2.09358826e-01 -8.18819225e-01 -6.37516916e-01
2.11203605e-01 1.02718568e+00 -7.26452887e-01 -3.91679674e-01
-4.15905893e-01 -6.61071956e-01 6.86573505e-01 5.02934813e-01
2.84708738e-01 -1.19396150e+00 -3.09647024e-01 -1.20673157e-01
-1.43123642e-01 -7.67698109e-01 -7.82835543e-01 7.31725916e-02
-8.60069573e-01 -6.40304804e-01 -8.52173805e-01 -5.99050522e-01
7.92114735e-01 -2.50132203e-01 1.46609187e+00 -8.26248825e-02
-2.22853851e-02 8.58767331e-03 -1.90663978e-01 -7.98963130e-01
-9.23128486e-01 5.93564928e-01 3.87293696e-02 -2.20011145e-01
1.87102407e-01 -6.06850922e-01 -4.44678813e-01 3.42269570e-01
-1.05910897e+00 3.44096154e-01 4.81260598e-01 8.89216781e-01
2.08835095e-01 -3.16889673e-01 8.96654546e-01 -1.04207182e+00
1.07272851e+00 -3.39135557e-01 -4.48813409e-01 2.06888899e-01
-6.14154041e-01 5.72870314e-01 7.29239583e-01 -6.89909577e-01
-1.26836419e+00 -2.65261829e-01 1.80679396e-01 -1.53536543e-01
-2.68593371e-01 5.56640439e-02 -4.56592739e-01 3.03461194e-01
9.54133391e-01 -1.29858494e-01 1.88922673e-01 -3.55768681e-01
5.45144916e-01 6.70591950e-01 1.76292524e-01 -1.23715067e+00
8.66397917e-01 9.79437083e-02 -1.25536114e-01 -6.58848703e-01
-6.43455744e-01 2.35718295e-01 -6.58866823e-01 -4.14574854e-02
6.91690445e-01 -7.78197706e-01 -9.73661914e-02 5.56953251e-01
-9.42722619e-01 -8.93929958e-01 -3.92610669e-01 1.29089326e-01
-5.92866838e-01 -5.64635433e-02 -4.71552134e-01 -5.30821145e-01
-3.14207762e-01 -1.22161412e+00 1.08954871e+00 1.11907996e-01
-8.21091771e-01 -9.76844907e-01 2.49252602e-01 3.16837490e-01
6.12708986e-01 1.85706183e-01 1.34603012e+00 -4.62742805e-01
-3.48268956e-01 1.64743841e-01 -2.16780946e-01 2.81844109e-01
5.10450065e-01 2.64893286e-02 -1.01833475e+00 -3.87452990e-01
-3.18607092e-01 -6.39748871e-01 6.12511694e-01 1.43460318e-01
1.09679508e+00 -3.63446511e-02 4.60147858e-04 5.45502603e-01
1.07152033e+00 -4.15610932e-02 5.45348585e-01 3.68292063e-01
8.43907118e-01 8.58157754e-01 5.75868785e-01 3.46378624e-01
1.41078949e-01 6.18938446e-01 -2.32242599e-01 -3.81976143e-02
-1.18382104e-01 -4.77878720e-01 2.00373515e-01 3.63674372e-01
2.74097711e-01 -4.01846498e-01 -7.40099251e-01 4.71976131e-01
-1.31089890e+00 -5.75111330e-01 1.92872524e-01 2.33022904e+00
1.46211255e+00 3.47150356e-01 3.29367429e-01 1.49481177e-01
6.20597959e-01 -1.05620593e-01 -4.73572224e-01 -6.77793860e-01
-1.34512186e-01 2.26777613e-01 3.24487954e-01 3.47417265e-01
-8.21020544e-01 9.68933046e-01 6.57406998e+00 7.57543504e-01
-1.17532909e+00 -4.75849539e-01 9.43388283e-01 -3.44449759e-01
-9.24605370e-01 -1.60996914e-01 -8.41060579e-01 7.07636833e-01
8.42065871e-01 -3.05888981e-01 4.62877959e-01 4.93659288e-01
2.48578548e-01 -4.12128940e-02 -1.47869337e+00 6.33050382e-01
-1.67689666e-01 -9.40377951e-01 3.86180580e-01 1.61108911e-01
8.97398293e-01 -3.97182763e-01 2.98666149e-01 2.80778706e-01
4.45249677e-01 -1.06047523e+00 8.63061488e-01 3.66755843e-01
1.20566797e+00 -8.50195050e-01 3.26494038e-01 5.16498387e-02
-5.25581777e-01 1.10341925e-02 -1.23732060e-01 -7.61451423e-02
4.05666158e-02 7.13299930e-01 -9.31414723e-01 9.57766548e-02
4.15411800e-01 2.34540984e-01 -8.35355580e-01 4.90844309e-01
-3.22615542e-02 6.78626716e-01 -5.11104651e-02 -2.22085174e-02
-2.20628917e-01 -4.18288082e-01 4.25822735e-01 1.18561780e+00
3.85915369e-01 -4.15599108e-01 -2.16960073e-01 1.16852117e+00
-2.47172117e-01 1.41108483e-01 -8.22126091e-01 -2.21928313e-01
5.94716370e-01 1.01614201e+00 -5.83040893e-01 -2.65899241e-01
-1.29933000e-01 9.53917980e-01 4.41192478e-01 2.99433380e-01
-4.49663043e-01 -2.44417101e-01 9.02565598e-01 2.08790377e-01
-1.69374291e-02 -2.15534657e-01 -1.09697020e+00 -1.01113129e+00
6.50687218e-02 -1.17935002e+00 1.81743920e-01 -6.40321434e-01
-1.67710674e+00 3.04797471e-01 6.38572574e-02 -9.06074643e-01
-2.22320318e-01 -4.25921410e-01 -7.79255509e-01 1.25995731e+00
-1.15255916e+00 -1.06051266e+00 -2.38341093e-01 2.06976041e-01
5.38735986e-01 -2.75409997e-01 8.12729716e-01 1.69062585e-01
-3.75907540e-01 1.03446496e+00 -3.14049050e-02 1.57045331e-02
1.40911603e+00 -1.41703224e+00 6.91586137e-01 4.59108055e-01
-2.40023732e-01 7.16251135e-01 8.92722905e-01 -7.75380790e-01
-1.17163146e+00 -1.14946628e+00 7.01164186e-01 -8.12790990e-01
4.36035752e-01 -7.06708074e-01 -1.06737232e+00 5.62353373e-01
3.70260745e-01 -5.81500053e-01 8.08912754e-01 4.15630698e-01
-3.29450846e-01 6.86489837e-03 -1.17993772e+00 8.98492694e-01
1.12239218e+00 -4.92875189e-01 -2.92519540e-01 -1.13754191e-01
7.80471563e-01 -2.35367581e-01 -8.51382136e-01 1.93201721e-01
5.57742238e-01 -7.36691117e-01 5.96085668e-01 -7.38257349e-01
8.13700020e-01 1.75107289e-02 7.20157996e-02 -1.91985691e+00
-2.55601108e-01 -3.66801828e-01 5.44896424e-01 1.79776609e+00
7.02619612e-01 -3.82553220e-01 7.50807047e-01 7.98640609e-01
1.21584803e-01 -4.45640802e-01 -4.35594499e-01 -5.26481152e-01
8.02881002e-01 8.51032697e-03 7.99828410e-01 8.28845084e-01
-2.72732198e-01 6.28200531e-01 -1.57942176e-01 -5.00873327e-01
3.42155814e-01 -5.60871288e-02 1.09894109e+00 -1.09666002e+00
-4.19510543e-01 -6.32648349e-01 1.55201167e-01 -5.93309104e-01
2.58010536e-01 -8.40926230e-01 9.39213783e-02 -1.15204060e+00
2.03200161e-01 -9.55463409e-01 8.85462910e-02 1.82825759e-01
-5.62708914e-01 -1.03689106e-02 3.93432707e-01 -7.60035291e-02
-4.79634665e-02 8.00454915e-01 1.59635305e+00 4.18994837e-02
-3.84106100e-01 -2.86087040e-02 -1.02114308e+00 2.74025857e-01
1.01260698e+00 -2.95631349e-01 -6.86241746e-01 -6.56249881e-01
3.48503590e-01 -2.10143596e-01 -4.26567122e-02 -6.77852750e-01
-4.29149032e-01 -3.72824609e-01 6.36297464e-01 -1.88144416e-01
2.70125382e-02 -5.86853981e-01 -1.76062614e-01 -3.88776809e-02
-7.97613144e-01 3.25111657e-01 5.42535365e-01 2.68833131e-01
1.65205374e-01 5.77594526e-02 8.67581904e-01 2.23617326e-03
-9.70705524e-02 5.97104020e-02 -3.10117036e-01 5.36492407e-01
6.29457653e-01 -4.25414070e-02 -1.13488659e-01 -2.89486140e-01
-1.51530638e-01 1.90306857e-01 8.91983092e-01 8.07037771e-01
6.32921010e-02 -1.49585128e+00 -7.88640559e-01 4.14606571e-01
2.33651534e-01 8.43510628e-02 -9.60275456e-02 1.14203975e-01
-1.98015124e-01 -6.74040467e-02 -6.20529592e-01 -4.85624969e-01
-1.01451528e+00 3.01712215e-01 7.01032057e-02 -1.78318933e-01
-1.09323695e-01 5.61602533e-01 3.36097121e-01 -6.76149487e-01
-1.58979720e-03 -2.26270765e-01 1.75329104e-01 1.32275224e-01
3.69299352e-01 4.30470288e-01 3.29663157e-02 -1.30027369e-01
1.67916298e-01 3.30261678e-01 -1.39122874e-01 -5.42732000e-01
1.08927476e+00 -4.73808721e-02 1.47795957e-02 6.96612477e-01
1.04460216e+00 -1.07106036e-02 -1.42145681e+00 -5.97868301e-02
-7.92189017e-02 -6.07645154e-01 -2.94856280e-01 -8.61507058e-01
-5.49795508e-01 8.55246007e-01 4.54235584e-01 -9.06912386e-02
9.04181480e-01 -3.46588373e-01 6.03586733e-01 7.00637400e-02
1.41286716e-01 -1.16967487e+00 4.19674635e-01 5.26637971e-01
1.06107855e+00 -1.31300366e+00 -3.37221146e-01 -1.66774020e-01
-8.88815284e-01 8.31200063e-01 9.80446160e-01 9.44837406e-02
2.10366264e-01 3.71974081e-01 3.89354318e-01 2.39501283e-01
-6.67349339e-01 2.85354674e-01 2.86934048e-01 5.90738356e-01
8.12332630e-01 2.24198863e-01 -2.12936968e-01 3.09466481e-01
-7.21828163e-01 -6.85307682e-02 3.35765988e-01 8.51798892e-01
-8.47139582e-02 -1.67702973e+00 -4.62831080e-01 6.41651392e-01
-1.77862480e-01 -1.08467065e-01 -7.03006327e-01 4.92868185e-01
-6.01827726e-02 7.58337677e-01 2.65548885e-01 -2.51509696e-01
4.22068089e-01 4.57092941e-01 6.15245223e-01 -7.17225075e-01
-6.71950161e-01 -1.20765880e-01 1.13475829e-01 1.80779118e-02
2.85137057e-01 -7.51659572e-01 -8.13929498e-01 -4.35403883e-01
-1.14352517e-01 -5.53835593e-02 5.92041075e-01 8.29195321e-01
4.79226053e-01 5.21198153e-01 6.14651203e-01 -8.38262677e-01
-6.82208896e-01 -1.29160929e+00 -3.88911098e-01 8.39885473e-01
2.27347776e-01 -6.05862558e-01 -2.63113797e-01 6.84804022e-02] | [11.52079963684082, 9.725409507751465] |
284e9333-4682-483b-9e64-33e05f26b640 | csg0-continual-urban-scene-generation-with | 2112.03252 | null | https://arxiv.org/abs/2112.03252v2 | https://arxiv.org/pdf/2112.03252v2.pdf | CSG0: Continual Urban Scene Generation with Zero Forgetting | With the rapid advances in generative adversarial networks (GANs), the visual quality of synthesised scenes keeps improving, including for complex urban scenes with applications to automated driving. We address in this work a continual scene generation setup in which GANs are trained on a stream of distinct domains; ideally, the learned models should eventually be able to generate new scenes in all seen domains. This setup reflects the real-life scenario where data are continuously acquired in different places at different times. In such a continual setup, we aim for learning with zero forgetting, \IE, with no degradation in synthesis quality over earlier domains due to catastrophic forgetting. To this end, we introduce a novel framework that not only (i) enables seamless knowledge transfer in continual training but also (ii) guarantees zero forgetting with a small overhead cost. While being more memory efficient, thanks to continual learning, our model obtains better synthesis quality as compared against the brute-force solution that trains one full model for each domain. Especially, under extreme low-data regimes, our approach outperforms the brute-force one by a large margin. | ['Matthieu Cord', 'Patrick Pérez', 'Tuan-Hung Vu', 'Himalaya Jain'] | 2021-12-06 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 5.35594583e-01 3.50525022e-01 2.05029234e-01 8.80882051e-03
-6.14212275e-01 -5.87326765e-01 9.26789522e-01 -1.56893060e-01
-3.31341028e-01 1.18226540e+00 -3.43688689e-02 -1.53192237e-01
1.24482132e-01 -1.14122999e+00 -1.12261057e+00 -7.52138674e-01
3.35779160e-01 6.10008657e-01 2.64827996e-01 -2.63094425e-01
-2.06091747e-01 5.38714230e-01 -1.59768951e+00 -1.46263063e-01
1.20852530e+00 6.87786460e-01 4.45169598e-01 7.24450409e-01
6.48180991e-02 8.36322844e-01 -6.14832878e-01 -7.34015405e-01
3.74109149e-01 -5.39079785e-01 -4.46894467e-01 4.15029913e-01
4.56432521e-01 -3.00296158e-01 -5.63321590e-01 8.94929051e-01
3.09683830e-01 1.69956937e-01 5.11474669e-01 -1.18788528e+00
-4.34529036e-01 1.80535093e-01 -4.53993618e-01 -1.74393728e-01
1.66072249e-01 5.95835626e-01 6.96446300e-01 -6.03851736e-01
7.45022476e-01 9.05808270e-01 4.31415081e-01 7.13447750e-01
-1.37950826e+00 -5.84404707e-01 2.20340982e-01 -1.70405221e-03
-1.09514725e+00 -5.40190279e-01 8.17401409e-01 -3.68685901e-01
3.82367611e-01 8.05538371e-02 7.26546168e-01 1.37992847e+00
2.01725826e-01 7.54442155e-01 9.80162442e-01 -2.16786012e-01
4.44015026e-01 3.65091473e-01 -5.97840607e-01 4.75987107e-01
2.52331913e-01 1.34424508e-01 -4.94426489e-01 2.67145455e-01
6.62109435e-01 8.33461210e-02 -1.78989872e-01 -4.94259268e-01
-1.18779135e+00 6.80457234e-01 3.18887293e-01 7.01875091e-02
-5.74988067e-01 2.06006885e-01 2.17856392e-01 5.98964870e-01
4.77505296e-01 3.81157458e-01 -8.14737603e-02 -6.10758662e-02
-1.10273910e+00 4.27075922e-01 5.47407150e-01 1.04258144e+00
8.93862724e-01 4.85123664e-01 -1.13998942e-01 5.84558249e-01
-2.41625726e-01 7.54382253e-01 3.68871182e-01 -7.06788361e-01
6.08204067e-01 2.17478350e-01 4.04460400e-01 -7.65097618e-01
-9.75863487e-02 -7.82318234e-01 -1.19332981e+00 5.10604680e-01
4.34968412e-01 -3.62450331e-01 -1.08154500e+00 2.10373092e+00
4.43468779e-01 3.56306255e-01 1.84229836e-01 4.90149319e-01
1.72567949e-01 7.20742285e-01 -9.41645056e-02 -2.31821731e-01
9.49602485e-01 -8.87735665e-01 -6.28966808e-01 -4.83351529e-01
2.52025217e-01 -6.33353472e-01 1.16441095e+00 5.97109973e-01
-9.94649649e-01 -7.72682607e-01 -1.18293619e+00 2.40121201e-01
-1.89241827e-01 -1.96098372e-01 3.92376482e-01 6.42593980e-01
-1.05807090e+00 5.93557656e-01 -7.47236192e-01 -2.98574060e-01
4.86151248e-01 1.67316243e-01 -4.02663708e-01 -2.15016246e-01
-1.08700323e+00 8.40312839e-01 3.58134866e-01 -9.94982198e-02
-1.34597397e+00 -7.79785037e-01 -6.72775924e-01 -1.15021050e-01
5.76825559e-01 -9.52176273e-01 1.13892496e+00 -1.34481335e+00
-1.69617558e+00 5.35048187e-01 -1.82190776e-01 -7.26695597e-01
1.06615877e+00 -4.24046129e-01 -3.30042452e-01 -1.03364877e-01
-1.59416143e-02 7.32728004e-01 1.28037035e+00 -1.69152093e+00
-5.40565073e-01 -4.87351157e-02 1.85380653e-01 3.02174568e-01
-2.68575042e-01 -6.77650392e-01 -2.25972608e-01 -7.08252311e-01
-2.72970885e-01 -1.06347668e+00 -2.60281742e-01 2.63297028e-04
-1.56772718e-01 2.50983328e-01 9.32538748e-01 -5.67604661e-01
7.06991434e-01 -2.08865118e+00 2.73417532e-01 -1.06584378e-01
2.13549733e-01 4.07367438e-01 -1.81868479e-01 5.21364570e-01
3.93540412e-02 -2.00317413e-01 -4.50401574e-01 -7.55673528e-01
-1.76697746e-01 4.94602203e-01 -6.53037429e-01 2.00116336e-01
4.07026559e-01 1.05243838e+00 -1.03989387e+00 -2.48068467e-01
3.88915956e-01 3.72024894e-01 -5.31723678e-01 4.38954771e-01
-4.10732299e-01 9.30413842e-01 -1.74364939e-01 2.09372178e-01
6.84399962e-01 -1.07977659e-01 6.61249682e-02 2.22912759e-01
4.97694090e-02 -2.55764753e-01 -1.10385799e+00 1.95194030e+00
-8.55143666e-01 5.97133994e-01 -1.16599105e-01 -9.13294196e-01
8.89111280e-01 3.37514520e-01 1.87072575e-01 -8.96648109e-01
-2.37891659e-01 1.51453957e-01 -2.15345666e-01 -2.14965075e-01
5.07572651e-01 -4.40698564e-01 -8.03383365e-02 2.67134577e-01
1.56877860e-01 -4.71468925e-01 1.11087516e-01 2.28667125e-01
1.19249535e+00 1.79184809e-01 1.32622972e-01 6.29631430e-02
4.05247152e-01 -1.81162968e-01 5.56682706e-01 8.24615479e-01
1.09806135e-01 7.88341463e-01 3.87716860e-01 -4.24401551e-01
-1.40785122e+00 -1.27134359e+00 2.98901707e-01 4.96512651e-01
1.82216123e-01 8.81986022e-02 -6.47591710e-01 -7.05702126e-01
-1.81313321e-01 8.77095520e-01 -6.46304309e-01 -3.85846734e-01
-6.24674499e-01 -4.66560930e-01 3.61071646e-01 2.34728843e-01
6.88520968e-01 -1.08480585e+00 -6.79945767e-01 2.23642990e-01
-7.35866055e-02 -1.13515508e+00 -3.28181982e-01 1.03136808e-01
-8.03246260e-01 -6.69253945e-01 -1.10276699e+00 -5.04107773e-01
5.67627370e-01 2.03856111e-01 1.26852155e+00 -1.96793020e-01
-5.41860750e-03 1.77286848e-01 -3.90625715e-01 -3.00726622e-01
-6.48223281e-01 1.51931196e-01 -5.50730079e-02 3.61321449e-01
-2.97258645e-01 -9.03354526e-01 -6.78030491e-01 3.75828110e-02
-1.27802324e+00 3.86091620e-01 8.74954820e-01 9.61744249e-01
5.22635281e-01 3.82802874e-01 8.78002524e-01 -9.45044279e-01
3.90222728e-01 -5.55385292e-01 -6.52192533e-01 2.33259499e-01
-6.32827640e-01 1.88296780e-01 1.21577621e+00 -4.94620591e-01
-1.37621963e+00 7.94179812e-02 -2.31422469e-01 -5.09705067e-01
-2.55255789e-01 -3.70737910e-02 -2.63930142e-01 3.30723301e-02
6.95738673e-01 6.08486116e-01 -1.48293272e-01 -2.04211846e-01
5.28280914e-01 2.79123843e-01 7.43385315e-01 -6.16710365e-01
1.38888359e+00 6.11455560e-01 7.66309351e-02 -7.36647487e-01
-6.00308239e-01 1.46031782e-01 -5.66512644e-01 -4.67261285e-01
6.71764314e-01 -8.77479792e-01 -4.66134906e-01 6.91999376e-01
-1.13972712e+00 -7.09205389e-01 -7.92017579e-01 1.16926186e-01
-7.22455084e-01 2.88327098e-01 -1.98850393e-01 -8.88004720e-01
-7.65905827e-02 -1.02118063e+00 1.08537483e+00 3.16886187e-01
5.57616539e-02 -1.01154137e+00 2.05712631e-01 1.06517337e-01
4.07846540e-01 6.98418319e-01 7.50231445e-01 -9.10862982e-02
-6.85394526e-01 -4.92285676e-02 -3.52288485e-02 4.92431045e-01
2.78521329e-01 -3.27396691e-01 -9.89996552e-01 -3.89283776e-01
6.38397969e-03 -3.49163920e-01 7.69861281e-01 -2.29247268e-02
1.01488996e+00 -3.59229058e-01 -1.41669363e-02 5.46651065e-01
1.57093453e+00 4.64724332e-01 8.56594741e-01 2.08493069e-01
5.95415533e-01 6.06646061e-01 5.71655214e-01 5.08852005e-01
1.88915774e-01 5.92905998e-01 6.65582716e-01 -3.35713774e-01
-4.14645880e-01 -5.26300490e-01 2.30487287e-01 5.53862035e-01
-1.02078794e-02 -6.14489079e-01 -8.21571767e-01 8.53946626e-01
-1.83646500e+00 -9.40972924e-01 2.60933191e-01 2.55081224e+00
8.15305710e-01 3.79103243e-01 1.18680425e-01 1.28457189e-01
5.88280916e-01 3.73151839e-01 -9.31105852e-01 -1.88285872e-01
-2.54816324e-01 4.25389171e-01 4.73406702e-01 4.34186459e-01
-8.28015387e-01 8.17370594e-01 5.74580526e+00 8.42985451e-01
-1.24539471e+00 3.15174878e-01 7.21886873e-01 -1.49384752e-01
-4.63850200e-01 1.94800925e-02 -3.69770348e-01 6.26093447e-01
9.26477671e-01 -2.77574360e-01 6.13367081e-01 7.19627559e-01
1.50217533e-01 -1.76512033e-01 -9.68966961e-01 7.81432509e-01
-1.17971875e-01 -1.38026345e+00 1.76730543e-01 9.82462317e-02
1.04260457e+00 -2.08117470e-01 2.67147422e-01 2.46583953e-01
4.95054811e-01 -9.26757753e-01 8.95511270e-01 6.38806343e-01
1.02153230e+00 -1.00401592e+00 5.29396117e-01 6.17882788e-01
-8.41307044e-01 1.32881161e-02 -2.06035614e-01 2.27185581e-02
4.59816784e-01 1.02982688e+00 -6.57817781e-01 9.31589127e-01
1.95942819e-01 5.72710812e-01 -3.98254633e-01 8.08292866e-01
-3.67132336e-01 5.11496305e-01 -4.11303230e-02 4.03311342e-01
1.85946852e-01 -2.83504188e-01 6.04137242e-01 9.99806941e-01
5.15552700e-01 -2.59743899e-01 -5.85083999e-02 6.42426968e-01
-1.15355931e-01 -2.76146650e-01 -9.93107796e-01 2.00359777e-01
3.58356714e-01 9.64981854e-01 -6.62635863e-01 -3.52423221e-01
-2.37196907e-01 1.32484949e+00 2.79104710e-01 3.99479210e-01
-1.07233298e+00 -3.15505445e-01 6.11424446e-01 3.69588643e-01
3.43861014e-01 -3.71031731e-01 -2.10002556e-01 -1.12921596e+00
1.78398237e-01 -8.64112854e-01 2.61781435e-03 -6.47306621e-01
-1.17642367e+00 8.21701050e-01 -2.62028426e-01 -1.32415080e+00
-3.08337748e-01 -1.66467458e-01 -5.70014894e-01 5.75780034e-01
-1.73901701e+00 -1.13950384e+00 -4.34457213e-01 7.14778781e-01
8.16011250e-01 -9.67093110e-02 6.53863132e-01 4.02212381e-01
-3.46904635e-01 6.14852428e-01 2.78798908e-01 -2.73234993e-01
7.64407635e-01 -1.24229765e+00 5.13919353e-01 1.19611442e+00
2.25664541e-01 1.29502386e-01 1.02374804e+00 -6.22258186e-01
-1.25481284e+00 -1.45152938e+00 4.37466711e-01 -2.02746347e-01
4.76012558e-01 -4.79366034e-01 -7.82997906e-01 5.70023417e-01
3.43772888e-01 -2.15121672e-01 1.56437114e-01 -1.50610939e-01
-2.10892692e-01 -4.66680497e-01 -1.26277304e+00 6.77998006e-01
1.08390832e+00 -2.78018862e-01 -2.69830555e-01 3.93576622e-01
7.67069638e-01 -4.36613202e-01 -6.00032628e-01 3.99051309e-01
2.99996227e-01 -1.02840757e+00 7.61252165e-01 -3.11950684e-01
6.08960867e-01 -4.07385558e-01 6.29831031e-02 -1.53496170e+00
-9.87034440e-02 -9.49473798e-01 -3.19236279e-01 1.12650120e+00
2.53125668e-01 -8.31919670e-01 7.62638152e-01 3.30059141e-01
-1.01224236e-01 -5.70400596e-01 -1.11264729e+00 -1.01874375e+00
1.21579729e-01 -3.76746744e-01 6.74548566e-01 4.66606200e-01
-7.29817927e-01 2.95641035e-01 -9.45753634e-01 1.60187960e-01
6.96385086e-01 8.39376375e-02 1.11677587e+00 -9.92658794e-01
-5.79580009e-01 -8.96112472e-02 -4.16000664e-01 -1.19544327e+00
8.12682509e-03 -4.21709210e-01 1.96883187e-01 -1.32199335e+00
2.13407874e-02 -6.10394657e-01 -5.24218567e-02 2.35094160e-01
-2.94514120e-01 3.36063504e-01 3.62035841e-01 1.99548721e-01
-4.78888065e-01 8.14989567e-01 1.39471364e+00 -5.39386682e-02
-5.13625108e-02 1.32826775e-01 -6.17092192e-01 3.65352541e-01
7.27193356e-01 -4.70686138e-01 -8.14322829e-01 -5.14565170e-01
2.85577565e-01 1.69056833e-01 4.47419405e-01 -1.50774825e+00
1.01714849e-01 -2.59524196e-01 2.96538919e-01 -2.69290149e-01
5.21127164e-01 -8.59697819e-01 6.58171475e-01 4.65288043e-01
-1.20476875e-02 -1.39825776e-01 8.88768658e-02 8.16940725e-01
-2.63947606e-01 -3.80036905e-02 1.02206814e+00 -1.97595611e-01
-5.53272724e-01 3.95606875e-01 -2.34827474e-01 9.49100591e-03
1.34919500e+00 -2.81096518e-01 -1.97609350e-01 -7.01140404e-01
-7.05748796e-01 2.71529388e-02 7.03194797e-01 3.79927605e-01
3.76818329e-01 -1.32139397e+00 -7.08304226e-01 1.85514703e-01
-6.21403940e-02 3.86466593e-01 4.99418199e-01 5.48909605e-01
-2.95213580e-01 1.32056698e-01 -1.89599097e-01 -5.06501973e-01
-8.12009633e-01 6.81769371e-01 1.28844082e-01 -6.58178031e-01
-7.14686334e-01 6.28804624e-01 6.26934111e-01 -1.46513477e-01
-1.15038529e-01 1.75283223e-01 1.87058717e-01 -4.58127744e-02
3.87794077e-01 1.56245992e-01 1.48194656e-01 -4.62469161e-01
1.38851598e-01 3.06994736e-01 -1.26196845e-02 -2.49720991e-01
1.39388597e+00 -2.12026507e-01 4.06910926e-01 5.32247663e-01
7.86705732e-01 1.73893303e-01 -1.87876880e+00 -2.14541718e-01
-3.97734255e-01 -5.48422635e-01 -2.22898573e-01 -7.00119257e-01
-1.23498952e+00 7.52431929e-01 5.36701918e-01 2.17433929e-01
1.40823102e+00 -2.63408631e-01 1.07527840e+00 1.18193716e-01
5.84492683e-01 -9.45625544e-01 2.62959003e-01 2.78970063e-01
7.46049047e-01 -1.10599113e+00 -2.83791453e-01 -1.93823501e-02
-9.25916433e-01 8.56679976e-01 4.95254397e-01 -3.37983429e-01
2.12213576e-01 2.19900489e-01 -1.96280837e-01 1.65279672e-01
-7.48116255e-01 -2.98712432e-01 1.83093660e-02 7.79718876e-01
-8.52261111e-02 -1.33941853e-02 7.01238140e-02 -1.90195628e-03
-2.64161706e-01 7.14189410e-02 5.12455702e-01 6.14900291e-01
-3.21965903e-01 -1.22036791e+00 -2.04744190e-01 2.92828113e-01
-8.77688527e-02 6.87821954e-02 -1.12937585e-01 8.31262231e-01
3.73185486e-01 9.01880920e-01 -2.53703073e-02 -3.22554618e-01
3.40438008e-01 5.74573800e-02 3.65138322e-01 -4.29400831e-01
-2.46284023e-01 -2.85619378e-01 -1.16701856e-01 -4.63165551e-01
-3.47731024e-01 -7.60579884e-01 -8.05165708e-01 -4.34701979e-01
3.60265449e-02 -8.89886450e-03 5.56032240e-01 9.46510553e-01
4.48850125e-01 8.45156670e-01 8.32577527e-01 -7.46691346e-01
-3.14990759e-01 -6.55064940e-01 -4.38831747e-01 4.76469100e-01
5.49971104e-01 -6.19487941e-01 -2.79471010e-01 2.64719218e-01] | [11.679048538208008, -0.403053879737854] |
33223977-a844-4a58-8942-44825b23f4c8 | dfdl-discriminative-feature-oriented | 1502.01032 | null | http://arxiv.org/abs/1502.01032v1 | http://arxiv.org/pdf/1502.01032v1.pdf | DFDL: Discriminative Feature-oriented Dictionary Learning for Histopathological Image Classification | In histopathological image analysis, feature extraction for classification is
a challenging task due to the diversity of histology features suitable for each
problem as well as presence of rich geometrical structure. In this paper, we
propose an automatic feature discovery framework for extracting discriminative
class-specific features and present a low-complexity method for classification
and disease grading in histopathology. Essentially, our Discriminative
Feature-oriented Dictionary Learning (DFDL) method learns class-specific
features which are suitable for representing samples from the same class while
are poorly capable of representing samples from other classes. Experiments on
three challenging real-world image databases: 1) histopathological images of
intraductal breast lesions, 2) mammalian lung images provided by the Animal
Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor
images from The Cancer Genome Atlas (TCGA) database, show the significance of
DFDL model in a variety problems over state-of-the-art methods | ['Hojjat S. Mousavi', 'Vishal Monga', 'UK Arvind Rao', 'Ganesh Rao', 'Tiep H. Vu'] | 2015-02-03 | null | null | null | null | ['histopathological-image-classification'] | ['medical'] | [ 1.75713316e-01 -3.20465773e-01 -1.80503696e-01 -2.01953158e-01
-9.18628633e-01 -4.06580895e-01 4.62368309e-01 7.61272490e-01
-4.03167844e-01 5.93104482e-01 9.27989110e-02 -2.31252149e-01
-5.08362651e-01 -6.19135499e-01 -9.75039080e-02 -1.34547007e+00
-4.56252009e-01 6.60040200e-01 2.91767806e-01 -2.57311221e-02
2.67158061e-01 1.12185085e+00 -1.08441842e+00 3.53098392e-01
3.88188958e-01 9.32092488e-01 4.12257701e-01 6.33224547e-01
1.51509732e-01 4.90374058e-01 -1.48508400e-01 1.25122085e-01
1.02413766e-01 -2.55934060e-01 -9.01126683e-01 3.22947830e-01
3.30352038e-01 2.17830781e-02 -4.43141520e-01 9.64240193e-01
5.08291185e-01 -2.84367204e-01 1.36792576e+00 -8.32722545e-01
-2.73859262e-01 -9.15413126e-02 -5.02636552e-01 6.71947300e-01
-6.96939677e-02 -1.95107564e-01 9.50919151e-01 -8.10157597e-01
1.05577314e+00 6.66924238e-01 6.19425654e-01 4.62808639e-01
-1.24151206e+00 -2.22804517e-01 -1.96852416e-01 3.55055958e-01
-1.51984417e+00 -4.14510310e-01 7.35689044e-01 -7.93961465e-01
4.15758789e-01 4.81679231e-01 5.74808240e-01 7.06805944e-01
7.89079368e-01 5.57515144e-01 1.52632856e+00 -2.38691270e-01
3.78681421e-01 1.05594687e-01 2.20307782e-01 1.10915732e+00
1.40482008e-01 6.35171309e-02 -1.07893825e-01 -5.35950124e-01
8.65981579e-01 2.67071962e-01 -2.45298505e-01 -7.25425243e-01
-1.07486284e+00 8.69714379e-01 3.76392215e-01 7.21033514e-01
-2.93934226e-01 -2.95417130e-01 3.16579252e-01 2.72119224e-01
2.74429053e-01 3.95239778e-02 -3.61728668e-01 5.69812775e-01
-7.50335038e-01 8.43145102e-02 5.80715895e-01 6.01713538e-01
6.52954102e-01 -3.17937851e-01 2.16831230e-02 7.63131678e-01
3.54118496e-01 1.06943049e-01 1.02306426e+00 -3.64160895e-01
-2.78062373e-01 7.47054100e-01 -4.25272018e-01 -1.12840140e+00
-8.00939679e-01 -5.86140096e-01 -1.14109480e+00 3.21577080e-02
4.97772634e-01 3.98937434e-01 -9.80342031e-01 1.22277820e+00
5.29652059e-01 -7.79929236e-02 4.35132943e-02 7.98373759e-01
1.00708508e+00 1.97040632e-01 -7.09536821e-02 -2.41075695e-01
1.56844115e+00 -4.70484793e-01 -1.29980162e-01 2.17348382e-01
9.92817342e-01 -5.33394992e-01 5.10599315e-01 3.03600609e-01
-4.69550222e-01 -2.08139256e-01 -7.40843654e-01 -3.62681635e-02
-3.87704253e-01 3.69726866e-01 9.46983039e-01 2.36381799e-01
-1.00407636e+00 2.81559646e-01 -9.71814930e-01 -8.70129526e-01
6.22746885e-01 5.23828328e-01 -1.02677488e+00 -1.52255997e-01
-3.19001615e-01 7.14975715e-01 1.05653800e-01 -2.10207269e-01
-1.14663839e+00 -5.98981678e-01 -6.93333089e-01 -1.42158985e-01
-2.51329184e-01 -5.94071269e-01 5.93116164e-01 -6.72186732e-01
-9.27197158e-01 1.27474582e+00 -1.03487931e-01 -2.19819576e-01
1.94286004e-01 7.85171330e-01 -3.96568894e-01 5.38985848e-01
3.13405842e-02 5.43381095e-01 4.98001754e-01 -1.29361987e+00
-5.41320622e-01 -8.36225390e-01 -2.89685309e-01 -1.36026651e-01
-3.71372551e-01 -2.54327595e-01 1.59177005e-01 -6.87043726e-01
3.04618657e-01 -8.94934952e-01 -6.57791853e-01 2.56651700e-01
-3.14158320e-01 -5.20565063e-02 9.49832201e-01 -6.17095113e-01
1.01219046e+00 -2.27230239e+00 3.60014051e-01 4.77276951e-01
3.79100055e-01 -3.93357947e-02 -1.08021855e-01 3.43114525e-01
-9.31006819e-02 -5.34359738e-02 -1.28229439e-01 1.30250484e-01
-2.77620316e-01 4.11167800e-01 3.32951754e-01 9.93421853e-01
2.11738706e-01 5.22415578e-01 -8.59789491e-01 -8.77017677e-01
1.87922399e-02 2.22544730e-01 -3.71505857e-01 -1.07551597e-01
4.07104790e-01 6.77878439e-01 -9.07889128e-01 1.02846980e+00
5.26012719e-01 -1.58395499e-01 2.56353915e-01 -4.88174886e-01
6.33735582e-02 -2.50504345e-01 -8.30278575e-01 1.50977015e+00
-2.17873618e-01 4.86293495e-01 2.48650029e-01 -1.12439477e+00
6.24192238e-01 2.16362223e-01 7.62389183e-01 -3.77364695e-01
3.22945386e-01 2.70522624e-01 2.01589927e-01 -6.26833916e-01
-3.08351461e-02 -4.20228362e-01 -5.84462250e-04 1.33296415e-01
3.16397697e-01 -4.63579372e-02 1.53444752e-01 -4.15685140e-02
1.42694044e+00 -5.76991558e-01 5.98120570e-01 -8.03475022e-01
6.46620095e-01 2.10145161e-01 6.35162771e-01 3.21380526e-01
-6.26350164e-01 6.30726576e-01 4.87274498e-01 -6.49095058e-01
-8.90200913e-01 -9.73918498e-01 -9.11855817e-01 6.29316628e-01
-7.85406083e-02 -1.05304830e-01 -4.49682683e-01 -9.10515666e-01
1.94551766e-01 -1.04125105e-01 -9.29458618e-01 6.79369047e-02
-2.90943176e-01 -9.76214468e-01 5.35955608e-01 2.88745135e-01
-4.46464866e-02 -5.91977179e-01 -4.07069921e-01 1.67235777e-01
2.75510967e-01 -9.04298604e-01 -3.27545792e-01 5.75090468e-01
-1.06395388e+00 -1.34434390e+00 -7.18175173e-01 -1.25141025e+00
1.37603366e+00 3.02462935e-01 8.69982183e-01 3.05540681e-01
-1.18450677e+00 3.01968038e-01 -1.85927093e-01 -2.96979219e-01
-4.48756516e-01 -1.03575541e-02 -1.65808070e-02 -1.84197761e-02
2.53960192e-01 -5.02354741e-01 -5.85407197e-01 2.74995267e-01
-1.17741477e+00 -3.26128304e-01 8.45671713e-01 1.21581900e+00
1.09800625e+00 3.25659364e-01 4.88297522e-01 -9.88965034e-01
2.17796847e-01 -6.40261054e-01 -2.41196617e-01 2.04602167e-01
-1.87028378e-01 -2.41863295e-01 6.75368309e-01 -1.87964484e-01
-4.64565963e-01 3.37501198e-01 -7.00845495e-02 -8.45542252e-02
-3.45100939e-01 6.65295482e-01 1.45554841e-01 -5.74671507e-01
7.10737109e-01 4.15902972e-01 3.13776493e-01 -3.12626749e-01
-2.45354190e-01 7.11360037e-01 4.42262679e-01 -5.03829360e-01
6.23004317e-01 8.25617611e-01 6.75868154e-01 -9.47838664e-01
-5.61085343e-01 -8.65845561e-01 -9.50589299e-01 1.21142842e-01
7.81536579e-01 -7.11354852e-01 -1.55092150e-01 4.76921082e-01
-4.84649658e-01 4.46474962e-02 -1.10195890e-01 4.39527810e-01
-5.46300113e-01 4.73286569e-01 -7.07818210e-01 -1.03103966e-01
-2.25055173e-01 -1.32777488e+00 1.19801629e+00 1.48158327e-01
3.86777036e-02 -1.36402249e+00 2.99065799e-01 2.05194980e-01
2.27569640e-01 4.89237845e-01 1.64617598e+00 -7.93594360e-01
-1.96008444e-01 -6.32964492e-01 2.06277985e-02 2.03407124e-01
4.88270342e-01 2.16371641e-01 -7.56378651e-01 -6.13494277e-01
-2.09340323e-02 -2.74184525e-01 8.45023155e-01 2.86735684e-01
1.31780708e+00 -1.90079078e-01 -7.01847970e-01 6.43567920e-01
1.74953139e+00 1.63231660e-02 3.67527097e-01 2.04027340e-01
5.47248781e-01 5.59306443e-01 3.74527991e-01 4.09664363e-01
3.46094012e-01 6.84910715e-01 2.28690475e-01 -3.80748868e-01
-1.30645916e-01 3.13290566e-01 -3.91257554e-02 8.22120368e-01
-6.62415177e-02 -1.79050881e-02 -8.93529534e-01 7.87600756e-01
-1.48848736e+00 -7.52483964e-01 -1.11800946e-01 1.88092864e+00
8.05611789e-01 -2.44329825e-01 1.09376550e-01 3.15401942e-01
3.36047828e-01 -1.39747515e-01 -2.03590542e-01 -1.79122120e-01
-7.67875090e-03 2.06058621e-01 3.94419491e-01 9.33425222e-03
-1.28336096e+00 4.29053932e-01 6.35908794e+00 1.06274796e+00
-1.28805721e+00 3.99710052e-02 8.39473903e-01 4.29014236e-01
-1.12956606e-01 -1.75580606e-01 -5.29612541e-01 2.02024445e-01
6.55572057e-01 -2.82351315e-01 -1.31952748e-01 6.89163685e-01
-4.72864583e-02 -1.91253111e-01 -1.19200051e+00 7.11787939e-01
-1.83182750e-02 -1.34784913e+00 2.38769010e-01 4.58736539e-01
6.02864504e-01 -3.47418748e-02 2.63366699e-02 -1.82574570e-01
-1.44203424e-01 -1.27357042e+00 4.20942567e-02 4.70926583e-01
8.24012101e-01 -7.07083523e-01 1.05557036e+00 4.37662929e-01
-9.32036936e-01 6.71423674e-02 -6.85595155e-01 4.73348320e-01
-5.51610410e-01 6.84492409e-01 -1.15541029e+00 6.59920812e-01
3.72136474e-01 7.38228142e-01 -8.54585946e-01 1.28839207e+00
3.57268304e-01 5.51428795e-01 -1.94117606e-01 1.63547724e-01
4.39425051e-01 2.48180684e-02 4.69095141e-01 1.47886658e+00
3.06817621e-01 2.87389189e-01 2.88587332e-01 2.91874617e-01
2.46525303e-01 2.96506912e-01 -7.28532374e-01 -7.92689398e-02
8.18811804e-02 1.84634054e+00 -1.11221349e+00 3.54670174e-02
-2.60818422e-01 6.46850824e-01 2.93293715e-01 -4.92957458e-02
-3.53821218e-01 -1.42529666e-01 4.07786459e-01 2.61162162e-01
1.65764138e-01 -2.23585561e-01 1.51802860e-02 -9.95091200e-01
-3.65945905e-01 -7.45773017e-01 5.62933207e-01 -1.96484268e-01
-1.54167759e+00 6.29478037e-01 -8.96828696e-02 -1.59691501e+00
1.30039781e-01 -9.24901605e-01 -4.21290368e-01 6.01459146e-01
-1.67573643e+00 -1.23238671e+00 -3.41714978e-01 6.35670841e-01
2.65087724e-01 -4.50400442e-01 1.33764946e+00 3.91859531e-01
-4.38212872e-01 5.51015735e-01 6.73162878e-01 2.74257541e-01
4.64250922e-01 -1.13908386e+00 -5.00645697e-01 2.51231700e-01
5.24954237e-02 4.18924779e-01 4.20937270e-01 -1.01492658e-01
-1.57258141e+00 -1.20195651e+00 7.85998881e-01 -2.81678408e-01
6.96315050e-01 -4.01126780e-02 -7.42119670e-01 3.40758592e-01
-1.89454123e-01 6.30739391e-01 1.30082679e+00 -4.89423156e-01
-1.83792010e-01 -1.57101199e-01 -1.69758928e+00 3.48284632e-01
5.70260465e-01 -3.38866442e-01 -2.87504077e-01 6.67003691e-01
-4.78345066e-01 -2.90109992e-01 -1.21948779e+00 3.16509575e-01
6.47195101e-01 -7.03477025e-01 9.15959716e-01 -6.60706878e-01
3.72550219e-01 -4.35904562e-01 -3.31297517e-01 -1.40158784e+00
-6.96819484e-01 9.77595598e-02 4.24023569e-01 8.03640783e-01
8.21325853e-02 -4.68740165e-01 7.33047664e-01 -1.17757045e-01
-2.17621341e-01 -1.22470558e+00 -1.31796169e+00 -6.65661633e-01
2.67859459e-01 3.10407788e-01 1.23879515e-01 9.68751311e-01
-7.32090231e-03 -4.84138243e-02 2.96733946e-01 1.08875521e-01
8.11533511e-01 1.82385877e-01 4.03339148e-01 -1.39420307e+00
-2.19029948e-01 -4.69890267e-01 -1.47753596e+00 -2.54767150e-01
1.57432124e-01 -1.28730440e+00 -2.19662085e-01 -1.45440185e+00
7.15382695e-01 -7.97573030e-01 -4.85720366e-01 4.14704233e-01
1.16644293e-01 3.94304305e-01 -2.07846820e-01 4.32551324e-01
-3.45075816e-01 9.18127820e-02 1.37829602e+00 -4.81301248e-01
3.48748595e-01 -1.56741038e-01 -7.28503466e-01 8.51454079e-01
3.56079966e-01 -6.75836861e-01 -2.30547026e-01 -1.53348133e-01
-3.92270446e-01 1.44662842e-01 5.24752796e-01 -1.11160958e+00
6.88895583e-02 -2.70361066e-01 6.79666162e-01 -3.47951025e-01
1.09983951e-01 -1.09278512e+00 2.62165815e-01 6.33090675e-01
-1.77686617e-01 -1.66203335e-01 4.27497886e-02 6.31960928e-01
-4.08513904e-01 -4.03042406e-01 1.21280587e+00 -2.88668543e-01
-6.55488372e-01 5.32584369e-01 -5.88918984e-01 -3.31278503e-01
1.35958672e+00 -3.75410527e-01 -3.51224929e-01 1.35335311e-01
-8.38973045e-01 -8.31434131e-02 6.07592702e-01 -2.03790665e-01
6.44835472e-01 -1.39238596e+00 -1.00801885e+00 2.75476694e-01
3.92559618e-01 -6.19890690e-02 1.87868506e-01 1.09938133e+00
-7.45206654e-01 3.30067247e-01 -4.99151766e-01 -7.63169825e-01
-1.45104027e+00 2.34792784e-01 3.73127580e-01 -7.05550492e-01
-5.54256141e-01 8.21345389e-01 5.44104934e-01 -2.47302324e-01
-1.73334926e-01 -3.54387492e-01 -5.11551082e-01 4.93465587e-02
2.75570929e-01 1.22539558e-01 5.87278366e-01 -1.02313054e+00
-5.69780827e-01 7.21314609e-01 -5.48614979e-01 5.43192983e-01
1.57640171e+00 1.64446145e-01 -2.69016951e-01 2.58141369e-01
1.56517577e+00 -1.22839116e-01 -4.99721557e-01 -1.97962999e-01
1.98564902e-01 -3.72696340e-01 3.58095080e-01 -7.12220728e-01
-1.20612979e+00 6.87990963e-01 8.52452874e-01 1.28090441e-01
1.24135995e+00 1.15833707e-01 5.17487884e-01 3.74747187e-01
7.63409078e-01 -8.23436141e-01 -1.25157624e-01 3.31856847e-01
7.67216802e-01 -1.02987361e+00 3.68641943e-01 -5.06691396e-01
-5.49986422e-01 1.42685282e+00 1.01361647e-01 -3.66768926e-01
9.58490908e-01 3.49539161e-01 1.58540338e-01 -4.70830262e-01
-8.45957816e-01 -9.71059203e-02 2.39883766e-01 7.10261226e-01
4.78185773e-01 1.41486913e-01 -4.89765286e-01 6.61383450e-01
2.00601563e-01 -6.23809621e-02 5.06599069e-01 1.29595506e+00
-5.23939431e-01 -1.05208254e+00 -1.34115517e-01 8.04629862e-01
-7.24187553e-01 1.26309544e-01 -5.39941370e-01 8.82141292e-01
-3.06936596e-02 4.35497850e-01 -1.29514143e-01 -1.66248158e-01
-3.79702225e-02 -2.49544382e-01 7.93224990e-01 -6.69917762e-01
-5.98511398e-01 2.38365829e-01 -1.54117972e-01 -9.79537666e-02
-4.28892910e-01 -7.55808413e-01 -1.47994602e+00 1.68812662e-01
-3.86410743e-01 1.01419896e-01 6.20547771e-01 8.83513212e-01
2.56448507e-01 3.01007688e-01 1.03958344e+00 -6.77567065e-01
-4.20197099e-01 -7.03725934e-01 -1.28956699e+00 5.13258278e-01
3.71243834e-01 -7.77999222e-01 -3.86949241e-01 2.77583659e-01] | [15.057138442993164, -2.881216049194336] |
9ed25fbe-ac36-46d8-a2fa-54012b63c0f2 | load-local-orientation-adaptive-descriptor | 1504.05809 | null | http://arxiv.org/abs/1504.05809v1 | http://arxiv.org/pdf/1504.05809v1.pdf | LOAD: Local Orientation Adaptive Descriptor for Texture and Material Classification | In this paper, we propose a novel local feature, called Local Orientation
Adaptive Descriptor (LOAD), to capture regional texture in an image. In LOAD,
we proposed to define point description on an Adaptive Coordinate System (ACS),
adopt a binary sequence descriptor to capture relationships between one point
and its neighbors and use multi-scale strategy to enhance the discriminative
power of the descriptor. The proposed LOAD enjoys not only discriminative power
to capture the texture information, but also has strong robustness to
illumination variation and image rotation. Extensive experiments on benchmark
data sets of texture classification and real-world material recognition show
that the proposed LOAD yields the state-of-the-art performance. It is worth to
mention that we achieve a 65.4\% classification accuracy-- which is, to the
best of our knowledge, the highest record by far --on Flickr Material Database
by using a single feature. Moreover, by combining LOAD with the feature
extracted by Convolutional Neural Networks (CNN), we obtain significantly
better performance than both the LOAD and CNN. This result confirms that the
LOAD is complementary to the learning-based features. | ['Xianbiao Qi', 'Qingquan Li', 'Linlin Shen', 'Matti Pietikainen', 'Guoying Zhao'] | 2015-04-22 | null | null | null | null | ['material-classification', 'material-recognition'] | ['computer-vision', 'computer-vision'] | [ 2.59546936e-01 -6.66133344e-01 -2.07578093e-01 -2.98594624e-01
-6.15737319e-01 -3.13404381e-01 6.61381662e-01 5.27741946e-02
-4.87058252e-01 4.32239711e-01 -9.89510417e-02 2.62462407e-01
-4.21977222e-01 -9.04350102e-01 -5.47198176e-01 -1.06938100e+00
-6.36531487e-02 -1.01524033e-01 4.02896672e-01 -3.69139344e-01
4.59467828e-01 8.88184369e-01 -1.76209092e+00 2.97151357e-01
5.00904977e-01 1.65338218e+00 2.02359572e-01 2.22093061e-01
1.65342137e-01 5.93654394e-01 -3.14790636e-01 -1.99397713e-01
1.74149841e-01 -4.77821194e-02 -6.62386894e-01 7.42169172e-02
5.35962939e-01 -1.26951873e-01 -3.48122209e-01 9.22568738e-01
4.50007468e-01 -2.08132844e-02 6.89992666e-01 -8.93949449e-01
-8.25471401e-01 3.17335390e-02 -5.99337697e-01 2.48864919e-01
4.68305260e-01 -3.22351605e-01 9.43957210e-01 -8.61938357e-01
8.10633659e-01 7.19649494e-01 7.21141398e-01 -2.44022757e-02
-7.46723652e-01 -3.00094068e-01 -4.75448519e-02 5.14421046e-01
-1.83498240e+00 -1.78116992e-01 1.23554862e+00 -2.33657733e-01
6.94333613e-01 3.86490881e-01 6.84579670e-01 8.68286133e-01
5.14707804e-01 7.48456597e-01 1.24753308e+00 -5.38127363e-01
-8.38589892e-02 -2.10999623e-01 5.01195155e-02 9.92517114e-01
-1.21045776e-01 -8.56246278e-02 -4.20073181e-01 3.76218595e-02
1.04732442e+00 1.49766043e-01 -2.68368214e-01 -4.91433114e-01
-1.35506678e+00 5.10028183e-01 6.27037644e-01 7.05612123e-01
-4.69129890e-01 9.62255150e-02 4.71814990e-01 3.22178543e-01
4.08641100e-01 4.87696081e-02 -3.27909470e-01 -3.03132236e-01
-5.06121278e-01 -4.63866442e-02 5.05306065e-01 5.81360936e-01
7.05811739e-01 -2.47355476e-02 -2.79854704e-02 9.38785672e-01
3.07895076e-02 8.68129492e-01 5.26547015e-01 -5.30172348e-01
1.40522361e-01 6.47881985e-01 -7.69004822e-02 -1.73530710e+00
-4.78730142e-01 -5.12951076e-01 -1.25561821e+00 2.51516309e-02
1.79734185e-01 5.63608408e-01 -5.18646359e-01 1.32689822e+00
1.87688425e-01 6.45657703e-02 -1.07950144e-01 1.00351608e+00
8.29103351e-01 5.24088204e-01 -3.32161516e-01 -1.33865923e-01
1.19298398e+00 -7.38511086e-01 -5.39760768e-01 5.21074355e-01
2.88667738e-01 -1.03415799e+00 8.31706524e-01 5.46827316e-01
-6.54554963e-01 -8.52885783e-01 -1.18110323e+00 1.67100251e-01
-3.92618537e-01 5.02184749e-01 6.17595375e-01 3.75065237e-01
-8.43763590e-01 5.81016719e-01 -6.99015915e-01 -6.50404990e-01
4.61908057e-02 3.41536403e-01 -7.86838949e-01 -1.68947145e-01
-9.18351054e-01 5.47110498e-01 9.94587690e-02 3.55607510e-01
-3.75067472e-01 -1.07225068e-01 -5.92230439e-01 -1.57327235e-01
1.97497413e-01 -2.91052222e-01 6.92798913e-01 -1.00396931e+00
-1.59319854e+00 6.40435159e-01 -2.86468595e-01 1.54211698e-02
3.10297251e-01 -9.96450782e-02 -8.08448911e-01 5.03009081e-01
-5.72008360e-03 3.24353337e-01 8.89723122e-01 -1.17248476e+00
-5.25904596e-01 -3.10703814e-01 1.83153793e-03 8.42760056e-02
-5.34305632e-01 1.17999073e-02 -6.51421964e-01 -6.62745833e-01
4.63785410e-01 -1.02493191e+00 8.19225311e-02 1.05195433e-01
-8.54033604e-02 -4.34747756e-01 9.60180998e-01 -5.53586006e-01
1.30385089e+00 -2.42756152e+00 -1.84643511e-02 5.42810380e-01
-7.82521591e-02 3.80845755e-01 -1.67267144e-01 5.03035665e-01
5.89166731e-02 -2.27187991e-01 8.13366026e-02 2.08098277e-01
-5.13312668e-02 1.10673115e-01 -1.46632912e-02 7.09366858e-01
1.97153181e-01 8.75256360e-01 -6.15434527e-01 -4.83011395e-01
6.30261481e-01 5.94068408e-01 -1.78004012e-01 -2.13807464e-01
3.08768988e-01 1.93982989e-01 -7.54722655e-01 7.96324849e-01
9.00749087e-01 -1.67542323e-01 1.87169164e-02 -7.00217009e-01
-8.67996588e-02 -5.00498056e-01 -1.31565833e+00 1.61950529e+00
-2.58597165e-01 4.83791977e-01 -2.65824378e-01 -1.03973341e+00
1.43920541e+00 8.38860422e-02 7.07623184e-01 -9.97558475e-01
8.16432387e-02 3.49230200e-01 -2.28161052e-01 -6.43972993e-01
6.34238124e-01 3.76990080e-01 -1.57173052e-02 1.92499328e-02
-1.60604864e-02 2.80370742e-01 5.76995350e-02 -3.50325644e-01
9.07200098e-01 1.97255015e-01 4.69155729e-01 -4.06381279e-01
9.64570045e-01 -2.99738735e-01 3.32573116e-01 6.69127941e-01
-1.62652135e-01 5.52484691e-01 2.25621998e-01 -8.50956440e-01
-1.05347586e+00 -8.94214153e-01 -3.20655167e-01 7.99015045e-01
4.94282722e-01 -2.35037580e-01 -4.78299290e-01 -5.72360694e-01
2.03447379e-02 -2.11033061e-01 -6.89261734e-01 3.23528983e-02
-7.29723573e-01 -6.22461677e-01 4.21584189e-01 5.10600448e-01
1.01230359e+00 -9.45694566e-01 -2.88208753e-01 9.42302942e-02
-5.34843653e-02 -1.21508515e+00 -3.88257712e-01 -1.20668419e-01
-5.92015266e-01 -1.02667642e+00 -7.30846345e-01 -1.12757635e+00
5.38210571e-01 4.16232437e-01 7.40634263e-01 6.28759861e-02
-2.68411368e-01 3.62796217e-01 -7.46562243e-01 2.19205573e-01
1.09059401e-01 1.78154230e-01 -1.43165380e-01 4.77888465e-01
3.15864265e-01 -3.59373480e-01 -7.64556348e-01 6.55313432e-01
-9.10370052e-01 -1.94983125e-01 8.29145014e-01 8.20601761e-01
7.76697457e-01 3.18130285e-01 3.72198433e-01 -1.42274275e-01
3.76289189e-01 -4.19839323e-02 -2.47980833e-01 3.46180677e-01
-3.78609419e-01 -7.98064992e-02 7.55477965e-01 -2.54831553e-01
-8.27512801e-01 8.62282142e-02 -2.07525775e-01 -1.23367228e-01
-3.62854481e-01 5.68735600e-01 1.37656286e-01 -6.93500042e-01
2.64604717e-01 5.50264597e-01 -6.16492108e-02 -5.45836210e-01
1.29558593e-01 8.33756268e-01 5.44451892e-01 -6.46937728e-01
6.09331012e-01 7.27977514e-01 4.43149298e-01 -1.04580498e+00
-7.31648505e-01 -7.71800935e-01 -8.92965376e-01 -3.03299755e-01
7.82186210e-01 -8.05754542e-01 -9.21528280e-01 7.47536242e-01
-8.99992764e-01 2.24985585e-01 7.18764216e-02 5.26013672e-01
-5.54165423e-01 5.50865293e-01 -6.67741716e-01 -5.28055549e-01
-3.85639250e-01 -1.12535906e+00 1.25523603e+00 7.53753185e-02
3.00810069e-01 -1.02140367e+00 -9.62911844e-02 2.82487608e-02
5.89142382e-01 5.34600973e-01 5.52565813e-01 -2.58401543e-01
-5.36187649e-01 -4.81220633e-01 -4.83882517e-01 3.71462524e-01
1.78810209e-01 9.71882641e-02 -6.86572254e-01 -3.50124389e-01
-1.53282657e-01 -1.30059555e-01 8.39491963e-01 1.71747893e-01
1.39767492e+00 4.67375219e-02 -1.34660766e-01 6.86906219e-01
1.84393799e+00 2.68285483e-01 6.53295994e-01 6.00270867e-01
6.59618437e-01 6.20721877e-02 8.13574553e-01 5.82334816e-01
2.94475645e-01 1.08621955e+00 2.39594907e-01 -3.33007216e-01
-1.60266861e-01 2.09406950e-02 1.80793226e-01 1.28259766e+00
-5.29818952e-01 -9.68026742e-02 -6.48769259e-01 2.52797663e-01
-1.93635309e+00 -9.80433643e-01 -1.13089450e-01 1.88738227e+00
3.56893629e-01 4.65875566e-02 -4.53730561e-02 2.32894197e-01
6.79156721e-01 3.12515467e-01 -7.19222650e-02 -3.57000083e-01
-4.88417417e-01 4.15797591e-01 6.50913775e-01 1.35452479e-01
-1.43438697e+00 8.34085822e-01 6.00303316e+00 1.31790972e+00
-1.55567551e+00 -1.39817521e-01 4.23584431e-01 4.53100920e-01
1.69528946e-01 -3.65293026e-01 -6.19540453e-01 2.15386078e-01
4.16697353e-01 1.63251936e-01 3.80888909e-01 7.93602169e-01
3.76230888e-02 2.53496617e-02 -5.32206059e-01 1.19448864e+00
5.70396602e-01 -1.11105442e+00 1.62578776e-01 1.12501755e-01
6.71336472e-01 -6.98940307e-02 2.57541001e-01 -7.08897412e-02
-4.03992772e-01 -8.73553455e-01 6.29011512e-01 9.06186819e-01
8.88518453e-01 -9.23162818e-01 1.24332464e+00 -7.08635449e-02
-1.74183834e+00 -4.51128483e-02 -5.79168022e-01 2.67293006e-02
-1.17824875e-01 5.21265805e-01 -4.33062077e-01 1.20413959e+00
6.79476380e-01 1.01038182e+00 -8.10415864e-01 1.11773801e+00
3.92356776e-02 2.75605500e-01 -1.54602051e-01 -2.97797650e-01
3.55452925e-01 -8.82822052e-02 2.63296008e-01 1.23212063e+00
5.18694937e-01 -1.82459816e-01 3.99449110e-01 2.72528917e-01
3.04653764e-01 5.59731603e-01 -6.62503242e-01 1.28864750e-01
2.13632181e-01 1.51305819e+00 -8.14898074e-01 -1.84450224e-01
-4.15956020e-01 1.04746425e+00 2.04937443e-01 1.92138076e-01
-6.49127901e-01 -7.12214649e-01 1.99726388e-01 -2.95345664e-01
7.00792313e-01 -6.00225687e-01 4.56314497e-02 -1.08100748e+00
2.64175653e-01 -7.80333936e-01 1.28599107e-01 -8.12668324e-01
-1.32389152e+00 7.82033741e-01 -2.28647962e-01 -1.61766553e+00
9.74402651e-02 -9.80535507e-01 -1.90484479e-01 5.63288689e-01
-1.47887850e+00 -1.48894167e+00 -6.19418144e-01 7.26767540e-01
1.34078056e-01 -1.94147289e-01 8.08864415e-01 3.28546852e-01
-3.74622256e-01 6.68563664e-01 4.93953496e-01 2.64523685e-01
6.50428891e-01 -9.29459095e-01 -3.95441614e-02 4.77517694e-01
-5.19378111e-02 4.90665227e-01 1.65356070e-01 -3.52147758e-01
-1.73424733e+00 -8.15944076e-01 7.35662460e-01 -1.20655380e-01
6.30445242e-01 -1.87910814e-02 -6.42799437e-01 1.80363536e-01
-5.79295084e-02 1.54281110e-01 4.81493711e-01 -9.22114849e-02
-4.10031468e-01 -6.99088275e-01 -1.08282399e+00 2.54762232e-01
9.19511318e-01 -7.11907327e-01 -3.63612801e-01 1.00710310e-01
4.73751128e-01 -2.71444082e-01 -1.42931139e+00 6.89396322e-01
1.03617084e+00 -1.02677250e+00 1.00274873e+00 7.93749169e-02
2.92376608e-01 -4.41482544e-01 -5.67474782e-01 -9.16613460e-01
-7.73858666e-01 -9.66285542e-02 2.17928857e-01 1.14938772e+00
-8.01678747e-02 -7.06749737e-01 5.80991745e-01 -2.66596228e-01
-6.81479424e-02 -9.40031767e-01 -9.74762440e-01 -1.02869213e+00
-1.92432329e-01 -2.50587910e-01 8.04157794e-01 9.81279016e-01
-1.39482960e-01 -1.12505674e-01 -4.82935935e-01 1.62749872e-01
4.89154726e-01 2.58293360e-01 6.13591731e-01 -1.08923900e+00
4.58908901e-02 -3.94607991e-01 -1.02841842e+00 -9.00258362e-01
7.05822483e-02 -7.16487110e-01 -2.10440710e-01 -1.24692464e+00
2.88279235e-01 -5.21521270e-01 -9.22412395e-01 2.92826265e-01
2.09405959e-01 8.30652118e-01 1.93938389e-01 2.92255729e-01
-9.24447060e-01 5.21729708e-01 1.59045470e+00 -6.72909915e-02
1.22720554e-01 -3.02865624e-01 -3.59819949e-01 6.51631415e-01
8.53102863e-01 -3.40459682e-02 1.01630501e-01 -3.90941173e-01
3.62985544e-02 -1.42386854e-01 4.80912626e-01 -1.23968410e+00
1.59531489e-01 -1.66042030e-01 5.96391797e-01 -6.20625556e-01
3.16534370e-01 -8.93362641e-01 1.91960722e-01 4.85130519e-01
-1.16047688e-01 2.37892866e-01 -6.94520772e-02 5.09918272e-01
-6.55283570e-01 -9.64569226e-02 5.92275679e-01 3.35753784e-02
-1.17119992e+00 4.53565538e-01 -7.19302148e-02 -6.83665633e-01
8.70071232e-01 -3.08108807e-01 -4.38969433e-01 -1.20387152e-01
-5.16254008e-01 -5.27008176e-01 5.95349014e-01 5.48911452e-01
7.16097534e-01 -1.81831598e+00 -4.68803108e-01 5.74942768e-01
4.56383467e-01 -5.21541357e-01 3.92077714e-01 9.73546743e-01
-8.84092748e-01 5.53845108e-01 -3.42910349e-01 -8.47140729e-01
-1.27505863e+00 5.08287549e-01 6.62996545e-02 -1.40353262e-01
-5.29067159e-01 4.42276835e-01 -1.21410869e-01 -2.45983690e-01
-2.09624358e-02 -2.48898387e-01 -3.82217616e-01 -1.20325021e-01
3.66540730e-01 3.65353733e-01 2.82227159e-01 -1.17040837e+00
-5.83600938e-01 1.47073889e+00 6.23104014e-02 1.78704694e-01
1.24497831e+00 -1.34228900e-01 -3.89160573e-01 3.39225948e-01
1.60319114e+00 1.11034736e-01 -7.89876521e-01 -4.92415935e-01
-1.15276642e-01 -7.49494791e-01 1.11509576e-01 -6.19029820e-01
-9.87961292e-01 6.05782688e-01 1.01967967e+00 2.02812359e-01
1.35383523e+00 -1.97180361e-01 7.73840725e-01 6.51582599e-01
6.39126062e-01 -1.14145970e+00 2.68109530e-01 5.35012841e-01
1.04054630e+00 -1.08506727e+00 4.33518067e-02 -3.80152643e-01
-4.14572269e-01 1.55700254e+00 4.50556636e-01 -5.40414453e-01
8.50179434e-01 -6.35979921e-02 1.72343269e-01 -1.56833053e-01
-4.24950302e-01 -2.09481135e-01 6.59650624e-01 4.10489142e-01
3.72189164e-01 1.28905699e-01 -4.38511282e-01 6.19045785e-03
-4.76028174e-02 2.05164030e-01 -2.40992103e-02 9.37942445e-01
-5.81793070e-01 -9.88274992e-01 -3.77710909e-01 2.74332553e-01
-4.60143298e-01 1.89778641e-01 -4.63340133e-02 8.93141568e-01
3.27374190e-01 8.13180864e-01 1.35043621e-01 -6.37491882e-01
3.03115845e-01 -3.03272307e-01 6.85536206e-01 2.15816423e-01
-3.37422818e-01 2.73627520e-01 -4.07249369e-02 -7.59974897e-01
-9.18711960e-01 -5.17387033e-01 -6.34213746e-01 -3.51605326e-01
-3.79379332e-01 -2.52344031e-02 7.40493417e-01 8.14059436e-01
2.60136276e-01 3.13379616e-01 1.00629330e+00 -8.11076522e-01
-3.97317678e-01 -8.10790718e-01 -9.85554278e-01 5.90356290e-01
2.26472080e-01 -7.22306609e-01 -1.83393091e-01 -1.17592521e-01] | [10.382233619689941, -0.3696097433567047] |
a8cb55a1-5473-4992-8acd-cbe5e8a7e025 | faster-tad-towards-temporal-action-detection | 2204.02674 | null | https://arxiv.org/abs/2204.02674v1 | https://arxiv.org/pdf/2204.02674v1.pdf | Faster-TAD: Towards Temporal Action Detection with Proposal Generation and Classification in a Unified Network | Temporal action detection (TAD) aims to detect the semantic labels and boundaries of action instances in untrimmed videos. Current mainstream approaches are multi-step solutions, which fall short in efficiency and flexibility. In this paper, we propose a unified network for TAD, termed Faster-TAD, by re-purposing a Faster-RCNN like architecture. To tackle the unique difficulty in TAD, we make important improvements over the original framework. We propose a new Context-Adaptive Proposal Module and an innovative Fake-Proposal Generation Block. What's more, we use atomic action features to improve the performance. Faster-TAD simplifies the pipeline of TAD and gets remarkable performance on lots of benchmarks, i.e., ActivityNet-1.3 (40.01% mAP), HACS Segments (38.39% mAP), SoccerNet-Action Spotting (54.09% mAP). It outperforms existing single-network detector by a large margin. | ['Yandong Guo', 'Xunqiang Tao', 'Wei Li', 'Chen Chen', 'Shimin Chen'] | 2022-04-06 | null | null | null | null | ['action-spotting'] | ['computer-vision'] | [ 2.63949394e-01 -9.81742069e-02 -4.92651194e-01 -1.31268755e-01
-5.64289808e-01 -3.65916997e-01 5.51163197e-01 -4.46267188e-01
-5.39411187e-01 4.89811063e-01 5.25860965e-01 5.93279526e-02
1.31336600e-01 -5.13836503e-01 -4.63609248e-01 -5.79016745e-01
-1.02015778e-01 -2.45520864e-02 1.05875027e+00 -2.21543938e-01
2.66957462e-01 3.31907153e-01 -1.32621562e+00 5.92299461e-01
2.80378848e-01 1.05540609e+00 -1.48588032e-01 2.60477424e-01
-7.82638267e-02 1.21807969e+00 -6.54325724e-01 -1.10644840e-01
3.24098170e-01 -7.19114840e-01 -9.73829150e-01 -3.48848343e-01
3.50802720e-01 -5.31852484e-01 -6.11464381e-01 9.02653873e-01
4.96399701e-01 2.19680697e-01 2.09850371e-01 -1.56767380e+00
-1.84457898e-01 8.50005507e-01 -8.55982363e-01 5.38777113e-01
4.35826600e-01 2.75007218e-01 8.35180283e-01 -7.87541568e-01
7.62668729e-01 1.53676426e+00 9.13645804e-01 7.91221261e-01
-7.33497739e-01 -7.25890040e-01 5.57666957e-01 5.06580412e-01
-1.22484446e+00 -4.12812054e-01 4.89620894e-01 -1.96777940e-01
9.75706041e-01 1.32129386e-01 5.88221073e-01 1.69173741e+00
-1.00592352e-01 1.40042043e+00 9.30710912e-01 -3.20628360e-02
1.18912511e-01 -5.76555014e-01 6.11722050e-03 5.26930332e-01
1.03628047e-01 -1.88218504e-01 -6.09614432e-01 3.04615766e-01
9.31074560e-01 1.62991136e-01 3.13422754e-02 -1.75693825e-01
-1.60510159e+00 4.95363504e-01 2.80804992e-01 4.43840742e-01
-2.27784485e-01 3.01201880e-01 1.00138664e+00 2.63117929e-03
3.06892902e-01 3.40266645e-01 -5.40343940e-01 -6.52430713e-01
-8.57256234e-01 3.90231848e-01 3.88452619e-01 9.44986284e-01
2.89001882e-01 7.62254521e-02 -7.92487562e-01 6.71877503e-01
-5.34299761e-02 1.61002204e-01 6.45999372e-01 -1.13893092e+00
4.53416169e-01 7.68690765e-01 1.24320120e-01 -1.03613973e+00
-6.33168697e-01 -3.55934113e-01 -7.63723135e-01 8.34561884e-02
5.27121544e-01 -1.24336734e-01 -1.00859678e+00 1.53128707e+00
4.93152678e-01 7.61281669e-01 -2.71737576e-01 1.16735649e+00
7.82556117e-01 4.91554230e-01 2.14153484e-01 4.29880880e-02
1.18098938e+00 -1.76226854e+00 -7.56444931e-01 -1.19038790e-01
7.92613506e-01 -5.26655138e-01 1.01989198e+00 3.98322582e-01
-8.95639956e-01 -7.44340718e-01 -1.05880964e+00 -4.40982468e-02
-4.49619651e-01 3.50023001e-01 5.58581173e-01 1.77911133e-01
-7.91891694e-01 7.62550831e-01 -9.10213828e-01 -5.92476428e-01
7.13779330e-01 8.89379531e-02 -4.03772652e-01 6.03178330e-02
-1.10848475e+00 7.38060534e-01 6.49305522e-01 3.23133200e-01
-1.29522192e+00 -4.10369962e-01 -6.87458515e-01 -2.21300796e-01
1.04060316e+00 -3.66701186e-01 1.49477971e+00 -1.09900653e+00
-1.67087448e+00 6.85732782e-01 5.71168885e-02 -8.06351364e-01
9.90024507e-01 -5.96063733e-01 -6.30397797e-01 2.13444620e-01
3.10614407e-01 7.79374301e-01 7.35427201e-01 -4.45650607e-01
-9.57561612e-01 -2.37340946e-02 2.40070969e-01 1.52816996e-01
-1.12441376e-01 2.91985810e-01 -7.02497602e-01 -8.86658311e-01
3.64545546e-02 -8.53030801e-01 -2.50613183e-01 -1.92044657e-02
-5.09597600e-01 -6.40014410e-01 1.07820511e+00 -4.70810682e-01
1.49484837e+00 -2.24707055e+00 -1.10447571e-01 -2.99093187e-01
3.31232846e-01 7.11424947e-01 -3.38396370e-01 2.83139020e-01
-1.21993810e-01 -1.16662020e-02 2.61804555e-02 -2.43674889e-01
1.43866315e-01 1.32054314e-01 9.76364221e-03 4.27355021e-01
3.59319240e-01 1.12587357e+00 -1.03027189e+00 -6.77383363e-01
3.00426573e-01 1.57090515e-01 -4.13597435e-01 -1.54543757e-01
-2.33895883e-01 2.26044998e-01 -6.77499235e-01 9.16252077e-01
6.71456456e-01 -1.15392789e-01 1.59520984e-01 -2.01704949e-01
-3.88705999e-01 4.30927753e-01 -1.36610210e+00 2.19883895e+00
1.43031195e-01 5.65293729e-01 -2.31343195e-01 -1.08374035e+00
7.06704259e-01 1.26466587e-01 8.10997486e-01 -9.48109746e-01
2.17449248e-01 9.37111080e-02 -7.87866190e-02 -6.84007466e-01
3.91645610e-01 4.12737668e-01 -2.13747159e-01 3.70079368e-01
1.83053166e-01 9.55765128e-01 3.28440428e-01 1.96307287e-01
1.70077813e+00 7.61357069e-01 2.95173377e-01 -1.26773059e-01
6.05628610e-01 -8.61707628e-02 1.10686088e+00 8.93294930e-01
-9.31143045e-01 5.03239751e-01 8.03408146e-01 -8.15307379e-01
-7.31001258e-01 -6.78186595e-01 4.00401443e-01 1.40586841e+00
3.56300384e-01 -5.77476084e-01 -8.76368284e-01 -1.36227691e+00
-5.89218475e-02 3.74778330e-01 -8.85616422e-01 -1.19847186e-01
-1.02869737e+00 -4.87874985e-01 8.68757367e-01 8.65909219e-01
1.17323148e+00 -1.38162816e+00 -6.07672691e-01 2.99018174e-01
-2.93149143e-01 -1.34124541e+00 -5.34682691e-01 -9.21714380e-02
-6.78977728e-01 -1.01734972e+00 -6.06454611e-01 -6.25912607e-01
6.98290542e-02 4.95587051e-01 9.52395260e-01 -2.97745585e-01
-1.41706571e-01 -9.76026580e-02 -6.75427377e-01 -1.62879348e-01
2.90004089e-02 3.00604731e-01 3.73998806e-02 1.91037208e-01
7.55976498e-01 -5.33102095e-01 -7.45303273e-01 8.54739428e-01
-5.43627501e-01 2.93632522e-02 8.34006011e-01 4.17136043e-01
5.94994962e-01 -1.49952278e-01 6.06818378e-01 -5.84456205e-01
2.77369678e-01 -5.04066825e-01 -2.75935143e-01 1.96473166e-01
-4.19942945e-01 -1.20929062e-01 3.62674117e-01 -5.27339756e-01
-1.13354158e+00 1.30261764e-01 -1.12771988e-01 -7.01387465e-01
-3.79176736e-01 -3.19149122e-02 -5.37137613e-02 1.72155917e-01
6.29082143e-01 1.24962114e-01 -1.54388905e-01 -8.99619699e-01
2.59058595e-01 3.64367038e-01 6.53366923e-01 -2.15334401e-01
5.36716998e-01 5.14157593e-01 -1.57947972e-01 -1.74393281e-01
-1.10952878e+00 -6.09903693e-01 -7.43186891e-01 -4.92809981e-01
1.11768174e+00 -1.03994048e+00 -8.14510882e-01 7.54745543e-01
-9.99460220e-01 -4.12427783e-01 -3.29591185e-01 5.73294580e-01
-3.61024767e-01 3.19763303e-01 -6.08065605e-01 -4.68067050e-01
-1.59602553e-01 -9.07606304e-01 1.09168816e+00 1.91281095e-01
-2.36168250e-01 -4.05958116e-01 4.99198250e-02 3.92480195e-01
3.34717184e-01 4.84483987e-01 7.05157816e-02 -7.46343136e-01
-5.55405736e-01 -3.16752004e-04 -3.87915552e-01 4.61723447e-01
-9.71363634e-02 3.81553709e-03 -8.54175150e-01 -5.16460948e-02
-4.05678213e-01 -4.27429050e-01 1.10295534e+00 4.29012567e-01
1.40056264e+00 -3.98176424e-02 -4.20729131e-01 3.97376001e-01
9.83945072e-01 2.33056888e-01 7.25006640e-01 5.80317736e-01
5.99076211e-01 2.98879266e-01 1.09292853e+00 4.73088771e-01
1.40517280e-01 9.26441967e-01 5.73925734e-01 -4.94460501e-02
-3.42606425e-01 -4.83935714e-01 7.51320601e-01 5.84126353e-01
-2.51085907e-01 -1.30520180e-01 -6.92654073e-01 6.41686141e-01
-2.35172915e+00 -1.23707223e+00 -2.82648027e-01 1.55715632e+00
4.09813643e-01 5.96202254e-01 6.73325717e-01 9.92430896e-02
9.55438793e-01 5.60381234e-01 -6.39492750e-01 -1.13045469e-01
-1.15977399e-01 -4.17196155e-02 4.72218782e-01 -1.35489210e-01
-1.66998625e+00 1.23789322e+00 6.03788471e+00 1.26065660e+00
-7.49671042e-01 4.11373198e-01 2.84870088e-01 -2.75370598e-01
5.31302214e-01 -1.15709223e-01 -1.05179536e+00 6.00632966e-01
7.00865030e-01 1.49884075e-01 -7.47448951e-02 9.59260464e-01
5.96705616e-01 -1.70664594e-01 -9.59479272e-01 1.03894424e+00
1.61493078e-01 -1.49794602e+00 -2.54091382e-01 -3.16093266e-01
5.43176413e-01 2.00495645e-01 -4.18125033e-01 6.75915301e-01
2.08660051e-01 -6.94585502e-01 8.05332184e-01 4.68665510e-01
5.88155448e-01 -5.73665619e-01 8.59033585e-01 1.69016838e-01
-1.39520264e+00 -2.91902751e-01 -2.02559769e-01 -1.77850962e-01
2.59356201e-01 3.76504004e-01 -4.29109991e-01 4.86147881e-01
1.09445417e+00 1.33280492e+00 -5.94249606e-01 1.16644931e+00
-3.74516338e-01 7.27640390e-01 -1.94879845e-01 -8.18270966e-02
7.70651102e-01 5.76336496e-02 4.31234807e-01 1.31087947e+00
2.51807068e-02 -1.65278539e-01 3.37579101e-01 6.10479593e-01
-1.88203156e-01 -1.63151234e-01 -3.84170234e-01 1.49534103e-02
2.86441475e-01 1.38885689e+00 -8.79724741e-01 -4.55016911e-01
-3.68981421e-01 1.16514421e+00 7.43003637e-02 6.94830865e-02
-1.48877048e+00 -4.12874341e-01 6.94937646e-01 -2.43298262e-02
4.25903767e-01 -2.14250386e-02 2.28761569e-01 -1.09057856e+00
1.12252429e-01 -9.69077289e-01 6.28541529e-01 -7.34006226e-01
-9.61982489e-01 2.79153883e-01 -4.77303751e-02 -1.53953278e+00
2.23698974e-01 -4.99244273e-01 -4.86088932e-01 1.92420915e-01
-1.19058323e+00 -1.12859941e+00 -4.63360369e-01 6.22941136e-01
9.41580713e-01 -5.29512353e-02 3.68240058e-01 7.17800438e-01
-1.22402203e+00 6.35741591e-01 -3.33458185e-01 5.64920604e-01
8.18507612e-01 -9.55239832e-01 7.38736689e-01 9.80987728e-01
8.16636011e-02 1.11729808e-01 2.60274500e-01 -5.25592685e-01
-8.56932938e-01 -1.39643729e+00 7.64311552e-01 -4.58369553e-01
8.40226650e-01 -3.58257890e-01 -5.69832087e-01 8.09158623e-01
5.89359216e-02 1.78581238e-01 1.86982065e-01 -1.34314135e-01
-3.35938990e-01 -1.97452411e-01 -8.69564235e-01 6.99751675e-01
1.79650462e+00 -2.69607276e-01 -5.75126410e-01 4.03259844e-01
9.26801264e-01 -4.19501305e-01 -6.57270074e-01 3.52302432e-01
6.43300653e-01 -1.15329826e+00 8.34493697e-01 -8.68257999e-01
3.94780695e-01 -7.07452178e-01 1.84796363e-01 -5.68992078e-01
-6.23823345e-01 -8.95083845e-01 -3.07305306e-01 1.08043408e+00
1.37176812e-01 -3.07017297e-01 9.20718849e-01 -4.31051403e-02
-4.60026294e-01 -7.58888960e-01 -9.88259614e-01 -1.06651437e+00
-4.86647218e-01 -4.93392944e-01 5.82647741e-01 1.00147712e+00
-8.54805186e-02 3.19372326e-01 -6.85479760e-01 -3.19679558e-01
3.12805265e-01 -1.97144777e-01 8.49991500e-01 -7.78987765e-01
-1.15390927e-01 -5.75357497e-01 -6.33509457e-01 -1.52465487e+00
-1.13907725e-01 -4.50257182e-01 -5.19977398e-02 -1.20532012e+00
1.83685154e-01 -3.78992148e-02 -6.98501647e-01 9.99156117e-01
1.30360037e-01 3.54041785e-01 1.59456208e-01 1.52238816e-01
-1.43518877e+00 4.64147270e-01 1.24547589e+00 7.77542293e-02
-1.17945999e-01 3.13410535e-02 -3.71429980e-01 9.29410458e-01
9.85960126e-01 -6.08623683e-01 -3.71855080e-01 -4.24192756e-01
9.71413124e-03 -3.01205426e-01 5.50399065e-01 -1.44259834e+00
1.82577297e-01 -2.54296958e-01 1.51055664e-01 -9.37975526e-01
7.82614946e-02 -4.70292747e-01 -2.04633966e-01 4.26880121e-01
-4.39311802e-01 1.12825856e-01 1.29370699e-02 7.56899476e-01
-2.81119466e-01 5.83670568e-03 6.98661447e-01 -1.71712041e-01
-1.40070903e+00 2.77176976e-01 -4.30979639e-01 1.90109208e-01
1.40158725e+00 -3.78737062e-01 -5.40362298e-01 -2.86856405e-02
-6.76373959e-01 2.41839290e-01 -5.57871014e-02 8.12122047e-01
4.61531997e-01 -1.48418629e+00 -3.86444509e-01 -1.74644753e-01
9.36713740e-02 -1.27983749e-01 5.39571583e-01 1.45031703e+00
-5.05915761e-01 4.03876245e-01 -3.72834742e-01 -4.51840699e-01
-1.12711048e+00 4.03392404e-01 5.39092720e-01 -2.91380465e-01
-8.73822153e-01 9.80133712e-01 -1.37014076e-01 -1.80992320e-01
4.72920209e-01 -4.96830046e-01 -3.04548442e-01 1.43209711e-01
5.91807425e-01 8.56433868e-01 -2.12198570e-01 -5.68971395e-01
-6.01958275e-01 3.35433275e-01 -1.64535016e-01 2.68106550e-01
1.31655443e+00 1.23010069e-01 1.10383019e-01 3.25746626e-01
1.02687287e+00 -3.35388571e-01 -1.61217749e+00 -2.92819709e-01
2.26757407e-01 -4.10193235e-01 -1.60265744e-01 -9.19027686e-01
-1.20928800e+00 8.06755602e-01 6.27562523e-01 1.16048478e-01
1.02608442e+00 -9.67327803e-02 1.00884736e+00 3.35141927e-01
2.83014029e-01 -1.61288559e+00 4.54707205e-01 4.76291418e-01
7.12484717e-01 -1.04885101e+00 2.56801676e-02 -1.32143334e-01
-6.16710246e-01 1.04039586e+00 1.13160384e+00 -1.81982815e-01
3.08710903e-01 -3.32737379e-02 -1.20984912e-01 -3.26339692e-01
-6.83881164e-01 -2.31215432e-01 7.27323145e-02 2.00255066e-01
5.87294288e-02 -1.04798399e-01 -5.08359730e-01 4.83037859e-01
3.81762087e-01 2.66955733e-01 3.30799222e-01 9.90017474e-01
-5.20501554e-01 -7.95547843e-01 6.60343003e-03 2.92767853e-01
-5.34503639e-01 2.33680233e-01 -5.29959857e-01 1.01486719e+00
6.95269823e-01 7.36536026e-01 5.30700311e-02 -6.95883989e-01
5.27081132e-01 1.28112644e-01 6.04436686e-03 -2.37105906e-01
-5.37015319e-01 1.36556074e-01 4.28834736e-01 -1.36570644e+00
-9.57801163e-01 -7.37136006e-01 -1.13047922e+00 -3.18478942e-01
-1.17796481e-01 -2.77718991e-01 1.52299091e-01 8.77295792e-01
6.57831788e-01 6.62209332e-01 3.12352329e-01 -6.10269129e-01
-4.35871750e-01 -1.09824753e+00 -4.14256364e-01 2.73934782e-01
-1.03543006e-01 -9.12098527e-01 -2.05043733e-01 -1.43519774e-01] | [8.383606910705566, 0.5133983492851257] |
ddb63b45-c1af-4540-98a3-32b958ba668d | sources-of-uncertainty-in-machine-learning-a | 2305.16703 | null | https://arxiv.org/abs/2305.16703v1 | https://arxiv.org/pdf/2305.16703v1.pdf | Sources of Uncertainty in Machine Learning -- A Statisticians' View | Machine Learning and Deep Learning have achieved an impressive standard today, enabling us to answer questions that were inconceivable a few years ago. Besides these successes, it becomes clear, that beyond pure prediction, which is the primary strength of most supervised machine learning algorithms, the quantification of uncertainty is relevant and necessary as well. While first concepts and ideas in this direction have emerged in recent years, this paper adopts a conceptual perspective and examines possible sources of uncertainty. By adopting the viewpoint of a statistician, we discuss the concepts of aleatoric and epistemic uncertainty, which are more commonly associated with machine learning. The paper aims to formalize the two types of uncertainty and demonstrates that sources of uncertainty are miscellaneous and can not always be decomposed into aleatoric and epistemic. Drawing parallels between statistical concepts and uncertainty in machine learning, we also demonstrate the role of data and their influence on uncertainty. | ['Göran Kauermann', 'Frauke Kreuter', 'Malte Schierholz', 'Patrick Oliver Schenk', 'Cornelia Gruber'] | 2023-05-26 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [-2.03512073e-01 4.18959051e-01 -1.45653710e-01 -6.09567702e-01
-7.67640889e-01 -5.35734236e-01 1.02618873e+00 5.03686368e-01
-5.08146286e-01 9.87626672e-01 2.30995014e-01 -4.74385560e-01
-7.86131322e-01 -9.37094808e-01 -6.07983589e-01 -7.87678540e-01
-4.95787710e-02 4.96260673e-01 -1.66535154e-01 1.61689714e-01
4.39656556e-01 4.09376055e-01 -1.70305157e+00 -2.15239793e-01
8.85888815e-01 1.37811792e+00 -5.15501261e-01 6.28386214e-02
-5.04997671e-01 9.05180633e-01 -6.74228907e-01 -7.52489686e-01
8.59417319e-02 -7.06950575e-02 -8.41505885e-01 -4.10673797e-01
1.22830682e-01 -1.36834204e-01 -2.26640493e-01 1.17019057e+00
2.44590759e-01 6.28612861e-02 9.94701385e-01 -1.21825504e+00
-6.48644149e-01 1.16570139e+00 -3.67613167e-01 1.82787135e-01
1.28260627e-01 -2.28888974e-01 1.23895359e+00 -5.27972579e-01
1.37734669e-03 1.31167090e+00 7.34803379e-01 3.00533414e-01
-8.78920674e-01 -4.94231850e-01 -2.18482539e-02 3.06471914e-01
-1.24733388e+00 -1.41188815e-01 7.00270593e-01 -5.71827829e-01
3.96757215e-01 2.62745202e-01 3.95271480e-01 8.84849966e-01
5.68732440e-01 8.67288828e-01 1.23788285e+00 -6.30170524e-01
8.35452616e-01 2.15067953e-01 2.76462168e-01 6.00834824e-02
6.91596150e-01 5.42199552e-01 -4.02012080e-01 -6.73044026e-02
4.73742187e-01 -1.27181843e-01 -1.72060639e-01 -4.28291500e-01
-1.02925861e+00 1.15137482e+00 8.79234076e-03 5.02675533e-01
-1.28943235e-01 3.73805881e-01 3.81761551e-01 3.13020498e-01
2.29060814e-01 5.17316639e-01 -5.59166968e-01 -4.96507078e-01
-9.57687020e-01 1.33855298e-01 9.41222966e-01 5.09094417e-01
3.81664842e-01 2.26813443e-02 1.22378163e-01 3.33815873e-01
6.43040836e-01 3.92358452e-01 2.72552460e-01 -1.02035367e+00
-4.56494605e-03 2.61837572e-01 3.50074351e-01 -9.43885803e-01
-3.18289936e-01 -6.06879532e-01 -6.79309428e-01 4.39530045e-01
7.51715481e-01 -2.98000723e-01 -6.15413487e-01 1.67034686e+00
-4.43882588e-03 -6.06363043e-02 6.88063726e-02 6.36725485e-01
5.89124739e-01 3.45434368e-01 1.20356143e-01 -2.26479486e-01
9.63702559e-01 -9.32130218e-02 -8.94778550e-01 -1.41941145e-01
4.25637275e-01 -3.55085909e-01 7.16625690e-01 6.45421505e-01
-9.02621150e-01 -9.16629359e-02 -1.12600470e+00 1.05381116e-01
-4.69767869e-01 -7.04545379e-01 8.65798354e-01 1.01324165e+00
-6.33248985e-01 9.72396016e-01 -7.66113460e-01 -3.83910239e-02
2.26964056e-01 5.76121509e-02 -9.36067849e-02 2.14903355e-01
-1.61153543e+00 1.47588420e+00 6.31289184e-01 1.93268359e-01
-6.33895695e-01 -6.19060755e-01 -8.40285242e-01 4.79578041e-02
7.23795414e-01 -3.55490297e-01 1.38079751e+00 -7.78789341e-01
-1.33842969e+00 5.04751265e-01 8.51913095e-02 -5.78938246e-01
7.33869493e-01 -3.70307803e-01 -6.20378733e-01 -8.87852162e-02
-2.54280627e-01 1.36785246e-02 5.30647635e-01 -1.16787863e+00
-7.00855255e-01 -4.07640427e-01 1.09471381e-01 -1.26768321e-01
1.93121642e-01 -8.03612396e-02 1.78293288e-01 -3.68285090e-01
3.17705035e-01 -5.11875272e-01 -1.35921195e-01 -1.43085629e-01
-3.68108988e-01 -4.64662462e-01 2.60569870e-01 -2.44695768e-01
1.13590872e+00 -1.84810889e+00 9.15458202e-02 4.83377635e-01
2.80592412e-01 -1.43419355e-01 4.93624389e-01 4.31099683e-01
-2.52952754e-01 2.98585385e-01 -6.03899419e-01 -9.64906961e-02
6.26932502e-01 2.29997307e-01 -4.39162046e-01 6.37636840e-01
1.48139045e-01 9.55561757e-01 -1.02072477e+00 -3.11729699e-01
4.72978354e-01 3.71142358e-01 6.04814291e-02 -3.08896720e-01
-3.35410625e-01 2.25174233e-01 -4.19062078e-01 6.09209657e-01
4.76555526e-01 -1.06556177e-01 1.56449005e-01 3.62114087e-02
-8.29031244e-02 4.52221334e-01 -1.32936001e+00 1.16205525e+00
-2.41920635e-01 7.09748983e-01 -2.17668578e-01 -1.16367924e+00
7.79775798e-01 2.11226404e-01 2.30407715e-01 -3.12867075e-01
5.57185054e-01 3.83671314e-01 2.20613942e-01 -1.65045157e-01
5.06454885e-01 -5.77472568e-01 -3.14416677e-01 5.72089255e-01
-2.67352704e-02 -4.60350662e-01 3.00417673e-02 2.44798306e-02
8.16867888e-01 9.77047607e-02 5.60333490e-01 -5.57663918e-01
3.39383841e-01 -1.85662478e-01 4.73719507e-01 8.26003671e-01
-3.97295743e-01 3.71411204e-01 7.71527767e-01 -4.35828239e-01
-8.29753220e-01 -1.42395747e+00 -7.58060515e-01 6.19184136e-01
-7.94171989e-02 -2.20989749e-01 -3.78014386e-01 -6.33106589e-01
3.32902938e-01 1.21835995e+00 -9.30096745e-01 -1.12432815e-01
1.23899028e-01 -9.06503201e-01 2.53520876e-01 4.39531863e-01
1.46797061e-01 -6.80192292e-01 -6.35717809e-01 -6.31622002e-02
1.65420339e-01 -6.95346892e-01 4.77594197e-01 4.70692337e-01
-8.69696736e-01 -1.12524319e+00 -2.96499759e-01 1.81988716e-01
1.84645906e-01 -3.41615975e-01 1.33546627e+00 -2.16979817e-01
-1.13629758e-01 6.35681570e-01 -2.57110357e-01 -7.76081920e-01
-5.16825318e-01 -5.27873516e-01 1.66098267e-01 -3.30876619e-01
1.02075171e+00 -7.24666417e-01 -2.31237993e-01 -2.31127322e-01
-1.05940235e+00 -5.55693448e-01 5.65561473e-01 6.02101684e-01
2.78626949e-01 4.34139639e-01 6.52498662e-01 -8.12093079e-01
5.89122772e-01 -7.19707251e-01 -6.81465805e-01 1.97371975e-01
-8.79396617e-01 3.38487446e-01 1.86617851e-01 1.97663516e-01
-1.08769083e+00 -4.63433027e-01 -3.79728302e-02 -7.54681081e-02
-3.71306956e-01 9.28700805e-01 -2.16659129e-01 2.17712685e-01
6.19303167e-01 8.63450542e-02 -9.45203006e-02 -2.62836784e-01
4.81350720e-01 7.10083663e-01 2.00830370e-01 -7.69483149e-01
6.09962463e-01 4.45525259e-01 1.83829308e-01 -7.45966017e-01
-1.12241185e+00 6.85212165e-02 -6.91256583e-01 -2.32764915e-01
5.75997710e-01 -5.55161536e-01 -6.59460545e-01 3.78398895e-01
-9.58309829e-01 1.67584717e-01 -8.34141850e-01 5.77348351e-01
-5.53209484e-01 5.15284419e-01 -3.21019679e-01 -1.20344245e+00
1.44737244e-01 -9.84210610e-01 5.45911312e-01 3.09365213e-01
-4.22231168e-01 -1.32315922e+00 -9.21416879e-02 7.51410648e-02
3.51274461e-01 3.64561260e-01 1.04419148e+00 -9.82692242e-01
-3.27116311e-01 -4.00757074e-01 -1.38173655e-01 4.95277971e-01
4.59020659e-02 1.61318317e-01 -1.10215795e+00 1.52741283e-01
3.26333225e-01 -3.86869103e-01 8.94830287e-01 4.98045534e-01
9.65383887e-01 3.54024172e-02 -9.01290565e-04 3.60472426e-02
1.57147062e+00 1.94306478e-01 6.80207074e-01 3.73659641e-01
5.67269698e-02 9.18563604e-01 4.55213130e-01 6.33340359e-01
4.18371499e-01 1.95593268e-01 6.40900850e-01 7.45978951e-01
3.60001892e-01 -6.15810826e-02 -4.08986099e-02 6.05115116e-01
-2.44421765e-01 1.49484336e-01 -1.10401440e+00 4.08317745e-01
-1.70171130e+00 -1.07595420e+00 -6.39664605e-02 2.53504658e+00
8.82141471e-01 4.56049293e-01 -1.67720467e-01 3.84646535e-01
5.96314490e-01 2.24032491e-01 -4.78179276e-01 -4.05139804e-01
-3.09182554e-01 1.02019727e-01 3.24426800e-01 6.44710004e-01
-1.00650179e+00 5.19353807e-01 7.67312479e+00 8.91510069e-01
-8.30397427e-01 -2.56804470e-02 6.39616609e-01 -1.21140987e-01
-8.77963841e-01 -3.75007540e-02 -5.73624134e-01 6.49316490e-01
1.03929806e+00 -4.18895632e-01 -6.58164732e-03 9.18023169e-01
-4.36902829e-02 -6.14625037e-01 -1.44123936e+00 7.15336978e-01
-2.17320159e-01 -1.07164359e+00 -2.41009906e-01 3.69874462e-02
6.60860240e-01 3.36017534e-02 2.31984183e-01 3.00986111e-01
7.34034419e-01 -1.51214802e+00 7.71015346e-01 6.14346683e-01
3.45864207e-01 -9.44290638e-01 1.15548170e+00 3.14467341e-01
-3.14024955e-01 -1.93344787e-01 -4.23851520e-01 -4.51251745e-01
1.02372624e-01 1.46426463e+00 -1.32536590e-01 6.56558096e-01
6.04675353e-01 3.70599091e-01 -1.99959710e-01 1.00554192e+00
-5.67668438e-01 4.55437273e-01 -4.92077947e-01 -1.95432335e-01
3.01762909e-01 -1.72697201e-01 4.88305420e-01 9.71671581e-01
4.27381545e-01 3.24389227e-02 -5.39626718e-01 1.09186304e+00
2.87263364e-01 -2.34128535e-01 -5.07791698e-01 -3.94233286e-01
7.47054577e-01 8.95021021e-01 -5.12894809e-01 -4.60508391e-02
-6.03646874e-01 2.75450885e-01 1.05550922e-01 1.78635851e-01
-5.97693622e-01 -3.52660000e-01 5.56638122e-01 -2.28914618e-01
-1.08580247e-01 -1.87577501e-01 -6.48310602e-01 -1.14706206e+00
-5.33674471e-02 -6.97234929e-01 2.76273727e-01 -2.38743678e-01
-1.62127209e+00 1.11460574e-01 1.94619879e-01 -8.49724948e-01
-5.70383668e-01 -9.03581381e-01 -5.10970473e-01 8.05067718e-01
-1.37557459e+00 -7.29969203e-01 1.47304803e-01 1.56696856e-01
-4.30824496e-02 -6.32969514e-02 8.17501545e-01 -2.80796796e-01
-3.60144824e-01 2.43922189e-01 6.46921754e-01 3.05692269e-03
4.13528860e-01 -1.50937474e+00 4.49874401e-02 6.60985112e-01
1.17134549e-01 6.34754360e-01 1.31384611e+00 -5.08085728e-01
-1.20199287e+00 -5.60819685e-01 1.04932237e+00 -7.18164444e-01
1.07421446e+00 8.06129426e-02 -7.15019226e-01 7.40498602e-01
4.48720716e-02 -2.33377129e-01 8.88648808e-01 6.14079058e-01
-5.52660465e-01 1.24578021e-01 -1.17678738e+00 5.27259231e-01
5.40803015e-01 -5.33532679e-01 -1.26928854e+00 6.74617663e-02
4.06181157e-01 -2.88791031e-01 -9.08334494e-01 5.76081336e-01
7.38901079e-01 -1.36079884e+00 6.43960059e-01 -5.47803819e-01
6.00366831e-01 3.68633233e-02 -4.02534604e-01 -1.36695755e+00
-2.11757678e-03 -3.49485904e-01 -2.21880823e-01 1.08356512e+00
4.16406184e-01 -7.85743237e-01 8.01213205e-01 1.21134055e+00
-3.35136689e-02 -5.91381252e-01 -1.28517318e+00 -9.77013111e-01
9.12558734e-01 -1.07597697e+00 6.91901982e-01 9.86713767e-01
5.42094588e-01 -7.11663812e-02 -8.30379948e-02 -7.25238547e-02
8.84011984e-01 1.46536350e-01 1.17200345e-01 -1.85597825e+00
-2.12210983e-01 -7.73032546e-01 -7.23197460e-01 -4.58314985e-01
2.30833694e-01 -6.31965697e-01 1.31599635e-01 -1.43537104e+00
7.67777488e-02 -4.41671371e-01 -6.84332669e-01 1.76026508e-01
1.50963143e-01 -1.04398698e-01 1.15360305e-01 -7.13798478e-02
-4.19835955e-01 5.47709107e-01 7.53606439e-01 3.25080231e-02
2.56346405e-01 2.02576265e-01 -1.00693011e+00 1.22912908e+00
8.39486420e-01 -2.85511166e-01 -3.20526034e-01 -2.99527198e-01
8.03355694e-01 -1.09675549e-01 3.01103026e-01 -9.08379853e-01
1.17787071e-01 -3.41431320e-01 3.93999428e-01 -4.88692999e-01
2.41139412e-01 -9.57267284e-01 2.87953038e-02 2.72537678e-01
-5.36784053e-01 -4.43202943e-01 2.76645392e-01 6.70280516e-01
-3.54351342e-01 -7.71618366e-01 7.22589076e-01 -2.15942234e-01
-7.77131796e-01 -7.89590552e-02 -3.14034551e-01 1.78989679e-01
1.13971615e+00 7.11562112e-02 -3.28538507e-01 -5.99930584e-01
-7.75211334e-01 1.17516987e-01 2.52357781e-01 2.35397324e-01
4.12021130e-01 -1.05190039e+00 -5.26519001e-01 -4.68088239e-01
-8.10143948e-02 -1.77159846e-01 3.68625820e-01 6.95004582e-01
-2.59644747e-01 8.87887955e-01 1.02089550e-02 -3.32548231e-01
-3.88318688e-01 5.18924296e-01 2.84437716e-01 7.79389739e-02
-2.87109286e-01 9.31310833e-01 -1.52376428e-01 -3.88128459e-01
5.68937242e-01 -2.39742234e-01 -8.40355381e-02 2.44166389e-01
6.02601111e-01 5.65642893e-01 -4.31793788e-03 -3.53190750e-01
-3.81511301e-01 3.11188668e-01 1.72293499e-01 -3.21566224e-01
1.17191541e+00 -2.36702219e-01 -3.78860563e-01 1.14439249e+00
6.50523126e-01 3.73192653e-02 -1.05376363e+00 -2.37523362e-01
4.67731059e-01 -3.35694343e-01 3.52850139e-01 -1.17753053e+00
-7.10206747e-01 1.35523820e+00 2.79000133e-01 4.98430014e-01
7.30273843e-01 1.14055529e-01 2.10012764e-01 3.01511317e-01
5.83530605e-01 -1.18770373e+00 -4.21265990e-01 4.67980415e-01
8.26802969e-01 -1.55329895e+00 8.32077041e-02 -8.97293389e-02
-3.12840879e-01 1.24230659e+00 3.02272558e-01 1.70894533e-01
9.45326924e-01 3.99354935e-01 -2.07145542e-01 1.56032136e-02
-6.51055396e-01 -1.46850020e-01 2.12250113e-01 5.46074331e-01
6.74291551e-01 1.17235102e-01 -4.76500899e-01 9.83679652e-01
-3.87275934e-01 2.05104306e-01 6.34421706e-01 8.34349155e-01
-8.24615300e-01 -1.06779253e+00 -5.75039268e-01 5.50762355e-01
-8.16084981e-01 -8.00572857e-02 -1.66841701e-01 9.29032266e-01
1.05737522e-01 9.48965251e-01 2.10144803e-01 -2.33979240e-01
-2.68330038e-01 2.42281795e-01 4.90938842e-01 -4.26103771e-01
-4.54760483e-03 -3.53625476e-01 1.58485323e-01 -3.97299737e-01
-3.66371989e-01 -7.79712200e-01 -9.99244690e-01 -5.25296688e-01
-2.31643677e-01 7.16572583e-01 9.22115147e-01 1.42830431e+00
-5.12657315e-02 3.86419117e-01 3.30637544e-02 -4.59737539e-01
-1.19416213e+00 -9.51511383e-01 -1.23571086e+00 -2.89919674e-01
2.76466846e-01 -7.93482065e-01 -9.53042209e-01 -4.88102466e-01] | [7.588131904602051, 4.09501838684082] |
862ceeba-d9fd-4474-90e3-2bac1c4c8bd2 | learning-summary-worthy-visual-representation | 2305.04824 | null | https://arxiv.org/abs/2305.04824v1 | https://arxiv.org/pdf/2305.04824v1.pdf | Learning Summary-Worthy Visual Representation for Abstractive Summarization in Video | Multimodal abstractive summarization for videos (MAS) requires generating a concise textual summary to describe the highlights of a video according to multimodal resources, in our case, the video content and its transcript. Inspired by the success of the large-scale generative pre-trained language model (GPLM) in generating high-quality textual content (e.g., summary), recent MAS methods have proposed to adapt the GPLM to this task by equipping it with the visual information, which is often obtained through a general-purpose visual feature extractor. However, the generally extracted visual features may overlook some summary-worthy visual information, which impedes model performance. In this work, we propose a novel approach to learning the summary-worthy visual representation that facilitates abstractive summarization. Our method exploits the summary-worthy information from both the cross-modal transcript data and the knowledge that distills from the pseudo summary. Extensive experiments on three public multimodal datasets show that our method outperforms all competing baselines. Furthermore, with the advantages of summary-worthy visual information, our model can have a significant improvement on small datasets or even datasets with limited training data. | ['Qun Liu', 'Xin Jiang', 'Zexuan Qiu', 'Qinliang Su', 'Yasheng Wang', 'Xiaojun Meng', 'Zenan Xu'] | 2023-05-08 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 4.06497717e-01 -2.70736264e-03 -2.72978306e-01 -2.11447701e-01
-1.39641154e+00 -6.05136096e-01 7.07105994e-01 1.09627936e-02
-1.64086193e-01 7.40912259e-01 8.80246580e-01 9.15913209e-02
4.85878557e-01 -3.48869890e-01 -9.50329840e-01 -6.71594381e-01
2.35548675e-01 -8.85755867e-02 -6.24409690e-02 -5.19902147e-02
3.10035855e-01 7.13041574e-02 -1.48162723e+00 7.74293184e-01
9.41088915e-01 9.65759039e-01 4.74584699e-01 7.23466218e-01
-2.33988926e-01 8.72424483e-01 -8.59137058e-01 -4.69301075e-01
-1.03531241e-01 -7.69015193e-01 -5.57548225e-01 4.58207875e-01
6.42140687e-01 -6.28377736e-01 -4.82579559e-01 8.03095460e-01
4.43362981e-01 1.38255507e-01 8.46840799e-01 -1.38918686e+00
-7.43799627e-01 5.67454755e-01 -8.18531871e-01 -1.18934564e-01
6.65570974e-01 3.92841965e-01 1.33244097e+00 -1.04987681e+00
9.23843980e-01 1.26041996e+00 2.40062833e-01 4.39786017e-01
-1.11664820e+00 -2.85849869e-01 3.62265259e-01 2.20770314e-01
-1.26563728e+00 -5.92027366e-01 1.02848220e+00 -2.10207283e-01
7.58853793e-01 4.93262559e-01 7.84061670e-01 1.43130505e+00
2.41167247e-02 1.43642414e+00 5.97003877e-01 -2.77593136e-01
-5.15419580e-02 6.48575425e-02 -4.09283727e-01 7.87023067e-01
1.28155664e-01 -5.34120321e-01 -8.03151011e-01 -1.55559003e-01
3.96427721e-01 -7.93847162e-03 -5.29546916e-01 -3.03773642e-01
-1.31145060e+00 7.04289258e-01 2.48047337e-01 9.00771469e-02
-6.09904528e-01 -2.27239206e-02 5.51381767e-01 -1.32865250e-01
4.97379422e-01 3.23278368e-01 5.46549857e-02 -1.09847337e-01
-1.28798759e+00 1.33755744e-01 6.59795225e-01 1.13995647e+00
7.58084774e-01 2.36884400e-01 -7.33017802e-01 7.69618690e-01
2.63338536e-01 6.40817165e-01 3.65669578e-01 -8.00198674e-01
8.96091998e-01 6.95764542e-01 6.58920482e-02 -1.20380008e+00
-1.78740099e-02 -2.20900685e-01 -7.90403962e-01 -3.39648008e-01
3.48413363e-02 -1.30929649e-01 -8.73937607e-01 1.65579057e+00
7.37830177e-02 2.67344229e-02 1.92423239e-01 8.33294034e-01
1.39173198e+00 1.16441226e+00 7.31205419e-02 -2.65346617e-01
1.32904053e+00 -1.07642877e+00 -7.80682206e-01 -3.58813375e-01
3.40131104e-01 -7.60681808e-01 1.20944440e+00 1.85829505e-01
-1.12715173e+00 -4.32157844e-01 -1.14127707e+00 -2.60806978e-01
2.16686744e-02 6.14249051e-01 2.59648472e-01 1.81713179e-01
-9.28886533e-01 1.47130728e-01 -4.79468077e-01 -3.90186131e-01
5.48753142e-01 -1.70900583e-01 -4.52397496e-01 -3.47244918e-01
-8.34277511e-01 4.16030347e-01 5.18860042e-01 9.15053710e-02
-1.06804729e+00 -4.36058789e-01 -1.14579988e+00 1.52969465e-01
6.40044630e-01 -9.49029744e-01 1.00119424e+00 -1.13660026e+00
-1.39499962e+00 4.93794769e-01 -4.38275158e-01 -1.74764350e-01
4.22411621e-01 -2.79105157e-01 1.80991702e-02 8.98696840e-01
8.08236152e-02 9.73561049e-01 1.15558112e+00 -1.71322930e+00
-5.71117938e-01 -3.81401964e-02 1.76954404e-01 3.79470587e-01
-5.72533190e-01 -1.80947974e-01 -9.33367968e-01 -8.67807269e-01
-3.21196079e-01 -8.54707003e-01 -4.45872247e-02 -2.13526368e-01
-7.75920928e-01 -1.01619035e-01 8.32304835e-01 -1.02714086e+00
1.26824558e+00 -2.17566729e+00 5.00068843e-01 -6.49873391e-02
2.63897240e-01 1.94108352e-01 -5.64304590e-01 9.11833942e-01
2.45554388e-01 9.01030656e-03 -2.56763905e-01 -6.73956454e-01
1.05888799e-01 6.71600029e-02 -5.44381976e-01 1.14282764e-01
4.31737095e-01 1.26887631e+00 -1.11927879e+00 -9.90403056e-01
7.21033439e-02 6.00911021e-01 -4.63697761e-01 4.52228516e-01
-3.41391265e-01 4.30775642e-01 -5.91706395e-01 6.70061290e-01
3.75255346e-01 -4.00309741e-01 1.22521088e-01 -6.15403593e-01
9.69993845e-02 1.09763471e-02 -6.75371408e-01 1.94996583e+00
-3.50926399e-01 9.17863309e-01 -2.30065182e-01 -9.47610378e-01
6.38124764e-01 3.68245870e-01 3.29025894e-01 -5.65053225e-01
-2.98480932e-02 -7.97916427e-02 -6.39469504e-01 -6.61847353e-01
9.09267962e-01 6.65450096e-02 -1.73594415e-01 4.62709814e-01
6.29333630e-02 -1.58629775e-01 4.33183074e-01 9.04772758e-01
8.26998055e-01 3.82268757e-01 3.68208557e-01 4.37455952e-01
3.74275595e-01 1.04882620e-01 3.72408032e-01 7.50403821e-01
6.00639321e-02 1.06441593e+00 7.86276639e-01 -2.36746818e-02
-9.40036714e-01 -8.22102308e-01 6.01618528e-01 1.07653260e+00
1.26579121e-01 -1.03232789e+00 -7.19699502e-01 -8.43075871e-01
-3.11318874e-01 6.06988192e-01 -5.08069634e-01 -3.43861252e-01
-4.50311422e-01 -4.73917216e-01 4.31847751e-01 5.33535242e-01
4.36792076e-01 -8.92315269e-01 -5.11106074e-01 -2.67234724e-02
-8.89398515e-01 -1.34272861e+00 -7.71436155e-01 -5.47789097e-01
-6.12047851e-01 -7.40605175e-01 -1.07652271e+00 -6.66811585e-01
7.79547751e-01 5.83676279e-01 1.13253295e+00 1.06770739e-01
-1.35798082e-01 7.18962073e-01 -5.93165338e-01 -2.75869370e-01
-3.23005050e-01 -3.54450056e-03 -4.27702725e-01 2.71822155e-01
-1.86868608e-02 -2.90649414e-01 -6.07919216e-01 -2.64157861e-01
-1.16768980e+00 6.09309435e-01 9.16296422e-01 7.48924375e-01
5.20239055e-01 -2.08305955e-01 6.60923421e-01 -5.38451910e-01
6.23133659e-01 -4.24159557e-01 6.31618276e-02 4.28511024e-01
4.77027036e-02 5.36900945e-02 5.42362988e-01 -5.08852780e-01
-1.23257172e+00 4.48654406e-02 1.64691642e-01 -5.69141805e-01
1.49770603e-01 8.00950587e-01 -3.62902313e-01 4.08203512e-01
2.09571466e-01 6.68381631e-01 -1.17478266e-01 -3.23678285e-01
5.78273773e-01 5.96978784e-01 5.77022552e-01 -4.50821906e-01
7.39517689e-01 4.90934610e-01 -2.01950409e-02 -1.04349530e+00
-9.82102275e-01 -3.77584308e-01 -3.78594846e-01 -3.72416019e-01
8.70320857e-01 -1.20513678e+00 -2.88029432e-01 1.34325236e-01
-1.30954432e+00 4.17963862e-02 -9.12022069e-02 3.17139000e-01
-6.02091134e-01 9.47268188e-01 -4.36951458e-01 -7.65682995e-01
-4.79767442e-01 -1.12761211e+00 1.46016574e+00 1.52422488e-01
-2.45294511e-01 -7.38954008e-01 -2.76812494e-01 4.79879707e-01
1.27098933e-01 4.35007751e-01 8.44677866e-01 -5.01312673e-01
-7.73792505e-01 -2.39173621e-01 -4.13685948e-01 4.37067151e-01
-2.91400007e-03 2.37770364e-01 -7.97144413e-01 -2.96617597e-01
-3.36811155e-01 -5.79270184e-01 1.29793739e+00 2.77505964e-01
1.14311707e+00 -6.83362484e-01 -2.69592881e-01 3.37811053e-01
1.11611819e+00 -1.54316798e-01 6.15376294e-01 2.02348500e-01
1.11084461e+00 5.85918665e-01 8.15355361e-01 6.68338418e-01
7.41686642e-01 5.51946700e-01 4.68481511e-01 -2.15057179e-01
-3.47866476e-01 -6.32302880e-01 6.15235448e-01 9.05927062e-01
-1.32848278e-01 -5.09300888e-01 -5.37933886e-01 5.29395282e-01
-2.03321743e+00 -1.18364060e+00 2.16983259e-01 1.82999539e+00
9.32259083e-01 -2.74428338e-01 2.33551949e-01 -2.13154972e-01
5.08796275e-01 6.08537853e-01 -3.13771129e-01 -7.53275305e-02
-3.07850897e-01 -4.73553181e-01 4.87662330e-02 9.98002440e-02
-9.61724401e-01 8.52236509e-01 5.50384140e+00 9.93918240e-01
-1.11661959e+00 -1.60651803e-01 4.41924185e-01 -3.18245679e-01
-5.75361550e-01 -1.52993649e-01 -5.20915210e-01 5.26103616e-01
6.19943857e-01 -2.84956247e-01 4.33952771e-02 7.06925273e-01
3.05762053e-01 -1.84927776e-01 -1.08386374e+00 1.24002135e+00
7.67711461e-01 -1.48983908e+00 8.32561433e-01 -4.06823680e-02
8.82413805e-01 -4.46860969e-01 2.97519863e-01 3.85814220e-01
-2.15777457e-01 -8.33790064e-01 9.79478240e-01 5.42406261e-01
9.67703223e-01 -7.76514947e-01 7.01956451e-01 2.39641085e-01
-1.23088503e+00 8.04008022e-02 -2.66839683e-01 2.85662234e-01
3.28585535e-01 3.25689524e-01 -8.89942288e-01 9.04733837e-01
3.49485040e-01 1.14765954e+00 -8.15366566e-01 9.46119368e-01
-4.02158409e-01 5.56619287e-01 1.43440574e-01 -8.96399841e-02
4.83408928e-01 -1.20518826e-01 7.98566580e-01 1.44165099e+00
3.82544786e-01 8.00704136e-02 1.77175552e-01 6.40838742e-01
-2.53026277e-01 3.32225114e-01 -6.84555352e-01 -5.92808008e-01
2.50326991e-01 1.41362596e+00 -5.37203789e-01 -6.47241473e-01
-5.63646674e-01 1.06010079e+00 2.17242807e-01 7.10644543e-01
-7.82835066e-01 -3.00954431e-01 1.83022469e-01 -1.68416038e-01
5.34352303e-01 -1.52398631e-01 2.37126369e-02 -1.54946005e+00
2.18234867e-01 -1.03059614e+00 3.42537731e-01 -1.19405508e+00
-1.00016785e+00 6.93692863e-01 2.30561808e-01 -1.50556660e+00
-4.06841964e-01 -2.05913112e-01 -7.01310337e-01 5.36823809e-01
-1.48321855e+00 -1.67076671e+00 -3.31696212e-01 4.14441735e-01
9.71258223e-01 -1.56912446e-01 4.22012120e-01 -2.60005742e-02
-6.02158010e-01 4.31035787e-01 -9.20338258e-02 5.10072820e-02
1.00540864e+00 -1.09592295e+00 5.58612831e-02 1.02333701e+00
2.97839105e-01 4.89231914e-01 8.91699374e-01 -7.42759645e-01
-1.78925562e+00 -1.14623153e+00 7.13253319e-01 -3.01474452e-01
4.53186452e-01 -2.80945361e-01 -8.23400021e-01 7.08179474e-01
6.35659397e-01 -4.32466626e-01 8.66747022e-01 -2.14958027e-01
-4.25727367e-01 3.82643268e-02 -6.18147731e-01 9.23650920e-01
8.98704588e-01 -4.92614418e-01 -7.92615652e-01 9.33532864e-02
6.67639852e-01 -2.86344081e-01 -3.86416912e-01 1.91391036e-01
7.37171590e-01 -6.75963044e-01 9.64215875e-01 -5.61750948e-01
1.06787014e+00 -2.93733060e-01 -1.94465369e-01 -1.47685480e+00
6.01276420e-02 -6.96530402e-01 -3.05507481e-01 1.53494108e+00
2.54591346e-01 -1.39322449e-02 3.30322593e-01 3.74039650e-01
-2.43926600e-01 -6.08351409e-01 -5.01484036e-01 -4.81587887e-01
-5.14235497e-01 -2.26395532e-01 4.79327112e-01 6.05133712e-01
9.14684162e-02 7.25069344e-01 -9.58832443e-01 -4.34946641e-02
4.91423488e-01 4.59530562e-01 1.07885170e+00 -7.44572878e-01
-7.94700086e-02 -3.31294477e-01 -3.17932338e-01 -1.26072490e+00
3.29482347e-01 -7.68800378e-01 1.06186256e-01 -1.99529505e+00
9.29801226e-01 3.22553307e-01 -7.37718567e-02 4.47700828e-01
-5.93794644e-01 3.84079903e-01 4.97523904e-01 1.22759648e-01
-1.26585078e+00 8.82950306e-01 1.46195495e+00 -3.94470274e-01
-1.43733740e-01 -4.64144975e-01 -9.65401471e-01 5.89521527e-01
4.86761719e-01 -2.85956204e-01 -5.17766118e-01 -3.84866804e-01
3.25469524e-01 1.94121122e-01 5.15437543e-01 -5.70635557e-01
1.41077265e-01 -2.59003282e-01 4.82392997e-01 -1.03612638e+00
6.36673808e-01 -5.08012235e-01 -5.03811166e-02 6.83823675e-02
-4.09667075e-01 -6.36774600e-02 1.07142746e-01 8.37866008e-01
-4.66785014e-01 -1.31585011e-02 2.96381444e-01 -1.98171332e-01
-7.90577233e-01 2.12296978e-01 -2.37918779e-01 7.05600977e-02
7.20519781e-01 -2.80780196e-01 -6.30451262e-01 -8.65752220e-01
-3.07198465e-01 2.71453172e-01 6.23117745e-01 4.59573627e-01
1.07162154e+00 -1.45433056e+00 -9.73674715e-01 -1.16951883e-01
4.75659847e-01 -6.49522021e-02 5.34799039e-01 9.55799520e-01
-2.16180086e-01 3.44976872e-01 -1.95772260e-01 -6.10238791e-01
-1.51594222e+00 6.00742996e-01 -3.89300346e-01 -6.18894324e-02
-7.55840242e-01 5.30995548e-01 7.19525933e-01 4.36008275e-01
7.48322904e-02 -2.20474362e-01 -3.59520763e-01 4.32862103e-01
8.62749040e-01 1.77447304e-01 -3.64794105e-01 -9.66424882e-01
-1.49113685e-01 4.58583772e-01 -1.36750370e-01 -2.46399030e-01
1.42271531e+00 -4.99285728e-01 4.08411734e-02 5.33705235e-01
1.34546912e+00 2.91964322e-01 -1.41476679e+00 -2.97226489e-01
-2.93289423e-01 -5.43294311e-01 -1.36112422e-01 -5.44389367e-01
-1.06993854e+00 9.22150254e-01 -2.26856276e-01 -5.91737740e-02
1.22295225e+00 1.91260904e-01 9.01765287e-01 3.56352210e-01
4.62488011e-02 -9.03829098e-01 6.93834424e-01 2.12192774e-01
1.27883875e+00 -1.35956597e+00 2.51525104e-01 -2.48053744e-01
-1.28968728e+00 1.08806205e+00 3.25605601e-01 2.56792754e-01
-1.32711798e-01 -3.39389712e-01 -1.59849841e-02 -7.84467459e-02
-9.36965466e-01 -2.05654025e-01 8.35021853e-01 6.32413805e-01
2.59568542e-01 -8.29024240e-02 -1.08532920e-01 8.00834179e-01
6.82987422e-02 -2.83434778e-01 7.02197433e-01 8.94911468e-01
-4.94067639e-01 -6.48097694e-01 -2.21814394e-01 3.39540213e-01
-4.26623046e-01 -1.27804622e-01 -6.84725344e-01 6.49704933e-01
-3.72700512e-01 1.09261501e+00 -1.66429535e-01 -1.85075060e-01
1.62519991e-01 -3.95592712e-02 4.22092944e-01 -5.47745585e-01
-2.27265492e-01 3.61182213e-01 2.08833545e-01 -5.98630130e-01
-5.46261609e-01 -5.50157368e-01 -1.13512611e+00 -6.08973950e-02
1.25682190e-01 5.94050959e-02 5.39551735e-01 9.41890359e-01
4.55908686e-01 5.12219369e-01 5.15075803e-01 -1.19973719e+00
-1.87477812e-01 -8.49926889e-01 -3.59545469e-01 5.69664836e-01
5.28884768e-01 -4.43477333e-01 -2.28648499e-01 4.54481840e-01] | [10.666755676269531, 0.6948280930519104] |
420ba012-c8f8-4dc1-a2ac-ccbc73d1e686 | a-deep-dive-into-dataset-imbalance-and-bias | 2203.08235 | null | https://arxiv.org/abs/2203.08235v1 | https://arxiv.org/pdf/2203.08235v1.pdf | A Deep Dive into Dataset Imbalance and Bias in Face Identification | As the deployment of automated face recognition (FR) systems proliferates, bias in these systems is not just an academic question, but a matter of public concern. Media portrayals often center imbalance as the main source of bias, i.e., that FR models perform worse on images of non-white people or women because these demographic groups are underrepresented in training data. Recent academic research paints a more nuanced picture of this relationship. However, previous studies of data imbalance in FR have focused exclusively on the face verification setting, while the face identification setting has been largely ignored, despite being deployed in sensitive applications such as law enforcement. This is an unfortunate omission, as 'imbalance' is a more complex matter in identification; imbalance may arise in not only the training data, but also the testing data, and furthermore may affect the proportion of identities belonging to each demographic group or the number of images belonging to each identity. In this work, we address this gap in the research by thoroughly exploring the effects of each kind of imbalance possible in face identification, and discuss other factors which may impact bias in this setting. | ['Tom Goldstein', 'Micah Goldblum', 'Hossein Souri', 'Samuel Dooley', 'Steven Reich', 'Valeriia Cherepanova'] | 2022-03-15 | null | null | null | null | ['face-identification'] | ['computer-vision'] | [ 0.29770455 -0.13551442 -0.30469325 -0.5444574 -0.30794957 -0.57206786
0.31736684 0.09171268 -0.36740765 0.53002465 0.33392057 -0.44135243
-0.02198026 -0.62931406 -0.44256976 -0.63981014 0.30663994 0.27402744
-0.53328943 -0.03414468 0.40655977 0.589834 -1.6167222 0.07096032
0.5521152 0.7903514 -0.5239237 0.18101566 -0.06092626 0.7265635
-1.025937 -0.915321 0.34474614 -0.4853669 -0.47403586 0.11922278
1.2097379 -0.4716921 -0.20431161 1.1553833 0.889665 -0.302005
0.5133261 -1.374677 -0.7732644 0.5538931 -1.0531453 0.4574465
0.2698257 0.22929902 0.7127105 -0.6603661 0.45014465 1.6559739
0.774663 0.6685521 -1.4085157 -1.257037 0.07557517 0.08247759
-1.5436931 -1.0811311 0.58912337 -0.69080335 0.34021866 0.45209572
0.53227675 1.0716298 -0.03176558 0.22928557 1.3967273 -0.2922623
-0.15947224 0.49300766 0.19353701 0.17783844 0.8002931 -0.09184584
-0.6147482 -0.42601746 0.6383032 -0.12252329 -0.33723295 0.03946697
-0.67842007 0.8970498 0.08404505 0.34893718 -0.22875431 -0.30424458
0.24940924 0.30263194 0.55497223 0.4508072 0.02329022 -0.07600852
-1.1577018 0.25036854 0.67785615 0.21055423 0.55958503 0.04970411
0.00753803 0.9998969 0.2736346 0.5580117 0.35810542 -0.6473
0.4184746 0.58583224 -0.06992821 -1.542164 -0.21331416 -0.5084164
-0.6070751 0.31771 0.8850642 -0.23953511 -0.5868298 2.0655842
0.3165257 -0.2709086 -0.3517759 1.0217842 0.6052571 0.05093528
0.45894742 -0.25735953 1.440558 -0.10813153 -0.6761278 -0.5494456
0.28493774 -1.0194732 0.78755325 0.07140183 -0.9559518 -0.45335513
-0.8603917 0.15286441 -0.14596274 0.09730199 0.41723222 1.3784162
-1.0384744 0.31801686 -0.26443112 -0.5895097 0.8605933 0.43731934
-0.6023692 -0.29735816 -0.8867614 0.99177533 -0.1644352 0.26508883
-0.21694434 -0.9241537 -0.7402902 -0.12233265 0.2250884 -0.27040052
0.79804015 -1.6711714 -0.5249069 1.2317067 -0.3284765 -0.13040684
0.5824264 0.14933428 -0.7237416 -0.29056773 0.18962316 0.5756323
0.9181009 -1.3142393 -0.30726683 -0.99113876 -0.09265906 0.16181575
-0.15958202 0.48968372 0.24985527 -0.67421013 -0.147244 -0.85124767
0.23383015 0.02188936 -0.1945255 -0.12836294 0.90592957 -0.7498622
1.4894904 -2.4886646 -0.43026727 0.23241836 0.47017547 0.31823918
0.14379926 0.19275908 -0.43269965 0.6453839 0.09467933 -0.17209585
-0.11547466 -0.27995753 -0.12067617 0.7644672 0.500286 0.3460856
-0.5278953 -0.3714613 -0.04154392 0.51656675 -0.47886792 -0.14114495
0.4356609 0.34906596 -0.05500437 0.84255266 1.0424407 -0.04576397
0.40840757 -0.1949045 -0.17735176 0.10031857 -1.0859039 0.60222435
0.15886432 1.023693 0.16564853 -0.7403535 0.864869 0.17216125
0.42676017 -0.6354892 0.29762957 0.24907029 0.7949282 -0.41669938
0.5427087 -0.45989275 0.45958608 0.57032996 -0.41433936 0.36997554
0.02936747 -0.18590505 0.706493 -0.4161039 0.02543724 -0.32124797
0.17872962 0.04884981 0.7933537 0.6487209 -0.61691415 0.67475843
0.72348833 -0.11310376 -1.0271953 -0.7536317 -0.591652 0.80386066
-0.06144929 -0.04071048 -0.8325062 -0.42351267 0.4686765 0.49352986
-0.78863734 -0.31447354 -0.33890682 -1.2118325 0.6441711 0.21150486
0.3700113 -0.70098263 -0.5685588 -0.16592252 -0.1493275 -0.9980747
-0.41725618 -0.6289089 -0.5250314 -1.6272142 -0.6407856 -0.5041475
0.9679895 0.3983412 1.2049233 0.51187474 -0.4291542 0.39203152
-0.17713444 -0.8614324 -0.5028273 -0.16051543 0.15489642 0.52465737
0.91582215 -0.15187739 -0.6920015 0.5386318 -0.8399372 -0.32882306
0.40483007 0.50534725 -0.18342908 0.12018634 0.85364485 -1.0926942
0.55423784 -0.6514155 -0.2933627 0.14966482 -0.56755847 -0.60573936
0.06567233 -0.60596013 -0.96647793 -0.37738952 0.04158778 -0.29370618
-0.1361195 0.26465577 -0.2237441 -0.35239604 0.74962014 -0.21592857
0.6836107 -0.32289755 -0.55429393 0.8978022 0.0461868 -0.38070193
0.843425 0.4718405 -0.26193008 -1.0341505 -0.5443115 -0.15877229
-0.26531148 -0.4833238 0.5357536 -0.9805905 -0.7035475 0.5066272
-0.70038104 -0.18662083 0.0634254 0.5505248 0.2528886 0.14509864
-0.22309545 -1.066438 -0.0723189 -1.4418465 0.6737244 0.43983898
-0.6297735 -0.57802206 -0.35999578 0.750644 0.54892015 0.36629775
0.9484719 -0.5570067 -0.17018533 -0.3699204 -0.5970996 0.34993836
0.4159458 0.33828473 -1.2376323 -0.41375998 0.02387837 -0.28118894
0.4057175 0.3829813 0.8775365 -0.24676906 -0.23389593 0.12883586
1.2937019 0.27666098 0.6175304 0.03144796 0.5794764 1.2432046
0.4561486 0.34938073 0.39855504 0.70273674 0.1562078 -0.15067497
-0.24992391 -0.04921088 0.2779313 0.06269556 0.02438914 0.09773322
-1.0735161 0.3371391 -1.2001829 -1.1839286 -0.16884345 2.3841703
0.71456033 -0.3086902 0.41708213 0.28125072 1.1847669 0.21712166
-0.5374743 -0.14671613 -0.41309583 -0.11722472 0.3928943 0.26308712
-0.7398458 0.4188593 6.765838 0.51631385 -1.576671 -0.0587375
1.3919206 -0.3046881 -0.24049063 -0.24534452 -0.7532531 0.719127
0.6421895 -0.13315725 0.3054502 0.45214096 0.4387681 -0.36719522
-1.0540403 1.2168758 0.46059424 -0.7271711 -0.12541564 0.5275534
0.37874672 -0.42138395 0.5444874 0.01897557 -0.1361414 -1.3407342
0.87047523 0.03210835 1.0635183 -0.58309984 0.7205357 -0.16786072
-0.49032 -0.20728707 -0.24492581 -0.25692004 -0.16142759 0.6774558
-0.6096135 -0.14445218 0.80007726 0.16167176 -0.82265276 0.9338043
0.37500358 0.65480816 -0.06722508 0.2894707 -0.208566 0.02443173
0.32833526 0.78130347 0.17234318 0.17367783 -0.2861481 0.70000243
-0.20791234 0.18655306 -0.70157456 -0.29388306 0.557043 1.2437407
-0.64437824 -0.01484025 -0.6295332 0.3141735 0.21721165 0.22008039
-0.6025234 0.19537133 1.1286546 0.4816369 -0.30094242 0.09211479
-0.39227828 -0.91834116 0.06210952 -1.5060632 0.45970175 -0.30679226
-1.2723399 0.26308239 -0.09996227 -0.79598075 0.06650104 -0.46626058
-0.1718417 1.0649089 -1.3255562 -0.7529818 -0.33950317 0.26539326
0.19684729 -0.19536722 0.47318915 0.7029678 -0.9866652 1.0132954
-0.19303952 0.41027156 1.1160975 -0.68849194 0.05860887 0.7040499
-0.05577338 0.86829215 0.61905843 -0.71147645 -1.2394063 -0.65286016
0.85477334 -0.637066 0.31047404 -0.27255028 -0.8171568 0.6234832
-0.0771994 -0.3463963 1.2366222 0.35412136 -0.5240537 -0.2629065
-1.6234965 0.77680224 1.0210553 -0.5755237 0.07345045 0.0361864
-0.08336069 -0.3416014 -0.956925 0.3734796 0.89235705 -1.2747895
0.8599091 -0.39921325 0.30334258 -0.1133576 0.02335966 -0.95341384
-0.339255 -0.10992087 0.42754263 1.6609457 0.4558281 -1.0066284
0.856738 1.1805447 0.35268912 -0.5639049 -0.7735921 -0.31496456
-0.05696483 -0.218293 0.85830516 1.3705442 -0.4546798 0.02151517
-0.17939873 0.12492363 0.56564057 -0.24861172 0.8478811 -1.2446798
0.10398538 -0.70821226 -0.60517615 -0.10448188 0.29223904 -0.49257705
-0.34322393 -0.8288997 0.43512246 -0.83195525 0.09753405 0.28257838
-0.28082165 0.6628709 0.48676163 0.3252649 0.27946845 -0.14613447
1.0092717 -0.12809306 0.09640747 -0.08246074 -1.4367543 0.50315076
0.79178214 -0.26279357 -0.35618532 -0.46619722 0.10750662 -0.31765467
0.5532583 -0.8636783 -0.0522994 -0.2641843 0.9255848 0.07830564
0.32093525 -0.93826383 0.4821497 0.34905303 -0.13246201 0.45036998
0.3604876 0.09031352 -0.03385802 -0.21768178 0.90996855 -0.02342747
-0.20671895 0.25243896 -0.07332528 0.1678847 0.92107755 -0.6277571
-0.6450568 -0.45793793 -0.2615521 -0.06836579 0.78748405 0.58159983
0.20383403 -1.1752859 -0.92789155 0.37716478 -0.04942887 -0.41897
0.39639822 1.0838364 -0.3537379 0.05659929 -0.2724976 -0.37728265
-1.5569574 0.16484404 0.49629194 0.35869914 -0.1604741 0.7288565
0.30422232 -0.0188864 0.17117688 0.52123857 -0.27907792 0.7085664
0.74664575 0.4802319 -0.02558997 -1.2649366 -0.56835437 0.38394716
-0.32871306 0.02819613 0.8502802 -0.08468228 -0.38480195 0.3205862
1.050999 0.17055765 -0.83531785 0.0315756 -0.16768916 -1.1801523
-0.22497672 -0.6257609 -1.2694843 0.5243676 0.6947064 0.31892803
0.96362925 -0.23435694 0.18650031 -0.47060266 0.17150128 -1.0163733
-0.21303016 -0.17818746 0.8578979 -1.5060816 0.02133273 -0.3842689
-0.5163016 0.8215298 0.82143414 0.3251665 0.5340687 -0.03529324
0.36587712 -0.0938656 -0.29058787 -0.03278885 0.04583885 0.6176441
0.96041006 0.03630267 -0.54299134 0.33605865 -0.5376275 -0.12110598
0.5186778 0.7659106 0.07675739 -1.134309 -1.0174943 0.95593244
-0.85380906 0.04296661 -0.7695177 0.7745215 0.39023876 1.1400659
0.5008195 -0.31213933 0.22186229 0.13704164 0.5429355 -0.59361106
-0.82995844 -0.36576647 0.2748031 -0.2057357 -0.37317473 -1.1748761
-0.4740607 -1.1220254 -0.34574312 -0.03927134 0.69481665 0.7786651
0.41176367 0.06982316 0.5891674 -0.4214233 -0.4304178 -0.87522143
-0.68501455 0.5459887 0.44490206 -0.78756094 -0.45450002 -0.32798517] | [12.984983444213867, 1.2172410488128662] |
a95f2cca-a31a-47f8-b057-48a8a009b0b9 | enhancing-visual-domain-adaptation-with | 2306.10142 | null | https://arxiv.org/abs/2306.10142v1 | https://arxiv.org/pdf/2306.10142v1.pdf | Enhancing Visual Domain Adaptation with Source Preparation | Robotic Perception in diverse domains such as low-light scenarios, where new modalities like thermal imaging and specialized night-vision sensors are increasingly employed, remains a challenge. Largely, this is due to the limited availability of labeled data. Existing Domain Adaptation (DA) techniques, while promising to leverage labels from existing well-lit RGB images, fail to consider the characteristics of the source domain itself. We holistically account for this factor by proposing Source Preparation (SP), a method to mitigate source domain biases. Our Almost Unsupervised Domain Adaptation (AUDA) framework, a label-efficient semi-supervised approach for robotic scenarios -- employs Source Preparation (SP), Unsupervised Domain Adaptation (UDA) and Supervised Alignment (SA) from limited labeled data. We introduce CityIntensified, a novel dataset comprising temporally aligned image pairs captured from a high-sensitivity camera and an intensifier camera for semantic segmentation and object detection in low-light settings. We demonstrate the effectiveness of our method in semantic segmentation, with experiments showing that SP enhances UDA across a range of visual domains, with improvements up to 40.64% in mIoU over baseline, while making target models more robust to real-world shifts within the target domain. We show that AUDA is a label-efficient framework for effective DA, significantly improving target domain performance with only tens of labeled samples from the target domain. | ['Jeff Schneider', 'Christoph Mertz', 'Anurag Ghosh', 'Anirudha Ramesh'] | 2023-06-16 | null | null | null | null | ['unsupervised-domain-adaptation'] | ['methodology'] | [ 7.02680647e-01 -2.62471437e-01 -2.00573444e-01 -5.71600854e-01
-9.94585037e-01 -9.96069312e-01 5.85524559e-01 -3.71461987e-01
-6.16745830e-01 5.89038372e-01 -2.98021510e-02 1.31443009e-01
2.87313983e-02 -3.31007987e-01 -7.75189877e-01 -1.00036287e+00
5.19479275e-01 5.31956792e-01 5.44028044e-01 -1.83212042e-01
8.51261541e-02 4.93961364e-01 -1.60325623e+00 2.06088498e-02
6.86932921e-01 9.73470628e-01 4.90280747e-01 5.74009180e-01
-5.32345884e-02 6.95352852e-01 -4.94451523e-01 1.54702380e-01
6.24335766e-01 -4.79657501e-01 -7.55854666e-01 4.07655567e-01
6.10741138e-01 -4.31819886e-01 -9.66387689e-02 8.98889065e-01
5.67993999e-01 2.78260112e-01 6.68847919e-01 -1.37867391e+00
-2.41636097e-01 -1.87578872e-02 -6.40537441e-01 1.42606661e-01
7.73871690e-02 4.98627931e-01 7.67633080e-01 -6.53980613e-01
8.14263821e-01 1.12115145e+00 5.75927734e-01 6.65065944e-01
-1.39930630e+00 -6.70478821e-01 -1.11346699e-01 1.20700933e-01
-8.84079516e-01 -5.57629645e-01 1.01302195e+00 -5.19883871e-01
9.35661793e-01 -2.77171344e-01 3.48429620e-01 1.31684470e+00
-2.28060395e-01 5.50952375e-01 1.71465182e+00 -5.59366167e-01
5.45601845e-01 1.42634913e-01 9.43164006e-02 2.80512184e-01
1.45428792e-01 3.87539297e-01 -7.29529202e-01 2.00873464e-01
5.65636873e-01 -1.98747054e-01 -3.84280123e-02 -6.47706032e-01
-1.34655297e+00 5.32280266e-01 5.01299679e-01 -1.37924060e-01
-2.45727181e-01 -1.52146034e-02 1.85906127e-01 3.84111218e-02
4.83934253e-01 5.11234462e-01 -5.84383726e-01 3.11725382e-02
-8.18660200e-01 1.59897748e-02 5.17906368e-01 1.22701943e+00
1.10278964e+00 2.38088537e-02 9.40913484e-02 1.08598721e+00
2.09166378e-01 1.10198224e+00 3.53302360e-01 -1.51554930e+00
1.06329322e-01 5.81738234e-01 3.01105142e-01 -2.90240288e-01
-5.98626971e-01 -1.30742818e-01 -1.36011884e-01 4.65430975e-01
8.00962150e-01 -9.19060931e-02 -1.30004525e+00 1.84243274e+00
5.60344398e-01 -1.29287153e-01 2.56959081e-01 1.13454258e+00
5.55144370e-01 3.87938559e-01 2.47001946e-01 -8.59815851e-02
1.25181472e+00 -9.94108438e-01 -2.67709464e-01 -9.29738402e-01
3.80972564e-01 -7.69599795e-01 1.34039199e+00 3.91923904e-01
-6.10795975e-01 -5.30768096e-01 -9.94411230e-01 -3.11842829e-01
-4.84789789e-01 8.27700421e-02 4.71254647e-01 6.19429827e-01
-8.82078886e-01 2.34291241e-01 -5.69974840e-01 -1.04426110e+00
5.18241823e-01 3.16430420e-01 -3.95152062e-01 -4.88033473e-01
-6.77793026e-01 8.10595334e-01 6.58960819e-01 -2.33608156e-01
-1.11870611e+00 -6.61687732e-01 -7.01808751e-01 -6.07541263e-01
6.41929924e-01 -5.43084443e-01 1.36697268e+00 -1.17730069e+00
-1.62936282e+00 1.25644267e+00 -2.01279938e-01 -4.64868993e-01
2.83844054e-01 -1.56143501e-01 -2.62114167e-01 5.45997620e-01
2.89867878e-01 1.05913377e+00 1.09461141e+00 -1.57788479e+00
-7.11227715e-01 -5.31167507e-01 5.77433445e-02 4.75821823e-01
-1.25276536e-01 4.12285998e-02 -1.92466021e-01 -1.40948817e-01
1.34162545e-01 -1.22491372e+00 -7.00446591e-02 8.35834220e-02
-7.02268183e-02 5.07279262e-02 1.04993916e+00 -4.66207892e-01
2.11520284e-01 -2.18777633e+00 -9.96199027e-02 -2.19113544e-01
-1.64526999e-01 2.74504423e-01 -1.21471569e-01 3.64743508e-02
1.69830248e-01 -5.21544218e-01 -6.13336265e-01 -3.06370556e-01
