Split (2)

train · 44.6k rows

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
896ef490-f89c-4c10-a0e7-b0ec6aab90f8	diving-deep-into-sentiment-understanding-fine	1508.05056	null	http://arxiv.org/abs/1508.05056v2	http://arxiv.org/pdf/1508.05056v2.pdf	Diving Deep into Sentiment: Understanding Fine-tuned CNNs for Visual Sentiment Prediction	Visual media are powerful means of expressing emotions and sentiments. The constant generation of new content in social networks highlights the need of automated visual sentiment analysis tools. While Convolutional Neural Networks (CNNs) have established a new state-of-the-art in several vision problems, their application to the task of sentiment analysis is mostly unexplored and there are few studies regarding how to design CNNs for this purpose. In this work, we study the suitability of fine-tuning a CNN for visual sentiment prediction as well as explore performance boosting techniques within this deep learning setting. Finally, we provide a deep-dive analysis into a benchmark, state-of-the-art network architecture to gain insight about how to design patterns for CNNs on the task of visual sentiment prediction.	['Xavier Giró-i-Nieto', 'Victor Campos', 'Brendan Jou', 'Amaia Salvador']	2015-08-20	null	null	null	null	['visual-sentiment-prediction']	['computer-vision']	[-3.90175618e-02 -8.17839336e-03 -5.30678481e-02 -6.24547899e-01 1.72307983e-01 -5.60069263e-01 6.20179415e-01 2.26066485e-01 -3.71168196e-01 2.95867860e-01 3.32658619e-01 -5.30786753e-01 4.20911580e-01 -6.60526752e-01 -5.98107755e-01 -4.39369678e-01 -6.98391497e-02 -1.30655076e-02 9.71809253e-02 -7.18512237e-01 3.43782097e-01 6.16875768e-01 -1.59050286e+00 8.98697078e-01 5.68148308e-02 1.27232146e+00 -1.38902515e-01 7.19853044e-01 -4.74585354e-01 1.26487505e+00 -8.32703531e-01 -7.05899775e-01 1.17385294e-02 -1.81865796e-01 -8.71671140e-01 -2.05795337e-02 5.77710152e-01 1.15645051e-01 -1.09166570e-01 7.57116020e-01 5.51859796e-01 -7.04033971e-02 5.43460667e-01 -1.43202972e+00 -1.15793395e+00 3.89095932e-01 -7.49025226e-01 4.84822839e-01 8.53192434e-02 5.64677306e-02 9.86526012e-01 -7.37681746e-01 8.66895616e-01 1.14046538e+00 6.98773205e-01 6.04410052e-01 -1.01521742e+00 -5.48028827e-01 4.54316765e-01 2.21271858e-01 -6.52651668e-01 -2.35416487e-01 9.65672791e-01 -6.41735315e-01 1.05149198e+00 2.11115792e-01 1.22900760e+00 1.38648522e+00 1.90208808e-01 1.04896522e+00 1.22342765e+00 -4.89330083e-01 1.61438867e-01 6.02550209e-01 8.27043578e-02 7.09045827e-01 -3.48563306e-02 -1.49389759e-01 -9.42096829e-01 2.10899845e-01 4.07610387e-01 -7.36434981e-02 2.92829331e-02 -4.88782823e-01 -8.44558537e-01 1.12204266e+00 9.40520644e-01 3.85684848e-01 -2.13235557e-01 4.54215169e-01 8.04821730e-01 4.58100379e-01 9.98002946e-01 4.80947256e-01 -5.14573812e-01 3.97934355e-02 -9.47884679e-01 1.46410719e-01 5.52824974e-01 5.97555280e-01 6.87447548e-01 3.56253564e-01 -2.15834573e-01 8.37811410e-01 2.84999520e-01 1.73048731e-02 3.76851648e-01 -2.98794419e-01 8.74574929e-02 1.00558615e+00 -1.29372910e-01 -1.13301849e+00 -8.43371093e-01 -4.45615768e-01 -7.53868222e-01 8.13980401e-01 4.25165802e-01 -2.94405758e-01 -9.03556168e-01 1.24547911e+00 2.72709161e-01 -2.90057898e-01 -1.88518628e-01 9.69444871e-01 1.13239324e+00 6.63218260e-01 2.63432175e-01 3.23007971e-01 1.41910267e+00 -1.09752715e+00 -3.06529731e-01 -6.62277639e-01 6.85479462e-01 -8.56339455e-01 1.25941491e+00 3.89275014e-01 -8.66636515e-01 -5.96435070e-01 -9.82288241e-01 -2.69247144e-01 -8.87319505e-01 7.48226196e-02 8.64979684e-01 9.04615462e-01 -1.25110328e+00 3.06763560e-01 -6.89766705e-01 -5.48568010e-01 8.11096966e-01 1.44703552e-01 -3.62720460e-01 2.52807826e-01 -7.90926039e-01 1.06032622e+00 -2.53585070e-01 2.03890696e-01 -6.88621938e-01 -7.13463783e-01 -6.77404284e-01 -9.40195993e-02 -2.07439572e-01 -6.91245615e-01 1.13895607e+00 -1.78589416e+00 -1.26662076e+00 1.22949898e+00 -6.85049444e-02 -3.00431281e-01 3.95978928e-01 -3.30069959e-02 -4.12683159e-01 2.79168598e-03 -1.93357006e-01 9.33640420e-01 8.72459590e-01 -1.36041379e+00 -5.09261727e-01 -2.94650882e-01 4.89479363e-01 -4.59027849e-02 -9.29444909e-01 3.83714736e-01 -3.63552690e-01 -6.41424954e-01 -4.94203359e-01 -8.76796842e-01 -4.49793786e-01 3.60189289e-01 -5.11191726e-01 -1.02926522e-01 1.11976802e+00 -1.95166230e-01 7.89940298e-01 -2.13598347e+00 -1.38465315e-01 1.59337312e-01 3.60163987e-01 1.49330899e-01 -2.94799566e-01 4.12493378e-01 -3.31374735e-01 3.26152742e-01 2.21945420e-01 -6.79042161e-01 -1.08453296e-01 -2.69845039e-01 -2.92898118e-01 4.84791249e-01 3.99988294e-01 1.11963618e+00 -6.10074580e-01 -3.16453487e-01 3.00421298e-01 9.87285793e-01 -4.38833475e-01 1.11854404e-01 -3.51315171e-01 3.51926029e-01 -3.61622393e-01 7.20400631e-01 2.99786925e-01 -5.80741942e-01 1.12664543e-01 -3.12643439e-01 -2.75898725e-01 2.38170847e-02 -4.37420964e-01 1.18557954e+00 -5.53959668e-01 1.53143179e+00 -3.39594372e-02 -1.00450647e+00 1.10441184e+00 -1.46149108e-02 3.13133478e-01 -9.23819304e-01 5.09027541e-01 -3.60356957e-01 -3.23504120e-01 -6.47390544e-01 7.86781907e-01 -3.13622087e-01 1.82741974e-02 4.06319082e-01 -2.10035276e-02 1.72372699e-01 1.47348806e-01 1.00617662e-01 6.88296378e-01 9.10728648e-02 5.14637455e-02 -2.74070084e-01 1.46991238e-01 3.73225063e-01 -1.39669731e-01 4.05985832e-01 -1.06654987e-01 7.21921623e-01 9.27586377e-01 -1.12488198e+00 -1.07234371e+00 -2.36920506e-01 5.40781803e-02 1.74251306e+00 2.60037221e-02 -4.26883906e-01 -6.40301943e-01 -7.77174830e-01 -7.47021660e-02 2.49099612e-01 -1.29647088e+00 6.34140894e-02 -2.94109017e-01 -8.95900846e-01 2.26006880e-01 6.58132136e-01 1.47829458e-01 -1.67023802e+00 -9.44528043e-01 -1.17748089e-01 2.08227962e-01 -9.55663562e-01 1.59168273e-01 4.31987286e-01 -5.24296761e-01 -9.99325454e-01 -9.02744114e-01 -1.09134638e+00 6.10308349e-01 2.87629068e-01 1.46940219e+00 2.84461737e-01 -1.64496005e-01 3.19385529e-01 -4.74786431e-01 -8.86895120e-01 -2.14101136e-01 3.52953345e-01 -5.36749423e-01 1.27661496e-01 5.11920750e-01 -3.75364751e-01 -9.29118991e-01 1.16361812e-01 -7.83383965e-01 1.70361564e-01 2.74592698e-01 4.95042861e-01 3.60217035e-01 -4.99450386e-01 3.03199947e-01 -1.09113729e+00 9.54556406e-01 -4.94320005e-01 -4.63528365e-01 4.52732630e-02 -2.94190377e-01 -2.92224228e-01 5.88945925e-01 -4.67994094e-01 -6.79351926e-01 3.82568911e-02 -1.19673848e-01 -3.82240951e-01 -1.72641173e-01 6.96667373e-01 5.62216043e-01 -3.22645932e-01 8.72206867e-01 -2.78004080e-01 -1.65926758e-02 -2.12320030e-01 5.01291394e-01 4.87571120e-01 7.92906154e-03 -1.06182352e-01 3.06751668e-01 7.95517623e-01 9.35579557e-03 -9.58598614e-01 -1.09574437e+00 -4.55163062e-01 -4.03013408e-01 -6.41682625e-01 1.15486717e+00 -8.08153808e-01 -9.60694849e-01 2.99259394e-01 -9.39288676e-01 -6.62038863e-01 -1.23047411e-01 -3.03814113e-01 -3.95485014e-01 4.60075177e-02 -4.97357994e-01 -7.47444570e-01 -5.69550991e-01 -1.19375849e+00 1.10998404e+00 2.41683513e-01 -4.52297956e-01 -1.27280056e+00 1.89787865e-01 3.83945614e-01 6.69745147e-01 3.93876135e-01 6.29770935e-01 -2.72790402e-01 -2.06426382e-01 -1.23093165e-01 -5.98859668e-01 2.75165439e-01 -5.09285331e-01 3.25035691e-01 -1.27836311e+00 -2.33907744e-01 -4.50301379e-01 -6.41008317e-01 1.07865155e+00 5.84931850e-01 1.39836180e+00 -6.85539991e-02 -2.90780425e-01 7.01180458e-01 1.36536431e+00 -6.55897334e-02 7.26317644e-01 9.66438770e-01 9.48657691e-01 9.69054401e-01 3.49327624e-01 4.21963871e-01 3.53910357e-01 5.22235394e-01 8.92661631e-01 -7.93282747e-01 -3.70370038e-02 7.98867568e-02 8.33760351e-02 2.91754365e-01 -8.45867693e-02 -4.28700775e-01 -1.13271964e+00 6.67330921e-01 -1.72157586e+00 -7.66175568e-01 -1.87501907e-01 1.24922729e+00 2.53496855e-01 2.04690471e-01 3.41975152e-01 -8.86534248e-03 4.83436346e-01 5.78222036e-01 -2.48472631e-01 -9.16333735e-01 -2.25644723e-01 3.22947681e-01 4.11610544e-01 3.38616855e-02 -1.18605340e+00 1.03356016e+00 6.59380865e+00 3.88214111e-01 -1.83921361e+00 -9.17751268e-02 1.19428349e+00 -2.73797214e-01 -3.00653011e-01 -2.41660729e-01 -6.60342753e-01 1.24554276e-01 5.79564691e-01 5.49071431e-01 3.13282795e-02 1.22500706e+00 1.68212593e-01 5.92757687e-02 -7.33171582e-01 8.50594938e-01 1.99731246e-01 -1.88843083e+00 6.19705021e-02 -8.69376063e-02 8.98292065e-01 2.28183448e-01 4.87688899e-01 1.34066314e-01 1.69191182e-01 -1.25239754e+00 1.08983827e+00 2.19560385e-01 7.24690974e-01 -8.74716759e-01 6.13648415e-01 -3.08516711e-01 -9.91163194e-01 -1.59149215e-01 -3.83677721e-01 -2.40493327e-01 1.59087986e-01 4.92780238e-01 -6.85608327e-01 -1.88822433e-01 1.21153808e+00 1.06173110e+00 -8.40909839e-01 8.58726084e-01 -1.19494438e-01 5.73297381e-01 2.21309513e-01 -6.48302197e-01 6.06166244e-01 1.93574741e-01 2.09433027e-02 1.67653990e+00 -2.39950065e-02 -6.42694533e-01 -1.20055899e-01 5.97500026e-01 -7.98508301e-02 3.24816406e-01 -5.64566374e-01 -1.25433281e-01 -2.09484890e-01 1.61406672e+00 -1.17445683e+00 -3.80320475e-02 -5.59821665e-01 6.49075508e-01 7.82925546e-01 4.60606307e-01 -5.31507373e-01 -7.24069625e-02 8.59309554e-01 2.28812635e-01 4.28250432e-01 -1.45439073e-01 -6.94756925e-01 -8.64397705e-01 -1.62311524e-01 -8.91508818e-01 1.84855968e-01 -1.29508424e+00 -1.36936736e+00 8.80749643e-01 -3.87994140e-01 -1.06170201e+00 9.62202400e-02 -1.23832142e+00 -8.43535185e-01 6.05041564e-01 -1.69226110e+00 -1.45340538e+00 -5.33796668e-01 7.30062127e-01 3.54664207e-01 -2.99617976e-01 7.73809016e-01 -5.57811148e-02 -3.47737104e-01 3.02279949e-01 -1.44726142e-01 1.62329510e-01 6.12301946e-01 -1.09168506e+00 5.55227876e-01 4.54673290e-01 1.80175662e-01 2.74245918e-01 8.63326252e-01 -2.33862311e-01 -1.27740109e+00 -9.04502273e-01 5.96707940e-01 -4.65551615e-01 9.15932894e-01 -6.95613742e-01 -5.47605455e-01 6.64008796e-01 5.10299623e-01 1.31334409e-01 8.57556343e-01 5.42396963e-01 -6.10941052e-01 -5.63465618e-02 -7.77550817e-01 7.65961945e-01 6.89771712e-01 -5.44656694e-01 2.23310851e-02 1.85056388e-01 4.01713282e-01 -1.97987318e-01 -5.28840661e-01 1.71526313e-01 8.44261825e-01 -1.40591025e+00 1.06450903e+00 -6.65771782e-01 1.11657596e+00 1.05357468e-02 1.66682065e-01 -1.43562043e+00 -2.52601415e-01 -2.53858626e-01 1.66182742e-01 8.36213827e-01 6.56362653e-01 -1.74169928e-01 1.27383518e+00 1.69509158e-01 -3.05731483e-02 -1.17635155e+00 -4.11047518e-01 2.33292542e-02 1.34890720e-01 -5.12332022e-01 2.26442024e-01 1.19227600e+00 5.65644167e-02 5.45054734e-01 -4.71692294e-01 -3.26513618e-01 -1.09986804e-01 1.93196595e-01 9.69455838e-01 -1.12023377e+00 1.77265704e-02 -7.84621894e-01 -5.85425496e-01 -6.94651067e-01 1.13793187e-01 -5.66260874e-01 -4.54076558e-01 -1.91051817e+00 2.04027027e-01 -2.44505048e-01 -3.09236944e-01 6.26953542e-01 9.71964970e-02 9.87890780e-01 4.22753662e-01 -3.07614714e-01 -7.12400258e-01 2.83621460e-01 1.43552208e+00 -3.00684571e-01 -6.04198724e-02 -2.54481405e-01 -1.09198511e+00 6.61809504e-01 1.07004976e+00 -3.54924560e-01 -3.08127075e-01 -4.27954167e-01 1.19683373e+00 -3.67926747e-01 3.13283384e-01 -6.17040694e-01 1.84513386e-02 -3.09567265e-02 7.91964889e-01 -5.75307310e-01 4.72380072e-01 -7.03601718e-01 -2.49421805e-01 1.38151139e-01 -4.22169328e-01 4.14437503e-01 5.71354687e-01 2.89044797e-01 -3.76463562e-01 3.71441282e-02 7.93053627e-01 -1.52706206e-01 -9.92618561e-01 1.04462065e-01 -6.08546853e-01 -1.50663108e-01 8.27312768e-01 -4.19865876e-01 -6.75200045e-01 -5.79976618e-01 -7.58040369e-01 6.99788928e-02 5.12079775e-01 8.10012698e-01 5.30947447e-01 -9.99438763e-01 -4.96161640e-01 4.89401408e-02 4.15172249e-01 -2.41717726e-01 2.67940611e-01 5.45966327e-01 -7.01691210e-01 1.54147623e-02 -6.41848326e-01 -6.11670792e-01 -1.57151210e+00 6.63070977e-01 3.66207212e-01 -2.31011286e-01 -3.50725651e-01 1.16102290e+00 5.19132257e-01 -3.11290681e-01 2.30201423e-01 -6.93377405e-02 -8.09605598e-01 4.97615516e-01 5.51715910e-01 3.33885103e-02 2.43858099e-01 -6.27485394e-01 -3.30465406e-01 5.07590592e-01 -3.60076688e-02 1.96135029e-01 1.66643500e+00 -4.24384959e-02 -7.30872378e-02 4.85032201e-01 1.39736879e+00 -1.87224016e-01 -1.13172245e+00 4.01552409e-01 -1.22744955e-01 -1.36523187e-01 1.85790092e-01 -8.11468184e-01 -1.58033669e+00 1.08031583e+00 5.33699989e-01 7.74820387e-01 1.08297539e+00 9.49975923e-02 2.64652997e-01 2.89947271e-01 -1.58604532e-01 -9.34283018e-01 5.50106466e-01 6.78483129e-01 1.00191414e+00 -1.43852842e+00 6.97935820e-02 -1.86708257e-01 -1.06379318e+00 1.25641274e+00 5.84047437e-01 -3.74149710e-01 9.36533213e-01 4.13584232e-01 6.60686016e-01 -6.95457458e-01 -7.06236720e-01 -3.05275023e-01 5.10863841e-01 7.50054002e-01 9.29359615e-01 -4.98827063e-02 2.29400516e-01 8.28990862e-02 -6.43538952e-01 -1.52624711e-01 5.66175103e-01 9.18401837e-01 -3.08069289e-01 -7.76263297e-01 -9.28858146e-02 4.08423752e-01 -8.55751216e-01 -1.66667253e-01 -8.43640447e-01 8.08577657e-01 -4.41641398e-02 8.57060254e-01 2.72682071e-01 -3.67997855e-01 3.77666861e-01 -1.60782725e-01 3.20541382e-01 -4.68558818e-01 -1.27605081e+00 -1.21516742e-01 3.85924667e-01 -4.90398318e-01 -8.63467097e-01 -3.46407026e-01 -6.58209920e-01 -4.44694817e-01 -7.40019185e-03 -3.72630358e-01 1.13591969e+00 6.58027232e-01 1.86951607e-01 6.69947922e-01 3.70207042e-01 -1.11479437e+00 3.61728728e-01 -7.53759503e-01 -4.02125061e-01 5.94141662e-01 4.55126286e-01 -3.98482919e-01 -1.96175098e-01 1.02138266e-01]	[11.023090362548828, 2.679241180419922]
bea18260-4ceb-4a65-9a44-5511400fef85	actions-speak-louder-than-goals-valuing	1802.07127	null	https://arxiv.org/abs/1802.07127v2	https://arxiv.org/pdf/1802.07127v2.pdf	Actions Speak Louder Than Goals: Valuing Player Actions in Soccer	Assessing the impact of the individual actions performed by soccer players during games is a crucial aspect of the player recruitment process. Unfortunately, most traditional metrics fall short in addressing this task as they either focus on rare actions like shots and goals alone or fail to account for the context in which the actions occurred. This paper introduces (1) a new language for describing individual player actions on the pitch and (2) a framework for valuing any type of player action based on its impact on the game outcome while accounting for the context in which the action happened. By aggregating soccer players' action values, their total offensive and defensive contributions to their team can be quantified. We show how our approach considers relevant contextual information that traditional player evaluation metrics ignore and present a number of use cases related to scouting and playing style characterization in the 2016/2017 and 2017/2018 seasons in Europe's top competitions.	['Tom Decroos', 'Lotte Bransen', 'Jesse Davis', 'Jan Van Haaren']	2018-02-18	null	null	null	null	['football-action-valuation']	['playing-games']	[ 1.19738758e-01 -1.73474804e-01 -8.31465274e-02 1.87054984e-02 -6.29102349e-01 -8.38941932e-01 5.43405175e-01 5.25609493e-01 -8.85950506e-01 6.47266626e-01 6.45148993e-01 -8.98197889e-02 -6.77114367e-01 -7.66957402e-01 -1.96249187e-01 -5.32244682e-01 2.25234941e-01 4.96345162e-01 4.37105507e-01 -8.05512071e-01 6.56959236e-01 4.92601931e-01 -1.83179331e+00 2.77908802e-01 1.47425592e-01 7.64973938e-01 9.88318399e-02 7.80510724e-01 -2.25476716e-02 1.30352962e+00 -1.17916775e+00 -7.50501692e-01 2.84161448e-01 -5.84294975e-01 -8.40024233e-01 -2.90645123e-01 3.28854695e-02 -1.17487170e-01 4.40676697e-02 8.67446244e-01 5.27940094e-01 2.76646584e-01 3.82604986e-01 -1.05357635e+00 4.90239769e-01 1.00747859e+00 -2.87256539e-01 6.96334124e-01 7.45230436e-01 1.69102419e-02 1.02615237e+00 -2.20012590e-01 6.39172018e-01 6.85727715e-01 6.78088427e-01 2.03819931e-01 -9.63169217e-01 -5.70413947e-01 1.19524717e-01 3.04232359e-01 -9.63857412e-01 -9.49151143e-02 6.15669847e-01 -8.35068345e-01 4.90602463e-01 4.37308639e-01 8.58235419e-01 9.43453193e-01 4.11042236e-02 4.57028449e-01 9.58890557e-01 -2.07878664e-01 1.64413005e-01 -2.34739006e-01 1.77212104e-01 -3.89285803e-01 2.26778448e-01 1.86820373e-01 -8.91399682e-01 -8.64405334e-02 4.54502791e-01 -3.65936309e-01 3.14790756e-01 9.70490500e-02 -1.05028427e+00 7.17754006e-01 -2.48536155e-01 3.90914828e-01 -4.87327695e-01 1.82293862e-01 9.04245555e-01 1.99163556e-01 1.40582025e-01 8.95842254e-01 -1.67290851e-01 -1.28262770e+00 -1.13681376e+00 1.05109823e+00 7.26314008e-01 2.89062262e-01 5.02769351e-01 -1.29574224e-01 -4.72615600e-01 5.15222132e-01 -4.60618526e-01 -1.68804109e-01 1.92836151e-01 -1.15276027e+00 3.24518174e-01 7.40779102e-01 3.57592613e-01 -9.87603605e-01 -4.66891855e-01 -5.61177969e-01 2.14970455e-01 5.38813472e-01 7.60659635e-01 -2.66811013e-01 -1.89598814e-01 1.67002225e+00 8.12856331e-02 -1.95920929e-01 -2.08636969e-01 1.00236440e+00 8.88194025e-01 -1.00371905e-01 3.77159655e-01 -9.52766165e-02 1.42499852e+00 -3.92407179e-01 -6.03392303e-01 -1.28953964e-01 6.01214945e-01 -8.19452763e-01 1.07504308e+00 6.59072638e-01 -1.49540913e+00 -5.55169225e-01 -8.41873646e-01 4.29712892e-01 -1.68473020e-01 -3.07441413e-01 4.84828770e-01 1.00327766e+00 -5.08471012e-01 7.97101080e-01 -4.64325279e-01 1.01309791e-01 -1.42547876e-01 3.44200999e-01 -1.02967255e-01 5.25709867e-01 -1.21971583e+00 1.07776380e+00 3.52396876e-01 -2.74982303e-01 -8.45522225e-01 -8.25548768e-01 -4.07963544e-01 5.53110093e-02 1.06376457e+00 1.87405959e-01 1.37500584e+00 -8.43923330e-01 -1.29605377e+00 9.36244547e-01 6.72449112e-01 -3.28340799e-01 8.30588579e-01 -3.79518360e-01 -1.94234252e-01 -1.51264504e-01 5.29778659e-01 -1.63097307e-01 -6.40487447e-02 -6.48006856e-01 -1.07832432e+00 -1.27291784e-01 8.40802372e-01 6.28319621e-01 -9.11644921e-02 7.83524930e-01 -2.55981863e-01 -6.87452614e-01 -2.11702272e-01 -7.15004325e-01 -1.44679949e-01 -9.32266831e-01 -2.00468048e-01 -1.82158723e-01 2.88738400e-01 -4.92659330e-01 1.90345752e+00 -2.09837365e+00 3.58271688e-01 9.88579094e-02 4.50720824e-02 2.27043808e-01 3.66663098e-01 9.65459645e-01 8.55736583e-02 6.80756122e-02 2.74173468e-01 9.88009870e-02 3.55291516e-01 8.37365761e-02 -1.85693115e-01 1.42542362e-01 -4.50845271e-01 5.74210584e-01 -9.59462881e-01 -2.97811657e-01 2.29188025e-01 4.24699532e-03 -4.76122856e-01 -6.15195781e-02 2.87294418e-01 2.97190547e-01 -2.96253294e-01 4.87138033e-01 1.37372240e-01 7.45439351e-01 3.02521586e-01 1.85920715e-01 -7.10123181e-01 6.63708270e-01 -1.46748853e+00 1.25900352e+00 -3.98479141e-02 3.76953602e-01 1.43422157e-01 -9.03358519e-01 6.97866261e-01 2.77406245e-01 7.35027134e-01 -6.03627443e-01 3.02653879e-01 3.24963301e-01 3.74513358e-01 -3.07100773e-01 1.06546056e+00 -5.47014832e-01 -8.62759590e-01 5.77960074e-01 1.80405490e-02 -6.32519275e-02 8.68897796e-01 2.12647796e-01 1.25709367e+00 4.47600991e-01 4.25810963e-01 -2.89330125e-01 3.66753727e-01 1.76881969e-01 6.66273713e-01 8.27160001e-01 -4.56789136e-01 4.44616795e-01 1.11252940e+00 -4.05380666e-01 -8.38760793e-01 -8.37872922e-01 2.08481237e-01 1.53503513e+00 2.28956416e-01 -8.01222861e-01 -7.24407494e-01 -1.65605411e-01 -6.57184348e-02 4.65539217e-01 -9.12320375e-01 -3.51132303e-01 -5.59198797e-01 -5.57961226e-01 7.63113558e-01 5.83370745e-01 2.74115622e-01 -1.23313999e+00 -1.18986797e+00 4.25633550e-01 -4.15290087e-01 -9.04300392e-01 -3.08641762e-01 1.22456118e-01 -3.75380784e-01 -1.38720715e+00 -3.65625381e-01 -1.85488343e-01 -2.26554915e-01 -1.02025181e-01 1.19312775e+00 -1.48245096e-01 -3.59928161e-01 3.40481400e-01 -7.23244786e-01 -6.34838223e-01 -2.51271158e-01 1.61041602e-01 1.55393630e-01 -2.05945596e-01 5.09097874e-01 -4.72225040e-01 -3.86288106e-01 5.31973064e-01 -6.07089460e-01 -1.17877223e-01 3.07294875e-01 3.35805237e-01 3.76029044e-01 1.30302310e-01 4.09702569e-01 -6.86364710e-01 1.10530031e+00 -3.39175582e-01 -8.19180831e-02 -8.36032704e-02 -1.49392277e-01 -5.30232787e-01 7.95340613e-02 -5.18641770e-01 -7.71049201e-01 -4.27796274e-01 -1.36238486e-01 2.50623792e-01 -6.41221255e-02 6.26686037e-01 -1.19688205e-01 6.03501126e-02 7.66776919e-01 -1.70580029e-01 -2.67251819e-01 -5.75124621e-01 -2.90200472e-01 3.31093013e-01 7.28066981e-01 -9.00043607e-01 6.62933588e-01 1.89851001e-01 2.76806094e-02 -4.21543628e-01 -7.50723779e-01 -8.06533515e-01 -6.04544342e-01 -1.07063150e+00 8.64664316e-01 -6.90679729e-01 -1.32224190e+00 3.16180110e-01 -4.47273493e-01 -2.66292989e-01 -6.80157125e-01 7.89082766e-01 -6.60840273e-01 2.79895633e-01 -3.07581604e-01 -1.06284499e+00 1.48242429e-01 -1.11178780e+00 4.44261760e-01 3.62713933e-01 -6.75415754e-01 -5.90720654e-01 3.29894274e-01 6.54184759e-01 3.02068174e-01 5.92910767e-01 3.44181031e-01 -6.50077343e-01 2.87306279e-01 -5.62177241e-01 3.46632481e-01 2.55648971e-01 -1.46957055e-01 -1.46845758e-01 -4.28644180e-01 1.88940644e-01 -7.29389116e-02 -1.95408195e-01 3.14636976e-01 3.48892689e-01 4.89007562e-01 1.31699845e-01 2.09874794e-01 1.46819219e-01 1.22232485e+00 2.90980320e-02 6.79972053e-01 8.86275768e-01 2.54928827e-01 1.07243979e+00 1.05698967e+00 6.73522174e-01 -1.23745631e-02 1.29346824e+00 7.11636007e-01 3.06614071e-01 4.27893503e-03 -1.24725290e-01 5.77571332e-01 2.09565744e-01 -8.53515565e-01 1.01007760e-01 -6.98236167e-01 6.71858609e-01 -1.78080940e+00 -1.37098062e+00 -3.71474981e-01 2.37656593e+00 5.17815351e-01 6.79050207e-01 1.09728348e+00 5.80143034e-01 7.62547612e-01 1.24909163e-01 2.86083929e-02 -8.78524840e-01 -1.14330560e-01 5.11156440e-01 7.78178096e-01 3.29840094e-01 -7.91424096e-01 8.50270808e-01 6.71982002e+00 1.12090349e+00 -5.93601823e-01 1.79111406e-01 9.07897651e-02 -9.35942888e-01 -2.98567768e-02 2.04870865e-01 -7.36193180e-01 4.45065171e-01 7.61960030e-01 -4.59169120e-01 1.08428128e-01 6.25418067e-01 3.72426093e-01 -4.54723775e-01 -6.25698149e-01 4.87299740e-01 -2.50730813e-02 -1.04156327e+00 -4.00057167e-01 3.66604477e-01 4.11850959e-01 -4.69098002e-01 -6.76112100e-02 5.66762984e-01 5.60595453e-01 -1.01175046e+00 1.44591701e+00 4.42920715e-01 6.03831172e-01 -9.32061195e-01 8.37398827e-01 1.45471528e-01 -1.04038060e+00 -3.51867825e-01 5.88326156e-02 -1.01387262e+00 3.90740991e-01 5.00062332e-02 -3.73184085e-01 5.66289544e-01 8.57057393e-01 9.41678211e-02 -3.50438863e-01 1.09500253e+00 -3.79124641e-01 7.75747359e-01 -1.86577693e-01 4.59930301e-02 5.13754964e-01 -1.87666625e-01 9.42006826e-01 8.57962847e-01 2.36329511e-01 3.36970054e-02 1.45407870e-01 5.33651829e-01 5.18680096e-01 4.46298897e-01 -3.25953066e-02 -1.42466411e-01 3.17296565e-01 1.31201851e+00 -1.01183987e+00 -1.06674083e-01 -1.63778707e-01 3.06995422e-01 -8.43639448e-02 -1.48005992e-01 -8.67971599e-01 -3.99141610e-01 1.09437490e+00 6.86740637e-01 4.57438305e-02 -4.70308661e-02 -4.90950197e-01 -4.61310327e-01 6.74416572e-02 -9.10788476e-01 4.75178778e-01 -3.70424986e-01 -6.03141844e-01 2.77251899e-01 4.19789284e-01 -1.27134585e+00 -2.67000467e-01 -6.08211696e-01 -8.56861651e-01 8.28413546e-01 -5.61056972e-01 -6.94696248e-01 -1.88270643e-01 1.19551562e-01 2.08073094e-01 -1.01967581e-01 4.42137927e-01 2.18829870e-01 -4.07113135e-01 4.89288360e-01 -3.81884992e-01 2.89975684e-02 5.24531543e-01 -1.22822762e+00 1.05540507e-01 7.07571030e-01 -1.76246718e-01 2.08658680e-01 1.21688008e+00 -7.16489196e-01 -5.44528902e-01 -4.03202064e-02 8.47972631e-01 -4.98340070e-01 8.11882973e-01 -2.14784309e-01 -2.97135711e-01 2.78307825e-01 -2.02059597e-01 -8.34723949e-01 1.04406178e+00 6.33907557e-01 4.57230173e-02 9.39797610e-02 -7.41919518e-01 7.26119280e-01 1.14312279e+00 -5.72245538e-01 -6.27400875e-01 -8.81867036e-02 -1.07640000e-02 -5.67704737e-01 -9.26769316e-01 3.46320808e-01 8.17059457e-01 -1.43651462e+00 9.38718438e-01 -8.34907889e-01 5.87924182e-01 -1.42867804e-01 -1.02809787e-01 -8.94092739e-01 -2.67722160e-01 -6.31965339e-01 5.28088510e-01 1.04761553e+00 8.69817138e-02 -2.22539958e-02 8.64649177e-01 5.72680473e-01 -3.97503942e-01 -7.38946438e-01 -9.97645080e-01 -7.09447920e-01 1.85889959e-01 -6.93108380e-01 7.21080661e-01 7.39670396e-01 5.18141389e-01 -3.04500967e-01 -5.22638917e-01 -6.10951722e-01 3.52321535e-01 -3.36462229e-01 9.37083602e-01 -1.38513935e+00 -5.60935855e-01 -1.03604341e+00 -9.51462388e-01 -1.26936540e-01 -3.44506085e-01 -4.99505848e-01 -3.45223725e-01 -1.33742499e+00 2.69509465e-01 -1.66983619e-01 -3.55109841e-01 1.54774353e-01 -2.07141206e-01 6.40450776e-01 4.59769428e-01 2.46930286e-01 -5.06129682e-01 -3.68648380e-01 8.79925072e-01 4.33169812e-01 -1.97576061e-01 3.35063785e-01 -9.65558529e-01 6.06560588e-01 7.26712167e-01 -3.84294152e-01 -6.76213279e-02 3.07729952e-02 8.70239258e-01 1.14766762e-01 3.96803290e-01 -1.30277991e+00 8.90815854e-02 -7.12380171e-01 -4.28546220e-01 -1.83652446e-01 2.72740245e-01 -1.94444343e-01 5.56252182e-01 5.26233435e-01 -4.18281198e-01 -1.40327543e-01 2.19284177e-01 9.18793902e-02 -4.66713697e-01 -5.86049557e-01 3.85048538e-01 -1.37436196e-01 -6.47456527e-01 -1.01859763e-01 -7.70010948e-01 2.69226849e-01 1.33557940e+00 -8.48243773e-01 4.86818217e-02 -5.01684904e-01 -1.17785048e+00 -3.85462157e-02 5.33155739e-01 2.94984162e-01 -1.93857238e-01 -1.15250242e+00 -9.54931557e-01 -4.67771888e-01 1.98888600e-01 -7.55444348e-01 6.21872365e-01 1.10922718e+00 -8.06271315e-01 3.11094113e-02 -7.63567686e-01 1.76008701e-01 -1.51536632e+00 2.34961316e-01 3.80154282e-01 -7.33230889e-01 -4.72603530e-01 9.24291134e-01 2.17909664e-01 -1.22998562e-02 1.58875868e-01 2.92383552e-01 -7.46791244e-01 6.73554659e-01 5.18532515e-01 9.25803542e-01 1.33035198e-01 -1.08059728e+00 -4.64267641e-01 2.07643077e-01 8.17712843e-02 -6.21404886e-01 1.12671900e+00 1.69638470e-01 1.30790398e-01 6.96531057e-01 3.50014508e-01 4.15191948e-01 -1.11094391e+00 1.80389278e-03 6.64811581e-02 -4.12554413e-01 -9.51421410e-02 -8.51279855e-01 -7.98253417e-01 6.17261529e-01 -7.76370913e-02 3.35269332e-01 1.04909122e+00 -2.16222137e-01 5.14417350e-01 -2.05866054e-01 4.40762699e-01 -1.84818661e+00 5.23055904e-02 5.04075229e-01 5.94928682e-01 -4.00242925e-01 5.03602065e-02 -2.67799497e-01 -9.83269095e-01 9.41518784e-01 4.70591486e-01 -1.77818239e-01 2.54160315e-01 3.76631290e-01 7.85529688e-02 -3.33941162e-01 -4.42175508e-01 -5.43657303e-01 1.22001790e-01 4.63295072e-01 5.91279566e-01 3.93904001e-01 -1.27495027e+00 1.18837500e+00 -8.85153115e-01 -2.05998778e-01 8.55566978e-01 1.12186146e+00 -5.13015807e-01 -1.32619834e+00 -5.28237820e-01 5.84535003e-01 -1.13158214e+00 1.85882121e-01 -9.14060235e-01 9.32869315e-01 6.11768007e-01 9.24944460e-01 -1.20634399e-02 -9.13057625e-01 7.23192811e-01 -1.05636399e-02 4.64349926e-01 -7.81004906e-01 -1.62793398e+00 -1.68175995e-01 4.07435954e-01 -5.93570113e-01 -3.45343113e-01 -1.16186488e+00 -9.76236105e-01 -7.38416970e-01 -1.94140941e-01 4.99927789e-01 5.83791852e-01 9.93624926e-01 -3.34139079e-01 8.43144238e-01 5.37009016e-02 -8.45624030e-01 -4.10844207e-01 -9.75409210e-01 -1.04460251e+00 5.83423555e-01 -4.75396216e-01 -1.14284241e+00 -4.02301073e-01 -4.31176245e-01]	[6.512857437133789, 0.3959474265575409]
9ed67807-24c7-40ae-86fd-5cc0abf7e489	towards-open-world-eeg-decoding-via-deep	2112.06654	null	https://arxiv.org/abs/2112.06654v2	https://arxiv.org/pdf/2112.06654v2.pdf	Toward Open-World Electroencephalogram Decoding Via Deep Learning: A Comprehensive Survey	Electroencephalogram (EEG) decoding aims to identify the perceptual, semantic, and cognitive content of neural processing based on non-invasively measured brain activity. Traditional EEG decoding methods have achieved moderate success when applied to data acquired in static, well-controlled lab environments. However, an open-world environment is a more realistic setting, where situations affecting EEG recordings can emerge unexpectedly, significantly weakening the robustness of existing methods. In recent years, deep learning (DL) has emerged as a potential solution for such problems due to its superior capacity in feature extraction. It overcomes the limitations of defining `handcrafted' features or features extracted using shallow architectures, but typically requires large amounts of costly, expertly-labelled data - something not always obtainable. Combining DL with domain-specific knowledge may allow for development of robust approaches to decode brain activity even with small-sample data. Although various DL methods have been proposed to tackle some of the challenges in EEG decoding, a systematic tutorial overview, particularly for open-world applications, is currently lacking. This article therefore provides a comprehensive survey of DL methods for open-world EEG decoding, and identifies promising research directions to inspire future studies for EEG decoding in real-world applications.	['Z. Jane Wang', 'Ruobing Qian', 'Martin J. McKeown', 'Aiping Liu', 'Chang Li', 'Xun Chen']	2021-12-08	null	null	null	null	['eeg-decoding', 'eeg-decoding']	['medical', 'time-series']	[ 3.82611901e-01 -3.69494617e-01 2.00425327e-01 -3.52830261e-01 -7.24060357e-01 -4.85705614e-01 3.78217131e-01 2.58882791e-01 -5.61968923e-01 1.02119434e+00 5.28509878e-02 -2.01948136e-02 -1.97856754e-01 -4.90206122e-01 -5.28614819e-01 -7.77739882e-01 -4.12775308e-01 1.18222803e-01 -6.56896457e-02 1.72266942e-02 4.35449064e-01 4.60340232e-01 -1.76039720e+00 3.47963542e-01 8.51443827e-01 1.04019761e+00 5.64726293e-01 2.86357015e-01 4.32520211e-02 3.66134346e-01 -9.99058902e-01 -2.71731108e-01 -1.03525624e-01 -5.37000358e-01 -5.66583931e-01 -2.28925064e-01 6.78396504e-03 -8.38091746e-02 -2.21757203e-01 1.06692684e+00 9.28059161e-01 -1.09135307e-01 4.77126896e-01 -1.03367507e+00 -4.24396843e-01 1.46337777e-01 -6.61582500e-02 8.04583430e-01 7.15438366e-01 5.12744263e-02 6.52195513e-01 -6.23078704e-01 3.32588851e-02 3.86219352e-01 3.82338434e-01 5.19926727e-01 -1.19460833e+00 -9.24764872e-01 1.05049476e-01 5.41799664e-01 -1.38409996e+00 -5.28474331e-01 7.34118223e-01 -3.90097946e-01 1.31145048e+00 3.13658357e-01 1.07397389e+00 1.69989955e+00 5.80305755e-01 8.04374099e-01 1.48026478e+00 -7.71180838e-02 3.92802924e-01 1.47556230e-01 7.01528713e-02 8.46203640e-02 2.05859900e-01 -5.15583307e-02 -9.92375910e-01 -4.73294556e-02 6.79193258e-01 -1.71419159e-01 -8.00302505e-01 -1.24371871e-01 -1.47844827e+00 3.88596147e-01 3.08499604e-01 6.89064384e-01 -5.02642989e-01 -2.85157979e-01 5.57414353e-01 5.57315588e-01 5.55159926e-01 8.29329848e-01 -5.13277233e-01 -6.69589758e-01 -1.19518518e+00 1.13321036e-01 8.06706011e-01 7.24929571e-01 4.86506760e-01 1.82381198e-01 3.28284539e-02 8.02148104e-01 -9.72489938e-02 2.06864432e-01 8.65718186e-01 -3.17604184e-01 3.65099162e-01 4.20425653e-01 -4.49824072e-02 -9.68331695e-01 -7.29501128e-01 -5.43786645e-01 -6.85911477e-01 -2.02785596e-01 2.43219584e-01 -2.22321987e-01 -5.36953986e-01 1.48736346e+00 -4.44521934e-01 4.56017643e-01 -4.57125567e-02 9.14109230e-01 7.95721114e-01 3.88419747e-01 8.92546494e-03 -3.49708438e-01 1.30071819e+00 -2.96967119e-01 -9.08176720e-01 -6.16231084e-01 2.31995091e-01 -3.40306401e-01 8.96991074e-01 8.17018330e-01 -8.57833803e-01 -1.77824184e-01 -1.21482742e+00 2.58980036e-01 -6.13281667e-01 -1.93213582e-01 7.51964629e-01 8.90853226e-01 -1.10726273e+00 3.63021940e-01 -8.77722740e-01 -3.80504102e-01 5.57597876e-01 7.95666993e-01 -5.64603031e-01 9.19662565e-02 -1.28035009e+00 1.23093283e+00 3.05036038e-01 3.41278255e-01 -8.19909453e-01 -4.31715429e-01 -7.26402819e-01 6.65391982e-02 2.35689208e-01 -3.52265805e-01 8.92316818e-01 -8.63336027e-01 -1.54410756e+00 7.85017192e-01 -1.86146960e-01 -2.63625592e-01 -1.67989440e-03 -1.51613504e-01 -5.54717898e-01 7.01652169e-02 -1.59746483e-01 5.45765460e-01 7.47014761e-01 -7.51097023e-01 -3.13987553e-01 -5.85814059e-01 -4.70175706e-02 1.84705317e-01 -4.96013761e-01 2.46102735e-01 -7.92471096e-02 -6.44443691e-01 -1.20988842e-02 -5.38935483e-01 1.35330439e-01 -5.77522635e-01 1.84005126e-01 -7.09308609e-02 3.67466837e-01 -5.68888783e-01 1.05634284e+00 -2.14250422e+00 2.13236019e-01 8.72287974e-02 3.16569299e-01 3.13686162e-01 3.79318856e-02 4.56778258e-01 -1.04375355e-01 -3.26841205e-01 -2.37560600e-01 1.11251168e-01 -7.93810710e-02 -4.54716273e-02 -1.42792836e-01 5.57910800e-01 1.13683015e-01 8.97400141e-01 -1.02117622e+00 1.40170246e-01 3.48267406e-01 4.27798897e-01 -2.19325259e-01 3.12523186e-01 2.88631380e-01 7.47898519e-01 -5.54549545e-02 5.55611968e-01 3.85154516e-01 3.12915631e-02 -1.25814909e-02 4.70241569e-02 -3.88841704e-02 5.57341099e-01 -9.38941419e-01 1.81137037e+00 -4.85354424e-01 1.16615510e+00 -2.79098619e-02 -1.30052722e+00 8.74092638e-01 6.50739491e-01 4.05882299e-01 -1.03386140e+00 4.86362696e-01 4.00910616e-01 4.08248901e-01 -7.91977286e-01 -9.63538736e-02 -9.72214490e-02 8.60736817e-02 3.54798019e-01 4.25607920e-01 9.44246131e-04 1.38746187e-01 -2.17050225e-01 1.33821118e+00 -1.48435697e-01 3.60456079e-01 -2.79376388e-01 4.09951955e-01 -5.23565650e-01 4.30830061e-01 6.35213614e-01 -2.98087060e-01 5.75432003e-01 3.84224236e-01 -3.86332929e-01 -3.77556175e-01 -8.76421690e-01 -5.00198722e-01 9.10263002e-01 8.41190070e-02 -5.11007965e-01 -9.08649862e-01 -3.35983455e-01 -3.45069855e-01 4.76561934e-01 -3.61031950e-01 -3.35485756e-01 -2.81281024e-01 -1.16935241e+00 4.66391206e-01 5.72348654e-01 4.20989394e-01 -1.25483394e+00 -1.12062502e+00 5.08515894e-01 -3.83056581e-01 -1.25860548e+00 1.95241526e-01 7.01731861e-01 -8.09380412e-01 -1.00482690e+00 -7.84313738e-01 -7.59808838e-01 5.17456055e-01 6.67065457e-02 8.05064440e-01 -1.88932836e-01 -2.44419754e-01 2.66306013e-01 -4.14052635e-01 -5.88465452e-01 3.73672724e-01 2.65736021e-02 2.33033791e-01 7.14310408e-02 1.01843989e+00 -7.95731187e-01 -6.35683894e-01 2.92901337e-01 -6.87952638e-01 -1.06124632e-01 7.02125728e-01 7.97958255e-01 1.87449709e-01 1.08069830e-01 1.09706604e+00 -3.15883368e-01 1.15915084e+00 -5.15830219e-01 -3.09413254e-01 3.24376822e-02 -3.39786083e-01 -1.96356073e-01 6.02220356e-01 -4.12764698e-01 -6.27175510e-01 -3.98811191e-01 -3.54023010e-01 1.91037402e-01 -5.69673359e-01 7.41425693e-01 -2.11949542e-01 -3.87757272e-01 5.93270719e-01 5.53242028e-01 -4.03212935e-01 -2.88663179e-01 -4.39511299e-01 1.07678795e+00 4.59494174e-01 -5.01767814e-01 1.85045838e-01 2.43029650e-02 -4.89101559e-01 -8.40487719e-01 -4.81325448e-01 -3.74031752e-01 -6.27375782e-01 -2.71866709e-01 7.18026459e-01 -9.01098251e-01 -5.21765411e-01 5.01063049e-01 -9.69503760e-01 -2.64056534e-01 2.04682246e-01 8.18289340e-01 -5.84895909e-01 1.01670690e-01 -3.64570051e-01 -6.64698482e-01 -2.41083845e-01 -1.46878493e+00 9.08919036e-01 9.36119109e-02 -6.38199687e-01 -8.51654232e-01 -1.70399155e-02 1.54986218e-01 4.51652706e-01 8.03761557e-02 7.69112289e-01 -6.80765390e-01 -1.90126762e-01 -5.67561805e-01 -8.12306479e-02 3.35608125e-01 2.13326707e-01 -7.38715291e-01 -1.44649172e+00 -3.72936666e-01 4.13776308e-01 -4.20735240e-01 3.82272810e-01 3.44117999e-01 1.59488261e+00 1.42932206e-01 -3.09480995e-01 6.04701817e-01 1.06469631e+00 4.91622865e-01 6.74376190e-01 4.08307821e-01 1.89611048e-01 4.27001178e-01 9.38470885e-02 4.57074612e-01 3.18274945e-01 4.90706354e-01 3.16590548e-01 1.37555674e-01 2.54384518e-01 1.77838206e-01 4.68429267e-01 9.23724890e-01 -2.59562433e-01 -2.23004177e-01 -9.60245371e-01 4.60464060e-01 -1.51782215e+00 -7.30175614e-01 -3.83695448e-03 2.31360364e+00 7.41431534e-01 1.32280454e-01 -8.42516348e-02 5.28461516e-01 3.41987461e-01 -1.88767328e-03 -5.42834640e-01 -3.07232022e-01 -9.86103117e-02 5.85174680e-01 1.18266031e-01 -2.12502107e-01 -8.29138756e-01 4.45569962e-01 7.03178692e+00 3.90115082e-01 -1.35375178e+00 3.70638102e-01 1.26129657e-01 -4.54089284e-01 7.44329765e-02 -4.38510895e-01 -4.22216147e-01 8.05820584e-01 1.46213806e+00 -7.94145688e-02 7.40261674e-01 3.47890645e-01 2.58238405e-01 -4.24164623e-01 -1.49181914e+00 1.73097622e+00 3.23646575e-01 -1.05375838e+00 -5.00977576e-01 1.10408410e-01 3.07988971e-01 3.05477262e-01 2.57151872e-02 3.07820648e-01 -5.57108700e-01 -1.11521125e+00 5.15475750e-01 2.82631159e-01 7.29926229e-01 -6.97864711e-01 9.64324892e-01 6.38724029e-01 -8.04607213e-01 -2.24005446e-01 -3.64504427e-01 -5.53851008e-01 -8.91242772e-02 5.31212509e-01 -3.99966925e-01 2.35829979e-01 8.64388585e-01 8.56690168e-01 -6.31365597e-01 1.36375976e+00 -3.02156001e-01 5.87703288e-01 -1.25968620e-01 -2.12280005e-01 8.97966847e-02 -4.49941829e-02 4.01604533e-01 1.27435791e+00 3.80126745e-01 1.75923854e-01 -2.63871312e-01 6.32892132e-01 2.86586545e-02 4.50516567e-02 -7.88629472e-01 -1.66842386e-01 2.94192731e-01 1.11489248e+00 -9.17553723e-01 2.00513583e-02 -6.94285512e-01 1.10518539e+00 3.35867375e-01 4.57764179e-01 -6.46414816e-01 -5.79452515e-01 5.20036697e-01 -1.91108622e-02 -1.86744466e-01 -3.32599312e-01 -4.22044933e-01 -1.47020340e+00 2.25752577e-01 -1.12175786e+00 5.93403019e-02 -7.93051243e-01 -1.13289785e+00 8.07418823e-01 8.69464576e-02 -1.12435269e+00 -2.68481612e-01 -7.94671774e-01 -4.77372080e-01 8.50452721e-01 -1.36336482e+00 -4.04204071e-01 -3.49180043e-01 7.46270061e-01 7.08976924e-01 -2.83098549e-01 1.38173628e+00 4.57159549e-01 -7.28775859e-01 3.76037300e-01 1.42525584e-01 -8.19874406e-02 7.64608622e-01 -1.09415162e+00 7.99450427e-02 6.09360874e-01 2.51916379e-01 7.57687688e-01 5.53310335e-01 -2.83327132e-01 -1.53983426e+00 -5.06742775e-01 7.22106397e-01 -4.80719328e-01 5.20721078e-01 -1.00011384e+00 -1.04551303e+00 5.16583323e-01 3.51361215e-01 -1.46191165e-01 1.25344706e+00 1.85992077e-01 1.25309914e-01 -3.13234746e-01 -9.93904769e-01 4.21996832e-01 9.97165024e-01 -7.33292997e-01 -8.39272618e-01 2.74208069e-01 -2.33211204e-01 -3.37480396e-01 -9.37801242e-01 1.86765537e-01 5.26062667e-01 -8.89806509e-01 5.97115040e-01 -1.56552568e-01 -1.39374495e-01 -1.72885433e-02 3.33098285e-02 -1.70921493e+00 -2.57130384e-01 -5.85215390e-01 -4.55066115e-02 7.60932982e-01 1.80532157e-01 -8.58066022e-01 4.64557678e-01 7.62008429e-01 -2.03724131e-01 -7.59489119e-01 -9.53188598e-01 -7.33848333e-01 -2.22019136e-01 -8.07213426e-01 6.85376048e-01 7.18230963e-01 7.24853158e-01 2.92487085e-01 -2.01025158e-01 3.49628888e-02 1.38810337e-01 -2.17857048e-01 3.54300886e-01 -1.33458483e+00 9.33025777e-02 -4.59493876e-01 -1.00010610e+00 -8.40286314e-01 3.97071391e-01 -9.30421948e-01 5.03855050e-02 -1.57755458e+00 1.79977670e-01 -1.26357777e-02 -7.35278368e-01 3.33657980e-01 5.33675477e-02 4.84667808e-01 -1.55521378e-01 -7.01068863e-02 -3.54015589e-01 4.27937031e-01 9.33784008e-01 -5.64662106e-02 -1.57566383e-01 -3.29485722e-02 -7.88735032e-01 6.59880459e-01 9.00901198e-01 -4.84127581e-01 -7.04158366e-01 -5.83242416e-01 2.43863523e-01 -1.03921719e-01 2.11355418e-01 -1.48590183e+00 2.91361421e-01 1.60884127e-01 7.87939429e-01 -2.78022945e-01 4.50678825e-01 -8.20101261e-01 5.66759221e-02 9.45787355e-02 -1.69262037e-01 1.00937828e-01 3.26505452e-01 4.07192975e-01 -4.22812015e-01 -2.82500774e-01 4.11043763e-01 -2.84755766e-01 -8.68088245e-01 1.32894754e-01 -8.61935854e-01 -6.38229251e-02 1.13245344e+00 -7.04214633e-01 -1.08633742e-01 -2.00274676e-01 -7.34268725e-01 -1.00489315e-02 3.31894681e-02 4.81503159e-01 8.04142535e-01 -9.40105796e-01 -4.34118807e-01 8.31661761e-01 1.86192930e-01 -2.80511588e-01 5.73109128e-02 1.19137871e+00 -1.58305287e-01 6.35828793e-01 -6.01089954e-01 -5.33479691e-01 -8.33386183e-01 3.18414062e-01 2.62112826e-01 2.95982480e-01 -7.77599275e-01 7.76076972e-01 1.48607403e-01 8.33180770e-02 4.46043253e-01 -2.64454305e-01 -4.38336462e-01 2.99212456e-01 8.38873029e-01 8.39236602e-02 8.64212155e-01 -3.88512582e-01 -5.71917653e-01 1.11206196e-01 -7.96462744e-02 -1.31070927e-01 1.53229558e+00 7.95901753e-03 -1.53750852e-01 7.48330832e-01 1.05915713e+00 -4.65570480e-01 -1.04014707e+00 1.87853619e-01 -4.23731990e-02 -5.38360476e-01 4.51074302e-01 -9.27209616e-01 -9.69497800e-01 1.15461302e+00 6.49555027e-01 8.90666321e-02 1.46497750e+00 -2.82616228e-01 6.44109309e-01 4.63375092e-01 1.01944935e+00 -1.15066111e+00 -6.26780046e-03 3.56854081e-01 8.88546586e-01 -1.04905689e+00 -2.13567302e-01 2.12208152e-01 -4.56969976e-01 1.22985613e+00 6.37258112e-01 -9.29250494e-02 7.16108859e-01 4.13368285e-01 -1.51264235e-01 -3.15107286e-01 -5.94063997e-01 1.02003776e-02 2.16592178e-01 8.91826928e-01 6.96230233e-01 8.57635736e-02 -3.59409988e-01 7.94523835e-01 -4.30133492e-01 6.73015341e-02 4.14075464e-01 1.02741480e+00 -2.79632479e-01 -1.01827753e+00 -3.55077237e-01 1.02197206e+00 -5.18880367e-01 -1.66915804e-01 -3.03217769e-01 5.09726405e-01 1.93197906e-01 1.01126099e+00 1.47291228e-01 -3.97542059e-01 2.81776309e-01 4.00748938e-01 8.95125866e-01 -7.29598641e-01 -6.03920639e-01 9.86153856e-02 -9.51870158e-02 -5.55031121e-01 -4.28882122e-01 -7.62142301e-01 -9.37048554e-01 -6.80381358e-02 -3.39886993e-01 1.89276654e-02 7.82957137e-01 1.20204234e+00 3.64128858e-01 6.76078498e-01 1.75584614e-01 -1.03322244e+00 2.98051536e-02 -9.56745386e-01 -7.64161646e-01 7.25557581e-02 3.23439300e-01 -1.04567194e+00 -1.20910086e-01 -1.42343417e-01]	[13.151094436645508, 3.431887626647949]
22445b16-9418-4b2a-bce2-9436edae2db0	weak-label-supervision-for-monaural-source	1810.13104	null	https://arxiv.org/abs/1810.13104v3	https://arxiv.org/pdf/1810.13104v3.pdf	Audio Source Separation Using Variational Autoencoders and Weak Class Supervision	In this paper, we propose a source separation method that is trained by observing the mixtures and the class labels of the sources present in the mixture without any access to isolated sources. Since our method does not require source class labels for every time-frequency bin but only a single label for each source constituting the mixture signal, we call this scenario as weak class supervision. We associate a variational autoencoder (VAE) with each source class within a non-negative (compositional) model. Each VAE provides a prior model to identify the signal from its associated class in a sound mixture. After training the model on mixtures, we obtain a generative model for each source class and demonstrate our method on one-second mixtures of utterances of digits from 0 to 9. We show that the separation performance obtained by source class supervision is as good as the performance obtained by source signal supervision.	['Serap Kırbız', 'Ertuğ Karamatlı', 'Ali Taylan Cemgil']	2018-10-31	null	null	null	null	['audio-source-separation']	['audio']	[ 4.56957072e-01 2.01425731e-01 -1.79639667e-01 -2.26756185e-01 -9.38400805e-01 -6.31749868e-01 5.70793688e-01 -1.07962780e-01 -2.12871544e-02 5.44442534e-01 1.39418110e-01 -1.43287078e-01 8.62743333e-02 -5.32455504e-01 -6.91233754e-01 -1.04555786e+00 7.02161156e-03 5.53018987e-01 1.01206906e-01 1.57816991e-01 -3.91443163e-01 4.01158363e-01 -1.46285284e+00 3.99376661e-01 7.47283876e-01 9.30542171e-01 2.10035041e-01 1.17052341e+00 -2.25526661e-01 9.13352787e-01 -1.11988413e+00 -4.38357331e-03 9.33343098e-02 -1.13989687e+00 -4.43496823e-01 5.09048641e-01 2.19815865e-01 -1.50457760e-02 -7.14261755e-02 1.29432285e+00 1.76812112e-01 5.98730706e-02 1.33338344e+00 -1.49365497e+00 -2.60528386e-01 9.71774161e-01 -5.00633419e-01 1.89579025e-01 -7.58702084e-02 -4.70957071e-01 9.27581429e-01 -7.19727099e-01 2.37203598e-01 1.02792215e+00 3.28529000e-01 6.03397608e-01 -1.56701040e+00 -6.58637345e-01 3.60475741e-02 -1.54423341e-01 -1.17553473e+00 -9.26566303e-01 1.11423290e+00 -6.79911554e-01 6.27239466e-01 1.73698291e-01 4.26409036e-01 1.06504679e+00 -1.39628351e-01 7.88997710e-01 7.28706956e-01 -6.70695722e-01 5.85856616e-01 3.36730063e-01 2.99239129e-01 3.44408453e-01 -3.11504483e-01 -1.50146857e-01 -4.61105078e-01 -2.34165013e-01 8.33282471e-01 -2.45891228e-01 -2.65947580e-01 -2.48880580e-01 -1.05783188e+00 7.89177299e-01 3.14950347e-02 5.38761675e-01 -4.75697249e-01 3.20266753e-01 3.11640301e-03 1.75899044e-01 4.78899986e-01 -1.83437914e-01 -2.24021256e-01 1.92471504e-01 -1.05565882e+00 -1.30547985e-01 1.06051445e+00 8.22919309e-01 6.97519660e-01 5.92824161e-01 2.04860836e-01 1.04710317e+00 6.23226345e-01 8.21039557e-01 4.33290690e-01 -1.36746585e+00 5.99835478e-02 -3.98856923e-02 6.87888637e-02 -4.40184146e-01 -1.67116504e-02 -7.51237094e-01 -7.46414900e-01 4.70530510e-01 5.44025421e-01 -1.74168885e-01 -1.01519203e+00 2.12442493e+00 3.52563292e-01 5.08302987e-01 5.01120627e-01 4.63884652e-01 5.57177126e-01 9.95104730e-01 -2.02904642e-01 -6.09674096e-01 1.12818408e+00 -9.48711932e-01 -9.52040493e-01 -1.43472716e-01 -1.45399556e-01 -8.40171635e-01 3.95639628e-01 6.16337657e-01 -1.00581181e+00 -6.77136838e-01 -1.13745177e+00 4.59166169e-01 -7.34493285e-02 1.90308705e-01 2.05123365e-01 6.77255630e-01 -7.69187570e-01 4.44785714e-01 -1.04941332e+00 1.43804699e-01 -1.27832323e-01 3.30734074e-01 -1.23370536e-01 4.61039275e-01 -9.58177567e-01 4.36765224e-01 3.37662771e-02 -2.32289657e-01 -1.36917877e+00 -4.02656555e-01 -8.61716688e-01 1.42425954e-01 -8.94147996e-03 -2.09243640e-01 1.50244975e+00 -1.49926829e+00 -1.67271817e+00 4.79221851e-01 -6.19622052e-01 -3.89784247e-01 2.05549560e-02 1.52141511e-01 -8.78759205e-01 4.13081259e-01 1.69120252e-01 4.33449000e-01 1.41038215e+00 -1.82707036e+00 -7.29586005e-01 1.63012501e-02 -4.34575915e-01 -7.04658329e-02 -1.13005638e-01 1.81668282e-01 -5.06218569e-03 -7.27917671e-01 3.95097733e-01 -8.07635903e-01 7.26860091e-02 -5.10615766e-01 -6.22035563e-01 -1.75668776e-01 7.15985239e-01 -6.98452711e-01 8.11082304e-01 -2.48161411e+00 3.11752647e-01 1.71814263e-01 1.86030447e-01 -1.62698761e-01 -2.70913783e-02 9.80793536e-02 -3.80376369e-01 -2.30943531e-01 -4.91330832e-01 -8.67510200e-01 1.31043524e-01 3.38659197e-01 -7.97665775e-01 5.38600683e-01 1.42722279e-01 1.86715260e-01 -8.99048567e-01 -4.07339543e-01 8.44787434e-02 8.56185377e-01 -5.18706739e-01 4.05276984e-01 -2.43942603e-01 5.51105857e-01 1.08827233e-01 2.68654376e-01 6.26660585e-01 -2.18915477e-01 2.17149019e-01 -4.45421436e-04 9.39510539e-02 3.86925668e-01 -1.54508829e+00 1.29997253e+00 -3.02256614e-01 6.65549457e-01 5.57193100e-01 -8.94623756e-01 7.59955466e-01 9.48867440e-01 5.56768715e-01 5.39364703e-02 5.68760410e-02 4.47008729e-01 2.88021684e-01 -2.04858959e-01 4.20593172e-02 -5.91840744e-01 4.26633395e-02 5.74771106e-01 8.09614360e-01 4.33503613e-02 1.81231365e-01 2.24766105e-01 6.61551774e-01 -8.98851603e-02 1.66207738e-02 -2.17951402e-01 2.02745974e-01 -5.55091023e-01 6.29739583e-01 5.35103917e-01 -7.43966177e-02 5.59565485e-01 2.66824692e-01 4.43531483e-01 -8.72390389e-01 -1.55451536e+00 -1.86903089e-01 1.14552176e+00 -1.62395000e-01 -2.01456502e-01 -9.06760991e-01 -3.69378865e-01 -2.93765932e-01 7.65142500e-01 -3.66000265e-01 3.98228616e-02 -4.95545417e-01 -5.83374321e-01 5.36283910e-01 5.03565550e-01 -7.93816242e-03 -9.42251444e-01 -2.88452119e-01 2.27259681e-01 -4.19688046e-01 -9.41533029e-01 -5.10756552e-01 9.48723972e-01 -7.11332381e-01 -7.92787492e-01 -8.18272352e-01 -9.89117384e-01 5.01263976e-01 -4.72125411e-02 9.22353745e-01 -7.47894466e-01 2.84275830e-01 3.32907677e-01 -5.66688552e-02 -5.25493920e-01 -1.02461672e+00 -3.68992776e-01 4.03202415e-01 5.71779013e-01 1.46839917e-01 -1.01528394e+00 -1.77492015e-02 9.43076387e-02 -7.67175496e-01 -2.57762104e-01 2.01318592e-01 4.93471414e-01 4.86336023e-01 5.47717988e-01 8.42706919e-01 -3.04896712e-01 2.38968194e-01 -7.50759482e-01 -4.68203068e-01 -6.46911785e-02 -7.76149705e-02 3.00731882e-02 7.02015698e-01 -7.85410643e-01 -1.10610366e+00 2.89980948e-01 -3.75948623e-02 -6.97850466e-01 -6.25368178e-01 3.11087102e-01 -4.55992252e-01 5.45662642e-01 6.87142730e-01 2.25375906e-01 -1.52808666e-01 -7.96476305e-01 4.58155066e-01 7.52551496e-01 1.04735184e+00 -4.34370875e-01 7.01057017e-01 3.35668564e-01 -3.03504229e-01 -1.05881071e+00 -7.36724615e-01 -3.74814630e-01 -6.63892388e-01 -1.83078468e-01 1.15233541e+00 -1.06428564e+00 -3.76946807e-01 5.61423600e-01 -1.21786833e+00 -3.32972884e-01 -5.63042164e-01 9.08696949e-01 -4.67260569e-01 1.63216636e-01 -7.92726934e-01 -1.38764966e+00 2.75012732e-01 -9.01719451e-01 8.62680674e-01 2.30323061e-01 -2.62442797e-01 -1.10710752e+00 2.80121863e-01 8.27820525e-02 3.00365668e-02 -2.53912807e-02 7.70376921e-01 -9.95252669e-01 -3.12039673e-01 -9.00381012e-04 5.26622713e-01 7.63305664e-01 5.12442172e-01 1.42742902e-01 -1.50321579e+00 -9.38147083e-02 6.27361298e-01 -1.13855656e-02 1.01003230e+00 6.42058372e-01 4.82638270e-01 -2.87964553e-01 -3.48864496e-01 3.39784116e-01 9.55007792e-01 6.82802439e-01 7.42238238e-02 -3.96721154e-01 6.85893655e-01 5.94738722e-01 -2.02332839e-01 1.73196077e-01 -1.01357371e-01 4.50146407e-01 1.37513816e-01 -1.94222748e-01 -2.44793847e-01 -1.81623131e-01 7.54884005e-01 1.10739422e+00 1.21700674e-01 -6.03160262e-01 -5.07978439e-01 7.62008131e-01 -1.56803811e+00 -1.06707466e+00 -9.75421146e-02 2.17738509e+00 1.06139863e+00 1.21321797e-01 4.27858800e-01 7.35721409e-01 9.90614653e-01 -2.83478126e-02 -4.04468417e-01 8.67628399e-03 -2.22320780e-01 2.85045534e-01 -4.68702875e-02 1.06257808e+00 -1.21416235e+00 3.61065507e-01 7.35662699e+00 7.72529244e-01 -9.76449609e-01 1.92879930e-01 2.96102554e-01 -1.89122498e-01 -3.55385035e-01 -1.10006139e-01 -6.59656465e-01 5.14392257e-01 1.24823415e+00 6.70317188e-02 5.16814232e-01 7.03133523e-01 -1.65511608e-01 -7.81462118e-02 -1.52022934e+00 8.07092130e-01 1.45510763e-01 -7.03695893e-01 -2.29784191e-01 1.78599104e-01 6.87774658e-01 -1.90074265e-01 1.12799712e-01 -7.53795877e-02 6.57248259e-01 -1.03670764e+00 1.13085139e+00 1.17832206e-01 5.37498236e-01 -7.25584030e-01 3.10500950e-01 7.04793036e-01 -1.06961966e+00 1.28415763e-01 -1.65471360e-01 1.36658726e-02 2.82496184e-01 7.47445464e-01 -7.46258140e-01 2.68176585e-01 1.35998189e-01 4.68188882e-01 1.12972707e-01 6.78639352e-01 -2.65282303e-01 1.22398674e+00 -4.48499233e-01 5.52856624e-01 -1.99381888e-01 -3.32596928e-01 7.68196523e-01 1.29270375e+00 4.19657350e-01 8.45396668e-02 3.16783398e-01 1.00208068e+00 -3.97888087e-02 -2.57358611e-01 -3.65901440e-01 -2.02000007e-01 5.33185780e-01 9.45261061e-01 -9.69222665e-01 -6.89385295e-01 -1.81789622e-01 1.08617961e+00 -1.68407381e-01 6.91891134e-01 -7.44243264e-01 -3.09487462e-01 7.28710353e-01 -2.68561840e-01 5.00604570e-01 -5.33815362e-02 -3.39999460e-02 -1.17470312e+00 -2.77294517e-01 -7.19679832e-01 3.24124545e-01 -7.05845833e-01 -1.27474320e+00 7.64141262e-01 1.38376161e-01 -1.26609409e+00 -7.94529974e-01 -3.81720364e-01 -6.15549326e-01 1.21389151e+00 -1.19419968e+00 -6.94548190e-01 2.34567553e-01 6.77289248e-01 4.17332917e-01 -3.27606231e-01 1.02701139e+00 2.61043519e-01 -3.30554396e-01 3.21910620e-01 2.71342635e-01 3.24213684e-01 5.07118165e-01 -1.52483475e+00 -9.05628130e-02 8.58245671e-01 6.60895348e-01 5.61206400e-01 9.85882521e-01 -4.64621723e-01 -6.86390221e-01 -8.20622206e-01 9.26216662e-01 -4.37071890e-01 5.41766226e-01 -5.28479695e-01 -8.61637652e-01 9.95822728e-01 6.06779039e-01 2.89125089e-02 1.21436274e+00 -1.47563443e-01 -4.04316306e-01 4.19262797e-02 -9.15772855e-01 1.05866596e-01 5.93876183e-01 -7.99632847e-01 -8.66475046e-01 1.26997173e-01 8.45527112e-01 -1.46371886e-01 -5.67349195e-01 7.26504102e-02 2.22393289e-01 -9.16822374e-01 9.04220760e-01 -3.53792608e-01 4.27288413e-01 -6.21925712e-01 -5.38787842e-01 -1.51224411e+00 -4.51874375e-01 -3.40423912e-01 -5.02467394e-01 1.49635816e+00 6.14322424e-01 -5.08241713e-01 4.86077994e-01 1.17711067e-01 -1.39592662e-01 3.13283950e-02 -1.03368461e+00 -9.09012437e-01 1.30879313e-01 -5.83968222e-01 3.52264315e-01 9.65748191e-01 -5.66369258e-02 6.33514047e-01 -4.22196269e-01 5.58003604e-01 9.35507715e-01 3.63098800e-01 3.60342324e-01 -1.24738550e+00 -1.02730095e+00 -5.04658759e-01 -3.92902270e-02 -1.14667773e+00 4.08043116e-01 -9.67757285e-01 4.86703426e-01 -1.25515187e+00 1.39298216e-01 -1.68385983e-01 -6.19844854e-01 3.47155452e-01 -8.86405781e-02 2.74689466e-01 3.12482119e-02 3.56319726e-01 -9.43802670e-02 4.57028031e-01 5.72008193e-01 -3.38633001e-01 -2.25315526e-01 2.34468654e-01 -5.31865656e-01 9.64745164e-01 5.41791022e-01 -6.81237459e-01 -5.80892324e-01 -1.93446442e-01 -3.23576093e-01 4.44328666e-01 3.58147442e-01 -1.11161351e+00 4.19152640e-02 -2.46467758e-02 5.57969093e-01 -4.49867934e-01 6.30474567e-01 -8.08539331e-01 4.33909327e-01 2.14797482e-01 -4.43309009e-01 -7.89582670e-01 1.82729252e-02 5.59862852e-01 -4.82770741e-01 -4.71514195e-01 8.63096833e-01 1.49350360e-01 -1.27984002e-01 -1.37897819e-01 -7.05758452e-01 -2.22134247e-01 5.25555849e-01 1.58383116e-01 9.89314541e-02 -6.76226437e-01 -1.24121428e+00 -2.37759367e-01 3.09431642e-01 3.08600124e-02 2.55046636e-01 -1.50862432e+00 -5.77622592e-01 4.63781625e-01 -2.78792500e-01 -3.85038070e-02 4.87509035e-02 4.82593536e-01 2.38869280e-01 3.33891660e-02 7.88075253e-02 -6.40398204e-01 -1.27390814e+00 6.63186491e-01 5.41307509e-01 8.01237822e-02 -2.13080063e-01 1.08665645e+00 6.30221009e-01 -2.21892104e-01 4.14382845e-01 -3.70694071e-01 -3.66110921e-01 1.31570160e-01 6.22856677e-01 3.75346094e-01 -3.39992076e-01 -9.27758992e-01 -3.77885342e-01 4.76404876e-01 6.24898851e-01 -9.62385714e-01 1.01109517e+00 1.45748720e-01 -2.09029898e-01 1.35399389e+00 1.24685788e+00 5.89688540e-01 -1.36596525e+00 -1.89714476e-01 -2.49774069e-01 2.07725521e-02 -1.06587775e-01 -7.34917879e-01 -1.01740718e+00 1.01023090e+00 4.63050753e-01 6.80563390e-01 1.14708662e+00 4.08521742e-01 4.35174435e-01 2.41909269e-02 -1.41148567e-02 -7.38017619e-01 8.88749436e-02 3.60435456e-01 7.19596624e-01 -8.90429258e-01 -7.10804522e-01 -5.39436758e-01 -4.37239468e-01 8.88731718e-01 6.25685975e-02 -2.50701934e-01 1.04577720e+00 6.74740553e-01 4.26653862e-01 6.43369630e-02 -6.10571265e-01 -1.09906010e-01 4.07353044e-01 6.20098352e-01 5.33559978e-01 2.87479520e-01 4.80683237e-01 8.58827770e-01 -2.95576602e-01 -2.77584702e-01 4.42597836e-01 7.11331725e-01 -7.16750622e-01 -1.21802688e+00 -6.91736341e-01 6.19531870e-02 -6.58771932e-01 -1.95292085e-02 -3.45098674e-01 2.01774994e-03 2.81917214e-01 1.47032046e+00 3.39936286e-01 -3.87847155e-01 -4.36701104e-02 6.53662920e-01 5.23867130e-01 -7.77366698e-01 -1.17271312e-01 1.16906226e+00 -3.81253362e-02 4.94967140e-02 -6.26495361e-01 -7.03340590e-01 -1.54040384e+00 1.98172614e-01 -2.89209962e-01 6.77253187e-01 5.93986511e-01 8.93474579e-01 -2.22537056e-01 8.74555588e-01 7.75269091e-01 -8.76720369e-01 -3.77907842e-01 -1.13609266e+00 -1.10360074e+00 2.39262015e-01 8.93681705e-01 -4.38098997e-01 -8.70593309e-01 8.08516264e-01]	[15.309370994567871, 5.626613616943359]
47f1f9e4-374d-4c0d-8394-36cd263d9a2d	pay-attention-when-required	2009.04534	null	https://arxiv.org/abs/2009.04534v3	https://arxiv.org/pdf/2009.04534v3.pdf	Pay Attention when Required	Transformer-based models consist of interleaved feed-forward blocks - that capture content meaning, and relatively more expensive self-attention blocks - that capture context meaning. In this paper, we explored trade-offs and ordering of the blocks to improve upon the current Transformer architecture and proposed PAR Transformer. It needs 35% lower compute time than Transformer-XL achieved by replacing ~63% of the self-attention blocks with feed-forward blocks, and retains the perplexity on WikiText-103 language modelling benchmark. We further validated our results on text8 and enwiki8 datasets, as well as on the BERT model.	['Szymon Migacz', 'Alex Fit Florea', 'Swetha Mandava']	2020-09-09	null	null	null	null	['paraphrase-identification']	['natural-language-processing']	[ 2.58285254e-02 3.12615931e-01 6.35020360e-02 -4.85095412e-01 -5.70194125e-01 -2.75798500e-01 6.93095982e-01 -8.77368003e-02 -6.91945195e-01 7.24619806e-01 8.74355912e-01 -7.99336851e-01 4.46987040e-02 -7.71001160e-01 -5.52995741e-01 -4.13218170e-01 -6.65689930e-02 6.45962179e-01 2.24658042e-01 -3.84561688e-01 3.74519303e-02 -1.65636435e-01 -1.20183313e+00 6.98177040e-01 6.31172597e-01 7.72219718e-01 5.52775383e-01 7.35881627e-01 -7.22254455e-01 1.41241252e+00 -2.57947177e-01 -5.50812542e-01 -3.86873454e-01 -1.76395476e-01 -9.89507496e-01 -5.96004605e-01 1.02861509e-01 -1.72532558e-01 -6.72360182e-01 6.11510992e-01 4.96190995e-01 5.49562760e-02 3.72404844e-01 -9.57831562e-01 -1.02707243e+00 1.36633968e+00 -2.47320756e-01 7.07629859e-01 2.30805531e-01 -3.17731574e-02 1.48773813e+00 -1.05057144e+00 4.88447130e-01 1.66428506e+00 6.37661815e-01 3.98050904e-01 -1.17690873e+00 -8.46323192e-01 6.26834214e-01 5.62769294e-01 -1.11149967e+00 -5.11999309e-01 3.77105862e-01 -1.03646673e-01 2.04440975e+00 2.84237266e-01 9.33447540e-01 1.13838375e+00 1.78726435e-01 1.22823763e+00 9.26088691e-01 -3.66742849e-01 -1.75738096e-01 -1.48346856e-01 5.05154014e-01 6.44008458e-01 -4.54256944e-02 -1.61240116e-01 -6.43314183e-01 -2.21472189e-01 5.12495995e-01 -2.04752870e-02 -1.70343563e-01 2.77873814e-01 -1.11513674e+00 7.34485984e-01 2.33979583e-01 4.91178393e-01 -4.13218826e-01 5.64229429e-01 2.54870385e-01 4.46064502e-01 6.81652546e-01 2.78002471e-01 -9.06423926e-01 -6.65824175e-01 -8.30711246e-01 8.95873178e-03 7.91195989e-01 1.36172962e+00 5.82206666e-01 1.20455325e-01 -4.92400318e-01 9.21334445e-01 4.34735417e-01 4.42935497e-01 7.87903726e-01 -3.53684753e-01 5.77315807e-01 5.98322392e-01 -3.30841333e-01 -4.90460813e-01 -2.18499765e-01 -7.36532032e-01 -7.57046282e-01 -7.24621773e-01 -1.45549979e-02 -2.24227756e-01 -1.07799757e+00 1.73855567e+00 -1.11413971e-01 2.48282462e-01 -1.87036648e-01 5.78815639e-01 8.65681708e-01 7.81730831e-01 4.00086820e-01 1.56003460e-01 1.60365081e+00 -1.11185968e+00 -9.06670272e-01 -5.58861375e-01 6.87900960e-01 -6.88667774e-01 1.36401296e+00 1.96314722e-01 -1.29314351e+00 -2.42892593e-01 -8.23885918e-01 -6.19746864e-01 -4.45873946e-01 -9.58039239e-03 6.81593776e-01 2.17410684e-01 -1.32902861e+00 3.81384611e-01 -6.62267685e-01 -3.66332650e-01 1.18690938e-01 2.08547488e-01 -1.11407124e-01 7.61168450e-02 -1.63388050e+00 8.40060472e-01 3.93145949e-01 1.34407550e-01 -8.08850288e-01 -1.09209085e+00 -8.14687908e-01 6.64262652e-01 1.65447190e-01 -8.38109553e-01 1.53051746e+00 -7.75616944e-01 -1.58830035e+00 4.89340007e-01 -6.30047739e-01 -6.32687092e-01 1.07748151e-01 -5.81633925e-01 -4.29563284e-01 -4.15257335e-01 -2.94835240e-01 6.23405755e-01 4.16333854e-01 -7.87741125e-01 -9.02275503e-01 5.57697490e-02 1.97975814e-01 2.39676341e-01 -4.56918895e-01 9.92657989e-02 -8.17309976e-01 -6.80779278e-01 -2.44871601e-01 -8.21393132e-01 3.68162878e-02 -5.52921414e-01 -2.92772681e-01 -2.19719589e-01 8.09597611e-01 -9.01370704e-01 1.81058311e+00 -2.06940770e+00 1.68336213e-01 -1.03218943e-01 4.40559387e-01 1.05787650e-01 -4.74584520e-01 6.95088565e-01 -2.55758703e-01 2.29164317e-01 -1.55433223e-01 -4.47080284e-01 1.79106429e-01 5.53879499e-01 -4.93557930e-01 -7.00133741e-02 3.19926083e-01 1.18013060e+00 -9.31690216e-01 -3.86351407e-01 1.19894303e-01 5.98749280e-01 -7.87820697e-01 1.66181758e-01 -2.77464181e-01 -4.39026117e-01 -2.00164229e-01 3.44941616e-01 3.74464750e-01 -4.34390515e-01 3.12371552e-01 2.71741170e-02 8.31712112e-02 1.14751208e+00 -5.45287251e-01 1.57297647e+00 -8.77818048e-01 9.27888632e-01 9.30616409e-02 -6.31160021e-01 6.92867100e-01 4.09021735e-01 2.68153936e-01 -1.02839839e+00 -6.24764301e-02 -2.83190817e-01 1.58427641e-01 -2.84143150e-01 6.51194394e-01 -3.17598224e-01 4.93451953e-02 4.18888152e-01 3.89752120e-01 2.89848834e-01 1.29515141e-01 4.74379599e-01 1.31486213e+00 8.58708099e-03 1.63903967e-01 -5.98126471e-01 3.99531394e-01 -2.26583973e-01 3.53072345e-01 5.34263909e-01 2.86619902e-01 2.32404217e-01 6.97960556e-01 -3.34156007e-01 -9.29897010e-01 -8.93343925e-01 2.62926549e-01 1.60252345e+00 -3.66395652e-01 -1.14869523e+00 -5.72673261e-01 -5.49613893e-01 -9.45234820e-02 1.28738141e+00 -6.09067500e-01 -5.37739210e-02 -6.00018620e-01 -8.15686047e-01 6.93092287e-01 8.83836627e-01 1.61348596e-01 -1.05363083e+00 -2.29454368e-01 5.22517145e-01 -4.51442391e-01 -9.90637004e-01 -4.00347382e-01 5.62607169e-01 -6.50213301e-01 -7.04108775e-01 -3.41180444e-01 -5.94388604e-01 3.42453301e-01 1.65305644e-01 1.75455642e+00 -7.62885511e-02 1.71479091e-01 5.97675592e-02 -3.83362979e-01 -4.38735545e-01 -5.81429005e-02 4.66733456e-01 -4.45479184e-01 -3.34322721e-01 4.25350815e-01 -6.43159747e-01 -3.06456983e-01 6.60308823e-02 -7.32713819e-01 6.22830629e-01 8.57043862e-01 1.20996165e+00 5.03528774e-01 -1.05774030e-01 2.10401177e-01 -1.01438773e+00 7.70204544e-01 -6.85695767e-01 -2.36398965e-01 3.54138941e-01 -7.10826397e-01 4.94412690e-01 5.77716947e-01 -2.80534834e-01 -1.32726336e+00 -5.21265328e-01 -4.90378618e-01 -1.60861209e-01 4.37521636e-01 8.12741041e-01 -5.53634390e-02 6.26758873e-01 2.46394854e-02 4.40548092e-01 -3.69398892e-01 -7.28197873e-01 5.49861610e-01 7.37527907e-01 2.05950812e-01 -7.21918464e-01 1.33849710e-01 6.78988621e-02 -5.98132730e-01 -8.84333432e-01 -7.67622232e-01 -4.00211126e-01 -9.98295173e-02 2.44605705e-01 5.81895173e-01 -1.07425666e+00 -4.82998669e-01 1.86246097e-01 -1.15915143e+00 -4.89264339e-01 -2.25331128e-01 1.99580252e-01 -4.33490098e-01 -2.87160277e-02 -1.04059124e+00 -5.45412838e-01 -8.59275341e-01 -9.18100715e-01 9.88011539e-01 -2.43545428e-01 -3.68207425e-01 -1.15922821e+00 2.52811741e-02 -3.20735085e-03 9.20065463e-01 -7.11324990e-01 1.40212250e+00 -7.31426239e-01 -3.05892885e-01 1.21606641e-01 -5.67802012e-01 6.43145759e-03 -4.67494465e-02 -2.18595728e-01 -1.13285935e+00 -2.41965294e-01 -3.08632135e-01 -9.43833590e-02 1.02180898e+00 2.66823709e-01 1.23229909e+00 -5.35923660e-01 -2.89084703e-01 7.27241695e-01 1.26464581e+00 1.33622587e-01 5.93733788e-01 1.78614527e-01 5.72716057e-01 1.96159080e-01 1.95316821e-01 3.04286808e-01 7.98686147e-01 4.68085766e-01 3.35288882e-01 -2.22363487e-01 -2.37507775e-01 -6.16358161e-01 4.75291461e-01 1.45942223e+00 6.35755137e-02 -6.46814227e-01 -1.05406058e+00 7.48643279e-01 -1.95005631e+00 -9.38448310e-01 -1.76307205e-02 1.61286199e+00 9.50382054e-01 2.37967759e-01 -8.32332075e-02 -9.89417881e-02 2.50549972e-01 1.61563963e-01 -2.04435766e-01 -5.16836345e-01 -1.21828072e-01 3.54743928e-01 3.83832008e-01 8.69305193e-01 -5.86702943e-01 1.28270471e+00 7.41126251e+00 1.04496586e+00 -1.02060032e+00 3.75252932e-01 5.81456602e-01 -5.85526884e-01 -8.97122920e-01 1.51274547e-01 -8.78365159e-01 4.41803068e-01 1.30719304e+00 -3.37815940e-01 4.27806437e-01 7.25795746e-01 1.29153252e-01 1.51868895e-01 -1.11019623e+00 7.63449788e-01 -2.45147556e-01 -1.24139309e+00 2.77292252e-01 -7.49653056e-02 1.30097091e-01 7.29905844e-01 -2.42524698e-01 8.20284963e-01 8.44041348e-01 -9.74362791e-01 7.71329880e-01 4.67818737e-01 7.60166049e-01 -6.93800867e-01 7.75524735e-01 2.94013858e-01 -1.41857696e+00 -4.19478528e-02 -3.98703426e-01 -2.34764844e-01 1.04062967e-01 5.74123621e-01 -9.11151648e-01 4.32188004e-01 7.94406712e-01 8.30824256e-01 -4.15166885e-01 4.89246696e-01 -3.75175774e-01 1.15309083e+00 -4.30896968e-01 -3.61528724e-01 5.40797353e-01 1.47640809e-01 3.68330449e-01 1.92013288e+00 2.19284862e-01 -1.45285223e-02 -1.85419664e-01 8.76003087e-01 -2.36639474e-02 -3.17784362e-02 -2.07486913e-01 -1.74082562e-01 5.62866926e-01 1.15819192e+00 -1.54706970e-01 -8.09849262e-01 -6.16413653e-01 9.32194054e-01 6.87726498e-01 4.80605930e-01 -9.23442423e-01 -3.11296850e-01 1.12742865e+00 3.02163392e-01 4.72537339e-01 -2.23526269e-01 -4.76263389e-02 -1.24432421e+00 -2.15762258e-01 -8.46165955e-01 4.80587006e-01 -1.03689313e+00 -1.07361865e+00 8.05234373e-01 1.36179179e-01 -4.41015899e-01 -3.55067134e-01 -5.58875203e-01 -5.94894052e-01 1.14373243e+00 -1.73427582e+00 -1.39739966e+00 1.29653877e-02 5.82139492e-01 7.82777727e-01 1.72031969e-01 9.42029238e-01 4.17692602e-01 -5.04630089e-01 6.64645970e-01 9.53708589e-02 8.18316638e-02 2.95766085e-01 -1.52042949e+00 1.02000582e+00 6.39818966e-01 3.46179068e-01 8.78913462e-01 5.72398841e-01 -4.04119015e-01 -1.48758388e+00 -1.00978875e+00 1.53515601e+00 -1.93268389e-01 1.01789725e+00 -6.64418519e-01 -8.54349256e-01 1.17164266e+00 6.89882874e-01 -3.55242163e-01 7.20486760e-01 6.78488612e-01 -2.71901339e-01 3.50074917e-02 -5.76718092e-01 6.98732376e-01 1.35998094e+00 -5.82279265e-01 -9.07530904e-01 -1.68591201e-01 1.14369953e+00 -1.94725722e-01 -8.55757594e-01 -6.07574731e-02 7.43955970e-01 -5.63236892e-01 8.02424490e-01 -8.13449264e-01 4.74462867e-01 1.77601010e-01 -1.99391440e-01 -1.52190816e+00 -9.48607564e-01 -6.28539562e-01 -3.69545043e-01 1.36837232e+00 6.01922214e-01 -5.71709335e-01 5.17318547e-01 3.93143237e-01 -2.32063338e-01 -9.58602428e-01 -8.86111081e-01 -5.23683488e-01 1.11683086e-01 -7.08875716e-01 7.76921093e-01 7.73999333e-01 2.81170309e-01 8.57511163e-01 -2.66359091e-01 -4.07208860e-01 1.53420761e-01 -2.84404457e-01 2.19885662e-01 -6.71282589e-01 -5.38738310e-01 -6.36064112e-01 -1.04944482e-02 -1.39416242e+00 2.35066414e-01 -9.51755524e-01 -1.57256305e-01 -1.61327648e+00 2.84781218e-01 -3.83365035e-01 -7.49673784e-01 1.01379931e+00 -4.03242767e-01 -2.15956807e-01 1.16065092e-01 -2.03150645e-01 -5.03600121e-01 8.09846401e-01 9.15644348e-01 -2.84069508e-01 1.20143577e-01 -4.59910274e-01 -8.71841073e-01 5.60151160e-01 5.59859097e-01 -4.78125691e-01 -8.45189631e-01 -1.00681746e+00 6.59571588e-01 -3.05024236e-02 -1.06695341e-02 -6.68956757e-01 1.64315864e-01 1.53764904e-01 2.57967710e-01 -8.08977544e-01 4.25880015e-01 -7.69034147e-01 7.87532702e-02 3.23363423e-01 -5.45210004e-01 4.74382401e-01 5.36286831e-01 3.33392859e-01 -1.98390827e-01 1.54186979e-01 2.05920115e-01 -1.34525388e-01 -7.06888199e-01 7.84611702e-02 -7.23352790e-01 2.55671293e-01 4.04333591e-01 7.20894709e-02 -2.52850860e-01 -4.75552976e-01 -7.11657047e-01 3.54843736e-01 -1.19880149e-02 7.14928031e-01 7.48157799e-01 -1.29090798e+00 -8.27600062e-01 4.23386484e-01 8.25944245e-02 -1.99140713e-01 1.25216143e-02 7.13279188e-01 -1.27051622e-01 8.36246371e-01 1.24705285e-02 -3.12481463e-01 -1.34756279e+00 4.19112682e-01 2.37712979e-01 -6.57140791e-01 -7.96165407e-01 1.06127024e+00 3.14117640e-01 -3.19351166e-01 -1.12098968e-02 -9.61887181e-01 -3.45469899e-02 -3.52884717e-02 5.78348815e-01 1.98468208e-01 1.84345305e-01 -2.54632622e-01 -4.10470486e-01 6.39195293e-02 -2.89927095e-01 -2.34784827e-01 1.40461457e+00 -2.22741723e-01 -2.49706537e-01 4.28271741e-01 1.00778759e+00 -2.60976106e-01 -9.20539439e-01 -4.86883253e-01 2.09478214e-01 -1.46011949e-01 5.00794530e-01 -1.11518323e+00 -1.05404246e+00 9.66044545e-01 9.12157521e-02 2.37342015e-01 1.19786251e+00 -6.51982203e-02 8.08667719e-01 3.63356352e-01 2.40531504e-01 -8.23785424e-01 -4.11750257e-01 1.02891171e+00 9.40893114e-01 -5.61626434e-01 -3.51938814e-01 -2.49783918e-01 -5.13065100e-01 7.45745718e-01 5.62899113e-01 -4.02114876e-02 8.45878363e-01 8.27688634e-01 -2.97087252e-01 -1.78349376e-01 -1.92034793e+00 -2.24072546e-01 3.47311795e-01 3.22276086e-01 1.12684667e+00 2.12823406e-01 -3.48204881e-01 1.07457483e+00 -3.99013788e-01 1.64470926e-01 1.04126157e-02 8.45932484e-01 -2.43627176e-01 -9.80891049e-01 1.73590079e-01 6.07019126e-01 -4.36824471e-01 -1.04748273e+00 -5.02335370e-01 8.35669935e-01 -1.19511418e-01 9.22955394e-01 3.33708137e-01 -5.46400189e-01 2.10515916e-01 3.87754261e-01 3.39865685e-01 -4.95240510e-01 -9.55830216e-01 2.87340045e-01 6.49368107e-01 -6.97667658e-01 1.14119537e-01 -2.92874694e-01 -1.15571749e+00 -6.67373598e-01 -2.61528134e-01 1.85614884e-01 4.61077064e-01 9.34850693e-01 7.14538693e-01 9.97861028e-01 7.24299550e-02 -4.69656706e-01 -4.97571260e-01 -1.57404089e+00 -2.75361180e-01 1.51561260e-01 1.36298195e-01 -3.87474567e-01 -8.35946277e-02 -6.64289743e-02]	[10.931312561035156, 7.370661735534668]
666d9f31-5335-466e-9c9a-2549d785c100	a-peek-at-peak-emotion-recognition	2205.09791	null	https://arxiv.org/abs/2205.09791v1	https://arxiv.org/pdf/2205.09791v1.pdf	A Peek at Peak Emotion Recognition	Despite much progress in the field of facial expression recognition, little attention has been paid to the recognition of peak emotion. Aviezer et al. [1] showed that humans have trouble discerning between positive and negative peak emotions. In this work we analyze how deep learning fares on this challenge. We find that (i) despite using very small datasets, features extracted from deep learning models can achieve results significantly better than humans. (ii) We find that deep learning models, even when trained only on datasets tagged by humans, still outperform humans in this task.	['Shmuel Peleg', 'Hillel Aviezer', 'Tzvi Michelson']	2022-05-19	null	null	null	null	['facial-expression-recognition']	['computer-vision']	[-6.27142340e-02 7.76436031e-02 -3.33730340e-01 -7.74185836e-01 -5.15295506e-01 -5.04183888e-01 6.33822143e-01 -5.50083108e-02 -6.39758706e-01 5.07048190e-01 3.95363271e-02 1.63842157e-01 3.09444159e-01 -4.49951291e-01 -2.42104933e-01 -4.02218372e-01 -3.11397642e-01 2.31358156e-01 -4.07737017e-01 -4.32212114e-01 -8.44478756e-02 5.86676896e-01 -1.43196607e+00 6.46804035e-01 1.65137157e-01 1.41820788e+00 -5.75911522e-01 5.47905385e-01 -1.41539171e-01 1.20539987e+00 -6.73684955e-01 -6.31304801e-01 2.20494628e-01 -4.95754868e-01 -1.02203763e+00 -6.10185377e-02 5.86311698e-01 -3.52188379e-01 -1.09195113e-01 9.28430617e-01 4.90515590e-01 -6.03430718e-02 5.99954069e-01 -1.56042802e+00 -4.74952489e-01 1.08009681e-01 -7.40298808e-01 2.35945165e-01 4.20356750e-01 -5.45229651e-02 1.13733530e+00 -9.62170720e-01 7.15870738e-01 9.97407317e-01 8.74505222e-01 6.68323159e-01 -1.03027201e+00 -8.73297513e-01 -1.08678512e-01 2.12225571e-01 -1.44578838e+00 -7.61368632e-01 7.42914498e-01 -4.27853614e-01 1.11025596e+00 -2.59557068e-02 5.67453682e-01 1.22253573e+00 7.00361133e-02 1.00651169e+00 1.31292248e+00 -3.56463343e-01 3.87868360e-02 1.37190342e-01 8.42057168e-02 7.16941416e-01 -3.94882202e-01 3.32179405e-02 -5.34442186e-01 -2.02897489e-01 3.05952698e-01 -1.71441451e-01 -8.52850229e-02 3.27692442e-02 -6.83921993e-01 8.98084700e-01 3.94989580e-01 8.65976691e-01 -6.97258472e-01 1.10170700e-01 6.55141890e-01 4.81692642e-01 5.87349296e-01 5.03863811e-01 -6.62135243e-01 -5.62521338e-01 -8.62457633e-01 1.93508819e-01 8.07444811e-01 4.94166553e-01 7.06910372e-01 1.49669766e-01 1.04881555e-01 1.07438731e+00 -2.27444440e-01 2.49156386e-01 4.68024790e-01 -9.97350454e-01 -3.53442520e-01 4.78047848e-01 2.08368152e-01 -1.29307997e+00 -7.89060235e-01 -2.31891572e-01 -8.29471231e-01 4.84008253e-01 5.43417037e-01 -5.17549992e-01 -7.44957566e-01 2.03051925e+00 -1.03171848e-01 -1.88858524e-01 -4.12229856e-04 8.87599289e-01 7.35403121e-01 4.74435747e-01 3.82758617e-01 -7.20570311e-02 1.35237539e+00 -6.65990531e-01 -7.95592844e-01 -3.52080613e-01 7.49811172e-01 -5.67132711e-01 7.67256498e-01 7.05045521e-01 -1.07566643e+00 -4.95485872e-01 -8.50531876e-01 -1.39084673e-02 -5.35539925e-01 -1.26761477e-02 1.23219311e+00 6.30283833e-01 -1.29757929e+00 5.07826567e-01 -4.07901675e-01 -5.87809563e-01 7.47977018e-01 5.27033389e-01 -7.64810920e-01 2.74754018e-01 -1.30561721e+00 1.05262506e+00 -1.22785576e-01 2.63059229e-01 -6.18937194e-01 -2.89308220e-01 -7.64330924e-01 5.95664643e-02 1.62685454e-01 -2.08051041e-01 1.58030248e+00 -2.09922481e+00 -1.42079389e+00 1.50225163e+00 -3.22134554e-01 -5.78023076e-01 2.57707953e-01 -2.42163703e-01 -4.74075526e-01 2.35902905e-01 -2.88046449e-01 8.98507774e-01 6.93644941e-01 -1.14467359e+00 -7.07517684e-01 -5.34625828e-01 1.46285454e-02 -1.55815452e-01 -2.89918393e-01 6.60867333e-01 -1.48933474e-02 -2.72941500e-01 -3.68879646e-01 -8.74309421e-01 -5.99284237e-03 1.69300139e-01 9.95051637e-02 -6.92880988e-01 7.93331087e-01 -4.31330949e-01 7.73349881e-01 -2.11745000e+00 -1.03928804e-01 1.39728904e-01 4.22066599e-01 5.39383948e-01 -1.66784599e-01 2.56872535e-01 -3.32334280e-01 2.46487305e-01 2.35716611e-01 -3.44041795e-01 2.82890648e-01 3.58170271e-01 -3.82045180e-01 5.85134149e-01 2.63365656e-01 1.03787160e+00 -8.32392812e-01 -3.92389238e-01 -6.94992691e-02 5.51282883e-01 -2.68010646e-01 2.64967799e-01 1.37866929e-01 1.13916166e-01 -1.27668932e-01 8.39649737e-01 5.99541545e-01 -3.02353799e-01 3.39305550e-01 -1.73056275e-01 7.26755783e-02 -1.70614406e-01 -3.61009091e-01 1.26203501e+00 -4.00881290e-01 1.13461614e+00 4.23803061e-01 -1.27673149e+00 8.33335161e-01 5.88880241e-01 6.51742399e-01 -1.08334088e+00 4.97362077e-01 2.38094125e-02 2.42116377e-01 -5.66253603e-01 3.00969005e-01 -7.47749150e-01 -4.42077704e-02 3.77116501e-01 2.58198589e-01 2.26917729e-01 -1.17908232e-01 -8.26450363e-02 1.09468567e+00 -2.95719773e-01 3.49237591e-01 -6.04583276e-03 3.83506238e-01 -6.11068942e-02 6.09125674e-01 4.37947482e-01 -8.11978996e-01 2.35767692e-01 9.31288362e-01 -8.30193937e-01 -7.19341218e-01 -6.65526867e-01 -8.96243528e-02 1.74248433e+00 -3.73136967e-01 -4.81811792e-01 -8.03691030e-01 -6.71058476e-01 -2.04144046e-02 3.08630437e-01 -1.09574664e+00 -7.31349289e-02 -3.64166975e-01 -6.41853333e-01 9.54941332e-01 6.89040184e-01 3.06074411e-01 -1.31865406e+00 -8.06101918e-01 -1.91883177e-01 1.47013694e-01 -1.09315383e+00 1.46616891e-01 2.40906447e-01 -4.06858772e-01 -9.62966442e-01 -7.67453134e-01 -7.64483273e-01 2.70022362e-01 -1.97636262e-01 1.38845026e+00 2.47922018e-01 -3.78162742e-01 5.51700056e-01 -4.16128665e-01 -7.64191031e-01 -2.69189000e-01 -1.45493627e-01 -6.33250102e-02 1.54448628e-01 9.77082014e-01 -3.82489353e-01 -4.01730925e-01 2.00475246e-01 -6.58953726e-01 -3.25706601e-01 6.00026429e-01 7.72858977e-01 7.47048929e-02 -1.68948382e-01 7.32778251e-01 -7.08451390e-01 7.12453365e-01 -5.60643017e-01 -1.88509762e-01 2.56761885e-03 -4.72952276e-01 -3.84703130e-01 4.50012237e-01 -3.13524455e-01 -6.06325567e-01 2.40622107e-02 -4.34957564e-01 -4.01400954e-01 -4.45838451e-01 3.23856592e-01 2.32083291e-01 -2.26916477e-01 6.43482745e-01 -2.05637142e-01 1.81684673e-01 -2.83103973e-01 -1.77318864e-02 7.06503451e-01 3.48791629e-01 -3.83759528e-01 1.48499325e-01 5.85021615e-01 4.32148464e-02 -9.89851058e-01 -1.04510462e+00 -2.16024384e-01 -6.29947424e-01 -4.63520736e-01 1.09909022e+00 -8.38230431e-01 -1.07152772e+00 5.49166501e-01 -1.08631623e+00 -5.00072300e-01 2.92033534e-02 1.31395563e-01 -5.47669768e-01 8.41591060e-02 -7.85222411e-01 -1.02315176e+00 -1.56439766e-01 -6.79069161e-01 1.03413248e+00 2.48688281e-01 -8.90848160e-01 -1.01288748e+00 2.13855684e-01 1.39362797e-01 5.26339948e-01 5.12241364e-01 5.90585947e-01 -7.39023983e-01 3.02953362e-01 -4.06759828e-01 -3.10936242e-01 4.76623148e-01 1.42970696e-01 8.61575827e-02 -1.41580224e+00 3.84190399e-03 -3.10982376e-01 -1.00366282e+00 7.40713418e-01 1.80057406e-01 1.21880281e+00 -3.32623683e-02 -6.94173127e-02 4.72036153e-01 8.92689705e-01 3.55830371e-01 5.53333640e-01 2.24463984e-01 3.52263272e-01 9.03202355e-01 4.27430719e-01 3.88840050e-01 2.67049909e-01 6.78330004e-01 2.48613596e-01 -4.72292662e-01 2.82299757e-01 -2.71560671e-03 2.80165344e-01 -5.87531179e-03 -3.20437253e-01 -1.01545669e-01 -1.15505672e+00 5.97560167e-01 -1.79648888e+00 -1.21968675e+00 1.15289636e-01 1.49205518e+00 7.61522770e-01 -2.25198530e-02 3.34624410e-01 2.43214164e-02 3.57950300e-01 4.40066606e-01 -3.25737208e-01 -9.82002854e-01 -2.76742548e-01 5.87417066e-01 9.34610516e-02 2.66878933e-01 -1.18714929e+00 1.06955421e+00 7.89934826e+00 4.48784918e-01 -1.55576468e+00 -6.45589083e-02 9.53233004e-01 -8.15495383e-03 1.71433032e-01 -4.51716870e-01 -3.89029533e-01 2.59237051e-01 9.92481947e-01 -5.62824868e-02 1.71455979e-01 1.05036521e+00 -7.52733126e-02 -3.41379307e-02 -1.29427671e+00 1.30704272e+00 3.22179556e-01 -7.72214830e-01 -2.21930429e-01 1.18286713e-04 5.14516294e-01 -1.15186244e-01 2.86864638e-01 6.54845178e-01 1.70155361e-01 -1.80808735e+00 4.77077723e-01 5.59824944e-01 3.27085912e-01 -9.37425435e-01 1.03084493e+00 2.10592628e-01 -5.29681623e-01 8.00957438e-03 -3.80918175e-01 -4.07823235e-01 -8.85019228e-02 3.77021670e-01 -8.18502784e-01 -1.93293244e-02 8.13681304e-01 5.26932538e-01 -4.12393153e-01 4.17558551e-01 -1.76756993e-01 6.88171268e-01 -1.13802560e-01 -1.27857625e-02 4.19242203e-01 1.19972594e-01 -1.78153515e-01 1.51189673e+00 7.16191530e-02 2.72974074e-01 4.77338135e-02 5.79617202e-01 -2.39855200e-01 1.21907189e-01 -7.58630931e-01 -5.44893026e-01 -2.37974465e-01 1.51888192e+00 -4.84311044e-01 -3.55091333e-01 -5.58451116e-01 1.01643574e+00 5.91109395e-01 3.03663969e-01 -5.71164548e-01 -3.07716638e-01 1.05342054e+00 -7.50553533e-02 1.06362276e-01 -1.88156858e-01 -1.27141967e-01 -7.29919970e-01 -2.86296934e-01 -1.00871253e+00 4.60908115e-01 -1.02596569e+00 -1.42804146e+00 7.67213285e-01 -3.88540059e-01 -5.61237216e-01 -6.94567740e-01 -1.01045120e+00 -6.75881207e-01 7.53741503e-01 -1.18319035e+00 -9.75113869e-01 -3.18279296e-01 5.58993578e-01 1.44685760e-01 -1.71562098e-02 1.08541155e+00 2.95386136e-01 -4.43223506e-01 7.14394152e-01 -5.12142003e-01 6.85223877e-01 8.47127616e-01 -1.17072105e+00 -1.33819833e-01 1.59047768e-01 3.16998065e-01 5.31244814e-01 7.31566250e-01 5.88478073e-02 -1.23944652e+00 -5.16151011e-01 1.03750408e+00 -3.84600192e-01 6.36329472e-01 -3.74842823e-01 -8.99878681e-01 8.16256583e-01 7.29002953e-01 2.41734385e-01 1.18221760e+00 6.17457390e-01 -6.66687548e-01 -4.59283032e-02 -1.14479637e+00 2.16954976e-01 7.52171516e-01 -7.32302308e-01 -4.36157942e-01 1.99049205e-01 -3.75674777e-02 -1.91533104e-01 -8.29845905e-01 6.48338914e-01 9.89856899e-01 -1.29823196e+00 6.22899830e-01 -1.26123726e+00 5.81447780e-01 2.21172392e-01 -1.48375005e-01 -1.26624262e+00 -2.39707261e-01 -3.41242313e-01 7.96648487e-02 8.63438487e-01 2.66608924e-01 -4.15865630e-01 9.09203410e-01 7.92673767e-01 2.79367805e-01 -9.62493122e-01 -6.86276913e-01 -5.38441420e-01 3.30464572e-01 -4.39374387e-01 3.96555305e-01 1.35834754e+00 1.85931563e-01 5.13013542e-01 -5.45209944e-01 -2.95139849e-01 7.72235468e-02 2.18044549e-01 8.65609288e-01 -1.26375544e+00 6.11786321e-02 -7.92061865e-01 -7.40738988e-01 -5.17993212e-01 8.41909349e-01 -6.47800505e-01 -1.80698410e-02 -1.23193264e+00 2.60901153e-01 2.34798472e-02 -6.11057043e-01 1.00131309e+00 1.13231048e-01 7.74643719e-01 2.79358029e-02 -2.39701807e-01 -7.59931207e-01 3.18464994e-01 7.36099660e-01 3.99776287e-02 3.59115779e-01 -4.01117861e-01 -8.20772409e-01 1.03999913e+00 9.62392211e-01 -1.01160400e-01 -9.45514906e-03 -2.67309487e-01 5.26384830e-01 -1.44176081e-01 4.23633337e-01 -7.55881250e-01 -5.61441518e-02 -1.98262200e-01 8.52304995e-01 -2.20469147e-01 5.19718230e-01 -5.81451416e-01 -3.11494797e-01 1.03694499e-01 -3.79256070e-01 1.29003510e-01 4.92123693e-01 5.69861270e-02 -5.97530425e-01 -4.99130972e-02 1.03816807e+00 -1.81138873e-01 -1.05162299e+00 1.95304573e-01 -5.95042229e-01 4.27940786e-02 8.27743649e-01 1.48045376e-01 -1.83566332e-01 -8.27268541e-01 -9.65595543e-01 9.17520300e-02 5.42676389e-01 3.73565137e-01 2.41240010e-01 -1.33785439e+00 -4.18659627e-01 -3.09212543e-02 1.27185047e-01 -6.16306841e-01 1.20165609e-01 9.59805071e-01 -2.26759091e-01 3.61570626e-01 -5.07135272e-01 -5.21284223e-01 -1.54870641e+00 4.02902633e-01 5.86127460e-01 2.06979997e-02 -1.91947341e-01 1.14238703e+00 1.72440410e-01 -2.36143366e-01 2.33156338e-01 9.60591808e-02 -1.93406090e-01 4.66167659e-01 7.21465528e-01 -1.08707566e-02 -5.85451908e-02 -1.00201142e+00 -5.25717020e-01 3.98434788e-01 -1.13554925e-01 -7.21719339e-02 1.37693310e+00 3.59914571e-01 -2.16555193e-01 4.96415585e-01 1.71277297e+00 4.16654907e-02 -8.00763905e-01 6.96354546e-03 1.88051715e-01 -3.87388080e-01 8.42920691e-02 -8.50195050e-01 -1.08269775e+00 1.19343472e+00 6.81737483e-01 4.01419014e-01 1.31319761e+00 4.24288251e-02 7.87352920e-01 5.73371172e-01 2.90525675e-01 -1.15329194e+00 4.22531664e-02 6.12248957e-01 7.15487778e-01 -1.48132300e+00 -2.18568981e-01 1.31302014e-01 -7.92730391e-01 1.07710409e+00 6.30983114e-01 -1.79687038e-01 6.61666155e-01 3.39857489e-01 4.77426201e-01 -5.91234028e-01 -9.69012201e-01 -4.17161971e-01 1.26402617e-01 4.83900517e-01 9.89507020e-01 -1.58347934e-02 -9.70444977e-02 6.83326662e-01 -3.60382259e-01 3.18255007e-01 1.13595068e-01 9.07362163e-01 -5.08448660e-01 -9.29837704e-01 -1.17708489e-01 2.91473866e-01 -1.05955386e+00 3.13378304e-01 -1.19270837e+00 9.17081654e-01 3.45775306e-01 8.88022721e-01 3.38721186e-01 -4.09028977e-01 2.80516118e-01 5.50164044e-01 6.56342268e-01 -3.58986527e-01 -6.46456540e-01 -3.02773595e-01 4.03547704e-01 -8.72316957e-01 -6.60510778e-01 -5.53987145e-01 -1.12026823e+00 -3.65184456e-01 3.25453192e-01 3.71231556e-01 4.49335843e-01 8.01412702e-01 3.26148868e-01 1.25443429e-01 6.58389091e-01 -7.98120737e-01 -3.47634614e-01 -1.01521790e+00 -7.98027754e-01 5.21814167e-01 4.45023477e-01 -6.84055090e-01 -4.73552495e-01 -2.56353598e-02]	[13.547638893127441, 1.8747297525405884]
18804102-8de7-4bbd-b409-041c3b7c123c	learnable-frontends-that-do-not-learn	2302.10014	null	https://arxiv.org/abs/2302.10014v1	https://arxiv.org/pdf/2302.10014v1.pdf	Learnable Frontends that do not Learn: Quantifying Sensitivity to Filterbank Initialisation	While much of modern speech and audio processing relies on deep neural networks trained using fixed audio representations, recent studies suggest great potential in acoustic frontends learnt jointly with a backend. In this study, we focus specifically on learnable filterbanks. Prior studies have reported that in frontends using learnable filterbanks initialised to a mel scale, the learned filters do not differ substantially from their initialisation. Using a Gabor-based filterbank, we investigate the sensitivity of a learnable filterbank to its initialisation using several initialisation strategies on two audio tasks: voice activity detection and bird species identification. We use the Jensen-Shannon Distance and analysis of the learned filters before and after training. We show that although performance is overall improved, the filterbanks exhibit strong sensitivity to their initialisation strategy. The limited movement from initialised values suggests that alternate optimisation strategies may allow a learnable frontend to reach better overall performance.	['Naomi Harte', 'Tomi Kinnunen', 'Mark Anderson']	2023-02-20	null	null	null	null	['activity-detection']	['computer-vision']	[ 3.98236215e-01 1.38971061e-01 3.43892068e-01 -4.21735317e-01 -8.19321990e-01 -7.91373670e-01 4.35549110e-01 6.47950247e-02 -8.39213967e-01 5.09648740e-01 4.57979053e-01 -1.10661715e-01 -3.10296118e-01 -4.35577840e-01 -5.48600793e-01 -6.35118663e-01 -6.23052716e-01 -1.05955705e-01 3.00425619e-01 -2.74380416e-01 -1.73824310e-01 3.86364669e-01 -1.84349787e+00 3.90350193e-01 1.87115103e-01 8.74282539e-01 1.21755362e-01 1.39107549e+00 4.05598521e-01 3.50659102e-01 -1.19103432e+00 -1.46613419e-01 6.57357872e-01 -5.13262331e-01 -7.22566009e-01 -2.43261322e-01 3.90654474e-01 -3.00737828e-01 -8.95127468e-03 1.08903027e+00 7.94436753e-01 5.56963384e-01 5.90303659e-01 -7.49463677e-01 -1.89148575e-01 8.56355548e-01 4.04518157e-01 5.46509683e-01 3.93619001e-01 2.99786747e-01 1.38757312e+00 -4.32862520e-01 2.80076433e-02 1.42717707e+00 1.24994707e+00 3.25847298e-01 -1.49376643e+00 -7.53653288e-01 3.50803174e-02 -2.45673373e-01 -1.23478925e+00 -8.31632674e-01 4.11388546e-01 -4.50976163e-01 9.93433475e-01 3.88778031e-01 5.97656786e-01 6.43937588e-01 9.21817347e-02 2.44579956e-01 1.04568708e+00 -5.57416916e-01 1.14975303e-01 4.96917628e-02 -3.58758956e-01 5.02119780e-01 -1.14472419e-01 5.85487068e-01 -6.07706845e-01 -1.30452693e-01 7.86708772e-01 -7.86258101e-01 -4.28082317e-01 2.23956496e-01 -1.08348811e+00 8.97014320e-01 4.33062911e-01 2.36268893e-01 -3.64987731e-01 3.76423925e-01 3.95901442e-01 7.43933558e-01 3.72966975e-01 1.05964613e+00 -6.37398303e-01 -2.27223441e-01 -1.10034513e+00 1.63571462e-01 1.13620174e+00 2.72031516e-01 6.50095344e-01 5.72364151e-01 -1.55069426e-01 1.12259936e+00 3.38557839e-01 3.94334167e-01 6.07412338e-01 -1.01945758e+00 9.40303728e-02 -2.27717713e-01 -1.78696662e-01 -8.10335815e-01 -5.45123041e-01 -8.10442805e-01 -2.77116954e-01 3.36492956e-01 5.92548311e-01 -7.05579400e-01 -1.00609934e+00 1.57415771e+00 1.10951170e-01 4.38677251e-01 5.20235151e-02 6.98863328e-01 6.72259748e-01 7.86345363e-01 7.16446200e-03 -1.62949264e-02 1.01120186e+00 -5.59431851e-01 -5.19900978e-01 -2.85353273e-01 2.77195334e-01 -9.54748869e-01 9.52481210e-01 6.26907527e-01 -9.84025598e-01 -8.19102585e-01 -1.30766237e+00 2.31364459e-01 -2.14657113e-01 -4.51906510e-02 3.16048265e-01 1.13990176e+00 -1.19024348e+00 9.06025290e-01 -7.12066174e-01 -1.09561346e-01 -1.19416766e-01 6.21309996e-01 -1.54692963e-01 6.77449048e-01 -1.25892162e+00 6.71736777e-01 6.60255134e-01 1.12811819e-01 -8.63377273e-01 -7.33301878e-01 -7.22439110e-01 3.20846662e-02 4.97400947e-02 -3.93888086e-01 1.86878502e+00 -1.08529723e+00 -2.10581803e+00 4.38703448e-01 4.49392647e-01 -1.01220417e+00 1.19036525e-01 -2.76534498e-01 -5.20667315e-01 1.78514138e-01 -3.35275531e-01 7.93251276e-01 1.38461983e+00 -7.40276694e-01 -8.30181718e-01 4.00074273e-01 7.88021088e-02 1.22466713e-01 -5.04492402e-01 1.59994029e-02 2.37429991e-01 -1.06946480e+00 -1.93177253e-01 -9.19412076e-01 -8.30368251e-02 -9.71465185e-02 1.16042588e-02 -1.61952116e-02 2.66977847e-01 -6.92632079e-01 1.37632489e+00 -2.12721276e+00 -8.64626840e-02 3.45908105e-01 -2.46542722e-01 5.22198379e-01 -3.74807566e-01 2.48701960e-01 -2.26559713e-02 6.69688061e-02 -1.11433588e-01 -1.10322513e-01 1.30220070e-01 3.03800285e-01 -2.00221732e-01 4.66342956e-01 4.91595954e-01 2.75550365e-01 -9.12020087e-01 6.63093245e-03 1.68998078e-01 7.78397083e-01 -9.83110607e-01 2.80077606e-01 5.66017814e-02 1.63168073e-01 2.88863599e-01 3.86595577e-02 1.88353121e-01 5.04878640e-01 -1.00402594e-01 -2.26248443e-01 -1.25478864e-01 7.46526062e-01 -1.48059726e+00 1.16871381e+00 -6.99602485e-01 8.86626244e-01 6.99390471e-01 -9.24378395e-01 1.03788674e+00 5.00747621e-01 2.13352337e-01 -2.91488200e-01 5.90779968e-02 2.77452737e-01 7.10047185e-01 -5.47701530e-02 3.96860540e-01 -5.87602615e-01 1.30959526e-01 2.85836905e-01 4.91964251e-01 -5.97097754e-01 -3.28130908e-02 -5.13794482e-01 9.71818447e-01 -1.36696264e-01 1.46714702e-01 -3.60299438e-01 4.17358249e-01 -5.33003211e-01 3.43809426e-01 9.30644453e-01 -4.17443365e-02 6.74868107e-01 -1.07065484e-01 -1.13196567e-01 -6.67732537e-01 -1.09575129e+00 -4.83799249e-01 1.57005048e+00 -5.32007098e-01 -7.22304106e-01 -9.76863027e-01 -8.71941894e-02 2.36395281e-03 5.53225219e-01 -3.40281188e-01 -4.14663374e-01 -5.73583186e-01 -5.12699425e-01 1.03632343e+00 4.21217024e-01 2.89524645e-01 -1.10369718e+00 -8.74335289e-01 6.62452936e-01 2.82004386e-01 -8.20166826e-01 -6.85614109e-01 8.26595426e-01 -8.25303435e-01 -5.26778877e-01 -7.23270953e-01 -5.47898412e-01 5.14578000e-02 -2.11350530e-01 9.65055943e-01 -8.90712142e-02 -1.02451183e-01 6.63160861e-01 -3.42593998e-01 -7.86427855e-01 -7.67464995e-01 2.83412904e-01 3.64097029e-01 -6.79674372e-02 -1.89856946e-01 -5.77580750e-01 -4.48679239e-01 3.78019005e-01 -8.47317517e-01 -5.10441720e-01 5.39603889e-01 1.04201758e+00 2.16319352e-01 5.48576653e-01 6.02212012e-01 -3.62712145e-01 1.12586892e+00 -2.26995423e-01 -4.47733134e-01 -3.08498651e-01 -2.94220299e-01 4.56419140e-01 6.20123148e-01 -7.07582176e-01 -8.01435113e-01 1.07009463e-01 -6.45877004e-01 -1.66163087e-01 -1.50414258e-01 5.40526569e-01 1.72526672e-01 -1.17926486e-01 1.08303034e+00 -2.61284560e-01 5.47282770e-02 -5.40638149e-01 3.46572220e-01 6.62897110e-01 6.60465837e-01 -6.34249508e-01 8.26023638e-01 -5.97314164e-02 -4.60702926e-01 -1.22801685e+00 -6.08229935e-01 -3.58708829e-01 -4.73836750e-01 -1.60988390e-01 6.15229011e-01 -6.90647483e-01 -5.74132323e-01 3.59742671e-01 -7.96862543e-01 -5.27269363e-01 -6.15786970e-01 8.70241821e-01 -6.25623107e-01 -1.63388848e-01 -4.81888503e-01 -9.70897377e-01 -2.02597603e-01 -9.13634598e-01 9.45622504e-01 1.02775976e-01 -5.46113133e-01 -1.19620156e+00 2.10215479e-01 -1.77448153e-01 6.55109942e-01 -7.03646541e-02 5.56687534e-01 -7.16239393e-01 1.84699520e-02 -1.07800893e-01 4.64830250e-01 8.72343302e-01 4.00154799e-01 6.25930652e-02 -1.52079225e+00 -4.88174766e-01 4.65760641e-02 -1.17446840e-01 9.23128784e-01 5.39971471e-01 8.11550200e-01 -6.74372792e-01 2.12044328e-01 8.22510242e-01 7.02531815e-01 3.54961187e-01 3.40299338e-01 3.44695270e-01 -2.54341103e-02 5.56820333e-01 1.33516923e-01 3.41892481e-01 -2.22993493e-01 4.68473762e-01 1.29381657e-01 -1.09308794e-01 -4.44568604e-01 -3.85360360e-01 6.96337223e-01 1.16499424e+00 -1.09156698e-01 -2.17430353e-01 -7.06967592e-01 5.32597899e-01 -1.20089138e+00 -9.47519541e-01 6.63782597e-01 2.39388824e+00 1.06202149e+00 6.30755365e-01 5.43705940e-01 6.42859936e-01 5.31836331e-01 1.83907539e-01 -3.04179072e-01 -8.25008810e-01 1.04481757e-01 9.48624194e-01 5.90045333e-01 7.48339534e-01 -1.06431234e+00 6.00108981e-01 7.92877245e+00 7.34358668e-01 -1.35554647e+00 -2.36815035e-01 1.02891758e-01 -1.70230418e-01 -1.01079561e-01 -2.31771469e-01 -7.40435123e-01 2.54416019e-01 1.58627951e+00 -9.26660821e-02 7.35106468e-01 5.39004922e-01 1.49446845e-01 9.96730626e-02 -1.18462586e+00 6.46358371e-01 -3.80214363e-01 -9.78236258e-01 -1.93230495e-01 4.74369638e-02 3.71815324e-01 3.98623534e-02 3.15701723e-01 5.34788847e-01 4.63944495e-01 -1.39599109e+00 8.76930773e-01 3.68168801e-01 5.55035174e-01 -8.70851398e-01 5.13441801e-01 1.78765982e-01 -1.32641733e+00 -2.66669869e-01 -1.15465820e-01 -3.79944295e-01 -1.60927415e-01 2.40640536e-01 -1.56984770e+00 -2.36927830e-02 6.91175044e-01 3.97545934e-01 -5.24750173e-01 1.36943364e+00 -1.07968099e-01 1.26581359e+00 -6.86600685e-01 -1.86700225e-01 3.47151697e-01 1.67400166e-01 7.91764975e-01 1.68661594e+00 3.20267767e-01 -1.43748552e-01 3.04970518e-02 3.44468236e-01 1.21370025e-01 -8.32849219e-02 -3.75587493e-01 -3.44065666e-01 4.37889248e-01 8.22367191e-01 -6.27547801e-01 5.54582514e-02 -1.40106618e-01 5.15107274e-01 -1.17435955e-01 2.93049634e-01 -4.91769820e-01 -8.27882707e-01 1.00734031e+00 2.32020766e-01 5.30751467e-01 -3.23484868e-01 2.58690685e-01 -6.73957586e-01 -3.86840165e-01 -1.03074193e+00 3.49002570e-01 -5.71740985e-01 -1.12783504e+00 7.60952592e-01 3.24639797e-01 -9.35973823e-01 -6.51272655e-01 -7.88407445e-01 -5.81127346e-01 9.32796299e-01 -1.00059474e+00 -4.61885631e-01 3.21261883e-01 3.35029513e-01 5.75254560e-01 -1.52944952e-01 9.67471659e-01 1.52088761e-01 -7.67787918e-02 8.07042897e-01 3.23680788e-02 1.69139758e-01 6.77522361e-01 -1.38338435e+00 4.37045604e-01 7.02947676e-01 7.08962381e-01 6.67674899e-01 1.08705592e+00 -2.60483176e-01 -8.45126808e-01 -8.42215419e-01 2.59356767e-01 -1.97514087e-01 7.27010608e-01 -3.99687767e-01 -9.44569170e-01 4.84492421e-01 4.28137451e-01 -2.36465428e-02 6.45024002e-01 3.07446569e-01 -9.76283848e-02 -2.95805663e-01 -8.55625510e-01 2.45044664e-01 8.22342634e-01 -9.60359275e-01 -8.62677336e-01 -1.15510039e-02 5.88737130e-01 -3.85768920e-01 -9.38038230e-01 4.57727134e-01 8.03146362e-01 -9.50469792e-01 1.10942042e+00 -5.29797137e-01 -3.05850685e-01 -2.14678735e-01 -1.83179229e-01 -1.91311955e+00 -3.95184338e-01 -1.04107738e+00 1.30907461e-01 1.21104896e+00 4.54700917e-01 -8.04678500e-01 4.97236967e-01 2.04700127e-01 -2.19308347e-01 -3.03054363e-01 -1.03572154e+00 -9.94219840e-01 1.64300621e-01 -6.25207663e-01 4.29844171e-01 4.84598786e-01 -3.00573051e-01 3.37719113e-01 -2.82427549e-01 3.13666224e-01 2.07765117e-01 -5.83777189e-01 5.44852495e-01 -1.41392553e+00 -7.49563754e-01 -5.55962205e-01 -5.65038443e-01 -1.08505476e+00 2.28308700e-02 -7.78844953e-01 4.69894558e-01 -9.36620057e-01 -8.65068138e-01 -2.80159712e-01 -6.22852385e-01 3.98010612e-01 -5.33308126e-02 1.83372170e-01 3.11193645e-01 -3.89139444e-01 -6.65804446e-02 3.17596704e-01 8.49394083e-01 -9.30833742e-02 -6.13773525e-01 4.03121948e-01 -5.06910145e-01 9.07922208e-01 8.44578087e-01 -6.12246215e-01 -6.60077870e-01 -3.81635040e-01 1.17966786e-01 -1.41556129e-01 3.01475376e-01 -1.46272457e+00 2.21885324e-01 1.44815430e-01 2.48470649e-01 -2.72891391e-02 6.21700943e-01 -4.89265323e-01 2.06643939e-01 4.38450694e-01 -7.08074450e-01 -3.55469547e-02 8.01437378e-01 5.54591179e-01 -3.23910952e-01 -6.13658786e-01 8.39151025e-01 -8.69640335e-02 -4.32946563e-01 -2.00025678e-01 -9.70677495e-01 2.36256495e-01 2.61790186e-01 -3.63978028e-01 2.10218817e-01 -5.70486426e-01 -8.98551881e-01 -3.23991567e-01 -1.27913162e-01 3.95656794e-01 3.79652888e-01 -1.13510108e+00 -7.48476863e-01 6.51678920e-01 -3.87396812e-01 -2.15079919e-01 -3.93036008e-01 3.39730024e-01 -3.36653709e-01 4.21415180e-01 -1.85216263e-01 -6.04477108e-01 -1.34101880e+00 -8.76677334e-02 7.54492998e-01 3.57014984e-01 -2.64704853e-01 1.32380140e+00 -7.82546215e-03 -4.99035776e-01 5.41096807e-01 -9.55363393e-01 -8.09874162e-02 2.73387104e-01 4.17753279e-01 2.82335579e-01 2.49870822e-01 -7.04454541e-01 -3.10584068e-01 4.59557384e-01 2.81759918e-01 -5.40545225e-01 1.32634282e+00 1.96244538e-01 4.34030473e-01 6.44213080e-01 1.21740198e+00 3.06369245e-01 -1.46561730e+00 -1.06159307e-01 4.12706658e-02 -3.28078866e-01 5.06483674e-01 -5.70170462e-01 -7.55835772e-01 9.57884789e-01 8.57135892e-01 5.36488831e-01 1.19160628e+00 -3.84591907e-01 5.19233167e-01 5.54618657e-01 9.64750797e-02 -1.07849967e+00 1.55281454e-01 6.10047579e-01 9.78134453e-01 -7.04524159e-01 -7.02986047e-02 2.75379959e-02 -2.15949818e-01 1.43659449e+00 1.46837339e-01 -2.26195246e-01 8.97482872e-01 4.89167869e-01 3.18360507e-01 1.86552867e-01 -6.35779262e-01 -3.98674577e-01 6.22949302e-01 7.89721012e-01 4.67991590e-01 -9.75449234e-02 2.46979326e-01 3.31143796e-01 -9.56513405e-01 -4.00912553e-01 4.39411372e-01 6.89231813e-01 -7.98762619e-01 -9.49792504e-01 -7.46763051e-01 5.14644623e-01 -6.59707665e-01 -2.91640073e-01 -3.18699211e-01 6.01849198e-01 2.92445660e-01 1.18207717e+00 1.15512475e-01 -5.08480370e-01 4.31503654e-01 1.53511316e-01 4.50090021e-01 -9.72138882e-01 -1.11330545e+00 2.40767032e-01 3.34365964e-01 -1.61581263e-01 -6.00037754e-01 -6.34508014e-01 -1.03009713e+00 1.41240865e-01 -8.26401234e-01 4.29767489e-01 6.32792771e-01 5.82469642e-01 -3.62535380e-02 8.27282071e-01 4.36287403e-01 -9.82547641e-01 -8.50587249e-01 -9.01399016e-01 -4.50934291e-01 -1.17661096e-01 9.88534093e-01 -5.17283857e-01 -7.21980453e-01 2.52315134e-01]	[15.211323738098145, 5.5144572257995605]
89579b3e-59ef-458f-b2e0-17caa5ee9f8e	discovering-the-representation-bottleneck-of	2205.07266	null	https://arxiv.org/abs/2205.07266v4	https://arxiv.org/pdf/2205.07266v4.pdf	Discovering and Explaining the Representation Bottleneck of Graph Neural Networks from Multi-order Interactions	Graph neural networks (GNNs) mainly rely on the message-passing paradigm to propagate node features and build interactions, and different graph learning tasks require different ranges of node interactions. In this work, we explore the capacity of GNNs to capture interactions between nodes under contexts with different complexities. We discover that GNNs are usually unable to capture the most informative kinds of interaction styles for diverse graph learning tasks, and thus name this phenomenon as GNNs' representation bottleneck. As a response, we demonstrate that the inductive bias introduced by existing graph construction mechanisms can prevent GNNs from learning interactions of the most appropriate complexity, i.e., resulting in the representation bottleneck. To address that limitation, we propose a novel graph rewiring approach based on interaction patterns learned by GNNs to adjust the receptive fields of each node dynamically. Extensive experiments on both real-world and synthetic datasets prove the effectiveness of our algorithm to alleviate the representation bottleneck and its superiority to enhance the performance of GNNs over state-of-the-art graph rewiring baselines.	['Stan Z. Li', 'Dragomir Radev', 'Lirong Wu', 'Siyuan Li', 'Fang Wu']	2022-05-15	null	null	null	null	['molecular-property-prediction']	['miscellaneous']	[ 2.22301304e-01 4.07578021e-01 -2.65644222e-01 -1.25219852e-01 3.74775261e-01 -5.10535657e-01 6.42887831e-01 2.85655588e-01 -2.69302398e-01 4.60629225e-01 7.34220594e-02 -4.42324221e-01 -2.44830072e-01 -1.22828758e+00 -8.99827778e-01 -6.31435394e-01 -3.86186779e-01 2.60868907e-01 4.86603409e-01 -5.30737996e-01 1.46904290e-01 4.73899692e-01 -1.22848737e+00 -8.56697187e-03 8.86125684e-01 3.30834508e-01 1.80849269e-01 5.13914883e-01 -4.04791385e-01 9.06836152e-01 -4.93576050e-01 -2.18980864e-01 2.57145345e-01 -4.86551970e-01 -6.29866958e-01 -1.84185788e-01 2.44517699e-01 -1.72454759e-01 -8.45117748e-01 9.18539703e-01 4.21065122e-01 1.91645026e-01 5.53863049e-01 -1.35043848e+00 -9.28402185e-01 1.45731580e+00 -5.70690393e-01 3.77732694e-01 1.51569650e-01 1.64886147e-01 1.34988034e+00 -4.93060917e-01 7.47657061e-01 1.22879601e+00 6.51523650e-01 8.89048815e-01 -1.46070313e+00 -7.73844302e-01 7.38014102e-01 -4.04574275e-02 -1.22235310e+00 -3.72019649e-01 1.15503943e+00 -5.21172844e-02 8.88395190e-01 1.28291726e-01 8.94010007e-01 1.38351297e+00 1.92514747e-01 6.70807302e-01 5.20670235e-01 -3.00654292e-01 1.31168023e-01 -3.15224439e-01 4.39531147e-01 8.91011894e-01 7.71078289e-01 -1.26976177e-01 -4.80806202e-01 -1.57534540e-01 1.06128371e+00 1.26405224e-01 -4.43907976e-01 -6.04319453e-01 -1.04114020e+00 7.34447837e-01 1.10951900e+00 3.68265778e-01 -2.17261553e-01 6.10149026e-01 2.38795474e-01 5.55860519e-01 2.58973330e-01 6.00082457e-01 -2.55062938e-01 4.08486485e-01 -9.36088245e-03 4.77480656e-03 8.90184820e-01 8.61176968e-01 9.03528333e-01 2.20575742e-02 -1.65727332e-01 7.22523689e-01 4.06167358e-01 2.33200908e-01 3.25294346e-01 -1.66110411e-01 4.76239979e-01 1.15429986e+00 -6.42972112e-01 -1.46454573e+00 -5.24879217e-01 -6.59029543e-01 -1.13328755e+00 -3.24824661e-01 2.68638998e-01 -9.10144374e-02 -9.16699708e-01 2.11583877e+00 2.85211414e-01 4.00706828e-01 -2.08652709e-02 5.02843082e-01 1.07647908e+00 3.46627891e-01 5.52688651e-02 1.91558659e-01 9.62035894e-01 -9.94244635e-01 -3.54133308e-01 -6.04508102e-01 9.80749369e-01 6.04945309e-02 1.01999998e+00 -1.56069338e-01 -8.14422905e-01 -2.99272835e-01 -1.10646057e+00 3.03511947e-01 -4.52361345e-01 -5.63478827e-01 1.09439278e+00 3.84762198e-01 -1.39362872e+00 8.76151025e-01 -7.88242936e-01 -5.58641374e-01 4.90873098e-01 4.94173676e-01 -4.07579601e-01 -1.73831545e-02 -1.33029950e+00 4.45061088e-01 4.39598143e-01 2.55398899e-01 -8.81549180e-01 -5.80340624e-01 -8.61521840e-01 2.20741302e-01 4.77667272e-01 -8.41131628e-01 7.20133662e-01 -9.58988488e-01 -1.16387689e+00 4.28772837e-01 2.62915015e-01 -4.14983094e-01 1.59143344e-01 1.85210228e-01 -1.53755695e-01 3.32798809e-02 -3.18841308e-01 5.50935805e-01 7.32995927e-01 -1.36351681e+00 -1.17690824e-01 -2.61817783e-01 3.43696952e-01 1.91134915e-01 -7.82688856e-01 -5.51555991e-01 -5.47949433e-01 -5.75226605e-01 3.39320272e-01 -1.07987761e+00 -4.52622831e-01 -3.10471296e-01 -7.23990977e-01 -4.43963677e-01 4.83289391e-01 1.73446551e-01 1.33113194e+00 -2.09097672e+00 3.78215939e-01 6.03645980e-01 9.97699797e-01 2.03427255e-01 -6.66790068e-01 6.77444160e-01 -5.93601093e-02 5.23662865e-01 9.35560465e-02 -5.33544645e-02 -1.73405811e-01 4.20379460e-01 -1.95146665e-01 3.49731863e-01 3.60618159e-02 1.30988503e+00 -1.15399373e+00 -2.70289719e-01 -8.55204985e-02 4.22091901e-01 -6.19288921e-01 2.04735816e-01 -2.05162033e-01 2.97855258e-01 -6.78711951e-01 2.84710228e-01 5.48606217e-01 -6.60398066e-01 6.47953391e-01 -8.03331882e-02 4.26194459e-01 4.17412609e-01 -9.29598272e-01 1.45265484e+00 -2.18042314e-01 4.89242136e-01 -5.51884361e-02 -1.05353630e+00 1.07347143e+00 -1.63641080e-01 2.85089105e-01 -6.20977879e-01 3.43280137e-02 -6.94760606e-02 4.99768674e-01 -5.38201742e-02 9.77017358e-02 5.71191072e-01 4.62979004e-02 7.13634849e-01 2.45529637e-02 3.50743622e-01 1.64917856e-01 7.17867672e-01 1.77236021e+00 -3.74062806e-01 1.51584536e-01 -4.85714823e-01 2.81122833e-01 -4.88344550e-01 4.07518536e-01 1.35012758e+00 -5.22891693e-02 1.21746063e-01 7.02356815e-01 -5.99935830e-01 -5.91031909e-01 -9.99251664e-01 3.49336445e-01 1.49218631e+00 3.62858474e-01 -5.94047785e-01 -7.01251745e-01 -8.89999270e-01 3.48129719e-02 3.04959416e-01 -9.36012506e-01 -7.24338472e-01 -7.70146191e-01 -7.61306643e-01 6.23687267e-01 4.98022616e-01 3.47353607e-01 -1.33435047e+00 -2.22974777e-01 2.24627808e-01 6.16739430e-02 -9.81499434e-01 -3.59219402e-01 2.70738989e-01 -9.79846299e-01 -1.16233242e+00 -3.22682798e-01 -9.06807959e-01 1.25119102e+00 6.37071490e-01 1.41836345e+00 9.46276546e-01 6.08302057e-02 3.67515594e-01 -5.42269826e-01 -1.09849073e-01 -4.16659594e-01 7.99385369e-01 -1.90816596e-01 -9.55943018e-02 1.49213284e-01 -1.10128069e+00 -6.92998052e-01 2.53747970e-01 -1.08065510e+00 3.39082599e-01 6.94074035e-01 7.50636041e-01 -9.85498820e-03 -6.46335930e-02 6.34110332e-01 -1.46265018e+00 1.15676928e+00 -6.02174878e-01 -3.48252773e-01 3.57237369e-01 -7.85740674e-01 5.30178726e-01 8.05071712e-01 -7.61671364e-01 -5.72240531e-01 -2.84956336e-01 2.35650748e-01 -7.23362789e-02 2.16546521e-01 7.06726134e-01 -1.08368255e-01 -5.41224718e-01 8.49759638e-01 2.24036276e-01 -1.02807008e-01 -2.18617454e-01 3.59692961e-01 -4.19900604e-02 1.62505791e-01 -5.42183876e-01 1.01703060e+00 3.85557145e-01 1.01880707e-01 -5.56134522e-01 -5.42408228e-01 -4.73985486e-02 -3.89840186e-01 -1.53211623e-01 3.35761279e-01 -5.40082932e-01 -6.75527632e-01 5.60785949e-01 -1.12327790e+00 -6.52185977e-01 -6.68877289e-02 7.26282820e-02 1.32590711e-01 2.45683759e-01 -8.33550334e-01 -3.63695472e-01 -4.00826186e-01 -9.62614894e-01 5.38878322e-01 3.07780147e-01 3.01526990e-02 -1.26604676e+00 1.89289063e-01 -3.47095788e-01 6.42380118e-01 1.55144244e-01 1.22613084e+00 -8.71590793e-01 -7.55444229e-01 1.15677729e-01 -3.51922661e-01 -3.32753509e-01 2.50096262e-01 -3.80930975e-02 -6.48406386e-01 -4.70430911e-01 -6.84719801e-01 -1.55680841e-02 1.23679101e+00 2.22020686e-01 1.20549655e+00 -3.57091546e-01 -7.62272716e-01 7.61774898e-01 1.26534116e+00 -1.76871821e-01 6.66535139e-01 1.43860921e-01 1.19580638e+00 5.13819814e-01 -3.15358549e-01 9.71900001e-02 6.25523686e-01 3.67743760e-01 7.18084455e-01 -2.83395380e-01 -2.40964219e-01 -5.77810109e-01 1.83174208e-01 1.04688966e+00 -8.15789774e-02 -6.32982314e-01 -1.02613664e+00 3.86641949e-01 -2.05069757e+00 -4.43209887e-01 1.51285967e-02 1.88464487e+00 6.81101084e-01 3.87935072e-01 -1.44479781e-01 -1.73353657e-01 8.10104489e-01 5.26142001e-01 -7.30173051e-01 -1.50886536e-01 -9.14102606e-03 4.41869125e-02 7.06665039e-01 3.38505030e-01 -6.31374121e-01 1.25604618e+00 6.11577463e+00 4.47951198e-01 -9.00942504e-01 -3.31739277e-01 3.50096136e-01 3.82532954e-01 -7.85282791e-01 1.23521492e-01 -6.31810188e-01 2.06429690e-01 6.89053237e-01 -1.51447922e-01 8.72258842e-01 4.81308401e-01 -3.15552920e-01 4.66698408e-01 -1.20018077e+00 7.92476416e-01 -5.03306463e-02 -1.34264326e+00 4.40393150e-01 1.26153663e-01 5.22004008e-01 1.55767694e-01 -1.89131364e-01 4.75772798e-01 8.93715322e-01 -1.02613509e+00 1.42319366e-01 3.13808113e-01 3.09632570e-01 -4.78413701e-01 4.62996930e-01 2.37177745e-01 -1.55705488e+00 -1.11479595e-01 -4.90046740e-01 -1.46766752e-01 -3.14593911e-01 4.64331180e-01 -9.42469060e-01 4.16326225e-01 3.35595220e-01 8.31238210e-01 -1.02013040e+00 7.21322238e-01 -4.46751714e-01 6.17088675e-01 -2.43322402e-01 -4.65188831e-01 1.97584555e-01 -6.63785636e-02 4.03726071e-01 9.57367063e-01 -8.50658584e-03 4.29613367e-02 1.12555042e-01 9.11048651e-01 -6.02287531e-01 3.59357633e-02 -1.05096769e+00 -3.02994847e-01 8.14239144e-01 1.24438226e+00 -1.05619788e+00 -6.07202644e-04 -2.04900101e-01 7.35708535e-01 1.08703625e+00 6.93309903e-01 -7.13128269e-01 -4.04348046e-01 5.67623377e-01 2.38523737e-01 1.92905456e-01 -2.41587371e-01 -4.48518246e-02 -1.11244607e+00 -2.20570683e-01 -6.89212918e-01 3.62596929e-01 -3.53304148e-01 -1.32065678e+00 7.07428038e-01 -7.19778463e-02 -6.59043014e-01 1.08402386e-01 -4.57448661e-01 -9.09281194e-01 2.57291317e-01 -1.31271100e+00 -1.10288846e+00 -5.49711764e-01 6.26227021e-01 2.15304401e-02 4.23590206e-02 5.84304869e-01 1.40318740e-02 -7.41933584e-01 7.96755075e-01 -2.44508818e-01 4.61474955e-01 3.68200719e-01 -1.11499715e+00 1.05705631e+00 7.47173190e-01 4.06293243e-01 9.27700162e-01 5.40392160e-01 -6.74147248e-01 -1.79870057e+00 -1.14664626e+00 5.59215605e-01 -6.50093257e-02 7.48050570e-01 -8.53834212e-01 -1.16101956e+00 8.82011116e-01 -1.80365276e-02 2.79298544e-01 4.89769876e-01 4.88196462e-01 -7.21320271e-01 -2.73198903e-01 -6.83342695e-01 1.08461916e+00 1.85639524e+00 -3.56416672e-01 -2.71877766e-01 1.16788901e-01 1.05078769e+00 -1.62045717e-01 -5.35872936e-01 4.49032336e-01 3.98981929e-01 -7.19485164e-01 1.14476347e+00 -7.80105114e-01 2.87589788e-01 3.79644446e-02 4.21396285e-01 -1.67845130e+00 -6.59391463e-01 -7.46221781e-01 -2.46244952e-01 1.17743170e+00 6.16637468e-01 -1.11635458e+00 7.28864551e-01 3.69887918e-01 2.34869257e-01 -7.75870621e-01 -4.56336468e-01 -4.03232098e-01 -1.55728608e-01 -1.39529660e-01 8.98571372e-01 1.05335641e+00 2.44222190e-02 7.57235408e-01 -2.19059944e-01 9.46678296e-02 6.91915095e-01 3.06800054e-03 9.89136696e-01 -1.60152352e+00 -3.49914491e-01 -6.24213159e-01 -4.35582489e-01 -1.22612071e+00 5.82201481e-01 -1.15278912e+00 -1.05094783e-01 -1.62949455e+00 2.89058506e-01 -6.73167825e-01 -6.60345197e-01 6.38205111e-01 -3.52197498e-01 -1.29620552e-01 1.31290123e-01 1.82630032e-01 -7.71683574e-01 4.32662785e-01 1.46530557e+00 -2.99856156e-01 -4.20456916e-01 -2.03782424e-01 -1.17606294e+00 5.76008260e-01 8.42052102e-01 -5.74317813e-01 -9.98666108e-01 -7.66949177e-01 7.61061549e-01 -2.69342959e-01 3.28826696e-01 -7.42396235e-01 5.08194804e-01 -2.73134857e-01 1.27256989e-01 -5.21419272e-02 -1.82937473e-01 -6.56864524e-01 5.49731180e-02 5.28889477e-01 -6.52679026e-01 5.37115410e-02 2.29186807e-02 9.63157773e-01 3.50488007e-01 9.06817764e-02 3.92229974e-01 -2.02351496e-01 -6.57826960e-01 5.51491082e-01 -2.29359269e-01 2.29471982e-01 8.13179076e-01 -1.03599794e-01 -7.62148023e-01 -4.67671603e-01 -3.45142573e-01 3.93288821e-01 4.48232502e-01 5.72125614e-01 7.53833652e-01 -1.19028676e+00 -5.92689276e-01 4.43785906e-01 2.54445761e-01 1.27487153e-01 -9.15404558e-02 5.33729970e-01 -3.81868154e-01 -4.63522375e-02 -8.13042969e-02 -4.59325165e-01 -1.02500069e+00 4.34913129e-01 3.81082565e-01 -5.70669770e-01 -8.54376316e-01 1.05477750e+00 5.39723098e-01 -5.68263710e-01 2.25638509e-01 -3.66232991e-01 -3.99937898e-01 -4.05395567e-01 2.40348190e-01 -6.02025818e-03 -5.34763858e-02 -1.96944356e-01 -3.23460996e-01 2.10916623e-01 -4.90035802e-01 3.56312126e-01 1.26783466e+00 -3.62170376e-02 -1.98887810e-01 1.59227893e-01 9.05280471e-01 -2.03401312e-01 -1.14842069e+00 -3.98516685e-01 1.17274381e-01 -3.18467349e-01 -1.97000325e-01 -3.16368520e-01 -1.32219625e+00 6.11921370e-01 1.01796702e-01 6.17780924e-01 9.99119818e-01 2.02806130e-01 7.80429602e-01 7.55549669e-01 4.00425285e-01 -6.57254338e-01 2.05364689e-01 6.05517268e-01 5.82007766e-01 -9.84499216e-01 -1.32920057e-01 -5.55529714e-01 -2.27324113e-01 9.20979619e-01 9.57587779e-01 -4.13634002e-01 7.65252590e-01 2.12278560e-01 -2.98358262e-01 -5.31061292e-01 -1.06034720e+00 -9.35363919e-02 1.23682059e-01 7.21481442e-01 2.81965315e-01 1.50315881e-01 -1.28479421e-01 3.33776891e-01 1.21384501e-01 -4.57317591e-01 4.56171781e-01 7.80586898e-01 -2.84171402e-01 -1.13936257e+00 3.27362627e-01 5.64123869e-01 -2.86390241e-02 -2.83007741e-01 -8.85681748e-01 8.30661297e-01 -4.34152067e-01 8.46431792e-01 2.25887094e-02 -6.85727119e-01 2.37784222e-01 -4.64239806e-01 4.61063325e-01 -6.69832766e-01 -7.03188896e-01 -4.51841980e-01 -3.10562737e-02 -6.73622727e-01 -1.64439961e-01 5.53117096e-02 -1.27616000e+00 -5.93080103e-01 -4.20592099e-01 7.39099309e-02 1.46703541e-01 7.42206514e-01 6.04260743e-01 8.20112705e-01 5.93750656e-01 -6.04224682e-01 -5.14659047e-01 -8.80911469e-01 -5.27920365e-01 6.40329540e-01 3.68093878e-01 -6.84572339e-01 -6.46309018e-01 -5.23574948e-01]	[7.018253326416016, 6.188572883605957]
05b36adf-9a54-4d2f-a1d4-d7c3528f2ada	shapes-of-emotions-multimodal-emotion	2112.01938	null	https://arxiv.org/abs/2112.01938v2	https://arxiv.org/pdf/2112.01938v2.pdf	Shapes of Emotions: Multimodal Emotion Recognition in Conversations via Emotion Shifts	Emotion Recognition in Conversations (ERC) is an important and active research area. Recent work has shown the benefits of using multiple modalities (e.g., text, audio, and video) for the ERC task. In a conversation, participants tend to maintain a particular emotional state unless some stimuli evokes a change. There is a continuous ebb and flow of emotions in a conversation. Inspired by this observation, we propose a multimodal ERC model and augment it with an emotion-shift component that improves performance. The proposed emotion-shift component is modular and can be added to any existing multimodal ERC model (with a few modifications). We experiment with different variants of the model, and results show that the inclusion of emotion shift signal helps the model to outperform existing models for ERC on MOSEI and IEMOCAP datasets.	['Ashutosh Modi', 'Abhinav Joshi', 'Keshav Bansal', 'Harsh Agarwal']	2021-12-03	null	https://aclanthology.org/2022.mmmpie-1.6	https://aclanthology.org/2022.mmmpie-1.6.pdf	mmmpie-coling-2022-10	['multimodal-emotion-recognition', 'multimodal-emotion-recognition']	['computer-vision', 'speech']	[-1.09637482e-02 -1.21925287e-01 -1.56766232e-02 -5.36541641e-01 -3.24062407e-01 -5.07840335e-01 6.87828600e-01 4.40618657e-02 -3.08133394e-01 5.16116142e-01 6.09469652e-01 1.64988056e-01 2.67988443e-01 -3.04458439e-01 -1.80416152e-01 -5.38177192e-01 1.10581994e-01 -2.26889357e-01 3.60960476e-02 -6.24658763e-01 1.57281131e-01 2.71291882e-01 -1.75474608e+00 7.19104767e-01 5.95141053e-01 1.04010034e+00 -1.26118094e-01 8.07198763e-01 -4.67421949e-01 9.17346179e-01 -6.79139555e-01 -5.88919699e-01 -4.24745709e-01 -6.05425537e-01 -8.32799852e-01 1.35405838e-01 -2.02327773e-01 1.29147872e-01 -3.42095196e-02 5.90662539e-01 8.65679324e-01 3.14895391e-01 4.33514953e-01 -1.56307626e+00 -2.64218241e-01 5.37208438e-01 -3.36932987e-01 -1.95924148e-01 8.95305514e-01 -4.50393856e-01 7.26363420e-01 -9.56695020e-01 6.34810328e-01 1.34377635e+00 6.60321593e-01 8.32326293e-01 -9.21382666e-01 -5.81343532e-01 3.88354927e-01 3.54460776e-01 -1.09264159e+00 -5.60629070e-01 1.17053962e+00 -6.55538440e-02 9.34380054e-01 4.84246910e-01 7.41611958e-01 1.68713844e+00 7.16261044e-02 9.83137071e-01 1.09291017e+00 -5.75852633e-01 3.46972913e-01 6.39238000e-01 2.57099092e-01 1.34664550e-01 -4.74343956e-01 -3.19991797e-01 -9.76148546e-01 -1.92285702e-01 -1.55973122e-01 -1.52373254e-01 -4.63107824e-01 -4.09878045e-02 -7.99111128e-01 6.01926267e-01 -4.92153279e-02 4.75743562e-01 -4.37816471e-01 -3.24825160e-02 8.70205045e-01 6.33308172e-01 4.60783213e-01 8.14863443e-02 -2.54226625e-01 -9.95252192e-01 -5.84172785e-01 1.52117284e-02 1.10442090e+00 7.55000830e-01 3.94327760e-01 -1.16824791e-01 -1.04559876e-01 1.22656417e+00 2.29008332e-01 3.06182802e-01 6.21152520e-01 -8.98358583e-01 1.69189125e-01 7.42523313e-01 1.50683671e-01 -1.24901450e+00 -6.26471281e-01 -3.31118032e-02 -7.37877309e-01 -1.58363238e-01 -1.02752626e-01 -4.84916061e-01 -2.67324388e-01 1.90593266e+00 2.93551654e-01 2.90953279e-01 3.32617372e-01 7.14800835e-01 1.15217650e+00 7.63214469e-01 1.71153188e-01 -3.71399909e-01 1.36067224e+00 -8.20230484e-01 -1.50164759e+00 4.42783311e-02 5.33196509e-01 -7.34225452e-01 9.24199283e-01 6.10891938e-01 -8.85752618e-01 -3.94158363e-01 -8.40038955e-01 1.19778141e-01 -7.14205682e-01 -1.14418276e-01 6.44601703e-01 1.05262792e+00 -9.87422645e-01 1.77972317e-01 -2.49121770e-01 -7.06476390e-01 -3.16040516e-01 1.10636361e-01 -5.01964450e-01 2.64967233e-01 -1.42796230e+00 6.84499800e-01 1.75872833e-01 3.50880623e-01 -1.06916562e-01 -6.75372258e-02 -7.76929975e-01 4.32822891e-02 2.58464485e-01 -9.64478627e-02 1.25755131e+00 -1.47163117e+00 -2.19807458e+00 6.63649082e-01 -4.56245601e-01 5.15721925e-02 1.66190803e-01 -3.14642757e-01 -1.01120031e+00 3.64994824e-01 -6.55657530e-01 6.98500752e-01 8.00444484e-01 -1.49846256e+00 -4.72721875e-01 -2.02177659e-01 -5.86109497e-02 2.95092434e-01 -6.71194613e-01 5.01364052e-01 -6.04806960e-01 -4.36553329e-01 -2.33815402e-01 -1.08856750e+00 1.88579246e-01 -5.33465803e-01 -9.84654352e-02 -3.54616225e-01 1.13380396e+00 -4.43406820e-01 1.56924653e+00 -2.35185766e+00 1.88318029e-01 2.55798310e-01 -8.22911039e-02 9.54927355e-02 -2.79179394e-01 8.55421364e-01 -2.03197256e-01 2.43264318e-01 1.06915280e-01 -7.59041548e-01 2.35841095e-01 2.25452825e-01 1.52248666e-02 1.31172478e-01 2.63342578e-02 8.23028564e-01 -5.66222668e-01 -4.69952226e-01 8.43111426e-02 7.26609826e-01 -5.38110375e-01 2.38173068e-01 2.30392098e-01 3.72475713e-01 -1.28706321e-01 5.07341266e-01 6.67278230e-01 1.70994941e-02 2.93693542e-01 -9.28436667e-02 -2.69637585e-01 -6.90577552e-02 -1.32510412e+00 1.40522671e+00 -5.11195481e-01 8.48204017e-01 3.30033898e-01 -1.03427756e+00 1.04521024e+00 9.63123977e-01 4.28405046e-01 -5.48218787e-01 4.95198637e-01 -2.00911537e-02 -6.15134872e-02 -8.47078204e-01 8.11505735e-01 -1.90157682e-01 -3.65130156e-01 4.14595336e-01 1.16734542e-01 3.91735807e-02 3.19767278e-03 1.92024022e-01 8.24511349e-01 -2.37160444e-01 1.96637988e-01 1.62994951e-01 8.52356315e-01 -5.54686248e-01 6.24947190e-01 5.75837553e-01 -6.41711950e-01 3.21862131e-01 5.78074932e-01 2.49928832e-02 -3.89499575e-01 -4.19049352e-01 9.89837870e-02 1.38140357e+00 1.97178468e-01 -6.15321696e-01 -6.57070398e-01 -5.20461559e-01 -3.56622487e-01 5.16374052e-01 -5.52132308e-01 -2.34294578e-01 -1.95911437e-01 -5.86495996e-01 4.82059032e-01 4.49240297e-01 5.82610369e-01 -1.34742665e+00 -5.48423111e-01 1.75849915e-01 -8.43013823e-01 -1.38067937e+00 -3.53847325e-01 5.27405329e-02 -5.77306747e-01 -4.84145939e-01 -4.70506966e-01 -6.27206683e-01 1.58606857e-01 1.84162527e-01 9.57066059e-01 -1.91985235e-01 8.91022235e-02 1.16042507e+00 -9.57691669e-01 -5.28848290e-01 -3.91106993e-01 -4.13978584e-02 -1.17108174e-01 5.82510173e-01 4.86591280e-01 -4.44503456e-01 -3.89483333e-01 3.69922966e-01 -8.73236358e-01 -1.21507226e-02 1.26311779e-01 6.16820872e-01 7.93644786e-03 7.26760691e-03 1.08286631e+00 -5.64352870e-01 1.17167318e+00 -5.74371874e-01 2.27193713e-01 2.07250476e-01 -2.69737512e-01 -3.62475097e-01 3.78712386e-01 -7.45128512e-01 -1.53876019e+00 -1.58344373e-01 -3.84728819e-01 -1.79498553e-01 -3.67289215e-01 6.69893324e-01 -3.15590322e-01 6.57886639e-02 1.09402947e-01 -6.88270479e-02 -9.09068361e-02 -3.30539048e-01 3.58389378e-01 1.26053035e+00 2.92430133e-01 -5.95297933e-01 -8.25621071e-04 3.06777716e-01 -4.17607218e-01 -1.01269615e+00 -3.09651226e-01 -5.98159194e-01 -1.17340244e-01 -9.88622129e-01 7.16865540e-01 -8.72432590e-01 -1.23040605e+00 6.06433988e-01 -1.15355337e+00 -1.43942535e-01 5.93026616e-02 6.97015047e-01 -3.06707948e-01 3.09409142e-01 -6.27605140e-01 -1.34046578e+00 -1.83167830e-01 -7.89824724e-01 9.33178246e-01 5.33922076e-01 -4.94687706e-01 -9.82000470e-01 1.36802047e-01 2.32243523e-01 6.83451414e-01 2.22140417e-01 4.67205733e-01 -6.92770660e-01 3.33341658e-01 -4.25581664e-01 1.22760899e-01 4.08490539e-01 6.40030578e-02 1.56864256e-01 -1.24101925e+00 4.87544527e-03 1.04829632e-01 -6.05708957e-01 5.55265784e-01 -2.56760549e-02 1.07698298e+00 3.30394804e-02 -1.07887067e-01 2.52547152e-02 1.02771735e+00 5.75396895e-01 8.57272983e-01 6.45820871e-02 3.76464962e-03 9.73827422e-01 2.92771339e-01 6.36080801e-01 6.07188880e-01 6.84805751e-01 2.54419476e-01 -1.05316609e-01 3.34434777e-01 1.12106949e-01 7.12008834e-01 1.26391196e+00 -1.86924055e-01 -6.88892722e-01 -5.65412939e-01 4.09180820e-01 -1.99228466e+00 -1.07990587e+00 -1.60362870e-01 1.75542498e+00 7.41978407e-01 -1.63279369e-01 9.11209583e-02 3.39994997e-01 6.96208298e-01 2.12651625e-01 -1.62747964e-01 -1.10104942e+00 -2.94066519e-01 3.77635546e-02 -2.23626480e-01 4.09891009e-01 -7.96153009e-01 7.22363710e-01 6.68296909e+00 5.15090466e-01 -1.31362641e+00 -2.67648604e-02 4.22880858e-01 -9.49186981e-02 -2.63650715e-01 -3.41067582e-01 -4.37915713e-01 4.26515996e-01 1.10120761e+00 1.58119900e-03 4.26926285e-01 6.17705286e-01 4.64492977e-01 -3.37756366e-01 -1.00123286e+00 1.39151525e+00 4.47422296e-01 -7.86209822e-01 -3.72982711e-01 -3.17420632e-01 3.33202183e-01 -4.45176661e-01 -1.38758227e-01 6.36674881e-01 -1.25616401e-01 -7.74470985e-01 5.29331684e-01 6.40627027e-01 3.63633126e-01 -7.69180119e-01 1.04813290e+00 1.06509522e-01 -1.24999368e+00 -8.40196609e-02 2.94371426e-01 -1.44250736e-01 3.84059310e-01 1.38361141e-01 -3.01379085e-01 6.98031962e-01 8.75954866e-01 6.16903603e-01 -3.49575400e-01 4.93152976e-01 -1.54219090e-03 7.32036293e-01 -1.26850098e-01 -4.26052511e-01 4.88268621e-02 -2.78649956e-01 5.27621567e-01 1.73420775e+00 3.76277596e-01 1.77901432e-01 -1.94204599e-01 3.59716237e-01 -1.50675118e-01 4.13547605e-01 -4.72970575e-01 -1.72934964e-01 4.54586267e-01 1.46353328e+00 -5.44756711e-01 -3.21833163e-01 -6.67861760e-01 1.36591649e+00 4.59594913e-02 5.38463533e-01 -9.60642576e-01 -5.07556081e-01 4.90723193e-01 -6.40772045e-01 1.92867890e-01 5.35700135e-02 4.91798343e-03 -9.58786368e-01 -7.09072426e-02 -9.15994108e-01 2.58024365e-01 -1.02358997e+00 -1.23346221e+00 6.49360836e-01 -2.09826753e-01 -1.08865154e+00 -7.29135051e-02 -4.07589048e-01 -7.87148356e-01 4.74385798e-01 -1.50580752e+00 -7.31871068e-01 -5.74652076e-01 7.93548048e-01 3.81837517e-01 5.55446632e-02 9.01438177e-01 5.02115846e-01 -7.33099401e-01 6.35114014e-01 -2.63187051e-01 -7.11786747e-02 1.08013856e+00 -1.03740454e+00 -5.72813690e-01 3.60614181e-01 -3.65840435e-01 5.39086819e-01 7.82461703e-01 -2.96867907e-01 -1.42190158e+00 -6.51931584e-01 1.11038923e+00 -1.33451745e-01 3.72915268e-01 -5.89275181e-01 -8.47778201e-01 4.75459814e-01 8.80444348e-01 -4.09448326e-01 1.18475151e+00 4.04159784e-01 -2.27263913e-01 -7.22546205e-02 -1.13699555e+00 7.18262732e-01 7.16644824e-01 -6.94175005e-01 -6.10303760e-01 -4.38067257e-01 5.80571771e-01 -5.58517687e-02 -8.64488065e-01 3.94875109e-01 9.57415760e-01 -1.07627904e+00 5.63202083e-01 -4.69607592e-01 1.37656197e-01 -3.78833413e-02 -3.03876221e-01 -1.45269525e+00 1.29459679e-01 -9.88827884e-01 -8.12322274e-02 1.63288248e+00 3.29805970e-01 -7.09405005e-01 2.54352808e-01 8.60462666e-01 -1.11819670e-01 -4.31252688e-01 -7.55867839e-01 -5.43010950e-01 -2.59257227e-01 -6.75978303e-01 4.62793589e-01 1.02048647e+00 9.53894138e-01 6.90257728e-01 -7.55312324e-01 -7.95319006e-02 -2.15501577e-01 -1.64693296e-01 9.18623447e-01 -1.25140035e+00 9.17878151e-02 -4.30657417e-01 -1.59865484e-01 -9.62994277e-01 2.62019753e-01 -5.54051638e-01 4.63515036e-02 -1.33738911e+00 1.66466311e-01 7.44091207e-03 -3.99156183e-01 3.13403755e-01 -1.45236582e-01 9.01977811e-03 3.79608065e-01 -4.31234598e-01 -8.82786095e-01 8.91517818e-01 8.38502526e-01 2.79299468e-01 -5.17990410e-01 -2.30488405e-01 -7.30446756e-01 5.99798381e-01 9.54058468e-01 -1.50032759e-01 -1.40985578e-01 3.20608020e-01 5.42940438e-01 1.90992624e-01 1.91083141e-02 -6.63604140e-01 2.55874515e-01 9.71443430e-02 4.09700945e-02 -5.60481548e-01 7.08329737e-01 -1.14974272e+00 1.52257442e-01 3.64801697e-02 -3.68592799e-01 2.59759009e-01 3.76734078e-01 4.64807898e-01 -6.35962665e-01 -1.28362209e-01 6.07633829e-01 2.61946827e-01 -6.54107749e-01 -3.92124712e-01 -1.02027869e+00 -2.71952182e-01 8.46150994e-01 -3.29245567e-01 -2.52199233e-01 -9.47615862e-01 -8.08582962e-01 4.03242052e-01 -2.27987040e-02 8.19208324e-01 5.84820330e-01 -1.43185079e+00 -4.40708667e-01 -1.42069338e-02 2.28071690e-01 -7.29852736e-01 3.75369072e-01 1.21663547e+00 2.93804526e-01 1.86768860e-01 -5.26745478e-03 -4.44722623e-01 -1.67553997e+00 2.39724502e-01 4.23507690e-01 -6.90847859e-02 -2.82983720e-01 4.69673872e-01 -2.97577977e-01 -6.03402197e-01 6.61975205e-01 -2.38594368e-01 -6.07924223e-01 5.48487723e-01 5.82389712e-01 4.31469917e-01 -3.95048745e-02 -7.14745224e-01 -4.42096651e-01 4.34818327e-01 2.54448086e-01 -3.80802423e-01 1.26158607e+00 -6.45276845e-01 -7.96022117e-02 1.07234895e+00 1.09261489e+00 3.33486319e-01 -5.72465241e-01 -1.47637755e-01 1.90140661e-02 -9.91957635e-02 -1.26540348e-01 -1.00133681e+00 -7.82012224e-01 7.07009852e-01 7.49378264e-01 5.44363678e-01 1.39441597e+00 -6.86363429e-02 6.14107966e-01 3.37998092e-01 -5.26537150e-02 -1.75079203e+00 4.48281676e-01 6.90732896e-01 1.05517375e+00 -1.21923530e+00 -5.69399238e-01 -2.86558688e-01 -1.15284169e+00 1.14948153e+00 3.96810949e-01 4.57097441e-01 9.21443164e-01 3.78133297e-01 2.74908721e-01 -2.18589559e-01 -1.08448625e+00 -2.06603512e-01 8.62748399e-02 3.86409402e-01 7.23618388e-01 -2.25993190e-02 -6.68655694e-01 1.00849724e+00 8.01322907e-02 1.10200882e-01 5.17046332e-01 9.54970777e-01 -2.31430367e-01 -1.13003421e+00 -3.71306926e-01 -1.30925015e-01 -4.54702973e-01 2.01254100e-01 -7.48719931e-01 8.03394616e-01 1.07405797e-01 1.69675398e+00 8.68223980e-02 -5.67920744e-01 5.10862708e-01 7.08040774e-01 4.62615192e-02 -2.71064453e-02 -8.81407499e-01 2.73273051e-01 4.92227882e-01 -6.83009088e-01 -1.17188168e+00 -6.51666343e-01 -1.24712706e+00 -1.99313492e-01 -3.33949059e-01 2.98437804e-01 8.42356086e-01 8.26329827e-01 6.15654171e-01 7.18313336e-01 9.77590919e-01 -6.44853115e-01 3.59541506e-01 -1.22082973e+00 -5.18999696e-01 7.11819291e-01 1.96201995e-01 -6.31042600e-01 -5.77942550e-01 1.14083186e-01]	[13.160859107971191, 5.58691930770874]
d226a141-156d-4261-9918-adf2044c2b89	robust-learning-through-cross-task	null	null	http://openaccess.thecvf.com/content_CVPR_2020/html/Zamir_Robust_Learning_Through_Cross-Task_Consistency_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Zamir_Robust_Learning_Through_Cross-Task_Consistency_CVPR_2020_paper.pdf	Robust Learning Through Cross-Task Consistency	Visual perception entails solving a wide set of tasks (e.g., object detection, depth estimation, etc). The predictions made for different tasks out of one image are not independent, and therefore, are expected to be 'consistent'. We propose a flexible and fully computational framework for learning while enforcing Cross-Task Consistency (X-TAC). The proposed formulation is based on 'inference path invariance' over an arbitrary graph of prediction domains. We observe that learning with cross-task consistency leads to more accurate predictions, better generalization to out-of-distribution samples, and improved sample efficiency. This framework also leads to a powerful unsupervised quantity, called 'Consistency Energy, based on measuring the intrinsic consistency of the system. Consistency Energy well correlates with the supervised error (r=0.67), thus it can be employed as an unsupervised robustness metric as well as for detection of out-of-distribution inputs (AUC=0.99). The evaluations were performed on multiple datasets, including Taskonomy, Replica, CocoDoom, and ApolloScape.	[' Leonidas J. Guibas', ' Jitendra Malik', ' Zhangjie Cao', ' Rohan Suri', ' Nikhil Cheerla', ' Alexander Sax', 'Amir R. Zamir']	2020-06-01	null	null	null	cvpr-2020-6	['single-view-3d-reconstruction', 'surface-normals-estimation']	['computer-vision', 'computer-vision']	[ 1.83830157e-01 -1.12172738e-01 1.46860301e-01 -5.14694571e-01 -6.40642583e-01 -4.78843451e-01 6.61057234e-01 1.64115816e-01 -3.50380689e-01 7.87569523e-01 -1.43949106e-01 2.32606083e-01 -4.63133723e-01 -3.56792629e-01 -7.25093186e-01 -8.14964414e-01 -2.47022882e-02 -6.16330542e-02 5.89051545e-01 2.56656170e-01 6.57440126e-01 4.10789877e-01 -1.66244519e+00 9.03047919e-02 9.68153417e-01 1.28458881e+00 3.40944469e-01 4.52272624e-01 4.95054156e-01 6.09653294e-01 -5.17965674e-01 -4.39159244e-01 2.39170626e-01 -2.96330392e-01 -7.13762701e-01 4.97064367e-02 5.96729219e-01 1.95354760e-01 7.97769055e-02 1.44128311e+00 4.67944115e-01 2.09375098e-01 1.04163408e+00 -1.32741654e+00 -6.05836093e-01 -7.19810873e-02 -5.78302681e-01 1.06183328e-01 2.46104658e-01 3.24645370e-01 9.43586767e-01 -7.89311945e-01 6.53160214e-01 1.17436349e+00 6.50956690e-01 3.97087514e-01 -1.38375533e+00 -6.12246394e-01 3.57192606e-02 3.93300712e-01 -1.40237498e+00 -2.98798174e-01 6.16266072e-01 -8.65577579e-01 6.34644270e-01 1.35435462e-01 3.25501651e-01 1.33233488e+00 8.51766229e-01 4.60122883e-01 1.74586523e+00 -3.07807982e-01 5.21647036e-01 3.46381694e-01 6.94341809e-02 5.13452947e-01 4.43221569e-01 2.44862005e-01 -7.03055322e-01 3.22875082e-01 6.87085271e-01 -3.76138836e-01 -2.78506130e-01 -5.63447535e-01 -1.08550715e+00 4.04496729e-01 4.23536927e-01 8.39050710e-02 4.32876125e-02 -1.90887958e-01 4.29513395e-01 4.02863413e-01 3.65824103e-01 3.97972107e-01 -3.35842401e-01 1.38438225e-01 -5.01592338e-01 1.60637543e-01 3.96591872e-01 8.23687434e-01 6.97030842e-01 -6.89953342e-02 -2.63253659e-01 7.97658205e-01 3.57918233e-01 4.47976440e-01 5.76697946e-01 -9.32183266e-01 3.32220763e-01 4.24897522e-01 1.29048809e-01 -1.03972650e+00 -6.26372755e-01 -5.46273708e-01 -1.18929970e+00 8.08848500e-01 4.52819139e-01 6.06248602e-02 -6.21374011e-01 1.99996459e+00 2.43971467e-01 -1.89996168e-01 1.27353460e-01 9.32615936e-01 4.70336348e-01 2.49204233e-01 3.27015519e-02 -3.90170783e-01 1.16113520e+00 -7.02091694e-01 -5.80625117e-01 -2.08908498e-01 1.08633034e-01 -6.59119546e-01 1.19016683e+00 8.86476159e-01 -6.46756172e-01 -1.05668902e+00 -1.04487014e+00 1.91651195e-01 -2.97013849e-01 1.33045927e-01 3.04964811e-01 3.40806693e-01 -7.86631286e-01 7.46080101e-01 -5.53386986e-01 -4.51089025e-01 5.32955647e-01 1.31053746e-01 -7.17918932e-01 9.59975198e-02 -5.44813931e-01 1.16076541e+00 7.15880692e-01 -4.25314158e-02 -9.71511066e-01 -3.12047809e-01 -4.18466091e-01 -2.31340170e-01 2.03896791e-01 -6.17461920e-01 7.32483566e-01 -1.00903594e+00 -1.21125984e+00 9.28689599e-01 4.59783450e-02 -2.16492817e-01 8.03234220e-01 -4.30773020e-01 -4.08378631e-01 -2.41935655e-01 1.79456279e-01 7.04741240e-01 1.15295219e+00 -1.47171152e+00 -5.12275994e-01 -5.31298995e-01 -1.68545887e-01 1.94807708e-01 -1.12025395e-01 -1.59406304e-01 -2.05794424e-01 -5.56746483e-01 4.07939643e-01 -8.63111377e-01 1.26537487e-01 1.83216289e-01 -6.63859248e-01 -2.22382709e-01 4.45536226e-01 -6.45200908e-01 1.14863324e+00 -2.20646191e+00 1.63212776e-01 3.74174207e-01 6.88091889e-02 -4.33545299e-02 1.40120700e-01 1.53387517e-01 -1.37497127e-01 -1.01793453e-01 -3.29996198e-01 -3.00078511e-01 2.71412823e-02 7.75619894e-02 -1.74068779e-01 6.88099146e-01 2.23000377e-01 5.30017376e-01 -6.50100768e-01 -4.97420937e-01 2.98636526e-01 6.42893463e-02 -2.66859949e-01 2.50395656e-01 -8.78388211e-02 7.95562446e-01 -1.96298048e-01 5.19907534e-01 6.93147242e-01 -3.18810403e-01 3.15074585e-02 -3.12349230e-01 -1.16987385e-01 -2.42863536e-01 -1.29617298e+00 1.63377738e+00 -2.56543934e-01 7.97259092e-01 -5.45342207e-01 -9.18247938e-01 1.06364548e+00 -9.65797976e-02 3.09862960e-02 -9.51163054e-01 1.44021690e-01 1.02347434e-01 1.36044100e-01 -7.00612485e-01 1.25591069e-01 2.31325030e-01 9.00928751e-02 2.20494017e-01 1.68201670e-01 -5.55155762e-02 3.08675934e-02 -2.39174798e-01 7.47103035e-01 3.88432831e-01 6.66360497e-01 -6.00287437e-01 5.89801013e-01 -4.47273850e-01 4.51301813e-01 6.92490876e-01 -4.21050131e-01 4.73418206e-01 6.00187540e-01 -3.24298471e-01 -1.06701612e+00 -1.23269701e+00 -6.03259265e-01 6.56275809e-01 3.98662627e-01 -4.03349809e-02 -6.54973865e-01 -6.20601833e-01 -5.95254488e-02 4.18614447e-01 -7.54242897e-01 -2.84617454e-01 6.67051449e-02 -5.07822931e-01 3.21788430e-01 5.02622962e-01 7.21445918e-01 -1.01591790e+00 -7.95657098e-01 -2.03081265e-01 -1.36579543e-01 -1.33343089e+00 -1.28520519e-01 2.35711813e-01 -8.42859209e-01 -1.25202584e+00 -1.18738823e-01 -5.00882983e-01 6.51992857e-01 5.92072494e-02 9.56804693e-01 -2.68118024e-01 -2.28740185e-01 2.66909540e-01 -2.03387201e-01 -3.24604124e-01 -1.44867674e-01 -3.92859429e-01 5.62913120e-01 2.28023291e-01 -7.46800676e-02 -5.05552530e-01 -5.51748157e-01 5.78031898e-01 -7.77907670e-01 -8.17114934e-02 5.41474164e-01 9.94123340e-01 8.94299924e-01 1.74519718e-01 5.42159557e-01 -6.87301278e-01 5.18365979e-01 -1.10036589e-01 -9.48197782e-01 4.92426902e-01 -9.34522331e-01 1.79887116e-01 7.01643527e-01 -5.04470229e-01 -1.05803883e+00 -3.79459113e-02 2.79813051e-01 -5.10360360e-01 -3.73167753e-01 -8.65862705e-03 -3.08500051e-01 -3.47636163e-01 1.04333937e+00 1.24067977e-01 -1.50465235e-01 -2.82101572e-01 3.41123939e-02 2.28210256e-01 6.42114937e-01 -4.67521340e-01 7.70469904e-01 3.06155264e-01 4.19018894e-01 -8.29681754e-01 -8.70011091e-01 -3.92324924e-01 -7.21038282e-01 -5.50595939e-01 9.74908471e-01 -7.68025160e-01 -9.78192627e-01 7.38573194e-01 -1.19889057e+00 -2.41774410e-01 -5.86490035e-02 7.08303452e-01 -7.15738773e-01 4.64890003e-01 3.10952719e-02 -9.93316650e-01 -8.66782516e-02 -9.03174818e-01 8.02733541e-01 2.07715765e-01 -2.64749855e-01 -1.15706515e+00 1.72177494e-01 5.32311201e-02 -6.08771704e-02 5.11269510e-01 8.06425452e-01 -5.50791860e-01 -5.22139728e-01 -1.27573088e-02 -5.47609508e-01 7.80890524e-01 1.39530119e-03 1.72708556e-01 -1.47756660e+00 -4.17236388e-01 1.31988779e-01 -5.54590166e-01 7.58211017e-01 4.92286116e-01 1.34070909e+00 -2.67681777e-01 -1.59546942e-01 5.60282767e-01 1.67498922e+00 -1.14957571e-01 7.91858435e-01 2.89776415e-01 5.30278563e-01 8.97287607e-01 9.20146227e-01 4.85696465e-01 9.19095147e-03 6.99820757e-01 6.67821169e-01 3.07974547e-01 2.84406263e-02 -5.68686724e-02 4.71818864e-01 5.63841462e-01 -1.65234521e-01 -1.82471886e-01 -8.04754317e-01 1.72658324e-01 -1.80844748e+00 -9.77772057e-01 -2.37071380e-01 2.42084503e+00 4.94580328e-01 5.59782922e-01 4.83174622e-02 2.50011057e-01 6.54042840e-01 -2.20167533e-01 -6.45193696e-01 -2.81178594e-01 -1.81174234e-01 3.88722084e-02 2.88126022e-01 4.11810756e-01 -1.21970034e+00 7.33676016e-01 6.10929585e+00 9.66411173e-01 -8.97184789e-01 5.53918593e-02 6.72126472e-01 3.61231834e-01 -3.33484523e-02 -1.08494028e-01 -7.17757404e-01 6.87858939e-01 5.17945051e-01 1.60240993e-01 1.28222093e-01 8.79962385e-01 2.50032414e-02 -4.61548835e-01 -1.21638250e+00 1.10189724e+00 2.57591426e-01 -8.15296769e-01 -2.29218140e-01 -9.87940207e-02 9.25327480e-01 -2.32028559e-01 2.19025895e-01 -1.61083024e-02 8.35109781e-03 -9.18628573e-01 8.51737261e-01 8.89402509e-01 9.65170085e-01 -6.07913375e-01 6.58500075e-01 5.24064541e-01 -9.57352102e-01 -2.24986076e-01 -5.69161654e-01 1.21400267e-01 -3.63510162e-01 7.44139373e-01 -4.61001962e-01 5.19641876e-01 1.07186162e+00 8.80470037e-01 -8.69965732e-01 1.19613135e+00 -2.66226262e-01 6.67724460e-02 -1.04966864e-01 9.31571200e-02 -2.01095879e-01 -2.00677201e-01 5.02918184e-01 1.15828872e+00 2.22893491e-01 -1.89590245e-01 -4.34824973e-02 8.46352518e-01 3.04772198e-01 2.00434458e-02 -7.44966805e-01 5.25804639e-01 3.55769038e-01 1.14431572e+00 -6.87197626e-01 -1.20928310e-01 -1.54929712e-01 1.00580513e+00 5.03727853e-01 2.64240533e-01 -9.13844705e-01 -1.97368637e-01 5.40964365e-01 -3.89764041e-01 1.00693367e-01 -1.03428811e-01 -5.73371649e-01 -1.07058334e+00 2.77752161e-01 -7.63526022e-01 2.15803012e-01 -9.71867383e-01 -1.65543735e+00 6.17013097e-01 -1.02173509e-02 -1.75470805e+00 8.53704754e-03 -9.45194185e-01 -5.54482698e-01 7.23405480e-01 -1.51204360e+00 -9.44639087e-01 -7.47583091e-01 8.58707666e-01 2.67898768e-01 -2.95562327e-01 6.72956347e-01 1.07692145e-02 -7.26689637e-01 6.32689178e-01 1.24055192e-01 -2.59871244e-01 9.60650086e-01 -1.23950756e+00 -9.53172222e-02 1.05465281e+00 9.10461992e-02 3.20234895e-01 7.48960316e-01 -4.52546030e-01 -8.70318115e-01 -1.12779951e+00 4.60047394e-01 -7.16930151e-01 5.80590725e-01 -1.79873258e-01 -1.05564332e+00 3.37002188e-01 -5.66230975e-02 1.40094101e-01 5.44422507e-01 -5.71866147e-02 -7.20215976e-01 -5.18006444e-01 -1.03022420e+00 3.56450409e-01 1.19314706e+00 -7.38727748e-01 -4.78341520e-01 4.80037451e-01 3.03202689e-01 -3.18094343e-01 -8.86454523e-01 4.97460365e-01 7.65843093e-01 -1.53396940e+00 8.09786141e-01 -4.45810258e-01 4.85624939e-01 -2.76837885e-01 -4.05270994e-01 -1.04418004e+00 -4.76750076e-01 -2.68842876e-01 -6.70604706e-02 1.13537025e+00 2.44477019e-01 -6.60110414e-01 2.24418536e-01 1.78867429e-01 -2.39783749e-01 -5.21627545e-01 -1.08120012e+00 -1.14914000e+00 -2.04865038e-01 -6.35307133e-01 6.52881637e-02 8.87809098e-01 -2.09214628e-01 2.29747549e-01 -3.41535896e-01 5.17909169e-01 9.46547389e-01 -7.29814097e-02 7.86672771e-01 -1.41556597e+00 -5.00936270e-01 -4.64071184e-01 -6.93913579e-01 -8.28925133e-01 1.92162678e-01 -7.53403783e-01 2.44644821e-01 -1.09449339e+00 1.83196574e-01 -3.92197669e-01 -4.45270479e-01 3.67592484e-01 -3.10273580e-02 3.43050361e-01 -1.65996403e-02 5.09064019e-01 -7.20110655e-01 3.66662502e-01 1.32750952e+00 1.14619747e-01 4.73713987e-02 1.26473337e-01 -3.12307209e-01 9.35268998e-01 8.87106836e-01 -2.86959857e-01 -4.51667756e-01 -2.16014802e-01 1.50533915e-01 -9.37779099e-02 7.95834780e-01 -1.52203631e+00 -5.79704307e-02 -3.11226279e-01 6.28417432e-01 -1.89032972e-01 2.46432647e-01 -9.12565410e-01 1.79665238e-01 5.14962375e-01 -3.22049499e-01 3.61576416e-02 2.01640561e-01 6.79732382e-01 -1.37925521e-01 -2.82768130e-01 1.30898309e+00 1.94312871e-01 -9.64749396e-01 6.41559483e-03 1.44026667e-01 6.56505153e-02 1.15377069e+00 -4.89554614e-01 -4.81762111e-01 -8.29320252e-02 -5.80315650e-01 -1.37428761e-01 4.25872117e-01 3.42296988e-01 7.36335635e-01 -1.23701847e+00 -5.10963738e-01 3.52161437e-01 5.11430204e-01 -1.94208175e-01 3.30983818e-01 8.75034928e-01 -2.65844971e-01 2.04819009e-01 -6.45698667e-01 -1.21416163e+00 -1.17381144e+00 5.09771585e-01 2.31367305e-01 -5.24641983e-02 -3.52684438e-01 8.47421229e-01 3.61654133e-01 -7.39740357e-02 3.23473662e-01 -4.09250975e-01 -2.12657750e-01 4.18526605e-02 2.27015093e-01 5.03093541e-01 -2.93317381e-02 -5.11260271e-01 -3.97871256e-01 9.65439379e-01 3.05862218e-01 1.97319970e-01 8.65320325e-01 -2.49070391e-01 -9.84409824e-02 7.16879845e-01 9.49255824e-01 -1.93925843e-01 -1.67524624e+00 -1.39669016e-01 9.11822990e-02 -6.54472470e-01 -3.47683519e-01 -8.80458355e-01 -6.03573799e-01 9.29048717e-01 1.02111411e+00 2.48102307e-01 1.20971656e+00 -1.00039355e-01 -1.39883026e-01 4.09608155e-01 3.95750523e-01 -1.15275764e+00 6.50444567e-01 5.20663083e-01 1.17881799e+00 -1.62417376e+00 1.82815045e-01 -2.57583171e-01 -9.44592774e-01 9.36702073e-01 1.04847205e+00 3.17884088e-02 5.35759747e-01 -6.10357746e-02 -2.48282582e-01 1.38567209e-01 -7.25178838e-01 -6.73425719e-02 6.85863078e-01 8.58347356e-01 1.08511314e-01 3.46369147e-02 5.07183336e-02 3.11139554e-01 -8.35992768e-02 -2.60237485e-01 1.74322531e-01 3.50843877e-01 -5.41858017e-01 -5.88366389e-01 -2.62685150e-01 3.93281817e-01 -1.67968068e-02 2.60492176e-01 -1.33973375e-01 8.14179003e-01 4.07997668e-01 7.26818085e-01 -1.44311693e-02 -5.52653432e-01 2.73306489e-01 -4.31181230e-02 6.78917706e-01 -3.92037243e-01 -7.18825459e-02 -1.38085276e-01 -2.29690313e-01 -7.13165402e-01 -6.07131481e-01 -6.51262522e-01 -8.21815372e-01 -1.64721802e-01 -3.06864172e-01 -2.96991289e-01 4.72999215e-01 9.96882319e-01 1.71062633e-01 4.76880848e-01 7.30302632e-01 -6.32865727e-01 -5.97724855e-01 -9.85279262e-01 -6.48467481e-01 6.85051203e-01 3.40800166e-01 -9.55944955e-01 -4.22139347e-01 9.17850658e-02]	[9.673563957214355, 2.0581531524658203]