-1.44050598e-01 6.33081973e-01 -4.24310446e-01 5.62562585e-01
2.75858581e-01 8.64284635e-01 -1.04230535e+00 -4.47695404e-01
6.83534563e-01 7.17339814e-02 -3.07460189e-01 3.16433340e-01
-6.74799681e-01 8.72323036e-01 -7.21596777e-02 9.11850572e-01
7.31355786e-01 7.07539022e-02 -1.53545318e-02 -2.84034967e-01
-1.06600583e-01 1.91021279e-01 -9.05459583e-01 2.05626154e+00
-2.71231860e-01 7.22177207e-01 2.85217941e-01 -8.17096174e-01
9.71605539e-01 -2.45901287e-01 6.31680071e-01 -1.08979785e+00
2.59574115e-01 5.43454051e-01 -2.73318678e-01 -5.93337774e-01
5.37950635e-01 -3.14364284e-01 -9.94796231e-02 4.71966803e-01
3.46036047e-01 -7.25559831e-01 7.14384690e-02 1.23062052e-01
9.63613212e-01 5.94168484e-01 1.65476859e-01 -1.31080166e-01
8.90529528e-02 7.26776898e-01 5.70921183e-01 7.91093707e-01
-8.08119655e-01 7.58681297e-01 -1.86543185e-02 -1.05426095e-01
-1.38335276e+00 -1.10835588e+00 -6.37422223e-03 1.34351695e+00
5.67178845e-01 3.77235413e-01 -7.35758424e-01 -5.24142504e-01
-7.54183531e-02 7.07016587e-01 -3.00284326e-01 -2.66215682e-01
-3.84358495e-01 -8.38380456e-01 4.16854262e-01 5.43514073e-01
9.11380947e-01 -1.07131755e+00 -9.89197433e-01 5.55786006e-02
-3.52142185e-01 -1.69863033e+00 -1.06952572e-02 6.55359745e-01
-8.12268674e-01 -1.07966399e+00 -6.36199415e-01 -5.92344105e-01
3.66015583e-01 7.50306487e-01 1.03682232e+00 -8.11038911e-01
-3.83374035e-01 8.74686539e-01 -4.01658446e-01 -5.74885190e-01
-4.08983201e-01 -1.64325282e-01 1.50895342e-01 -1.25164405e-01
7.13702381e-01 -2.98365414e-01 -6.94460869e-01 6.20500684e-01
-8.74748230e-01 -6.46563470e-02 5.30492961e-01 6.37804925e-01
6.38817191e-01 -2.97006488e-01 4.20137584e-01 -7.40839124e-01
5.33986511e-03 -2.70943642e-01 -6.08084023e-01 1.94644947e-02
-6.31759048e-01 -1.19141564e-01 3.30670536e-01 -4.04416740e-01
-1.48919678e+00 4.97746766e-01 3.90928686e-01 -4.94208753e-01
-7.94079423e-01 -2.78962076e-01 -1.42822012e-01 -3.57948124e-01
1.27371621e+00 1.24191433e-01 9.44370776e-02 -1.56231508e-01
6.29083633e-01 7.68824697e-01 9.59288001e-01 -7.95458972e-01
7.52267778e-01 1.05096149e+00 6.33977205e-02 -9.31364954e-01
-1.08403420e+00 -1.08691144e+00 -9.33656096e-01 -4.97051746e-01
8.97631943e-01 -1.22998393e+00 -1.22661643e-01 7.07482100e-01
-8.35781217e-01 -8.94225419e-01 -4.78299797e-01 5.39661527e-01
-8.06826532e-01 1.90099716e-01 -1.85025215e-01 -8.04932356e-01
8.44061524e-02 -9.74136710e-01 1.32399821e+00 5.45674264e-01
9.95055288e-02 -7.74093807e-01 -3.29570509e-02 8.34226489e-01
3.28536004e-01 3.70157003e-01 5.29781222e-01 -4.37110335e-01
-5.71176469e-01 3.30793679e-01 -5.64842820e-01 7.22730935e-01
9.92489085e-02 -3.75942498e-01 -1.62982345e+00 -1.51961282e-01
7.94272944e-02 -7.21197188e-01 8.55154335e-01 3.99860412e-01
6.49090707e-01 4.29799974e-01 -2.34062806e-01 6.34439170e-01
1.45295572e+00 1.94746271e-01 3.15973967e-01 6.80518627e-01
7.62661934e-01 7.53490269e-01 1.02142310e+00 2.57197887e-01
4.02274340e-01 6.82135642e-01 5.88716567e-01 -2.51559883e-01
-5.90050519e-01 5.04990593e-02 4.94066328e-01 1.32566571e-01
9.33104828e-02 3.25872637e-02 -9.74235475e-01 8.32751095e-01
-1.70054722e+00 -5.94081759e-01 -1.92727551e-01 2.10377479e+00
7.47414708e-01 -4.19128984e-02 4.60703254e-01 -1.50620323e-02
5.63098431e-01 -5.49503081e-02 -1.19241118e+00 -4.32188399e-02
-5.05252898e-01 9.06451941e-02 1.13147855e+00 2.19669998e-01
-1.10475349e+00 1.12758052e+00 6.42432070e+00 4.63777632e-01
-1.09818578e+00 3.31391364e-01 2.06769437e-01 -1.52113691e-01
1.12789974e-01 -1.31072970e-02 -6.52173281e-01 3.41417491e-01
8.47143412e-01 2.69069672e-01 7.14968920e-01 1.10441530e+00
1.61243856e-01 -6.14162266e-01 -9.89674032e-01 1.05131376e+00
3.34059507e-01 -5.90251029e-01 -4.65436131e-01 1.01802021e-03
8.89850616e-01 4.82600570e-01 3.73294130e-02 1.48796573e-01
6.33330524e-01 -4.00750220e-01 8.54235709e-01 1.63461864e-01
7.81585336e-01 -2.65723825e-01 2.89013803e-01 1.91929251e-01
-7.55095720e-01 -4.06559408e-01 -4.78097200e-01 7.32130483e-02
5.93491271e-02 4.84936863e-01 -1.15959263e+00 3.77255946e-01
1.00642705e+00 8.08499694e-01 -6.93842053e-01 7.17505276e-01
-2.87546605e-01 6.69346452e-01 -6.51071370e-01 3.99825424e-01
2.03092307e-01 -2.19698116e-01 5.44021666e-01 1.13895452e+00
7.41439611e-02 1.26206219e-01 1.51119635e-01 8.49431217e-01
9.72485989e-02 -4.76908654e-01 -7.68372655e-01 1.30146921e-01
4.29525584e-01 1.29553139e+00 -8.39047790e-01 -1.47987589e-01
-2.70278513e-01 1.17082334e+00 -9.12162196e-03 5.50159156e-01
-7.24931002e-01 -8.43787193e-02 5.65587640e-01 -5.81407659e-02
1.47865847e-01 -5.45841396e-01 -6.21365666e-01 -9.96298432e-01
-7.98042268e-02 -6.81396186e-01 4.18908119e-01 -1.50457489e+00
-1.21340382e+00 1.89310089e-01 2.63672411e-01 -1.40364504e+00
-1.35217592e-01 -9.19181943e-01 9.28667337e-02 5.68269014e-01
-2.02832198e+00 -1.49780488e+00 -8.83479893e-01 9.31811810e-01
6.51548982e-01 -7.24775270e-02 4.94937658e-01 1.80237517e-01
-3.14765841e-01 8.77290964e-02 3.07380646e-01 -2.99995899e-01
1.26717103e+00 -1.37469280e+00 -1.24285787e-01 1.07017076e+00
9.09998789e-02 2.07605928e-01 7.06762433e-01 -3.19207042e-01
-1.32356668e+00 -1.19170523e+00 9.50129852e-02 -8.12933505e-01
5.85645735e-01 -4.26701516e-01 -6.35114193e-01 5.65010130e-01
1.33850917e-01 1.02065146e-01 4.49909002e-01 -3.49241376e-01
-5.81612349e-01 -4.32591856e-01 -1.40386450e+00 3.49974364e-01
1.16640759e+00 -7.78598607e-01 -6.05822504e-01 4.84131068e-01
6.72372222e-01 -4.44301575e-01 -6.12396300e-01 4.75671113e-01
4.14765269e-01 -1.07575405e+00 1.13255858e+00 2.23607887e-02
2.36005604e-01 -5.95158219e-01 -3.76455575e-01 -1.22720218e+00
1.71733182e-02 -3.14912856e-01 2.48237744e-01 1.27023661e+00
7.71726575e-03 -6.04937375e-01 6.92088664e-01 6.52763247e-01
-1.90212250e-01 2.65223622e-01 -8.22419167e-01 -9.62176919e-01
-9.28374976e-02 -5.46322823e-01 1.89738363e-01 8.76255929e-01
-5.79029202e-01 3.34976584e-01 -1.54386416e-01 2.51271576e-01
1.01492476e+00 1.67522341e-01 9.78278637e-01 -1.13506019e+00
-8.18796381e-02 -1.07577175e-01 -1.51316985e-01 -7.27606177e-01
1.48038000e-01 -5.86143911e-01 5.67541122e-01 -1.57272148e+00
1.06012911e-01 -5.33521771e-01 -2.06722498e-01 6.14694118e-01
1.21603213e-01 5.29942811e-01 1.45258412e-01 6.18257344e-01
-7.08217323e-01 3.27771991e-01 8.86654615e-01 -1.84520319e-01
-7.85628334e-02 -5.01219392e-01 -4.35649127e-01 6.89170182e-01
8.27036738e-01 -5.48752964e-01 -4.68669087e-01 -6.07564688e-01
-2.10028946e-01 -6.08174264e-01 6.48705542e-01 -1.37454891e+00
9.96841863e-02 -2.94025004e-01 2.91961133e-01 -3.27539176e-01
4.93359059e-01 -1.05801594e+00 -1.83280751e-01 1.54759794e-01
-1.89134017e-01 -6.64115727e-01 3.80226880e-01 6.79346204e-01
7.24881887e-02 -3.47022377e-02 1.16725111e+00 -2.21991554e-01
-1.53199780e+00 -1.68843091e-01 -9.59177613e-02 1.73860729e-01
1.13527966e+00 -4.92552072e-01 -6.37594163e-01 1.85163990e-02
-3.77522886e-01 8.05253088e-02 8.91388297e-01 3.78752798e-01
1.91635877e-01 -9.87528563e-01 -2.46141762e-01 1.47520468e-01
6.32091880e-01 4.45153415e-01 1.20744012e-01 7.71651924e-01
-4.48345095e-01 2.24278778e-01 -4.10111308e-01 -1.12963080e+00
-1.01861179e+00 3.48161399e-01 3.96605015e-01 2.66163230e-01
-3.51702929e-01 8.28123152e-01 2.41158828e-01 -7.06386209e-01
2.12428309e-02 -3.18724871e-01 2.63715655e-01 -5.83004504e-02
1.80253699e-01 4.41163003e-01 1.17263034e-01 -6.64780259e-01
-3.66409123e-01 7.46922493e-01 2.41582051e-01 -2.11156741e-01
1.17068887e+00 -5.34063816e-01 8.98680016e-02 5.51351547e-01
8.43566537e-01 -4.45179731e-01 -1.93413937e+00 -5.63239217e-01
-8.09133127e-02 -3.71012211e-01 1.13492683e-01 -1.23419881e+00
-6.62434280e-01 9.23608124e-01 9.86773551e-01 -1.70316696e-01
1.44680452e+00 2.60215074e-01 6.50147796e-01 4.29066062e-01
7.57238984e-01 -1.54205346e+00 3.91543835e-01 4.51341987e-01
3.85360628e-01 -1.77981842e+00 -1.58763438e-01 -3.07971179e-01
-9.32811856e-01 8.62591207e-01 7.11269796e-01 3.92263234e-02
1.17414430e-01 1.86826631e-01 4.71296906e-01 -1.30629614e-01
-2.73004562e-01 -6.99632108e-01 -1.39044791e-01 1.10760403e+00
-2.56000161e-01 -4.75775898e-02 3.82988065e-01 1.83807850e-01
1.46714419e-01 9.55813080e-02 5.10951579e-01 1.02183998e+00
-6.21057928e-01 -8.29274118e-01 -6.05422795e-01 4.68631238e-02
3.65777947e-02 2.47775555e-01 -5.36028624e-01 6.43109024e-01
3.84381950e-01 1.27502859e+00 -1.10221151e-02 -2.73105651e-01
3.88195246e-01 1.82963327e-01 6.92118168e-01 -6.35738313e-01
-1.33438930e-01 2.06095561e-01 -4.42174561e-02 -7.35316575e-01
-1.10411739e+00 -8.47865164e-01 -1.18909264e+00 1.85918793e-01
-2.33762771e-01 -5.75650990e-01 1.04945707e+00 1.02915084e+00
2.72772402e-01 2.99330384e-01 5.47648191e-01 -1.14563775e+00
-2.28537664e-01 -8.93915892e-01 -7.00560510e-01 5.76079190e-01
3.54977936e-01 -1.04402161e+00 -4.93657619e-01 5.13309419e-01] | [8.701216697692871, -1.993804931640625] |
8646372d-b2cc-4bd1-a3d9-f1e34f314921 | superpixel-perception-graph-neural-network | 2210.07539 | null | https://arxiv.org/abs/2210.07539v1 | https://arxiv.org/pdf/2210.07539v1.pdf | Superpixel Perception Graph Neural Network for Intelligent Defect Detection | Aero-engine is the core component of aircraft and other spacecraft. The high-speed rotating blades provide power by sucking in air and fully combusting, and various defects will inevitably occur, threatening the operation safety of aero-engine. Therefore, regular inspections are essential for such a complex system. However, existing traditional technology which is borescope inspection is labor-intensive, time-consuming, and experience-dependent. To endow this technology with intelligence, a novel superpixel perception graph neural network (SPGNN) is proposed by utilizing a multi-stage graph convolutional network (MSGCN) for feature extraction and superpixel perception region proposal network (SPRPN) for region proposal. First, to capture complex and irregular textures, the images are transformed into a series of patches, to obtain their graph representations. Then, MSGCN composed of several GCN blocks extracts graph structure features and performs graph information processing at graph level. Last but not least, the SPRPN is proposed to generate perceptual bounding boxes by fusing graph representation features and superpixel perception features. Therefore, the proposed SPGNN always implements feature extraction and information transmission at the graph level in the whole SPGNN pipeline, and SPRPN and MSGNN mutually benefit from each other. To verify the effectiveness of SPGNN, we meticulously construct a simulated blade dataset with 3000 images. A public aluminum dataset is also used to validate the performances of different methods. The experimental results demonstrate that the proposed SPGNN has superior performance compared with the state-of-the-art methods. The source code will be available at https://github.com/githbshang/SPGNN. | ['Ruqiang Yan', 'Xuefeng Chen', 'Chuang Sun', 'Qixiu Yang', 'Hongbing Shang'] | 2022-10-14 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 2.85483059e-02 -7.73321614e-02 2.89936215e-01 1.38711348e-01
1.21028975e-01 -2.93816060e-01 6.00661784e-02 -2.47855857e-01
1.30373031e-01 2.36100271e-01 -3.32036525e-01 -3.60957384e-01
-6.31094053e-02 -1.14985943e+00 -4.62490678e-01 -8.67211044e-01
-1.62761565e-02 -9.13203508e-02 4.43145663e-01 -3.11961979e-01
1.82343975e-01 6.79189622e-01 -1.50201154e+00 2.24127799e-01
8.98137629e-01 1.40245998e+00 5.48636675e-01 4.33987051e-01
1.69720218e-01 3.45307618e-01 -6.91471815e-01 5.35114408e-02
4.34170991e-01 -4.20700341e-01 -2.30392739e-01 5.17050207e-01
-1.18354537e-01 -2.76549667e-01 -2.95776069e-01 1.45833373e+00
2.30025053e-01 1.95040882e-01 4.24811900e-01 -1.16703010e+00
-7.96559870e-01 2.20566347e-01 -5.89330375e-01 5.43662943e-02
9.90503877e-02 4.45844412e-01 6.91378295e-01 -8.58042419e-01
2.06114903e-01 1.24297166e+00 5.03701508e-01 1.07098185e-02
-6.08871698e-01 -5.94833076e-01 2.22526360e-02 5.63289039e-02
-1.46836150e+00 1.74309701e-01 1.04672956e+00 -2.30686858e-01
6.80667400e-01 2.09988639e-01 1.11936116e+00 4.92844343e-01
7.05088258e-01 4.82907861e-01 8.94573569e-01 -1.46599069e-01
4.88163717e-02 -2.52842188e-01 -1.45032570e-01 1.32827115e+00
5.23267150e-01 2.15414196e-01 -1.45633966e-01 3.96264076e-01
1.34291947e+00 2.54272461e-01 -5.70332766e-01 -2.10355036e-02
-1.15888095e+00 4.49275553e-01 9.53654945e-01 2.13406608e-01
-3.01498324e-01 -6.97002038e-02 2.63692617e-01 1.81433812e-01
4.66816753e-01 3.21071446e-01 -1.36282057e-01 2.92647660e-01
-5.68040133e-01 -9.84478816e-02 6.15694046e-01 1.05577552e+00
8.52644444e-01 3.30248564e-01 -8.49173516e-02 7.02507019e-01
4.53730106e-01 4.55359250e-01 3.64932835e-01 -6.88207865e-01
1.43057212e-01 9.78537142e-01 -1.18810810e-01 -1.49339592e+00
-5.65899789e-01 -6.58574700e-01 -1.19928467e+00 3.55385333e-01
-8.18507094e-03 -1.51033521e-01 -8.70884597e-01 8.84002924e-01
3.90190750e-01 -3.04434616e-02 -1.26339778e-01 1.51375914e+00
1.12538183e+00 9.78955507e-01 -3.33551884e-01 -1.66122898e-01
1.63785613e+00 -1.20938885e+00 -5.53408146e-01 -1.54812619e-01
2.57845998e-01 -8.08647752e-01 1.19308901e+00 6.78512275e-01
-8.83889854e-01 -1.05580640e+00 -1.47631454e+00 9.58282053e-02
-2.79660434e-01 5.25004029e-01 6.67125046e-01 2.98428684e-01
-7.98472404e-01 5.37631333e-01 -6.80254042e-01 -8.24765563e-02
3.45654368e-01 1.92327455e-01 -2.53473908e-01 -2.01565653e-01
-1.10191917e+00 2.37970382e-01 5.62032580e-01 7.77181327e-01
-9.60744262e-01 -2.72994608e-01 -9.06208813e-01 2.71767646e-01
5.21863759e-01 -7.46398747e-01 8.55848134e-01 -7.71788657e-01
-1.36740351e+00 3.08610052e-01 3.96209687e-01 9.27037522e-02
1.35911107e-01 8.50953311e-02 -4.98092622e-01 3.72490138e-01
-1.62859216e-01 3.20274800e-01 1.00994134e+00 -1.18714631e+00
-5.00504136e-01 -1.15094580e-01 1.28633544e-01 3.86185974e-01
-2.18098372e-01 -1.03757709e-01 -5.10626078e-01 -6.98451221e-01
2.87308335e-01 -3.97407681e-01 -1.36947230e-01 -6.33964613e-02
-5.69378376e-01 -3.84949386e-01 9.83124197e-01 -7.81234264e-01
1.05644929e+00 -2.43140030e+00 3.86222489e-02 1.27875969e-01
3.71983498e-01 2.24362597e-01 4.82067131e-02 2.41680399e-01
-6.24576882e-02 -1.24660216e-01 -2.57378072e-01 4.82690297e-02
-1.47575185e-01 8.53452459e-02 4.82738726e-02 4.23692852e-01
1.95710376e-01 7.08987415e-01 -8.78878832e-01 -5.80500424e-01
4.00279284e-01 1.50354207e-01 -3.35458994e-01 2.96642244e-01
-2.48810291e-01 4.60028559e-01 -6.96433067e-01 9.74186957e-01
9.33999479e-01 -3.39354336e-01 -4.65812534e-02 -6.66148961e-01
-3.46653193e-01 -3.06153804e-01 -1.02412319e+00 1.64430439e+00
-1.76128864e-01 2.41413131e-01 4.33034152e-01 -1.06753922e+00
1.30367064e+00 5.26435860e-02 2.97370046e-01 -4.98928845e-01
4.30240721e-01 1.83504224e-01 -6.26300201e-02 -6.93824589e-01
4.06364590e-01 -1.70319349e-01 3.21920663e-02 -5.25646247e-02
-1.45634845e-01 -4.57529813e-01 1.30042747e-01 1.91935264e-02
9.02306974e-01 9.58586782e-02 4.31644060e-02 -5.14657855e-01
7.80145586e-01 3.09750080e-01 8.89938653e-01 7.93623254e-02
-8.90434161e-02 6.15605772e-01 4.58015680e-01 -4.43358660e-01
-8.36216390e-01 -1.06624722e+00 1.69180781e-01 2.64792144e-01
7.93194890e-01 -2.25388810e-01 -7.92945683e-01 -6.58242583e-01
-9.18757766e-02 3.71807426e-01 -1.56899557e-01 -3.75201941e-01
-3.26893359e-01 -5.53791344e-01 4.26538698e-02 4.90416110e-01
1.08194554e+00 -1.12738931e+00 -4.31277633e-01 7.34686926e-02
1.32492051e-01 -9.85735714e-01 -5.01988530e-01 -4.30436969e-01
-6.86152279e-01 -1.25454664e+00 -4.55152690e-01 -1.09161353e+00
1.03369319e+00 7.05200255e-01 9.77402329e-01 7.06899881e-01
-6.28032446e-01 1.47892818e-01 -4.61263478e-01 -3.72301251e-01
-1.53848365e-01 -2.65137851e-01 -6.60676956e-02 -1.57221537e-02
-3.93038616e-02 -4.04006302e-01 -1.03762078e+00 4.91906554e-01
-9.95274782e-01 4.65644300e-01 8.92742932e-01 8.89594078e-01
6.79134905e-01 7.71049201e-01 2.87532270e-01 -5.34034371e-01
6.78395927e-01 -2.79386640e-01 -1.01398897e+00 9.23504233e-02
-3.95998150e-01 -6.02644444e-01 1.20908225e+00 4.76673692e-02
-1.07237172e+00 -3.01647872e-01 -4.88415249e-02 -7.55166352e-01
-2.11769715e-01 6.77153885e-01 -4.76101011e-01 -1.69402555e-01
2.20370054e-01 1.37467310e-01 3.50237042e-01 -2.32557759e-01
8.90982598e-02 6.88979268e-01 4.79620755e-01 -1.07941203e-01
9.06356752e-01 3.19539875e-01 2.12718248e-01 -9.72687900e-01
-6.55909836e-01 -2.55065292e-01 -3.54500204e-01 -7.52776563e-01
1.04331207e+00 -7.76273608e-01 -8.86079013e-01 6.87486827e-01
-1.04834390e+00 -3.61440271e-01 -1.62962258e-01 5.89794874e-01
-2.57663310e-01 7.46822298e-01 -7.20957100e-01 -5.65077364e-01
-3.95875961e-01 -1.30344152e+00 1.01809335e+00 7.26534724e-01
6.14803672e-01 -8.77361178e-01 -6.15398347e-01 4.88761514e-01
2.27907225e-01 2.41299912e-01 7.44835913e-01 -6.22613616e-02
-9.85777020e-01 -1.48959965e-01 -6.46952033e-01 7.00808406e-01
4.44934249e-01 1.40058428e-01 -5.68852603e-01 -5.36360621e-01
3.19929987e-01 -1.35260567e-01 7.26730406e-01 3.37337494e-01
1.54764080e+00 -2.21010581e-01 -1.83693156e-01 8.13587725e-01
1.55715859e+00 3.38751823e-01 5.46926081e-01 1.40358865e-01
8.57889771e-01 4.32643831e-01 1.13911176e+00 4.29705709e-01
1.10554785e-01 1.32221624e-01 9.34894383e-01 -5.77565551e-01
-3.32370251e-01 7.03112036e-02 3.56874943e-01 1.21654785e+00
-3.35005999e-01 -3.95206511e-01 -5.88869810e-01 2.98996180e-01
-1.55319524e+00 -5.02726555e-01 -2.02637315e-01 1.86179614e+00
2.38495648e-01 1.92662090e-01 -4.43333656e-01 2.97551341e-02
7.76196063e-01 2.47890830e-01 -5.03431320e-01 -1.78832725e-01
4.50733863e-02 5.55264540e-02 4.08419639e-01 1.43458754e-01
-1.04242599e+00 7.31160879e-01 4.69761705e+00 1.01686573e+00
-1.04596353e+00 -1.90272659e-01 6.32574916e-01 3.98515910e-01
-2.36055970e-01 -6.31298646e-02 -4.17853266e-01 3.89906824e-01
1.53760493e-01 1.43013140e-02 7.12955236e-01 8.00166309e-01
1.82714865e-01 -5.34517830e-03 -3.61395657e-01 9.45926428e-01
1.54988065e-01 -1.10860932e+00 8.51674825e-02 -1.89669073e-01
5.54014027e-01 -3.10730010e-01 -1.33960068e-01 2.14713272e-02
8.20917413e-02 -7.58611262e-01 5.00449657e-01 5.40030241e-01
8.18047225e-01 -7.14425564e-01 9.24602211e-01 1.58520132e-01
-1.70289516e+00 -1.67762354e-01 -9.36207592e-01 -3.69421504e-02
3.92010026e-02 9.56906199e-01 -7.00417399e-01 1.42780030e+00
7.52052426e-01 8.32325161e-01 -4.42583293e-01 9.89104688e-01
-4.68396753e-01 5.65099359e-01 -1.68495685e-01 -3.43002230e-02
3.42642516e-01 -8.26295376e-01 6.47399187e-01 7.87295938e-01
6.45091653e-01 7.86518529e-02 5.58479130e-01 1.12643051e+00
-2.66032130e-03 1.27384678e-01 -5.99338293e-01 -1.62463576e-01
2.61649221e-01 1.90630352e+00 -1.15527630e+00 -4.16512519e-01
-5.71223795e-01 8.31505001e-01 7.77541175e-02 4.49138552e-01
-9.44910586e-01 -8.32639694e-01 3.61968875e-01 5.48711121e-02
2.44262740e-01 -5.65826237e-01 -4.20493260e-02 -8.60343635e-01
1.30631879e-03 -7.37173200e-01 3.97281885e-01 -1.14850307e+00
-1.30785179e+00 6.55737281e-01 -1.87840447e-01 -1.72883487e+00
5.81028163e-01 -1.10837078e+00 -8.41249764e-01 9.22839165e-01
-1.47225893e+00 -1.25671303e+00 -1.05556488e+00 3.36359471e-01
7.22875535e-01 -1.43986613e-01 2.63083458e-01 3.76763903e-02
-8.60055029e-01 3.71629074e-02 -2.44028538e-01 2.19984904e-01
3.82838100e-01 -9.46885884e-01 3.43428075e-01 1.09930599e+00
-4.24140245e-01 4.40061718e-01 2.92077303e-01 -8.62021148e-01
-1.68996692e+00 -1.44312048e+00 -1.34578824e-01 4.10493791e-01
6.00574136e-01 -2.05377936e-01 -8.97513449e-01 4.05762345e-01
4.22520101e-01 1.84044287e-01 2.09018365e-01 -6.04826391e-01
3.41931731e-01 -3.11160982e-01 -8.59021306e-01 5.88304102e-01
1.08300459e+00 -1.08669743e-01 -3.37837607e-01 6.33938849e-01
1.07543087e+00 -5.50925851e-01 -1.07528436e+00 7.00950682e-01
1.50067106e-01 -1.10567212e+00 7.89951980e-01 -2.18364503e-02
4.67474252e-01 -9.46137965e-01 2.22254634e-01 -1.31354845e+00
-6.00737929e-01 -4.46364790e-01 3.73119652e-01 9.28075433e-01
1.27753198e-01 -9.88222837e-01 5.72305322e-01 -1.34035692e-01
-1.04695070e+00 -9.24999058e-01 -5.64158082e-01 -8.44226122e-01
-4.89960909e-01 -9.18363556e-02 8.20643902e-01 7.39634037e-01
-1.44690350e-01 2.86680073e-01 1.18854448e-01 6.09667897e-01
6.16071761e-01 4.88341749e-01 5.78678370e-01 -1.03694022e+00
-3.01633716e-01 -4.14925575e-01 -4.34277624e-01 -1.19400132e+00
-2.12662354e-01 -9.12434518e-01 2.66979039e-01 -1.94727373e+00
-2.65698165e-01 -3.46470892e-01 -3.18229288e-01 2.95624703e-01
-2.92604685e-01 1.61075279e-01 -7.96250254e-02 1.63081035e-01
-4.30899441e-01 7.79331505e-01 2.15497589e+00 -3.01615834e-01
3.99182960e-02 -2.05388665e-01 -4.32858557e-01 6.24567926e-01
9.32189584e-01 2.35043570e-01 -5.90307236e-01 -2.87734807e-01
1.38989523e-01 5.55913299e-02 5.54226995e-01 -1.28946614e+00
3.17860454e-01 -8.31334740e-02 5.14914572e-01 -7.64593005e-01
1.60758734e-01 -8.06232035e-01 8.88839290e-02 7.52277017e-01
6.39198482e-01 1.82693750e-01 2.07523867e-01 5.13202429e-01
-4.27718908e-01 -7.62833804e-02 5.42923868e-01 -3.37855637e-01
-8.64773333e-01 5.28178692e-01 -3.70875061e-01 -4.87064272e-01
1.15869427e+00 -3.75261277e-01 -5.67294061e-01 1.52956277e-01
-2.28634283e-01 3.14045936e-01 6.56873524e-01 1.60975650e-01
1.11181092e+00 -1.19221294e+00 -4.23777014e-01 8.49891663e-01
-5.98971285e-02 7.12039888e-01 6.49240136e-01 9.48454320e-01
-1.21280527e+00 9.95055959e-02 -2.88481951e-01 -6.70209706e-01
-1.10351574e+00 7.74024904e-01 2.68508732e-01 -9.85992849e-02
-8.06215763e-01 8.64205062e-01 5.52327871e-01 -3.43434244e-01
-2.99828559e-01 -6.87232196e-01 -2.97997802e-01 -4.31510389e-01
4.63151075e-02 3.22850108e-01 3.96845415e-02 -2.62474090e-01
3.03204618e-02 5.68732917e-01 3.01507533e-01 5.27633667e-01
8.86949420e-01 8.60972097e-04 -6.50147915e-01 -6.06218874e-02
7.66591728e-01 -1.22132912e-01 -1.27888548e+00 3.01222742e-01
-7.80002296e-01 -5.11802316e-01 1.94174469e-01 -2.26145059e-01
-1.56035340e+00 8.21334243e-01 3.11759055e-01 5.22647083e-01
1.64905632e+00 -1.76010966e-01 8.80441785e-01 1.05814055e-01
4.48162407e-01 -8.63960922e-01 2.82395363e-01 2.27887556e-01
1.04955637e+00 -8.33186507e-01 2.71387011e-01 -1.01639616e+00
-4.00051624e-01 1.37564075e+00 9.91485178e-01 -4.16352689e-01
6.46573246e-01 6.25745021e-03 -5.66897616e-02 -7.07980871e-01
-3.82412642e-01 7.77698457e-02 3.50494653e-01 3.11597854e-01
8.35177377e-02 1.84014022e-01 -2.22456172e-01 7.25967884e-01
-2.41962180e-01 -2.63167948e-01 3.40646148e-01 7.99397051e-01
-5.71199000e-01 -6.67141855e-01 -5.06695509e-01 5.90983510e-01
1.22320494e-02 -4.80826534e-02 2.57577319e-02 7.20536888e-01
2.70356148e-01 9.98879015e-01 1.99992314e-01 -7.49175310e-01
3.14897954e-01 -5.68318188e-01 2.24612147e-01 -5.89352906e-01
-3.18493187e-01 9.91534814e-02 6.08130395e-02 -6.59374654e-01
-2.48949304e-01 -2.44209707e-01 -1.52359223e+00 -6.43745298e-04
-4.32173938e-01 2.68616557e-01 3.83893281e-01 5.41466713e-01
3.57356697e-01 1.21397316e+00 9.30471539e-01 -7.43929923e-01
-2.20848948e-01 -9.95053947e-01 -9.42233682e-01 1.41552076e-01
-1.06504425e-01 -8.26504290e-01 -4.00929421e-01 -1.54896881e-02] | [9.603386878967285, -0.8162592053413391] |
ebd9836d-fd97-4ed7-a42c-d173b31742fa | understanding-user-behavior-in-carousel | 2307.01866 | null | https://arxiv.org/abs/2307.01866v1 | https://arxiv.org/pdf/2307.01866v1.pdf | Understanding User Behavior in Carousel Recommendation Systems for Click Modeling and Learning to Rank | Carousels (also-known as multilists) have become the standard user interface for e-commerce platforms replacing the ranked list, the previous standard for recommender systems. While the research community has begun to focus on carousels, there are many unanswered questions and undeveloped areas when compared to the literature for ranked lists, which includes information retrieval research on the presentation of web search results. This work is an extended abstract for the RecSys 2023 Doctoral Symposium outlining a PhD project, with the main contribution of addressing the undeveloped areas in carousel recommenders: 1) the formulation of new click models and 2) learning to rank with click data. We present two significant barriers for this contribution and the field: lack of public datasets and lack of eye tracking user studies of browsing behavior. Clicks, the standard feedback collected by recommender systems, are insufficient to understand the whole interaction process of a user with a recommender requiring system designers to make assumptions, especially on browsing behavior. Eye tracking provides a means to elucidate the process and test these assumptions. Thus, to address these barriers and encourage future work, we will conduct an eye tracking user study within a carousel movie recommendation setting and make the dataset publicly available. Moreover, the insights learned on browsing behavior will help motivate the formulation of new click models and learning to rank. | ['Santiago de Leon-Martinez'] | 2023-07-04 | null | null | null | null | ['learning-to-rank', 'learning-to-rank', 'movie-recommendation', 'information-retrieval'] | ['graphs', 'miscellaneous', 'miscellaneous', 'natural-language-processing'] | [-4.77145314e-02 -1.18615001e-01 -6.10293508e-01 -4.48285282e-01
-7.41634220e-02 -8.58483374e-01 4.46220040e-01 1.33454455e-02
-4.54999596e-01 1.08442418e-01 2.50684589e-01 -9.11332846e-01
-7.79919386e-01 -4.39598411e-01 -2.57525206e-01 -3.15233655e-02
-4.95421030e-02 2.02991113e-01 4.80032533e-01 -4.24286276e-01
8.41924131e-01 8.30206648e-02 -2.06687307e+00 6.34350300e-01
7.21128464e-01 7.90956736e-01 4.01373863e-01 5.91604292e-01
-1.88108295e-01 4.68823105e-01 -2.74369001e-01 -5.76038063e-01
4.83891845e-01 -2.27717832e-01 -4.62636471e-01 3.28554213e-02
8.80406260e-01 -6.68176770e-01 -1.67507201e-01 7.16187418e-01
3.40096921e-01 4.25001591e-01 3.32293987e-01 -1.28130364e+00
-1.00579655e+00 6.25332117e-01 -2.44079694e-01 5.20310104e-01
6.87205851e-01 -3.07576656e-01 1.69381750e+00 -5.87999582e-01
7.96516180e-01 1.14135337e+00 4.75266665e-01 3.87379736e-01
-1.13354564e+00 -5.08585215e-01 5.24353743e-01 2.81285852e-01
-1.00945497e+00 -2.58659363e-01 2.24333704e-01 -6.45237386e-01
4.59878027e-01 6.85459018e-01 6.86664045e-01 1.11288023e+00
-1.50977746e-01 1.00259662e+00 1.19807935e+00 -5.67153096e-01
1.34387657e-01 8.68177533e-01 9.65176761e-01 2.59556442e-01
5.81541777e-01 4.98675495e-01 -7.33683586e-01 -3.68139505e-01
6.15526855e-01 2.69633383e-01 -2.51777202e-01 -7.53975511e-01
-6.19917214e-01 1.01911271e+00 1.70342639e-01 2.48654693e-01
-8.54218453e-02 -4.69120562e-01 -2.48148534e-02 7.86264896e-01
2.71097183e-01 6.84346974e-01 -4.26729470e-01 -2.35507041e-01
-8.56215298e-01 4.89231408e-01 1.26735139e+00 1.03565204e+00
2.51631916e-01 -5.34761965e-01 -1.61333233e-01 9.94249105e-01
9.47595060e-01 3.28755051e-01 2.84779102e-01 -9.03806150e-01
1.52037382e-01 4.38615173e-01 6.15798533e-01 -9.64714229e-01
-3.55639160e-01 -4.82154220e-01 4.20109510e-01 2.78815508e-01
9.02619183e-01 -8.67068321e-02 -3.38078171e-01 1.11225104e+00
-1.28443688e-01 -5.30110598e-01 -7.81807005e-01 1.10826325e+00
7.84358799e-01 1.04053162e-01 9.20257941e-02 -1.71399921e-01
1.41158652e+00 -8.56103241e-01 -7.25345135e-01 1.98532268e-02
8.79883468e-01 -1.15135384e+00 1.36648273e+00 7.14662850e-01
-1.07999587e+00 -5.07408559e-01 -9.44197834e-01 -3.85344476e-02
-5.09179235e-01 -6.33048192e-02 1.18078601e+00 1.38685453e+00
-9.89108682e-01 5.65135121e-01 -4.41538006e-01 -9.70671952e-01
-2.16997769e-02 1.87492624e-01 2.62184978e-01 -1.41233563e-01
-1.21563077e+00 8.33068430e-01 -4.61129248e-01 -3.09203714e-01
-3.47331315e-01 -8.55155826e-01 -1.94541246e-01 1.89759135e-02
5.03790557e-01 -3.10221314e-01 1.62852275e+00 -7.02566803e-01
-1.34061265e+00 3.76672894e-01 1.07715465e-01 -6.17062859e-02
3.14448237e-01 -3.87576520e-01 -7.59247363e-01 -4.53095585e-01
3.16210985e-02 1.07403465e-01 2.44788572e-01 -1.17610025e+00
-1.19118547e+00 -4.26371723e-01 5.38439453e-01 2.16995478e-01
-5.00743091e-01 2.83159673e-01 -5.61386049e-01 -4.39494759e-01
-4.22065258e-02 -9.98266280e-01 -3.15526724e-02 -7.56155103e-02
6.89567104e-02 -5.58691204e-01 3.92099798e-01 -2.39804119e-01
1.93610871e+00 -1.99863982e+00 -4.93413597e-01 4.45309371e-01
2.63995767e-01 2.58662999e-01 -1.29654884e-01 8.04592669e-01
9.71895903e-02 2.99219131e-01 1.03464901e+00 1.85860574e-01
2.63102233e-01 -5.25843024e-01 -4.42478657e-01 1.20181240e-01
-7.77975678e-01 6.24160528e-01 -9.94066775e-01 5.91617860e-02
1.20694377e-01 2.84033984e-01 -8.04610074e-01 1.48580791e-02
-1.61854681e-02 -2.11944371e-01 -5.39655328e-01 4.56911862e-01
4.25639540e-01 -4.64236140e-01 1.55536518e-01 -1.13353617e-01
-6.43618703e-01 6.77051187e-01 -1.27504194e+00 1.30590630e+00
-3.95535469e-01 6.11942053e-01 -1.09190047e-01 -2.43320525e-01
5.48907816e-01 1.64746553e-01 3.96323949e-01 -9.32388961e-01
1.85107857e-01 1.11988999e-01 4.28630114e-01 -5.89986384e-01
5.93479037e-01 3.57556075e-01 3.98350120e-01 1.03863859e+00
-2.95333117e-01 4.32142526e-01 6.79793954e-01 4.35060740e-01
1.02628899e+00 2.24720642e-01 -1.22711405e-01 -4.86426979e-01
8.64706561e-02 9.05811638e-02 -8.37078020e-02 1.36723030e+00
-1.19523428e-01 1.43105194e-01 1.63050875e-01 -2.85646081e-01
-7.67976999e-01 -8.65961313e-01 -3.86223346e-01 1.95873582e+00
1.90598011e-01 -9.85574782e-01 -6.29705966e-01 -5.84432244e-01
1.57870889e-01 1.11067438e+00 -4.54463720e-01 1.72692329e-01
-5.80176376e-02 -5.02163351e-01 -1.64615408e-01 2.84332782e-01
2.05121692e-02 -6.91119015e-01 -3.28088135e-01 1.18731655e-01
-6.30837306e-02 -5.55327594e-01 -7.30111778e-01 -1.64358243e-01
-1.08395767e+00 -1.34361517e+00 -5.85137367e-01 -4.52978194e-01
4.25236791e-01 1.12572396e+00 1.18079615e+00 4.01087791e-01
-1.26459643e-01 8.68247569e-01 -5.55035472e-01 -3.00191790e-01
3.49866971e-02 2.81902272e-02 6.42575044e-03 -2.96514094e-01
1.23946226e+00 -2.38541320e-01 -1.09253633e+00 1.06051040e+00
-5.87549090e-01 -3.68517637e-01 7.05848098e-01 3.17098916e-01
-2.34519735e-01 -9.42073315e-02 2.39582092e-01 -1.46720791e+00
1.23025513e+00 -7.33398199e-01 -6.84765100e-01 2.77228415e-01
-1.47597039e+00 -2.18465909e-01 -1.36057083e-02 -4.63644475e-01
-1.15484130e+00 -4.34467167e-01 2.55818099e-01 5.67490637e-01
-4.82664146e-02 5.88168263e-01 3.46989304e-01 -9.44615155e-02
1.14549196e+00 -3.65819484e-01 -6.89425394e-02 -1.07859838e+00
4.67807919e-01 1.17435133e+00 -2.38246664e-01 -2.33775541e-01
5.65696418e-01 3.37016433e-01 -6.80158615e-01 -8.86755943e-01
-8.78796160e-01 -1.05498207e+00 -3.66541892e-01 -3.13983619e-01
4.13394451e-01 -6.00309491e-01 -1.09095490e+00 -2.37194479e-01
-4.63196576e-01 -2.91130722e-01 1.70565829e-01 6.58286989e-01
-2.48456582e-01 4.75880742e-01 -7.42214561e-01 -9.39246297e-01
6.59781098e-02 -8.72068405e-01 4.53009456e-01 3.59269798e-01
-7.00505078e-01 -9.51078534e-01 1.78773463e-01 1.02792323e+00
7.04807281e-01 -8.58368337e-01 7.69835413e-01 -8.75632882e-01
-6.99090123e-01 -4.98492002e-01 -2.00764939e-01 -9.75737497e-02
-1.50133064e-02 2.13663429e-02 -6.61479890e-01 -4.08035845e-01
-1.78842276e-01 3.48826386e-02 4.28739190e-01 2.40743652e-01
7.77545333e-01 1.55888677e-01 -5.43061316e-01 -2.75556073e-02
9.47863519e-01 4.64633942e-01 4.65269476e-01 6.16556764e-01
7.10988268e-02 7.02243567e-01 8.27171564e-01 4.47098702e-01
3.83294433e-01 1.00216687e+00 1.35805115e-01 4.40319806e-01
-2.80528724e-01 -4.74600077e-01 3.28907937e-01 4.77772892e-01
-3.42037141e-01 -4.90455359e-01 -2.44771019e-01 -1.49371848e-02
-1.69767392e+00 -9.89058673e-01 -4.80526000e-01 2.69936800e+00
2.76169777e-01 2.13378876e-01 4.92306352e-01 1.94964372e-02
6.66422844e-01 -2.07724229e-01 -2.89998591e-01 -2.50139266e-01
4.62630153e-01 -2.79282052e-02 5.81436038e-01 6.14202201e-01
-8.49501550e-01 6.53642893e-01 7.27918911e+00 2.73237586e-01
-5.13135493e-01 -8.18764791e-02 -4.00660560e-02 -2.03565270e-01
-3.81834477e-01 1.38435259e-01 -1.18719256e+00 5.85303068e-01
1.11822200e+00 -1.45443350e-01 9.09381270e-01 1.04324687e+00
5.88507771e-01 -1.50638223e-01 -1.33193505e+00 7.70124555e-01
1.35301933e-01 -9.22087848e-01 -2.40184680e-01 4.91615117e-01
5.88707030e-01 -1.95590369e-02 3.62996906e-01 5.23398161e-01
6.50821269e-01 -5.70428789e-01 3.66717368e-01 4.08481389e-01
1.37283430e-01 -9.68887878e-04 4.17551994e-01 1.56073421e-01
-5.79775751e-01 -4.51221943e-01 -3.57690126e-01 -3.46017420e-01
1.84835404e-01 2.57371694e-01 -5.44072390e-01 -1.96437448e-01
9.60057616e-01 6.96744680e-01 -7.62885094e-01 1.71845329e+00
7.75105730e-02 6.55076742e-01 -4.58549932e-02 -5.68952322e-01
-1.22910435e-03 -3.76440555e-01 4.07804400e-01 7.85562277e-01
2.91707397e-01 5.34294471e-02 7.09335646e-03 5.93284786e-01
2.73808062e-01 4.63717192e-01 -3.81482899e-01 -3.84897441e-01
4.44329858e-01 1.36211240e+00 -6.72254920e-01 1.59769475e-01
-1.00144100e+00 6.43620491e-01 -4.64220271e-02 5.22764206e-01
-3.92069519e-01 -3.92972946e-01 6.34928703e-01 9.88086462e-01
1.57442302e-01 -2.58726567e-01 -2.20198914e-01 -9.65125740e-01
-2.08956257e-01 -1.07238388e+00 5.15841603e-01 -7.67423511e-01
-1.44340682e+00 1.68728992e-01 -1.57291524e-03 -1.16156411e+00
4.60604578e-02 -8.70295525e-01 -3.72543544e-01 8.35889161e-01
-1.14587629e+00 -5.25999725e-01 -5.60347199e-01 2.16382921e-01
6.58049762e-01 -1.27221107e-01 5.30767143e-01 7.29282677e-01
-2.62600690e-01 6.55903876e-01 4.02402073e-01 -4.17446882e-01
1.21042240e+00 -1.23785102e+00 2.16623858e-01 1.38923973e-01
3.19200635e-01 1.58184457e+00 6.98567390e-01 -7.31855571e-01
-1.53156507e+00 -2.05550194e-01 9.64189649e-01 -1.06833684e+00
9.47492361e-01 -3.38756204e-01 -4.41763252e-01 6.78614557e-01
1.54900149e-01 -7.33055711e-01 1.36963499e+00 9.03513193e-01
-2.52006263e-01 6.27990514e-02 -5.93270183e-01 8.41057241e-01
1.36520302e+00 -3.53531778e-01 -5.41184485e-01 4.80202287e-01
8.13801587e-02 1.22244172e-01 -8.50933254e-01 -1.18854135e-01
1.19318354e+00 -1.21008432e+00 8.92450154e-01 -8.47529113e-01
6.98745400e-02 -1.44267499e-01 -3.45383920e-02 -1.10813987e+00
-7.94284344e-01 -7.04853117e-01 1.09430969e-01 1.01033831e+00
8.20323110e-01 -3.74093294e-01 1.16292953e+00 1.24806130e+00
3.95878293e-02 -4.47914839e-01 -3.11943069e-02 -6.31201029e-01
-3.16366553e-01 -4.66494918e-01 1.38451353e-01 5.84879637e-01
4.72876221e-01 5.46261132e-01 -3.64253491e-01 -2.19882026e-01
5.34016073e-01 -8.84300694e-02 9.01174724e-01 -1.89194906e+00
-4.32503045e-01 -6.51026726e-01 1.05493488e-02 -1.78966081e+00
-5.60606122e-01 -8.28359306e-01 -3.05981249e-01 -1.43731487e+00
3.01754534e-01 -6.48927033e-01 -4.93936568e-01 -1.52525306e-01
1.66989014e-01 2.44541258e-01 2.28577435e-01 4.27210003e-01
-8.64540875e-01 -2.44975820e-01 1.15996635e+00 2.38852143e-01
-4.01475400e-01 8.67051959e-01 -1.40762866e+00 5.12153566e-01
3.60486686e-01 -1.88599870e-01 -9.64783132e-01 -1.54236838e-01
9.48277831e-01 -2.23569334e-01 -1.06828891e-01 -4.42341447e-01
4.07955348e-01 -1.51382387e-01 7.19316006e-02 -5.45602441e-01
1.10202499e-01 -1.04554331e+00 2.88101695e-02 1.09076768e-01
-8.21510673e-01 -5.90085723e-02 -2.32070357e-01 7.52028584e-01
3.56419027e-01 -7.09528029e-01 6.65739700e-02 -8.71732160e-02
-6.73813999e-01 8.23751241e-02 -7.35135436e-01 -1.15206115e-01
7.11351097e-01 -4.99458700e-01 -4.19759661e-01 -7.98957050e-01
-9.02796388e-01 2.24489063e-01 4.23790783e-01 1.00037444e+00
4.62271124e-02 -1.08838737e+00 -1.90457508e-01 1.02664895e-01
3.20843369e-01 -1.32083905e+00 3.30804512e-02 6.86297238e-01
-1.62272021e-01 1.05459142e+00 -1.16312124e-01 -1.15422033e-01
-1.31230688e+00 6.40149415e-01 -1.22523479e-01 6.79245815e-02
-2.33307108e-01 5.77468753e-01 3.04421186e-02 -2.33955607e-01
6.95206344e-01 -2.96182279e-02 -7.33587861e-01 2.63774633e-01
8.41672480e-01 8.27898264e-01 1.32650971e-01 -9.38485414e-02
-2.07257476e-02 1.71101838e-01 -6.96699798e-01 -9.05543864e-02
9.95292068e-01 -5.83416820e-01 2.20042288e-01 5.25811493e-01
7.11250126e-01 2.49757975e-01 -8.45912814e-01 -1.90789416e-01
3.55714440e-01 -1.01797962e+00 3.09236079e-01 -1.15874660e+00
-6.94384038e-01 5.51251769e-01 9.86023843e-01 8.44932854e-01
3.58920515e-01 -1.21333368e-01 4.48459983e-01 6.30539000e-01
3.87689739e-01 -1.34559536e+00 1.06580779e-01 2.17889220e-01
5.41756272e-01 -1.16679096e+00 1.57067165e-01 -6.45211101e-01
-6.07098281e-01 1.01235378e+00 6.06224775e-01 3.08099180e-01
1.27640724e+00 -2.90866047e-01 3.47631425e-01 -4.72912431e-01
-7.70745516e-01 -2.24804401e-01 5.09749949e-01 4.92492229e-01
1.35714793e+00 -2.57222652e-01 -1.05286515e+00 8.33167613e-01
-1.67177022e-01 3.31250340e-01 4.68468338e-01 7.46816576e-01
-6.44506633e-01 -1.57001126e+00 -1.19711999e-02 1.00939846e+00
-4.21256632e-01 -3.57726991e-01 -3.98770243e-01 7.37121940e-01
-3.49175602e-01 1.63375878e+00 -9.12588462e-02 -5.38264513e-01
5.68582475e-01 8.74138176e-02 1.20706655e-01 -8.76839280e-01
-6.66046381e-01 4.13825773e-02 3.82105738e-01 -7.02525556e-01
-1.08165413e-01 -8.78505826e-01 -2.99156457e-01 -4.41859692e-01
-1.00738788e+00 5.55050135e-01 8.38698924e-01 6.24442399e-01
5.69711387e-01 1.51065782e-01 4.48644638e-01 -4.19801712e-01
-9.15101111e-01 -9.94669795e-01 -9.13789093e-01 7.09160030e-01
-4.02508169e-01 -1.03533387e+00 -5.89871824e-01 -2.69765824e-01] | [10.044998168945312, 5.806351661682129] |
d7adf715-2d1f-4aaf-a761-d805e71cf728 | keyword-aware-influential-community-search-in | 1912.02114 | null | https://arxiv.org/abs/1912.02114v1 | https://arxiv.org/pdf/1912.02114v1.pdf | Keyword Aware Influential Community Search in Large Attributed Graphs | We introduce a novel keyword-aware influential community query KICQ that finds the most influential communities from an attributed graph, where an influential community is defined as a closely connected group of vertices having some dominance over other groups of vertices with the expertise (a set of keywords) matching with the query terms (words or phrases). We first design the KICQ that facilitates users to issue an influential CS query intuitively by using a set of query terms, and predicates (AND or OR). In this context, we propose a novel word-embedding based similarity model that enables semantic community search, which substantially alleviates the limitations of exact keyword based community search. Next, we propose a new influence measure for a community that considers both the cohesiveness and influence of the community and eliminates the need for specifying values of internal parameters of a network. Finally, we propose two efficient algorithms for searching influential communities in large attributed graphs. We present detailed experiments and a case study to demonstrate the effectiveness and efficiency of the proposed approaches. | ['Farhana M. Choudhury', 'Yong-Bin Kang', 'Md. Saiful Islam', 'Mohammed Eunus Ali', 'Timos Sellis'] | 2019-12-04 | null | null | null | null | ['community-search'] | ['graphs'] | [-7.43508488e-02 9.61382464e-02 -2.38834172e-01 5.06709963e-02
-2.23205194e-01 -7.21978188e-01 7.64720559e-01 7.69468188e-01
-4.37715679e-01 2.87992835e-01 4.33965355e-01 -6.56128749e-02
-7.23981917e-01 -1.25406039e+00 -2.90433258e-01 -4.76370186e-01
-5.46753287e-01 6.95768833e-01 7.30264783e-01 -2.55844355e-01
5.91485381e-01 1.97155669e-01 -1.22299135e+00 -2.39262193e-01
9.47046161e-01 3.31786275e-01 4.09711957e-01 4.30729508e-01
-4.47916925e-01 6.08268857e-01 -5.48595250e-01 -5.31070650e-01
-9.16176215e-02 -1.48421898e-01 -8.37578356e-01 -2.01139405e-01
-3.99678111e-01 2.06465185e-01 -2.11799696e-01 8.98191631e-01
3.07668209e-01 -1.09392814e-01 7.43295610e-01 -1.65881312e+00
-6.48962796e-01 9.66392040e-01 -5.71248055e-01 3.52324426e-01
4.86714602e-01 -4.81513649e-01 1.53229952e+00 -8.51577759e-01
9.05160189e-01 1.11122310e+00 3.86010230e-01 -8.36876407e-02
-9.07274365e-01 -5.76539755e-01 2.98863798e-01 1.56767085e-01
-1.76000273e+00 8.60312060e-02 9.36192811e-01 -3.05000067e-01
7.86632001e-01 2.98504144e-01 9.79649544e-01 5.17472744e-01
-1.91795930e-01 5.17322004e-01 3.61432850e-01 -4.08661425e-01
5.12674510e-01 1.87604636e-01 2.30863512e-01 5.41853309e-01
7.73682296e-01 -5.62180400e-01 -6.14935100e-01 -1.06332397e+00
3.91983509e-01 2.78874159e-01 -4.88181323e-01 -8.53880346e-01
-1.05972886e+00 1.14174402e+00 6.03627622e-01 7.51308918e-01
-3.65419716e-01 4.54037100e-01 8.37072656e-02 1.96866348e-01
4.62014973e-01 4.66260374e-01 4.41514477e-02 2.86367148e-01
-7.61054099e-01 -1.11573585e-03 1.11325157e+00 1.07492840e+00
7.79077888e-01 -7.00658023e-01 3.53692472e-02 5.59833229e-01
8.08449090e-01 5.04813612e-01 -2.92269945e-01 -8.32474947e-01
2.16235384e-01 1.09945726e+00 1.27174601e-01 -1.75952542e+00
2.50400722e-01 -5.25724590e-01 -5.61059415e-01 -5.69835305e-01
-2.18746394e-01 4.65813816e-01 -8.59326571e-02 1.49232483e+00
3.60226214e-01 1.79151341e-01 1.38032027e-02 7.37529695e-01
5.93687713e-01 4.00653511e-01 1.36835855e-02 -3.01067144e-01
1.12765932e+00 -7.81849742e-01 -5.93250990e-01 2.18387112e-01
3.62971365e-01 -6.94735885e-01 7.31594980e-01 -2.62801319e-01
-8.44438910e-01 2.84283072e-01 -9.53612983e-01 3.18863183e-01
-4.62735862e-01 -7.35175788e-01 5.57157636e-01 3.46629530e-01
-1.47137868e+00 3.61979008e-01 -3.19123387e-01 -7.47891843e-01
1.79094464e-01 2.34530374e-01 -6.86805174e-02 -1.22369386e-01
-1.46725166e+00 3.58689785e-01 2.65020192e-01 -4.60452735e-01
-7.72644341e-01 -7.15062141e-01 -4.32408214e-01 4.61476564e-01
7.01328516e-01 -8.03034484e-01 4.83606815e-01 -6.77933097e-01
-4.61748451e-01 7.90729880e-01 -2.53579348e-01 -1.96549326e-01
9.22875628e-02 2.19853029e-01 -4.38941061e-01 6.42134190e-01
5.38724661e-01 2.97398806e-01 5.52836776e-01 -1.42629766e+00
-6.40137017e-01 -2.55353451e-01 4.32089031e-01 2.78576046e-01
-8.68445158e-01 2.94867724e-01 -1.05477571e+00 -5.45310497e-01
1.84936881e-01 -6.24907374e-01 -3.52518946e-01 -5.18982001e-02
-2.53284067e-01 -7.03265667e-01 7.16104686e-01 -5.45589924e-02
1.81940305e+00 -1.93890619e+00 1.78847745e-01 1.12492430e+00
9.98707592e-01 -1.98784217e-01 -2.22651929e-01 1.07734859e+00
2.68557936e-01 7.03673244e-01 -1.39649391e-01 1.16831042e-01
-2.80614048e-01 2.30539050e-02 -5.13560362e-02 2.74973333e-01
-2.39178151e-01 8.98995638e-01 -1.25988734e+00 -7.81282783e-01
-4.88458753e-01 4.40536082e-01 -5.14475346e-01 -3.11091512e-01
7.63112828e-02 -3.03427875e-01 -1.04478514e+00 6.52172446e-01
3.72612000e-01 -8.12745810e-01 4.79707003e-01 1.03887670e-01
-3.99355777e-02 -1.80464134e-01 -1.30955172e+00 1.11298823e+00
9.05347243e-03 1.41197696e-01 3.50965977e-01 -8.12277973e-01
8.66561890e-01 2.99932718e-01 8.09665859e-01 -6.13001697e-02
-1.32971004e-01 3.43422174e-01 -4.16698635e-01 -1.23763122e-01
4.59615439e-01 4.48075384e-01 -2.29817256e-02 1.15635872e+00
-2.85010517e-01 2.75922418e-01 2.82907784e-01 1.36762106e+00
1.54019225e+00 -9.67195392e-01 4.44516450e-01 -7.32826233e-01
7.30336130e-01 1.57426223e-01 4.07877207e-01 8.18784833e-01
-2.97911495e-01 2.80274078e-02 6.68288410e-01 5.19639477e-02
-9.43275630e-01 -9.55871403e-01 4.07723308e-01 9.96027172e-01
6.27110183e-01 -8.93190563e-01 -5.20131528e-01 -4.58176404e-01
5.00994563e-01 1.52434230e-01 -6.01810753e-01 -9.90094501e-04
-2.28349984e-01 -2.94403315e-01 3.25414181e-01 2.71030426e-01
1.50869146e-01 -7.84907997e-01 2.44236691e-03 1.48518726e-01
-4.15590554e-01 -6.77123129e-01 -9.93667305e-01 -1.96691290e-01
-7.85114467e-01 -1.61607409e+00 -6.43207908e-01 -9.96637881e-01
8.84571731e-01 7.71631181e-01 1.22764158e+00 8.57161403e-01
-7.89840892e-02 9.96555984e-01 -6.05922878e-01 3.39783281e-02
-5.00172451e-02 2.05477923e-01 1.15433879e-01 4.71374765e-02
4.30263072e-01 -7.65118182e-01 -7.66562045e-01 3.42070162e-01
-8.67166102e-01 -3.07453573e-01 2.83645421e-01 2.84052223e-01
3.70468676e-01 2.90349305e-01 7.19070137e-01 -8.26543629e-01
1.18521392e+00 -8.06494415e-01 -4.04554546e-01 6.39092028e-01
-1.12580752e+00 6.02133870e-02 9.43824649e-02 -2.39464328e-01
-5.82253397e-01 -2.00178862e-01 5.57931364e-01 -7.60071352e-02
6.67474806e-01 8.61887336e-01 -1.53429419e-01 -1.57939568e-01
4.22276527e-01 5.98707236e-02 -1.19040959e-01 -3.69801909e-01
5.47126949e-01 6.84720933e-01 -2.34087184e-03 -5.71075201e-01
1.09378970e+00 7.83279896e-01 -1.65808678e-01 -6.34516537e-01
-1.15459368e-01 -1.27411008e+00 -4.80623871e-01 -3.99075240e-01
4.18528467e-01 -8.70658278e-01 -9.10341203e-01 3.41698900e-02
-1.10750580e+00 4.73852634e-01 4.92779277e-02 2.50492543e-01
2.96282582e-02 6.24523401e-01 -4.59204942e-01 -7.54385412e-01
-5.98428249e-01 -6.19196653e-01 4.18103844e-01 -3.55328806e-02
-3.14229339e-01 -1.20378542e+00 3.08828861e-01 2.38502815e-01
5.11220574e-01 1.06643653e-02 1.07588887e+00 -9.43302155e-01
-1.09500778e+00 -2.31185853e-01 -3.51045758e-01 -4.90657955e-01
2.52053827e-01 9.86585617e-02 -2.82468408e-01 -3.99869800e-01
-6.31446540e-01 3.71523231e-01 8.05738509e-01 5.78450859e-02
3.80047321e-01 -2.44806066e-01 -1.12822127e+00 1.61364619e-02
1.67244649e+00 2.22385630e-01 2.01351285e-01 3.90661865e-01
5.52928030e-01 5.89497387e-01 3.98059607e-01 5.07816434e-01
5.24560392e-01 3.29396278e-01 4.18887049e-01 2.15129092e-01
5.40373266e-01 -4.67744529e-01 -1.23989023e-01 1.17012966e+00
-3.90587986e-01 -5.23986578e-01 -9.21699047e-01 1.06553745e+00
-1.83905983e+00 -9.56377208e-01 -3.87766153e-01 2.04694819e+00
8.18632662e-01 -2.14166213e-02 3.30415033e-02 1.42631933e-01
1.28723657e+00 1.05150357e-01 -3.13134372e-01 1.46237284e-01
-1.64104417e-01 1.24694435e-02 4.39107895e-01 8.13079357e-01
-6.55275702e-01 9.61930692e-01 6.27971888e+00 8.21440160e-01
-2.93182254e-01 1.95176914e-01 1.12023614e-01 1.29644528e-01
-1.28377485e+00 3.43271285e-01 -5.79523861e-01 1.62768409e-01
4.29257780e-01 -9.26637828e-01 4.85334814e-01 8.90160561e-01
2.73640335e-01 -7.96912685e-02 -8.01598489e-01 6.27277493e-01
1.95265904e-01 -1.52076125e+00 2.25945413e-01 1.94970369e-01
7.88605213e-01 -2.01371208e-01 -3.80381554e-01 -1.40934855e-01
5.35457909e-01 -5.20809948e-01 3.63603473e-01 4.83072430e-01
4.32307810e-01 -7.51429439e-01 4.05083179e-01 2.88767457e-01
-1.60151672e+00 -9.03198123e-02 -3.00123930e-01 1.89584494e-01
9.32004005e-02 7.66420543e-01 -7.09119856e-01 5.39982975e-01
7.86038518e-01 8.07904661e-01 -6.05113029e-01 9.31576073e-01
-1.83743685e-01 5.32373905e-01 -2.85553455e-01 -6.52199984e-01
2.82588869e-01 -4.21301663e-01 8.38871062e-01 9.14374113e-01
2.97302663e-01 2.35065296e-01 1.04169324e-01 9.86440301e-01
-3.46583337e-01 4.57792431e-01 -8.59637916e-01 -3.95979077e-01
1.22653437e+00 1.24687195e+00 -1.19493890e+00 -2.96533227e-01
-2.74882406e-01 8.90451252e-01 1.83904052e-01 6.26684189e-01
-5.02545655e-01 -5.36150992e-01 5.77278852e-01 6.23477697e-01
2.42233545e-01 8.10836069e-03 2.06537023e-01 -8.34212065e-01
-1.02551833e-01 -4.26101089e-01 6.00283206e-01 -5.81575632e-01
-1.23222411e+00 3.25775594e-01 4.87750731e-02 -7.06198275e-01
2.69615144e-01 1.85458422e-01 -7.67245650e-01 7.70436883e-01
-1.25049758e+00 -8.44282866e-01 -2.85283327e-01 9.54337001e-01
-6.12721927e-02 -2.46237189e-01 5.41562676e-01 2.51026779e-01
1.94895808e-02 1.21119857e-01 1.21131115e-01 1.07598282e-01
5.57905376e-01 -1.06525218e+00 2.34854907e-01 7.53607213e-01
-9.60919540e-03 1.23615956e+00 4.34744567e-01 -1.02843070e+00
-1.09843600e+00 -9.09054339e-01 1.78102529e+00 -2.78376669e-01
8.72985482e-01 -3.46707284e-01 -6.42681718e-01 3.72168779e-01
2.92705357e-01 -3.83520097e-01 7.74153173e-01 3.52294356e-01
-3.15595299e-01 -2.31629640e-01 -9.50265229e-01 6.11113131e-01
1.43437493e+00 -5.96600711e-01 -4.48412389e-01 2.70589948e-01
1.18914914e+00 8.00907671e-01 -7.94398606e-01 2.20888227e-01
3.01579565e-01 -6.17503524e-01 1.17553782e+00 -3.50395113e-01
5.05214743e-03 -6.07232511e-01 -1.65480167e-01 -1.06298351e+00
-7.79833674e-01 -4.74515200e-01 -7.68228620e-02 1.29140007e+00
2.81906456e-01 -6.55795813e-01 7.37060249e-01 1.77943841e-01
5.04042447e-01 -4.81842816e-01 -5.53955972e-01 -4.74655479e-01
-4.15900886e-01 -3.26032825e-02 6.98285699e-01 1.22478914e+00
2.47810021e-01 5.88436007e-01 1.70864224e-01 1.86022595e-01
9.55602169e-01 1.84170112e-01 2.05050781e-01 -1.93957877e+00
1.34333178e-01 -2.40981624e-01 -1.99050099e-01 -5.93230784e-01
-3.22364047e-02 -9.59140122e-01 -4.04854953e-01 -1.83158660e+00
7.60590553e-01 -8.40499580e-01 -3.46054256e-01 2.65273482e-01
-1.89778686e-01 -1.20341748e-01 1.80034786e-02 7.92391777e-01
-1.03197467e+00 2.88626254e-01 1.00696349e+00 -4.11936730e-01
-3.13699424e-01 6.40359707e-03 -1.05966341e+00 4.59103167e-01
4.54477638e-01 -7.12542653e-01 -9.08273757e-01 2.66451342e-03
9.44446325e-01 -8.98963511e-02 2.22134441e-01 -3.18904132e-01
8.42795253e-01 -9.21216235e-02 -4.11782295e-01 -5.89976132e-01
-1.80711582e-01 -9.14225399e-01 4.56682533e-01 5.18640757e-01
-4.97652680e-01 9.97593701e-02 -6.97384417e-01 1.15293062e+00
-2.30767697e-01 -3.30666959e-01 2.35300660e-01 -1.25413895e-01
-5.89490771e-01 3.31152827e-01 -4.66478467e-01 2.26252720e-01
1.15231049e+00 -2.79521108e-01 -1.46767393e-01 -6.85269594e-01
-5.82751334e-01 6.77701116e-01 6.93328798e-01 4.56072450e-01
7.92386174e-01 -1.50524998e+00 -7.42727399e-01 -2.81783372e-01
5.93091547e-01 -3.92125160e-01 -3.31969023e-01 6.62202477e-01
-4.47283864e-01 5.18969834e-01 4.27704871e-01 -2.94793546e-01
-1.64593923e+00 7.55902410e-01 -1.11575149e-01 -3.47286373e-01
-2.75753736e-01 7.75947094e-01 6.05237633e-02 -2.93536156e-01
3.22579563e-01 2.11289421e-01 -5.30702293e-01 2.37288669e-01
3.50275010e-01 4.36414123e-01 -2.87769169e-01 -5.14609158e-01
-8.16113174e-01 4.14102674e-01 1.47151649e-01 -1.49173617e-01
1.17637122e+00 -3.34272951e-01 -7.15070903e-01 1.20640345e-01
1.04247952e+00 2.12654188e-01 -3.35860223e-01 -4.87871140e-01
3.43101740e-01 -3.71533602e-01 -3.90132740e-02 -4.16535586e-01
-1.03557837e+00 1.21244147e-01 4.20650691e-02 3.65798712e-01
9.05807972e-01 4.36246008e-01 3.81507635e-01 4.08253938e-01
5.34797013e-01 -1.12350631e+00 2.34811291e-01 2.92055964e-01
6.00207984e-01 -6.28010333e-01 -4.13422696e-02 -7.92640507e-01
-4.85169917e-01 7.43902624e-01 1.68480530e-01 2.36315995e-01
1.14784229e+00 2.26276852e-02 -2.30630517e-01 -7.28475511e-01
-7.41649687e-01 -4.41052258e-01 -2.19144821e-02 5.23843706e-01
1.38117567e-01 -3.34523134e-02 -8.88878942e-01 5.43054640e-01
3.87520581e-01 -2.15144992e-01 4.48645085e-01 9.41107810e-01
-9.72819149e-01 -1.25570405e+00 -2.99821973e-01 2.05342487e-01
-1.84563220e-01 -4.67548221e-01 -9.60910022e-01 6.96105659e-01
-9.89880972e-03 1.25736415e+00 1.89471856e-01 -3.93401206e-01
-1.75397042e-02 -2.14299127e-01 -2.11666897e-01 -5.52860439e-01
-3.88618916e-01 1.02828085e-01 -2.56448817e-02 -2.66422391e-01
-7.77804255e-01 -2.92962790e-01 -1.20786619e+00 -6.79779828e-01
-6.01143062e-01 1.02469981e+00 1.88622236e-01 4.72133189e-01
5.05394876e-01 1.08254552e-01 9.58405912e-01 2.16464713e-01
7.95807410e-03 -7.93407977e-01 -1.00980353e+00 3.51340055e-01
-2.83834577e-01 -5.97121954e-01 -6.45801306e-01 -2.97092949e-03] | [7.304025173187256, 5.878608226776123] |
c3ee841f-94a5-40b7-b2d4-af4effc4b538 | towards-dense-volumetric-pancreas | 1711.06439 | null | http://arxiv.org/abs/1711.06439v2 | http://arxiv.org/pdf/1711.06439v2.pdf | Towards dense volumetric pancreas segmentation in CT using 3D fully convolutional networks | Pancreas segmentation in computed tomography imaging has been historically
difficult for automated methods because of the large shape and size variations
between patients. In this work, we describe a custom-build 3D fully
convolutional network (FCN) that can process a 3D image including the whole
pancreas and produce an automatic segmentation. We investigate two variations
of the 3D FCN architecture; one with concatenation and one with summation skip
connections to the decoder part of the network. We evaluate our methods on a
dataset from a clinical trial with gastric cancer patients, including 147
contrast enhanced abdominal CT scans acquired in the portal venous phase. Using
the summation architecture, we achieve an average Dice score of 89.7 $\pm$ 3.8
(range [79.8, 94.8]) % in testing, achieving the new state-of-the-art
performance in pancreas segmentation on this dataset. | ['Kensaku MORI', 'Kazunari Misawa', 'Hirohisa ODA', 'Yuichiro Hayashi', 'Takayuki Kitasaka', 'Michitaka Fujiwara', 'Holger Roth', 'Masahiro Oda', 'Natsuki Shimizu'] | 2017-11-17 | null | null | null | null | ['pancreas-segmentation'] | ['medical'] | [ 5.96094877e-02 2.15445504e-01 6.85702786e-02 -4.29654151e-01
-5.66547751e-01 -6.69684470e-01 1.89050540e-01 2.53853053e-01
-4.95060146e-01 3.58247042e-01 2.28324607e-01 -6.84286118e-01
1.34864952e-02 -3.92881066e-01 -6.95517957e-01 -6.23936594e-01
-6.90145433e-01 9.04730439e-01 9.61918384e-02 2.97175109e-01
-2.73313582e-01 6.08665764e-01 -1.70152813e-01 3.04793745e-01
8.93621385e-01 9.67114508e-01 -2.10741967e-01 9.00317252e-01
-7.97272548e-02 6.61772668e-01 -2.30436519e-01 -2.77996749e-01
7.12168217e-01 -7.30093598e-01 -5.97326398e-01 1.61704332e-01
4.62860614e-01 -6.06419325e-01 -5.03141105e-01 8.61663461e-01
7.16977298e-01 -3.60885143e-01 6.62912190e-01 -8.34712148e-01
-1.77892327e-01 1.04808211e+00 -3.56169939e-01 3.16455960e-01
-3.42005603e-02 3.77513915e-01 2.62249351e-01 -3.61165285e-01
6.26390636e-01 6.13793850e-01 1.16138709e+00 4.74169433e-01
-1.27888799e+00 -5.28765500e-01 -2.79709280e-01 -5.18128276e-01
-1.09027910e+00 1.77165493e-01 2.01882273e-01 -4.66170222e-01
9.50326443e-01 -1.17658205e-01 1.21442401e+00 6.64397180e-01
7.74958909e-01 6.32139981e-01 1.06750882e+00 -6.48681745e-02
1.34166241e-01 -4.37492758e-01 1.74311087e-01 8.68576467e-01
6.62645996e-01 2.41741091e-01 1.75732732e-01 -2.06764236e-01
1.13531268e+00 2.07396001e-02 -5.68860471e-01 -8.06379497e-01
-1.62537336e+00 9.25453961e-01 8.11275423e-01 1.41630143e-01
-7.41747081e-01 3.38900030e-01 4.74498928e-01 1.32574722e-01
7.63357356e-02 3.51346403e-01 -5.46667218e-01 -2.71883700e-02
-1.03793323e+00 4.41078134e-02 1.41190434e+00 1.11931801e+00
-2.32267097e-01 -1.71445385e-01 -9.55651104e-02 3.53624165e-01
4.78040814e-01 2.73443729e-01 5.73748708e-01 -9.65284884e-01
1.86785564e-01 4.94073510e-01 -3.46108466e-01 -1.51874527e-01
-1.10811436e+00 -7.29797006e-01 -1.04242527e+00 3.04794818e-01
8.04240346e-01 -6.86564684e-01 -1.43064606e+00 1.31990409e+00
1.52227297e-01 -1.03146106e-01 8.71836171e-02 1.20353222e+00
1.16709399e+00 3.87792140e-02 2.20626652e-01 -3.16139720e-02
1.43633795e+00 -1.10030782e+00 -3.77803773e-01 8.63823518e-02
7.57919729e-01 -6.55299246e-01 4.50667471e-01 4.43899035e-01
-1.29932845e+00 1.37675643e-01 -1.03506219e+00 2.16662958e-01
1.33308515e-01 -1.70046791e-01 6.22132242e-01 7.34168410e-01
-1.15616333e+00 8.31875563e-01 -1.44102561e+00 -4.14098412e-01
7.98149645e-01 7.34660745e-01 -3.07115585e-01 -7.91712478e-02
-7.07365811e-01 1.01173997e+00 3.53297442e-01 -7.96290860e-02
-8.95117402e-01 -1.02170992e+00 -7.57914424e-01 1.17043905e-01
1.36523589e-01 -1.20545578e+00 1.70691121e+00 -8.49546432e-01
-1.52719903e+00 9.99165714e-01 5.67651749e-01 -8.34698260e-01
1.16878045e+00 1.46024957e-01 4.24931571e-02 4.36818570e-01
-2.98830062e-01 8.99056613e-01 1.89745739e-01 -1.00149620e+00
-2.63517022e-01 -3.73180091e-01 -3.81185204e-01 3.22990805e-01
5.56333065e-01 -8.13131779e-02 -4.96644944e-01 -5.55468440e-01
6.15591824e-01 -1.27956808e+00 -7.24602759e-01 5.64148009e-01
-3.75399441e-01 4.81309772e-01 1.63907647e-01 -9.08005476e-01
5.04003823e-01 -1.69117892e+00 -1.55490324e-01 3.68791521e-01
5.57576776e-01 4.18640897e-02 1.09337069e-01 -1.62454903e-01
-1.68106139e-01 -7.79601932e-02 -6.97178900e-01 -4.73597981e-02
-4.74196002e-02 2.05404282e-01 4.72098708e-01 7.91925132e-01
-1.86748505e-01 1.20386565e+00 -8.01547945e-01 -5.66433430e-01
1.70537800e-01 6.06796503e-01 -6.48674786e-01 -3.49598657e-03
5.55924140e-02 6.32911503e-01 -2.64963418e-01 7.58070588e-01
8.57799232e-01 -6.05292082e-01 5.05339384e-01 -1.00403026e-01
-6.67340159e-02 8.82615447e-02 -6.88953280e-01 2.32572365e+00
-3.22231986e-02 3.63100708e-01 4.20524508e-01 -4.73762244e-01
4.64819998e-01 5.31555593e-01 1.19888067e+00 -5.53300738e-01
5.01340389e-01 4.91328895e-01 7.18036532e-01 -2.73438692e-01
-1.76693425e-01 -4.38366592e-01 -6.58844551e-03 4.94869888e-01
3.08204859e-01 -4.59345698e-01 2.03440890e-01 -3.05410307e-02
1.30206776e+00 7.78494775e-02 3.57652903e-01 -7.20164776e-01
8.13251585e-02 5.10358751e-01 3.14374268e-01 7.50114441e-01
-6.41479671e-01 1.00571978e+00 7.43318558e-01 -6.36262596e-01
-1.14313555e+00 -1.25394094e+00 -4.62270588e-01 1.79897830e-01
3.30670103e-02 1.36418179e-01 -8.68444562e-01 -9.75459933e-01
1.77606255e-01 4.90872920e-01 -5.69110632e-01 1.35477155e-01
-9.49333251e-01 -1.08009851e+00 7.73251295e-01 7.89430916e-01
4.43751782e-01 -7.56745100e-01 -9.16539133e-01 5.34965396e-01
8.72798115e-02 -1.04199851e+00 -6.00809693e-01 7.07861662e-01
-1.44322801e+00 -1.26944804e+00 -1.15703261e+00 -9.31638062e-01
7.97745764e-01 -1.87826693e-01 1.66453755e+00 -6.66489899e-02
-2.77875990e-01 9.81435701e-02 -1.73612118e-01 -2.78921008e-01
-6.47894859e-01 1.60515085e-01 -4.68865544e-01 -1.03833103e+00
2.17186809e-01 -2.94105709e-01 -1.02575660e+00 2.43744582e-01
-8.78903925e-01 2.41792351e-01 7.72574604e-01 8.61726463e-01
6.70272768e-01 -5.88036895e-01 -3.12685445e-02 -7.46231258e-01
4.05501544e-01 -4.25876409e-01 -7.02393949e-01 -1.25142589e-01
-4.94518250e-01 -3.10491920e-02 3.79871279e-01 -3.15934479e-01
-5.25311351e-01 4.76136297e-01 -3.48725349e-01 -3.73892754e-01
-2.37999782e-01 4.17252481e-01 4.34302300e-01 -3.98753703e-01
4.72637683e-01 -1.67737319e-03 4.99560237e-01 -3.28503937e-01
2.64705926e-01 9.42352042e-02 4.94040847e-01 -2.14232326e-01
3.10683161e-01 4.23854828e-01 2.91840166e-01 -5.19824028e-02
-3.06702942e-01 -1.14130281e-01 -8.39036405e-01 9.48292613e-02
1.09312713e+00 -8.24533880e-01 -6.23190403e-01 3.29475045e-01
-7.77870774e-01 -8.10831308e-01 -4.74537969e-01 1.04191041e+00
-6.14611685e-01 2.81419724e-01 -1.22763336e+00 2.96852678e-01
-9.98275578e-01 -1.63128555e+00 6.28486216e-01 1.82118446e-01
-2.45608613e-01 -1.12948918e+00 4.96129878e-02 -2.26666823e-01
7.49196768e-01 6.59229934e-01 9.12742019e-01 -1.01064885e+00
-4.99935865e-01 -1.46291405e-01 -5.07747531e-01 1.34876460e-01
-6.28716126e-02 -3.67258132e-01 -3.24860692e-01 -3.29184771e-01
4.29598764e-02 1.29333213e-01 7.50864029e-01 1.14709127e+00
9.81893361e-01 1.30110130e-01 -2.82904923e-01 1.13262784e+00
1.55457127e+00 4.41347450e-01 3.44550580e-01 3.20567578e-01
4.13607746e-01 -1.85001194e-01 -1.37703180e-01 3.30391735e-01
3.21246475e-01 1.47420406e-01 7.03189790e-01 -4.09142226e-01
-4.31962103e-01 1.22027405e-01 -4.58611473e-02 7.37448514e-01
1.52408838e-01 -2.99084514e-01 -1.25546765e+00 2.62082368e-01
-1.27780545e+00 -2.43376702e-01 -4.57784474e-01 1.85644531e+00
7.21890390e-01 1.09893091e-01 -3.96684259e-02 -5.01104712e-01
5.07018685e-01 -3.60886216e-01 -6.81123435e-01 -3.68193537e-01
1.55106232e-01 3.76189142e-01 9.51008141e-01 2.66024858e-01
-1.38793468e+00 2.67902195e-01 6.69588327e+00 -9.73290429e-02
-1.22964919e+00 -5.54012731e-02 6.49376690e-01 -1.60579249e-01
9.58729014e-02 -2.38357410e-01 -2.11226344e-01 3.96499425e-01
8.82907212e-01 6.13377728e-02 2.45393425e-01 5.94554603e-01
-8.68656486e-02 -2.88743109e-01 -1.35028434e+00 7.93105721e-01
1.36981875e-01 -1.22880006e+00 -1.48114473e-01 2.30244204e-01
7.68340111e-01 6.74406946e-01 -1.19657442e-01 2.10557744e-01
4.21372950e-01 -1.22460961e+00 4.52757329e-01 2.39769682e-01
9.23973501e-01 -5.61223209e-01 1.16615415e+00 1.07158855e-01
-7.54050791e-01 2.41339996e-01 2.04897851e-01 5.69512963e-01
4.73723412e-01 5.30092418e-01 -1.34183228e+00 3.95121664e-01
4.97393578e-01 4.25875664e-01 -2.74645269e-01 1.74889266e+00
-7.30302483e-02 3.60514343e-01 -7.09424913e-01 1.96710065e-01
5.75240850e-01 -2.47560859e-01 6.14820063e-01 1.43553126e+00
5.14449239e-01 2.58368880e-01 8.58676359e-02 8.77480149e-01
-4.77873653e-01 3.30650471e-02 -2.74635494e-01 3.39210749e-01
-1.98820695e-01 1.29159617e+00 -1.19077826e+00 -4.38746005e-01
-5.29330671e-01 9.03607845e-01 -3.06746095e-01 -9.56083238e-02
-1.09341562e+00 -8.24208185e-02 2.43398219e-01 -1.74001634e-01
4.20002103e-01 -1.89130083e-01 -6.90513492e-01 -1.18736577e+00
-3.17019016e-01 -7.05135703e-01 6.19171321e-01 -5.46608031e-01
-1.41674173e+00 6.83162630e-01 -1.89321339e-01 -1.23967695e+00
-1.66583180e-01 -8.33826840e-01 -4.45161551e-01 8.54047477e-01
-1.52504635e+00 -8.61486912e-01 -6.26327693e-01 3.77642512e-01
2.21744478e-01 2.55797237e-01 8.51796865e-01 4.34768885e-01
-1.48185596e-01 4.62679654e-01 1.66893139e-01 6.31819665e-01
5.62975407e-01 -1.59352934e+00 3.04852694e-01 5.36053300e-01
-4.96874779e-01 3.45974386e-01 2.25157455e-01 -6.97450519e-01
-1.62491703e+00 -1.09443557e+00 4.74840820e-01 -2.42486030e-01
3.72946918e-01 1.64247170e-01 -5.20345509e-01 9.81196344e-01
5.60956061e-01 3.18836570e-01 8.71715009e-01 -5.89296520e-01
9.59515125e-02 3.46320063e-01 -1.78879178e+00 4.48846072e-01
8.14581513e-01 4.36210155e-01 -6.39272094e-01 2.00846344e-01
5.04682839e-01 -1.29112661e+00 -1.61304641e+00 6.73416376e-01
8.09618294e-01 -7.84624875e-01 9.34414625e-01 -2.21690357e-01
5.22501349e-01 -2.06996519e-02 1.13903709e-01 -1.46051490e+00
-2.27085248e-01 -3.61143649e-01 -6.41345652e-03 7.97188804e-02
5.22932410e-01 -6.68308854e-01 9.92766321e-01 8.58146608e-01
-8.01495492e-01 -8.30569386e-01 -9.16925669e-01 -3.35456520e-01
7.81938076e-01 -9.22005996e-03 4.09994930e-01 9.31935608e-01
-2.98495516e-02 -2.30184108e-01 5.26525974e-01 1.17030507e-03
8.50125968e-01 2.48472363e-01 2.13696346e-01 -1.17150080e+00
-4.41879816e-02 -8.70513558e-01 -2.28387207e-01 -9.44611788e-01
-4.70722854e-01 -1.31758189e+00 3.19969803e-02 -1.81852245e+00
5.51500440e-01 -4.07524526e-01 -1.68554708e-01 4.72035795e-01
1.97053522e-01 2.26909384e-01 2.89031118e-01 1.17214665e-01
-3.33635807e-01 -1.60250906e-02 1.83391130e+00 3.01558282e-02
-1.90008312e-01 -2.59381253e-02 -4.84069228e-01 7.24524438e-01
9.27672625e-01 -5.55176735e-01 1.45585731e-01 -5.00691056e-01
-3.77516925e-01 4.88654315e-01 3.39318842e-01 -9.85818505e-01
1.56513989e-01 4.40944254e-01 1.07227302e+00 -8.69456410e-01
-9.90866348e-02 -1.15312958e+00 2.71511257e-01 1.20277393e+00
-2.33980328e-01 2.53872603e-01 4.84747797e-01 -1.49266282e-02
-1.61980931e-02 -7.18842745e-02 1.06548476e+00 -7.34458148e-01
-1.84066951e-01 6.19966924e-01 -4.65637624e-01 1.60152614e-01
9.81582642e-01 -1.77315865e-02 -1.40776649e-01 -5.66120893e-02
-9.23924208e-01 3.61215472e-01 5.58097839e-01 -1.93199143e-01
5.07401168e-01 -1.13407481e+00 -9.05385673e-01 3.69312674e-01
-2.99786568e-01 4.49307352e-01 1.50511563e-01 1.47136080e+00
-1.52135813e+00 5.15213370e-01 -4.28833038e-01 -9.28156912e-01
-7.66249239e-01 2.06839144e-01 9.37520206e-01 -6.21663809e-01
-1.17357957e+00 5.81636190e-01 -2.23219946e-01 -7.22185314e-01
1.28404438e-01 -1.06811750e+00 2.60124773e-01 -3.19480300e-01
6.84885308e-03 7.67521411e-02 3.16827834e-01 -2.74511486e-01
-4.25120026e-01 2.37462446e-01 1.12107575e-01 -4.70446907e-02
1.32015944e+00 1.76165938e-01 -5.44428453e-02 -3.15540910e-01
1.23784089e+00 -4.96965945e-01 -1.37871540e+00 -3.70900258e-02
-1.27863064e-01 -9.66337398e-02 1.94896787e-01 -1.40179074e+00
-1.48242879e+00 6.21827185e-01 8.86479676e-01 4.12884131e-02
9.80065823e-01 -1.52780339e-01 1.05554795e+00 2.25730962e-03
1.18301608e-01 -4.13445234e-01 -3.70333731e-01 6.45585537e-01
4.95632380e-01 -1.31623840e+00 1.32151827e-01 -1.41310409e-01
-6.60871804e-01 1.44675815e+00 3.65516067e-01 -4.76399869e-01
7.51415133e-01 7.47550189e-01 4.30974156e-01 -3.50641012e-01
-3.04814398e-01 2.12789014e-01 2.12497666e-01 2.40774393e-01
7.48112738e-01 2.84727395e-01 -3.82904738e-01 5.91038108e-01
-3.12299244e-02 2.22795278e-01 5.48079431e-01 1.18241775e+00
-8.85075480e-02 -7.51960695e-01 -2.29894742e-01 6.48893297e-01
-7.58429468e-01 -2.50119478e-01 -4.31978740e-02 1.16677845e+00
-1.72119975e-01 3.16958308e-01 2.18258455e-01 3.59270632e-01
4.49889094e-01 2.63493299e-01 8.61471653e-01 -2.80444115e-01
-1.22674990e+00 3.92054796e-01 -2.16568471e-03 -5.84544718e-01
-2.37602100e-01 -8.99159610e-01 -1.68972576e+00 -2.90060699e-01
-1.41327623e-02 -1.14507355e-01 1.01864767e+00 4.35935378e-01
1.77642971e-01 8.97621870e-01 9.58406702e-02 -1.03839481e+00
-7.45642304e-01 -1.00576150e+00 -5.90172291e-01 3.43149304e-01
3.80613506e-01 -2.18805030e-01 -1.68378472e-01 -1.32166296e-01] | [14.494793891906738, -2.610658645629883] |
961b169e-01e2-42df-aed4-c3ba71739846 | inversion-of-1d-frequency-and-time-domain | 1912.00612 | null | https://arxiv.org/abs/1912.00612v1 | https://arxiv.org/pdf/1912.00612v1.pdf | Inversion of 1D frequency- and time-domain electromagnetic data with convolutional neural networks | Inversion of electromagnetic data finds applications in many areas of geophysics. The inverse problem is commonly solved with either deterministic optimization methods (such as the nonlinear conjugate gradient or Gauss-Newton) which are prone to getting trapped in a local minimum, or probabilistic methods which are very computationally demanding. A recently emerging alternative is to employ deep neural networks for predicting subsurface model properties from measured data. This approach is entirely data-driven, does not employ traditional gradient-based techniques and provides a guess to the model instantaneously. In this study, we apply deep convolutional neural networks for 1D inversion of marine frequency-domain controlled-source electromagnetic (CSEM) data as well as onshore time-domain electromagnetic (TEM) data. Our approach yields accurate results both on synthetic and real data and provides them instantaneously. Using several networks and combining their outputs from various training epochs can also provide insights into the uncertainty distribution, which is found to be higher in the regions where resistivity anomalies are present. The proposed method opens up possibilities to estimate the subsurface resistivity distribution in exploration scenarios in real time. | ['Andrei Swidinsky', 'Vladimir Puzyrev'] | 2019-12-02 | null | null | null | null | ['geophysics'] | ['miscellaneous'] | [ 8.72159600e-02 -2.11218253e-01 4.87744242e-01 -4.98720378e-01
-7.97832549e-01 -4.84897941e-01 6.01703107e-01 1.05257519e-01
-6.85223579e-01 1.12369812e+00 -3.06298733e-01 -7.77985632e-01
-5.47234893e-01 -1.07248640e+00 -7.71982193e-01 -9.62566912e-01
-2.99166620e-01 7.24525273e-01 -5.83264083e-02 -5.78354657e-01
5.34502804e-01 7.22855747e-01 -1.20859337e+00 -3.53869587e-01
1.17388916e+00 1.23329914e+00 9.55599174e-02 4.74162310e-01
-7.11689368e-02 5.27188838e-01 -3.40152383e-01 2.03465506e-01
1.77760392e-01 -2.04478592e-01 -5.60288668e-01 -6.14094138e-01
-4.40064110e-02 -2.60096788e-01 5.92579134e-02 9.36181903e-01
5.48674285e-01 2.65969187e-01 8.57931376e-01 -5.55981994e-01
-5.29620796e-02 2.63204694e-01 -6.15193427e-01 3.14330488e-01
-2.71583488e-03 -1.07898474e-01 3.78305107e-01 -8.81651580e-01
1.36515573e-01 6.52949035e-01 1.01490998e+00 -7.77051747e-02
-1.14819753e+00 -5.64027250e-01 -4.81483191e-01 2.65943080e-01
-1.25740278e+00 -6.13085628e-01 9.50587511e-01 -4.65333521e-01
8.43518794e-01 6.85216486e-02 4.66792017e-01 7.97404468e-01
2.18806222e-01 2.98284024e-01 1.30174351e+00 -5.74622452e-01
4.80216533e-01 1.07614465e-01 -2.95128465e-01 4.43249792e-01
3.64674628e-01 4.18123841e-01 -4.04018223e-01 -1.20057762e-01
2.87023187e-01 -3.00250441e-01 -4.05468017e-01 -6.18383437e-02
-8.47832203e-01 8.82052660e-01 3.74930739e-01 3.28785121e-01
-7.65350759e-01 1.18426681e-01 2.65052795e-01 3.07587445e-01
7.68570423e-01 5.72786093e-01 -7.47833669e-01 -1.21995442e-01
-1.40307510e+00 3.82928729e-01 8.89430463e-01 4.46789898e-02
8.64570737e-01 4.43009973e-01 5.99314868e-01 6.46100283e-01
6.67581558e-01 8.87507856e-01 3.55218887e-01 -6.85486853e-01
2.97987521e-01 -1.79981161e-02 3.71801406e-01 -1.12330556e+00
-5.99213183e-01 -3.73906910e-01 -8.16128910e-01 5.60938895e-01
3.91638756e-01 -6.92013800e-01 -7.70047784e-01 1.53479064e+00
2.09938988e-01 1.23538494e-01 2.86575943e-01 9.07022238e-01
4.80000794e-01 7.48974383e-01 -1.36968493e-01 8.51966590e-02
8.69340241e-01 -1.97871760e-01 -6.42938495e-01 -3.96541417e-01
6.08514905e-01 -4.83449876e-01 2.35399693e-01 4.62278128e-01
-6.62390530e-01 -1.30146742e-01 -1.29915822e+00 4.04819399e-01
-6.05248094e-01 1.03563495e-01 5.64507663e-01 6.10082865e-01
-1.04830515e+00 1.11858702e+00 -1.08155334e+00 1.53083891e-01
1.66339815e-01 2.63075739e-01 -3.82248372e-01 2.34634951e-01
-1.59915233e+00 1.09645498e+00 3.85936201e-01 8.44782829e-01
-7.56409347e-01 -7.31550276e-01 -1.03342211e+00 -1.28078878e-01
-9.73396003e-02 1.46768782e-02 9.70657289e-01 -8.60443830e-01
-1.71591592e+00 3.19525450e-01 -2.41653044e-02 -6.24427617e-01
6.17772102e-01 -2.22097322e-01 -3.57861131e-01 -8.73549189e-03
-6.76137283e-02 1.86391994e-01 6.90184653e-01 -1.17303717e+00
-1.86245516e-01 -2.98145920e-01 -2.23088473e-01 -1.71324328e-01
-5.47057018e-02 -3.63715410e-01 4.83518004e-01 -2.18767017e-01
5.71408808e-01 -8.45878005e-01 -2.82947481e-01 -1.25793174e-01
-3.57016295e-01 1.81298584e-01 6.70039415e-01 -9.58809912e-01
4.34506595e-01 -1.61484599e+00 -5.38673140e-02 5.06402791e-01
-1.42725244e-01 3.02037388e-01 2.03070402e-01 5.86893380e-01
-2.37155303e-01 2.27450952e-02 -6.75183535e-01 -3.99516486e-02
-2.26173401e-01 -2.52839308e-02 -2.91951269e-01 8.80533755e-01
3.14188868e-01 5.88526726e-01 -7.91558206e-01 -5.75245242e-04
4.74542916e-01 6.96898162e-01 -2.69810706e-01 1.16536371e-01
-2.15568438e-01 9.95864213e-01 -4.96801972e-01 2.41911799e-01
1.27923107e+00 3.97633985e-02 1.69485599e-01 -9.21047553e-02
-5.86243451e-01 2.92115629e-01 -1.07270312e+00 1.33965266e+00
-1.08338165e+00 1.01407778e+00 1.57299206e-01 -1.66569304e+00
1.26669657e+00 2.42787614e-01 2.31082827e-01 -9.52588499e-01
1.78147644e-01 6.67689860e-01 -3.20008658e-02 -7.25520372e-01
4.29780781e-01 -5.97149849e-01 1.89391389e-01 3.32118094e-01
-2.65139760e-03 -3.32085878e-01 -1.12058595e-01 -2.57498264e-01
7.00238585e-01 3.80841494e-01 -6.63112774e-02 -7.66434491e-01
5.11391401e-01 -1.15125857e-01 2.80555338e-01 7.99274921e-01
4.44347054e-01 5.14373720e-01 5.55613101e-01 -5.79397798e-01
-8.40577483e-01 -6.96442544e-01 -6.33664787e-01 3.99808913e-01
-8.96822382e-03 4.55869466e-01 -3.03216249e-01 -7.96153694e-02
-1.50645643e-01 7.46556580e-01 -6.29942358e-01 -1.46343976e-01
-3.54842305e-01 -1.18345165e+00 3.48525405e-01 3.40850025e-01
6.07089639e-01 -9.70620096e-01 -6.84787452e-01 4.71329331e-01
-2.71039575e-01 -9.11851287e-01 6.43763363e-01 6.27309322e-01
-1.14328384e+00 -7.44076073e-01 -8.06343436e-01 -1.59992322e-01
5.42708516e-01 -4.73543793e-01 1.09479809e+00 -9.70385820e-02
3.85745540e-02 -1.60860479e-01 -3.63267690e-01 -6.27236426e-01
-4.59975332e-01 -2.96955947e-02 1.16426937e-01 1.12252444e-01
2.63024360e-01 -8.06990087e-01 -5.12187958e-01 1.60355136e-01
-7.96207547e-01 -4.94149625e-01 4.81994808e-01 9.10057783e-01
1.88648537e-01 2.27669150e-01 7.46405423e-01 -8.78080606e-01
5.80680549e-01 -8.31529975e-01 -1.16855001e+00 -7.60354772e-02
-5.47375381e-01 3.09903413e-01 7.57812500e-01 9.86148641e-02
-1.32468987e+00 -1.30335763e-01 -7.04833925e-01 2.36896724e-02
-2.45995119e-01 1.08511722e+00 3.44995856e-01 -5.62889636e-01
9.59205449e-01 2.74952650e-01 -5.62807769e-02 -6.73133671e-01
-2.78239876e-01 6.62980974e-01 3.42600584e-01 -7.17204571e-01
7.35815346e-01 6.49176061e-01 3.33680451e-01 -1.14453185e+00
-7.31537759e-01 -2.17370857e-02 -3.98772538e-01 -1.99110970e-01
4.74844366e-01 -8.31515491e-01 -6.28155172e-01 7.03752875e-01
-1.07154047e+00 -3.19726348e-01 1.89694241e-01 6.46027029e-01
-2.68114567e-01 3.49316120e-01 -2.32638225e-01 -1.20974267e+00
-3.53716284e-01 -9.87891018e-01 9.19195294e-01 4.07411486e-01
1.12891361e-01 -1.27390409e+00 2.30507955e-01 -1.41233683e-01
8.64972651e-01 6.64618015e-01 7.16793060e-01 -4.06971782e-01
-2.42771089e-01 -5.08170068e-01 -1.15767397e-01 4.09223557e-01
-4.94479090e-02 -3.72782797e-02 -1.18431604e+00 -1.78440213e-01
2.45627925e-01 -3.44888389e-01 9.56782520e-01 8.37868869e-01
1.06344306e+00 -1.05035737e-01 -1.55883789e-01 8.85962784e-01
1.68978333e+00 1.16108999e-01 7.29653478e-01 5.01078188e-01
2.02178150e-01 6.98568821e-01 3.69129658e-01 6.04364991e-01
1.06811166e-01 3.31538975e-01 6.30304933e-01 -1.92178711e-01
8.17701936e-01 1.40138403e-01 -1.40627384e-01 6.57144308e-01
-3.59057277e-01 -1.83809981e-01 -1.26629353e+00 7.77560949e-01
-1.45117843e+00 -6.95101261e-01 -3.11582267e-01 2.31883860e+00
5.65396488e-01 2.01166958e-01 -5.42425334e-01 2.89783388e-01
2.96724945e-01 8.95179883e-02 -3.47517669e-01 -5.17019093e-01
-2.20399629e-02 4.98453945e-01 8.14879298e-01 4.87819850e-01
-9.43447709e-01 3.44633132e-01 5.95689058e+00 4.12474871e-01
-1.52928591e+00 3.67530212e-02 6.26904666e-01 4.21586901e-01
-4.09717262e-01 3.05730104e-02 -2.85129696e-01 4.85143274e-01
1.22022557e+00 7.20749199e-02 3.57644647e-01 5.59468567e-01
3.82692844e-01 -7.72464812e-01 -6.22098863e-01 6.16060793e-01
-4.06828284e-01 -1.51032209e+00 -4.96367902e-01 -8.51766467e-02
8.16545486e-01 4.12228882e-01 2.54164804e-02 1.37564046e-02
3.00299060e-02 -1.08419216e+00 5.74062765e-01 9.44524288e-01
8.61492455e-01 -9.30203199e-01 1.19031358e+00 4.83636111e-01
-6.92648053e-01 6.23057112e-02 -3.43784511e-01 -1.90942481e-01
3.43118548e-01 1.16498458e+00 -7.52917469e-01 6.68438196e-01
9.50619698e-01 5.06046653e-01 2.46438980e-02 9.39618766e-01
-1.57627493e-01 7.75283813e-01 -8.53762567e-01 -1.58364385e-01
3.43310505e-01 -5.80483496e-01 5.60657024e-01 1.02941847e+00
7.33203530e-01 -1.56493768e-01 -4.69448328e-01 1.06548488e+00
3.24603796e-01 -2.86339819e-01 -8.26536000e-01 3.82766873e-02
3.01532000e-01 1.27320123e+00 -6.16065919e-01 2.94217654e-03
-3.19949649e-02 3.16856056e-01 -1.00304954e-01 5.14855564e-01
-6.36067986e-01 -8.78448486e-01 3.32897216e-01 1.67001367e-01
2.01215789e-01 -4.81618494e-01 -2.38780841e-01 -7.16499031e-01
4.35991734e-02 -5.10290742e-01 -2.31381133e-01 -6.47349119e-01
-9.49253261e-01 7.84544468e-01 1.48881093e-01 -1.10189891e+00
-6.42971098e-01 -9.67451036e-01 -9.08023238e-01 1.30105114e+00
-2.01534343e+00 -4.63062137e-01 -4.14444089e-01 1.97769105e-01
-5.18716164e-02 -7.95482099e-02 6.92549646e-01 4.04324472e-01
-8.65051746e-02 -4.12840322e-02 7.26337492e-01 7.71364421e-02
2.51408905e-01 -1.18531930e+00 2.92947680e-01 7.58684337e-01
-2.79387444e-01 4.33665127e-01 1.31765032e+00 -4.32247818e-01
-1.29463923e+00 -6.46454096e-01 6.81649506e-01 -7.98986107e-03
7.23291636e-01 -4.97755781e-02 -1.21811664e+00 2.49187142e-01
-4.50728163e-02 3.95262748e-01 2.58609474e-01 -5.08413017e-02
2.87751496e-01 -1.84653327e-01 -1.29695141e+00 9.11815464e-02
2.38852635e-01 -4.09984827e-01 -2.94060826e-01 4.58572894e-01
2.93436572e-02 -4.78374302e-01 -7.03589022e-01 7.27212369e-01
4.28730756e-01 -1.22363138e+00 6.44222438e-01 -3.01599503e-01
5.46649933e-01 -2.16687039e-01 -9.01305526e-02 -1.74236667e+00
3.10056448e-01 -4.17795688e-01 1.08247884e-01 7.94889927e-01
6.60918295e-01 -1.11130762e+00 7.19149828e-01 2.49615774e-01
1.00780040e-01 -7.15974867e-01 -1.22070539e+00 -5.31861007e-01
2.80766159e-01 -5.55363059e-01 3.84201288e-01 8.51794064e-01
-3.76681358e-01 -3.09522450e-01 -3.55377793e-01 5.56824148e-01
8.66348028e-01 3.37214507e-02 2.52679497e-01 -1.44987381e+00
-8.61702766e-03 -6.12211488e-02 -2.23909780e-01 -6.48287177e-01
3.90719652e-01 -5.98815620e-01 5.58122337e-01 -1.36695826e+00
-6.70797348e-01 -7.80571342e-01 -2.59183228e-01 2.74124384e-01
3.41240466e-01 2.63731122e-01 -6.43408656e-01 -2.77186811e-01
1.70555398e-01 7.65001893e-01 6.58680737e-01 -3.98676135e-02
-1.70595627e-02 1.11969486e-01 -1.98873118e-01 7.79169619e-01
9.10173833e-01 -7.56786704e-01 -5.75737655e-02 -6.49204373e-01
7.60996580e-01 3.33424568e-01 4.37938243e-01 -1.23389864e+00
2.82256573e-01 3.66089940e-02 6.34457767e-01 -4.49822128e-01
1.06582396e-01 -6.75506830e-01 1.31791502e-01 3.46984416e-01
1.32552221e-01 -1.22509979e-01 5.00530541e-01 4.28294122e-01
-4.62344319e-01 -6.73722386e-01 1.05220127e+00 -2.14103073e-01
-6.34992301e-01 1.72951873e-02 -5.51634014e-01 -1.44473284e-01
3.91488284e-01 1.89676601e-02 1.28007889e-01 -6.20307207e-01
-4.80889529e-01 9.39254239e-02 1.10678654e-02 -2.83014728e-03
4.75191891e-01 -8.31906974e-01 -8.65052879e-01 3.19924414e-01
-1.10662788e-01 1.09369129e-01 4.63385016e-01 1.02954650e+00
-1.03305113e+00 2.73118585e-01 -1.60570219e-01 -8.04428875e-01
-2.41723791e-01 -1.67161539e-01 1.01692688e+00 -5.53745441e-02
-3.85615826e-01 8.92099321e-01 -3.41657728e-01 -9.00845289e-01
-4.66265142e-01 1.71274785e-02 -3.68447006e-01 8.93312171e-02
2.92325944e-01 2.77158558e-01 5.69188595e-01 -5.30376613e-01
-4.08435017e-01 6.47342801e-01 3.06788087e-01 -2.82627046e-01
1.73545671e+00 2.36547012e-02 -3.39443028e-01 4.04992521e-01
1.31196892e+00 -1.01030022e-01 -1.23595202e+00 -2.43978631e-02
2.18413994e-01 -2.55487233e-01 7.63596117e-01 -7.00831652e-01
-1.27947211e+00 1.11105239e+00 6.74180388e-01 2.59918958e-01
1.01377773e+00 -4.55541521e-01 5.17947376e-01 7.85459578e-01
3.94447684e-01 -1.23227525e+00 -5.65238297e-01 6.28272355e-01
8.41089070e-01 -1.39796853e+00 9.05172061e-03 4.12810236e-01
-1.67846680e-01 1.44059873e+00 1.91517815e-01 -2.64537841e-01
1.03073692e+00 5.53966761e-01 3.69698018e-01 -2.31297046e-01
-3.15899998e-01 2.54858822e-01 1.15060769e-01 3.89327139e-01
4.63268787e-01 -1.13583773e-01 -1.34858802e-01 1.26850337e-01
-9.16398019e-02 7.80862123e-02 6.08432174e-01 9.10190284e-01
-3.05213392e-01 -8.23235452e-01 -5.27218461e-01 5.83112180e-01
-5.91111422e-01 -1.47412986e-01 4.64512289e-01 5.61993122e-01
-1.39412820e-01 8.55539203e-01 1.69811413e-01 -3.11929081e-03
6.56157210e-02 -6.45819157e-02 2.24680990e-01 3.39984670e-02
6.15508296e-02 -3.24867308e-01 1.18925251e-01 -2.82932550e-01
-3.80175650e-01 -6.05977416e-01 -1.09724844e+00 -2.36784235e-01
-5.82192361e-01 5.68555534e-01 1.27073658e+00 1.18844581e+00
1.56412162e-02 6.34013116e-01 8.07212770e-01 -1.54270828e+00
-5.64828694e-01 -1.15370500e+00 -8.20276916e-01 -3.41768384e-01
8.72744560e-01 -8.74038994e-01 -7.97281861e-01 -4.79283184e-01] | [6.709049701690674, 2.8370156288146973] |
686205ba-3d0b-4166-a65a-7673213a0f1e | pc2-projection-conditioned-point-cloud | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Melas-Kyriazi_PC2_Projection-Conditioned_Point_Cloud_Diffusion_for_Single-Image_3D_Reconstruction_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Melas-Kyriazi_PC2_Projection-Conditioned_Point_Cloud_Diffusion_for_Single-Image_3D_Reconstruction_CVPR_2023_paper.pdf | PC2: Projection-Conditioned Point Cloud Diffusion for Single-Image 3D Reconstruction | Reconstructing the 3D shape of an object from a single RGB image is a long-standing problem in computer vision. In this paper, we propose a novel method for single-image 3D reconstruction which generates a sparse point cloud via a conditional denoising diffusion process. Our method takes as input a single RGB image along with its camera pose and gradually denoises a set of 3D points, whose positions are initially sampled randomly from a three-dimensional Gaussian distribution, into the shape of an object. The key to our method is a geometrically-consistent conditioning process which we call projection conditioning: at each step in the diffusion process, we project local image features onto the partially-denoised point cloud from the given camera pose. This projection conditioning process enables us to generate high-resolution sparse geometries that are well-aligned with the input image and can additionally be used to predict point colors after shape reconstruction. Moreover, due to the probabilistic nature of the diffusion process, our method is naturally capable of generating multiple different shapes consistent with a single input image. In contrast to prior work, our approach not only performs well on synthetic benchmarks but also gives large qualitative improvements on complex real-world data. | ['Andrea Vedaldi', 'Christian Rupprecht', 'Luke Melas-Kyriazi'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['3d-reconstruction'] | ['computer-vision'] | [ 5.05649745e-01 -1.30674437e-01 4.52406794e-01 -1.48859158e-01
-9.86514926e-01 -7.29742467e-01 7.32040763e-01 -1.04122661e-01
-1.97279438e-01 2.59316921e-01 -6.12502545e-02 1.06141761e-01
5.21981232e-02 -1.09553075e+00 -1.13875961e+00 -8.98568809e-01
6.69801474e-01 1.14991236e+00 1.70447335e-01 1.82001591e-01
2.72330850e-01 9.83810067e-01 -1.58247697e+00 1.02889821e-01
6.75689161e-01 9.71938550e-01 3.82784814e-01 6.63450837e-01
-2.21360907e-01 3.50241244e-01 -7.40344301e-02 -3.41040820e-01
4.02385622e-01 -3.07462364e-01 -6.65331304e-01 7.53271043e-01
5.20000577e-01 -1.40971899e-01 8.38490948e-02 1.18724513e+00
1.55144140e-01 -3.46991532e-02 9.22470748e-01 -7.58018315e-01
-4.07679915e-01 -1.66982025e-01 -7.31092811e-01 -6.37812793e-01
4.01343912e-01 1.17053859e-01 8.06552172e-01 -1.10661995e+00
8.60234082e-01 1.27638745e+00 6.71926677e-01 2.94301927e-01
-1.86329687e+00 -2.59095788e-01 -1.63796470e-01 -2.92561322e-01
-1.42655742e+00 -2.76079118e-01 1.12264538e+00 -5.77376664e-01
4.60226029e-01 2.13912204e-01 8.84880245e-01 7.48410165e-01
6.19090274e-02 4.24561113e-01 1.31342006e+00 -3.64088356e-01
5.99071681e-01 -1.15950719e-01 -3.99263144e-01 5.41554928e-01
-6.55186623e-02 2.88595557e-02 -5.26991308e-01 -3.93799603e-01
1.18218374e+00 3.09765548e-01 -3.93401980e-01 -9.78844643e-01
-1.34021306e+00 7.47891247e-01 5.75885057e-01 -4.91744094e-02
-8.02786946e-01 2.38220692e-01 -4.89497691e-01 -8.87116343e-02
6.43941045e-01 3.93434241e-02 -9.74424630e-02 -5.58341481e-02
-9.08750713e-01 2.54532397e-01 7.27876425e-01 8.03687871e-01
1.23197567e+00 -2.40841463e-01 3.27695459e-01 4.85706717e-01
5.57656407e-01 1.06888902e+00 -2.79965580e-01 -1.44528258e+00
1.66217163e-01 6.01576984e-01 3.13811868e-01 -9.31610763e-01
1.07760295e-01 -1.86543062e-01 -9.63676155e-01 6.96731329e-01
3.68396014e-01 2.65792936e-01 -1.02267385e+00 1.39799738e+00
6.01029873e-01 3.22384596e-01 -2.45114416e-02 8.79982710e-01
2.82919675e-01 7.78156877e-01 -5.34970880e-01 -9.07606259e-02
9.72046256e-01 -2.58661419e-01 -1.37472942e-01 -1.38323918e-01
-2.11613312e-01 -9.29244518e-01 7.14774251e-01 7.22090960e-01
-1.36570907e+00 -3.14360231e-01 -6.89488590e-01 -1.65435106e-01
2.64699012e-01 -1.34636730e-01 2.19088912e-01 4.47533876e-01
-1.11375368e+00 5.86771846e-01 -1.05536163e+00 -7.06589371e-02
4.44300413e-01 6.23978600e-02 -5.60636997e-01 -5.91004848e-01
-2.29411304e-01 6.96623385e-01 -1.12092786e-01 -9.61442664e-02
-9.45474088e-01 -6.50502026e-01 -7.60629714e-01 -2.52185762e-01
2.74830788e-01 -1.07239711e+00 9.77687776e-01 -8.22087526e-01
-1.46263313e+00 1.04606366e+00 -5.24780571e-01 -6.18438944e-02
4.95388031e-01 -5.15672937e-03 3.68860781e-01 4.05302972e-01
1.17004290e-01 7.28439569e-01 1.36764562e+00 -2.05263901e+00
-2.38373399e-01 -7.11852252e-01 -3.73745918e-01 3.00273865e-01
4.06058699e-01 -3.93302530e-01 -7.36831963e-01 -4.49675232e-01
7.90992141e-01 -1.08218193e+00 -4.48236763e-01 4.31709230e-01
-3.47030401e-01 2.06698149e-01 5.67562759e-01 -3.92687380e-01
2.85527676e-01 -2.24244189e+00 8.35308433e-01 5.06373465e-01
2.32821807e-01 -3.94623518e-01 1.12179935e-01 1.78292602e-01
-4.87449951e-02 -8.58875960e-02 -9.03537333e-01 -9.26936030e-01
-9.90533456e-02 4.14324075e-01 -3.08667690e-01 8.39848936e-01
2.65424699e-01 7.86320150e-01 -8.15599322e-01 -1.90254629e-01
5.06043971e-01 9.27784443e-01 -6.30365849e-01 3.20617169e-01
-4.79379177e-01 8.01945627e-01 -3.11389506e-01 5.60337961e-01
1.01394629e+00 -2.85064369e-01 -3.28312904e-01 -3.01429152e-01
-8.03261250e-02 -2.93067455e-01 -1.44536936e+00 2.12757111e+00
-4.90437925e-01 4.03422862e-02 4.81679320e-01 -7.12533832e-01
9.88558531e-01 1.76006377e-01 7.00833678e-01 -2.01442003e-01
7.14727864e-02 2.57384092e-01 -6.06840968e-01 1.18790120e-01
3.96667808e-01 -6.17725551e-01 1.99248090e-01 6.61500573e-01
-1.21622004e-01 -1.03867352e+00 -3.78568888e-01 2.43528515e-01
9.17975664e-01 2.77689129e-01 -1.60939515e-01 4.62436713e-02
3.67165059e-01 8.84253606e-02 3.71355385e-01 4.66281086e-01
4.93274957e-01 1.47271848e+00 2.49750257e-01 -2.32128426e-01
-1.26758158e+00 -1.51147366e+00 -1.20166928e-01 4.35915217e-02
1.64541095e-01 -1.09159634e-01 -6.39178216e-01 -4.46071655e-01
9.26267952e-02 6.16448283e-01 -5.91818392e-01 -1.17315259e-03
-3.32362264e-01 -3.70261639e-01 -1.98288471e-01 1.41090646e-01
2.19057992e-01 -8.51832569e-01 -6.17563307e-01 1.15777776e-01
-2.61706054e-01 -1.05127072e+00 -3.12826306e-01 1.32412255e-01
-1.10986340e+00 -1.04441512e+00 -9.39410806e-01 -6.25153959e-01
1.19313669e+00 3.92173111e-01 1.24408925e+00 -1.00548856e-01
-1.81523204e-01 7.14984477e-01 -1.99907959e-01 -4.10164893e-03
-5.51621616e-01 -6.17143571e-01 -1.26221776e-01 6.26589417e-01
-7.66987279e-02 -7.32121825e-01 -5.36182582e-01 1.33864120e-01
-1.18160856e+00 2.86845446e-01 6.04738414e-01 6.29038811e-01
1.30601549e+00 1.57395110e-01 -3.39214355e-01 -7.12415934e-01
1.71674460e-01 -4.23247308e-01 -7.89024532e-01 -8.76358803e-03
-2.96534061e-01 2.14282259e-01 2.51648813e-01 -2.65166223e-01
-1.09057260e+00 8.90632808e-01 -1.01003565e-01 -8.47542107e-01
-3.23588192e-01 2.05056265e-01 -2.06718609e-01 -1.36753544e-01
5.32772124e-01 5.04307270e-01 4.48466480e-01 -8.03769767e-01
6.92165017e-01 2.31956303e-01 7.02011466e-01 -7.58175313e-01
1.15401983e+00 1.12533522e+00 3.96988243e-01 -8.47201228e-01
-4.28598046e-01 -4.09104049e-01 -9.45410073e-01 -2.42413789e-01
8.79385114e-01 -9.13011670e-01 -5.18878698e-01 6.99967086e-01
-1.26074898e+00 -3.20195228e-01 -4.69048858e-01 1.87317193e-01
-8.20953071e-01 5.46468675e-01 -4.01344270e-01 -8.05639982e-01
-9.43478718e-02 -1.16804659e+00 1.54613698e+00 -2.01306894e-01
7.41119832e-02 -8.27504754e-01 7.24019036e-02 3.74155253e-01
1.24347523e-01 4.93775308e-01 7.79315054e-01 5.16552269e-01
-9.81645346e-01 -1.86641470e-01 -6.57885671e-02 6.12864912e-01
7.40325972e-02 1.00936212e-01 -7.21570849e-01 -1.11381881e-01
5.50759733e-01 -2.11943880e-01 7.25382209e-01 4.49114829e-01
8.83139849e-01 5.87709248e-02 -1.00782648e-01 7.50577927e-01
1.69131982e+00 -4.25469756e-01 5.92240393e-01 -3.75664420e-02
9.04831409e-01 6.02821946e-01 3.54742169e-01 5.05542457e-01
3.69319648e-01 6.63636327e-01 8.00831258e-01 -3.96372937e-02
-1.44944206e-01 -3.36163878e-01 2.23578900e-01 6.12488449e-01
-2.60031283e-01 9.15324613e-02 -8.86988640e-01 3.60364556e-01
-1.61491418e+00 -6.00654900e-01 -4.98998165e-01 2.49614048e+00
8.14969003e-01 -6.65266141e-02 -4.28220108e-02 2.85178959e-01
5.73807776e-01 -1.70222342e-01 -5.66868484e-01 2.28431031e-01
-2.75556296e-01 4.19017136e-01 4.43157434e-01 8.29060912e-01
-5.85245669e-01 6.85721219e-01 5.40333176e+00 5.68914711e-01
-1.08923745e+00 8.76929425e-03 6.19063795e-01 -2.68705864e-03
-6.73873007e-01 1.52671263e-01 -4.31239069e-01 2.91406035e-01
4.62412775e-01 7.22119436e-02 6.37087524e-01 5.81333756e-01
-4.58105467e-02 -4.13648278e-01 -1.06749845e+00 1.32318056e+00
1.09644920e-01 -1.41948378e+00 2.18755767e-01 4.40410018e-01
9.73671675e-01 1.86430424e-01 1.65695354e-01 -6.45492017e-01
5.17579257e-01 -9.46289539e-01 1.10180557e+00 8.67268801e-01
8.73866379e-01 -7.83521593e-01 2.82120466e-01 7.11869240e-01
-8.39658976e-01 4.23400223e-01 -3.14631045e-01 1.38458014e-01
4.79951113e-01 1.12335169e+00 -6.20244026e-01 5.85659921e-01
8.03442597e-01 7.91593492e-01 -3.53755504e-01 9.75322545e-01
-4.31120038e-01 3.76194924e-01 -7.80996382e-01 4.49027836e-01
-6.52891472e-02 -7.14012980e-01 5.85962236e-01 6.04270637e-01
6.25148356e-01 2.08023101e-01 6.74109906e-02 1.35739422e+00
2.46282984e-02 -2.61676759e-01 -6.83325648e-01 3.39281112e-01
2.05200225e-01 1.26863623e+00 -9.11984026e-01 -1.51765272e-01
-1.48131236e-01 1.26604581e+00 2.40389645e-01 4.37487245e-01
-3.25214565e-01 4.08723801e-01 3.74448895e-01 3.11892211e-01
6.28547370e-01 -6.50215089e-01 -7.09208250e-01 -1.14048374e+00
2.15962991e-01 -6.53959632e-01 -1.62167653e-01 -1.12020195e+00
-1.35055649e+00 4.99182940e-01 -1.23457663e-01 -1.29620528e+00
-8.62240866e-02 -5.00776291e-01 -4.41705793e-01 1.25883269e+00
-1.42324650e+00 -1.11306989e+00 -4.42493737e-01 7.78549254e-01
2.42116168e-01 4.12069708e-01 8.32825780e-01 -1.63956791e-01
7.65878856e-02 -4.61821675e-01 1.79068968e-01 -2.41402507e-01
4.12564963e-01 -1.43694675e+00 4.53487158e-01 7.90761113e-01
4.34946209e-01 5.15717864e-01 6.15052104e-01 -7.18729377e-01
-1.75031745e+00 -9.19663072e-01 4.39479351e-01 -7.77994335e-01
2.58719891e-01 -4.24523562e-01 -9.05414999e-01 4.52472329e-01
-2.35295705e-02 2.77535796e-01 1.25565514e-01 -4.55396831e-01
-2.37083256e-01 1.76758729e-02 -1.33071876e+00 3.25882614e-01
8.44352782e-01 -4.71452773e-01 -5.15715003e-01 2.47501880e-01
2.34947875e-01 -6.31941319e-01 -8.08349252e-01 2.41906300e-01
1.02813378e-01 -1.10868430e+00 1.21478748e+00 6.00488260e-02
5.42491674e-01 -6.34167135e-01 -2.90685952e-01 -1.47770548e+00
-2.76655853e-01 -6.15774930e-01 5.57355303e-03 9.88173425e-01
7.96541497e-02 -3.93621176e-01 1.01746738e+00 7.32588053e-01
8.62367898e-02 -5.34767509e-01 -9.50544655e-01 -5.32014668e-01
2.15801299e-01 -7.63884306e-01 5.35813689e-01 5.41923285e-01
-8.36727738e-01 2.37217367e-01 -1.64697945e-01 4.06898052e-01
1.29465711e+00 5.30300438e-01 9.42156672e-01 -1.40284312e+00
-3.36299896e-01 -2.95610100e-01 -2.39621773e-01 -1.34073520e+00
-7.25089461e-02 -8.15944612e-01 3.03787053e-01 -1.57845759e+00
1.48147076e-01 -6.62260652e-01 4.42504466e-01 1.32751942e-01
5.35675175e-02 5.73879719e-01 8.15350637e-02 3.14682454e-01
-2.12389976e-01 7.16801465e-01 1.21393108e+00 -3.66104916e-02
3.54727320e-02 1.42128408e-01 -4.79481250e-01 6.80636108e-01
2.62054741e-01 -5.82793713e-01 -1.34494185e-01 -6.23052895e-01
3.85489136e-01 1.55144557e-01 7.12090254e-01 -8.19973707e-01
2.44971290e-01 -1.79146260e-01 5.06698251e-01 -7.88035929e-01
7.41527617e-01 -1.26453149e+00 6.73440218e-01 2.51808047e-01
2.05502048e-01 -1.08326308e-01 -1.70741111e-01 8.99383008e-01
-5.30057698e-02 -2.12412059e-01 9.16203320e-01 -3.45849931e-01
-2.93865502e-01 4.34712201e-01 -6.99211434e-02 -1.01173222e-01
8.75965655e-01 -3.31712037e-01 3.14244360e-01 -4.08824652e-01
-7.72251427e-01 -1.62665546e-01 1.35258245e+00 1.14233583e-01
9.42451835e-01 -1.44979084e+00 -7.87178993e-01 5.15358984e-01
1.32187605e-02 9.58923340e-01 8.60736147e-02 5.20496070e-01
-7.56978333e-01 -1.39547780e-01 7.51018524e-02 -1.16556835e+00
-1.07324255e+00 4.36741799e-01 2.42684945e-01 2.97679037e-01
-9.73245442e-01 8.36836159e-01 1.84961319e-01 -5.30956328e-01
-9.76437796e-03 -4.22737867e-01 4.19060767e-01 -3.88392389e-01
2.83138126e-01 1.44285522e-02 1.41153321e-01 -1.11930919e+00
-1.34520933e-01 1.08689487e+00 3.94992262e-01 -6.66219711e-01
1.74846959e+00 -7.33712092e-02 -4.58212912e-01 5.11544824e-01
1.24939752e+00 2.45778382e-01 -1.82029939e+00 -4.76692915e-01
-2.34070912e-01 -8.51734877e-01 2.98486590e-01 -4.68283951e-01
-1.30564582e+00 8.58392775e-01 2.55416781e-01 8.20041150e-02
9.57749903e-01 3.37078393e-01 5.80956459e-01 -2.88946535e-02
6.21555805e-01 -5.75158179e-01 1.78256780e-01 2.35951081e-01
1.06554699e+00 -1.13582039e+00 1.81187510e-01 -6.11293316e-01
-5.35530806e-01 1.10430217e+00 -8.14445782e-03 -4.69704479e-01
6.74761593e-01 1.44996971e-01 -6.43174574e-02 -4.84657615e-01
-4.09325212e-01 -1.30008712e-01 3.73763978e-01 7.59573042e-01
-1.14403829e-01 -4.83269989e-02 4.47407424e-01 1.13753937e-01
-1.74426466e-01 -1.10303573e-01 4.82290119e-01 9.27690625e-01
-3.22484493e-01 -1.14943898e+00 -8.82772982e-01 1.52935833e-01
1.54441828e-02 1.67008176e-01 -5.35673440e-01 1.25034198e-01
2.71251462e-02 6.92256629e-01 1.46134079e-01 -4.96390052e-02
3.40427458e-01 -6.30039945e-02 7.19360232e-01 -8.34798634e-01
-2.45437361e-02 3.45837831e-01 -4.86774266e-01 -6.54318094e-01
-4.96560276e-01 -1.09861898e+00 -1.28329062e+00 -8.62458944e-02
1.21811200e-02 9.96694807e-03 9.92281497e-01 7.18435585e-01
2.84094602e-01 -6.37936220e-02 7.27216721e-01 -1.44898689e+00
-2.54533023e-01 -4.30015594e-01 -6.74117684e-01 6.59314811e-01
3.90479296e-01 -6.58508599e-01 -6.74741805e-01 2.80726790e-01] | [8.7964448928833, -3.3324708938598633] |
f1c15332-1217-4b16-822b-cb987fa114cb | ji-yu-duo-tou-zhu-yi-li-he-bilstmgai-jin | null | null | https://aclanthology.org/2020.ccl-1.30 | https://aclanthology.org/2020.ccl-1.30.pdf | 基于多头注意力和BiLSTM改进DAM模型的中文问答匹配方法(Chinese question answering method based on multi-head attention and BiLSTM improved DAM model) | 针对目前检索式多轮对话深度注意力机制模型DAM(Deep Attention Matching Network)候选回复细节不匹配和语义混淆的问题,本文提出基于多头注意力和双向长短时记忆网络(BiLSTM)改进DAM模型的中文问答匹配方法,该方法采用多头注意力机制,使模型有能力建模较长的多轮对话,更好的处理目标回复与上下文的匹配关系。此外,本文在特征融合过程中采用BiLSTM模型,通过捕获多轮对话中的序列依赖关系,进一步提升选择目标候选回复的准确率。本文在豆瓣和电商两个开放数据集上进行实验,实验性能均优于DAM基线模型,R10@1指标在含有词向量增强的情况下提升了1.5%。 | ['Xia Zhao', 'Weijie Jiang', 'Chongchong Yu', 'Hanzhong Qin'] | null | null | null | null | ccl-2020-10 | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [-0.81465846 -0.53620666 0.57658917 0.63949645 -0.03455365 -0.08120593
-0.06266747 1.298765 -0.3264723 0.166293 0.8044529 0.11080449
0.04242913 -1.2629861 -0.3989925 -1.2384853 -0.7696726 1.6158489
1.2553394 -0.91242003 0.29202372 0.9180892 -0.8747693 0.36349005
0.8449636 0.7818563 0.8309246 0.45368207 0.3013518 0.47732154
-0.979481 0.0042621 -0.69175804 0.18225566 -0.22339965 0.54909754
-0.00437371 -1.2982976 -1.7311195 1.1111348 0.7022971 0.20403811
1.1425512 -0.65580857 -1.050601 0.6025626 -0.17783453 1.1952593
-0.5960217 0.18342805 0.72656655 -0.9133998 -0.19741249 1.8500112
1.7677293 0.19868217 -1.4272828 -0.73612195 0.64238554 -0.01301237
-1.6104966 0.5304822 0.30246508 -0.36448961 1.265352 0.03851958
1.0912198 1.7070669 0.92727804 1.1882036 0.9191838 0.4360432
0.12069525 0.7527714 -0.37897766 -0.10169529 1.2106584 0.40791166
0.03415578 0.50026095 0.38724315 0.15892047 0.2880235 1.0393932
-1.3736719 1.8677009 0.8506983 1.3360755 -0.41592145 -0.91759694
-0.46782377 0.2083017 0.21755403 -0.5439519 0.9115228 0.7422095
0.4036272 0.12470414 0.4701143 1.2211003 1.2585866 0.89139706
-0.7998804 0.44173425 1.6790485 0.69539857 1.3480958 -0.5233291
-0.99592954 0.33172533 -0.96183705 -2.017893 -1.0832046 -1.2522218
-0.98938197 -1.0146298 -0.344452 -0.49072987 -0.9362246 1.2325659
0.2728534 -0.46436492 0.5851445 0.8203238 0.63331753 1.4808776
0.29644868 0.15749158 1.417095 -0.42775035 -0.16776624 -0.56467956
-0.7016595 -1.4474436 1.0623344 -0.2831825 -0.67050993 -0.38444623
-1.560336 0.29007313 -0.26344642 -0.69977134 0.73639727 0.90914625
-1.5035312 0.39919433 -1.0791162 -1.2048553 0.18056203 0.38087985
0.34288037 -0.07061915 -1.6563015 1.2472154 -0.06930052 0.56163365
-1.9196028 -0.4927352 -0.8164554 0.28060815 0.42651698 -0.37032884
1.906662 -0.9758836 -0.27564642 0.62118804 -0.29477945 -0.5816355
0.19156785 0.5967694 -0.10904221 0.15103923 -0.13481641 0.47922188
0.7453315 -1.9174154 -0.60765344 -1.2454488 0.40356985 -0.00469128
0.13553539 0.8029314 0.01401126 -0.81789464 -0.37961018 -1.708481
-0.13351959 -0.88572663 -0.25127366 -0.02386715 0.8285374 -0.00764082
1.233845 -2.2601922 -0.43574116 0.6966736 0.49442965 0.85915744
-0.08737785 2.006646 0.6871997 0.72304577 -0.86196655 0.42182085
-0.12600309 0.27441153 1.3315 -0.01150117 -0.29999474 -0.26217106
-0.33431408 -0.22152427 0.46023652 0.7832086 0.42125714 0.6294466
0.54588515 -0.21850348 0.03064586 0.91096205 1.1226531 -0.4259041
0.2542349 -0.15641913 -0.27746868 -0.33760196 -2.18612 1.3254954
0.14917621 0.8166068 0.32892168 -0.7531529 1.130428 1.1250037
0.8121123 -0.09730576 0.70421606 0.4048399 0.76372427 -0.08195222
1.0544716 -1.2385752 0.77125996 0.28804088 -0.42535743 0.612239
0.33480474 -0.73304397 1.4743638 -1.2870486 -0.33539197 -0.6812328
0.35806468 -0.5813771 0.56494963 -0.04887911 -0.85324234 0.55713063
-0.74390185 0.4480131 -0.2095279 -1.6592124 -0.26185587 1.4772238
0.02243108 -0.76767826 -0.8148211 -0.15062214 -0.08017144 0.6584647
0.03782485 -0.7469309 -0.79273087 -1.0649614 -0.06954502 0.41326514
1.5329069 -1.5929962 -0.37704757 -0.17942522 0.03044497 -0.3580366
-0.89561486 -0.3908802 -1.6070799 -0.62261367 -0.15010291 -1.5902442
1.2452824 0.2719264 0.7100004 0.3422391 0.11300151 0.36956787
-0.36705256 -1.0964743 -0.27948058 0.5845799 0.3013197 1.2830456
0.9612582 -0.666617 -1.3918357 0.3225336 -1.3571671 0.21895848
0.54720813 0.08157918 0.65961367 0.04374915 0.12701364 -0.6443971
1.2428615 0.11970076 -1.0464257 0.10729417 -1.3586806 -0.8485853
0.9460471 -0.780234 -1.0927339 -1.1914579 -0.6551097 -0.63368994
0.33549687 0.15040965 -0.16602772 0.796379 -0.10451455 -0.00322521
0.5421789 -1.0053424 -0.29537642 1.0143567 0.05645523 -0.818678
0.51019603 -0.06567021 -0.34713 -0.23069336 0.20980094 0.05735805
-0.6300914 0.9293821 1.2161236 -1.5330256 -0.17672704 0.81006765
-0.6327826 0.63039327 -0.4551724 1.5824568 0.00301796 -0.43681055
-1.2206705 -0.47205055 -0.40164873 -2.2673266 0.22013435 2.0008245
0.00542182 -0.9519347 0.24195987 0.2768957 1.1050837 -0.09922557
1.1268139 -0.4769787 -0.74197227 -0.36381677 0.02867314 1.284249
0.698645 0.29496855 0.18487604 -0.44268295 -0.5587217 0.7168208
0.72874755 0.6238995 -0.00352129 -0.37372035 0.02572801 0.44442722
2.1491153 1.1008407 1.1387414 2.0454738 -0.36742926 -0.23753747
0.0106519 0.3827327 0.35193652 1.1365216 0.30219355 0.7998798
-1.2550938 -0.14717129 0.998545 1.2320784 0.3042119 0.15979493
-0.18240513 0.9445269 -2.5470045 -1.6846874 0.361121 2.4932418
0.33255222 -0.09311702 0.22578807 -0.09677143 1.6426923 0.04084115
0.01000961 -0.67677724 0.64758694 0.8983684 0.49167463 0.26540428
-0.6717341 0.36237192 0.93387485 0.71389157 -1.9621402 0.06512253
0.3680993 -1.0126026 -0.69570524 0.5082946 -0.6808733 1.9116387
1.3379849 -1.4469705 0.18755457 0.05367108 0.74403185 0.32530704
-0.6775388 1.4101692 0.23338692 -1.4394768 0.23450132 0.1251807
0.36327276 0.08787427 -0.24879953 0.5215873 0.26171723 -0.92013633
0.79434514 -0.1345652 -0.05590701 -1.2807231 1.7362535 -0.12086429
-1.314232 -0.68494284 -0.22211447 -0.37544575 0.73666614 0.20565563
0.10544989 0.25388715 -0.02138747 0.07749013 -0.692421 2.3889134
-0.77802306 -0.37333548 -0.14531997 -1.3670349 0.9542459 -0.01884288
0.66376096 1.5629984 0.41277733 0.75715405 0.20228098 0.7843677
-0.76011896 -0.06442285 -0.07442535 0.84625584 0.61066955 1.1438384
-1.1868385 -0.49501586 -0.3156189 -0.7168069 -0.14676267 -0.58712107
-0.07186892 -0.5343785 0.27901608 0.26521417 -0.26549262 -0.7059419
0.7101749 -0.23378055 -0.38591442 -1.0056723 0.21570285 -0.9544659
-1.1744837 0.56107974 0.35596302 -0.9505853 0.62901384 -1.5297817
-0.46415478 0.609918 -0.00386961 -0.72654814 -0.15733202 -0.18185419
1.2391974 0.26867175 0.855626 1.1038392 -1.2008715 0.5043242
1.3606279 0.67099816 0.05705998 -2.0023012 -1.047272 0.6365157
0.7936037 -0.2404403 -0.11268921 -0.29048333 -0.94210064 -0.9048215
0.47588938 0.5493876 0.20762473 -0.31869867 -0.54080176 1.5928081
0.6244705 -0.03265209 0.3990691 -0.43578503 0.623426 -1.4568508
-1.7176063 1.3092891 0.43996343 0.37906176 -0.8009092 -0.17113324
0.4216089 -1.1948375 -1.5876365 0.35684144 1.5571165 -0.71145064
-0.01574331 -0.29701644 -0.05790573 -0.13602316 -0.4155359 -1.5176392
-0.07705872 -0.67090434 0.87837255 1.3037933 0.2967991 -0.9034262
0.7988683 1.5629545 -0.9043115 -0.8145228 -2.1559074 -1.0335162
0.5489745 -0.21031696 0.07837716 -0.52602565 -0.6057838 0.70538527
0.4644589 0.09211316 0.08879338 -0.7857111 -0.1807775 -1.2316247
1.0098201 -0.6470396 -0.8068445 0.0860486 -0.95943177 -0.23352304
-1.6475441 -2.3640156 0.43865842 -0.3153082 -0.65090466 -0.13918374
1.2600822 0.20752929 1.1462896 0.8569339 0.0446053 -0.03676297
1.2229517 -0.69438875 -0.29601356 0.16014504 -0.76384693 0.87934244
0.51654875 -0.17146255 -0.16588113 -0.8998905 -0.23799442 -1.3264186
-1.221132 -1.104678 0.17204185 0.39730147 0.15198228 0.64624435
0.39350834 -0.5903643 0.28673685 0.7343643 0.27526295 1.1035953
0.56851965 -0.5980017 0.01122796 -1.1712676 1.0803144 -0.20282216
-0.609291 -0.14226833 -0.82972634 -0.7669367 1.5545801 0.08674552
-0.55887586 0.445098 -1.0409685 -0.16106656 0.2255304 1.0108917
1.6056299 -1.7334222 -2.2968192 0.10679547 -0.5027582 0.4010171
0.41862068 1.001249 -0.5937758 0.1424321 -0.7844789 -0.39427614
-2.3395607 0.84013975 -0.03111615 -0.22477649 -0.7588489 0.4135659
-0.39295697 0.74962574 0.36857238 -0.41072023 -0.51322365 0.22575073
0.6219652 0.27693686 0.17150442 -1.3557581 -0.7022352 1.5287708
-1.0136641 -0.10826155 0.5320629 0.02616455 -0.4037517 -0.15274432
1.1121424 0.2240906 -0.49489602 0.41205975 -0.90379405 -0.08091142
-0.5412779 -1.4604101 -0.67589897 1.124358 0.9808613 -0.7466744
0.8294686 -0.482729 1.4217741 -0.0272975 1.7929646 -1.6361473
-0.65743 0.96689415 1.1970872 -1.846709 0.14893329 -0.03048366
0.46253973 1.472717 1.1721072 -0.67335886 0.8425958 -0.03163088
-0.7271092 -0.08154786 -0.11810396 -0.8658485 -0.03773214 0.8722963
0.7777941 -0.1331599 -1.2031455 0.3750295 -0.4209856 0.06747667
1.023897 0.89754015 -1.0130979 -0.26811874 -1.3736346 0.3877262
-0.9608443 0.5823574 0.48500288 1.0129669 0.52737546 2.2070777
0.0275012 -1.1890613 1.8274504 -0.46413764 0.55545914 -0.43175963
-1.1646198 -0.6379448 0.12566988 0.6672643 -0.04478366 0.15560704
-1.1278577 -1.6049699 -0.10864354 1.8949552 0.6550473 0.604493
-0.0194614 1.1380144 0.868647 -1.0842185 -0.90946263 -1.4260215
-0.88347805 -0.33153504 0.22921874 -1.0128496 -1.3252676 -0.8254979 ] | [-3.3160908222198486, 6.907607555389404] |
3e694808-1caf-4722-ba68-c9012edbad2c | improving-interpretability-via-regularization | 2211.08686 | null | https://arxiv.org/abs/2211.08686v1 | https://arxiv.org/pdf/2211.08686v1.pdf | Improving Interpretability via Regularization of Neural Activation Sensitivity | State-of-the-art deep neural networks (DNNs) are highly effective at tackling many real-world tasks. However, their wide adoption in mission-critical contexts is hampered by two major weaknesses - their susceptibility to adversarial attacks and their opaqueness. The former raises concerns about the security and generalization of DNNs in real-world conditions, whereas the latter impedes users' trust in their output. In this research, we (1) examine the effect of adversarial robustness on interpretability and (2) present a novel approach for improving the interpretability of DNNs that is based on regularization of neural activation sensitivity. We evaluate the interpretability of models trained using our method to that of standard models and models trained using state-of-the-art adversarial robustness techniques. Our results show that adversarially robust models are superior to standard models and that models trained using our proposed method are even better than adversarially robust models in terms of interpretability. | ['Asaf Shabtai', 'Ron Bitton', 'Gil Fidel', 'Ofir Moshe'] | 2022-11-16 | null | null | null | null | ['explanation-fidelity-evaluation', 'interpretability-techniques-for-deep-learning'] | ['methodology', 'miscellaneous'] | [ 7.05161318e-02 3.87040645e-01 4.53269869e-01 -3.75833392e-01
-2.22631305e-01 -8.91219914e-01 7.11884260e-01 -2.08330646e-01
-6.34029329e-01 6.48503363e-01 8.30142796e-02 -6.80915833e-01
-7.90506676e-02 -6.20366156e-01 -9.53537166e-01 -5.20610213e-01
-1.09770276e-01 1.26297280e-01 1.49183556e-01 -6.03806734e-01
6.42678812e-02 8.72337043e-01 -1.08615291e+00 2.10506022e-01
8.33952963e-01 1.13072646e+00 -5.63369453e-01 6.43044114e-01
3.37756276e-01 8.89220893e-01 -1.08837497e+00 -7.42468238e-01
6.22503042e-01 1.62632228e-03 -7.72219241e-01 -4.29222941e-01
5.36764324e-01 -5.43583810e-01 -5.97837389e-01 1.30128932e+00
3.76045376e-01 2.06355210e-02 5.47399580e-01 -1.56243503e+00
-1.12397707e+00 4.55639035e-01 3.46273668e-02 3.38713199e-01
-1.79482549e-01 4.02813107e-01 6.66215658e-01 -2.63460726e-01
1.18057974e-01 1.46096683e+00 7.56390929e-01 9.83969867e-01
-1.00432312e+00 -8.80707979e-01 4.00240362e-01 -1.87709942e-01
-9.39823747e-01 -5.34357965e-01 6.68204486e-01 -4.34539288e-01
7.01653898e-01 4.28029329e-01 1.55359015e-01 1.61661434e+00
5.32337308e-01 2.25547135e-01 1.11655164e+00 -7.81619921e-02
3.73601675e-01 1.74783662e-01 4.80227619e-02 6.52708888e-01
3.77805620e-01 6.03528917e-01 -1.61626965e-01 -1.69613853e-01
6.97384477e-01 -1.54297603e-02 -1.99970126e-01 9.28587541e-02
-8.28714430e-01 9.20393288e-01 8.11451197e-01 1.54063925e-01
-2.83307612e-01 5.23526907e-01 6.03485882e-01 4.87639070e-01
4.93108749e-01 7.48591185e-01 -3.56190592e-01 3.18360090e-01
-3.71643215e-01 2.43631586e-01 5.87348878e-01 7.23178864e-01
2.11993128e-01 6.79297686e-01 1.07971609e-01 4.77293015e-01
6.37285829e-01 4.38435018e-01 3.90288323e-01 -6.92303479e-01
5.92588246e-01 4.37092066e-01 -1.52732462e-01 -9.53136623e-01
-2.24722430e-01 -3.73587698e-01 -9.17592168e-01 9.91828024e-01
3.37234199e-01 -5.39693773e-01 -1.25462079e+00 1.86414766e+00
-2.43101463e-01 -3.05482168e-02 5.59459925e-01 7.60332465e-01
8.81423652e-01 5.27434587e-01 3.04683506e-01 5.31609237e-01
1.09072697e+00 -8.25635910e-01 -7.05429971e-01 -6.38758242e-01
1.30330563e-01 -5.14962375e-01 8.52530241e-01 3.69215548e-01
-8.52364302e-01 -5.50936460e-01 -1.50264502e+00 2.41244659e-01
-6.31091058e-01 -3.70784223e-01 5.39108157e-01 1.37101042e+00
-1.07532001e+00 5.87169349e-01 -6.96434379e-01 -1.27120158e-02
7.21407294e-01 7.70607293e-01 -4.73550707e-01 4.30659562e-01
-1.43658721e+00 1.29522324e+00 6.10702157e-01 3.36469054e-01
-1.33207428e+00 -4.13644880e-01 -8.72246027e-01 -1.19195081e-01
6.82975799e-02 -5.28284371e-01 1.16293752e+00 -1.38275087e+00
-1.36288941e+00 5.70453405e-01 4.16020751e-01 -8.64653111e-01
7.39508808e-01 -5.44682324e-01 -6.80113971e-01 -2.12601423e-02
-4.61294562e-01 4.49076533e-01 1.04673696e+00 -1.63913989e+00
-2.10185096e-01 -1.75080046e-01 6.39451385e-01 -4.48359773e-02
-5.86466372e-01 -4.45460714e-02 9.87699479e-02 -8.93491745e-01
-2.11068571e-01 -1.07403600e+00 -3.34982842e-01 3.38872135e-01
-4.99036551e-01 4.49741066e-01 1.19335592e+00 -6.81969285e-01
9.11520481e-01 -2.14781427e+00 -1.88158363e-01 2.85492599e-01
3.80951762e-01 9.14482057e-01 -1.19381405e-01 1.48596659e-01
-4.38965768e-01 7.22754598e-01 -2.26038367e-01 -2.47931749e-01
-3.25826779e-02 5.99238217e-01 -6.13738477e-01 4.42830682e-01
4.52498257e-01 7.41382778e-01 -6.86408401e-01 -5.67712747e-02
2.55368084e-01 4.40611303e-01 -4.11341846e-01 3.52408707e-01
6.17261492e-02 5.29896021e-01 -5.39943874e-01 5.21125376e-01
6.84395850e-01 3.50645840e-01 -2.33147055e-01 -1.21894896e-01
3.83728117e-01 -1.55151367e-01 -8.85017991e-01 9.16711569e-01
-5.22622287e-01 9.64899302e-01 -5.32765761e-02 -7.88870215e-01
7.87851095e-01 5.67125082e-01 -2.35175952e-01 -4.20580715e-01
3.94322157e-01 -5.03597818e-02 2.10851610e-01 -4.46191698e-01
4.98828202e-01 -3.48479837e-01 -9.32138935e-02 3.77601445e-01
5.62302843e-02 -1.17498906e-02 -5.12546957e-01 -2.95815971e-02
1.08004820e+00 -1.88163668e-01 1.08626455e-01 -4.16257083e-01
5.90961218e-01 -4.77147669e-01 4.21624094e-01 8.40211034e-01
-4.53052282e-01 5.49542546e-01 5.80623388e-01 -7.86419392e-01
-1.06176853e+00 -1.37522721e+00 8.16784613e-03 7.61687934e-01
4.17843722e-02 1.32867709e-01 -1.08459127e+00 -9.99168396e-01
-5.08382656e-02 9.87361908e-01 -9.23413396e-01 -7.15754449e-01
-3.91494304e-01 -6.71059370e-01 1.37643492e+00 8.21788192e-01
7.11996317e-01 -9.41316545e-01 -5.40792465e-01 -1.70612320e-01
1.27099724e-02 -1.21467113e+00 -7.89104253e-02 1.39258280e-01
-9.38224316e-01 -8.30719471e-01 -2.80046821e-01 -2.96315819e-01
1.07533991e+00 -6.05879501e-02 1.04456592e+00 4.02385622e-01
1.58171698e-01 2.04322621e-01 -2.83906639e-01 -9.75729346e-01
-1.09315908e+00 -2.21517712e-01 4.01430696e-01 -1.52668711e-02
-1.60291597e-01 -4.25178766e-01 -3.38922918e-01 5.14323056e-01
-1.60789061e+00 -4.17201489e-01 5.71976304e-01 6.70755744e-01
-9.81281251e-02 1.94038391e-01 7.45845973e-01 -1.14201498e+00
1.01517177e+00 -3.28466237e-01 -5.55381596e-01 1.74982548e-01
-7.16537952e-01 3.83277893e-01 1.12221038e+00 -6.14691734e-01
-1.15101779e+00 -2.53380030e-01 -3.85270983e-01 -3.68817866e-01
-1.82118833e-01 1.51484266e-01 -2.42520139e-01 -7.07523525e-01
1.26986217e+00 -4.26967531e-01 -1.77365821e-02 -1.55167520e-01
1.53221115e-01 6.07225597e-01 6.47255838e-01 -6.47185683e-01
1.39219403e+00 4.49847609e-01 -6.03758767e-02 -6.88206732e-01
-6.74272001e-01 3.30443561e-01 -4.11969334e-01 -2.59231180e-01
7.58601248e-01 -8.42022836e-01 -5.92639685e-01 7.34565675e-01
-1.28137088e+00 -1.12598702e-01 -7.30660856e-02 1.09607548e-01
-1.79577142e-01 4.01649028e-01 -5.60178518e-01 -8.87538314e-01
-5.01571715e-01 -1.39344239e+00 4.99419630e-01 1.53460070e-01
-2.45224684e-01 -1.30295730e+00 -1.73878253e-01 5.39740264e-01
6.34193897e-01 8.64347100e-01 8.65814745e-01 -9.85977650e-01
-3.49371791e-01 -5.00088632e-01 -5.86748309e-02 1.08909702e+00
2.20259409e-02 9.80604440e-02 -1.52418685e+00 -3.73579323e-01
2.89527386e-01 -3.35472018e-01 5.96848905e-01 -7.73678254e-03
1.17526674e+00 -7.53608882e-01 2.00450853e-01 6.68678284e-01
1.42271912e+00 2.52686471e-01 9.82109129e-01 6.48870707e-01
6.68794930e-01 4.02722090e-01 2.70036578e-01 8.03630725e-02
-2.16164514e-01 1.31658331e-01 1.19711673e+00 -4.05381769e-01
4.27677959e-01 -1.06226742e-01 5.46399713e-01 1.18566610e-01
-4.73030180e-01 -5.80394268e-01 -8.70516062e-01 2.37199083e-01
-1.83241796e+00 -8.19681227e-01 -4.34650071e-02 2.12642527e+00
4.73565638e-01 7.91297317e-01 -2.29371026e-01 1.95504963e-01
6.60623074e-01 3.31149817e-01 -4.82180357e-01 -9.43624437e-01
-1.40811697e-01 1.71408653e-01 8.17033172e-01 4.32004482e-01
-1.14848661e+00 7.33089387e-01 7.13080645e+00 4.01803046e-01
-9.82320964e-01 4.42711785e-02 9.23801124e-01 1.12548582e-01
-2.05079839e-01 -2.25157887e-01 -3.02127540e-01 3.58936608e-01
1.12541604e+00 1.60676748e-01 4.04566258e-01 1.01469254e+00
3.08789462e-02 2.84856439e-01 -1.21382940e+00 3.82320851e-01
6.90954402e-02 -1.24133396e+00 2.48176306e-01 4.43039984e-02
6.50085151e-01 -1.70639288e-02 3.96270514e-01 1.96424559e-01
5.35888433e-01 -1.45736575e+00 1.11927593e+00 3.49943757e-01
4.28441703e-01 -9.68460262e-01 1.16036320e+00 1.50574207e-01
-6.47600234e-01 -2.37472072e-01 -3.02060246e-01 -6.69526756e-02
-1.75568596e-01 4.39828932e-02 -6.11510992e-01 3.16741973e-01
7.23566413e-01 -3.21591087e-02 -8.27375174e-01 4.91899252e-01
-5.40587544e-01 5.51356792e-01 -1.00357473e-01 1.39595225e-01
4.77970898e-01 1.41683877e-01 5.55039883e-01 1.04586601e+00
-5.74322902e-02 -1.44691736e-01 -4.84034508e-01 9.77396309e-01
-1.44285396e-01 -5.78452587e-01 -9.49272752e-01 -1.14861637e-01
2.53203154e-01 9.88167822e-01 -3.94087195e-01 -9.56382453e-02
-1.04507677e-01 9.06115294e-01 1.23799495e-01 4.39603418e-01
-1.15691042e+00 -2.02026322e-01 8.58925760e-01 -1.52626440e-01
-9.13381353e-02 -1.62516117e-01 -6.14797831e-01 -8.51893187e-01
1.59007490e-01 -1.23764563e+00 3.01732093e-01 -7.23755956e-01
-1.31682527e+00 1.17549956e+00 1.69237107e-02 -1.26048934e+00
-8.96789655e-02 -1.06000090e+00 -8.56631756e-01 8.89113843e-01
-1.29583371e+00 -1.33032298e+00 -1.70890316e-01 6.62746608e-01
1.99474409e-01 -4.76047069e-01 1.06344151e+00 1.00358121e-01
-5.52012026e-01 9.72168505e-01 1.88511238e-02 5.11806726e-01
4.51958507e-01 -1.04547763e+00 9.07106459e-01 1.40565014e+00
-1.70898393e-01 7.90936291e-01 9.84169841e-01 -3.32908034e-01
-1.04723454e+00 -1.18576014e+00 3.01972300e-01 -6.47548676e-01
6.02291405e-01 -4.03932422e-01 -8.32269490e-01 9.54485655e-01
2.08551019e-01 8.16117972e-02 6.80800080e-01 -1.25360698e-01
-5.76876342e-01 -1.63540274e-01 -1.64459407e+00 9.57158029e-01
5.67111313e-01 -6.43818915e-01 -8.97255719e-01 1.42837435e-01
8.43391716e-01 -4.35411215e-01 -7.35074699e-01 3.73254806e-01
4.93731469e-01 -1.06928980e+00 1.22686100e+00 -9.05245960e-01
5.63957155e-01 -2.43188858e-01 -9.29977596e-02 -1.45140183e+00
-9.75456014e-02 -7.09916592e-01 8.48899484e-02 1.00648952e+00
5.65703452e-01 -1.12627721e+00 5.71202338e-01 1.18509579e+00
-2.56555349e-01 -4.86206770e-01 -9.43498790e-01 -8.74574125e-01
1.84796125e-01 -4.75897253e-01 5.74812055e-01 7.94268191e-01
-4.75858957e-01 8.60558227e-02 -4.59420592e-01 7.98908412e-01
5.45840859e-01 -8.29560757e-01 6.08294129e-01 -1.10037184e+00
-2.27358967e-01 -1.80894703e-01 -8.37913990e-01 -3.22190553e-01
2.59391904e-01 -2.98968166e-01 -1.51200173e-02 -1.05039442e+00
-3.70661736e-01 -2.57090539e-01 -3.82074803e-01 6.25341535e-01
-1.70984700e-01 2.45238155e-01 3.78339946e-01 -5.52003868e-02
-5.38492948e-02 2.81335741e-01 8.61134648e-01 -1.33302018e-01
2.09271237e-01 1.70269355e-01 -9.73927319e-01 1.04616868e+00
9.49934721e-01 -6.49227202e-01 -5.62765300e-01 -7.44245470e-01
1.90151498e-01 -2.77534097e-01 7.05354929e-01 -1.31030774e+00
-8.59324075e-03 -1.63760528e-01 4.57140207e-01 5.47355637e-02
3.66271675e-01 -1.17678297e+00 2.58353204e-01 7.04850435e-01
-2.84250498e-01 3.12027991e-01 7.16929018e-01 6.22746050e-01
-1.10624768e-01 -5.68529069e-01 1.03385246e+00 -6.83169439e-02
-5.59464812e-01 1.52568847e-01 -6.16162419e-01 7.46957511e-02
1.04490745e+00 -2.58517683e-01 -5.83327234e-01 -5.06357908e-01
-4.16818917e-01 -4.95744944e-02 4.85629916e-01 6.18837714e-01
7.95127571e-01 -1.26899290e+00 -4.82266992e-01 2.56792516e-01
8.23794678e-02 6.61517084e-02 -4.62017721e-03 1.38891473e-01
-1.06943512e+00 1.88599378e-01 -5.92423677e-01 -6.99273497e-02
-1.22211564e+00 5.72514772e-01 5.57059824e-01 -1.95343882e-01
-2.42092550e-01 8.97692323e-01 4.29751903e-01 -4.19993043e-01
4.85937893e-01 -2.82560617e-01 -1.08450954e-03 -5.62525511e-01
3.53876501e-01 2.63330042e-01 1.11513264e-01 -6.10197544e-01
-3.17436725e-01 8.42208639e-02 -2.09546790e-01 -1.21056825e-01
1.23137057e+00 3.49411249e-01 6.94432035e-02 8.38881265e-03
9.37908649e-01 -2.52941698e-01 -1.35222387e+00 8.58334452e-02
-3.30490112e-01 -3.96218300e-01 1.17111281e-01 -9.40819323e-01
-1.34420621e+00 9.51312542e-01 7.99457550e-01 3.18593740e-01
1.23023462e+00 -5.66878676e-01 6.79302812e-01 6.46703660e-01
-3.80490795e-02 -9.79919672e-01 1.14637397e-01 5.01729906e-01
9.76702273e-01 -1.29897511e+00 3.89128141e-02 -1.92926109e-01
-8.34359288e-01 1.12442350e+00 6.07679725e-01 -3.26171100e-01
4.85929668e-01 2.59890378e-01 4.96912867e-01 -7.20565096e-02
-3.83291483e-01 4.17079329e-01 3.89876395e-01 8.78222108e-01
-2.23647114e-02 -1.33834302e-01 2.66511410e-01 5.24818480e-01
-2.59632677e-01 -4.24053580e-01 6.22560024e-01 9.66760695e-01
-4.95052561e-02 -9.21497047e-01 -7.63415217e-01 1.38396725e-01
-9.07027185e-01 -1.99877545e-01 -7.26091444e-01 8.91469777e-01
1.62494004e-01 1.10089123e+00 -2.95628965e-01 -7.95893908e-01
4.79931653e-01 -1.94938645e-01 -3.96113619e-02 -2.67562330e-01
-1.14259458e+00 -6.90821171e-01 2.68314421e-01 -2.73760736e-01
-1.79721177e-01 -2.18875140e-01 -1.04033875e+00 -5.59818149e-01
-2.83779621e-01 -1.59936756e-01 8.67206275e-01 1.09129643e+00
1.46322519e-01 7.93335855e-01 6.59438312e-01 -7.06735253e-01
-9.88052607e-01 -7.14289308e-01 -1.98888287e-01 5.91897905e-01
6.71961308e-01 -4.06068534e-01 -7.12239146e-01 8.65552649e-02] | [5.678408622741699, 7.841391086578369] |
b5066723-fbbd-4cb2-b05b-06bdb3a819a2 | sub-pixel-face-landmarks-using-heatmaps-and-a | 2103.03059 | null | https://arxiv.org/abs/2103.03059v2 | https://arxiv.org/pdf/2103.03059v2.pdf | Sub-pixel face landmarks using heatmaps and a bag of tricks | Accurate face landmark localization is an essential part of face recognition, reconstruction and morphing. To accurately localize face landmarks, we present our heatmap regression approach. Each model consists of a MobileNetV2 backbone followed by several upscaling layers, with different tricks to optimize both performance and inference cost. We use five na\"ive face landmarks from a publicly available face detector to position and align the face instead of using the bounding box like traditional methods. Moreover, we show by adding random rotation, displacement and scaling -- after alignment -- that the model is more sensitive to the face position than orientation. We also show that it is possible to reduce the upscaling complexity by using a mixture of deconvolution and pixel-shuffle layers without impeding localization performance. We present our state-of-the-art face landmark localization model (ranking second on The 2nd Grand Challenge of 106-Point Facial Landmark Localization validation set). Finally, we test the effect on face recognition using these landmarks, using a publicly available model and benchmarks. | ['Siwa Boonpunmongkol', 'Pavit Noinongyao', 'Sanjana Jain', 'Aubin Samacoits', 'Samuel W. F. Earp'] | 2021-03-04 | null | null | null | null | ['face-alignment'] | ['computer-vision'] | [-2.67340094e-01 2.04422791e-02 -4.11386713e-02 -7.15173304e-01
-1.04410684e+00 -6.25511289e-01 6.42669678e-01 -2.72775412e-01
-5.56761444e-01 2.92355478e-01 2.76216511e-02 2.84405444e-02
3.16709548e-01 -3.45944136e-01 -1.07616591e+00 -5.05403399e-01
-3.74643505e-01 7.02858090e-01 4.63965870e-02 8.89592469e-02
2.84986198e-01 1.05490196e+00 -1.47963238e+00 7.74700418e-02
-1.23764433e-01 1.13205981e+00 -3.95016640e-01 5.62554836e-01
7.25655109e-02 1.68533117e-01 -3.43185633e-01 -3.50269794e-01
5.37258863e-01 -1.79278180e-01 -9.41019297e-01 -2.07048222e-01
1.31063294e+00 -4.11414027e-01 -2.01515201e-02 8.65513206e-01
8.86887431e-01 -2.55564712e-02 4.03090000e-01 -1.09128499e+00
-3.68526489e-01 2.79349029e-01 -7.98774242e-01 1.12249546e-01
3.99413258e-01 -9.55654755e-02 6.81433916e-01 -1.32495165e+00
9.88960266e-01 1.43907785e+00 1.43439436e+00 8.25326324e-01
-1.44254732e+00 -7.51127541e-01 6.86911913e-03 -1.40336752e-01
-2.01799679e+00 -1.42153430e+00 5.54961324e-01 -2.21033186e-01
1.06035006e+00 4.61016335e-02 2.85843611e-01 9.20570076e-01
-7.30309188e-02 1.64030761e-01 8.39752257e-01 -3.83353323e-01
1.52851552e-01 -1.28175735e-01 -2.42224351e-01 1.38567305e+00
-1.62839204e-01 2.40315106e-02 -5.75933218e-01 -1.93448618e-01
8.50059092e-01 -1.52592465e-01 -1.85655594e-01 -5.74486792e-01
-9.52055514e-01 4.90817100e-01 6.65725648e-01 1.35458574e-01
-8.75710919e-02 7.50338256e-01 1.35170221e-01 2.25516222e-02
5.06106675e-01 2.89196670e-02 -4.49814588e-01 4.66842242e-02
-1.48068142e+00 1.10881895e-01 7.90687263e-01 9.02312517e-01
1.04198468e+00 -1.68373704e-01 -9.89025608e-02 6.87935293e-01
7.01073706e-01 5.20102143e-01 1.73168212e-01 -1.13664079e+00
1.21887945e-01 2.27843598e-01 -1.13460362e-01 -1.05150867e+00
-7.57631958e-01 -2.66540349e-02 -3.71895850e-01 4.55876499e-01
6.30501807e-01 -1.71834931e-01 -1.14946938e+00 1.82670271e+00
4.00013804e-01 8.01322103e-01 -5.78336418e-01 7.95734346e-01
8.07427168e-01 1.24707393e-01 1.08411282e-01 2.18750790e-01
1.41618407e+00 -8.90289187e-01 -1.63969889e-01 -1.73610225e-01
6.16434872e-01 -8.77666235e-01 7.79011548e-01 -7.62207806e-02
-1.18681824e+00 -4.86923426e-01 -1.06315422e+00 -4.04734701e-01
-4.48481351e-01 1.78436100e-01 6.01310372e-01 9.15108740e-01
-1.89275134e+00 8.22106242e-01 -1.06739306e+00 -6.51921690e-01
7.94356525e-01 9.65752780e-01 -1.03315175e+00 9.94192623e-03
-4.33305860e-01 9.29779112e-01 -3.18894267e-01 1.29983634e-01
-9.81016457e-01 -9.72903967e-01 -1.03248060e+00 -3.45129818e-02
-1.74539492e-01 -4.50139284e-01 1.10478127e+00 -7.66364455e-01
-1.55067468e+00 1.14534593e+00 -7.11858332e-01 -1.84525833e-01
4.67993766e-01 6.57196045e-02 -2.03778252e-01 1.00891709e-01
6.64640218e-02 1.29457521e+00 9.02105570e-01 -1.05085731e+00
-3.00695032e-01 -7.28331804e-01 -3.42623502e-01 -9.28672925e-02
-7.40499329e-03 2.42255270e-01 -8.77711892e-01 -1.50554165e-01
2.75164038e-01 -8.89191270e-01 -7.02288300e-02 4.34902608e-01
-2.64197022e-01 -9.43038333e-03 8.88687789e-01 -7.18753576e-01
6.12736166e-01 -2.11997342e+00 -2.17284724e-01 5.60528934e-01
1.35849416e-01 -6.45779371e-02 -4.90656108e-01 -4.64959927e-02
-2.74197131e-01 2.69679040e-01 -5.09651527e-02 -1.17019141e+00
7.74229243e-02 -1.46756098e-01 -2.99718194e-02 1.09070361e+00
1.46355599e-01 1.12084556e+00 -4.03339237e-01 -4.93715793e-01
7.69967437e-02 1.12374687e+00 -7.93785155e-01 -2.76933104e-01
-3.24959792e-02 3.90287340e-01 6.03695102e-02 9.96033132e-01
9.53241765e-01 -2.94804759e-02 5.53744137e-02 -2.43298635e-01
-1.37888324e-02 2.77740061e-01 -1.21428430e+00 2.13928199e+00
-5.29844224e-01 6.06823087e-01 5.39104939e-01 -4.59373862e-01
7.39121199e-01 2.62782902e-01 5.58533311e-01 -5.17984509e-01
1.06870309e-01 6.19454645e-02 -4.54329699e-01 7.69278854e-02
6.81493357e-02 3.89709473e-02 3.88083220e-01 4.56073046e-01
4.96505588e-01 1.15358233e-01 -2.25458324e-01 -2.32283697e-01
9.68897521e-01 4.14339393e-01 -1.56247169e-01 -5.26192427e-01
3.38955849e-01 -3.01016390e-01 3.20171654e-01 3.97440821e-01
-2.95070350e-01 8.84077132e-01 5.66094518e-01 -6.17564499e-01
-7.99462795e-01 -8.45310271e-01 -4.19879258e-01 1.26400626e+00
-2.53322005e-01 -6.31667614e-01 -1.21729672e+00 -8.36121082e-01
2.19271377e-01 8.33263099e-02 -8.71673644e-01 2.52332956e-01
-7.69387424e-01 -7.47147441e-01 1.08362401e+00 4.71782982e-01
3.95777136e-01 -8.64882171e-01 -2.38394126e-01 -2.40829393e-01
2.14985579e-01 -8.90923917e-01 -7.41135478e-01 9.40224603e-02
-4.81120914e-01 -1.14290595e+00 -4.94687498e-01 -9.71507668e-01
9.71482158e-01 2.65599601e-02 1.24172568e+00 3.82945001e-01
-5.21124065e-01 5.66160619e-01 2.05289245e-01 -1.22571521e-01
1.88613459e-01 1.69032931e-01 2.49653324e-01 -1.79898012e-02
4.65871602e-01 -4.44498003e-01 -6.62208498e-01 3.32674652e-01
-2.16733962e-01 -6.03925169e-01 1.92009687e-01 5.37547767e-01
8.23193908e-01 -4.12817568e-01 1.06332660e-01 -6.52335167e-01
1.77472457e-01 -2.26903558e-01 -9.63554382e-01 2.41836235e-01
-4.66781318e-01 7.56181031e-02 3.09210837e-01 -4.54059727e-02
-4.44900185e-01 7.53392756e-01 -5.20328462e-01 -4.88129586e-01
-1.37609214e-01 -2.72639036e-01 -2.28031844e-01 -1.05744636e+00
6.27972662e-01 -1.00599870e-01 3.43764424e-01 -6.07488215e-01
6.26634061e-01 2.43747368e-01 5.52017093e-01 -6.28968537e-01
7.05948830e-01 7.39336491e-01 3.62492114e-01 -7.37397015e-01
-3.06178302e-01 -2.08214745e-01 -1.03944254e+00 7.27373213e-02
7.95014858e-01 -7.97481298e-01 -1.27850461e+00 1.50248975e-01
-1.37574160e+00 -3.65883052e-01 -5.53154312e-02 1.09419428e-01
-4.37927186e-01 7.89006799e-02 -5.30338705e-01 -3.35720301e-01
-3.62071186e-01 -1.41930604e+00 1.55623746e+00 1.43196866e-01
-1.03900454e-03 -9.38491404e-01 8.69272426e-02 -8.46868530e-02
6.56494141e-01 7.33193532e-02 4.16673034e-01 -6.17759705e-01
-5.56503594e-01 -2.36101508e-01 -2.74299920e-01 -3.25273760e-02
-9.70164537e-02 6.25259951e-02 -1.34892821e+00 -3.79689991e-01
-3.39227736e-01 -2.46513739e-01 8.45613003e-01 3.73847455e-01
1.20825171e+00 -2.30696529e-01 -5.91982305e-01 1.33525836e+00
1.18198586e+00 -3.97882283e-01 5.89148879e-01 9.78997424e-02
8.04144800e-01 5.92565835e-01 -4.07148711e-02 6.80656135e-02
4.39926893e-01 9.60490406e-01 3.28861535e-01 -1.97311834e-01
-4.25404131e-01 -3.42241526e-01 2.64837474e-01 1.00388207e-01
-1.13111153e-01 3.70255202e-01 -1.12780476e+00 2.00107142e-01
-1.38589478e+00 -9.11199391e-01 2.48668268e-01 2.24639463e+00
6.52300417e-01 -4.26263839e-01 1.87787727e-01 -4.03693140e-01
4.69103754e-01 1.75178185e-01 -2.57168114e-01 -7.09129795e-02
9.63466689e-02 7.15920985e-01 8.00015450e-01 1.15054929e+00
-1.31756592e+00 1.42889428e+00 7.55545235e+00 5.28681397e-01
-1.35820222e+00 3.56389284e-01 8.78167808e-01 -3.19328457e-01
1.38132513e-01 -1.25991017e-01 -1.35035276e+00 1.75513968e-01
9.97445822e-01 4.85550076e-01 7.77648330e-01 9.85920012e-01
-1.56900555e-01 1.07663676e-01 -1.37489724e+00 1.19574952e+00
5.05334914e-01 -1.41545987e+00 -3.32750440e-01 9.04841274e-02
3.78965229e-01 4.15823668e-01 1.54224724e-01 8.46533477e-02
-2.62402110e-02 -1.57364488e+00 6.26937568e-01 3.44507575e-01
1.21519327e+00 -7.02773571e-01 5.26376367e-01 -2.74648756e-01
-1.38771975e+00 2.96335429e-01 -4.44782913e-01 4.92858946e-01
-1.42735109e-01 2.79832240e-02 -1.11395156e+00 2.62135663e-03
6.66110039e-01 3.58310014e-01 -1.02998853e+00 8.03955078e-01
-1.16422296e-01 1.09353766e-01 -7.51970887e-01 4.29602146e-01
-8.91578570e-02 6.97394982e-02 5.18199168e-02 1.39429808e+00
3.69064122e-01 -1.71543032e-01 -4.99190623e-03 7.51338899e-01
-5.55135310e-01 6.13898272e-03 -4.87834007e-01 4.34307158e-01
5.93489945e-01 1.51118374e+00 -7.98641562e-01 6.91054985e-02
-3.04816276e-01 1.15273201e+00 4.90152299e-01 2.88827240e-01
-7.74371266e-01 -2.48413950e-01 9.80449677e-01 4.31141764e-01
2.95525044e-01 -2.50892967e-01 -1.56668648e-01 -8.12719584e-01
-6.47113025e-02 -5.84988415e-01 1.88671853e-02 -3.24377954e-01
-8.56741428e-01 7.88467467e-01 -3.15091193e-01 -4.04375881e-01
-2.47448891e-01 -7.47814894e-01 -4.80978936e-01 1.10127199e+00
-1.58194065e+00 -1.44672203e+00 -3.62554222e-01 8.09772134e-01
3.27405632e-02 -1.06853068e-01 1.07326651e+00 6.48023009e-01
-6.50701761e-01 1.16451561e+00 -3.48772556e-01 4.13997412e-01
9.98028040e-01 -1.03036225e+00 9.52689528e-01 6.61990047e-01
5.02891958e-01 1.00694442e+00 1.77078590e-01 -3.98688883e-01
-1.63599861e+00 -1.17441571e+00 9.63231623e-01 -9.92403448e-01
3.20727557e-01 -8.84205997e-01 -5.37479401e-01 1.18382287e+00
-8.67237598e-02 8.89481306e-01 4.67265278e-01 3.20399225e-01
-6.40127718e-01 -3.11209202e-01 -1.54198468e+00 5.12763381e-01
1.32356179e+00 -7.37039804e-01 -5.72487190e-02 4.55099761e-01
2.37962082e-01 -5.17647922e-01 -5.73649108e-01 2.94852972e-01
7.79694438e-01 -9.53574777e-01 1.29817951e+00 -4.41050380e-01
-2.00842768e-01 -4.67966557e-01 -1.69576913e-01 -1.01531851e+00
-2.72960037e-01 -6.67892456e-01 9.27459523e-02 1.31030595e+00
4.77160841e-01 -4.80752766e-01 1.16000164e+00 5.55731416e-01
1.39268547e-01 -6.54996574e-01 -1.45750201e+00 -4.92015421e-01
-1.65592045e-01 -2.85907507e-01 8.18528056e-01 6.88272297e-01
-1.86945975e-01 6.48026168e-02 -3.00117712e-02 3.69067132e-01
5.69770575e-01 -4.06589806e-01 8.77830982e-01 -9.57398593e-01
1.19287275e-01 -5.73609412e-01 -8.11291575e-01 -9.29903984e-01
4.85164255e-01 -1.07962298e+00 8.51722360e-02 -1.03937018e+00
-2.15224639e-01 -5.65272331e-01 -1.84071705e-01 9.73945856e-01
3.38987321e-01 1.00903189e+00 2.68035624e-02 4.48229350e-02
-6.57682717e-01 1.19800903e-01 5.88692546e-01 9.53916460e-02
-1.33082261e-02 -3.32633615e-01 -4.35822397e-01 7.72657573e-01
4.69876885e-01 -4.44649398e-01 -4.14732285e-02 -6.73259795e-01
-1.75428465e-01 -3.11729372e-01 5.56991994e-01 -1.15482044e+00
4.51331586e-01 3.68576288e-01 1.05206943e+00 -1.86082810e-01
6.81891799e-01 -8.87276053e-01 4.96509932e-02 2.49399364e-01
1.91609897e-02 4.36233491e-01 4.42773759e-01 6.53514713e-02
1.68651685e-01 4.47509810e-02 1.08257449e+00 1.70526300e-02
-5.83603501e-01 6.61939144e-01 4.92036581e-01 -3.14136863e-01
9.55188870e-01 3.95245738e-02 -3.23778152e-01 -5.11648953e-02
-7.33686626e-01 -1.44116193e-01 8.72635543e-01 3.29095721e-01
4.21244234e-01 -1.31361818e+00 -6.60245061e-01 8.35974932e-01
-2.04020694e-01 -3.19873273e-01 -7.67125189e-02 9.35250342e-01
-1.08404934e+00 4.79839623e-01 -1.21330984e-01 -6.39954209e-01
-1.33471286e+00 4.16497797e-01 8.50910544e-01 3.13645661e-01
-4.16126996e-01 1.38297367e+00 -1.51223019e-01 -6.72897279e-01
4.83951181e-01 -7.74623007e-02 1.64758965e-01 -1.66837908e-02
8.71646404e-01 2.53570229e-01 4.75360453e-01 -9.99161601e-01
-1.05415499e+00 1.17409527e+00 5.81182949e-02 -2.57363200e-01
1.08936822e+00 1.38644844e-01 -4.20411259e-01 -2.35094249e-01
1.52246344e+00 2.75020450e-01 -1.25345016e+00 1.95827052e-01
8.72242600e-02 -3.72431457e-01 1.01455748e-01 -7.02839136e-01
-1.25856829e+00 8.49105418e-01 9.75544453e-01 -4.87215847e-01
7.57585049e-01 1.14245145e-02 3.32225233e-01 2.96209961e-01
5.49628079e-01 -8.17915261e-01 -4.11179513e-01 4.13432747e-01
7.89465547e-01 -1.28579307e+00 1.08276226e-03 -3.02152663e-01
-3.96989025e-02 9.92849410e-01 6.00665271e-01 -1.55936852e-01
1.02588201e+00 6.45636201e-01 1.91545233e-01 -3.22184712e-01
-2.13141784e-01 -7.41525665e-02 3.37360948e-01 6.54636323e-01
6.72329247e-01 -2.20217794e-01 2.36154959e-01 2.30398938e-01
-2.70218313e-01 -2.26186246e-01 -4.03073400e-01 6.70737922e-01
-2.18493044e-01 -1.27587950e+00 -4.84879702e-01 8.06436613e-02
-5.90966582e-01 -2.39919513e-01 -4.33559626e-01 8.28604341e-01
2.51563400e-01 6.68979287e-01 3.30438435e-01 -3.12904716e-01
1.62068605e-01 3.89306188e-01 7.51240015e-01 -5.90094388e-01
-5.12454212e-01 4.90141392e-04 -2.96917647e-01 -1.20347643e+00
-5.59853427e-02 -5.45985937e-01 -1.29932404e+00 -6.84362113e-01
-6.36479184e-02 -5.03488258e-02 1.23693001e+00 6.71693325e-01
8.93667996e-01 5.66067621e-02 4.16257560e-01 -1.43098307e+00
-3.20738375e-01 -8.76716733e-01 -4.16336089e-01 9.94652044e-03
4.23986077e-01 -5.90065420e-01 -3.09793293e-01 -7.25741088e-02] | [13.493762969970703, 0.33908575773239136] |
9627b965-49d6-4345-8cd4-b1be94166e55 | deep-semantics-aware-photo-adjustment | 1706.08260 | null | http://arxiv.org/abs/1706.08260v1 | http://arxiv.org/pdf/1706.08260v1.pdf | Deep Semantics-Aware Photo Adjustment | Automatic photo adjustment is to mimic the photo retouching style of
professional photographers and automatically adjust photos to the learned
style. There have been many attempts to model the tone and the color adjustment
globally with low-level color statistics. Also, spatially varying photo
adjustment methods have been studied by exploiting high-level features and
semantic label maps. Those methods are semantics-aware since the color mapping
is dependent on the high-level semantic context. However, their performance is
limited to the pre-computed hand-crafted features and it is hard to reflect
user's preference to the adjustment. In this paper, we propose a deep neural
network that models the semantics-aware photo adjustment. The proposed network
exploits bilinear models that are the multiplicative interaction of the color
and the contexual features. As the contextual features we propose the semantic
adjustment map, which discovers the inherent photo retouching presets that are
applied according to the scene context. The proposed method is trained using a
robust loss with a scene parsing task. The experimental results show that the
proposed method outperforms the existing method both quantitatively and
qualitatively. The proposed method also provides users a way to retouch the
photo by their own likings by giving customized adjustment maps. | ['Seonghyeon Nam', 'Seon Joo Kim'] | 2017-06-26 | null | null | null | null | ['photo-retouching'] | ['computer-vision'] | [ 2.83648700e-01 -2.00563222e-01 -4.97634038e-02 -8.35198045e-01
-4.06123430e-01 -4.28220361e-01 4.40784216e-01 1.08446933e-01
-4.62966919e-01 1.69191927e-01 2.80062079e-01 2.70548254e-01
-3.90410013e-02 -7.92158604e-01 -7.10263252e-01 -5.61223090e-01
6.37862384e-01 -9.82572064e-02 3.47484201e-01 -4.44742978e-01
6.10811174e-01 2.47696519e-01 -1.73168957e+00 3.52875710e-01
8.58043194e-01 1.00788963e+00 3.84122312e-01 6.10802889e-01
-3.56219321e-01 5.93709290e-01 -3.56792182e-01 -4.52524841e-01
4.72466230e-01 -4.74519730e-01 -3.84747773e-01 1.81211099e-01
9.19025719e-01 -1.88602954e-01 1.23370672e-02 1.38247871e+00
4.79751647e-01 2.88535714e-01 3.91476154e-01 -1.01632118e+00
-1.08918273e+00 3.68855983e-01 -6.83041394e-01 -1.10370897e-01
3.88032317e-01 1.06635861e-01 6.99124992e-01 -7.27650166e-01
4.26110715e-01 1.38233089e+00 5.83608091e-01 4.35573995e-01
-9.04980063e-01 -6.52603149e-01 4.24188852e-01 5.18302500e-01
-1.22097206e+00 -1.90740049e-01 1.17627919e+00 -4.22594190e-01
3.01980376e-01 4.74145859e-01 6.89976931e-01 7.14704633e-01
1.67466804e-01 4.42969978e-01 1.66940320e+00 -6.43493295e-01
2.86452562e-01 5.95302820e-01 8.13662037e-02 7.27080941e-01
-2.94504166e-01 -1.13629431e-01 -3.54292154e-01 3.80840480e-01
7.36742914e-01 1.55660659e-01 -2.16274351e-01 -4.72384304e-01
-7.83343792e-01 4.88624960e-01 8.44308257e-01 3.12122524e-01
-1.05131768e-01 1.11379877e-01 1.82649061e-01 -9.47960615e-02
3.83616924e-01 4.30749565e-01 -4.36286658e-01 2.42129713e-01
-5.90359926e-01 3.54690775e-02 4.25502151e-01 8.78000796e-01
1.24179387e+00 -2.65188873e-01 -4.61119860e-01 1.15385377e+00
2.11977601e-01 1.71379060e-01 6.68086469e-01 -8.74839067e-01
3.24542135e-01 7.42122293e-01 2.81900406e-01 -1.55744243e+00
-3.83883536e-01 -2.23622248e-01 -6.82651162e-01 4.51457560e-01
4.03318703e-01 -3.13335992e-02 -1.06289291e+00 1.91267121e+00
3.84017885e-01 -4.60346323e-03 -1.84066668e-01 1.29965365e+00
7.05942631e-01 5.14841378e-01 4.68464255e-01 7.63877854e-02
1.42830610e+00 -9.25128162e-01 -8.72552156e-01 -3.18057895e-01
1.27857774e-01 -1.03108573e+00 1.74449670e+00 2.13504940e-01
-1.00951827e+00 -8.40429842e-01 -1.17498446e+00 -2.57611096e-01
-6.45926654e-01 3.90930742e-01 5.62489867e-01 7.01448917e-01
-1.18415141e+00 4.31341916e-01 -2.44200140e-01 -7.35984683e-01
1.12562418e-01 1.30091041e-01 -8.08701366e-02 -1.72556549e-01
-1.35099912e+00 9.13512170e-01 3.24551046e-01 4.78508025e-01
-2.17545062e-01 -4.85213399e-01 -7.26414800e-01 1.19394451e-01
1.59749225e-01 -6.42593443e-01 1.03123820e+00 -1.66165888e+00
-1.89362240e+00 8.88742924e-01 5.00196591e-02 5.81712350e-02
7.87170172e-01 -8.26633871e-02 -4.48971510e-01 1.41296946e-02
5.41860387e-02 7.65289545e-01 9.13555920e-01 -1.44583237e+00
-7.11969614e-01 -4.12119269e-01 4.92584348e-01 7.15337694e-01
-5.50419569e-01 -2.21216112e-01 -8.78151357e-01 -7.33031154e-01
1.27262071e-01 -8.36839497e-01 -2.05909073e-01 1.81294933e-01
-4.67741489e-01 -4.37727869e-02 6.08342230e-01 -5.80600142e-01
1.15119588e+00 -2.10286546e+00 -1.35620922e-01 1.83482781e-01
-1.36322275e-01 1.46764874e-01 -1.41936362e-01 3.14974666e-01
6.19576089e-02 4.55012769e-02 -1.84552073e-01 -1.74360767e-01
7.50019550e-02 -2.37086743e-01 2.05955207e-01 1.73815861e-01
-1.55752018e-01 5.03584564e-01 -8.47322226e-01 -4.79954273e-01
5.81714332e-01 4.84841347e-01 -5.52190602e-01 4.06595618e-01
-1.87816441e-01 2.33426601e-01 -3.53072941e-01 3.79411459e-01
1.04374564e+00 9.56710353e-02 5.72077036e-02 -7.71768153e-01
-1.61873952e-01 -2.81835526e-01 -1.20811033e+00 1.80972922e+00
-7.23188639e-01 5.32334864e-01 -3.12973648e-01 -4.87297028e-01
9.41158354e-01 -3.31644475e-01 2.06131220e-01 -9.35200214e-01
1.22688018e-01 -1.15670986e-01 -5.64467907e-01 -8.28242898e-01
6.51084721e-01 3.01730982e-03 -2.14208230e-01 9.11213979e-02
-5.58545709e-01 -2.71730840e-01 -7.30085280e-03 5.48467450e-02
5.39164662e-01 2.29936272e-01 3.03077847e-01 -1.59927592e-01
6.65059149e-01 -1.54301569e-01 4.93635565e-01 7.11718738e-01
-2.22402677e-01 8.03415954e-01 1.28733099e-01 -5.71899414e-01
-8.02572608e-01 -9.61935580e-01 -1.64169595e-02 1.27295041e+00
8.27174127e-01 -4.52810563e-02 -1.16666424e+00 -5.04979968e-01
-2.00646907e-01 6.35581315e-01 -7.66385555e-01 -3.13680470e-01
-4.12945777e-01 -6.95936143e-01 -1.63452268e-01 2.83301175e-01
1.11748397e+00 -1.10751283e+00 -2.61202842e-01 -5.75128244e-03
-1.09384373e-01 -1.00170243e+00 -9.38991427e-01 -4.07460183e-01
-3.64321202e-01 -9.05889452e-01 -5.29711127e-01 -9.28053141e-01
8.53216529e-01 3.79453331e-01 7.85316408e-01 3.90586704e-02
-3.83787274e-01 5.12659311e-01 -4.56419826e-01 -1.59722582e-01
-1.64885446e-01 -7.89163560e-02 -1.62040383e-01 5.47519743e-01
4.84355956e-01 -3.95912826e-01 -1.22623825e+00 4.03271765e-01
-1.06633174e+00 3.53577882e-01 8.18021238e-01 3.68698895e-01
4.80914831e-01 6.24674037e-02 3.03438723e-01 -1.21618378e+00
6.18553877e-01 -1.00697754e-02 -4.33188736e-01 4.40200686e-01
-7.88475513e-01 8.92965421e-02 4.90321606e-01 -4.33312535e-01
-1.59844577e+00 3.29469204e-01 1.54360011e-01 -1.60842836e-01
-2.13442415e-01 7.31074437e-02 -4.73983020e-01 -3.80436569e-01
5.41937470e-01 -1.23499162e-01 -5.17079532e-01 -4.05219257e-01
8.66633832e-01 6.10985279e-01 5.28650224e-01 -2.90677279e-01
8.44103694e-01 4.06413704e-01 -3.49588424e-01 -4.66444612e-01
-1.10024142e+00 -3.05330336e-01 -6.03406549e-01 -5.20093143e-01
1.22288060e+00 -6.76330090e-01 -5.82632184e-01 6.85662389e-01
-9.43367660e-01 -2.45707914e-01 -1.14461000e-03 2.63099313e-01
-4.04848039e-01 5.85113347e-01 -3.68171692e-01 -5.76791584e-01
-1.26095429e-01 -1.01562250e+00 9.19227660e-01 6.96978748e-01
1.94992647e-02 -7.77275920e-01 2.87818946e-02 3.86004001e-01
5.60238361e-01 2.73804694e-01 1.02365816e+00 1.72355279e-01
-7.93145537e-01 -2.43138328e-01 -6.88276052e-01 4.03269231e-01
3.40781510e-01 3.12868766e-02 -1.12620008e+00 1.57810077e-01
-7.82871172e-02 4.00106497e-02 7.40868330e-01 6.25297427e-01
1.75517559e+00 -4.47433025e-01 5.48220426e-02 8.25974226e-01
1.87424624e+00 2.11060986e-01 8.00516248e-01 5.00479102e-01
1.10705936e+00 7.48131514e-01 6.03141308e-01 2.87441343e-01
5.92714727e-01 8.27575445e-01 4.88910109e-01 -5.09558082e-01
-3.12736511e-01 -5.59590042e-01 2.24822268e-01 5.52990854e-01
1.11826785e-01 -2.15379104e-01 -3.56186211e-01 2.70192564e-01
-1.90421116e+00 -7.32093334e-01 -1.57457799e-01 2.17041278e+00
9.33846056e-01 7.02242032e-02 -3.23910743e-01 -1.62890062e-01
1.08264625e+00 1.77125305e-01 -4.94959295e-01 -6.93481684e-01
-1.02178767e-01 5.11220936e-03 8.16963077e-01 6.94734216e-01
-1.15506661e+00 1.14326537e+00 5.59823465e+00 8.57795358e-01
-1.16593683e+00 -5.99986641e-04 8.50796342e-01 2.05352172e-01
-5.78387916e-01 1.50387259e-02 -5.90569556e-01 6.11934066e-01
1.64476156e-01 1.75302908e-01 6.07789695e-01 6.32567942e-01
5.42670488e-01 -2.80758411e-01 -8.30795467e-01 1.13386917e+00
3.94690692e-01 -9.61477578e-01 1.71619043e-01 -5.68902254e-01
7.81876028e-01 -6.25825465e-01 4.96466637e-01 4.06255834e-02
1.86503917e-01 -7.18731582e-01 7.41445959e-01 9.26769018e-01
7.52369881e-01 -5.15292823e-01 4.73029822e-01 -2.00162813e-01
-1.16558218e+00 -1.91690698e-01 -3.67145300e-01 -9.91383493e-02
-6.24701791e-02 3.92452896e-01 -7.83516705e-01 9.76631492e-02
8.07196617e-01 6.83056891e-01 -9.72914159e-01 9.68055606e-01
-3.43678474e-01 1.59431398e-01 5.41779324e-02 -1.36486337e-01
6.40162900e-02 -4.35378343e-01 1.27277792e-01 1.15540135e+00
3.66056323e-01 1.63369238e-01 4.42766733e-02 7.45451510e-01
-5.48019856e-02 5.09899914e-01 -1.58172354e-01 4.14622009e-01
3.75662923e-01 1.45311904e+00 -5.89305937e-01 -9.11789834e-02
-1.61100149e-01 1.35269046e+00 2.82635719e-01 5.84862173e-01
-6.81743383e-01 -4.37865347e-01 5.23641348e-01 1.59473598e-01
-1.17173925e-01 1.75588906e-01 -4.77456659e-01 -9.52611983e-01
6.39038831e-02 -3.35537434e-01 2.74351239e-01 -1.36867833e+00
-1.52855706e+00 6.38028026e-01 -1.29467368e-01 -1.15859210e+00
2.19471768e-01 -4.51181352e-01 -7.02529371e-01 7.74110615e-01
-1.66141379e+00 -1.37028861e+00 -1.08836043e+00 4.69313592e-01
6.72580242e-01 9.77886990e-02 4.51234251e-01 3.85404497e-01
-6.00733936e-01 7.17129290e-01 -1.15240566e-01 -1.07295901e-01
1.11492002e+00 -1.38124287e+00 -8.29576775e-02 8.75450253e-01
-1.32426023e-01 4.41910833e-01 1.00863421e+00 -3.35788995e-01
-8.04599047e-01 -1.05902672e+00 7.18709946e-01 -4.12074685e-01
1.33041307e-01 -2.63052076e-01 -7.59757459e-01 2.88246304e-01
3.02312762e-01 -1.35444745e-01 3.97031516e-01 -2.10316569e-01
-3.30934107e-01 -7.67455578e-01 -1.29830396e+00 7.12124169e-01
8.74092162e-01 -3.65248889e-01 -2.09474996e-01 2.56105185e-01
7.19555259e-01 -3.20655614e-01 -5.07991552e-01 1.56046242e-01
7.51036286e-01 -1.23648977e+00 9.26450312e-01 -1.17502987e-01
4.00681138e-01 -5.85443854e-01 -7.96117187e-02 -1.27574420e+00
-4.57046628e-01 -2.50193894e-01 7.72032201e-01 1.45419347e+00
1.80019855e-01 -4.09182906e-01 5.87823153e-01 1.03555155e+00
5.27729541e-02 -3.76775652e-01 -4.28784072e-01 -4.59584594e-02
-4.36242402e-01 -1.96077257e-01 8.39646518e-01 9.63541746e-01
-4.23033983e-01 6.55567497e-02 -4.89801347e-01 2.98684984e-01
4.96721268e-01 8.32295418e-02 6.66148245e-01 -8.36049199e-01
-1.51550651e-01 -4.98737782e-01 -4.02736157e-01 -6.46291375e-01
-3.37443016e-02 -5.19407809e-01 1.80473641e-01 -1.49805999e+00
3.59388679e-01 -6.60081089e-01 -6.41402245e-01 1.72359779e-01
-5.60574412e-01 5.27926087e-01 1.93470582e-01 1.87166519e-02
-5.34256697e-01 2.65831709e-01 1.30145383e+00 -2.58996457e-01
-4.87823993e-01 -7.26208389e-02 -9.10955250e-01 8.43322814e-01
9.54313457e-01 -1.53483927e-01 -5.59803128e-01 -6.83158815e-01
3.35579425e-01 -3.70371252e-01 4.95198369e-01 -9.96251881e-01
2.01432630e-01 -5.44279814e-01 5.32214999e-01 -2.22371712e-01
2.98151255e-01 -8.52623463e-01 9.92449373e-02 2.03573741e-02
-7.89289057e-01 2.69040447e-02 -1.54840693e-01 8.20331514e-01
-5.68411918e-03 -1.13428794e-01 1.06196105e+00 -3.63453239e-01
-1.19164860e+00 3.30096215e-01 3.60072590e-02 -2.14099362e-01
9.58660543e-01 -3.38176668e-01 -5.07145636e-02 -5.66988528e-01
-5.20660400e-01 -1.42718017e-01 6.93892777e-01 6.38037980e-01
5.30454338e-01 -1.30843246e+00 -3.22301447e-01 2.87072416e-02
4.18310672e-01 -3.37747574e-01 6.48599982e-01 2.35635445e-01
-6.94773853e-01 -1.62864536e-01 -4.68932539e-01 -3.85786086e-01
-1.14625406e+00 5.96248865e-01 4.55591440e-01 2.08938912e-01
-3.95204991e-01 7.75265098e-01 5.93864858e-01 -2.83082157e-01
2.22201705e-01 -5.23303568e-01 -5.95896959e-01 -1.60603553e-01
2.57738590e-01 1.68615326e-01 -8.72435942e-02 -5.14125526e-01
-1.10730268e-01 1.13564754e+00 1.25563055e-01 -8.78232718e-03
1.00000060e+00 -7.34401941e-01 -1.35312930e-01 4.08865988e-01
1.32258081e+00 5.54099977e-02 -1.39103174e+00 -3.81530941e-01
-3.30540955e-01 -8.94837976e-01 8.05811435e-02 -1.17615819e+00
-1.17999911e+00 8.29930067e-01 1.22259796e+00 2.17142403e-02
1.52851963e+00 -3.57562453e-01 7.13566840e-01 -8.72564763e-02
8.76766965e-02 -1.52330625e+00 2.46693343e-01 -1.78735942e-01
7.67760873e-01 -1.37007761e+00 -6.10183068e-02 -5.24333715e-01
-8.13296556e-01 1.06960678e+00 1.02170515e+00 -1.88843310e-01
5.03292143e-01 -2.77281672e-01 4.80152935e-01 4.55330079e-03
-9.02118310e-02 -2.10129872e-01 5.67387700e-01 6.17511809e-01
2.77376443e-01 2.88099498e-01 -4.84944403e-01 5.77315331e-01
-1.78565145e-01 -1.10428736e-01 6.02454901e-01 1.60262033e-01
-5.67195535e-01 -1.07438111e+00 -2.82116711e-01 1.71451807e-01
-1.40199140e-01 7.32543692e-02 -4.77594286e-01 5.65083265e-01
4.40962315e-01 9.17250812e-01 -3.12394332e-02 -4.21678424e-01
4.25714523e-01 -3.12522173e-01 3.62040609e-01 -3.93793195e-01
-4.04172927e-01 -2.25675210e-01 -2.65072107e-01 -6.68241680e-01
-5.35179973e-01 -2.35550180e-01 -9.48622048e-01 -1.48612976e-01
1.50000425e-02 -2.24209011e-01 8.70054066e-01 7.71609306e-01
1.81243047e-01 4.08747137e-01 9.23755109e-01 -7.61986613e-01
-1.42676160e-01 -8.52855980e-01 -4.78964686e-01 8.95934463e-01
1.91803351e-01 -4.61401254e-01 -3.21843654e-01 1.69775411e-01] | [11.366817474365234, -0.9952687621116638] |
305968ef-a7c3-45b7-b194-91a2267641db | latentkeypointgan-controlling-gans-via-latent | 2103.15812 | null | https://arxiv.org/abs/2103.15812v4 | https://arxiv.org/pdf/2103.15812v4.pdf | LatentKeypointGAN: Controlling GANs via Latent Keypoints | Generative adversarial networks (GANs) have attained photo-realistic quality in image generation. However, how to best control the image content remains an open challenge. We introduce LatentKeypointGAN, a two-stage GAN which is trained end-to-end on the classical GAN objective with internal conditioning on a set of space keypoints. These keypoints have associated appearance embeddings that respectively control the position and style of the generated objects and their parts. A major difficulty that we address with suitable network architectures and training schemes is disentangling the image into spatial and appearance factors without domain knowledge and supervision signals. We demonstrate that LatentKeypointGAN provides an interpretable latent space that can be used to re-arrange the generated images by re-positioning and exchanging keypoint embeddings, such as generating portraits by combining the eyes, nose, and mouth from different images. In addition, the explicit generation of keypoints and matching images enables a new, GAN-based method for unsupervised keypoint detection. | ['Helge Rhodin', 'Bastian Wandt', 'Xingzhe He'] | 2021-03-29 | latentkeypointgan-controlling-gans-via-latent-1 | https://openreview.net/forum?id=y_tIL5vki1l | https://openreview.net/pdf?id=y_tIL5vki1l | null | ['unsupervised-facial-landmark-detection'] | ['computer-vision'] | [ 4.69359875e-01 3.54855478e-01 1.31624117e-02 -7.98916742e-02
-7.36323118e-01 -9.45706844e-01 9.56222594e-01 -4.93284851e-01
-2.21640263e-02 5.44860363e-01 1.12909645e-01 1.09797217e-01
4.85846773e-02 -8.55522513e-01 -9.54476953e-01 -9.84938085e-01
4.16543245e-01 3.97458404e-01 -3.26637119e-01 -1.15001708e-01
3.03865466e-02 6.74859583e-01 -1.23471618e+00 7.64363334e-02
7.93879926e-01 9.13102865e-01 1.86220735e-01 9.45645869e-01
1.49838910e-01 4.80723947e-01 -7.18688369e-01 -6.79023147e-01
5.64972937e-01 -9.19770241e-01 -2.55459070e-01 3.34849000e-01
8.56346130e-01 -4.11446601e-01 -4.38969940e-01 8.99952888e-01
3.31919193e-01 -1.88462548e-02 9.18086648e-01 -1.58994067e+00
-1.28151512e+00 1.39011979e-01 -4.31335896e-01 -3.79225492e-01
1.43105149e-01 2.76758105e-01 9.86390650e-01 -7.09384143e-01
6.50227666e-01 1.12205207e+00 3.53916794e-01 9.28054333e-01
-1.56428289e+00 -6.64639115e-01 -5.73816299e-02 1.23464661e-02
-1.29610932e+00 -4.19154048e-01 1.14379478e+00 -4.89575714e-01
1.42555028e-01 5.13226569e-01 7.49163389e-01 1.42250526e+00
1.50762022e-01 6.46902800e-01 1.00252759e+00 -6.22757971e-01
2.88424760e-01 1.36798814e-01 -7.81726658e-01 6.78002298e-01
-7.15103447e-02 1.22418784e-01 -4.07317877e-01 -6.46083951e-02
1.27887905e+00 3.38974893e-01 -3.26279014e-01 -7.87846148e-01
-1.36144304e+00 8.72498691e-01 6.78055108e-01 1.78660359e-02
-5.68222165e-01 4.68473822e-01 -3.00619990e-01 4.83512506e-02
2.32653186e-01 7.82084584e-01 -1.54916823e-01 3.55310142e-01
-7.28532076e-01 2.57882267e-01 2.42125720e-01 1.02217269e+00
7.91385114e-01 2.43673533e-01 -4.28592443e-01 5.96096575e-01
1.61015451e-01 7.68816650e-01 5.67943931e-01 -1.10051763e+00
3.10277134e-01 6.25957012e-01 1.87377974e-01 -1.01383018e+00
4.30604964e-01 -3.03523570e-01 -1.05566621e+00 5.92967689e-01
2.07103372e-01 -7.08609745e-02 -1.51689863e+00 1.71183300e+00
3.27735603e-01 2.80346274e-01 1.02404691e-01 5.67106009e-01
4.99596536e-01 7.35910296e-01 -4.76376861e-02 1.92410335e-01
1.34702146e+00 -1.12095463e+00 -7.07022965e-01 -4.07133818e-01
-1.64721981e-01 -6.92147791e-01 1.01838410e+00 1.13667421e-01
-1.29690194e+00 -6.72357619e-01 -1.08478200e+00 -1.58554167e-01
-4.60218161e-01 3.03931504e-01 3.56280297e-01 4.17501330e-01
-1.18195772e+00 3.44126225e-01 -6.52535617e-01 -2.10239049e-02
3.66421789e-01 2.44257137e-01 -6.37366712e-01 4.13135700e-02
-9.13805962e-01 5.77859223e-01 1.66884288e-01 2.94767171e-01
-1.10159636e+00 -7.45698988e-01 -1.01863730e+00 -6.80752695e-02
1.32055283e-01 -1.13303375e+00 1.02661109e+00 -1.29503107e+00
-1.83762884e+00 1.03171504e+00 3.63026001e-02 -1.76914215e-01
6.52196765e-01 -4.53910530e-02 -3.30227762e-01 2.34306574e-01
1.91103697e-01 1.03701317e+00 1.79694366e+00 -1.52844048e+00
-4.02612716e-01 -3.27908397e-01 -1.41646340e-01 1.70714587e-01
-1.76531270e-01 -3.37079108e-01 -6.20308876e-01 -1.11601675e+00
-2.54699718e-02 -1.02281153e+00 -2.13054895e-01 3.78392905e-01
-6.77299321e-01 2.02868775e-01 8.44895005e-01 -5.99148273e-01
5.83448648e-01 -2.23713684e+00 7.58225381e-01 1.24403842e-01
5.33208728e-01 2.57647693e-01 -5.05138516e-01 4.98269022e-01
-2.69287974e-01 1.61637142e-01 -2.14964107e-01 -6.22403920e-01
8.66356939e-02 2.77764440e-01 -5.19993424e-01 3.36085498e-01
4.39496875e-01 1.34424436e+00 -8.38896215e-01 -1.85537070e-01
4.00880694e-01 7.29240119e-01 -4.52983171e-01 6.90289080e-01
-2.43129194e-01 6.34144425e-01 -3.39606494e-01 4.41175640e-01
6.44544125e-01 -1.64707676e-01 -1.83313359e-02 -4.33801025e-01
2.58056223e-01 -2.26552919e-01 -8.39126647e-01 1.66912591e+00
-5.58436453e-01 7.44903028e-01 1.03313677e-01 -5.89603901e-01
7.86967635e-01 1.42703012e-01 1.29080385e-01 -5.46316385e-01
4.60615344e-02 -1.00607209e-01 -4.05168772e-01 -1.69009894e-01
4.17996496e-01 -1.18251391e-01 -1.89661890e-01 3.82940412e-01
1.28182828e-01 -4.49194044e-01 -2.21780628e-01 2.40397170e-01
7.29406416e-01 1.77100487e-02 1.16245393e-02 2.22345620e-01
2.57055551e-01 -3.87215644e-01 2.75612444e-01 4.64246422e-01
3.62604260e-01 1.20809686e+00 4.52762842e-01 -3.95105481e-01
-1.38731503e+00 -1.35571074e+00 3.36872667e-01 7.89480150e-01
7.95058608e-02 -1.82709202e-01 -9.07460690e-01 -7.73275614e-01
-2.90353857e-02 6.30251586e-01 -1.06487739e+00 -3.95171434e-01
-6.39972746e-01 -2.10936040e-01 3.64354193e-01 5.07642269e-01
2.84463644e-01 -1.05838299e+00 -1.78828463e-01 -3.00856251e-02
-2.33729091e-02 -1.00223100e+00 -8.93640280e-01 -7.69609679e-03
-4.13724273e-01 -9.14801300e-01 -1.23504317e+00 -7.41408706e-01
1.35053122e+00 5.28506897e-02 1.02904749e+00 -1.55272946e-01
-2.70559788e-01 5.59216738e-01 -3.65047425e-01 -1.73409253e-01
-6.36874199e-01 3.94313224e-03 -1.80694506e-01 5.08243680e-01
-3.93343806e-01 -6.78012073e-01 -9.98523474e-01 3.77305508e-01
-1.23127902e+00 3.22189867e-01 6.75327897e-01 9.80957091e-01
7.00500786e-01 -1.86627265e-02 2.26591036e-01 -8.23804379e-01
4.90927756e-01 -1.08665317e-01 -6.16815329e-01 4.63308960e-01
-2.95565128e-01 1.51030973e-01 7.38347352e-01 -4.66995418e-01
-7.30121970e-01 1.54981315e-01 3.36231734e-03 -7.67558992e-01
-6.85021952e-02 -3.20390075e-01 -5.28598845e-01 -2.74766654e-01
4.45097744e-01 5.68721175e-01 1.67991757e-01 -2.36528993e-01
1.06574965e+00 3.68026644e-01 8.15064609e-01 -3.31786960e-01
1.37543011e+00 6.34085536e-01 -4.57391553e-02 -6.53728724e-01
-3.99407119e-01 6.69735065e-03 -7.54741371e-01 1.10936858e-01
1.18586826e+00 -8.26251626e-01 -4.47541177e-01 6.96425498e-01
-1.08906436e+00 -5.68319499e-01 -6.90690219e-01 -9.05788839e-02
-6.80303097e-01 1.32294834e-01 -2.86196738e-01 -4.20464158e-01
-4.21874702e-01 -9.83602047e-01 1.46841145e+00 2.40541339e-01
-1.00193769e-01 -1.02905738e+00 1.86248962e-02 1.44999281e-01
3.29516143e-01 5.64904749e-01 9.16236699e-01 -6.84650913e-02
-9.80682433e-01 -3.51173639e-01 -8.77208933e-02 4.73295659e-01
4.10872877e-01 1.47520021e-01 -8.70176733e-01 -3.71868432e-01
-1.76993355e-01 -1.50209635e-01 6.90034151e-01 2.59531051e-01
1.03420258e+00 -6.83547795e-01 -3.73292327e-01 1.07738721e+00
1.30972004e+00 2.57291228e-01 8.07906508e-01 1.18092217e-01
1.14240837e+00 4.59112048e-01 2.54172813e-02 -3.74002494e-02
1.74381196e-01 8.43272090e-01 5.13183117e-01 -5.35891950e-01
-3.53570640e-01 -9.21938002e-01 1.55760184e-01 4.10039127e-01
9.23562497e-02 -5.87149978e-01 -3.86691064e-01 4.20690954e-01
-1.46011019e+00 -8.58137786e-01 4.43383157e-01 1.93642473e+00
5.60424387e-01 -3.26727688e-01 -3.64279449e-02 -5.83867216e-03
7.82055855e-01 2.21476898e-01 -7.44189680e-01 7.79717565e-02
-1.07410319e-01 3.30918819e-01 5.57577312e-01 3.89036953e-01
-8.81988049e-01 9.28851187e-01 6.47199917e+00 7.91445792e-01
-1.31096351e+00 -7.55696669e-02 6.91201150e-01 2.15182602e-01
-7.24004388e-01 -1.00082509e-01 -5.12231588e-01 5.11292040e-01
2.96216547e-01 1.24406636e-01 5.33055484e-01 7.58985639e-01
-2.07338557e-01 3.98485959e-01 -1.12752366e+00 1.17337012e+00
2.88591504e-01 -1.56506991e+00 4.75698233e-01 2.04657346e-01
1.06733167e+00 -4.91821676e-01 6.70089543e-01 -1.07578091e-01
4.26562279e-01 -1.14264464e+00 9.03353095e-01 6.78831279e-01
1.30663121e+00 -5.04120827e-01 1.18002392e-01 1.86106134e-02
-8.65934193e-01 -2.69421004e-02 -1.90535784e-01 5.15006661e-01
2.45215207e-01 4.75463644e-02 -7.35951126e-01 4.25676674e-01
3.17975581e-01 4.76107627e-01 -5.17636418e-01 5.44726193e-01
-8.57506692e-01 1.35837138e-01 4.14022543e-02 3.62767935e-01
6.90850541e-02 -4.58983094e-01 4.09442008e-01 5.84193647e-01
5.49435318e-01 -4.26460616e-02 -2.32395917e-01 1.19455969e+00
-3.38924795e-01 -3.20590883e-01 -6.01352990e-01 -2.20088467e-01
4.69931573e-01 1.41361308e+00 -6.68863833e-01 -2.48653859e-01
4.06158827e-02 1.66938436e+00 1.50159433e-01 7.19298959e-01
-6.81596339e-01 -4.35076684e-01 8.60488236e-01 4.25133288e-01
6.08981490e-01 -2.43158907e-01 3.63791138e-02 -1.25105774e+00
-2.37114802e-02 -9.01026845e-01 -2.43718904e-02 -1.20848358e+00
-1.31844056e+00 8.16949487e-01 -1.87657088e-01 -1.12997556e+00
-4.76099342e-01 -6.00811779e-01 -8.29852700e-01 9.79415298e-01
-1.30495918e+00 -1.93085384e+00 -4.64304298e-01 6.71620786e-01
3.04271877e-01 -1.87372848e-01 9.27813888e-01 -2.75518037e-02
-3.40376824e-01 9.49707568e-01 2.79890150e-01 2.93278784e-01
6.13187313e-01 -1.44058168e+00 8.16241860e-01 8.22989523e-01
3.13959688e-01 5.25882542e-01 5.70661783e-01 -2.86897898e-01
-1.45205390e+00 -1.29547417e+00 3.60341012e-01 -7.04092205e-01
2.31564388e-01 -8.00481737e-01 -4.73447829e-01 8.05697262e-01
4.68484193e-01 9.32402536e-02 5.12246609e-01 -4.04858232e-01
-3.05250406e-01 -3.79210442e-01 -1.09381807e+00 8.38156700e-01
8.43358457e-01 -7.03232646e-01 -3.08983773e-01 1.68159902e-01
7.66717374e-01 -4.88200843e-01 -4.67803419e-01 7.21184015e-02
5.97343445e-01 -7.75433362e-01 1.27755284e+00 -4.43787634e-01
5.32181382e-01 -4.17390853e-01 2.98092607e-02 -1.63847768e+00
-4.25722271e-01 -8.78449857e-01 5.23609519e-02 1.34677052e+00
1.07299358e-01 -7.06552982e-01 9.00731921e-01 5.26902497e-01
1.10285893e-01 -4.63870585e-01 -5.92068672e-01 -4.17466193e-01
-5.67651950e-02 1.73131555e-01 9.49352503e-01 8.24303508e-01
-7.25735664e-01 3.29433531e-01 -7.53655851e-01 1.24577969e-01
6.73527241e-01 1.95314139e-01 1.21991265e+00 -7.61535645e-01
-3.12683433e-01 -2.47789532e-01 -6.82753146e-01 -1.14539969e+00
1.61100686e-01 -5.68299770e-01 -1.25570342e-01 -1.30566013e+00
-1.96505755e-01 -4.92417216e-01 -5.22053763e-02 4.75469917e-01
-1.74188346e-01 6.81648552e-01 2.41190195e-01 1.98163465e-01
-1.05284370e-01 8.81009698e-01 1.81723726e+00 -2.76272416e-01
-9.69164521e-02 -1.28152475e-01 -8.29931378e-01 3.92998278e-01
5.64557076e-01 -2.62737691e-01 -5.99092543e-01 -5.78601658e-01
2.79768854e-01 1.34301424e-01 7.78045416e-01 -7.47583449e-01
1.46100283e-01 -1.79034159e-01 8.19405377e-01 -1.76713720e-01
6.17072403e-01 -9.08353448e-01 6.17645085e-01 1.67572036e-01
-3.81487906e-01 1.61230549e-01 -6.30179122e-02 7.70756543e-01
-2.33159825e-01 -4.01347950e-02 6.89270496e-01 -1.80230647e-01
-4.26609784e-01 6.72812521e-01 -1.28410026e-01 -2.04037353e-01
1.35944474e+00 -3.71256649e-01 -6.56315871e-03 -5.52791893e-01
-6.83877110e-01 -1.55248135e-01 8.84456992e-01 4.76239413e-01
7.07226932e-01 -1.73629534e+00 -5.92167556e-01 8.49648654e-01
1.36000946e-01 2.48612180e-01 3.28372061e-01 8.58477727e-02
-6.14729345e-01 5.44193015e-02 -4.32064295e-01 -5.32740474e-01
-1.09435725e+00 6.72201991e-01 3.46946120e-01 5.03116986e-03
-4.60393131e-01 8.65527928e-01 6.42888844e-01 -3.50527972e-01
-8.66279826e-02 -1.82411641e-01 1.48654819e-01 -3.94853801e-02
4.42414403e-01 -4.06575017e-02 -2.79169321e-01 -6.33167207e-01
7.91004151e-02 7.49490321e-01 -5.28996848e-02 -1.52495921e-01
1.31826591e+00 2.05446756e-03 -9.26634669e-02 -1.00283250e-02
1.31826472e+00 2.69133747e-01 -1.59308779e+00 -1.69950910e-02
-7.73018777e-01 -7.58840621e-01 -1.43948361e-01 -6.21487319e-01
-1.19938350e+00 8.55048239e-01 5.43935597e-01 1.58773333e-01
1.19853437e+00 -1.61319226e-02 8.07877958e-01 -2.73890764e-01
5.17594106e-02 -6.41467392e-01 4.56836104e-01 -9.41899493e-02
1.17328489e+00 -9.56632376e-01 -2.91160673e-01 -2.77136803e-01
-5.32481015e-01 9.02225733e-01 4.43122715e-01 -3.72449279e-01
3.06605458e-01 -1.66654050e-01 3.28608185e-01 -1.84327051e-01
-4.00816113e-01 1.51732951e-01 5.69253027e-01 9.37316000e-01
-1.16325445e-01 2.52753913e-01 3.49492610e-01 1.24704517e-01
-2.94783652e-01 -4.69500661e-01 1.31745860e-01 5.47055960e-01
2.56502420e-01 -1.52692449e+00 -3.98127228e-01 7.08249658e-02
-2.49362707e-01 -5.88826723e-02 -4.64220017e-01 6.70969129e-01
3.83178115e-01 3.98102701e-01 1.84148297e-01 -3.81350935e-01
2.39039868e-01 -2.37597182e-01 6.60662472e-01 -6.48969591e-01
-7.96693340e-02 -9.89637747e-02 -5.64064205e-01 -2.97022462e-01
-2.48657867e-01 -2.31282026e-01 -5.29689193e-01 -6.59586787e-02
-2.72505999e-01 1.73053235e-01 6.21178031e-01 5.66708386e-01
5.87611198e-01 4.87167746e-01 8.32190096e-01 -1.13335776e+00
-4.09937769e-01 -6.75176442e-01 -5.14088750e-01 7.04707146e-01
6.22716069e-01 -3.82619888e-01 -4.57535863e-01 5.16489327e-01] | [11.724371910095215, -0.48492559790611267] |
88a1c00b-5a1a-4845-a398-b325e40c2fa2 | deepfake-network-architecture-attribution | 2202.13843 | null | https://arxiv.org/abs/2202.13843v2 | https://arxiv.org/pdf/2202.13843v2.pdf | Deepfake Network Architecture Attribution | With the rapid progress of generation technology, it has become necessary to attribute the origin of fake images. Existing works on fake image attribution perform multi-class classification on several Generative Adversarial Network (GAN) models and obtain high accuracies. While encouraging, these works are restricted to model-level attribution, only capable of handling images generated by seen models with a specific seed, loss and dataset, which is limited in real-world scenarios when fake images may be generated by privately trained models. This motivates us to ask whether it is possible to attribute fake images to the source models' architectures even if they are finetuned or retrained under different configurations. In this work, we present the first study on Deepfake Network Architecture Attribution to attribute fake images on architecture-level. Based on an observation that GAN architecture is likely to leave globally consistent fingerprints while traces left by model weights vary in different regions, we provide a simple yet effective solution named DNA-Det for this problem. Extensive experiments on multiple cross-test setups and a large-scale dataset demonstrate the effectiveness of DNA-Det. | ['Xirong Li', 'Lei LI', 'Juan Cao', 'Ziyao Huang', 'Tianyun Yang'] | 2022-02-28 | null | null | null | null | ['fake-image-attribution'] | ['computer-vision'] | [ 4.78482664e-01 1.61816761e-01 -6.02790639e-02 -4.17049855e-01
-6.77661836e-01 -8.35907638e-01 6.98623776e-01 -5.23068130e-01
6.93793371e-02 7.97997653e-01 -2.36666456e-01 -3.65214646e-01
3.64603698e-01 -7.25652635e-01 -1.06126308e+00 -6.60302639e-01
3.12764943e-01 4.83424157e-01 5.02768643e-02 -2.09674329e-01
1.19339898e-01 5.42317033e-01 -1.10834241e+00 5.99754393e-01
6.71172380e-01 7.44034767e-01 -3.91269863e-01 5.45455277e-01
1.32853597e-01 8.05683494e-01 -1.11169910e+00 -1.11971939e+00
4.83007282e-01 -8.86231959e-01 -6.36559010e-01 6.71442673e-02
8.38783324e-01 -4.37647849e-01 -4.68665659e-01 1.27384365e+00
2.99270153e-01 -5.14481246e-01 7.61397898e-01 -1.84040117e+00
-1.18050086e+00 5.48900783e-01 -3.07383299e-01 -6.12768717e-02
-1.64484009e-01 2.97980100e-01 4.55368280e-01 -4.31616992e-01
6.86393261e-01 1.16662574e+00 7.15271413e-01 1.07837784e+00
-1.43539906e+00 -9.89765227e-01 -1.40454486e-01 -7.28982734e-03
-1.25730264e+00 -4.14198965e-01 7.79704392e-01 -3.63581121e-01
4.28612709e-01 2.77781725e-01 2.38973901e-01 2.03839207e+00
4.58150566e-01 3.99262697e-01 1.54529965e+00 -2.22264588e-01
1.29045360e-02 6.19347155e-01 -3.25398475e-01 5.72203934e-01
4.87306148e-01 3.06189150e-01 -4.89346892e-01 -3.81318897e-01
7.50237525e-01 -1.81219950e-01 -2.79072762e-01 -4.82663304e-01
-9.79351461e-01 1.11135852e+00 5.78754008e-01 1.57224983e-01
-1.50694892e-01 6.98273838e-01 3.63481432e-01 6.04897141e-01
3.93589437e-01 8.20821345e-01 -1.59031048e-01 3.06571871e-01
-9.78766680e-01 1.55989274e-01 5.41603565e-01 1.10036349e+00
5.80789089e-01 3.56337786e-01 -8.79979059e-02 5.86263359e-01
6.03956990e-02 4.44376975e-01 6.79014266e-01 -5.31251311e-01
2.76183367e-01 2.45398238e-01 7.98370168e-02 -1.24504781e+00
2.86816597e-01 -5.23531735e-01 -9.51282322e-01 3.15253794e-01
5.05072951e-01 2.35652812e-02 -1.17745149e+00 1.78600395e+00
-1.41802635e-02 3.07172418e-01 8.67417753e-02 9.23462391e-01
4.18384492e-01 2.93556780e-01 3.11927013e-02 4.34596241e-01
1.20619512e+00 -9.40979660e-01 -5.88094592e-01 -3.77200514e-01
4.00730371e-01 -7.73577273e-01 1.06459332e+00 2.61144876e-01
-5.58014512e-01 -4.23106909e-01 -1.17638409e+00 2.55517036e-01
-6.48535669e-01 1.28654644e-01 6.74479067e-01 1.34769976e+00
-9.96747732e-01 5.59252918e-01 -4.69479650e-01 -2.26747692e-01
8.94773424e-01 3.08298200e-01 -5.17636478e-01 4.70065884e-02
-1.35920811e+00 9.97703433e-01 3.41039956e-01 1.00688241e-01
-1.46670473e+00 -3.06300402e-01 -4.27369505e-01 -1.74238697e-01
-5.96240461e-02 -6.62581980e-01 7.07072318e-01 -1.70960796e+00
-1.31583107e+00 1.13696074e+00 2.06768960e-01 -7.00885296e-01
9.71415997e-01 1.46816283e-01 -6.46196902e-01 -3.91805694e-02
1.59847513e-02 7.49568045e-01 1.39690447e+00 -1.79767764e+00
-1.06253922e-01 -2.51868606e-01 -7.34057277e-02 -4.20027077e-01
-5.26258409e-01 -3.62044424e-02 -5.41877048e-03 -8.54838133e-01
-1.49886623e-01 -1.28738523e+00 2.36964356e-02 -2.30673596e-01
-7.45163023e-01 4.95923400e-01 1.07284260e+00 -6.77142739e-01
6.59876704e-01 -2.02371955e+00 -7.65730515e-02 1.14694171e-01
2.55965382e-01 3.91064733e-01 -3.10014635e-01 2.61129439e-01
-3.01950842e-01 6.06132150e-01 -3.09933901e-01 -2.97632605e-01
4.80275229e-02 1.12948142e-01 -8.59095633e-01 6.32224679e-01
4.32390660e-01 1.11033654e+00 -7.04195678e-01 -3.65137547e-01
-1.01537339e-01 4.67181176e-01 -3.01932544e-01 2.66812623e-01
-1.01140454e-01 7.07962513e-01 -4.12516177e-01 7.68656850e-01
9.21776354e-01 -1.66101128e-01 1.01151532e-02 -3.46877798e-02
6.77331448e-01 -9.42812562e-02 -5.02730489e-01 1.27951908e+00
-3.26944709e-01 8.36903036e-01 -1.49166062e-01 -8.13615978e-01
1.00555408e+00 2.66016155e-01 -2.94328164e-02 -5.63716352e-01
1.24631561e-01 4.85122979e-01 2.70278733e-02 -1.84566095e-01
6.69040501e-01 -3.62141013e-01 -4.39412266e-01 5.31193256e-01
1.80975392e-01 -1.82697162e-01 -6.00198507e-01 2.73894221e-01
1.10919654e+00 1.76529065e-01 -1.99227124e-01 2.54804026e-02
1.60116822e-01 8.50093812e-02 2.08028138e-01 1.02758384e+00
-3.48901004e-01 9.18492079e-01 5.97637653e-01 -4.55752313e-01
-1.47364283e+00 -1.02020693e+00 -9.10379440e-02 6.31875098e-01
1.95680499e-01 1.21962719e-01 -9.80553985e-01 -1.10712850e+00
2.57051270e-02 7.46657372e-01 -9.80690479e-01 -6.13999367e-01
-5.19701183e-01 -9.04883444e-01 1.29309809e+00 1.51041135e-01
5.98857820e-01 -1.07312071e+00 -2.79392451e-01 1.57053888e-01
-1.39749900e-01 -1.08194518e+00 -3.24617058e-01 4.23767930e-03
-5.92298508e-01 -8.25517774e-01 -6.88470840e-01 -5.36873579e-01
7.71003544e-01 -1.66639709e-03 1.16438174e+00 3.00346822e-01
-6.65708333e-02 8.11709911e-02 -3.60167414e-01 -4.75529552e-01
-1.11829507e+00 3.18033248e-01 -1.54638529e-01 4.08694655e-01
6.65480420e-02 -4.70270336e-01 -4.43164855e-01 5.98201036e-01
-1.16700661e+00 2.61177886e-02 6.31104827e-01 1.04705513e+00
2.27172047e-01 -8.33294764e-02 8.01690042e-01 -1.22198164e+00
6.91753030e-01 -6.69407666e-01 -5.25119364e-01 4.66711521e-01
-6.63239658e-01 3.20558846e-02 7.56033897e-01 -7.11506784e-01
-7.83326805e-01 -1.21493220e-01 1.08902223e-01 -8.72134745e-01
-1.49846494e-01 -1.15753934e-01 -3.23903173e-01 -4.28611755e-01
7.96212137e-01 3.46214890e-01 -1.49385303e-01 -1.02618439e-02
3.93724054e-01 7.52719998e-01 5.42213440e-01 -5.34440756e-01
1.15340674e+00 6.33445561e-01 -2.76401229e-02 -2.57385671e-01
-6.34202957e-01 3.37830573e-01 -4.10075158e-01 -2.09404767e-01
7.16879725e-01 -6.84126854e-01 -4.01829720e-01 8.08344603e-01
-1.24266064e+00 -3.11276257e-01 7.84997270e-03 -8.31908584e-02
-4.65002418e-01 1.66608632e-01 -4.54522759e-01 -6.09315574e-01
-9.37390849e-02 -1.25484431e+00 9.51196194e-01 -1.40093133e-01
-1.86527856e-02 -9.42808926e-01 -2.22913116e-01 5.54443181e-01
7.22014964e-01 7.34730840e-01 7.95174360e-01 -7.15954244e-01
-8.16370726e-01 -4.86160427e-01 -2.69648939e-01 5.75099468e-01
1.62421882e-01 -1.48704126e-01 -1.20654368e+00 -4.08116877e-01
9.20005217e-02 -6.50081694e-01 9.96283054e-01 -1.44213408e-01
1.42381465e+00 -5.42763054e-01 -3.37565333e-01 8.40655804e-01
1.58095419e+00 -9.62422118e-02 1.04478848e+00 4.07720774e-01
9.12377715e-01 5.38601816e-01 3.12342942e-01 -1.15899988e-01
-9.05377865e-02 7.52782345e-01 1.03577268e+00 -1.94540381e-01
-2.40080237e-01 -5.69718540e-01 2.69037575e-01 2.23775208e-01
1.88924879e-01 -8.18785012e-01 -6.02401018e-01 5.60151577e-01
-1.61543238e+00 -1.10995603e+00 -2.06221253e-01 2.17843914e+00
6.01611376e-01 5.44717237e-02 -9.12318975e-02 -2.85874873e-01
9.86524761e-01 1.20883167e-01 -5.76009572e-01 -5.63783586e-01
-5.34689546e-01 2.13522837e-01 9.95844066e-01 1.92052826e-01
-1.20170736e+00 1.18615282e+00 6.36461020e+00 1.00719190e+00
-1.38209367e+00 4.94314253e-01 9.27884638e-01 1.10987522e-01
-5.02419174e-01 1.12991855e-01 -4.79767531e-01 9.68375087e-01
1.06514287e+00 2.53304631e-01 5.40397346e-01 7.06572056e-01
-2.63753504e-01 2.61392206e-01 -9.74479377e-01 7.06713915e-01
2.07877964e-01 -1.40660083e+00 2.97717482e-01 4.39055979e-01
9.52540159e-01 -1.40903816e-01 5.03846228e-01 1.54680908e-01
3.87147009e-01 -1.52464521e+00 1.00159526e+00 4.51339751e-01
1.04800785e+00 -6.93031132e-01 8.93098891e-01 1.07223205e-01
-2.78918654e-01 1.97215423e-01 -5.06248951e-01 5.02260029e-01
2.02657096e-02 3.14084351e-01 -1.14426327e+00 3.95429015e-01
4.11003888e-01 2.13989630e-01 -8.80153775e-01 5.42305410e-01
-3.53713393e-01 8.90668392e-01 8.93705264e-02 2.33923793e-01
1.88051254e-01 1.58023257e-02 3.47970933e-01 1.01407719e+00
5.09482205e-01 -5.24284840e-01 -2.62885660e-01 1.26397169e+00
-4.69555348e-01 -4.77539927e-01 -9.13210154e-01 -1.98480770e-01
3.65271837e-01 1.02696455e+00 -6.81401730e-01 -2.47028366e-01
1.49095178e-01 1.64659107e+00 3.25916350e-01 2.17795685e-01
-1.41894245e+00 -1.24924645e-01 5.88747025e-01 2.31583580e-01
2.08688229e-01 6.47883788e-02 -3.29632908e-01 -1.16337562e+00
-6.41640350e-02 -1.11766899e+00 6.98144212e-02 -7.87793338e-01
-1.56075704e+00 9.48948026e-01 -2.79173702e-01 -1.20973921e+00
-1.80075929e-01 -5.37793815e-01 -3.93732041e-01 8.86714578e-01
-1.30437613e+00 -1.71701801e+00 -2.61719555e-01 5.52818179e-01
2.17335448e-02 -4.79261160e-01 1.09697509e+00 2.07928076e-01
-4.06855553e-01 1.11267364e+00 2.85169542e-01 3.34324867e-01
9.18372750e-01 -1.03094947e+00 6.53487742e-01 1.00330567e+00
2.92707533e-01 5.22088647e-01 8.03358495e-01 -7.29449093e-01
-1.05548489e+00 -1.35161829e+00 6.13582253e-01 -8.45306516e-01
5.92069685e-01 -6.49020255e-01 -1.00859606e+00 8.65586936e-01
3.03958744e-01 2.72551119e-01 6.04222953e-01 -5.47506690e-01
-8.25453997e-01 4.71663550e-02 -1.59301269e+00 2.58382082e-01
8.80581081e-01 -7.42942631e-01 -8.06172118e-02 4.36599672e-01
5.32916367e-01 -4.50813234e-01 -6.53112352e-01 8.04926977e-02
5.54844081e-01 -1.09692705e+00 9.73069191e-01 -8.78340602e-01
7.18745112e-01 -3.53823364e-01 -6.82862550e-02 -1.52377701e+00
-8.03769901e-02 -4.86549526e-01 2.06658989e-01 1.40491128e+00
4.38244820e-01 -9.50643241e-01 8.97864580e-01 4.28704381e-01
8.50748271e-03 -3.48774880e-01 -1.05624402e+00 -9.66108203e-01
2.58462876e-01 -9.00872424e-02 1.01601648e+00 1.33978248e+00
-8.17075968e-01 -7.25168884e-02 -1.14142954e+00 3.28290880e-01
7.55525708e-01 4.91919480e-02 9.76671457e-01 -8.37656081e-01
-3.30497652e-01 -2.87443042e-01 -6.05318069e-01 -4.17860955e-01
4.44171995e-01 -8.51940393e-01 -1.39035344e-01 -8.21496904e-01
1.46265104e-01 -5.88917851e-01 -2.19745263e-01 4.86046582e-01
3.36115137e-02 9.38971102e-01 1.70399427e-01 4.77292955e-01
-5.42958789e-02 3.68769228e-01 1.24459314e+00 -4.02127892e-01
4.33929294e-01 -7.35268146e-02 -7.08729148e-01 3.31204474e-01
1.03701115e+00 -1.03013062e+00 -3.01578760e-01 -5.12893856e-01
3.80931818e-03 -2.67502278e-01 1.01583552e+00 -9.23796535e-01
-1.92378789e-01 -2.17742532e-01 5.09462118e-01 -1.82668492e-02
3.29124868e-01 -7.72218168e-01 6.75500512e-01 4.70007062e-01
-2.88746864e-01 -1.17424622e-01 1.05552472e-01 5.81350029e-01
-7.16260299e-02 -2.29140043e-01 1.08776081e+00 -2.42160141e-01
-5.50272167e-01 1.93368956e-01 -1.48751527e-01 -1.70031667e-01
1.06819713e+00 -4.24665809e-01 -6.66894436e-01 -4.69400644e-01
-3.76329631e-01 -4.70659375e-01 1.02624702e+00 5.87256074e-01
5.01016438e-01 -1.51962519e+00 -8.15994859e-01 2.04329759e-01
3.90381277e-01 -5.16063571e-01 1.35548562e-01 3.01602930e-01
-6.39805794e-01 2.68911421e-01 -5.96259117e-01 -3.95910203e-01
-1.00189114e+00 6.30577803e-01 6.72449589e-01 -1.86144128e-01
-1.99563578e-01 8.79560888e-01 2.11634263e-01 -3.80632609e-01
-4.27309692e-01 2.58551627e-01 4.74125713e-01 -2.99126178e-01
4.36245091e-02 -5.36691099e-02 1.69643983e-01 -9.45379972e-01
-3.11261833e-01 5.91483526e-02 -2.22674366e-02 -4.49724272e-02
9.64304984e-01 2.16295198e-01 -1.05811298e-01 -9.66374390e-03
1.18845463e+00 6.19822741e-02 -1.21595132e+00 1.87800869e-01
-3.62957090e-01 -8.57705593e-01 -2.22179070e-01 -1.06161869e+00
-1.32485044e+00 7.98139572e-01 7.41336405e-01 3.36299658e-01
9.22291875e-01 -1.59243355e-03 7.28178978e-01 -1.09466910e-01
7.16134012e-01 -6.89565837e-01 1.90104976e-01 -7.99028017e-03
9.62231755e-01 -1.19161618e+00 -2.90257961e-01 -2.67928481e-01
-6.41853034e-01 7.66551852e-01 7.67431140e-01 -2.49055237e-01
5.88256828e-02 -6.04982972e-02 2.56980956e-01 -1.23898804e-01
-2.94153303e-01 3.78840029e-01 4.24019285e-02 8.78231645e-01
9.75776464e-02 3.64023089e-01 1.28057390e-01 2.74146706e-01
-5.00329494e-01 -3.19964260e-01 9.01831567e-01 4.85416085e-01
1.71072230e-01 -1.53492165e+00 -6.40890121e-01 4.15432483e-01
-6.65791333e-01 3.22758369e-02 -8.82385910e-01 9.17491257e-01
3.02187204e-01 8.61119926e-01 -7.78552815e-02 -5.47186077e-01
-2.49062348e-02 -4.58760597e-02 5.60673654e-01 -3.57712328e-01
-8.93665433e-01 -4.55332875e-01 -5.77417351e-02 -3.72705638e-01
-2.76644439e-01 -4.16129470e-01 -5.53914070e-01 -4.44079429e-01
-5.43470919e-01 -1.78126823e-02 8.54747176e-01 6.74606919e-01
5.43654919e-01 3.95164698e-01 7.41268218e-01 -5.08125424e-01
-6.48500144e-01 -1.01441181e+00 -5.39294124e-01 8.03152144e-01
2.84081519e-01 -5.19732296e-01 -4.99447227e-01 1.36249721e-01] | [12.520745277404785, 1.0452675819396973] |
2684c6b7-4968-4bda-8d4d-062e5cfb802c | omdet-language-aware-object-detection-with | 2209.05946 | null | https://arxiv.org/abs/2209.05946v1 | https://arxiv.org/pdf/2209.05946v1.pdf | OmDet: Language-Aware Object Detection with Large-scale Vision-Language Multi-dataset Pre-training | Advancing object detection to open-vocabulary and few-shot transfer has long been a challenge for computer vision research. This work explores a continual learning approach that enables a detector to expand its zero/few-shot capabilities via multi-dataset vision-language pre-training. Using natural language as knowledge representation, we explore methods to accumulate "visual vocabulary" from different training datasets and unify the task as a language-conditioned detection framework. Specifically, we propose a novel language-aware detector OmDet and a novel training mechanism. The proposed multimodal detection network can resolve the technical challenges in multi-dataset joint training and it can generalize to arbitrary number of training datasets without the requirements for manual label taxonomy merging. Experiment results on COCO, Pascal VOC, and Wider Face/Pedestrian confirmed the efficacy by achieving on par or higher scores in joint training compared to training separately. Moreover, we pre-train on more than 20 million images with 4 million unique object vocabulary, and the resulting model is evaluated on 35 downstream tasks of ODinW. Results show that OmDet is able to achieve the state-of-the-art fine-tuned performance on ODinW. And analysis shows that by scaling up the proposed pre-training method, OmDet continues to improve its zero/few-shot tuning performance, suggesting a promising way for further scaling. | ['Kyusong Lee', 'Xiaopeng Lu', 'Peng Liu', 'Tiancheng Zhao'] | 2022-09-10 | null | null | null | null | ['open-vocabulary-object-detection'] | ['computer-vision'] | [ 8.61147940e-02 -1.05756432e-01 -1.83674812e-01 -1.68295071e-01
-1.05648375e+00 -4.15734798e-01 7.57321417e-01 -1.72387674e-01
-9.49168503e-01 4.19665962e-01 -7.58544952e-02 -9.78412107e-02
2.95380265e-01 -4.15807545e-01 -7.08311558e-01 -4.61140484e-01
3.77350986e-01 4.92286593e-01 8.20143461e-01 -2.93580055e-01
-1.66031584e-01 3.27188313e-01 -1.78178775e+00 2.54496217e-01
3.47568244e-01 7.72410274e-01 5.24682760e-01 7.66516626e-01
-2.13175386e-01 6.36098921e-01 -3.77590388e-01 -5.60861945e-01
4.28472757e-01 6.51265457e-02 -6.49653614e-01 1.38377786e-01
1.09069991e+00 -4.82903004e-01 -1.62547126e-01 9.94347632e-01
7.60371864e-01 -6.01715855e-02 6.62702978e-01 -1.27858424e+00
-7.14616835e-01 2.62847424e-01 -6.65683568e-01 3.36285740e-01
2.78046001e-02 5.38624525e-01 9.94887769e-01 -1.28129280e+00
8.59507203e-01 1.30333662e+00 7.60588050e-01 8.25151622e-01
-1.30002725e+00 -8.52161765e-01 -8.50719027e-03 2.35105589e-01
-1.63298821e+00 -5.26976645e-01 3.57783139e-01 -6.97708547e-01
1.18536925e+00 -1.46204934e-01 3.79336327e-01 1.19231808e+00
-2.67530560e-01 8.18319678e-01 9.76114511e-01 -6.61752641e-01
-2.99328733e-02 4.05232340e-01 4.36904311e-01 9.67742562e-01
3.96141589e-01 1.85972437e-01 -4.79565263e-01 6.78781047e-02
3.83814782e-01 -2.47041091e-01 6.26453832e-02 -7.16093838e-01
-9.44674909e-01 1.04325950e+00 3.45315635e-01 2.32022256e-01
5.61184958e-02 1.91463545e-01 6.60543203e-01 1.78175583e-01
4.35049921e-01 4.06049997e-01 -3.52642149e-01 3.16787273e-01
-1.08189797e+00 1.85410324e-02 5.42257071e-01 1.08409989e+00
9.91651714e-01 1.80301704e-02 -5.21684587e-01 8.74049366e-01
2.79270232e-01 6.94369733e-01 2.92067617e-01 -7.84010768e-01
2.44886667e-01 5.63221931e-01 -9.72773321e-03 -3.58487904e-01
-4.87603098e-01 -3.59362602e-01 -4.42135572e-01 3.48075956e-01
4.23244357e-01 -1.18095793e-01 -1.31606710e+00 1.71064174e+00
3.34640235e-01 4.04722184e-01 3.66335106e-03 8.02788675e-01
1.14966404e+00 4.29771543e-01 5.31916678e-01 -5.31828254e-02
1.73932338e+00 -1.04203844e+00 -3.11962456e-01 -3.09768677e-01
6.66225731e-01 -7.15568423e-01 9.62582052e-01 2.75185317e-01
-6.55497253e-01 -9.91993010e-01 -1.10495567e+00 -1.74442112e-01
-5.12803793e-01 4.43793714e-01 5.82168937e-01 8.41237128e-01
-1.11337316e+00 -3.66378166e-02 -5.37105918e-01 -1.01054096e+00
6.38389349e-01 2.91276991e-01 -4.67763841e-01 -3.19454402e-01
-1.00174868e+00 1.09150410e+00 8.70633602e-01 -4.51285750e-01
-1.29309642e+00 -7.11744785e-01 -8.56192529e-01 -1.25577435e-01
6.18997157e-01 -8.31449270e-01 1.20884073e+00 -5.73413014e-01
-1.19080365e+00 1.27373588e+00 2.81645376e-02 -6.74246192e-01
4.41303879e-01 -1.60900414e-01 -4.20831323e-01 1.54546455e-01
3.03321034e-01 1.36591995e+00 1.04829156e+00 -1.12129354e+00
-9.20514524e-01 -1.60492450e-01 1.14655346e-01 -5.05836261e-03
-7.15512991e-01 3.08404237e-01 -8.54506910e-01 -2.85203278e-01
-5.35468161e-01 -9.16109324e-01 -2.36144252e-02 1.86077967e-01
2.93336175e-02 -5.72259724e-01 8.47745121e-01 -3.23528558e-01
8.12293470e-01 -2.19336104e+00 -2.57940926e-02 -2.95018554e-01
3.25233698e-01 7.70340681e-01 -6.38320804e-01 2.64217794e-01
2.01952577e-01 -2.49567181e-01 -2.92191319e-02 -6.17734849e-01
-4.53900322e-02 3.31715912e-01 -1.93606779e-01 4.16692823e-01
5.40816307e-01 1.17035472e+00 -8.56954515e-01 -7.17979968e-01
3.84397477e-01 3.45591873e-01 -5.08896887e-01 9.88719538e-02
-3.21259677e-01 1.05152845e-01 -7.99818411e-02 5.99688530e-01
6.37727499e-01 -3.14503551e-01 -5.19998819e-02 -3.52329671e-01
-1.92008227e-01 -5.01129389e-01 -1.23018718e+00 1.83273852e+00
-3.61988038e-01 7.92041600e-01 -7.06954375e-02 -8.73604834e-01
9.26757872e-01 3.12752053e-02 1.13282137e-01 -6.04439080e-01
3.19115669e-01 -1.01492340e-02 -6.39777631e-02 -6.52684271e-01
5.00411153e-01 -2.12437481e-01 -1.27461597e-01 1.00825988e-01
9.29583132e-01 3.02255712e-02 5.05479217e-01 3.62282693e-01
9.57421541e-01 -3.40749174e-02 4.49770570e-01 -1.67441905e-01
6.94028497e-01 1.42467231e-01 3.44800383e-01 1.03102875e+00
-5.31330287e-01 4.23269570e-01 2.53700092e-02 -1.54520899e-01
-1.05637848e+00 -1.12378037e+00 -2.62972504e-01 1.83179665e+00
7.48278573e-02 -2.26525351e-01 -5.55540204e-01 -6.64522767e-01
1.19292133e-01 6.74535990e-01 -6.91178501e-01 -3.04741673e-02
-2.27519244e-01 -6.48835719e-01 9.62660134e-01 5.96974909e-01
5.12735605e-01 -8.60431314e-01 -6.10755861e-01 3.67714204e-02
8.75203758e-02 -1.57348454e+00 -3.64848405e-01 1.69600680e-01
-3.34105492e-01 -1.01135302e+00 -9.27369297e-01 -1.02150333e+00
2.09217876e-01 7.22230494e-01 9.34158385e-01 -1.73439667e-01
-8.91258180e-01 8.30532730e-01 -3.70411128e-01 -5.11765599e-01
-4.87511963e-01 2.50645895e-02 1.27442881e-01 5.64765707e-02
6.77212656e-01 -1.49757452e-02 -3.46055329e-01 1.64204031e-01
-6.91705763e-01 -1.54653952e-01 6.90444887e-01 9.09898400e-01
2.23615333e-01 -5.28723836e-01 5.78054667e-01 -4.80582654e-01
3.27782899e-01 -1.69240221e-01 -8.39467704e-01 4.76487160e-01
-4.98938918e-01 8.58665854e-02 7.48025477e-02 -6.43645406e-01
-1.08018470e+00 3.81824106e-01 -1.25252651e-02 -7.62832582e-01
-2.58335114e-01 -9.65600982e-02 -4.57365736e-02 -3.35052073e-01
9.21137989e-01 1.39962733e-01 2.05539949e-02 -4.23544556e-01
1.08016074e+00 6.86214685e-01 7.67707527e-01 -2.97697812e-01
8.89935911e-01 6.38131320e-01 -3.24610531e-01 -1.06126130e+00
-8.99333656e-01 -1.21089983e+00 -7.93727100e-01 -1.72733232e-01
1.32301521e+00 -1.36793184e+00 -5.48313677e-01 3.78644556e-01
-1.28809500e+00 -1.09773271e-01 -2.72968262e-01 4.00620580e-01
-3.12573075e-01 4.05723363e-01 -3.88045818e-01 -7.71225989e-01
-4.56896693e-01 -1.02508390e+00 1.42746437e+00 1.87058493e-01
7.95050338e-02 -7.66001999e-01 1.99855953e-01 4.75463599e-01
2.11916402e-01 -2.66741127e-01 4.37892616e-01 -7.12910116e-01
-5.77988803e-01 -1.44068018e-01 -7.99022377e-01 4.27644521e-01
-2.50576109e-01 -2.45572224e-01 -1.30415165e+00 -5.68649292e-01
-4.51622158e-01 -7.75977969e-01 1.45596325e+00 5.11195064e-02
4.42573011e-01 3.92773390e-01 -4.43309218e-01 5.19149065e-01
1.58509874e+00 -3.30615491e-01 3.72465640e-01 2.90934026e-01
7.47612178e-01 4.09553528e-01 5.93053401e-01 3.51962149e-01
3.61786664e-01 8.34245801e-01 3.28726918e-01 -1.76274106e-01
-7.36990988e-01 -1.04291782e-01 3.92203480e-01 3.12146306e-01
6.65691197e-02 1.68983396e-02 -1.00915909e+00 7.44430542e-01
-1.72615957e+00 -1.08153021e+00 -4.97439206e-02 1.88660610e+00
6.12849176e-01 1.66483298e-01 3.68457228e-01 -3.61180335e-01
7.32491314e-01 2.16727033e-02 -5.06482959e-01 -1.14314437e-01
-2.13372707e-01 1.79042965e-01 8.25013220e-01 3.84386063e-01
-1.38693523e+00 1.52360058e+00 5.98650122e+00 1.06452525e+00
-1.11926270e+00 6.96631491e-01 -4.08457369e-02 -1.12250663e-01
3.18976969e-01 -1.85725003e-01 -1.55780160e+00 1.32853417e-02
6.52910292e-01 -1.32186577e-01 7.33164176e-02 9.69759881e-01
-1.95305720e-01 -1.07127048e-01 -9.84661281e-01 1.17009044e+00
5.10992944e-01 -1.41145575e+00 1.18291907e-01 -1.61367990e-02
7.77307153e-01 6.57958686e-01 -5.84291928e-02 8.56611550e-01
3.65820169e-01 -6.95864916e-01 5.69172561e-01 2.88773000e-01
1.10642838e+00 -2.70273745e-01 6.85246110e-01 5.51436007e-01
-1.26712382e+00 -3.98261994e-01 -6.12305760e-01 6.50214478e-02
1.69624388e-01 7.39153707e-03 -1.29070866e+00 4.07960773e-01
7.24386096e-01 4.40056503e-01 -1.11721480e+00 1.07392073e+00
-7.61942565e-02 4.17973578e-01 -2.26039439e-01 -2.84785070e-02
2.80982822e-01 4.08149153e-01 4.39755768e-01 1.62096488e+00
-1.94387380e-02 -9.87116992e-02 4.49215531e-01 5.72381735e-01
-2.12870881e-01 1.74237803e-01 -6.62910163e-01 2.15242311e-01
2.50019133e-01 1.44840920e+00 -6.59170032e-01 -6.01690114e-01
-8.09100926e-01 9.94392633e-01 6.69869721e-01 2.82533973e-01
-1.04269087e+00 9.57112107e-03 5.52800238e-01 -9.63313803e-02
7.38757610e-01 -2.34269932e-01 8.45442042e-02 -1.04237163e+00
-2.90139675e-01 -5.42512774e-01 4.50225890e-01 -6.77227736e-01
-1.35952640e+00 4.72343087e-01 1.46437749e-01 -1.15119350e+00
-7.75062814e-02 -9.44823980e-01 -3.54856163e-01 5.59480071e-01
-1.72421753e+00 -1.72337723e+00 -4.36346292e-01 7.37969458e-01
7.75370121e-01 -5.56964338e-01 7.25737274e-01 5.17874658e-01
-3.79429221e-01 8.01495254e-01 -4.28554090e-03 2.89156973e-01
9.76722717e-01 -9.75969255e-01 2.90718883e-01 9.65310097e-01
5.86545587e-01 2.95064211e-01 4.82829541e-01 -5.49818993e-01
-1.23830986e+00 -1.43970692e+00 5.18871546e-01 -6.54439986e-01
8.09680700e-01 -5.56401730e-01 -8.78057301e-01 5.33520460e-01
3.31418753e-01 3.12279969e-01 4.50830758e-01 4.82549295e-02
-7.79602408e-01 -1.05224170e-01 -9.33221221e-01 3.45152080e-01
1.21647239e+00 -6.53028548e-01 -8.63333523e-01 3.91924292e-01
9.87591863e-01 -1.60618946e-02 -4.54892665e-01 4.93702114e-01
4.19601172e-01 -6.51034117e-01 1.10659635e+00 -6.37933254e-01
-1.08937144e-01 -3.69664907e-01 -3.57187837e-01 -8.06053698e-01
-4.60080534e-01 -2.99679130e-01 2.10004523e-02 1.43428433e+00
1.83936790e-01 -4.54068631e-01 6.10860825e-01 2.10872471e-01
9.28886142e-03 -1.73834667e-01 -1.09730291e+00 -1.10036016e+00
-1.69533323e-02 -6.52170539e-01 -1.09307937e-01 7.08116710e-01
-4.86772776e-01 8.04176092e-01 -5.28464139e-01 2.22701684e-01
8.66289020e-01 -1.98198959e-01 1.19341636e+00 -1.26608491e+00
-3.47863644e-01 -3.97799432e-01 -6.73428535e-01 -9.70046401e-01
2.28560254e-01 -1.15982258e+00 1.52549386e-01 -1.42089820e+00
5.63465238e-01 -1.59447622e-02 -3.33381504e-01 6.47430897e-01
-1.83005288e-01 7.64475942e-01 6.39159024e-01 1.02441289e-01
-1.12166274e+00 5.38229942e-01 9.35002387e-01 -4.47248578e-01
-4.80743423e-02 -4.59162444e-01 -4.75427091e-01 7.04659224e-01
4.16703522e-01 -4.01060104e-01 -2.32745662e-01 -3.07753742e-01
-2.91760772e-01 -4.35688317e-01 7.62825012e-01 -1.22504997e+00
4.13648605e-01 3.13458979e-01 1.31447881e-01 -6.28361225e-01
4.95451748e-01 -6.16679430e-01 -2.72198886e-01 4.38019753e-01
-9.96988565e-02 -5.39109468e-01 5.48131824e-01 8.39363754e-01
6.07619286e-02 -2.52585620e-01 1.02247286e+00 8.75239670e-02
-1.69889140e+00 1.02031261e-01 -1.83633924e-01 1.35494009e-01
1.28426707e+00 -2.13257819e-01 -3.90891731e-01 2.04039916e-01
-7.75794387e-01 4.51350749e-01 2.70267367e-01 8.13494682e-01
5.13281345e-01 -1.09886992e+00 -8.79435360e-01 1.22272931e-01
8.41482580e-01 -4.99313742e-01 2.31555790e-01 6.88455284e-01
-6.98841736e-02 5.37803531e-01 -2.19094843e-01 -1.00946176e+00
-1.64779139e+00 9.25474703e-01 2.72490740e-01 -1.82625368e-01
-5.73379457e-01 1.16192877e+00 3.70061845e-01 -3.12882036e-01
3.17451209e-01 -9.20442492e-02 -2.57316351e-01 4.19875145e-01
6.11448705e-01 2.29642779e-01 -5.69114387e-02 -7.08497107e-01
-3.86572361e-01 7.67929256e-01 -2.71224082e-01 -6.62510023e-02
1.05805361e+00 -2.21131250e-01 3.61427635e-01 3.13095272e-01
1.15051603e+00 -3.61343890e-01 -1.29057825e+00 -5.21075606e-01
5.51023968e-02 -1.52768552e-01 7.73544759e-02 -5.96457303e-01
-6.93310976e-01 1.02939260e+00 1.07902789e+00 -1.79021940e-01
7.68068492e-01 5.52879333e-01 5.39314270e-01 5.68446517e-01
5.09254575e-01 -1.10412169e+00 4.11084294e-01 6.90006971e-01
6.46216691e-01 -1.74401402e+00 -1.04943737e-01 -2.69964904e-01
-6.79198384e-01 9.95551407e-01 7.27219045e-01 -5.98130748e-02
5.00733256e-01 8.03690851e-02 1.55765548e-01 -2.17647403e-01
-6.02481306e-01 -1.17128444e+00 4.58344638e-01 7.42438495e-01
7.00890794e-02 -6.35387301e-02 -1.57490879e-01 2.95395851e-01
3.58994603e-01 1.62328228e-01 2.45981708e-01 4.93490517e-01
-8.56191516e-01 -9.21983123e-01 -3.77150625e-01 2.10449263e-01
1.18225478e-02 -2.76750296e-01 -1.50920123e-01 1.05943203e+00
5.89827836e-01 9.66808140e-01 -8.27405676e-02 -2.44308352e-01
3.24713051e-01 3.41742992e-01 5.72280049e-01 -8.57868731e-01
-3.05514753e-01 1.67398434e-02 6.08984381e-02 -4.17181224e-01
-5.37620366e-01 -6.07213855e-01 -1.01879478e+00 2.65046746e-01
-6.37124717e-01 -4.20135915e-01 5.42738914e-01 9.88256276e-01
2.55007416e-01 5.32409489e-01 2.89129354e-02 -9.22531188e-01
-6.50801659e-01 -1.05433738e+00 -3.09869468e-01 4.14260983e-01
3.15027475e-01 -1.06269801e+00 -2.57774349e-02 1.61207303e-01] | [9.596418380737305, 1.492775321006775] |
ba42f51a-2de1-44b0-a208-29d29e976a4d | a-survey-on-causal-discovery-methods-for | 2303.15027 | null | https://arxiv.org/abs/2303.15027v2 | https://arxiv.org/pdf/2303.15027v2.pdf | A Survey on Causal Discovery Methods for Temporal and Non-Temporal Data | Causal Discovery (CD) is the process of identifying the cause-effect relationships among the variables of a system from data. Over the years, several methods have been developed primarily based on the statistical properties of data to uncover the underlying causal mechanism. In this study, we present an extensive discussion on the methods designed to perform causal discovery from both independent and identically distributed (i.i.d.) data and time series data. For this purpose, we first introduce the common terminologies in causal discovery, and then provide a comprehensive discussion of the algorithms designed to identify the causal edges in different settings. We further discuss some of the benchmark datasets available for evaluating the performance of the causal discovery methods, available tools or software packages to perform causal discovery readily, and the common metrics used to evaluate these methods. We also test some common causal discovery algorithms on different benchmark datasets, and compare their performances. Finally, we conclude by presenting the common challenges involved in causal discovery, and also, discuss the applications of causal discovery in multiple areas of interest. | ['Md Osman Gani', 'Emam Hossain', 'Uzma Hasan'] | 2023-03-27 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 3.26714069e-01 -1.09718762e-01 -4.11913067e-01 -3.34495604e-01
-3.67819279e-01 -5.58303475e-01 8.69299531e-01 3.07168990e-01
4.71869648e-01 1.05820572e+00 4.85175729e-01 -6.34731710e-01
-1.06320369e+00 -1.02500105e+00 -5.24354517e-01 -6.28625512e-01
-1.13565922e+00 3.84588748e-01 1.20154366e-01 2.45328844e-01
5.05067229e-01 2.84487575e-01 -1.67030168e+00 1.87743336e-01
7.85431027e-01 4.87644553e-01 -1.62148654e-01 7.03684211e-01
1.26685768e-01 9.14204478e-01 -6.44181013e-01 9.85832885e-02
-1.75544575e-01 -7.18639612e-01 -9.61304128e-01 -6.19503736e-01
-6.81225732e-02 -1.08617701e-01 -4.88218963e-01 5.40824294e-01
4.98642594e-01 -3.30016941e-01 7.44479835e-01 -1.89735675e+00
-4.19060290e-01 1.21380889e+00 -6.61772490e-01 7.59427488e-01
8.83494735e-01 -1.68671191e-01 1.21166992e+00 -8.39087784e-01
7.23959982e-01 1.66842270e+00 5.07479787e-01 -3.17261815e-02
-1.54730630e+00 -8.23290944e-01 1.97774515e-01 4.88106400e-01
-1.39157844e+00 -3.14401150e-01 6.12444282e-01 -7.31760919e-01
7.60995746e-01 4.66525584e-01 3.71183544e-01 1.21223366e+00
4.65950966e-01 5.22038758e-01 1.11659861e+00 -4.64769423e-01
2.43418694e-01 -6.14016235e-01 5.17597139e-01 4.50764984e-01
5.55846751e-01 6.44089937e-01 -8.31184685e-01 -9.63475466e-01
6.64363503e-01 -4.23080921e-01 -1.96140140e-01 -1.18147410e-01
-1.18102467e+00 9.83384192e-01 1.53418314e-02 3.24012727e-01
-3.42758060e-01 5.11688828e-01 5.21673262e-01 4.93238449e-01
3.64245623e-01 3.93195003e-01 -6.03659868e-01 2.39653401e-02
-4.00963575e-01 4.68471348e-01 9.47467387e-01 7.91803777e-01
1.77391976e-01 -1.99930921e-01 -3.34559262e-01 2.96236366e-01
3.00329000e-01 4.67544198e-01 -3.57679307e-01 -6.37064815e-01
7.91732073e-02 4.94728297e-01 5.39827906e-02 -1.05325007e+00
-3.71056736e-01 -1.04929646e-02 -7.68459737e-01 -1.39867514e-01
2.27436289e-01 -3.82178098e-01 -5.85475385e-01 1.38830185e+00
4.14460152e-01 6.06354058e-01 -2.86440581e-01 6.39098167e-01
9.44994986e-01 4.06331211e-01 2.84548968e-01 -7.35407472e-01
1.13505912e+00 -1.36104990e-02 -1.13695633e+00 3.98036897e-01
2.46196494e-01 -9.37017739e-01 5.91060460e-01 1.09212570e-01
-6.86650217e-01 -1.90005615e-01 -5.13243258e-01 6.39577448e-01
-1.99229062e-01 -3.65188092e-01 1.07912505e+00 5.50864100e-01
-5.94104290e-01 6.07120633e-01 -8.12424123e-01 -4.42148715e-01
2.38121882e-01 1.79601371e-01 4.12156843e-02 -7.19950125e-02
-1.60239553e+00 5.13038456e-01 2.16650143e-01 -3.70459765e-01
-1.40445721e+00 -1.36951518e+00 -1.98767990e-01 -8.18720981e-02
4.36480701e-01 -8.95127058e-01 9.24095273e-01 -1.94612905e-01
-6.76622093e-01 3.67492795e-01 -3.56046975e-01 -2.21001327e-01
3.76976222e-01 -2.82681763e-01 -8.96606743e-01 -3.05055678e-01
3.73425603e-01 -1.98840037e-01 3.68023127e-01 -1.35215807e+00
-1.08200979e+00 -2.58585989e-01 -2.62131412e-02 -4.51142579e-01
-7.51763815e-03 5.00578105e-01 -1.14065498e-01 -6.28324151e-01
-2.83838660e-01 -6.29727602e-01 -2.56224960e-01 -7.07295716e-01
-1.04922116e+00 -5.79360247e-01 1.04749954e+00 -1.87547132e-01
1.70444167e+00 -1.80812001e+00 3.68743762e-02 4.50243473e-01
4.29182112e-01 -5.00186801e-01 6.06236905e-02 1.10261452e+00
-6.66698694e-01 5.46906233e-01 -1.06439546e-01 2.63266087e-01
-4.19785857e-01 1.98692769e-01 -5.74470580e-01 5.61712503e-01
2.66497493e-01 6.29978240e-01 -1.23831558e+00 -4.24777478e-01
1.21038437e-01 1.46730259e-01 -6.95046410e-02 3.23658764e-01
-9.73565578e-02 9.21954393e-01 -6.30967975e-01 5.84260046e-01
2.61762917e-01 -3.44274312e-01 6.98163688e-01 3.17081027e-02
-5.44132292e-01 5.10969639e-01 -1.38766313e+00 8.63488555e-01
-1.04137309e-01 6.56805038e-01 -2.87611961e-01 -1.14900696e+00
7.31072068e-01 6.93832338e-01 9.88755286e-01 -4.40834939e-01
-3.15858945e-02 1.78703755e-01 8.34463984e-02 -7.36362040e-01
-3.08090389e-01 1.75591022e-01 -2.54609585e-01 7.41712272e-01
-3.28894496e-01 3.40693533e-01 4.27400321e-01 2.87781656e-01
2.02037168e+00 -3.90726388e-01 6.24088645e-01 -5.20900190e-01
1.49493903e-01 3.79093617e-01 6.52543604e-01 8.27737510e-01
2.38832653e-01 -1.81458503e-01 8.14202845e-01 -6.93687439e-01
-7.01012015e-01 -1.18992221e+00 -4.77677405e-01 7.50586271e-01
2.10956901e-01 -7.29520082e-01 -1.64665040e-02 -6.87398314e-01
5.66927493e-01 9.76079583e-01 -9.91278350e-01 1.12587325e-01
-4.01755035e-01 -1.35933387e+00 5.34733713e-01 4.01394069e-01
1.98064700e-01 -7.17409849e-01 -4.26738620e-01 3.14462543e-01
-2.85700381e-01 -6.32453501e-01 3.23473632e-01 3.49942267e-01
-9.47335958e-01 -1.66661274e+00 3.32531899e-01 -9.18455049e-02
4.30131704e-01 2.48258442e-01 1.44230318e+00 5.13540357e-02
-7.49703407e-01 3.44278455e-01 -3.86288494e-01 -4.86065745e-01
-4.40011382e-01 -4.27525729e-01 2.43942097e-01 -3.90403718e-01
4.14072990e-01 -7.42870629e-01 -3.07879478e-01 5.76685786e-01
-4.84328955e-01 -4.46092784e-01 2.60161251e-01 6.16054237e-01
4.89931583e-01 8.83460701e-01 6.61553323e-01 -1.02156329e+00
1.01619995e+00 -1.19617403e+00 -6.89114809e-01 1.41740456e-01
-1.08012664e+00 3.55159789e-02 1.38927951e-01 -1.43820614e-01
-1.09125280e+00 3.82468179e-02 4.33288366e-01 -1.07875161e-01
-1.61653459e-01 8.78558815e-01 -6.58232421e-02 3.63587588e-01
6.90768182e-01 -5.40482104e-01 -4.30897295e-01 -5.72470903e-01
5.00168502e-01 1.77759498e-01 2.08642036e-01 -5.80574870e-01
7.57026672e-01 5.84532797e-01 2.80565947e-01 -3.25226426e-01
-6.01364255e-01 -5.70036530e-01 -7.34615386e-01 -3.90764922e-01
5.22373259e-01 -5.11786878e-01 -8.69445801e-01 -3.74639854e-02
-1.39097476e+00 -8.15809593e-02 3.68090644e-02 6.00925684e-01
-3.27018440e-01 -3.83096546e-01 -4.16959703e-01 -8.96044374e-01
-4.34282348e-02 -5.96875906e-01 7.44429588e-01 -9.27071124e-02
-8.42613161e-01 -1.32621944e+00 5.80053627e-01 -1.31836981e-01
-1.33484229e-01 7.30960846e-01 1.31424892e+00 -3.52447957e-01
-4.35850143e-01 -8.02544281e-02 -3.41574460e-01 -6.51103318e-01
5.82025290e-01 6.83817029e-01 -7.69159257e-01 3.41640078e-02
-4.39548701e-01 3.98355693e-01 6.48118675e-01 8.96025002e-01
1.04996431e+00 -2.77009666e-01 -1.49722493e+00 -3.62348333e-02
1.45800185e+00 4.73936975e-01 4.68198776e-01 9.17303562e-02
5.73263705e-01 8.77870560e-01 1.05096495e+00 8.15865576e-01
1.75019965e-01 4.24077481e-01 6.94478691e-01 -2.89695770e-01
-2.41519585e-02 -3.00911963e-01 6.36059940e-02 3.73130292e-01
-5.34085929e-01 -4.83705819e-01 -1.23537445e+00 7.48008013e-01
-2.05291414e+00 -1.18213189e+00 -1.51119554e+00 1.93063903e+00
8.80708635e-01 -1.71188936e-01 2.36383647e-01 2.99950898e-01
1.10651064e+00 -4.59780812e-01 -5.05650759e-01 -8.14218447e-03
-5.15701734e-02 1.41866520e-01 7.13746965e-01 3.56399596e-01
-1.20776367e+00 5.01499414e-01 8.40791035e+00 3.78895402e-01
-5.13275981e-01 1.63440168e-01 3.47569019e-01 1.38081601e-02
-3.93179089e-01 4.49436069e-01 -7.10176349e-01 2.94255465e-01
1.15952408e+00 -7.24065483e-01 -6.69517145e-02 3.71575803e-01
1.05544055e+00 -1.78607285e-01 -1.43491280e+00 3.76826137e-01
-8.88135850e-01 -1.52475858e+00 -1.97385937e-01 1.97949663e-01
1.04497731e+00 -9.09654200e-02 -3.03406239e-01 -4.24362123e-01
1.22419345e+00 -1.10557210e+00 3.45738441e-01 5.91367364e-01
7.00852334e-01 -6.21582270e-01 6.64405107e-01 -1.93663344e-01
-1.18904662e+00 -3.34958375e-01 -1.01741664e-02 -5.33803523e-01
4.07194734e-01 1.66253495e+00 -9.28716540e-01 1.04429638e+00
1.22019231e+00 1.01345897e+00 -4.47375700e-02 1.18573046e+00
-5.97941995e-01 1.15509725e+00 -1.23337597e-01 1.01575084e-01
-3.81871521e-01 2.84210265e-01 7.67314076e-01 9.86927927e-01
2.65293032e-01 2.80246496e-01 2.17938609e-02 1.05533481e+00
3.11945707e-01 -1.94016963e-01 -7.82158196e-01 -8.31037089e-02
1.07692385e+00 7.84160733e-01 -6.08364522e-01 -2.10085019e-01
-3.49323571e-01 2.20313102e-01 -3.26454222e-01 2.63130724e-01
-1.06552196e+00 -6.71189874e-02 9.18691218e-01 2.42301717e-01
-2.96658635e-01 -2.14583233e-01 -5.42862952e-01 -5.01487195e-01
-5.05501032e-01 -7.15824783e-01 1.14435697e+00 -3.79191250e-01
-1.64821446e+00 1.71739757e-02 5.76548994e-01 -1.04491019e+00
-1.01164781e-01 -2.31020078e-02 -8.90511632e-01 8.97470474e-01
-1.23402965e+00 -4.59074944e-01 -3.85682076e-01 6.24217272e-01
2.18586862e-01 1.38027638e-01 9.01338577e-01 4.10076439e-01
-7.75730848e-01 -2.74704099e-01 7.61750937e-02 -1.50995344e-01
9.09883499e-01 -1.32283461e+00 5.49212217e-01 6.47424102e-01
-1.99275494e-01 9.04046714e-01 1.21922696e+00 -1.32297575e+00
-1.42084301e+00 -1.16190207e+00 9.89443541e-01 -6.08644247e-01
1.24989176e+00 -2.59681612e-01 -5.36920667e-01 6.46857262e-01
3.01555276e-01 -3.13602120e-01 8.17090869e-01 9.03201401e-01
-1.52447492e-01 5.16009424e-03 -7.95531869e-01 3.36178035e-01
1.52572083e+00 1.07246198e-01 -4.76443410e-01 5.24776936e-01
6.99058652e-01 3.52991104e-01 -1.27242529e+00 4.74976808e-01
3.46466571e-01 -7.58125722e-01 9.15713072e-01 -7.73925364e-01
6.75760686e-01 -5.18522382e-01 8.60972106e-02 -1.33145702e+00
-7.32902110e-01 -7.08060563e-01 -9.57437009e-02 1.47203624e+00
4.99574035e-01 -5.51764011e-01 2.33337983e-01 2.66844273e-01
2.18381107e-01 -1.01593800e-01 -7.80277789e-01 -8.53286028e-01
-2.45728433e-01 -5.21436632e-01 6.93659067e-01 1.51453614e+00
2.31773451e-01 7.05519021e-01 -2.61971116e-01 5.59304833e-01
1.05927813e+00 5.15935481e-01 7.68995702e-01 -1.83482146e+00
1.39640629e-01 -3.03427696e-01 -2.29604036e-01 -2.45108634e-01
-7.98090026e-02 -6.50483668e-01 -2.16548353e-01 -1.46996605e+00
5.24907947e-01 -9.47739959e-01 -1.28699750e-01 6.69724226e-01
-4.27188128e-01 -3.23846787e-01 -7.34348714e-01 5.80684304e-01
4.22985703e-02 2.52820849e-01 9.16463554e-01 -1.13724075e-01
-3.51809710e-01 5.27453572e-02 -5.18857658e-01 4.95135307e-01
7.30067492e-01 -9.18726206e-01 -7.05740869e-01 -3.27950791e-02
4.51945812e-01 2.58111358e-01 8.41434360e-01 -2.23332971e-01
1.95656955e-01 -6.82386994e-01 1.07788965e-01 -7.42602766e-01
-5.99529147e-01 -7.61034310e-01 7.77980745e-01 7.06700265e-01
-3.23040187e-01 1.65830344e-01 1.13556057e-01 9.11257684e-01
-2.02057600e-01 1.72095388e-01 2.93130457e-01 1.70404211e-01
-9.23530877e-01 3.14159468e-02 -4.04151261e-01 -2.24217162e-01
1.33652246e+00 3.63881171e-01 -5.38605750e-01 -3.24117362e-01
-7.04949737e-01 5.77307940e-01 -2.41747648e-01 7.70194888e-01
4.21912223e-01 -1.44126534e+00 -1.13623416e+00 -4.08169597e-01
2.40255386e-01 -6.27951562e-01 -7.00988844e-02 1.07672000e+00
1.78201035e-01 6.79112554e-01 -3.08401454e-02 -5.39924920e-01
-1.52335155e+00 8.62540781e-01 5.15715256e-02 -1.56034306e-01
-5.04007936e-01 5.89518547e-01 2.76000857e-01 -9.49535668e-02
-2.18331680e-01 8.88695344e-02 -2.46827781e-01 1.31449029e-01
4.81061667e-01 9.14960623e-01 -8.36948231e-02 1.81362703e-01
-8.79571319e-01 1.41033918e-01 4.78476077e-01 1.23553514e-01
1.54580486e+00 -2.44917065e-01 -6.58158541e-01 7.45781183e-01
7.88596869e-01 -1.16392458e-02 -7.37469137e-01 1.59599796e-01
4.38820809e-01 -4.88867462e-01 1.06417015e-01 -9.67899024e-01
-9.99512315e-01 3.03593695e-01 3.82125199e-01 9.34650958e-01
1.10293663e+00 4.46219504e-01 -1.61842406e-01 -4.41839695e-01
5.33838332e-01 -7.43666410e-01 -2.58710325e-01 8.62703845e-02
9.87701654e-01 -8.30604553e-01 1.85980543e-01 -1.22171712e+00
9.94145200e-02 1.11300266e+00 1.21165402e-01 -7.01405620e-03
9.75889325e-01 7.25902140e-01 -4.08769667e-01 -7.68977821e-01
-1.19286847e+00 -1.20510735e-01 1.15069866e-01 6.64591134e-01
9.49570715e-01 4.09968734e-01 -6.62706494e-01 3.34482819e-01
8.56236927e-03 2.77433861e-02 4.93761420e-01 8.03305209e-01
1.85094699e-02 -1.28981066e+00 -8.05637062e-01 6.27977371e-01
-3.77756476e-01 -1.93557888e-01 -8.80479395e-01 1.03851151e+00
2.81145200e-02 1.80178010e+00 1.89064831e-01 -4.97763783e-01
5.11323392e-01 -2.99799949e-01 1.63206488e-01 -4.55145836e-01
-1.58200756e-01 -9.67020169e-02 6.37064815e-01 -8.22189927e-01
-6.59832895e-01 -1.20724046e+00 -1.32248163e+00 -9.57755148e-01
-3.91501844e-01 2.08356157e-01 3.11370850e-01 7.30956137e-01
3.77414405e-01 1.14121294e+00 7.60402977e-01 4.41521406e-02
1.93986148e-01 -7.96736181e-01 -5.85182667e-01 8.38547200e-02
-2.00355649e-02 -1.26156294e+00 -5.21107912e-01 5.12983024e-01] | [7.835007667541504, 5.359640121459961] |
b7eb60d1-73fb-4995-9260-eeeb989762c1 | adaptive-knowledge-sharing-in-multi-task | null | null | https://aclanthology.org/P18-2104 | https://aclanthology.org/P18-2104.pdf | Adaptive Knowledge Sharing in Multi-Task Learning: Improving Low-Resource Neural Machine Translation | Neural Machine Translation (NMT) is notorious for its need for large amounts of bilingual data. An effective approach to compensate for this requirement is Multi-Task Learning (MTL) to leverage different linguistic resources as a source of inductive bias. Current MTL architectures are based on the Seq2Seq transduction, and (partially) share different components of the models among the tasks. However, this MTL approach often suffers from task interference and is not able to fully capture commonalities among subsets of tasks. We address this issue by extending the recurrent units with multiple {``}blocks{''} along with a trainable {``}routing network{''}. The routing network enables adaptive collaboration by dynamic sharing of blocks conditioned on the task at hand, input, and model state. Empirical evaluation of two low-resource translation tasks, English to Vietnamese and Farsi, show +1 BLEU score improvements compared to strong baselines. | ['Poorya Zaremoodi', 'Gholamreza Haffari', 'Wray Buntine'] | 2018-07-01 | null | null | null | acl-2018-7 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 3.43385667e-01 -1.49698019e-01 -3.67037654e-01 -4.28758085e-01
-1.42345977e+00 -8.91872466e-01 7.90892601e-01 -2.85107642e-01
-6.46617115e-01 1.03246808e+00 6.03570700e-01 -8.39666069e-01
3.11699450e-01 -2.20610470e-01 -9.61669624e-01 -4.47614551e-01
3.33509833e-01 8.35646808e-01 -1.07792318e-01 -4.06641096e-01
1.31583020e-01 8.17259848e-02 -7.30612040e-01 7.33237088e-01
9.56383169e-01 3.02008569e-01 5.19867480e-01 4.44471508e-01
-2.62399167e-01 5.76638103e-01 -5.45894265e-01 -4.89947498e-01
3.54353845e-01 -7.03614354e-01 -9.51814473e-01 -4.02147651e-01
3.99055600e-01 -1.11281097e-01 -1.05491288e-01 7.64203668e-01
7.39623547e-01 6.89003691e-02 6.04603171e-01 -8.01888168e-01
-6.76255107e-01 1.08854640e+00 -5.41133285e-01 4.11057234e-01
-3.05960830e-02 2.13538483e-01 1.23384416e+00 -1.16170120e+00
7.41303742e-01 1.17655659e+00 5.37984371e-01 7.60115385e-01
-1.58477330e+00 -7.63345420e-01 6.43690974e-02 -1.03091948e-01
-1.03842032e+00 -7.63220906e-01 4.53757226e-01 -3.15478206e-01
1.53385437e+00 -4.77279164e-02 1.83245465e-01 1.69559765e+00
3.97183567e-01 8.54479015e-01 1.29742301e+00 -4.27023560e-01
-1.77138537e-01 1.41431421e-01 -1.57726929e-01 4.23240781e-01
-6.59372807e-02 1.29126117e-01 -7.85046101e-01 -4.81995195e-03
7.36398935e-01 -2.71576107e-01 -4.77743298e-02 9.83933173e-03
-1.56137145e+00 6.47197366e-01 1.68883041e-01 5.92654467e-01
-2.76491463e-01 2.21925527e-01 6.13251686e-01 7.40331888e-01
6.17423475e-01 8.24533761e-01 -7.32069433e-01 -3.38049084e-01
-9.11155522e-01 -1.84427481e-02 7.04450250e-01 1.11402404e+00
9.00326312e-01 1.41303122e-01 -4.70208049e-01 9.94015276e-01
-7.32923299e-03 6.00058973e-01 5.97866893e-01 -6.59777999e-01
1.04073787e+00 2.55737722e-01 1.47132166e-02 -3.10861140e-01
-1.94524556e-01 -6.32518828e-01 -6.40113056e-01 -1.64137468e-01
4.17196810e-01 -4.75226998e-01 -9.34377670e-01 2.19686818e+00
-1.75184548e-01 -2.71599472e-01 2.28257999e-01 6.75087035e-01
3.53092670e-01 8.17139745e-01 1.37109935e-01 -2.15988651e-01
9.90112603e-01 -1.29385412e+00 -5.30483067e-01 -7.67573893e-01
9.98988390e-01 -1.02716446e+00 1.34036887e+00 -3.40396464e-02
-1.29052484e+00 -5.11904538e-01 -6.36683702e-01 -2.12861523e-01
-2.29559600e-01 1.88214913e-01 4.35537368e-01 3.31688076e-01
-1.26474285e+00 5.11286199e-01 -7.94413865e-01 -3.83394182e-01
8.02290663e-02 5.32204390e-01 -3.26707780e-01 -8.85167941e-02
-1.35488737e+00 1.43778551e+00 2.36140713e-01 1.85892776e-01
-9.81398582e-01 -5.78576446e-01 -7.17223287e-01 -1.39928833e-02
1.79724708e-01 -6.89347804e-01 1.44114745e+00 -1.43964434e+00
-1.58170950e+00 7.47623086e-01 -3.27637672e-01 -3.18300366e-01
4.42133933e-01 -3.47282767e-01 -1.59933120e-02 -3.88161480e-01
2.85974175e-01 6.70963228e-01 7.15979993e-01 -9.80277419e-01
-4.09535557e-01 -1.78253964e-01 -2.27988586e-01 5.35578430e-01
-2.54081130e-01 5.38590908e-01 -3.34984452e-01 -7.79430866e-01
-2.40738124e-01 -1.14833999e+00 -2.60555178e-01 -6.90618098e-01
-1.50823101e-01 -1.65945828e-01 3.34253639e-01 -9.23322022e-01
1.16848540e+00 -1.88347006e+00 6.70121193e-01 -1.66694999e-01
-3.19322884e-01 3.07258457e-01 -5.78529418e-01 6.82051420e-01
1.18864246e-01 2.40187183e-01 -1.90061092e-01 -4.52230483e-01
-1.11126728e-01 2.93837488e-01 -1.75787255e-01 9.12058130e-02
3.61405045e-01 1.14418089e+00 -9.45129812e-01 -4.07305598e-01
-1.36250451e-01 2.84982860e-01 -3.98524135e-01 1.44944340e-01
-4.53747094e-01 7.91164935e-01 -3.58332038e-01 4.33604151e-01
6.22591749e-02 -1.48116127e-01 4.61768359e-01 8.18001255e-02
-2.62122661e-01 9.85142350e-01 -6.68273747e-01 2.10761786e+00
-7.83108592e-01 5.47193050e-01 7.23293275e-02 -7.58170366e-01
7.59991705e-01 6.72144473e-01 2.35485002e-01 -7.52743006e-01
3.17432694e-02 7.36239851e-01 3.87493283e-01 -2.85229951e-01
3.19415838e-01 -3.39195609e-01 -2.59402245e-01 8.46148610e-01
3.04733455e-01 -1.49170440e-02 8.07053596e-02 1.22908112e-02
1.21335578e+00 6.08026505e-01 -7.68471602e-03 -4.36818987e-01
1.59785271e-01 2.18578279e-01 8.73460829e-01 6.24542773e-01
-1.09601216e-02 4.03371632e-01 2.69006848e-01 -4.20264155e-01
-1.30647969e+00 -8.51972401e-01 2.92364746e-01 1.52809036e+00
-3.91756654e-01 -2.92055607e-01 -6.42684877e-01 -7.19133794e-01
-2.74956405e-01 8.90119672e-01 -4.70248699e-01 -1.59908623e-01
-1.19495368e+00 -8.26084733e-01 7.66631782e-01 6.27160847e-01
3.63414079e-01 -1.24918652e+00 -2.61675686e-01 5.75889766e-01
-7.19359577e-01 -1.05929649e+00 -8.50630224e-01 5.10980904e-01
-8.84054720e-01 -2.76616871e-01 -7.01938868e-01 -7.75461435e-01
4.26255763e-01 2.72911519e-01 1.50245178e+00 -3.17906499e-01
3.37999791e-01 -3.01185250e-01 -1.54285476e-01 -2.79746145e-01
-7.24080503e-01 7.86191106e-01 1.03675440e-01 -1.55379817e-01
2.93706119e-01 -5.84469318e-01 -2.53979057e-01 3.41863871e-01
-6.07358396e-01 4.87666190e-01 1.16189516e+00 1.07224166e+00
4.52586025e-01 -7.88946629e-01 8.37693810e-01 -1.15208149e+00
5.97130179e-01 -3.81057352e-01 -2.95848697e-01 4.83299524e-01
-6.11445606e-01 2.77223647e-01 6.20554745e-01 -5.32474995e-01
-1.21003807e+00 -1.76151574e-01 9.41170752e-02 -3.23895276e-01
1.69982463e-01 5.21111131e-01 -1.86728224e-01 3.29643846e-01
6.50129914e-01 2.01065317e-01 -1.81728467e-01 -5.03437221e-01
3.82590026e-01 6.15913928e-01 2.35184282e-01 -7.77676165e-01
6.27214789e-01 -1.59346774e-01 -3.41099322e-01 -3.27456266e-01
-6.60212040e-01 -1.63067922e-01 -8.02544832e-01 8.35707337e-02
7.63209105e-01 -1.03715408e+00 -1.01439096e-01 2.63487726e-01
-1.43779361e+00 -8.39380682e-01 6.00355938e-02 6.82307601e-01
-5.51429629e-01 -1.24738030e-01 -9.54706252e-01 -4.05747712e-01
-6.31576955e-01 -1.41203272e+00 9.60408866e-01 -1.34483978e-01
-4.05666441e-01 -1.01187479e+00 1.36014506e-01 4.48576212e-01
7.84725785e-01 -2.34073341e-01 1.27150273e+00 -9.25323665e-01
-5.01281977e-01 2.01311663e-01 -1.67023331e-01 3.93775105e-01
1.44770846e-01 -3.78232241e-01 -7.90508866e-01 -5.37404120e-01
-1.39806509e-01 -5.31171560e-01 7.48382390e-01 1.61572143e-01
4.70632374e-01 -4.11616594e-01 -2.54445225e-01 3.85337085e-01
1.24069822e+00 1.59182996e-02 2.89628744e-01 2.27064520e-01
7.55298793e-01 5.93285501e-01 2.61774510e-01 -1.78339094e-01
4.70360398e-01 8.09996009e-01 -2.67126001e-02 -2.14442387e-01
-1.80547744e-01 -2.71848857e-01 9.70031083e-01 1.31245255e+00
-1.42193720e-01 -2.21644983e-01 -1.12006152e+00 4.87879544e-01
-1.70119536e+00 -7.73734093e-01 7.14905336e-02 2.18928432e+00
1.25581646e+00 1.97639421e-01 -4.05356623e-02 -5.67404807e-01
6.22970164e-01 7.95102865e-02 -4.91226286e-01 -8.56561065e-01
-2.02973157e-01 2.29875386e-01 5.79693079e-01 5.81738174e-01
-6.42538965e-01 1.55132747e+00 6.18590355e+00 6.22455478e-01
-1.14996135e+00 5.15413821e-01 3.56572151e-01 -2.32238725e-01
-4.39286947e-01 1.62869453e-01 -9.23006833e-01 3.67725551e-01
1.27361751e+00 -9.20706540e-02 5.51445067e-01 1.88603565e-01
2.12977991e-01 2.56102830e-01 -1.36624408e+00 3.65594596e-01
7.09981769e-02 -1.06430590e+00 2.68490940e-01 6.18139021e-02
8.62596035e-01 6.61789238e-01 3.43433022e-02 5.89365005e-01
7.75353551e-01 -9.44739103e-01 5.69432199e-01 1.28431246e-01
1.08461082e+00 -4.55290645e-01 5.58016598e-01 5.21453619e-01
-7.83841550e-01 7.54125565e-02 -2.18786031e-01 -4.21949252e-02
2.50261545e-01 7.59256035e-02 -9.68871772e-01 5.56055069e-01
3.82223517e-01 4.52827483e-01 -2.63884157e-01 3.44935954e-01
-3.50785285e-01 8.75456274e-01 -5.34149073e-02 5.01331082e-03
5.26726842e-01 -2.33827099e-01 4.96941984e-01 1.51320171e+00
2.66823590e-01 -2.73183584e-01 2.50953168e-01 5.89188159e-01
-3.90033871e-01 2.71591693e-01 -9.26147699e-01 -2.19184086e-01
3.80654752e-01 9.64632928e-01 -4.32172179e-01 -2.69945860e-01
-7.48424053e-01 1.28078687e+00 6.95540130e-01 5.36174893e-01
-6.43624067e-01 -2.72693634e-02 7.05335498e-01 -7.08788857e-02
-2.65687313e-02 -5.51200151e-01 -4.18336421e-01 -1.26648533e+00
-4.72777933e-02 -1.29765642e+00 1.41873822e-01 -5.80601394e-01
-1.12344122e+00 7.86214292e-01 -2.53840655e-01 -8.64051700e-01
-5.14567137e-01 -4.27730888e-01 -3.40232879e-01 1.40983415e+00
-1.37464011e+00 -1.62019074e+00 5.56358695e-01 4.23802584e-01
1.02668869e+00 -2.72148341e-01 1.02027988e+00 3.45314682e-01
-6.87006891e-01 7.21727550e-01 2.62163132e-01 1.76743180e-01
1.31964672e+00 -1.21207225e+00 8.35653424e-01 9.11961794e-01
2.28091255e-01 6.43926561e-01 5.81533790e-01 -6.95194721e-01
-1.53111446e+00 -1.09386015e+00 1.71809626e+00 -8.44259560e-01
6.70522988e-01 -7.25784481e-01 -8.42489839e-01 1.18915653e+00
4.90723163e-01 -5.08010745e-01 7.50720382e-01 4.24008012e-01
-5.95307887e-01 -6.21401239e-03 -5.81761897e-01 7.60114610e-01
1.18249094e+00 -7.39411950e-01 -6.38166130e-01 3.57152224e-01
8.22704971e-01 -3.63902479e-01 -6.17751122e-01 4.21414644e-01
4.74639267e-01 -4.44494396e-01 6.57418191e-01 -9.20669615e-01
6.55667484e-01 1.50314271e-01 -2.12544009e-01 -1.69956231e+00
-4.39852506e-01 -8.07969332e-01 3.85666370e-01 1.24656749e+00
1.16493797e+00 -5.38729310e-01 2.68949360e-01 3.74079943e-01
-4.53008890e-01 -6.53927505e-01 -9.57165778e-01 -7.66433060e-01
6.67366326e-01 -1.56688496e-01 3.18944931e-01 1.01374042e+00
-5.84519655e-02 1.13276160e+00 -6.33906066e-01 -9.78597850e-02
2.70650476e-01 4.41107228e-02 6.75631344e-01 -9.01396871e-01
-5.53833067e-01 -5.75900137e-01 4.73805189e-01 -9.39344525e-01
4.44029391e-01 -1.35598540e+00 2.30881661e-01 -1.53882635e+00
3.45155537e-01 -5.95476210e-01 -3.42310160e-01 7.64374852e-01
-2.79109776e-01 1.25170767e-01 1.57388046e-01 5.19540489e-01
-3.37192565e-01 3.33829582e-01 1.19632602e+00 -2.70661199e-03
-2.42183477e-01 -1.57645315e-01 -6.33519948e-01 3.09535891e-01
8.45658839e-01 -6.16584957e-01 -2.73102045e-01 -1.51951337e+00
4.48370904e-01 2.37923771e-01 -1.46156698e-01 -4.73793685e-01
2.48620525e-01 -2.37435907e-01 1.49144143e-01 -1.41695485e-01
1.87106252e-01 -3.80638957e-01 1.67918861e-01 3.70227635e-01
-7.20099807e-01 6.96979523e-01 2.93887049e-01 1.68559268e-01
-6.29437715e-02 4.26536985e-02 6.92241311e-01 -4.36048955e-01
-2.76921988e-01 1.83980495e-01 -5.63357472e-01 1.70683756e-01
4.45917457e-01 1.88240796e-01 -2.35637963e-01 -1.09226286e-01
-3.77721846e-01 4.26747799e-01 4.19660330e-01 6.02867067e-01
7.87531212e-02 -1.25987077e+00 -1.15241361e+00 1.38428807e-01
1.38972723e-03 -2.48566851e-01 -1.95664346e-01 9.11995113e-01
2.12977286e-02 5.77744544e-01 -3.52026314e-01 -2.45452568e-01
-9.66256022e-01 2.83673912e-01 3.77360731e-01 -8.16502631e-01
-3.13950956e-01 8.30264986e-01 3.19592021e-02 -6.70482934e-01
-7.49276057e-02 1.82682723e-02 2.05930695e-01 5.75682521e-02
1.03371330e-01 2.01713130e-01 2.81407505e-01 -6.25666678e-01
-1.91276610e-01 2.65579820e-01 -3.80084187e-01 -7.43578315e-01
1.11644328e+00 -1.89769536e-01 -1.83605567e-01 8.14642310e-01
9.53857958e-01 -1.30529240e-01 -1.10312831e+00 -6.76448226e-01
2.70055503e-01 3.08930129e-02 -2.44623080e-01 -1.19113624e+00
-6.09827280e-01 1.14925897e+00 3.07157170e-02 -5.15384793e-01
8.49057138e-01 -1.76304668e-01 7.87334561e-01 5.16256273e-01
5.86147666e-01 -1.11161816e+00 -7.04125082e-03 9.65638161e-01
8.30416977e-01 -1.26231003e+00 -5.02033889e-01 3.94669771e-02
-6.49792850e-01 8.03118885e-01 6.73764408e-01 3.95857729e-02
1.53209582e-01 2.38458365e-01 3.80939573e-01 2.01069877e-01