794b2da7-a75c-4c25-8534-396159df8004	cardigraphormer-unveiling-the-power-of-self	2307.00859	null	https://arxiv.org/abs/2307.00859v2	https://arxiv.org/pdf/2307.00859v2.pdf	CardiGraphormer: Unveiling the Power of Self-Supervised Learning in Revolutionizing Drug Discovery	In the expansive realm of drug discovery, with approximately 15,000 known drugs and only around 4,200 approved, the combinatorial nature of the chemical space presents a formidable challenge. While Artificial Intelligence (AI) has emerged as a powerful ally, traditional AI frameworks face significant hurdles. This manuscript introduces CardiGraphormer, a groundbreaking approach that synergizes self-supervised learning (SSL), Graph Neural Networks (GNNs), and Cardinality Preserving Attention to revolutionize drug discovery. CardiGraphormer, a novel combination of Graphormer and Cardinality Preserving Attention, leverages SSL to learn potent molecular representations and employs GNNs to extract molecular fingerprints, enhancing predictive performance and interpretability while reducing computation time. It excels in handling complex data like molecular structures and performs tasks associated with nodes, pairs of nodes, subgraphs, or entire graph structures. CardiGraphormer's potential applications in drug discovery and drug interactions are vast, from identifying new drug targets to predicting drug-to-drug interactions and enabling novel drug discovery. This innovative approach provides an AI-enhanced methodology in drug development, utilizing SSL combined with GNNs to overcome existing limitations and pave the way for a richer exploration of the vast combinatorial chemical space in drug discovery.	['Arnab Mukherjee', 'Abhijit Gupta']	2023-07-03	null	null	null	null	['self-supervised-learning', 'drug-discovery']	['computer-vision', 'medical']	[ 6.04503214e-01 8.20918307e-02 -8.40060830e-01 1.55236244e-01 -2.46691599e-01 -7.20908403e-01 2.56403387e-01 7.25169003e-01 2.89815422e-02 1.02747226e+00 -9.78399441e-02 -8.48511934e-01 -5.22697747e-01 -8.88349056e-01 -4.22191620e-01 -8.23585212e-01 -4.87689108e-01 4.59201992e-01 -3.97894345e-02 -2.86511451e-01 2.11791053e-01 1.03560710e+00 -9.06417012e-01 9.00716335e-02 1.15599239e+00 7.11368561e-01 9.02549084e-03 2.32158631e-01 -1.72151774e-01 7.01502383e-01 -3.76810521e-01 -3.09763461e-01 -3.29876132e-02 -3.93805444e-01 -5.99721134e-01 -2.67983407e-01 3.60951751e-01 3.93325180e-01 -3.61209959e-01 8.31651330e-01 6.59834266e-01 -1.60659283e-01 5.85681558e-01 -8.05316448e-01 -8.37865889e-01 2.49024734e-01 -4.74467784e-01 2.77665019e-01 5.39617956e-01 3.98361623e-01 1.20447123e+00 -8.32305193e-01 6.01030827e-01 9.71436858e-01 7.86760330e-01 5.26277900e-01 -1.35647535e+00 -7.57394135e-01 -1.96551532e-02 2.22192660e-01 -1.22968853e+00 -1.24477930e-01 6.68148756e-01 -5.20282388e-01 1.14862955e+00 3.37091595e-01 7.61667430e-01 1.03923237e+00 4.75009114e-01 5.56744814e-01 7.37356961e-01 -2.16940731e-01 5.10648906e-01 -2.53639907e-01 7.13248327e-02 8.28049600e-01 5.74676871e-01 2.00221673e-01 -4.35802728e-01 -6.05325282e-01 5.67444146e-01 5.02228677e-01 -3.70548487e-01 -3.37896138e-01 -9.95604992e-01 1.00360489e+00 6.89970613e-01 3.75926346e-01 -4.90238696e-01 -8.42340887e-02 1.86349079e-01 3.68413441e-02 2.39579424e-01 1.36900222e+00 -5.46924651e-01 3.07903498e-01 -5.28522253e-01 1.27225429e-01 8.50444734e-01 3.67724240e-01 6.13406003e-01 3.09047729e-01 1.96902663e-01 5.74621379e-01 7.78103322e-02 2.65767336e-01 3.11887920e-01 -2.44516894e-01 9.20762047e-02 1.10485435e+00 -4.90864247e-01 -1.18683827e+00 -7.84566939e-01 -7.84682870e-01 -9.59700108e-01 -2.49067933e-04 1.96359545e-01 -3.01330052e-02 -8.67188692e-01 1.49698853e+00 4.42067772e-01 1.70469120e-01 -2.58036032e-02 4.63605255e-01 9.17564929e-01 4.59909022e-01 5.44422150e-01 -3.61547619e-01 1.23803258e+00 -6.74494624e-01 -4.33453172e-01 -4.46436629e-02 7.70855188e-01 -4.10915583e-01 5.92765391e-01 4.17976141e-01 -7.60477722e-01 -5.33192372e-03 -1.21630311e+00 3.07016075e-01 -8.22661877e-01 -3.67163509e-01 1.38374114e+00 6.34114981e-01 -6.27335668e-01 1.17407441e+00 -4.58321184e-01 -4.29089107e-02 1.05693030e+00 1.01155210e+00 -5.93187213e-01 -3.48109871e-01 -1.19508374e+00 9.42826629e-01 4.67795849e-01 -3.63875553e-02 -6.19369149e-01 -1.08810401e+00 -8.60444069e-01 -3.28699388e-02 6.92023277e-01 -7.94404387e-01 4.96696174e-01 -4.97468174e-01 -1.36835146e+00 1.74265727e-01 2.60994583e-01 -5.37696660e-01 -1.15735300e-01 2.59076864e-01 -5.40661514e-01 9.48226377e-02 -8.44827667e-02 1.79258153e-01 4.42227036e-01 -6.97100043e-01 -1.08378887e-01 -6.52717054e-01 -2.91691065e-01 2.99036931e-02 -3.33706170e-01 -4.04828995e-01 1.25035107e-01 -5.48340440e-01 -7.65249804e-02 -8.48183393e-01 -6.99494720e-01 -3.16950709e-01 -6.04338169e-01 -3.37269157e-01 8.61292958e-01 -2.08005354e-01 1.36411512e+00 -1.72967482e+00 3.64508092e-01 5.81666708e-01 9.27950859e-01 7.47175932e-01 -3.51141155e-01 9.02370572e-01 -4.16167855e-01 2.54238725e-01 -2.53023177e-01 4.78102207e-01 -4.69948262e-01 3.57753318e-03 1.61316708e-01 5.21610916e-01 3.44037294e-01 1.29157960e+00 -1.28406537e+00 7.62718963e-03 2.21127257e-01 5.09693921e-01 -4.76539433e-01 -1.35663316e-01 -6.02034330e-01 5.72114289e-01 -8.10767591e-01 1.13927960e+00 4.37552005e-01 -8.27366650e-01 5.80167532e-01 -6.60479814e-02 8.74843970e-02 5.02152182e-02 -5.91645718e-01 1.32642365e+00 -4.49822992e-02 2.24463597e-01 -6.29592717e-01 -9.11123335e-01 9.09507573e-01 2.46792674e-01 9.06209350e-01 -8.12214077e-01 -1.88768748e-02 2.42886245e-01 2.83448786e-01 -4.09357280e-01 -1.94164872e-01 -1.61733314e-01 2.99726605e-01 1.00975357e-01 8.12541917e-02 1.72964528e-01 2.64294654e-01 2.26221979e-02 1.57547796e+00 -9.43759978e-02 7.97248244e-01 -1.54273272e-01 5.06905973e-01 1.62190020e-01 4.00811672e-01 3.84010911e-01 2.59160429e-01 -4.66254316e-02 5.25402665e-01 -9.20792937e-01 -8.54855835e-01 -8.97211611e-01 -2.45954707e-01 7.96084404e-01 -1.14950687e-01 -6.97763979e-01 -3.75858366e-01 -8.09879363e-01 2.26003483e-01 1.59237698e-01 -7.06534088e-01 -4.99809980e-01 -3.40830922e-01 -9.43996906e-01 3.66979897e-01 5.05975410e-02 1.11528173e-01 -9.19624746e-01 2.24643856e-01 5.23364127e-01 5.90450287e-01 -8.21026564e-01 -3.42894465e-01 3.04533660e-01 -9.00802255e-01 -1.67846489e+00 -4.90412325e-01 -7.32476175e-01 6.39422178e-01 2.49601826e-01 1.05730987e+00 1.55987501e-01 -8.07965040e-01 -1.75585642e-01 -1.90841228e-01 -6.14671588e-01 -4.76571769e-01 1.59858391e-01 -1.57512072e-02 -2.30653554e-01 3.55556518e-01 -8.86643410e-01 -8.19446862e-01 9.79677588e-02 -7.24872231e-01 -2.69444346e-01 8.75917554e-01 9.39069629e-01 6.05731547e-01 1.59223676e-02 1.01458073e+00 -1.36494231e+00 6.74830258e-01 -8.63298059e-01 -4.71334815e-01 2.33384743e-01 -8.78129780e-01 1.07658923e-01 1.00147462e+00 -6.00541949e-01 -1.83818206e-01 2.45917946e-01 -2.17162460e-01 -2.63489783e-01 9.55039263e-02 8.99945617e-01 -1.94127992e-01 -6.07743859e-01 9.98212695e-01 6.81355819e-02 3.33622277e-01 -4.11212176e-01 2.05272406e-01 3.35688531e-01 1.46101847e-01 -1.89550653e-01 5.74608028e-01 1.56205937e-01 6.84957206e-01 -1.06247473e+00 -5.36874592e-01 -3.58564973e-01 -2.88312346e-01 2.39420459e-01 6.46187484e-01 -6.08601511e-01 -1.36009598e+00 -2.01384738e-01 -8.55740130e-01 1.22576363e-01 -1.22231089e-01 4.47613746e-01 -9.43857804e-02 5.00470698e-01 -3.38323802e-01 -3.86852473e-01 -5.72668910e-01 -1.07899749e+00 5.55827379e-01 2.34843403e-01 -3.31949055e-01 -1.16154206e+00 3.31512928e-01 2.98984557e-01 2.48204142e-01 7.84216702e-01 1.46844053e+00 -1.04583859e+00 -7.84981906e-01 -4.81699973e-01 -1.13623142e-01 -8.61616619e-03 5.85506380e-01 -2.28437141e-01 -6.41095638e-01 -2.26769999e-01 -4.86866266e-01 -7.93091953e-02 6.61419749e-01 3.49100739e-01 1.24497747e+00 -6.70142472e-01 -6.37742758e-01 5.62079310e-01 1.36741161e+00 8.15746903e-01 6.95196927e-01 1.60260454e-01 9.71062064e-01 1.94598183e-01 6.61538467e-02 4.52736437e-01 4.28598970e-02 5.06744087e-01 6.95429862e-01 -5.71934164e-01 -7.02958182e-02 -3.29887092e-01 -1.88536271e-01 3.59035641e-01 -2.35794544e-01 -2.78243214e-01 -9.24174964e-01 1.03723975e-02 -1.50988936e+00 -9.19915378e-01 -6.01761714e-02 2.12536383e+00 1.03179383e+00 -1.09979421e-01 2.20583841e-01 -1.56788863e-02 4.97416764e-01 -1.14519015e-01 -1.03705370e+00 -3.89930725e-01 -2.82812536e-01 7.75986671e-01 4.40830827e-01 2.45068923e-01 -9.41260278e-01 1.03108728e+00 6.44169283e+00 9.24290240e-01 -1.23733389e+00 -5.79679668e-01 7.70450115e-01 3.75169247e-01 -3.57383639e-01 -4.13905233e-02 -5.43742537e-01 3.71081024e-01 9.06115890e-01 -1.89237744e-01 4.62373555e-01 8.55507493e-01 2.31055960e-01 2.14530349e-01 -1.22455215e+00 8.17630172e-01 -1.04414329e-01 -2.21243978e+00 5.35750747e-01 3.90184283e-01 7.15858638e-01 -4.01709862e-02 1.71049759e-01 -1.25738516e-01 2.87679911e-01 -1.63799322e+00 -4.74554896e-01 2.58401871e-01 8.71202946e-01 -8.54424536e-01 5.34491420e-01 -3.37980315e-02 -9.33050334e-01 -1.99328512e-01 -1.36431828e-01 -1.25324028e-02 -2.84360588e-01 6.10360563e-01 -1.26635373e+00 6.85640216e-01 1.55448727e-02 1.19163120e+00 -5.00578046e-01 1.25848222e+00 1.68316141e-01 3.67382944e-01 8.89469758e-02 -3.48766237e-01 2.70085335e-01 -3.77297908e-01 5.27977765e-01 9.38189030e-01 -1.22834124e-01 1.64684594e-01 3.30318302e-01 7.21459329e-01 -2.29325786e-01 3.22459549e-01 -9.07910705e-01 -7.80566633e-01 4.98974591e-01 1.02926159e+00 -6.60088062e-01 -1.05566263e-01 -2.87016630e-01 4.84902054e-01 7.77568892e-02 3.77213240e-01 -6.10866845e-01 -5.00627458e-01 6.41142607e-01 2.39983365e-01 9.21687782e-02 -3.88812833e-02 -1.52151898e-01 -6.51886523e-01 -5.75037062e-01 -1.33473635e+00 4.83549386e-01 -1.72487378e-01 -1.34091306e+00 7.59972990e-01 -4.48330849e-01 -1.22373319e+00 1.24121584e-01 -8.46692324e-01 -4.64992821e-01 5.53084671e-01 -1.43579853e+00 -9.59955513e-01 3.51818092e-02 4.45905596e-01 3.07356566e-01 -3.97530854e-01 1.06548595e+00 3.39067847e-01 -7.99552619e-01 4.27875936e-01 3.14363688e-01 -3.40633661e-01 2.32114866e-01 -9.77907598e-01 2.56719828e-01 1.24134924e-02 1.64342567e-01 7.74468780e-01 4.23347116e-01 -7.51934111e-01 -1.84612489e+00 -9.77866828e-01 5.43727756e-01 -2.95062989e-01 8.40952098e-01 -2.06486508e-01 -8.05930674e-01 1.64431989e-01 -2.96981961e-01 -2.83466130e-02 1.22967029e+00 1.17795311e-01 -5.48842072e-01 -1.34466672e-02 -9.27563012e-01 7.91511297e-01 9.46039557e-01 -3.58266175e-01 -7.81466588e-02 9.11129713e-01 8.01435232e-01 -2.36017779e-01 -1.27838778e+00 6.53666794e-01 4.06283140e-01 -5.52234054e-01 1.22418308e+00 -1.06372941e+00 3.07013661e-01 -2.87106603e-01 2.58165658e-01 -1.06946051e+00 -5.09113252e-01 -1.06086981e+00 -3.04690808e-01 3.67820859e-01 8.73170614e-01 -8.34002197e-01 1.09039164e+00 4.50834572e-01 -1.87773705e-01 -1.50072193e+00 -7.71619678e-01 -6.76888049e-01 -4.67794985e-02 1.48737654e-01 7.27476299e-01 1.16258144e+00 3.39742333e-01 7.86582530e-01 -3.18807095e-01 -2.47518986e-01 4.52041864e-01 -1.34430947e-02 4.11476046e-01 -1.54995000e+00 -5.90267956e-01 -5.86460114e-01 -7.16783822e-01 -5.54778576e-01 -5.25050573e-02 -1.15274513e+00 -7.88776696e-01 -1.46870387e+00 2.73238659e-01 -4.56662029e-01 -3.21690112e-01 7.29534984e-01 5.58560248e-03 1.27439961e-01 -4.60269041e-02 1.34594083e-01 -4.95579243e-01 2.69990176e-01 1.59098995e+00 -5.01830220e-01 -5.03741980e-01 1.78402573e-01 -1.11217022e+00 3.85301232e-01 7.61608183e-01 -2.73141235e-01 -4.27087307e-01 3.60326916e-01 3.57218176e-01 -1.79928336e-02 4.92380653e-03 -6.49639785e-01 1.38296291e-01 -4.00096178e-01 5.95589340e-01 -1.65854126e-01 1.16024785e-01 -6.88672960e-01 4.98599082e-01 8.21174085e-01 -7.52161965e-02 -1.76068097e-01 3.46100807e-01 8.02351356e-01 -6.68224692e-02 1.77005127e-01 7.12385297e-01 -2.65441239e-01 -4.10223126e-01 9.44541872e-01 -1.63932756e-01 -1.97220013e-01 1.11327529e+00 -7.38285184e-01 -4.58593667e-01 1.61268219e-01 -8.27732146e-01 3.62820439e-02 1.98453322e-01 2.37194777e-01 8.39748740e-01 -9.75882351e-01 -2.99825072e-01 2.15430260e-01 2.48793080e-01 -1.00829385e-01 2.03811496e-01 7.34198391e-01 -5.71225166e-01 6.61257863e-01 -6.44621477e-02 -4.08496559e-01 -1.23434293e+00 8.00706267e-01 1.86683327e-01 -2.87762433e-01 -5.54421008e-01 7.19984770e-01 2.33448505e-01 -1.06642105e-01 1.68762311e-01 2.78290749e-01 -2.98198372e-01 -9.73125994e-02 4.40075964e-01 3.50247234e-01 1.20172188e-01 -1.13714397e-01 -4.73603070e-01 5.82742393e-01 -4.83718872e-01 9.76854801e-01 1.71419501e+00 6.18580401e-01 -2.80138820e-01 -1.85733005e-01 1.14410782e+00 -8.51780921e-02 -1.00124240e+00 1.56424078e-03 2.87524551e-01 -1.64538652e-01 6.01609498e-02 -1.04718864e+00 -7.42280960e-01 4.58764941e-01 3.36543828e-01 7.74113014e-02 8.50812197e-01 1.75269127e-01 7.43312597e-01 5.09671330e-01 2.25104347e-01 -6.26610219e-01 5.62098324e-01 3.59256923e-01 7.37349391e-01 -1.07724440e+00 5.08912146e-01 -5.35862148e-01 -2.98437983e-01 1.28092003e+00 2.94313878e-01 9.28815976e-02 5.13589203e-01 -7.38075897e-02 -5.56770205e-01 -7.54610240e-01 -5.75932741e-01 -8.90593454e-02 4.81164306e-01 7.32619405e-01 6.29017532e-01 -4.60892310e-03 -2.77707696e-01 2.11902007e-01 3.00972939e-01 -9.60046891e-03 1.45287856e-01 8.78999591e-01 -4.67552274e-01 -1.61360931e+00 -9.95419454e-03 7.09911048e-01 -4.89623904e-01 -6.58733964e-01 -8.44708264e-01 8.06126952e-01 8.30580965e-02 7.43787110e-01 -5.59361398e-01 -4.91368890e-01 1.46879256e-01 -3.86784166e-01 5.25461614e-01 -7.43254483e-01 -5.75121522e-01 -2.54682787e-02 3.79697755e-02 -4.78548437e-01 -1.01648331e-01 -1.03495397e-01 -1.25666523e+00 -3.82999897e-01 -6.97166622e-01 2.37130627e-01 6.59790993e-01 8.62254798e-01 9.55869436e-01 4.83865440e-01 8.92499983e-01 -4.95234996e-01 -2.43399486e-01 -3.53107274e-01 -4.94972229e-01 -5.96050881e-02 3.74917716e-01 -5.71768343e-01 -4.19311784e-02 -3.97691250e-01]	[5.16267204284668, 5.831884860992432]
d42b0a30-d1f7-41e3-b591-6fc2a33cc4cf	unsupervised-geometry-aware-representation	1804.01110	null	http://arxiv.org/abs/1804.01110v1	http://arxiv.org/pdf/1804.01110v1.pdf	Unsupervised Geometry-Aware Representation for 3D Human Pose Estimation	Modern 3D human pose estimation techniques rely on deep networks, which require large amounts of training data. While weakly-supervised methods require less supervision, by utilizing 2D poses or multi-view imagery without annotations, they still need a sufficiently large set of samples with 3D annotations for learning to succeed. In this paper, we propose to overcome this problem by learning a geometry-aware body representation from multi-view images without annotations. To this end, we use an encoder-decoder that predicts an image from one viewpoint given an image from another viewpoint. Because this representation encodes 3D geometry, using it in a semi-supervised setting makes it easier to learn a mapping from it to 3D human pose. As evidenced by our experiments, our approach significantly outperforms fully-supervised methods given the same amount of labeled data, and improves over other semi-supervised methods while using as little as 1% of the labeled data.	['Pascal Fua', 'Mathieu Salzmann', 'Helge Rhodin']	2018-04-03	unsupervised-geometry-aware-representation-1	http://openaccess.thecvf.com/content_ECCV_2018/html/Helge_Rhodin_Unsupervised_Geometry-Aware_Representation_ECCV_2018_paper.html	http://openaccess.thecvf.com/content_ECCV_2018/papers/Helge_Rhodin_Unsupervised_Geometry-Aware_Representation_ECCV_2018_paper.pdf	eccv-2018-9	['weakly-supervised-3d-human-pose-estimation']	['computer-vision']	[ 1.08478732e-01 4.91843015e-01 -5.25926590e-01 -6.60334468e-01 -7.64212608e-01 -6.10194981e-01 4.11681056e-01 -6.21425211e-02 -4.80914056e-01 5.61325133e-01 3.09152395e-01 1.76774144e-01 5.42978108e-01 -6.81183100e-01 -1.23956800e+00 -1.97451085e-01 1.24490336e-01 9.43892360e-01 1.22644743e-02 -7.04724118e-02 -2.34156176e-01 1.50570095e-01 -1.35899365e+00 3.42848301e-02 3.57908577e-01 8.62648904e-01 9.07727331e-02 5.16491830e-01 3.87168676e-01 5.58411241e-01 -9.83871743e-02 -3.31756175e-01 4.78252172e-01 -3.83146554e-01 -8.57640028e-01 5.16747117e-01 8.71516645e-01 -8.27853143e-01 -4.22236085e-01 7.63318717e-01 5.51000953e-01 -6.04176596e-02 5.97574294e-01 -8.52143466e-01 -3.34978461e-01 2.21772805e-01 -5.46932340e-01 -1.77171573e-01 7.17617631e-01 -3.64374518e-02 9.28900957e-01 -8.16538036e-01 7.26168394e-01 1.15672934e+00 8.04443121e-01 6.60738885e-01 -1.34991741e+00 -3.05408865e-01 2.75519013e-01 -3.21473747e-01 -1.27920651e+00 -3.59108835e-01 9.69828069e-01 -5.69967568e-01 7.97150135e-01 -2.20735326e-01 1.06749547e+00 1.12425005e+00 -9.03324410e-02 1.08300984e+00 9.29139972e-01 -4.08962190e-01 -4.67786901e-02 -2.85243848e-03 -1.85677007e-01 1.01604319e+00 3.04886580e-01 7.40330964e-02 -5.93452811e-01 1.17571734e-01 1.10768366e+00 6.26544356e-02 -1.28599033e-01 -1.00799620e+00 -1.13255382e+00 7.99640536e-01 6.19798183e-01 -1.61618561e-01 -1.70426384e-01 3.21931988e-01 3.99343550e-01 1.02443621e-01 8.42861056e-01 2.75305927e-01 -6.28699720e-01 5.96229397e-02 -9.93748844e-01 2.44248390e-01 5.49142182e-01 9.70023990e-01 9.35298502e-01 -3.94313410e-02 4.59903806e-01 6.32034063e-01 5.29349923e-01 6.78146482e-01 4.02673721e-01 -8.85206580e-01 6.52305543e-01 6.71923280e-01 2.09650233e-01 -9.10806715e-01 -7.29162455e-01 -5.17413437e-01 -5.43758154e-01 1.94898114e-01 5.42207241e-01 -2.18789831e-01 -9.77837980e-01 1.87109387e+00 4.72157151e-01 -2.96401471e-01 -2.36224651e-01 1.25367987e+00 5.59628308e-01 2.10948288e-01 -3.10953081e-01 2.29267225e-01 1.12946188e+00 -1.08376014e+00 -3.29730213e-01 -7.54528642e-01 6.72468305e-01 -3.91147375e-01 9.66096938e-01 3.82532686e-01 -1.13200092e+00 -7.42395997e-01 -1.18480361e+00 -3.28582644e-01 2.38550287e-02 4.42281604e-01 4.96203452e-01 4.83337969e-01 -8.75266731e-01 6.30114079e-01 -1.20987594e+00 -4.59071875e-01 3.61535221e-01 5.38127005e-01 -7.81867445e-01 -2.08102703e-01 -9.25356984e-01 9.54779029e-01 2.24773899e-01 5.08904271e-02 -1.01853728e+00 -3.19234401e-01 -1.42457604e+00 -3.86153340e-01 4.68039095e-01 -9.81141686e-01 1.29539454e+00 -9.63609874e-01 -1.38899970e+00 1.42737877e+00 7.38336286e-03 -3.96404117e-01 7.55392373e-01 -9.10050869e-01 2.04208121e-01 3.61282647e-01 1.81778640e-01 9.40567732e-01 8.87781560e-01 -1.40092504e+00 -1.15280092e-01 -9.09091353e-01 3.72655123e-01 4.92700785e-01 -1.12731978e-01 -4.61195707e-01 -6.25006080e-01 -4.52251941e-01 3.95704478e-01 -1.26014006e+00 -3.06452066e-01 3.70882779e-01 -4.54735339e-01 9.54885334e-02 4.76960808e-01 -6.90933108e-01 4.47041601e-01 -1.84662890e+00 4.98982847e-01 1.32266069e-02 3.13405931e-01 -4.82307523e-02 1.42720029e-01 2.32446209e-01 8.49395543e-02 -2.06329718e-01 -8.25466886e-02 -8.29679966e-01 -1.20312810e-01 3.80707026e-01 1.11723863e-01 9.37193811e-01 2.11107776e-01 8.56385171e-01 -1.01242566e+00 -4.94651347e-01 4.54842389e-01 4.48800981e-01 -9.05356586e-01 3.44104379e-01 -2.75892675e-01 7.83817530e-01 -2.30950221e-01 3.46072137e-01 3.62332016e-01 -6.66231811e-01 3.89701068e-01 -2.80761838e-01 3.60401720e-01 3.43710601e-01 -9.61868882e-01 2.46440697e+00 -6.30069017e-01 2.93486178e-01 -1.37474397e-02 -1.23492539e+00 8.37055087e-01 4.48444545e-01 6.28260374e-01 -3.56886417e-01 1.83837876e-01 1.95177391e-01 -3.68952781e-01 -4.97155279e-01 1.49406016e-01 -3.80470067e-01 -2.45100543e-01 5.60139775e-01 4.44555759e-01 -1.24154106e-01 -1.17201090e-01 1.16569279e-02 7.97884405e-01 7.48647511e-01 4.03242052e-01 1.50212392e-01 2.37261921e-01 -7.19691440e-02 5.72849572e-01 3.43676150e-01 -1.34619735e-02 8.86911750e-01 2.18150601e-01 -8.60693991e-01 -1.24831688e+00 -1.07901764e+00 9.95715782e-02 8.79020095e-01 1.72210813e-01 -5.36332071e-01 -7.30409801e-01 -9.97077227e-01 1.40101254e-01 1.49773985e-01 -7.57577181e-01 -8.49215090e-02 -7.11425424e-01 -3.44880581e-01 3.99722964e-01 9.08416092e-01 4.72561657e-01 -4.41485107e-01 -7.90692806e-01 -4.30150516e-02 -2.71555483e-01 -1.39808452e+00 -4.68533188e-01 2.01307535e-01 -1.14014971e+00 -1.14254212e+00 -9.31551218e-01 -6.96416080e-01 1.10540473e+00 1.19119383e-01 1.27352571e+00 -1.43345535e-01 -1.56460106e-01 4.29424644e-01 -2.57316917e-01 -1.09005988e-01 -5.45734391e-02 1.44918516e-01 3.89781773e-01 -2.27285430e-01 2.55319715e-01 -6.79179072e-01 -5.92570186e-01 2.97786266e-01 -3.80530030e-01 3.10926378e-01 5.44011533e-01 9.83496249e-01 7.01287210e-01 -4.93007392e-01 2.42942557e-01 -8.69501531e-01 -1.24233223e-01 -1.34296730e-01 -3.80956173e-01 -8.38474706e-02 -2.50301003e-01 1.62768617e-01 5.99890113e-01 -2.97932118e-01 -7.73706377e-01 7.62636602e-01 -1.38971627e-01 -6.27556622e-01 -4.02025282e-01 4.27947164e-01 -2.09061727e-01 8.42416601e-04 7.91187286e-01 5.94544485e-02 2.60681659e-01 -7.69287944e-01 4.33393091e-01 3.57366025e-01 4.63965595e-01 -4.24305767e-01 7.45159745e-01 7.21755385e-01 -5.64452112e-02 -3.93795729e-01 -1.43992651e+00 -3.48343879e-01 -1.38907731e+00 -2.21225560e-01 1.02774000e+00 -1.41473222e+00 -4.30984676e-01 2.48161137e-01 -8.82119358e-01 -3.99696589e-01 -1.85839206e-01 7.87494719e-01 -8.63510847e-01 4.80582952e-01 -5.53835630e-01 -5.86891711e-01 -4.73251864e-02 -1.02319086e+00 1.62353861e+00 -2.57699013e-01 -4.98473227e-01 -1.01378691e+00 1.50252238e-01 7.80088663e-01 -2.72219270e-01 3.57003331e-01 3.52404028e-01 -4.74208266e-01 -2.38180384e-01 -5.68832278e-01 1.24612838e-01 4.45620984e-01 1.55646652e-01 -7.12745845e-01 -9.34631407e-01 -5.09579539e-01 -6.16429187e-03 -1.00344920e+00 6.85284436e-01 3.95156235e-01 1.06024897e+00 -4.60107066e-02 -3.05988461e-01 7.09814131e-01 1.08287501e+00 -4.80898708e-01 8.09219033e-02 1.40605003e-01 1.01596785e+00 5.84814429e-01 5.06146789e-01 4.03707296e-01 6.88024879e-01 8.86854291e-01 5.21442413e-01 -1.82625964e-01 -4.51318547e-02 -7.88605392e-01 2.37962261e-01 7.77165592e-01 -2.11793989e-01 1.35082155e-01 -9.47604120e-01 3.29361439e-01 -1.70873129e+00 -6.17501438e-01 3.53814125e-01 2.29695916e+00 7.93536723e-01 2.41601691e-01 3.69231880e-01 2.13327527e-01 3.55001122e-01 2.35777989e-01 -7.52963364e-01 1.29471749e-01 2.71262825e-01 -7.43823275e-02 5.56779385e-01 4.14143056e-01 -1.36825681e+00 8.45320225e-01 6.59877491e+00 8.02068487e-02 -1.19567454e+00 4.78356332e-02 4.34734434e-01 -3.69005024e-01 -1.73997030e-01 -1.61839396e-01 -6.39792860e-01 1.38456270e-01 6.36464119e-01 4.35415238e-01 1.34427577e-01 9.84733045e-01 5.08681796e-02 -1.11812405e-01 -1.46357715e+00 1.27431047e+00 5.98158658e-01 -8.86903346e-01 -9.11851004e-02 1.92272544e-01 6.69770002e-01 1.42307222e-01 -2.16492012e-01 3.87834981e-02 2.57086813e-01 -1.04703224e+00 8.51865113e-01 3.27036977e-01 9.12628412e-01 -4.90628451e-01 6.01179242e-01 7.96222270e-01 -9.78697240e-01 1.91815019e-01 -2.51914024e-01 -4.73378271e-01 4.44850475e-01 5.80490351e-01 -6.50318801e-01 5.45666456e-01 5.20056427e-01 1.04200876e+00 -5.10799587e-01 6.14680707e-01 -5.65193355e-01 3.67032200e-01 -5.25945306e-01 3.17670763e-01 1.68262403e-02 7.33294785e-02 2.36836448e-01 6.04331434e-01 4.95261513e-02 -1.22706458e-01 7.18343318e-01 6.14014030e-01 -2.34752491e-01 -9.11535695e-02 -8.87911558e-01 2.25114584e-01 5.26245572e-02 9.56149042e-01 -5.94131827e-01 -3.71138990e-01 -4.90056336e-01 1.21588612e+00 5.50230980e-01 1.68832541e-02 -6.63341939e-01 1.34672686e-01 1.98658705e-01 3.05254102e-01 1.96530804e-01 -5.93419194e-01 -3.00984144e-01 -1.44027269e+00 1.70847878e-01 -6.51284039e-01 1.82737067e-01 -9.13588166e-01 -1.03675985e+00 4.15630281e-01 1.10178664e-01 -1.46363986e+00 -6.54329658e-01 -7.63288677e-01 1.09644711e-01 4.98683780e-01 -1.25078070e+00 -1.42879903e+00 -2.58172691e-01 4.04947400e-01 4.61093456e-01 5.80960922e-02 8.67524326e-01 1.92637712e-01 -1.71238244e-01 5.44745803e-01 -3.76708418e-01 4.84646022e-01 9.32934225e-01 -1.42704785e+00 5.18236518e-01 4.10440058e-01 5.46417236e-01 5.16849101e-01 5.02035260e-01 -5.78735650e-01 -1.56406116e+00 -7.68638670e-01 7.43299007e-01 -8.18637669e-01 8.70605335e-02 -5.81072509e-01 -3.87360662e-01 1.04015410e+00 -1.76620543e-01 5.21834970e-01 8.85178268e-01 4.66811061e-01 -5.32124043e-01 6.84137419e-02 -9.73412514e-01 2.83271492e-01 1.40495276e+00 -6.97272897e-01 -6.78963125e-01 4.63869303e-01 3.92993093e-01 -8.63075197e-01 -9.84327734e-01 4.53047007e-01 6.72605455e-01 -8.75295341e-01 1.01711428e+00 -5.75784802e-01 6.63022399e-01 -2.07287312e-01 -2.28466481e-01 -1.38715506e+00 4.94553223e-02 -2.65092254e-01 -2.95613289e-01 4.10507232e-01 2.65521348e-01 -1.58738375e-01 1.25543475e+00 2.91730762e-01 -3.32667753e-02 -8.57692838e-01 -7.47880340e-01 -7.04156160e-01 1.17194183e-01 -3.24297637e-01 1.23916537e-01 8.18478823e-01 7.43519068e-02 6.26773655e-01 -8.11954975e-01 8.27630982e-02 8.36785913e-01 2.90575087e-01 1.22109437e+00 -1.24403131e+00 -4.59903985e-01 2.98923403e-01 -7.73048043e-01 -1.68431449e+00 2.67639458e-01 -8.65705013e-01 1.64622828e-01 -1.48253369e+00 2.11125836e-01 -1.93710923e-01 2.53125548e-01 6.32057190e-01 -1.32079772e-03 7.61560380e-01 1.28738195e-01 3.54634464e-01 -7.80388236e-01 6.14371955e-01 1.32894039e+00 -9.47329157e-04 1.10016577e-01 4.03996557e-02 -4.00287926e-01 1.20691979e+00 5.44250667e-01 -3.32927525e-01 -5.16684413e-01 -8.52276385e-01 2.91333228e-01 1.34006813e-01 5.93861282e-01 -9.85350728e-01 -2.74143834e-02 2.09898874e-01 9.12306845e-01 -7.42091715e-01 7.43794382e-01 -6.93169534e-01 -7.40841404e-02 3.65103483e-01 -4.24765110e-01 -2.59724874e-02 -7.12305009e-02 7.05419898e-01 -1.82196394e-01 -1.43571906e-02 6.01107955e-01 -5.24466753e-01 -4.90663260e-01 3.29945922e-01 -1.24934122e-01 2.92840630e-01 7.32843578e-01 -2.92904645e-01 3.44258428e-01 -6.31184816e-01 -1.18294907e+00 1.78222507e-01 9.10225868e-01 3.14950943e-01 5.50083280e-01 -1.37344790e+00 -3.48406851e-01 4.08552259e-01 1.53697759e-01 4.95218664e-01 1.85113251e-01 6.53043509e-01 -5.67795455e-01 4.52144355e-01 -2.88392484e-01 -9.50783789e-01 -1.11196768e+00 3.93582493e-01 3.96051109e-01 -2.38281399e-01 -8.80963326e-01 7.26471424e-01 2.11230472e-01 -9.73906159e-01 2.37362429e-01 -2.55971253e-01 1.39392138e-01 -2.26504743e-01 2.02473328e-01 -6.65187389e-02 -3.46719213e-02 -9.51087773e-01 -2.18946010e-01 8.26347470e-01 -2.81459698e-03 -2.34692544e-01 1.29951572e+00 -1.42042175e-01 3.64789963e-01 6.91021979e-01 1.45144665e+00 -2.22209543e-02 -1.78152978e+00 -3.92000079e-01 -2.33483106e-01 -5.85877359e-01 -1.00541100e-01 -5.74900627e-01 -1.11046565e+00 1.11604059e+00 3.27848226e-01 -3.92952085e-01 7.41323531e-01 2.26036862e-01 8.85012388e-01 5.48813760e-01 6.96180761e-01 -1.04628146e+00 5.13292074e-01 4.54327077e-01 7.13485360e-01 -1.60936046e+00 3.21540296e-01 -3.79597515e-01 -6.43330395e-01 9.20664370e-01 7.67156422e-01 -5.54613531e-01 7.08548069e-01 9.28251967e-02 9.62292627e-02 -3.11506271e-01 -4.26037282e-01 -2.03824103e-01 4.41851228e-01 6.63353443e-01 6.02399111e-01 -3.15172039e-02 1.64636970e-01 4.24372494e-01 -3.67157400e-01 1.11976065e-01 1.57961026e-01 1.03067577e+00 -2.77287632e-01 -1.10766459e+00 -2.35599563e-01 3.36701214e-01 -4.25669372e-01 3.33453357e-01 -4.74400401e-01 7.73328781e-01 7.75081664e-02 4.71601576e-01 4.05651033e-02 -5.26175380e-01 3.01768482e-01 1.18685663e-01 9.79347825e-01 -1.05077863e+00 -1.08375654e-01 2.59136558e-01 2.56016344e-01 -7.19433784e-01 -7.56587863e-01 -6.37356460e-01 -1.10025334e+00 8.46994147e-02 -2.83463895e-01 -1.74465775e-01 4.58741397e-01 1.30078554e+00 1.37368113e-01 1.74690291e-01 3.94253880e-01 -1.38101792e+00 -6.91825092e-01 -8.29567969e-01 -5.53520262e-01 5.48585773e-01 3.21337849e-01 -1.07058489e+00 -1.50957555e-01 3.12485784e-01]	[6.988636493682861, -1.0220003128051758]
813c2e9a-ede3-4736-9502-499714770797	how-to-sift-out-a-clean-data-subset-in-the	2210.06516	null	https://arxiv.org/abs/2210.06516v2	https://arxiv.org/pdf/2210.06516v2.pdf	How to Sift Out a Clean Data Subset in the Presence of Data Poisoning?	Given the volume of data needed to train modern machine learning models, external suppliers are increasingly used. However, incorporating external data poses data poisoning risks, wherein attackers manipulate their data to degrade model utility or integrity. Most poisoning defenses presume access to a set of clean data (or base set). While this assumption has been taken for granted, given the fast-growing research on stealthy poisoning attacks, a question arises: can defenders really identify a clean subset within a contaminated dataset to support defenses? This paper starts by examining the impact of poisoned samples on defenses when they are mistakenly mixed into the base set. We analyze five defenses and find that their performance deteriorates dramatically with less than 1% poisoned points in the base set. These findings suggest that sifting out a base set with high precision is key to these defenses' performance. Motivated by these observations, we study how precise existing automated tools and human inspection are at identifying clean data in the presence of data poisoning. Unfortunately, neither effort achieves the precision needed. Worse yet, many of the outcomes are worse than random selection. In addition to uncovering the challenge, we propose a practical countermeasure, Meta-Sift. Our method is based on the insight that existing attacks' poisoned samples shifts from clean data distributions. Hence, training on the clean portion of a dataset and testing on the corrupted portion will result in high prediction loss. Leveraging the insight, we formulate a bilevel optimization to identify clean data and further introduce a suite of techniques to improve efficiency and precision. Our evaluation shows that Meta-Sift can sift a clean base set with 100% precision under a wide range of poisoning attacks. The selected base set is large enough to give rise to successful defenses.	['Ruoxi Jia', 'Lingjuan Lyu', 'Ming Jin', 'Himanshu Jahagirdar', 'Minzhou Pan', 'Yi Zeng']	2022-10-12	null	null	null	null	['data-poisoning']	['adversarial']	[ 1.24922238e-01 -4.19257879e-01 -2.66929597e-01 1.17028534e-01 -1.09486949e+00 -1.29141498e+00 5.18254042e-01 3.89825493e-01 -4.35068041e-01 4.72881436e-01 -6.03688210e-02 -6.27242088e-01 -9.99745578e-02 -7.96682715e-01 -8.22289228e-01 -8.33934247e-01 -2.01833189e-01 2.66960591e-01 1.53885394e-01 -1.35011300e-01 5.55706382e-01 6.12861574e-01 -9.91719604e-01 2.67580599e-01 7.67636180e-01 5.60117424e-01 -4.91240084e-01 4.41196918e-01 2.54748225e-01 6.35834992e-01 -1.05390334e+00 -6.68360233e-01 8.82317781e-01 -1.20009124e-01 -6.66171551e-01 -3.92499924e-01 3.96848410e-01 -7.54906952e-01 -3.02919567e-01 1.31087506e+00 3.95732224e-01 -3.10098886e-01 4.44930166e-01 -1.72448647e+00 -3.69198203e-01 7.39652753e-01 -7.46673822e-01 3.14349592e-01 1.21410489e-01 8.15947533e-01 9.44213867e-01 -4.68595624e-01 3.35970014e-01 9.99886513e-01 6.51195586e-01 5.39605856e-01 -1.18463683e+00 -9.80001986e-01 -4.40079980e-02 -1.95813492e-01 -1.25892878e+00 -5.81941843e-01 5.56896269e-01 -2.32965559e-01 5.35864592e-01 6.29190087e-01 1.86491266e-01 1.45870721e+00 1.81461155e-01 5.70586145e-01 9.96194720e-01 -1.61918283e-01 5.02558708e-01 1.98972851e-01 5.08828580e-01 3.71433556e-01 1.18498087e+00 4.15714145e-01 -6.20482743e-01 -9.76363778e-01 4.39305510e-03 7.63619840e-02 -3.79849672e-01 -3.09567899e-01 -6.26044035e-01 1.04527009e+00 2.61925817e-01 -7.62161613e-02 -3.18705827e-01 2.04449832e-01 5.16211450e-01 3.33669662e-01 1.60394832e-01 9.50813472e-01 -3.67806405e-01 -3.42720398e-03 -6.75375700e-01 3.35563183e-01 9.25247908e-01 4.26638365e-01 3.14412445e-01 7.00302571e-02 1.32429570e-01 1.13606257e-02 -7.56491199e-02 8.20116580e-01 1.16658248e-01 -4.87359941e-01 7.45256007e-01 4.08880562e-01 3.23625535e-01 -1.11208677e+00 6.05858304e-02 -3.38742435e-01 -4.75680798e-01 3.39939266e-01 7.60312080e-01 -3.33921701e-01 -7.72406340e-01 1.89070904e+00 5.90237617e-01 -1.34324148e-01 -6.33403137e-02 6.64940178e-01 -3.02959085e-01 2.72279412e-01 1.17592424e-01 3.85926105e-02 1.21747875e+00 -2.44685367e-01 -3.19714516e-01 -6.25121519e-02 7.21896648e-01 -2.45305464e-01 1.14180791e+00 8.18796813e-01 -7.80560911e-01 -8.44794326e-03 -1.13850284e+00 4.48670805e-01 -6.22507751e-01 -7.89927900e-01 6.10636652e-01 1.25145280e+00 -4.19854283e-01 6.25033438e-01 -8.89683962e-01 -1.12715423e-01 7.89733231e-01 4.08622086e-01 -5.29639006e-01 -1.34352580e-01 -1.08583868e+00 8.60098660e-01 9.15879197e-03 -4.00497317e-01 -1.37030041e+00 -1.03837168e+00 -2.97041178e-01 1.21831633e-01 5.89528203e-01 -5.51617265e-01 9.92781281e-01 -3.19409847e-01 -5.14853895e-01 2.94258863e-01 1.96691781e-01 -8.82526815e-01 7.04431713e-01 -4.66606498e-01 -1.45993859e-01 3.06341082e-01 -2.03909376e-03 5.89177012e-03 1.13516724e+00 -1.49342418e+00 -6.65216446e-01 -5.86787343e-01 1.72944993e-01 -2.25613967e-01 -9.55328047e-01 2.07747698e-01 1.66035384e-01 -4.44366246e-01 -3.99200410e-01 -7.58338869e-01 -3.86797041e-01 -3.75583112e-01 -1.08186281e+00 3.03759485e-01 1.06663668e+00 -3.36311966e-01 1.29507637e+00 -2.13756204e+00 -2.38097742e-01 5.88482738e-01 5.22824168e-01 5.81972957e-01 -1.58592716e-01 7.54697919e-01 -3.81361581e-02 8.01744580e-01 -3.16873461e-01 -2.36113459e-01 8.47449675e-02 -1.05007015e-01 -1.22139537e+00 8.31292689e-01 -6.05112500e-02 6.23176873e-01 -8.43766987e-01 1.22579038e-01 -7.31145367e-02 1.85414270e-01 -6.34627402e-01 2.38574609e-01 -6.66613057e-02 2.01887339e-01 -6.04883075e-01 7.95738280e-01 7.14119852e-01 5.11070453e-02 1.12144798e-01 -2.51621902e-01 1.63437307e-01 2.50204831e-01 -1.13028872e+00 7.81703770e-01 8.57535079e-02 5.73746264e-02 3.28409284e-01 -6.32846355e-01 5.77721119e-01 1.08852070e-02 4.61323857e-01 -3.41843069e-01 2.66205877e-01 1.99306816e-01 8.63601789e-02 -3.23526889e-01 3.02290648e-01 -1.02662720e-01 -2.96159208e-01 9.22070920e-01 -5.25281966e-01 -6.37155101e-02 -1.05554044e-01 4.60202098e-01 1.83198524e+00 -5.58551013e-01 1.09632902e-01 -1.70156255e-01 -1.89682439e-01 4.69446450e-01 4.47339326e-01 1.17508721e+00 -3.25563610e-01 2.31782407e-01 5.68669498e-01 -5.14763951e-01 -1.10445189e+00 -1.09519839e+00 8.40557292e-02 7.66614139e-01 1.78430781e-01 -5.60522318e-01 -1.00887692e+00 -1.27188349e+00 3.93020153e-01 7.93066323e-01 -6.62232816e-01 -5.79338372e-01 -3.05367172e-01 -1.08378565e+00 1.12966800e+00 2.59047270e-01 2.46081039e-01 -5.79836547e-01 -9.73583519e-01 -1.68361813e-01 1.61675781e-01 -7.99617827e-01 -4.15574431e-01 4.60560054e-01 -5.23272216e-01 -1.70615828e+00 1.22291386e-01 1.94615677e-01 8.15808833e-01 6.97383642e-01 7.16333270e-01 4.51712191e-01 -4.09359127e-01 3.72338176e-01 -4.56866264e-01 -7.08897412e-01 -5.51558733e-01 5.36171570e-02 4.59253639e-01 -1.75352246e-01 5.83685338e-01 -5.90002894e-01 -3.18913996e-01 9.18719471e-02 -1.31834888e+00 -6.23167515e-01 3.83231282e-01 5.79743981e-01 2.15894073e-01 5.13771951e-01 7.29213297e-01 -1.04983425e+00 9.78461742e-01 -8.36293697e-01 -5.77620208e-01 2.12866887e-01 -3.32727849e-01 6.94311336e-02 1.01177514e+00 -6.48506224e-01 -3.59667778e-01 -9.25313160e-02 -1.75021011e-02 -6.41540051e-01 -1.67856202e-01 2.42187545e-01 -4.39644605e-01 -8.12121704e-02 1.00957501e+00 -5.28410636e-03 1.39757544e-01 -5.95497847e-01 4.13343966e-01 5.08153319e-01 5.42418480e-01 -8.09251010e-01 1.54852855e+00 5.88940442e-01 6.05817586e-02 -5.80296278e-01 -9.20770407e-01 -2.74599433e-01 -3.05297971e-01 3.82799327e-01 3.78026605e-01 -5.22732019e-01 -7.89723694e-01 4.22580659e-01 -7.17322588e-01 -4.55328822e-01 -2.49973685e-01 9.33665782e-03 -3.34551819e-02 6.96143985e-01 -3.34815800e-01 -1.01967108e+00 -6.18803799e-01 -1.20088935e+00 7.45888650e-01 -1.90454334e-01 -2.51531780e-01 -6.66913331e-01 4.78330515e-02 2.79057741e-01 3.70902270e-01 6.18556142e-01 8.99080336e-01 -1.44692016e+00 -4.77265418e-01 -7.07180679e-01 1.53375864e-01 2.54166871e-01 3.22583079e-01 1.68651398e-02 -1.01113856e+00 -6.81871057e-01 4.31594580e-01 -4.71849263e-01 6.76045477e-01 -1.67593732e-01 1.24608588e+00 -7.28349090e-01 -3.71457100e-01 6.50889933e-01 1.38474727e+00 3.05183440e-01 4.70337301e-01 4.92706895e-01 6.97783887e-01 5.35496891e-01 5.31251848e-01 5.93249083e-01 -7.42443502e-02 3.66434753e-01 8.40601921e-01 1.11088566e-01 3.60312283e-01 -4.85639691e-01 4.80040073e-01 -5.32105304e-02 6.04993224e-01 -3.54434609e-01 -8.93674552e-01 4.94297981e-01 -1.22922623e+00 -1.25143743e+00 6.51000114e-03 2.64156818e+00 9.62838233e-01 5.04026353e-01 4.58749413e-01 4.82275873e-01 4.67329353e-01 2.32254760e-03 -7.87567735e-01 -2.96941996e-01 1.05397895e-01 2.33051777e-01 9.96115685e-01 4.30690169e-01 -1.21100390e+00 8.13404143e-01 6.28640413e+00 9.23301160e-01 -1.05464482e+00 -8.73936415e-02 6.58151865e-01 -3.67940158e-01 -2.78355837e-01 1.89737365e-01 -9.42224026e-01 6.83799326e-01 1.23231006e+00 -2.57950544e-01 5.52554131e-01 8.45176399e-01 6.98029026e-02 5.99292815e-02 -1.12247813e+00 5.66308558e-01 1.59287229e-02 -1.25121367e+00 9.82331336e-02 5.81855416e-01 3.26822340e-01 -9.40938964e-02 4.48313177e-01 1.02627344e-01 1.03892851e+00 -1.13027465e+00 5.95053315e-01 4.85524051e-02 5.73658288e-01 -1.04926562e+00 4.10676479e-01 6.48933113e-01 -6.84731007e-01 -4.11026090e-01 -2.72962600e-01 2.12506399e-01 -4.86991648e-03 6.25208199e-01 -9.24236238e-01 4.38083142e-01 5.81680238e-01 -4.56653088e-02 -8.18201780e-01 9.42787766e-01 -2.05670983e-01 1.04142404e+00 -6.25901222e-01 3.35629910e-01 2.06163600e-01 2.81309247e-01 5.97400904e-01 8.76415908e-01 -5.16081452e-02 2.40700301e-02 3.30234200e-01 7.05896974e-01 -1.18757628e-01 -4.33836997e-01 -9.70863342e-01 -1.68148831e-01 1.12966704e+00 1.01430583e+00 -4.23325151e-01 -1.93688795e-01 1.29185513e-01 5.34234047e-01 1.93531349e-01 1.11824580e-01 -9.22488093e-01 -3.73885632e-01 9.52879667e-01 1.57825246e-01 1.33569501e-02 -1.92597523e-01 -6.27261162e-01 -1.12075400e+00 -1.67392164e-01 -1.60588932e+00 5.14140248e-01 -3.84911299e-02 -1.64806855e+00 3.84259611e-01 1.03754513e-01 -9.87961769e-01 3.14326696e-02 -3.28904271e-01 -7.80524373e-01 7.13335693e-01 -1.07545495e+00 -1.00408471e+00 1.07976206e-01 4.38723564e-01 2.15476602e-01 -9.01635289e-02 6.52375221e-01 -5.75881004e-02 -8.54940593e-01 9.81993079e-01 5.30192778e-02 2.40821645e-01 6.58720076e-01 -1.05117452e+00 8.47933233e-01 1.35233533e+00 1.66646659e-01 1.21789825e+00 8.65181267e-01 -1.12592649e+00 -1.85356665e+00 -1.06768167e+00 1.83165669e-01 -1.13162577e+00 8.11561286e-01 -5.08929551e-01 -1.04999781e+00 5.46184659e-01 -1.05935715e-01 -1.54754281e-01 7.02100933e-01 -1.37516439e-01 -1.06155169e+00 6.74515367e-02 -1.69432366e+00 5.87116480e-01 7.03610182e-01 -4.83446002e-01 -4.87741530e-01 3.07549596e-01 7.53906429e-01 -1.45325447e-02 -6.92031980e-01 2.90699989e-01 2.82825381e-01 -8.36941361e-01 1.12999976e+00 -1.14056158e+00 9.42882895e-02 -3.02462637e-01 -3.31663221e-01 -1.19722700e+00 1.46104440e-01 -8.26317489e-01 -3.46429437e-01 1.25591278e+00 8.32115561e-02 -6.75332427e-01 7.70336032e-01 8.48322392e-01 1.60863459e-01 -6.03051424e-01 -7.44373381e-01 -1.05784523e+00 3.53768259e-01 -2.87114590e-01 8.45835388e-01 1.08935714e+00 2.44213101e-02 -4.38483544e-02 -5.27021646e-01 6.02604508e-01 1.25111747e+00 -1.28524408e-01 1.26212108e+00 -1.01002026e+00 -3.18185300e-01 -1.77989498e-01 -8.06968566e-03 -4.48079228e-01 -2.28782937e-01 -4.98167753e-01 -1.98641911e-01 -8.86491954e-01 3.55086237e-01 -7.03008294e-01 -1.49612561e-01 9.41028237e-01 -4.76800978e-01 3.91020715e-01 3.66488665e-01 3.80951345e-01 -3.34941357e-01 7.35039860e-02 5.32272041e-01 -1.04599871e-01 -1.03612766e-01 -1.85614377e-01 -1.27370787e+00 6.05675936e-01 1.05950522e+00 -8.27199280e-01 -4.06739801e-01 -1.55697316e-01 1.36290188e-03 -4.56260949e-01 4.72251624e-01 -9.68862832e-01 1.97780579e-01 -4.83418763e-01 2.26009279e-01 -3.67405027e-01 2.10271761e-01 -8.06999445e-01 1.52076244e-01 8.54970276e-01 -3.67200732e-01 2.34548375e-01 1.23270065e-01 6.08404875e-01 3.21559906e-01 -2.95134604e-01 9.38682020e-01 -1.16872825e-01 -6.63235784e-02 3.78900886e-01 -1.24848433e-01 3.03998291e-01 1.21004665e+00 -6.84568211e-02 -1.01734626e+00 -9.61584672e-02 -1.39274016e-01 1.92261189e-02 8.77298057e-01 1.37286365e-01 3.81707817e-01 -7.52233624e-01 -4.84799743e-01 3.07816148e-01 -8.28276277e-02 -2.68095493e-01 -1.39925271e-01 6.16095006e-01 -2.01222390e-01 -2.18538661e-03 -6.48830757e-02 -7.19711334e-02 -1.31325638e+00 1.09272468e+00 1.68485612e-01 -2.04965442e-01 -5.21115720e-01 6.51345909e-01 2.25884859e-02 1.09536536e-01 3.60399365e-01 4.51826863e-02 3.56914848e-01 -1.33797273e-01 9.36881900e-01 3.34169984e-01 -3.60301957e-02 -2.94000864e-01 -3.08732897e-01 -9.27211791e-02 -3.31431150e-01 4.50306907e-02 1.33337450e+00 2.56852210e-01 7.93929771e-02 -2.65666217e-01 9.98862267e-01 6.21958792e-01 -1.05263376e+00 2.14855283e-01 1.75921321e-01 -9.50702131e-01 -2.72097379e-01 -7.94711411e-01 -9.91701961e-01 7.78641820e-01 3.10223669e-01 7.82966375e-01 9.10470247e-01 -3.39500934e-01 1.00955808e+00 5.29904902e-01 6.11275434e-01 -5.19380331e-01 2.08582208e-01 2.62823161e-02 4.34755892e-01 -9.92576599e-01 1.46730155e-01 -2.34317005e-01 -6.80676758e-01 6.13054514e-01 6.28909469e-01 -2.02323213e-01 3.18542838e-01 5.53997934e-01 -1.54666424e-01 -3.55471909e-01 -8.38851452e-01 2.95407057e-01 -3.97002995e-01 7.87756443e-01 -5.03971696e-01 -8.75962302e-02 8.49362463e-02 6.14329994e-01 -2.78671175e-01 -4.99892890e-01 7.73397624e-01 1.19725049e+00 -7.36066639e-01 -1.22393906e+00 -8.95662725e-01 6.09212101e-01 -8.49388957e-01 -9.52566266e-02 -6.85904682e-01 7.27066100e-01 -9.00044739e-02 1.30574811e+00 -3.66555929e-01 -7.64762759e-01 2.72002697e-01 -5.61873019e-02 -2.95500048e-02 -6.33161128e-01 -1.01167536e+00 -2.86379158e-01 -8.66621826e-03 -6.03184581e-01 2.96908677e-01 -5.34162939e-01 -9.09838676e-01 -8.01876843e-01 -5.08997440e-01 5.12593150e-01 6.41042829e-01 7.66674697e-01 4.86121386e-01 -4.20475788e-02 9.15279031e-01 -4.71953571e-01 -1.56632924e+00 -4.72109407e-01 -4.36476856e-01 6.26795590e-01 4.29797769e-01 -5.96275687e-01 -8.72637570e-01 -1.57901376e-01]	[5.8097686767578125, 7.561938285827637]
2bfca8ea-0a54-4973-bca8-eba8840b7a39	recbaselines2023-a-new-dataset-for-choosing	2306.14292	null	https://arxiv.org/abs/2306.14292v1	https://arxiv.org/pdf/2306.14292v1.pdf	RecBaselines2023: a new dataset for choosing baselines for recommender models	The number of proposed recommender algorithms continues to grow. The authors propose new approaches and compare them with existing models, called baselines. Due to the large number of recommender models, it is difficult to estimate which algorithms to choose in the article. To solve this problem, we have collected and published a dataset containing information about the recommender models used in 903 papers, both as baselines and as proposed approaches. This dataset can be seen as a typical dataset with interactions between papers and previously proposed models. In addition, we provide a descriptive analysis of the dataset and highlight possible challenges to be investigated with the data. Furthermore, we have conducted extensive experiments using a well-established methodology to build a good recommender algorithm under the dataset. Our experiments show that the selection of the best baselines for proposing new recommender approaches can be considered and successfully solved by existing state-of-the-art collaborative filtering models. Finally, we discuss limitations and future work.	['Sergey Kolesnikov', 'Marina Ananyeva', 'Oleg Lashinin', 'Veronika Ivanova']	2023-06-25	null	null	null	null	['collaborative-filtering']	['miscellaneous']	[-1.94408581e-01 -1.16619997e-01 -2.30605721e-01 -3.97833169e-01 -2.42216378e-01 -7.35470831e-01 6.54518247e-01 -1.01817325e-01 -8.01501349e-02 5.04535735e-01 6.14836454e-01 -2.03316703e-01 -8.44712913e-01 -7.70855725e-01 -3.93250465e-01 -3.86200219e-01 3.44902538e-02 6.88241720e-01 1.77620098e-01 -5.04419744e-01 1.05698276e+00 4.17308599e-01 -2.08775282e+00 6.04596615e-01 1.06951916e+00 5.92993557e-01 3.73996049e-01 3.95504326e-01 -2.14883298e-01 5.59449196e-01 -6.87614918e-01 -5.91023028e-01 5.06159484e-01 -2.56367981e-01 -6.50933743e-01 -4.22614485e-01 6.28791392e-01 5.14821522e-02 -2.50375301e-01 7.03926146e-01 4.53110099e-01 6.35220349e-01 8.22828114e-01 -1.32457900e+00 -1.17427421e+00 1.30357063e+00 -9.08361822e-02 2.22029850e-01 8.45869362e-01 -6.13777459e-01 1.17483830e+00 -1.08601665e+00 9.53700244e-01 1.05873764e+00 7.78365672e-01 5.73792934e-01 -8.52924228e-01 -7.10419178e-01 5.92232049e-01 3.43767673e-01 -1.27664101e+00 -1.86649323e-01 5.64365923e-01 -5.11031926e-01 7.61693060e-01 8.01648438e-01 6.51121199e-01 1.14019442e+00 -1.46757603e-01 6.13041222e-01 1.06637752e+00 -4.84330922e-01 2.19579816e-01 4.66660023e-01 7.42903888e-01 1.25928253e-01 3.04672509e-01 1.89993992e-01 -2.62676597e-01 -5.10445952e-01 3.21391404e-01 3.22169274e-01 -3.05632830e-01 -2.22261652e-01 -9.56855834e-01 7.05976009e-01 1.51165918e-01 5.78425527e-01 -1.41903684e-01 -5.67223489e-01 -9.26449224e-02 6.11157000e-01 4.15410250e-01 9.67394888e-01 -5.43022573e-01 -3.33326533e-02 -1.21838534e+00 5.84649980e-01 1.19317961e+00 1.19556248e+00 2.24463999e-01 -4.30571139e-01 -1.75631627e-01 1.05406833e+00 7.26207435e-01 2.47027129e-01 2.97055572e-01 -1.08030474e+00 8.43355879e-02 4.79841292e-01 4.09331232e-01 -1.07424402e+00 -2.33004481e-01 -5.84629595e-01 -3.81300122e-01 -2.26206228e-01 2.41856724e-01 3.36357467e-02 -1.71767652e-01 9.67413664e-01 7.30527416e-02 5.46827197e-01 -1.11395702e-01 8.39192927e-01 1.29753053e+00 5.66909015e-01 -4.09058660e-01 -6.01512253e-01 1.09382021e+00 -1.55338204e+00 -7.89395630e-01 4.14581954e-01 4.84499305e-01 -1.30170202e+00 8.10663104e-01 9.43325222e-01 -9.91108060e-01 -7.79630661e-01 -9.97575879e-01 4.05051023e-01 -5.73347330e-01 2.58752495e-01 8.59618843e-01 1.05314171e+00 -1.06529713e+00 9.82364953e-01 -2.89375871e-01 -5.81308961e-01 -9.52883065e-02 5.24443984e-01 1.31993890e-01 -1.27489761e-01 -1.15901029e+00 9.68641281e-01 1.22360904e-02 -1.22972250e-01 -5.97340763e-01 -9.66816366e-01 1.33665606e-01 -1.41820043e-01 4.80400205e-01 -7.61186481e-01 1.02622509e+00 -5.48954785e-01 -1.67162991e+00 1.46394312e-01 1.24464959e-01 -2.84177333e-01 1.06160514e-01 -5.61970592e-01 -9.21253383e-01 -4.89962727e-01 -3.74520004e-01 1.30381221e-02 3.01325202e-01 -1.41940761e+00 -9.07279253e-01 -1.36741459e-01 5.66788018e-01 1.71450362e-01 -4.49566901e-01 5.38324177e-01 -5.17387688e-01 -8.40354621e-01 -2.51202226e-01 -1.15298474e+00 -4.94070262e-01 -5.36503077e-01 5.31021915e-02 -5.48907876e-01 4.27570134e-01 1.90207399e-02 2.00816011e+00 -1.76071858e+00 2.04953924e-01 2.49834880e-01 -2.53092758e-02 2.96315759e-01 -3.06560665e-01 1.00240731e+00 2.39015311e-01 3.19589913e-01 3.92655700e-01 -1.92008644e-01 4.40600701e-02 2.06239134e-01 -5.39445519e-01 2.96770990e-01 -8.04209352e-01 3.86010706e-01 -9.39306021e-01 -1.44618362e-01 2.48526365e-01 6.04818523e-01 -7.45258152e-01 3.59334290e-01 -3.88964787e-02 2.00294077e-01 -5.90452313e-01 4.98035550e-01 7.34904051e-01 1.01109602e-01 3.23306948e-01 -3.67037803e-01 -4.24251199e-01 5.30818045e-01 -1.79453254e+00 1.66541374e+00 -3.55324805e-01 6.29655719e-02 -2.76793987e-01 -8.74225020e-01 1.09882045e+00 5.78375682e-02 7.13623703e-01 -2.75507987e-01 2.22480491e-01 1.27906531e-01 1.48113787e-01 -1.81706563e-01 8.34796011e-01 5.51132381e-01 3.47272426e-01 8.30216110e-01 -5.08793294e-02 2.75330037e-01 6.34348571e-01 4.20031369e-01 1.02890766e+00 1.83576360e-01 1.38120025e-01 -6.70581698e-01 8.30629528e-01 -5.23766205e-02 3.54566246e-01 1.16606641e+00 4.14512992e-01 4.98725355e-01 -3.69353563e-01 -5.39006710e-01 -4.13700432e-01 -5.66148043e-01 -1.54686078e-01 1.43059099e+00 1.45295203e-01 -1.27836847e+00 -4.28093076e-01 -8.19178760e-01 -1.70768239e-02 8.05791676e-01 -7.87802339e-01 2.82081842e-01 -4.74427730e-01 -7.29596078e-01 1.80341095e-01 3.50811809e-01 -1.99537173e-01 -8.46830606e-01 6.07117414e-02 2.43837863e-01 9.94703993e-02 -6.69044554e-01 -5.10623336e-01 -1.96015432e-01 -1.03803170e+00 -1.19187510e+00 -3.22559208e-01 -3.37408572e-01 5.06161571e-01 1.10123968e+00 1.45281982e+00 5.71095347e-01 2.88478941e-01 4.42772239e-01 -1.27593565e+00 -4.36125904e-01 -3.41247469e-01 2.88732320e-01 2.08741233e-01 -3.23752314e-01 3.92381132e-01 -4.52149391e-01 -6.76888943e-01 9.40764427e-01 -4.10891086e-01 -5.09673834e-01 3.35116357e-01 4.31874961e-01 3.67265791e-01 -7.72704110e-02 4.51233566e-01 -1.59061217e+00 1.01883280e+00 -7.83938169e-01 -4.09604996e-01 5.24221838e-01 -1.33075476e+00 -2.13514164e-01 7.14192867e-01 -4.75750089e-01 -1.07391226e+00 -2.74406821e-01 -2.30795205e-01 -4.48945612e-02 -9.25096497e-02 6.53778493e-01 2.07639728e-02 -1.93892270e-01 8.58202159e-01 -3.17543417e-01 -3.84491324e-01 -1.07464516e+00 8.01581144e-01 9.89294052e-01 2.81436462e-02 -8.32166195e-01 6.48696840e-01 2.09495679e-01 -4.35117453e-01 -6.27659202e-01 -1.14885736e+00 -8.89238358e-01 -7.45480001e-01 -3.77838790e-01 2.19214540e-02 -7.21849620e-01 -4.31592852e-01 -3.67127389e-01 -6.26737177e-01 1.30152047e-01 -1.74468458e-01 5.77046752e-01 -2.00985804e-01 5.21833777e-01 -2.44717523e-01 -8.43905628e-01 -5.62135637e-01 -1.18196344e+00 3.97426635e-01 3.09594005e-01 -4.43411946e-01 -1.02580822e+00 4.49861735e-01 7.50857770e-01 7.08240092e-01 -4.55495626e-01 3.82977813e-01 -1.03368247e+00 -1.91595614e-01 1.17571779e-01 2.41751984e-01 2.78654303e-02 5.43627255e-02 5.73335528e-01 -6.23946548e-01 -4.71585006e-01 -1.42585129e-01 3.57948065e-01 7.84030735e-01 2.44311288e-01 1.10145676e+00 -9.38024297e-02 -6.86823368e-01 3.61436397e-01 1.17360675e+00 4.36570048e-01 7.35016525e-01 5.70946038e-01 2.96335131e-01 4.34959024e-01 1.24706280e+00 4.60959375e-01 4.83444899e-01 8.34460557e-01 1.67627633e-01 4.62258041e-01 -1.42616495e-01 -5.93074895e-02 3.99764627e-01 1.39009929e+00 -8.46553743e-01 -5.69694698e-01 -4.30488825e-01 9.38492715e-02 -2.06736970e+00 -1.09771216e+00 -6.72950029e-01 2.22364402e+00 3.39928806e-01 -2.19345540e-01 5.43581843e-01 2.11073279e-01 5.89486837e-01 -2.55084544e-01 -3.21120732e-02 -5.64199090e-01 -9.59893465e-02 3.13479781e-01 1.51179060e-01 2.49407634e-01 -9.99258399e-01 7.91579247e-01 7.76294184e+00 6.32977426e-01 -6.12125039e-01 8.56567100e-02 -2.92397439e-01 -7.81878829e-02 -3.92637074e-01 2.81047046e-01 -1.06577229e+00 4.84574020e-01 1.41525912e+00 -3.73469144e-01 5.40097952e-01 8.04272890e-01 1.13980293e-01 1.69206843e-01 -1.04341221e+00 7.81119764e-01 3.35512698e-01 -1.51285374e+00 2.74802029e-01 2.24678621e-01 1.09760535e+00 1.72908589e-01 -4.46778834e-02 4.25769955e-01 6.53657436e-01 -6.67779386e-01 3.05800498e-01 8.99552166e-01 -1.35156587e-01 -5.24981856e-01 8.26623440e-01 2.48906687e-01 -1.09361804e+00 -4.32605743e-01 -8.63038898e-01 -3.76276910e-01 -1.85974076e-01 4.72312927e-01 -1.42149910e-01 1.18917692e+00 7.89120734e-01 1.27734637e+00 -5.42696238e-01 1.57406521e+00 -7.12636635e-02 8.00678194e-01 -1.84430510e-01 -2.34525666e-01 -9.18854475e-02 -7.17704594e-01 3.43409091e-01 1.18544221e+00 6.31380916e-01 2.62791336e-01 4.29623574e-01 3.06342363e-01 -2.38417578e-03 7.52628982e-01 -4.07884061e-01 5.91078810e-02 9.26653147e-01 1.48984981e+00 -7.13625431e-01 -1.99452609e-01 -7.79357433e-01 3.32591563e-01 2.18731567e-01 -1.10462949e-01 -7.54103005e-01 1.04418784e-01 7.12191463e-01 2.12025851e-01 1.82359770e-01 -1.43428603e-02 9.23817977e-03 -1.25205171e+00 -5.46802700e-01 -1.12924266e+00 7.92775273e-01 -4.95552868e-01 -1.71501684e+00 6.37311041e-01 6.28618225e-02 -1.57253814e+00 2.70481825e-01 -3.27256650e-01 -4.82555985e-01 4.62453097e-01 -1.14581311e+00 -9.50091422e-01 -2.00648904e-01 4.13049161e-01 6.88292742e-01 -6.70789242e-01 9.78763938e-01 7.32673585e-01 -4.37157869e-01 6.16232038e-01 6.60569847e-01 -5.44012427e-01 1.32216954e+00 -1.13891685e+00 2.00809479e-01 5.54446399e-01 7.61088789e-01 1.44806778e+00 8.89310658e-01 -5.11563122e-01 -1.73766029e+00 -8.51911604e-01 6.91949606e-01 -7.82034516e-01 7.40206480e-01 -6.72913417e-02 -8.73199046e-01 3.27268094e-01 4.92576838e-01 -4.08409119e-01 1.35251975e+00 8.79013360e-01 -2.86230624e-01 -1.80694968e-01 -9.75484133e-01 3.31337988e-01 1.47837400e+00 1.28731757e-01 -9.21637118e-01 4.05223310e-01 2.43801638e-01 -2.69318104e-01 -1.37028813e+00 2.09326297e-01 1.01691175e+00 -1.03124321e+00 1.12247479e+00 -7.42894709e-01 2.67724395e-01 -3.76472235e-01 -2.79714435e-01 -1.54793215e+00 -7.87153363e-01 -6.61419570e-01 -4.22423422e-01 1.41626942e+00 5.80233455e-01 -3.02365929e-01 6.21516109e-01 5.16087353e-01 -2.10084260e-01 -7.90907443e-01 -1.16362944e-01 -7.66726196e-01 3.18358362e-01 -2.90960878e-01 8.43022823e-01 1.18660629e+00 1.22504957e-01 4.17752564e-01 -6.10917389e-01 -5.06408140e-02 4.07871693e-01 5.85140169e-01 1.03946698e+00 -2.00078535e+00 -6.74267635e-02 -5.82202435e-01 7.78662711e-02 -1.21807444e+00 1.86486784e-02 -1.07274687e+00 -4.99816328e-01 -1.84089744e+00 2.37043247e-01 -8.17630887e-01 -8.22928846e-01 -3.99621613e-02 -6.13180362e-02 2.33550757e-01 3.65143359e-01 4.89012420e-01 -7.86139131e-01 5.74514791e-02 1.20476055e+00 -9.47804656e-03 -3.19754064e-01 3.38419378e-01 -1.31001258e+00 6.21739686e-01 6.00062668e-01 -6.16856933e-01 -8.05203795e-01 -1.44202679e-01 7.36005664e-01 -1.93721846e-01 -5.70039392e-01 -8.35372925e-01 5.93325853e-01 -3.87380004e-01 -1.03546925e-01 -9.47103262e-01 6.07130118e-02 -9.21850264e-01 5.76607227e-01 2.81090382e-02 -5.60440838e-01 1.99359447e-01 -4.01601940e-01 4.94363278e-01 1.71459734e-01 -5.21638036e-01 3.76185536e-01 -1.30344272e-01 -5.25828898e-01 1.39031097e-01 -4.73708212e-01 -1.62164748e-01 8.36566865e-01 -1.55002296e-01 -3.92512590e-01 -1.55204549e-01 -9.06723917e-01 2.42958516e-01 3.92815709e-01 1.08915019e+00 4.89713967e-01 -1.02772808e+00 -7.03431129e-01 -2.41886303e-01 4.69645746e-02 -1.15216672e+00 3.28504026e-01 6.65698051e-01 -1.24987513e-01 3.98669362e-01 -3.32366794e-01 1.06939338e-01 -1.57045257e+00 9.22935426e-01 -7.15995580e-02 -2.80725271e-01 -5.14435112e-01 4.85809535e-01 -4.71828490e-01 -6.88975394e-01 3.31299067e-01 -4.27210853e-02 -1.20167875e+00 3.32923442e-01 8.37477028e-01 8.75941038e-01 1.97675586e-01 -6.89163983e-01 -3.52034748e-01 5.48717916e-01 -4.63669151e-01 5.30391514e-01 1.64260066e+00 -2.85205901e-01 -1.97630227e-01 3.49747986e-01 5.26152670e-01 3.90154481e-01 -1.34562165e-01 -2.12505922e-01 -5.57029918e-02 -7.69177139e-01 1.08962916e-01 -1.19605935e+00 -1.15462744e+00 2.44495630e-01 5.78488588e-01 4.69727337e-01 7.57460594e-01 -2.68738419e-01 4.21310544e-01 6.27477288e-01 6.27772629e-01 -1.19433236e+00 -4.23534304e-01 6.55793130e-01 8.95767689e-01 -1.00413501e+00 6.71701431e-01 -7.90219367e-01 -3.67498547e-01 1.20232868e+00 4.67988580e-01 -4.76550281e-01 1.44908559e+00 4.38832730e-01 1.69855163e-01 -1.20224863e-01 -1.07050705e+00 -2.16982305e-01 7.53609240e-01 5.83804905e-01 1.08203459e+00 7.90619627e-02 -1.26876521e+00 1.14195895e+00 -3.79952371e-01 8.16831365e-02 8.23667645e-01 7.32574224e-01 -2.71324188e-01 -2.03900313e+00 -2.00547636e-01 7.04056680e-01 -4.42910701e-01 6.73042983e-02 -8.54701400e-01 5.96968293e-01 1.43871322e-01 1.67069554e+00 -5.66071987e-01 -7.80989885e-01 6.50800228e-01 -3.57585251e-01 4.90889460e-01 -7.44795144e-01 -1.32293832e+00 -7.38331228e-02 3.70881885e-01 -4.02402610e-01 -1.12686419e+00 -7.09996104e-01 -7.48165190e-01 -3.09836805e-01 -7.90647984e-01 9.28569734e-01 4.98394042e-01 8.79749477e-01 6.19082808e-01 4.84807909e-01 6.67320430e-01 -5.89843392e-01 -3.50210488e-01 -1.10541618e+00 -5.45661628e-01 2.39367321e-01 -6.26930714e-01 -1.24979317e+00 -2.52567858e-01 -9.06784832e-02]	[10.049314498901367, 5.774102687835693]
c4785505-c270-4176-8844-9d605685acbf	regression-based-model-error-compensation-for	2306.09080	null	https://arxiv.org/abs/2306.09080v1	https://arxiv.org/pdf/2306.09080v1.pdf	Regression-Based Model Error Compensation for Hierarchical MPC Building Energy Management System	One of the major challenges in the development of energy management systems (EMSs) for complex buildings is accurate modeling. To address this, we propose an EMS, which combines a Model Predictive Control (MPC) approach with data-driven model error compensation. The hierarchical MPC approach consists of two layers: An aggregator controls the overall energy flows of the building in an aggregated perspective, while a distributor distributes heating and cooling powers to individual temperature zones. The controllers of both layers employ regression-based error estimation to predict and incorporate the model error. The proposed approach is evaluated in a software-in-the-loop simulation using a physics-based digital twin model. Simulation results show the efficacy and robustness of the proposed approach	['Tobias Rodemann', 'Jens Engel', 'Thomas Schmitt']	2023-06-15	null	null	null	null	['management', 'energy-management']	['miscellaneous', 'time-series']	[-7.23261088e-02 3.92185263e-02 9.71474871e-02 3.77502851e-02 -2.05758780e-01 -5.72752357e-01 5.23516297e-01 4.49050963e-01 6.00025833e-01 9.03047442e-01 -1.85886651e-01 -1.18213117e-01 -5.19246459e-01 -1.01367831e+00 -3.19306523e-01 -1.00616753e+00 3.23329240e-01 3.37218046e-01 2.66801625e-01 -2.23385930e-01 -1.19681172e-01 3.83425564e-01 -1.26490414e+00 -2.69083112e-01 1.00695419e+00 1.15001464e+00 1.71260133e-01 8.43337059e-01 7.06475556e-01 1.00494242e+00 -1.43865973e-01 8.63584876e-01 1.85904026e-01 -4.63005900e-01 -1.40363216e-01 -1.33779058e-02 -6.52794063e-01 -3.65132987e-01 2.58582532e-01 6.95671618e-01 4.35134143e-01 4.67590541e-01 7.21260250e-01 -1.46755385e+00 1.17771566e-01 -1.58587232e-01 -8.26079473e-02 -1.27181381e-01 -1.01839714e-01 3.86026770e-01 4.66773868e-01 -8.75431597e-02 -1.50939420e-01 7.29762137e-01 4.17371154e-01 3.16235721e-02 -1.49908197e+00 -2.68036693e-01 -5.17716967e-02 2.06339061e-02 -1.50711477e+00 -1.17733382e-01 7.48578608e-01 -5.47914505e-01 1.25719941e+00 7.43290961e-01 1.19388378e+00 -8.27058926e-02 4.92368370e-01 1.79627329e-01 1.33866525e+00 -5.59059858e-01 9.02958095e-01 3.42147201e-01 7.43855461e-02 1.72721699e-01 5.20498693e-01 5.38681030e-01 1.79193765e-01 -3.13283533e-01 6.12835169e-01 -1.65641725e-01 -1.19271941e-01 -5.04962444e-01 -5.09737849e-01 4.95538622e-01 4.78577822e-01 2.17828810e-01 -6.61679745e-01 4.35067743e-01 -4.43910714e-03 -1.43464416e-01 4.05459613e-01 4.29838561e-02 -5.74841797e-01 2.10187256e-01 -1.24582016e+00 3.10823321e-01 8.75964224e-01 1.07242036e+00 2.51266539e-01 4.03037131e-01 -7.83088207e-02 2.54534096e-01 9.10484195e-01 4.78163838e-01 2.52438039e-01 -1.04801297e+00 -1.28575176e-01 8.33650231e-01 5.90274990e-01 -5.68136573e-01 -4.73415524e-01 -3.31364036e-01 -9.44387794e-01 5.76231658e-01 -4.29352552e-01 -5.79945922e-01 -7.34837174e-01 1.49017632e+00 6.78699613e-01 -2.26224616e-01 -1.50818303e-01 7.08662570e-01 -3.41532499e-01 1.19954407e+00 5.14304578e-01 -5.70253789e-01 1.24381065e+00 -7.45066285e-01 -9.98701453e-01 2.78360248e-01 1.21025883e-01 -5.81099391e-01 6.17724322e-02 1.24281883e-01 -1.21861291e+00 -5.42403638e-01 -1.30100513e+00 4.33464438e-01 -6.91979945e-01 4.15105164e-01 -3.81717116e-01 4.99375582e-01 -1.13404894e+00 7.71966577e-01 -1.20718908e+00 -5.33128500e-01 -2.30430603e-01 3.72376531e-01 5.99186659e-01 7.84484804e-01 -1.06778836e+00 1.26585901e+00 7.60692835e-01 2.61066973e-01 -7.18666077e-01 -1.00757849e+00 -4.38908458e-01 4.76931423e-01 7.03196451e-02 -1.06820261e+00 1.49902821e+00 -4.84584242e-01 -1.85043585e+00 -2.89513886e-01 -8.18455068e-04 -4.10549998e-01 5.66320181e-01 8.31757952e-03 -4.79402840e-01 -1.51936784e-01 -4.69578058e-01 -6.71974868e-02 4.20470089e-01 -1.40268552e+00 -8.68177891e-01 -1.00507736e-01 -3.45285922e-01 1.50982529e-01 2.03961596e-01 -4.59449559e-01 6.23737931e-01 -2.47038782e-01 -1.37198493e-01 -9.18125510e-01 -4.82779473e-01 -3.21977496e-01 -3.54789406e-01 -1.45884315e-02 1.04342067e+00 -8.16952109e-01 1.55195844e+00 -1.58921456e+00 1.67113528e-01 7.76175439e-01 -4.44310963e-01 2.73973078e-01 9.59481657e-01 9.87792552e-01 -1.67976409e-01 -1.14497952e-01 -3.54174197e-01 6.45307824e-02 2.68993229e-01 2.82413423e-01 7.66163915e-02 1.17853984e-01 9.97857973e-02 2.44755790e-01 -5.57516932e-01 -2.73290932e-01 1.01589978e+00 5.25852144e-01 -1.85055152e-01 4.39620227e-01 -4.76968527e-01 3.67246717e-01 -8.34460020e-01 1.20173179e-01 4.75892842e-01 -6.51537208e-03 5.48794687e-01 -3.77799630e-01 -7.19509780e-01 -1.14213996e-01 -1.72090077e+00 8.98199916e-01 -6.42848074e-01 1.13232940e-01 6.79508209e-01 -8.65386426e-01 6.36494040e-01 7.21385360e-01 7.09108651e-01 -2.92566180e-01 2.47646362e-01 2.84596175e-01 -3.37850630e-01 -2.94315130e-01 4.34043437e-01 -2.94067264e-01 2.99187660e-01 2.36759603e-01 -3.70605439e-01 -6.71483278e-01 1.47185912e-02 -1.64945453e-01 9.19582307e-01 3.29895049e-01 6.99593961e-01 -7.70480871e-01 1.00592673e+00 3.42072338e-01 7.44112194e-01 -2.71427274e-01 -3.21809739e-01 -3.13474536e-01 -3.24985348e-02 -9.79385749e-02 -1.29543841e+00 -6.74852550e-01 1.51797682e-02 4.52742338e-01 -4.23196778e-02 -5.33655770e-02 -8.31579506e-01 1.15652606e-01 1.18860155e-01 1.53680110e+00 -1.41094431e-01 -3.00675541e-01 -3.87792259e-01 -8.97334158e-01 -5.51868260e-01 4.21504974e-01 4.55548018e-01 -1.78022668e-01 -1.21721303e+00 6.67495191e-01 1.79371774e-01 -7.08328485e-01 4.66782898e-02 3.82197708e-01 -8.44465017e-01 -1.07335007e+00 1.74597222e-02 -3.64236265e-01 8.30583036e-01 -3.07153910e-01 9.28230584e-01 7.97304660e-02 -2.51594603e-01 6.42701566e-01 8.87872744e-03 -6.20090246e-01 -7.58047223e-01 -6.67197183e-02 -1.06116399e-01 -1.71181306e-01 -1.80445611e-01 -5.87131977e-01 -8.45329583e-01 4.52069372e-01 -6.27756357e-01 4.63670701e-01 3.07134002e-01 4.11377788e-01 7.42133439e-01 9.52529252e-01 5.34603059e-01 -5.22376299e-01 6.23217225e-01 -4.87310290e-01 -1.35639429e+00 6.35514200e-01 -1.01858330e+00 5.61111458e-02 8.55448306e-01 1.17820635e-01 -1.17056000e+00 6.88167930e-01 3.04713517e-01 3.75671797e-02 -1.92794532e-01 -1.01565480e-01 -3.61619413e-01 -1.18727110e-01 -3.02310526e-01 2.71595210e-01 -1.70432016e-01 -4.33602870e-01 -3.15241478e-02 3.95819962e-01 2.14413553e-01 -5.77214777e-01 1.03054416e+00 -1.30720019e-01 6.55687094e-01 -6.35563493e-01 1.13498859e-01 -3.21346432e-01 -4.66173261e-01 -6.86716497e-01 8.78015161e-01 -1.01424146e+00 -9.72188056e-01 3.59263510e-01 -7.36475408e-01 -4.76524204e-01 -3.94269049e-01 3.21236938e-01 -9.09398615e-01 -4.07060504e-01 -1.99597150e-01 -1.43654716e+00 -6.90579116e-01 -1.06006563e+00 5.77904701e-01 6.22662306e-01 -3.36986214e-01 -8.62263560e-01 4.73228663e-01 2.22723000e-02 8.94702971e-01 1.06192827e+00 1.17126346e+00 -7.16211423e-02 -9.48932171e-01 -2.14381799e-01 3.98995250e-01 5.38215876e-01 4.80853803e-02 4.77664471e-01 -7.82700539e-01 -5.33323705e-01 2.51211226e-01 3.17805976e-01 -2.14437097e-01 4.66980755e-01 6.92099988e-01 -4.34908122e-01 -5.23244262e-01 -1.58509091e-01 2.52698541e+00 7.29680598e-01 1.38236582e-01 1.13028318e-01 1.90574065e-01 3.65515560e-01 6.04874372e-01 7.88746953e-01 5.05099654e-01 6.74005389e-01 5.82826078e-01 -4.93685454e-02 2.34176323e-01 -3.73678878e-02 6.68866858e-02 8.57256770e-01 -2.02998236e-01 -9.20771062e-02 -6.79480016e-01 4.19769764e-01 -1.96078742e+00 -7.35257626e-01 -3.57899904e-01 2.17611170e+00 6.68101847e-01 -1.05015352e-01 -8.19894448e-02 4.42791522e-01 6.74739122e-01 -2.35679626e-01 -3.81672144e-01 -7.98188925e-01 7.32379973e-01 1.08541608e-01 9.37958598e-01 4.46272016e-01 -7.27944672e-01 -4.35918532e-02 5.89264822e+00 6.00049496e-01 -8.00504267e-01 1.06176876e-01 5.29729784e-01 -9.62586328e-02 2.01925114e-01 2.52602935e-01 -3.78507942e-01 6.95480525e-01 1.73575294e+00 -5.56648672e-01 6.95439577e-01 1.12282467e+00 1.25947905e+00 -7.18230426e-01 -1.03710461e+00 5.99677265e-02 -8.12260747e-01 -1.03990304e+00 -5.42398512e-01 2.98857298e-02 1.05606329e+00 -3.29772860e-01 -6.02852106e-01 7.19006062e-02 6.47304893e-01 -2.60613978e-01 8.40051115e-01 9.85511065e-01 -1.66476801e-01 -1.05989265e+00 6.51818395e-01 7.52963245e-01 -1.68746722e+00 -2.20870107e-01 2.88882330e-02 -2.15070486e-01 4.95808631e-01 4.73860294e-01 -7.21334279e-01 9.71137047e-01 5.57140172e-01 -8.15972015e-02 -3.29879522e-01 9.02258694e-01 -1.04314856e-01 6.09410882e-01 -6.85830295e-01 -3.10929179e-01 -1.46743014e-01 -5.52575231e-01 1.72746852e-01 7.61246383e-01 3.13238055e-01 5.75513005e-01 1.34316683e-01 1.00805736e+00 6.35793328e-01 -5.10036536e-02 -1.02719866e-01 2.48912111e-01 4.85963434e-01 1.39633620e+00 -3.22502285e-01 -6.25918269e-01 -1.08414881e-01 3.20817858e-01 -5.00734746e-01 3.24255854e-01 -1.00389934e+00 -1.55011818e-01 5.14775395e-01 3.45086366e-01 2.99303979e-01 -2.90134519e-01 -3.89894664e-01 -2.50678271e-01 -2.31012791e-01 -2.43246213e-01 3.78301032e-02 -9.19163525e-01 -9.30970728e-01 -7.54915625e-02 4.15286690e-01 -1.06844878e+00 -5.96085250e-01 -3.30983102e-01 -9.07186031e-01 9.81721878e-01 -1.46348536e+00 -9.71277714e-01 -3.66834968e-01 3.24962109e-01 3.26179713e-01 2.74527073e-01 7.93926477e-01 3.66565943e-01 -1.12819350e+00 -4.86534953e-01 8.40399325e-01 -5.26935637e-01 -4.52318713e-02 -1.46384227e+00 -2.62883246e-01 9.88884270e-01 -1.18918753e+00 4.38184589e-02 1.12455189e+00 -4.97747511e-01 -1.43978739e+00 -1.43473554e+00 6.55334234e-01 2.74208277e-01 5.36108136e-01 -4.11862209e-02 -6.16548002e-01 5.15494108e-01 8.79958391e-01 -2.99358666e-01 3.09091121e-01 -8.51949096e-01 7.15311408e-01 -4.71484363e-01 -1.61810267e+00 5.96945845e-02 -8.14772025e-02 -1.65722564e-01 -4.24259603e-01 1.57194272e-01 2.83390135e-01 -2.78665096e-01 -1.29256284e+00 5.12597680e-01 3.64847481e-01 -5.36021113e-01 5.97123027e-01 1.83215499e-01 -1.83607683e-01 -9.60738897e-01 -2.17199937e-01 -1.63481295e+00 -3.53413254e-01 -5.16530633e-01 -4.96184140e-01 1.57699883e+00 7.39889964e-02 -6.46170616e-01 2.83352584e-01 1.38632512e+00 -1.17953405e-01 -5.83443642e-01 -1.06639409e+00 -4.43901598e-01 -1.64512277e-01 -3.59865092e-02 7.27380931e-01 4.76396948e-01 2.70760935e-02 6.91368580e-02 2.07519397e-01 7.18175888e-01 7.86768138e-01 -1.13142513e-01 3.07485044e-01 -1.00043654e+00 -1.18497610e-01 -1.92343161e-01 5.79753108e-02 5.19652925e-02 -5.81467390e-01 -8.69990289e-02 2.27736741e-01 -2.01047349e+00 -4.22948562e-02 -5.98647483e-02 -4.46298152e-01 1.64656550e-01 4.33857851e-02 -6.75755620e-01 2.92078674e-01 -8.68528709e-02 2.91551556e-02 9.62037623e-01 5.86153448e-01 1.36930402e-03 -2.75192589e-01 2.93475181e-01 4.64785658e-02 6.29376173e-01 1.11910939e+00 -4.56100732e-01 -7.08001435e-01 1.06888562e-01 -3.26039582e-01 1.13946781e-01 5.33622682e-01 -1.77911294e+00 3.40190947e-01 -5.17581701e-01 3.68071407e-01 -1.05282378e+00 -8.60483386e-03 -1.93205321e+00 1.42153883e+00 1.11771238e+00 -5.22603281e-02 2.75742590e-01 3.13774198e-01 6.11903071e-01 -8.60557053e-03 2.90226519e-01 1.11995041e+00 2.16841735e-02 -3.63947213e-01 -3.33617747e-01 -6.39642477e-01 -7.64310122e-01 1.87208974e+00 1.13363020e-01 -3.80933970e-01 1.54568836e-01 -4.65441823e-01 7.58317649e-01 3.12275738e-01 -7.02896491e-02 -1.90447807e-01 -1.31377816e+00 -4.03054684e-01 -8.96061286e-02 -3.75114709e-01 1.02836929e-01 3.06894332e-01 6.60494268e-01 -7.82800138e-01 6.63428783e-01 -1.16200842e-01 -4.85019416e-01 -1.04573560e+00 5.65119982e-01 9.69059467e-01 -5.75930893e-01 3.15043703e-02 -1.67968974e-01 -3.34624290e-01 -1.81381822e-01 -3.76244307e-01 -8.24154496e-01 1.30465552e-01 -9.25479680e-02 5.58308512e-02 9.52733099e-01 2.04627484e-01 -4.58269924e-01 -3.07436109e-01 5.64949989e-01 8.99320722e-01 4.66869725e-03 1.35421634e+00 -3.96652162e-01 -8.96211639e-02 4.67264920e-01 8.12860906e-01 -7.14356005e-01 -1.29154992e+00 2.76820868e-01 2.49028169e-02 2.87077595e-02 5.83642125e-01 -1.18362784e+00 -9.20949876e-01 1.98074088e-01 8.85899365e-01 7.15172768e-01 1.77274537e+00 -8.23049128e-01 4.27392095e-01 -5.73523715e-02 3.36848050e-01 -2.00108886e+00 -7.76023090e-01 -1.59978420e-01 7.20679998e-01 -6.38907135e-01 5.15288651e-01 -3.23253453e-01 -2.48304784e-01 1.04101157e+00 5.66463232e-01 -3.36136311e-01 1.12064373e+00 4.08155829e-01 -6.72453165e-01 1.60720795e-01 -1.12471795e+00 2.17970267e-01 -5.75454980e-02 1.31011650e-01 1.53077006e-01 4.81459379e-01 -5.42443991e-01 4.78471726e-01 3.58211845e-01 4.41843688e-01 2.95922786e-01 1.50280988e+00 -6.10098422e-01 -7.82406390e-01 -8.73968780e-01 1.15656191e-02 8.65095109e-02 5.06531835e-01 1.82057008e-01 1.00358534e+00 3.23294729e-01 1.25774944e+00 -1.23984143e-01 -1.62700102e-01 1.09247887e+00 2.33920142e-01 -9.25523192e-02 -1.17393412e-01 -9.21545804e-01 2.18523502e-01 5.92688285e-02 -4.92589027e-01 -4.09854472e-01 -5.63153148e-01 -1.27202058e+00 -6.32318437e-01 -3.89579117e-01 3.80154073e-01 1.20711219e+00 8.46103072e-01 4.15603817e-01 1.39544451e+00 1.18337619e+00 -6.52264297e-01 -7.22643971e-01 -7.82993913e-01 -7.86665380e-01 -2.63801455e-01 2.92006910e-01 -4.75886047e-01 -3.62612575e-01 6.35533780e-02]	[5.705544471740723, 2.473729372024536]
08bc404d-f516-4f90-ae25-36d318e7ab60	pixel-aware-deep-function-mixture-network-for	1903.10501	null	http://arxiv.org/abs/1903.10501v1	http://arxiv.org/pdf/1903.10501v1.pdf	Pixel-aware Deep Function-mixture Network for Spectral Super-Resolution	Spectral super-resolution (SSR) aims at generating a hyperspectral image (HSI) from a given RGB image. Recently, a promising direction for SSR is to learn a complicated mapping function from the RGB image to the HSI counterpart using a deep convolutional neural network. This essentially involves mapping the RGB context within a size-specific receptive field centered at each pixel to its spectrum in the HSI. The focus thereon is to appropriately determine the receptive field size and establish the mapping function from RGB context to the corresponding spectrum. Due to their differences in category or spatial position, pixels in HSIs often require different-sized receptive fields and distinct mapping functions. However, few efforts have been invested to explicitly exploit this prior. To address this problem, we propose a pixel-aware deep function-mixture network for SSR, which is composed of a new class of modules, termed function-mixture (FM) blocks. Each FM block is equipped with some basis functions, i.e., parallel subnets of different-sized receptive fields. Besides, it incorporates an extra subnet as a mixing function to generate pixel-wise weights, and then linearly mixes the outputs of all basis functions with those generated weights. This enables us to pixel-wisely determine the receptive field size and the mapping function. Moreover, we stack several such FM blocks to further increase the flexibility of the network in learning the pixel-wise mapping. To encourage feature reuse, intermediate features generated by the FM blocks are fused in late stage, which proves to be effective for boosting the SSR performance. Experimental results on three benchmark HSI datasets demonstrate the superiority of the proposed method.	['Yanning Zhang', 'Shengcai Liao', 'Peng Wang', 'Zhiqiang Lang', 'Wei Wei', 'Lei Zhang', 'Ling Shao']	2019-03-24	null	null	null	null	['spectral-super-resolution']	['computer-vision']	[ 7.99895942e-01 -3.67812753e-01 1.61908850e-01 -3.96103144e-01 -5.19422352e-01 -4.66260344e-01 3.47640157e-01 -4.12203461e-01 -2.31508866e-01 5.90254962e-01 -7.17603043e-02 -1.47662923e-01 -7.50349462e-02 -1.28962076e+00 -7.61643827e-01 -1.27664959e+00 4.02873099e-01 -3.96569014e-01 1.92868814e-01 -2.31102854e-01 5.47300167e-02 6.67184234e-01 -1.59477270e+00 3.26423675e-01 1.31632054e+00 1.26779151e+00 7.77459800e-01 3.57642204e-01 -3.46272886e-01 6.27764285e-01 -4.73749578e-01 1.76452711e-01 5.52102387e-01 -5.61825514e-01 -4.91564453e-01 3.54798108e-01 2.92062670e-01 -2.20961079e-01 -3.85081172e-01 1.30362558e+00 2.33881533e-01 4.35806751e-01 4.66992527e-01 -9.69501913e-01 -7.40831971e-01 4.98469412e-01 -6.86920702e-01 -6.16446286e-02 -1.97523057e-01 3.04933459e-01 7.89281070e-01 -6.52269065e-01 4.11513038e-02 9.24407482e-01 4.15419281e-01 2.82085747e-01 -1.20575249e+00 -8.69041681e-01 1.38184264e-01 -4.05908888e-03 -1.56058729e+00 -2.09674686e-01 1.04755282e+00 -7.68639743e-02 4.45053190e-01 3.67424846e-01 6.46494567e-01 5.09672403e-01 -1.41084775e-01 3.90782863e-01 1.40944195e+00 -2.62420237e-01 1.02902137e-01 2.92612910e-02 -1.85124055e-01 4.32637632e-01 -2.58084126e-02 -2.16702521e-01 -2.05444381e-01 2.59614944e-01 1.10858178e+00 2.89947480e-01 -6.91353321e-01 -7.59349018e-03 -1.04963613e+00 5.48781276e-01 1.23591709e+00 4.82041746e-01 -5.13831139e-01 -2.62306575e-02 -2.33868048e-01 -1.73688114e-01 1.18228599e-01 3.26699972e-01 -2.49681532e-01 7.24218965e-01 -7.06443012e-01 -1.60794243e-01 2.00885698e-01 6.09518290e-01 1.41260564e+00 -2.54286006e-02 -2.80439407e-01 1.04582083e+00 1.67982176e-01 3.42441022e-01 4.02678192e-01 -8.59098494e-01 5.54975212e-01 8.45344186e-01 7.82169998e-02 -8.38890493e-01 -3.36804837e-01 -4.98224050e-01 -1.02346563e+00 2.58988976e-01 2.46187076e-01 3.48236337e-02 -1.07283640e+00 1.65005946e+00 3.94892186e-01 2.94172496e-01 1.12452798e-01 1.05777717e+00 7.98874140e-01 1.00824320e+00 9.58626643e-02 -2.08889768e-01 1.20324087e+00 -9.24204409e-01 -2.22921297e-01 -3.88255447e-01 3.84914055e-02 -5.87160826e-01 1.18356633e+00 7.50455633e-02 -8.53907228e-01 -9.04288590e-01 -1.17677724e+00 1.70771256e-02 -4.72558618e-01 5.22619843e-01 6.18750215e-01 3.78386706e-01 -9.58898723e-01 5.57976484e-01 -5.36154509e-01 8.58307641e-04 4.68392968e-01 3.83695424e-01 -2.42453590e-01 -4.20772843e-02 -1.19555080e+00 4.58529979e-01 7.95956969e-01 5.50724804e-01 -5.75074375e-01 -6.60590470e-01 -7.52975523e-01 3.03769737e-01 1.93986490e-01 -3.92858535e-01 6.31691217e-01 -1.25784791e+00 -1.52448857e+00 4.91937280e-01 3.02720610e-02 4.24018241e-02 -2.91579831e-02 5.29683650e-01 -6.31600559e-01 1.80607870e-01 3.28404792e-02 7.35039771e-01 1.12780607e+00 -1.43901193e+00 -9.91895139e-01 -4.36840832e-01 3.32119018e-01 3.57757419e-01 -7.95003235e-01 -2.89412528e-01 -4.96820867e-01 -5.40325701e-01 4.22323257e-01 -6.52214289e-01 -2.02721804e-01 -1.96240187e-01 -4.16330546e-01 5.80192879e-02 5.10506749e-01 -6.95160866e-01 1.15852916e+00 -2.31312704e+00 9.00573954e-02 3.36986601e-01 1.01526290e-01 2.79196411e-01 -2.38515660e-01 -4.48038941e-03 -2.84728587e-01 -5.34465685e-02 -6.54624045e-01 2.60144234e-01 -3.18759054e-01 -6.40809014e-02 -1.76642686e-01 4.06795859e-01 4.38690931e-01 7.16172457e-01 -7.35139549e-01 -2.64671892e-01 3.69365573e-01 6.62514150e-01 -1.93509102e-01 2.57643521e-01 -2.53646046e-01 5.75351000e-01 -5.90554595e-01 6.29551768e-01 1.27519488e+00 -2.46460244e-01 1.08242862e-01 -6.82002187e-01 -5.03683269e-01 1.38326082e-02 -1.22287464e+00 1.54772496e+00 -6.09315455e-01 1.53076097e-01 3.10979247e-01 -1.11317933e+00 1.23081112e+00 -1.21825613e-01 5.14756620e-01 -6.24865055e-01 7.23390877e-02 2.52531230e-01 -1.09282792e-01 -3.02700579e-01 3.09698343e-01 -1.64602295e-01 2.33258590e-01 5.41810095e-02 -1.11406073e-01 -1.01591684e-02 3.96326035e-02 -5.13265610e-01 5.89847744e-01 2.25595266e-01 -1.17555186e-02 -1.45072401e-01 1.06216705e+00 8.57320428e-03 5.68148911e-01 4.32571828e-01 1.50184408e-01 5.97600162e-01 -1.53073315e-02 -3.91331494e-01 -8.33843470e-01 -8.35120738e-01 -3.54580075e-01 1.00824142e+00 4.41404283e-01 1.08167738e-01 -7.60629356e-01 -4.47305292e-01 -1.33766949e-01 4.77367252e-01 -6.35085762e-01 -2.48600617e-01 -5.22219718e-01 -1.02927470e+00 3.06535572e-01 5.38946033e-01 1.29286981e+00 -1.21455252e+00 -3.86884212e-01 1.65282875e-01 -2.30269819e-01 -9.40701723e-01 -2.79862553e-01 3.59040231e-01 -7.09159255e-01 -1.00583589e+00 -7.05009639e-01 -8.22852075e-01 9.51903164e-01 7.33889520e-01 4.99808729e-01 -1.32615447e-01 -1.67089701e-01 -1.55015379e-01 -3.18321556e-01 -4.11357172e-02 -2.10613176e-01 9.59893242e-02 -5.07700205e-01 6.34970009e-01 1.15199551e-01 -5.69604993e-01 -9.11828816e-01 2.99460888e-01 -1.48721933e+00 2.73727268e-01 8.23368251e-01 7.33091891e-01 7.37061083e-01 7.12114871e-01 3.07505429e-01 -5.76566815e-01 3.12944978e-01 -4.05833781e-01 -7.50453234e-01 4.11072999e-01 -1.32379249e-01 -4.41627614e-02 1.08030486e+00 -3.04190665e-01 -1.33073354e+00 4.62154657e-01 -4.65552993e-02 -3.07150304e-01 -2.64126480e-01 5.61724067e-01 -5.60540318e-01 -4.19150084e-01 6.00807667e-01 6.53835773e-01 -3.32391225e-02 -3.12105924e-01 3.57938588e-01 8.22320104e-01 6.10457182e-01 -3.27844083e-01 9.84050214e-01 5.42059779e-01 -2.21155602e-02 -6.50448143e-01 -7.62898743e-01 -3.29374999e-01 -4.15512174e-01 -1.35525659e-01 1.01302862e+00 -9.55666065e-01 -5.96599638e-01 6.70229614e-01 -8.33945453e-01 -3.76018941e-01 -1.09694958e-01 2.82412857e-01 -1.95643783e-01 1.38340458e-01 -3.56340498e-01 -6.21482551e-01 -2.40851417e-01 -1.18239605e+00 1.00048792e+00 8.71406198e-01 7.02849984e-01 -6.45549595e-01 -3.18874568e-01 2.82643020e-01 5.21076560e-01 1.13987774e-01 8.96114230e-01 4.05879803e-02 -7.21626520e-01 1.54593410e-02 -7.14831352e-01 6.54212534e-01 5.82681358e-01 2.11780034e-02 -1.19516659e+00 -1.47920385e-01 7.91611448e-02 -1.22097403e-01 1.24246633e+00 4.68711078e-01 1.64054704e+00 1.75439883e-02 -1.61858335e-01 1.09141886e+00 1.77559316e+00 3.62972796e-01 7.30894208e-01 4.76533443e-01 9.93235350e-01 5.17445266e-01 4.38691109e-01 3.37283880e-01 2.60729641e-01 4.44707364e-01 6.08717203e-01 -4.19619650e-01 -2.03378662e-01 -5.95146678e-02 2.62302190e-01 2.83319950e-01 -2.75366068e-01 -4.24350463e-02 -4.49535817e-01 5.98923117e-02 -1.49991190e+00 -8.48169148e-01 1.75711792e-02 2.25183153e+00 8.89965475e-01 -1.73537910e-01 -8.92171264e-02 5.94365895e-02 1.10322213e+00 3.43406141e-01 -8.59559774e-01 2.75943249e-01 -3.77982438e-01 3.80950898e-01 6.30315661e-01 2.28388518e-01 -1.19080377e+00 9.11578059e-01 5.00670767e+00 1.09150434e+00 -1.62200129e+00 -2.27321386e-01 6.94348574e-01 3.61793250e-01 -4.74294871e-01 -1.32350801e-02 -7.11188436e-01 5.27052224e-01 3.88288260e-01 2.50057638e-01 1.03864908e+00 5.07966459e-01 1.75976858e-01 -8.37086812e-02 -3.80784929e-01 8.81657600e-01 -2.01487735e-01 -1.27779984e+00 3.05640027e-02 4.83357301e-03 8.31170201e-01 -1.31698519e-01 8.15862790e-02 1.56691134e-01 -1.29945362e-02 -8.86972249e-01 5.22087336e-01 5.57482839e-01 1.03715718e+00 -8.00770223e-01 3.75457019e-01 1.92932203e-01 -1.65092468e+00 -4.79631096e-01 -8.92649233e-01 2.31909469e-01 -4.72015768e-01 5.09855330e-01 -5.09919226e-01 6.46396160e-01 7.63272643e-01 5.18869221e-01 -5.48396468e-01 7.70645201e-01 -2.34520584e-01 3.46777260e-01 -1.60819143e-01 2.71040022e-01 2.99426377e-01 -7.36013710e-01 3.76764014e-02 8.64629090e-01 5.37585795e-01 2.07038045e-01 1.07532419e-01 1.39412618e+00 -1.44155681e-01 8.69287476e-02 -3.27010095e-01 -1.54743627e-01 5.33614457e-01 1.81450021e+00 -8.83458138e-01 -8.12306181e-02 -4.98223275e-01 8.57255459e-01 2.80474931e-01 6.82061255e-01 -9.19957876e-01 -5.69821239e-01 5.19604623e-01 -5.48962988e-02 5.85900366e-01 9.01626274e-02 -2.34290570e-01 -9.60522950e-01 1.04865424e-01 -6.81080043e-01 2.43493453e-01 -9.99381602e-01 -1.10673785e+00 8.52311134e-01 -2.33944625e-01 -1.47131312e+00 3.51480991e-01 -5.72941303e-01 -7.37836540e-01 1.44231713e+00 -1.85287333e+00 -1.39864194e+00 -9.58361149e-01 8.47788870e-01 1.93495780e-01 6.90715834e-02 6.34159744e-01 1.32475063e-01 -8.28651190e-01 2.24858969e-01 1.30019948e-01 6.73078699e-03 4.62408960e-01 -9.85594213e-01 -1.57305989e-02 8.42376828e-01 -2.89809018e-01 5.96431255e-01 1.67035267e-01 -3.68790686e-01 -1.30844963e+00 -1.49683642e+00 2.43921682e-01 1.78897053e-01 5.10814965e-01 -1.01579696e-01 -9.84480619e-01 2.58114934e-01 -8.96178186e-02 2.92311937e-01 4.98140842e-01 -4.97824490e-01 -2.46834263e-01 -8.15370321e-01 -1.13383567e+00 4.22831595e-01 8.77820849e-01 -5.71917832e-01 -2.55441777e-02 4.06364739e-01 6.92565024e-01 -2.73483843e-01 -8.38270187e-01 7.26782739e-01 2.84884810e-01 -1.15536523e+00 1.05383790e+00 -1.15202151e-01 5.77419400e-01 -8.42003286e-01 -1.57297194e-01 -1.33035815e+00 -6.41889453e-01 -8.27127397e-02 3.97352457e-01 1.36764646e+00 2.21702814e-01 -7.91481495e-01 6.98424697e-01 4.84227210e-01 -4.24434245e-01 -6.78016067e-01 -5.44244468e-01 -4.84951973e-01 -1.39947623e-01 -1.78114563e-01 1.14891422e+00 8.37273180e-01 -6.22086287e-01 7.79345185e-02 3.25455368e-02 6.39805853e-01 4.43072021e-01 6.44490480e-01 4.20208246e-01 -1.07820618e+00 -2.04600900e-01 -7.63570309e-01 -2.54693218e-02 -8.31338942e-01 1.66012153e-01 -9.30149257e-01 2.35962421e-01 -1.63424826e+00 1.23752408e-01 -7.41136074e-01 -6.49422467e-01 6.11113966e-01 -5.90871334e-01 4.24497932e-01 1.62871107e-02 2.35762686e-01 -4.44700830e-02 5.86405993e-01 1.56241226e+00 -3.98509324e-01 -4.32394087e-01 9.18689668e-02 -8.81106675e-01 4.06202883e-01 1.02350378e+00 -1.07298736e-02 -4.10060674e-01 -3.63585353e-01 -2.70029884e-02 -3.67764644e-02 4.04625237e-01 -1.09279251e+00 2.05614373e-01 -5.14993370e-01 7.75378168e-01 -3.98572594e-01 2.38780826e-01 -9.59742367e-01 3.24877709e-01 8.40810686e-02 -1.09294459e-01 -6.28818870e-01 1.25631124e-01 2.79332429e-01 -2.31782854e-01 -2.92939812e-01 7.86713660e-01 -2.73524493e-01 -9.59277213e-01 5.07970989e-01 8.40588585e-02 -5.38873672e-01 9.35063660e-01 -4.08717811e-01 -2.87282497e-01 1.38701098e-02 -3.90456229e-01 8.08547214e-02 4.36131001e-01 1.28529608e-01 6.19088173e-01 -1.44615519e+00 -5.24518430e-01 5.84989250e-01 2.12474063e-01 3.86826247e-01 5.88913023e-01 6.75237000e-01 -4.72459793e-01 1.08744621e-01 -5.00272214e-01 -4.91557270e-01 -6.91949129e-01 4.30843681e-01 6.12460434e-01 5.53770214e-02 -2.69081712e-01 9.39953089e-01 5.59345186e-01 -5.59642553e-01 -6.22644685e-02 -3.47069174e-01 -5.30321002e-01 -1.49396315e-01 5.76876044e-01 2.46837020e-01 9.10614505e-02 -6.93705261e-01 -1.03002355e-01 6.24920964e-01 3.72372359e-01 1.64843380e-01 1.37701464e+00 1.07540032e-02 -5.55144727e-01 1.31862834e-01 1.14998639e+00 -6.99094161e-02 -1.66936040e+00 -3.31466913e-01 -6.03835464e-01 -5.10443211e-01 3.40589762e-01 -6.39135480e-01 -1.60141432e+00 6.32630825e-01 4.77457643e-01 2.99114108e-01 1.89287078e+00 -2.94041574e-01 6.05190635e-01 8.72315466e-02 1.53263167e-01 -1.01393294e+00 -2.54191875e-01 2.18489200e-01 7.28344083e-01 -1.06912673e+00 -2.77196676e-01 -5.46047509e-01 -3.74042392e-01 1.43323445e+00 7.98071146e-01 -8.40274692e-02 4.13966656e-01 2.67617702e-02 -4.45539318e-02 4.54151705e-02 7.86013976e-02 -5.36339879e-01 4.35250938e-01 5.70306420e-01 3.10127705e-01 1.70465946e-01 5.18447384e-02 5.80363631e-01 4.63830456e-02 -2.77364656e-04 1.20775320e-01 6.27248049e-01 -6.17265165e-01 -9.48097825e-01 -5.55566370e-01 4.23046470e-01 8.74321461e-02 -2.03771979e-01 -6.32303357e-02 4.22137111e-01 5.66887259e-01 9.16314244e-01 9.32886451e-02 -7.07959175e-01 2.88500339e-01 -2.28279620e-01 4.29327726e-01 -5.23792326e-01 -4.90104556e-01 3.52378428e-01 -5.19448400e-01 -4.02854860e-01 -5.85649431e-01 -3.44964534e-01 -1.49309194e+00 -7.35894293e-02 -4.47974443e-01 3.10983509e-02 6.60511613e-01 8.53295386e-01 3.35769318e-02 7.35461533e-01 1.20436716e+00 -9.86166835e-01 -5.09527802e-01 -9.18756425e-01 -8.34216118e-01 1.81895480e-01 2.84068912e-01 -3.80483061e-01 -3.08988154e-01 -5.85159063e-02]	[10.128504753112793, -1.8206275701522827]
ace1c359-5914-481c-8aec-d5a7c2b048ef	deep-learning-hyperspectral-image	1807.10574	null	http://arxiv.org/abs/1807.10574v1	http://arxiv.org/pdf/1807.10574v1.pdf	Deep Learning Hyperspectral Image Classification Using Multiple Class-based Denoising Autoencoders, Mixed Pixel Training Augmentation, and Morphological Operations	Herein, we present a system for hyperspectral image segmentation that utilizes multiple class--based denoising autoencoders which are efficiently trained. Moreover, we present a novel hyperspectral data augmentation method for labelled HSI data using linear mixtures of pixels from each class, which helps the system with edge pixels which are almost always mixed pixels. Finally, we utilize a deep neural network and morphological hole-filling to provide robust image classification. Results run on the Salinas dataset verify the high performance of the proposed algorithm.	['Wei Pan', 'Ball John E.']	2018-07-11	null	null	null	null	['hyperspectral-image-segmentation']	['computer-vision']	[ 7.22264469e-01 -1.85671300e-01 2.63055980e-01 -3.24444145e-01 -4.44448292e-01 -4.84884709e-01 1.67741656e-01 -2.74572760e-01 -4.56995934e-01 7.18776166e-01 -2.53042430e-01 -3.90220731e-01 -1.25827193e-01 -1.18231106e+00 -6.54548049e-01 -1.14125001e+00 1.65859938e-01 2.15587262e-02 -1.94728836e-01 -2.24622875e-01 -5.21060899e-02 8.32892895e-01 -1.84039855e+00 2.15464473e-01 1.21079481e+00 1.22193944e+00 2.51237959e-01 6.95258021e-01 -5.36042564e-02 4.88478720e-01 -5.09688199e-01 2.21712112e-01 8.46524417e-01 -2.35960424e-01 -5.48570156e-01 8.68980110e-01 7.36856937e-01 -7.79425144e-01 -3.59737784e-01 1.51261616e+00 2.58044422e-01 4.94925559e-01 6.50384724e-01 -1.09448409e+00 -4.83817577e-01 7.09435105e-01 -7.66545594e-01 -2.65303664e-02 -4.74499136e-01 -6.88998587e-03 5.47963142e-01 -7.13771164e-01 2.61960953e-01 6.81522965e-01 7.86908090e-01 8.37118477e-02 -8.35545421e-01 -5.69953024e-01 -3.61878648e-02 2.38987982e-01 -1.39426327e+00 -1.66335985e-01 8.35663021e-01 -3.01890373e-01 7.46025681e-01 4.83755946e-01 9.03502643e-01 4.12915170e-01 -2.45172724e-01 8.52328598e-01 1.37812030e+00 -5.28935552e-01 3.02423745e-01 -1.03189752e-01 4.07492578e-01 5.07034123e-01 3.33890140e-01 2.14885339e-01 1.65765747e-01 2.16782227e-01 7.44360983e-01 4.81856704e-01 -4.85255212e-01 1.81714371e-01 -3.89102370e-01 9.28214133e-01 6.93112254e-01 3.74508381e-01 -8.02896380e-01 -2.48661086e-01 7.73618668e-02 -4.73198406e-02 5.51352739e-01 2.17431605e-01 -3.06087017e-01 8.79047632e-01 -1.38910508e+00 -3.11142147e-01 4.29459304e-01 7.79379666e-01 1.26705360e+00 6.53027833e-01 1.09396748e-01 1.01376355e+00 2.91554868e-01 6.54828250e-01 5.05633771e-01 -8.92353296e-01 -2.53128380e-01 7.29724824e-01 -4.72128354e-02 -8.20686281e-01 -4.87463027e-01 -6.38567328e-01 -1.17065644e+00 6.45520389e-01 -2.33493999e-01 -2.13788405e-01 -1.62036943e+00 1.16649163e+00 2.13451520e-01 3.95977646e-01 6.02270603e-01 8.42844069e-01 8.70180368e-01 1.05224907e+00 2.07775265e-01 -1.99829936e-01 9.24046516e-01 -1.26694059e+00 -8.66549969e-01 -3.10473412e-01 2.03001440e-01 -3.47650290e-01 7.12855160e-01 6.57332778e-01 -7.27948785e-01 -6.36613131e-01 -1.29933798e+00 7.61066452e-02 -9.65774238e-01 6.30012453e-01 1.01532471e+00 8.72310996e-01 -1.05721867e+00 6.44247174e-01 -8.08170259e-01 -3.02576452e-01 5.64251781e-01 3.99182260e-01 -3.42480242e-01 5.79226539e-02 -8.41139615e-01 5.12618542e-01 1.04070437e+00 5.68423152e-01 -1.00244343e+00 -3.71097863e-01 -9.27752376e-01 2.27352083e-01 -1.18953688e-02 -8.75316486e-02 7.71424413e-01 -1.37937927e+00 -1.41966760e+00 7.11929619e-01 9.26610902e-02 -5.54653645e-01 9.34989899e-02 -1.11801758e-01 -6.30431235e-01 5.96035659e-01 -2.34024793e-01 8.79916608e-01 9.41487789e-01 -1.76877177e+00 -7.90837288e-01 -3.96692902e-01 -2.15772703e-01 3.34585488e-01 -7.79665530e-01 -4.60658878e-01 1.06146663e-01 -6.05948567e-01 6.81501210e-01 -6.23276293e-01 -4.10101354e-01 -4.91627976e-02 -5.96000433e-01 3.07330251e-01 1.35904002e+00 -1.12495410e+00 7.10213780e-01 -2.24977612e+00 -2.03042075e-01 4.73666400e-01 -1.25731051e-01 3.95123065e-01 -2.51901716e-01 -6.96846247e-02 -4.58751976e-01 1.77461833e-01 -1.02968335e+00 -1.95007548e-01 -1.26979291e-01 4.99861181e-01 -2.07331166e-01 5.01361251e-01 2.31199130e-01 5.68635285e-01 -3.06757122e-01 -7.06354007e-02 6.78928018e-01 4.50759202e-01 -2.08683133e-01 3.11437339e-01 -1.68593049e-01 4.50964570e-02 5.51195629e-02 1.02381933e+00 1.46017325e+00 1.64624572e-01 -2.65625238e-01 -6.64480448e-01 -4.28747565e-01 -5.65643609e-01 -1.28291321e+00 1.38324106e+00 -3.13665681e-02 4.03731465e-01 6.13079548e-01 -1.21841490e+00 9.53437805e-01 1.80010512e-01 4.87471253e-01 -1.53440073e-01 4.11299020e-01 1.85471117e-01 -3.15123737e-01 -4.56560254e-01 7.42997169e-01 -1.74879417e-01 7.98379064e-01 1.47881031e-01 2.21695140e-01 -5.16833961e-01 2.95047849e-01 -2.23939031e-01 4.44578409e-01 1.19859360e-01 8.67600739e-03 -3.56842190e-01 6.31786048e-01 4.37858343e-01 4.38422531e-01 8.91737759e-01 -2.36050189e-01 8.20086181e-01 -3.25638950e-01 -4.09140617e-01 -1.04258502e+00 -5.46725690e-01 -3.51712435e-01 8.68218660e-01 1.50730401e-01 6.15887582e-01 -8.92446458e-01 -3.53327215e-01 -2.33845472e-01 6.63747907e-01 -6.05220914e-01 1.88973024e-02 5.78057542e-02 -1.50592995e+00 5.21089792e-01 6.09059155e-01 1.05547035e+00 -1.17153072e+00 -3.18138570e-01 1.65872350e-01 -4.67259549e-02 -1.13291883e+00 3.01678240e-01 7.84177899e-01 -1.08742344e+00 -1.13120842e+00 -6.46511555e-01 -1.04201281e+00 8.64839315e-01 5.08567870e-01 5.51915169e-01 8.24473500e-02 -3.84286612e-01 1.65611982e-01 -4.90469009e-01 -3.33690047e-01 -3.40354502e-01 -9.91423205e-02 -3.09561104e-01 1.09223343e-01 3.74886245e-01 -6.54412210e-01 -5.83210647e-01 -1.20177023e-01 -1.60046566e+00 1.37040308e-02 6.93226218e-01 9.23471034e-01 7.40015686e-01 7.42534399e-01 1.44375309e-01 -8.65059197e-01 2.31968492e-01 -2.80549675e-01 -8.26489151e-01 3.29084516e-01 -4.17046040e-01 -4.41981286e-01 5.34500778e-01 -1.83898926e-01 -1.33630848e+00 5.53069115e-01 -4.94078964e-01 -3.36470574e-01 -9.40771341e-01 7.23862231e-01 -2.05596983e-01 -5.30024230e-01 7.96504140e-01 5.94606459e-01 6.77469419e-03 -3.11426282e-01 3.94161791e-01 9.84681666e-01 8.75609159e-01 -3.44125628e-02 8.40615571e-01 7.88160801e-01 -2.35491693e-01 -1.27333558e+00 -7.53742874e-01 -5.78984737e-01 -7.03957140e-01 -1.65614814e-01 9.25968409e-01 -1.19460022e+00 -3.35342318e-01 1.00084317e+00 -8.10453534e-01 -4.59250718e-01 -3.63184988e-01 4.10981804e-01 -1.52759582e-01 5.74341059e-01 -8.78255963e-01 -9.60721195e-01 -6.88762486e-01 -1.08036053e+00 8.34979057e-01 7.69481719e-01 9.46641684e-01 -9.31404710e-01 -4.69821811e-01 3.76067519e-01 4.07102406e-01 2.95880854e-01 5.78238964e-01 -7.23257482e-01 -5.16043067e-01 -1.51268750e-01 -5.23515224e-01 1.08166575e+00 2.83771932e-01 4.07016605e-01 -1.33724523e+00 -1.01310693e-01 6.21387474e-02 -3.44653159e-01 1.27437961e+00 8.55683625e-01 1.62097979e+00 -1.49606233e-02 -6.65706173e-02 1.20358634e+00 1.94813836e+00 4.36797500e-01 8.43020320e-01 5.45688391e-01 6.89170897e-01 4.39162761e-01 2.79867500e-01 4.89415735e-01 -1.40757844e-01 -5.31287074e-01 1.00329208e+00 -9.46515322e-01 1.33556738e-01 3.41718137e-01 -1.69654652e-01 4.70481485e-01 -1.12893067e-01 -3.57506067e-01 -5.82382739e-01 5.12826085e-01 -1.45381117e+00 -8.55542183e-01 -5.59662521e-01 1.56935930e+00 5.90940535e-01 -4.71664459e-01 -4.40536976e-01 6.20143354e-01 8.17257524e-01 1.36912927e-01 -5.79584360e-01 1.12227030e-01 -7.97391832e-01 5.88368714e-01 9.77354884e-01 6.37229025e-01 -1.76856458e+00 1.15906465e+00 6.98804712e+00 6.34889722e-01 -1.27289951e+00 -1.07995443e-01 6.98360145e-01 7.92279243e-01 -8.02972466e-02 -2.77498633e-01 -4.53310251e-01 -3.78310382e-02 4.58318293e-01 3.71273726e-01 6.02227867e-01 8.70370686e-01 1.06018029e-01 -4.74364519e-01 -1.68306619e-01 8.23873162e-01 2.09914073e-01 -1.16268146e+00 2.03156158e-01 -2.15196222e-01 1.13138926e+00 -3.63071519e-03 3.20438631e-02 -2.86761131e-02 3.47809434e-01 -8.19333255e-01 3.04046541e-01 4.33751494e-01 3.62704486e-01 -8.39628100e-01 9.01234925e-01 1.82822019e-01 -8.81145835e-01 -4.36465532e-01 -7.82357335e-01 1.33221790e-01 -1.85789928e-01 7.82232106e-01 -5.48922837e-01 7.34357476e-01 7.11524069e-01 7.51412809e-01 -6.38173819e-01 1.25622833e+00 -3.75267923e-01 7.82540023e-01 -5.34275651e-01 6.02338970e-01 4.28625405e-01 -8.55532944e-01 2.73258626e-01 1.20498836e+00 5.59907258e-01 5.36249816e-01 2.75799125e-01 9.25271153e-01 4.25169058e-02 5.23352958e-02 -5.64858258e-01 -2.33631775e-01 1.33453801e-01 1.69427896e+00 -9.32988703e-01 -5.28849006e-01 -1.84296101e-01 1.13892674e+00 -3.52921963e-01 6.74848497e-01 -4.83333707e-01 -2.88237840e-01 4.01825100e-01 -5.29577196e-01 1.48867756e-01 -1.95452422e-01 -5.00239909e-01 -9.30498481e-01 -5.06117821e-01 -8.14851344e-01 4.05955106e-01 -1.34737265e+00 -1.21627641e+00 8.72308314e-01 -4.10683423e-01 -1.06128645e+00 3.45795572e-01 -1.13172960e+00 -4.22077626e-01 7.94605315e-01 -2.04629374e+00 -1.39173496e+00 -1.03914928e+00 6.96873248e-01 3.37031096e-01 -2.37164646e-01 1.05061233e+00 3.28473777e-01 -8.81490231e-01 -6.42564613e-03 4.00971055e-01 1.99038938e-01 2.16741115e-01 -1.29414129e+00 -2.84068584e-01 1.41357493e+00 -5.74465282e-02 2.07231969e-01 4.04823899e-01 -5.47919273e-01 -1.07799172e+00 -1.52470851e+00 -1.10127181e-01 6.43490195e-01 3.86555463e-01 3.15898746e-01 -1.09945488e+00 8.23881686e-01 4.95836079e-01 -3.61770689e-02 9.30132329e-01 -4.64177459e-01 4.15486917e-02 -2.22782955e-01 -1.55786705e+00 2.44670451e-01 4.16185290e-01 -3.13256353e-01 -3.53406280e-01 7.97915518e-01 4.65858132e-01 -3.02556187e-01 -6.93978131e-01 8.10611367e-01 -5.02188085e-03 -9.12083030e-01 8.18589449e-01 -2.17780098e-01 4.16358054e-01 -6.45182073e-01 -3.95858616e-01 -1.42960787e+00 -3.19449693e-01 6.31852001e-02 3.08765233e-01 9.89418566e-01 3.80855531e-01 -4.78192270e-01 1.12326539e+00 3.12783152e-01 -5.19640684e-01 2.39395633e-01 -3.07374805e-01 -4.55923706e-01 -1.15887776e-01 -2.06118122e-01 4.97480512e-01 1.22118902e+00 -6.56707168e-01 -2.39739805e-01 -3.06399465e-01 9.37686265e-01 7.97816575e-01 1.93811804e-01 1.64592683e-01 -1.33910716e+00 3.16320434e-02 -4.01468992e-01 -8.88164937e-02 -6.84318781e-01 3.38623643e-01 -6.92992985e-01 3.77868801e-01 -1.75453901e+00 3.53655182e-02 -1.89078644e-01 -2.65205920e-01 9.51755762e-01 -2.67396927e-01 7.11707711e-01 -3.33408117e-01 -1.68143079e-01 1.76528364e-01 6.19126320e-01 9.37961817e-01 -6.41381681e-01 -3.99763733e-01 -1.46625459e-01 -6.90787613e-01 7.43346035e-01 1.02298188e+00 -2.25097537e-01 -1.37056783e-01 -6.02438688e-01 -2.78866738e-01 -3.12242389e-01 3.20966840e-01 -1.37008727e+00 3.95198733e-01 -1.97851568e-01 7.69030571e-01 -9.73971367e-01 3.57402116e-01 -1.37056601e+00 2.43001133e-01 3.89231473e-01 1.36044389e-02 -5.68948984e-01 5.74073553e-01 1.55122966e-01 -4.38558459e-01 -7.35243618e-01 1.19195104e+00 -1.73637047e-01 -1.09987891e+00 3.96708161e-01 -6.20671213e-01 -8.55123878e-01 9.04395223e-01 -4.11920071e-01 -2.26370871e-01 -2.42517397e-01 -9.20214713e-01 -5.48163475e-03 2.51288205e-01 -2.40192130e-01 6.26235902e-01 -8.98843527e-01 -6.77070558e-01 5.87121189e-01 -1.04375780e-01 1.75409257e-01 4.84688193e-01 3.85399997e-01 -1.14334869e+00 -2.39557490e-01 -5.88680029e-01 -5.86280286e-01 -1.07172227e+00 2.89387077e-01 8.00438225e-01 3.55828464e-01 -3.36359173e-01 9.77100670e-01 -2.27707401e-01 -6.75483227e-01 1.65391728e-01 -3.21439058e-01 -5.12802362e-01 2.09584519e-01 5.35463989e-01 3.17765743e-01 3.00007343e-01 -4.86967266e-01 8.55531618e-02 2.28352681e-01 4.59611893e-01 -4.07218784e-02 1.79956174e+00 1.03832604e-02 -6.43649280e-01 -2.01351494e-02 8.22434902e-01 -3.49470764e-01 -1.42808807e+00 2.15745140e-02 -4.30518866e-01 -1.82698309e-01 6.58713222e-01 -1.01270819e+00 -1.70693159e+00 9.10910964e-01 1.12473404e+00 3.48154843e-01 1.83212590e+00 -8.65372479e-01 3.56570303e-01 6.11852705e-01 -1.96073800e-01 -1.26232505e+00 -4.53157157e-01 4.90585029e-01 6.09365404e-01 -1.45553744e+00 1.21287160e-01 -5.43487608e-01 -4.05370891e-01 1.47224247e+00 6.02571726e-01 -1.59946814e-01 7.98905611e-01 4.58592296e-01 4.23349202e-01 4.95892502e-02 1.83046535e-01 -7.41970420e-01 -3.99214365e-02 8.79879355e-01 8.54294449e-02 1.68357998e-01 -3.14612150e-01 4.82660711e-01 -6.46213861e-03 -1.67454675e-01 8.55092049e-01 8.96715701e-01 -9.43457663e-01 -7.46839166e-01 -7.56694973e-01 4.02129591e-01 -2.54417419e-01 -3.58559966e-01 -2.11277127e-01 6.47741437e-01 2.96799034e-01 1.17641175e+00 1.20504379e-01 -3.08849871e-01 6.39516413e-02 2.76258200e-01 6.65780231e-02 -3.40163738e-01 -5.85518241e-01 4.44377929e-01 -4.49161112e-01 4.77698371e-02 -8.30824971e-01 -3.57447207e-01 -1.14530730e+00 3.09985783e-02 -6.39515877e-01 6.97132722e-02 1.06521070e+00 8.59867156e-01 -1.39362827e-01 6.70205951e-01 6.81923091e-01 -1.16106808e+00 -3.35221082e-01 -1.25365233e+00 -1.29512382e+00 1.17382981e-01 3.62475753e-01 -1.06465198e-01 -7.01680064e-01 4.96026009e-01]	[9.98850154876709, -1.841872215270996]
0dc0642e-01cb-4d30-835d-37d0a629fb89	talktomodel-understanding-machine-learning	2207.04154	null	https://arxiv.org/abs/2207.04154v4	https://arxiv.org/pdf/2207.04154v4.pdf	TalkToModel: Explaining Machine Learning Models with Interactive Natural Language Conversations	Machine Learning (ML) models are increasingly used to make critical decisions in real-world applications, yet they have become more complex, making them harder to understand. To this end, researchers have proposed several techniques to explain model predictions. However, practitioners struggle to use these explainability techniques because they often do not know which one to choose and how to interpret the results of the explanations. In this work, we address these challenges by introducing TalkToModel: an interactive dialogue system for explaining machine learning models through conversations. Specifically, TalkToModel comprises of three key components: 1) a natural language interface for engaging in conversations, making ML model explainability highly accessible, 2) a dialogue engine that adapts to any tabular model and dataset, interprets natural language, maps it to appropriate explanations, and generates text responses, and 3) an execution component that constructs the explanations. We carried out extensive quantitative and human subject evaluations of TalkToModel. Overall, we found the conversational system understands user inputs on novel datasets and models with high accuracy, demonstrating the system's capacity to generalize to new situations. In real-world evaluations with humans, 73% of healthcare workers (e.g., doctors and nurses) agreed they would use TalkToModel over baseline point-and-click systems for explainability in a disease prediction task, and 85% of ML professionals agreed TalkToModel was easier to use for computing explanations. Our findings demonstrate that TalkToModel is more effective for model explainability than existing systems, introducing a new category of explainability tools for practitioners. Code & demo released here: https://github.com/dylan-slack/TalkToModel.	['Sameer Singh', 'Himabindu Lakkaraju', 'Satyapriya Krishna', 'Dylan Slack']	2022-07-08	null	null	null	null	['disease-prediction']	['medical']	[ 2.49126311e-02 7.86411643e-01 -3.10488045e-01 -8.39562595e-01 -7.01556206e-01 -5.41024685e-01 3.02518189e-01 3.93081307e-01 1.87642455e-01 7.09697783e-01 5.42162538e-01 -8.51791382e-01 -7.89673850e-02 -4.47050571e-01 -4.30198848e-01 9.73664001e-02 2.87274659e-01 9.18779433e-01 -3.57856601e-01 -1.03454523e-01 3.04500937e-01 9.43611488e-02 -1.23510098e+00 1.05376875e+00 1.03743637e+00 2.72657871e-01 5.57327867e-02 9.83095944e-01 -3.73321325e-01 9.94570613e-01 -5.38555324e-01 -4.64474350e-01 -2.53340662e-01 -6.79947793e-01 -1.34219873e+00 -1.84782147e-01 6.21401779e-02 -5.08608103e-01 1.05886325e-01 3.42521012e-01 1.79253921e-01 -3.47152315e-02 3.59877616e-01 -1.59775722e+00 -7.05020726e-01 7.53404498e-01 2.04123437e-01 -1.92635581e-01 8.48036468e-01 5.51692903e-01 8.93711686e-01 -7.17173815e-01 4.63474900e-01 1.45882869e+00 7.31289864e-01 1.07652318e+00 -1.39963520e+00 -8.02483678e-01 3.57390270e-02 1.02479026e-01 -7.24307597e-01 -3.42043757e-01 1.36468753e-01 -6.48924887e-01 1.20248878e+00 8.97350252e-01 6.41058803e-01 1.14648616e+00 4.49948251e-01 5.70057869e-01 1.08842134e+00 -3.90403956e-01 2.22543314e-01 7.78441966e-01 6.67401791e-01 6.06738627e-01 4.43307031e-03 -5.06414995e-02 -6.29543602e-01 -4.64552402e-01 4.21653718e-01 2.22057492e-01 -3.37156951e-01 1.07919671e-01 -1.06750798e+00 8.36554527e-01 4.05742109e-01 6.42425418e-02 -3.75137866e-01 -3.69419344e-02 2.01632947e-01 3.68818164e-01 4.70868051e-01 9.10625219e-01 -7.08518326e-01 -4.97523546e-01 -2.87211299e-01 4.66710031e-01 1.33184171e+00 9.70873892e-01 7.29747832e-01 -5.01111746e-01 -1.31039768e-01 7.20049679e-01 2.26933017e-01 2.73842961e-01 3.34950477e-01 -1.02937996e+00 2.50157624e-01 9.41480935e-01 1.96717769e-01 -1.00335705e+00 -7.71864057e-01 -1.85219660e-01 -5.86404979e-01 -4.26673628e-02 2.79016286e-01 -3.05488467e-01 -4.59503800e-01 1.63237143e+00 2.64577687e-01 -3.11833143e-01 1.72814757e-01 7.85018981e-01 1.06451464e+00 5.82720637e-01 3.89356732e-01 2.51675323e-02 1.39890778e+00 -9.95724678e-01 -7.17811167e-01 -5.91822267e-01 1.06557941e+00 -7.04981625e-01 1.57991028e+00 4.41305637e-01 -9.28020239e-01 -6.45253241e-01 -6.55150592e-01 -1.06796190e-01 -1.36873648e-01 -2.21572682e-01 8.06967258e-01 4.69407022e-01 -9.11643445e-01 5.48186541e-01 -7.79871762e-01 -6.71793222e-01 6.42699450e-02 3.20416719e-01 -2.62036681e-01 -7.57985264e-02 -1.07634962e+00 1.02266061e+00 6.90836906e-02 -1.41712412e-01 -4.24678236e-01 -9.45034981e-01 -7.84408748e-01 2.34852597e-01 8.41854215e-02 -1.13560808e+00 1.92498636e+00 -9.15069759e-01 -1.14026570e+00 5.09488702e-01 -4.75207031e-01 -3.25322449e-01 4.37425405e-01 -4.50754821e-01 -2.76029110e-01 -2.33114913e-01 2.04095528e-01 6.92858160e-01 6.22984804e-02 -1.13215744e+00 -4.34576690e-01 1.30413948e-02 3.89766335e-01 8.18976015e-02 -9.15939659e-02 -1.57692134e-01 -1.29101083e-01 -1.25651985e-01 -8.99378508e-02 -1.03408480e+00 -3.15930486e-01 3.75139825e-02 -7.38465488e-01 -2.02948630e-01 5.65524340e-01 -6.90576851e-01 1.36102808e+00 -1.81104577e+00 -2.02795669e-01 5.26359342e-02 5.72671354e-01 2.28309229e-01 1.83228135e-01 8.43864858e-01 -2.38265991e-01 6.50996983e-01 2.96214018e-02 -3.58659387e-01 2.00213581e-01 2.19761029e-01 -2.96720862e-01 -3.28973740e-01 9.46467891e-02 8.72389674e-01 -8.69227588e-01 -2.61254519e-01 3.52345049e-01 3.86507154e-01 -8.98019671e-01 5.52321792e-01 -4.48284507e-01 7.08291948e-01 -5.10073364e-01 2.02548221e-01 2.45647162e-01 -7.34196246e-01 2.47602999e-01 1.42201483e-01 1.15493797e-01 6.84708059e-01 -7.94669211e-01 1.21399546e+00 -6.80208266e-01 7.35164762e-01 -2.55178809e-01 -3.25753957e-01 7.92882860e-01 4.58138615e-01 3.32278162e-02 -2.90613830e-01 -2.85379887e-02 8.16684738e-02 5.12536168e-02 -9.62537527e-01 1.34850457e-01 -1.60268530e-01 -1.88723728e-02 8.89108717e-01 -4.35818732e-01 -2.95533597e-01 -1.62830278e-01 4.95985210e-01 1.11241996e+00 -3.45254779e-01 6.95789933e-01 -6.54457808e-02 2.66562045e-01 4.05413032e-01 2.39598542e-01 9.28588808e-01 1.54019639e-01 4.33698475e-01 5.60153365e-01 -9.56762910e-01 -6.52848423e-01 -7.62312949e-01 2.22891524e-01 9.39554632e-01 -3.31163742e-02 -7.35192776e-01 -1.02006757e+00 -7.57036626e-01 6.76811859e-02 1.50940442e+00 -5.79094410e-01 -2.85717010e-01 -1.29582465e-01 -4.70748395e-02 2.72837967e-01 5.02295494e-01 2.80701131e-01 -1.15407848e+00 -7.00715601e-01 1.58379957e-01 -6.83714390e-01 -7.72683084e-01 -7.20733047e-01 -3.64410616e-02 -8.52045119e-01 -1.31744373e+00 9.19865891e-02 -2.41311714e-01 7.48479068e-01 3.14844251e-01 1.26228726e+00 7.67970324e-01 -1.77579001e-01 6.74089611e-01 -3.73817176e-01 -7.78405309e-01 -1.03782749e+00 7.84892365e-02 -1.78476155e-01 -4.00106162e-01 6.33768141e-01 -1.91696212e-01 -5.64784586e-01 6.82267249e-01 -7.78234959e-01 9.39882338e-01 4.22732413e-01 8.24598074e-01 8.70025605e-02 -5.92096210e-01 7.11602390e-01 -1.47647309e+00 1.20101810e+00 -5.61176658e-01 1.12640128e-01 3.31338257e-01 -9.93840873e-01 2.39974391e-02 6.31421626e-01 -3.00569326e-01 -9.19057965e-01 -1.00500919e-02 -2.35503748e-01 2.60554612e-01 -4.23789978e-01 6.13482654e-01 5.64024486e-02 4.11585063e-01 1.13079441e+00 -2.38167226e-01 3.24024320e-01 -2.87167221e-01 3.46744925e-01 1.08573520e+00 1.45687148e-01 -4.16817993e-01 6.18387103e-01 -7.19066858e-02 -7.42445827e-01 -4.61043209e-01 -8.60305429e-01 -1.48550406e-01 -2.78415591e-01 -3.48671615e-01 8.62433076e-01 -5.60599267e-01 -1.22457790e+00 -2.22542077e-01 -1.41854465e+00 -6.93315625e-01 -8.36628005e-02 4.89710957e-01 -3.97942394e-01 -1.09394625e-01 -4.98589188e-01 -8.71110082e-01 -5.28897285e-01 -1.16922104e+00 7.32435405e-01 4.13869560e-01 -1.56173956e+00 -1.14849007e+00 -1.12878919e-01 8.59004736e-01 6.41750693e-01 1.13589264e-01 1.32602692e+00 -1.20802557e+00 -3.92160773e-01 -2.28747174e-01 -9.00279805e-02 2.58767959e-02 3.24879527e-01 -1.30132467e-01 -1.05273402e+00 6.27753213e-02 -6.22977316e-02 -3.15685660e-01 3.68867666e-02 1.25168338e-01 1.26676869e+00 -8.52216661e-01 -6.82598114e-01 1.00467280e-01 5.95024943e-01 3.82760167e-01 3.18958372e-01 1.59097627e-01 3.73113006e-01 8.23062956e-01 6.56952858e-01 2.81726390e-01 7.65619397e-01 5.46851695e-01 1.85203582e-01 -4.61029589e-01 1.90658703e-01 -4.28695947e-01 1.06403641e-01 5.90166211e-01 3.09485972e-01 -2.44827464e-01 -1.17821288e+00 1.92855150e-01 -2.05706143e+00 -6.25077307e-01 -3.40483695e-01 1.86396718e+00 8.41896057e-01 1.88717782e-01 -9.86177996e-02 -3.19256097e-01 3.47518355e-01 -4.93757069e-01 -8.47913980e-01 -8.88153315e-01 5.55023611e-01 -3.17110755e-02 -1.50565997e-01 9.46375132e-01 -4.71442729e-01 6.10630631e-01 6.13839912e+00 3.70040685e-02 -8.87485266e-01 8.07454716e-03 9.31502700e-01 7.82618225e-02 -6.30721688e-01 1.50603980e-01 -4.07734752e-01 1.74049258e-01 1.00194502e+00 -4.28402036e-01 4.25389767e-01 1.05746019e+00 7.05816686e-01 4.00817767e-02 -1.74468982e+00 7.41615057e-01 -1.77493423e-01 -1.47569156e+00 1.56064272e-01 -2.59646565e-01 2.43609443e-01 -5.12813210e-01 -1.77368283e-01 4.16365296e-01 3.83612067e-01 -1.45596766e+00 3.30711007e-01 6.56463981e-01 4.32594031e-01 -4.26766902e-01 7.97049344e-01 7.31487155e-01 -6.72382116e-01 -7.20457956e-02 5.66948615e-02 -5.88248789e-01 1.01342686e-01 2.23889023e-01 -1.79050303e+00 1.83087155e-01 5.81452906e-01 5.22678196e-01 -4.64104950e-01 6.98258042e-01 -3.45982283e-01 7.31982172e-01 8.83113667e-02 -2.98970103e-01 -2.31306314e-01 3.80238920e-01 1.99920028e-01 1.37597990e+00 9.09333006e-02 5.14129400e-01 1.55517891e-01 1.10805774e+00 1.38635874e-01 5.09326793e-02 -5.53951502e-01 -6.47379830e-02 6.16904438e-01 1.12152815e+00 -4.33670402e-01 -5.07690012e-01 -1.76491231e-01 6.62716985e-01 1.11472242e-01 3.53318453e-01 -7.32958436e-01 -1.59389928e-01 7.63260067e-01 3.95079583e-01 -7.10062683e-01 1.16394930e-01 -6.65762365e-01 -8.34376156e-01 -1.41683623e-01 -1.50785708e+00 3.69616300e-01 -1.29904628e+00 -1.20543253e+00 9.79818285e-01 1.25192448e-01 -1.00124311e+00 -6.35632634e-01 -3.46083581e-01 -6.39825761e-01 1.08279991e+00 -8.55642974e-01 -9.16728735e-01 -8.24570119e-01 2.30432376e-01 9.72865999e-01 2.15781271e-01 1.23128009e+00 -1.77488774e-01 -3.11054111e-01 3.34015101e-01 -5.99413395e-01 -2.75625974e-01 9.31198120e-01 -1.17987287e+00 7.90814400e-01 1.30715281e-01 -1.00598522e-01 1.27701080e+00 1.14449680e+00 -7.70430386e-01 -1.10730088e+00 -8.12267900e-01 1.24101722e+00 -8.92277122e-01 2.62270957e-01 -4.63064313e-01 -1.32391644e+00 1.05546904e+00 3.92460316e-01 -7.46355355e-01 1.08029699e+00 3.49119753e-01 -2.15730086e-01 1.33279249e-01 -1.11184120e+00 8.02113831e-01 8.41111541e-01 -4.71789420e-01 -6.14634693e-01 7.89810002e-01 9.49543059e-01 -6.43432260e-01 -7.11118877e-01 7.69063458e-02 7.60991633e-01 -1.09739840e+00 5.93143344e-01 -1.06245816e+00 7.33176291e-01 4.18416336e-02 2.36701608e-01 -1.48172045e+00 -7.10669234e-02 -8.99271250e-01 2.07717001e-01 1.00971007e+00 9.95590210e-01 -9.43851054e-01 5.17777264e-01 1.74011815e+00 -1.33850977e-01 -9.40979958e-01 -3.31182867e-01 -6.42701387e-02 -1.99032024e-01 -5.64175129e-01 7.58025050e-01 9.86406446e-01 6.92129016e-01 6.53225899e-01 -3.32630694e-01 2.02245995e-01 2.24290639e-02 -4.36367393e-02 1.30010414e+00 -1.19299388e+00 -5.91003537e-01 -2.13257551e-01 1.23468757e-01 -1.18573046e+00 -7.76151717e-02 -8.62962663e-01 1.01503901e-01 -2.02135801e+00 4.70586747e-01 -4.02474880e-01 3.28920096e-01 1.00002170e+00 -4.32183504e-01 -5.50245941e-01 2.28020817e-01 4.08050358e-01 -2.52802551e-01 8.03754926e-02 1.02746165e+00 -6.45982251e-02 -4.76193726e-01 2.84126222e-01 -1.27821863e+00 7.00771511e-01 9.23439384e-01 -5.94865918e-01 -6.65257215e-01 -4.09271955e-01 1.93603188e-01 3.13040614e-01 4.58908975e-01 -7.15105116e-01 3.17847729e-01 -4.82659757e-01 2.65172839e-01 -1.11481398e-01 1.37560174e-01 -6.81051552e-01 5.98903894e-01 7.62670457e-01 -8.47374380e-01 4.27362263e-01 6.53103411e-01 1.93824321e-01 4.50451262e-02 -2.56398413e-02 3.52681100e-01 -2.47991271e-02 -1.34843618e-01 -1.74164265e-01 -6.48581743e-01 -1.33030936e-01 8.68238330e-01 -2.02999160e-01 -6.56625986e-01 -1.11823905e+00 -8.79006863e-01 6.12006783e-01 2.96880007e-01 6.92232370e-01 7.31948912e-01 -8.01849425e-01 -5.39670825e-01 1.38550982e-01 1.85746953e-01 -2.28922069e-01 3.50365877e-01 8.16942990e-01 -5.71779191e-01 5.26030362e-01 7.17657954e-02 -6.16155863e-01 -1.51194000e+00 3.12858403e-01 5.25229692e-01 -1.58083916e-01 -6.63971245e-01 6.68550730e-01 5.95539212e-01 -1.03330076e+00 1.71374664e-01 -6.07465863e-01 3.64823788e-02 -4.82591331e-01 7.17324376e-01 1.85731739e-01 -1.05833083e-01 8.44223574e-02 -3.59720796e-01 7.75113627e-02 -2.37275496e-01 -4.25771400e-02 1.18962061e+00 -1.31683514e-01 1.17897213e-01 6.21143401e-01 6.96125269e-01 -2.59303987e-01 -9.14939702e-01 1.17747881e-01 4.24232967e-02 -3.89462501e-01 -7.65552521e-01 -1.41039455e+00 -3.00684154e-01 9.64719355e-01 1.91441208e-01 5.73068738e-01 7.71251440e-01 1.70330152e-01 6.71922088e-01 5.57393432e-01 1.44305617e-01 -4.95045960e-01 1.62938222e-01 1.43611059e-01 1.27401483e+00 -1.25676525e+00 -1.33861646e-01 -5.54942310e-01 -1.03426659e+00 1.24577618e+00 8.33373070e-01 6.88937247e-01 3.72783214e-01 5.90056442e-02 6.18689656e-01 -3.54587793e-01 -1.31822383e+00 5.80548048e-01 2.85485685e-01 4.67112601e-01 8.09847713e-01 2.46396214e-01 -1.23777248e-01 1.04046917e+00 -5.59073091e-01 1.70849368e-01 6.16075277e-01 5.82536399e-01 -3.58241320e-01 -1.10429287e+00 -2.92684257e-01 5.36666155e-01 4.47986722e-02 1.53937340e-02 -9.42453623e-01 1.02707446e+00 -2.74027228e-01 1.55507445e+00 -3.32633853e-01 -7.10475385e-01 6.43361449e-01 2.81785101e-01 -2.92160660e-01 -1.06230247e+00 -1.06575656e+00 -5.23235142e-01 5.47613382e-01 -7.38371015e-01 1.10157602e-01 -4.54145908e-01 -1.56862855e+00 -5.71601152e-01 -1.15893297e-01 5.71431160e-01 5.01956344e-01 1.14498651e+00 7.29292154e-01 5.62258840e-01 3.63877267e-01 -3.00821096e-01 -4.99283493e-01 -9.67655241e-01 1.99250638e-01 4.86568183e-01 3.22263837e-01 -2.59139478e-01 -3.30858022e-01 2.31820971e-01]	[9.318374633789062, 6.783052921295166]
b5245095-6f90-454a-a879-9e886abc4373	head-detection-with-depth-images-in-the-wild	1707.06786	null	http://arxiv.org/abs/1707.06786v2	http://arxiv.org/pdf/1707.06786v2.pdf	Head Detection with Depth Images in the Wild	Head detection and localization is a demanding task and a key element for many computer vision applications, like video surveillance, Human Computer Interaction and face analysis. The stunning amount of work done for detecting faces on RGB images, together with the availability of huge face datasets, allowed to setup very effective systems on that domain. However, due to illumination issues, infrared or depth cameras may be required in real applications. In this paper, we introduce a novel method for head detection on depth images that exploits the classification ability of deep learning approaches. In addition to reduce the dependency on the external illumination, depth images implicitly embed useful information to deal with the scale of the target objects. Two public datasets have been exploited: the first one, called Pandora, is used to train a deep binary classifier with face and non-face images. The second one, collected by Cornell University, is used to perform a cross-dataset test during daily activities in unconstrained environments. Experimental results show that the proposed method overcomes the performance of state-of-art methods working on depth images.	['Guido Borghi', 'Roberto Vezzani', 'Rita Cucchiara', 'Diego Ballotta']	2017-07-21	null	null	null	null	['head-detection']	['computer-vision']	[ 7.10902587e-02 -4.54087295e-02 2.21366867e-01 -5.83479643e-01 -1.84415162e-01 -2.81022727e-01 3.37241292e-01 -2.37945229e-01 -7.13862240e-01 5.60337663e-01 -1.16888098e-01 1.65189564e-01 1.66039079e-01 -6.33269072e-01 -4.94351596e-01 -9.23809528e-01 3.56437229e-02 2.17020050e-01 3.18718255e-01 1.12533703e-01 1.43868938e-01 8.48891079e-01 -2.03706264e+00 1.09222911e-01 1.13255747e-01 1.24330425e+00 1.56080008e-01 3.48414302e-01 7.73243373e-03 4.90364730e-01 -5.44111907e-01 -3.87138367e-01 3.75377476e-01 -6.77631572e-02 -3.13786596e-01 3.76190752e-01 5.77781558e-01 -5.61144829e-01 -3.50574374e-01 8.86000872e-01 9.44881260e-01 2.88939383e-02 3.16834271e-01 -1.31802225e+00 5.74957505e-02 -5.50596453e-02 -6.86622262e-01 2.63134390e-01 5.46410561e-01 1.07990086e-01 2.31451690e-01 -7.84239590e-01 4.23287332e-01 1.30097866e+00 4.36731488e-01 7.83760428e-01 -7.21081972e-01 -8.68514717e-01 -1.63334563e-01 4.24586207e-01 -1.37800205e+00 -7.90436387e-01 7.90123940e-01 -2.77635485e-01 6.20858014e-01 1.57024171e-02 5.67033410e-01 1.06670821e+00 -1.07778370e-01 5.91294587e-01 1.29834378e+00 -5.73677361e-01 3.67234170e-01 5.56364536e-01 -1.20887756e-02 7.77475953e-01 2.64074236e-01 -7.64668733e-02 -5.35619676e-01 8.05227160e-02 5.37193596e-01 9.30695832e-02 -4.22103316e-01 -5.31083286e-01 -5.47719359e-01 7.63356447e-01 3.59221309e-01 4.43082035e-01 -2.95645088e-01 -3.21496904e-01 3.46095532e-01 5.97525481e-03 4.10230190e-01 -3.33230674e-01 -2.91634262e-01 1.67365987e-02 -8.80203962e-01 -1.04638703e-01 8.60974312e-01 5.92334270e-01 5.77750802e-01 -3.06846976e-01 2.30475202e-01 6.10109866e-01 4.72991616e-01 4.67468560e-01 5.81474185e-01 -4.48791176e-01 2.40093425e-01 7.79108763e-01 -1.39521137e-01 -8.26222956e-01 -7.56462336e-01 4.33175191e-02 -7.11426854e-01 3.55755895e-01 5.91235638e-01 1.61289272e-03 -7.40944147e-01 1.37069845e+00 7.13156581e-01 -9.87156630e-02 -2.31543586e-01 9.35972571e-01 8.84644210e-01 1.70498639e-01 -8.08291733e-02 -1.59351245e-01 1.49180591e+00 -5.68316162e-01 -5.63128471e-01 -2.78192133e-01 3.95505875e-01 -6.74946725e-01 7.54129589e-01 6.00727975e-01 -6.92302823e-01 -4.34369385e-01 -8.72064233e-01 3.72171439e-02 -5.72260439e-01 3.04963112e-01 5.42209506e-01 1.27080238e+00 -1.19193316e+00 4.43755277e-02 -6.20688438e-01 -7.82943428e-01 5.37517369e-01 7.27474868e-01 -8.63246262e-01 -3.34445626e-01 -7.71514595e-01 8.54953349e-01 2.71177799e-01 2.86039025e-01 -7.05494106e-01 3.54996473e-02 -8.07524800e-01 -5.82827777e-02 1.12709910e-01 -1.17606118e-01 8.37929010e-01 -9.04711485e-01 -1.58742976e+00 1.34661555e+00 -7.50097856e-02 -2.14278877e-01 6.39132679e-01 -1.16255820e-01 -1.75349370e-01 2.08251342e-01 -9.03262571e-02 6.48719847e-01 1.15764987e+00 -7.45119393e-01 -4.56474960e-01 -1.06916463e+00 -6.98218793e-02 7.37236291e-02 -5.49168885e-01 2.18397021e-01 -6.02030516e-01 1.99501179e-02 -6.09889477e-02 -1.01023185e+00 4.27904934e-01 1.13056593e-01 -1.62384957e-01 -3.89823407e-01 1.19792438e+00 -6.01791143e-01 6.63275301e-01 -2.32037306e+00 1.15488693e-01 2.05965057e-01 -1.23737797e-01 3.51185530e-01 1.52581379e-01 6.35275170e-02 -1.27702266e-01 -5.97058892e-01 -8.44707489e-02 -6.54305458e-01 -2.21522570e-01 1.42791867e-01 1.90759629e-01 9.82461035e-01 -3.99657711e-02 2.83033013e-01 -4.04710680e-01 -5.24409294e-01 4.24884468e-01 8.41762781e-01 -2.51723200e-01 2.93466836e-01 2.56634921e-01 6.61832273e-01 -1.04043961e-01 7.00470328e-01 8.54276240e-01 2.92476445e-01 1.00541249e-01 -9.00819898e-02 -6.39088005e-02 -1.52847290e-01 -1.31444204e+00 1.45925856e+00 -3.78293484e-01 5.15827954e-01 5.18736362e-01 -1.02116764e+00 7.96948433e-01 4.11528617e-01 2.82347947e-01 -8.82788956e-01 4.36004788e-01 1.31308228e-01 -2.42929041e-01 -7.66844511e-01 1.53448120e-01 -5.89235015e-02 3.06415856e-01 3.87751758e-01 2.89144069e-02 5.64432610e-03 1.37477443e-01 -2.66292572e-01 8.55216861e-01 1.83699638e-01 1.59447148e-01 -1.24774240e-01 8.99271429e-01 -6.62073433e-01 3.49861830e-01 1.32091060e-01 -4.33869869e-01 5.61621308e-01 3.54606420e-01 -7.01817393e-01 -6.52471960e-01 -4.37230676e-01 -3.12763393e-01 1.15705311e+00 -2.07284749e-01 1.11500964e-01 -1.11330891e+00 -6.52897060e-01 -6.53101653e-02 1.48519948e-01 -6.85853899e-01 2.97973268e-02 -4.90819424e-01 -8.40815187e-01 4.35503870e-01 3.53754997e-01 7.71413803e-01 -1.12000608e+00 -1.17577767e+00 -1.09387867e-01 8.83872733e-02 -1.37555850e+00 -4.54601757e-02 5.09397164e-02 -6.74556077e-01 -1.26134086e+00 -8.71002913e-01 -6.21659756e-01 6.74003601e-01 3.39528769e-01 7.61268735e-01 8.97384733e-02 -6.83439374e-01 5.22787333e-01 -1.69406727e-01 -5.40756702e-01 1.00461103e-01 1.42862156e-01 3.63262862e-01 3.42115372e-01 7.39861727e-01 -3.08512300e-01 -5.98575354e-01 2.99789041e-01 -9.65932250e-01 -5.00035465e-01 5.59818506e-01 5.05498946e-01 3.51890400e-02 8.00150484e-02 3.93966347e-01 -5.42634130e-01 1.71271011e-01 -1.77683637e-01 -9.10205007e-01 2.68855449e-02 -7.61536360e-02 -8.99677128e-02 1.15855351e-01 -2.01079831e-01 -8.61097932e-01 4.40459460e-01 -2.76427448e-01 -1.82934985e-01 -6.16050422e-01 -1.44380301e-01 -6.09418333e-01 -3.92543197e-01 4.68637705e-01 9.80677903e-02 1.76206499e-01 -5.28377473e-01 -9.43748951e-02 1.00987303e+00 3.53701115e-01 -4.03503776e-02 4.68846589e-01 6.32966638e-01 2.10096791e-01 -1.28790593e+00 -6.52090192e-01 -5.98535776e-01 -9.20438349e-01 -3.26714545e-01 9.10032213e-01 -8.98128033e-01 -8.04445028e-01 8.99525821e-01 -1.08025932e+00 -1.51615754e-01 3.15494657e-01 4.30024803e-01 -2.23315105e-01 2.83178002e-01 -2.61749268e-01 -1.06823015e+00 -3.34405750e-01 -1.11604881e+00 1.24913776e+00 4.10771072e-01 2.54361868e-01 -7.21652746e-01 -1.89995736e-01 4.93404537e-01 2.99888462e-01 2.17403844e-01 4.33323264e-01 -3.34466308e-01 -3.75831634e-01 -6.71877265e-01 -3.03107232e-01 3.77069712e-01 1.63560614e-01 -2.03434750e-01 -1.65464973e+00 -4.36819285e-01 3.19569677e-01 -4.47331429e-01 6.02572024e-01 2.86956191e-01 1.09385037e+00 5.20293079e-02 -2.64413714e-01 4.57425207e-01 1.06732106e+00 4.09283414e-02 8.18513930e-01 3.61344278e-01 4.96642172e-01 9.87769067e-01 2.84093827e-01 5.63218892e-01 3.51034462e-01 8.65630329e-01 5.97110271e-01 -1.35186329e-01 -1.13123348e-02 2.47779220e-01 3.15600395e-01 1.26822278e-01 -1.30079895e-01 5.99871911e-02 -7.40799308e-01 1.90357625e-01 -1.40859473e+00 -7.53946066e-01 6.92251921e-02 2.45427847e+00 3.39843005e-01 4.34421338e-02 4.17215019e-01 7.26695359e-01 6.72847152e-01 -2.04188153e-01 -2.47632697e-01 -1.34084344e-01 5.10019658e-04 3.39772969e-01 3.12556446e-01 2.63808668e-02 -1.28041208e+00 5.89659572e-01 5.20508480e+00 2.44865239e-01 -1.57241619e+00 2.81867206e-01 4.57525104e-01 -1.63864285e-01 7.01004446e-01 -7.27206230e-01 -1.06897438e+00 5.61121762e-01 8.55695903e-01 5.24068952e-01 2.00687900e-01 9.65137601e-01 -9.24267545e-02 -6.20480835e-01 -1.19216216e+00 1.31716347e+00 5.33753514e-01 -4.16072786e-01 -5.54958403e-01 2.19854370e-01 2.52890557e-01 -5.39568439e-02 1.04858905e-01 2.02515498e-01 -6.85452461e-01 -1.02432132e+00 4.77657199e-01 1.84037283e-01 6.49045587e-01 -9.21732843e-01 1.11220884e+00 4.98661280e-01 -9.30305421e-01 -2.09546700e-01 -4.58275050e-01 -2.58108854e-01 -1.88053623e-01 5.28178573e-01 -8.76923203e-01 7.18883276e-02 9.58677888e-01 2.58374363e-01 -7.30312765e-01 1.04543042e+00 -2.93244869e-01 3.64952087e-02 -6.40492797e-01 -1.05221309e-02 -1.13815211e-01 1.07214963e-02 1.28312498e-01 1.03974128e+00 2.37956792e-01 6.88469857e-02 -1.87190652e-01 2.30114639e-01 -1.94858938e-01 1.13847084e-01 -9.62924123e-01 3.48910898e-01 3.44267637e-02 1.56783664e+00 -9.16635811e-01 3.70155238e-02 -5.10547936e-01 1.13054085e+00 1.32191733e-01 3.73559222e-02 -6.83946490e-01 -2.13582292e-01 4.14275259e-01 3.31558257e-01 3.37618440e-01 -2.22639427e-01 2.11112320e-01 -9.63305593e-01 9.20429677e-02 -6.52118623e-01 2.17245340e-01 -4.42276567e-01 -7.81010509e-01 6.98364615e-01 8.86082873e-02 -8.87706816e-01 -2.83034950e-01 -9.48749304e-01 -4.40539032e-01 6.36646867e-01 -1.49903369e+00 -1.19332182e+00 -7.38095164e-01 8.69756103e-01 4.64847326e-01 -1.73448548e-01 8.30139160e-01 6.53776288e-01 -8.48802090e-01 7.26779699e-01 -1.69377401e-01 2.55950183e-01 7.86325753e-01 -7.26557195e-01 -8.55423883e-02 7.26523757e-01 -4.41872925e-02 2.45050371e-01 5.00386059e-01 -1.62504807e-01 -1.59275460e+00 -7.04236686e-01 7.66480803e-01 -4.48622435e-01 -6.09510625e-03 -6.30128741e-01 -7.18575180e-01 4.72182006e-01 8.66043121e-02 4.04456437e-01 6.88920498e-01 -2.25697488e-01 -1.97961330e-01 -4.01381791e-01 -1.61543584e+00 2.59784292e-02 6.63356483e-01 -4.34464633e-01 -3.95032853e-01 3.77011597e-01 -9.97877419e-02 -3.83992106e-01 -3.33781809e-01 2.52691805e-01 7.11786568e-01 -1.44756818e+00 7.83545494e-01 2.38841046e-02 -1.07037075e-01 -1.81425795e-01 -8.92627686e-02 -8.86141360e-01 3.04402739e-01 -1.17571734e-01 -4.46868427e-02 1.23566401e+00 -2.16204628e-01 -6.90239727e-01 9.81094480e-01 7.95058370e-01 3.95350307e-01 -3.79892230e-01 -1.19555712e+00 -4.82829154e-01 -4.59852844e-01 -1.96885794e-01 4.33743566e-01 5.62613666e-01 -3.79954547e-01 2.89373338e-01 -1.05692193e-01 2.15120986e-01 9.13552821e-01 -1.69934079e-01 9.03927445e-01 -1.49708831e+00 -6.57158792e-02 -9.62222815e-02 -1.00429595e+00 -4.48758036e-01 3.07612419e-01 -2.82444835e-01 -1.00199461e-01 -1.08330059e+00 1.19799323e-01 -1.67556435e-01 -1.47743560e-02 4.52145755e-01 2.43729264e-01 8.33422959e-01 1.40830815e-01 -1.37065008e-01 -3.61421078e-01 2.91989207e-01 6.21315062e-01 2.83821560e-02 -4.38276082e-02 2.22237989e-01 -1.17744870e-01 8.57890189e-01 6.02629304e-01 -3.36319387e-01 -1.35120153e-01 -3.57266635e-01 -1.03283621e-01 -2.88278669e-01 3.80659789e-01 -1.33678496e+00 3.88356149e-01 4.05452609e-01 8.27439368e-01 -4.28289354e-01 6.68800652e-01 -9.82744873e-01 -8.66708457e-02 5.73827147e-01 3.02290440e-01 2.95389473e-04 3.28292251e-01 2.68043101e-01 -1.29483908e-01 -3.18623632e-01 1.02996707e+00 -2.02180281e-01 -7.32573628e-01 1.50443703e-01 -9.34850499e-02 -4.26981241e-01 1.26791775e+00 -3.16644192e-01 -2.52882987e-02 -2.60302603e-01 -5.41992247e-01 -1.54569864e-01 5.21683097e-01 4.32918698e-01 6.64014459e-01 -9.30496991e-01 -4.93323356e-01 7.84910023e-01 7.38043860e-02 -8.71661976e-02 1.34223029e-01 8.96256447e-01 -5.29935956e-01 4.39157784e-01 -5.45828998e-01 -6.49675429e-01 -1.67102337e+00 5.42383850e-01 3.10684860e-01 1.13370560e-01 -4.81902212e-01 1.01685762e+00 1.81469500e-01 -1.77743852e-01 6.91822946e-01 -7.24042356e-02 -4.08906639e-01 3.32654923e-01 9.46163118e-01 3.03067565e-01 5.67771852e-01 -9.06617761e-01 -6.07693315e-01 7.27054477e-01 4.17852849e-02 1.12117026e-02 1.41206694e+00 -2.20826194e-01 -2.97953755e-01 1.99951231e-01 1.13437569e+00 -1.96238950e-01 -1.12223220e+00 -3.23125385e-02 7.54340142e-02 -5.39188683e-01 2.54681498e-01 -3.48647296e-01 -1.34254038e+00 1.24794173e+00 1.36621749e+00 1.67222455e-01 1.32963824e+00 -1.21527843e-01 4.36410785e-01 4.12734121e-01 8.04643393e-01 -9.78180766e-01 -6.93659019e-03 2.10547686e-01 6.55879200e-01 -1.60148036e+00 -2.27329526e-02 -2.87258178e-01 -3.01913261e-01 1.21407473e+00 5.53901076e-01 2.01762334e-01 5.01377761e-01 2.63816804e-01 1.76621024e-02 -5.66642173e-02 -1.62114471e-01 -3.20338845e-01 -2.21189521e-02 6.44537389e-01 4.12838370e-01 -1.60982788e-01 6.29180670e-02 7.29123875e-02 9.00695920e-02 2.54168302e-01 2.88841158e-01 9.37663913e-01 -4.32533205e-01 -1.00677621e+00 -8.76395524e-01 1.21825494e-01 -6.83375835e-01 3.43169481e-01 -4.41234976e-01 8.24091375e-01 3.28754663e-01 8.65706682e-01 1.11375593e-01 -4.74503031e-03 2.80939341e-01 1.66267067e-01 8.38972032e-01 -5.15839398e-01 -4.58049089e-01 -7.58366808e-02 -3.54012638e-01 -4.95781481e-01 -5.53640783e-01 -4.90998089e-01 -8.22327852e-01 -2.21808955e-01 -2.32265487e-01 -1.50833488e-01 1.16632092e+00 9.58005965e-01 2.66139749e-02 5.35648018e-02 6.70049667e-01 -1.34883249e+00 -2.97506005e-01 -1.16821313e+00 -7.40428150e-01 3.70726228e-01 2.96950549e-01 -8.80403817e-01 -3.96329284e-01 -6.15030266e-02]	[13.60761547088623, 0.36205101013183594]
2019ceac-f771-49c4-95b3-cd65a7f0b1e9	on-the-importance-of-video-action-recognition	1903.09616	null	https://arxiv.org/abs/1903.09616v2	https://arxiv.org/pdf/1903.09616v2.pdf	On the Importance of Video Action Recognition for Visual Lipreading	We focus on the word-level visual lipreading, which requires to decode the word from the speaker's video. Recently, many state-of-the-art visual lipreading methods explore the end-to-end trainable deep models, involving the use of 2D convolutional networks (e.g., ResNet) as the front-end visual feature extractor and the sequential model (e.g., Bi-LSTM or Bi-GRU) as the back-end. Although a deep 2D convolution neural network can provide informative image-based features, it ignores the temporal motion existing between the adjacent frames. In this work, we investigate the spatial-temporal capacity power of I3D (Inflated 3D ConvNet) for visual lipreading. We demonstrate that, after pre-trained on the large-scale video action recognition dataset (e.g., Kinetics), our models show a considerable improvement of performance on the task of lipreading. A comparison between a set of video model architectures and input data representation is also reported. Our extensive experiments on LRW shows that a two-stream I3D model with RGB video and optical flow as the inputs achieves the state-of-the-art performance.	['Xinshuo Weng']	2019-03-22	null	null	null	null	['lipreading']	['computer-vision']	[ 1.28959656e-01 -2.62792315e-02 -5.47277212e-01 -1.42726555e-01 -6.58734262e-01 -6.03043176e-02 5.70127010e-01 -7.16571510e-01 -4.19928402e-01 3.49170625e-01 6.22102976e-01 -5.55121958e-01 6.54394150e-01 -9.40708257e-03 -8.37038040e-01 -5.87805152e-01 2.28866488e-01 -4.10885096e-01 2.41280243e-01 8.27682465e-02 5.66709042e-01 3.98779601e-01 -1.91971850e+00 4.64553714e-01 3.37810695e-01 1.45684659e+00 3.18914652e-01 1.14974773e+00 -2.47573555e-01 1.01867461e+00 -3.43808889e-01 -7.34262988e-02 4.52575609e-02 -2.83380777e-01 -6.74243510e-01 1.05683901e-03 5.91366649e-01 -8.72569203e-01 -9.87878501e-01 6.99949503e-01 1.11099672e+00 -3.59427510e-03 5.51776111e-01 -1.49767745e+00 -8.77487123e-01 -1.84921145e-01 -4.04802501e-01 1.57605842e-01 5.78263700e-01 7.48211563e-01 5.60718596e-01 -1.36660278e+00 4.16642934e-01 1.39176714e+00 5.59981942e-01 1.12886262e+00 -8.29854667e-01 -5.42606711e-01 -1.61942132e-02 6.93515778e-01 -1.16440892e+00 -1.49259484e+00 8.98761451e-01 -2.47806773e-01 1.24978542e+00 -1.41538456e-01 5.23552597e-01 1.44820261e+00 1.51342198e-01 1.27972317e+00 8.44779491e-01 -4.14534390e-01 7.04084784e-02 -2.30432063e-01 -1.75940558e-01 6.50491893e-01 -2.74964333e-01 2.73822606e-01 -1.22076750e+00 3.45213354e-01 7.11745858e-01 -1.10680975e-01 -6.70445979e-01 -2.75202185e-01 -1.05066037e+00 4.63331550e-01 2.39920676e-01 -8.83204415e-02 -2.66678184e-01 4.31967169e-01 7.03515232e-01 -1.72297880e-02 5.20194113e-01 -4.68509138e-01 -3.96420807e-01 -3.45319748e-01 -1.19321537e+00 -3.65208745e-01 5.14811337e-01 9.58042026e-01 4.56476599e-01 2.32247010e-01 -3.67932260e-01 9.20004010e-01 7.10479140e-01 4.62812126e-01 8.77388358e-01 -1.00826693e+00 5.39025545e-01 4.13446240e-02 -3.76605522e-03 -4.74083602e-01 -2.37247378e-01 3.88959438e-01 -8.19847584e-01 6.15650833e-01 3.05996478e-01 2.28663087e-02 -1.29515636e+00 1.82614124e+00 5.07126376e-02 4.21416640e-01 3.39854687e-01 1.08324814e+00 1.35590744e+00 5.74590981e-01 7.09347129e-02 -2.89605647e-01 1.17569578e+00 -1.28135240e+00 -8.98886979e-01 -7.40467161e-02 9.97626111e-02 -7.32463419e-01 1.08796251e+00 -3.17713395e-02 -1.51907051e+00 -9.14672792e-01 -7.99137592e-01 -6.29750609e-01 -4.61218894e-01 1.95392981e-01 2.98736811e-01 4.79306817e-01 -1.74127877e+00 1.99488625e-01 -5.94225109e-01 -4.65962440e-01 4.93308485e-01 3.25796723e-01 -5.25935292e-01 -1.21375218e-01 -1.17683125e+00 7.91040480e-01 -2.29600489e-01 2.08697885e-01 -1.23416817e+00 -5.39751112e-01 -1.11623394e+00 3.03479172e-02 6.01812974e-02 -7.19098866e-01 1.39466441e+00 -9.39781666e-01 -2.26421857e+00 1.03598654e+00 -8.17198396e-01 -3.59388649e-01 6.24291062e-01 5.37283644e-02 -1.42666712e-01 6.34195268e-01 -2.67332852e-01 1.29375613e+00 1.42131960e+00 -9.71761405e-01 -3.93292338e-01 -4.89291176e-02 -6.42888248e-02 3.08067471e-01 -8.85520354e-02 2.61668772e-01 -6.50918543e-01 -4.57347482e-01 -1.32758528e-01 -6.75271869e-01 5.62749267e-01 5.44717133e-01 -2.98962474e-01 -4.76708800e-01 1.05613697e+00 -9.64213490e-01 8.73644769e-01 -2.26069379e+00 -9.99379978e-02 -5.60642123e-01 2.45914280e-01 5.52356303e-01 -3.85639399e-01 2.26607263e-01 -1.33867964e-01 2.98434258e-01 9.25657600e-02 -8.24201643e-01 1.40393451e-01 -1.40795603e-01 -1.83084533e-01 6.61161542e-01 1.37068883e-01 1.30779505e+00 -5.75420916e-01 -5.87645233e-01 4.46481377e-01 9.17726517e-01 -4.17432487e-01 6.11070216e-01 8.79389048e-03 1.23629577e-01 3.99179347e-02 7.80824006e-01 7.43394554e-01 -1.81796119e-01 -2.65220344e-01 -3.07845235e-01 -1.21848486e-01 5.69242001e-01 -4.99117404e-01 2.00786161e+00 -6.10611022e-01 1.38595188e+00 3.15483481e-01 -7.87696362e-01 5.89072704e-01 7.51754999e-01 4.83259320e-01 -9.68776703e-01 -1.55524969e-01 2.60300357e-02 -3.57775688e-01 -9.62842524e-01 3.99668664e-01 1.16692081e-01 5.98786056e-01 4.52257365e-01 1.50063083e-01 1.25148088e-01 -4.40562695e-01 -1.81432385e-02 6.69019580e-01 2.85585821e-01 5.28262332e-02 -6.88958820e-03 7.39680290e-01 -7.21248209e-01 1.58854336e-01 4.27044809e-01 -8.00318360e-01 8.52269948e-01 4.08090442e-01 -2.23705918e-01 -1.09757972e+00 -9.46107030e-01 6.67358860e-02 1.17997658e+00 2.60236681e-01 -1.81311399e-01 -7.59965301e-01 -4.71137553e-01 -1.06576860e-01 3.22268158e-01 -6.82627678e-01 -2.78645158e-01 -3.82474601e-01 6.51866123e-02 8.15819621e-01 5.62814653e-01 9.05193985e-01 -1.27615750e+00 -7.23432004e-01 9.63127892e-03 -5.32818854e-01 -1.30708051e+00 -1.00374007e+00 -2.56249011e-01 -3.78283858e-01 -8.57621551e-01 -1.26084125e+00 -1.06886399e+00 3.21773440e-01 5.87523222e-01 7.28961766e-01 1.97039060e-02 -2.20751297e-02 5.66743970e-01 2.32826686e-03 -2.02130765e-01 -3.44016075e-01 -3.58169049e-01 1.43385604e-01 1.49342939e-01 4.01812315e-01 -2.50902861e-01 -9.44503188e-01 3.34458232e-01 -7.00336814e-01 3.76514286e-01 3.86670858e-01 8.80560160e-01 2.88757235e-01 -7.35496283e-01 5.52864134e-01 5.66531181e-01 6.75606728e-01 -1.07174471e-01 -1.76220924e-01 3.00404936e-01 -1.36717156e-01 8.74711424e-02 1.78735048e-01 -7.59949207e-01 -1.00269020e+00 -1.33306369e-01 -3.77122611e-01 -9.93533015e-01 -1.76883236e-01 -6.46073138e-03 -2.51632661e-01 -1.81191266e-01 1.06136300e-01 7.60708869e-01 5.73094845e-01 -4.88007665e-01 2.69469053e-01 1.30018222e+00 6.12140775e-01 7.43992701e-02 8.29930007e-02 3.83792698e-01 -1.86244056e-01 -1.26633024e+00 -1.97552368e-01 -5.59288263e-01 -4.69887555e-01 -4.88230735e-01 1.17205298e+00 -1.12093890e+00 -1.39054763e+00 1.13085270e+00 -1.54768789e+00 -8.10662150e-01 -2.09762063e-02 4.99480724e-01 -1.04643607e+00 4.15064663e-01 -7.03660429e-01 -8.61570954e-01 -3.24078888e-01 -1.48262441e+00 1.28062832e+00 2.63718247e-01 2.50172257e-01 -7.68702030e-01 -1.45191774e-01 4.64517951e-01 6.25330746e-01 -3.46637785e-01 6.54193580e-01 -3.10205668e-01 -5.68588495e-01 3.06739628e-01 -5.73460698e-01 5.36586642e-01 5.76585382e-02 -1.03524618e-01 -1.57024777e+00 -2.02379659e-01 -1.36049435e-01 -6.84449792e-01 1.24062693e+00 8.76920700e-01 1.31648457e+00 -4.40453589e-01 -1.63497746e-01 8.30305517e-01 9.49648380e-01 3.60519700e-02 1.05568469e+00 -2.54883677e-01 4.90246177e-01 5.28791964e-01 8.47198293e-02 1.10833831e-01 6.57063067e-01 8.79439890e-01 4.87669677e-01 -3.64342183e-01 -9.50020254e-01 -7.01325417e-01 8.40322256e-01 6.02054119e-01 -1.12382956e-01 -4.60093528e-01 -7.35640168e-01 4.18277949e-01 -1.67361510e+00 -1.14680493e+00 5.10996044e-01 2.01160383e+00 6.68881834e-01 -2.33727396e-02 2.43231326e-01 1.89542800e-01 8.85029316e-01 4.58777994e-01 -7.96942472e-01 -5.39152145e-01 -1.50553718e-01 -7.99174383e-02 2.91764498e-01 8.58268619e-01 -8.26760113e-01 1.27699792e+00 6.48821545e+00 7.01662362e-01 -1.65408719e+00 1.43950135e-01 5.82744777e-01 -1.44132853e-01 1.41702667e-01 -5.57945430e-01 -6.77785397e-01 5.53263962e-01 9.74174440e-01 1.23340018e-01 5.27208745e-01 4.66641843e-01 7.45026112e-01 -1.52692482e-01 -1.05932570e+00 1.48454607e+00 5.31799912e-01 -1.43619084e+00 -1.60422716e-02 3.12748641e-01 2.16069266e-01 2.83386409e-01 2.88258046e-01 8.88037011e-02 -2.75303423e-01 -1.25698745e+00 9.29379463e-01 6.50764108e-01 1.69208539e+00 -2.83719152e-01 3.03769231e-01 9.22681987e-02 -1.25550663e+00 -2.30134148e-02 -2.84832299e-01 2.02904046e-01 1.52928174e-01 -1.11262254e-01 -7.54513979e-01 -1.22507133e-01 6.56022012e-01 9.02755737e-01 -1.92625463e-01 9.28836703e-01 -1.88131317e-01 5.03107309e-01 -1.39694363e-01 -4.60997373e-02 1.91393420e-01 4.99620140e-01 5.18232644e-01 1.29273105e+00 4.89947014e-02 6.59062341e-02 -4.02093917e-01 6.78531289e-01 -4.11885291e-01 -1.90153718e-01 -7.62757480e-01 1.22734204e-01 -1.65323950e-02 7.04427958e-01 4.49400060e-02 -3.23915958e-01 -6.22417390e-01 1.17670488e+00 9.96828899e-02 8.96860659e-01 -7.47638762e-01 -4.06946033e-01 1.24556220e+00 1.49144277e-01 4.87516671e-01 -3.58003736e-01 8.88159946e-02 -1.17954695e+00 1.81204379e-02 -7.16305077e-01 -2.25490049e-01 -1.39087176e+00 -8.61831725e-01 4.52401936e-01 -5.34383357e-01 -1.03899229e+00 -4.68861610e-01 -8.08045268e-01 -6.34301543e-01 9.88959551e-01 -2.18489051e+00 -1.08321488e+00 -4.00211722e-01 1.15002704e+00 1.00566459e+00 -2.49451831e-01 5.01691103e-01 -3.20131853e-02 -3.28133494e-01 9.16796982e-01 -1.59286976e-01 3.32691073e-01 7.73817241e-01 -6.00032687e-01 5.52844048e-01 6.97752953e-01 -1.69561073e-01 1.19308576e-01 2.39828408e-01 -2.85713434e-01 -1.59416568e+00 -7.70009220e-01 1.21041262e+00 -2.63613332e-02 3.68994206e-01 -6.50019765e-01 -6.85006201e-01 3.46260875e-01 5.09997308e-01 3.89002562e-01 3.67531061e-01 -6.21599734e-01 -4.24738437e-01 -2.47940477e-02 -1.17663956e+00 6.92536592e-01 1.36103559e+00 -1.18616366e+00 -4.62519258e-01 3.48417251e-03 8.79018545e-01 -1.91018641e-01 -3.70485306e-01 1.47587627e-01 9.34753299e-01 -1.11001217e+00 1.15536606e+00 -7.68722236e-01 4.63934630e-01 5.45491986e-02 -2.84910321e-01 -9.67461228e-01 1.49292856e-01 -1.02208889e+00 -3.96132767e-01 8.64371121e-01 1.19759127e-01 -5.58284938e-01 5.78880191e-01 5.08399308e-01 -1.52844772e-01 -7.60911524e-01 -1.27312231e+00 -6.34915888e-01 -8.15540403e-02 -6.19271219e-01 3.12251389e-01 1.65847436e-01 2.63126403e-01 1.35039836e-01 -6.41177595e-01 -1.63872853e-01 4.54263628e-01 -2.91240841e-01 6.83823466e-01 -7.35792816e-01 1.56686828e-01 -5.18106699e-01 -5.70319235e-01 -1.80602109e+00 7.16622055e-01 -7.10189998e-01 3.51490527e-01 -1.57289958e+00 7.50104487e-02 1.09984092e-01 -1.88307375e-01 5.56256115e-01 -1.18067721e-02 2.51887351e-01 4.34631735e-01 2.51111537e-01 -4.00306493e-01 7.28002369e-01 1.44162166e+00 -3.73280615e-01 -2.62473971e-01 -2.13220958e-02 -2.23149404e-01 4.34149176e-01 7.33549595e-01 1.01644829e-01 -3.77659172e-01 -4.72464859e-01 -4.46732908e-01 3.88416976e-01 6.81907058e-01 -7.40784168e-01 6.57237947e-01 4.16654628e-03 3.46234202e-01 -5.40713072e-01 8.96357656e-01 -3.79710585e-01 -6.33632898e-01 2.49906346e-01 -5.50293505e-01 -1.30362660e-01 3.59578103e-01 3.82793546e-01 -2.59972245e-01 1.28847435e-01 8.97738338e-01 7.98301101e-02 -8.40775907e-01 5.99365711e-01 -4.72360581e-01 2.14136168e-01 6.10067070e-01 -5.65259993e-01 -5.13816297e-01 -8.43531489e-01 -5.74150801e-01 -2.11172536e-01 4.91931945e-01 5.35999000e-01 1.18822205e+00 -1.17295372e+00 -6.68125987e-01 5.51113009e-01 -9.52132046e-02 -5.28509378e-01 2.34873474e-01 8.03698123e-01 -1.28492936e-01 8.22028875e-01 -3.58207583e-01 -8.36700022e-01 -1.36694503e+00 7.25369632e-01 6.77784026e-01 2.67544568e-01 -6.65463090e-01 8.06468606e-01 3.13862890e-01 1.69464245e-01 7.31777251e-01 -2.88109273e-01 -8.56156722e-02 -2.29277350e-02 6.73591912e-01 2.24509194e-01 -2.39400029e-01 -7.95991421e-01 -5.56538045e-01 7.43362784e-01 3.11637819e-01 -3.38338107e-01 1.04403031e+00 -4.82664585e-01 4.38237160e-01 2.56994218e-01 1.72960162e+00 -2.35732079e-01 -1.84937191e+00 5.25624380e-02 -5.32250106e-01 -2.95552820e-01 2.71323174e-01 -6.30750000e-01 -1.14920926e+00 1.61038888e+00 6.66478932e-01 -1.87542692e-01 1.17719555e+00 1.62180550e-02 7.50885725e-01 9.17126387e-02 -1.80889592e-02 -1.01079261e+00 4.06740367e-01 4.31161553e-01 1.14568043e+00 -1.50795138e+00 -5.82673073e-01 6.29739910e-02 -7.12188482e-01 1.18262589e+00 3.59593928e-01 4.76452231e-01 6.92207098e-01 1.76637441e-01 2.07876012e-01 3.84933203e-01 -9.27440941e-01 -4.82721448e-01 2.13909283e-01 9.18886602e-01 1.99566185e-01 -2.06617415e-01 2.81764060e-01 3.11748013e-02 2.48897642e-01 5.06579876e-01 3.74844342e-01 4.98284519e-01 -2.80275643e-01 -4.66160387e-01 -3.78846191e-02 -5.14724776e-02 -3.33421171e-01 -4.69077736e-01 -1.85683802e-01 6.90543830e-01 -1.36781141e-01 1.17685091e+00 1.31325930e-01 -4.43261951e-01 6.78442940e-02 4.87542510e-01 4.00865018e-01 3.76042649e-02 -1.39466092e-01 -3.80001962e-02 -1.18118718e-01 -8.82138968e-01 -4.83699471e-01 -4.10933673e-01 -9.75363195e-01 -4.63470489e-01 -1.47061544e-02 -5.04938602e-01 1.11887753e+00 6.56498790e-01 6.21742547e-01 6.04135171e-02 6.04884624e-01 -1.32334638e+00 -3.33233714e-01 -1.08565414e+00 -2.86340445e-01 2.55507350e-01 1.21090198e+00 -6.80004001e-01 -6.47541702e-01 2.89174110e-01]	[14.336807250976562, 5.006595134735107]