-1.20874441e+00 -4.36047614e-02 4.13192250e-02 2.03045502e-01
9.70326245e-01 8.20082054e-02 -6.17234707e-01 4.93318439e-01
6.64776042e-02 -1.82097182e-01 1.83768094e-01 9.05027568e-01
-1.23762608e-01 -1.50772858e+00 -3.64503153e-02 2.83759326e-01
-7.86618531e-01 -5.50022900e-01 -5.34322858e-01 5.76560915e-01
-9.11039487e-02 8.11404347e-01 -1.06293008e-01 -3.41813087e-01
2.22164363e-01 4.91341472e-01 5.13214111e-01 -9.77486253e-01
-1.26622772e+00 2.63171852e-01 2.48940319e-01 -3.91012460e-01
-1.32937953e-01 -8.06262076e-01 -9.18050826e-01 -1.39720947e-01
-1.50542796e-01 1.59093276e-01 6.67244017e-01 1.04664993e+00
4.77643102e-01 3.19305986e-01 6.17602050e-01 -7.54341781e-01
-9.68197465e-01 -1.37597752e+00 3.04375943e-02 2.99812078e-01
9.68859717e-02 -2.89334804e-01 -4.33427505e-02 1.58831552e-02] | [11.604574203491211, 10.188304901123047] |
35ed3768-7c3b-42b6-8bc9-f92b430650ca | learning-to-measure-the-point-cloud | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Huang_Learning_To_Measure_the_Point_Cloud_Reconstruction_Loss_in_a_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Huang_Learning_To_Measure_the_Point_Cloud_Reconstruction_Loss_in_a_CVPR_2023_paper.pdf | Learning To Measure the Point Cloud Reconstruction Loss in a Representation Space | For point cloud reconstruction-related tasks, the reconstruction losses to evaluate the shape differences between reconstructed results and the ground truths are typically used to train the task networks. Most existing works measure the training loss with point-to-point distance, which may introduce extra defects as predefined matching rules may deviate from the real shape differences. Although some learning-based works have been proposed to overcome the weaknesses of manually-defined rules, they still measure the shape differences in 3D Euclidean space, which may limit their ability to capture defects in reconstructed shapes. In this work, we propose a learning-based Contrastive Adversarial Loss (CALoss) to measure the point cloud reconstruction loss dynamically in a non-linear representation space by combining the contrastive constraint with the adversarial strategy. Specifically, we use the contrastive constraint to help CALoss learn a representation space with shape similarity, while we introduce the adversarial strategy to help CALoss mine differences between reconstructed results and ground truths. According to experiments on reconstruction-related tasks, CALoss can help task networks improve reconstruction performances and learn more representative representations. | ['Yong liu', 'Chengjie Wang', 'Mingang Chen', 'Zhenyu Zhang', 'Ying Tai', 'Jiangning Zhang', 'Zhonggan Ding', 'Tianxin Huang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['point-cloud-reconstruction'] | ['computer-vision'] | [ 3.71617936e-02 -6.02239966e-02 1.93011731e-01 -4.21879351e-01
-7.36421347e-01 -3.54094863e-01 3.84532958e-01 -8.66338387e-02
-2.13088036e-01 4.01345313e-01 -2.48491302e-01 8.12393799e-02
-2.27338523e-01 -1.21403074e+00 -1.07414186e+00 -6.18026137e-01
1.69313908e-01 5.57156622e-01 3.52274060e-01 -3.09400201e-01
2.80703098e-01 8.68676066e-01 -1.23208964e+00 2.59886891e-01
1.10931528e+00 1.23665357e+00 1.48198992e-01 -2.76019592e-02
-3.47333580e-01 4.45061594e-01 -5.82769632e-01 -4.29482430e-01
6.38817489e-01 -1.70368895e-01 -3.92859399e-01 -1.48247704e-01
4.31282967e-01 -1.26564920e-01 -5.60994864e-01 1.27562046e+00
5.79057693e-01 8.42515901e-02 8.30144048e-01 -1.26316428e+00
-8.41276765e-01 8.64864439e-02 -5.19034088e-01 -3.31430957e-02
3.49169254e-01 1.40284523e-01 5.90484560e-01 -1.02845562e+00
4.93721396e-01 1.45150900e+00 1.07660961e+00 3.40434849e-01
-9.99210835e-01 -8.51318896e-01 1.91343613e-02 1.18216552e-01
-1.48698235e+00 -1.66333362e-01 1.33124506e+00 -2.55858213e-01
5.42980433e-01 1.91818208e-01 5.14176130e-01 7.97703505e-01
1.77147329e-01 6.69063628e-01 7.88548470e-01 -1.57426313e-01
-4.20865826e-02 2.99189836e-02 -5.29928386e-01 6.33567989e-01
8.00914690e-02 3.98954213e-01 2.36398596e-02 1.49419736e-02
1.27508974e+00 3.86504233e-01 -6.33835137e-01 -7.37920105e-01
-9.96442795e-01 9.05186832e-01 1.12924647e+00 4.28495377e-01
-2.28872791e-01 2.17989255e-02 3.18472922e-01 5.73810041e-01
5.51348388e-01 4.84084845e-01 -2.64909774e-01 2.88070381e-01
-6.76842570e-01 2.03129321e-01 2.96299279e-01 9.11828518e-01
7.88739264e-01 3.62111121e-01 -2.08675131e-01 1.12389457e+00
4.23331916e-01 5.09363115e-01 5.82575798e-01 -8.89291763e-01
6.15641594e-01 8.30859423e-01 -1.42216623e-01 -1.45288241e+00
-1.19278096e-01 -5.74403048e-01 -9.90687609e-01 6.02286696e-01
2.94861883e-01 3.30305427e-01 -7.85442591e-01 1.52165473e+00
2.23427385e-01 3.99494082e-01 -8.63476917e-02 1.10982275e+00
9.85774338e-01 5.56346476e-01 -3.50858897e-01 6.76338002e-02
6.83809996e-01 -7.53383040e-01 -3.89059842e-01 8.13287571e-02
2.33649686e-01 -7.83085287e-01 1.29925299e+00 2.26780713e-01
-1.30686486e+00 -7.38644063e-01 -1.17366743e+00 1.27121314e-01
-1.07427903e-01 -1.85087010e-01 2.03428209e-01 3.13147753e-01
-6.10673249e-01 1.04324448e+00 -7.26221919e-01 1.20385751e-01
7.41381705e-01 4.76643071e-02 -3.24668229e-01 -3.87690544e-01
-1.02434254e+00 8.69659841e-01 1.82274148e-01 9.87745449e-02
-7.92293131e-01 -8.85869205e-01 -7.67502844e-01 -5.30678853e-02
1.32791683e-01 -5.30957401e-01 6.82660937e-01 -1.04598737e+00
-1.38007498e+00 8.79831314e-01 5.15882552e-01 -2.25030199e-01
8.89455795e-01 1.01309076e-01 -3.48197132e-01 3.77708226e-02
-3.21920216e-02 5.44334948e-01 9.89152670e-01 -1.64382088e+00
-1.48597717e-01 -4.72884178e-01 7.77635872e-02 1.80512577e-01
-5.50234392e-02 -4.66723561e-01 -3.99170369e-01 -1.06240439e+00
6.20378852e-01 -6.23825669e-01 -1.13851093e-01 6.82892680e-01
-1.63968891e-01 -4.42083143e-02 9.45178270e-01 -5.13591290e-01
5.01476645e-01 -2.27437639e+00 -3.72651964e-03 2.20878348e-01
-8.47959053e-03 3.11629891e-01 -3.89962971e-01 2.70896647e-02
-1.33947313e-01 1.07889295e-01 -6.09177232e-01 -3.06821942e-01
-1.24292538e-01 4.60236758e-01 -3.02914172e-01 5.71794033e-01
3.67922097e-01 9.17658448e-01 -9.08805668e-01 -3.66335422e-01
3.22477579e-01 6.67848289e-01 -4.12671626e-01 3.06687444e-01
-1.05333105e-01 4.79985237e-01 -5.06763935e-01 5.21535397e-01
1.23524880e+00 2.17171684e-01 -5.82394123e-01 -3.31304669e-01
3.64298314e-01 -1.14022128e-01 -1.17250657e+00 1.85881436e+00
-6.88535750e-01 2.83875912e-01 1.09163433e-01 -1.26550734e+00
1.37222528e+00 7.35469386e-02 5.83798707e-01 -8.47769618e-01
2.23578531e-02 2.87231028e-01 -1.33831128e-01 -1.89725131e-01
8.89665261e-02 -6.06637716e-01 2.42473245e-01 -5.54823466e-02
-1.64743230e-01 -6.37096465e-01 -7.10071802e-01 -3.34639043e-01
9.72603858e-01 2.82634497e-01 -2.93643773e-02 1.21225178e-01
6.77032053e-01 -1.87198460e-01 8.32050264e-01 3.96484107e-01
-1.69863611e-01 1.26064658e+00 1.72427043e-01 -6.70299053e-01
-1.08070278e+00 -1.49019754e+00 -4.22027797e-01 3.18206161e-01
4.72897202e-01 1.57345027e-01 -4.31055963e-01 -1.06167936e+00
2.24681720e-01 6.38714612e-01 -3.87946427e-01 -6.74662828e-01
-7.37326384e-01 -1.27919465e-01 5.89964688e-01 5.96283793e-01
8.37253392e-01 -1.18063259e+00 -1.62341103e-01 1.18087374e-01
-8.18146914e-02 -8.68702769e-01 -5.67190647e-01 -2.40053669e-01
-1.09187245e+00 -1.15489578e+00 -9.98038113e-01 -8.14612806e-01
9.43816960e-01 2.07946196e-01 1.05434775e+00 3.06432098e-01
-8.85529146e-02 3.98474932e-01 -4.02162164e-01 -4.46932286e-01
-4.37585026e-01 -3.73937190e-01 -3.21805745e-01 -2.15017609e-02
9.82192531e-03 -8.57076168e-01 -5.74866295e-01 6.38473928e-01
-1.09670579e+00 -2.30084732e-01 4.99088407e-01 8.56456995e-01
9.22520041e-01 2.53712624e-01 5.71223497e-01 -6.31833017e-01
6.13186657e-01 -3.64646524e-01 -3.68460298e-01 2.38230914e-01
-6.41379118e-01 1.59377217e-01 8.80334318e-01 -5.79117835e-01
-8.06651056e-01 -1.29555494e-01 -4.61271524e-01 -1.30054235e+00
7.52747804e-02 3.15184236e-01 -3.51511687e-01 -4.74692404e-01
6.76440597e-01 4.42304432e-01 1.69385776e-01 -4.29435939e-01
5.55982552e-02 2.09115013e-01 4.88143235e-01 -7.52072036e-01
1.14656293e+00 5.63364148e-01 1.54362023e-01 -2.73195982e-01
-5.59041381e-01 -2.05894053e-01 -4.78917956e-01 -1.36294737e-01
4.57090765e-01 -7.27326691e-01 -4.18142527e-01 3.95868927e-01
-1.26674378e+00 -4.82013933e-02 -7.73227930e-01 3.68515491e-01
-8.01610172e-01 6.81666195e-01 -4.06044155e-01 -5.35973966e-01
-4.61571872e-01 -1.12123179e+00 1.10850370e+00 -2.63891220e-02
4.72446293e-01 -8.72040987e-01 -3.33937183e-02 9.49920267e-02
3.83012086e-01 7.42152035e-01 8.94410074e-01 -2.75025159e-01
-5.11919618e-01 -3.69704902e-01 -3.58030438e-01 8.33875000e-01
1.59395486e-01 -2.85271913e-01 -7.00173020e-01 -4.19069201e-01
3.90007287e-01 -2.86690533e-01 7.94890106e-01 2.10290074e-01
1.59571826e+00 -2.39758581e-01 -1.26323149e-01 9.40601528e-01
1.47467184e+00 2.04527602e-01 1.09518015e+00 2.40634069e-01
7.43281662e-01 3.95074159e-01 5.87098956e-01 1.19925857e-01
7.62768239e-02 7.66321301e-01 7.17073441e-01 -2.47083586e-02
-3.26803833e-01 -5.58887959e-01 1.32429495e-01 8.20435107e-01
-2.09255949e-01 2.46335845e-02 -6.91933632e-01 2.96682239e-01
-1.69655037e+00 -7.50001132e-01 5.04993349e-02 2.45213962e+00
5.91070950e-01 3.74091268e-01 -2.35780001e-01 2.73420215e-01
6.87581778e-01 1.51499897e-01 -7.46513128e-01 -1.00341626e-01
-1.51461601e-01 2.53631115e-01 3.17475259e-01 2.34056562e-01
-7.89448917e-01 6.76753461e-01 5.29614353e+00 1.22260845e+00
-1.31586742e+00 1.79532945e-01 2.72933602e-01 1.49904862e-01
-7.42570698e-01 -2.41372824e-01 -8.32502171e-02 5.79681575e-01
1.86883986e-01 3.40420082e-02 2.84647733e-01 1.01091588e+00
-1.04502939e-01 5.63308120e-01 -1.01461959e+00 1.36791003e+00
4.76795509e-02 -1.18906510e+00 4.28332359e-01 -7.32961595e-02
6.56521499e-01 -9.06433761e-02 1.41638979e-01 4.58420366e-01
4.76538436e-03 -1.16690683e+00 6.75248861e-01 9.23909068e-01
7.97776878e-01 -8.21723700e-01 8.43314469e-01 5.07812977e-01
-1.25852287e+00 6.93742335e-02 -9.25512731e-01 3.58119398e-01
-2.84277275e-02 5.95135748e-01 -5.84264576e-01 9.66845393e-01
6.22628212e-01 8.97052884e-01 -2.98271388e-01 1.27926040e+00
-1.35364667e-01 2.24645689e-01 -2.29788736e-01 3.62215728e-01
1.82052895e-01 -4.84182268e-01 7.36616671e-01 7.21061349e-01
5.65510333e-01 -9.16095302e-02 2.04657897e-01 1.30029762e+00
-1.85349822e-01 6.93510547e-02 -8.60030949e-01 3.36718947e-01
3.50711972e-01 8.30916584e-01 -4.49007213e-01 1.49729803e-01
-1.59110293e-01 1.04349077e+00 2.98475862e-01 1.84304222e-01
-8.14089715e-01 -3.57288837e-01 5.56143582e-01 5.72896719e-01
1.36897847e-01 -1.77450001e-01 -5.02844334e-01 -1.00813437e+00
3.41538489e-01 -7.53360510e-01 1.43672213e-01 -8.81889641e-01
-1.71380937e+00 6.40437841e-01 -1.77389920e-01 -1.81763029e+00
2.03327820e-01 -3.51737797e-01 -1.02764368e+00 7.60704756e-01
-1.63717937e+00 -1.02821863e+00 -5.20012736e-01 7.37460017e-01
5.66579223e-01 -3.64996493e-01 4.55757499e-01 5.86646438e-01
-2.27210894e-01 8.68950725e-01 5.32277413e-02 2.53465950e-01
5.53362548e-01 -9.17297482e-01 1.96151391e-01 4.73494023e-01
2.89205108e-02 2.73257434e-01 3.95635307e-01 -4.32689726e-01
-1.13258195e+00 -1.21038675e+00 8.16369876e-02 -3.23684394e-01
6.07214344e-04 -9.45141073e-03 -1.45662332e+00 3.49917293e-01
-3.72220427e-01 6.70497537e-01 1.24421669e-02 -4.40472692e-01
-4.91069674e-01 -3.47555667e-01 -1.70564866e+00 2.99637049e-01
1.33356357e+00 -4.03535903e-01 -6.92278981e-01 2.23308653e-01
7.90849268e-01 -5.27917266e-01 -1.02834809e+00 9.05278504e-01
3.78548741e-01 -9.07070637e-01 1.40511680e+00 -4.14829582e-01
6.16496563e-01 -3.91188473e-01 -2.49312624e-01 -1.45857954e+00
-3.06068897e-01 6.36355430e-02 1.18388891e-01 1.12219381e+00
5.94828092e-02 -7.12972522e-01 8.59123528e-01 1.96528822e-01
-4.25209016e-01 -9.96225893e-01 -1.17768216e+00 -1.15560508e+00
5.65612435e-01 -4.32307392e-01 8.89323771e-01 1.06807494e+00
-6.36339903e-01 -1.14956453e-01 -5.35553321e-02 1.86472088e-01
6.14892304e-01 9.12240222e-02 6.49864495e-01 -1.33446932e+00
-1.25373214e-01 -6.10796213e-01 -8.10590863e-01 -1.09014714e+00
2.79498935e-01 -1.25744927e+00 -1.42301992e-02 -1.61429095e+00
-2.62099534e-01 -9.55174148e-01 -3.76040429e-01 1.96373165e-01
6.41124472e-02 8.64536166e-02 3.09660643e-01 4.24345374e-01
-1.46156862e-01 1.19068658e+00 1.79457641e+00 -6.02358282e-01
-5.29387444e-02 1.48487210e-01 -4.66357559e-01 7.56717920e-01
5.89455485e-01 -7.65878022e-01 -5.55193543e-01 -5.96771300e-01
5.31183220e-02 2.17623152e-02 4.73407358e-01 -1.28032088e+00
4.38852794e-02 -1.08743243e-01 5.28125286e-01 -5.48142016e-01
4.84242737e-01 -1.18043017e+00 2.44184345e-01 3.97286743e-01
-5.42573221e-02 -1.59799129e-01 7.97906220e-02 7.17549205e-01
-5.09014487e-01 -3.58635873e-01 1.11577654e+00 -2.74381012e-01
-3.37721109e-01 6.83873117e-01 4.79223132e-01 7.75604397e-02
9.43302095e-01 -6.27547622e-01 6.05094694e-02 -3.65196496e-01
-4.26710159e-01 4.13280874e-02 6.77305520e-01 3.91170561e-01
1.26754797e+00 -1.83769321e+00 -8.25144589e-01 3.83333713e-01
1.71888560e-01 5.31795144e-01 2.29853526e-01 4.02626067e-01
-7.04210818e-01 -1.08266428e-01 -4.08547282e-01 -7.20160842e-01
-7.48595059e-01 6.86995864e-01 7.58850515e-01 -2.78083235e-01
-8.59581470e-01 7.78782070e-01 2.79319286e-01 -9.62405145e-01
2.58642763e-01 -4.15663540e-01 -1.20735979e-02 -4.46692526e-01
1.61611304e-01 3.67719680e-01 1.97473392e-01 -4.30311710e-01
-2.08608329e-01 9.39691007e-01 2.79672295e-01 2.60746241e-01
1.25562465e+00 2.18140274e-01 1.86275199e-01 2.61922836e-01
1.46643007e+00 -5.10967411e-02 -1.46943974e+00 -4.90842998e-01
-3.62402469e-01 -8.04391861e-01 2.02514470e-01 -5.37054121e-01
-1.61092710e+00 8.98870707e-01 9.36979532e-01 1.05410717e-01
1.07355177e+00 -1.44857764e-01 1.09284031e+00 2.65898556e-01
6.18624389e-01 -7.78052151e-01 3.58844042e-01 3.20926577e-01
1.66536415e+00 -1.33185041e+00 -4.35641222e-02 -4.07436341e-01
-5.43722570e-01 1.07632077e+00 6.61464155e-01 -6.33049071e-01
8.80452991e-01 1.22816181e-02 -7.88193792e-02 -2.04800591e-01
-5.16322218e-02 8.87121037e-02 4.82893348e-01 9.04650509e-01
8.07290711e-03 -7.61051401e-02 -2.70922005e-01 5.93499482e-01
-1.37300372e-01 -1.39686570e-01 6.68935403e-02 5.90120614e-01
-3.37834954e-01 -1.11182022e+00 -5.49428999e-01 4.13556993e-01
3.13863046e-02 3.14449698e-01 -2.61713237e-01 7.36662865e-01
3.95161748e-01 6.19137049e-01 2.01393500e-01 -4.94656384e-01
9.59025502e-01 -2.83715814e-01 6.06076181e-01 -4.18213516e-01
-5.20609975e-01 -2.91162044e-01 -4.81081694e-01 -4.34949160e-01
-2.27450699e-01 -3.79136294e-01 -1.41385925e+00 -2.89125532e-01
-2.87080944e-01 -3.93845774e-02 4.70815033e-01 5.57052672e-01
2.35177532e-01 5.51147878e-01 1.08693326e+00 -8.57156634e-01
-7.07794070e-01 -8.42135727e-01 -5.49209237e-01 8.96184444e-01
1.08428076e-01 -9.88025784e-01 -4.56406206e-01 -4.85621601e-01] | [8.379057884216309, -3.4644813537597656] |
5d6f450a-3f0b-4148-937e-964d4a1311d7 | multi-target-regression-via-output-space | 2003.09896 | null | https://arxiv.org/abs/2003.09896v1 | https://arxiv.org/pdf/2003.09896v1.pdf | Multi-target regression via output space quantization | Multi-target regression is concerned with the prediction of multiple continuous target variables using a shared set of predictors. Two key challenges in multi-target regression are: (a) modelling target dependencies and (b) scalability to large output spaces. In this paper, a new multi-target regression method is proposed that tries to jointly address these challenges via a novel problem transformation approach. The proposed method, called MRQ, is based on the idea of quantizing the output space in order to transform the multiple continuous targets into one or more discrete ones. Learning on the transformed output space naturally enables modeling of target dependencies while the quantization strategy can be flexibly parameterized to control the trade-off between prediction accuracy and computational efficiency. Experiments on a large collection of benchmark datasets show that MRQ is both highly scalable and also competitive with the state-of-the-art in terms of accuracy. In particular, an ensemble version of MRQ obtains the best overall accuracy, while being an order of magnitude faster than the runner up method. | ['Ioannis Vlahavas', 'Konstantinos Sechidis', 'Eleftherios Spyromitros-Xioufis'] | 2020-03-22 | null | null | null | null | ['multi-target-regression'] | ['miscellaneous'] | [ 4.98896807e-01 -1.71526670e-01 -4.50012565e-01 -4.92259324e-01
-1.39773023e+00 -2.99529463e-01 6.46045446e-01 2.84586579e-01
-1.21160783e-01 7.19763696e-01 -1.76423401e-01 -7.68340379e-02
-4.00938839e-01 -7.94403613e-01 -5.21916509e-01 -9.08499658e-01
1.70374848e-02 7.35286534e-01 6.87370673e-02 -2.10111976e-01
1.83327496e-01 3.38090986e-01 -1.51909840e+00 1.59508914e-01
9.88614500e-01 1.21755958e+00 -4.94955806e-03 6.60694838e-01
1.68076858e-01 7.58039594e-01 -5.09624183e-01 -2.86323547e-01
2.04728946e-01 -4.09792334e-01 -3.60561103e-01 -2.51403928e-01
3.11546326e-01 4.08166379e-01 2.86161631e-01 8.16363335e-01
3.42233539e-01 1.48589388e-01 7.83090532e-01 -1.46690309e+00
-4.97456968e-01 3.70017529e-01 -4.85782504e-01 1.19257346e-01
-1.32674277e-01 -2.17555776e-01 1.34952211e+00 -9.21063006e-01
7.15735927e-02 1.35249734e+00 4.15883124e-01 1.62808374e-01
-1.82791972e+00 -7.72501171e-01 1.65417701e-01 5.60186766e-02
-1.50449502e+00 -2.59955704e-01 5.77298462e-01 -6.63549483e-01
9.27570760e-01 5.83430052e-01 9.23976153e-02 8.93766463e-01
5.13309896e-01 4.33195770e-01 1.32753909e+00 -4.92558569e-01
2.75454104e-01 2.02610090e-01 2.27263883e-01 3.99982244e-01
-1.11610360e-01 2.23628551e-01 -6.92356586e-01 -4.24366355e-01
5.70821524e-01 1.09804403e-02 -7.92522058e-02 -5.40249109e-01
-1.27175772e+00 1.24086106e+00 2.20251977e-01 2.29306117e-01
-2.95337558e-01 1.61977693e-01 3.54009688e-01 5.38232088e-01
7.23507464e-01 6.07358813e-01 -7.82921493e-01 1.17521510e-01
-8.21750998e-01 1.71704888e-01 7.41463542e-01 7.82386124e-01
7.56086946e-01 -4.33465056e-02 -5.21773577e-01 9.84039307e-01
1.26112044e-01 4.37090009e-01 4.91583675e-01 -4.18658286e-01
8.08335364e-01 6.99480057e-01 2.04111859e-01 -9.03722167e-01
-4.92516786e-01 -4.76581573e-01 -9.97752428e-01 2.57153362e-01
2.98145205e-01 -4.43199389e-02 -7.36640871e-01 1.66777885e+00
4.15606290e-01 2.21551314e-01 8.02856758e-02 6.29573345e-01
3.85905564e-01 7.28294611e-01 3.46834809e-01 -4.63747770e-01
1.07465529e+00 -1.06533742e+00 -4.51957554e-01 -3.04831296e-01
6.89516664e-01 -5.91629863e-01 8.00511062e-01 5.36880076e-01
-6.01266086e-01 -6.94880962e-01 -1.05489433e+00 1.63146600e-01
-3.14434618e-01 4.32707548e-01 4.80255395e-01 6.58736289e-01
-7.33936548e-01 6.78908587e-01 -8.68431330e-01 -2.67362986e-02
3.08913868e-02 8.06376994e-01 -3.88098389e-01 1.75228100e-02
-1.12371504e+00 1.06972694e+00 4.12809461e-01 -1.69972911e-01
-5.66056013e-01 -8.66570413e-01 -8.33507776e-01 9.07734782e-02
4.37729746e-01 -3.99006635e-01 9.73849118e-01 -1.02312636e+00
-1.65407789e+00 3.71014863e-01 -1.87332526e-01 -2.41883919e-01
3.51093650e-01 -3.19390237e-01 -5.12053311e-01 -4.43040788e-01
1.25592351e-01 2.88091213e-01 9.93182600e-01 -8.04734707e-01
-9.95382667e-01 -6.29277945e-01 -3.99094552e-01 1.91448286e-01
-5.80590546e-01 2.30840743e-01 -3.56624126e-01 -6.87376678e-01
-2.22566918e-01 -9.34565246e-01 -6.24936104e-01 -4.47652012e-01
-3.31205875e-01 -3.78448963e-01 7.05448508e-01 -4.90548551e-01
1.46224082e+00 -1.89296198e+00 7.49842763e-01 3.22519243e-01
1.70467228e-01 9.98952761e-02 -3.43075812e-01 4.17251170e-01
-2.96585679e-01 -1.79344844e-02 -1.57786250e-01 -3.88752103e-01
-2.34394625e-01 1.24212027e-01 -2.96275765e-01 5.69252431e-01
5.38808405e-01 8.57962072e-01 -8.43653977e-01 -3.64343435e-01
1.52695209e-01 5.15768826e-01 -2.33211413e-01 3.47027600e-01
-2.33729050e-01 7.60919392e-01 -6.14594281e-01 7.06132233e-01
4.50351298e-01 -3.30810249e-01 3.16166639e-01 -2.84084398e-02
-1.38048440e-01 2.35671893e-01 -1.19289386e+00 1.24286735e+00
-6.07873261e-01 2.43530273e-01 -1.80815354e-01 -1.21089387e+00
1.45350516e+00 3.03602666e-01 6.25996053e-01 -5.11830449e-01
1.13252243e-02 2.93723196e-01 -7.81838074e-02 2.62425132e-02
3.69413912e-01 -1.33757457e-01 -4.31913704e-01 9.16863009e-02
-8.90415534e-02 1.35191217e-01 1.27216429e-01 -4.98759925e-01
1.12891495e+00 6.09653443e-02 6.41253054e-01 -2.53114223e-01
6.64336562e-01 4.61935475e-02 8.68789434e-01 5.66376269e-01
9.07151327e-02 2.02493891e-01 6.26760542e-01 -4.72739398e-01
-8.77428234e-01 -7.50723004e-01 -5.91372624e-02 1.36133182e+00
-1.92751765e-01 -5.05577683e-01 -2.69178659e-01 -6.58117771e-01
2.26822808e-01 8.01429093e-01 -8.99983227e-01 -1.31831691e-01
-7.65471578e-01 -9.54503417e-01 2.32461572e-01 5.85372925e-01
-3.46137248e-02 -7.26884246e-01 -5.71327746e-01 5.40423095e-01
-1.32153675e-01 -1.03106678e+00 -3.53416443e-01 6.99908674e-01
-1.01246440e+00 -9.75723386e-01 -4.65662390e-01 -4.96871740e-01
3.58184010e-01 -7.22648054e-02 1.10856438e+00 -3.03393960e-01
-7.62355402e-02 -1.85337603e-01 -4.28106844e-01 -3.69550020e-01
-6.30552053e-01 3.35758328e-01 -9.01919529e-02 2.74921924e-01
3.75470161e-01 -6.12797618e-01 -5.50146364e-02 4.98465449e-01
-7.54746675e-01 -4.96792002e-03 8.97491872e-01 1.10423934e+00
9.62609351e-01 4.08906192e-02 6.69488847e-01 -9.97053564e-01
5.07188380e-01 -6.85022593e-01 -8.76780212e-01 5.86118281e-01
-9.41544175e-01 3.55228990e-01 1.09674549e+00 -6.95092797e-01
-5.66544175e-01 4.15540755e-01 3.37624401e-01 -6.20212436e-01
2.38790348e-01 5.51470995e-01 -2.37773865e-01 -9.29083377e-02
5.49107492e-01 1.55088782e-01 -1.06757022e-01 -5.41796684e-01
4.53497678e-01 5.20570338e-01 2.22941890e-01 -4.61524218e-01
8.12795699e-01 -8.01370889e-02 4.60353822e-01 -4.74384606e-01
-6.80309772e-01 -4.88620877e-01 -9.79525983e-01 1.38082311e-01
7.03008950e-01 -7.93794155e-01 -4.18864101e-01 2.99013883e-01
-8.91472459e-01 -3.65893751e-01 -3.15417051e-02 3.94926280e-01
-7.22590148e-01 -1.24834947e-01 -3.21838945e-01 -5.31555891e-01
-4.40612316e-01 -1.29073203e+00 1.14512944e+00 -9.16188359e-02
-1.71407193e-01 -8.86238933e-01 2.35347778e-01 2.81560928e-01
3.34845901e-01 5.63155890e-01 1.15525436e+00 -1.03246284e+00
-3.53591502e-01 -2.50375330e-01 -7.68933818e-02 2.58688331e-01
3.01379591e-01 -6.48439303e-02 -5.73774099e-01 -3.84574145e-01
-1.68680742e-01 -3.82244319e-01 6.98317170e-01 2.58617431e-01
9.89577532e-01 -2.54441231e-01 -5.15019119e-01 4.65020031e-01
1.56204021e+00 1.06089488e-01 4.22370344e-01 2.55152017e-01
6.44959033e-01 3.89361531e-01 1.26006210e+00 6.07687473e-01
4.25864965e-01 1.19046009e+00 3.66463959e-01 -6.04372583e-02
3.08043957e-01 -6.18428439e-02 3.68011445e-01 6.32403851e-01
5.80475442e-02 -5.59489280e-02 -9.69080806e-01 2.40027383e-01
-2.03932929e+00 -5.64870894e-01 -9.85525176e-02 2.50080538e+00
8.31375659e-01 -5.14177307e-02 2.36531749e-01 1.99573234e-01
6.33455157e-01 -7.88696781e-02 -5.24184525e-01 -5.40204763e-01
6.83237910e-02 4.63992745e-01 7.20840991e-01 4.53072637e-01
-1.42757821e+00 8.94186795e-01 6.24403334e+00 8.70013177e-01
-1.29426765e+00 1.81409359e-01 8.33862662e-01 -3.90745550e-02
1.52802095e-01 -1.43385768e-01 -1.33110893e+00 3.46760571e-01
1.34674478e+00 -2.51247525e-01 3.07874173e-01 9.35377836e-01
5.93903176e-02 2.16005757e-01 -1.40982616e+00 6.49128139e-01
2.09335797e-03 -9.78201866e-01 -9.80628282e-02 3.01333163e-02
6.01855755e-01 -2.15175785e-02 9.57048312e-02 5.57050288e-01
4.48351383e-01 -1.38245666e+00 5.98749638e-01 5.30756295e-01
8.97014439e-01 -8.40490937e-01 4.76964712e-01 4.99101967e-01
-1.42324626e+00 -3.90705079e-01 -2.47635260e-01 8.57480913e-02
-3.16218257e-01 4.74495232e-01 -6.68459117e-01 6.70363784e-01
6.53801560e-01 9.07320499e-01 -6.11456037e-01 7.02946186e-01
-6.44681230e-02 5.23852825e-01 -2.72211820e-01 -1.66619383e-02
3.69806625e-02 -2.95198917e-01 2.93348193e-01 1.29354656e+00
4.23367769e-01 -8.74852687e-02 5.14472842e-01 5.54456472e-01
2.32752353e-01 4.36390847e-01 -3.82178664e-01 1.68073267e-01
5.69390953e-01 1.24585044e+00 -3.91535550e-01 -1.03058748e-01
-4.55504715e-01 7.08849430e-01 7.44917512e-01 7.84627274e-02
-9.82160091e-01 -8.26314241e-02 7.01290846e-01 -2.52297997e-01
7.57429600e-01 -8.91138315e-02 -4.59344119e-01 -8.58948946e-01
6.53192177e-02 -1.17272055e+00 6.29811764e-01 -1.68004856e-01
-1.33155644e+00 7.63078988e-01 -8.82329494e-02 -1.44662738e+00
-3.56423765e-01 -5.49303532e-01 -2.69577980e-01 1.13504374e+00
-1.58966482e+00 -1.30319476e+00 -1.61189348e-01 6.11550868e-01
5.45296073e-01 -1.67732790e-01 1.23053229e+00 2.19667107e-01
-7.91156650e-01 7.97055781e-01 5.01029432e-01 -3.20658863e-01
8.04131448e-01 -1.23122132e+00 2.65299022e-01 5.44959962e-01
1.02309845e-02 1.57038048e-01 5.46126902e-01 -4.80953991e-01
-1.40154743e+00 -1.56380427e+00 8.54608655e-01 -5.65945506e-01
6.90945327e-01 -3.40393424e-01 -8.03506076e-01 7.04471350e-01
-4.32764798e-01 2.42065281e-01 9.59261179e-01 2.89029866e-01
-6.58027053e-01 -4.69923556e-01 -9.04902160e-01 3.14477652e-01
2.63382316e-01 -3.24627757e-02 -2.27558956e-01 2.93205231e-01
6.04556620e-01 -3.22956234e-01 -1.44949222e+00 6.45615816e-01
4.07129228e-01 -6.33125842e-01 1.00581133e+00 -7.25764871e-01
4.95011479e-01 -2.55338311e-01 -5.40714800e-01 -1.44423962e+00
-6.36114299e-01 -3.99495602e-01 -3.37723136e-01 1.24917185e+00
5.97437441e-01 -6.45560980e-01 5.09840608e-01 7.41995573e-01
2.15236634e-01 -1.11854064e+00 -1.21824646e+00 -9.38995659e-01
2.14926884e-01 -2.45280102e-01 4.75566715e-01 7.28269637e-01
-1.92695796e-01 6.30996585e-01 -8.30159485e-01 2.60287315e-01
5.30358136e-01 3.72399598e-01 1.00386596e+00 -1.45645607e+00
-5.23352325e-01 -3.07876080e-01 -5.73063850e-01 -8.38369727e-01
1.06525548e-01 -7.63859153e-01 -5.93737178e-02 -9.89151239e-01
9.06242505e-02 -8.40073824e-01 -7.22051203e-01 7.40960300e-01
-4.97258514e-01 1.02333233e-01 2.47782975e-01 5.40594995e-01
-4.84795690e-01 5.59455931e-01 9.34058011e-01 -1.02481321e-01
-6.12451077e-01 3.62704873e-01 -8.45303535e-01 2.57361442e-01
8.02417815e-01 -7.74728000e-01 -2.84589261e-01 -6.03502505e-02
-1.79673508e-01 4.93197978e-01 1.09402999e-01 -8.64560485e-01
2.40928363e-02 -6.20006263e-01 3.02834392e-01 -5.11122763e-01
6.20594263e-01 -8.02861273e-01 2.34638661e-01 4.00257409e-01
-5.07723808e-01 1.28490224e-01 1.80602565e-01 7.21077323e-01
-3.58233124e-01 1.67881638e-01 9.60516572e-01 3.88475090e-01
-4.65569735e-01 1.89912081e-01 -1.53351232e-01 -1.81094974e-01
1.36814523e+00 1.82198316e-01 -4.77653481e-02 -7.06127286e-02
-5.92170656e-01 3.95095825e-01 2.63310056e-02 6.90918684e-01
3.76822531e-01 -1.28257036e+00 -9.62894201e-01 2.40255371e-01
2.67379582e-01 -3.18688959e-01 -3.22163850e-01 7.93603122e-01
1.76466122e-01 7.56690919e-01 2.26538833e-02 -7.19582736e-01
-1.53239942e+00 6.38142049e-01 3.33052844e-01 -1.22055495e+00
-3.92759591e-01 6.00645483e-01 1.16481766e-01 -5.60861886e-01
1.10536464e-01 -3.17090183e-01 -3.30988020e-01 -5.54840453e-02
4.40394193e-01 4.46408272e-01 1.16400950e-01 -6.40882790e-01
-3.63384545e-01 5.83095193e-01 -6.24647290e-02 2.29988322e-01
1.55435562e+00 2.30535179e-01 -1.32085353e-01 5.64370036e-01
1.22490144e+00 -2.50009090e-01 -1.17183435e+00 -4.99522984e-01
3.03017229e-01 -4.96903211e-01 2.79390011e-02 -8.87623012e-01
-9.95583832e-01 5.68139791e-01 5.51077247e-01 7.82629028e-02
1.46224320e+00 -9.25652757e-02 3.71635735e-01 3.33006889e-01
2.48275727e-01 -8.23755562e-01 6.92123547e-02 4.64890420e-01
9.51608360e-01 -1.32169557e+00 5.39549207e-03 -6.52301729e-01
-7.28654861e-01 1.22719193e+00 6.96516156e-01 -1.87870741e-01
5.99104226e-01 2.06862763e-01 -6.89005107e-03 5.77106066e-02
-1.09075928e+00 1.53723210e-01 5.29428124e-01 3.20975751e-01
5.12230456e-01 2.91969299e-01 -2.76947111e-01 7.14717388e-01
9.80697107e-03 -8.98403227e-02 -6.57967925e-02 6.32050812e-01
-2.51544893e-01 -1.70790398e+00 -6.62905335e-01 6.70400083e-01
-4.09315765e-01 -8.61719027e-02 -2.01444075e-01 8.69989216e-01
4.05935831e-02 1.27011549e+00 -1.99156776e-01 -6.08566940e-01
4.28120375e-01 2.66178809e-02 2.32914358e-01 -7.15122342e-01
-7.94318557e-01 1.65505886e-01 4.57920246e-02 -5.73397100e-01
-2.62933940e-01 -6.34172618e-01 -7.94353068e-01 1.70167640e-01
-4.80082899e-01 1.68114930e-01 6.99624836e-01 9.30253983e-01
4.09272343e-01 5.10412395e-01 1.10716546e+00 -5.55876672e-01
-1.04303432e+00 -1.04418099e+00 -5.27454555e-01 1.67323455e-01
2.75603861e-01 -8.58847141e-01 -1.21511385e-01 -1.15327537e-01] | [9.08290958404541, 4.229432582855225] |
6ca444ab-a1bf-428f-ba84-ee9a346eae3c | improving-word-mover-s-distance-by-leveraging | 2211.06229 | null | https://arxiv.org/abs/2211.06229v1 | https://arxiv.org/pdf/2211.06229v1.pdf | Improving word mover's distance by leveraging self-attention matrix | Measuring the semantic similarity between two sentences is still an important task. The word mover's distance (WMD) computes the similarity via the optimal alignment between the sets of word embeddings. However, WMD does not utilize word order, making it difficult to distinguish sentences with large overlaps of similar words, even if they are semantically very different. Here, we attempt to improve WMD by incorporating the sentence structure represented by BERT's self-attention matrix (SAM). The proposed method is based on the Fused Gromov-Wasserstein distance, which simultaneously considers the similarity of the word embedding and the SAM for calculating the optimal transport between two sentences. Experiments on paraphrase identification and semantic textual similarity show that the proposed method improves WMD and its variants. Our code is available at https://github.com/ymgw55/WSMD. | ['Hidetoshi Shimodaira', 'Sho Yokoi', 'Hiroaki Yamagiwa'] | 2022-11-11 | null | null | null | null | ['paraphrase-identification'] | ['natural-language-processing'] | [-8.05305615e-02 -3.07793587e-01 -1.51411101e-01 -3.56937975e-01
-6.53401673e-01 -4.51834142e-01 3.66032481e-01 6.16546988e-01
-6.55991614e-01 1.76974013e-01 6.57143176e-01 -3.08580548e-01
-3.69741052e-01 -7.15599597e-01 -3.64417881e-02 -5.64868748e-01
3.52633506e-01 7.67403319e-02 2.24772573e-01 -2.83892632e-01
6.92069054e-01 1.52258828e-01 -1.33501136e+00 2.00329408e-01
1.10082626e+00 7.27319121e-01 7.16182351e-01 4.75361586e-01
-6.88944280e-01 2.97331691e-01 -2.96084821e-01 -4.19176847e-01
7.44365379e-02 -6.11124933e-01 -1.15719807e+00 -2.62774557e-01
3.21039051e-01 7.28872195e-02 -3.95370901e-01 1.44555473e+00
5.50814867e-01 4.76829141e-01 5.70106447e-01 -1.15333354e+00
-1.01604187e+00 4.35041964e-01 -4.43316668e-01 8.13287675e-01
6.61868036e-01 -2.76809812e-01 1.47127831e+00 -1.03212631e+00
4.02682424e-01 1.30934393e+00 6.76517427e-01 3.34094673e-01
-1.00218904e+00 -2.48665169e-01 -3.66738513e-02 7.98315287e-01
-1.46710992e+00 -2.24456832e-01 9.58765030e-01 -3.90370339e-01
1.00105548e+00 3.66880924e-01 4.90791023e-01 7.31432498e-01
1.98245600e-01 7.38168895e-01 8.00307691e-01 -5.09353101e-01
1.85128540e-01 1.65414855e-01 5.51172674e-01 5.09540379e-01
2.61505872e-01 -4.96576488e-01 -3.52634251e-01 -1.97955638e-01
2.22468376e-01 2.74147362e-01 -4.77032572e-01 -3.18546325e-01
-1.08137536e+00 9.96104896e-01 3.62805545e-01 9.55617726e-01
-1.43991560e-01 -2.53949821e-01 3.94770443e-01 3.59560102e-01
5.37960351e-01 5.22790313e-01 9.53731872e-03 -2.88035035e-01
-8.28741729e-01 7.39650354e-02 4.41217005e-01 7.70654440e-01
6.83891833e-01 -5.51438749e-01 -1.70993060e-01 1.11475313e+00
5.12725174e-01 2.95950472e-01 1.07350075e+00 -6.85885251e-01
6.38995528e-01 7.03401446e-01 -1.88312724e-01 -1.54622197e+00
-2.14346156e-01 -7.41546601e-02 -4.84303892e-01 -3.20371091e-01
3.54224980e-01 2.55930513e-01 -1.90411106e-01 1.69370377e+00
2.97986448e-01 2.57711798e-01 -1.52366189e-02 1.01176465e+00
1.00812054e+00 5.79439700e-01 -3.31108384e-02 -4.94655408e-03
1.36254060e+00 -1.03116572e+00 -9.37575340e-01 -3.39748800e-01
9.13576066e-01 -8.77610683e-01 1.31816232e+00 -4.31067050e-01
-1.15435338e+00 -5.44746280e-01 -1.11343753e+00 -3.37041616e-01
-4.96851057e-01 -4.12944019e-01 8.49164799e-02 4.29892004e-01
-9.98867869e-01 8.02791476e-01 -5.39191365e-01 -7.23944724e-01
1.07387759e-01 -4.39931639e-02 -4.32591945e-01 -7.18154460e-02
-1.61585569e+00 1.20651710e+00 5.03586471e-01 -4.78857867e-02
6.75742105e-02 -4.72808510e-01 -1.04997563e+00 1.81899369e-01
-1.11537658e-01 -5.90342879e-01 9.16625321e-01 -6.85104966e-01
-1.19103825e+00 1.06915867e+00 -5.07568002e-01 -2.50481814e-01
2.26734892e-01 -1.14813201e-01 -4.26198721e-01 2.02859551e-01
1.26056477e-01 3.10306013e-01 5.05441010e-01 -7.83117175e-01
-2.80238092e-01 -4.73746985e-01 1.28929779e-01 5.37533104e-01
-7.96077549e-01 9.56076756e-02 -9.18651223e-02 -7.45558918e-01
3.17942858e-01 -7.02120245e-01 8.67171511e-02 1.15271984e-02
-1.52591065e-01 -5.79622865e-01 5.93315661e-01 -8.68180931e-01
1.59407020e+00 -2.30373049e+00 3.21226239e-01 6.26715794e-02
2.11915955e-01 3.31078768e-01 -5.03033757e-01 9.41384077e-01
-1.84207365e-01 1.75437734e-01 -4.10491735e-01 -2.67404526e-01
1.50343359e-01 -3.99269834e-02 -4.92394827e-02 4.32587087e-01
-7.84713253e-02 8.10760677e-01 -1.21549320e+00 -6.97495639e-01
1.43706664e-01 1.72967300e-01 -3.60465884e-01 2.01610073e-01
4.34989959e-01 -1.27944145e-02 -4.36821610e-01 9.98605695e-03
7.77791262e-01 -1.65231720e-01 2.78044730e-01 -1.85993731e-01
3.53423096e-02 4.91227478e-01 -1.04859042e+00 1.91726875e+00
-5.48638642e-01 7.74589658e-01 -5.33690155e-01 -1.17230844e+00
1.04264462e+00 2.10251004e-01 3.85328829e-01 -7.33859181e-01
2.17341676e-01 8.59853402e-02 1.21973015e-01 -8.19154859e-01
6.28460884e-01 -2.52666503e-01 1.61911957e-02 6.65252626e-01
-8.14055428e-02 -1.27314329e-01 1.82357833e-01 3.43293935e-01
1.11328351e+00 -3.34595233e-01 6.87973976e-01 -5.73671818e-01
7.39345074e-01 -4.15231198e-01 3.99598479e-01 3.71734351e-01
-4.55782205e-01 6.02414846e-01 3.48959357e-01 -6.86804876e-02
-1.08008802e+00 -1.47011852e+00 -3.84091854e-01 7.07787454e-01
6.80913806e-01 -6.15960479e-01 -8.24612439e-01 -6.31144822e-01
1.14954323e-01 1.01273572e+00 -4.81528521e-01 -6.70680881e-01
-3.63806665e-01 -4.27350760e-01 3.16511393e-01 5.05199254e-01
3.06327403e-01 -8.28126848e-01 -2.72024840e-01 3.46279368e-02
-6.72730923e-01 -8.46298158e-01 -9.53197300e-01 -4.14396405e-01
-8.48365843e-01 -9.67738211e-01 -6.40310705e-01 -1.08999598e+00
5.73169351e-01 8.00180972e-01 8.35414886e-01 2.30833635e-01
-2.53819674e-01 3.39171112e-01 -7.24488556e-01 1.20095927e-02
-3.64452779e-01 -6.15017489e-02 2.60385782e-01 -1.62019115e-02
8.96271110e-01 -4.37121689e-01 -6.28469825e-01 2.83755094e-01
-9.92170215e-01 -7.85858706e-02 1.51849762e-01 8.26800883e-01
3.51741821e-01 -7.23217130e-02 6.12036228e-01 -3.44219774e-01
1.06945133e+00 -6.80261135e-01 4.43058610e-02 4.33663398e-01
-6.46133363e-01 2.96549410e-01 4.69316393e-01 -3.24500769e-01
-7.44864702e-01 -6.32395923e-01 -2.61205614e-01 -1.97981000e-01
-2.09049862e-02 3.98372889e-01 -4.77957763e-02 2.23170593e-01
2.50688046e-01 2.39044532e-01 5.84337711e-02 -5.13268113e-01
3.63919646e-01 1.08845234e+00 7.94112086e-02 -2.11925566e-01
6.12190008e-01 2.78029472e-01 -4.63414997e-01 -8.93541217e-01
-7.53134549e-01 -8.55493128e-01 -7.76148260e-01 5.65188006e-02
9.58462834e-01 -4.06436980e-01 -4.56119061e-01 1.56140894e-01
-1.21950865e+00 2.81862408e-01 -1.94770917e-01 6.92180872e-01
-4.02675956e-01 1.02603579e+00 -5.23112178e-01 -4.04287785e-01
-4.38154727e-01 -9.31221306e-01 7.01600969e-01 6.29286915e-02
-6.02732241e-01 -1.38988149e+00 4.68330115e-01 5.38698614e-01
4.07405406e-01 -1.51685923e-01 1.11481678e+00 -1.06110060e+00
1.87509373e-01 -3.51981610e-01 -2.42654383e-01 4.19315457e-01
5.30340552e-01 -2.35007778e-01 -6.32685840e-01 -3.68260980e-01
3.93546075e-01 1.50695488e-01 8.57858300e-01 1.13903560e-01
1.00020194e+00 -2.17661113e-01 -8.23185146e-02 2.04609916e-01
1.28738046e+00 1.60959646e-01 7.22803056e-01 5.32529116e-01
6.09416187e-01 6.01150692e-01 6.15623176e-01 3.53392482e-01
2.84001142e-01 8.00041497e-01 1.30605057e-01 3.84168178e-01
-7.28535801e-02 -2.21212775e-01 3.82410496e-01 1.60868645e+00
2.75626630e-01 -1.69409469e-01 -9.11827624e-01 5.72652578e-01
-1.84566712e+00 -1.27381456e+00 -3.19643587e-01 2.24593019e+00
7.36738861e-01 1.44585511e-02 -2.45800316e-01 2.57545799e-01
1.02707386e+00 4.97428715e-01 -2.19058111e-01 -7.35897839e-01
-2.10613292e-02 1.38064429e-01 6.66976860e-03 8.81230175e-01
-8.33237469e-01 6.98939979e-01 5.34313965e+00 1.04737484e+00
-6.26411378e-01 3.10441822e-01 -1.83332916e-02 -1.13523044e-01
-6.66734517e-01 -4.34295386e-02 -4.04603273e-01 8.74445677e-01
8.24734211e-01 -6.20940030e-01 9.05251503e-02 5.74543357e-01
4.31896091e-01 -5.35784923e-02 -9.56001341e-01 9.07994270e-01
3.94462109e-01 -1.07849252e+00 5.95204160e-02 -3.46117407e-01
4.14727569e-01 -2.90004015e-01 2.99438415e-03 -3.94089893e-02
-1.62929446e-01 -6.89135790e-01 3.10960382e-01 5.47746062e-01
2.92584956e-01 -6.29567027e-01 1.04306960e+00 1.77566096e-01
-1.30703378e+00 5.21829799e-02 -5.82102835e-01 -1.68066192e-02
1.49851814e-01 5.60937047e-01 -5.26167393e-01 4.87432241e-01
5.16626477e-01 9.55882072e-01 -6.42952442e-01 9.36990976e-01
-8.06819573e-02 1.73117653e-01 1.94973901e-01 -4.22818482e-01
2.68776983e-01 -6.08501256e-01 7.02916920e-01 1.18206084e+00
5.33759117e-01 -8.00064430e-02 -9.93649065e-02 7.30530977e-01
3.10168341e-02 5.50532222e-01 -7.02912807e-01 -8.78870636e-02
7.47911572e-01 1.04934299e+00 -4.49042886e-01 -3.11062276e-01
-5.55158556e-01 1.37075698e+00 4.64253008e-01 3.93038057e-02
-8.63076746e-01 -8.95271182e-01 1.17451632e+00 -4.53495942e-02
7.44641051e-02 -2.07923234e-01 -1.84138313e-01 -1.17405879e+00
2.13724792e-01 -4.01038051e-01 4.00536805e-01 -8.18334818e-01
-1.65353072e+00 5.61230481e-01 -1.94419816e-01 -1.43303108e+00
1.54065028e-01 -4.29641098e-01 -8.82571578e-01 9.56241548e-01
-1.21383107e+00 -5.20183146e-01 -2.33289406e-01 4.21449035e-01
7.79816687e-01 2.30398122e-02 8.21409941e-01 3.20959926e-01
-6.22249961e-01 7.54496157e-01 5.09329677e-01 6.54544160e-02
7.03113735e-01 -1.19633853e+00 4.23077643e-01 6.28348947e-01
1.83375612e-01 7.60770857e-01 7.69152701e-01 -5.10541737e-01
-1.01749682e+00 -8.45044553e-01 1.46795523e+00 -2.91344106e-01
9.86013651e-01 -1.34434238e-01 -1.22798383e+00 2.55450279e-01
4.91893351e-01 -5.38808465e-01 9.77179408e-01 -8.97631645e-02
-4.75110829e-01 -1.55117018e-02 -1.08943427e+00 7.65199125e-01
1.20015931e+00 -8.47585559e-01 -1.23868454e+00 4.82480258e-01
8.28599811e-01 1.99192196e-01 -1.00450873e+00 8.53321776e-02
3.54755104e-01 -9.49032247e-01 9.53734159e-01 -7.61957765e-01
5.64915597e-01 -2.00049460e-01 -4.10393625e-01 -1.48596227e+00
-6.47427678e-01 -1.92748010e-02 1.66423112e-01 1.25963771e+00
2.58752286e-01 -8.01977217e-01 3.72090548e-01 3.90505373e-01
-6.24290220e-02 -8.07202756e-01 -9.80383515e-01 -1.13755906e+00
2.21335411e-01 -2.59497970e-01 5.70913672e-01 1.20052636e+00
6.34982288e-01 3.13661844e-01 2.01642662e-02 -6.81903958e-02
3.88443410e-01 2.38876030e-01 8.95382315e-02 -1.00544870e+00
-1.11984000e-01 -8.34598660e-01 -8.34428906e-01 -9.12553668e-01
4.56745863e-01 -1.42677414e+00 -1.22680560e-01 -1.81077874e+00
4.74562794e-01 1.52820855e-01 -5.27426839e-01 3.18875872e-02
-5.36356390e-01 -8.16146843e-03 3.02018702e-01 2.44314775e-01
-5.63990772e-01 8.17845345e-01 1.04310453e+00 -1.83068573e-01
5.69212437e-02 -2.34705850e-01 -5.44960082e-01 7.71821082e-01
1.15731108e+00 -4.63922203e-01 -4.15804684e-01 -6.55512393e-01
-2.07731519e-02 -3.19629252e-01 1.43506989e-01 -8.86318088e-01
3.27731401e-01 -7.59734958e-02 -2.22605411e-02 -3.13743919e-01
3.04092020e-01 -7.58596063e-01 -1.15851261e-01 6.35468960e-01
-6.86843276e-01 6.32783175e-01 -1.49347335e-01 5.26206493e-01
-4.01354283e-01 -8.16916168e-01 8.25370431e-01 -2.06317287e-02
-7.17011154e-01 2.87620183e-02 -2.89718032e-01 2.60953307e-01
1.09076321e+00 -3.00715476e-01 -2.86433309e-01 -2.74813801e-01
-4.81047899e-01 1.42066717e-01 5.45138478e-01 7.97733665e-01
1.00111604e+00 -1.79930723e+00 -6.49320662e-01 -6.14248514e-02
3.68919939e-01 -7.65532434e-01 5.38764715e-01 8.93053353e-01
-3.93308640e-01 3.87143224e-01 -8.49313438e-02 -1.54907510e-01
-1.59052396e+00 6.63628578e-01 1.76245078e-01 -1.20898776e-01
-5.66405058e-01 7.87058890e-01 -2.05307659e-02 -4.30226237e-01
-1.28778845e-01 -5.93922623e-02 -3.38986158e-01 3.18477482e-01
6.07342184e-01 6.05212569e-01 -5.62052950e-02 -8.72066319e-01
-5.77863038e-01 8.11098218e-01 -1.42404109e-01 -1.13747711e-03
1.02128351e+00 -5.09185553e-01 -4.07448977e-01 6.44219756e-01
1.89878261e+00 -6.45057932e-02 -3.91390234e-01 -5.39029956e-01
3.81993115e-01 -8.92528713e-01 -1.27044618e-01 6.04209909e-03
-7.31291711e-01 1.08567595e+00 5.42642415e-01 4.56008732e-01
8.43589067e-01 1.48159564e-01 1.24004245e+00 2.83740640e-01
3.41611244e-02 -1.13801003e+00 2.44238511e-01 4.47652161e-01
8.39407623e-01 -1.08353102e+00 -8.08244944e-02 -2.02441558e-01
-7.14022756e-01 1.12963760e+00 4.68328714e-01 -1.15731753e-01
6.56899154e-01 -3.30541700e-01 7.08011463e-02 4.40133922e-02
-3.56725395e-01 -2.53553927e-01 4.99072492e-01 1.78127140e-01
5.40651858e-01 -1.39360666e-01 -9.66273546e-01 3.45548272e-01
-2.63338476e-01 -5.78289092e-01 2.82649577e-01 7.90454090e-01
-6.90402567e-01 -1.16234767e+00 -1.44857988e-01 4.73265111e-01
-1.34974107e-01 -3.65053058e-01 -3.23531628e-01 3.25368822e-01
-1.97329372e-01 1.17449808e+00 3.36749375e-01 -6.08545542e-01
2.98611850e-01 3.01561445e-01 3.26843023e-01 -5.61134577e-01
-2.67058551e-01 -5.72317898e-01 -1.61116481e-01 -5.09245813e-01
-2.99049944e-01 -6.29966974e-01 -1.17453492e+00 -5.00998139e-01
-3.02541822e-01 4.54113573e-01 5.45185268e-01 9.70019221e-01
4.09324557e-01 2.21980602e-01 9.52422082e-01 -2.99271911e-01
-7.32128799e-01 -9.87167776e-01 -6.92727268e-01 1.00563216e+00
1.06545344e-01 -5.12970924e-01 -5.57591379e-01 -2.67445445e-01] | [10.770050048828125, 8.722293853759766] |
39c0bf9e-2eb7-4d16-9694-4b1fd0809c12 | kgpool-dynamic-knowledge-graph-context | 2106.00459 | null | https://arxiv.org/abs/2106.00459v2 | https://arxiv.org/pdf/2106.00459v2.pdf | KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction | We present a novel method for relation extraction (RE) from a single sentence, mapping the sentence and two given entities to a canonical fact in a knowledge graph (KG). Especially in this presumed sentential RE setting, the context of a single sentence is often sparse. This paper introduces the KGPool method to address this sparsity, dynamically expanding the context with additional facts from the KG. It learns the representation of these facts (entity alias, entity descriptions, etc.) using neural methods, supplementing the sentential context. Unlike existing methods that statically use all expanded facts, KGPool conditions this expansion on the sentence. We study the efficacy of KGPool by evaluating it with different neural models and KGs (Wikidata and NYT Freebase). Our experimental evaluation on standard datasets shows that by feeding the KGPool representation into a Graph Neural Network, the overall method is significantly more accurate than state-of-the-art methods. | ['Vijay Saraswat', 'Saeedeh Shekarpour', 'Johannes Hoffart', "Isaiah Onando Mulang'", 'Kuldeep Singh', 'Anson Bastos', 'Abhishek Nadgeri'] | 2021-06-01 | null | https://aclanthology.org/2021.findings-acl.48 | https://aclanthology.org/2021.findings-acl.48.pdf | findings-acl-2021-8 | ['relationship-extraction-distant-supervised'] | ['natural-language-processing'] | [ 1.77121371e-01 8.67431700e-01 -4.77859735e-01 -4.22272444e-01
-4.25775349e-01 -4.05313075e-01 6.13765240e-01 9.18271482e-01
-6.44090772e-01 1.20596492e+00 4.66424108e-01 -3.19070071e-01
-1.35726333e-01 -1.24942684e+00 -1.07203126e+00 -1.81591120e-02
-3.61560404e-01 5.84217250e-01 3.17703158e-01 -1.66325539e-01
-1.58646777e-01 2.32549626e-02 -1.23206937e+00 4.48229223e-01
7.24165022e-01 8.79158139e-01 1.27314419e-01 4.25768077e-01
-5.05910516e-01 1.25577223e+00 -7.14822173e-01 -9.74635959e-01
1.42529264e-01 -1.08546443e-01 -1.29622185e+00 -4.62918401e-01
8.18473697e-01 -8.53534937e-02 -5.19769311e-01 1.05810273e+00
3.43234062e-01 2.33752981e-01 2.90403873e-01 -1.08842456e+00
-9.65138078e-01 1.51791108e+00 -3.08269560e-01 3.19814503e-01
7.03261912e-01 -5.97101331e-01 1.35843050e+00 -8.85610402e-01
1.17005038e+00 1.09628963e+00 8.08582902e-01 2.18305841e-01
-9.28101957e-01 -4.53944266e-01 3.99240166e-01 4.83715445e-01
-1.36255896e+00 -2.85292923e-01 5.54319143e-01 -2.69245282e-02
1.83977640e+00 1.65742308e-01 5.20872116e-01 7.47318745e-01
-4.13747169e-02 6.73495531e-01 7.04814672e-01 -7.06003308e-01
2.89285854e-02 1.06417239e-01 5.71067631e-01 8.33958209e-01
7.04257071e-01 -3.88108760e-01 -7.51184344e-01 -2.56168038e-01
1.66272774e-01 -4.43857819e-01 -4.66122478e-01 -1.98695391e-01
-1.03010035e+00 6.50938511e-01 5.52871823e-01 2.93279141e-01
-5.03940344e-01 1.79480955e-01 4.43844020e-01 2.90233284e-01
5.76907873e-01 6.96240187e-01 -9.49799478e-01 2.10980684e-01
-7.86189675e-01 3.53811800e-01 1.48662126e+00 1.02838719e+00
9.08268034e-01 -3.68637562e-01 -2.43252695e-01 5.02294719e-01
-1.30429463e-02 1.09891675e-01 2.72279918e-01 -3.50912154e-01
1.01827073e+00 8.98000598e-01 -2.00921491e-01 -1.16994321e+00
-4.86300111e-01 -5.22255361e-01 -5.84009409e-01 -4.78271872e-01
1.22770026e-01 -4.16627705e-01 -8.96907806e-01 1.81378233e+00
5.05806625e-01 4.02353317e-01 4.25723583e-01 4.92867827e-01
1.60468435e+00 5.47393620e-01 3.91056925e-01 -2.86337137e-01
1.24161911e+00 -8.27451348e-01 -9.36248839e-01 -4.72899139e-01
8.56321216e-01 -2.09927976e-01 5.30439138e-01 -3.85408588e-02
-8.89803231e-01 -1.62324533e-01 -1.17286718e+00 -3.85225922e-01
-1.00764692e+00 -3.88405845e-03 9.31084335e-01 2.43551031e-01
-1.14773905e+00 9.38488841e-01 -4.19573694e-01 -4.81464773e-01
3.27962816e-01 4.72817421e-01 -7.25385308e-01 -9.12986696e-02
-1.88371944e+00 1.29585719e+00 1.15135539e+00 -1.81505308e-02
-3.67895961e-01 -8.36095631e-01 -1.37914276e+00 3.77428472e-01
1.00215364e+00 -1.01652873e+00 1.06936920e+00 -4.08039719e-01
-9.49788451e-01 7.58687496e-01 -2.59312004e-01 -9.08302903e-01
-1.84092626e-01 -4.30735588e-01 -5.58662772e-01 6.96666688e-02
2.38916129e-02 3.52049112e-01 4.85358626e-01 -1.27134633e+00
-4.76110905e-01 -2.46654749e-01 6.05231822e-01 3.44592303e-01
-2.72466660e-01 1.58727437e-01 -4.86672908e-01 -4.22565758e-01
-2.80899018e-01 -5.10657609e-01 -1.18896879e-01 -6.79078519e-01
-8.43666673e-01 -5.06238759e-01 6.36703432e-01 -9.33359861e-01
1.67434323e+00 -1.61601865e+00 2.33871743e-01 2.03003958e-01
5.35549343e-01 3.38199586e-01 1.59001779e-02 7.56967902e-01
-2.50155598e-01 1.71262607e-01 -2.76675433e-01 -3.33174825e-01
-7.04062879e-02 4.11555380e-01 -1.99665174e-01 -9.11626443e-02
2.33299941e-01 1.26793814e+00 -1.15762234e+00 -5.29058516e-01
-3.63065541e-01 3.50838453e-01 -3.64607215e-01 -7.31019154e-02
-3.96692157e-01 -4.58761990e-01 -2.97823846e-01 4.89627182e-01
3.99560243e-01 -4.54879761e-01 7.24878669e-01 -5.19806981e-01
4.27903414e-01 7.90590823e-01 -1.13717997e+00 1.61991310e+00
-3.92266870e-01 4.67162073e-01 -3.35680395e-01 -9.20692563e-01
7.62081146e-01 3.63119572e-01 1.63947552e-01 -3.50072742e-01
3.34240915e-03 -1.46186873e-01 -2.76942402e-01 -5.46175539e-01
9.04111803e-01 -2.68213395e-02 -7.09243268e-02 3.27612489e-01
6.48354232e-01 1.16687573e-01 7.04048276e-01 8.58522117e-01
1.29494214e+00 1.36538848e-01 7.21538186e-01 -4.82939109e-02
3.97166908e-01 -1.17119774e-01 5.33742726e-01 7.80396640e-01
3.07852268e-01 3.47196460e-02 6.84691250e-01 -4.30999994e-01
-5.14103413e-01 -6.78180337e-01 2.34737143e-01 8.35101187e-01
-7.09836483e-02 -1.17224026e+00 -5.60295463e-01 -1.38388026e+00
2.63322949e-01 9.25775111e-01 -7.54886031e-01 -1.36754200e-01
-7.16018498e-01 -8.08039546e-01 5.03492653e-01 7.27499843e-01
4.92743999e-01 -1.24188626e+00 -3.20066988e-01 2.57803380e-01
-2.60015190e-01 -1.61609554e+00 -1.06805883e-01 3.44152927e-01
-4.66086537e-01 -1.20575202e+00 9.41447020e-02 -8.64838600e-01
6.06918752e-01 -3.28569770e-01 1.67680037e+00 2.78973758e-01
1.24609597e-01 2.25057915e-01 -4.33858871e-01 -3.48469168e-01
-2.57052362e-01 2.47204453e-01 -4.66752276e-02 -3.61138463e-01
6.44764304e-01 -6.21050179e-01 8.47089454e-04 -5.64467311e-01
-8.47440004e-01 1.70038007e-02 4.92207736e-01 5.88546455e-01
4.47868496e-01 4.31110293e-01 8.27884257e-01 -1.67525089e+00
8.75372350e-01 -7.23746955e-01 -3.12497437e-01 6.38916552e-01
-7.17092395e-01 2.42261514e-01 5.58331251e-01 -1.46967933e-01
-1.13457119e+00 -9.94810015e-02 9.03038681e-02 -1.95434559e-02
6.71767518e-02 1.42809522e+00 -2.02722833e-01 1.49889022e-01
6.92324042e-01 -1.54922754e-01 -5.52574575e-01 -3.71740103e-01
7.52947569e-01 1.72862232e-01 7.40871847e-01 -7.49233842e-01
6.28128946e-01 -4.84804139e-02 8.13373402e-02 -3.67368430e-01
-1.18276024e+00 -3.76478612e-01 -7.65440762e-01 1.47709727e-01
5.84920466e-01 -8.26874077e-01 -4.87895340e-01 -8.02124739e-02
-1.31368423e+00 -2.32411966e-01 -5.25944829e-01 4.16709214e-01
-7.39965886e-02 4.24478173e-01 -8.13415349e-01 -4.35218304e-01
-5.53616643e-01 -4.68173057e-01 8.96492839e-01 2.27094918e-01
-2.21841112e-01 -1.18202674e+00 1.72338098e-01 4.25537862e-02
8.81889239e-02 4.08076316e-01 1.12334633e+00 -1.05536520e+00
-4.72688735e-01 -3.98428142e-01 -3.10483515e-01 4.57476974e-02
2.89618075e-01 -1.55133158e-01 -7.95780241e-01 2.43648961e-02
-4.68773097e-01 -3.51859689e-01 1.02165103e+00 -2.13581938e-02
8.04060459e-01 -7.95545042e-01 -5.90704620e-01 3.88161719e-01
1.73740268e+00 -7.32532442e-02 4.66664732e-01 4.39365357e-01
8.91027272e-01 5.28808594e-01 2.10728154e-01 8.48393813e-02
8.61938000e-01 3.25305760e-01 3.95722449e-01 3.19962688e-02
-3.89144331e-01 -4.82424021e-01 1.64742425e-01 8.22924376e-01
-2.07035914e-01 -4.99362528e-01 -8.63061547e-01 7.86111772e-01
-1.92246032e+00 -9.64745164e-01 5.55954166e-02 1.94443202e+00
1.36992478e+00 2.30650693e-01 -2.78979540e-01 -4.04822407e-03
6.24134839e-01 1.17080487e-01 -2.37843052e-01 -2.92372853e-01
-3.80338192e-01 3.89835507e-01 5.62259257e-01 7.14856565e-01
-1.16994381e+00 1.20875907e+00 6.30339098e+00 5.65376341e-01
-7.05085337e-01 -1.95206925e-02 1.31223649e-01 3.23374756e-02
-2.81870067e-01 1.83481693e-01 -1.16368914e+00 6.66109174e-02
9.52027798e-01 -4.83182609e-01 2.54181832e-01 6.57242537e-01
-5.71246743e-01 -1.52484238e-01 -1.26945126e+00 3.87793899e-01
3.44079077e-01 -1.69147158e+00 3.08392048e-01 -1.94492221e-01
5.64153194e-01 1.57017902e-01 -5.28008401e-01 6.61163032e-01
7.03885913e-01 -8.30873191e-01 3.92312080e-01 4.75882888e-01
7.04737186e-01 -6.53898597e-01 1.09612203e+00 1.25857562e-01
-1.23949850e+00 2.14865774e-01 -2.81682849e-01 -9.84402597e-02
2.38873854e-01 8.31088841e-01 -1.24263668e+00 1.42704296e+00
7.05188334e-01 8.64797711e-01 -8.06875288e-01 7.03526795e-01
-7.48055577e-01 4.65044022e-01 -4.37244833e-01 -8.08766633e-02
2.16485262e-02 3.09812129e-01 6.07651174e-01 1.61556268e+00
-7.87648559e-02 1.25725001e-01 2.76238263e-01 5.23278236e-01
-6.19931102e-01 2.30372041e-01 -8.46919537e-01 -1.22124925e-01
5.26398659e-01 1.33228397e+00 -5.14076591e-01 -7.79330730e-01
-6.92845106e-01 6.34372413e-01 1.08278239e+00 5.53339005e-01
-3.86589080e-01 -6.48066938e-01 1.10961735e-01 -1.00049362e-01
6.00541770e-01 1.92629602e-02 4.05046828e-02 -1.29704666e+00
3.69120687e-01 -5.57873964e-01 8.83182526e-01 -8.66495371e-01
-1.37727630e+00 8.41401100e-01 4.11884010e-01 -4.49674249e-01
-3.57006639e-01 -5.34035802e-01 -3.86767596e-01 7.79929698e-01
-1.80960381e+00 -1.21040237e+00 -4.60195029e-03 5.15723288e-01
5.93901938e-03 7.43626133e-02 1.02051723e+00 3.81166756e-01
-5.70009410e-01 5.59847355e-01 -6.55668736e-01 4.75880235e-01
5.66185832e-01 -1.61988282e+00 5.66929579e-01 1.00516856e+00
4.96648908e-01 9.72148538e-01 5.69851041e-01 -1.28917944e+00
-1.19461155e+00 -1.18313265e+00 1.66282189e+00 -5.96028388e-01
1.03167760e+00 -3.24734539e-01 -1.06046295e+00 1.15162861e+00
7.45718122e-01 1.40664622e-01 7.71323800e-01 6.83646083e-01
-5.01026988e-01 7.52055794e-02 -9.62336183e-01 4.64542359e-01
1.18470120e+00 -5.86908937e-01 -1.01820934e+00 4.56047773e-01
1.12820137e+00 -6.12256348e-01 -1.06433475e+00 6.00974739e-01
1.36456817e-01 -3.73367816e-01 8.50136876e-01 -9.95606601e-01
4.04156476e-01 -3.86412710e-01 -6.78534359e-02 -1.51373672e+00
-2.23800197e-01 -4.60801095e-01 -1.13049197e+00 1.29465818e+00
8.91827941e-01 -4.91693795e-01 7.68165171e-01 6.35637224e-01
-5.57744429e-02 -8.85369956e-01 -8.16428840e-01 -7.29118466e-01
-2.04106182e-01 -1.30285800e-01 8.65347564e-01 1.30803049e+00
5.88786244e-01 9.39008474e-01 -9.31158140e-02 3.57692540e-01
3.86191338e-01 1.18052162e-01 3.56211871e-01 -1.25201404e+00
-3.86403680e-01 -6.20224923e-02 -3.60558391e-01 -4.92975056e-01
5.72869182e-01 -1.35830641e+00 -1.13447160e-01 -2.10492802e+00
2.73281783e-01 -2.47411802e-01 -3.59650373e-01 1.08087969e+00
-7.49290407e-01 -2.85036385e-01 2.29772460e-02 -2.95875937e-01
-6.65831864e-01 2.71392077e-01 8.70973647e-01 -3.32612306e-01
-7.32335895e-02 -4.87270147e-01 -8.26929271e-01 6.01423621e-01
7.22498596e-01 -6.05805576e-01 -5.84535718e-01 -5.08296669e-01
9.02688622e-01 -1.27377689e-01 2.64953285e-01 -6.44952536e-01
7.07144320e-01 1.72806934e-01 1.83284655e-01 -5.30230045e-01
2.06825539e-01 -7.46198654e-01 -5.05594052e-02 -2.95959488e-02
-4.42165554e-01 1.24261007e-01 4.06144381e-01 6.00275517e-01
-4.10407424e-01 -3.27360839e-01 -1.10566154e-01 -3.38934600e-01
-8.67675602e-01 2.76514083e-01 1.20367572e-01 2.61492610e-01
5.39460480e-01 1.40398264e-01 -7.72782087e-01 -2.79257238e-01
-9.30575013e-01 3.10180783e-01 -1.91863906e-02 2.93477714e-01
6.71954215e-01 -1.12679291e+00 -7.00716972e-01 -1.68141797e-01
8.36793184e-02 2.43930712e-01 -1.17525488e-01 5.49757302e-01
-1.11620560e-01 3.73134673e-01 3.35994869e-01 1.45034671e-01
-1.23624158e+00 9.84189272e-01 1.98110044e-01 -8.72001231e-01
-7.91169703e-01 9.02436554e-01 -1.78932488e-01 -4.45154309e-01
1.59505591e-01 -4.88625318e-01 -7.83448040e-01 8.08086097e-02
4.04725254e-01 -3.93435024e-02 4.68376160e-01 -4.19367433e-01
-3.66973549e-01 1.11968137e-01 -3.38359147e-01 1.23629928e-01
1.44332838e+00 8.35702270e-02 -5.45436978e-01 1.81263149e-01
8.59449446e-01 3.28468472e-01 -5.92931390e-01 -6.01433396e-01
4.88228202e-01 -5.24655022e-02 -6.43387958e-02 -9.78040397e-01
-1.08887994e+00 9.44726169e-02 -4.65111494e-01 4.55753952e-01
1.00915647e+00 2.33208671e-01 6.82876885e-01 9.97880995e-01
4.08635944e-01 -8.10959578e-01 -5.13737500e-01 6.84222221e-01
9.12555277e-01 -9.42713976e-01 4.92671818e-01 -9.72057283e-01
-5.42570055e-01 9.03245270e-01 7.06408858e-01 -1.80784911e-02
7.94728041e-01 3.98766279e-01 -2.44970292e-01 -6.11141086e-01
-9.87844288e-01 -3.60234708e-01 3.48072827e-01 6.42798007e-01
3.88836086e-01 -5.51701486e-02 -2.58036673e-01 7.70928264e-01
-3.58895183e-01 7.09568113e-02 6.80674732e-01 1.23290014e+00
-5.67502864e-02 -1.14656878e+00 3.02040607e-01 6.39371932e-01
-6.63814723e-01 -7.87703276e-01 -7.06840158e-01 9.90915000e-01
1.68408960e-01 7.80756116e-01 -2.47877941e-01 -3.60609412e-01
4.38533634e-01 3.51394385e-01 5.88828266e-01 -1.03780007e+00
-8.88616204e-01 -5.70336282e-01 1.01738262e+00 -4.59312290e-01
-7.10929811e-01 -4.44649249e-01 -1.29914796e+00 1.93330850e-02
-4.86905932e-01 3.66697431e-01 3.19884002e-01 1.08415437e+00
4.68907207e-01 6.19340897e-01 -1.08197056e-01 -1.40857384e-01
2.44599767e-02 -9.57414746e-01 -4.23911452e-01 3.79218310e-01
2.19608963e-01 -6.16652012e-01 -4.84780334e-02 1.14770465e-01] | [9.316832542419434, 8.414727210998535] |
fb96dc9b-c3be-4718-9fd5-56621f37a784 | towards-end-to-end-prosody-transfer-for | 1803.09047 | null | http://arxiv.org/abs/1803.09047v1 | http://arxiv.org/pdf/1803.09047v1.pdf | Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron | We present an extension to the Tacotron speech synthesis architecture that
learns a latent embedding space of prosody, derived from a reference acoustic
representation containing the desired prosody. We show that conditioning
Tacotron on this learned embedding space results in synthesized audio that
matches the prosody of the reference signal with fine time detail even when the
reference and synthesis speakers are different. Additionally, we show that a
reference prosody embedding can be used to synthesize text that is different
from that of the reference utterance. We define several quantitative and
subjective metrics for evaluating prosody transfer, and report results with
accompanying audio samples from single-speaker and 44-speaker Tacotron models
on a prosody transfer task. | ['Ron J. Weiss', 'RJ Skerry-Ryan', 'Ying Xiao', 'Daisy Stanton', 'Yuxuan Wang', 'Rif A. Saurous', 'Eric Battenberg', 'Rob Clark', 'Joel Shor'] | 2018-03-24 | towards-end-to-end-prosody-transfer-for-1 | https://icml.cc/Conferences/2018/Schedule?showEvent=2281 | http://proceedings.mlr.press/v80/skerry-ryan18a/skerry-ryan18a.pdf | icml-2018-7 | ['expressive-speech-synthesis'] | ['speech'] | [ 1.24578506e-01 2.25087881e-01 -2.46865168e-01 -1.83730751e-01
-1.18494463e+00 -7.36543298e-01 4.74581689e-01 -4.89310831e-01
1.53507501e-01 5.23573875e-01 1.13131523e+00 4.22189683e-02
1.82736024e-01 -4.63260323e-01 -6.03574455e-01 -7.36233711e-01
1.08888231e-01 2.95537889e-01 -1.83885068e-01 -3.16061020e-01
-5.69968879e-01 1.68522328e-01 -1.20359218e+00 4.48686093e-01
6.38465211e-02 6.68987870e-01 3.12460661e-01 1.14387333e+00
8.70133564e-02 6.12113297e-01 -1.09256959e+00 -1.14335962e-01
1.33825898e-01 -9.06022727e-01 -5.75661361e-01 -1.22655228e-01
4.35799152e-01 -2.18938902e-01 -6.18019938e-01 8.33460689e-01
7.20954239e-01 2.53057986e-01 6.36702716e-01 -1.04350090e+00
-8.40807140e-01 1.42917669e+00 4.29482043e-01 1.11839853e-01
2.85653383e-01 -4.80452403e-02 1.48616028e+00 -7.27187693e-01
4.90235299e-01 1.39635384e+00 8.06807280e-01 9.41835225e-01
-1.87042451e+00 -8.80623519e-01 -2.73383766e-01 -1.97786942e-01
-8.43116462e-01 -9.05066490e-01 1.22247231e+00 -4.53211367e-01
8.69916856e-01 4.94467318e-01 6.05205476e-01 1.70728779e+00
1.90191701e-01 6.35333955e-01 7.03291893e-01 -4.99775261e-01
1.36180922e-01 2.45289117e-01 1.51938438e-01 -7.47064427e-02
-7.95112908e-01 6.38510883e-01 -8.64709914e-01 -1.08333312e-01
6.23250365e-01 -5.69116652e-01 -4.40002173e-01 -1.44335479e-01
-1.23274541e+00 8.31026316e-01 1.71909049e-01 5.99032819e-01
-5.85193157e-01 6.83456123e-01 6.13149285e-01 8.17737460e-01
3.28153282e-01 6.10721111e-01 -2.38972470e-01 -3.02790284e-01
-9.58559096e-01 4.90332171e-02 7.65798688e-01 7.73072898e-01
1.25819772e-01 1.10873306e+00 -1.09169222e-01 1.18558621e+00
1.07972704e-01 6.96227789e-01 8.16409469e-01 -9.70244050e-01
1.53604731e-01 -4.32992995e-01 6.94761649e-02 -2.92199045e-01
-4.37530428e-02 -2.76262105e-01 -3.57828349e-01 1.16354235e-01
8.06273073e-02 -3.84108841e-01 -6.08592689e-01 2.05527091e+00
-1.67703107e-01 4.80010062e-01 4.95353818e-01 8.72059762e-01
9.28739071e-01 1.31477749e+00 -1.16806343e-01 -4.22320187e-01
1.06374681e+00 -1.03938472e+00 -1.29903877e+00 5.42871887e-03
7.17590190e-03 -1.06115901e+00 1.40984190e+00 1.84235424e-01
-1.29994559e+00 -8.92350674e-01 -1.29354393e+00 -1.50127381e-01
5.79390787e-02 5.19530810e-02 1.09179743e-01 9.04044807e-01
-1.12636459e+00 6.01058006e-01 -5.86680591e-01 1.24988936e-01
-4.75401193e-01 1.39524087e-01 -2.21053839e-01 8.64533424e-01
-1.49745679e+00 6.21678054e-01 3.20821494e-01 -4.00749803e-01
-1.33196592e+00 -1.29403472e+00 -7.82510519e-01 3.65422249e-01
-1.99995890e-01 -3.55588764e-01 1.84571099e+00 -1.09245586e+00
-2.23751163e+00 3.90191257e-01 -2.01407466e-02 -7.04627037e-01
2.47265548e-02 -3.78333360e-01 -9.35681164e-01 3.86603981e-01
-1.37710795e-01 4.09653306e-01 1.34190822e+00 -1.09712148e+00
-1.88283101e-01 3.80193353e-01 -3.61433834e-01 2.03092724e-01
-1.73877656e-01 2.95510799e-01 1.26013130e-01 -1.04342282e+00
-1.50263071e-01 -9.81870472e-01 3.18610042e-01 -2.67546684e-01
-5.37384927e-01 8.00704435e-02 8.76617730e-01 -7.20609486e-01
1.13569951e+00 -2.51229978e+00 5.45642197e-01 -2.62707114e-01
-1.32592618e-01 -8.10199156e-02 -2.01869011e-01 7.55491853e-01
-6.00982010e-01 3.20330337e-02 8.00454244e-02 -6.00925684e-01
3.04863125e-01 -9.95952636e-02 -1.50009310e+00 1.19101606e-01
-2.39764042e-02 6.64212525e-01 -6.83728993e-01 1.16831418e-02
2.37477377e-01 7.88399339e-01 -4.42964405e-01 3.85522634e-01
-2.52688020e-01 4.23373401e-01 9.18340534e-02 8.57768282e-02
-3.37857492e-02 5.01431048e-01 4.73504141e-02 -3.38135958e-01
-5.33694811e-02 1.00221717e+00 -7.62324750e-01 1.36505365e+00
-8.92062485e-01 9.23956752e-01 4.27905023e-01 -4.16020244e-01
1.07703745e+00 1.22657132e+00 4.42616165e-01 -6.43014684e-02
1.63643416e-02 1.91637367e-01 9.21146274e-02 -3.93717401e-02
4.23994184e-01 -1.13304269e+00 -1.55956343e-01 6.92109168e-01
6.21592164e-01 -1.00874341e+00 -5.07002294e-01 -8.16707984e-02
6.38968527e-01 -2.96971411e-01 2.47365702e-02 -2.66762227e-01
2.57825673e-01 -5.96171379e-01 1.86258063e-01 4.37701732e-01
-2.55635798e-01 7.33694136e-01 2.62873292e-01 -8.80799443e-02
-1.30928755e+00 -1.76176691e+00 -1.21559061e-01 1.32120705e+00
-2.86213845e-01 -4.99539942e-01 -5.53832531e-01 -6.57430887e-02
-1.67222679e-01 1.33771610e+00 -4.85124648e-01 -1.99458718e-01
-7.26482153e-01 1.29392399e-02 1.01359832e+00 5.37749946e-01
-2.71372229e-01 -1.47397792e+00 -1.29692271e-01 5.68511784e-01
-1.77538633e-01 -9.27560627e-01 -1.31239545e+00 4.69947159e-01
-5.70061386e-01 -3.17986965e-01 -6.00976825e-01 -1.10132921e+00
-3.39291036e-01 -1.75503924e-01 9.03785884e-01 -4.41432834e-01
1.65229723e-01 5.98445117e-01 -4.11860757e-02 -4.32836801e-01
-1.23396480e+00 -3.16508383e-01 6.04714572e-01 1.20758703e-02
-1.45726174e-01 -1.00899696e+00 8.12696218e-02 2.56911516e-01
-7.66974688e-01 8.51620510e-02 -2.75499463e-01 1.07667148e+00
5.06527364e-01 9.02731810e-03 1.20389366e+00 -2.00949013e-01
9.52051461e-01 -2.37080500e-01 -3.97373646e-01 -1.13517217e-01
-4.54295166e-02 9.10688415e-02 9.66334760e-01 -8.70948672e-01
-9.84902382e-01 -1.43819734e-01 -4.40379649e-01 -7.53745496e-01
2.24924654e-01 2.16317043e-01 -9.90204215e-02 6.99454963e-01
8.52659047e-01 2.99633890e-01 3.07331420e-02 -5.24260461e-01
8.24972093e-01 8.57512593e-01 8.89848948e-01 -5.20335734e-01
8.25605392e-01 1.31101862e-01 -6.18260980e-01 -1.24482441e+00
-5.84325194e-01 -2.20841765e-01 -3.67492348e-01 -6.30944669e-02
9.60029602e-01 -9.18243408e-01 -5.44450700e-01 1.96437120e-01
-1.19342947e+00 -6.69019401e-01 -9.59669471e-01 9.67527688e-01
-1.34940231e+00 -1.64906964e-01 -1.01600027e+00 -9.01886046e-01
-4.37491030e-01 -1.16161084e+00 1.09983408e+00 -1.49101034e-01
-7.10535526e-01 -1.03308964e+00 5.33108056e-01 -7.71895871e-02
4.27254319e-01 -1.08118616e-01 1.10448456e+00 -6.03820801e-01
-2.46651709e-01 1.14010856e-01 5.34538805e-01 6.95770204e-01
6.28584743e-01 5.48383631e-02 -1.38510656e+00 -2.35526934e-01
5.94518363e-01 -5.46742439e-01 5.87518752e-01 5.95053315e-01
2.97294050e-01 -5.19375026e-01 8.79457071e-02 5.29185951e-01
7.68591821e-01 4.14199114e-01 1.83155447e-01 -4.40647185e-01
2.58892417e-01 5.34929395e-01 -7.43077463e-03 1.30737443e-02
-3.50022018e-01 8.69953394e-01 -2.14633197e-01 2.72703618e-01
-7.62338340e-01 -8.24971497e-01 1.02502728e+00 1.64489400e+00
5.06580472e-01 -1.42831564e-01 -4.22524691e-01 8.74629915e-01
-1.21860898e+00 -1.39883769e+00 4.61364686e-01 1.98390901e+00
1.32029724e+00 1.55536264e-01 2.09089831e-01 3.28879446e-01
6.80082023e-01 5.45488298e-01 -4.48250711e-01 -7.84817338e-01
-2.73378640e-01 5.80141306e-01 -1.36225238e-01 1.15470874e+00
-8.50667000e-01 1.06070876e+00 8.27520561e+00 7.57498980e-01
-1.40605295e+00 2.13198110e-01 -7.25973099e-02 -3.90210956e-01
-7.64884710e-01 -2.07617760e-01 -4.23899859e-01 2.36827791e-01
1.66912091e+00 -9.52427804e-01 9.60504889e-01 8.14965367e-01
2.31734738e-01 7.92355478e-01 -1.79302025e+00 9.92503703e-01
-9.86401811e-02 -1.47683370e+00 6.69963611e-03 -3.72663856e-01
5.80666125e-01 -6.96530640e-02 5.28198957e-01 6.83563471e-01
2.70850033e-01 -1.25255895e+00 1.07886350e+00 1.86965391e-01
9.68814552e-01 -5.47958612e-01 1.77701877e-04 2.15785310e-01
-1.23475921e+00 2.07941979e-02 -5.27673885e-02 1.43111721e-01
5.03316522e-01 -1.11695714e-01 -1.35141027e+00 2.22848371e-01
2.93718874e-01 3.66269588e-01 2.35415459e-01 3.54571074e-01
-2.06206024e-01 1.30853856e+00 -1.90930650e-01 1.08949468e-01
-3.83367799e-02 7.97647387e-02 1.07139194e+00 1.11673927e+00
3.25798035e-01 -6.43052682e-02 -2.00518444e-01 1.14237642e+00
-3.36790144e-01 1.70962542e-01 -7.92980313e-01 -6.03977561e-01
7.67389357e-01 6.33125603e-01 1.29808607e-02 -2.76391655e-01
-6.38338327e-02 8.07067096e-01 -1.10992700e-01 5.48750639e-01
-7.46716559e-01 -2.71911502e-01 1.05487323e+00 -2.56855398e-01
1.67210490e-01 -2.77267665e-01 -9.64015722e-02 -8.03804755e-01
-4.51334029e-01 -8.59836459e-01 -6.31904788e-03 -1.14651012e+00
-1.29858744e+00 8.83421957e-01 3.43133993e-02 -1.21297777e+00
-8.04335058e-01 -2.79188871e-01 -8.28181446e-01 1.28596938e+00
-7.34271586e-01 -8.70737016e-01 4.17792022e-01 3.18538517e-01
9.73615825e-01 -5.59156358e-01 1.39376974e+00 -1.65508303e-03
1.45083955e-02 6.04276001e-01 9.15111378e-02 7.09241629e-02
6.60019457e-01 -1.32197952e+00 6.40847802e-01 3.50530386e-01
6.89788163e-01 7.05563545e-01 1.23742354e+00 -2.89870232e-01
-1.02424932e+00 -8.73422027e-01 7.98591077e-01 -5.97962499e-01
1.03561819e+00 -5.40941000e-01 -9.69213665e-01 1.06117773e+00
8.11043084e-01 -3.58397633e-01 9.98706579e-01 3.65511551e-02
-4.28348601e-01 -1.53011650e-01 -7.71496117e-01 9.32606280e-01
5.77626169e-01 -1.42831969e+00 -1.32786119e+00 1.21745490e-01
1.66096187e+00 -2.87672788e-01 -7.86840737e-01 3.14388424e-02
6.32798910e-01 -6.58720136e-01 1.16189861e+00 -8.12043190e-01
1.59965143e-01 -1.25282556e-01 -6.18338406e-01 -1.74004710e+00
-6.93270266e-02 -1.27868176e+00 1.18280791e-01 1.16439629e+00
2.96642631e-01 -3.10830861e-01 3.11197877e-01 1.47115573e-01
-4.30747598e-01 -6.38856664e-02 -1.20716166e+00 -1.20406651e+00
4.12838310e-01 -6.51808798e-01 5.85431993e-01 7.44965732e-01
2.51740128e-01 8.55759621e-01 -7.52637982e-01 2.54109204e-01
1.69212148e-01 1.83285177e-01 5.99101067e-01 -7.13945389e-01
-6.97774172e-01 -4.84736532e-01 -1.23298921e-01 -1.04899454e+00
6.12535775e-01 -1.05634785e+00 3.33028018e-01 -8.27779710e-01
-5.04421234e-01 -1.56048518e-02 -5.05384922e-01 1.91339448e-01
3.01544964e-01 6.25309795e-02 5.52278280e-01 4.04589176e-02
5.01969159e-01 1.25360680e+00 9.27703321e-01 -2.80605972e-01
-5.51086307e-01 1.83063284e-01 -3.26467365e-01 5.45577526e-01
6.73333406e-01 -5.62465906e-01 -7.85343051e-01 -3.37356031e-02
-5.29421806e-01 8.00830901e-01 1.15518868e-01 -9.84469175e-01
-4.66912128e-02 -2.01897789e-02 -1.89282849e-01 -5.06102264e-01
1.09268773e+00 -6.93559647e-01 5.78002751e-01 2.80294895e-01
-9.71374273e-01 -3.77118915e-01 5.06493807e-01 3.68254006e-01
-4.83778477e-01 -3.26371610e-01 9.14771140e-01 4.45593685e-01
4.82252799e-02 -1.73466280e-01 -4.66249943e-01 -2.33779848e-02
5.41954935e-01 1.35501459e-01 -1.18531361e-01 -9.04619694e-01
-1.22184861e+00 -3.86014730e-01 6.58603981e-02 7.75711775e-01
5.17671287e-01 -1.86064649e+00 -7.41729259e-01 2.79402226e-01
-2.02742249e-01 -8.02792370e-01 7.69420862e-02 2.78200835e-01
-7.29611292e-02 5.48029423e-01 -2.30460033e-01 -4.30458277e-01
-1.35178435e+00 6.37145698e-01 5.74382484e-01 2.24941954e-01
-8.17398846e-01 7.36158431e-01 5.85749745e-01 -5.57035089e-01
3.20287883e-01 -5.78993797e-01 1.93972319e-01 5.89085631e-02
5.59508681e-01 3.06775160e-02 -3.57667923e-01 -8.76862764e-01
-1.41374588e-01 2.40040317e-01 4.10398275e-01 -1.04167235e+00
1.13372827e+00 -4.37476188e-02 3.48749012e-01 1.37357295e+00
1.25474191e+00 8.38515580e-01 -1.19412184e+00 -1.54368043e-01
-2.14007720e-01 3.90683711e-02 5.33155091e-02 -7.59793580e-01
-5.42332709e-01 9.76338744e-01 3.68729055e-01 5.23621261e-01
9.32960749e-01 5.28376102e-02 8.02952707e-01 1.94954365e-01
9.18994322e-02 -9.21903253e-01 4.05331463e-01 5.38653433e-01
1.61134529e+00 -4.94254529e-01 -6.04350746e-01 -1.28971562e-01
-8.77901137e-01 1.23374319e+00 3.30802388e-02 -2.96918035e-01
9.17150021e-01 3.40039015e-01 5.51250160e-01 1.72984377e-01
-1.00665104e+00 3.04917157e-01 3.51595521e-01 5.46042323e-01
5.31493366e-01 3.79372418e-01 3.10491920e-01 1.01274812e+00
-1.16534185e+00 -4.12861079e-01 6.23188436e-01 3.58083993e-02
-3.52909237e-01 -1.07211053e+00 -5.32962680e-01 -1.53181598e-01
-3.67143869e-01 -2.02979550e-01 -5.00207484e-01 5.10221064e-01
-5.29276192e-01 1.02447593e+00 9.04133096e-02 -5.53979814e-01
2.82515347e-01 7.75682151e-01 3.21129978e-01 -9.23932850e-01
-6.19994581e-01 6.66368127e-01 3.29480022e-01 -3.24211240e-01
-8.58967155e-02 -4.72965777e-01 -1.49485803e+00 1.90930933e-01
-2.00722933e-01 4.77802277e-01 5.29879451e-01 4.50047731e-01
-9.69908834e-02 8.24519873e-01 9.71996844e-01 -9.09589708e-01
-1.03007901e+00 -1.14473784e+00 -8.83828759e-01 3.74211460e-01
1.02793097e+00 -1.92964390e-01 -6.15771711e-01 4.71451789e-01] | [15.011167526245117, 6.555191516876221] |
0d1550b4-89c4-40b1-9e91-212e2fd0242d | specializing-multilingual-language-models-an | 2106.09063 | null | https://arxiv.org/abs/2106.09063v4 | https://arxiv.org/pdf/2106.09063v4.pdf | Specializing Multilingual Language Models: An Empirical Study | Pretrained multilingual language models have become a common tool in transferring NLP capabilities to low-resource languages, often with adaptations. In this work, we study the performance, extensibility, and interaction of two such adaptations: vocabulary augmentation and script transliteration. Our evaluations on part-of-speech tagging, universal dependency parsing, and named entity recognition in nine diverse low-resource languages uphold the viability of these approaches while raising new questions around how to optimally adapt multilingual models to low-resource settings. | ['Noah A. Smith', 'Ethan C. Chau'] | 2021-06-16 | null | https://aclanthology.org/2021.mrl-1.5 | https://aclanthology.org/2021.mrl-1.5.pdf | emnlp-mrl-2021-11 | ['transliteration', 'pretrained-multilingual-language-models'] | ['natural-language-processing', 'natural-language-processing'] | [-2.25396112e-01 -9.64756534e-02 -6.45547032e-01 -4.87667352e-01
-9.40041363e-01 -1.15318215e+00 5.07628500e-01 7.53718102e-03
-8.25778544e-01 1.04883623e+00 4.76344913e-01 -8.21033418e-01
4.33461219e-01 -3.47104907e-01 -6.21004283e-01 -3.67693906e-03
6.79419041e-02 6.65103734e-01 1.51104003e-01 -2.61838019e-01
-3.53336662e-01 5.70292830e-01 -5.05957723e-01 2.01477215e-01
1.03919804e+00 9.64739323e-02 2.20391750e-01 4.72193033e-01
-6.46936893e-01 7.74544775e-01 -5.66711843e-01 -7.07344115e-01
6.71756044e-02 -1.86550111e-01 -1.05224800e+00 -3.41924638e-01
1.91076785e-01 -3.16935703e-02 -1.17153004e-01 8.57836962e-01
5.74039638e-01 4.49892767e-02 2.83066243e-01 -7.15830803e-01
-9.09804344e-01 1.17365301e+00 -2.46504508e-02 5.41142046e-01
5.64629853e-01 5.48693053e-02 1.01018536e+00 -7.29842782e-01
1.05875206e+00 1.19464445e+00 8.14420223e-01 4.10059690e-01
-1.12705016e+00 -5.99035740e-01 3.43437493e-01 -2.57712722e-01
-1.54168987e+00 -7.36236930e-01 2.30515122e-01 -3.56395036e-01
1.72038090e+00 -8.76557156e-02 3.88356633e-02 1.22322965e+00
7.00043561e-03 6.49744749e-01 1.00184953e+00 -8.68750989e-01
-2.37936348e-01 4.04492319e-01 1.05024174e-01 6.44730508e-01
3.51717591e-01 -2.99092352e-01 -3.55884820e-01 -1.16238229e-01
7.17355609e-01 -3.94663215e-01 -1.78898200e-02 9.18829888e-02
-1.21895385e+00 7.58434772e-01 -6.22606464e-02 8.04689467e-01
-1.99373513e-01 -2.00423107e-01 6.41225338e-01 4.09466267e-01
6.24524057e-01 8.24235022e-01 -1.10925162e+00 -2.88274676e-01
-5.22085547e-01 -3.96639943e-01 1.20275772e+00 1.27344394e+00
6.90342069e-01 2.95017242e-01 1.34495050e-01 1.20907640e+00
9.31219682e-02 8.55572999e-01 6.70240700e-01 -4.91194218e-01
8.87676299e-01 4.48341548e-01 -4.66017090e-02 -1.86655283e-01
-5.68666995e-01 -2.23771200e-01 -2.19485223e-01 -5.90062141e-01
4.19687420e-01 -7.04067707e-01 -8.64296496e-01 1.71883512e+00
1.68876827e-01 -4.49258715e-01 4.91055220e-01 2.59168833e-01
6.28889382e-01 4.49026525e-01 7.61698306e-01 -1.40244603e-01
1.26608992e+00 -9.31150734e-01 -7.10965991e-01 -6.98192477e-01
1.10387504e+00 -1.20615494e+00 1.22217667e+00 -1.74336538e-01
-9.67034340e-01 -3.69498193e-01 -6.34598792e-01 -2.43178442e-01
-5.49749374e-01 2.08926722e-01 9.46650743e-01 8.39138091e-01
-9.19160187e-01 3.05839896e-01 -9.71496880e-01 -8.38900924e-01
-1.82625651e-01 1.79111481e-01 -7.63278782e-01 -2.02578813e-01
-1.44545257e+00 1.26463127e+00 6.43283308e-01 -2.56801337e-01
-3.33772570e-01 -4.94956136e-01 -1.10912621e+00 -2.37914816e-01
3.81876200e-01 -3.31859916e-01 1.29719031e+00 -8.39657426e-01
-1.54965210e+00 1.01565349e+00 -6.34163171e-02 -1.04730330e-01
1.91038325e-01 -4.94563013e-01 -7.65124738e-01 -4.52756912e-01
2.47391090e-01 3.65142643e-01 1.98205963e-01 -6.30590081e-01
-5.77728212e-01 -1.65548488e-01 -7.06898719e-02 4.05617952e-01
-3.38815451e-01 8.87638867e-01 -6.23767078e-01 -6.69517934e-01
-3.75497848e-01 -1.05752969e+00 -3.39698583e-01 -8.90683591e-01
-1.53124467e-01 -2.44523078e-01 2.00445309e-01 -1.18146503e+00
1.31151021e+00 -2.10573196e+00 4.55778316e-02 -7.03196675e-02
-5.79423904e-01 4.57436562e-01 -3.48030299e-01 6.38876319e-01
7.21936524e-02 4.96144474e-01 2.37032510e-02 -1.37590274e-01
-1.74815074e-01 6.98346913e-01 -9.06282365e-02 1.04280800e-01
4.64501739e-01 9.20456231e-01 -8.99632275e-01 -6.57914758e-01
1.46951815e-02 5.48492372e-01 -3.45461011e-01 2.27948040e-01
-1.70054048e-01 6.34655774e-01 -4.48499113e-01 8.05183470e-01
1.18281826e-01 1.79422110e-01 7.43180335e-01 1.96615577e-01
-4.88538027e-01 8.68834496e-01 -9.27757502e-01 1.75045907e+00
-8.90241206e-01 4.27730769e-01 -7.70194307e-02 -3.35788250e-01
6.61389649e-01 4.43204999e-01 6.24276437e-02 -7.61907876e-01
-3.22607048e-02 4.56027359e-01 1.14526913e-01 -6.61370099e-01
5.84130943e-01 -1.92628384e-01 -5.24307549e-01 3.66290569e-01
4.64272350e-01 2.47232597e-02 3.01053941e-01 -4.81523201e-02
9.76957858e-01 4.17763591e-01 8.32459807e-01 -1.56260833e-01
3.19048136e-01 2.34613582e-01 5.72011292e-01 6.71661675e-01
-1.40607581e-01 7.68105164e-02 1.66745037e-01 -3.36088985e-01
-1.05708826e+00 -9.54358280e-01 -1.94753945e-01 1.81797302e+00
-5.72753787e-01 -3.49932790e-01 -4.66515303e-01 -1.06977320e+00
-2.52530873e-01 8.29007924e-01 -7.29595721e-02 2.61426449e-01
-1.20329428e+00 -6.97683156e-01 1.14679837e+00 8.20720851e-01
6.60671527e-03 -1.19809937e+00 6.08461052e-02 4.59816277e-01
-3.72524291e-01 -1.85205460e+00 -5.88294387e-01 5.52378058e-01
-6.74863398e-01 -7.58642137e-01 -4.31238443e-01 -1.05284059e+00
2.84364551e-01 -1.87572181e-01 1.39653122e+00 -2.34148085e-01
2.19550982e-01 2.50997156e-01 -5.48578262e-01 -2.81378329e-01
-8.59105229e-01 8.41597319e-01 2.24730685e-01 -6.71137512e-01
4.60933298e-01 -3.40080053e-01 2.49299482e-01 2.03615680e-01
-7.58952916e-01 -4.32377577e-01 8.63349557e-01 4.87671614e-01
5.66020191e-01 -6.21198654e-01 3.80534112e-01 -1.34950125e+00
5.77498496e-01 -4.70212817e-01 -4.32622999e-01 6.36548281e-01
-3.66604716e-01 4.29213166e-01 9.49031591e-01 -5.10318220e-01
-1.41609144e+00 6.74791336e-02 -5.99302709e-01 1.73966467e-01
-2.76393473e-01 7.43170738e-01 -5.34487188e-01 -1.81443319e-01
6.24754071e-01 -4.88787331e-02 -8.16283286e-01 -9.18441772e-01
8.48289132e-01 7.22686887e-01 5.15479386e-01 -8.06549966e-01
5.94915688e-01 -1.06858477e-01 -6.32105350e-01 -1.01863098e+00
-7.87653029e-01 -6.56716943e-01 -1.02803278e+00 4.61017311e-01
8.98711920e-01 -1.19169939e+00 1.01422466e-01 2.18510032e-01
-1.21490586e+00 -6.41401172e-01 -2.87536889e-01 6.63587928e-01
5.96644096e-02 4.02115732e-01 -8.41835380e-01 -3.31476986e-01
-2.80618370e-01 -9.46361721e-01 6.82310402e-01 1.40178557e-02
-3.72556090e-01 -1.45300055e+00 5.35860598e-01 3.15670073e-01
3.89470279e-01 -2.52594352e-01 9.91588175e-01 -1.22492993e+00
2.49896385e-02 -9.50921550e-02 -5.80108091e-02 3.44702780e-01
1.39891401e-01 -1.24433801e-01 -5.93581140e-01 -2.91148931e-01
-5.57757556e-01 -4.88531053e-01 2.76716441e-01 -1.18768975e-01
2.63267070e-01 -4.16420490e-01 -1.80615276e-01 8.55746686e-01
1.33305466e+00 1.85443565e-01 2.80173719e-01 5.36761940e-01
8.95207286e-01 3.12957406e-01 3.47084075e-01 1.64682567e-02
5.75422525e-01 5.73835909e-01 -4.19679701e-01 -7.40016401e-02
-3.04201812e-01 -3.94109964e-01 5.85230350e-01 1.38869107e+00
-1.08930245e-02 -2.50989467e-01 -1.45874023e+00 5.32092452e-01
-1.55389047e+00 -3.86697620e-01 7.21589103e-02 2.01271081e+00
1.25765538e+00 -3.06987241e-02 -1.23991102e-01 -8.23163211e-01
6.02919519e-01 2.21833110e-01 -1.72531068e-01 -6.94746375e-01
-3.49308491e-01 4.19927359e-01 7.69771457e-01 5.45849383e-01
-1.14901495e+00 1.73002839e+00 7.12781334e+00 4.86765146e-01
-1.28550708e+00 5.72954059e-01 1.44169822e-01 4.33776081e-01
-1.50953636e-01 2.14341089e-01 -1.39201665e+00 1.07961826e-01
1.39799750e+00 7.12665170e-02 3.74543667e-01 1.01547551e+00
-1.91315934e-01 2.17954397e-01 -1.03953898e+00 3.95897120e-01
6.63722530e-02 -8.69107783e-01 7.24088624e-02 -3.65504891e-01
7.62523592e-01 7.63860941e-01 -3.68181348e-01 8.07717681e-01
9.62717414e-01 -8.38293433e-01 4.34280723e-01 -4.69293334e-02
1.12715411e+00 -4.45418924e-01 9.06160831e-01 2.00034291e-01
-1.14883339e+00 3.57832521e-01 -2.37007037e-01 -4.75451574e-02
5.49141049e-01 -1.96097828e-02 -1.03387630e+00 4.47352856e-01
5.73917925e-01 2.00766668e-01 -7.03843355e-01 5.95972180e-01
-7.19809711e-01 8.42132688e-01 -4.51299816e-01 5.96266389e-02
2.47306064e-01 5.60963750e-02 3.23096842e-01 1.98276567e+00
-1.85125582e-02 -2.13105559e-01 5.67040563e-01 8.70487317e-02
-3.61990333e-01 7.03911602e-01 -6.19706750e-01 -4.51103806e-01
5.48348427e-01 1.14887297e+00 -6.54486835e-01 -2.30571970e-01
-8.50040257e-01 1.00962162e+00 9.69228685e-01 2.85641164e-01
-5.80757618e-01 -2.74316639e-01 6.55177832e-01 -9.37023908e-02
3.41091484e-01 -6.87018812e-01 -1.28035188e-01 -1.48782432e+00
-4.29992639e-02 -1.12093198e+00 5.90419173e-01 -3.67118478e-01
-1.22371709e+00 8.24705005e-01 7.77710229e-03 -6.06184304e-01
-3.22019964e-01 -6.44305110e-01 -2.73595572e-01 9.28760767e-01
-1.65940189e+00 -1.61426270e+00 2.84282655e-01 4.43489075e-01
4.79818225e-01 -1.70238748e-01 1.10879624e+00 5.00100434e-01
-7.67723978e-01 7.96433210e-01 1.86664984e-01 6.28525555e-01
1.14190495e+00 -1.12072206e+00 9.02453780e-01 1.12375140e+00
5.17602086e-01 8.09274137e-01 4.74474967e-01 -7.77635336e-01
-1.07997739e+00 -1.11415350e+00 1.59281814e+00 -8.23188245e-01
1.14956033e+00 -5.52770913e-01 -1.01528513e+00 1.38204241e+00
3.90084863e-01 -4.86865528e-02 9.79868591e-01 7.92168617e-01
-6.10099494e-01 2.96438098e-01 -8.04959536e-01 5.99806547e-01
1.12201703e+00 -7.95816422e-01 -6.82692468e-01 3.29620302e-01
9.17045832e-01 -4.21496361e-01 -1.28828216e+00 1.71721086e-01
4.77224141e-01 -2.77283281e-01 7.88765490e-01 -1.00842750e+00
-1.46799356e-01 1.98179588e-01 -1.65078148e-01 -1.45286477e+00
-3.18429202e-01 -7.96224058e-01 4.78675127e-01 1.66716135e+00
9.78695095e-01 -7.82803416e-01 1.91332161e-01 5.30849397e-01
-2.47392923e-01 -1.94433391e-01 -8.13693762e-01 -9.15419936e-01
5.30633926e-01 -3.76118153e-01 4.48000640e-01 1.38169193e+00
1.92429885e-01 9.09656763e-01 -2.38176107e-01 1.68315813e-01
6.60504622e-04 -4.19535249e-01 6.76260233e-01 -9.97214615e-01
-5.54004788e-01 -9.67804864e-02 -1.12929702e-01 -7.15276957e-01
6.80587769e-01 -9.84630048e-01 9.97589082e-02 -1.45239842e+00
1.04669705e-01 -7.76752889e-01 -1.52891129e-01 9.57965732e-01
-4.01495278e-01 -1.97699517e-02 2.50584662e-01 2.42007971e-01
-7.13305533e-01 1.11922711e-01 6.06331110e-01 2.61095166e-01
-5.01870751e-01 -1.91302449e-01 -6.46612108e-01 8.56676936e-01
8.28024507e-01 -7.41848707e-01 5.46547025e-02 -1.00665164e+00
4.74267513e-01 1.13622248e-01 -6.11539006e-01 -6.72204435e-01
3.35034728e-02 -2.91027963e-01 2.25336209e-01 1.31378993e-01
-1.67182207e-01 -5.56977689e-01 -1.46209419e-01 7.42155090e-02
-1.32877439e-01 5.67482650e-01 5.14191806e-01 6.69913962e-02
-1.02582172e-01 -2.98364520e-01 5.94698071e-01 -4.49227333e-01
-1.01578569e+00 4.47001308e-02 -4.77096915e-01 7.57889807e-01
7.95335531e-01 2.33077526e-01 -3.28664303e-01 5.07913902e-02
-6.89598143e-01 5.79758268e-03 5.13592303e-01 7.98455536e-01
-1.50151104e-01 -8.18898797e-01 -6.43885016e-01 3.09032708e-01
2.06279427e-01 -4.36469197e-01 -2.17307821e-01 6.90272033e-01
-5.73641002e-01 8.63927484e-01 -2.33809009e-01 -1.43659770e-01
-1.17624795e+00 5.47908723e-01 1.60493761e-01 -8.00179183e-01
-2.25486860e-01 8.11101437e-01 -1.28540114e-01 -1.08776128e+00
-7.66827017e-02 -3.12520534e-01 -2.53553480e-01 -1.02986500e-01
4.59681116e-02 3.90771031e-02 2.83951163e-01 -8.38175476e-01
-6.62670851e-01 2.74216741e-01 -3.96543086e-01 -3.00588489e-01
1.12309122e+00 -1.72991991e-01 -6.40601814e-02 5.52764475e-01
8.96633446e-01 6.44044697e-01 -7.80612409e-01 -4.26438153e-01
5.16742289e-01 1.13953002e-01 -2.77983487e-01 -9.75330234e-01
-5.58671653e-01 6.09955847e-01 7.60549307e-02 -2.00275391e-01
6.36580408e-01 2.82960325e-01 8.12324882e-01 8.00803781e-01
5.92281163e-01 -1.16739333e+00 -5.10329485e-01 1.19665849e+00
4.27901119e-01 -1.40675104e+00 -2.04433277e-01 -2.93450862e-01
-8.76084268e-01 8.12063932e-01 6.73607171e-01 2.95518816e-01
4.80868310e-01 6.22602761e-01 5.44159770e-01 1.05497345e-01
-5.17805219e-01 -3.71512353e-01 6.08776733e-02 6.35492802e-01
1.14517486e+00 2.85448462e-01 -5.12506306e-01 5.34632564e-01
-2.20218956e-01 -2.68581539e-01 2.97935814e-01 1.16101897e+00
-3.29396911e-02 -1.61471009e+00 -1.75601572e-01 -1.89645756e-02
-9.88917708e-01 -6.96455121e-01 -4.43652213e-01 1.16890228e+00
7.52965212e-02 7.91697562e-01 -1.06208295e-01 8.56106654e-02
6.20015264e-01 6.28583848e-01 4.45188731e-01 -1.07358849e+00
-9.63260055e-01 -6.00668676e-02 7.02169240e-01 -4.26596105e-01
-1.84006542e-01 -7.67848074e-01 -1.15753722e+00 8.11619014e-02
-3.44618738e-01 2.01475531e-01 7.99688876e-01 1.07207394e+00
3.07556957e-01 2.39392325e-01 1.62445486e-01 -2.62811095e-01
-5.59397876e-01 -1.12737226e+00 -1.25745997e-01 2.64981747e-01
-1.17513366e-01 -1.89232633e-01 -1.49970710e-01 1.76996097e-01] | [10.458941459655762, 9.943202018737793] |
f29fcb12-cc22-48c7-b0ec-d8ff0ebf8b5c | mmea-entity-alignment-for-multi-modal | null | null | https://link.springer.com/chapter/10.1007/978-3-030-55130-8_12 | http://home.ustc.edu.cn/~liyichen/assets/files/LiyiChen_KSEM20.pdf | MMEA: Entity Alignment for Multi-Modal Knowledge Graphs | Entity alignment plays an essential role in the knowledge graph (KG) integration. Though large efforts have been made on exploring the association of relational embeddings between different knowledge graphs, they may fail to effectively describe and integrate the multimodal knowledge in the real application scenario. To that end, in this paper, we propose a novel solution called Multi-Modal Entity Alignment (MMEA) to address the problem of entity alignment in a multi-modal view. Specifically, we first design a novel multi-modal knowledge embedding method to generate the entity representations of relational, visual and numerical knowledge, respectively. Along this line, multiple representations of different types of knowledge will be integrated via a multimodal knowledge fusion module. Extensive experiments on two public datasets clearly demonstrate the effectiveness of the MMEA model with a significant margin compared with the state-of-the-art methods. | ['Enhong Chen', 'Zhefeng Wang', 'Tong Xu', 'Yijun Wang', 'Zhi Li', 'Liyi Chen'] | 2020-08-20 | null | null | null | null | ['multi-modal-entity-alignment'] | ['knowledge-base'] | [-3.38131756e-01 4.15427610e-02 -3.41483831e-01 -1.21537752e-01
-6.88671291e-01 -5.38126767e-01 5.56838095e-01 4.35973227e-01
-6.57595620e-02 3.03635597e-01 4.67706710e-01 -7.34659582e-02
-3.78332973e-01 -1.01382923e+00 -4.96138543e-01 -5.71990252e-01
2.17366174e-01 1.37071371e-01 -2.77176183e-02 -3.20030123e-01
-1.02739207e-01 2.95378566e-01 -1.40711546e+00 2.12697774e-01
9.45535123e-01 8.12263787e-01 -2.20521346e-01 1.20495178e-01
-3.74548525e-01 7.62382925e-01 -2.07548872e-01 -1.07719803e+00
-1.15902849e-01 -8.60359520e-02 -8.82042885e-01 9.05571207e-02
2.67704248e-01 -4.74172756e-02 -5.26656210e-01 1.21916735e+00
5.59640110e-01 2.23074734e-01 7.15913117e-01 -1.53715456e+00
-1.19050610e+00 8.51911724e-01 -7.58559108e-01 -1.66266963e-01
6.27729893e-01 -3.53969872e-01 1.27216768e+00 -1.16250145e+00
6.49858475e-01 1.32756603e+00 5.95364332e-01 -5.24144582e-02
-1.15274262e+00 -4.09553468e-01 1.72681093e-01 5.64121246e-01
-1.85251021e+00 -1.32494057e-02 1.05167508e+00 -3.94717276e-01
5.70168376e-01 1.71679616e-01 7.66294777e-01 8.95131767e-01
-2.34765455e-01 8.67986083e-01 7.45056987e-01 -3.50761950e-01
-2.94019163e-01 3.98622543e-01 2.02849165e-01 8.63979340e-01
5.78162491e-01 -5.57274461e-01 -6.04941666e-01 -2.20933720e-01
6.05879009e-01 6.70774430e-02 -4.87786293e-01 -7.73032308e-01
-1.45208848e+00 8.92145634e-01 6.18309796e-01 4.05527413e-01
-4.58984643e-01 -2.80911550e-02 4.09728736e-01 2.92730667e-02
2.23578185e-01 3.27812821e-01 -7.21751451e-02 2.51959503e-01
-3.44980150e-01 9.34434608e-02 5.70872307e-01 1.04195905e+00
9.68570173e-01 -7.58149102e-02 -1.17396228e-01 7.62213469e-01
6.25607371e-01 3.87598693e-01 2.82978982e-01 -4.03887272e-01
6.56698704e-01 1.29123974e+00 -3.29483859e-02 -1.59373736e+00
-3.62702101e-01 -3.02078933e-01 -8.20550859e-01 -3.30746889e-01
5.31267636e-02 5.66843012e-03 -5.07724702e-01 1.60444915e+00
6.94076061e-01 3.72770816e-01 6.25311434e-01 7.63680339e-01
1.30124247e+00 6.06390417e-01 2.59832263e-01 1.21324189e-01
1.73710990e+00 -9.18811142e-01 -1.08500171e+00 9.88949910e-02
6.34845197e-01 -7.68689930e-01 7.43159950e-01 -2.85399020e-01
-7.72857666e-01 -5.81465483e-01 -1.04334331e+00 -1.15951255e-01
-8.74955595e-01 3.13420802e-01 7.66440153e-01 6.01490140e-01
-6.80109322e-01 -9.40410513e-03 -5.61132491e-01 -4.13541764e-01
1.90858990e-01 2.25914381e-02 -8.42889905e-01 -3.39204043e-01
-1.47125733e+00 1.05672300e+00 8.63845348e-01 2.51053452e-01
-1.89715803e-01 -6.70988619e-01 -1.28451645e+00 6.10958412e-02
5.42434335e-01 -1.04658675e+00 5.99869072e-01 -4.67463017e-01
-9.36444342e-01 5.70240796e-01 1.09214867e-02 -1.35848001e-02
5.73904775e-02 -2.02550992e-01 -8.40460896e-01 1.86518162e-01
-2.64052209e-02 4.50942278e-01 4.27787542e-01 -1.76347125e+00
-5.21563411e-01 -4.04144019e-01 5.26432931e-01 5.50625920e-01
-6.89326644e-01 -2.25288168e-01 -9.04676080e-01 -6.81988955e-01
1.29863963e-01 -7.73190856e-01 6.24769814e-02 -4.73144710e-01
-5.64434052e-01 -3.33159775e-01 6.55667424e-01 -8.01533282e-01
1.47898209e+00 -2.15328503e+00 7.15716362e-01 4.63316977e-01
3.56953561e-01 7.87049159e-02 -2.85172965e-02 8.07124019e-01
-2.11727549e-03 1.41767994e-01 -8.58884752e-02 -4.28696662e-01
3.45675588e-01 2.08086267e-01 -2.67680526e-01 2.26670176e-01
2.37102211e-01 1.30363142e+00 -9.64303732e-01 -8.22287142e-01
8.29869807e-02 8.19811463e-01 -3.05516422e-01 1.28034472e-01
1.94583923e-01 5.20196594e-02 -6.06889427e-01 1.07314289e+00
6.41587198e-01 -4.54569250e-01 6.10321701e-01 -1.08951521e+00
2.29980037e-01 -4.28971052e-01 -1.39330673e+00 1.78702807e+00
-2.03400284e-01 1.10474989e-01 -2.58465260e-01 -9.21222210e-01
7.67187893e-01 3.19483757e-01 5.06129324e-01 -3.96330982e-01
1.54009059e-01 -2.35667117e-02 -2.02111512e-01 -5.53449810e-01
1.00271785e+00 -2.07988426e-01 -2.76297361e-01 9.50612947e-02
3.06675732e-01 3.85500073e-01 1.62861586e-01 5.05769968e-01
6.79124296e-01 1.25201732e-01 3.93820494e-01 2.01169983e-01
6.11466229e-01 3.79993506e-02 3.10426623e-01 1.93240985e-01
-6.01025745e-02 2.90117025e-01 4.10273701e-01 6.34187907e-02
-5.99490881e-01 -1.11714292e+00 -1.20882280e-02 6.64972425e-01
6.12134218e-01 -8.07707489e-01 -4.57777858e-01 -7.87161827e-01
2.37068087e-01 4.40520257e-01 -6.93430960e-01 -4.27502751e-01
-2.24940643e-01 -7.93592393e-01 7.14610577e-01 7.12238193e-01
4.18499827e-01 -7.00724959e-01 -1.75134130e-02 -2.18229555e-02
-4.19403136e-01 -1.35716724e+00 -2.71982789e-01 -3.28251541e-01
-3.85581613e-01 -1.24577892e+00 -5.57146609e-01 -8.23559821e-01
7.53193617e-01 4.24941033e-01 9.11780894e-01 -1.08167216e-01
-6.85510039e-02 9.15404379e-01 -6.87301934e-01 -5.78174181e-02
-5.06731980e-02 4.29898947e-02 -4.12405236e-03 4.04589504e-01
4.74139512e-01 -4.10588503e-01 -4.61575478e-01 5.21014668e-02
-1.24101865e+00 1.53361544e-01 7.22100794e-01 7.07240343e-01
6.49768353e-01 3.66739631e-01 6.90373421e-01 -6.82697475e-01
7.69649446e-01 -8.20609748e-01 -2.00317115e-01 8.98669541e-01
-4.12894070e-01 1.27336338e-01 3.58589500e-01 -3.94111693e-01
-9.38433409e-01 -7.85745233e-02 2.23690942e-01 -7.39631116e-01
1.62865505e-01 1.23141205e+00 -5.11356175e-01 -1.57534897e-01
1.04837812e-01 1.35139629e-01 -1.96076408e-01 -5.09731233e-01
1.03891790e+00 3.92709464e-01 4.32166457e-01 -6.44196749e-01
1.10990000e+00 4.19540644e-01 3.38970721e-02 -4.48104143e-01
-5.83872437e-01 -5.25998116e-01 -6.35141969e-01 -1.39014840e-01
1.00880671e+00 -1.06369710e+00 -6.25555158e-01 1.75302953e-01
-1.06315970e+00 4.90300924e-01 -1.42507434e-01 6.24173164e-01
-1.77484274e-01 7.35772848e-01 -1.55837610e-01 -5.65079391e-01
-2.18644366e-01 -1.02169120e+00 9.08148706e-01 4.24099267e-01
3.51127237e-02 -1.28679693e+00 1.23491615e-01 6.31350517e-01
1.26261055e-01 4.03356880e-01 1.18492758e+00 -7.06262648e-01
-6.29336476e-01 -2.27404580e-01 -5.37335932e-01 9.94969755e-02
3.23015779e-01 1.55129015e-01 -6.52231097e-01 -5.50884940e-02
-6.41021192e-01 -1.96722195e-01 7.15397477e-01 -1.81779519e-01
6.63671494e-01 -7.18803778e-02 -3.78108978e-01 3.41809571e-01
1.60865378e+00 4.18349765e-02 5.84178746e-01 2.71392733e-01
1.28017271e+00 6.37611032e-01 7.66026437e-01 4.00602341e-01
1.22854936e+00 8.23061764e-01 3.69739473e-01 -1.16457820e-01
-2.61548031e-02 -5.16445696e-01 1.76193312e-01 1.34551430e+00
-9.07988697e-02 -2.49320671e-01 -1.00745797e+00 8.30081940e-01
-2.19680023e+00 -9.40466881e-01 -1.24393754e-01 1.83254135e+00
7.04883039e-01 -4.00020987e-01 -6.75382419e-03 -1.67622920e-02
8.05039167e-01 1.12147383e-01 -1.05685636e-01 -3.18059907e-03
-3.85255843e-01 -2.15375692e-01 2.67703712e-01 2.76721627e-01
-1.20416474e+00 8.81281376e-01 5.31791544e+00 9.55353141e-01
-6.37752950e-01 6.60319030e-02 -2.76264474e-02 4.67457801e-01
-8.04128408e-01 1.25771612e-01 -7.57228792e-01 1.12873599e-01
5.01905203e-01 -3.76114547e-01 2.63580590e-01 6.26535118e-01
-5.48759580e-01 1.29977524e-01 -9.03828382e-01 1.16223300e+00
2.71282703e-01 -1.29392982e+00 5.01780927e-01 -6.58899248e-02
7.09691882e-01 -5.59497118e-01 -1.79634262e-02 4.42991436e-01
1.56641036e-01 -9.27569330e-01 4.80923504e-01 9.82941568e-01
5.33754885e-01 -1.08889163e+00 9.45901573e-01 -6.78477883e-02
-1.78938174e+00 2.14474738e-01 -1.60032257e-01 6.08518004e-01
4.39350367e-01 3.70969117e-01 -5.05299687e-01 1.73222649e+00
5.81390917e-01 7.98610151e-01 -8.90616715e-01 8.68959665e-01
-1.63769290e-01 -1.49583649e-02 2.51731370e-03 2.22622529e-01
1.00914642e-01 -2.60382831e-01 2.85797298e-01 1.13209426e+00
4.33600336e-01 4.56677563e-03 1.37459680e-01 8.03080380e-01
-2.53828049e-01 5.25542259e-01 -8.13909650e-01 -5.06608069e-01
6.04408205e-01 1.52182102e+00 -3.82927358e-01 -4.09852058e-01
-7.81430483e-01 7.67279088e-01 5.35531759e-01 5.16630590e-01
-1.03766358e+00 -5.64198732e-01 6.39591038e-01 -4.08877075e-01
4.63505208e-01 -1.78744867e-01 -1.38439285e-02 -1.44321561e+00
1.64272889e-01 -7.28320599e-01 6.77642405e-01 -8.80569637e-01
-1.56760561e+00 4.77719188e-01 2.32965544e-01 -1.28969884e+00
7.90744424e-02 -4.02542830e-01 -3.02084476e-01 7.20953882e-01
-1.68133235e+00 -1.73296034e+00 -4.13150728e-01 8.20000827e-01
-1.50842041e-01 -1.90746278e-01 8.91910911e-01 6.52468324e-01
-7.27730989e-01 6.32190883e-01 -5.36031835e-02 3.24240297e-01
6.39047325e-01 -1.12378526e+00 -1.47640526e-01 7.93853402e-01
4.71196234e-01 9.42047119e-01 3.56113255e-01 -6.82627022e-01
-1.97102308e+00 -1.11676347e+00 6.79943204e-01 -4.07854617e-01
9.56364632e-01 3.61223295e-02 -1.12949359e+00 8.22531700e-01
4.52993453e-01 6.15585819e-02 1.22174752e+00 2.07533911e-01
-6.64623022e-01 -3.70925404e-02 -9.12124932e-01 7.40842998e-01
8.10074508e-01 -7.87132204e-01 -8.01517904e-01 -2.83792596e-02
8.70188773e-01 -2.52653927e-01 -1.68742096e+00 8.05283785e-01
4.90073293e-01 -5.13403893e-01 1.28598416e+00 -7.05952406e-01
4.56426769e-01 -7.29663968e-01 -5.46186030e-01 -1.31149459e+00
-2.79483140e-01 -3.61750536e-02 -6.12039447e-01 1.82216871e+00
2.78348476e-01 -4.97082770e-01 2.58693546e-01 4.30607051e-01
1.35444567e-01 -8.03318381e-01 -8.62151742e-01 -5.32943249e-01
-2.14186609e-01 -2.33229399e-01 7.78352201e-01 1.50521827e+00
3.63829851e-01 4.72297043e-01 -4.12712634e-01 6.51099086e-01
5.20923853e-01 4.26309764e-01 8.15984786e-01 -1.00248992e+00
-5.46922982e-02 -4.48763967e-01 -9.32676494e-01 -5.08559406e-01
2.88418889e-01 -1.12078953e+00 -6.62837446e-01 -1.96042609e+00
4.26581979e-01 -2.24395037e-01 -6.48958266e-01 6.05373025e-01
-4.76143211e-01 1.11321263e-01 1.74894869e-01 6.42133728e-02
-7.86776543e-01 9.96041298e-01 1.10950959e+00 -2.92724103e-01
1.46678403e-01 -7.47471750e-01 -1.12009060e+00 4.88935649e-01
6.83192074e-01 -1.09920524e-01 -5.81144154e-01 -2.74887860e-01
4.66297835e-01 -5.35052158e-02 4.85006332e-01 -6.65057421e-01
3.98267299e-01 -1.38585225e-01 1.30570382e-01 -6.66577518e-01
4.51560557e-01 -1.05295265e+00 5.14924884e-01 -7.62273818e-02
9.41007584e-02 3.20185214e-01 2.95228422e-01 8.48467648e-01
-7.08683729e-01 3.71464044e-02 2.15770140e-01 2.75391936e-01
-1.01269913e+00 1.62874386e-01 1.03651635e-01 -6.65875077e-02
1.20718420e+00 -2.54759118e-02 -7.15239406e-01 -2.16691777e-01
-6.45431757e-01 4.98680055e-01 4.40784127e-01 6.93660736e-01
8.35446894e-01 -2.08018398e+00 -4.06125546e-01 -8.41093063e-02
6.80422783e-01 -2.87709504e-01 5.70728660e-01 1.06957459e+00
-1.56022668e-01 2.64556348e-01 -1.41127720e-01 -1.43004805e-01
-1.26243448e+00 8.09548616e-01 8.75700340e-02 -3.77575338e-01
-4.56349850e-01 4.97146755e-01 3.61804999e-02 -5.69885731e-01
1.40936896e-01 7.36927763e-02 -7.01134920e-01 5.69803476e-01
1.52171344e-01 3.07933331e-01 -2.29211792e-01 -1.14930356e+00
-4.74695772e-01 8.43069017e-01 8.89056399e-02 1.75247043e-01
1.04014993e+00 -1.84731245e-01 -2.95534968e-01 5.97098470e-01
1.18258643e+00 2.46593088e-01 -4.40255076e-01 -5.27987480e-01
-7.72554502e-02 -4.05128419e-01 -1.00502759e-01 -4.78746861e-01
-1.22713542e+00 6.96888685e-01 3.74609590e-01 2.92307734e-01
9.81557250e-01 1.57431722e-01 7.08755672e-01 3.44559759e-01
2.03527197e-01 -8.91877949e-01 3.13026197e-02 7.28750676e-02
7.63176918e-01 -1.27483201e+00 1.48125038e-01 -7.42249012e-01
-1.03731322e+00 1.04747534e+00 7.69059658e-01 3.20288837e-01
6.37995481e-01 -2.88615286e-01 -1.45208817e-02 -6.16245449e-01
-3.91975343e-01 -5.87816656e-01 8.26215506e-01 4.47045863e-01
2.59979069e-01 1.57465175e-01 -2.32233495e-01 8.24338675e-01
9.65919867e-02 -3.39194179e-01 2.12732717e-01 9.53484952e-01
-7.71418586e-02 -1.21386993e+00 -3.05973351e-01 5.04042283e-02
-2.17251614e-01 -4.35636938e-02 -4.28334713e-01 8.99560153e-01
7.49527067e-02 8.14997196e-01 -3.43854964e-01 -7.47616768e-01
4.93938744e-01 1.89342380e-01 3.25540394e-01 -2.35683709e-01
-4.03317600e-01 -1.46992132e-01 2.13321522e-01 -2.57329226e-01
-8.79012764e-01 -3.43075097e-01 -1.30940342e+00 -3.49931121e-01
-3.14992517e-01 9.93355960e-02 4.12752509e-01 8.12010825e-01
5.70567012e-01 7.00100780e-01 3.20794910e-01 -4.74431545e-01
-1.08474091e-01 -5.77977240e-01 -6.66625977e-01 7.12743282e-01
-1.69321507e-01 -1.04048014e+00 -2.03763366e-01 -1.29257128e-01] | [8.674450874328613, 7.728212833404541] |
a0dc17cf-01d0-4974-9b53-8ccb1e691861 | on-projection-methods-for-functional-time | 2105.04399 | null | https://arxiv.org/abs/2105.04399v1 | https://arxiv.org/pdf/2105.04399v1.pdf | On projection methods for functional time series forecasting | Two nonparametric methods are presented for forecasting functional time series (FTS). The FTS we observe is a curve at a discrete-time point. We address both one-step-ahead forecasting and dynamic updating. Dynamic updating is a forward prediction of the unobserved segment of the most recent curve. Among the two proposed methods, the first one is a straightforward adaptation to FTS of the $k$-nearest neighbors methods for univariate time series forecasting. The second one is based on a selection of curves, termed \emph{the curve envelope}, that aims to be representative in shape and magnitude of the most recent functional observation, either a whole curve or the observed part of a partially observed curve. In a similar fashion to $k$-nearest neighbors and other projection methods successfully used for time series forecasting, we ``project'' the $k$-nearest neighbors and the curves in the envelope for forecasting. In doing so, we keep track of the next period evolution of the curves. The methods are applied to simulated data, daily electricity demand, and NOx emissions and provide competitive results with and often superior to several benchmark predictions. The approach offers a model-free alternative to statistical methods based on FTS modeling to study the cyclic or seasonal behavior of many FTS. | ['Hanlin Shang', 'Raúl Jiménez', 'Antonio Elías'] | 2021-05-10 | null | null | null | null | ['univariate-time-series-forecasting'] | ['time-series'] | [ 1.49121985e-01 -1.62883028e-01 -1.71298981e-02 -3.86288673e-01
-3.94614607e-01 -5.49908638e-01 6.28492057e-01 1.00590251e-01
-4.71415780e-02 9.94338691e-01 1.30670354e-01 -3.68946642e-01
-5.41636407e-01 -9.41785812e-01 -7.51999617e-01 -1.10700715e+00
-3.69923115e-01 3.58655274e-01 9.60531458e-02 -3.05689186e-01
2.04335541e-01 7.05645561e-01 -1.61686039e+00 4.08145934e-02
8.19004953e-01 1.27945387e+00 6.83423206e-02 2.68458128e-01
-3.36476147e-01 1.88099429e-01 -3.00933301e-01 9.85449851e-02
3.25347304e-01 -2.07304522e-01 -1.06696993e-01 -1.77381411e-02
-2.06497520e-01 -8.24654251e-02 -1.96664080e-01 6.82540417e-01
6.18698681e-03 3.15402478e-01 8.75212431e-01 -1.27798474e+00
-2.61387303e-02 4.17226791e-01 -3.96180719e-01 2.11009532e-01
2.71257550e-01 -2.30048314e-01 4.25959051e-01 -9.10117328e-01
4.38985229e-01 8.49029839e-01 1.02520192e+00 -9.71158743e-02
-1.60710609e+00 -4.41620469e-01 -2.26471946e-02 -3.57670039e-02
-1.38472986e+00 -1.10564806e-01 1.11828375e+00 -9.52503324e-01
6.46464348e-01 4.49202269e-01 5.31487882e-01 6.49020135e-01
4.57639247e-01 2.76589155e-01 1.36038065e+00 -3.18813086e-01
2.88765788e-01 3.00188214e-01 1.64632082e-01 1.07025661e-01
-2.56480813e-01 5.78315973e-01 -6.44941777e-02 -6.74711049e-01
3.84581149e-01 2.36510277e-01 -3.82845730e-01 -2.99529970e-01
-1.04745436e+00 7.18311429e-01 1.02791496e-01 5.56466937e-01
-7.58974910e-01 -3.27388830e-02 2.56140083e-01 4.53641981e-01
1.07090271e+00 -1.03947543e-01 -6.99124932e-01 -1.88642532e-01
-1.31860912e+00 4.18559015e-01 1.01031613e+00 7.58659542e-01
9.73995149e-01 3.37290585e-01 -4.09110665e-01 4.80787754e-01
-1.11301251e-01 7.37413526e-01 4.06361759e-01 -7.86158442e-01
3.36816788e-01 3.05939078e-01 6.19952261e-01 -8.40342283e-01
-3.67754906e-01 -5.97821653e-01 -7.56242096e-01 1.99302986e-01
5.72807968e-01 -2.42889971e-01 -4.38602954e-01 1.58599615e+00
2.83848435e-01 2.68413872e-01 -2.81397939e-01 1.89705864e-02
-9.42724571e-03 1.09183049e+00 -3.29390228e-01 -8.90925825e-01
8.18116426e-01 -4.66582119e-01 -6.27619088e-01 7.24680781e-01
2.61195421e-01 -7.83076584e-01 4.32453901e-01 4.60604012e-01
-8.94523203e-01 -8.19472134e-01 -5.37059724e-01 7.40698874e-01
-5.09589732e-01 1.67273447e-01 -7.94993639e-02 5.74188709e-01
-1.05624413e+00 1.31040049e+00 -6.78495884e-01 -5.36399037e-02
-3.63569111e-01 4.09144014e-02 1.34062439e-01 4.08986390e-01
-9.83058929e-01 7.41587698e-01 3.81551206e-01 2.39736140e-01
-3.61955285e-01 -9.25094366e-01 -3.76267433e-01 1.03204422e-01
9.32938159e-02 -6.26661777e-02 1.09244657e+00 -7.38945305e-01
-1.57929790e+00 2.64497191e-01 -6.58585012e-01 -6.18825316e-01
7.88582563e-01 1.96278989e-01 -1.03050983e+00 -3.74662369e-01
-1.53284177e-01 8.74437764e-03 1.32996976e+00 -1.04095626e+00
-5.37930131e-01 -2.02043161e-01 -8.58915687e-01 -2.14602426e-01
-7.32887089e-02 -3.06823224e-01 2.32370675e-01 -7.73186445e-01
3.04646730e-01 -1.01362634e+00 -8.75452310e-02 -3.18418980e-01
-1.05922125e-01 -4.72845763e-01 1.21486831e+00 -9.55849171e-01
1.42312849e+00 -2.30347443e+00 -2.19786108e-01 6.50512218e-01
-3.15560371e-01 -2.07846627e-01 3.56006354e-01 1.12192237e+00
-4.05585378e-01 -2.94501185e-01 -4.64231163e-01 -2.66623855e-01
6.18200935e-02 2.34487981e-01 -1.02907419e+00 6.86255157e-01
-1.24933027e-01 5.41656673e-01 -6.55957758e-01 3.17555130e-01
3.01500529e-01 2.71555692e-01 7.78300092e-02 -3.76038975e-03
-3.13076645e-01 7.54384041e-01 -2.30228946e-01 2.22444311e-01
8.73322904e-01 1.21460378e-01 -7.26297274e-02 -4.97461781e-02
-1.03860295e+00 -8.49321559e-02 -1.10482395e+00 1.01185274e+00
-2.78138727e-01 4.74838912e-01 -2.13388622e-01 -1.21204984e+00
1.57879710e+00 5.76233745e-01 8.36624801e-01 -5.03657401e-01
-2.16564164e-01 5.53372741e-01 -3.86914134e-01 -2.04674318e-01
4.33326334e-01 -1.79980427e-01 9.87895280e-02 3.15959811e-01
-4.21463847e-01 -2.93421805e-01 1.93894610e-01 -5.82234621e-01
6.40199065e-01 3.34263951e-01 4.17469084e-01 -7.09033668e-01
8.12024295e-01 -1.24953501e-01 5.73717237e-01 4.53031093e-01
6.91838237e-03 1.79468662e-01 6.13493025e-01 -6.73018932e-01
-1.13618970e+00 -1.11195123e+00 -3.94750774e-01 8.70791256e-01
-4.78339732e-01 7.56087303e-02 -3.18693399e-01 -1.11873582e-01
5.05969882e-01 1.29913747e+00 -8.21288347e-01 2.36123517e-01
-7.14532018e-01 -4.37277526e-01 -5.00827059e-02 3.75990957e-01
1.99534908e-01 -1.04145622e+00 -2.68950701e-01 6.85671329e-01
6.69105276e-02 -6.72064841e-01 -4.85331893e-01 1.96464062e-01
-1.11721897e+00 -5.83207846e-01 -9.27052736e-01 -3.32049549e-01
4.26470250e-01 -3.39878313e-02 8.42581809e-01 -6.17651224e-01
3.15769404e-01 3.39251220e-01 6.27243444e-02 -7.55067348e-01
-5.01667857e-01 -4.37398732e-01 1.24218956e-01 4.85956788e-01
2.57536024e-02 -1.02572191e+00 -4.82673228e-01 5.73934317e-01
-5.26070058e-01 -4.45926785e-01 2.13459522e-01 6.02146566e-01
9.57382500e-01 6.16879404e-01 5.83019555e-01 -6.71408117e-01
5.60722232e-01 -7.96782553e-01 -1.17048788e+00 1.13616638e-01
-9.78068352e-01 5.14779054e-02 1.15876341e+00 -3.74834806e-01
-9.39453602e-01 8.74640942e-02 2.09839895e-01 -5.56172371e-01
-2.13664547e-01 6.29403889e-01 5.08587480e-01 1.39018819e-01
4.14623469e-01 7.84225345e-01 -2.43957881e-02 -8.75349581e-01
1.26813933e-01 3.58538538e-01 7.29336202e-01 -5.87589979e-01
1.02695608e+00 7.35406458e-01 2.26536736e-01 -1.05762637e+00
-4.17392969e-01 -8.59761953e-01 -7.10992038e-01 -3.53600591e-01
4.91002321e-01 -5.43495655e-01 -7.16189802e-01 4.22541767e-01
-1.09155607e+00 -1.38440609e-01 -8.70084941e-01 5.02424955e-01
-8.31362486e-01 2.66440600e-01 -1.13985986e-01 -1.12831354e+00
-1.95537701e-01 -5.95493078e-01 8.63363385e-01 3.88232283e-02
-1.13224201e-01 -1.20842612e+00 6.60152018e-01 -6.11075282e-01
6.46375835e-01 7.89645135e-01 1.03533709e+00 -4.70918596e-01
-9.85626783e-03 -4.25357282e-01 1.41295180e-01 6.25550985e-01
2.15390995e-01 1.98049739e-01 -8.44239354e-01 -5.16804814e-01
5.67848563e-01 5.05924106e-01 6.31005824e-01 7.52210677e-01
1.28565133e+00 -4.29857552e-01 -4.68600065e-01 4.20041740e-01
1.42969155e+00 7.06424892e-01 4.19429362e-01 -1.07277445e-01
-4.46321443e-02 6.78615868e-01 4.06599730e-01 6.33742213e-01
8.91042948e-02 6.69995308e-01 1.05328932e-01 1.86564937e-01
5.03164470e-01 -2.39448935e-01 4.35340226e-01 1.03541446e+00
-4.09347892e-01 1.05857797e-01 -8.93597126e-01 4.75325763e-01
-1.79184198e+00 -1.30113757e+00 -4.17366236e-01 2.70371938e+00
2.82678127e-01 2.06558615e-01 5.16189396e-01 2.83300698e-01
9.54065144e-01 2.76013225e-01 -5.09128451e-01 -5.59674263e-01
-2.23231148e-02 2.51403630e-01 5.13411820e-01 3.10090512e-01
-9.08909976e-01 -1.39860198e-01 6.55922079e+00 8.78423989e-01
-1.05801344e+00 -5.58292791e-02 3.58691603e-01 4.90533769e-01
-3.97962898e-01 7.86286741e-02 -8.62502635e-01 8.79976749e-01
1.32489789e+00 -6.11346483e-01 6.34667516e-01 8.81128609e-01
7.88360417e-01 -5.38614877e-02 -9.24195707e-01 6.49842143e-01
-4.42812055e-01 -1.34717906e+00 -3.39052260e-01 2.91469634e-01
1.06324661e+00 5.25294580e-02 1.16506629e-01 3.79399419e-01
-1.92955419e-01 -7.15336859e-01 7.99567997e-01 1.25790334e+00
6.78890109e-01 -7.30679512e-01 6.20501995e-01 8.27059150e-01
-1.61986268e+00 -2.69147515e-01 -2.36653775e-01 -1.10137261e-01
4.07926947e-01 9.35373008e-01 -6.76802635e-01 8.42502296e-01
5.45888066e-01 6.70361161e-01 3.54022011e-02 1.00848222e+00
3.09908092e-01 7.49431670e-01 -5.22552371e-01 1.96211729e-02
2.20123842e-01 -7.84486115e-01 7.64382839e-01 9.47039545e-01
1.02072287e+00 5.56935631e-02 1.73432738e-01 9.21420872e-01
3.49383861e-01 -9.45710577e-03 -7.62197912e-01 3.48636657e-02
4.44492459e-01 8.36941004e-01 -3.76993001e-01 -3.44173133e-01
-5.21528125e-01 3.80391330e-01 -2.63035297e-01 4.46347743e-01
-6.80710733e-01 -2.04736039e-01 2.59955496e-01 2.56979465e-01
6.66197300e-01 -4.34061259e-01 -1.43717617e-01 -5.56210458e-01
1.17081761e-01 -3.10613930e-01 3.50519180e-01 -6.51512563e-01
-1.45909071e+00 5.33113480e-01 3.38686645e-01 -1.68469083e+00
-6.50255740e-01 -4.23380494e-01 -1.05063963e+00 1.13072431e+00
-1.37489104e+00 -5.31485438e-01 -1.53118938e-01 8.01284969e-01
3.54975820e-01 5.44641279e-02 9.57310319e-01 -1.30896583e-01
-9.56845358e-02 -2.01483220e-01 1.20351970e+00 -5.78961492e-01
9.08110514e-02 -1.32092237e+00 3.76856387e-01 4.94025916e-01
-2.51672536e-01 2.32106552e-01 1.01990843e+00 -8.03558528e-01
-8.92332315e-01 -1.05870092e+00 9.86438990e-01 -1.94908217e-01
9.30130661e-01 -1.77331299e-01 -1.05777800e+00 5.76772451e-01
-1.91902950e-01 -4.76329364e-02 3.35175514e-01 -2.66250253e-01
1.60172120e-01 -4.30638552e-01 -1.00912833e+00 -1.97119489e-02
3.73456776e-01 -3.63494337e-01 -5.85700393e-01 2.89694279e-01
3.45055461e-01 -8.37775841e-02 -1.20783782e+00 5.72292864e-01
6.33052230e-01 -1.01309550e+00 7.76171505e-01 -1.30287707e-01
-1.34567648e-01 -3.20867062e-01 -3.75581086e-01 -1.41234148e+00
-3.95558447e-01 -1.12351203e+00 -2.59551883e-01 1.01559341e+00
2.07647115e-01 -1.09875000e+00 5.80963850e-01 2.45951802e-01
-1.81401506e-01 -7.00554848e-01 -1.26787245e+00 -1.29827380e+00
1.56734869e-01 -4.62214023e-01 9.27297354e-01 7.97514200e-01
-3.31441104e-01 -5.04321277e-01 -3.68590772e-01 1.45679548e-01
7.46614814e-01 7.08445251e-01 6.24653935e-01 -1.51718128e+00
-3.40974092e-01 -4.49401200e-01 -2.14723833e-02 -7.72468626e-01
-2.83823926e-02 -6.57619834e-01 -5.56624718e-02 -9.14944589e-01
-3.90950978e-01 -3.31151754e-01 -4.84162271e-01 -1.06657535e-01
4.72271651e-01 -2.77760297e-01 -7.12509304e-02 4.31677580e-01
5.24583876e-01 7.65890062e-01 8.71469438e-01 1.37278274e-01
-5.60773551e-01 8.54992390e-01 3.42565000e-01 8.31061900e-01
6.87017858e-01 -4.06341016e-01 -3.49128544e-01 3.57299179e-01
-1.29805073e-01 6.90488815e-01 3.30640465e-01 -8.99953544e-01
1.12287037e-01 -2.90807784e-01 3.24818462e-01 -1.48288953e+00
3.59683514e-01 -9.60880697e-01 8.24795663e-01 6.18737042e-01
9.98549312e-02 3.83876443e-01 1.62446395e-01 9.03459489e-01
-2.35215291e-01 -9.12280679e-02 5.63421786e-01 1.15925111e-02
-5.47591507e-01 3.41512471e-01 -3.90348881e-01 -4.74024385e-01
9.58172917e-01 -3.69057477e-01 -1.92434326e-01 -5.58514237e-01
-1.15912962e+00 -6.78821728e-02 1.28491163e-01 -1.57261714e-02
1.63933933e-01 -1.29153252e+00 -6.47804737e-01 2.70688504e-01
-1.70492053e-01 -4.99730885e-01 3.11105907e-01 1.09820533e+00
-1.20251261e-01 8.38432908e-01 -8.99364501e-02 -6.59962177e-01
-5.68099499e-01 6.06733382e-01 3.23672205e-01 -3.98552924e-01
-6.08065248e-01 1.44343108e-01 3.84450406e-02 -3.17147225e-01
3.07628419e-02 -7.41583645e-01 -1.50913641e-01 2.95667648e-01
3.28275651e-01 9.38864172e-01 1.91476539e-01 -6.11581326e-01
-1.27416730e-01 6.47034883e-01 5.86379468e-01 -1.19396225e-01
1.60198569e+00 -2.71760553e-01 -3.89766902e-01 1.27074659e+00
1.29899096e+00 1.85544044e-01 -1.52262664e+00 -3.24921608e-01
3.35663289e-01 -1.83516234e-01 -2.00672075e-01 -5.85498810e-01
-5.55444837e-01 4.51198608e-01 5.18502831e-01 9.12115574e-01
1.11691010e+00 -4.84817743e-01 5.59635580e-01 1.55129001e-01
4.26662952e-01 -1.04480183e+00 -6.60620153e-01 3.82473618e-01
1.16284323e+00 -5.96515059e-01 -7.13499933e-02 -3.32369000e-01
-1.84912339e-01 1.62759423e+00 -2.88419932e-01 -2.24704310e-01
1.22427797e+00 -4.75900657e-02 -3.77374768e-01 1.01096928e-01
-7.08353043e-01 7.50346556e-02 5.89179933e-01 2.24406630e-01
1.62160993e-01 4.24587697e-01 -4.83288288e-01 1.98028833e-01
-4.56982434e-01 2.22617432e-01 2.48662651e-01 6.66208982e-01
-3.78639549e-01 -8.86261642e-01 -6.34329498e-01 6.04479909e-01
-8.61281455e-02 2.06104234e-01 2.93177187e-01 9.36283529e-01
1.90807395e-02 7.59452581e-01 2.01283202e-01 -5.59781492e-03
6.05570316e-01 6.17094040e-01 6.69641346e-02 -3.07153333e-02
-1.85975268e-01 3.45414817e-01 -2.79310811e-02 -5.08316815e-01
-2.14785188e-01 -1.18078661e+00 -7.46256173e-01 -5.54013073e-01
-2.71400303e-01 3.24565321e-01 8.76545131e-01 9.70150173e-01
-6.32338300e-02 1.09774634e-01 1.41285479e+00 -1.00012922e+00
-9.42697048e-01 -8.14984858e-01 -1.06122661e+00 1.96799412e-01
4.27536786e-01 -5.65007687e-01 -6.86546981e-01 -5.06249405e-02] | [6.852479934692383, 3.2490885257720947] |
9234517b-bf4a-46cd-8513-edfefc2feb55 | the-many-faces-of-1-lipschitz-neural-networks | 2104.05097 | null | https://arxiv.org/abs/2104.05097v6 | https://arxiv.org/pdf/2104.05097v6.pdf | Pay attention to your loss: understanding misconceptions about 1-Lipschitz neural networks | Lipschitz constrained networks have gathered considerable attention in the deep learning community, with usages ranging from Wasserstein distance estimation to the training of certifiably robust classifiers. However they remain commonly considered as less accurate, and their properties in learning are still not fully understood. In this paper we clarify the matter: when it comes to classification 1-Lipschitz neural networks enjoy several advantages over their unconstrained counterpart. First, we show that these networks are as accurate as classical ones, and can fit arbitrarily difficult boundaries. Then, relying on a robustness metric that reflects operational needs we characterize the most robust classifier: the WGAN discriminator. Next, we show that 1-Lipschitz neural networks generalize well under milder assumptions. Finally, we show that hyper-parameters of the loss are crucial for controlling the accuracy-robustness trade-off. We conclude that they exhibit appealing properties to pave the way toward provably accurate, and provably robust neural networks. | ['Alberto González-Sanz', 'Corentin Friedrich', 'Mathieu Serrurier', 'Thibaut Boissin', 'Franck Mamalet', 'Louis Béthune'] | 2021-04-11 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [ 7.70585537e-02 1.38199791e-01 -4.50092226e-01 -6.76023483e-01
-7.36938477e-01 -7.13703334e-01 3.59394848e-01 1.33889422e-01
-7.35213935e-01 9.52081323e-01 -2.16880292e-01 -1.18497908e-01
-5.65019965e-01 -6.19974732e-01 -9.29455698e-01 -1.03267324e+00
-2.20896795e-01 2.49677479e-01 7.28262216e-02 -6.46862984e-02
9.69654471e-02 7.61937261e-01 -1.43181574e+00 -3.62107366e-01
8.21252763e-01 1.42721403e+00 -4.31515932e-01 5.08698463e-01
3.84708852e-01 6.46126211e-01 -3.90471041e-01 -6.09573901e-01
4.06117857e-01 -1.69535220e-01 -7.25015402e-01 -2.99662828e-01
6.56299949e-01 -2.10158974e-01 -1.76923007e-01 1.26277530e+00
4.52131063e-01 2.94562191e-01 1.03302753e+00 -1.38057506e+00
-7.86144137e-01 6.75327003e-01 -1.23965353e-01 -2.69076005e-02
-2.64394164e-01 -1.83346316e-01 1.33577418e+00 -6.12679660e-01
3.82923394e-01 9.61809814e-01 9.46210206e-01 7.93505371e-01
-1.04834771e+00 -4.55155492e-01 1.56824306e-01 2.58506000e-01
-1.33458233e+00 -5.93248546e-01 6.48756623e-01 -4.83155131e-01
3.35206956e-01 2.05537498e-01 1.79126456e-01 1.12768257e+00
5.78731634e-02 7.97145963e-01 1.01787651e+00 -3.77908766e-01
3.40605080e-01 2.26374403e-01 1.42737895e-01 9.80209410e-01
4.91489679e-01 2.94269882e-02 -2.22495392e-01 1.84614807e-01
7.22682118e-01 -1.93747386e-01 -6.21875525e-01 -6.27705932e-01
-6.88917994e-01 1.02203846e+00 5.91533959e-01 4.83293474e-01
1.07296705e-01 2.96323150e-01 4.05340165e-01 4.62745756e-01
6.55389309e-01 5.39980948e-01 -4.71209466e-01 8.86310413e-02
-7.68720448e-01 1.96516663e-01 7.88188398e-01 9.19876993e-01
4.80885953e-01 3.41204144e-02 9.09210369e-02 8.24419200e-01
1.56072844e-02 1.61061868e-01 9.08968151e-02 -1.09010053e+00
2.89443076e-01 1.92754880e-01 1.68639541e-01 -9.10013437e-01
-5.18480539e-01 -7.37341225e-01 -1.05196786e+00 2.26063758e-01
8.79326761e-01 -9.84864756e-02 -3.40167224e-01 2.15124512e+00
-6.87129200e-02 -1.38919905e-01 -6.12620562e-02 8.75163496e-01
8.92640948e-02 9.37017649e-02 -1.46935716e-01 -1.18818089e-01
6.47598803e-01 -6.58744752e-01 -5.24483681e-01 1.54811844e-01
6.57521486e-01 -1.19124651e-01 1.13432324e+00 6.40821934e-01
-1.16708601e+00 -2.45629162e-01 -1.35719562e+00 -2.64366716e-01
-5.49800992e-01 -4.25635986e-02 3.78827542e-01 8.47049594e-01
-1.01565409e+00 1.08255804e+00 -7.66755104e-01 3.40284482e-02
7.45213807e-01 3.55939031e-01 -3.58204663e-01 -1.69299264e-02
-1.30694628e+00 1.05126524e+00 3.96395713e-01 4.43076700e-01
-9.51127052e-01 -5.97320855e-01 -7.72508740e-01 2.54552662e-02
2.38690943e-01 -4.12573367e-01 1.16777444e+00 -9.23855126e-01
-1.50340641e+00 8.93241584e-01 2.42391005e-01 -7.79033363e-01
1.23660731e+00 -2.02252403e-01 3.32051259e-03 1.91749558e-02
-2.62992144e-01 4.66843843e-01 7.93460429e-01 -9.64564919e-01
-5.72324514e-01 -5.40097117e-01 4.05916244e-01 -1.81468174e-01
-5.85269988e-01 -1.65425256e-01 1.02827445e-01 -5.20258009e-01
-8.36996883e-02 -6.68315232e-01 -1.38611883e-01 6.04626238e-01
-3.68105918e-01 -4.47115242e-01 4.95780021e-01 -1.46327808e-01
7.45378017e-01 -2.07219553e+00 1.73683390e-01 1.45528674e-01
3.00349295e-01 2.29829133e-01 -9.47706923e-02 -6.87135085e-02
1.01235658e-01 4.30481285e-01 -4.28392589e-01 -4.14995849e-01
5.37801266e-01 3.37375760e-01 -1.99283198e-01 1.22513092e+00
3.14060867e-01 8.87246072e-01 -7.61257291e-01 -3.17907244e-01
-1.10743552e-01 6.28980160e-01 -3.82344067e-01 -6.30411059e-02
-1.85360551e-01 2.72735626e-01 -4.70565081e-01 3.79903644e-01
6.32917881e-01 -3.75858881e-02 -1.98042214e-01 6.71669170e-02
1.06530741e-01 -7.07027912e-02 -9.25066471e-01 1.33296788e+00
-5.18657744e-01 8.43452036e-01 3.97534132e-01 -1.47779882e+00
8.59655738e-01 1.93694308e-02 7.86998272e-02 -1.90264851e-01
4.20614839e-01 3.88892770e-01 -2.36937627e-01 -4.08326656e-01
1.92352191e-01 -4.33623463e-01 8.40133280e-02 2.35443562e-01
-4.16761963e-04 4.25880961e-03 5.28264716e-02 -2.61319458e-01
6.93734586e-01 -3.72496210e-02 2.81216241e-02 -7.03452647e-01
5.79017460e-01 -3.71639729e-01 4.84946966e-01 8.39380026e-01
-5.26688933e-01 6.54659331e-01 8.17152381e-01 -3.27725232e-01
-9.63660657e-01 -9.82531726e-01 -6.63134754e-01 1.15447807e+00
1.20033592e-01 2.60846794e-01 -9.86826658e-01 -8.47894311e-01
8.80506113e-02 2.83309251e-01 -9.98658180e-01 -3.43804300e-01
-3.77011031e-01 -6.32275403e-01 1.00360334e+00 8.15200806e-01
5.23103356e-01 -5.40104449e-01 -4.43592221e-01 -1.29088998e-01
1.55380234e-01 -1.08507836e+00 -3.02309483e-01 4.81005579e-01
-8.66981268e-01 -1.15454423e+00 -1.05093074e+00 -7.46620893e-01
6.79293931e-01 -1.80673733e-01 9.35773253e-01 1.18331544e-01
-7.40623847e-02 2.64428794e-01 -5.68840131e-02 -4.19944435e-01
-3.00626844e-01 4.73853260e-01 4.22498047e-01 2.58430298e-02
6.82646334e-02 -5.42031646e-01 -3.44070345e-01 3.96409124e-01
-9.93984640e-01 -5.48468113e-01 3.41833651e-01 7.09209263e-01
5.53956449e-01 9.42131206e-02 7.61655092e-01 -8.45439315e-01
6.36763334e-01 -2.60866195e-01 -8.74179244e-01 4.01130676e-01
-6.73557460e-01 2.67430276e-01 1.19370914e+00 -4.87551808e-01
-6.04529798e-01 -1.37097865e-01 -2.82508403e-01 -2.91699529e-01
-1.39494568e-01 2.84013450e-01 -3.13326180e-01 -4.83973920e-01
7.90313780e-01 -9.99291241e-02 -1.10286489e-01 -4.43789542e-01
4.14962381e-01 4.20145303e-01 5.92231095e-01 -8.75590622e-01
8.10118556e-01 5.57495534e-01 2.88392931e-01 -7.60162830e-01
-1.19119155e+00 -4.79472196e-03 -8.07571232e-01 -1.81651890e-01
6.78109765e-01 -4.33987588e-01 -8.53297353e-01 4.28915650e-01
-9.40970063e-01 -4.73331839e-01 -4.57352042e-01 3.02751869e-01
-7.96393812e-01 2.28442296e-01 -6.08767271e-01 -1.04236197e+00
1.63235068e-02 -1.11975145e+00 6.62151158e-01 7.84802660e-02
7.71873444e-02 -1.38864136e+00 -2.02933341e-01 5.56898303e-02
5.58539629e-01 5.79108775e-01 8.40812922e-01 -7.61517167e-01
-2.22854257e-01 -4.15911973e-01 -3.48045111e-01 8.56684387e-01
-1.26374111e-01 1.04728058e-01 -1.21127093e+00 -2.53470421e-01
9.79214683e-02 -5.83342075e-01 1.04597116e+00 4.56529468e-01
1.56031430e+00 -5.63176274e-01 7.93658197e-02 1.02770221e+00
1.39126813e+00 -3.08356285e-01 1.88327461e-01 3.48668158e-01
5.17167270e-01 7.91269422e-01 1.23644598e-01 1.60316408e-01
8.96842927e-02 2.62083709e-01 6.27972364e-01 3.35588783e-01
3.08753610e-01 9.08225589e-03 1.87346891e-01 6.99435592e-01
-1.35440871e-01 -1.53746650e-01 -6.36710942e-01 4.47367936e-01
-1.73886883e+00 -7.33167648e-01 6.75285235e-02 2.25061989e+00
9.89440382e-01 4.46599454e-01 9.87501740e-02 5.18998265e-01
6.46100283e-01 1.08046137e-01 -7.99923539e-01 -4.05019492e-01
-4.97134209e-01 -3.42011340e-02 7.54724264e-01 7.91541517e-01
-1.18759441e+00 6.11345172e-01 6.68259811e+00 8.86605859e-01
-1.00513005e+00 2.17770875e-01 9.25902128e-01 3.45777981e-02
-2.52529413e-01 -5.18019676e-01 -7.17006028e-01 2.40153044e-01
6.98981166e-01 3.91208157e-02 2.69134074e-01 1.05985522e+00
4.18825559e-02 2.07795516e-01 -1.46275854e+00 7.50113964e-01
-3.32667492e-02 -1.18613410e+00 -3.26374680e-01 1.56407226e-02
8.57995927e-01 2.82494031e-04 4.45248425e-01 1.00606516e-01
2.67960906e-01 -1.37288785e+00 8.71881902e-01 4.89042103e-01
8.11734796e-01 -9.52042103e-01 8.50274324e-01 4.69771445e-01
-7.85509765e-01 -1.16838284e-01 -6.99908793e-01 -8.69502034e-03
-2.82378882e-01 7.72851825e-01 -2.05752164e-01 3.00075591e-01
3.76977473e-01 5.96229076e-01 -3.64164889e-01 9.85887885e-01
-3.00147295e-01 4.12077099e-01 -4.07360345e-01 -3.18744272e-01
4.35614228e-01 -1.86260343e-01 2.20700949e-01 1.20120132e+00
9.26877484e-02 -7.70694166e-02 -2.46557370e-01 1.04747987e+00
-6.50865257e-01 3.31190564e-02 -5.99246442e-01 2.05562592e-01
2.07273588e-01 1.06409669e+00 -7.22755253e-01 -1.82932913e-02
-3.10645401e-02 6.57650948e-01 7.00265050e-01 3.63635182e-01
-9.24127519e-01 -5.77552199e-01 7.65434682e-01 -8.09606984e-02
1.72440082e-01 -2.63601005e-01 -3.77926677e-01 -1.17514443e+00
3.43571961e-01 -5.71477771e-01 1.51267469e-01 -2.52457738e-01
-1.54177666e+00 5.14195919e-01 -1.22010693e-01 -7.79946566e-01
2.72619352e-02 -1.22992885e+00 -3.44430029e-01 5.60370147e-01
-1.84038436e+00 -6.72645628e-01 -8.08444172e-02 4.33769912e-01
2.11779565e-01 1.26130998e-01 5.83892047e-01 2.56444633e-01
-8.49092364e-01 1.04839027e+00 4.65080857e-01 3.85008603e-01
3.91209722e-01 -1.39560068e+00 9.08675697e-03 8.36603165e-01
5.26941791e-02 5.41372895e-01 7.31877863e-01 1.54177591e-01
-1.24164689e+00 -1.09686613e+00 7.40889966e-01 -4.36086476e-01
8.83706331e-01 -4.01358902e-01 -1.01047528e+00 5.10297537e-01
-3.19580525e-01 3.98580253e-01 3.65957797e-01 1.73531435e-02
-7.14798152e-01 -2.81825423e-01 -1.40164888e+00 5.11186659e-01
1.12128162e+00 -6.36896133e-01 -2.50095397e-01 3.50520968e-01
4.84204918e-01 -1.64797351e-01 -9.86289561e-01 3.98893207e-01
6.49330854e-01 -1.15313780e+00 7.44846523e-01 -7.80443370e-01
4.06955123e-01 1.78768232e-01 -4.09635991e-01 -1.15036988e+00
5.31722233e-02 -7.80272067e-01 -1.35205999e-01 8.43500495e-01
4.70336646e-01 -9.78401363e-01 9.02475357e-01 6.09453261e-01
-9.55453962e-02 -1.16733444e+00 -1.18635988e+00 -1.28255117e+00
8.20682406e-01 -6.42140925e-01 3.44230384e-01 1.05291593e+00
2.17425488e-02 -1.72963291e-01 -1.92641750e-01 -3.33292484e-02
9.02682602e-01 -3.79685611e-01 2.11314514e-01 -1.57870901e+00
-3.57585698e-02 -1.18561816e+00 -5.07470727e-01 -1.27653837e+00
6.22651219e-01 -8.32047284e-01 3.52715701e-01 -9.70458984e-01
-1.54091701e-01 -6.87630773e-01 -3.81942958e-01 3.39835167e-01
1.58223972e-01 3.84271204e-01 -1.22411929e-01 2.18817852e-02
-6.08145773e-01 4.33493078e-01 1.08957613e+00 -1.74308315e-01
1.27920583e-01 2.16774836e-01 -7.63611615e-01 8.47394586e-01
9.93845224e-01 -2.82841772e-01 -2.92387128e-01 -6.05351567e-01
4.04937506e-01 -4.46431845e-01 3.51365089e-01 -8.76924217e-01
1.89502388e-01 -4.39411476e-02 2.52432793e-01 1.35012582e-01
1.74216658e-01 -7.59293258e-01 -6.16523504e-01 3.32408935e-01
-9.89075780e-01 -1.85955375e-01 -2.20457897e-01 5.08568943e-01
7.56246299e-02 -6.43591285e-01 1.29446411e+00 1.17982410e-01
-4.65065651e-02 7.11948335e-01 -4.98646796e-02 6.39365435e-01
7.85754144e-01 -1.22145019e-01 -1.93828538e-01 -3.94640982e-01
-6.51649296e-01 1.36408195e-01 3.49472374e-01 1.78472847e-01
4.84393120e-01 -1.22223639e+00 -6.97872758e-01 6.65317252e-02
-2.12122090e-02 1.91897020e-01 -7.96665028e-02 8.95175815e-01
-5.32373786e-01 4.40522373e-01 1.89225916e-02 -4.79246348e-01
-7.13264406e-01 4.11314875e-01 7.47935653e-01 7.32966289e-02
-5.02407134e-01 1.09494483e+00 -8.78130049e-02 -3.21342915e-01
1.11464751e+00 -7.27339625e-01 1.19907081e-01 9.84656904e-03
5.36082089e-01 5.15035510e-01 2.50252604e-01 -5.50647855e-01
-3.58213633e-01 6.37189746e-01 1.92756012e-01 -3.78483012e-02
1.42623866e+00 3.50088887e-02 4.08104695e-02 6.07319474e-01
1.68275917e+00 -3.90678376e-01 -1.59189105e+00 -2.18676403e-01
3.49661887e-01 -1.22637987e-01 -1.79881565e-02 -3.96321982e-01
-1.23256850e+00 1.20401263e+00 2.50111639e-01 6.63010716e-01
9.62568402e-01 -1.83075424e-02 5.41307092e-01 6.49052083e-01
2.04102084e-01 -1.11907756e+00 -2.84372121e-01 5.78865349e-01
7.84135997e-01 -1.16090465e+00 -1.36177301e-01 9.89322830e-03
-1.18869580e-01 1.34360075e+00 3.13942641e-01 -3.88233036e-01
7.45023072e-01 2.74926811e-01 -9.21921283e-02 3.25275093e-01
-5.60209870e-01 -1.39123127e-02 2.96535492e-01 7.50381291e-01
2.88013011e-01 -1.19336985e-01 -1.15682550e-01 6.21461093e-01
-3.22398335e-01 -2.35080034e-01 4.40553933e-01 3.59955549e-01
-6.23400748e-01 -6.80607736e-01 -1.34758521e-02 1.94073409e-01
-8.11295688e-01 1.87114820e-01 -3.92059863e-01 9.85392094e-01
5.12686595e-02 7.57077694e-01 -1.69169962e-01 -1.45564854e-01
1.64808705e-01 -9.96193849e-03 7.98210561e-01 -8.72814804e-02
-1.32581934e-01 -5.23484170e-01 -2.19184920e-01 -4.06985164e-01
-4.05200690e-01 -4.28951293e-01 -8.65419328e-01 -4.87532377e-01
-3.98782283e-01 2.96147913e-01 6.97457075e-01 1.22178698e+00
-2.20426202e-01 1.29889846e-01 8.03694308e-01 -8.13253760e-01
-1.29661036e+00 -7.12364018e-01 -7.32625723e-01 3.23983207e-02
8.99597943e-01 -6.30999327e-01 -8.85510683e-01 -3.04565549e-01] | [7.878880500793457, 3.8396413326263428] |
c4ac8ee3-eeee-4ed0-bca1-0e70dc9038fe | cliff-carrying-location-information-in-full | 2208.00571 | null | https://arxiv.org/abs/2208.00571v2 | https://arxiv.org/pdf/2208.00571v2.pdf | CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation | Top-down methods dominate the field of 3D human pose and shape estimation, because they are decoupled from human detection and allow researchers to focus on the core problem. However, cropping, their first step, discards the location information from the very beginning, which makes themselves unable to accurately predict the global rotation in the original camera coordinate system. To address this problem, we propose to Carry Location Information in Full Frames (CLIFF) into this task. Specifically, we feed more holistic features to CLIFF by concatenating the cropped-image feature with its bounding box information. We calculate the 2D reprojection loss with a broader view of the full frame, taking a projection process similar to that of the person projected in the image. Fed and supervised by global-location-aware information, CLIFF directly predicts the global rotation along with more accurate articulated poses. Besides, we propose a pseudo-ground-truth annotator based on CLIFF, which provides high-quality 3D annotations for in-the-wild 2D datasets and offers crucial full supervision for regression-based methods. Extensive experiments on popular benchmarks show that CLIFF outperforms prior arts by a significant margin, and reaches the first place on the AGORA leaderboard (the SMPL-Algorithms track). The code and data are available at https://github.com/huawei-noah/noah-research/tree/master/CLIFF. | ['Youliang Yan', 'Songcen Xu', 'Zhensong Zhang', 'Jianzhuang Liu', 'Zhihao LI'] | 2022-08-01 | null | null | null | null | ['3d-human-pose-and-shape-estimation', 'unsupervised-3d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-2.46507198e-01 -3.52490656e-02 -1.55847654e-01 -2.29330122e-01
-7.32218802e-01 -5.28808177e-01 3.27825725e-01 -3.22726816e-01
-3.80776316e-01 4.01504934e-01 4.70289826e-01 2.23631620e-01
3.70131612e-01 -4.10573334e-01 -7.21731603e-01 -5.73678374e-01
3.49144220e-01 7.37637877e-01 3.25886421e-02 -2.96806782e-01
-2.38846630e-01 2.39605322e-01 -1.16963446e+00 6.89761713e-03
5.07045388e-01 7.88101852e-01 1.00926243e-01 4.68973368e-01
5.10392547e-01 3.25998038e-01 -3.29693794e-01 -6.14109814e-01
6.74568772e-01 -1.57939509e-01 -6.69986486e-01 3.59688103e-01
7.45748460e-01 -6.33311093e-01 -5.28431594e-01 7.85994828e-01
6.86481237e-01 6.74745589e-02 2.95550883e-01 -1.21318388e+00
-1.60765469e-01 5.73347956e-02 -9.29417729e-01 -1.82951063e-01
9.28250134e-01 4.35603827e-01 8.62297237e-01 -1.32821703e+00
8.26884389e-01 1.43003452e+00 9.22485650e-01 4.38671857e-01
-1.10052216e+00 -5.37854612e-01 2.88719535e-01 -1.71874896e-01
-1.52829683e+00 -3.87373567e-01 7.78441131e-01 -5.83639801e-01
5.26679993e-01 1.85096011e-01 8.66740704e-01 1.37785316e+00
-1.97397411e-01 1.05916715e+00 7.70738065e-01 -2.55721569e-01
-2.33097821e-01 -3.95494342e-01 5.99054210e-02 8.78594220e-01
3.33641410e-01 8.83895606e-02 -6.98350489e-01 9.08234864e-02
8.87939453e-01 3.12317401e-01 -4.46170330e-01 -7.17702270e-01
-1.46219170e+00 4.44173962e-01 6.07028663e-01 -7.03173131e-02
-3.56043845e-01 6.48543164e-02 2.08180338e-01 -1.29677933e-02
4.28946853e-01 1.32905543e-01 -5.15879095e-01 -1.30202323e-01
-7.47314692e-01 6.31086349e-01 4.99386013e-01 1.07343972e+00
6.10098660e-01 -3.86441380e-01 -1.87921807e-01 5.51580966e-01
1.88711673e-01 7.71532476e-01 -3.04644331e-02 -1.12250948e+00
7.75111198e-01 5.53550065e-01 4.31973606e-01 -1.07763875e+00
-6.13166273e-01 -5.70876896e-01 -7.99838126e-01 2.14674938e-02
9.26049948e-01 -1.63661584e-01 -7.55363941e-01 1.74384773e+00
7.97376215e-01 -1.65817365e-01 -4.75688636e-01 1.52918851e+00
4.25401449e-01 4.91327018e-01 -3.31970781e-01 1.57754883e-01
1.61438346e+00 -1.24513757e+00 -3.55668098e-01 -6.17893159e-01
5.35655439e-01 -5.88913739e-01 9.57390606e-01 5.47269285e-01
-8.38186204e-01 -7.01722085e-01 -7.80249715e-01 -2.57492334e-01
1.12388246e-01 5.02537251e-01 3.82142395e-01 3.48956585e-01
-6.97678268e-01 3.85265261e-01 -1.03776455e+00 -5.13591647e-01
1.52706116e-01 1.35499418e-01 -7.75540650e-01 -3.08089286e-01
-9.44833577e-01 8.75954390e-01 1.02966659e-01 4.52654749e-01
-6.80364013e-01 -5.41775107e-01 -1.07813954e+00 -3.63853335e-01
9.38397944e-01 -9.64851260e-01 1.19905651e+00 -4.88159269e-01
-1.33649564e+00 9.03318882e-01 -1.99759394e-01 -1.16678312e-01
1.07981300e+00 -9.30985272e-01 2.68575490e-01 1.18690208e-01
2.58792698e-01 7.23146975e-01 8.37028444e-01 -1.30849421e+00
-3.97695929e-01 -7.52404332e-01 4.79471957e-04 4.17792767e-01
-4.68750782e-02 -2.20761165e-01 -1.07737136e+00 -7.84709096e-01
2.38945916e-01 -1.23842275e+00 -2.39022419e-01 2.97345459e-01
-6.84669137e-01 4.53000441e-02 4.89584386e-01 -9.90455091e-01
1.01112723e+00 -2.07622051e+00 5.47195256e-01 -3.54713276e-02
3.49163055e-01 9.84578431e-02 5.82701564e-02 3.88219416e-01
7.30297863e-02 -4.39912230e-01 -1.92207664e-01 -8.64497602e-01
3.41283642e-02 3.09296865e-02 -7.29538947e-02 8.07887375e-01
6.04128605e-03 9.91209269e-01 -9.81237113e-01 -4.44491923e-01
3.66328090e-01 5.29756308e-01 -7.04383433e-01 2.66959965e-01
1.30713344e-01 8.81914914e-01 -4.45266873e-01 7.16355443e-01
6.36575937e-01 -1.77498460e-01 8.03701282e-02 -4.46963847e-01
-3.97485420e-02 5.08113243e-02 -1.41045022e+00 2.16570640e+00
-1.41306236e-01 1.86734900e-01 9.69081149e-02 -6.68499231e-01
6.73564315e-01 2.01796189e-01 5.88126659e-01 -4.28900778e-01
2.13460714e-01 8.99377614e-02 -4.28108752e-01 -2.49850065e-01
3.13897759e-01 2.37338126e-01 -2.87629277e-01 1.58473074e-01
-1.39637413e-02 1.19604588e-01 2.75826119e-02 2.59835478e-02
9.45052445e-01 7.61428654e-01 3.38587165e-01 1.12052172e-01
4.00551349e-01 -6.63471296e-02 8.48897755e-01 4.30613369e-01
-3.75699103e-01 1.05033171e+00 2.40702450e-01 -6.12908661e-01
-1.12612236e+00 -1.07659304e+00 2.00794101e-01 9.34769690e-01
3.58995825e-01 -6.97993279e-01 -9.64441597e-01 -7.72095799e-01
1.71793193e-01 1.42219514e-01 -7.03780651e-01 6.03804663e-02
-8.32633972e-01 -4.35278296e-01 3.66519719e-01 5.67738295e-01
5.24858832e-01 -6.00254059e-01 -6.75161183e-01 5.96063817e-03
-6.79338276e-01 -1.37551475e+00 -9.30871546e-01 -1.85592294e-01
-5.20090163e-01 -1.16294658e+00 -1.03521085e+00 -3.99720937e-01
7.09748268e-01 3.27057451e-01 8.32967162e-01 4.26759794e-02
-3.28960121e-01 3.34612012e-01 -3.54263216e-01 -4.79018055e-02
2.34665066e-01 6.23434298e-02 3.32994103e-01 1.89416364e-01
9.43706110e-02 -4.35099483e-01 -7.75503695e-01 6.26986623e-01
-3.59155945e-02 3.54276270e-01 5.16951084e-01 8.40539813e-01
5.85647762e-01 -4.78522539e-01 -6.03383817e-02 -4.66877192e-01
-5.48346788e-02 -3.77019756e-02 -4.72263128e-01 -5.45431487e-02
-1.18022360e-01 -4.57427725e-02 2.78011531e-01 -3.20914477e-01
-8.64560544e-01 5.75481057e-01 -1.36974901e-01 -7.18394220e-01
-1.84176758e-01 7.33070001e-02 -4.46103036e-01 2.55670130e-01
5.25367320e-01 6.45567179e-02 2.87362281e-02 -7.39562929e-01
2.65165061e-01 2.50826955e-01 9.90889311e-01 -6.74050927e-01
1.13449430e+00 6.63096070e-01 -7.02095404e-02 -5.89239836e-01
-1.23935318e+00 -7.12397456e-01 -1.04150891e+00 -3.03480178e-01
9.20989037e-01 -1.20925939e+00 -7.63832271e-01 6.86815679e-01
-1.23672116e+00 -2.31114462e-01 -1.80775791e-01 5.15337288e-01
-5.57411134e-01 4.77004647e-01 -5.07928491e-01 -8.04473579e-01
-2.20922634e-01 -1.13928390e+00 1.67688429e+00 -7.49743879e-02
-3.58337164e-01 -4.53269869e-01 1.21424623e-01 7.09821761e-01
-1.96609855e-01 5.39135933e-01 6.21338598e-02 -2.76314050e-01
-4.34436202e-01 -3.98151547e-01 1.39359059e-02 2.13635758e-01
-1.41184613e-01 -3.17295700e-01 -9.42596436e-01 -4.14544493e-01
-1.37198865e-01 -2.60420442e-01 7.71061599e-01 2.69793570e-01
7.09581792e-01 -1.25485048e-01 -4.24664646e-01 8.04429114e-01
8.88845205e-01 -7.23651886e-01 2.90151358e-01 2.52536565e-01
1.12002850e+00 6.61173463e-01 9.41677392e-01 6.24757648e-01
6.88202918e-01 1.16238654e+00 5.13536692e-01 -1.14390559e-01
-2.39492208e-01 -6.81034029e-01 5.72665572e-01 5.27980685e-01
-4.81043637e-01 -3.46037745e-02 -8.99002016e-01 3.37949038e-01
-2.00104356e+00 -8.64052176e-01 -1.39411330e-01 2.26395059e+00
7.17519403e-01 1.34481415e-01 5.69421470e-01 1.21992782e-01
8.51874590e-01 2.57132828e-01 -4.25740808e-01 5.44443309e-01
7.35203326e-02 -3.27471375e-01 5.99750400e-01 6.33610725e-01
-1.21976936e+00 9.69386160e-01 4.76005459e+00 6.43005431e-01
-7.65770674e-01 1.52637079e-01 3.45261395e-01 -2.88784206e-01
2.35127494e-01 1.09861799e-01 -1.10128546e+00 4.28518862e-01
3.11897904e-01 3.63856047e-01 4.04067218e-01 7.88156092e-01
3.08764875e-01 -7.37820715e-02 -1.16018379e+00 1.17846918e+00
9.64756459e-02 -8.19289923e-01 -2.17826784e-01 3.16388488e-01
4.98316944e-01 -4.86992486e-03 -1.95609838e-01 1.64105624e-01
9.70181674e-02 -6.92114532e-01 1.16264677e+00 5.98809481e-01
6.61681533e-01 -6.67334437e-01 6.57966495e-01 6.44930065e-01
-1.46562231e+00 -3.60958278e-02 -1.69493243e-01 -1.74549073e-01
3.76631558e-01 6.77873611e-01 -5.27338982e-01 7.31621385e-01
8.72671485e-01 8.60582292e-01 -5.28959513e-01 8.49053621e-01
-5.11938989e-01 2.23203436e-01 -5.24634123e-01 4.29419547e-01
-1.73648566e-01 -1.23015352e-01 7.75775492e-01 1.06368303e+00
2.88273901e-01 1.31842360e-01 7.13876426e-01 6.06192708e-01
-3.56739499e-02 -9.08606201e-02 -3.55876893e-01 4.14817512e-01
4.17787254e-01 1.25800037e+00 -5.31129718e-01 -1.03803977e-01
-3.31128210e-01 1.18846965e+00 2.92079926e-01 1.72363013e-01
-9.64102149e-01 1.87558606e-02 7.15040386e-01 5.44196963e-01
2.62574494e-01 -4.87752050e-01 -6.62042946e-02 -1.45995200e+00
4.20325935e-01 -9.14665341e-01 3.10520619e-01 -7.18000352e-01
-1.08253431e+00 3.85037750e-01 1.63037196e-01 -1.33383477e+00
-3.09539974e-01 -7.30961740e-01 -2.15553775e-01 6.45746946e-01
-1.08311117e+00 -1.53660417e+00 -6.77833617e-01 6.72399223e-01
5.41606665e-01 2.18451470e-01 4.37587380e-01 3.18947345e-01
-6.45783186e-01 6.92243338e-01 -3.82676482e-01 5.58818758e-01
1.03263962e+00 -1.12134230e+00 6.14396572e-01 9.13849175e-01
1.42402887e-01 5.28124392e-01 7.66335487e-01 -7.09468722e-01
-1.57372117e+00 -8.50190699e-01 8.07571471e-01 -9.68780637e-01
2.74841398e-01 -6.53511524e-01 -5.18762469e-01 7.82996714e-01
-4.15586203e-01 2.51923949e-01 1.67562991e-01 1.36024430e-01
-3.43857437e-01 3.42309661e-02 -8.12570333e-01 5.41869283e-01
1.56479180e+00 -3.28599483e-01 -5.65874755e-01 2.86515623e-01
5.83625793e-01 -9.30377841e-01 -6.40723228e-01 2.78785378e-01
8.10605347e-01 -9.79568303e-01 1.23568106e+00 -1.68333545e-01
2.01330751e-01 -5.91140211e-01 -2.36025035e-01 -9.86039221e-01
-1.89021915e-01 -6.90887630e-01 -2.63747126e-01 9.80443120e-01
-8.74433443e-02 -3.17543328e-01 9.02463317e-01 3.39827985e-01
1.62652740e-03 -7.50989974e-01 -8.18180561e-01 -6.87750518e-01
-3.24712038e-01 -4.71002370e-01 3.74284029e-01 6.01081431e-01
-2.32894480e-01 2.98305780e-01 -7.93559372e-01 2.68712640e-01
9.69376385e-01 6.66189566e-02 1.47044790e+00 -1.22196841e+00
-4.44727570e-01 -3.99311706e-02 -4.00152713e-01 -1.59034228e+00
-6.18826114e-02 -6.69493079e-01 1.69111878e-01 -1.24423039e+00
2.71741152e-01 -8.75030458e-02 1.49187371e-01 6.29057944e-01
-3.98581564e-01 4.97646809e-01 5.33587694e-01 4.04021293e-01
-5.92564881e-01 5.79460561e-01 1.34341049e+00 4.30335365e-02
3.03795878e-02 9.07966271e-02 -5.64751685e-01 1.08354950e+00
3.15035820e-01 -4.79390115e-01 -2.87907105e-03 -5.71928799e-01
6.43743202e-02 1.13128098e-02 9.12739754e-01 -1.11244023e+00
2.12507248e-01 -7.55690709e-02 6.73505843e-01 -7.21915424e-01
5.99995375e-01 -7.46233642e-01 2.37949312e-01 5.16827524e-01
-6.19819053e-02 1.03913486e-01 -2.70674109e-01 4.98673111e-01
1.17990695e-01 1.42120272e-01 5.71771562e-01 -1.65486410e-01
-4.85526860e-01 5.89802921e-01 3.70188713e-01 2.63912797e-01
8.33668470e-01 -2.39681050e-01 4.96778600e-02 -4.70318317e-01
-8.26764226e-01 4.12241876e-01 8.79976630e-01 4.93255347e-01
3.80242854e-01 -1.29538929e+00 -7.57233858e-01 1.88420236e-01
1.86314702e-01 4.92708385e-01 1.75320342e-01 1.16698623e+00
-4.46519405e-01 2.79350787e-01 -5.07852100e-02 -9.02337611e-01
-1.07428479e+00 4.67309743e-01 4.26837891e-01 -4.49469596e-01
-8.96937251e-01 7.29784429e-01 3.72417927e-01 -5.92137039e-01
1.60288230e-01 -1.56920835e-01 2.44388044e-01 4.70867530e-02
5.09450614e-01 3.54852974e-01 -2.86501180e-02 -1.01223183e+00
-5.64697981e-01 1.00020742e+00 2.48646662e-01 -1.93631977e-01
1.31852233e+00 -2.80304968e-01 3.11929494e-01 1.98513672e-01
1.10291493e+00 3.42386037e-01 -1.78195953e+00 -4.24241871e-01
-2.39300728e-01 -7.79203832e-01 -2.95020998e-01 -5.71956098e-01
-1.11859632e+00 9.07236755e-01 3.49945992e-01 -4.43200380e-01
8.28940332e-01 7.77601823e-02 8.99927676e-01 3.50119710e-01
6.59091353e-01 -1.04146910e+00 1.93033576e-01 4.48353946e-01
1.03159678e+00 -1.30254018e+00 3.18433583e-01 -5.46486914e-01
-6.41270638e-01 9.78000700e-01 7.30232358e-01 -1.78727970e-01
2.69526213e-01 1.42137691e-01 -5.51602878e-02 -6.61968514e-02
-3.31571043e-01 -2.55011111e-01 3.60116899e-01 5.88035226e-01
1.99406430e-01 3.91043238e-02 6.98515475e-02 7.35130847e-01
-4.67709601e-01 -7.17579946e-02 9.49526429e-02 9.11821067e-01
-3.13169241e-01 -1.06837749e+00 -7.92729199e-01 -5.94144054e-02
-1.70490414e-01 2.20212474e-01 -4.02725279e-01 7.81681240e-01
2.76149690e-01 6.11519754e-01 -1.66151240e-01 -5.24780095e-01
6.50332272e-01 -1.92250200e-02 6.53950989e-01 -4.60296333e-01
-2.88914472e-01 2.51277924e-01 8.71606693e-02 -1.01758897e+00
-2.25132003e-01 -9.72830176e-01 -1.19276941e+00 -3.99440140e-01
-2.74669528e-01 -3.00726704e-02 3.34843397e-01 9.68205273e-01
3.11463177e-01 1.65566847e-01 4.09921020e-01 -1.53122401e+00
-5.58198094e-01 -8.30336034e-01 -2.53563225e-01 5.80686271e-01
4.47356701e-01 -9.89823818e-01 -2.63495028e-01 5.88442236e-02] | [7.06966495513916, -0.889286458492279] |
80ca3600-f4a9-46e2-9b8a-1a3672691259 | classi-fly-inferring-aircraft-categories-from | 1908.01061 | null | https://arxiv.org/abs/1908.01061v2 | https://arxiv.org/pdf/1908.01061v2.pdf | Classi-Fly: Inferring Aircraft Categories from Open Data using Machine Learning | In recent years, air traffic communication data has become easy to access, enabling novel research in many fields. Exploiting this new data source, a wide range of applications have emerged, from weather forecasting to stock market prediction, or the collection of information about military and government movements. Typically these applications require knowledge about the metadata of the aircraft, specifically its operator and the aircraft category. armasuisse Science + Technology, the R\&D agency for the Swiss Armed Forces, has been developing Classi-Fly, a novel approach to obtain metadata about aircraft based on their movement patterns. We validate Classi-Fly using several hundred thousand flights collected through open source means, in conjunction with ground truth from publicly available aircraft registries containing more than two million aircraft. Classi-Fly obtains the correct aircraft category with an accuracy of over 88%, demonstrating that it can improve the meta data necessary for applications working with air traffic communication. Finally, we show that it is feasible to automatically detect specific flights such as police and surveillance missions. | ['Ivan Martinovic', 'Vincent Lenders', 'Matthew Smith', 'Martin Strohmeier'] | 2019-07-30 | null | null | null | null | ['stock-market-prediction'] | ['time-series'] | [-1.77699938e-01 -2.97163457e-01 -5.66946208e-01 -2.77008504e-01
-5.49366653e-01 -1.00029194e+00 6.51998162e-01 5.96668720e-01
-4.56976593e-01 7.53373921e-01 3.32476497e-01 -5.20841062e-01
-3.46343786e-01 -1.10490906e+00 -5.18282473e-01 -2.41809472e-01
-3.60544980e-01 5.98265886e-01 3.19172412e-01 -6.50777593e-02
1.71536300e-02 7.77851522e-01 -1.52166820e+00 2.95231581e-01
2.69701809e-01 1.70281684e+00 -2.93957770e-01 5.12216330e-01
-3.95490974e-02 4.51171607e-01 -7.61609137e-01 -3.50455374e-01
6.69800401e-01 -5.88090681e-02 -6.90299451e-01 -2.36984223e-01
2.06313267e-01 -1.53666273e-01 9.40216556e-02 8.42306435e-01
-2.26237513e-02 -8.03122893e-02 3.41167927e-01 -1.07843220e+00
5.93926422e-02 3.61913323e-01 -2.22094521e-01 6.62018716e-01
5.40042937e-01 -1.99322738e-02 1.07164621e+00 -2.54222363e-01
8.65705371e-01 6.36296391e-01 6.53636813e-01 -2.24969834e-02
-8.64385605e-01 -8.54040086e-01 1.86299756e-02 -2.28206053e-01
-1.51799297e+00 -2.11799398e-01 1.78035751e-01 -8.69180381e-01
6.45137966e-01 8.55428100e-01 6.62977338e-01 6.14878058e-01
2.51740128e-01 1.26606911e-01 1.09087157e+00 -1.53252944e-01
1.18986197e-01 3.70589018e-01 1.60935819e-01 8.16016912e-01
4.89437968e-01 3.92497689e-01 -6.47309303e-01 -5.42503774e-01
5.52423000e-01 1.15314499e-01 -1.27539784e-01 -2.22918659e-01
-1.62614179e+00 4.72378880e-01 4.08946723e-01 4.79392231e-01
-5.91118872e-01 -5.63331135e-02 2.26080805e-01 5.04174888e-01
5.39430261e-01 7.32555687e-01 -7.08747089e-01 -2.27234840e-01
-1.11902404e+00 4.99775320e-01 1.00592232e+00 1.04181993e+00
6.60801709e-01 -1.62288055e-01 3.38225394e-01 1.48782268e-01
1.32181600e-01 6.26306653e-01 1.59981772e-01 -7.47595191e-01
5.12154818e-01 5.62683523e-01 3.52470666e-01 -1.29312456e+00
-5.47222376e-01 -6.68606102e-01 -6.10456228e-01 -3.30425829e-01
2.45818287e-01 -3.18920165e-01 -4.07283157e-01 1.23631251e+00
5.60785294e-01 5.61862141e-02 -2.35121429e-01 8.81180227e-01
4.29382503e-01 4.40816134e-01 -3.01970065e-01 -2.17915788e-01
1.50738406e+00 -1.47912204e-01 -4.42589462e-01 1.89405188e-01
7.93177664e-01 -6.48707926e-01 1.77472517e-01 6.31737530e-01
-6.91344857e-01 -3.80349427e-01 -6.12831235e-01 8.20641577e-01
-8.64724219e-01 -1.30316883e-01 9.09585357e-01 7.82424986e-01
-6.41494155e-01 3.37881625e-01 -7.27366388e-01 -2.13759407e-01
5.56703210e-01 4.91199523e-01 -6.43846333e-01 1.38735428e-01
-1.03799486e+00 5.36089778e-01 1.82518750e-01 -1.45048082e-01
-7.53904939e-01 -8.93843174e-01 -6.21385157e-01 -2.17734594e-02
5.30407548e-01 -7.00636089e-01 9.61834133e-01 -5.71423888e-01
-7.54616022e-01 5.28833091e-01 2.94069350e-01 -8.18635046e-01
5.81345521e-02 -8.02776366e-02 -1.02690065e+00 -1.26402587e-01
2.30402768e-01 1.72397032e-01 7.19591975e-01 -5.85334182e-01
-1.21755111e+00 -4.80041862e-01 9.66612995e-02 -4.20921355e-01
-3.54109198e-01 3.23367596e-01 9.52970013e-02 -7.26076365e-01
-2.14219168e-01 -1.13201475e+00 -6.33681417e-02 -3.99595320e-01
-4.10946518e-01 1.09961480e-01 5.70746422e-01 -6.75264001e-01
1.61995101e+00 -1.94696665e+00 -1.28725916e-03 6.97788596e-01
9.43171233e-02 1.32988140e-01 2.04072997e-01 5.68750620e-01
-1.35563388e-01 2.09112063e-01 -1.20225720e-01 3.83143216e-01
-1.81485116e-01 6.69422671e-02 -6.83826327e-01 3.72859120e-01
7.39819333e-02 4.96814013e-01 -8.44279647e-01 5.83105907e-03
8.98281559e-02 -9.98847187e-02 -6.27206624e-01 2.51295809e-02
-2.06457615e-01 5.94171941e-01 -6.93733156e-01 9.12851930e-01
2.28164986e-01 -4.05773707e-02 1.45733923e-01 8.92310143e-02
-4.89831418e-01 4.03291643e-01 -1.01640415e+00 1.44756639e+00
-4.43634510e-01 5.99491477e-01 1.95741028e-01 -6.52082801e-01
7.15765834e-01 3.29823554e-01 7.31272399e-01 -1.33930713e-01
2.89974689e-01 1.43779546e-01 -7.12646618e-02 -1.99592948e-01
5.71655691e-01 -2.11404756e-01 -3.69286001e-01 4.03296828e-01
-1.99733883e-01 4.86038178e-02 3.29489738e-01 -5.82771469e-03
1.46613395e+00 -4.81925100e-01 2.37476662e-01 -3.55031520e-01
4.97633547e-01 5.71577311e-01 5.18841386e-01 1.69861436e-01
1.42163098e-01 7.43411481e-02 2.77702361e-01 -7.65129268e-01
-4.86470729e-01 -9.81846631e-01 -2.38895178e-01 6.88152552e-01
-3.27003151e-01 -6.32323802e-01 -6.55004442e-01 -7.34995186e-01
3.34086567e-01 4.67733532e-01 -4.04631317e-01 -1.93948969e-02
-2.89750546e-01 -5.69393754e-01 3.63034070e-01 1.39260113e-01
4.11704600e-01 -5.39131999e-01 -5.88430643e-01 2.37138063e-01
-4.43949625e-02 -1.22606719e+00 -3.08033645e-01 -2.48671561e-01
-7.55410790e-01 -1.28454423e+00 -4.19416189e-01 -1.68254092e-01
6.63800061e-01 1.09289318e-01 1.05431676e+00 6.32479563e-02
-6.62245274e-01 6.26523972e-01 -2.37221658e-01 -5.22312462e-01
-2.95300514e-01 4.26502734e-01 4.84666586e-01 2.62066633e-01
2.81212687e-01 -2.51372725e-01 -2.66053349e-01 5.73871732e-01
-7.57320821e-01 -5.03582537e-01 3.84085804e-01 2.89442450e-01
4.94590968e-01 5.68397760e-01 5.70730448e-01 -8.72962892e-01
5.56458116e-01 -7.78177202e-01 -1.20885491e+00 -1.03561794e-02
-6.26161277e-01 -2.15725377e-01 3.72309566e-01 2.08253950e-01
-5.61896801e-01 8.40392858e-02 -1.56972818e-02 -2.19454899e-01
-6.52633071e-01 6.87839806e-01 1.62853539e-01 -7.42394254e-02
2.83138216e-01 -1.63390711e-01 -5.53979427e-02 -6.88049078e-01
-5.26519865e-03 9.99264479e-01 5.01678765e-01 -4.15749609e-01
1.00198162e+00 5.45319438e-01 2.25353092e-01 -8.86553943e-01
-1.02584660e+00 -8.29025686e-01 -6.16282344e-01 -2.81865239e-01
9.25735414e-01 -7.91208684e-01 -8.64881933e-01 -3.14225331e-02
-9.90894616e-01 1.85165510e-01 -3.96750599e-01 9.47426677e-01
-2.60045171e-01 -5.80923036e-02 -1.98958755e-01 -6.10734880e-01
1.13133773e-01 -8.88718963e-01 9.24031258e-01 -4.22520697e-01
-4.35555279e-01 -9.01376426e-01 2.36275420e-01 5.54787636e-01
4.89779025e-01 5.36016524e-01 7.04719186e-01 -7.78633654e-01
-1.03456974e+00 -7.89525747e-01 7.35498071e-02 2.56586730e-01
4.14881051e-01 -2.18903795e-01 -6.25898182e-01 -3.72632146e-01
-2.47241691e-01 4.30836380e-01 6.09920263e-01 3.01762316e-02
1.07025480e+00 -4.07654911e-01 -5.43074906e-01 3.60999256e-01
8.84335279e-01 1.36429459e-01 1.19139418e-01 1.74104825e-01
2.11342260e-01 8.41971040e-01 1.10792518e+00 6.46122634e-01
8.42202753e-02 7.71194041e-01 4.57152814e-01 1.85261205e-01
2.72075444e-01 -5.72009683e-02 1.04606502e-01 7.65974402e-01
-3.83061767e-01 -1.45993292e-01 -1.07758236e+00 4.00588185e-01
-1.42101455e+00 -1.10908914e+00 -2.17671748e-02 2.64586306e+00
5.03212214e-01 1.98617175e-01 2.84622908e-01 4.05605398e-02
4.25869375e-01 5.33459634e-02 2.61221807e-02 -7.62485936e-02
4.57125545e-01 7.00971857e-02 1.06164634e+00 1.35768175e-01
-1.03192639e+00 5.14191806e-01 7.18530941e+00 4.77098078e-01
-9.12842095e-01 1.50781661e-01 1.92762345e-01 -2.19740003e-01
-1.18894696e-01 -1.30491272e-01 -1.07734263e+00 5.48815608e-01
1.36110198e+00 -2.44115636e-01 5.67499459e-01 6.75612390e-01
6.72792047e-02 -7.38239735e-02 -7.14110434e-01 6.27726257e-01
-6.58681244e-02 -1.71124256e+00 -5.68622258e-03 6.88378036e-01
4.48239356e-01 1.17359608e-01 1.19225822e-01 -1.06521636e-01
1.37988091e-01 -6.19119525e-01 6.54122412e-01 8.15838039e-01
8.06958139e-01 -9.37255979e-01 7.83555269e-01 4.78796750e-01
-1.30740905e+00 -2.63278186e-01 -7.80582279e-02 -1.57631174e-01
2.87460983e-01 9.43273783e-01 -1.03991723e+00 8.34171712e-01
7.42384613e-01 8.05142343e-01 -4.33742791e-01 9.72342789e-01
2.80594260e-01 6.06789947e-01 -3.54945332e-01 2.07192212e-01
1.18366368e-01 -1.61935300e-01 7.56842732e-01 8.75709772e-01
6.21481419e-01 1.24762915e-01 2.57222682e-01 5.42498827e-01
-6.98041320e-02 -1.22155771e-01 -1.00535345e+00 -6.26015365e-01
2.58747041e-01 1.22127151e+00 -6.36731565e-01 -8.79872143e-02
-5.06320417e-01 3.08947444e-01 -3.77965480e-01 -4.21757586e-02
-7.95960665e-01 -5.29789984e-01 1.12704635e+00 7.01388597e-01
2.89634287e-01 -6.99395597e-01 5.27770042e-01 -8.01768839e-01
-2.19530970e-01 -8.42642725e-01 4.20974106e-01 -4.58667934e-01
-1.00061369e+00 9.52074885e-01 2.53885001e-01 -1.52792788e+00
-5.49379706e-01 -9.45007205e-01 1.06355697e-01 6.63685739e-01
-1.19572318e+00 -7.96539903e-01 -8.90745968e-02 3.86086553e-01
9.61098000e-02 -6.99059367e-01 8.11817586e-01 5.87620556e-01
-5.71869433e-01 -7.80365765e-02 -1.52277261e-01 2.57889509e-01
5.58262289e-01 -7.71315992e-01 4.58753079e-01 6.35087669e-01
5.76719582e-01 6.84364736e-01 5.26797414e-01 -9.68968213e-01
-1.51196146e+00 -1.35533714e+00 8.35899711e-01 -9.37392950e-01
1.06606877e+00 -2.70946294e-01 -3.42738926e-01 8.35769594e-01
-6.30657300e-02 -8.22894573e-02 1.05640841e+00 2.83469230e-01
-1.63694799e-01 -5.14390647e-01 -9.94627893e-01 5.19782826e-02
9.16358829e-01 -3.85376334e-01 -4.37705874e-01 7.20973611e-01
6.36029005e-01 -2.62418598e-01 -1.36688614e+00 2.39543751e-01
2.62980103e-01 -8.91120136e-01 9.63393927e-01 -9.51565027e-01
-1.91336513e-01 -4.24766421e-01 -2.48651788e-01 -1.32689655e+00
-1.32685989e-01 -7.11117923e-01 1.50456047e-02 1.13914347e+00
4.53348398e-01 -9.62192535e-01 4.87275571e-01 6.46804154e-01
-1.66968033e-01 -3.98195803e-01 -1.04472268e+00 -1.14132130e+00
-6.30027592e-01 -7.37572551e-01 1.28315067e+00 9.63433981e-01
-1.76443443e-01 -1.18132278e-01 -2.81767875e-01 4.55315530e-01
1.37162238e-01 3.53926748e-01 8.40899825e-01 -1.79634464e+00
-3.21403921e-01 -9.22199562e-02 -8.25841486e-01 -7.50436902e-01
2.25788027e-01 -9.48588431e-01 -3.66718411e-01 -1.02203834e+00
-4.82392251e-01 -7.50602186e-01 -2.44987518e-01 4.31219101e-01
8.03515971e-01 1.81432769e-01 -1.04878157e-01 2.13023394e-01
-4.58595127e-01 -9.79328975e-02 8.11615944e-01 -2.62346528e-02
1.01996988e-01 5.45627892e-01 -4.66161519e-01 7.04670668e-01
6.64798021e-01 -7.25206017e-01 1.01331711e-01 -2.41545066e-01
5.88944435e-01 1.65531620e-01 5.45924485e-01 -1.36227179e+00
2.79473096e-01 -2.28030458e-01 1.53120369e-01 -7.27932274e-01
3.19345325e-01 -1.43841481e+00 7.18196511e-01 6.35274529e-01
-2.13001832e-01 5.03583133e-01 2.68367052e-01 8.40243876e-01
-5.03726244e-01 -1.15113139e-01 1.17229909e-01 -9.63309035e-02
-4.72864956e-01 5.27377009e-01 -5.10203838e-01 -1.67039305e-01
1.30156755e+00 3.30407798e-01 -2.10769519e-01 -1.05838895e-01
-5.52016735e-01 8.31118152e-02 4.09103870e-01 5.78172624e-01
2.44734362e-01 -1.22413027e+00 -3.20505738e-01 7.49439240e-01
1.76253974e-01 -6.02069914e-01 -1.21219285e-01 9.24923658e-01
-4.12999064e-01 9.90223587e-01 -3.24207880e-02 -3.74040097e-01
-1.30067885e+00 8.11545908e-01 8.29778984e-02 -4.21940498e-02
-1.33868203e-01 3.78336698e-01 -2.29298398e-01 -2.84613848e-01
-2.93389019e-02 -7.74559438e-01 1.29193902e-01 3.03720623e-01
7.48669863e-01 3.88599634e-01 3.75962853e-01 -7.05105007e-01
-5.82724571e-01 3.91117603e-01 8.98036808e-02 -8.84354785e-02
1.12314308e+00 1.33738503e-01 -1.85821787e-01 4.31509852e-01
8.19951892e-01 4.01847064e-01 -4.90447789e-01 1.05126001e-01
2.07609147e-01 -8.50080073e-01 2.76118964e-01 -6.51857972e-01
-1.20154750e+00 7.38647521e-01 3.30569804e-01 8.90877187e-01
9.76484716e-01 -7.86883906e-02 9.96673286e-01 4.18402553e-01
1.15166545e+00 -7.69987226e-01 -7.76209533e-01 2.35283673e-01
6.55837476e-01 -1.00769663e+00 6.98108971e-02 -5.32390177e-01
-2.38439530e-01 8.37983489e-01 -2.32686862e-01 2.14644417e-01
1.01621985e+00 1.87267512e-01 -1.27745256e-01 -3.89670908e-01
-8.17983508e-01 -3.84626627e-01 5.58223426e-01 5.98360479e-01
2.93852508e-01 2.07412288e-01 3.26446667e-02 6.74192607e-01
-3.84766400e-01 6.67723939e-02 2.83208847e-01 8.74495924e-01
-5.52637815e-01 -1.23560560e+00 -4.00576264e-01 1.03008687e+00
-4.86777216e-01 -4.71284352e-02 -5.43236554e-01 9.18614626e-01
1.76080897e-01 8.81088555e-01 3.63310903e-01 -5.28710067e-01
5.66570282e-01 -1.26799598e-01 3.40326615e-02 -8.98381829e-01
-9.43535328e-01 -4.47107643e-01 5.29936135e-01 -7.89400280e-01
-4.90033180e-01 -8.40947509e-01 -1.03280294e+00 -3.90112311e-01
-2.63806194e-01 6.07921720e-01 8.22954595e-01 7.09016502e-01
7.05921292e-01 5.49999535e-01 7.32669711e-01 -4.08645779e-01
-3.00755829e-01 -3.16307634e-01 -9.54621255e-01 -1.85600430e-01
3.37403685e-01 -9.57975090e-01 -3.08892876e-01 -2.01828461e-02] | [7.409072399139404, 3.1328537464141846] |
50a76c64-8091-4872-838c-3fc0019b2c51 | music-composition-with-deep-learning-a-review | 2108.12290 | null | https://arxiv.org/abs/2108.12290v2 | https://arxiv.org/pdf/2108.12290v2.pdf | Music Composition with Deep Learning: A Review | Generating a complex work of art such as a musical composition requires exhibiting true creativity that depends on a variety of factors that are related to the hierarchy of musical language. Music generation have been faced with Algorithmic methods and recently, with Deep Learning models that are being used in other fields such as Computer Vision. In this paper we want to put into context the existing relationships between AI-based music composition models and human musical composition and creativity processes. We give an overview of the recent Deep Learning models for music composition and we compare these models to the music composition process from a theoretical point of view. We have tried to answer some of the most relevant open questions for this task by analyzing the ability of current Deep Learning models to generate music with creativity or the similarity between AI and human composition processes, among others. | ['Jose R. Beltran', 'Carlos Hernandez-Olivan'] | 2021-08-27 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 1.90684408e-01 1.50727943e-01 3.13873082e-01 2.91556239e-01
1.65504396e-01 -8.02003205e-01 9.30964351e-01 -3.39329481e-01
-1.34742439e-01 3.63013595e-01 4.26887184e-01 6.99909702e-02
-6.12067044e-01 -9.68731105e-01 -3.47628295e-01 -5.88671327e-01
8.50318223e-02 8.40636075e-01 -2.92203903e-01 -7.72048473e-01
5.10062575e-01 4.64996338e-01 -1.66536713e+00 6.92187488e-01
6.13905787e-01 6.93527758e-01 -2.21878439e-02 8.56291175e-01
-4.88011360e-01 1.03285134e+00 -8.67957115e-01 -6.81547999e-01
3.28710914e-01 -1.04862678e+00 -9.22991872e-01 -1.02887109e-01
3.50406647e-01 1.50349483e-01 8.58260170e-02 9.64467824e-01
4.47392464e-01 -1.27715049e-02 8.84652138e-01 -1.05702972e+00
-8.66990447e-01 1.12834167e+00 -8.39039236e-02 -1.54660016e-01
2.62630999e-01 2.04900697e-01 1.15120351e+00 -5.86165249e-01
6.65575087e-01 1.14454103e+00 9.49523926e-01 8.15305710e-01
-1.28211474e+00 -7.29357839e-01 -4.10064220e-01 3.06231022e-01
-1.05308783e+00 -1.17472909e-01 1.08919966e+00 -7.50083506e-01
5.27913928e-01 2.24294782e-01 1.41870964e+00 1.25033534e+00
5.91037646e-02 9.21289623e-01 1.25024343e+00 -5.98805130e-01
1.79233193e-01 -2.47480437e-01 -4.07790661e-01 3.65626037e-01
-6.12999424e-02 4.29109573e-01 -9.57441449e-01 4.32840735e-02
1.37214208e+00 -5.40538311e-01 9.59791988e-02 -2.38590077e-01
-1.56351054e+00 8.72178912e-01 4.83770609e-01 1.18387628e+00
-4.33711916e-01 6.07418418e-01 4.19283479e-01 5.26219368e-01
-2.08082557e-01 1.47634733e+00 -3.22991461e-01 -1.20509624e-01
-1.31392455e+00 6.37906671e-01 9.54961896e-01 5.43693781e-01
3.27535808e-01 4.53415692e-01 -1.93237066e-01 6.43267810e-01
7.35460296e-02 -3.71830687e-02 8.20362449e-01 -1.13911808e+00
-1.91222176e-01 6.12215161e-01 -2.26358294e-01 -8.53994071e-01
-2.91052908e-01 -5.85114360e-01 -1.02329004e+00 7.41448522e-01
5.01888633e-01 6.97977981e-03 -3.50319594e-01 1.64813673e+00
-2.87529498e-01 1.74499869e-01 2.82957815e-02 9.15247262e-01
7.71617055e-01 4.55643982e-01 -4.73555736e-02 -2.50723101e-02
1.20711863e+00 -8.51328492e-01 -6.71273649e-01 3.31002325e-01
2.78337188e-02 -9.81498718e-01 1.16908908e+00 7.95083582e-01
-1.48761892e+00 -9.41770196e-01 -1.04183757e+00 -1.32150427e-01
-2.01719761e-01 5.63077554e-02 9.80432808e-01 6.78111255e-01
-1.26156104e+00 1.18573225e+00 -3.39757621e-01 -4.69957650e-01
2.57951796e-01 3.96742821e-01 3.24832499e-02 7.31302619e-01
-1.08640981e+00 8.47250760e-01 5.11483192e-01 -3.31772156e-02
-7.33956218e-01 -5.54611504e-01 -2.02435687e-01 1.17541984e-01
1.14669658e-01 -1.43160784e+00 1.62264943e+00 -1.61539042e+00
-1.90931320e+00 1.12590110e+00 3.20300967e-01 -4.83602136e-01
7.32468486e-01 -1.39365509e-01 -1.92046613e-01 -5.39606996e-02
-2.54906178e-01 7.53266215e-01 7.19320118e-01 -1.32002676e+00
-3.08522165e-01 -3.43880236e-01 -3.35087404e-02 8.27166289e-02
-8.02706927e-02 -7.22113475e-02 2.57589757e-01 -1.11329806e+00
2.05697189e-03 -1.05314887e+00 -1.69135883e-01 -9.75736231e-02
-2.25679830e-01 -3.89349759e-01 8.80227387e-02 -2.11013511e-01
1.05923939e+00 -1.85957336e+00 7.43956923e-01 2.91166045e-02
1.85932308e-01 3.06233764e-01 -2.65526831e-01 7.71523476e-01
-7.23200515e-02 2.84910172e-01 -2.67796069e-01 -1.08291723e-01
2.68554538e-01 -1.22999623e-01 -6.19521499e-01 -2.12693408e-01
1.15558937e-01 1.26960909e+00 -7.50312269e-01 -2.98272759e-01
-6.06827484e-03 5.07051945e-01 -5.61635196e-01 8.27149898e-02
-6.74472570e-01 8.12755704e-01 -2.62922108e-01 3.30898732e-01
-6.99531659e-02 1.75292373e-01 1.65813372e-01 -5.25340550e-02
-2.71324486e-01 2.32118279e-01 -1.07553089e+00 2.04829073e+00
-2.61027157e-01 7.01670110e-01 -1.63507462e-01 -7.29126096e-01
1.04308772e+00 7.22476900e-01 4.88189220e-01 -5.35151243e-01
3.13934028e-01 5.65719068e-01 6.63071036e-01 -2.78965294e-01
4.83966649e-01 -9.87853706e-01 -1.90511756e-02 8.26083958e-01
1.83375522e-01 -7.43934870e-01 3.20235163e-01 -4.35555279e-01
8.79467785e-01 5.54643154e-01 4.57959980e-01 -2.29299024e-01
6.18804157e-01 1.02381706e-01 -1.39854793e-02 5.98790586e-01
7.66430348e-02 3.70205522e-01 4.11859214e-01 -8.11370969e-01
-1.31870019e+00 -1.10101104e+00 2.54728407e-01 1.05272782e+00
-5.33856511e-01 -3.20620239e-01 -8.06501269e-01 6.14752583e-02
-2.89133877e-01 7.61090219e-01 -7.66703963e-01 -2.14036182e-01
-8.27138066e-01 -3.98870081e-01 9.32224274e-01 4.31284249e-01
3.95249784e-01 -2.18000674e+00 -7.43174195e-01 4.35990244e-01
1.79840207e-01 -4.30310577e-01 1.99614257e-01 4.35052887e-02
-1.11086500e+00 -8.27988505e-01 -8.42000782e-01 -9.37262535e-01
-2.39402771e-01 -4.19275671e-01 1.60129547e+00 1.63563430e-01
-3.38034391e-01 2.75573432e-01 -1.99313015e-01 -8.68291676e-01
-8.79912555e-01 3.67326945e-01 -1.00143574e-01 -3.34448904e-01
2.89930850e-01 -1.15701699e+00 -4.08387899e-01 4.93550021e-03
-1.23152697e+00 2.74605453e-01 7.55664945e-01 5.71264267e-01
3.57564509e-01 6.98337927e-02 8.30982566e-01 -5.04873693e-01
1.16260350e+00 -6.83802590e-02 -1.57197699e-01 -6.00808337e-02
-2.75755316e-01 1.05516233e-01 5.07724643e-01 -5.77020586e-01
-7.03333080e-01 2.92803650e-03 -1.52629510e-01 -2.65559554e-01
-2.62154192e-01 4.44198072e-01 6.33959249e-02 9.75723490e-02
6.56984687e-01 3.85972053e-01 -8.55621845e-02 -4.50731426e-01
6.71899915e-01 1.73306197e-01 6.61356688e-01 -7.64401913e-01
8.78022969e-01 3.76402199e-01 3.07253540e-01 -6.97044492e-01
-7.18005180e-01 3.07247695e-02 -9.68582511e-01 -2.43203491e-01
1.03504550e+00 -3.79957259e-01 -1.07658422e+00 4.09831226e-01
-1.29466093e+00 -5.47484398e-01 -8.74380410e-01 2.18435615e-01
-1.39330101e+00 2.60466151e-02 -8.53647709e-01 -8.05545151e-01
-6.57950759e-01 -6.87959969e-01 9.68628287e-01 2.32086271e-01
-9.24265087e-01 -9.49943900e-01 6.45000041e-01 1.94028899e-01
4.42140192e-01 3.42158943e-01 1.35776806e+00 -3.49007517e-01
-6.01463139e-01 1.70393765e-01 3.12871635e-01 2.07356840e-01
-1.48006722e-01 -3.17856148e-02 -8.88128459e-01 3.20820153e-01
1.78148776e-01 -3.55346859e-01 9.60103393e-01 1.08075358e-01
8.77270222e-01 -3.28526124e-02 2.91109502e-01 4.28187698e-01
1.38069665e+00 3.17082703e-01 7.95987725e-01 4.31925297e-01
5.47934651e-01 8.41693282e-01 1.16089657e-01 2.98814863e-01
-2.43859366e-01 7.35089481e-01 2.07379222e-01 1.28725722e-01
-4.83612061e-01 -3.94985437e-01 2.71093696e-01 1.05533004e+00
-8.58356357e-01 1.04257181e-01 -9.02776897e-01 2.86739171e-01
-1.92156446e+00 -1.53934264e+00 -1.52995944e-01 1.94698179e+00
9.51254725e-01 -1.70402408e-01 5.26255965e-01 7.21507072e-01
4.39523071e-01 -1.21879920e-01 -3.14319730e-01 -6.61635518e-01
-3.60822350e-01 8.75247180e-01 -4.50379848e-01 1.51518986e-01
-7.66340613e-01 1.30521238e+00 7.28122711e+00 4.92377132e-01
-1.11654162e+00 -8.97059590e-02 1.13215772e-02 -1.49834841e-01
-4.10566986e-01 -2.88921386e-01 -1.27355624e-02 6.78632269e-03
5.80807865e-01 -1.91078901e-01 1.00929189e+00 4.71114069e-01
1.89313944e-02 4.72368628e-01 -1.40902078e+00 1.10702932e+00
2.44660586e-01 -1.47317183e+00 5.18540263e-01 1.75486386e-01
9.93310452e-01 -4.81039852e-01 2.46301174e-01 4.18023527e-01
2.77505547e-01 -1.60624194e+00 1.14338470e+00 7.80928254e-01
3.48270833e-01 -7.03056216e-01 3.92093062e-01 2.76260287e-01
-1.01357913e+00 -1.95749611e-01 -1.09110735e-01 -5.85298657e-01
1.65155321e-01 2.41660088e-01 -6.66520357e-01 2.73844153e-01
4.73269641e-01 5.44513583e-01 -3.55538189e-01 1.13394654e+00
-4.57690805e-01 3.63154769e-01 2.73009479e-01 -2.35520333e-01
2.34164327e-01 -4.74727720e-01 6.49218440e-01 9.51089859e-01
7.11871922e-01 -1.50059864e-01 -3.10540676e-01 1.61269689e+00
1.08800337e-01 3.65262300e-01 -5.25717378e-01 -6.30873322e-01
-9.44555923e-02 1.12188363e+00 -8.12272191e-01 -3.37386072e-01
1.71888784e-01 1.00658035e+00 1.45454451e-01 4.16576490e-02
-4.73342508e-01 3.88747416e-02 6.43426418e-01 2.07465500e-01
1.06448039e-01 -5.96190751e-01 -7.11350322e-01 -8.53423476e-01
-4.41287041e-01 -1.09791279e+00 -1.21166192e-01 -1.17725456e+00
-1.43083119e+00 5.55849314e-01 -5.28551579e-01 -9.52127278e-01
-4.33888763e-01 -7.28582501e-01 -9.23359275e-01 7.68126607e-01
-8.98334384e-01 -1.28858316e+00 -6.43480271e-02 5.79204917e-01
4.80175734e-01 -5.32984376e-01 1.22939563e+00 -9.42593999e-03
1.02357984e-01 1.68510936e-02 -3.75451386e-01 1.41777635e-01
4.48120266e-01 -1.44109082e+00 5.86554885e-01 -5.01025468e-04
1.05241692e+00 6.13878608e-01 8.13087761e-01 -3.27695787e-01
-1.08196139e+00 -4.19981241e-01 1.13728976e+00 -6.35288715e-01
6.39027953e-01 -1.82977870e-01 -4.50160682e-01 4.47674185e-01
6.36845946e-01 -9.00627732e-01 1.08977187e+00 2.32968479e-01
-3.70253861e-01 2.85330981e-01 -6.64011896e-01 9.80366528e-01
1.30174136e+00 -4.97595072e-01 -1.10133779e+00 8.01480040e-02
2.95529366e-01 2.53417253e-01 -7.78996527e-01 3.11800897e-01
9.72451568e-01 -1.37576520e+00 9.38530922e-01 -7.82138169e-01
9.39013004e-01 -4.87084866e-01 1.93798929e-01 -1.33882642e+00
-6.88655078e-01 -7.97900319e-01 2.76458953e-02 9.00151610e-01
1.37120694e-01 8.06299299e-02 7.16825128e-01 -2.47395843e-01
-7.37112388e-02 -4.25874770e-01 -6.60751462e-01 -6.99739695e-01
4.52335835e-01 -4.85684633e-01 7.44074285e-01 1.11662030e+00
3.92443277e-02 8.15513253e-01 -2.42374375e-01 -6.66482568e-01
2.59570628e-01 4.79747444e-01 9.23093021e-01 -1.97074854e+00
-6.44094884e-01 -1.37714124e+00 -4.57142413e-01 -1.80882066e-01
2.42132545e-01 -1.31753230e+00 -3.80083740e-01 -1.70002782e+00
4.03517410e-02 -3.15947384e-02 -3.74124140e-01 1.33694887e-01
3.23406547e-01 6.15232229e-01 6.17073715e-01 3.97260934e-01
-2.37977117e-01 5.58663666e-01 1.59053063e+00 -5.88567033e-02
-3.14985454e-01 4.94323820e-02 -8.40075970e-01 9.49309349e-01
1.00582409e+00 -2.12299705e-01 -2.39207894e-01 -3.98510993e-01
1.07551098e+00 -1.61504552e-01 3.27329427e-01 -1.41271281e+00
5.29256649e-02 3.22999172e-02 6.01637542e-01 -6.52478188e-02
3.22461605e-01 -6.41970813e-01 3.57739002e-01 8.20369542e-01
-6.20571315e-01 1.97479889e-01 8.93294364e-02 1.87887594e-01
-3.82085115e-01 -3.12953413e-01 6.54500484e-01 -7.06901968e-01
-4.98632133e-01 -1.48883864e-01 -6.37919784e-01 -2.58917719e-01
7.44494915e-01 -3.68745923e-01 1.66910082e-01 -3.89973611e-01
-1.10723352e+00 -5.45594573e-01 4.22204733e-01 6.77341044e-01
1.48028389e-01 -1.55051541e+00 -7.62604594e-01 -1.71472266e-01
-2.84663551e-02 -6.46210670e-01 6.63540279e-03 5.11103332e-01
-7.30585396e-01 4.55325365e-01 -8.70565772e-01 -2.49104545e-01
-1.06742752e+00 8.21202099e-01 4.34345156e-01 -3.48683238e-01
-3.41945082e-01 6.38404012e-01 1.61009908e-01 -4.43460077e-01
7.03517348e-02 -3.80754709e-01 -4.73731130e-01 1.27870396e-01
4.52424139e-01 4.22640979e-01 -4.91613805e-01 -5.42512536e-01
1.37592793e-01 9.99509811e-01 6.77118659e-01 -5.33787549e-01
1.26083827e+00 4.92415726e-01 -5.14562488e-01 9.46101964e-01
3.70051056e-01 9.47062150e-02 -4.88095194e-01 1.00510433e-01
3.51029158e-01 7.60449320e-02 -3.76377165e-01 -1.12809145e+00
-8.81368458e-01 1.17684400e+00 4.20617014e-01 2.95226842e-01
1.04017138e+00 8.48845392e-02 7.09654450e-01 4.18558359e-01
4.72560704e-01 -1.04531932e+00 6.29827201e-01 6.70156956e-01
1.44305873e+00 -6.52694225e-01 -3.71272832e-01 3.89748742e-03
-6.07730329e-01 1.43191767e+00 2.57234633e-01 -6.57883525e-01
3.97057176e-01 1.34434193e-01 3.79457660e-02 -3.22861731e-01
-6.25544667e-01 -5.26528895e-01 5.30529618e-01 5.43206930e-01
1.10908008e+00 2.59185225e-01 -7.06126451e-01 8.92573237e-01
-1.11156845e+00 5.35677373e-01 2.52337635e-01 4.09851730e-01
-5.02471745e-01 -1.49538898e+00 -4.04209316e-01 -9.78556052e-02
-2.83295214e-01 -1.00964189e-01 -1.23309112e+00 5.92519879e-01
8.99502635e-01 6.33700490e-01 7.58618563e-02 -4.29747880e-01
1.90694451e-01 4.79531348e-01 1.11823535e+00 -5.90794444e-01
-1.41018176e+00 1.33291408e-01 -1.41569361e-01 -1.45268008e-01
-6.67902350e-01 -6.37714028e-01 -1.06280363e+00 -3.28781605e-01
3.49271595e-01 -1.04907595e-01 7.04063952e-01 7.68649697e-01
4.10860069e-02 7.85073280e-01 -2.43943602e-01 -1.18343461e+00
-1.96515754e-01 -1.01623893e+00 -8.32532525e-01 4.39960241e-01
-2.26701736e-01 -2.49303579e-01 2.10287739e-02 2.11781144e-01] | [16.08775520324707, 5.516983985900879] |
67fb8b98-4a67-472e-8a9b-b1f0722c3001 | lightweight-multi-drone-detection-and-3d | 2202.09097 | null | https://arxiv.org/abs/2202.09097v1 | https://arxiv.org/pdf/2202.09097v1.pdf | Lightweight Multi-Drone Detection and 3D-Localization via YOLO | In this work, we present and evaluate a method to perform real-time multiple drone detection and three-dimensional localization using state-of-the-art tiny-YOLOv4 object detection algorithm and stereo triangulation. Our computer vision approach eliminates the need for computationally expensive stereo matching algorithms, thereby significantly reducing the memory footprint and making it deployable on embedded systems. Our drone detection system is highly modular (with support for various detection algorithms) and capable of identifying multiple drones in a system, with real-time detection accuracy of up to 77\% with an average FPS of 332 (on Nvidia Titan Xp). We also test the complete pipeline in AirSim environment, detecting drones at a maximum distance of 8 meters, with a mean error of $23\%$ of the distance. We also release the source code for the project, with pre-trained models and the curated synthetic stereo dataset. | ['Mangal Kothari', 'Nitik Jain', 'Aryan Sharma'] | 2022-02-18 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [-8.83405954e-02 -4.00702417e-01 7.09321856e-01 -1.72675192e-01
-3.88048559e-01 -1.03364503e+00 3.59054476e-01 3.51012908e-02
-7.44156599e-01 2.83981532e-01 -7.17079937e-01 -2.42301390e-01
2.71651536e-01 -8.49168837e-01 -5.99917948e-01 -2.68991023e-01
-5.94718099e-01 5.61352015e-01 9.36506987e-01 -3.55737865e-01
1.95645764e-01 6.81846738e-01 -2.11265707e+00 -1.57895461e-01
1.39981017e-01 1.12501812e+00 -1.71257257e-01 1.42802715e+00
9.72928464e-01 2.46859014e-01 -9.53572273e-01 -1.98140442e-01
1.04616892e+00 2.05303416e-01 -1.13414577e-03 -4.65880707e-02
1.20575035e+00 -6.97237194e-01 -2.58988172e-01 7.62557566e-01
7.25932479e-01 -1.46798566e-01 1.78804472e-01 -1.30558133e+00
5.52216649e-01 -3.08279723e-01 -6.05991662e-01 5.63007832e-01
7.25915253e-01 4.71220940e-01 4.38549072e-01 -7.69174099e-01
3.95478457e-01 9.77078557e-01 1.02420413e+00 1.24923669e-01
-1.01604056e+00 -9.69115555e-01 -5.64766288e-01 -3.45386684e-01
-1.87086093e+00 -3.70465219e-01 -1.65536106e-02 -6.21768355e-01
1.31000161e+00 2.01577649e-01 9.50727701e-01 4.22506511e-01
7.99474344e-02 1.14909649e-01 7.84686804e-01 -1.40401632e-01
-7.73098599e-03 1.37879834e-01 -3.29229921e-01 1.17010856e+00
7.32200325e-01 5.98296463e-01 -1.33931696e-01 -3.20359886e-01
7.82868743e-01 -1.07835561e-01 -2.54222099e-02 -5.66906989e-01
-1.39090157e+00 6.58378661e-01 3.23912352e-01 -2.07324937e-01
-9.11363289e-02 4.22836155e-01 3.31213295e-01 1.95088536e-01
1.23242639e-01 3.47920328e-01 -2.42973164e-01 -2.46996477e-01
-1.13939917e+00 5.21016538e-01 5.90718806e-01 1.42658567e+00
8.42982709e-01 2.37747893e-01 4.90790069e-01 -6.83571622e-02
3.88591468e-01 1.05681205e+00 8.34247768e-02 -1.04787707e+00
4.18716669e-01 8.41838360e-01 4.09781635e-01 -1.03587425e+00
-4.44586903e-01 -1.65480122e-01 -1.37643784e-01 8.46244037e-01
2.70174205e-01 -7.26674378e-01 -5.91274559e-01 9.32156086e-01
5.98383963e-01 3.03916670e-02 -2.13337854e-01 1.06039715e+00
5.11717021e-01 5.06907165e-01 -4.22648549e-01 3.72309685e-01
1.20779955e+00 -5.06334424e-01 4.67363745e-02 -5.22254169e-01
6.64689124e-01 -9.72250819e-01 2.97634155e-01 4.70569313e-01
-7.87664235e-01 -4.84742492e-01 -1.53818834e+00 2.30913401e-01
-3.61512721e-01 4.42398071e-01 5.19410968e-01 7.85356879e-01
-1.32665992e+00 1.56811818e-01 -8.56849968e-01 -7.10084379e-01
-1.01045743e-01 7.50458896e-01 -1.95515200e-01 3.97028774e-01
-7.20102608e-01 9.01748478e-01 3.03180605e-01 -1.83317557e-01
-1.20478523e+00 -4.93338287e-01 -8.50525737e-01 -3.89227659e-01
2.43149012e-01 -5.83110332e-01 1.12788320e+00 -6.32510066e-01
-8.84704649e-01 1.09688199e+00 2.75572389e-01 -8.66506279e-01
6.98906362e-01 -4.03732866e-01 -4.52829301e-01 2.59169549e-01
2.27282807e-01 1.07275808e+00 9.22622263e-01 -8.30871880e-01
-1.37936759e+00 -4.25272793e-01 2.19104186e-01 1.64212406e-01
5.36391027e-02 2.29940470e-02 -2.35769525e-01 -1.85018912e-01
-2.12537616e-01 -1.24936628e+00 -1.60825714e-01 2.89625168e-01
1.08741276e-01 3.56393158e-01 1.09920871e+00 -3.45749736e-01
9.39557433e-01 -2.07010365e+00 -3.77595454e-01 1.58517778e-01
3.64981554e-02 4.98390883e-01 2.68898934e-01 4.91362959e-01
2.15245217e-01 -3.85191172e-01 -8.16429853e-02 -1.57566741e-01
-1.34332106e-01 -1.59340829e-01 -2.29503974e-01 8.54936540e-01
-2.70370811e-01 1.52484894e-01 -8.52946997e-01 -3.84256274e-01
7.99003661e-01 5.09023666e-01 -4.01960373e-01 1.72316045e-01
2.65669048e-01 -1.03202993e-02 -2.33172742e-03 9.91322160e-01
8.17369878e-01 4.42550808e-01 -1.71435401e-01 9.71965492e-02
-8.51337016e-01 4.54968261e-03 -1.63727891e+00 1.45454562e+00
-2.22000256e-01 1.09490705e+00 4.92803127e-01 -1.95746168e-01
9.90920782e-01 -2.18139179e-02 2.62323141e-01 -2.83666790e-01
3.60874921e-01 2.00116068e-01 -2.72311747e-01 -1.62587866e-01
7.84416914e-01 2.98154980e-01 -1.88605875e-01 1.74982175e-01
-1.48486674e-01 -5.23206711e-01 6.10468686e-01 -1.86747033e-02
1.27842879e+00 -3.31244841e-02 2.68359035e-01 -4.88760531e-01
2.80919433e-01 7.62941122e-01 1.86036676e-01 6.13471031e-01
-2.77058870e-01 4.28566098e-01 -1.89992070e-01 -8.14562976e-01
-1.02012300e+00 -9.35882032e-01 -2.33491406e-01 7.61193812e-01
4.85186368e-01 -5.51324725e-01 -8.09257865e-01 -3.72778118e-01
2.37162620e-01 3.49867284e-01 -3.17473829e-01 2.11072192e-01
-5.01466870e-01 -5.10840535e-01 9.80007291e-01 4.02879953e-01
7.23343730e-01 -4.16863739e-01 -1.95141053e+00 3.14825797e-03
3.68542075e-01 -1.14122224e+00 -5.14212325e-02 -1.59415811e-01
-7.39933968e-01 -1.36433756e+00 -2.52492994e-01 -6.00980341e-01
6.91593766e-01 7.81625092e-01 1.15832853e+00 2.21840918e-01
-1.05207789e+00 6.44799411e-01 -1.28096566e-01 -4.83963788e-01
2.32060120e-01 -3.46719801e-01 2.55690277e-01 -5.82736492e-01
5.65221548e-01 2.70025209e-02 -7.93629885e-01 5.74564517e-01
-4.93824273e-01 -2.61561066e-01 2.45458290e-01 2.28714719e-01
3.18864018e-01 6.87408075e-02 -5.50915122e-01 -1.60827190e-02
1.24409683e-01 -5.22178933e-02 -1.82042491e+00 -3.79035413e-01
-4.14142907e-01 -3.90202671e-01 4.58465695e-01 -2.51826718e-02
-3.05359602e-01 6.74026906e-01 2.64392793e-01 -4.16362464e-01
-2.79514879e-01 -2.22953975e-01 5.80204010e-01 -6.93206310e-01
7.90538549e-01 -1.28089879e-02 -7.33060315e-02 5.92659600e-02
6.37922958e-02 5.77552617e-01 5.82064807e-01 1.41888529e-01
1.08581758e+00 9.08871531e-01 2.02539146e-01 -1.18343115e+00
-3.19050491e-01 -7.27342367e-01 -8.18428099e-01 -3.12572330e-01
6.59130037e-01 -1.41752195e+00 -8.55587006e-01 3.14113379e-01
-8.79944265e-01 -1.67716414e-01 1.11077346e-01 5.14840245e-01
-2.09673285e-01 2.05477133e-01 -7.40344375e-02 -7.28839695e-01
-3.98663729e-01 -1.14968753e+00 1.47483468e+00 1.75816163e-01
-2.26708919e-01 -7.51222789e-01 4.04981315e-01 1.47686705e-01
2.49333754e-01 5.40971279e-01 -3.76992553e-01 4.46923971e-02
-8.91569614e-01 -8.62182438e-01 -3.43318641e-01 -9.07534212e-02
-4.02067989e-01 2.44946480e-01 -7.39043057e-01 -7.77234435e-01
-4.06974018e-01 -5.97706661e-02 6.04316652e-01 1.74508408e-01
2.56734550e-01 1.94713175e-01 -6.48568511e-01 6.98323429e-01
1.73450756e+00 2.75182575e-02 2.74020076e-01 5.24906218e-01
4.06612486e-01 3.49003315e-01 1.26478970e+00 7.98058569e-01
3.91706109e-01 8.27099323e-01 9.70698416e-01 -1.31366596e-01
1.52656063e-01 -5.40205278e-02 4.08631086e-01 -3.37191641e-01
-2.32569009e-01 7.12363422e-03 -1.10047615e+00 5.85062206e-01
-1.43380177e+00 -8.22162926e-01 -5.86741567e-01 2.62467766e+00
-3.31723616e-02 9.80339721e-02 4.28099126e-01 -4.73116437e-04
7.56577015e-01 -8.34123045e-02 1.35656893e-02 -2.42678687e-01
4.04222429e-01 7.00453937e-04 1.46007085e+00 6.51550770e-01
-1.56273854e+00 9.64667201e-01 6.63581276e+00 6.02144599e-02
-1.19747329e+00 -2.98680335e-01 -2.69353420e-01 -3.92214656e-01
7.65118718e-01 2.30876654e-01 -1.40094960e+00 3.62057716e-01
1.06804037e+00 -6.03742562e-02 3.78574908e-01 1.22412217e+00
2.11647823e-02 -5.63068151e-01 -8.09603810e-01 1.11997271e+00
3.41944903e-01 -1.14310658e+00 -4.45266962e-01 3.81681532e-01
3.15947324e-01 5.64257205e-01 -2.85579920e-01 -7.61838444e-03
3.45344514e-01 -7.27108777e-01 9.16122675e-01 -7.79568404e-02
9.30714250e-01 -1.04898500e+00 9.37687457e-01 4.65359956e-01
-1.65227008e+00 -4.81000319e-02 -6.34185374e-01 -3.55694443e-01
-3.25873084e-02 -8.12012032e-02 -1.31959963e+00 -2.01814398e-02
1.25270498e+00 5.33145249e-01 -9.30917084e-01 1.20527589e+00
-9.42171514e-02 5.29065616e-02 -1.07248747e+00 -1.22787595e-01
3.12658459e-01 -9.94346961e-02 7.58921146e-01 1.35478926e+00
7.09051847e-01 1.41407521e-02 3.38125408e-01 3.40201557e-01
3.91448051e-01 -4.34852868e-01 -9.75885034e-01 4.01275486e-01
5.96540451e-01 1.48019826e+00 -7.17164874e-01 -3.06430787e-01
-1.40604287e-01 7.88347185e-01 -2.16375738e-01 -3.87602866e-01
-1.15093124e+00 -8.67431700e-01 7.98197746e-01 5.96028924e-01
4.82902616e-01 -6.76016092e-01 1.57392576e-01 -8.55348110e-01
-1.15656696e-01 -4.69382882e-01 1.59560591e-01 -5.85079074e-01
-3.36209744e-01 6.42128408e-01 8.31418782e-02 -1.82973611e+00
-3.66845429e-01 -7.84273028e-01 -3.82239699e-01 4.03715134e-01
-1.07757652e+00 -1.10259569e+00 -7.79290438e-01 4.53311175e-01
2.34459385e-01 -2.51415133e-01 7.93881476e-01 5.40658414e-01
-3.44794303e-01 3.05845141e-01 -2.34621972e-01 1.33533999e-01
7.20899940e-01 -1.27746344e+00 6.45900846e-01 1.20535278e+00
-1.48251146e-01 5.50622225e-01 8.95077825e-01 -6.80045903e-01
-1.82276225e+00 -1.15999305e+00 4.32439983e-01 -7.79768288e-01
6.85909271e-01 -6.15278065e-01 1.37795294e-02 9.19359744e-01
2.64727503e-01 1.12178802e-01 3.36406171e-01 -5.15506864e-01
2.26993099e-01 -3.99201244e-01 -1.39729106e+00 3.63481700e-01
8.12107980e-01 9.21511948e-02 -3.95225853e-01 4.74414229e-01
1.14345372e-01 -8.53186369e-01 -7.10495472e-01 4.54440922e-01
5.93613923e-01 -1.55451500e+00 1.22837257e+00 5.10366023e-01
-5.50836802e-01 -9.66624856e-01 -5.09906234e-03 -8.63889158e-01
-1.41556352e-01 -6.77220404e-01 2.41315305e-01 8.02113950e-01
2.71771014e-01 -5.99737883e-01 7.54361749e-01 2.77657449e-01
-1.82923675e-01 3.60919647e-02 -1.06510425e+00 -6.58721626e-01
-8.91643405e-01 -3.72370273e-01 3.04783702e-01 3.25488001e-01
-2.23004013e-01 1.38958201e-01 -3.11841935e-01 8.74783337e-01
8.25719774e-01 4.97747064e-02 1.47630298e+00 -1.17750132e+00
-1.05545670e-01 7.73702655e-03 -1.06986475e+00 -8.70219946e-01
-4.35661793e-01 -2.93726653e-01 -7.71797895e-02 -1.06574047e+00
-3.95497888e-01 -1.51506320e-01 6.89670920e-01 3.11085016e-01
5.08576512e-01 8.66986811e-01 -5.28147854e-02 6.64125057e-03
-8.08358550e-01 -1.21666744e-01 2.04025909e-01 2.39975035e-01
1.37939408e-01 -3.42682679e-03 1.01404525e-01 9.76309478e-01
4.78537977e-01 -5.35278380e-01 -4.55256402e-02 -4.60619658e-01
1.81675792e-01 -1.95912033e-01 8.63190174e-01 -1.95201278e+00
3.26823592e-01 4.23490644e-01 7.08518207e-01 -1.02683139e+00
7.74354160e-01 -1.16930127e+00 1.54974565e-01 1.00964069e+00
4.95801002e-01 6.66343808e-01 6.78102612e-01 2.56861180e-01
-7.30576292e-02 3.69384438e-02 9.58459616e-01 -2.70982862e-01
-1.14058185e+00 1.27814665e-01 -6.41656697e-01 -1.29895210e-01
1.78614748e+00 -5.10387361e-01 -3.40854287e-01 -9.27254930e-02
-4.63526100e-02 4.09488261e-01 1.17233026e+00 2.73520827e-01
6.46929562e-01 -7.36288071e-01 -4.98403192e-01 6.90534711e-01
3.29922199e-01 -1.06340118e-01 -2.24792719e-01 5.19025564e-01
-1.74837649e+00 6.71683848e-01 -2.34980956e-01 -9.35681522e-01
-1.76414597e+00 3.37030739e-01 5.08172393e-01 4.46190566e-01
-4.44243580e-01 8.72844219e-01 -4.14450198e-01 -2.01348096e-01
5.35136312e-02 -2.65030056e-01 2.37778634e-01 -1.04000404e-01
7.83946216e-01 7.86621571e-01 1.21036775e-01 -7.96629071e-01
-7.88043618e-01 1.05336010e+00 4.06395733e-01 -2.10258648e-01
8.51130188e-01 -1.88808382e-01 1.05889760e-01 -1.17757134e-01
6.58297122e-01 1.83030903e-01 -1.42983997e+00 5.91984808e-01
-1.91815898e-01 -7.44633257e-01 3.64558637e-01 -4.30081636e-01
-5.90848565e-01 7.51831472e-01 1.32185996e+00 1.30090535e-01
6.84474945e-01 -3.04629087e-01 6.77150488e-01 5.63386440e-01
9.03282285e-01 -9.10322905e-01 -5.06402731e-01 4.68315005e-01
4.38076735e-01 -1.42892218e+00 4.52080458e-01 -2.82111973e-01
-3.64520371e-01 1.23776889e+00 7.65756428e-01 -7.62998283e-01
1.62728891e-01 6.36926889e-01 2.05226958e-01 -4.46403712e-01
-3.58973265e-01 -3.89560848e-01 1.68120898e-02 7.40730584e-01
1.16795316e-01 6.00462630e-02 1.71673581e-01 -5.12602627e-01
-4.99794453e-01 -3.24086756e-01 4.48536694e-01 1.31587434e+00
-8.26140642e-01 -6.26184821e-01 -7.06023693e-01 -1.01949155e-01
-2.24282697e-01 -5.83918430e-02 -6.11770868e-01 1.36068189e+00
3.67632180e-01 9.81586576e-01 2.02397376e-01 -6.10604644e-01
5.09039223e-01 -5.80532849e-01 4.46376204e-01 -7.49373317e-01
-7.82799542e-01 4.18356620e-02 1.80080235e-01 -7.81601369e-01
-6.08211495e-02 -6.93438351e-01 -1.12499714e+00 -4.27887380e-01
-2.23674759e-01 -1.53025255e-01 9.05128777e-01 4.87393320e-01
5.91541529e-01 -1.26433372e-01 4.18776542e-01 -1.27439809e+00
-1.51703775e-01 -6.97950721e-01 -4.12046403e-01 -1.85994267e-01
3.46864820e-01 -6.46424234e-01 -5.01328647e-01 -3.80666032e-02] | [8.27503776550293, -1.2591378688812256] |
bf041729-5535-4e98-8380-68df420023d7 | improving-iterative-text-revision-by-learning | 2212.01350 | null | https://arxiv.org/abs/2212.01350v1 | https://arxiv.org/pdf/2212.01350v1.pdf | Improving Iterative Text Revision by Learning Where to Edit from Other Revision Tasks | Iterative text revision improves text quality by fixing grammatical errors, rephrasing for better readability or contextual appropriateness, or reorganizing sentence structures throughout a document. Most recent research has focused on understanding and classifying different types of edits in the iterative revision process from human-written text instead of building accurate and robust systems for iterative text revision. In this work, we aim to build an end-to-end text revision system that can iteratively generate helpful edits by explicitly detecting editable spans (where-to-edit) with their corresponding edit intents and then instructing a revision model to revise the detected edit spans. Leveraging datasets from other related text editing NLP tasks, combined with the specification of editable spans, leads our system to more accurately model the process of iterative text refinement, as evidenced by empirical results and human evaluations. Our system significantly outperforms previous baselines on our text revision tasks and other standard text revision tasks, including grammatical error correction, text simplification, sentence fusion, and style transfer. Through extensive qualitative and quantitative analysis, we make vital connections between edit intentions and writing quality, and better computational modeling of iterative text revisions. | ['Dongyeop Kang', 'Dhruv Kumar', 'Vipul Raheja', 'Wanyu Du', 'Zae Myung Kim'] | 2022-12-02 | null | null | null | null | ['grammatical-error-correction'] | ['natural-language-processing'] | [ 8.12640429e-01 5.93751848e-01 -1.40434466e-02 -6.36870027e-01
-7.78570592e-01 -4.64362562e-01 5.55235505e-01 8.13886583e-01
-4.14051682e-01 6.96613848e-01 7.73164034e-01 -4.67761129e-01
-2.00041570e-02 -3.81917655e-01 -6.10408843e-01 5.32561481e-01
7.12021351e-01 5.90230346e-01 -1.82853535e-01 -6.62890911e-01
9.27237153e-01 -1.97498545e-01 -1.25410163e+00 3.75638038e-01
1.77777648e+00 2.08854869e-01 4.84101206e-01 1.01869929e+00
-2.19497472e-01 7.63377070e-01 -8.06733847e-01 -8.40439260e-01
-6.65772930e-02 -5.81609249e-01 -1.15922475e+00 1.10926561e-01
8.28338385e-01 -4.12631571e-01 1.35064512e-01 9.29214120e-01
5.00522077e-01 1.91634953e-01 6.41240478e-01 -4.82373059e-01
-1.29394221e+00 1.32998645e+00 -2.76658088e-01 1.33577198e-01
8.63608539e-01 -4.93075214e-02 1.10794878e+00 -1.12946165e+00
8.16190779e-01 1.45140922e+00 1.00593472e+00 5.65153658e-01
-1.26162970e+00 -3.17537695e-01 3.69673043e-01 -1.06287666e-01
-8.22859943e-01 -7.41375685e-01 4.14683163e-01 -4.43203062e-01
1.23201597e+00 4.40356046e-01 4.80090350e-01 9.64207947e-01
5.11679828e-01 8.57924879e-01 4.03825223e-01 -8.06250870e-01
-3.44538182e-01 2.65992916e-04 4.15774703e-01 7.00740159e-01
2.15160578e-01 -3.75137717e-01 -5.82508922e-01 6.47102371e-02
1.67219818e-01 -6.30496368e-02 -5.01255214e-01 2.47368962e-01
-1.25457215e+00 5.04519880e-01 -8.32110569e-02 7.98591040e-03
-2.87030727e-01 -1.39648870e-01 6.40829086e-01 6.58770144e-01
7.76086032e-01 1.16999400e+00 -5.97605884e-01 -4.50221956e-01
-1.28763568e+00 4.22215819e-01 8.10152948e-01 1.40617824e+00
7.04531133e-01 -3.31366718e-01 -7.15314865e-01 1.12850010e+00
2.08875000e-01 5.12496293e-01 3.02402526e-01 -9.80591476e-01
9.85727847e-01 7.83236563e-01 1.52386889e-01 -8.94138455e-01
-4.30659980e-01 -2.97942996e-01 -7.38989890e-01 -2.12413967e-01
2.11170465e-02 -1.61524966e-01 -6.80313647e-01 1.44687390e+00
-1.63635418e-01 -8.33861649e-01 -2.21926942e-01 4.00337279e-01
6.08073950e-01 2.27778897e-01 -1.01423495e-01 -2.56158620e-01
1.12948918e+00 -1.04704523e+00 -1.04928517e+00 -2.00705960e-01
1.10140967e+00 -1.28649771e+00 1.61001134e+00 5.11873960e-01
-1.40896094e+00 -6.56667471e-01 -8.02269101e-01 -8.55911016e-01
-1.27875293e-02 5.17156780e-01 2.48965099e-01 3.05894911e-01
-1.12133968e+00 9.21323836e-01 -7.03975022e-01 -5.78296542e-01
2.69650310e-01 -7.83490986e-02 -7.45027885e-02 -1.71055067e-02
-1.05691552e+00 1.13728642e+00 8.30642506e-02 2.15575714e-02
-1.17151037e-01 -1.26732838e+00 -9.81620252e-01 -4.99494225e-02
1.63215041e-01 -1.20863342e+00 1.72181165e+00 -1.01386499e+00
-1.38296878e+00 6.58768475e-01 -6.09716773e-01 -3.36330533e-01
6.60375595e-01 -8.58744144e-01 -2.61142015e-01 -4.93866026e-01
2.17510924e-01 7.07154870e-01 7.77724147e-01 -9.15293753e-01
-7.89603114e-01 -1.36615127e-01 5.33960238e-02 5.51050901e-01
-2.61406988e-01 2.74741203e-01 -4.24154222e-01 -1.21036673e+00
-2.19371423e-01 -7.40804255e-01 -8.44787732e-02 -1.37248397e-01
-8.24927390e-01 -2.59601444e-01 4.08459395e-01 -1.36305785e+00
2.01628733e+00 -1.79000902e+00 7.85162389e-01 -4.08157073e-02
6.35399401e-01 1.01790413e-01 -2.30162308e-01 5.33845723e-01
2.55119145e-01 5.10694146e-01 -5.08573890e-01 -1.09791470e+00
1.06493473e-01 -2.21217334e-01 -4.85297114e-01 -1.08270362e-01
1.13202997e-01 1.00679302e+00 -9.67325270e-01 -4.56698149e-01
-8.29492509e-02 7.90853798e-02 -9.20111299e-01 1.81537256e-01
-2.07114160e-01 1.64821267e-01 1.17964379e-01 5.57250440e-01
3.27748328e-01 9.94865447e-02 -1.51432008e-01 -7.99661204e-02
-1.01118438e-01 7.62564480e-01 -7.32882857e-01 1.94142520e+00
-5.98897219e-01 9.74700153e-01 -2.20108882e-01 -6.14789277e-02
8.62657607e-01 -1.32197052e-01 -7.70990327e-02 -7.75215864e-01
-1.51565835e-01 1.92835418e-04 -2.83169806e-01 -4.62695986e-01
1.49662125e+00 3.41809511e-01 -2.57476360e-01 9.37374890e-01
-1.30818069e-01 -6.34429991e-01 6.04115665e-01 8.06514621e-01
1.03986049e+00 -1.47166541e-02 2.92648673e-01 -2.04609022e-01
2.95648038e-01 -1.00341812e-01 3.49527806e-01 1.10549474e+00
8.66448656e-02 6.32896900e-01 3.21518034e-01 -1.78748339e-01
-1.33725572e+00 -7.88795471e-01 6.73901737e-02 1.30859160e+00
-1.36418432e-01 -1.05029368e+00 -8.84299517e-01 -7.05227494e-01
1.49510875e-01 1.25995326e+00 -5.70865512e-01 -5.04307866e-01
-6.10072970e-01 -9.27371010e-02 7.87134111e-01 4.57414508e-01
3.21629569e-02 -1.07287860e+00 -1.95677698e-01 4.50319171e-01
-4.92601275e-01 -5.47573566e-01 -1.27246010e+00 -2.02149495e-01
-9.03821468e-01 -1.08758390e+00 -3.40848625e-01 -6.42126560e-01
9.06211853e-01 1.53753415e-01 1.45637715e+00 5.75538635e-01
-1.27592027e-01 4.66304064e-01 -5.58954716e-01 -8.27414870e-01
-9.37685609e-01 5.01259506e-01 1.56905234e-01 -7.55387008e-01
4.82969694e-02 -1.21165387e-01 -2.54236639e-01 1.42746359e-01
-8.03413451e-01 5.43829560e-01 4.79601800e-01 7.54345834e-01
2.88609743e-01 -4.92454201e-01 3.99934560e-01 -1.48724341e+00
1.54072928e+00 -1.62278273e-04 7.42411427e-03 7.50708222e-01
-1.11620867e+00 2.63076037e-01 6.75620556e-01 -1.00692943e-01
-1.41902077e+00 -5.40609181e-01 -4.96802740e-02 3.09082687e-01
2.06761032e-01 6.98321521e-01 1.98511645e-01 3.41712356e-01
8.17044735e-01 2.97461264e-02 -1.37927800e-01 -6.17215753e-01
6.22217715e-01 7.62244642e-01 5.98294199e-01 -5.42196035e-01
7.12796569e-01 -5.55840805e-02 -7.02705264e-01 -6.06243551e-01
-1.03541875e+00 -5.29853441e-03 -1.06505692e+00 -1.07026309e-01
4.41932321e-01 -7.76421487e-01 -3.09791397e-02 5.81901312e-01
-1.50143015e+00 -3.23586732e-01 -4.34821099e-01 3.89670767e-02
-2.11805463e-01 8.54522109e-01 -8.10286403e-01 -4.46801841e-01
-8.68599474e-01 -1.01930213e+00 1.25076497e+00 1.49204284e-01
-1.21611381e+00 -1.07593191e+00 1.66041687e-01 5.14547944e-01
5.58377445e-01 -2.91847438e-01 1.20234060e+00 -2.76621401e-01
-8.38974416e-02 4.05070856e-02 -1.20002426e-01 3.56238008e-01
2.83586562e-01 5.29957533e-01 -1.80308282e-01 -2.07766235e-01
-4.17954862e-01 -2.61526674e-01 9.86882448e-01 2.19460636e-01
1.17259598e+00 -4.49356318e-01 -1.65499732e-01 6.60008907e-01
5.01356304e-01 -1.84139445e-01 6.00628734e-01 2.38868326e-01
8.99987578e-01 4.59213138e-01 9.02703702e-01 7.03390837e-01
9.01798606e-01 5.29458821e-01 -1.17947675e-01 -6.62858039e-02
-2.93975651e-01 -4.14908290e-01 3.91896009e-01 1.30023479e+00
2.12445259e-01 -6.69076979e-01 -7.57103741e-01 4.25582618e-01
-1.79863191e+00 -8.08857024e-01 -3.20896417e-01 1.89970124e+00
1.47444940e+00 3.27833802e-01 -3.76556605e-01 -7.48118833e-02
7.49443054e-01 -6.91416934e-02 -5.69761097e-01 -1.02525568e+00
-2.11892024e-01 2.44715303e-01 2.70293802e-01 9.99328434e-01
-8.15688610e-01 1.12396693e+00 6.72645950e+00 2.82158405e-01
-6.97141647e-01 -1.47744462e-01 4.28679883e-01 -2.64681607e-01
-8.26326966e-01 2.11445764e-01 -1.08418643e+00 3.93771678e-01
4.85655189e-01 -3.34154189e-01 6.97072387e-01 2.17979401e-01
6.45851016e-01 -8.90574977e-02 -1.51253128e+00 6.20663226e-01
6.15331233e-01 -1.30288541e+00 6.49416327e-01 -4.11546379e-01
8.76214802e-01 -4.16938096e-01 -1.71614125e-01 5.41855514e-01
4.63461131e-01 -9.71616924e-01 1.17183399e+00 1.18455887e+00
7.00207591e-01 -4.41932678e-01 5.20995855e-01 2.72241384e-01
-7.43244588e-01 8.51757750e-02 -2.78620481e-01 -3.37436736e-01
4.23049599e-01 8.17171037e-01 -7.62449205e-01 3.71397227e-01
3.44663560e-01 1.32801402e+00 -1.22669864e+00 6.22671843e-01
-4.16255414e-01 4.95747298e-01 6.62601143e-02 1.09985612e-01
-4.60672468e-01 -8.27798992e-02 6.26378834e-01 1.52326334e+00
3.64577591e-01 -1.30307958e-01 -1.65445230e-03 9.52845514e-01
-6.19921505e-01 2.14785740e-01 -1.43194050e-01 -2.35358194e-01
8.11337411e-01 8.99766147e-01 -1.18408665e-01 -4.51067299e-01
-1.29539192e-01 1.31676447e+00 6.33734882e-01 2.31085137e-01
-7.30469644e-01 -9.35840189e-01 5.75815558e-01 -4.81770597e-02
5.47593869e-02 -1.57238960e-01 -9.61746335e-01 -1.35624230e+00
3.73343498e-01 -1.22102046e+00 1.16744816e-01 -8.81668508e-01
-9.01067197e-01 2.42565900e-01 -3.80280733e-01 -7.86287963e-01
6.47047535e-02 -1.57825425e-02 -8.04290533e-01 1.03806448e+00
-1.23314619e+00 -9.49413240e-01 -3.40746254e-01 8.61682817e-02
1.12536097e+00 3.42093334e-02 5.04073679e-01 2.17940971e-01
-7.82253385e-01 1.10199523e+00 5.25709093e-02 -9.18490738e-02
1.40377307e+00 -1.53701472e+00 1.01357114e+00 1.11737788e+00
-1.40828267e-01 1.11476350e+00 8.87131751e-01 -1.16932333e+00
-1.10430682e+00 -1.19942617e+00 1.62961090e+00 -1.02894771e+00
4.28916156e-01 -3.88379693e-01 -1.03109622e+00 8.98327112e-01
3.78123313e-01 -1.16808295e+00 4.11102563e-01 5.09921491e-01
-2.21972972e-01 2.41039217e-01 -7.18798101e-01 8.58930409e-01
1.44586957e+00 -6.99595690e-01 -1.15221548e+00 4.00687724e-01
1.01856971e+00 -7.57380784e-01 -8.73246253e-01 2.32519343e-01
3.87423754e-01 -5.53501070e-01 5.34942806e-01 -6.90957487e-01
1.05723536e+00 -1.42275512e-01 4.34592336e-01 -1.84343696e+00
-5.41420817e-01 -9.36110675e-01 -8.30545574e-02 1.53340399e+00
8.42371106e-01 -1.61825940e-01 6.61887527e-02 7.77929485e-01
-7.97873735e-01 -6.45354569e-01 -3.64495248e-01 -2.00962856e-01
-3.78662385e-02 -3.51231515e-01 6.14247143e-01 8.64996493e-01
4.18340594e-01 3.78207386e-01 -4.14565444e-01 -3.51790428e-01
2.73340136e-01 -2.92296439e-01 9.78173018e-01 -1.36188328e+00
-1.42332971e-01 -8.00448298e-01 2.97895014e-01 -1.38358676e+00
1.46673828e-01 -9.88504350e-01 1.58684313e-01 -1.92230344e+00
3.40119421e-01 -2.85002679e-01 3.36676031e-01 5.62609434e-01
-7.80101001e-01 -3.23202461e-01 2.14391962e-01 2.80651003e-01
-9.45521295e-01 6.46038175e-01 1.25386715e+00 -3.45416754e-01
-6.08625591e-01 -2.14051381e-01 -1.29607475e+00 5.91730058e-01
4.17814553e-01 -2.86338866e-01 -2.14648068e-01 -1.08928883e+00
8.44536364e-01 -1.88975856e-01 -1.38522506e-01 -6.08220994e-01
4.23625708e-01 -6.06637588e-03 2.52124012e-01 -6.42225683e-01
-3.21760505e-01 -2.47041330e-01 -2.89110392e-01 1.37346402e-01
-1.14378667e+00 5.68722486e-01 7.16391578e-02 4.16029155e-01
6.56305999e-02 -3.26171994e-01 5.49463928e-01 1.67302176e-01
-3.50021690e-01 -1.34152442e-01 -6.17259860e-01 4.30099607e-01
5.68722010e-01 -3.67931664e-01 -6.19049191e-01 -5.00130892e-01
-5.49073994e-01 5.98562717e-01 8.97910357e-01 8.53664398e-01
5.61197340e-01 -9.42014754e-01 -9.94838297e-01 2.11714283e-01
3.10683280e-01 1.05362833e-02 4.93600853e-02 7.26677954e-01
-4.30973321e-01 3.58048737e-01 8.09649304e-02 -3.80326629e-01
-1.45971799e+00 -8.27439055e-02 1.83394462e-01 -3.25348169e-01
-6.43740416e-01 9.63115156e-01 -3.21569443e-01 -9.10495222e-01
4.62994665e-01 -9.81470704e-01 -4.01843749e-02 1.14558693e-02
4.41328466e-01 5.25327444e-01 3.15306425e-01 -1.03424497e-01
-1.92991067e-02 4.03603017e-01 -7.61277616e-01 8.06106180e-02
1.02365172e+00 -8.41359258e-01 -3.56541991e-01 3.63222450e-01
6.86905622e-01 4.21925992e-01 -9.61036563e-01 -3.94174129e-01
3.29418242e-01 -3.08726341e-01 -1.36046141e-01 -1.23919857e+00
-3.51729900e-01 5.91621995e-01 -1.19913429e-01 -2.18722537e-01
8.58619809e-01 -1.94775119e-01 1.13988829e+00 9.04215097e-01
2.41751187e-02 -1.65136564e+00 8.43326619e-04 1.21195042e+00
1.22319865e+00 -1.35292470e+00 2.87660211e-01 -2.14821622e-01
-8.08591485e-01 1.31742013e+00 8.11957121e-01 4.03492898e-01
2.77381390e-01 -8.51173140e-03 -3.27729359e-02 -1.00686498e-01
-8.92994344e-01 4.11554992e-01 6.10444844e-01 2.97497511e-01
1.01871336e+00 -7.03138299e-03 -5.73701262e-01 6.59878969e-01
-7.25180686e-01 -3.01480889e-02 9.04911041e-01 9.93347406e-01
-6.09204590e-01 -1.24218225e+00 -9.33366939e-02 1.03699660e+00
1.96397305e-02 -5.76661885e-01 -1.04537618e+00 3.08474660e-01
-1.22827865e-01 1.01006413e+00 8.14945921e-02 -2.77379781e-01
7.30078876e-01 8.12257547e-03 4.67825472e-01 -1.07150197e+00
-1.04163992e+00 -5.02413273e-01 3.56439799e-01 -4.67631787e-01
9.12226364e-02 -9.30274189e-01 -1.25029361e+00 -6.01317167e-01
-4.35635984e-01 -1.57780629e-02 4.84071523e-01 1.12107062e+00
7.83890009e-01 7.38087356e-01 2.89407015e-01 -6.47267401e-01
-5.27781248e-01 -1.31804967e+00 -1.01209722e-01 8.01240563e-01
2.62661099e-01 -1.45047158e-01 -2.26414084e-01 3.30199063e-01] | [11.902214050292969, 9.33910846710205] |
2b67b8f8-04b3-4bcb-b33b-1a18f89baa79 | deep-scattering-spectrum-germaneness-to-fault | 2210.09837 | null | https://arxiv.org/abs/2210.09837v3 | https://arxiv.org/pdf/2210.09837v3.pdf | Deep Scattering Spectrum germaneness to Fault Detection and Diagnosis for Component-level Prognostics and Health Management (PHM) | In fault detection and diagnosis of prognostics and health management (PHM) systems, most of the methodologies utilize machine learning (ML) or deep learning (DL) through which either some features are extracted beforehand (in the case of ML) or filters are used to extract features autonomously (in case of DL) to perform the critical classification task. Particularly in the fault detection and diagnosis of industrial robots where electric current, vibration or acoustic emissions signals are the primary sources of information, a feature domain that can map the signals into their constituent components with compressed information at different levels can reduce the complexities and size of typical ML and DL-based frameworks. The Deep Scattering Spectrum (DSS) is one of the strategies that use the Wavelet Transform (WT) analogy to separate and extract the information encoded in a signal's various temporal and frequency domains. As a result, the focus of this work is on the study of the DSS's relevance to fault detection and daignosis for mechanical components of industrail robots. We used multiple industrial robots and distinct mechanical faults to build an approach for classifying the faults using low-variance features extracted from the input signals. The presented approach was implemented on the practical test benches and demonstrated satisfactory performance in fault detection and diagnosis for simple and complex classification problems with a classification accuracy of 99.7% and 88.1%, respectively. | ['Ali Rohan'] | 2022-10-18 | null | null | null | null | ['fault-detection', 'industrial-robots'] | ['miscellaneous', 'robots'] | [ 8.18889886e-02 -1.81817397e-01 4.97970849e-01 -1.18518462e-02
-3.14656556e-01 2.55320203e-02 3.39652359e-01 2.65855163e-01
1.07314341e-01 6.37192011e-01 -4.26609904e-01 -1.60783917e-01
-7.88607478e-01 -6.90176189e-01 -4.07053143e-01 -9.89019513e-01
-4.19256270e-01 3.76222014e-01 1.57313675e-01 -2.59147733e-01
3.30582887e-01 8.11097205e-01 -1.97294486e+00 4.81582910e-01
8.02809238e-01 1.39373577e+00 6.07348800e-01 6.76164210e-01
-6.43237820e-03 6.85318530e-01 -9.56607759e-01 6.60154343e-01
1.40415341e-01 -4.66100872e-01 -8.33876312e-01 1.84780523e-01
-4.66490716e-01 -1.55007184e-01 -2.03040078e-01 8.69823456e-01
4.29397851e-01 -8.10455313e-05 9.04984057e-01 -1.19179785e+00
-1.63440496e-01 9.73257646e-02 -2.98143417e-01 3.54446590e-01
3.53417248e-01 4.14693467e-02 3.50690722e-01 -1.07534683e+00
2.77193874e-01 1.08865714e+00 6.95195079e-01 -5.33672310e-02
-8.04423213e-01 -2.43884593e-01 -5.21657944e-01 8.61809790e-01
-1.07105041e+00 -1.21316135e-01 8.95889640e-01 -6.98211432e-01
9.83150542e-01 1.50267646e-01 4.98761773e-01 7.80490577e-01
1.08620703e+00 3.97941947e-01 1.20243537e+00 -5.60156524e-01
2.44359702e-01 -5.49410917e-02 3.07165325e-01 5.52387536e-01
3.80644202e-01 2.19912305e-01 -3.49379212e-01 -9.41229388e-02
4.87520128e-01 1.15769647e-01 -4.01355743e-01 1.91683576e-01
-7.96106756e-01 8.57204556e-01 1.64906144e-01 7.75265992e-01
-9.15209413e-01 -6.30658269e-02 5.93862712e-01 8.43244374e-01
2.52429068e-01 5.11754215e-01 -6.27109289e-01 5.69033884e-02
-6.97761834e-01 5.57584837e-02 8.13328207e-01 3.53140205e-01
6.43874168e-01 4.15776283e-01 1.49622902e-01 5.69063902e-01
3.93963248e-01 4.49529439e-01 9.86424744e-01 -6.86206818e-01
-7.02091604e-02 5.17581820e-01 4.41563800e-02 -1.02037823e+00
-6.23674035e-01 -5.31616449e-01 -8.39825332e-01 5.91182232e-01
1.36125222e-01 -3.20368081e-01 -7.98847556e-01 9.10818696e-01
2.12786734e-01 1.93957642e-01 2.27983624e-01 7.79292703e-01
4.17519897e-01 8.47643256e-01 -4.99421656e-01 -2.60152161e-01
1.35618687e+00 -3.23321998e-01 -9.07827020e-01 -1.52620152e-02
3.54062855e-01 -8.47461164e-01 5.35942435e-01 9.13593173e-01
-6.42638922e-01 -8.63633335e-01 -1.44363916e+00 5.04453301e-01
-4.30975080e-01 3.27013850e-01 2.19987482e-01 1.91951707e-01
-8.06213975e-01 1.08278215e+00 -8.41627717e-01 -1.98685169e-01
-2.76565440e-02 2.86604941e-01 -3.95620912e-01 -3.24224204e-01
-1.35895360e+00 1.29168987e+00 1.61789581e-01 4.92664486e-01
-1.05093622e+00 -3.87511879e-01 -4.90484238e-01 2.82105897e-03
1.37390010e-02 -2.96176910e-01 8.68574679e-01 -7.72205770e-01
-1.34430373e+00 1.23162650e-01 3.46967161e-01 -5.83920896e-01
2.62945414e-01 -3.66594762e-01 -6.48052633e-01 7.82288253e-01
-4.31795754e-02 -3.27383995e-01 1.22487783e+00 -1.13748753e+00
-7.33454943e-01 -3.12070876e-01 -4.24979895e-01 -2.99982518e-01
-2.11233944e-01 -1.57663643e-01 8.51819873e-01 -3.96959424e-01
4.67876881e-01 -5.77483177e-01 1.07106365e-01 -5.67771912e-01
-1.94604874e-01 -3.11136454e-01 1.25717640e+00 -1.08687353e+00
6.87511086e-01 -2.29304147e+00 2.69960046e-01 3.93923759e-01
1.09090246e-02 1.64254159e-01 2.66660869e-01 9.64367390e-01
-1.67046800e-01 -5.47682345e-01 -2.81336278e-01 1.13950640e-01
-1.80955604e-01 3.50042135e-01 -3.01392768e-02 8.06501389e-01
5.05484462e-01 9.55056995e-02 -6.40068114e-01 -1.92124933e-01
4.93879735e-01 3.56464595e-01 -5.09848669e-02 3.23418200e-01
1.47826374e-01 5.27778804e-01 -5.26414573e-01 4.41965252e-01
5.24035454e-01 3.13430756e-01 -1.86002791e-01 -5.94058394e-01
-1.40219703e-01 -2.58728340e-02 -1.31231689e+00 1.12265265e+00
-6.48188710e-01 6.08668804e-01 4.01558995e-01 -1.75285649e+00
1.21072137e+00 8.43336403e-01 1.03710127e+00 -3.90330046e-01
2.29859531e-01 6.27295196e-01 1.83795273e-01 -1.17687011e+00
-7.45262355e-02 -3.01566184e-01 1.41368151e-01 1.38401657e-01
4.46805358e-01 -2.18247980e-01 1.59242935e-02 -5.32800019e-01
1.36041260e+00 -3.12690705e-01 1.52442798e-01 -5.08005917e-01
8.38653266e-01 -6.38829097e-02 3.80933404e-01 1.47216901e-01
5.14864437e-02 2.43619680e-01 3.28574181e-01 -4.47547674e-01
-6.81583643e-01 -6.84682727e-01 -2.77777433e-01 3.02148491e-01
-5.55671751e-02 2.22467095e-01 -5.12263358e-01 -3.01556200e-01
2.87350774e-01 6.48505449e-01 -3.11523378e-01 -7.48792768e-01
-5.54998696e-01 -7.03069806e-01 3.10140878e-01 1.81882575e-01
3.68184716e-01 -1.15455115e+00 -1.18558800e+00 5.07959485e-01
2.56191194e-01 -7.08700180e-01 6.18982434e-01 8.93828094e-01
-9.95309412e-01 -1.56460345e+00 -3.95387262e-01 -9.01128352e-01
4.45181131e-01 -7.86819756e-02 6.41281426e-01 -2.13909633e-02
-7.39952564e-01 4.89338577e-01 -6.87110603e-01 -5.16401947e-01
-7.09834158e-01 -3.24438572e-01 1.26300529e-01 1.90824300e-01
6.83652610e-02 -6.46700561e-01 -3.36905777e-01 8.48523378e-02
-9.29496586e-01 -6.52930081e-01 1.02582741e+00 1.07997191e+00
2.34489236e-02 9.93035793e-01 9.88890290e-01 -5.40949404e-01
9.03629959e-01 -8.07757676e-01 -2.66872764e-01 -1.93154737e-01
-7.53986180e-01 -1.58143342e-01 8.45928431e-01 -2.54813254e-01
-9.03962910e-01 -2.20063418e-01 -2.43622929e-01 -4.86702055e-01
-4.55493152e-01 8.79573107e-01 -2.79329456e-02 -7.30082451e-04
5.43979526e-01 3.05411100e-01 4.50428933e-01 -7.35740781e-01
-3.39066327e-01 8.82981539e-01 4.27827865e-01 -4.12493944e-01
5.87507129e-01 3.25739011e-02 3.90576810e-01 -1.14573300e+00
-1.48244768e-01 -4.57462281e-01 -6.20997906e-01 -5.42878032e-01
7.50640750e-01 -5.33696473e-01 -5.51615298e-01 8.25810611e-01
-1.10653496e+00 1.20729275e-01 -3.90181482e-01 7.39749968e-01
-4.72267509e-01 4.35872823e-01 -1.02307653e+00 -8.84850442e-01
-3.08237940e-01 -1.28425336e+00 8.56951416e-01 -1.57641284e-02
-9.73970741e-02 -7.78756022e-01 -2.57392645e-01 -1.45118386e-01
4.21181679e-01 6.63907826e-01 1.19766068e+00 -7.05038011e-01
-8.74964967e-02 -6.03487551e-01 3.83217394e-01 9.85584795e-01
4.67925757e-01 -2.10271552e-01 -8.57517600e-01 -3.17444682e-01
9.89363551e-01 -6.14765659e-02 5.51631570e-01 3.38804275e-01
5.81266999e-01 -6.03145771e-02 -2.17356220e-01 7.56161241e-03
1.54645920e+00 6.49221838e-01 4.25576895e-01 3.03332210e-01
2.92663962e-01 6.93060696e-01 8.45948160e-01 6.50926411e-01
-3.65126252e-01 3.01086813e-01 5.40425599e-01 1.36865109e-01
-2.57893689e-02 4.91534829e-01 6.19893432e-01 1.01405334e+00
4.14815694e-02 4.66304719e-02 -7.49108732e-01 4.11473185e-01
-1.56166077e+00 -7.75127113e-01 -5.04256546e-01 1.77064395e+00
4.32789057e-01 2.34329000e-01 -2.13686034e-01 1.36934972e+00
7.68268228e-01 -3.18501890e-01 -3.13679546e-01 -4.23624814e-01
1.75232038e-01 4.74843651e-01 3.50706995e-01 2.17172563e-01
-8.81971240e-01 -1.36082694e-01 5.20868635e+00 6.05589449e-01
-1.17745566e+00 6.89257234e-02 6.94483519e-02 4.30740476e-01
3.56493503e-01 -4.41037744e-01 -1.08767428e-01 5.32508373e-01
1.33859885e+00 2.77116179e-01 2.74903238e-01 7.07351208e-01
2.23582223e-01 -4.08163160e-01 -1.04272556e+00 7.53436267e-01
-2.43852749e-01 -6.58678710e-01 -3.09739292e-01 -1.51257873e-01
3.09038609e-01 -2.40756422e-01 -7.81256333e-02 1.30050618e-03
-3.73451352e-01 -8.46343100e-01 7.32576549e-01 8.51119697e-01
3.24725479e-01 -9.48479593e-01 1.44991624e+00 3.56661946e-01
-1.04369652e+00 -7.24308908e-01 -3.80258709e-01 -2.25831628e-01
2.29872212e-01 1.13592827e+00 -9.38339055e-01 1.11789870e+00
7.54873097e-01 6.49021089e-01 -1.67933673e-01 9.20706928e-01
9.80078653e-02 7.33439803e-01 -1.61326364e-01 1.65748879e-01
1.43331885e-01 -1.41852528e-01 6.35595202e-01 9.57429409e-01
7.35383928e-01 -2.52195179e-01 1.80486575e-01 5.13717949e-01
6.62598729e-01 -1.76491618e-01 -6.03478611e-01 9.90410447e-02
3.61079037e-01 9.87387776e-01 -8.09426248e-01 -1.41344756e-01
-2.75599390e-01 7.53356516e-01 -5.17642558e-01 1.93197623e-01
-4.93094504e-01 -7.64203370e-01 5.84174573e-01 7.02176392e-02
2.87698239e-01 -2.66267329e-01 -9.95057523e-02 -3.90394807e-01
-5.84168285e-02 -7.26877451e-01 1.93213254e-01 -5.93637705e-01
-1.49486113e+00 6.05873644e-01 1.47863105e-01 -1.35315323e+00
-3.71969998e-01 -9.43127155e-01 -6.12938046e-01 9.75954235e-01
-1.37383485e+00 -6.72022462e-01 -2.85523891e-01 6.74226880e-01
7.50409484e-01 -3.60531032e-01 8.14180791e-01 3.98893505e-01
-3.46746832e-01 -3.39966387e-01 4.71762091e-01 -2.63952464e-01
3.99825156e-01 -1.30735397e+00 -2.98060387e-01 6.94037676e-01
-3.53095680e-01 1.42942443e-02 1.07913959e+00 -5.99340618e-01
-1.65543580e+00 -7.85325229e-01 4.02031183e-01 2.61894852e-01
5.53855717e-01 1.31440789e-01 -9.26299930e-01 1.44165233e-01
2.61665970e-01 1.05598364e-02 3.53677124e-01 -6.28156304e-01
5.30975282e-01 -3.31618369e-01 -1.53496325e+00 -2.59934455e-01
1.95859835e-01 -4.22801226e-01 -9.29879308e-01 3.60917479e-01
2.94438660e-01 -5.63501380e-03 -1.19902885e+00 6.03447855e-01
1.86661661e-01 -1.09375834e+00 8.60344648e-01 -1.25168055e-01
2.22364277e-01 -3.70212197e-01 -7.85894468e-02 -1.61740804e+00
-3.31509203e-01 -8.73452872e-02 -2.73931056e-01 9.73320127e-01
-5.31341769e-02 -9.55968320e-01 6.70552105e-02 -2.95067757e-01
-6.65801883e-01 -8.37533116e-01 -1.03762257e+00 -6.67374730e-01
-4.23534125e-01 -2.78103292e-01 2.91884869e-01 7.85561323e-01
-1.17182592e-02 1.21049486e-01 -4.91697267e-02 5.10666966e-01
2.63237029e-01 1.31143644e-01 9.35422555e-02 -1.72446978e+00
-2.72831976e-01 -5.31289950e-02 -7.71092594e-01 2.90547987e-03
6.17549084e-02 -6.84208691e-01 3.89420480e-01 -1.71685362e+00
-7.73505449e-01 -2.35482827e-01 -4.94263470e-01 1.20103031e-01
2.94016033e-01 -2.06119254e-01 -5.34807630e-02 2.41204157e-01
3.32937986e-01 5.19474626e-01 1.00716114e+00 -1.62031159e-01
1.84034333e-01 2.63654798e-01 1.11106515e-01 5.93877196e-01
8.18677902e-01 -3.84727389e-01 -4.59836394e-01 1.17860153e-01
-2.45525047e-01 4.18842971e-01 3.34300578e-01 -1.58634210e+00
9.43285301e-02 1.30030811e-01 5.95536411e-01 -5.81189454e-01
1.76549718e-01 -1.22820604e+00 3.44785392e-01 1.14063931e+00
1.71624213e-01 3.35350335e-01 8.98168162e-02 4.46581513e-01
-7.10961521e-01 -5.55393457e-01 7.34417975e-01 -1.73066810e-01
-7.63279736e-01 -3.84661913e-01 -9.69762504e-01 -7.16355085e-01
1.10597146e+00 -7.31861126e-03 -9.78522077e-02 3.02739185e-03
-7.22384810e-01 -1.86789066e-01 -1.26294091e-01 1.99977800e-01
9.49423254e-01 -1.03070855e+00 -6.65751517e-01 4.30360317e-01
-4.05556858e-01 -1.22466506e-02 3.21170181e-01 1.11389518e+00
-7.77721286e-01 4.10643458e-01 -6.24831676e-01 -5.95131636e-01
-8.15252841e-01 5.64579785e-01 5.10578871e-01 5.41269733e-03
-7.29300559e-01 5.33100307e-01 -4.23804104e-01 1.64240941e-01
-1.48381397e-01 -5.90855896e-01 -5.24386168e-01 2.58259684e-01
1.69533834e-01 8.77125323e-01 6.29663885e-01 -5.72765887e-01
-3.10116172e-01 4.20257330e-01 4.95841175e-01 1.37642235e-01
1.61913908e+00 1.08475052e-01 -4.17574495e-01 7.26462245e-01
1.27885640e+00 -3.06754380e-01 -9.47179019e-01 3.27907830e-01
4.68061328e-01 -1.78748015e-02 2.14420140e-01 -7.27199376e-01
-1.07108366e+00 8.45793784e-01 9.17742074e-01 7.90904462e-01
1.43241549e+00 -2.41566464e-01 7.96046853e-01 2.83664912e-01
5.26152492e-01 -1.09540784e+00 1.15472578e-01 2.94934005e-01
1.06430459e+00 -5.43236434e-01 -2.13880718e-01 -2.34098300e-01
-1.48839086e-01 1.67404449e+00 2.33504340e-01 -5.45740247e-01
1.06555510e+00 6.09302342e-01 -7.76581019e-02 -4.92647797e-01
-5.28781295e-01 8.01155493e-02 1.76412523e-01 5.84507167e-01
2.32068613e-01 -5.45056537e-02 -4.00177896e-01 7.43559301e-01
3.74383405e-02 5.22528077e-03 4.06491160e-01 1.36691988e+00
-8.91541839e-01 -9.20320570e-01 -8.78456891e-01 6.88467741e-01
-5.54663479e-01 5.27418435e-01 1.77967519e-01 6.99591339e-01
5.37401021e-01 1.34737539e+00 -2.81363636e-01 -6.54974878e-01
4.59690273e-01 4.21698570e-01 4.39105660e-01 -5.05836666e-01
-4.67235863e-01 -1.67860150e-01 -3.46532390e-02 -4.53307152e-01
-2.87118196e-01 -7.11560845e-01 -1.50556266e+00 2.52783000e-01
-3.47007006e-01 4.17893350e-01 9.27690983e-01 1.25815761e+00
8.68541598e-02 1.22282565e+00 9.01693344e-01 -1.05981576e+00
-8.65574419e-01 -1.48054564e+00 -1.05538285e+00 2.60636866e-01
6.28026128e-01 -1.11341262e+00 -5.67138553e-01 3.60024050e-02] | [6.753728866577148, 2.3765451908111572] |
18d97aa1-9f40-4cc4-878b-89827b3c1de4 | temporally-guided-articulated-hand-pose | 2101.04281 | null | https://arxiv.org/abs/2101.04281v2 | https://arxiv.org/pdf/2101.04281v2.pdf | Temporally Guided Articulated Hand Pose Tracking in Surgical Videos | Articulated hand pose tracking is an under-explored problem that carries the potential for use in an extensive number of applications, especially in the medical domain. With a robust and accurate tracking system on in-vivo surgical videos, the motion dynamics and movement patterns of the hands can be captured and analyzed for many rich tasks. In this work, we propose a novel hand pose estimation model, Res152-CondPose, which improves detection and tracking accuracy by incorporating a hand pose prior into its pose prediction. We show improvements over state-of-the-art methods which provide frame-wise independent predictions, by following a temporally guided approach that effectively leverages past predictions. Additionally, we collect the first dataset, Surgical Hands, that provides multi-instance articulated hand pose annotations for in-vivo videos. Our dataset contains 76 video clips from 28 publicly available surgical videos and over 8.1k annotated hand pose instances. We provide bounding boxes, articulated hand pose annotations, and tracking IDs to enable multi-instance area-based and articulated tracking. When evaluated on Surgical Hands, we show our method outperforms the state-of-the-art method using mean Average Precision (mAP), to measure pose estimation accuracy, and Multiple Object Tracking Accuracy (MOTA), to assess pose tracking performance. Both the code and dataset are available at https://github.com/MichiganCOG/Surgical_ Hands_RELEASE. | ['Jason J. Corso', 'Donald S. Likosky', 'Francis D. Pagani', 'Milisa Manojlovich', 'Roger D. Dias', 'Steven J. Yule', 'Luowei Zhou', 'Nathan Louis'] | 2021-01-12 | null | null | null | null | ['skills-assessment'] | ['computer-vision'] | [-1.15217730e-01 -1.71367124e-01 -6.32996023e-01 1.96436018e-01
-1.12630785e+00 -8.48918855e-01 1.94248885e-01 -1.23833247e-01
-5.72080076e-01 5.35635948e-01 4.99896914e-01 -9.47160646e-02
-3.03093970e-01 1.64085656e-01 -5.30143440e-01 -8.18119824e-01
-3.95449698e-01 5.75775802e-01 2.58053571e-01 4.03193757e-02
9.16694403e-02 7.53205597e-01 -9.55489993e-01 1.12273932e-01
1.23863824e-01 8.84856701e-01 3.18110585e-01 1.02387440e+00
5.40016532e-01 7.97429562e-01 -2.63484120e-01 -6.70103133e-02
4.15933341e-01 -2.72669829e-02 -9.21822369e-01 -1.43825725e-01
5.42176366e-01 -6.41139746e-01 -7.34213591e-01 5.51815867e-01
1.12853849e+00 -1.34710863e-01 1.27967224e-01 -8.38922381e-01
4.55197752e-01 2.80390799e-01 -6.15854144e-01 3.26327413e-01
4.41028833e-01 4.42369521e-01 4.08339322e-01 -8.17678928e-01
1.18022597e+00 7.44857371e-01 9.89604056e-01 7.12910175e-01
-9.47182596e-01 -1.01392686e+00 7.85318688e-02 -2.32825920e-01
-1.46992683e+00 -3.31077874e-01 4.33022380e-01 -8.51014435e-01
7.34062970e-01 2.74769932e-01 9.80875731e-01 1.14254510e+00
7.74596095e-01 1.06480956e+00 7.66204178e-01 -2.55074292e-01
-2.22053975e-01 -5.10025561e-01 -1.42108425e-01 9.72518146e-01
7.32492730e-02 2.75197983e-01 -6.10983014e-01 -2.80692309e-01
1.21397173e+00 4.13698792e-01 -6.43835664e-01 -6.50996685e-01
-1.78460157e+00 2.39084363e-01 5.79384744e-01 2.70000011e-01
-6.62255585e-01 4.91274327e-01 4.56180602e-01 -2.41134271e-01
-1.49192510e-03 2.84007818e-01 -4.68134999e-01 -5.31046748e-01
-7.80689836e-01 1.92017674e-01 7.49018371e-01 1.19598174e+00
-1.68705165e-01 -4.39175248e-01 -8.09700191e-01 1.67810991e-01
3.52936715e-01 3.86214584e-01 4.98533994e-01 -8.35896134e-01
2.04462871e-01 3.50637615e-01 1.63490355e-01 -5.49670160e-01
-9.28284943e-01 -5.81336439e-01 -4.86735195e-01 8.02418813e-02
6.13247752e-01 -2.63928026e-01 -1.13401437e+00 1.48584676e+00
5.84398150e-01 7.34616742e-02 -5.09578705e-01 1.04717851e+00
8.35254371e-01 -1.52039960e-01 1.95822626e-01 -3.80453229e-01
1.40605879e+00 -1.13085163e+00 -7.93840587e-01 -5.62205277e-02
7.41357565e-01 -8.97283375e-01 7.38942027e-01 5.33641577e-01
-8.73510420e-01 -2.14500695e-01 -5.91786027e-01 3.66919339e-01
2.39117712e-01 4.39773887e-01 7.28025079e-01 4.97518152e-01
-6.46697819e-01 5.72512031e-01 -1.79251754e+00 -2.42285609e-01
7.48755693e-01 7.95298994e-01 -5.31389058e-01 -4.57138866e-02
-4.64015067e-01 8.92187297e-01 8.47812444e-02 1.69039875e-01
-1.13315558e+00 -1.05112946e+00 -7.71632493e-01 -4.94285583e-01
5.38491786e-01 -5.54527938e-01 1.51977456e+00 -1.84224382e-01
-1.32965171e+00 7.61563301e-01 -1.37044221e-01 5.63601516e-02
1.03289390e+00 -4.93842810e-01 -5.94153181e-02 3.74073148e-01