62d722d6-3c73-4997-b129-c8d146f40471	improving-pre-trained-vision-and-language	null	null	https://aclanthology.org/2021.emnlp-main.513	https://aclanthology.org/2021.emnlp-main.513.pdf	Improving Pre-trained Vision-and-Language Embeddings for Phrase Grounding	Phrase grounding aims to map textual phrases to their associated image regions, which can be a prerequisite for multimodal reasoning and can benefit tasks requiring identifying objects based on language. With pre-trained vision-and-language models achieving impressive performance across tasks, it remains unclear if we can directly utilize their learned embeddings for phrase grounding without fine-tuning. To this end, we propose a method to extract matched phrase-region pairs from pre-trained vision-and-language embeddings and propose four fine-tuning objectives to improve the model phrase grounding ability using image-caption data without any supervised grounding signals. Experiments on two representative datasets demonstrate the effectiveness of our objectives, outperforming baseline models in both weakly-supervised and supervised phrase grounding settings. In addition, we evaluate the aligned embeddings on several other downstream tasks and show that we can achieve better phrase grounding without sacrificing representation generality.	['Nanyun Peng', 'Zi-Yi Dou']	null	null	null	null	emnlp-2021-11	['phrase-grounding']	['natural-language-processing']	[ 3.39252204e-01 2.05000594e-01 -5.18756270e-01 -4.59111333e-01 -1.24395585e+00 -7.09006310e-01 8.35564792e-01 2.39068508e-01 -6.24482632e-01 4.58789766e-01 6.14889562e-01 -2.53576785e-01 1.52543515e-01 -5.30062318e-01 -9.48976874e-01 -3.01487088e-01 3.11317682e-01 4.16404516e-01 -2.70232521e-02 -7.37605942e-03 3.38464946e-01 1.15560941e-01 -1.08655918e+00 5.86292446e-01 4.87990320e-01 7.44882464e-01 3.90156329e-01 5.80315828e-01 -2.53123850e-01 5.18508911e-01 -2.55419195e-01 -6.08394206e-01 1.40197232e-01 -2.30951577e-01 -7.93330133e-01 3.01131517e-01 8.27431381e-01 -5.63478589e-01 -4.27903205e-01 9.64960694e-01 1.18496783e-01 1.90026369e-02 8.45407903e-01 -1.13023865e+00 -1.40572023e+00 5.18415570e-01 -5.76636970e-01 8.91749337e-02 3.93408686e-01 2.96950340e-01 1.71576786e+00 -1.31691194e+00 4.82474566e-01 1.44835770e+00 4.76735204e-01 6.43245816e-01 -1.44476688e+00 -4.45523769e-01 4.00121689e-01 4.23911549e-02 -1.42342031e+00 -3.01897854e-01 5.04074693e-01 -4.38096344e-01 9.98941422e-01 -1.70890763e-01 4.11769003e-01 1.33323920e+00 -2.09023997e-01 9.18841720e-01 8.69150460e-01 -4.34181094e-01 -1.76624432e-01 1.84119657e-01 2.50421166e-01 1.08285880e+00 2.72116840e-01 -2.01652437e-01 -8.71540546e-01 -1.85151640e-02 8.91669571e-01 -8.11872184e-02 -3.22632909e-01 -3.99405897e-01 -1.74962234e+00 9.64509904e-01 6.88221276e-01 5.63809313e-02 -2.62310266e-01 6.91066623e-01 1.76902145e-01 -2.07082063e-01 2.05294088e-01 6.85170054e-01 -3.21099579e-01 1.35765567e-01 -8.16825330e-01 3.00837815e-01 3.26688617e-01 1.18549371e+00 8.30904961e-01 -3.89944673e-01 -7.44997084e-01 6.94194674e-01 5.84986925e-01 7.69726336e-01 7.47492164e-02 -9.13336635e-01 7.66566396e-01 5.57828486e-01 3.25008839e-01 -8.26118767e-01 -2.73913532e-01 -2.96880484e-01 -2.52592027e-01 -2.41007403e-01 5.66274703e-01 8.95896703e-02 -1.07816112e+00 2.00138807e+00 -1.21695004e-01 9.25397202e-02 4.29044217e-02 1.09086752e+00 1.02162063e+00 6.00497484e-01 3.55534583e-01 3.16843390e-01 1.84147990e+00 -1.32558560e+00 -5.31301141e-01 -5.20248711e-01 6.40640020e-01 -5.69978774e-01 1.73025155e+00 -8.19235221e-02 -8.75127614e-01 -5.20855963e-01 -1.00848484e+00 -5.34563839e-01 -2.87970454e-01 2.21328780e-01 6.65555418e-01 2.00588241e-01 -1.09326255e+00 3.87501791e-02 -7.30833352e-01 -6.43585205e-01 5.10238171e-01 2.37234756e-01 -5.22115409e-01 -2.18640387e-01 -8.84070456e-01 9.61718142e-01 3.15200835e-01 -2.74540130e-02 -9.05926526e-01 -7.66083419e-01 -1.09485185e+00 4.44992930e-02 2.48518273e-01 -1.26015735e+00 1.21362555e+00 -5.73257029e-01 -9.39853132e-01 1.37785208e+00 -5.22224963e-01 -4.96909052e-01 -2.00087689e-02 -3.56342643e-01 1.43966794e-01 5.19699752e-01 2.47738391e-01 1.48871541e+00 9.80800331e-01 -1.46124303e+00 -6.85399413e-01 -8.52633342e-02 5.17737091e-01 4.64907646e-01 -6.15671575e-01 -1.45177498e-01 -8.09236586e-01 -6.26879930e-01 5.67187332e-02 -9.28087413e-01 -3.08828615e-02 5.18969297e-01 -4.12015796e-01 -4.24158931e-01 4.21976596e-01 -4.52467263e-01 7.36229658e-01 -2.10515237e+00 2.90327758e-01 -1.43316895e-01 3.17460209e-01 -8.91448632e-02 -6.42283976e-01 3.07942867e-01 2.69215763e-01 3.55046004e-01 -1.67944297e-01 -6.20136380e-01 4.36115026e-01 5.45243323e-01 -7.35076725e-01 2.21957698e-01 8.37756395e-01 1.56501257e+00 -1.10694110e+00 -7.36781001e-01 1.42019898e-01 2.58252710e-01 -6.74305677e-01 2.53964543e-01 -5.25689244e-01 3.71605963e-01 -4.84236628e-01 6.84387624e-01 1.53519303e-01 -5.83974004e-01 -1.61706030e-01 -5.30492544e-01 4.18746352e-01 2.82666862e-01 -3.94645870e-01 2.12061143e+00 -5.73675752e-01 8.57231796e-01 -1.26686454e-01 -8.59294415e-01 6.21524692e-01 1.74643338e-01 2.13988602e-01 -7.12845683e-01 -1.10752314e-01 8.27174168e-04 -2.22001508e-01 -6.57773912e-01 6.12486720e-01 -2.36025497e-01 -3.41479182e-01 6.15230441e-01 2.22680733e-01 -3.30526710e-01 -2.18596198e-02 4.15613174e-01 9.09029484e-01 1.96605936e-01 -2.18835529e-02 -9.49240178e-02 2.81664610e-01 2.41551146e-01 8.27892348e-02 8.50596547e-01 -3.03274840e-01 9.56813395e-01 3.05112213e-01 -1.17759809e-01 -1.04901600e+00 -1.42151487e+00 -2.14526802e-02 1.52555716e+00 3.94977212e-01 -4.70415235e-01 -4.24080938e-01 -8.04892361e-01 1.69471383e-01 6.99612439e-01 -7.02637076e-01 -8.94862935e-02 -3.49443436e-01 -4.12562847e-01 5.55820525e-01 8.58549058e-01 2.84040213e-01 -8.66918921e-01 -1.81321919e-01 -1.04741134e-01 -3.69620085e-01 -1.72386515e+00 -7.37616241e-01 -4.18755487e-02 -5.41207850e-01 -6.95825160e-01 -9.33856606e-01 -1.09776473e+00 8.56706202e-01 5.36984384e-01 1.43526530e+00 1.59958154e-01 8.51791129e-02 9.59961414e-01 -3.67562234e-01 -4.29162711e-01 3.34381266e-03 1.12397313e-01 -9.67356786e-02 -2.44759381e-01 6.41896307e-01 -1.98302194e-01 -7.20293581e-01 1.72450006e-01 -7.45445907e-01 2.88472533e-01 7.45829523e-01 9.86614823e-01 6.38693392e-01 -6.95082247e-01 4.15047586e-01 -2.88766861e-01 6.59875095e-01 -2.01905653e-01 -3.62369001e-01 4.31144178e-01 -2.66503364e-01 3.84820461e-01 6.99244887e-02 -3.57164919e-01 -5.01627147e-01 -5.49634546e-02 9.96515229e-02 -5.61269879e-01 -1.58329215e-02 4.62880522e-01 1.24603407e-02 6.71120137e-02 4.30000395e-01 2.14019418e-01 -9.49504524e-02 -2.56596692e-02 1.01974082e+00 4.10165846e-01 7.81433105e-01 -1.00487065e+00 1.05217266e+00 6.50590062e-01 -8.79006237e-02 -5.40082395e-01 -1.46032071e+00 -5.86455703e-01 -5.41397035e-01 1.65785179e-01 1.39480472e+00 -1.22778761e+00 -4.84455556e-01 -2.93313831e-01 -1.56855714e+00 -2.57118940e-01 -1.37502760e-01 5.07509172e-01 -6.93328917e-01 2.32250333e-01 -4.98245537e-01 -4.97905284e-01 -2.09083855e-01 -1.14805758e+00 1.60535359e+00 1.64304093e-01 -4.12152112e-01 -1.16987062e+00 -5.06444536e-02 6.67170227e-01 5.01303710e-02 -1.54074192e-01 9.56664979e-01 -5.56086004e-01 -7.18501568e-01 1.89330041e-01 -7.86436796e-01 9.61651355e-02 5.56040406e-02 -3.37101877e-01 -1.08299243e+00 -2.43023172e-01 -4.33537096e-01 -7.15478241e-01 1.25538826e+00 2.12840050e-01 1.07580209e+00 -1.45694688e-01 -3.17051411e-01 5.70295274e-01 1.30479848e+00 -4.39720124e-01 2.33317465e-01 3.44432980e-01 9.75499630e-01 6.86944544e-01 6.60618186e-01 5.05496003e-02 6.21779978e-01 6.76137447e-01 4.56614047e-01 -2.86532909e-01 -3.81355584e-01 -5.95844388e-01 3.39026570e-01 3.71111661e-01 2.73015559e-01 -2.98386782e-01 -9.89161193e-01 9.54158187e-01 -1.98301065e+00 -8.55541170e-01 1.55530870e-01 1.75313938e+00 1.04681122e+00 1.50495365e-01 -6.57520490e-03 -4.69897032e-01 4.88663465e-01 1.42616943e-01 -4.94849980e-01 -3.46134007e-01 1.53775113e-02 1.91847160e-01 4.18631971e-01 5.71240366e-01 -1.18357265e+00 1.28787386e+00 6.54530573e+00 4.80432838e-01 -9.23553526e-01 1.97039202e-01 3.37650537e-01 -9.63055864e-02 -8.14750314e-01 -7.50271156e-02 -9.16520417e-01 1.78492051e-02 5.83050191e-01 1.07867055e-01 3.08322132e-01 5.07345140e-01 2.07759485e-01 1.48129836e-01 -1.71187425e+00 1.28469634e+00 3.04934680e-01 -1.45094454e+00 4.08008635e-01 8.61163959e-02 7.26446390e-01 -1.68368649e-02 4.50142950e-01 3.97014052e-01 2.31946275e-01 -1.39696407e+00 8.68950129e-01 3.26036245e-01 8.21381092e-01 -1.69824764e-01 5.62932491e-01 -6.99319467e-02 -1.02514648e+00 1.25397786e-01 -3.26494336e-01 -3.78280543e-02 3.03622663e-01 1.28606528e-01 -1.04135299e+00 3.83388370e-01 3.23604643e-01 7.51501858e-01 -6.41840577e-01 6.87470317e-01 -6.55433536e-01 5.87067902e-01 -2.10856020e-01 -8.12714845e-02 6.46560907e-01 4.90293354e-02 3.78710002e-01 1.32956064e+00 1.16974860e-01 -8.53565261e-02 2.33251706e-01 1.37188733e+00 -2.88532615e-01 -1.10722698e-01 -6.43318415e-01 -4.66405302e-01 4.65214461e-01 1.22607827e+00 -5.69182575e-01 -2.45348096e-01 -8.73861074e-01 1.04850578e+00 4.35103744e-01 6.92181230e-01 -8.49965811e-01 -1.71095096e-02 7.75682807e-01 -3.01304925e-02 4.81553823e-01 -7.09991276e-01 -5.22970855e-01 -1.23394513e+00 3.44379656e-02 -5.07100701e-01 2.20432281e-01 -1.10054946e+00 -1.67380250e+00 1.42687470e-01 1.83958542e-02 -9.55810905e-01 -1.34455919e-01 -1.09258723e+00 -3.29834193e-01 9.12882924e-01 -1.77303112e+00 -1.91631377e+00 -3.65439832e-01 6.59032047e-01 6.03685737e-01 7.58029595e-02 8.84093046e-01 -1.27248824e-01 -3.15109491e-01 8.59567106e-01 -4.16801155e-01 4.24539894e-01 9.94086862e-01 -1.28109193e+00 2.16658533e-01 7.66648948e-01 8.82630885e-01 9.56497788e-01 7.36260712e-01 -2.69826323e-01 -1.51953983e+00 -1.17727244e+00 7.71950901e-01 -1.10545993e+00 9.44685042e-01 -6.35747075e-01 -7.88784981e-01 8.54891717e-01 4.81082261e-01 1.81464516e-02 7.86743343e-01 4.90468055e-01 -8.29414129e-01 7.19850883e-02 -7.13635325e-01 8.36777151e-01 1.15737343e+00 -1.06951714e+00 -1.09583175e+00 5.57547748e-01 1.05626667e+00 -2.14104205e-01 -6.59526765e-01 1.97886676e-01 5.49947321e-01 -3.66026521e-01 1.23367155e+00 -7.28745103e-01 7.02809811e-01 -2.89203674e-01 -6.02953732e-01 -9.26091135e-01 -3.47635806e-01 -2.54369587e-01 -4.66513149e-02 1.21745980e+00 6.50218248e-01 -2.44958907e-01 8.05748343e-01 6.66949868e-01 -9.26265717e-02 -7.89146125e-01 -5.98988414e-01 -7.57407188e-01 3.02087575e-01 -4.60616887e-01 2.87150443e-01 5.97561657e-01 3.91656272e-02 7.69612491e-01 -3.80960070e-02 5.01225352e-01 6.56626523e-01 2.62911320e-01 7.88877964e-01 -5.87442458e-01 -2.67475605e-01 -5.45110106e-01 -4.21097726e-01 -1.51434660e+00 5.75850487e-01 -1.05995131e+00 2.91536838e-01 -2.10722518e+00 6.68701589e-01 -2.64944941e-01 -5.36815643e-01 7.60019362e-01 -5.05690455e-01 6.48796141e-01 4.07686681e-01 2.64039487e-01 -9.85409498e-01 6.34099662e-01 1.36675000e+00 -5.28514862e-01 2.14181021e-01 -7.01485634e-01 -9.97716665e-01 5.97518027e-01 4.80813920e-01 -2.46431425e-01 -6.36357486e-01 -1.04731023e+00 2.45805725e-01 -4.08727944e-01 8.13164294e-01 -5.72820365e-01 1.22085273e-01 -2.98762679e-01 1.14715442e-01 -5.82722962e-01 5.97246230e-01 -6.03073597e-01 -7.59088516e-01 4.79406491e-02 -6.30566776e-01 -2.42466219e-02 3.50598067e-01 7.31186986e-01 -1.79616600e-01 -2.63559043e-01 4.06635672e-01 -6.89467117e-02 -1.04838014e+00 2.75309712e-01 -9.53370854e-02 2.15860233e-01 8.71710837e-01 -1.51494429e-01 -4.05678838e-01 -3.92786354e-01 -5.52560389e-01 4.71445829e-01 6.26618624e-01 5.43943644e-01 8.67180228e-01 -1.52194405e+00 -6.94942176e-01 -1.22010760e-01 7.29780614e-01 2.09644493e-02 -3.11001360e-01 7.19857335e-01 -2.25335389e-01 9.08167303e-01 4.20177951e-02 -1.08062994e+00 -1.09796190e+00 6.61041260e-01 6.91184774e-02 -6.66046217e-02 -4.79993492e-01 1.10809767e+00 7.08152235e-01 -3.39521259e-01 3.12830031e-01 -6.34891212e-01 -4.45385017e-02 -2.10062653e-01 4.41833913e-01 -5.53081453e-01 -3.79595339e-01 -4.88569111e-01 -3.86334926e-01 8.76990616e-01 -1.34044886e-01 -6.13613546e-01 9.92675602e-01 -3.44436675e-01 1.32033378e-01 4.19430554e-01 1.24240327e+00 -1.50693953e-01 -1.37771618e+00 -4.33685988e-01 1.37764648e-01 -3.42414469e-01 2.28695255e-02 -5.75419903e-01 -5.50405145e-01 1.20435631e+00 3.13433766e-01 -3.02421808e-01 7.90701151e-01 5.72345734e-01 8.17339063e-01 6.40706360e-01 4.36155021e-01 -6.30285025e-01 7.34698713e-01 5.22388160e-01 1.02665615e+00 -1.54618812e+00 -1.95316002e-01 -3.16422462e-01 -6.63342953e-01 9.06419098e-01 6.29219532e-01 -6.59671873e-02 2.25734591e-01 -1.23418480e-01 -4.36478555e-02 -2.88012385e-01 -8.33509564e-01 -5.86635530e-01 6.71828985e-01 6.97359920e-01 4.33071315e-01 -1.24465249e-01 -1.15669839e-01 5.68965614e-01 -1.66373737e-02 -1.81919202e-01 6.06295690e-02 6.86106205e-01 -5.75320780e-01 -9.85329807e-01 -4.16491777e-01 3.18094879e-01 -2.37334490e-01 -3.94660443e-01 -4.76398021e-01 6.78774059e-01 4.16427031e-02 8.83135319e-01 2.16120571e-01 -2.00922385e-01 1.57729745e-01 1.42039480e-02 8.75465870e-01 -1.10676825e+00 -1.36915118e-01 -1.42143056e-01 1.74329817e-01 -5.52074194e-01 -4.69748110e-01 -3.87263417e-01 -1.42613089e+00 1.18178330e-01 -1.29060879e-01 -1.12521715e-01 3.88059109e-01 1.14776659e+00 3.64123106e-01 4.05264199e-01 6.64779358e-03 -7.53326237e-01 -5.50483584e-01 -8.60797346e-01 -1.09894350e-01 6.70581639e-01 2.92508066e-01 -7.46919513e-01 -1.09784439e-01 3.31143081e-01]	[10.589125633239746, 1.5363458395004272]
5fcc8865-f5fe-46ec-9141-43d333042b23	pedhunter-occlusion-robust-pedestrian	1909.06826	null	https://arxiv.org/abs/1909.06826v1	https://arxiv.org/pdf/1909.06826v1.pdf	PedHunter: Occlusion Robust Pedestrian Detector in Crowded Scenes	Pedestrian detection in crowded scenes is a challenging problem, because occlusion happens frequently among different pedestrians. In this paper, we propose an effective and efficient detection network to hunt pedestrians in crowd scenes. The proposed method, namely PedHunter, introduces strong occlusion handling ability to existing region-based detection networks without bringing extra computations in the inference stage. Specifically, we design a mask-guided module to leverage the head information to enhance the feature representation learning of the backbone network. Moreover, we develop a strict classification criterion by improving the quality of positive samples during training to eliminate common false positives of pedestrian detection in crowded scenes. Besides, we present an occlusion-simulated data augmentation to enrich the pattern and quantity of occlusion samples to improve the occlusion robustness. As a consequent, we achieve state-of-the-art results on three pedestrian detection datasets including CityPersons, Caltech-USA and CrowdHuman. To facilitate further studies on the occluded pedestrian detection in surveillance scenes, we release a new pedestrian dataset, called SUR-PED, with a total of over 162k high-quality manually labeled instances in 10k images. The proposed dataset, source codes and trained models will be released.	['Xudong Zou', 'Shifeng Zhang', 'Junliang Xing', 'Zhen Lei', 'Stan Z. Li', 'Cheng Chi']	2019-09-15	null	null	null	null	['occlusion-handling']	['computer-vision']	[-2.32309699e-01 -4.06723201e-01 6.97218105e-02 -4.73437607e-01 -2.29699314e-01 -3.14347774e-01 4.32981908e-01 -2.03026995e-01 -7.63220429e-01 7.52764583e-01 6.74236789e-02 -1.87075451e-01 5.89957952e-01 -8.85275483e-01 -6.63555920e-01 -7.88128018e-01 7.64053373e-04 -2.06761975e-02 9.29503262e-01 -1.09973893e-01 -2.06729338e-01 4.36910093e-01 -1.54476511e+00 3.22105199e-01 7.43596733e-01 8.38777602e-01 2.51196116e-01 5.98822653e-01 3.42097282e-01 6.59916103e-01 -7.04818606e-01 -7.57691443e-01 5.06579161e-01 9.28518176e-02 -1.59451738e-01 4.15960014e-01 6.60724938e-01 -6.87872052e-01 -6.67949498e-01 1.05932641e+00 8.50107849e-01 1.34432256e-01 5.31602561e-01 -1.40421891e+00 -3.56880248e-01 2.26127177e-01 -1.11545575e+00 6.45022869e-01 1.46308109e-01 8.86268973e-01 4.71180022e-01 -1.08672535e+00 3.50645512e-01 1.53966093e+00 6.66661859e-01 3.83135319e-01 -7.80108213e-01 -7.90516019e-01 4.48011786e-01 4.34069186e-01 -1.59581518e+00 -5.86816370e-01 7.05759525e-01 -5.72466075e-01 4.56293136e-01 2.03974754e-01 8.69233131e-01 1.10883152e+00 -2.36167282e-01 1.23739815e+00 7.42508411e-01 -2.07336769e-01 -1.19874608e-02 3.05512935e-01 3.39691669e-01 8.29452217e-01 7.70129561e-01 4.87696618e-01 -1.23586565e-01 3.82920131e-02 6.59041524e-01 9.84969214e-02 -7.08732978e-02 -3.89055610e-01 -8.56332004e-01 8.37367415e-01 7.54051089e-01 -2.41479099e-01 -1.55433849e-01 -1.59583420e-01 6.93519473e-01 -3.20811331e-01 2.00405821e-01 -1.66229337e-01 -1.67598456e-01 4.22765821e-01 -7.45751321e-01 4.25785601e-01 2.76351482e-01 1.26883149e+00 5.78977227e-01 7.40458369e-02 -7.79685140e-01 7.02489138e-01 4.25178856e-01 7.35738039e-01 -1.13020102e-02 -6.89892173e-01 7.67677248e-01 7.21123934e-01 3.40700805e-01 -1.33309996e+00 -5.19156575e-01 -7.89586365e-01 -1.08483553e+00 5.28056063e-02 4.40437526e-01 -4.66478795e-01 -6.66603446e-01 1.44821310e+00 7.07925856e-01 1.15245990e-01 -6.51261657e-02 1.32502806e+00 1.22810507e+00 5.77365816e-01 3.13354492e-01 -8.74407589e-02 1.78952491e+00 -1.31544590e+00 -4.46461946e-01 -5.28920531e-01 3.29559922e-01 -6.93427145e-01 6.68779790e-01 1.61644548e-01 -7.81418562e-01 -9.46851313e-01 -9.27482367e-01 5.02103381e-02 -3.28859061e-01 8.02832127e-01 6.78852975e-01 9.27383006e-01 -5.36998451e-01 -3.48674983e-01 -4.07889962e-01 -4.54489112e-01 9.16494370e-01 -1.42297978e-02 -1.49037361e-01 -3.76458108e-01 -1.04753935e+00 7.37483382e-01 5.29667139e-01 3.41783375e-01 -1.07347834e+00 -4.14700449e-01 -9.24257159e-01 -1.23125337e-01 5.18077195e-01 -7.64854193e-01 9.09231246e-01 -3.30789119e-01 -8.90988231e-01 8.68648708e-01 -1.98516190e-01 -6.65645361e-01 9.98508632e-01 -1.74518183e-01 -3.76826644e-01 -3.42768151e-03 4.23182338e-01 9.85475302e-01 6.20779991e-01 -1.26253545e+00 -1.11469209e+00 -1.55617416e-01 5.22886477e-02 -8.48622620e-02 -2.47561097e-01 3.63417596e-01 -8.51054430e-01 -5.99813640e-01 -3.25330734e-01 -6.29989564e-01 -5.97586751e-01 2.76610494e-01 -6.07727289e-01 -2.41088107e-01 8.32378983e-01 -5.20400226e-01 9.16412115e-01 -2.01813483e+00 -3.42513978e-01 1.41747678e-02 4.04976040e-01 8.01243484e-01 -1.85633957e-01 -2.00434893e-01 2.39866421e-01 -3.58748764e-01 -9.39674973e-02 -5.16767085e-01 -4.25488316e-02 6.86956868e-02 -1.36474788e-01 7.07430482e-01 4.85226899e-01 8.80385339e-01 -8.53047431e-01 -9.27636087e-01 7.15981841e-01 3.83185923e-01 -4.70306844e-01 2.89540261e-01 2.12524191e-01 3.34839374e-01 -4.49417561e-01 7.99903572e-01 1.22265327e+00 6.80371374e-02 -4.98786211e-01 -2.97317386e-01 -4.10987496e-01 -4.33883220e-01 -1.44482923e+00 1.13279021e+00 1.30552173e-01 5.75980425e-01 9.07585993e-02 -8.06527674e-01 9.75918651e-01 -7.16561675e-02 3.20357904e-02 -5.79076231e-01 3.94233614e-01 -2.89810658e-01 -9.19843465e-02 -6.63428724e-01 6.23932540e-01 5.32630384e-01 1.26871288e-01 -2.14727193e-01 -2.75448501e-01 5.80676258e-01 8.43039989e-01 1.56447291e-01 7.57394850e-01 -1.91133007e-01 2.60889977e-01 -1.80520490e-01 9.28347647e-01 4.32203300e-02 9.06862199e-01 1.05985296e+00 -9.04606760e-01 5.08499265e-01 2.85520762e-01 -7.71622419e-01 -1.04841328e+00 -1.01013792e+00 -2.92841017e-01 1.08483756e+00 3.09112728e-01 -1.93244815e-01 -8.04588258e-01 -5.42631269e-01 -4.71003987e-02 2.05568582e-01 -4.85985726e-01 -4.49497215e-02 -7.08268523e-01 -1.37611604e+00 7.99871206e-01 7.90937006e-01 1.16182029e+00 -8.67182076e-01 -6.16843104e-01 8.21699426e-02 -3.48565459e-01 -1.47343135e+00 -5.58890462e-01 -3.21403921e-01 -1.17019065e-01 -1.45703530e+00 -9.15715158e-01 -8.93615425e-01 6.37291729e-01 8.22998941e-01 9.24175560e-01 3.98394503e-02 -7.46001720e-01 -9.45977196e-02 -1.35848433e-01 -5.23881078e-01 1.68409511e-01 -4.93383892e-02 1.92164153e-01 1.69449359e-01 5.99305272e-01 6.81318715e-02 -9.31500077e-01 6.42035067e-01 -3.49155962e-01 -2.90180487e-03 6.50462687e-01 6.62442923e-01 2.13957563e-01 1.72131702e-01 3.20944577e-01 -2.00733706e-01 2.07786143e-01 -1.88822195e-01 -9.57180440e-01 8.68795216e-02 2.85308182e-01 -5.23959458e-01 5.40115654e-01 -4.10195976e-01 -1.22956288e+00 4.03217852e-01 -7.98467249e-02 -1.84343234e-01 -4.21434700e-01 -4.13096517e-01 -4.88444537e-01 -2.14492053e-01 7.42213070e-01 2.49470085e-01 -4.48121727e-01 -3.37537587e-01 4.92557704e-01 6.01968408e-01 8.70929837e-01 -5.10767639e-01 8.80835295e-01 6.32819533e-01 -1.56007066e-01 -1.02310860e+00 -8.12988162e-01 -8.46097946e-01 -6.81251466e-01 -4.93424028e-01 9.49128270e-01 -1.41037309e+00 -8.68071437e-01 6.41412735e-01 -1.46820998e+00 1.59203395e-01 -6.94405809e-02 4.31519359e-01 1.04475096e-01 6.16497755e-01 -7.36851454e-01 -1.16088331e+00 -2.83966720e-01 -1.21594894e+00 1.09844077e+00 5.28355300e-01 3.95653576e-01 -4.47106510e-01 -3.68479192e-01 3.95747304e-01 1.57293901e-01 2.82910675e-01 -1.46328434e-02 -2.39337996e-01 -8.81746590e-01 -3.90239507e-01 -9.57527757e-01 2.92737901e-01 -2.65140772e-01 8.60705525e-02 -9.94998574e-01 -4.54455167e-01 -4.58819687e-01 -6.22506626e-02 1.25354886e+00 6.77018523e-01 9.36073899e-01 -6.80347458e-02 -6.76065922e-01 5.38759589e-01 9.65633273e-01 -1.39153704e-01 4.37080353e-01 3.68062288e-01 8.68790090e-01 7.39847004e-01 6.34786367e-01 6.65184617e-01 4.52426523e-01 7.33421206e-01 2.21461639e-01 -4.69666392e-01 -4.32488650e-01 -2.04914615e-01 1.85074151e-01 2.43037909e-01 -7.35813603e-02 -2.39474729e-01 -6.97224319e-01 4.61867094e-01 -1.99150670e+00 -1.12052166e+00 -5.11649609e-01 1.91963029e+00 4.17804241e-01 3.67794394e-01 5.90355575e-01 5.40935434e-03 1.26422977e+00 1.68935910e-01 -3.60016823e-01 5.07155657e-01 -4.95484143e-01 -5.44599175e-01 6.51101887e-01 4.51081008e-01 -1.75153148e+00 1.06529725e+00 6.11090326e+00 9.43109930e-01 -4.98722523e-01 1.02360666e-01 7.52125323e-01 -2.30355617e-02 6.99688494e-01 -3.96359861e-01 -1.54252601e+00 6.97726786e-01 8.69438052e-02 2.08368614e-01 -2.58736730e-01 1.16765821e+00 6.14377499e-01 -1.73826009e-01 -6.86063766e-01 1.13536370e+00 1.90687254e-02 -1.11657989e+00 -1.00541867e-01 -1.12918884e-01 5.47919571e-01 -2.23975942e-01 9.57147498e-03 3.65986675e-01 4.25115794e-01 -6.04153693e-01 5.96795559e-01 2.77836710e-01 3.58349502e-01 -8.03768814e-01 9.42258000e-01 4.48205531e-01 -1.66550922e+00 -2.72388488e-01 -9.10229445e-01 -2.83187121e-01 4.25379217e-01 8.25542629e-01 -4.93002236e-01 3.44108701e-01 8.06021154e-01 7.15910077e-01 -1.18215168e+00 1.72756934e+00 -3.45182687e-01 3.05947781e-01 -3.75487864e-01 -1.95896868e-02 2.72452950e-01 7.29287118e-02 5.62714875e-01 1.64072633e+00 -2.24925682e-01 6.39228895e-02 7.97378480e-01 7.68278897e-01 2.19343215e-01 -6.47641495e-02 -4.71361458e-01 9.50398922e-01 4.90788072e-01 1.37091827e+00 -6.55820251e-01 -6.21613264e-01 -4.52109933e-01 6.27884328e-01 2.60922849e-01 5.00623882e-01 -1.21577716e+00 -1.27690494e-01 6.02539122e-01 1.62404720e-02 3.92530203e-01 -1.65941551e-01 -1.04407445e-02 -1.18097389e+00 2.90737540e-01 -5.96041858e-01 2.89420426e-01 -4.30760056e-01 -1.45236218e+00 4.42508876e-01 8.52134898e-02 -1.04240537e+00 3.43666881e-01 -7.50075519e-01 -7.45398819e-01 6.79450333e-01 -1.61844683e+00 -1.25653553e+00 -8.11491668e-01 5.73158205e-01 5.40437043e-01 -3.70420903e-01 -2.63056736e-02 8.33441138e-01 -1.27940404e+00 6.63975298e-01 -3.07666332e-01 8.43396425e-01 6.60774052e-01 -7.30047345e-01 5.80841243e-01 1.41296375e+00 -4.27917928e-01 2.69988447e-01 6.62031889e-01 -6.23298824e-01 -8.06149662e-01 -1.70841098e+00 4.24646437e-01 -3.62588406e-01 1.05741933e-01 -4.55351800e-01 -5.36288917e-01 4.17224139e-01 3.10740024e-02 4.28293347e-01 2.66677409e-01 -2.93905824e-01 -2.21079454e-01 -1.92257583e-01 -1.16028309e+00 6.29844189e-01 1.23939288e+00 1.58834517e-01 -6.02969289e-01 5.25777161e-01 7.86170781e-01 -3.10637355e-01 -1.68228984e-01 3.99237931e-01 3.31652522e-01 -1.01353741e+00 1.45257318e+00 -5.51138222e-01 3.96889336e-02 -8.51410151e-01 -1.82267725e-01 -7.51307070e-01 -6.17316425e-01 -1.95112661e-01 -1.21558376e-01 1.33499551e+00 -9.11064073e-02 -5.12926936e-01 1.09014082e+00 4.71375436e-01 -4.09036241e-02 -4.43613708e-01 -8.94902945e-01 -8.04873109e-01 -3.09883088e-01 -2.14140207e-01 5.27750611e-01 4.10403639e-01 -5.41114748e-01 3.29831123e-01 -7.02082932e-01 4.66167659e-01 1.18454874e+00 -3.06525111e-01 1.27553153e+00 -1.05795920e+00 -4.90265042e-02 -3.81484866e-01 -6.43948376e-01 -1.41245031e+00 -2.06609115e-01 -3.22074741e-01 4.24212366e-02 -1.28069615e+00 5.54442108e-01 -3.49242747e-01 1.60427690e-01 1.79459721e-01 -5.81410527e-01 3.92394841e-01 3.33857298e-01 2.59462427e-02 -1.08258212e+00 6.80511594e-01 1.22016954e+00 -3.46734524e-01 -1.67248659e-02 1.76939845e-01 -4.45723623e-01 9.51490343e-01 6.09217584e-01 -2.24509120e-01 -5.12454845e-02 -3.68489057e-01 -5.66519737e-01 -3.85149658e-01 9.54293907e-01 -1.45326185e+00 5.63214242e-01 1.36569757e-02 1.06868887e+00 -1.14551771e+00 4.72536206e-01 -5.16408980e-01 -3.35367620e-01 8.07455003e-01 -9.32799503e-02 -1.69896614e-02 1.35091648e-01 6.06003284e-01 4.28490788e-02 -8.20090473e-02 1.04357111e+00 -1.67074367e-01 -1.05208707e+00 6.28900409e-01 -3.74556005e-01 1.60473570e-01 1.31398177e+00 -6.71924129e-02 -5.48513353e-01 1.86890453e-01 -2.63010740e-01 7.23335981e-01 1.46726549e-01 4.74534571e-01 7.01999903e-01 -1.38421464e+00 -1.02845812e+00 2.90860176e-01 2.06705421e-01 2.20202461e-01 4.21257883e-01 6.04761899e-01 -4.67791766e-01 5.12406170e-01 -1.69661880e-01 -8.38031173e-01 -1.36890280e+00 8.96756887e-01 3.62482280e-01 4.91893180e-02 -6.91582501e-01 8.92763019e-01 5.06852508e-01 -2.76999384e-01 4.90913600e-01 -9.37914625e-02 -2.67388076e-01 -3.78694981e-02 1.01001871e+00 6.15173161e-01 -4.01589870e-01 -8.15019131e-01 -5.43991506e-01 3.53833020e-01 -2.33373672e-01 3.54555309e-01 7.51370907e-01 -2.18638062e-01 1.90011367e-01 -3.24871361e-01 9.01541531e-01 -2.60681987e-01 -1.58724785e+00 -3.59903783e-01 -4.18868482e-01 -8.48566055e-01 -2.33595699e-01 -3.47051978e-01 -1.19954383e+00 7.83050656e-01 8.29916954e-01 -2.36860320e-01 7.85195768e-01 -1.28275454e-01 8.00491571e-01 4.81633484e-01 2.24012092e-01 -9.96642172e-01 1.15339689e-01 2.68801719e-01 4.90417421e-01 -1.65687096e+00 9.85420644e-02 -7.71514177e-01 -4.38557863e-01 7.26248085e-01 1.20544410e+00 -1.39877990e-01 3.40172291e-01 2.10998788e-01 -1.04757287e-01 6.30174130e-02 -2.41548374e-01 -6.07613564e-01 1.03602603e-01 1.09048808e+00 -5.45563828e-03 1.72452345e-01 -1.17918760e-01 6.87904537e-01 -1.54439341e-02 -1.91256508e-01 3.23081642e-01 6.53145373e-01 -9.16756034e-01 -5.91916144e-01 -9.34081018e-01 2.25971386e-01 1.20346965e-02 -7.08087459e-02 -1.71910062e-01 7.32557118e-01 6.90028250e-01 1.27653456e+00 -1.18276656e-01 -1.48932368e-01 6.18508220e-01 -5.47415733e-01 2.52896100e-01 -2.24919692e-01 -2.04770759e-01 -1.26802519e-01 8.05449709e-02 -3.61164182e-01 -4.43747848e-01 -6.90170705e-01 -5.94411790e-01 -5.25819480e-01 -4.65222985e-01 -7.70555362e-02 1.24601960e-01 8.21209788e-01 1.53255373e-01 6.47300839e-01 4.25336689e-01 -1.11772716e+00 -2.19379351e-01 -8.59430254e-01 -2.75303304e-01 2.89708376e-01 2.15053022e-01 -9.80738223e-01 -1.89197287e-02 -8.79547447e-02]	[8.065807342529297, -0.5993462204933167]
9babea93-9b3b-40cf-95a6-f9eb532c1760	multilingual-language-model-adaptive-fine	2204.06487	null	https://arxiv.org/abs/2204.06487v3	https://arxiv.org/pdf/2204.06487v3.pdf	Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning	Multilingual pre-trained language models (PLMs) have demonstrated impressive performance on several downstream tasks for both high-resourced and low-resourced languages. However, there is still a large performance drop for languages unseen during pre-training, especially African languages. One of the most effective approaches to adapt to a new language is \textit{language adaptive fine-tuning} (LAFT) -- fine-tuning a multilingual PLM on monolingual texts of a language using the pre-training objective. However, adapting to a target language individually takes a large disk space and limits the cross-lingual transfer abilities of the resulting models because they have been specialized for a single language. In this paper, we perform \textit{multilingual adaptive fine-tuning} on 17 most-resourced African languages and three other high-resource languages widely spoken on the African continent to encourage cross-lingual transfer learning. To further specialize the multilingual PLM, we removed vocabulary tokens from the embedding layer that corresponds to non-African writing scripts before MAFT, thus reducing the model size by around 50%. Our evaluation on two multilingual PLMs (AfriBERTa and XLM-R) and three NLP tasks (NER, news topic classification, and sentiment classification) shows that our approach is competitive to applying LAFT on individual languages while requiring significantly less disk space. Additionally, we show that our adapted PLM also improves the zero-shot cross-lingual transfer abilities of parameter efficient fine-tuning methods.	['Dietrich Klakow', 'Marius Mosbach', 'David Ifeoluwa Adelani', 'Jesujoba O. Alabi']	2022-04-13	null	https://aclanthology.org/2022.coling-1.382	https://aclanthology.org/2022.coling-1.382.pdf	coling-2022-10	['xlm-r']	['natural-language-processing']	[-3.20012301e-01 -1.64946601e-01 -3.59492779e-01 -4.51072335e-01 -1.26492071e+00 -8.32221806e-01 5.96521795e-01 -5.86504452e-02 -1.10987663e+00 9.89230394e-01 3.13851774e-01 -6.64603174e-01 3.11021000e-01 -5.42462826e-01 -9.35810387e-01 -2.93048650e-01 1.16086155e-01 7.42459953e-01 5.25492877e-02 -2.23267302e-01 -1.19899325e-01 3.12587440e-01 -8.32303464e-01 3.22107136e-01 1.05088270e+00 -5.31632788e-02 6.02938056e-01 5.48848450e-01 -4.30216461e-01 3.07230335e-02 -5.28961241e-01 -6.27710462e-01 2.15287566e-01 -1.03186965e-01 -9.54119384e-01 -3.59815240e-01 4.87002075e-01 1.12175532e-01 9.72046852e-02 8.39209557e-01 5.65292716e-01 1.32021308e-01 6.75272644e-01 -7.00477600e-01 -7.59725988e-01 1.12374306e+00 -6.21703625e-01 3.24878424e-01 -5.92924878e-02 -2.87485942e-02 8.25821936e-01 -1.25110197e+00 7.69080937e-01 1.57138944e+00 8.84147227e-01 7.07754254e-01 -1.28098309e+00 -9.66329336e-01 3.63760561e-01 -1.69491366e-01 -1.57218623e+00 -7.94882953e-01 2.30616152e-01 -3.75380754e-01 1.52623379e+00 -5.16651804e-03 2.07451031e-01 1.00330758e+00 2.27728128e-01 7.55537212e-01 1.19839072e+00 -7.74933159e-01 -6.52769580e-02 6.77174866e-01 -2.74600089e-01 4.92453337e-01 3.11996490e-02 -2.52605438e-01 -6.15768850e-01 -5.52503504e-02 4.28151548e-01 -5.49855053e-01 -8.42649862e-02 3.27101685e-02 -1.31552923e+00 1.10509086e+00 1.80027150e-02 6.82072520e-01 -4.56075966e-02 -1.62426785e-01 7.52600491e-01 6.18856430e-01 9.51833785e-01 5.58679521e-01 -1.18480802e+00 -1.69793546e-01 -9.51144576e-01 -3.61002803e-01 8.25447798e-01 9.14422870e-01 1.01122272e+00 2.54681796e-01 1.41447604e-01 1.35084772e+00 2.00881913e-01 7.24273384e-01 8.80653381e-01 -5.10863662e-01 7.69220471e-01 2.03240484e-01 -3.76724541e-01 -6.23201989e-02 -2.46088952e-01 -2.60847390e-01 -6.20615959e-01 -1.55778259e-01 3.77655625e-01 -6.37129784e-01 -1.01047146e+00 2.07391524e+00 2.87298858e-01 -1.66428849e-01 5.06783545e-01 2.93593735e-01 2.61187822e-01 1.04050517e+00 4.99916583e-01 -1.80533826e-01 1.33781087e+00 -1.16682148e+00 -3.85909855e-01 -6.12500846e-01 1.06822145e+00 -1.19933903e+00 1.61408639e+00 1.22208975e-01 -8.64277959e-01 -5.50024211e-01 -8.19616675e-01 -2.15229437e-01 -7.55913258e-01 1.59054101e-01 4.56470221e-01 6.72687054e-01 -1.21662271e+00 1.72687948e-01 -7.90118337e-01 -7.08671749e-01 -5.88446967e-02 4.42972034e-01 -6.00380063e-01 -3.54135901e-01 -1.59269762e+00 1.17898715e+00 6.47671580e-01 -3.33311349e-01 -7.12223530e-01 -9.37035739e-01 -8.96878660e-01 -1.56960189e-01 7.64419558e-03 -3.61056000e-01 1.10419083e+00 -1.01484668e+00 -1.69206500e+00 1.00451231e+00 -1.66236952e-01 -3.15254778e-01 4.12422985e-01 -4.05226558e-01 -5.58712304e-01 -4.09931004e-01 3.75668079e-01 8.08413148e-01 7.55645871e-01 -7.92827427e-01 -7.48704433e-01 -2.63003148e-02 -2.91617215e-01 6.96966767e-01 -8.80866766e-01 4.60609555e-01 -7.84978688e-01 -8.37401271e-01 -6.39166236e-01 -9.89612222e-01 -1.43835902e-01 -7.76363671e-01 1.04695320e-01 -3.95145416e-01 6.34780645e-01 -8.25808942e-01 1.36980259e+00 -2.22738457e+00 1.16504245e-01 4.00711643e-03 -5.20829260e-01 5.25380731e-01 -4.91914302e-01 4.27698910e-01 6.02555536e-02 3.55836540e-01 -3.76421064e-02 -3.97148758e-01 -7.86155611e-02 5.24077475e-01 -2.36946985e-01 2.09231332e-01 1.75910130e-01 7.54668713e-01 -7.69336641e-01 -3.49122018e-01 -2.38434467e-02 7.14269459e-01 -6.62020564e-01 -4.12655249e-02 -1.33257598e-01 4.23609853e-01 -1.45345584e-01 2.36925736e-01 3.53363633e-01 2.42398560e-01 3.41279387e-01 1.17015563e-01 -3.06174815e-01 5.98656654e-01 -9.27026153e-01 1.80992723e+00 -1.25493467e+00 4.33193237e-01 9.86565873e-02 -5.53036153e-01 7.22335339e-01 3.90466452e-01 1.13457963e-01 -4.62678850e-01 -2.85660535e-01 6.99823022e-01 6.20587170e-02 -2.05016047e-01 5.88795424e-01 -2.39552528e-01 -4.20595258e-01 6.79167628e-01 3.81336719e-01 -1.70672774e-01 1.99107930e-01 -9.63574927e-03 6.48933232e-01 1.96582302e-01 4.28218454e-01 -7.63362646e-01 5.75521052e-01 -1.85055453e-02 5.17365277e-01 5.92624247e-01 -3.07440199e-02 -4.77693230e-02 -1.34180874e-01 -2.75328606e-01 -1.14717007e+00 -1.05877090e+00 -3.45829457e-01 2.04902220e+00 -5.60337842e-01 -4.91023958e-01 -9.39720511e-01 -8.44942629e-01 1.12063680e-02 8.34645808e-01 -3.64377499e-01 7.85318539e-02 -1.02193975e+00 -9.86774445e-01 8.50676417e-01 2.80319422e-01 2.45223075e-01 -1.22635591e+00 9.68009606e-02 4.85574543e-01 -1.95659086e-01 -1.08776045e+00 -9.45402384e-01 4.72810298e-01 -6.09688818e-01 -3.11119974e-01 -8.80598962e-01 -1.10042357e+00 5.91782808e-01 -1.57374471e-01 1.15135884e+00 -3.88708711e-01 7.50384256e-02 1.42936572e-01 -2.98245139e-02 -3.37485701e-01 -7.71229088e-01 9.69024837e-01 7.03024983e-01 -1.24120004e-01 7.81590760e-01 -1.67037070e-01 7.09496140e-02 2.21802682e-01 -6.77641928e-01 -4.30619419e-02 6.36091828e-01 7.22528636e-01 5.94868004e-01 -7.67151192e-02 7.97344208e-01 -1.20586717e+00 6.60720527e-01 -4.45496470e-01 -4.97198880e-01 4.59724933e-01 -5.25262654e-01 3.32530856e-01 8.38693798e-01 -8.44912410e-01 -1.35188913e+00 -9.66569111e-02 -2.30905205e-01 -4.64240126e-02 2.54809968e-02 6.01454496e-01 -1.51559949e-01 1.99232534e-01 7.18447030e-01 -1.28792403e-02 -5.33894479e-01 -7.38600016e-01 6.06975138e-01 9.69178200e-01 3.24563205e-01 -7.99094379e-01 8.40838015e-01 -1.46760792e-01 -8.34587276e-01 -1.04949975e+00 -8.65258574e-01 -3.91144484e-01 -8.62590969e-01 2.85526723e-01 8.94223154e-01 -1.32766914e+00 -4.55165468e-02 4.33447987e-01 -1.00799119e+00 -8.96552980e-01 -2.26740018e-01 7.97330976e-01 -1.61122829e-01 1.60521269e-02 -8.01772714e-01 -3.69504660e-01 -5.45289457e-01 -1.20766759e+00 9.61134076e-01 -6.94082677e-02 -3.47432733e-01 -1.59958172e+00 4.33865249e-01 1.10417850e-01 6.89788640e-01 -5.54982960e-01 1.16583598e+00 -8.21734011e-01 1.16059273e-01 2.85173595e-01 -9.07374173e-03 4.65226203e-01 1.89240575e-01 -2.49797076e-01 -9.38685715e-01 -8.12725246e-01 -2.88836002e-01 -5.83287239e-01 7.39129126e-01 1.56293884e-01 4.50644463e-01 -3.72427404e-01 -3.40424329e-01 7.80121505e-01 1.31702459e+00 -8.03452656e-02 -1.60186607e-02 5.84701240e-01 6.96399510e-01 4.76699740e-01 4.88594651e-01 -1.19820237e-01 5.20962000e-01 4.88357991e-01 -5.70419729e-01 -2.58471489e-01 -2.29357228e-01 -2.70509899e-01 1.23201871e+00 1.59957886e+00 2.59631842e-01 -1.61400154e-01 -1.06498861e+00 8.80254865e-01 -1.52037740e+00 -3.24118137e-01 2.94649929e-01 2.28364515e+00 1.60411084e+00 2.46139001e-02 -2.08582997e-01 -6.88466668e-01 6.13259673e-01 -1.55429736e-01 -5.55937946e-01 -8.78095746e-01 -3.15070719e-01 5.14840245e-01 6.70842826e-01 1.11169791e+00 -1.12389064e+00 1.87107480e+00 6.03256273e+00 1.13553643e+00 -1.31844831e+00 6.55264795e-01 4.19658452e-01 -9.72738117e-02 -2.94509292e-01 -5.58319204e-02 -1.53790843e+00 2.02094182e-01 1.48969710e+00 -2.93739974e-01 6.23487175e-01 7.70581245e-01 -2.77683325e-02 1.97190017e-01 -9.95847106e-01 5.96935630e-01 1.04122646e-01 -8.97832394e-01 1.79063663e-01 -3.33640464e-02 1.00975978e+00 9.92454529e-01 1.51743628e-02 8.71497631e-01 7.76210248e-01 -9.19706106e-01 4.20120448e-01 -3.22957598e-02 1.35535634e+00 -8.94486308e-01 5.72909057e-01 4.23135906e-01 -1.15489733e+00 3.63965869e-01 -5.87981284e-01 3.31496537e-01 8.15674439e-02 2.64898717e-01 -1.08962810e+00 1.77358687e-01 8.96035254e-01 3.84875178e-01 -7.50072300e-01 2.85009265e-01 -3.30212802e-01 9.41089153e-01 -6.00304544e-01 2.68634290e-01 4.52720851e-01 5.01832515e-02 4.07124400e-01 1.87686825e+00 3.18120807e-01 -5.32489479e-01 3.88086200e-01 1.34533957e-01 -3.89266878e-01 7.02438235e-01 -6.31335735e-01 -5.54918423e-02 3.88401866e-01 1.21355820e+00 -3.10328007e-01 -4.30697620e-01 -5.74218690e-01 1.14107728e+00 8.84711683e-01 3.64271134e-01 -4.57921892e-01 -4.12664324e-01 6.64528191e-01 -1.38209211e-02 1.82531223e-01 -3.86412442e-01 9.19964835e-02 -1.50305164e+00 -3.14490855e-01 -1.08513415e+00 5.09359658e-01 -2.82196641e-01 -1.49094272e+00 9.42051113e-01 1.42347384e-02 -6.15670145e-01 -5.97171009e-01 -6.55497313e-01 -1.27657264e-01 1.35743558e+00 -1.74328279e+00 -1.46939468e+00 5.39069831e-01 8.56968105e-01 9.23008680e-01 -5.51817358e-01 1.17900085e+00 5.35285056e-01 -4.78996605e-01 1.02094364e+00 4.48501199e-01 2.06413388e-01 1.42981684e+00 -1.18555677e+00 6.92045391e-01 9.21118915e-01 2.43280515e-01 8.36233199e-01 3.50743771e-01 -6.80237412e-01 -1.07455003e+00 -1.46789765e+00 1.51575458e+00 -5.37140846e-01 1.04554152e+00 -8.66657555e-01 -1.18591881e+00 1.12405753e+00 5.73172033e-01 -3.78687352e-01 8.91949773e-01 5.10568678e-01 -3.80914837e-01 -2.34580543e-02 -7.76928902e-01 7.94873655e-01 6.10042453e-01 -7.97497571e-01 -6.63084030e-01 5.72097719e-01 8.27821016e-01 -5.79425916e-02 -1.11628842e+00 1.22589417e-01 3.19268614e-01 -1.39846757e-01 6.52019143e-01 -7.09967613e-01 -1.06717110e-01 -6.28636107e-02 -1.91630542e-01 -1.85250771e+00 -2.75983483e-01 -8.90671968e-01 3.48226905e-01 1.61928248e+00 8.31683815e-01 -8.74288678e-01 1.61303639e-01 1.08723111e-01 -3.66776399e-02 -2.94406623e-01 -9.42951202e-01 -9.49634731e-01 8.97923231e-01 -4.76205498e-01 3.24768454e-01 1.40371931e+00 -1.74509794e-01 9.87932086e-01 -2.49215335e-01 3.76749709e-02 3.60505641e-01 -3.42390269e-01 8.38230491e-01 -9.48237896e-01 -4.77768004e-01 -2.92644799e-01 3.52039427e-01 -7.28647351e-01 6.49433613e-01 -1.40222967e+00 8.63165781e-02 -1.09536517e+00 6.89601004e-02 -8.58401358e-01 -3.97891015e-01 9.73175943e-01 -3.30774486e-01 4.79556531e-01 7.91913942e-02 2.48449713e-01 -2.12311879e-01 2.61377484e-01 7.10775375e-01 3.90704013e-02 -4.74182338e-01 -2.42306367e-01 -6.30897462e-01 8.69197607e-01 7.45067418e-01 -7.43829489e-01 -1.57332793e-01 -1.02133787e+00 2.08926350e-01 -4.78776127e-01 -6.22329831e-01 -5.55489779e-01 7.05355685e-03 -1.51877970e-01 3.29705596e-01 -8.05038139e-02 8.24469998e-02 -5.10623753e-01 -8.15312788e-02 3.26984525e-01 -1.08119369e-01 2.57786393e-01 8.33847463e-01 -5.50680123e-02 -1.00912049e-01 -3.14447075e-01 1.01819730e+00 -2.12833434e-01 -8.38917077e-01 2.48554975e-01 -6.62555635e-01 4.86813486e-01 7.10515022e-01 2.55638242e-01 -1.96429238e-01 4.98040020e-02 -5.70953548e-01 2.69600302e-01 4.83367711e-01 8.37819874e-01 -9.59179550e-02 -1.11768484e+00 -1.11446321e+00 5.56807518e-01 1.14311874e-01 -2.01388121e-01 -1.03688473e-03 6.40169919e-01 -4.01135206e-01 5.92949450e-01 -8.69205073e-02 -4.28393841e-01 -1.05434513e+00 3.71364057e-01 2.37435654e-01 -7.13608384e-01 -2.91935086e-01 9.16047990e-01 4.43581581e-01 -1.29628885e+00 -9.49930474e-02 8.66592955e-03 -9.43029765e-03 1.43807709e-01 3.06099534e-01 1.68369338e-02 1.66253015e-01 -8.24528098e-01 -4.22842175e-01 7.21306682e-01 -5.77568412e-01 -5.78072011e-01 1.31698954e+00 -3.00203800e-01 -1.62241399e-01 6.61216199e-01 1.25823295e+00 7.30339289e-01 -1.01766992e+00 -6.13204598e-01 2.21737012e-01 1.59038395e-01 -9.82554443e-03 -9.27480936e-01 -6.50222600e-01 9.78102505e-01 2.56030560e-01 -3.89712185e-01 9.04803157e-01 -5.79147451e-02 8.19504023e-01 6.34894907e-01 3.73463452e-01 -1.47208595e+00 -4.41336155e-01 1.12296414e+00 6.53759360e-01 -1.16968620e+00 -1.39388025e-01 1.64554223e-01 -7.87474394e-01 8.63392055e-01 7.13841617e-01 1.65381700e-01 6.62883699e-01 4.64191049e-01 5.45793235e-01 3.36457521e-01 -8.06474984e-01 1.30346000e-01 4.30480272e-01 3.79710793e-01 8.72832000e-01 3.10802341e-01 -1.84103057e-01 2.99495995e-01 -5.70256114e-01 -3.88488829e-01 2.09064081e-01 6.30487323e-01 -3.18526536e-01 -1.51709855e+00 -3.26200306e-01 1.04004122e-01 -8.01414311e-01 -7.47383773e-01 -1.58321410e-01 1.06435311e+00 2.15459436e-01 5.80305040e-01 7.70388842e-02 5.54078594e-02 -1.43422838e-02 6.40322328e-01 2.97563434e-01 -1.06739676e+00 -9.41751540e-01 4.66715217e-01 1.47603765e-01 -1.43207952e-01 -1.08968750e-01 -7.16119349e-01 -1.14515960e+00 -1.66371301e-01 -3.00126165e-01 3.76676351e-01 7.45938957e-01 8.95418882e-01 2.28338912e-01 2.18988746e-01 3.71807486e-01 -6.50358677e-01 -3.98560315e-01 -1.27217376e+00 -2.95315415e-01 8.73078108e-02 7.53839538e-02 -2.01599553e-01 -2.79208183e-01 2.16046646e-01]	[10.97188949584961, 9.956121444702148]
aca36137-49ba-468c-84b3-02690f77cf54	learning-credit-assignment-for-cooperative	2210.05367	null	https://arxiv.org/abs/2210.05367v2	https://arxiv.org/pdf/2210.05367v2.pdf	Learning Explicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning via Polarization Policy Gradient	Cooperative multi-agent policy gradient (MAPG) algorithms have recently attracted wide attention and are regarded as a general scheme for the multi-agent system. Credit assignment plays an important role in MAPG and can induce cooperation among multiple agents. However, most MAPG algorithms cannot achieve good credit assignment because of the game-theoretic pathology known as \textit{centralized-decentralized mismatch}. To address this issue, this paper presents a novel method, \textit{\underline{M}ulti-\underline{A}gent \underline{P}olarization \underline{P}olicy \underline{G}radient} (MAPPG). MAPPG takes a simple but efficient polarization function to transform the optimal consistency of joint and individual actions into easily realized constraints, thus enabling efficient credit assignment in MAPG. Theoretically, we prove that individual policies of MAPPG can converge to the global optimum. Empirically, we evaluate MAPPG on the well-known matrix game and differential game, and verify that MAPPG can converge to the global optimum for both discrete and continuous action spaces. We also evaluate MAPPG on a set of StarCraft II micromanagement tasks and demonstrate that MAPPG outperforms the state-of-the-art MAPG algorithms.	['Yang Gao', 'Shangdong Yang', 'Xiao Liu', 'Wenbin Li', 'Wubing Chen']	2022-10-10	null	null	null	null	['starcraft-ii', 'starcraft']	['playing-games', 'playing-games']	[-5.15181124e-01 3.12059879e-01 -2.41551921e-01 2.13435024e-01 -4.74243611e-01 -5.45653582e-01 4.10892785e-01 1.43749177e-01 -7.68256545e-01 1.22466791e+00 -2.46250153e-01 -3.84902060e-01 -6.20252669e-01 -7.45906472e-01 -6.70032263e-01 -9.16046619e-01 -5.40969074e-01 9.17153358e-01 2.60756254e-01 -6.66741431e-01 3.01058650e-01 5.20023219e-02 -1.02734375e+00 -3.59155118e-01 1.48378837e+00 7.78126001e-01 3.36100489e-01 6.66445434e-01 3.29018503e-01 9.29044783e-01 -7.97251523e-01 -3.76759142e-01 6.61202073e-01 -4.29488271e-01 -5.47613859e-01 8.03192481e-02 -1.83881372e-01 -4.31446493e-01 -2.72831440e-01 1.23739898e+00 7.07143903e-01 3.17751348e-01 7.21859217e-01 -1.86735356e+00 -3.54142576e-01 8.21178675e-01 -9.88081753e-01 3.73850703e-01 -3.78206000e-02 1.01624370e-01 1.30096066e+00 -6.14770532e-01 5.89729130e-01 1.14730000e+00 1.13624372e-01 3.91049117e-01 -8.68963957e-01 -5.64913690e-01 5.09958565e-01 1.74233034e-01 -1.25579607e+00 2.02728331e-01 3.41924757e-01 -1.99784592e-01 7.39932358e-01 3.52956653e-01 6.83685303e-01 2.46654049e-01 4.61759329e-01 1.17770529e+00 1.06363988e+00 -3.70069385e-01 6.12698972e-01 -2.04978958e-01 -4.88564879e-01 8.18090260e-01 3.74029100e-01 3.15987140e-01 -7.82478929e-01 -3.64463449e-01 7.79628158e-01 -4.22721297e-01 -6.30633980e-02 -5.36121130e-01 -1.01078951e+00 1.05663002e+00 2.91768670e-01 -1.42512903e-01 -6.95996404e-01 4.25526321e-01 1.90706551e-02 5.55014849e-01 4.90750968e-01 6.69114590e-01 -4.22865264e-02 -2.26268962e-01 -5.58938324e-01 6.22393191e-01 9.97006714e-01 8.45517933e-01 7.03973472e-01 3.56032968e-01 -4.13492359e-02 5.99321902e-01 2.27026463e-01 4.49883997e-01 1.13722093e-01 -1.52694106e+00 8.13634813e-01 2.17757538e-01 5.62767267e-01 -1.00323474e+00 -4.93187696e-01 -5.75000107e-01 -8.10147226e-01 6.41672969e-01 4.37002569e-01 -8.30895841e-01 -1.20931372e-01 1.78851151e+00 2.93814451e-01 1.56009868e-01 1.20274171e-01 1.21921170e+00 1.33847399e-02 7.05914795e-01 -1.56811655e-01 -4.95064586e-01 7.70862401e-01 -1.01696587e+00 -4.16007489e-01 -5.98999262e-01 5.41763067e-01 -1.78575203e-01 7.55020261e-01 5.67305803e-01 -1.26857138e+00 2.67328262e-01 -1.18208504e+00 7.76584506e-01 1.33243442e-01 -1.21922523e-01 6.84566855e-01 6.27208948e-01 -1.43445873e+00 5.46074331e-01 -4.14668471e-01 -1.46684974e-01 1.71556383e-01 7.39428282e-01 -6.39961287e-02 1.55497491e-01 -1.23685944e+00 8.75074446e-01 7.01026559e-01 1.08397007e-01 -1.10650373e+00 -4.50216055e-01 -4.62518215e-01 6.13763705e-02 1.10041296e+00 -4.43307132e-01 1.31169224e+00 -8.04362774e-01 -1.92743194e+00 5.31985879e-01 5.73518276e-01 -4.64053065e-01 7.76316941e-01 1.86387300e-02 9.32713151e-02 8.86262581e-02 2.28340015e-01 5.15965462e-01 6.41560018e-01 -1.03569615e+00 -1.20155799e+00 -1.69395044e-01 5.52305579e-01 1.00988257e+00 -5.02873480e-01 2.24063862e-02 2.31191404e-02 -5.16205072e-01 -2.29547337e-01 -1.08540905e+00 -5.48076034e-01 -4.59832221e-01 -3.70266765e-01 -1.89513475e-01 3.43840957e-01 -2.54084975e-01 1.19230807e+00 -1.79167795e+00 6.27888203e-01 3.40189666e-01 4.08158809e-01 3.57176214e-02 2.92773098e-02 6.27833366e-01 5.28251708e-01 3.73362713e-02 9.15168524e-02 -1.96093678e-01 3.95225406e-01 5.08704722e-01 -3.66587862e-02 6.50923371e-01 -5.87381124e-01 6.92771912e-01 -1.12155986e+00 -3.42557281e-01 -3.32922302e-02 -5.88277817e-01 -9.45621729e-01 2.66654994e-02 -3.87779653e-01 2.22741172e-01 -7.36511469e-01 3.21631700e-01 5.05689383e-01 -1.13998964e-01 5.74736774e-01 7.51151264e-01 -2.85784245e-01 -3.54243249e-01 -1.42237949e+00 1.26522470e+00 -1.17038690e-01 1.74456120e-01 7.21444309e-01 -1.23361397e+00 8.48268211e-01 1.65503994e-01 9.07309771e-01 -5.56573927e-01 2.73971677e-01 4.79670942e-01 2.70674415e-02 9.78940129e-02 7.22697973e-01 2.79195327e-02 -4.54625845e-01 8.19217205e-01 -1.98453754e-01 -3.01808208e-01 4.48560029e-01 5.12472570e-01 9.15808797e-01 -2.65777409e-01 4.48464304e-01 -8.58456254e-01 3.13834727e-01 -3.25029641e-02 8.59832048e-01 1.25199938e+00 -4.93851185e-01 -4.42496538e-02 1.06865871e+00 -2.01581776e-01 -8.50941420e-01 -8.38835061e-01 7.40239263e-01 1.49477303e+00 7.06706285e-01 -2.22651139e-01 -8.00401032e-01 -4.89377469e-01 3.40218723e-01 5.04890323e-01 -6.65506780e-01 1.52396038e-02 -4.42494005e-01 -1.27449632e+00 3.00730675e-01 1.07918926e-01 8.44323456e-01 -9.22888339e-01 -7.67584443e-01 4.74392414e-01 -1.47765800e-01 -9.04573381e-01 -8.14299047e-01 8.19780901e-02 -4.22549397e-01 -9.70450878e-01 -8.08477342e-01 -4.14333194e-01 5.00543416e-01 2.34745488e-01 9.29438055e-01 3.54899727e-02 2.80617177e-01 2.91037023e-01 -3.32745582e-01 -5.82030118e-01 -3.09916407e-01 3.30363125e-01 4.45737273e-01 1.63844481e-01 -2.53975838e-01 -6.30713403e-01 -6.60162508e-01 7.38702595e-01 -5.17388582e-01 1.48365587e-01 5.15913785e-01 7.63405502e-01 4.07204419e-01 -1.46616818e-02 8.06505859e-01 -4.93057191e-01 1.16415179e+00 -5.01338243e-01 -1.08694291e+00 2.62479603e-01 -6.74789608e-01 2.05999725e-02 6.69111371e-01 -2.25166693e-01 -7.68295228e-01 -2.37508312e-01 4.16701496e-01 -3.00811589e-01 6.32419467e-01 6.84403181e-01 -1.22978009e-01 -2.14197367e-01 6.47750437e-01 1.59066349e-01 2.24861220e-01 3.60376686e-02 3.36630344e-01 5.34238219e-01 3.88564497e-01 -1.13769293e+00 4.86112595e-01 3.83058459e-01 1.76994517e-01 -4.57403749e-01 -4.66250449e-01 -6.73133433e-02 2.94844322e-02 -6.86175466e-01 5.78095496e-01 -7.21466482e-01 -1.35948050e+00 7.15302706e-01 -6.52057588e-01 -7.66300321e-01 -1.42663613e-01 3.98894250e-01 -9.23150957e-01 3.32419366e-01 -6.44165158e-01 -9.13803816e-01 -1.46960318e-01 -9.84343529e-01 4.99733537e-01 5.17376661e-01 4.16445941e-01 -9.03746903e-01 1.45107418e-01 9.53801051e-02 3.52367014e-01 1.07283510e-01 4.25617039e-01 -4.96897101e-01 -7.56651998e-01 1.94384620e-01 1.21081792e-01 -7.72819817e-02 -1.91079557e-01 -2.76174963e-01 -1.79921865e-01 -8.30538094e-01 -2.86492944e-01 -3.52326602e-01 3.37484777e-01 5.40106237e-01 4.44718122e-01 -5.92915952e-01 -2.87558109e-01 4.03785199e-01 1.32725430e+00 4.77298737e-01 3.91254008e-01 8.40676904e-01 1.84706450e-01 1.56089842e-01 1.09200263e+00 1.03423941e+00 6.05835974e-01 9.33450401e-01 1.02892113e+00 3.53501230e-01 7.34669447e-01 -8.28994587e-02 4.76084262e-01 5.13339281e-01 -3.80570233e-01 -5.24016440e-01 -7.14480340e-01 3.20239395e-01 -2.58310342e+00 -8.34761202e-01 1.54062197e-01 2.07519436e+00 6.22179985e-01 1.32221341e-01 4.37473476e-01 -2.52921999e-01 9.65594351e-01 1.03989989e-01 -8.28929305e-01 -4.87349659e-01 -4.59375799e-01 -2.53673762e-01 9.51203525e-01 8.02516401e-01 -9.51759219e-01 1.07827926e+00 5.83342457e+00 1.09773231e+00 -7.66423523e-01 2.06391290e-01 6.81452990e-01 -3.10059637e-01 -4.72702123e-02 2.49841087e-03 -5.61774731e-01 6.19670868e-01 3.43092084e-01 -1.01269388e+00 7.75346994e-01 8.05103302e-01 4.50617373e-01 -5.82904041e-01 -4.75855649e-01 8.69348228e-01 -2.15829343e-01 -1.23663354e+00 -4.32215214e-01 4.75739002e-01 1.02012718e+00 -3.66283432e-02 3.03292200e-02 3.89885128e-01 1.21683133e+00 -6.69021964e-01 1.08210540e+00 9.10097063e-02 5.25499284e-01 -1.06340158e+00 5.50086975e-01 6.26933753e-01 -1.13331795e+00 -5.32006323e-01 -4.93586570e-01 -5.12759924e-01 3.25366914e-01 2.06214875e-01 -5.63340783e-01 9.94622648e-01 4.77986932e-01 2.57457912e-01 -6.32667616e-02 1.03043711e+00 -3.66203010e-01 1.60773680e-01 -4.28814232e-01 -4.74801451e-01 6.14838660e-01 -6.32744551e-01 7.93558180e-01 3.91388357e-01 2.39351884e-01 4.22913671e-01 8.46518576e-01 3.97833198e-01 4.52656299e-02 2.35417873e-01 -1.19122170e-01 1.00767121e-01 6.07869089e-01 9.70837891e-01 -8.03721547e-01 -7.91573822e-02 6.99837580e-02 9.18593884e-01 4.34211880e-01 4.21374053e-01 -1.02195752e+00 -4.37692940e-01 9.81256425e-01 -3.09663147e-01 7.07186610e-02 -3.00255567e-01 -1.56147018e-01 -1.08220267e+00 -1.29586160e-01 -8.28361869e-01 6.48755133e-01 -4.99951810e-01 -9.13320601e-01 3.02711546e-01 -5.66828772e-02 -1.10400581e+00 -5.78212023e-01 -3.01289082e-01 -6.01316750e-01 5.24336159e-01 -1.06768429e+00 -4.77715433e-01 1.72208250e-02 6.54707372e-01 1.96318805e-01 -5.83267331e-01 3.47995192e-01 2.54748553e-01 -6.40370607e-01 5.34356058e-01 6.01346910e-01 -2.59954333e-01 3.38897049e-01 -1.46214926e+00 5.12774847e-02 9.05662894e-01 -1.67590141e-01 -1.67939901e-01 9.93173659e-01 -6.16043389e-01 -1.38442481e+00 -7.91568875e-01 1.66556612e-01 1.90611064e-01 9.80320811e-01 -2.47255147e-01 -2.33624175e-01 4.87505943e-01 2.61379421e-01 -1.59191281e-01 -4.32089604e-02 -2.70838410e-01 4.32134390e-01 -1.86644271e-01 -9.49049830e-01 9.02617753e-01 1.09681129e+00 1.77755039e-02 1.49466936e-02 4.57579911e-01 4.11951840e-01 -7.33968258e-01 -5.43347776e-01 6.51620403e-02 5.81554398e-02 -1.13160956e+00 6.34573519e-01 -4.99890119e-01 1.10827416e-01 -2.55419403e-01 -3.20462771e-02 -1.93274808e+00 -2.59514987e-01 -1.40481365e+00 -8.84101391e-02 6.47020519e-01 3.69706690e-01 -9.96479511e-01 1.23295605e+00 4.81226504e-01 -2.12774828e-01 -7.60143936e-01 -1.39586294e+00 -1.09474277e+00 5.62973619e-01 -1.67659633e-02 3.20338279e-01 8.37580502e-01 5.92148662e-01 -4.05663848e-02 -8.10813665e-01 2.14450464e-01 9.32140708e-01 5.45375980e-03 7.10345924e-01 -9.69482005e-01 -6.88140869e-01 -6.47097826e-01 -1.32425323e-01 -1.11393297e+00 1.76523358e-01 -7.32220709e-01 -7.50844041e-03 -1.53059196e+00 1.32010639e-01 -7.56593406e-01 -7.66948760e-02 3.96873832e-01 -1.08068056e-01 -6.53139427e-02 7.12680995e-01 3.62840742e-01 -1.09235895e+00 7.91984856e-01 1.34904063e+00 -1.72985479e-01 -3.50997508e-01 1.09695897e-01 -8.12823355e-01 4.33668762e-01 1.02291477e+00 -3.38943720e-01 -5.90760410e-01 -3.63040715e-01 5.14870644e-01 5.53248405e-01 -1.44532975e-02 -7.76670277e-01 5.57594597e-01 -8.09490800e-01 -6.32788360e-01 4.59969714e-02 2.84777731e-01 -2.12856442e-01 8.05275068e-02 7.68899262e-01 -4.87524718e-02 2.09149927e-01 -1.67176753e-01 5.50356090e-01 -1.41640127e-01 -2.05493093e-01 6.26728892e-01 -1.76181018e-01 -7.66716003e-01 4.25989926e-01 -7.60366023e-01 4.57428515e-01 1.39107311e+00 2.84672398e-02 -5.01192629e-01 -9.87689734e-01 -6.15415037e-01 1.02171195e+00 3.25266510e-01 1.25324160e-01 3.31035793e-01 -1.15575576e+00 -9.51372802e-01 -4.17856783e-01 -4.46967781e-01 -3.41537356e-01 2.22972363e-01 9.95044351e-01 -6.71212316e-01 2.87324399e-01 -3.37413043e-01 -3.42259049e-01 -9.66959417e-01 3.24941278e-01 8.17942441e-01 -6.23267949e-01 -2.10316196e-01 8.14523518e-01 2.75344223e-01 -3.58657300e-01 1.07148020e-02 1.87411830e-01 8.42119455e-02 -9.55654159e-02 2.10829854e-01 6.48552537e-01 -3.09707165e-01 -1.86079696e-01 -2.19955623e-01 5.53894378e-02 -1.11912563e-02 -6.19584322e-01 1.17713976e+00 -2.69846886e-01 -8.79007354e-02 -2.90588737e-01 2.20994547e-01 -1.88161224e-01 -1.55105782e+00 -2.17867941e-02 -1.50127951e-02 -5.53753853e-01 -2.20836088e-01 -5.69184482e-01 -1.34034300e+00 3.12545329e-01 7.59602562e-02 4.78762418e-01 8.37032616e-01 -1.95476696e-01 1.78162262e-01 5.35544276e-01 1.27204013e+00 -1.60303915e+00 3.17728460e-01 6.70712113e-01 8.37567806e-01 -7.22943902e-01 8.89464170e-02 -3.91602933e-01 -1.00837147e+00 6.96366847e-01 8.76555324e-01 -2.84348220e-01 5.14553905e-01 1.98804826e-01 -1.43865183e-01 -1.52789995e-01 -7.59899378e-01 -1.84966192e-01 -6.25626564e-01 5.83323181e-01 -3.79410028e-01 3.57727975e-01 -8.32631111e-01 5.76355994e-01 -1.85181856e-01 -5.68862595e-02 1.18204296e+00 1.14855838e+00 -7.13464439e-01 -1.20033777e+00 -4.43053097e-01 4.55501139e-01 -3.88513058e-01 1.83093682e-01 -2.60355741e-01 7.79960036e-01 -2.97679961e-01 1.12798727e+00 -2.30676234e-01 -2.25842357e-01 2.13618517e-01 -6.58602417e-01 3.78427893e-01 -1.87078759e-01 -4.62128669e-01 -2.88020074e-02 1.82787657e-01 -6.13579571e-01 -2.80397594e-01 -6.52672648e-01 -1.16035247e+00 -7.77282953e-01 -1.72937036e-01 6.97196305e-01 1.06786825e-01 1.00792968e+00 3.60301107e-01 3.42982441e-01 7.60341406e-01 -7.78929710e-01 -9.88150597e-01 -6.01525068e-01 -1.30053258e+00 -1.00686885e-01 -2.39971906e-01 -9.48622406e-01 -3.84771109e-01 -7.38241673e-01]	[4.121977806091309, 2.5387468338012695]
50056bf2-e726-4352-8595-5db3416b9fb6	cross-cbam-a-lightweight-network-for-scene	2306.02306	null	https://arxiv.org/abs/2306.02306v1	https://arxiv.org/pdf/2306.02306v1.pdf	Cross-CBAM: A Lightweight network for Scene Segmentation	Scene parsing is a great challenge for real-time semantic segmentation. Although traditional semantic segmentation networks have made remarkable leap-forwards in semantic accuracy, the performance of inference speed is unsatisfactory. Meanwhile, this progress is achieved with fairly large networks and powerful computational resources. However, it is difficult to run extremely large models on edge computing devices with limited computing power, which poses a huge challenge to the real-time semantic segmentation tasks. In this paper, we present the Cross-CBAM network, a novel lightweight network for real-time semantic segmentation. Specifically, a Squeeze-and-Excitation Atrous Spatial Pyramid Pooling Module(SE-ASPP) is proposed to get variable field-of-view and multiscale information. And we propose a Cross Convolutional Block Attention Module(CCBAM), in which a cross-multiply operation is employed in the CCBAM module to make high-level semantic information guide low-level detail information. Different from previous work, these works use attention to focus on the desired information in the backbone. CCBAM uses cross-attention for feature fusion in the FPN structure. Extensive experiments on the Cityscapes dataset and Camvid dataset demonstrate the effectiveness of the proposed Cross-CBAM model by achieving a promising trade-off between segmentation accuracy and inference speed. On the Cityscapes test set, we achieve 73.4% mIoU with a speed of 240.9FPS and 77.2% mIoU with a speed of 88.6FPS on NVIDIA GTX 1080Ti.	['Juan Xiong', 'Xingsheng Gu', 'Zhenhao Xu', 'Zhengbin Zhang']	2023-06-04	null	null	null	null	['scene-parsing', 'scene-segmentation', 'real-time-semantic-segmentation', 'edge-computing']	['computer-vision', 'computer-vision', 'computer-vision', 'time-series']	[ 1.19935587e-01 -1.25073090e-01 -1.18350536e-01 -5.49907267e-01 -5.89502752e-01 -1.51731730e-01 1.77060172e-01 -2.15623543e-01 -6.44017696e-01 3.24005127e-01 -2.80293912e-01 -3.68669361e-01 1.13629080e-01 -1.13262999e+00 -7.84941196e-01 -5.92417777e-01 2.64799923e-01 9.63419378e-02 7.51500547e-01 -7.94164613e-02 2.85900719e-02 4.34350193e-01 -1.54958713e+00 4.17982012e-01 1.14285755e+00 1.22796905e+00 5.46288073e-01 6.09342813e-01 -3.79023373e-01 6.34179115e-01 -4.85922575e-01 -3.77041519e-01 1.97809219e-01 -1.56379700e-01 -8.29163849e-01 8.98075029e-02 3.35887849e-01 -2.79324740e-01 -2.65158147e-01 1.30608642e+00 4.90307897e-01 1.90287679e-02 7.65457153e-02 -1.33455670e+00 -2.38049418e-01 5.67832828e-01 -9.60355461e-01 3.55358630e-01 -1.23846471e-01 1.18989376e-02 7.12481201e-01 -5.99559307e-01 2.73384452e-01 1.42008710e+00 5.03993094e-01 3.00417125e-01 -9.17586446e-01 -8.65059435e-01 2.57762194e-01 3.06489527e-01 -1.37052226e+00 -1.29315034e-01 5.33795774e-01 -3.88266966e-02 9.90943730e-01 1.36222601e-01 6.53342426e-01 5.16932786e-01 4.55657840e-02 1.07524264e+00 8.74570012e-01 -1.64851770e-02 5.10440394e-02 -2.34089345e-01 2.11611450e-01 8.74540567e-01 2.10718378e-01 -2.87879050e-01 -3.72395664e-01 3.50500882e-01 1.09860718e+00 1.97011366e-01 -1.29814640e-01 2.40192279e-01 -9.26641047e-01 6.85446501e-01 8.92669857e-01 3.29862446e-01 -3.56059998e-01 2.82214791e-01 4.27746207e-01 -1.08009994e-01 3.36494803e-01 -1.30433559e-01 -6.13874257e-01 -4.42306101e-02 -9.39362526e-01 4.30225581e-02 3.39498580e-01 7.78445601e-01 6.82351649e-01 1.80586368e-01 -7.25332135e-03 8.97955894e-01 1.41496643e-01 6.21139407e-01 4.76713836e-01 -9.72298741e-01 5.96350729e-01 6.95198655e-01 -3.07070524e-01 -1.11054730e+00 -6.13381982e-01 -4.58115160e-01 -1.05882251e+00 2.49734595e-01 2.84905463e-01 -1.63465068e-01 -1.32176375e+00 1.39913082e+00 5.51500559e-01 4.15503502e-01 -6.95894798e-03 1.02182949e+00 1.07414198e+00 9.31594908e-01 3.86948079e-01 2.30058551e-01 1.63165438e+00 -1.38993156e+00 -4.23984200e-01 -3.03264052e-01 6.12308741e-01 -6.69275165e-01 1.09350669e+00 2.96317488e-01 -1.16760147e+00 -9.43045199e-01 -1.01912141e+00 -4.46918756e-01 -2.56039739e-01 1.97012514e-01 8.53413999e-01 5.33214033e-01 -9.62982953e-01 5.03230214e-01 -1.18988311e+00 -1.05357490e-01 8.13854873e-01 5.50829351e-01 -1.91915482e-01 -9.18700248e-02 -9.47309494e-01 2.66935796e-01 6.25865102e-01 2.56979018e-01 -3.63455892e-01 -6.12415135e-01 -7.99315274e-01 3.77383709e-01 4.52229530e-01 -5.83587766e-01 1.11112547e+00 -1.09317148e+00 -1.42410719e+00 6.10548913e-01 -8.13362002e-02 -5.06787539e-01 2.99096227e-01 -1.57795325e-01 -4.22267079e-01 3.09950024e-01 2.40028501e-01 1.18845868e+00 5.88538766e-01 -7.73447990e-01 -1.25315988e+00 -5.19229829e-01 9.92340371e-02 2.51017541e-01 -1.97220802e-01 -7.15276822e-02 -9.30636764e-01 -5.13719320e-01 4.14036930e-01 -5.54124892e-01 -4.27340209e-01 -1.03030637e-01 -2.99547762e-01 -2.20590308e-01 1.12408304e+00 -7.00559080e-01 1.05367553e+00 -2.12828374e+00 -2.75188833e-01 -7.31547251e-02 3.59211192e-02 6.76250160e-01 -7.94809461e-02 -3.67184579e-01 1.23708792e-01 1.55772105e-01 -3.22724372e-01 -2.26180285e-01 -2.19920248e-01 3.11954379e-01 -4.97437119e-02 1.23795986e-01 2.95182168e-01 1.03815317e+00 -6.46418333e-01 -6.90969586e-01 3.84298354e-01 6.83950841e-01 -6.98421419e-01 1.58846211e-02 -2.13608574e-02 2.73899347e-01 -7.40734100e-01 7.80963421e-01 9.96249437e-01 -3.15129369e-01 -1.93200931e-01 -2.13121161e-01 -1.70643702e-01 7.44372904e-02 -1.10764813e+00 1.93774331e+00 -5.37297904e-01 3.73041213e-01 2.93753147e-01 -1.08902884e+00 7.90051937e-01 -6.13478050e-02 2.91979581e-01 -1.10954285e+00 4.81136262e-01 2.52043039e-01 -1.81010868e-02 -4.15065706e-01 4.69755232e-01 8.38710368e-02 -1.39559090e-01 -1.17505051e-01 -7.59004280e-02 -4.20763828e-02 2.81230152e-01 -4.44121100e-02 8.01148772e-01 1.51457936e-01 -6.68491945e-02 -3.54015559e-01 6.85764909e-01 8.31902847e-02 9.18620288e-01 4.14204389e-01 -1.60021499e-01 6.04692757e-01 5.43652415e-01 -5.15699983e-01 -8.25624526e-01 -9.12940025e-01 -1.65982485e-01 8.59991968e-01 4.59729731e-01 -1.93288699e-01 -1.09501851e+00 -6.36390984e-01 -3.44167411e-01 3.16066444e-01 -1.82675660e-01 1.10613383e-01 -6.70630395e-01 -9.66775537e-01 4.37588453e-01 9.55387056e-01 1.51915240e+00 -1.10815275e+00 -9.90947783e-01 3.05726975e-01 -2.25883991e-01 -1.50109065e+00 -3.50665569e-01 -1.44312561e-01 -1.01125777e+00 -8.58524799e-01 -5.65379202e-01 -1.04711473e+00 5.59947670e-01 3.98684651e-01 9.14678693e-01 8.72984454e-02 -3.86827558e-01 -3.73241425e-01 -2.34416157e-01 -2.03365102e-01 1.98663682e-01 2.98397154e-01 -5.48906982e-01 -9.50551108e-02 3.71565670e-01 -5.24777710e-01 -7.68289328e-01 2.89173126e-01 -9.32601511e-01 5.37902951e-01 5.66775382e-01 7.79487073e-01 8.12237263e-01 3.39512855e-01 4.34840024e-01 -7.61945367e-01 6.96742013e-02 -1.80881485e-01 -8.54911864e-01 2.10635290e-02 -2.95275062e-01 -2.07565188e-01 6.88925862e-01 -4.89976592e-02 -1.20625305e+00 2.09246784e-01 -6.71524942e-01 -2.75789499e-01 -1.65742159e-01 2.62351930e-01 -4.56335247e-01 1.38791010e-01 6.39164746e-02 9.21347886e-02 -1.58441380e-01 -3.87160033e-01 9.96930897e-02 8.64380836e-01 7.31408119e-01 -4.77746159e-01 2.91111380e-01 6.10633254e-01 -1.25742167e-01 -8.01034331e-01 -8.78670633e-01 -3.37004453e-01 -3.99810314e-01 1.34437323e-01 1.31283379e+00 -1.16888404e+00 -9.00059462e-01 8.77790928e-01 -9.70999360e-01 -4.79697138e-01 -9.59367864e-03 2.99622953e-01 -2.79306978e-01 2.15313211e-01 -7.33467102e-01 -4.81867522e-01 -6.09450996e-01 -1.52576590e+00 1.17117202e+00 8.33798945e-01 2.96373725e-01 -6.31841898e-01 -7.04418063e-01 5.74533343e-01 3.02269787e-01 1.64074883e-01 5.61373830e-01 -2.22506523e-01 -7.68017948e-01 5.06264567e-02 -9.59489346e-01 4.62130308e-01 -1.87202364e-01 -7.00241327e-02 -9.50878799e-01 2.73025092e-02 -5.36157042e-02 -9.77949202e-02 1.01374221e+00 6.34912133e-01 1.78834558e+00 1.71136379e-01 -4.04993027e-01 9.72293317e-01 1.60133564e+00 4.74123329e-01 7.84254849e-01 2.16978714e-01 9.64022994e-01 2.38812670e-01 5.81258893e-01 1.67661890e-01 5.51959932e-01 4.84677374e-01 4.15739030e-01 -4.94688332e-01 -2.04665840e-01 -1.67575300e-01 -4.39051539e-02 6.98986292e-01 -3.60575058e-02 -2.80824959e-01 -9.07779098e-01 5.31285465e-01 -1.85294747e+00 -6.33258760e-01 -3.26626211e-01 1.81748652e+00 4.70420659e-01 4.57132041e-01 -9.78543013e-02 1.61440164e-01 7.80894756e-01 2.11923793e-01 -5.97506106e-01 -4.42071617e-01 6.81220321e-03 4.92305696e-01 6.16908848e-01 4.68722910e-01 -1.31650794e+00 1.15810204e+00 4.42744160e+00 1.29316866e+00 -1.25967526e+00 2.50049353e-01 1.11060107e+00 9.10236388e-02 1.36040404e-01 -2.72415757e-01 -9.61868167e-01 6.14710629e-01 7.22714484e-01 3.71707529e-01 1.11221679e-01 1.00895894e+00 9.74618122e-02 -3.44681174e-01 -3.76255989e-01 1.13648760e+00 -2.00792074e-01 -1.26161587e+00 -1.74202338e-01 -1.70085922e-01 5.78258157e-01 1.84555829e-01 -1.56250253e-01 3.52488697e-01 2.54976779e-01 -8.89257014e-01 5.04794359e-01 -3.74103873e-03 7.14156210e-01 -1.07775331e+00 8.51727486e-01 3.26073617e-01 -1.50652766e+00 -5.18444553e-02 -4.81037647e-01 -1.25256041e-03 3.27384800e-01 5.95717072e-01 -2.49073818e-01 5.09258151e-01 1.11020851e+00 6.88063681e-01 -2.65800148e-01 8.57770622e-01 -3.36928993e-01 5.19701302e-01 -6.35784507e-01 7.51432329e-02 6.53081000e-01 -2.08547920e-01 -8.13812762e-02 1.13747776e+00 3.23520571e-01 3.24369729e-01 3.51070732e-01 6.73634470e-01 -2.23667890e-01 -4.33673076e-02 4.86118123e-02 2.24539056e-01 1.70574158e-01 1.60926235e+00 -1.32086563e+00 -5.64943492e-01 -4.07923818e-01 1.16731906e+00 2.46084526e-01 1.79792792e-01 -1.29358315e+00 -5.92575550e-01 7.26126850e-01 -8.67801085e-02 6.12695575e-01 -6.71643913e-02 -5.60349822e-01 -1.20258558e+00 -1.83199774e-02 -5.11603653e-01 3.62326622e-01 -7.89581478e-01 -7.95104563e-01 6.76882327e-01 -3.38432372e-01 -7.78831959e-01 4.59927708e-01 -5.54073751e-01 -5.77055037e-01 7.84934223e-01 -1.77233338e+00 -1.26025164e+00 -5.53594172e-01 5.94498158e-01 8.81777823e-01 2.83650935e-01 3.95005792e-01 6.79723561e-01 -8.81128132e-01 5.07857323e-01 -1.69851199e-01 3.92151386e-01 1.08865686e-01 -9.32495892e-01 7.40104198e-01 9.44638133e-01 3.31083387e-02 1.10937089e-01 1.34253919e-01 -5.43803215e-01 -9.79854524e-01 -1.39080119e+00 6.60271466e-01 2.08859369e-01 3.05492669e-01 -2.50104636e-01 -9.80181992e-01 4.91838872e-01 -1.29206115e-02 4.29699421e-01 1.00210547e-01 -2.44984463e-01 -2.03015700e-01 -1.88130811e-01 -1.17636132e+00 4.82356369e-01 1.10962355e+00 -7.85480812e-02 -2.96233416e-01 1.09586857e-01 1.01737404e+00 -6.77790642e-01 -6.41899168e-01 5.72695971e-01 4.43778962e-01 -1.07601416e+00 1.06805539e+00 -1.89198300e-01 5.98088145e-01 -4.27069187e-01 -1.21700712e-01 -8.06716502e-01 -1.14484392e-01 -6.39172867e-02 3.71424079e-01 1.15587664e+00 1.63351849e-01 -6.88132346e-01 1.10166037e+00 3.88785750e-01 -4.62423116e-01 -1.01284957e+00 -9.48701978e-01 -6.25189602e-01 -3.48872244e-02 -6.57597005e-01 7.36245990e-01 6.20731533e-01 -4.30968553e-01 4.16287512e-01 1.04662009e-01 3.13297987e-01 4.60476547e-01 3.32127064e-01 5.41301489e-01 -1.03567016e+00 -1.66121110e-01 -5.24119914e-01 -4.79143113e-01 -1.28275287e+00 4.89732660e-02 -7.52930641e-01 -7.67304525e-02 -1.67374671e+00 1.22548915e-01 -4.85197514e-01 -1.75097480e-01 5.38516462e-01 -2.90172070e-01 4.06398952e-01 2.76055664e-01 -2.60234535e-01 -6.64447784e-01 4.62240368e-01 1.44456732e+00 -6.95568770e-02 -2.15144515e-01 -4.63258885e-02 -5.00247717e-01 1.08793902e+00 9.34580207e-01 -3.45525116e-01 -5.15941918e-01 -8.20122600e-01 -1.30778119e-01 1.13607883e-01 4.65141654e-01 -1.27743268e+00 2.69867957e-01 1.07182667e-01 4.47979629e-01 -9.55547154e-01 4.27012324e-01 -6.78405881e-01 -1.15313403e-01 7.30973184e-01 2.76478082e-01 4.92135137e-02 3.75932455e-01 2.90485859e-01 -4.95150894e-01 -5.70812151e-02 9.04612005e-01 -1.96665213e-01 -1.15856969e+00 5.49607515e-01 -9.55975279e-02 8.82181674e-02 9.87868071e-01 -3.18417609e-01 -2.37226382e-01 1.10914126e-01 -5.44032514e-01 5.09637594e-01 1.38789564e-01 3.76947761e-01 5.93559027e-01 -1.09542203e+00 -4.28142428e-01 4.96084601e-01 -3.57636333e-01 7.02678204e-01 8.15214217e-01 6.82754576e-01 -8.68942797e-01 3.57753873e-01 -3.25842977e-01 -7.48044193e-01 -1.18207335e+00 2.73863047e-01 2.82642484e-01 -2.27618456e-01 -7.11716056e-01 1.15545440e+00 3.45170557e-01 -2.73520052e-01 -4.28722426e-02 -5.50183952e-01 -1.74016416e-01 -1.70640815e-02 6.22716248e-01 4.00220305e-01 1.08223401e-01 -6.42627239e-01 -3.16980183e-01 7.21707761e-01 4.70288657e-02 9.93992090e-02 1.22021759e+00 -1.34111866e-01 -7.57035241e-02 -1.43081710e-01 1.19243622e+00 -4.68942106e-01 -1.47680092e+00 -9.52498689e-02 -2.81570166e-01 -4.77778316e-01 4.84563738e-01 -6.48695230e-01 -1.72690976e+00 1.22608590e+00 6.97104871e-01 -9.29922462e-02 1.49147177e+00 -1.26641959e-01 1.39048672e+00 -3.40382047e-02 4.14940149e-01 -1.10340691e+00 -1.89299241e-01 3.86787653e-01 3.36428702e-01 -1.36323702e+00 -1.13783650e-01 -7.89395630e-01 -4.81643975e-01 9.28139269e-01 9.68505085e-01 -2.27072850e-01 7.13473380e-01 4.04573917e-01 -4.34225090e-02 -1.67439312e-01 -2.61263311e-01 -3.75920504e-01 4.51564509e-03 2.15557382e-01 1.99248016e-01 1.81653589e-01 -3.83446187e-01 8.01970482e-01 -2.10711911e-01 3.30610536e-02 2.72591293e-01 8.17995489e-01 -5.41148186e-01 -8.27221513e-01 -2.06164509e-01 3.82544249e-01 -7.76127696e-01 -1.07116751e-01 4.58932847e-01 8.35746646e-01 4.42182362e-01 9.55982208e-01 6.04980469e-01 -3.77542734e-01 3.20498198e-01 -2.44060501e-01 1.65492162e-01 -3.42322409e-01 -6.42597437e-01 2.53342539e-01 -1.60582691e-01 -8.14357102e-01 -3.92341018e-01 -4.00933295e-01 -1.78524029e+00 -4.16058153e-01 -2.65072823e-01 -8.43348056e-02 8.60376954e-01 8.06340873e-01 4.63545263e-01 8.66242826e-01 2.82703996e-01 -7.05910444e-01 9.44608226e-02 -6.92123830e-01 -4.63028580e-01 1.45569310e-01 -3.25977989e-02 -4.33232009e-01 1.13577344e-01 2.43127495e-02]	[9.358586311340332, -0.4793314039707184]
21bdf747-c09d-4ca1-8b8e-9b6740090a2c	accented-text-to-speech-synthesis-with	2305.04816	null	https://arxiv.org/abs/2305.04816v1	https://arxiv.org/pdf/2305.04816v1.pdf	Accented Text-to-Speech Synthesis with Limited Data	This paper presents an accented text-to-speech (TTS) synthesis framework with limited training data. We study two aspects concerning accent rendering: phonetic (phoneme difference) and prosodic (pitch pattern and phoneme duration) variations. The proposed accented TTS framework consists of two models: an accented front-end for grapheme-to-phoneme (G2P) conversion and an accented acoustic model with integrated pitch and duration predictors for phoneme-to-Mel-spectrogram prediction. The accented front-end directly models the phonetic variation, while the accented acoustic model explicitly controls the prosodic variation. Specifically, both models are first pre-trained on a large amount of data, then only the accent-related layers are fine-tuned on a limited amount of data for the target accent. In the experiments, speech data of three English accents, i.e., General American English, Irish English, and British English Received Pronunciation, are used for pre-training. The pre-trained models are then fine-tuned with Scottish and General Australian English accents, respectively. Both objective and subjective evaluation results show that the accented TTS front-end fine-tuned with a small accented phonetic lexicon (5k words) effectively handles the phonetic variation of accents, while the accented TTS acoustic model fine-tuned with a limited amount of accented speech data (approximately 3 minutes) effectively improves the prosodic rendering including pitch and duration. The overall accent modeling contributes to improved speech quality and accent similarity.	['Haizhou Li', 'Zhizheng Wu', 'Yi Zhou', 'Mingyang Zhang', 'Xuehao Zhou']	2023-05-08	null	null	null	null	['text-to-speech-synthesis', 'speech-synthesis']	['speech', 'speech']	[-1.48790047e-01 1.20577596e-01 -7.05762655e-02 -4.98735785e-01 -8.01714659e-01 -6.62082255e-01 3.53792943e-02 -1.06021941e-01 -3.58206570e-01 6.65733814e-01 4.98133987e-01 -4.07334685e-01 3.56667608e-01 -5.31734109e-01 -3.22805077e-01 -6.39939964e-01 1.90796569e-01 3.63125950e-01 1.53685108e-01 -4.51192290e-01 1.47344219e-03 3.76459926e-01 -1.43746090e+00 -1.54874390e-02 9.77267802e-01 1.01586056e+00 4.97263670e-01 1.02273834e+00 -2.16118217e-01 4.21335757e-01 -7.99164832e-01 -1.23394459e-01 6.44941628e-02 -4.15357947e-01 -4.20852214e-01 2.90532932e-02 5.78502752e-03 1.53040104e-02 2.31598124e-01 9.22214806e-01 9.10977423e-01 5.07346392e-01 4.63918209e-01 -6.54898584e-01 -6.36735022e-01 8.19754124e-01 -1.29656941e-01 1.57092497e-01 8.42929780e-02 1.42296776e-01 8.71687233e-01 -7.96189725e-01 1.95154458e-01 1.36407089e+00 4.49055910e-01 5.01658261e-01 -1.03507483e+00 -7.87022412e-01 1.58485502e-01 -4.90626097e-02 -1.31223154e+00 -5.45221746e-01 8.17962825e-01 -3.97514403e-01 1.00647044e+00 4.19285148e-01 5.59021056e-01 8.59590769e-01 3.08627784e-01 3.52457047e-01 1.23187041e+00 -7.02724516e-01 3.75877768e-01 2.94255823e-01 1.48800388e-01 8.51313621e-02 -8.82457435e-01 6.00001574e-01 -3.30406249e-01 3.52056801e-01 7.02662885e-01 -6.93398297e-01 -1.47355303e-01 3.94000173e-01 -9.35906708e-01 8.47110212e-01 3.41310864e-04 3.12518209e-01 -5.66598475e-01 -4.30355936e-01 7.21726000e-01 2.39216849e-01 3.60124379e-01 2.69852132e-01 -8.46919119e-01 -3.41845572e-01 -9.65743601e-01 5.38098253e-02 8.88673484e-01 1.01770186e+00 5.68509579e-01 6.93356991e-01 -2.00030118e-01 1.59177005e+00 3.68558854e-01 9.04146016e-01 8.65234196e-01 -6.92495525e-01 4.03943807e-01 -2.38990560e-01 -1.51629106e-03 -4.68784958e-01 -2.82244384e-01 -2.65128553e-01 -6.55425847e-01 2.39063010e-01 2.88082033e-01 -6.59924388e-01 -1.29717994e+00 1.83524573e+00 3.39836925e-01 -1.46531120e-01 2.27199376e-01 8.46148670e-01 5.48846006e-01 1.31418025e+00 4.38168406e-01 -5.97770810e-01 1.66542053e+00 -1.17669249e+00 -1.25655890e+00 -3.54491800e-01 5.99117503e-02 -1.45533228e+00 1.63763583e+00 1.77090034e-01 -1.43768430e+00 -1.30292749e+00 -9.38544333e-01 4.43667807e-02 -4.60231841e-01 7.13335276e-02 -2.29291439e-01 9.97266114e-01 -9.68428671e-01 5.55007756e-02 -5.60792446e-01 -8.13239347e-03 -5.09173334e-01 4.59732920e-01 -6.47166232e-03 5.37135005e-01 -1.69208574e+00 7.71759987e-01 5.81297457e-01 7.54176825e-02 -5.63085437e-01 -7.84425855e-01 -1.00261998e+00 1.63720518e-01 3.16948593e-02 -2.13752866e-01 1.57465124e+00 -9.70092654e-01 -2.41027379e+00 4.34393376e-01 -1.48763150e-01 -2.38516673e-01 -2.23103371e-02 -2.44900897e-01 -9.05243814e-01 -1.70152947e-01 -1.65585592e-01 7.59218931e-01 1.02481949e+00 -1.09594655e+00 -9.37837720e-01 -5.23534641e-02 -5.68400025e-01 6.28044009e-01 -1.78223982e-01 3.67958277e-01 -3.50608021e-01 -9.98707473e-01 -1.53319180e-01 -8.91765773e-01 -1.49927601e-01 -9.64929521e-01 -5.70050001e-01 2.79541202e-02 6.96148992e-01 -1.13307822e+00 1.44320512e+00 -2.43807268e+00 -3.31965834e-02 1.10406727e-01 -6.30738020e-01 5.07766783e-01 -9.37801227e-02 1.03461012e-01 -2.05479905e-01 -4.61815149e-02 7.37160444e-03 -4.98916626e-01 2.92226166e-01 2.61866063e-01 -3.68833721e-01 -2.47227713e-01 1.54557884e-01 6.37183130e-01 -6.31285250e-01 -4.37443584e-01 7.11765349e-01 6.44884825e-01 -5.03153026e-01 7.65692413e-01 -1.04911610e-01 6.88231885e-01 3.60006839e-02 2.25018814e-01 7.27027893e-01 1.01784050e+00 -1.92723945e-02 -2.67009109e-01 -5.09152830e-01 7.39519358e-01 -1.38160169e+00 1.11843169e+00 -9.27200437e-01 8.94698203e-02 4.40584928e-01 -3.27351809e-01 1.26409650e+00 6.81144238e-01 -9.18022394e-02 -7.54617691e-01 1.04676448e-01 3.04613888e-01 3.16785038e-01 -3.01340997e-01 7.14811385e-01 -5.34376860e-01 -2.58634150e-01 -1.28584474e-01 1.74874127e-01 -6.61568463e-01 1.17114663e-01 -4.67452943e-01 1.84258595e-01 -1.30391465e-02 3.23593825e-01 -3.89236152e-01 6.62405968e-01 -3.96639258e-01 7.47880578e-01 1.94581136e-01 -2.64206260e-01 6.62136912e-01 2.61684563e-02 -4.26670909e-02 -1.09025323e+00 -1.39480746e+00 -9.15642679e-02 1.67001927e+00 -2.18097821e-01 -3.24207097e-01 -1.28621936e+00 -2.44914830e-01 -4.90508586e-01 1.22571826e+00 -1.86214238e-01 4.44702655e-02 -7.77752995e-01 -3.44614208e-01 5.16429603e-01 6.88410223e-01 3.60541135e-01 -1.66584659e+00 1.17307574e-01 5.52173197e-01 -1.16496809e-01 -1.02941215e+00 -1.27536345e+00 5.37263215e-01 -3.18421692e-01 -1.24107532e-01 -5.90056777e-01 -1.16865766e+00 3.73819880e-02 -6.47397876e-01 8.42673600e-01 -6.36210084e-01 3.78767371e-01 -6.19780906e-02 -3.85370821e-01 -6.19521201e-01 -1.05567348e+00 2.86729157e-01 2.59761423e-01 1.49029315e-01 2.74626195e-01 -6.37992620e-01 -2.78379261e-01 4.45931911e-01 -5.84927797e-01 -1.20718263e-01 4.35998082e-01 6.89312458e-01 9.76261556e-01 1.01716377e-01 8.97643089e-01 -6.66751623e-01 5.96156836e-01 -5.51565215e-02 -6.93739474e-01 -2.22460791e-01 -3.01869929e-01 -3.43806148e-01 1.17544818e+00 -5.86912930e-01 -1.51740706e+00 5.86897805e-02 -9.90520597e-01 -4.01215285e-01 -6.34437859e-01 4.56088096e-01 -8.46993744e-01 4.33659792e-01 3.64155710e-01 1.39872745e-01 -1.44745633e-01 -5.04365861e-01 4.67784852e-01 1.12484229e+00 9.04135048e-01 -5.50393283e-01 6.51070297e-01 -5.01330018e-01 -5.63283920e-01 -1.26019907e+00 -5.99521577e-01 -3.31752658e-01 -8.18321347e-01 2.61697778e-03 1.00276458e+00 -9.15610015e-01 -3.50464106e-01 6.08928740e-01 -1.09540927e+00 -5.77545285e-01 -7.31696367e-01 8.45309019e-01 -7.90476203e-01 4.19487059e-02 -9.96695697e-01 -1.01730967e+00 -4.17755514e-01 -1.33364534e+00 9.20563638e-01 3.77203822e-01 -4.49581921e-01 -1.16278529e+00 2.23351896e-01 3.90448004e-01 4.60097581e-01 -1.09244458e-01 9.68109250e-01 -5.79570949e-01 1.31153315e-01 2.03487217e-01 3.03473055e-01 1.02723789e+00 4.76911277e-01 1.57650579e-02 -1.34440768e+00 -3.35193388e-02 2.44097888e-01 -7.01492727e-02 1.79989621e-01 8.33477318e-01 6.04331732e-01 -2.38957107e-01 2.92403460e-01 5.20042658e-01 8.94477129e-01 7.98624992e-01 7.23575175e-01 -1.83064386e-01 7.93959916e-01 6.13175094e-01 9.03836489e-01 1.19330779e-01 3.59190196e-01 7.40819812e-01 -1.32407784e-01 -3.98657680e-01 -4.62449133e-01 -2.34105825e-01 6.01066530e-01 1.88303542e+00 4.49620299e-02 -4.14307743e-01 -6.12799346e-01 7.20234990e-01 -1.19639122e+00 -4.52851027e-01 4.88565415e-02 2.26969147e+00 1.37002444e+00 3.41790378e-01 1.90749690e-01 1.93676367e-01 1.09755456e+00 3.13491702e-01 -2.59158105e-01 -1.32679236e+00 -7.20262453e-02 6.02613091e-01 2.56189436e-01 1.14941609e+00 -1.13832045e+00 1.36907399e+00 5.92697859e+00 1.19720817e+00 -1.29662454e+00 -8.19235966e-02 5.25042236e-01 1.65072963e-01 -7.73481280e-02 -1.99126720e-01 -1.04763865e+00 6.87365532e-01 1.60737658e+00 7.05961883e-02 5.94507813e-01 7.78709888e-01 7.41379082e-01 2.64106303e-01 -8.98229241e-01 6.74805343e-01 -3.85854393e-01 -7.72417247e-01 -1.49068981e-02 -1.22318953e-01 5.92716813e-01 -2.49512032e-01 2.59983957e-01 5.70047379e-01 1.96234077e-01 -1.03787589e+00 1.08689582e+00 2.60207623e-01 1.03140903e+00 -1.13587081e+00 8.04796755e-01 1.23538911e-01 -1.45061624e+00 2.32840016e-01 -2.12400228e-01 5.73903583e-02 6.60645485e-01 3.90968859e-01 -9.43991184e-01 4.51573610e-01 3.98083329e-01 6.64585978e-02 3.27836499e-02 5.36833286e-01 -3.78302813e-01 1.25370204e+00 -2.73373514e-01 2.76552111e-01 2.27893144e-01 -2.51023144e-01 5.29512763e-01 1.73066509e+00 2.59926438e-01 1.71897605e-01 3.70638430e-01 5.11219740e-01 2.98579216e-01 6.24370396e-01 1.36980072e-01 -2.49815565e-02 8.68498683e-01 1.14612675e+00 -4.56126034e-01 -2.27891311e-01 -2.51983941e-01 8.64863813e-01 -3.23844254e-02 5.13598025e-01 -7.44318068e-01 -6.84112251e-01 7.66867638e-01 -9.05112699e-02 6.03243649e-01 -5.26959524e-02 -3.42892379e-01 -4.03718084e-01 -4.56191003e-01 -9.40076768e-01 3.76498364e-02 -7.87897050e-01 -1.27627671e+00 1.03482378e+00 -2.51891017e-01 -6.84261084e-01 -8.42486262e-01 -5.69245756e-01 -7.66709268e-01 1.69729757e+00 -1.56356394e+00 -9.89636540e-01 1.85657710e-01 3.31335366e-01 9.83029544e-01 -8.33021775e-02 1.04545796e+00 3.63832384e-01 -5.26113391e-01 8.42837870e-01 5.30027747e-02 1.39197737e-01 8.32012773e-01 -1.56704521e+00 6.26676381e-01 6.79029286e-01 -2.17054471e-01 2.96173334e-01 7.45692790e-01 -5.59288323e-01 -6.54646158e-01 -1.34014416e+00 1.14517975e+00 -1.19831204e-01 5.32699525e-01 -5.01420557e-01 -1.08132684e+00 6.03103518e-01 5.52919984e-01 -2.44435668e-01 8.13610494e-01 2.99986117e-02 1.85894579e-01 -1.40868053e-01 -9.46390808e-01 6.25904143e-01 3.56843889e-01 -6.55857205e-01 -8.62113714e-01 -2.06797078e-01 1.18333375e+00 -7.31877387e-01 -1.11315250e+00 2.39602119e-01 4.06145930e-01 -8.18170607e-01 7.08887994e-01 -2.23338634e-01 -1.91682726e-02 -4.98720080e-01 -5.17327309e-01 -1.92558336e+00 -3.03100824e-01 -9.53863978e-01 4.17112380e-01 1.73920143e+00 5.61662376e-01 -4.44010556e-01 2.23985791e-01 2.41065592e-01 -7.41789997e-01 -3.69028181e-01 -7.22251117e-01 -8.04915249e-01 3.05155247e-01 -4.66037601e-01 5.95894217e-01 7.18298972e-01 -3.44653018e-02 7.27567732e-01 -2.40888909e-01 4.74751890e-01 6.83516636e-02 -2.10324690e-01 4.55271006e-01 -7.93023646e-01 -1.99617043e-01 -2.67673820e-01 -4.19562981e-02 -9.19918418e-01 1.64892271e-01 -3.35316658e-01 5.06755292e-01 -9.30467486e-01 -6.97717309e-01 -3.62385750e-01 -3.97525996e-01 2.57359505e-01 -5.47999620e-01 -5.02300821e-02 3.61359775e-01 -4.66500610e-01 2.56995887e-01 7.64642537e-01 1.23208570e+00 8.38782489e-02 -8.10299158e-01 3.60449672e-01 -1.98294297e-01 7.83731639e-01 9.26034451e-01 -1.63259387e-01 -6.19775057e-01 -1.28245065e-02 -7.41325438e-01 2.02880815e-01 -2.82871664e-01 -8.89095068e-01 5.16522750e-02 -2.97624618e-01 1.39683232e-01 -5.64164937e-01 5.61304927e-01 -6.03419125e-01 -1.06173707e-02 -3.61457393e-02 -3.89482141e-01 -1.15709804e-01 6.86947405e-01 -2.78474428e-02 -5.63264191e-01 -1.78600326e-01 1.34807825e+00 1.41759083e-01 -4.65524375e-01 9.63802859e-02 -6.11395061e-01 2.12406293e-01 6.52821898e-01 -2.18601480e-01 2.13818878e-01 -3.77366692e-01 -1.14320028e+00 -1.47949979e-01 3.23126912e-02 3.93964320e-01 2.25814730e-01 -1.37190902e+00 -7.94988632e-01 6.72176242e-01 -3.36404294e-01 -7.36971572e-02 5.81283152e-01 3.46158206e-01 -1.01363316e-01 5.25295436e-01 -2.04625651e-01 -4.17481035e-01 -1.12847924e+00 6.83964312e-01 3.70241672e-01 -1.19493410e-01 -8.61814469e-02 9.03530359e-01 6.57827556e-01 -9.03344214e-01 4.48448777e-01 -6.36454523e-01 -3.45595121e-01 -3.12977210e-02 4.35606331e-01 3.75040263e-01 -1.81382836e-03 -1.01947010e+00 -2.05923334e-01 6.80873573e-01 -9.70316976e-02 -5.38980126e-01 7.72472084e-01 -5.74738681e-01 2.25173369e-01 7.72910476e-01 9.80040073e-01 7.98699796e-01 -1.37464619e+00 1.65182218e-01 -1.79484770e-01 1.02814034e-01 1.69309020e-01 -1.21198142e+00 -7.23487914e-01 1.04830384e+00 5.31500578e-01 2.65947104e-01 1.34647572e+00 -3.91182691e-01 1.09237337e+00 -2.56679893e-01 -1.48448795e-01 -1.60020697e+00 -3.45070422e-01 1.03764558e+00 8.77884507e-01 -7.59127438e-01 -9.36009467e-01 -6.63939178e-01 -1.10397959e+00 9.50501621e-01 6.25352085e-01 7.16352016e-02 8.10775042e-01 7.18141794e-01 7.33994246e-01 3.58234793e-01 -5.02084315e-01 -1.24761373e-01 4.04125571e-01 9.05937672e-01 6.13636494e-01 4.19954807e-01 -6.65695444e-02 9.41343606e-01 -1.19209802e+00 -4.88457173e-01 3.26664776e-01 2.09090173e-01 -5.79020619e-01 -1.08930886e+00 -7.10900605e-01 -9.64118019e-02 -4.71720010e-01 -5.58726311e-01 1.04597155e-02 5.80919981e-01 3.76056999e-01 1.10668123e+00 3.10736716e-01 -3.89582157e-01 7.78906286e-01 5.04123986e-01 5.74404513e-03 -8.23265493e-01 -8.29312980e-01 8.07543457e-01 1.78698272e-01 -2.62144953e-01 6.93750829e-02 -5.59516847e-01 -1.51644313e+00 -1.22469679e-01 -4.12883967e-01 5.76599777e-01 6.85164034e-01 8.53235066e-01 -1.78928375e-02 9.50915039e-01 1.02103615e+00 -7.34097600e-01 -4.28044498e-01 -1.28730381e+00 -1.15084362e+00 5.35860397e-02 4.10904378e-01 -1.53651595e-01 -4.19900417e-01 4.96642113e-01]	[14.730019569396973, 6.693434715270996]
80b2e86e-d7cf-4101-aa75-26804fe5a134	are-pre-trained-cnns-good-feature-extractors	1811.08495	null	http://arxiv.org/abs/1811.08495v1	http://arxiv.org/pdf/1811.08495v1.pdf	Are pre-trained CNNs good feature extractors for anomaly detection in surveillance videos?	Recently, several techniques have been explored to detect unusual behaviour in surveillance videos. Nevertheless, few studies leverage features from pre-trained CNNs and none of then present a comparison of features generate by different models. Motivated by this gap, we compare features extracted by four state-of-the-art image classification networks as a way of describing patches from security video frames. We carry out experiments on the Ped1 and Ped2 datasets and analyze the usage of different feature normalization techniques. Our results indicate that choosing the appropriate normalization is crucial to improve the anomaly detection performance when working with CNN features. Also, in the Ped2 dataset our approach was able to obtain results comparable to the ones of several state-of-the-art methods. Lastly, as our method only considers the appearance of each frame, we believe that it can be combined with approaches that focus on motion patterns to further improve performance.	['Tiago S. Nazare', 'Rodrigo F. de Mello', 'Moacir A. Ponti']	2018-11-20	null	null	null	null	['anomaly-detection-in-surveillance-videos', 'anomaly-detection-in-surveillance-videos']	['computer-vision', 'methodology']	[ 9.15945172e-02 -1.45088926e-01 1.38569340e-01 -2.45200470e-01 8.88189822e-02 -3.78645658e-01 9.59658623e-01 4.10920560e-01 -7.43517876e-01 2.97887862e-01 1.20789930e-01 -1.07581437e-01 -1.49617210e-01 -7.43770003e-01 -5.14088631e-01 -6.39934003e-01 -3.83860439e-01 -1.92719668e-01 7.35419571e-01 -3.17630887e-01 2.34380990e-01 7.71467924e-01 -2.03165889e+00 5.77743351e-01 1.27923772e-01 1.29286683e+00 -5.52074075e-01 9.15905058e-01 2.07011759e-01 8.98864150e-01 -8.77317309e-01 -3.83655369e-01 4.21322554e-01 -3.72093290e-01 -6.81079328e-01 4.22508456e-03 7.81027377e-01 -4.59448576e-01 -4.96315837e-01 8.16934705e-01 3.27784777e-01 -3.13400701e-02 7.37760603e-01 -1.32550967e+00 -2.53982842e-01 -2.25739684e-02 -1.92000985e-01 1.10930669e+00 5.83124697e-01 2.57162064e-01 3.92712265e-01 -3.90392423e-01 7.91837037e-01 8.62216175e-01 9.74067926e-01 4.52636451e-01 -9.94369507e-01 -3.60242426e-01 1.71411246e-01 4.77199286e-01 -1.05133009e+00 -4.69646454e-01 6.61701441e-01 -6.12219870e-01 1.33373284e+00 1.70441717e-01 8.32802594e-01 1.68123174e+00 1.68492928e-01 6.29889488e-01 8.07109892e-01 -4.26726490e-01 2.07674392e-02 -1.14267087e-02 3.19618136e-02 7.70234346e-01 4.84822243e-01 1.97579399e-01 -2.28026658e-01 -3.57950687e-01 4.66679901e-01 1.57795697e-01 -2.07679212e-01 -4.23287809e-01 -1.00672317e+00 9.42101121e-01 -2.97502801e-03 7.91187346e-01 -5.51690340e-01 -2.99485251e-02 9.66825008e-01 5.44529319e-01 6.11087561e-01 6.08090878e-01 -4.59259838e-01 -5.75160801e-01 -1.22905684e+00 4.10173386e-01 7.66392171e-01 3.64835352e-01 4.42401618e-01 -3.43637317e-02 -2.18820915e-01 4.02611673e-01 6.52136579e-02 1.65233091e-01 4.14808750e-01 -1.01031494e+00 5.04167303e-02 6.82526708e-01 -4.29257564e-02 -1.53330684e+00 -6.78892195e-01 -2.55171675e-02 -7.89279342e-01 3.53054076e-01 7.46599317e-01 -1.19566850e-01 -8.46376479e-01 1.40042651e+00 4.84620295e-02 2.70755678e-01 -6.83540255e-02 6.20072067e-01 7.69459009e-01 3.56489480e-01 -3.94665077e-02 -5.77320196e-02 1.16339946e+00 -7.55010843e-01 -6.33632481e-01 1.66921049e-01 8.67120683e-01 -7.75019526e-01 4.76162076e-01 6.97879314e-01 -7.78267920e-01 -6.59161866e-01 -1.01271474e+00 5.43284416e-01 -8.00611734e-01 1.75860703e-01 4.56144035e-01 7.89645910e-01 -1.08725345e+00 7.64985323e-01 -1.13442218e+00 -9.67824042e-01 4.54610348e-01 2.88007438e-01 -6.64785147e-01 2.38738075e-01 -9.08068776e-01 8.89793932e-01 4.61590081e-01 -1.73321530e-01 -9.18284416e-01 -2.09030241e-01 -8.23824823e-01 -2.46785954e-01 4.12398010e-01 -5.41479945e-01 1.01341939e+00 -1.28492868e+00 -1.27097893e+00 1.00046146e+00 -1.77542549e-02 -8.88775289e-01 6.58313811e-01 -3.68927270e-01 -6.49028003e-01 4.49417949e-01 -2.85401106e-01 5.48600018e-01 9.53888476e-01 -8.03969264e-01 -8.54653478e-01 -4.83756289e-02 3.56790662e-01 -5.26189744e-01 -4.72257704e-01 4.05296057e-01 -2.13979319e-01 -8.50429296e-01 -5.00528753e-01 -8.40307951e-01 -6.98436648e-02 1.27521949e-02 -7.30962818e-03 -1.87432930e-01 1.18685710e+00 -4.65038031e-01 1.25205863e+00 -2.27304173e+00 1.94270909e-02 2.28983313e-01 2.76302490e-02 9.02210236e-01 -7.48855993e-02 4.76963431e-01 -2.33011082e-01 1.88279390e-01 -1.63905546e-02 -3.55847925e-01 -2.94411778e-01 2.94221044e-01 -3.36053111e-02 6.79926515e-01 6.63567007e-01 5.48439145e-01 -7.63398826e-01 -2.87279159e-01 4.82664704e-01 5.64046621e-01 -5.34615099e-01 2.12004825e-01 1.08713694e-01 2.29458511e-01 -3.25378925e-01 5.69663167e-01 5.57755947e-01 1.53628662e-01 -2.35061139e-01 -9.56834406e-02 -1.10989146e-01 5.82218543e-02 -8.33597660e-01 1.29768360e+00 -4.61024698e-03 1.09718597e+00 -3.46539855e-01 -1.24850190e+00 7.41841614e-01 5.35868526e-01 6.60041273e-01 -4.63830054e-01 4.69648540e-01 -1.72796436e-02 2.30451092e-01 -8.68796468e-01 4.49912757e-01 6.00233436e-01 4.06664610e-01 8.23303312e-02 4.80837256e-01 4.83362675e-01 6.46604657e-01 -1.93408117e-01 1.42459822e+00 1.30281806e-01 4.29682612e-01 -2.04587325e-01 8.65060925e-01 4.47241142e-02 3.42008710e-01 1.10199571e+00 -6.36807323e-01 7.68367350e-01 6.91197813e-01 -1.26510906e+00 -1.07129800e+00 -6.68265045e-01 3.64932269e-02 9.77868795e-01 -3.98351133e-01 -5.65538347e-01 -9.53724682e-01 -1.01976001e+00 -2.95920223e-01 2.27745697e-01 -1.11330795e+00 -3.10850944e-02 -7.48095274e-01 -8.95874023e-01 9.79593039e-01 5.03071904e-01 4.55849200e-01 -1.32474160e+00 -1.27121866e+00 1.81281775e-01 3.16514634e-02 -1.34828544e+00 2.04179913e-01 3.73040475e-02 -8.25865865e-01 -1.40904224e+00 -4.98270005e-01 -3.91335964e-01 4.92765307e-01 1.00382209e-01 1.12595737e+00 5.59741378e-01 -4.34805185e-01 6.80681467e-01 -9.09650326e-01 -5.80558896e-01 -5.26815057e-01 3.47158045e-01 1.65197283e-01 1.10895455e-01 9.35977161e-01 -4.04532880e-01 -4.50492859e-01 2.88668513e-01 -1.17713237e+00 -4.70191687e-01 4.19525862e-01 6.19862676e-01 -6.75520301e-02 -3.62359770e-02 4.72513102e-02 -6.06570065e-01 4.76578444e-01 -4.57329303e-01 -4.97349203e-01 -2.97543667e-02 -2.04536051e-01 -4.03603651e-02 7.30892181e-01 -5.56177378e-01 -5.40371358e-01 -3.59224938e-02 -2.66437769e-01 -6.48594439e-01 -1.01409388e+00 -4.23830822e-02 3.93924296e-01 -2.55294293e-01 8.03119719e-01 1.62095547e-01 1.70063168e-01 -1.83118567e-01 -1.34655505e-01 3.55545372e-01 4.48857784e-01 -7.27318004e-02 6.78912044e-01 7.32574463e-01 9.17486846e-03 -1.06569302e+00 -5.03869176e-01 -5.49274504e-01 -8.67930770e-01 -2.47571051e-01 1.13100278e+00 -4.97139603e-01 -5.35142362e-01 8.24936926e-01 -1.24656010e+00 -3.32170501e-02 -1.83512881e-01 4.12763298e-01 -5.02948821e-01 5.71072757e-01 -4.45458472e-01 -8.98596048e-01 2.31464040e-02 -1.21918905e+00 9.67758179e-01 9.45387110e-02 -4.39327925e-01 -1.04414499e+00 3.57094139e-01 -3.46712358e-02 6.67968094e-01 7.67732263e-01 3.28976005e-01 -1.06520379e+00 -5.05671091e-02 -4.07039642e-01 2.26984639e-02 4.20428306e-01 2.40002632e-01 5.54074347e-01 -1.24590504e+00 -4.33094114e-01 -7.46213943e-02 5.82003482e-02 1.12256575e+00 4.39480931e-01 1.14145935e+00 -3.30164075e-01 -3.30214858e-01 6.24487221e-01 1.04893196e+00 2.62630731e-01 9.14352953e-01 1.00055242e+00 3.83297563e-01 5.57561994e-01 5.93276799e-01 4.23864245e-01 -1.16223335e-01 7.84424782e-01 6.57274544e-01 -8.65563005e-02 7.50916153e-02 3.81021887e-01 5.81945658e-01 5.95823769e-03 -6.40523374e-01 -4.50844318e-01 -8.17084908e-01 7.38510966e-01 -1.87211740e+00 -1.37846470e+00 -4.55780290e-02 1.90971577e+00 -4.83078100e-02 3.43482286e-01 7.02116072e-01 4.20113802e-01 4.82854813e-01 3.98017675e-01 5.88827245e-02 -6.46650434e-01 -1.78609431e-01 6.05091304e-02 4.72263664e-01 -1.19589634e-01 -1.83697832e+00 4.91283804e-01 7.01828432e+00 4.00323451e-01 -1.22856402e+00 -8.91092271e-02 4.52378124e-01 -7.86746889e-02 5.54479122e-01 -4.16524768e-01 -5.43817401e-01 5.66121995e-01 1.24200690e+00 6.06087267e-01 4.24061790e-02 8.31042290e-01 7.77605623e-02 -1.78185329e-01 -1.06898952e+00 8.03988397e-01 4.46876705e-01 -1.21594512e+00 -3.93011682e-02 3.91505249e-02 4.53493297e-01 2.26708651e-01 -3.83222699e-02 -7.43472856e-03 -1.67395398e-01 -9.62942958e-01 5.48600852e-01 5.17451584e-01 2.18142327e-02 -7.15980709e-01 1.38760126e+00 -2.27152742e-02 -9.97262895e-01 -2.18873844e-01 -2.55597383e-01 -2.31959343e-01 5.88099181e-04 2.87383527e-01 -7.41591811e-01 7.25746930e-01 1.35449457e+00 9.23785210e-01 -1.14496982e+00 1.21276140e+00 1.17209703e-01 7.05868423e-01 -3.70317250e-01 1.21678621e-01 6.56197429e-01 1.82397902e-01 6.70830786e-01 1.68341637e+00 2.96807826e-01 -4.84436631e-01 2.81824507e-02 2.60814130e-01 4.94367808e-01 1.00162104e-01 -1.13341069e+00 -8.32675323e-02 -1.55819386e-01 1.20626056e+00 -8.18070233e-01 -3.41690868e-01 -8.36616874e-01 8.88090789e-01 8.10879022e-02 2.21483950e-02 -8.19736302e-01 -3.35809052e-01 9.05480087e-01 2.43268579e-01 7.81846642e-01 -2.14176938e-01 3.03671867e-01 -1.11687231e+00 8.27073306e-02 -1.01012206e+00 5.96230388e-01 -3.78970325e-01 -1.24269497e+00 9.86609340e-01 4.19900894e-01 -1.40937483e+00 -6.51190341e-01 -1.06508458e+00 -7.44430959e-01 1.26099169e-01 -1.34688318e+00 -9.60141301e-01 -5.07288337e-01 4.94343221e-01 4.32735175e-01 -5.56878686e-01 8.68779182e-01 2.43408963e-01 -5.37046552e-01 4.45613950e-01 -1.90759644e-01 4.77765918e-01 6.59174442e-01 -8.74860466e-01 6.36937678e-01 1.20868480e+00 1.79232210e-01 4.21716392e-01 8.24362755e-01 -2.97653556e-01 -6.52907193e-01 -9.29172575e-01 5.98302186e-01 -7.15330422e-01 5.10433793e-01 -1.65954679e-01 -1.13960719e+00 6.07804060e-01 5.57989836e-01 3.15157115e-01 6.51248395e-01 -1.00923836e-01 -3.21207255e-01 2.15746388e-01 -1.19305408e+00 3.42673957e-01 9.72573161e-01 -2.42351025e-01 -7.73636401e-01 -1.39035091e-01 1.80253014e-01 -2.50293761e-01 -7.07284987e-01 5.65261126e-01 6.00914836e-01 -1.68589330e+00 9.27006781e-01 -8.91074121e-01 3.39398712e-01 -3.28863502e-01 8.60371143e-02 -1.19166231e+00 -3.23294103e-01 -3.70721698e-01 -3.01841944e-01 1.07004404e+00 2.27485001e-01 -6.76790059e-01 7.10631967e-01 8.32627863e-02 3.60321775e-02 -5.37554622e-01 -8.77491593e-01 -8.90042603e-01 -3.45302105e-01 -4.70188469e-01 3.86349440e-01 8.46758425e-01 -3.66607964e-01 -3.88429314e-01 -6.15588307e-01 6.13552928e-02 6.63901344e-02 -5.58444440e-01 1.00734329e+00 -1.40145350e+00 -3.41319628e-02 -5.97236693e-01 -1.36509538e+00 -2.75130928e-01 9.45907906e-02 -2.12416738e-01 -2.75802404e-01 -1.01336455e+00 2.75020655e-02 1.67537495e-01 -4.29024667e-01 6.67625070e-01 6.35223836e-02 5.91307521e-01 1.80891082e-01 -5.00042401e-02 -6.39942348e-01 5.27894534e-02 5.54561555e-01 4.33793403e-02 -6.19771853e-02 -9.33903158e-02 -1.15588069e-01 1.12108064e+00 9.42038834e-01 -5.83853006e-01 -7.82567635e-02 -2.59072661e-01 2.30585281e-02 -6.68108225e-01 6.93602443e-01 -1.56553578e+00 1.32474497e-01 1.20190069e-01 6.50483549e-01 -4.72977817e-01 2.48618841e-01 -1.05867982e+00 -8.35924894e-02 6.79956734e-01 -4.95912172e-02 5.92257202e-01 4.92591977e-01 3.24846596e-01 -3.82739246e-01 -3.06142777e-01 7.20607579e-01 -1.86816707e-01 -8.44888628e-01 -1.96864605e-02 -1.05593979e+00 -9.26950425e-02 1.12842453e+00 -4.19986546e-01 -2.30793968e-01 -4.99967486e-01 -5.93733788e-01 -2.70512342e-01 6.43602431e-01 8.71852100e-01 3.79519492e-01 -1.08389926e+00 -5.87584615e-01 4.41523403e-01 3.39592069e-01 -4.09580827e-01 7.05426512e-03 1.04916644e+00 -8.32190812e-01 5.56643188e-01 -7.34032869e-01 -7.91149318e-01 -1.63675094e+00 7.17513978e-01 4.44149524e-01 -3.63502085e-01 -4.73437279e-01 2.29637638e-01 -1.57985032e-01 -3.92449759e-02 2.04917043e-01 -5.17523766e-01 -6.43348217e-01 2.51085252e-01 7.94938505e-01 4.68908101e-01 2.58261442e-01 -8.44328225e-01 -6.22010231e-01 5.68924606e-01 -2.20077813e-01 2.33612746e-01 1.43745458e+00 2.79444724e-01 8.06325302e-02 8.87564570e-02 1.18952274e+00 -1.45334631e-01 -1.12955439e+00 2.00493023e-01 2.29032382e-01 -5.94492435e-01 -2.31520951e-01 -2.47709185e-01 -1.22072852e+00 7.29409099e-01 1.02565575e+00 7.11726546e-01 1.35150707e+00 -2.74463832e-01 2.72634953e-01 5.70576191e-01 3.50607857e-02 -9.55170214e-01 7.97419921e-02 6.26887202e-01 5.53075850e-01 -1.54878712e+00 -9.00548697e-02 -1.46319270e-01 -4.67125326e-01 1.54282975e+00 5.42740941e-01 -4.02625442e-01 5.78014791e-01 2.38264382e-01 2.02741563e-01 -3.87488991e-01 -5.27559161e-01 -3.38492423e-01 4.86505210e-01 8.89941394e-01 4.55163419e-01 -5.44502497e-01 -1.88911855e-01 -1.23609111e-01 4.92586233e-02 1.77039281e-01 6.17941558e-01 1.16191554e+00 -2.11543441e-01 -1.15858185e+00 -4.02394027e-01 4.73534405e-01 -1.06923926e+00 2.25501314e-01 -6.11205101e-01 1.26150990e+00 3.95726770e-01 1.03139424e+00 3.78823727e-01 -5.15710950e-01 5.14071107e-01 1.67047292e-01 3.67595941e-01 -2.46602803e-01 -9.24699724e-01 -4.80477899e-01 1.81493893e-01 -1.02419174e+00 -1.08633649e+00 -7.61783004e-01 -4.52716321e-01 -1.71537295e-01 1.10321101e-02 -1.61901385e-01 3.04185539e-01 1.04780388e+00 3.68907601e-01 4.98587638e-01 2.40855023e-01 -1.03739774e+00 8.72373581e-02 -1.02834201e+00 -6.74764514e-02 8.59338701e-01 7.16326237e-01 -8.81049573e-01 -4.95693207e-01 9.60254446e-02]	[7.926241397857666, 1.453847885131836]
bd89bb3c-6c54-42e2-b5ae-024546ae292f	small-language-models-improve-giants-by	2305.13514	null	https://arxiv.org/abs/2305.13514v1	https://arxiv.org/pdf/2305.13514v1.pdf	Small Language Models Improve Giants by Rewriting Their Outputs	Large language models (LLMs) have demonstrated impressive few-shot learning capabilities, but they often underperform compared to fine-tuned models on challenging tasks. Furthermore, their large size and restricted access only through APIs make task-specific fine-tuning impractical. Moreover, LLMs are sensitive to different aspects of prompts (e.g., the selection and order of demonstrations) and can thus require time-consuming prompt engineering. In this light, we propose a method to correct LLM outputs without relying on their weights. First, we generate a pool of candidates by few-shot prompting an LLM. Second, we refine the LLM-generated outputs using a smaller model, the LM-corrector (LMCor), which is trained to rank, combine and rewrite the candidates to produce the final target output. Our experiments demonstrate that even a small LMCor model (250M) substantially improves the few-shot performance of LLMs (62B) across diverse tasks. Moreover, we illustrate that the LMCor exhibits robustness against different prompts, thereby minimizing the need for extensive prompt engineering. Finally, we showcase that the LMCor can be seamlessly integrated with different LLMs at inference time, serving as a plug-and-play module to improve their performance.	['Eric Malmi', 'Aliaksei Severyn', 'Jonathan Mallinson', 'Jakub Adamek', 'Arthur Bražinskas', 'Giorgos Vernikos']	2023-05-22	null	null	null	null	['prompt-engineering']	['natural-language-processing']	[ 1.08157322e-01 -5.55682071e-02 -6.03263751e-02 -2.90877968e-01 -1.09534669e+00 -7.45659471e-01 8.66357207e-01 -1.17760068e-02 -5.11762917e-01 4.31444168e-01 1.59570068e-01 -5.27803957e-01 1.51447833e-01 -3.36738080e-01 -7.79493809e-01 -1.05454832e-01 4.18313116e-01 3.66948664e-01 5.00747740e-01 -1.76096916e-01 1.88818797e-01 1.67153060e-01 -1.75811768e+00 4.12668079e-01 1.02830780e+00 5.94511926e-01 7.33925700e-01 9.33715522e-01 -2.30120674e-01 7.83460975e-01 -9.60143089e-01 -2.98231035e-01 1.98150184e-02 -3.75041455e-01 -5.07678866e-01 -3.19866478e-01 5.39086461e-01 -4.98087555e-01 -8.79155174e-02 6.88766897e-01 6.14517868e-01 4.84809369e-01 5.18859744e-01 -1.32913589e+00 -4.79305655e-01 7.93560505e-01 -5.97649626e-02 -1.63414720e-02 3.44540089e-01 6.91596985e-01 1.06699336e+00 -1.16864431e+00 5.06925702e-01 1.17159605e+00 4.62455153e-01 9.21797812e-01 -1.37459373e+00 -7.29200363e-01 2.46349111e-01 1.57663628e-01 -1.17310500e+00 -8.22764754e-01 4.09962624e-01 -4.68502551e-01 1.24957621e+00 2.77930021e-01 2.40440428e-01 1.38163435e+00 -3.48892435e-02 8.42810035e-01 8.80585134e-01 -4.56448525e-01 3.82778287e-01 1.14076167e-01 2.16671705e-01 7.56782234e-01 2.78803539e-02 -7.54178986e-02 -6.54480577e-01 -2.66618639e-01 4.56310868e-01 5.44730015e-02 -8.98496658e-02 -1.41366109e-01 -1.19636619e+00 5.18099725e-01 -5.05913347e-02 1.55878738e-01 -1.36411518e-01 2.32377112e-01 1.87554315e-01 3.71469438e-01 2.78725754e-02 8.44558060e-01 -5.53007305e-01 -5.19137621e-01 -1.12194967e+00 2.93922693e-01 9.58603203e-01 1.18460429e+00 7.56039560e-01 3.04058492e-02 -8.20643842e-01 1.05941761e+00 1.73754811e-01 3.31083089e-01 5.27998924e-01 -1.08336914e+00 5.63988984e-01 4.35444027e-01 3.16887677e-01 -3.54409754e-01 -5.02279162e-01 -2.63857514e-01 -3.99222642e-01 3.12542558e-01 3.86711270e-01 -2.45156392e-01 -9.21470881e-01 1.91064107e+00 1.06676407e-02 2.44504377e-01 -2.03279078e-01 7.25084066e-01 8.02908123e-01 7.28615522e-01 5.16598642e-01 -1.72101334e-02 1.15609396e+00 -1.26745701e+00 -3.76237839e-01 -5.50810933e-01 6.10535920e-01 -8.18373978e-01 1.81857169e+00 3.63256037e-01 -1.29279613e+00 -6.31986558e-01 -9.43364263e-01 -1.17881715e-01 -1.17474400e-01 3.05500180e-01 4.16007429e-01 1.70153528e-01 -9.41282630e-01 7.69811273e-01 -7.89004147e-01 -2.91893661e-01 4.20372188e-02 1.95424303e-01 7.78431371e-02 4.97578606e-02 -1.02956474e+00 1.05118585e+00 1.08484372e-01 -3.75647366e-01 -1.21730685e+00 -9.56242919e-01 -7.95041919e-01 4.57056820e-01 4.50027376e-01 -8.87233317e-01 2.01074457e+00 -3.73343498e-01 -1.83125365e+00 5.26669919e-01 -1.92546278e-01 -2.25756824e-01 5.38015604e-01 -3.11075151e-01 -2.29688436e-01 -9.24430192e-02 6.54023737e-02 7.83810437e-01 9.96186376e-01 -9.87465680e-01 -7.86077499e-01 3.41019273e-01 4.21766281e-01 -7.64746103e-04 -3.83957654e-01 1.97358429e-01 -6.71876550e-01 -4.83116001e-01 -4.23038989e-01 -9.31178510e-01 -2.09116250e-01 -3.15030962e-01 -2.94269770e-01 -4.47722614e-01 4.15093541e-01 -4.02785242e-01 1.52551055e+00 -2.16648722e+00 7.18957633e-02 -2.39170149e-01 8.10883343e-02 5.47722518e-01 -7.29353189e-01 4.75990564e-01 1.23686388e-01 1.92712635e-01 3.64611261e-02 -7.88756192e-01 3.68450135e-01 6.58087134e-02 -3.74641359e-01 -1.11615518e-02 2.45316461e-01 1.06298542e+00 -1.07039058e+00 -4.55928147e-01 3.57251346e-01 1.90886214e-01 -7.47950137e-01 5.44421196e-01 -6.56143129e-01 2.32010916e-01 -1.40814155e-01 4.11064893e-01 1.33565038e-01 -5.77666759e-01 8.44989996e-03 -8.29720497e-02 -1.93349108e-01 6.00774169e-01 -1.11041141e+00 1.84492505e+00 -1.13813651e+00 4.81643260e-01 -9.96556580e-02 -5.34404874e-01 7.78168201e-01 3.21787417e-01 -1.04866754e-02 -5.78263879e-01 -1.08265042e-01 1.52060345e-01 -7.15027303e-02 -4.67220813e-01 6.42127156e-01 2.98189253e-01 -3.73638988e-01 8.65586340e-01 2.36178115e-01 -2.66027510e-01 5.48338294e-01 4.96489555e-01 1.20758104e+00 3.53897810e-01 2.38661110e-01 2.43959442e-01 1.89874858e-01 -1.19659469e-01 4.31459576e-01 1.29990637e+00 -1.14551175e-03 5.78281343e-01 3.47380847e-01 -1.14288889e-01 -1.09907162e+00 -1.07321346e+00 3.99506092e-01 1.75515389e+00 -1.83384299e-01 -6.75696015e-01 -6.84056163e-01 -6.29129469e-01 -9.47094932e-02 1.22074890e+00 -1.56487942e-01 -3.03933024e-01 -5.47974110e-01 -2.92985231e-01 5.01454055e-01 4.36696738e-01 -3.39911953e-02 -1.20302379e+00 -8.54500771e-01 3.05013210e-01 -2.31122658e-01 -9.16710854e-01 -7.79246986e-01 2.78073370e-01 -6.29954040e-01 -1.01204848e+00 -5.86559534e-01 -6.91508293e-01 6.66913331e-01 4.14105296e-01 1.30362141e+00 5.63161671e-02 -8.45669433e-02 4.81220514e-01 -2.12611005e-01 -2.79446691e-01 -7.33190238e-01 3.66437018e-01 6.38588667e-02 -4.19856846e-01 1.93006784e-01 -5.96323311e-01 -4.39121246e-01 2.91492909e-01 -6.12212777e-01 3.64691347e-01 6.15205884e-01 9.22914386e-01 3.50459516e-01 -3.52306098e-01 5.65570951e-01 -8.91040564e-01 1.04376531e+00 -2.14979857e-01 -7.30204582e-01 4.86650735e-01 -7.71249950e-01 1.30568087e-01 7.88470864e-01 -8.98245990e-01 -1.19000149e+00 8.43168516e-03 5.62643521e-02 -4.59622949e-01 -1.01637259e-01 2.82622516e-01 8.76617208e-02 2.58939594e-01 8.56873989e-01 1.67255998e-01 -3.37308228e-01 -7.83914089e-01 7.25295842e-01 7.11588383e-01 5.40785074e-01 -7.44461179e-01 7.44376361e-01 -2.14677230e-01 -5.05091667e-01 -4.25930202e-01 -9.58703041e-01 -3.56699854e-01 -4.40509804e-02 3.36123630e-02 4.50796723e-01 -1.00987720e+00 -7.50356495e-01 2.66825229e-01 -1.45830369e+00 -9.52605009e-01 -2.28745282e-01 2.97019631e-01 -5.68907738e-01 -4.26492430e-02 -8.06397080e-01 -7.06697941e-01 -5.07944763e-01 -1.22864604e+00 1.05561388e+00 3.39865386e-01 -8.33360672e-01 -6.40392125e-01 -1.35364547e-01 1.44029304e-01 5.01387298e-01 -4.87027198e-01 1.05841088e+00 -7.43580461e-01 -6.38991177e-01 -1.28765553e-01 -2.39234455e-02 1.00085787e-01 -1.32165253e-01 1.39315471e-01 -9.69964087e-01 -1.39873341e-01 -3.49977136e-01 -4.19321924e-01 7.42918909e-01 2.15506509e-01 1.12851751e+00 -3.38772237e-01 -2.49086604e-01 5.87959945e-01 8.83304536e-01 -5.06214239e-03 2.27260023e-01 3.79816860e-01 5.57670534e-01 3.16676885e-01 6.88337982e-01 5.62634110e-01 3.02306801e-01 7.61673212e-01 7.56398216e-03 1.81110024e-01 -4.86056983e-01 -5.99129379e-01 6.22962952e-01 9.20816362e-01 2.05443725e-01 -1.71229362e-01 -6.85164034e-01 3.59419376e-01 -2.02370405e+00 -8.65906954e-01 1.66172445e-01 2.09813499e+00 1.21080136e+00 3.23090762e-01 -5.29911518e-02 -3.01262468e-01 4.71100032e-01 1.90168738e-01 -6.34927750e-01 -4.47229743e-01 2.68625200e-01 4.31340039e-01 9.57298502e-02 6.29479349e-01 -7.14113414e-01 1.19607162e+00 6.83701277e+00 9.67410505e-01 -9.72005665e-01 3.70563686e-01 1.37509748e-01 -5.86829960e-01 -5.27969658e-01 2.05973789e-01 -1.10241771e+00 6.08337045e-01 9.82734561e-01 -3.66428852e-01 8.50148022e-01 9.99819219e-01 4.13300037e-01 3.37174647e-02 -1.32144225e+00 8.99109542e-01 -7.23713860e-02 -1.34157205e+00 -7.44209960e-02 -4.61393028e-01 7.93682933e-01 1.46098584e-01 -9.80282500e-02 9.97073710e-01 6.73583865e-01 -6.93932176e-01 9.40808773e-01 5.11911929e-01 8.48456025e-01 -4.72718239e-01 9.97052118e-02 7.87696660e-01 -9.54820752e-01 -1.83215201e-01 -2.96250284e-01 -2.72763073e-01 2.71927506e-01 3.79925311e-01 -9.67852831e-01 -9.27497596e-02 5.07134080e-01 1.62832707e-01 -6.47275984e-01 1.02534902e+00 -8.43045235e-01 6.28728688e-01 -1.12426467e-01 -3.00857663e-01 -5.29917367e-02 2.53687739e-01 4.75637406e-01 1.31787694e+00 3.81094903e-01 -7.83493146e-02 2.73416907e-01 1.08031082e+00 -1.58175677e-01 -1.61944658e-01 -2.86167830e-01 -2.04370767e-01 8.35169315e-01 1.56845033e+00 -2.17274234e-01 -6.65680051e-01 -4.33476061e-01 8.58135581e-01 5.95684111e-01 6.22877479e-01 -9.24683690e-01 -4.71481383e-01 6.70596898e-01 1.17776833e-01 1.03284001e-01 -2.12811738e-01 -2.13190168e-01 -1.22295797e+00 -1.49961337e-01 -1.07525980e+00 1.96563140e-01 -9.72471297e-01 -1.08127165e+00 5.54774940e-01 -8.41439962e-02 -1.18864822e+00 -5.30365527e-01 -2.66719133e-01 -9.36674416e-01 9.73934591e-01 -1.32958889e+00 -8.74830008e-01 -2.75287628e-01 3.97258759e-01 8.63758087e-01 -1.54938236e-01 9.47385848e-01 2.67481267e-01 -6.59067810e-01 7.82683492e-01 -2.76315421e-01 -2.79048383e-01 1.15207744e+00 -1.18327618e+00 7.34011710e-01 9.07146513e-01 1.73544809e-01 1.06846583e+00 8.47965539e-01 -6.29005134e-01 -1.39221501e+00 -1.11104417e+00 1.14056349e+00 -5.66029847e-01 7.41849184e-01 -5.48796594e-01 -7.52188742e-01 5.95339000e-01 1.19451113e-01 -1.96292728e-01 5.84190309e-01 3.51680994e-01 -4.11980569e-01 -4.72493730e-02 -6.95065200e-01 1.06198525e+00 1.03181767e+00 -6.53327823e-01 -7.31387198e-01 3.32737774e-01 1.07567644e+00 -5.07010579e-01 -5.95980167e-01 1.02725543e-01 5.30374825e-01 -7.79074132e-01 8.19658160e-01 -6.42416716e-01 5.08203268e-01 -2.99457937e-01 1.12499326e-01 -1.52870750e+00 -5.16137421e-01 -8.99249911e-01 -6.05361223e-01 1.30926907e+00 5.50114334e-01 -3.50446075e-01 4.77709651e-01 1.00682592e+00 -2.90346861e-01 -5.96170127e-01 -4.79423940e-01 -8.37634563e-01 -3.53525221e-01 -5.73670983e-01 7.07974374e-01 4.88676935e-01 1.91478804e-01 7.23451674e-01 -5.14717460e-01 -1.60775200e-01 3.44638616e-01 2.05327615e-01 1.11545229e+00 -1.10107744e+00 -8.47472489e-01 -6.18319988e-01 5.68806767e-01 -1.29736447e+00 1.28030598e-01 -8.65886450e-01 4.44132894e-01 -1.58760691e+00 2.81750798e-01 -6.01871371e-01 -3.90390843e-01 7.63989389e-01 -5.17947733e-01 -1.04416490e-01 6.92823410e-01 3.66556853e-01 -9.78821635e-01 3.06681424e-01 1.11583233e+00 -2.83799022e-01 -5.10897398e-01 2.12753579e-01 -7.78288245e-01 7.44463682e-01 6.44256830e-01 -5.24426818e-01 -4.54556465e-01 -6.52237952e-01 2.51452684e-01 4.80893105e-02 1.80079967e-01 -8.99851263e-01 5.83813071e-01 -3.19567770e-01 1.65910751e-01 -3.70830774e-01 3.69812071e-01 -3.30885768e-01 2.93234140e-02 1.96738467e-01 -6.36275589e-01 -7.22932145e-02 1.55786023e-01 2.58607328e-01 9.79680568e-02 -6.24177456e-01 5.44606864e-01 -2.52714068e-01 -8.59763682e-01 2.15700790e-01 -4.55324888e-01 1.55836552e-01 8.17920744e-01 1.06648371e-01 -4.60308075e-01 -3.08944702e-01 -5.57350457e-01 2.14183912e-01 5.00177503e-01 6.31985009e-01 3.67440939e-01 -1.19118321e+00 -3.55797261e-01 6.66224211e-02 3.28519672e-01 4.05877195e-02 9.25345421e-02 6.68907821e-01 2.17567191e-01 3.67285490e-01 1.69344082e-01 -4.04382974e-01 -1.16380858e+00 4.97308731e-01 -1.27965137e-02 -3.55621725e-01 -5.50383091e-01 8.27852070e-01 1.00368544e-01 -4.58143681e-01 5.04192472e-01 -3.26117724e-01 1.60592757e-02 7.80040747e-04 7.35663056e-01 3.58568877e-01 -1.77339315e-02 1.53655306e-01 -3.32719505e-01 2.10683003e-01 -1.50602698e-01 -2.94628680e-01 1.29046023e+00 7.93326348e-02 2.46758506e-01 5.13856769e-01 7.15123534e-01 1.17339589e-01 -1.70637274e+00 -2.54700005e-01 2.74232011e-02 -2.34714612e-01 -2.53459960e-01 -1.03750646e+00 -4.00296837e-01 9.06824708e-01 2.79602688e-02 -8.92165452e-02 9.06999350e-01 -7.45038316e-02 9.08130825e-01 5.25645077e-01 4.29230720e-01 -1.32251740e+00 3.24919671e-01 6.82393253e-01 6.33559048e-01 -1.19809365e+00 -2.62511283e-01 1.52346492e-02 -7.37529218e-01 9.73146856e-01 8.36424351e-01 1.80119187e-01 9.34277102e-03 3.91092926e-01 -1.72655061e-02 2.90806174e-01 -1.42930520e+00 -1.08625889e-01 2.34266937e-01 5.42662501e-01 4.46711153e-01 1.57502979e-01 -2.17651099e-01 9.77182865e-01 -9.15515423e-02 1.95253134e-01 4.46534365e-01 7.75653839e-01 -6.48704290e-01 -1.23486185e+00 -1.79501116e-01 4.28363174e-01 -1.46190897e-01 -3.94439965e-01 -8.30865055e-02 3.14992517e-01 2.42360570e-02 1.08069658e+00 -1.27125159e-01 -3.71412069e-01 5.27293146e-01 2.71676719e-01 4.72760558e-01 -1.06494462e+00 -6.97672963e-01 -6.83656856e-02 1.36061773e-01 -6.06423795e-01 2.60649472e-01 -3.28656375e-01 -1.17371547e+00 -1.60061330e-01 -2.06721127e-01 2.56291721e-02 6.37950301e-01 9.24479604e-01 6.67434096e-01 7.32449114e-01 3.85965019e-01 -9.19844627e-01 -1.11826634e+00 -1.08812642e+00 -7.45388344e-02 4.69209701e-01 9.01747644e-02 -6.19092226e-01 -2.29547933e-01 1.19188607e-01]	[10.963512420654297, 8.368489265441895]
65e23f67-73fb-473f-935d-6feec673899b	syntactically-look-ahead-attention-network	2002.01145	null	https://arxiv.org/abs/2002.01145v2	https://arxiv.org/pdf/2002.01145v2.pdf	Syntactically Look-Ahead Attention Network for Sentence Compression	Sentence compression is the task of compressing a long sentence into a short one by deleting redundant words. In sequence-to-sequence (Seq2Seq) based models, the decoder unidirectionally decides to retain or delete words. Thus, it cannot usually explicitly capture the relationships between decoded words and unseen words that will be decoded in the future time steps. Therefore, to avoid generating ungrammatical sentences, the decoder sometimes drops important words in compressing sentences. To solve this problem, we propose a novel Seq2Seq model, syntactically look-ahead attention network (SLAHAN), that can generate informative summaries by explicitly tracking both dependency parent and child words during decoding and capturing important words that will be decoded in the future. The results of the automatic evaluation on the Google sentence compression dataset showed that SLAHAN achieved the best kept-token-based-F1, ROUGE-1, ROUGE-2 and ROUGE-L scores of 85.5, 79.3, 71.3 and 79.1, respectively. SLAHAN also improved the summarization performance on longer sentences. Furthermore, in the human evaluation, SLAHAN improved informativeness without losing readability.	['Hidetaka Kamigaito', 'Manabu Okumura']	2020-02-04	null	null	null	null	['sentence-compression']	['natural-language-processing']	[ 4.79252189e-01 2.14969397e-01 -1.93603076e-02 -4.61337030e-01 -8.92842174e-01 -3.32220286e-01 1.95078403e-01 3.86871845e-01 -5.26149511e-01 1.13375843e+00 1.07306266e+00 -1.75158054e-01 1.44856006e-01 -6.52017534e-01 -7.14736819e-01 -4.05846447e-01 2.04494104e-01 2.62793750e-01 -1.57319345e-02 -3.93925816e-01 4.95842874e-01 5.12094283e-03 -1.50967324e+00 6.52602613e-01 1.24457431e+00 3.54920655e-01 7.58670986e-01 9.79686081e-01 -4.07266378e-01 6.98979437e-01 -8.39522898e-01 -6.40728056e-01 -1.79762259e-01 -8.74314845e-01 -7.77956963e-01 -3.86574596e-01 1.75961226e-01 -5.06046474e-01 -3.80147129e-01 1.15485322e+00 5.73679864e-01 1.13103483e-02 3.40982288e-01 -4.68724936e-01 -7.31968939e-01 1.43370795e+00 -3.70265931e-01 3.73122275e-01 6.30971730e-01 2.56429780e-02 1.10660946e+00 -7.11557329e-01 6.29894853e-01 1.24138427e+00 3.02923113e-01 6.15868330e-01 -6.37888908e-01 -4.10974413e-01 2.15127572e-01 1.56478927e-01 -9.87945974e-01 -6.13038063e-01 3.78108889e-01 7.14901537e-02 1.61214924e+00 6.92457855e-01 6.18336558e-01 1.04125714e+00 6.59810662e-01 9.36135530e-01 2.58301884e-01 -5.67917168e-01 9.02837664e-02 -2.61938840e-01 4.35706288e-01 2.67258883e-01 4.10498142e-01 -1.87743396e-01 -5.85943162e-01 3.21625412e-01 5.96410371e-02 -1.01569049e-01 -3.86664063e-01 6.31206751e-01 -1.10380006e+00 6.19935572e-01 4.64066826e-02 3.78292948e-01 -6.63264215e-01 7.10137784e-02 7.57386148e-01 2.76371539e-01 4.74384427e-01 4.91402537e-01 -4.23116237e-01 -5.35011947e-01 -1.03271413e+00 1.10737361e-01 6.81942999e-01 1.33678412e+00 3.12489867e-01 1.69629946e-01 -7.49742091e-01 7.56891370e-01 -4.63392735e-02 5.56152523e-01 1.07720971e+00 -7.56560624e-01 8.02049816e-01 3.32137227e-01 -2.29114488e-01 -8.25838268e-01 -1.93242952e-01 -7.45338678e-01 -1.06656182e+00 -8.20024908e-01 -4.69832838e-01 -2.44973615e-01 -7.22249985e-01 1.66705942e+00 -4.08040494e-01 -2.05406442e-01 3.35643381e-01 5.82562983e-01 1.04736924e+00 1.16836798e+00 6.51868107e-03 -7.71513224e-01 1.06590140e+00 -1.17545271e+00 -1.09046018e+00 -5.48770189e-01 8.59319925e-01 -7.85442770e-01 1.12665880e+00 2.99532026e-01 -1.47548223e+00 -7.08149731e-01 -9.32738245e-01 -1.99751779e-01 3.27899419e-02 8.04567430e-03 1.63123101e-01 3.70672405e-01 -1.01965737e+00 7.35065281e-01 -6.13722920e-01 -1.65716037e-01 2.31301278e-01 1.25461454e-02 -1.39431745e-01 -2.38244981e-01 -1.41212416e+00 7.72263348e-01 9.63512599e-01 -1.23509504e-02 -6.28142297e-01 -5.35813868e-01 -8.54684412e-01 7.08684802e-01 1.25284895e-01 -6.67278230e-01 1.43297243e+00 -9.26370323e-01 -1.26934171e+00 3.41404885e-01 -5.17731249e-01 -8.42653692e-01 7.55459443e-02 -6.06232941e-01 -5.11732519e-01 1.07864588e-01 1.13388330e-01 7.57149279e-01 5.08374751e-01 -7.02857256e-01 -6.90782547e-01 -2.19522387e-01 -2.16209903e-01 4.27689672e-01 -2.96721429e-01 1.66149400e-02 -4.05951977e-01 -8.72905374e-01 -1.69435829e-01 -4.95940089e-01 -4.09379043e-02 -8.72167230e-01 -6.73495471e-01 -1.46340266e-01 5.38171172e-01 -1.26407814e+00 1.85572517e+00 -2.07855964e+00 2.56803304e-01 -3.55021834e-01 -1.82628095e-01 7.85380304e-01 -5.08027852e-01 6.72191501e-01 -1.83458682e-02 2.58578569e-01 -5.10558426e-01 -3.04313570e-01 -2.24828288e-01 2.67230004e-01 -4.50578004e-01 -1.81120068e-01 1.95308700e-01 1.14173257e+00 -1.14407206e+00 -4.18060869e-01 -4.02229354e-02 1.73442230e-01 -6.96620405e-01 2.58780062e-01 -2.53115535e-01 2.72625848e-03 -5.46525195e-02 2.52567321e-01 5.31204879e-01 3.14951569e-01 -6.24344908e-02 1.76948592e-01 -6.30087107e-02 7.74035037e-01 -5.74937224e-01 1.82146871e+00 -5.83095133e-01 7.17828453e-01 -2.96819061e-01 -7.86034107e-01 8.57243299e-01 2.47491375e-01 3.32757016e-03 -1.03142929e+00 2.88209133e-02 7.29080513e-02 -3.01873945e-02 -7.90050685e-01 1.20275593e+00 -8.69778395e-02 -3.23373199e-01 3.82302523e-01 -5.09951115e-02 1.07117156e-02 5.59020936e-01 4.23378348e-01 1.04508662e+00 -1.93887085e-01 4.97137606e-01 1.62505396e-02 7.44010508e-01 -2.31881484e-01 6.25617564e-01 8.17031920e-01 1.21464491e-01 7.60107219e-01 5.56199610e-01 -4.79497798e-02 -1.15840566e+00 -7.00902939e-01 2.22917572e-01 1.06505215e+00 -1.58955067e-01 -8.67597044e-01 -1.06734645e+00 -5.66386878e-01 -3.90174955e-01 1.74614120e+00 2.41205888e-03 -7.51310229e-01 -8.66739929e-01 -3.26859981e-01 7.24483967e-01 4.39551324e-01 3.24503601e-01 -1.44973385e+00 -5.57163358e-01 5.60192823e-01 -7.32574344e-01 -8.12408626e-01 -8.46760809e-01 -1.19406693e-01 -9.69392419e-01 -5.68080723e-01 -6.38773859e-01 -8.00679088e-01 5.88102102e-01 2.90719867e-01 1.12340057e+00 1.12649970e-01 9.94584337e-02 -2.61658758e-01 -9.00894105e-01 -4.01217282e-01 -7.45133519e-01 3.83681476e-01 -1.51264697e-01 -4.45457935e-01 4.16217595e-01 -3.19054127e-01 -4.12902713e-01 -2.91770786e-01 -1.06461620e+00 3.01525801e-01 7.87529349e-01 8.89133751e-01 5.64835191e-01 -4.56345305e-02 9.66297746e-01 -8.37848485e-01 1.07689309e+00 -3.50821853e-01 -5.83601706e-02 3.76685053e-01 -4.99266624e-01 1.97629347e-01 1.15231097e+00 -2.61332397e-03 -1.02156639e+00 -2.67399997e-01 -6.64602637e-01 4.75391261e-02 -4.30720188e-02 8.29981446e-01 -2.27466613e-01 8.38440835e-01 3.08265775e-01 8.46810162e-01 -2.38680348e-01 -5.50018251e-01 2.79676884e-01 1.10129106e+00 7.48531461e-01 7.31956912e-03 1.34757102e-01 -3.08204383e-01 -5.57441473e-01 -9.70976532e-01 -8.12403560e-01 -3.66703361e-01 -4.85494882e-01 4.18880098e-02 6.93349898e-01 -7.74192274e-01 -1.76350519e-01 2.86429614e-01 -1.57823932e+00 1.98154196e-01 -4.83839512e-01 3.59948307e-01 -3.52267563e-01 9.23895657e-01 -5.37585020e-01 -6.44642591e-01 -1.08354390e+00 -1.07526660e+00 9.89040971e-01 2.09282979e-01 -6.50605679e-01 -5.68880081e-01 -1.11592218e-01 1.91718102e-01 4.39738810e-01 -2.44183809e-01 1.10568023e+00 -7.90669322e-01 -2.25594327e-01 -3.21239710e-01 5.76323010e-02 5.89055300e-01 -4.57192026e-02 -1.45868048e-01 -5.26631713e-01 -3.68465483e-01 2.64211427e-02 -3.96487191e-02 1.16275370e+00 3.88657868e-01 1.34857643e+00 -8.58362913e-01 3.25152501e-02 4.59588498e-01 1.13117397e+00 3.91639501e-01 1.13070023e+00 -1.37685716e-01 5.51557541e-01 4.03384298e-01 6.45611823e-01 4.92379278e-01 2.54524171e-01 2.65134364e-01 3.84924471e-01 4.88637179e-01 -2.54840434e-01 -7.30596125e-01 8.31333816e-01 1.65337086e+00 3.60230386e-01 -8.82033885e-01 -6.02471530e-01 5.39609253e-01 -1.56110144e+00 -1.11576796e+00 -3.28596056e-01 2.26170754e+00 9.26095903e-01 4.13709909e-01 -3.50985289e-01 1.75530776e-01 8.49507809e-01 2.66759604e-01 -2.95201063e-01 -9.54627395e-01 -2.63222396e-01 1.83829084e-01 4.75413948e-01 7.25655913e-01 -6.67319536e-01 1.14318466e+00 5.78592968e+00 9.11961854e-01 -9.79412794e-01 -8.33349377e-02 6.74877226e-01 -2.90005684e-01 -6.47776902e-01 -6.93436638e-02 -9.76317942e-01 8.82217467e-01 1.42644191e+00 -6.40525639e-01 3.54184687e-01 5.94164848e-01 5.02373993e-01 -1.57595277e-01 -9.24715638e-01 7.76595116e-01 4.13359344e-01 -1.34319842e+00 6.35964632e-01 -3.83768082e-01 6.08614981e-01 -1.35501787e-01 -3.34676951e-01 5.23979068e-01 -1.80062816e-01 -1.05888915e+00 7.00059116e-01 7.15796709e-01 8.24643552e-01 -1.02822506e+00 1.12648880e+00 8.46344709e-01 -6.82616055e-01 -1.03778705e-01 -6.53949022e-01 -1.71118960e-01 6.33700013e-01 8.12408268e-01 -8.04520667e-01 7.15167284e-01 1.93240494e-01 8.79354656e-01 -4.65973049e-01 9.48015153e-01 -4.65355784e-01 7.06862867e-01 3.17344069e-02 -4.00267452e-01 2.68605500e-01 -5.67335337e-02 8.44297409e-01 1.65227568e+00 7.04016924e-01 1.59537762e-01 -2.77180880e-01 5.21573186e-01 -3.31263006e-01 1.94095716e-01 -5.10974050e-01 -2.64991701e-01 5.86827993e-01 7.90364742e-01 -2.31621876e-01 -4.78182733e-01 -5.27091790e-03 1.23233390e+00 1.46989420e-01 1.85182393e-01 -7.77260780e-01 -8.75594139e-01 2.62440562e-01 -9.85632986e-02 4.77796257e-01 -2.67501678e-02 -3.27047795e-01 -1.12181175e+00 2.14292035e-01 -9.19193387e-01 1.29088283e-01 -9.42026317e-01 -7.03560829e-01 7.03804493e-01 -1.36414096e-01 -1.00533688e+00 -3.69311303e-01 9.88854840e-02 -7.58392453e-01 8.55171859e-01 -1.41421807e+00 -5.14847040e-01 -1.27692865e-02 -1.53926328e-01 1.31828988e+00 -2.03618541e-01 8.07359815e-01 3.00872177e-01 -6.94759429e-01 8.25281322e-01 2.94008315e-01 -1.70597821e-01 4.81908351e-01 -9.24612999e-01 6.56795323e-01 1.13050771e+00 -1.30008698e-01 6.49533033e-01 7.17720151e-01 -9.41033661e-01 -1.24128687e+00 -1.33184779e+00 1.76823187e+00 2.82840878e-01 -1.08201923e-02 -1.65998384e-01 -1.07975304e+00 5.53628922e-01 4.45181698e-01 -8.57186317e-01 4.89314049e-01 -3.27182919e-01 9.37938318e-02 -1.43762827e-01 -9.11206305e-01 6.20082557e-01 1.06742811e+00 -2.62361228e-01 -8.88336539e-01 3.95289391e-01 1.37394166e+00 -3.32350641e-01 -4.64961082e-01 1.93513185e-01 2.80700177e-01 -9.54957187e-01 6.53474808e-01 -6.78146183e-01 9.79288697e-01 1.25052035e-01 -2.29635105e-01 -1.57233453e+00 -4.61936802e-01 -6.55987322e-01 -3.97515327e-01 1.32692146e+00 5.65318346e-01 -2.25636274e-01 4.35849786e-01 1.60758436e-01 -8.11498344e-01 -6.33173704e-01 -9.63751197e-01 -7.46789157e-01 1.41665980e-01 -4.18382287e-01 9.55445766e-01 2.89409667e-01 2.07275286e-01 6.34357333e-01 -5.28086722e-01 -2.17369080e-01 1.35128871e-01 -9.63104963e-02 3.22812349e-01 -6.39884353e-01 -1.06962562e-01 -5.47655582e-01 -5.68935685e-02 -1.51399386e+00 2.19708636e-01 -1.12757456e+00 3.88860881e-01 -1.68311620e+00 2.91392058e-01 1.51632816e-01 -5.00202179e-02 8.83867443e-02 -5.11146247e-01 -4.70001847e-01 1.51803464e-01 -2.45409366e-02 -7.02341378e-01 1.01848435e+00 1.22083819e+00 -2.02849984e-01 -1.28234208e-01 -7.45017752e-02 -9.21246290e-01 3.26589257e-01 1.04338455e+00 -5.88936925e-01 -3.61031651e-01 -8.50176811e-01 2.22731024e-01 3.78573745e-01 -2.88787991e-01 -8.47966731e-01 2.70031333e-01 6.43764287e-02 7.07969591e-02 -1.14265454e+00 -4.05957364e-02 -4.02591079e-01 -6.65195584e-02 8.43084633e-01 -8.31119001e-01 4.15036798e-01 2.46721804e-02 5.07806540e-01 -3.23094338e-01 -8.41778040e-01 4.40592110e-01 -2.62544453e-01 -6.19416893e-01 -7.37032816e-02 -5.76079428e-01 2.51806349e-01 9.36159253e-01 -2.25757107e-01 -2.59012878e-01 -6.69018865e-01 -3.24071497e-01 3.66890371e-01 1.67398050e-01 4.83828902e-01 1.01252854e+00 -1.07542610e+00 -1.25498056e+00 4.34612840e-01 -5.20737059e-02 1.18418925e-01 4.30905968e-01 4.91620481e-01 -7.78375030e-01 8.58589292e-01 -3.23968306e-02 -1.93769813e-01 -1.43144178e+00 4.78596836e-01 -6.92600459e-02 -4.12975281e-01 -6.14490986e-01 8.72053742e-01 -1.56327859e-01 -2.03451693e-01 3.94291222e-01 -5.11584520e-01 -3.13364774e-01 3.29944007e-02 8.64036918e-01 5.73360562e-01 3.09219867e-01 -5.46634674e-01 -3.71494852e-02 1.13533095e-01 -4.67656404e-01 1.16484694e-01 1.36104143e+00 -3.68473768e-01 -4.12419409e-01 2.48552680e-01 1.36741781e+00 -1.11215515e-02 -6.20711088e-01 2.24666931e-02 1.33323073e-01 -4.16441411e-01 -9.03467387e-02 -7.86262631e-01 -7.99949586e-01 9.01662767e-01 -4.89895232e-02 7.79127628e-02 1.22386730e+00 -2.78940976e-01 1.46933305e+00 4.74925667e-01 5.58485761e-02 -1.16268194e+00 -1.73150256e-01 1.14526308e+00 1.13782990e+00 -7.30256081e-01 -8.47593024e-02 -2.38408789e-01 -7.53163874e-01 1.15400803e+00 5.14412642e-01 2.83343405e-01 2.23710239e-02 -3.97440307e-02 -4.79408652e-01 1.76681072e-01 -9.39742088e-01 2.68488288e-01 7.51903933e-03 3.86992842e-01 6.33316934e-01 1.12428069e-01 -8.28827739e-01 7.77437925e-01 -6.70591593e-01 -8.77517238e-02 8.12054813e-01 7.79263437e-01 -8.72222543e-01 -1.00731695e+00 -6.02891147e-02 7.20640421e-01 -5.04953563e-01 -5.44067025e-01 -3.27103883e-01 1.70689762e-01 -7.58476257e-02 1.07768345e+00 2.50122279e-01 -4.15007055e-01 4.98665959e-01 1.60085127e-01 1.17298670e-01 -7.74022043e-01 -6.89954281e-01 -6.62307665e-02 4.03809369e-01 -3.01309019e-01 5.38602248e-02 -5.47677994e-01 -1.31095099e+00 -4.90088880e-01 -3.11508298e-01 3.41021299e-01 5.16995430e-01 7.94193923e-01 6.73320591e-01 9.79124963e-01 6.13074899e-01 -4.61657256e-01 -7.17202663e-01 -1.22779870e+00 -3.00034434e-01 3.19341421e-01 3.89318675e-01 2.85414189e-01 -1.09872870e-01 -1.17843424e-03]	[12.24384593963623, 9.370707511901855]
013a232f-ac91-4967-9b48-d7d1dd76bc71	tensor-program-optimization-with	2205.13603	null	https://arxiv.org/abs/2205.13603v2	https://arxiv.org/pdf/2205.13603v2.pdf	Tensor Program Optimization with Probabilistic Programs	Automatic optimization for tensor programs becomes increasingly important as we deploy deep learning in various environments, and efficient optimization relies on a rich search space and effective search. Most existing efforts adopt a search space which lacks the ability to efficiently enable domain experts to grow the search space. This paper introduces MetaSchedule, a domain-specific probabilistic programming language abstraction to construct a rich search space of tensor programs. Our abstraction allows domain experts to analyze the program, and easily propose stochastic choices in a modular way to compose program transformation accordingly. We also build an end-to-end learning-driven framework to find an optimized program for a given search space. Experimental results show that MetaSchedule can cover the search space used in the state-of-the-art tensor program optimization frameworks in a modular way. Additionally, it empowers domain experts to conveniently grow the search space and modularly enhance the system, which brings 48% speedup on end-to-end deep learning workloads.	['Tianqi Chen', 'Cody Hao Yu', 'Masahiro Masuda', 'Wuwei Lin', 'Hongyi Jin', 'Ruihang Lai', 'Bohan Hou', 'Siyuan Feng', 'Xiyou Zhou', 'Junru Shao']	2022-05-26	null	null	null	null	['probabilistic-programming']	['methodology']	[-6.23898983e-01 -5.79817593e-01 -6.54489398e-01 -5.06370664e-01 -6.78770244e-01 -5.89982808e-01 -1.75191425e-02 1.33770853e-01 -4.85017926e-01 5.35892099e-02 -4.62765284e-02 -5.30084729e-01 9.39626098e-02 -9.39141095e-01 -7.19944894e-01 -4.24143374e-01 -1.95905939e-01 3.99629653e-01 5.10926366e-01 -1.73717380e-01 2.21840099e-01 3.65485013e-01 -1.65964746e+00 4.16667074e-01 1.01101828e+00 8.59169424e-01 3.91112804e-01 8.59258771e-01 -5.77697933e-01 9.36093509e-01 -3.38816226e-01 -4.22722399e-01 2.77986676e-01 4.42219257e-01 -9.69561934e-01 -2.69978166e-01 6.73412204e-01 -3.22560430e-01 -2.46866047e-01 1.03872323e+00 4.70773965e-01 4.52166013e-02 -9.53964964e-02 -1.35398734e+00 -4.45761800e-01 1.22259271e+00 -4.66979206e-01 3.93880516e-01 -2.14478850e-01 5.25985599e-01 1.45956230e+00 -8.52047145e-01 2.94176400e-01 1.08884883e+00 5.44302344e-01 3.29692066e-01 -1.37637460e+00 -5.92935860e-01 2.64886498e-01 1.98535725e-01 -1.18082070e+00 -2.55065054e-01 8.97648454e-01 -5.28389394e-01 1.32908821e+00 4.53030646e-01 7.91868865e-01 5.34760177e-01 8.55964124e-02 1.32273376e+00 5.90440750e-01 -2.33930171e-01 2.90257901e-01 -1.93510234e-01 5.67056239e-01 1.19445574e+00 -2.35509887e-01 -1.05154710e-02 -6.08689904e-01 -4.59178090e-01 4.31421250e-01 9.07622874e-02 2.33012103e-02 -6.19721353e-01 -1.33949864e+00 7.52597213e-01 5.81578195e-01 1.26383692e-01 -3.04404587e-01 7.27784395e-01 9.73661423e-01 1.64658338e-01 9.43658948e-02 8.35834384e-01 -7.48574793e-01 -6.66343749e-01 -1.15748000e+00 6.22810841e-01 9.53797281e-01 9.56694603e-01 9.88185048e-01 2.88124859e-01 -4.87462550e-01 7.57278740e-01 1.91228420e-01 4.25796032e-01 3.38880837e-01 -1.14451301e+00 6.86753094e-01 8.38181078e-01 -4.18517172e-01 -8.54907215e-01 -2.18033582e-01 -7.42564619e-01 -5.07190645e-01 1.43969312e-01 1.03273645e-01 -1.75117552e-02 -5.73410749e-01 1.60343242e+00 4.22104359e-01 -1.49640590e-01 -2.25546181e-01 8.58466923e-01 5.58167756e-01 6.28286302e-01 -8.93356949e-02 2.98243403e-01 1.59951425e+00 -1.72164178e+00 -1.20942757e-01 -2.75768518e-01 1.07739711e+00 -6.96160376e-01 1.82154143e+00 5.38344920e-01 -1.06922710e+00 -3.88592958e-01 -1.00835443e+00 -2.89145976e-01 8.91226828e-02 2.48764545e-01 1.31483698e+00 7.19525576e-01 -1.24729908e+00 5.56826949e-01 -1.34724951e+00 2.84347087e-01 5.70564032e-01 4.23295140e-01 -1.79387052e-02 1.75745606e-01 -6.04218125e-01 2.62860805e-01 5.85498393e-01 -2.79125661e-01 -8.84368360e-01 -1.35043204e+00 -6.92340732e-01 3.53327721e-01 3.94434541e-01 -8.78100932e-01 1.61257923e+00 -6.68050408e-01 -1.65369725e+00 6.53725564e-01 -2.25848094e-01 -4.09156799e-01 3.44950199e-01 -2.22449586e-01 2.33573645e-01 -1.87125772e-01 -2.83047538e-02 2.97722846e-01 7.49874651e-01 -6.75659657e-01 -6.09101593e-01 -1.99243650e-01 2.49156877e-01 -4.02009524e-02 -7.68248320e-01 3.40693355e-01 -7.86251605e-01 -6.48885846e-01 -9.60556865e-02 -1.11194181e+00 -4.76712584e-01 2.64262222e-02 -2.91824311e-01 -5.37986219e-01 6.33922875e-01 -1.14030480e-01 1.47569573e+00 -2.14558029e+00 2.95871139e-01 3.04349922e-02 8.52687418e-01 4.27382663e-02 -1.27153173e-01 -3.62832536e-04 1.50722355e-01 2.62395382e-01 6.86384887e-02 -4.73721623e-01 5.17607927e-01 1.66708559e-01 -5.37453592e-01 1.64822310e-01 -1.28715545e-01 9.02546704e-01 -1.05954862e+00 -6.49618268e-01 -8.12705159e-02 -9.25555080e-02 -1.36045599e+00 3.13103735e-01 -8.17676604e-01 -2.28791818e-01 -7.79646337e-01 7.32555687e-01 6.79709077e-01 -5.51782846e-01 -9.22933314e-03 -2.78181732e-01 -2.73902655e-01 4.71616477e-01 -9.96893167e-01 1.86127043e+00 -7.93210387e-01 5.61052382e-01 7.81822279e-02 -7.09339440e-01 8.62395763e-01 -2.41379648e-01 4.46889490e-01 -2.65721321e-01 -2.22095326e-01 2.94229180e-01 -4.46153805e-02 -3.46909821e-01 8.04997027e-01 4.59632277e-01 -2.29410157e-01 9.46917355e-01 -3.32869105e-02 -1.34006366e-01 5.36155760e-01 9.26379189e-02 1.30165911e+00 -1.38313845e-02 -3.50438386e-01 -4.03140754e-01 5.44760764e-01 1.84105277e-01 6.38993740e-01 8.13987672e-01 -1.93941340e-01 -1.72317699e-02 7.39535511e-01 -1.00262880e+00 -1.00760841e+00 -9.74934220e-01 9.29778814e-02 2.08512211e+00 -3.05331618e-01 -9.49723303e-01 -7.33085394e-01 -7.96546936e-01 -1.56162500e-01 5.06917715e-01 -2.28871316e-01 9.52942818e-02 -9.34193552e-01 -8.92012894e-01 5.73354125e-01 5.81614017e-01 6.71453416e-01 -8.84424448e-01 -8.23192179e-01 2.29748815e-01 -3.17253470e-02 -8.51256847e-01 -1.00617850e+00 3.19548249e-01 -1.12257326e+00 -5.71716845e-01 -2.20193595e-01 -7.57423341e-01 5.39327383e-01 1.98030099e-01 1.65914726e+00 2.10583284e-01 -2.68340826e-01 8.17244649e-02 -4.73741442e-02 3.17078270e-02 -4.73214954e-01 7.08640337e-01 -1.75285846e-01 -3.81788224e-01 3.52789611e-01 -7.09837615e-01 -5.12094378e-01 7.68156573e-02 -7.41629541e-01 2.21060604e-01 3.42604816e-01 9.57445621e-01 5.87503552e-01 4.10065025e-01 -1.88164562e-01 -6.57406449e-01 7.09319055e-01 -3.25028598e-01 -1.11540341e+00 3.45307857e-01 -8.37866843e-01 9.01923120e-01 7.24112153e-01 -5.95340908e-01 -6.33321404e-01 1.38556376e-01 -2.41748076e-02 -6.48602843e-01 3.26373160e-01 7.92431414e-01 4.20791395e-02 -3.53459537e-01 9.45019960e-01 3.73074651e-01 -1.34681165e-01 -5.11947691e-01 5.05640388e-01 1.73493832e-01 3.59140813e-01 -1.57428825e+00 7.09542811e-01 8.38939026e-02 -6.62647411e-02 -2.37924010e-01 -5.07406592e-01 -4.82766300e-01 -3.00690293e-01 1.35703996e-01 4.88467664e-01 -8.72425377e-01 -1.15285957e+00 4.49888259e-01 -1.23703921e+00 -6.01704955e-01 7.42303878e-02 1.89191639e-01 -3.42190444e-01 2.72560090e-01 -7.00523138e-01 -2.02187181e-01 -8.87226760e-01 -2.17568135e+00 1.14233577e+00 1.28775567e-01 -4.74749804e-02 -9.04863179e-01 9.08756182e-02 4.64442849e-01 6.32642627e-01 -2.13714212e-01 1.22539568e+00 -3.64186972e-01 -1.36322618e+00 8.02376792e-02 -2.44367868e-01 1.69336319e-01 -5.27492881e-01 3.72710794e-01 -5.97200930e-01 -1.07000656e-01 -2.51871318e-01 -2.73379445e-01 7.48142064e-01 6.98619261e-02 1.82131708e+00 -4.78873581e-01 -2.34557018e-01 1.28299308e+00 1.33887100e+00 -2.74684876e-01 2.70294666e-01 5.87617517e-01 1.06332409e+00 5.76889478e-02 3.03346246e-01 5.61270773e-01 7.81471789e-01 8.24469328e-01 4.66016710e-01 1.13552243e-01 2.71105945e-01 -9.76429507e-02 3.78140807e-01 1.05580568e+00 1.79455474e-01 2.44665653e-01 -1.27528596e+00 5.73271215e-01 -1.92112207e+00 -6.33080542e-01 4.59481403e-02 1.84763265e+00 1.50936997e+00 1.20582201e-01 1.49990633e-01 -1.35355383e-01 1.37599006e-01 3.54207695e-01 -5.43475688e-01 -5.92789948e-01 4.13292289e-01 4.68730420e-01 8.45361233e-01 2.54274815e-01 -1.18041468e+00 1.22855115e+00 6.04706573e+00 1.26361001e+00 -1.52282619e+00 2.30083033e-01 4.35508817e-01 -2.40159974e-01 -6.43492281e-01 7.69801065e-02 -9.23727751e-01 3.25955272e-01 8.69920731e-01 -5.31308115e-01 9.38859463e-01 1.85417271e+00 -1.33400619e-01 3.48306328e-01 -1.34015894e+00 1.19069409e+00 -4.55325454e-01 -1.82672083e+00 -3.13746065e-01 -5.73190227e-02 6.49521351e-01 4.48423564e-01 2.93039620e-01 8.04219842e-01 6.56228125e-01 -9.04274166e-01 9.32590902e-01 1.65454492e-01 4.19742942e-01 -7.93507814e-01 3.19438338e-01 3.40070665e-01 -1.44288290e+00 -7.88809806e-02 -2.07001090e-01 -8.10736604e-03 -3.25372368e-01 7.02380598e-01 -9.43971753e-01 2.44384725e-02 9.03796792e-01 3.01166654e-01 -9.23437357e-01 9.39286351e-01 -1.11689910e-01 6.32924199e-01 -3.99890870e-01 -5.72507083e-01 3.42499554e-01 1.81906104e-01 5.08039832e-01 1.21492195e+00 2.33584642e-01 -5.15525997e-01 6.98015094e-01 1.29888284e+00 -3.77128750e-01 2.95837253e-01 -5.91607997e-03 -3.09968978e-01 5.84883749e-01 1.25754666e+00 -4.59714174e-01 -3.09747040e-01 -3.44652712e-01 6.77294552e-01 7.01719999e-01 1.66243315e-01 -1.15404356e+00 -4.38243032e-01 9.79385674e-01 -4.52346168e-02 2.45021075e-01 -4.71772879e-01 -7.78217673e-01 -1.38111389e+00 3.89391154e-01 -1.36600280e+00 2.51704425e-01 -3.66638333e-01 -9.31736290e-01 7.50812948e-01 1.84860863e-02 -7.32855201e-01 -7.77108446e-02 -5.34412444e-01 -5.53507566e-01 9.13963258e-01 -1.29638731e+00 -9.52897847e-01 -2.41401240e-01 3.28528464e-01 5.27736425e-01 -4.64803249e-01 6.82413459e-01 3.76322240e-01 -7.19321966e-01 1.11823976e+00 -6.84271678e-02 1.08432084e-01 3.56365055e-01 -1.49234891e+00 7.29360342e-01 9.41180229e-01 1.57704782e-02 1.11514282e+00 6.49997652e-01 -2.18201473e-01 -2.15081692e+00 -1.09260893e+00 6.59156859e-01 -3.36875051e-01 1.06332946e+00 -3.97488236e-01 -7.93636024e-01 3.40225607e-01 -4.63715382e-02 4.84209448e-01 5.87250888e-01 6.19675338e-01 -8.32065046e-01 -5.38417280e-01 -7.67636955e-01 1.01720703e+00 9.46727812e-01 -7.36795485e-01 -1.99943379e-01 5.66146135e-01 1.20368648e+00 -8.23998988e-01 -1.05040133e+00 3.36518109e-01 4.45662826e-01 -8.36316228e-01 1.10044754e+00 -5.87584972e-01 5.08345723e-01 -3.74859333e-01 -2.51534522e-01 -1.00278211e+00 -3.72463346e-01 -9.50025797e-01 -5.18258631e-01 8.72671068e-01 3.78151804e-01 -5.07036328e-01 1.18293476e+00 9.34771061e-01 -4.23324436e-01 -1.11303377e+00 -6.22962296e-01 -6.35272384e-01 5.93685694e-02 -9.42898989e-01 1.30930746e+00 6.70863926e-01 -1.75680339e-01 1.27011582e-01 7.64747709e-02 2.22325563e-01 7.09355950e-01 5.85578918e-01 1.09701669e+00 -9.39317048e-01 -9.34670210e-01 -1.08751738e+00 -1.89259008e-01 -1.20661795e+00 3.75147313e-01 -1.20803738e+00 -8.62428993e-02 -8.60070288e-01 3.32149625e-01 -9.19340134e-01 -2.18394503e-01 6.97334707e-01 -2.28143796e-01 -3.69945079e-01 2.98379242e-01 3.16086382e-01 -6.66598976e-01 5.83993018e-01 1.08381379e+00 -3.02823871e-01 -4.23762679e-01 -8.14228959e-04 -7.78238356e-01 5.46380758e-01 5.67710340e-01 -5.89518726e-01 -4.03231978e-01 -8.47589314e-01 6.37139559e-01 -8.54769573e-02 1.81552529e-01 -8.29706252e-01 2.82262206e-01 -5.10543227e-01 -3.94502848e-01 -4.93328065e-01 -1.40590191e-01 -5.25074899e-01 -5.34034818e-02 4.59669769e-01 -4.42618370e-01 4.45123643e-01 4.69505072e-01 1.15996644e-01 -8.53778422e-02 -3.37974995e-01 7.00083077e-01 -9.34021920e-02 -9.77382064e-01 8.07791650e-01 -4.43588197e-03 2.72746533e-01 8.11152339e-01 2.74433911e-01 -3.95757228e-01 3.00920635e-01 -4.20134544e-01 3.84597301e-01 5.90703607e-01 2.12376997e-01 3.36164325e-01 -1.36151302e+00 -5.12982488e-01 2.91747212e-01 3.01645845e-01 8.72001573e-02 1.73271924e-01 7.06652939e-01 -1.27058649e+00 3.10726970e-01 6.88594207e-02 -8.43751490e-01 -1.28910828e+00 6.77207649e-01 3.46968383e-01 -9.24443841e-01 -3.40215951e-01 1.28401184e+00 2.66743638e-02 -7.34586120e-01 3.28532219e-01 -9.59101439e-01 4.89289671e-01 -4.36567903e-01 5.35409212e-01 1.61996156e-01 5.51909916e-02 1.13082416e-01 -3.94665241e-01 1.45776853e-01 -1.94944695e-01 7.55556673e-02 1.35783494e+00 4.79443073e-01 -6.79672420e-01 2.31296401e-02 1.24768519e+00 8.90370905e-02 -1.06915331e+00 -6.29270732e-01 3.76035906e-02 -5.40631771e-01 7.16219962e-01 -3.60160440e-01 -1.25549150e+00 8.71399164e-01 3.44782889e-01 -2.60098819e-02 1.15670884e+00 -2.04255506e-01 9.92764831e-01 8.90618265e-01 4.21367884e-01 -8.71603251e-01 2.86883831e-01 7.99272656e-01 6.81399345e-01 -1.06801486e+00 -7.65395984e-02 -3.08059454e-01 -4.16378111e-01 1.48935938e+00 7.96397746e-01 3.54184136e-02 7.26502120e-01 6.47094667e-01 -3.11785340e-01 -2.50886947e-01 -1.08019471e+00 1.43683508e-01 3.91020119e-01 2.54058540e-01 5.69214821e-01 2.64695764e-01 1.32985696e-01 6.92308426e-01 -5.60569584e-01 -6.14482649e-02 2.17290550e-01 8.36066246e-01 -3.65222335e-01 -1.38313687e+00 -2.97562838e-01 2.98213810e-01 -3.39083374e-01 -4.99018043e-01 5.47671199e-01 3.13995034e-01 -6.36878982e-02 3.64569962e-01 -2.27908716e-02 -5.92590153e-01 7.89563507e-02 -1.01989508e-01 4.00298595e-01 -7.00413823e-01 -8.59866440e-01 -3.38289946e-01 8.32640976e-02 -8.53314996e-01 2.63670176e-01 -3.48332405e-01 -1.26152742e+00 -7.21950829e-01 -1.04715186e-03 5.05688936e-02 8.35346043e-01 5.99589765e-01 6.56094491e-01 5.79001307e-01 6.72180831e-01 -6.57242358e-01 -1.02384162e+00 -4.29368377e-01 -8.80666301e-02 -2.58786976e-01 2.84792155e-01 -4.42381799e-01 3.44144702e-02 1.78689901e-02]	[8.472899436950684, 3.4681551456451416]
a704f006-1464-4bd8-80e5-f773e8c89cfc	forming-a-sparse-representation-for-visual	2109.14916	null	https://arxiv.org/abs/2109.14916v1	https://arxiv.org/pdf/2109.14916v1.pdf	Forming a sparse representation for visual place recognition using a neurorobotic approach	This paper introduces a novel unsupervised neural network model for visual information encoding which aims to address the problem of large-scale visual localization. Inspired by the structure of the visual cortex, the model (namely HSD) alternates layers of topologic sparse coding and pooling to build a more compact code of visual information. Intended for visual place recognition (VPR) systems that use local descriptors, the impact of its integration in a bio-inpired model for self-localization (LPMP) is evaluated. Our experimental results on the KITTI dataset show that HSD improves the runtime speed of LPMP by a factor of at least 2 and its localization accuracy by 10%. A comparison with CoHog, a state-of-the-art VPR approach, showed that our method achieves slightly better results.	['Olivier Romain', 'Guillaume Bresson', 'Nicolas Cuperlier', 'Sylvain Colomer']	2021-09-30	null	null	null	null	['visual-place-recognition']	['computer-vision']	[-2.67233878e-01 -1.09152362e-01 -2.27006465e-01 -2.44730964e-01 -4.82776970e-01 -3.17618519e-01 7.90529251e-01 3.84370536e-01 -6.26489401e-01 5.04641593e-01 2.55300641e-01 7.77138025e-02 1.51799828e-01 -6.29164219e-01 -9.23634470e-01 -6.17923975e-01 -3.76186132e-01 4.05022688e-02 6.25605345e-01 8.96522552e-02 5.53201079e-01 9.39348757e-01 -1.51630068e+00 7.09675491e-01 2.39032865e-01 1.12075019e+00 5.89769423e-01 5.60174465e-01 -1.62995085e-01 1.29422832e+00 -5.19761443e-01 2.55982012e-01 1.45414576e-01 -9.89692360e-02 -8.46570492e-01 -3.46953154e-01 6.81201816e-01 -8.92085806e-02 -8.38349700e-01 1.01690972e+00 3.11227500e-01 1.66151971e-01 6.94927394e-01 -1.12022066e+00 -8.31297338e-01 4.87999946e-01 -3.22850257e-01 4.15300548e-01 2.15421155e-01 -2.57043749e-01 9.31762874e-01 -9.98815835e-01 9.58247542e-01 9.14293468e-01 9.19305682e-01 1.52934924e-01 -1.59820557e+00 -1.87796950e-01 -5.34344949e-02 3.08019608e-01 -2.14558816e+00 -4.74770993e-01 6.51454508e-01 -3.46837610e-01 1.58620048e+00 1.06692575e-02 6.11467719e-01 7.87404418e-01 3.95653814e-01 7.05380738e-01 1.07551503e+00 -3.14629436e-01 6.61190808e-01 1.42717615e-01 3.20982821e-02 8.27333033e-01 2.57704377e-01 1.30658463e-01 -9.47676301e-01 -4.29117791e-02 9.77706373e-01 -1.69348046e-02 -5.65043315e-02 -6.91441596e-01 -1.40496504e+00 6.87067807e-01 1.34544337e+00 7.73630321e-01 -3.21232200e-01 5.71009934e-01 1.96568355e-01 -1.45587102e-02 7.27251694e-02 5.44341505e-01 -1.03118189e-01 1.31019160e-01 -1.46882176e+00 1.57489583e-01 6.67949319e-01 8.75408053e-01 1.01598442e+00 5.40084429e-02 -2.29510039e-01 8.89750540e-01 5.49279749e-01 3.31428736e-01 8.06554794e-01 -9.71949637e-01 1.64772168e-01 4.40370053e-01 -1.46416202e-01 -1.33410943e+00 -4.29075062e-01 -1.95959374e-01 -6.52697861e-01 3.65042001e-01 1.47844717e-01 4.22653049e-01 -1.24046338e+00 1.37950587e+00 -4.62820590e-01 1.41480580e-01 8.70340392e-02 8.96504164e-01 9.10953879e-01 7.45021045e-01 4.82247584e-03 4.39114690e-01 1.06482041e+00 -1.02531779e+00 -3.76470685e-01 -3.44107836e-01 4.34065402e-01 -3.55174631e-01 3.91246825e-01 5.01321219e-02 -7.58736610e-01 -6.40201449e-01 -1.40092993e+00 -4.23475087e-01 -9.07086968e-01 2.01839119e-01 7.56738186e-01 3.51936281e-01 -1.74269176e+00 6.17831111e-01 -8.50195408e-01 -8.21117461e-01 6.46778822e-01 4.17032599e-01 -8.45968544e-01 5.20143732e-02 -6.16927505e-01 9.78565812e-01 5.47240913e-01 2.90022418e-02 -1.14500272e+00 -4.81109142e-01 -1.17736864e+00 2.41236508e-01 -2.78970063e-01 -2.77107298e-01 7.19772875e-01 -7.20610499e-01 -1.32664621e+00 9.71318185e-01 -4.89129186e-01 -1.10105979e+00 1.24660227e-02 1.04734987e-01 -3.72852534e-02 5.12746990e-01 1.90585777e-01 1.21161175e+00 5.67111194e-01 -1.20457256e+00 -3.38952512e-01 -4.01134491e-01 -9.63457748e-02 -5.80210388e-02 -1.85530502e-02 -1.31049216e-01 -4.33416009e-01 -5.09990811e-01 4.00369704e-01 -6.85384691e-01 -3.25254023e-01 1.13066688e-01 -1.08061753e-01 1.04940623e-01 6.39332831e-01 -4.79771733e-01 7.90558219e-01 -2.35712171e+00 -8.60157609e-02 3.90572935e-01 4.59207326e-01 1.36152864e-01 -2.71111846e-01 4.85067338e-01 -1.89833000e-01 -1.45691969e-02 -2.27092177e-01 -4.49594051e-01 -2.03232870e-01 3.44802439e-01 -4.30922508e-01 7.73022294e-01 1.46027207e-01 1.08598053e+00 -6.88527822e-01 -4.84671980e-01 2.99004674e-01 6.12587333e-01 -4.57939327e-01 4.83699041e-05 4.71881703e-02 3.50316241e-02 1.14743132e-02 7.63048351e-01 5.29355109e-01 -3.94374788e-01 -2.58892849e-02 -1.93160847e-01 -5.06382763e-01 1.94924504e-01 -8.79615426e-01 2.09108400e+00 -3.00149858e-01 1.31089091e+00 -2.46881083e-01 -8.96455765e-01 1.15787923e+00 2.43678037e-02 3.28996330e-01 -1.06916523e+00 1.11741714e-01 1.06883481e-01 -1.63454756e-01 3.66669334e-02 6.57494128e-01 3.80956829e-01 1.37031645e-01 -8.90716538e-02 7.82792568e-01 4.03868228e-01 3.60976905e-02 3.65380764e-01 1.47616231e+00 8.69424343e-02 3.86989564e-01 -6.25591218e-01 4.36337024e-01 1.09933503e-01 2.81074584e-01 9.40756917e-01 -4.75550324e-01 1.08111191e+00 3.41127276e-01 -6.00352526e-01 -1.00337088e+00 -1.30169690e+00 -2.73747265e-01 8.22516620e-01 2.52598524e-01 -7.03879118e-01 -6.31854832e-01 -3.50139529e-01 1.05022937e-01 3.66781771e-01 -8.68970990e-01 1.43286973e-01 -5.04479766e-01 -5.00190079e-01 7.61985481e-01 7.28860199e-01 7.57116556e-01 -1.21757853e+00 -8.54375362e-01 1.51957095e-01 2.52942033e-02 -1.14974189e+00 9.77942795e-02 7.60063469e-01 -6.24536455e-01 -6.85412705e-01 -9.30316150e-01 -1.14264369e+00 7.77285099e-01 2.23187804e-01 8.50321829e-01 -1.55817285e-01 -4.43924934e-01 2.94760913e-01 -3.10784966e-01 5.61911799e-02 5.70398159e-02 2.17957392e-01 -1.11518510e-01 -8.54823331e-04 3.73951733e-01 -6.61141932e-01 -5.86293995e-01 -6.25289530e-02 -7.47509539e-01 -3.17734271e-01 4.67956543e-01 7.65486717e-01 7.64390945e-01 -3.97587240e-01 8.76714066e-02 -4.21012133e-01 2.85580397e-01 -4.18655306e-01 -8.49445701e-01 9.93737802e-02 -3.16516697e-01 3.43594611e-01 5.46723723e-01 -2.57349640e-01 -6.02668524e-01 4.24209416e-01 -2.54939079e-01 -6.20531917e-01 -3.70873570e-01 2.95938909e-01 1.90066367e-01 -9.47283506e-01 8.30987990e-01 7.05181122e-01 -2.22836182e-01 -3.87271315e-01 5.97102225e-01 3.54738712e-01 6.18125618e-01 -1.95345968e-01 4.89103347e-01 5.72939217e-01 8.98304805e-02 -1.12998319e+00 -1.77256256e-01 -7.29009748e-01 -7.25882947e-01 3.87636460e-02 9.06787634e-01 -1.03379583e+00 -5.32017469e-01 3.12025845e-01 -1.29382098e+00 -4.50703412e-01 -2.66292483e-01 3.50158066e-01 -9.20843780e-01 6.09807484e-02 -5.87344766e-01 -4.57403630e-01 4.31354940e-02 -9.45909679e-01 9.60818529e-01 1.88475341e-01 -4.07964224e-03 -8.41326654e-01 6.03461444e-01 -2.08551943e-01 6.26371622e-01 -1.82413869e-02 4.78568971e-01 -7.99379110e-01 -9.58810508e-01 -2.71819234e-02 -5.33535838e-01 1.70784891e-01 -3.42306644e-01 -4.11526471e-01 -1.27525151e+00 -1.71995282e-01 -2.63272941e-01 -3.67649913e-01 1.41399658e+00 2.88997501e-01 1.16842687e+00 -2.30341092e-01 -5.34849107e-01 9.03700113e-01 2.00422359e+00 1.56325877e-01 1.07063746e+00 4.84991342e-01 7.40042090e-01 9.35411900e-02 5.99158145e-02 2.20220536e-01 3.93832803e-01 6.43427551e-01 5.48997879e-01 -1.81147307e-02 -5.41067600e-01 -5.67715466e-01 1.20783314e-01 6.09947085e-01 -9.81167555e-02 5.94927110e-02 -1.03360474e+00 1.11600149e+00 -1.91330445e+00 -9.39387262e-01 2.98403800e-01 2.07890153e+00 4.43688393e-01 -3.09912413e-01 -2.31931850e-01 -6.68939874e-02 3.79319757e-01 5.73023856e-01 -4.51353639e-02 -5.63646734e-01 -1.59762323e-01 2.87616700e-01 9.88891423e-01 4.07974243e-01 -1.23671627e+00 1.15166688e+00 7.43496799e+00 7.68674791e-01 -1.29346085e+00 1.98565617e-01 3.50416809e-01 2.20188424e-01 2.61781871e-01 -1.04284525e-01 -7.49260128e-01 3.39502156e-01 1.17669547e+00 2.48073101e-01 7.00509906e-01 1.07604277e+00 -3.61094087e-01 -2.69235671e-01 -1.07597578e+00 1.34996367e+00 4.82468307e-01 -1.78802383e+00 8.14830661e-02 1.41136825e-01 6.82962775e-01 7.50436962e-01 9.05858278e-02 1.62176743e-01 2.43134331e-02 -1.14513195e+00 8.62319171e-01 4.93800610e-01 3.66823673e-01 -7.31364012e-01 7.20768332e-01 4.19856980e-02 -1.38382936e+00 -1.99101478e-01 -8.58995795e-01 -5.31220511e-02 -2.36353159e-01 1.20070189e-01 -8.64170969e-01 1.45387962e-01 8.03704917e-01 7.74410844e-01 -1.09022188e+00 1.60155952e+00 -2.26366118e-01 2.05056220e-01 -2.95228332e-01 -1.46940947e-01 5.37645042e-01 4.56329435e-01 4.27847594e-01 1.39824975e+00 9.86424237e-02 -2.59841293e-01 -5.58620505e-02 1.11128271e+00 -1.65821433e-01 1.68032944e-01 -1.02046859e+00 -2.03814302e-02 4.00517315e-01 1.18504429e+00 -9.48457599e-01 -3.54262292e-01 -1.49631307e-01 1.39465809e+00 8.45182180e-01 3.40985149e-01 -6.06153309e-01 -7.69166172e-01 4.46400017e-01 1.58575594e-01 9.62362230e-01 -4.13418949e-01 -4.16187704e-01 -1.11783195e+00 -1.99327141e-01 -2.96946168e-01 -1.92111313e-01 -9.52048421e-01 -9.57066298e-01 8.31894875e-01 -2.57027388e-01 -9.34546471e-01 -1.37893274e-01 -8.78378987e-01 -2.63601273e-01 7.65117288e-01 -1.50543535e+00 -1.16554940e+00 -1.44856811e-01 7.10821807e-01 9.18733105e-02 -2.68728971e-01 1.07275641e+00 9.86359939e-02 7.35254437e-02 5.25229275e-01 3.44752431e-01 5.23624420e-01 3.70379150e-01 -1.26465166e+00 5.17376542e-01 8.80821347e-01 9.51342344e-01 8.32934916e-01 3.91962081e-01 -3.53546649e-01 -1.22621679e+00 -1.14689505e+00 1.22862351e+00 -4.51704949e-01 5.87395668e-01 -7.15660930e-01 -7.88395822e-01 6.85971081e-01 3.04661423e-01 4.68569249e-01 4.11555499e-01 -1.06653105e-03 -8.81674707e-01 -3.37470680e-01 -1.39701867e+00 5.24737060e-01 9.37127590e-01 -1.01948774e+00 -9.18094218e-01 4.21084054e-02 6.52258515e-01 -1.24583550e-01 -6.95419490e-01 -2.99590025e-02 4.82509792e-01 -1.00211847e+00 1.15118086e+00 -1.16012245e-01 1.95919156e-01 -5.74158967e-01 -5.70713103e-01 -1.07964575e+00 -7.43421316e-01 -8.59393775e-02 -4.89873253e-02 8.32998395e-01 2.06394166e-01 -5.74172437e-01 6.40355051e-01 5.21899574e-02 1.22926086e-01 -3.78302157e-01 -1.09983122e+00 -7.29169428e-01 -3.14859897e-01 -4.41925526e-01 1.45892240e-02 7.11980641e-01 1.96293786e-01 9.47836488e-02 -2.63588931e-02 4.00553584e-01 6.79779291e-01 -3.77487834e-03 2.11515009e-01 -1.00826597e+00 -6.72323033e-02 -1.64063931e-01 -1.41242504e+00 -1.01802444e+00 1.42900214e-01 -1.10518932e+00 4.62230176e-01 -1.72795260e+00 1.20106332e-01 -8.73263106e-02 -7.03351259e-01 8.13277423e-01 7.62854576e-01 8.72038543e-01 6.19931936e-01 2.36418366e-01 -1.00912952e+00 3.86964083e-01 3.97608429e-01 -1.99539557e-01 -1.06914870e-01 -7.95763671e-01 -3.14775348e-01 6.70018315e-01 6.88603342e-01 -6.31960213e-01 -1.38219893e-01 -4.71390039e-01 -1.22437820e-01 -3.80788624e-01 7.92037189e-01 -1.67328274e+00 6.65077925e-01 5.33858240e-01 8.35871279e-01 -7.20731020e-01 3.73588234e-01 -8.49896371e-01 -2.73131847e-01 5.17682910e-01 -4.31483150e-01 6.04636632e-02 3.56637657e-01 4.53822702e-01 -4.89193857e-01 -1.44598588e-01 7.74551928e-01 -2.91945457e-01 -1.24397230e+00 7.52522126e-02 -6.83763087e-01 -2.98571765e-01 8.96472692e-01 -2.05842108e-01 -5.07825613e-01 7.70288035e-02 -7.45318234e-01 -2.23736659e-01 5.64159930e-01 3.92877370e-01 8.97444367e-01 -1.40434933e+00 -2.46634468e-01 4.65099096e-01 3.92367184e-01 -4.81229722e-01 -7.45400228e-03 5.22806644e-01 -9.72483039e-01 1.03086567e+00 -5.58104277e-01 -6.66553915e-01 -8.89002085e-01 7.40455091e-01 3.43079001e-01 -8.82462263e-02 -7.87834644e-01 7.52680302e-01 1.56252652e-01 -9.55991670e-02 3.85927081e-01 -5.15325725e-01 -2.95826793e-01 -9.44102854e-02 8.75779510e-01 2.41262481e-01 1.31539196e-01 -9.07082260e-01 -8.36251140e-01 4.47507471e-01 -6.04190752e-02 -2.41191775e-01 1.35956776e+00 6.64886534e-02 -3.89578879e-01 5.91661930e-01 1.52068138e+00 -1.21036917e-01 -1.13946342e+00 -1.12406015e-01 1.06973581e-01 -2.77186543e-01 2.74529546e-01 -8.37018311e-01 -8.66553605e-01 9.06304717e-01 8.85410428e-01 -9.53130722e-02 7.88809478e-01 1.88395694e-01 2.24869862e-01 7.96399534e-01 8.55691314e-01 -7.63546348e-01 -5.89320101e-02 7.25603819e-01 1.01461279e+00 -9.96440828e-01 -2.38736570e-01 1.26384705e-01 -3.37197632e-01 9.22792614e-01 2.92107135e-01 -8.69050980e-01 7.16934979e-01 3.24399084e-01 -5.91557100e-02 -6.99128211e-02 -4.33336377e-01 -3.36592913e-01 2.19090819e-01 9.13475871e-01 8.13813880e-02 -1.32890344e-01 6.96973056e-02 2.36397162e-01 8.37858692e-02 1.70247138e-01 1.41893297e-01 1.00864697e+00 -6.13649607e-01 -6.82440281e-01 -2.82688290e-01 -9.07328948e-02 -2.60890037e-01 -3.43372524e-01 -3.78118336e-01 6.09479904e-01 3.10921848e-01 4.76742834e-01 3.48065704e-01 -3.57485801e-01 -6.59320224e-03 4.02559340e-03 5.32364070e-01 -4.93930757e-01 -6.15931273e-01 -2.72885948e-01 -3.07409346e-01 -1.17638206e+00 -4.53202099e-01 -2.21041456e-01 -1.33590138e+00 3.39877568e-02 3.84125680e-01 -1.60428077e-01 9.02364492e-01 6.82174444e-01 5.81720829e-01 5.63511431e-01 3.08031768e-01 -1.14324903e+00 1.19671999e-02 -7.44656384e-01 -7.74549663e-01 -9.96785089e-02 5.58213592e-01 -5.39512157e-01 -1.85749784e-01 1.47524076e-02]	[7.730695724487305, -1.7970095872879028]
ebdf2c1d-05d5-407d-83a9-3b2ebc58a3d6	snipper-a-spatiotemporal-transformer-for	2207.04320	null	https://arxiv.org/abs/2207.04320v2	https://arxiv.org/pdf/2207.04320v2.pdf	Snipper: A Spatiotemporal Transformer for Simultaneous Multi-Person 3D Pose Estimation Tracking and Forecasting on a Video Snippet	Multi-person pose understanding from RGB videos includes three complex tasks: pose estimation, tracking and motion forecasting. Among these three tasks, pose estimation and tracking are correlated, and tracking is crucial to motion forecasting. Most existing works either focus on a single task or employ cascaded methods to solve each individual task separately. In this paper, we propose Snipper, a framework to perform multi-person 3D pose estimation, tracking and motion forecasting simultaneously in a single inference. Specifically, we first propose a deformable attention mechanism to aggregate spatiotemporal information from video snippets. Building upon this deformable attention, a visual transformer is learned to encode the spatiotemporal features from multi-frame images and to decode informative pose features to update multi-person pose queries. Last, these queries are regressed to predict multi-person pose trajectories and future motions in one forward pass. In the experiments, we show the effectiveness of Snipper on three challenging public datasets where a generic model rivals specialized state-of-art baselines for pose estimation, tracking, and forecasting. Code is available at https://github.com/JimmyZou/Snipper	['Minh Vo', 'Li Cheng', 'Lingni Ma', 'Chao Li', 'Yuanlu Xu', 'Shihao Zou']	2022-07-09	null	null	null	null	['3d-pose-estimation']	['computer-vision']	[-1.90516904e-01 -4.59614307e-01 -1.98532298e-01 -4.62345511e-01 -9.54220414e-01 -6.45804942e-01 4.57638174e-01 -5.14243841e-01 -4.09150571e-01 6.24629378e-01 6.05008960e-01 4.23402697e-01 1.80866659e-01 -2.74449676e-01 -8.45784843e-01 -5.11449516e-01 2.47506738e-01 5.77805340e-01 1.65216878e-01 7.37446249e-02 -1.57775432e-01 4.82452542e-01 -1.54672825e+00 2.37556309e-01 5.07650554e-01 1.07564330e+00 -4.61609289e-03 9.88262653e-01 3.41917515e-01 6.49965227e-01 -3.96008909e-01 -6.41152442e-01 8.15732703e-02 5.99357970e-02 -5.98865509e-01 1.52127534e-01 1.02217507e+00 -7.14128137e-01 -7.56233454e-01 7.55200446e-01 7.23385930e-01 3.17129046e-01 3.85925412e-01 -1.39996803e+00 -5.78605771e-01 4.24216799e-02 -4.24599916e-01 2.40422860e-01 9.33771014e-01 5.81704855e-01 7.25074768e-01 -1.00605941e+00 5.08733690e-01 1.62719071e+00 7.82155275e-01 8.56636167e-01 -7.62917876e-01 -6.20274425e-01 6.85100615e-01 2.03308880e-01 -1.43191493e+00 -6.51446640e-01 5.72785020e-01 -6.53494895e-01 6.91500366e-01 3.76047552e-01 1.05735648e+00 1.43363249e+00 1.14885569e-02 1.16156316e+00 3.62484962e-01 1.74339786e-01 -3.71588260e-01 -2.93462068e-01 -7.01219216e-02 8.05476069e-01 1.31529748e-01 1.36123911e-01 -8.15569282e-01 1.09188609e-01 7.24416077e-01 3.27072084e-01 -2.82889903e-01 -1.91132531e-01 -1.28339171e+00 5.40165901e-01 4.85556394e-01 -1.06320389e-01 -2.92601049e-01 6.89386249e-01 1.78222105e-01 -2.28338599e-01 6.81596041e-01 -1.10891275e-01 -4.26587999e-01 -1.69234678e-01 -7.90214539e-01 7.71251738e-01 4.28649127e-01 9.91859257e-01 3.66055459e-01 -2.26423368e-01 -8.09899807e-01 2.64236897e-01 8.04944634e-01 9.01161969e-01 8.10961798e-02 -1.05617607e+00 6.15523636e-01 4.26055044e-01 6.40480936e-01 -1.05956268e+00 -4.68989164e-01 -2.53834188e-01 -3.49166781e-01 -4.46363509e-01 5.59740543e-01 -3.20009381e-01 -9.55777049e-01 1.77977777e+00 7.30738997e-01 4.97136921e-01 -5.69785655e-01 1.30555570e+00 1.05568683e+00 5.75137615e-01 3.66401017e-01 1.75750434e-01 1.41131389e+00 -1.31936347e+00 -6.04683578e-01 -2.74614096e-01 2.58564144e-01 -6.78854346e-01 5.42107165e-01 1.37608601e-02 -1.10247195e+00 -7.90552557e-01 -4.67843711e-01 -4.87179130e-01 -1.15360059e-01 6.04027450e-01 5.12531877e-01 5.20298243e-01 -9.00115907e-01 3.23234618e-01 -1.12377465e+00 -2.63469040e-01 4.06065404e-01 5.14277160e-01 -1.34034112e-01 -5.63402623e-02 -1.06068683e+00 6.94766283e-01 3.53518478e-03 6.18909419e-01 -8.98806095e-01 -5.71968138e-01 -9.02236164e-01 -2.43383542e-01 3.86930555e-01 -1.31893682e+00 1.28727877e+00 -4.49663192e-01 -1.37006032e+00 7.89728403e-01 -6.40563190e-01 -2.58108079e-01 8.96575749e-01 -9.96879041e-01 -2.55430818e-01 -1.13596901e-01 2.13779345e-01 8.75884771e-01 9.52848136e-01 -9.62275863e-01 -7.35387385e-01 -5.67780912e-01 -8.05089250e-02 3.85164291e-01 -1.62322551e-01 1.01690829e-01 -1.25485694e+00 -6.55048311e-01 -1.20699689e-01 -1.20642245e+00 -1.61881492e-01 3.00608397e-01 -5.43210566e-01 -5.06575227e-01 6.78164184e-01 -8.55686188e-01 1.20820701e+00 -1.71322727e+00 6.68666005e-01 -2.04557493e-01 3.07047695e-01 1.35344863e-01 -4.37922552e-02 1.88953783e-02 3.20098758e-01 -3.64319205e-01 2.71444619e-01 -8.89978468e-01 2.89538592e-01 -1.84034914e-01 -1.56970337e-01 6.88848794e-01 1.31699458e-01 1.44049358e+00 -8.22401106e-01 -5.30497909e-01 4.63201493e-01 8.60503852e-01 -6.87602699e-01 3.43301415e-01 -4.16952997e-01 1.07836068e+00 -5.88279426e-01 9.23608661e-01 4.92610037e-01 -4.77994233e-01 -1.39085650e-01 -4.59635943e-01 -1.79707065e-01 -1.14986360e-01 -1.16106403e+00 2.03214192e+00 -8.10497925e-02 5.12301683e-01 -2.47710407e-01 -4.94745463e-01 4.71905023e-01 2.15232059e-01 8.57370436e-01 -2.18326300e-01 2.60544062e-01 -1.64386138e-01 -7.06539214e-01 -6.61837697e-01 6.45349145e-01 3.86338115e-01 -2.28177130e-01 1.27034172e-01 4.33743596e-02 4.50719237e-01 2.54030656e-02 -2.38374937e-02 6.76746547e-01 7.90427685e-01 -3.50435376e-02 4.09340650e-01 6.61637664e-01 -2.10301936e-01 7.45577216e-01 5.17861962e-01 -5.29002130e-01 6.18130267e-01 -1.27624765e-01 -7.84416080e-01 -8.03719580e-01 -1.17999566e+00 2.45739773e-01 1.42258608e+00 4.13448066e-01 -4.45364624e-01 -5.17956257e-01 -6.31786942e-01 3.25858533e-01 -1.70325469e-02 -5.88811636e-01 1.41268268e-01 -8.06563318e-01 -5.40488422e-01 3.63919526e-01 7.30124891e-01 4.09298807e-01 -8.61459851e-01 -5.19861162e-01 1.27360541e-02 -5.64576387e-01 -1.33674955e+00 -1.15584552e+00 -7.60329902e-01 -4.33912784e-01 -1.10455561e+00 -1.09917617e+00 -5.68246603e-01 4.64788049e-01 2.61229843e-01 1.09266245e+00 9.52714011e-02 -3.32985997e-01 7.94826269e-01 -5.91921732e-02 -2.91926801e-01 3.64170313e-01 1.20904036e-01 2.74876684e-01 3.30242366e-01 3.99125218e-01 -1.26487032e-01 -1.08480656e+00 3.34857345e-01 -2.11976349e-01 5.72137423e-02 4.03450638e-01 4.15365338e-01 6.43271625e-01 -6.53807819e-01 1.53302431e-01 -4.23357636e-01 1.19439930e-01 -3.87548119e-01 -6.32302701e-01 3.63167584e-01 2.88273674e-02 -1.53591424e-01 3.61059280e-03 -5.00178397e-01 -1.04473913e+00 7.80736089e-01 -2.12606356e-01 -8.46338689e-01 -2.32681379e-01 -8.22031572e-02 -3.50365311e-01 -7.63244927e-02 1.93277702e-01 1.91348150e-01 -2.90803134e-01 -5.90187550e-01 5.08139670e-01 4.34145033e-01 8.42829227e-01 -4.83287930e-01 9.31237280e-01 6.00688517e-01 -2.89346099e-01 -5.03982484e-01 -1.16718793e+00 -6.24004424e-01 -9.42842662e-01 -7.84670293e-01 1.35181642e+00 -1.46700919e+00 -1.32781184e+00 6.84475899e-01 -1.24993122e+00 -1.61051556e-01 2.49428913e-01 5.18224776e-01 -4.68010545e-01 2.54877537e-01 -6.40097320e-01 -1.01761997e+00 -3.50048184e-01 -1.09898901e+00 1.88986361e+00 3.79304767e-01 -2.21119851e-01 -9.53484297e-01 8.93471837e-02 7.52059877e-01 7.40087926e-02 4.89919424e-01 -2.75607824e-01 -2.43944488e-02 -1.03984189e+00 -3.84720176e-01 -9.28190127e-02 -3.50459546e-01 -5.94143234e-02 -1.24854051e-01 -9.43981051e-01 -5.30292034e-01 -4.28960592e-01 -2.96673715e-01 1.06938410e+00 7.89143860e-01 1.30598772e+00 -3.07810575e-01 -6.87361956e-01 1.10129976e+00 9.48339581e-01 -1.11188665e-01 2.93720365e-01 6.61153272e-02 1.25460863e+00 3.51152062e-01 7.00158834e-01 6.95346773e-01 9.77307975e-01 1.18764770e+00 3.05334002e-01 1.69973567e-01 -1.32170439e-01 -4.05297101e-01 5.46011388e-01 4.55390424e-01 -4.71378565e-01 -2.03364223e-01 -6.09227657e-01 2.43506119e-01 -2.13278913e+00 -1.24344730e+00 4.42194603e-02 1.96132624e+00 4.42552716e-01 -1.91641510e-01 6.59923494e-01 -5.09230077e-01 9.40950155e-01 3.26245368e-01 -8.54803741e-01 6.14524424e-01 1.64579093e-01 -4.19104397e-01 3.92195106e-01 5.70682764e-01 -1.67024124e+00 1.17966628e+00 5.32321548e+00 2.71868855e-01 -8.82178724e-01 9.99445021e-02 3.84349048e-01 -7.00540960e-01 -1.48551255e-01 -3.37418348e-01 -1.38829195e+00 7.02537298e-01 5.95851362e-01 7.71913826e-02 3.29620212e-01 5.86770535e-01 2.68740714e-01 1.52107120e-01 -1.09369576e+00 1.43462849e+00 1.63331166e-01 -1.19071543e+00 -3.74263562e-02 -6.94771782e-02 5.80843449e-01 -1.11238226e-01 2.73153603e-01 2.41014287e-01 2.19082624e-01 -9.49800253e-01 1.03152692e+00 1.14652121e+00 5.30073047e-01 -5.98818958e-01 3.84799629e-01 1.67204812e-01 -1.81715238e+00 -2.11845443e-01 6.92451671e-02 7.83129409e-02 7.00278699e-01 6.19316287e-02 -2.13469550e-01 5.08858144e-01 8.91869068e-01 1.06293976e+00 -6.03593946e-01 1.21984088e+00 -8.84461254e-02 -3.52843329e-02 -2.35788748e-01 1.23836901e-02 -2.59184428e-02 1.03359737e-01 6.28387690e-01 1.11432052e+00 4.14082617e-01 9.96882617e-02 5.29314160e-01 6.98886096e-01 -2.18974408e-02 -3.94505441e-01 -1.96939692e-01 1.62062228e-01 5.38343072e-01 1.15502918e+00 -3.25220525e-01 -3.34696084e-01 -5.74602604e-01 1.27385879e+00 3.39178473e-01 3.50156039e-01 -1.37670887e+00 2.95059860e-01 1.02233279e+00 4.94780056e-02 4.81846631e-01 -4.01646018e-01 1.81521803e-01 -1.58059490e+00 1.75369009e-01 -4.44085389e-01 5.56593060e-01 -7.09281027e-01 -1.21995330e+00 4.15421337e-01 -4.95544076e-02 -1.41010737e+00 -3.42703670e-01 -4.88675207e-01 -3.23899299e-01 7.30293155e-01 -1.21693945e+00 -1.56789851e+00 -7.17522502e-01 9.59488988e-01 6.00800812e-01 4.46699038e-02 4.35234666e-01 6.33404672e-01 -8.88539076e-01 6.81983113e-01 -4.32499528e-01 4.82273936e-01 8.36735308e-01 -1.03901958e+00 7.88216054e-01 8.44513059e-01 1.12676896e-01 5.54254055e-01 5.69666326e-01 -8.98446143e-01 -1.68091214e+00 -1.43895864e+00 7.25971937e-01 -1.13563728e+00 3.18248838e-01 -4.31283414e-01 -3.93780798e-01 1.01149035e+00 -2.66326129e-01 3.74680847e-01 5.05056083e-01 -8.70214105e-02 -9.45398062e-02 7.62737691e-02 -6.95692539e-01 5.44591606e-01 1.53613698e+00 -3.69983077e-01 -1.50576860e-01 4.68569547e-01 7.22828507e-01 -9.73765075e-01 -9.03810859e-01 1.98659450e-01 1.00776339e+00 -6.80860043e-01 1.40582764e+00 -6.82052076e-01 2.77104415e-02 -4.77594912e-01 -3.18158306e-02 -7.90448844e-01 -3.77620935e-01 -6.94665194e-01 -7.70517290e-01 9.60568428e-01 4.22506668e-02 -2.54584014e-01 1.11308336e+00 9.18824017e-01 -1.88241415e-02 -6.92509651e-01 -8.31236780e-01 -4.87990618e-01 -4.35455978e-01 -2.70478964e-01 6.47708714e-01 4.16753918e-01 -6.61409974e-01 2.86392272e-01 -1.08284283e+00 3.95218939e-01 9.54555333e-01 2.54838586e-01 1.10438669e+00 -1.15397799e+00 -3.28956485e-01 -1.22331977e-01 -3.64400566e-01 -1.69356215e+00 1.72369763e-01 -6.05136335e-01 1.21581271e-01 -1.49606299e+00 2.92025089e-01 1.38457343e-02 3.98738831e-02 2.53720850e-01 -5.95959306e-01 3.38952422e-01 6.24623954e-01 3.23710740e-01 -1.24065125e+00 7.27711201e-01 1.33737934e+00 -2.18697309e-01 -3.09930056e-01 4.71319884e-01 -4.65659499e-01 6.90529168e-01 3.25830966e-01 -2.30385050e-01 -9.16858688e-02 -7.72590756e-01 -2.39176191e-02 3.00654322e-01 9.21430767e-01 -1.26881361e+00 6.10559821e-01 -1.18183360e-01 1.00768626e+00 -1.02726328e+00 9.23080444e-01 -5.32981098e-01 2.96716958e-01 5.07503450e-01 -2.10788101e-01 2.88281649e-01 1.34027917e-02 8.20731163e-01 1.38530731e-01 4.99351829e-01 3.21353465e-01 -1.60023153e-01 -1.10494876e+00 1.20267236e+00 1.55550584e-01 4.77151461e-02 1.01920497e+00 -7.86565095e-02 -1.14275262e-01 -4.78985459e-01 -1.10460663e+00 6.16179585e-01 2.09281623e-01 8.64885986e-01 7.56676495e-01 -1.71835959e+00 -6.93907320e-01 -9.86289829e-02 -3.01136356e-02 8.65190774e-02 6.57095850e-01 9.23360407e-01 -2.26613894e-01 6.40333652e-01 -9.88618210e-02 -8.65701020e-01 -1.46094167e+00 4.79155958e-01 4.79917943e-01 -2.16657314e-02 -6.00610912e-01 1.25536621e+00 9.47686136e-02 -2.70242989e-01 5.62944770e-01 -1.66858450e-01 -2.04715043e-01 7.38012493e-02 6.81000948e-01 3.62197816e-01 -4.28311825e-01 -1.10291564e+00 -6.91954494e-01 1.08733153e+00 1.58678085e-01 1.08559970e-02 1.15967572e+00 -5.82419932e-01 3.53201330e-01 2.59589255e-01 1.07285166e+00 -1.98467091e-01 -1.91559613e+00 -2.20426515e-01 -2.33845189e-01 -7.02777684e-01 -3.37738782e-01 -4.02411968e-01 -1.16084123e+00 6.45955563e-01 6.18560255e-01 -3.74544919e-01 9.35843647e-01 2.10978583e-01 1.03255725e+00 2.64519274e-01 2.69730419e-01 -9.72614467e-01 2.75923312e-01 4.30070519e-01 9.47906077e-01 -1.36273110e+00 9.57071036e-02 -4.43035364e-01 -7.14716494e-01 8.41383219e-01 9.14380431e-01 4.77166809e-02 4.95885432e-01 4.88755591e-02 -7.34478831e-02 -9.23779383e-02 -7.18540132e-01 -5.78636050e-01 8.47477138e-01 5.01727521e-01 4.74692136e-01 8.10978934e-03 2.59242415e-01 6.56576872e-01 -2.20500797e-01 2.47861892e-01 -2.96912551e-01 5.74981570e-01 -3.18959892e-01 -8.66808653e-01 -6.21928871e-01 1.28800049e-01 -3.34560752e-01 2.77492404e-01 -3.07928234e-01 5.24321079e-01 3.06918025e-01 7.48942494e-01 7.39835799e-02 -6.04947805e-01 2.94572473e-01 -9.48670506e-02 6.98093116e-01 -3.57164055e-01 -4.68107492e-01 1.25415191e-01 2.40482390e-02 -1.02125359e+00 -5.86834669e-01 -9.62949216e-01 -8.89201641e-01 -4.36182618e-01 1.26569360e-01 -2.29542777e-01 2.30341092e-01 1.04738426e+00 3.44671696e-01 6.14823461e-01 2.01848269e-01 -1.47363925e+00 -3.73382449e-01 -7.33730793e-01 -6.18452206e-02 5.18700838e-01 5.97739398e-01 -1.04190087e+00 1.98864594e-01 1.99956477e-01]	[7.037853240966797, -0.8500020503997803]
ca630eb9-29ad-467e-bb10-9aa8f89fa1c8	dive-into-the-resolution-augmentations-and	2302.05621	null	https://arxiv.org/abs/2302.05621v1	https://arxiv.org/pdf/2302.05621v1.pdf	Dive into the Resolution Augmentations and Metrics in Low Resolution Face Recognition: A Plain yet Effective New Baseline	Although deep learning has significantly improved Face Recognition (FR), dramatic performance deterioration may occur when processing Low Resolution (LR) faces. To alleviate this, approaches based on unified feature space are proposed with the sacrifice under High Resolution (HR) circumstances. To deal with the huge domain gap between HR and LR domains and achieve the best on both domains, we first took a closer look at the impacts of several resolution augmentations and then analyzed the difficulty of LR samples from the perspective of the model gradient produced by different resolution samples. Besides, we also find that the introduction of some resolutions could help the learning of lower resolutions. Based on these, we divide the LR samples into three difficulties according to the resolution and propose a more effective Multi-Resolution Augmentation. Then, due to the rapidly increasing domain gap as the resolution decreases, we carefully design a novel and effective metric loss based on a LogExp distance function that provides decent gradients to prevent oscillation near the convergence point or tolerance to small distance errors; it could also dynamically adjust the penalty for errors in different dimensions, allowing for more optimization of dimensions with large errors. Combining these two insights, our model could learn more general knowledge in a wide resolution range of images and balanced results can be achieved by our extremely simple framework. Moreover, the augmentations and metrics are the cornerstones of LRFR, so our method could be considered a new baseline for the LRFR task. Experiments on the LRFR datasets: SCface, XQLFW, and large-scale LRFR dataset: TinyFace demonstrate the effectiveness of our methods, while the degradation on HRFR datasets is significantly reduced.	['Dongchao Wen', 'Hongzhi Shi', 'Xingchen Cui', 'Yingjie Zhang', 'Weihong Deng', 'Wenqi Xu', 'Yichen Lu', 'Xu Ling']	2023-02-11	null	null	null	null	['general-knowledge']	['miscellaneous']	[-8.28758776e-02 -6.88399002e-02 -1.30170763e-01 -4.69520062e-01 -7.94336081e-01 -2.37953141e-01 2.84078926e-01 -4.43704545e-01 -1.47672176e-01 6.33136630e-01 2.27535993e-01 3.10981840e-01 -3.61095876e-01 -9.34963584e-01 -5.92199624e-01 -7.80401170e-01 1.27672791e-01 -3.13610844e-02 2.15151444e-01 -4.12232548e-01 9.53944772e-02 7.10933387e-01 -1.61522913e+00 1.99198052e-01 1.06872058e+00 1.26396906e+00 1.68801382e-01 -1.39227435e-02 -1.84610695e-01 3.64190131e-01 -5.79706311e-01 -5.10693073e-01 6.32342994e-01 -1.74131244e-01 -4.20862257e-01 -1.93714648e-02 6.54939711e-01 -4.15305108e-01 -4.47022170e-01 1.11446106e+00 6.27038121e-01 5.08125462e-02 4.87957358e-01 -9.28289473e-01 -9.27267373e-01 3.71598095e-01 -1.10141444e+00 1.54295415e-01 3.45909357e-01 5.58516122e-02 7.29026914e-01 -1.18243003e+00 3.84122640e-01 1.66167819e+00 7.92157888e-01 7.16792107e-01 -1.10784400e+00 -9.46063519e-01 3.78784001e-01 1.67257860e-01 -1.53890455e+00 -5.16554832e-01 7.92430043e-01 -2.23534003e-01 3.47442210e-01 1.21690169e-01 8.55977908e-02 1.26081705e+00 -1.37430519e-01 2.94799209e-01 1.16404033e+00 -1.14129484e-01 -4.43131700e-02 1.93651065e-01 8.56677722e-03 6.09800100e-01 2.33369052e-01 5.76982051e-02 -3.19373995e-01 2.35885978e-02 1.12978816e+00 3.96258850e-03 -6.19282782e-01 -2.69877493e-01 -8.08562100e-01 8.09322059e-01 6.30993485e-01 2.19034806e-01 7.19354972e-02 -4.61384833e-01 2.78234243e-01 3.71576875e-01 4.40493315e-01 3.98592412e-01 -5.03970027e-01 2.64190435e-01 -7.76111484e-01 2.62044985e-02 3.17473561e-01 6.13113761e-01 7.23316491e-01 -2.23510191e-02 -3.31860334e-01 1.33582878e+00 1.34286225e-01 3.75272900e-01 3.39735508e-01 -8.19177091e-01 7.37401843e-01 6.22616410e-01 6.65002018e-02 -1.28583419e+00 -3.85866523e-01 -4.73964751e-01 -1.07510018e+00 2.73895442e-01 5.76926291e-01 8.73018056e-02 -8.26096773e-01 1.93947530e+00 3.81698638e-01 4.65943590e-02 4.69419397e-02 1.19331229e+00 8.27695906e-01 4.83892769e-01 -1.56964019e-01 -3.63578826e-01 1.41202319e+00 -7.06877053e-01 -7.91790485e-01 -3.22743319e-02 1.97716370e-01 -6.09724760e-01 1.34954095e+00 4.47755575e-01 -8.47352207e-01 -9.55277205e-01 -1.15633535e+00 -7.42196590e-02 -2.04810396e-01 3.74236852e-01 5.64131916e-01 6.37995422e-01 -8.80786180e-01 7.50247598e-01 -5.08675635e-01 -1.41966194e-01 6.22427166e-01 2.79806376e-01 -3.36148560e-01 -2.65050948e-01 -1.26112664e+00 6.68655694e-01 1.10476241e-01 4.32613552e-01 -4.16470319e-01 -8.08205962e-01 -7.17205465e-01 -3.53604415e-03 4.33405161e-01 -2.87529498e-01 5.53657889e-01 -1.00606608e+00 -1.42319262e+00 6.10410094e-01 7.42502734e-02 -9.81616080e-02 7.52065778e-01 -3.66113007e-01 -6.28076732e-01 1.41557857e-01 -8.96353871e-02 5.14494717e-01 1.11516762e+00 -1.20695603e+00 -4.53763545e-01 -6.95834637e-01 1.42021477e-01 1.68968812e-01 -6.58066988e-01 -1.03234462e-01 -3.65740508e-01 -6.50806129e-01 3.69825996e-02 -6.26743436e-01 -2.10890807e-02 7.80259147e-02 3.29968147e-02 -2.26834267e-01 7.47958422e-01 -6.50623858e-01 1.19548655e+00 -2.46996403e+00 1.39326647e-01 -2.36985255e-02 2.86587626e-01 3.82778674e-01 -3.79792839e-01 -2.54442524e-02 -2.31333822e-01 2.26754770e-01 -7.64164850e-02 -1.71832852e-02 -3.64029706e-01 -1.34377599e-01 -4.89268601e-01 3.50868642e-01 5.47681093e-01 5.52386582e-01 -5.87960720e-01 -2.50123799e-01 -1.87459186e-01 7.76003599e-01 -5.38425088e-01 1.90261766e-01 1.20273381e-01 5.35831153e-01 -5.63562453e-01 5.71164966e-01 1.19221711e+00 -1.67762458e-01 -5.03060222e-03 -6.71177328e-01 -6.52051419e-02 -8.46196115e-02 -1.41496003e+00 1.39693701e+00 -4.47210729e-01 2.59339064e-01 2.15051398e-01 -8.51708412e-01 1.37501121e+00 4.49796580e-03 2.76339173e-01 -8.88186395e-01 -1.25427321e-01 1.95828691e-01 -8.11499730e-02 -3.01684260e-01 2.87453055e-01 -5.82424328e-02 2.01221064e-01 -1.08187340e-01 -2.11221039e-01 4.46045667e-01 -5.53353056e-02 -1.93040341e-01 7.91041315e-01 1.50461510e-01 5.43613657e-02 -1.74612015e-01 7.45211184e-01 -6.29515409e-01 8.46323550e-01 4.13248390e-01 -2.16543853e-01 8.25264096e-01 4.38748479e-01 -5.69596410e-01 -8.09708178e-01 -9.76003408e-01 -5.66059232e-01 9.47261930e-01 4.25508082e-01 -3.30838054e-01 -6.34289145e-01 -8.63137960e-01 -1.36979092e-02 1.26815133e-03 -6.12952948e-01 -2.43906558e-01 -7.99231350e-01 -1.08778477e+00 4.14005220e-01 6.04498208e-01 1.01032758e+00 -8.37357104e-01 -1.49100870e-01 -1.60873443e-01 -4.19595242e-02 -1.26141846e+00 -3.64609152e-01 -3.10605794e-01 -7.60206044e-01 -9.91213620e-01 -8.18848670e-01 -5.62046766e-01 6.17587209e-01 4.74841058e-01 8.10145855e-01 4.52765375e-02 -2.99303025e-01 5.08793034e-02 -4.06194001e-01 1.64209023e-01 -1.87347028e-02 -5.38459867e-02 2.59006500e-01 1.44738212e-01 8.54413733e-02 -6.07126117e-01 -8.25025022e-01 5.21495938e-01 -7.41706729e-01 -2.28285491e-01 6.03871882e-01 8.73660028e-01 4.97670949e-01 2.88508058e-01 8.51069391e-01 -5.92693210e-01 5.93288779e-01 -3.49414974e-01 -6.16977513e-01 3.61767918e-01 -6.16444528e-01 1.30020216e-01 1.03709412e+00 -6.37373447e-01 -1.31602859e+00 -2.91585833e-01 -2.15176508e-01 -5.78740478e-01 3.32321785e-02 -1.56290457e-02 -4.73387569e-01 -3.16926569e-01 7.38134801e-01 -3.10285985e-02 -2.15097759e-02 -7.47881413e-01 3.43101501e-01 5.43159783e-01 2.58987159e-01 -6.76049948e-01 9.71644163e-01 4.30954456e-01 2.10194476e-02 -7.42869079e-01 -9.01577830e-01 1.00471891e-01 -4.02599096e-01 2.09854450e-02 5.36665261e-01 -1.09780383e+00 -6.66997015e-01 3.09565097e-01 -8.26774955e-01 -6.47023669e-04 -1.39146194e-01 1.90636471e-01 -7.45133534e-02 5.87182045e-01 -6.88267767e-01 -7.30164409e-01 -2.97239810e-01 -1.16448450e+00 9.97932971e-01 5.50131619e-01 3.51194859e-01 -5.57091892e-01 -1.48461610e-01 2.71042317e-01 4.91526455e-01 1.61421135e-01 8.62015486e-01 -2.91605711e-01 -5.14805019e-01 1.57270804e-01 -7.10767329e-01 4.69777077e-01 2.99621046e-01 -2.93579921e-02 -1.03000617e+00 -6.25262439e-01 1.57390773e-01 -4.34279919e-01 1.09276402e+00 7.83470869e-02 1.54782677e+00 -3.74774337e-01 -1.60659820e-01 9.54632282e-01 1.58140373e+00 7.59884194e-02 8.73965383e-01 3.08167607e-01 6.22468054e-01 6.91868126e-01 8.83525014e-01 2.91180581e-01 2.12969944e-01 1.01569223e+00 3.09178740e-01 -2.57161021e-01 -2.87909865e-01 -2.48890474e-01 4.84905601e-01 4.03751850e-01 -2.11776018e-01 1.43337756e-01 -4.71005827e-01 5.65146580e-02 -1.66996968e+00 -8.18169355e-01 2.36921072e-01 2.40848470e+00 7.72125959e-01 5.57068139e-02 2.26925448e-01 1.48122415e-01 7.46006310e-01 2.59772360e-01 -6.50702000e-01 -1.99458793e-01 -2.42144465e-01 1.21279158e-01 2.58842885e-01 2.00165451e-01 -9.39622760e-01 8.32704246e-01 5.81299400e+00 1.19695997e+00 -1.39260292e+00 -4.31380905e-02 8.92381251e-01 -1.07475236e-01 -1.07309483e-01 -3.22447896e-01 -1.28690469e+00 5.67947924e-01 6.47720993e-01 2.69852579e-01 6.34291887e-01 8.84243727e-01 7.82598779e-02 2.60049820e-01 -8.29522491e-01 1.14717364e+00 -8.19850266e-02 -9.68292952e-01 1.55699417e-01 1.37400925e-01 5.29527009e-01 -3.56059015e-01 3.03034633e-01 5.36686778e-01 -2.29581803e-01 -1.18196666e+00 3.54589522e-01 3.76731932e-01 1.04559207e+00 -9.12202775e-01 5.54458559e-01 1.60306275e-01 -1.32540035e+00 -3.89149874e-01 -8.80971193e-01 8.14895034e-02 -3.57450277e-01 7.48390079e-01 -3.17032278e-01 7.36075521e-01 8.54792893e-01 6.42352581e-01 -8.07725966e-01 6.66198134e-01 9.53327771e-03 1.33202597e-01 -1.34071887e-01 6.03549123e-01 -3.23623180e-01 -4.08143848e-01 4.01436597e-01 1.01305962e+00 3.87984544e-01 1.59194544e-01 8.59147757e-02 8.65504920e-01 -1.82778612e-01 1.80310428e-01 -4.78535622e-01 2.27902785e-01 6.68063819e-01 1.47978175e+00 -4.54113007e-01 4.95062619e-02 -5.50129414e-01 8.59544516e-01 6.63119972e-01 4.52544332e-01 -1.02976203e+00 -3.14487249e-01 8.74863029e-01 3.59821886e-01 3.66744488e-01 4.34183494e-05 -2.22588286e-01 -1.41019380e+00 3.47896516e-01 -1.02150905e+00 3.66404653e-01 -2.12902889e-01 -1.48870742e+00 9.64017928e-01 -1.70292735e-01 -1.28382444e+00 1.44169390e-01 -5.80923080e-01 -2.55817324e-01 7.08448291e-01 -1.86500156e+00 -8.03397655e-01 -4.21117544e-01 6.40314877e-01 5.47813535e-01 -1.32668346e-01 4.88036603e-01 5.33413887e-01 -9.35496092e-01 1.16665888e+00 -6.67660013e-02 1.36858076e-01 9.18190956e-01 -8.73426497e-01 5.37025854e-02 7.21644223e-01 -1.77739248e-01 7.07750082e-01 3.38028461e-01 -4.36249256e-01 -1.26245248e+00 -1.15565991e+00 2.32645959e-01 -2.83481777e-01 3.02657694e-01 -4.80451524e-01 -1.35785997e+00 2.13480875e-01 -3.79516691e-01 2.54388154e-01 2.96222925e-01 2.71333188e-01 -7.28873193e-01 -6.24350786e-01 -1.50827456e+00 5.83814502e-01 1.38359344e+00 -3.22407275e-01 -3.25436056e-01 2.93954704e-02 8.65561783e-01 -3.17329258e-01 -1.18177032e+00 9.73532498e-01 6.04810178e-01 -1.14671350e+00 1.26123941e+00 -1.99116841e-01 4.86024261e-01 -3.63518566e-01 -5.01294471e-02 -1.02571344e+00 -5.56939483e-01 -2.28239939e-01 -1.04072340e-01 1.65022540e+00 8.13287273e-02 -7.60740817e-01 4.93010879e-01 4.61967111e-01 2.63659745e-01 -1.00522935e+00 -9.64206874e-01 -8.85006487e-01 6.21449202e-02 8.15577134e-02 7.62744069e-01 8.93699288e-01 -3.63673389e-01 2.32608333e-01 -5.10552406e-01 3.13756883e-01 6.47969186e-01 1.66267559e-01 6.76561892e-01 -1.26509285e+00 -1.39691666e-01 -3.95993710e-01 -1.68052703e-01 -1.12321460e+00 2.74388008e-02 -5.15620232e-01 -1.79827303e-01 -9.80664015e-01 3.36423606e-01 -6.01976931e-01 -3.46569151e-01 4.14797992e-01 -4.09552515e-01 4.22244847e-01 2.78233141e-01 4.31419462e-01 -4.40763623e-01 8.43576968e-01 1.52117324e+00 2.26109251e-01 -2.53478110e-01 -1.92008212e-01 -1.00295115e+00 6.58530593e-01 5.99909723e-01 -8.81272927e-02 -3.39532375e-01 -3.65467221e-01 1.36120975e-01 5.57641387e-02 1.72596753e-01 -9.49499011e-01 -1.62052482e-01 -1.02624997e-01 8.42565417e-01 -2.20978528e-01 3.26587200e-01 -7.44299233e-01 -1.99099351e-02 -3.54678333e-02 -2.46674657e-01 -1.67327046e-01 1.89324141e-01 5.06477892e-01 -2.16002539e-01 2.37323686e-01 1.31670082e+00 2.37081610e-02 -5.84848166e-01 4.67673659e-01 3.88492674e-01 2.86893547e-02 7.08724022e-01 -4.36399132e-01 -4.27857697e-01 -5.68862371e-02 -4.37239379e-01 2.50819653e-01 6.38311565e-01 7.23743558e-01 7.00745821e-01 -1.48649132e+00 -7.06818998e-01 4.85512048e-01 -8.33416581e-02 1.51054906e-02 4.24983799e-01 6.70797884e-01 4.52252999e-02 2.70038784e-01 -3.27234000e-01 -4.41867173e-01 -9.88742530e-01 5.57810307e-01 4.10984397e-01 -4.24594223e-01 -7.05156446e-01 7.82273471e-01 6.47005498e-01 -3.39869380e-01 2.93240756e-01 -3.84641141e-02 -6.58545077e-01 2.43503332e-01 9.94469166e-01 3.39506239e-01 1.32304892e-01 -5.55842757e-01 -3.89151841e-01 1.03437102e+00 -3.97243232e-01 3.68269533e-01 1.38507950e+00 -3.65604639e-01 1.37963325e-01 -1.01336362e-02 1.12301862e+00 1.97391555e-01 -1.71948051e+00 -1.74040467e-01 -1.79831475e-01 -8.44546139e-01 -1.28574491e-01 -7.01998413e-01 -1.45367730e+00 6.70632124e-01 9.71421659e-01 6.93754256e-02 1.55220091e+00 -2.43066832e-01 7.24857748e-01 -9.13115740e-02 4.63763595e-01 -9.30584788e-01 3.47864509e-01 3.04358751e-01 1.07969475e+00 -1.31562567e+00 -4.14130613e-02 -6.17781878e-01 -5.41608691e-01 1.28373456e+00 9.68366921e-01 -1.51649550e-01 2.63184398e-01 1.61754698e-01 -8.44834596e-02 4.48255986e-02 -5.07534564e-01 -5.88707477e-02 3.07885230e-01 6.13058329e-01 3.40998024e-01 -2.79583484e-01 -5.47472119e-01 7.61554420e-01 -9.52982306e-02 -4.05875109e-02 2.08202183e-01 2.10052460e-01 -3.79893690e-01 -1.09355497e+00 -4.90625799e-01 2.79012442e-01 -5.35063088e-01 5.62554002e-02 -9.75740701e-02 8.83258164e-01 2.65163332e-01 8.58318210e-01 -1.93565279e-01 -4.42577183e-01 5.04651368e-01 -2.82249570e-01 6.79945648e-01 -3.13239574e-01 -2.43176952e-01 -9.06100776e-03 -2.01325372e-01 -8.47303629e-01 -1.42643884e-01 -3.35572720e-01 -1.11571610e+00 -2.57607311e-01 -3.35338503e-01 -3.56483646e-02 2.66916901e-01 7.65379131e-01 4.64226186e-01 2.95148760e-01 1.06395280e+00 -6.84468746e-01 -8.20849359e-01 -8.08508694e-01 -6.62104368e-01 5.04629731e-01 3.82927537e-01 -8.54323030e-01 -6.29028201e-01 -4.71966267e-01]	[13.070780754089355, 0.4186554551124573]
22eae513-22e1-4b82-878e-dac1bfd79ada	babel-bodies-action-and-behavior-with-english	2106.09696	null	https://arxiv.org/abs/2106.09696v2	https://arxiv.org/pdf/2106.09696v2.pdf	BABEL: Bodies, Action and Behavior with English Labels	Understanding the semantics of human movement -- the what, how and why of the movement -- is an important problem that requires datasets of human actions with semantic labels. Existing datasets take one of two approaches. Large-scale video datasets contain many action labels but do not contain ground-truth 3D human motion. Alternatively, motion-capture (mocap) datasets have precise body motions but are limited to a small number of actions. To address this, we present BABEL, a large dataset with language labels describing the actions being performed in mocap sequences. BABEL consists of action labels for about 43 hours of mocap sequences from AMASS. Action labels are at two levels of abstraction -- sequence labels describe the overall action in the sequence, and frame labels describe all actions in every frame of the sequence. Each frame label is precisely aligned with the duration of the corresponding action in the mocap sequence, and multiple actions can overlap. There are over 28k sequence labels, and 63k frame labels in BABEL, which belong to over 250 unique action categories. Labels from BABEL can be leveraged for tasks like action recognition, temporal action localization, motion synthesis, etc. To demonstrate the value of BABEL as a benchmark, we evaluate the performance of models on 3D action recognition. We demonstrate that BABEL poses interesting learning challenges that are applicable to real-world scenarios, and can serve as a useful benchmark of progress in 3D action recognition. The dataset, baseline method, and evaluation code is made available, and supported for academic research purposes at https://babel.is.tue.mpg.de/.	['Michael J. Black', 'Alejandra Quiros-Ramirez', 'Nikos Athanasiou', 'Arjun Chandrasekaran', 'Abhinanda R. Punnakkal']	2021-06-17	null	http://openaccess.thecvf.com//content/CVPR2021/html/Punnakkal_BABEL_Bodies_Action_and_Behavior_With_English_Labels_CVPR_2021_paper.html	http://openaccess.thecvf.com//content/CVPR2021/papers/Punnakkal_BABEL_Bodies_Action_and_Behavior_With_English_Labels_CVPR_2021_paper.pdf	cvpr-2021-1	['3d-human-action-recognition']	['computer-vision']	[ 1.77580357e-01 -3.68398875e-01 -6.47268176e-01 -2.28277504e-01 -6.72152519e-01 -5.80972254e-01 6.63437903e-01 -4.25644189e-01 -3.19158494e-01 5.68311989e-01 8.16002786e-01 1.36070877e-01 3.22670341e-01 -3.07032883e-01 -6.71608329e-01 -7.49753356e-01 -1.94802716e-01 3.93145263e-01 3.90796214e-01 -1.03472009e-01 -1.48643390e-03 4.92404521e-01 -1.25100672e+00 6.90640092e-01 -2.29414120e-01 9.16050553e-01 -5.32493591e-02 8.88815165e-01 -1.85340755e-02 1.39179850e+00 -6.03083491e-01 -2.15384681e-02 3.58711332e-01 -7.32033670e-01 -1.11003172e+00 3.50146264e-01 5.36929607e-01 -6.52638197e-01 -7.92994559e-01 6.24938488e-01 2.98088193e-01 4.55170125e-01 6.91941619e-01 -1.70755374e+00 -2.79936194e-01 2.40870416e-01 -1.97994649e-01 3.78049821e-01 7.56432295e-01 5.35796583e-01 1.00094461e+00 -5.45505583e-01 9.57685173e-01 1.33241737e+00 5.78702152e-01 1.08241177e+00 -7.99329638e-01 -4.79228467e-01 4.63934764e-02 4.63440806e-01 -1.02972817e+00 -5.56774318e-01 5.15715003e-01 -7.63739228e-01 1.20367420e+00 8.09966251e-02 8.56994092e-01 1.71843815e+00 1.02085002e-01 1.27638209e+00 5.15051365e-01 -1.19974248e-01 1.72816321e-01 -7.67799079e-01 7.91363344e-02 6.34809136e-01 -2.63559818e-01 -2.78421603e-02 -8.34719002e-01 1.00982703e-01 9.32272673e-01 1.94404557e-01 -5.88708445e-02 -5.22644341e-01 -1.82914126e+00 4.39493746e-01 1.37284353e-01 2.79612809e-01 -2.44797036e-01 7.82716751e-01 7.51339734e-01 1.02831483e-01 1.04749218e-01 1.19354442e-01 -4.42148685e-01 -8.15461218e-01 -5.85413814e-01 6.57636166e-01 6.09808743e-01 1.10826993e+00 6.01707876e-01 5.99807724e-02 -3.84938836e-01 6.42029345e-01 1.08881881e-02 7.14597344e-01 5.22531688e-01 -1.66688848e+00 5.85025191e-01 4.96980697e-01 3.88507396e-01 -8.34869564e-01 -5.56050658e-01 5.49362302e-01 -4.91176963e-01 2.80939098e-02 7.40741670e-01 -3.17056552e-02 -9.98174489e-01 1.72590196e+00 4.32320386e-01 4.59457457e-01 1.16233528e-02 1.08184111e+00 8.08460295e-01 6.60500944e-01 2.63327777e-01 1.51447982e-01 1.34804440e+00 -1.32201529e+00 -7.36231267e-01 -3.43055993e-01 1.06656063e+00 -4.57051277e-01 9.50611591e-01 4.45775241e-02 -8.84832263e-01 -6.21258616e-01 -6.55941904e-01 -2.03625277e-01 -1.68819606e-01 9.11763981e-02 5.27535081e-01 3.33013415e-01 -7.65868723e-01 3.07253927e-01 -1.29887366e+00 -7.12607861e-01 4.50756580e-01 6.46006539e-02 -7.25828469e-01 -4.49831970e-02 -9.75414395e-01 8.69847238e-01 3.97929907e-01 -2.23799989e-01 -1.36692917e+00 -2.74281204e-01 -1.17801178e+00 -3.52636307e-01 4.97817636e-01 -4.71266180e-01 1.67688942e+00 -9.81946111e-01 -1.24441385e+00 9.85232174e-01 -1.68163791e-01 -5.92390120e-01 4.22526300e-01 -3.28641832e-01 -4.64552999e-01 4.60562408e-01 3.01233143e-01 9.45407093e-01 6.16591215e-01 -6.45452142e-01 -8.50500882e-01 -1.90806970e-01 1.87349781e-01 1.16844036e-01 1.66609094e-01 2.59220213e-01 -6.28446758e-01 -8.07776511e-01 -1.19640373e-01 -1.27394760e+00 -6.21607564e-02 -7.38634393e-02 -1.66020006e-01 -1.56575635e-01 6.79599166e-01 -6.97571158e-01 1.16054273e+00 -2.35057378e+00 1.87967226e-01 -4.21529025e-01 -1.16746187e-01 1.43517643e-01 -2.95383364e-01 4.93470103e-01 -3.97100225e-02 -4.31822762e-02 -1.63700864e-01 -1.70901433e-01 2.83337802e-01 4.92185116e-01 -3.08399528e-01 5.84012866e-01 6.19331188e-03 1.19660747e+00 -9.85775888e-01 -6.30259216e-01 4.15837437e-01 1.78320095e-01 -4.02610183e-01 1.54062524e-01 -3.08295459e-01 7.79838264e-01 -6.70690298e-01 8.84694099e-01 -8.22605789e-02 -2.52134383e-01 -4.75000776e-02 -8.64480212e-02 2.58132428e-01 3.31343591e-01 -1.15872860e+00 2.13804340e+00 9.00619775e-02 7.58577228e-01 -3.61382633e-01 -9.30852413e-01 5.19877672e-01 5.43772876e-01 1.15630758e+00 -5.46192467e-01 4.92724665e-02 -1.43565228e-02 -1.71410933e-01 -7.71284819e-01 3.12958062e-01 -8.21080357e-02 -4.77634579e-01 6.40547037e-01 2.58732975e-01 2.56774165e-02 6.03756666e-01 -4.15792204e-02 1.52987826e+00 6.43193603e-01 5.11606991e-01 3.22835416e-01 4.21646357e-01 4.14246053e-01 7.60963678e-01 6.57922029e-01 -8.39580953e-01 5.74655414e-01 5.10120034e-01 -8.43458235e-01 -1.16547632e+00 -9.95104492e-01 2.59062141e-01 1.18826938e+00 1.37019053e-01 -6.33474767e-01 -8.03607821e-01 -8.02860558e-01 -4.37151492e-02 4.51024115e-01 -5.97135961e-01 -2.25959904e-02 -9.71072078e-01 -1.29165143e-01 8.92843425e-01 9.20015275e-01 5.78028679e-01 -1.49188733e+00 -9.18375313e-01 1.57420173e-01 -5.73532581e-01 -1.55399334e+00 -7.32807636e-01 -2.15702742e-01 -7.45229542e-01 -1.33108699e+00 -7.78569996e-01 -6.13062918e-01 2.56248713e-01 2.41160020e-01 1.15943968e+00 -2.03024834e-01 -3.11393797e-01 7.79262125e-01 -6.39309764e-01 -1.10148601e-01 -3.51652592e-01 -1.62688851e-01 1.87471434e-01 -2.85724252e-02 7.59025037e-01 -2.90871799e-01 -5.42702019e-01 6.73457623e-01 -5.91471374e-01 1.21402025e-01 2.26725146e-01 5.41422069e-01 6.08863950e-01 -3.91024113e-01 1.43397689e-01 -4.71114546e-01 -1.45088121e-01 -4.39094782e-01 -9.54856053e-02 6.00975454e-02 3.66573423e-01 -1.49669528e-01 1.76017553e-01 -4.61405754e-01 -8.15853834e-01 4.33379471e-01 -1.40799642e-01 -6.00320041e-01 -7.12544143e-01 -4.80697751e-02 -1.78435028e-01 4.15409654e-01 6.05148971e-01 2.26229385e-01 -7.68509284e-02 -4.90206838e-01 3.76195371e-01 3.28984708e-01 7.05788195e-01 -6.21597528e-01 2.77246922e-01 6.78219616e-01 3.92171964e-02 -7.49342680e-01 -9.24759269e-01 -6.50294542e-01 -8.40678394e-01 -4.94258016e-01 1.46959424e+00 -1.06747973e+00 -6.54165268e-01 6.92112267e-01 -9.46005940e-01 -8.67778063e-01 -3.01048011e-01 7.31774271e-01 -1.25501955e+00 3.19021851e-01 -8.34234416e-01 -4.52052295e-01 2.29142725e-01 -1.14768839e+00 1.27904701e+00 -1.73543885e-01 -6.22782886e-01 -7.93909550e-01 1.14018887e-01 6.34379685e-01 -3.03861737e-01 4.98164535e-01 4.92844582e-01 -5.54756165e-01 -5.02794087e-01 -1.75795034e-01 2.26937622e-01 3.69561791e-01 2.10046187e-01 -1.32631585e-01 -5.32433212e-01 -1.09352052e-01 -2.38233238e-01 -5.73488712e-01 6.80729687e-01 5.32731950e-01 9.18407381e-01 -1.17250167e-01 -3.01528692e-01 4.81082648e-01 8.07757378e-01 3.20128858e-01 5.88614523e-01 3.74617040e-01 8.97788167e-01 4.28191572e-01 9.13710654e-01 5.20661473e-01 3.00307035e-01 1.05797350e+00 1.89235106e-01 3.42744768e-01 -3.44159782e-01 -3.53413790e-01 8.19697380e-01 5.81602275e-01 -2.89309621e-01 -2.64567971e-01 -1.18249905e+00 4.97951835e-01 -2.15725088e+00 -1.48373556e+00 -9.60382298e-02 1.87207210e+00 6.34855866e-01 -1.75880976e-02 5.74537873e-01 -9.38521102e-02 5.92929244e-01 6.15291655e-01 -4.99996036e-01 -5.41234203e-02 7.99117461e-02 -1.16869986e-01 5.50792992e-01 2.32024074e-01 -1.52464998e+00 1.13257062e+00 6.57868958e+00 6.92106605e-01 -7.64440238e-01 1.84541628e-01 2.12128565e-01 -4.61575150e-01 4.07199502e-01 -9.62726772e-02 -8.56402874e-01 4.94431376e-01 1.00561011e+00 1.27413020e-01 2.46422693e-01 9.34170067e-01 4.07188267e-01 -8.75381678e-02 -1.43218708e+00 1.25391257e+00 1.89435989e-01 -1.30856383e+00 -2.49366295e-02 1.75799001e-02 6.99965954e-01 8.00734162e-02 -4.17162806e-01 4.37482625e-01 4.11688894e-01 -9.56087291e-01 1.05288267e+00 5.80446005e-01 7.51639545e-01 -3.74620050e-01 4.14597601e-01 4.59499419e-01 -1.37340569e+00 -1.50730610e-01 -1.45085737e-01 -2.69848436e-01 6.47870779e-01 -1.46725222e-01 -4.86938924e-01 1.76539242e-01 7.62281597e-01 1.38169658e+00 -3.67336869e-01 6.96212053e-01 -3.56415570e-01 5.59012651e-01 -8.21072832e-02 1.43088162e-01 5.18245578e-01 1.74517161e-03 3.85196120e-01 1.20399570e+00 2.79006809e-01 3.34517419e-01 5.33786058e-01 3.27080935e-01 5.83318360e-02 -2.75979012e-01 -7.94640958e-01 -4.35539901e-01 2.72211879e-01 7.19994545e-01 -6.51096404e-01 -4.10356492e-01 -6.69122636e-01 1.08687055e+00 -5.78650273e-02 3.75709593e-01 -1.07516253e+00 1.27310485e-01 1.16136789e+00 7.98558071e-02 2.49991864e-01 -6.11134171e-01 1.29005253e-01 -1.28929448e+00 -9.46623459e-02 -1.11845529e+00 5.79470992e-01 -9.47537720e-01 -9.25007999e-01 5.54339811e-02 2.73384809e-01 -1.62657583e+00 -6.63410783e-01 -7.78172314e-01 -1.45644918e-01 3.01616162e-01 -7.14487076e-01 -1.20484102e+00 -4.27318335e-01 7.66580045e-01 9.69144225e-01 -2.62035131e-01 7.09355414e-01 3.64197344e-01 -4.07227755e-01 7.92831481e-02 -2.13197872e-01 6.82538092e-01 7.64217496e-01 -9.85812187e-01 7.38229632e-01 6.33939803e-01 6.30036592e-01 1.78722534e-02 4.74873781e-01 -6.28986716e-01 -1.30045521e+00 -1.06596851e+00 8.86924088e-01 -1.15813434e+00 7.30516016e-01 -2.20381111e-01 -5.81069410e-01 1.23943818e+00 -2.53278017e-01 2.25298092e-01 7.00392365e-01 -3.11453283e-01 -2.11191565e-01 1.58314809e-01 -6.57025814e-01 6.52980626e-01 1.61960340e+00 -6.63285375e-01 -8.08592081e-01 4.08574522e-01 5.56175709e-01 -7.08212435e-01 -7.56738484e-01 2.69871563e-01 7.47909486e-01 -9.56863046e-01 1.23535693e+00 -1.03973937e+00 6.54175103e-01 -4.72106606e-01 -4.38408345e-01 -8.48594368e-01 -2.49272719e-01 -2.49074906e-01 -3.81609529e-01 8.88178825e-01 -4.34199199e-02 5.72275892e-02 8.73960197e-01 5.48425078e-01 -2.20802695e-01 -4.54241365e-01 -1.04376853e+00 -9.72623169e-01 -1.72619477e-01 -7.58712947e-01 6.04089379e-01 8.00539672e-01 -7.96500668e-02 6.33481750e-03 -7.33303964e-01 -2.69862860e-01 3.57378811e-01 -1.22563615e-01 1.25376177e+00 -5.46672225e-01 -2.58665323e-01 -4.41880286e-01 -8.55688810e-01 -1.33813238e+00 4.93345052e-01 -6.77259922e-01 1.76901743e-01 -1.44326580e+00 5.09306379e-02 3.31454203e-02 -1.30770966e-01 8.74959350e-01 1.11701779e-01 3.28679025e-01 4.37653244e-01 4.83475834e-01 -8.76055956e-01 3.72861356e-01 1.26429605e+00 -2.81537354e-01 8.92352015e-02 -9.62615684e-02 1.95636466e-01 9.23050404e-01 6.22287810e-01 -4.31139648e-01 -3.72706771e-01 -5.23837149e-01 -3.03530306e-01 3.40521067e-01 5.59432507e-01 -1.31298184e+00 4.00258861e-02 -5.82737327e-01 2.18290046e-01 -4.13178146e-01 6.12867355e-01 -8.05347681e-01 3.43567312e-01 4.13182974e-01 -5.05293608e-01 -9.80228372e-03 -1.04680873e-01 5.83145022e-01 -3.27306122e-01 -2.91118100e-02 6.03982508e-01 -4.22079116e-01 -1.54841340e+00 4.41971004e-01 -4.13368583e-01 3.69736552e-01 1.31051123e+00 -2.72064775e-01 -1.89404964e-01 -4.06856596e-01 -1.01151800e+00 1.61962628e-01 5.78772068e-01 7.19447017e-01 3.31965178e-01 -1.70600820e+00 -5.50980508e-01 -9.03871283e-02 3.81860256e-01 -3.55811417e-01 3.38633984e-01 7.91078687e-01 -7.57421911e-01 5.01374424e-01 -5.80426335e-01 -7.33055890e-01 -1.23162544e+00 5.67085922e-01 3.96365523e-01 -2.92168669e-02 -9.98780489e-01 6.53369188e-01 2.18330801e-01 -1.78364441e-01 2.72600055e-01 -3.51830989e-01 -9.10322964e-02 -3.18658166e-02 7.47504473e-01 5.19769490e-01 -4.81365621e-01 -1.21039557e+00 -5.74750245e-01 5.60997248e-01 4.79913861e-01 -9.77432281e-02 1.05581832e+00 -9.29527655e-02 1.35055065e-01 8.57670426e-01 1.08800864e+00 -4.89685923e-01 -1.64652002e+00 -8.65705162e-02 2.30188668e-01 -6.11495316e-01 -4.36581671e-01 -4.77987766e-01 -9.76799190e-01 8.70187938e-01 3.81511539e-01 -2.25543261e-01 8.42352450e-01 2.07519785e-01 1.08241212e+00 4.81975883e-01 6.15148067e-01 -1.20045745e+00 4.02609855e-01 6.96084380e-01 7.13120997e-01 -1.28955615e+00 -2.07715139e-01 -5.24055809e-02 -1.13385677e+00 8.87375057e-01 6.22218847e-01 3.80723155e-03 3.72174680e-01 9.43600908e-02 1.60414368e-01 -1.97498664e-01 -6.75812244e-01 -3.94248396e-01 1.66673839e-01 4.97986764e-01 2.94104010e-01 1.27778322e-01 -1.56768471e-01 4.68025714e-01 3.90937738e-02 2.61674941e-01 3.89006287e-01 1.14813328e+00 -4.42402303e-01 -1.00535715e+00 -2.74235547e-01 1.51317880e-01 -3.54426295e-01 3.81358415e-01 -3.29632729e-01 8.69708121e-01 2.16155410e-01 8.28773379e-01 1.71843648e-01 -4.26606834e-01 4.69470829e-01 5.14382064e-01 5.36350548e-01 -5.29335737e-01 -2.03388348e-01 -9.38220322e-02 3.11122030e-01 -1.18294704e+00 -8.82254720e-01 -9.34930980e-01 -1.54322064e+00 -3.02282363e-01 5.18988192e-01 -1.99474663e-01 2.16151297e-01 1.04942381e+00 1.75382704e-01 2.80372381e-01 1.18269175e-01 -1.04425871e+00 -2.51614243e-01 -8.72791708e-01 -7.33473539e-01 9.73224401e-01 3.79117936e-01 -9.07169461e-01 -2.28641838e-01 7.77767122e-01]	[8.141790390014648, 0.4831882119178772]
b45cd340-1075-4cc3-9476-bef5559caa9f	few-shot-class-incremental-learning-for-3d	2205.15225	null	https://arxiv.org/abs/2205.15225v2	https://arxiv.org/pdf/2205.15225v2.pdf	Few-shot Class-incremental Learning for 3D Point Cloud Objects	Few-shot class-incremental learning (FSCIL) aims to incrementally fine-tune a model (trained on base classes) for a novel set of classes using a few examples without forgetting the previous training. Recent efforts address this problem primarily on 2D images. However, due to the advancement of camera technology, 3D point cloud data has become more available than ever, which warrants considering FSCIL on 3D data. This paper addresses FSCIL in the 3D domain. In addition to well-known issues of catastrophic forgetting of past knowledge and overfitting of few-shot data, 3D FSCIL can bring newer challenges. For example, base classes may contain many synthetic instances in a realistic scenario. In contrast, only a few real-scanned samples (from RGBD sensors) of novel classes are available in incremental steps. Due to the data variation from synthetic to real, FSCIL endures additional challenges, degrading performance in later incremental steps. We attempt to solve this problem using Microshapes (orthogonal basis vectors) by describing any 3D objects using a pre-defined set of rules. It supports incremental training with few-shot examples minimizing synthetic to real data variation. We propose new test protocols for 3D FSCIL using popular synthetic datasets (ModelNet and ShapeNet) and 3D real-scanned datasets (ScanObjectNN and CO3D). By comparing state-of-the-art methods, we establish the effectiveness of our approach in the 3D domain.	['Shafin Rahman', 'Morteza Saberi', 'Sahar Ahmadi', 'Sameera Ramasinghe', 'Ali Cheraghian', 'Townim Chowdhury']	2022-05-30	null	null	null	null	['few-shot-class-incremental-learning']	['methodology']	[ 3.50082606e-01 -9.55033582e-03 -6.30500354e-03 -4.21954334e-01 -5.52658498e-01 -5.02737343e-01 6.72158897e-01 -6.60530850e-02 -2.18285322e-01 6.10101998e-01 -4.10732120e-01 -5.05117550e-02 -2.66095817e-01 -8.48117769e-01 -1.08087921e+00 -4.38105583e-01 -1.66482672e-01 8.21492076e-01 8.05319607e-01 -1.92183346e-01 1.47739977e-01 1.03141928e+00 -2.29874134e+00 2.98681051e-01 6.17713273e-01 8.75998020e-01 2.39320278e-01 5.42588830e-01 -4.54561979e-01 2.85769671e-01 -6.71139300e-01 -1.03536002e-01 5.23894012e-01 -1.64899722e-01 -2.69859314e-01 4.37375307e-01 7.81777978e-01 -3.38576823e-01 -3.14469606e-01 8.54373455e-01 6.55488610e-01 2.58903772e-01 5.25843740e-01 -1.36793602e+00 -6.21597946e-01 1.71101928e-01 -3.77876133e-01 -3.92527739e-03 3.31362158e-01 5.72904110e-01 2.72448689e-01 -1.30449903e+00 9.69488919e-01 1.08814144e+00 9.69176650e-01 8.43549192e-01 -1.21199477e+00 -5.16772330e-01 1.97999254e-01 3.71956229e-01 -1.40822995e+00 -2.72211194e-01 9.22364771e-01 -2.09464535e-01 1.18741989e+00 1.60727710e-01 1.05521321e+00 1.40092587e+00 -8.12872872e-02 7.60994434e-01 1.05019486e+00 -4.86800224e-01 7.60395110e-01 2.16772184e-01 1.37260705e-01 3.17655116e-01 4.47068006e-01 4.24880892e-01 -6.07421279e-01 6.16138466e-02 6.56802416e-01 2.69987524e-01 6.78104833e-02 -9.19457495e-01 -9.29049015e-01 3.10336709e-01 1.66522235e-01 2.02778056e-01 -2.16684252e-01 -6.96330443e-02 1.83743238e-01 5.30027032e-01 4.76004928e-01 2.85417467e-01 -7.27943003e-01 -2.28497908e-01 -9.74748969e-01 2.58687973e-01 5.99375248e-01 1.48742867e+00 1.05360281e+00 2.22011447e-01 2.74945027e-03 8.78503203e-01 -2.81622082e-01 7.11327314e-01 5.77096820e-01 -7.50042498e-01 1.62129045e-01 6.68384194e-01 6.65649911e-03 -5.85106432e-01 -1.96916550e-01 -3.63373995e-01 -6.17805839e-01 5.84537804e-01 1.52661921e-02 3.90880376e-01 -1.49097490e+00 1.32535326e+00 6.16876364e-01 4.93549556e-01 -7.11200833e-02 5.07091463e-01 6.46188557e-01 5.96637428e-01 -3.76754284e-01 -3.12056571e-01 5.61382830e-01 -5.88032067e-01 -1.91711858e-01 -2.36356571e-01 2.49740943e-01 -3.25607926e-01 1.59443521e+00 4.96136665e-01 -7.25116193e-01 -8.63469481e-01 -1.15632975e+00 3.72178555e-01 -6.83489859e-01 -5.36568105e-01 4.39122051e-01 6.61364198e-01 -7.62144268e-01 9.09008503e-01 -8.76695395e-01 -6.34442270e-01 7.06024289e-01 1.83027089e-01 -3.48858893e-01 -6.58894122e-01 -8.83379340e-01 7.99768388e-01 4.51679856e-01 -3.29910100e-01 -1.15648878e+00 -1.25720274e+00 -6.54325843e-01 -3.39518070e-01 5.75179935e-01 -4.27102655e-01 1.29503179e+00 -6.04219854e-01 -1.47963679e+00 9.26554799e-01 1.59981221e-01 -3.44269156e-01 6.89429045e-01 -2.18526900e-01 -5.09225667e-01 -5.83073348e-02 3.88391479e-03 5.50047159e-01 1.15539634e+00 -1.45949686e+00 -4.31241691e-01 -3.30514491e-01 6.67826831e-02 5.08541837e-02 -2.10598439e-01 -7.78299272e-01 -4.48578268e-01 -4.25886571e-01 3.33129913e-01 -9.18462515e-01 -1.53433606e-01 4.50656325e-01 -1.10861566e-03 -5.06580211e-02 1.16952145e+00 1.33803159e-01 8.32026601e-01 -2.14865851e+00 -2.18603522e-01 -1.42476007e-01 -9.53540653e-02 5.22092342e-01 -2.21097469e-01 1.79447487e-01 -7.93138891e-02 -1.05307288e-02 -4.07860935e-01 -2.15008199e-01 -1.01327084e-01 6.87296212e-01 -3.18445951e-01 1.82461247e-01 3.43049884e-01 8.01333427e-01 -1.10998213e+00 -3.03628504e-01 5.22474706e-01 2.85173088e-01 -4.21543926e-01 6.70498759e-02 -5.54761052e-01 2.04515278e-01 1.14778364e-02 1.07166374e+00 9.70659435e-01 -1.81852281e-01 -4.60581928e-01 -1.89679220e-01 -7.28104040e-02 -4.09476459e-01 -1.36964440e+00 2.01371050e+00 -5.45248091e-01 2.24204272e-01 -7.76798308e-01 -9.97824788e-01 1.07672322e+00 -2.36485582e-02 4.96267498e-01 -7.37789631e-01 -1.55013174e-01 2.69368857e-01 -3.36874992e-01 -7.01509774e-01 2.66603529e-01 -4.03655618e-01 -6.23364709e-02 5.23960054e-01 5.02660930e-01 -7.74394691e-01 1.57832116e-01 -3.23784500e-02 1.23458862e+00 4.79680210e-01 2.65819788e-01 2.82283068e-01 4.49761413e-02 2.56325185e-01 4.87909973e-01 1.19640124e+00 -2.50020266e-01 1.04050791e+00 -6.84525594e-02 -9.35881317e-01 -1.29560733e+00 -1.45194447e+00 -1.75793216e-01 5.46103001e-01 2.68672347e-01 -7.39359483e-02 -2.78777957e-01 -9.02694106e-01 3.21438998e-01 1.35284960e+00 -7.75541544e-01 -4.47193712e-01 -4.33571607e-01 -6.15587533e-01 1.80339068e-01 2.71736503e-01 4.20000911e-01 -9.03459311e-01 -1.10151279e+00 3.61121088e-01 5.98190546e-01 -9.78966475e-01 -1.38432560e-02 3.68071675e-01 -1.24108922e+00 -1.22836339e+00 -6.41243339e-01 -3.84393960e-01 6.73999906e-01 5.42282820e-01 1.31266022e+00 -1.87750071e-01 -6.94812119e-01 1.00680459e+00 -6.29540265e-01 -7.54353404e-01 -5.22788584e-01 -7.47822896e-02 2.71597743e-01 -1.15022436e-01 5.06485760e-01 -9.15909529e-01 -3.35523635e-01 2.03575984e-01 -1.17661929e+00 8.30262527e-02 6.40559435e-01 7.76619613e-01 1.03614604e+00 1.50758833e-01 3.22948337e-01 -1.01603758e+00 1.34754762e-01 -4.23028231e-01 -4.00859445e-01 4.51288044e-01 -7.68926084e-01 5.36512583e-04 5.41180372e-01 -1.15124238e+00 -9.96514618e-01 1.30900323e-01 1.90761104e-01 -1.13943160e+00 -4.40889925e-01 1.21395864e-01 -5.37826531e-02 -1.15695633e-01 1.12661862e+00 4.48225856e-01 -4.15442176e-02 -5.08470774e-01 4.19772148e-01 3.17310005e-01 4.80262905e-01 -6.23079360e-01 1.17500591e+00 5.75649619e-01 8.37501734e-02 -1.09917831e+00 -1.04200602e+00 -1.89508930e-01 -1.01241469e+00 -5.96151292e-01 1.10598229e-01 -5.69801688e-01 8.28689113e-02 5.86806715e-01 -9.06231225e-01 -4.44036663e-01 -1.24857223e+00 1.94719627e-01 -8.14426124e-01 9.99637991e-02 4.77790721e-02 -7.75709271e-01 -6.88515604e-02 -6.59175575e-01 1.03898990e+00 1.17483288e-01 2.95782797e-02 -6.14982367e-01 1.97420537e-01 -3.72834325e-01 3.49890530e-01 6.17348015e-01 9.29773927e-01 -4.44555849e-01 -6.36162579e-01 -6.17189050e-01 2.14772671e-01 5.70311308e-01 2.14984581e-01 -1.12032071e-01 -9.97403264e-01 -3.20728689e-01 2.30766490e-01 -4.72240478e-01 6.85078263e-01 9.67967212e-02 1.22506833e+00 1.23849548e-01 -2.67887026e-01 6.00612223e-01 1.63087678e+00 3.24105442e-01 6.08657539e-01 2.73259670e-01 3.95155102e-01 1.61627248e-01 9.30633366e-01 6.44619823e-01 -5.33019416e-02 3.72940123e-01 5.39130449e-01 3.77278417e-01 -6.37590468e-01 -4.09542680e-01 3.10370116e-03 6.89229548e-01 -1.88777056e-02 -7.65052438e-02 -1.02959955e+00 6.54349744e-01 -1.61327422e+00 -1.03472471e+00 3.27726781e-01 2.41775179e+00 6.90269589e-01 4.77130115e-01 -3.00774693e-01 3.19989622e-01 6.69289470e-01 -6.53449148e-02 -1.34204614e+00 5.64137809e-02 -3.28978777e-01 5.53064942e-01 2.39650264e-01 2.92294472e-02 -7.39571750e-01 7.51570642e-01 5.70652914e+00 7.73837149e-01 -1.08843791e+00 2.61686981e-01 3.69741440e-01 -5.74846804e-01 -2.98244685e-01 6.70426805e-03 -1.01532650e+00 3.22944701e-01 6.58246636e-01 -3.11603129e-01 3.90293628e-01 1.12180483e+00 -2.63568968e-01 -1.60433069e-01 -1.35159755e+00 1.37545204e+00 3.92856419e-01 -1.44435263e+00 2.93440074e-01 -4.01441157e-01 1.04955447e+00 2.03652531e-01 9.56257209e-02 7.68301785e-01 2.78721228e-02 -5.84826887e-01 8.63799334e-01 8.38013113e-01 1.30831337e+00 -6.01306438e-01 3.26002538e-01 7.08688200e-01 -8.54590416e-01 -1.20133698e-01 -6.51401520e-01 9.72206295e-02 6.90684095e-02 8.34947526e-01 -1.02008307e+00 3.74816537e-01 9.40682769e-01 7.45862603e-01 -7.23671675e-01 1.22127545e+00 -9.04025137e-02 4.06056702e-01 -6.12669706e-01 -1.01522215e-01 -2.01734398e-02 3.11136365e-01 7.38459110e-01 7.20829844e-01 8.26309323e-01 1.78744242e-01 -8.46345201e-02 7.39087820e-01 1.53102167e-02 -3.52578461e-01 -9.08077121e-01 2.15382293e-01 5.93507051e-01 6.61721587e-01 -7.12003946e-01 -4.37557489e-01 -3.78430605e-01 1.13796365e+00 1.85716003e-01 1.87023342e-01 -7.02154040e-01 -3.60758960e-01 5.05557835e-01 4.78139728e-01 5.64592481e-01 -2.05712706e-01 -4.58745845e-02 -1.15750885e+00 1.20409422e-01 -6.18937314e-01 1.88256904e-01 -8.97831500e-01 -1.55813599e+00 4.93945122e-01 3.02769780e-01 -1.86032557e+00 -2.09053501e-01 -5.82266629e-01 -4.00953978e-01 1.61402419e-01 -1.40205956e+00 -1.15452468e+00 -6.59299374e-01 5.95941663e-01 1.11977792e+00 -2.45172486e-01 8.77092540e-01 1.76615432e-01 3.39297615e-02 4.62600648e-01 1.96740463e-01 -6.86933279e-01 6.50805652e-01 -9.67496037e-01 7.52468944e-01 4.58290398e-01 2.86019683e-01 1.55479878e-01 6.89594030e-01 -7.83921957e-01 -1.47392142e+00 -1.34964323e+00 3.67509127e-01 -6.56124473e-01 3.85187060e-01 -5.01544416e-01 -1.17853034e+00 3.91083449e-01 -5.79790115e-01 5.48777580e-01 4.27845120e-01 -2.42235079e-01 -4.20943916e-01 -3.68592590e-01 -1.37800133e+00 4.03720587e-01 1.72143793e+00 -3.13447416e-01 -6.61746681e-01 3.75233173e-01 1.01548862e+00 -5.50351024e-01 -6.08081520e-01 7.68616438e-01 5.53021312e-01 -1.12969375e+00 1.03496039e+00 -6.55930102e-01 8.55168477e-02 -3.29377353e-01 -3.03229719e-01 -1.42067051e+00 -1.43824220e-01 -4.09979254e-01 -6.68127000e-01 8.45503867e-01 1.77527145e-01 -2.67707437e-01 1.04410422e+00 4.65613186e-01 -3.50674808e-01 -6.98943615e-01 -1.09154379e+00 -1.51758909e+00 -1.25018328e-01 -9.53887463e-01 8.25594366e-01 8.01183105e-01 -6.89348400e-01 -9.19705024e-04 -2.97707707e-01 3.61325815e-02 9.02772367e-01 2.23970994e-01 1.03378081e+00 -1.44488585e+00 -2.56371051e-01 -2.17499919e-02 -6.34454846e-01 -7.52464533e-01 -2.13633284e-01 -7.89472461e-01 1.18856188e-02 -1.29985166e+00 -5.71363457e-02 -6.73603475e-01 -2.81818330e-01 4.06305760e-01 1.86348155e-01 1.67556167e-01 1.97450668e-01 1.89711154e-01 -6.54261708e-01 7.04577923e-01 1.26509023e+00 -4.14046258e-01 -5.12256444e-01 1.77637771e-01 -1.63945526e-01 5.78005612e-01 7.07810998e-01 -7.18556702e-01 -7.59731233e-01 -4.97651517e-01 2.24768192e-01 -3.11196655e-01 3.12780142e-01 -1.63427222e+00 3.57298732e-01 -2.21132487e-01 5.75826347e-01 -1.17866170e+00 5.35531521e-01 -9.90370512e-01 4.37524706e-01 4.19531047e-01 1.36413604e-01 -3.47635388e-01 4.12703902e-01 8.10991228e-01 1.33594260e-01 -4.85399008e-01 8.82712066e-01 -5.64900756e-01 -1.12286496e+00 6.43433452e-01 7.62596130e-02 6.55824132e-03 1.30603170e+00 -7.53099024e-01 -8.03825259e-02 4.85499948e-02 -9.62997019e-01 -2.33369648e-01 6.81344926e-01 6.31175399e-01 1.12181103e+00 -1.41182733e+00 -5.04383326e-01 7.03487933e-01 5.85708797e-01 4.35571522e-01 5.30126333e-01 1.56996131e-01 -3.83819044e-01 -1.44451782e-01 -2.65420765e-01 -9.46853936e-01 -8.45836997e-01 7.73432374e-01 2.25713521e-01 1.75608277e-01 -7.22052217e-01 8.47944915e-01 -2.42584974e-01 -7.06586003e-01 3.15250754e-01 -1.63963437e-01 3.55996013e-01 2.54078843e-02 5.34997642e-01 4.75427389e-01 4.19850796e-01 -3.95090180e-03 -1.44749612e-01 6.38066769e-01 -1.07826062e-01 1.90861553e-01 1.86847281e+00 8.90366212e-02 3.58636916e-01 1.19830716e+00 9.83225346e-01 -4.91561055e-01 -1.63649917e+00 -4.84293878e-01 -1.15681939e-01 -6.25063121e-01 -3.19694102e-01 -8.60335410e-01 -5.98286629e-01 8.94073308e-01 1.09834456e+00 -7.83511102e-02 9.25558150e-01 1.26804933e-01 6.25782251e-01 7.28363514e-01 1.03060091e+00 -1.28868723e+00 5.54535031e-01 6.65267646e-01 9.65700209e-01 -1.30253875e+00 7.88088813e-02 2.20118109e-02 -2.54359901e-01 1.23225260e+00 7.92410791e-01 -1.00620098e-01 9.28953111e-01 1.68715268e-01 -2.56327003e-01 -2.51316041e-01 -8.01708281e-01 -1.07334614e-01 -3.65029946e-02 1.07395208e+00 -4.89890426e-01 -2.20167756e-01 3.91657084e-01 3.84651899e-01 -6.06912468e-03 2.17794314e-01 5.28374076e-01 1.25634527e+00 -5.43599367e-01 -8.92089844e-01 -3.62731248e-01 6.28924906e-01 4.57267612e-01 1.97529212e-01 -1.06928229e-01 1.05933940e+00 5.61209798e-01 3.55383694e-01 2.72957534e-01 -5.36322415e-01 9.08457816e-01 2.74756044e-01 8.77403021e-01 -9.75201488e-01 1.26440153e-01 -4.13377196e-01 -3.75862509e-01 -4.47989374e-01 -4.11528140e-01 -8.20453167e-01 -9.00219142e-01 -3.40961069e-02 -2.55565763e-01 -2.54939109e-01 7.90861547e-01 5.33962429e-01 4.65384662e-01 3.77837390e-01 7.81337857e-01 -1.17706978e+00 -7.04101562e-01 -8.38570595e-01 -5.80386341e-01 4.83157456e-01 1.44905895e-01 -9.38957572e-01 -5.09813488e-01 1.28660098e-01]	[7.961592674255371, -3.211730718612671]
132a996b-4283-4c4b-bd79-f6bcdaa79af2	electronic-excited-states-in-deep-variational	2203.09472	null	https://arxiv.org/abs/2203.09472v3	https://arxiv.org/pdf/2203.09472v3.pdf	Electronic excited states in deep variational Monte Carlo	Obtaining accurate ground and low-lying excited states of electronic systems is crucial in a multitude of important applications. One ab initio method for solving the Schr\"odinger equation that scales favorably for large systems is variational quantum Monte Carlo (QMC). The recently introduced deep QMC approach uses ansatzes represented by deep neural networks and generates nearly exact ground-state solutions for molecules containing up to a few dozen electrons, with the potential to scale to much larger systems where other highly accurate methods are not feasible. In this paper, we extend one such ansatz (PauliNet) to compute electronic excited states. We demonstrate our method on various small atoms and molecules and consistently achieve high accuracy for low-lying states. To highlight the method's potential, we compute the first excited state of the much larger benzene molecule, as well as the conical intersection of ethylene, with PauliNet matching results of more expensive high-level methods.	['Frank Noé', 'Jan Hermann', 'Paolo A. Erdman', 'Zeno Schätzle', 'Mike Entwistle']	2022-03-17	null	null	null	null	['variational-monte-carlo']	['miscellaneous']	[ 6.74443925e-03 -2.77273208e-01 -1.85383260e-02 -9.71179381e-02 -1.08126950e+00 -3.93459409e-01 4.96693134e-01 2.81922162e-01 -5.79357743e-01 1.43275321e+00 -7.45723322e-02 -4.97985840e-01 1.25116706e-01 -1.01011312e+00 -5.39511919e-01 -1.08421290e+00 -8.09872597e-02 7.10596800e-01 -1.76516756e-01 -5.07705212e-01 4.84385848e-01 8.99377108e-01 -1.51995659e+00 4.36024576e-01 9.84178901e-01 8.27560723e-01 -2.70706654e-01 5.16519010e-01 9.88902748e-02 3.12116623e-01 -1.50270894e-01 -4.30842608e-01 1.32671937e-01 -4.73979205e-01 -1.00854051e+00 -6.92372680e-01 4.95547473e-01 1.57390756e-03 -3.85773748e-01 1.15455890e+00 8.23322356e-01 5.81349134e-01 9.88509655e-01 -6.50562704e-01 -4.62225288e-01 5.55601120e-01 -1.55853570e-01 -5.16365580e-02 2.20399529e-01 5.57903230e-01 1.19204950e+00 -9.41099763e-01 5.46448588e-01 8.46328676e-01 9.74769890e-01 7.15061486e-01 -1.53451073e+00 -6.82563126e-01 -9.85448480e-01 2.57007599e-01 -1.79369974e+00 -5.30350268e-01 7.06262708e-01 -2.31914699e-01 1.74115038e+00 3.31149884e-02 7.49799192e-01 9.99229252e-01 6.64962888e-01 2.74840891e-02 1.00621200e+00 -6.51041389e-01 5.44888020e-01 -1.49531052e-01 2.00864673e-01 5.21074593e-01 4.66530383e-01 6.06550634e-01 -2.92301476e-01 -5.52220345e-01 2.74625272e-01 4.03225608e-02 -1.06362760e-01 -4.51166123e-01 -1.09627855e+00 1.25641060e+00 7.96430886e-01 2.70599037e-01 -8.23239267e-01 6.19955838e-01 3.98173034e-01 -3.17291141e-01 3.64872925e-02 1.05592716e+00 -1.24897391e-01 -2.77277768e-01 -1.14565730e+00 6.60747230e-01 8.96031380e-01 4.09975737e-01 9.67313886e-01 1.46833703e-01 -1.87624663e-01 2.37611204e-01 5.90258762e-02 2.78375208e-01 -7.32415244e-02 -1.22277570e+00 8.40874091e-02 -9.92218964e-03 6.24190867e-01 -4.56625789e-01 -4.24200028e-01 -2.01547921e-01 -1.17453992e+00 2.49616832e-01 2.28419319e-01 -4.16629046e-01 -6.22879684e-01 1.21312833e+00 2.88751185e-01 -3.23028237e-01 3.45062345e-01 7.77299941e-01 9.75692570e-01 1.16389525e+00 -4.11352701e-02 -6.55091286e-01 8.94961596e-01 -6.97250366e-01 -5.76026320e-01 1.87236831e-01 7.12946653e-01 -5.94529271e-01 4.48238134e-01 4.37101662e-01 -1.43405199e+00 -4.08635288e-01 -8.53285372e-01 -2.33440012e-01 -4.77429330e-01 -2.00743467e-01 1.19927073e+00 6.48762584e-01 -9.03783679e-01 1.61177003e+00 -8.63338232e-01 1.08203720e-02 3.57278287e-01 6.99080706e-01 -4.14275706e-01 1.48176908e-01 -1.36768711e+00 1.03420246e+00 8.03000271e-01 1.28347218e-01 -9.06802893e-01 -5.61264455e-01 -6.31214619e-01 2.17801601e-01 6.43713921e-02 -6.57046080e-01 1.30133426e+00 -3.60552043e-01 -1.60709345e+00 5.67077518e-01 -4.92812753e-01 -5.64641595e-01 5.30050807e-02 3.65641624e-01 -2.84678161e-01 9.88062620e-02 -3.08197364e-02 8.40268075e-01 1.38397694e-01 -8.53211999e-01 1.20117106e-01 -3.71353149e-01 -1.75011337e-01 4.68116216e-02 1.03160292e-01 -1.57981738e-01 3.13729495e-01 3.77278060e-01 1.11850597e-01 -9.19747710e-01 -7.75801659e-01 -6.63889766e-01 -4.89204288e-01 -4.49153364e-01 8.89306888e-02 -1.57027924e-03 9.87525940e-01 -1.61541474e+00 2.41078526e-01 3.22702110e-01 2.36989453e-01 2.52665877e-01 1.09668344e-01 1.08400464e+00 -5.22622108e-01 4.81478572e-02 -2.99841523e-01 -2.58061439e-01 4.42148522e-02 1.16676494e-01 9.63695422e-02 3.97614956e-01 1.45383209e-01 1.16001725e+00 -7.10294425e-01 -1.99676275e-01 1.76397398e-01 7.52656400e-01 -1.06346834e+00 -2.60092080e-01 -1.43431708e-01 3.67456555e-01 -1.09151088e-01 4.56874698e-01 7.87978649e-01 -4.76019830e-01 6.76129758e-02 -5.00419199e-01 -8.60270858e-01 5.17099917e-01 -1.02001560e+00 1.60795224e+00 -1.19402371e-01 4.08174753e-01 -1.02863617e-01 -9.85763848e-01 6.04341686e-01 4.98157591e-01 5.33606887e-01 -3.89228404e-01 2.13180333e-01 5.85455239e-01 5.19326329e-01 -1.86513573e-01 7.68377244e-01 -8.74943852e-01 3.30151916e-02 -3.44390459e-02 1.60639092e-01 -7.91205227e-01 4.34191763e-01 2.35355422e-01 6.82715058e-01 -2.81709552e-01 6.59380674e-01 -5.94203591e-01 2.94339389e-01 4.53621715e-01 -3.97583842e-02 9.52588260e-01 -2.90799946e-01 5.07138371e-01 3.47187281e-01 -7.69170642e-01 -1.34049094e+00 -7.56737173e-01 -7.52772212e-01 4.91100907e-01 -6.87440783e-02 -6.96042955e-01 -8.77444327e-01 2.02197626e-01 1.28453299e-01 8.48104775e-01 -4.62550998e-01 -3.00958872e-01 -5.77916503e-02 -1.21048367e+00 3.13168734e-01 2.20940605e-01 3.89842898e-01 -1.47938597e+00 -2.48167440e-01 5.22944927e-01 8.28254074e-02 -6.36984587e-01 6.38547465e-02 7.07630455e-01 -8.31405342e-01 -7.24019885e-01 -5.42684257e-01 -2.17035800e-01 1.12470493e-01 -2.24261433e-01 1.09658599e+00 -2.50277132e-01 -5.96877754e-01 -3.96804601e-01 1.69339642e-01 -1.23457603e-01 -6.55401826e-01 1.77877054e-01 4.89459306e-01 -4.24427032e-01 6.25186920e-01 -5.77236474e-01 -5.95029891e-01 -4.77390856e-01 -4.43908453e-01 -1.10217258e-01 3.85786802e-01 9.74109590e-01 7.21768498e-01 1.74188003e-01 6.91733733e-02 -7.15899825e-01 6.98586643e-01 -2.38583788e-01 -8.33818316e-01 -2.49081373e-01 -4.21728671e-01 4.17854160e-01 8.97653937e-01 1.60131454e-01 -7.73325562e-01 2.43013754e-01 -9.19613481e-01 -1.09747335e-01 -3.11009914e-01 5.58184326e-01 2.78476924e-01 -5.07229686e-01 1.13305879e+00 2.20971003e-01 -3.94266903e-01 -3.30818921e-01 1.52855441e-01 6.01337910e-01 6.24435782e-01 -8.97735119e-01 4.20968443e-01 3.45339268e-01 7.50189126e-01 -1.01021409e+00 -1.03678000e+00 -3.30389500e-01 -8.91018808e-01 2.72021204e-01 1.11326730e+00 -6.82252526e-01 -1.52242148e+00 1.06319368e-01 -1.54969335e+00 -6.55129999e-02 -4.05341685e-01 5.98691404e-01 -5.18518865e-01 3.91457707e-01 -8.30148995e-01 -1.07600176e+00 -4.40512508e-01 -1.53769541e+00 1.17186940e+00 2.23921984e-01 -7.83227980e-02 -8.68407965e-01 3.92387718e-01 1.65283829e-01 4.17043656e-01 5.18102586e-01 9.78875041e-01 -1.94593444e-01 -5.41209340e-01 -4.98265564e-01 -1.74976274e-01 1.11911215e-01 -4.14433867e-01 2.67355502e-01 -8.85438979e-01 -4.40954626e-01 -4.42125410e-01 -4.81666863e-01 1.15578866e+00 6.77342355e-01 1.14413917e+00 -9.17515997e-03 -3.67003977e-01 5.94793260e-01 1.55371511e+00 3.70831192e-01 8.02859426e-01 -3.20322603e-01 7.69519091e-01 9.92835537e-02 1.70177165e-02 5.55226862e-01 -1.26455009e-01 5.60592651e-01 5.77455223e-01 1.09785974e-01 4.82293516e-01 -9.67836678e-02 8.41264203e-02 7.84829915e-01 -8.24284315e-01 -1.64158478e-01 -1.02156663e+00 7.05730766e-02 -1.36410332e+00 -1.42933714e+00 -8.14980030e-01 2.23686838e+00 9.51951206e-01 9.24609080e-02 -1.15239404e-01 4.00756896e-02 3.41300726e-01 -3.53874057e-05 -6.37063086e-01 -7.13665605e-01 1.97864190e-01 1.02569234e+00 2.88947016e-01 7.40124404e-01 -9.74948168e-01 1.11001861e+00 7.85135984e+00 8.33577514e-01 -9.95132923e-01 1.37110129e-01 4.30904359e-01 -1.63000539e-01 -1.03686387e-02 2.55291015e-01 -1.01373100e+00 3.73727351e-01 1.39142787e+00 1.16820289e-02 6.24043882e-01 7.51623929e-01 2.14945212e-01 -1.95688680e-01 -1.10405850e+00 1.22275221e+00 -6.09264553e-01 -2.08255100e+00 2.81364694e-02 4.29323554e-01 1.18315387e+00 3.31438065e-01 -1.86196595e-01 3.64943653e-01 -5.25139235e-02 -1.33315802e+00 2.22855747e-01 4.25295472e-01 1.04515457e+00 -1.28753138e+00 7.27445424e-01 2.79893309e-01 -8.39308500e-01 2.43756667e-01 -7.42430687e-01 -5.05084693e-01 7.97140226e-02 5.86110234e-01 -3.03179175e-01 2.45230898e-01 4.19388413e-01 5.43389201e-01 -4.63570841e-02 7.81710446e-01 2.28721350e-01 4.32960987e-01 -3.30266505e-01 -5.92807710e-01 6.80388212e-01 -7.09502220e-01 9.97854099e-02 8.01684439e-01 4.53211486e-01 4.05913979e-01 -9.84035060e-02 1.42637062e+00 -2.18813732e-01 5.84629700e-02 -7.80812681e-01 -4.41412777e-01 3.49359095e-01 1.29983687e+00 -3.02626401e-01 -4.16435391e-01 -5.69950379e-02 1.09059632e+00 3.58887374e-01 3.42570215e-01 -6.47263288e-01 -6.99531972e-01 6.73219800e-01 -7.09064528e-02 1.63845330e-01 -3.52185845e-01 1.90880314e-01 -1.28917015e+00 -4.78293896e-01 -7.21825778e-01 -1.31277770e-01 -8.02775741e-01 -1.20224917e+00 4.34902281e-01 -1.58379763e-01 -3.73881727e-01 -3.35586101e-01 -1.19036067e+00 -8.05362761e-01 1.23912370e+00 -1.34660733e+00 -4.06380713e-01 1.09548494e-02 3.61136079e-01 -2.65914261e-01 -1.09247770e-02 1.20518470e+00 1.57450587e-01 -7.99463987e-01 1.74469098e-01 8.83079231e-01 -2.83851206e-01 2.02223569e-01 -1.37994194e+00 4.67225730e-01 4.22820240e-01 5.68389297e-02 9.64189708e-01 9.21213686e-01 -3.25548708e-01 -1.67454398e+00 -6.06894851e-01 9.21548724e-01 -4.16864634e-01 5.28887928e-01 -2.97181219e-01 -7.85149455e-01 2.62591451e-01 2.22434506e-01 2.56692410e-01 6.70408428e-01 8.13482627e-02 8.55760798e-02 2.64690757e-01 -1.15993333e+00 4.47459608e-01 8.11449468e-01 -8.22767198e-01 -1.79600894e-01 8.85802567e-01 2.79160768e-01 -4.05047625e-01 -8.82844627e-01 2.44752422e-01 5.02619624e-01 -1.55830646e+00 1.01553249e+00 -9.20285165e-01 5.85483670e-01 1.04325324e-01 -2.69422054e-01 -1.29597545e+00 -3.67017955e-01 -1.00858533e+00 -9.50465202e-02 2.64591575e-01 3.54435205e-01 -7.50998318e-01 9.10353482e-01 5.51135123e-01 -3.75568300e-01 -6.89538360e-01 -1.19672430e+00 -5.31243861e-01 7.48491824e-01 -2.82067269e-01 5.85087538e-01 7.91419685e-01 2.13994682e-01 4.71378744e-01 -2.78576523e-01 1.97473601e-01 7.28192925e-01 3.66706520e-01 2.50331223e-01 -1.48652530e+00 -3.05667460e-01 -3.79821122e-01 -1.36409044e-01 -6.57375634e-01 4.15745348e-01 -1.21247911e+00 2.41089519e-02 -1.26601827e+00 5.69886744e-01 1.16286604e-02 -1.05056763e-01 2.62517244e-01 2.05946371e-01 6.49844885e-01 -2.85913557e-01 3.94042619e-02 -6.00606978e-01 7.61012971e-01 1.20422781e+00 2.91703567e-02 -4.18083631e-02 -2.40707844e-01 -4.36829150e-01 5.79266548e-01 5.92865884e-01 -5.24594963e-01 5.09549141e-01 2.63302833e-01 4.98124987e-01 1.88535839e-01 4.90490109e-01 -1.36760712e+00 1.42283812e-01 -4.85990159e-02 4.60721046e-01 -6.96431577e-01 6.89734936e-01 -3.59133363e-01 2.32598156e-01 7.54477501e-01 -2.54793882e-01 -1.50251284e-01 2.26511210e-01 1.66349597e-02 -2.47390866e-01 -7.46999443e-01 1.26416838e+00 -5.86722612e-01 -3.07514250e-01 5.55218041e-01 -4.26445574e-01 -1.88125163e-01 8.15186024e-01 -1.00800209e-01 1.12369962e-01 -2.77757764e-01 -8.09699595e-01 -1.17766179e-01 5.64578295e-01 -6.00885034e-01 4.00359303e-01 -1.42166257e+00 -3.23456019e-01 2.03988001e-01 8.96263644e-02 -9.10738036e-02 3.31180215e-01 6.92324936e-01 -1.02679098e+00 9.13433135e-01 -1.25397265e-01 -3.66778851e-01 -8.12088609e-01 5.08223295e-01 9.48435426e-01 -1.77275866e-01 -9.85219702e-03 7.45429516e-01 -2.95280039e-01 -6.27991796e-01 -4.14876640e-01 -2.35137284e-01 2.72835761e-01 -1.26747742e-01 3.14699709e-01 3.81176740e-01 2.63267726e-01 -8.02322865e-01 -2.57669896e-01 3.81299287e-01 3.95649206e-03 1.64149567e-01 1.33144939e+00 5.97025514e-01 -4.49748576e-01 1.13916881e-01 1.46864307e+00 -1.22854941e-01 -1.04826987e+00 3.39472532e-01 -4.53753412e-01 6.72292337e-02 5.06180406e-01 -4.04377997e-01 -5.22720098e-01 1.56297541e+00 6.31288469e-01 3.35947633e-01 2.23663881e-01 -2.29912937e-01 7.87414372e-01 1.25919342e+00 4.73107427e-01 -7.41777599e-01 -3.26089770e-01 6.51985884e-01 2.99452931e-01 -1.65727532e+00 7.94668868e-02 -2.69923359e-02 -1.89952567e-01 1.44902527e+00 2.56609946e-01 -2.95676421e-02 2.52946526e-01 -3.04492302e-02 -6.48755968e-01 -5.63492119e-01 -4.83733147e-01 -2.07716107e-01 2.69308627e-01 2.74430603e-01 8.96051109e-01 2.01144233e-01 -2.33754784e-01 1.72493890e-01 -2.63810694e-01 -1.91934295e-02 6.77726448e-01 5.77676892e-01 -5.32659233e-01 -1.21458852e+00 -1.64907381e-01 3.62616450e-01 -4.61804479e-01 -7.20211089e-01 -2.51936227e-01 7.61155486e-01 1.27008066e-01 7.57768035e-01 3.00770141e-02 1.63547605e-01 -2.43349466e-03 4.08104986e-01 8.25946391e-01 -6.97910607e-01 -4.72033679e-01 -2.95312107e-01 -6.66947961e-02 -7.22531676e-01 -3.73371124e-01 -5.94576538e-01 -1.42867362e+00 -7.55692422e-01 -5.76910436e-01 8.91117752e-01 5.20042002e-01 8.57245922e-01 4.40079898e-01 1.79281533e-01 2.82630146e-01 -1.64688885e+00 -8.58644664e-01 -7.03966498e-01 -8.53019714e-01 2.97765702e-01 3.98630440e-01 -4.59650725e-01 -4.09238458e-01 -6.64411187e-01]	[5.356338024139404, 5.165130138397217]
03119499-f861-40f9-b6c3-e5d151e26ba3	model-based-learning-for-accelerated-limited	1708.09832	null	http://arxiv.org/abs/1708.09832v3	http://arxiv.org/pdf/1708.09832v3.pdf	Model based learning for accelerated, limited-view 3D photoacoustic tomography	Recent advances in deep learning for tomographic reconstructions have shown great potential to create accurate and high quality images with a considerable speed-up. In this work we present a deep neural network that is specifically designed to provide high resolution 3D images from restricted photoacoustic measurements. The network is designed to represent an iterative scheme and incorporates gradient information of the data fit to compensate for limited view artefacts. Due to the high complexity of the photoacoustic forward operator, we separate training and computation of the gradient information. A suitable prior for the desired image structures is learned as part of the training. The resulting network is trained and tested on a set of segmented vessels from lung CT scans and then applied to in-vivo photoacoustic measurement data.	['Marta Betcke', 'Sebastien Ourselin', 'Jonas Adler', 'Simon Arridge', 'Felix Lucka', 'Ben Cox', 'Andreas Hauptmann', 'Paul Beard', 'Nam Huynh']	2017-08-31	null	null	null	null	['tomographic-reconstructions']	['medical']	[ 4.90859866e-01 3.60406786e-02 3.07410657e-01 -3.26118708e-01 -7.34858394e-01 5.95532507e-02 2.56150275e-01 -1.26521096e-01 -6.27472639e-01 4.70099032e-01 1.29127875e-01 -3.45096797e-01 -1.50617629e-01 -8.07316899e-01 -5.95440984e-01 -8.74961734e-01 -3.46474499e-02 6.32965744e-01 4.28503960e-01 2.60109961e-01 4.05018143e-02 8.49995852e-01 -1.15050125e+00 2.02947199e-01 4.43060428e-01 1.05918658e+00 3.49333614e-01 9.45305586e-01 -7.07764700e-02 7.88880229e-01 -3.24488550e-01 -2.77428068e-02 3.23295057e-01 -5.96669316e-01 -8.35086167e-01 2.60760576e-01 5.63498616e-01 -7.11802244e-01 -4.11407053e-01 6.67001903e-01 1.06878841e+00 1.68504953e-01 8.12068701e-01 -4.48718488e-01 -3.16041619e-01 3.47262353e-01 -2.83230513e-01 5.31660438e-01 5.42790219e-02 6.03129491e-02 6.45521462e-01 -1.03524065e+00 4.63333815e-01 6.53615654e-01 7.13144720e-01 7.93764114e-01 -1.26834571e+00 -1.96670368e-01 -7.15931118e-01 1.10681705e-01 -1.00312865e+00 -4.89716560e-01 7.59527624e-01 -4.17969912e-01 9.13482666e-01 2.40992382e-02 7.76479721e-01 6.73274636e-01 2.52354026e-01 3.64704311e-01 1.08891082e+00 -4.98625040e-01 2.91632086e-01 -3.95012572e-02 -2.92963266e-01 8.66065800e-01 -2.94398926e-02 4.71747190e-01 -1.81760967e-01 -1.59618072e-02 1.33125091e+00 -2.97032535e-01 -5.72023809e-01 -5.18104315e-01 -8.97290349e-01 6.82097733e-01 7.70434141e-01 3.55549604e-01 -5.72152615e-01 2.47516423e-01 2.01131225e-01 -4.71518971e-02 1.39759794e-01 4.89047736e-01 -4.06397395e-02 1.10714689e-01 -9.05913055e-01 -1.63212940e-01 8.03474963e-01 1.96844473e-01 5.30089676e-01 4.36298251e-01 1.82074428e-01 9.22284245e-01 3.19650352e-01 1.80636004e-01 7.86170721e-01 -1.23516035e+00 -8.75562653e-02 6.58136159e-02 -2.05720216e-01 -5.12484372e-01 -5.60690165e-01 -3.45235050e-01 -8.90443981e-01 6.67619586e-01 5.60219824e-01 3.07607912e-02 -1.22788358e+00 1.21839190e+00 6.54918492e-01 3.19997370e-01 1.54631227e-01 1.15142453e+00 1.09159577e+00 6.08067632e-01 -6.42956868e-02 -2.12842092e-01 9.52554762e-01 -6.90906942e-01 -3.84759724e-01 -1.12421557e-01 3.68449152e-01 -7.46854842e-01 7.68370807e-01 4.65409577e-01 -1.53612554e+00 -5.95790386e-01 -1.10490501e+00 -2.26974115e-01 8.32590237e-02 -5.57915606e-02 1.80175856e-01 5.39416254e-01 -1.19013596e+00 8.31725419e-01 -9.10115182e-01 5.91175146e-02 6.59488201e-01 3.98059160e-01 -3.53748381e-01 3.17054652e-02 -8.66739690e-01 9.81016517e-01 1.19241156e-01 2.50539660e-01 -1.08572721e+00 -9.30896521e-01 -6.88884079e-01 5.77876344e-02 9.66786444e-02 -9.77452397e-01 1.45934927e+00 -5.56741774e-01 -2.06715369e+00 1.00602841e+00 2.48429492e-01 -6.34676874e-01 6.28965676e-01 2.06499830e-01 1.16757326e-01 7.88525105e-01 -2.73379236e-01 5.35799026e-01 8.75777006e-01 -1.34704840e+00 -2.61195838e-01 -1.17363945e-01 -2.62392163e-01 3.07616740e-01 1.34652242e-01 -1.79594412e-01 -1.82257667e-01 -3.86662692e-01 2.91786313e-01 -8.07297409e-01 -4.74684685e-01 3.86017412e-01 -1.84147149e-01 3.13407749e-01 4.93877351e-01 -6.63653135e-01 5.76716125e-01 -1.77080631e+00 1.33964360e-01 2.25503474e-01 2.88914710e-01 4.02833164e-01 -6.05949648e-02 8.70610699e-02 -1.97560236e-01 -1.40596002e-01 -6.21331751e-01 -3.31774473e-01 -4.37131941e-01 3.25628370e-01 4.15009893e-02 5.84079802e-01 -3.34290531e-03 7.73186684e-01 -5.97859502e-01 -4.83139157e-01 5.81849635e-01 7.66145706e-01 -4.53548491e-01 5.05085528e-01 -5.19027859e-02 7.53223300e-01 -3.54829639e-01 2.32494771e-01 7.63730109e-01 -3.34687740e-01 -3.26987095e-02 -3.28661919e-01 -2.38315344e-01 3.51156890e-01 -7.82038629e-01 1.58050597e+00 -6.64558470e-01 5.30833840e-01 4.32638854e-01 -1.11748731e+00 5.96984923e-01 6.66792750e-01 9.27050889e-01 -7.18334675e-01 4.66942370e-01 3.62327039e-01 1.74990043e-01 -7.22908556e-01 1.69543701e-03 -7.80461609e-01 7.01347589e-01 7.06301332e-01 1.79441236e-02 -7.72757232e-01 -1.33449614e-01 -1.30104750e-01 7.14672565e-01 -2.78911591e-02 2.30560988e-01 -1.41757712e-01 8.73932779e-01 -1.54563747e-02 6.88047782e-02 3.74221444e-01 -1.65370241e-01 9.94849861e-01 6.64135143e-02 -7.20850468e-01 -1.45523322e+00 -8.80069911e-01 -5.16290426e-01 3.76445353e-01 -2.33393818e-01 3.35349083e-01 -6.92833006e-01 -4.46538627e-01 -2.50055283e-01 4.64639753e-01 -4.61446375e-01 -1.34613318e-02 -9.51825261e-01 -7.16234207e-01 2.29528919e-01 4.86217380e-01 4.54786956e-01 -1.19889700e+00 -9.86776888e-01 4.92656857e-01 -6.44346103e-02 -1.21053278e+00 -1.93941996e-01 3.62929463e-01 -1.05993021e+00 -1.07772684e+00 -9.71585393e-01 -9.92756665e-01 6.09483480e-01 2.30986029e-01 9.83908117e-01 1.94870561e-01 -5.78067124e-01 3.45874071e-01 6.38570860e-02 -2.85775572e-01 -9.26836312e-01 -4.13100988e-01 -1.80196241e-01 -2.47124135e-01 -9.13738757e-02 -8.62347126e-01 -8.97739589e-01 3.04528594e-01 -9.53355968e-01 -1.71191357e-02 7.39305377e-01 1.04370368e+00 8.58023047e-01 1.08173050e-01 2.17174560e-01 -5.61264575e-01 4.16810602e-01 -4.80961874e-02 -6.64796472e-01 -2.11420819e-01 -4.59878683e-01 -1.82919241e-02 6.83534145e-01 -5.21277010e-01 -1.22927964e+00 2.22506329e-01 -8.32609773e-01 -4.27264810e-01 -3.70589554e-01 4.52761412e-01 4.37612355e-01 -5.57945788e-01 7.95331538e-01 3.43039423e-01 2.57308334e-01 -3.62296522e-01 2.82022692e-02 3.97995323e-01 7.03095555e-01 -3.62356603e-01 6.14486933e-01 8.02334011e-01 4.57258195e-01 -9.81456816e-01 -8.87138903e-01 -3.76975864e-01 -8.68603110e-01 -2.95680165e-01 1.01792932e+00 -5.11248291e-01 -6.03829265e-01 5.18467546e-01 -8.19317877e-01 -8.12191069e-01 -7.28487849e-01 7.88340032e-01 -7.48003304e-01 6.35953605e-01 -8.38054776e-01 -4.58433390e-01 -6.09880745e-01 -1.20064759e+00 7.05623448e-01 2.01619312e-01 3.49482805e-01 -1.14362681e+00 3.73829097e-01 2.89279610e-01 4.56645608e-01 2.03129262e-01 9.35494542e-01 -2.40406632e-01 -5.70938230e-01 -2.08150178e-01 -2.07282260e-01 7.26348639e-01 -3.29679325e-02 -8.16595331e-02 -1.17296052e+00 -1.17383696e-01 4.05492812e-01 -5.33637166e-01 8.67468417e-01 1.00838089e+00 1.25387359e+00 3.27678248e-02 7.84755033e-03 8.66278410e-01 1.43899357e+00 1.29529893e-01 6.04881585e-01 1.31564185e-01 5.50215542e-01 4.69745696e-01 2.11190376e-02 1.63454846e-01 -2.01786757e-02 4.27615404e-01 5.57857275e-01 -3.37268710e-01 -6.65369928e-01 -1.46259859e-01 -1.50951911e-02 7.78758764e-01 -1.46928310e-01 -1.54688163e-02 -7.56158173e-01 4.22805518e-01 -1.09566963e+00 -7.51272559e-01 -1.43864930e-01 2.17534471e+00 7.45949090e-01 9.38729048e-02 -8.98450427e-03 3.11026722e-01 2.87622005e-01 -6.88777268e-02 -3.55012566e-01 -4.40231472e-01 2.30551735e-01 6.21715128e-01 3.39808911e-01 7.27301776e-01 -8.69938612e-01 4.24313545e-01 7.75730181e+00 4.95114833e-01 -1.31312120e+00 -5.57818413e-02 5.37116587e-01 2.52223909e-01 -1.04913734e-01 -3.58186334e-01 -2.54286319e-01 1.32665960e-02 9.20891106e-01 8.53213370e-02 2.69028544e-01 3.48287195e-01 3.06719750e-01 -3.21698487e-01 -8.80599439e-01 6.86488867e-01 -1.61150798e-01 -1.43420684e+00 6.17427155e-02 2.44463548e-01 6.28045738e-01 2.29454041e-01 1.38030902e-01 -3.61408442e-01 1.85272604e-01 -1.03260136e+00 3.28805536e-01 3.34572971e-01 8.18239272e-01 -5.41423976e-01 6.55891299e-01 3.73585790e-01 -7.00224578e-01 -3.58335711e-02 -7.34131277e-01 1.69653401e-01 5.04685462e-01 5.92216134e-01 -1.31675935e+00 3.39914560e-01 5.74537098e-01 4.45491850e-01 1.35804117e-02 1.37427115e+00 -2.29353845e-01 5.99820256e-01 -4.38728392e-01 1.90051630e-01 3.81881505e-01 -2.44205579e-01 5.02420604e-01 1.01502514e+00 5.90422511e-01 2.64851630e-01 -1.37071058e-01 8.60636890e-01 -2.40956601e-02 1.44028991e-01 -4.45823669e-01 2.50200510e-01 -5.79713918e-02 1.37779498e+00 -6.55312538e-01 -2.71196753e-01 -3.55038166e-01 5.95315754e-01 1.89810276e-01 2.76369572e-01 -4.67592627e-01 1.90538719e-01 5.06490357e-02 3.76934499e-01 4.92175579e-01 -2.33386829e-01 -1.65765345e-01 -7.55295277e-01 -4.41887438e-01 -3.59942019e-01 4.11612779e-01 -1.05112159e+00 -1.15229595e+00 7.38924742e-01 -1.27155542e-01 -9.81403828e-01 -2.45388970e-01 -7.92095423e-01 -8.75222504e-01 9.69513893e-01 -1.92173409e+00 -9.07660067e-01 -5.23124874e-01 5.33690453e-01 3.84363323e-01 5.28970324e-02 8.86675477e-01 2.69048125e-01 -2.65764058e-01 9.09633934e-02 -1.81125309e-02 6.72521591e-02 4.10792559e-01 -1.50189400e+00 1.71167538e-01 6.76693320e-01 8.85756761e-02 2.33214438e-01 7.28338003e-01 -2.82426178e-01 -8.79621506e-01 -6.83849573e-01 5.84914327e-01 -1.94132775e-01 5.29253900e-01 2.54100531e-01 -1.08357477e+00 3.89922321e-01 2.22238570e-01 3.42411995e-01 7.58745670e-01 -7.33619809e-01 1.12345278e-01 1.69550460e-02 -1.32653463e+00 5.94835589e-03 3.83047819e-01 -4.01000500e-01 -5.13742685e-01 3.18959028e-01 3.15518856e-01 -8.79639745e-01 -1.10595274e+00 2.71325827e-01 6.56521559e-01 -1.04201722e+00 1.25154734e+00 -1.48405448e-01 6.36766613e-01 -1.28395766e-01 2.67426491e-01 -1.38833094e+00 -3.64943981e-01 -4.54889476e-01 3.93511578e-02 3.53885591e-01 3.26060802e-01 -4.28982556e-01 1.11456025e+00 5.01529157e-01 -5.02432823e-01 -7.21477032e-01 -1.31999934e+00 -2.54123271e-01 4.57721144e-01 -2.28044823e-01 1.54034764e-01 4.51098084e-01 -2.74143219e-01 2.02654660e-01 -2.89763927e-01 1.55923426e-01 8.83707047e-01 7.32695013e-02 3.97202641e-01 -1.16011822e+00 -3.88756037e-01 -4.11743790e-01 -1.76553532e-01 -1.26030445e+00 -7.80385137e-02 -7.13585317e-01 2.28726715e-01 -1.64978170e+00 1.12150714e-01 -4.17707622e-01 -4.90591899e-02 1.50982708e-01 2.41985559e-01 5.85399866e-01 -2.12186038e-01 1.77316651e-01 2.05070123e-01 4.11276162e-01 1.97902465e+00 1.41347826e-01 -1.92506775e-01 4.47904766e-01 -2.74327904e-01 6.47995651e-01 7.35529363e-01 -3.53684455e-01 -2.58329302e-01 -3.50203902e-01 -1.66924015e-01 3.40355396e-01 4.55256790e-01 -1.06521857e+00 3.71899396e-01 2.10041046e-01 5.67508876e-01 -5.79918802e-01 5.67932427e-01 -1.06300581e+00 3.82195339e-02 6.78699255e-01 -5.57471812e-01 -5.35722077e-01 9.23115835e-02 4.29689974e-01 -2.24380046e-01 -5.72845995e-01 1.29407501e+00 -5.16752958e-01 -2.35805616e-01 4.68588412e-01 -5.05703628e-01 -3.27660650e-01 6.13939166e-01 -3.44181776e-01 2.04022035e-01 -4.32293266e-01 -1.08231902e+00 -2.57523954e-01 3.13756436e-01 -3.61045718e-01 8.75371337e-01 -1.23321402e+00 -7.48582542e-01 3.28529447e-01 -3.93131882e-01 3.85400325e-01 4.70698893e-01 9.74724889e-01 -9.62769806e-01 2.06871524e-01 -4.15059626e-01 -7.53248990e-01 -1.13093925e+00 4.97038752e-01 1.07941508e+00 -3.21136087e-01 -1.07337010e+00 9.74996150e-01 1.83319092e-01 -3.64125580e-01 7.58403093e-02 -4.77776408e-01 -2.69217908e-01 -4.96645004e-01 5.34003913e-01 2.86342800e-01 4.25985940e-02 -7.40397513e-01 -4.57972027e-02 8.45259130e-01 3.04779470e-01 -1.64834827e-01 1.51832271e+00 -1.15236998e-01 8.32499415e-02 1.42983869e-02 1.29872084e+00 -3.00560534e-01 -1.65087259e+00 -3.98421615e-01 -3.96903276e-01 -3.99082363e-01 5.79138219e-01 -7.24333048e-01 -1.35150635e+00 1.33247709e+00 6.21603668e-01 1.92546904e-01 1.13761735e+00 -1.88892350e-01 9.85938907e-01 4.21419814e-02 -1.25987336e-01 -8.61639500e-01 4.00009781e-01 1.20151415e-01 7.25645483e-01 -1.30608153e+00 3.66658717e-01 -3.92584980e-01 -3.84463519e-01 1.58430314e+00 3.23570877e-01 -1.68974504e-01 8.60339344e-01 4.56321448e-01 2.07913652e-01 -3.76838297e-01 -4.59094852e-01 -7.03557059e-02 3.82226765e-01 8.62637699e-01 4.31654036e-01 -3.81265223e-01 -1.26929535e-02 -2.23928526e-01 -4.78009284e-02 1.72815561e-01 7.87409008e-01 7.66295016e-01 -6.63039327e-01 -7.89908946e-01 -2.45732635e-01 1.68872103e-01 -6.58377111e-01 1.53776377e-01 -3.15393656e-02 6.45055413e-01 -1.51435614e-01 3.74320656e-01 -3.42409573e-02 1.44383654e-01 3.68353188e-01 -2.38632932e-01 8.09785903e-01 -3.58802021e-01 -4.52573180e-01 6.78232551e-01 4.31112051e-02 -3.99767339e-01 -7.38946497e-01 -5.48446655e-01 -1.44760084e+00 -3.56319826e-04 -2.55954713e-01 1.17542081e-01 7.13479161e-01 6.93486214e-01 -2.15825588e-01 6.42449498e-01 6.94123209e-01 -1.20360970e+00 -6.87769771e-01 -8.45061958e-01 -5.82533121e-01 2.57864505e-01 6.02060437e-01 -3.82888913e-01 -5.78307271e-01 9.28549170e-02]	[13.180741310119629, -2.6288888454437256]
2199f63e-3a8c-4e15-ba1b-ef539e3d4093	locally-transferred-fisher-vectors-for	null	null	http://openaccess.thecvf.com/content_iccv_2017/html/Song_Locally-Transferred_Fisher_Vectors_ICCV_2017_paper.html	http://openaccess.thecvf.com/content_ICCV_2017/papers/Song_Locally-Transferred_Fisher_Vectors_ICCV_2017_paper.pdf	Locally-Transferred Fisher Vectors for Texture Classification	Texture classification has been extensively studied in computer vision. Recent research shows that the combination of Fisher vector (FV) encoding and convolutional neural network (CNN) provides significant improvement in texture classification over the previous feature representation methods. However, by truncating the CNN model at the last convolutional layer, the CNN-based FV descriptors would not incorporate the full capability of neural networks in feature learning. In this study, we propose that we can further transform the CNN-based FV descriptors in a neural network model to obtain more discriminative feature representations. In particular, we design a locally-transferred Fisher vector (LFV) method, which involves a multi-layer neural network model containing locally connected layers to transform the input FV descriptors with filters of locally shared weights. The network is optimized based on the hinge loss of classification, and transferred FV descriptors are then used for image classification. Our results on three challenging texture image datasets show improved performance over the state of the art.	["Lauren J. O'Donnell", 'Heng Huang', 'Qing Li', 'Weidong Cai', 'Yang Song', 'Fan Zhang']	2017-10-01	null	null	null	iccv-2017-10	['texture-classification']	['computer-vision']	[ 2.55734831e-01 -3.14431399e-01 -3.59273672e-01 -7.20885336e-01 -5.29133439e-01 -1.63543895e-01 4.02752548e-01 -2.37579063e-01 -1.95458218e-01 3.43814790e-01 5.87222204e-02 -8.41350853e-02 -2.10876048e-01 -8.91722202e-01 -6.11185670e-01 -8.03393304e-01 -9.02943462e-02 -2.67768413e-01 1.67043239e-01 -3.58030759e-02 3.55272651e-01 7.67862678e-01 -1.49990618e+00 6.84702218e-01 5.16727924e-01 1.81665969e+00 -7.17215016e-02 3.92821252e-01 -1.33178964e-01 1.00479841e+00 -2.34002024e-01 -1.20737284e-01 1.30060211e-01 -2.97827393e-01 -9.56275582e-01 -6.67872578e-02 5.30623317e-01 -1.74661070e-01 -7.03396082e-01 9.62642789e-01 3.06783199e-01 2.93742925e-01 8.16199899e-01 -1.02893901e+00 -9.25999165e-01 2.44939998e-01 -3.40271950e-01 2.43078656e-02 -1.20153606e-01 -4.52746958e-01 9.85456526e-01 -1.00912046e+00 4.88946676e-01 1.37332678e+00 8.38320374e-01 2.76085496e-01 -1.13887608e+00 -5.42589188e-01 -2.86777057e-02 4.03287172e-01 -1.58700895e+00 -2.55031735e-01 1.01908815e+00 -3.76635313e-01 1.19746077e+00 1.84115708e-01 6.19762897e-01 7.95272827e-01 7.99484670e-01 8.53306174e-01 7.79968202e-01 -5.21581829e-01 -6.81164414e-02 -2.28816628e-01 7.37277940e-02 1.19333661e+00 -1.13516107e-01 -1.80100612e-02 -5.14105499e-01 -1.68998480e-01 9.40496325e-01 3.11147153e-01 2.70865280e-02 -3.44654888e-01 -8.43109190e-01 1.12879229e+00 7.15498030e-01 3.39761198e-01 -2.13744834e-01 4.15866762e-01 5.73036313e-01 5.52875519e-01 8.11538279e-01 9.77307558e-02 -4.24414515e-01 1.57219917e-01 -8.81381035e-01 -4.06413786e-02 5.81674933e-01 4.40028727e-01 9.06088948e-01 1.51218534e-01 -3.84802103e-01 1.10072851e+00 3.50650162e-01 4.72575605e-01 5.60940266e-01 -9.06569779e-01 1.35045022e-01 7.50325799e-01 -5.32545805e-01 -1.55561161e+00 -2.38917768e-01 -3.15455973e-01 -1.08931029e+00 1.63160846e-01 1.83968425e-01 3.21661085e-01 -8.74242961e-01 1.33604884e+00 -1.15183957e-01 -4.08885293e-02 -4.92194705e-02 8.39946032e-01 8.53913307e-01 5.65621018e-01 -1.48840562e-01 3.74942005e-01 1.14470768e+00 -1.19378102e+00 -5.44142306e-01 2.21226454e-01 7.63615072e-01 -8.43942523e-01 7.78542817e-01 2.95810878e-01 -6.35366857e-01 -9.70686257e-01 -1.07500601e+00 -2.88020313e-01 -4.10462588e-01 5.41253269e-01 8.73487532e-01 4.76178944e-01 -1.01375782e+00 9.59701955e-01 -1.05294633e+00 -2.42195368e-01 7.62158155e-01 5.12110531e-01 -7.29544222e-01 -1.43013120e-01 -1.00564897e+00 5.79921424e-01 2.40333557e-01 4.57250625e-01 -4.91275847e-01 -2.33476177e-01 -1.26468313e+00 3.01996887e-01 -1.13642968e-01 -3.56245756e-01 7.97316551e-01 -1.41234040e+00 -1.63512886e+00 5.47451973e-01 -3.50368083e-01 -1.68510213e-01 2.11780369e-01 3.91982906e-02 -2.17963338e-01 2.39739463e-01 -1.71651185e-01 7.44635522e-01 9.71435845e-01 -6.82291329e-01 -5.48711300e-01 -7.48205110e-02 -1.56381845e-01 -3.87484580e-01 -5.87282181e-01 -8.47723261e-02 -2.93189883e-01 -7.06207037e-01 3.49540323e-01 -7.40878284e-01 -4.72888313e-02 4.36393172e-01 -1.93456322e-01 -5.29102504e-01 1.04044104e+00 -4.26368833e-01 1.11498547e+00 -2.31961679e+00 -5.47518209e-02 5.57499111e-01 2.14758188e-01 1.33302882e-01 -5.34290552e-01 2.39905551e-01 -3.35939601e-02 -1.36095444e-02 1.36533707e-01 -3.12285662e-01 -9.50499550e-02 3.32266450e-01 -1.50633261e-01 6.15081668e-01 6.09862089e-01 1.00011492e+00 -5.26779294e-01 -4.68559951e-01 3.12347502e-01 8.92878354e-01 -8.55078876e-01 -7.13913590e-02 2.09079444e-01 1.58346683e-01 -6.94127202e-01 8.93781722e-01 7.94309795e-01 -3.61509621e-01 5.70811145e-02 -7.36338198e-01 -3.06525752e-02 -2.49765649e-01 -7.23322034e-01 1.48632777e+00 -4.30395156e-01 8.63795280e-01 -4.65904504e-01 -1.35526371e+00 1.26800990e+00 -5.59397079e-02 5.42880774e-01 -6.94306910e-01 3.96277934e-01 2.58727789e-01 -4.53837924e-02 -4.41240162e-01 3.68370652e-01 1.34880722e-01 -7.33962059e-02 1.67837232e-01 4.56534505e-01 4.18428183e-01 -1.43209472e-01 -4.55168068e-01 9.36444044e-01 1.62183106e-01 7.02298060e-02 -3.65480602e-01 8.37646186e-01 -1.40884876e-01 3.92204553e-01 6.95672452e-01 -1.78274825e-01 6.53451979e-01 6.78891778e-01 -1.02494562e+00 -9.18907523e-01 -7.21948504e-01 -4.49446559e-01 1.17620230e+00 -3.70792113e-02 -3.29465091e-01 -5.45038104e-01 -8.45992267e-01 3.19911748e-01 -2.35892743e-01 -1.02717018e+00 -5.07684529e-01 -4.24631000e-01 -4.03582036e-01 6.75145805e-01 7.16979921e-01 7.92901635e-01 -1.03320014e+00 -3.21174502e-01 1.97814316e-01 -9.47527662e-02 -7.37980604e-01 -4.92203474e-01 4.21912104e-01 -8.48195791e-01 -1.02318239e+00 -6.47233069e-01 -1.10988605e+00 8.47253442e-01 2.78620601e-01 5.04774332e-01 1.46303505e-01 -3.65988165e-01 7.88272321e-02 -5.33207774e-01 1.24138318e-01 7.92902783e-02 2.65480250e-01 -2.67291129e-01 4.75664467e-01 5.06945491e-01 -7.00168014e-02 -6.71092987e-01 2.41118029e-01 -9.80454862e-01 -1.56785250e-01 6.56887829e-01 1.58689725e+00 7.97217011e-01 -2.34338194e-02 2.96246141e-01 -6.22103393e-01 3.74689221e-01 -3.18811238e-01 -3.52916300e-01 3.45169067e-01 -2.90802032e-01 3.90549421e-01 5.48327148e-01 -5.69449902e-01 -8.46533954e-01 1.57434359e-01 -3.09049636e-01 -7.32210159e-01 7.74007589e-02 7.76231885e-01 2.19689161e-01 -9.99355614e-01 3.60961288e-01 2.75541693e-01 2.77384698e-01 -5.65566361e-01 8.76753628e-02 6.98137522e-01 1.36535674e-01 -5.75495601e-01 2.28116661e-01 4.85386968e-01 1.71893612e-01 -6.46281004e-01 -9.55207229e-01 -1.98756665e-01 -7.33704090e-01 -8.40169266e-02 7.79992640e-01 -5.62267363e-01 -8.87176514e-01 6.96193993e-01 -9.28455532e-01 -2.68708736e-01 -7.52153471e-02 5.81194162e-01 -5.50526261e-01 3.39567333e-01 -6.67142034e-01 -3.65001261e-01 -3.16339552e-01 -1.31671417e+00 1.07814074e+00 2.39293262e-01 9.02677178e-02 -1.16768289e+00 -5.71728610e-02 -9.47711021e-02 8.73994172e-01 2.59118408e-01 1.05746734e+00 -4.92072850e-01 -2.35042945e-01 -4.62632477e-01 -5.71619809e-01 8.34658146e-01 3.23353976e-01 3.63702662e-02 -1.00950396e+00 -3.39327037e-01 -1.59285679e-01 -4.84346867e-01 1.55027938e+00 5.64742625e-01 1.80301845e+00 -2.60070950e-01 -3.06073755e-01 1.22339070e+00 1.60840178e+00 7.86471143e-02 6.42598867e-01 4.38333005e-01 6.55799568e-01 3.41722578e-01 2.25735903e-01 4.18186575e-01 3.09456885e-01 4.18854892e-01 1.58844680e-01 -3.87138963e-01 -1.49031505e-01 -3.13841105e-02 1.35371163e-01 7.90387630e-01 -3.93127084e-01 -9.59867537e-02 -5.95879495e-01 5.16111106e-02 -1.77887154e+00 -7.50205457e-01 2.02132195e-01 1.89861691e+00 4.71436590e-01 -6.38216510e-02 -4.40803885e-01 1.42931901e-02 4.57037508e-01 3.34194839e-01 -3.24543029e-01 -6.48673236e-01 -2.92642385e-01 7.30321765e-01 3.97145301e-01 3.02890331e-01 -1.66682982e+00 1.13851941e+00 6.91434383e+00 1.07156038e+00 -1.64179838e+00 4.23307084e-02 7.97470152e-01 3.02547842e-01 1.44303024e-01 -2.06339166e-01 -4.53279555e-01 2.30423689e-01 6.07197285e-01 2.55574495e-01 1.51763797e-01 9.26254213e-01 -2.31051579e-01 1.26056537e-01 -8.45765769e-01 9.67144728e-01 2.78860062e-01 -1.29966438e+00 3.56040925e-01 7.72895515e-02 6.17776096e-01 -7.43166730e-02 2.64419645e-01 3.27508420e-01 -1.85896233e-01 -1.29265153e+00 6.04823112e-01 9.29819226e-01 9.30974185e-01 -9.11424875e-01 1.18876231e+00 -3.51618499e-01 -1.43215644e+00 -1.17377736e-01 -9.10421133e-01 8.01375427e-04 -3.40565175e-01 4.05213594e-01 -7.84768239e-02 5.05753636e-01 7.74197340e-01 1.04568207e+00 -6.27246499e-01 8.47534001e-01 2.25469545e-01 5.29691100e-01 -4.11310159e-02 -4.67238091e-02 4.83252853e-01 -3.66106443e-02 -1.17775224e-01 1.25292873e+00 3.52126032e-01 -2.54175276e-01 3.08475405e-01 6.29153728e-01 6.07118662e-03 1.65215254e-01 -5.68275511e-01 -3.26155238e-02 -1.09548256e-01 1.42303050e+00 -6.89655483e-01 -1.65267378e-01 -5.75939953e-01 1.05163729e+00 5.82802892e-01 3.10495436e-01 -4.88181055e-01 -8.87663186e-01 7.27097034e-01 -2.15376034e-01 6.92876458e-01 -2.34787941e-01 5.99289089e-02 -1.10272539e+00 -9.62210670e-02 -4.90585804e-01 1.26397580e-01 -3.61744374e-01 -1.51684046e+00 8.20951641e-01 -4.36102509e-01 -1.26193845e+00 -9.04289074e-03 -1.12251890e+00 -4.39592689e-01 8.03252995e-01 -1.69084382e+00 -1.56882513e+00 -3.02667111e-01 7.03867495e-01 2.56786883e-01 -3.22535455e-01 1.00326431e+00 2.67998934e-01 -4.33794707e-01 1.03494036e+00 4.42726016e-01 4.59222376e-01 7.55368471e-01 -8.62542748e-01 1.22182583e-02 2.57710338e-01 -1.72491506e-01 4.77217257e-01 -8.92550871e-02 -5.46624005e-01 -1.51413608e+00 -1.22560871e+00 1.00617206e+00 1.22061208e-01 6.41673386e-01 -3.09786081e-01 -9.69945312e-01 4.38977093e-01 -1.45134926e-01 7.85138249e-01 7.45946050e-01 -9.76381972e-02 -6.08160138e-01 -2.65838295e-01 -1.00771570e+00 6.38253763e-02 8.07231963e-01 -7.35485196e-01 -1.63502902e-01 8.93800557e-02 2.42828771e-01 -2.29712278e-01 -1.07805467e+00 5.33989727e-01 1.17386246e+00 -5.81009567e-01 9.04651105e-01 -8.24629724e-01 6.35443151e-01 -3.31353992e-02 -5.13578713e-01 -9.97603178e-01 -7.57731795e-01 4.82099988e-02 1.35603145e-01 8.05664837e-01 1.41237646e-01 -8.26677799e-01 7.41246104e-01 4.15808409e-02 -5.82779087e-02 -1.00487578e+00 -1.02815068e+00 -6.71319783e-01 1.66887879e-01 -1.90363333e-01 2.31636509e-01 7.87321150e-01 -1.59678578e-01 -2.22548038e-01 -4.24186498e-01 -2.36071140e-01 4.26718235e-01 1.40397355e-01 3.14988017e-01 -1.31429815e+00 3.34644578e-02 -6.63055718e-01 -1.03839266e+00 -9.94141877e-01 4.75530565e-01 -1.21054351e+00 2.58481111e-02 -1.19133770e+00 4.42423970e-01 -4.96787459e-01 -8.17910492e-01 9.64893699e-01 1.58673316e-01 6.19965732e-01 -4.47232053e-02 2.81087756e-01 -5.61269522e-01 7.72146165e-01 1.44642949e+00 -4.34318334e-01 1.64920270e-01 -2.21400678e-01 -4.27475691e-01 5.68647027e-01 6.02082431e-01 -4.20485020e-01 -5.48823923e-02 -3.85852873e-01 -1.06191844e-01 -2.50356585e-01 4.80889440e-01 -9.18162882e-01 3.13482285e-01 -1.20458461e-01 1.10940456e+00 -3.23721081e-01 1.75185591e-01 -6.20084047e-01 -1.65936470e-01 5.22516072e-01 -4.65149730e-01 -1.78412750e-01 1.89613044e-01 3.73073727e-01 -7.90868461e-01 -1.49209827e-01 5.93077958e-01 9.66028944e-02 -6.91366255e-01 5.24742782e-01 -5.16266167e-01 -5.93895674e-01 5.39395690e-01 -2.47068629e-01 -1.88048795e-01 2.00306810e-02 -7.19821334e-01 -2.15751126e-01 1.43521160e-01 4.99983311e-01 8.87978971e-01 -1.88100314e+00 -5.39356172e-01 7.56856620e-01 1.50149435e-01 -4.21568453e-01 3.88327539e-01 8.15696836e-01 -7.58241117e-01 4.91282344e-01 -6.09823406e-01 -7.04390943e-01 -1.03124332e+00 1.49962246e-01 6.09070599e-01 -2.15145797e-01 -5.61859906e-01 8.58145416e-01 2.26909950e-01 -4.30756897e-01 1.78610861e-01 -3.86163294e-01 -3.90104562e-01 -1.58531010e-01 4.90603715e-01 1.16289705e-01 1.33487418e-01 -7.46923387e-01 -3.65903556e-01 9.79814947e-01 -1.86823979e-01 4.23174322e-01 1.42893910e+00 1.31446779e-01 -3.19378167e-01 1.04652315e-01 1.92780817e+00 -4.21736747e-01 -1.21944845e+00 -4.50228542e-01 -1.56763390e-01 -5.60765326e-01 3.83753836e-01 -3.48999590e-01 -1.55687618e+00 9.01882648e-01 8.67967069e-01 -1.51317850e-01 1.17424834e+00 -5.52403405e-02 6.78107083e-01 7.19347477e-01 2.46186823e-01 -9.59087849e-01 2.39962831e-01 9.75093722e-01 8.33368242e-01 -1.30893302e+00 -2.62915164e-01 -3.00793529e-01 -2.85319358e-01 1.81107664e+00 4.38219011e-01 -7.34449804e-01 1.23379242e+00 1.57400057e-01 3.45074534e-02 -3.10959309e-01 -6.21765912e-01 -3.03708613e-02 7.34861195e-01 2.93008804e-01 5.95962763e-01 7.74134174e-02 -1.66418165e-01 7.95129836e-01 8.69407356e-02 9.58886966e-02 -1.57753095e-01 1.11030757e+00 -5.10139346e-01 -1.15967512e+00 -4.21134979e-02 7.59908080e-01 -6.05507135e-01 -9.20259282e-02 -8.11623260e-02 5.12872159e-01 2.40888387e-01 5.49188972e-01 3.32332999e-01 -8.22484732e-01 2.06339046e-01 -2.59172916e-03 6.98312461e-01 -1.62300125e-01 -5.23495793e-01 1.25408202e-01 -4.14721549e-01 -8.32624316e-01 -4.57099050e-01 -3.47658008e-01 -7.64812112e-01 -3.64728600e-01 -5.79375327e-01 -4.10411879e-02 6.18594348e-01 1.00791180e+00 3.22144449e-01 6.03682160e-01 8.40739012e-01 -1.06665885e+00 -4.65901315e-01 -1.00852525e+00 -7.01331437e-01 2.93222964e-01 3.53427023e-01 -9.65639055e-01 -3.49427223e-01 7.11537376e-02]	[10.21349811553955, -0.1315307319164276]
c7c11b1c-ae5f-4af9-bc9e-c98746121a8b	hyperspectral-image-classification-of	null	null	http://doi.org/10.1088/1742-6596/1549/5/052011	https://iopscience.iop.org/article/10.1088/1742-6596/1549/5/052011/pdf	Hyperspectral Image Classification of Convolutional Neural Network Combined with Valuable Samples	Aiming at the problem that the manual labeling of samples in the hyperspectral image classification is expensive and laborious, a large number of unlabeled samples are not effectively utilized and the classification results are not ideal. A method which can provide valuable samples and employ convolutional neural network to extract spectral spatial features for classification is proposed. Active learning method is used to construct a valuable training sample set by iteratively selecting the most uncertain samples through support vector machine which performs well in small sample classification, and labeling them. Then the 3D convolutional neural network is used to extract the spectral spatial features of hyperspectral image. The experimental results of the hyperspectral classification on Indian Pines and PaviaU datasets show that the proposed method (3D VS-CNN) is better than traditional classification methods.	['Yufan Wei', 'Xiaobo Luo', 'Lixin Hu']	2020-06-01	null	null	null	journal-of-physics-conference-series-2020-6	['few-shot-image-classification']	['computer-vision']	[ 5.34949124e-01 -3.41814488e-01 -4.35936272e-01 -4.56605583e-01 -4.51537460e-01 -6.34388030e-01 1.42535135e-01 6.98407441e-02 -4.22222018e-01 8.80265176e-01 -2.84580737e-01 -3.91642869e-01 -5.73674202e-01 -1.12016237e+00 -7.15912879e-02 -1.09064662e+00 -1.35896042e-01 3.25047046e-01 4.63711657e-03 1.36507347e-01 3.68477106e-01 9.57332313e-01 -1.73883867e+00 5.10367639e-02 1.25480473e+00 1.42468119e+00 3.99656445e-01 2.97538638e-01 -4.74701315e-01 7.07062662e-01 -4.99097466e-01 5.00539660e-01 6.13869250e-01 -4.13936302e-02 -9.51406360e-01 8.05680037e-01 1.03745885e-01 -5.42170048e-01 2.02903435e-01 1.26780748e+00 4.38756347e-01 4.08002436e-01 8.13047051e-01 -1.07622147e+00 -6.38835311e-01 4.90687907e-01 -7.16043413e-01 1.24176010e-01 -5.14778852e-01 -6.13050088e-02 7.28442013e-01 -9.94295001e-01 1.38997152e-01 7.30957329e-01 5.04279554e-01 1.36597142e-01 -8.48704755e-01 -7.41859853e-01 1.18278578e-01 2.66815126e-01 -1.61277246e+00 -2.22125262e-01 9.13196027e-01 -4.49793845e-01 4.77549314e-01 4.19565558e-01 9.07098174e-01 3.10576349e-01 -4.00528252e-01 9.22727942e-01 1.10780466e+00 -4.57062751e-01 4.13456738e-01 -1.35072123e-03 6.90030694e-01 6.58961833e-01 3.55238169e-01 3.42297375e-01 -8.61138925e-02 -8.94589424e-02 5.03579199e-01 4.67451423e-01 -5.29727817e-01 -2.24858984e-01 -5.35651207e-01 9.17699993e-01 7.96716094e-01 1.88496083e-01 -6.42785251e-01 -6.84247196e-01 3.01419497e-01 1.38542280e-01 9.40031767e-01 4.34615403e-01 -6.54936612e-01 4.44659233e-01 -1.21611953e+00 -1.93359852e-01 2.21444547e-01 5.68294704e-01 1.13026810e+00 3.26532513e-01 2.05470771e-01 1.05118072e+00 4.20158625e-01 3.87961149e-01 3.71734470e-01 -5.94826758e-01 5.06520420e-02 1.15636337e+00 3.44647914e-01 -7.14652061e-01 -2.97584474e-01 -4.92845803e-01 -6.80574894e-01 9.04618561e-01 1.58621427e-02 -3.02299857e-01 -1.33509231e+00 7.05055952e-01 1.19275361e-01 -9.16248411e-02 2.18186557e-01 1.19465613e+00 9.92205441e-01 8.95646513e-01 5.86721003e-02 -6.15480661e-01 5.05850732e-01 -9.45840061e-01 -4.81695652e-01 -1.59475729e-01 3.93663079e-01 -6.50615692e-01 9.30268228e-01 5.77900231e-01 -5.04160225e-01 -6.43883586e-01 -1.32107401e+00 4.74902540e-01 -7.28275180e-01 7.31607139e-01 1.00606000e+00 5.97332299e-01 -4.12404865e-01 5.48929751e-01 -6.78822160e-01 9.23732482e-03 1.05339372e+00 6.11566842e-01 -4.33612317e-01 -1.58309370e-01 -7.37605870e-01 4.53604430e-01 1.21741784e+00 6.61079049e-01 -8.72418404e-01 -2.46930942e-01 -6.38373733e-01 1.03883334e-01 2.59571016e-01 2.57172316e-01 9.86397147e-01 -1.69834828e+00 -1.47656274e+00 4.79844511e-01 1.09753676e-01 -8.28119740e-02 -3.35092400e-03 2.43890882e-02 -4.52507854e-01 2.46029586e-01 -1.18801929e-01 4.51508164e-01 6.85565412e-01 -1.26703644e+00 -7.82363415e-01 -6.07341886e-01 -1.47458971e-01 4.59357083e-01 -5.67711830e-01 -2.29005337e-01 4.41646457e-01 -1.95102915e-01 8.04443896e-01 -7.19617307e-01 -3.51074696e-01 1.55508816e-01 -3.52888733e-01 -1.53905392e-01 1.70304108e+00 -4.58362907e-01 4.99871939e-01 -2.14775896e+00 -2.63182133e-01 4.38582599e-01 4.25394103e-02 8.03327680e-01 -1.17562758e-02 -2.20510662e-02 -4.69800383e-01 2.66475946e-01 -5.37610352e-01 3.46697122e-01 -3.24413121e-01 4.21529040e-02 -9.57522467e-02 3.05724502e-01 3.68072778e-01 6.02923989e-01 -9.00999427e-01 -2.94714630e-01 4.63887274e-01 2.10235104e-01 2.93417603e-01 3.35896999e-01 -2.27737918e-01 3.18600357e-01 -6.25907600e-01 1.13996124e+00 1.04408240e+00 -2.36270875e-01 -2.80765116e-01 -3.47221047e-01 -3.79103005e-01 -2.99070626e-01 -1.17567468e+00 1.13736212e+00 -1.85223278e-02 4.86787856e-01 9.71227810e-02 -1.33900857e+00 1.22135603e+00 3.68221402e-01 4.24461067e-01 -2.13170201e-01 2.54719645e-01 3.67317796e-01 -2.04617400e-02 -8.00963759e-01 2.89212078e-01 -7.43299946e-02 7.31883943e-01 2.63974875e-01 -2.92115603e-02 -1.52156651e-01 3.78241166e-02 -3.23943496e-01 2.78052926e-01 1.46922305e-01 2.54905343e-01 -3.37732732e-01 5.09523451e-01 4.72063482e-01 6.48308218e-01 2.63445914e-01 -2.80419022e-01 2.40920871e-01 -4.02893648e-02 -5.47787786e-01 -8.65276217e-01 -5.96655250e-01 -2.56168038e-01 6.57264948e-01 5.06104939e-02 4.52792615e-01 -3.41872394e-01 -8.65013242e-01 -2.32534304e-01 4.53241467e-01 -4.43970919e-01 -3.10300421e-02 2.07803488e-01 -1.14142632e+00 3.03376596e-02 5.49148083e-01 1.07972121e+00 -9.21474338e-01 -2.28296384e-01 1.39018521e-01 2.47628614e-01 -3.06384563e-01 3.22815567e-01 5.33332884e-01 -1.13347936e+00 -1.24142599e+00 -5.55482090e-01 -9.24437463e-01 8.72435331e-01 7.11016655e-01 5.09244442e-01 -1.08306743e-02 -3.38756114e-01 -3.23568642e-01 -7.89265394e-01 -9.24811184e-01 4.98293415e-02 1.79213241e-01 -3.90028447e-01 1.71096265e-01 5.40344477e-01 -4.08009708e-01 -3.90453547e-01 -9.67082530e-02 -9.64989662e-01 -9.03811753e-02 6.62299275e-01 1.07425833e+00 6.17122352e-01 8.83888125e-01 6.43849015e-01 -1.02774787e+00 3.84829640e-01 -3.22415918e-01 -9.44275081e-01 1.80270121e-01 -7.76536047e-01 -5.12058318e-01 7.08753228e-01 -3.11082721e-01 -1.21261120e+00 6.80236220e-01 1.42132819e-01 -2.17949897e-01 -3.85823488e-01 9.73826528e-01 -2.33740807e-01 -4.37843680e-01 9.68613744e-01 3.46751511e-01 5.57258464e-02 -3.82560790e-01 -1.09400854e-01 1.25518596e+00 1.31048739e-01 -8.16048905e-02 7.40951657e-01 2.50760198e-01 -8.35176930e-02 -9.67169583e-01 -1.18074238e+00 -6.83376670e-01 -1.03074992e+00 -3.07183683e-01 6.65453076e-01 -7.85360634e-01 -2.28672057e-01 6.32509649e-01 -7.23717391e-01 -2.69028693e-01 -2.33601570e-01 9.99971032e-01 -1.22249447e-01 3.53405386e-01 -2.57616341e-01 -1.31145549e+00 -5.71091712e-01 -1.18071032e+00 7.13416398e-01 6.63760245e-01 4.13810968e-01 -8.63159597e-01 -4.95571703e-01 4.11322862e-01 1.52741715e-01 1.20931640e-01 1.12786567e+00 -7.58467078e-01 -6.11436188e-01 -4.72858101e-01 -4.84592646e-01 7.48562992e-01 5.35551012e-01 5.83618224e-01 -1.28418660e+00 -2.00608179e-01 -4.14429270e-02 -6.76645577e-01 8.95973325e-01 5.58419406e-01 1.70516121e+00 1.17061986e-03 -2.62375385e-01 5.59117198e-01 1.79764557e+00 9.07625079e-01 8.25451612e-01 2.02123914e-02 5.45251846e-01 5.46207726e-01 1.05500543e+00 2.92532504e-01 -5.48006356e-01 -5.41807592e-01 8.10833275e-01 -6.59792423e-01 5.69067001e-01 3.96478921e-01 -2.67652035e-01 5.10789752e-01 -5.02324581e-01 -3.06576550e-01 -8.49208832e-01 3.03938299e-01 -1.71139312e+00 -1.06140256e+00 -3.74442726e-01 2.15590262e+00 6.01174712e-01 6.64545503e-03 -1.67171702e-01 8.83647025e-01 8.21144760e-01 5.12878895e-02 -7.27922261e-01 3.30417991e-01 -3.00693572e-01 4.82749313e-01 7.26326227e-01 3.24304163e-01 -1.54250097e+00 8.83576214e-01 6.39042234e+00 6.60089731e-01 -1.70911312e+00 -1.39831856e-01 7.91703641e-01 2.50877678e-01 2.34955519e-01 4.36638258e-02 -3.91641378e-01 1.49561003e-01 3.84658754e-01 1.62828162e-01 3.68381023e-01 8.40668917e-01 4.53157574e-01 -2.48597994e-01 -4.24772888e-01 9.80001450e-01 -2.43150547e-01 -1.31888330e+00 6.38447097e-03 -3.74748558e-02 8.68846059e-01 -1.90554410e-02 -5.91183007e-02 2.38561146e-02 3.14444512e-01 -9.86594439e-01 3.59336406e-01 7.58041739e-01 5.95871389e-01 -9.13899660e-01 9.18302476e-01 4.83965486e-01 -1.06798220e+00 -5.02368093e-01 -8.09595346e-01 -9.19418335e-02 -5.63387871e-01 7.49181151e-01 -9.46378648e-01 6.60359263e-01 4.86905307e-01 7.99813747e-01 -4.16876763e-01 1.43575048e+00 4.21583652e-02 1.00650930e+00 -4.38664556e-02 -1.65859491e-01 5.51021039e-01 -6.64602757e-01 2.20341295e-01 5.47355294e-01 4.10005182e-01 4.08158392e-01 5.11507511e-01 8.67676973e-01 1.30222872e-01 4.71888781e-01 -4.92584914e-01 -7.91778564e-01 5.27743459e-01 1.47426379e+00 -9.44774985e-01 -3.37948263e-01 -1.41340986e-01 7.92941809e-01 9.57611054e-02 4.81510639e-01 -3.29830676e-01 -6.00247502e-01 -1.46370009e-01 -3.13472986e-01 7.24416152e-02 -3.81276220e-01 -2.85022467e-01 -8.16512585e-01 -3.36809039e-01 -5.14620125e-01 3.09207112e-01 -1.31759691e+00 -1.34157932e+00 3.74315053e-01 -3.74370068e-01 -1.48301399e+00 2.19977796e-01 -9.99223053e-01 -6.69438839e-01 1.15353882e+00 -1.54267144e+00 -1.18867707e+00 -8.86312544e-01 4.71539319e-01 5.90549648e-01 -6.78346336e-01 1.13520277e+00 8.49455446e-02 -7.67673552e-01 -2.04945788e-01 5.75565040e-01 3.23395163e-01 2.51158506e-01 -1.08195722e+00 -5.17160356e-01 7.41148889e-01 -1.61186397e-01 2.36194044e-01 -7.57483067e-03 -5.85618258e-01 -8.99426043e-01 -1.40007484e+00 1.56074181e-01 5.48642755e-01 2.97685862e-01 2.76205212e-01 -7.31854200e-01 5.56800842e-01 3.43841016e-02 2.42330879e-01 1.14670336e+00 -7.60635287e-02 2.42516696e-02 -3.18356872e-01 -1.35414231e+00 1.68832183e-01 4.50667828e-01 -3.11660916e-01 -1.59250438e-01 8.51069868e-01 4.62200820e-01 9.07507353e-03 -7.52795517e-01 5.71974695e-01 2.37950504e-01 -6.51257932e-01 7.14224935e-01 -7.09808230e-01 2.39385217e-01 -4.41993088e-01 -2.35773295e-01 -1.44541800e+00 -6.37915134e-01 2.03875750e-01 5.62643886e-01 1.01351047e+00 7.32697546e-01 -7.56397665e-01 1.11366355e+00 2.23241299e-01 -3.86148512e-01 -5.47882199e-01 -2.47807994e-01 -4.96361554e-01 -2.60291755e-01 5.40864207e-02 7.16484606e-01 1.46500909e+00 -4.78285760e-01 3.81905973e-01 1.79161370e-01 4.42650765e-01 5.54050863e-01 3.07581604e-01 1.56616107e-01 -1.94356704e+00 3.23836356e-01 -2.63420910e-01 -4.15207893e-01 -4.62306798e-01 3.50834519e-01 -8.34201515e-01 -3.01072672e-02 -1.71743631e+00 4.28679362e-02 -7.93310881e-01 -4.19670165e-01 8.06490421e-01 -1.31610200e-01 3.10081631e-01 -4.52753901e-01 4.29408163e-01 1.64575264e-01 5.31301141e-01 1.42109728e+00 -8.09868693e-01 -2.72124469e-01 2.93073088e-01 -4.95520025e-01 5.91201067e-01 9.81995821e-01 -1.23176120e-01 -8.99277627e-01 -2.15193465e-01 -1.84779242e-01 1.49846934e-02 2.23561347e-01 -1.10424435e+00 -4.24030088e-02 -6.75777793e-01 1.08942616e+00 -1.16139758e+00 4.32134509e-01 -1.16252017e+00 -4.54020500e-03 2.83211499e-01 -2.76263237e-01 -8.65954876e-01 3.31026204e-02 2.63446599e-01 -2.77952969e-01 -9.93695080e-01 9.47688758e-01 -4.80584621e-01 -1.16195834e+00 9.44958091e-01 -4.09027636e-01 -6.56217813e-01 9.41878557e-01 -5.79682410e-01 -2.32844964e-01 1.26137370e-02 -9.39792931e-01 -4.36764695e-02 1.27001882e-01 -2.11648583e-01 6.96980953e-01 -1.25770307e+00 -7.15542972e-01 3.02874058e-01 3.37324977e-01 3.38264108e-01 1.21332392e-01 3.31103921e-01 -1.06556869e+00 -1.35251703e-02 -4.12152946e-01 -6.19174659e-01 -1.32439744e+00 3.29410583e-01 6.63367510e-01 4.25135851e-01 -7.21530020e-02 1.02651441e+00 -4.23428535e-01 -6.37395382e-01 -2.48862393e-02 5.68263456e-02 -8.10476184e-01 2.94085383e-01 4.14071411e-01 2.74673015e-01 3.48678291e-01 -4.81084168e-01 8.82863849e-02 1.04619183e-01 1.75990477e-01 2.63417065e-01 1.80103564e+00 1.88105389e-01 -5.33474743e-01 4.29991931e-01 1.22596407e+00 -3.10738832e-01 -1.04422247e+00 -3.53008091e-01 -2.06747726e-01 -7.09855378e-01 7.68057764e-01 -1.01793253e+00 -1.30973029e+00 1.02374828e+00 1.25454330e+00 5.60914397e-01 1.33850241e+00 -8.31242502e-01 1.95296317e-01 7.88686454e-01 9.55769941e-02 -1.22311580e+00 -3.26261431e-01 4.01222736e-01 5.52227676e-01 -1.59488583e+00 2.76152521e-01 -7.72157252e-01 -2.59638935e-01 1.72889066e+00 7.79374063e-01 3.71151976e-02 8.60048056e-01 7.69498870e-02 2.65801400e-01 -2.76384026e-01 -1.05895489e-01 -3.85622650e-01 1.34957418e-01 7.40477026e-01 5.63847125e-01 3.32315087e-01 -2.94009298e-01 3.13478708e-01 3.02429572e-02 5.79492524e-02 3.78072530e-01 1.15323889e+00 -1.07881057e+00 -7.59714842e-01 -5.60845256e-01 9.00462270e-01 -4.92267683e-02 -4.87696975e-02 -6.90645754e-01 5.27600825e-01 4.04968858e-01 1.01140487e+00 2.08132684e-01 -5.10999143e-01 -1.58210739e-01 3.22628200e-01 3.13566774e-01 -7.70794988e-01 -1.02197438e-01 1.12380847e-01 6.11810386e-02 3.11132848e-01 -9.47356403e-01 -1.57948896e-01 -1.22617424e+00 -2.32228916e-02 -1.01391125e+00 3.78456652e-01 6.26200020e-01 8.97108316e-01 -1.45704329e-01 2.64947802e-01 1.16035831e+00 -9.11922157e-01 -4.61323291e-01 -1.16346943e+00 -1.11360931e+00 -5.90927377e-02 6.95675611e-02 -7.90265560e-01 -4.03981596e-01 1.09729826e-01]	[9.863030433654785, -1.5641884803771973]
ec9c3256-2f3b-4fbf-bbc5-7e44ea58a154	diminishing-return-of-value-expansion-methods	2303.03955	null	https://arxiv.org/abs/2303.03955v1	https://arxiv.org/pdf/2303.03955v1.pdf	Diminishing Return of Value Expansion Methods in Model-Based Reinforcement Learning	Model-based reinforcement learning is one approach to increase sample efficiency. However, the accuracy of the dynamics model and the resulting compounding error over modelled trajectories are commonly regarded as key limitations. A natural question to ask is: How much more sample efficiency can be gained by improving the learned dynamics models? Our paper empirically answers this question for the class of model-based value expansion methods in continuous control problems. Value expansion methods should benefit from increased model accuracy by enabling longer rollout horizons and better value function approximations. Our empirical study, which leverages oracle dynamics models to avoid compounding model errors, shows that (1) longer horizons increase sample efficiency, but the gain in improvement decreases with each additional expansion step, and (2) the increased model accuracy only marginally increases the sample efficiency compared to learned models with identical horizons. Therefore, longer horizons and increased model accuracy yield diminishing returns in terms of sample efficiency. These improvements in sample efficiency are particularly disappointing when compared to model-free value expansion methods. Even though they introduce no computational overhead, we find their performance to be on-par with model-based value expansion methods. Therefore, we conclude that the limitation of model-based value expansion methods is not the model accuracy of the learned models. While higher model accuracy is beneficial, our experiments show that even a perfect model will not provide an un-rivalled sample efficiency but that the bottleneck lies elsewhere.	['Jan Peters', 'Joao Carvalho', 'Michael Lutter', 'Daniel Palenicek']	2023-03-07	null	null	null	null	['continuous-control']	['playing-games']	[-1.29840299e-01 2.98922360e-01 -7.13191807e-01 2.96262741e-01 -8.93282175e-01 -6.71577871e-01 4.18322980e-01 8.06134194e-02 -6.07204378e-01 1.30972862e+00 -1.13947853e-01 -5.08001566e-01 -5.33879876e-01 -7.03390062e-01 -6.16404951e-01 -6.16588473e-01 -3.49762172e-01 4.01253104e-01 1.36849657e-01 -2.34416753e-01 3.71106565e-01 3.27015102e-01 -1.21748960e+00 -3.41628790e-01 8.56472731e-01 9.99869764e-01 -1.12613425e-01 7.32054353e-01 2.51770675e-01 1.08534408e+00 -4.61048305e-01 -1.37522846e-01 4.62763369e-01 -4.64552820e-01 -5.92403233e-01 -1.66483119e-01 -9.72093642e-02 -8.88753295e-01 -3.26250285e-01 7.73320794e-01 4.11948681e-01 4.26811337e-01 3.84978622e-01 -1.36944044e+00 -7.67211393e-02 5.95969021e-01 -3.09738010e-01 2.17836142e-01 1.75180845e-02 7.04022408e-01 9.58294213e-01 -3.28931391e-01 4.30107623e-01 1.07056224e+00 9.82114315e-01 4.38104123e-01 -1.42926419e+00 -3.75273019e-01 3.30263585e-01 -1.21359892e-01 -1.05723226e+00 -2.48228580e-01 2.92387187e-01 -3.62616003e-01 1.17628300e+00 2.85078168e-01 1.17094803e+00 7.02693820e-01 3.17373544e-01 5.82541823e-01 1.20270169e+00 -8.22733417e-02 6.06027544e-01 1.89957336e-01 -1.51225358e-01 4.52045023e-01 4.19530302e-01 9.66813385e-01 -6.74417838e-02 -2.81193823e-01 1.18658280e+00 1.37317419e-01 -2.10195452e-01 -5.86663365e-01 -1.03656256e+00 9.83253717e-01 2.82822639e-01 3.15794237e-02 -5.73918402e-01 7.26643503e-01 4.22531277e-01 6.75315142e-01 2.33631060e-01 1.04744089e+00 -7.28708565e-01 -8.97104025e-01 -9.22413766e-01 8.77621830e-01 9.49790180e-01 7.65013337e-01 4.68515128e-01 4.76100415e-01 -1.22154266e-01 3.84396076e-01 -3.92428227e-02 5.21162868e-01 4.50692594e-01 -1.65592957e+00 3.10604632e-01 4.23004866e-01 7.62369633e-01 -6.56124771e-01 -3.89831990e-01 -5.69185078e-01 -3.17005277e-01 6.08002603e-01 8.85961294e-01 -7.02111423e-01 -5.35317004e-01 1.89195573e+00 2.57019192e-01 1.51164019e-02 2.53855772e-02 5.32079279e-01 -3.70663673e-01 6.56759858e-01 -1.97042108e-01 -6.27865553e-01 7.94780552e-01 -9.80723679e-01 -6.27808273e-01 -4.36686948e-02 8.16836834e-01 -3.52905661e-01 1.17472565e+00 3.45968843e-01 -1.19687605e+00 -2.53470957e-01 -8.05101633e-01 6.65707231e-01 -2.96337577e-03 -4.80614007e-01 7.13799119e-01 5.22098839e-01 -7.58932054e-01 1.16830480e+00 -1.23431468e+00 -4.79519255e-02 2.15290725e-01 4.55049455e-01 2.11627468e-01 3.87906045e-01 -1.00266373e+00 1.16058874e+00 3.05808038e-01 -3.18067342e-01 -1.04119933e+00 -1.13089299e+00 -5.79044223e-01 3.39858294e-01 8.61397445e-01 -4.93487030e-01 1.82799494e+00 -9.76953089e-01 -1.66025615e+00 -3.80661458e-01 9.10628065e-02 -1.00579894e+00 8.73659611e-01 -3.94632928e-02 1.65768549e-01 7.61884376e-02 -4.24745768e-01 4.54860508e-01 7.54325211e-01 -1.14038622e+00 -7.58598268e-01 3.71272862e-02 3.58519793e-01 1.65606469e-01 -2.93026656e-01 -4.37933713e-01 4.19866443e-01 -4.53864753e-01 -5.11101067e-01 -1.06101751e+00 -7.17003405e-01 -9.72242579e-02 3.04232121e-01 5.97904399e-02 5.71923554e-01 -6.40700042e-01 1.58801425e+00 -1.59629774e+00 -2.13273525e-01 1.35109633e-01 4.34005708e-02 2.02031210e-01 -3.06230485e-02 7.46064425e-01 1.15516499e-01 1.53821394e-01 -2.66978234e-01 2.25005180e-01 1.94039091e-01 2.31300548e-01 -5.94654441e-01 1.54931307e-01 1.29143119e-01 1.07470179e+00 -1.06245220e+00 -6.40756488e-02 3.36273938e-01 1.09930508e-01 -8.28118861e-01 -1.07186414e-01 -4.71498638e-01 2.19415396e-01 -3.98923099e-01 2.78200895e-01 1.62872478e-01 -2.10131258e-01 2.42252439e-01 4.01837140e-01 2.22920370e-03 4.03187685e-02 -1.25604761e+00 7.82871842e-01 -3.73854965e-01 4.13011044e-01 -4.46650349e-02 -1.22917819e+00 6.47761524e-01 2.78132737e-01 6.69639051e-01 -7.56365120e-01 -1.06421523e-01 3.03964764e-01 2.60521263e-01 -4.14096326e-01 4.98924762e-01 -4.39581722e-01 1.86792925e-01 4.46416408e-01 -3.09238106e-01 -5.05406678e-01 1.67905942e-01 -1.97680846e-01 1.07322967e+00 4.47897106e-01 3.96874815e-01 -3.07460010e-01 -6.93728030e-02 4.13555294e-01 7.19768107e-01 8.25138867e-01 -3.03491473e-01 -1.37516618e-01 9.85313177e-01 -2.94059604e-01 -1.35696352e+00 -7.75858343e-01 -6.89116865e-02 6.77594423e-01 -9.05311182e-02 -3.41277421e-01 -6.29801810e-01 -5.85844994e-01 4.89183515e-01 8.42359602e-01 -6.04457378e-01 -1.47602722e-01 -6.05232656e-01 -6.33097053e-01 2.90466487e-01 8.04461718e-01 3.41500223e-01 -6.76249683e-01 -1.09701312e+00 5.30847847e-01 1.35675430e-01 -5.20855069e-01 -3.97896051e-01 5.70731834e-02 -1.45978189e+00 -1.03403056e+00 -8.00031006e-01 -1.18568473e-01 5.51028728e-01 -8.19881931e-02 9.51078296e-01 3.99335101e-03 1.53003469e-01 8.48279536e-01 -6.54445589e-02 -6.20300353e-01 -4.21760172e-01 -7.87706599e-02 1.46024764e-01 -6.44396544e-01 -1.04349211e-01 -4.79734391e-01 -6.03019178e-01 2.23528326e-01 -6.22147977e-01 -3.33502740e-01 2.74356842e-01 1.31856811e+00 4.81406808e-01 2.57707298e-01 1.09956217e+00 -5.49057484e-01 9.14889395e-01 -2.46089295e-01 -1.15761876e+00 1.26941383e-01 -1.33811831e+00 2.20930070e-01 7.80214131e-01 -8.63329351e-01 -9.31429267e-01 -1.75065190e-01 1.48156360e-01 -4.04523075e-01 4.47720617e-01 7.50717759e-01 6.94314063e-01 9.71412286e-02 6.14833593e-01 1.60885975e-01 5.85751832e-01 -2.68847406e-01 1.13749027e-01 6.28340542e-02 -3.57281277e-03 -7.63565540e-01 5.31922996e-01 7.22083077e-02 1.79723874e-01 -5.83087981e-01 -3.81464452e-01 -2.50571296e-02 3.50446738e-02 -1.74836472e-01 2.78646201e-01 -8.46714973e-01 -9.43005860e-01 9.52940360e-02 -4.97657329e-01 -1.08612812e+00 -9.98458803e-01 6.15649998e-01 -1.13836968e+00 1.94130674e-01 -7.32463062e-01 -1.30942428e+00 -4.42680866e-02 -9.54253197e-01 3.95705760e-01 2.26498425e-01 -2.46971026e-01 -1.13795733e+00 2.14429989e-01 -8.54484066e-02 7.11072564e-01 3.93933535e-01 9.70907986e-01 -5.51894128e-01 -5.98205626e-01 -2.75102854e-01 2.82458395e-01 3.96934539e-01 -1.19002260e-01 -1.38085768e-01 -5.76567531e-01 -7.28583574e-01 1.60679251e-01 -4.17669773e-01 4.77732897e-01 7.07968116e-01 8.18752825e-01 -8.55840027e-01 -1.48534194e-01 1.11066245e-01 1.58506703e+00 6.51841402e-01 3.35636288e-01 5.14512658e-01 1.39002010e-01 3.83079767e-01 1.02318215e+00 8.25113118e-01 8.11643153e-02 5.06342411e-01 3.41517299e-01 1.69979110e-01 3.58632565e-01 -4.82551932e-01 5.96127689e-01 4.17252213e-01 -1.47736520e-01 3.02357703e-01 -9.08248961e-01 6.31265998e-01 -2.14344001e+00 -1.31928074e+00 3.07814360e-01 2.75305009e+00 9.29448426e-01 3.17534983e-01 8.99898052e-01 -1.56868307e-03 2.58249193e-01 -2.47066483e-01 -1.16320062e+00 -5.50862670e-01 4.69739884e-01 6.13303967e-02 6.80439651e-01 7.63270974e-01 -5.24036407e-01 5.20948112e-01 7.26145744e+00 8.54099929e-01 -1.02501559e+00 -1.18220985e-01 6.94887519e-01 -5.35698116e-01 -1.88100189e-01 3.00621450e-01 -7.30787754e-01 5.92166424e-01 1.41033041e+00 -7.92269647e-01 8.75855565e-01 1.34804559e+00 4.15109277e-01 -2.98071116e-01 -1.11572957e+00 5.59943438e-01 -6.11218214e-01 -1.33378065e+00 -3.44094425e-01 4.81576234e-01 9.82328475e-01 -2.72878706e-01 -3.95888463e-02 9.92618740e-01 4.32550192e-01 -1.12417352e+00 6.30793929e-01 5.98822832e-01 3.52167338e-01 -1.16611266e+00 7.76284337e-01 6.89393520e-01 -9.90902305e-01 -7.13012040e-01 -3.93510371e-01 -6.03258729e-01 8.24704021e-02 1.60255894e-01 -7.20804572e-01 1.45009741e-01 2.29720041e-01 2.85019070e-01 -2.31082708e-01 1.19233489e+00 1.70698822e-01 8.93872499e-01 -6.01790905e-01 -3.69364291e-01 3.87110263e-01 -2.19307244e-01 4.95642662e-01 7.25002170e-01 3.10144961e-01 -1.38328388e-01 1.57264084e-01 8.61004770e-01 4.68058974e-01 -3.52250040e-01 -8.24662685e-01 -4.77168173e-01 6.75145209e-01 7.49365091e-01 -4.03584629e-01 -4.72998291e-01 -3.47389221e-01 2.49509320e-01 1.05285786e-01 4.02163178e-01 -9.13851917e-01 -4.61123288e-01 6.00830495e-01 2.82089114e-01 3.75319302e-01 -3.43576610e-01 -1.86185732e-01 -9.05811965e-01 -1.42656609e-01 -1.18037724e+00 2.20496014e-01 -3.18487883e-01 -7.93049037e-01 -1.28525734e-01 3.79135936e-01 -1.23056340e+00 -1.03460014e+00 -3.20553541e-01 -2.96954036e-01 5.54445624e-01 -1.15827990e+00 -5.71843207e-01 2.63782352e-01 1.83910757e-01 6.07589662e-01 2.32420906e-01 6.25717521e-01 -1.09904841e-01 -2.36637756e-01 6.19046867e-01 6.73960984e-01 -3.25312048e-01 1.47951677e-01 -1.37500095e+00 -5.11451215e-02 5.81174552e-01 -3.69869262e-01 5.42782068e-01 8.64656746e-01 -5.76868713e-01 -1.52303350e+00 -8.00460577e-01 3.50971490e-01 -7.10779548e-01 8.34410667e-01 1.98083878e-01 -7.40833759e-01 8.20120692e-01 -9.86853167e-02 -3.80000085e-01 1.39075086e-01 1.35397613e-01 2.19351053e-01 -1.33008823e-01 -1.14838803e+00 7.55529106e-01 7.32464254e-01 -2.64811575e-01 -4.47832316e-01 3.07412036e-02 8.00866783e-01 -3.53495866e-01 -1.40859008e+00 3.59328061e-01 7.49477088e-01 -7.70776689e-01 9.54192042e-01 -7.90303826e-01 3.16310763e-01 6.86444864e-02 -8.66248757e-02 -1.41895652e+00 -6.23230152e-02 -9.12220359e-01 -6.09769285e-01 9.35362041e-01 3.55128258e-01 -9.82880056e-01 7.69352436e-01 1.07397914e+00 3.50564867e-01 -1.29356360e+00 -8.75211060e-01 -1.55949068e+00 7.26311147e-01 -4.21961337e-01 6.95860445e-01 8.62765670e-01 3.64116460e-01 1.74632490e-01 -3.88363361e-01 -2.94128001e-01 6.23431146e-01 1.27447665e-01 8.31483364e-01 -7.69146919e-01 -7.69729912e-01 -6.72023594e-01 4.19228487e-02 -9.80709195e-01 -2.22308919e-01 -2.51205266e-01 -2.39098653e-01 -1.30237818e+00 1.53775975e-01 -5.94143629e-01 -2.46379733e-01 4.74875093e-01 -1.51321903e-01 -4.19467539e-01 5.26409090e-01 1.40790179e-01 -3.27780783e-01 7.29711533e-01 1.34158981e+00 1.32816195e-01 -7.47638583e-01 2.69717604e-01 -5.43300688e-01 6.49532259e-01 9.06317532e-01 -3.41553122e-01 -9.36119139e-01 9.44507718e-02 2.65991122e-01 7.01838851e-01 4.64754790e-01 -1.00337291e+00 -5.68962097e-02 -7.13642895e-01 2.36256823e-01 -2.44496405e-01 2.29250371e-01 -7.23283947e-01 1.80507615e-01 1.18211699e+00 -5.10958254e-01 2.99534827e-01 5.09159327e-01 7.88380742e-01 8.55851695e-02 -4.39305514e-01 7.47205198e-01 -2.46651128e-01 -2.58933604e-01 2.47275442e-01 -7.33268023e-01 2.60337144e-01 1.00370979e+00 -3.74089330e-01 -5.26234992e-02 -7.00524747e-01 -6.77228093e-01 4.02271718e-01 5.85078716e-01 2.25655679e-02 2.10832909e-01 -1.25238025e+00 -2.12119415e-01 -1.37993917e-01 -3.77026498e-01 -2.05362782e-01 -1.10884011e-02 9.95177686e-01 -5.38930595e-02 5.45932651e-01 8.34208727e-02 -3.48500490e-01 -8.20922434e-01 5.83911061e-01 7.02193320e-01 -7.17722833e-01 -5.16982138e-01 3.38166058e-01 -1.88302144e-01 -3.53305012e-01 3.54324996e-01 -7.14372158e-01 4.31917131e-01 1.89478565e-02 4.85704511e-01 8.94695818e-01 -3.86895001e-01 3.86888802e-01 -1.17066046e-02 3.13771158e-01 7.35060796e-02 -4.49859142e-01 1.49010932e+00 1.13885671e-01 5.02473295e-01 4.88566041e-01 6.21828139e-01 -3.49194616e-01 -2.03297949e+00 3.40970717e-02 6.83508143e-02 -4.85960066e-01 2.31624208e-02 -9.33318257e-01 -7.05723107e-01 8.13249886e-01 6.08452976e-01 4.35377479e-01 8.65631938e-01 -6.87835395e-01 5.66764295e-01 7.51718819e-01 6.98991120e-01 -1.48388386e+00 6.22300990e-02 5.18713295e-01 8.23701262e-01 -1.01689398e+00 1.77911788e-01 4.34063375e-01 -8.18226755e-01 8.77896130e-01 7.00374484e-01 -3.18639755e-01 4.36648369e-01 1.86802119e-01 -4.49379265e-01 1.64931461e-01 -1.14954650e+00 -1.39661327e-01 -4.56794649e-01 6.52102053e-01 -2.81787217e-02 -1.46593088e-02 -4.31626201e-01 5.66995680e-01 -1.47830218e-01 4.15547490e-01 5.35096705e-01 1.03269303e+00 -6.49853289e-01 -9.22775626e-01 -3.51690382e-01 7.41751730e-01 -3.24325144e-01 1.80720642e-01 2.20600478e-02 1.27886391e+00 -4.95926380e-01 9.80781674e-01 5.57218231e-02 -1.19864233e-01 3.71842563e-01 6.59051314e-02 6.63564086e-01 -1.26427338e-01 -5.91851830e-01 3.64720561e-02 2.04425529e-01 -7.77326643e-01 1.57546803e-01 -5.70873976e-01 -1.28780138e+00 -6.83130682e-01 -5.37860751e-01 2.60072857e-01 3.49843591e-01 7.37250328e-01 4.82151270e-01 3.93419027e-01 7.29129732e-01 -6.55366838e-01 -1.89432728e+00 -6.98546946e-01 -4.88059223e-01 2.25955602e-02 4.86830115e-01 -8.66376758e-01 -5.97583890e-01 -3.44520390e-01]	[4.210270881652832, 2.329653263092041]