-2.05829486e-01 4.01933014e-01 5.50152063e-01 -9.60087359e-01
-3.67642045e-01 -7.14139521e-01 -2.57628262e-01 1.04785442e-01
-1.48969650e-01 1.39848650e-01 -9.20324981e-01 -9.29527521e-01
6.39228150e-02 -1.55084419e+00 -3.34674537e-01 6.07413828e-01
-3.81303132e-01 9.97179300e-02 5.99217236e-01 -1.17469144e+00
1.54078221e+00 -1.97835517e+00 3.36346090e-01 9.09214318e-02
4.16122437e-01 1.60437971e-01 2.07512066e-01 1.06052063e-01
6.85370266e-02 -3.80620569e-01 2.40807161e-01 -4.46646243e-01
-3.32381338e-01 -1.50683010e-02 2.14125469e-01 8.44127655e-01
-3.55693012e-01 9.62044895e-01 -1.07324791e+00 -8.53398621e-01
1.89928129e-01 5.91958880e-01 -6.75565779e-01 2.05304354e-01
1.78623274e-02 1.28448677e+00 -5.17961383e-01 1.06204820e+00
2.28763074e-01 -3.94636095e-01 5.19434154e-01 -6.05440497e-01
8.40898976e-03 -1.99768454e-01 -9.87787604e-01 2.46899414e+00
-3.31226975e-01 3.96374941e-01 4.64953445e-02 1.14407651e-01
2.41439611e-01 6.30574822e-01 1.27692914e+00 -6.30425150e-03
6.11748278e-01 2.55719364e-01 2.28905767e-01 -4.92226332e-01
5.27284332e-02 -4.95808199e-02 7.71006495e-02 8.52720067e-02
1.66996062e-01 1.70909435e-01 1.48513098e-03 3.67194787e-02
1.33822584e+00 3.67673248e-01 5.14838219e-01 -1.79580733e-01
1.40501812e-01 2.25339845e-01 3.40524793e-01 6.26630425e-01
-5.51465273e-01 6.83958173e-01 1.66161135e-01 -3.75686049e-01
-6.64183736e-01 -9.88410354e-01 -2.22339541e-01 8.95283103e-01
1.30677551e-01 -4.35159683e-01 -4.51832175e-01 -8.67934883e-01
3.13984305e-01 -2.14013383e-01 -8.60141158e-01 2.74520159e-01
-9.02099848e-01 -2.38279164e-01 4.17800784e-01 1.06506181e+00
-3.29934925e-01 -9.47242558e-01 -8.75790894e-01 3.53860170e-01
-1.19712867e-01 -1.16232693e+00 -1.02195191e+00 -6.25601113e-02
-7.89046824e-01 -1.45202291e+00 -1.23923266e+00 -7.42558539e-01
6.90593183e-01 -1.47557676e-01 7.28127539e-01 6.36387840e-02
-9.76008713e-01 8.95523608e-01 -1.94172084e-01 -5.08932948e-01
-2.22426325e-01 1.58033028e-01 2.54415512e-01 -5.41806400e-01
-2.07796291e-01 -1.55451775e-01 -1.08437109e+00 5.30539215e-01
-3.47363442e-01 8.07973519e-02 7.80533969e-01 8.94803226e-01
8.24188232e-01 -8.96998048e-01 -2.66184546e-02 -5.67251623e-01
1.53817505e-01 -2.97556631e-02 -5.49849510e-01 4.08168554e-01
-3.35439712e-01 -8.46450925e-02 5.61136901e-02 -7.83711016e-01
-7.25729585e-01 6.82220042e-01 -9.69399810e-02 -1.02691317e+00
1.57977283e-01 2.71916002e-01 3.63498569e-01 -5.03843904e-01
5.44799626e-01 5.63201234e-02 4.42507714e-01 -2.83179104e-01
-3.22933234e-02 3.76602173e-01 8.01007748e-01 -5.30570328e-01
4.06599194e-01 5.11712134e-01 1.01079717e-01 -3.73763710e-01
-8.42312574e-01 -8.72110665e-01 -8.43215823e-01 -6.64064825e-01
7.19866753e-01 -7.86113083e-01 -1.33209014e+00 4.32878226e-01
-1.12868917e+00 -5.23856044e-01 1.10115975e-01 9.35436010e-01
-7.82227457e-01 1.48675397e-01 -7.05281675e-01 -6.86167777e-01
-7.90489376e-01 -1.50758779e+00 1.39069057e+00 -1.31027843e-03
-4.41704422e-01 -8.95785868e-01 3.27109843e-01 3.38508971e-02
2.08163366e-01 7.11613178e-01 1.08992085e-01 -5.13154149e-01
-5.52370965e-01 -6.85810864e-01 1.25550777e-01 -2.59615272e-01
3.15440476e-01 -3.22301805e-01 -6.11839652e-01 -9.95099783e-01
-4.83477920e-01 3.96625958e-02 3.96070987e-01 7.64397144e-01
1.35765362e+00 -1.25447765e-01 -9.26747680e-01 6.28624558e-01
1.17034721e+00 -8.56010243e-04 3.04422110e-01 1.19701169e-01
8.88144732e-01 6.56184331e-02 8.48719299e-01 7.42941022e-01
8.88859779e-02 9.34554756e-01 4.02517229e-01 2.54754163e-02
-3.15355808e-01 2.07122285e-02 -1.51255965e-01 5.95967174e-01
-6.42802536e-01 1.83457807e-01 -1.33879673e+00 5.03047645e-01
-1.71221399e+00 -6.86186969e-01 2.80150622e-01 2.24828982e+00
1.05759168e+00 -1.52515486e-01 3.92854095e-01 -1.45101860e-01
5.23845375e-01 -1.26732960e-01 -6.96762502e-01 8.31758082e-01
8.01447570e-01 3.67595762e-01 8.62786651e-01 3.59140933e-01
-1.24553370e+00 6.39780104e-01 5.96103668e+00 4.46989864e-01
-1.16834462e+00 2.93903977e-01 -4.14958969e-02 -5.98412871e-01
5.04740477e-01 -3.37954223e-01 -8.99066508e-01 2.04295665e-01
2.21634269e-01 4.85412739e-02 1.53804719e-01 8.21584284e-01
8.11574683e-02 -8.21323767e-02 -1.19673705e+00 1.12176120e+00
1.20614208e-01 -1.40630066e+00 -3.66706997e-01 2.03006670e-01
4.68380809e-01 -3.10993269e-02 -1.03817950e-03 -1.60919372e-02
-2.85061151e-02 -8.71000826e-01 5.42787373e-01 8.07249665e-01
1.18963325e+00 -3.60530972e-01 7.42267549e-01 1.28491268e-01
-1.44946444e+00 -3.86006273e-02 4.56534922e-01 4.55885708e-01
1.57599509e-01 -2.22528338e-01 -1.08399093e+00 4.74560499e-01
7.09133267e-01 7.82214284e-01 -3.52982908e-01 1.46700883e+00
-2.06343159e-02 1.03645682e-01 -2.72221208e-01 2.42620319e-01
-1.15923420e-01 8.02061439e-01 6.15354180e-01 1.22933519e+00
1.27065301e-01 3.84131104e-01 4.69394177e-01 1.33671075e-01
1.34257928e-01 -8.39602500e-02 -5.86406849e-02 6.18348345e-02
4.42687482e-01 1.30673981e+00 -7.32575715e-01 2.54474524e-02
-1.65857702e-01 8.15341175e-01 -7.25734383e-02 -1.31955266e-01
-8.71212006e-01 -1.31363049e-01 6.79818451e-01 3.72647762e-01
1.35458454e-01 -4.53216285e-01 5.98560758e-02 -9.47691500e-01
8.49876478e-02 -8.18783998e-01 6.21051610e-01 -4.65822458e-01
-9.11721587e-01 4.62864578e-01 -1.04926512e-01 -1.75582623e+00
-3.88798952e-01 -9.07180905e-01 -1.84494257e-01 6.66911721e-01
-1.06860542e+00 -1.36637425e+00 -1.02576840e+00 7.11145580e-01
4.49489623e-01 1.28385723e-01 9.88687396e-01 1.94144636e-01
-4.23103184e-01 8.97289813e-01 -2.17297934e-02 6.27175152e-01
1.11688626e+00 -8.97975445e-01 -3.97935845e-02 4.13154632e-01
-2.05877021e-01 8.65025103e-01 5.82159877e-01 -8.79088581e-01
-2.01906204e+00 -8.09810519e-01 -1.06976770e-01 -1.09360611e+00
6.45016730e-01 -2.60421671e-02 -2.78007358e-01 1.07514012e+00
-3.13795865e-01 6.54659331e-01 7.22936869e-01 -1.03234023e-01
1.52406290e-01 2.88078368e-01 -1.14478350e+00 3.38120639e-01
1.38011527e+00 -1.62774161e-01 -2.56492198e-01 5.91605544e-01
2.54667819e-01 -1.67712057e+00 -1.47824144e+00 6.84742510e-01
1.38873291e+00 -4.09442782e-01 1.00699174e+00 -6.21973217e-01
1.79433331e-01 -1.56298399e-01 8.39326680e-02 -8.30407321e-01
-1.73774928e-01 -7.34539449e-01 -6.14453733e-01 4.05580848e-01
4.35224781e-03 -4.45549756e-01 1.05484295e+00 5.21663547e-01
-1.76187322e-01 -1.18095732e+00 -1.03961933e+00 -8.12405646e-01
-1.29554123e-01 -2.23430067e-01 6.64911941e-02 5.22173166e-01
2.84191310e-01 -5.17237484e-01 -1.87140346e-01 1.99967876e-01
7.37424314e-01 1.78275872e-02 8.18726003e-01 -9.44488287e-01
-3.57036978e-01 -3.78462374e-01 -5.48232555e-01 -8.51558685e-01
-1.35236219e-01 -8.12329769e-01 2.00804695e-01 -1.54458034e+00
3.86713892e-01 -4.21735406e-01 -2.69682884e-01 8.02262664e-01
-1.42784715e-01 3.59377295e-01 3.73512685e-01 5.29387176e-01
-6.89402640e-01 5.66936918e-02 1.66548979e+00 1.16337493e-01
-2.09023207e-01 2.21667126e-01 8.60184524e-03 6.09698653e-01
4.84624892e-01 -5.27883887e-01 2.04222083e-01 -1.84925020e-01
-3.96602273e-01 5.69200754e-01 5.87514579e-01 -1.11916351e+00
5.91338038e-01 1.62871748e-01 6.69037402e-01 -7.33334303e-01
3.61565590e-01 -9.17317629e-01 3.64158779e-01 1.11274862e+00
-9.13385972e-02 -4.67893444e-02 5.27794898e-01 4.55917209e-01
1.19817093e-01 3.11484665e-01 7.87539601e-01 -1.53604686e-01
-5.25023282e-01 9.38797295e-01 1.26513928e-01 5.54197980e-03
1.25270474e+00 -1.70295641e-01 -4.32000905e-02 1.13218715e-02
-1.21133792e+00 2.94681668e-01 3.80493730e-01 5.20172238e-01
5.08912861e-01 -1.26229942e+00 -5.99538684e-01 -1.31610125e-01
2.19629407e-01 1.39995515e-01 5.65253913e-01 1.63498437e+00
-6.11366332e-01 4.14220303e-01 -1.78063661e-01 -1.00486243e+00
-1.54910243e+00 4.97010142e-01 5.91829181e-01 -3.99480283e-01
-9.14664090e-01 7.12267041e-01 3.00932173e-02 -3.44833553e-01
5.83881617e-01 -3.78726572e-01 1.73393860e-01 -2.28606358e-01
6.76995873e-01 3.40079367e-01 1.69672519e-01 -4.81563598e-01
-8.37592721e-01 7.87181914e-01 -1.84467584e-01 2.16382504e-01
1.11722827e+00 3.87253702e-01 3.52092028e-01 2.79348176e-02
1.00579607e+00 3.63078993e-03 -1.51492620e+00 -1.94983095e-01
-2.51958996e-01 -6.17257833e-01 1.19525306e-01 -1.09110880e+00
-1.12228525e+00 5.33868313e-01 1.07567799e+00 -6.56877816e-01
8.65498006e-01 4.09594119e-01 6.21695876e-01 5.96039705e-02
8.10455024e-01 -4.57479149e-01 1.80272609e-01 2.84126073e-01
1.13110864e+00 -1.26535690e+00 2.86252975e-01 -4.79685843e-01
-6.50510788e-01 1.03613281e+00 6.86879933e-01 1.94690004e-01
7.50310302e-01 8.53186667e-01 1.88678771e-01 -3.00349742e-01
-1.80607855e-01 6.84127351e-03 5.43453813e-01 2.71943748e-01
6.53151214e-01 1.83286190e-01 -1.03817031e-01 7.10321724e-01
2.64262734e-03 4.06257629e-01 4.43730131e-02 1.54717541e+00
2.49941647e-02 -7.90889502e-01 -4.48435336e-01 5.52361667e-01
-6.12499237e-01 2.07793474e-01 2.54220098e-01 1.09286559e+00
-4.03137684e-01 2.52480447e-01 -2.30590239e-01 -2.99348146e-01
6.69970334e-01 -2.33107775e-01 1.19356501e+00 -6.27930105e-01
-7.17770040e-01 1.53675094e-01 -2.64248163e-01 -9.55513239e-01
-2.01232970e-01 -8.56655300e-01 -1.43683398e+00 -1.29324675e-01
-4.59764928e-01 -3.86605024e-01 6.46025836e-01 7.57463813e-01
3.10948998e-01 9.10311401e-01 2.00230882e-01 -1.51252568e+00
-6.36583984e-01 -9.64725971e-01 -4.26600486e-01 1.76817492e-01
5.17147601e-01 -1.20543742e+00 -7.26104006e-02 -1.41174808e-01] | [14.02945613861084, -3.3355019092559814] |
d27a280a-bb92-4891-8601-cae1f9ee8b46 | multi-view-deep-subspace-clustering-networks | 1908.01978 | null | https://arxiv.org/abs/1908.01978v1 | https://arxiv.org/pdf/1908.01978v1.pdf | Multi-view Deep Subspace Clustering Networks | Multi-view subspace clustering aims to discover the inherent structure by fusing multi-view complementary information. Most existing methods first extract multiple types of hand-crafted features and then learn a joint affinity matrix for clustering. The disadvantage lies in two aspects: 1) Multi-view relations are not embedded into feature learning. 2) The end-to-end learning manner of deep learning is not well used in multi-view clustering. To address the above issues, we propose a novel multi-view deep subspace clustering network (MvDSCN) by learning a multi-view self-representation matrix in an end-to-end manner. MvDSCN consists of two sub-networks, i.e., diversity network (Dnet) and universality network (Unet). A latent space is built upon deep convolutional auto-encoders and a self-representation matrix is learned in the latent space using a fully connected layer. Dnet learns view-specific self-representation matrices while Unet learns a common self-representation matrix for all views. To exploit the complementarity of multi-view representations, Hilbert Schmidt Independence Criterion (HSIC) is introduced as a diversity regularization, which can capture the non-linear and high-order inter-view relations. As different views share the same label space, the self-representation matrices of each view are aligned to the common one by a universality regularization. Experiments on both multi-feature and multi-modality learning validate the superiority of the proposed multi-view subspace clustering model. | ['QinGhua Hu', 'Dawei Du', 'Longyin Wen', 'Binyuan Hui', 'Pengfei Zhu', 'Changqing Zhang'] | 2019-08-06 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-3.26243967e-01 -3.62722129e-01 -2.14323863e-01 -5.01459301e-01
-5.50728977e-01 -7.29648948e-01 5.10803819e-01 -5.61631143e-01
-2.68639643e-02 1.11840159e-01 4.39725608e-01 3.10073078e-01
-3.26031834e-01 -4.66695905e-01 -4.91248429e-01 -9.66041088e-01
2.29408890e-01 4.75848794e-01 -2.61250973e-01 -1.63872056e-02
-9.37523022e-02 3.20914179e-01 -1.36306560e+00 5.74277043e-01
7.48303294e-01 6.92875147e-01 1.04588866e-01 3.49638253e-01
1.02474488e-01 6.30465508e-01 1.24398097e-01 7.65300868e-03
3.70108068e-01 -6.11780405e-01 -6.33602381e-01 5.25539100e-01
1.90660149e-01 -3.76118831e-02 -4.64689285e-01 1.06045151e+00
5.52817822e-01 5.16221598e-02 8.46666336e-01 -1.43158579e+00
-7.08963573e-01 3.32534581e-01 -8.13336134e-01 -8.25452209e-02
1.52995721e-01 -9.19546559e-02 1.23087084e+00 -1.12600088e+00
5.88230193e-01 1.18156886e+00 3.87081474e-01 4.24241453e-01
-1.36582506e+00 -5.96986055e-01 1.87432885e-01 1.68239564e-01
-1.56138015e+00 -2.70188808e-01 1.14806783e+00 -6.64608657e-01
5.32912791e-01 1.93189114e-01 7.12880194e-01 1.12379897e+00
1.90000832e-01 9.59957182e-01 1.11933124e+00 -7.82213211e-02
5.57490773e-02 1.83365569e-01 -1.37762010e-01 7.76622713e-01
-6.34638965e-02 2.87394854e-03 -4.04007703e-01 -1.26171589e-01
7.92580366e-01 4.82470810e-01 -2.97600299e-01 -1.32345319e+00
-1.38025701e+00 1.10766697e+00 5.13920248e-01 5.76718688e-01
-2.60609925e-01 -3.58821869e-01 5.95829964e-01 2.61476547e-01
2.66899794e-01 9.36260074e-03 -2.80387521e-01 3.76174569e-01
-7.48161316e-01 -3.02875042e-01 6.35711610e-01 8.47549438e-01
9.41174090e-01 -4.17333143e-03 1.94996938e-01 8.38307142e-01
5.81545174e-01 4.45253432e-01 8.02799106e-01 -8.10060561e-01
4.41841811e-01 9.72265959e-01 -2.95743734e-01 -9.62268710e-01
-4.80289459e-01 -7.13651240e-01 -1.41657615e+00 8.74707177e-02
8.94283354e-02 -5.24426997e-02 -6.21681273e-01 1.86820173e+00
2.79059827e-01 6.75212815e-02 2.98690766e-01 1.09819770e+00
7.87759781e-01 3.39953572e-01 -4.21512902e-01 -2.61335313e-01
1.15789080e+00 -1.08755863e+00 -5.31830907e-01 -8.84748921e-02
4.86700416e-01 -5.28880954e-01 6.83601320e-01 2.38803744e-01
-7.57995427e-01 -5.71430087e-01 -1.13810933e+00 2.50822175e-02
-2.69418538e-01 2.53369510e-01 5.37752748e-01 2.16881618e-01
-7.92492330e-01 1.85773224e-01 -8.60764444e-01 -3.26804757e-01
3.77627879e-01 4.29679364e-01 -9.33881879e-01 -3.05443764e-01
-8.88244331e-01 4.12141740e-01 4.13043618e-01 6.02244586e-02
-8.87114882e-01 -3.93033206e-01 -9.24640417e-01 3.15552838e-02
3.17246467e-01 -1.02432871e+00 3.18791211e-01 -1.20315528e+00
-1.37274802e+00 1.01033890e+00 -5.36991358e-02 2.58077979e-01
2.75567800e-01 -1.08408347e-01 -6.25689328e-01 2.57262886e-01
4.77410316e-01 2.97410697e-01 1.01345289e+00 -1.68107700e+00
-3.67372423e-01 -7.02173829e-01 -2.10287794e-01 6.05801463e-01
-4.36755776e-01 -2.41374627e-01 -6.02883816e-01 -4.37276870e-01
6.50207222e-01 -1.14268589e+00 -4.88930382e-02 -3.27663213e-01
-5.21624684e-01 1.01135358e-01 9.88591790e-01 -4.02362287e-01
1.08731055e+00 -2.31619573e+00 8.95592630e-01 2.99038142e-01
4.95487154e-01 -7.37236440e-02 -1.66116610e-01 6.55109286e-01
-5.37911475e-01 -3.35375547e-01 -1.92969099e-01 -1.43409401e-01
-1.08157478e-01 3.17851514e-01 6.19441736e-03 8.21907938e-01
-6.50673881e-02 7.18130231e-01 -1.01236987e+00 -3.38976353e-01
3.00566941e-01 6.19833887e-01 -5.99209964e-01 4.09366816e-01
2.79068232e-01 8.63993049e-01 -3.54951441e-01 4.81605530e-01
6.44477963e-01 -7.01333582e-01 3.84463519e-01 -6.36155248e-01
2.31630309e-03 -3.07921618e-01 -1.43112504e+00 2.24177814e+00
-4.34709519e-01 6.26572296e-02 1.66321442e-01 -1.02140844e+00
6.15893900e-01 4.70196575e-01 9.53156292e-01 -1.67806000e-01
9.79713872e-02 1.99219689e-01 1.42568663e-01 -4.41934943e-01
-1.38432652e-01 -4.14325356e-01 7.01679587e-02 6.24800324e-01
5.68200827e-01 5.36721766e-01 -7.66100362e-02 3.11694980e-01
6.89835608e-01 8.08516145e-03 3.91873360e-01 -3.56250703e-01
8.54892194e-01 -4.70647991e-01 6.63556755e-01 1.32764265e-01
2.00797664e-03 8.56467187e-01 4.50173318e-01 -4.41626608e-01
-9.24461246e-01 -1.14106429e+00 3.51221673e-02 1.04416645e+00
1.87259004e-01 -3.42704892e-01 -4.39770848e-01 -1.01094091e+00
-1.09641246e-01 2.63834447e-01 -7.70620644e-01 -3.00894439e-01
-1.62474364e-01 -6.25794649e-01 1.44894332e-01 6.32061362e-01
4.99597222e-01 -5.99305391e-01 -2.91898519e-01 7.38250315e-02
-2.21147239e-01 -1.05759668e+00 -7.69050002e-01 3.46846193e-01
-8.64522636e-01 -1.27142572e+00 -6.86964273e-01 -7.48510003e-01
8.45370591e-01 6.88815594e-01 7.83184946e-01 -2.80007362e-01
5.37641570e-02 7.01854408e-01 -2.08110362e-01 2.04745531e-01
-1.43938214e-01 2.84455791e-02 4.23690587e-01 7.02523470e-01
5.81501126e-01 -1.04652774e+00 -6.17735803e-01 4.66977417e-01
-1.05114245e+00 1.70279592e-01 7.05327690e-01 1.31262088e+00
7.52578855e-01 1.71956476e-02 4.26949054e-01 -9.21710849e-01
1.37110308e-01 -5.72578847e-01 -3.11242133e-01 3.82655233e-01
-5.66343009e-01 6.04163259e-02 7.66422093e-01 -2.25045785e-01
-7.56112337e-01 5.55220962e-01 2.20093623e-01 -1.23907256e+00
-2.43360400e-01 5.18919885e-01 -6.85795128e-01 1.33530602e-01
3.81308466e-01 6.22078657e-01 2.45390385e-01 -3.98344606e-01
7.80555367e-01 4.12055761e-01 3.21545303e-01 -3.16174060e-01
1.00345826e+00 8.17019582e-01 -6.07954562e-02 -6.19875729e-01
-1.05397749e+00 -9.28504229e-01 -1.35662508e+00 -6.38280883e-02
1.11956108e+00 -1.49649978e+00 -5.17158806e-01 3.53323787e-01
-6.92653954e-01 3.14603478e-01 -1.95672274e-01 8.23694170e-01
-6.85795844e-01 5.87173760e-01 -3.99908543e-01 -3.25120419e-01
-1.79315284e-01 -1.07693350e+00 1.04478598e+00 1.99125335e-02
1.12352856e-01 -1.15987527e+00 1.64563596e-01 5.13385892e-01
-1.00182720e-01 2.57939637e-01 9.14760232e-01 -8.22528541e-01
-2.13862956e-01 -1.54058188e-01 -1.92069635e-01 4.99631941e-01
3.34470779e-01 -3.12116653e-01 -1.05708694e+00 -7.71358907e-01
3.16508114e-01 -4.05609936e-01 8.88898969e-01 3.10566902e-01
8.98268044e-01 -1.94024175e-01 -3.08079183e-01 9.08567369e-01
1.72631049e+00 -7.31263217e-03 2.59353697e-01 1.62403703e-01
1.36554825e+00 4.86847430e-01 5.47133503e-04 3.92850667e-01
5.91453433e-01 4.36407298e-01 4.29416716e-01 -9.80745554e-02
2.68170893e-01 -2.08083704e-01 3.91903281e-01 1.45941138e+00
-9.45945233e-02 9.29377154e-02 -7.57357717e-01 4.79864836e-01
-2.05789709e+00 -1.12361348e+00 -1.92800499e-02 2.11986995e+00
2.86734581e-01 -1.37372896e-01 3.44166845e-01 -2.31409315e-02
6.67211652e-01 2.94891059e-01 -8.00402880e-01 2.24187478e-01
-3.80433619e-01 -6.04480267e-01 7.54540339e-02 2.53140152e-01
-1.37057054e+00 6.33913279e-01 4.72915697e+00 4.11176741e-01
-1.13387883e+00 2.13652626e-01 2.77089059e-01 -9.11904275e-02
-4.39613044e-01 -5.98137314e-03 -3.58799309e-01 2.08812147e-01
4.88976419e-01 1.57878488e-01 4.98837858e-01 7.94523418e-01
-2.86496788e-01 4.33782071e-01 -1.23933792e+00 1.36358464e+00
4.30983692e-01 -1.13171101e+00 6.94079697e-02 2.44899228e-01
9.46822703e-01 2.29485005e-01 7.81244412e-02 2.39168838e-01
3.06194216e-01 -7.65021026e-01 4.14198458e-01 5.16925395e-01
8.33380759e-01 -8.80920589e-01 6.44043982e-01 3.55421633e-01
-1.42701733e+00 -1.97239265e-01 -3.65812570e-01 2.75463909e-01
-9.97067764e-02 3.96681458e-01 -2.17767745e-01 1.16202044e+00
5.87979913e-01 1.35452831e+00 -5.38007021e-01 5.23817301e-01
-9.29835159e-03 1.19426630e-01 -1.35141999e-01 7.37351179e-01
4.74985182e-01 -7.40334213e-01 5.78400373e-01 8.10618639e-01
1.15064844e-01 4.90287691e-02 3.77738118e-01 8.87288690e-01
9.59771052e-02 7.78583949e-03 -8.22340667e-01 -4.72115427e-02
1.13264285e-01 1.58718598e+00 -6.36312723e-01 -1.07935980e-01
-9.60712790e-01 1.44591475e+00 4.69273955e-01 5.49695671e-01
-4.47114438e-01 1.10754952e-01 5.03134131e-01 -2.50418574e-01
5.62209487e-01 -1.39101505e-01 -2.67645836e-01 -1.77744162e+00
-1.46764889e-01 -9.76726651e-01 5.93602836e-01 -4.27465379e-01
-1.83130085e+00 5.95858514e-01 -2.04986811e-01 -1.84892535e+00
-2.15190664e-01 -4.13856864e-01 -5.40788949e-01 8.65037382e-01
-1.15264118e+00 -1.57459569e+00 -1.86063454e-01 1.09412932e+00
2.70749718e-01 -7.65228093e-01 1.00067890e+00 3.42493117e-01
-7.83202708e-01 5.53618670e-01 6.00791693e-01 3.90148938e-01
6.97504938e-01 -1.34955668e+00 -3.39935720e-01 6.88554645e-01
3.54326874e-01 8.17538857e-01 1.01477884e-01 -3.80685240e-01
-1.74984837e+00 -1.11903059e+00 4.24787164e-01 -5.10590851e-01
6.44306779e-01 -4.63899672e-01 -8.79399061e-01 8.60960543e-01
3.04549843e-01 3.51932615e-01 1.41895902e+00 3.87134016e-01
-8.21486771e-01 -1.08197398e-01 -7.92179942e-01 3.15341115e-01
8.42842102e-01 -1.04074025e+00 -3.81180137e-01 2.67656207e-01
3.67499113e-01 -1.63587660e-01 -1.20738220e+00 2.76189148e-01
6.35936439e-01 -1.29774916e+00 9.47727919e-01 -7.95795858e-01
5.53510427e-01 -5.06607354e-01 -5.95689416e-01 -1.51455808e+00
-8.10211658e-01 -9.32805538e-02 -2.18648046e-01 1.21621370e+00
5.24771437e-02 -4.34136689e-01 6.20944738e-01 2.46207058e-01
-1.28042400e-01 -9.03147399e-01 -9.45319235e-01 -7.63785839e-01
4.15296368e-02 4.13542800e-02 3.74238342e-01 1.50818431e+00
-1.03688188e-01 1.04689598e+00 -5.07706404e-01 4.86393511e-01
9.03094590e-01 7.04232275e-01 6.24593198e-01 -1.33169198e+00
-3.42884570e-01 -2.91435689e-01 -3.89110416e-01 -6.61546826e-01
2.92571843e-01 -1.27563870e+00 -3.45436692e-01 -1.39650416e+00
6.83158994e-01 -8.03050846e-02 -6.55155063e-01 2.51351446e-01
-1.74951881e-01 -7.67255351e-02 1.90928429e-01 6.37437522e-01
-7.99564242e-01 9.09153342e-01 1.27632022e+00 -4.33330350e-02
-1.79291561e-01 -1.29644573e-01 -7.56092548e-01 8.77779365e-01
4.10205960e-01 -3.39867055e-01 -6.37087762e-01 -3.79812956e-01
7.36023039e-02 3.50940943e-01 3.63638163e-01 -1.03365386e+00
3.57302219e-01 1.89879313e-02 7.08971024e-01 -6.91644251e-01
3.10880244e-01 -1.19124281e+00 1.95895672e-01 2.75421262e-01
-1.50288254e-01 -6.70842230e-02 -2.73319721e-01 1.04239905e+00
-5.34964800e-01 1.98513776e-01 8.48255217e-01 -3.33831549e-01
-5.06167412e-01 5.04420161e-01 -3.89464945e-02 -5.50564192e-03
8.94028902e-01 -2.54872739e-01 1.09112829e-01 -3.71654302e-01
-9.11176503e-01 3.26268733e-01 5.36246359e-01 5.65569818e-01
6.11705780e-01 -1.91460741e+00 -5.11547804e-01 6.23527169e-01
4.93703127e-01 2.57447232e-02 7.00976312e-01 9.61339772e-01
-1.57756284e-02 2.58887649e-01 -4.48391706e-01 -9.68728125e-01
-9.94542301e-01 8.54990959e-01 3.04064035e-01 -3.97002518e-01
-6.40546739e-01 7.90968835e-01 7.65724480e-01 -8.78412604e-01
-1.61011070e-02 3.64821136e-01 -4.22120035e-01 3.54328215e-01
2.86955833e-01 1.75096631e-01 -2.99170554e-01 -1.17491961e+00
-3.97057414e-01 8.54153931e-01 -1.51468098e-01 6.10012338e-02
1.52929926e+00 -3.97716731e-01 -3.35322738e-01 8.55808377e-01
1.78084254e+00 -1.89348742e-01 -1.31559861e+00 -5.88288069e-01
-2.34718919e-01 -2.95442402e-01 6.91767186e-02 -4.93329972e-01
-1.45737541e+00 9.57575202e-01 7.12383270e-01 -1.48678064e-01
1.24315298e+00 -2.51954813e-02 5.14836371e-01 2.96243161e-01
8.12267140e-02 -9.50757921e-01 3.03897381e-01 5.10563731e-01
9.63606477e-01 -1.54372382e+00 7.20191896e-02 -3.04882854e-01
-1.02539420e+00 1.12348413e+00 7.44521081e-01 -1.89541250e-01
1.05471659e+00 -3.46339285e-01 1.55015156e-01 -7.07686961e-01
-5.97277284e-01 -2.51814425e-01 5.68165243e-01 4.47243631e-01
3.65748405e-01 7.18383342e-02 1.39349505e-01 7.30899394e-01
1.82673067e-01 -3.59029651e-01 1.74237415e-01 5.20819843e-01
1.72075145e-02 -1.01193202e+00 -2.67544150e-01 3.94394219e-01
-8.37915838e-02 1.76256105e-01 -3.94977182e-01 5.60244977e-01
1.54713243e-01 7.77988732e-01 -1.37741804e-01 -7.97214210e-01
1.29909590e-01 1.38804927e-01 2.87135959e-01 -6.98123217e-01
-4.75651473e-01 6.62344098e-01 -4.88655031e-01 -6.43389821e-01
-7.49251902e-01 -6.52679324e-01 -1.04119277e+00 6.47686049e-02
-2.17746630e-01 9.76369083e-02 2.41316959e-01 8.29648018e-01
5.00018418e-01 5.11969209e-01 1.16840291e+00 -7.03339100e-01
-4.54388857e-01 -6.32059872e-01 -1.13526320e+00 7.75659561e-01
2.34762639e-01 -6.85373962e-01 -3.34625393e-01 1.29871396e-02] | [8.374654769897461, 4.542566776275635] |
6853f3d9-891f-47c0-ad18-2e4faa5c2d50 | exploring-the-impact-of-noise-and | 2211.07445 | null | https://arxiv.org/abs/2211.07445v1 | https://arxiv.org/pdf/2211.07445v1.pdf | Exploring the Impact of Noise and Degradations on Heart Sound Classification Models | The development of data-driven heart sound classification models has been an active area of research in recent years. To develop such data-driven models in the first place, heart sound signals need to be captured using a signal acquisition device. However, it is almost impossible to capture noise-free heart sound signals due to the presence of internal and external noises in most situations. Such noises and degradations in heart sound signals can potentially reduce the accuracy of data-driven classification models. Although different techniques have been proposed in the literature to address the noise issue, how and to what extent different noise and degradations in heart sound signals impact the accuracy of data-driven classification models remains unexplored. To answer this question, we produced a synthetic heart sound dataset including normal and abnormal heart sounds contaminated with a large variety of noise and degradations. We used this dataset to investigate the impact of noise and degradation in heart sound recordings on the performance of different classification models. The results show different noises and degradations affect the performance of heart sound classification models to a different extent; some are more problematic for classification models, and others are less destructive. Comparing the findings of this study with the results of a survey we previously carried out with a group of clinicians shows noise and degradations that are more detrimental to classification models are also more disruptive to accurate auscultation. The findings of this study can be leveraged to develop targeted heart sound quality enhancement approaches - which adapt the type and aggressiveness of quality enhancement based on the characteristics of noise and degradation in heart sound signals. | ['Susan Mckeever', 'Andrew Hines', 'Davoud Shariat Panah'] | 2022-11-14 | null | null | null | null | ['sound-classification'] | ['audio'] | [ 2.89031267e-01 -2.27223188e-01 4.35303718e-01 -9.55911279e-02
-4.90850121e-01 -4.71796453e-01 8.03212076e-02 1.97474137e-01
8.08195770e-02 3.48521978e-01 4.88205254e-01 -3.40736389e-01
-5.22941887e-01 -6.59847260e-01 -2.03619182e-01 -8.18654895e-01
4.67391172e-03 -1.06058039e-01 1.81542695e-01 -1.70912564e-01
2.40154937e-01 4.23121959e-01 -1.73481858e+00 1.53345257e-01
4.32936937e-01 7.68776834e-01 -6.78912476e-02 1.06656301e+00
-4.69355844e-02 6.83271587e-01 -1.08668387e+00 2.30779350e-01
1.96084499e-01 -8.47409725e-01 -3.27543974e-01 -1.88133061e-01
1.60726786e-01 -1.44733727e-01 2.65828781e-02 5.74379265e-01
1.14744210e+00 -1.34807348e-01 4.43561375e-01 -8.30204606e-01
-6.02369942e-02 5.45063198e-01 6.96726665e-02 6.01003945e-01
3.05520743e-01 3.77186805e-01 7.02686667e-01 -3.96878332e-01
2.83065021e-01 8.32571626e-01 1.01560712e+00 4.27849174e-01
-1.39849639e+00 -7.20912337e-01 -4.73733872e-01 -1.03113033e-01
-1.00465047e+00 -5.78992069e-01 1.19190371e+00 -6.52471244e-01
5.28950930e-01 5.10110378e-01 9.33388650e-01 1.03174472e+00
2.95583010e-01 -2.77093947e-01 1.33769584e+00 -6.44503236e-01
2.71869808e-01 2.83932984e-01 1.28737882e-01 1.50085226e-01
4.35728788e-01 3.80231202e-01 -5.80860615e-01 -3.86603206e-01
3.33116144e-01 -3.56647342e-01 -4.40056503e-01 1.41997293e-01
-9.49775994e-01 5.94197094e-01 -1.62229836e-01 6.99316442e-01
-4.26437825e-01 2.83484254e-02 4.39740747e-01 3.77000839e-01
4.50447440e-01 9.39349532e-01 -3.65714997e-01 -5.39892733e-01
-9.32395101e-01 2.14991629e-01 1.03458011e+00 1.07869394e-01
1.30045429e-01 3.66069645e-01 -2.67347634e-01 9.97810364e-01
2.73923665e-01 5.36298275e-01 3.22872013e-01 -1.00149393e+00
8.84324461e-02 2.18091860e-01 3.37988399e-02 -1.50071144e+00
-6.73441708e-01 -7.66945720e-01 -5.03055155e-01 5.65106571e-02
5.88260710e-01 -3.05214226e-01 -4.92970258e-01 1.46629572e+00
2.18076557e-01 2.13523790e-01 -1.50522739e-02 9.44419324e-01
8.76486123e-01 3.18444222e-01 1.76886380e-01 -3.47355366e-01
1.15082216e+00 -1.30693242e-01 -9.35540378e-01 -7.59213045e-02
5.06483257e-01 -1.00313342e+00 1.27944362e+00 5.03670931e-01
-9.29432154e-01 -7.74841189e-01 -1.07164443e+00 6.22701049e-01
5.99219091e-02 -4.70637381e-01 -4.21206281e-03 1.39464140e+00
-6.75248146e-01 6.24193609e-01 -7.27977753e-01 -3.35404754e-01
1.82534590e-01 -1.60057649e-01 8.55995342e-02 2.44098768e-01
-1.18040383e+00 7.16725409e-01 -3.02912921e-01 1.19124480e-01
-6.37445450e-01 -1.12440014e+00 -3.58745575e-01 5.23879379e-02
1.20334579e-02 -5.87833285e-01 1.14681423e+00 -6.12135231e-01
-1.32221735e+00 4.20524657e-01 4.37526584e-01 -2.75642425e-01
4.04845119e-01 -1.55837566e-01 -7.29466259e-01 1.67164281e-01
-7.84245729e-02 -9.55744088e-02 7.44177938e-01 -1.25718021e+00
-2.62343049e-01 -2.32712999e-01 -3.63497376e-01 -1.60767078e-01
-4.55578089e-01 -3.63500603e-03 6.34396151e-02 -7.88402677e-01
2.29011118e-01 -9.48041022e-01 -1.45853299e-03 -1.52182236e-01
-2.37337068e-01 5.42782128e-01 8.09434354e-01 -5.74370265e-01
1.38611054e+00 -2.43013334e+00 -4.85335112e-01 2.40400940e-01
8.55057612e-02 4.75580156e-01 8.43975022e-02 5.86876690e-01
6.18345141e-02 4.21962887e-01 -1.32204220e-01 2.46527027e-02
-2.15587378e-01 2.19178617e-01 -2.38184884e-01 4.07887876e-01
3.45337801e-02 2.49509290e-01 -6.59318209e-01 -3.71073186e-01
4.35282916e-01 7.44830608e-01 -6.20157838e-01 4.61716920e-01
2.81737506e-01 7.16842175e-01 -2.96986312e-01 4.39658225e-01
3.13615203e-01 3.08469564e-01 -4.72894795e-02 -3.16211343e-01
-1.02063879e-01 5.19629300e-01 -1.16883016e+00 1.04398835e+00
-4.91562039e-01 7.51920998e-01 -8.82885139e-03 -8.25867951e-01
1.18952906e+00 7.24195361e-01 6.13296747e-01 -7.12782085e-01
4.27577235e-02 2.59596080e-01 5.91395497e-01 -1.06620085e+00
-1.77660603e-02 -7.38716841e-01 2.18012005e-01 4.99663830e-01
-4.01486784e-01 -5.82269788e-01 -1.52126819e-01 -3.15146983e-01
1.17709458e+00 -2.69864649e-01 -2.74268109e-02 -3.01154435e-01
3.19133401e-01 1.95088219e-02 7.29167163e-01 8.09516370e-01
-5.38088322e-01 8.86974752e-01 2.98051804e-01 -3.87591541e-01
-7.06025362e-01 -8.00882101e-01 -2.02435359e-01 8.37570906e-01
-2.59904236e-01 -3.31433594e-01 -7.41796851e-01 -1.35023445e-01
-9.33752060e-02 7.88418949e-01 -3.52324128e-01 -6.03036106e-01
-4.10492480e-01 -8.46594632e-01 1.00518119e+00 4.37440038e-01
1.83325648e-01 -1.17616713e+00 -1.24644852e+00 5.52870393e-01
-1.72767401e-01 -8.85279298e-01 -1.34062350e-01 2.37936854e-01
-8.45473528e-01 -1.18336427e+00 -4.05543864e-01 -3.00786018e-01
3.04730922e-01 5.90425218e-03 1.20648551e+00 3.00330758e-01
-7.76680231e-01 7.76916206e-01 -6.55138433e-01 -1.19987977e+00
-9.50708926e-01 -1.64874583e-01 -3.36163538e-03 -7.71406889e-02
9.83059220e-03 -7.33452201e-01 -8.08732808e-01 3.47754270e-01
-9.03359771e-01 -4.54170734e-01 2.33309090e-01 5.87740123e-01
2.32740819e-01 5.06222129e-01 9.15407836e-01 -7.21403658e-01
1.02591181e+00 -4.00785357e-01 -1.75734639e-01 -2.97985345e-01
-7.20243812e-01 -4.59057182e-01 7.09618926e-01 -5.38670301e-01
-7.83067465e-01 -3.61354321e-01 -2.93329895e-01 -2.90613949e-01
-3.64630193e-01 3.77398193e-01 4.55846218e-03 2.55653292e-01
9.01033342e-01 -1.22805117e-02 2.41137296e-01 -5.88042498e-01
-2.75997877e-01 8.28298688e-01 3.22624564e-01 -3.94450158e-01
6.24680877e-01 2.70204365e-01 1.21387981e-01 -1.20122600e+00
-6.08625293e-01 -3.81435543e-01 -1.41389877e-01 -4.29212362e-01
7.25356102e-01 -4.64933008e-01 -1.75624683e-01 6.55671477e-01
-6.79849625e-01 -3.82657170e-01 -5.27789056e-01 5.43531060e-01
-1.28625721e-01 2.16793194e-01 -3.20404679e-01 -1.16562760e+00
-6.08482540e-01 -1.02282107e+00 5.13826609e-01 7.29172081e-02
-7.01248050e-01 -1.00412738e+00 1.95328549e-01 5.43694377e-01
8.61873925e-01 5.59161603e-01 1.08839464e+00 -5.45239627e-01
7.66626373e-02 -7.52315074e-02 4.92427021e-01 6.30988240e-01
5.21669745e-01 2.39699185e-01 -1.16971958e+00 -1.97605640e-01
4.12014186e-01 -1.06611876e-02 3.17822039e-01 6.15569592e-01
8.07901204e-01 -1.05157174e-01 8.48268196e-02 4.28340018e-01
1.20887876e+00 4.17293817e-01 5.77261090e-01 2.61742324e-01
2.23188952e-01 8.37222874e-01 6.74648106e-01 4.80246454e-01
-1.49059057e-01 5.52282035e-01 2.42517889e-01 -3.69459778e-01
-4.67485666e-01 -3.95255862e-03 9.65497568e-02 7.05783963e-01
1.24956705e-01 -9.21376497e-02 -1.01805544e+00 4.97329772e-01
-9.61284697e-01 -8.24476838e-01 -4.12276596e-01 2.20233607e+00
7.24735618e-01 1.33230373e-01 3.31275880e-01 9.40077186e-01
4.24629033e-01 4.39516790e-02 -1.83880672e-01 -6.55468106e-01
1.50038227e-01 2.71012634e-01 3.27663459e-02 2.11994529e-01
-7.75946915e-01 -3.28909382e-02 6.97889948e+00 -1.09600119e-01
-1.59258878e+00 -7.80525208e-02 6.45537496e-01 -1.68484524e-01
-2.75905281e-01 -1.82832718e-01 -2.07703918e-01 7.06886530e-01
1.17588544e+00 1.57211348e-01 6.41799048e-02 5.90319037e-01
8.94000649e-01 2.89738961e-02 -8.72484207e-01 8.09465826e-01
-1.29119277e-01 -9.44396675e-01 -3.97442132e-01 -1.95012793e-01
2.49079123e-01 -4.52483743e-01 -1.05588451e-01 -4.64177653e-02
-3.60117853e-01 -9.19230402e-01 5.26682556e-01 6.56640768e-01
5.72761655e-01 -4.69296813e-01 7.93691158e-01 1.69288516e-01
-8.27594638e-01 -3.51366788e-01 1.54513657e-01 -2.80547678e-01
7.03934655e-02 1.06298220e+00 -1.01251781e+00 1.06652997e-01
8.43156934e-01 2.02511489e-01 -4.86232460e-01 1.10197699e+00
1.17837474e-01 1.52501070e+00 -2.31484815e-01 1.08829178e-01
-4.09431279e-01 1.47101566e-01 7.59593725e-01 1.24067760e+00
3.90005291e-01 1.31812066e-01 -1.43411145e-01 8.06968153e-01
4.97514725e-01 2.24431738e-01 -6.12432420e-01 -3.83650437e-02
7.24890053e-01 8.85869920e-01 -6.20557308e-01 4.46430728e-04
-3.82854879e-01 1.22433580e-01 -5.59986174e-01 1.89498946e-01
-6.59678221e-01 -3.40758473e-01 6.81575477e-01 6.73313439e-01
-1.21021256e-01 4.62403707e-02 -5.81092179e-01 -3.46546531e-01
-9.22783837e-02 -1.16402233e+00 3.54060829e-01 -5.53867519e-01
-1.17163277e+00 4.31385726e-01 -1.02500640e-01 -1.28304493e+00
-1.04604013e-01 -3.58580723e-02 -7.65470862e-01 8.90136719e-01
-1.23420036e+00 -4.44617867e-01 -5.11901140e-01 1.39896825e-01
4.09295171e-01 5.16939396e-03 9.23478782e-01 4.51422453e-01
-4.02636945e-01 2.59621859e-01 -2.12165609e-01 9.17646438e-02
6.37083113e-01 -1.02624977e+00 6.16055839e-02 6.88856781e-01
-1.23613067e-02 5.35886824e-01 1.13140488e+00 -5.64013422e-01
-1.28266430e+00 -1.04012156e+00 7.63952732e-01 -2.35322416e-01
1.72439381e-01 2.70465463e-02 -9.89979625e-01 -1.70525089e-01
-2.52422631e-01 1.34907365e-01 9.09484565e-01 -4.70457152e-02
-5.78911342e-02 -4.89620000e-01 -1.16165841e+00 2.38084301e-01
5.31478047e-01 -4.57908005e-01 -6.47427797e-01 -1.13750860e-01
3.52671027e-01 -1.32994251e-02 -1.08164585e+00 4.65819627e-01
7.62938559e-01 -1.28529775e+00 8.55188489e-01 -1.25686035e-01
1.39678195e-01 -2.37946495e-01 8.42674598e-02 -1.42455328e+00
-2.41509005e-01 -5.82811952e-01 3.02151561e-01 1.48828483e+00
2.94648647e-01 -7.24804282e-01 3.27683181e-01 7.03550160e-01
-7.07436129e-02 -5.82384109e-01 -6.31319761e-01 -6.87952042e-01
-4.03528661e-02 -7.27360189e-01 4.12595540e-01 7.25565910e-01
-1.09682724e-01 8.80007520e-02 -1.09373212e-01 1.62922949e-01
4.23660517e-01 -1.06552012e-01 6.21487319e-01 -1.35909569e+00
-4.16520923e-01 -3.34184706e-01 -3.67683440e-01 1.26219288e-01
-4.74603891e-01 -3.52160156e-01 2.07190394e-01 -1.41362882e+00
-1.57068595e-01 -8.13448489e-01 -3.89360964e-01 2.17732325e-01
-3.67337584e-01 3.91502559e-01 3.68052006e-01 2.50025600e-01
2.95939863e-01 -1.16280895e-02 9.99658108e-01 1.44920737e-01
-5.78067422e-01 2.80451089e-01 -7.33598053e-01 6.07810438e-01
8.41579437e-01 -6.89013422e-01 -7.38589585e-01 -1.05601199e-01
1.04541421e-01 3.03643972e-01 3.35620821e-01 -1.40194762e+00
-1.22236371e-01 1.20139070e-01 5.87938242e-02 -1.03018314e-01
2.27314204e-01 -9.83665049e-01 6.18723094e-01 7.64324129e-01
-4.87027973e-01 -1.09001048e-01 3.83135349e-01 3.52995813e-01
-1.18896261e-01 -1.82509385e-02 9.08489466e-01 -1.00469254e-01
1.56447157e-01 -2.86225677e-01 -6.93944275e-01 3.03849757e-01
7.56418824e-01 -3.05722386e-01 -1.23632647e-01 -6.33168876e-01
-6.92481935e-01 -5.37519574e-01 1.61350265e-01 4.85099137e-01
5.06278992e-01 -7.59537578e-01 -8.18562746e-01 3.35580528e-01
-1.04192264e-01 -2.41508260e-01 2.05870986e-01 1.05683565e+00
-4.72677141e-01 1.35338426e-01 -1.80698976e-01 -6.43807590e-01
-1.46804368e+00 1.36237025e-01 8.68073463e-01 9.77621675e-02
-7.62477160e-01 5.55112362e-01 -2.09081769e-01 -6.88785687e-02
3.23693484e-01 -3.27900350e-01 -2.25354016e-01 1.95382029e-01
4.01101291e-01 6.23823225e-01 3.50338995e-01 -2.87162900e-01
-3.16900849e-01 4.96047914e-01 4.31650102e-01 -1.03995856e-02
1.18392766e+00 -7.79476166e-02 2.40937218e-01 7.71723092e-01
8.43972147e-01 1.43199295e-01 -7.77847707e-01 2.07053766e-01
-8.39596689e-02 -5.87960660e-01 3.13717455e-01 -9.11727250e-01
-1.21402848e+00 8.42351317e-01 9.98155594e-01 6.53809845e-01
1.38904309e+00 -3.70114386e-01 6.84654534e-01 -1.91706076e-01
-7.10484385e-02 -1.03577864e+00 3.34933311e-01 -2.47704804e-01
6.75332308e-01 -7.37657726e-01 -1.62745312e-01 -3.05842906e-01
-3.71850789e-01 9.29649234e-01 2.28415340e-01 2.55566210e-01
9.14369047e-01 6.71476424e-01 7.67751694e-01 -2.90753722e-01
-6.10795259e-01 8.04935619e-02 -5.53736240e-02 7.14107752e-01
7.05549955e-01 -9.55762565e-02 -5.60056627e-01 6.38268173e-01
-3.18916529e-01 2.30642900e-01 6.59925878e-01 1.05452228e+00
-3.36610228e-01 -1.04140985e+00 -8.26445937e-01 6.00852907e-01
-7.98839092e-01 5.84692433e-02 -4.08406079e-01 4.51745719e-01
4.20942158e-01 1.71605539e+00 -1.85566679e-01 -3.68805677e-01
6.79257393e-01 4.45291638e-01 3.95537317e-02 -6.82694674e-01
-8.70830178e-01 1.44049659e-01 3.56833875e-01 -1.32913932e-01
-3.85692239e-01 -7.96316087e-01 -9.87122536e-01 5.94804585e-02
-5.00826061e-01 1.18786559e-01 7.83937514e-01 6.86805308e-01
4.26850885e-01 1.04184306e+00 6.91637635e-01 -4.34825778e-01
-7.00243771e-01 -9.92091358e-01 -7.53590226e-01 6.81474626e-01
5.74946940e-01 -4.63044047e-01 -8.10121357e-01 1.88507676e-01] | [14.290766716003418, 3.312272071838379] |
fda42197-ce7f-41ec-82ef-7798e213924f | reference-based-video-super-resolution-using | 2203.14537 | null | https://arxiv.org/abs/2203.14537v1 | https://arxiv.org/pdf/2203.14537v1.pdf | Reference-based Video Super-Resolution Using Multi-Camera Video Triplets | We propose the first reference-based video super-resolution (RefVSR) approach that utilizes reference videos for high-fidelity results. We focus on RefVSR in a triple-camera setting, where we aim at super-resolving a low-resolution ultra-wide video utilizing wide-angle and telephoto videos. We introduce the first RefVSR network that recurrently aligns and propagates temporal reference features fused with features extracted from low-resolution frames. To facilitate the fusion and propagation of temporal reference features, we propose a propagative temporal fusion module. For learning and evaluation of our network, we present the first RefVSR dataset consisting of triplets of ultra-wide, wide-angle, and telephoto videos concurrently taken from triple cameras of a smartphone. We also propose a two-stage training strategy fully utilizing video triplets in the proposed dataset for real-world 4x video super-resolution. We extensively evaluate our method, and the result shows the state-of-the-art performance in 4x super-resolution. | ['Seungyong Lee', 'Sunghyun Cho', 'Myeonghee Lee', 'Junyong Lee'] | 2022-03-28 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Lee_Reference-Based_Video_Super-Resolution_Using_Multi-Camera_Video_Triplets_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Lee_Reference-Based_Video_Super-Resolution_Using_Multi-Camera_Video_Triplets_CVPR_2022_paper.pdf | cvpr-2022-1 | ['video-super-resolution', 'reference-based-video-super-resolution'] | ['computer-vision', 'computer-vision'] | [ 3.69200110e-01 -6.16742730e-01 -1.53124154e-01 -2.06186324e-01
-1.33524692e+00 -2.55323142e-01 5.77122271e-01 -8.61065388e-01
-2.65621215e-01 8.52156401e-01 4.98294502e-01 2.36621082e-01
-3.56786132e-01 -4.05113548e-01 -9.24669862e-01 -4.67574418e-01
-1.31704271e-01 -2.51884997e-01 5.50588071e-01 -2.96542972e-01
6.58435002e-02 6.15545690e-01 -1.92385817e+00 6.41854703e-01
4.13771361e-01 8.27912927e-01 3.94197285e-01 1.15034461e+00
6.05526507e-01 8.38778436e-01 -3.51232320e-01 -9.37892571e-02
4.93240148e-01 -2.40733683e-01 -7.41591632e-01 5.10040969e-02
1.29603660e+00 -8.68944466e-01 -8.27676773e-01 7.34112799e-01
4.31912124e-01 5.20627022e-01 1.79872825e-03 -7.00111985e-01
-6.46345854e-01 5.01357019e-01 -9.23801780e-01 9.07999218e-01
9.06437755e-01 6.45047054e-02 8.17833602e-01 -1.05811667e+00
1.10010290e+00 1.26921201e+00 7.21818924e-01 5.90970159e-01
-1.07901013e+00 -8.80854547e-01 -3.11163515e-02 2.86759228e-01
-1.52525342e+00 -7.60592937e-01 5.52805245e-01 -1.35329187e-01
9.25196826e-01 8.78459215e-02 7.07425833e-01 1.45062745e+00
2.45066509e-01 2.36001208e-01 1.15459013e+00 -1.90543719e-02
-1.04476698e-01 -5.47765791e-01 -1.83804452e-01 4.80760962e-01
-1.44471563e-02 5.46441436e-01 -1.30804467e+00 1.87140957e-01
1.49531507e+00 5.79810739e-02 -5.51696062e-01 2.55380303e-01
-1.44661510e+00 3.31879228e-01 3.07279050e-01 4.21356201e-01
-3.98630202e-01 1.10623568e-01 -2.14257687e-02 1.57716885e-01
5.04706740e-01 1.30104661e-01 -3.31454009e-01 -2.69706517e-01
-1.41060758e+00 2.06105456e-01 8.19801986e-02 1.07255590e+00
7.04654574e-01 2.56824762e-01 -7.23181888e-02 8.05489540e-01
-1.08797200e-01 3.92570913e-01 4.33325946e-01 -1.75636041e+00
5.46061277e-01 -2.29100794e-01 4.81608778e-01 -7.07997262e-01
-2.57413000e-01 -2.05744207e-01 -8.40559423e-01 6.47283718e-02
1.04551122e-01 2.64006406e-02 -7.86955059e-01 1.60687768e+00
2.99017489e-01 8.96671832e-01 2.14527860e-01 1.40718246e+00
1.00575411e+00 7.76771128e-01 -4.36084121e-01 -8.05705905e-01
1.21225476e+00 -9.28007007e-01 -8.27410996e-01 2.75413543e-01
-1.44340172e-01 -6.11441612e-01 8.14175010e-01 4.35864508e-01
-1.46263516e+00 -1.03593910e+00 -1.11217666e+00 -5.06949008e-01
2.01725021e-01 -5.70780523e-02 1.53120801e-01 -1.30227000e-01
-1.32473445e+00 1.04408371e+00 -8.34896922e-01 -3.16923559e-01
2.34254003e-01 2.64769018e-01 -7.55006313e-01 -2.77457863e-01
-1.22077072e+00 5.00935435e-01 2.88878858e-01 -1.60373729e-02
-8.54951084e-01 -1.15824497e+00 -7.67078698e-01 -6.70662820e-02
5.41067421e-01 -7.53664255e-01 1.11392128e+00 -6.05724812e-01
-1.52730477e+00 5.83451867e-01 -3.08241665e-01 -3.43092740e-01
2.76781440e-01 -5.73559523e-01 -7.76190817e-01 6.64698780e-01
9.24336016e-02 5.33535361e-01 1.12225866e+00 -1.37314343e+00
-1.08807600e+00 -1.97238758e-01 1.76243097e-01 1.99417949e-01
6.81845145e-03 9.59880129e-02 -6.03188217e-01 -5.54137647e-01
-2.66492460e-02 -7.80515015e-01 7.32397661e-02 -2.80099511e-01
-1.15636483e-01 1.43075347e-01 1.11013341e+00 -7.73007095e-01
1.20240986e+00 -2.11999035e+00 3.20049495e-01 -4.55871373e-01
4.68154758e-01 2.90783674e-01 -2.87620425e-01 -5.39303720e-02
-2.59957761e-01 -9.98724401e-02 1.89760670e-01 -4.46782708e-01
-6.22471035e-01 6.77703843e-02 -4.42770988e-01 3.57979953e-01
1.13576941e-01 5.62900722e-01 -1.10195267e+00 -6.03717446e-01
3.92492592e-01 1.22153044e+00 -4.80855405e-01 3.53743315e-01
1.86619505e-01 7.46996939e-01 -1.82717696e-01 7.10224450e-01
7.74788558e-01 -5.18957853e-01 3.17282155e-02 -9.78556693e-01
-6.33661389e-01 -1.74393877e-01 -1.13177824e+00 2.18601155e+00
-3.57544988e-01 7.38553822e-01 2.14316417e-02 -1.24567769e-01
7.02948749e-01 1.76901907e-01 6.87267482e-01 -8.90626311e-01
-1.86214432e-01 5.02446778e-02 -6.78262174e-01 -4.96528953e-01
1.24240685e+00 1.14736378e-01 2.57990479e-01 1.93483129e-01
3.65933567e-01 1.48194388e-01 3.33749473e-01 3.13451827e-01
1.19527233e+00 7.19799757e-01 3.44370663e-01 2.70863831e-01
6.54456258e-01 -4.18025911e-01 6.43694401e-01 5.96003771e-01
-2.07348228e-01 1.13398802e+00 -4.42492925e-02 -5.36315858e-01
-1.41864216e+00 -1.19461226e+00 2.33973172e-02 1.07246029e+00
4.05530840e-01 -6.28976047e-01 -3.81545514e-01 -1.28851891e-01
-3.93246502e-01 7.87079856e-02 -6.62573934e-01 4.17875081e-01
-1.01316082e+00 -2.90171087e-01 1.06233247e-01 4.38180804e-01
7.76337504e-01 -4.60600317e-01 -7.04454362e-01 1.44591797e-02
-5.70858955e-01 -1.85237026e+00 -7.17647135e-01 -3.33163768e-01
-8.38147402e-01 -9.78341758e-01 -6.59524202e-01 -1.14451259e-01
-3.16080116e-02 9.87496674e-01 1.09290421e+00 -1.52582467e-01
-1.74466535e-01 5.89341283e-01 -3.81639063e-01 7.29226887e-01
-1.59628317e-01 -5.41203916e-02 4.44298267e-01 1.96451604e-01
-1.47658929e-01 -1.03232133e+00 -7.87989795e-01 3.53495687e-01
-8.08145940e-01 2.45925829e-01 4.81589735e-01 6.63328230e-01
1.01355112e+00 -9.95161533e-02 1.31557569e-01 -3.92001599e-01
2.42178887e-01 -3.20304543e-01 -7.12956190e-01 9.16091651e-02
-2.25266472e-01 -1.77149743e-01 6.86058462e-01 -6.32901609e-01
-1.38775718e+00 9.11221374e-03 4.28667553e-02 -1.29019392e+00
1.11418560e-01 -1.47472695e-02 2.89422929e-01 -3.57271463e-01
6.53362393e-01 1.05504669e-01 -3.86895269e-01 -4.02277678e-01
4.92789179e-01 4.06555206e-01 1.32666421e+00 -3.11692536e-01
9.97767925e-01 8.34219337e-01 1.49859473e-01 -8.49022567e-01
-1.11990356e+00 -3.95553291e-01 -8.19959521e-01 -4.42235738e-01
1.05135059e+00 -1.50072587e+00 -9.47195411e-01 6.51075095e-02
-9.73196328e-01 -1.19350821e-01 -2.78271466e-01 7.27594793e-01
-7.85021722e-01 4.12069112e-01 -7.98763573e-01 -4.46703911e-01
-3.53709310e-01 -9.66111720e-01 1.45269084e+00 5.38371921e-01
2.17755064e-01 -5.44410050e-01 3.41037959e-01 5.23920536e-01
5.40889978e-01 2.21190274e-01 -3.72431837e-02 6.54596835e-02
-1.23413455e+00 4.62376684e-01 -6.27892435e-01 1.64376438e-01
9.56805795e-02 2.30920941e-01 -9.27850008e-01 -4.71459687e-01
-1.47560433e-01 -3.33914310e-01 8.59972358e-01 5.29290080e-01
8.78730178e-01 -5.28590046e-02 -2.41284072e-02 1.32956553e+00
1.64455307e+00 -8.40974823e-02 6.40756071e-01 3.03004533e-01
9.77527022e-01 8.20541233e-02 9.75715756e-01 6.82950258e-01
4.35196877e-01 1.10926974e+00 3.67823601e-01 1.75278410e-01
-5.26910186e-01 -1.51098654e-01 5.26843071e-01 7.90405154e-01
-9.30753887e-01 3.10117565e-02 -2.75345951e-01 3.42807621e-01
-1.66056454e+00 -1.59696627e+00 1.26943886e-01 1.97236800e+00
7.64953911e-01 -3.19219738e-01 1.48905188e-01 -1.88544899e-01
8.55659425e-01 7.33764768e-01 -5.83809376e-01 2.44792566e-01
-2.52369374e-01 1.93326384e-01 2.77703285e-01 6.84666336e-01
-1.08865726e+00 9.09836769e-01 6.68129921e+00 6.89725041e-01
-1.22512460e+00 3.35506827e-01 2.35155165e-01 -7.25055397e-01
-6.67266697e-02 -2.36938149e-01 -1.01813567e+00 3.13937664e-01
1.21281803e+00 -2.17486560e-01 8.33784342e-01 4.13362235e-01
4.25577074e-01 -4.71848026e-02 -1.04798365e+00 1.40546346e+00
1.64299697e-01 -1.77516079e+00 3.75524387e-02 -1.61479175e-01
1.01870668e+00 2.34001577e-01 1.69779688e-01 8.38840976e-02
7.05295205e-02 -7.99471855e-01 5.06766737e-01 8.26002181e-01
1.39167690e+00 -6.94956005e-01 2.03150317e-01 -1.86072841e-01
-1.62547338e+00 -2.44903281e-01 -2.80175388e-01 4.12735283e-01
4.96476084e-01 3.13673913e-01 -1.96845040e-01 9.82210815e-01
1.20088530e+00 1.28534055e+00 -4.83525664e-01 4.46762383e-01
2.34422386e-01 7.74926320e-02 -1.27363622e-01 9.61687267e-01
-1.31910980e-01 -9.69965756e-02 6.53801739e-01 1.12621939e+00
7.44399428e-01 7.37158418e-01 -6.09857589e-02 6.91949964e-01
-2.17274085e-01 -5.16464472e-01 -4.44298208e-01 2.95933276e-01
6.72133267e-01 1.44053996e+00 -2.31774613e-01 -4.17851120e-01
-7.42845118e-01 8.85903776e-01 2.57873476e-01 5.12113571e-01
-1.01872456e+00 -7.44273607e-03 7.63919890e-01 1.45627558e-01
5.76847792e-01 -2.31218472e-01 4.77248192e-01 -1.67748368e+00
-3.49421389e-02 -8.46520483e-01 3.14694941e-01 -1.47574639e+00
-8.82603884e-01 8.99749875e-01 1.86817721e-01 -1.57701337e+00
-5.83042979e-01 -1.37956247e-01 -5.65451942e-02 6.65989757e-01
-1.93471301e+00 -1.18829274e+00 -9.89638805e-01 1.13554490e+00
8.68117154e-01 -1.00886717e-01 2.37503052e-01 5.08242905e-01
-3.96881998e-01 2.71796703e-01 -3.93414758e-02 -2.13419199e-01
1.02379453e+00 -6.92476511e-01 2.11827442e-01 1.20061600e+00
-1.06544621e-01 5.99638283e-01 7.66300976e-01 -6.70154631e-01
-1.65413523e+00 -1.22615600e+00 3.62520307e-01 -3.29719841e-01
5.64561486e-01 -6.25878349e-02 -1.05549884e+00 9.56312895e-01
1.39347330e-01 7.49900162e-01 2.46299773e-01 -1.98719218e-01
-5.63497961e-01 -4.40166384e-01 -1.09878099e+00 3.00538749e-01
1.49647689e+00 -7.92578399e-01 -4.27626640e-01 -2.45725065e-02
1.17711306e+00 -8.28937232e-01 -1.46594405e+00 5.35097778e-01
8.91343117e-01 -1.59932923e+00 1.34277248e+00 -5.74072823e-02
7.70208299e-01 -4.70331818e-01 -5.10210037e-01 -8.59900057e-01
-5.77251434e-01 -9.75812197e-01 -5.84198892e-01 9.48780775e-01
-3.28422517e-01 -1.87752202e-01 5.85533202e-01 1.06592208e-01
-1.81263089e-01 -4.14473087e-01 -1.05616653e+00 -6.53935671e-01
-6.80035293e-01 -1.53262004e-01 4.63330686e-01 8.20935249e-01
-4.87881571e-01 3.43054891e-01 -1.05779791e+00 4.53569442e-01
1.06457734e+00 1.38464227e-01 7.12548614e-01 -9.46859717e-01
-2.87836760e-01 1.96714446e-01 -2.59630769e-01 -1.15781677e+00
-5.26563264e-02 -1.70590222e-01 -1.34854183e-01 -9.96303380e-01
3.84350687e-01 2.39828542e-01 -2.00432077e-01 -1.06871799e-01
-4.91863117e-04 7.56236017e-01 3.98675770e-01 4.64425266e-01
-1.00665200e+00 3.62043798e-01 1.37439966e+00 3.55513752e-01
-2.96955496e-01 -4.53006238e-01 -4.98663813e-01 4.92346317e-01
1.74209893e-01 2.02817842e-02 -5.31213701e-01 -6.26245439e-01
1.25668094e-01 9.19330299e-01 4.70286041e-01 -1.11523128e+00
3.91992837e-01 -1.91311538e-01 6.22292399e-01 -9.27142501e-01
8.67078900e-01 -5.79661250e-01 5.88227332e-01 -2.62329131e-01
-3.36491406e-01 3.51973832e-01 -5.31624407e-02 6.53664470e-01
-3.31924319e-01 4.63816136e-01 1.11148620e+00 -9.94449705e-02
-8.78797233e-01 6.62740290e-01 1.64207071e-01 9.56184417e-03
7.25793302e-01 -3.23461652e-01 -7.61897564e-01 -3.18138093e-01
-8.24676096e-01 -6.77360594e-02 6.78057790e-01 4.63672221e-01
1.16570187e+00 -1.51463079e+00 -8.27466846e-01 2.04576284e-01
-2.09549725e-01 -5.60869314e-02 9.46351171e-01 7.32316434e-01
-2.89338619e-01 2.78867394e-01 -5.64429283e-01 -7.87036002e-01
-1.57470536e+00 6.83527410e-01 2.99758404e-01 -2.75238872e-01
-1.09232008e+00 5.75916052e-01 2.91251112e-02 3.48925412e-01
-1.49306044e-01 -1.46587625e-01 -4.67312485e-01 -1.55718058e-01
1.07661378e+00 6.38987720e-01 -2.86588877e-01 -9.98559177e-01
-1.72123551e-01 1.08431053e+00 -2.95280427e-01 -3.58973324e-01
1.57645357e+00 -7.36136973e-01 1.41974002e-01 4.14056927e-01
1.21352327e+00 3.54400203e-02 -1.79734695e+00 -5.10506630e-01
-6.29376769e-01 -1.02296889e+00 1.40346169e-01 -3.16235244e-01
-1.17005372e+00 5.13154805e-01 6.83212042e-01 -1.76185191e-01
1.60019052e+00 -6.61737621e-02 8.48913491e-01 1.26006380e-01
6.11898959e-01 -8.31100762e-01 3.54920506e-01 4.57787156e-01
7.79997230e-01 -1.17735219e+00 4.68810111e-01 -3.25581819e-01
-3.22924554e-01 1.29591107e+00 6.36060536e-01 -2.30932668e-01
2.56738693e-01 3.29416543e-01 -1.95990443e-01 6.07289299e-02
-1.32675171e+00 -1.33411884e-01 1.74918875e-01 6.93122625e-01
2.29007617e-01 -4.69506025e-01 8.91314372e-02 2.14351952e-01
-8.05687010e-02 4.55189377e-01 9.76234317e-01 5.89404404e-01
-2.89464921e-01 -5.18712521e-01 -4.57770616e-01 -3.61214727e-02
-4.85961497e-01 1.24046011e-02 3.39476883e-01 8.97357762e-01
-3.87755483e-02 6.79068685e-01 2.00167775e-01 -8.21487069e-01
2.50911623e-01 -4.87307817e-01 8.49423707e-01 3.54042724e-02
-2.57374555e-01 1.99860096e-01 -7.05535663e-03 -1.42687726e+00
-1.11686265e+00 -6.09661579e-01 -9.46368277e-01 -5.98894775e-01
1.63305700e-01 -1.08771995e-01 3.47528040e-01 6.64167225e-01
5.64728081e-01 6.39119208e-01 8.71435881e-01 -1.59614933e+00
-1.69817999e-01 -7.91985571e-01 -6.03164256e-01 3.55488986e-01
1.04825735e+00 -6.28147900e-01 -4.23871130e-01 4.07399356e-01] | [11.004864692687988, -1.9430314302444458] |
dcb8eac9-fc12-4b5c-b89a-dc97d5dcea39 | a-new-information-theory-of-certainty-for | 2304.12833 | null | https://arxiv.org/abs/2304.12833v1 | https://arxiv.org/pdf/2304.12833v1.pdf | A New Information Theory of Certainty for Machine Learning | Claude Shannon coined entropy to quantify the uncertainty of a random distribution for communication coding theory. We observe that the uncertainty nature of entropy also limits its direct usage in mathematical modeling. Therefore we propose a new concept troenpy,as the canonical dual of entropy, to quantify the certainty of the underlying distribution. We demonstrate two applications in machine learning. The first is for the classical document classification, we develop a troenpy based weighting scheme to leverage the document class label. The second is a self-troenpy weighting scheme for sequential data and show that it can be easily included in neural network based language models and achieve dramatic perplexity reduction. We also define quantum troenpy as the dual of the Von Neumann entropy to quantify the certainty of quantum systems. | ['Arthur Jun Zhang'] | 2023-04-25 | null | null | null | null | ['document-classification'] | ['natural-language-processing'] | [ 2.25513846e-01 1.74454868e-01 -1.33082017e-01 -1.60380512e-01
-5.11577010e-01 -6.29410625e-01 9.03914034e-01 2.70058513e-01
-5.03676355e-01 8.77439559e-01 1.48442954e-01 -5.46189129e-01
-2.48214141e-01 -9.24871027e-01 -2.66508609e-01 -9.60357785e-01
-2.72466332e-01 2.70855278e-01 -3.11408788e-01 -2.66794205e-01
3.66934896e-01 2.69482404e-01 -1.14998376e+00 8.01310912e-02
5.27895093e-01 1.05008388e+00 -3.64218913e-02 9.65116441e-01
-1.88789710e-01 1.03572297e+00 -3.68034214e-01 -5.16252935e-01
1.51513219e-01 -4.44485456e-01 -8.50270092e-01 -7.22553074e-01
-2.94608921e-01 -2.48581082e-01 -8.11090350e-01 1.54369557e+00
1.74186870e-01 -3.75865437e-02 1.14635718e+00 -1.11611640e+00
-5.18079042e-01 1.08303654e+00 -9.83222499e-02 1.77787468e-01
2.73691922e-01 -3.12871784e-01 1.52426445e+00 -2.77487487e-01
4.18630451e-01 1.11825860e+00 5.10289907e-01 5.46353281e-01
-1.17888582e+00 -5.55614471e-01 -7.70300746e-01 2.03231320e-01
-1.57761073e+00 -1.04418263e-01 3.41762632e-01 -5.18754423e-01
5.49219370e-01 3.31741095e-01 5.66810787e-01 8.90458107e-01
6.81163371e-01 6.79771841e-01 1.31917489e+00 -6.06647372e-01
6.91192031e-01 2.62152106e-02 3.33365113e-01 8.88099074e-01
2.91611046e-01 5.16418755e-01 -4.94113326e-01 -2.67259747e-01
3.54042500e-01 -6.97047915e-03 -2.65121788e-01 -1.65626511e-01
-1.08004439e+00 1.18857038e+00 2.13876456e-01 3.01666737e-01
9.80372280e-02 6.40827477e-01 3.60750079e-01 3.79964232e-01
1.91815883e-01 3.74925941e-01 -2.28001714e-01 -4.49823618e-01
-7.40331233e-01 1.87269211e-01 1.29234850e+00 8.10552180e-01
6.18642867e-01 -5.04859149e-01 -2.52218306e-01 1.70817330e-01
5.06943345e-01 6.53619111e-01 3.61107439e-01 -1.02287292e+00
1.65690839e-01 8.43783692e-02 -1.13300271e-01 -4.52235639e-01
-3.47029269e-01 -2.72342384e-01 -1.21175623e+00 9.58212316e-02
2.45898128e-01 9.60930064e-03 -7.41441607e-01 1.86542058e+00
-4.23521191e-01 -6.83354512e-02 3.11192244e-01 5.38398504e-01
3.22272241e-01 6.56146049e-01 -1.25795126e-01 -2.83527195e-01
1.25499034e+00 -3.20586026e-01 -7.19993174e-01 2.65278310e-01
9.16296363e-01 -5.17671466e-01 3.54546279e-01 4.88015503e-01
-6.82809770e-01 4.06436026e-01 -1.37324226e+00 -3.10187042e-02
-5.44129789e-01 -4.94126260e-01 9.92112994e-01 1.28304851e+00
-8.33923757e-01 1.10295999e+00 -7.73004174e-01 -1.57836825e-02
2.38829494e-01 2.98730791e-01 -2.82171726e-01 -1.61945401e-03
-1.55638278e+00 1.13899779e+00 8.27769935e-01 -4.48703229e-01
-5.65455973e-01 -2.17076913e-01 -6.86827302e-01 4.09817606e-01
-5.70325963e-02 -6.60637558e-01 1.24445736e+00 -1.73008829e-01
-1.86105537e+00 6.03218317e-01 1.32835463e-01 -7.89130688e-01
2.47834131e-01 3.38996232e-01 -2.38083452e-01 8.18469599e-02
-3.00894350e-01 2.21093744e-01 5.33112049e-01 -5.43875456e-01
-2.68833160e-01 -6.82260916e-02 4.56528440e-02 -8.41402784e-02
-2.00636834e-01 -5.00836968e-01 1.98043853e-01 -4.42030966e-01
1.93308711e-01 -1.04547751e+00 -3.27252746e-02 -2.46176556e-01
-3.87011200e-01 -1.54462159e-01 2.92831033e-01 -1.98384181e-01
1.26022947e+00 -2.10351300e+00 1.82957068e-01 5.27510107e-01
6.91511512e-01 -2.61137355e-02 9.89560597e-03 6.51627481e-01
6.37442023e-02 4.22316581e-01 -4.40753162e-01 -6.52479008e-02
4.91110295e-01 3.46861809e-01 -4.07181919e-01 5.14185011e-01
2.41292417e-02 7.25090683e-01 -9.38833594e-01 -2.20551461e-01
1.56754963e-02 4.76504743e-01 -6.27882659e-01 -5.80134690e-02
-1.00096641e-02 1.11576863e-01 -3.04297924e-01 1.68250024e-01
7.91894197e-01 -4.18374002e-01 2.11992383e-01 -4.49026227e-02
1.58131018e-01 5.72364211e-01 -8.63911152e-01 1.50928628e+00
-3.96433890e-01 8.40198934e-01 -2.31457248e-01 -6.77308083e-01
5.42468369e-01 1.85944587e-01 2.50117421e-01 -3.59245896e-01
6.23897433e-01 2.23252401e-01 2.78184444e-01 -8.52022991e-02
7.55620897e-01 -5.42094707e-01 -4.34720665e-01 8.29132199e-01
4.38289642e-01 -5.62538922e-01 2.94557124e-01 5.96630514e-01
1.40747273e+00 -4.43433911e-01 7.59261012e-01 -6.06747270e-01
3.46934915e-01 -6.66869521e-01 -1.65349860e-02 1.05076659e+00
-3.81535947e-01 4.45343226e-01 7.51949668e-01 4.99120988e-02
-1.16364694e+00 -1.30504525e+00 -4.96118486e-01 8.33720088e-01
2.04036251e-01 -9.03253794e-01 -7.13924229e-01 -2.75693357e-01
6.78479578e-03 7.91876495e-01 -5.32193899e-01 -4.21681374e-01
4.05997485e-01 -9.82303202e-01 1.01417136e+00 7.41832182e-02
4.49831218e-01 -2.74819970e-01 -3.32142681e-01 -1.20571971e-01
-1.31734982e-01 -1.01119912e+00 -2.52692461e-01 6.85345352e-01
-5.15864670e-01 -7.81651139e-01 -4.10743922e-01 5.42434230e-02
2.57905629e-02 -3.50394070e-01 6.88075900e-01 -1.87210500e-01
-2.17068985e-01 4.29800183e-01 -3.38855624e-01 -2.50673294e-01
-9.60210383e-01 4.52756993e-02 3.03826660e-01 -5.01486361e-01
5.88298321e-01 -6.28502667e-01 -3.47479045e-01 -3.29941928e-01
-9.67902541e-01 -1.13217331e-01 5.51133454e-01 9.15485680e-01
-2.58317720e-02 2.16374248e-01 -1.06824912e-01 -5.93164325e-01
9.86768365e-01 -4.94478673e-01 -5.80744982e-01 1.96144253e-01
-6.77550018e-01 1.06787264e+00 3.69029760e-01 -6.23046681e-02
-4.18714434e-01 -4.53967124e-01 -1.01874098e-01 2.02677473e-01
3.45365047e-01 7.05010951e-01 3.90429825e-01 -3.66546243e-01
4.21128601e-01 1.70160130e-01 5.46092875e-02 -1.39501952e-02
6.17492676e-01 9.28372622e-01 2.94148028e-01 -5.99695444e-01
5.81465185e-01 3.12057734e-01 7.07814276e-01 -7.97435641e-01
-8.76867831e-01 -2.77423531e-01 -4.71041739e-01 2.98529714e-01
8.82890344e-01 -7.73810923e-01 -1.51729155e+00 1.89272463e-01
-1.27854276e+00 1.86484754e-01 -2.27852508e-01 9.21908081e-01
-8.87156963e-01 6.25220954e-01 -7.69360125e-01 -1.29078650e+00
-3.15452516e-01 -9.00928319e-01 7.79487371e-01 1.13363512e-01
-1.01532601e-01 -1.18561256e+00 2.34368175e-01 -2.04793051e-01
3.04222703e-01 -1.66455563e-02 1.06518388e+00 -5.95192611e-01
-5.91233373e-01 -3.96842033e-01 -4.60539311e-01 4.85568225e-01
-3.79234910e-01 -1.84477329e-01 -9.90681767e-01 -1.79855660e-01
1.19161077e-01 -2.56890416e-01 1.25898612e+00 -1.02921754e-01
8.16483021e-01 -2.68385261e-01 3.86730172e-02 5.40665329e-01
1.63416839e+00 -6.34951815e-02 7.60140836e-01 -9.44502205e-02
2.84288079e-01 8.96963701e-02 -2.04319373e-01 8.47361445e-01
2.44642451e-01 3.65256995e-01 2.20202133e-01 9.04269874e-01
1.94516346e-01 -3.40474635e-01 5.17794251e-01 1.51870883e+00
-9.74363834e-02 -3.28715384e-01 -9.42639887e-01 -5.55422716e-02
-1.57583070e+00 -1.22597647e+00 9.86879095e-02 2.22458386e+00
9.76997554e-01 1.05700471e-01 -4.77981269e-01 1.92969322e-01
5.49761832e-01 8.33750516e-02 -7.82746002e-02 -6.39586449e-01
-2.20968440e-01 5.44319808e-01 9.48185921e-01 8.50754440e-01
-1.06758308e+00 5.56077302e-01 7.51539803e+00 1.20526242e+00
-8.24932992e-01 4.45327759e-01 1.44396365e-01 2.24131078e-01
-4.65107650e-01 2.14599565e-01 -5.81883729e-01 5.56530416e-01
1.20266557e+00 -6.13709867e-01 8.24752808e-01 5.29812217e-01
-5.80090225e-01 -2.76085287e-01 -1.34117377e+00 1.34033918e+00
-1.41986206e-01 -1.18142450e+00 -1.67550460e-01 4.27478164e-01
6.30258024e-01 3.93016726e-01 1.35579407e-01 1.90264732e-01
7.78814256e-01 -1.16784728e+00 5.81586778e-01 6.81727111e-01
9.28841531e-01 -7.08825648e-01 7.63350785e-01 4.08471227e-01
-8.39059114e-01 -1.02449544e-01 -5.25691867e-01 -4.13818508e-01
1.12461515e-01 8.71208847e-01 -7.94022024e-01 3.62977654e-01
5.53498184e-03 2.71441996e-01 -1.82811588e-01 9.16371047e-01
-1.76174313e-01 2.76263416e-01 -5.49601674e-01 -5.72554767e-01
2.44982794e-01 -3.24042588e-01 6.64259791e-01 1.18810630e+00
5.96124530e-01 7.80078769e-02 -3.45894009e-01 9.70211804e-01
-3.30031425e-01 -2.93002754e-01 -5.68420589e-01 -7.22138703e-01
5.37075937e-01 9.75881040e-01 -5.22236049e-01 -2.21263319e-01
-2.38196049e-02 1.04220879e+00 1.69356450e-01 -2.23599281e-02
-5.47750831e-01 -6.58694983e-01 5.14221847e-01 -6.67937815e-01
1.21244207e-01 -5.67996144e-01 -4.26873237e-01 -1.58330238e+00
-4.20071542e-01 -2.48649970e-01 4.36165929e-02 -3.96023631e-01
-1.44498968e+00 3.34971845e-01 -1.58259705e-01 -1.02562284e+00
-2.44481310e-01 -1.03011954e+00 -2.39338189e-01 8.67038071e-01
-1.23972499e+00 -3.78067344e-01 2.17023700e-01 2.88417071e-01
-5.63337862e-01 -3.85618836e-01 1.36075866e+00 4.04188856e-02
-2.40076751e-01 4.55369413e-01 1.05488622e+00 1.34742171e-01
2.22255558e-01 -1.53393221e+00 1.39817551e-01 5.26449442e-01
2.03546718e-01 8.65724027e-01 9.80503559e-01 -2.99593449e-01
-1.35349500e+00 -6.26293957e-01 8.23756993e-01 -5.23379087e-01
1.08709526e+00 -5.42906523e-01 -2.01182410e-01 3.66622448e-01
4.83511724e-02 -2.64030606e-01 9.38321352e-01 1.52156666e-01
-8.48979294e-01 1.96878091e-01 -1.15003681e+00 3.85055721e-01
5.35241425e-01 -1.20895553e+00 -6.07425869e-01 3.88134897e-01
7.53998756e-01 -6.03356175e-02 -1.05436373e+00 1.18250519e-01
9.23782349e-01 -1.01017082e+00 4.98164237e-01 -2.29206309e-01
5.50364316e-01 -8.43207166e-02 -7.23043919e-01 -1.19543672e+00
-2.44810298e-01 -8.33624721e-01 -3.16301823e-01 6.58408046e-01
2.38154039e-01 -6.63082659e-01 5.17844856e-01 5.66915810e-01
4.62614000e-01 -3.88709694e-01 -1.34535956e+00 -9.96192753e-01
4.26742077e-01 -6.77473426e-01 4.35313702e-01 7.24649489e-01
8.81354690e-01 3.17938596e-01 -3.69116724e-01 -1.87280014e-01
9.35018003e-01 -4.21804637e-01 5.69512583e-02 -1.35247946e+00
-3.89758199e-01 -7.47343242e-01 -1.08088744e+00 -1.03168738e+00
1.13182813e-01 -1.38668656e+00 8.93582702e-02 -8.83408785e-01
5.52020431e-01 -1.83274716e-01 -4.77008134e-01 -8.52217227e-02
4.04991150e-01 1.18203349e-01 4.37183231e-01 1.78598791e-01
-5.20230532e-01 7.69143224e-01 7.64816880e-01 -2.37338409e-01
3.42670113e-01 -1.24433592e-01 -5.76246619e-01 5.04138172e-01
6.58922672e-01 -6.77850544e-01 -6.74281791e-02 1.45332918e-01
8.73058021e-01 3.41146588e-02 3.96892011e-01 -1.20499384e+00
2.61695743e-01 1.43388242e-01 -1.80945173e-01 -2.27452770e-01
2.85264224e-01 -6.14717364e-01 -3.35709125e-01 7.11597323e-01
-5.22566915e-01 -4.08743978e-01 -2.77152687e-01 8.37195158e-01
-1.22141996e-02 -6.84867084e-01 7.55988836e-01 -1.24522209e-01
-3.88713151e-01 2.95029312e-01 -4.61540699e-01 1.00994945e-01
7.45263517e-01 4.82605278e-01 -5.73193312e-01 -5.63286483e-01
-7.60274768e-01 -1.97881922e-01 2.38369569e-01 1.79513488e-02
4.20789808e-01 -1.39683175e+00 -3.65851581e-01 7.53672272e-02
-3.77499796e-02 -8.19370389e-01 3.99231315e-02 8.37293565e-01
-7.19398081e-01 9.02990401e-01 -5.04919067e-02 -3.17589134e-01
-7.86893487e-01 4.13039327e-01 1.82897121e-01 -4.42165852e-01
-1.01625226e-01 7.77992666e-01 -6.83572376e-03 -2.32235074e-01
2.81384718e-02 -4.07015979e-01 1.66288719e-01 -1.07821345e-01
7.06268728e-01 3.60636741e-01 -2.20855787e-01 -4.34801579e-01
-4.47712094e-01 2.06730813e-01 3.40013355e-02 -7.56027281e-01
9.91699398e-01 -3.32333058e-01 -6.64617658e-01 8.66842031e-01
1.58356011e+00 -2.72692323e-01 -6.65409565e-01 -2.73964375e-01
5.59214689e-02 -5.70955761e-02 2.88639277e-01 -6.74070776e-01
-2.65970200e-01 1.30162442e+00 6.52444780e-01 6.90259218e-01
6.45361543e-01 -9.82703641e-02 5.87591290e-01 1.05270326e+00
7.18193531e-01 -1.00223053e+00 -3.93021524e-01 1.07475734e+00
3.67123932e-01 -9.64953721e-01 4.68107872e-02 -1.21925563e-01
-3.98322433e-01 1.50912845e+00 -4.52758580e-01 2.70372536e-02
1.14360964e+00 4.11094517e-01 -6.46025121e-01 -8.02613422e-03
-8.16488326e-01 -3.63654464e-01 1.56860679e-01 4.76640165e-01
5.68820596e-01 5.62770188e-01 -5.07003486e-01 5.99313676e-01
-6.87557101e-01 1.95280924e-01 1.09598005e+00 6.47629678e-01
-6.77043140e-01 -1.18421710e+00 -4.44484577e-02 5.32238364e-01
-5.05694747e-01 -6.95481837e-01 -7.58212432e-02 1.69636652e-01
9.34128556e-03 8.68545949e-01 -1.81400664e-02 -8.80699158e-01
-6.64717972e-01 3.30992848e-01 7.70474076e-01 -4.86213624e-01
1.39185965e-01 -5.45468330e-01 -1.51715055e-01 -2.15084657e-01
-3.76616031e-01 -2.80551314e-01 -1.21102905e+00 -9.72431004e-01
-5.31438172e-01 4.45505470e-01 1.02661228e+00 1.27449548e+00
1.55209467e-01 2.41583750e-01 4.49671030e-01 -4.01453972e-01
-1.15545201e+00 -9.23421025e-01 -1.22472847e+00 4.68575843e-02
6.50649011e-01 -6.22219265e-01 -6.71493411e-01 -3.70426089e-01] | [5.635475158691406, 4.939809799194336] |
6ea3dac7-e3dd-4ddf-9cad-4b8207763a65 | audio-to-intent-using-acoustic-textual | 2210.12134 | null | https://arxiv.org/abs/2210.12134v1 | https://arxiv.org/pdf/2210.12134v1.pdf | Audio-to-Intent Using Acoustic-Textual Subword Representations from End-to-End ASR | Accurate prediction of the user intent to interact with a voice assistant (VA) on a device (e.g. on the phone) is critical for achieving naturalistic, engaging, and privacy-centric interactions with the VA. To this end, we present a novel approach to predict the user's intent (the user speaking to the device or not) directly from acoustic and textual information encoded at subword tokens which are obtained via an end-to-end ASR model. Modeling directly the subword tokens, compared to modeling of the phonemes and/or full words, has at least two advantages: (i) it provides a unique vocabulary representation, where each token has a semantic meaning, in contrast to the phoneme-level representations, (ii) each subword token has a reusable "sub"-word acoustic pattern (that can be used to construct multiple full words), resulting in a largely reduced vocabulary space than of the full words. To learn the subword representations for the audio-to-intent classification, we extract: (i) acoustic information from an E2E-ASR model, which provides frame-level CTC posterior probabilities for the subword tokens, and (ii) textual information from a pre-trained continuous bag-of-words model capturing the semantic meaning of the subword tokens. The key to our approach is the way it combines acoustic subword-level posteriors with text information using the notion of positional-encoding in order to account for multiple ASR hypotheses simultaneously. We show that our approach provides more robust and richer representations for audio-to-intent classification, and is highly accurate with correctly mitigating 93.3% of unintended user audio from invoking the smart assistant at 99% true positive rate. | ['Ahmed Tewfik', 'Xiaochuan Niu', 'Erik Marchi', 'Oggi Rudovic', 'Prateeth Nayak', 'Pranay Dighe'] | 2022-10-21 | null | null | null | null | ['intent-classification'] | ['natural-language-processing'] | [ 4.91247118e-01 1.69856861e-01 -1.33353949e-01 -2.86601245e-01
-1.40921342e+00 -6.68698251e-01 3.36222947e-01 5.14352368e-03
-1.14644237e-01 1.73127815e-01 8.22729886e-01 -4.61841613e-01
1.28220990e-01 -2.43341163e-01 -5.49387395e-01 -4.45087016e-01
4.66019809e-02 1.48998141e-01 -2.66768754e-01 -8.43399689e-02
-2.90902525e-01 2.80824065e-01 -1.89893973e+00 4.85349864e-01
3.20888937e-01 1.35686231e+00 3.84712696e-01 1.18855619e+00
-4.42953818e-02 5.46850801e-01 -7.62839556e-01 -2.07803547e-01
-3.66112739e-02 -3.73112261e-01 -6.95844412e-01 -1.60134107e-01
2.20510125e-01 -3.31041127e-01 -4.27878439e-01 5.83119750e-01
7.64757633e-01 1.45602003e-01 5.34762561e-01 -1.00429499e+00
1.24639608e-01 4.15109485e-01 1.11851776e-02 -2.20710918e-01
1.04395533e+00 1.32979318e-01 1.50328982e+00 -7.19793975e-01
8.75750259e-02 1.35812688e+00 6.97075129e-01 5.29583991e-01
-1.18197894e+00 -7.04510152e-01 1.66451946e-01 -5.98669089e-02
-1.74337769e+00 -8.86047900e-01 7.51996458e-01 -2.68293709e-01
1.08118820e+00 7.78246820e-01 7.31615365e-01 1.69847536e+00
8.99079368e-02 8.67566824e-01 6.20760262e-01 -3.62142116e-01
2.25740224e-01 3.46483052e-01 2.63206840e-01 4.27260458e-01
-4.58439350e-01 7.87651166e-02 -7.79330790e-01 -6.16527081e-01
1.30591795e-01 -9.43684280e-02 -4.46934968e-01 1.26767084e-01
-7.86220193e-01 5.85979283e-01 -3.13525826e-01 3.58399957e-01
-4.80605751e-01 3.19237500e-01 4.95606154e-01 -2.93999035e-02
1.64414361e-01 1.52647436e-01 -7.53182352e-01 -9.39656258e-01
-7.24908233e-01 1.49005875e-01 1.11401474e+00 1.11848855e+00
4.17226195e-01 2.04783022e-01 -3.73055398e-01 8.80621135e-01
4.86550093e-01 7.71841109e-01 4.92814988e-01 -5.64148188e-01
3.37931812e-01 -8.41163471e-02 2.19948813e-01 -8.62288415e-01
-2.56967783e-01 -3.81448627e-01 -4.52493757e-01 -3.11855495e-01
2.05770880e-02 -2.41748765e-01 -6.18639469e-01 2.00602627e+00
-4.06145491e-02 3.44374925e-01 3.52290347e-02 4.06871915e-01
7.64682591e-01 8.95213306e-01 1.33885682e-01 -3.49444479e-01
1.74502718e+00 -2.86518753e-01 -1.25447369e+00 -3.53588581e-01
5.88474274e-01 -7.28478789e-01 1.40658343e+00 5.72030067e-01
-9.10069466e-01 -7.42080629e-01 -9.36045408e-01 9.91698354e-03
-2.90039450e-01 2.10359886e-01 2.87324160e-01 1.09832013e+00
-1.07855213e+00 8.23353082e-02 -4.42205340e-01 -1.47279903e-01
1.31232422e-02 5.38437247e-01 -1.77560180e-01 1.45904496e-01
-1.23243225e+00 4.58461136e-01 8.29072297e-02 -1.24912806e-01
-8.52359176e-01 -6.32138193e-01 -1.25867331e+00 3.70211214e-01
2.28495315e-01 -2.80397773e-01 1.44826055e+00 -7.31342137e-01
-1.88938415e+00 6.08575284e-01 -9.10246611e-01 -2.35887289e-01
-1.56028513e-02 -2.30101958e-01 -7.91355908e-01 -1.15602966e-02
-1.57921299e-01 4.59590733e-01 1.14038968e+00 -1.22078741e+00
-8.80804300e-01 -2.11761639e-01 -1.74689204e-01 1.37121737e-01
-6.29628301e-01 3.89440320e-02 -5.29031336e-01 -8.21067274e-01
-2.10399568e-01 -1.00902998e+00 3.19794685e-01 -4.03571904e-01
-4.52979326e-01 -2.07479060e-01 1.05071521e+00 -1.05201530e+00
1.64587951e+00 -2.52129984e+00 3.52448449e-02 2.77137697e-01
1.37021810e-01 2.52248645e-01 -1.10690013e-01 4.24918950e-01
-8.89300108e-02 6.14830889e-02 1.21138602e-01 -9.05438840e-01
3.59791666e-02 1.34002224e-01 -8.89422357e-01 2.53691733e-01
6.58440888e-02 8.07704747e-01 -6.46160603e-01 -9.39146280e-02
5.42035520e-01 8.83525312e-01 -7.52632976e-01 4.48218316e-01
3.16002592e-02 3.07947904e-01 -3.42228025e-01 3.83924335e-01
3.57904345e-01 3.97185594e-01 2.72321433e-01 -3.49422067e-01
2.26760775e-01 9.20943558e-01 -1.11177111e+00 1.47178829e+00
-1.16933727e+00 6.42606676e-01 3.07252347e-01 -6.81487858e-01
7.83104300e-01 8.91242683e-01 5.24976194e-01 -2.83174396e-01
1.95062399e-01 1.44894496e-01 -4.56115633e-01 -5.08498192e-01
5.13281763e-01 -1.86099455e-01 -5.41748166e-01 3.04758072e-01
3.36850196e-01 -1.84231251e-01 -7.56451428e-01 -7.51396716e-02
1.12255752e+00 -3.83614153e-01 3.67953330e-01 1.94481276e-02
5.25737941e-01 -7.23998368e-01 2.56303072e-01 8.85590613e-01
-1.59308061e-01 6.00459456e-01 1.46958083e-01 2.07664054e-02
-5.03870368e-01 -1.04336751e+00 -6.21733516e-02 1.20831966e+00
-1.14620745e-01 -9.56812680e-01 -6.71745539e-01 -4.00659561e-01
-1.79295421e-01 1.23639107e+00 -1.57023281e-01 -4.22448367e-01
-3.72999519e-01 -1.42522389e-02 8.10441196e-01 3.80121142e-01
-7.19157681e-02 -8.37548137e-01 -1.74833283e-01 3.14683020e-01
-8.00244868e-01 -1.46250904e+00 -8.27276289e-01 4.81751412e-01
-4.62888062e-01 -4.98495311e-01 -1.51845664e-01 -6.26760900e-01
-5.65760136e-02 2.43153274e-01 7.92294741e-01 -3.49264830e-01
-2.01285809e-01 1.02203846e+00 -4.30869132e-01 -6.18227899e-01
-5.64922631e-01 -1.33056685e-01 4.25688833e-01 4.40669000e-01
5.09881794e-01 -5.56419790e-01 -1.33240178e-01 2.08761722e-01
-6.47342622e-01 -2.94325620e-01 2.35660002e-01 7.49953866e-01
3.47312301e-01 3.30916166e-01 4.39339250e-01 -1.99806124e-01
7.86109269e-01 -2.94536859e-01 -1.00937886e-02 -1.61299363e-01
-1.58149585e-01 -2.15331942e-01 7.90551782e-01 -7.98641443e-01
-8.02173138e-01 1.86357707e-01 -8.22598755e-01 -4.15411830e-01
-4.59843040e-01 1.60033509e-01 -7.17008948e-01 3.52093697e-01
2.60482281e-01 5.01232445e-01 -3.39332595e-02 -4.44312662e-01
5.00454128e-01 1.55483246e+00 5.69820225e-01 -4.62378889e-01
4.79163527e-01 3.26511860e-01 -5.95791459e-01 -1.34282136e+00
-3.95955086e-01 -9.46033657e-01 -2.66787261e-01 -1.81325853e-01
8.65849316e-01 -1.04983115e+00 -1.13731670e+00 2.23131686e-01
-1.29510939e+00 2.66245678e-02 -4.48454827e-01 5.80314398e-01
-8.15688848e-01 3.28212500e-01 -4.52077210e-01 -1.65830851e+00
-2.48152286e-01 -1.34525955e+00 1.66568041e+00 -3.15008461e-01
-9.33344603e-01 -8.14015508e-01 -3.87321204e-01 5.74661672e-01
2.42208123e-01 -3.42692256e-01 8.97829115e-01 -8.32078636e-01
3.93127389e-02 -6.42518163e-01 4.25459117e-01 4.74188954e-01
5.01613021e-01 -3.91822100e-01 -1.65495634e+00 -4.05063897e-01
5.13514757e-01 -2.37344831e-01 3.61110508e-01 5.02519369e-01
1.40475786e+00 -6.90865338e-01 -3.66683930e-01 3.06959599e-01
8.23720217e-01 3.45100731e-01 6.87680364e-01 -6.45545125e-01
5.58243155e-01 4.67108577e-01 5.56284547e-01 7.62685061e-01
2.65566766e-01 1.11596751e+00 2.22706243e-01 5.61897866e-02
-9.70821679e-02 -6.20661497e-01 9.44430709e-01 9.93541539e-01
5.94674885e-01 -6.64506555e-01 -5.06513953e-01 4.59923804e-01
-1.44965005e+00 -8.82580280e-01 1.38217896e-01 2.43345737e+00
6.85214877e-01 6.25399053e-02 2.50469178e-01 4.46974099e-01
5.67701697e-01 2.51925945e-01 -2.00688660e-01 -5.76065004e-01
1.71462998e-01 3.77182633e-01 1.92330435e-01 9.45097089e-01
-9.47836339e-01 7.48007059e-01 6.35746145e+00 1.33415639e+00
-9.34386373e-01 2.28918731e-01 4.63439256e-01 -4.38064039e-02
-4.61675882e-01 -3.70376199e-01 -1.01900482e+00 4.12059158e-01
1.40775490e+00 2.34270871e-01 6.55282557e-01 7.64098763e-01
4.97840792e-01 1.08850680e-01 -1.38379872e+00 1.34113884e+00
2.47047886e-01 -8.44824016e-01 1.05354808e-01 2.55901873e-01
-1.44387633e-01 -4.05075163e-01 3.10024589e-01 4.51937377e-01
-8.94960165e-02 -1.07172728e+00 1.01061821e+00 2.42181152e-01
1.17718983e+00 -7.36526251e-01 6.04605794e-01 4.86116201e-01
-1.53732300e+00 -3.85456204e-01 2.02825636e-01 3.06005962e-02
2.52135128e-01 3.41181487e-01 -1.08260500e+00 4.04907614e-01
5.44194639e-01 2.95358628e-01 1.55910179e-01 1.79118276e-01
8.51111487e-02 1.01772964e+00 -4.74885553e-01 -1.82348192e-01
-4.62322757e-02 1.30828559e-01 8.69426191e-01 1.40491223e+00
3.47200692e-01 1.49520278e-01 1.53238460e-01 7.29705811e-01
6.02624193e-02 9.21416283e-02 -9.10079896e-01 -2.93293267e-01
6.68487132e-01 8.87353897e-01 -1.26969710e-01 -7.82315657e-02
-3.13370228e-01 1.27610159e+00 -2.47598022e-01 4.44924414e-01
-7.90954530e-01 -4.81124073e-01 1.15693617e+00 6.77392110e-02
2.26459101e-01 -3.22263479e-01 -1.46567717e-01 -7.10965693e-01
1.06701940e-01 -1.11719036e+00 1.37152404e-01 -6.98406279e-01
-1.08319652e+00 6.20487392e-01 -1.02450341e-01 -1.32166171e+00
-5.42185366e-01 -5.82261086e-01 -5.28815150e-01 1.11483622e+00
-1.07255924e+00 -1.11379910e+00 -7.33343512e-02 5.68570554e-01
7.40885317e-01 -1.66743413e-01 1.55690420e+00 3.31655145e-01
-1.05292551e-01 1.05213737e+00 -1.10236742e-01 1.20583102e-02
4.68856990e-01 -9.53276992e-01 4.36601222e-01 3.90619338e-01
3.62985373e-01 7.35205770e-01 7.77350843e-01 -4.70632464e-01
-1.78948021e+00 -1.01384830e+00 1.15299964e+00 -9.19593513e-01
4.44364548e-01 -1.13069534e+00 -4.61712450e-01 7.12712526e-01
-2.86866695e-01 -3.54901671e-01 1.25376081e+00 3.75949383e-01
-1.80227906e-01 -9.70076472e-02 -1.02930880e+00 7.22756982e-01
8.60814035e-01 -1.32526553e+00 -5.18824458e-01 1.88046679e-01
1.10185301e+00 -2.02414364e-01 -6.01924598e-01 1.89522430e-01
9.74100828e-01 -5.22334456e-01 1.11488569e+00 -4.19933736e-01
-2.91958064e-01 -3.24363336e-02 -7.45672464e-01 -8.95072401e-01
2.08759382e-02 -1.13828135e+00 -3.58142376e-01 1.34406471e+00
3.65347147e-01 -5.35639167e-01 4.52048957e-01 6.51859224e-01
-1.02533735e-01 -6.31542087e-01 -1.20270360e+00 -6.06845617e-01
-4.86688524e-01 -1.43417645e+00 5.39565802e-01 3.98173660e-01
2.77085215e-01 4.91446555e-01 -8.45734060e-01 4.18923825e-01
9.27727483e-03 -4.89929497e-01 6.39611661e-01 -1.04027498e+00
-4.86410469e-01 -1.26528233e-01 -4.29413319e-01 -1.68168771e+00
3.63735855e-01 -7.80730009e-01 3.84963393e-01 -9.59192276e-01
-2.87370026e-01 -4.76635635e-01 -1.36532500e-01 3.23738098e-01
-6.92396238e-02 -8.16524774e-02 1.32576630e-01 -1.58455446e-01
-2.71356493e-01 8.53146493e-01 4.38818902e-01 -3.01190883e-01
-5.36736786e-01 5.31945527e-01 -7.26985574e-01 6.09759033e-01
4.08266604e-01 -3.14131290e-01 -4.37401384e-01 2.05282539e-01
-2.46752709e-01 4.09757078e-01 2.13819444e-01 -9.41363215e-01
-1.00059144e-01 2.31872946e-01 1.49111506e-02 -4.77733463e-01
1.15206110e+00 -1.22013545e+00 9.84805003e-02 2.87324667e-01
-5.18090963e-01 -4.95214373e-01 4.29355383e-01 8.31970394e-01
-4.15561125e-02 -7.71066025e-02 4.23032194e-01 3.76446009e-01
-2.56702244e-01 1.73397698e-02 -1.00334215e+00 -2.47683778e-01
4.77477551e-01 -2.67412156e-01 5.06666064e-01 -1.18605447e+00
-6.81047916e-01 -1.66762069e-01 -1.42408088e-01 8.88193488e-01
5.76238513e-01 -1.34371305e+00 -3.00486565e-01 5.53451240e-01
2.73406953e-01 -4.09029871e-01 2.09549204e-01 2.88800031e-01
3.42823744e-01 6.94312930e-01 5.28065622e-01 -7.85607517e-01
-1.54901671e+00 2.43416473e-01 2.43490726e-01 -6.38066158e-02
-5.99877954e-01 8.21674347e-01 3.45499575e-01 -5.15793622e-01
8.34845006e-01 -3.95970434e-01 -1.64565638e-01 -4.51879064e-03
6.25400901e-01 3.78251933e-02 2.62691766e-01 -1.04357839e+00
-4.20108646e-01 3.86028916e-01 2.05213070e-01 -5.04830658e-01
8.24439645e-01 -3.44996721e-01 4.41750854e-01 8.34151566e-01
1.65520251e+00 4.73422974e-01 -6.77547336e-01 -2.10206956e-01
-3.40182036e-01 -3.94340843e-01 2.70581454e-01 -6.64087474e-01
-3.95580351e-01 9.87976730e-01 8.57420623e-01 9.76653323e-02
1.06253099e+00 1.15040720e-01 1.10826302e+00 2.65458167e-01
5.49774110e-01 -1.02253664e+00 6.55263215e-02 4.38642532e-01
8.61847937e-01 -8.63309324e-01 -5.96214890e-01 -4.21347409e-01
-7.47187257e-01 8.96431506e-01 2.06469130e-02 6.39660478e-01
8.54472816e-01 4.50967073e-01 -4.47373204e-02 6.14932701e-02
-5.56249738e-01 -1.13757856e-01 4.43684846e-01 7.86329210e-01
3.89410734e-01 4.30334300e-01 3.16074431e-01 1.27826273e+00
-5.08649766e-01 -4.21945870e-01 1.37174830e-01 7.32922614e-01
-2.68399060e-01 -1.06273103e+00 -3.96113485e-01 4.64560241e-01
-4.24883366e-01 -2.50542879e-01 -4.47295368e-01 1.15172416e-01
7.27764238e-03 1.72550559e+00 -2.65506818e-03 -1.06469440e+00
5.47511697e-01 5.85885406e-01 -5.74516952e-02 -6.91433847e-01
-6.34174883e-01 2.70898134e-01 3.13772708e-01 -6.94598138e-01
2.38735199e-01 -6.57602310e-01 -1.18241143e+00 -1.02298684e-01
-4.63335425e-01 2.59455800e-01 9.15133774e-01 9.86720741e-01
5.53558171e-01 7.92618811e-01 1.00710297e+00 -1.04709411e+00
-5.65017164e-01 -9.25622344e-01 -6.52862191e-01 3.35108340e-01
6.77899003e-01 -3.59922409e-01 -6.13629878e-01 -9.81780291e-02] | [14.631667137145996, 6.2125115394592285] |
fb029d96-2b8f-4968-9abe-b2414bc28a18 | edge-cloud-collaborative-learning-with | 2304.05871 | null | https://arxiv.org/abs/2304.05871v1 | https://arxiv.org/pdf/2304.05871v1.pdf | Edge-cloud Collaborative Learning with Federated and Centralized Features | Federated learning (FL) is a popular way of edge computing that doesn't compromise users' privacy. Current FL paradigms assume that data only resides on the edge, while cloud servers only perform model averaging. However, in real-life situations such as recommender systems, the cloud server has the ability to store historical and interactive features. In this paper, our proposed Edge-Cloud Collaborative Knowledge Transfer Framework (ECCT) bridges the gap between the edge and cloud, enabling bi-directional knowledge transfer between both, sharing feature embeddings and prediction logits. ECCT consolidates various benefits, including enhancing personalization, enabling model heterogeneity, tolerating training asynchronization, and relieving communication burdens. Extensive experiments on public and industrial datasets demonstrate ECCT's effectiveness and potential for use in academia and industry. | ['Chao Wu', 'Guannan Zhang', 'Wenliang Zhong', 'Yi Zhou', 'Qunwei Li', 'Zexi Li'] | 2023-04-12 | null | null | null | null | ['edge-computing'] | ['time-series'] | [-6.04812086e-01 -1.72813669e-01 -5.32856405e-01 -2.61580408e-01
-3.31452250e-01 -6.29191518e-01 2.16942519e-01 -9.82500762e-02
-1.51687622e-01 6.20615423e-01 -3.99648063e-02 -5.38599432e-01
-2.85003573e-01 -8.58645976e-01 -5.31951249e-01 -5.07302463e-01
-2.10068733e-01 5.67159466e-02 -9.65883806e-02 2.03801230e-01
-1.44183829e-01 3.18877339e-01 -1.25138402e+00 3.31838787e-01
8.72280300e-01 1.43418002e+00 -1.78570852e-01 4.18208480e-01
-1.10772364e-01 8.59413683e-01 4.24108319e-02 -7.78652251e-01
6.01371765e-01 3.70805740e-01 -3.27059299e-01 -5.33824325e-01
1.88046649e-01 -2.76313245e-01 -6.21172667e-01 9.67074335e-01
5.50099492e-01 4.63539548e-02 5.03416583e-02 -1.77165067e+00
-1.12371719e+00 6.36933565e-01 -2.33824581e-01 9.10789147e-02
-7.37829134e-02 1.40764816e-02 7.83241093e-01 -8.32271576e-01
4.83294338e-01 3.44748735e-01 1.08157432e+00 5.19823611e-01
-8.27905178e-01 -1.05309534e+00 3.73353422e-01 5.62657118e-01
-1.60088480e+00 -5.22658467e-01 5.06443322e-01 -3.10124040e-01
8.30319643e-01 5.74118793e-01 7.31134057e-01 7.36923993e-01
3.26414764e-01 9.17413712e-01 8.29975367e-01 -1.77175954e-01
4.98755008e-01 8.44098985e-01 2.28513658e-01 2.69883990e-01
4.68756050e-01 1.64743334e-01 -8.74526322e-01 -6.36890709e-01
3.88514102e-01 4.90449697e-01 -3.51906955e-01 -5.79533756e-01
-5.83210766e-01 4.01917428e-01 2.18567431e-01 5.32629266e-02
-7.55069375e-01 1.37763456e-01 5.19035220e-01 5.31207740e-01
8.16251040e-01 -1.82646587e-01 -8.74814808e-01 -2.29284272e-01
-7.59224176e-01 -1.80366799e-01 9.72938001e-01 1.31525540e+00
7.62516737e-01 -9.35703963e-02 -8.24530795e-02 2.40704164e-01
1.37900844e-01 2.43064031e-01 4.65872645e-01 -7.35537529e-01
1.22148059e-01 3.93923372e-01 3.32642645e-01 -8.15662682e-01
1.18414477e-01 -6.53699934e-01 -5.49207985e-01 -1.84215918e-01
-2.70787686e-01 -6.37864351e-01 -1.55188039e-01 1.69188178e+00
5.79514086e-01 1.10865796e+00 -2.65307669e-02 7.90612519e-01
4.76783901e-01 1.79323331e-01 1.47084877e-01 -6.68129548e-02
1.08947492e+00 -1.13480163e+00 -6.28331423e-01 1.29100338e-01
7.01214731e-01 -4.08709764e-01 5.64004660e-01 3.29460233e-01
-7.74546981e-01 -1.56918839e-01 -6.70918167e-01 2.88160771e-01
-5.48786461e-01 9.11057666e-02 1.30151236e+00 1.11819541e+00
-1.23710859e+00 6.93481565e-01 -8.36890459e-01 -7.58660436e-02
7.15852559e-01 6.79195404e-01 -5.83803713e-01 -1.44730061e-01
-1.13818991e+00 4.19602364e-01 5.41953035e-02 -1.63778231e-01
-2.77588755e-01 -1.40391421e+00 -2.80599535e-01 5.10106206e-01
2.24959210e-01 -1.19067621e+00 1.25670338e+00 -1.00703001e+00
-1.61649811e+00 1.75349534e-01 9.72399563e-02 -5.26984572e-01
5.11722445e-01 -4.39743638e-01 -8.96111727e-01 -2.71717787e-01
-4.57792699e-01 -3.10924184e-02 7.37892449e-01 -9.75883901e-01
-9.78324771e-01 -4.99586523e-01 -2.02567410e-02 1.04438230e-01
-1.21528339e+00 -2.19072238e-01 -4.93035525e-01 -2.43828759e-01
-4.52340245e-01 -1.00673211e+00 -1.92119032e-01 6.06801622e-02
3.06257177e-02 -2.81970501e-01 1.37407374e+00 -5.06190181e-01
1.41809630e+00 -2.46813965e+00 -7.01053858e-01 3.45641941e-01
5.17541587e-01 5.35975158e-01 1.32001445e-01 6.28023684e-01
1.61073089e-01 8.35939795e-02 6.52420640e-01 -5.66663384e-01
1.34998620e-01 4.96115237e-02 -1.61925942e-01 3.01289618e-01
-6.31737947e-01 1.05304062e+00 -8.10511112e-01 -3.28077555e-01
3.37154302e-03 6.89604402e-01 -7.43038833e-01 1.24457683e-02
1.26285046e-01 3.15656692e-01 -6.07147217e-01 4.41667229e-01
8.02956820e-01 -5.09432733e-01 3.16511124e-01 -2.05005985e-02
-9.83353257e-02 6.16861545e-02 -1.28969884e+00 1.39464962e+00
-8.47015917e-01 2.94831216e-01 3.90929282e-01 -5.07307351e-01
5.33919990e-01 6.23068810e-01 7.75461614e-01 -2.68559784e-01
2.50934213e-01 -9.63869467e-02 -4.67862636e-01 -2.34824359e-01
5.97333610e-01 3.91058087e-01 3.48458648e-01 7.60552526e-01
-2.45049186e-02 8.81635427e-01 -4.81573194e-01 2.46769160e-01
1.14976132e+00 -1.74102738e-01 3.67075391e-03 -2.71372616e-01
7.43759796e-02 -4.16646093e-01 8.28847706e-01 5.61161339e-01
-3.30805093e-01 -5.11239246e-02 -2.81836092e-01 -5.21480799e-01
-4.54545796e-01 -9.72392440e-01 8.49160999e-02 1.22872901e+00
1.78382233e-01 -8.44079256e-01 -6.58357441e-01 -8.74131441e-01
5.84995091e-01 8.97610545e-01 -5.17777264e-01 -5.25177002e-01
1.37858883e-01 -2.91187018e-01 1.07773602e-01 6.16132617e-01
3.50614429e-01 -3.08689982e-01 -3.03682148e-01 2.28748145e-03
6.72114119e-02 -9.32765961e-01 -1.01505315e+00 -9.27966610e-02
-7.80022264e-01 -8.17805111e-01 6.47373870e-02 -3.53511870e-01
5.84006369e-01 9.41963315e-01 1.00866640e+00 -9.61410254e-02
-6.87517375e-02 9.43695366e-01 -2.64156908e-01 -5.82533181e-01
2.72265255e-01 1.30649516e-02 3.76027226e-01 4.26123500e-01
7.50360012e-01 -1.05618942e+00 -1.00924885e+00 2.35504672e-01
-5.50029457e-01 -1.57245934e-01 3.06609541e-01 6.81634724e-01
3.46582055e-01 3.09770554e-01 7.98091412e-01 -1.40637839e+00
6.01066291e-01 -9.99679744e-01 -3.57104480e-01 4.95756030e-01
-1.43408108e+00 -6.56539559e-01 9.28838432e-01 -5.29843390e-01
-1.21612775e+00 -3.45776491e-02 5.10776639e-01 -1.01853037e+00
1.11831687e-01 3.15283805e-01 -2.01831743e-01 -6.12962663e-01
4.16450173e-01 -1.15260417e-02 -1.50197104e-01 -4.29113656e-01
7.12267101e-01 1.24069917e+00 4.38047409e-01 -4.78957742e-01
6.77099586e-01 4.02996749e-01 -4.83048618e-01 -2.83199281e-01
-2.60545135e-01 -5.58435023e-01 -8.97128135e-02 -1.00770399e-01
1.60959810e-01 -1.31121588e+00 -1.29313266e+00 7.60645196e-02
-5.80936372e-01 -1.05049275e-03 -5.04426897e-01 7.24876702e-01
-2.36431316e-01 1.54348627e-01 -6.40428066e-01 -8.29386353e-01
-9.79683697e-01 -5.46272516e-01 3.76424313e-01 6.97091341e-01
1.23553552e-01 -1.11721957e+00 -1.09339193e-01 4.47161913e-01
1.09385180e+00 -3.99161041e-01 4.92203683e-01 -9.12228703e-01
-8.47641826e-01 -7.04780698e-01 -7.87236392e-02 2.27446944e-01
1.80390850e-01 -2.03391239e-01 -1.29921412e+00 -6.13264322e-01
-3.08063984e-01 1.58840701e-01 2.38379732e-01 7.05529228e-02
1.36685479e+00 -4.36636239e-01 -5.70341527e-01 7.75466442e-01
1.36703920e+00 4.19871882e-02 2.67170429e-01 -1.90324306e-01
6.20976150e-01 6.00599498e-02 2.74730474e-01 7.69604146e-01
7.38450229e-01 3.71824652e-01 2.92307884e-01 1.76536720e-02
9.51980278e-02 -4.72044021e-01 2.50743061e-01 9.91601408e-01
7.92429522e-02 -9.90167633e-02 -5.03364027e-01 5.53431869e-01
-2.10926843e+00 -8.80014598e-01 2.74550557e-01 2.44436693e+00
5.69835663e-01 -2.00606480e-01 -6.95770979e-02 -4.47544128e-01
7.21656203e-01 -4.85808194e-01 -9.72432494e-01 -3.63948494e-01
3.83951157e-01 8.12402815e-02 9.10016954e-01 -8.97242278e-02
-9.21048939e-01 7.24174380e-01 5.87841797e+00 7.04799891e-01
-1.40851331e+00 6.35624588e-01 4.11702484e-01 -3.75480980e-01
-4.32651669e-01 1.53879523e-01 -5.94573736e-01 6.23260617e-01
1.13360131e+00 -9.23931777e-01 7.35168695e-01 1.59438455e+00
-1.44967884e-01 4.27401483e-01 -1.05323243e+00 1.29259491e+00
-3.07486117e-01 -1.55696356e+00 -2.99742073e-01 1.52855009e-01
9.62244153e-01 4.62716043e-01 2.74055362e-01 5.28344452e-01
7.38627136e-01 -5.74214935e-01 3.80281776e-01 7.93456256e-01
7.43832529e-01 -8.97163928e-01 6.68778896e-01 4.19568956e-01
-1.17678738e+00 -4.26313430e-01 -2.30133951e-01 -3.44953942e-03
-5.17838709e-02 8.09536338e-01 -6.80895209e-01 9.61351812e-01
9.65147078e-01 5.77843785e-01 -2.83941895e-01 1.10264015e+00
3.30365747e-01 6.29751265e-01 -2.98657715e-01 1.90924034e-01
-3.48890841e-01 -4.51722264e-01 1.33482218e-01 8.86405170e-01
4.46497262e-01 1.47682279e-01 3.38524431e-01 3.73748630e-01
-3.85061920e-01 3.97025168e-01 -7.13393569e-01 -7.47102275e-02
1.19331944e+00 1.67874634e+00 -1.48644030e-01 -2.94868201e-01
-7.73810446e-01 1.00292230e+00 4.11799520e-01 2.96689004e-01
-6.86619401e-01 -2.99480051e-01 1.30207515e+00 -4.18795384e-02
3.68815094e-01 -2.21337825e-02 -2.52451956e-01 -1.22214222e+00
-1.31241962e-01 -5.42807579e-01 4.76882666e-01 -4.28261310e-01
-1.57984781e+00 5.13821721e-01 -5.31470060e-01 -1.20847285e+00
1.06290840e-01 -2.84091085e-01 -7.83659160e-01 7.70829618e-01
-1.48714268e+00 -1.54002464e+00 -1.63760945e-01 9.29047465e-01
-1.35833383e-01 -1.62046894e-01 1.10003293e+00 8.84206176e-01
-6.74698353e-01 1.20425975e+00 5.10641813e-01 -2.40416080e-01
8.51076066e-01 -8.86684299e-01 3.88567418e-01 6.05851591e-01
2.50166804e-01 9.67380762e-01 2.82133549e-01 -3.73431087e-01
-2.00075126e+00 -1.43677211e+00 6.91037357e-01 -4.15995032e-01
5.97923100e-01 -2.92001575e-01 -7.06490099e-01 9.68462229e-01
9.05399919e-02 8.09013784e-01 1.38706458e+00 4.84674275e-01
-5.27215004e-01 -5.20178437e-01 -1.24786198e+00 5.52019775e-01
1.14408553e+00 -8.46777499e-01 3.92288446e-01 4.46549356e-01
6.64909244e-01 -2.23038748e-01 -1.27236354e+00 1.05144627e-01
5.60288906e-01 -8.83432090e-01 6.63962245e-01 -9.64882493e-01
-4.26326543e-01 -7.99896717e-02 -1.56453326e-01 -1.33415794e+00
-6.04105353e-01 -1.19720173e+00 -6.34674132e-01 1.40882552e+00
1.81207791e-01 -1.07755971e+00 1.11790001e+00 1.32807326e+00
-1.22443266e-01 -7.09315598e-01 -8.68189871e-01 -9.01900947e-01
-2.22327337e-01 -5.39285600e-01 1.23012590e+00 1.31298816e+00
2.45481998e-01 -4.57605720e-02 -4.46580440e-01 4.58206743e-01
5.60044467e-01 2.80722916e-01 8.92622709e-01 -1.37775433e+00
-5.52488983e-01 4.02194895e-02 -3.07475328e-01 -6.06092751e-01
2.16377184e-01 -1.10850239e+00 -7.48730600e-01 -9.40756023e-01
3.41887064e-02 -1.04767621e+00 -9.29356217e-01 6.38924837e-01
8.08684006e-02 1.23493159e-02 2.74620444e-01 1.42704889e-01
-9.36723769e-01 3.97909015e-01 6.75322890e-01 2.62651980e-01
-2.29636356e-01 3.07754576e-01 -1.07428873e+00 4.65269536e-01
9.44724262e-01 -4.22575355e-01 -8.57751966e-01 -4.60593283e-01
1.85973048e-01 -9.00571123e-02 -2.53855139e-02 -8.50884914e-01
9.34505463e-01 -1.12353705e-01 6.90089390e-02 4.19436917e-02
9.39663351e-02 -1.43048882e+00 8.82587254e-01 1.37117237e-01
2.38449827e-01 -1.36843324e-02 1.54634062e-02 9.42651093e-01
1.61998272e-01 2.06858039e-01 3.26369107e-01 2.30202734e-01
-6.44708157e-01 1.00236440e+00 1.27510309e-01 -3.01717669e-01
1.42168307e+00 -1.29094590e-02 -4.12478209e-01 -4.85059172e-01
-6.08166933e-01 4.56457645e-01 6.52346551e-01 4.04399723e-01
2.47384563e-01 -1.36004865e+00 -1.60794571e-01 4.47049111e-01
1.97519809e-01 -5.76048136e-01 6.73708200e-01 1.00089228e+00
8.37248117e-02 3.40091079e-01 6.04161583e-02 -1.07302494e-01
-1.17512763e+00 9.38289523e-01 9.87135097e-02 9.31504369e-02
-6.81750059e-01 7.56230295e-01 -1.53063491e-01 -5.42951703e-01
3.31698388e-01 4.54743683e-01 2.25584760e-01 -3.63470852e-01
7.47146964e-01 6.10556781e-01 4.11430955e-01 1.38150025e-02
-2.66713023e-01 -6.92820624e-02 -3.67236465e-01 4.49213505e-01
1.21007645e+00 -3.92940402e-01 -1.89742483e-02 2.03044161e-01
1.01989257e+00 3.17019284e-01 -1.17134333e+00 -6.83948755e-01
-1.28688812e-01 -7.04111695e-01 5.97105742e-01 -6.80213809e-01
-1.49315822e+00 2.76014268e-01 8.59617293e-01 1.38985574e-01
1.19490480e+00 -4.66157645e-01 1.17590654e+00 3.41232300e-01
9.55928862e-01 -1.11770761e+00 -6.40297890e-01 -1.87493134e-02
-1.18452273e-01 -1.03155577e+00 -2.28836641e-01 -5.88444173e-01
-6.57170594e-01 6.85415328e-01 5.62371910e-01 1.31806120e-01
1.46270692e+00 4.95518029e-01 9.52240005e-02 1.62633453e-02
-1.42451358e+00 2.43161723e-01 -1.38798982e-01 7.05016196e-01
1.59534454e-01 4.75060672e-01 -1.65959761e-01 1.41049504e+00
1.03328556e-01 5.59166372e-01 1.52977228e-01 9.91345942e-01
8.76474753e-02 -1.30324376e+00 3.76371667e-02 7.75095582e-01
-5.73766053e-01 -1.75474092e-01 5.72587103e-02 2.12827012e-01
2.07557514e-01 9.34283316e-01 3.89976166e-02 -8.09565246e-01
1.10221863e-01 3.82423162e-01 -3.32751349e-02 -3.09868366e-01
-9.42826509e-01 -3.86036098e-01 -1.13031283e-01 -7.37690568e-01
2.32999131e-01 -5.09958088e-01 -8.59405518e-01 -8.12291265e-01
-7.40452647e-01 6.70724511e-01 9.74796414e-01 4.15021926e-01
1.56742537e+00 3.43737841e-01 1.20812893e+00 -3.70072395e-01
-8.40431452e-01 -3.97854775e-01 -9.60928798e-01 1.11089990e-01
-9.61442515e-02 -4.84234780e-01 -2.91970998e-01 -5.75608164e-02] | [5.897154808044434, 6.231959819793701] |
13550ee7-84fb-4fd5-abac-26a067ae98a0 | audio-visual-efficient-conformer-for-robust | 2301.01456 | null | https://arxiv.org/abs/2301.01456v1 | https://arxiv.org/pdf/2301.01456v1.pdf | Audio-Visual Efficient Conformer for Robust Speech Recognition | End-to-end Automatic Speech Recognition (ASR) systems based on neural networks have seen large improvements in recent years. The availability of large scale hand-labeled datasets and sufficient computing resources made it possible to train powerful deep neural networks, reaching very low Word Error Rate (WER) on academic benchmarks. However, despite impressive performance on clean audio samples, a drop of performance is often observed on noisy speech. In this work, we propose to improve the noise robustness of the recently proposed Efficient Conformer Connectionist Temporal Classification (CTC)-based architecture by processing both audio and visual modalities. We improve previous lip reading methods using an Efficient Conformer back-end on top of a ResNet-18 visual front-end and by adding intermediate CTC losses between blocks. We condition intermediate block features on early predictions using Inter CTC residual modules to relax the conditional independence assumption of CTC-based models. We also replace the Efficient Conformer grouped attention by a more efficient and simpler attention mechanism that we call patch attention. We experiment with publicly available Lip Reading Sentences 2 (LRS2) and Lip Reading Sentences 3 (LRS3) datasets. Our experiments show that using audio and visual modalities allows to better recognize speech in the presence of environmental noise and significantly accelerate training, reaching lower WER with 4 times less training steps. Our Audio-Visual Efficient Conformer (AVEC) model achieves state-of-the-art performance, reaching WER of 2.3% and 1.8% on LRS2 and LRS3 test sets. Code and pretrained models are available at https://github.com/burchim/AVEC. | ['Radu Timofte', 'Maxime Burchi'] | 2023-01-04 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 3.36843789e-01 1.37701035e-01 -1.21488914e-01 -3.36847007e-01
-1.45400739e+00 -1.37636885e-01 5.46199262e-01 -1.63103744e-01
-5.88434041e-01 4.21706289e-01 6.07993186e-01 -4.52604502e-01
3.83902639e-01 -1.45731807e-01 -8.03395212e-01 -4.99460429e-01
4.87650722e-01 1.01628065e-01 2.93724149e-01 1.75939761e-02
-3.92663255e-02 1.91652134e-01 -1.80878162e+00 7.94324756e-01
5.53407311e-01 1.14311159e+00 4.07627702e-01 1.07605243e+00
4.02862467e-02 6.97503507e-01 -5.15594780e-01 -2.20509470e-01
-5.48161380e-02 -3.63812059e-01 -7.94080615e-01 -1.94748566e-01
6.46527827e-01 -1.08820744e-01 -4.88095760e-01 7.71472991e-01
1.21361911e+00 3.61339629e-01 3.69310707e-01 -1.01825452e+00
-7.77271748e-01 7.10156918e-01 -4.61456984e-01 1.16142422e-01
2.78332949e-01 5.30241489e-01 1.12418747e+00 -1.20006549e+00
3.14607561e-01 1.28720152e+00 6.87366903e-01 9.59995508e-01
-1.30531979e+00 -6.11185789e-01 7.50671551e-02 7.61864603e-01
-1.40044761e+00 -1.35461891e+00 6.01952016e-01 1.70491952e-02
1.54479754e+00 3.56659204e-01 3.77449304e-01 1.59070635e+00
-1.65038079e-01 1.17146945e+00 1.09439909e+00 -5.74798584e-01
1.58989936e-01 4.63270508e-02 5.03995549e-03 3.10644358e-01
-4.68953222e-01 1.97683975e-01 -8.97589326e-01 2.60821939e-01
1.10859834e-01 -4.73573983e-01 -5.43687463e-01 1.79646000e-01
-9.80448186e-01 5.85349560e-01 4.15272802e-01 4.45477962e-01
-2.01260388e-01 2.86647886e-01 6.54717326e-01 2.40845501e-01
5.37089527e-01 -2.04318821e-01 -5.74836850e-01 -2.09767073e-01
-1.16250777e+00 -4.46147859e-01 5.08772314e-01 6.89218283e-01
2.22314805e-01 3.37727427e-01 -5.45159101e-01 1.45772779e+00
4.48329568e-01 5.09333670e-01 7.58520007e-01 -6.98981881e-01
6.03832662e-01 -1.87036291e-01 -4.92985278e-01 -2.41302863e-01
-2.99945772e-01 -4.31205899e-01 -1.01780331e+00 3.30007762e-01
2.74207115e-01 1.16469324e-01 -1.37602973e+00 1.84548652e+00
-9.40569937e-02 1.76828608e-01 3.87124047e-02 6.93687916e-01
1.02590525e+00 7.96865821e-01 2.97928363e-01 -7.10649788e-02
1.30662227e+00 -1.17057669e+00 -7.86622763e-01 -1.14563003e-01
4.85380977e-01 -8.27302337e-01 1.38907230e+00 4.10079956e-01
-1.32575488e+00 -8.22669983e-01 -8.75010490e-01 -5.01160979e-01
-2.33734339e-01 4.36211079e-01 -6.37825504e-02 7.66912341e-01
-1.66286623e+00 4.63471919e-01 -7.93023288e-01 -3.18648130e-01
6.08609676e-01 5.11089563e-01 -4.16667670e-01 -3.31544224e-03
-9.95769620e-01 7.78968453e-01 1.30142923e-02 1.84630245e-01
-1.00738358e+00 -7.37531006e-01 -9.85867620e-01 1.90187082e-01
2.87298530e-01 -5.00682950e-01 1.47307122e+00 -1.05813134e+00
-2.12771106e+00 8.34555924e-01 -6.01390600e-01 -6.10413492e-01
4.96467739e-01 -1.39128193e-01 -4.44649577e-01 2.01813392e-02
-3.51043463e-01 1.09487474e+00 1.00070083e+00 -9.09288168e-01
-3.26698542e-01 -1.58069506e-02 -4.57803607e-01 1.18906215e-01
-2.31646940e-01 2.06475228e-01 -4.01468664e-01 -5.79090834e-01
-1.66018561e-01 -7.95710921e-01 2.90653110e-01 3.38751730e-03
-4.12425041e-01 -5.43456078e-01 7.00378597e-01 -1.14282084e+00
8.13086808e-01 -2.30435538e+00 -7.82294199e-02 -1.40899986e-01
5.10929637e-02 7.31921375e-01 -7.15049326e-01 1.58548132e-01
-4.50307369e-01 2.45720237e-01 -1.01268917e-01 -9.55420613e-01
5.33768423e-02 -1.40487492e-01 -2.41409719e-01 3.86687130e-01
3.00627470e-01 9.79401112e-01 -4.62669283e-01 -2.01288998e-01
5.11343420e-01 1.08362186e+00 -5.26970923e-01 8.08616653e-02
-1.39190666e-02 2.93604553e-01 3.67519617e-01 5.99317908e-01
5.29048800e-01 -2.68555060e-02 -1.82508767e-01 -2.26144761e-01
7.07901688e-03 8.78012240e-01 -8.50598097e-01 1.70759487e+00
-9.16809738e-01 1.30416250e+00 2.12228850e-01 -8.09368014e-01
8.13481271e-01 7.69558012e-01 1.25618652e-01 -1.03409708e+00
1.09661013e-01 2.18270615e-01 5.03197573e-02 -3.80163759e-01
2.27083042e-01 -8.83871615e-02 4.82131541e-01 4.76975851e-02
3.73512477e-01 6.12634793e-02 -2.62223870e-01 -3.23672593e-02
1.10831547e+00 -8.65677074e-02 4.15485017e-02 -3.61873619e-02
8.78502548e-01 -6.98319018e-01 2.46849388e-01 5.33958793e-01
-4.03382272e-01 9.36158955e-01 2.39952311e-01 6.73674121e-02
-9.69418824e-01 -1.16010380e+00 -1.76913753e-01 1.36055696e+00
-5.32138944e-01 -4.33622062e-01 -7.39448726e-01 -5.12480080e-01
-3.23925495e-01 8.03553283e-01 -4.64977622e-01 -9.89898741e-02
-5.08324385e-01 -3.36181492e-01 8.89033318e-01 7.57153034e-01
5.31368315e-01 -1.25906229e+00 -4.15229052e-02 5.47649190e-02
-5.03566325e-01 -1.13568461e+00 -5.13050973e-01 2.78929114e-01
-3.06086034e-01 -5.96410573e-01 -1.13617623e+00 -6.93111539e-01
1.40416116e-01 2.63869911e-02 9.65110123e-01 -1.01102442e-01
-3.22516739e-01 3.92669588e-01 -3.25499326e-01 -3.67818475e-01
-6.05655789e-01 2.38583580e-01 1.11976393e-01 1.43577397e-01
2.18673706e-01 -3.65548015e-01 -4.97081399e-01 2.27903619e-01
-5.66455603e-01 1.22309603e-01 5.36499739e-01 1.07953382e+00
5.10759354e-01 -4.80919302e-01 7.35345066e-01 -4.06009778e-02
4.11621004e-01 -9.71802622e-02 -4.57172722e-01 1.33228987e-01
-4.64141607e-01 7.79836699e-02 5.62459350e-01 -6.15443170e-01
-1.07223725e+00 -2.37167161e-02 -8.89968336e-01 -5.77986181e-01
-4.92414355e-01 -1.52459834e-02 -2.42469266e-01 2.04443961e-01
5.18980205e-01 2.46737599e-01 -1.07004628e-01 -7.19210207e-01
2.20458850e-01 1.20035172e+00 5.19403219e-01 -1.02914408e-01
3.60118538e-01 1.42264768e-01 -3.25216860e-01 -1.17659771e+00
-5.70622802e-01 -4.98199463e-01 -2.68094599e-01 -1.95223913e-01
9.05051529e-01 -1.04388869e+00 -9.61902857e-01 6.46196663e-01
-1.16698074e+00 -8.51676226e-01 -3.39862883e-01 5.77044308e-01
-6.42234683e-01 2.73919672e-01 -5.83903134e-01 -1.03029239e+00
-5.21748304e-01 -1.33550680e+00 1.22136629e+00 -1.13323681e-01
-2.29312614e-01 -6.77128315e-01 -2.71977466e-02 7.10670292e-01
7.42277861e-01 -5.21028519e-01 5.20158350e-01 -6.67302668e-01
-3.48199129e-01 1.92859873e-01 -3.34113717e-01 8.75707507e-01
-1.53434291e-01 -8.85890499e-02 -1.86269116e+00 -3.47055078e-01
-2.92277217e-01 -4.83193278e-01 1.39600635e+00 5.86855412e-01
1.32738447e+00 -2.33461380e-01 -6.54837042e-02 5.89643300e-01
1.10260010e+00 9.59944427e-02 9.66815174e-01 -1.58555046e-01
4.87014204e-01 6.43741548e-01 3.27817462e-02 -1.06963282e-02
1.15307793e-01 9.65696573e-01 1.68417752e-01 -2.17744455e-01
-1.11606896e+00 -2.74897426e-01 8.84345889e-01 9.79577482e-01
6.20178431e-02 -6.26750231e-01 -9.60838318e-01 7.03778803e-01
-1.49254787e+00 -1.02210140e+00 -3.86189262e-04 2.20406365e+00
8.81182551e-01 -6.97371438e-02 1.12727307e-01 5.11534989e-01
7.75390804e-01 1.82056516e-01 -3.65909666e-01 -5.58454096e-01
-2.07455829e-01 6.11716330e-01 3.00202847e-01 9.76874173e-01
-8.83589804e-01 1.03382170e+00 5.68094349e+00 1.27162850e+00
-1.38524878e+00 4.72654223e-01 8.58012021e-01 -3.48715723e-01
-1.29923746e-02 -4.98015612e-01 -9.08249259e-01 3.39226186e-01
1.45885217e+00 2.25459203e-01 4.52677250e-01 5.22154808e-01
4.61645097e-01 -6.90139830e-02 -1.07676053e+00 1.27541173e+00
1.86553374e-01 -1.14104211e+00 -2.20493659e-01 -1.48838516e-02
4.34566736e-01 7.24603355e-01 3.99627090e-01 4.03313726e-01
-4.39184085e-02 -1.29998636e+00 8.01875472e-01 2.97171831e-01
1.28805149e+00 -5.41776657e-01 6.12597167e-01 1.60623305e-02
-1.35313678e+00 -5.98318130e-02 -2.21331567e-01 3.61091256e-01
-4.35594209e-02 2.80908465e-01 -1.04014122e+00 2.13583216e-01
8.31552505e-01 5.49472272e-01 -4.16475773e-01 1.07560802e+00
-3.15210491e-01 1.02920699e+00 -5.06088972e-01 -3.36783156e-02
1.11353979e-01 5.59823930e-01 5.76164126e-01 1.49928260e+00
1.81019410e-01 -2.00231940e-01 -5.61387241e-01 6.70817912e-01
-3.78184110e-01 1.82140976e-01 -4.36675608e-01 2.88421720e-01
2.08604440e-01 1.02432668e+00 -2.79747725e-01 -2.62086660e-01
-3.73238951e-01 1.04129410e+00 2.15447024e-01 4.97294426e-01
-8.78357768e-01 -4.62480754e-01 6.14913642e-01 5.25092036e-02
4.48490083e-01 -4.75995801e-02 -1.95028648e-01 -1.06636500e+00
1.18564136e-01 -9.88562882e-01 3.81052159e-02 -1.01967609e+00
-9.86738384e-01 7.84820914e-01 -4.36080277e-01 -8.91320169e-01
-2.61513114e-01 -7.48157918e-01 -4.85118777e-01 9.70298111e-01
-1.72793782e+00 -1.06297946e+00 -1.46909684e-01 8.49159718e-01
1.05156207e+00 -1.43542469e-01 8.46942425e-01 4.24659997e-01
-5.43849945e-01 1.17216957e+00 6.44288585e-02 1.26504570e-01
8.26590836e-01 -9.91269767e-01 5.49882710e-01 6.84313595e-01
4.29094702e-01 2.01230142e-02 3.21563363e-01 -2.85697073e-01
-9.56193030e-01 -1.03167343e+00 1.23070204e+00 -1.50403306e-01
4.60601151e-01 -6.68685675e-01 -9.07740116e-01 4.91553664e-01
6.21272981e-01 8.60930905e-02 5.25878012e-01 1.62289932e-01
-7.25815296e-01 -2.60609627e-01 -9.64252770e-01 6.32835090e-01
1.13254082e+00 -9.52779293e-01 -4.26500618e-01 2.87081122e-01
7.89444089e-01 -1.05601907e-01 -4.54096705e-01 4.72050875e-01
5.04729509e-01 -9.64413464e-01 8.71884286e-01 -2.69390881e-01
-3.15380432e-02 -7.06937909e-02 -3.21130902e-01 -1.25107324e+00
3.08541618e-02 -9.16533053e-01 4.06609029e-02 1.24838817e+00
6.80682421e-01 -6.64927721e-01 4.89086419e-01 2.63968557e-01
-3.89900774e-01 -6.20945692e-01 -1.43322897e+00 -9.31575537e-01
5.12719899e-02 -1.00955617e+00 5.06797284e-02 4.24185038e-01
-2.92320475e-02 5.71556389e-01 -3.19398493e-01 -3.32103856e-02
3.20417970e-01 -6.80222750e-01 4.45506722e-01 -9.30449128e-01
-3.85885626e-01 -5.91534495e-01 -3.56956631e-01 -1.13727152e+00
4.92293566e-01 -1.02058637e+00 2.99669206e-01 -1.49378502e+00
-1.64244492e-02 -1.12196669e-01 -3.54102731e-01 9.84524727e-01
1.15932636e-01 5.31998217e-01 4.07673389e-01 -1.70994058e-01
-4.36906606e-01 8.59811604e-01 6.98006392e-01 -3.92394185e-01
-2.32673571e-01 -1.68889295e-02 -1.56730101e-01 6.11038089e-01
9.64892924e-01 -2.25958511e-01 -2.13074505e-01 -5.97979605e-01
-2.23677114e-01 -8.50630403e-02 5.42602837e-01 -1.37331951e+00
4.18910235e-01 5.41733503e-01 3.24512720e-01 -6.61827207e-01
8.76870096e-01 -5.62174678e-01 -2.37152770e-01 2.56393492e-01
-7.78549910e-01 -3.87419462e-01 6.20499849e-01 3.64453465e-01
-2.23286361e-01 4.56589088e-02 1.17486978e+00 3.66399169e-01
-3.66812795e-01 8.23609307e-02 -6.10149264e-01 -3.85497743e-03
5.26130497e-01 -6.64176568e-02 -2.55848318e-01 -6.74013913e-01
-9.98827636e-01 -1.74139574e-01 -5.88739812e-02 5.98434687e-01
8.31313252e-01 -1.11164320e+00 -1.05480337e+00 4.43203032e-01
-6.93662688e-02 -4.42921698e-01 4.90687311e-01 9.91316617e-01
7.21607311e-03 6.31681323e-01 1.05384357e-01 -7.84583926e-01
-1.71716368e+00 2.36698791e-01 5.98152459e-01 -1.25436941e-02
-6.93958461e-01 1.15320325e+00 1.72760874e-01 -2.08555058e-01
7.57905781e-01 -4.25341696e-01 3.80175910e-03 -7.07357451e-02