4c0e673b-cf32-41f0-9c3d-4400eb6127fe	optimal-feature-transport-for-cross-view	1907.05021	null	https://arxiv.org/abs/1907.05021v3	https://arxiv.org/pdf/1907.05021v3.pdf	Optimal Feature Transport for Cross-View Image Geo-Localization	This paper addresses the problem of cross-view image geo-localization, where the geographic location of a ground-level street-view query image is estimated by matching it against a large scale aerial map (e.g., a high-resolution satellite image). State-of-the-art deep-learning based methods tackle this problem as deep metric learning which aims to learn global feature representations of the scene seen by the two different views. Despite promising results are obtained by such deep metric learning methods, they, however, fail to exploit a crucial cue relevant for localization, namely, the spatial layout of local features. Moreover, little attention is paid to the obvious domain gap (between aerial view and ground view) in the context of cross-view localization. This paper proposes a novel Cross-View Feature Transport (CVFT) technique to explicitly establish cross-view domain transfer that facilitates feature alignment between ground and aerial images. Specifically, we implement the CVFT as network layers, which transports features from one domain to the other, leading to more meaningful feature similarity comparison. Our model is differentiable and can be learned end-to-end. Experiments on large-scale datasets have demonstrated that our method has remarkably boosted the state-of-the-art cross-view localization performance, e.g., on the CVUSA dataset, with significant improvements for top-1 recall from 40.79% to 61.43%, and for top-10 from 76.36% to 90.49%. We expect the key insight of the paper (i.e., explicitly handling domain difference via domain transport) will prove to be useful for other similar problems in computer vision as well.	['Xin Yu', 'Yujiao Shi', 'Tong Zhang', 'Liu Liu', 'Hongdong Li']	2019-07-11	null	null	null	null	['image-based-localization']	['computer-vision']	[-1.50096700e-01 -5.15961051e-01 -3.79531085e-02 -6.03393853e-01 -1.11567283e+00 -8.49772751e-01 6.78576767e-01 6.10615574e-02 -3.92611057e-01 3.53238165e-01 1.94738302e-02 2.94240471e-02 -2.80027956e-01 -8.99808228e-01 -9.29269671e-01 -5.89582622e-01 -8.95290971e-02 2.07257330e-01 8.43395293e-02 -1.86056256e-01 2.02952236e-01 5.18270254e-01 -1.40862679e+00 9.87908617e-02 6.85676575e-01 1.30147791e+00 3.44137818e-01 2.04962015e-01 2.30522513e-01 3.78929824e-01 -2.67215133e-01 -2.52897292e-01 3.90059948e-01 -3.35065126e-02 -9.05534208e-01 9.54448357e-02 1.06605303e+00 -3.43779773e-01 -3.12163919e-01 1.12521446e+00 4.92389739e-01 1.31515175e-01 4.48961407e-01 -1.22001410e+00 -8.79203618e-01 -8.79860073e-02 -9.15478170e-01 1.55677080e-01 3.48574847e-01 -8.16461891e-02 1.16529751e+00 -1.06787789e+00 5.82968295e-01 9.82862294e-01 7.03852952e-01 -3.25319506e-02 -1.13565338e+00 -5.62035859e-01 3.57347041e-01 2.77272493e-01 -1.79923761e+00 -1.52596727e-01 6.75200343e-01 -5.49794972e-01 8.37154031e-01 1.75773781e-02 2.65429735e-01 7.13776588e-01 1.04370974e-01 5.98860025e-01 1.01778805e+00 -7.44548962e-02 4.33299690e-04 -4.80240248e-02 -3.01219046e-01 9.24883366e-01 5.95965870e-02 2.12402362e-02 -5.70555866e-01 4.80954461e-02 6.67402148e-01 2.49936670e-01 -4.26101297e-01 -9.59822655e-01 -1.50770605e+00 7.71160901e-01 1.07228720e+00 4.16589677e-01 -3.07434112e-01 1.38944805e-01 2.89358288e-01 3.01714540e-01 6.36040807e-01 5.71767807e-01 -5.60492933e-01 2.20660046e-01 -9.47621524e-01 8.17843452e-02 3.43978584e-01 1.16899371e+00 1.18240142e+00 -3.04593712e-01 8.03574771e-02 5.81256270e-01 1.96537703e-01 8.14685166e-01 8.35044608e-02 -6.94858968e-01 8.52254152e-01 6.83688223e-01 3.48330736e-01 -1.44817078e+00 -4.84138280e-01 -5.52481592e-01 -8.08515787e-01 -1.62608638e-01 4.31244791e-01 1.54054940e-01 -5.98370314e-01 1.83477199e+00 3.75105619e-01 3.43357950e-01 -7.68096074e-02 1.13706732e+00 6.54136539e-01 4.83603925e-01 -3.28224123e-01 5.75413048e-01 1.30801976e+00 -8.63387227e-01 4.29824367e-02 -4.06378031e-01 8.91966343e-01 -7.14604855e-01 1.10816002e+00 -8.50633681e-02 -6.01745367e-01 -6.17873311e-01 -1.09211588e+00 -6.50906488e-02 -7.40564227e-01 4.68583405e-01 4.74192262e-01 2.34342590e-01 -1.15088367e+00 4.49722260e-01 -7.31175721e-01 -7.98236549e-01 3.63852948e-01 3.01802963e-01 -8.06452692e-01 -5.67631066e-01 -1.19990814e+00 7.48378456e-01 8.85385871e-02 5.44831455e-02 -7.40222692e-01 -8.65890563e-01 -1.07817662e+00 3.95957418e-02 2.36286357e-01 -8.78004432e-01 8.56585026e-01 -7.06639349e-01 -7.96017766e-01 1.15394843e+00 -1.61007687e-01 -2.88726419e-01 4.73296940e-01 -3.34012628e-01 -4.35391963e-01 1.51957542e-01 7.75830567e-01 6.43440247e-01 5.58477998e-01 -1.10217619e+00 -1.05942452e+00 -7.11114526e-01 4.10039037e-01 2.65225679e-01 -2.62511700e-01 -2.27345437e-01 -5.20310998e-01 -4.37293589e-01 5.14149666e-01 -9.88203824e-01 3.41904350e-02 3.43526185e-01 -1.45712510e-01 -1.52409405e-01 6.52330697e-01 -5.09203434e-01 8.60755146e-01 -2.19422197e+00 3.80549207e-02 1.20929621e-01 9.72978473e-02 -1.10183463e-01 -1.51346326e-01 5.88340640e-01 -1.23872206e-01 7.56521076e-02 -1.99856445e-01 -1.53100058e-01 9.75614935e-02 -1.47036806e-01 -4.70866472e-01 9.43638206e-01 2.22761989e-01 8.73656154e-01 -1.15223885e+00 -6.16339706e-02 4.60192353e-01 4.21321332e-01 -3.56071651e-01 1.56991407e-01 2.78249472e-01 4.80510443e-01 -4.66825068e-01 7.77907550e-01 1.01365769e+00 -4.60881084e-01 -1.42349511e-01 -5.59908569e-01 -1.99559510e-01 2.54063398e-01 -9.97442603e-01 2.26042676e+00 -8.88805151e-01 7.09057629e-01 -1.72692567e-01 -9.91353750e-01 9.10872459e-01 -1.55523703e-01 4.75726247e-01 -8.92794073e-01 -2.63310313e-01 2.06802607e-01 -4.68903035e-01 -1.46248281e-01 5.27077436e-01 2.17731267e-01 -3.76565695e-01 1.84320882e-01 1.84747782e-02 -8.74136481e-03 -7.17766955e-02 7.96561763e-02 7.79799044e-01 1.98773503e-01 4.18209791e-01 -4.17065144e-01 6.83389723e-01 2.52429973e-02 3.12020719e-01 5.71308792e-01 -4.47556637e-02 7.65018761e-01 1.63186565e-01 -7.19953954e-01 -7.00270593e-01 -1.18803823e+00 -1.35230079e-01 1.00294304e+00 6.95097685e-01 -3.01235080e-01 -4.90918398e-01 -8.73602688e-01 2.01962397e-01 2.83376455e-01 -6.60129488e-01 -2.07199052e-01 -5.65616369e-01 -4.34162557e-01 3.60850155e-01 7.01418698e-01 9.63548303e-01 -3.01425755e-01 -5.98942459e-01 2.10762266e-02 -3.91130269e-01 -1.35914278e+00 -6.96641684e-01 -1.42659307e-01 -7.03521729e-01 -1.18706715e+00 -7.56871998e-01 -8.02333474e-01 6.42183900e-01 1.02167356e+00 1.21859515e+00 -1.30726144e-01 -3.81931476e-02 4.95095700e-01 -2.61460006e-01 3.11098874e-01 3.52718443e-01 3.18903655e-01 1.70654744e-01 2.34589517e-01 5.77908158e-01 -5.26053786e-01 -8.70631278e-01 7.59570241e-01 -6.45189941e-01 -2.06216395e-01 5.65950394e-01 8.59069645e-01 7.92438745e-01 -1.70099884e-01 1.21098645e-01 -4.27114069e-01 6.53836206e-02 -5.67545831e-01 -8.84459853e-01 4.27458107e-01 -5.00706732e-01 5.57706226e-04 5.75908899e-01 4.55835164e-02 -5.09249330e-01 1.23628363e-01 1.15291350e-01 -3.37705791e-01 -3.28673601e-01 6.11705840e-01 -3.80098164e-01 -3.74450862e-01 5.08073986e-01 4.55921948e-01 -4.18411911e-01 -3.69233698e-01 4.53785837e-01 5.88995636e-01 4.84686404e-01 -4.42038119e-01 1.00167143e+00 8.52266908e-01 -1.19872661e-02 -6.06534719e-01 -1.06353378e+00 -8.41856956e-01 -9.40652490e-01 1.09495737e-01 8.10638666e-01 -1.44609416e+00 -5.21928549e-01 2.93465137e-01 -1.07467926e+00 -2.03514937e-02 1.14215337e-01 5.70703089e-01 -6.36380911e-01 2.57675081e-01 -3.33399773e-02 -1.40143394e-01 -2.61652023e-02 -1.12815046e+00 1.58661532e+00 1.33115083e-01 1.38763279e-01 -9.87068474e-01 3.11971754e-02 1.95342347e-01 2.71666914e-01 4.08799678e-01 6.42947018e-01 -2.95533776e-01 -9.38590646e-01 -2.24818900e-01 -7.32341707e-01 7.70710707e-02 3.27701747e-01 -4.87130135e-01 -1.05877423e+00 -5.73308468e-01 -2.46052027e-01 -1.69875368e-01 9.28993762e-01 2.61600494e-01 9.30702329e-01 1.35896057e-01 -5.30364990e-01 1.05956209e+00 1.65734434e+00 -2.10669294e-01 3.62123311e-01 6.26198888e-01 8.42905998e-01 5.17840803e-01 1.03979194e+00 3.98976505e-01 7.19755232e-01 1.01189280e+00 7.35113740e-01 -3.54307175e-01 1.38191609e-02 -4.30318505e-01 1.34687603e-01 2.66562313e-01 2.82536060e-01 -1.51160911e-01 -9.90911484e-01 9.36827898e-01 -1.90093195e+00 -7.26817131e-01 3.71028446e-02 2.27596259e+00 1.84875712e-01 -2.24946931e-01 -1.30310014e-01 -3.59400123e-01 7.87669480e-01 5.12705147e-01 -6.90180063e-01 2.94144690e-01 -1.31256253e-01 -2.93507159e-01 8.98886263e-01 3.09096098e-01 -1.68821657e+00 1.06447577e+00 4.34031773e+00 6.43063605e-01 -1.31944311e+00 6.97768331e-02 4.13618177e-01 -4.00723740e-02 9.16266367e-02 -1.30821824e-01 -7.72956431e-01 3.23090941e-01 4.71400857e-01 8.69397223e-02 4.18197155e-01 9.86342013e-01 -4.00890149e-02 -1.38812169e-01 -1.26268840e+00 1.38630462e+00 1.41204447e-01 -1.35944998e+00 -5.64491339e-02 3.02888811e-01 8.77498329e-01 4.55746830e-01 3.11282337e-01 1.40252158e-01 4.96308357e-02 -7.43614078e-01 7.99440980e-01 2.59900063e-01 1.10709655e+00 -8.02117348e-01 8.77771378e-01 1.94729701e-01 -1.71295321e+00 -5.49566075e-02 -6.55173302e-01 1.81792587e-01 7.69135058e-02 4.64445204e-01 -6.30206525e-01 8.89142454e-01 9.57624853e-01 1.14619577e+00 -7.08440781e-01 9.76065695e-01 -9.11772251e-02 4.63904738e-02 -2.93679684e-01 4.14166123e-01 6.94192529e-01 -7.28098825e-02 2.17160776e-01 9.47389126e-01 6.11793220e-01 -3.50380927e-01 2.62727439e-01 9.09167588e-01 -1.53902486e-01 -1.23241238e-01 -1.12143946e+00 2.58719563e-01 4.59175527e-01 1.28892803e+00 -5.39688051e-01 9.29767266e-03 -6.57163620e-01 1.19605422e+00 4.57011133e-01 3.09442103e-01 -1.00682521e+00 -5.01753926e-01 1.16110265e+00 2.31844485e-01 6.77711844e-01 -4.02460396e-01 6.88438187e-04 -1.32523453e+00 2.70515829e-01 -4.15585786e-01 2.28587374e-01 -7.83533752e-01 -1.46724272e+00 6.51186287e-01 -1.06750697e-01 -1.63684070e+00 -1.90564662e-01 -6.26579583e-01 -2.95586914e-01 1.12356007e+00 -1.93308234e+00 -1.60437655e+00 -6.75618351e-01 7.15859532e-01 2.81428844e-01 -1.57677174e-01 7.43797779e-01 4.87641454e-01 -5.30653596e-02 7.08639443e-01 4.54474211e-01 3.33942473e-01 9.81196523e-01 -1.20678127e+00 7.86449909e-01 9.31820154e-01 4.04220015e-01 5.96686959e-01 9.25369933e-02 -1.79836914e-01 -1.42759240e+00 -1.55041039e+00 9.69389558e-01 -7.57490754e-01 7.40305662e-01 -5.18581092e-01 -6.13760471e-01 4.68318701e-01 -7.68461227e-02 4.55588341e-01 5.23633003e-01 8.10213909e-02 -7.01749861e-01 -4.61084306e-01 -1.09640872e+00 3.48681390e-01 1.26443064e+00 -9.68362093e-01 -2.43140712e-01 2.76309788e-01 7.22290456e-01 -4.56515402e-01 -9.40281034e-01 3.42333317e-01 4.89946544e-01 -1.05775571e+00 1.20589864e+00 -4.45967346e-01 2.84908235e-01 -6.62795901e-01 -8.13562512e-01 -1.44794726e+00 -5.09218037e-01 -9.97295752e-02 2.02945113e-01 1.17260706e+00 1.56236127e-01 -7.60800004e-01 5.61737835e-01 1.88767418e-01 -8.83343071e-02 -7.08312750e-01 -8.99559796e-01 -1.01983309e+00 -4.38040085e-02 -1.91202104e-01 8.79934072e-01 1.16901863e+00 -4.99437690e-01 2.90508389e-01 -2.80173331e-01 8.15621257e-01 3.84297818e-01 7.44168162e-01 8.53056788e-01 -1.22721040e+00 1.16787024e-01 -2.55677462e-01 -7.17728674e-01 -1.38007343e+00 1.80203110e-01 -9.26015437e-01 -8.80042687e-02 -1.67895806e+00 -6.54032528e-02 -4.48549628e-01 -4.67480153e-01 1.15420528e-01 -2.89228354e-02 4.07054305e-01 2.37765253e-01 1.98503345e-01 -8.97236466e-01 5.58529258e-01 9.43551540e-01 -3.27560127e-01 1.26801416e-01 -5.44794165e-02 -8.08595181e-01 5.65750241e-01 7.29573131e-01 -5.20891309e-01 -3.28050226e-01 -9.35239315e-01 1.89296797e-01 -1.47898838e-01 7.43910789e-01 -1.11125815e+00 3.24415654e-01 -1.67682245e-02 3.82016629e-01 -7.21325815e-01 3.21123570e-01 -1.00939178e+00 -1.69227868e-01 1.61449254e-01 -4.95294742e-02 4.75420535e-01 3.84674631e-02 8.87063086e-01 -4.40608352e-01 2.41748333e-01 5.98754168e-01 -1.43369228e-01 -1.19972682e+00 5.62648058e-01 2.51784801e-01 3.25919181e-01 9.57566142e-01 -2.46623486e-01 -3.73508990e-01 -2.73986071e-01 -2.96734124e-01 2.81588644e-01 8.14763665e-01 5.08976102e-01 5.23744881e-01 -1.50323081e+00 -6.04742050e-01 3.27883452e-01 7.32185245e-01 3.29335257e-02 2.28993356e-01 9.48238969e-01 -4.10087347e-01 7.08214641e-01 -1.65119201e-01 -9.98487115e-01 -9.48247910e-01 5.26293874e-01 4.66431648e-01 -2.34860927e-01 -4.34327066e-01 9.00148511e-01 6.57427967e-01 -6.81998551e-01 4.92586680e-02 -4.92607266e-01 2.41395727e-01 1.18935488e-01 5.79208612e-01 2.21468493e-01 3.03390592e-01 -1.00730300e+00 -8.27175140e-01 1.15194988e+00 9.68311168e-03 1.45091355e-01 1.33127391e+00 -4.00123358e-01 -3.46322507e-02 1.56271949e-01 1.69606864e+00 -2.08364278e-01 -1.40084016e+00 -6.06622696e-01 9.17034745e-02 -8.55041742e-01 1.05977684e-01 -6.76261485e-01 -1.17013943e+00 1.18129063e+00 9.75426137e-01 -8.28431770e-02 1.07843268e+00 1.52069762e-01 8.50741386e-01 6.70865774e-01 7.92658567e-01 -7.80887365e-01 -3.96258123e-02 5.89422703e-01 7.83940971e-01 -1.67842734e+00 -6.82475641e-02 -3.98693383e-02 -4.81802106e-01 9.10843372e-01 6.45828307e-01 -2.26780146e-01 6.63928211e-01 -3.23790252e-01 -4.43815216e-02 -3.60859692e-01 -2.57519394e-01 -4.26606327e-01 4.81591851e-01 7.75050581e-01 2.89788395e-01 7.14880154e-02 3.78321618e-01 2.82990783e-01 1.65194515e-02 -2.60063380e-01 2.27538496e-03 7.02268541e-01 -2.68089890e-01 -6.20524764e-01 -1.65633649e-01 8.96550938e-02 -1.08006023e-01 -2.26241842e-01 -3.52474451e-01 9.94118929e-01 2.80816704e-01 8.30776215e-01 1.91222325e-01 -5.13132155e-01 6.01876974e-01 -3.61014366e-01 3.65667731e-01 -5.01015961e-01 -3.38669240e-01 -2.70157874e-01 -2.86708683e-01 -1.01054752e+00 -5.50220072e-01 -5.31204641e-01 -8.48129392e-01 -3.23707372e-01 -1.61216542e-01 -8.71251300e-02 6.01028979e-01 8.73401523e-01 7.45268881e-01 1.07553400e-01 8.55062246e-01 -8.78426492e-01 -4.62113708e-01 -5.72461605e-01 -6.34743154e-01 2.11945608e-01 5.89909256e-01 -9.33378160e-01 -1.76338390e-01 -1.72845230e-01]	[7.733282089233398, -1.9070545434951782]
e23cc124-f1a0-4305-a561-71e25b2982ef	dense-resolution-network-for-point-cloud	2005.06734	null	https://arxiv.org/abs/2005.06734v2	https://arxiv.org/pdf/2005.06734v2.pdf	Dense-Resolution Network for Point Cloud Classification and Segmentation	Point cloud analysis is attracting attention from Artificial Intelligence research since it can be widely used in applications such as robotics, Augmented Reality, self-driving. However, it is always challenging due to irregularities, unorderedness, and sparsity. In this article, we propose a novel network named Dense-Resolution Network (DRNet) for point cloud analysis. Our DRNet is designed to learn local point features from the point cloud in different resolutions. In order to learn local point groups more effectively, we present a novel grouping method for local neighborhood searching and an error-minimizing module for capturing local features. In addition to validating the network on widely used point cloud segmentation and classification benchmarks, we also test and visualize the performance of the components. Comparing with other state-of-the-art methods, our network shows superiority on ModelNet40, ShapeNet synthetic and ScanObjectNN real point cloud datasets.	['Shi Qiu', 'Nick Barnes', 'Saeed Anwar']	2020-05-14	null	null	null	null	['3d-part-segmentation']	['computer-vision']	[-2.92761326e-01 -4.61014032e-01 -1.09121576e-01 -2.65793860e-01 -2.23468289e-01 -2.94591159e-01 3.49335343e-01 3.09885144e-01 -1.59832552e-01 3.91551346e-01 -3.62335443e-01 -1.40905797e-01 -5.31508148e-01 -1.15153396e+00 -7.84958482e-01 -2.71160513e-01 -3.54817688e-01 7.54449010e-01 4.39153612e-01 -2.99429506e-01 3.92728686e-01 1.33986449e+00 -1.69597542e+00 -9.42212809e-03 1.06103504e+00 1.02228868e+00 3.66290659e-01 -1.72168210e-01 -3.31275702e-01 6.33871257e-02 -2.46016100e-01 2.63848782e-01 5.16774178e-01 1.94312066e-01 -5.28458416e-01 3.68622988e-02 5.41826606e-01 4.42160890e-02 5.23668788e-02 1.22287977e+00 3.96315813e-01 4.21191901e-01 5.52582800e-01 -1.09377027e+00 -3.69055063e-01 2.37903282e-01 -8.23217213e-01 9.68714133e-02 6.47567734e-02 -6.30044490e-02 7.67517745e-01 -1.05112338e+00 6.58271968e-01 1.44020140e+00 8.26042950e-01 1.73344940e-01 -1.00500190e+00 -9.77191985e-01 2.25289911e-01 2.85294890e-01 -1.67715728e+00 1.48635909e-01 9.51412976e-01 -2.75595993e-01 7.05393493e-01 2.22474501e-01 6.46027625e-01 5.04520416e-01 -6.69466006e-03 3.76793623e-01 7.44665384e-01 -1.98563165e-03 4.41848427e-01 -3.04268569e-01 1.04210936e-01 5.25454342e-01 2.93297827e-01 8.13415796e-02 -1.49771616e-01 -2.96506315e-01 1.37242544e+00 4.94456857e-01 -7.66150728e-02 -6.37626350e-01 -1.27939904e+00 7.60324240e-01 9.99560118e-01 3.86415690e-01 -5.59865355e-01 1.46888778e-01 1.21939361e-01 7.86579177e-02 5.64860284e-01 4.92154360e-01 -3.65229070e-01 -5.44191785e-02 -7.16440856e-01 2.94269413e-01 4.77287591e-01 1.07459319e+00 1.05354643e+00 -4.16725203e-02 2.42813528e-01 1.03487694e+00 2.85372406e-01 4.09150511e-01 1.64881781e-01 -9.73653615e-01 3.29168469e-01 1.05163527e+00 -1.75412014e-01 -1.50429738e+00 -6.51334822e-01 -5.82298160e-01 -1.25238121e+00 3.31152856e-01 -1.28282100e-01 3.20871294e-01 -8.08030427e-01 9.39302385e-01 4.64761227e-01 9.36395109e-01 -3.09203506e-01 1.03740120e+00 1.02013290e+00 6.77754700e-01 -2.03231722e-01 1.87669665e-01 9.43787634e-01 -6.39693916e-01 -2.07288414e-01 1.32780164e-01 2.72919506e-01 -4.96242493e-01 7.90587187e-01 4.10270691e-01 -8.30735922e-01 -7.33159423e-01 -8.52467179e-01 1.31592393e-01 -2.21733034e-01 -1.17901608e-01 5.87228537e-01 6.15590438e-02 -8.81935418e-01 9.23226953e-01 -9.68589246e-01 -4.38369125e-01 6.54673517e-01 5.76754630e-01 -2.88391739e-01 -1.74008787e-01 -5.36622941e-01 4.68982220e-01 3.68369132e-01 -5.67330047e-02 -3.34421486e-01 -9.62559879e-01 -7.42321372e-01 1.63964763e-01 1.82849288e-01 -6.73863530e-01 7.03868032e-01 -3.43994141e-01 -1.25857675e+00 4.82493967e-01 -5.94917908e-02 -4.59413141e-01 2.89851367e-01 3.94313037e-02 -2.97963589e-01 2.36634061e-01 1.47811696e-01 8.00599158e-01 6.20539188e-01 -1.44600618e+00 -8.01512837e-01 -6.16595447e-01 -1.00474149e-01 2.33280525e-01 -2.45215688e-02 -2.11521775e-01 -5.81697404e-01 -5.86793423e-01 8.53506327e-01 -8.64956677e-01 -5.20071447e-01 4.62512746e-02 -3.28631729e-01 -6.67719662e-01 1.33369291e+00 -3.96535136e-02 7.40295887e-01 -2.35880208e+00 -2.93893293e-02 7.95054376e-01 4.40530986e-01 1.08624272e-01 -1.67367443e-01 9.12822708e-02 -8.80997255e-02 3.25956941e-01 -1.88163236e-01 -3.21775645e-01 -1.80588737e-01 4.24299628e-01 -1.87479649e-02 6.24430776e-01 1.17941126e-01 7.57842362e-01 -7.83962011e-01 -4.42035288e-01 6.72317863e-01 5.53260326e-01 -5.11027634e-01 -3.03990990e-01 -2.44715571e-01 4.87565100e-01 -5.70720971e-01 9.44394946e-01 1.14888787e+00 -3.34519267e-01 -4.45437193e-01 -2.97541440e-01 -3.22058588e-01 -2.60755032e-01 -1.35925877e+00 1.76538479e+00 -2.97668695e-01 4.05470371e-01 6.72725379e-04 -7.69706249e-01 1.27494574e+00 -5.03062047e-02 7.49727428e-01 -6.87683523e-01 1.38728574e-01 2.29210466e-01 -1.02319583e-01 -1.06577836e-01 4.50676769e-01 7.82997310e-02 3.14769506e-01 -5.27474992e-02 -2.74898112e-01 -2.31088117e-01 -8.98402482e-02 -1.87735662e-01 9.93070543e-01 -3.08217496e-01 -2.99444720e-02 -3.44684750e-01 5.42415500e-01 2.64726549e-01 5.70940077e-01 5.58552802e-01 9.29415822e-02 1.01429796e+00 8.56976584e-03 -7.10290492e-01 -9.27882552e-01 -9.60341990e-01 -4.39470917e-01 4.25045133e-01 7.71050394e-01 -2.96325773e-01 -3.16386431e-01 -3.75598550e-01 3.61500174e-01 4.31268483e-01 -2.84850746e-01 1.81237370e-01 -7.96320796e-01 -2.43181095e-01 1.46841750e-01 5.81836045e-01 7.06438005e-01 -1.09372270e+00 -1.89546958e-01 1.52371511e-01 1.32800713e-01 -1.21164846e+00 -3.46578211e-02 -2.62123704e-01 -1.35035038e+00 -9.73552883e-01 -3.14524055e-01 -7.72569001e-01 7.21893907e-01 6.16628349e-01 1.21048868e+00 3.60985339e-01 -7.90323988e-02 1.29855096e-01 -3.59382242e-01 -4.61724639e-01 8.48079994e-02 4.50726420e-01 6.72257096e-02 -1.78896472e-01 4.50621873e-01 -8.90734076e-01 -6.91062868e-01 5.99026859e-01 -8.73283863e-01 -1.94640443e-01 5.56835592e-01 3.81386340e-01 1.13076687e+00 3.62799615e-01 2.34114930e-01 -5.81313252e-01 5.90653062e-01 -6.63721740e-01 -8.87061357e-01 -1.52178660e-01 -3.13224524e-01 -3.16714346e-01 5.53907812e-01 -1.30195230e-01 -3.70260090e-01 1.27873853e-01 -9.56273973e-02 -1.02456975e+00 -4.45098370e-01 5.23846328e-01 -3.55239622e-02 -6.86462224e-01 5.68318665e-01 -9.18238331e-03 1.49541765e-01 -7.13656068e-01 1.13076255e-01 3.88184518e-01 3.99369389e-01 -5.99418581e-01 1.13631904e+00 8.46093714e-01 4.81698930e-01 -1.10263622e+00 -3.32803100e-01 -8.29835951e-01 -7.11835563e-01 -9.28499103e-02 7.10005701e-01 -7.89160728e-01 -1.02624297e+00 9.97122899e-02 -1.26157749e+00 2.66220719e-01 -4.06807035e-01 4.02355671e-01 -2.87449509e-01 2.31336519e-01 -2.03956440e-01 -3.92305672e-01 -2.24578336e-01 -1.14721406e+00 1.13550937e+00 2.94073045e-01 1.30949005e-01 -9.80951488e-01 5.32781817e-02 -1.26855988e-02 3.45384240e-01 6.58611953e-01 6.85865104e-01 -5.28231561e-01 -1.09156096e+00 -1.54538825e-01 -4.56205666e-01 2.96322912e-01 2.02839717e-01 5.51378727e-02 -3.59801352e-01 -1.91568241e-01 -1.33860871e-01 2.36524090e-01 5.80694020e-01 4.73451406e-01 1.64194548e+00 -4.08172905e-02 -5.64883888e-01 9.66853499e-01 1.69249713e+00 8.16260725e-02 5.85834920e-01 5.39816439e-01 1.01164055e+00 2.64279872e-01 6.10284269e-01 3.27816725e-01 3.20212632e-01 6.43880308e-01 9.29581523e-01 -3.07809532e-01 2.15016901e-01 -2.43943483e-02 -3.70142549e-01 7.55269825e-01 -3.41108590e-01 1.64688766e-01 -1.31333053e+00 5.10187209e-01 -2.08052015e+00 -8.05793405e-01 -3.65709752e-01 1.95553875e+00 8.44068378e-02 -1.56557262e-02 -2.64969431e-02 -4.15620767e-02 7.31920183e-01 -3.02269571e-02 -5.37183225e-01 1.56765094e-03 -2.04876140e-01 4.95732754e-01 6.62366688e-01 1.41549692e-01 -1.00091588e+00 9.63736355e-01 5.65071964e+00 1.10455739e+00 -1.33412421e+00 1.04416855e-01 3.49806488e-01 8.08527842e-02 -1.60393536e-01 -2.13150322e-01 -7.20595717e-01 3.93444836e-01 3.24471414e-01 -1.99309047e-02 3.19274485e-01 1.03179288e+00 3.73819411e-01 1.45566061e-01 -8.61101031e-01 1.30790579e+00 -1.12981036e-01 -1.77874076e+00 2.54360884e-01 2.05266610e-01 8.46624732e-01 6.44407451e-01 -5.20446114e-02 7.81867877e-02 7.18303099e-02 -1.22165036e+00 3.97005379e-01 4.80771661e-01 5.16533077e-01 -1.16365469e+00 7.96043336e-01 5.25397599e-01 -1.43431187e+00 8.16850364e-03 -8.04136515e-01 -5.15390560e-02 3.30874659e-02 7.11347878e-01 -7.50584900e-01 8.90442073e-01 9.81635153e-01 1.12645555e+00 -6.28280044e-01 1.55709183e+00 2.15726569e-01 2.98483670e-01 -7.94800639e-01 1.46621764e-01 4.22349423e-01 -6.09689593e-01 7.34094262e-01 7.46871889e-01 4.94753540e-01 2.86176115e-01 5.02528250e-01 1.08737516e+00 -1.21592909e-01 2.19318584e-01 -8.11974108e-01 4.30218577e-01 7.33457685e-01 1.23617840e+00 -9.73622680e-01 -7.15060607e-02 -1.97858185e-01 2.39998743e-01 2.66285777e-01 1.55165419e-01 -4.72377241e-01 -3.18730980e-01 9.64963973e-01 4.12037343e-01 2.88331389e-01 -6.87800884e-01 -5.98511934e-01 -9.97687221e-01 4.60815318e-02 -6.85305715e-01 -1.29357129e-01 -6.89966440e-01 -1.31108356e+00 6.95368409e-01 2.08671942e-01 -1.75439250e+00 5.69907762e-02 -5.07023871e-01 -7.47798443e-01 7.77567804e-01 -1.68063104e+00 -9.15736914e-01 -8.35249364e-01 7.13477850e-01 4.76146370e-01 -2.38096923e-01 3.77705902e-01 3.82992834e-01 -2.60799706e-01 9.62150320e-02 2.25189507e-01 7.18399882e-02 2.02305973e-01 -9.45270002e-01 5.58832049e-01 4.94938970e-01 7.38913268e-02 7.34305203e-01 2.90035009e-01 -7.12765157e-01 -1.23042893e+00 -1.39802253e+00 1.59012109e-01 -1.78111672e-01 3.60517263e-01 -2.32973903e-01 -1.21751404e+00 3.30320001e-01 -1.46452829e-01 3.92947257e-01 1.05402552e-01 6.79556578e-02 4.47234735e-02 -3.27898741e-01 -1.39193165e+00 4.07496721e-01 1.27892244e+00 5.19934110e-02 -2.85501271e-01 4.62272614e-01 8.48637342e-01 -6.69285417e-01 -9.26063180e-01 8.31029236e-01 2.09029149e-02 -9.85400558e-01 1.34050667e+00 -1.71399102e-01 2.85407871e-01 -6.30262077e-01 -6.40848875e-02 -1.29875803e+00 -6.71014786e-01 -1.64808109e-01 1.83286384e-01 9.49248254e-01 1.01477817e-01 -8.11686635e-01 1.09755707e+00 6.62220791e-02 -3.48461002e-01 -8.61213446e-01 -1.17840338e+00 -9.85659003e-01 -5.02297096e-02 -4.61200058e-01 1.22164214e+00 1.13220620e+00 -6.48412466e-01 9.18110386e-02 2.36166224e-01 5.75668812e-01 8.48312616e-01 1.71520472e-01 9.40372050e-01 -1.89532626e+00 4.80665356e-01 -5.90344191e-01 -9.45337057e-01 -9.93899167e-01 1.67203382e-01 -8.80165875e-01 -2.84134120e-01 -1.70971632e+00 -2.71207035e-01 -1.05834341e+00 -1.31817967e-01 2.25737602e-01 2.05724895e-01 3.68954331e-01 3.38782758e-01 6.29892230e-01 -5.53572953e-01 6.28231347e-01 1.30501080e+00 -5.97582869e-02 -4.21077847e-01 1.47523046e-01 -2.17216074e-01 8.49625885e-01 9.57894742e-01 -4.12100524e-01 -2.40057871e-01 -5.96291542e-01 2.52372205e-01 -2.02927202e-01 5.68519652e-01 -1.55546916e+00 5.97223461e-01 -2.13886976e-01 2.58210808e-01 -1.33618438e+00 4.16555911e-01 -1.28683853e+00 3.23487937e-01 7.82482997e-02 2.72837460e-01 4.60556030e-01 2.86679775e-01 5.28632164e-01 -5.19400239e-01 -8.02640840e-02 7.46092260e-01 -2.27721542e-01 -7.54954457e-01 7.93869078e-01 3.13030273e-01 -3.40869576e-01 1.09997070e+00 -5.08183181e-01 -3.38180244e-01 -6.56739995e-02 -5.10315299e-01 5.29477179e-01 8.43644857e-01 4.89492714e-01 1.06655288e+00 -1.52457774e+00 -6.37732506e-01 3.21992010e-01 1.19560413e-01 8.42126667e-01 1.95087448e-01 6.03431761e-01 -1.13362455e+00 1.90943390e-01 -1.96674719e-01 -1.30467057e+00 -1.11075318e+00 3.46582144e-01 2.35740200e-01 2.19969437e-01 -9.20956671e-01 4.23334330e-01 -5.92726767e-02 -6.90919280e-01 1.37714282e-01 -6.65332615e-01 -3.48751247e-01 -3.85945857e-01 3.61161195e-02 6.04025424e-01 2.55527139e-01 -6.79984987e-01 -4.25518572e-01 1.08647180e+00 7.88399875e-02 3.38749290e-01 1.51026666e+00 1.87401637e-01 -5.18735290e-01 2.15631768e-01 1.16160917e+00 -1.51823014e-01 -9.07353342e-01 -2.97850549e-01 4.16746885e-02 -7.66790628e-01 1.19128846e-01 -1.59137353e-01 -1.28752494e+00 6.21368706e-01 7.41866052e-01 3.51179838e-01 8.53235245e-01 1.67337298e-01 6.84160113e-01 5.13866127e-01 7.33251095e-01 -8.01306784e-01 -2.09329829e-01 7.01314211e-01 8.69548798e-01 -1.31336617e+00 1.11858010e-01 -7.29811013e-01 -1.41478777e-01 1.06091321e+00 7.16266096e-01 -6.55770123e-01 9.72470284e-01 7.99486879e-03 -7.52126500e-02 -6.01235926e-01 -3.81784648e-01 -1.17997173e-02 3.18362296e-01 6.14024222e-01 -1.24158368e-01 -3.86730395e-02 6.68359697e-02 -3.57412808e-02 -4.66216445e-01 -3.92524488e-02 1.09159440e-01 8.09741437e-01 -4.66992408e-01 -8.81673574e-01 -6.57998741e-01 6.16075575e-01 -6.15997091e-02 1.66508242e-01 -1.06351033e-01 1.00994658e+00 2.94642508e-01 5.72987437e-01 6.57803893e-01 -3.74058932e-01 6.71536863e-01 -6.50512636e-01 3.00029106e-02 -6.89902365e-01 -5.97009718e-01 -1.43230319e-01 -5.16226530e-01 -7.66146839e-01 -5.99672794e-01 -5.89259684e-01 -1.43638182e+00 -4.63775665e-01 -2.66406536e-01 2.40439087e-01 1.06713867e+00 7.20626056e-01 7.78189659e-01 5.12215793e-01 7.04968333e-01 -1.24428988e+00 -1.03599250e-01 -7.77689755e-01 -5.45290291e-01 2.53990173e-01 2.04185784e-01 -7.39096940e-01 -2.45177090e-01 -5.44535577e-01]	[7.851707935333252, -3.225336790084839]
649e5ab4-4e8e-4eed-81b2-e6e69e417a0d	single-and-multi-task-architectures-for-1	1610.08844	null	http://arxiv.org/abs/1610.08844v2	http://arxiv.org/pdf/1610.08844v2.pdf	Single- and Multi-Task Architectures for Surgical Workflow Challenge at M2CAI 2016	The surgical workflow challenge at M2CAI 2016 consists of identifying 8 surgical phases in cholecystectomy procedures. Here, we propose to use deep architectures that are based on our previous work where we presented several architectures to perform multiple recognition tasks on laparoscopic videos. In this technical report, we present the phase recognition results using two architectures: (1) a single-task architecture designed to perform solely the surgical phase recognition task and (2) a multi-task architecture designed to perform jointly phase recognition and tool presence detection. On top of these architectures we propose to use two different approaches to enforce the temporal constraints of the surgical workflow: (1) HMM-based and (2) LSTM-based pipelines. The results show that the LSTM-based approach is able to outperform the HMM-based approach and also to properly enforce the temporal constraints into the recognition process.	['Michel de Mathelin', 'Didier Mutter', 'Andru P. Twinanda', 'Nicolas Padoy', 'Jacques Marescaux']	2016-10-27	null	null	null	null	['surgical-phase-recognition']	['computer-vision']	[ 4.04340893e-01 2.59188712e-01 1.84831023e-02 -1.59846365e-01 -7.27331519e-01 -4.46357608e-01 9.29754794e-01 2.02854440e-01 -6.60847068e-01 -6.13813885e-02 3.11866462e-01 -5.98165691e-01 -3.40415776e-01 -2.00934708e-01 -3.88718188e-01 -5.62465429e-01 -3.39498758e-01 6.30194426e-01 3.13725442e-01 1.27768889e-01 4.99308288e-01 6.02125406e-01 -1.51341784e+00 8.81349683e-01 1.25156254e-01 8.00757349e-01 3.54698718e-01 1.16339862e+00 -1.51353434e-01 1.00552297e+00 -5.42958617e-01 3.35203171e-01 3.00465733e-01 -3.75424594e-01 -8.20837200e-01 -5.83239309e-02 1.99875221e-01 -1.34705335e-01 -1.41830608e-01 6.68078005e-01 4.77694958e-01 -9.98178422e-02 2.11481810e-01 -6.04001045e-01 4.50817615e-01 5.37035346e-01 1.45300766e-02 2.79675990e-01 6.59453273e-01 2.84268290e-01 1.94893181e-01 -6.15838289e-01 8.94659758e-01 9.77123141e-01 6.78933084e-01 5.99229634e-01 -8.51460338e-01 -3.73061359e-01 -6.88295066e-02 2.30591431e-01 -9.98118222e-01 -3.87290299e-01 3.06135654e-01 -7.87201822e-01 1.24513721e+00 1.36033431e-01 1.07575333e+00 1.60990250e+00 5.80788374e-01 7.02205300e-01 1.32865369e+00 -6.35300994e-01 1.47025898e-01 -3.26322876e-02 1.00841425e-01 9.48319435e-01 2.59389430e-01 4.83219445e-01 -7.61441588e-01 7.87065402e-02 7.42112339e-01 5.26635759e-02 6.67819008e-02 -4.08484310e-01 -1.91774237e+00 4.54821646e-01 -3.93956602e-02 8.14297199e-01 -5.25063038e-01 2.91678280e-01 8.30273688e-01 9.17955786e-02 -1.50456354e-01 7.74235666e-01 -3.17905068e-01 -5.48814714e-01 -1.27457130e+00 -1.88861653e-01 1.29470575e+00 1.02910781e+00 3.68382365e-01 -3.58087778e-01 -6.21380448e-01 2.33231589e-01 4.77083832e-01 -1.69217288e-01 6.49562180e-01 -8.13891947e-01 1.92398772e-01 4.07083482e-01 1.14375226e-01 -4.91305858e-01 -9.13255692e-01 -2.13722229e-01 -4.54877675e-01 2.05883965e-01 3.04333746e-01 -1.72179282e-01 -1.58622348e+00 9.91655946e-01 -1.36552662e-01 2.27292567e-01 1.01545870e-01 4.76599783e-01 1.02050543e+00 1.33842364e-01 3.59038562e-01 -7.44575858e-02 1.52892613e+00 -1.27638686e+00 -1.04222703e+00 -1.88198000e-01 9.99094665e-01 -1.14313698e+00 4.04224753e-01 8.43187630e-01 -1.02744555e+00 -6.64754391e-01 -1.20742691e+00 1.98792055e-01 -4.10556823e-01 5.56919277e-01 5.80789089e-01 7.75161684e-01 -1.22664237e+00 7.03478217e-01 -1.49150884e+00 -5.99237204e-01 -1.20840713e-01 6.10334814e-01 -4.71133679e-01 3.24747831e-01 -6.08198583e-01 1.37797070e+00 7.24780858e-01 5.58476031e-01 -1.37271488e+00 -2.63778508e-01 -1.00293446e+00 -3.10728955e-03 1.83878124e-01 -6.50745392e-01 1.50288832e+00 -6.28344178e-01 -1.84518802e+00 1.26139128e+00 -1.82846442e-01 -5.93732834e-01 5.94244301e-01 -4.30633366e-01 -8.54752287e-02 2.47670725e-01 -4.64465022e-01 4.67259794e-01 4.89361048e-01 -1.10689092e+00 -7.77742982e-01 6.80592433e-02 -1.58909276e-01 4.05299999e-02 9.74949896e-02 1.99087068e-01 -7.27063179e-01 -3.55963320e-01 -1.74439978e-02 -1.41647744e+00 -5.74081004e-01 -1.98087946e-01 -4.61881459e-01 7.28922188e-02 5.28408825e-01 -8.33014429e-01 1.17514801e+00 -2.11831474e+00 2.11487651e-01 4.69176807e-02 -5.05785495e-02 7.01043010e-01 1.50785306e-02 5.04586458e-01 -3.72570269e-02 -1.38715386e-01 6.91963732e-02 -8.41390669e-01 -1.78268015e-01 5.99901557e-01 2.32050255e-01 3.44062030e-01 -5.37974015e-02 5.52161515e-01 -9.40891266e-01 -5.36989987e-01 7.37315476e-01 3.65892857e-01 -3.63600373e-01 4.93330210e-01 6.44751042e-02 7.89559424e-01 -1.34040743e-01 6.01708174e-01 3.66670221e-01 1.26198158e-01 6.98717058e-01 -2.51815081e-01 -5.00303447e-01 4.65827465e-01 -1.12258911e+00 2.48356628e+00 -6.00817084e-01 5.45380950e-01 3.61672312e-01 -7.34340847e-01 6.46399856e-01 8.19303155e-01 6.43505573e-01 -3.45539659e-01 1.67266503e-01 3.84583831e-01 4.38005894e-01 -9.52264607e-01 3.53284687e-01 3.92515175e-02 -4.59523359e-03 -5.70065416e-02 5.95473051e-01 6.23795092e-02 2.56465435e-01 -3.34154129e-01 1.48899710e+00 5.84313810e-01 5.80771923e-01 -3.88469160e-01 5.32288313e-01 2.21252218e-01 5.41349769e-01 1.02541304e+00 -6.10336244e-01 4.90277886e-01 4.86135006e-01 -6.83305025e-01 -6.07104719e-01 -8.66689205e-01 3.27266693e-01 6.84920132e-01 -4.50763479e-03 -5.22331834e-01 -3.74704987e-01 -7.77014971e-01 -4.18086946e-01 3.73537123e-01 -9.65050757e-01 -8.45218301e-02 -9.81959164e-01 -3.39840144e-01 5.01305342e-01 5.07543623e-01 -2.10939138e-03 -1.27605832e+00 -1.19352639e+00 2.75091022e-01 -8.44092220e-02 -1.10720766e+00 -3.11141890e-02 9.27416801e-01 -7.81280935e-01 -1.21027875e+00 -4.91077721e-01 -8.91778409e-01 3.68908435e-01 -1.49173334e-01 8.03820729e-01 -3.30464868e-03 -5.71810484e-01 4.24987972e-01 -5.11154592e-01 -6.99090183e-01 -7.54518330e-01 2.30633199e-01 -3.28757226e-01 -3.97146642e-01 2.72142857e-01 -1.73929453e-01 -7.19512701e-01 9.71763581e-02 -7.51650155e-01 3.20314497e-01 1.23054194e+00 6.70170784e-01 1.61183581e-01 -8.21701109e-01 -2.97737032e-01 -1.09221780e+00 1.07532047e-01 -3.47423881e-01 -5.04413903e-01 2.93988705e-01 -5.54928958e-01 1.47582993e-01 8.22938755e-02 -4.44070488e-01 -8.51972044e-01 6.47208571e-01 -3.87575597e-01 -7.77590573e-01 -5.71761370e-01 5.64864755e-01 2.53879517e-01 -2.74575114e-01 3.09687614e-01 1.73007742e-01 6.49836734e-02 -6.58146560e-01 -2.03828171e-01 4.81012762e-01 5.01581848e-01 -2.65072167e-01 3.92984807e-01 4.21151638e-01 1.20406158e-01 -6.09688520e-01 -5.30812144e-01 -1.05572760e+00 -8.11970830e-01 -4.32813466e-01 1.34658253e+00 -7.26159990e-01 -7.80799389e-01 3.74835640e-01 -1.35665607e+00 -6.46450758e-01 -8.79372209e-02 9.52092469e-01 -7.58154631e-01 4.32351649e-01 -9.39051509e-01 -8.32759500e-01 -3.98728997e-01 -1.54442239e+00 1.18963444e+00 7.60131255e-02 -3.31000268e-01 -1.06455982e+00 3.82709354e-01 1.81405753e-01 5.19580483e-01 5.80681860e-01 4.96694893e-01 -1.09260178e+00 -4.41399932e-01 -1.95063218e-01 1.94588006e-01 -8.69893376e-03 8.94435570e-02 1.89042881e-01 -9.64543104e-01 -2.99439162e-01 1.70286685e-01 3.91944498e-01 6.94080353e-01 2.59204328e-01 8.78388226e-01 3.86460237e-02 -5.58468640e-01 6.97957337e-01 1.20081127e+00 5.12971461e-01 8.16927135e-01 8.63983750e-01 4.62700844e-01 5.65614760e-01 8.01871836e-01 2.52589852e-01 1.01177655e-01 6.86955333e-01 5.59828877e-01 -2.71085650e-01 -3.49377573e-01 -6.40746430e-02 3.99695307e-01 8.02895784e-01 -1.90989330e-01 6.66158646e-02 -1.33342791e+00 6.30777597e-01 -2.00524521e+00 -6.85519040e-01 -3.83290768e-01 2.25756621e+00 4.09280658e-01 2.23219693e-01 -1.08100742e-01 -1.07643358e-01 2.84527272e-01 -2.26060804e-02 3.03617030e-01 -7.54872441e-01 5.28401256e-01 5.64199269e-01 6.30164683e-01 2.94584483e-01 -1.53869629e+00 6.77648067e-01 6.88819075e+00 2.99494117e-01 -1.23779225e+00 3.50747257e-01 -3.39536697e-01 -1.26754567e-01 4.50434297e-01 2.28454739e-01 -6.69641435e-01 4.34217006e-01 1.28413618e+00 4.71730262e-01 -4.82207276e-02 7.13023067e-01 1.68839946e-01 -4.09630746e-01 -1.38526380e+00 7.60746002e-01 1.31883070e-01 -1.34130037e+00 -9.20845941e-02 1.80380881e-01 2.31647819e-01 3.98541182e-01 -3.89604658e-01 4.90264416e-01 7.99536332e-02 -9.38825428e-01 7.02167451e-01 7.16673493e-01 3.98883969e-01 -1.97332744e-02 1.05127585e+00 3.23116004e-01 -1.18102884e+00 -6.49397895e-02 4.37169582e-01 1.76699519e-01 2.85409123e-01 3.14609818e-02 -1.24561012e+00 9.50718462e-01 6.91999793e-01 5.73096275e-01 -3.62458020e-01 1.67490268e+00 -3.50475550e-01 1.95745528e-01 -1.40164867e-01 2.91487217e-01 5.66489458e-01 1.18870847e-01 7.23937750e-01 2.05768299e+00 5.43523371e-01 -5.98517776e-01 4.45809186e-01 1.15428336e-01 5.94902873e-01 -3.29979688e-01 -6.11507833e-01 1.32060423e-01 -2.42035259e-02 1.50385273e+00 -9.18101966e-01 -3.66852641e-01 -4.54570055e-01 1.04136539e+00 5.30623505e-03 -9.67539381e-03 -5.18056452e-01 -2.84288168e-01 3.71923745e-01 -1.84287921e-01 5.27978539e-01 -6.37579501e-01 -2.15012863e-01 -8.07068467e-01 -1.71283886e-01 -7.36292005e-01 6.73831344e-01 -5.68865061e-01 -3.95478249e-01 6.34534061e-01 -8.90479609e-02 -1.43170214e+00 -4.71614599e-01 -1.00291014e+00 -5.47588170e-01 7.81212866e-01 -1.70638216e+00 -1.40189195e+00 -5.19250154e-01 2.63359427e-01 5.80828369e-01 1.29382938e-01 1.28059924e+00 3.11710447e-01 -3.93070072e-01 -4.67249788e-02 -2.51170099e-01 1.30897745e-01 1.02606416e+00 -1.42072499e+00 2.98030712e-02 9.35996354e-01 3.88671681e-02 9.24809217e-01 8.61169934e-01 -5.45685172e-01 -1.70661008e+00 -8.92778516e-01 9.96397197e-01 -6.45127535e-01 4.67088878e-01 -3.20380330e-01 -5.23210585e-01 1.19945931e+00 5.06039798e-01 -2.09999308e-01 9.69447136e-01 1.23533107e-01 1.34231582e-01 2.80226052e-01 -8.41624320e-01 1.25375912e-01 6.30583942e-01 -2.47198179e-01 -9.25103486e-01 3.87378365e-01 5.06073050e-03 -1.08916724e+00 -1.17146099e+00 6.77604556e-01 7.48161614e-01 -9.68511283e-01 6.29210114e-01 -3.33504111e-01 3.69200796e-01 -2.99691260e-01 2.94447690e-01 -1.02744365e+00 -7.85496011e-02 -8.27246785e-01 -3.61127645e-01 4.80988085e-01 1.47895232e-01 -3.33725363e-01 7.25988030e-01 2.88050801e-01 -7.26306021e-01 -5.51445067e-01 -1.13258231e+00 -7.27046847e-01 -4.10795778e-01 -1.03645541e-01 -2.43223310e-01 6.19908929e-01 1.45013109e-01 -1.42251715e-01 -3.08841646e-01 2.96526790e-01 6.94212243e-02 -7.49672502e-02 8.21084142e-01 -1.02396977e+00 -4.24180567e-01 -6.90278053e-01 -5.35134733e-01 -5.52063406e-01 -3.15875590e-01 -5.97456157e-01 5.33740222e-01 -1.93036306e+00 2.40878820e-01 -1.00517549e-01 -5.02641797e-01 6.92060053e-01 1.43328637e-01 -1.13879636e-01 3.74725699e-01 3.67881477e-01 -7.28701591e-01 -1.99338689e-01 8.15023899e-01 1.86827570e-01 -2.58061469e-01 4.71175276e-02 -6.19564168e-02 7.11197853e-01 3.20252120e-01 -5.47764897e-01 2.82030344e-01 -5.83634317e-01 -4.69915345e-02 2.06787989e-01 3.25751513e-01 -1.37039399e+00 7.67803371e-01 3.11421633e-01 2.04602227e-01 -6.28485084e-01 4.14025784e-01 -8.03952456e-01 2.76719153e-01 1.43171370e+00 -6.87603001e-03 -5.36298715e-02 4.91819859e-01 4.42846715e-01 -3.17488164e-01 -2.23036155e-01 6.34946465e-01 -4.98306185e-01 -8.65666747e-01 -2.38114640e-01 -1.22180927e+00 -7.48432517e-01 1.12440717e+00 -2.87997276e-01 -2.13202596e-01 3.14692855e-01 -1.28949833e+00 1.43597737e-01 2.79651523e-01 6.20441198e-01 2.81134218e-01 -6.40503168e-01 -3.91544461e-01 1.41600236e-01 3.45286965e-01 -2.04067051e-01 2.07669854e-01 1.61485469e+00 -9.24935102e-01 1.00009835e+00 -3.61342371e-01 -8.67419422e-01 -1.51757729e+00 6.57778502e-01 4.21675593e-01 -1.05016351e+00 -6.75548792e-01 6.58125341e-01 -2.31046334e-01 -4.06977147e-01 5.48845708e-01 -6.81085944e-01 -4.19877321e-01 1.37481555e-01 2.53540128e-01 9.41937938e-02 6.72787964e-01 -1.62341580e-01 -7.27394581e-01 2.68289119e-01 -1.09393790e-01 -7.03047365e-02 1.13842595e+00 2.97334582e-01 1.77889213e-01 6.00677669e-01 7.95129716e-01 -2.78349906e-01 -7.67421186e-01 -2.21344456e-02 7.60741293e-01 -2.80079506e-02 1.47923395e-01 -1.12422907e+00 -5.62432528e-01 9.25217211e-01 9.56020176e-01 7.33794719e-02 8.87291968e-01 -3.37601066e-01 4.54008609e-01 2.16362908e-01 4.32271481e-01 -1.00121915e+00 -2.27027759e-01 6.48913205e-01 5.70900440e-01 -1.03651869e+00 -2.01869980e-01 -2.32533872e-01 -3.66985261e-01 1.59989119e+00 3.42081040e-01 3.79550979e-02 5.05755723e-01 6.05660737e-01 3.09434950e-01 -4.79264051e-01 -7.47084260e-01 -5.19248068e-01 4.62140948e-01 3.30750585e-01 7.48826385e-01 1.97826885e-02 -5.45953870e-01 4.76108581e-01 2.83158481e-01 4.60981697e-01 3.64889532e-01 1.47113526e+00 -8.93326998e-02 -1.19304872e+00 -1.74905092e-01 3.96701843e-01 -5.89644969e-01 6.44721985e-02 -3.08513582e-01 1.05288005e+00 1.88062564e-01 7.57524312e-01 -7.09710047e-02 -3.62860262e-01 6.06162786e-01 2.01266691e-01 7.27905869e-01 -1.07204700e+00 -1.56782901e+00 2.13036329e-01 4.93568540e-01 -1.21382380e+00 -5.44855773e-01 -7.98661411e-01 -9.15199161e-01 4.01884586e-01 6.63779229e-02 -4.62098271e-02 1.13952041e+00 9.67967629e-01 3.98905277e-01 1.09024882e+00 -2.22235415e-02 -1.45604575e+00 -3.58534664e-01 -1.20393586e+00 -1.80981264e-01 1.77367479e-02 4.09973532e-01 -5.91075182e-01 -1.64283171e-01 -7.06309592e-03]	[14.075703620910645, -3.3587512969970703]
b86cae94-2f7b-427f-87e6-d25647259f70	ct-dqn-control-tutored-deep-reinforcement	2212.01343	null	https://arxiv.org/abs/2212.01343v1	https://arxiv.org/pdf/2212.01343v1.pdf	CT-DQN: Control-Tutored Deep Reinforcement Learning	One of the major challenges in Deep Reinforcement Learning for control is the need for extensive training to learn the policy. Motivated by this, we present the design of the Control-Tutored Deep Q-Networks (CT-DQN) algorithm, a Deep Reinforcement Learning algorithm that leverages a control tutor, i.e., an exogenous control law, to reduce learning time. The tutor can be designed using an approximate model of the system, without any assumption about the knowledge of the system's dynamics. There is no expectation that it will be able to achieve the control objective if used stand-alone. During learning, the tutor occasionally suggests an action, thus partially guiding exploration. We validate our approach on three scenarios from OpenAI Gym: the inverted pendulum, lunar lander, and car racing. We demonstrate that CT-DQN is able to achieve better or equivalent data efficiency with respect to the classic function approximation solutions.	['Mario di Bernardo', 'Mirco Musolesi', 'Giovanni Russo', 'Marco Coraggio', 'Francesco De Lellis']	2022-12-02	null	null	null	null	['carracing-v0']	['playing-games']	[-2.56286025e-01 6.49811983e-01 -2.40843967e-01 1.85609356e-01 -4.27414268e-01 -6.70410097e-01 4.94519293e-01 7.51639083e-02 -4.55427319e-01 1.13755810e+00 -2.23799914e-01 -6.50730133e-01 -2.88507372e-01 -7.79837668e-01 -1.11993992e+00 -5.81208050e-01 -8.80265385e-02 5.26784062e-01 4.80974242e-02 -7.24826217e-01 1.55104965e-01 3.58744979e-01 -1.58709335e+00 -3.05091411e-01 1.06975055e+00 6.67723179e-01 2.82766968e-01 9.61784840e-01 3.09392720e-01 1.22345889e+00 -6.25846922e-01 2.37575486e-01 3.81242573e-01 -7.73113847e-01 -8.83120060e-01 -1.52268484e-01 8.13455433e-02 -5.27553618e-01 -3.99222940e-01 8.23206663e-01 5.22578895e-01 4.38710421e-01 4.58239585e-01 -1.14253891e+00 -9.96515620e-03 5.86381137e-01 -6.69808537e-02 1.68185636e-01 2.39437863e-01 6.59338892e-01 7.94638216e-01 -7.51551911e-02 4.93375480e-01 1.32296419e+00 5.10800600e-01 6.59697294e-01 -1.41533041e+00 -2.41175577e-01 1.12449281e-01 6.98233396e-03 -1.00514603e+00 -2.14467850e-02 3.62099409e-01 -1.59356117e-01 6.85388029e-01 1.26235634e-02 1.13325465e+00 9.54306662e-01 3.39057356e-01 9.73148525e-01 1.29558647e+00 -5.48201442e-01 7.63638735e-01 8.63146856e-02 -3.20490092e-01 6.93504393e-01 -1.26693121e-04 7.34095037e-01 -2.66753793e-01 -8.75358954e-02 8.65348279e-01 -3.57895225e-01 -3.97793651e-01 -9.13117170e-01 -6.36590004e-01 8.70413542e-01 4.46241319e-01 -9.70039740e-02 -6.08755112e-01 5.94973266e-01 2.81219363e-01 8.25630963e-01 -2.32500210e-01 1.04639447e+00 -7.19067693e-01 -6.57830954e-01 -4.76186574e-01 1.03366029e+00 1.07074285e+00 6.87617421e-01 6.29351974e-01 5.20228803e-01 -1.13246433e-01 3.03187042e-01 4.58623022e-02 2.81690538e-01 5.58979571e-01 -1.44773090e+00 8.20512921e-02 4.79420483e-01 5.36459565e-01 -3.22994113e-01 -3.03213567e-01 -4.25628006e-01 -1.75641105e-02 8.62961352e-01 6.57268703e-01 -7.62724459e-01 -7.70005882e-01 1.65189624e+00 5.63883424e-01 1.52452916e-01 2.29801416e-01 8.92703235e-01 1.89475417e-02 4.96202201e-01 -1.80314273e-01 -1.42748117e-01 9.75955129e-01 -6.88563287e-01 -6.33657694e-01 -7.41529241e-02 5.58997810e-01 -1.06154576e-01 1.19760513e+00 4.55418408e-01 -1.36182439e+00 -5.73328495e-01 -1.13725495e+00 4.29298788e-01 -2.73326188e-01 -3.72452021e-01 2.24919096e-01 4.79147941e-01 -1.02574885e+00 1.28938723e+00 -1.20882869e+00 -3.35214078e-01 -5.80187254e-02 6.65850937e-01 1.37603506e-01 2.58270890e-01 -1.16325533e+00 1.11185896e+00 4.35994267e-01 -2.33209386e-01 -1.39007258e+00 -9.14176404e-01 -6.99230313e-01 3.29387337e-01 7.41458118e-01 -5.69635749e-01 2.09603429e+00 -9.48998690e-01 -2.12351465e+00 5.70251010e-02 4.05828297e-01 -7.74398506e-01 8.38451326e-01 -2.71890461e-01 2.36138374e-01 1.90585673e-01 -1.35839418e-01 6.76250100e-01 7.84250498e-01 -1.22269142e+00 -7.65471339e-01 -5.48128097e-04 5.11410534e-01 5.42253733e-01 -1.92104466e-02 -7.05677152e-01 1.53914720e-01 -3.50079060e-01 -5.37068844e-01 -9.98053372e-01 -4.18991685e-01 -1.49622455e-01 1.22738024e-02 -2.61870474e-01 9.16727185e-01 -2.99608320e-01 8.97030294e-01 -1.80680931e+00 1.02020122e-01 3.62128258e-01 -8.01900104e-02 3.38762790e-01 -7.94343650e-02 6.77629411e-01 -9.66634229e-02 -1.30667061e-01 -1.58798695e-03 2.18492523e-01 2.34898940e-01 5.65818310e-01 -3.03022861e-01 2.75364608e-01 2.16423228e-01 9.36342120e-01 -1.15172970e+00 -1.49821132e-01 1.72348134e-02 1.13904528e-01 -6.58708692e-01 4.80837494e-01 -7.74963200e-01 5.69051623e-01 -6.64612055e-01 2.43157431e-01 1.57997370e-01 4.92548086e-02 6.29207075e-01 3.48788053e-01 -3.33177000e-01 3.49346846e-01 -1.20776260e+00 1.30493343e+00 -5.13292909e-01 4.15863752e-01 3.26585740e-01 -1.35565472e+00 8.05964887e-01 2.85219133e-01 4.83931839e-01 -1.07457078e+00 1.71087101e-01 2.47396126e-01 3.47527564e-01 -5.49970508e-01 1.92914620e-01 -1.71956539e-01 2.12122619e-01 5.32168806e-01 1.29376113e-01 -5.82980871e-01 3.01410884e-01 -2.11364761e-01 1.45696652e+00 6.27643645e-01 2.61836112e-01 -6.33662105e-01 3.26445937e-01 3.30559105e-01 5.12292266e-01 1.06492603e+00 -1.50797099e-01 -1.22504588e-02 9.44616139e-01 -4.78765637e-01 -1.04311633e+00 -7.81384945e-01 1.21898770e-01 1.02659094e+00 -1.42788097e-01 -1.93603188e-01 -9.30031717e-01 -6.60667598e-01 3.66255671e-01 8.77652705e-01 -7.34074950e-01 -3.07072818e-01 -8.54079008e-01 -9.83133987e-02 1.81975380e-01 3.80120009e-01 3.58699352e-01 -1.16538548e+00 -1.25261164e+00 5.32655597e-01 3.29672039e-01 -5.10181129e-01 -3.10027242e-01 7.04108298e-01 -1.01960170e+00 -1.27949548e+00 -5.86437404e-01 -5.78469992e-01 3.90201777e-01 -2.68575341e-01 1.06623507e+00 7.08922967e-02 6.68828422e-03 7.43066430e-01 -8.70875344e-02 -3.64583939e-01 -6.29717708e-01 1.38220876e-01 1.39587268e-01 -6.18401647e-01 -2.58274674e-02 -7.21931696e-01 -7.39299893e-01 2.02492639e-01 -6.28052831e-01 -2.35924557e-01 4.25348729e-01 1.19331098e+00 3.52583081e-01 1.14205271e-01 7.05557644e-01 -6.52189851e-01 1.02954650e+00 -2.81709731e-01 -1.01770163e+00 1.38502881e-01 -8.98834050e-01 4.94180292e-01 1.06592178e+00 -6.56765759e-01 -6.73457026e-01 1.50392666e-01 1.40897647e-01 -4.45198774e-01 2.41958238e-02 4.87232089e-01 1.85390547e-01 -2.61698097e-01 7.41088152e-01 2.73384243e-01 4.63410676e-01 -2.77948350e-01 2.71060705e-01 2.09853038e-01 3.32972139e-01 -1.01764655e+00 6.46159828e-01 -1.30153894e-01 1.50845155e-01 -6.44897282e-01 -6.07835531e-01 9.87565368e-02 -3.97967011e-01 -2.66745865e-01 4.99183506e-01 -6.57182932e-01 -1.56502473e+00 6.68571591e-02 -5.70596457e-01 -1.20472169e+00 -8.22118402e-01 2.12271541e-01 -1.27957928e+00 -1.42979935e-01 -5.06504714e-01 -1.06219578e+00 -3.61749791e-02 -1.03765392e+00 5.54755688e-01 6.08562291e-01 -1.22984946e-01 -1.01338375e+00 3.36028337e-01 -1.04655556e-01 4.48972851e-01 4.07024175e-01 8.88267577e-01 -4.67525452e-01 -3.95866156e-01 1.18440807e-01 4.62755144e-01 1.97217435e-01 -7.44289011e-02 -2.40792200e-01 -7.34635949e-01 -5.91035128e-01 3.34456302e-02 -8.44840765e-01 2.16797724e-01 3.43288183e-01 1.06108212e+00 -4.87928718e-01 -7.37542228e-04 2.75020003e-01 1.26714706e+00 5.47557294e-01 3.85749489e-01 6.85844481e-01 2.77539253e-01 4.23159301e-01 7.96356201e-01 6.12991631e-01 3.37489396e-01 5.02507746e-01 6.49389267e-01 1.91805214e-01 3.96203458e-01 -6.44694984e-01 5.00402093e-01 1.22299835e-01 6.04958795e-02 1.73598647e-01 -1.00317049e+00 4.53792810e-01 -2.00413442e+00 -6.72604024e-01 2.90395021e-01 2.19803095e+00 9.47720170e-01 2.66868025e-01 5.42926133e-01 9.20320004e-02 2.36557350e-01 -3.26240361e-01 -1.14279616e+00 -6.37292266e-01 5.50393581e-01 4.80212212e-01 4.20246661e-01 5.99439502e-01 -7.88790643e-01 7.05124378e-01 6.89350843e+00 5.00930250e-01 -1.09294033e+00 -4.20917869e-01 5.65301239e-01 1.35835866e-02 -6.87234104e-02 8.62421021e-02 -4.37619030e-01 3.33203405e-01 1.40731537e+00 -4.58764255e-01 9.76010799e-01 9.76348102e-01 5.76979935e-01 -3.11944991e-01 -1.20896280e+00 3.21734577e-01 -5.34050882e-01 -1.20778644e+00 -4.96979237e-01 1.62842184e-01 6.78313732e-01 -2.92019933e-01 -1.01840608e-01 1.07983398e+00 7.48394430e-01 -9.41700816e-01 7.18795002e-01 4.90300387e-01 3.27378035e-01 -1.13900852e+00 5.39487123e-01 8.11381757e-01 -7.22160399e-01 -5.54121494e-01 -1.33185849e-01 -4.18693781e-01 -3.63882780e-01 -2.78372258e-01 -9.66063082e-01 2.10297510e-01 3.84194583e-01 1.77766517e-01 -2.08518922e-01 9.10272539e-01 -2.24975631e-01 7.87441134e-01 -4.11588013e-01 -6.12930775e-01 5.92988849e-01 -3.60920757e-01 2.52080321e-01 5.16138434e-01 1.32988259e-01 2.18295813e-01 5.19372404e-01 8.16821456e-01 3.43670726e-01 -1.69476315e-01 -7.16083944e-01 -6.18873537e-02 1.98942140e-01 8.78694773e-01 -4.29608971e-01 -8.41665566e-02 9.11513269e-02 5.72645187e-01 4.92092699e-01 4.57752913e-01 -7.09794641e-01 -3.36832702e-01 6.43830895e-01 2.23436996e-01 4.87109542e-01 -7.76587203e-02 5.19182608e-02 -6.72517538e-01 -3.01488101e-01 -1.23890316e+00 1.75721884e-01 -6.64917767e-01 -7.12850451e-01 -5.52563705e-02 -3.54793742e-02 -1.07391083e+00 -8.28613341e-01 -4.70637232e-01 -6.74018562e-01 8.08658957e-01 -1.55717421e+00 -3.39655668e-01 -2.44236086e-02 5.15212476e-01 2.68737078e-01 -1.25491127e-01 7.70823419e-01 -1.01816446e-01 -4.42856759e-01 4.13311541e-01 3.05025727e-01 -2.96450198e-01 2.23076656e-01 -1.95643568e+00 7.08705783e-02 2.52424955e-01 -5.17853856e-01 2.95657724e-01 1.19556117e+00 -3.59258950e-01 -1.97369611e+00 -7.59966731e-01 6.44144509e-03 -1.03249460e-01 8.82366478e-01 -2.58892655e-01 -8.53042305e-01 4.95994717e-01 3.18515509e-01 -6.53935075e-02 7.26052150e-02 -1.74448937e-01 5.01933217e-01 -2.85924822e-01 -1.05936301e+00 7.56644785e-01 4.44732815e-01 -1.50481671e-01 -5.04944742e-01 2.29803905e-01 7.23726153e-01 -8.72404933e-01 -7.68040061e-01 8.85380358e-02 3.87452215e-01 -7.26918280e-01 6.03968024e-01 -9.44549382e-01 4.30914134e-01 -9.54161659e-02 2.35374615e-01 -1.75516868e+00 -3.32536944e-03 -1.00053537e+00 -6.51129007e-01 6.47401452e-01 1.37224212e-01 -3.75354171e-01 1.05828869e+00 5.98397017e-01 -8.09993818e-02 -1.02065408e+00 -9.59997714e-01 -8.00920010e-01 5.33732474e-01 8.65791738e-02 3.59019637e-01 8.16929877e-01 1.89575955e-01 3.29225361e-01 -1.94722280e-01 9.54729170e-02 5.01087785e-01 6.44690990e-02 9.63279665e-01 -8.61664951e-01 -6.61477566e-01 -3.35537761e-01 -1.49984257e-02 -1.13876104e+00 2.16179237e-01 -3.22236747e-01 2.19852969e-01 -1.36536252e+00 -5.42601824e-01 -2.60763943e-01 -5.09139933e-02 5.83661973e-01 -9.69059467e-02 -5.83858311e-01 1.90384194e-01 -3.21234316e-01 -4.24566329e-01 9.14206564e-01 1.63938034e+00 1.36887565e-01 -5.80621839e-01 3.39620322e-01 -4.67821628e-01 5.54356396e-01 1.03342223e+00 -3.92173916e-01 -7.10815728e-01 7.47733116e-02 5.55777811e-02 7.22626507e-01 2.10182637e-01 -1.16869676e+00 1.88550755e-01 -4.30710077e-01 2.67726332e-01 -2.20073640e-01 9.66360793e-02 -8.80295753e-01 -2.62982368e-01 1.14331293e+00 -4.83738035e-01 2.74795383e-01 4.83766228e-01 5.27106404e-01 -8.79325867e-02 -3.54198098e-01 8.99608314e-01 -2.43862897e-01 -3.70974958e-01 -6.52996153e-02 -8.34396422e-01 6.11397102e-02 1.08654630e+00 7.77368783e-04 -2.25964755e-01 -6.43352926e-01 -8.52158725e-01 1.02890348e+00 1.44211039e-01 7.63798282e-02 2.92371571e-01 -1.12263763e+00 -3.33278745e-01 1.14432864e-01 -2.66900837e-01 -3.33940238e-02 -1.53211087e-01 4.65788275e-01 -6.89149797e-01 2.90164262e-01 -3.06088090e-01 -3.68228704e-01 -6.57979846e-01 4.79537249e-01 1.13249469e+00 -4.72161055e-01 -7.23771513e-01 -2.73406673e-02 -2.05003515e-01 -8.29937339e-01 3.63955200e-01 -4.63522971e-01 6.45675650e-03 -1.88075423e-01 2.81388342e-01 2.85344243e-01 -5.67973070e-02 5.65402925e-01 1.89271733e-01 4.71099354e-02 4.36081290e-02 -2.93997079e-01 1.45496845e+00 2.02498987e-01 4.28309232e-01 4.86243755e-01 4.49424654e-01 -6.25014782e-01 -1.85203767e+00 1.18326709e-01 1.68774992e-01 -1.03800982e-01 1.21446207e-01 -9.72700715e-01 -6.69718027e-01 6.19496882e-01 7.12148428e-01 4.58741605e-01 9.26614761e-01 -6.90251827e-01 2.86785573e-01 9.75290477e-01 3.91192198e-01 -1.32778239e+00 2.50587374e-01 7.75337279e-01 7.43358910e-01 -9.56444919e-01 -1.56202525e-01 4.73075867e-01 -5.91916680e-01 1.33137882e+00 1.12082636e+00 -5.44092774e-01 4.90622520e-01 3.23832452e-01 1.54588632e-02 -1.72213335e-02 -1.09444499e+00 -1.87490121e-01 -1.94143355e-01 6.39720440e-01 1.09720632e-01 -2.16475561e-01 -2.10548118e-01 7.90024623e-02 -2.04148069e-01 1.80046260e-01 7.01561749e-01 1.25196469e+00 -6.88668251e-01 -1.05067265e+00 -4.22248393e-01 2.51207769e-01 -1.30532533e-01 4.18442786e-01 -1.88695401e-01 1.34799612e+00 -1.89970598e-01 5.09049237e-01 -2.24125367e-02 9.17148218e-02 7.07226574e-01 3.74335945e-02 5.47493935e-01 -4.70010132e-01 -8.10911655e-01 1.09011427e-01 -4.70834076e-02 -7.82401621e-01 5.14124222e-02 -6.75545633e-01 -1.37350559e+00 -3.46539199e-01 3.29776690e-03 4.66592282e-01 5.04640996e-01 8.76903474e-01 2.21331105e-01 8.90156686e-01 6.77264750e-01 -6.74357831e-01 -1.33963466e+00 -7.99373090e-01 -6.28352821e-01 -8.69574398e-02 6.50464177e-01 -7.13364184e-01 -2.19985425e-01 -3.13836306e-01]	[4.295992851257324, 1.8983036279678345]
274bbdf6-61d8-4317-ac1a-4d08dcb1a852	transfer-learning-from-speaker-verification	1806.04558	null	http://arxiv.org/abs/1806.04558v4	http://arxiv.org/pdf/1806.04558v4.pdf	Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis	Clone a voice in 5 seconds to generate arbitrary speech in real-time	['Patrick Nguyen', 'Ron J. Weiss', 'Ye Jia', 'Fei Ren', 'Yonghui Wu', 'Ruoming Pang', 'Jonathan Shen', 'Ignacio Lopez Moreno', 'Zhifeng Chen', 'Yu Zhang', 'Quan Wang']	2018-06-12	transfer-learning-from-speaker-verification-1	http://papers.nips.cc/paper/7700-transfer-learning-from-speaker-verification-to-multispeaker-text-to-speech-synthesis	http://papers.nips.cc/paper/7700-transfer-learning-from-speaker-verification-to-multispeaker-text-to-speech-synthesis.pdf	neurips-2018-12	['voice-cloning']	['speech']	[ 7.17807189e-02 3.57013881e-01 5.89586735e-01 -1.20749678e-02 -9.45443213e-01 -1.30160999e+00 3.23132932e-01 -1.53527999e+00 3.19853783e-01 1.30055022e+00 4.75652516e-01 -8.94604325e-01 4.76105750e-01 -5.88925540e-01 -3.17172617e-01 -5.73979378e-01 -5.80303855e-02 4.94717568e-01 1.80852354e-01 -2.83555180e-01 -5.77923954e-01 7.85751998e-01 -1.20941114e+00 4.59284276e-01 5.46575308e-01 1.32771403e-01 4.16489452e-01 2.21667004e+00 -2.60791242e-01 8.78377438e-01 -1.94276476e+00 -2.07870409e-01 3.24262828e-01 -1.72043276e+00 -5.50166726e-01 6.49463892e-01 9.67155099e-02 6.38618274e-03 -3.67369622e-01 1.12533474e+00 1.36187303e+00 2.63209999e-01 7.89600790e-01 -1.04123640e+00 -3.82829756e-01 1.14953279e+00 5.91159701e-01 -8.19750801e-02 1.00360036e+00 1.98786229e-01 5.70197761e-01 -7.03380942e-01 6.53368235e-01 1.41328239e+00 8.61704946e-01 1.19592404e+00 -1.71256316e+00 -7.03929782e-01 -1.02595949e+00 -7.66938031e-01 -1.54954195e+00 -1.16309965e+00 7.61807621e-01 1.50689870e-01 1.28318787e+00 1.27695763e+00 8.30030322e-01 1.56444263e+00 4.35721695e-01 3.88285488e-01 5.31698406e-01 -5.55308342e-01 2.79489011e-01 -4.96305406e-01 -8.58097255e-01 3.85500699e-01 -5.21145761e-01 6.62847936e-01 -6.51641339e-02 -1.03590623e-01 1.12503839e+00 -9.01239991e-01 -3.38248789e-01 7.07766593e-01 -1.47921145e+00 6.81906104e-01 -8.75213966e-02 2.05147266e-01 -6.95095599e-01 5.97654819e-01 4.38568115e-01 9.83530402e-01 -5.27250357e-02 1.18563330e+00 -1.22946784e-01 -8.47793698e-01 -8.73824000e-01 5.75705826e-01 1.13595104e+00 1.69220161e+00 -1.59141704e-01 1.50285625e+00 -1.46807596e-01 7.76917338e-01 -7.44682476e-02 1.03897893e+00 1.22168291e+00 -1.65174913e+00 2.28886560e-01 -4.69956845e-01 2.98850119e-01 -1.19494170e-01 -2.22710043e-01 -2.17793390e-01 -6.31717324e-01 2.72138059e-01 2.83607185e-01 -1.09217799e+00 -1.04591012e+00 1.07305765e+00 9.57631618e-02 2.33528733e-01 4.22876596e-01 7.30749965e-01 8.90419424e-01 1.09354734e+00 -6.50450230e-01 -2.72921681e-01 1.04534197e+00 -9.59040999e-01 -1.35045421e+00 1.36277348e-01 1.70558855e-01 -1.74297488e+00 1.36196458e+00 -8.40824619e-02 -1.08228183e+00 -8.35388422e-01 -1.15971017e+00 3.90437543e-01 2.01479904e-02 8.15828145e-02 5.21766543e-01 1.65292370e+00 -1.31686521e+00 3.27895582e-01 -2.82552838e-01 -2.94931419e-02 -7.47258306e-01 9.54403821e-03 -3.11915517e-01 6.46873116e-01 -1.53931403e+00 2.27259055e-01 2.73554087e-01 -5.32573700e-01 -8.87217104e-01 -3.94473583e-01 -7.57493734e-01 -2.15945125e-01 -2.06304997e-01 -8.87475967e-01 2.27481079e+00 -8.48477840e-01 -2.80586767e+00 2.67157942e-01 -5.49356639e-02 -3.70945156e-01 8.47693801e-01 3.39662790e-01 -1.15050101e+00 2.78062522e-01 -3.21836829e-01 5.29896379e-01 1.61530185e+00 -1.06470346e+00 -1.72260642e-01 6.95855916e-01 -3.79254878e-01 -1.73782259e-01 5.59639990e-01 5.34897506e-01 1.37744337e-01 -1.28532648e+00 8.32569450e-02 -1.21844232e+00 -1.35185972e-01 -7.42676735e-01 -9.52391744e-01 3.69071424e-01 1.03950989e+00 -3.80513489e-01 1.15972507e+00 -1.92251921e+00 -3.81500840e-01 -5.98574579e-01 -7.43099988e-01 1.77138358e-01 -7.28720546e-01 3.67806911e-01 -6.11419559e-01 5.87092638e-01 -3.96509208e-02 -5.87192833e-01 1.94651540e-02 4.90286350e-01 -6.72002435e-01 -1.29958376e-01 4.50545043e-01 1.15629053e+00 -1.14268446e+00 -4.68311667e-01 1.95596516e-01 7.24193990e-01 -7.59217978e-01 6.06516778e-01 -2.79047102e-01 6.25168800e-01 -3.67200188e-02 5.66844881e-01 2.77907610e-01 7.02302337e-01 -3.42291296e-01 6.54915929e-01 -6.42039701e-02 7.64254034e-01 -1.26888406e+00 1.28515875e+00 -9.08507347e-01 1.21443939e+00 1.94996372e-01 -1.79171160e-01 7.98176885e-01 1.29402125e+00 1.00654215e-01 8.36985372e-03 -2.56335419e-02 3.06653142e-01 3.76462042e-01 -5.51581979e-01 7.84264505e-01 -2.58665413e-01 -3.25233161e-01 8.34701955e-01 -1.13380201e-01 -1.69686151e+00 2.04560325e-01 -1.88003272e-01 1.36801386e+00 -3.55709463e-01 2.89356589e-01 2.30025172e-01 3.47589999e-01 -4.31539297e-01 3.82333606e-01 9.32878137e-01 -2.01975971e-01 1.75270224e+00 4.23621945e-02 1.42033935e-01 -1.40244079e+00 -1.39911628e+00 3.73692572e-01 9.74268377e-01 -1.02827835e+00 -4.06497456e-02 -1.11838150e+00 -4.57003534e-01 -4.71659213e-01 1.70948637e+00 -8.15286115e-02 3.40425283e-01 -1.00692534e+00 -2.61891574e-01 1.48490667e+00 2.96988845e-01 -1.73362195e-01 -2.17829227e+00 -2.57221490e-01 7.46920645e-01 -3.12928498e-01 -1.15115404e+00 -1.43801165e+00 3.78014535e-01 -2.87277907e-01 -3.15016061e-01 -1.09872675e+00 -8.86525512e-01 2.39293933e-01 3.41790080e-01 1.42333233e+00 -3.08616400e-01 -3.39343727e-01 1.08107492e-01 -7.50438273e-01 -4.52741325e-01 -1.96640921e+00 4.04355563e-02 7.52870888e-02 -4.55281436e-01 -4.00013328e-01 -7.72435129e-01 -9.38870944e-04 6.20685577e-01 -1.04812372e+00 -1.52430072e-01 -1.65444538e-02 8.64591479e-01 -1.25966594e-03 2.98959911e-01 7.42359400e-01 -3.43373239e-01 1.54403293e+00 -4.98749986e-02 -3.65704775e-01 -1.04792207e-01 4.00845498e-01 1.62002891e-02 1.20205152e+00 -9.69956756e-01 -1.00682330e+00 1.99698865e-01 -9.15214300e-01 -5.01046598e-01 -4.96738613e-01 -2.77434170e-01 -2.87818909e-01 1.22900575e-01 8.93637180e-01 7.70052552e-01 1.19964778e-03 1.80609301e-02 1.12643814e+00 1.33027244e+00 9.68902349e-01 -4.83989745e-01 1.13754344e+00 -4.85481739e-01 -7.18317866e-01 -1.13041449e+00 1.75265580e-01 2.85316408e-01 -4.23466861e-01 -1.84051290e-01 4.68964696e-01 -7.80314267e-01 -5.26947260e-01 4.06253725e-01 -1.81214154e+00 -8.56215954e-01 -9.92183805e-01 5.31877398e-01 -1.15425658e+00 -2.31401138e-02 -4.30720329e-01 -1.11183202e+00 -4.75819200e-01 -1.24296045e+00 1.47549403e+00 3.20035458e-01 -9.51313615e-01 -6.01654291e-01 2.17963070e-01 -5.10128796e-01 3.98479223e-01 -8.56325179e-02 4.38909471e-01 -1.15551010e-01 -3.41836959e-01 -6.29503012e-01 6.15916729e-01 4.60077196e-01 9.37703013e-01 6.69116139e-01 -6.98489666e-01 1.96331650e-01 -8.13973323e-02 6.91966265e-02 -2.22434074e-01 4.88558590e-01 4.35789704e-01 -7.74625063e-01 2.02129260e-01 6.31454051e-01 6.87579811e-01 9.11674500e-01 7.96443224e-01 -3.72106194e-01 1.85103253e-01 -1.31224498e-01 2.69403219e-01 4.94035333e-01 -5.58603585e-01 1.01531994e+00 -3.14515829e-01 7.19883144e-02 -1.14676571e+00 -4.17628050e-01 7.93401957e-01 1.15970123e+00 1.10648029e-01 -5.65813243e-01 1.92786939e-03 3.71549964e-01 -6.16659284e-01 -1.96440852e+00 -2.16673926e-01 1.76288342e+00 1.44958043e+00 -4.38583791e-02 3.62421006e-01 8.12506139e-01 1.22923589e+00 4.29472744e-01 5.24321869e-02 -1.16468346e+00 -2.76871234e-01 5.53428411e-01 4.51520979e-02 1.11733174e+00 -5.28553069e-01 1.44023645e+00 8.45155239e+00 9.34534073e-01 -1.28394628e+00 8.49938542e-02 4.04799044e-01 -3.46373856e-01 -8.69535685e-01 -5.63393712e-01 -5.63364983e-01 5.73725641e-01 1.61448145e+00 -7.75981247e-01 1.50617266e+00 1.00727129e+00 1.00550985e+00 8.55040669e-01 -1.11582708e+00 8.15314889e-01 -4.45169769e-02 -1.41096056e+00 -2.35635657e-02 -8.52304995e-02 1.08748198e+00 -3.25790286e-01 1.21038735e-01 5.27737379e-01 6.11203849e-01 -1.27339959e+00 1.33228958e+00 -7.74957016e-02 1.26749265e+00 -1.18870568e+00 4.70762327e-02 2.91926742e-01 -1.05576181e+00 2.92978585e-01 -3.64196897e-01 1.65126562e-01 7.00103760e-01 5.13123274e-01 -1.96428323e+00 -2.40678430e-01 -3.11915964e-01 -4.67363536e-01 -4.43605036e-02 1.31378293e+00 -6.40196800e-01 9.92966831e-01 -4.37297970e-02 -6.67922735e-01 2.35507965e-01 2.04433069e-01 1.28551257e+00 1.22878361e+00 1.09389913e+00 2.26411700e-01 -3.80274713e-01 6.77067459e-01 -1.23939052e-01 9.67328716e-03 -8.27424228e-01 -3.86293530e-01 9.45717394e-01 5.24611712e-01 -5.58327258e-01 4.60362695e-02 4.92008120e-01 1.82838178e+00 -5.10410547e-01 7.46081233e-01 -8.35163414e-01 -1.46672773e+00 8.50026250e-01 -1.52096927e-01 4.01742756e-01 -7.43298471e-01 -2.25172743e-01 -5.78562737e-01 -2.74792373e-01 -9.83849883e-01 -3.95389915e-01 -1.26537156e+00 -8.27044129e-01 1.57282162e+00 -9.04695451e-01 -1.63050556e+00 -1.59649110e+00 -2.24517792e-01 -9.53737557e-01 1.01428843e+00 -2.97351956e-01 -4.68218684e-01 1.07420877e-01 3.29828292e-01 1.06243932e+00 -3.50859940e-01 1.37768185e+00 1.04190689e-02 -2.01709524e-01 9.93738711e-01 1.58481970e-01 9.33534950e-02 2.00532734e-01 -1.34864116e+00 1.94969189e+00 7.58502603e-01 6.68511629e-01 3.33542705e-01 1.36627340e+00 -2.68974841e-01 -1.08395314e+00 -1.23997974e+00 1.37326860e+00 -5.44229865e-01 4.21455979e-01 -6.51168704e-01 -7.30425596e-01 4.15089190e-01 2.21331999e-01 -2.39617378e-03 8.28693271e-01 -7.91877806e-01 1.18789256e-01 2.99749613e-01 -1.51646209e+00 1.26993358e+00 1.24039304e+00 -7.96681166e-01 -6.89686120e-01 4.53191608e-01 1.86867511e+00 -9.34367895e-01 -4.88772869e-01 -4.74019170e-01 1.90586746e-01 -8.35837126e-01 1.09824824e+00 -1.00772750e+00 1.14901543e-01 -6.24527097e-01 -1.41639560e-01 -2.12128949e+00 -4.02723581e-01 -2.33719730e+00 3.96660149e-01 1.25725532e+00 7.86558568e-01 -9.88122344e-01 5.41004658e-01 2.12485641e-01 -5.11313200e-01 4.94525313e-01 -1.66047072e+00 -1.33986604e+00 4.97375056e-03 -1.01191771e+00 1.07306409e+00 4.46593881e-01 5.44964299e-02 6.41780794e-01 -5.24182975e-01 2.28028119e-01 -2.52495289e-01 -8.99356091e-04 1.00250757e+00 -3.37211967e-01 -2.32451886e-01 -6.41684890e-01 -2.11008430e-01 -1.08600473e+00 4.12888318e-01 -4.85344857e-01 4.67749983e-01 -8.90269220e-01 -1.17951787e+00 -3.33667815e-01 4.18558508e-01 -1.44261315e-01 -4.22983497e-01 1.19211286e-01 5.27729034e-01 -4.49735999e-01 1.98041350e-01 9.44437027e-01 1.37863767e+00 -2.76818514e-01 -7.17786431e-01 8.54092479e-01 -1.67409614e-01 2.07825135e-02 3.96482706e-01 -9.78331327e-01 -8.02008450e-01 1.25905843e-02 -3.48193198e-01 6.96210921e-01 -2.60786116e-01 -1.35140347e+00 -2.77439326e-01 -3.27146262e-01 7.31295720e-02 -4.02148187e-01 4.25389320e-01 -4.62938547e-01 6.65908575e-01 5.93267024e-01 -4.83040363e-01 2.97187269e-01 1.36830360e-01 5.88658731e-03 -1.34101421e-01 -6.78289294e-01 7.31611371e-01 -3.21005553e-01 1.60691570e-02 -2.91592568e-01 -1.29926991e+00 4.45073068e-01 6.80801153e-01 -3.88505757e-02 5.35724685e-02 -1.22157145e+00 -5.83720863e-01 -1.10045564e+00 4.52100635e-01 7.14286089e-01 4.85462397e-01 -1.48286748e+00 -1.06274629e+00 2.37881213e-01 -6.69227958e-01 -6.49249256e-01 -1.10331602e-01 -4.13306415e-01 -1.22545934e+00 7.11846828e-01 3.62803936e-01 -3.49358976e-01 -1.24196422e+00 2.60969728e-01 8.47240448e-01 4.85684335e-01 -7.82817841e-01 1.07366192e+00 -6.59561694e-01 -4.54311490e-01 -1.52518004e-01 -2.67858446e-01 2.26301834e-01 -5.18317103e-01 8.81139636e-01 1.15647927e-01 -1.47385463e-01 -7.52041757e-01 1.98376358e-01 4.22130115e-02 9.55573559e-01 -1.12596309e+00 2.97549784e-01 2.08732709e-01 4.12670761e-01 3.37743074e-01 9.95095968e-01 7.81303048e-01 -8.34228277e-01 7.86405206e-01 -3.63627106e-01 -5.40406168e-01 -2.92112470e-01 -5.61435997e-01 -7.24355638e-01 3.54518890e-01 1.38221622e-01 9.46840644e-01 9.91351664e-01 -4.24856573e-01 1.22073090e+00 5.39136112e-01 4.49375451e-01 -7.08131313e-01 5.72822988e-02 6.38622761e-01 1.69867742e+00 -4.57560748e-01 -5.77987552e-01 -6.73237801e-01 -6.42072737e-01 1.16868901e+00 -1.95312977e-01 4.06411618e-01 5.04659772e-01 8.42903435e-01 9.61304665e-01 7.35804200e-01 -1.00268531e+00 1.95966229e-01 -1.23511046e-01 1.27255464e+00 6.21856809e-01 5.62680840e-01 1.17324665e-01 4.55625385e-01 -1.61334443e+00 -2.52823740e-01 8.83509278e-01 2.50192970e-01 -1.48140909e-02 -9.13873911e-01 -8.71680498e-01 -4.54948455e-01 -2.44955212e-01 -4.82122809e-01 -5.36474943e-01 2.09438249e-01 -2.35308290e-01 1.93181360e+00 3.23190331e-01 -4.15172577e-01 3.57804656e-01 5.92051208e-01 3.39463890e-01 -6.03105068e-01 -8.87245178e-01 3.27259362e-01 6.04449689e-01 3.93274128e-02 4.27269459e-01 -7.10502863e-01 -1.51958728e+00 -3.23405892e-01 -1.45241156e-01 5.42511702e-01 8.46233189e-01 3.89085293e-01 1.85329095e-01 9.60891485e-01 1.44024682e+00 -9.42430913e-01 -4.95959699e-01 -1.13645673e+00 -7.07822323e-01 -2.37677880e-02 5.48252940e-01 2.77817279e-01 -9.97711241e-01 2.62661278e-01]	[15.105846405029297, 6.437905311584473]
e8f969fc-4544-4578-8335-7e861cc96c0f	bi-directional-relationship-inferring-network	null	null	http://openaccess.thecvf.com/content_CVPR_2020/html/Hu_Bi-Directional_Relationship_Inferring_Network_for_Referring_Image_Segmentation_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Hu_Bi-Directional_Relationship_Inferring_Network_for_Referring_Image_Segmentation_CVPR_2020_paper.pdf	Bi-Directional Relationship Inferring Network for Referring Image Segmentation	Most existing methods do not explicitly formulate the mutual guidance between vision and language. In this work, we propose a bi-directional relationship inferring network (BRINet) to model the dependencies of cross-modal information. In detail, the vision-guided linguistic attention is used to learn the adaptive linguistic context corresponding to each visual region. Combining with the language-guided visual attention, a bi-directional cross-modal attention module (BCAM) is built to learn the relationship between multi-modal features. Thus, the ultimate semantic context of the target object and referring expression can be represented accurately and consistently. Moreover, a gated bi-directional fusion module (GBFM) is designed to integrate the multi-level features where a gate function is used to guide the bi-directional flow of multi-level information. Extensive experiments on four benchmark datasets demonstrate that the proposed method outperforms other state-of-the-art methods under different evaluation metrics.	[' Huchuan Lu', ' Lihe Zhang', ' Jiayu Sun', ' Guang Feng', 'Zhiwei Hu']	2020-06-01	null	null	null	cvpr-2020-6	['referring-expression-segmentation']	['computer-vision']	[-2.31074318e-01 -3.20715189e-01 -3.65450561e-01 -5.74585378e-01 -5.81859350e-01 -1.49523929e-01 8.18353534e-01 1.05577953e-01 -4.28292006e-01 4.70829934e-01 3.79389703e-01 -8.00390169e-02 -1.24459537e-02 -7.34398723e-01 -5.45523524e-01 -5.07807851e-01 3.41934204e-01 -1.29536510e-01 3.78088534e-01 -2.02492669e-01 2.26079568e-01 3.35591048e-01 -1.36976993e+00 5.93166888e-01 8.37806880e-01 1.04116297e+00 5.67217827e-01 2.49722943e-01 -4.77235019e-01 9.57635283e-01 -1.08099341e-01 -1.35765299e-01 -2.64045626e-01 -3.65107149e-01 -7.55814970e-01 2.37728238e-01 2.64588207e-01 -1.36174247e-01 -4.34926867e-01 1.21917081e+00 3.02069604e-01 1.99323937e-01 5.93275130e-01 -1.04543519e+00 -1.13563120e+00 4.69848722e-01 -7.95821011e-01 4.24659491e-01 2.61192083e-01 3.66978914e-01 1.18364167e+00 -1.05388010e+00 3.53933811e-01 1.57494974e+00 6.25074431e-02 2.43392393e-01 -1.08633006e+00 -5.40124834e-01 7.42846131e-01 5.82566142e-01 -1.53370833e+00 -1.87160164e-01 1.12831891e+00 -5.44227183e-01 7.58908927e-01 -1.33129790e-01 6.36855781e-01 5.92405379e-01 2.35712349e-01 9.99308169e-01 9.79727805e-01 -4.18224663e-01 -2.53142536e-01 1.15703687e-01 2.47940838e-01 1.03781855e+00 -4.89401110e-02 -3.89616825e-02 -5.99554598e-01 3.29721570e-01 6.99238718e-01 8.94349068e-02 -2.22751170e-01 -2.00797543e-01 -1.32985950e+00 7.99342394e-01 8.98624659e-01 7.28420317e-01 -5.53383768e-01 2.81286597e-01 4.33422774e-01 -1.36930391e-01 2.01875016e-01 -9.95029882e-02 -2.47745886e-01 5.20174146e-01 -6.47859275e-01 -2.37569079e-01 1.76777944e-01 9.35051918e-01 1.15939379e+00 -1.13453969e-01 -8.34647954e-01 8.14821959e-01 1.09793591e+00 1.95520237e-01 3.37425649e-01 -7.68930137e-01 5.63679039e-01 1.06949234e+00 -5.47510199e-02 -1.31432557e+00 -3.10970604e-01 -4.33706284e-01 -7.22651541e-01 -7.02944919e-02 -4.78065424e-02 1.65152594e-01 -8.27111125e-01 1.90416253e+00 4.24666345e-01 2.44398892e-01 1.58975139e-01 1.15023685e+00 1.25553405e+00 6.74096763e-01 5.68046331e-01 -1.95968300e-01 1.61757696e+00 -1.20657456e+00 -8.02350283e-01 -5.30177474e-01 2.96503216e-01 -7.52009749e-01 1.11865294e+00 -1.40694186e-01 -8.84052336e-01 -8.82075548e-01 -9.54984605e-01 -4.42657143e-01 -4.95666176e-01 3.23894173e-01 6.43882275e-01 -1.15712956e-01 -1.01762652e+00 -2.32183293e-01 -5.45830190e-01 -3.17170978e-01 5.52683949e-01 2.53016680e-01 -3.67623687e-01 -3.73172283e-01 -1.38499355e+00 9.17655468e-01 5.13695836e-01 4.47676867e-01 -7.25669026e-01 -2.70348877e-01 -1.06913209e+00 -8.95839781e-02 2.81085551e-01 -8.89088809e-01 7.48824775e-01 -1.03579307e+00 -1.15849161e+00 1.04570425e+00 -4.22918558e-01 -6.21479973e-02 5.42360283e-02 -1.65489748e-01 -4.86308903e-01 2.50815302e-01 2.61000663e-01 9.66163397e-01 4.60674137e-01 -1.68085945e+00 -9.77093816e-01 -4.63026971e-01 4.88946438e-01 6.75339818e-01 -3.63276839e-01 -6.72605187e-02 -1.10990894e+00 -5.09250343e-01 1.16095960e-01 -2.43208706e-01 2.46717576e-02 -4.50943261e-02 -4.12387758e-01 -5.94656110e-01 8.89333427e-01 -5.57172596e-01 1.40064991e+00 -2.03194547e+00 4.18471396e-01 -1.98109180e-01 1.69006616e-01 8.09454173e-02 -2.54175395e-01 1.87366992e-01 2.02707782e-01 -6.04702681e-02 -8.88061300e-02 -4.76434827e-01 -6.62164316e-02 4.22213197e-01 -4.01039124e-02 3.85060102e-01 3.11121672e-01 1.12596416e+00 -1.02391064e+00 -8.61544907e-01 4.90217507e-01 7.44374871e-01 -3.63596380e-01 2.70460784e-01 -1.73492312e-01 6.64585412e-01 -7.16713667e-01 6.60934746e-01 4.24089879e-01 -4.67341006e-01 -5.80374040e-02 -8.36371660e-01 -1.93924025e-01 -5.03959469e-02 -6.96057677e-01 2.09383225e+00 -7.73012340e-01 3.35967183e-01 1.33619606e-01 -1.11936414e+00 1.04441166e+00 -4.68864851e-02 2.47990787e-01 -8.47885966e-01 3.11661988e-01 -2.11434543e-01 -1.03307031e-01 -7.85642684e-01 2.91310042e-01 2.48435959e-02 -1.06875189e-01 2.58058887e-02 1.55011371e-01 2.61027873e-01 1.08844958e-01 1.45773143e-01 3.59518141e-01 3.21980178e-01 3.91122699e-01 -2.88556039e-01 1.33545125e+00 -2.62566954e-01 7.04616308e-01 2.80145884e-01 -3.86669606e-01 4.45662066e-02 1.82395026e-01 -3.99531782e-01 -3.30084771e-01 -8.73048723e-01 9.14375111e-02 1.28313017e+00 7.17682540e-01 -2.24043608e-01 -4.93891925e-01 -6.44352138e-01 -1.45410076e-01 7.84658968e-01 -8.52334619e-01 -1.87974587e-01 -2.57011563e-01 -4.68684226e-01 4.36703898e-02 5.45244575e-01 8.61604095e-01 -1.17526221e+00 -4.51447010e-01 5.26981354e-02 -3.41642976e-01 -1.32272196e+00 -5.72718441e-01 -2.48249218e-01 -4.39317346e-01 -9.37814772e-01 -3.60534519e-01 -1.15205264e+00 7.90027142e-01 2.81070411e-01 1.04057336e+00 2.40932629e-01 -1.03719354e-01 5.07322848e-01 -2.25801572e-01 -1.16452351e-01 3.40356193e-02 -2.05987751e-01 -4.23450112e-01 5.06053507e-01 6.75604761e-01 -4.49207395e-01 -7.58484602e-01 1.79677665e-01 -8.99408221e-01 4.10307288e-01 6.43486917e-01 9.89234626e-01 1.03257227e+00 -9.69319344e-02 5.52995801e-01 -5.81669450e-01 4.63155597e-01 -5.04217327e-01 -4.84443247e-01 6.05826914e-01 -2.40448564e-01 7.46720880e-02 4.73359287e-01 -2.56791890e-01 -1.19093823e+00 1.19718775e-01 1.62248746e-01 -4.97245789e-01 -1.99358314e-01 7.00802267e-01 -5.90090334e-01 1.36361748e-01 -1.35318087e-02 3.68828952e-01 -4.03454244e-01 -2.24637181e-01 9.38073814e-01 5.73367298e-01 6.89420640e-01 -6.08675599e-01 3.82815987e-01 5.74128151e-01 -1.21996291e-01 -4.18723077e-01 -1.28568769e+00 -3.64162683e-01 -8.28608513e-01 -3.42110276e-01 1.37189341e+00 -9.66321170e-01 -7.53203273e-01 3.30350608e-01 -1.37969160e+00 -9.03471559e-02 4.31307629e-02 5.29333293e-01 -5.62190592e-01 2.52957672e-01 -3.55119407e-01 -7.48408854e-01 -2.88696498e-01 -1.39612758e+00 1.17114437e+00 5.83664656e-01 3.31032217e-01 -1.16813922e+00 -3.44145745e-01 4.34561372e-01 7.15933219e-02 1.40448257e-01 9.48566675e-01 -2.94157475e-01 -6.61608994e-01 1.17607147e-01 -8.63089204e-01 1.44753352e-01 3.94708961e-01 -3.47588100e-02 -8.45152199e-01 3.03828213e-02 -1.76575720e-01 -3.11390221e-01 1.03176057e+00 3.40213686e-01 1.25993252e+00 -2.52555683e-02 -4.10232455e-01 5.91424167e-01 1.47457039e+00 1.18982658e-01 3.87032598e-01 2.09203228e-01 1.05071223e+00 6.57610118e-01 8.31815004e-01 1.32854506e-01 1.21352732e+00 6.58007205e-01 6.49261236e-01 -5.30506857e-02 -2.06545889e-01 -2.14585289e-01 1.52285308e-01 8.30347121e-01 1.29911602e-01 -1.30284831e-01 -7.58267641e-01 6.17639959e-01 -2.07362771e+00 -7.88995206e-01 -3.57804727e-03 1.76758003e+00 8.09599340e-01 6.65659383e-02 -1.97384730e-01 -3.15530598e-01 8.41254234e-01 2.56319463e-01 -6.78807855e-01 -6.13908656e-02 -1.82356849e-01 -5.36174834e-01 2.17824522e-02 7.02943981e-01 -1.22856581e+00 1.24440348e+00 5.47412682e+00 8.71486425e-01 -1.19335759e+00 2.49632984e-01 5.33300817e-01 2.89586365e-01 -4.86521631e-01 -5.62671907e-02 -7.56397545e-01 3.19465131e-01 2.27233440e-01 -8.50546584e-02 3.05352658e-01 4.27377164e-01 2.80437499e-01 -1.05011612e-01 -8.75281572e-01 1.08472228e+00 3.52812558e-01 -1.11076033e+00 3.62324178e-01 -1.58538550e-01 4.48629409e-01 5.86391799e-02 1.48525061e-02 3.08461845e-01 1.71997562e-01 -8.89680266e-01 6.78521216e-01 1.13263154e+00 7.81488121e-01 -8.86394024e-01 5.73069334e-01 2.38193303e-01 -1.78365695e+00 -1.81133837e-01 -3.40683460e-01 2.66443163e-01 2.97228217e-01 3.46442878e-01 -9.51198414e-02 9.40936863e-01 6.16878510e-01 1.09017050e+00 -5.58615088e-01 6.16047025e-01 -5.87745070e-01 2.31231004e-01 5.63428849e-02 7.75033906e-02 5.84715486e-01 -1.06150508e-01 4.00987595e-01 1.33326626e+00 -2.33856421e-02 2.41259277e-01 4.29583728e-01 1.07643950e+00 6.01247288e-02 2.10061505e-01 -4.00814831e-01 7.94481933e-02 3.65739495e-01 1.58302617e+00 -4.95633543e-01 -4.46592450e-01 -9.37138021e-01 9.38617468e-01 6.40615642e-01 5.31947196e-01 -8.14006388e-01 -3.01539570e-01 5.74725688e-01 -3.51090908e-01 4.56149071e-01 -1.90051347e-01 -2.00247765e-01 -1.13320935e+00 -1.57583266e-01 -3.29432338e-01 6.17277265e-01 -1.09059083e+00 -1.54138243e+00 6.84765935e-01 -1.74113452e-01 -1.03366530e+00 1.00556634e-01 -5.41936457e-01 -5.75396955e-01 1.33342755e+00 -2.04916048e+00 -1.85311174e+00 -5.09957552e-01 9.21191573e-01 6.10949755e-01 -2.02552408e-01 6.21632040e-01 2.80425817e-01 -6.22949839e-01 4.69386905e-01 -5.90229988e-01 2.22078040e-01 3.93758535e-01 -8.91315222e-01 -3.08118492e-01 9.38701332e-01 1.66624740e-01 7.83361077e-01 3.09268773e-01 -5.08870125e-01 -1.28488159e+00 -1.35022092e+00 7.32458651e-01 -1.33903533e-01 7.22426474e-01 2.94081308e-02 -9.52898204e-01 5.80900908e-01 3.61019880e-01 4.64094698e-01 5.64588726e-01 -2.26800233e-01 -5.67254245e-01 -3.88360441e-01 -8.29419076e-01 4.22607660e-01 9.97027159e-01 -7.94574142e-01 -7.99619615e-01 1.17183320e-01 1.07289982e+00 -2.52306074e-01 -8.51582050e-01 4.87719744e-01 3.52100164e-01 -8.54592443e-01 9.77685988e-01 -5.74799299e-01 4.73439187e-01 -8.49576890e-01 -5.53106904e-01 -9.00824904e-01 -5.57681441e-01 -3.11753284e-02 -2.20259398e-01 1.57644427e+00 1.37750626e-01 -3.57579082e-01 -4.92160767e-03 2.10686967e-01 -2.19650432e-01 -1.09550846e+00 -6.59600019e-01 -2.28827044e-01 -2.20906079e-01 -3.87498975e-01 5.25127888e-01 8.81089091e-01 -2.39317626e-01 8.06534052e-01 -2.35897914e-01 3.80633742e-01 5.09517610e-01 4.61855948e-01 3.18444073e-01 -8.81089151e-01 -7.48646632e-02 -6.27413094e-01 -5.70369482e-01 -1.30932224e+00 6.34467781e-01 -1.00831378e+00 -1.29584884e-02 -1.96107221e+00 4.44315165e-01 -2.85863340e-01 -8.91971529e-01 5.47457814e-01 -5.37757814e-01 1.32401183e-01 1.31610528e-01 1.04328834e-01 -9.41303968e-01 7.39143610e-01 1.53213763e+00 -2.48494551e-01 -1.10754713e-01 -4.30433750e-01 -1.04054034e+00 8.89615715e-01 4.90629166e-01 1.76868569e-02 -5.78171074e-01 -8.11412096e-01 -4.51804176e-02 -1.05263427e-01 5.67843616e-01 -6.48103654e-01 5.15436053e-01 -4.16302413e-01 3.01231742e-01 -9.51345861e-01 3.69509339e-01 -9.12084401e-01 -4.61469680e-01 1.66215837e-01 -4.99968439e-01 3.15514281e-02 1.56924248e-01 7.92775691e-01 -5.28108239e-01 2.93525666e-01 7.84850121e-01 -4.27209735e-02 -1.10074651e+00 6.24992788e-01 4.70213220e-02 1.96460988e-02 1.09170449e+00 1.46069854e-01 -2.70032555e-01 -1.24450967e-01 -7.47810304e-01 7.35209823e-01 2.28321925e-01 6.71152711e-01 6.85494661e-01 -1.66783309e+00 -6.14563942e-01 9.26698521e-02 5.50062358e-01 1.10515818e-01 5.26415944e-01 8.78013551e-01 -5.57179563e-02 4.37107772e-01 -1.62586421e-01 -7.25794017e-01 -1.01397157e+00 7.53979623e-01 5.39446354e-01 -1.93751439e-01 -3.37603897e-01 9.54881787e-01 6.62574053e-01 -2.64569391e-02 4.42622863e-02 -2.03149512e-01 -7.88090467e-01 1.17060378e-01 6.74385607e-01 -1.82459652e-01 -3.98340583e-01 -1.42091668e+00 -6.58847988e-01 8.93685043e-01 6.35979995e-02 -8.74295533e-02 1.15767944e+00 -7.29576290e-01 -5.75829446e-01 7.63388872e-01 1.07219708e+00 -2.97619343e-01 -1.30120814e+00 -7.67212570e-01 -1.97733000e-01 -2.45638669e-01 3.41250390e-01 -7.90106237e-01 -1.41940713e+00 1.19035435e+00 4.68162864e-01 -9.67065915e-02 1.49355090e+00 2.99554378e-01 3.88837278e-01 3.33584868e-03 1.97203457e-01 -7.68175006e-01 1.06287904e-01 4.35031146e-01 1.07954395e+00 -1.32501900e+00 -1.37925863e-01 -4.45683897e-01 -7.30900764e-01 8.87423575e-01 1.15009391e+00 -2.20031738e-02 7.44669974e-01 -7.12592527e-02 7.44635835e-02 -3.11629593e-01 -7.28078663e-01 -5.41358948e-01 7.67277956e-01 5.65610468e-01 5.16871154e-01 -6.35295212e-02 -3.59727621e-01 8.25312853e-01 4.16921407e-01 7.75953084e-02 -2.12195784e-01 5.00677288e-01 -4.86144245e-01 -8.93430769e-01 -1.82171673e-01 1.05590008e-01 -5.27495928e-02 -2.58493513e-01 -1.99013546e-01 5.86476326e-01 5.56554496e-01 1.15362811e+00 1.40436634e-01 -4.69309956e-01 1.11464269e-01 -1.10515878e-01 3.48153204e-01 -6.41709447e-01 -3.65307599e-01 2.32002333e-01 -2.51010120e-01 -7.27283239e-01 -1.03645289e+00 -3.53597194e-01 -1.61984837e+00 2.32381299e-01 -5.00462875e-02 -1.55253425e-01 3.45769554e-01 1.17474484e+00 4.88623559e-01 9.56957817e-01 4.65285689e-01 -6.52346551e-01 2.48241261e-01 -7.65194774e-01 -2.69177616e-01 4.17186707e-01 3.13232064e-01 -1.00307322e+00 6.73455000e-03 1.65515944e-01]	[10.340043067932129, 1.197568416595459]
96443da8-8c3a-4f13-a1e3-8f244dcbb97f	towards-precision-in-appearance-based-gaze	2302.02353	null	https://arxiv.org/abs/2302.02353v2	https://arxiv.org/pdf/2302.02353v2.pdf	Towards Precision in Appearance-based Gaze Estimation in the Wild	Appearance-based gaze estimation systems have shown great progress recently, yet the performance of these techniques depend on the datasets used for training. Most of the existing gaze estimation datasets setup in interactive settings were recorded in laboratory conditions and those recorded in the wild conditions display limited head pose and illumination variations. Further, we observed little attention so far towards precision evaluations of existing gaze estimation approaches. In this work, we present a large gaze estimation dataset, PARKS-Gaze, with wider head pose and illumination variation and with multiple samples for a single Point of Gaze (PoG). The dataset contains 974 minutes of data from 28 participants with a head pose range of 60 degrees in both yaw and pitch directions. Our within-dataset and cross-dataset evaluations and precision evaluations indicate that the proposed dataset is more challenging and enable models to generalize on unseen participants better than the existing in-the-wild datasets. The project page can be accessed here: https://github.com/lrdmurthy/PARKS-Gaze	['Pradipta Biswas', 'Ketan Anand', 'Shambhavi Aggarwal', 'Abhishek Mukhopadhyay', 'Murthy L. R. D.']	2023-02-05	null	null	null	null	['gaze-estimation']	['computer-vision']	[-9.06696171e-02 -4.43935469e-02 -1.95081398e-01 -7.47376800e-01 -3.60432148e-01 -4.23760027e-01 3.34781170e-01 -3.60338032e-01 -2.52346903e-01 6.22071624e-01 -8.00883211e-03 9.97645035e-02 2.30876744e-01 3.62417489e-01 -5.86550355e-01 -5.91586173e-01 -3.91448997e-02 -4.08304594e-02 1.36092320e-01 -3.68127488e-02 5.22179067e-01 1.47465179e-02 -2.30782437e+00 -2.23474145e-01 6.79595232e-01 9.53266561e-01 -8.58897865e-02 1.03107226e+00 6.38601959e-01 3.25661123e-01 -6.81687713e-01 -2.88130850e-01 1.56165231e-02 -2.11079389e-01 -5.43064773e-01 -2.08488479e-01 1.49585795e+00 -4.34855551e-01 6.17343560e-02 7.29381204e-01 1.08829939e+00 1.37608856e-01 1.70681551e-01 -1.87408650e+00 -6.49027705e-01 -2.55073696e-01 -1.05431378e+00 3.92212361e-01 1.03517199e+00 3.32202941e-01 6.20704770e-01 -8.78692508e-01 4.88865227e-01 1.11804223e+00 6.66617334e-01 1.08058667e+00 -1.12407827e+00 -1.17232072e+00 9.82616544e-02 4.61965203e-01 -1.39465523e+00 -1.02633786e+00 5.14599085e-01 -3.25044572e-01 7.67859221e-01 3.62238020e-01 5.42416453e-01 1.57136488e+00 -1.67549197e-02 8.91945481e-01 1.50557816e+00 -5.46495616e-01 -3.79688293e-02 3.24299902e-01 3.20700824e-01 4.11470652e-01 2.30417088e-01 5.48506528e-02 -1.26356161e+00 1.53388396e-01 2.63299048e-01 -1.36155337e-01 -7.08054721e-01 -4.32112306e-01 -1.03873575e+00 3.78281534e-01 4.46536660e-01 -1.15101434e-01 9.66514125e-02 -1.02518173e-02 1.63059384e-01 1.48008510e-01 7.16517031e-01 2.10757315e-01 -4.84116048e-01 -6.07270181e-01 -1.07920146e+00 3.25502336e-01 8.31392467e-01 1.38544023e+00 5.51500857e-01 -2.89544910e-01 -6.76438510e-02 6.26019657e-01 5.13418257e-01 8.12781870e-01 3.58055055e-01 -8.67020547e-01 3.77411932e-01 2.73411304e-01 2.21797019e-01 -8.18322957e-01 -6.66397393e-01 2.85116673e-01 -4.97522019e-02 3.65967453e-01 9.09677327e-01 -4.09607410e-01 -7.01046526e-01 1.93693113e+00 6.59119189e-01 1.69853657e-01 -5.68878591e-01 1.09903419e+00 9.78634119e-01 1.11715093e-01 7.24577457e-02 -3.16133767e-01 1.55301583e+00 -8.68817151e-01 -1.07434583e+00 -3.22637469e-01 5.13229251e-01 -9.24839795e-01 1.46780658e+00 6.45872414e-01 -1.06193745e+00 -4.81242806e-01 -1.01098943e+00 -3.00000966e-01 -3.68615896e-01 1.26809627e-01 5.44010222e-01 1.16399240e+00 -1.48082149e+00 5.43993786e-02 -7.84661174e-01 -8.96402299e-01 3.45931262e-01 6.46449387e-01 -4.34210956e-01 1.37750000e-01 -6.95183754e-01 8.49172652e-01 -2.41203487e-01 2.71584451e-01 -3.98262769e-01 -7.04529464e-01 -9.22434688e-01 -3.74041796e-01 2.58368582e-01 -3.09137911e-01 1.60047007e+00 -8.60196888e-01 -1.63163877e+00 1.11719966e+00 -9.07515347e-01 2.39064023e-02 4.68134969e-01 -7.76555538e-01 -4.96940821e-01 -1.20782837e-01 -2.60150850e-01 1.00033510e+00 9.36742842e-01 -9.56377506e-01 -4.74329203e-01 -8.14935029e-01 -3.22255306e-02 2.62238920e-01 -9.46280137e-02 4.84185308e-01 -3.51114154e-01 1.35361450e-02 -2.57945120e-01 -1.17040575e+00 8.03497553e-01 1.51819929e-01 -3.66138816e-01 -3.22042733e-01 8.85235965e-01 -4.28810477e-01 1.40864861e+00 -1.87346792e+00 -1.47819191e-01 -2.06461191e-01 4.79053766e-01 1.59457639e-01 3.06330979e-01 1.55570358e-01 -3.59952688e-01 5.65277738e-03 4.95082676e-01 -9.01208997e-01 1.63518369e-01 -3.43809873e-01 2.31063720e-02 7.18908608e-01 -1.55532673e-01 6.86145961e-01 -6.65719509e-01 -5.49073815e-01 6.56530261e-02 7.17514634e-01 -3.89752239e-01 3.25611591e-01 2.51674652e-01 5.67358315e-01 -7.61947334e-02 8.59216034e-01 9.49894607e-01 -1.95159927e-01 -2.62772501e-01 -9.07774419e-02 -2.74668157e-01 5.62497750e-02 -9.25401509e-01 1.64269960e+00 -2.26673722e-01 1.43844557e+00 -1.40402287e-01 1.90480500e-01 5.94500005e-01 1.26553521e-01 4.77742180e-02 -7.75859594e-01 5.63785672e-01 -1.00488558e-01 6.79775551e-02 -7.24898279e-01 7.81826854e-01 5.21694362e-01 2.61776894e-01 5.68343282e-01 1.17695078e-01 2.12030098e-01 1.75071359e-01 -1.38485968e-01 5.52639246e-01 4.56821501e-01 1.31491825e-01 -1.21543571e-01 4.19327825e-01 -4.92925704e-01 9.08756629e-02 3.43052775e-01 -8.80815685e-01 8.75791848e-01 4.62969720e-01 -1.84233069e-01 -6.98103547e-01 -7.12102115e-01 -4.66211051e-01 1.70147943e+00 2.33034000e-01 -5.24293423e-01 -1.32068861e+00 -5.50850570e-01 -2.77554512e-01 5.59015214e-01 -1.03514600e+00 2.57248878e-01 -3.04710358e-01 -4.24197435e-01 4.58807021e-01 3.43682855e-01 2.89788008e-01 -1.03705990e+00 -8.35465014e-01 -8.03231895e-01 -8.19661394e-02 -8.57388079e-01 -7.95017719e-01 -3.49726200e-01 -4.50896710e-01 -1.37841630e+00 -8.22447538e-01 -4.06113893e-01 6.96355879e-01 3.06166023e-01 1.11422718e+00 -1.15800634e-01 -2.24275276e-01 5.14074147e-01 -2.72092730e-01 -9.65150952e-01 3.86177510e-01 1.76297203e-01 3.84297371e-01 -1.10299684e-01 1.16927695e+00 -1.27166778e-01 -9.38224554e-01 7.15222359e-01 -1.03456303e-01 -1.55666128e-01 9.93934199e-02 5.57444394e-01 7.75207579e-02 -9.15746748e-01 1.53842703e-01 -7.09537089e-01 6.11983716e-01 -3.24905545e-01 -6.88820422e-01 1.78806707e-01 -7.74580717e-01 -3.24618697e-01 -3.94598395e-01 -5.06060362e-01 -1.08388627e+00 -3.41282904e-01 2.45513365e-01 -3.48985463e-01 -6.02343142e-01 -3.37817788e-01 -8.76905099e-02 -1.40960544e-01 9.84552503e-01 -3.61349851e-01 1.39094949e-01 -4.29702014e-01 5.56975715e-02 9.94259655e-01 2.19539195e-01 -3.52848947e-01 3.15458357e-01 2.14729458e-01 -1.07229069e-01 -8.40588629e-01 -7.23686755e-01 -4.60181594e-01 -9.29366231e-01 -5.66390455e-01 7.25759864e-01 -9.68388677e-01 -1.37630439e+00 9.52382147e-01 -6.10249102e-01 -4.35730934e-01 3.77007186e-01 5.57310998e-01 -3.86968255e-01 -1.07953899e-01 7.82058574e-03 -1.07991409e+00 -4.51880842e-01 -1.19846010e+00 1.41026998e+00 9.34340239e-01 -6.11449301e-01 -7.94017136e-01 2.06914186e-01 4.69537556e-01 4.57610458e-01 2.11602807e-01 -2.43043810e-01 -2.57659554e-01 -2.79697299e-01 -3.46790135e-01 -1.63367435e-01 -1.60232112e-01 5.08653782e-02 4.44966376e-01 -1.69651723e+00 -5.16611695e-01 -2.81003535e-01 -7.88622379e-01 1.89994961e-01 6.61722124e-01 8.96867454e-01 -4.68930863e-02 -3.65735680e-01 7.35779405e-01 9.55159009e-01 -1.90998077e-01 6.32111609e-01 4.11403477e-01 8.08231533e-01 6.66646123e-01 7.53560364e-01 3.56136888e-01 6.89859688e-01 8.38077605e-01 2.44164735e-01 2.00159848e-01 -8.86352286e-02 -1.32350683e-01 3.32415283e-01 1.54457703e-01 -3.55880588e-01 -5.22864282e-01 -1.01628125e+00 2.68965930e-01 -1.44163823e+00 -1.03915989e+00 -2.85400718e-01 2.47965288e+00 9.01902676e-01 -2.43816972e-01 6.68445945e-01 -5.42638823e-02 7.31503844e-01 1.93347842e-01 -7.06519723e-01 -5.41509092e-01 2.61039197e-01 2.16950905e-02 3.16379696e-01 3.40334624e-01 -9.15702581e-01 5.85267305e-01 6.61148262e+00 8.60281810e-02 -1.49697459e+00 1.31706998e-01 3.93877447e-01 -8.52607429e-01 3.62209886e-01 -3.57236922e-01 -1.33665085e+00 7.29782522e-01 1.34439063e+00 2.18583271e-01 3.28714132e-01 7.95227647e-01 1.38706073e-01 -6.68751240e-01 -1.29729271e+00 1.31288517e+00 5.86617291e-01 -3.15322846e-01 -1.01114154e+00 2.75522619e-01 3.68475825e-01 3.21929187e-01 5.37465155e-01 2.60095924e-01 -3.49634916e-01 -1.07749212e+00 6.72980428e-01 7.46929884e-01 1.27323341e+00 -3.57444733e-01 5.11272967e-01 1.72821715e-01 -7.67641664e-01 7.64729977e-02 -4.91933897e-03 -1.74757719e-01 -2.22056117e-02 -3.67288828e-01 -9.14843202e-01 -1.21521160e-01 1.19836068e+00 7.99977303e-01 -1.23106861e+00 1.14977276e+00 -1.95999920e-01 5.95347524e-01 -5.68613231e-01 -3.41419339e-01 -4.26977664e-01 2.56796747e-01 4.14091468e-01 9.28409755e-01 1.82465360e-01 -1.76770046e-01 -5.70522070e-01 5.96002340e-01 -1.72040425e-02 -1.84841976e-01 -4.80733126e-01 4.65838104e-01 6.50736034e-01 1.23275900e+00 -1.35893762e-01 2.64065653e-01 -4.36315417e-01 5.77920258e-01 3.47314328e-01 6.73365295e-01 -1.05510736e+00 -4.04551059e-01 9.19944823e-01 3.42103004e-01 4.26098257e-02 -8.82706419e-02 -3.02632779e-01 -1.15544891e+00 2.25440532e-01 -7.82648206e-01 9.98219773e-02 -1.26830852e+00 -7.97174573e-01 5.90558410e-01 4.23762798e-01 -1.37449837e+00 -4.98146564e-01 -7.00521350e-01 -5.56561708e-01 1.27843273e+00 -1.46978784e+00 -1.30005920e+00 -1.07237434e+00 7.05361664e-01 4.55985755e-01 8.40568915e-03 7.62747169e-01 1.15295745e-01 -1.00135469e+00 1.43644059e+00 -1.72978491e-01 -3.22844118e-01 1.62770617e+00 -1.32491982e+00 2.70650178e-01 6.67527974e-01 -2.75287777e-01 1.05508733e+00 9.33980107e-01 -1.12368740e-01 -1.27464664e+00 -2.76744336e-01 8.61301899e-01 -1.27043521e+00 3.31018925e-01 -7.88607121e-01 -8.33449721e-01 7.89709091e-01 7.79118955e-01 1.98360205e-01 8.44743311e-01 6.86882138e-01 -3.49475831e-01 -1.30798832e-01 -1.20118582e+00 4.58287060e-01 9.77750301e-01 -4.51826304e-01 -3.66657138e-01 1.54697234e-02 1.00564606e-01 -1.04243982e+00 -6.55106127e-01 9.80802774e-02 1.28380513e+00 -1.23501468e+00 5.57724714e-01 -2.30787352e-01 -4.56921151e-03 -2.75968075e-01 2.39415288e-01 -1.00753868e+00 8.63916874e-02 -9.18121636e-01 -6.08331561e-01 1.28342283e+00 3.02382410e-01 -7.71273315e-01 7.02038944e-01 1.20126343e+00 2.54992783e-01 -6.52735531e-01 -4.90996271e-01 -2.62734324e-01 -3.21109831e-01 -3.14328879e-01 6.38711154e-01 5.71130991e-01 1.75702810e-01 4.29088593e-01 -3.51725489e-01 5.38051724e-02 7.23545432e-01 -3.73168886e-01 1.45460320e+00 -1.26572669e+00 2.60970652e-01 -3.34530205e-01 -4.90501553e-01 -9.69260514e-01 8.80603120e-02 8.05566162e-02 3.01821958e-02 -7.15607345e-01 9.31211784e-02 -1.21979319e-01 -2.97971372e-03 4.70094591e-01 -3.31746519e-01 4.93943125e-01 1.09284133e-01 3.31502289e-01 -7.60554016e-01 1.77314386e-01 1.07103562e+00 3.11163485e-01 -2.58513629e-01 1.47656322e-01 -6.75737619e-01 5.84672928e-01 6.12423480e-01 -2.21939057e-01 -6.22650385e-01 -4.59753990e-01 2.62587637e-01 -3.98844242e-01 1.95032746e-01 -9.96190608e-01 5.08402407e-01 7.45447576e-02 6.48850441e-01 -6.60624683e-01 5.91245174e-01 -5.31871676e-01 -1.77523822e-01 -3.42640817e-01 -1.74073234e-01 2.95826375e-01 5.30052960e-01 3.95860612e-01 1.24455109e-01 9.57545415e-02 6.11945033e-01 5.04551053e-01 -7.00416327e-01 2.21619025e-01 3.94452661e-01 3.32387984e-01 1.01614738e+00 -7.89329827e-01 -7.66375184e-01 -5.17272830e-01 -5.84027648e-01 3.14218491e-01 9.73900795e-01 8.32853615e-01 3.19406599e-01 -9.57304001e-01 -3.96870881e-01 5.69227815e-01 5.60501754e-01 -8.57111737e-02 2.23516181e-01 1.38352776e+00 -3.44945669e-01 4.54263896e-01 -4.90225405e-01 -1.09012556e+00 -1.82818472e+00 2.42776230e-01 3.67519498e-01 6.83573842e-01 -6.00123443e-02 1.20491469e+00 1.80243850e-01 -1.15284346e-01 4.36648846e-01 -2.54239559e-01 -4.46457267e-01 3.50090653e-01 1.09310019e+00 5.68781435e-01 4.46743257e-02 -1.18419611e+00 -5.26962996e-01 6.93933666e-01 -2.58741498e-01 1.18166670e-01 9.90808904e-01 -7.30795562e-01 3.19493264e-01 6.42727375e-01 1.13764060e+00 8.60341638e-02 -1.44065487e+00 2.80391481e-02 -3.10803711e-01 -9.24250424e-01 -2.90786326e-02 -8.05664957e-01 -7.91126966e-01 9.95394647e-01 1.16126955e+00 -5.88867515e-02 1.18778956e+00 -6.66470006e-02 2.49484256e-01 1.93422109e-01 3.57485801e-01 -1.00382423e+00 -2.25181907e-01 3.84794563e-01 8.50959659e-01 -1.82468522e+00 7.69763216e-02 -4.51194346e-02 -8.72365177e-01 8.05309236e-01 1.14461219e+00 2.84249276e-01 7.59235859e-01 -2.77443766e-03 2.66916811e-01 -4.04841453e-01 -7.17811584e-01 -2.09627971e-01 7.37299383e-01 9.47046101e-01 9.36605155e-01 -1.63432330e-01 2.05126166e-01 6.12997748e-02 -6.00023150e-01 1.84034511e-01 4.84947145e-01 8.46134245e-01 -4.17235382e-02 -6.74375832e-01 -5.27055621e-01 2.26849228e-01 -5.72234690e-01 9.71090794e-02 -4.39987689e-01 1.11425817e+00 -1.32564783e-01 1.03037584e+00 1.79801315e-01 -2.99905449e-01 4.64139611e-01 2.60819376e-01 8.15874994e-01 -4.51658785e-01 -3.62409353e-01 -1.03928752e-01 6.68379990e-03 -9.61815178e-01 -6.92228138e-01 -1.06094325e+00 -6.69421017e-01 -7.50672281e-01 -5.95047712e-01 -3.10963750e-01 7.73081899e-01 5.83199084e-01 5.64088106e-01 2.02037305e-01 4.21465099e-01 -1.44423020e+00 -7.03355744e-02 -1.56502092e+00 -6.23581767e-01 3.48901778e-01 8.06959331e-01 -1.11684501e+00 -5.22797465e-01 2.44761825e-01]	[14.111180305480957, 0.09774181246757507]
92930ebc-0e8e-4278-b0c0-5ca6f4d0a1ae	feature-decoupling-in-self-supervised	2209.14385	null	https://arxiv.org/abs/2209.14385v1	https://arxiv.org/pdf/2209.14385v1.pdf	Feature Decoupling in Self-supervised Representation Learning for Open Set Recognition	Assuming unknown classes could be present during classification, the open set recognition (OSR) task aims to classify an instance into a known class or reject it as unknown. In this paper, we use a two-stage training strategy for the OSR problems. In the first stage, we introduce a self-supervised feature decoupling method that finds the content features of the input samples from the known classes. Specifically, our feature decoupling approach learns a representation that can be split into content features and transformation features. In the second stage, we fine-tune the content features with the class labels. The fine-tuned content features are then used for the OSR problems. Moreover, we consider an unsupervised OSR scenario, where we cluster the content features learned from the first stage. To measure representation quality, we introduce intra-inter ratio (IIR). Our experimental results indicate that our proposed self-supervised approach outperforms others in image and malware OSR problems. Also, our analyses indicate that IIR is correlated with OSR performance.	['Philip K. Chan', 'Jingyun Jia']	2022-09-28	null	null	null	null	['open-set-learning']	['miscellaneous']	[ 4.89120007e-01 -3.07177663e-01 -3.81981641e-01 -3.62444699e-01 -6.07415617e-01 -7.65731573e-01 7.52653182e-01 1.69018984e-01 -1.71847299e-01 6.16383135e-01 -2.03641787e-01 -8.97392631e-02 -1.80427477e-01 -7.86649406e-01 -4.22339112e-01 -7.80246735e-01 9.75230485e-02 4.10798490e-01 1.21047750e-01 -1.05215214e-01 4.33553100e-01 7.02074707e-01 -1.86588573e+00 3.93568844e-01 7.53246784e-01 1.24243772e+00 -1.06365755e-01 6.58223331e-01 -1.96298867e-01 7.18941510e-01 -7.51741886e-01 -8.60084146e-02 5.25368571e-01 -2.19839856e-01 -8.10388803e-01 2.28260368e-01 7.81051368e-02 3.21286991e-02 -1.34190321e-01 1.15074170e+00 1.33053213e-01 6.06630743e-02 9.91836667e-01 -1.48362041e+00 -4.71914023e-01 2.18289346e-01 -5.21976948e-01 1.29188597e-01 3.09407264e-01 1.56425506e-01 9.34386075e-01 -7.51301765e-01 5.25133133e-01 1.01725221e+00 5.16573250e-01 5.51676035e-01 -1.20718312e+00 -7.86104262e-01 2.56231073e-02 2.09779635e-01 -1.50389397e+00 -3.65367502e-01 7.34283984e-01 -6.75127089e-01 4.86665726e-01 5.97195566e-01 2.70214319e-01 8.54706526e-01 5.58182970e-02 5.25836170e-01 1.46200252e+00 -4.04021621e-01 2.31531560e-01 6.73746586e-01 7.75750041e-01 5.89789391e-01 2.86174446e-01 1.88900441e-01 -1.89994529e-01 -4.26676512e-01 4.54745531e-01 3.75156730e-01 -2.11717024e-01 -1.63752273e-01 -9.64294016e-01 9.47772264e-01 2.84560740e-01 4.89085972e-01 -1.30394977e-02 -4.03064668e-01 5.52042484e-01 6.74441397e-01 5.16573429e-01 3.74025285e-01 -4.41337615e-01 1.39390811e-01 -7.42529392e-01 -4.29605961e-01 7.80456185e-01 5.85515797e-01 1.12764120e+00 -2.71292955e-01 -2.77057528e-01 9.10550892e-01 2.07608372e-01 3.76548737e-01 9.55314517e-01 -3.27408671e-01 8.05585310e-02 7.45171010e-01 -7.48640671e-02 -1.20505154e+00 -3.53565291e-02 -5.17035782e-01 -7.49959588e-01 -1.30518023e-02 1.79950997e-01 3.23736817e-01 -9.09106255e-01 1.38679290e+00 4.01337177e-01 3.81638259e-01 2.75934130e-01 5.39273620e-01 8.44350696e-01 7.93228507e-01 -2.49449804e-01 -5.40004849e-01 1.39357889e+00 -9.37181532e-01 -6.15625858e-01 7.25143924e-02 4.77284610e-01 -6.88543320e-01 7.65873432e-01 4.43863034e-01 -5.78381643e-02 -5.85125983e-01 -9.96510506e-01 4.37372625e-01 -5.78659773e-01 6.42874479e-01 3.32198203e-01 7.57681906e-01 -7.08334684e-01 6.42252624e-01 -4.67700750e-01 -3.55133265e-01 3.18781674e-01 4.50616896e-01 -5.97623169e-01 -5.32446196e-03 -9.21799839e-01 4.45018589e-01 6.17166817e-01 -2.85583064e-02 -1.05464840e+00 -2.50508994e-01 -6.94706142e-01 9.11176354e-02 4.41807389e-01 -2.74997149e-02 5.34526527e-01 -1.26895094e+00 -1.46179175e+00 1.04461849e+00 -1.10478168e-02 -1.78586245e-01 2.96885401e-01 9.39480811e-02 -5.26968658e-01 1.28593922e-01 -1.53078195e-02 -5.66885844e-02 1.44412982e+00 -1.56051004e+00 -6.69110477e-01 -5.21619141e-01 -6.03297688e-02 -1.76476389e-01 -8.03037941e-01 6.94541484e-02 -2.24210531e-01 -6.37817621e-01 1.36772752e-01 -7.81404793e-01 7.04826564e-02 -2.19929084e-01 -5.79016924e-01 -3.23712558e-01 9.84230459e-01 -5.02849102e-01 1.25251484e+00 -2.44980574e+00 8.91507119e-02 7.07366645e-01 4.96045381e-01 3.46821517e-01 -2.10272044e-01 1.44408807e-01 -3.77297729e-01 1.41128287e-01 -3.16378504e-01 -1.98211357e-01 -2.38473386e-01 1.01092152e-01 -6.90030754e-01 6.70556903e-01 4.07972842e-01 2.84552842e-01 -7.64858842e-01 -5.27693689e-01 2.17009202e-01 2.91362554e-01 -2.97193587e-01 6.39631212e-01 4.00175638e-02 4.85523254e-01 -5.12625992e-01 5.85248709e-01 6.93202972e-01 -2.33875111e-01 2.00778350e-01 -3.55469584e-01 7.85959959e-02 -1.06788911e-01 -1.24130309e+00 8.43655229e-01 -4.39249754e-01 4.49214429e-01 -2.82597423e-01 -1.22052932e+00 1.47800517e+00 7.16544613e-02 3.81536722e-01 -1.37806401e-01 4.18596357e-01 4.42157388e-01 -1.40086502e-01 -2.70048827e-01 5.01896977e-01 7.49368891e-02 1.78400055e-02 4.77862805e-01 2.35948190e-01 2.39986867e-01 -1.55051686e-02 -4.21746299e-02 1.08371854e+00 -1.59590378e-01 7.47627616e-01 -1.66346043e-01 1.05219114e+00 -1.16851509e-01 5.84759355e-01 6.61889553e-01 -3.01962733e-01 5.57261527e-01 5.83050489e-01 -5.33960760e-01 -7.36449957e-01 -8.79247129e-01 -3.46856087e-01 8.63934636e-01 2.12577954e-01 -4.10979748e-01 -6.41331851e-01 -1.16004372e+00 -2.72472333e-02 2.64328063e-01 -8.04022372e-01 -3.07120770e-01 -1.97210655e-01 -7.44814396e-01 5.08409739e-01 5.53589426e-02 2.32923880e-01 -7.41320908e-01 -2.89589554e-01 -2.36606464e-01 -4.69572470e-02 -7.99841821e-01 -2.18799651e-01 4.16245967e-01 -5.47917247e-01 -1.31836855e+00 -4.41241980e-01 -7.90372193e-01 9.53885436e-01 3.29982698e-01 6.16083026e-01 3.54094237e-01 -3.22762668e-01 2.53552556e-01 -8.46224308e-01 1.03314579e-01 -6.23166203e-01 1.87890455e-02 2.04722971e-01 8.25171888e-01 3.73995602e-01 -3.60645682e-01 -1.85778856e-01 5.78121185e-01 -9.43317950e-01 -3.55457336e-01 4.84360784e-01 1.00775957e+00 7.14612901e-01 3.33017319e-01 4.81725901e-01 -1.00233912e+00 4.25685972e-01 -6.42229855e-01 -4.35010970e-01 4.73099113e-01 -5.85995853e-01 1.28163546e-01 1.08233702e+00 -8.32298517e-01 -7.75045812e-01 2.84781814e-01 9.54090431e-03 -5.50620317e-01 -2.39997238e-01 1.00323513e-01 -2.76220530e-01 -2.24619702e-01 6.65884852e-01 5.12290955e-01 2.78326534e-02 -4.00892466e-01 9.83737186e-02 1.34100091e+00 2.73757488e-01 -5.31552434e-01 1.19496465e+00 4.33981001e-01 -3.86210114e-01 -8.85941565e-01 -8.46587539e-01 -6.26410007e-01 -5.66151321e-01 -7.18797147e-02 6.00474179e-01 -7.70948470e-01 -5.39621174e-01 4.00050133e-01 -9.03102279e-01 1.18907899e-01 -4.33662832e-01 2.64821291e-01 -4.15116489e-01 5.51439524e-01 -4.30891484e-01 -8.19484234e-01 -3.50193053e-01 -1.40655327e+00 1.08830988e+00 2.43525445e-01 -4.03554812e-02 -7.44038761e-01 1.64011747e-01 1.08563758e-01 7.04106838e-02 1.07513107e-01 7.53465950e-01 -1.65312302e+00 -9.87173989e-02 -4.31879431e-01 -2.83398777e-01 4.98557597e-01 5.02241671e-01 1.18744522e-01 -1.24787045e+00 -5.50204635e-01 1.40339687e-01 -4.98371452e-01 9.48635876e-01 -1.64920494e-01 1.39507198e+00 -3.43597770e-01 -4.74502534e-01 7.11736619e-01 1.27133965e+00 1.70323759e-01 4.53217804e-01 3.81957173e-01 7.42820621e-01 6.07245564e-01 7.10623324e-01 4.86554801e-01 -1.15521528e-01 4.60524112e-01 7.76711702e-02 4.47776727e-02 1.33721873e-01 -1.67570695e-01 5.50160408e-01 8.88006985e-01 2.11528450e-01 -6.23049960e-02 -6.56184316e-01 8.53037089e-02 -1.59258449e+00 -7.91980982e-01 1.24105856e-01 2.37701702e+00 8.35887372e-01 2.28524342e-01 5.61797693e-02 5.47269166e-01 1.03819573e+00 2.52517057e-03 -3.79330575e-01 -3.15460682e-01 -1.87577643e-02 1.53693676e-01 2.46305168e-01 3.04282784e-01 -1.29690158e+00 6.95329010e-01 5.44880581e+00 1.27819765e+00 -1.24530506e+00 1.84189510e-02 7.35681772e-01 5.41117370e-01 -2.20781207e-01 1.19546510e-01 -8.01214218e-01 4.93336469e-01 7.54906833e-01 -7.00527132e-02 4.71414179e-01 9.36582267e-01 -3.31040025e-01 1.72271162e-01 -1.27199805e+00 1.06565213e+00 2.94352204e-01 -9.23753619e-01 2.77972907e-01 1.05632097e-01 5.62808990e-01 -4.25100148e-01 6.54669404e-02 4.31086421e-01 6.59395978e-02 -8.83945644e-01 4.42683786e-01 4.05617535e-01 9.28502262e-01 -6.89484775e-01 7.98066676e-01 4.39977884e-01 -1.38551509e+00 -3.86764944e-01 -3.05570304e-01 1.78236187e-01 -6.15867019e-01 5.54173172e-01 -6.96244895e-01 7.55285859e-01 3.76515120e-01 9.33425546e-01 -8.61033797e-01 8.73548031e-01 6.24857359e-02 6.43963635e-01 -9.51447040e-02 1.06382526e-01 -2.48064473e-01 -2.87604600e-01 5.41878760e-01 9.78030860e-01 4.43775253e-03 -1.35042313e-02 4.41188842e-01 6.37263060e-01 6.99457452e-02 3.21081787e-01 -6.21242821e-01 -2.58763641e-01 4.62313235e-01 1.41866255e+00 -1.03486776e+00 -5.22203147e-01 -1.17581263e-02 1.01920450e+00 3.14617127e-01 -7.24323913e-02 -7.59660482e-01 -7.07234263e-01 4.65600938e-01 -2.49362931e-01 2.31232390e-01 2.11699411e-01 -5.35601042e-02 -1.55898118e+00 -2.41117671e-01 -1.04971504e+00 5.30492365e-01 -3.10573161e-01 -1.63233685e+00 9.18165267e-01 -2.05152035e-01 -1.58793867e+00 -2.54394151e-02 -7.76829541e-01 -5.30789673e-01 4.10707921e-01 -1.62595606e+00 -1.07012939e+00 -3.30561608e-01 5.78153372e-01 5.03608584e-01 -3.74084622e-01 9.32170689e-01 2.88913310e-01 -8.56631219e-01 8.87081802e-01 4.68347400e-01 3.84000242e-01 6.36637092e-01 -9.14067209e-01 -1.48710921e-01 6.64103568e-01 1.27299622e-01 7.58151233e-01 4.68565494e-01 -6.58007026e-01 -1.26268733e+00 -1.20099306e+00 5.07323861e-01 -2.75431573e-01 7.35392094e-01 -3.16914469e-01 -9.75107253e-01 6.02895141e-01 -5.20849049e-01 4.68966067e-01 9.29918647e-01 -2.02708811e-01 -8.12927306e-01 -2.42566720e-01 -1.37956953e+00 4.00323346e-02 5.69818437e-01 -6.56613290e-01 -6.59761906e-01 3.03484261e-01 7.25618839e-01 -3.38595621e-02 -9.17555928e-01 3.00242484e-01 5.87967396e-01 -5.87378383e-01 8.51595104e-01 -6.98462367e-01 2.80953258e-01 -5.27027249e-01 -3.80633354e-01 -1.15960133e+00 -1.76517963e-01 -4.25369680e-01 -2.36159358e-02 1.55194831e+00 2.21544161e-01 -9.37321246e-01 3.79117578e-01 -4.79548350e-02 2.31910422e-01 -4.45779711e-01 -7.59926856e-01 -8.72388840e-01 -1.99910671e-01 -1.03540212e-01 4.56141084e-01 1.16487157e+00 -1.44323155e-01 2.14697286e-01 -4.21080947e-01 2.47894451e-01 6.64813936e-01 4.57164168e-01 7.07566023e-01 -1.53935158e+00 -4.87121791e-01 -9.73647684e-02 -7.12427974e-01 -5.12263834e-01 3.90622646e-01 -1.11482024e+00 5.02158739e-02 -7.51152873e-01 6.80040658e-01 -7.59872198e-01 -5.44455051e-01 4.87307698e-01 -2.48545811e-01 3.52431834e-01 1.90271080e-01 6.54432237e-01 -6.05305254e-01 4.73197758e-01 7.46264160e-01 -1.94493681e-01 -3.58711272e-01 4.08462435e-02 -4.25911367e-01 5.84633708e-01 8.59118164e-01 -6.22113347e-01 -1.03728481e-01 1.86442420e-01 -2.71503657e-01 -1.58342615e-01 2.46866241e-01 -9.80138779e-01 -2.20720157e-01 -3.52858394e-01 4.42397326e-01 -4.84725595e-01 1.58210129e-01 -1.03410733e+00 1.04964845e-01 6.69846416e-01 -4.02910382e-01 -4.13512200e-01 -6.55923486e-02 6.32799208e-01 -1.34565726e-01 -4.38270211e-01 8.92006695e-01 1.81604788e-01 -3.83223802e-01 2.11496949e-01 -3.11163247e-01 -8.27268139e-02 1.22236252e+00 -2.68846989e-01 -2.87273228e-01 1.33724764e-01 -6.46050870e-01 -1.31474510e-02 4.16980237e-01 3.32375795e-01 6.94389164e-01 -1.19394827e+00 -4.91567999e-01 5.52643120e-01 5.82579255e-01 -2.67928272e-01 -9.84215513e-02 6.92284822e-01 -2.87149459e-01 9.65789780e-02 -2.13011518e-01 -7.35698879e-01 -1.43876100e+00 7.86419272e-01 1.01022579e-01 -4.52445865e-01 -2.72228032e-01 4.25990492e-01 1.50603905e-01 -7.76337981e-01 2.09162802e-01 1.53273776e-01 -6.58971965e-01 1.79494590e-01 6.91237986e-01 3.35581243e-01 -4.32463214e-02 -9.80135739e-01 -2.66469181e-01 6.57128990e-01 -1.97107539e-01 3.18464518e-01 1.36886787e+00 3.96619774e-02 -3.35223615e-01 5.88156760e-01 1.63328397e+00 8.54880586e-02 -7.57571697e-01 -3.85199606e-01 4.15873453e-02 -6.99795485e-01 -2.18848720e-01 -4.03452665e-01 -8.88649464e-01 6.39106810e-01 9.78491306e-01 3.41579676e-01 1.27065730e+00 -4.71503213e-02 4.68638331e-01 3.71935666e-01 3.00232083e-01 -7.38132596e-01 1.81850776e-01 4.43241209e-01 4.88304257e-01 -1.27886426e+00 -8.08106363e-02 -6.92889690e-01 -3.87955606e-01 1.40514576e+00 6.22796774e-01 -2.46672630e-01 5.89696407e-01 1.86397627e-01 -2.87670493e-01 -3.62122171e-02 -5.50620794e-01 -1.45537436e-01 3.23482335e-01 6.37141883e-01 4.71239500e-02 2.48911098e-01 -3.64819229e-01 6.62480295e-01 -1.66759208e-01 -1.77025408e-01 4.39717770e-01 8.07792485e-01 -5.87086201e-01 -1.03638113e+00 -6.65519834e-01 6.18753195e-01 -2.74764657e-01 1.72370657e-01 -6.27686501e-01 2.66358674e-01 1.17439590e-01 9.42252636e-01 9.43956152e-02 -1.10836899e+00 1.34263813e-01 -1.23072872e-02 1.25844359e-01 -7.64634430e-01 -7.33806670e-01 -1.78388149e-01 -1.49749056e-01 -3.76058906e-01 -2.19385862e-01 -2.77054101e-01 -1.10512114e+00 2.66593620e-02 -8.21126163e-01 3.54519457e-01 5.56276321e-01 9.17327583e-01 1.88714609e-01 3.79882812e-01 1.57095647e+00 -5.19691288e-01 -9.92415547e-01 -6.52113318e-01 -4.66951430e-01 5.80213666e-01 5.64054608e-01 -7.41319656e-01 -8.60185385e-01 1.22830264e-01]	[9.704136848449707, 3.0193428993225098]
3efb4a7f-7fbd-4911-98fe-3496b1ceb976	improving-native-language-identification-with	null	null	https://aclanthology.org/W13-1728	https://aclanthology.org/W13-1728.pdf	Improving Native Language Identification with TF-IDF Weighting	null	['Peter Wittenburg', 'Binyam Gebrekidan Gebre', 'Tom Heskes', 'Marcos Zampieri']	2013-06-01	null	null	null	ws-2013-6	['native-language-identification']	['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]	[-7.164094924926758, 3.59242582321167]
501b8c8c-bf47-493d-8895-f6c2a3785625	multiview-regenerative-morphing-with-dual	2208.01287	null	https://arxiv.org/abs/2208.01287v1	https://arxiv.org/pdf/2208.01287v1.pdf	Multiview Regenerative Morphing with Dual Flows	This paper aims to address a new task of image morphing under a multiview setting, which takes two sets of multiview images as the input and generates intermediate renderings that not only exhibit smooth transitions between the two input sets but also ensure visual consistency across different views at any transition state. To achieve this goal, we propose a novel approach called Multiview Regenerative Morphing that formulates the morphing process as an optimization to solve for rigid transformation and optimal-transport interpolation. Given the multiview input images of the source and target scenes, we first learn a volumetric representation that models the geometry and appearance for each scene to enable the rendering of novel views. Then, the morphing between the two scenes is obtained by solving optimal transport between the two volumetric representations in Wasserstein metrics. Our approach does not rely on user-specified correspondences or 2D/3D input meshes, and we do not assume any predefined categories of the source and target scenes. The proposed view-consistent interpolation scheme directly works on multiview images to yield a novel and visually plausible effect of multiview free-form morphing.	['Hwann-Tzong Chen', 'Cheng Sun', 'Chih-Jung Tsai']	2022-08-02	null	null	null	null	['image-morphing']	['computer-vision']	[ 1.52021676e-01 2.16749206e-01 1.22281164e-01 -4.52685386e-01 -7.44575024e-01 -6.06532931e-01 7.44648695e-01 1.20776474e-01 5.22081666e-02 4.14155573e-01 -2.67455041e-01 3.64256091e-02 1.12586796e-01 -1.00462079e+00 -9.79000866e-01 -3.95637810e-01 3.37223947e-01 4.76453900e-01 3.62611800e-01 -1.43578127e-01 1.42642602e-01 5.94964087e-01 -1.67116559e+00 1.93920240e-01 1.07567024e+00 8.42081666e-01 4.42787468e-01 8.19352865e-01 -2.42126092e-01 3.24216366e-01 1.71733536e-02 -3.50894541e-01 5.18945575e-01 -5.81129491e-01 -8.36716235e-01 7.57924199e-01 7.49323308e-01 -2.18496516e-01 -3.45750432e-03 1.13183677e+00 3.30545940e-02 3.22716653e-01 8.33193541e-01 -1.21780503e+00 -8.38004053e-01 -1.31082967e-01 -7.72174060e-01 -2.65472710e-01 5.13011634e-01 1.93332940e-01 9.19787526e-01 -1.03579092e+00 9.91250813e-01 1.30768156e+00 2.10559279e-01 3.87624890e-01 -1.66308653e+00 -1.17585227e-01 4.68716383e-01 -1.81429818e-01 -1.26878536e+00 -3.42057377e-01 9.98331726e-01 -8.32781315e-01 1.56058609e-01 4.63839680e-01 9.36093569e-01 5.69470644e-01 3.60477448e-01 2.93042839e-01 1.15661430e+00 -3.38280559e-01 3.55984837e-01 3.42211694e-01 -1.30710557e-01 9.00555253e-01 -9.78681371e-02 -3.09561521e-01 -3.17378402e-01 -2.05942363e-01 1.02534986e+00 3.09634268e-01 -4.20131177e-01 -9.19187248e-01 -1.30562723e+00 6.79836810e-01 2.62058794e-01 1.36393279e-01 -4.59875733e-01 -4.83395270e-04 -8.94513633e-03 1.03192106e-01 6.13366187e-01 5.31501807e-02 -4.37943526e-02 6.91012800e-01 -7.87057638e-01 3.40116292e-01 2.66598642e-01 9.66736615e-01 1.30995727e+00 1.28604069e-01 1.27833545e-01 6.37257636e-01 5.91654062e-01 4.21994627e-01 7.57638738e-02 -1.41629362e+00 6.25600874e-01 6.58039629e-01 3.29134345e-01 -1.26009333e+00 1.76107869e-01 4.48210090e-02 -7.30579674e-01 4.45248306e-01 2.63675690e-01 2.75459498e-01 -8.73843789e-01 1.60315275e+00 8.21164727e-01 1.57409292e-02 -2.49192894e-01 6.85677111e-01 5.80103755e-01 8.00100446e-01 -1.66711763e-01 -2.84721643e-01 1.07093441e+00 -7.66826868e-01 -6.66284978e-01 -6.48344904e-02 1.97294906e-01 -6.82139814e-01 1.16147625e+00 9.08206329e-02 -1.74010456e+00 -5.78398764e-01 -1.11160421e+00 -1.96014643e-01 -1.39008284e-01 -3.06166232e-01 6.30467385e-02 1.14242621e-01 -1.09251368e+00 5.95800161e-01 -6.12619340e-01 -1.11609548e-01 2.69338936e-02 6.97459802e-02 -5.40147185e-01 -6.36921078e-02 -7.70243049e-01 6.40889823e-01 8.65622424e-03 9.92966369e-02 -9.34976101e-01 -7.38434672e-01 -1.09939170e+00 -2.32404187e-01 2.70109288e-02 -1.20383489e+00 7.68213272e-01 -1.26724136e+00 -1.51179695e+00 1.09681320e+00 -3.60078871e-01 2.29464605e-01 7.87525594e-01 2.79048949e-01 4.78168018e-02 3.74767818e-02 2.14992151e-01 4.94762927e-01 1.09043133e+00 -1.99586487e+00 -3.92456740e-01 -5.74500561e-01 1.98109567e-01 4.33585256e-01 1.41930759e-01 -4.52162653e-01 -6.96211874e-01 -6.41435623e-01 5.36224961e-01 -6.28241718e-01 -2.68941104e-01 4.51728135e-01 -4.55738157e-01 3.26301634e-01 8.09400737e-01 -7.72636592e-01 4.40281421e-01 -2.01088309e+00 7.81865418e-01 2.26533324e-01 2.97927439e-01 -3.27193528e-01 -9.45697203e-02 2.88944513e-01 3.80158462e-02 1.16304860e-01 -4.29174125e-01 -8.07174563e-01 -3.65685046e-01 2.84106821e-01 -1.04907237e-01 6.38179898e-01 -1.44449979e-01 5.85500479e-01 -8.98168504e-01 -7.40703702e-01 5.97393394e-01 6.08250439e-01 -7.26624906e-01 7.08419085e-01 -3.94485623e-01 1.17263007e+00 -5.36800265e-01 1.71233475e-01 9.02536273e-01 -2.55873501e-01 5.78986518e-02 -3.92319977e-01 -3.41735482e-01 -3.55941713e-01 -1.35841072e+00 1.95292449e+00 -7.09567070e-01 2.08037645e-01 4.49679881e-01 -8.93722236e-01 8.32774401e-01 1.80198103e-01 6.33136094e-01 -4.22607064e-01 4.98506846e-03 9.43296626e-02 -7.07073450e-01 -5.61083078e-01 5.24420619e-01 -4.62764353e-01 1.75071865e-01 3.50155026e-01 -3.14378627e-02 -6.69622660e-01 -3.15114260e-01 8.24534521e-02 2.18002751e-01 1.80922464e-01 1.85043663e-01 -1.60321251e-01 6.99953377e-01 -3.60355467e-01 5.89877009e-01 1.40355468e-01 3.81859571e-01 1.00054014e+00 1.87319115e-01 -5.26651204e-01 -1.25916374e+00 -1.50083828e+00 -8.15066397e-02 5.23807943e-01 5.71084797e-01 -9.48166624e-02 -6.22609317e-01 -4.52717453e-01 -1.50248766e-01 4.75152433e-01 -6.53676808e-01 2.06930280e-01 -4.72039998e-01 -2.31672116e-02 -2.36769974e-01 4.30585109e-02 4.15866405e-01 -5.34575522e-01 -5.84445000e-01 -1.25241131e-01 -5.25748909e-01 -1.02766538e+00 -8.11719477e-01 -2.70464778e-01 -1.04948938e+00 -1.11166298e+00 -1.03482878e+00 -9.18132067e-01 1.02733910e+00 3.62950712e-01 8.58166695e-01 -8.49571601e-02 -1.10893302e-01 5.98620653e-01 7.73872761e-03 3.22239727e-01 -6.04218900e-01 -3.74717951e-01 -9.58113447e-02 7.94370353e-01 -9.50837672e-01 -9.76614714e-01 -6.25941992e-01 2.74728984e-01 -1.09714735e+00 5.56821585e-01 5.83473127e-03 6.56298101e-01 1.10571957e+00 -5.15473664e-01 1.28099814e-01 -7.97147155e-01 1.68789849e-01 -3.80563766e-01 -6.71877146e-01 4.43889320e-01 -2.72956163e-01 1.08557597e-01 8.25621843e-01 -2.43137434e-01 -1.04250956e+00 1.92954749e-01 2.44722143e-02 -7.88876116e-01 -4.71857302e-02 9.10409912e-02 -5.62262297e-01 -9.34103951e-02 3.68604273e-01 2.63496816e-01 -2.01194528e-02 -4.14673328e-01 8.26522529e-01 4.04129684e-01 5.28048933e-01 -5.74843168e-01 1.00128317e+00 8.68457437e-01 -3.54522914e-02 -8.79237533e-01 -4.05154943e-01 -6.40201792e-02 -1.12300003e+00 -5.19076228e-01 1.28820860e+00 -6.40149117e-01 -6.06667876e-01 2.09298044e-01 -1.40322983e+00 -3.73037130e-01 -4.14375961e-01 2.16536090e-01 -1.01688313e+00 6.72985017e-01 -2.13477209e-01 -5.99348485e-01 -1.33112237e-01 -1.53086591e+00 1.23084867e+00 -1.04860641e-01 -3.45493555e-02 -1.27470303e+00 3.70331630e-02 2.77225465e-01 8.00603777e-02 9.18568373e-01 1.24607122e+00 2.59731025e-01 -1.08307183e+00 1.30747706e-01 6.08436242e-02 4.03145283e-01 4.69038486e-01 2.10383266e-01 -8.62319231e-01 -2.84427196e-01 8.88751373e-02 -3.34871747e-02 4.41485286e-01 3.86576474e-01 1.05312467e+00 -4.68707085e-01 -2.07625479e-01 9.69595969e-01 1.53471470e+00 1.59831151e-01 4.04648244e-01 1.17261209e-01 1.00991964e+00 1.01152039e+00 4.82484430e-01 3.93450886e-01 7.67634034e-01 9.57253397e-01 7.90880919e-01 -2.16436341e-01 -1.32404896e-03 -4.30883437e-01 5.05824573e-02 1.02352166e+00 -1.37815282e-01 8.70126337e-02 -6.34213805e-01 5.65766394e-01 -1.63159525e+00 -7.39493549e-01 -1.89376205e-01 2.71270514e+00 5.72549403e-01 -4.27090347e-01 -9.57818106e-02 -2.56980240e-01 8.66276741e-01 4.92725819e-01 -6.15452051e-01 -4.48874801e-01 2.79618114e-01 -1.73608378e-01 8.88834223e-02 1.00421977e+00 -6.57246053e-01 5.93092680e-01 5.63244295e+00 3.40480566e-01 -1.15959132e+00 1.48348629e-01 7.54083931e-01 2.53061086e-01 -1.03840625e+00 2.05440730e-01 -2.51081258e-01 1.89916298e-01 4.21202868e-01 -2.84456104e-01 4.79729116e-01 4.71044034e-01 3.02740514e-01 8.51992592e-02 -1.23680985e+00 1.01906621e+00 3.27907503e-02 -1.57290637e+00 6.66975141e-01 9.39716920e-02 9.03328955e-01 -4.90577877e-01 2.77870357e-01 -3.93282294e-01 2.54101098e-01 -7.89131761e-01 1.16183019e+00 8.25426519e-01 9.43568468e-01 -6.59972191e-01 -2.84239417e-03 4.74333346e-01 -1.43986928e+00 3.01083773e-01 -2.37152994e-01 4.77038890e-01 5.65380692e-01 3.71653676e-01 -7.46332780e-02 9.09486771e-01 4.47395980e-01 8.36302400e-01 -3.06144714e-01 6.16178453e-01 2.96884149e-01 -2.95149207e-01 1.91749141e-01 8.67464662e-01 -1.45624965e-01 -6.98042095e-01 7.16163993e-01 6.11685932e-01 3.31334114e-01 -1.57238916e-01 4.21603203e-01 1.14833939e+00 6.51262049e-03 3.37550223e-01 -1.01062036e+00 6.17860675e-01 1.70096219e-01 1.11639881e+00 -5.97334743e-01 -3.65503877e-01 -3.65929306e-01 1.25531685e+00 4.75872934e-01 6.33287609e-01 -8.18302035e-01 9.70451161e-02 6.04311883e-01 4.54563648e-01 3.09875458e-02 -2.99980760e-01 -2.75005579e-01 -1.60777628e+00 1.68806970e-01 -6.53622985e-01 5.41982315e-02 -8.55604708e-01 -9.83857989e-01 6.28618419e-01 2.02766240e-01 -1.42255783e+00 -1.85893148e-01 7.23508559e-03 -7.24752069e-01 1.18517280e+00 -1.35009205e+00 -1.31152439e+00 -5.74698150e-01 7.96342611e-01 7.75122344e-01 4.27026212e-01 5.46561778e-01 1.31146610e-01 -1.60154194e-01 1.95535749e-01 2.88710147e-02 -4.01908875e-01 6.39914572e-01 -1.05704463e+00 3.11991066e-01 7.46290326e-01 -2.36735970e-01 4.36010331e-01 6.49807096e-01 -5.29846072e-01 -1.40479505e+00 -1.44636834e+00 4.73586440e-01 -3.82918149e-01 1.52197555e-01 -1.79488167e-01 -1.04731631e+00 9.56802964e-01 2.70082086e-01 3.14629227e-01 2.43318647e-01 -4.98177677e-01 -2.83457011e-01 -1.03381179e-01 -1.25291371e+00 7.40629196e-01 9.58308160e-01 -5.32091796e-01 -2.30379701e-01 1.83902606e-01 7.20342934e-01 -5.75767159e-01 -1.09826291e+00 2.78098404e-01 4.94004995e-01 -1.03053474e+00 1.24985921e+00 -4.21514928e-01 5.13383269e-01 -3.87263387e-01 -4.06076103e-01 -1.56825912e+00 -8.22731629e-02 -4.67812181e-01 2.18970552e-01 1.21056652e+00 2.06351608e-01 -7.59869814e-01 3.37699831e-01 6.23969257e-01 -2.04940930e-01 -7.11193800e-01 -8.05207968e-01 -6.07129097e-01 1.78686127e-01 -1.97603598e-01 5.05475521e-01 1.04350686e+00 -4.18366432e-01 1.08437657e-01 -3.96921098e-01 3.72027904e-01 9.81540978e-01 4.92808878e-01 9.39736128e-01 -1.23306787e+00 -3.99099916e-01 -9.58328471e-02 -2.34981179e-01 -1.05212164e+00 2.30631545e-01 -1.11122692e+00 -2.15984751e-02 -1.79242718e+00 4.43239510e-02 -5.10232449e-01 5.14076531e-01 -3.30263429e-04 1.38680227e-02 1.35658890e-01 3.23147684e-01 4.37055230e-01 -2.14749858e-01 9.69208717e-01 1.83246183e+00 -1.82084188e-01 -3.90048921e-01 -8.48821402e-02 -4.34333831e-01 7.95808136e-01 4.14521545e-01 -1.10510819e-01 -6.81171715e-01 -7.02676475e-01 1.21465675e-03 6.96234941e-01 5.45403540e-01 -5.23540795e-01 -7.79063627e-02 -5.98128438e-01 2.03530148e-01 -3.75513494e-01 5.19461274e-01 -7.92577088e-01 6.63624823e-01 1.23195373e-01 -3.56921792e-01 3.56524557e-01 -1.19862892e-01 8.19877148e-01 -5.99743687e-02 -1.77754834e-02 1.25567460e+00 -3.00610721e-01 -2.22750559e-01 6.01078868e-01 -2.69903928e-01 1.11858539e-01 1.30719411e+00 -3.80674154e-01 1.09636046e-01 -4.10421759e-01 -1.16791940e+00 1.35912865e-01 1.21385038e+00 3.09847504e-01 9.52173471e-01 -1.57912076e+00 -4.78039324e-01 4.06995952e-01 1.40874997e-01 4.39224303e-01 5.54292917e-01 6.92654252e-01 -6.36653244e-01 -1.67304069e-01 -2.94413835e-01 -8.46826792e-01 -1.26018405e+00 5.80042243e-01 7.13963330e-01 -7.81808794e-02 -8.61101985e-01 3.14945519e-01 8.06126893e-01 -6.52758241e-01 -3.74374501e-02 -4.01782900e-01 -1.01410963e-01 -6.12113252e-02 3.24491292e-01 3.54445070e-01 -1.41260073e-01 -1.02726138e+00 -5.15113957e-02 1.03660858e+00 1.64900675e-01 -4.87209529e-01 1.15841103e+00 -4.85770583e-01 -2.89343148e-01 7.95142174e-01 1.49524617e+00 3.24007198e-02 -1.40599966e+00 1.26896268e-02 -5.37027597e-01 -8.75917733e-01 -1.97802395e-01 1.44733548e-01 -1.26939356e+00 9.79050934e-01 3.75247240e-01 1.11689009e-01 7.91657686e-01 1.68632492e-01 6.79674566e-01 -3.14432442e-01 5.29322743e-01 -6.46176696e-01 3.08917850e-01 1.89199910e-01 1.20867980e+00 -9.13173318e-01 -1.65557340e-01 -5.44813633e-01 -3.81387562e-01 1.00734115e+00 5.38216472e-01 -1.56298473e-01 6.20980084e-01 -2.45412976e-01 -1.61619544e-01 -2.88594395e-01 -4.64225709e-01 3.40436846e-02 3.78182560e-01 6.68748021e-01 2.56741643e-01 -4.42441832e-03 -1.15842439e-01 -1.66815028e-01 1.62096217e-01 -3.11478615e-01 5.80695391e-01 6.45439506e-01 -2.66836494e-01 -1.04041874e+00 -4.83725876e-01 1.20255940e-01 -5.09736352e-02 2.77372479e-01 -2.39667073e-01 5.67708135e-01 1.30171239e-01 6.87053561e-01 1.68937027e-01 -3.17681938e-01 5.91038048e-01 -8.01684260e-02 6.16476953e-01 -8.90332282e-01 -2.11425573e-01 8.41209292e-02 -4.37665790e-01 -6.65776849e-01 -4.53962386e-01 -7.34175920e-01 -1.14730906e+00 -2.72561014e-01 -8.37361906e-03 1.51766703e-01 4.72119302e-01 7.49495924e-01 2.66768247e-01 4.26878959e-01 1.01954055e+00 -1.50982118e+00 -1.25314385e-01 -3.26918870e-01 -7.46939242e-01 6.51489377e-01 6.70430422e-01 -6.57636940e-01 -3.83776337e-01 5.07138073e-01]	[9.153169631958008, -3.1434760093688965]
c5882e5b-fc22-43f6-8e96-da79d5240915	pure-transformers-are-powerful-graph-learners	2207.02505	null	https://arxiv.org/abs/2207.02505v2	https://arxiv.org/pdf/2207.02505v2.pdf	Pure Transformers are Powerful Graph Learners	We show that standard Transformers without graph-specific modifications can lead to promising results in graph learning both in theory and practice. Given a graph, we simply treat all nodes and edges as independent tokens, augment them with token embeddings, and feed them to a Transformer. With an appropriate choice of token embeddings, we prove that this approach is theoretically at least as expressive as an invariant graph network (2-IGN) composed of equivariant linear layers, which is already more expressive than all message-passing Graph Neural Networks (GNN). When trained on a large-scale graph dataset (PCQM4Mv2), our method coined Tokenized Graph Transformer (TokenGT) achieves significantly better results compared to GNN baselines and competitive results compared to Transformer variants with sophisticated graph-specific inductive bias. Our implementation is available at https://github.com/jw9730/tokengt.	['Seunghoon Hong', 'Honglak Lee', 'Moontae Lee', 'Sungjun Cho', 'Seonwoo Min', 'Tien Dat Nguyen', 'Jinwoo Kim']	2022-07-06	null	null	null	null	['graph-regression']	['graphs']	[ 1.10949032e-01 6.58796251e-01 -4.59789813e-01 7.91725144e-02 -5.48182487e-01 -6.75234795e-01 9.89665329e-01 2.26042211e-01 -3.22029859e-01 4.35581535e-01 3.84125710e-01 -8.08407009e-01 2.42417336e-01 -1.30080664e+00 -1.09157407e+00 -5.62107027e-01 -5.73669016e-01 5.10831296e-01 1.03543699e-01 -1.24013789e-01 -2.30179250e-01 2.53936261e-01 -6.03413939e-01 -2.19137445e-01 4.61011499e-01 5.16132176e-01 -4.20950800e-01 9.35570002e-01 -1.75102934e-01 1.17670929e+00 -1.82580426e-02 -7.57035375e-01 2.65694559e-01 -6.45497888e-02 -1.28745496e+00 -1.60612851e-01 7.03856587e-01 -1.69114873e-01 -9.59676325e-01 9.63721216e-01 3.68108213e-01 1.02963690e-02 5.17042220e-01 -1.45967257e+00 -1.06392550e+00 1.46855426e+00 -2.78289586e-01 6.73865676e-02 2.18207166e-01 2.35858291e-01 1.65199375e+00 -7.75343418e-01 7.59731054e-01 1.25846696e+00 1.06217217e+00 4.06016052e-01 -1.66982770e+00 -5.30174792e-01 3.26810628e-01 6.82838559e-02 -1.14748049e+00 -1.63086414e-01 7.34165013e-01 -2.03670010e-01 1.20887363e+00 2.14697747e-03 9.05525684e-01 1.19783616e+00 2.75644809e-01 8.13840032e-01 5.77441514e-01 -1.87343553e-01 -1.40704475e-02 -3.41837645e-01 3.79741043e-01 1.19535136e+00 4.64088023e-01 -6.51333407e-02 -2.18791127e-01 -1.64547995e-01 6.02896333e-01 3.69842425e-02 -1.61062151e-01 -7.23780572e-01 -1.32552922e+00 9.88025010e-01 9.58709359e-01 3.83924067e-01 -1.35703877e-01 1.07051623e+00 7.27206588e-01 7.29708314e-01 5.34022212e-01 3.39196384e-01 -2.29050472e-01 2.30694354e-01 -2.54156470e-01 2.11951584e-02 8.25835049e-01 1.04037380e+00 9.80422616e-01 4.73731488e-01 -9.83326361e-02 3.27598989e-01 1.33452907e-01 5.26947916e-01 6.32346570e-02 -6.23341322e-01 4.91408527e-01 6.56227946e-01 -4.81709599e-01 -8.13443244e-01 -4.97809619e-01 -4.73785728e-01 -9.45086420e-01 -1.23652540e-01 3.11975718e-01 -1.06412701e-01 -9.95358646e-01 1.96360445e+00 2.72831116e-02 3.84794563e-01 -3.59289609e-02 3.57118070e-01 9.11220193e-01 5.19858479e-01 2.31598943e-01 4.37003911e-01 1.20078063e+00 -1.00273800e+00 -2.62497902e-01 -2.09874436e-01 1.25269306e+00 -2.90341765e-01 1.13900101e+00 2.17760671e-02 -1.03670573e+00 -1.75696686e-01 -9.30728316e-01 -4.17565227e-01 -6.59439981e-01 -5.37616014e-01 1.13824821e+00 5.26042044e-01 -1.80461633e+00 7.03580797e-01 -9.12510931e-01 -4.46207255e-01 4.99264956e-01 4.61081624e-01 -6.72848165e-01 -2.86807775e-01 -1.38181889e+00 6.99706137e-01 3.63973051e-01 -4.02187109e-02 -1.35743368e+00 -7.64437616e-01 -1.45550704e+00 1.86494365e-01 2.84254730e-01 -9.24279571e-01 1.28060567e+00 -7.56689966e-01 -1.15266001e+00 8.74801874e-01 5.26469946e-02 -7.54623413e-01 3.77808034e-01 2.02293605e-01 -1.53627083e-01 5.32836355e-02 -6.38109911e-03 4.00561243e-01 6.43780589e-01 -8.70523810e-01 -1.75413251e-01 -5.86983636e-02 6.13311827e-01 -6.04732037e-02 -4.90728170e-01 -2.94274092e-01 -2.39876047e-01 -3.27412963e-01 -1.89753577e-01 -8.93832505e-01 -4.84949231e-01 -1.78094402e-01 -7.94212520e-01 -3.98763031e-01 7.22337663e-01 -3.63453656e-01 8.45895112e-01 -1.89498234e+00 2.42429271e-01 3.52761596e-01 1.13199615e+00 3.26213181e-01 -6.90993428e-01 8.03799033e-01 -3.64969492e-01 3.93511266e-01 -2.02059522e-01 -5.29406667e-01 4.55656826e-01 3.13837767e-01 -1.87003762e-01 7.84683406e-01 8.11788291e-02 1.58090901e+00 -1.25808036e+00 -3.93687367e-01 4.34370428e-01 6.09863639e-01 -7.58591175e-01 -9.14793287e-04 -4.28618401e-01 -1.23090938e-01 -4.55430001e-01 4.45309728e-01 5.85239470e-01 -6.96004629e-01 5.47786534e-01 -1.56998411e-01 4.02465612e-01 5.96768796e-01 -6.90042257e-01 1.63149166e+00 -6.50911391e-01 8.17875564e-01 -1.92418788e-02 -1.16161001e+00 3.99877250e-01 4.33216721e-01 2.47007787e-01 -5.02884746e-01 1.85786515e-01 -5.37063777e-02 -2.12298200e-01 1.84461661e-02 5.00694215e-01 -2.12234408e-01 -1.95550933e-01 5.97247541e-01 5.56638539e-01 7.24079832e-02 1.69944271e-01 1.02007174e+00 1.70981038e+00 -1.26216561e-01 1.68103412e-01 -1.86738715e-01 2.53631383e-01 -3.58092934e-01 8.07630792e-02 7.78792500e-01 -2.07602195e-02 1.31790385e-01 1.08493936e+00 -4.78965759e-01 -1.18559134e+00 -1.42018771e+00 3.30744445e-01 1.11726677e+00 -1.77674249e-01 -9.98400092e-01 -4.91704613e-01 -8.16423655e-01 1.75048560e-01 3.62354368e-01 -7.34352589e-01 -3.22220474e-01 -5.15525401e-01 -3.90017182e-01 9.50945556e-01 6.75747275e-01 2.60756940e-01 -9.07382607e-01 3.33158493e-01 1.56470656e-01 2.66995281e-02 -1.14823627e+00 -5.80828786e-01 2.56848782e-01 -7.19763458e-01 -1.24553072e+00 -4.17689860e-01 -1.01545954e+00 8.40582788e-01 2.10033745e-01 1.41525400e+00 4.66745943e-01 -2.60335263e-02 6.09509349e-01 -2.31913686e-01 1.30895600e-01 -5.08569241e-01 6.39788568e-01 -2.44753584e-01 -2.48953193e-01 8.52437019e-02 -9.47684884e-01 -3.61265182e-01 -3.49450231e-01 -8.72935236e-01 -6.15883507e-02 3.40207398e-01 7.19446540e-01 2.09462911e-01 -1.68279216e-01 1.54742509e-01 -1.47704732e+00 6.99070454e-01 -4.32210147e-01 -7.46472418e-01 4.57919054e-02 -7.94473708e-01 2.80269176e-01 1.18735278e+00 -2.15894237e-01 -3.04990679e-01 -3.46298665e-01 -1.09344460e-01 -3.13760757e-01 3.29128087e-01 7.21821964e-01 9.67539102e-03 -4.71449226e-01 5.62609911e-01 1.27380267e-01 -4.81834635e-02 -1.00646682e-01 9.78366852e-01 -1.03372918e-03 3.88128608e-01 -5.92167616e-01 1.35309827e+00 3.69168162e-01 2.97154456e-01 -6.11394107e-01 -5.52674830e-01 -2.11312965e-01 -4.72350836e-01 7.36947581e-02 6.73457503e-01 -8.88450146e-01 -8.05612147e-01 5.06380141e-01 -9.55903649e-01 -9.69208598e-01 -3.34712058e-01 2.76045918e-01 -3.76392573e-01 5.17179310e-01 -1.13138974e+00 -2.87095577e-01 -5.33220470e-01 -7.31622100e-01 7.87822783e-01 -4.23942000e-01 1.12395622e-01 -1.90488803e+00 1.71208739e-01 -1.09005637e-01 6.02575123e-01 4.30190086e-01 1.20507300e+00 -8.33256841e-01 -8.07343364e-01 -3.53221685e-01 -3.97136867e-01 3.10461491e-01 4.03234288e-02 -9.05513987e-02 -7.56034374e-01 -6.00504458e-01 -8.04295897e-01 -5.14396966e-01 1.04303324e+00 2.49806985e-01 8.72242987e-01 -6.30777121e-01 -3.73457938e-01 9.41121995e-01 1.65811062e+00 -7.78368056e-01 5.71624160e-01 -4.29326333e-02 1.44444144e+00 -1.86773837e-01 -4.61350560e-01 1.66096799e-02 8.10740411e-01 1.28505230e-01 7.25816727e-01 -3.69901657e-01 -3.19693804e-01 -7.42293179e-01 7.24793613e-01 1.10032642e+00 -6.95561096e-02 -5.69682658e-01 -9.12804246e-01 7.36102700e-01 -1.71023691e+00 -8.76964092e-01 -2.08336785e-01 1.87687588e+00 5.84225118e-01 2.71948040e-01 1.64214551e-01 -2.62201875e-02 6.10186100e-01 7.53476322e-01 -3.21304709e-01 -5.81539154e-01 -1.04728326e-01 7.32919753e-01 1.04589474e+00 1.00848007e+00 -1.06348896e+00 1.32062924e+00 6.80384207e+00 5.57986975e-01 -1.04534245e+00 1.76431373e-01 1.20412730e-01 2.61874378e-01 -9.39050257e-01 4.18539047e-01 -1.93259865e-01 -2.50955466e-02 1.24798119e+00 -5.36837697e-01 7.42793679e-01 6.19223773e-01 -3.62577856e-01 6.90667331e-01 -1.35956180e+00 4.16302502e-01 -8.41347724e-02 -1.62152660e+00 2.66686499e-01 1.77428916e-01 4.49385941e-01 7.07718074e-01 9.09949765e-02 6.04733765e-01 1.30225205e+00 -1.35175824e+00 3.45658302e-01 -4.16115411e-02 8.16412449e-01 -4.84697998e-01 6.62415981e-01 -3.07588488e-01 -1.72633696e+00 2.71911323e-01 -5.43399572e-01 -1.62936643e-01 -4.76238951e-02 4.52227712e-01 -1.05134594e+00 8.29846740e-01 1.61040321e-01 1.28820586e+00 -5.90900362e-01 6.42970383e-01 -6.63907647e-01 1.04726613e+00 -2.20216170e-01 3.26262414e-02 6.26425683e-01 -1.26017690e-01 4.58129227e-01 1.25679004e+00 -1.48421049e-01 -3.18518996e-01 2.04859734e-01 8.34982514e-01 -7.89047003e-01 -4.54556830e-02 -1.03364921e+00 -5.54454207e-01 3.58818710e-01 1.42255116e+00 -6.21103644e-01 -4.77417737e-01 -5.48973441e-01 9.10393596e-01 9.41637099e-01 6.31259501e-01 -8.21605504e-01 -5.30785382e-01 4.77917701e-01 1.22921228e-01 4.32218462e-01 -2.48700663e-01 3.91223073e-01 -1.37376356e+00 -1.08549058e-01 -6.90266907e-01 7.08917439e-01 -6.12881184e-01 -1.24438894e+00 4.72497970e-01 -3.29082698e-01 -7.25840390e-01 -7.01534525e-02 -8.59381497e-01 -9.33005691e-01 5.63841403e-01 -1.67502820e+00 -1.58720148e+00 -3.92811671e-02 8.55168164e-01 -3.11551988e-01 1.78567633e-01 9.63927805e-01 3.36872101e-01 -3.69220912e-01 9.08303320e-01 -1.09784596e-01 7.89130032e-01 2.47349322e-01 -1.73937654e+00 1.20016909e+00 1.03463185e+00 4.20792013e-01 5.57332516e-01 4.60250109e-01 -3.70790243e-01 -2.01163411e+00 -1.40844357e+00 1.08780837e+00 -4.83865917e-01 1.32649159e+00 -8.85863900e-01 -7.11119056e-01 1.76380169e+00 4.25870389e-01 5.10545909e-01 3.23560506e-01 5.18582165e-01 -9.82255101e-01 5.47064506e-02 -7.75652707e-01 8.75429571e-01 1.52515233e+00 -9.00765598e-01 -2.95402437e-01 6.22890830e-01 1.12862253e+00 -2.65452087e-01 -1.20570338e+00 5.85465208e-02 1.63408816e-01 -6.03498220e-01 9.68806565e-01 -8.96570861e-01 1.00733697e-01 -3.17009375e-03 -7.80160129e-02 -1.54307628e+00 -6.71705306e-01 -8.92382145e-01 -1.89667416e-03 8.94185245e-01 5.45372963e-01 -1.28364003e+00 7.43472695e-01 1.50285840e-01 -1.32853955e-01 -4.67701524e-01 -7.35725164e-01 -8.70207191e-01 4.62989688e-01 -3.99797112e-01 6.59141600e-01 1.09143698e+00 3.80462945e-01 5.16343057e-01 -3.22570026e-01 1.58238724e-01 8.22224796e-01 2.06235051e-03 9.19619858e-01 -1.04847181e+00 -1.33685628e-02 -6.48860633e-01 -8.32498491e-01 -8.74446929e-01 7.71876633e-01 -1.90934193e+00 -4.82530028e-01 -2.00644445e+00 2.25450471e-01 -3.62176806e-01 -5.54570735e-01 1.02216947e+00 4.83659767e-02 3.18552285e-01 1.12749793e-01 -3.80320102e-01 -7.86697149e-01 5.92869580e-01 1.13914001e+00 -5.02495468e-01 4.12237495e-01 -2.72646338e-01 -8.02974522e-01 3.61932665e-01 8.69854748e-01 -4.07360911e-01 -4.73628819e-01 -5.58354557e-01 5.53228199e-01 -1.46384329e-01 5.91917634e-01 -5.90114474e-01 1.85298026e-01 1.58543020e-01 -5.86057939e-02 -4.04853225e-02 -3.27952462e-03 -4.52767402e-01 4.07230370e-02 6.82179928e-01 -4.80993479e-01 3.34718585e-01 6.81116506e-02 4.77896899e-01 4.57924567e-02 2.71678001e-01 4.62703526e-01 -1.22195885e-01 -4.45318997e-01 1.00433135e+00 -3.46629769e-01 5.17347991e-01 5.24544239e-01 8.87427106e-03 -6.64971411e-01 -6.51149392e-01 -5.25801659e-01 3.82909775e-01 6.48429513e-01 1.91723555e-01 4.67554063e-01 -1.48298740e+00 -7.97583520e-01 5.43422475e-02 1.27073482e-01 -1.25567287e-01 7.47764111e-03 8.57001424e-01 -5.13833404e-01 2.85242260e-01 7.60905892e-02 -2.00113386e-01 -8.63814294e-01 7.55842268e-01 5.29002547e-01 -5.89695930e-01 -1.08384323e+00 7.83816934e-01 2.13341713e-01 -9.10654962e-01 2.57884085e-01 -6.52719915e-01 3.03882688e-01 -3.86113495e-01 7.07552508e-02 1.26201406e-01 -1.35202944e-01 -4.36611414e-01 -4.34427261e-01 4.82214361e-01 -5.40192388e-02 2.13435844e-01 1.24444509e+00 2.73159206e-01 -4.24359858e-01 2.03363523e-01 1.55053759e+00 1.43051773e-01 -8.08180690e-01 -5.32108665e-01 -1.20562494e-01 -4.74090164e-04 -7.96076804e-02 -2.23642394e-01 -1.44856215e+00 8.41089487e-01 -1.21933401e-01 4.02900070e-01 6.03679061e-01 2.00231299e-01 8.75333786e-01 6.77125394e-01 3.24708104e-01 -5.07424653e-01 1.04387542e-02 6.26742184e-01 3.95764381e-01 -8.40540349e-01 -2.81852633e-02 -2.86506236e-01 -1.20811827e-01 8.51074278e-01 2.30581701e-01 -6.82026088e-01 7.25217760e-01 3.29121053e-01 -1.75545260e-01 -4.04462993e-01 -1.04240882e+00 -1.83509365e-01 1.09240450e-02 8.73419404e-01 4.43491906e-01 3.52837771e-01 7.92837441e-02 -3.91074941e-02 -3.32131803e-01 -1.75435692e-01 7.64937341e-01 5.02360463e-01 -1.21717930e-01 -1.34964788e+00 4.11850005e-01 5.04460037e-01 -4.01478469e-01 -5.68744302e-01 -3.93410385e-01 1.13210177e+00 -5.75945258e-01 6.39471292e-01 2.20643566e-03 -6.04407609e-01 7.67787322e-02 -9.49967429e-02 6.74607635e-01 -5.98250031e-01 -5.86451352e-01 -5.27997375e-01 4.50645953e-01 -7.66580522e-01 -2.54802644e-01 -2.19856068e-01 -1.47452641e+00 -1.08767104e+00 -1.31493047e-01 2.18558505e-01 7.42904693e-02 6.02625072e-01 2.84740597e-01 6.32286012e-01 4.61086512e-01 -5.76451480e-01 -3.08182746e-01 -7.61998773e-01 -6.84346676e-01 3.78300130e-01 4.86466497e-01 -2.26000577e-01 -6.73718393e-01 -3.10938329e-01]	[6.931245803833008, 6.2589430809021]