6.84697926e-01 5.02257884e-01 4.51817155e-01 -7.67109394e-01
-3.96554589e-01 5.12713790e-01 3.06417933e-03 -5.46118081e-01
1.23618615e+00 -2.04838127e-01 4.48885709e-01 3.83570760e-01
1.48632717e+00 1.78502858e-01 -1.10898685e+00 -1.54049814e-01
-2.71111399e-01 -5.03276289e-02 4.44280654e-01 -1.15350926e+00
-1.10818064e+00 1.50525761e+00 9.64939058e-01 -1.20225204e-02
1.25194323e+00 1.64133027e-01 8.27107728e-01 2.62198240e-01
-2.08381057e-01 -1.02927458e+00 -9.25872102e-03 6.33716881e-01
1.15741408e+00 -1.19023609e+00 -6.94626451e-01 -1.98844627e-01
-5.80297709e-01 9.48061645e-01 3.30277532e-01 2.56283253e-01
4.79480147e-01 4.28972691e-01 1.22443087e-01 4.46515471e-01
-1.06740749e+00 -5.13994575e-01 5.05725384e-01 7.65843987e-01
5.46866059e-01 -4.64762077e-02 2.84857243e-01 3.24250460e-01
-9.97895598e-02 -9.28027555e-02 3.63378599e-02 2.33039513e-01
-3.21993560e-01 -1.02476990e+00 -2.50981301e-01 4.77448106e-02
-6.59008622e-01 -6.22287035e-01 -3.07547182e-01 5.86217046e-01
-6.08147308e-02 1.36171079e+00 1.53085291e-01 -4.60327893e-01
3.14050138e-01 4.47152108e-01 2.78493702e-01 -3.78425002e-01
-6.86315060e-01 3.06767285e-01 1.95096895e-01 -7.10084140e-01
-7.18036592e-02 -5.77105403e-01 -1.06763208e+00 -1.65744618e-01
-4.18005049e-01 -2.37264663e-01 9.75991130e-01 7.21377194e-01
6.23080730e-01 6.99217439e-01 4.63791996e-01 -9.22463596e-01
-5.99938273e-01 -1.27849233e+00 -1.17364421e-01 5.04466295e-02
7.65856087e-01 -2.86422193e-01 -6.27888680e-01 3.66114154e-02] | [14.36329174041748, 5.125904560089111] |
f64680e0-bd68-40ed-bf81-d14cb0db39c0 | parkinson-gait-modelling-from-an-anomaly-deep | 2301.11418 | null | https://arxiv.org/abs/2301.11418v1 | https://arxiv.org/pdf/2301.11418v1.pdf | Parkinson gait modelling from an anomaly deep representation | Parkinson's Disease is associated with gait movement disorders, such as postural instability, stiffness, and tremors. Today, some approaches implemented learning representations to quantify kinematic patterns during locomotion, supporting clinical procedures such as diagnosis and treatment planning. These approaches assumes a large amount of stratified and labeled data to optimize discriminative representations. Nonetheless, these considerations may restrict the operability of approaches in real scenarios during clinical practice. This work introduces a self-supervised generative representation, under the pretext of video reconstruction and anomaly detection framework. This architecture is trained following a one-class weakly supervised learning to avoid inter-class variance and approach the multiple relationships that represent locomotion. For validation 14 PD patients and 23 control subjects were recorded, and trained with the control population only, achieving an AUC of 86.9%, homoscedasticity level of 80% and shapeness level of 70% in the classification task considering its generalization. | ['Fabio Martinez', 'Edgar Rangel'] | 2023-01-26 | null | null | null | null | ['video-reconstruction'] | ['computer-vision'] | [ 1.78113252e-01 4.56397504e-01 -4.15985733e-01 -3.83319199e-01
-4.50516671e-01 -9.61987227e-02 3.03397268e-01 -9.29157436e-02
-5.09259701e-01 1.03954947e+00 4.32824045e-01 2.71307558e-01
-3.50900531e-01 -6.06523156e-01 -3.23285133e-01 -7.62506902e-01
-6.94818974e-01 7.98560262e-01 1.70847103e-01 -1.00970373e-01
-6.86902255e-02 2.85748303e-01 -1.75409794e+00 5.13428330e-01
1.04095840e+00 6.47195995e-01 1.18775792e-01 7.56755054e-01
3.91656101e-01 7.89093673e-01 -3.87427539e-01 -1.21965937e-01
2.79741418e-02 -4.55060393e-01 -6.30975246e-01 1.74468204e-01
9.39972922e-02 -4.49522346e-01 -3.06076676e-01 6.96213186e-01
7.10339725e-01 -6.52820244e-02 1.16763282e+00 -1.19632161e+00
-3.58907908e-01 4.12884682e-01 -2.14636132e-01 4.24360812e-01
4.01315808e-01 5.41658223e-01 9.18297052e-01 -4.08070177e-01
6.62593067e-01 9.41356659e-01 7.04035878e-01 6.11741424e-01
-1.26122153e+00 -1.33958220e-01 -1.37941793e-01 5.25517344e-01
-9.12194967e-01 -9.84834209e-02 3.81656826e-01 -8.87773991e-01
1.04936469e+00 6.74930364e-02 1.13627720e+00 1.42802393e+00
5.12475193e-01 7.78046131e-01 8.24667454e-01 -3.70688774e-02
5.58309495e-01 -3.13661397e-01 3.23433578e-01 4.05231714e-01
6.53385878e-01 8.20644349e-02 -3.41604769e-01 -1.93186611e-01
5.23440301e-01 -1.07766390e-01 -2.27829099e-01 -3.60960424e-01
-1.04888582e+00 8.66388083e-01 2.06875131e-01 5.33299983e-01
-6.77298069e-01 -4.27563190e-02 6.40774131e-01 1.80935532e-01
1.38980225e-01 9.09797102e-03 -9.04993713e-02 -5.01009583e-01
-1.04616594e+00 4.34166521e-01 4.78587568e-01 5.72432637e-01
2.07507133e-01 2.32448623e-01 -1.46737695e-01 6.01664901e-01
4.70278978e-01 6.01280332e-01 1.20299911e+00 -7.24047601e-01
5.01031101e-01 9.68802929e-01 -2.50053704e-01 -7.53872335e-01
-8.93322349e-01 -4.67521489e-01 -7.46729314e-01 3.81361425e-01
3.35165143e-01 -5.81385382e-02 -9.36190307e-01 1.64518011e+00
7.58775920e-02 -2.79268146e-01 9.88273397e-02 7.79140890e-01
3.87444288e-01 1.08696654e-01 2.81683534e-01 -3.72087568e-01
1.03680527e+00 -3.44559640e-01 -6.09193504e-01 -4.59389716e-01
8.75273645e-01 -2.20750317e-01 1.16514754e+00 4.94021177e-01
-8.57536376e-01 -3.22367221e-01 -9.50052261e-01 2.12877974e-01
-1.92301404e-02 4.82252538e-01 3.51471454e-01 6.92215204e-01
-6.60110950e-01 8.90574276e-01 -1.35997236e+00 -5.94612777e-01
6.78458035e-01 5.77678382e-01 -4.68749553e-01 2.69271135e-01
-8.47562194e-01 1.13116300e+00 5.62920511e-01 1.43642083e-01
-4.75566030e-01 -2.98726201e-01 -4.75243360e-01 -3.32108557e-01
-4.03879672e-01 -9.61850703e-01 6.90946817e-01 -5.31051815e-01
-1.33577442e+00 9.49187636e-01 3.01769018e-01 -8.10788751e-01
1.07358658e+00 -6.31708086e-01 -3.59421194e-01 1.73610464e-01
2.64569461e-01 2.81687558e-01 7.49210835e-01 -7.13147283e-01
-4.51509237e-01 -8.05185258e-01 -4.06220734e-01 3.18464756e-01
3.22822705e-02 -4.99216616e-01 2.43279740e-01 -5.96567333e-01
3.77510160e-01 -1.11607897e+00 -2.41592616e-01 8.29920247e-02
-3.60369861e-01 8.40850994e-02 7.30595410e-01 -8.13600123e-01
1.11883497e+00 -1.94143343e+00 4.59009141e-01 2.35395700e-01
2.63203919e-01 2.53164649e-01 3.65869105e-01 2.09133983e-01
-2.32373610e-01 -3.98429632e-01 -7.04064906e-01 -3.05366646e-02
1.21728040e-03 4.44915354e-01 8.86501670e-02 8.53279114e-01
2.29314536e-01 7.26124167e-01 -8.44927430e-01 -3.49180400e-01
5.15399337e-01 2.53950387e-01 -7.73495138e-01 2.01451257e-01
-1.16597012e-01 5.78120589e-01 -5.19172251e-01 6.90329909e-01
1.34532481e-01 -1.13423802e-01 3.07276547e-01 -2.24002883e-01
3.30282450e-01 -4.99821119e-02 -1.06254864e+00 1.36209941e+00
-8.24233517e-03 6.12283051e-01 -3.70020092e-01 -1.25254118e+00
7.96623826e-01 1.22023918e-01 1.01918471e+00 -5.17791688e-01
3.99436742e-01 5.25957942e-01 5.50506949e-01 -1.01716197e+00
-1.61073953e-01 3.19216475e-02 2.33621493e-01 2.42087111e-01
3.42187546e-02 3.12551498e-01 4.12327230e-01 -1.51060775e-01
1.26581311e+00 4.91200328e-01 5.15185714e-01 -3.03686798e-01
4.20772314e-01 4.26028669e-02 3.59202802e-01 2.69551456e-01
-3.68585676e-01 7.41678178e-01 4.41163182e-01 -4.14235860e-01
-1.01653290e+00 -1.27572441e+00 -2.85951525e-01 5.31912506e-01
-3.84900630e-01 -1.99456155e-01 -7.09292889e-01 -3.95998567e-01
1.08526096e-01 6.55820787e-01 -6.65971279e-01 -5.72108090e-01
-7.47287393e-01 -1.19762158e+00 5.40492535e-01 8.79910171e-01
1.30664229e-01 -1.07089663e+00 -1.16774464e+00 4.18430537e-01
-1.55196160e-01 -7.83659399e-01 3.96057546e-01 3.98451626e-01
-1.31834531e+00 -1.51210284e+00 -8.73043299e-01 -5.48236847e-01
4.96646017e-01 -6.61914408e-01 6.15154624e-01 -4.41098958e-01
-4.58105326e-01 3.53636354e-01 -3.31946611e-01 1.36662498e-01
-4.52128857e-01 -8.36930498e-02 3.21194440e-01 -2.83430099e-01
2.94087589e-01 -1.05132711e+00 -7.28096068e-01 2.35938832e-01
-4.97705549e-01 -4.00872946e-01 7.37768590e-01 8.41948330e-01
7.22183883e-01 -4.01081741e-01 3.90691042e-01 -3.99742663e-01
6.51977956e-01 -5.53734422e-01 -2.49403110e-03 -4.60555032e-02
-7.10436523e-01 3.56550999e-02 3.70927066e-01 -5.80350459e-01
-3.72567922e-01 2.83546358e-01 -1.49447709e-01 -4.41793770e-01
-2.52940625e-01 3.83842647e-01 -1.53555870e-01 3.36034596e-01
1.03229022e+00 3.19993407e-01 4.47762787e-01 -2.55406946e-01
1.73426256e-01 6.78571284e-01 6.72062755e-01 -3.23412925e-01
4.98155385e-01 3.54647845e-01 6.63468465e-02 -1.01866388e+00
-2.22692057e-01 -3.14063638e-01 -8.53770375e-01 -4.44503993e-01
9.48185563e-01 -7.34481037e-01 -4.29817289e-01 4.35332835e-01
-5.78003824e-01 -3.48433256e-01 -6.23721421e-01 1.06829202e+00
-1.36442673e+00 6.08774662e-01 -2.53112406e-01 -7.57497191e-01
-5.17789721e-01 -1.15477765e+00 8.58853042e-01 -6.48354664e-02
-8.47660303e-01 -6.94294453e-01 4.27311927e-01 2.10953563e-01
2.17115656e-01 7.88230062e-01 9.17081594e-01 -8.72057736e-01
7.81903267e-02 -3.60418856e-01 2.46530443e-01 2.56875485e-01
3.72346371e-01 -3.34772728e-02 -8.10227275e-01 -2.43390843e-01
-6.09980300e-02 -3.53251427e-01 5.49730897e-01 6.83032572e-01
6.51938260e-01 -2.11262241e-01 -2.25859687e-01 2.30434582e-01
1.05848396e+00 3.61514866e-01 9.52304184e-01 5.42544723e-01
6.18840635e-01 5.94945908e-01 2.38311887e-01 5.27679503e-01
1.00441247e-01 6.52758539e-01 4.60278302e-01 4.88275766e-01
-1.39230996e-01 6.97811916e-02 5.47439396e-01 5.67908823e-01
-5.94130635e-01 -8.86307880e-02 -9.71023560e-01 3.86825681e-01
-2.02260804e+00 -1.22822607e+00 -3.19567025e-01 2.14503050e+00
1.52064562e-01 3.80872905e-01 6.92455471e-01 6.42248273e-01
6.18220329e-01 -1.86053380e-01 -7.81453013e-01 -5.87071814e-02
-1.06561154e-01 -6.58833012e-02 3.49001765e-01 5.44960648e-02
-9.47672963e-01 4.20989007e-01 6.38513231e+00 8.13444033e-02
-1.07515192e+00 -1.56271756e-01 2.14356527e-01 -6.41152933e-02
1.80175588e-01 -5.68947971e-01 -3.33200693e-01 8.58168483e-01
1.26042140e+00 -2.40184274e-02 1.09500699e-01 9.85975623e-01
5.86988449e-01 -7.85575993e-03 -1.10640144e+00 9.71677423e-01
-9.49925780e-02 -7.85637736e-01 -4.61624600e-02 2.99289316e-01
1.96109980e-01 2.42708474e-01 -1.04730323e-01 3.06314439e-01
-2.02765182e-01 -9.70512390e-01 5.65387726e-01 7.87781119e-01
4.89713281e-01 -4.15465236e-01 8.82224977e-01 2.76606113e-01
-1.03348589e+00 -2.76555568e-01 1.40220523e-02 1.34504005e-01
4.15357709e-01 2.95833230e-01 -7.25082397e-01 4.51037109e-01
5.76389790e-01 9.57435250e-01 -4.70274806e-01 1.20240664e+00
-1.54690787e-01 5.55345356e-01 -6.72137618e-01 6.49229735e-02
1.89626545e-01 -2.86575973e-01 7.47686803e-01 8.64149690e-01
3.96654248e-01 -1.66625455e-01 -1.77230705e-02 6.12413764e-01
7.11561203e-01 3.28579515e-01 -5.84658146e-01 -1.63505599e-01
-4.04329076e-02 4.43178743e-01 -7.76138783e-01 1.48015656e-02
-1.66188836e-01 8.70813310e-01 7.45903626e-02 -5.61060123e-02
-9.09161270e-01 1.71650827e-01 4.34407741e-01 4.43459481e-01
-4.27118577e-02 1.12121331e-03 -3.82889032e-01 -1.05450702e+00
2.86817878e-01 -8.00433278e-01 6.73805058e-01 -4.58799779e-01
-1.21866143e+00 5.24690032e-01 2.79432327e-01 -1.93385565e+00
-8.66800785e-01 -8.96208227e-01 -6.93634808e-01 4.15528268e-01
-6.55447662e-01 -1.08126605e+00 -2.45880619e-01 7.12503195e-01
3.40495139e-01 -4.30298775e-01 8.80787134e-01 2.90333182e-01
-5.05823612e-01 1.78533807e-01 1.93628669e-01 -3.34121194e-03
3.20535421e-01 -1.23083210e+00 -2.15483040e-01 5.49768567e-01
-2.39408910e-01 4.76923913e-01 8.41034770e-01 -8.98235142e-01
-9.30254757e-01 -9.65213597e-01 5.78756571e-01 -1.76370770e-01
7.43418336e-01 3.49714935e-01 -8.81583810e-01 5.69271982e-01
-5.23300231e-01 -9.27638412e-02 8.98845851e-01 -1.63211063e-01
1.50993407e-01 2.89008379e-01 -1.40349019e+00 5.72040498e-01
1.13494313e+00 -2.69163668e-01 -1.13003016e+00 5.42566717e-01
-3.37027222e-01 -4.91393119e-01 -1.16854846e+00 5.91904819e-01
9.59168911e-01 -9.54005122e-01 8.85980546e-01 -8.19185317e-01
4.88176316e-01 -1.90856099e-01 -3.59110832e-01 -9.20217931e-01
-1.62205413e-01 -1.14947379e-01 -4.94450122e-01 4.60313052e-01
4.09497589e-01 -4.62681323e-01 1.12109184e+00 4.75191772e-01
-3.21996927e-01 -8.59891117e-01 -1.09865689e+00 -8.05289209e-01
1.41021445e-01 -4.47044462e-01 -1.24117248e-01 5.07955492e-01
1.38896555e-01 1.61011189e-01 -2.95606226e-01 4.82723750e-02
6.23106480e-01 -2.01516643e-01 4.88043010e-01 -1.54268146e+00
-1.86383128e-01 -4.54127133e-01 -1.44993007e+00 -9.54089388e-02
-3.30162421e-02 -9.37278926e-01 -2.72098273e-01 -1.92036176e+00
-1.45990863e-01 8.26236680e-02 -7.07898587e-02 4.43150342e-01
1.99316725e-01 2.37834118e-02 -2.23402813e-01 6.77466214e-01
-2.17754114e-02 6.78698421e-01 9.83482182e-01 -1.61690429e-01
-5.51544785e-01 4.23085749e-01 -5.52821457e-02 9.55136657e-01
1.04061127e+00 -4.52500552e-01 -8.17302704e-01 9.59655643e-02
-1.64902434e-01 -2.09297851e-01 4.13632095e-01 -1.51009297e+00
-3.21519762e-01 5.46011552e-02 5.22575676e-01 -6.23957098e-01
4.10545915e-01 -7.45780051e-01 4.83665973e-01 1.22078097e+00
-8.17711949e-02 1.40791608e-03 -8.43185037e-02 6.95278466e-01
1.20452698e-02 -1.34014741e-01 9.47451532e-01 -1.34089634e-01
-6.15537226e-01 1.43576100e-01 -6.44026399e-01 2.00182825e-01
1.16781723e+00 -8.07895064e-01 -6.98905811e-02 -1.34865746e-01
-1.51558888e+00 -6.67328089e-02 2.05744326e-01 1.98806018e-01
4.42683101e-01 -1.16632092e+00 -5.31021953e-01 -4.13493346e-03
2.72376090e-01 -2.10665554e-01 3.59721184e-01 1.03363299e+00
-6.89632654e-01 1.84356362e-01 -9.14220393e-01 -9.00610864e-01
-9.69964087e-01 -1.11083739e-01 4.82575446e-01 -2.45754153e-01
-1.30544424e+00 1.31496072e-01 -4.42384720e-01 -3.76532897e-02
1.69908062e-01 -5.89116573e-01 -5.62791109e-01 3.47128212e-01
3.15342337e-01 7.46283889e-01 2.88675457e-01 -8.47348273e-01
-5.28956950e-01 5.60591221e-01 7.43179843e-02 2.52099186e-02
1.34854162e+00 6.78571090e-02 4.27100480e-01 5.93576729e-01
8.47108781e-01 -5.82938910e-01 -9.73953664e-01 4.53151464e-01
1.76934928e-01 6.89844266e-02 -1.61533982e-01 -7.03728080e-01
-8.46194565e-01 6.00282848e-01 1.45152104e+00 -4.62318510e-02
7.34504282e-01 -1.57804340e-01 4.23048943e-01 6.16709769e-01
4.26303208e-01 -1.12517083e+00 -1.17114894e-01 1.62032589e-01
8.42351079e-01 -1.02128458e+00 1.19616464e-01 -1.40918523e-01
-7.71887004e-01 1.24780285e+00 4.93929654e-01 -5.32602310e-01
3.87440056e-01 1.40598476e-01 -1.13493197e-01 -2.47011930e-01
-3.51562679e-01 -3.03160667e-01 3.12482834e-01 1.01153684e+00
4.52728838e-01 1.77490026e-01 -8.22485745e-01 6.68335795e-01
-3.97757322e-01 3.01363528e-01 2.34218165e-01 1.19805110e+00
-5.80436468e-01 -7.42144048e-01 -2.68008243e-02 8.98132801e-01
-1.79346338e-01 4.47659105e-01 -1.84892550e-01 1.18186998e+00
1.38153329e-01 3.88094246e-01 1.04962341e-01 -3.81721675e-01
6.57538712e-01 2.54763544e-01 5.97617447e-01 -5.07847190e-01
-3.84456724e-01 -5.16091734e-02 1.82840139e-01 -6.51009500e-01
-6.17857754e-01 -9.17760789e-01 -1.49754918e+00 3.95258702e-02
8.93442854e-02 -9.45434123e-02 1.65230840e-01 1.06111217e+00
8.05739015e-02 5.10972500e-01 1.49459466e-01 -7.72798359e-01
-6.26161218e-01 -1.20952761e+00 -6.23802900e-01 5.34023106e-01
1.03877813e-01 -1.26831126e+00 -4.29689914e-01 2.50237435e-01] | [7.1380205154418945, 0.3367210924625397] |
1eae87c0-7fa0-4f4d-a982-d8c98575c1b2 | constructing-a-multi-hop-qa-dataset-for | 2011.01060 | null | https://arxiv.org/abs/2011.01060v2 | https://arxiv.org/pdf/2011.01060v2.pdf | Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps | A multi-hop question answering (QA) dataset aims to test reasoning and inference skills by requiring a model to read multiple paragraphs to answer a given question. However, current datasets do not provide a complete explanation for the reasoning process from the question to the answer. Further, previous studies revealed that many examples in existing multi-hop datasets do not require multi-hop reasoning to answer a question. In this study, we present a new multi-hop QA dataset, called 2WikiMultiHopQA, which uses structured and unstructured data. In our dataset, we introduce the evidence information containing a reasoning path for multi-hop questions. The evidence information has two benefits: (i) providing a comprehensive explanation for predictions and (ii) evaluating the reasoning skills of a model. We carefully design a pipeline and a set of templates when generating a question-answer pair that guarantees the multi-hop steps and the quality of the questions. We also exploit the structured format in Wikidata and use logical rules to create questions that are natural but still require multi-hop reasoning. Through experiments, we demonstrate that our dataset is challenging for multi-hop models and it ensures that multi-hop reasoning is required. | ['Akiko Aizawa', 'Saku Sugawara', 'Anh-Khoa Duong Nguyen', 'Xanh Ho'] | 2020-11-02 | null | https://aclanthology.org/2020.coling-main.580 | https://aclanthology.org/2020.coling-main.580.pdf | coling-2020-8 | ['multi-hop-question-answering'] | ['knowledge-base'] | [-5.18569350e-02 8.53326201e-01 9.72804800e-02 -7.01576710e-01
-1.00367427e+00 -8.05751681e-01 4.92996544e-01 2.19759807e-01
2.26756245e-01 7.61010706e-01 2.87910104e-01 -9.36734200e-01
-4.89645243e-01 -1.21793294e+00 -9.47527230e-01 4.89779860e-01
4.76480454e-01 7.96216011e-01 7.84759283e-01 -5.37305415e-01
3.11689228e-01 -1.85123011e-01 -1.68510723e+00 9.80460763e-01
1.47104430e+00 7.62769520e-01 -5.56775555e-03 1.07393336e+00
-5.59257269e-01 1.73132908e+00 -5.61487079e-01 -9.97991085e-01
8.60618800e-02 -7.70550132e-01 -1.68814003e+00 -5.00560343e-01
8.31662178e-01 -5.07347465e-01 5.80004528e-02 5.20987153e-01
4.54777339e-03 1.91086791e-02 4.34815019e-01 -1.55688667e+00
-7.99405277e-01 9.37778354e-01 4.48905557e-01 3.50198224e-02
1.02461910e+00 2.75143325e-01 1.37739527e+00 -5.53412199e-01
7.76889145e-01 1.16986930e+00 7.06918955e-01 6.64612770e-01
-7.03459203e-01 -3.06072354e-01 3.70964175e-03 8.21755052e-01
-6.75821662e-01 -3.80995482e-01 5.65050006e-01 -5.21490276e-01
1.14988148e+00 5.90994895e-01 5.09983182e-01 9.13687170e-01
1.81440234e-01 5.74785531e-01 1.01481462e+00 -5.02172351e-01
1.53675824e-01 2.68945158e-01 5.86219251e-01 1.04263723e+00
5.27083874e-02 -4.45992082e-01 -5.72821736e-01 -9.52290669e-02
2.73583442e-01 -3.96373391e-01 1.33349448e-02 3.97716388e-02
-9.67291892e-01 7.80351698e-01 2.22796828e-01 6.36080131e-02
-1.72977000e-01 -1.45081416e-01 6.47473335e-02 6.16755962e-01
-3.99693280e-01 9.68250096e-01 -8.62800419e-01 -2.99600005e-01
-5.95277548e-01 7.15512276e-01 1.45249653e+00 1.22218168e+00
7.11711645e-01 -6.50751114e-01 -5.68442166e-01 3.74231130e-01
3.65719318e-01 3.78483623e-01 2.27936953e-01 -1.59142423e+00
8.47892702e-01 1.28411329e+00 2.56931961e-01 -9.64418948e-01
-3.92890424e-01 -1.62954867e-01 -1.57324255e-01 -7.41721168e-02
6.75113320e-01 -8.11629370e-02 -3.25001627e-01 1.64741099e+00
4.91462648e-01 -9.86765549e-02 4.29150760e-01 6.29817724e-01
1.52246261e+00 4.51633632e-01 -7.80317485e-02 2.31622472e-01
1.54051244e+00 -1.36701906e+00 -6.96124971e-01 -4.21583951e-01
8.32466066e-01 -4.69816595e-01 1.61221552e+00 3.31672490e-01
-1.43094110e+00 -6.23845041e-01 -8.56905162e-01 -6.22126341e-01
-3.95254880e-01 -3.74974728e-01 5.13496220e-01 1.76351681e-01
-7.31149137e-01 3.37373883e-01 -1.99421123e-01 -2.13411227e-01
-1.01273350e-01 -2.32817844e-01 -2.38399535e-01 -4.09129739e-01
-1.67787743e+00 1.18891668e+00 3.09727341e-01 -3.32710713e-01
-5.69645762e-01 -1.09178400e+00 -1.05100870e+00 3.66808295e-01
5.81860542e-01 -1.05670011e+00 1.65279150e+00 -3.72522116e-01
-1.27175379e+00 4.44883138e-01 -3.41036856e-01 -3.46931547e-01
5.84370732e-01 -2.33305678e-01 -3.13923925e-01 3.01561207e-01
4.16640997e-01 6.34525537e-01 8.80815983e-02 -1.03308749e+00
-6.10770404e-01 -1.40423596e-01 1.02042580e+00 -6.63491189e-02
8.64348933e-02 -1.58159748e-01 -5.15432656e-01 -3.37717608e-02
3.02717034e-02 -3.86005312e-01 1.78495318e-01 -1.35523781e-01
-5.27305961e-01 -4.87675637e-01 4.31298643e-01 -9.47915435e-01
1.50573349e+00 -1.40170085e+00 -1.57211557e-01 -6.64409921e-02
3.03423166e-01 4.86932546e-02 -3.48538399e-01 7.41501808e-01
2.61008352e-01 4.48442966e-01 -1.73209071e-01 9.40118060e-02
4.42878932e-01 3.80194426e-01 -4.70068902e-01 -7.45090961e-01
4.77610767e-01 1.23345697e+00 -8.69299531e-01 -9.62823272e-01
-1.73311949e-01 -1.83796585e-01 -1.04928708e+00 5.90630114e-01
-1.04705393e+00 2.08089769e-01 -3.89323384e-01 5.35137951e-01
4.62842256e-01 -6.66651487e-01 7.99258202e-02 9.93028190e-03
2.23167583e-01 9.43506062e-01 -1.03127265e+00 1.52333641e+00
-6.44249141e-01 4.58680272e-01 -3.95760804e-01 -2.60322154e-01
8.20875943e-01 2.54757166e-01 -1.75313145e-01 -9.51567829e-01
-3.65559757e-01 2.38723770e-01 6.17735386e-02 -1.24111164e+00
4.42943513e-01 2.88279988e-02 2.46992633e-02 6.56559706e-01
-7.79794157e-02 -4.66711640e-01 7.84384847e-01 4.72664058e-01
1.53468037e+00 1.87006369e-01 1.37870416e-01 9.86661017e-02
7.82447994e-01 5.03953874e-01 5.73604584e-01 1.01321125e+00
1.42577365e-01 3.87383491e-01 7.94838786e-01 -3.78102481e-01
-7.57148206e-01 -9.68203306e-01 6.16579987e-02 8.83932889e-01
2.62899809e-02 -8.67320359e-01 -7.61248708e-01 -1.08082998e+00
-4.25413661e-02 1.38583493e+00 -5.50724745e-01 5.93598410e-02
-5.17484486e-01 3.68548661e-01 7.94857383e-01 5.90507269e-01
7.92058647e-01 -1.09064162e+00 -7.85233438e-01 5.47073968e-02
-8.50170612e-01 -1.23373449e+00 2.12379433e-02 -1.72932357e-01
-6.50955081e-01 -1.67924583e+00 1.87644348e-01 -5.78015745e-01
5.70234716e-01 -1.03549818e-02 1.74129963e+00 8.41265440e-01
3.24892014e-01 4.91971731e-01 -6.32094324e-01 -4.81448144e-01
-7.01583505e-01 9.44656134e-02 -6.93144977e-01 -7.29809701e-01
5.35837412e-01 -3.78149778e-01 -4.35421556e-01 5.34514010e-01
-9.38285112e-01 5.03515959e-01 4.00186121e-01 5.27510405e-01
2.78261214e-01 3.89526389e-03 6.03538573e-01 -1.05554533e+00
8.71274829e-01 -6.21189177e-01 -4.21509296e-01 9.94971812e-01
-7.43544340e-01 3.53532404e-01 9.87621307e-01 1.42505199e-01
-1.12218809e+00 -5.68993151e-01 -3.69446367e-01 3.69639963e-01
-1.97660923e-01 8.25130939e-01 -1.73649639e-01 4.27743912e-01
7.99018025e-01 -1.18490890e-01 -2.31915936e-01 -2.66892105e-01
5.37415564e-01 4.35650021e-01 5.26840389e-01 -9.62975800e-01
8.90649498e-01 -1.39032990e-01 -1.89365014e-01 -2.19168030e-02
-1.40672398e+00 -1.84749529e-01 -4.34897214e-01 -2.62931496e-01
8.32524061e-01 -4.78792816e-01 -1.16623187e+00 -3.29332471e-01
-1.35911167e+00 -7.02035248e-01 -2.83377588e-01 7.06383958e-02
-6.12915635e-01 2.02385291e-01 -4.50026006e-01 -5.61367691e-01
-3.41868490e-01 -9.62364733e-01 5.86936474e-01 2.68014163e-01
-7.48581588e-01 -1.09215271e+00 8.26526359e-02 1.14968431e+00
3.78426611e-01 -5.45661263e-02 1.68433452e+00 -8.21326554e-01
-6.68115854e-01 1.16921470e-01 -2.08458290e-01 1.50976911e-01
-1.53279722e-01 1.98823720e-01 -7.69913614e-01 5.74539423e-01
-1.97948158e-01 -7.50665903e-01 1.60617769e-01 -2.25863114e-01
1.18641949e+00 -7.01512754e-01 2.67193228e-01 5.09573109e-02
1.04283834e+00 -1.81024984e-01 8.43162060e-01 5.94602764e-01
1.64460987e-01 9.93599296e-01 6.95890427e-01 4.49222513e-02
1.24939942e+00 3.19383264e-01 3.72163117e-01 3.43790472e-01
-9.13355649e-02 -7.10321724e-01 1.22552879e-01 8.38408172e-01
4.27109629e-01 -1.74936220e-01 -1.29887950e+00 6.75302505e-01
-1.88531983e+00 -1.13746285e+00 -6.59900844e-01 1.64402807e+00
1.18464553e+00 8.45896155e-02 -2.59873748e-01 1.18962474e-01
-4.23723608e-02 -2.95289755e-01 -5.40326893e-01 -5.77773452e-01
-2.64593530e-02 2.52245426e-01 -4.62789625e-01 9.60331321e-01
-2.46913925e-01 8.51521611e-01 6.62227392e+00 2.94758230e-01
-2.81424820e-01 -2.33471110e-01 1.02070319e-02 2.96748310e-01
-1.10433912e+00 3.23097020e-01 -8.02239001e-01 9.36426744e-02
1.07658947e+00 -2.17764631e-01 2.96482682e-01 7.36249387e-01
4.49507385e-02 -1.92339912e-01 -1.37808919e+00 2.83738941e-01
-4.10509594e-02 -1.55266392e+00 5.02078593e-01 -5.49943388e-01
4.65144485e-01 -6.63130403e-01 -3.46526355e-01 8.49250138e-01
4.98417199e-01 -1.20292675e+00 8.10715020e-01 9.83696401e-01
2.90458083e-01 -4.40737367e-01 8.22611690e-01 7.74028420e-01
-8.59222054e-01 -2.76228666e-01 -1.52846485e-01 -4.51904714e-01
1.48236483e-01 4.07579601e-01 -9.69959080e-01 7.35238850e-01
6.68443859e-01 2.21359715e-01 -1.13330138e+00 8.14829707e-01
-9.77489293e-01 5.88528395e-01 -1.59833636e-02 -1.83820367e-01
9.44115967e-02 4.38778773e-02 -5.16424794e-03 8.69268775e-01
2.93057650e-01 4.29553270e-01 -5.87039702e-02 1.15349591e+00
-1.84876367e-01 -6.51947856e-02 -3.16262007e-01 -9.15277079e-02
6.98588312e-01 1.01757681e+00 -1.64595980e-03 -5.12757599e-01
-5.15117109e-01 5.22634387e-01 6.85126483e-01 1.26581699e-01
-8.79808009e-01 -4.76472348e-01 4.64825779e-01 2.71510273e-01
-5.30126737e-03 3.27658392e-02 -5.10583103e-01 -1.12526405e+00
5.19259274e-01 -1.40316141e+00 6.27590179e-01 -1.56689787e+00
-1.34253907e+00 4.72442031e-01 -1.77917127e-02 -7.14406788e-01
-5.55169702e-01 -6.23944998e-01 -7.21423507e-01 8.90046239e-01
-1.85821033e+00 -1.19082046e+00 -8.80491912e-01 4.94315803e-01
5.21735251e-01 3.08286369e-01 1.02351367e+00 1.36026591e-01
-2.95892417e-01 4.66829598e-01 -8.35235953e-01 1.39072612e-01
7.17553973e-01 -1.60161543e+00 4.18312669e-01 7.05577016e-01
-8.08329210e-02 1.09723508e+00 7.97121823e-01 -6.99536562e-01
-1.40851843e+00 -9.34937835e-01 1.42530835e+00 -9.59300160e-01
6.99542940e-01 7.95224458e-02 -1.20786524e+00 9.55059886e-01
3.94330323e-01 -4.98992056e-01 1.05910778e+00 3.36110979e-01
-5.82181156e-01 2.20868550e-02 -1.14545667e+00 6.75139725e-01
9.38710511e-01 -6.85211480e-01 -1.47791970e+00 4.24413174e-01
1.04219151e+00 -5.44197023e-01 -1.03740811e+00 4.10505325e-01
6.14978313e-01 -1.22195017e+00 6.05747342e-01 -1.22526276e+00
1.23281014e+00 -5.46789169e-01 -1.08222522e-01 -1.00574577e+00
-1.78534731e-01 -4.05931652e-01 -5.19697309e-01 1.24424696e+00
1.07938063e+00 -3.55220318e-01 5.42008877e-01 1.28890955e+00
-1.32625923e-01 -1.00206172e+00 -3.72162163e-01 -5.54576159e-01
2.24356636e-01 -7.83789217e-01 1.05864727e+00 8.29583108e-01
3.06288987e-01 5.02548635e-01 1.34683639e-01 4.22002494e-01
2.36282110e-01 3.89438689e-01 1.10101342e+00 -1.22817957e+00
-4.69918489e-01 -1.46915257e-01 2.97830671e-01 -1.20892382e+00
1.52196079e-01 -8.87030900e-01 -1.91163383e-02 -2.42002869e+00
1.68071851e-01 -3.63782346e-01 2.76852190e-01 5.40008485e-01
-3.86378139e-01 -4.81573969e-01 4.34015617e-02 -7.09095970e-02
-9.25989628e-01 8.59375298e-02 1.41158855e+00 -6.22946862e-03
2.14883596e-01 -1.96375951e-01 -1.09427428e+00 7.94035614e-01
6.56261206e-01 -3.53235036e-01 -7.43703663e-01 -8.63406122e-01
1.00742972e+00 2.42086649e-01 5.86862445e-01 -9.99118507e-01
5.74673474e-01 -4.16701645e-01 1.29959270e-01 -6.04643762e-01
1.93641633e-01 -6.80761576e-01 -1.32463306e-01 1.92394212e-01
-8.72502685e-01 3.65033507e-01 1.39367670e-01 7.03526586e-02
-3.86487991e-01 -7.05400586e-01 3.86387438e-01 -3.07398468e-01
-5.96879005e-01 -1.47915661e-01 -1.60398841e-01 6.08523786e-01
9.17918563e-01 9.07566175e-02 -8.82276118e-01 -5.50617814e-01
-4.66915727e-01 9.83436167e-01 2.83895046e-01 3.58749211e-01
6.58518851e-01 -1.07115161e+00 -7.11364508e-01 -1.36621401e-01
4.53150243e-01 1.05869532e-01 2.69672155e-01 5.05583942e-01
-7.35156357e-01 5.77337682e-01 -1.91690162e-01 -2.16403127e-01
-1.20040858e+00 3.02098125e-01 4.30825382e-01 -5.91440260e-01
-4.00321096e-01 7.08818853e-01 -4.44709808e-01 -1.15680516e+00
-3.10197398e-02 -5.55429339e-01 -3.65039170e-01 -1.24502324e-01
7.69632041e-01 3.19745690e-01 2.21156463e-01 2.11371005e-01
-1.13348596e-01 3.77918035e-01 1.38391078e-01 -8.73072743e-02
1.18307316e+00 -8.36911649e-02 -2.79395252e-01 4.60450560e-01
6.30991936e-01 1.76262364e-01 -8.04273486e-01 -1.77447721e-01
2.62842029e-01 -3.77746910e-01 -5.41345239e-01 -1.49576807e+00
-1.94187999e-01 9.08512831e-01 -4.61847007e-01 5.83030581e-01
7.11502910e-01 1.95612118e-01 9.50255454e-01 9.90382552e-01
2.16987386e-01 -9.30920601e-01 3.44806820e-01 1.04806173e+00
1.36461592e+00 -1.21218276e+00 -3.34932595e-01 -5.13232648e-01
-6.52021885e-01 1.24035668e+00 1.32559502e+00 5.80842733e-01
7.64020383e-02 1.89843684e-01 4.30576265e-01 -5.13601840e-01
-1.45825887e+00 -4.83232886e-02 3.67734402e-01 4.72208053e-01
5.25739610e-01 -3.24397951e-01 -5.11863679e-02 1.01103771e+00
-9.72348690e-01 1.11709952e-01 6.40300214e-01 1.00873077e+00
-6.18789732e-01 -1.07682312e+00 -2.97606021e-01 3.99880767e-01
3.46487686e-02 -1.04983799e-01 -8.90878975e-01 8.06400418e-01
1.54893827e-02 1.57940054e+00 -3.04820776e-01 -3.00165653e-01
7.18886077e-01 5.88236511e-01 4.22870010e-01 -7.23984063e-01
-8.28915238e-01 -1.23334742e+00 6.20344639e-01 -6.88872099e-01
-1.19157331e-02 -6.11212887e-02 -1.56821001e+00 -6.20973825e-01
-1.94164533e-02 5.32751083e-01 3.27317268e-01 1.32631814e+00
6.03675902e-01 6.51684284e-01 1.00017548e-01 3.82714242e-01
-5.76064110e-01 -9.22303617e-01 1.66433856e-01 4.72039312e-01
6.84170285e-04 -2.97490090e-01 -2.60463417e-01 1.05537832e-01] | [10.992527961730957, 7.908002853393555] |
5887fac9-2784-4066-8562-4623bc2bdede | learning-speaker-representation-with-semi | 2110.13653 | null | https://arxiv.org/abs/2110.13653v1 | https://arxiv.org/pdf/2110.13653v1.pdf | Learning Speaker Representation with Semi-supervised Learning approach for Speaker Profiling | Speaker profiling, which aims to estimate speaker characteristics such as age and height, has a wide range of applications inforensics, recommendation systems, etc. In this work, we propose a semisupervised learning approach to mitigate the issue of low training data for speaker profiling. This is done by utilizing external corpus with speaker information to train a better representation which can help to improve the speaker profiling systems. Specifically, besides the standard supervised learning path, the proposed framework has two more paths: (1) an unsupervised speaker representation learning path that helps to capture the speaker information; (2) a consistency training path that helps to improve the robustness of the system by enforcing it to produce similar predictions for utterances of the same speaker.The proposed approach is evaluated on the TIMIT and NISP datasets for age, height, and gender estimation, while the Librispeech is used as the unsupervised external corpus. Trained both on single-task and multi-task settings, our approach was able to achieve state-of-the-art results on age estimation on the TIMIT Test dataset with Root Mean Square Error(RMSE) of6.8 and 7.4 years and Mean Absolute Error(MAE) of 4.8 and5.0 years for male and female speakers respectively. | ['Chng Eng Siong', 'Pham Van Tung', 'Shangeth Rajaa'] | 2021-10-24 | null | null | null | null | ['age-estimation', 'age-estimation', 'speaker-profiling'] | ['computer-vision', 'miscellaneous', 'speech'] | [ 1.29875541e-01 4.66123611e-01 -1.46042362e-01 -7.43001640e-01
-8.84254813e-01 -1.34062499e-01 6.88729644e-01 3.97519886e-01
-2.78671503e-01 7.02968955e-01 3.52056235e-01 5.64930476e-02
-2.61165828e-01 -4.68307763e-01 -2.45415792e-01 -8.33708644e-01
2.72578862e-03 5.74605644e-01 4.86884937e-02 8.23585242e-02
2.48717397e-01 1.61974117e-01 -1.90714586e+00 -8.68947878e-02
1.18812096e+00 1.06025624e+00 -1.18411586e-01 4.80256379e-01
-2.50675797e-01 7.98889995e-01 -5.55350065e-01 -5.30633926e-01
-2.57465452e-01 -5.50302006e-02 -8.14286530e-01 1.66933119e-01
5.77094436e-01 5.63315414e-02 2.73132443e-01 8.25616956e-01
7.27914631e-01 7.21553117e-02 8.84395003e-01 -1.17652321e+00
-1.76100314e-01 1.10200286e+00 -6.41960144e-01 5.89173026e-02
2.40081519e-01 -4.89763588e-01 6.68879390e-01 -5.76024771e-01
1.31510615e-01 1.45344138e+00 4.99656230e-01 6.98808551e-01
-9.21885073e-01 -9.37435389e-01 -4.02669510e-04 8.95538777e-02
-1.47745883e+00 -9.31357563e-01 7.81817138e-01 -6.36364281e-01
3.36160898e-01 2.33606279e-01 2.70207822e-01 1.05682635e+00
-1.70801774e-01 6.36360466e-01 1.28688073e+00 -5.12329042e-01
1.95367634e-01 6.62301183e-01 4.73236501e-01 3.00547034e-01
-1.09168030e-01 -8.83160010e-02 -7.70972133e-01 -1.30842358e-01
2.18182728e-01 -4.52782482e-01 1.81144953e-01 -1.53954804e-01
-7.58071184e-01 8.40285122e-01 -1.21250585e-01 2.88534909e-01
-2.49575302e-01 -3.12783211e-01 5.12155235e-01 1.55182872e-02
8.16589594e-01 -1.41829038e-02 -3.79387170e-01 -2.31354997e-01
-1.16256249e+00 2.03893229e-01 8.51185143e-01 5.73484421e-01
4.35323447e-01 2.24128291e-01 -2.71576867e-02 1.30968392e+00
6.64375722e-01 6.19125903e-01 9.57838476e-01 -4.89623666e-01
6.15682185e-01 6.41426384e-01 1.16589654e-03 -7.16949224e-01
-5.28406858e-01 -6.41371906e-01 -8.01229119e-01 -1.27698436e-01
5.76601624e-01 -2.44063556e-01 -8.96705627e-01 1.85527921e+00
5.07148325e-01 2.60767430e-01 2.79802740e-01 5.19658685e-01
9.43129003e-01 4.95646656e-01 2.95407861e-01 -2.62816072e-01
1.51268744e+00 -8.17804933e-01 -6.02165341e-01 -4.06590849e-01
6.29288614e-01 -8.42586696e-01 7.88059235e-01 3.22453946e-01
-9.53986526e-01 -6.20522916e-01 -9.17083025e-01 4.37976331e-01
-3.77789497e-01 4.26898062e-01 3.63185734e-01 1.28594458e+00
-9.28198695e-01 3.74814332e-01 -7.03204453e-01 -6.29527569e-01
1.03577375e-01 5.16872883e-01 -2.80919760e-01 3.13912958e-01
-1.04995859e+00 6.04097009e-01 3.99116188e-01 -4.14186269e-02
-6.82998955e-01 -8.99732828e-01 -8.83266687e-01 1.05570227e-01
3.76795977e-03 -1.91449404e-01 1.08581591e+00 -9.82569695e-01
-1.75017834e+00 9.73915875e-01 -2.10290223e-01 -6.64031982e-01
6.34715974e-01 -2.53283381e-01 -6.93893373e-01 -2.57854283e-01
3.69396918e-02 4.91953671e-01 9.19315577e-01 -8.21116924e-01
-7.23470867e-01 -9.71988082e-01 -6.52366400e-01 3.15545678e-01
-6.32164121e-01 2.96547651e-01 -2.03232512e-01 -3.95822227e-01
2.61698931e-01 -8.80043209e-01 2.45448485e-01 -7.86869705e-01
-4.26759750e-01 -3.24321598e-01 6.43091023e-01 -1.00533080e+00
1.34564722e+00 -2.10283494e+00 5.27382046e-02 3.89386475e-01
-1.20289221e-01 2.44689375e-01 3.76616836e-01 2.66972363e-01
-1.04942337e-01 -2.08304510e-01 -1.15499049e-01 -7.60793865e-01
-6.02575243e-02 -1.75686076e-01 2.05613658e-01 4.13864553e-01
-6.11641780e-02 1.10864677e-01 -3.57518137e-01 -6.56520247e-01
1.17931038e-01 4.35147166e-01 -2.70745307e-01 2.57929534e-01
2.69843608e-01 6.77261949e-01 -3.29780549e-01 5.07927179e-01
7.78585255e-01 4.41036403e-01 1.35430470e-02 2.58226573e-01
-2.79639274e-01 2.76611090e-01 -1.52691770e+00 1.25605977e+00
-6.17985487e-01 3.02381992e-01 2.98259288e-01 -9.86982405e-01
1.47746944e+00 3.56077373e-01 3.91233027e-01 -2.69823492e-01
3.41770202e-01 2.51540989e-01 1.62214443e-01 -3.38808805e-01
3.77030194e-01 -4.62871045e-02 -2.52557307e-01 1.41946688e-01
8.13679025e-03 2.90071070e-01 2.68031489e-02 -1.66025609e-02
3.64602357e-01 -1.99083820e-01 1.59629300e-01 -5.15549242e-01
1.32106984e+00 -5.75733721e-01 5.11086583e-01 3.34630817e-01
-2.24182159e-01 1.91183999e-01 3.54510874e-01 2.42722873e-02
-7.90228426e-01 -9.03285205e-01 -5.12559116e-01 1.38382781e+00
-4.44382042e-01 -2.82177776e-01 -8.72214854e-01 -4.25331682e-01
-7.91554078e-02 7.19743490e-01 -6.47637784e-01 -1.64024457e-02
-1.69960842e-01 -7.18533278e-01 7.00278163e-01 4.34069991e-01
5.83863378e-01 -7.16929018e-01 -1.95179537e-01 4.75749709e-02
-3.98686714e-02 -1.01530063e+00 -3.13633174e-01 6.30303696e-02
-8.39946270e-01 -7.23155439e-01 -8.29095423e-01 -7.35737145e-01
4.65958625e-01 -5.41145146e-01 8.48449111e-01 -3.07357609e-01
3.61166507e-01 2.05750346e-01 -2.29219720e-01 -6.25612020e-01
-6.28602743e-01 5.86339593e-01 3.21557254e-01 4.01035517e-01
5.47019660e-01 -6.56545699e-01 -3.05591643e-01 4.42199051e-01
-3.14334691e-01 -1.74787879e-01 5.84822476e-01 6.92954957e-01
8.21561664e-02 -6.48277998e-02 1.03283596e+00 -1.08694243e+00
4.79169667e-01 -4.66778457e-01 -5.48450172e-01 5.72135560e-02
-8.93178642e-01 1.06555514e-01 3.47617775e-01 -4.27994370e-01
-1.32813573e+00 1.67532355e-01 -4.66742963e-01 1.36774406e-01
-4.87000287e-01 3.75498980e-01 -3.27874720e-01 2.64899939e-01
4.67671692e-01 2.94857055e-01 6.63978681e-02 -7.98125386e-01
-1.37006238e-01 1.32871294e+00 4.91970032e-01 -6.38793170e-01
6.02939785e-01 -9.40887332e-02 -1.44053623e-01 -1.03442001e+00
-7.75970459e-01 -7.12681293e-01 -7.27813482e-01 -1.45806521e-01
8.43727171e-01 -1.15665233e+00 -6.87259555e-01 9.35064495e-01
-5.65581679e-01 -4.66920212e-02 3.73607039e-01 6.26370072e-01
-3.22368830e-01 2.99857944e-01 -4.54344779e-01 -1.41000402e+00
-7.89846241e-01 -9.50863063e-01 9.88374293e-01 4.02840227e-01
-2.69995421e-01 -1.00296223e+00 3.34254913e-02 6.90830112e-01
3.53422910e-01 5.26054390e-02 6.45646274e-01 -1.21858215e+00
2.64684379e-01 -2.16698766e-01 -4.64737834e-03 4.15372550e-01
9.56583843e-02 -2.73050293e-02 -1.33169293e+00 -2.07341298e-01
-1.97461843e-01 -2.74664313e-01 4.93301541e-01 3.74490470e-01
1.00792837e+00 -2.23774105e-01 -1.67849466e-01 2.88455367e-01
9.75431979e-01 4.57789637e-02 5.13566077e-01 1.36297405e-01
6.70442998e-01 1.01491714e+00 5.64362645e-01 5.60905576e-01
5.98667443e-01 8.60859811e-01 7.54508749e-02 2.27071598e-01
1.49494663e-01 -2.83510149e-01 4.32648480e-01 9.04203296e-01
8.87439027e-02 2.53695309e-01 -1.13561594e+00 5.62504053e-01
-1.71905375e+00 -6.70389593e-01 -1.36061266e-01 2.63863897e+00
7.40493596e-01 2.51749068e-01 6.95930123e-01 6.15527689e-01
8.49084556e-01 1.08089536e-01 -3.01331878e-01 -6.69594467e-01
2.40125552e-01 3.91029045e-02 4.12170708e-01 4.85939205e-01
-1.16208911e+00 7.43307710e-01 5.14741230e+00 5.94625592e-01
-1.25666690e+00 3.74854207e-02 7.41343081e-01 2.43714288e-01
2.95941889e-01 -5.07919073e-01 -1.22203422e+00 6.13714814e-01
1.50251436e+00 -2.16649756e-01 1.18300490e-01 1.00957000e+00
2.18037516e-01 -6.23083897e-02 -9.27877724e-01 1.07523620e+00
2.31635094e-01 -5.76024652e-01 -3.60805929e-01 1.55471593e-01
5.55673778e-01 -2.62401581e-01 2.16335222e-01 5.39077461e-01
-1.75377131e-01 -9.15020347e-01 7.97249496e-01 2.66741574e-01
5.06918788e-01 -1.07501352e+00 1.12168038e+00 5.86733639e-01
-1.10336053e+00 -1.85959160e-01 -9.15000737e-02 -1.70242917e-02
-1.76586851e-01 5.62895596e-01 -1.05356252e+00 4.87327158e-01
6.91793025e-01 5.18532813e-01 -7.27331042e-01 9.67078745e-01
6.72338381e-02 7.75519669e-01 -3.23925912e-01 -1.20776240e-02
-2.92952713e-02 -3.15548405e-02 2.90936708e-01 9.73052025e-01
4.55915958e-01 -3.72421563e-01 5.68210185e-02 3.07370126e-01
-1.25895953e-03 6.91552758e-01 -2.55902588e-01 6.41040877e-02
6.99859262e-01 1.18451786e+00 -4.27097410e-01 -2.81440318e-01
-1.28271922e-01 5.23074269e-01 2.57031858e-01 -4.02368158e-02
-6.94643855e-01 -3.13190281e-01 4.51303840e-01 2.71924496e-01
1.96689680e-01 1.18864536e-01 -2.88245887e-01 -7.80691981e-01
-2.64947325e-01 -8.16987872e-01 5.16646743e-01 -1.63317725e-01
-1.07084310e+00 4.40828770e-01 9.87946391e-02 -9.50633347e-01
-6.43952608e-01 -3.64391983e-01 -7.91455805e-01 1.06470370e+00
-1.34211791e+00 -1.29492474e+00 -2.59074241e-01 5.27251244e-01
5.34661710e-01 -5.04966736e-01 7.86064625e-01 5.45923412e-01
-9.18546200e-01 1.10511518e+00 1.24487169e-01 5.39497994e-02
1.01258981e+00 -1.37516212e+00 -1.14230916e-01 5.90541065e-01
-1.95483401e-01 4.48750466e-01 8.69899035e-01 -4.34560269e-01
-1.04631495e+00 -1.02336359e+00 1.14223397e+00 -4.02084053e-01
5.04073322e-01 -3.90474379e-01 -7.43620872e-01 5.23502469e-01
-1.46787375e-01 -5.91726959e-01 8.99515629e-01 7.81791151e-01
-2.29590610e-01 -3.67761642e-01 -1.40839314e+00 1.41564131e-01
4.03069526e-01 -2.93499470e-01 -4.27573115e-01 1.10472962e-01
1.96981013e-01 -4.91469622e-01 -1.21075225e+00 2.15565145e-01
6.92377567e-01 -9.58782375e-01 8.28089952e-01 -2.03175560e-01
4.18764174e-01 4.78416234e-02 9.71971266e-03 -1.23116565e+00
7.35004023e-02 -2.37728432e-01 -2.27568716e-01 2.15319133e+00
6.15545750e-01 -6.93279207e-01 9.83575940e-01 7.13768423e-01
-3.61361019e-02 -6.49705231e-01 -8.63647878e-01 -6.54501796e-01
1.53945148e-01 -3.18089813e-01 7.76469290e-01 6.73687994e-01
-2.29281913e-02 6.79197967e-01 -4.95552987e-01 3.71931851e-01
8.18350255e-01 -3.52352172e-01 9.48777258e-01 -1.67963529e+00
-5.11802360e-02 -2.15697467e-01 -5.35114586e-01 -5.21350801e-01
4.13747489e-01 -6.05484545e-01 1.22146844e-03 -1.04758716e+00
1.07057028e-01 -5.14499664e-01 -3.03890228e-01 2.25977167e-01
-1.45046636e-01 -4.40170355e-02 -1.03828974e-01 -6.75837174e-02
-2.09561512e-01 4.42238837e-01 4.96354669e-01 -8.46757274e-03
-5.11386275e-01 6.68800414e-01 -6.80682898e-01 7.02487051e-01
9.01664972e-01 -3.85288149e-01 -5.90232193e-01 9.10566449e-02
-3.14231217e-01 3.30225192e-02 -1.99641630e-01 -1.22025383e+00
-9.64071043e-03 1.91829011e-01 5.02302825e-01 -7.03555405e-01
4.98662442e-01 -6.66953087e-01 -9.70784500e-02 4.71871436e-01
-3.73923898e-01 -1.97847560e-01 1.13140553e-01 3.02995682e-01
-2.44304314e-01 -3.83394152e-01 8.58272076e-01 2.78025746e-01
-3.78130257e-01 1.72259342e-02 -2.06797674e-01 -9.14624929e-02
8.45729947e-01 -2.92816479e-02 -2.42670283e-01 -4.59551334e-01
-7.79786587e-01 4.50231850e-01 1.86762497e-01 4.61160690e-01
1.35704279e-01 -1.16073799e+00 -9.55464005e-01 1.91014796e-01
2.58136541e-01 -3.99156511e-01 3.38577777e-01 9.35576320e-01
-7.86552280e-02 4.22224820e-01 -1.55230865e-01 -7.37837553e-01
-1.84085751e+00 2.49436647e-01 2.17348889e-01 -3.68764788e-01
-2.01354977e-02 8.27378035e-01 -6.08644672e-02 -6.95071399e-01
6.70603573e-01 -1.24351673e-01 -8.04089844e-01 5.19103765e-01
6.13992810e-01 5.67991495e-01 2.08084077e-01 -1.03206015e+00
-5.71908832e-01 4.60153490e-01 -2.90231675e-01 -2.05119684e-01
1.14152539e+00 -3.79336119e-01 7.63191134e-02 6.51522338e-01
9.82797682e-01 3.22962642e-01 -7.12704659e-01 -1.96472526e-01
3.65020156e-01 -3.59211445e-01 8.78502354e-02 -7.86342740e-01
-7.98524559e-01 7.82099426e-01 1.06803131e+00 2.61072516e-01
8.77257943e-01 4.45452407e-02 4.34943080e-01 -5.90958819e-02
-6.31500594e-03 -1.17808056e+00 -3.27421486e-01 3.80247355e-01
6.08314633e-01 -1.49104512e+00 -1.86209679e-02 -5.13027310e-01
-8.17615449e-01 8.59927177e-01 6.23725474e-01 4.89193201e-01
6.93552613e-01 -1.63517445e-01 2.26827607e-01 8.43890309e-02
-4.89230156e-01 -1.13939397e-01 5.01908481e-01 6.14752531e-01
8.83377194e-01 3.10484529e-01 -4.82894897e-01 9.49778140e-01
-7.09062397e-01 -2.02033341e-01 1.55126661e-01 4.47244436e-01
-5.08339942e-01 -1.21085680e+00 -6.43951476e-01 5.71302891e-01
-7.51257300e-01 2.10222617e-01 -2.83764958e-01 5.07772267e-01
5.76097816e-02 1.22128701e+00 3.60149369e-02 -3.87481749e-01
2.00540394e-01 5.70437074e-01 1.95437923e-01 -5.46103597e-01
-7.27956593e-01 4.92505208e-02 5.81229270e-01 -6.37098476e-02
-5.23648024e-01 -8.79555285e-01 -8.36800635e-01 -1.89270347e-01
-2.61427283e-01 4.50979918e-01 1.06434536e+00 9.90829408e-01
4.33868989e-02 4.84717280e-01 8.24610353e-01 -5.63864291e-01
-5.88040054e-01 -1.44647288e+00 -7.98003972e-01 3.43476504e-01
3.90578881e-02 -6.94993794e-01 -3.53483111e-01 -9.52658579e-02] | [14.213830947875977, 6.097212791442871] |
4fb2b67c-c06a-4021-945b-654a69db09af | dual-domain-self-supervised-learning-for | 2302.09244 | null | https://arxiv.org/abs/2302.09244v1 | https://arxiv.org/pdf/2302.09244v1.pdf | Dual-Domain Self-Supervised Learning for Accelerated Non-Cartesian MRI Reconstruction | While enabling accelerated acquisition and improved reconstruction accuracy, current deep MRI reconstruction networks are typically supervised, require fully sampled data, and are limited to Cartesian sampling patterns. These factors limit their practical adoption as fully-sampled MRI is prohibitively time-consuming to acquire clinically. Further, non-Cartesian sampling patterns are particularly desirable as they are more amenable to acceleration and show improved motion robustness. To this end, we present a fully self-supervised approach for accelerated non-Cartesian MRI reconstruction which leverages self-supervision in both k-space and image domains. In training, the undersampled data are split into disjoint k-space domain partitions. For the k-space self-supervision, we train a network to reconstruct the input undersampled data from both the disjoint partitions and from itself. For the image-level self-supervision, we enforce appearance consistency obtained from the original undersampled data and the two partitions. Experimental results on our simulated multi-coil non-Cartesian MRI dataset demonstrate that DDSS can generate high-quality reconstruction that approaches the accuracy of the fully supervised reconstruction, outperforming previous baseline methods. Finally, DDSS is shown to scale to highly challenging real-world clinical MRI reconstruction acquired on a portable low-field (0.064 T) MRI scanner with no data available for supervised training while demonstrating improved image quality as compared to traditional reconstruction, as determined by a radiologist study. | ['Michal Sofka', 'James S. Duncan', 'Chi Liu', 'Kevin Sheth', 'Seyed Sadegh Mohseni Salehi', 'Neel Dey', 'Jo Schlemper', 'Bo Zhou'] | 2023-02-18 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [ 6.21177673e-01 1.35072872e-01 -3.53251338e-01 -6.22726738e-01
-1.11639178e+00 -3.09419394e-01 2.12704867e-01 -1.67046353e-01
-3.54508519e-01 6.91689968e-01 3.17151099e-01 -3.56990010e-01
-3.26435417e-01 -3.65092367e-01 -8.06175768e-01 -7.99993634e-01
-5.02864540e-01 5.67878842e-01 1.55944929e-01 2.20796198e-01
-3.47907692e-01 4.51220334e-01 -7.91253626e-01 2.39860401e-01
7.79564261e-01 7.96427548e-01 5.40248752e-01 3.83628577e-01
6.49037540e-01 6.46560252e-01 -8.74764770e-02 2.77842730e-01
3.95388007e-01 -4.22810525e-01 -9.96977985e-01 2.44607851e-01
5.04559696e-01 -9.01499867e-01 -4.70390916e-01 8.47112715e-01
7.02464223e-01 -7.13631278e-03 4.56079304e-01 -6.84397221e-01
-4.01542634e-01 6.77357018e-01 -7.33245969e-01 5.30305028e-01
-1.40314460e-01 2.77783900e-01 2.71836042e-01 -5.49377918e-01
7.88588345e-01 4.61791873e-01 9.27306592e-01 6.05169892e-01
-1.78000379e+00 -7.53624022e-01 -1.50781661e-01 -1.19384736e-01
-1.01101875e+00 -4.73848313e-01 7.67096102e-01 -4.82722431e-01
7.31252015e-01 1.21325798e-01 7.76173472e-01 1.05885661e+00
7.20149279e-01 6.84908807e-01 1.61524212e+00 -4.13136221e-02
3.16568345e-01 -3.98355424e-01 -1.33660346e-01 4.11055297e-01
5.16966060e-02 1.34923205e-01 -1.81542605e-01 -1.04344398e-01
1.32377207e+00 7.19531327e-02 -6.37301385e-01 -8.82452369e-01
-1.65423000e+00 5.47592819e-01 7.98288584e-01 4.32218969e-01
-6.57151997e-01 -1.09776944e-01 5.39230108e-01 -3.01701557e-02
4.85523701e-01 5.63078761e-01 -1.64034829e-01 1.22424595e-01
-1.34521616e+00 -1.54909655e-01 1.20875925e-01 6.81651235e-01
3.39923091e-02 2.72245258e-01 8.47254321e-02 7.41170883e-01
2.93671656e-02 4.59749371e-01 9.20288622e-01 -8.41797829e-01
8.10411721e-02 -3.26532051e-02 -1.00212522e-01 -7.45976806e-01
-8.53827536e-01 -8.32976878e-01 -1.32616270e+00 1.41936779e-01
2.73067802e-01 -2.12718341e-02 -1.03665006e+00 1.77672899e+00
5.95702052e-01 3.03770155e-01 -1.12541892e-01 1.64036822e+00
7.22076237e-01 1.89287275e-01 1.49993086e-02 -4.60622162e-01
1.24271202e+00 -9.22152638e-01 -8.29127133e-01 -1.60130605e-01
6.86085582e-01 -3.76943052e-01 1.05722976e+00 4.81976688e-01
-1.28859675e+00 -3.52639169e-01 -1.28677917e+00 3.14500123e-01
5.77557802e-01 8.46477523e-02 6.80638731e-01 3.47358644e-01
-1.11535454e+00 7.42569745e-01 -1.38039577e+00 2.45168343e-01
5.81389010e-01 5.09989440e-01 -7.00078845e-01 -3.69903833e-01
-1.16538405e+00 9.38734770e-01 3.90095487e-02 -5.93038127e-02
-1.13357627e+00 -1.24309433e+00 -8.81741405e-01 -5.43913960e-01
1.44792661e-01 -5.52908480e-01 1.34130013e+00 -8.37387621e-01
-1.21840012e+00 8.80678713e-01 2.88787454e-01 -8.01038444e-01
5.86740434e-01 1.02372207e-01 -6.03079438e-01 8.07719827e-01
4.73438472e-01 6.52875006e-01 1.07680750e+00 -1.13088918e+00
2.98901916e-01 -5.45519412e-01 -3.90323788e-01 3.70716751e-01
2.07886234e-01 -1.34958580e-01 9.09092948e-02 -8.02501500e-01
6.39706850e-01 -1.04024601e+00 -4.35902745e-01 3.38834524e-02
-3.47377509e-01 6.75615728e-01 6.20366514e-01 -9.84639883e-01
6.61491096e-01 -2.17550993e+00 -1.61327288e-01 7.60569945e-02
6.78551376e-01 -8.94472972e-02 8.94271657e-02 -3.69013816e-01
-8.18836510e-01 -3.77981871e-01 -5.53765297e-01 -1.29355147e-01
-7.04842269e-01 1.47829071e-01 -2.54770704e-02 1.02858353e+00
-8.42246339e-02 8.37196052e-01 -1.19955397e+00 -3.45126331e-01
2.08332703e-01 5.55892289e-01 -5.09142697e-01 2.88313270e-01
4.11034912e-01 1.24928129e+00 -2.46734560e-01 2.09821939e-01
7.53199637e-01 -6.48443997e-01 5.60340583e-01 -7.16317654e-01
1.96654052e-01 3.77475411e-01 -7.74377584e-01 2.25263786e+00
-5.05027473e-01 2.25947097e-01 4.45906579e-01 -1.18490374e+00
4.95637327e-01 4.97475266e-01 1.13662612e+00 -1.03096163e+00
8.86897072e-02 3.19295883e-01 3.33001554e-01 -3.59818399e-01
1.00539505e-01 -8.70525181e-01 2.27255210e-01 8.22592854e-01
-2.08384581e-02 -3.79732639e-01 -3.32623810e-01 2.74935454e-01
1.09398210e+00 7.66039342e-02 -3.12071983e-02 -5.68533659e-01
-7.43851364e-02 1.69516712e-01 4.73139793e-01 6.44900799e-01
-4.69907403e-01 1.02877092e+00 -1.96583513e-02 -4.84652668e-01
-1.34847295e+00 -1.26243484e+00 -7.89109886e-01 3.90381128e-01
2.10395783e-01 1.00271262e-01 -9.04676855e-01 -7.81588495e-01
-3.47761840e-01 3.88996422e-01 -5.85950315e-01 -2.33539924e-01
-8.09779286e-01 -8.43595743e-01 3.28107804e-01 5.55618525e-01
2.76670158e-01 -6.40762150e-01 -9.31378543e-01 5.67766964e-01
-4.18102652e-01 -1.15887344e+00 -6.51543260e-01 4.34892029e-01
-1.37307227e+00 -9.42694783e-01 -1.06250930e+00 -5.98991394e-01
9.25351858e-01 3.16162229e-01 1.08475780e+00 -1.52848199e-01
-4.60927188e-01 2.38266662e-01 -4.39124107e-02 2.07028314e-01
-2.90060550e-01 -1.98186174e-01 4.98561978e-01 -2.48321205e-01
-3.55571985e-01 -7.98007429e-01 -1.16336954e+00 3.31039041e-01
-9.53218937e-01 5.33375382e-01 6.93752825e-01 1.21305144e+00
8.81245673e-01 3.35173719e-02 6.94756508e-01 -7.52182186e-01
3.18036199e-01 -5.62774718e-01 -3.83606702e-02 -3.84838283e-02
-4.97726053e-01 1.09244287e-01 6.45533025e-01 -6.13125324e-01
-9.25279558e-01 1.38380453e-01 -1.38367221e-01 -4.95730698e-01
-1.99175090e-01 2.59537011e-01 3.77977133e-01 -1.85163140e-01
8.16709340e-01 5.29544115e-01 6.97225571e-01 -2.73001254e-01
6.61899000e-02 3.76283228e-01 8.04214776e-01 -5.59803963e-01
3.69110793e-01 8.02280724e-01 -2.64152829e-02 -5.66831112e-01
-7.02037632e-01 -1.02575451e-01 -7.32227921e-01 -3.50073427e-01
8.32883537e-01 -1.00308061e+00 -2.64635533e-01 3.40996176e-01
-5.07397592e-01 -5.93146741e-01 -4.31169063e-01 9.56194997e-01
-5.75891972e-01 5.89295805e-01 -9.66901302e-01 -2.16667950e-01
-6.35447323e-01 -1.75547576e+00 1.15527177e+00 -2.00467661e-01
-3.06382358e-01 -8.78770769e-01 -1.49020329e-01 5.20593643e-01
6.56193793e-01 3.37629467e-01 9.46698487e-01 -3.66961479e-01
-3.01078051e-01 1.36519507e-01 -7.84020647e-02 2.13123307e-01
3.35363626e-01 -9.38527048e-01 -5.48218310e-01 -6.90388203e-01
4.80447263e-01 -6.83944523e-01 2.52149105e-01 9.36686099e-01
1.32285333e+00 7.15893880e-02 -9.71026570e-02 8.15071940e-01
1.15622008e+00 8.48801434e-02 3.69453073e-01 2.45117024e-01
5.60842037e-01 3.54818672e-01 3.76786411e-01 1.90559909e-01
4.35133159e-01 7.30502903e-01 1.98418394e-01 -5.79859853e-01
-4.62324828e-01 -1.83193848e-01 -1.39830068e-01 1.06383705e+00
2.70645440e-01 7.99881577e-01 -1.08240664e+00 6.13171339e-01
-1.27769792e+00 -4.55152065e-01 -9.03532878e-02 2.17669940e+00
1.25262558e+00 -1.14303492e-01 2.13274106e-01 1.23436511e-01
5.43889642e-01 7.38980696e-02 -9.18036640e-01 2.61067599e-01
4.83667813e-02 2.37984613e-01 5.63428342e-01 4.58566159e-01
-1.02648652e+00 2.78031617e-01 6.73743439e+00 4.99621868e-01
-1.65617657e+00 7.58357286e-01 9.44257498e-01 -4.64402258e-01
-2.80476481e-01 -3.13887864e-01 -1.95150618e-02 3.69027853e-01
8.43395352e-01 2.73176610e-01 4.10615891e-01 6.89730287e-01
3.38078558e-01 -3.38125765e-01 -1.06586528e+00 9.64106143e-01
-1.21721603e-01 -1.28715944e+00 -4.07656968e-01 -7.63498396e-02
7.27361679e-01 3.30034941e-01 1.05599925e-01 -6.31241053e-02
6.76808283e-02 -1.20448446e+00 5.56723893e-01 2.16946393e-01
1.51004338e+00 -4.88624781e-01 2.94232965e-01 4.75980729e-01
-6.93451285e-01 4.09872502e-01 -4.46927100e-02 2.41293147e-01
3.24908346e-01 7.99786687e-01 -8.95654500e-01 5.93055606e-01
7.30724633e-01 7.41356611e-01 -1.74504191e-01 6.91073537e-01
1.42067164e-01 5.79553902e-01 -2.32782200e-01 7.67314076e-01
2.51923084e-01 -1.29497781e-01 5.20530641e-01 8.75502169e-01
-8.57485011e-02 4.48022813e-01 2.04014957e-01 7.37146080e-01
2.86219865e-01 -1.55831844e-01 -5.45425713e-01 5.31592667e-01
1.94344055e-02 1.17021739e+00 -8.75793576e-01 -4.11727160e-01
-1.13566130e-01 8.67635250e-01 2.68842727e-01 3.33837658e-01
-7.77325273e-01 1.57529488e-01 2.28637189e-01 6.55799747e-01
-1.24229714e-01 -2.46659189e-01 -5.50670445e-01 -1.27878070e+00
4.10197228e-02 -1.05323732e+00 1.72654107e-01 -8.58044505e-01
-1.24402928e+00 1.02514911e+00 2.22011626e-01 -1.28834474e+00
-2.10056916e-01 -2.30784208e-01 -1.56059906e-01 8.49421263e-01
-1.35295427e+00 -7.76245594e-01 -2.35181615e-01 5.49427509e-01
2.09089607e-01 1.57234773e-01 7.27937698e-01 4.68592674e-01
-1.58703715e-01 3.99202883e-01 9.34466645e-02 2.22188886e-02
6.63186908e-01 -1.12516356e+00 1.24219410e-01 7.13283360e-01
-3.46176147e-01 7.93466508e-01 5.14927626e-01 -9.06165957e-01
-1.52598095e+00 -8.61474812e-01 2.30423994e-02 -1.78850293e-02
6.21579468e-01 -2.52495825e-01 -1.04465353e+00 5.17695427e-01
1.38124228e-02 6.88173532e-01 6.59595311e-01 -3.98238987e-01
2.21918095e-02 -1.59607366e-01 -1.61729193e+00 4.05497700e-01
9.79617476e-01 -5.27176678e-01 -5.98057091e-01 5.40038168e-01
5.23762226e-01 -1.00535798e+00 -1.50215471e+00 5.86392879e-01
5.33060610e-01 -7.80413568e-01 1.06019723e+00 -3.36528122e-01
6.68384492e-01 -2.54603386e-01 1.48342669e-01 -1.54746377e+00
-4.21155661e-01 -3.57713640e-01 -6.79440945e-02 2.43854851e-01
2.27614827e-02 -5.97621858e-01 9.37402725e-01 6.59093380e-01
-3.02708596e-01 -1.06548798e+00 -1.20312631e+00 -6.27305984e-01
2.18355447e-01 -4.66104656e-01 4.83639896e-01 1.31846130e+00
1.26569942e-01 2.18635887e-01 -4.62846994e-01 1.45555034e-01
1.23970294e+00 2.18332723e-01 7.05596954e-02 -5.93799770e-01
-5.43969512e-01 -5.29608727e-02 -2.15827063e-01 -1.23481131e+00
7.96880573e-02 -1.15919936e+00 5.80826215e-02 -1.47303450e+00
2.85321742e-01 -1.04005802e+00 -2.65014797e-01 4.13830012e-01
3.82907279e-02 6.19595408e-01 -1.64451495e-01 4.07315940e-01
-2.89604455e-01 5.21447062e-01 1.96682906e+00 -9.90738347e-02
1.78423494e-01 -3.40433806e-01 -5.91881156e-01 3.62794101e-01
5.53985059e-01 -4.33891714e-01 -6.84128463e-01 -7.13735580e-01
-4.91643876e-01 7.29685605e-01 3.08032125e-01 -1.00336921e+00
9.88747552e-02 2.25242108e-01 7.81580269e-01 -3.87691021e-01
1.46711245e-01 -9.19237137e-01 4.51350093e-01 6.61670864e-01
-3.17427546e-01 -1.37382164e-03 1.65659189e-01 1.87431186e-01
-1.98044125e-02 9.18951482e-02 1.21904767e+00 -2.41032854e-01
-2.76715662e-02 4.96210873e-01 -2.90096760e-01 1.26069695e-01
8.50799024e-01 -2.80597270e-01 3.38296920e-01 -2.55535692e-01
-1.06932402e+00 -8.47688839e-02 4.02107269e-01 1.32899180e-01
7.49915183e-01 -1.45379114e+00 -6.02417052e-01 4.87807095e-01
-2.65110880e-01 2.91576803e-01 8.88930738e-01 1.56042206e+00
-4.77601826e-01 2.56803602e-01 -3.78021181e-01 -1.25604677e+00
-7.82891154e-01 3.97720397e-01 6.96507752e-01 -5.87612331e-01
-1.40014708e+00 4.27908808e-01 3.50880802e-01 -7.74964690e-01
-5.61969690e-02 -3.59196693e-01 2.40069807e-01 -6.42561495e-01
5.79981506e-01 -1.72904998e-01 3.66301894e-01 -6.67822957e-01
-5.24643779e-01 2.97474176e-01 -3.48196536e-01 -1.74061015e-01
1.58436275e+00 -8.30276236e-02 1.13992743e-01 1.07550144e-01
1.20494378e+00 -4.51202720e-01 -1.63517809e+00 -5.23603439e-01
-3.91932875e-01 -3.91262740e-01 5.81383348e-01 -9.57582474e-01
-1.36533630e+00 7.04946518e-01 9.18694317e-01 -3.27925384e-01
1.22671688e+00 -1.17040470e-01 9.72635388e-01 -3.68997574e-01
7.46301472e-01 -6.25872254e-01 -1.03060380e-02 2.11795829e-02
8.79725575e-01 -1.20712066e+00 2.19587788e-01 -1.79312646e-01
-9.05877113e-01 7.50780106e-01 5.93262255e-01 -2.70397127e-01
5.05856574e-01 7.78342187e-01 1.24723397e-01 -3.37014049e-01
-4.43056792e-01 6.96714044e-01 3.44493657e-01 7.27637589e-01
5.31366408e-01 3.06681663e-01 -1.32231325e-01 6.13346159e-01
-1.52769551e-01 1.63594693e-01 4.02689070e-01 9.57627833e-01
2.33619392e-01 -6.82568789e-01 -4.29604709e-01 7.64213979e-01
-5.86078227e-01 -3.50334793e-02 4.35548216e-01 4.11138177e-01
-2.00405359e-01 4.29286391e-01 4.13644239e-02 6.08141050e-02
1.66061893e-01 -3.69859189e-01 9.14852738e-01 -4.84776020e-01
-6.12912059e-01 2.72585839e-01 -6.02947138e-02 -7.08572626e-01
-3.34681243e-01 -7.36416161e-01 -1.50063848e+00 7.94128627e-02
-1.66972727e-02 1.55647784e-01 8.06368291e-01 8.60364199e-01
4.31563586e-01 6.79119349e-01 7.43021548e-01 -1.02830434e+00
-7.59101093e-01 -9.06699896e-01 -7.76044846e-01 5.50198734e-01
4.21085417e-01 -6.97536170e-01 -1.56961702e-04 -1.95249215e-01] | [13.543734550476074, -2.410111427307129] |
da9507eb-2580-4df8-b7c2-083abaee5210 | learning-better-representation-for-tables-by | 2010.07606 | null | https://arxiv.org/abs/2010.07606v3 | https://arxiv.org/pdf/2010.07606v3.pdf | Learning Better Representation for Tables by Self-Supervised Tasks | Table-to-text generation aims at automatically generating natural text to help people to conveniently obtain the important information in tables. Although neural models for table-to-text have achieved remarkable progress, some problems still overlooked. The first is that the values recorded in many tables are mostly numbers in practice. The existing approaches do not do special treatment for these, and still regard these as words in natural language text. Secondly, the target texts in training dataset may contain redundant information or facts do not exist in the input tables. These may give wrong supervision signals to some methods based on content selection and planning and auxiliary supervision. To solve these problems, we propose two self-supervised tasks, Number Ordering and Significance Ordering, to help to learn better table representation. The former works on the column dimension to help to incorporate the size property of numbers into table representation. The latter acts on row dimension and help to learn a significance-aware table representation. We test our methods on the widely used dataset ROTOWIRE which consists of NBA game statistic and related news. The experimental results demonstrate that the model trained together with these two self-supervised tasks can generate text that contains more salient and well-organized facts, even without modeling context selection and planning. And we achieve the state-of-the-art performance on automatic metrics. | ['Dayong Hu', 'Linjun Shou', 'Yinliang Yue', 'Can Ma', 'Liang Li'] | 2020-10-15 | null | null | null | null | ['table-to-text-generation'] | ['natural-language-processing'] | [ 2.32768446e-01 4.20055777e-01 -5.15319109e-01 -3.21196944e-01
-6.98629916e-01 -3.81253988e-01 5.75499535e-01 5.25168121e-01
-2.55723327e-01 1.29563820e+00 7.65134513e-01 -2.34513074e-01
1.71399862e-01 -1.22111988e+00 -8.38700533e-01 -4.23817307e-01
1.25990778e-01 8.34180892e-01 3.18805158e-01 -8.23585868e-01
4.50038910e-01 -2.56952047e-01 -1.69908333e+00 7.62923241e-01
1.14264727e+00 9.37401772e-01 2.39321455e-01 3.05131465e-01
-7.75083780e-01 1.35660684e+00 -8.70974422e-01 -7.46174157e-01
2.02725902e-01 -6.36100411e-01 -7.46632516e-01 5.20194657e-02
-2.06736103e-02 -2.94430465e-01 -2.75685471e-02 1.13357735e+00
3.94340038e-01 7.51463547e-02 8.50634873e-01 -1.15514994e+00
-5.80047607e-01 1.56212330e+00 -5.93475461e-01 3.09569031e-01
4.12751406e-01 -2.41432011e-01 1.37631762e+00 -7.02740312e-01
6.54434741e-01 1.33726311e+00 3.53722751e-01 4.33277488e-01
-7.42625356e-01 -4.27581668e-01 2.94185519e-01 2.37063885e-01
-1.04967892e+00 -3.21619719e-01 8.20908308e-01 -9.18663219e-02
8.01937580e-01 4.31425691e-01 5.42616427e-01 1.15643966e+00
1.66389436e-01 1.01171017e+00 8.72800112e-01 -5.12842178e-01
1.40178591e-01 5.00005424e-01 -7.38602206e-02 5.17784715e-01
6.03327155e-01 -4.17697906e-01 -8.00216317e-01 2.50989199e-01
6.61449194e-01 -2.25898549e-01 -5.10177389e-02 -2.62155067e-02
-1.48996997e+00 9.95698750e-01 2.12035209e-01 3.68246943e-01
-2.31724665e-01 -3.25705409e-01 6.93892300e-01 1.14736684e-01
3.82431984e-01 6.94590390e-01 -6.28883004e-01 -2.37834066e-01
-4.75803226e-01 5.79837084e-01 8.81421745e-01 1.42384934e+00
6.53686762e-01 9.30632651e-03 -4.42537248e-01 7.53243864e-01
-1.25409020e-02 4.01515156e-01 7.31587291e-01 -3.21202070e-01
1.22805750e+00 9.35737491e-01 1.73791304e-01 -1.21570635e+00
-3.75803322e-01 -4.22012329e-01 -1.21525967e+00 -3.27429622e-01
4.74885583e-01 -1.23519778e-01 -7.60102808e-01 1.45303881e+00
1.52077094e-01 -8.02669942e-01 2.90677100e-01 5.78772128e-01
1.10988688e+00 8.66859913e-01 -9.72639844e-02 -1.99323446e-01
1.67203438e+00 -1.08188498e+00 -1.27242684e+00 -4.42320287e-01
8.93331587e-01 -6.16526186e-01 1.49261105e+00 5.01406074e-01
-9.27975655e-01 -5.61713576e-01 -1.17621052e+00 -2.66874552e-01
-9.03514683e-01 4.78116065e-01 7.73013115e-01 4.80521709e-01
-5.00865340e-01 4.96322393e-01 -1.32470742e-01 -1.64547324e-01
3.10472548e-01 -6.81004487e-03 -1.00562498e-01 1.91392705e-01
-1.72377264e+00 8.78967881e-01 8.50558221e-01 3.27026062e-02
-2.41599575e-01 -4.09443319e-01 -1.02421427e+00 1.26386866e-01
1.12960899e+00 -5.59981883e-01 1.18049574e+00 -6.38068199e-01
-1.19352818e+00 7.17320859e-01 -3.83243151e-02 -5.45334697e-01
5.38268447e-01 -2.23210663e-01 -3.38552535e-01 -2.04472959e-01
6.08870804e-01 6.03016853e-01 3.44438046e-01 -1.14255440e+00
-9.09659922e-01 -3.25629443e-01 8.42250809e-02 5.51068246e-01
-3.55876625e-01 -1.76521912e-01 -3.66485476e-01 -1.08426356e+00
1.65582553e-01 -4.27980244e-01 -2.78234720e-01 -5.63509226e-01
-9.61613953e-01 -4.11434025e-01 2.45112389e-01 -6.67837918e-01
1.62824273e+00 -1.52211249e+00 -7.22445920e-02 -8.55927728e-03
8.14101025e-02 -2.57329673e-01 2.87945151e-01 6.22974575e-01
5.82162254e-02 2.49682054e-01 -2.08107755e-02 -3.37626450e-02
1.68664455e-02 2.15084806e-01 -6.41324162e-01 -3.49797517e-01
2.33269915e-01 7.91519880e-01 -8.70060921e-01 -8.69501233e-01
-3.06108236e-01 -2.82150060e-01 -6.05435133e-01 1.14161082e-01
-7.16400862e-01 1.32019490e-01 -5.23123860e-01 5.77403128e-01
3.27731669e-01 -2.11118951e-01 1.54647857e-01 -1.50856227e-01
1.60151765e-01 9.18297052e-01 -1.45130193e+00 1.52428365e+00
-1.79988652e-01 9.42521244e-02 -6.40696883e-01 -9.39630151e-01
1.03180087e+00 -3.19600012e-03 5.35002537e-02 -6.77723885e-01
3.01503420e-01 1.62177205e-01 1.10604703e-01 -5.51847398e-01
9.43245292e-01 -4.78010029e-02 -3.56007844e-01 3.06886673e-01
-1.38162315e-01 -1.83813214e-01 7.95089602e-01 4.55850005e-01
7.51404703e-01 1.52132019e-01 6.09446824e-01 -3.39358777e-01
7.08526134e-01 3.09505641e-01 6.42314136e-01 6.63942337e-01
3.32599938e-01 5.06301403e-01 1.07979047e+00 -5.15917122e-01
-1.04598749e+00 -5.78849554e-01 2.07366601e-01 1.22228098e+00
6.48594201e-02 -8.91775191e-01 -6.56179309e-01 -9.42090571e-01
-2.86347419e-01 1.06708038e+00 -8.90810966e-01 -2.86872461e-02
-4.72457826e-01 -8.61594796e-01 2.17454121e-01 7.39134908e-01
6.51107907e-01 -1.38003147e+00 -2.78975606e-01 4.28900242e-01
-6.29085541e-01 -1.08185947e+00 -4.46326166e-01 6.16164207e-01
-7.67977297e-01 -8.96954656e-01 -3.34773332e-01 -7.89052069e-01
8.04645956e-01 1.69658661e-02 1.36834562e+00 -3.09797954e-02
1.55926332e-01 -4.63629246e-01 -5.80567777e-01 -9.99161482e-01
-5.11643291e-01 4.35086727e-01 -1.30712271e-01 -2.77662992e-01
4.31645930e-01 -4.16978836e-01 -3.01792733e-02 2.52912313e-01
-8.65442097e-01 5.23822308e-01 7.48383939e-01 9.55160201e-01
4.87437576e-01 4.27874029e-01 7.41205752e-01 -1.48317468e+00
8.52584362e-01 -3.45988214e-01 -3.36924702e-01 2.52761275e-01
-6.09713256e-01 5.79451025e-01 1.06566417e+00 -1.30308300e-01
-9.64871943e-01 -1.85209438e-01 -8.69668722e-02 3.17313075e-01
1.00256214e-02 7.71080613e-01 -7.50669777e-01 9.19903040e-01
7.77080536e-01 5.73172987e-01 -1.60721824e-01 -2.47677565e-01
2.57919312e-01 5.21884561e-01 3.67448181e-01 -7.54293025e-01
8.17249477e-01 1.72512218e-01 -5.36857359e-02 -2.75023729e-01
-1.35856259e+00 -1.97238103e-01 -5.57955205e-01 2.50681564e-02
6.71783864e-01 -8.81234527e-01 -3.83108884e-01 8.07672516e-02
-1.01377749e+00 -8.77329782e-02 -4.40291733e-01 1.19495712e-01
-4.96279925e-01 1.65710747e-01 -5.41024566e-01 -8.45447004e-01
-3.06918442e-01 -9.04954374e-01 9.64406788e-01 3.28860372e-01
-1.53836116e-01 -7.81723619e-01 -3.40262860e-01 2.96902984e-01
2.03195691e-01 7.32634291e-02 1.37304878e+00 -1.01408958e+00
-6.69702291e-01 -4.67477702e-02 -3.90741825e-02 -5.25701568e-02
3.84322912e-01 -1.26768261e-01 -6.46586895e-01 4.19810504e-01
-4.40044627e-02 -6.56132340e-01 8.96287560e-01 8.63001402e-03
1.26346493e+00 -7.97664523e-01 -1.71674952e-01 5.55028468e-02
1.04211569e+00 4.51818407e-01 7.97553420e-01 5.94844818e-01
6.76174402e-01 9.43131208e-01 1.02154028e+00 5.73591173e-01
7.88640022e-01 4.49579716e-01 2.13253304e-01 8.69059786e-02
2.03835234e-01 -9.11612689e-01 1.46138430e-01 1.03866816e+00
-1.18633434e-01 -3.35000902e-01 -7.11269259e-01 5.15235424e-01
-1.94804633e+00 -1.01672554e+00 -1.06848352e-01 2.01633477e+00
1.45535350e+00 9.81110990e-01 3.08318198e-01 5.83016515e-01
6.71608806e-01 2.47411609e-01 -2.98707336e-01 -1.75908834e-01
-4.07423139e-01 -2.81823456e-01 2.99815357e-01 1.63535371e-01
-1.12053537e+00 1.14584601e+00 5.15534544e+00 1.16364288e+00
-5.85140646e-01 -4.61074471e-01 1.15484905e+00 2.29353756e-01
-6.04600191e-01 -7.10313991e-02 -1.29498684e+00 3.60034525e-01
6.46449149e-01 -3.96344155e-01 2.76794761e-01 1.08291030e+00
-3.66748013e-02 -3.19598973e-01 -1.14342952e+00 9.87532854e-01
2.51032501e-01 -1.45970356e+00 6.71659648e-01 -1.80039614e-01
6.59516871e-01 -8.77831161e-01 -1.31028146e-01 6.98846400e-01
2.70936936e-01 -1.01087463e+00 9.72952664e-01 2.58248091e-01
6.67089641e-01 -9.67485368e-01 1.01194739e+00 5.72345734e-01
-1.13060319e+00 1.38088197e-01 -8.14049423e-01 -3.14605027e-01
-2.20038205e-01 6.56946361e-01 -1.03223681e+00 6.39458358e-01
5.75305820e-01 6.92472875e-01 -9.02678907e-01 5.26072681e-01
-5.73558509e-01 3.24132264e-01 -1.83402114e-02 -7.77073979e-01
1.62330255e-01 -1.53063133e-01 2.75895447e-01 9.73742306e-01
1.03624314e-01 1.04141794e-02 1.08859375e-01 8.55981171e-01
-3.33235770e-01 5.24478316e-01 -7.48883843e-01 -1.32250682e-01
4.08108413e-01 1.14469266e+00 -1.21006918e+00 -6.97389185e-01
-2.63359308e-01 5.16089857e-01 2.81683326e-01 -2.42060050e-03
-8.48178566e-01 -7.14197099e-01 1.03555627e-01 2.78580487e-01
2.40021810e-01 1.21629946e-01 -9.17692721e-01 -1.41020274e+00
3.38397801e-01 -1.27483678e+00 5.80870867e-01 -7.46914804e-01
-1.19436920e+00 7.72368491e-01 1.60725534e-01 -1.36488390e+00
-5.13028562e-01 -5.28764248e-01 -4.24983412e-01 5.46227455e-01
-1.19812119e+00 -6.91359639e-01 -7.51942992e-02 4.16256875e-01
9.44292068e-01 -4.76005554e-01 7.01975346e-01 3.94290574e-02
-5.74754059e-01 6.70170963e-01 -1.99374765e-01 5.05702496e-01
7.51336277e-01 -1.69625604e+00 5.61275840e-01 9.39417839e-01
2.90709645e-01 6.85004175e-01 8.86597037e-01 -9.75335777e-01
-1.24983203e+00 -9.45151508e-01 1.18252718e+00 -5.02850294e-01
5.80057740e-01 -7.82033503e-01 -7.07762539e-01 6.48343861e-01
2.85853446e-01 -4.70010251e-01 5.78930378e-01 2.43832931e-01
-3.34093958e-01 -2.97767699e-01 -8.51471782e-01 9.11688089e-01
1.11564410e+00 -1.30974099e-01 -9.53310966e-01 4.57488835e-01
8.68290246e-01 -7.23953485e-01 -2.30419874e-01 1.43475965e-01
8.79264995e-02 -8.71273041e-01 5.58461428e-01 -7.39108801e-01
1.19053066e+00 -3.21066320e-01 -1.41722867e-02 -1.41068995e+00
-1.11712351e-01 -5.44489384e-01 -6.04658574e-02 1.52549720e+00
8.66208732e-01 -2.43247524e-01 9.92870212e-01 3.99285048e-01
-6.69023916e-02 -9.40617442e-01 -4.85088050e-01 -5.42345107e-01
-2.11663499e-01 -1.31588235e-01 8.92624021e-01 9.01495755e-01
3.58947009e-01 1.04475999e+00 -5.38743019e-01 -3.55180919e-01
3.13536257e-01 2.24627554e-01 8.21147859e-01 -1.10654831e+00
-7.33968094e-02 -3.85616928e-01 2.11833813e-03 -1.13530517e+00
-2.64219940e-01 -7.10419714e-01 1.19797103e-01 -1.69817150e+00
2.24965885e-01 -3.01471442e-01 3.28116193e-02 6.26169622e-01
-5.63916385e-01 -3.24517399e-01 9.75669846e-02 -2.28774119e-02
-6.99765682e-01 7.79388964e-01 1.52776682e+00 -2.14466259e-01
-1.97055474e-01 6.57750070e-02 -1.49740112e+00 6.52948081e-01
8.22620153e-01 -5.42607069e-01 -6.09765053e-01 -5.69446124e-02
7.67611861e-01 2.26954639e-01 -3.17780405e-01 -1.07725096e+00
1.26344800e-01 -3.80871922e-01 6.72884405e-01 -9.97436047e-01
-9.12182927e-02 -6.75989449e-01 -4.77179259e-01 2.88280427e-01
-5.92878401e-01 3.25873554e-01 1.44640565e-01 4.88043934e-01
-4.14930105e-01 -3.29155952e-01 3.35477829e-01 -4.97645527e-01
-4.56717551e-01 1.16434451e-02 -3.67135942e-01 5.19174695e-01
5.14014542e-01 5.89895174e-02 -4.58041906e-01 -6.48583889e-01
-2.33690351e-01 2.89549679e-01 1.20968334e-02 5.70664167e-01
4.42518443e-01 -1.43105388e+00 -7.17085838e-01 2.08242968e-01
3.28153878e-01 3.83568794e-01 -4.42467928e-02 2.61025339e-01
-5.57405055e-01 6.06930017e-01 -3.05455565e-01 -1.73585892e-01
-7.81220555e-01 7.09397078e-01 -9.00966153e-02 -9.04649198e-01
-4.70241606e-01 7.89924860e-01 5.52715063e-02 -4.53165919e-01
5.54571927e-01 -9.17458296e-01 -8.04055631e-01 4.14833367e-01
7.56901801e-01 -9.60981250e-02 2.69956708e-01 -7.11322948e-02
9.16429143e-03 4.04965878e-02 -4.64140564e-01 -8.89800936e-02
1.23856926e+00 -2.58003008e-02 -3.17640007e-02 8.30116510e-01
4.90804493e-01 3.93423259e-01 -8.27423751e-01 -3.12230825e-01
3.82179618e-01 -2.96585649e-01 -4.69379693e-01 -1.07305598e+00
-8.26115668e-01 7.23902285e-01 -3.18561763e-01 5.29789925e-01
1.04885638e+00 -2.02611774e-01 7.77582169e-01 8.63420486e-01
5.18334806e-01 -1.23285878e+00 2.05738246e-01 7.76631236e-01
1.16253102e+00 -1.31891704e+00 1.46561295e-01 -7.69719720e-01
-1.20145559e+00 1.24340141e+00 1.13755965e+00 1.98971882e-01
1.63358569e-01 3.18286657e-01 2.92974133e-02 7.83810578e-03
-1.02610981e+00 -3.28741699e-01 2.38743737e-01 5.07820964e-01
6.07545674e-01 -2.16910660e-01 -5.43221653e-01 1.25369132e+00
-9.14492011e-01 -3.49730700e-01 9.24325228e-01 9.18009043e-01
-5.25880218e-01 -1.20970631e+00 -3.71155232e-01 8.98217857e-01
-5.13510168e-01 -3.71922046e-01 -5.15841365e-01 7.42865980e-01
3.03810798e-02 8.23191226e-01 -1.82172716e-01 -4.64434952e-01
4.09881473e-01 1.77951053e-01 2.52016366e-01 -8.57527971e-01
-5.66151261e-01 1.12401873e-01 4.33502406e-01 -1.73258066e-01
-1.59352824e-01 -4.86440957e-01 -1.30817068e+00 -2.28259787e-01
-1.77481949e-01 5.49142540e-01 2.12552845e-01 8.64000916e-01
-1.28288521e-02 8.24778795e-01 4.92797792e-01 -4.24561173e-01
-7.11120129e-01 -1.29521370e+00 -7.88524151e-01 3.76207203e-01
2.26835138e-03 -6.85269356e-01 -2.58246660e-01 7.40774050e-02] | [11.677376747131348, 8.820026397705078] |
dc6b8da7-54eb-4c8b-982b-ccfbce354561 | revisiting-domain-generalized-stereo-matching | 2203.10887 | null | https://arxiv.org/abs/2203.10887v1 | https://arxiv.org/pdf/2203.10887v1.pdf | Revisiting Domain Generalized Stereo Matching Networks from a Feature Consistency Perspective | Despite recent stereo matching networks achieving impressive performance given sufficient training data, they suffer from domain shifts and generalize poorly to unseen domains. We argue that maintaining feature consistency between matching pixels is a vital factor for promoting the generalization capability of stereo matching networks, which has not been adequately considered. Here we address this issue by proposing a simple pixel-wise contrastive learning across the viewpoints. The stereo contrastive feature loss function explicitly constrains the consistency between learned features of matching pixel pairs which are observations of the same 3D points. A stereo selective whitening loss is further introduced to better preserve the stereo feature consistency across domains, which decorrelates stereo features from stereo viewpoint-specific style information. Counter-intuitively, the generalization of feature consistency between two viewpoints in the same scene translates to the generalization of stereo matching performance to unseen domains. Our method is generic in nature as it can be easily embedded into existing stereo networks and does not require access to the samples in the target domain. When trained on synthetic data and generalized to four real-world testing sets, our method achieves superior performance over several state-of-the-art networks. | ['Edwin R. Hancock', 'Tatsuya Harada', 'Jun Zhou', 'Lin Gu', 'Yimin Chen', 'Lei Huang', 'Chen Wang', 'Xiao Bai', 'Xiang Wang', 'Jiawei Zhang'] | 2022-03-21 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhang_Revisiting_Domain_Generalized_Stereo_Matching_Networks_From_a_Feature_Consistency_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhang_Revisiting_Domain_Generalized_Stereo_Matching_Networks_From_a_Feature_Consistency_CVPR_2022_paper.pdf | cvpr-2022-1 | ['stereo-matching-1'] | ['computer-vision'] | [ 3.74608099e-01 -1.31729692e-01 -1.30549476e-01 -5.90716660e-01
-4.79387224e-01 -5.25023282e-01 7.51233697e-01 -1.75362766e-01
-4.05260414e-01 7.24068642e-01 2.45883539e-01 8.72865170e-02
1.49295563e-02 -7.49154687e-01 -8.79267037e-01 -7.45626330e-01
3.14952582e-01 1.53358296e-01 6.06362939e-01 -3.07443053e-01
1.68628797e-01 6.53526545e-01 -1.62705266e+00 3.26935738e-01
8.40929866e-01 1.05977976e+00 3.34206820e-01 5.99802770e-02
2.01184273e-01 4.75008219e-01 -1.28160208e-01 -2.07931787e-01
8.13743711e-01 -1.89565271e-01 -6.57569706e-01 3.78785312e-01
1.35460305e+00 -3.75036955e-01 -7.23975897e-01 1.22518742e+00
3.31894845e-01 1.98250547e-01 5.31620443e-01 -1.27071452e+00
-7.92960882e-01 -1.74080998e-01 -6.18987799e-01 2.17696756e-01
2.51037091e-01 1.63750291e-01 1.10276294e+00 -7.80815065e-01
7.97722936e-01 1.28409016e+00 6.41193986e-01 6.23656273e-01
-1.55486393e+00 -6.95218801e-01 3.32826257e-01 2.11090520e-01
-1.18849719e+00 -3.49842578e-01 1.07251334e+00 -3.87544125e-01
7.71206558e-01 3.93099966e-04 6.80524111e-01 1.11328959e+00
2.78204292e-01 6.44138277e-01 1.16388905e+00 -2.64071792e-01
2.47034833e-01 -2.24173279e-03 -2.55320877e-01 4.06399220e-01
2.86382616e-01 5.45031488e-01 -7.92262018e-01 2.64476649e-02
1.10799181e+00 2.82910943e-01 -5.32752275e-01 -1.22922075e+00
-1.14891100e+00 7.59207487e-01 8.59409928e-01 1.29412383e-01
-1.89535275e-01 -1.14836544e-01 1.31986380e-01 5.20282149e-01
4.94572878e-01 4.92590338e-01 -3.97898078e-01 3.40718836e-01
-8.04591417e-01 3.16753834e-01 4.14036810e-01 1.06723869e+00
1.08002019e+00 5.03916591e-02 1.21877035e-02 8.48803401e-01
7.23534897e-02 4.27950412e-01 3.33825022e-01 -1.32238424e+00
6.21579170e-01 7.71200120e-01 4.67763692e-02 -1.02276802e+00
-2.36691386e-01 -5.17046869e-01 -8.56777787e-01 5.21174967e-01
5.69442749e-01 2.16057271e-01 -8.40831876e-01 2.08073092e+00
2.37526402e-01 5.00921533e-02 7.18548074e-02 1.11588109e+00
7.10182190e-01 3.03857088e-01 -1.79185092e-01 2.59215713e-01
9.42080021e-01 -8.38571429e-01 -2.02385075e-02 -7.41390944e-01
2.26383150e-01 -7.84948468e-01 9.30365562e-01 2.28292141e-02
-1.12335646e+00 -7.63534486e-01 -1.19983554e+00 -2.98646480e-01
-3.53745371e-01 -4.74294096e-01 5.61990082e-01 2.80574292e-01
-1.10668302e+00 6.17405534e-01 -4.88356262e-01 -5.73747873e-01
4.13414031e-01 3.12869579e-01 -7.21572340e-01 -4.68514949e-01
-1.17373359e+00 7.95955062e-01 2.22076043e-01 -1.83459654e-01
-7.04109013e-01 -8.58330429e-01 -1.14198422e+00 -9.64227840e-02
7.56226406e-02 -1.01132536e+00 9.98747706e-01 -1.26991272e+00
-9.90164220e-01 1.26547575e+00 -2.46866643e-01 -2.35183701e-01
7.35654771e-01 -5.24914861e-02 -3.57158482e-01 9.78689864e-02
3.97499919e-01 1.19349980e+00 8.52647424e-01 -1.43376684e+00
-8.42399657e-01 -5.92647314e-01 3.33429515e-01 4.58807498e-01
-2.97802299e-01 -5.45277894e-01 -4.49606031e-01 -7.17835963e-01
6.01532102e-01 -9.08044934e-01 -1.03256352e-01 5.72393060e-01
-1.99076563e-01 8.05870891e-02 8.44833255e-01 -1.96145386e-01
4.54075336e-01 -2.31405067e+00 4.91749868e-02 2.95495018e-02
8.95407796e-02 -3.62907201e-02 -3.49675685e-01 2.51857877e-01
-2.85006285e-01 -5.57953775e-01 -2.33798414e-01 -4.15323675e-02
-1.33595914e-01 2.91929126e-01 -4.00446028e-01 7.19931066e-01
3.21833402e-01 8.22611094e-01 -1.00897396e+00 -2.78759986e-01
4.18940485e-01 4.40256476e-01 -7.76447237e-01 1.76068828e-01
-6.69985125e-03 6.94538593e-01 -3.40266705e-01 3.97969425e-01
1.03536773e+00 -3.13953310e-01 -9.47754905e-02 -2.96748877e-01
-8.26296676e-03 2.81260639e-01 -1.18875539e+00 1.97296274e+00
-4.15916741e-01 7.59722769e-01 -2.81391200e-02 -9.95355189e-01
8.69992018e-01 -1.03321098e-01 3.74479115e-01 -1.19650400e+00
-1.79273561e-01 2.98570096e-01 -1.22749403e-01 -1.92429900e-01
4.49801505e-01 -3.27022284e-01 1.12413272e-01 4.39661220e-02
-3.29422951e-02 -3.78203422e-01 4.09744494e-03 -1.68231264e-01
8.22807193e-01 2.42876291e-01 2.60731071e-01 -4.11339939e-01
5.41629970e-01 1.63438711e-02 7.85767555e-01 7.50762880e-01
-2.23839983e-01 9.79539096e-01 6.28076345e-02 -7.20239401e-01
-1.36900079e+00 -1.41143119e+00 -4.12902325e-01 9.49300706e-01
7.55836010e-01 1.54087484e-01 -3.42112154e-01 -6.19905949e-01
3.81054729e-01 3.31721991e-01 -6.57211721e-01 -2.78445512e-01
-5.74271619e-01 -1.56362027e-01 8.52255523e-02 7.17031837e-01
9.80002463e-01 -7.06522644e-01 -5.63979387e-01 1.18916892e-01
-7.78182596e-02 -1.29441190e+00 -5.64596891e-01 2.73927778e-01
-9.85037804e-01 -1.13645184e+00 -9.56189454e-01 -1.05098188e+00
7.96710014e-01 8.11785221e-01 1.19263494e+00 -2.08728060e-01
-1.65436834e-01 1.60492301e-01 3.00193243e-02 -1.69109497e-02
-1.46608755e-01 -3.21326666e-02 -4.18538973e-03 -1.25430897e-01
5.15761614e-01 -8.18001747e-01 -8.77573788e-01 6.16435587e-01
-9.75854278e-01 2.17041388e-01 3.36590558e-01 1.15378773e+00
4.71995652e-01 -2.05084145e-01 9.95695889e-02 -6.78510845e-01
2.99381744e-02 -9.84588191e-02 -5.86648822e-01 4.31610756e-02
-3.50377500e-01 1.76977858e-01 6.69791102e-01 -2.58796453e-01
-1.04565692e+00 1.27079621e-01 1.47805482e-01 -4.81463790e-01
-2.64250457e-01 -1.14281371e-01 -1.22757293e-01 -3.22750568e-01
7.17045724e-01 2.82163292e-01 7.86117241e-02 -3.03281337e-01
1.66063666e-01 2.85704464e-01 5.34265161e-01 -5.53453207e-01
1.07206094e+00 1.11951661e+00 2.07731307e-01 -6.22063994e-01
-1.07432520e+00 -5.41692615e-01 -6.99203253e-01 -7.33764537e-05
6.48846388e-01 -1.20613635e+00 -2.98108876e-01 4.83271778e-01
-9.69882846e-01 -2.69663036e-01 -3.06601644e-01 3.83982956e-01
-7.66025066e-01 5.91736436e-01 -3.53124678e-01 -2.47466847e-01
1.10342667e-01 -1.09414160e+00 1.33156228e+00 1.43271327e-01
-1.69389158e-01 -1.05774379e+00 1.95176143e-03 2.58188128e-01
2.67664373e-01 1.67391151e-01 9.09010351e-01 -1.97634786e-01
-7.03544438e-01 -3.07496004e-02 -5.84771752e-01 4.72760886e-01
2.89992779e-01 -3.64133775e-01 -1.18360555e+00 -5.88954031e-01
5.25125712e-02 -4.19898987e-01 1.07411087e+00 3.93064499e-01
1.06536579e+00 5.06981537e-02 -3.00174385e-01 8.75655353e-01
1.46765888e+00 -6.84971437e-02 5.11320174e-01 7.07250118e-01
6.62125111e-01 8.11345220e-01 4.91969436e-01 1.55964300e-01
2.06479222e-01 9.29826617e-01 5.71769118e-01 -4.54966247e-01
-2.93415725e-01 -4.38593805e-01 1.23567514e-01 1.41646937e-01
1.96365714e-01 4.36664112e-02 -6.61762297e-01 5.95848739e-01
-1.75263751e+00 -9.66269553e-01 1.98317543e-01 2.36714244e+00
6.51025355e-01 2.15081438e-01 -8.54962915e-02 -3.79182734e-02
8.46728683e-01 4.82786238e-01 -9.01249111e-01 -6.38447627e-02
-4.55343366e-01 -6.65529221e-02 5.86475432e-01 3.69403303e-01
-1.17236912e+00 7.62004137e-01 6.06907129e+00 6.87202275e-01
-1.22697532e+00 -2.05515102e-01 6.45871460e-01 -3.60772014e-02
-4.79608268e-01 -5.85322306e-02 -6.27254546e-01 2.56052643e-01
1.61681063e-02 1.05661782e-03 2.49085262e-01 9.01853919e-01
1.32805452e-01 2.00637560e-02 -1.44177663e+00 1.07405365e+00
8.99911076e-02 -1.31453264e+00 2.49731109e-01 1.56159833e-01
1.11832750e+00 1.64853111e-01 3.19501281e-01 -5.29735200e-02
2.61241853e-01 -8.32726598e-01 7.50855505e-01 8.69764686e-02
6.63499236e-01 -4.62490112e-01 4.70867515e-01 2.74992466e-01
-1.00864148e+00 -2.10068792e-01 -6.53554320e-01 -2.40042850e-01
8.74428451e-03 4.98133272e-01 -4.67417955e-01 3.95976782e-01
8.83237362e-01 1.07797241e+00 -6.23905182e-01 1.09040976e+00
-7.48725012e-02 -2.03118995e-01 -2.03178212e-01 4.60564524e-01
4.89896208e-01 -1.57188088e-01 5.34383535e-01 7.94037282e-01
2.46514812e-01 -3.98169160e-01 1.21598288e-01 9.86036897e-01
2.17073876e-02 -1.71880588e-01 -9.71703351e-01 6.20278418e-01
5.14421225e-01 8.71822238e-01 -2.85503030e-01 -9.91384909e-02
-7.01730609e-01 1.12682593e+00 4.32972491e-01 5.48129320e-01
-5.53275466e-01 -3.87145840e-02 9.40221965e-01 3.75255644e-01
4.27325606e-01 -5.25504239e-02 -4.74265188e-01 -1.31866145e+00
4.22874749e-01 -7.51566052e-01 3.59180778e-01 -8.20051551e-01
-1.69831824e+00 5.00212669e-01 -1.25013590e-01 -1.75321269e+00
-8.01618174e-02 -8.31939578e-01 -4.91768003e-01 9.26838875e-01
-1.89261484e+00 -1.08767188e+00 -6.72630131e-01 8.53479981e-01
4.54335272e-01 -2.62553185e-01 4.84687805e-01 2.44381741e-01
-8.09986815e-02 5.91274321e-01 2.16387600e-01 1.69476978e-02
1.02267635e+00 -1.17994690e+00 5.62342823e-01 8.57825577e-01
-9.31303576e-02 5.67122102e-01 6.67223930e-01 -3.01268607e-01
-1.01303470e+00 -1.07987571e+00 8.46623361e-01 -2.92518526e-01
5.67910373e-01 -3.25992525e-01 -9.29156125e-01 3.89800310e-01
2.08162010e-01 3.09237093e-01 2.53718555e-01 -1.10697150e-01
-8.09274375e-01 -4.73556519e-01 -1.22305965e+00 7.72368073e-01
1.45093226e+00 -6.75869942e-01 -6.43392086e-01 1.20066412e-01
3.88752848e-01 -4.80373859e-01 -5.87543964e-01 6.96025848e-01
4.84355539e-01 -1.48321640e+00 1.19832325e+00 -6.26470923e-01
6.29568279e-01 -3.60499948e-01 -4.46385026e-01 -1.23047483e+00
-4.91270363e-01 -2.54648030e-01 4.67832118e-01 8.96047354e-01
2.37240911e-01 -7.82011449e-01 9.65529740e-01 5.88430464e-01
-1.28290340e-01 -4.26233411e-01 -1.22439384e+00 -1.12788868e+00
3.05321664e-01 -6.85973838e-02 4.97657806e-01 9.95730162e-01
-1.95731685e-01 1.35146856e-01 -2.14952648e-01 2.12206081e-01
8.73009801e-01 4.95816588e-01 7.47840345e-01 -1.25706017e+00
-3.45277727e-01 -4.59245235e-01 -7.29573786e-01 -1.47579813e+00
2.84824908e-01 -7.84425378e-01 -5.91995977e-02 -1.22842383e+00
4.23907965e-01 -3.71277511e-01 -1.67758167e-01 2.39999324e-01
2.56225187e-02 5.14648259e-01 1.14275865e-01 2.86808342e-01
-3.37092727e-01 7.03990459e-01 1.64095318e+00 -2.50374258e-01
1.50108128e-03 -6.04246072e-02 -6.13350093e-01 7.32444525e-01
6.22227669e-01 -1.99300945e-01 -5.37180722e-01 -6.36441410e-01
8.89611170e-02 -2.50343263e-01 6.91599011e-01 -1.13389003e+00
2.22105697e-01 -2.35331476e-01 6.86148643e-01 -3.61362845e-01
5.45580864e-01 -1.04598427e+00 7.75331631e-02 3.26818228e-01
-3.78794819e-01 2.69747283e-02 1.42641619e-01 7.06437349e-01
-4.27834004e-01 3.97402830e-02 1.18447793e+00 -1.71295300e-01
-1.08575451e+00 4.15413737e-01 1.25569776e-01 3.65058839e-01
9.62140262e-01 -8.04909945e-01 -4.46013868e-01 -3.91825080e-01
-3.79599899e-01 5.61027601e-02 1.18201518e+00 6.22145653e-01
5.51519275e-01 -1.52551234e+00 -5.13824821e-01 6.29630446e-01
5.42735100e-01 1.79355651e-01 2.78516322e-01 4.63387787e-01
-5.20834208e-01 4.76217240e-01 -5.99664390e-01 -9.41839099e-01
-1.06945932e+00 5.32028556e-01 5.82617760e-01 1.27262309e-01
-6.82132244e-01 7.37690628e-01 8.74729156e-01 -4.54148680e-01
3.41980636e-01 -2.37614036e-01 1.95913583e-01 -3.36242944e-01
2.65193969e-01 7.89886937e-02 1.96236279e-03 -5.85201085e-01
-3.75500321e-01 9.42746460e-01 -2.46243328e-01 1.27402861e-02
1.32367659e+00 -2.19531894e-01 6.57976940e-02 2.16866553e-01
1.43190908e+00 -2.38616660e-01 -1.92949462e+00 -5.82878351e-01
-2.94901133e-01 -8.37891877e-01 -5.43517582e-02 -4.64309365e-01
-1.17822075e+00 8.08213532e-01 6.03414714e-01 -4.06830609e-02
1.16793752e+00 8.52344111e-02 6.75719738e-01 2.70462960e-01
4.70271736e-01 -1.09392130e+00 1.92960650e-01 5.38528204e-01
8.06851327e-01 -1.70862138e+00 -8.67502615e-02 -5.79088271e-01
-4.39491063e-01 1.00030005e+00 8.89554858e-01 -4.26926225e-01
4.86166894e-01 -1.24158815e-01 5.55049554e-02 -8.11992511e-02
-4.86749262e-01 -1.59735769e-01 4.28624004e-01 8.06193888e-01
3.87458771e-01 -2.98588991e-01 2.28148345e-02 -2.03988850e-01
-4.82029766e-02 -1.39290094e-01 1.23905130e-01 8.13207924e-01
-4.16075408e-01 -1.03946137e+00 -2.15544581e-01 9.01398435e-02
-5.10885417e-02 -9.16971713e-02 -3.82667482e-01 9.89115000e-01
1.25660494e-01 6.94077253e-01 2.81660885e-01 -1.53260916e-01
4.84558463e-01 -2.63346314e-01 6.08943760e-01 -5.68023682e-01
-1.38941824e-01 -9.06611457e-02 -1.45701900e-01 -6.33945525e-01
-6.60544038e-01 -6.69057906e-01 -7.46437490e-01 -3.74791354e-01
1.44786105e-01 -3.28886747e-01 9.65347067e-02 8.08347046e-01
4.01432335e-01 8.35420936e-02 8.14986169e-01 -9.95920837e-01
-5.53526044e-01 -4.91479099e-01 -6.84579551e-01 9.76692319e-01
5.47127545e-01 -9.16139543e-01 -5.00308633e-01 -9.38508660e-02] | [8.735492706298828, -2.299800395965576] |
9f30915d-9ec2-445a-9b60-acd125931fb5 | zegot-zero-shot-segmentation-through-optimal | 2301.12171 | null | https://arxiv.org/abs/2301.12171v2 | https://arxiv.org/pdf/2301.12171v2.pdf | ZegOT: Zero-shot Segmentation Through Optimal Transport of Text Prompts | Recent success of large-scale Contrastive Language-Image Pre-training (CLIP) has led to great promise in zero-shot semantic segmentation by transferring image-text aligned knowledge to pixel-level classification. However, existing methods usually require an additional image encoder or retraining/tuning the CLIP module. Here, we propose a novel Zero-shot segmentation with Optimal Transport (ZegOT) method that matches multiple text prompts with frozen image embeddings through optimal transport. In particular, we introduce a novel Multiple Prompt Optimal Transport Solver (MPOT), which is designed to learn an optimal mapping between multiple text prompts and visual feature maps of the frozen image encoder hidden layers. This unique mapping method facilitates each of the multiple text prompts to effectively focus on distinct visual semantic attributes. Through extensive experiments on benchmark datasets, we show that our method achieves the state-of-the-art (SOTA) performance over existing Zero-shot Semantic Segmentation (ZS3) approaches. | ['Jong Chul Ye', 'Yujin Oh', 'Kwanyoung Kim'] | 2023-01-28 | null | null | null | null | ['zero-shot-segmentation'] | ['computer-vision'] | [ 7.37217963e-01 1.15678795e-01 -4.26245451e-01 -5.92692912e-01
-1.30950356e+00 -2.95072049e-01 3.86466473e-01 -2.64769495e-01
-5.31345606e-01 2.48435155e-01 -1.68687999e-01 -1.06453687e-01
2.43008524e-01 -5.23393929e-01 -1.13358104e+00 -5.34098506e-01
5.73557794e-01 6.09070957e-01 4.84328032e-01 7.56009221e-02
1.94084510e-01 -4.26492721e-01 -1.51583874e+00 4.32288617e-01
9.50822830e-01 1.24905956e+00 6.86981916e-01 6.64583981e-01
-6.48626804e-01 9.99726474e-01 -2.57101715e-01 -3.37452739e-01
2.97518998e-01 -6.26842260e-01 -1.13289416e+00 5.31992197e-01
8.13606679e-01 -5.54197371e-01 -3.34326476e-02 1.07657182e+00
2.23036170e-01 3.63868594e-01 4.68862534e-01 -1.56738806e+00
-1.09024060e+00 6.22732699e-01 -7.03759074e-01 -2.84047686e-02
-7.27189556e-02 4.07799572e-01 1.16544294e+00 -1.01650500e+00
8.22436750e-01 1.15529299e+00 4.83212739e-01 7.95962334e-01
-1.35580516e+00 -4.32967126e-01 3.32618058e-01 2.45132983e-01
-1.34431386e+00 -2.60331511e-01 6.35075390e-01 -4.72683191e-01
9.24419284e-01 -1.01480246e-01 7.19136834e-01 1.12558472e+00
-4.73680608e-02 1.31372881e+00 1.11276186e+00 -3.56807262e-01
2.69655108e-01 1.56690165e-01 1.20975278e-01 1.02538347e+00
-2.99052864e-01 -4.59995329e-01 -6.97536886e-01 5.20340383e-01
6.90009713e-01 9.42286775e-02 -3.25197615e-02 -4.22512680e-01
-1.21326292e+00 7.56467044e-01 4.93859231e-01 -2.56891139e-02
-8.82840157e-02 5.32856822e-01 4.00080919e-01 9.41792205e-02
6.33311093e-01 2.22119540e-01 -4.66201246e-01 5.42323366e-02
-1.22707319e+00 -3.15265846e-03 4.35188532e-01 1.28124785e+00
1.14233875e+00 6.79491758e-02 -7.44111478e-01 8.68243992e-01
-2.30786521e-02 4.63790268e-01 3.79610777e-01 -1.36593020e+00
4.56054717e-01 6.04087472e-01 -2.18769193e-01 -2.21942306e-01
8.69724229e-02 2.09053252e-02 -5.51500320e-01 -2.48626843e-02
2.27968827e-01 5.05410023e-02 -1.55489695e+00 1.63810861e+00
2.21040517e-01 6.20057404e-01 2.16776773e-01 1.03654957e+00
6.49838984e-01 8.91684353e-01 4.45964903e-01 2.20310926e-01
1.34209383e+00 -1.52232242e+00 -5.91394663e-01 -5.39890826e-01
5.85424423e-01 -5.64519405e-01 1.69211316e+00 -1.04679167e-01
-1.17766106e+00 -4.48420018e-01 -9.69965339e-01 -7.42523909e-01
-6.31974936e-01 -1.00039817e-01 5.26127994e-01 3.29664856e-01
-1.15455925e+00 4.90855843e-01 -7.40565956e-01 -4.91898209e-01
8.12675953e-01 2.69649267e-01 -3.00282612e-02 -2.98545659e-01
-1.09651065e+00 3.91921222e-01 5.49640656e-01 -3.43330145e-01
-1.17421567e+00 -1.11800992e+00 -1.04555345e+00 1.64869577e-01
7.55994916e-01 -7.98447490e-01 1.37443614e+00 -1.34225392e+00
-1.57362616e+00 1.23415923e+00 -3.03352714e-01 -5.04465401e-01
4.82530534e-01 -1.24190085e-01 3.11151624e-01 3.98782909e-01
6.63923442e-01 1.65842056e+00 1.06015861e+00 -1.19445562e+00
-5.90985537e-01 -1.73116580e-01 -8.94760266e-02 2.89764941e-01
-5.09820342e-01 8.75767320e-02 -1.05739844e+00 -4.66687590e-01
-2.41704062e-01 -7.02032745e-01 -2.23242283e-01 4.97462690e-01
-5.34427226e-01 -3.11641932e-01 9.04248834e-01 -5.20659208e-01
7.87685752e-01 -2.03551388e+00 2.66926289e-01 -3.54669690e-01
1.06401011e-01 1.01082474e-01 -3.29628497e-01 -7.23391864e-03
2.49038368e-01 1.18304975e-01 -7.23226070e-01 -8.78424466e-01
1.65525153e-01 5.25174499e-01 -2.35052258e-01 1.81797087e-01
4.87530082e-01 1.53801870e+00 -8.76601875e-01 -1.18509531e+00
5.90625465e-01 4.03872252e-01 -5.89453697e-01 1.79156989e-01
-7.30749011e-01 1.46680549e-01 -4.08095032e-01 8.11673701e-01
5.56564033e-01 -7.29549050e-01 -2.23155126e-01 5.39280334e-03
-7.96195567e-02 -3.85238737e-01 -5.97274065e-01 2.28111863e+00
-2.79895335e-01 7.78604627e-01 -9.20364782e-02 -1.11805093e+00
4.77256805e-01 1.29051194e-01 6.91320896e-01 -1.01770771e+00
3.30802560e-01 7.19522685e-02 -7.17792332e-01 -5.39387465e-01
6.13995373e-01 5.23925759e-02 -2.29785413e-01 3.99055481e-01
5.71995080e-01 -1.56476438e-01 1.07815087e-01 5.24286509e-01
6.46710575e-01 3.24948609e-01 -1.78536102e-01 -2.86335021e-01
1.98491260e-01 3.13843787e-01 4.76183623e-01 8.49144399e-01
-4.03668463e-01 9.15136218e-01 5.10305583e-01 -1.05891727e-01
-1.38270044e+00 -1.06006908e+00 1.01695638e-02 1.38729548e+00
4.57063466e-01 -1.81245759e-01 -1.35638309e+00 -6.17225289e-01
-2.31266007e-01 7.16910183e-01 -9.38365281e-01 9.97495055e-02
-2.58420050e-01 -3.22653323e-01 4.58606154e-01 8.65920544e-01
7.51114964e-01 -9.61306334e-01 -6.32069409e-01 2.67663412e-02
-2.87851781e-01 -1.68089521e+00 -9.29551482e-01 4.43410099e-01
-6.15306318e-01 -7.47526824e-01 -1.08571231e+00 -1.06991577e+00
7.74433911e-01 6.93140626e-01 8.26588869e-01 -1.44276107e-02
-5.34348190e-01 5.79450071e-01 -3.91427368e-01 -1.26244187e-01
-3.71606834e-02 2.90325135e-01 -5.79882383e-01 1.45139977e-01
3.48299593e-01 -1.96618196e-02 -5.94015658e-01 1.59361556e-01
-1.20478427e+00 7.01702476e-01 3.54576766e-01 8.76501262e-01
8.76691639e-01 -4.63280976e-01 2.19007522e-01 -1.00026798e+00
2.04343259e-01 -3.21085364e-01 -5.31768322e-01 5.88236988e-01
-6.11605942e-01 2.51701325e-01 5.38611472e-01 -2.72811770e-01
-1.16839027e+00 3.60371709e-01 1.07102409e-01 -1.02320457e+00
-6.10221811e-02 6.86036199e-02 -4.92590703e-02 -1.24715768e-01
1.95863172e-01 3.80092829e-01 -1.67488530e-01 -1.41706184e-01
7.70746171e-01 6.84863269e-01 7.52267301e-01 -7.16002941e-01
5.23504257e-01 6.28771186e-01 -4.03791010e-01 -8.55373502e-01
-1.45130360e+00 -8.22389424e-01 -8.30657184e-01 -2.52596527e-01
1.68702602e+00 -1.07417107e+00 -3.10218543e-01 6.65066481e-01
-1.21408093e+00 -9.45404768e-01 -3.04397345e-01 -1.09640032e-01
-9.30948019e-01 1.83286324e-01 -5.64838290e-01 -5.33311784e-01
-5.66800296e-01 -1.37481189e+00 1.73283052e+00 3.18373352e-01
1.14145234e-01 -1.01842499e+00 -3.49615097e-01 6.79847062e-01
1.64596573e-01 -6.00846671e-02 8.62170458e-01 -2.38601893e-01
-9.81899381e-01 3.53530347e-01 -8.59256506e-01 4.18757975e-01
-4.22593683e-01 -3.07953656e-01 -1.10453403e+00 -6.18939921e-02
-3.74930799e-01 -7.08306730e-01 1.16592097e+00 5.89815378e-01
1.40198076e+00 -2.65335012e-02 -2.08793685e-01 1.19828081e+00
1.72459507e+00 -3.09090447e-02 7.15518773e-01 3.78776014e-01
1.10596335e+00 5.11109948e-01 7.73462594e-01 1.37004510e-01
5.44336557e-01 5.75490475e-01 2.92325377e-01 -4.48567510e-01
-4.35288876e-01 -5.27039349e-01 4.31040585e-01 5.74729145e-01
3.07454765e-01 -2.63600916e-01 -6.79078996e-01 7.65109777e-01
-2.10756540e+00 -6.78687096e-01 1.56079173e-01 1.82931066e+00
8.16302359e-01 -6.10906631e-02 -2.16753930e-01 -3.54427755e-01
7.13959038e-01 3.55411977e-01 -9.71277714e-01 -3.68862897e-01
-1.34178922e-01 3.10863823e-01 9.12996531e-01 4.69319999e-01
-1.15120578e+00 1.80117190e+00 5.84362984e+00 1.06499434e+00
-1.04235816e+00 5.50246239e-01 8.69900763e-01 -1.04257397e-01
-3.28285605e-01 1.39725894e-01 -8.40024233e-01 4.58638370e-01
6.84164762e-01 -2.69506961e-01 6.21692955e-01 9.22678769e-01
-6.04468398e-02 -1.08631715e-01 -8.48872006e-01 9.68010604e-01
4.17461336e-01 -1.60098803e+00 2.83052862e-01 -1.61898062e-01
1.19724166e+00 1.60571009e-01 1.63029388e-01 3.20112169e-01
2.65053988e-01 -8.10657382e-01 8.71489525e-01 2.83626229e-01
1.22671640e+00 -4.41457838e-01 2.01734066e-01 2.00949353e-03
-1.22941220e+00 -5.43697998e-02 -3.63716632e-01 3.95993739e-01
3.11726868e-01 1.20321780e-01 -6.83643222e-01 2.21015751e-01
8.06722164e-01 9.93168473e-01 -4.98839796e-01 5.48369825e-01
-1.55766636e-01 4.60525990e-01 5.96394427e-02 8.46694335e-02
8.01352859e-01 -4.23510820e-02 1.15845412e-01 1.06206620e+00
1.57708481e-01 1.14875793e-01 3.91382873e-01 1.27773118e+00
-3.63023698e-01 1.27935320e-01 -3.48921478e-01 -2.47036189e-01
1.46101460e-01 1.17634237e+00 -1.12970161e+00 -6.52628660e-01
-6.02299333e-01 1.68985200e+00 2.97717899e-01 6.41572595e-01
-9.85627949e-01 -3.11964989e-01 7.27427125e-01 -5.51730208e-02
5.02768099e-01 -1.14162348e-01 -4.32649553e-01 -1.30501997e+00
-2.01647609e-01 -2.56054014e-01 2.39580750e-01 -1.22224796e+00
-9.64688659e-01 2.62317270e-01 -1.11144997e-01 -9.57805336e-01
1.24362841e-01 -7.01336026e-01 -5.14527142e-01 6.15479112e-01
-1.71338820e+00 -1.60123467e+00 -4.56389874e-01 8.66535068e-01
1.30947578e+00 2.07609609e-01 5.68664789e-01 2.06915408e-01
-7.10189521e-01 5.70479691e-01 2.56756712e-02 2.18448266e-01
6.48474991e-01 -1.38579202e+00 6.58213198e-01 8.72292459e-01
4.48013023e-02 6.16714470e-02 4.27552730e-01 -5.20657539e-01
-1.32627058e+00 -1.43334270e+00 5.28833508e-01 -3.99499655e-01
7.47991562e-01 -5.91228127e-01 -9.14904058e-01 9.66909170e-01
4.81655806e-01 9.41308662e-02 3.60986859e-01 -3.92864943e-01
-3.11686605e-01 1.06664680e-01 -7.19628215e-01 5.48646867e-01
9.90047216e-01 -6.72259331e-01 -3.43061268e-01 3.11503708e-01
1.30375433e+00 -3.37227553e-01 -6.41094804e-01 1.44674089e-02
2.67632544e-01 -5.10650039e-01 1.00548089e+00 -4.68928218e-01
7.93672681e-01 -9.84432101e-02 -2.06353515e-01 -9.64797735e-01
2.63179708e-02 -5.36356330e-01 2.44847059e-01 1.08732712e+00
2.65049785e-01 -1.75816923e-01 8.84721935e-01 6.86246753e-01
-4.47573662e-01 -7.24636972e-01 -8.64820659e-01 -6.73093855e-01
6.87236264e-02 -5.49758971e-01 2.61772037e-01 9.87398624e-01
-3.08455139e-01 5.00926375e-01 -5.71133673e-01 -1.38294563e-01
9.48326826e-01 1.01785623e-01 5.76463521e-01 -9.57610846e-01
-1.51317403e-01 -4.28956419e-01 -2.86579043e-01 -1.19173205e+00
4.05013174e-01 -1.14426315e+00 4.79762346e-01 -1.81826794e+00
6.24969244e-01 -1.77568212e-01 -3.92694026e-01 7.81244755e-01
-2.85022199e-01 3.93812507e-01 2.62594342e-01 5.15048020e-03
-1.33951962e+00 6.47896647e-01 1.51656103e+00 -4.39564705e-01
-9.00283083e-02 -6.05210841e-01 -6.16809189e-01 4.26772773e-01
5.63841701e-01 -5.36797404e-01 -6.45885229e-01 -7.98456907e-01
-4.84435335e-02 -1.24085313e-02 6.42691195e-01 -8.69203687e-01
4.55990553e-01 -2.32545972e-01 2.12431267e-01 -5.40011466e-01
4.04930025e-01 -4.77201432e-01 -4.68376130e-01 1.34289384e-01
-6.07605696e-01 -4.73642886e-01 4.83869649e-02 6.17264509e-01
-1.59843832e-01 -2.56101787e-01 9.78286922e-01 -2.89585799e-01
-1.43326998e+00 6.06478214e-01 -1.03951395e-01 4.46735471e-01
1.23139858e+00 -3.68120074e-01 -4.19122994e-01 -1.62468292e-02
-4.88740802e-01 7.59591758e-01 6.79919302e-01 5.81179082e-01
7.66371191e-01 -1.00154078e+00 -3.51289392e-01 2.30131939e-01
4.64876205e-01 1.73769802e-01 6.29017770e-01 9.82309103e-01
-4.15940344e-01 4.35079187e-01 -2.53842592e-01 -9.04514909e-01
-1.15451527e+00 6.10731781e-01 3.78990978e-01 1.56686381e-01
-1.04822659e+00 1.02991843e+00 5.49254656e-01 -1.90257385e-01
3.10436666e-01 -3.43794316e-01 3.40513021e-01 -3.94130610e-02
3.81539911e-01 6.04140498e-02 -3.45451057e-01 -5.11904836e-01
-2.81340512e-03 8.81411970e-01 -3.22344124e-01 -3.31167072e-01
1.04334414e+00 -2.84632534e-01 1.10635534e-01 5.68485796e-01
1.53742266e+00 -1.00897229e+00 -1.88065708e+00 -3.27661514e-01
-1.18540727e-01 -4.80770946e-01 3.85504484e-01 -5.53020060e-01
-1.17844605e+00 1.28370345e+00 4.81167018e-01 -1.99234262e-01
1.06795919e+00 2.99968630e-01 1.35833693e+00 2.61129647e-01
2.60519236e-01 -1.54814017e+00 4.76826847e-01 5.30885160e-01
1.27270073e-01 -1.55861402e+00 -5.22410214e-01 -3.50637704e-01
-1.08630049e+00 7.88234413e-01 8.30846548e-01 -5.26670180e-02
2.85839021e-01 1.36785582e-01 1.96656391e-01 -1.30136341e-01
-7.90176749e-01 -7.46671975e-01 1.19776376e-01 5.33125520e-01
4.59883362e-02 -5.31456545e-02 8.89158100e-02 4.58491921e-01
3.39079738e-01 1.63852364e-01 2.80200869e-01 7.31733918e-01
-7.25805283e-01 -8.47021818e-01 -2.03266870e-02 2.96589464e-01
-2.38714725e-01 -3.91015053e-01 -3.34275067e-01 6.36861682e-01
3.02648693e-02 5.10713398e-01 3.57005388e-01 -2.12391570e-01
-3.43903527e-02 2.87743419e-01 4.92397249e-01 -6.92680955e-01
-2.04433888e-01 2.02867791e-01 -4.77936476e-01 -8.18990231e-01
-3.96158218e-01 -5.28504014e-01 -1.61899579e+00 5.15049696e-02
4.29190136e-02 -2.28619084e-01 7.59142280e-01 1.13720429e+00
2.35384449e-01 8.39550018e-01 2.56015837e-01 -8.53180528e-01
-5.60641214e-02 -5.15516460e-01 -5.84762335e-01 4.88869220e-01
1.63320258e-01 -5.85900962e-01 -9.97617394e-02 4.75873530e-01] | [9.7290678024292, 0.8257309198379517] |
893b210d-21a1-4545-8a37-3f75b2f9061c | on-the-relation-between-sharpness-aware | 2305.05392 | null | https://arxiv.org/abs/2305.05392v2 | https://arxiv.org/pdf/2305.05392v2.pdf | Sharpness-Aware Minimization Alone can Improve Adversarial Robustness | Sharpness-Aware Minimization (SAM) is an effective method for improving generalization ability by regularizing loss sharpness. In this paper, we explore SAM in the context of adversarial robustness. We find that using only SAM can achieve superior adversarial robustness without sacrificing clean accuracy compared to standard training, which is an unexpected benefit. We also discuss the relation between SAM and adversarial training (AT), a popular method for improving the adversarial robustness of DNNs. In particular, we show that SAM and AT differ in terms of perturbation strength, leading to different accuracy and robustness trade-offs. We provide theoretical evidence for these claims in a simplified model. Finally, while AT suffers from decreased clean accuracy and computational overhead, we suggest that SAM can be regarded as a lightweight substitute for AT under certain requirements. Code is available at https://github.com/weizeming/SAM_AT. | ['Yihao Zhang', 'Jingyu Zhu', 'Zeming Wei'] | 2023-05-09 | null | null | null | null | ['mathematical-proofs'] | ['miscellaneous'] | [ 1.29404619e-01 -1.46704791e-02 -9.34248939e-02 -1.53182030e-01
-1.06980836e+00 -1.06615245e+00 3.66605699e-01 -7.63026671e-03
-3.09564590e-01 7.81290472e-01 3.44229639e-01 -5.06901860e-01
-8.91777724e-02 -6.63382530e-01 -9.53029275e-01 -7.28767872e-01
5.52335009e-02 -3.43115211e-01 -9.85088423e-02 -3.20128053e-01
1.12051815e-01 6.61999404e-01 -1.06763458e+00 -6.57400936e-02
1.10140944e+00 8.41315627e-01 -2.48145610e-01 7.02405214e-01
2.91047126e-01 7.03240216e-01 -7.06906378e-01 -9.15043950e-01
9.32578981e-01 -3.13425332e-01 -7.05709517e-01 -2.73548633e-01
8.14842701e-01 -3.81427705e-01 -7.57155478e-01 1.39580059e+00
7.05913186e-01 3.14548105e-01 4.20857787e-01 -1.30767202e+00
-7.02940345e-01 7.89671361e-01 -2.30172306e-01 2.97319412e-01
-6.60272688e-02 3.36074412e-01 9.42362189e-01 -5.87350488e-01
2.65272349e-01 9.97025132e-01 9.11434829e-01 1.00911224e+00
-1.14692080e+00 -7.55377293e-01 2.35753283e-01 -2.77070124e-02
-1.22738397e+00 -8.77327144e-01 7.14768529e-01 -8.90660584e-02
5.23246408e-01 6.74106836e-01 8.33379626e-02 1.24468541e+00
1.44796595e-01 7.82294750e-01 9.25283015e-01 -1.91630661e-01
3.31037462e-01 5.00245430e-02 5.45516089e-02 3.63183856e-01
5.25024891e-01 3.68072867e-01 -2.22982198e-01 -1.84005722e-01
5.82184970e-01 -3.18695828e-02 -6.60860181e-01 -9.16560367e-02
-6.35357320e-01 6.06251955e-01 7.63891697e-01 1.95554495e-01
-1.16510421e-01 4.31483477e-01 5.92996776e-01 7.06243336e-01
4.07897055e-01 7.64809847e-01 -4.75542247e-01 -5.99421896e-02
-7.72447050e-01 3.52672637e-01 6.40808165e-01 1.00276899e+00
4.36405361e-01 3.82966191e-01 -1.73937663e-01 7.10557997e-01
-6.52455240e-02 4.22809780e-01 1.24569096e-01 -1.31374693e+00
6.30020618e-01 -2.20639724e-02 -7.95582533e-02 -6.80299103e-01
-1.30655691e-02 -6.48496032e-01 -9.12608564e-01 4.12197590e-01
5.07673323e-01 -4.09086585e-01 -8.82531226e-01 2.24285889e+00
-1.27963088e-02 1.56479493e-01 1.23870216e-01 6.82120383e-01
3.68243486e-01 4.60814685e-01 -5.07519171e-02 -1.37162298e-01
7.77794719e-01 -8.81109893e-01 -6.07932985e-01 -1.76396623e-01
2.96822339e-01 -5.88840663e-01 1.13098311e+00 4.05557036e-01
-1.44617260e+00 -2.28479728e-01 -1.12600267e+00 -1.01824716e-01
-3.70955527e-01 -4.44979042e-01 5.55208564e-01 9.22827184e-01
-1.12774253e+00 1.01866162e+00 -7.34656513e-01 -2.04503208e-01
6.37224436e-01 2.41439000e-01 -3.20756197e-01 -9.95374024e-02
-1.09363818e+00 9.10948873e-01 5.13595119e-02 -7.11167753e-02
-9.78124499e-01 -1.09171939e+00 -8.80189121e-01 1.29247932e-02
2.69791037e-01 -7.56191969e-01 1.18228459e+00 -9.58557725e-01
-1.50247753e+00 4.64403272e-01 -1.01153858e-01 -7.42399633e-01
8.75261903e-01 -4.48224723e-01 -3.99125844e-01 4.41509634e-02
-1.94116026e-01 3.29635084e-01 9.69853997e-01 -1.32704747e+00
-1.35768354e-01 -2.76708335e-01 4.13535655e-01 -2.69131884e-02
-5.53578019e-01 3.71449511e-03 -3.04710299e-01 -1.04816771e+00
-1.85034886e-01 -8.52528393e-01 -3.23988646e-01 2.34231412e-01
-6.15257919e-01 2.77090758e-01 5.92706382e-01 -7.24390447e-01
1.18868303e+00 -2.28683567e+00 1.53577745e-01 3.40332568e-01
2.57261634e-01 5.85486174e-01 -2.37256587e-01 3.26220721e-01
-1.89720854e-01 5.62803030e-01 -6.17368221e-01 -3.41782957e-01
9.07469168e-02 1.58248082e-01 -6.21779203e-01 6.57272995e-01
2.20417723e-01 8.57533813e-01 -8.09623539e-01 -6.39339611e-02
1.83253407e-01 5.00938892e-01 -6.38621390e-01 1.26462147e-01
5.39896451e-02 3.87061864e-01 -1.51801050e-01 7.25945294e-01
1.00435138e+00 3.14598441e-01 -1.19497441e-01 -1.78943947e-01
1.62573248e-01 2.58096308e-01 -9.27401960e-01 1.50242710e+00
-5.25119305e-01 7.61694610e-01 4.10401136e-01 -8.86365831e-01
3.86353403e-01 2.31755912e-01 3.16550247e-02 -6.02492392e-01
1.31831378e-01 1.78882092e-01 -8.67520273e-02 -6.89114183e-02
3.19754362e-01 -1.89466789e-01 -4.17084806e-02 1.86002418e-01
1.69862993e-02 -9.25638527e-02 1.11118458e-01 3.28158915e-01
1.36802804e+00 -2.20599979e-01 -2.18412075e-02 -3.57983053e-01
2.26696968e-01 -5.09291708e-01 7.40271270e-01 9.00245547e-01
-4.29757297e-01 5.98243177e-01 3.19815487e-01 -3.40609588e-02
-1.23043263e+00 -1.31861436e+00 -3.12323630e-01 8.09098840e-01
2.04966277e-01 -3.23026359e-01 -8.55484068e-01 -7.68944383e-01
2.12550893e-01 7.90395796e-01 -5.58702588e-01 -4.21510965e-01
-5.48854709e-01 -6.30611241e-01 1.04607666e+00 5.47342241e-01
4.86273974e-01 -4.98843074e-01 -1.46807149e-01 -2.70447969e-01
-3.73922177e-02 -9.55175698e-01 -7.31548786e-01 3.12406629e-01
-8.73022079e-01 -8.51153374e-01 -7.02368081e-01 -3.13263357e-01
7.26199687e-01 4.39279497e-01 9.75043774e-01 3.59648108e-01
-3.95567566e-02 3.56133938e-01 -3.02480847e-01 -3.82148594e-01
-6.11772597e-01 6.77648708e-02 3.68227303e-01 -4.94411081e-01
-3.46087009e-01 -9.82155800e-01 -6.27154887e-01 2.50034869e-01
-1.30869842e+00 -4.41087514e-01 4.14731562e-01 6.81775808e-01
3.98890942e-01 1.64329320e-01 6.45293951e-01 -7.47366667e-01
7.73087263e-01 -4.81467426e-01 -4.46985632e-01 7.97973648e-02
-7.21291959e-01 2.37958983e-01 1.05320776e+00 -3.11826766e-01