06cf9d20-a79c-4dae-82bd-7bbc755fb734	simplifying-deep-learning-based-model-for	2005.14373	null	https://arxiv.org/abs/2005.14373v2	https://arxiv.org/pdf/2005.14373v2.pdf	CodeMatcher: Searching Code Based on Sequential Semantics of Important Query Words	To accelerate software development, developers frequently search and reuse existing code snippets from a large-scale codebase, e.g., GitHub. Over the years, researchers proposed many information retrieval based models for code search, but they fail to connect the semantic gap between query and code. An early successful deep learning based model DeepCS solved this issue by learning the relationship between pairs of code methods and corresponding natural language descriptions. Two major advantages of DeepCS are the capability of understanding irrelevant/noisy keywords and capturing sequential relationships between words in query and code. In this paper, we proposed an IR-based model CodeMatcher that inherits the advantages of DeepCS, while it can leverage the indexing technique in the IR-based model to accelerate the search response time substantially. CodeMatcher first collects metadata for query words to identify irrelevant/noisy ones, then iteratively performs fuzzy search with important query words on the codebase that is indexed by the Elasticsearch tool, and finally reranks a set of returned candidate code according to how the tokens in the candidate code snippet sequentially matched the important words in a query. We verified its effectiveness on a large-scale codebase with ~41k repositories. Experimental results showed that CodeMatcher achieves an MRR of 0.60, outperforming DeepCS, CodeHow, and UNIF by 82%, 62%, and 46% respectively. Our proposed model is over 1.2k times faster than DeepCS. Moreover, CodeMatcher outperforms GitHub and Google search by 46% and 33% respectively in terms of MRR. We also observed that: fusing the advantages of IR-based and DL-based models is promising; improving the quality of method naming helps code search, since method name plays an important role in connecting query and code.	['Chao Liu', 'Ahmed E. Hassan', 'Xin Xia', 'Zhiwei Liu', 'David Lo', 'Shanping Li']	2020-05-29	null	null	null	null	['code-search', 'code-search']	['computer-code', 'computer-vision']	[-6.39644563e-01 -6.70089006e-01 -6.78016782e-01 1.94762219e-02 -9.84956920e-01 -7.35959947e-01 9.47610289e-02 4.88063246e-01 -3.35770279e-01 1.80121046e-02 1.83304116e-01 -2.70367265e-01 -5.10783851e-01 -6.62088215e-01 -6.39645100e-01 -8.22468773e-02 -1.04419284e-01 2.76743978e-01 5.51286101e-01 -1.89035371e-01 8.49197567e-01 -5.17949052e-02 -1.81098723e+00 5.18873811e-01 1.22971690e+00 7.25636959e-01 7.26949215e-01 3.10970366e-01 -8.93536925e-01 8.68631840e-01 -5.31543791e-01 -4.13377911e-01 -2.42502298e-02 1.21541560e-01 -1.01239514e+00 -8.97122860e-01 3.99928808e-01 -2.10192174e-01 -3.46439034e-01 1.27073681e+00 3.29593688e-01 -1.53294936e-01 2.08414182e-01 -1.15107453e+00 -1.19701505e+00 9.29139197e-01 -8.60062540e-01 4.34627116e-01 7.74609268e-01 -3.96819770e-01 1.33711421e+00 -1.13112152e+00 5.97028136e-01 8.40336740e-01 6.91505313e-01 5.73035121e-01 -6.43804550e-01 -9.68317449e-01 -5.01460135e-02 3.47858638e-01 -1.74488759e+00 -8.57132152e-02 3.97291243e-01 -7.01938629e-01 1.50968575e+00 3.00519168e-01 4.17385519e-01 5.36981761e-01 1.09235100e-01 8.64396095e-01 2.17051938e-01 -6.53696597e-01 1.53239176e-04 1.96621597e-01 3.41874331e-01 9.09086704e-01 2.38601223e-01 -2.70171195e-01 -4.09333140e-01 -6.11369908e-01 2.33049095e-01 5.33265889e-01 -2.68779755e-01 7.03236163e-02 -9.67583239e-01 5.42588532e-01 3.65124583e-01 8.06734502e-01 8.46139796e-04 3.57829839e-01 6.62126362e-01 4.66446549e-01 1.46405056e-01 7.31468797e-01 -7.42555678e-01 -5.28713644e-01 -1.02646279e+00 2.04178184e-01 7.88027048e-01 1.56990457e+00 1.07608294e+00 -4.21446502e-01 -1.35661930e-01 1.19617379e+00 5.76509476e-01 6.83395743e-01 9.26263511e-01 -8.37478697e-01 6.56340241e-01 1.29160786e+00 -3.07613045e-01 -1.16515338e+00 7.45324865e-02 -3.65676969e-01 -2.46288896e-01 -5.53635299e-01 -2.88470209e-01 5.06147325e-01 -4.85080034e-01 1.28017783e+00 -2.49453828e-01 1.68076307e-02 -1.00373924e-01 4.33358699e-01 1.04221880e+00 4.64980572e-01 -1.57579079e-01 2.43110508e-02 1.20068836e+00 -9.80508864e-01 -4.78215605e-01 -2.31308132e-01 1.03714788e+00 -1.06498611e+00 1.47295797e+00 2.65803486e-01 -7.62874722e-01 -4.84681636e-01 -7.54396677e-01 -3.12422924e-02 -4.24301803e-01 3.68891120e-01 6.47271395e-01 3.87040466e-01 -1.29954207e+00 2.83007413e-01 -6.92846060e-01 -5.14712274e-01 1.90612808e-01 2.09044620e-01 -2.31800284e-02 -3.59067589e-01 -9.40715194e-01 4.11049366e-01 2.96670228e-01 -6.54022694e-01 -9.41487134e-01 -9.90621567e-01 -6.30432606e-01 4.08133119e-01 3.91258419e-01 -1.08279593e-01 1.24637723e+00 -7.20982492e-01 -6.73592269e-01 7.44298398e-01 -2.30882600e-01 1.58817515e-01 -2.70971328e-01 -4.27092671e-01 -7.21235216e-01 -1.53859273e-01 5.20845711e-01 1.01428688e-01 1.80421457e-01 -1.00015414e+00 -8.75666857e-01 -1.43805936e-01 3.44646305e-01 -2.14825645e-01 -8.31598043e-01 3.66648346e-01 -1.21521139e+00 -5.68513393e-01 -8.71476606e-02 -8.37366521e-01 2.42795616e-01 -1.24159835e-01 -1.33398233e-03 -7.85463274e-01 5.02239943e-01 -4.61858720e-01 2.12911296e+00 -2.36877418e+00 -1.93937927e-01 8.10913667e-02 3.25518280e-01 3.05959255e-01 -5.14875650e-01 9.34716523e-01 3.17304991e-02 4.69185978e-01 -6.15215569e-04 2.32490078e-01 6.32076934e-02 -1.04458436e-01 -3.38225991e-01 9.68265310e-02 -3.24747562e-01 9.06930804e-01 -1.16026616e+00 -6.73516989e-01 -3.97611320e-01 1.85582310e-01 -9.40982342e-01 9.78385806e-02 -1.95436642e-01 -6.15106702e-01 -7.54178822e-01 9.85849261e-01 4.50220197e-01 -5.08904696e-01 -4.94018346e-02 2.24527121e-01 -2.03725547e-01 4.41766083e-01 -7.91312277e-01 2.22540665e+00 -8.72016370e-01 7.03965783e-01 -2.66540706e-01 -7.14045227e-01 9.98936772e-01 2.35931680e-01 5.51348627e-01 -1.09065139e+00 -6.08708739e-01 6.50056005e-01 -3.05089504e-01 -9.55940723e-01 3.84610891e-01 5.83905876e-01 -2.48603448e-01 6.43577158e-01 -1.83340430e-01 1.40023604e-01 3.91057879e-01 6.06305540e-01 1.66620696e+00 -6.14734851e-02 5.45461178e-02 -5.65620005e-01 8.92924011e-01 8.90709907e-02 4.82639790e-01 8.09050202e-01 3.05926651e-01 2.60051072e-01 3.05826038e-01 -5.49001575e-01 -4.03989017e-01 -7.13025272e-01 -3.15604880e-02 1.50988674e+00 3.91718924e-01 -1.05585015e+00 -5.18007994e-01 -9.38572168e-01 2.61939943e-01 4.75592554e-01 -3.74847531e-01 -3.87099355e-01 -7.53504455e-01 -2.81658411e-01 7.75913954e-01 4.92797613e-01 2.84339547e-01 -8.81763160e-01 -2.75650680e-01 1.98801205e-01 -2.97843874e-01 -4.56549913e-01 -8.42403591e-01 -1.49320662e-01 -5.37824512e-01 -1.39393210e+00 -4.95665461e-01 -1.07017219e+00 6.33548319e-01 6.61501110e-01 1.71110785e+00 1.02307546e+00 -4.21547204e-01 4.97476876e-01 -8.66240740e-01 -2.13890485e-02 -5.28071821e-01 3.93598974e-01 -2.36580908e-01 -8.89918149e-01 8.50807607e-01 -4.86169279e-01 -7.38082767e-01 3.59014302e-01 -1.21293855e+00 -5.59534848e-01 5.72688341e-01 5.87003291e-01 1.46839008e-01 -1.20481271e-02 5.27703345e-01 -4.99027371e-01 7.49832451e-01 -9.99274373e-01 -9.53773141e-01 7.48461306e-01 -1.25632179e+00 3.38277727e-01 5.39303303e-01 -3.58700067e-01 -8.72097909e-01 -1.29383042e-01 -1.00932203e-01 -1.94463179e-01 3.89765382e-01 1.08369827e+00 3.65528226e-01 -1.58805668e-01 7.59842873e-01 5.16172230e-01 -5.21452129e-01 -9.39098179e-01 1.34967819e-01 1.01994383e+00 2.32332632e-01 -1.01506770e+00 6.54679477e-01 3.36055346e-02 -8.53724599e-01 -3.88728194e-02 -1.93841621e-01 -1.13274777e+00 -1.95361912e-01 1.68409236e-02 5.32933116e-01 -1.00539923e+00 -6.44722641e-01 3.46333869e-02 -1.31546998e+00 2.22394168e-01 1.07707679e-01 3.80322963e-01 6.97210059e-02 6.50359333e-01 -7.07062662e-01 -2.93859601e-01 -5.22517920e-01 -1.37099099e+00 1.17459941e+00 1.60030410e-01 1.71691366e-02 -7.27485001e-01 5.39622426e-01 1.71304896e-01 6.85411513e-01 -6.11955285e-01 1.46035135e+00 -8.05263638e-01 -7.76121736e-01 -3.82090032e-01 -3.75779927e-01 8.00879747e-02 3.33608925e-01 1.86213881e-01 -4.88341719e-01 -5.11298478e-01 -3.92665952e-01 -2.71320164e-01 6.10931993e-01 -2.27409855e-01 1.28330076e+00 -2.03284532e-01 -6.38004959e-01 4.28319126e-01 2.03192663e+00 6.21099353e-01 3.54156405e-01 6.00291371e-01 6.22278154e-01 1.71541423e-01 5.35467148e-01 5.07778287e-01 5.45237720e-01 7.35786676e-01 4.79642302e-01 4.35213178e-01 -7.51601607e-02 -1.45125270e-01 4.50249404e-01 1.62341309e+00 2.58271188e-01 -8.40857998e-03 -1.55993187e+00 9.70656455e-01 -1.75336206e+00 -6.00309312e-01 -6.53075054e-02 2.16793728e+00 1.22159183e+00 -1.64939448e-01 -3.65888059e-01 -3.02818030e-01 4.37625825e-01 -2.84181565e-01 -4.56590354e-01 -1.46825716e-01 2.49803945e-01 1.36237875e-01 3.96127313e-01 1.81958392e-01 -5.05915344e-01 7.90849864e-01 5.45906067e+00 1.18242860e+00 -7.56410837e-01 1.67827860e-01 -1.02972224e-01 -1.69419334e-03 -7.08039463e-01 1.68586731e-01 -9.04584348e-01 7.07672060e-01 7.17220008e-01 -7.43636310e-01 7.52965987e-01 1.44729733e+00 -4.00183618e-01 8.10644776e-03 -1.29184723e+00 1.09558308e+00 1.89394534e-01 -1.65296614e+00 7.50589892e-02 -1.28930673e-01 9.89113986e-01 5.57698905e-01 -2.19807163e-01 7.65425682e-01 5.63092709e-01 -7.36849666e-01 5.94952345e-01 4.84538257e-01 7.20510602e-01 -5.56708276e-01 8.54698122e-01 3.83885711e-01 -1.65653503e+00 -3.32392037e-01 -4.97837007e-01 1.56834036e-01 -8.28111291e-01 4.81468052e-01 -5.92624664e-01 4.43647921e-01 1.23406661e+00 9.09840643e-01 -8.30799699e-01 1.28362894e+00 1.47232682e-01 3.25619280e-01 -1.07668079e-02 -1.09578304e-01 2.24532425e-01 2.94219613e-01 1.03238940e-01 1.61392438e+00 6.02387190e-01 -2.84895211e-01 2.86093712e-01 1.18149626e+00 -3.64901155e-01 2.97891200e-01 -4.94570851e-01 -1.06379911e-01 1.09404004e+00 1.01284587e+00 -5.38017631e-01 -3.64975333e-01 -9.82368588e-01 5.74528039e-01 2.21661165e-01 3.68857086e-01 -7.86510944e-01 -9.58525062e-01 8.00370395e-01 -1.81980114e-02 3.14788163e-01 1.21240780e-01 3.27520430e-01 -1.21593976e+00 6.20086014e-01 -1.10073853e+00 4.79506522e-01 -5.36227286e-01 -1.21144581e+00 6.75411344e-01 -3.55563648e-02 -1.40277088e+00 -7.81240687e-02 -1.41433492e-01 -4.09029156e-01 8.23389590e-01 -1.53014588e+00 -6.95903063e-01 -3.98219645e-01 5.84110379e-01 8.35443318e-01 -5.27256370e-01 6.76963687e-01 9.60270405e-01 -3.02335441e-01 7.96034873e-01 5.46383560e-01 2.71176189e-01 6.59906805e-01 -1.23635793e+00 6.54310465e-01 7.73767173e-01 3.40599626e-01 1.44539320e+00 6.04369938e-02 -7.79157460e-01 -1.79161239e+00 -7.84857631e-01 9.58417773e-01 -5.46005249e-01 7.85999000e-01 -2.21384719e-01 -1.11682332e+00 2.02027276e-01 1.73168421e-01 1.95047513e-01 6.51818693e-01 1.01179704e-02 -8.61822426e-01 -4.09449726e-01 -7.09701598e-01 7.43626505e-02 1.08429718e+00 -1.07635045e+00 -5.93297541e-01 3.87990594e-01 1.13650417e+00 -1.40895084e-01 -9.76687729e-01 2.12522507e-01 4.47784990e-01 -7.49845207e-01 8.93283367e-01 -3.97814423e-01 5.72211385e-01 -4.13284510e-01 -3.10746580e-01 -8.64200234e-01 -3.57880145e-01 -3.33534181e-01 -7.70872906e-02 1.41789353e+00 4.83063906e-01 -2.02642471e-01 5.22597909e-01 5.16271412e-01 -2.55406171e-01 -6.47451699e-01 -6.50152922e-01 -8.86798203e-01 1.39465421e-01 -4.80969429e-01 9.56235230e-01 1.00314343e+00 3.88791382e-01 -1.73826933e-01 2.40345240e-01 2.15763934e-02 6.71032816e-02 4.20369059e-01 3.02221268e-01 -1.23923683e+00 -2.65941292e-01 -6.31880224e-01 -7.87782371e-02 -1.13496554e+00 3.19408685e-01 -1.28365934e+00 4.04059924e-02 -1.46513486e+00 7.46027529e-01 -7.23021150e-01 -7.14984119e-01 6.45148873e-01 -1.57312617e-01 -2.91504651e-01 -1.16208717e-01 8.30963194e-01 -1.02014279e+00 7.76168704e-02 6.16701663e-01 -5.66691518e-01 -9.25123617e-02 -1.55148610e-01 -7.91009665e-01 4.75482792e-01 1.83517784e-01 -9.21421766e-01 -6.08977079e-01 -1.08376193e+00 9.99739468e-01 3.77492756e-02 -1.08346410e-01 -9.35072660e-01 7.81113029e-01 2.36078426e-02 -4.14126754e-01 -3.40258241e-01 -6.71697021e-01 -8.77651334e-01 -2.12358180e-02 5.26702642e-01 -6.33216977e-01 5.22227645e-01 3.73591274e-01 5.77198446e-01 -4.41797137e-01 -6.56164348e-01 2.58645535e-01 -4.12427455e-01 -1.04931366e+00 1.78007320e-01 -2.70445853e-01 4.95642900e-01 5.06044626e-01 8.40899423e-02 -4.31043506e-01 3.07247519e-01 1.12833224e-01 1.82583988e-01 6.45460725e-01 1.00528133e+00 7.67208278e-01 -1.40390217e+00 -4.74960417e-01 1.05532780e-01 8.15732121e-01 -3.54640156e-01 -1.80543154e-01 5.15739679e-01 -6.63027823e-01 6.68546617e-01 2.19258144e-01 -4.86825377e-01 -1.29132521e+00 6.48146987e-01 6.16277792e-02 -1.30469546e-01 -3.77989650e-01 1.04597187e+00 1.21047489e-01 -5.47522664e-01 6.10992491e-01 -6.86250508e-01 -1.25467807e-01 -2.27796078e-01 7.78414428e-01 3.49531889e-01 5.27496219e-01 -1.78970546e-01 -9.12483692e-01 9.24008250e-01 -4.29700851e-01 5.40075362e-01 1.36421669e+00 -1.19766025e-02 -8.07024539e-01 5.95044978e-02 1.57202709e+00 3.45512092e-01 -5.37323177e-01 -6.13422811e-01 7.52978802e-01 -7.38119841e-01 1.49304271e-01 -1.00700152e+00 -1.47620881e+00 4.67036515e-01 6.28663480e-01 2.40812033e-01 1.20806968e+00 3.52290541e-01 9.07266915e-01 7.27634013e-01 7.38391578e-01 -9.34345663e-01 2.62023777e-01 5.37659109e-01 7.17032254e-01 -1.03090930e+00 -3.95772532e-02 -2.92938538e-02 1.78328436e-03 1.29436862e+00 8.17581296e-01 1.76062167e-01 6.22907937e-01 4.90441948e-01 -6.51541501e-02 -6.60179079e-01 -9.79716480e-01 8.53079334e-02 5.36169350e-01 2.28566572e-01 8.40252399e-01 -4.17429477e-01 -2.55356580e-01 5.96199572e-01 3.89081836e-01 8.00330043e-02 2.48528525e-01 1.17235434e+00 -5.67713857e-01 -1.23823249e+00 3.77682447e-02 4.78736967e-01 -5.90099454e-01 -7.44893372e-01 -2.96068698e-01 5.98498821e-01 2.93945432e-01 8.60507488e-01 -1.50661975e-01 -6.67408168e-01 3.96393061e-01 -2.12603927e-01 -1.78048283e-01 -9.27691042e-01 -9.17417943e-01 -2.34308034e-01 -4.29284394e-01 -9.81362760e-01 -1.60129517e-01 -2.64587313e-01 -1.41064823e+00 -1.56212553e-01 -7.16432214e-01 7.55805552e-01 5.69010079e-01 6.82801187e-01 7.71633804e-01 3.26900125e-01 5.85393488e-01 9.47675705e-02 -3.27722937e-01 -5.39833009e-01 -2.14867979e-01 1.90761209e-01 4.29357767e-01 -5.81674993e-01 -4.97447699e-01 8.27030912e-02]	[7.504638195037842, 8.079793930053711]
676115fe-972f-4c96-937b-3b4cbd0a523f	frequency-domain-blind-quality-assessment-of	2303.02753	null	https://arxiv.org/abs/2303.02753v1	https://arxiv.org/pdf/2303.02753v1.pdf	Frequency-domain Blind Quality Assessment of Blurred and Blocking-artefact Images using Gaussian Process Regression model	Most of the standard image and video codecs are block-based and depending upon the compression ratio the compressed images/videos suffer from different distortions. At low ratios, blurriness is observed and as compression increases blocking artifacts occur. Generally, in order to reduce blockiness, images are low-pass filtered which leads to more blurriness. Also, in bokeh mode images they are commonly seen: blurriness as a result of intentional blurred background while blocking artifact and global blurriness arising due to compression. Therefore, such visual media suffer from both blockiness and blurriness distortions. Along with this, noise is also commonly encountered distortion. Most of the existing works on quality assessment quantify these distortions individually. This paper proposes a methodology to blindly measure overall quality of an image suffering from these distortions, individually as well as jointly. This is achieved by considering the sum of absolute values of low and high-frequency Discrete Frequency Transform (DFT) coefficients defined as sum magnitudes. The number of blocks lying in specific ranges of sum magnitudes including zero-valued AC coefficients and mean of 100 maximum and 100 minimum values of these sum magnitudes are used as feature vectors. These features are then fed to the Machine Learning (ML) based Gaussian Process Regression (GPR) model, which quantifies the image quality. The simulation results show that the proposed method can estimate the quality of images distorted with the blockiness, blurriness, noise and their combinations. It is relatively fast compared to many state-of-art methods, and therefore is suitable for real-time quality monitoring applications.	['M. Ghanbari', 'Ekram Khan', 'Athar A. Moinuddin', 'Maryam Viqar']	2023-03-05	null	null	null	null	['gpr', 'gpr', 'blocking']	['computer-vision', 'miscellaneous', 'natural-language-processing']	[ 3.21848392e-01 -7.87371695e-01 2.77084764e-02 -5.99177089e-03 -4.09848839e-01 -3.72020245e-01 5.27268052e-01 1.53650150e-01 -1.73813641e-01 7.05095470e-01 3.84781063e-01 6.93593174e-02 -3.46900791e-01 -6.24439061e-01 -4.20639277e-01 -9.57994342e-01 -2.76335716e-01 -2.59889215e-01 4.99928482e-02 2.74949968e-01 6.18364036e-01 3.31088811e-01 -1.85169411e+00 1.92358166e-01 1.14714015e+00 1.08938754e+00 4.05400455e-01 8.38605881e-01 1.10971928e-01 8.81799221e-01 -9.49244857e-01 -5.76867461e-02 2.33502507e-01 -4.50059116e-01 -2.85960227e-01 4.56507027e-01 1.11741282e-01 -5.99762619e-01 -4.14899647e-01 1.53429461e+00 4.84569371e-01 9.20671076e-02 8.17766011e-01 -8.78404737e-01 -7.45826542e-01 -3.56100127e-02 -7.95775712e-01 5.40969372e-01 5.09340107e-01 3.13496649e-01 3.02643538e-01 -9.36081529e-01 1.17750503e-01 1.28629875e+00 4.75001395e-01 -4.13451344e-02 -1.24875700e+00 -3.61734211e-01 -7.12145925e-01 7.34406888e-01 -1.24624658e+00 -5.77810168e-01 7.24157453e-01 -4.84021366e-01 6.49637222e-01 4.94279861e-01 5.28309703e-01 5.93785882e-01 6.85641170e-01 2.78385222e-01 1.53569221e+00 -5.25114238e-01 2.69745857e-01 7.85686821e-02 -2.26473570e-01 1.75989270e-01 4.73990530e-01 3.24141324e-01 -3.19775552e-01 -5.87567464e-02 7.25965679e-01 1.51902840e-01 -9.41737294e-01 -2.03539699e-01 -1.11366868e+00 3.09017986e-01 2.42672414e-01 3.42653096e-01 -6.05182946e-01 1.50101513e-01 1.81070656e-01 3.18587273e-01 1.58755630e-01 8.12352449e-02 -1.64084867e-01 -3.49588484e-01 -1.18346095e+00 7.28854984e-02 5.97139060e-01 6.26660049e-01 7.14436412e-01 -3.29532241e-03 -2.39946216e-01 1.10612941e+00 5.33539474e-01 6.49525940e-01 7.29525983e-01 -9.69549358e-01 3.87579978e-01 2.35437676e-01 2.94997513e-01 -1.44126964e+00 1.14719048e-01 -3.22088182e-01 -1.15438485e+00 4.97178465e-01 2.99625635e-01 1.92500263e-01 -8.22364450e-01 9.84730124e-01 -1.00123763e-01 3.08725297e-01 5.80540225e-02 9.89826381e-01 5.88555694e-01 9.86715674e-01 -1.18450142e-01 -7.47938514e-01 1.37108016e+00 -6.37254834e-01 -1.24545503e+00 -1.43925592e-01 -1.39270395e-01 -1.47728848e+00 7.05353737e-01 7.45796859e-01 -1.13905501e+00 -9.02790070e-01 -1.26641572e+00 3.47869843e-01 -6.38433099e-02 1.51896477e-01 -1.21684328e-01 8.41612995e-01 -8.85398209e-01 8.42370868e-01 -5.29641747e-01 -5.62584028e-02 2.32240587e-01 -2.03514099e-03 -9.86041874e-02 -5.07176042e-01 -9.05788779e-01 1.13365221e+00 6.78242445e-02 2.27080062e-01 -6.52729988e-01 -5.62716126e-01 -7.01378524e-01 -5.27998507e-02 4.09829691e-02 -2.81823158e-01 9.02675629e-01 -9.50862050e-01 -1.15109921e+00 4.31665897e-01 -2.80043274e-01 -3.23757350e-01 6.20469987e-01 -1.93482786e-01 -8.04481149e-01 2.70433247e-01 -2.47162908e-01 -6.65632263e-02 1.33331800e+00 -1.43348336e+00 -4.58359808e-01 -2.65286505e-01 -4.11638260e-01 2.64091671e-01 -4.78313938e-02 7.78504163e-02 -2.91584909e-01 -8.05541813e-01 1.73540831e-01 -4.15824890e-01 1.07784249e-01 3.56410407e-02 -6.84481412e-02 3.66082191e-01 1.07914209e+00 -1.11528754e+00 1.60227704e+00 -2.29399061e+00 -8.54911804e-02 5.71489148e-02 -8.68507624e-02 4.04740810e-01 1.72220081e-01 4.55396026e-01 -3.65280025e-02 -2.51918323e-02 -4.29729074e-01 2.53324240e-01 -3.00505102e-01 7.15073124e-02 9.55422744e-02 7.36500740e-01 2.69739516e-02 1.96414024e-01 -7.66029000e-01 -2.70888239e-01 6.29932582e-01 6.55669689e-01 9.71855503e-03 3.55151474e-01 3.68088871e-01 2.40241766e-01 -8.25348683e-03 5.05913377e-01 1.20528865e+00 2.14461893e-01 -2.16467172e-01 -6.72540545e-01 -2.18109325e-01 -1.26097903e-01 -1.50857890e+00 9.40491676e-01 -4.31261450e-01 7.19419599e-01 2.01667503e-01 -8.80942166e-01 9.08262730e-01 5.77412248e-01 2.64658391e-01 -5.53490877e-01 1.46741122e-01 4.28860903e-01 2.53215730e-02 -9.80342746e-01 2.96750158e-01 -9.90735441e-02 6.23946607e-01 -4.16672975e-03 -3.55672598e-01 -3.48108828e-01 1.35777667e-01 -2.05293372e-01 1.06296325e+00 -1.78727716e-01 5.93407333e-01 -1.64165452e-01 9.02452290e-01 -6.04819417e-01 3.48268420e-01 3.61216694e-01 -4.37434733e-01 7.03826666e-01 1.07129350e-01 -1.34631963e-02 -1.24334967e+00 -1.02014518e+00 -4.14625466e-01 1.38197154e-01 4.88546073e-01 -4.30197939e-02 -7.41215706e-01 4.73137088e-02 -1.63862128e-02 6.63767934e-01 -1.06120445e-01 -1.87680855e-01 -3.41624916e-01 -8.44053745e-01 3.34194228e-02 2.34862487e-03 8.37165236e-01 -1.01682067e+00 -7.13721275e-01 1.98954165e-01 -2.01545864e-01 -8.82198513e-01 -4.44145143e-01 -2.15176389e-01 -9.84203994e-01 -1.18726385e+00 -9.61729228e-01 -6.07847333e-01 5.08144498e-01 5.82621276e-01 7.43534088e-01 1.04179680e-01 -4.48972791e-01 1.97728604e-01 -3.69054735e-01 -6.19292744e-02 -6.95464015e-01 -1.07828999e+00 -9.37369540e-02 3.50916445e-01 2.61425495e-01 -4.14155573e-01 -9.75637317e-01 2.81456858e-01 -1.05550575e+00 -2.27193683e-01 8.13448310e-01 7.37646461e-01 3.98550570e-01 8.58129144e-01 2.56438583e-01 -1.89709559e-01 9.34499621e-01 -3.72263312e-01 -5.89069366e-01 1.82800703e-02 -7.41771340e-01 -2.86849529e-01 6.65638983e-01 -2.92433828e-01 -1.15978301e+00 -5.64881682e-01 3.86804998e-01 -5.15049636e-01 -3.99186790e-01 3.51606816e-01 -2.08924532e-01 -5.94946668e-02 5.79733431e-01 4.69283015e-01 1.03353575e-01 -5.82761467e-01 -4.81731296e-02 1.14012444e+00 5.94952583e-01 4.87062484e-02 7.62871087e-01 2.39382908e-01 8.81398320e-02 -1.11065507e+00 2.71417089e-02 -5.74177444e-01 -1.16529033e-01 -5.02863526e-01 7.75874972e-01 -8.13063920e-01 -4.17831481e-01 1.02513099e+00 -1.04913974e+00 2.39162371e-01 1.96082160e-01 7.41686285e-01 -4.60174382e-01 9.18422103e-01 -7.45946288e-01 -1.17450547e+00 -2.91046560e-01 -1.39120412e+00 3.81451726e-01 4.74660963e-01 -6.31346181e-02 -7.82736182e-01 -3.39984417e-01 3.49136889e-01 6.50725901e-01 1.71897441e-01 9.28421795e-01 1.93070203e-01 -5.90385318e-01 -1.94218785e-01 -2.67435968e-01 1.01475239e+00 6.65530920e-01 4.78254253e-04 -7.11072564e-01 -4.08942223e-01 6.17435277e-01 2.82637328e-01 5.56280971e-01 7.11342633e-01 1.10416567e+00 -6.21992588e-01 6.15151040e-02 5.04309952e-01 1.93597436e+00 5.43319345e-01 1.06757700e+00 4.42873001e-01 3.00806195e-01 3.34143072e-01 7.18868256e-01 5.19305110e-01 -3.33870888e-01 6.50276542e-01 5.62524796e-01 -3.30868550e-02 -3.25478226e-01 2.28395790e-01 3.28612179e-01 8.39206040e-01 -2.46334121e-01 -1.73126847e-01 -6.19381070e-01 6.27674282e-01 -1.33686161e+00 -1.22580123e+00 -5.20471931e-01 2.57603836e+00 8.97687078e-01 -5.86385839e-02 -4.03837770e-01 8.14738929e-01 1.02244437e+00 2.41471648e-01 -1.51173204e-01 -5.10326028e-01 -9.01508927e-02 1.81895196e-01 7.53175259e-01 4.51792538e-01 -1.04351997e+00 5.13099506e-02 5.71992636e+00 1.07024455e+00 -9.43120956e-01 -1.77841976e-01 6.68934464e-01 1.45305350e-01 2.49338523e-03 -1.08010247e-01 -1.14513859e-01 1.03380609e+00 8.33110631e-01 -1.06392860e-01 8.30769420e-01 4.64074314e-01 8.11603725e-01 -7.19112754e-01 -6.48141742e-01 1.35718191e+00 6.62991852e-02 -6.59099638e-01 -6.34849519e-02 9.57849026e-02 8.05508435e-01 -5.52209020e-01 2.02241689e-01 -4.31002706e-01 -3.61248195e-01 -1.11548686e+00 6.39698386e-01 7.73868382e-01 7.60883808e-01 -7.67073810e-01 1.05945587e+00 2.83861727e-01 -9.06988442e-01 -2.08820611e-01 -4.92632210e-01 -5.63748106e-02 2.70409405e-01 1.05312479e+00 -4.56983089e-01 4.69706714e-01 7.01122582e-01 7.01604068e-01 -3.81139904e-01 1.67375302e+00 -1.30062506e-01 6.65345311e-01 9.61678177e-02 2.59880960e-01 -1.62154153e-01 -4.41049755e-01 7.76556790e-01 1.21173728e+00 8.61829460e-01 8.74720588e-02 -3.61637890e-01 7.25612044e-01 1.95894212e-01 2.65543461e-02 -2.17141062e-01 2.06730694e-01 5.51702023e-01 1.04264474e+00 -4.13804710e-01 -3.91827255e-01 -4.51423168e-01 1.03555417e+00 -7.11223125e-01 4.96151596e-01 -7.27287650e-01 -4.90837991e-01 6.64715648e-01 1.42896026e-01 3.04128468e-01 2.85697039e-02 -4.16060925e-01 -8.64366233e-01 1.18694171e-01 -1.11657870e+00 -1.39932305e-01 -1.01758349e+00 -1.25455356e+00 2.25934178e-01 -7.94749036e-02 -1.68860948e+00 1.19954027e-01 -3.26271862e-01 -4.79375035e-01 1.25017202e+00 -1.64300823e+00 -3.06504875e-01 -5.07383466e-01 4.22618479e-01 8.46373737e-01 -9.07870978e-02 5.53633392e-01 4.99480814e-01 -2.90928692e-01 9.63254422e-02 4.63975728e-01 -2.31672004e-01 7.61518598e-01 -9.99073029e-01 -1.36936754e-01 1.09022272e+00 -2.51335204e-01 4.03498083e-01 1.26856780e+00 -6.00041330e-01 -1.26371884e+00 -9.26258087e-01 7.25537002e-01 1.61507562e-01 3.32259417e-01 4.22132105e-01 -1.06272948e+00 -1.79879218e-02 3.61022770e-01 1.51751039e-03 2.85137266e-01 -7.79722035e-01 1.96723975e-02 -3.79201859e-01 -1.43053854e+00 2.29440019e-01 3.37355673e-01 -3.94246429e-01 -6.92875385e-01 -1.09528683e-01 7.57833570e-02 -1.32309631e-01 -8.63217711e-01 4.99921232e-01 3.80427361e-01 -1.43873656e+00 1.04940772e+00 4.21237856e-01 7.50917375e-01 -7.36713052e-01 -1.81555271e-01 -1.44416845e+00 -4.75360870e-01 -4.42229271e-01 -3.98013741e-01 1.29458904e+00 -6.14103749e-02 -4.20699745e-01 8.19695666e-02 8.13274607e-02 1.83864623e-01 -4.24641520e-01 -8.52607727e-01 -7.32134402e-01 -5.72372079e-01 -1.00458980e-01 4.08197790e-01 6.99665904e-01 -2.79142316e-02 -2.04772860e-01 -5.07436872e-01 3.29918504e-01 1.01817703e+00 -3.34543049e-01 4.53649610e-01 -8.18725407e-01 -3.36116999e-01 -4.36002910e-01 -6.87890708e-01 -8.50697160e-01 -8.90957654e-01 -2.27894932e-01 5.74589968e-02 -1.65168643e+00 1.69981495e-01 -1.62070796e-01 -2.72743076e-01 -4.26958859e-01 -2.50219822e-01 2.91405767e-01 1.45636976e-01 5.24567485e-01 1.54394656e-01 3.02048028e-01 1.28281045e+00 -8.75743851e-02 -4.22300473e-02 1.19715787e-01 -1.24584295e-01 7.24905789e-01 6.71909034e-01 -2.02209115e-01 -3.02044332e-01 -3.12103301e-01 -1.78598478e-01 3.19170803e-01 5.11972904e-01 -1.41971374e+00 2.94694696e-02 -1.71172425e-01 6.83775067e-01 -6.89529300e-01 2.34812587e-01 -1.09920180e+00 5.18114805e-01 6.25986397e-01 4.70815301e-02 -2.51066387e-01 -3.64206135e-02 7.59700954e-01 -4.98420060e-01 -4.77387607e-01 1.21410918e+00 -1.47433475e-01 -6.10694170e-01 -2.74193913e-01 -7.26740658e-01 -5.73970199e-01 1.05245030e+00 -6.69816971e-01 -2.68497288e-01 -5.85417509e-01 -4.61913198e-01 -4.46273565e-01 5.59073150e-01 2.22678632e-01 8.10710073e-01 -1.25628603e+00 -7.19770074e-01 2.44881436e-01 -1.99716330e-01 -4.68282551e-01 5.96652806e-01 1.01417089e+00 -9.34226811e-01 1.34945035e-01 -2.54313767e-01 -5.21111131e-01 -1.46300197e+00 6.51796162e-01 2.73829848e-01 -1.12607582e-02 -2.21859738e-01 5.54308295e-01 -1.43631130e-01 6.00297451e-01 1.61630809e-01 -3.63735020e-01 -4.83940691e-01 -1.64169624e-01 7.13100731e-01 9.72019792e-01 5.32731302e-02 -8.63496602e-01 -1.35988086e-01 8.72500598e-01 3.86675209e-01 -4.78559509e-02 9.30702567e-01 -5.21141171e-01 -3.81032825e-01 3.46123517e-01 1.28929460e+00 2.17604592e-01 -1.18162560e+00 3.73982862e-02 -1.53443143e-01 -9.87611949e-01 2.28681445e-01 -8.88966739e-01 -9.28361297e-01 7.90061772e-01 1.07444620e+00 4.59271997e-01 1.64570785e+00 -3.78414929e-01 6.06695175e-01 -4.48609531e-01 2.25841850e-01 -1.11651492e+00 2.68788282e-02 -1.27848193e-01 1.00250423e+00 -9.08778965e-01 2.99276948e-01 -3.91723335e-01 -3.11800182e-01 1.20350170e+00 1.26719818e-01 -1.92942396e-01 5.41434348e-01 1.42923862e-01 5.65769486e-02 2.32853889e-01 -4.09620583e-01 9.32857543e-02 2.66799361e-01 6.85653031e-01 3.79369646e-01 -4.93084341e-02 -7.96904743e-01 1.17375135e-01 -5.53332344e-02 6.84457645e-02 8.27780724e-01 7.82250881e-01 -8.14940095e-01 -7.67180800e-01 -1.22494781e+00 4.87771332e-01 -7.23724186e-01 -5.23555502e-02 3.53029579e-01 2.16512293e-01 5.53658366e-01 1.58673871e+00 8.23407806e-03 -2.21811682e-01 2.83963919e-01 -2.40862340e-01 4.54113066e-01 -3.11173555e-02 -1.54727861e-01 3.06729138e-01 -1.75964355e-01 -4.05258805e-01 -4.47481930e-01 -6.38387799e-01 -9.24101353e-01 -4.26124871e-01 -3.72734010e-01 -1.16352558e-01 8.25267315e-01 7.37069309e-01 -1.25229806e-01 5.57927132e-01 7.55728424e-01 -7.11406708e-01 -5.84077179e-01 -1.23502505e+00 -8.07015240e-01 8.10932994e-01 7.00617909e-01 -5.35623014e-01 -9.12857175e-01 4.89826769e-01]	[11.69080924987793, -2.1015031337738037]
c37e9622-fce8-4b01-9343-02ba0e24b14f	query-based-video-summarization-with-pseudo	2307.01945	null	https://arxiv.org/abs/2307.01945v1	https://arxiv.org/pdf/2307.01945v1.pdf	Query-based Video Summarization with Pseudo Label Supervision	Existing datasets for manually labelled query-based video summarization are costly and thus small, limiting the performance of supervised deep video summarization models. Self-supervision can address the data sparsity challenge by using a pretext task and defining a method to acquire extra data with pseudo labels to pre-train a supervised deep model. In this work, we introduce segment-level pseudo labels from input videos to properly model both the relationship between a pretext task and a target task, and the implicit relationship between the pseudo label and the human-defined label. The pseudo labels are generated based on existing human-defined frame-level labels. To create more accurate query-dependent video summaries, a semantics booster is proposed to generate context-aware query representations. Furthermore, we propose mutual attention to help capture the interactive information between visual and textual modalities. Three commonly-used video summarization benchmarks are used to thoroughly validate the proposed approach. Experimental results show that the proposed video summarization algorithm achieves state-of-the-art performance.	['Marcel Worring', 'Marta Mrak', 'Luka Murn', 'Jia-Hong Huang']	2023-07-04	null	null	null	null	['video-summarization', 'pseudo-label']	['computer-vision', 'miscellaneous']	[ 7.41711438e-01 7.90139362e-02 -6.33784473e-01 -6.43327534e-01 -1.01742494e+00 -2.61966974e-01 6.19371951e-01 2.07501039e-01 -3.88722777e-01 5.48669755e-01 8.74630213e-01 2.48743474e-01 4.52649534e-01 -3.61850858e-01 -9.51625228e-01 -4.63814557e-01 1.79104358e-01 2.51729608e-01 9.35874283e-02 -9.57039930e-03 2.49912620e-01 -3.11102092e-01 -1.66445756e+00 8.71506989e-01 1.10212338e+00 8.44487131e-01 4.12552088e-01 7.19026148e-01 8.77134129e-03 1.28702927e+00 -7.54890382e-01 -1.11330621e-01 7.28472620e-02 -1.02068865e+00 -9.86108482e-01 5.80846131e-01 9.39723611e-01 -6.39169991e-01 -6.43782735e-01 8.85927320e-01 3.69761616e-01 5.30446351e-01 7.74047256e-01 -1.02780032e+00 -6.00406468e-01 7.42735624e-01 -6.03119612e-01 2.06636786e-01 6.75559223e-01 2.45457202e-01 1.19640636e+00 -7.53225863e-01 9.26992774e-01 1.13423908e+00 2.97853768e-01 5.64032853e-01 -1.05738342e+00 -5.05293071e-01 3.15830588e-01 5.23676038e-01 -1.13645267e+00 -7.36861765e-01 1.02409768e+00 -3.13212633e-01 8.73431861e-01 4.54338878e-01 7.60807097e-01 1.27572167e+00 -2.74136007e-01 1.21729326e+00 4.43673253e-01 -2.45490134e-01 1.34940282e-01 -4.65990901e-02 8.20339397e-02 7.62087047e-01 -5.59459031e-02 -4.92824823e-01 -7.84340322e-01 3.99968810e-02 5.81296921e-01 2.55418330e-01 -4.24086452e-01 -4.24577802e-01 -1.27859271e+00 7.97848463e-01 5.08744240e-01 2.56101459e-01 -4.21084225e-01 2.13743612e-01 8.25848401e-01 3.57115306e-02 5.84057450e-01 4.05141741e-01 -1.55120224e-01 2.27512121e-02 -1.53864861e+00 8.29172656e-02 6.45100355e-01 1.29728544e+00 7.97798038e-01 1.66326538e-01 -9.55095708e-01 8.22282612e-01 1.37287453e-01 3.27671319e-01 5.32751322e-01 -1.24349332e+00 6.97746038e-01 6.62166595e-01 1.55603644e-02 -1.08487356e+00 -5.50674051e-02 -2.87935227e-01 -8.21434677e-01 -6.66290045e-01 -1.86678559e-01 2.01364696e-01 -1.09127021e+00 1.59741354e+00 7.66151696e-02 5.13838768e-01 1.37199029e-01 1.03784227e+00 1.37985897e+00 1.03645277e+00 3.51921409e-01 -5.75853348e-01 1.12281859e+00 -1.72262883e+00 -8.55982840e-01 -2.27493539e-01 7.20421374e-01 -4.50091332e-01 1.11568332e+00 -1.98973179e-01 -1.16767335e+00 -7.83459485e-01 -9.31611001e-01 -5.04182041e-01 3.15446645e-01 3.05107087e-01 1.12741418e-01 -1.65730208e-01 -1.15915501e+00 3.18154782e-01 -8.01168442e-01 -4.72370088e-01 6.57553017e-01 -3.35232951e-02 -2.27814943e-01 -3.46686721e-01 -9.60790992e-01 3.69701028e-01 6.53667867e-01 -1.73987150e-01 -1.27484870e+00 -4.17884022e-01 -1.22411430e+00 2.76392490e-01 5.97006142e-01 -9.60456967e-01 1.30604768e+00 -1.58818483e+00 -1.31684124e+00 9.92037714e-01 -4.94460285e-01 -6.20948911e-01 4.13404971e-01 -4.06933337e-01 -4.14027460e-02 7.33603597e-01 5.20762920e-01 1.09371841e+00 1.11693251e+00 -1.34462821e+00 -6.86631978e-01 -8.42661783e-02 1.30737439e-01 6.92682087e-01 -4.44750607e-01 -2.55616456e-01 -9.16057289e-01 -9.67849493e-01 -2.70029724e-01 -7.34808505e-01 -1.59912333e-01 -5.57808399e-01 -6.71832144e-01 -3.74901831e-01 9.38578844e-01 -8.87344122e-01 1.42007875e+00 -2.22210717e+00 4.33263034e-01 -3.98504198e-01 3.35211784e-01 2.59911448e-01 -5.24397552e-01 3.40831488e-01 4.38532904e-02 -8.94741863e-02 -3.79143417e-01 -8.18574905e-01 -2.20289648e-01 1.24382168e-01 -2.49122083e-01 2.70802796e-01 1.28730118e-01 1.00281584e+00 -1.19038010e+00 -8.11473966e-01 1.60353497e-01 3.21171761e-01 -5.81177652e-01 6.72164261e-01 -6.71888113e-01 5.54855704e-01 -4.54959154e-01 3.72836351e-01 2.12199852e-01 -3.05351198e-01 8.08804557e-02 -6.98341310e-01 1.47146136e-01 2.45205134e-01 -5.88572741e-01 2.19679976e+00 -2.14039460e-01 8.08701158e-01 -2.04251885e-01 -1.33529615e+00 6.12567425e-01 2.83652991e-01 6.76874697e-01 -7.21709788e-01 2.53326625e-01 -2.69099832e-01 -6.40496373e-01 -7.86574960e-01 9.19640362e-01 1.91792428e-01 -1.42913759e-01 4.71134007e-01 4.26824957e-01 -2.17124429e-02 5.14147580e-01 6.49937391e-01 9.33188677e-01 3.36255550e-01 2.56610185e-01 9.23044905e-02 5.90320766e-01 1.83312356e-01 6.90240204e-01 6.83276236e-01 -1.63646489e-01 8.03253889e-01 4.28198010e-01 -3.59802246e-01 -9.10907149e-01 -4.94764745e-01 4.18704301e-01 1.58567262e+00 4.10026371e-01 -7.20150471e-01 -8.92971218e-01 -9.37221944e-01 -3.15376997e-01 8.39107454e-01 -5.87696552e-01 -3.55204135e-01 -5.58875918e-01 -1.13484882e-01 2.67508686e-01 4.53557163e-01 6.38969004e-01 -1.20270383e+00 -7.09795535e-01 9.63826776e-02 -7.57086754e-01 -1.32821131e+00 -9.81355608e-01 -3.01123053e-01 -8.53968918e-01 -1.04631686e+00 -8.75828385e-01 -1.11651754e+00 7.12333620e-01 6.57530725e-01 1.28878176e+00 1.20679453e-01 1.00688040e-01 6.00715220e-01 -5.94054639e-01 -2.32913787e-03 -4.33185309e-01 2.16791183e-01 -2.96792358e-01 2.36997068e-01 1.16211027e-01 -1.55558899e-01 -7.49448597e-01 -1.24530829e-02 -1.13422692e+00 7.06629634e-01 5.62297523e-01 6.93028450e-01 7.70758212e-01 -3.62644702e-01 7.13024139e-01 -1.00800824e+00 3.94346058e-01 -3.85143816e-01 -9.32419032e-04 3.77665401e-01 -1.29486471e-01 6.48031235e-02 5.08534193e-01 -3.73478889e-01 -1.17776477e+00 2.41364285e-01 3.28718692e-01 -7.12091684e-01 -1.00807950e-01 6.46942794e-01 -1.06107518e-01 5.10879636e-01 3.80979806e-01 3.92342091e-01 -2.97301978e-01 -1.89544171e-01 6.69046819e-01 5.73122799e-01 7.65391409e-01 -2.47378424e-01 3.85632396e-01 3.97623837e-01 -3.57173979e-01 -9.27821457e-01 -1.41868746e+00 -7.36303389e-01 -4.48282391e-01 -3.45340371e-01 1.17027366e+00 -1.25512636e+00 -3.67416232e-03 2.77434796e-01 -1.18808401e+00 -4.30099130e-01 -4.95606393e-01 1.55867159e-01 -8.66163313e-01 6.29605949e-01 -5.19773245e-01 -3.61950010e-01 -6.50914431e-01 -1.12920499e+00 1.34845352e+00 3.80108118e-01 -2.13951290e-01 -8.30151141e-01 6.47309190e-03 8.82303536e-01 1.82196081e-01 2.67116964e-01 5.97642899e-01 -8.31656039e-01 -6.50633574e-01 -7.26843486e-04 -4.64226395e-01 3.67459625e-01 1.50482580e-01 -1.00152865e-01 -6.49634898e-01 -3.90345663e-01 3.21285650e-02 -6.88994765e-01 1.30529010e+00 5.62257290e-01 1.12754977e+00 -7.18466759e-01 -2.86359727e-01 5.45005441e-01 1.03857923e+00 -1.98933020e-01 4.35706466e-01 8.95818397e-02 1.25988710e+00 5.60472727e-01 8.68617952e-01 5.33615947e-01 8.34213257e-01 5.62409699e-01 2.96654642e-01 -1.33498371e-01 -3.51675689e-01 -5.79825580e-01 4.97182339e-01 9.73295927e-01 1.52292438e-02 -4.54439700e-01 -4.91201192e-01 5.64324558e-01 -2.24489355e+00 -1.15732932e+00 -5.33115584e-04 1.91824269e+00 1.12205184e+00 -1.34415373e-01 1.63384244e-01 -3.95598322e-01 8.54701221e-01 6.23090327e-01 -6.26295686e-01 4.16654833e-02 -7.31577352e-02 -3.26888740e-01 1.77311376e-01 3.78149569e-01 -1.36677122e+00 1.21320963e+00 5.33629179e+00 7.38928020e-01 -1.11802781e+00 1.81349203e-01 7.11113930e-01 -3.57996017e-01 -4.36892450e-01 7.97101937e-04 -3.56642038e-01 4.40460265e-01 7.77601123e-01 -3.06816995e-01 1.34561986e-01 6.62938118e-01 5.16003907e-01 -1.17049411e-01 -1.50290537e+00 1.14720714e+00 8.69796634e-01 -1.51063383e+00 6.56019866e-01 -3.66670042e-01 1.28021979e+00 -7.19122291e-02 -1.51465893e-01 5.88891983e-01 -1.53258204e-01 -6.92340374e-01 7.65297115e-01 6.20428085e-01 9.89632785e-01 -5.75191796e-01 6.50061488e-01 1.93416461e-01 -1.17826879e+00 9.32228491e-02 -1.01175651e-01 1.03399754e-01 2.93881714e-01 1.77038088e-01 -7.75658607e-01 6.25745535e-01 4.08552259e-01 1.44853961e+00 -7.18706846e-01 8.52657497e-01 -3.37127030e-01 6.71999693e-01 7.15599731e-02 2.89035290e-01 3.79264116e-01 -1.37134701e-01 6.35493875e-01 1.47006929e+00 -7.53030134e-03 4.41320315e-02 5.98526001e-01 5.37527680e-01 -4.54922497e-01 2.05949888e-01 -4.70553577e-01 -1.81702480e-01 2.61093825e-01 9.86467004e-01 -5.67878246e-01 -8.34796607e-01 -3.94356787e-01 1.24594617e+00 2.01228112e-01 7.35048234e-01 -9.39891219e-01 -1.42689228e-01 7.72315115e-02 8.68603215e-02 2.71569639e-01 -8.26300308e-02 4.90001477e-02 -1.50318241e+00 -7.36171156e-02 -9.93429422e-01 5.92732728e-01 -8.47091019e-01 -8.08602095e-01 3.90486807e-01 1.42562315e-01 -1.25462413e+00 -3.63036007e-01 3.39554340e-01 -6.70874178e-01 2.65081465e-01 -1.38253319e+00 -1.35679781e+00 -6.65429235e-01 5.35579324e-01 1.35380030e+00 -9.05083641e-02 5.23268819e-01 2.02529997e-01 -7.10717499e-01 3.69272977e-01 -1.93125308e-01 1.06994562e-01 9.65191066e-01 -9.55672681e-01 2.17661988e-02 9.25435007e-01 2.91059941e-01 1.33276045e-01 8.72222900e-01 -8.13583136e-01 -1.27286792e+00 -1.46379578e+00 5.94010830e-01 -2.03877792e-01 2.21439227e-01 1.11250877e-02 -8.83807361e-01 8.34438562e-01 5.77400148e-01 -1.77723303e-01 7.88926482e-01 -3.91488582e-01 -1.47915363e-01 7.79228285e-03 -7.14276433e-01 4.46201861e-01 1.17686164e+00 -5.18272161e-01 -7.82905161e-01 5.73530853e-01 1.13473940e+00 -3.54870111e-01 -3.81783098e-01 3.81565362e-01 2.58806646e-01 -7.85775781e-01 7.12914288e-01 -6.21573031e-01 9.00978804e-01 -2.01312736e-01 -3.99703532e-02 -1.15638816e+00 -1.84664205e-01 -4.84388709e-01 -4.61653799e-01 1.38591135e+00 5.68822734e-02 3.01148653e-01 8.43870819e-01 4.28662479e-01 -3.51515770e-01 -5.71296930e-01 -4.95898962e-01 -3.21947753e-01 -6.87498689e-01 4.41150852e-02 1.14695087e-01 1.04045355e+00 5.46298361e-05 9.15789425e-01 -5.57790577e-01 -1.67044759e-01 6.91154063e-01 2.76604027e-01 9.60578084e-01 -7.39761412e-01 -5.33602647e-02 -3.48472416e-01 -3.26246113e-01 -1.35657239e+00 5.97217739e-01 -8.99155796e-01 1.28887370e-01 -1.96501613e+00 7.12232947e-01 9.00224671e-02 -2.39420071e-01 3.30693007e-01 -4.66578126e-01 1.90807819e-01 2.37526849e-01 3.96210074e-01 -1.61505747e+00 1.02156019e+00 1.30122221e+00 -5.13513386e-01 -4.69804645e-01 -2.24878773e-01 -6.53181732e-01 6.26300216e-01 5.22019327e-01 -3.80245686e-01 -7.61957109e-01 -7.75433064e-01 -9.89259705e-02 3.34523559e-01 1.83142722e-01 -8.70493352e-01 2.72044688e-01 -3.26611102e-01 5.68735152e-02 -7.36509323e-01 2.57323116e-01 -4.88246202e-01 -2.54736245e-01 1.65198013e-01 -9.03698087e-01 -2.38491714e-01 -1.85232133e-01 9.01802301e-01 -5.79525530e-01 -1.84320629e-01 5.61144292e-01 -5.28166555e-02 -9.06455874e-01 4.90038007e-01 -1.18228413e-01 3.30990642e-01 1.08205211e+00 -1.35768563e-01 -1.52835295e-01 -8.86135936e-01 -6.02003813e-01 6.25639260e-01 6.56638265e-01 4.50159967e-01 8.25435817e-01 -1.17907226e+00 -9.43376660e-01 -8.13967362e-02 3.64377588e-01 2.75497645e-01 5.49450219e-01 5.93329847e-01 -5.08264482e-01 3.60669017e-01 -1.57492518e-01 -7.05356658e-01 -1.37154961e+00 6.04193985e-01 -8.18306655e-02 -1.32889703e-01 -6.72873020e-01 8.02639425e-01 5.19150496e-01 1.66873276e-01 6.05222881e-01 -3.79822254e-01 -5.78375578e-01 1.53069571e-01 5.81945717e-01 3.12392086e-01 -4.07888263e-01 -9.34742570e-01 -9.48877633e-02 1.69212371e-01 -1.60850227e-01 9.86882821e-02 1.38237214e+00 -4.17048901e-01 -7.30261952e-02 4.15982813e-01 1.36915135e+00 -3.52010667e-01 -1.44610608e+00 -4.65347648e-01 -1.44673318e-01 -3.26639622e-01 7.99216609e-03 -3.89492065e-01 -1.09211838e+00 5.61845601e-01 1.43211514e-01 3.25659402e-02 1.26028156e+00 1.95287600e-01 1.09832406e+00 4.39149499e-01 -1.66765839e-01 -1.19300532e+00 6.33570552e-01 3.73191208e-01 1.10044074e+00 -1.33366168e+00 1.45160273e-01 -2.86354840e-01 -1.05428243e+00 7.94466197e-01 8.44561398e-01 -1.67962268e-01 -7.06189945e-02 -4.58272636e-01 -1.85095981e-01 -9.10661966e-02 -8.03565621e-01 -1.19866058e-01 5.27513444e-01 3.86898369e-01 4.21758294e-01 -2.21242905e-02 -2.74862111e-01 6.93264544e-01 1.10507399e-01 1.56686217e-01 5.65893769e-01 8.97283196e-01 -5.87079167e-01 -6.34876668e-01 8.70418921e-03 6.79176509e-01 -2.41627187e-01 3.42713036e-02 -3.98614496e-01 1.40923873e-01 -1.91083968e-01 8.60089064e-01 1.73819274e-01 -2.58636892e-01 2.74910897e-01 -7.94201568e-02 3.99371862e-01 -9.84077394e-01 -3.00945312e-01 1.30105764e-01 1.98409051e-01 -6.33002758e-01 -1.03715551e+00 -4.99482661e-01 -1.18928301e+00 1.34546012e-01 -8.72143507e-02 2.18505725e-01 2.50220537e-01 1.01946771e+00 5.96230805e-01 6.84190452e-01 6.41747355e-01 -1.24480462e+00 -3.42911869e-01 -1.09973502e+00 -1.09917730e-01 9.39874589e-01 4.25026357e-01 -3.61621290e-01 -1.50562495e-01 7.49256730e-01]	[10.441788673400879, 0.5152862668037415]
ace358a9-9966-4658-945d-33ac46e87ba2	eider-evidence-enhanced-document-level	2106.08657	null	https://arxiv.org/abs/2106.08657v2	https://arxiv.org/pdf/2106.08657v2.pdf	Eider: Empowering Document-level Relation Extraction with Efficient Evidence Extraction and Inference-stage Fusion	Document-level relation extraction (DocRE) aims to extract semantic relations among entity pairs in a document. Typical DocRE methods blindly take the full document as input, while a subset of the sentences in the document, noted as the evidence, are often sufficient for humans to predict the relation of an entity pair. In this paper, we propose an evidence-enhanced framework, Eider, that empowers DocRE by efficiently extracting evidence and effectively fusing the extracted evidence in inference. We first jointly train an RE model with a lightweight evidence extraction model, which is efficient in both memory and runtime. Empirically, even training the evidence model on silver labels constructed by our heuristic rules can lead to better RE performance. We further design a simple yet effective inference process that makes RE predictions on both extracted evidence and the full document, then fuses the predictions through a blending layer. This allows Eider to focus on important sentences while still having access to the complete information in the document. Extensive experiments show that Eider outperforms state-of-the-art methods on three benchmark datasets (e.g., by 1.37/1.26 Ign F1/F1 on DocRED).	['Jiawei Han', 'Yuning Mao', 'Sha Li', 'Jiaming Shen', 'Yiqing Xie']	2021-06-16	null	https://aclanthology.org/2022.findings-acl.23	https://aclanthology.org/2022.findings-acl.23.pdf	findings-acl-2022-5	['document-level-relation-extraction']	['natural-language-processing']	[ 4.57064770e-02 5.66494524e-01 -4.83597875e-01 -2.09580734e-01 -7.94666767e-01 -5.58334351e-01 6.49959207e-01 4.72026527e-01 -5.65589845e-01 8.19325268e-01 2.50919998e-01 -3.28164279e-01 -1.98756605e-01 -9.49355483e-01 -6.72339141e-01 -2.39740640e-01 1.30751371e-01 6.63114548e-01 5.13107777e-01 -2.24267002e-02 2.41631836e-01 1.48872048e-01 -1.52267838e+00 4.97173905e-01 1.02973950e+00 1.10606754e+00 2.40782589e-01 6.74757183e-01 -3.39328140e-01 1.11281812e+00 -6.08155847e-01 -9.20776606e-01 -8.38196650e-02 -6.09826148e-02 -1.10903692e+00 4.04885076e-02 2.74606973e-01 -4.17404503e-01 -2.67338336e-01 1.07671618e+00 1.24115229e-01 -1.11616917e-01 6.67751133e-01 -1.04622984e+00 -5.24106324e-01 1.06506145e+00 -5.03255844e-01 2.17917129e-01 3.14719915e-01 -4.47105914e-01 1.47782445e+00 -1.29805052e+00 7.98075795e-01 1.06044590e+00 3.36918861e-01 2.69099325e-01 -8.06794703e-01 -6.02009177e-01 3.61026615e-01 3.58391315e-01 -1.20978749e+00 -4.30797607e-01 6.44711852e-01 -1.01754598e-01 1.25603831e+00 3.83689851e-01 1.52788967e-01 8.25194359e-01 -7.42673352e-02 1.00573063e+00 8.85083973e-01 -5.97109973e-01 9.78526622e-02 1.80663496e-01 6.07012689e-01 7.39610791e-01 5.56498349e-01 -2.57101893e-01 -8.19723785e-01 -1.65749565e-01 2.30923951e-01 -6.15549348e-02 -3.09960991e-01 2.66668588e-01 -9.15339231e-01 4.73984063e-01 3.48914087e-01 1.36750534e-01 -5.31074226e-01 -3.23817492e-01 3.12412232e-01 3.72588113e-02 4.60333943e-01 4.11091089e-01 -6.45441711e-01 1.37535289e-01 -8.89256895e-01 3.00940603e-01 9.78059292e-01 1.08657837e+00 6.08289242e-01 -9.33729172e-01 -5.27824163e-01 9.92544293e-01 4.12364185e-01 3.46612841e-01 2.01152429e-01 -7.33850777e-01 9.58823562e-01 9.23453391e-01 2.58789033e-01 -9.48867381e-01 -1.52774096e-01 -5.85789323e-01 -6.44157708e-01 -3.29665124e-01 1.13843113e-01 -2.37852171e-01 -7.18013883e-01 1.36822104e+00 4.72748131e-01 1.98928550e-01 3.34653944e-01 7.43369758e-01 1.09805334e+00 5.47039926e-01 5.59974089e-02 -1.38208851e-01 1.79502606e+00 -1.28231061e+00 -9.26118135e-01 -6.23965442e-01 6.71466351e-01 -6.52470887e-01 6.21315181e-01 4.39521581e-01 -9.74877834e-01 -1.79621071e-01 -1.31757367e+00 -3.47209156e-01 -3.74714941e-01 5.21820843e-01 4.57888335e-01 4.23901454e-02 -4.39204812e-01 5.68226278e-01 -6.36900365e-01 9.94802266e-02 4.50693816e-01 2.09253326e-01 -4.38123912e-01 -3.18125635e-01 -1.34240878e+00 9.89239395e-01 7.69566119e-01 1.04435936e-01 -4.27006692e-01 -2.72175759e-01 -9.06617284e-01 4.20034438e-01 1.11964941e+00 -6.40095115e-01 1.31506526e+00 -7.47455331e-03 -1.02127326e+00 6.67589962e-01 -5.28396547e-01 -4.68119413e-01 1.96588457e-01 -7.23685563e-01 -5.67147791e-01 2.90559977e-01 1.93374321e-01 4.11358237e-01 4.48207527e-01 -1.35035622e+00 -9.22389567e-01 -3.34028006e-01 2.52870440e-01 6.00767545e-02 -4.64710325e-01 2.45700523e-01 -8.70953083e-01 -5.80844641e-01 1.76609784e-01 -7.32913852e-01 9.55184251e-02 -4.05383587e-01 -8.52304637e-01 -6.25664353e-01 5.85396767e-01 -8.23677778e-01 1.62008917e+00 -1.81984031e+00 -5.72057813e-02 2.57897764e-01 6.05800092e-01 3.85225862e-01 2.58267652e-02 3.97636473e-01 2.56140620e-01 9.75913033e-02 -1.93897232e-01 -5.17502725e-01 6.58162460e-02 3.43726873e-01 -6.37298107e-01 -1.47842556e-01 3.55071843e-01 9.77690637e-01 -1.05078769e+00 -8.81061196e-01 -3.33102256e-01 1.63168475e-01 -4.48449999e-01 3.57147902e-01 -3.55297983e-01 -1.34854123e-01 -5.09399533e-01 4.97015595e-01 7.11822867e-01 -7.15963900e-01 4.59281474e-01 -1.94233388e-01 2.31847435e-01 9.45352674e-01 -1.22882938e+00 1.14981914e+00 -5.30041575e-01 4.39649552e-01 -2.53520578e-01 -6.15920067e-01 8.98418963e-01 3.69039863e-01 -5.99526502e-02 -4.70766366e-01 -2.92332973e-02 3.19723338e-01 -1.27778217e-01 -5.60874522e-01 6.95551872e-01 2.31075183e-01 8.09833407e-02 4.72360015e-01 2.25265384e-01 2.35050827e-01 5.73084474e-01 5.66338897e-01 1.24386930e+00 -9.58745033e-02 3.80979419e-01 2.61201233e-01 6.37468636e-01 -1.80103049e-01 7.23119795e-01 7.21509039e-01 4.12412405e-01 1.51163831e-01 6.96736097e-01 -8.74855928e-03 -5.64302325e-01 -7.83639550e-01 7.23522604e-02 9.13575888e-01 4.06049043e-01 -1.20092905e+00 -6.40910566e-01 -1.40906668e+00 -7.72535652e-02 1.07870102e+00 -3.72925907e-01 -2.58522443e-02 -5.09284675e-01 -5.95279276e-01 3.91340077e-01 8.36650372e-01 7.18296707e-01 -9.24260378e-01 -3.20275277e-01 2.70139337e-01 -5.61727226e-01 -1.65670705e+00 -1.85258061e-01 2.22871989e-01 -6.32799745e-01 -1.10950220e+00 -4.95803729e-02 -5.19399583e-01 7.12662041e-01 2.02336937e-01 1.23595357e+00 4.11196053e-01 2.14759335e-01 -1.57011896e-01 -5.24813056e-01 -3.66609067e-01 -2.82315493e-01 1.25092164e-01 -2.08442718e-01 -2.78599381e-01 7.64773309e-01 -3.66068125e-01 -4.09226805e-01 4.21529800e-01 -7.06098616e-01 2.01772735e-01 8.63439500e-01 8.81094813e-01 6.87889934e-01 3.84362727e-01 3.83285820e-01 -1.31546950e+00 6.80096865e-01 -6.03688180e-01 -4.06943232e-01 7.56341100e-01 -9.50598836e-01 3.92845094e-01 5.35947084e-01 -1.01178244e-01 -1.25854230e+00 -4.31806296e-01 -1.85826253e-02 -3.44401672e-02 1.30691916e-01 8.72773290e-01 -1.54482082e-01 7.20969379e-01 4.04059172e-01 -1.10448413e-01 -6.58199370e-01 -6.05439484e-01 3.47797602e-01 1.15177476e+00 8.84111464e-01 -8.00284982e-01 6.78514063e-01 1.12681672e-01 -1.38001308e-01 -1.57160327e-01 -1.67243207e+00 -6.15240276e-01 -4.92953539e-01 1.22280970e-01 5.17275810e-01 -8.62852514e-01 -4.68868285e-01 1.04227498e-01 -1.36001790e+00 9.50367451e-02 -3.99018489e-02 4.16704655e-01 8.50034952e-02 1.39375955e-01 -7.16228008e-01 -7.94353604e-01 -5.18515468e-01 -8.43860209e-01 1.18795872e+00 2.91248143e-01 -2.95924395e-01 -6.57750010e-01 -6.74423650e-02 6.29490852e-01 -2.58310288e-01 -4.01244998e-01 8.11533451e-01 -9.80597496e-01 -6.43266439e-01 -3.68214846e-01 -5.86950243e-01 3.02336782e-01 1.20731480e-01 -1.25057157e-03 -1.14763141e+00 2.44717881e-01 -2.09416032e-01 -3.55340213e-01 1.12873471e+00 -3.07532012e-01 1.36259854e+00 -5.79022348e-01 -7.03056037e-01 -4.02502976e-02 1.08264863e+00 -6.18215352e-02 4.98392612e-01 3.15632731e-01 6.16378784e-01 6.55822039e-01 1.09347963e+00 3.62839848e-01 7.22583115e-01 6.59579039e-01 2.86620259e-01 2.52151042e-01 -2.33705416e-01 -6.25036299e-01 1.17386714e-01 9.12571430e-01 -1.43884644e-01 -5.95168233e-01 -7.76159048e-01 5.54393172e-01 -2.09218192e+00 -7.45051563e-01 -1.70945805e-02 1.82793152e+00 1.19177091e+00 6.16834521e-01 -3.62752736e-01 4.16630864e-01 6.96226954e-01 -3.96443717e-02 -2.90189058e-01 -1.46958232e-01 -4.66471314e-02 1.91245601e-01 3.64760280e-01 5.67676127e-01 -1.11560714e+00 9.07808065e-01 5.53058100e+00 9.01243150e-01 -5.51452160e-01 -4.58946861e-02 4.52525735e-01 1.05777346e-01 -4.22943830e-01 2.60499954e-01 -1.40337849e+00 4.98407483e-01 8.10778260e-01 -1.01158142e-01 6.88771009e-02 8.04014325e-01 -2.90375859e-01 -3.05156857e-01 -1.23977494e+00 7.21887767e-01 7.67214075e-02 -1.41241932e+00 -6.66043162e-02 6.96362257e-02 6.11529171e-01 -2.22679943e-01 -3.91865879e-01 3.02742720e-01 5.44564664e-01 -9.11381245e-01 5.92539787e-01 4.33631986e-01 5.63801110e-01 -6.46177530e-01 1.23052275e+00 7.08003640e-01 -1.10499454e+00 -1.06154755e-01 -1.59780070e-01 5.35032190e-02 1.34734765e-01 1.03032482e+00 -9.83057797e-01 8.10752571e-01 6.23570859e-01 7.15573668e-01 -6.42768621e-01 6.97404325e-01 -1.12807822e+00 6.07461214e-01 -3.21099907e-01 -9.56472382e-02 7.08876038e-03 1.53108239e-01 5.06756008e-01 1.37386405e+00 1.23948619e-01 3.05005878e-01 1.39935076e-01 7.26094127e-01 -5.82748175e-01 -1.73248842e-01 -1.92591950e-01 -1.81486309e-01 1.00168133e+00 1.46368027e+00 -5.75657725e-01 -6.18735611e-01 -4.94323522e-01 7.87421763e-01 8.16224992e-01 9.77248922e-02 -5.65398276e-01 -5.91086209e-01 1.99469224e-01 -2.30184972e-01 6.45228744e-01 5.86154982e-02 -1.81012675e-01 -1.18718243e+00 4.92200881e-01 -7.08356440e-01 6.68665469e-01 -8.47475827e-01 -1.28828621e+00 6.20690942e-01 2.33912468e-02 -9.19310391e-01 -4.83236700e-01 -6.88331842e-01 -3.82803440e-01 7.10169017e-01 -1.77416277e+00 -9.01858091e-01 -1.47253558e-01 1.71558056e-02 3.93112928e-01 -3.30231972e-02 7.43563592e-01 2.58170485e-01 -7.82750368e-01 6.39227390e-01 -4.80463177e-01 5.07422566e-01 6.66489124e-01 -1.52882731e+00 3.29153240e-01 1.10317338e+00 5.25720596e-01 9.28171039e-01 5.28182447e-01 -8.19322586e-01 -9.43557501e-01 -9.43117738e-01 1.60066569e+00 -5.00843644e-01 6.02829278e-01 -1.90699205e-01 -1.10610557e+00 8.09101343e-01 1.15639783e-01 1.51102260e-01 6.20808303e-01 5.54058969e-01 -6.07983410e-01 1.19442306e-02 -9.65760469e-01 7.05118716e-01 1.14363492e+00 -6.96328163e-01 -1.18460739e+00 5.55550121e-02 8.03381145e-01 -6.35007739e-01 -8.74931276e-01 5.69375038e-01 4.23089474e-01 -6.48368895e-01 8.50225985e-01 -5.69250345e-01 9.09312189e-01 -4.07445937e-01 -1.87764660e-01 -1.11662340e+00 -6.64244220e-02 -3.29281390e-01 -1.09167528e+00 1.58577085e+00 7.98655570e-01 -4.50925201e-01 5.21204889e-01 6.83491766e-01 2.30195224e-02 -1.07458854e+00 -5.16354144e-01 -6.58680201e-01 -4.56547201e-01 -5.53179204e-01 8.86359274e-01 6.34408414e-01 2.76386082e-01 7.03384280e-01 -5.51944263e-02 4.79823351e-01 4.87070650e-01 4.47466075e-01 5.34618139e-01 -1.19892418e+00 -4.40398604e-01 -9.91695672e-02 1.05490595e-01 -1.41159260e+00 3.93661082e-01 -9.44252074e-01 1.34988904e-01 -1.82693207e+00 5.72065413e-01 -5.59810221e-01 -5.11374176e-01 7.94663846e-01 -8.41185451e-01 -9.11370516e-02 -9.30081755e-02 2.05861375e-01 -9.02079642e-01 4.07863647e-01 1.14500010e+00 -9.59924832e-02 3.69419754e-02 -1.30616978e-01 -1.02886772e+00 9.54877377e-01 5.39902866e-01 -7.11743236e-01 -3.87737453e-01 -4.14263368e-01 5.39647996e-01 -1.70835033e-02 3.57426345e-01 -5.84844530e-01 5.61237812e-01 6.33343831e-02 3.10670644e-01 -8.32028449e-01 1.30538598e-01 -7.08709002e-01 -4.55249995e-01 -3.42489183e-02 -5.46615362e-01 -2.65989959e-01 -7.35107139e-02 5.91578245e-01 -4.31211233e-01 -5.55576265e-01 1.29909068e-01 1.58273131e-01 -6.16634130e-01 1.39094830e-01 2.00488135e-01 2.37855479e-01 5.02867341e-01 2.14483529e-01 -8.56323242e-01 -1.34612722e-02 -4.89694327e-01 5.36782324e-01 -6.66199252e-02 3.49654049e-01 6.42071962e-01 -1.20401561e+00 -7.36758173e-01 -9.67255980e-02 3.68761808e-01 4.26023692e-01 -3.07868961e-02 6.70184970e-01 -7.01563619e-03 5.41774631e-01 4.32591826e-01 -2.13537604e-01 -1.57055259e+00 5.42698145e-01 -9.51226428e-02 -9.26117837e-01 -6.73170865e-01 9.76972342e-01 -9.07988250e-02 -2.84198433e-01 1.19781025e-01 -3.50279242e-01 -5.04798055e-01 1.20375052e-01 8.01668942e-01 3.73248219e-01 4.14993107e-01 -1.82650119e-01 -4.22314465e-01 2.19073668e-01 -5.76379955e-01 -2.30657026e-01 1.31538928e+00 -1.05576180e-01 -3.70761305e-01 2.57043131e-02 7.59882748e-01 6.24015570e-01 -1.11325133e+00 -7.59071171e-01 4.78956729e-01 -4.13532853e-01 2.69281447e-01 -1.15955245e+00 -8.21295381e-01 5.39179444e-01 -2.44808137e-01 1.72987774e-01 1.01222956e+00 3.84335130e-01 1.03895390e+00 6.89674139e-01 1.80406183e-01 -1.12468088e+00 1.21620558e-02 4.69714493e-01 7.65064061e-01 -1.20744669e+00 2.64925659e-01 -1.08670318e+00 -6.13822341e-01 9.27326798e-01 6.47130430e-01 1.66109443e-01 5.46570599e-01 5.42592824e-01 -3.72568816e-01 -3.43558550e-01 -1.16448641e+00 -2.60794699e-01 6.14551902e-01 1.15826055e-01 4.20145780e-01 -4.39601690e-02 -3.61733794e-01 1.19052589e+00 -2.45658413e-01 8.39109272e-02 9.60790813e-02 9.55447495e-01 -5.12103260e-01 -1.38144422e+00 -1.55365959e-01 6.42300963e-01 -5.52343547e-01 -3.28513145e-01 -6.27128899e-01 4.29222554e-01 3.24447244e-01 1.21688592e+00 -7.55436420e-02 -3.43323320e-01 1.76793277e-01 1.24622665e-01 4.02304828e-01 -7.68960893e-01 -2.76412606e-01 -1.80856377e-01 8.45423460e-01 -3.25394511e-01 -3.36188585e-01 -4.00841206e-01 -1.56122947e+00 -9.63109210e-02 -6.43877745e-01 3.66869360e-01 4.29311514e-01 1.47702157e+00 5.41644752e-01 6.52948380e-01 4.96770263e-01 -2.64868159e-02 -4.79616970e-01 -1.07084858e+00 -2.95748621e-01 1.63141042e-01 2.54762888e-01 -7.82700181e-01 -1.73883051e-01 -5.31688053e-03]	[9.34903335571289, 8.623761177062988]
3684815c-fbb2-4624-96dc-1b9ae47ac706	predicting-issue-types-with-sebert	2205.01335	null	https://arxiv.org/abs/2205.01335v1	https://arxiv.org/pdf/2205.01335v1.pdf	Predicting Issue Types with seBERT	Pre-trained transformer models are the current state-of-the-art for natural language models processing. seBERT is such a model, that was developed based on the BERT architecture, but trained from scratch with software engineering data. We fine-tuned this model for the NLBSE challenge for the task of issue type prediction. Our model dominates the baseline fastText for all three issue types in both recall and precisio} to achieve an overall F1-score of 85.7%, which is an increase of 4.1% over the baseline.	['Steffen Herbold', 'Alexander Trautsch']	2022-05-03	null	null	null	null	['type-prediction']	['computer-code']	[ 7.17732981e-02 5.42709410e-01 -4.18213814e-01 -1.60415947e-01 -1.32405877e+00 -4.92052495e-01 7.70947456e-01 3.74801755e-01 -3.48057657e-01 3.52598250e-01 2.30753496e-01 -8.06096792e-01 2.61247694e-01 -5.95287263e-01 -8.28802228e-01 4.81300473e-01 2.71681070e-01 4.17274147e-01 7.36778378e-01 -6.21306598e-01 4.09797430e-01 -2.75548875e-01 -1.17137289e+00 1.02832496e+00 8.42218757e-01 9.99267995e-01 -2.25794828e-03 6.45688653e-01 -8.13659906e-01 1.33366990e+00 -6.17655814e-01 -8.50218356e-01 -2.66235322e-01 4.54639018e-01 -1.11140907e+00 -7.05179513e-01 5.99035263e-01 -1.96929201e-01 -3.03386003e-01 7.48923659e-01 -8.17895606e-02 -6.39048159e-01 5.08588433e-01 -1.21297073e+00 -5.90163112e-01 1.26101899e+00 -5.55876970e-01 5.29659450e-01 3.79134297e-01 -1.40795663e-01 1.23095834e+00 -1.09297264e+00 7.36447215e-01 1.04072297e+00 8.99841249e-01 5.51730275e-01 -1.28514969e+00 -7.20646620e-01 2.56409556e-01 2.01047286e-01 -9.16993856e-01 -4.30558115e-01 3.08536738e-01 -4.56058860e-01 1.89328802e+00 1.27298474e-01 2.78225571e-01 1.11945403e+00 8.68338764e-01 9.81024742e-01 1.21371627e+00 -7.31559277e-01 -8.66849571e-02 2.55801171e-01 8.38863850e-01 6.76684916e-01 -3.23572196e-02 -1.17925495e-01 -7.02553868e-01 -4.20232832e-01 3.00648004e-01 -2.94387937e-01 1.69666123e-03 1.99515417e-01 -1.05771649e+00 7.17278242e-01 2.80459404e-01 4.38821346e-01 -3.06925565e-01 2.09378913e-01 5.37787735e-01 6.41237855e-01 8.66997600e-01 6.88525140e-01 -9.92535532e-01 -5.58249831e-01 -9.90037024e-01 5.09334385e-01 1.11088526e+00 1.33664978e+00 4.36079800e-01 -1.77312434e-01 -2.64637053e-01 7.21823037e-01 4.31639850e-01 2.64218241e-01 2.19825149e-01 -5.81583977e-01 9.41191673e-01 1.09160960e+00 -1.57886013e-01 -4.83005553e-01 -1.30893201e-01 -3.79561454e-01 -3.62212002e-01 -8.62996280e-02 2.85031438e-01 -9.28383172e-02 -1.06356931e+00 1.38308084e+00 -1.65295541e-01 2.04133421e-01 -8.65432546e-02 4.76685651e-02 9.77101505e-01 9.16693270e-01 3.94510895e-01 1.17016762e-01 1.65199828e+00 -1.02894819e+00 -7.75627553e-01 -7.95279145e-01 6.27626359e-01 -1.13417244e+00 1.30875850e+00 5.27458906e-01 -1.29269326e+00 -2.61042476e-01 -1.23590624e+00 -1.81199685e-01 -3.93023193e-01 2.16799766e-01 6.03941083e-01 5.62156916e-01 -1.34562802e+00 2.37039387e-01 -7.37913191e-01 -4.68349636e-01 2.30347469e-01 6.06017970e-02 -9.73479375e-02 1.35006264e-01 -1.23339951e+00 1.08173931e+00 2.43386086e-02 -5.21009564e-01 -7.14718103e-01 -1.74178588e+00 -6.98398471e-01 1.29951790e-01 5.84718764e-01 -4.32538778e-01 1.97103858e+00 -1.50313318e-01 -1.28771186e+00 8.78127396e-01 -4.48799938e-01 -7.71781325e-01 -4.76571433e-02 -7.14566946e-01 -6.86682284e-01 -2.79045105e-01 9.12738815e-02 3.15215528e-01 5.11110783e-01 -7.72007287e-01 -6.23959661e-01 -3.27204019e-02 4.49121296e-01 -4.88310307e-01 -3.78740937e-01 8.72990608e-01 -2.72102237e-01 -8.79397035e-01 -4.50550079e-01 -6.41350985e-01 -2.64263332e-01 -2.40356535e-01 -9.24050137e-02 -6.64611280e-01 5.06869435e-01 -1.02540588e+00 1.73590004e+00 -1.80149162e+00 -2.34019056e-01 -1.89290702e-01 2.75340229e-01 1.73823401e-01 -3.75466377e-01 5.34505665e-01 -4.52359915e-02 6.63396001e-01 -3.03188730e-02 -3.96152675e-01 3.71194035e-01 -4.44559842e-01 -8.85861099e-01 -2.80258209e-01 6.74373984e-01 1.11326087e+00 -6.42152488e-01 -4.32960629e-01 -3.90519761e-02 4.52434719e-02 -7.22518563e-01 4.42131042e-01 -6.69345140e-01 -5.23012757e-01 -4.00739700e-01 2.65012741e-01 5.83421648e-01 -8.12306255e-02 3.68106328e-02 4.51167859e-02 -9.20232683e-02 1.51246202e+00 -6.76219225e-01 1.83992434e+00 -8.53122950e-01 7.55714238e-01 -9.09073576e-02 -1.84552535e-01 7.99785972e-01 5.21426201e-01 1.63370281e-01 -6.74463093e-01 -7.11633042e-02 9.00219157e-02 -2.25338623e-01 -2.78655678e-01 7.44233191e-01 -4.77067716e-02 -5.34124613e-01 6.94725037e-01 5.06062210e-01 -1.49458781e-01 1.27039835e-01 4.80512947e-01 1.55840814e+00 1.47159174e-01 3.37406129e-01 -5.29950976e-01 3.39694560e-01 3.85435112e-02 3.63832027e-01 5.94121635e-01 3.22440445e-01 3.95708829e-01 6.40715480e-01 -4.19087738e-01 -9.75176454e-01 -1.12117207e+00 1.04492448e-01 1.25668120e+00 -4.53712583e-01 -1.19334054e+00 -8.32942188e-01 -1.06888354e+00 -3.37747186e-01 1.33894181e+00 -4.12721127e-01 -2.02887252e-01 -9.22923326e-01 -4.79952425e-01 8.13640237e-01 7.78019905e-01 1.21366023e-03 -1.08689260e+00 -2.66151220e-01 5.49379110e-01 -2.87135124e-01 -1.42316306e+00 -3.45245719e-01 1.45892039e-01 -4.79406565e-01 -1.00031149e+00 -5.83066046e-02 -6.66745543e-01 5.98531365e-02 -4.10390705e-01 2.02250624e+00 1.36039481e-01 7.71389231e-02 6.83658337e-03 -3.37346286e-01 -8.96112263e-01 -7.75955558e-01 5.47204375e-01 -4.39930588e-01 -5.87713242e-01 8.46230686e-01 -3.12367767e-01 -8.63836855e-02 7.02975020e-02 -6.51795506e-01 3.49863432e-02 4.60859537e-01 4.83105659e-01 1.69548929e-01 -4.54225779e-01 4.74317461e-01 -1.22289908e+00 6.46981537e-01 -4.00779158e-01 -4.66945887e-01 6.74497247e-01 -7.69182444e-01 1.88125640e-01 6.93274617e-01 -2.90833980e-01 -1.28799546e+00 -6.13269567e-01 -4.75940675e-01 1.81013450e-01 -1.23645052e-01 5.81265509e-01 -1.57746505e-02 1.68753222e-01 5.07731140e-01 6.93774670e-02 -4.03552502e-01 -7.77595520e-01 3.69361669e-01 8.44650865e-01 2.12536409e-01 -7.28709817e-01 6.49581254e-01 -2.10064054e-01 -6.92557514e-01 -3.66356134e-01 -8.72376502e-01 -3.33544910e-01 -2.71182030e-01 1.31839871e-01 5.72066605e-01 -9.25527573e-01 -2.16505975e-01 4.25041854e-01 -1.54749846e+00 -1.89544529e-01 -1.13279603e-01 -1.41462004e-02 -3.31380010e-01 4.35182378e-02 -1.12887681e+00 -4.51945633e-01 -7.93468416e-01 -1.02228463e+00 1.09440696e+00 -1.43117517e-01 -6.35280907e-01 -8.04341972e-01 7.48989582e-02 2.34582454e-01 8.07722032e-01 -2.86743343e-01 1.38487995e+00 -8.98550332e-01 -3.60470921e-01 -1.45774096e-01 -1.65535748e-01 1.61023065e-01 -1.07195079e-01 -2.94377226e-02 -8.61731946e-01 -2.26009581e-02 2.49256045e-01 -4.04687315e-01 6.30008340e-01 8.19671750e-02 8.86904120e-01 -2.60853708e-01 -5.28856337e-01 4.52455468e-02 1.36332607e+00 -6.80262316e-03 6.90690100e-01 5.84459245e-01 3.91534567e-01 1.94488168e-01 6.43207073e-01 1.99785188e-01 9.82057869e-01 5.49795270e-01 1.53915539e-01 -3.02317347e-02 -3.77846099e-02 -5.01824617e-01 5.94354570e-01 1.18048990e+00 5.48469603e-01 -1.19627178e-01 -1.24543214e+00 7.05873847e-01 -1.63608420e+00 -5.43203235e-01 -2.66696990e-01 1.60480356e+00 1.22408664e+00 7.28781402e-01 -1.35228068e-01 -1.62368014e-01 2.70060956e-01 1.96051404e-01 -1.99717768e-02 -9.53409731e-01 2.96176255e-01 8.84091377e-01 2.80887425e-01 3.07399184e-01 -9.58370328e-01 1.15751266e+00 7.36443186e+00 1.03221440e+00 -9.25377369e-01 1.99254170e-01 4.42988724e-01 1.93216845e-01 -5.74101985e-01 4.93717432e-01 -1.18050945e+00 3.15353006e-01 1.74803734e+00 -5.83234549e-01 2.20927522e-01 7.27887213e-01 -1.00108579e-01 2.15113103e-01 -1.31346405e+00 3.98742497e-01 1.04895793e-01 -1.50675678e+00 2.96144903e-01 -2.06206411e-01 2.65998781e-01 2.06183016e-01 -1.80020571e-01 9.77704406e-01 5.98973215e-01 -9.11681116e-01 8.95415723e-01 3.22455317e-01 6.31974101e-01 -5.22170067e-01 6.80696309e-01 5.62403798e-01 -1.26926398e+00 2.62678474e-01 -3.15192848e-01 1.62489921e-01 2.53035098e-01 4.53710407e-01 -8.99143994e-01 4.36202288e-01 6.91588819e-01 7.68819153e-01 -8.48352730e-01 8.36542785e-01 -3.54216278e-01 1.10686707e+00 -5.22412360e-01 -1.59745872e-01 1.59952447e-01 6.35122418e-01 4.42140937e-01 1.44107795e+00 1.81728378e-01 -1.31545767e-01 2.62588114e-01 1.00852811e+00 -2.17322946e-01 -6.53067157e-02 -3.27760547e-01 -1.86972041e-02 6.44361019e-01 1.22913802e+00 -7.87110534e-03 -4.69245374e-01 -6.94281697e-01 2.72452086e-01 5.31799495e-01 -6.57241121e-02 -9.95460451e-01 -4.21376199e-01 4.27724361e-01 5.14743924e-01 4.50457454e-01 5.81629425e-02 -3.36035609e-01 -1.28015268e+00 3.49764079e-01 -1.42907250e+00 4.83168840e-01 -5.30856669e-01 -1.15030777e+00 1.01592696e+00 6.38422370e-02 -1.09390163e+00 -4.36669856e-01 -6.74287736e-01 -7.63961434e-01 7.21024692e-01 -1.63840389e+00 -1.61430454e+00 2.08809003e-01 8.58594477e-02 9.18999434e-01 -2.63597816e-01 9.64056790e-01 2.57927448e-01 -3.17114890e-01 9.83859539e-01 -3.68718296e-01 -1.17481565e-02 7.29569733e-01 -1.47533226e+00 1.30249345e+00 1.03875744e+00 7.19690649e-03 1.02481890e+00 5.66833496e-01 -4.98227298e-01 -1.40761137e+00 -1.17231178e+00 1.57757068e+00 -8.06017399e-01 1.32929635e+00 -8.08366179e-01 -9.53812778e-01 1.07220852e+00 6.87759221e-01 -5.30048132e-01 7.20123947e-01 4.56002802e-01 -7.81934500e-01 4.53843847e-02 -9.53880429e-01 5.51820993e-01 6.69669688e-01 -6.57097876e-01 -1.18490922e+00 7.51732523e-03 1.11781645e+00 -4.23712224e-01 -1.46664381e+00 5.82570612e-01 3.29874456e-01 -5.06721437e-01 6.96128905e-01 -9.06862438e-01 8.54836702e-01 -1.85883604e-02 -9.43157673e-02 -1.17965567e+00 -4.05725360e-01 -8.11253428e-01 -2.42992565e-01 1.69191468e+00 1.16096270e+00 -4.47887123e-01 5.57260275e-01 6.39284372e-01 -8.53048936e-02 -9.69189167e-01 -8.17063332e-01 -5.37637353e-01 5.01205683e-01 -9.85846639e-01 8.14373672e-01 6.10305667e-01 4.24121082e-01 8.39020133e-01 1.46072924e-01 -2.17189312e-01 5.06784439e-01 7.99922943e-02 6.92712545e-01 -1.11077702e+00 -3.34371746e-01 -3.00201952e-01 8.91058743e-02 -1.19405413e+00 4.76531744e-01 -9.04252768e-01 -1.15023345e-01 -1.46880317e+00 2.96409130e-01 -3.55047315e-01 -3.87052864e-01 7.05109417e-01 -3.24311435e-01 -2.16576636e-01 2.89804339e-01 -1.15118392e-01 -6.04545653e-01 3.65047574e-01 3.82494748e-01 -3.77294689e-01 2.15428591e-01 -8.24615061e-02 -1.00350082e+00 8.63588929e-01 6.81946576e-01 -6.67465568e-01 -1.51884705e-01 -8.50765944e-01 4.68709320e-01 5.65585643e-02 1.74652822e-02 -8.78779650e-01 2.12088868e-01 -1.41909933e-02 -1.41624257e-01 -7.01203585e-01 9.77043957e-02 -4.57129359e-01 -3.53426337e-01 3.28855366e-01 -7.40968645e-01 5.02342641e-01 7.50879765e-01 1.44121617e-01 -1.30136490e-01 -2.58306533e-01 5.02435863e-01 6.97339466e-03 -7.64739335e-01 5.72759174e-02 -8.43757689e-01 4.95869011e-01 5.84968984e-01 1.82135150e-01 -9.07789171e-01 -1.13507032e-01 -1.51341304e-01 1.59464508e-01 2.66756326e-01 1.02564836e+00 6.27749383e-01 -9.01401818e-01 -9.99042869e-01 1.94547568e-02 4.65696901e-01 -3.16423535e-01 -2.63451725e-01 4.93287921e-01 -1.95956752e-01 4.82573539e-01 5.61244152e-02 -3.07892084e-01 -1.29316425e+00 2.79270887e-01 8.69018584e-02 -1.13267267e+00 -5.50551057e-01 7.39540279e-01 7.12189302e-02 -6.98493123e-01 -5.31417169e-02 -9.16702390e-01 -3.65511775e-01 -4.98497993e-01 8.53809893e-01 1.21827133e-01 6.09286964e-01 4.00759093e-02 -7.06248105e-01 3.20868641e-01 -8.66530180e-01 1.69343036e-02 1.56496656e+00 4.26391482e-01 -5.11335135e-01 5.39473951e-01 1.06321299e+00 -1.32461071e-01 -2.86626995e-01 -5.96775115e-01 6.84947789e-01 5.50187863e-02 2.43804067e-01 -1.23610175e+00 -5.53170264e-01 7.01645672e-01 2.09018886e-01 1.41822353e-01 8.74827087e-01 2.15832293e-01 1.02281284e+00 3.62900287e-01 6.12787664e-01 -8.23269010e-01 -8.45944658e-02 1.17250204e+00 1.04387569e+00 -7.59607136e-01 -1.51020765e-01 -7.85173476e-01 -3.72102290e-01 9.86575842e-01 8.58983934e-01 -3.79916519e-01 9.37808871e-01 1.25810683e+00 3.34479958e-02 -3.12399358e-01 -1.69132292e+00 3.89607698e-01 4.30527836e-01 3.23092073e-01 1.00754917e+00 -8.34292024e-02 -2.58901089e-01 1.23353767e+00 -4.19801533e-01 1.85742050e-01 5.73884845e-01 1.25511396e+00 -2.23279506e-01 -1.30766046e+00 -8.30426533e-03 8.10020804e-01 -1.02099991e+00 -8.28320444e-01 -6.90746784e-01 6.92091048e-01 -5.52906871e-01 1.03375554e+00 -7.06057921e-02 -6.50408268e-01 8.29933643e-01 1.53782591e-01 3.71347904e-01 -1.00218582e+00 -1.49352229e+00 -2.37433612e-01 4.92355734e-01 -5.48353910e-01 4.82822359e-02 -3.64578247e-01 -9.45952058e-01 -4.54341710e-01 -5.29700458e-01 1.63574398e-01 3.67311895e-01 9.52808201e-01 5.85666716e-01 7.24596262e-01 2.95444667e-01 -1.90948844e-01 -7.15140581e-01 -1.41943038e+00 3.91933285e-02 -3.63508798e-02 1.69363558e-01 -3.01172942e-01 -1.48740217e-01 1.29159003e-01]	[10.723470687866211, 8.892197608947754]
b4c41d5a-3243-46ce-97d2-8b3e37031c2c	rnn-based-early-cyber-attack-detection-for	1709.02232	null	http://arxiv.org/abs/1709.02232v1	http://arxiv.org/pdf/1709.02232v1.pdf	RNN-based Early Cyber-Attack Detection for the Tennessee Eastman Process	An RNN-based forecasting approach is used to early detect anomalies in industrial multivariate time series data from a simulated Tennessee Eastman Process (TEP) with many cyber-attacks. This work continues a previously proposed LSTM-based approach to the fault detection in simpler data. It is considered necessary to adapt the RNN network to deal with data containing stochastic, stationary, transitive and a rich variety of anomalous behaviours. There is particular focus on early detection with special NAB-metric. A comparison with the DPCA approach is provided. The generated data set is made publicly available.	['Andrey Lavrentyev', 'Pavel Filonov', 'Fedor Kitashov']	2017-09-07	null	null	null	null	['cyber-attack-detection']	['miscellaneous']	[ 1.90927237e-01 -2.76004940e-01 6.21662199e-01 -2.31059968e-01 -2.88910806e-01 -1.92701906e-01 4.94011432e-01 1.32639930e-01 1.88770536e-02 6.56008780e-01 -1.21100597e-01 -7.84093797e-01 -4.67336625e-01 -5.83515406e-01 -1.74988613e-01 -7.93382049e-01 -7.81412184e-01 3.36658716e-01 9.33719650e-02 -2.81635135e-01 5.81528187e-01 1.04399478e+00 -1.49053478e+00 5.03776848e-01 3.17876101e-01 1.07845199e+00 7.62962503e-03 1.26800489e+00 1.57008708e-01 1.16594350e+00 -1.23756456e+00 2.26797864e-01 3.96796018e-01 -5.27927801e-02 -8.30435574e-01 -7.30769560e-02 -2.10918635e-01 -1.52478486e-01 -3.40699285e-01 9.70733404e-01 3.32539976e-01 5.26821136e-01 3.63909692e-01 -1.51979411e+00 -2.73517761e-02 5.03084183e-01 -1.97842866e-01 1.05746853e+00 4.40912396e-01 2.68875420e-01 2.24209279e-01 -8.37843418e-01 2.69508362e-01 1.23809075e+00 7.45675504e-01 1.07196890e-01 -6.68312252e-01 -6.00539088e-01 2.11188167e-01 4.50760782e-01 -6.92772627e-01 1.47160918e-01 9.87802684e-01 -3.35218281e-01 1.65722525e+00 2.73137808e-01 4.76003766e-01 1.71582437e+00 1.14370537e+00 5.75307786e-01 1.00843883e+00 -3.44448984e-01 4.25529957e-01 -6.54234171e-01 5.23933530e-01 2.57926494e-01 -8.95336345e-02 6.29534185e-01 -2.87077010e-01 -1.90195546e-01 6.84933126e-01 6.54191196e-01 1.56650543e-01 4.76855636e-01 -1.18866014e+00 6.68125451e-01 3.45978215e-02 8.82654190e-01 -1.12088335e+00 -7.94889405e-02 1.20090294e+00 1.15418732e+00 7.54965544e-01 6.24065638e-01 -1.04497898e+00 -7.25014210e-01 -6.72935247e-01 1.39329508e-01 1.05553424e+00 6.24460816e-01 -2.15118065e-01 1.22728097e+00 2.64200598e-01 2.50387669e-01 7.97480494e-02 2.62973666e-01 7.49452114e-01 -8.29322219e-01 3.02117050e-01 4.63876665e-01 -4.43914486e-03 -1.15444386e+00 -4.83568788e-01 -3.47384036e-01 -1.02540040e+00 7.57691741e-01 1.88739181e-01 -5.84426105e-01 -1.23832583e+00 7.45184660e-01 -1.72967076e-01 4.76297051e-01 4.74673420e-01 1.18003331e-01 2.38815337e-01 1.04077876e+00 -1.04538895e-01 -3.52080375e-01 8.08606148e-01 -4.67556596e-01 -1.17739379e+00 2.84673244e-01 5.11705995e-01 -7.13780224e-01 3.72314274e-01 1.09088778e+00 -6.13049448e-01 -5.39538324e-01 -9.87705708e-01 8.96251380e-01 -9.81972575e-01 -3.53701770e-01 3.47560942e-01 4.01452512e-01 -8.43973100e-01 1.23475432e+00 -1.09348989e+00 -5.42710721e-01 2.75199255e-03 4.10452068e-01 -1.40768990e-01 2.30075330e-01 -1.47919333e+00 1.13271427e+00 8.39918375e-01 7.37487972e-01 -1.22698116e+00 -1.90137580e-01 -6.81704342e-01 -2.95490444e-01 2.05499053e-01 1.41886830e-01 1.43842018e+00 -7.96319127e-01 -1.72728646e+00 -2.87441343e-01 3.11562985e-01 -8.38561654e-01 4.76299524e-01 -2.94944674e-01 -1.34603190e+00 -2.47475460e-01 -4.16785747e-01 -4.14490759e-01 8.68559062e-01 -8.69249463e-01 -9.39314246e-01 -2.42155761e-01 -5.08590639e-01 -3.62420678e-01 2.37848788e-01 4.40855354e-01 9.83641624e-01 -9.20543969e-01 1.76648214e-01 -6.76015675e-01 -4.44471478e-01 -1.09355700e+00 -5.49431443e-01 -4.92668897e-01 1.45405030e+00 -1.07335532e+00 9.89283264e-01 -1.84424865e+00 -3.80362064e-01 6.23707771e-01 -2.40307465e-01 3.92091393e-01 9.82489511e-02 9.37235713e-01 -9.26828325e-01 -2.64830619e-01 -7.93293715e-02 1.32941499e-01 -2.61523098e-01 6.28705978e-01 -7.86108971e-01 5.18876672e-01 3.21881711e-01 2.32576221e-01 -8.50420475e-01 2.97163397e-01 5.44921339e-01 1.68707415e-01 4.41429585e-01 1.24873489e-01 5.85729405e-02 3.77751529e-01 -3.12694907e-01 1.08178103e+00 5.00241518e-01 4.34991330e-01 -5.21588862e-01 1.36747986e-01 -3.58610958e-01 -1.64080665e-01 -1.13571727e+00 1.08556795e+00 -1.61458001e-01 9.14969563e-01 -1.43069893e-01 -1.27490282e+00 1.21709538e+00 1.11757708e+00 7.27974653e-01 -3.78905833e-01 4.00011659e-01 4.59164679e-01 3.30714643e-01 -5.90083659e-01 4.28605676e-01 -3.69826853e-02 1.09911956e-01 2.33930573e-01 1.08302444e-01 3.24328870e-01 1.96123213e-01 -3.55363816e-01 1.79216886e+00 -1.95240579e-03 1.55389914e-02 -4.52667087e-01 7.73197055e-01 1.47506431e-01 8.75466347e-01 5.69121182e-01 -3.27312440e-01 -4.63642478e-02 4.76193011e-01 -1.01747358e+00 -9.85317707e-01 -8.49034607e-01 9.67804864e-02 6.97930455e-01 -5.73434055e-01 -4.44006175e-02 -1.54788390e-01 -8.55199754e-01 -6.65579662e-02 1.09999943e+00 -5.67465782e-01 -1.41032308e-01 -7.95900047e-01 -7.40940034e-01 5.37255108e-01 6.57280922e-01 3.51711631e-01 -1.67843759e+00 -6.66500390e-01 8.67956936e-01 6.26130104e-01 -8.21330249e-01 3.97384316e-01 8.83834958e-01 -1.30514336e+00 -1.07700038e+00 -4.69307423e-01 -8.00917685e-01 3.42183739e-01 -3.82397383e-01 1.07967186e+00 -5.52691400e-01 -3.47721010e-01 3.97128642e-01 -4.37505513e-01 -1.05127645e+00 -9.51477706e-01 -3.68325174e-01 3.28229845e-01 -3.27920377e-01 8.09420466e-01 -6.22278631e-01 8.10191706e-02 1.63319618e-01 -8.72114718e-01 -9.50721741e-01 3.70237261e-01 7.83216655e-01 -3.99406720e-03 1.01125860e+00 8.10791790e-01 -6.80351734e-01 1.20488679e+00 -7.16814339e-01 -6.35337234e-01 -1.87195167e-02 -7.67717242e-01 -2.21545577e-01 9.30677354e-01 -5.07737100e-01 -1.07583642e+00 2.51492169e-02 -1.69563204e-01 -6.64024174e-01 -8.52138221e-01 5.76137781e-01 2.12915376e-01 1.62628815e-01 4.23283845e-01 2.06609964e-01 -9.21809860e-03 -5.62538862e-01 -3.50625634e-01 6.65531337e-01 5.60846388e-01 -2.07079858e-01 6.63506746e-01 -3.65225896e-02 4.26694900e-02 -9.95201945e-01 5.02364226e-02 -3.74346018e-01 -6.97236419e-01 -5.57213187e-01 4.60778415e-01 -5.79511046e-01 -9.09049451e-01 1.12753761e+00 -1.30528975e+00 -7.41883144e-02 -4.59055811e-01 6.24123991e-01 -3.29992473e-01 2.36084118e-01 -1.23899055e+00 -1.17427790e+00 -5.20047247e-01 -7.33446538e-01 5.85761070e-01 -2.66908258e-01 -2.24160746e-01 -1.21832371e+00 3.18213582e-01 -5.32239556e-01 6.21425927e-01 8.49980354e-01 7.03850806e-01 -1.46114111e+00 1.22544140e-01 -8.19234490e-01 4.95619684e-01 9.35573459e-01 4.20202047e-01 5.31045496e-01 -8.55841100e-01 -3.84946674e-01 7.05296159e-01 4.64393228e-01 3.98094833e-01 3.46019626e-01 9.12623405e-01 -1.85413808e-01 -6.50761649e-02 -2.44352445e-02 1.27286887e+00 1.25822639e+00 4.57775533e-01 6.31245971e-01 6.43638372e-01 5.46512842e-01 6.49224997e-01 4.43594098e-01 -5.26564062e-01 -2.95775205e-01 5.62551200e-01 1.04575500e-01 8.77669513e-01 5.16143501e-01 9.68531907e-01 1.22957361e+00 -2.98512518e-01 -4.10755247e-01 -1.34560919e+00 3.24387431e-01 -1.76995027e+00 -1.13753235e+00 -4.92479026e-01 1.59528148e+00 -1.89987198e-02 5.73102891e-01 -5.68889193e-02 9.16420519e-01 8.76966715e-01 1.19561635e-01 -5.02243519e-01 -1.00802779e+00 2.61990563e-03 6.12521693e-02 6.59484565e-01 1.97146371e-01 -1.37436831e+00 4.18662012e-01 7.28315067e+00 3.17953676e-01 -9.71913457e-01 -6.90079406e-02 3.21982175e-01 1.07723475e-01 4.90144759e-01 -4.62658525e-01 -2.05252066e-01 6.45263553e-01 1.88051486e+00 6.00592606e-02 -5.00757992e-02 9.66666818e-01 4.73935753e-01 1.89262450e-01 -9.60435748e-01 8.62009645e-01 1.34554307e-03 -7.43606329e-01 -3.24294120e-01 1.56265005e-01 5.06899476e-01 3.25664759e-01 -2.10894421e-01 4.06521559e-01 7.53861248e-01 -8.05538297e-01 1.01107255e-01 7.85366952e-01 -1.46171957e-01 -1.33084965e+00 1.49117231e+00 -1.00631990e-01 -1.02099609e+00 -7.11668253e-01 -1.88123435e-01 -3.50494623e-01 3.02412003e-01 8.32346022e-01 -8.96888554e-01 7.49249458e-01 8.84943187e-01 1.02101946e+00 -2.40214944e-01 8.31676245e-01 1.10878356e-01 7.21316040e-01 -2.47760817e-01 2.07900077e-01 5.60057521e-01 -4.40934971e-02 9.96222913e-01 9.95263875e-01 6.86107755e-01 -5.55061400e-01 3.67815286e-01 3.25471729e-01 6.81405962e-01 -4.01149392e-01 -1.19658303e+00 -7.46609271e-02 1.34273410e-01 8.47406685e-01 -8.98672640e-01 -4.77447093e-01 -2.61443347e-01 9.62779045e-01 -7.29643762e-01 5.47563910e-01 -4.82551903e-01 -7.34279990e-01 6.75979614e-01 -5.13498843e-01 8.41509029e-02 -3.34619790e-01 -9.23984051e-02 -6.47042394e-01 -9.88117009e-02 -9.03256953e-01 7.07790971e-01 -5.85036278e-01 -1.92475057e+00 1.09790373e+00 1.81509927e-02 -1.48823428e+00 -8.35508466e-01 -1.08297467e+00 -1.19000185e+00 7.68471897e-01 -7.49992430e-01 -6.51162684e-01 9.95249674e-02 7.00991988e-01 1.06870782e+00 -9.02120709e-01 8.46086502e-01 7.10353032e-02 -1.10759604e+00 -4.52308714e-01 3.65513772e-01 6.86904266e-02 1.80547222e-01 -1.65481722e+00 9.63233769e-01 1.46999300e+00 -4.53683794e-01 1.80166394e-01 1.23471129e+00 -1.00603473e+00 -9.44640100e-01 -1.20108485e+00 6.82142735e-01 -3.56683224e-01 1.07817781e+00 6.38884380e-02 -1.13308585e+00 1.00245214e+00 6.55195177e-01 -2.80935258e-01 4.14843142e-01 -2.37329274e-01 5.01358390e-01 -1.91568777e-01 -1.21474469e+00 2.13323995e-01 2.15014681e-01 -4.08421755e-01 -9.73993957e-01 4.54161942e-01 3.82546037e-01 -1.02761932e-01 -1.19499576e+00 5.39953649e-01 -1.49573103e-01 -5.82769811e-01 5.51922798e-01 -7.10069954e-01 -1.20009743e-01 -2.51196593e-01 9.47780535e-02 -1.59834671e+00 -3.51871848e-01 -1.06384003e+00 -4.33293134e-01 8.77807796e-01 2.03534096e-01 -1.05958569e+00 6.49699509e-01 5.10680974e-01 -5.47449470e-01 -3.75320166e-01 -1.21072733e+00 -1.02121043e+00 -2.40406603e-01 -6.96412623e-01 5.16544044e-01 1.17462182e+00 -1.58787984e-02 -1.25608727e-01 -3.65214348e-01 4.32558686e-01 2.89681137e-01 -3.06169152e-01 1.13977157e-01 -1.74063742e+00 4.61332113e-01 -2.16381714e-01 -8.36872399e-01 1.25840008e-01 3.36911865e-02 -2.92581826e-01 2.25171357e-01 -1.07609534e+00 -1.11654842e+00 4.09360230e-01 -1.02282441e+00 3.36533308e-01 4.25635874e-01 -5.29624462e-01 -7.58354589e-02 5.74189536e-02 -5.09515852e-02 4.13012773e-01 4.83722150e-01 -1.86203942e-02 -1.66242898e-01 4.44195300e-01 2.52259225e-01 7.34712362e-01 1.27570617e+00 -6.19310021e-01 -3.54597777e-01 1.19124755e-01 -4.22635814e-03 2.38284275e-01 2.77331620e-01 -1.58315063e+00 3.87428671e-01 1.55682206e-01 6.36290491e-01 -1.21387827e+00 -1.41951039e-01 -1.37873054e+00 1.92206755e-01 1.10294044e+00 -1.56091839e-01 1.39920175e+00 3.18565637e-01 8.83146524e-01 -5.87294042e-01 -1.46881223e-01 2.62853861e-01 -4.96972144e-01 -1.07534158e+00 1.03926025e-01 -1.24932909e+00 -7.02923298e-01 1.37762868e+00 -3.16606075e-01 5.97549090e-03 -1.54872537e-01 -1.04188991e+00 3.54665332e-02 -1.81761920e-01 6.73768580e-01 7.78069437e-01 -1.24420142e+00 -6.35412037e-01 5.57402849e-01 -3.88305515e-01 -3.26454222e-01 1.94427520e-01 5.97174883e-01 -6.97674870e-01 5.11867344e-01 -8.07137430e-01 -4.64916468e-01 -1.06735706e+00 7.11449623e-01 4.91260767e-01 -2.54381388e-01 -1.00520551e+00 4.74784672e-01 -9.27768469e-01 -2.91114420e-01 3.67648989e-01 -8.96180332e-01 -4.54071552e-01 8.22279230e-02 4.77082640e-01 1.12130535e+00 4.20180589e-01 -3.69989157e-01 -2.65341133e-01 -2.25944951e-01 -1.32720560e-01 2.74454981e-01 1.68238080e+00 1.85086355e-01 -2.83600956e-01 1.30355501e+00 9.73241270e-01 -7.44884253e-01 -1.11115134e+00 -1.55043965e-02 8.85600269e-01 -1.07618958e-01 1.29778251e-01 -8.87654245e-01 -1.24518657e+00 6.36924863e-01 1.21034896e+00 7.51666427e-01 1.11610031e+00 -7.16990769e-01 7.92534232e-01 7.07451582e-01 1.63900554e-01 -1.41784954e+00 -1.67766303e-01 1.15914357e+00 7.87996292e-01 -9.74469423e-01 -3.20775956e-01 1.85658008e-01 -5.77288926e-01 1.80781651e+00 4.23960716e-01 -6.50622070e-01 9.76656258e-01 5.60923457e-01 1.08195215e-01 -6.51523232e-01 -1.10386968e+00 3.74505073e-01 -1.69709370e-01 6.64831042e-01 3.01327735e-01 -3.51916328e-02 3.67150724e-01 9.55844373e-02 -2.76782885e-02 5.20699508e-02 8.15654337e-01 1.63070989e+00 -2.47252002e-01 -8.70336175e-01 -6.00341916e-01 7.29148269e-01 -9.44576740e-01 1.05549432e-01 -1.63914487e-01 9.82194424e-01 -2.22265422e-01 1.16056597e+00 4.27656621e-01 -7.00279832e-01 5.73698223e-01 5.79650342e-01 -4.51942444e-01 -2.08645031e-01 -1.02824795e+00 -1.23520987e-02 7.26728663e-02 -8.27759922e-01 -2.67496079e-01 -8.67041945e-01 -9.12899613e-01 -2.89838344e-01 -8.12614188e-02 1.39654800e-01 7.10101664e-01 1.00979543e+00 2.19162419e-01 1.21737909e+00 7.04033732e-01 -9.44358647e-01 -5.61007738e-01 -1.56184387e+00 -9.71556723e-01 1.37826681e-01 7.03096688e-01 -4.47611332e-01 -7.48644710e-01 -1.32330969e-01]	[6.957039833068848, 2.5726842880249023]
22676740-414d-4354-90f5-af0733ef52eb	perturbation-of-deep-autoencoder-weights-for	2205.08358	null	https://arxiv.org/abs/2205.08358v1	https://arxiv.org/pdf/2205.08358v1.pdf	Perturbation of Deep Autoencoder Weights for Model Compression and Classification of Tabular Data	Fully connected deep neural networks (DNN) often include redundant weights leading to overfitting and high memory requirements. Additionally, the performance of DNN is often challenged by traditional machine learning models in tabular data classification. In this paper, we propose periodical perturbations (prune and regrow) of DNN weights, especially at the self-supervised pre-training stage of deep autoencoders. The proposed weight perturbation strategy outperforms dropout learning in four out of six tabular data sets in downstream classification tasks. The L1 or L2 regularization of weights at the same pretraining stage results in inferior classification performance compared to dropout or our weight perturbation routine. Unlike dropout learning, the proposed weight perturbation routine additionally achieves 15% to 40% sparsity across six tabular data sets for the compression of deep pretrained models. Our experiments reveal that a pretrained deep autoencoder with weight perturbation or dropout can outperform traditional machine learning in tabular data classification when fully connected DNN fails miserably. However, traditional machine learning models appear superior to any deep models when a tabular data set contains uncorrelated variables. Therefore, the success of deep models can be attributed to the inevitable presence of correlated variables in real-world data sets.	['Sakib Abrar', 'Manar Samad']	2022-05-17	null	null	null	null	['l2-regularization']	['methodology']	[ 1.85366049e-02 2.45790616e-01 -2.26271793e-01 -2.67380327e-01 -2.69023359e-01 -2.53549218e-01 7.54374340e-02 1.71201732e-02 -5.04173338e-01 8.59914243e-01 -5.40937111e-02 -3.37911189e-01 -2.46840373e-01 -9.40793037e-01 -9.90523815e-01 -9.14971530e-01 3.45579498e-02 6.62971437e-01 -1.49738997e-01 -8.93104821e-02 -1.02339394e-01 5.58824718e-01 -1.39035952e+00 4.02805150e-01 6.32506430e-01 1.25653052e+00 4.75179628e-02 4.63667512e-01 -2.54012883e-01 1.14864469e+00 -8.37017775e-01 -8.55571687e-01 3.44461262e-01 -4.84277643e-02 -5.21417320e-01 -1.27651319e-01 8.88700843e-01 -5.16340315e-01 -8.32099080e-01 1.17140830e+00 4.41471666e-01 1.52369924e-02 4.63302553e-01 -1.37440324e+00 -8.49536359e-01 1.03099120e+00 -1.64185777e-01 1.86633810e-01 -6.08442307e-01 8.79182294e-02 1.13581097e+00 -7.57781327e-01 2.38483325e-01 1.37512374e+00 1.00422883e+00 6.47587419e-01 -1.41823721e+00 -8.57871592e-01 1.60611168e-01 2.55550295e-01 -1.06384504e+00 -2.47135937e-01 6.80544376e-01 -3.69763434e-01 1.09431446e+00 1.95333987e-01 5.49182117e-01 1.54058635e+00 3.89385968e-01 8.46341550e-01 3.07497710e-01 1.02486990e-01 2.08658159e-01 3.81358042e-02 2.69760549e-01 7.00821221e-01 8.96187544e-01 2.06189573e-01 -5.35676062e-01 -5.32661080e-02 6.78789616e-01 4.70749021e-01 -2.35779941e-01 -1.48863912e-01 -9.37835097e-01 8.59905660e-01 5.67841947e-01 -7.19462633e-02 -1.55695975e-01 5.49043357e-01 6.48153901e-01 7.90453553e-01 4.12892044e-01 7.13500440e-01 -1.00985253e+00 -1.56850874e-01 -7.13978469e-01 5.47024980e-02 5.06861210e-01 1.09969056e+00 6.70839727e-01 8.16679537e-01 -1.08366355e-01 9.39292073e-01 -8.49786401e-02 1.89837888e-01 9.25000310e-01 -6.56529486e-01 7.82693028e-01 8.44412625e-01 -4.32809114e-01 -8.53895187e-01 -3.68933320e-01 -7.46825159e-01 -1.36982346e+00 1.63873807e-01 4.48882937e-01 -5.20129502e-01 -1.23029923e+00 1.56636655e+00 -2.53256023e-01 -2.33075738e-01 3.45932961e-01 7.46623456e-01 1.05232263e+00 6.96328998e-01 -1.49514318e-01 1.97169363e-01 1.01428926e+00 -9.33374941e-01 -9.98028040e-01 -4.66125160e-01 7.97956407e-01 -4.75772023e-01 9.68053162e-01 6.54801548e-01 -1.03558075e+00 -5.18900156e-01 -1.20402765e+00 -4.11299407e-01 -5.21671355e-01 2.40739346e-01 6.37958884e-01 4.73066509e-01 -8.74368429e-01 1.03878522e+00 -7.66387939e-01 1.96443692e-01 8.56754482e-01 8.05649936e-01 -4.71097380e-01 -1.97164699e-01 -1.16242266e+00 8.48143280e-01 6.87828124e-01 2.46568292e-01 -8.32741737e-01 -9.29948926e-01 -7.85266936e-01 5.07096171e-01 1.89645723e-01 -4.64696795e-01 1.09487426e+00 -1.09341514e+00 -1.24928653e+00 4.15148735e-01 2.27146864e-01 -8.67628038e-01 3.78823370e-01 -4.24607694e-01 -2.03185111e-01 -3.10476087e-02 -5.23200572e-01 7.29637027e-01 1.00655901e+00 -9.16533947e-01 -2.38434121e-01 -3.23649943e-01 -3.12290907e-01 -5.02306484e-02 -8.90907824e-01 -6.01773500e-01 -1.12128891e-01 -9.30108070e-01 3.54892135e-01 -5.99116325e-01 -1.91607401e-01 4.61109914e-02 -5.16101778e-01 -9.24702510e-02 1.10050595e+00 -3.19547713e-01 1.05196083e+00 -2.24354124e+00 2.19354391e-01 -1.53678492e-01 5.89862585e-01 2.78511912e-01 -2.46721908e-01 1.35337353e-01 -4.45874065e-01 3.35224420e-01 -8.33055843e-03 -4.88449603e-01 -1.61544979e-01 6.28650725e-01 -2.31761634e-01 3.80226254e-01 5.89328945e-01 8.37299764e-01 -6.52936459e-01 -2.83434745e-02 5.53294495e-02 4.33362782e-01 -7.35283971e-01 4.57333028e-02 -1.74625263e-01 -2.62010276e-01 -4.15915512e-02 6.49967968e-01 6.66963458e-01 -2.64723748e-01 -5.49999438e-02 -2.08786547e-01 2.61421025e-01 4.47174519e-01 -6.51794672e-01 1.14300883e+00 -1.35842204e-01 9.24437582e-01 -3.94730300e-01 -1.16693890e+00 1.16332757e+00 4.23144966e-01 3.39491248e-01 -6.52689219e-01 3.24649334e-01 8.16045180e-02 3.13394815e-01 -3.42934847e-01 4.71828640e-01 -8.97592530e-02 1.71655357e-01 -2.03202516e-01 6.50253654e-01 1.33337796e-01 3.56019363e-02 4.56118211e-03 1.12911117e+00 -5.35115719e-01 -3.11737806e-01 -8.63585398e-02 -7.38928840e-03 9.60581973e-02 8.74079525e-01 7.64768779e-01 -7.04793707e-02 8.38614762e-01 1.08039999e+00 -1.02817643e+00 -1.41139686e+00 -6.05922818e-01 -3.50119293e-01 8.70825648e-01 -2.65961349e-01 -3.99466395e-01 -5.99057674e-01 -5.52251875e-01 2.78592318e-01 6.71343029e-01 -9.65461910e-01 -7.51361728e-01 -4.53456551e-01 -1.13992679e+00 7.13002086e-01 7.71235049e-01 7.75305390e-01 -1.05941069e+00 -2.65509754e-01 1.61179081e-01 3.29651415e-01 -1.00888908e+00 -7.46227652e-02 1.17661583e+00 -1.52550542e+00 -1.05140746e+00 -5.15042722e-01 -8.16107631e-01 8.77688050e-01 -9.65385213e-02 1.01925731e+00 6.96598366e-02 -2.34846603e-02 -3.92414510e-01 -3.27206612e-01 -2.28500649e-01 -3.06140691e-01 4.31366146e-01 1.53367653e-01 -2.13207662e-01 5.79156578e-01 -3.89468879e-01 -4.55337614e-01 2.05772549e-01 -9.27821159e-01 -5.75264655e-02 5.86272836e-01 1.46075225e+00 4.47222292e-01 3.30947071e-01 3.37686300e-01 -1.03203380e+00 4.57960546e-01 -5.80677092e-01 -8.30059588e-01 -4.58524376e-02 -5.93539596e-01 5.74568570e-01 1.08467293e+00 -8.02458525e-01 -5.28769493e-01 -1.87200442e-01 -1.47932872e-01 -9.95664716e-01 9.07414556e-02 5.97318828e-01 -6.05484508e-02 2.28996500e-01 6.83004618e-01 -5.04907779e-02 4.03775498e-02 -7.45154083e-01 -1.65650651e-01 1.74340591e-01 4.17701364e-01 -2.22641170e-01 6.66659117e-01 6.57274425e-02 4.38693687e-02 -2.61915892e-01 -8.07109177e-01 7.98070431e-02 -5.32468498e-01 2.11323559e-01 6.89817011e-01 -8.73182118e-01 -7.82344103e-01 4.33769166e-01 -9.50878620e-01 -6.45691156e-01 -4.04404968e-01 4.17022139e-01 -1.18890978e-01 -2.71523029e-01 -8.22970390e-01 -4.41675335e-01 -5.37152052e-01 -1.30746555e+00 5.83071709e-01 1.05832340e-02 -9.27145183e-02 -1.00709021e+00 -4.69274551e-01 1.09916158e-01 3.95926565e-01 1.28246680e-01 1.23953307e+00 -8.46912980e-01 -2.36741930e-01 -6.13123357e-01 -9.96024068e-03 6.60902381e-01 1.36269271e-01 4.02481109e-02 -1.01298273e+00 -4.31487888e-01 -1.12917554e-03 -6.96308851e-01 1.30430222e+00 3.57761294e-01 1.70660543e+00 -7.52148688e-01 8.62413719e-02 1.06398618e+00 1.35951793e+00 2.16044873e-01 6.46699011e-01 6.73695147e-01 9.42907453e-01 3.33326042e-01 -3.18956077e-02 3.48373055e-01 -2.61145830e-01 8.69609565e-02 8.95070910e-01 -4.94030714e-02 6.37439936e-02 -2.89602816e-01 3.66969377e-01 8.81002545e-01 2.76531905e-01 -3.76632392e-01 -8.56154561e-01 3.46296936e-01 -1.63186324e+00 -8.96102786e-01 -2.32503742e-01 2.05795431e+00 8.03780138e-01 5.90103507e-01 -3.83283526e-01 5.84194660e-01 3.74143988e-01 6.72860071e-03 -8.69762540e-01 -6.83754444e-01 -4.52436596e-01 9.82399732e-02 1.05976856e+00 9.39167440e-02 -1.00109708e+00 9.94567692e-01 6.21753311e+00 7.17597485e-01 -1.30458653e+00 -4.43475395e-02 1.04520702e+00 -5.34698725e-01 -3.04610003e-02 -5.65190017e-01 -1.08077133e+00 4.85456526e-01 1.14066589e+00 1.67910412e-01 4.32356149e-01 1.14900589e+00 -3.15277018e-02 3.34323972e-01 -1.31209290e+00 1.13726759e+00 -2.08361059e-01 -1.47228682e+00 5.27685404e-01 -8.58948529e-02 7.67374933e-01 2.04455435e-01 5.63804924e-01 5.95251441e-01 4.52182114e-01 -1.44941235e+00 7.28650570e-01 1.57834619e-01 7.71830261e-01 -1.12427473e+00 1.07547796e+00 1.33704677e-01 -4.75706518e-01 -3.78566265e-01 -1.08431673e+00 -7.60987327e-02 -4.72084492e-01 6.17633462e-01 -7.57812858e-01 5.73916025e-02 9.58337843e-01 8.39470983e-01 -5.98101854e-01 9.90482092e-01 -2.42557712e-02 8.66275966e-01 -2.55667478e-01 -8.17951113e-02 5.06951213e-01 -1.73570439e-02 1.96493492e-01 9.15154576e-01 2.12885439e-01 -5.77530973e-02 -5.45743048e-01 8.52515936e-01 -6.78207099e-01 -3.25590223e-01 -5.82761943e-01 -9.16219130e-02 2.84923792e-01 8.36671889e-01 -2.60477811e-01 -3.42877477e-01 -4.50041473e-01 7.87007809e-01 6.58606768e-01 6.06302679e-01 -4.73572075e-01 -2.93933600e-01 9.57243621e-01 2.13926360e-02 6.26877069e-01 -3.70063819e-03 -7.64734507e-01 -1.20681715e+00 -4.99289297e-02 -9.45106685e-01 3.84398371e-01 -6.21686876e-01 -1.18834651e+00 7.92821884e-01 -2.16074482e-01 -1.10976303e+00 1.02039099e-01 -1.11448073e+00 -4.41030174e-01 4.92703438e-01 -1.30521429e+00 -3.72140199e-01 -2.75993288e-01 5.47118902e-01 6.05813503e-01 -7.30292618e-01 7.73245096e-01 4.68941718e-01 -1.21336651e+00 1.07587004e+00 5.94140232e-01 5.03169954e-01 4.43529487e-01 -1.29881203e+00 4.11703467e-01 5.91780484e-01 -6.72935396e-02 6.78377450e-01 6.72186553e-01 -4.59009260e-01 -1.61498702e+00 -1.40677834e+00 6.16231740e-01 -6.15271777e-02 7.55383253e-01 -7.08259165e-01 -1.17529666e+00 9.76282001e-01 3.02968472e-02 3.15865993e-01 5.46720803e-01 1.33215785e-01 -4.08335418e-01 -4.51713085e-01 -1.06661272e+00 7.09485233e-01 6.41521037e-01 -2.30297923e-01 -5.42781234e-01 3.75946045e-01 8.04367542e-01 -5.16281486e-01 -7.69579649e-01 4.41126555e-01 3.68949354e-01 -6.79731548e-01 7.60007143e-01 -9.74062979e-01 9.95662391e-01 1.86412811e-01 -1.35778129e-01 -1.37443137e+00 -2.60105163e-01 -4.65657473e-01 -4.58929986e-01 9.18214500e-01 6.98258579e-01 -5.19253790e-01 1.33857417e+00 7.23065734e-01 -2.44259372e-01 -7.79537916e-01 -1.00753820e+00 -7.91251779e-01 5.51397681e-01 -4.77725774e-01 4.53905731e-01 1.09699154e+00 -3.11251700e-01 1.78902358e-01 -4.95869428e-01 4.05926742e-02 5.67192316e-01 -5.22621691e-01 4.41159487e-01 -1.29848123e+00 -5.88370152e-02 -6.89167500e-01 -7.10204959e-01 -9.89081204e-01 8.33142549e-02 -9.02111411e-01 -3.12081754e-01 -1.09720778e+00 3.64493504e-02 -3.25946808e-01 -4.90698725e-01 8.00254762e-01 -1.72574595e-01 2.04338223e-01 -3.00871097e-02 1.16736174e-01 -1.82696030e-01 8.63018274e-01 1.21290827e+00 -5.55809140e-01 -3.59714776e-02 -1.41426772e-01 -5.79969227e-01 4.99600768e-01 1.04580283e+00 -8.78312588e-01 -3.54194850e-01 -9.87899959e-01 3.63523245e-01 -1.35657534e-01 2.93354630e-01 -1.07389307e+00 3.40783983e-01 2.09186032e-01 8.43043745e-01 -7.72156596e-01 3.28000873e-01 -1.18112111e+00 8.67732335e-03 6.35377228e-01 -4.62208003e-01 3.50306004e-01 7.31606185e-01 2.98955977e-01 -4.07305777e-01 -4.60406512e-01 6.92924201e-01 -1.15624040e-01 -2.15884924e-01 5.31560123e-01 -4.70717192e-01 -2.25280300e-02 4.88702536e-01 -3.10995609e-01 -4.23460186e-01 -4.26956676e-02 -6.91241920e-01 2.73895264e-01 -7.88484048e-03 4.43909317e-01 8.48568141e-01 -1.39698184e+00 -5.92309475e-01 4.18039471e-01 -1.73291907e-01 4.73641187e-01 3.31264213e-02 5.74657679e-01 -8.20707083e-01 4.16704863e-01 -3.88253361e-01 -3.33095640e-01 -1.01940882e+00 5.93884647e-01 6.68195248e-01 -9.81672332e-02 -7.31308699e-01 1.15918291e+00 7.48244822e-02 -2.88087726e-01 8.69177341e-01 -7.38084137e-01 1.18048973e-01 1.05187118e-01 2.90418476e-01 2.29527831e-01 3.69877845e-01 -1.40904412e-02 4.84242104e-02 -6.40981123e-02 -6.51013732e-01 4.58711565e-01 1.68720055e+00 4.30182159e-01 1.79180592e-01 6.33763552e-01 1.51795697e+00 -7.06721723e-01 -1.48417103e+00 -1.61366776e-01 -4.70279194e-02 -7.87734091e-02 5.45179069e-01 -6.10886693e-01 -1.73183477e+00 1.06726933e+00 3.90969753e-01 7.28977323e-02 9.04142082e-01 -5.00537097e-01 7.70168960e-01 9.74546790e-01 -2.14626029e-01 -9.57939744e-01 1.52221695e-01 7.65899777e-01 9.06204760e-01 -1.47647834e+00 -4.79140282e-02 1.38158172e-01 -5.98999381e-01 1.55629694e+00 8.80065024e-01 -3.02301854e-01 5.50841153e-01 6.57307446e-01 3.31943370e-02 -2.37453640e-01 -1.28919625e+00 2.87316233e-01 2.73752324e-02 3.25713307e-01 2.62576193e-01 -3.76411378e-01 2.64148593e-01 6.47771716e-01 -6.46535635e-01 -3.81710619e-01 3.79939765e-01 5.41262805e-01 -2.22763613e-01 -6.52982891e-01 -4.05120999e-01 8.84199679e-01 -6.50224388e-01 -5.74904382e-01 -4.13145125e-01 1.01949179e+00 -9.08680111e-02 6.22405529e-01 4.36087936e-01 -7.23661840e-01 3.49667013e-01 1.68379307e-01 8.38699862e-02 -3.00954551e-01 -1.02805305e+00 -1.60607159e-01 -2.11240754e-01 -2.38924906e-01 3.07200432e-01 -4.61379111e-01 -1.04681599e+00 -9.03564215e-01 -2.86008328e-01 -8.21593180e-02 3.43855858e-01 6.95593178e-01 8.99679735e-02 8.70705724e-01 3.08161527e-01 -5.01031041e-01 -7.44935989e-01 -1.16610157e+00 -5.84268034e-01 5.16622663e-01 7.18533874e-01 -6.51928544e-01 -7.06024468e-01 -1.03258260e-01]	[8.736376762390137, 3.068572759628296]
6eb70c24-cca8-4faf-8a79-276aae5c56cf	taming-detection-transformers-for-medical	2306.15472	null	https://arxiv.org/abs/2306.15472v1	https://arxiv.org/pdf/2306.15472v1.pdf	Taming Detection Transformers for Medical Object Detection	The accurate detection of suspicious regions in medical images is an error-prone and time-consuming process required by many routinely performed diagnostic procedures. To support clinicians during this difficult task, several automated solutions were proposed relying on complex methods with many hyperparameters. In this study, we investigate the feasibility of DEtection TRansformer (DETR) models for volumetric medical object detection. In contrast to previous works, these models directly predict a set of objects without relying on the design of anchors or manual heuristics such as non-maximum-suppression to detect objects. We show by conducting extensive experiments with three models, namely DETR, Conditional DETR, and DINO DETR on four data sets (CADA, RibFrac, KiTS19, and LIDC) that these set prediction models can perform on par with or even better than currently existing methods. DINO DETR, the best-performing model in our experiments demonstrates this by outperforming a strong anchor-based one-stage detector, Retina U-Net, on three out of four data sets.	['Klaus H. Maier-Hein', 'Tassilo Wald', 'Saikat Roy', 'Michael Baumgartner', 'Marc K. Ickler']	2023-06-27	null	null	null	null	['medical-object-detection']	['computer-vision']	[ 2.41328076e-01 3.48218679e-01 -1.10434704e-01 -1.73707351e-01 -6.80750668e-01 -3.02364707e-01 6.15542710e-01 4.14678037e-01 -4.62332010e-01 6.43777311e-01 -2.01558694e-01 -5.26592791e-01 -1.03909753e-01 -5.18944502e-01 -2.27935195e-01 -5.85524976e-01 -1.72587544e-01 7.40137458e-01 1.08763301e+00 2.41364643e-01 2.78596491e-01 6.22052372e-01 -1.19303346e+00 4.03102458e-01 5.40620148e-01 9.54008520e-01 2.83354133e-01 7.37216890e-01 5.73060736e-02 8.03807139e-01 -2.84217209e-01 -3.32308829e-01 2.78340757e-01 -3.63908052e-01 -5.96749246e-01 1.85544237e-01 8.73307064e-02 -2.69336611e-01 1.89958438e-01 8.24525297e-01 5.94601870e-01 -3.51597965e-01 1.12547731e+00 -7.16593802e-01 -1.14049837e-01 5.12267113e-01 -9.25198734e-01 5.35646021e-01 2.07914069e-01 1.10367715e-01 6.91797614e-01 -1.08398139e+00 6.31532192e-01 9.31170225e-01 1.15661192e+00 4.60554749e-01 -9.69702482e-01 -3.81712288e-01 -1.20390989e-01 9.98610258e-02 -1.46116936e+00 -2.37533525e-01 2.96399236e-01 -6.49956942e-01 7.46963322e-01 5.04798055e-01 4.86064136e-01 7.67876744e-01 2.74632186e-01 8.32738400e-01 1.10592687e+00 -6.84702575e-01 2.33896315e-01 5.59255838e-01 1.58902526e-01 1.11581886e+00 6.18164897e-01 -1.83319956e-01 -1.38392597e-01 -4.70489979e-01 7.47939527e-01 -7.02130273e-02 -2.80314863e-01 -3.23361367e-01 -1.05599761e+00 8.21909428e-01 2.88505167e-01 4.66116220e-01 -4.35128987e-01 -1.67124152e-01 3.18195760e-01 -3.05840135e-01 4.09835041e-01 3.40780556e-01 -1.74350902e-01 2.42909878e-01 -1.07221544e+00 -1.61772847e-01 7.23513484e-01 7.13802040e-01 1.36447266e-01 -4.70187277e-01 -5.02790570e-01 5.99813521e-01 4.50034708e-01 1.05356462e-01 6.52493119e-01 -4.79921460e-01 1.96604595e-01 8.43691051e-01 6.51694834e-02 -8.65388334e-01 -9.11316574e-01 -6.47161007e-01 -8.73602033e-01 2.28477925e-01 5.20871341e-01 -1.22657448e-01 -1.26054811e+00 9.44206893e-01 4.31967676e-01 1.51259109e-01 -3.30891699e-01 7.96698749e-01 9.92716789e-01 2.52862632e-01 1.24285080e-01 -3.86438638e-01 1.52704775e+00 -8.09344053e-01 -4.53514636e-01 -9.93395597e-02 8.12791109e-01 -7.39360332e-01 7.06201017e-01 5.93669951e-01 -9.66055751e-01 -3.17247629e-01 -1.09271765e+00 2.54859835e-01 -2.29968071e-01 4.80572939e-01 6.97644174e-01 9.34438586e-01 -9.21386480e-01 3.54060650e-01 -1.05299723e+00 -5.49117684e-01 5.87062299e-01 3.08714598e-01 -1.82812572e-01 3.39871310e-02 -5.37102103e-01 1.12796557e+00 3.14829558e-01 1.15778267e-01 -7.95399666e-01 -4.48898703e-01 -4.36978072e-01 -7.79552758e-02 6.01608098e-01 -7.75476336e-01 1.18307674e+00 -5.53678274e-01 -1.10442579e+00 1.19966376e+00 8.64076689e-02 -8.11398089e-01 1.05774355e+00 8.46473873e-02 -5.02644442e-02 3.24773937e-01 -4.01925258e-02 5.91316819e-01 6.38536036e-01 -1.29571342e+00 -7.69462764e-01 -3.63184810e-01 -2.28022367e-01 -5.41555658e-02 -1.90991044e-01 9.70011503e-02 -6.08151019e-01 -4.90492910e-01 2.64158964e-01 -9.03719008e-01 -6.84202135e-01 2.44531974e-01 -8.16052318e-01 -1.67518422e-01 8.03643465e-01 -3.57403994e-01 1.31572258e+00 -1.97714579e+00 -3.23006511e-01 3.96113575e-01 5.59456110e-01 4.86433566e-01 3.42909157e-01 -9.32774246e-02 5.50214909e-02 2.08832875e-01 -1.23266846e-01 -4.36780363e-01 -3.66849065e-01 2.28502616e-01 3.03060830e-01 7.61490941e-01 3.86937186e-02 5.35131931e-01 -7.68096566e-01 -1.13855445e+00 3.45320523e-01 4.07342672e-01 -4.44291204e-01 -5.70711028e-03 8.67552385e-02 2.49830693e-01 -6.09649003e-01 9.24535871e-01 5.12196660e-01 -7.48680055e-01 1.75849169e-01 -1.44683152e-01 3.11417058e-02 -2.46675119e-01 -1.30852056e+00 1.10580039e+00 -1.56315371e-01 4.89058852e-01 1.19039938e-01 -8.01208794e-01 6.12458646e-01 4.27412927e-01 6.30240440e-01 -2.84438312e-01 3.86992484e-01 3.04575801e-01 9.41740423e-02 -6.45880282e-01 3.20634216e-01 -1.58182636e-01 2.95312613e-01 4.25298512e-02 -1.58954278e-01 2.59371977e-02 2.62881100e-01 1.14992291e-01 1.42214608e+00 -3.19088668e-01 7.50275731e-01 -2.64572591e-01 6.43083334e-01 -2.53307708e-02 6.98698878e-01 1.05541587e+00 -4.79472339e-01 1.09994996e+00 5.78438818e-01 -3.68071645e-01 -6.41570747e-01 -9.24529254e-01 -5.23169219e-01 4.60363388e-01 7.73739442e-02 -3.37158442e-01 -6.89372480e-01 -9.82529521e-01 -5.20944111e-02 5.40756702e-01 -8.52833331e-01 1.84924364e-01 -3.40676278e-01 -1.18523192e+00 4.43142533e-01 4.71667856e-01 2.38708422e-01 -9.43795621e-01 -1.08007562e+00 3.44097048e-01 -2.39033327e-02 -1.07947886e+00 -1.25637010e-01 3.14229518e-01 -9.92057621e-01 -1.36850572e+00 -9.00468111e-01 -5.15851080e-01 8.87735724e-01 9.39211920e-02 1.13984203e+00 4.28889394e-01 -9.08582509e-01 1.65433530e-02 -3.72113794e-01 -7.86666691e-01 -4.20299977e-01 4.15984839e-02 -2.01923743e-01 -2.23480407e-02 2.51387626e-01 -9.14234295e-02 -7.39338160e-01 5.16495347e-01 -7.30316341e-01 2.80922532e-01 1.01112568e+00 7.28748918e-01 8.31390619e-01 -1.96628854e-01 3.89433771e-01 -1.28046012e+00 4.39353377e-01 -5.80584586e-01 -5.37498057e-01 3.05723101e-01 -6.28761053e-01 -2.27530256e-01 2.24065736e-01 -2.36470610e-01 -7.37218440e-01 5.36567271e-01 -1.18944928e-01 -4.54653054e-01 -1.13193683e-01 2.03712001e-01 5.71956873e-01 -1.87908888e-01 9.64767694e-01 -2.49597654e-02 -4.25000675e-02 -4.25237834e-01 -1.96947366e-01 6.54065371e-01 3.61385107e-01 -9.31965262e-02 4.73803282e-01 7.43079364e-01 3.34709138e-01 -7.63730526e-01 -8.14106226e-01 -8.36775839e-01 -7.24487245e-01 -2.90266663e-01 9.76840615e-01 -5.35809100e-01 -3.50231498e-01 1.86146125e-01 -9.83588398e-01 1.88795314e-03 -2.58736879e-01 4.60595071e-01 -2.54710913e-01 3.15069616e-01 -5.12264788e-01 -9.27227557e-01 -3.96014839e-01 -1.30270481e+00 1.08763576e+00 1.31405458e-01 -9.94130969e-02 -9.07276511e-01 -1.32027894e-01 3.11225623e-01 3.12247634e-01 6.79756224e-01 6.93224132e-01 -6.79766119e-01 -4.44236606e-01 -4.33840722e-01 -4.48486894e-01 4.39208336e-02 1.42680332e-01 2.02067509e-01 -9.48331118e-01 -1.52845994e-01 -1.73860401e-01 -4.85501112e-03 1.07912862e+00 7.66077757e-01 1.45748901e+00 1.56340942e-01 -9.07414734e-01 4.22573984e-01 1.45076871e+00 1.60485506e-01 4.82793331e-01 3.70687395e-01 3.09770286e-01 3.89316708e-01 5.54640174e-01 5.56181431e-01 2.19639122e-01 4.73762631e-01 6.83734655e-01 -4.63812858e-01 -2.63095886e-01 2.41845638e-01 -3.03236842e-01 2.31385350e-01 -4.80742246e-01 -2.83621669e-01 -1.18274844e+00 6.62632525e-01 -1.81511283e+00 -6.78902447e-01 -5.58911026e-01 2.05092597e+00 5.80223858e-01 5.70317686e-01 1.60263360e-01 2.35377297e-01 5.81358016e-01 -3.59069794e-01 -3.05890769e-01 -6.36410266e-02 1.79973453e-01 -1.21760271e-01 6.96322799e-01 7.29233250e-02 -1.27620292e+00 4.41437781e-01 7.05345201e+00 7.27136791e-01 -8.54238331e-01 3.20726216e-01 1.01062703e+00 -1.01676978e-01 2.04035625e-01 -2.80770421e-01 -9.73063767e-01 4.83195484e-01 7.11133480e-01 2.78831601e-01 -4.39586908e-01 9.11838531e-01 1.74223512e-01 -6.43524766e-01 -1.11223209e+00 7.91794717e-01 1.48113117e-01 -1.45204008e+00 -2.70232111e-01 1.05795540e-01 5.76893210e-01 -1.28194010e-02 -3.56970616e-02 2.33386710e-01 1.61154360e-01 -9.43263650e-01 3.31111431e-01 4.43472534e-01 5.92601836e-01 -3.87093633e-01 1.03000438e+00 4.38257098e-01 -8.38672459e-01 -1.86065945e-03 -2.20705926e-01 4.77370173e-01 1.42365828e-01 9.97470498e-01 -1.47436345e+00 3.37151408e-01 7.66321063e-01 2.19601467e-01 -9.84246790e-01 1.57385647e+00 -9.91261471e-03 8.20494533e-01 -4.44032043e-01 -1.71773612e-01 2.93327659e-01 4.07866985e-01 5.77108502e-01 1.62688041e+00 2.80682445e-01 1.56372964e-01 1.61004558e-01 4.98871595e-01 1.07444629e-01 2.25098252e-01 -3.85904402e-01 5.06639421e-01 -1.89380273e-02 1.55154419e+00 -1.30052817e+00 -4.79129821e-01 -3.93630564e-01 4.98483837e-01 -1.18215680e-02 -2.58353740e-01 -9.99183893e-01 -3.03526670e-02 -3.03952098e-01 6.04516625e-01 3.28039259e-01 8.98639336e-02 -5.27420878e-01 -7.43523300e-01 -2.65095681e-02 -4.77071226e-01 7.60206163e-01 -5.77953696e-01 -1.11106682e+00 6.26467228e-01 6.58336654e-02 -1.50304306e+00 -2.83677876e-01 -8.13961685e-01 -5.39527059e-01 4.26237822e-01 -1.67653906e+00 -1.05186176e+00 -4.11692679e-01 4.34004515e-01 4.74956214e-01 -6.49740770e-02 6.03635371e-01 2.30628446e-01 -5.12044370e-01 3.78237993e-01 -3.26439813e-02 4.94403318e-02 6.84986889e-01 -1.36638153e+00 -2.34602869e-01 6.74461603e-01 -7.01741129e-02 2.49102160e-01 7.08354056e-01 -4.68439668e-01 -9.34957623e-01 -9.20754075e-01 5.58290958e-01 -3.92529130e-01 3.20993543e-01 -2.13274807e-02 -8.62452388e-01 5.65032542e-01 8.11640639e-03 4.95919883e-01 6.31699681e-01 -2.36389786e-02 2.34330788e-01 1.40890718e-01 -1.52857435e+00 3.06186587e-01 8.10698867e-01 3.58800769e-01 -5.38421631e-01 6.97591364e-01 9.72123370e-02 -6.33950353e-01 -5.83431125e-01 6.81930602e-01 4.17430639e-01 -1.29966497e+00 9.61309552e-01 -2.30980664e-01 2.26704359e-01 -2.59418160e-01 1.91117302e-01 -9.51995254e-01 -4.44294304e-01 -1.53697878e-01 -2.03842834e-01 8.56654823e-01 6.65627778e-01 -4.54157919e-01 1.07383990e+00 3.31346750e-01 -7.00622052e-02 -1.04367352e+00 -9.55018461e-01 -5.70982456e-01 -2.55960077e-01 -2.76288003e-01 3.74228433e-02 7.49570668e-01 -2.92787850e-01 5.49595207e-02 2.67867856e-02 2.41987005e-01 6.02595747e-01 -4.02927101e-02 4.62386072e-01 -1.38400805e+00 -4.55940783e-01 -5.25758982e-01 -6.81817353e-01 -4.72047120e-01 -5.21900773e-01 -6.49659395e-01 -5.78385731e-03 -1.80793583e+00 4.63754207e-01 -6.57165051e-01 -3.41885209e-01 5.16754210e-01 -2.66045809e-01 4.80628222e-01 -2.05481306e-01 2.47415185e-01 -6.81340218e-01 -1.21353105e-01 1.34135973e+00 -7.75010511e-02 -1.59516573e-01 3.50461692e-01 -4.70302880e-01 1.32476628e+00 6.95548773e-01 -5.35328507e-01 -2.93694198e-01 -1.25790834e-02 7.70849138e-02 2.37109572e-01 3.56498301e-01 -1.35998440e+00 3.87920648e-01 -1.72886194e-03 6.91952407e-01 -9.32935238e-01 2.74608195e-01 -8.05221796e-01 -2.98471868e-01 8.28501821e-01 5.24777994e-02 5.93912937e-02 1.72131881e-01 4.73979503e-01 -7.76511580e-02 -4.77880359e-01 1.14452457e+00 -4.63539809e-01 -7.11912811e-01 4.80647804e-03 -4.92364556e-01 -1.71630427e-01 1.56729174e+00 -4.65458661e-01 -8.34176317e-02 -5.70770726e-02 -1.02923870e+00 2.95007765e-01 -1.22418523e-01 6.69363812e-02 6.79072976e-01 -8.76980782e-01 -8.31944704e-01 3.25608961e-02 1.59061700e-01 2.10750923e-01 1.38457909e-01 1.31705666e+00 -7.69775689e-01 4.42108572e-01 7.69563112e-03 -1.00280464e+00 -1.64605415e+00 5.18589377e-01 6.37951851e-01 -6.88656211e-01 -7.21391439e-01 9.21891093e-01 1.04216106e-01 -7.72440284e-02 2.25840732e-01 -5.85956097e-01 -4.59209681e-01 4.51415852e-02 4.81409639e-01 4.16051835e-01 4.20273423e-01 -3.64370972e-01 -5.64508677e-01 3.69123071e-01 -4.17071462e-01 2.20180869e-01 1.08975697e+00 1.38185516e-01 1.27622619e-01 4.82529849e-02 7.02734113e-01 -1.04241990e-01 -9.43759680e-01 -1.06841959e-01 1.56904876e-01 -3.32065672e-01 2.86865503e-01 -8.97815347e-01 -1.05693519e+00 6.32426083e-01 8.90038371e-01 3.30578029e-01 1.07823920e+00 1.29636541e-01 1.71214938e-01 2.51504779e-01 4.08573896e-01 -1.01253891e+00 -3.71315628e-02 -4.93755341e-02 7.74633169e-01 -1.50565350e+00 3.55431020e-01 -6.74406767e-01 -4.49988931e-01 1.08675921e+00 7.57209539e-01 -2.04099715e-02 8.00585270e-01 5.05474627e-01 -1.44210979e-02 -3.28004092e-01 -7.41186857e-01 -2.19694927e-01 4.24927384e-01 3.95287871e-01 4.27645296e-01 1.13154881e-01 -5.48508644e-01 2.36267671e-01 2.74412006e-01 1.17385119e-01 6.95487618e-01 1.01040685e+00 -7.32953846e-01 -7.42276192e-01 -6.89024210e-01 8.75607491e-01 -8.38333309e-01 1.05309695e-01 -3.71115178e-01 1.22306347e+00 2.98130184e-01 8.20981503e-01 -7.75161609e-02 -9.65145975e-02 3.54773641e-01 -3.27085465e-01 3.57900083e-01 -7.65397489e-01 -7.08911955e-01 1.72602937e-01 1.08950503e-01 -3.94940436e-01 -3.48787695e-01 -8.42588902e-01 -1.28896677e+00 1.56092122e-01 -6.75519586e-01 -1.88243330e-01 5.98987699e-01 8.37995470e-01 6.58625588e-02 6.95087135e-01 2.89042085e-01 -7.03215897e-01 -5.12105346e-01 -1.06856430e+00 -4.85657603e-01 3.00438792e-01 2.00545996e-01 -7.86087632e-01 -2.57816374e-01 1.46105047e-02]	[15.050665855407715, -2.3602144718170166]
eaaa7ee7-14f9-4b70-be63-d8804da754ce	the-use-of-second-life-for-deception	null	null	https://aclanthology.org/W16-0805	https://aclanthology.org/W16-0805.pdf	The Use of Second Life for Deception Detection Research	null	['Kevin McCabe', 'Stephen Kunath']	2016-06-01	null	null	null	ws-2016-6	['deception-detection']	['miscellaneous']	[-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]	[-7.401474952697754, 3.6845099925994873]
33544e91-57fb-4aad-a702-a2e8a75f367e	you-are-catching-my-attention-are-vision	null	null	http://openaccess.thecvf.com//content/CVPR2023/html/Yuan_You_Are_Catching_My_Attention_Are_Vision_Transformers_Bad_Learners_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Yuan_You_Are_Catching_My_Attention_Are_Vision_Transformers_Bad_Learners_CVPR_2023_paper.pdf	You Are Catching My Attention: Are Vision Transformers Bad Learners Under Backdoor Attacks?	Vision Transformers (ViTs), which made a splash in the field of computer vision (CV), have shaken the dominance of convolutional neural networks (CNNs). However, in the process of industrializing ViTs, backdoor attacks have brought severe challenges to security. The success of ViTs benefits from the self-attention mechanism. However, compared with CNNs, we find that this mechanism of capturing global information within patches makes ViTs more sensitive to patch-wise triggers. Under such observations, we delicately design a novel backdoor attack framework for ViTs, dubbed BadViT, which utilizes a universal patch-wise trigger to catch the model's attention from patches beneficial for classification to those with triggers, thereby manipulating the mechanism on which ViTs survive to confuse itself. Furthermore, we propose invisible variants of BadViT to increase the stealth of the attack by limiting the strength of the trigger perturbation. Through a large number of experiments, it is proved that BadViT is an efficient backdoor attack method against ViTs, which is less dependent on the number of poisons, with satisfactory convergence, and is transferable for downstream tasks. Furthermore, the risks inside of ViTs to backdoor attacks are also explored from the perspective of existing advanced defense schemes.	['Yu Cheng', 'Kai Zou', 'Pan Zhou', 'Zenghui Yuan']	2023-01-01	null	null	null	cvpr-2023-1	['backdoor-attack']	['adversarial']	[-4.25818227e-02 -1.34023294e-01 -1.23709723e-01 2.74910808e-01 -5.38137555e-02 -8.45767081e-01 5.52709103e-01 -2.44013876e-01 -2.70348966e-01 9.04391259e-02 -2.03980550e-01 -5.51214397e-01 1.77103415e-01 -9.31398988e-01 -8.57334912e-01 -1.05625057e+00 1.18859872e-01 -6.40697837e-01 7.05883384e-01 -4.73112911e-01 2.43154466e-01 5.73254883e-01 -1.01200914e+00 9.01741832e-02 3.95730525e-01 1.18394315e+00 -6.87669665e-02 3.04005712e-01 2.84592211e-01 8.09961915e-01 -9.06652451e-01 -7.84552932e-01 5.71691632e-01 -1.96448982e-01 -5.51680028e-01 -4.22071427e-01 3.61660004e-01 -2.90779531e-01 -8.94487143e-01 1.29776359e+00 3.73569757e-01 -3.93879294e-01 2.55995482e-01 -1.53868330e+00 -8.29155862e-01 5.56758821e-01 -8.50190818e-01 4.94515598e-01 -4.83800948e-01 5.97705245e-01 9.02677894e-01 -4.72313762e-01 1.81263804e-01 1.25200200e+00 7.50036359e-01 6.45322502e-01 -8.32140565e-01 -1.11549675e+00 3.52862448e-01 2.42761344e-01 -1.13845420e+00 -1.75253570e-01 7.95727730e-01 -1.54839501e-01 4.52309072e-01 3.74082983e-01 4.84488636e-01 1.45722508e+00 5.30996919e-01 6.67492390e-01 9.82738733e-01 -1.56613544e-01 2.61094540e-01 1.37528867e-01 2.88495511e-01 6.58861279e-01 3.70591640e-01 5.36781371e-01 -4.47998643e-01 -4.31411535e-01 7.28299439e-01 -1.79369338e-02 -4.32074130e-01 -2.63701886e-01 -6.86311960e-01 1.07162845e+00 8.09507728e-01 1.94418691e-02 -1.26297206e-01 2.57112205e-01 5.17966568e-01 1.67462528e-01 1.97143570e-01 3.30960304e-01 -3.94812644e-01 3.04694504e-01 -3.68838608e-01 -6.83743358e-02 4.09183919e-01 5.22508264e-01 4.79508489e-01 1.27244070e-01 -3.28134596e-01 2.86202252e-01 2.41965845e-01 6.12510502e-01 2.95845121e-01 -6.01247847e-01 2.62837082e-01 5.97073019e-01 -4.10990328e-01 -1.27423680e+00 -7.94150084e-02 -7.46067882e-01 -1.02692235e+00 3.54827762e-01 3.65934342e-01 -2.40742594e-01 -7.22733557e-01 2.06168985e+00 4.15722728e-01 5.06405473e-01 -1.18126690e-01 7.18141496e-01 4.66716379e-01 5.65424740e-01 -1.28216922e-01 -3.90596651e-02 1.54202282e+00 -7.08189785e-01 -2.45506108e-01 -9.01194736e-02 4.51755524e-01 -3.46013397e-01 1.10307741e+00 3.65228772e-01 -6.06677055e-01 -2.38825545e-01 -1.17027819e+00 4.28803891e-01 -3.23352396e-01 -4.91517752e-01 5.18443882e-01 1.16542840e+00 -7.54704595e-01 2.14513406e-01 -7.32499659e-01 -2.88592838e-02 8.75729382e-01 3.18796843e-01 -7.29386732e-02 1.72359943e-01 -1.37156928e+00 6.58435345e-01 -4.70730774e-02 2.10582018e-01 -1.44932926e+00 -6.66389227e-01 -4.06991690e-01 2.73870170e-01 4.77462411e-01 -5.22392273e-01 8.95044267e-01 -7.19026685e-01 -1.34974194e+00 4.21453655e-01 2.36852825e-01 -7.94813752e-01 3.85936111e-01 -5.92491180e-02 2.08113659e-02 2.50834882e-01 -4.17648889e-02 4.78316545e-01 1.33824694e+00 -1.03175771e+00 -4.05154616e-01 -4.38546270e-01 4.29068625e-01 -4.15466428e-01 -9.99715865e-01 2.17508286e-01 -2.89430350e-01 -5.35102904e-01 -3.41797858e-01 -9.36584711e-01 -3.40370297e-01 1.92806497e-02 -5.63919544e-01 -9.20893103e-02 1.29951382e+00 -3.68049145e-02 1.07170796e+00 -2.50311995e+00 -2.48966053e-01 1.33633450e-01 6.22141182e-01 8.92867506e-01 -1.08027302e-01 3.39917183e-01 4.37588505e-02 3.60693902e-01 -4.02383879e-03 -5.81596047e-02 -2.81690508e-01 2.60792851e-01 -1.03834403e+00 6.19765759e-01 1.01434395e-01 8.31215918e-01 -6.84738100e-01 -1.29671231e-01 -3.55865508e-02 4.34887886e-01 -5.77963829e-01 8.31295177e-02 1.56839434e-02 2.28361383e-01 -6.85739875e-01 5.89615643e-01 7.76314557e-01 -2.32724637e-01 -2.61717230e-01 -1.79072514e-01 -9.56480280e-02 -1.10447936e-01 -5.74622571e-01 6.91544831e-01 -2.57251784e-02 6.16168916e-01 2.15678170e-01 -9.97484922e-01 8.40011001e-01 2.44439319e-01 1.90986246e-01 -5.83962321e-01 5.30306995e-01 -1.70515656e-01 -9.10880715e-02 -3.27106297e-01 1.50205761e-01 3.77200618e-02 -3.80529948e-02 1.69773445e-01 -2.27431148e-01 4.34140325e-01 -4.33143556e-01 3.95024896e-01 1.32184780e+00 -4.67581362e-01 -1.09312929e-01 -1.97185352e-01 4.46999848e-01 -2.24582125e-02 7.68525779e-01 9.08320725e-01 -5.19558728e-01 1.62250131e-01 7.27522552e-01 -4.36231941e-01 -5.91930628e-01 -9.07872379e-01 -5.65428734e-02 6.72314703e-01 6.55717790e-01 -4.27388340e-01 -9.44192529e-01 -9.44639146e-01 -9.54695344e-02 2.36169145e-01 -6.66943312e-01 -9.01713073e-01 -3.04414719e-01 -8.80169868e-01 1.34270895e+00 4.75372076e-01 9.44971502e-01 -8.15067291e-01 -7.05535591e-01 -1.53947800e-01 -5.90414740e-02 -1.18571615e+00 -5.50980985e-01 1.50455222e-01 -6.14876449e-01 -1.33653891e+00 -5.36291242e-01 -4.98916090e-01 4.65698212e-01 8.39141369e-01 6.10108614e-01 2.40925580e-01 -2.70653367e-01 2.23375916e-01 -3.04458231e-01 -7.13914275e-01 -3.14490616e-01 2.98230117e-03 9.84532014e-02 3.39966923e-01 2.10215840e-02 -6.72251821e-01 -6.65760279e-01 5.33658981e-01 -1.02325773e+00 -3.87622058e-01 4.93306607e-01 9.24973845e-01 8.57531130e-02 2.79303312e-01 4.28821504e-01 -5.88694930e-01 5.16149521e-01 -4.11956638e-01 -8.55995178e-01 1.82410762e-01 -4.57845420e-01 -1.71061680e-01 8.35260272e-01 -7.90096104e-01 -6.76666498e-01 -3.81955832e-01 -6.89197425e-03 -1.06290329e+00 2.29702815e-01 8.59327428e-03 -2.79368579e-01 -5.59980154e-01 7.86517143e-01 2.32548431e-01 8.70821774e-02 -2.98118770e-01 3.32555860e-01 4.75457609e-01 5.71746290e-01 -3.90714973e-01 1.13740551e+00 8.69797409e-01 1.05659187e-01 -9.62312758e-01 -5.97159386e-01 -8.47769529e-03 2.08748460e-01 -1.41661480e-01 8.29872310e-01 -7.67834067e-01 -1.16140437e+00 9.99055564e-01 -1.17397892e+00 -1.26481026e-01 6.17982857e-02 2.74060350e-02 1.25991344e-01 4.81169492e-01 -7.81748891e-01 -7.95271218e-01 -3.95867527e-01 -1.27088010e+00 5.51253736e-01 3.89550000e-01 3.71329665e-01 -6.94930851e-01 -1.68644160e-01 3.97844106e-01 3.84036899e-01 2.13801637e-01 9.77453232e-01 -4.85390395e-01 -8.53959620e-01 -1.45535931e-01 -2.68109649e-01 7.02784956e-01 -9.85612348e-02 9.08412859e-02 -1.21131611e+00 -4.52880383e-01 3.96846056e-01 -1.58373922e-01 9.80195582e-01 2.42904961e-01 1.21925628e+00 -6.81640387e-01 -4.26309884e-01 9.06847119e-01 1.35347164e+00 2.52171487e-01 7.58933306e-01 5.58675885e-01 7.51428246e-01 3.27834725e-01 2.01706544e-01 2.30289429e-01 7.48015046e-02 4.59650099e-01 1.21801269e+00 -2.75878370e-01 1.60317078e-01 -2.27195919e-01 6.69034898e-01 2.48123229e-01 2.80408442e-01 -2.78897494e-01 -5.27032018e-01 4.04568583e-01 -1.56745911e+00 -9.30100262e-01 8.65428429e-03 2.05756235e+00 5.89648485e-01 4.20846134e-01 3.41838524e-02 5.16338274e-02 8.20908368e-01 2.25786522e-01 -5.78358948e-01 -3.94130826e-01 -1.04263216e-01 7.41617456e-02 8.79436016e-01 -3.61464662e-03 -9.60418940e-01 1.05261719e+00 6.37690067e+00 1.13901138e+00 -1.45709836e+00 2.28486389e-01 7.50796556e-01 -2.09877584e-02 -4.74692881e-02 1.50097430e-01 -9.69497323e-01 6.47238553e-01 5.31023979e-01 4.76745293e-02 3.22916478e-01 9.04502094e-01 1.25001475e-01 2.74976343e-01 -6.55535161e-01 5.96771419e-01 -2.23656192e-01 -1.42061436e+00 9.36436728e-02 3.52259994e-01 4.29211974e-01 2.44515821e-01 4.53182012e-01 1.74124151e-01 3.77450407e-01 -7.37944543e-01 7.32218206e-01 -2.79835373e-01 3.77165467e-01 -8.32400501e-01 5.05930007e-01 4.53854382e-01 -9.03015733e-01 -5.26708722e-01 -5.41895270e-01 4.45936769e-02 -2.84001082e-01 5.30168772e-01 -4.39163148e-01 3.49216729e-01 9.23840880e-01 1.71187073e-01 -5.71386695e-01 7.33551145e-01 -2.42836595e-01 9.47885573e-01 -2.27602318e-01 1.30833924e-01 5.32343388e-01 9.99780148e-02 8.53151858e-01 8.02700162e-01 -9.77719054e-02 -8.31406843e-03 4.26467434e-02 9.47460055e-01 -9.76852477e-02 -4.50583667e-01 -8.46437395e-01 6.69346079e-02 6.69986069e-01 1.28113377e+00 -6.04938507e-01 -1.00711817e-02 -1.87870562e-01 5.98850727e-01 1.06079921e-01 2.27875099e-01 -1.24507725e+00 -3.13061059e-01 1.10805976e+00 -6.31583929e-02 4.31226194e-01 -7.44178444e-02 -3.62461179e-01 -8.60481679e-01 -6.14170954e-02 -1.02738309e+00 2.22944915e-01 -4.67315674e-01 -1.29087269e+00 6.43026531e-01 -1.92664608e-01 -1.04668462e+00 5.92556059e-01 -4.93096352e-01 -1.12913060e+00 4.76345628e-01 -1.48925340e+00 -1.10752869e+00 -3.68185714e-02 9.72656250e-01 1.92458436e-01 -2.75103033e-01 3.83339703e-01 1.31360665e-01 -9.43704903e-01 1.04901206e+00 -1.47466809e-01 4.19266731e-01 4.81081039e-01 -4.89821583e-01 4.62941945e-01 1.27990329e+00 2.54614532e-01 9.02066708e-01 5.61090112e-01 -4.04541552e-01 -1.44660378e+00 -1.13794363e+00 1.50265083e-01 -2.52435863e-01 7.28997469e-01 -4.14543718e-01 -7.61848450e-01 3.87009889e-01 2.03224480e-01 3.79432216e-02 2.40008667e-01 -4.59975936e-02 -9.78278160e-01 -3.21371168e-01 -1.10292721e+00 8.83619010e-01 8.76110256e-01 -4.72582221e-01 -2.63315320e-01 1.66866973e-01 1.11426759e+00 -8.63463879e-02 -2.33459815e-01 3.69986534e-01 3.78102332e-01 -1.12177873e+00 1.08976328e+00 -5.03072262e-01 2.66777217e-01 -1.56233221e-01 5.16118519e-02 -1.03756166e+00 -3.85713726e-01 -9.74394202e-01 -7.02674165e-02 1.13365877e+00 3.14972140e-02 -1.05677867e+00 7.02163875e-01 -6.62662694e-03 -1.42104581e-01 -8.08056355e-01 -1.08982480e+00 -1.10607719e+00 1.99006498e-01 -2.45692521e-01 6.02725089e-01 8.47744763e-01 -1.52366176e-01 2.41211697e-01 -5.38585544e-01 7.62813270e-01 7.20196724e-01 -4.06755477e-01 6.76405370e-01 -9.61582720e-01 -4.24719304e-01 -5.59506238e-01 -5.00423193e-01 -8.53359520e-01 -1.67214006e-01 -5.97039223e-01 -1.97610021e-01 -6.02625370e-01 9.29715410e-02 -3.60122651e-01 -5.61747491e-01 7.67630637e-01 -1.99459627e-01 5.08061707e-01 5.05699337e-01 3.65688562e-01 -1.96928993e-01 4.53451216e-01 1.25526536e+00 -3.37404519e-01 -4.86556217e-02 1.83247596e-01 -1.09665573e+00 6.45057678e-01 8.03132176e-01 -6.84062779e-01 -6.38641775e-01 -3.64658564e-01 2.08895087e-01 -3.28477114e-01 7.87764788e-01 -8.56518447e-01 2.82199621e-01 -1.47449106e-01 -9.29445997e-02 -2.88370073e-01 2.60907590e-01 -7.56712258e-01 -2.67233431e-01 7.89507747e-01 -8.66935849e-02 -2.91883177e-03 2.67844260e-01 7.83500493e-01 6.29907623e-02 3.63433957e-02 1.02724338e+00 1.57820620e-02 -3.84306043e-01 5.72808564e-01 -5.21894872e-01 8.62122849e-02 1.11900818e+00 -2.35149637e-01 -6.49058402e-01 -1.04744516e-01 -1.43998340e-01 2.16044173e-01 1.79582298e-01 3.59529644e-01 5.50399899e-01 -1.00955987e+00 -3.62503648e-01 4.12358046e-01 -1.45856738e-01 -1.04682505e-01 2.14041963e-01 8.22999179e-01 -1.05215266e-01 2.85020143e-01 -1.53674364e-01 -7.18482912e-01 -1.46397388e+00 9.14880157e-01 3.64058584e-01 -1.15101241e-01 -8.75107884e-01 1.11040783e+00 9.43276107e-01 2.11610317e-01 4.84245420e-01 -1.01424173e-01 -8.07312429e-02 -1.99175432e-01 6.96644306e-01 1.95712149e-01 -6.71044737e-02 -2.42665663e-01 -4.75591719e-01 2.14831844e-01 -2.83509970e-01 1.43381253e-01 1.03793871e+00 1.07149975e-02 -9.34296250e-02 -2.16025025e-01 9.58622694e-01 7.48236477e-02 -1.48823488e+00 -1.95132017e-01 -3.58027726e-01 -4.81961071e-01 2.28066832e-01 -4.91515458e-01 -1.55331922e+00 9.45095956e-01 4.15850103e-01 4.76374328e-01 1.07386553e+00 -1.66469708e-01 1.15197766e+00 2.96733916e-01 4.45542604e-01 -5.23874044e-01 4.15739357e-01 5.47938645e-01 3.58125955e-01 -6.78331614e-01 -5.34016430e-01 -4.22474414e-01 -5.71773946e-01 6.77865446e-01 6.63043737e-01 -2.89868623e-01 6.86918259e-01 4.83147621e-01 1.07297987e-01 -2.91707754e-01 -7.79890656e-01 1.91920772e-01 -3.51576388e-01 7.32562721e-01 -4.83514130e-01 -2.55496591e-01 5.49147464e-02 6.86429799e-01 1.07519314e-01 -3.20598394e-01 5.86298764e-01 5.95754206e-01 -5.44022739e-01 -1.08771074e+00 -6.16537690e-01 -7.75149390e-02 -6.55565977e-01 -1.64321005e-01 -5.37330806e-01 6.44584715e-01 3.82727861e-01 1.07437134e+00 -2.99236327e-01 -7.85557270e-01 4.27600183e-02 -3.78942907e-01 1.52424514e-01 -2.44225144e-01 -8.93431664e-01 -1.41304433e-01 -4.33518648e-01 -6.23750806e-01 1.27539679e-01 -2.49405637e-01 -7.52655447e-01 -6.12162113e-01 -7.39675581e-01 1.24526642e-01 4.62420732e-01 9.25703108e-01 4.12805438e-01 4.57648546e-01 1.18216813e+00 -3.52898628e-01 -1.12273860e+00 -5.03121078e-01 -4.90269363e-01 -5.92922270e-02 3.60749066e-01 -7.17884064e-01 -5.21737874e-01 -3.75643790e-01]	[5.647037982940674, 7.768864154815674]
2804ab8e-8dae-42a3-8dae-e625b81e2517	a-note-on-the-regularity-of-images-generated	2204.10588	null	https://arxiv.org/abs/2204.10588v2	https://arxiv.org/pdf/2204.10588v2.pdf	A Note on the Regularity of Images Generated by Convolutional Neural Networks	The regularity of images generated by convolutional neural networks, such as the U-net, generative networks, or the deep image prior, is analyzed. In a resolution-independent, infinite dimensional setting, it is shown that such images, represented as functions, are always continuous and, in some circumstances, even continuously differentiable, contradicting the widely accepted modeling of sharp edges in images via jump discontinuities. While such statements require an infinite dimensional setting, the connection to (discretized) neural networks used in practice is made by considering the limit as the resolution approaches infinity. As practical consequence, the results of this paper in particular provide analytical evidence that basic L2 regularization of network weights might lead to over-smoothed outputs.	['Martin Holler', 'Andreas Habring']	2022-04-22	null	null	null	null	['l2-regularization']	['methodology']	[ 3.17203373e-01 6.88781142e-01 1.22559384e-01 -6.64167106e-03 -1.94380045e-01 -4.36075121e-01 6.63577020e-01 -3.93836856e-01 -2.88256198e-01 8.71335745e-01 1.65495187e-01 -1.70670763e-01 -3.81533474e-01 -8.48349571e-01 -8.65352750e-01 -8.57340932e-01 2.48530079e-02 -7.36172348e-02 -1.71602353e-01 1.91927273e-02 1.74726173e-02 7.13567257e-01 -1.39748478e+00 -1.66162714e-01 6.41888201e-01 1.10961998e+00 -3.07461739e-01 7.97097623e-01 -1.38479695e-01 7.55422890e-01 -1.88115567e-01 -5.10365903e-01 4.65753973e-01 -3.48127246e-01 -5.69357693e-01 4.89143908e-01 6.08857036e-01 -3.13948363e-01 -5.84294260e-01 1.41193223e+00 7.91735873e-02 3.85534108e-01 9.68524992e-01 -7.24880517e-01 -1.39156246e+00 3.49654466e-01 -4.07264650e-01 2.03961447e-01 -1.31182775e-01 -5.67354560e-02 6.17131710e-01 -1.00546205e+00 5.56105196e-01 1.17744088e+00 1.24910462e+00 5.28582275e-01 -1.59454656e+00 1.72291264e-01 1.29137784e-01 -5.94758153e-01 -1.22947216e+00 -4.45850164e-01 8.74088109e-01 -5.67953110e-01 6.69225156e-01 1.87114403e-01 2.81564057e-01 1.09184539e+00 3.79210532e-01 2.83670843e-01 8.28381658e-01 -7.48899996e-01 9.53755304e-02 8.81417170e-02 3.16846669e-01 7.65784681e-01 6.39826894e-01 1.30421549e-01 1.23942174e-01 -1.80866808e-01 1.63832366e+00 -1.67239960e-02 -5.30696571e-01 -2.82966554e-01 -7.66203642e-01 9.71147537e-01 3.59724551e-01 3.96454930e-01 -5.64477384e-01 1.98322013e-01 3.28212678e-01 3.45513105e-01 8.12468708e-01 1.72381848e-01 -1.18656708e-02 3.44494343e-01 -7.87046432e-01 2.49005295e-02 8.49403620e-01 9.59476113e-01 6.36657894e-01 4.70920295e-01 1.97453797e-01 5.43721259e-01 7.58906230e-02 1.19647332e-01 2.62604892e-01 -1.35696542e+00 -1.24635035e-02 -1.43304661e-01 3.09454590e-01 -1.22249961e+00 -4.60282117e-01 -5.70823252e-01 -1.24732614e+00 5.53745091e-01 9.00862038e-01 -3.44524503e-01 -7.34321356e-01 1.81221437e+00 -2.24210680e-01 3.35226744e-01 7.50921145e-02 8.96948040e-01 3.59848797e-01 4.15698677e-01 -9.77499485e-02 -3.62103969e-01 8.87782395e-01 -3.95014942e-01 -9.74308312e-01 2.20406115e-01 2.34925419e-01 -6.87189639e-01 1.06664681e+00 4.53159600e-01 -1.36930478e+00 -6.92138672e-01 -1.04107940e+00 -8.31335634e-02 -2.77346045e-01 -5.06736562e-02 6.69798076e-01 5.10312676e-01 -1.18807828e+00 9.34689999e-01 -6.26470506e-01 -2.96013892e-01 5.53191721e-01 1.50828427e-02 -1.97064608e-01 1.29205704e-01 -1.09102213e+00 7.39362478e-01 3.28227244e-02 5.17501712e-01 -5.08051813e-01 -6.75292552e-01 -8.94974589e-01 1.93395823e-01 -8.30686018e-02 -7.32143700e-01 1.03117073e+00 -1.37366748e+00 -1.34774935e+00 9.87142026e-01 -3.17091718e-02 -8.00168276e-01 9.30746615e-01 -2.15527549e-01 -2.08745360e-01 4.32035297e-01 -3.46638411e-01 2.36740112e-01 1.17782271e+00 -1.40386188e+00 -7.52858147e-02 -2.41157100e-01 3.18460107e-01 -6.53347746e-02 -3.54150198e-02 -4.04307365e-01 9.57875699e-02 -7.23495185e-01 1.03284322e-01 -5.16720116e-01 -5.33081412e-01 2.98003554e-01 -3.84697020e-01 1.34037569e-01 5.24574578e-01 -7.84562588e-01 8.41060400e-01 -2.14858747e+00 -1.04163483e-01 2.78455913e-01 4.19025838e-01 -5.50268888e-02 5.71482405e-02 7.86937699e-02 -2.26849198e-01 2.40993798e-01 -3.36779118e-01 -3.64736974e-01 1.80844307e-01 3.24180245e-01 -4.42881256e-01 1.18911076e+00 3.62892836e-01 8.82880390e-01 -4.20655042e-01 -2.98446715e-01 4.10772175e-01 1.03995049e+00 -5.36089301e-01 -3.52665424e-01 3.31866145e-02 3.28987777e-01 -4.46693391e-01 3.36374901e-02 8.40806663e-01 -5.46052098e-01 -4.31219637e-02 3.87700796e-02 -3.11146080e-01 -3.68900836e-01 -1.10140061e+00 1.30447555e+00 -5.61275125e-01 9.91880715e-01 4.25028145e-01 -1.50646460e+00 6.75708711e-01 5.52612126e-01 3.47007573e-01 -5.31260610e-01 1.65103644e-01 1.57379974e-02 -2.81067312e-01 -3.81824285e-01 3.43360215e-01 -4.80933726e-01 4.36398178e-01 -7.53655210e-02 5.80582954e-02 3.51762205e-01 -7.88624808e-02 2.30654757e-02 6.74380898e-01 1.90176144e-01 -4.19677086e-02 -5.80504179e-01 3.71187747e-01 -1.80932596e-01 3.45377743e-01 9.62650776e-01 7.11161830e-03 9.73453879e-01 6.76640511e-01 -4.87402141e-01 -1.52811503e+00 -1.12562323e+00 -8.19251776e-01 3.63305271e-01 -5.09156659e-03 2.31718093e-01 -1.12148833e+00 -1.73172027e-01 -8.74690861e-02 5.22899330e-01 -9.96499956e-01 5.07940091e-02 -5.51362813e-01 -6.02776289e-01 5.53437114e-01 4.64098394e-01 4.71481204e-01 -8.56950343e-01 -5.08161187e-01 3.91356438e-01 3.00297439e-01 -1.22085381e+00 -2.22253352e-01 1.00011207e-01 -1.04135799e+00 -1.08016276e+00 -1.45752215e+00 -7.21124232e-01 9.86238062e-01 -7.24977180e-02 1.14984870e+00 -1.09654078e-02 -1.29484519e-01 8.44512343e-01 2.20557675e-01 4.13140170e-02 -2.52213418e-01 -5.31788707e-01 1.18157975e-01 1.45565659e-01 3.34404521e-02 -8.25395823e-01 -5.97969830e-01 8.07294771e-02 -1.07732940e+00 -1.89241007e-01 3.51504058e-01 8.95295799e-01 6.61085546e-01 3.01805943e-01 7.21403420e-01 -1.00240600e+00 6.36041462e-01 -1.92386016e-01 -7.24193454e-01 2.80717555e-02 -4.07579243e-01 4.74783853e-02 9.76338446e-01 -5.54325938e-01 -1.30639708e+00 -2.46858999e-01 -1.93584785e-01 -7.01448262e-01 -4.57611203e-01 4.98542994e-01 7.51675069e-02 -4.36355025e-01 9.40547109e-01 9.99957919e-02 4.55180705e-02 -5.48345387e-01 3.23252112e-01 -2.60194354e-02 7.72300661e-01 -4.47026700e-01 6.67685151e-01 8.59981894e-01 3.44310462e-01 -1.37562537e+00 -9.58859503e-01 4.01897170e-02 -6.05995536e-01 -1.62562460e-01 1.05369949e+00 -6.42668843e-01 -6.84869766e-01 2.63905644e-01 -1.32562697e+00 -4.23785359e-01 -7.37701654e-01 5.25256753e-01 -1.05365455e+00 4.38956797e-01 -9.12805378e-01 -1.16605926e+00 2.03240052e-01 -8.27944458e-01 4.91521001e-01 2.65979886e-01 8.78629312e-02 -1.64697194e+00 -3.22061032e-01 -2.29364097e-01 5.22638261e-01 6.01314008e-01 8.13413739e-01 -2.56878316e-01 -2.19696939e-01 -2.42891237e-01 -3.38956922e-01 7.42810488e-01 -5.35649844e-02 1.99810103e-01 -1.09782362e+00 9.81972218e-02 5.04751205e-01 -4.37144749e-02 8.85881603e-01 1.18882692e+00 1.12172711e+00 -5.72659433e-01 4.92122993e-02 7.25512326e-01 1.78989744e+00 -1.73758417e-01 7.50449419e-01 -3.14996243e-02 4.65444803e-01 6.67997897e-01 -2.36674786e-01 3.18544298e-01 -3.72022361e-01 2.45999157e-01 4.26827043e-01 -3.63324344e-01 -2.04459324e-01 -1.56799212e-01 -6.44786879e-02 4.65491056e-01 -4.96495247e-01 4.19254377e-02 -4.24289644e-01 4.60481882e-01 -1.70938158e+00 -1.17890334e+00 -3.79100472e-01 2.26305461e+00 4.36951131e-01 3.68325353e-01 -6.01502471e-02 -1.88471901e-03 1.02258360e+00 6.37087179e-03 -4.29151177e-01 -3.33059013e-01 -5.17222285e-01 1.77144229e-01 8.11623752e-01 8.74376953e-01 -9.92745399e-01 3.68701965e-01 7.95180178e+00 7.68981934e-01 -9.74807084e-01 1.81612577e-02 9.34582233e-01 2.57254541e-01 -4.79764640e-01 -1.95936501e-01 -3.70345801e-01 2.92618871e-01 6.86397016e-01 -2.89996684e-01 3.05442631e-01 6.41077042e-01 4.14072841e-01 6.26586974e-02 -7.70199001e-01 9.50580537e-01 -1.38903260e-01 -1.70244932e+00 -2.12566163e-02 3.43713671e-01 8.95909667e-01 -3.37685823e-01 4.58762527e-01 -6.06394410e-02 1.78989172e-01 -1.41161561e+00 5.47481596e-01 1.03112733e+00 7.30484545e-01 -1.01210296e+00 5.00575304e-01 4.23565567e-01 -7.11746693e-01 3.28997254e-01 -7.52431333e-01 -2.56717891e-01 2.16415986e-01 9.52411354e-01 1.26824960e-01 4.49716538e-01 2.97424197e-01 5.53003311e-01 -1.82828367e-01 8.82091224e-01 2.09306717e-01 6.39877141e-01 -3.66247654e-01 4.68774945e-01 5.85383236e-01 -6.51407242e-01 5.12623310e-01 1.19608879e+00 2.56573409e-01 1.29583418e-01 -1.99644521e-01 1.17913949e+00 7.80761912e-02 -1.46181166e-01 -1.03922951e+00 3.60396504e-02 -2.51205862e-01 9.99985814e-01 -8.29065323e-01 -1.96470290e-01 -9.17729795e-01 8.38489532e-01 1.41639978e-01 1.16727161e+00 -7.14646637e-01 -3.65746886e-01 5.92211783e-01 3.92921954e-01 3.20541501e-01 -2.96826482e-01 -6.32363260e-01 -1.14665270e+00 7.41066337e-02 -1.98894829e-01 5.11218607e-03 -6.35778606e-01 -1.46054363e+00 4.72370535e-01 -2.14185417e-01 -8.99340391e-01 -1.46970719e-01 -9.42682266e-01 -7.57957101e-01 1.15605044e+00 -1.33233750e+00 -7.10403144e-01 1.59611940e-01 4.95003819e-01 2.98806787e-01 1.92311957e-01 6.57734513e-01 2.34421477e-01 -2.45481893e-01 3.12645912e-01 3.41625810e-01 2.54114598e-01 -7.47165233e-02 -1.21457505e+00 2.52599299e-01 8.72042358e-01 1.86263472e-01 7.48090267e-01 1.14265978e+00 -3.88258666e-01 -9.93642330e-01 -8.29100072e-01 5.63112259e-01 -2.31648330e-03 6.74715042e-01 -6.29659146e-02 -1.33217859e+00 8.97947371e-01 3.71101379e-01 4.79174346e-01 2.61695176e-01 -7.82081038e-02 4.14971299e-02 6.28385544e-01 -1.23855162e+00 5.74751318e-01 9.53099012e-01 -4.86232370e-01 -3.97796482e-01 2.55633444e-01 4.54778492e-01 -1.71585679e-01 -1.01047456e+00 1.35451823e-01 4.40471828e-01 -1.19931734e+00 1.12965631e+00 -8.79505754e-01 4.83774096e-01 1.98582932e-01 -1.53343365e-01 -1.15210128e+00 -5.58039248e-01 -7.31809258e-01 -2.12211341e-01 8.64966750e-01 3.97503853e-01 -8.42880964e-01 8.02879930e-01 7.88766563e-01 -1.69282854e-01 -6.72219694e-01 -1.01897490e+00 -8.56225252e-01 5.58749080e-01 -3.96870196e-01 -1.11164108e-01 1.08653855e+00 -2.46532276e-01 1.77741051e-02 -3.41087133e-01 2.23275393e-01 1.07422245e+00 -3.05300564e-01 2.01208577e-01 -1.41116714e+00 -2.95498013e-01 -7.49744773e-01 -4.97823864e-01 -1.34227610e+00 2.58764505e-01 -6.30797803e-01 -2.49199152e-01 -1.47084808e+00 -3.21602821e-01 -3.40991616e-01 -1.57488376e-01 -3.12516659e-01 2.99170136e-01 3.27087730e-01 -1.63370907e-01 8.49988684e-02 -4.33676876e-02 4.08125043e-01 1.58116698e+00 2.08885521e-01 7.87148178e-02 2.12643579e-01 -5.98803699e-01 1.39455938e+00 6.97826087e-01 9.34306905e-03 -4.20096666e-01 -2.96556979e-01 3.83430332e-01 1.97146475e-01 8.00259471e-01 -8.31399620e-01 1.37068808e-01 -3.12365945e-02 7.40172148e-01 -2.29233429e-01 2.90058285e-01 -8.89383316e-01 1.52811438e-01 5.04254252e-02 -5.32326162e-01 -2.17889473e-01 3.00897777e-01 6.43861890e-01 -1.44915402e-01 -6.36177897e-01 1.02574277e+00 -3.46456349e-01 -3.45148921e-01 3.17823827e-01 -4.45310444e-01 1.41662672e-01 5.96528351e-01 -6.22043908e-01 -1.09733395e-01 -6.33574307e-01 -1.47192061e+00 -4.22205746e-01 4.91421431e-01 -3.60106438e-01 5.78316927e-01 -1.50922561e+00 -5.49275994e-01 1.84929520e-01 -4.78499144e-01 8.95852000e-02 5.33765554e-01 1.07782280e+00 -5.48343182e-01 3.63440603e-01 -9.79194045e-02 -3.35052758e-01 -5.35438240e-01 6.31016672e-01 7.08770573e-01 1.46997394e-02 -1.37695158e+00 6.14909828e-01 7.49650478e-01 1.73847303e-01 2.69973427e-01 -3.44270200e-01 6.80082962e-02 -1.58173680e-01 4.85063136e-01 3.59028250e-01 -2.15778440e-01 -6.54631555e-01 2.00303927e-01 4.64095592e-01 2.54397303e-01 -2.13465348e-01 1.26099122e+00 -2.85102904e-01 1.14746243e-01 4.72614706e-01 1.27485824e+00 -2.58798394e-02 -1.98731351e+00 -2.73412287e-01 -1.22035526e-01 -2.81842083e-01 1.48014277e-01 -1.62553087e-01 -1.11316109e+00 8.41648400e-01 2.01705366e-01 9.10998285e-01 8.58115494e-01 -1.37401372e-01 3.18279684e-01 2.97637194e-01 -6.71179742e-02 -1.14006388e+00 -2.58283019e-01 4.57191050e-01 1.05770266e+00 -8.96694481e-01 -2.14680731e-01 -2.94491559e-01 -3.11448812e-01 1.26434362e+00 1.84206501e-01 -8.99266541e-01 8.43795121e-01 4.10843223e-01 -1.52645856e-01 8.78950860e-03 -4.30923849e-01 -3.83185558e-02 2.67558515e-01 8.61791730e-01 6.57440722e-01 -2.97853798e-01 -3.23597342e-01 3.47784340e-01 1.66089147e-01 4.38685678e-02 7.31613278e-01 6.15079939e-01 -4.70616609e-01 -2.96571881e-01 -5.06476760e-01 4.02024984e-01 -7.56274819e-01 -7.16841891e-02 5.79388998e-02 1.01637280e+00 -1.29107296e-01 4.86527711e-01 3.89274269e-01 5.50856352e-01 3.15730512e-01 1.35190800e-01 8.25448692e-01 -7.06642866e-02 -9.67826396e-02 2.49681368e-01 -1.35542646e-01 -2.66029894e-01 -4.28684086e-01 -4.64464903e-01 -1.23317170e+00 -4.55210835e-01 -1.74625784e-01 -7.33546093e-02 2.29366899e-01 8.06668818e-01 -6.09801523e-02 6.50595367e-01 2.10334584e-01 -1.06942427e+00 -6.64174080e-01 -1.00545835e+00 -9.79083836e-01 4.64125454e-01 6.97283328e-01 -6.56289220e-01 -8.77330363e-01 3.56261790e-01]	[11.829960823059082, -2.4065184593200684]