-8.69681776e-01 -4.20796365e-01 -5.53374946e-01 -5.55873215e-01
-4.09609526e-02 6.94057718e-02 -3.63051504e-01 -5.15007734e-01
9.05270398e-01 1.19565018e-02 -2.12251954e-02 -4.79574770e-01
4.80206221e-01 2.58498967e-01 5.47404349e-01 -7.64227748e-01
1.31587720e+00 3.36466998e-01 -2.23861281e-02 -4.56307888e-01
-9.52306151e-01 -2.32750969e-03 -2.38904163e-01 7.48296082e-02
2.81167299e-01 -7.83870578e-01 -4.67296690e-01 4.50674653e-01
-6.98113918e-01 -5.79820454e-01 -5.27540445e-01 1.74643490e-02
-5.19296587e-01 5.44457912e-01 -9.02599990e-01 -6.90829575e-01
-5.84410429e-01 -8.94608140e-01 5.48976958e-01 2.34830007e-01
1.31686419e-01 -1.16680324e+00 -1.39512986e-01 1.24232173e-01
6.94480538e-01 5.23937345e-01 5.12544751e-01 -5.75114369e-01
-2.97669858e-01 -3.03548425e-01 -1.29496858e-01 8.86676490e-01
1.11373246e-01 -7.77729554e-03 -1.16547394e+00 -6.14455700e-01
2.25533873e-01 -2.48728454e-01 9.90784407e-01 3.02572846e-01
1.47890830e+00 -8.10045540e-01 1.74802691e-01 1.12960029e+00
1.67759693e+00 -1.12451687e-01 7.59032786e-01 4.75573957e-01
7.94644773e-01 1.36674091e-01 2.65096575e-01 2.96488672e-01
-1.65775150e-01 5.09563684e-01 6.41608477e-01 -6.77509159e-02
-2.93404341e-01 -2.18971655e-01 7.75021791e-01 5.40723503e-01
-1.06806809e-03 -2.74363548e-01 -6.47318542e-01 4.68159586e-01
-1.57892299e+00 -1.20655274e+00 1.91003978e-01 2.38207626e+00
1.07797253e+00 3.96859646e-01 2.10961699e-01 4.08301324e-01
6.53650403e-01 2.25214347e-01 -7.23478734e-01 -6.03385985e-01
-3.24533403e-01 2.62466460e-01 1.07744873e+00 7.46243536e-01
-1.01656199e+00 7.88409233e-01 7.03878164e+00 1.06936038e+00
-1.04990649e+00 7.66431242e-02 7.04399645e-01 -3.77167076e-01
-5.66960812e-01 -1.55841321e-01 -4.71872628e-01 5.65249860e-01
9.38052118e-01 -4.41139966e-01 8.16962838e-01 7.12428212e-01
-4.40100133e-02 4.11261380e-01 -1.18390417e+00 5.35155773e-01
-9.68971029e-02 -1.33341265e+00 2.44393051e-01 -6.08191267e-02
8.51318121e-01 2.78865565e-02 4.26120520e-01 1.18040361e-01
4.04073060e-01 -9.49621677e-01 6.63008809e-01 2.81075507e-01
7.33439565e-01 -1.11032736e+00 6.78598344e-01 2.11684063e-01
-8.93711865e-01 -2.66649753e-01 -4.23132718e-01 1.34451792e-01
-3.00592743e-02 8.24075460e-01 -3.18700910e-01 7.09854364e-01
6.32142901e-01 4.33788449e-01 -5.12591600e-01 9.95094657e-01
-3.31363589e-01 8.22691083e-01 -2.74843961e-01 5.36612332e-01
1.24331146e-01 6.78238720e-02 8.79760921e-01 1.36422467e+00
2.62458771e-01 -3.54594626e-02 -2.13985458e-01 8.97439718e-01
-4.57796842e-01 -3.73776078e-01 -7.48591959e-01 1.24686971e-01
8.52447331e-01 1.01269901e+00 -2.39124671e-01 -1.73719585e-01
-5.97679578e-02 1.16441500e+00 3.69080812e-01 5.23733020e-01
-1.06665361e+00 -6.49986148e-01 1.17259324e+00 -1.05235562e-01
9.06525254e-02 -1.76094115e-01 -6.03789687e-01 -1.19148862e+00
1.89748079e-01 -1.02190483e+00 1.37696967e-01 -2.39260733e-01
-1.48540962e+00 3.86890471e-01 -2.28732795e-01 -1.28925216e+00
2.95878053e-01 -5.45504391e-01 -8.60068500e-01 7.36227989e-01
-1.47976315e+00 -6.90816283e-01 -7.34385774e-02 7.92384207e-01
4.69992340e-01 1.42520905e-01 5.45166850e-01 4.09382343e-01
-8.30085218e-01 1.45169246e+00 3.71956289e-01 5.29437400e-02
7.27559626e-01 -1.34959567e+00 8.31230640e-01 1.38040519e+00
-3.86651866e-02 7.94986844e-01 1.00049007e+00 -2.30336159e-01
-1.48229110e+00 -1.43592191e+00 3.97760451e-01 -6.28828704e-01
6.44186199e-01 -2.09115297e-01 -8.72427166e-01 6.39629960e-01
1.80748716e-01 -7.59842023e-02 7.24379599e-01 -7.00865537e-02
-7.34242380e-01 -2.07868397e-01 -1.73550236e+00 1.01337814e+00
1.33367300e+00 -6.37228727e-01 -3.02070081e-01 2.78593063e-01
1.02172792e+00 -4.16507691e-01 -1.01384640e+00 4.17960823e-01
2.62630522e-01 -1.04396844e+00 1.20301294e+00 -4.25065011e-01
4.07387674e-01 -1.65266857e-01 -4.10538465e-01 -1.47910666e+00
-3.62066418e-01 -9.76536572e-01 -2.82615423e-01 1.34241617e+00
3.96929502e-01 -8.32357824e-01 4.32620943e-01 8.37949932e-01
-3.37439328e-01 -6.88218892e-01 -9.12223101e-01 -1.31714439e+00
6.37820065e-01 -5.63833714e-01 5.54822803e-01 9.58596647e-01
3.73579226e-02 -2.58471251e-01 -4.48637217e-01 4.32639211e-01
7.74613678e-01 -4.35522109e-01 5.93394041e-01 -6.97832406e-01
-2.50379354e-01 -6.40741706e-01 -3.33813548e-01 -9.49920475e-01
2.72094965e-01 -9.30166781e-01 -3.00104860e-02 -1.22819424e+00
-1.09068137e-02 -4.31991667e-01 -5.43322980e-01 5.23673832e-01
-4.56175059e-01 3.86690348e-01 3.82454127e-01 7.49210715e-02
-3.80409271e-01 3.80300909e-01 1.01749635e+00 -9.66346040e-02
-8.80645774e-03 1.67705849e-01 -1.23959494e+00 5.78426421e-01
1.35953319e+00 -3.21679384e-01 -3.34594190e-01 -7.03759432e-01
8.32517371e-02 -3.09812337e-01 3.50016236e-01 -1.24774337e+00
5.54921143e-02 -1.11496769e-01 2.16575772e-01 1.46828383e-01
1.83836833e-01 -7.47368395e-01 -1.22965686e-01 4.49102789e-01
-6.15714490e-01 -7.99640082e-03 5.99660099e-01 4.84879434e-01
8.04797222e-04 -1.81663468e-01 1.11443913e+00 7.46597052e-02
-2.97148228e-01 5.22857487e-01 -1.56406775e-01 2.74441987e-01
8.39053094e-01 1.65226625e-03 -6.94564521e-01 -4.27274585e-01
-4.87979084e-01 1.17405854e-01 8.15485477e-01 1.61145896e-01
5.81209779e-01 -1.46366155e+00 -6.57494962e-01 -1.21189817e-03
-1.76809549e-01 -1.90233037e-01 2.22061887e-01 5.77960551e-01
-3.43418181e-01 5.92576005e-02 -1.54980242e-01 1.34478509e-01
-1.16157126e+00 7.57455945e-01 5.54973722e-01 -2.55952571e-02
-5.24804533e-01 1.25129783e+00 -1.38008909e-03 -2.84270942e-01
5.25498807e-01 -2.35760570e-01 5.07483900e-01 -5.60741305e-01
6.17588222e-01 6.53568268e-01 -3.18327569e-03 -2.33653754e-01
-3.00097436e-01 4.03827250e-01 -8.61880854e-02 -1.20454393e-01
1.05844152e+00 -5.85213117e-02 -7.30610592e-03 3.15904357e-02
1.29070544e+00 4.56381977e-01 -1.51177573e+00 -1.33639127e-01
-2.49366775e-01 -7.08496034e-01 4.63178903e-02 -6.82207823e-01
-1.38655496e+00 6.76349759e-01 4.53952879e-01 2.98193187e-01
1.41545558e+00 -1.95243880e-01 9.63690639e-01 3.92738819e-01
1.91248477e-01 -8.97318602e-01 -2.26291478e-01 4.29571211e-01
9.71904993e-01 -1.11125016e+00 4.08642553e-02 -3.18048626e-01
-4.99152809e-01 6.78744793e-01 5.19771814e-01 -4.37327057e-01
5.80245912e-01 4.24683541e-01 -5.43549992e-02 3.16337258e-01
-6.79193437e-01 -1.95475668e-02 1.42395228e-01 7.03099191e-01
2.44383708e-01 -6.64416980e-03 -2.08351135e-01 4.19295043e-01
-4.92261559e-01 -4.06888247e-01 3.61376077e-01 8.12005043e-01
-3.79611939e-01 -1.21558070e+00 -3.56335849e-01 3.40895027e-01
-9.09562230e-01 -4.07025129e-01 -4.08212662e-01 4.08826441e-01
8.82765949e-02 1.04040933e+00 -3.89709055e-01 -6.64028883e-01
3.30782712e-01 -1.59767896e-01 5.84866762e-01 -2.08891511e-01
-7.09858775e-01 -5.59793830e-01 3.43009867e-02 -7.52434075e-01
3.65908891e-02 -4.09718305e-01 -9.66448963e-01 -9.63362575e-01
-2.54769772e-01 1.36150077e-01 4.97618109e-01 7.37644076e-01
4.02003825e-01 5.27897894e-01 9.94183242e-01 -7.43736863e-01
-9.08682108e-01 -5.30153215e-01 -3.74567121e-01 4.52773958e-01
8.18110645e-01 -2.55126357e-01 -8.43444169e-01 -1.65853143e-01] | [5.655416488647461, 7.829474925994873] |
60f63592-dda8-4ae8-86aa-21b57e58c22a | brundlefly-at-semeval-2016-task-12-recurrent-1 | null | null | https://aclanthology.org/S16-1198 | https://aclanthology.org/S16-1198.pdf | Brundlefly at SemEval-2016 Task 12: Recurrent Neural Networks vs. Joint Inference for Clinical Temporal Information Extraction | null | ['Jason Fries'] | 2016-06-01 | null | null | null | semeval-2016-6 | ['temporal-information-extraction'] | ['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] | [-7.221503734588623, 3.6570255756378174] |
c278567b-39a0-4f79-b35f-85779da2cb50 | robust-multimodal-fusion-for-human-activity | 2303.04636 | null | https://arxiv.org/abs/2303.04636v1 | https://arxiv.org/pdf/2303.04636v1.pdf | Robust Multimodal Fusion for Human Activity Recognition | The proliferation of IoT and mobile devices equipped with heterogeneous sensors has enabled new applications that rely on the fusion of time-series data generated by multiple sensors with different modalities. While there are promising deep neural network architectures for multimodal fusion, their performance falls apart quickly in the presence of consecutive missing data and noise across multiple modalities/sensors, the issues that are prevalent in real-world settings. We propose Centaur, a multimodal fusion model for human activity recognition (HAR) that is robust to these data quality issues. Centaur combines a data cleaning module, which is a denoising autoencoder with convolutional layers, and a multimodal fusion module, which is a deep convolutional neural network with the self-attention mechanism to capture cross-sensor correlation. We train Centaur using a stochastic data corruption scheme and evaluate it on three datasets that contain data generated by multiple inertial measurement units. Centaur's data cleaning module outperforms 2 state-of-the-art autoencoder-based models and its multimodal fusion module outperforms 4 strong baselines. Compared to 2 related robust fusion architectures, Centaur is more robust, achieving 11.59-17.52% higher accuracy in HAR, especially in the presence of consecutive missing data in multiple sensor channels. | ['Omid Ardakanian', 'Xin Yang', 'Sanju Xaviar'] | 2023-03-08 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [-4.81292494e-02 -3.08614463e-01 3.34018141e-01 -2.67602324e-01
-1.09578383e+00 -1.43254921e-01 6.14204645e-01 2.01769710e-01
-5.74667037e-01 6.99284434e-01 8.63931596e-01 2.42388353e-01
-2.93228626e-02 -5.51765382e-01 -1.02289343e+00 -7.01938987e-01
-1.95732657e-02 5.23458160e-02 -3.78245950e-01 -5.17313540e-01
-4.60019469e-01 -3.10526416e-02 -1.67455339e+00 2.47909039e-01
6.16934478e-01 1.45495784e+00 -4.17579710e-01 8.35324347e-01
3.50532562e-01 9.77932990e-01 -8.28887701e-01 -2.20326483e-01
7.77763054e-02 -1.34641841e-01 -3.62295210e-01 -2.04707518e-01
5.86489022e-01 -1.04743212e-01 -4.70712900e-01 7.28261769e-01
9.04699326e-01 1.37423843e-01 2.02907890e-01 -1.44609284e+00
-6.79091275e-01 4.56354380e-01 -2.17650786e-01 2.34448567e-01
6.74464703e-01 1.93671048e-01 4.68649656e-01 -6.88059449e-01
1.16004013e-01 1.18014538e+00 1.30769193e+00 2.50930756e-01
-1.13708317e+00 -3.69085401e-01 -1.97573811e-01 3.07780821e-02
-1.19915473e+00 -6.05504751e-01 6.58494532e-01 -3.84971023e-01
1.32935095e+00 1.22878857e-01 4.50946689e-01 2.03943992e+00
5.86231470e-01 7.17732489e-01 6.61765337e-01 6.83410540e-02
3.34037840e-01 -5.38401842e-01 2.41303906e-01 2.49820247e-01
4.39043045e-01 9.54663604e-02 -8.83105218e-01 -2.60370612e-01
4.10796165e-01 5.66885710e-01 -1.35848448e-01 -1.30365983e-01
-1.69674850e+00 4.26907778e-01 3.65471780e-01 4.54657584e-01
-8.78425241e-01 3.62350047e-01 5.46705663e-01 3.30179125e-01
1.35987490e-01 3.59462112e-01 -7.67682493e-01 -4.42768335e-01
-7.81354487e-01 1.52787983e-01 6.95151150e-01 6.05042636e-01
3.18349987e-01 4.51839745e-01 -6.60289004e-02 6.55960679e-01
3.57416868e-01 9.06302094e-01 7.94417977e-01 -9.96883750e-01
6.53539240e-01 7.20579565e-01 5.61022051e-02 -8.83472204e-01
-7.74634182e-01 -4.58229870e-01 -1.44361377e+00 -2.83825994e-02
1.41103178e-01 -6.54159904e-01 -8.78188372e-01 1.98355901e+00
1.37464240e-01 4.29062754e-01 4.90717739e-01 8.07992816e-01
9.97456968e-01 3.62477899e-01 3.09026331e-01 5.84579408e-02
1.38363552e+00 -6.17084682e-01 -1.15066874e+00 -2.64039338e-01
3.18896502e-01 -4.23181653e-01 5.45971930e-01 5.62253892e-01
-1.01108170e+00 -9.86011624e-01 -1.47055686e+00 -2.56827652e-01
-8.85408759e-01 -1.33712620e-01 4.75780308e-01 5.69416761e-01
-7.94232070e-01 4.90709662e-01 -1.24071014e+00 -4.88118708e-01
2.80433357e-01 3.09250772e-01 -9.01639342e-01 1.00202166e-01
-1.32702291e+00 9.11433280e-01 5.12691796e-01 2.49543816e-01
-8.94676089e-01 -6.38460040e-01 -1.33115375e+00 4.32176329e-02
4.74977568e-02 -1.00782216e+00 1.09225512e+00 -7.53141463e-01
-1.09671712e+00 4.59007360e-02 3.77091579e-02 -8.05522323e-01
1.92749366e-01 -7.99762845e-01 -1.22400069e+00 -2.49682635e-01
7.39896894e-02 4.09226477e-01 8.82838070e-01 -9.52749133e-01
-6.31202519e-01 -3.04003209e-01 -3.86296779e-01 -1.44274626e-02
-2.13683426e-01 -3.10020298e-01 -1.82381719e-01 -6.68932557e-01
-1.69340089e-01 -6.12671554e-01 3.95762697e-02 -7.75958776e-01
-2.31610119e-01 2.59837694e-02 9.42671359e-01 -8.81269038e-01
1.26019335e+00 -2.14850521e+00 3.26838702e-01 -1.83399748e-02
1.80778816e-01 1.95872903e-01 -7.63884857e-02 5.56554258e-01
-2.58870900e-01 -2.15020195e-01 -3.24108422e-01 -8.98020864e-01
3.00401330e-01 4.05467480e-01 -7.43023008e-02 3.58501881e-01
2.84243345e-01 1.00612605e+00 -7.31882274e-01 -7.29007944e-02
5.96408963e-01 1.07219923e+00 -1.73801407e-01 3.23158890e-01
2.06617355e-01 5.10544002e-01 1.94974288e-01 1.07763624e+00
5.21225452e-01 -2.41496637e-01 -4.93955538e-02 -6.20999157e-01
1.17475621e-01 -4.80506048e-02 -1.33599854e+00 2.06629395e+00
-2.44867638e-01 5.29270411e-01 1.18876532e-01 -7.57025123e-01
4.53939140e-01 5.55356383e-01 9.23601329e-01 -8.98962796e-01
4.91538256e-01 -9.39151943e-02 -4.71137047e-01 -5.91636300e-01
7.52288699e-01 3.89257312e-01 -7.30182648e-01 9.76807252e-02
6.25845790e-01 3.92962188e-01 4.06445563e-02 3.47573124e-02
1.54977596e+00 6.50808439e-02 8.10757130e-02 1.46131337e-01
3.67165178e-01 -5.04521430e-01 8.75909448e-01 9.40956950e-01
-4.29888844e-01 9.01383579e-01 -5.89990839e-02 -6.79628491e-01
-8.14666331e-01 -1.06565452e+00 1.55706614e-01 1.02007461e+00
3.52093391e-02 -7.37454712e-01 -5.74673235e-01 -3.53973478e-01
1.90116212e-01 2.33632028e-01 -1.05123234e+00 -4.07206863e-01
-5.07745147e-01 -1.05951786e+00 8.65490556e-01 9.27411735e-01
8.79350960e-01 -8.86852980e-01 -7.90715277e-01 4.05897319e-01
-7.22859621e-01 -1.29096913e+00 -5.91936037e-02 4.74955291e-01
-5.66135883e-01 -1.28834188e+00 -2.80522913e-01 -9.67087150e-02
-4.70162230e-03 1.54406363e-02 1.35125732e+00 -2.93154985e-01
1.36084724e-02 8.75340521e-01 -4.02367324e-01 -7.43645847e-01
-3.22431810e-02 1.41280681e-01 4.56068784e-01 2.12409139e-01
6.83309972e-01 -6.14915907e-01 -5.76318622e-01 1.72808766e-02
-1.20723724e+00 -3.59200537e-01 4.62431312e-01 8.46815288e-01
5.29393017e-01 -1.14076555e-01 4.53405201e-01 4.28231470e-02
7.17402518e-01 -9.78300035e-01 -2.78323684e-02 6.01828806e-02
-2.89672673e-01 4.39123176e-02 2.46083841e-01 -3.35204661e-01
-8.18588734e-01 8.96185935e-02 -2.82324463e-01 -5.21707237e-01
-5.25803626e-01 6.06828034e-01 -3.63238156e-01 1.97453380e-01
8.46549213e-01 -1.01620398e-01 -1.22356445e-01 -6.65866971e-01
1.61764503e-01 5.90871751e-01 1.20447111e+00 -2.37943769e-01
3.75138611e-01 5.26175380e-01 -1.83265388e-01 -7.43278921e-01
-7.71337569e-01 -4.18642551e-01 -3.29730630e-01 -6.76306710e-02
1.31443620e+00 -1.49829888e+00 -7.02923298e-01 9.55480874e-01
-1.01525486e+00 -8.22423100e-02 -2.65341967e-01 6.17134690e-01
-3.65456879e-01 1.63901955e-01 -5.45598686e-01 -6.46375239e-01
-6.24353349e-01 -9.93830860e-01 1.61287093e+00 3.85849625e-01
-3.68479520e-01 -7.77627170e-01 3.68628770e-01 5.15688300e-01
6.50876343e-01 9.34110105e-01 -4.14383933e-02 -6.98604941e-01
-1.54864267e-01 -4.50288415e-01 3.42680037e-01 6.14142895e-01
1.98262051e-01 -3.06956172e-01 -1.18704295e+00 -3.58496159e-01
1.87964551e-02 -3.26761425e-01 8.72981191e-01 4.21220779e-01
8.94993126e-01 -6.43316731e-02 -1.63540810e-01 7.74369657e-01
1.18483365e+00 -5.12626618e-02 8.06559384e-01 3.33123922e-01
9.27272856e-01 -1.69086203e-01 1.12017572e-01 5.18171132e-01
8.57724071e-01 3.38410199e-01 8.51748705e-01 -1.96441874e-01
6.79012761e-02 5.95187731e-02 6.10617578e-01 9.81853306e-01
-2.06793360e-02 -1.95638910e-01 -8.99593651e-01 6.88592732e-01
-2.30955505e+00 -1.05529904e+00 -5.37267774e-02 2.00007463e+00
3.73292476e-01 -9.97605622e-02 1.81703180e-01 5.09086370e-01
2.70445734e-01 1.69213578e-01 -4.33429450e-01 -1.12176895e-01
-6.91873789e-01 1.78073287e-01 4.74084407e-01 1.75280362e-01
-1.79895985e+00 1.27110779e-01 6.51153755e+00 2.09935177e-02
-8.74368250e-01 3.54157835e-01 -8.62595718e-03 -2.38939956e-01
4.11489666e-01 -6.37831509e-01 -4.39317167e-01 8.44915926e-01
1.49979842e+00 6.23524368e-01 3.05722594e-01 5.52974105e-01
-5.64572252e-02 -2.59074539e-01 -1.05898106e+00 1.47868907e+00
3.49115580e-01 -9.96192336e-01 -3.98359865e-01 -9.08829570e-02
7.98611403e-01 6.75171912e-01 -1.00529499e-01 5.76349556e-01
4.84009504e-01 -1.00612938e+00 6.07319713e-01 1.22693038e+00
1.06296159e-01 -6.74458146e-01 1.32847655e+00 1.37750715e-01
-1.16568136e+00 -4.27622259e-01 2.37200648e-01 -1.15090325e-01
2.44682893e-01 7.03709900e-01 -7.70595297e-02 8.88554215e-01
1.40602362e+00 9.03032720e-01 -8.55393827e-01 8.56120944e-01
2.13755235e-01 3.75615269e-01 -5.09855509e-01 6.34605169e-01
-1.92767754e-01 2.24646851e-01 4.09091711e-01 1.25627816e+00
5.96224725e-01 -1.91317171e-01 -1.31212566e-02 1.73241526e-01
-1.85804904e-01 -6.04655504e-01 -6.49347484e-01 4.30098698e-02
4.94088620e-01 1.04172862e+00 2.00523034e-01 -6.05243921e-01
-7.12715805e-01 1.03910816e+00 -3.80071811e-02 3.40895265e-01
-9.83995378e-01 -2.62221754e-01 1.11881769e+00 -4.72377479e-01
3.79875004e-01 -3.90109837e-01 -3.74621689e-01 -1.62446272e+00
1.10801965e-01 -1.18615818e+00 7.50073612e-01 -1.09293568e+00
-1.47162867e+00 4.85784113e-01 -1.58238530e-01 -1.26404822e+00
-4.70165014e-01 -4.04436141e-01 -3.41834784e-01 5.52514970e-01
-1.07764232e+00 -1.37487566e+00 -7.59025812e-01 1.03525543e+00
1.68969527e-01 -2.88819164e-01 1.06871378e+00 7.58436739e-01
-7.78975427e-01 4.44711119e-01 1.42286122e-01 4.01322275e-01
8.92321825e-01 -1.38124287e+00 4.28218216e-01 1.24146152e+00
-9.53138918e-02 6.67666912e-01 6.64741874e-01 -5.63318968e-01
-1.83297074e+00 -1.19922924e+00 7.81682193e-01 -1.04298604e+00
5.32420635e-01 -2.57890373e-01 -9.47745562e-01 9.20578003e-01
6.51170731e-01 1.40334696e-01 9.35091436e-01 1.90616950e-01
-4.41879213e-01 -4.11145896e-01 -9.94409978e-01 1.51792035e-01
8.05370569e-01 -6.91351771e-01 -1.01494682e+00 -1.31276160e-01
5.00014246e-01 -5.45903981e-01 -1.61531937e+00 9.11296964e-01
6.37813985e-01 -9.05736506e-01 1.16661930e+00 -4.29667860e-01
1.64283812e-03 -6.56524420e-01 -5.99886119e-01 -1.50766587e+00
-3.00494641e-01 -5.29059231e-01 -9.56179142e-01 1.01070547e+00
-1.13144875e-01 -4.22001243e-01 1.79546803e-01 6.68565452e-01
-3.33755434e-01 8.31111073e-02 -1.13800573e+00 -4.81625915e-01
-3.44938904e-01 -8.09791803e-01 9.80746388e-01 1.01611304e+00
-1.88409343e-01 4.12130862e-01 -8.68803620e-01 3.93798590e-01
5.83186984e-01 -5.28482378e-01 9.89059389e-01 -1.39776111e+00
-1.09760441e-01 -1.30271971e-01 -5.14744759e-01 -4.58114743e-01
-2.93695629e-01 -2.17845231e-01 -5.17535284e-02 -1.39034808e+00
-3.95107895e-01 4.87409800e-01 -9.04118061e-01 7.26906955e-01
-7.48505294e-02 3.38984460e-01 1.04287550e-01 -1.36544749e-01
-1.03559542e+00 8.47474277e-01 3.53251994e-01 -3.48205596e-01
-2.12681428e-01 -4.90315706e-01 -8.54745328e-01 7.17990160e-01
7.88197935e-01 -1.18282080e-01 1.38041442e-02 -8.01223874e-01
3.71709526e-01 -6.02089018e-02 6.94262028e-01 -1.87370944e+00
3.69047791e-01 3.58478278e-01 9.78695929e-01 -8.00195336e-01
6.31768525e-01 -1.19309878e+00 7.01370537e-01 1.42308652e-01
4.54737246e-02 5.66802979e-01 3.59433919e-01 6.23728991e-01
-3.82172793e-01 8.14195037e-01 2.84477651e-01 2.27387026e-01
-7.44228125e-01 1.06724866e-01 -4.82169449e-01 -2.97020376e-01
5.13546705e-01 -6.31658882e-02 -5.38522661e-01 -3.79143596e-01
-9.04957235e-01 3.33984524e-01 1.11087561e-01 1.04942751e+00
4.23041195e-01 -1.84061432e+00 -5.75336695e-01 5.10335684e-01
3.06384087e-01 1.80902872e-02 4.28611398e-01 1.07895994e+00
1.48494408e-01 1.64039895e-01 -3.47355187e-01 -6.77781820e-01
-8.70363772e-01 4.26156074e-01 5.89439094e-01 -8.32216218e-02
-2.48959884e-01 2.64754415e-01 -4.84132022e-01 -4.32451606e-01
5.21033049e-01 -7.68714964e-01 -7.07580969e-02 2.59665370e-01
7.56419539e-01 6.37266695e-01 5.95370531e-01 -8.99472296e-01
-5.58132112e-01 2.57722348e-01 4.64865953e-01 -1.72641128e-02
1.39884412e+00 -2.51320988e-01 6.36467040e-02 6.66905940e-01
1.07922447e+00 -3.96967053e-01 -1.22792280e+00 -2.85706848e-01
-1.56869382e-01 1.27125248e-01 1.06657632e-01 -1.14600909e+00
-9.70058262e-01 5.40505111e-01 1.17825282e+00 3.55439872e-01
1.37095082e+00 -3.39613140e-01 1.02333343e+00 3.98346871e-01
1.02641664e-01 -1.46153581e+00 -5.76060116e-02 8.81132424e-01
9.02808785e-01 -1.72252929e+00 -2.01823041e-01 5.97307563e-01
-5.45280337e-01 6.42445982e-01 5.00689089e-01 -9.14714113e-02
7.84750521e-01 4.09648448e-01 2.13882998e-01 -2.31248572e-01
-7.85771549e-01 -4.25424635e-01 3.44598979e-01 6.79274797e-01
2.28405014e-01 1.24647185e-01 2.10795328e-01 1.04793179e+00
-7.18719661e-02 1.74912527e-01 7.46814860e-03 1.23846781e+00
2.16472317e-02 -8.15819383e-01 -8.34559858e-01 2.92389721e-01
-6.19595706e-01 1.79693744e-01 -2.40959033e-01 7.08208561e-01
6.73400879e-01 1.36047459e+00 1.27339646e-01 -8.59430611e-01
6.63507223e-01 3.50255340e-01 1.14468560e-01 2.55635053e-01
-1.03339064e+00 6.04784191e-02 1.19528480e-01 -1.20583439e+00
-8.63201022e-01 -8.67381394e-01 -8.05758357e-01 -4.44454193e-01
2.28792682e-01 -2.81804919e-01 8.19591045e-01 1.11712945e+00
9.68980670e-01 9.89800751e-01 1.26198724e-01 -1.07698655e+00
-1.28478155e-01 -1.26429653e+00 -1.23861142e-01 8.20538938e-01
9.49303448e-01 -7.22327471e-01 -7.56447464e-02 2.22098857e-01] | [7.638155937194824, 0.8345587849617004] |
c082e7c7-d0ce-4c00-8925-d4d8e4f8752a | fast-exploration-and-learning-of-latent | 2303.07397 | null | https://arxiv.org/abs/2303.07397v3 | https://arxiv.org/pdf/2303.07397v3.pdf | Fast exploration and learning of latent graphs with aliased observations | We consider the problem of recovering a latent graph where the observations at each node are \emph{aliased}, and transitions are stochastic. Observations are gathered by an agent traversing the graph. Aliasing means that multiple nodes emit the same observation, so the agent can not know in which node it is located. The agent needs to uncover the hidden topology as accurately as possible and in as few steps as possible. This is equivalent to efficient recovery of the transition probabilities of a partially observable Markov decision process (POMDP) in which the observation probabilities are known. An algorithm for efficiently exploring (and ultimately recovering) the latent graph is provided. Our approach is exponentially faster than naive exploration in a variety of challenging topologies with aliased observations while remaining competitive with existing baselines in the unaliased regime. | ['Dileep George', 'Meet Dave', 'Sivaramakrishnan Swaminathan', 'Ishan Deshpande', 'Miguel Lazaro-Gredilla'] | 2023-03-13 | null | null | null | null | ['efficient-exploration'] | ['methodology'] | [ 3.94444972e-01 9.21959937e-01 -4.96351302e-01 1.43830806e-01
-8.93792629e-01 -1.01595879e+00 6.13768280e-01 4.42805678e-01
-3.61323714e-01 7.35701978e-01 2.61968911e-01 -3.18246543e-01
-2.12598935e-01 -7.98225522e-01 -6.85694933e-01 -9.09324169e-01
-7.91790783e-01 1.57242393e+00 2.40402505e-01 5.45792639e-01
-1.19471207e-01 4.38667208e-01 -8.18700910e-01 -3.13116610e-01
2.52574813e-02 5.00334918e-01 2.93023139e-01 1.08633602e+00
2.58743465e-01 9.65668023e-01 -3.41663033e-01 1.35591969e-01
2.53981799e-01 -3.84493202e-01 -1.10761404e+00 6.99920118e-01
-2.88186520e-01 -4.60959554e-01 -6.05585456e-01 1.15139437e+00
-2.29928002e-01 1.81938931e-02 5.22557855e-01 -1.32944739e+00
-7.77985230e-02 1.08764243e+00 -8.87526155e-01 4.16015565e-01
6.90315545e-01 5.53585738e-02 1.25601029e+00 -6.64386973e-02
9.82731462e-01 1.27875090e+00 5.46959005e-02 1.20767735e-01
-1.55668485e+00 -3.48253548e-01 5.95711410e-01 9.10642594e-02
-1.15820992e+00 -3.94372880e-01 5.73230922e-01 -6.76373988e-02
5.39190233e-01 2.05205068e-01 6.21295989e-01 1.36935973e+00
1.79221794e-01 7.97229350e-01 1.04247487e+00 -3.28235686e-01
6.08299315e-01 -2.45435625e-01 -9.12160203e-02 7.92253852e-01
3.83298308e-01 1.39542520e-01 -7.76688874e-01 -5.94868243e-01
7.08469748e-01 3.14763486e-01 6.60736561e-02 -6.42341435e-01
-1.42958093e+00 6.88718140e-01 -4.84026968e-03 -3.74555767e-01
-6.84106708e-01 5.56804240e-01 -1.29844591e-01 5.36796212e-01
1.02100700e-01 1.52796715e-01 -1.33055359e-01 -3.13786834e-01
-7.91693687e-01 1.39595404e-01 1.19304729e+00 1.08176827e+00
7.98047781e-01 -1.63247809e-01 1.71588361e-01 -3.31829786e-01
5.08704722e-01 7.84000516e-01 -2.76026666e-01 -1.44251513e+00
6.67701840e-01 3.42899889e-01 5.74829102e-01 -5.84045112e-01
-3.96844476e-01 -1.95600033e-01 -6.30077958e-01 2.23731622e-01
5.96123934e-01 -3.49889785e-01 -1.08535731e+00 1.92497504e+00
5.49149334e-01 2.17400640e-01 1.28523767e-01 6.79725468e-01
-1.54162019e-01 7.63875067e-01 -2.04252079e-01 -5.83744168e-01
1.18119633e+00 -7.95060813e-01 -5.86723387e-01 -6.36847913e-01
2.48610582e-02 -2.73635000e-01 4.03391004e-01 4.58589852e-01
-1.14791930e+00 1.13106564e-01 -7.89573789e-01 2.32988894e-01
1.86988607e-01 -7.03809202e-01 6.76536679e-01 3.28501105e-01
-1.15546811e+00 5.14155924e-01 -1.79050922e+00 -2.95935571e-01
1.75168857e-01 4.12292957e-01 -3.89444947e-01 -2.01730132e-01
-5.81984937e-01 5.63578784e-01 4.88896787e-01 -3.20456550e-02
-1.96222115e+00 1.79471046e-01 -7.52821624e-01 1.46186858e-01
1.12172449e+00 -4.66525316e-01 1.30291808e+00 -4.46624517e-01
-1.15416408e+00 3.72052640e-01 -4.78660762e-01 -7.03959644e-01
4.96386290e-01 5.50328828e-02 -3.16456556e-01 4.57720965e-01
3.83026391e-01 2.46952102e-01 9.50150847e-01 -1.24421322e+00
-6.99155271e-01 -4.83189255e-01 2.10553035e-01 4.95541811e-01
2.07729205e-01 -3.08718890e-01 -5.30370951e-01 1.42451808e-01
8.36180389e-01 -1.32300830e+00 -6.16522431e-01 -1.44993439e-01
-9.49864626e-01 1.31915018e-01 8.69992375e-01 -4.37612832e-01
7.34553754e-01 -1.72190499e+00 3.10365617e-01 4.84012365e-01
6.33585036e-01 -6.65572345e-01 7.48964027e-04 1.15816677e+00
3.94575059e-01 1.17883921e-01 -2.82619536e-01 -7.64991403e-01
1.08525669e-02 5.23618639e-01 -6.36718094e-01 9.54180479e-01
-5.13569236e-01 7.40086377e-01 -1.21019161e+00 -5.35609186e-01
2.25388095e-01 -8.39067400e-02 -2.23732725e-01 2.04533655e-02
-6.22427583e-01 9.82721686e-01 -9.77019727e-01 6.14082932e-01
2.84864724e-01 -7.33599186e-01 8.27639520e-01 8.97990286e-01
1.03214264e-01 3.98166180e-01 -1.48937905e+00 1.65293276e+00
-1.58007592e-01 4.45846796e-01 3.69492859e-01 -4.58391458e-01
3.67598444e-01 5.99109113e-01 5.74133992e-01 -8.28178823e-02
-1.84186727e-01 -1.47775486e-01 -1.78387731e-01 -1.71741799e-01
2.46033490e-01 -9.64950547e-02 -3.49276751e-01 1.25302553e+00
-2.54795343e-01 2.35440567e-01 1.65785700e-02 7.41106987e-01
1.82207143e+00 -6.11781999e-02 3.46645117e-01 2.92481910e-02
-3.72650117e-01 1.30024612e-01 5.36234140e-01 1.41466546e+00
-1.12181865e-01 2.02731475e-01 8.72905135e-01 -4.10121351e-01
-8.10917616e-01 -1.61903965e+00 4.85060424e-01 7.11934328e-01
5.67423105e-01 -5.23252010e-01 -4.90192652e-01 -5.37155807e-01
-3.05021018e-01 6.20268166e-01 -7.68582702e-01 2.49798357e-01
-3.23302090e-01 -2.88432121e-01 -3.63097489e-02 1.90469712e-01
2.39464611e-01 -1.14335704e+00 -8.11912537e-01 4.45244312e-01
-3.91928077e-01 -1.27966464e+00 -2.04950258e-01 5.06083131e-01
-1.17708313e+00 -1.16264570e+00 1.78046241e-01 -2.85893470e-01
9.22146618e-01 3.14775109e-01 9.92197871e-01 -9.72476080e-02
1.15096055e-01 3.59326214e-01 -1.28647760e-01 -1.61698833e-01
-3.69315773e-01 2.54509151e-01 2.58033723e-03 -5.85313961e-02
1.82679072e-02 -9.12727475e-01 -3.52184355e-01 -9.00594145e-02
-6.76128149e-01 1.92617606e-02 5.09480536e-01 2.84971207e-01
7.04970002e-01 5.00439823e-01 -3.56622785e-01 -1.08534169e+00
2.85737544e-01 -9.45505977e-01 -9.13520694e-01 1.38678804e-01
-3.61826897e-01 4.06396598e-01 4.54308063e-01 -2.60081351e-01
-9.39430714e-01 2.42777690e-01 6.77943826e-01 -4.28092122e-01
-4.20515776e-01 4.79629695e-01 -5.01795523e-02 4.07667100e-01
2.19133750e-01 1.04787886e-01 -3.17240208e-01 -5.37442565e-01
2.62054056e-01 4.31432985e-02 5.60819089e-01 -6.06923819e-01
1.01978970e+00 1.18254876e+00 5.51984251e-01 -6.11180782e-01
-7.24009633e-01 -2.63509870e-01 -6.60715103e-01 1.40367551e-02
4.33367610e-01 -9.28322971e-01 -1.10095906e+00 1.32299781e-01
-8.78981769e-01 -5.97932398e-01 -7.99472034e-01 4.82984513e-01
-5.41705072e-01 3.86592031e-01 -6.23649836e-01 -1.32825983e+00
4.87675041e-01 -9.65330899e-01 1.22628462e+00 1.08659483e-01
-4.71475214e-01 -1.20551205e+00 3.13994467e-01 1.87988773e-01
-1.22321390e-01 4.88834143e-01 5.97365379e-01 -5.98924935e-01
-1.43150043e+00 -3.42158884e-01 2.64485747e-01 -8.92885745e-01
4.85681482e-02 -1.94188207e-01 -7.88597107e-01 -7.48864055e-01
-8.73015150e-02 -2.21015438e-01 7.27955759e-01 4.54729348e-01
7.95775890e-01 -7.29925931e-01 -8.82463634e-01 2.56842583e-01
1.19043124e+00 4.72502820e-02 1.48319557e-01 1.29068166e-01
3.44001383e-01 4.22214657e-01 3.73966277e-01 6.74532413e-01
7.26187885e-01 3.74817789e-01 9.95716512e-01 1.55425861e-01
4.03587162e-01 -8.33707273e-01 4.60949570e-01 4.12914038e-01
9.11786407e-02 -8.93051028e-01 -8.69516253e-01 8.69741201e-01
-1.89571941e+00 -8.83188486e-01 -2.05981489e-02 2.17336440e+00
2.37174422e-01 3.55708808e-01 3.91472131e-02 -9.94718000e-02
6.00645721e-01 6.06960535e-01 -8.24994385e-01 -1.41218632e-01
1.22012116e-01 -7.89668784e-02 1.00049567e+00 1.17330027e+00
-7.71338940e-01 8.33191693e-01 6.97112656e+00 2.87509233e-01
-2.90282428e-01 3.10622662e-01 3.96300554e-01 -3.24288458e-01
-5.78051925e-01 9.24755335e-01 -7.57192254e-01 2.39240557e-01
1.14095211e+00 1.08493261e-01 9.07847106e-01 5.27660251e-01
2.69160241e-01 -6.60915792e-01 -1.15292048e+00 4.35471475e-01
-3.77657235e-01 -1.06638122e+00 -3.53131086e-01 8.60750079e-01
8.53530109e-01 4.31004763e-01 -2.75874641e-02 -2.68780947e-01
1.55682313e+00 -8.77268851e-01 7.80620515e-01 3.96918058e-01
5.32214582e-01 -5.93875468e-01 1.41269997e-01 9.73754764e-01
-1.31532133e+00 -2.57595390e-01 -9.25690755e-02 -4.02767926e-01
7.47862637e-01 3.75022650e-01 -1.15528584e+00 4.35435534e-01
5.61371148e-01 4.23505962e-01 -2.76241712e-02 7.11102962e-01
-8.32260311e-01 9.94672596e-01 -7.92218268e-01 2.80206054e-01
4.62157488e-01 -4.85879779e-01 1.20412230e+00 4.13607746e-01
1.79556668e-01 1.64400369e-01 9.05873597e-01 7.73036003e-01
-1.37756377e-01 -7.61008680e-01 -6.15655661e-01 -3.29056054e-01
9.15687203e-01 9.68793333e-01 -1.28426218e+00 -3.13641667e-01
1.08220661e-02 1.07411575e+00 3.40713412e-01 5.77188969e-01
-4.68679458e-01 3.12467128e-01 3.10981035e-01 9.40560400e-02
2.45855972e-01 -4.79592890e-01 7.97099695e-02 -8.74475241e-01
-3.46870236e-02 -4.50375319e-01 8.34816813e-01 -5.71000516e-01
-7.06939518e-01 3.47557425e-01 1.81958035e-01 -8.35545838e-01
-6.77620709e-01 2.82902181e-01 -6.35616422e-01 5.68692625e-01
-1.27998722e+00 -1.09581256e+00 1.39840385e-02 5.30437052e-01
3.69757652e-01 1.64379656e-01 9.23088729e-01 -6.38970435e-01
-3.98109704e-01 -4.77946609e-01 1.53964803e-01 -1.83355212e-01
-1.47938922e-01 -1.48236775e+00 8.46994460e-01 1.18042219e+00
7.90083647e-01 3.70468169e-01 1.02873683e+00 -1.01893842e+00
-1.67198825e+00 -8.00965369e-01 7.51857638e-01 -5.84490478e-01
9.07517970e-01 -5.63320935e-01 -5.00475705e-01 1.60504186e+00
1.26123756e-01 7.00464174e-02 1.23029053e-01 7.13610202e-02
-2.39277035e-02 3.79232407e-01 -9.39792037e-01 5.42571247e-01
1.06871414e+00 -4.72092062e-01 -2.97167003e-01 5.30778229e-01
6.28858447e-01 -4.43843782e-01 -5.75038373e-01 -1.40172303e-01
1.98061198e-01 -7.53596425e-01 6.11618817e-01 -6.13813221e-01
-2.58873791e-01 -2.99484581e-01 -6.89551383e-02 -1.13636482e+00
-2.53712088e-01 -1.38579726e+00 -8.07927191e-01 7.39933133e-01
5.20404875e-01 -6.89018130e-01 1.28496587e+00 5.79463661e-01
4.99221891e-01 -2.29787454e-01 -1.26976264e+00 -6.89513564e-01
-6.11027777e-01 -2.25810930e-01 7.34302640e-01 6.36756241e-01
-1.95323285e-02 2.37984464e-01 -5.58373153e-01 1.01112211e+00
1.26061821e+00 5.23708105e-01 7.13013947e-01 -1.26883638e+00
-6.75741613e-01 3.52805406e-01 2.00426411e-02 -1.25269294e+00
1.40710130e-01 -5.98366797e-01 2.14381181e-02 -1.58576894e+00
6.15824461e-01 -4.16711032e-01 -1.02892734e-01 5.45852184e-01
2.11535320e-01 -3.06223452e-01 9.13007930e-02 6.12009048e-01
-1.17194676e+00 2.78265536e-01 1.08927536e+00 1.50553226e-01
-1.77233055e-01 4.43599463e-01 -5.12882054e-01 8.42146695e-01
5.88634491e-01 -1.05280256e+00 -6.37023926e-01 -4.41734791e-01
3.14386636e-01 1.01089299e+00 4.84576553e-01 -6.07427418e-01
5.35156369e-01 -3.67244601e-01 1.75666630e-01 -8.46168637e-01
7.98272192e-01 -9.67250884e-01 7.58882284e-01 6.66843474e-01
-5.07408679e-01 1.43058673e-01 -4.63082492e-01 1.40525258e+00
1.77513659e-01 -1.14746556e-01 3.63146245e-01 -3.87369454e-01
-4.28286701e-01 6.67883396e-01 -6.81615949e-01 2.08863363e-01
1.09202909e+00 -2.17588961e-01 -2.24017784e-01 -1.08912885e+00
-1.10833490e+00 6.09408617e-01 8.63822103e-01 5.22871222e-03
3.27037305e-01 -7.61974096e-01 -3.52220774e-01 -5.48004322e-02
-1.94342360e-01 4.23694849e-01 1.77174032e-01 7.18488932e-01
-2.22577035e-01 2.70433128e-01 1.08648911e-01 -4.45843130e-01
-9.79818106e-01 7.12029934e-01 -3.71420814e-04 -8.34222496e-01
-1.06449401e+00 5.36422193e-01 -4.91874665e-03 -2.60203689e-01
2.95854688e-01 -1.63003318e-02 2.56505013e-01 -1.88328922e-01
3.84820014e-01 6.31117523e-01 -5.68484366e-01 -3.35459650e-01
-1.49002671e-01 7.98473433e-02 -4.82736118e-02 -8.48741114e-01
1.33182287e+00 -7.16183484e-01 -3.59069207e-03 8.42458546e-01
7.28078604e-01 2.94328839e-01 -1.91969371e+00 -5.95249057e-01
1.35491788e-01 -4.01620567e-01 -1.44909574e-02 -5.11006534e-01
-9.01919901e-01 6.31078959e-01 -1.65814489e-01 6.15395546e-01
5.40636420e-01 4.41354543e-01 6.88711405e-01 5.49817681e-01
9.61238086e-01 -7.62438536e-01 -7.12248981e-02 9.86702666e-02
1.22010075e-01 -8.83637488e-01 2.55263329e-01 -3.04929674e-01
-7.24593639e-01 7.29476810e-01 -1.38202950e-01 -1.51088923e-01
4.58453953e-01 5.79446137e-01 -3.58124405e-01 -6.58180177e-01
-1.17623425e+00 -9.36627984e-02 -6.17208004e-01 5.24137437e-01
-7.40569472e-01 4.51069683e-01 8.40079904e-01 -2.02210858e-01
-1.59832865e-01 -4.18369800e-01 9.19575155e-01 1.18554354e+00
-7.10319877e-01 -1.04495037e+00 -4.91803497e-01 3.05932969e-01
-4.22582000e-01 1.98329508e-01 -4.19550896e-01 7.27442682e-01
-5.02984226e-01 1.13828528e+00 2.21470296e-01 8.81891772e-02
-2.73420155e-01 -2.13269934e-01 2.97614098e-01 -8.20851386e-01
2.22068176e-01 2.59172052e-01 2.21156150e-01 -8.71200919e-01
-1.56406626e-01 -1.15711462e+00 -1.28997016e+00 -4.19231921e-01
-1.94592953e-01 4.67105597e-01 3.65030318e-01 1.05613780e+00
1.16998851e-01 1.45704061e-01 8.81456733e-01 -5.37064075e-01
-5.63164115e-01 -6.16842628e-01 -1.01482642e+00 -1.19034439e-01
7.21402109e-01 -5.46302974e-01 -5.33683300e-01 -1.12456843e-01] | [4.308643817901611, 2.1260082721710205] |
8e21e55b-eeb5-45d3-b4a1-a2d9ccd9db01 | goferbot-a-visual-guided-human-robot | 2304.08840 | null | https://arxiv.org/abs/2304.08840v2 | https://arxiv.org/pdf/2304.08840v2.pdf | GoferBot: A Visual Guided Human-Robot Collaborative Assembly System | The current transformation towards smart manufacturing has led to a growing demand for human-robot collaboration (HRC) in the manufacturing process. Perceiving and understanding the human co-worker's behaviour introduces challenges for collaborative robots to efficiently and effectively perform tasks in unstructured and dynamic environments. Integrating recent data-driven machine vision capabilities into HRC systems is a logical next step in addressing these challenges. However, in these cases, off-the-shelf components struggle due to generalisation limitations. Real-world evaluation is required in order to fully appreciate the maturity and robustness of these approaches. Furthermore, understanding the pure-vision aspects is a crucial first step before combining multiple modalities in order to understand the limitations. In this paper, we propose GoferBot, a novel vision-based semantic HRC system for a real-world assembly task. It is composed of a visual servoing module that reaches and grasps assembly parts in an unstructured multi-instance and dynamic environment, an action recognition module that performs human action prediction for implicit communication, and a visual handover module that uses the perceptual understanding of human behaviour to produce an intuitive and efficient collaborative assembly experience. GoferBot is a novel assembly system that seamlessly integrates all sub-modules by utilising implicit semantic information purely from visual perception. | ['Robert Mahony', 'Stephen Gould', 'Jiahao Zhang', 'Yizhak Ben-Shabat', 'Zheyu Zhuang'] | 2023-04-18 | null | null | null | null | ['action-recognition-in-videos'] | ['computer-vision'] | [ 2.74740607e-01 3.67087126e-01 4.16123033e-01 -4.17630374e-01
7.33719319e-02 -5.94276726e-01 5.80661416e-01 3.02270025e-01
-1.66480869e-01 2.26297036e-01 -1.41426831e-01 4.38141562e-02
-3.23258936e-01 -3.34354103e-01 -3.94954056e-01 -3.16131741e-01
1.04211941e-01 8.05498064e-01 4.83429134e-01 -7.28143215e-01
4.57953721e-01 6.88576043e-01 -2.02998352e+00 5.69818497e-01
6.42470121e-01 9.52139676e-01 1.26904178e+00 9.68751490e-01
-3.95854972e-02 9.85578895e-01 -4.73481596e-01 1.99034691e-01
2.71755934e-01 -3.10269177e-01 -8.99159014e-01 3.78195345e-01
-1.34361401e-01 -3.57416179e-03 3.60575289e-01 6.65814221e-01
2.45883986e-01 2.35121354e-01 2.45027855e-01 -1.35788620e+00
-4.37702924e-01 2.81309098e-01 1.80722430e-01 -4.45390463e-01
1.00004661e+00 5.18559873e-01 5.06337762e-01 -4.30554003e-01
7.75158465e-01 1.45825696e+00 4.82720733e-01 4.97146547e-01
-1.00148726e+00 7.80338496e-02 8.34409222e-02 2.38319889e-01
-8.23122621e-01 -4.23285812e-01 5.91984391e-01 -6.26418114e-01
1.32514143e+00 8.84352326e-02 7.49520242e-01 9.83222783e-01
4.16341335e-01 5.14394701e-01 1.19302154e+00 -6.95611656e-01
3.51558000e-01 2.45251626e-01 -1.79209799e-01 5.12153685e-01
9.77921560e-02 2.10304737e-01 -4.14647549e-01 4.63262796e-01
1.00760639e+00 1.95135534e-01 -7.00535178e-02 -1.12487030e+00
-1.44941795e+00 3.21946502e-01 4.56735939e-01 5.33154190e-01
-7.21868992e-01 2.89911151e-01 4.04027909e-01 5.89559674e-01
-3.49387318e-01 9.32608724e-01 -4.66502607e-01 -5.79773068e-01
-4.80984338e-03 1.55188948e-01 1.21691084e+00 1.27118325e+00
4.45675850e-01 -2.67368317e-01 3.01002115e-01 7.10740566e-01
4.65770662e-01 2.86224097e-01 1.84829727e-01 -1.65479302e+00
1.33832872e-01 8.34929824e-01 4.87946123e-01 -9.57254171e-01
-5.06918430e-01 1.28908157e-01 -2.69186854e-01 9.32152152e-01
1.73093870e-01 2.19894215e-01 -9.21494901e-01 9.67787564e-01
4.57951516e-01 -7.85420954e-01 2.76335895e-01 1.41115546e+00
4.67465520e-01 2.87902951e-01 1.20820366e-01 1.28824055e-01
1.41358638e+00 -1.17895269e+00 -9.20555592e-01 -3.30604702e-01
4.36268240e-01 -1.01961195e+00 8.82402837e-01 6.07400119e-01
-1.04486239e+00 -9.79859173e-01 -1.21131563e+00 -1.95886657e-01
-6.26509488e-01 1.33434637e-02 7.02667117e-01 7.95102268e-02
-9.64940667e-01 7.53492534e-01 -9.27437484e-01 -9.64370728e-01
-2.99335003e-01 5.11053920e-01 -7.07285464e-01 -2.75455892e-01
-6.45356894e-01 1.47844553e+00 6.00272357e-01 3.18529904e-01
-8.09188545e-01 -8.50399211e-02 -1.09045625e+00 -3.06049973e-01
7.02553689e-01 -7.07083881e-01 1.58704138e+00 -8.62033248e-01
-1.99666870e+00 6.91323102e-01 3.73652309e-01 -2.71912158e-01
6.01919949e-01 -4.58379209e-01 -1.99990019e-01 2.45215058e-01
9.96943042e-02 7.00182259e-01 7.87770987e-01 -1.89299917e+00
-7.91415393e-01 -3.41976941e-01 1.65518776e-01 4.96192247e-01
6.69749081e-01 -2.00265378e-01 -3.56097132e-01 -3.59857976e-02
1.87991634e-01 -1.06606042e+00 -3.25668544e-01 -4.14358899e-02
1.55945867e-01 -1.27958804e-01 1.17775297e+00 -3.14701021e-01
2.05657333e-01 -2.12182927e+00 3.00414950e-01 -8.02642405e-02
-1.06890313e-01 4.07091558e-01 5.84689192e-02 1.12621057e+00
2.80812323e-01 -7.95420766e-01 1.95681185e-01 -4.11614746e-01
2.12723613e-01 5.97040772e-01 5.90966679e-02 1.74307629e-01
8.42561796e-02 6.56262934e-01 -1.12943602e+00 -2.62741566e-01
1.05946159e+00 2.71240532e-01 -4.64009158e-02 5.49137950e-01
-3.25125933e-01 8.68448853e-01 -2.89527506e-01 5.61193407e-01
2.29693905e-01 1.00888431e-01 4.66734707e-01 1.99482944e-02
-4.43462640e-01 -2.42682263e-01 -1.24755549e+00 2.12226152e+00
-8.69192064e-01 2.63373882e-01 7.88992524e-01 -1.01415849e+00
1.15394187e+00 4.48047101e-01 2.12012157e-01 -8.56942892e-01
1.58388689e-01 4.96773034e-01 5.19052036e-02 -1.07922292e+00
5.52913368e-01 -1.67602330e-01 -1.72542125e-01 6.54168660e-03
1.51553497e-01 -8.10789227e-01 -8.75216257e-03 -4.37319167e-02
1.13153338e+00 7.51966476e-01 2.85746723e-01 1.02674186e-01
4.31095332e-01 5.97449243e-01 2.02044696e-01 6.58557415e-01
-4.49314982e-01 4.60304648e-01 -1.28279209e-01 -5.33943951e-01
-8.90001714e-01 -9.87784743e-01 5.98690093e-01 1.00189924e+00
6.85617983e-01 -8.70791301e-02 -4.37626153e-01 -3.58656108e-01
8.25698823e-02 7.88830757e-01 -2.56960124e-01 -6.44350424e-02
-4.54018801e-01 4.35223043e-01 -3.26074988e-01 5.79541266e-01
3.02043110e-01 -1.62467349e+00 -1.65959692e+00 4.90766108e-01
6.44573867e-02 -1.36377966e+00 1.29670739e-01 3.99637550e-01
-5.70211828e-01 -1.14236200e+00 -2.39259735e-01 -9.79238868e-01
6.61916435e-01 5.20301461e-01 8.67704809e-01 -6.41647279e-02
-6.65333211e-01 1.10255122e+00 -8.03580880e-01 -5.98412275e-01
-9.48250115e-01 -3.63530904e-01 -5.44709787e-02 -4.37893629e-01
-1.02672637e-01 -3.13784361e-01 -7.06308544e-01 3.96291763e-01
-4.74491537e-01 3.48200440e-01 9.04246151e-01 5.05558610e-01
2.04204425e-01 -6.61555827e-02 3.23773205e-01 -4.15289193e-01
6.53217971e-01 -1.11326464e-01 -3.26855183e-01 1.71914876e-01
-4.51941818e-01 -2.35324815e-01 6.30939543e-01 -2.80189037e-01
-1.35584676e+00 5.45430481e-01 2.50583202e-01 -3.19965839e-01
-8.47937047e-01 2.94691801e-01 -6.86176866e-02 9.81131345e-02
2.24006444e-01 -1.15544654e-01 5.48684061e-01 -4.58090633e-01
6.70564175e-01 8.42069507e-01 9.21279311e-01 -3.92879963e-01
3.30076277e-01 4.45014268e-01 -1.04806619e-02 -7.29565740e-01
-3.82462859e-01 -9.34661448e-01 -1.01262271e+00 -6.28011405e-01
9.97138500e-01 -7.07377553e-01 -1.33368838e+00 4.43531305e-01
-1.30492234e+00 -6.62022233e-01 -3.80938560e-01 5.07154405e-01
-1.16734719e+00 1.34775966e-01 -5.99624693e-01 -1.07996964e+00
-2.52972469e-02 -1.22750223e+00 1.28836846e+00 9.61404592e-02
-6.29942477e-01 -6.15202308e-01 -1.82282984e-01 8.11166465e-01
5.03885746e-01 2.70781040e-01 4.55363780e-01 -4.76539522e-01
-6.22872055e-01 -3.47349614e-01 -6.32675141e-02 3.58790070e-01
4.58703071e-01 -2.80902147e-01 -5.89947164e-01 -1.74849242e-01
8.97474661e-02 -3.80244702e-01 8.73524547e-02 4.81243283e-02
3.95015717e-01 2.66672462e-01 -4.01121497e-01 -1.79162353e-01
1.46793807e+00 6.92514002e-01 3.96978885e-01 2.90169686e-01
6.35055840e-01 1.26972127e+00 1.50199687e+00 2.32838914e-01
4.38508689e-01 8.56965899e-01 8.12645316e-01 5.06151356e-02
-1.19004056e-01 -1.62182376e-02 4.34716433e-01 9.28127050e-01
-3.76131535e-01 4.41526324e-02 -8.80925715e-01 4.67073500e-01
-2.29152679e+00 -7.91893005e-01 -3.12465459e-01 1.84698629e+00
2.74655432e-01 1.23062551e-01 -2.58035570e-01 2.10381210e-01
4.52524424e-01 -3.17843556e-01 -3.03320378e-01 -1.11261630e+00
5.73114932e-01 3.97371966e-03 1.92629114e-01 2.79067129e-01
-9.11577106e-01 9.58955646e-01 5.33916664e+00 1.84282333e-01
-8.75424087e-01 1.09163836e-01 -3.61921370e-01 3.07419240e-01
5.32825589e-01 4.86185253e-02 -2.40396261e-01 1.60628855e-01
6.84650958e-01 4.51494038e-01 5.10375142e-01 1.10866594e+00
3.69982749e-01 -7.45881855e-01 -1.30948150e+00 8.72801542e-01
1.09888896e-01 -1.06368506e+00 -5.18570423e-01 -1.81170985e-01
3.52171898e-01 -1.50876030e-01 -2.95494407e-01 3.31782252e-01
6.17834926e-01 -9.04453039e-01 1.02637208e+00 8.07815194e-01
1.43864855e-01 -4.09081131e-01 9.39150453e-01 6.18094146e-01
-1.11151147e+00 -5.23754776e-01 4.31946628e-02 -5.09639204e-01
6.21687233e-01 4.13011983e-02 -1.14144075e+00 7.85979986e-01
8.38773489e-01 2.40880057e-01 -4.20105942e-02 7.77412474e-01
-1.31276116e-01 -4.70696777e-01 -3.86544913e-02 -5.83704412e-02
2.28935272e-01 -2.76054949e-01 4.62048054e-01 9.21280921e-01
7.53441304e-02 -2.55878065e-02 6.53908730e-01 6.32063925e-01
7.65184104e-01 -3.06116521e-01 -9.38914835e-01 -1.02401264e-02
2.18931034e-01 1.23510206e+00 -9.61029530e-01 -1.23741098e-01
-2.62356758e-01 1.58551276e+00 1.83143765e-01 9.77298990e-02
-5.13331354e-01 -5.07513404e-01 5.78275919e-01 -5.69879897e-02
4.30813819e-01 -8.50137472e-01 3.09667662e-02 -3.43047261e-01
4.11040597e-02 -8.11200857e-01 -1.76225781e-01 -1.22895598e+00
-8.92486215e-01 2.36938924e-01 -6.53968155e-02 -1.17150414e+00
-4.26631927e-01 -8.09906602e-01 -2.07363188e-01 6.72919512e-01
-1.03741479e+00 -1.54130685e+00 -7.46022403e-01 1.72434017e-01
1.00948393e+00 1.04315951e-01 1.02408683e+00 -2.71763176e-01
6.04196563e-02 -4.78293806e-01 -4.17824507e-01 -4.49599385e-01
6.20909333e-01 -1.34547758e+00 7.00667277e-02 3.08054537e-01
-3.30912262e-01 5.37287772e-01 9.62709188e-01 -7.85788119e-01
-1.96991885e+00 -5.41390419e-01 4.64761794e-01 -6.17233217e-01
4.18052524e-01 -3.69252026e-01 -6.26213908e-01 5.44845521e-01
3.98918301e-01 -1.91433325e-01 1.78191721e-01 -2.30239198e-01
1.83103710e-01 -3.34639437e-02 -1.18356740e+00 5.43385446e-01
1.24432182e+00 -3.97018313e-01 -1.14265680e+00 1.66365832e-01
7.43921936e-01 -4.94162947e-01 -1.06889033e+00 5.40666759e-01
6.48542106e-01 -1.08937895e+00 8.28692734e-01 -7.35409930e-02
2.83666134e-01 -7.33416855e-01 -1.24348223e-01 -1.28265452e+00
-2.36412168e-01 -6.26263320e-01 8.37466940e-02 9.64011371e-01
-5.88267818e-02 -6.49514496e-01 3.65979165e-01 5.13468444e-01
-7.23741055e-01 -3.98880333e-01 -4.76191312e-01 -8.69658828e-01
-9.39631462e-01 -5.91963589e-01 2.72698969e-01 6.17757857e-01
4.00471598e-01 3.40969443e-01 -8.40524063e-02 2.20343500e-01
2.51134634e-01 2.17989534e-01 1.07427800e+00 -1.37605000e+00
-2.09391922e-01 2.15029176e-02 -6.95011973e-01 -7.71803200e-01
-2.12939784e-01 -4.27118331e-01 7.67786801e-01 -2.19092894e+00
-3.54499668e-01 -2.90938497e-01 1.78582400e-01 2.49556601e-01
5.57494283e-01 -1.65497348e-01 6.96123064e-01 2.70309448e-01
-8.50356042e-01 4.64879632e-01 1.69533861e+00 2.52584666e-01
-3.20667833e-01 -9.36768129e-02 -1.42596498e-01 7.19476283e-01
6.82562232e-01 1.56282738e-01 -5.70094824e-01 -1.99053347e-01
-9.07998160e-03 1.77957982e-01 6.31091833e-01 -1.35866284e+00
7.19812512e-01 -2.13465676e-01 2.11829081e-01 -4.98948276e-01
7.65835583e-01 -1.45082414e+00 4.33075011e-01 7.93971479e-01
-1.86343983e-01 9.49945524e-02 -7.98659995e-02 6.65260375e-01
-2.31218830e-01 -1.82427287e-01 3.14252645e-01 -6.27609670e-01
-1.33472526e+00 -5.59093535e-01 -7.42832899e-01 -7.61990488e-01
1.56523585e+00 -7.15079963e-01 -2.90958434e-01 -1.84275702e-01
-1.12648165e+00 4.11495179e-01 7.86436915e-01 9.03410673e-01
6.51836574e-01 -8.10247421e-01 -1.91373751e-02 4.95804727e-01
3.35627168e-01 2.34049439e-01 3.06888580e-01 9.14564788e-01
-8.86520088e-01 5.53093255e-01 -5.80502689e-01 -7.47576535e-01
-1.33286071e+00 7.26772547e-01 4.36812602e-02 1.41469553e-01
-7.59931564e-01 4.22261268e-01 -1.45512149e-01 -7.55252540e-01
1.96886539e-01 -4.76944774e-01 -1.17487289e-01 -2.94094026e-01
1.08758591e-01 4.53845769e-01 5.41035943e-02 -6.23928010e-01
-3.17779899e-01 4.51717228e-01 2.08431542e-01 -2.84748733e-01
1.15438461e+00 -4.70189601e-01 -9.19662043e-02 8.33870351e-01
6.50543511e-01 -5.04349530e-01 -1.28899252e+00 2.77779698e-01
4.65454698e-01 -2.92196244e-01 -4.09926295e-01 -1.11234295e+00
-3.49207491e-01 6.15845978e-01 7.23139822e-01 4.09975111e-01
7.46347010e-01 3.36968780e-01 3.99217010e-01 4.95161295e-01
1.11705232e+00 -1.70484936e+00 3.84727210e-01 5.63948095e-01
1.38506818e+00 -1.27652085e+00 -1.11110389e-01 -8.62421989e-01
-1.03617644e+00 1.16176474e+00 7.37128854e-01 5.05168736e-02
2.52127111e-01 3.44570011e-01 4.31275696e-01 -6.16404772e-01
-8.05025280e-01 -4.48155612e-01 -1.77667603e-01 1.10753131e+00
1.08418047e-01 2.76903987e-01 -4.50064018e-02 7.81289190e-02
-3.49917747e-02 1.44861773e-01 3.53855580e-01 1.59850788e+00
-5.62462449e-01 -1.02708495e+00 -4.16948944e-01 -1.75617039e-01
1.14496320e-01 8.73493552e-01 -4.57904398e-01 9.44038570e-01
4.39350814e-01 1.31454313e+00 1.05455451e-01 -3.29275697e-01
8.43697071e-01 -7.64513910e-02 7.59500861e-01 -9.79979575e-01
-7.96987593e-01 -1.96041450e-01 4.25503492e-01 -1.10579693e+00
-6.34894013e-01 -6.35729313e-01 -1.63898849e+00 1.46447062e-01
5.41951042e-03 -2.14468867e-01 1.33085871e+00 9.63223219e-01
4.87036318e-01 6.91600382e-01 3.12006772e-01 -1.39076233e+00
-5.51559746e-01 -8.77047062e-01 -3.84384990e-01 6.33099496e-01
9.02535915e-02 -7.57163584e-01 1.69192571e-02 1.52811974e-01] | [4.907749652862549, 0.7604445219039917] |
3962e3ac-447c-4b3f-97e7-9c0b1e0a5418 | category-specific-semantic-coherency-learning | null | null | https://dl.acm.org/doi/pdf/10.1145/3394171.3413871 | https://dl.acm.org/doi/pdf/10.1145/3394171.3413871 | Category-specific Semantic Coherency Learning for Fine-grained Image Recognition | Existing deep learning based weakly supervised fine-grained image recognition (WFGIR) methods usually pick out the discriminative
regions from the high-level feature (HLF) maps directly. However, as HLF maps are derived based on spatial aggregation of convolution which is basically a pattern matching process that applies fixed filters, it is ineffective to model visual contents of same semantic but varying posture or perspective. We argue that this will cause the selected discriminative regions of same sub-category are not semantically corresponding and thus degrade the WFGIR performance. To address this issue, we propose an end-to-end Category-specific Semantic Coherency Network (CSC-Net) to semantically align the discriminative regions of the same subcategory. Specifically, CSCNet consists of: 1) Local-to-Attribute Projecting Module (LPM), which automatically learns a set of latent attributes via collecting the category-specific semantic details while eliminating the varying spatial distributions from the local regions. 2) Latent Attribute Aligning (LAA), which aligns the latent attributes to specific semantic via graph convolution based on their discriminability, to achieve category-specific semantic coherency; 3) Attribute-to-Local Resuming Module (ARM), which resumes the original Euclidean space of latent attributes and construct latent attribute aligned feature maps by a location-embedding graph unpooling operation. Finally, the new feature maps are used which applies the category-specific semantic coherency implicitly for more accurate discriminative regions localization. Extensive experiments verify that CSC-Net yields the best performance under the same settings with most competitive approaches, on CUB Bird, Stanford-Cars, and FGVC Aircraft datasets. | ['Wanli Ouyang', 'Haojie Li', 'Zhihui Wang', 'Shijie Wang'] | 2020-10-12 | null | null | null | null | ['fine-grained-image-recognition'] | ['computer-vision'] | [ 8.38491321e-03 -2.86625713e-01 -2.00669944e-01 -7.83749700e-01
-5.92373312e-01 -6.84434772e-01 6.81740820e-01 -1.02134556e-01
-1.02238856e-01 2.52867669e-01 3.79837215e-01 9.45309084e-03
-4.38880265e-01 -8.00154686e-01 -7.17182159e-01 -9.02020037e-01
4.29007001e-02 3.33622098e-01 2.24305987e-01 -5.28587028e-02
2.22058952e-01 7.28459716e-01 -1.53286815e+00 5.28564990e-01
7.07451820e-01 1.29892147e+00 3.87130409e-01 4.87601422e-02
-1.59795210e-01 5.99382997e-01 -3.39696169e-01 1.34047896e-01
4.35189784e-01 -2.60551900e-01 -6.49435163e-01 1.67178988e-01
8.07281792e-01 -1.27356142e-01 -3.75022471e-01 1.15012646e+00
1.88487291e-01 2.38415971e-01 7.15655148e-01 -1.31934822e+00
-9.92154717e-01 1.52393758e-01 -4.94424731e-01 2.50804946e-02
5.65902621e-04 1.28024176e-01 1.23288536e+00 -1.06413853e+00
3.84307623e-01 1.37252903e+00 4.01350588e-01 3.30259025e-01
-1.37261856e+00 -7.66624868e-01 7.08940625e-01 1.42938435e-01
-1.83942401e+00 -3.96900140e-02 9.79423344e-01 -4.28359807e-01
8.37793291e-01 2.82445252e-01 4.29989100e-01 8.72468233e-01
1.29726812e-01 5.37149489e-01 1.22958755e+00 2.62263156e-02
8.80387947e-02 6.04722463e-02 1.94189489e-01 7.00514317e-01
-9.88837332e-02 2.34934762e-01 -5.33878148e-01 -3.92055959e-02
8.56656015e-01 5.64070940e-01 -1.93798631e-01 -8.63413334e-01
-1.54051948e+00 9.18887734e-01 9.54239309e-01 2.36624986e-01
-3.50887150e-01 9.80472714e-02 1.07784353e-01 2.92911917e-01
3.28925103e-01 3.20323408e-01 -6.24116063e-01 6.45475864e-01
-7.79070497e-01 1.83054075e-01 4.18968052e-01 1.27032292e+00
1.43045092e+00 -5.45149334e-02 -4.68084693e-01 8.79767001e-01
5.16517162e-01 3.82257909e-01 5.45396149e-01 -6.29512131e-01
3.94820303e-01 8.98901582e-01 -3.07812333e-01 -1.24807835e+00
-2.96256036e-01 -9.14188802e-01 -6.94990456e-01 2.03368932e-01
4.33423510e-03 3.45813990e-01 -1.15302408e+00 1.93614089e+00
2.25287497e-01 3.04561079e-01 -2.12447569e-02 1.26493967e+00
9.06082094e-01 6.71527326e-01 3.98804218e-01 3.66180569e-01
1.34591770e+00 -1.10443294e+00 -2.12087914e-01 -4.23152834e-01
5.10997534e-01 -7.00384855e-01 1.24250174e+00 -2.39989549e-01
-3.23362708e-01 -8.43052566e-01 -1.13187468e+00 -1.84410196e-02
-7.71122456e-01 2.54145652e-01 7.62358069e-01 1.20205179e-01
-1.02520883e+00 4.68474239e-01 -2.63893008e-01 -4.22883719e-01
4.53755230e-01 3.49121869e-01 -7.75760293e-01 -1.78476021e-01
-1.06563592e+00 5.86195230e-01 4.48064685e-01 4.84391078e-02
-1.16136074e+00 -8.11395884e-01 -1.00330305e+00 1.82863355e-01
2.83529609e-01 -7.74160743e-01 4.92272556e-01 -1.13964868e+00
-9.86758828e-01 9.97535050e-01 -7.48911649e-02 -2.38410267e-03
-2.58309785e-02 -3.95608582e-02 -5.68289340e-01 -3.94461453e-02
4.12141621e-01 8.46347392e-01 9.59430695e-01 -1.37690663e+00
-8.08935404e-01 -6.98496163e-01 3.17622200e-02 4.37603682e-01
-2.65346646e-01 -2.72677302e-01 -5.09605229e-01 -9.74355698e-01
5.60267270e-01 -9.30325150e-01 -5.46550304e-02 -2.03742564e-01
-4.34165299e-01 -2.92437911e-01 1.08481300e+00 -5.92954457e-01
1.03399348e+00 -2.49953032e+00 2.26346657e-01 4.27717865e-01
3.67347777e-01 -2.53915370e-01 -5.00571609e-01 1.78788617e-01
-4.54244435e-01 -1.68716654e-01 -1.71744823e-01 2.76053380e-02
4.09615301e-02 1.66071847e-01 -4.43140745e-01 5.72847903e-01
2.48075977e-01 1.06631780e+00 -8.80179167e-01 -4.01739180e-01
4.51231778e-01 1.88287154e-01 -4.18819487e-01 2.90812820e-01
2.54848357e-02 3.72660637e-01 -8.31024706e-01 7.76822090e-01
7.60540485e-01 -6.12573475e-02 -6.77224174e-02 -7.70474792e-01
-5.99923264e-03 1.96929444e-02 -9.86362934e-01 1.97197938e+00
-4.61262375e-01 2.48413816e-01 -1.15541004e-01 -1.08531225e+00
1.26997912e+00 -1.93428710e-01 3.88172239e-01 -6.06759310e-01
-2.67214149e-01 1.10133588e-01 -1.61612615e-01 -5.50293811e-02
2.38721669e-01 -1.23151980e-01 -5.25952041e-01 3.03950608e-02
4.19927478e-01 1.07292086e-01 -5.26107609e-01 2.75561631e-01
8.67774129e-01 2.79392540e-01 2.02811673e-01 -7.06142187e-01
5.78648210e-01 -1.21805444e-02 7.71981657e-01 6.27820849e-01
-2.41595745e-01 7.76681781e-01 1.37416974e-01 -6.93849921e-01
-8.68775487e-01 -1.54483759e+00 -3.76872905e-02 1.20885921e+00
6.88851774e-01 -2.11445555e-01 -5.09957969e-01 -1.02384722e+00
1.95632160e-01 7.17416346e-01 -7.42663980e-01 -4.37185198e-01
-3.72824401e-01 -4.91452873e-01 1.18904747e-01 7.55921125e-01
6.30907297e-01 -1.00986242e+00 1.89315192e-02 -5.71203651e-04
-1.20948963e-01 -9.09679353e-01 -7.84168124e-01 3.73703331e-01
-5.85708439e-01 -7.95335174e-01 -4.36761975e-01 -9.72711802e-01
8.99072051e-01 4.95375544e-01 9.39664185e-01 -3.13957445e-02
-6.27374053e-02 2.45515719e-01 -3.43194813e-01 1.73487961e-01
1.65312529e-01 2.50174291e-02 1.73119195e-02 4.09045517e-01
6.38751626e-01 -6.40009761e-01 -7.29591250e-01 6.65921569e-01
-7.09277093e-01 1.49372578e-01 9.84349251e-01 1.00627601e+00
1.05317605e+00 4.64043766e-02 5.18853247e-01 -7.92789102e-01
2.38426805e-01 -4.19985920e-01 -4.83391464e-01 3.51441979e-01
-4.25492108e-01 5.68932444e-02 8.01563680e-01 -2.86539793e-01
-8.39180231e-01 3.15061688e-01 8.45846087e-02 -8.29082251e-01
-6.46245480e-01 2.02807456e-01 -6.90164268e-01 -6.66423738e-02
3.90365541e-01 5.75292170e-01 -2.37024143e-01 -3.72219414e-01
5.36609054e-01 4.44471896e-01 5.92091680e-01 -4.83397752e-01
1.08318710e+00 5.05452394e-01 -6.53029680e-02 -3.83546978e-01
-1.13887942e+00 -8.11869502e-01 -8.35075319e-01 -1.09469909e-02
1.26772809e+00 -1.14882374e+00 -2.80668229e-01 2.85010546e-01
-8.12787473e-01 -6.91539124e-02 -3.73219132e-01 5.72276711e-01
-5.22396028e-01 4.40212376e-02 -2.79466093e-01 -3.73957269e-02
-8.17339867e-02 -1.10728419e+00 1.50252008e+00 2.49676868e-01
-3.11230663e-02 -8.13805282e-01 -2.61399418e-01 1.78001449e-01
3.38131011e-01 2.41677225e-01 1.08053267e+00 -7.27573991e-01
-8.02910686e-01 -1.81258813e-01 -5.24549127e-01 3.67222220e-01
3.08492541e-01 -4.64490563e-01 -1.05074453e+00 -5.19444406e-01
-2.30579272e-01 -3.78272347e-02 1.10324931e+00 2.27662921e-01
1.44829786e+00 -2.49274403e-01 -6.01839542e-01 1.09599864e+00
1.57956016e+00 -2.17724853e-04 4.71246809e-01 2.33492896e-01
1.27844489e+00 6.58311069e-01 8.03880692e-01 2.50193216e-02
2.33634055e-01 8.84470344e-01 5.32866120e-01 -3.74480009e-01
-4.60647941e-01 -5.15381157e-01 2.60989964e-01 6.76120222e-01
9.01747271e-02 1.46835893e-01 -4.05259609e-01 5.49195170e-01
-1.92211461e+00 -8.06659877e-01 2.43167311e-01 2.16157079e+00
3.64711136e-01 -5.85343018e-02 -7.02013001e-02 -5.05099177e-01
9.02406633e-01 3.85928780e-01 -5.48215270e-01 -1.96120143e-01
-2.84399003e-01 2.53276862e-02 6.66201293e-01 1.80449620e-01
-1.39613998e+00 1.18898618e+00 4.46078587e+00 1.32562196e+00
-1.08731842e+00 2.24417105e-01 6.61375821e-01 1.62437692e-01
-5.54290414e-01 2.32856959e-01 -8.67028236e-01 4.39313322e-01
3.67682070e-01 4.08895761e-01 4.44731891e-01 1.12610686e+00
-2.74693638e-01 4.43829179e-01 -1.06733656e+00 9.64732289e-01
8.59324858e-02 -1.21161032e+00 5.91786921e-01 8.10167938e-02
6.41045570e-01 -9.33261961e-02 1.49584085e-01 4.98262614e-01
2.84896225e-01 -1.10484779e+00 8.93964708e-01 6.03821933e-01
9.63493645e-01 -8.18845272e-01 7.10084319e-01 5.20858541e-02
-1.66931522e+00 -2.51807421e-01 -7.86828756e-01 3.34192395e-01
-2.20340237e-01 4.03943688e-01 -5.05256414e-01 6.96147561e-01
8.74063969e-01 9.00729358e-01 -6.94903255e-01 6.15115881e-01
-3.14390987e-01 4.20702100e-01 1.46486238e-03 2.03496486e-01
5.73685467e-01 -3.49128366e-01 5.38723350e-01 1.09200239e+00
2.43716493e-01 -7.73518905e-02 4.05778229e-01 1.16324866e+00
9.15275961e-02 -5.56050502e-02 -6.70723736e-01 2.76625454e-01
6.38117492e-01 1.65859663e+00 -7.47845173e-01 -1.18388765e-01
-6.09235764e-01 1.21292067e+00 4.13101971e-01 5.84382176e-01
-8.60261381e-01 -4.36386943e-01 9.25220013e-01 2.04979658e-01
5.25533020e-01 -2.07306325e-01 -1.97160602e-01 -9.88490045e-01
-7.49481842e-02 -5.88255346e-01 5.94383299e-01 -7.18713224e-01
-1.66141009e+00 6.99987769e-01 -6.78900182e-02 -1.49324536e+00
1.78371698e-01 -5.76552689e-01 -5.65577805e-01 1.14341092e+00
-1.54893768e+00 -1.83032334e+00 -6.61488652e-01 9.40451562e-01
5.57934284e-01 -4.52478737e-01 8.94772649e-01 2.50922799e-01
-2.72376060e-01 7.53101587e-01 -8.58284906e-02 1.94213063e-01
6.82120025e-01 -1.25596249e+00 1.73005864e-01 7.78893292e-01
2.61783987e-01 7.56738663e-01 1.36434719e-01 -6.61293805e-01
-1.18539822e+00 -1.73220944e+00 6.09756052e-01 -5.94393849e-01
6.01413488e-01 -6.20878577e-01 -8.44949901e-01 6.76447928e-01
-2.13036612e-01 3.51487219e-01 4.57390636e-01 1.25385359e-01
-7.22615540e-01 -5.70217133e-01 -9.32673454e-01 4.33170915e-01
1.27316785e+00 -8.24592471e-01 -6.98304892e-01 3.30725580e-01
1.11540353e+00 8.86866376e-02 -7.99297512e-01 6.59530401e-01
2.79640555e-01 -7.47909963e-01 1.29977608e+00 -4.91038740e-01
2.29206115e-01 -9.55459535e-01 -6.05748177e-01 -1.43981385e+00
-1.04389369e+00 4.98295389e-02 4.26382482e-01 1.41893470e+00
2.99466610e-01 -7.68506408e-01 4.72788572e-01 1.57561451e-01
-5.40059566e-01 -7.07759202e-01 -8.60531747e-01 -9.60281789e-01
-1.49975047e-01 -7.99949840e-02 9.56417203e-01 1.19268453e+00
-5.77447772e-01 4.59459275e-01 -1.37561530e-01 5.00886321e-01
6.62463844e-01 7.84068644e-01 5.64509690e-01 -1.09699821e+00
-1.96405172e-01 -4.06924695e-01 -7.86791742e-01 -9.44828629e-01
3.63844275e-01 -1.26498032e+00 1.09127663e-01 -1.34896588e+00
3.47844362e-01 -7.50053704e-01 -8.20959032e-01 7.25292325e-01
-1.07510336e-01 2.89646268e-01 1.80400386e-02 4.66853887e-01
-7.35738814e-01 6.62256896e-01 1.30512333e+00 -2.87643671e-01
8.95852745e-02 -2.59655744e-01 -8.28423500e-01 5.97445667e-01
5.57343841e-01 -3.03821921e-01 -4.32000518e-01 -2.82685339e-01
-2.32257873e-01 -3.95156592e-01 7.95955598e-01 -1.04299986e+00
7.76597410e-02 -2.61631608e-01 7.90894866e-01 -6.25760078e-01
1.77659065e-01 -9.99994874e-01 1.36945978e-01 6.84907138e-02
-3.45739454e-01 -4.73848283e-02 2.17972863e-02 6.67373598e-01
-5.43313205e-01 1.85889676e-01 7.95053840e-01 -6.70574158e-02
-1.47556913e+00 7.23158956e-01 6.02567904e-02 -2.08235115e-01
1.22914469e+00 -5.02943337e-01 -2.95280188e-01 -7.29233921e-02
-6.67287648e-01 1.03240736e-01 4.85657185e-01 6.34036422e-01
8.11007917e-01 -1.72178924e+00 -5.50960898e-01 5.52047670e-01
6.53988242e-01 -4.03978787e-02 4.60589886e-01 5.66282988e-01
-1.80926502e-01 4.49878633e-01 -4.04031098e-01 -8.17971826e-01
-9.76561427e-01 8.78264904e-01 2.02431530e-01 -1.94882452e-02
-6.31948292e-01 1.00747287e+00 1.34043562e+00 -7.21734464e-01
-1.85942292e-01 -1.04726501e-01 -3.42854530e-01 -1.62666723e-01
1.55693382e-01 -6.69531152e-02 1.15789525e-01 -1.05133462e+00
-7.05277205e-01 8.00651848e-01 -9.43356007e-02 2.99226791e-01
1.14133835e+00 -2.55291611e-01 -2.54467010e-01 9.59434360e-02
1.51473558e+00 2.95550600e-02 -1.42236829e+00 -2.91633695e-01
-1.74341366e-01 -5.81062973e-01 1.80562884e-01 -7.17826426e-01
-1.27008712e+00 7.43478000e-01 7.87835002e-01 -2.51619369e-01
1.32686579e+00 3.50480676e-01 5.34787178e-01 -3.60128433e-02
5.40677845e-01 -9.61454988e-01 8.25078487e-02 4.40957963e-01
8.58451426e-01 -1.02339756e+00 -2.01977745e-01 -6.06016994e-01
-6.85441971e-01 8.78632247e-01 9.07844782e-01 -3.76541793e-01
7.76274741e-01 -7.97021389e-02 5.22132628e-02 -4.41147506e-01
-3.80824447e-01 -2.59950280e-01 8.42429757e-01 6.43601358e-01
4.54290397e-02 5.49761534e-01 1.22611038e-01 8.15404832e-01
-1.05103992e-01 -6.75790131e-01 -2.53833503e-01 4.70531106e-01
-3.24041367e-01 -7.54889667e-01 -9.75632519e-02 5.70343137e-01
5.58821596e-02 -2.97748566e-01 -3.05066735e-01 7.55258858e-01
4.52143848e-01 6.37513638e-01 2.98277467e-01 -8.09669495e-01
3.24955791e-01 -1.76562145e-01 1.39993429e-01 -8.04191172e-01
-3.67203951e-01 1.19021624e-01 -1.65222004e-01 -9.05325294e-01
-1.31332532e-01 -4.10456568e-01 -1.26152360e+00 7.68217221e-02
-2.16726974e-01 -4.64716032e-02 2.95000225e-01 8.72574568e-01
4.62574631e-01 6.01443350e-01 7.91728616e-01 -5.61513841e-01
-3.79231006e-01 -7.65040517e-01 -9.14714634e-01 8.76220584e-01
1.14304118e-01 -9.82471645e-01 -4.04351324e-01 -3.92071307e-02] | [9.715191841125488, 2.037099599838257] |
0ba6eddd-2b93-4a77-9712-8d9372ff0d60 | explaining-rl-decisions-with-trajectories | 2305.04073 | null | https://arxiv.org/abs/2305.04073v1 | https://arxiv.org/pdf/2305.04073v1.pdf | Explaining RL Decisions with Trajectories | Explanation is a key component for the adoption of reinforcement learning (RL) in many real-world decision-making problems. In the literature, the explanation is often provided by saliency attribution to the features of the RL agent's state. In this work, we propose a complementary approach to these explanations, particularly for offline RL, where we attribute the policy decisions of a trained RL agent to the trajectories encountered by it during training. To do so, we encode trajectories in offline training data individually as well as collectively (encoding a set of trajectories). We then attribute policy decisions to a set of trajectories in this encoded space by estimating the sensitivity of the decision with respect to that set. Further, we demonstrate the effectiveness of the proposed approach in terms of quality of attributions as well as practical scalability in diverse environments that involve both discrete and continuous state and action spaces such as grid-worlds, video games (Atari) and continuous control (MuJoCo). We also conduct a human study on a simple navigation task to observe how their understanding of the task compares with data attributed for a trained RL policy. Keywords -- Explainable AI, Verifiability of AI Decisions, Explainable RL. | ['Jayakumar Subramanian', 'Georgios Theocharous', 'Chirag Agarwal', 'Nan Jiang', 'Balaji Krishnamurthy', 'Arpan Dasgupta', 'Shripad Vilasrao Deshmukh'] | 2023-05-06 | null | null | null | null | ['offline-rl', 'continuous-control'] | ['playing-games', 'playing-games'] | [ 2.12509051e-01 4.39310044e-01 -4.70670730e-01 -1.97056472e-01
-2.95092136e-01 -5.58740437e-01 7.88022459e-01 2.81394571e-01
-4.10174757e-01 1.01519454e+00 3.28116119e-01 -3.58865321e-01
-2.30783418e-01 -4.71000046e-01 -9.38276112e-01 -5.15390933e-01
-2.90052176e-01 5.28160334e-01 9.81160030e-02 -2.88455248e-01
5.34654200e-01 3.43214333e-01 -1.77015269e+00 -8.70818496e-02
9.18989658e-01 7.27546155e-01 3.67060125e-01 8.06904078e-01
1.22360691e-01 1.16611290e+00 -3.65205109e-01 -6.25732467e-02
2.01996475e-01 -5.30209422e-01 -7.56315231e-01 1.63080677e-01
-1.38670504e-01 -4.04593259e-01 -4.44960535e-01 1.04340243e+00
3.64183597e-02 5.72966278e-01 7.00191617e-01 -1.75604033e+00
-6.49487436e-01 5.47876954e-01 -1.04476899e-01 2.45625004e-01
4.05163884e-01 5.20293772e-01 8.25990915e-01 -2.80728579e-01
6.49451137e-01 1.28402746e+00 9.98363048e-02 7.44947910e-01
-9.51960444e-01 -4.36868191e-01 6.31697774e-01 5.80085337e-01
-6.25658512e-01 -4.17007267e-01 5.27370572e-01 -3.77687454e-01
7.04819143e-01 1.40238013e-02 7.16549218e-01 1.21227443e+00
3.82709354e-01 9.75593269e-01 1.32973242e+00 -2.25654572e-01
8.09575438e-01 1.13458456e-02 4.30320762e-03 8.06982756e-01
5.12854941e-02 6.09464824e-01 -6.01979852e-01 -1.37608219e-02
8.76311123e-01 2.08852932e-01 -1.60527050e-01 -5.27448177e-01
-1.34412181e+00 8.46094787e-01 5.83244085e-01 -1.05586864e-01
-8.67729604e-01 5.85206211e-01 1.95405975e-01 2.73649603e-01
2.36228988e-01 7.13408470e-01 -2.85025805e-01 -3.02715480e-01
-2.46721357e-01 5.23598909e-01 5.75282156e-01 1.10311425e+00
8.10370684e-01 3.10284942e-01 -5.12876272e-01 7.63221681e-02
3.65127742e-01 3.78596902e-01 7.30072439e-01 -1.26166594e+00
4.49342787e-01 4.48860258e-01 6.97811127e-01 -9.17259812e-01
-3.84113014e-01 -1.30917504e-01 -2.77448326e-01 6.00611627e-01
3.55632216e-01 -2.75401622e-01 -9.69019711e-01 2.01046658e+00
4.95814592e-01 4.85079110e-01 4.15165782e-01 1.27668953e+00
1.22122914e-01 4.36161876e-01 2.48973414e-01 -9.82973054e-02
1.06136239e+00 -1.16889036e+00 -9.11534190e-01 -4.25542116e-01
3.97454619e-01 -5.82035966e-02 1.30619848e+00 1.62850767e-01
-7.67813385e-01 -4.86884207e-01 -9.82239246e-01 2.34925658e-01
-1.71032250e-01 1.28426760e-01 7.34067619e-01 1.11931086e-01
-8.65823269e-01 8.91154408e-01 -9.89805758e-01 -6.01287425e-01
3.92720014e-01 3.66897136e-01 -8.15414637e-02 1.85843289e-01
-1.11977041e+00 1.00095093e+00 4.11697507e-01 -1.57940164e-01
-1.61735570e+00 -1.91962957e-01 -7.15974391e-01 6.34563435e-03
8.01091373e-01 -4.88765895e-01 1.58538723e+00 -1.24431181e+00
-1.82012641e+00 2.65771836e-01 -1.95366129e-01 -8.61368656e-01
5.19123971e-01 -4.15301830e-01 -2.07964346e-01 1.73101071e-02
1.95284486e-01 7.62616634e-01 1.06832898e+00 -1.20336378e+00
-9.46776390e-01 -3.77839565e-01 6.70902967e-01 6.04162157e-01
1.48199096e-01 -5.37233353e-01 3.19632292e-02 -3.59861135e-01
-8.77463222e-02 -1.27762699e+00 -6.11575365e-01 -1.70898095e-01
-3.64320606e-01 -3.30318838e-01 7.05838144e-01 -4.54741538e-01
8.16743791e-01 -1.97287333e+00 2.59409249e-01 -4.40784218e-03
3.81487906e-01 9.45515931e-03 -6.08267188e-02 4.49877262e-01
1.94058910e-01 -4.96434793e-03 -7.03425929e-02 -2.01764122e-01
2.83330083e-01 4.41243798e-01 -6.72604620e-01 6.20217502e-01
-7.89739005e-03 8.47263813e-01 -1.28565645e+00 -1.50396088e-02
3.38270843e-01 2.31890311e-03 -4.32084143e-01 4.90558237e-01
-6.46950364e-01 9.93677557e-01 -9.43485856e-01 2.72103190e-01
-1.73232049e-01 -2.33666506e-02 -1.24955559e-02 3.95161241e-01
-2.10104093e-01 2.43264601e-01 -9.72051859e-01 1.57357264e+00
-2.41336390e-01 6.38132572e-01 -4.39248741e-01 -6.18522704e-01
7.22572029e-01 3.68303299e-01 2.48302892e-01 -6.46863759e-01
1.26729473e-01 6.25129556e-04 2.30293259e-01 -5.15703559e-01
6.32500231e-01 1.56506062e-01 -3.51496646e-03 7.65741229e-01
-1.82554960e-01 2.52940476e-01 -1.27680898e-01 1.25144750e-01
1.02875388e+00 4.17448461e-01 5.41307271e-01 -4.35025655e-02
2.29298964e-01 3.58915329e-01 4.38087255e-01 1.07903719e+00
-7.04181850e-01 2.01366380e-01 5.59036493e-01 -5.63515186e-01
-1.07371140e+00 -6.88881278e-01 5.54885864e-01 1.15842950e+00
5.69946349e-01 -3.93039323e-02 -8.23995769e-01 -9.26857114e-01
5.74443154e-02 1.35309935e+00 -9.67624784e-01 -5.51949918e-01
-2.64913052e-01 -2.79567409e-02 1.93029180e-01 5.99087298e-01
6.01396918e-01 -1.54580045e+00 -1.46689212e+00 1.06378645e-01
-2.92918198e-02 -9.17608738e-01 -4.77483898e-01 1.60913780e-01
-7.58282185e-01 -1.15812385e+00 -2.94451684e-01 -2.26504043e-01
6.85205638e-01 1.54998243e-01 8.31515729e-01 -3.50205563e-02
1.77443236e-01 7.72367716e-01 -5.13726294e-01 -4.96195883e-01
-4.03302848e-01 -2.14922383e-01 5.27990878e-01 4.02881801e-02
1.43382788e-01 -9.74478051e-02 -6.35138273e-01 3.67644876e-01
-4.73616898e-01 2.91454762e-01 4.88976508e-01 5.75566590e-01
6.81633294e-01 -4.48646396e-02 5.47322214e-01 -7.80746758e-01
9.13541198e-01 -8.24953437e-01 -6.46390438e-01 2.07494497e-01
-8.19188535e-01 4.01143372e-01 6.27843976e-01 -6.08617723e-01
-1.02075088e+00 9.85602736e-02 5.09014010e-01 -7.56747723e-01
-5.15063703e-01 3.79701406e-01 1.96282879e-01 2.39170626e-01
6.31866276e-01 2.46366039e-01 5.04360832e-02 -1.60582140e-01
6.11046135e-01 3.82870376e-01 4.65079606e-01 -4.94312167e-01
5.96450984e-01 3.32644969e-01 -3.06477398e-02 -3.79481882e-01
-7.01872289e-01 -9.19725820e-02 -4.73274767e-01 -5.22619009e-01
9.87001717e-01 -6.03316963e-01 -1.11112082e+00 1.96182472e-03
-9.15395379e-01 -7.17842877e-01 -4.99816149e-01 6.99744403e-01
-1.12772608e+00 -1.80963650e-01 -1.33115903e-01 -1.08246100e+00
1.60244063e-01 -1.40068018e+00 8.11265767e-01 4.66938078e-01
-2.63024062e-01 -9.88977492e-01 6.77073523e-02 3.34373228e-02
3.58525455e-01 2.21919000e-01 9.15746927e-01 -8.97440732e-01
-5.88359118e-01 1.59936383e-01 -2.36728974e-02 -3.40924799e-01
1.65464520e-01 -4.23810840e-01 -9.58166659e-01 -2.63843268e-01
-6.95868209e-02 -4.09747303e-01 4.88343477e-01 4.62466091e-01
1.01240766e+00 -6.84785843e-01 -3.05614531e-01 1.70863658e-01
1.07122827e+00 6.17401242e-01 3.55224490e-01 5.39156556e-01
5.21328866e-01 6.61842465e-01 1.30422556e+00 5.63219488e-01
5.91216624e-01 6.24071538e-01 1.04342186e+00 1.12202168e-01
1.24700949e-01 -7.73303151e-01 5.79990387e-01 1.55837446e-01
-2.08037496e-01 -2.66674489e-01 -6.61574006e-01 3.98987859e-01
-2.39574838e+00 -9.16420102e-01 1.32980809e-01 2.35620308e+00
3.15239757e-01 -4.93385978e-02 1.93801343e-01 -1.73706397e-01
7.13474989e-01 4.44689617e-02 -1.38840270e+00 -5.17312050e-01
2.83191919e-01 -4.47339535e-01 6.10140264e-01 5.90420544e-01
-9.80871558e-01 1.24894726e+00 6.34901285e+00 4.57740188e-01
-1.05318916e+00 3.82363088e-02 6.40728593e-01 1.16753638e-01
-1.49036229e-01 1.14386261e-01 -4.41711307e-01 3.23546171e-01
1.03810132e+00 -2.59884059e-01 8.79864633e-01 8.69945288e-01
4.63278234e-01 -3.04104179e-01 -1.41026282e+00 6.04876399e-01
-2.55680770e-01 -1.14710224e+00 -1.31033063e-01 7.25114197e-02
6.94519043e-01 -1.82600290e-01 3.85627955e-01 5.27384877e-01
7.19930232e-01 -1.21908402e+00 1.09100389e+00 6.11133754e-01
4.28014755e-01 -7.33231604e-01 5.35006106e-01 6.42733216e-01
-8.34056795e-01 -4.39687133e-01 -3.46752822e-01 -3.53527635e-01
-2.37010881e-01 -3.23322177e-01 -1.23153949e+00 1.78791121e-01
3.99456620e-01 7.98442364e-01 -2.58263260e-01 7.89706886e-01
-7.46440589e-01 6.19231522e-01 1.46621227e-01 -3.28850776e-01
6.13305330e-01 -3.00047427e-01 6.57511830e-01 4.85009670e-01
2.72939891e-01 3.21460515e-01 3.15806806e-01 9.89234626e-01
2.24082768e-01 -2.63210416e-01 -8.57499897e-01 -1.60075873e-01
6.24009609e-01 7.74656951e-01 -7.27977216e-01 -3.83408070e-01
1.41023412e-01 1.03133988e+00 4.88298327e-01 6.59730971e-01
-9.92703915e-01 1.21793114e-01 8.90022933e-01 -2.88163185e-01
2.16877475e-01 -9.96541828e-02 -5.84217794e-02 -8.32382262e-01
-3.94267887e-01 -7.98848391e-01 1.49773180e-01 -9.38966811e-01
-7.28770256e-01 5.44146359e-01 3.73581126e-02 -1.29426694e+00
-5.55812895e-01 -2.35059053e-01 -5.89448690e-01 6.70330107e-01
-1.55608070e+00 -6.62751496e-01 -2.16408432e-01 6.23366356e-01
9.18026745e-01 -4.54316646e-01 6.90123618e-01 -4.37482208e-01
-5.52350938e-01 1.28619418e-01 7.13267028e-02 -3.83600891e-01
3.73987347e-01 -1.47187340e+00 4.06713456e-01 5.73633790e-01
2.48428315e-01 3.70667189e-01 1.16500318e+00 -8.06484520e-01
-1.47985291e+00 -1.13053548e+00 3.38470519e-01 -4.31427449e-01
4.99227047e-01 7.08896667e-02 -7.43658602e-01 1.03842783e+00
2.18450323e-01 -2.21627042e-01 2.02839896e-01 -9.58180875e-02
1.47857636e-01 2.96240151e-01 -1.28723478e+00 9.93920326e-01
1.06428218e+00 -3.79177749e-01 -6.22665346e-01 3.45602006e-01
8.61462533e-01 -6.35205626e-01 -4.16979074e-01 -1.34934604e-01
4.29845303e-01 -9.35464919e-01 6.55424356e-01 -1.13142967e+00
5.29577255e-01 -5.42823017e-01 -1.33712232e-01 -1.81271482e+00
-4.00189102e-01 -6.07578397e-01 -3.20643663e-01 7.93125629e-01
2.31940761e-01 -6.63662791e-01 7.55192101e-01 6.80462241e-01
-1.49904534e-01 -7.29031742e-01 -7.53569782e-01 -5.03590465e-01
-4.05534387e-01 -4.11344975e-01 8.17174613e-01 6.21905327e-01
-1.34269446e-02 3.75544205e-02 -5.42350411e-01 4.87361223e-01
6.25041306e-01 8.23599175e-02 7.80893803e-01 -8.37617338e-01
-1.98139042e-01 -1.64536312e-01 -2.98812717e-01 -1.02723861e+00
3.87274146e-01 -6.48530185e-01 3.96338791e-01 -1.47477639e+00
-1.15297973e-01 -6.51614547e-01 -3.66343200e-01 5.49743056e-01
-2.49125108e-01 -4.68337268e-01 4.62225139e-01 5.54966390e-01
-8.72383058e-01 8.62767756e-01 1.36275136e+00 1.50758713e-01
-3.83313656e-01 1.15759917e-01 -5.32197714e-01 8.84683549e-01
9.74512517e-01 -5.55042088e-01 -8.43809307e-01 -4.48690206e-01
-1.50713339e-01 5.59649229e-01 4.93243843e-01 -1.03659379e+00
3.12174261e-01 -6.38297856e-01 1.30734414e-01 -2.82108426e-01
4.07906950e-01 -9.60429609e-01 4.83840257e-02 7.04161942e-01
-9.01904583e-01 3.86224896e-01 4.39833924e-02 1.14982319e+00
1.39351144e-01 -1.31178588e-01 3.69889498e-01 -1.25302538e-01
-1.18258321e+00 3.32899034e-01 -5.49237251e-01 -4.00419859e-03
1.45116961e+00 -1.56642899e-01 -3.71862203e-01 -8.62360775e-01
-7.01834083e-01 5.20873249e-01 4.42462027e-01 4.48006839e-01
7.88117945e-01 -1.17193997e+00 -3.46582770e-01 5.87023124e-02
7.19572231e-02 -3.11934233e-01 -1.72844511e-02 7.86456645e-01
-1.37908250e-01 5.12166321e-01 -6.39836192e-01 -4.42156851e-01
-8.08350563e-01 5.93280852e-01 2.94831961e-01 -4.25351895e-02
-7.10053146e-01 4.91778046e-01 2.62598276e-01 -2.87301034e-01
4.16528106e-01 -4.15956527e-01 -6.21477962e-01 -2.95637518e-01
2.67025083e-01 4.41313297e-01 -5.41585624e-01 -5.41719019e-01
-1.25935793e-01 1.44801483e-01 1.16554998e-01 -3.79757643e-01
1.01044786e+00 -3.08227092e-01 5.73865473e-01 8.73575747e-01
4.57613796e-01 -4.71294016e-01 -2.07738805e+00 -9.02102217e-02
1.72042146e-01 -4.78807122e-01 -9.91447456e-03 -9.77249444e-01
-8.41434181e-01 7.45880067e-01 6.67339265e-01 8.13624412e-02
7.03846276e-01 -1.86842337e-01 3.13968211e-01 4.76588756e-01
6.87009633e-01 -1.02867711e+00 1.76232740e-01 4.18315768e-01
8.11780095e-01 -1.39100432e+00 -3.02635401e-01 2.82130331e-01
-1.34843946e+00 8.77887428e-01 6.85692966e-01 -3.31012785e-01
2.13097662e-01 -4.33845222e-01 2.90309153e-02 -1.81047052e-01
-1.12191737e+00 -4.92699295e-01 1.12978928e-01 7.51579762e-01
-8.39267895e-02 3.11458290e-01 1.29103392e-01 4.29329962e-01
-1.28468379e-01 9.58250389e-02 7.87498951e-01 8.66610765e-01
-5.98235965e-01 -5.81253886e-01 -2.76376635e-01 3.34900618e-01
4.23217267e-02 3.23200196e-01 -3.53955418e-01 8.13842297e-01
-9.57722962e-02 1.03406668e+00 6.15153089e-02 -5.48504889e-01
2.68234879e-01 -1.19081445e-01 1.62231192e-01 -5.84086061e-01
-4.66701955e-01 -3.48937869e-01 -3.22569720e-02 -9.89431798e-01
-3.43501359e-01 -7.91749418e-01 -1.73354506e+00 -1.50154889e-01
4.45279814e-02 2.31004864e-01 7.25716114e-01 1.29241133e+00
4.97241229e-01 7.68971324e-01 5.93584776e-01 -9.49916840e-01
-7.58182108e-01 -6.34739816e-01 -4.17809248e-01 4.80345428e-01
7.09117293e-01 -1.13528776e+00 -2.72960037e-01 -1.11701928e-01] | [4.188821315765381, 1.701714038848877] |
6ce07795-6e9f-4182-b218-219f261c723b | the-limitations-of-large-width-in-neural | 2106.06529 | null | https://arxiv.org/abs/2106.06529v2 | https://arxiv.org/pdf/2106.06529v2.pdf | The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective | Large width limits have been a recent focus of deep learning research: modulo computational practicalities, do wider networks outperform narrower ones? Answering this question has been challenging, as conventional networks gain representational power with width, potentially masking any negative effects. Our analysis in this paper decouples capacity and width via the generalization of neural networks to Deep Gaussian Processes (Deep GP), a class of nonparametric hierarchical models that subsume neural nets. In doing so, we aim to understand how width affects (standard) neural networks once they have sufficient capacity for a given modeling task. Our theoretical and empirical results on Deep GP suggest that large width can be detrimental to hierarchical models. Surprisingly, we prove that even nonparametric Deep GP converge to Gaussian processes, effectively becoming shallower without any increase in representational power. The posterior, which corresponds to a mixture of data-adaptable basis functions, becomes less data-dependent with width. Our tail analysis demonstrates that width and depth have opposite effects: depth accentuates a model's non-Gaussianity, while width makes models increasingly Gaussian. We find there is a "sweet spot" that maximizes test performance before the limiting GP behavior prevents adaptability, occurring at width = 1 or width = 2 for nonparametric Deep GP. These results make strong predictions about the same phenomenon in conventional neural networks trained with L2 regularization (analogous to a Gaussian prior on parameters): we show that such neural networks may need up to 500 - 1000 hidden units for sufficient capacity - depending on the dataset - but further width degrades performance. | ['John P. Cunningham', 'Geoff Pleiss'] | 2021-06-11 | null | http://proceedings.neurips.cc/paper/2021/hash/1b9f38268c50805669fd8caf8f3cc84a-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/1b9f38268c50805669fd8caf8f3cc84a-Paper.pdf | neurips-2021-12 | ['l2-regularization'] | ['methodology'] | [ 6.06155284e-02 4.69369709e-01 -1.18982047e-01 -1.06969262e-02
-3.77430499e-01 -7.14005709e-01 5.69222033e-01 1.29982056e-02
-5.22088349e-01 7.09815204e-01 1.97148532e-01 -5.54042578e-01
-3.98667663e-01 -8.77039194e-01 -9.10708666e-01 -1.15739346e+00
-1.92487001e-01 4.70463514e-01 4.59274828e-01 1.42772332e-01
1.07749216e-01 5.12700558e-01 -1.40744305e+00 -2.23887712e-02
7.11085081e-01 9.66820240e-01 5.04066534e-02 8.11184168e-01
1.14210747e-01 4.22236353e-01 -5.25103629e-01 -4.34624255e-01
3.06385189e-01 -6.50931448e-02 -4.35545594e-01 -2.15568513e-01
4.78988886e-01 -7.10166767e-02 -3.62027347e-01 1.14693105e+00
5.62060714e-01 3.55633765e-01 1.19417608e+00 -1.11864400e+00
-9.65165913e-01 8.23341072e-01 -5.82212269e-01 3.87397230e-01
-5.62370896e-01 4.20749128e-01 1.01114917e+00 -5.05357683e-01
-1.14412479e-01 1.45798612e+00 1.28791773e+00 5.80681741e-01
-1.77352774e+00 -4.31479722e-01 7.22206086e-02 -7.05039620e-01
-1.36293733e+00 -4.46331114e-01 2.68526793e-01 -6.19406939e-01
9.90303636e-01 2.62227766e-02 4.78794217e-01 1.26773798e+00
4.59305495e-01 3.70525092e-01 8.69623959e-01 -2.42379650e-01
4.69557881e-01 -1.27114756e-02 2.57780850e-01 4.69804198e-01
8.18837225e-01 1.85524493e-01 -2.57695049e-01 -3.29785019e-01
1.41375101e+00 -1.66137174e-01 -3.82164478e-01 -5.14718592e-01
-5.39367378e-01 1.09013200e+00 1.40136525e-01 1.49026185e-01
-2.98936814e-01 6.57749832e-01 2.69868135e-01 3.26766521e-01
2.85815984e-01 8.06930065e-01 -6.67289793e-01 -3.55985522e-01
-9.46299672e-01 2.37185895e-01 9.61561143e-01 8.01213920e-01
5.22117138e-01 2.58173645e-01 -2.51628578e-01 9.03270125e-01
2.40107059e-01 3.76536459e-01 7.22431242e-01 -1.14409113e+00
3.58779728e-01 2.00394168e-01 -2.20181067e-02 -7.19443381e-01
-6.36579394e-01 -8.35977435e-01 -9.27440524e-01 1.24327406e-01
9.35571551e-01 -5.04235804e-01 -8.34342122e-01 2.13764763e+00
-3.60123485e-01 -2.15331033e-01 2.23395936e-02 3.84982318e-01
2.81983405e-01 5.71155131e-01 4.64566916e-01 -2.63161361e-02
1.16735554e+00 -7.26419091e-01 -2.50314295e-01 -3.76044244e-01
5.64023972e-01 -3.11920822e-01 1.41380143e+00 5.49616158e-01
-1.35143650e+00 -4.88887638e-01 -9.85859334e-01 -8.82141143e-02
-2.45466903e-01 -3.25259238e-01 7.04938114e-01 1.14897549e+00
-1.45898104e+00 9.73923862e-01 -9.49762821e-01 -3.37579548e-01
6.41211092e-01 6.52456939e-01 -1.33655593e-01 2.56496727e-01
-9.97825682e-01 8.19852650e-01 6.16577625e-01 5.36781102e-02
-6.80734694e-01 -7.47001767e-01 -6.58352733e-01 5.85245430e-01
-2.78446302e-02 -8.93479764e-01 1.21155274e+00 -9.79391396e-01
-1.43759942e+00 5.29695630e-01 7.17471838e-02 -8.11399817e-01
5.10958672e-01 -2.43679464e-01 -1.78811029e-02 -1.72902510e-01
-3.66775602e-01 8.98830354e-01 8.85921121e-01 -1.30690873e+00
-3.12240452e-01 -3.86076182e-01 -2.90292501e-02 7.41702989e-02
-4.70519483e-01 -4.23806757e-01 -3.68549019e-01 -5.60641587e-01
2.17674524e-02 -9.35175121e-01 -1.45057976e-01 -1.57897860e-01
-2.73401737e-01 -2.59378552e-01 3.91403884e-01 -2.72688240e-01
1.19451284e+00 -2.06073380e+00 -1.07728332e-01 2.80347556e-01
3.76406729e-01 -7.29675889e-02 -2.87339896e-01 1.39793098e-01
-1.05277739e-01 5.27427137e-01 -2.51346022e-01 -3.75977963e-01
4.42855030e-01 5.77620029e-01 -3.41738671e-01 5.72572231e-01
2.52002805e-01 9.21553135e-01 -5.17570198e-01 -1.65336385e-01
-2.96909779e-01 4.50180650e-01 -8.89663339e-01 -1.99386299e-01
-1.63598746e-01 -5.97549453e-02 -9.98950079e-02 2.81904489e-01
7.30750859e-01 -3.65503281e-01 -1.11709312e-02 6.45573437e-02
4.03423011e-02 -6.66361600e-02 -8.02905440e-01 8.56094003e-01
-3.55930150e-01 7.82757401e-01 -1.12128379e-02 -1.10961998e+00
7.20027685e-01 6.18733466e-02 -7.24032670e-02 -3.54156196e-01
1.81046769e-01 1.64562985e-01 4.93398100e-01 -1.43458232e-01
4.21050817e-01 -5.60389400e-01 6.01040609e-02 1.12263337e-01
1.26157507e-01 -2.23232463e-01 1.15685999e-01 -3.67023736e-01
1.14322150e+00 -1.45190805e-01 8.35442841e-02 -7.31509387e-01
-6.97193369e-02 -4.26791608e-01 5.19819081e-01 1.36944973e+00
-1.51743859e-01 6.19225323e-01 1.32469010e+00 -1.39281452e-01
-1.25438178e+00 -1.48222363e+00 -5.00813663e-01 1.44015539e+00
-2.14500546e-01 9.83285457e-02 -7.26881206e-01 -3.38673383e-01
2.81982511e-01 8.12481284e-01 -9.20365036e-01 -3.90600830e-01
-5.09939015e-01 -1.16659403e+00 9.45325792e-01 8.66420686e-01
2.43802994e-01 -1.00299489e+00 -4.19214219e-01 1.66906416e-02
4.48400646e-01 -7.71829009e-01 -2.72502542e-01 7.50489652e-01
-1.34700835e+00 -6.50940955e-01 -9.35980558e-01 -5.85409045e-01
5.85769415e-01 -3.66864771e-01 1.27260888e+00 -1.18166037e-01
3.88323963e-01 2.98619062e-01 2.23536432e-01 -3.85754764e-01
-4.19046074e-01 3.66422683e-01 1.36760414e-01 -5.95844507e-01
1.83592811e-01 -1.02030647e+00 -6.35342598e-01 1.84077233e-01
-1.09184265e+00 -4.14175928e-01 8.48034978e-01 9.82283115e-01
3.46639931e-01 3.24938387e-01 5.95533133e-01 -7.96066940e-01
1.09973526e+00 -6.36778712e-01 -6.36956453e-01 -3.17573808e-02
-4.42280114e-01 4.41799015e-01 6.81674123e-01 -7.88069248e-01
-8.53870332e-01 -2.82588601e-01 -9.07862261e-02 -4.52211946e-01
-1.11951530e-02 4.98683661e-01 -1.15277715e-01 7.75223672e-02
8.61938179e-01 8.63237604e-02 -6.08604262e-03 -3.31930876e-01
3.09916258e-01 1.26832753e-01 5.01529098e-01 -9.94567454e-01
5.68189263e-01 2.87698150e-01 1.60256922e-01 -7.97627032e-01
-7.81838357e-01 -9.21821818e-02 -3.76546293e-01 4.17774618e-01
7.98677206e-01 -7.58478403e-01 -9.01700914e-01 3.09096694e-01
-9.69946623e-01 -8.69544506e-01 -6.35944903e-01 2.95781761e-01
-7.72007287e-01 2.28573039e-01 -8.24265182e-01 -7.67549872e-01
-3.40614393e-02 -1.06074893e+00 7.96062410e-01 2.87264138e-01
-2.59257466e-01 -1.47913051e+00 3.39009874e-02 -2.09294125e-01
5.36099195e-01 -2.11940303e-01 1.36298978e+00 -9.70923424e-01
-4.75714765e-02 3.37306894e-02 -2.84427643e-01 7.24926710e-01
-2.59825617e-01 -2.36403681e-02 -1.11967599e+00 -2.07570925e-01
1.47839874e-01 -1.48871839e-01 1.20001721e+00 8.80004466e-01
1.45105875e+00 -5.76281011e-01 -8.10003579e-02 7.19844878e-01
1.38088417e+00 5.62939346e-02 7.44167447e-01 2.45024592e-01
5.92222869e-01 3.90693277e-01 -4.56752717e-01 2.54412681e-01
-2.75929153e-01 1.34377465e-01 2.59338409e-01 1.06239595e-01
1.59567103e-01 -2.47171551e-01 3.29350263e-01 4.52367187e-01
-2.23567426e-01 -4.23632890e-01 -1.08857381e+00 3.37888449e-01
-1.67599142e+00 -8.29394042e-01 1.90425497e-02 2.56980395e+00
1.05830908e+00 7.06904411e-01 1.31602451e-01 -2.02347174e-01
5.17472863e-01 -4.36524265e-02 -5.28865397e-01 -6.48135543e-01
-2.71391034e-01 3.93246472e-01 9.34794784e-01 4.59766090e-01
-1.06002939e+00 7.33126462e-01 7.27786684e+00 1.12717295e+00
-1.03478587e+00 9.59034041e-02 9.95015621e-01 -1.59083888e-01
-3.29378903e-01 -3.61359030e-01 -8.88810039e-01 5.47900617e-01
1.38464832e+00 -1.14966914e-01 3.98929976e-02 1.03546286e+00
1.40115693e-01 -1.24217935e-01 -1.37619901e+00 6.70485079e-01
-4.27127361e-01 -1.24988294e+00 1.84312657e-01 4.40654248e-01
8.47778201e-01 2.49219462e-01 5.62650025e-01 8.17045331e-01
7.37324536e-01 -1.53302932e+00 6.12306595e-01 4.97244060e-01
5.69867730e-01 -8.73024523e-01 8.38947237e-01 4.83705103e-01
-5.47654033e-01 -3.32338005e-01 -9.58678186e-01 -1.36971474e-01
-1.27882019e-01 6.65358067e-01 -4.77532297e-01 -3.33966196e-01
3.47020090e-01 -5.27862534e-02 -6.23488247e-01 1.09501886e+00
3.40965576e-02 7.81313062e-01 -5.13351560e-01 1.51423007e-01
3.47100973e-01 -8.79618749e-02 2.84499139e-01 1.36325943e+00
5.18433630e-01 -1.48090377e-01 -2.35285357e-01 9.99010801e-01
-2.07592517e-01 -3.40080738e-01 -6.30954027e-01 -1.91267371e-01
4.96922404e-01 7.56593406e-01 -9.09322262e-01 -1.44990563e-01
-2.90650964e-01 6.02687657e-01 3.82083148e-01 6.76710367e-01
-6.41053557e-01 -8.85105133e-03 5.53874254e-01 1.71073198e-01
4.14003402e-01 -2.16607779e-01 -7.06584036e-01 -8.11605871e-01
-4.47315514e-01 -5.75293601e-01 9.94113237e-02 -5.70168853e-01
-1.58784068e+00 2.47851640e-01 -1.52361030e-02 -6.52316809e-01
-1.23889439e-01 -9.68440473e-01 -6.65284336e-01 9.88307476e-01
-8.95601451e-01 -8.12316895e-01 1.76114634e-01 2.55361348e-01
2.69742548e-01 1.36626035e-01 5.92698932e-01 -7.01433495e-02
-5.23231626e-01 8.22489500e-01 4.31442797e-01 -1.27608040e-02
3.49894226e-01 -1.52986026e+00 5.46767533e-01 5.64469934e-01
-5.84886894e-02 1.01362455e+00 9.56682622e-01 -5.49805164e-01
-1.06211531e+00 -8.90559316e-01 3.33017200e-01 -7.68486917e-01
9.70459104e-01 -2.76837170e-01 -1.17775702e+00 7.01767027e-01
-9.22400281e-02 -7.48962760e-02 6.62674725e-01 5.97324312e-01
-4.87172663e-01 2.32225522e-01 -1.00489831e+00 8.00647736e-01
9.94337916e-01 -3.80258232e-01 -5.99749625e-01 -4.23207209e-02
9.25159872e-01 -1.44681901e-01 -8.23087931e-01 4.66992199e-01
6.26594663e-01 -1.06765687e+00 8.42507005e-01 -6.36252463e-01
3.79731148e-01 2.15494663e-01 -2.06363633e-01 -1.37558031e+00
-4.72370148e-01 -6.49524629e-01 -3.51037055e-01 7.95130312e-01
6.39485419e-01 -9.63673174e-01 9.94876027e-01 8.87237072e-01
-3.15436184e-01 -9.08494890e-01 -9.33295012e-01 -1.14293492e+00
9.05996025e-01 -5.67104638e-01 1.79467916e-01 7.42766023e-01
-2.47861683e-01 2.11620122e-01 2.58633882e-01 2.11783439e-01
4.28311318e-01 -5.37615359e-01 4.27245498e-01 -1.42885709e+00
-6.99479222e-01 -1.20327592e+00 -3.59937161e-01 -1.36514151e+00
1.74273085e-02 -4.81910020e-01 7.07012117e-02 -1.20189762e+00
1.91396341e-01 -4.35508400e-01 -2.11246803e-01 3.92862827e-01
7.56205320e-02 1.11598238e-01 2.99865957e-02 2.39418432e-01
-1.63203850e-01 4.87502098e-01 1.13131773e+00 2.00713590e-01
-3.60505760e-01 1.89180046e-01 -1.02700996e+00 1.14016294e+00
7.76522994e-01 -2.45921522e-01 -6.93145931e-01 -2.78502882e-01
4.92905915e-01 -2.25544631e-01 3.61136973e-01 -1.13532233e+00
1.30854845e-01 -6.95271138e-03 7.22903848e-01 -1.99916035e-01
3.60433102e-01 -5.74072838e-01 -1.87832832e-01 3.98451567e-01
-3.92004460e-01 1.02382459e-01 6.65819228e-01 6.15492105e-01
1.67107180e-01 -4.75111365e-01 8.85842204e-01 -1.57946404e-02
-1.66650981e-01 2.78613299e-01 -6.04848862e-01 3.26121062e-01
5.55445790e-01 -3.95277947e-01 -5.25318921e-01 -3.71558458e-01
-9.98751879e-01 6.43824190e-02 4.62255925e-01 9.64101055e-04
9.25484896e-02 -9.43566322e-01 -4.06340718e-01 1.06498770e-01
-3.50689471e-01 1.58180699e-01 4.12511677e-01 9.02795434e-01
-5.09937823e-01 3.39285731e-01 -7.35357106e-02 -4.46670771e-01
-6.13235950e-01 3.86085123e-01 6.75333619e-01 -2.44946465e-01
-3.77786219e-01 1.26779950e+00 1.02015650e+00 -1.59084439e-01
3.21571589e-01 -7.58946836e-01 -3.72514986e-02 1.74498871e-01
1.52852267e-01 1.93534434e-01 -9.08371732e-02 -2.10457090e-02
2.11081743e-01 2.13851631e-01 -1.41877681e-01 -6.22687265e-02
1.19966590e+00 1.05244577e-01 6.13582730e-02 5.82720518e-01
9.63056207e-01 -8.56868103e-02 -1.79192042e+00 1.94515720e-01
8.17111880e-02 1.22116573e-01 -3.51683572e-02 -4.89031523e-01
-6.98495865e-01 1.16015029e+00 4.30125684e-01 7.26035774e-01
7.86791861e-01 1.08086005e-01 3.02888304e-01 4.46272314e-01
-8.35600048e-02 -9.92243290e-01 9.02015194e-02 8.45430076e-01
8.45777094e-01 -8.42487276e-01 -1.72063053e-01 -2.32453700e-02
-4.23041135e-01 1.03460181e+00 6.97234094e-01 -1.97281405e-01
7.14072168e-01 3.87794673e-01 -6.03067279e-01 -1.34088024e-01
-8.34396541e-01 7.53608570e-02 2.48279780e-01 7.81884730e-01
3.35024506e-01 5.51153533e-02 1.03007801e-01 1.02786231e+00
-5.76808035e-01 -2.48442516e-01 4.61434096e-01 4.93011773e-01
-8.23958039e-01 -6.63937449e-01 -3.72151494e-01 6.57291591e-01
-5.61895788e-01 -5.45280337e-01 1.07915275e-01 1.16793406e+00
9.51399207e-02 4.51117009e-01 6.94986522e-01 7.75951967e-02
1.05392158e-01 2.31629267e-01 6.06016159e-01 -7.32923448e-01
-2.33618021e-01 9.01870131e-02 -4.03959508e-04 -6.19437695e-02
1.17789142e-01 -4.91082996e-01 -1.01072526e+00 -5.99519014e-01
-2.40891799e-01 1.06431963e-02 2.48475805e-01 8.37963998e-01
7.28260204e-02 4.20570105e-01 -1.48135006e-01 -9.06002879e-01
-9.89830196e-01 -1.07109559e+00 -7.62047112e-01 -5.93529679e-02
2.63340503e-01 -6.64214015e-01 -7.98177421e-01 -5.36545645e-03] | [7.820926666259766, 3.6821727752685547] |
286e8258-a7b9-4b73-97b0-1e0a326a4fe3 | matt-a-multiple-instance-attention-mechanism | 2209.04109 | null | https://arxiv.org/abs/2209.04109v1 | https://arxiv.org/pdf/2209.04109v1.pdf | MATT: A Multiple-instance Attention Mechanism for Long-tail Music Genre Classification | Imbalanced music genre classification is a crucial task in the Music Information Retrieval (MIR) field for identifying the long-tail, data-poor genre based on the related music audio segments, which is very prevalent in real-world scenarios. Most of the existing models are designed for class-balanced music datasets, resulting in poor performance in accuracy and generalization when identifying the music genres at the tail of the distribution. Inspired by the success of introducing Multi-instance Learning (MIL) in various classification tasks, we propose a novel mechanism named Multi-instance Attention (MATT) to boost the performance for identifying tail classes. Specifically, we first construct the bag-level datasets by generating the album-artist pair bags. Second, we leverage neural networks to encode the music audio segments. Finally, under the guidance of a multi-instance attention mechanism, the neural network-based models could select the most informative genre to match the given music segment. Comprehensive experimental results on a large-scale music genre benchmark dataset with long-tail distribution demonstrate MATT significantly outperforms other state-of-the-art baselines. | ['Menghua Zhang', 'Xiaokai Liu'] | 2022-09-09 | null | null | null | null | ['genre-classification', 'music-information-retrieval'] | ['computer-vision', 'music'] | [ 1.48319885e-01 -5.70496380e-01 -4.37535912e-01 -1.30174294e-01
-1.32658005e+00 -4.70007032e-01 8.72564875e-03 1.11686006e-01
-4.23326008e-02 2.58832425e-01 4.66160029e-01 2.09790856e-01
-5.16759098e-01 -5.42990506e-01 -5.73329151e-01 -7.20111787e-01
7.37395212e-02 6.04740977e-01 -3.05119038e-01 -1.56294823e-01
4.99680668e-01 -7.74707720e-02 -1.69578803e+00 7.04562604e-01
6.24531865e-01 1.52834702e+00 -2.97566447e-02 4.48454440e-01
-2.63766468e-01 7.98169196e-01 -7.46297896e-01 -4.41579401e-01
1.39881656e-01 -5.04577458e-01 -6.72847390e-01 -1.05266012e-01
5.45115411e-01 -6.42749071e-02 -2.66362816e-01 9.10854757e-01
9.19659436e-01 1.27178803e-01 7.82567620e-01 -1.26671898e+00
-3.08066845e-01 1.10001910e+00 -8.40663552e-01 3.70143980e-01
1.87046885e-01 -1.20966673e-01 1.55408764e+00 -6.44640803e-01
3.73938456e-02 1.26507843e+00 6.33233488e-01 2.73237199e-01
-9.82808530e-01 -1.34420216e+00 4.12129879e-01 7.11706996e-01
-1.37956595e+00 -4.24677908e-01 1.21786773e+00 -4.93426830e-01
3.65144879e-01 4.39725727e-01 6.45075381e-01 9.42755997e-01
-2.33812839e-01 1.16167057e+00 5.08008540e-01 -1.99318960e-01
-8.03853720e-02 -3.59012812e-01 1.52340874e-01 -1.14752822e-01
-1.89322695e-01 -3.88087243e-01 -9.48381960e-01 -1.86443955e-01
4.97456789e-01 2.40660999e-02 -2.88168699e-01 -4.03793575e-03
-1.29748094e+00 6.49282813e-01 5.05751073e-01 2.27887943e-01
-4.22533721e-01 2.26381138e-01 9.04681742e-01 2.21746713e-01
6.82773888e-01 7.07387328e-01 -4.35338497e-01 -3.07861358e-01
-9.33126748e-01 5.38828015e-01 4.06243294e-01 6.04368508e-01
1.07643060e-01 -2.08656751e-02 -6.69592857e-01 1.24049652e+00
1.11808643e-01 6.32522255e-02 7.72068858e-01 -6.86390698e-01
8.20200443e-01 6.80377364e-01 -1.39435723e-01 -9.23664391e-01
-7.83865303e-02 -1.16344225e+00 -9.58393991e-01 -3.08636755e-01
3.43314201e-01 3.20087194e-01 -4.29313421e-01 1.64953005e+00
2.13576958e-01 4.97677177e-01 -2.64286339e-01 9.46873069e-01
8.50822568e-01 6.47398472e-01 -3.93285826e-02 -2.48449549e-01
1.36355889e+00 -1.06401837e+00 -4.32756245e-01 -5.66140749e-02
1.32756427e-01 -7.76028216e-01 1.26965368e+00 8.01807940e-01
-9.80284989e-01 -9.28766370e-01 -9.69832420e-01 1.17509484e-01
7.90400878e-02 2.35802993e-01 4.24283475e-01 1.83923349e-01
-1.02007389e-01 6.32145464e-01 -2.88076192e-01 3.91993344e-01
6.58086479e-01 2.28618056e-01 2.08414659e-01 1.46304116e-01
-1.06816745e+00 -2.21776634e-01 3.96494418e-01 1.31301969e-01
-1.09026468e+00 -9.81355727e-01 -3.86340767e-01 2.79239058e-01
1.55126736e-01 -4.61833060e-01 1.34923053e+00 -1.45094371e+00
-1.21270978e+00 8.56203616e-01 1.95486754e-01 -4.73466456e-01
3.43756199e-01 -3.16224992e-01 -2.83491462e-01 -9.57622901e-02
2.52629697e-01 4.74289089e-01 1.00496221e+00 -9.51789081e-01
-6.89537883e-01 -6.23112321e-01 -1.22189917e-01 4.59167391e-01
-6.56016052e-01 -2.85252538e-02 -5.23453593e-01 -1.21088350e+00
2.57209986e-01 -7.39005625e-01 1.92885771e-01 -6.53232276e-01
-6.43458366e-01 -5.31103730e-01 3.41902047e-01 -6.73160136e-01
1.77674115e+00 -2.43035960e+00 3.72693866e-01 -9.85541344e-02
-7.94074163e-02 1.18692979e-01 -2.04386279e-01 2.62170464e-01
-3.64398271e-01 -1.00343823e-01 1.31473020e-01 -3.02026212e-01
2.20784262e-01 -3.57527405e-01 -8.40736747e-01 5.99130802e-02
-9.98620465e-02 5.72011113e-01 -7.61432350e-01 -3.17355901e-01
-1.85617581e-01 1.79049626e-01 -7.79442191e-01 4.92048532e-01
-5.25251806e-01 8.05579603e-01 -4.00422305e-01 7.78437078e-01
3.42651635e-01 8.11558776e-03 -1.01642422e-01 -3.45071584e-01
2.13995978e-01 6.69835746e-01 -1.12976170e+00 1.94850373e+00
-3.12215865e-01 3.75550300e-01 -2.80647486e-01 -1.03317130e+00
9.55315709e-01 3.81183207e-01 7.30933547e-01 -7.12253749e-01
1.78263023e-01 4.58882421e-01 3.05722177e-01 -5.31680107e-01
3.98540527e-01 -3.09415042e-01 -4.02540952e-01 3.41131717e-01
-9.31943506e-02 1.99075311e-01 2.35911265e-01 -1.92522839e-01
7.08736718e-01 1.41857281e-01 -5.89060187e-02 1.94149956e-01
5.45574367e-01 -4.16372031e-01 8.69043052e-01 5.82503140e-01
-7.61478767e-02 8.34937572e-01 4.95580733e-01 -5.17261147e-01
-7.55066693e-01 -5.72377026e-01 -2.27719381e-01 1.81520975e+00
7.39610717e-02 -5.91147125e-01 -6.84707105e-01 -4.57778186e-01
-1.38243446e-02 2.78601795e-01 -4.17931169e-01 -3.25525016e-01
-4.84514236e-01 -8.56400788e-01 7.37237275e-01 5.89546323e-01
3.12855572e-01 -1.54003060e+00 -1.17942654e-01 4.36130345e-01
-6.30210996e-01 -5.12496293e-01 -6.93436921e-01 2.01883093e-01
-7.49996483e-01 -1.04078877e+00 -9.30994987e-01 -9.59334791e-01
-8.71302113e-02 2.12948062e-02 1.48052943e+00 -2.06269905e-01
-1.38650730e-01 -9.24255252e-02 -5.24149001e-01 -7.00620472e-01
1.01220429e-01 4.75795001e-01 -1.11659154e-01 5.49311101e-01
4.15145397e-01 -7.57016122e-01 -7.73983657e-01 3.40961128e-01
-6.68259084e-01 2.09671669e-02 2.70547539e-01 8.00034702e-01
9.60958183e-01 2.05117449e-01 1.24495208e+00 -6.14654720e-01
5.97477555e-01 -5.42039871e-01 -3.38377915e-02 -1.55542448e-01
-2.13615879e-01 -4.51409042e-01 6.55097842e-01 -7.03457713e-01
-4.51781005e-01 -2.31292382e-01 -3.66494626e-01 -6.32559896e-01
-1.18417189e-01 5.38703024e-01 -5.39789498e-01 5.20970404e-01
4.09695297e-01 1.79377660e-01 -5.00984728e-01 -9.39550996e-01
-1.16832875e-01 1.17714882e+00 5.17496705e-01 -7.70389974e-01
1.91072226e-01 7.53532723e-02 -3.94076735e-01 -3.39780807e-01
-1.49804783e+00 -7.47548878e-01 -2.70116031e-01 -1.75199583e-01
6.90782666e-01 -1.16119885e+00 -7.71227717e-01 7.53669739e-01
-8.41605246e-01 -1.75203398e-01 -3.67553800e-01 4.57229823e-01
-8.27380061e-01 -1.10047264e-02 -5.35850883e-01 -1.00518668e+00
-7.31325030e-01 -1.11602187e+00 1.42294335e+00 5.20759486e-02
-2.00552747e-01 -2.47203872e-01 2.07515359e-01 8.03477287e-01
5.06612472e-02 -6.45312816e-02 1.32478034e+00 -8.53480995e-01
-3.83278370e-01 -2.17227727e-01 -1.21953852e-01 3.74243319e-01
-7.89621919e-02 -4.49878216e-01 -1.29405057e+00 -3.09129775e-01
-2.62800366e-01 -5.44323146e-01 1.14727283e+00 5.06212294e-01
2.13453650e+00 -2.11366862e-01 3.85925546e-02 8.72622013e-01
1.00364232e+00 2.29531899e-01 4.39248085e-01 4.49621528e-01
9.03230667e-01 3.45725536e-01 8.04182529e-01 5.69158375e-01
2.85931408e-01 9.36010301e-01 5.82518458e-01 1.98948577e-01
-1.94727570e-01 -5.11501193e-01 3.09148312e-01 1.21663201e+00
1.27670079e-01 -1.61434814e-01 -6.31648183e-01 6.95930541e-01
-2.00241494e+00 -9.41435695e-01 2.85210639e-01 2.44846845e+00
1.03238428e+00 1.60496518e-01 6.37378693e-01 8.43951643e-01
8.16657662e-01 2.25216866e-01 -8.54429722e-01 6.01978600e-02
1.29228696e-01 5.25901988e-02 -1.74070835e-01 -2.28380069e-01
-1.39525592e+00 6.17219329e-01 5.05079412e+00 1.44577956e+00
-1.01924133e+00 1.20088840e-02 7.92445183e-01 -6.18273735e-01
-2.18717009e-02 -3.53620619e-01 -7.66204476e-01 8.36815953e-01
6.60887063e-01 1.04916148e-01 5.45585513e-01 5.60038865e-01
7.38653317e-02 5.78619003e-01 -1.00949013e+00 1.34336221e+00
2.13554859e-01 -1.00745356e+00 2.61369199e-01 -1.34089971e-02
6.60023868e-01 -1.29315719e-01 2.05097646e-01 7.53668368e-01
-5.01784980e-01 -9.00188565e-01 1.02696967e+00 4.23206270e-01
7.56605923e-01 -1.21728051e+00 6.18351281e-01 4.04300064e-01
-1.23199844e+00 -5.46206951e-01 -4.32736874e-01 -2.10621487e-02
-1.71918720e-01 4.88215536e-01 -5.15730321e-01 4.13457692e-01
8.51724088e-01 1.09559417e+00 -3.99613231e-01 1.54649985e+00
2.68848985e-01 1.10162890e+00 4.58799414e-02 3.28458071e-01
-3.28761712e-02 -1.54430375e-01 7.34850883e-01 9.38496113e-01
2.84410387e-01 -4.30677652e-01 3.15735757e-01 5.42346120e-01
-4.13854599e-01 5.17876685e-01 -1.02915931e-02 -2.70829856e-01
3.04586679e-01 1.04285157e+00 -4.14811194e-01 -1.09372959e-01
1.24753304e-01 6.22123718e-01 2.20863268e-01 1.62231728e-01
-8.08307111e-01 -5.15844107e-01 7.55900979e-01 1.42512426e-01
4.15340096e-01 3.93142700e-01 -3.46936911e-01 -9.73689497e-01
2.43163686e-02 -1.34673274e+00 7.85826385e-01 -7.67947078e-01
-1.71409059e+00 5.62944472e-01 -4.53310072e-01 -1.78494048e+00
-6.48961514e-02 -2.08833322e-01 -6.53195858e-01 6.36172950e-01
-1.42735422e+00 -1.32345665e+00 -2.55929619e-01 4.98733073e-01
9.28728223e-01 -4.76328582e-01 8.43461335e-01 8.17677617e-01
-6.83217883e-01 9.20307100e-01 1.96472645e-01 4.55297530e-01
1.04268527e+00 -1.29892993e+00 7.08569884e-02 3.62114906e-02
5.83070397e-01 3.48928392e-01 2.69032896e-01 -3.46591085e-01
-1.14975047e+00 -1.11348808e+00 4.74051267e-01 -2.12619141e-01
5.63567102e-01 -2.70620644e-01 -8.52172673e-01 3.34002197e-01
-9.29643288e-02 -3.62445503e-01 1.09014833e+00 5.83372653e-01
-6.08940661e-01 -6.26409471e-01 -4.29154634e-01 2.79458344e-01
9.09661591e-01 -5.90691030e-01 -4.50406760e-01 3.41969281e-01
6.39455020e-01 -3.50679725e-01 -7.72005260e-01 5.30294478e-01
8.82315755e-01 -7.34728158e-01 1.14111674e+00 -8.67118895e-01
6.56472623e-01 -3.32364708e-01 -3.22992533e-01 -1.26992011e+00
-4.12562430e-01 -4.11023855e-01 -2.35664234e-01 1.57387602e+00
1.44073099e-01 6.17628694e-02 5.86224437e-01 -4.25247908e-01
-2.84154356e-01 -8.19190562e-01 -9.58133101e-01 -4.86677587e-01
1.28923682e-02 -7.10911870e-01 9.36318278e-01 7.05657959e-01
-1.04993857e-01 5.90935290e-01 -5.44268668e-01 -2.21458465e-01
4.79982793e-01 8.61886978e-01 8.58985901e-01 -1.46166337e+00
-8.14481199e-01 -8.08546126e-01 -5.53066730e-01 -9.82270896e-01
2.74577677e-01 -1.20144629e+00 1.13382459e-01 -1.18644810e+00
4.61247027e-01 -5.88932157e-01 -1.10061157e+00 3.13196599e-01
-3.26624721e-01 7.65942216e-01 2.15763256e-01 5.36886156e-01
-1.05632603e+00 6.07639432e-01 1.14851975e+00 -5.48860967e-01
-3.11820835e-01 5.34535468e-01 -1.02529550e+00 8.44710648e-01
5.84724724e-01 -5.54541945e-01 -2.76073009e-01 -3.53457212e-01
5.35458505e-01 4.13493775e-02 1.51755884e-01 -1.13589561e+00
-1.32046258e-02 4.24972177e-02 3.21324348e-01 -9.16267991e-01
3.68616581e-01 -5.17994463e-01 -6.12330772e-02 -1.71849877e-02
-7.88815022e-01 -3.22429657e-01 6.49983585e-02 6.74502075e-01
-6.00573421e-01 -2.42144600e-01 5.51815689e-01 8.40748772e-02
-2.38372445e-01 5.73872983e-01 2.27122560e-01 3.67115706e-01
3.69930476e-01 1.33342102e-01 6.60939962e-02 -3.51571858e-01
-6.49123192e-01 1.94415804e-02 -6.41052499e-02 7.36117244e-01
1.51758894e-01 -1.56898034e+00 -9.13621128e-01 3.61800641e-01
5.54560304e-01 1.45290032e-01 5.36673605e-01 7.69632339e-01
-8.17718133e-02 1.20906934e-01 -3.84658165e-02 -6.62194848e-01
-1.17567885e+00 4.29764688e-01 3.21638823e-01 -3.04981709e-01
-3.79926860e-01 1.02146840e+00 5.23257375e-01 -2.93076098e-01
8.06175768e-01 -7.27972016e-02 -4.07296389e-01 5.82504272e-01
7.99928546e-01 3.27771902e-01 2.42586136e-01 -5.64051390e-01
-1.31810591e-01 7.30910361e-01 -9.33665559e-02 1.05276741e-01
1.42230380e+00 1.17992118e-01 -1.09811231e-01 9.31870341e-01
9.97216344e-01 1.05807982e-01 -1.08298385e+00 -1.59916908e-01
-1.23702265e-01 -5.28933942e-01 2.00109214e-01 -9.01664436e-01
-1.20535409e+00 1.18252194e+00 6.32094264e-01 3.95783156e-01
1.36450827e+00 -1.29980937e-01 1.01623368e+00 -5.43029420e-02
1.59684822e-01 -1.07285321e+00 2.30064973e-01 4.15671647e-01
1.06457961e+00 -9.56720889e-01 -2.62908697e-01 9.53689218e-02
-5.92529476e-01 7.80587077e-01 6.22721732e-01 -2.32457981e-01
4.86263186e-01 -1.39940143e-01 1.01805426e-01 6.44757375e-02
-5.85077405e-01 -1.89332515e-01 7.23664582e-01 1.67411372e-01
7.02508330e-01 1.43194631e-01 -1.17554493e-01 1.49243295e+00
-3.86465967e-01 -1.33845016e-01 -1.27104476e-01 2.43832380e-01
-3.79031271e-01 -1.05309725e+00 -3.50227773e-01 6.92769051e-01
-9.08466220e-01 -1.20352037e-01 -5.09488523e-01 -9.24763642e-03
4.19250488e-01 9.03251648e-01 2.46549100e-01 -5.27421355e-01
3.43058527e-01 2.33569756e-01 4.75093544e-01 -5.08099198e-01
-1.15211403e+00 6.76605046e-01 -3.22040826e-01 -1.87635973e-01
-1.41974404e-01 -5.24471462e-01 -7.98287511e-01 1.69316739e-01
-3.79949778e-01 2.00565934e-01 4.63011265e-01 7.23931134e-01
5.20495355e-01 9.64038253e-01 8.64163518e-01 -1.07535934e+00
-5.22783339e-01 -1.32467568e+00 -8.08382332e-01 7.83045232e-01
2.91779965e-01 -6.16868854e-01 -3.21520299e-01 -1.05222650e-01] | [15.759607315063477, 5.2430572509765625] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.