f7546f87-6c64-49bb-8f16-a9e579cc311c	extending-label-smoothing-regularization-with	2009.05226	null	https://arxiv.org/abs/2009.05226v1	https://arxiv.org/pdf/2009.05226v1.pdf	Extending Label Smoothing Regularization with Self-Knowledge Distillation	Inspired by the strong correlation between the Label Smoothing Regularization(LSR) and Knowledge distillation(KD), we propose an algorithm LsrKD for training boost by extending the LSR method to the KD regime and applying a softer temperature. Then we improve the LsrKD by a Teacher Correction(TC) method, which manually sets a constant larger proportion for the right class in the uniform distribution teacher. To further improve the performance of LsrKD, we develop a self-distillation method named Memory-replay Knowledge Distillation (MrKD) that provides a knowledgeable teacher to replace the uniform distribution one in LsrKD. The MrKD method penalizes the KD loss between the current model's output distributions and its copies' on the training trajectory. By preventing the model learning so far from its historical output distribution space, MrKD can stabilize the learning and find a more robust minimum. Our experiments show that LsrKD can improve LSR performance consistently at no cost, especially on several deep neural networks where LSR is ineffectual. Also, MrKD can significantly improve single model training. The experiment results confirm that the TC can help LsrKD and MrKD to boost training, especially on the networks they are failed. Overall, LsrKD, MrKD, and their TC variants are comparable to or outperform the LSR method, suggesting the broad applicability of these KD methods.	['Wen-feng Pang', 'Ji-Yue Wang', 'Pei Zhang', 'Jie Li']	2020-09-11	null	null	null	null	['self-knowledge-distillation']	['computer-vision']	[-1.29319534e-01 2.68893331e-01 -4.97942597e-01 -1.32892504e-01 -4.01398748e-01 -4.38530296e-01 6.26366854e-01 -8.98925401e-03 -7.03202546e-01 1.01773024e+00 -1.24423809e-01 -4.56214905e-01 -2.22578451e-01 -6.58578634e-01 -9.76704299e-01 -1.22556543e+00 2.68595874e-01 3.72173429e-01 7.04069376e-01 -1.34513617e-01 -1.92696471e-02 5.71077466e-01 -1.37083054e+00 -6.48191869e-02 1.08537042e+00 7.78168440e-01 3.18468809e-01 4.55591619e-01 -4.61969599e-02 7.57427633e-01 -5.41547298e-01 -2.85306126e-01 1.35612428e-01 -3.07814449e-01 -7.91443765e-01 -5.13568997e-01 4.12771583e-01 -1.24072179e-01 -4.84048724e-01 9.91867065e-01 6.11077011e-01 5.62698126e-01 7.22766757e-01 -1.04327142e+00 -8.43173683e-01 8.23958516e-01 -8.08324516e-01 1.64951220e-01 -5.70991516e-01 7.93764293e-02 4.77426410e-01 -6.50712311e-01 2.94711858e-01 1.26743984e+00 7.66866028e-01 9.80232894e-01 -1.39892924e+00 -8.24334443e-01 4.45569366e-01 -6.22838475e-02 -1.46454072e+00 -2.69897908e-01 5.74438930e-01 -4.43896413e-01 8.74533713e-01 -4.80747223e-02 2.65228689e-01 7.76301801e-01 3.22158113e-02 8.64408195e-01 1.25863278e+00 -5.20864010e-01 5.29999733e-01 5.65065086e-01 5.20771027e-01 8.11601639e-01 1.62734777e-01 3.05823535e-01 -4.79978681e-01 -2.50223756e-01 7.75326669e-01 -1.36185184e-01 -2.56372333e-01 -3.51008266e-01 -5.93408823e-01 7.11656034e-01 5.58373153e-01 3.21179539e-01 1.55228442e-02 3.00851703e-01 2.74156332e-01 4.04481053e-01 8.44132543e-01 3.24225783e-01 -8.60292137e-01 1.07787475e-01 -8.40459049e-01 1.41067669e-01 6.35968983e-01 6.07908964e-01 9.94648635e-01 1.66506112e-01 -3.76104951e-01 1.03614926e+00 1.57595173e-01 5.69316328e-01 6.71954572e-01 -8.17730248e-01 2.02052578e-01 6.12356603e-01 -4.14261036e-03 -4.37919021e-01 -3.43235373e-01 -5.34666896e-01 -9.57338572e-01 4.07846570e-01 3.66552025e-01 -2.06296280e-01 -1.26385176e+00 1.92678177e+00 4.28414941e-01 6.72650278e-01 3.93232107e-01 7.53332198e-01 5.23309410e-01 7.71272540e-01 1.95040196e-01 -3.37728083e-01 7.94482052e-01 -9.70166445e-01 -5.74329615e-01 -6.47984520e-02 9.57762837e-01 -2.28410795e-01 1.26220894e+00 3.18366945e-01 -8.57938349e-01 -5.57487786e-01 -1.01246452e+00 2.36379147e-01 -3.79626960e-01 1.74167708e-01 5.24308860e-01 5.33065856e-01 -1.19649673e+00 1.04734659e+00 -9.71224666e-01 2.45328359e-02 5.90349257e-01 4.48595673e-01 -7.21191019e-02 4.88535911e-02 -1.32299674e+00 1.11484396e+00 6.73419893e-01 -2.26935759e-01 -8.77863646e-01 -1.07162619e+00 -5.45894384e-01 -5.63371368e-02 3.43014240e-01 -3.62149835e-01 1.22207141e+00 -9.21526670e-01 -1.96490431e+00 6.54143095e-01 -2.76301708e-02 -7.13115513e-01 4.18516368e-01 -3.26614022e-01 -1.49974585e-01 -2.22316161e-01 -4.38747078e-01 7.91886866e-01 1.00008035e+00 -1.35449898e+00 -5.20287216e-01 -2.10613504e-01 -2.65613914e-01 1.40907392e-01 -4.41569984e-01 -4.94797826e-01 -3.21285307e-01 -3.93282652e-01 -1.84636354e-01 -9.62499082e-01 -2.47150585e-01 -3.58036399e-01 -2.86355853e-01 -5.65566182e-01 9.10849273e-01 -3.34145874e-01 1.55840087e+00 -2.27524281e+00 1.07378043e-01 2.82959372e-01 2.27754056e-01 1.02872038e+00 -7.74866715e-02 -4.78972606e-02 -8.63326807e-03 9.59223956e-02 -4.09445465e-01 -4.59201336e-01 2.42755320e-02 7.68696964e-01 -5.51357090e-01 4.35591102e-01 1.89279020e-01 8.27871680e-01 -9.26889300e-01 -1.90281570e-01 -1.12417497e-01 4.72135603e-01 -4.95114744e-01 2.53725231e-01 -5.29310942e-01 4.29573148e-01 -2.09966317e-01 4.87633012e-02 9.08686221e-01 -3.87622356e-01 4.77229282e-02 8.58738124e-02 -1.18579388e-01 1.22284040e-01 -9.83364224e-01 1.25751853e+00 -3.54473799e-01 4.43482101e-01 -1.63414255e-01 -1.05691946e+00 1.18961143e+00 2.66260598e-02 7.82083273e-02 -7.06922770e-01 -9.09401700e-02 2.00402856e-01 -1.42466232e-01 -2.44208276e-01 4.47474808e-01 -2.68953681e-01 3.43254894e-01 4.19546276e-01 7.26551116e-02 1.63737927e-02 -2.07829744e-01 3.64706159e-01 8.50902259e-01 2.39452496e-01 -2.45362651e-02 -4.50500607e-01 4.20195222e-01 -2.90509105e-01 7.50218153e-01 9.82643545e-01 -1.98623344e-01 6.05840273e-02 4.21787322e-01 -1.76807523e-01 -1.04852247e+00 -1.20661438e+00 -3.70173156e-01 1.42866230e+00 1.22144274e-01 3.44420820e-02 -8.47118676e-01 -9.98366892e-01 2.85468936e-01 8.94691169e-01 -6.03801727e-01 -7.93584526e-01 -6.41520798e-01 -9.93504405e-01 7.24571407e-01 4.68589455e-01 7.44113922e-01 -7.50886142e-01 -5.70316724e-02 5.36420494e-02 2.41075575e-01 -4.82644826e-01 -4.05235022e-01 7.27069080e-01 -9.32617843e-01 -7.40942538e-01 -8.87032092e-01 -7.62958348e-01 7.88162231e-01 -1.59844663e-02 9.11263347e-01 8.87899846e-02 1.23648591e-01 1.40942633e-01 -1.87404141e-01 -2.65233636e-01 -7.63838053e-01 3.72894853e-01 4.38225120e-01 -3.05547565e-01 3.66622716e-01 -5.70628405e-01 -4.74641740e-01 3.57749879e-01 -8.84365976e-01 -2.17937082e-02 4.21070993e-01 8.14565897e-01 5.65558255e-01 3.88699263e-01 1.18325746e+00 -1.02113867e+00 6.05276465e-01 -5.59404075e-01 -8.02169681e-01 2.55643547e-01 -1.36859345e+00 7.14429975e-01 7.89333999e-01 -1.03970218e+00 -1.24200022e+00 5.43247685e-02 -2.05206558e-01 -8.61347556e-01 -3.46386619e-02 1.30620033e-01 1.80000126e-01 -4.00875956e-02 9.30301905e-01 2.76507914e-01 1.35527954e-01 -7.95725107e-01 6.87929392e-01 4.40436661e-01 5.98092318e-01 -7.31860101e-01 7.27292478e-01 1.67621925e-01 -1.43605962e-01 -4.89421219e-01 -1.18320680e+00 -2.84726620e-01 -4.89958614e-01 4.04540356e-03 5.89522660e-01 -8.21813166e-01 -8.04719388e-01 7.92831659e-01 -7.58954883e-01 -1.03926384e+00 -6.11296237e-01 3.88003558e-01 -2.63053179e-01 5.26138879e-02 -6.31078005e-01 -9.98544157e-01 -2.33818263e-01 -9.34182048e-01 4.89782184e-01 5.87034225e-01 3.07724833e-01 -1.39327002e+00 3.76241565e-01 -1.07318304e-01 5.57194293e-01 -1.34042442e-01 1.20056856e+00 -7.79309571e-01 -2.44203195e-01 3.55175018e-01 -1.57737315e-01 7.95145690e-01 5.92281111e-03 -8.58172551e-02 -1.13260865e+00 -4.71038818e-01 -1.69625059e-01 -6.00129902e-01 1.42050600e+00 4.85001892e-01 9.95807648e-01 -4.89842206e-01 -4.54543114e-01 4.07691121e-01 1.35651720e+00 1.94143578e-01 6.29192054e-01 2.66798824e-01 6.64257646e-01 1.46511585e-01 3.97616208e-01 1.64627835e-01 2.51412243e-01 4.70443040e-01 1.68511257e-01 -2.75338620e-01 -2.64773548e-01 -3.07184815e-01 6.82651341e-01 1.03403044e+00 1.14755645e-01 4.60460735e-03 -6.76473200e-01 3.17136198e-01 -2.07241654e+00 -6.63542628e-01 -6.90224990e-02 2.40270567e+00 1.59044671e+00 2.28078872e-01 3.55405658e-02 -9.30858478e-02 6.38577223e-01 -1.57292355e-02 -1.02541709e+00 -6.54027402e-01 -4.60035577e-02 2.84196138e-01 7.90721238e-01 7.11560249e-01 -8.71627927e-01 1.21634126e+00 6.47883606e+00 1.36971200e+00 -1.14696074e+00 2.79731274e-01 6.34299159e-01 9.31050405e-02 -1.85376585e-01 -1.20351233e-01 -1.40249932e+00 4.94927824e-01 1.20133066e+00 -2.31744675e-03 6.42740309e-01 1.01958144e+00 1.10971451e-01 -4.50384974e-01 -9.74206805e-01 6.58683896e-01 -3.04795146e-01 -1.36598396e+00 -1.42783135e-01 -2.10773364e-01 1.08034456e+00 6.23644777e-02 2.60555595e-01 7.68621624e-01 9.20686066e-01 -8.35384011e-01 3.61762732e-01 7.31134593e-01 5.88409305e-01 -9.26077545e-01 5.06026685e-01 6.42933905e-01 -8.47184896e-01 -4.78414483e-02 -7.38016844e-01 1.72921121e-01 -2.04867899e-01 8.62161875e-01 -9.46008265e-01 2.18086407e-01 5.94159365e-01 5.13689399e-01 -4.46135730e-01 6.93131983e-01 -3.80373061e-01 1.04038548e+00 -2.93476313e-01 8.91088769e-02 1.70045510e-01 -2.32605278e-01 3.66576046e-01 1.20345199e+00 -1.62337676e-01 2.85923518e-02 1.63690984e-01 8.86234164e-01 -2.39835426e-01 -2.61147499e-01 -4.13397729e-01 1.49397552e-01 6.27927899e-01 9.64078665e-01 -4.53256458e-01 -5.59391379e-01 6.45640120e-02 8.29652071e-01 8.76170814e-01 4.60534215e-01 -9.11922216e-01 -2.53227085e-01 6.51692927e-01 -2.04702485e-02 2.97828257e-01 -3.88076231e-02 -8.67286101e-02 -8.00871134e-01 -3.07996869e-01 -4.69779849e-01 3.94304127e-01 -5.06904721e-01 -1.41174972e+00 5.05297363e-01 1.25104666e-01 -7.73561120e-01 4.93190698e-02 -3.60990673e-01 -4.77293760e-01 8.10545743e-01 -1.91987526e+00 -5.18229187e-01 6.42909482e-02 6.30933702e-01 6.05698153e-02 -1.10201053e-01 6.47730947e-01 3.36290032e-01 -7.60328472e-01 8.72681379e-01 7.37234652e-01 -2.32932195e-01 8.79506350e-01 -1.51669478e+00 9.17918906e-02 4.81801718e-01 -3.10703814e-01 6.15273356e-01 5.53880811e-01 -6.80141687e-01 -9.41400528e-01 -1.51653123e+00 3.42287064e-01 -3.32327724e-01 5.78217745e-01 -2.59429455e-01 -1.54332018e+00 5.67917824e-01 -1.07916884e-01 -1.35460198e-01 3.18773627e-01 1.12257719e-01 -3.75242829e-01 -3.06904316e-01 -1.22455800e+00 4.66467738e-01 7.14529395e-01 -3.69510919e-01 -6.45518184e-01 3.97694468e-01 1.17281580e+00 -4.26026255e-01 -9.19764400e-01 3.21744919e-01 1.33721322e-01 -5.75069845e-01 7.67727792e-01 -6.70113981e-01 9.92190093e-02 -2.82861620e-01 2.38796741e-01 -1.64304245e+00 -3.05334121e-01 -5.30402422e-01 -5.29333532e-01 1.56412745e+00 5.78216434e-01 -7.62150884e-01 7.91195035e-01 7.47625232e-01 -8.00914094e-02 -8.40613961e-01 -9.42701459e-01 -1.27243209e+00 6.49122179e-01 -1.22969680e-01 4.34454829e-01 1.03913879e+00 -2.32924014e-01 2.64758527e-01 -5.06868660e-01 1.58092663e-01 4.93018568e-01 -1.92218035e-01 4.93298382e-01 -1.21692419e+00 -4.04643416e-01 -3.51558834e-01 2.06510007e-01 -1.19627047e+00 3.87484074e-01 -1.13844812e+00 1.83932856e-01 -1.24746513e+00 2.45748937e-01 -1.10341346e+00 -5.65545619e-01 8.46827745e-01 -4.41873401e-01 -7.85469711e-02 -1.32362917e-01 2.46061325e-01 -6.22090876e-01 7.53666878e-01 1.37302780e+00 1.06192820e-01 -8.28440905e-01 5.17009571e-02 -5.71113825e-01 6.74274206e-01 9.26394582e-01 -8.18869472e-01 -6.04655087e-01 -1.47165284e-01 5.33397347e-02 -4.34387356e-01 1.89879864e-01 -7.39643633e-01 2.16144383e-01 -2.75285989e-01 3.03397387e-01 -4.04072702e-01 3.51492502e-02 -5.51063299e-01 -2.10467726e-02 5.64750433e-01 -5.26841879e-01 -4.16142821e-01 3.37823808e-01 6.18422091e-01 3.41093421e-01 -4.21799690e-01 1.38766968e+00 1.37752771e-01 -5.95387757e-01 2.47231409e-01 -3.85534346e-01 2.63597071e-01 8.59920800e-01 1.30001962e-01 -7.50533223e-01 6.02203086e-02 -7.85648048e-01 4.81133133e-01 2.73977071e-01 1.84622750e-01 3.33317965e-01 -1.26227486e+00 -2.93449730e-01 1.85498983e-01 -2.79054791e-01 5.03239632e-01 1.44624218e-01 8.21342349e-01 7.60377198e-03 1.33969128e-01 2.22655907e-01 -5.91449201e-01 -8.86444271e-01 7.43003190e-01 6.00405812e-01 -4.43205237e-01 -7.61931717e-01 9.79807734e-01 1.86237901e-01 -6.69173956e-01 5.26223302e-01 -4.12645191e-01 -2.76734624e-02 -2.07332343e-01 5.82336843e-01 4.60342079e-01 6.59651384e-02 4.62074466e-02 -2.24642143e-01 3.44011456e-01 -5.58045387e-01 6.58365861e-02 1.27248430e+00 -1.29547760e-01 7.69484341e-02 5.22575915e-01 1.09542036e+00 -2.63885230e-01 -1.89529753e+00 -6.00483060e-01 -2.01720223e-02 -8.08864981e-02 2.71528125e-01 -9.76934671e-01 -1.13898075e+00 7.40121424e-01 7.72328138e-01 1.45670786e-01 1.06919646e+00 2.71417201e-01 6.29598320e-01 4.79211956e-01 -7.30826473e-03 -1.39272380e+00 3.19172412e-01 8.49885821e-01 5.05312920e-01 -9.80650246e-01 -3.41459457e-03 1.74546376e-01 -8.37617874e-01 7.81633615e-01 9.12240863e-01 -1.55552447e-01 8.45699251e-01 3.30451548e-01 4.58817780e-02 7.94109777e-02 -9.14412379e-01 -1.03330538e-01 1.64093569e-01 4.34519172e-01 -1.35220047e-02 -1.52655840e-01 -1.59890756e-01 6.43557847e-01 2.57389881e-02 4.23081666e-02 2.65516609e-01 7.56886363e-01 -8.93288195e-01 -1.05012131e+00 -6.97279423e-02 3.82812619e-01 -1.71981633e-01 -5.02982624e-02 -1.32364705e-01 6.00338101e-01 1.77632645e-01 6.30693316e-01 2.64863223e-02 -4.05498147e-01 2.65680104e-01 3.68541598e-01 3.08261186e-01 -2.48166636e-01 -5.71603179e-01 -1.02796458e-01 -2.54056305e-01 -1.96741953e-01 -1.83430105e-01 -9.88694727e-02 -1.56104326e+00 -5.08725703e-01 -7.03472137e-01 2.35370874e-01 4.95073110e-01 1.01653290e+00 4.10760403e-01 5.71379602e-01 7.56876647e-01 -4.88868088e-01 -9.94209945e-01 -8.08270752e-01 -7.81452000e-01 2.07668543e-01 4.39721942e-01 -9.36582565e-01 -6.09627068e-01 7.96291530e-02]	[9.445252418518066, 3.420530319213867]
4b0afed6-3a95-407e-88ef-866df39fb1ac	deep-matching-prior-test-time-optimization	2106.03090	null	https://arxiv.org/abs/2106.03090v3	https://arxiv.org/pdf/2106.03090v3.pdf	Deep Matching Prior: Test-Time Optimization for Dense Correspondence	Conventional techniques to establish dense correspondences across visually or semantically similar images focused on designing a task-specific matching prior, which is difficult to model. To overcome this, recent learning-based methods have attempted to learn a good matching prior within a model itself on large training data. The performance improvement was apparent, but the need for sufficient training data and intensive learning hinders their applicability. Moreover, using the fixed model at test time does not account for the fact that a pair of images may require their own prior, thus providing limited performance and poor generalization to unseen images. In this paper, we show that an image pair-specific prior can be captured by solely optimizing the untrained matching networks on an input pair of images. Tailored for such test-time optimization for dense correspondence, we present a residual matching network and a confidence-aware contrastive loss to guarantee a meaningful convergence. Experiments demonstrate that our framework, dubbed Deep Matching Prior (DMP), is competitive, or even outperforms, against the latest learning-based methods on several benchmarks for geometric matching and semantic matching, even though it requires neither large training data nor intensive learning. With the networks pre-trained, DMP attains state-of-the-art performance on all benchmarks.	['Seungryong Kim', 'Sunghwan Hong']	2021-06-06	null	http://openaccess.thecvf.com//content/ICCV2021/html/Hong_Deep_Matching_Prior_Test-Time_Optimization_for_Dense_Correspondence_ICCV_2021_paper.html	http://openaccess.thecvf.com//content/ICCV2021/papers/Hong_Deep_Matching_Prior_Test-Time_Optimization_for_Dense_Correspondence_ICCV_2021_paper.pdf	iccv-2021-1	['geometric-matching', 'dense-pixel-correspondence-estimation']	['computer-vision', 'computer-vision']	[ 3.41140956e-01 4.66337986e-02 -9.63210091e-02 -4.50931728e-01 -1.01675832e+00 -3.01768005e-01 5.48183560e-01 8.32672417e-02 -4.24089968e-01 2.74439037e-01 -2.31473714e-01 -1.22062184e-01 -1.28603339e-01 -7.56724656e-01 -1.19589961e+00 -4.47162002e-01 1.87689722e-01 6.62664175e-01 4.55749810e-01 -4.80039828e-02 2.96996474e-01 5.46357274e-01 -1.70809174e+00 1.08040325e-01 8.91495407e-01 1.20907164e+00 6.30156577e-01 1.81776509e-01 -4.62363251e-02 3.39147896e-01 -2.31635973e-01 -5.83562016e-01 8.03721547e-01 -2.48097286e-01 -7.78536677e-01 1.66887686e-01 1.10552859e+00 -3.14938843e-01 -4.00791347e-01 1.12216628e+00 4.38739121e-01 7.54458979e-02 5.53968966e-01 -1.21549177e+00 -6.46346748e-01 1.29483745e-01 -6.32079840e-01 -1.65797055e-01 2.55630940e-01 1.66405722e-01 1.03633976e+00 -1.14551508e+00 4.85723555e-01 1.05930662e+00 8.99352312e-01 4.86461401e-01 -1.22135317e+00 -5.43543637e-01 1.08675249e-01 2.63666719e-01 -1.56581855e+00 -4.08378452e-01 8.23196590e-01 -4.99482036e-01 7.05752909e-01 6.47738427e-02 5.13580918e-01 7.58495748e-01 3.41291465e-02 7.16510177e-01 9.83261883e-01 -5.14507592e-01 8.17173496e-02 1.93421334e-01 -1.12484202e-01 7.92665482e-01 1.45144373e-01 5.97455025e-01 -3.55405241e-01 -3.30523588e-02 9.85777259e-01 4.91692014e-02 -3.87498587e-01 -8.55853260e-01 -1.06250942e+00 7.17609048e-01 8.85731816e-01 1.07645154e-01 -2.19245151e-01 -1.40463570e-02 1.27886072e-01 4.28186715e-01 3.54873717e-01 5.45125246e-01 -3.44458163e-01 3.78251016e-01 -1.13603806e+00 1.21737622e-01 4.76689100e-01 1.23794305e+00 1.19799423e+00 -2.58033216e-01 -1.46190390e-01 8.49442601e-01 1.06305011e-01 2.96453834e-01 3.01894188e-01 -8.53485823e-01 5.28047383e-01 7.18353033e-01 1.54267207e-01 -1.14705896e+00 -2.08138987e-01 -6.24301195e-01 -9.00780380e-01 4.15012836e-01 6.86435401e-01 3.59502673e-01 -1.05234861e+00 1.85663319e+00 1.80092484e-01 4.66275364e-01 -1.13500342e-01 1.02251852e+00 6.44316912e-01 2.85668284e-01 -1.90137208e-01 2.02840030e-01 9.84640479e-01 -1.19473255e+00 -2.23943144e-01 -5.21373212e-01 4.43434685e-01 -9.88342941e-01 1.23788512e+00 9.67889652e-02 -1.31870079e+00 -8.29668939e-01 -1.17515171e+00 -3.28883827e-02 -2.23768920e-01 -2.35643368e-02 5.32943189e-01 4.27838415e-01 -1.21154678e+00 7.66254544e-01 -5.82896411e-01 -4.33524460e-01 4.67572778e-01 4.83612269e-01 -5.12054741e-01 -4.65649992e-01 -9.18805182e-01 1.08562732e+00 2.57328898e-01 2.05824047e-01 -7.71033466e-01 -9.13849711e-01 -9.80250657e-01 8.85799825e-02 2.51171291e-01 -1.01774430e+00 8.30810130e-01 -9.89417136e-01 -1.34040248e+00 1.27248251e+00 -3.10204700e-02 -3.41209263e-01 9.10341859e-01 -1.59732059e-01 -6.66502267e-02 1.08603798e-01 -4.55255108e-03 1.05819643e+00 9.56695080e-01 -1.57825327e+00 -4.78318602e-01 -2.17787042e-01 1.02115355e-01 7.45103285e-02 -2.85943508e-01 -1.86205193e-01 -7.04002023e-01 -6.36639535e-01 3.50783288e-01 -8.64823639e-01 -2.35509336e-01 5.79305232e-01 -1.45533592e-01 -1.10416770e-01 5.88786066e-01 -4.92793679e-01 7.19164073e-01 -2.01879358e+00 8.16107318e-02 3.54433447e-01 6.19911514e-02 3.65074247e-01 -5.75312376e-01 2.07155168e-01 -6.17974959e-02 -2.17220351e-01 -3.46527755e-01 -5.51482499e-01 -5.21733332e-03 2.12758496e-01 -1.37112886e-01 6.74121797e-01 3.36532623e-01 9.38078940e-01 -8.92610312e-01 -4.97237682e-01 4.55445081e-01 4.14482951e-01 -7.60957181e-01 5.36451399e-01 -6.21536858e-02 4.72591847e-01 -2.20918983e-01 5.93220711e-01 1.04385602e+00 -4.64416385e-01 -1.48723915e-01 -5.48329473e-01 1.89047903e-01 -5.19698970e-02 -1.35534680e+00 2.01754022e+00 -6.26902342e-01 5.19892871e-01 1.10674258e-02 -1.33704114e+00 1.02784264e+00 -3.87004111e-03 3.96134168e-01 -8.89922500e-01 2.77457051e-02 5.25676489e-01 -1.51631311e-01 -2.47987956e-01 1.80750340e-01 -1.60215853e-03 2.48723567e-01 1.59469008e-01 1.45276725e-01 -2.35314682e-01 -6.41460763e-03 -1.01396896e-01 9.99242604e-01 1.07842274e-01 5.82071841e-02 -3.27711374e-01 7.37047374e-01 -1.36445478e-01 6.06262505e-01 8.87980580e-01 -8.55909809e-02 1.07304823e+00 -1.44356221e-01 -5.76049805e-01 -1.24920046e+00 -1.18021631e+00 -3.79986852e-01 6.89964175e-01 6.65849209e-01 -9.57689211e-02 -6.01469219e-01 -6.58057868e-01 5.10210693e-02 1.10723615e-01 -4.76479739e-01 -1.42548874e-01 -6.99704349e-01 -3.73838872e-01 1.81752250e-01 5.31692564e-01 7.22844005e-01 -7.70221353e-01 -2.69753247e-01 1.34109616e-01 -9.46958587e-02 -1.37561882e+00 -6.79001629e-01 -7.12770745e-02 -8.44455302e-01 -1.29054451e+00 -9.15027440e-01 -1.11612260e+00 1.06740761e+00 4.25773770e-01 1.32887185e+00 4.05994833e-01 -3.37677598e-01 3.83337438e-01 -6.24278896e-02 8.82873088e-02 -2.91654050e-01 6.80012181e-02 -2.25298196e-01 6.23806417e-02 9.69524458e-02 -6.73071325e-01 -7.50590146e-01 7.47327268e-01 -7.95614243e-01 2.07868129e-01 8.16352308e-01 1.20074344e+00 6.08142734e-01 -2.69888133e-01 4.16985005e-01 -5.18157423e-01 2.00871564e-03 -1.99338332e-01 -8.27428162e-01 5.44114351e-01 -7.12565005e-01 1.89040020e-01 5.44821084e-01 -4.37471449e-01 -6.60900950e-01 2.11158812e-01 -1.58354163e-01 -7.70805597e-01 -1.73752718e-02 1.85002491e-01 -2.14917615e-01 -6.71187520e-01 5.67269981e-01 5.84540069e-02 1.37711719e-01 -4.49851841e-01 1.94167793e-01 2.52649903e-01 6.94649518e-01 -8.50498259e-01 1.18655157e+00 4.73335922e-01 8.25043023e-02 -2.72958398e-01 -9.25168395e-01 -6.85587108e-01 -7.78685570e-01 -3.86473089e-02 6.81854665e-01 -1.00140786e+00 -3.97791922e-01 5.09820282e-01 -1.20867896e+00 -4.22936857e-01 -2.36989588e-01 4.90279764e-01 -7.63441682e-01 4.47823465e-01 -3.69223684e-01 -1.90730616e-01 -1.94678307e-01 -1.19429636e+00 1.03539717e+00 4.86291051e-02 -1.98256411e-02 -1.12584209e+00 -1.26927480e-01 3.88856739e-01 6.05107963e-01 1.23310633e-01 7.85577416e-01 -4.09357339e-01 -1.06734455e+00 -3.54286730e-01 -6.83005691e-01 5.83378196e-01 1.71182349e-01 -3.51308107e-01 -8.46323013e-01 -5.69896579e-01 -6.06129020e-02 -4.46304798e-01 8.61949801e-01 2.09038764e-01 1.29280186e+00 -5.90605997e-02 -2.86275893e-01 1.01250219e+00 1.69406319e+00 -3.15756798e-01 7.26108730e-01 4.39382851e-01 8.24954867e-01 7.51077652e-01 5.56007504e-01 6.69287443e-02 2.98854768e-01 9.51709747e-01 6.57883108e-01 -5.25334060e-01 -3.31373334e-01 -3.79801810e-01 3.55347879e-02 4.23166275e-01 5.64354137e-02 -1.02029935e-01 -7.32604563e-01 5.79472125e-01 -1.91324687e+00 -7.51677692e-01 2.84573864e-02 2.53513026e+00 7.26661265e-01 7.44594038e-02 -1.76342174e-01 8.80350024e-02 7.98157334e-01 1.59212798e-02 -4.96693164e-01 1.01940595e-01 -2.42048755e-01 3.57989818e-01 5.53290784e-01 7.08405793e-01 -1.01322508e+00 9.17852640e-01 6.10540962e+00 9.91216242e-01 -1.05735540e+00 6.46662340e-02 4.98870343e-01 2.35582739e-01 -4.31589901e-01 1.53330907e-01 -7.33356893e-01 3.16594779e-01 4.01941657e-01 6.12539686e-02 2.52730101e-01 6.96126938e-01 4.39851321e-02 -3.91143076e-02 -1.45813322e+00 1.22524583e+00 6.63640574e-02 -1.41011643e+00 2.09941790e-01 -5.17690107e-02 8.91887546e-01 9.93343070e-02 1.30845591e-01 6.91060871e-02 3.41449454e-02 -1.02916849e+00 6.78200126e-01 3.87418300e-01 6.91284537e-01 -4.66813385e-01 8.31259489e-01 2.41745174e-01 -1.09094346e+00 2.10864037e-01 -6.27867222e-01 6.19086102e-02 6.22290708e-02 5.76329887e-01 -3.62548888e-01 6.82343245e-01 5.89533746e-01 8.02411616e-01 -6.98046803e-01 1.38261616e+00 -6.12817742e-02 1.50136963e-01 -3.26943308e-01 4.93313760e-01 3.33929628e-01 -2.11830989e-01 3.67016107e-01 1.04462421e+00 4.30729508e-01 -2.37325281e-01 4.06960189e-01 1.02798712e+00 -1.05464920e-01 6.49009123e-02 -4.62452471e-01 5.07501483e-01 3.75163555e-01 1.09125555e+00 -4.96389717e-01 -2.11260751e-01 -4.66317147e-01 1.06057262e+00 5.10972738e-01 2.70159036e-01 -7.60697544e-01 6.15940839e-02 5.75169027e-01 4.02606100e-01 2.15741962e-01 -8.55670571e-02 -3.58921528e-01 -1.17916870e+00 3.52188885e-01 -7.17384636e-01 3.39084238e-01 -5.89795530e-01 -1.57709324e+00 5.89216888e-01 -6.93442747e-02 -1.48183346e+00 -1.56692594e-01 -6.07937217e-01 -6.08061075e-01 1.02254653e+00 -1.96838641e+00 -1.32608497e+00 -7.16457665e-01 7.99904346e-01 3.43460828e-01 -1.17523320e-01 5.96360147e-01 7.29412675e-01 -2.58779466e-01 9.38737214e-01 -1.55277267e-01 1.47496045e-01 9.96142983e-01 -1.16793036e+00 3.29994649e-01 8.05123091e-01 1.29641280e-01 3.89598936e-01 5.45770824e-01 -1.96792901e-01 -1.14764941e+00 -1.03470492e+00 9.10804927e-01 -4.18461114e-01 3.55796248e-01 -3.02867025e-01 -1.18618166e+00 4.92726684e-01 8.66862833e-02 4.64090228e-01 3.39791328e-01 -7.36270994e-02 -5.14349222e-01 -2.54653037e-01 -1.18509197e+00 4.39012706e-01 1.40756774e+00 -4.90043730e-01 -4.92859215e-01 4.07852381e-01 4.89576757e-01 -6.00102007e-01 -7.97234952e-01 7.49391079e-01 4.51069921e-01 -1.19771659e+00 1.24963379e+00 -2.80293941e-01 3.27992439e-01 -4.04956281e-01 -1.82881087e-01 -1.05990803e+00 -2.33567834e-01 -5.18380284e-01 2.50676602e-01 1.16748190e+00 2.00470135e-01 -5.92101693e-01 9.99438167e-01 6.30895793e-01 -3.76954645e-01 -7.24286497e-01 -7.64203787e-01 -1.17355740e+00 2.10802063e-01 -3.04933995e-01 6.15569949e-01 1.04529083e+00 -5.25633872e-01 2.52071582e-02 -3.81226778e-01 4.20971125e-01 8.58103573e-01 2.09462225e-01 9.59590852e-01 -1.18921816e+00 -3.52939844e-01 -6.12925112e-01 -7.00141490e-01 -1.42503238e+00 3.84382457e-01 -9.62044656e-01 4.85735424e-02 -1.34488976e+00 2.25107253e-01 -9.53942239e-01 -2.64051288e-01 3.40795994e-01 -3.47439200e-01 3.53743911e-01 1.21890284e-01 2.91044503e-01 -3.23418349e-01 5.72340310e-01 1.44119656e+00 -3.60871315e-01 -6.73385104e-03 2.91296914e-02 -4.02620226e-01 5.68448842e-01 5.58555782e-01 -4.02246773e-01 -4.69996810e-01 -6.18857622e-01 2.08912361e-02 -2.84179270e-01 7.45386839e-01 -1.16124618e+00 5.34512103e-01 -2.56296229e-02 2.83919364e-01 -5.44777155e-01 3.04817647e-01 -9.74722207e-01 2.69824922e-01 4.29053009e-01 -2.18857065e-01 6.66653365e-02 1.82996720e-01 4.72517669e-01 -5.66526651e-01 -3.32236588e-01 1.07807362e+00 -1.82503164e-02 -9.27224338e-01 6.59878850e-01 4.74071920e-01 1.49339914e-01 8.24441195e-01 -4.95884836e-01 -7.62521923e-02 -2.03041211e-01 -5.48309326e-01 3.18301231e-01 8.31178904e-01 4.25682992e-01 7.40969479e-01 -1.44153130e+00 -8.29218447e-01 3.34718883e-01 3.23641747e-01 2.22740188e-01 2.87461787e-01 5.92971981e-01 -4.91902292e-01 5.97395673e-02 -3.36601853e-01 -9.41187322e-01 -1.06522310e+00 7.76777446e-01 5.66565692e-01 -3.54461044e-01 -7.06235647e-01 8.36642921e-01 5.25308371e-01 -5.11425376e-01 4.58257794e-01 -2.49035284e-01 2.54430830e-01 -3.58624250e-01 2.49436170e-01 -1.86644993e-05 2.14481547e-01 -3.99037868e-01 -2.59553343e-01 1.10477471e+00 -7.82424137e-02 1.85551643e-01 1.02504337e+00 -1.16227284e-01 1.06006622e-01 -2.44193766e-02 1.54417062e+00 -3.51083994e-01 -1.56316245e+00 -5.97744823e-01 3.86443473e-02 -9.02721405e-01 2.82630399e-02 -4.47892189e-01 -1.32905865e+00 8.73836040e-01 6.28005743e-01 -2.62044191e-01 1.09911537e+00 -1.69223659e-02 9.19910133e-01 3.17361653e-01 5.51990151e-01 -8.91878128e-01 3.11065972e-01 3.22657555e-01 9.56496060e-01 -1.81246257e+00 -7.61965886e-02 -6.33042216e-01 -1.79539412e-01 1.08485603e+00 6.94704115e-01 -4.19944972e-01 7.52284586e-01 6.43813834e-02 2.89324857e-02 -3.14764343e-02 -3.96866858e-01 -3.14496487e-01 7.02389359e-01 6.12965345e-01 1.30956233e-01 -3.13599437e-01 -2.13248171e-02 -7.06424788e-02 -8.94602612e-02 -1.27032906e-01 6.77123889e-02 6.87522531e-01 -2.39259332e-01 -1.42255521e+00 -1.86413527e-01 2.12422401e-01 -5.45428582e-02 -1.04404256e-01 -9.43769813e-02 1.01804864e+00 1.64247692e-01 7.19666958e-01 2.78179228e-01 -2.79417098e-01 4.60385412e-01 -3.83497804e-01 8.71071339e-01 -4.75392580e-01 -5.05963743e-01 -2.02916965e-01 -2.48998031e-01 -7.73942888e-01 -6.49011433e-01 -3.20531666e-01 -8.61054480e-01 -4.93482143e-01 -4.61821079e-01 1.39178745e-02 4.39534962e-01 9.32658613e-01 2.54233867e-01 1.20040886e-01 8.09603989e-01 -1.00920939e+00 -5.69715440e-01 -6.03216827e-01 -3.15861583e-01 6.23009026e-01 3.31859618e-01 -7.87856519e-01 -5.30319929e-01 -3.95426713e-02]	[8.533226013183594, -2.1891167163848877]
b4fba6da-279a-423e-a819-76fdd9b169e1	a-deep-neural-network-model-for-the-task-of	null	null	https://www.researchgate.net/publication/330556058_A_Deep_Neural_Network_Model_for_the_task_of_Named_Entity_Recognition	http://www.ijmlc.org/vol9/758-ML0025.pdf	A Deep Neural Network Model for the Task of Named Entity Recognition	One of the most important factors which directly and significantly affects the quality of the neural sequence labeling is the selection and encoding the input features to generate rich semantic and grammatical representation vectors. In this paper, we propose a deep neural network model to address a particular task of sequence labeling problem, the task of Named Entity Recognition (NER). The model consists of three sub-networks to fully exploit character-level and capitalization features as well as word-level contextual representation. To show the ability of our model to generalize to different languages, we evaluated the model in Russian, Vietnamese, English and Chinese and obtained state-of-the-art performances: 91.10%, 94.43%, 91.22%, 92.95% of F-Measure on Gareev's dataset, VLSP-2016, CoNLL-2003 and MSRA datasets, respectively. Besides that, our model also obtained a good performance (about 70% of F1) with using only 100 samples for training and development sets.	['Anh Le. Mikhail S. Burtsev']	2018-02-01	null	null	null	international-journal-of-machine-learning-and-1	['named-entity-recognition-in-vietnamese']	['natural-language-processing']	[ 7.65921324e-02 -2.40480185e-01 -2.95228790e-03 -6.45365655e-01 -4.86773133e-01 -7.27530718e-01 3.81991535e-01 1.70927197e-01 -1.03166556e+00 1.04466546e+00 1.25183791e-01 -3.81300986e-01 3.85433853e-01 -8.97634506e-01 -6.00827157e-01 -2.56880343e-01 7.40483254e-02 2.89288104e-01 -2.97841895e-02 -2.62168854e-01 2.76936054e-01 5.61831415e-01 -1.32415068e+00 4.40368325e-01 1.20097089e+00 7.16751099e-01 2.58080065e-01 5.24027765e-01 -5.68633497e-01 8.40449572e-01 -8.19159508e-01 -5.82027197e-01 -1.44338578e-01 -2.57526934e-01 -1.01845086e+00 -2.48312950e-01 1.45751014e-01 1.36829674e-01 -1.51220903e-01 1.12214279e+00 5.84626913e-01 4.28590983e-01 7.50976980e-01 -6.70137525e-01 -1.08836389e+00 1.04675591e+00 -3.41089100e-01 3.52585852e-01 4.22393344e-02 -1.24546207e-01 9.33749259e-01 -9.04542029e-01 9.46579576e-01 1.09478426e+00 5.18029630e-01 8.27885270e-01 -6.71333194e-01 -8.45522344e-01 -5.13195293e-03 3.41375142e-01 -1.42725575e+00 -3.00609142e-01 3.83055687e-01 -2.49223739e-01 1.33586693e+00 3.14149149e-02 1.29322946e-01 1.07791626e+00 -7.27964193e-02 8.19625795e-01 1.13768935e+00 -6.35330141e-01 3.62921460e-03 6.42427951e-02 6.56983674e-01 7.55137563e-01 2.68998533e-01 -4.35139947e-02 -7.44134337e-02 1.68589443e-01 5.92364907e-01 -4.26562577e-01 -2.29390472e-01 4.02309477e-01 -9.85420525e-01 8.31596971e-01 3.81660521e-01 8.61379921e-01 -4.12082016e-01 -1.26609102e-01 5.25958359e-01 1.40855148e-01 3.27695251e-01 4.79685515e-01 -8.17808449e-01 -2.83891410e-01 -6.03387594e-01 1.99839640e-02 6.78509474e-01 1.08006215e+00 5.23321390e-01 4.72412586e-01 -4.94304329e-01 1.14864540e+00 2.37708658e-01 5.33695459e-01 8.13679338e-01 -3.55733722e-01 7.69657731e-01 5.34157455e-01 1.81521568e-02 -8.29198599e-01 -5.13421416e-01 -6.78005159e-01 -8.68701041e-01 -3.93161416e-01 2.33977765e-01 -3.32893580e-01 -1.27530015e+00 2.03916574e+00 4.61505651e-02 1.51297703e-01 4.95676488e-01 6.09723866e-01 1.27900136e+00 1.16582894e+00 5.39611280e-01 -6.01339573e-03 1.44420695e+00 -1.06052351e+00 -7.07117796e-01 -2.49662817e-01 8.23673964e-01 -5.03352404e-01 9.88866568e-01 3.54231745e-02 -8.38700593e-01 -7.39725709e-01 -9.39099073e-01 -5.98398671e-02 -6.06126130e-01 7.47589529e-01 2.98631728e-01 6.69882834e-01 -8.68188977e-01 6.90253973e-01 -5.15090823e-01 -5.32410622e-01 2.18424827e-01 1.93408489e-01 -4.96733308e-01 -1.82715282e-01 -1.67013955e+00 9.46364641e-01 1.19628155e+00 1.64621219e-01 -7.44784296e-01 -5.77576041e-01 -8.06725860e-01 3.02217662e-01 -3.30382548e-02 -2.08330214e-01 1.07403338e+00 -7.28845000e-01 -1.35367000e+00 9.27399993e-01 -2.44612060e-02 -3.44597697e-01 1.20107099e-01 -2.34598368e-01 -8.57108474e-01 -1.22860394e-01 -2.46941727e-02 6.79347873e-01 -2.37786956e-02 -9.12199616e-01 -6.88773215e-01 -3.95775437e-01 -2.07788616e-01 9.83296260e-02 -4.76385623e-01 3.16441089e-01 -2.37250105e-01 -6.15060389e-01 -5.01001120e-01 -8.15805018e-01 -2.90674865e-01 -9.45800781e-01 -5.02433777e-01 -6.00534320e-01 3.22922498e-01 -1.16764474e+00 1.41231370e+00 -1.96164906e+00 -1.26820639e-01 -2.13979799e-02 -3.43472987e-01 9.37525034e-01 -5.31049311e-01 5.42644083e-01 -2.14446917e-01 5.53981781e-01 -3.41215551e-01 -2.01410055e-02 2.70680822e-02 5.47594056e-02 -3.05022802e-02 1.55471474e-01 5.19409597e-01 1.00217283e+00 -8.51114869e-01 -2.55152673e-01 3.11904270e-02 5.10116637e-01 -2.35314116e-01 3.53337079e-01 -8.29209909e-02 3.50566536e-01 -4.36378688e-01 3.88320267e-01 5.84725142e-01 1.53530901e-02 3.59751165e-01 -6.31484017e-02 -1.92867681e-01 4.90873337e-01 -1.03290832e+00 1.62727737e+00 -5.99413216e-01 4.18417275e-01 -4.26639557e-01 -8.51833642e-01 1.32095647e+00 4.52016622e-01 -1.62410006e-01 -9.90246832e-01 3.66437703e-01 3.97427499e-01 4.34068963e-02 -4.49370444e-01 6.63210332e-01 -8.51961076e-02 -3.52885097e-01 1.77508205e-01 3.74551743e-01 6.41066968e-01 4.05250251e-01 -1.37816399e-01 8.87322426e-01 9.23522711e-02 5.87799668e-01 -3.68688256e-01 8.44295800e-01 -7.82758445e-02 9.03187215e-01 6.34186804e-01 -2.13056430e-01 4.19260174e-01 3.52298558e-01 -4.37241048e-01 -1.10706365e+00 -6.00593388e-01 -1.78798791e-02 1.19772780e+00 -1.96746200e-01 -5.66085614e-02 -9.45875585e-01 -9.38071549e-01 -4.18769181e-01 1.23374736e+00 -5.71208715e-01 -1.28567874e-01 -1.00167453e+00 -7.89093912e-01 1.18806946e+00 6.89569533e-01 6.72526658e-01 -1.60953856e+00 -2.08446354e-01 3.50110710e-01 -2.06014812e-01 -1.29714751e+00 -2.69793510e-01 1.02355823e-01 -7.17856467e-01 -7.29992211e-01 -7.92897284e-01 -1.10391259e+00 4.79391098e-01 -3.96053016e-01 1.23374295e+00 3.37503268e-03 -2.30391119e-02 -4.38469291e-01 -6.04271770e-01 -3.05665195e-01 -4.58038479e-01 6.00268722e-01 -3.04717362e-01 -9.19069424e-02 3.26204658e-01 -1.79309145e-01 2.28062868e-02 7.83609003e-02 -8.68232012e-01 -1.77263156e-01 6.49901330e-01 9.55206037e-01 4.22533333e-01 -9.78346393e-02 9.62283075e-01 -1.17134464e+00 5.48371911e-01 -4.41568434e-01 -5.31922936e-01 5.44724405e-01 -2.96788663e-01 2.61120588e-01 9.39678907e-01 -2.67209798e-01 -1.34151328e+00 -4.64964611e-03 -7.68528998e-01 3.46621312e-02 -4.96801674e-01 6.30046725e-01 -4.41288650e-01 3.80375355e-01 5.65530360e-01 4.33753699e-01 -6.45525694e-01 -8.77691865e-01 4.06857461e-01 9.52359855e-01 3.86653602e-01 -5.21940231e-01 2.19633684e-01 -2.30721965e-01 -4.18478698e-01 -8.35553825e-01 -8.51536810e-01 -2.61525452e-01 -6.96977913e-01 2.38478407e-01 1.00570381e+00 -7.74130464e-01 -4.90939468e-01 6.75851703e-01 -1.55117977e+00 -8.74417946e-02 7.06538409e-02 4.58359599e-01 8.49828422e-02 3.15721571e-01 -9.05929148e-01 -7.19855666e-01 -8.32210660e-01 -1.11096847e+00 6.59185410e-01 4.32300866e-01 6.18940331e-02 -9.09994423e-01 1.25710472e-01 1.60516992e-01 4.33770388e-01 2.85179824e-01 1.28311706e+00 -1.26130176e+00 6.21932046e-03 3.19168754e-02 -2.61968881e-01 4.83366162e-01 -5.06129004e-02 -1.24111488e-01 -1.04648447e+00 -1.94648519e-01 -3.73104274e-01 -2.83131808e-01 8.11857641e-01 -7.73058506e-03 1.03296840e+00 -2.44745359e-01 -8.65129754e-02 4.56161797e-01 1.59438384e+00 6.72003984e-01 8.39882016e-01 3.62084419e-01 8.03585112e-01 5.33024549e-01 4.95776176e-01 1.65513217e-01 3.15749645e-01 4.60187972e-01 1.25782266e-01 6.50672391e-02 -1.21086873e-01 -3.58067721e-01 3.12693000e-01 1.17126966e+00 8.13478157e-02 -6.72738612e-01 -1.18410408e+00 6.61648214e-01 -1.46144402e+00 -8.63880575e-01 -2.28721544e-01 1.97290349e+00 9.22661245e-01 -1.37952566e-01 -8.55772346e-02 -1.44655451e-01 1.17004561e+00 1.34792119e-01 -3.16245675e-01 -7.19250023e-01 -1.88629299e-01 4.90439385e-01 5.82974672e-01 1.53163940e-01 -1.28013873e+00 1.50163949e+00 5.63148022e+00 1.13203001e+00 -1.09599948e+00 1.52678862e-01 8.61491561e-01 5.96435726e-01 -2.74892747e-01 -4.22096282e-01 -1.29699636e+00 4.85106766e-01 1.46942699e+00 3.76304463e-02 2.19691455e-01 6.86456084e-01 -2.12257639e-01 5.46124101e-01 -6.32571638e-01 7.13629186e-01 -1.40414545e-02 -1.22230828e+00 2.28599235e-01 -2.22527951e-01 8.46261561e-01 2.36811310e-01 -4.51669097e-01 7.34763801e-01 4.12569463e-01 -1.30175447e+00 5.98547280e-01 3.09924603e-01 8.58689606e-01 -1.25197744e+00 1.10259116e+00 4.65532750e-01 -1.12104881e+00 8.07604119e-02 -6.41095221e-01 2.31227666e-01 2.73563445e-01 5.00958979e-01 -7.99834013e-01 8.06511045e-01 5.03829896e-01 5.86515427e-01 -4.78219092e-01 8.79002035e-01 -5.51294208e-01 9.82028425e-01 -5.96745573e-02 -5.19500554e-01 5.16220629e-01 -1.93124206e-03 1.08044341e-01 1.72533178e+00 3.96866471e-01 5.79469353e-02 3.96712348e-02 6.76083028e-01 -4.26394403e-01 5.92367589e-01 -4.07825470e-01 -4.96951312e-01 6.33574903e-01 1.21012938e+00 -5.77164233e-01 -4.04513359e-01 -2.32893094e-01 7.47862279e-01 9.47468400e-01 2.60385454e-01 -9.21400070e-01 -9.96424913e-01 4.62044209e-01 -5.11931717e-01 6.22825325e-01 -2.75655389e-01 -2.62874663e-01 -1.23320186e+00 -2.41934955e-01 -9.47876692e-01 4.27015632e-01 -4.02090520e-01 -1.28015256e+00 1.27935028e+00 -3.71218055e-01 -7.13258684e-01 -2.31848016e-01 -9.12681997e-01 -4.81041163e-01 1.12436831e+00 -1.64152670e+00 -1.15478051e+00 -8.46869797e-02 2.80951709e-01 4.53546971e-01 -5.18455923e-01 8.67742538e-01 6.63068473e-01 -9.07004654e-01 9.26871538e-01 2.70854712e-01 7.68144727e-01 3.42317790e-01 -1.07793725e+00 9.37800288e-01 1.02100611e+00 2.53883034e-01 6.42198682e-01 1.23477131e-01 -6.08642817e-01 -1.08730197e+00 -1.33776212e+00 1.41851568e+00 -3.75792682e-02 4.01243806e-01 -4.56149459e-01 -9.55546916e-01 6.02712572e-01 2.84530610e-01 -1.91836521e-01 6.53395295e-01 -3.27340066e-02 -2.58627623e-01 1.86282784e-01 -1.18655872e+00 4.17602748e-01 1.16650784e+00 -3.08970660e-01 -7.44222641e-01 -8.00325200e-02 9.33956087e-01 -3.23193759e-01 -1.01382387e+00 4.84435827e-01 3.28244954e-01 -6.45012915e-01 6.67631149e-01 -1.10982156e+00 3.80260795e-01 -1.46969870e-01 -2.54634708e-01 -1.48463035e+00 -5.69149137e-01 -5.58524318e-02 3.05590212e-01 1.73630261e+00 8.36189866e-01 -5.84147811e-01 3.92912239e-01 9.20313150e-02 -4.34561253e-01 -7.21756697e-01 -6.84188008e-01 -8.12497854e-01 3.41750741e-01 -3.00338298e-01 8.99916887e-01 1.22517729e+00 -4.27550673e-01 5.68958938e-01 -2.84713089e-01 -5.70902787e-03 2.13516384e-01 -1.18149906e-01 1.83500260e-01 -1.11423171e+00 8.10122490e-03 -2.03438759e-01 -2.98475325e-01 -7.53267229e-01 6.16757691e-01 -1.12467873e+00 1.65372536e-01 -1.58560586e+00 1.03189789e-01 -4.49232101e-01 -7.30133474e-01 7.45854259e-01 -3.23364466e-01 1.01228701e-02 3.53055209e-01 -1.90402731e-01 -3.57184350e-01 5.24934232e-01 1.02204895e+00 8.59449431e-02 1.09298984e-02 -3.27787697e-01 -6.76847875e-01 3.50887656e-01 9.98079062e-01 -6.07315958e-01 -1.16926074e-01 -9.25869644e-01 4.78005670e-02 -4.28574458e-02 -3.68594646e-01 -8.05439830e-01 -2.24196404e-01 -2.50729531e-01 4.69854861e-01 -5.95506907e-01 -1.29202843e-01 -3.09002906e-01 2.32985057e-02 5.60550392e-01 -6.95275426e-01 3.45081776e-01 2.63547868e-01 1.68648094e-01 -2.77747005e-01 -7.05605924e-01 8.34684491e-01 -2.20551193e-01 -1.26564610e+00 2.41324097e-01 -3.43476295e-01 4.78580475e-01 8.46389890e-01 9.00440067e-02 -5.04523635e-01 1.41438529e-01 -3.77059281e-01 1.15584902e-01 1.27058089e-01 6.81041598e-01 3.67742807e-01 -1.39790642e+00 -1.07821548e+00 1.49404004e-01 1.27668828e-01 -3.79817277e-01 3.04570198e-01 1.43846601e-01 -7.73553252e-01 7.11575270e-01 -3.66444796e-01 -1.35151446e-01 -9.69404101e-01 3.31775159e-01 2.10989445e-01 -5.99920690e-01 -2.36206204e-01 8.93006444e-01 7.71519616e-02 -1.04497600e+00 4.55332808e-02 -1.83783039e-01 -8.90977979e-01 -8.33250284e-02 5.29009342e-01 5.79180360e-01 2.18200177e-01 -9.15134072e-01 -6.68824255e-01 4.39387679e-01 -1.35838449e-01 -4.31367904e-02 1.38877618e+00 2.36862585e-01 -2.93231588e-02 2.08754689e-01 1.45639634e+00 -1.16806582e-01 -5.55379331e-01 -1.52561128e-01 3.81326675e-01 9.75411311e-02 -2.15738863e-01 -1.22318721e+00 -1.17312407e+00 1.17672849e+00 5.29761374e-01 -1.50303140e-01 8.72709274e-01 -3.96594346e-01 1.05038512e+00 5.05948901e-01 2.66832530e-01 -1.00006485e+00 -4.69460368e-01 1.25716984e+00 4.80412602e-01 -9.73387659e-01 -5.45227647e-01 -2.73539096e-01 -7.80143976e-01 1.07803285e+00 7.52795458e-01 -9.64867920e-02 2.20014170e-01 3.50842625e-02 1.55189767e-01 2.02578917e-01 -5.83978057e-01 -9.08500254e-02 4.78591889e-01 4.22673345e-01 9.10805047e-01 1.31299257e-01 -8.24623227e-01 9.66538429e-01 -1.22458830e-01 -3.31132226e-02 4.10830438e-01 8.35721016e-01 -5.02066910e-01 -1.12393427e+00 7.27487952e-02 2.84191966e-01 -9.18151140e-01 -3.26161236e-01 -2.77251214e-01 7.87173510e-01 1.58878788e-01 9.42921877e-01 -2.54653059e-02 -3.87495041e-01 4.88703072e-01 3.47496688e-01 1.01714849e-01 -7.41079450e-01 -9.67953324e-01 -3.78529400e-01 6.56256974e-01 -2.31665060e-01 -2.13535562e-01 -3.63187194e-01 -1.58640587e+00 -4.06883918e-02 -2.95891583e-01 5.27329803e-01 8.25268388e-01 9.84008789e-01 5.31021416e-01 6.06262267e-01 5.27654052e-01 -1.95261478e-01 -6.63779199e-01 -1.28214514e+00 -4.83236313e-01 5.85845411e-01 -2.46741951e-01 -4.08381641e-01 9.15344805e-02 -1.28476202e-01]	[9.856161117553711, 9.635419845581055]
456f7b72-41b2-4337-9e7a-e7af4115cea0	proppy-a-system-to-unmask-propaganda-in	1912.06810	null	https://arxiv.org/abs/1912.06810v1	https://arxiv.org/pdf/1912.06810v1.pdf	Proppy: A System to Unmask Propaganda in Online News	We present proppy, the first publicly available real-world, real-time propaganda detection system for online news, which aims at raising awareness, thus potentially limiting the impact of propaganda and helping fight disinformation. The system constantly monitors a number of news sources, deduplicates and clusters the news into events, and organizes the articles about an event on the basis of the likelihood that they contain propagandistic content. The system is trained on known propaganda sources using a variety of stylistic features. The evaluation results on a standard dataset show state-of-the-art results for propaganda detection.	['Alberto Barrón-Cedeño', 'Israa Jaradat', 'Giovanni Da San Martino', 'Preslav Nakov']	2019-12-14	null	null	null	null	['propaganda-detection']	['natural-language-processing']	[-3.31296861e-01 -2.06447795e-01 -9.05465901e-01 1.39767766e-01 -7.75504887e-01 -8.34364653e-01 1.48690081e+00 8.16204190e-01 -3.15079868e-01 4.51340973e-01 1.05001163e+00 -5.19847989e-01 1.38831660e-01 -9.93035376e-01 -3.52873623e-01 -4.03268665e-01 -1.90467328e-01 5.02228141e-01 3.39201689e-01 -3.76148731e-01 7.54847109e-01 2.97258586e-01 -8.13669384e-01 5.23681998e-01 6.39457405e-01 4.30726171e-01 -3.56486827e-01 7.08273232e-01 -3.50474387e-01 1.73575938e+00 -1.43567455e+00 -5.31268597e-01 -1.14334188e-02 -6.06714010e-01 -7.94529855e-01 -3.14630032e-01 4.57578778e-01 -3.45763534e-01 -6.18996918e-01 1.14633512e+00 2.55582064e-01 -3.16637099e-01 7.29982436e-01 -4.92887348e-01 -9.03865278e-01 1.12722695e+00 -8.33150446e-01 1.01201653e+00 7.36984432e-01 -2.46234775e-01 6.80084050e-01 -5.03374338e-01 1.12557352e+00 1.62761557e+00 4.34069812e-01 3.92640799e-01 -9.03748035e-01 -5.70571840e-01 3.92941162e-02 5.81942722e-02 -8.53258371e-01 -2.78882623e-01 7.85298765e-01 -8.48970473e-01 4.48396623e-01 1.62103504e-01 6.28446817e-01 1.61227238e+00 5.64224124e-01 6.78177118e-01 1.14092934e+00 -3.90346974e-01 3.01642895e-01 -3.37226056e-02 5.40212214e-01 9.71665263e-01 2.30302855e-01 -2.46104375e-01 -8.02462339e-01 -8.73236775e-01 3.46369416e-01 -4.23600495e-01 1.79960668e-01 5.56225419e-01 -9.83907640e-01 1.33801293e+00 4.73095745e-01 3.99668723e-01 -3.91871244e-01 -1.78686097e-01 6.66274726e-01 8.88287425e-02 1.17123652e+00 5.38705587e-01 2.35714585e-01 -2.83803254e-01 -9.32186306e-01 4.83162135e-01 8.98932517e-01 -6.64644167e-02 1.45102680e-01 3.26951942e-03 -5.28706193e-01 4.57426339e-01 -6.08117543e-02 9.32673633e-01 2.59654433e-01 -3.80661696e-01 5.16461670e-01 5.24381518e-01 1.24675900e-01 -1.54379666e+00 -4.53465879e-01 -3.36929262e-01 -4.43941921e-01 5.01666851e-02 3.32399935e-01 -2.90726721e-01 -8.60018015e-01 1.29050815e+00 5.43988347e-01 -7.70185739e-02 -5.62073112e-01 6.95491016e-01 1.02704167e+00 1.20430076e+00 3.65453571e-01 -2.99148887e-01 1.39671123e+00 -5.87322414e-01 -9.90867615e-01 -8.35285261e-02 4.00148898e-01 -9.49551344e-01 7.31612206e-01 3.08808804e-01 -6.38363838e-01 2.13184536e-01 -7.14524388e-01 2.79302776e-01 -5.05624175e-01 -3.09046600e-02 7.23346412e-01 6.52493358e-01 -6.87986538e-02 4.04556096e-01 -5.90072036e-01 -3.31226707e-01 4.64378536e-01 -7.24658847e-01 -8.57393146e-02 4.42161620e-01 -1.20017385e+00 1.14760411e+00 2.31037706e-01 -5.21800041e-01 -1.11040640e+00 -6.67790353e-01 -6.62254333e-01 -2.36481428e-01 4.16499376e-01 -5.30303307e-02 9.89511073e-01 -5.41302443e-01 -1.18529296e+00 8.60535681e-01 3.57311890e-02 -7.24452198e-01 4.83371973e-01 -5.91633737e-01 -8.68774414e-01 4.04192209e-01 6.30601168e-01 -2.96301782e-01 1.16421580e+00 -9.04121220e-01 -5.11198759e-01 -1.52425123e-02 -3.09761185e-02 -2.35219240e-01 -2.80793011e-01 9.95112360e-01 2.97886163e-01 -1.06654692e+00 -4.56880093e-01 -5.24348199e-01 2.23370984e-01 -6.05896890e-01 -9.13032115e-01 -3.21364522e-01 1.13276255e+00 -1.13782334e+00 1.54013097e+00 -1.96100426e+00 -8.64021033e-02 2.08921269e-01 4.50061798e-01 2.88111597e-01 7.54624754e-02 7.01846898e-01 3.47261280e-01 3.88135582e-01 6.31822824e-01 2.81731606e-01 -3.27058375e-01 -3.38761359e-01 -9.04384851e-01 9.93463278e-01 -2.82341130e-02 3.96076262e-01 -1.14120269e+00 -5.63463926e-01 9.92249399e-02 1.69485748e-01 -3.21742088e-01 -1.71375155e-01 -4.18378592e-01 2.91651219e-01 -6.66296542e-01 4.99827534e-01 4.31682259e-01 -3.93909782e-01 7.92128816e-02 3.72988790e-01 -5.65805495e-01 9.65871394e-01 -5.59986472e-01 8.63250256e-01 -7.11379498e-02 9.94161069e-01 1.24189541e-01 -4.15414959e-01 4.58453268e-01 -7.38867512e-03 2.15678692e-01 -4.96079594e-01 5.19782126e-01 -3.69134486e-01 -4.77594882e-01 -3.14880073e-01 5.37147939e-01 3.44729960e-01 -5.59931338e-01 1.06571805e+00 -1.70506403e-01 2.68269300e-01 6.14619017e-01 1.09799862e+00 1.35319507e+00 -4.79598761e-01 7.05105364e-01 -1.44076690e-01 1.29346237e-01 4.86564845e-01 1.36116713e-01 1.07579315e+00 -1.57294452e-01 -8.86218324e-02 6.02108300e-01 -7.36333549e-01 -7.52737820e-01 -1.38827467e+00 5.60756736e-02 1.59925210e+00 6.12539016e-02 -8.96526992e-01 -6.64163888e-01 -9.92590189e-01 -2.16899484e-01 9.01311100e-01 -1.14773357e+00 1.78448662e-01 -5.48562944e-01 -7.56179214e-01 5.29292285e-01 -2.68739194e-01 4.72986788e-01 -9.18754995e-01 -6.20527387e-01 2.31462508e-01 -3.36663663e-01 -6.39063895e-01 -2.03909114e-01 -2.48798192e-01 -2.22171266e-02 -1.40466905e+00 -2.85736233e-01 -4.45807040e-01 4.60911840e-01 3.27806026e-01 8.27852964e-01 1.08629964e-01 -3.82143617e-01 -6.32725209e-02 -6.47486627e-01 -4.42721635e-01 -1.12579012e+00 -1.48373032e-02 1.43742040e-01 -2.76208043e-01 1.03373855e-01 -8.01054463e-02 -1.40920475e-01 -2.02604339e-01 -6.00906491e-01 -4.98284027e-02 1.80088177e-01 6.40342414e-01 -1.63604811e-01 1.22607231e-01 4.70494241e-01 -1.23710096e+00 1.10710204e+00 -9.47569132e-01 -4.76001412e-01 2.01538607e-01 -8.92393291e-02 -2.11532474e-01 6.58545017e-01 -6.84913695e-01 -1.18311369e+00 -5.48112273e-01 1.30803257e-01 4.53479618e-01 1.22206267e-02 7.13547587e-01 7.53892779e-01 2.44985998e-01 1.45036459e+00 3.25314477e-02 -3.75440925e-01 -6.54001474e-01 8.53743374e-01 6.53787434e-01 5.29785872e-01 -1.31661937e-01 1.06077993e+00 8.09600592e-01 -6.48868859e-01 -8.27443659e-01 -1.59692037e+00 -7.55651712e-01 -9.35659707e-02 -4.02828425e-01 5.35683751e-01 -7.03941166e-01 -4.25109982e-01 3.27749789e-01 -1.46412408e+00 6.91751912e-02 7.33799264e-02 2.46029317e-01 9.80003849e-02 2.92797804e-01 -1.18474448e+00 -7.97820687e-01 -5.19365191e-01 -1.02539383e-01 4.31397289e-01 2.07516700e-01 -5.98315477e-01 -8.23414087e-01 6.56331599e-01 1.47413746e-01 4.08449739e-01 4.75466073e-01 7.97982335e-01 -9.81747508e-01 1.22804664e-01 -4.54197228e-01 -2.55107880e-01 -2.47420937e-01 3.15989643e-01 3.12420726e-01 -5.82103074e-01 1.73224941e-01 -7.72586986e-02 -3.30092520e-01 9.56816554e-01 4.09128547e-01 3.78606349e-01 -1.32053912e+00 -5.71315348e-01 -7.08939433e-02 6.32711589e-01 -2.56800532e-01 3.30482543e-01 8.04436862e-01 3.67251128e-01 4.43144917e-01 3.55636120e-01 9.00039077e-01 -7.10554793e-02 2.57613063e-01 3.17611039e-01 -8.37401450e-02 -2.64487982e-01 -6.87422693e-01 3.67548823e-01 4.14720684e-01 1.16788514e-01 -3.61729652e-01 -7.43258595e-01 5.70004284e-01 -1.57870722e+00 -1.90974116e+00 -1.84209064e-01 1.38522482e+00 1.04658592e+00 3.86387020e-01 6.44543409e-01 -1.22610532e-01 9.94562447e-01 7.82913566e-01 1.46730646e-01 -5.05479574e-01 -1.84731662e-01 2.21669003e-02 6.03673220e-01 6.17839694e-01 -1.52747571e+00 1.10565424e+00 7.78895855e+00 1.19811511e+00 -1.24682927e+00 2.73253381e-01 5.30667543e-01 -3.13078105e-01 1.33845136e-01 -2.63585001e-01 -6.84359789e-01 6.63405418e-01 8.74462783e-01 -4.71620768e-01 2.26134092e-01 1.19147313e+00 4.70276773e-01 -3.07979733e-01 -1.41466424e-01 2.53140628e-01 2.97778815e-01 -2.15027285e+00 2.82934040e-01 -2.75314152e-01 6.86344326e-01 2.16837242e-01 1.26980683e-02 3.00768651e-02 1.00117910e+00 -6.29284143e-01 1.24114335e+00 -6.02455810e-02 3.31566513e-01 -6.51641607e-01 4.27805454e-01 5.43770134e-01 -5.59022725e-01 1.00496486e-01 -1.83596313e-02 -3.74411106e-01 6.94793880e-01 1.03951228e+00 -9.20133412e-01 -1.28421411e-02 5.16532481e-01 6.23272717e-01 -8.00434768e-01 8.60391915e-01 -7.57709920e-01 1.37490046e+00 -3.20121288e-01 -5.22131324e-01 2.66709387e-01 4.20058906e-01 1.04529023e+00 1.69583297e+00 -2.04701468e-01 1.72816947e-01 2.55355656e-01 6.46643639e-01 5.18728048e-03 1.71511978e-01 -4.12406027e-01 -4.00731534e-01 5.28991759e-01 1.20420802e+00 -8.43596816e-01 -5.75881958e-01 1.85308531e-01 4.59559768e-01 3.86573017e-01 -5.48821539e-02 -9.09564197e-01 -2.99317062e-01 1.84595227e-01 5.55512309e-01 -1.08653620e-01 -2.16069013e-01 -1.30761310e-01 -1.17737114e+00 -5.34647942e-01 -7.15086460e-01 7.90359974e-01 -3.51046115e-01 -1.59078765e+00 6.17014706e-01 1.52371272e-01 -7.34985590e-01 -3.60779047e-01 -3.37708533e-01 -1.03526914e+00 4.08244103e-01 -8.83941233e-01 -9.87517893e-01 1.82801679e-01 2.56738156e-01 4.67361838e-01 -1.96904853e-01 6.32968962e-01 -2.39725441e-01 -3.20021152e-01 -9.66989994e-02 -5.20423939e-03 6.26875639e-01 6.87892139e-01 -7.32296884e-01 6.83144748e-01 9.15010452e-01 2.35462070e-01 7.35148370e-01 1.31581724e+00 -1.37339735e+00 -8.49943638e-01 -6.76142275e-01 1.05537069e+00 -6.86524987e-01 1.63446331e+00 -4.64484513e-01 -5.95510840e-01 3.16561610e-01 4.50397223e-01 -6.90187812e-01 7.07645774e-01 4.20792639e-01 -1.10060179e+00 7.14424670e-01 -1.15263903e+00 6.92636192e-01 7.75328875e-01 -3.30688894e-01 -1.08697295e+00 9.65094566e-01 3.75934452e-01 -2.61660963e-01 7.46576563e-02 -4.16067600e-01 4.51094210e-01 -5.49417317e-01 7.02098727e-01 -9.21731889e-01 8.70813251e-01 1.04294978e-01 5.32743335e-01 -1.32735753e+00 -7.15836644e-01 -7.57004023e-01 -3.63648832e-01 9.83145416e-01 3.01356673e-01 -3.88342798e-01 5.93666255e-01 -2.18173683e-01 4.07333255e-01 -5.91497235e-02 -9.87927496e-01 -7.59506226e-01 -2.04708189e-01 -1.06313825e-01 -1.62426502e-01 1.37788737e+00 5.29344618e-01 7.02024937e-01 -8.04827034e-01 2.37670839e-02 6.71386182e-01 1.45622477e-01 7.81189680e-01 -1.09023380e+00 -1.92459717e-01 -5.53380787e-01 -1.45045891e-01 -7.73440301e-01 -4.22616564e-02 -6.50667310e-01 -2.11712092e-01 -1.57731426e+00 4.10335869e-01 8.12912211e-02 2.85032213e-01 3.99793983e-01 2.40750201e-02 1.62130371e-01 9.37032178e-02 5.27388096e-01 -8.21470499e-01 8.88451841e-03 6.98769867e-01 -3.97427291e-01 -2.98844457e-01 -1.98877469e-01 -7.46886790e-01 1.05369806e+00 9.08983409e-01 -9.17653441e-01 4.11655217e-01 -4.15722765e-02 5.02455533e-01 -4.20721531e-01 3.65027100e-01 -4.86555248e-01 1.23820916e-01 -7.94200361e-01 3.09050500e-01 -9.11210835e-01 -2.22910009e-02 1.82228372e-01 -2.57708758e-01 7.70431101e-01 -4.38519567e-01 -1.25204518e-01 1.63888440e-01 8.29880714e-01 4.85670902e-02 -6.08210526e-02 8.02802026e-01 -4.36330698e-02 -2.99007058e-01 -1.70924619e-01 -1.09613883e+00 3.26761901e-01 6.94753289e-01 5.52357852e-01 -1.70104468e+00 -5.88009000e-01 -3.80260795e-01 -3.25352490e-01 2.09224641e-01 4.31269139e-01 1.42531365e-01 -1.18660200e+00 -1.18786740e+00 -3.63447756e-01 1.12945000e-02 -1.05814981e+00 -1.75195225e-02 4.81728882e-01 -1.00567758e+00 1.37058347e-01 -1.27795860e-01 1.28426403e-01 -1.34058166e+00 6.53545499e-01 -2.76453078e-01 -5.40623248e-01 -7.73753047e-01 7.83893883e-01 -3.93046066e-02 4.12284195e-01 -1.05803259e-01 3.62744570e-01 -4.58914608e-01 1.73796773e-01 1.46805632e+00 6.44041717e-01 -2.74626374e-01 -8.38701725e-01 -5.66102505e-01 -6.01444393e-02 -5.07331431e-01 -2.70679474e-01 1.29104853e+00 2.37878725e-01 -1.54663995e-01 1.54173970e-01 7.44978070e-01 1.08488286e+00 -6.55435443e-01 -2.52594978e-01 -2.17049271e-02 -6.03807628e-01 1.43282324e-01 -1.02855885e+00 -4.14341033e-01 1.37549236e-01 -1.93312719e-01 8.24269533e-01 2.75750339e-01 6.22785747e-01 6.65333807e-01 2.70866752e-01 1.14318684e-01 -1.39787757e+00 4.98059779e-01 8.08533430e-01 1.04519677e+00 -7.99815118e-01 6.84447587e-01 -6.19625807e-01 -5.38037896e-01 8.46118450e-01 -8.80482420e-02 -5.92349529e-01 8.25233281e-01 2.75311172e-01 3.31707269e-01 -7.55725503e-01 -5.91509938e-01 2.43065938e-01 4.14182752e-01 5.10313153e-01 2.37394229e-01 2.70066202e-01 -8.98661911e-01 4.15068835e-01 -3.87362212e-01 -6.46611273e-01 5.77320993e-01 9.83709276e-01 -1.05995262e+00 -3.12208086e-01 -7.94282675e-01 4.62047964e-01 -1.01489520e+00 -1.95533454e-01 -1.27315950e+00 5.99275827e-01 -1.53060675e-01 1.13333523e+00 -4.88706194e-02 -3.44124317e-01 -1.15310185e-01 -5.71160257e-01 1.78545609e-01 -7.00937033e-01 -1.02708590e+00 1.73465043e-01 6.78042293e-01 -2.75047034e-01 -3.22088063e-01 -4.08265531e-01 -9.58164930e-01 -1.01272392e+00 -2.82949001e-01 4.05541629e-01 8.21494162e-01 8.16146433e-01 1.24038324e-01 -7.40157664e-02 5.96677184e-01 -5.56152880e-01 -5.03978312e-01 -1.01889265e+00 -3.27220351e-01 6.38982654e-01 1.16790555e-01 -7.14436471e-01 -6.36604130e-01 4.26565297e-02]	[8.468461990356445, 10.63969612121582]
e5b7beef-91ef-4866-9b58-3cbeb5385e0f	sample-complexity-of-variance-reduced	2305.18420	null	https://arxiv.org/abs/2305.18420v1	https://arxiv.org/pdf/2305.18420v1.pdf	Sample Complexity of Variance-reduced Distributionally Robust Q-learning	Dynamic decision making under distributional shifts is of fundamental interest in theory and applications of reinforcement learning: The distribution of the environment on which the data is collected can differ from that of the environment on which the model is deployed. This paper presents two novel model-free algorithms, namely the distributionally robust Q-learning and its variance-reduced counterpart, that can effectively learn a robust policy despite distributional shifts. These algorithms are designed to efficiently approximate the $q$-function of an infinite-horizon $\gamma$-discounted robust Markov decision process with Kullback-Leibler uncertainty set to an entry-wise $\epsilon$-degree of precision. Further, the variance-reduced distributionally robust Q-learning combines the synchronous Q-learning with variance-reduction techniques to enhance its performance. Consequently, we establish that it attains a minmax sample complexity upper bound of $\tilde O(\|S\|\|A\|(1-\gamma)^{-4}\epsilon^{-2})$, where $S$ and $A$ denote the state and action spaces. This is the first complexity result that is independent of the uncertainty size $\delta$, thereby providing new complexity theoretic insights. Additionally, a series of numerical experiments confirm the theoretical findings and the efficiency of the algorithms in handling distributional shifts.	['Zhengyuan Zhou', 'Jose Blanchet', 'Nian Si', 'Shengbo Wang']	2023-05-28	null	null	null	null	['q-learning']	['methodology']	[-8.44629258e-02 6.22824989e-02 -2.39321291e-01 -1.40297160e-01 -1.16824293e+00 -5.91603696e-01 2.73892973e-02 4.83661056e-01 -1.03001666e+00 1.04413974e+00 -4.34534162e-01 -6.95731401e-01 -9.17530477e-01 -7.33390093e-01 -6.58866048e-01 -1.02054119e+00 -6.50048912e-01 4.79110271e-01 -1.81849301e-02 -4.78238985e-02 2.58131027e-01 9.88827460e-03 -1.35636914e+00 -5.59441388e-01 8.81347299e-01 1.63613605e+00 1.66758314e-01 7.66466022e-01 2.41157204e-01 7.51328707e-01 -6.68529689e-01 -1.77753583e-01 5.57857871e-01 -3.77936482e-01 -2.59101927e-01 -6.35749474e-02 -3.48688722e-01 -3.48140955e-01 -2.73932844e-01 1.53696787e+00 5.84053218e-01 5.85643530e-01 6.44570768e-01 -1.24328244e+00 -1.87743604e-01 5.61876714e-01 -7.48323679e-01 4.79122192e-01 -3.70616429e-02 3.62467021e-01 6.69691741e-01 -9.23450887e-02 6.04746044e-02 1.33638966e+00 3.65753412e-01 4.49490666e-01 -9.39935148e-01 -7.47871339e-01 5.28119206e-01 -1.12238832e-01 -1.23469424e+00 3.15411277e-02 4.00424093e-01 -4.49909121e-01 8.17232788e-01 -8.60046372e-02 4.46361810e-01 5.15886843e-01 7.26529598e-01 5.77878892e-01 1.42388320e+00 -3.60462785e-01 1.02082741e+00 -7.58950859e-02 -3.71469051e-01 4.43603277e-01 4.72562462e-01 7.08994746e-01 -6.67529553e-02 -2.25415245e-01 6.08792782e-01 1.65557563e-02 4.72265556e-02 -4.89342690e-01 -6.23974681e-01 9.71037030e-01 -2.15062752e-01 -2.47060627e-01 -6.65946960e-01 5.32583714e-01 3.69215101e-01 6.11916184e-01 2.75121808e-01 1.95373118e-01 -5.15701532e-01 -5.60097575e-01 -5.41588962e-01 5.90973139e-01 7.28525400e-01 1.05419159e+00 2.66963899e-01 4.57746416e-01 -3.32216591e-01 2.92384744e-01 5.34466624e-01 1.17444599e+00 2.88563967e-01 -1.28979516e+00 7.54319906e-01 -7.29295462e-02 8.28221142e-01 -5.39369226e-01 -2.80707747e-01 -4.24079716e-01 -4.96312618e-01 4.65039432e-01 6.73841894e-01 -7.31366992e-01 -5.08974016e-01 2.04761410e+00 2.79535025e-01 -3.14604998e-01 1.23190314e-01 5.39823532e-01 -5.60942352e-01 5.05280256e-01 8.20164233e-02 -9.27634001e-01 1.09484279e+00 -8.94936249e-02 -8.58343780e-01 -1.43554345e-01 3.08183998e-01 -4.80050981e-01 1.07805145e+00 4.15775895e-01 -1.28796256e+00 -1.27111658e-01 -1.05049634e+00 1.05448318e+00 1.73476011e-01 -7.13025033e-01 1.94376290e-01 8.19505513e-01 -7.24387050e-01 6.53209031e-01 -8.20080936e-01 2.76975542e-01 3.95217061e-01 2.49356270e-01 3.59210372e-01 9.82576907e-02 -1.16291022e+00 7.62971222e-01 5.31506717e-01 -1.05502695e-01 -1.18168592e+00 -5.41831017e-01 -5.74279904e-01 -1.35308817e-01 1.11457753e+00 -3.39895219e-01 1.83785272e+00 -4.37250972e-01 -1.78097737e+00 8.25440232e-03 1.98643565e-01 -7.15065181e-01 7.58194327e-01 3.74050811e-02 -2.62176454e-01 2.43165702e-01 1.20583668e-01 -2.44405791e-01 9.48052287e-01 -8.46024990e-01 -1.21762824e+00 -6.80033863e-01 7.28170052e-02 3.08916748e-01 1.18442036e-01 -1.48307411e-02 3.43722939e-01 -5.58915317e-01 -1.54090181e-01 -9.84487295e-01 -7.32067585e-01 -3.15253705e-01 6.84053302e-02 -2.01627880e-01 2.30798125e-01 -2.47042462e-01 1.38819802e+00 -2.07265115e+00 -2.23638102e-01 5.23603201e-01 -2.63912261e-01 -1.46785267e-02 1.92656651e-01 6.10995352e-01 2.45629191e-01 1.49276227e-01 -2.32870623e-01 1.05719127e-01 3.73351932e-01 1.95019707e-01 -4.16262448e-01 7.70900071e-01 -2.38218457e-01 3.63891393e-01 -1.00851512e+00 -1.04063421e-01 1.29481688e-01 -2.13325396e-01 -2.21667558e-01 2.33931132e-02 -4.79914695e-01 1.58550665e-01 -8.56661022e-01 3.72296065e-01 5.22417128e-01 3.79713699e-02 1.71384096e-01 5.72763026e-01 -2.51178771e-01 -3.07911545e-01 -1.76725757e+00 1.16886508e+00 -4.34098452e-01 -1.55767784e-01 4.09873456e-01 -1.23509741e+00 7.83027053e-01 1.68164760e-01 6.25691295e-01 -9.33770597e-01 4.11172986e-01 2.67317504e-01 -3.06984708e-02 -3.02685529e-01 2.77704597e-01 -8.55193853e-01 -4.79155272e-01 7.97394037e-01 -1.64529651e-01 -2.81728804e-01 3.54920983e-01 -2.48058677e-01 1.04377222e+00 -6.76952973e-02 3.95578712e-01 -6.18200779e-01 9.51591507e-02 -4.33170408e-01 1.00400555e+00 1.07289290e+00 -8.10447454e-01 -5.48566401e-01 8.53451550e-01 3.89727652e-02 -7.81762302e-01 -1.26480246e+00 -1.23680755e-01 1.12567222e+00 2.47108072e-01 4.40463610e-02 -7.23402977e-01 -5.27422011e-01 3.12984526e-01 1.10361493e+00 -5.67731738e-01 -3.73458475e-01 -1.15779653e-01 -6.82447791e-01 1.70311227e-01 5.41687310e-01 4.54024285e-01 -7.97291100e-01 -1.22309244e+00 3.45599204e-01 3.29752803e-01 -4.76083398e-01 -4.62120682e-01 5.39600015e-01 -8.23549807e-01 -1.05715621e+00 -5.24784923e-01 -1.84850946e-01 4.34063166e-01 -1.62631407e-01 5.60516179e-01 -1.06745994e+00 1.66873232e-01 8.90553951e-01 -4.53964546e-02 -1.08350682e+00 -2.80251443e-01 -5.29463410e-01 4.63807702e-01 -1.08919449e-01 2.35061541e-01 -3.49436790e-01 -7.79448211e-01 1.79197997e-01 -1.01247346e+00 -8.71999562e-01 4.71758872e-01 7.50951648e-01 9.22637343e-01 8.14178169e-01 8.81281435e-01 -4.85770077e-01 1.04570842e+00 -3.25346172e-01 -1.33833158e+00 2.05700204e-01 -8.27280998e-01 3.67618859e-01 7.45379984e-01 -4.20103997e-01 -1.00499129e+00 -2.06113800e-01 3.77681404e-01 -3.18613380e-01 2.44377241e-01 5.22955120e-01 -6.60009608e-02 4.33156967e-01 4.32691544e-01 2.86220759e-01 3.25495154e-01 -2.42623016e-01 2.80284673e-01 5.77849150e-01 3.96638244e-01 -9.55395997e-01 5.83145022e-01 3.23741555e-01 3.30770642e-01 -4.20066446e-01 -7.13816702e-01 -1.21992990e-01 1.75094735e-02 -1.18085988e-01 4.34932947e-01 -8.33447218e-01 -1.24903095e+00 2.49424592e-01 -2.59939253e-01 -5.09173036e-01 -8.16673756e-01 7.53037751e-01 -1.45370400e+00 1.26172110e-01 -2.84626096e-01 -1.68740070e+00 -1.80432200e-01 -8.61943662e-01 3.45488906e-01 4.36109543e-01 3.08210433e-01 -8.03808212e-01 2.35181689e-01 1.21167302e-02 3.07387143e-01 3.04313391e-01 9.28291857e-01 -5.71279585e-01 -2.15673849e-01 -2.10858181e-01 2.40435392e-01 5.20953774e-01 -4.25602347e-02 -2.73621827e-01 -4.65041846e-01 -7.23841667e-01 4.42137569e-01 -2.31262073e-01 4.47174847e-01 7.13001609e-01 1.08213413e+00 -7.07804799e-01 3.95300798e-02 3.79135609e-02 1.48457551e+00 1.09599459e+00 6.89051151e-02 4.59566653e-01 -2.26973951e-01 3.98017645e-01 1.12179852e+00 1.29688632e+00 2.90196151e-01 1.42048284e-01 7.30246127e-01 6.85000777e-01 9.41137731e-01 -3.78476590e-01 5.87017417e-01 2.85439879e-01 2.91093409e-01 -6.86719939e-02 -7.24554718e-01 4.56963181e-01 -1.95324218e+00 -1.07075512e+00 6.60747945e-01 2.96664095e+00 8.47684443e-01 4.07769829e-01 4.36534286e-01 5.88925704e-02 7.06345856e-01 -1.12386860e-01 -1.13027716e+00 -7.37063229e-01 1.48865700e-01 4.52284485e-01 8.90452564e-01 4.91889238e-01 -9.85840976e-01 3.68034482e-01 5.35415792e+00 1.16871333e+00 -7.68927217e-01 -1.21641368e-01 5.92471838e-01 -4.07506675e-01 -1.51148304e-01 -1.87716320e-01 -5.41971445e-01 8.62850666e-01 1.38770115e+00 -8.47511411e-01 3.84595215e-01 1.12134564e+00 5.98458171e-01 -5.61970592e-01 -1.00639760e+00 8.88604224e-01 -6.43707156e-01 -8.29418600e-01 -5.24801016e-01 2.06499964e-01 7.85763919e-01 -3.25753748e-01 5.54718435e-01 5.67436099e-01 7.55088985e-01 -8.41784596e-01 9.22122478e-01 5.18711269e-01 7.77084649e-01 -1.61805892e+00 8.45724940e-01 6.28509104e-01 -1.15674698e+00 -1.00219953e+00 -3.74549419e-01 -2.03582063e-01 1.44931510e-01 5.28036892e-01 -3.81954551e-01 3.70449036e-01 7.10682213e-01 -7.98811391e-02 2.21578807e-01 9.27042782e-01 -1.28494799e-01 3.69535744e-01 -5.06612122e-01 -3.35069746e-01 4.97215718e-01 -3.32031995e-01 5.55826366e-01 7.60679603e-01 4.79510397e-01 2.62160301e-01 5.73350728e-01 5.68285286e-01 2.57133365e-01 4.10913443e-03 -4.38068807e-01 -1.28123909e-01 8.72816563e-01 5.87245762e-01 -6.44148290e-01 -9.18428600e-02 -7.37653300e-02 2.79889733e-01 -1.32036418e-01 3.92431319e-01 -8.29650521e-01 -7.77561426e-01 7.60758579e-01 -6.32819757e-02 5.58490157e-01 -3.29607725e-01 7.54484609e-02 -5.62017202e-01 2.11232871e-01 -7.88970411e-01 7.55736530e-01 4.46784459e-02 -1.38821280e+00 4.15804982e-02 2.67861903e-01 -1.08398795e+00 -6.10162377e-01 -3.82124364e-01 -2.90910214e-01 8.77445102e-01 -1.24099505e+00 -1.87150106e-01 7.11010993e-01 7.12575197e-01 1.82487503e-01 -1.72325686e-01 5.71671784e-01 -2.06704557e-01 -6.97871149e-01 5.28196990e-01 1.00318396e+00 -1.98789701e-01 2.77617782e-01 -1.41926432e+00 -3.33264709e-01 6.56719506e-01 -5.44600546e-01 2.88524002e-01 9.44850266e-01 -4.52423006e-01 -1.55726373e+00 -1.10585344e+00 5.60981110e-02 -2.20329929e-02 9.25513029e-01 7.41995573e-02 -5.49126685e-01 3.80101234e-01 -1.40114576e-01 3.11900619e-02 5.64953983e-01 -2.76201814e-01 -8.61269683e-02 -4.87748146e-01 -1.46562564e+00 6.28670454e-01 6.40154958e-01 -4.10025656e-01 -4.81066585e-01 -2.32278556e-02 7.45357156e-01 -1.57909304e-01 -1.02635217e+00 4.23770458e-01 2.97031611e-01 -7.61987269e-01 4.67403114e-01 -7.57520020e-01 -2.62643695e-01 -9.40806568e-02 -5.75641274e-01 -1.42418206e+00 4.90823807e-03 -1.30763781e+00 -4.16777462e-01 7.64054596e-01 1.72353685e-01 -9.05662179e-01 4.93975788e-01 6.83164299e-01 2.67290115e-01 -8.21854651e-01 -1.69251955e+00 -1.32367468e+00 7.35816240e-01 -6.02971971e-01 3.67500693e-01 2.77306050e-01 3.25165451e-01 -2.74999976e-01 -1.46330781e-02 -4.12286781e-02 1.18717515e+00 5.90818785e-02 1.56685650e-01 -7.43095160e-01 -4.82353866e-01 -5.64934850e-01 -4.33668345e-02 -8.86189163e-01 2.27069780e-02 -2.80021161e-01 4.85256612e-01 -8.70386183e-01 1.00698583e-01 -6.72236621e-01 -9.44405317e-01 8.01144168e-02 -2.68825758e-02 -8.31493378e-01 3.91298860e-01 -3.22378516e-01 -9.29234326e-01 1.08472633e+00 1.10920012e+00 3.12910043e-02 -4.28094327e-01 6.77136838e-01 -7.34746993e-01 8.30900729e-01 9.43475187e-01 -4.67504561e-01 -9.39129770e-01 9.67856646e-02 3.36419284e-01 7.46616066e-01 -1.45930380e-01 -8.81239653e-01 -3.17465030e-02 -9.04179096e-01 -2.14003071e-01 -4.58003640e-01 -1.05096243e-01 -7.46530294e-01 -2.86354959e-01 8.81633103e-01 -6.41276538e-01 2.81970352e-01 2.00696453e-01 1.24123228e+00 1.52631015e-01 -2.95707941e-01 1.02481842e+00 -2.43422434e-01 -3.30685586e-01 5.19824207e-01 -8.36216807e-01 5.93836665e-01 1.34394264e+00 2.14832142e-01 -7.97403380e-02 -6.94409192e-01 -4.57023263e-01 5.46898484e-01 2.72708237e-02 5.23672998e-02 3.38589966e-01 -9.75010097e-01 -4.51318115e-01 -1.57401145e-01 -1.99505270e-01 9.83681083e-02 4.40428019e-01 7.04476357e-01 8.71823505e-02 4.40132916e-01 -2.47712154e-02 -3.93833131e-01 -5.60005307e-01 7.56513715e-01 5.19699991e-01 -3.84080142e-01 -1.22091249e-01 6.53147459e-01 -1.51974097e-01 -1.23395473e-01 4.81070071e-01 -4.71265137e-01 4.55440372e-01 1.08044796e-01 8.18281531e-01 7.66563892e-01 -1.91131458e-01 -1.50234681e-02 -1.74097165e-01 1.28212690e-01 2.19968977e-04 -7.58777916e-01 1.04832733e+00 -3.87047648e-01 3.39954644e-01 6.89657867e-01 6.88711762e-01 -4.79895383e-01 -1.99581802e+00 -5.32942653e-01 2.31265381e-01 -3.81305665e-01 -7.19347745e-02 -7.24254608e-01 -6.53092384e-01 8.19494188e-01 9.27164912e-01 2.46464089e-01 9.38502848e-01 -3.62154335e-01 2.27929890e-01 4.19153422e-01 7.50694573e-01 -1.88265681e+00 1.19186498e-01 6.01305485e-01 5.61661839e-01 -8.77933800e-01 -2.42707774e-01 5.68449378e-01 -7.57728636e-01 7.32068777e-01 4.57971424e-01 -1.13706179e-01 9.54751134e-01 2.99161375e-01 -2.87916988e-01 3.35373282e-01 -9.40906703e-01 -2.22720370e-01 -2.93866694e-01 7.56496251e-01 -8.77886713e-02 4.58533376e-01 -3.63488168e-01 8.52640629e-01 -5.43122329e-02 5.08391336e-02 5.29780149e-01 1.44161248e+00 -8.58846903e-01 -7.51615107e-01 -4.06709164e-01 3.72915268e-01 -5.33003092e-01 3.12058538e-01 3.93349200e-01 7.93867588e-01 -1.90278530e-01 1.18419456e+00 5.93392812e-02 1.45082995e-01 4.71865416e-01 7.16278255e-02 5.26165068e-01 -2.74756759e-01 1.75329298e-02 2.45931134e-01 -3.80170792e-01 -7.42826343e-01 -3.92939150e-02 -7.46515989e-01 -1.52185774e+00 -3.36466908e-01 2.35005394e-02 3.96433979e-01 5.81949055e-01 1.07756162e+00 2.75113642e-01 3.27606797e-01 1.08953929e+00 -2.98312843e-01 -1.98231626e+00 -7.67264843e-01 -1.10203898e+00 -5.81346117e-02 3.17858905e-01 -8.57484460e-01 -4.44149047e-01 -6.01632774e-01]	[4.357432842254639, 2.7879159450531006]
80f2775d-8ee9-4fe7-ae97-1215e2f423c2	efficient-splitting-based-method-for-global	1604.07681	null	http://arxiv.org/abs/1604.07681v1	http://arxiv.org/pdf/1604.07681v1.pdf	Efficient Splitting-based Method for Global Image Smoothing	Edge-preserving smoothing (EPS) can be formulated as minimizing an objective function that consists of data and prior terms. This global EPS approach shows better smoothing performance than a local one that typically has a form of weighted averaging, at the price of high computational cost. In this paper, we introduce a highly efficient splitting-based method for global EPS that minimizes the objective function of ${l_2}$ data and prior terms (possibly non-smooth and non-convex) in linear time. Different from previous splitting-based methods that require solving a large linear system, our approach solves an equivalent constrained optimization problem, resulting in a sequence of 1D sub-problems. This enables linear time solvers for weighted-least squares and -total variation problems. Our solver converges quickly, and its runtime is even comparable to state-of-the-art local EPS approaches. We also propose a family of fast iteratively re-weighted algorithms using a non-convex prior term. Experimental results demonstrate the effectiveness and flexibility of our approach in a range of computer vision and image processing tasks.	['Bumsub Ham', 'Youngjung Kim', 'Kwanghoon Sohn', 'Dongbo Min']	2016-04-26	null	null	null	null	['image-smoothing']	['computer-vision']	[ 1.09070092e-01 2.93619316e-02 3.88197780e-01 -4.45788354e-01 -1.10911024e+00 -1.50286928e-01 1.95859358e-01 2.03835174e-01 -6.02534115e-01 6.54201865e-01 -1.06212027e-01 9.95118544e-02 -6.23920858e-02 -4.60599184e-01 -6.78987861e-01 -8.06749701e-01 -6.93432167e-02 1.64331540e-01 6.39628232e-01 -1.06710918e-01 4.84974116e-01 5.13810754e-01 -1.31084073e+00 -2.30419457e-01 1.25389469e+00 1.12720668e+00 3.04008842e-01 5.20985305e-01 -6.78021610e-02 4.58876163e-01 -1.06137514e-01 -1.53799653e-01 4.20601308e-01 -3.27941209e-01 -8.35756838e-01 5.42927206e-01 9.43557739e-01 -2.83962046e-03 1.01804115e-01 1.28709245e+00 5.95654368e-01 6.16301119e-01 3.11526477e-01 -9.11551416e-01 -4.53981817e-01 4.26302031e-02 -1.04838896e+00 -4.91819307e-02 3.46431397e-02 -2.03580573e-01 7.44825006e-01 -1.17607141e+00 5.35211802e-01 1.14560187e+00 1.11995053e+00 1.16625741e-01 -1.72735596e+00 7.13160187e-02 4.15728807e-01 -1.61017880e-01 -1.37721586e+00 -3.74616295e-01 6.86645627e-01 -4.19392556e-01 6.78511918e-01 4.70672935e-01 4.93117720e-01 4.00634520e-02 1.53418347e-01 7.10375547e-01 1.24985743e+00 -4.87313867e-01 3.52126509e-01 5.60693666e-02 3.68699104e-01 9.22348857e-01 2.97331475e-02 -2.62060255e-01 -5.01367629e-01 -4.16223466e-01 8.25424433e-01 -9.87875536e-02 -4.01950479e-01 -6.25750303e-01 -1.14697134e+00 7.88834453e-01 2.71202236e-01 1.47006020e-01 -3.19891393e-01 1.46882415e-01 1.63952798e-01 1.00444071e-01 1.09052145e+00 1.42098442e-01 -5.16529739e-01 1.57945439e-01 -1.45805037e+00 4.90891993e-01 9.32351589e-01 7.54812896e-01 1.08209610e+00 1.68365732e-01 -7.36365393e-02 1.03971863e+00 2.88291901e-01 5.22524059e-01 8.18099603e-02 -1.14140809e+00 8.13681781e-02 3.45479906e-01 3.74223322e-01 -1.16928148e+00 -5.46488881e-01 -1.32970288e-01 -7.23553479e-01 7.16680110e-01 6.16441786e-01 -1.91209450e-01 -8.93336535e-01 1.56659722e+00 5.44853687e-01 6.90897644e-01 -5.18872142e-01 8.61123383e-01 5.01022100e-01 5.83262384e-01 -1.43675208e-01 -6.27492666e-01 1.08101547e+00 -1.24668324e+00 -7.62051642e-01 -3.39310706e-01 3.75735968e-01 -1.00793672e+00 8.54920983e-01 3.41014355e-01 -1.64929819e+00 -2.84421802e-01 -7.14242876e-01 -3.99545819e-01 -9.09527168e-02 1.03122532e-01 3.59524280e-01 3.64117593e-01 -1.48437178e+00 9.66998994e-01 -9.85070944e-01 -1.11454584e-01 2.12109700e-01 3.42398107e-01 -1.51304528e-01 1.89893112e-01 -4.38034624e-01 1.10709405e+00 -4.24182601e-02 2.62627721e-01 -3.28278929e-01 -1.26261699e+00 -9.73071575e-01 2.79766228e-02 6.28119349e-01 -4.27597463e-01 1.02999485e+00 -8.06386530e-01 -1.77213335e+00 7.87988782e-01 -7.04448044e-01 -2.72572488e-01 6.80275381e-01 -3.14417154e-01 1.04398057e-01 -6.25974685e-02 -7.25891665e-02 1.51063859e-01 1.19279850e+00 -1.26790833e+00 -4.13691163e-01 -2.73592889e-01 -2.39244014e-01 8.86175036e-02 -1.75764650e-01 2.07833707e-01 -7.01790333e-01 -8.16389382e-01 1.05712257e-01 -9.49750364e-01 -8.95712912e-01 3.67487937e-01 -1.27627924e-01 8.55981335e-02 6.49732828e-01 -9.65842187e-01 1.40500748e+00 -2.10431767e+00 4.00208652e-01 3.94842774e-01 2.46202052e-01 3.12916428e-01 -1.11043297e-01 2.60084391e-01 -5.46656065e-02 -1.24788940e-01 -9.83654499e-01 -7.69245207e-01 -6.34795800e-02 2.03732010e-02 -3.04890778e-02 7.41447985e-01 7.91016296e-02 7.11682379e-01 -8.58849764e-01 -5.15121460e-01 3.52811962e-01 7.28741109e-01 -4.46220666e-01 -2.23021299e-01 6.16544392e-04 1.44305885e-01 -2.13170335e-01 3.25541228e-01 1.12591267e+00 -2.24988014e-01 -9.38516185e-02 -2.48791829e-01 -7.11198926e-01 -3.52652222e-01 -1.79742110e+00 1.86207592e+00 -3.97831380e-01 5.30562162e-01 8.36499035e-01 -1.33692777e+00 8.96406233e-01 -1.93734877e-02 7.35765219e-01 -2.26043880e-01 3.01955491e-02 3.11479300e-01 -6.70382440e-01 -1.80632383e-01 6.17985308e-01 -2.51046062e-01 3.71502489e-01 6.69594184e-02 -1.05510883e-01 -3.36740166e-01 3.82621348e-01 1.36379689e-01 9.72545743e-01 1.15567572e-01 3.04324538e-01 -9.51005101e-01 7.68316627e-01 -5.12459800e-02 6.82512760e-01 6.07972622e-01 -5.52820675e-02 7.91490078e-01 4.81220782e-01 -3.16667616e-01 -8.81995678e-01 -7.24433959e-01 -4.86781657e-01 8.93236279e-01 3.34019482e-01 -3.48759621e-01 -8.04620385e-01 -2.64429688e-01 2.25835726e-01 4.88936812e-01 -5.84756494e-01 4.52156901e-01 -7.61529803e-01 -8.38768840e-01 -2.43239820e-01 4.35588688e-01 3.36426347e-01 -6.32757545e-01 -4.73877788e-01 4.56359357e-01 1.15861855e-01 -9.82224762e-01 -1.04852617e+00 -8.10669810e-02 -1.07520962e+00 -8.72111440e-01 -1.22409296e+00 -9.18930471e-01 9.77728367e-01 4.12572324e-01 8.85024309e-01 1.26440108e-01 -5.36764026e-01 5.36466360e-01 1.11563794e-01 -2.32218146e-01 2.40139142e-01 -3.60212147e-01 -2.11077765e-01 4.86759722e-01 -9.98995230e-02 -3.35875124e-01 -5.95569611e-01 3.70925188e-01 -7.59938121e-01 -1.78361967e-01 2.02839881e-01 8.21595132e-01 1.13752437e+00 -3.16871534e-04 2.32670531e-01 -9.36762214e-01 6.98535562e-01 -5.23033366e-02 -1.07830596e+00 3.23561698e-01 -5.16406894e-01 1.85134396e-01 3.82705539e-01 -4.68527615e-01 -1.39382255e+00 5.07900476e-01 1.12468950e-01 -3.33252221e-01 2.95260221e-01 5.68705916e-01 3.39938968e-01 -8.00370812e-01 5.96964598e-01 2.09879130e-02 3.40074122e-01 -7.01226830e-01 4.22195941e-01 6.25256151e-02 4.61910993e-01 -4.89830911e-01 7.36817896e-01 8.75341892e-01 4.06675071e-01 -1.18173456e+00 -7.41443515e-01 -8.80395353e-01 -5.16629696e-01 -1.92259684e-01 8.58596027e-01 -6.27955735e-01 -6.68089211e-01 7.21588433e-01 -9.35378253e-01 -5.70431530e-01 -4.43264395e-01 2.54193723e-01 -5.66764116e-01 7.77241111e-01 -5.04910529e-01 -7.33256161e-01 -9.96584371e-02 -1.12016582e+00 1.07814765e+00 1.55897006e-01 1.07640125e-01 -1.37986898e+00 3.28390390e-01 -2.24877931e-02 7.20480382e-01 4.66654569e-01 1.32531360e-01 1.18373446e-01 -4.03943956e-01 -2.79903024e-01 -3.78305227e-01 4.41571683e-01 -1.50609031e-01 6.26833588e-02 -5.44439554e-01 -4.85652417e-01 3.10955882e-01 5.70934825e-02 1.01104546e+00 1.03871453e+00 1.11444962e+00 -1.29381731e-01 -3.06550473e-01 7.98544168e-01 1.89666748e+00 -3.06443334e-01 4.40237850e-01 -7.17002749e-02 5.84044397e-01 5.19420028e-01 4.95807886e-01 4.60776865e-01 2.86156595e-01 7.78490722e-01 8.75407308e-02 -4.72917527e-01 1.68657433e-02 5.48808515e-01 3.29267353e-01 6.62143469e-01 -4.46924984e-01 3.40149909e-01 -7.71061242e-01 7.71782339e-01 -2.38528848e+00 -9.78031993e-01 -6.26034439e-01 2.37978506e+00 7.76359081e-01 -4.09240991e-01 2.00402007e-01 -1.28913671e-01 7.09022880e-01 2.11574137e-01 -4.36358124e-01 -5.74288666e-01 6.89207464e-02 5.03355682e-01 9.09793377e-01 1.17834246e+00 -1.33599937e+00 6.54862583e-01 7.02428198e+00 8.68022680e-01 -9.74177897e-01 1.40468419e-01 2.67216682e-01 -1.77506357e-01 -1.48201972e-01 -3.32809705e-03 -7.39109337e-01 4.56266820e-01 5.04103959e-01 -3.38541627e-01 6.60740912e-01 8.09872150e-01 3.74753237e-01 -4.37378436e-01 -6.55155420e-01 8.57731760e-01 2.41236567e-01 -1.36782289e+00 -6.97214425e-01 2.91633829e-02 1.06051052e+00 1.30801409e-01 -2.36233249e-01 -2.53517538e-01 2.94860333e-01 -6.67908847e-01 5.78268886e-01 7.14202166e-01 4.92105633e-01 -5.32342613e-01 3.89557004e-01 1.66986644e-01 -1.54839265e+00 1.55749395e-01 -5.04378915e-01 3.60361747e-02 6.86338782e-01 1.11408222e+00 2.58588165e-01 4.16467607e-01 8.32136512e-01 7.12666929e-01 -1.75821021e-01 1.39331162e+00 3.55012566e-02 1.88607693e-01 -5.73959112e-01 2.31361449e-01 1.72958791e-01 -8.32115531e-01 7.61653066e-01 1.42790961e+00 3.72003555e-01 4.10885364e-01 3.85485262e-01 7.52486467e-01 2.57376224e-01 3.15336853e-01 -2.00729653e-01 5.05630612e-01 -5.42930141e-03 1.48644567e+00 -7.05804169e-01 -2.49900505e-01 -8.20945203e-01 9.15117919e-01 5.65717399e-01 5.09194911e-01 -6.86795056e-01 -5.11490583e-01 5.06961524e-01 2.00002924e-01 6.53298259e-01 -5.19866645e-01 -5.25991380e-01 -1.31386089e+00 2.55915612e-01 -4.70730275e-01 2.13637650e-01 -4.35723454e-01 -1.34325945e+00 4.61195618e-01 -5.38447266e-03 -9.36815798e-01 3.34824592e-01 -7.03629553e-01 -7.58990407e-01 1.12620187e+00 -1.69321227e+00 -9.39225435e-01 -3.20885122e-01 6.81518257e-01 5.52129865e-01 4.36414480e-01 5.16968429e-01 2.52344280e-01 -5.49387038e-01 2.79259145e-01 2.84294248e-01 -4.04758006e-01 8.49245012e-01 -1.55509329e+00 1.54306799e-01 1.15175521e+00 -4.42059755e-01 5.29168606e-01 7.80192673e-01 -6.51148379e-01 -1.20514762e+00 -7.91757345e-01 1.09390962e+00 -3.89522798e-02 8.27694178e-01 8.33840948e-03 -1.24337292e+00 7.30583072e-01 2.08164915e-01 3.76922280e-01 3.71136934e-01 4.17639911e-02 1.51108012e-01 -1.23365462e-01 -1.19238842e+00 3.91661912e-01 9.41062808e-01 -3.96886617e-02 -3.72789532e-01 3.13359857e-01 1.64628103e-01 -6.57260656e-01 -8.63942444e-01 2.55366117e-01 3.12959880e-01 -7.65369475e-01 1.24613476e+00 -2.35698387e-01 1.08435348e-01 -4.49639857e-01 1.29179403e-01 -1.41558599e+00 -4.81465369e-01 -1.02349579e+00 -4.08377722e-02 9.25644219e-01 3.04779410e-01 -7.57890642e-01 5.84280550e-01 1.13522339e+00 -2.89742231e-01 -7.71670580e-01 -9.39867556e-01 -8.24144304e-01 -7.13741854e-02 -1.67515725e-01 -2.70195995e-02 9.01014388e-01 1.27318889e-01 -9.66238901e-02 -5.56264818e-01 2.10952252e-01 1.19558167e+00 4.28560041e-02 6.25236213e-01 -1.27858102e+00 -1.54834852e-01 -5.65020442e-01 -2.73090780e-01 -1.25372565e+00 6.88760281e-02 -7.86394656e-01 3.71412784e-01 -1.62557530e+00 -6.05576597e-02 -4.21931028e-01 -1.89989880e-02 4.07585710e-01 -4.73550081e-01 2.06122965e-01 1.32914558e-01 -1.11327924e-01 -3.18822503e-01 3.47029805e-01 1.21878839e+00 1.09587617e-01 -4.81532604e-01 -8.34043622e-02 -4.18496877e-01 1.05968535e+00 5.09413004e-01 -3.08229178e-01 -2.45728180e-01 -6.19094014e-01 7.79914334e-02 3.17565240e-02 3.12590778e-01 -6.39280260e-01 4.71339852e-01 -4.48136359e-01 -2.23265156e-01 -4.45366234e-01 3.87319058e-01 -8.24426889e-01 -2.12684888e-02 2.06477985e-01 -4.18852381e-02 -2.02074751e-01 2.18604058e-01 4.37786102e-01 -3.75881016e-01 -4.82138604e-01 1.19031811e+00 -1.15666837e-01 -7.00563490e-01 4.47600633e-01 -2.52925724e-01 1.09356694e-01 1.15169764e+00 -1.80969507e-01 -1.02251180e-01 -1.51420057e-01 -1.08491969e+00 3.47536415e-01 4.48798448e-01 -1.92514807e-01 4.26473200e-01 -1.21282172e+00 -8.75464737e-01 3.79309803e-02 -4.90994692e-01 2.14031473e-01 4.64194626e-01 1.35207927e+00 -7.83338785e-01 -7.27788061e-02 1.11729376e-01 -6.00102365e-01 -1.37238479e+00 4.06635225e-01 3.41017753e-01 -3.38842779e-01 -7.24995792e-01 1.04151511e+00 6.24159090e-02 -1.78685009e-01 7.90734664e-02 -3.19356352e-01 2.70788759e-01 9.27259475e-02 6.57202601e-01 1.01136541e+00 -7.53787719e-03 -5.18269777e-01 -4.69024539e-01 1.01473761e+00 1.26337513e-01 -1.50273189e-01 1.54799652e+00 -1.41758323e-01 -5.97445905e-01 1.29983783e-01 1.22687995e+00 2.49005795e-01 -1.60036170e+00 -4.49986219e-01 -1.07863821e-01 -6.75816357e-01 5.00970423e-01 -2.88227975e-01 -1.27400041e+00 5.30236363e-01 3.36059362e-01 3.49528491e-01 1.16753256e+00 -2.40714490e-01 7.98421204e-01 8.72901175e-03 3.31826955e-02 -1.47597027e+00 -2.03324184e-01 4.36696857e-01 1.15194488e+00 -1.29540873e+00 4.53711063e-01 -9.29793298e-01 -4.87096578e-01 9.80481684e-01 2.47710884e-01 -6.99183941e-01 1.22408807e+00 5.87520957e-01 1.19467797e-02 -1.58156324e-02 -2.19512969e-01 -2.20564619e-01 6.69639111e-01 2.52358913e-01 4.31480914e-01 -4.65972424e-02 -9.38083887e-01 7.41117448e-02 3.39915454e-01 1.64864451e-01 2.70371944e-01 1.04133952e+00 -5.85865855e-01 -1.19632506e+00 -3.48997116e-01 3.44337255e-01 -2.98602492e-01 -6.65302873e-02 9.00435075e-02 5.12113929e-01 -1.61160335e-01 8.05932105e-01 -2.47406643e-02 4.14218456e-01 4.06056523e-01 -5.95972203e-02 4.52095985e-01 -3.29006791e-01 -4.76087242e-01 4.93407995e-01 -6.04035929e-02 -1.01419055e+00 -7.79539585e-01 -1.00049758e+00 -1.19933760e+00 -3.07290524e-01 -6.15237176e-01 -1.67807505e-01 5.58253586e-01 8.70123267e-01 2.57171720e-01 3.39142054e-01 5.27394474e-01 -1.34813678e+00 -5.03097832e-01 -5.25349379e-01 -7.74147868e-01 4.44773346e-01 3.81134242e-01 -5.53708553e-01 -5.03767848e-01 2.71148741e-01]	[11.53538990020752, -2.5561835765838623]
c13a425b-dc5f-4c3a-a29b-5c85f5ea17c7	gaussian-processes-meet-neuralodes-a-bayesian	2103.03385	null	https://arxiv.org/abs/2103.03385v1	https://arxiv.org/pdf/2103.03385v1.pdf	Gaussian processes meet NeuralODEs: A Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy data	This paper presents a machine learning framework (GP-NODE) for Bayesian systems identification from partial, noisy and irregular observations of nonlinear dynamical systems. The proposed method takes advantage of recent developments in differentiable programming to propagate gradient information through ordinary differential equation solvers and perform Bayesian inference with respect to unknown model parameters using Hamiltonian Monte Carlo sampling and Gaussian Process priors over the observed system states. This allows us to exploit temporal correlations in the observed data, and efficiently infer posterior distributions over plausible models with quantified uncertainty. Moreover, the use of sparsity-promoting priors such as the Finnish Horseshoe for free model parameters enables the discovery of interpretable and parsimonious representations for the underlying latent dynamics. A series of numerical studies is presented to demonstrate the effectiveness of the proposed GP-NODE method including predator-prey systems, systems biology, and a 50-dimensional human motion dynamical system. Taken together, our findings put forth a novel, flexible and robust workflow for data-driven model discovery under uncertainty. All code and data accompanying this manuscript are available online at \url{https://github.com/PredictiveIntelligenceLab/GP-NODEs}.	['Paris Perdikaris', 'Mohamed Aziz Bhouri']	2021-03-04	null	null	null	null	['model-discovery']	['miscellaneous']	[-1.44210488e-01 -5.87316006e-02 -1.72799826e-02 1.62094593e-01 -5.23564577e-01 -4.07908887e-01 7.15432703e-01 -1.77968591e-02 -2.96918042e-02 1.14740002e+00 -1.93775788e-01 -3.60776484e-01 -6.56376541e-01 -4.35514838e-01 -5.25451422e-01 -1.02133965e+00 -6.00408196e-01 6.28648698e-01 -2.52196193e-01 4.19565663e-02 1.31583512e-01 6.04791462e-01 -1.16290367e+00 -6.40392065e-01 7.87451744e-01 5.08238673e-01 1.93071678e-01 6.49286747e-01 6.46616936e-01 3.27877939e-01 -9.05203521e-02 -6.91040605e-02 3.15456241e-01 -3.05040758e-02 2.55345982e-02 1.13174608e-02 -3.32319289e-01 -3.74767095e-01 -4.84192818e-01 9.87399757e-01 4.70274836e-01 2.69146055e-01 1.01012790e+00 -1.22449708e+00 -3.36963862e-01 4.36048895e-01 -3.18634301e-01 2.18834251e-01 9.65629667e-02 7.59772122e-01 6.40043199e-01 -7.75298953e-01 3.34353894e-01 1.39167154e+00 7.90975749e-01 1.58326060e-01 -1.44453132e+00 -6.10472679e-01 -8.87634903e-02 -5.63526573e-03 -1.59491861e+00 -3.04038435e-01 6.33147538e-01 -8.49144995e-01 7.00219631e-01 8.23770016e-02 8.89292419e-01 1.34692907e+00 6.77769125e-01 4.68610018e-01 1.00128162e+00 -9.08029750e-02 6.22407854e-01 -1.64932534e-01 1.85172975e-01 8.32017660e-01 5.47744870e-01 7.61286378e-01 -3.12638730e-01 -8.42320144e-01 7.91402459e-01 9.81509909e-02 -1.03472367e-01 -4.24053408e-02 -1.00934255e+00 8.15362513e-01 -2.17567503e-01 -2.72758752e-01 -6.73914015e-01 3.44374627e-01 2.78254170e-02 -1.94793299e-01 4.67501432e-01 1.66835949e-01 -3.27846646e-01 -3.57459277e-01 -7.77844310e-01 5.16302764e-01 1.18416786e+00 7.08886087e-01 5.69389462e-01 4.62000430e-01 2.50778466e-01 2.69725889e-01 8.79113019e-01 1.12636614e+00 -1.29212057e-02 -1.29626954e+00 -1.42947435e-01 2.14418381e-01 5.14296114e-01 -1.02145219e+00 -3.37824583e-01 -4.28600371e-01 -1.07827032e+00 1.19664177e-01 1.67215943e-01 -5.86484194e-01 -7.43931711e-01 1.64998937e+00 6.04011595e-01 6.43596172e-01 -1.94390619e-03 8.61617923e-01 2.10716665e-01 1.07725227e+00 3.05349496e-03 -4.91318762e-01 1.09855127e+00 -1.92218408e-01 -6.43978298e-01 1.28018588e-01 1.24488659e-01 -3.98659080e-01 5.00682116e-01 3.22095633e-01 -9.42646265e-01 -1.72766849e-01 -7.75274217e-01 6.21053636e-01 -6.58498779e-02 2.26753518e-01 4.50487167e-01 4.09838289e-01 -8.89789224e-01 6.92487001e-01 -1.59100986e+00 -1.47309169e-01 1.95568383e-01 2.13779792e-01 -5.30741410e-04 3.37882251e-01 -1.13686538e+00 9.06533897e-01 3.18089873e-01 5.46105146e-01 -1.52066386e+00 -7.29552150e-01 -7.58052170e-01 -4.37267460e-02 3.22646439e-01 -9.10846710e-01 9.90620911e-01 -2.29098141e-01 -1.77114570e+00 1.38278857e-01 -2.26969436e-01 -4.62713271e-01 5.05921423e-01 -7.93422237e-02 1.76979043e-02 4.47684340e-02 -1.56917974e-01 1.69532567e-01 9.66456950e-01 -9.23700392e-01 7.59431720e-02 -1.65440455e-01 -4.38074291e-01 -9.02504995e-02 1.90801710e-01 -2.91822165e-01 -1.81643497e-02 -5.21003127e-01 -2.36720704e-02 -1.16737056e+00 -5.21900535e-01 -1.54616050e-02 -5.11756718e-01 -7.28768203e-03 6.02368593e-01 -9.43432629e-01 1.16894317e+00 -1.90920568e+00 5.97095072e-01 3.51732701e-01 -9.42111164e-02 8.58708322e-02 3.04100603e-01 9.25107360e-01 2.55777210e-01 -3.07861492e-02 -7.97572255e-01 -3.49475890e-01 1.97155252e-01 3.48181218e-01 -5.45309365e-01 7.63811886e-01 2.79994547e-01 3.96583796e-01 -8.39868784e-01 -1.59862280e-01 5.31375945e-01 7.04647064e-01 -3.22251946e-01 6.11888096e-02 -2.90108472e-01 8.81365180e-01 -6.82922363e-01 5.03654480e-01 6.56854093e-01 -2.58880079e-01 1.83513656e-01 1.41123593e-01 -3.31678063e-01 -1.61242932e-01 -1.73380351e+00 9.65644300e-01 -2.05360830e-01 2.52820492e-01 3.04474413e-01 -8.94193351e-01 8.78526032e-01 4.33179975e-01 3.54216963e-01 2.36278564e-01 2.96195954e-01 2.11026415e-01 2.56717484e-02 -5.71438849e-01 2.07152620e-01 -7.35713691e-02 3.04929148e-02 2.81413555e-01 6.17076568e-02 -4.36694205e-01 1.49418831e-01 1.01778023e-01 7.74986804e-01 3.67264718e-01 4.63394582e-01 -5.60316265e-01 4.02434826e-01 -8.22436064e-02 7.53925920e-01 6.74978554e-01 -5.92074357e-02 -3.01124435e-02 5.26134133e-01 -2.87794143e-01 -1.18689251e+00 -1.04033089e+00 -4.72691447e-01 2.71423548e-01 -4.58509661e-02 -2.94158041e-01 -4.89622414e-01 3.53828073e-01 2.65907884e-01 7.71946609e-01 -5.33261716e-01 -2.03874201e-01 -2.67177701e-01 -1.23611236e+00 3.72196883e-01 9.34252068e-02 -6.75529125e-04 -6.99561954e-01 -6.14096820e-01 3.59753937e-01 1.34001672e-01 -6.78160548e-01 1.82201564e-01 8.31366554e-02 -9.37501431e-01 -9.03621912e-01 -5.51036298e-01 -8.26940313e-02 5.48087537e-01 -5.97434282e-01 5.18146276e-01 -4.72564012e-01 -6.93162084e-01 4.80584353e-01 2.96969533e-01 -1.58524200e-01 -6.26026869e-01 -4.61477488e-01 6.29962742e-01 -1.10008635e-01 -1.47375211e-01 -6.86092377e-01 -3.88445914e-01 2.40583330e-01 -6.32978737e-01 4.39866893e-02 2.58680284e-01 9.45958972e-01 6.49120748e-01 2.43924651e-02 3.60160649e-01 -1.06255792e-01 7.45491266e-01 -1.01579297e+00 -1.41849720e+00 5.10218479e-02 -4.08211499e-01 1.69742376e-01 3.37633729e-01 -5.56349277e-01 -1.06374800e+00 1.41327545e-01 9.35970545e-02 -6.16363645e-01 -2.41079316e-01 9.34116483e-01 2.60438025e-01 3.37401330e-01 3.45574945e-01 3.13216031e-01 3.35739344e-01 -4.54681486e-01 3.09504390e-01 4.74594206e-01 2.16074556e-01 -8.32151532e-01 5.00820875e-01 6.03234768e-01 5.50115764e-01 -1.17240000e+00 -1.69201151e-01 -7.69226700e-02 -4.75261271e-01 -2.64090627e-01 5.90690911e-01 -1.03213823e+00 -1.07947552e+00 8.69129002e-01 -9.21551764e-01 -5.07720351e-01 -2.05988601e-01 7.88384795e-01 -6.87147379e-01 4.48085427e-01 -6.79828107e-01 -1.59209800e+00 -1.43073380e-01 -1.09477866e+00 9.32931244e-01 2.99540520e-01 -2.13997588e-01 -1.29810941e+00 5.66321015e-01 -1.10068388e-01 3.46147269e-01 4.89655465e-01 6.07210696e-01 -2.25350454e-01 -5.54388225e-01 -3.63194227e-01 2.81698197e-01 1.86233550e-01 -4.45630588e-02 7.69737542e-01 -5.54041028e-01 -3.43957216e-01 8.30971450e-02 -4.25325986e-03 6.77724719e-01 9.62735653e-01 4.99026746e-01 -4.38685507e-01 -5.05832791e-01 4.79646981e-01 1.16121697e+00 1.00092940e-01 -3.82972360e-02 -1.93084016e-01 3.95960152e-01 4.78885859e-01 2.61073768e-01 1.23117483e+00 3.25375646e-01 4.10393924e-01 5.28897524e-01 5.22264063e-01 5.88429928e-01 -1.38298526e-01 4.72543061e-01 8.08071792e-01 -1.08428216e-02 -2.09305763e-01 -1.19695282e+00 4.99302864e-01 -1.87339473e+00 -1.03669441e+00 -2.34329522e-01 2.09866309e+00 7.47087061e-01 -1.66268185e-01 8.41960981e-02 -3.17092299e-01 8.51844251e-01 -2.81580091e-01 -8.69009614e-01 -2.89433841e-02 3.21513638e-02 -2.17706084e-01 4.74189013e-01 9.84091640e-01 -9.76992428e-01 5.56763053e-01 6.07038784e+00 8.03605318e-01 -9.11402047e-01 -2.77493019e-02 4.45533931e-01 -1.54941091e-02 -5.93005829e-02 2.72574961e-01 -1.03276241e+00 6.64189458e-01 1.23680508e+00 -4.62473631e-01 5.25107384e-01 5.02915084e-01 1.02238202e+00 -2.21795395e-01 -4.87231761e-01 6.39256418e-01 -5.19336164e-01 -1.10959542e+00 -3.24342936e-01 2.56912351e-01 7.96888649e-01 2.61110157e-01 2.23962858e-01 -1.00439541e-01 6.30839705e-01 -6.54604435e-01 6.73121870e-01 1.30437338e+00 2.03040868e-01 -4.96571839e-01 5.45562863e-01 6.63947701e-01 -9.26291049e-01 -1.87854782e-01 -4.09038961e-01 -4.47105646e-01 5.62623739e-01 8.00860763e-01 -8.11794460e-01 3.73830795e-01 4.97453421e-01 9.40735579e-01 3.26456758e-03 1.22660267e+00 -1.48646504e-01 1.10767090e+00 -9.75660205e-01 -7.93216825e-02 3.73580446e-03 -7.22921848e-01 1.21841514e+00 8.96970391e-01 6.46776259e-01 1.23372465e-01 1.55433923e-01 1.29436994e+00 6.53999865e-01 -4.66607660e-01 -6.07558787e-01 -3.74002159e-01 6.97692096e-01 1.08825612e+00 -5.57784796e-01 -1.50749713e-01 1.31545961e-01 1.63729310e-01 -1.22689717e-01 5.78674138e-01 -8.93979609e-01 2.52936687e-02 5.14682651e-01 -8.85414258e-02 3.01592469e-01 -7.14427233e-01 -9.60717499e-02 -1.16440237e+00 -2.78264731e-01 -5.86287141e-01 2.70530015e-01 -6.22647643e-01 -1.43022943e+00 2.04880998e-01 7.22254634e-01 -1.04921722e+00 -6.46252453e-01 -5.70148051e-01 -6.69606447e-01 9.87088025e-01 -9.98737693e-01 -7.87432253e-01 2.23197833e-01 2.64137685e-01 1.93497747e-01 -1.58515200e-01 7.47063339e-01 -2.64692903e-01 -1.07871306e+00 -2.52838790e-01 9.06767607e-01 -4.89118427e-01 1.40377089e-01 -9.60326433e-01 3.84164780e-01 8.18342030e-01 -4.17771190e-01 8.60174537e-01 1.12914324e+00 -1.08707929e+00 -1.66003144e+00 -8.31851065e-01 1.73875079e-01 -2.64283448e-01 1.21500218e+00 -7.27245957e-02 -8.87275934e-01 6.10043943e-01 1.07876278e-01 -1.77582845e-01 4.45926607e-01 -3.42246592e-01 2.12502480e-01 2.17008635e-01 -1.07285559e+00 5.73877990e-01 3.80353898e-01 -1.80964634e-01 -4.91375685e-01 3.34139943e-01 1.91272557e-01 -1.69258013e-01 -9.16805267e-01 3.25472444e-01 5.53360462e-01 -3.43096912e-01 8.87582362e-01 -4.57373351e-01 -4.00981260e-03 -4.77583915e-01 -8.88892263e-02 -1.22936118e+00 -1.30014941e-01 -1.14154196e+00 -5.84065437e-01 9.64736342e-01 3.37577939e-01 -8.66603851e-01 9.39724445e-02 5.77703297e-01 -7.54146501e-02 -6.53517306e-01 -1.21800387e+00 -8.75328720e-01 7.00344294e-02 -3.81577462e-01 1.38788864e-01 6.57863140e-01 -5.31862862e-02 -2.22664341e-01 -7.15424836e-01 7.57383525e-01 1.23127115e+00 -1.41758457e-01 5.41611671e-01 -1.21702933e+00 -6.60114527e-01 -2.74041474e-01 -2.06788629e-01 -6.30608618e-01 3.10191154e-01 -5.52460611e-01 9.18262675e-02 -1.09555328e+00 1.29071340e-01 -1.72898903e-01 8.58577043e-02 2.38060623e-01 -1.63492143e-01 -1.46093160e-01 2.75496747e-02 4.05128390e-01 -2.11864144e-01 9.35688078e-01 8.64160180e-01 1.19364500e-01 -1.29395053e-01 3.18136513e-01 -1.45260356e-02 7.23153651e-01 8.85288239e-01 -6.63179815e-01 -2.62491941e-01 5.60059361e-02 3.02585393e-01 4.57959801e-01 9.11339223e-01 -7.42254078e-01 2.37211972e-01 -4.60831642e-01 -4.37619956e-03 -5.20823121e-01 5.82568705e-01 -5.50762594e-01 8.18218291e-01 8.87800872e-01 -1.39686745e-02 -3.61343287e-03 6.04239345e-01 1.01854753e+00 9.73137394e-02 -3.17711383e-01 6.37200952e-01 -1.51326815e-02 -2.87014872e-01 2.46653616e-01 -9.74184752e-01 -1.42209336e-01 8.80960822e-01 2.13907316e-01 -2.50951648e-01 -5.72519958e-01 -1.13055348e+00 4.28824782e-01 3.10753703e-01 -1.61009178e-01 4.85812366e-01 -7.38244176e-01 -9.75654840e-01 1.61702767e-01 -4.31710392e-01 -3.18476349e-01 5.11483490e-01 1.12592030e+00 -5.41745484e-01 3.62290829e-01 -1.38698369e-01 -8.25342596e-01 -6.76821232e-01 2.59770900e-01 4.67493951e-01 9.86297801e-02 -4.47930217e-01 5.19572079e-01 -3.11036259e-01 -5.50435364e-01 -1.87603042e-01 -3.61465782e-01 1.95812404e-01 -1.34453043e-01 1.71651170e-01 8.42277467e-01 -5.89440346e-01 -5.94830155e-01 -2.95671880e-01 4.06996459e-01 4.02070284e-01 -3.85728180e-01 1.37485969e+00 -3.53157550e-01 -1.91199914e-01 7.92871833e-01 6.27273381e-01 -3.88248265e-01 -1.96103847e+00 7.65094012e-02 -4.08139639e-02 -8.26283842e-02 1.29565790e-01 -5.50991714e-01 -4.58815306e-01 7.35672653e-01 5.12786150e-01 2.93600053e-01 6.22701228e-01 -2.00043514e-01 6.22895546e-02 4.99127120e-01 2.26233318e-01 -6.24929368e-01 -3.93440545e-01 5.64321280e-01 8.74524891e-01 -8.92018855e-01 1.82519391e-01 3.09776533e-02 -4.40200120e-01 1.12155545e+00 1.07748173e-01 -3.77823293e-01 1.10542548e+00 4.39515054e-01 -3.57440054e-01 -8.04576129e-02 -9.77518976e-01 1.13815442e-01 6.57790601e-02 3.68007183e-01 -7.84017369e-02 1.96345210e-01 -2.00887635e-01 4.74626333e-01 7.95399696e-02 -4.17235829e-02 6.76330566e-01 8.06535244e-01 -2.81079590e-01 -7.27663815e-01 -5.49282491e-01 2.01334536e-01 -1.91238195e-01 -9.77867767e-02 1.06921226e-01 7.11406350e-01 -4.07377243e-01 7.60656178e-01 -9.23437551e-02 1.35457978e-01 -8.94360915e-02 1.28847957e-01 1.51789054e-01 -3.09456855e-01 2.32990384e-02 2.81055957e-01 -1.20804347e-01 -2.77359098e-01 -1.84371382e-01 -1.11902630e+00 -9.28209603e-01 -4.37836379e-01 -5.43091297e-01 2.81322747e-01 7.69132555e-01 9.83347833e-01 6.21942282e-01 2.89234966e-01 2.80465633e-01 -1.25233257e+00 -9.79572356e-01 -1.09671557e+00 -6.03398025e-01 -3.40656698e-01 3.94864798e-01 -9.25294042e-01 -9.30478036e-01 1.44546822e-01]	[6.602405071258545, 3.603386163711548]
2328a386-e74a-49bf-9873-18b949af5aea	elf-opengo-an-analysis-and-open	1902.04522	null	https://arxiv.org/abs/1902.04522v5	https://arxiv.org/pdf/1902.04522v5.pdf	ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero	The AlphaGo, AlphaGo Zero, and AlphaZero series of algorithms are remarkable demonstrations of deep reinforcement learning's capabilities, achieving superhuman performance in the complex game of Go with progressively increasing autonomy. However, many obstacles remain in the understanding of and usability of these promising approaches by the research community. Toward elucidating unresolved mysteries and facilitating future research, we propose ELF OpenGo, an open-source reimplementation of the AlphaZero algorithm. ELF OpenGo is the first open-source Go AI to convincingly demonstrate superhuman performance with a perfect (20:0) record against global top professionals. We apply ELF OpenGo to conduct extensive ablation studies, and to identify and analyze numerous interesting phenomena in both the model training and in the gameplay inference procedures. Our code, models, selfplay datasets, and auxiliary data are publicly available at https://ai.facebook.com/tools/elf-opengo/.	['James Pinkerton', 'Yuandong Tian', 'Qucheng Gong', 'Jerry Ma', 'C. Lawrence Zitnick', 'Zhuoyuan Chen', 'Shubho Sengupta']	2019-02-12	null	null	null	null	['game-of-go']	['playing-games']	[-3.78363281e-01 4.40611303e-01 -9.00219232e-02 -9.88703445e-02 -3.87315691e-01 -3.67618650e-01 4.02480334e-01 -3.00541550e-01 -4.91104394e-01 9.33119178e-01 1.19317904e-01 -3.53610307e-01 -3.75212133e-01 -5.76796412e-01 -5.57001412e-01 -3.94034594e-01 -5.01210749e-01 6.68140352e-01 -9.65038016e-02 -7.65685081e-01 2.13663518e-01 -2.32223347e-01 -1.57525146e+00 4.42655012e-03 1.00021207e+00 4.67112303e-01 1.41356587e-01 9.19320762e-01 7.04733491e-01 1.19900727e+00 -5.43479025e-01 -8.05433393e-01 5.14556170e-01 -5.08772969e-01 -8.89377832e-01 -6.11645520e-01 2.65853554e-01 -7.73586929e-01 -4.73924845e-01 9.53673542e-01 7.89529979e-01 2.25275248e-01 2.20633566e-01 -1.43231988e+00 -6.20664060e-01 9.95127141e-01 -4.52074856e-01 4.33499664e-01 5.36093771e-01 8.82242620e-01 1.23346782e+00 -1.77070513e-01 6.62424386e-01 1.08351791e+00 7.82848358e-01 8.70593905e-01 -1.05377293e+00 -9.67003703e-01 -3.62982363e-01 1.46091789e-01 -1.23248768e+00 -6.03315592e-01 5.05743504e-01 -5.52926540e-01 8.51551831e-01 1.41765758e-01 1.37224746e+00 1.68167555e+00 1.88402712e-01 8.59898388e-01 1.02668893e+00 -1.13680795e-01 3.41933578e-01 -4.21084285e-01 6.17259294e-02 1.10991776e+00 2.73011893e-01 8.41858447e-01 -8.28885138e-01 -2.15811238e-01 1.23766005e+00 -5.99399626e-01 -2.37310026e-02 -3.23057920e-01 -9.53370810e-01 9.54063237e-01 5.16863108e-01 1.66601881e-01 -5.21739602e-01 8.08120787e-01 2.04501808e-01 4.89501983e-01 1.44958884e-01 1.05744565e+00 -2.53086001e-01 -9.26720738e-01 -3.98818463e-01 9.58098888e-01 7.30767131e-01 7.46617496e-01 4.59287912e-01 1.54258251e-01 2.23866433e-01 6.67620540e-01 9.98619273e-02 1.74245745e-01 6.19862974e-01 -1.72126842e+00 1.88646048e-01 2.00327069e-01 2.13184252e-01 -1.08451819e+00 -7.23729312e-01 -7.10929215e-01 -3.09050113e-01 5.67216098e-01 7.19158471e-01 -4.58896786e-01 -4.22188222e-01 2.07686162e+00 1.61172912e-01 1.77133977e-02 2.36875634e-03 1.03875768e+00 7.27840245e-01 1.88701257e-01 4.27336931e-01 3.45390916e-01 1.06970608e+00 -8.89649212e-01 -1.54795870e-01 -4.78593290e-01 8.89358461e-01 7.38691613e-02 1.59890282e+00 5.11112869e-01 -1.29215789e+00 -4.33389157e-01 -1.04283607e+00 -4.94482182e-02 4.47704867e-02 -2.82367289e-01 1.48742723e+00 6.97816432e-01 -9.30468619e-01 7.73705900e-01 -9.56581056e-01 -4.28539932e-01 7.29163229e-01 5.41909099e-01 -3.13718915e-01 2.44187593e-01 -1.46157753e+00 7.63979077e-01 3.78083527e-01 -3.56434137e-01 -1.39130521e+00 -6.34937406e-01 -4.99192953e-01 2.41124973e-01 8.40429068e-01 -9.90330696e-01 1.66665447e+00 -1.00104821e+00 -1.48486423e+00 1.08894122e+00 7.24687934e-01 -6.35094285e-01 5.94575286e-01 -3.33716094e-01 -7.46400505e-02 -1.90650418e-01 2.64293760e-01 7.53493011e-01 3.55790228e-01 -8.38240027e-01 -5.89670181e-01 -3.40272248e-01 4.41546440e-01 4.75015849e-01 -3.27881612e-02 -2.29725018e-01 -7.29447231e-02 -5.59904218e-01 -5.32955229e-01 -9.34387505e-01 -3.73704612e-01 -2.28630498e-01 -1.35336310e-01 -4.54399079e-01 -3.11132312e-01 -5.24515808e-01 1.18904233e+00 -2.11192584e+00 1.97145686e-01 -1.99401118e-02 8.37214768e-01 8.51371884e-02 -1.34731114e-01 2.86932379e-01 1.36078894e-01 1.70135453e-01 1.31426215e-01 2.07737297e-01 2.40378201e-01 5.45360893e-02 3.40020686e-01 2.71904379e-01 -5.63174605e-01 1.28487766e+00 -1.21946478e+00 -3.41993272e-01 4.89411801e-02 -1.73901841e-01 -1.18682921e+00 1.45571619e-01 -8.40257108e-02 3.30339104e-01 -5.00314891e-01 4.94755596e-01 -6.23201951e-02 -1.22754902e-01 1.61600903e-01 4.64276969e-01 2.25287210e-02 1.96540207e-01 -6.48177385e-01 1.85504150e+00 1.44089125e-02 3.61207068e-01 -8.68007913e-03 -7.84824789e-01 4.51304436e-01 -7.65286908e-02 3.79718959e-01 -9.72172499e-01 6.51226282e-01 9.43644270e-02 6.37031734e-01 -6.50185704e-01 5.87380767e-01 -1.48011312e-01 -4.50040489e-01 3.86138529e-01 3.52185220e-01 -1.31827325e-01 4.54403102e-01 2.55335867e-01 1.32820392e+00 3.77870888e-01 5.28530478e-01 -6.26211882e-01 -3.36984575e-01 3.56715053e-01 5.56356490e-01 1.26566255e+00 -6.76247776e-01 7.89374113e-02 7.86375642e-01 -5.38848877e-01 -1.10297525e+00 -1.02089167e+00 3.65048438e-01 1.55098891e+00 7.35297054e-02 -7.58560598e-01 -1.03752029e+00 -3.11523944e-01 1.47392333e-01 8.23233426e-01 -9.84245181e-01 -5.63749909e-01 -3.33489507e-01 -4.39674973e-01 1.06519663e+00 3.97845387e-01 6.74997509e-01 -1.22981894e+00 -1.05486321e+00 1.83133677e-01 -3.85960430e-01 -5.33520341e-01 -8.70588943e-02 -7.89899901e-02 -4.58775520e-01 -1.25854301e+00 -3.79650086e-01 -5.05604625e-01 -7.84951299e-02 -7.24715889e-02 1.14446914e+00 3.39705944e-01 -3.60180825e-01 3.02395344e-01 -2.56135613e-01 -4.66341138e-01 -4.05302614e-01 3.14671516e-01 2.59643823e-01 -8.92013848e-01 3.28501821e-01 -9.16155517e-01 -4.12789136e-01 1.81969389e-01 -2.07034081e-01 4.74676937e-01 3.84110838e-01 8.19114804e-01 -1.50197878e-01 -1.55483544e-01 6.27604902e-01 -9.09374595e-01 1.17178881e+00 -6.88780665e-01 -5.10552108e-01 -1.36549577e-01 -5.04581213e-01 -7.33046681e-02 3.11900467e-01 -3.05782020e-01 -7.69469261e-01 -3.56410623e-01 -3.60428810e-01 -1.66477501e-01 -8.20206478e-02 6.24982297e-01 2.24920571e-01 7.72962794e-02 1.26460743e+00 -6.57817125e-02 8.71906057e-02 -2.51345873e-01 3.28995287e-01 3.34554166e-01 8.40324044e-01 -9.23537791e-01 5.75170994e-01 -9.49153230e-02 -4.46773350e-01 -7.07953513e-01 -7.00337589e-01 1.48597077e-01 -1.79910749e-01 -6.14375412e-01 8.75151336e-01 -1.06485641e+00 -1.49711967e+00 4.84067857e-01 -3.40241939e-01 -1.10796952e+00 -4.33294624e-01 3.48828763e-01 -1.05626547e+00 -1.43916443e-01 -7.72381067e-01 -5.55160344e-01 -9.09346938e-02 -8.17367136e-01 5.52816629e-01 5.37299633e-01 -7.17669845e-01 -7.14233398e-01 3.19739997e-01 8.26133907e-01 3.82739961e-01 1.25475407e-01 6.36860847e-01 -6.30200446e-01 -1.50695503e-01 1.38861462e-01 4.40124303e-01 -1.24575704e-01 -3.31926346e-01 -1.52184799e-01 -6.27620339e-01 -1.92981347e-01 -2.69310832e-01 -1.07959151e+00 3.80391121e-01 3.08332205e-01 9.34022427e-01 -2.58711994e-01 -5.59554324e-02 8.47455144e-01 7.95120478e-01 3.01104069e-01 6.92402661e-01 6.51303768e-01 4.75785553e-01 3.48768920e-01 6.76029623e-01 6.97818398e-01 4.90518391e-01 4.13956910e-01 3.73572707e-01 8.16093162e-02 2.71988124e-01 -7.35092461e-01 2.74781168e-01 4.49693382e-01 -5.82777798e-01 -1.49207205e-01 -1.05203414e+00 3.69777709e-01 -1.85935688e+00 -1.38501740e+00 1.90611348e-01 1.89943326e+00 7.45113134e-01 4.83251959e-01 6.28380179e-01 -2.01966777e-01 4.31251049e-01 9.58609357e-02 -7.86491096e-01 -3.31819654e-01 1.21878974e-01 3.44614506e-01 2.36800045e-01 5.03549635e-01 -9.44019735e-01 1.53614175e+00 7.19484568e+00 8.32638919e-01 -5.72698832e-01 7.32953325e-02 6.12183273e-01 -3.80229115e-01 4.73064221e-02 -2.66402531e-02 -3.25146407e-01 2.52740949e-01 9.44275379e-01 -7.51178384e-01 1.10390830e+00 1.11541140e+00 8.08102712e-02 -2.45228395e-01 -9.03830171e-01 9.95972216e-01 -1.49507046e-01 -1.07651448e+00 -4.53313351e-01 3.17181319e-01 4.96257126e-01 1.69214889e-01 1.22730948e-01 8.73596013e-01 1.28172529e+00 -1.14995646e+00 9.39026415e-01 3.40542823e-01 5.89106917e-01 -7.54535377e-01 3.37746561e-01 4.23256338e-01 -5.07163167e-01 -4.87308800e-01 -4.15129453e-01 -7.27401316e-01 -3.79883200e-01 -1.91162929e-01 -4.56168473e-01 1.11477211e-01 9.10925448e-01 6.28022909e-01 -7.13157833e-01 7.31991947e-01 -4.10013407e-01 8.16735744e-01 -2.49381453e-01 -1.89760417e-01 2.30033621e-01 1.71434972e-02 5.47771215e-01 3.88802946e-01 -5.90300746e-02 5.47018051e-01 6.80735782e-02 1.19677603e+00 -9.37047228e-03 -1.01051740e-01 -6.59722269e-01 -5.40504336e-01 5.06111741e-01 1.17459190e+00 -4.07132268e-01 -1.31974399e-01 7.66177997e-02 7.41395593e-01 6.10062361e-01 5.72673604e-02 -1.06593454e+00 -2.54075378e-01 1.04461706e+00 1.28945738e-01 -3.05502504e-01 -2.66011357e-01 -2.54984647e-01 -1.14268982e+00 -4.01280552e-01 -1.31440294e+00 4.30893332e-01 -1.02967978e+00 -9.57005441e-01 3.01098764e-01 -3.22463252e-02 -7.50312686e-01 -5.13129592e-01 -4.57222015e-01 -5.46508610e-01 3.40476662e-01 -5.05350769e-01 -9.08470452e-01 -3.99044812e-01 6.63058698e-01 3.65554452e-01 -3.66184652e-01 6.01334035e-01 9.89998877e-03 -6.41798019e-01 8.56620908e-01 -1.03798859e-01 8.28429386e-02 3.55141848e-01 -1.01454258e+00 7.92711437e-01 4.27883238e-01 -2.14680135e-01 5.91125429e-01 9.33718741e-01 -6.38521194e-01 -1.14283085e+00 -2.28588358e-01 -3.07202339e-02 -5.57100713e-01 8.51297677e-01 -4.79405373e-01 -4.04987901e-01 1.04635847e+00 8.88626873e-02 -5.69861174e-01 6.84523106e-01 7.01088786e-01 4.32523526e-02 2.84945846e-01 -9.01123226e-01 1.09456742e+00 1.69820881e+00 -2.67279863e-01 -7.77812958e-01 2.42354292e-02 5.30205071e-01 -4.87499267e-01 -9.09201443e-01 3.04060224e-02 9.52259541e-01 -1.33198309e+00 8.57121229e-01 -9.15144384e-01 8.70056808e-01 2.92253286e-01 3.07813399e-02 -1.41803980e+00 -8.42713654e-01 -8.27650666e-01 1.30707845e-01 7.86587358e-01 2.57946104e-01 -4.71076131e-01 1.08693051e+00 6.30731285e-01 -2.41186440e-01 -7.14467108e-01 -7.54149318e-01 -6.53823674e-01 3.08607966e-01 -4.19525862e-01 3.49546820e-01 1.16260588e+00 5.97261965e-01 3.33487242e-01 -7.94230282e-01 -2.54338741e-01 7.78239310e-01 -3.14519882e-01 1.24975693e+00 -1.38564181e+00 -8.57617080e-01 -5.62688291e-01 -3.57363552e-01 -8.17674994e-01 7.86734447e-02 -9.85284984e-01 -1.07985295e-01 -1.29116559e+00 2.57913619e-01 -4.29530144e-01 2.72841919e-02 7.32851446e-01 -1.73960686e-01 7.47523308e-02 2.58116573e-01 2.35055000e-01 -7.93538749e-01 6.82552397e-01 1.23852122e+00 1.98372558e-01 -1.97876960e-01 -2.66633123e-01 -1.52452159e+00 9.13926184e-01 1.22910202e+00 -4.53134030e-01 -4.98492986e-01 -3.92421186e-01 3.17625999e-01 1.72130644e-01 5.28579712e-01 -1.52718627e+00 1.38622537e-01 -5.50807834e-01 4.20633554e-01 3.63519669e-01 3.05868030e-01 -1.91413447e-01 1.09411575e-01 7.54528880e-01 -5.81410348e-01 9.13844854e-02 1.27102017e-01 1.19698800e-01 4.58985597e-01 -1.88196497e-03 7.82489657e-01 -4.78462964e-01 -9.16892588e-01 1.78142250e-01 -7.32613146e-01 5.22494555e-01 1.12919533e+00 -1.41133159e-01 -5.16011178e-01 -8.16433847e-01 -9.79619920e-01 6.44628286e-01 5.48060179e-01 4.21557933e-01 2.20719084e-01 -1.08697343e+00 -6.41137958e-01 1.65968046e-01 2.47621909e-02 -4.45682943e-01 4.82011557e-01 5.55066049e-01 -8.22309434e-01 1.19538859e-01 -9.45925593e-01 1.41769778e-02 -1.09624183e+00 4.81247574e-01 7.43349373e-01 -3.01565200e-01 -6.39115393e-01 1.07627654e+00 3.31179589e-01 -6.31280184e-01 5.00631332e-02 1.51961461e-01 6.87884614e-02 -6.34321809e-01 4.54277545e-01 4.90180075e-01 -6.08805895e-01 -2.70618588e-01 -7.55880848e-02 -1.18124858e-01 -1.03670191e-02 -3.62183839e-01 1.40892649e+00 7.10408688e-02 4.26076055e-01 3.48404318e-01 3.89667928e-01 -2.20426977e-01 -1.31466031e+00 3.60957414e-01 -1.78621575e-01 -4.54573452e-01 -1.72546744e-01 -1.03746307e+00 -7.66369402e-01 6.01091743e-01 3.97012830e-01 -8.32522660e-02 7.67398834e-01 8.87770206e-02 4.87858146e-01 6.14038885e-01 8.25811744e-01 -1.18072498e+00 1.96248963e-01 4.66321826e-01 7.53401458e-01 -9.81018007e-01 -1.78252663e-02 1.20664902e-01 -9.08085287e-01 4.22939122e-01 1.38804436e+00 -2.78891653e-01 2.97591329e-01 1.26299128e-01 -1.08571075e-01 -6.34936035e-01 -1.17019165e+00 -2.40693837e-01 -4.70757276e-01 7.05841362e-01 3.70663136e-01 2.02126488e-01 -3.95857871e-01 1.13725150e+00 -1.16556847e+00 2.64811635e-01 5.41255236e-01 8.34930420e-01 -6.48295105e-01 -7.99701095e-01 -2.24210415e-02 5.56781292e-01 -4.57381099e-01 -1.80038940e-02 -6.59194112e-01 1.14001799e+00 6.15111962e-02 7.25896597e-01 -1.57991081e-01 -9.54509974e-01 3.15920115e-01 -4.35360111e-02 7.14326203e-01 -2.76896954e-01 -7.68945992e-01 -3.38382781e-01 3.53286892e-01 -9.35327590e-01 1.47946194e-01 -6.29444659e-01 -1.13782430e+00 -1.01016796e+00 -7.20220730e-02 2.94157475e-01 1.85855553e-01 6.54041946e-01 2.46743292e-01 5.25019169e-01 1.75189659e-01 -8.08940232e-01 -5.66109061e-01 -8.73403013e-01 -8.93937230e-01 4.76438642e-01 -2.25607708e-01 -9.11036730e-01 -2.88172126e-01 -4.63204890e-01]	[3.613926649093628, 1.4338847398757935]
2aa693ab-9944-4dd5-87a4-1beac4345809	multi-scenario-ranking-with-adaptive-feature	2306.16732	null	https://arxiv.org/abs/2306.16732v1	https://arxiv.org/pdf/2306.16732v1.pdf	Multi-Scenario Ranking with Adaptive Feature Learning	Recently, Multi-Scenario Learning (MSL) is widely used in recommendation and retrieval systems in the industry because it facilitates transfer learning from different scenarios, mitigating data sparsity and reducing maintenance cost. These efforts produce different MSL paradigms by searching more optimal network structure, such as Auxiliary Network, Expert Network, and Multi-Tower Network. It is intuitive that different scenarios could hold their specific characteristics, activating the user's intents quite differently. In other words, different kinds of auxiliary features would bear varying importance under different scenarios. With more discriminative feature representations refined in a scenario-aware manner, better ranking performance could be easily obtained without expensive search for the optimal network structure. Unfortunately, this simple idea is mainly overlooked but much desired in real-world systems.Further analysis also validates the rationality of adaptive feature learning under a multi-scenario scheme. Moreover, our A/B test results on the Alibaba search advertising platform also demonstrate that Maria is superior in production environments.	['Chenliang Li', 'Qian Wang', 'Bo Zheng', 'Jian Xu', 'Hongbo Deng', 'Xubin Li', 'Si Chen', 'Bofang Li', 'Yu Tian']	2023-06-29	null	null	null	null	['retrieval', 'transfer-learning']	['methodology', 'miscellaneous']	[-4.27926108e-02 -3.33325326e-01 -8.39767814e-01 -5.23488164e-01 -2.52912611e-01 -5.76668501e-01 3.37902725e-01 -1.56100616e-01 1.33620948e-02 4.70907569e-01 1.76862851e-01 -3.58532906e-01 -1.27214646e+00 -7.62436986e-01 -3.93842012e-01 -5.75349569e-01 -2.71602571e-01 4.53287035e-01 2.00747773e-02 -7.35792220e-01 2.79490769e-01 4.98510718e-01 -1.73119712e+00 5.50538123e-01 7.89971709e-01 1.04739344e+00 3.84953618e-01 -1.03738919e-01 -9.61963162e-02 5.04090607e-01 -6.09820187e-01 -5.65381527e-01 6.87152684e-01 1.44038528e-01 -4.51667517e-01 2.71207057e-02 2.97125012e-01 -1.79316491e-01 -4.57198560e-01 8.24137807e-01 4.30339545e-01 1.10340543e-01 4.21571791e-01 -1.27248895e+00 -5.31122208e-01 1.02062535e+00 -5.33110559e-01 1.95994943e-01 2.16453955e-01 -5.49853072e-02 1.38061404e+00 -5.48571527e-01 1.82452038e-01 1.19155037e+00 5.15292645e-01 8.60219821e-02 -1.09066701e+00 -9.92383242e-01 5.66103458e-01 5.72566152e-01 -1.33719647e+00 -1.25459030e-01 1.16137075e+00 -1.14255287e-01 1.99962229e-01 4.94982094e-01 7.17743993e-01 1.17380071e+00 1.20849058e-01 8.81972790e-01 1.04606688e+00 -2.83884883e-01 -8.51206407e-02 6.55811012e-01 2.29365066e-01 4.75772500e-01 4.60559815e-01 1.75780907e-01 -5.48207760e-01 -2.86870211e-01 6.83633983e-01 3.61549288e-01 -2.33145893e-01 -7.25738823e-01 -1.03043687e+00 9.55049455e-01 3.15432549e-01 4.51167136e-01 -4.13083315e-01 -4.09408599e-01 3.23144883e-01 8.29326153e-01 1.38793349e-01 6.99932814e-01 -7.71503150e-01 3.00763786e-01 -7.05533266e-01 1.34232774e-01 8.36136818e-01 1.10879517e+00 8.07738483e-01 1.73813790e-01 -2.54007459e-01 9.52012181e-01 2.85797864e-01 2.56518364e-01 5.48592746e-01 -9.70150411e-01 3.04001749e-01 6.84440315e-01 9.12143383e-03 -1.28508759e+00 -4.33720142e-01 -1.03391099e+00 -8.29091311e-01 -3.63087118e-01 2.63288587e-01 -1.43709138e-01 -4.05309111e-01 1.64578450e+00 6.47202134e-02 1.91257402e-01 -1.63053200e-01 8.10824752e-01 4.83611941e-01 3.87670338e-01 1.78095698e-02 -3.49553078e-01 1.19058228e+00 -8.99454296e-01 -5.45527339e-01 -2.98761837e-02 3.71071607e-01 -8.57666969e-01 1.08953786e+00 7.91068971e-01 -7.45640159e-01 -7.87540257e-01 -9.23748314e-01 6.69065237e-01 -4.09534633e-01 1.26725078e-01 1.46147430e+00 7.74155498e-01 -8.20589066e-01 4.87262547e-01 3.65711586e-03 -2.68779337e-01 8.70511979e-02 6.72130764e-01 9.88639309e-04 -2.03607559e-01 -1.65904355e+00 6.65195525e-01 4.35943693e-01 9.51635987e-02 -6.45679772e-01 -7.34684467e-01 -3.88147771e-01 3.48504573e-01 6.34278238e-01 -6.02832913e-01 1.09117949e+00 -9.17240798e-01 -1.37786186e+00 2.17286199e-01 3.23853076e-01 -4.19858307e-01 1.63452923e-01 -1.40060604e-01 -1.22775435e+00 -5.76370321e-02 -9.78839546e-02 1.69576317e-01 1.28003073e+00 -1.43288481e+00 -7.30082870e-01 -2.89462358e-01 4.65739012e-01 2.29203150e-01 -8.90397787e-01 -2.52495885e-01 -5.28969228e-01 -6.94245696e-01 -5.70624471e-02 -8.03140819e-01 -5.02217770e-01 -2.24555120e-01 -1.17722347e-01 -3.43783438e-01 8.35989416e-01 -2.69586265e-01 1.70387304e+00 -2.07605481e+00 -6.73374310e-02 9.50898588e-01 2.27971628e-01 4.82320726e-01 -3.10611516e-01 3.05354059e-01 -9.93227214e-03 6.60248706e-03 7.26857483e-01 6.02761567e-01 -1.12430923e-01 2.38895968e-01 -1.22336317e-02 1.15316417e-02 -1.31283045e-01 7.75287449e-01 -9.10882115e-01 -4.48025972e-01 2.41274267e-01 1.00941554e-01 -8.44665229e-01 -1.86470628e-01 -5.82766682e-02 3.89373690e-01 -9.76735532e-01 8.53069007e-01 4.24362957e-01 -6.15057409e-01 7.26866961e-01 -7.17332959e-01 1.22276537e-01 -1.78396314e-01 -1.33982849e+00 1.45392036e+00 -8.64430308e-01 8.84707123e-02 1.14893474e-01 -1.38120091e+00 9.72724915e-01 1.21326238e-01 8.17203581e-01 -1.07657719e+00 1.62487209e-01 1.11112982e-01 3.48692626e-01 -5.09702086e-01 5.74162126e-01 3.19206476e-01 -2.25139305e-01 2.32530341e-01 3.86166908e-02 3.99397492e-01 1.55713111e-01 6.55105412e-02 7.47358561e-01 -3.27290893e-01 2.91241884e-01 -3.83180380e-01 6.09999716e-01 -2.42330641e-01 5.77970207e-01 9.48573112e-01 -5.42757064e-02 -7.64901042e-02 4.66635413e-02 -4.20221418e-01 -5.64366221e-01 -1.03456044e+00 -1.72879383e-01 1.30786383e+00 4.57213342e-01 -4.50759709e-01 7.84664527e-02 -7.72698462e-01 2.80762821e-01 7.38778472e-01 -3.17396224e-01 -4.48418766e-01 -4.71942723e-01 -4.24378902e-01 2.69493330e-02 7.76927322e-02 3.82840127e-01 -7.64600098e-01 -3.07238176e-02 2.57341683e-01 -8.00394192e-02 -7.44913638e-01 -3.67152989e-01 1.12276867e-01 -8.88460696e-01 -1.07138944e+00 -5.06163239e-01 -7.45573699e-01 3.58529299e-01 9.14316952e-01 1.21964324e+00 1.18344441e-01 2.43440345e-02 4.69185889e-01 -4.73520070e-01 -2.56880939e-01 -1.12424351e-01 1.57042548e-01 3.39530796e-01 1.04275368e-01 4.62307125e-01 -7.31764674e-01 -8.45748186e-01 8.51069212e-01 -7.98414588e-01 -4.89632040e-01 1.09645176e+00 8.33230197e-01 3.98562610e-01 6.93036854e-01 9.98865783e-01 -1.11291885e+00 9.19755518e-01 -8.42036247e-01 -4.35630709e-01 7.22260475e-01 -1.13674605e+00 -1.35508612e-01 8.04312170e-01 -7.40600586e-01 -1.27126288e+00 -3.72562647e-01 2.65267253e-01 -5.21709800e-01 8.11988488e-02 5.42306900e-01 -2.24229217e-01 -1.20444961e-01 6.27926409e-01 1.01062030e-01 2.65378039e-02 -5.04330158e-01 5.56495607e-01 8.32599044e-01 -5.82125597e-02 -6.88993812e-01 9.37163234e-01 2.24615306e-01 -1.39128864e-01 -6.15855575e-01 -1.13254082e+00 -6.33575201e-01 -2.60226011e-01 -3.30423117e-01 4.18337658e-02 -9.77355003e-01 -6.41288996e-01 2.85018445e-03 -4.35871035e-01 1.56152248e-01 -2.42175713e-01 5.39961815e-01 -1.52662680e-01 2.76292384e-01 -2.04476520e-01 -2.87303030e-01 -1.78198338e-01 -1.04996014e+00 5.70290387e-01 4.22644496e-01 -1.48465112e-01 -9.07894731e-01 -4.47884440e-01 5.59933364e-01 7.26385653e-01 -5.37481427e-01 1.21733510e+00 -9.54488575e-01 -5.10875583e-01 -1.83251888e-01 -2.21786156e-01 2.84070581e-01 4.38063025e-01 -2.48224050e-01 -6.01464927e-01 -4.96546894e-01 -1.11902356e-01 -7.25523457e-02 4.48547930e-01 2.45683461e-01 1.71267605e+00 -3.98055345e-01 -4.04826701e-01 2.73045331e-01 1.30598569e+00 4.65681434e-01 3.84239346e-01 3.36851090e-01 3.95737529e-01 6.69510067e-01 1.00218058e+00 4.39510047e-01 1.49726883e-01 8.30701530e-01 2.42695525e-01 -1.14016332e-01 -3.12727764e-02 -2.43340194e-01 2.58286983e-01 8.89519513e-01 -1.88981891e-02 -2.50921428e-01 -2.13015631e-01 2.62651801e-01 -1.89364529e+00 -1.08808124e+00 2.74267375e-01 2.11272740e+00 6.41792119e-01 3.13712627e-01 5.23868538e-02 1.45428643e-01 7.60701954e-01 8.17005560e-02 -6.79984570e-01 -1.82856798e-01 -6.96999878e-02 1.40280649e-01 3.85115743e-01 -1.22541532e-01 -8.49172592e-01 6.51662111e-01 6.30698633e+00 1.36642528e+00 -1.13905334e+00 -1.17203230e-02 5.25661647e-01 -2.09203646e-01 -6.25406384e-01 -1.67082161e-01 -9.07722414e-01 5.49872279e-01 7.96857119e-01 -4.41116959e-01 4.93407965e-01 1.06553757e+00 2.62823522e-01 4.00573552e-01 -8.16921711e-01 1.01371670e+00 -4.18861285e-02 -1.42703128e+00 4.83073086e-01 7.46765509e-02 8.19291413e-01 -2.67562836e-01 3.06020319e-01 8.14057171e-01 3.02010715e-01 -4.98750657e-01 2.47714624e-01 4.95383203e-01 4.83172566e-01 -8.04972470e-01 5.59157729e-01 2.61792809e-01 -1.10031509e+00 -7.14923739e-01 -3.71949166e-01 1.39463812e-01 8.53381604e-02 6.67313874e-01 -6.10375524e-01 1.06121206e+00 6.71503067e-01 6.87999249e-01 -6.57025397e-01 1.08436763e+00 1.29389256e-01 7.50898421e-01 -6.43365532e-02 -8.44780728e-02 1.65916398e-01 -3.67516398e-01 4.33223993e-01 7.26424396e-01 4.24208701e-01 -2.40118578e-01 4.09633100e-01 1.70548186e-01 -4.82983924e-02 3.20992678e-01 -7.12569892e-01 1.85172647e-01 5.59851348e-01 1.49714279e+00 -4.03330177e-01 2.11705710e-03 -7.14445174e-01 3.42147171e-01 7.57308826e-02 4.32241797e-01 -7.95945704e-01 -1.45888492e-01 5.20455003e-01 2.30335429e-01 4.24140096e-01 6.97482601e-02 1.44528255e-01 -9.99814749e-01 -3.50822836e-01 -1.18689179e+00 4.92585242e-01 -4.10426915e-01 -1.73108959e+00 5.10135829e-01 1.79783285e-01 -1.67513001e+00 -1.15136117e-01 -5.47301292e-01 -3.14754575e-01 3.28669429e-01 -1.72049201e+00 -1.07638872e+00 -7.30800554e-02 1.01345265e+00 7.02882230e-01 -6.45034552e-01 6.70701146e-01 7.76715040e-01 -4.95471030e-01 9.82370675e-01 2.56797016e-01 -4.88409877e-01 8.82664442e-01 -6.58065438e-01 -5.58130264e-01 1.92465007e-01 3.36573094e-01 9.16269541e-01 3.88245523e-01 -2.77857810e-01 -1.47135329e+00 -1.13094282e+00 4.34134215e-01 -2.60655042e-02 9.89372432e-01 -2.82218214e-02 -5.68477988e-01 4.09990221e-01 2.10266650e-01 -4.16118652e-01 9.82189596e-01 6.59335375e-01 -3.77448022e-01 -6.03082001e-01 -1.02039218e+00 8.30440164e-01 1.10821521e+00 -2.28238419e-01 -2.55751640e-01 6.02851927e-01 7.24367142e-01 1.42079696e-01 -1.17304015e+00 5.90848148e-01 7.25113511e-01 -9.47018147e-01 1.26847315e+00 -7.77958870e-01 1.22884132e-01 -7.54345655e-02 -3.21106017e-01 -1.25554252e+00 -7.97913373e-01 -6.50545657e-01 -4.97543305e-01 1.09954202e+00 5.58927298e-01 -6.87268436e-01 7.45999396e-01 2.28800490e-01 1.22921526e-01 -9.58220959e-01 -4.87878382e-01 -1.04146218e+00 -2.99175143e-01 -1.60141766e-01 8.40357125e-01 1.05288315e+00 -3.12407017e-01 4.90112156e-01 -8.06122184e-01 1.52910993e-01 5.31759143e-01 5.65258324e-01 6.21360183e-01 -1.72041464e+00 -5.39694130e-01 -4.34841514e-01 -6.29502535e-02 -1.23352420e+00 1.97543263e-01 -8.73299003e-01 -5.86617768e-01 -9.88442957e-01 8.63611549e-02 -1.00396466e+00 -1.07422042e+00 3.58819515e-01 6.50330037e-02 -9.51168314e-03 -9.75476354e-02 4.13276374e-01 -8.66021216e-01 2.55104572e-01 1.60621178e+00 -3.20665419e-01 -1.64728686e-01 6.78612828e-01 -1.28046250e+00 6.74071968e-01 9.80313718e-01 -1.54433921e-01 -1.02569151e+00 -1.73469752e-01 3.12956691e-01 9.69503894e-02 7.98788201e-03 -6.43109381e-01 1.02164313e-01 -3.01469475e-01 2.06580579e-01 -5.70845962e-01 3.24971139e-01 -1.36782134e+00 3.55254114e-01 2.49765381e-01 -3.33189517e-01 -1.18450224e-01 -3.41456123e-02 9.79743779e-01 -2.46081233e-01 -3.63427311e-01 2.22064823e-01 -1.73101246e-01 -1.16732967e+00 4.90552425e-01 -4.39190231e-02 -3.91029119e-01 1.03202069e+00 -3.19399178e-01 -1.30503729e-01 -6.14981711e-01 -7.19227195e-01 4.72136825e-01 -1.36524320e-01 7.66968071e-01 3.52345407e-01 -1.32645643e+00 -4.44628060e-01 2.27802008e-01 3.28073561e-01 -5.85728884e-01 4.91302371e-01 6.61850333e-01 2.14433268e-01 5.09652555e-01 -1.79312691e-01 -4.35815573e-01 -1.10281312e+00 5.61858118e-01 -1.75983936e-01 -7.07738400e-01 -1.24388330e-01 4.77014780e-01 -2.77228449e-02 -4.32850689e-01 2.54489094e-01 2.74638385e-01 -6.34961188e-01 3.25006902e-01 2.46360168e-01 4.58819598e-01 2.15112045e-01 -2.49476388e-01 -1.05832569e-01 4.12473738e-01 -6.69659138e-01 6.55470431e-01 1.26708984e+00 -3.11101913e-01 2.79948354e-01 3.52905132e-02 9.23618019e-01 2.25001216e-01 -7.51428008e-01 -5.37602603e-01 5.85479252e-02 -6.20905578e-01 1.26603082e-01 -8.06639850e-01 -1.50072074e+00 1.64617330e-01 5.82388699e-01 6.51072502e-01 1.33385646e+00 -6.48981109e-02 6.65996671e-01 7.69607246e-01 9.18693304e-01 -1.22172844e+00 3.28928173e-01 1.39924616e-01 8.17607045e-01 -1.24992955e+00 2.59098589e-01 -5.17450750e-01 -6.33763313e-01 9.52267945e-01 7.68277407e-01 8.37804079e-02 1.08216560e+00 -2.47305661e-01 -1.63757745e-02 -3.86143625e-01 -6.87780619e-01 -5.39002083e-02 3.65580708e-01 5.66643476e-01 2.27770567e-01 9.80436429e-02 -4.59879160e-01 7.25211024e-01 -4.63513695e-02 -2.64320940e-01 1.02542721e-01 6.97454751e-01 -2.74396747e-01 -1.46228969e+00 1.88671649e-02 1.11886477e+00 -2.48656914e-01 -9.48917866e-02 1.53772369e-01 1.04161108e+00 1.07936360e-01 1.13238096e+00 -2.66474456e-01 -8.09964657e-01 5.59386849e-01 -1.40506148e-01 3.34294975e-01 -4.86921519e-01 -6.73995972e-01 1.90554008e-01 2.41036192e-01 -7.30216622e-01 -6.22103512e-01 -4.44835305e-01 -5.48441291e-01 -1.56049460e-01 -8.51353526e-01 4.33292001e-01 4.25936311e-01 6.86385930e-01 6.64228499e-01 6.89297438e-01 1.21822846e+00 -4.80151713e-01 -9.53444481e-01 -7.24558949e-01 -7.59396791e-01 3.18113357e-01 -2.60331243e-01 -9.38425958e-01 -3.22418302e-01 -4.37822372e-01]	[10.073874473571777, 5.482692241668701]
7b5eb436-25a1-4958-9983-efa45dcae66b	analysis-and-forecasting-of-financial-time	2011.08011	null	https://arxiv.org/abs/2011.08011v2	https://arxiv.org/pdf/2011.08011v2.pdf	Robust Analysis of Stock Price Time Series Using CNN and LSTM-Based Deep Learning Models	Prediction of stock price and stock price movement patterns has always been a critical area of research. While the well-known efficient market hypothesis rules out any possibility of accurate prediction of stock prices, there are formal propositions in the literature demonstrating accurate modeling of the predictive systems that can enable us to predict stock prices with a very high level of accuracy. In this paper, we present a suite of deep learning-based regression models that yields a very high level of accuracy in stock price prediction. To build our predictive models, we use the historical stock price data of a well-known company listed in the National Stock Exchange (NSE) of India during the period December 31, 2012 to January 9, 2015. The stock prices are recorded at five minutes intervals of time during each working day in a week. Using these extremely granular stock price data, we build four convolutional neural network (CNN) and five long- and short-term memory (LSTM)-based deep learning models for accurate forecasting of the future stock prices. We provide detailed results on the forecasting accuracies of all our proposed models based on their execution time and their root mean square error (RMSE) values.	['Subhasis Dasgupta', 'Jaydip Sen', 'Sidra Mehtab']	2020-11-07	null	null	null	null	['stock-price-prediction']	['time-series']	[-7.62320399e-01 -6.65354013e-01 -2.23697990e-01 -3.67444664e-01 -2.40987822e-01 -4.84589159e-01 6.76255703e-01 -1.20251648e-01 -2.70021141e-01 8.84983659e-01 9.00088325e-02 -8.42408776e-01 -2.10342959e-01 -1.32638156e+00 -5.71255684e-01 -4.38972890e-01 -5.21874428e-01 1.88678220e-01 3.28078941e-02 -5.60971260e-01 8.19549799e-01 5.94560504e-01 -1.42600703e+00 7.50613362e-02 3.62226158e-01 1.94756639e+00 -1.61936451e-02 4.09565806e-01 -3.83858353e-01 1.40044546e+00 -6.61588669e-01 -4.71518010e-01 9.67792034e-01 4.64529693e-02 -2.24432066e-01 -4.25563037e-01 -4.04809080e-02 -8.14998984e-01 -2.24236742e-01 7.43754745e-01 6.91479221e-02 -2.35744357e-01 2.63463080e-01 -1.35642922e+00 -9.89500999e-01 8.48409951e-01 -4.15748179e-01 8.71247113e-01 -4.20267731e-01 3.74505669e-02 1.19887137e+00 -8.44599426e-01 8.97329897e-02 5.34098804e-01 8.55516970e-01 -2.71202505e-01 -6.64046288e-01 -1.17015898e+00 1.84537787e-02 3.86506766e-02 -1.00823474e+00 -6.15948848e-02 7.15463638e-01 -6.42784536e-01 1.33489156e+00 3.71450000e-03 1.17368174e+00 4.34683740e-01 9.87617731e-01 4.46722656e-01 1.38476479e+00 -6.72881529e-02 1.36669636e-01 1.65692016e-01 2.14128215e-02 5.56744970e-02 5.15885651e-01 5.06465316e-01 -4.01989400e-01 -6.13399334e-02 1.03220081e+00 4.98774707e-01 3.15740407e-01 4.14673984e-01 -1.19235468e+00 1.03440034e+00 5.50628126e-01 6.59993708e-01 -9.57177043e-01 3.65885228e-01 3.18822980e-01 8.94209921e-01 8.10203075e-01 3.40880901e-01 -1.02320886e+00 -1.93379462e-01 -1.37178636e+00 6.45389438e-01 9.60784972e-01 5.58735669e-01 2.03262314e-01 9.28751826e-01 1.88522860e-01 1.99616134e-01 1.93986684e-01 5.02917767e-01 9.21496272e-01 -4.97203648e-01 2.50625074e-01 4.94896293e-01 4.92735475e-01 -1.31943500e+00 -5.04226089e-01 -9.02339816e-01 -6.66238129e-01 3.79961699e-01 1.65611923e-01 -4.07741606e-01 -5.00163078e-01 1.03151023e+00 -4.70976502e-01 6.25061214e-01 2.16324538e-01 3.58062327e-01 2.39447176e-01 1.10412812e+00 -1.72876939e-01 -4.30668443e-01 9.32743430e-01 -7.01924920e-01 -7.58526444e-01 1.25452086e-01 3.27455938e-01 -5.02823412e-01 2.33686224e-01 3.40011150e-01 -1.00087833e+00 -5.04669607e-01 -1.12883556e+00 4.37027872e-01 -9.23928320e-01 -2.59215176e-01 6.47916138e-01 3.15587640e-01 -1.03661728e+00 1.15386617e+00 -6.38752997e-01 4.84791279e-01 6.69641867e-02 4.80529249e-01 2.10407734e-01 8.18040192e-01 -1.55391526e+00 1.27923059e+00 5.32273948e-01 3.65120620e-01 -2.70832628e-01 -7.98241377e-01 -2.90402144e-01 1.56856760e-01 -1.83524802e-01 -9.17571560e-02 1.49160087e+00 -1.09039950e+00 -1.28963673e+00 2.55828768e-01 4.59512591e-01 -1.31642079e+00 3.85515273e-01 -3.02347541e-02 -9.21832442e-01 -6.12838149e-01 -2.03688487e-01 1.53048411e-01 5.83273411e-01 -4.62547868e-01 -1.17941475e+00 -1.78988367e-01 -2.19373941e-01 -2.22554013e-01 -1.29441872e-01 4.64849807e-02 6.55045629e-01 -9.77230370e-01 -8.94711446e-03 -6.86513245e-01 -4.17523906e-02 -6.63200796e-01 1.24378696e-01 -1.84137091e-01 5.91337621e-01 -9.48197782e-01 1.40160012e+00 -1.69357598e+00 -7.27387428e-01 2.75139630e-01 -1.69953167e-01 1.75505877e-02 2.56780803e-01 6.32975578e-01 -4.17826176e-01 2.16987386e-01 2.43039839e-02 1.53845176e-01 2.91136980e-01 1.92485854e-01 -1.12884128e+00 3.39937001e-01 1.34574577e-01 1.31882751e+00 -3.14748943e-01 2.63600439e-01 3.09599817e-01 2.00529188e-01 2.45100945e-01 -1.43740550e-02 -3.00208896e-01 -3.66752930e-02 -2.31521711e-01 6.21016920e-01 6.37493193e-01 -2.24491671e-01 -3.61253142e-01 1.45500794e-01 -7.94669271e-01 5.47953844e-01 -1.06704032e+00 6.07683957e-01 -3.92195940e-01 7.06459403e-01 -6.59503579e-01 -8.41755986e-01 1.27738261e+00 4.17933851e-01 7.49193907e-01 -1.09010553e+00 1.88121974e-01 8.69527876e-01 2.39059821e-01 1.05163574e-01 7.10851431e-01 -5.48032463e-01 1.01596266e-02 6.72972739e-01 -3.65123600e-01 2.47583985e-01 1.21376120e-01 -5.89778244e-01 6.09329879e-01 -1.22690752e-01 3.17225635e-01 -4.04825240e-01 2.08029717e-01 -5.66556379e-02 4.96711463e-01 2.99002528e-01 -1.24528937e-01 4.40988652e-02 3.40769351e-01 -1.33482945e+00 -1.23373580e+00 -5.48536301e-01 -3.74568373e-01 8.16444755e-01 -6.21133447e-01 3.35744560e-01 -1.23715810e-01 1.88076466e-01 5.79601347e-01 9.94845569e-01 -7.38419294e-01 4.09690112e-01 -3.26744229e-01 -9.17513669e-01 2.35339448e-01 7.04892159e-01 7.53048897e-01 -1.45838833e+00 -1.00681412e+00 6.47254884e-01 5.81346095e-01 -7.87772775e-01 7.32471347e-02 3.72898042e-01 -1.04184318e+00 -7.57093549e-01 -8.96681666e-01 -4.04970556e-01 -6.36528134e-02 -2.17066333e-01 1.33182168e+00 1.30787343e-02 3.65878046e-01 -2.55805671e-01 -3.14790867e-02 -1.20061445e+00 3.99188474e-02 8.01831707e-02 8.66439864e-02 -4.70254607e-02 7.14440584e-01 -4.69745547e-01 -5.44824898e-01 -1.09166540e-01 -7.57624745e-01 -1.74318448e-01 6.29635632e-01 4.90129471e-01 4.45803732e-01 5.48777640e-01 1.06559372e+00 -4.01088566e-01 9.05567765e-01 -8.91640902e-01 -1.47595215e+00 -3.20928320e-02 -1.23317707e+00 -1.53190762e-01 4.59401459e-01 -3.63001861e-02 -5.52258790e-01 -2.43190929e-01 -1.89956069e-01 -3.57496083e-01 3.72576088e-01 1.08288074e+00 9.18304324e-01 7.07446113e-02 -1.53078318e-01 7.86924303e-01 -1.55546218e-01 -4.24245000e-01 -1.49327800e-01 5.41620493e-01 4.86523360e-01 1.19936600e-01 9.48271394e-01 4.43006717e-02 -4.54281233e-02 -3.53107899e-01 -7.58792102e-01 -6.59585372e-02 -8.02977443e-01 -1.86035499e-01 5.33177972e-01 -1.08757043e+00 -7.88884103e-01 8.95565569e-01 -7.09045529e-01 -1.20739542e-01 -2.10702419e-01 3.02926540e-01 -3.84500265e-01 -5.78834236e-01 -8.78704131e-01 -1.30861259e+00 -7.24507272e-01 -8.26819837e-01 4.04630184e-01 1.54344037e-01 -1.94949791e-01 -1.37980628e+00 1.48096651e-01 -3.12555730e-01 1.06521904e+00 7.29987025e-01 6.98750615e-01 -1.13517356e+00 -6.35155261e-01 -8.31928253e-01 -1.91485539e-01 5.66174269e-01 1.89551473e-01 1.32363409e-01 -7.08748639e-01 -1.08884089e-01 3.39127630e-01 7.26526976e-02 6.51832402e-01 7.29177058e-01 6.31697237e-01 -5.56128263e-01 3.14170986e-01 4.24365789e-01 1.78822398e+00 8.15404415e-01 6.03460431e-01 1.11429846e+00 2.23562330e-01 2.26124480e-01 4.08451974e-01 7.46762514e-01 3.20801139e-01 1.13788974e-02 3.21935683e-01 2.53909320e-01 9.17849779e-01 -1.11596137e-01 4.30529773e-01 1.03243673e+00 -2.86805123e-01 1.57118589e-01 -8.72240841e-01 4.40926015e-01 -1.51256919e+00 -1.21459544e+00 -8.81958306e-02 1.95259714e+00 5.25572538e-01 5.68183064e-01 4.21795368e-01 4.54549909e-01 2.14658350e-01 5.07064521e-01 -6.81634665e-01 -4.73103613e-01 -9.96395051e-02 1.31828666e-01 1.23417115e+00 4.78220098e-02 -1.15361977e+00 5.56164682e-01 7.05625248e+00 1.66253299e-01 -1.52164447e+00 -2.67227441e-01 1.01160753e+00 -1.54406250e-01 -2.34023780e-01 -4.03805584e-01 -9.16696310e-01 9.78324294e-01 1.75502038e+00 -8.13379467e-01 1.65734619e-01 1.17019618e+00 3.29307586e-01 3.19083109e-02 -6.85030937e-01 7.42720962e-01 -4.38161314e-01 -1.96735013e+00 -3.84056717e-02 4.24786091e-01 8.75811458e-01 3.16869974e-01 4.80576754e-01 3.85141253e-01 2.17306986e-01 -1.08540499e+00 9.61815059e-01 1.00052845e+00 2.34536827e-01 -1.23552597e+00 1.31649947e+00 5.49069881e-01 -1.16436255e+00 -5.99874496e-01 -4.49302584e-01 -7.34508097e-01 2.08387021e-02 6.32502198e-01 -2.49273762e-01 2.84965247e-01 8.18760574e-01 8.21612060e-01 -7.20410571e-02 7.18918622e-01 4.25377458e-01 3.95337611e-01 -3.24739069e-01 -2.27140069e-01 7.69424140e-01 -3.97193223e-01 -2.39157021e-01 8.18238497e-01 8.06054890e-01 2.49465674e-01 -2.54585832e-01 9.05722320e-01 3.38424221e-02 -8.17491114e-03 -7.54679143e-01 -3.55513513e-01 3.37094426e-01 7.00182080e-01 -5.70019007e-01 -3.67652297e-01 -8.86957526e-01 2.54199684e-01 -2.59920985e-01 -1.01629250e-01 -5.78515172e-01 -3.07618618e-01 7.28108883e-01 2.83253551e-01 4.61031705e-01 -4.16944861e-01 -6.36024058e-01 -9.80309904e-01 -6.42824098e-02 -4.91862833e-01 6.00219443e-02 -5.88131607e-01 -1.50834703e+00 5.64997315e-01 -8.76759067e-02 -1.32251251e+00 -5.99316120e-01 -8.49602103e-01 -8.76921833e-01 1.27533185e+00 -2.08408356e+00 -5.28850079e-01 3.15231264e-01 3.99482101e-01 5.57697594e-01 -8.43735039e-01 6.62575245e-01 5.25802299e-02 -4.39628065e-01 -7.25974739e-02 6.26219988e-01 5.63127756e-01 -1.78824395e-01 -1.26470363e+00 1.25113034e+00 6.53644085e-01 3.65873910e-02 4.61604714e-01 6.28884256e-01 -8.83617222e-01 -1.18830109e+00 -1.06765449e+00 1.08290935e+00 -1.82994351e-01 1.41043317e+00 2.58634031e-01 -9.82979357e-01 1.11284781e+00 3.37263256e-01 -1.05915368e-01 5.75266004e-01 -5.11704326e-01 2.14054435e-02 -4.20916200e-01 -9.42917466e-01 1.00300916e-01 -7.51668662e-02 -2.20119715e-01 -7.81465590e-01 -1.40505787e-02 3.91745508e-01 -2.50572830e-01 -1.14687002e+00 2.40784943e-01 7.79854894e-01 -1.29038799e+00 7.46172726e-01 -2.32697874e-01 3.61827075e-01 1.15333401e-01 -2.29612485e-01 -1.31301975e+00 -4.50746089e-01 -3.38870615e-01 -2.53618270e-01 7.77452052e-01 6.29448712e-01 -1.21723461e+00 5.71517527e-01 1.15970671e+00 1.38021663e-01 -1.00579023e+00 -8.98978412e-01 -9.42423820e-01 4.75532591e-01 -6.04617774e-01 1.33699811e+00 1.01432955e+00 -2.59464204e-01 -3.39058936e-01 -5.82519948e-01 -1.21671120e-02 2.95635402e-01 4.98832226e-01 3.36139381e-01 -1.55726588e+00 1.10236578e-01 -6.97698355e-01 -3.06081563e-01 -4.12712127e-01 9.71943438e-02 -3.64815414e-01 -5.62247157e-01 -1.48154831e+00 -4.07499433e-01 -2.80546457e-01 -1.05064559e+00 3.90620112e-01 5.25954127e-01 -8.27595126e-03 3.24624568e-01 6.82021797e-01 2.99911231e-01 2.21924961e-01 8.01106215e-01 5.01427613e-02 -3.97737212e-02 3.96282732e-01 -4.37806696e-01 7.09271193e-01 1.12464952e+00 -8.33951756e-02 -1.39326975e-01 -2.15886667e-01 6.22282445e-01 2.11053833e-01 3.96664254e-02 -9.35248077e-01 8.82532150e-02 -3.65124494e-01 9.31410789e-01 -1.17942917e+00 9.37276855e-02 -9.14530873e-01 5.22556841e-01 8.96351457e-01 -2.17646062e-01 1.01504183e+00 3.42394680e-01 7.89454207e-02 -6.30855203e-01 -7.37443492e-02 5.55654168e-01 -4.71348643e-01 -9.99210238e-01 5.32467604e-01 -3.82990211e-01 -6.37418449e-01 1.22597861e+00 -3.78432453e-01 -5.53315133e-02 -5.41552126e-01 -3.07231277e-01 1.84625551e-01 -7.72396550e-02 5.82276821e-01 5.47597229e-01 -1.52375710e+00 -7.22380817e-01 2.88937807e-01 -2.87108094e-01 -5.89729011e-01 -2.38875300e-01 4.83264387e-01 -8.73643339e-01 1.02198780e+00 -6.10120714e-01 7.11691380e-02 -2.74488389e-01 5.99729717e-01 6.76805735e-01 -2.87317097e-01 -6.90826535e-01 5.69909632e-01 -4.82080549e-01 2.06482619e-01 4.50486131e-02 -9.26344991e-01 -4.03077543e-01 6.03701770e-01 8.74401987e-01 3.65045488e-01 1.90438941e-01 -7.07960129e-01 -7.91347995e-02 5.31928182e-01 1.05288781e-01 -7.10996762e-02 2.01752806e+00 4.10320088e-02 -1.13401264e-01 1.20482922e+00 1.08717561e+00 -6.67367637e-01 -1.18453860e+00 -7.81537667e-02 7.84284234e-01 -2.49996394e-01 4.42153990e-01 -7.28546441e-01 -1.55911267e+00 5.60173333e-01 5.53715765e-01 8.74951303e-01 1.02437687e+00 -7.67831802e-01 1.30314696e+00 3.01939726e-01 4.00393188e-01 -1.21937084e+00 -6.29664063e-01 7.15281487e-01 9.37371194e-01 -1.26378989e+00 -4.54663523e-02 5.09189963e-01 -5.91384232e-01 1.36337280e+00 -1.37804411e-02 -7.77654827e-01 1.29074740e+00 6.06079400e-01 3.20276350e-01 -7.47705549e-02 -1.15352178e+00 1.24962717e-01 1.68784589e-01 7.99908265e-02 5.95343053e-01 1.60618305e-01 1.12668879e-01 7.73453832e-01 -7.90732443e-01 3.64490926e-01 6.04012907e-01 1.02476799e+00 -6.65084898e-01 -5.42999566e-01 -2.98125833e-01 8.57606947e-01 -1.00804102e+00 -2.43496925e-01 -1.26865357e-01 1.08698571e+00 -3.40648085e-01 5.28371274e-01 8.57080281e-01 -3.46466422e-01 3.47921580e-01 3.84978890e-01 -2.88043171e-01 -1.51707828e-01 -8.87033463e-01 5.50126247e-02 -2.58582264e-01 -1.87375307e-01 -2.08668202e-01 -6.63754106e-01 -1.19564807e+00 -1.02834284e+00 9.28857550e-02 -9.25071612e-02 7.28348672e-01 1.10545361e+00 -7.01868581e-03 3.97396505e-01 1.03447402e+00 -9.26677108e-01 -9.05031800e-01 -1.04189086e+00 -1.33959150e+00 -1.22555047e-02 6.25519872e-01 -4.25642341e-01 -4.05173510e-01 -1.16566166e-01]	[4.4630913734436035, 4.233850955963135]
c4306c0a-7a56-42fd-b0c6-fb57bb3eabbe	contrastmask-contrastive-learning-to-segment	2203.09775	null	https://arxiv.org/abs/2203.09775v2	https://arxiv.org/pdf/2203.09775v2.pdf	ContrastMask: Contrastive Learning to Segment Every Thing	Partially-supervised instance segmentation is a task which requests segmenting objects from novel unseen categories via learning on limited seen categories with annotated masks thus eliminating demands of heavy annotation burden. The key to addressing this task is to build an effective class-agnostic mask segmentation model. Unlike previous methods that learn such models only on seen categories, in this paper, we propose a new method, named ContrastMask, which learns a mask segmentation model on both seen and unseen categories under a unified pixel-level contrastive learning framework. In this framework, annotated masks of seen categories and pseudo masks of unseen categories serve as a prior for contrastive learning, where features from the mask regions (foreground) are pulled together, and are contrasted against those from the background, and vice versa. Through this framework, feature discrimination between foreground and background is largely improved, facilitating learning of the class-agnostic mask segmentation model. Exhaustive experiments on the COCO dataset demonstrate the superiority of our method, which outperforms previous state-of-the-arts.	['Wei Shen', 'Yan Wang', 'Shouhong Ding', 'Ruixin Zhang', 'Kai Zhao', 'Xuehui Wang']	2022-03-18	null	http://openaccess.thecvf.com//content/CVPR2022/html/Wang_ContrastMask_Contrastive_Learning_To_Segment_Every_Thing_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_ContrastMask_Contrastive_Learning_To_Segment_Every_Thing_CVPR_2022_paper.pdf	cvpr-2022-1	['semi-supervised-instance-segmentation']	['computer-vision']	[ 8.43705475e-01 2.92297751e-01 -3.03311825e-01 -6.09080732e-01 -5.04364610e-01 -7.55065203e-01 5.88219404e-01 -2.43280679e-01 -3.86319250e-01 5.80108523e-01 -2.03065321e-01 -1.23290047e-02 3.85331482e-01 -5.62765658e-01 -7.79092729e-01 -9.00720477e-01 2.45628625e-01 5.48380435e-01 7.51372278e-01 1.74896166e-01 1.58652127e-01 4.22480106e-01 -1.72667563e+00 6.13861978e-01 8.83352160e-01 9.71413195e-01 4.48744923e-01 4.17197257e-01 -3.74174118e-01 6.31406963e-01 -6.00062013e-01 -9.13805068e-02 5.68510890e-01 -4.79129016e-01 -8.63094628e-01 6.48284972e-01 8.65472972e-01 -2.81419575e-01 -1.28811434e-01 1.05958664e+00 6.74902927e-03 1.38810024e-01 7.00536251e-01 -1.26623392e+00 -3.25778335e-01 5.67277193e-01 -8.29507470e-01 3.90772760e-01 -1.82898611e-01 3.74925375e-01 8.31065655e-01 -1.01365900e+00 4.52965170e-01 1.07631946e+00 2.20587254e-01 9.85015810e-01 -1.49445832e+00 -7.50629902e-01 7.65725017e-01 -1.84349949e-03 -1.14405906e+00 -3.08658361e-01 8.89453113e-01 -4.91781890e-01 3.61257076e-01 4.70377594e-01 7.14347422e-01 7.24079490e-01 -3.39405000e-01 1.30064178e+00 1.31919110e+00 -3.86562288e-01 2.78020263e-01 3.55137795e-01 2.85616815e-01 5.93958020e-01 2.90066242e-01 1.00275360e-01 -3.12730193e-01 2.96575606e-01 6.13957584e-01 2.79819071e-01 -2.65828878e-01 -6.67068541e-01 -1.13840067e+00 5.17893076e-01 5.79160452e-01 1.78323701e-01 -1.42319486e-01 8.31393227e-02 8.84351060e-02 -4.69868593e-02 6.19014204e-01 1.42427236e-01 -8.04089725e-01 4.23127085e-01 -1.28176367e+00 -7.04897121e-02 6.46731555e-01 9.54799235e-01 1.27241874e+00 1.02075972e-01 -2.71722734e-01 7.51795352e-01 2.54264116e-01 4.92841840e-01 2.43732229e-01 -7.17935443e-01 1.88584670e-01 9.34359252e-01 -7.80740604e-02 -4.35737222e-01 -2.06105769e-01 -6.35283232e-01 -5.56266665e-01 4.30847108e-01 5.79305232e-01 -1.69030145e-01 -1.83868933e+00 1.64682996e+00 6.94691420e-01 5.22458494e-01 4.67114076e-02 8.77329528e-01 9.45541918e-01 5.59760273e-01 1.26531869e-01 -1.16975456e-01 1.24546993e+00 -1.41056216e+00 -4.88275439e-01 -7.92043030e-01 1.16585128e-01 -6.27870202e-01 1.06639004e+00 3.55231613e-01 -1.07677007e+00 -9.81296897e-01 -9.94121552e-01 9.28564519e-02 -5.14517903e-01 -3.54233943e-02 6.88943684e-01 5.86520195e-01 -8.55884910e-01 4.03389364e-01 -8.13597023e-01 7.14479154e-03 1.00978339e+00 3.86980712e-01 -5.27321249e-02 -8.08986947e-02 -6.32933557e-01 5.40751755e-01 8.65325987e-01 1.93348929e-01 -1.47792423e+00 -5.83604753e-01 -8.59651983e-01 8.68299082e-02 7.75239348e-01 -2.99248219e-01 1.16880298e+00 -1.64835966e+00 -1.13381720e+00 1.27355587e+00 -1.89179197e-01 -3.53593081e-01 6.20882392e-01 -1.49180561e-01 -1.54961437e-01 3.57946008e-01 1.81850210e-01 1.14484632e+00 1.33850276e+00 -2.03602409e+00 -1.07604349e+00 -1.29712865e-01 5.79916127e-02 2.78922051e-01 -8.19441900e-02 -3.35837394e-01 -6.58450186e-01 -5.86287677e-01 4.23948467e-01 -7.42870271e-01 -3.40163797e-01 3.82696874e-02 -6.76018178e-01 -6.33950830e-02 1.32410538e+00 -5.80217719e-01 8.25088382e-01 -2.35157084e+00 1.27735645e-01 1.05875663e-01 4.90143836e-01 3.05684656e-01 -1.77182645e-01 -2.80313164e-01 -3.57441939e-02 -6.06309064e-02 -7.05577374e-01 -2.15665534e-01 -7.37763792e-02 4.89591241e-01 -2.36178920e-01 3.67886543e-01 6.79651797e-01 9.33169723e-01 -9.66571987e-01 -7.38305330e-01 3.79010111e-01 1.73794106e-01 -4.27078575e-01 4.26109672e-01 -5.65288186e-01 8.67870450e-01 -2.52070576e-01 1.00329852e+00 9.83684659e-01 -1.37862161e-01 1.43093066e-02 -3.25753354e-02 5.36903599e-03 -1.39559492e-01 -1.18361521e+00 1.38109422e+00 -1.38322040e-01 4.34453458e-01 2.22802117e-01 -1.18631506e+00 7.48773396e-01 -4.68070060e-02 1.95480004e-01 -4.81252968e-01 1.59200713e-01 1.60961702e-01 2.18702793e-01 -3.78944665e-01 6.21506162e-02 -1.38939738e-01 -8.08620080e-03 2.76982099e-01 1.39644414e-01 -3.82256657e-01 2.81600028e-01 4.37466577e-02 6.56437278e-01 4.38150734e-01 2.53815856e-02 -4.20099884e-01 5.71209610e-01 1.00455187e-01 7.85466611e-01 8.89483213e-01 -5.01503408e-01 7.90940583e-01 1.68782249e-01 -4.50165957e-01 -6.47039652e-01 -1.39231598e+00 -2.41690621e-01 1.30169833e+00 5.99700987e-01 2.85237640e-01 -1.00172985e+00 -1.27446163e+00 1.05642654e-01 5.37144840e-01 -7.93487966e-01 1.66641306e-02 -7.70563066e-01 -7.35801160e-01 6.38435706e-02 6.31727219e-01 6.77470803e-01 -1.24919724e+00 -6.24381304e-01 5.85474223e-02 1.09742703e-02 -1.10683537e+00 -5.27171612e-01 6.53679669e-01 -8.05809736e-01 -1.26636827e+00 -7.04679787e-01 -1.32306838e+00 1.00079703e+00 4.83721584e-01 1.19459450e+00 2.53293782e-01 -5.80227554e-01 2.65110880e-01 -8.03058147e-02 -5.20435333e-01 -2.86651999e-01 -2.15512201e-01 -3.24648112e-01 5.08281529e-01 2.71578223e-01 -3.99559826e-01 -7.33174026e-01 4.94546771e-01 -1.07536292e+00 3.17696303e-01 8.49340141e-01 8.46344352e-01 1.01324475e+00 -8.14006478e-02 5.97530365e-01 -1.38226831e+00 -3.31980079e-01 -3.86694849e-01 -5.85595250e-01 2.65452445e-01 -2.27996260e-01 -2.63291508e-01 4.34222788e-01 -6.65697575e-01 -1.37766325e+00 4.87683207e-01 2.43678704e-01 -2.82605916e-01 -4.81225461e-01 -1.69701427e-01 -6.45947635e-01 -1.27425566e-01 4.07746613e-01 3.07090729e-01 -2.43141979e-01 -4.34433848e-01 7.39422083e-01 5.65182030e-01 8.37013066e-01 -4.82129484e-01 1.06786048e+00 8.86002839e-01 -3.03530872e-01 -5.62074900e-01 -1.24183273e+00 -7.42015541e-01 -1.16895890e+00 -2.36616954e-01 9.96608198e-01 -8.75968397e-01 5.68036996e-02 4.70338732e-01 -7.01296568e-01 -8.28912020e-01 -7.22057998e-01 4.99143526e-02 -6.11720920e-01 2.33779326e-01 -3.86040390e-01 -7.19208777e-01 -2.05842890e-02 -1.04462564e+00 1.19290996e+00 6.89736485e-01 4.92047518e-02 -9.39771295e-01 -4.52158511e-01 5.95651209e-01 1.95081979e-01 4.71524984e-01 8.19443524e-01 -9.74849164e-01 -9.18442070e-01 1.92036144e-02 -5.60082972e-01 6.46568239e-01 3.38875026e-01 -1.08244851e-01 -1.36737466e+00 -2.27449924e-01 4.27711830e-02 -2.44873405e-01 1.21622682e+00 2.19140694e-01 1.46885836e+00 -9.80415717e-02 -6.75161421e-01 6.93515301e-01 1.43273747e+00 2.61340201e-01 2.97895074e-01 -1.82420965e-02 9.45430636e-01 5.30610502e-01 6.79203749e-01 -5.54369912e-02 -5.28388768e-02 2.29138002e-01 5.65050364e-01 -7.22913921e-01 -6.04239047e-01 -3.21616381e-02 -6.96534812e-02 3.58753920e-01 2.18882844e-01 -2.02943578e-01 -6.88735366e-01 7.27795482e-01 -1.60125685e+00 -7.41967499e-01 -3.07511147e-02 1.93924737e+00 9.30504918e-01 5.26015162e-01 3.53445411e-02 4.06029150e-02 1.01281583e+00 7.11441189e-02 -9.39983904e-01 -1.93176290e-03 -1.82964593e-01 5.67160130e-01 2.13938475e-01 2.85471886e-01 -1.54701483e+00 1.19591677e+00 6.20824623e+00 8.02711070e-01 -9.88089800e-01 7.75109529e-02 1.03638124e+00 1.14919253e-01 -3.48371007e-02 1.30389392e-01 -7.21362889e-01 4.53730106e-01 2.51817465e-01 1.99091941e-01 2.21329644e-01 1.04715204e+00 -1.24008268e-01 -2.95557648e-01 -1.32243145e+00 7.03949690e-01 2.03584984e-01 -9.74165916e-01 6.62998930e-02 -6.88169822e-02 1.14689565e+00 -1.93861216e-01 1.58192575e-01 5.02722621e-01 1.74339205e-01 -7.86298692e-01 8.31345856e-01 2.32479557e-01 7.41017163e-01 -4.68570530e-01 6.01111352e-01 2.31829509e-01 -1.21935189e+00 -1.95084646e-01 -2.39301860e-01 -2.35629175e-02 -3.51048075e-02 5.22980869e-01 -7.15707898e-01 3.75726283e-01 5.95564544e-01 6.62752926e-01 -7.42652774e-01 1.10847235e+00 -4.39482838e-01 8.45631599e-01 -8.63343626e-02 3.36598128e-01 3.48286301e-01 -1.25650749e-01 3.06433290e-01 1.43965840e+00 -3.39960933e-01 3.28597962e-03 7.64116526e-01 1.00084174e+00 -1.93037912e-01 -2.04465881e-01 -1.38013020e-01 1.20129853e-01 2.25622073e-01 1.55619085e+00 -1.48517680e+00 -7.23312438e-01 -4.98877376e-01 1.15712941e+00 2.28600979e-01 4.27676260e-01 -9.11178231e-01 -1.31714702e-01 3.21150959e-01 1.17971048e-01 7.27369964e-01 1.94090903e-01 -4.45468366e-01 -1.05485487e+00 -2.31303632e-01 -7.59701967e-01 5.72421730e-01 -4.66061264e-01 -1.47191191e+00 3.78543466e-01 2.02920780e-01 -1.22362578e+00 3.53810102e-01 -7.00543582e-01 -7.78088450e-01 6.29599273e-01 -1.73625684e+00 -1.34097159e+00 -6.22095346e-01 2.94636279e-01 9.05415118e-01 5.61142378e-02 3.59784007e-01 1.96593732e-01 -4.97680873e-01 3.24986398e-01 -7.86866173e-02 2.61458904e-01 4.71455693e-01 -1.59044242e+00 2.18793809e-01 1.03710043e+00 3.11014712e-01 2.13090703e-01 3.94295931e-01 -6.34820223e-01 -7.30499506e-01 -1.42867351e+00 2.45495006e-01 -5.49961865e-01 3.32330465e-01 -6.38235569e-01 -9.65076506e-01 6.31094038e-01 2.11710975e-01 4.70766634e-01 5.90999305e-01 -2.76187599e-01 -2.64434844e-01 -1.03026614e-01 -1.16886330e+00 5.38966119e-01 1.14794207e+00 -1.50688574e-01 -8.13730896e-01 3.91653836e-01 8.25469434e-01 -4.61878151e-01 -2.07102805e-01 6.35157406e-01 2.89690137e-01 -8.63518834e-01 8.99125516e-01 -5.30948400e-01 4.71104421e-02 -6.01294696e-01 -8.77552107e-02 -1.02625430e+00 -9.12008360e-02 -4.17694539e-01 -2.10127547e-01 1.32185793e+00 3.01564425e-01 -4.74265337e-01 1.03838098e+00 2.84858942e-01 -4.59155589e-01 -6.76560700e-01 -7.25010335e-01 -7.96970129e-01 4.01761103e-03 -1.18609242e-01 3.90809447e-01 9.49414253e-01 -7.16195166e-01 2.65700817e-01 -2.92150918e-02 2.08967432e-01 7.34057724e-01 3.81353050e-01 8.15405309e-01 -1.25790453e+00 -3.66568089e-01 -4.15381402e-01 -3.49604130e-01 -1.10814631e+00 1.50217518e-01 -8.89608562e-01 5.63034177e-01 -1.54437506e+00 6.18427336e-01 -4.96251643e-01 -4.49640006e-01 7.32850373e-01 -6.83999360e-01 7.51632631e-01 2.54806012e-01 6.65082037e-02 -7.80570865e-01 2.59103477e-01 1.59405601e+00 -5.16410232e-01 -2.23688513e-01 5.89187853e-02 -7.10602999e-01 1.02779531e+00 6.71661258e-01 -5.64250469e-01 -4.52844560e-01 -2.83448905e-01 -7.32171595e-01 -5.61444104e-01 4.37617034e-01 -1.09068632e+00 -1.40663713e-01 -3.01510960e-01 7.78759539e-01 -7.59489954e-01 2.68964082e-01 -8.79953325e-01 -1.68168992e-01 5.60899436e-01 -1.56283796e-01 -6.48423195e-01 3.69801074e-01 7.87712216e-01 -8.62029567e-02 -3.22210431e-01 1.04265082e+00 -4.42913443e-01 -1.14716089e+00 3.71881574e-01 -1.69484511e-01 3.70305657e-01 1.24027359e+00 -4.68295366e-01 -2.29320064e-01 7.17119351e-02 -1.11746979e+00 3.26153010e-01 4.46432531e-01 2.12725595e-01 5.79596639e-01 -9.13154542e-01 -5.89011252e-01 4.39452529e-01 1.42964080e-01 6.72471941e-01 2.58183300e-01 5.64422607e-01 -4.70965445e-01 -1.10996723e-01 -1.60753056e-01 -9.54920292e-01 -1.22964370e+00 9.03347552e-01 3.81606251e-01 5.23608264e-07 -5.40125012e-01 1.12284100e+00 1.05061591e+00 -4.03470904e-01 3.64832431e-01 -2.81070799e-01 -7.71951973e-02 -1.29004391e-02 5.82889616e-01 8.20466056e-02 -2.17844218e-01 -5.99437535e-01 -2.29799658e-01 4.95224595e-01 -3.03696364e-01 1.47002012e-01 1.06262064e+00 -1.66160986e-01 -6.31724149e-02 5.21280706e-01 9.00462270e-01 -8.49887356e-03 -1.89272416e+00 -3.12239438e-01 1.92843318e-01 -5.40160894e-01 -1.95700943e-01 -9.60222006e-01 -1.30470204e+00 9.20532584e-01 8.38421106e-01 1.81107461e-01 1.28680074e+00 2.88830429e-01 6.63755298e-01 8.45489129e-02 1.47322088e-01 -1.08695829e+00 5.65916538e-01 1.40940428e-01 4.39203888e-01 -1.45657778e+00 -1.30740806e-01 -8.95731270e-01 -6.16073370e-01 8.64878237e-01 1.03493023e+00 -2.38105595e-01 5.32470107e-01 1.59595072e-01 3.77925903e-01 -1.03283711e-01 -3.01994592e-01 -7.72107482e-01 5.74745059e-01 8.45849752e-01 -3.13484855e-02 -1.04282545e-02 -1.04435557e-03 5.46551585e-01 3.22357476e-01 -5.84430099e-01 2.90088266e-01 1.18120277e+00 -7.35056877e-01 -9.61908221e-01 -3.48887235e-01 5.25366187e-01 -3.28012407e-01 -6.92142397e-02 -5.79139948e-01 9.83643174e-01 7.42596209e-01 9.43198144e-01 3.08114946e-01 -6.07808940e-02 1.16804481e-01 1.87724397e-01 5.37466526e-01 -1.20619404e+00 -5.21733761e-01 3.08900535e-01 -2.15503603e-01 -2.84057736e-01 -6.26348913e-01 -5.27829528e-01 -1.39367986e+00 5.32156825e-01 -5.19704461e-01 3.99406515e-02 3.21683615e-01 1.06524479e+00 -7.49181733e-02 7.88631022e-01 6.47077203e-01 -1.19182634e+00 -2.78625309e-01 -6.59841061e-01 -5.77591538e-01 7.61392534e-01 4.40644830e-01 -7.63323188e-01 -4.89539713e-01 4.95980829e-01]	[9.59369945526123, 0.6701580882072449]
7ff0473a-cb88-45d7-9d51-03317fb8174e	analysis-of-semi-supervised-methods-for	2208.00544	null	https://arxiv.org/abs/2208.00544v1	https://arxiv.org/pdf/2208.00544v1.pdf	Analysis of Semi-Supervised Methods for Facial Expression Recognition	Training deep neural networks for image recognition often requires large-scale human annotated data. To reduce the reliance of deep neural solutions on labeled data, state-of-the-art semi-supervised methods have been proposed in the literature. Nonetheless, the use of such semi-supervised methods has been quite rare in the field of facial expression recognition (FER). In this paper, we present a comprehensive study on recently proposed state-of-the-art semi-supervised learning methods in the context of FER. We conduct comparative study on eight semi-supervised learning methods, namely Pi-Model, Pseudo-label, Mean-Teacher, VAT, MixMatch, ReMixMatch, UDA, and FixMatch, on three FER datasets (FER13, RAF-DB, and AffectNet), when various amounts of labeled samples are used. We also compare the performance of these methods against fully-supervised training. Our study shows that when training existing semi-supervised methods on as little as 250 labeled samples per class can yield comparable performances to that of fully-supervised methods trained on the full labeled datasets. To facilitate further research in this area, we make our code publicly available at: https://github.com/ShuvenduRoy/SSL_FER	['Ali Etemad', 'Shuvendu Roy']	2022-07-31	null	null	null	null	['facial-expression-recognition']	['computer-vision']	[ 8.41646865e-02 1.01167440e-01 -3.26270044e-01 -1.05400276e+00 -6.72364652e-01 -6.78052977e-02 3.79198372e-01 -3.26365560e-01 -4.98543918e-01 9.26110923e-01 -2.19340906e-01 4.50179391e-02 2.13651076e-01 -3.55698317e-01 -6.24877691e-01 -5.95910013e-01 1.79294646e-02 4.63921994e-01 -1.99076697e-01 -1.40643209e-01 -3.43988597e-01 4.38533187e-01 -1.76923525e+00 4.92923379e-01 5.90676844e-01 1.31914783e+00 -4.14224714e-01 2.11310238e-01 -5.90064935e-02 1.33632052e+00 -5.27333558e-01 -7.37920463e-01 1.26080573e-01 -4.66324180e-01 -9.51508760e-01 3.16544384e-01 6.07807040e-01 -4.47084367e-01 -1.90487191e-01 1.01550388e+00 7.00328410e-01 -9.60766822e-02 7.37177074e-01 -1.50032675e+00 -4.06262785e-01 4.34476793e-01 -6.86999798e-01 -6.32900447e-02 -1.45760933e-02 -7.58854896e-02 6.40581369e-01 -1.11836278e+00 6.85103357e-01 1.20271385e+00 6.96483672e-01 8.49672735e-01 -1.10845375e+00 -1.19592166e+00 -1.83275491e-01 1.10174045e-01 -1.57970393e+00 -8.80413175e-01 9.34792817e-01 -4.73756015e-01 7.04410434e-01 -1.25452712e-01 3.32992256e-01 1.43384731e+00 -3.59633416e-01 1.10094023e+00 1.65874028e+00 -6.03329659e-01 2.13782370e-01 3.52960974e-01 4.05200869e-01 9.65185940e-01 -2.24998593e-01 1.46380320e-01 -6.50744081e-01 -2.34410107e-01 5.12609899e-01 -1.84797153e-01 -6.27506450e-02 -1.76799640e-01 -5.00119150e-01 8.29450309e-01 7.06548020e-02 3.55437934e-01 -2.61384487e-01 2.66135950e-03 7.43616402e-01 3.42979461e-01 1.12475801e+00 -3.73708680e-02 -5.68959177e-01 -2.59294212e-01 -1.19917548e+00 -1.95995510e-01 6.82807684e-01 9.28644121e-01 9.77598131e-01 3.64736795e-01 4.23122384e-02 1.15725350e+00 3.17265421e-01 2.89308220e-01 4.95925307e-01 -1.03019679e+00 -7.03585073e-02 5.32183290e-01 -1.06347568e-01 -6.67160153e-01 -2.44851485e-01 -4.48063239e-02 -9.61705625e-01 2.69621968e-01 3.38519782e-01 -4.22882855e-01 -9.39023077e-01 1.70719695e+00 2.05865875e-01 2.76523530e-01 2.66318452e-02 7.54211485e-01 1.20359695e+00 3.73377562e-01 3.47431809e-01 -3.69186342e-01 7.79592574e-01 -1.29536331e+00 -7.81127810e-01 6.73271902e-03 8.16348970e-01 -6.22532785e-01 8.69225204e-01 6.22158647e-01 -7.77527750e-01 -6.05512023e-01 -7.34903276e-01 1.71142444e-01 -4.43071991e-01 5.87645054e-01 8.32548857e-01 8.18106949e-01 -1.00292742e+00 4.72185850e-01 -7.29518294e-01 -4.05631244e-01 9.91279542e-01 4.87403870e-01 -7.18183160e-01 -7.66209215e-02 -1.07549715e+00 7.42039859e-01 1.73668295e-01 2.18887761e-01 -1.20060015e+00 -3.21119368e-01 -7.83012211e-01 -2.50027239e-01 1.77133068e-01 -1.04964515e-02 1.40364540e+00 -1.99914765e+00 -1.55659771e+00 1.58536947e+00 -2.27822706e-01 -2.57175535e-01 4.58311856e-01 -2.10446760e-01 -4.69387561e-01 1.19567931e-01 -3.14027011e-01 9.18788970e-01 8.29246342e-01 -1.23286760e+00 -2.03067765e-01 -3.41582388e-01 -5.21220565e-02 -1.69575185e-01 -3.51906657e-01 5.54275990e-01 -9.69170406e-02 -3.38174224e-01 -6.26949430e-01 -8.37801814e-01 4.92174551e-03 2.68087983e-01 -3.57635379e-01 -5.19407272e-01 8.06838870e-01 -3.88906181e-01 1.02183235e+00 -2.53709388e+00 -2.05455810e-01 -3.79976854e-02 7.68244192e-02 7.45261133e-01 -2.00849444e-01 9.18270573e-02 -5.00137687e-01 2.28759330e-02 -2.79622942e-01 -7.19238758e-01 2.12200917e-02 3.71491104e-01 9.62639302e-02 5.98744452e-01 2.81841815e-01 7.17672527e-01 -7.49005139e-01 -7.17445850e-01 2.71952916e-02 6.45198762e-01 -2.07355738e-01 4.30672228e-01 -5.86626306e-02 3.42285544e-01 -2.66942650e-01 1.05567026e+00 6.88737035e-01 -2.68618971e-01 1.29960433e-01 -1.47754624e-01 3.40115368e-01 -4.07565296e-01 -8.56358826e-01 1.53073287e+00 -3.18261176e-01 7.66496658e-01 1.01711668e-01 -1.26465034e+00 1.22071540e+00 4.53347147e-01 5.31261444e-01 -4.79748040e-01 5.79240799e-01 3.29614639e-01 -3.14830691e-01 -4.97389555e-01 1.49128348e-01 -2.25948051e-01 3.23309541e-01 4.27705884e-01 8.00156593e-01 5.18144548e-01 3.44717115e-01 -5.36752827e-02 7.23198354e-01 4.44135904e-01 3.12612563e-01 -8.78741592e-02 4.46516097e-01 -1.18995182e-01 5.47680020e-01 1.82020068e-01 -6.69250667e-01 4.98300254e-01 5.33084691e-01 -5.86064041e-01 -8.46625268e-01 -5.97030401e-01 -3.36443752e-01 1.39616859e+00 -4.68945295e-01 -3.02401692e-01 -1.11491406e+00 -1.01857436e+00 -1.77912697e-01 3.37517858e-01 -8.93703043e-01 -5.00074401e-02 -4.47760895e-02 -8.03781688e-01 9.36658740e-01 5.36907554e-01 7.90136039e-01 -1.31579673e+00 -4.20050740e-01 -2.40323350e-01 -6.01741411e-02 -1.10953319e+00 4.25254256e-02 3.91271263e-01 -6.01048946e-01 -1.12495244e+00 -8.82747829e-01 -7.20338941e-01 1.02066207e+00 -2.66759366e-01 1.15787339e+00 1.37381434e-01 -2.03752935e-01 3.28937232e-01 -5.57865381e-01 -4.87222493e-01 -2.79866844e-01 -9.00772065e-02 5.82596473e-02 4.68546718e-01 7.96046853e-01 -4.34308618e-01 -2.09234998e-01 5.02214491e-01 -7.82441139e-01 3.05150282e-02 3.92595917e-01 1.04573977e+00 8.18209589e-01 -3.35350603e-01 5.43778241e-01 -1.36425197e+00 2.64543980e-01 -5.37604749e-01 -3.95212114e-01 2.27712020e-01 -4.50012982e-01 -2.85785407e-01 3.65659624e-01 -6.62588060e-01 -1.19063854e+00 4.06168520e-01 -3.12187999e-01 -9.03994560e-01 -6.54307544e-01 4.86032337e-01 -1.11075655e-01 -2.74008572e-01 8.76108170e-01 -6.11943565e-02 1.69230700e-01 -3.65414530e-01 2.83047736e-01 1.18757260e+00 2.38827705e-01 -5.54898679e-01 2.18904391e-01 3.99396807e-01 -1.98438138e-01 -7.48703182e-01 -1.21432090e+00 -3.12622428e-01 -8.52509379e-01 -4.84676450e-01 7.31520891e-01 -1.11957920e+00 -1.68959066e-01 9.12823796e-01 -8.86074305e-01 -9.18006241e-01 -3.51281583e-01 2.79589236e-01 -6.12009108e-01 1.13192610e-01 -6.94778025e-01 -8.77062201e-01 -4.09683645e-01 -1.05568230e+00 1.00365567e+00 2.62250453e-01 -3.85408819e-01 -9.67894852e-01 9.21065509e-02 5.48392355e-01 4.29292828e-01 5.04483104e-01 3.07662308e-01 -1.11293209e+00 2.22131073e-01 -1.57890141e-01 -3.17734241e-01 8.98354232e-01 1.25449181e-01 2.56099135e-01 -1.46887326e+00 -1.75227001e-01 -4.05927390e-01 -1.41777527e+00 7.59868085e-01 1.17753953e-01 1.26400149e+00 -2.65071564e-03 -1.00340076e-01 6.79443479e-01 1.16044879e+00 5.26940338e-02 6.77237332e-01 1.83104500e-01 4.89606410e-01 5.41037738e-01 8.16088796e-01 5.76719046e-01 1.55543387e-01 3.29702944e-01 3.14645052e-01 -4.26769435e-01 -9.83359665e-02 -7.44877104e-03 2.44314283e-01 5.38755536e-01 -2.64166653e-01 -9.72566307e-02 -9.72357690e-01 3.33127350e-01 -1.71694791e+00 -8.11585903e-01 1.00459181e-01 1.84565675e+00 1.08417511e+00 -1.86599582e-01 1.73734576e-01 1.71148807e-01 6.49371803e-01 3.37278023e-02 -4.45567518e-01 -4.82710302e-01 -2.50059545e-01 5.59974015e-01 1.11158989e-01 6.55876920e-02 -1.41467690e+00 1.18828392e+00 6.40924263e+00 1.12796366e+00 -1.43595624e+00 3.82779390e-01 1.33267915e+00 -3.43132205e-02 3.83958250e-01 -3.92879099e-01 -7.75926411e-01 2.68846005e-01 1.26074159e+00 3.54062110e-01 2.08571315e-01 1.33364058e+00 8.75759125e-02 -1.76652178e-01 -1.12666738e+00 1.18838847e+00 3.42746168e-01 -8.09742928e-01 -1.38593271e-01 -1.63119569e-01 7.86125362e-01 5.02677597e-02 -3.58346850e-02 4.60514784e-01 2.44019195e-01 -1.23844993e+00 4.88670975e-01 4.92312461e-01 1.17385042e+00 -9.27898586e-01 9.99821365e-01 1.72448441e-01 -5.75296223e-01 1.92247257e-01 -4.56277937e-01 3.30681726e-02 -6.27613664e-02 6.31173551e-01 -3.69608045e-01 3.39653015e-01 8.00976992e-01 9.15372849e-01 -5.74088931e-01 6.38069212e-01 -4.14686143e-01 1.14902651e+00 -2.22261950e-01 8.51108357e-02 1.79469138e-01 -1.69328731e-02 -1.53191566e-01 1.33060455e+00 -3.62035781e-02 1.23503044e-01 6.24170601e-02 5.97383022e-01 -1.37488976e-01 3.46426070e-01 -3.99576724e-01 -3.46657097e-01 -6.34266250e-03 1.42349339e+00 -4.42452729e-01 -4.73414719e-01 -5.38638592e-01 8.17267478e-01 6.28920674e-01 3.81224036e-01 -7.83794880e-01 -1.63463965e-01 3.40577811e-01 1.46782875e-01 2.14532949e-02 9.57727060e-02 1.58941522e-01 -1.00750971e+00 -3.14431071e-01 -1.08097506e+00 5.03953516e-01 -1.13357997e+00 -1.35391927e+00 1.02476323e+00 1.97531953e-01 -9.97677565e-01 -3.26459467e-01 -8.30129564e-01 -3.25903922e-01 4.92457539e-01 -1.59949493e+00 -1.40399194e+00 -4.30852413e-01 9.65313911e-01 4.10128891e-01 -6.62470758e-01 1.24906421e+00 6.93590403e-01 -7.34498978e-01 9.34549034e-01 -1.15127362e-01 6.77088141e-01 1.09143126e+00 -7.52598763e-01 -2.91866511e-01 4.16061580e-01 3.46538574e-01 3.31673294e-01 3.49272996e-01 -1.85193360e-01 -9.16318417e-01 -1.10346782e+00 7.06182361e-01 -1.39047252e-02 5.64761221e-01 -2.34191075e-01 -7.67442584e-01 8.33490789e-01 4.54005152e-01 6.44507766e-01 1.21658146e+00 3.23474556e-02 -5.34060001e-01 -1.87971771e-01 -1.33452046e+00 1.00319050e-01 8.87033224e-01 -4.94321734e-01 -8.70323926e-02 4.49649870e-01 1.25630870e-01 -2.85977483e-01 -8.65072548e-01 6.35512590e-01 4.63309765e-01 -1.10326874e+00 3.15620065e-01 -7.01294780e-01 6.48250520e-01 -5.95101621e-03 -2.12513030e-01 -1.28659141e+00 -3.55725922e-03 -4.89714146e-01 -1.00084394e-01 1.41603696e+00 4.04983580e-01 -4.87474203e-01 1.02522957e+00 4.45959687e-01 1.11982770e-01 -1.13581502e+00 -8.75233948e-01 -6.19825065e-01 3.33577022e-02 -3.36787194e-01 2.24573612e-01 1.35393560e+00 -1.09476343e-01 1.33524507e-01 -5.47835112e-01 -4.41717952e-01 5.38992345e-01 -1.45364687e-01 9.10649121e-01 -1.12695360e+00 -1.15404412e-01 -1.13925591e-01 -5.02882183e-01 -5.02334118e-01 8.00085008e-01 -8.47227275e-01 -1.02347434e-01 -1.07151020e+00 4.70770031e-01 -2.93334961e-01 -3.43687356e-01 1.24984121e+00 1.93578616e-01 6.14642024e-01 -1.08324230e-01 1.32106513e-01 -7.57646203e-01 5.98600388e-01 9.72444952e-01 -9.31613520e-02 1.65647164e-01 -2.02452004e-01 -3.83750737e-01 1.01314938e+00 1.03913093e+00 -5.43995023e-01 -3.26403946e-01 -2.75549978e-01 -2.70682037e-01 -2.92731941e-01 1.33072972e-01 -8.41172278e-01 -6.85388371e-02 -6.33821487e-02 3.80367011e-01 -1.15975313e-01 3.44033897e-01 -7.04576969e-01 6.42617568e-02 -7.41784424e-02 -3.89448404e-01 -3.17792565e-01 2.94947296e-01 -2.43008416e-02 -6.16793990e-01 -2.99530447e-01 1.10367477e+00 -1.40748531e-01 -9.79068637e-01 5.24814665e-01 -3.94552410e-01 5.48511520e-02 1.02069163e+00 -7.10085332e-02 -2.22164735e-01 -5.09738564e-01 -9.53962207e-01 -4.94172759e-02 1.72122598e-01 1.97435305e-01 5.54146886e-01 -1.33035862e+00 -6.47181273e-01 -9.58388299e-03 3.54326248e-01 -1.63648829e-01 1.42119333e-01 8.14038754e-01 -4.11489367e-01 9.05134082e-02 -5.93358219e-01 -5.13527572e-01 -1.58667362e+00 3.78682226e-01 5.38330078e-01 -1.78521648e-01 1.71659067e-01 1.00964296e+00 -2.44796518e-02 -6.87132120e-01 4.47643757e-01 3.43893081e-01 -2.46301591e-01 1.64653540e-01 4.95903194e-01 1.47382453e-01 -1.54971674e-01 -1.17131877e+00 -1.81845799e-01 3.14628571e-01 -6.44398406e-02 1.17682584e-01 1.31920159e+00 2.35731199e-01 -1.68512210e-01 5.14277399e-01 1.41115570e+00 -4.93343115e-01 -1.16083276e+00 -3.61850470e-01 -2.54753768e-01 -2.41459921e-01 1.77001178e-01 -7.84901261e-01 -1.52343547e+00 1.07811439e+00 9.06868160e-01 -4.66001123e-01 1.42924750e+00 -6.23861887e-02 2.69103408e-01 3.36175501e-01 5.83524644e-01 -1.02667642e+00 1.58374459e-01 3.54574263e-01 5.87998867e-01 -1.46368670e+00 -4.88804877e-02 -4.52831984e-01 -9.27278280e-01 1.03704429e+00 9.72408891e-01 -7.40450174e-02 9.99347329e-01 3.95981997e-01 4.34306592e-01 -1.72991902e-01 -6.90298975e-01 -2.68351823e-01 6.88646734e-02 4.54093874e-01 8.47269237e-01 -1.40181527e-01 -1.49410784e-01 7.24573195e-01 1.45556387e-02 6.35155261e-01 2.35501632e-01 1.21326935e+00 -1.29765466e-01 -1.13208771e+00 2.20458303e-02 3.53040367e-01 -7.65252173e-01 1.57646891e-02 -7.09687233e-01 8.28873158e-01 2.73685277e-01 8.11564207e-01 -3.70927565e-02 -3.92772526e-01 1.49999440e-01 3.87440592e-01 5.12259364e-01 -5.67909241e-01 -3.57466280e-01 2.36825347e-02 4.54463214e-01 -5.10154605e-01 -1.20874476e+00 -5.17878830e-01 -1.10039175e+00 -1.18452117e-01 -2.92696267e-01 9.79724079e-02 4.80819494e-01 9.39191520e-01 2.55736709e-01 1.58183388e-02 5.29107571e-01 -8.45277548e-01 -3.22495818e-01 -1.27731812e+00 -7.51279414e-01 5.46442568e-01 -5.53321540e-02 -9.15231884e-01 -4.52154130e-01 3.73707205e-01]	[13.57992172241211, 1.669561743736267]
3417430a-a008-483b-adf3-7b19f461f16a	material-identification-from-radiographs	2303.06005	null	https://arxiv.org/abs/2303.06005v1	https://arxiv.org/pdf/2303.06005v1.pdf	Material Identification From Radiographs Without Energy Resolution	We propose a method for performing material identification from radiographs without energy-resolved measurements. Material identification has a wide variety of applications, including in biomedical imaging, nondestructive testing, and security. While existing techniques for radiographic material identification make use of dual energy sources, energy-resolving detectors, or additional (e.g., neutron) measurements, such setups are not always practical-requiring additional hardware and complicating imaging. We tackle material identification without energy resolution, allowing standard X-ray systems to provide material identification information without requiring additional hardware. Assuming a setting where the geometry of each object in the scene is known and the materials come from a known set of possible materials, we pose the problem as a combinatorial optimization with a loss function that accounts for the presence of scatter and an unknown gain and propose a branch and bound algorithm to efficiently solve it. We present experiments on both synthetic data and real, experimental data with relevance to security applications-thick, dense objects imaged with MeV X-rays. We show that material identification can be efficient and accurate, for example, in a scene with three shells (two copper, one aluminum), our algorithm ran in six minutes on a consumer-level laptop and identified the correct materials as being among the top 10 best matches out of 8,000 possibilities.	['Marc L. Klasky', 'Jennifer L. Schei', 'Lauren A. Misurek', 'Samuel M. Gonzales', 'Elena Guardincerri', 'Michael T. McCann']	2023-03-10	null	null	null	null	['combinatorial-optimization']	['methodology']	[ 5.32175720e-01 -1.65133551e-01 9.38340127e-02 -3.03049266e-01 -1.03024852e+00 -3.04891914e-01 1.02605127e-01 4.89952326e-01 -6.31942272e-01 5.99577904e-01 -3.81468356e-01 -3.09476376e-01 -4.57809418e-01 -6.22247398e-01 -7.59080052e-01 -8.43822718e-01 1.06331855e-01 1.19355333e+00 4.63945836e-01 1.92217395e-01 2.90248901e-01 7.52452374e-01 -1.23196805e+00 -3.88277881e-02 1.07987858e-01 1.14516401e+00 3.17135155e-01 6.65483773e-01 2.74215490e-01 5.74953854e-01 -4.16883647e-01 -3.30872983e-01 4.55172211e-01 -2.21516460e-01 -8.87430072e-01 1.70608297e-01 7.31568187e-02 -6.03458464e-01 5.17531820e-02 1.04045630e+00 5.01646936e-01 -2.41974927e-02 8.28793406e-01 -8.09878886e-01 3.58523168e-02 6.15261674e-01 -7.76070654e-01 1.36456490e-01 4.72712666e-01 -2.46848628e-01 7.05514729e-01 -5.60961306e-01 3.48645002e-01 8.42954040e-01 7.17630088e-01 7.44204819e-01 -1.03012443e+00 -4.84257668e-01 -5.10964274e-01 1.52843431e-01 -1.29315758e+00 -3.32953721e-01 7.17740715e-01 -3.69780064e-01 5.85566878e-01 7.38547683e-01 4.11686927e-01 8.70178163e-01 9.94327068e-02 3.39828044e-01 1.06705868e+00 -4.93106037e-01 4.13505822e-01 3.49689990e-01 2.24103525e-01 7.60162473e-01 5.85608304e-01 -1.19372860e-01 -3.08563650e-01 -5.34113586e-01 5.89760959e-01 2.13984698e-02 -3.07812601e-01 -4.26918387e-01 -1.09882283e+00 3.95366162e-01 1.50149465e-01 3.84733155e-02 -3.17145884e-01 1.91290975e-02 2.24689439e-01 -1.22992873e-01 2.57413924e-01 6.52979910e-01 -2.51437336e-01 2.72343099e-01 -6.75223947e-01 1.10046118e-01 6.53078377e-01 5.34393787e-01 5.45781553e-01 -2.09187150e-01 4.20642376e-01 7.91161060e-01 2.19407514e-01 5.40544450e-01 1.99337557e-01 -5.17829418e-01 2.74431735e-01 1.25367701e-01 1.27901137e-01 -6.56836212e-01 -6.24837339e-01 -4.26834524e-02 -4.42089349e-01 2.81859398e-01 5.41186273e-01 5.36432974e-02 -8.24552834e-01 1.26854134e+00 5.93063593e-01 2.37505749e-01 -1.13432240e-02 1.16418612e+00 6.08806133e-01 4.50212508e-01 -1.65157273e-01 -3.54691654e-01 1.69461846e+00 -3.77927363e-01 -2.44336307e-01 -2.61598825e-01 2.95423090e-01 -9.66071129e-01 5.03152192e-01 6.66023493e-01 -1.33958495e+00 -1.11557096e-01 -1.45962501e+00 2.17071384e-01 1.67049021e-01 1.24452576e-01 4.90944773e-01 7.53165960e-01 -4.82871532e-01 5.50662279e-01 -1.09369934e+00 1.17306458e-02 1.41791865e-01 7.79491246e-01 -2.50427067e-01 -1.67264476e-01 -6.34116173e-01 7.58313894e-01 1.75424263e-01 8.12510625e-02 -6.11789465e-01 -7.39723027e-01 -5.08090854e-01 -1.38264328e-01 7.91423321e-01 -7.05949843e-01 1.26364613e+00 -3.69168162e-01 -1.21265483e+00 7.43381023e-01 3.27254534e-01 -1.99686334e-01 5.42513669e-01 -7.29757268e-03 -2.62773335e-01 6.71902895e-01 1.68673888e-01 2.61301659e-02 6.74759746e-01 -1.39313829e+00 -1.06843054e-01 -3.72546852e-01 -2.32547484e-02 1.99645162e-01 -1.28384814e-01 2.39273623e-01 -2.11565599e-01 -1.65455326e-01 6.96830511e-01 -1.02464938e+00 -4.18335944e-01 7.31831491e-02 -7.08035171e-01 4.59056914e-01 5.21646142e-01 -7.68160760e-01 4.68447804e-01 -1.96248460e+00 -8.71259496e-02 5.59830010e-01 9.74785164e-02 -3.26259524e-01 5.67030907e-01 1.30038843e-01 2.32292842e-02 -2.44022548e-01 -4.33955461e-01 -2.90399373e-01 -3.28752816e-01 -8.60071629e-02 1.28147647e-01 8.41456175e-01 -3.01540196e-01 1.10087320e-01 -8.22016358e-01 -5.30689478e-01 2.10342526e-01 3.75033349e-01 -1.72840327e-01 -6.47112876e-02 -6.24159314e-02 4.12334561e-01 -6.77053869e-01 6.68097258e-01 8.26861858e-01 -4.58205134e-01 2.65202433e-01 -6.34524524e-01 7.77752325e-02 6.90667555e-02 -1.65991092e+00 1.17415464e+00 -4.69730198e-01 2.10007429e-01 4.52166766e-01 -6.59318984e-01 4.66556340e-01 1.89638659e-01 1.03850281e+00 -5.97505510e-01 4.44716603e-01 5.73328972e-01 -9.34563950e-02 -3.62305999e-01 5.19642889e-01 -5.42701483e-01 -4.37105522e-02 6.60187542e-01 -3.17396224e-01 -3.83867681e-01 -1.81410760e-01 2.03874871e-01 1.30306339e+00 -4.52746242e-01 1.49699003e-01 -3.11723411e-01 1.25233844e-01 -2.22295597e-02 2.25619555e-01 4.99008596e-01 5.18731177e-01 8.20320249e-01 -1.85204655e-01 -2.38464460e-01 -1.09425163e+00 -1.24999225e+00 -5.16851127e-01 3.39083046e-01 5.76982439e-01 6.12877542e-03 -7.59465873e-01 -1.02006190e-01 -2.24284723e-01 3.60216528e-01 -1.64517343e-01 -1.02669701e-01 -7.32201755e-01 -1.20813870e+00 1.14073388e-01 5.42425394e-01 2.30599225e-01 -5.61200619e-01 -1.00593424e+00 1.12137631e-01 -1.78608224e-01 -1.39974928e+00 -1.68144628e-01 3.95402312e-01 -6.64523244e-01 -1.31799638e+00 -4.41559315e-01 -3.61392409e-01 1.09823632e+00 1.43780246e-01 1.10683298e+00 3.51477683e-01 -1.02076423e+00 7.34806538e-01 -7.28276819e-02 -2.71215916e-01 -6.53237641e-01 -5.86333275e-01 3.82413596e-01 -1.45925730e-01 -1.78500637e-01 -1.05800554e-01 -7.02008426e-01 4.73435342e-01 -7.60794520e-01 -9.17585865e-02 5.04163325e-01 9.04944003e-01 7.87571609e-01 3.86344910e-01 8.26817602e-02 -9.61403728e-01 1.47865176e-01 -4.03741837e-01 -8.44817162e-01 2.96836257e-01 -3.57276410e-01 1.21854655e-01 4.90665793e-01 -3.72465849e-01 -9.79219139e-01 1.99902311e-01 -3.04029703e-01 -2.15815842e-01 4.25997637e-02 1.81268841e-01 -2.19505817e-01 -7.41911888e-01 7.29936659e-01 -1.12407491e-01 -2.37649947e-01 -4.18911278e-01 -3.27049136e-01 5.65117836e-01 8.60328197e-01 -9.13333893e-01 5.06020725e-01 5.50846815e-01 6.24726474e-01 -1.05415571e+00 -7.37087548e-01 -6.51763201e-01 -1.83056578e-01 -2.70719200e-01 8.38153958e-01 -6.23272419e-01 -1.18179977e+00 2.85939723e-01 -8.92013669e-01 5.74716404e-02 -1.81456715e-01 9.84343350e-01 -4.69609857e-01 6.76791012e-01 -7.32289612e-01 -9.49394822e-01 -2.27591857e-01 -1.38624763e+00 1.15156364e+00 -5.92739396e-02 -1.29774705e-01 -7.26669848e-01 -2.33232111e-01 6.03292525e-01 5.30969054e-02 1.82790667e-01 1.07230902e+00 -3.69061142e-01 -7.23534107e-01 -2.43591800e-01 1.66098494e-02 1.59149170e-01 7.65440091e-02 -2.96512097e-01 -7.74397969e-01 -2.51950324e-01 7.35370755e-01 -2.95982182e-01 4.55794543e-01 5.12658000e-01 1.43567109e+00 -9.30621848e-02 -5.46326220e-01 4.58381146e-01 1.63516617e+00 4.24260914e-01 3.17423910e-01 2.05886260e-01 7.19345331e-01 5.21578252e-01 4.86882180e-01 4.57273841e-01 1.00677148e-01 8.42425704e-01 5.25183737e-01 -2.38725115e-02 2.05431655e-01 4.80662435e-01 -2.10579261e-01 4.72223341e-01 -2.03425720e-01 -3.46369267e-01 -1.05904078e+00 3.52167934e-01 -1.20223701e+00 -8.13526809e-01 -3.60893041e-01 2.53719258e+00 6.46051586e-01 1.39007911e-01 2.34120078e-02 4.20931846e-01 5.89733183e-01 -7.10478485e-01 -5.80365717e-01 7.44033307e-02 9.58781466e-02 4.66376394e-01 1.07820129e+00 4.32178795e-01 -8.93691182e-01 -3.09717864e-01 6.46174240e+00 5.40472090e-01 -1.04452288e+00 5.06262574e-03 6.75768495e-01 -2.54513144e-01 -3.51943851e-01 -1.86056525e-01 -7.12807655e-01 5.53550005e-01 7.86596417e-01 1.55514389e-01 1.90561906e-01 6.77734137e-01 -4.19558495e-01 -6.28376484e-01 -1.22713780e+00 1.16944408e+00 9.68419164e-02 -1.05985308e+00 -4.20855135e-01 7.35273734e-02 3.16214800e-01 -2.10719988e-01 -1.75837856e-02 -5.63972652e-01 9.56879258e-02 -5.84660590e-01 9.18585956e-01 2.05495864e-01 7.18131006e-01 -4.72385257e-01 5.27080774e-01 2.63913482e-01 -8.64440978e-01 -5.97881079e-02 -2.32897878e-01 4.78007108e-01 4.17973727e-01 8.27799261e-01 -1.11890948e+00 7.59491563e-01 6.96417272e-01 -2.40108266e-01 -1.86044440e-01 1.11780500e+00 3.78193468e-01 3.50853175e-01 -8.18873763e-01 -8.86779875e-02 -2.20496848e-01 -2.04019114e-01 6.02716386e-01 8.52678001e-01 4.92946267e-01 6.16294146e-01 2.58135706e-01 2.54628152e-01 -2.03902069e-02 -5.35171330e-02 -1.68734595e-01 4.35225010e-01 2.07726359e-01 1.11976099e+00 -1.32690084e+00 -1.40509322e-01 -2.28738576e-01 7.63900936e-01 -2.72795767e-01 -2.07936734e-01 -7.37523079e-01 8.78305174e-04 1.46663144e-01 5.54364860e-01 -7.98334479e-02 -1.29954144e-01 -3.46118569e-01 -7.68314600e-01 2.57627994e-01 -5.58702648e-01 3.63425732e-01 -6.26879275e-01 -1.29774547e+00 5.52778602e-01 5.44643283e-01 -1.14810276e+00 -3.13956171e-01 -8.61921012e-01 -2.05610946e-01 5.52046478e-01 -9.90398288e-01 -8.15735638e-01 -2.87941784e-01 2.26839364e-01 2.58691341e-01 2.27039695e-01 6.39329672e-01 6.37100995e-01 -1.66897714e-01 3.07023138e-01 1.55063570e-01 -1.85854241e-01 3.34662586e-01 -1.23194814e+00 1.34473115e-01 4.15817261e-01 6.19437918e-02 3.54530364e-01 1.00286913e+00 -5.92408061e-01 -1.79987454e+00 -4.85488325e-01 1.56830158e-02 -2.43615001e-01 5.42262077e-01 -4.91966009e-01 -7.55428910e-01 3.77455920e-01 -1.75345108e-01 1.08800992e-01 7.08258510e-01 -2.34622747e-01 1.45865669e-02 2.70483773e-02 -1.52981102e+00 1.77434027e-01 7.29223549e-01 -4.11667824e-01 -3.96757014e-02 9.69430506e-01 8.77848789e-02 -9.93621826e-01 -8.17274630e-01 4.68826413e-01 4.83935416e-01 -7.09544182e-01 1.37717092e+00 3.32278549e-03 2.36282367e-02 -9.14200321e-02 -2.15743423e-01 -7.99242079e-01 1.40599599e-02 -1.29185423e-01 1.95753038e-01 6.74304962e-01 3.39110851e-01 -3.84354621e-01 1.06492805e+00 9.03460085e-01 -3.61945122e-01 -7.97584414e-01 -9.64074254e-01 -9.06357944e-01 -5.56338489e-01 -3.30090165e-01 4.04530346e-01 6.53260767e-01 -3.43688965e-01 1.38462767e-01 -4.71274644e-01 5.69001198e-01 9.60013092e-01 1.47014797e-01 2.69474208e-01 -1.18322563e+00 -9.44152057e-01 1.44507527e-01 -5.80871940e-01 -8.55761349e-01 -9.33889449e-02 -5.25737822e-01 3.37974638e-01 -1.28663778e+00 5.15277624e-01 -9.60260987e-01 9.84292850e-02 2.89676696e-01 2.09549993e-01 4.71442223e-01 -2.23173693e-01 1.73706897e-02 -2.80934513e-01 -4.41010147e-02 8.65466595e-01 -3.98544610e-01 2.93569922e-01 4.37446475e-01 -3.36519897e-01 9.13814545e-01 4.54682410e-01 -6.68589056e-01 -3.79745305e-01 -5.36349237e-01 5.14262557e-01 6.08438075e-01 6.32189572e-01 -1.30555975e+00 3.66834581e-01 -1.68791655e-02 3.68997067e-01 -6.78305387e-01 1.03187978e+00 -1.12379026e+00 7.65149891e-01 5.57008266e-01 7.70008042e-02 3.77799049e-02 5.02582043e-02 4.12710279e-01 1.14032373e-01 -1.10907173e+00 9.08735156e-01 -3.80651057e-01 -3.28707337e-01 1.95634775e-02 8.79010092e-03 -4.82798696e-01 1.09888804e+00 -2.03930989e-01 -2.30081648e-01 6.89856932e-02 -7.74594843e-01 -1.80650651e-01 5.58202803e-01 -2.42948651e-01 8.01835895e-01 -9.39255118e-01 -5.13100922e-01 2.76578572e-02 -1.42210498e-01 3.91411483e-02 4.79708701e-01 5.61296105e-01 -7.48583376e-01 -6.94238022e-02 -6.61455914e-02 -7.38411784e-01 -1.55996907e+00 4.47531879e-01 2.24337205e-01 -7.52636418e-02 -5.78990221e-01 8.44111145e-01 4.79484983e-02 9.97306630e-02 -1.80526614e-01 -2.41622522e-01 2.16163814e-01 -2.46167302e-01 3.79029572e-01 5.93346894e-01 8.21927726e-01 -4.66922402e-01 -4.73700047e-01 7.90851593e-01 -1.15290396e-01 -1.32819355e-01 1.31040907e+00 1.26808733e-01 -5.92042245e-02 3.68745923e-01 1.13498962e+00 1.82643473e-01 -6.74376488e-01 -2.35618763e-02 -2.51736552e-01 -4.45829093e-01 -4.15867791e-02 -5.20965457e-01 -9.00979400e-01 4.50480551e-01 5.96368909e-01 2.27222726e-01 1.16237223e+00 2.84046292e-01 7.47928858e-01 5.53614736e-01 7.97551215e-01 -9.41527009e-01 2.32818589e-01 -2.45740399e-01 3.98855954e-01 -1.11597586e+00 7.86437392e-01 -8.59343231e-01 -1.58098742e-01 1.18419731e+00 5.10421395e-01 6.99647889e-02 7.12908566e-01 7.30463564e-01 -2.26505741e-01 -4.96599436e-01 -3.76912355e-01 5.12125671e-01 9.08083320e-02 1.31630108e-01 -8.25903416e-02 1.11131974e-01 1.99163146e-02 1.87061742e-01 -6.45008162e-02 -5.88900447e-01 7.62983859e-01 1.16829538e+00 -2.86324859e-01 -1.07411420e+00 -8.01093280e-01 8.53440464e-01 -8.56074572e-01 2.81667531e-01 7.40437908e-03 4.49306041e-01 -1.73816338e-01 6.92477703e-01 -4.36746851e-02 -4.99055944e-02 3.41341078e-01 -6.91992462e-01 9.27758992e-01 -6.07104182e-01 -4.24392641e-01 1.61853388e-01 1.45874128e-01 -2.44567633e-01 -4.97128695e-01 -8.99052382e-01 -1.49848080e+00 -9.79250446e-02 -8.75186801e-01 2.03261688e-01 1.11811316e+00 9.10701394e-01 -4.16522622e-01 5.00728965e-01 6.47621393e-01 -7.28388309e-01 -7.98124492e-01 -4.57366377e-01 -7.23424375e-01 8.96841064e-02 2.31370583e-01 -8.50441694e-01 -3.21674168e-01 -2.66231429e-02]	[13.0095796585083, -2.710577964782715]
1b9cfe20-673f-48f0-a63f-18f84513b063	localization-using-multi-focal-spatial	2305.01905	null	https://arxiv.org/abs/2305.01905v1	https://arxiv.org/pdf/2305.01905v1.pdf	Localization using Multi-Focal Spatial Attention for Masked Face Recognition	Since the beginning of world-wide COVID-19 pandemic, facial masks have been recommended to limit the spread of the disease. However, these masks hide certain facial attributes. Hence, it has become difficult for existing face recognition systems to perform identity verification on masked faces. In this context, it is necessary to develop masked Face Recognition (MFR) for contactless biometric recognition systems. Thus, in this paper, we propose Complementary Attention Learning and Multi-Focal Spatial Attention that precisely removes masked region by training complementary spatial attention to focus on two distinct regions: masked regions and backgrounds. In our method, standard spatial attention and networks focus on unmasked regions, and extract mask-invariant features while minimizing the loss of the conventional Face Recognition (FR) performance. For conventional FR, we evaluate the performance on the IJB-C, Age-DB, CALFW, and CPLFW datasets. We evaluate the MFR performance on the ICCV2021-MFR/Insightface track, and demonstrate the improved performance on the both MFR and FR datasets. Additionally, we empirically verify that spatial attention of proposed method is more precisely activated in unmasked regions.	['Junmo Kim', 'JungWoo Chang', 'Dongmin Cho', 'Jaesung Ahn', 'Hyeong Gwon Hong', 'Hanbyel Cho', 'Yooshin Cho']	2023-05-03	null	null	null	null	['face-recognition']	['computer-vision']	[ 4.31269586e-01 -1.53775796e-01 5.14539331e-02 -4.18682963e-01 -5.30596614e-01 -4.75544989e-01 4.42099661e-01 -5.95225096e-01 -3.29312235e-01 6.71993971e-01 1.01035960e-01 -2.51624554e-01 -2.93350909e-02 -3.40466976e-01 -4.13434505e-01 -8.26740205e-01 5.09583019e-02 -1.46730185e-01 -3.75163615e-01 -2.04460502e-01 2.61774343e-02 1.09285331e+00 -1.48592401e+00 5.17362654e-01 6.71607256e-01 1.10092688e+00 -1.31563827e-01 3.15918535e-01 3.01040649e-01 3.29749793e-01 -6.62118912e-01 -6.00109696e-01 3.40354294e-01 -3.38003874e-01 -4.16495979e-01 -8.57175980e-03 6.29069924e-01 -4.18533266e-01 -3.53497177e-01 9.92174208e-01 5.83618045e-01 -1.73854485e-01 6.58734143e-01 -1.13193846e+00 -1.10135102e+00 8.09583347e-03 -1.09248066e+00 4.33137327e-01 2.23641917e-01 2.82511890e-01 2.45975375e-01 -1.24004745e+00 2.09713414e-01 1.36869895e+00 6.04559302e-01 1.00218797e+00 -1.08860910e+00 -1.03033650e+00 1.84066489e-01 1.55140936e-01 -1.93797898e+00 -9.45777059e-01 5.43984711e-01 -3.78589004e-01 7.59340525e-01 4.34066385e-01 7.99703225e-02 1.05634344e+00 1.64650202e-01 3.60675663e-01 1.26354074e+00 -2.99722672e-01 -3.38137805e-01 1.14694029e-01 -1.10784754e-01 6.60179615e-01 2.84935921e-01 3.89303982e-01 -4.07752335e-01 -8.93733129e-02 7.55114257e-01 3.06402534e-01 -5.90137780e-01 3.83888155e-01 -6.22218788e-01 5.49360216e-01 3.51434082e-01 3.78863305e-01 -3.49203110e-01 -1.86336085e-01 -6.49965778e-02 -5.07452432e-03 5.82461476e-01 -1.51135966e-01 -2.30625957e-01 4.71887380e-01 -1.01070786e+00 -1.88069329e-01 7.93543607e-02 4.76728112e-01 6.37272954e-01 1.14781119e-01 -5.37687361e-01 9.57820714e-01 4.86268371e-01 9.45336044e-01 2.43312687e-01 -4.11995977e-01 1.44599304e-01 3.62498641e-01 2.00765714e-01 -1.23907673e+00 -2.51766771e-01 -3.61225277e-01 -1.24046409e+00 1.54655889e-01 1.01925641e-01 -7.79403076e-02 -1.35594451e+00 1.78597629e+00 2.15244338e-01 5.42823970e-01 3.00417636e-02 9.41449881e-01 9.15151000e-01 3.10079247e-01 -3.17474967e-03 -3.51981819e-01 1.46688139e+00 -5.20421982e-01 -9.65534866e-01 -1.65894836e-01 1.35682791e-01 -7.46309936e-01 6.88498557e-01 7.28140920e-02 -7.56605208e-01 -7.74841845e-01 -1.00681210e+00 1.29353896e-01 -3.47389519e-01 3.46888363e-01 1.35528117e-01 1.23781264e+00 -1.17437100e+00 1.39845252e-01 -4.83644366e-01 -1.18227735e-01 8.64702106e-01 6.94035769e-01 -7.08043396e-01 -3.75536233e-01 -1.20929420e+00 8.33200037e-01 -2.79897153e-01 7.58320034e-01 -1.03346884e+00 -6.63987160e-01 -6.81962192e-01 -3.91515810e-03 3.22800018e-02 9.89675075e-02 4.50430661e-01 -8.94560397e-01 -1.12640238e+00 1.21775746e+00 -5.39644778e-01 -1.34629354e-01 1.79215446e-01 -1.61342978e-01 -8.96523654e-01 -1.22329826e-02 -2.08116651e-01 6.78941548e-01 1.15967703e+00 -1.27162671e+00 -2.19099894e-01 -7.75781155e-01 -2.61105657e-01 -1.70567498e-01 -2.37078279e-01 5.07881463e-01 -3.08908582e-01 -7.17157006e-01 -3.56330186e-01 -6.95170045e-01 1.70372009e-01 -2.09480569e-01 -4.23596352e-01 -1.08902201e-01 1.02174687e+00 -1.09657001e+00 9.77387369e-01 -2.57965994e+00 -2.46820524e-01 2.41398051e-01 2.06291065e-01 6.43425703e-01 -4.63839382e-01 -2.46033683e-01 -2.98346162e-01 3.08391511e-01 -3.28853756e-01 -2.90944517e-01 -4.09647137e-01 -1.14358820e-01 -4.06914473e-01 8.58531594e-01 6.27251625e-01 9.13222671e-01 -2.36605376e-01 -2.98461735e-01 -3.38158682e-02 1.03955984e+00 -4.45178121e-01 1.89957500e-01 1.89139113e-01 6.26856863e-01 -1.36569351e-01 1.00444531e+00 1.32726395e+00 -3.99212427e-02 7.35395728e-03 -3.49933982e-01 1.57840133e-01 -4.50892836e-01 -8.96894276e-01 1.07282841e+00 -1.04631893e-01 5.08438408e-01 4.57447022e-01 -6.66890562e-01 9.59828198e-01 4.63092595e-01 2.47915551e-01 -7.72401035e-01 3.38753670e-01 1.35263512e-02 1.51813522e-01 -4.47736740e-01 2.13879555e-01 -1.61293224e-01 4.37168300e-01 2.57135063e-01 -2.72292584e-01 6.63169324e-01 -4.92690474e-01 -3.32731217e-01 7.10426509e-01 -2.21236706e-01 1.17717152e-02 -3.28617454e-01 8.95007312e-01 -5.63101590e-01 6.10014975e-01 4.86715943e-01 -4.92571354e-01 7.83113003e-01 5.37926704e-02 -2.97741979e-01 -3.27187836e-01 -1.00708652e+00 -4.15686429e-01 9.60634232e-01 3.52540705e-03 1.22895718e-01 -8.94347787e-01 -8.20780694e-01 1.23754464e-01 2.63851434e-01 -9.73209739e-01 -2.43607804e-01 -7.09153473e-01 -9.76409078e-01 9.22789991e-01 3.09475601e-01 6.77135766e-01 -1.11324227e+00 -1.53921708e-01 -3.34219813e-01 8.53008125e-03 -9.51070130e-01 -8.29821646e-01 -4.28321928e-01 -2.83880174e-01 -1.12713003e+00 -1.00781465e+00 -7.89925635e-01 8.31613302e-01 3.96577805e-01 5.31918406e-01 4.95288968e-01 -7.65957594e-01 1.26872137e-01 -1.09212108e-01 -3.81918401e-01 1.37636373e-02 -1.26822025e-01 2.65459687e-01 8.92660677e-01 5.49486697e-01 -4.65274304e-02 -8.67895603e-01 7.12358534e-01 -7.50497580e-01 -4.35809940e-01 5.13642609e-01 7.65638530e-01 4.02265608e-01 -2.84896880e-01 9.66323793e-01 -6.25673771e-01 3.78912807e-01 -3.42352539e-01 -5.25321305e-01 5.61064005e-01 -2.38212943e-01 -4.06261653e-01 1.85223386e-01 -3.95760655e-01 -1.15467441e+00 -2.47843750e-03 -2.52314329e-01 -6.66824460e-01 -2.11715341e-01 4.80632223e-02 -5.93431711e-01 -2.80211926e-01 5.43943107e-01 3.23727310e-01 1.54624566e-01 -5.01222730e-01 1.07171975e-01 1.15721917e+00 5.46689570e-01 -1.52248710e-01 9.04116750e-01 7.46876419e-01 -1.81486085e-01 -1.04734004e+00 -5.72640538e-01 -2.56215483e-01 -2.90028453e-01 -1.10492222e-01 1.04059196e+00 -8.90367210e-01 -9.33666766e-01 7.16430843e-01 -1.06060731e+00 -1.11308340e-02 2.02091873e-01 2.33359635e-01 4.68810424e-02 1.53389141e-01 -3.53299826e-01 -1.13077521e+00 -3.89102459e-01 -1.21297669e+00 1.26661193e+00 2.91790038e-01 1.06786136e-02 -5.44594407e-01 -1.39619753e-01 3.83951187e-01 7.74055719e-01 1.08243570e-01 5.38542509e-01 -4.68160242e-01 -5.36477983e-01 -1.94463193e-01 -6.08286917e-01 3.48262846e-01 8.41125965e-01 -1.12580888e-01 -1.58896208e+00 -6.21047437e-01 8.37759152e-02 -8.89129490e-02 1.02456594e+00 3.14959943e-01 1.33208871e+00 -2.72116482e-01 -5.54069936e-01 8.35688591e-01 1.09183502e+00 6.26823843e-01 1.08659708e+00 -4.31886494e-01 6.84658766e-01 7.17104197e-01 3.14947158e-01 9.69199464e-02 -2.45879233e-01 6.75582290e-01 1.92586750e-01 -4.41014349e-01 -1.47156090e-01 1.44834891e-01 2.24458441e-01 6.93217516e-02 -1.65325418e-01 -2.14557230e-01 -6.89670265e-01 4.90432411e-01 -1.27869999e+00 -9.68977928e-01 4.84537810e-01 2.12276816e+00 6.18356943e-01 -4.65692252e-01 -1.31249875e-01 -1.92682028e-01 1.06744206e+00 2.49175414e-01 -5.55137455e-01 -1.04584031e-01 -4.27111715e-01 6.16245031e-01 2.85611778e-01 7.07546830e-01 -1.07158351e+00 9.45245028e-01 6.23164511e+00 8.44313681e-01 -1.38750839e+00 3.51271093e-01 1.27146041e+00 -4.02682841e-01 -1.37344506e-02 -7.65260339e-01 -1.01789844e+00 4.73171055e-01 7.92731166e-01 2.88296223e-01 6.34441793e-01 3.69974196e-01 1.94687754e-01 3.70737195e-01 -8.93158615e-01 1.28646600e+00 4.26062584e-01 -1.22905743e+00 -1.30623400e-01 2.33181074e-01 5.31126320e-01 -3.72893244e-01 6.30481422e-01 1.46372393e-01 -1.79053515e-01 -1.93412244e+00 1.35821447e-01 5.74684262e-01 1.48250246e+00 -8.43348145e-01 8.19191098e-01 -2.18705416e-01 -1.15741718e+00 1.09137306e-02 -2.24158227e-01 4.02914494e-01 -1.74155101e-01 3.17508936e-01 -5.05333006e-01 3.78105432e-01 6.96183383e-01 3.32583219e-01 -5.56485474e-01 5.23038149e-01 4.48615253e-02 3.09406132e-01 -8.98638666e-02 3.65040779e-01 -3.72207940e-01 7.99225271e-02 1.76644400e-01 1.06439257e+00 2.68279880e-01 3.07751328e-01 -2.80644983e-01 9.38254416e-01 -4.20438886e-01 -5.34013845e-02 -6.37474597e-01 1.23969959e-02 4.12643611e-01 1.17476737e+00 -3.89855206e-01 7.49982670e-02 -3.89745206e-01 1.06612766e+00 1.66520536e-01 5.42491078e-01 -1.05482423e+00 -1.29716948e-01 1.16275239e+00 2.29871303e-01 3.68948907e-01 1.77646890e-01 -1.42172217e-01 -8.74415398e-01 -1.16232494e-02 -1.07422888e+00 2.58706301e-01 -4.83895272e-01 -1.29761088e+00 9.84660685e-01 -2.41505519e-01 -6.75819874e-01 1.80718824e-01 -6.54361784e-01 -3.59388024e-01 1.49807000e+00 -1.62554359e+00 -1.09309947e+00 -3.28032881e-01 7.73359239e-01 5.08815907e-02 -3.91640186e-01 7.61406422e-01 5.93350053e-01 -9.58265126e-01 1.33847117e+00 -9.69959199e-02 4.30192798e-01 6.80862427e-01 -3.55335355e-01 3.41389596e-01 8.80624831e-01 -1.19057283e-01 9.45537210e-01 8.41781646e-02 -8.36692214e-01 -1.27462065e+00 -1.38110578e+00 5.17214954e-01 -3.28844279e-01 -6.33731782e-02 -5.14721155e-01 -9.52133656e-01 6.55546784e-01 2.86366105e-01 3.35811079e-01 6.19924366e-01 -2.74779558e-01 -5.83060622e-01 -4.37352896e-01 -1.70278168e+00 4.79498059e-01 1.07554710e+00 -7.96698868e-01 -2.18393773e-01 2.76604980e-01 5.36967814e-01 5.26483431e-02 -5.45158863e-01 7.46512532e-01 6.15211248e-01 -7.53773510e-01 9.93603647e-01 -7.35580266e-01 2.44754925e-02 -4.87735629e-01 -4.16999668e-01 -9.48403895e-01 -2.70626009e-01 -7.00539529e-01 1.13333561e-01 1.38925827e+00 3.07792574e-01 -8.99717152e-01 8.80013645e-01 3.88983577e-01 1.72283351e-01 -5.37580848e-01 -1.17737877e+00 -7.50270069e-01 -9.29564312e-02 4.52065058e-02 7.98916698e-01 9.75500941e-01 -5.52370608e-01 -1.02414176e-01 -6.52288377e-01 6.12624168e-01 6.22355998e-01 -7.03110397e-02 3.49211812e-01 -8.77680421e-01 -5.74931502e-02 -2.41253391e-01 -3.24061483e-01 -5.93295276e-01 1.94960833e-01 -6.70317173e-01 2.15258580e-02 -8.31438839e-01 2.41319969e-01 -2.85092890e-01 -6.13255262e-01 6.30470037e-01 -3.71034354e-01 7.88284183e-01 8.80985484e-02 1.42991273e-02 -8.89983377e-04 3.98925066e-01 1.07411146e+00 -2.37314746e-01 -9.65747237e-03 -3.08567345e-01 -8.12329412e-01 3.15824807e-01 8.59770179e-01 -2.28158578e-01 -2.05771312e-01 -4.13076729e-01 -4.07181740e-01 -2.26385742e-01 3.67850900e-01 -8.32911909e-01 -4.70970012e-02 -2.18734100e-01 8.26048374e-01 -5.01356840e-01 5.70563674e-01 -7.58694828e-01 2.46905297e-01 5.10679603e-01 -1.21743465e-03 -1.00196442e-02 5.46845794e-01 3.75369310e-01 1.58565324e-02 2.60542691e-01 1.16674984e+00 2.38001212e-01 -1.88970625e-01 5.55449724e-01 -2.87652373e-01 -3.42740342e-02 1.07662737e+00 -3.51065069e-01 -6.56027615e-01 4.25945851e-04 -6.25087678e-01 3.14275958e-02 2.03719944e-01 5.41972220e-01 1.06218445e+00 -1.25646126e+00 -1.00733709e+00 9.27712083e-01 9.24583431e-03 -4.68974411e-01 7.78375626e-01 7.58915484e-01 -4.08224136e-01 5.03435314e-01 -3.71625423e-01 -4.57073867e-01 -1.61956275e+00 8.90750766e-01 6.80639982e-01 2.44343996e-01 -1.49336904e-01 1.06253362e+00 6.96429908e-01 -1.60063639e-01 2.47943223e-01 1.03875272e-01 -3.01492780e-01 -6.92166686e-02 1.18706107e+00 1.55983016e-01 2.23108023e-01 -9.34719145e-01 -7.75900066e-01 7.17077851e-01 -3.87675673e-01 1.93972737e-01 1.01246881e+00 -5.61947301e-02 -2.43471235e-01 -3.48873883e-01 1.17850804e+00 2.80858636e-01 -1.11376905e+00 2.15312783e-02 -2.52167732e-01 -7.42051661e-01 -1.31815940e-01 -8.02677870e-01 -1.47259593e+00 8.98694754e-01 1.30396032e+00 -6.32181540e-02 1.38901675e+00 -3.49744782e-02 4.74893183e-01 -1.71426386e-01 1.45138800e-01 -3.09543133e-01 -1.55450717e-01 1.49203911e-01 1.11649036e+00 -1.21422338e+00 -2.29705095e-01 -6.01111948e-01 -2.53008395e-01 4.95980680e-01 7.13042378e-01 1.90160677e-01 9.25589144e-01 3.72365057e-01 2.81891584e-01 -2.66636223e-01 -2.86599845e-01 -6.17401116e-02 5.30375183e-01 8.53321195e-01 3.87105256e-01 -1.10604549e-02 1.66670427e-01 6.42711878e-01 1.78076729e-01 -9.71854478e-02 2.96550454e-03 7.19543636e-01 -2.00502187e-01 -7.81391859e-01 -6.83431685e-01 4.80553925e-01 -8.24153721e-01 -1.93142161e-01 -6.67782426e-01 7.01212525e-01 3.47939521e-01 1.12955678e+00 2.01686382e-01 -4.98497754e-01 1.40001193e-01 1.60001010e-01 5.16474366e-01 -4.58502859e-01 -5.92698216e-01 4.83025424e-03 -2.58217424e-01 -4.44664717e-01 -3.09438556e-01 -3.66873056e-01 -8.09498727e-01 -5.05225956e-01 -2.76840150e-01 -8.86128917e-02 2.93952048e-01 8.07945430e-01 6.96042955e-01 3.57281148e-01 7.19287574e-01 -5.30372262e-01 -2.56502151e-01 -1.15279520e+00 -6.08308017e-01 2.64526188e-01 6.33312166e-01 -6.82827830e-01 -2.16784969e-01 -1.01758242e-01]	[13.124866485595703, 0.6656903624534607]
162c53ca-450d-4500-8286-71d0229012ec	contravening-esotery-cryptanalysis-of	1606.06047	null	http://arxiv.org/abs/1606.06047v1	http://arxiv.org/pdf/1606.06047v1.pdf	Contravening Esotery: Cryptanalysis of Knapsack Cipher using Genetic Algorithms	Cryptanalysis of knapsack cipher is a fascinating problem which has eluded the computing fraternity for decades. However, in most of the cases either the time complexity of the proposed algorithm is colossal or an insufficient number of samples have been taken for verification. The present work proposes a Genetic Algorithm based technique for cryptanalysis of knapsack cipher. The experiments conducted prove the validity of the technique. The results prove that the technique is better than the existing techniques. An extensive review has been carried out in order to find the gaps in the existing techniques. The work paves the way of the application of computational intelligence techniques to the discipline of cryptanalysis.	['Harmeet Singh']	2016-06-20	null	null	null	null	['cryptanalysis']	['miscellaneous']	[ 3.66322786e-01 -3.02911788e-01 7.73189887e-02 1.11140171e-02 3.38530429e-02 -7.16596603e-01 2.61470258e-01 3.90774637e-01 -5.16262293e-01 9.94205117e-01 -3.63320887e-01 -7.40338743e-01 -5.94163239e-01 -8.72934580e-01 -2.52678990e-01 -9.81879532e-01 -1.89888969e-01 1.73995793e-01 1.22739092e-01 -4.20174837e-01 9.72461462e-01 3.72828811e-01 -1.60917389e+00 1.50342256e-01 5.62137008e-01 7.74185061e-01 9.59584042e-02 1.01803350e+00 -1.90758049e-01 5.81071079e-01 -7.34150410e-01 -3.38165551e-01 5.62825739e-01 -6.34135723e-01 -9.42221701e-01 -2.10699245e-01 -3.29904974e-01 3.28985989e-01 1.24649495e-01 1.14077532e+00 1.57281265e-01 -1.08518362e-01 5.41939139e-01 -1.28255963e+00 -9.40258503e-02 4.77745324e-01 -4.24340934e-01 4.87687737e-01 5.53633511e-01 -2.48613775e-01 6.67064428e-01 -1.86191082e-01 3.31650466e-01 8.12336445e-01 5.77411294e-01 -1.87496349e-01 -5.22272646e-01 -7.74914026e-01 -3.17238748e-01 7.18705416e-01 -1.31913197e+00 1.48955330e-01 6.08369946e-01 8.27003270e-02 8.95431995e-01 4.55509126e-01 8.14581037e-01 -3.65795679e-02 8.04409802e-01 2.88689375e-01 1.70570016e+00 -8.31202745e-01 3.24221194e-01 1.22738793e-01 3.14318359e-01 3.52815181e-01 1.03467846e+00 2.29756042e-01 -1.71424478e-01 -1.49834543e-01 -7.08427653e-03 1.60161760e-02 -4.89120893e-02 -6.07818514e-02 -6.00868344e-01 8.78014982e-01 -1.47464499e-02 9.51488972e-01 -2.82128096e-01 -1.43019706e-01 6.16196156e-01 5.03716767e-01 -2.19619632e-01 6.34096086e-01 -3.03089201e-01 -4.40861195e-01 -8.99342358e-01 3.79034251e-01 9.80480433e-01 5.48073113e-01 4.50611830e-01 2.20277570e-02 6.47562265e-01 -1.93092376e-01 1.33611515e-01 2.54356384e-01 2.86762774e-01 -2.08014846e-01 2.64547080e-01 7.49612272e-01 -8.76472592e-02 -1.08966851e+00 -1.94826156e-01 -2.02747390e-01 -5.66111863e-01 5.28083563e-01 4.85530674e-01 -2.72318274e-01 -6.41776860e-01 8.13587666e-01 3.64295244e-01 2.29898542e-02 5.85397720e-01 4.91454363e-01 5.17866790e-01 9.86798108e-01 -8.90008137e-02 -2.67397374e-01 1.31153035e+00 -7.14364886e-01 -9.18086648e-01 4.54293013e-01 2.83324063e-01 -1.34234130e+00 2.70660520e-01 9.19447839e-01 -9.02880073e-01 -2.51333565e-01 -1.46548104e+00 6.12227082e-01 -5.72178245e-01 -3.14268202e-01 9.36707795e-01 1.57521069e+00 -7.08150864e-01 4.46521223e-01 -4.95291531e-01 -2.48451650e-01 3.47907655e-02 9.01660681e-01 -3.66572380e-01 -1.87031776e-01 -8.41787040e-01 8.04386616e-01 9.61073697e-01 3.43135446e-01 -1.62493736e-01 -2.31786862e-01 -4.30680215e-01 -1.31851360e-01 3.75837266e-01 -5.99296577e-02 7.69711554e-01 -1.08180225e+00 -1.34833825e+00 6.12961829e-01 1.54420152e-01 -5.27418375e-01 3.25319529e-01 5.56467295e-01 -3.04952383e-01 5.02312556e-03 -4.42292303e-01 -2.69203871e-01 2.67233282e-01 -7.87272573e-01 -1.04398322e+00 -3.69298488e-01 6.83117211e-02 -1.01227565e-02 5.59270307e-02 1.06717452e-01 1.84434608e-01 -6.71925664e-01 2.04108968e-01 -8.97675753e-01 -3.66986990e-01 -8.55683267e-01 1.77051667e-02 -1.71051193e-02 8.29630733e-01 -4.74885255e-01 1.28344631e+00 -1.83318830e+00 -1.86546713e-01 8.48738968e-01 -3.76429707e-01 6.11816049e-01 4.42391574e-01 1.33892715e+00 -9.15097073e-02 9.72928926e-02 -1.52526408e-01 6.02780282e-01 -1.03198491e-01 2.42866173e-01 1.46974280e-01 5.85481286e-01 -4.26350504e-01 2.23236069e-01 -5.55240393e-01 -3.15245867e-01 1.04323566e-01 1.97915271e-01 -2.52966642e-01 -1.09408200e-01 1.35459140e-01 -2.75710458e-03 -5.80911517e-01 7.76114285e-01 1.19343758e+00 4.24924463e-01 4.24974054e-01 7.51194283e-02 -4.61521298e-01 8.83495137e-02 -1.82160211e+00 9.54287589e-01 2.73606349e-02 3.34764332e-01 -2.18093842e-01 -1.23007870e+00 8.88490617e-01 4.47770327e-01 3.35320711e-01 -3.94893825e-01 7.24509358e-01 4.56291020e-01 4.20747697e-01 -5.74540913e-01 6.31818771e-01 -2.61258304e-01 1.34805799e-01 7.13047802e-01 -3.45491707e-01 -1.13105677e-01 6.16873324e-01 -2.55516976e-01 9.00583267e-01 -1.40886297e-02 8.14453781e-01 -3.78892273e-01 1.10485590e+00 4.62111086e-01 4.29701865e-01 4.98309702e-01 -1.08584128e-01 -2.86343813e-01 4.13802058e-01 -5.53214550e-01 -1.10446179e+00 -4.90140647e-01 -1.87271506e-01 2.43990839e-01 3.04059476e-01 -1.95602402e-01 -8.48758399e-01 -3.41904461e-01 -2.42313549e-01 2.99430013e-01 -4.14251506e-01 2.08724752e-01 -4.71492767e-01 -1.04982352e+00 6.34757102e-01 -5.52839898e-02 8.63605618e-01 -1.04488850e+00 -8.60341311e-01 3.33240569e-01 4.61149395e-01 -8.90624464e-01 4.58888650e-01 3.39121670e-02 -1.11038888e+00 -1.41454601e+00 -1.58411011e-01 -1.07528579e+00 9.23093259e-01 3.24751228e-01 5.80794930e-01 6.56574130e-01 -4.79072243e-01 -1.25502199e-01 -9.81421411e-01 -1.07288945e+00 -6.58402920e-01 7.22075924e-02 -3.28650177e-01 -4.87986743e-01 8.72168243e-01 -3.13102543e-01 -3.29971254e-01 1.46139517e-01 -9.97810900e-01 -4.22084123e-01 7.95937777e-01 6.39158785e-01 1.23167120e-01 1.34316182e+00 5.42968214e-01 -9.50348914e-01 8.09226453e-01 -3.72057289e-01 -1.19199085e+00 2.75329977e-01 -7.46521711e-01 1.47805721e-01 7.23624527e-01 -1.08940259e-01 -7.57366478e-01 -1.39349714e-01 -1.02118671e-01 5.35026073e-01 -2.45919377e-02 4.77116913e-01 -3.47875357e-02 -8.32221866e-01 2.10236296e-01 4.62782443e-01 2.37924993e-01 -2.85679042e-01 -4.91312981e-01 7.74277806e-01 3.46778691e-01 -3.50445300e-01 8.27448785e-01 2.33447656e-01 5.13596952e-01 -6.53338134e-01 1.49869046e-03 -7.02659786e-01 5.36022596e-02 -1.62997812e-01 4.37418669e-01 -1.52126595e-01 -1.14689398e+00 6.57948554e-01 -6.74556613e-01 3.51710886e-01 2.83132315e-01 5.96722841e-01 -1.38744473e-01 6.11595392e-01 -2.64054596e-01 -1.26150846e+00 -5.52035034e-01 -1.36657941e+00 -5.36111416e-03 4.15210962e-01 7.94514939e-02 -7.75889039e-01 5.16469106e-02 4.32603508e-01 2.28619695e-01 4.72929329e-01 8.56531858e-01 -7.14349866e-01 -6.40017748e-01 -8.46373618e-01 2.22966477e-01 2.39673108e-01 2.21549898e-01 1.46263450e-01 -6.35209680e-01 -1.93661302e-01 4.21858966e-01 -9.98106822e-02 2.21635148e-01 1.16690183e-02 7.19144821e-01 -1.56569734e-01 -1.30992964e-01 3.58418614e-01 2.00475311e+00 8.56649101e-01 9.51303601e-01 9.42562580e-01 -2.18525201e-01 3.77386987e-01 9.58476484e-01 5.26773930e-01 6.78495988e-02 2.21102983e-01 3.13574463e-01 3.55472565e-01 5.10660946e-01 1.76022708e-01 -6.14973791e-02 6.93967044e-01 -4.40397829e-01 -3.42720419e-01 -9.10436153e-01 2.94127703e-01 -1.25591910e+00 -9.64883804e-01 -5.07043302e-01 2.05608940e+00 4.40691769e-01 1.48338631e-01 5.67800216e-02 1.37984776e+00 6.10486269e-01 -3.32417488e-01 3.18941176e-01 -1.31012011e+00 -3.65792736e-02 7.40483999e-01 9.16856110e-01 5.93069971e-01 -7.74866223e-01 6.78910136e-01 6.32589102e+00 7.10506082e-01 -1.10683417e+00 -3.48116696e-01 1.67649940e-01 5.25787234e-01 1.20275736e-01 4.66902614e-01 -5.76032937e-01 5.09988308e-01 7.62303710e-01 -3.69539768e-01 5.16087711e-01 5.19650042e-01 4.56138477e-02 -9.76246119e-01 -3.89861047e-01 6.65689826e-01 2.20582142e-01 -1.16306996e+00 -1.88455895e-01 1.95013210e-01 7.84457743e-01 -6.37711048e-01 -6.15451187e-02 -1.22302853e-01 -4.34023775e-02 -1.02719593e+00 2.50036687e-01 1.54703677e-01 -1.07461192e-01 -1.41595662e+00 1.47366381e+00 4.04723167e-01 -7.91389346e-01 -2.60450631e-01 -4.94757205e-01 -5.00892580e-01 -2.24597231e-01 1.61335431e-02 -1.45326388e+00 1.02763617e+00 4.32374686e-01 -3.46477330e-01 -3.59829009e-01 1.64533508e+00 1.45383421e-02 6.09854341e-01 -4.44869459e-01 -8.32192898e-01 6.71831667e-01 -5.42682111e-01 3.74554366e-01 9.79242980e-01 2.95108169e-01 2.14170814e-01 -3.14446718e-01 9.44687277e-02 4.35236692e-01 7.14354038e-01 -5.33886075e-01 -2.05479145e-01 3.71019870e-01 8.10482025e-01 -1.02442718e+00 -2.46252000e-01 -3.06385010e-01 4.47664946e-01 -5.35218954e-01 -1.51634350e-01 -3.12202215e-01 -7.61806309e-01 9.37654153e-02 2.26397905e-02 4.14991945e-01 -1.95850492e-01 -4.76635218e-01 -4.08193350e-01 -8.05075094e-03 -1.15736747e+00 5.23169637e-01 -1.61546201e-01 -4.53631133e-01 4.27771270e-01 2.50258267e-01 -9.91934597e-01 -2.24447623e-01 -8.30427825e-01 -7.72150278e-01 7.55564272e-01 -1.32105386e+00 -8.01168978e-01 -1.50809465e-02 1.84131965e-01 3.27688217e-01 -6.57960892e-01 8.93662214e-01 4.45894003e-02 -2.60184884e-01 4.00505513e-01 3.60198200e-01 -2.20270991e-01 1.03467748e-01 -8.03895831e-01 -9.92270112e-02 1.28086603e+00 -1.74901351e-01 7.23868608e-01 1.22904432e+00 -6.25616431e-01 -1.89066672e+00 -1.21441685e-01 1.01865101e+00 2.24015445e-01 5.29587448e-01 -2.87214667e-02 -4.75700945e-01 1.70505419e-01 5.85069299e-01 -3.71673793e-01 8.24354649e-01 -2.51772046e-01 2.92070895e-01 -2.20102623e-01 -1.47753549e+00 1.32525176e-01 -7.87226558e-02 2.24847525e-01 -6.42731190e-01 -4.40390445e-02 -2.15032935e-01 -3.91733289e-01 -6.75821781e-01 1.52065143e-01 6.10046148e-01 -1.17682970e+00 5.80634058e-01 -3.09625775e-01 -2.26687878e-01 -5.36658883e-01 4.11714092e-02 -5.99367678e-01 2.04900816e-01 -8.83158386e-01 5.56119919e-01 8.59568179e-01 3.95523310e-01 -6.97875857e-01 1.10870183e+00 1.12049080e-01 2.54521400e-01 -6.97456896e-01 -8.63047898e-01 -6.31238461e-01 -3.57022136e-02 -4.61443782e-01 8.35695803e-01 7.12426305e-01 1.99997678e-01 -2.44033530e-01 1.60074290e-02 1.30922154e-01 6.06295288e-01 1.63103089e-01 7.20812201e-01 -9.35759187e-01 -3.77418339e-01 -1.30282819e-01 -1.10893774e+00 1.23643570e-01 -2.09116906e-01 -4.53530401e-01 -5.53568423e-01 -1.24992955e+00 -3.86715904e-02 -6.14641428e-01 -1.90067321e-01 1.01737015e-01 2.79666036e-02 3.19939107e-01 2.64904797e-01 -3.15470517e-01 -7.83730447e-02 -4.12223876e-01 9.82195914e-01 1.49537429e-01 -9.40769538e-02 3.53418231e-01 -6.17952228e-01 5.29810131e-01 1.13960242e+00 -7.73442984e-01 -6.01601124e-01 1.68927804e-01 6.54062450e-01 -1.60398986e-02 -1.29948735e-01 -1.21079302e+00 3.09644520e-01 -3.26986104e-01 5.02366796e-02 -7.80946970e-01 -7.47713819e-02 -1.22809017e+00 4.72219408e-01 9.70786154e-01 2.60312557e-01 7.29588807e-01 6.67071640e-02 4.25624847e-01 -3.82910728e-01 -9.17489350e-01 7.01293826e-01 -3.00218046e-01 -7.07208872e-01 -2.30514809e-01 -5.49716473e-01 -3.17450345e-01 1.48302412e+00 -9.64124322e-01 1.72110915e-01 4.25511003e-02 -1.35598972e-01 -2.51813605e-02 6.48493052e-01 -6.45945519e-02 4.75362659e-01 -6.60147190e-01 -4.48318422e-01 2.14963317e-01 -1.48685008e-01 -3.15200776e-01 1.07650027e-01 7.18813658e-01 -1.58002293e+00 7.85972476e-01 -8.19714844e-01 -3.05939522e-02 -1.87824559e+00 6.72303557e-01 -3.75651047e-02 -2.82725513e-01 -4.33096260e-01 4.63713914e-01 -8.46437454e-01 3.91747296e-01 1.01469070e-01 -1.68679446e-01 -4.84367043e-01 -2.74528265e-01 5.39593637e-01 5.32077193e-01 2.64777511e-01 -4.52166587e-01 -2.25525513e-01 6.23318195e-01 1.18194692e-01 -1.76584780e-01 1.47539032e+00 1.92802310e-01 -6.99029028e-01 -1.94880385e-02 9.35188174e-01 1.70797840e-01 -2.20843479e-01 3.17073166e-01 1.45515338e-01 -7.20044613e-01 -2.56024748e-02 -6.53068900e-01 -4.68667299e-01 4.92284447e-01 6.36325121e-01 2.32172772e-01 1.39130914e+00 -9.46473598e-01 6.27523065e-01 6.78110242e-01 5.29204071e-01 -1.23625338e+00 -6.78022444e-01 4.49357659e-01 2.06660718e-01 -9.25729990e-01 3.72632593e-01 -5.62499464e-01 -4.50688928e-01 1.51303124e+00 -1.85713451e-02 -4.48163658e-01 6.13854289e-01 8.50710154e-01 -1.80822507e-01 4.62400578e-02 -5.39039612e-01 1.59259960e-01 -2.49204189e-01 6.06461048e-01 4.61081445e-01 -6.61150441e-02 -1.37052870e+00 4.13947374e-01 -6.07467234e-01 2.54489988e-01 9.53731060e-01 1.66951919e+00 -5.63415706e-01 -1.93093383e+00 -1.24265742e+00 -3.59222442e-02 -1.10586822e+00 1.05730165e-02 -4.02423680e-01 1.29698741e+00 4.33276951e-01 1.05322003e+00 -4.01496023e-01 -2.41542220e-01 -9.21495929e-02 -2.14813873e-01 7.60558188e-01 8.52138698e-02 -9.58537161e-01 -2.15421677e-01 1.98115811e-01 2.73081124e-01 -6.90843940e-01 -7.09404826e-01 -1.18283737e+00 -7.41729975e-01 -4.17059273e-01 1.06310523e+00 1.25834680e+00 8.80482197e-01 -3.21305215e-01 1.01520248e-01 6.01911306e-01 -2.34672770e-01 -1.78197607e-01 -6.42373443e-01 -5.65295398e-01 -2.45755166e-01 -5.09644896e-02 -3.79582584e-01 -1.10279664e-01 -1.05913453e-01]	[5.749305248260498, 4.515336513519287]
24cdd531-8367-4ce1-b906-0f2cc4e26917	how-to-train-your-agent-to-read-and-write	2101.00916	null	https://arxiv.org/abs/2101.00916v1	https://arxiv.org/pdf/2101.00916v1.pdf	How to Train Your Agent to Read and Write	Reading and writing research papers is one of the most privileged abilities that a qualified researcher should master. However, it is difficult for new researchers (\eg{students}) to fully {grasp} this ability. It would be fascinating if we could train an intelligent agent to help people read and summarize papers, and perhaps even discover and exploit the potential knowledge clues to write novel papers. Although there have been existing works focusing on summarizing (\emph{i.e.}, reading) the knowledge in a given text or generating (\emph{i.e.}, writing) a text based on the given knowledge, the ability of simultaneously reading and writing is still under development. Typically, this requires an agent to fully understand the knowledge from the given text materials and generate correct and fluent novel paragraphs, which is very challenging in practice. In this paper, we propose a Deep ReAder-Writer (DRAW) network, which consists of a \textit{Reader} that can extract knowledge graphs (KGs) from input paragraphs and discover potential knowledge, a graph-to-text \textit{Writer} that generates a novel paragraph, and a \textit{Reviewer} that reviews the generated paragraph from three different aspects. Extensive experiments show that our DRAW network outperforms considered baselines and several state-of-the-art methods on AGENDA and M-AGENDA datasets. Our code and supplementary are released at https://github.com/menggehe/DRAW.	['Qi Wu', 'Mingkui Tan', 'Guanghui Xu', 'Mengge He', 'Li Liu']	2021-01-04	null	null	null	null	['kg-to-text']	['natural-language-processing']	[ 2.33073160e-01 6.93458736e-01 -2.03582793e-01 -1.93604857e-01 -4.89591092e-01 -9.16360140e-01 6.87791467e-01 1.89247116e-01 -5.24824783e-02 1.00079703e+00 1.25990823e-01 -6.42751515e-01 -3.40105832e-01 -1.08824515e+00 -9.57340419e-01 -3.37438941e-01 4.88214195e-01 6.37418926e-01 6.70935139e-02 -1.21100739e-01 5.22536099e-01 2.28750229e-01 -1.24110222e+00 1.81530043e-01 1.27811301e+00 2.32105657e-01 7.14744687e-01 8.45644891e-01 -4.12184149e-01 8.83670807e-01 -9.51376855e-01 -8.25236380e-01 -1.55815557e-01 -6.84209406e-01 -1.26344013e+00 -1.70095086e-01 4.36024725e-01 -1.24321826e-01 -3.29062670e-01 1.16772318e+00 3.07996899e-01 2.57089019e-01 7.09869862e-01 -1.03180408e+00 -1.28181100e+00 1.44350934e+00 -6.00960255e-01 5.10592639e-01 3.19564313e-01 1.83016881e-01 9.64505434e-01 -6.20019436e-01 6.99722648e-01 9.79942322e-01 1.40687689e-01 7.32097745e-01 -6.22834086e-01 -6.64600492e-01 5.32185376e-01 2.56121278e-01 -1.01194060e+00 -2.64493883e-01 9.96431053e-01 -3.91771764e-01 7.80622184e-01 3.26023936e-01 5.37131429e-01 1.50478721e+00 -6.58931509e-02 1.08467066e+00 9.65846002e-01 -5.07166803e-01 1.53717604e-02 3.05465221e-01 4.36009347e-01 7.34905601e-01 6.60991073e-01 -2.75113821e-01 -6.37321651e-01 3.84626597e-01 5.40965736e-01 -1.60052419e-01 -5.50530374e-01 3.08770090e-01 -1.26609325e+00 5.74991703e-01 3.04521471e-01 2.97934890e-01 -3.32249075e-01 -3.93788107e-02 -1.77369062e-02 1.30869806e-01 2.54671156e-01 8.71891797e-01 -3.95103276e-01 -2.60248393e-01 -1.05709863e+00 2.89974451e-01 1.11755228e+00 1.28105485e+00 6.21502101e-01 -6.99330419e-02 -3.95593345e-01 3.93875331e-01 5.83562739e-02 5.36745191e-01 2.50905991e-01 -7.51482189e-01 7.44907379e-01 8.31847787e-01 -6.01064339e-02 -1.04664075e+00 -3.62871230e-01 -7.19965160e-01 -1.13835299e+00 -1.98759437e-01 4.44727808e-01 -3.40399951e-01 -7.78089762e-01 1.50582385e+00 -4.20319411e-04 1.77785426e-01 2.20382169e-01 7.79589355e-01 1.54207051e+00 8.17018509e-01 -2.09405527e-01 -2.95894057e-01 1.24618304e+00 -1.29597127e+00 -6.36922538e-01 -4.29439694e-01 3.24711442e-01 -6.51240826e-01 1.00957012e+00 5.57855189e-01 -1.43284845e+00 -6.99792981e-01 -8.47208321e-01 -2.76203215e-01 -6.37047708e-01 3.80805343e-01 3.28541696e-01 3.46008450e-01 -9.54018533e-01 6.18742764e-01 -4.25732523e-01 -2.67616272e-01 5.65925777e-01 1.52577370e-01 -4.93734367e-02 -1.15281008e-01 -1.38046885e+00 7.37588346e-01 5.85812032e-01 4.03262347e-01 -7.05058277e-01 -8.57629597e-01 -5.46077907e-01 8.89906660e-02 7.15939522e-01 -1.20633018e+00 1.25196922e+00 -5.86010277e-01 -1.52944005e+00 7.68076062e-01 -1.76177859e-01 -1.12292841e-01 6.38335705e-01 -2.29949251e-01 -1.53328970e-01 7.49159828e-02 1.28099620e-01 4.89899069e-01 6.03979886e-01 -1.16942573e+00 -5.29342175e-01 -3.71626228e-01 4.99758422e-01 2.18207583e-01 -3.66536587e-01 -2.93805212e-01 -6.14479065e-01 -7.36415386e-01 -4.17403579e-01 -7.05168545e-01 9.04794857e-02 -5.42940259e-01 -9.67236578e-01 -7.04601586e-01 4.78236407e-01 -9.54672158e-01 1.46073818e+00 -1.62768555e+00 5.38281202e-01 1.45428911e-01 5.94109535e-01 3.93262774e-01 -7.26664532e-03 6.11793220e-01 2.88152754e-01 5.02444446e-01 7.64537677e-02 -2.50197239e-02 1.10865973e-01 2.76614800e-02 -5.24148166e-01 -2.41540879e-01 8.80287662e-02 1.35061526e+00 -1.23015153e+00 -2.39240766e-01 -1.66534752e-01 1.97901130e-01 -2.71402597e-02 1.65958330e-01 -6.07151806e-01 3.76691937e-01 -8.62327874e-01 4.48914617e-01 4.23920274e-01 -6.65687203e-01 2.14952976e-02 9.81815606e-02 4.70575551e-03 3.00108045e-01 -1.06145239e+00 1.44370735e+00 -3.23067605e-01 8.15535069e-01 -2.50503629e-01 -9.88304853e-01 1.12698364e+00 3.64868343e-02 -4.12143111e-01 -5.87791324e-01 3.12992819e-02 1.42343894e-01 -6.29219040e-02 -6.46274745e-01 5.73563099e-01 2.91939288e-01 1.28452495e-01 7.66995251e-01 -2.47367710e-01 -1.20601527e-01 5.00019550e-01 7.18913615e-01 1.20312703e+00 -2.22000317e-03 5.07766120e-02 -1.57989696e-01 6.00357771e-01 -4.14073579e-02 1.40971571e-01 1.21408117e+00 3.15866411e-01 3.75406861e-01 7.96370208e-01 -2.79580265e-01 -7.95383453e-01 -9.34972167e-01 5.25842130e-01 9.54855859e-01 1.16737127e-01 -4.09165442e-01 -7.11447775e-01 -7.19519436e-01 -2.03331128e-01 1.07562876e+00 -6.27311885e-01 -1.05014019e-01 -5.43420613e-01 -2.40520582e-01 6.08936310e-01 7.19691217e-01 7.36560643e-01 -1.42864406e+00 -4.06828523e-01 1.47663414e-01 -2.82934427e-01 -9.83658433e-01 -3.75866801e-01 -7.43684545e-02 -4.97611493e-01 -9.49124873e-01 -7.24434912e-01 -8.91129732e-01 9.00912225e-01 2.77174026e-01 1.22924423e+00 3.94666106e-01 -1.04857303e-01 3.53554636e-01 -4.48579073e-01 -9.41352546e-01 -4.11772996e-01 6.21042252e-01 -2.79726177e-01 -5.13502419e-01 3.18840176e-01 -4.76024032e-01 -4.44526106e-01 -7.27532282e-02 -8.27366650e-01 6.69622600e-01 9.59482789e-01 5.99246383e-01 4.65913326e-01 3.42133284e-01 7.91510761e-01 -1.25696659e+00 1.13584018e+00 -4.72187608e-01 -4.41285878e-01 6.01535499e-01 -5.67649364e-01 1.77569017e-01 8.77737045e-01 -3.21823448e-01 -1.09721780e+00 -4.83148187e-01 1.31934300e-01 -2.04570591e-01 -1.69830427e-01 8.71810496e-01 -1.75242826e-01 5.28012037e-01 6.43468142e-01 5.32946110e-01 -3.34069133e-01 -3.77298623e-01 4.60478842e-01 6.22340918e-01 6.18383527e-01 -8.53241146e-01 1.05367482e+00 -3.78576713e-03 -1.15924336e-01 -7.63860226e-01 -1.36965740e+00 -1.75752640e-01 -5.76581478e-01 -2.81479627e-01 7.16301084e-01 -6.25797987e-01 -8.52298915e-01 2.95345515e-01 -1.44695401e+00 -4.35136288e-01 -2.35202387e-01 1.29538059e-01 -1.82253212e-01 2.29790494e-01 -2.05698937e-01 -5.19886732e-01 -6.24000788e-01 -9.21665609e-01 7.28656113e-01 9.98111725e-01 -2.86157221e-01 -1.26420915e+00 -1.58936694e-01 8.85182917e-01 2.94139087e-01 1.89034745e-01 1.00917923e+00 -8.36348593e-01 -8.93235862e-01 6.25007078e-02 -3.61226082e-01 2.29664624e-01 1.40091464e-01 2.30233952e-01 -8.13926458e-01 -1.13253996e-01 -4.88263726e-01 -2.55780280e-01 1.06830883e+00 2.27457002e-01 1.42601764e+00 -6.95985973e-01 -5.86806238e-01 2.47663379e-01 8.33059132e-01 8.06841552e-02 6.01234198e-01 3.62840414e-01 9.28406775e-01 5.40327787e-01 4.20047790e-02 1.47519588e-01 8.79712343e-01 1.89531982e-01 9.36492234e-02 1.25368088e-01 -2.68139094e-01 -3.17153364e-01 3.37149620e-01 1.00991535e+00 -3.79991382e-01 -7.81681418e-01 -1.03724837e+00 7.38974690e-01 -1.82018459e+00 -1.11021829e+00 -3.60733092e-01 1.59157562e+00 1.27089894e+00 3.14902306e-01 -1.30766749e-01 -1.18010044e-01 6.57172680e-01 1.27664909e-01 -6.75577819e-01 -6.40239835e-01 -1.66229293e-01 4.20251369e-01 -8.08610246e-02 4.49344337e-01 -6.89346492e-01 1.00497091e+00 4.17404842e+00 7.77404666e-01 -7.60869801e-01 -3.98032129e-01 4.74554807e-01 1.38292983e-01 -5.42888582e-01 7.66892657e-02 -1.18463385e+00 3.60660553e-01 6.72376215e-01 -7.64737368e-01 5.47863424e-01 5.56233585e-01 8.35855231e-02 -1.08831957e-01 -1.14584386e+00 6.53095841e-01 3.53009105e-01 -1.63584161e+00 5.74011326e-01 -9.33500379e-02 9.90160108e-01 -4.70040321e-01 1.87281653e-01 4.79433507e-01 4.84145910e-01 -1.22968638e+00 4.40924704e-01 9.67876732e-01 3.54322195e-01 -5.85328341e-01 6.11478090e-01 8.28057468e-01 -9.30356383e-01 2.13288561e-01 -3.61167908e-01 -3.03556114e-01 -2.68257320e-01 8.21330130e-01 -9.66985583e-01 1.08047640e+00 5.43437600e-01 7.63054967e-01 -9.74827528e-01 7.81824410e-01 -1.00960481e+00 7.34339654e-01 5.28449118e-02 -5.66216230e-01 1.31325021e-01 7.74540678e-02 6.06168389e-01 1.21776402e+00 2.75303692e-01 4.84190375e-01 2.14227095e-01 1.26002359e+00 -7.01706111e-01 -1.02772266e-01 -4.98266816e-01 -5.22106946e-01 4.80516493e-01 1.35709560e+00 -7.82631397e-01 -4.84853089e-01 -2.08871692e-01 9.58959877e-01 5.53952038e-01 4.73173231e-01 -4.39837754e-01 -8.54006529e-01 2.35143844e-02 5.48928417e-02 2.98702270e-01 -3.21273953e-02 -4.80432272e-01 -1.08917761e+00 4.55310225e-01 -8.32296193e-01 4.39076066e-01 -1.07071590e+00 -1.42297769e+00 4.59695727e-01 -7.34343156e-02 -6.17947936e-01 -6.63302541e-02 -4.36775416e-01 -1.01133108e+00 1.01838386e+00 -1.73697019e+00 -1.15011585e+00 -7.19681084e-01 3.39800864e-01 7.68485606e-01 -2.21228316e-01 3.98801833e-01 -2.56557822e-01 -7.40462601e-01 7.38064885e-01 -1.53786376e-01 3.55899423e-01 4.11532044e-01 -1.44436228e+00 6.06377840e-01 9.23878908e-01 3.31509203e-01 9.97366071e-01 6.09687150e-01 -9.34342325e-01 -1.55021250e+00 -1.24653089e+00 1.14032423e+00 -8.43851984e-01 7.07669735e-01 -1.36412606e-01 -1.11813259e+00 8.75994742e-01 6.62500203e-01 -6.19310558e-01 4.40896481e-01 -6.73625246e-02 -7.42126927e-02 1.00482553e-01 -4.54630196e-01 8.00099134e-01 1.18551791e+00 -2.01390520e-01 -9.86051857e-01 6.03740454e-01 8.38538945e-01 -6.49438977e-01 -5.41211665e-01 6.43192381e-02 2.57879168e-01 -7.31565058e-01 6.45650864e-01 -6.45241439e-01 8.95291984e-01 -3.26870471e-01 7.04530895e-01 -1.59364545e+00 -2.82606125e-01 -9.69682515e-01 -3.43752205e-01 1.53866065e+00 8.03513050e-01 -3.15199435e-01 6.20754182e-01 6.03085279e-01 -3.34817708e-01 -7.81605542e-01 -3.88899535e-01 -7.69460261e-01 4.07756746e-01 -1.42082110e-01 6.39759660e-01 1.07983780e+00 1.06438637e-01 6.51048660e-01 -1.63703069e-01 2.88426310e-01 5.35950303e-01 4.13541406e-01 9.44644213e-01 -1.49919212e+00 -2.35016778e-01 -7.58759439e-01 2.55354524e-01 -1.15829325e+00 4.36625332e-01 -1.24932146e+00 -1.56311110e-01 -2.48490500e+00 4.85215932e-01 -5.76145165e-02 -1.43067077e-01 7.63425767e-01 -6.24156654e-01 -3.23448271e-01 1.14348438e-02 2.74867546e-02 -7.76904225e-01 1.42999589e-01 1.64762020e+00 -3.49806845e-01 -3.57622862e-01 1.15929343e-01 -1.35792828e+00 6.07739329e-01 8.60690296e-01 -2.23932743e-01 -6.19911671e-01 -7.37215817e-01 8.61134350e-01 3.99544798e-02 5.51762223e-01 -5.74827015e-01 8.23573887e-01 -3.73746425e-01 6.46439314e-01 -6.03160918e-01 -2.68964171e-01 -7.88596645e-02 -6.87843487e-02 9.55606103e-02 -5.78562796e-01 1.74879268e-01 3.01085979e-01 4.74824697e-01 1.03046179e-01 -3.31802875e-01 2.76290178e-01 -4.93132442e-01 -5.63566148e-01 9.46891159e-02 -1.54577821e-01 4.23052788e-01 9.43899274e-01 -1.60504714e-01 -1.10528862e+00 -3.49004060e-01 -5.15592277e-01 6.18369460e-01 1.63193330e-01 6.55816555e-01 7.57967889e-01 -8.56374264e-01 -1.01159906e+00 -1.89500764e-01 -4.75213937e-02 4.89135534e-01 2.58363307e-01 5.77142656e-01 -4.11296427e-01 4.75584418e-01 -8.51184875e-03 -2.13762131e-02 -8.88449132e-01 5.47540426e-01 -5.71020134e-02 -5.12751102e-01 -6.68592930e-01 1.13216043e+00 6.33639246e-02 -3.10840875e-01 1.81552842e-01 -2.57799149e-01 -5.29013038e-01 1.32198036e-01 7.39763439e-01 5.22688508e-01 -1.05885468e-01 -2.88702752e-02 -1.62311047e-01 4.59188461e-01 -6.19669378e-01 1.83977261e-01 1.31235707e+00 5.46220411e-03 -2.89602369e-01 2.80857980e-01 4.99362051e-01 1.55625865e-01 -6.64093614e-01 -2.52946973e-01 -6.52658045e-02 -2.55616251e-02 -4.87633049e-02 -1.35780394e+00 -9.48443949e-01 9.58291888e-01 -3.99471998e-01 3.39959741e-01 9.18985546e-01 1.24745607e-01 7.33159542e-01 8.10019314e-01 2.46154685e-02 -1.07861042e+00 3.29316378e-01 5.96261561e-01 1.03718877e+00 -1.12074256e+00 2.94909626e-01 -3.29152972e-01 -6.70117676e-01 1.40620983e+00 9.15001690e-01 3.23425949e-01 2.64236897e-01 1.70771461e-02 -1.07912064e-01 -4.14912313e-01 -8.31428826e-01 -1.19316541e-01 5.35660386e-01 5.18132687e-01 2.77298689e-01 -4.97812172e-03 -2.17928663e-01 9.92323637e-01 -6.53409183e-01 1.15708575e-01 9.90033090e-01 8.47619593e-01 -4.13325697e-01 -9.73881066e-01 -2.87197735e-02 5.99737763e-01 -2.95717150e-01 -1.86876595e-01 -1.02294660e+00 6.98243916e-01 -2.10786220e-02 1.14312673e+00 -4.41041946e-01 -1.40357599e-01 4.08844650e-01 8.57695267e-02 4.25954223e-01 -8.41024220e-01 -6.54673636e-01 -4.45594698e-01 -4.01607901e-03 7.44616464e-02 -3.46757889e-01 -4.00170952e-01 -1.44448447e+00 -4.85322952e-01 -1.98704824e-01 3.10644209e-01 3.72531652e-01 1.05228794e+00 4.76131946e-01 8.62118959e-01 2.68965483e-01 -2.50890404e-01 -2.55523443e-01 -9.40748811e-01 -2.92030424e-01 6.55522719e-02 4.30356711e-02 -3.05069447e-01 -2.91289747e-01 3.23103368e-01]	[11.922272682189941, 8.961125373840332]
3cc9d7eb-697a-4610-ada7-6aa2c0fd1a11	itcm-a-real-time-internet-traffic-classifier	1501.01321	null	http://arxiv.org/abs/1501.01321v1	http://arxiv.org/pdf/1501.01321v1.pdf	ITCM: A Real Time Internet Traffic Classifier Monitor	The continual growth of high speed networks is a challenge for real-time network analysis systems. The real time traffic classification is an issue for corporations and ISPs (Internet Service Providers). This work presents the design and implementation of a real time flow-based network traffic classification system. The classifier monitor acts as a pipeline consisting of three modules: packet capture and pre-processing, flow reassembly, and classification with Machine Learning (ML). The modules are built as concurrent processes with well defined data interfaces between them so that any module can be improved and updated independently. In this pipeline, the flow reassembly function becomes the bottleneck of the performance. In this implementation, was used a efficient method of reassembly which results in a average delivery delay of 0.49 seconds, approximately. For the classification module, the performances of the K-Nearest Neighbor (KNN), C4.5 Decision Tree, Naive Bayes (NB), Flexible Naive Bayes (FNB) and AdaBoost Ensemble Learning Algorithm are compared in order to validate our approach.	['José Everardo Bessa Maia', 'Silas Santiago Lopes Pereira', 'Jorge Luiz de Castro e Silva']	2015-01-06	null	null	null	null	['traffic-classification']	['miscellaneous']	[-2.11715639e-01 -5.11192739e-01 -1.64315507e-01 -5.91429770e-01 2.35961318e-01 -6.02430582e-01 3.95631433e-01 5.87520540e-01 -4.48273659e-01 6.64006412e-01 -6.53548658e-01 -8.95878136e-01 -4.70232725e-01 -1.09225547e+00 1.69673949e-01 -3.56956482e-01 -1.78232163e-01 1.07505858e+00 1.06474352e+00 7.68691972e-02 6.18837714e-01 1.13039315e+00 -1.82821739e+00 5.41617155e-01 4.40547317e-01 1.44951093e+00 -2.14419782e-01 1.14218485e+00 -6.06820345e-01 8.05809021e-01 -6.41767442e-01 -2.31161997e-01 5.34808576e-01 1.04354419e-01 -1.10283494e+00 -1.86821014e-01 5.13126291e-02 -5.06041050e-01 4.69689183e-02 4.97609496e-01 2.76975006e-01 1.27956197e-01 5.15669286e-01 -1.87995577e+00 4.61527377e-01 4.52514023e-01 -3.59644264e-01 6.60739601e-01 1.07975282e-01 1.31682754e-01 5.94190657e-01 -2.77200133e-01 3.77362132e-01 1.24908221e+00 4.82576728e-01 4.24575545e-02 -1.18908489e+00 -8.18622291e-01 -3.35253596e-01 8.59672546e-01 -1.10244322e+00 -5.75562596e-01 4.21152562e-01 -7.82985687e-01 9.46543396e-01 5.16114116e-01 5.47552168e-01 3.73577178e-01 1.39841303e-01 2.51908526e-02 7.05708563e-01 -6.30991042e-01 5.78155220e-01 8.74634206e-01 5.95961511e-01 3.07109296e-01 3.07358235e-01 -1.08632848e-01 1.57871582e-02 -3.11437279e-01 6.11149609e-01 2.05878153e-01 4.21471536e-01 9.85909104e-02 -5.77563465e-01 5.15546739e-01 9.33533013e-02 4.50551480e-01 -6.30790412e-01 -1.15549546e-02 8.36448371e-01 4.92494583e-01 8.64679739e-02 -4.46844548e-02 -5.97968936e-01 -4.36068326e-01 -9.66995955e-01 -2.04164349e-03 1.16009605e+00 9.40414846e-01 8.37810934e-01 -2.40898058e-01 -5.33063747e-02 6.26576602e-01 4.56698656e-01 7.48567507e-02 2.98343390e-01 -7.15323031e-01 5.74639104e-02 7.59020567e-01 -1.16871677e-01 -1.20103931e+00 -4.56077009e-01 -3.71394694e-01 -7.21382558e-01 5.51631808e-01 5.70323884e-01 -1.51339218e-01 -2.80436754e-01 7.81471133e-01 4.58714873e-01 3.29493135e-01 -3.57866257e-01 4.14526343e-01 2.85095036e-01 8.75192165e-01 3.83758128e-01 -4.29296821e-01 1.32859099e+00 -7.81919122e-01 -6.07693732e-01 5.41961551e-01 4.32184786e-01 -1.25871456e+00 2.16693997e-01 3.94767970e-01 -8.30568016e-01 -9.02694404e-01 -6.22015715e-01 5.68444729e-01 -5.31270206e-01 -3.28980833e-01 5.26291966e-01 1.05852306e+00 -9.03672159e-01 6.94224775e-01 -5.19915283e-01 -7.18155980e-01 3.82537425e-01 5.08007467e-01 -2.99100727e-01 2.29865834e-01 -8.08874071e-01 6.21188998e-01 6.41643941e-01 -1.50032729e-01 -4.67549711e-01 -8.38371158e-01 3.75392921e-02 1.86056048e-01 1.68238655e-02 -4.42898333e-01 9.76627886e-01 -8.03605497e-01 -1.42882979e+00 7.04688489e-01 3.51514667e-02 -3.91937315e-01 5.08233666e-01 3.66775692e-01 -9.15721059e-01 2.29328468e-01 -8.72450173e-02 1.05840698e-01 6.99785471e-01 -9.17889476e-01 -1.43365955e+00 -2.96991348e-01 -8.68611187e-02 -6.15023434e-01 -1.29749134e-01 4.24270421e-01 1.15744658e-01 -2.37300679e-01 4.60643433e-02 -4.37892079e-01 -5.76649159e-02 -9.91627797e-02 -8.60341936e-02 -4.21760947e-01 1.12461388e+00 -4.14932072e-01 1.47364068e+00 -1.95343196e+00 -8.46992195e-01 8.56508374e-01 1.12550251e-01 7.01229632e-01 5.99318683e-01 4.91154909e-01 -3.08194339e-01 1.56243026e-01 4.01406527e-01 7.09158182e-02 -1.11670934e-01 1.43403664e-01 -1.31705981e-02 1.88305378e-01 -5.73480390e-02 -5.42719709e-03 -5.47937036e-01 -7.05300450e-01 6.66686475e-01 -1.74261462e-02 -4.61724281e-01 3.00019234e-01 1.28781065e-01 1.13029659e-01 -2.62244433e-01 4.72983330e-01 1.10206509e+00 2.60850359e-02 2.07260221e-01 -4.38666731e-01 -5.54901958e-01 -4.14685756e-02 -1.63801134e+00 9.67587948e-01 -3.58028203e-01 5.56659639e-01 6.41429722e-02 -1.28223121e+00 1.19761288e+00 5.51492929e-01 7.06575334e-01 -5.25286794e-01 3.22337478e-01 3.32193434e-01 -1.05794966e-01 -8.13360989e-01 7.91012421e-02 3.56627293e-02 6.98073626e-01 5.98684371e-01 -1.49688823e-02 7.24055946e-01 5.22452652e-01 -8.46600533e-02 1.30869067e+00 -3.26220781e-01 3.41107011e-01 -3.40919167e-01 1.14636123e+00 1.28063187e-01 7.00488031e-01 5.14820635e-01 -7.09885478e-01 -2.74141759e-01 6.83005571e-01 -8.11322749e-01 -1.12669730e+00 -1.04557920e+00 -1.80021152e-01 1.07388139e+00 -1.46064788e-01 -3.21366280e-01 -4.95219916e-01 -5.56884110e-01 -7.16801919e-03 5.70053875e-01 2.05945238e-01 1.91746399e-01 -6.45398557e-01 -5.04307330e-01 2.48495281e-01 5.42934518e-03 5.02577424e-01 -8.32960784e-01 -5.49574375e-01 6.66530490e-01 4.16514367e-01 -1.04545546e+00 2.66711205e-01 -8.27960446e-02 -1.08412099e+00 -1.17143548e+00 2.15935811e-01 -5.74685335e-01 2.40733907e-01 4.16543446e-02 1.07821071e+00 2.49759391e-01 -5.92215478e-01 -2.31719501e-02 -5.23321569e-01 -2.36043796e-01 -6.89014733e-01 1.60389826e-01 -5.62751926e-02 4.36885566e-01 5.04411399e-01 -1.01076674e+00 -6.33638918e-01 5.82943618e-01 -5.86719275e-01 -2.94305921e-01 5.04949689e-01 2.41606355e-01 3.70001867e-02 5.67741990e-01 6.24230325e-01 -1.11756921e+00 3.93132359e-01 -8.48778188e-01 -9.45091605e-01 1.32371351e-01 -8.91109705e-01 -3.46662670e-01 8.06961298e-01 4.49258313e-02 -1.01434243e+00 -1.86369404e-01 -2.45084062e-01 -8.97765830e-02 -6.73148096e-01 -1.96473479e-01 -1.42217994e-01 -1.34368585e-02 7.01635003e-01 -2.93840080e-01 2.77438700e-01 -6.99520230e-01 -2.74619371e-01 1.35235226e+00 -5.81529923e-02 -4.48420167e-01 6.28655493e-01 3.68135571e-01 3.18732113e-01 -7.89989352e-01 -2.46616692e-04 -7.83518612e-01 -6.69491351e-01 -8.35165143e-01 5.63693106e-01 -3.03634852e-01 -1.38883376e+00 3.90896469e-01 -1.42008102e+00 3.00364077e-01 -3.22440155e-02 5.34569085e-01 -1.45176739e-01 1.92466483e-01 -6.45281255e-01 -1.02878165e+00 -4.24597979e-01 -1.11053288e+00 1.37916625e-01 3.57368112e-01 -2.48239130e-01 -9.44312632e-01 -1.83930650e-01 6.53229475e-01 8.54739010e-01 -1.13999413e-03 1.18015802e+00 -1.13758707e+00 -6.35879099e-01 -3.61936539e-01 -6.04045451e-01 5.51636636e-01 -6.99458458e-03 7.37172663e-01 -1.06153500e+00 -1.39334062e-02 -2.47019261e-01 4.09405917e-01 2.86930233e-01 1.22554317e-01 1.24050605e+00 -2.33978584e-01 -4.15880829e-01 4.05462980e-01 1.50918210e+00 8.94503832e-01 6.71247959e-01 3.04182798e-01 2.09870473e-01 8.25343788e-01 4.94506001e-01 6.82951212e-01 -3.14618163e-02 5.16198277e-01 4.74384099e-01 2.39484340e-01 3.24727669e-02 3.28140050e-01 1.91310029e-02 7.04312027e-01 -2.98363455e-02 -2.17847645e-01 -1.10640872e+00 -8.07516128e-02 -1.74891198e+00 -1.39769650e+00 -6.87881172e-01 2.41906118e+00 3.68352085e-01 6.17943645e-01 5.00044465e-01 9.20170128e-01 1.03220057e+00 -4.48742360e-01 3.59774381e-02 -8.94183874e-01 7.27766395e-01 4.31321770e-01 6.20857596e-01 6.15399182e-01 -8.97127211e-01 4.62310940e-01 5.43133545e+00 1.09832668e+00 -1.15710723e+00 3.44683938e-02 4.30354327e-01 5.05566716e-01 3.57869536e-01 2.99481153e-01 -8.39036584e-01 9.45132315e-01 1.40326607e+00 -1.23773413e-02 5.04234254e-01 8.94569933e-01 6.00082755e-01 -2.50103921e-01 -9.15963233e-01 1.04694796e+00 -5.92302740e-01 -1.52242756e+00 1.07295819e-01 -1.69718251e-01 -1.49040483e-02 -1.83238700e-01 -7.56534457e-01 2.54161060e-01 -3.02590337e-02 -5.20338714e-01 2.58446127e-01 6.28363788e-01 4.11550790e-01 -8.27967763e-01 9.41266537e-01 1.01529971e-01 -1.14093018e+00 -4.29923117e-01 -1.43482223e-01 -3.10572475e-01 2.76977479e-01 1.12101030e+00 -1.18025720e+00 5.74014962e-01 6.91841245e-01 4.24782395e-01 -3.62878054e-01 1.40690541e+00 6.83676124e-01 7.13807821e-01 -3.70756000e-01 7.93096721e-02 -2.11292237e-01 -1.41061366e-01 5.22530079e-01 1.34305251e+00 -1.22645393e-03 -2.08161190e-01 2.09657669e-01 4.38212603e-01 3.24163020e-01 3.12739909e-01 2.38434933e-02 4.11488861e-01 7.51109779e-01 1.59229493e+00 -1.01000464e+00 -4.02604252e-01 -2.19264060e-01 3.62826526e-01 -2.18692467e-01 1.95042212e-02 -6.90620124e-01 -9.24861193e-01 8.80560577e-01 8.05993199e-01 -7.53780231e-02 6.36515766e-02 -2.25189745e-01 -4.02776003e-01 -1.45004019e-01 -4.17133123e-01 6.31451070e-01 -3.42432290e-01 -1.47362673e+00 5.86670816e-01 -1.31045178e-01 -1.27480650e+00 7.52866268e-02 -8.62769842e-01 -7.48926282e-01 9.00224626e-01 -1.16981530e+00 -5.60803175e-01 -6.78997040e-01 6.08425856e-01 2.48750597e-01 -6.26954377e-01 8.67953002e-01 1.07506084e+00 -9.95670319e-01 3.23812217e-01 1.41168023e-02 8.99294540e-02 5.34221768e-01 -9.64809299e-01 -1.90075129e-01 6.06694937e-01 -5.42653143e-01 1.98564887e-01 5.23573339e-01 -4.39551860e-01 -8.72171342e-01 -9.13962185e-01 8.08736742e-01 1.28448173e-01 5.66406667e-01 -6.02521114e-02 -6.45338118e-01 3.92757088e-01 -1.26449078e-01 2.38893017e-01 8.73319685e-01 -1.20143414e-01 -1.76642314e-01 -1.21405566e+00 -1.57273626e+00 -2.36586928e-02 4.17083234e-01 1.19632259e-02 -3.27650062e-03 2.62242377e-01 1.30797669e-01 4.22778368e-01 -1.10350895e+00 -2.20705266e-03 6.91497564e-01 -1.44793928e+00 8.58403444e-01 -7.93883860e-01 -3.66784245e-01 -6.00915015e-01 -6.80370480e-02 -5.77505589e-01 -3.59858125e-01 -7.23752558e-01 8.16285908e-02 1.79597270e+00 2.97863126e-01 -8.30345154e-01 7.77933478e-01 5.62166333e-01 3.10241699e-01 -2.22355589e-01 -8.19670975e-01 -5.99824965e-01 -6.43932223e-01 -5.96797585e-01 5.87449014e-01 8.84907305e-01 2.27774307e-02 5.17374039e-01 2.14071408e-01 1.03676178e-01 6.60537302e-01 -2.03247890e-01 8.54174078e-01 -1.87992859e+00 -1.67240113e-01 -6.60504222e-01 -1.20902789e+00 -4.96642143e-01 -1.69551954e-01 -6.99860334e-01 -6.38742924e-01 -1.11649656e+00 -1.92555249e-01 -1.16152275e+00 -3.16871703e-01 5.77701479e-02 4.52261209e-01 -3.94819587e-01 -2.30989978e-02 3.12431842e-01 -4.25079376e-01 -3.87902707e-01 7.18646526e-01 4.64286387e-01 -2.37252866e-03 6.40073240e-01 6.30993918e-02 4.72226739e-01 1.24858296e+00 -6.51879609e-01 -4.90622878e-01 -2.14983467e-02 -1.09883122e-01 1.39484286e-01 2.81073153e-01 -1.30545557e+00 6.39240265e-01 -2.87900507e-01 4.46455270e-01 -7.04551458e-01 -5.28203212e-02 -1.59486723e+00 4.67816025e-01 8.61432731e-01 6.62194146e-03 3.64375800e-01 -1.24358304e-01 3.38153839e-01 -7.34709576e-02 -4.30810720e-01 1.01851869e+00 1.50544522e-02 -7.00007737e-01 3.83590013e-01 -8.02054644e-01 -2.82329619e-01 1.66979980e+00 -4.64788020e-01 -4.23043132e-01 1.22263487e-02 -4.83766168e-01 1.04085147e-01 1.68835782e-02 3.08826398e-02 9.12436098e-02 -9.10775661e-01 -4.71547544e-01 5.01870036e-01 -1.26692757e-01 -3.18965286e-01 2.77840942e-01 7.87465394e-01 -1.42441022e+00 3.73359054e-01 -7.86733568e-01 -5.30736983e-01 -1.32353747e+00 4.84082550e-01 4.13725406e-01 -2.24583134e-01 6.11968897e-02 4.94639367e-01 -1.04751527e+00 -1.09891616e-01 4.28756684e-01 8.84328485e-02 -6.21114314e-01 2.02292174e-01 7.58498430e-01 1.58268988e+00 4.59096193e-01 -2.09140852e-01 -6.24376953e-01 2.71596521e-01 -1.45469576e-01 1.97110921e-02 1.19647586e+00 -1.45507514e-01 -7.14100599e-01 3.03157955e-01 1.14315259e+00 -2.48963669e-01 -2.88142085e-01 -8.36748183e-02 4.10708040e-01 -7.48922706e-01 1.23119846e-01 -5.66026807e-01 -1.16452622e+00 7.84927905e-01 7.53324389e-01 8.16118717e-01 1.24479961e+00 -6.51590407e-01 5.53612232e-01 9.50596333e-02 2.32099697e-01 -1.12815547e+00 -5.22061944e-01 3.49686652e-01 3.93061724e-04 -1.00375128e+00 -1.65454701e-01 -6.34542108e-01 -6.35530874e-02 1.72700024e+00 6.69279993e-01 3.97602171e-02 1.42607546e+00 4.96440738e-01 -1.15286671e-01 1.59787685e-01 -1.11261547e+00 -4.96821441e-02 -3.84860635e-01 6.25345349e-01 3.13619941e-01 2.20906287e-01 -4.28895354e-01 2.16693237e-01 9.02841017e-02 3.12049508e-01 3.72319460e-01 8.85805428e-01 -8.27124715e-01 -1.30156779e+00 -3.44157904e-01 6.72527015e-01 -4.80243415e-01 2.89179653e-01 2.62732446e-01 3.84000838e-01 5.17011344e-01 1.35675383e+00 7.24481821e-01 -6.24627829e-01 3.08830678e-01 -1.80649478e-02 -1.44439071e-01 -2.38404348e-01 -9.04300630e-01 -5.35901427e-01 1.91050932e-01 -6.18005216e-01 4.64798696e-02 -4.96939987e-01 -1.08920872e+00 -1.24681008e+00 -1.67054927e-03 4.72040117e-01 1.12707794e+00 7.56404877e-01 3.73306841e-01 4.06445473e-01 1.18001580e+00 -2.54707664e-01 -1.45021409e-01 -7.68399715e-01 -5.78621149e-01 3.06560695e-01 1.67862818e-01 -5.08688867e-01 -4.47528869e-01 2.22862124e-01]	[5.110086441040039, 7.183934688568115]
dc50ac39-9fe5-4ba2-ba18-cd94a204e4db	fusing-multiple-features-for-depth-based	null	null	https://doi.org/10.1145/2629483	http://xperzy.github.io/paper/zhu_rgbdaction_tist.pdf	Fusing multiple features for depth-based action recognition	Human action recognition is a very active research topic in computer vision and pattern recognition. Recently, it has shown a great potential for human action recognition using the three-dimensional (3D) depth data captured by the emerging RGB-D sensors. Several features and/or algorithms have been proposed for depth-based action recognition. A question is raised: Can we find some complementary features and combine them to improve the accuracy significantly for depth-based action recognition? To address the question and have a better understanding of the problem, we study the fusion of different features for depth-based action recognition. Although data fusion has shown great success in other areas, it has not been well studied yet on 3D action recognition. Some issues need to be addressed, for example, whether the fusion is helpful or not for depth-based action recognition, and how to do the fusion properly. In this article, we study different fusion schemes comprehensively, using diverse features for action characterization in depth videos. Two different levels of fusion schemes are investigated, that is, feature level and decision level. Various methods are explored at each fusion level. Four different features are considered to characterize the depth action patterns from different aspects. The experiments are conducted on four challenging depth action databases, in order to evaluate and find the best fusion methods generally. Our experimental results show that the four different features investigated in the article can complement each other, and appropriate fusion methods can improve the recognition accuracies significantly over each individual feature. More importantly, our fusion-based action recognition outperforms the state-of-the-art approaches on these challenging databases.	['Wenbin Chen', 'Guodong Guo', 'Yu Zhu']	2015-05-01	null	null	null	acm-transactions-on-intelligent-systems-and	['multimodal-activity-recognition', '3d-human-action-recognition']	['computer-vision', 'computer-vision']	[ 5.26988626e-01 -4.87215757e-01 -4.16539580e-01 -3.07119131e-01 -5.69230676e-01 4.56348509e-02 5.50831854e-01 -2.07833007e-01 -2.42551059e-01 4.68171507e-01 4.33993161e-01 3.04132640e-01 -2.89814651e-01 -6.67211711e-01 4.14669402e-02 -1.16532123e+00 1.91154987e-01 -2.13572457e-02 6.08176112e-01 -1.53366596e-01 6.12105012e-01 7.57654011e-01 -2.20334196e+00 4.85591501e-01 7.42601216e-01 1.34724653e+00 -4.61544329e-03 4.92598146e-01 -2.10266754e-01 1.01731420e+00 -6.63381338e-01 1.27197936e-01 4.53351796e-01 -6.82007194e-01 -6.37502372e-01 6.22318149e-01 2.14941025e-01 -3.39184910e-01 -3.66750240e-01 9.16896343e-01 5.76383710e-01 1.09888390e-01 6.40561521e-01 -1.45322466e+00 -2.36305445e-01 2.12759878e-02 -5.98922610e-01 4.37187582e-01 9.14994419e-01 1.87817305e-01 5.66201210e-01 -6.81220174e-01 2.69707739e-01 1.34215140e+00 2.07132488e-01 5.21357000e-01 -5.44833541e-01 -5.04766583e-01 -2.20424086e-02 7.41459906e-01 -1.16113043e+00 -2.63724804e-01 9.09348011e-01 -5.09700596e-01 9.35725987e-01 2.13636905e-01 8.69580269e-01 8.63122821e-01 4.82660621e-01 1.26916122e+00 1.30230451e+00 -5.12383759e-01 1.75547451e-01 -3.19702893e-01 2.19748929e-01 6.31811619e-01 2.37401441e-01 9.73577239e-03 -7.34128058e-01 1.67599887e-01 7.99861312e-01 3.12388182e-01 -3.96765172e-01 -4.62971777e-01 -1.14144444e+00 6.91731811e-01 1.61446869e-01 7.90580988e-01 -5.54337680e-01 -1.29905090e-01 3.29766899e-01 7.17112869e-02 1.50496140e-01 8.58699605e-02 -2.69025415e-01 -5.87644100e-01 -4.80356604e-01 1.70283675e-01 4.66919601e-01 4.89818513e-01 7.28604078e-01 -5.59634455e-02 -2.02887490e-01 7.54251242e-01 3.16824973e-01 5.51266968e-01 7.56797493e-01 -9.36811626e-01 4.69135106e-01 1.11949801e+00 -5.67803383e-02 -1.18034422e+00 -4.16288763e-01 4.72390562e-01 -8.64355862e-01 6.13903522e-01 5.90079367e-01 1.83511168e-01 -9.10123229e-01 1.08019948e+00 3.86042863e-01 1.20054744e-01 2.60694683e-01 1.04674911e+00 9.78985667e-01 4.40576643e-01 -8.91201571e-02 -2.77218997e-01 1.36443985e+00 -8.58430684e-01 -9.33010280e-01 -1.41615003e-01 7.94492126e-01 -6.77488863e-01 5.50259709e-01 6.08806908e-01 -7.30222881e-01 -7.92540312e-01 -1.16841447e+00 1.91343993e-01 -4.70508486e-01 3.40715498e-01 7.71653771e-01 7.39881337e-01 -6.81942821e-01 4.90448952e-01 -1.04517245e+00 -5.91409326e-01 2.48321965e-01 3.47933233e-01 -7.73100197e-01 -3.63326252e-01 -1.26500428e+00 9.52688396e-01 4.07055080e-01 9.15720016e-02 -4.77438629e-01 5.13903424e-02 -8.45282197e-01 -4.71165985e-01 4.54539835e-01 -3.86368096e-01 1.02333045e+00 -9.68377471e-01 -1.64085734e+00 7.36694336e-01 -1.32647872e-01 -2.69461423e-01 1.66879818e-01 -2.05146864e-01 -5.90964317e-01 4.69963253e-01 -6.34708256e-02 4.15010989e-01 6.86951280e-01 -8.22985172e-01 -1.08844697e+00 -8.36462796e-01 3.56343597e-01 4.40219104e-01 -2.57895380e-01 1.70987565e-02 -3.81816059e-01 -4.61363554e-01 6.48204803e-01 -9.30132866e-01 -6.14046864e-02 1.46059185e-01 1.57638907e-01 -4.18556869e-01 9.92524803e-01 -3.53508085e-01 1.06936204e+00 -2.10806727e+00 3.23502392e-01 -1.47353381e-01 -9.60203558e-02 6.05660558e-01 1.57310650e-01 4.24695402e-01 1.18890807e-01 -1.70987397e-01 -1.22374609e-01 -6.07391782e-02 -3.51694524e-01 5.58768153e-01 2.06050679e-01 5.33363640e-01 8.64840224e-02 6.13907933e-01 -7.32489228e-01 -5.74037373e-01 7.81567693e-01 5.82887948e-01 -9.90164131e-02 2.01622441e-01 3.41509819e-01 6.09833837e-01 -9.78938580e-01 1.16663074e+00 4.88525867e-01 1.87346965e-01 -1.13431454e-01 -4.22506809e-01 3.51963192e-03 -1.25582069e-01 -1.57139421e+00 1.58656275e+00 6.25004643e-04 3.92425835e-01 -1.87159032e-01 -1.28590584e+00 1.15382922e+00 3.51715237e-01 9.77858245e-01 -8.57292175e-01 3.08128685e-01 3.19058597e-01 5.25436178e-02 -8.72235656e-01 2.80382812e-01 -1.72983840e-01 4.80774939e-02 2.40580946e-01 -6.81802183e-02 -3.94911505e-02 2.81592309e-01 -4.27671582e-01 1.22757602e+00 2.12035701e-01 7.83721924e-01 1.85571745e-01 1.12271571e+00 -2.69940905e-02 7.78713524e-01 3.61804098e-01 -7.30625153e-01 5.54717660e-01 2.21594378e-01 -4.51536417e-01 -2.76463926e-01 -5.01878262e-01 -4.27634344e-02 4.06518281e-01 5.73491216e-01 -2.65322626e-01 -4.94386911e-01 -8.69757473e-01 -7.54005928e-03 8.34720805e-02 -6.35070384e-01 -1.89292699e-01 -4.12393510e-01 -6.43180251e-01 5.39379299e-01 7.81670630e-01 1.23643064e+00 -1.14388490e+00 -1.05587184e+00 1.51488096e-01 -1.16035745e-01 -1.10393584e+00 1.58807784e-01 2.03371309e-02 -1.14080274e+00 -1.46506894e+00 -8.06958318e-01 -3.74552697e-01 4.44694519e-01 8.01080883e-01 5.32184899e-01 3.57833877e-02 -1.66604444e-01 7.59332240e-01 -1.12109911e+00 -3.52049530e-01 -2.02943325e-01 -3.86106640e-01 1.38823956e-01 3.34336102e-01 8.59053493e-01 -4.42150772e-01 -6.15596056e-01 7.17385411e-01 -1.07982004e+00 -2.59566665e-01 8.76948237e-01 5.17199337e-01 5.44199467e-01 3.33825201e-01 1.75136730e-01 -1.79742709e-01 3.62800777e-01 -8.94906521e-02 -1.95992798e-01 2.92524070e-01 -2.31201142e-01 1.41852871e-02 1.70041651e-01 -2.09149048e-01 -9.21332538e-01 2.01719329e-01 -1.65489241e-01 -5.01150966e-01 -5.77025235e-01 3.46771270e-01 -5.77664137e-01 -4.09917980e-01 4.99468267e-01 4.18079942e-01 3.76753598e-01 -5.39661288e-01 -1.39489826e-02 8.95719528e-01 1.84442505e-01 -2.87056297e-01 2.46566683e-01 5.94858706e-01 2.59927332e-01 -8.84429574e-01 -7.09380925e-01 -7.63890564e-01 -9.69697297e-01 -5.23686528e-01 1.17013192e+00 -6.22347355e-01 -5.75069070e-01 1.10400069e+00 -9.05478656e-01 1.61917880e-01 -2.58767635e-01 8.25206876e-01 -7.90183306e-01 7.28056133e-01 -1.98578790e-01 -9.65706468e-01 7.88242221e-02 -1.44657588e+00 1.14736974e+00 3.69616508e-01 6.62544370e-02 -7.63183415e-01 1.47127256e-01 7.55433440e-01 1.16478562e-01 4.24667835e-01 4.05802727e-01 -5.07558465e-01 -5.31951189e-01 -3.83851230e-01 1.21613145e-02 6.62237048e-01 6.89903617e-01 2.13188771e-02 -7.49015033e-01 1.29296914e-01 3.58360291e-01 -2.80972898e-01 9.49657440e-01 3.68632168e-01 1.02300477e+00 2.20102578e-01 -3.83488715e-01 2.57500291e-01 1.17502785e+00 8.61945152e-01 1.17261147e+00 4.49685574e-01 5.07156849e-01 5.49385726e-01 1.22994828e+00 5.83792031e-01 8.18563476e-02 8.10665786e-01 5.32388806e-01 1.85630053e-01 -1.07988313e-01 1.62975952e-01 5.07945418e-01 4.90689576e-01 -6.36507154e-01 -1.87653720e-01 -7.79290140e-01 1.23083964e-01 -2.00301600e+00 -1.16206098e+00 -1.27506569e-01 2.05712748e+00 2.44191498e-01 -5.94884902e-03 1.72492027e-01 9.80604351e-01 5.35149395e-01 4.52323347e-01 -3.99658144e-01 -3.37137431e-02 -2.95774877e-01 9.51712579e-02 1.86312303e-01 1.18310094e-01 -1.46175885e+00 5.72957695e-01 5.83060074e+00 7.83623874e-01 -1.18721533e+00 -2.15402171e-01 1.90449670e-01 2.55516559e-01 3.70994896e-01 -5.36529049e-02 -1.00827503e+00 2.64851451e-01 3.42474550e-01 7.88598582e-02 -1.17995322e-01 8.10214877e-01 1.53113857e-01 -7.78337359e-01 -9.35683608e-01 1.59489977e+00 4.95963991e-01 -1.02390885e+00 1.26761064e-01 1.29016384e-01 4.97456461e-01 -5.40994346e-01 -3.21399838e-01 1.96488112e-01 -2.07052320e-01 -7.78963268e-01 1.61726475e-01 8.71668696e-01 1.44999877e-01 -6.26186848e-01 9.85775650e-01 4.40954983e-01 -1.35190523e+00 -2.51481324e-01 -2.24077746e-01 -3.98670167e-01 2.22259328e-01 5.07887423e-01 -1.39902800e-01 1.03960490e+00 6.18351579e-01 1.33309102e+00 -7.00050354e-01 1.09806168e+00 -2.83648133e-01 1.12461165e-01 -2.34607771e-01 -1.41993716e-01 1.61504596e-01 -1.81705400e-01 4.48001385e-01 6.41199470e-01 4.92231369e-01 7.28692114e-01 2.47624710e-01 2.32868150e-01 7.15007901e-01 1.70618609e-01 -7.49716341e-01 -1.77845210e-01 -1.01069212e-01 1.02567065e+00 -7.44501889e-01 -3.17860276e-01 -6.16315007e-01 1.02999699e+00 -2.54130989e-01 3.55712464e-03 -6.48970425e-01 -1.43056899e-01 8.29679668e-01 5.92398420e-02 3.18555504e-01 -3.81800711e-01 -1.70552686e-01 -1.13520622e+00 7.50652105e-02 -9.52269077e-01 6.05847180e-01 -7.82051980e-01 -1.03200269e+00 3.31042469e-01 1.85721636e-01 -1.87172437e+00 -2.38807678e-01 -9.21045661e-01 -3.08861077e-01 4.17921931e-01 -1.20076835e+00 -8.84050488e-01 -5.73794365e-01 8.80882561e-01 5.92166603e-01 -2.99737096e-01 6.54317677e-01 2.56687403e-01 -6.29310429e-01 1.67912424e-01 -2.03800961e-01 -1.95767288e-03 5.01978278e-01 -7.23247707e-01 -3.85086715e-01 7.73470521e-01 2.04717070e-01 -7.15764463e-02 4.22672927e-01 -5.85423291e-01 -1.70141816e+00 -4.84037220e-01 5.46968281e-01 -5.52621007e-01 1.80294991e-01 3.68865192e-01 -7.56128430e-01 2.26747125e-01 -2.23678872e-01 6.78849071e-02 6.97589219e-01 -2.42902488e-01 1.05376378e-01 -2.41158172e-01 -1.23057103e+00 2.14412600e-01 1.09481966e+00 -1.74495205e-01 -9.07607079e-01 4.78419289e-02 7.45154172e-02 -1.92231625e-01 -9.57973182e-01 9.26736474e-01 5.84829688e-01 -1.56008840e+00 8.87791634e-01 -2.19599679e-01 2.86772668e-01 -6.65676236e-01 -5.74828744e-01 -8.96154046e-01 -7.75166154e-02 1.33083507e-01 -2.06518650e-01 1.11945271e+00 -2.66914934e-01 -6.31649315e-01 8.83663058e-01 5.05704403e-01 3.24698840e-03 -1.04151702e+00 -1.07480013e+00 -8.44889283e-01 -3.33028883e-01 -5.69084346e-01 3.48655999e-01 5.71674109e-01 4.74442765e-02 6.04962893e-02 -2.92649984e-01 -1.01644874e-01 3.34011704e-01 1.20182931e-01 9.84211624e-01 -1.18492019e+00 4.48015071e-02 -5.04767716e-01 -1.43899477e+00 -1.16752505e+00 -6.77811205e-02 -3.50637704e-01 -2.94777155e-01 -1.89248157e+00 -1.52426213e-02 -5.14991069e-03 -2.33211443e-01 6.21036351e-01 -1.26060501e-01 2.77121127e-01 1.79118186e-01 2.74773419e-01 -5.26479542e-01 8.50755095e-01 1.45410860e+00 -2.81359971e-01 -8.32838863e-02 1.18994199e-01 -3.58210146e-01 8.95218551e-01 6.49477065e-01 -7.72309899e-02 -4.21775639e-01 1.05245411e-03 -3.62857968e-01 2.76800841e-01 2.76385605e-01 -1.62495577e+00 3.10858786e-01 -4.48178858e-01 3.67416501e-01 -9.06726480e-01 7.60978937e-01 -9.67870891e-01 2.90070213e-02 4.87299770e-01 2.05275968e-01 -3.56577098e-01 -8.86153430e-02 4.50736851e-01 -8.06186676e-01 -1.41901523e-01 8.43388379e-01 -2.02688649e-01 -1.44928050e+00 1.91521019e-01 -5.28266430e-01 -2.70493299e-01 1.50827396e+00 -1.04514778e+00 8.26465040e-02 -3.95152867e-01 -7.87754416e-01 -4.70561758e-02 3.55342329e-01 5.86104810e-01 8.49142671e-01 -1.66967618e+00 -4.57548469e-01 3.92285943e-01 3.46115500e-01 -3.00537199e-01 4.71734703e-01 1.14704537e+00 -3.64969313e-01 5.39265037e-01 -6.84816957e-01 -7.94249415e-01 -1.58188307e+00 4.94660676e-01 4.03886795e-01 -2.88940907e-01 -4.57559913e-01 4.93774652e-01 -8.75027403e-02 9.09643024e-02 2.92947501e-01 -5.17141759e-01 -6.94947124e-01 2.86727875e-01 7.80050397e-01 5.97446322e-01 -5.25759682e-02 -1.00769329e+00 -5.67346692e-01 1.23252368e+00 2.37119913e-01 1.14816047e-01 9.85065281e-01 -1.15954041e-01 1.19935706e-01 5.03989577e-01 9.57015216e-01 -4.83578771e-01 -1.17268848e+00 -1.57754377e-01 -1.10678673e-01 -9.53855693e-01 -8.93036351e-02 -4.95168209e-01 -1.35205865e+00 9.45817113e-01 8.97313714e-01 3.09369832e-01 1.54011345e+00 -1.84404969e-01 6.45818055e-01 2.65038043e-01 8.87753487e-01 -1.08303380e+00 3.58306497e-01 5.10242581e-01 1.00993562e+00 -1.45084620e+00 2.84213960e-01 -6.34484768e-01 -7.92838693e-01 1.35923672e+00 7.48927593e-01 -1.26897067e-01 7.25435913e-01 8.88180882e-02 1.10835031e-01 -2.95553297e-01 -2.98722982e-01 -6.45728588e-01 3.67471874e-01 6.78751826e-01 2.75386333e-01 -1.17284134e-01 -6.97452724e-01 3.95711958e-01 3.58608246e-01 3.02493989e-01 2.25175142e-01 1.38908112e+00 -8.24351668e-01 -1.42297840e+00 -6.74645722e-01 3.43692929e-01 -2.74210960e-01 6.70575619e-01 -6.49522960e-01 8.80785942e-01 3.45831633e-01 1.16269398e+00 -1.91061690e-01 -8.03307056e-01 5.94812512e-01 1.25950173e-01 7.57994294e-01 -3.31185162e-01 -2.01534957e-01 -1.61895007e-01 -6.18666783e-03 -8.79309654e-01 -1.27901816e+00 -9.11073089e-01 -1.20308185e+00 -6.05050027e-02 -2.80208141e-01 -1.52811050e-01 4.70797360e-01 1.18892992e+00 2.26447865e-01 2.63943017e-01 6.42699420e-01 -1.03840256e+00 -2.88906395e-01 -9.47692215e-01 -8.48771453e-01 3.68251801e-01 -1.98862385e-02 -1.35725844e+00 -5.81139743e-01 -1.68089628e-01]	[7.901707649230957, 0.3697359263896942]
22fffb0b-b60f-417b-9384-5e66656353f7	use-cases-of-quantum-optimization-for-finance	2010.01312	null	https://arxiv.org/abs/2010.01312v1	https://arxiv.org/pdf/2010.01312v1.pdf	Use Cases of Quantum Optimization for Finance	In this paper we briefly review two recent use-cases of quantum optimization algorithms applied to hard problems in finance and economy. Specifically, we discuss the prediction of financial crashes as well as dynamic portfolio optimization. We comment on the different types of quantum strategies to carry on these optimizations, such as those based on quantum annealers, universal gate-based quantum processors, and quantum-inspired Tensor Networks.	['Roman Orus', 'Enrique Lizaso', 'Samuel Mugel']	2020-10-03	null	null	null	null	['tensor-networks']	['methodology']	[-4.26131725e-01 2.52650641e-02 1.60738170e-01 -2.72899240e-01 -2.98456311e-01 -4.10400301e-01 4.09722120e-01 -1.52637109e-01 -4.83008534e-01 9.30917144e-01 2.74566486e-02 -5.46044648e-01 -4.74345267e-01 -1.43822432e+00 -3.42809886e-01 -8.05550098e-01 -4.65285867e-01 9.65064168e-01 -1.35531556e-02 -9.44061756e-01 7.94192076e-01 3.89352500e-01 -1.09480095e+00 3.31618369e-01 3.62635732e-01 9.84892845e-01 -5.48282385e-01 1.00989771e+00 2.11071149e-01 7.16380537e-01 -1.45635158e-01 -1.21832752e+00 7.17774868e-01 6.21269643e-03 -7.77926803e-01 -5.56232035e-01 -4.21556264e-01 -4.22390699e-01 -1.17122030e+00 1.40640700e+00 7.29375780e-01 5.20407140e-01 5.03587723e-01 -7.06322372e-01 -6.69305563e-01 9.82272685e-01 3.40444058e-01 8.10203731e-01 2.13181630e-01 6.47437394e-01 1.58227801e+00 -3.81861806e-01 4.33985591e-01 1.08269572e+00 4.38762903e-01 4.88534868e-01 -8.70841682e-01 -2.79037654e-01 -1.03175414e+00 9.45977867e-01 -1.18662417e+00 -4.95088369e-01 3.81751359e-01 -8.00609365e-02 1.82922471e+00 1.60275325e-01 6.95749044e-01 3.72937769e-01 1.05486929e+00 7.59589747e-02 1.27089858e+00 -4.88895804e-01 5.57263792e-01 -3.88719618e-01 3.70428562e-01 7.25551724e-01 3.17885041e-01 1.34596407e+00 -1.03459716e+00 -4.57393259e-01 4.26750898e-01 -3.39083672e-01 2.00381011e-01 4.83323753e-01 -1.33965814e+00 1.17762458e+00 1.38157263e-01 4.24634442e-02 -4.39849198e-01 8.10220063e-01 3.01766604e-01 5.43669879e-01 1.90697119e-01 9.53237057e-01 -4.01575446e-01 -4.66534048e-01 -4.60121989e-01 6.41326964e-01 1.27745485e+00 5.58434665e-01 7.70489335e-01 9.14657712e-02 -1.62819415e-01 -2.75716722e-01 4.03591096e-01 8.40995669e-01 1.66069716e-01 -1.27182424e+00 4.03654784e-01 -5.04676640e-01 6.53075799e-02 -6.52665317e-01 -7.70725369e-01 -2.26897057e-02 -6.82080150e-01 -3.33560407e-02 1.54508352e-01 -3.07053328e-01 -4.01806772e-01 9.10868704e-01 1.16888314e-01 3.59590203e-01 4.35417324e-01 6.99872792e-01 6.06381536e-01 6.32349491e-01 -5.78055263e-01 -1.60071507e-01 1.37642276e+00 -9.23934460e-01 -8.28149676e-01 3.49442363e-01 8.10992122e-01 -9.08263981e-01 1.89054534e-02 4.41917896e-01 -1.10646915e+00 7.34834448e-02 -1.05210280e+00 1.76900283e-01 -5.20816147e-01 -8.79605055e-01 1.36871135e+00 1.34610677e+00 -1.11254132e+00 1.65511370e+00 -9.96445656e-01 1.72138721e-01 3.22057419e-02 9.54456210e-01 2.32727051e-01 4.48576927e-01 -1.79636145e+00 1.09157217e+00 5.65567195e-01 -2.52606701e-02 -7.69796133e-01 -1.30227521e-01 -1.67051405e-01 1.29751369e-01 4.92676049e-02 -1.23801935e+00 1.34559703e+00 1.95248187e-01 -2.45533824e+00 6.18881583e-01 3.38152558e-01 -1.06798351e+00 -8.99799243e-02 6.83139870e-03 -5.99730670e-01 1.13149136e-01 -5.18335879e-01 -7.44417831e-02 5.01698434e-01 5.40678144e-01 -3.10342431e-01 -2.34342322e-01 4.02097166e-01 2.62290449e-03 -4.01045308e-02 3.31508636e-01 3.17592561e-01 -3.42111647e-01 -5.57693914e-02 -1.30850911e+00 -7.27718711e-01 -9.62810934e-01 -5.72901785e-01 -2.38729730e-01 -9.82538536e-02 -2.27079809e-01 1.27628529e+00 -1.46345186e+00 3.90552491e-01 4.39040661e-01 2.42912576e-01 -1.46173716e-01 1.48085749e-03 6.58452988e-01 6.90239444e-02 1.43922389e-01 1.03369772e-01 -2.30027959e-02 4.84593809e-01 4.99640465e-01 -2.68483043e-01 5.62018037e-01 -1.44963944e-02 1.49123359e+00 -7.12784827e-01 -1.02114156e-01 -3.77335623e-02 -3.13118130e-01 -8.99218976e-01 -1.21799976e-01 -2.50742316e-01 4.27481234e-01 -8.41553032e-01 9.50798512e-01 3.84521335e-01 -3.03092122e-01 -1.58153117e-01 9.49414819e-03 -2.18996003e-01 8.97695065e-01 -1.04701900e+00 1.15332913e+00 1.61645599e-02 2.62891889e-01 -3.21088165e-01 -9.66114283e-01 3.88381183e-01 4.34579641e-01 4.55678314e-01 -8.86622667e-01 4.15342450e-01 5.81046522e-01 3.48885983e-01 -5.28798759e-01 8.08483720e-01 -7.28727639e-01 -1.67257726e-01 1.13324535e+00 2.35301927e-01 -6.94831729e-01 5.44583738e-01 -1.34647176e-01 1.33805597e+00 -2.31720164e-01 1.68502647e-02 -3.18224072e-01 5.17683685e-01 -1.87780708e-01 2.46805072e-01 1.08439577e+00 -4.75646347e-01 3.15906227e-01 3.24485630e-01 -8.63699853e-01 -1.42647767e+00 -7.75530696e-01 -3.19483191e-01 9.28304613e-01 1.55545548e-01 -1.02820373e+00 -7.16735482e-01 -5.25627211e-02 -2.23339722e-02 5.88469446e-01 -4.24320072e-01 -3.09431016e-01 -6.19561017e-01 -1.94190812e+00 6.11661851e-01 -2.23210212e-02 2.31337249e-01 -1.03544617e+00 -2.96463013e-01 5.58165014e-01 2.13833228e-01 -1.23731029e+00 9.67706591e-02 5.18518865e-01 -1.04627573e+00 -9.03515875e-01 2.30128206e-02 2.34226182e-01 -2.03322232e-01 -4.47823972e-01 1.26786518e+00 -1.19128875e-01 -1.72626302e-01 -1.64741110e-02 -3.68953109e-01 -1.27023950e-01 -6.90050960e-01 1.22587919e-01 6.91296637e-01 -2.37417415e-01 5.36116540e-01 -4.59076732e-01 -3.54681343e-01 -1.54047668e-01 -7.03934848e-01 -5.31158328e-01 2.88437217e-01 1.04378879e+00 5.41260839e-01 3.85554463e-01 -1.78093910e-01 -6.49390936e-01 7.79225647e-01 -2.38013253e-01 -9.23899829e-01 3.93323272e-01 -6.31483018e-01 8.45747769e-01 6.77753985e-01 9.19330418e-02 -4.36820239e-01 -3.02573651e-01 -3.33557606e-01 -4.13159877e-02 6.51393235e-01 5.25986671e-01 5.91629148e-01 -9.84904349e-01 6.54486239e-01 -6.96024392e-03 -1.83562726e-01 2.70410301e-03 4.35344547e-01 3.71289551e-01 7.77668655e-02 -7.14561224e-01 9.77118969e-01 4.94346052e-01 8.34507883e-01 -6.95003748e-01 -8.03308666e-01 1.91260204e-01 -4.68777090e-01 3.07745412e-02 1.20009410e+00 -3.22387755e-01 -1.12240350e+00 3.97393227e-01 -1.22273076e+00 -8.32802430e-02 -4.74383563e-01 1.08747005e+00 -9.89314318e-01 5.05984843e-01 -1.18053019e+00 -6.97181165e-01 -7.38745213e-01 -1.39116347e+00 7.40504205e-01 4.97270286e-01 3.21322978e-01 -1.24101257e+00 6.20276570e-01 4.05804724e-01 5.67633629e-01 -5.50755672e-02 6.56013906e-01 -6.32140338e-01 -1.10685205e+00 -2.61149526e-01 2.56823987e-01 1.48442253e-01 -8.36273313e-01 6.28776625e-02 -7.40210056e-01 -2.92777661e-02 9.33970809e-02 -3.30168039e-01 8.12005758e-01 2.26918250e-01 7.07750082e-01 -3.04790363e-02 1.68757573e-01 1.12033093e+00 1.20675778e+00 5.79774380e-04 6.21733904e-01 5.96566498e-01 3.53506476e-01 6.68428093e-02 2.39543840e-01 7.94188976e-01 2.76845217e-01 6.20667040e-01 5.44553339e-01 8.93634021e-01 4.71147329e-01 4.02105361e-01 6.18926227e-01 1.57705891e+00 -8.15840185e-01 -7.16937333e-03 -7.06690311e-01 -4.21131283e-01 -1.79022574e+00 -9.91269231e-01 -2.29344308e-01 2.04582167e+00 4.26909834e-01 3.96056741e-01 -1.07144475e-01 -1.38695285e-01 7.03778744e-01 3.62764171e-04 -4.01436687e-01 -1.10443664e+00 -5.11073172e-01 1.27704144e+00 1.36300814e+00 3.98336619e-01 -1.19319761e+00 1.11597908e+00 8.27988625e+00 1.01040351e+00 -7.10914433e-01 7.07788467e-01 4.63641703e-01 -7.21231848e-02 -3.47126663e-01 5.45734048e-01 -9.80928004e-01 2.50273883e-01 1.80615914e+00 -5.35957575e-01 1.37149525e+00 3.35507959e-01 -1.33130610e-01 2.22802714e-01 -9.94440734e-01 1.04549301e+00 -6.10790610e-01 -1.70690203e+00 -2.46210143e-01 4.18603718e-01 1.02403772e+00 7.82924891e-01 1.72121078e-01 2.69169122e-01 3.40336800e-01 -1.13117564e+00 7.29219198e-01 8.53068590e-01 2.86935925e-01 -9.21189368e-01 1.02513516e+00 -7.29593039e-02 -6.60546124e-01 -2.46113628e-01 -8.65943372e-01 -7.15767682e-01 5.30825317e-01 7.24547744e-01 -5.59583642e-02 8.32608938e-01 6.67928219e-01 5.60788810e-01 -2.18411431e-01 1.07758832e+00 -3.23163480e-01 5.12004316e-01 -8.87531400e-01 -7.14453220e-01 4.97926503e-01 -8.17389190e-01 8.98921430e-01 5.52279055e-01 4.89240110e-01 7.06706464e-01 -2.14489177e-01 9.54874098e-01 1.68890685e-01 -1.11370675e-01 -3.67443144e-01 -7.03671753e-01 1.22521557e-02 1.04735351e+00 -4.89582151e-01 -4.81117576e-01 -2.70852089e-01 5.54594874e-01 -7.66611705e-03 -8.28765556e-02 -8.66557658e-01 -5.58324456e-01 6.14329100e-01 -3.60562444e-01 2.17693940e-01 -5.08791983e-01 -7.31788516e-01 -1.75352859e+00 -4.94817525e-01 -6.33589983e-01 2.92907864e-01 -3.00411463e-01 -1.29624379e+00 4.90315795e-01 -4.24747646e-01 -8.75820875e-01 -2.37162739e-01 -1.14455855e+00 -7.73487806e-01 5.49351752e-01 -1.46585858e+00 -2.35490635e-01 7.40611970e-01 4.44346189e-01 -4.45695370e-01 -6.70049965e-01 1.17982590e+00 3.10306281e-01 -7.39442706e-01 3.63097429e-01 9.17967319e-01 -2.26564094e-01 7.16863647e-02 -1.13519180e+00 7.11708605e-01 6.86694622e-01 2.74913311e-01 4.39788252e-01 1.05783033e+00 -3.27643335e-01 -2.12829494e+00 -4.77290034e-01 8.05306613e-01 -4.98205483e-01 1.43510854e+00 3.40667786e-04 -1.04589790e-01 3.05422187e-01 1.42257094e-01 6.99934065e-02 5.85899830e-01 2.49052551e-02 4.75668423e-02 -4.16302644e-02 -9.14644957e-01 3.35539937e-01 7.84806073e-01 -8.36037338e-01 -4.57879812e-01 1.26340139e+00 5.92650056e-01 -5.31537831e-01 -1.19159973e+00 3.09610039e-01 5.11823416e-01 -1.13472855e+00 1.16831386e+00 -1.02415955e+00 3.62375230e-01 1.21572107e-01 -3.78684729e-01 -8.85888755e-01 -4.52322602e-01 -1.60313964e+00 -5.94595611e-01 -2.97290962e-02 3.72822881e-01 -1.15780318e+00 9.42949951e-01 7.40604341e-01 -1.24571681e-01 -7.96603918e-01 -1.56270874e+00 -8.92738819e-01 4.53295201e-01 -5.82580924e-01 8.78562450e-01 4.06853199e-01 3.13875169e-01 1.44825071e-01 -4.94697303e-01 8.74859840e-02 8.17542374e-01 2.60111511e-01 1.62927479e-01 -7.08618939e-01 -9.78764594e-01 -6.96849465e-01 -8.30061913e-01 -6.23309731e-01 8.04538950e-02 -1.21473277e+00 -4.81171608e-01 -4.30765033e-01 1.15985014e-01 -4.30650681e-01 -6.46023810e-01 -2.01259986e-01 1.58601433e-01 4.31916893e-01 2.11365521e-01 2.28285283e-01 -9.27458584e-01 6.52751148e-01 1.07491839e+00 -1.58920199e-01 1.13184765e-01 2.68926352e-01 -5.36246896e-02 4.10582185e-01 8.04942667e-01 -8.47556233e-01 4.98099327e-01 -2.89272815e-01 1.26769078e+00 3.93766522e-01 1.28180012e-02 -9.13046777e-01 3.68985951e-01 -4.00150806e-01 -4.17609125e-01 -3.65989625e-01 4.05810595e-01 1.03415221e-01 -3.24715651e-03 1.10868764e+00 6.67741001e-02 4.72727537e-01 -4.93441641e-01 2.69206941e-01 -1.12049550e-01 -8.47621202e-01 6.58784807e-01 -5.07016480e-01 -4.74414915e-01 5.47717512e-01 -3.17478806e-01 1.05539307e-01 9.83539641e-01 2.61590242e-01 -4.64022845e-01 -2.71313667e-01 -1.05171132e+00 1.62414953e-01 1.55196935e-01 -9.14334804e-02 4.22881693e-01 -1.09745979e+00 -8.02687287e-01 -2.19874397e-01 -4.07751590e-01 -8.15936267e-01 1.89893544e-01 1.15608180e+00 -1.34237230e+00 1.21359754e+00 -1.70914799e-01 -1.48846824e-02 -3.01335782e-01 5.69209158e-01 7.50768483e-01 -7.47888148e-01 -2.19595924e-01 1.09330034e+00 -3.68453324e-01 -5.17206252e-01 -5.72643876e-01 -1.43153176e-01 -4.48133936e-03 -3.82280827e-01 4.60558951e-01 7.94308305e-01 3.34566385e-01 -5.89357376e-01 -4.82398421e-01 7.40269721e-01 5.02110839e-01 -2.79365361e-01 1.43116331e+00 3.80827188e-02 -9.18963790e-01 2.96055943e-01 1.01541209e+00 -4.68515754e-01 -3.45351189e-01 5.00671146e-03 1.51216492e-01 1.30732544e-02 2.34844774e-01 -2.05903962e-01 -1.04519677e+00 9.78015363e-01 4.22115743e-01 5.98273873e-01 6.41511917e-01 -3.13752621e-01 1.53133357e+00 1.31155086e+00 1.02167177e+00 -1.48800266e+00 -1.41980261e-01 1.17240644e+00 5.31529486e-02 -9.75039244e-01 3.85034502e-01 -3.96457873e-03 -5.82033694e-02 1.94772232e+00 -3.24458122e-01 -6.08295441e-01 1.31046474e+00 -1.38242021e-01 -6.33404076e-01 -5.17913282e-01 -1.15615690e+00 -3.75964850e-01 1.97215393e-01 4.10591587e-02 9.19471830e-02 5.88833213e-01 -5.22819817e-01 4.84564424e-01 -8.78447235e-01 -4.50668931e-02 1.07878053e+00 9.50409949e-01 -4.56043988e-01 -1.39663005e+00 -6.89604223e-01 5.80108404e-01 -9.40899312e-01 -5.60682178e-01 9.13916379e-02 -7.26947114e-02 3.77925187e-02 8.11469495e-01 -1.33866057e-01 -9.11279917e-01 -5.02047129e-02 1.67120565e-02 8.57265115e-01 -6.20216608e-01 -8.76594543e-01 -4.60743457e-01 3.60662341e-01 -8.25963497e-01 -3.86059523e-01 -8.53387892e-01 -1.27437091e+00 -1.09395647e+00 -8.47443402e-01 1.65847972e-01 7.15049744e-01 1.20186985e+00 2.53580153e-01 4.24310178e-01 7.31630981e-01 -9.54954743e-01 -1.44674253e+00 -5.33615708e-01 -1.16149521e+00 -2.58051425e-01 -1.35912508e-01 -6.61533117e-01 -5.47117889e-01 -5.90872526e-01]	[5.565478324890137, 4.930283546447754]
96c3c952-8a22-4d47-8038-2af0935ac5fc	robust-website-fingerprinting-through-the	1811.07153	null	http://arxiv.org/abs/1811.07153v3	http://arxiv.org/pdf/1811.07153v3.pdf	Robust Website Fingerprinting Through the Cache Occupancy Channel	Website fingerprinting attacks, which use statistical analysis on network traffic to compromise user privacy, have been shown to be effective even if the traffic is sent over anonymity-preserving networks such as Tor. The classical attack model used to evaluate website fingerprinting attacks assumes an on-path adversary, who can observe all traffic traveling between the user's computer and the Tor network. In this work we investigate these attacks under a different attack model, in which the adversary is capable of running a small amount of unprivileged code on the target user's computer. Under this model, the attacker can mount cache side-channel attacks, which exploit the effects of contention on the CPU's cache, to identify the website being browsed. In an important special case of this attack model, a JavaScript attack is launched when the target user visits a website controlled by the attacker. The effectiveness of this attack scenario has never been systematically analyzed, especially in the open-world model which assumes that the user is visiting a mix of both sensitive and non-sensitive sites. In this work we show that cache website fingerprinting attacks in JavaScript are highly feasible, even when they are run from highly restrictive environments, such as the Tor Browser. Specifically, we use machine learning techniques to classify traces of cache activity. Unlike prior works, which try to identify cache conflicts, our work measures the overall occupancy of the last-level cache. We show that our approach achieves high classification accuracy in both the open-world and the closed-world models. We further show that our techniques are resilient both to network-based defenses and to side-channel countermeasures introduced to modern browsers as a response to the Spectre attack.	['Anatoly Shusterman', 'Yuval Yarom', 'Yossi Oren', 'Yarden Haskal', 'Yosef Meltser', 'Prateek Mittal', 'Lachlan Kang']	2018-11-17	null	null	null	null	['website-fingerprinting-attacks']	['adversarial']	[ 1.81656964e-02 -3.38507712e-01 -6.09126687e-01 1.39587089e-01 -5.82728684e-01 -1.33152258e+00 5.82415164e-01 -3.29444408e-02 -3.83782268e-01 2.58919686e-01 -2.22396910e-01 -1.12986553e+00 2.44312927e-01 -1.31868291e+00 -7.07248569e-01 -4.06801969e-01 -3.70001167e-01 4.07619417e-01 9.59403157e-01 -1.56828031e-01 3.33235949e-01 5.66024601e-01 -1.09667480e+00 2.07455769e-01 1.90835655e-01 5.52684426e-01 -6.99074745e-01 8.81405711e-01 -1.23789594e-01 4.67036247e-01 -5.98097503e-01 -6.32937491e-01 3.84031624e-01 -1.26472294e-01 -9.00825560e-01 -4.67253894e-01 3.71466577e-01 -5.33677042e-01 -6.00956023e-01 1.19443417e+00 1.05797902e-01 -3.79474670e-01 8.80058098e-04 -1.64273965e+00 4.23883855e-01 7.65333235e-01 -4.46697295e-01 2.23523855e-01 5.72219312e-01 1.20849878e-01 1.01291513e+00 2.42689326e-01 6.82114065e-01 9.53695834e-01 5.99238932e-01 3.03109437e-01 -1.63582003e+00 -8.29311907e-01 -2.50929981e-01 -9.47840139e-02 -1.10464394e+00 -2.07229972e-01 5.58783889e-01 -1.70347705e-01 4.06469077e-01 7.05704272e-01 2.68162824e-02 1.49868894e+00 7.99292047e-03 2.73364782e-01 1.10782886e+00 -4.51751739e-01 3.16468507e-01 4.38258320e-01 5.63736916e-01 4.35143143e-01 6.79763794e-01 3.13331574e-01 -7.41133764e-02 -1.35138726e+00 5.10488868e-01 -3.26425791e-01 -3.70716780e-01 -5.46970963e-01 -9.35562551e-01 9.77020025e-01 -1.64069980e-01 3.28142822e-01 -2.98244115e-02 5.42617813e-02 9.87004817e-01 5.60281038e-01 -1.76634178e-01 3.21043342e-01 -6.25273407e-01 -4.68360603e-01 -6.23844504e-01 1.39468282e-01 1.72826076e+00 5.57300806e-01 4.91381615e-01 -1.89875215e-01 5.46952546e-01 1.32449478e-01 2.58564919e-01 4.95184451e-01 2.51897901e-01 -1.02816117e+00 3.64264637e-01 3.65136296e-01 2.16410637e-01 -1.03247321e+00 1.33559993e-02 -5.41560389e-02 -1.62496176e-02 2.17556864e-01 1.13697088e+00 7.01473802e-02 -6.97690472e-02 1.76618671e+00 4.12239730e-01 2.09825903e-01 -1.68114796e-01 4.80924755e-01 -2.17807040e-01 2.11430833e-01 9.13804919e-02 -2.57449858e-02 1.67130697e+00 -3.91311169e-01 -2.64967918e-01 3.84416670e-01 9.06447232e-01 -6.79014981e-01 1.08554482e+00 2.59271622e-01 -5.95042825e-01 1.64497450e-01 -9.38627899e-01 5.14046133e-01 -8.14867556e-01 -7.33876944e-01 4.52500582e-01 1.86135638e+00 -7.57572949e-01 3.31337333e-01 -9.01342332e-01 -6.43408954e-01 3.65312509e-02 4.15826201e-01 -2.67919630e-01 1.25634372e-01 -1.33222330e+00 3.55641216e-01 1.25551164e-01 -7.86137164e-01 -9.05028224e-01 -4.48592007e-01 -3.16230834e-01 2.08191231e-01 8.54033709e-01 -1.87104732e-01 1.25118232e+00 -7.44960368e-01 -1.24864769e+00 7.57766962e-01 1.35416240e-01 -4.91636425e-01 6.98753476e-01 2.95220107e-01 -6.81266367e-01 1.11783542e-01 -5.48030101e-02 -6.08054698e-01 4.56019759e-01 -1.18879485e+00 -3.53207767e-01 -4.84212786e-01 5.12009919e-01 -6.05783582e-01 -4.28810209e-01 3.63242805e-01 -1.35664731e-01 -1.82963476e-01 -3.33289534e-01 -1.17604887e+00 6.53263479e-02 -3.08198243e-01 -7.26449370e-01 3.72180730e-01 1.42825365e+00 -4.10293609e-01 1.37246549e+00 -2.02863407e+00 -7.66786814e-01 9.96578872e-01 7.50357881e-02 6.13458872e-01 2.26707131e-01 6.77489877e-01 4.39349636e-02 9.14449334e-01 1.09978497e-01 2.33046561e-01 9.06656608e-02 1.50486946e-01 -5.96384585e-01 7.34347939e-01 -8.83998990e-01 1.96823135e-01 -7.19484568e-01 -2.97177851e-01 -1.35646448e-01 3.03891718e-01 -4.96899813e-01 1.51420787e-01 -2.29827419e-01 1.65650621e-01 -7.58660138e-01 5.57830811e-01 6.85699999e-01 -2.04567015e-01 9.13613915e-01 1.17467806e-01 -2.73516715e-01 7.31025934e-01 -1.17319214e+00 8.44078660e-01 -5.29094696e-01 3.36294591e-01 3.51217955e-01 -4.56854671e-01 5.62369704e-01 4.31476057e-01 3.29617530e-01 -4.58224535e-01 2.16538116e-01 3.72249365e-01 -8.62618461e-02 -2.09639937e-01 2.24099588e-02 4.66306090e-01 -2.17660636e-01 1.23321617e+00 -5.59219897e-01 8.88447940e-01 -1.21572670e-02 2.39892155e-01 1.82285869e+00 -2.12445945e-01 2.56898642e-01 -2.08506942e-01 7.26004303e-01 7.10085854e-02 2.70867079e-01 1.18646646e+00 -4.35180217e-01 -1.05655804e-01 1.29463255e+00 -3.90749454e-01 -1.00847733e+00 -1.22197962e+00 -9.09861817e-04 1.16186929e+00 6.38157353e-02 -5.56509316e-01 -1.09029329e+00 -1.21472454e+00 -5.99408150e-03 7.29055405e-01 -1.20839976e-01 -1.85025498e-01 -9.60462689e-01 -6.16056740e-01 1.39165175e+00 7.27363676e-02 8.12894285e-01 -2.01183096e-01 -5.24724722e-01 1.92291245e-01 -3.76600057e-01 -1.40459776e+00 -6.77419841e-01 -1.94838867e-01 -7.53619611e-01 -1.70336962e+00 1.94928363e-01 -1.84797496e-01 1.11168042e-01 3.58801186e-01 8.35778058e-01 2.46033862e-01 -2.19439268e-01 8.01684260e-01 -1.15951039e-01 2.31282189e-01 -8.65863144e-01 3.07741255e-01 -1.69625748e-02 2.89376587e-01 8.20993423e-01 -7.24525213e-01 -2.55550295e-01 9.88825619e-01 -8.90430629e-01 -9.75925803e-01 -1.82242226e-02 4.50384408e-01 -2.10225135e-02 5.47964394e-01 2.09048558e-02 -1.38188922e+00 4.26705867e-01 -8.75805020e-01 -9.68257070e-01 8.28509107e-02 -6.95096731e-01 1.03708338e-02 9.11981642e-01 -6.06909335e-01 -7.87725568e-01 -1.21806756e-01 -1.76145881e-02 2.39984374e-02 -3.77731413e-01 -5.14922924e-02 -7.16475546e-01 -5.97629011e-01 7.07770646e-01 2.43031085e-01 6.76072538e-02 -5.50565720e-01 4.02487768e-03 6.87433362e-01 2.13854775e-01 -8.81020069e-01 1.14425957e+00 8.46488118e-01 1.04879379e-01 -9.68068898e-01 1.55108079e-01 -3.62450063e-01 -2.22758755e-01 -3.73823754e-02 5.23522973e-01 -2.61523217e-01 -1.46379042e+00 5.59896231e-01 -9.89496469e-01 -1.25563487e-01 4.68248934e-01 1.87171519e-01 -3.44434649e-01 8.39706838e-01 -6.91796541e-01 -7.35745192e-01 1.74137011e-01 -1.13549256e+00 3.53255302e-01 -1.29425898e-02 -2.78946251e-01 -1.24537003e+00 2.60031134e-01 4.65521634e-01 6.92746520e-01 3.59561414e-01 1.12291789e+00 -1.42670846e+00 -7.84953237e-01 -7.07676768e-01 -2.34732658e-01 -4.79147546e-02 -1.48778319e-01 1.71893150e-01 -9.92394924e-01 -3.42324078e-01 1.01325668e-01 2.91964948e-01 4.24314365e-02 -3.46405894e-01 1.03071570e+00 -7.50180840e-01 -4.89574730e-01 5.55405080e-01 1.58669901e+00 2.73493201e-01 7.79490232e-01 7.64650047e-01 5.25576174e-01 5.58561146e-01 6.51243106e-02 2.48090714e-01 1.11336261e-01 9.73706722e-01 5.75704336e-01 2.56846666e-01 5.77604592e-01 -4.58926260e-01 5.73793173e-01 -2.00281352e-01 1.03636339e-01 -2.97622025e-01 -8.81514013e-01 -1.72758270e-02 -1.21646273e+00 -1.08898044e+00 -5.29099345e-01 2.85340452e+00 4.63269860e-01 5.35825074e-01 7.53779769e-01 1.22056663e-01 9.44627643e-01 3.20961207e-01 -7.99802914e-02 -8.59014273e-01 2.54661590e-01 1.27447978e-01 1.37555981e+00 5.39310455e-01 -9.83807325e-01 5.13566554e-01 5.78273487e+00 4.72465724e-01 -9.79121387e-01 3.06758940e-01 3.42172921e-01 1.66656345e-01 -2.23124459e-01 6.56595111e-01 -7.42627978e-01 8.40054214e-01 1.76754463e+00 -3.59523646e-03 6.57203913e-01 1.00884354e+00 1.80810198e-01 -7.82309622e-02 -9.69317496e-01 3.29888016e-01 -3.46398056e-01 -9.77728009e-01 -2.61157513e-01 8.22235525e-01 -1.41000956e-01 -1.23836800e-01 1.37160430e-02 1.68027058e-01 5.25066733e-01 -5.29691219e-01 2.19155103e-01 -1.68280438e-01 5.15990376e-01 -7.36349463e-01 5.27423859e-01 2.57454306e-01 -1.15405118e+00 -1.15937516e-01 1.62024736e-01 2.80681074e-01 1.13209449e-02 2.96560735e-01 -6.53369367e-01 1.71684369e-01 4.29723561e-01 -3.58378410e-01 -3.55233550e-01 8.05713892e-01 7.16723129e-02 1.23184645e+00 -5.75196981e-01 2.06034616e-01 2.96182394e-01 -6.84392527e-02 8.97948861e-01 1.14176774e+00 -7.47403577e-02 -2.42789820e-01 1.34372637e-01 7.98655331e-01 -8.95110052e-03 -9.54309013e-03 -8.06268215e-01 -6.64245263e-02 6.86408579e-01 9.17956829e-01 -6.43818915e-01 -2.28237137e-02 -6.31307065e-01 7.39118338e-01 -1.86934099e-01 3.82400155e-01 -8.95871282e-01 -6.31808400e-01 9.50520217e-01 7.64676809e-01 2.09228203e-01 -4.21942115e-01 -2.37721577e-02 -1.06737280e+00 -5.23420945e-02 -1.21682012e+00 6.91709757e-01 2.43443828e-02 -9.51482713e-01 2.02074081e-01 -1.30392119e-01 -7.86946118e-01 -1.17995568e-01 -4.31629509e-01 -8.89129817e-01 7.64354050e-01 -1.11762071e+00 -9.49967563e-01 1.92992851e-01 7.90727198e-01 -3.66954952e-01 -9.55435112e-02 9.25241351e-01 4.74538624e-01 -5.75457871e-01 9.29321110e-01 2.82263577e-01 5.41212916e-01 5.75516522e-01 -1.12664080e+00 6.50297642e-01 8.72203290e-01 1.05842404e-01 1.03903377e+00 8.09009910e-01 -7.83189178e-01 -1.77020752e+00 -6.43556178e-01 7.74463117e-01 -4.44767326e-01 9.89448488e-01 -7.32753932e-01 -1.09325039e+00 8.69293213e-01 -2.95828521e-01 1.94673806e-01 8.06843340e-01 2.27272119e-02 -1.24722815e+00 -1.09104909e-01 -1.34229898e+00 5.96669078e-01 5.66408157e-01 -9.74138200e-01 6.37027174e-02 1.33900598e-01 4.59211886e-01 9.31527019e-02 -8.38261068e-01 -2.98276454e-01 9.62442398e-01 -1.26613009e+00 9.15780663e-01 -8.85587990e-01 -4.26003069e-01 -6.71821013e-02 -4.03543651e-01 -4.36917961e-01 1.72409192e-01 -1.06052041e+00 -2.08720118e-01 1.40441227e+00 2.15778247e-01 -1.14771140e+00 9.88958478e-01 6.27174497e-01 8.51777971e-01 -1.60280824e-01 -9.81466293e-01 -1.08650887e+00 -3.66281234e-02 -2.41335869e-01 7.44613588e-01 1.05478382e+00 1.37246940e-02 -2.88468868e-01 -2.43999630e-01 6.10308588e-01 1.11019528e+00 -2.91500360e-01 1.05275297e+00 -1.09680736e+00 -5.13179421e-01 -3.79380763e-01 -3.70946378e-01 -6.08895123e-01 2.15159088e-01 -5.14175534e-01 -7.35491514e-01 -2.48081982e-01 -3.65767069e-02 -4.29729134e-01 -1.21319987e-01 1.96549609e-01 6.39040470e-01 -2.76407674e-02 -1.14629656e-01 3.62490147e-01 -2.14854881e-01 -4.46228117e-01 2.72585958e-01 8.54483321e-02 -2.32250229e-01 5.41905999e-01 -3.84427905e-01 6.21596575e-01 8.89108300e-01 -7.89328933e-01 -2.07688019e-01 4.43139404e-01 9.55641419e-02 2.74792790e-01 6.94324791e-01 -7.43167043e-01 7.28065521e-02 -1.61576480e-01 -4.22915876e-01 8.48058015e-02 -8.41884986e-02 -1.14253020e+00 3.98222566e-01 7.47412562e-01 -2.26402834e-01 3.12258992e-02 -1.80232033e-01 9.12239194e-01 2.35884055e-01 -1.23342350e-01 8.89067709e-01 -3.18607628e-01 -2.62287766e-01 1.66362450e-01 -7.07618654e-01 6.29420727e-02 1.14180648e+00 -2.53752768e-01 -9.44642603e-01 -4.94237125e-01 -4.06635880e-01 -1.88574910e-01 1.08112299e+00 1.41615316e-01 -9.51374620e-02 -8.17840219e-01 -1.27663940e-01 2.78257608e-01 3.31003852e-02 -1.35650933e+00 -2.37145439e-01 7.63470352e-01 -6.69882178e-01 3.68196100e-01 -1.39386520e-01 -1.94823250e-01 -1.56566286e+00 8.43586504e-01 5.04957080e-01 -4.17549253e-01 -3.57641935e-01 -9.87056568e-02 -2.67148286e-01 -3.41452569e-01 2.84607440e-01 4.22848970e-01 1.90018907e-01 -1.46286234e-01 6.64330840e-01 9.54870939e-01 -2.35217493e-02 -6.51883245e-01 -4.23815966e-01 1.15687177e-01 -2.94042289e-01 -2.06210732e-01 5.42009234e-01 -2.14145109e-01 -4.28797364e-01 1.26774713e-01 1.66693258e+00 4.64984864e-01 -5.01353800e-01 -2.68190473e-01 4.40606892e-01 -1.01179457e+00 -1.12396628e-01 -4.92878288e-01 -9.87471938e-01 5.86636722e-01 5.07611334e-01 9.09356117e-01 6.25868261e-01 -3.81298929e-01 1.15494430e+00 3.28891546e-01 8.90995383e-01 -6.48911119e-01 -2.32965544e-01 1.47192270e-01 -3.57953906e-01 -8.17749441e-01 -3.25150996e-01 -6.39285743e-01 1.81628559e-02 1.30004084e+00 5.30375659e-01 1.50394775e-02 6.93568528e-01 4.20801073e-01 -4.86237518e-02 -3.93082127e-02 -3.67772877e-01 4.11709458e-01 -6.05476618e-01 6.58711672e-01 -8.65466073e-02 2.37324536e-01 -3.29485536e-01 3.63932014e-01 1.87124372e-01 -3.94939899e-01 1.23429394e+00 1.10514820e+00 -4.61833149e-01 -1.69660354e+00 -7.63791680e-01 2.15886638e-01 -1.00114286e+00 1.83991030e-01 -6.05501533e-01 1.10429835e+00 -4.80654776e-01 8.71185899e-01 -2.80815601e-01 -5.02445042e-01 2.25065261e-01 3.79289657e-01 -3.31417024e-01 -2.44915247e-01 -8.46636713e-01 -2.61020541e-01 4.59897041e-01 -1.06146872e+00 1.17794171e-01 -6.33997202e-01 -8.25149953e-01 -1.07650864e+00 -2.43145779e-01 3.65278840e-01 7.22038150e-01 4.60385263e-01 1.23138227e-01 -1.51226580e-01 1.01255560e+00 7.05263317e-02 -8.16739976e-01 -5.17128743e-02 -1.01977026e+00 2.08819926e-01 2.11657315e-01 -6.37984812e-01 -9.82451141e-01 -3.36276889e-01]	[5.552857875823975, 7.360994815826416]
b2086a03-53c1-4a04-9108-0fb272ffda92	multi-modality-multi-scale-cardiovascular	2304.09322	null	https://arxiv.org/abs/2304.09322v1	https://arxiv.org/pdf/2304.09322v1.pdf	Multi-Modality Multi-Scale Cardiovascular Disease Subtypes Classification Using Raman Image and Medical History	Raman spectroscopy (RS) has been widely used for disease diagnosis, e.g., cardiovascular disease (CVD), owing to its efficiency and component-specific testing capabilities. A series of popular deep learning methods have recently been introduced to learn nuance features from RS for binary classifications and achieved outstanding performance than conventional machine learning methods. However, these existing deep learning methods still confront some challenges in classifying subtypes of CVD. For example, the nuance between subtypes is quite hard to capture and represent by intelligent models due to the chillingly similar shape of RS sequences. Moreover, medical history information is an essential resource for distinguishing subtypes, but they are underutilized. In light of this, we propose a multi-modality multi-scale model called M3S, which is a novel deep learning method with two core modules to address these issues. First, we convert RS data to various resolution images by the Gramian angular field (GAF) to enlarge nuance, and a two-branch structure is leveraged to get embeddings for distinction in the multi-scale feature extraction module. Second, a probability matrix and a weight matrix are used to enhance the classification capacity by combining the RS and medical history data in the multi-modality data fusion module. We perform extensive evaluations of M3S and found its outstanding performance on our in-house dataset, with accuracy, precision, recall, specificity, and F1 score of 0.9330, 0.9379, 0.9291, 0.9752, and 0.9334, respectively. These results demonstrate that the M3S has high performance and robustness compared with popular methods in diagnosing CVD subtypes.	['Xianling Cong', 'Jianhui Zhuang', 'Xiankai Li', 'Lele Cong', 'Hongren Zhou', 'Chengyou Jia', 'Hechang Chen', 'Bo Yu']	2023-04-18	null	null	null	null	['specificity']	['natural-language-processing']	[ 2.08794385e-01 -4.62675244e-01 -2.25704968e-01 -2.76017010e-01 -8.32145452e-01 -1.93990678e-01 3.66202474e-01 2.00357810e-01 -7.23751411e-02 6.81261182e-01 3.27499181e-01 -1.17655627e-01 -3.60201269e-01 -9.45692539e-01 -1.65932313e-01 -1.11455631e+00 3.78386863e-02 1.19246401e-01 6.03133924e-02 -1.00794874e-01 1.05339894e-02 4.87595469e-01 -1.25781739e+00 3.39748800e-01 1.01161659e+00 1.23455811e+00 3.35171632e-02 3.95072252e-01 -8.55740234e-02 5.86161792e-01 -3.05828899e-01 -2.93018013e-01 -3.73830609e-02 -2.90066004e-01 -4.44859892e-01 -2.01566011e-01 -8.44577979e-03 -3.73117924e-01 -6.11109376e-01 7.97069132e-01 9.03182507e-01 -8.45335871e-02 8.94852877e-01 -8.58205914e-01 -9.66085732e-01 2.50745595e-01 -7.83333063e-01 2.14510813e-01 1.62468210e-01 1.45563066e-01 7.86662638e-01 -7.23488569e-01 3.55633020e-01 1.05433559e+00 7.26088464e-01 4.95060384e-01 -1.05773771e+00 -7.88283467e-01 -3.60041797e-01 4.12204802e-01 -1.45812118e+00 -3.38604718e-01 6.65518463e-01 -5.80599904e-01 5.09745061e-01 4.72276598e-01 5.19394994e-01 1.20929372e+00 3.81565899e-01 4.89775181e-01 1.08670342e+00 -6.64196163e-02 5.12479283e-02 7.01098843e-03 -1.11760106e-02 8.79591823e-01 3.59177083e-01 2.26394266e-01 -3.28877568e-01 -3.40975463e-01 9.08609331e-01 5.70304632e-01 -2.46188909e-01 -2.31308099e-02 -1.19884598e+00 8.93774807e-01 6.30067885e-01 2.01950282e-01 -4.49470252e-01 -3.54305595e-01 4.31347549e-01 -9.29993093e-02 3.07717353e-01 3.36487353e-01 -3.52462769e-01 1.57360733e-01 -5.96768141e-01 -1.64870895e-03 1.73407927e-01 3.88202429e-01 2.76200593e-01 -9.05292928e-02 -2.31675282e-01 1.25508213e+00 2.64782935e-01 5.75026095e-01 5.97898602e-01 -8.11054111e-01 1.20387003e-01 7.67635882e-01 -2.96355188e-01 -1.18035746e+00 -7.55729437e-01 -5.76917291e-01 -1.60467136e+00 -2.94320166e-01 -8.03709216e-03 3.71714458e-02 -6.43910110e-01 1.57365155e+00 4.05988157e-01 4.73433733e-01 1.33356899e-01 9.60315585e-01 1.34943795e+00 4.16450351e-01 2.14609474e-01 -2.12749407e-01 1.59479451e+00 -5.35344064e-01 -4.44457203e-01 2.01903805e-01 4.77125078e-01 -5.95957756e-01 7.62876987e-01 2.31662825e-01 -6.46010041e-01 -5.97309828e-01 -1.07680106e+00 -5.02723269e-02 -1.81103155e-01 4.50326771e-01 7.15036452e-01 5.95957816e-01 -4.58631068e-01 5.66908419e-01 -7.88400114e-01 -1.52261272e-01 7.49323905e-01 1.08867168e-01 -5.05396307e-01 -4.32864755e-01 -1.38992071e+00 5.63983738e-01 1.07897535e-01 9.12855417e-02 -4.12012219e-01 -9.08089161e-01 -7.68889666e-01 -6.80994689e-02 6.96600601e-02 -8.18259716e-01 6.39167309e-01 -1.59304574e-01 -1.37700355e+00 7.08772838e-01 -3.26576107e-03 1.29336581e-01 2.74369359e-01 -1.13231085e-01 -8.98994923e-01 4.72340047e-01 1.07094318e-01 2.38835424e-01 5.08826137e-01 -8.04452181e-01 -4.80495214e-01 -6.58361018e-01 -6.54087663e-02 -1.59451574e-01 -5.15087664e-01 -6.68180883e-02 -1.03279382e-01 -8.67668569e-01 3.67745787e-01 -8.41252804e-01 -2.52432019e-01 6.87721446e-02 -4.85697180e-01 -2.84286857e-01 4.66439635e-01 -6.84637368e-01 1.21670961e+00 -2.15262365e+00 2.22487431e-02 3.88049670e-02 7.79376626e-01 4.49231893e-01 -1.63745478e-01 5.69499582e-02 -1.47897348e-01 2.60181189e-01 -1.91383436e-01 3.02064329e-01 -2.68566787e-01 -1.16315231e-01 1.80476382e-01 4.43939567e-01 5.43682516e-01 9.20782506e-01 -8.82995486e-01 -4.46866333e-01 2.22959667e-01 7.10392237e-01 -3.65423977e-01 1.40761465e-01 3.75570774e-01 5.61265111e-01 -7.23150134e-01 8.64560306e-01 6.82599127e-01 -5.98731875e-01 1.97623283e-01 -7.05992401e-01 7.74179846e-02 -4.15952206e-02 -1.09210813e+00 1.43122435e+00 -1.54167697e-01 1.06094882e-01 -3.10499519e-01 -1.26727819e+00 1.06830335e+00 3.09095442e-01 1.05143201e+00 -8.03743184e-01 8.81021470e-02 1.77960262e-01 1.07489295e-01 -8.67544293e-01 -1.95706580e-02 -4.84643519e-01 7.43124709e-02 2.65876681e-01 -1.60796806e-01 4.45421755e-01 -1.12810470e-02 4.28316072e-02 1.21219897e+00 -1.61316350e-01 3.79868358e-01 -5.17726094e-02 7.41080225e-01 -2.01320216e-01 8.66039038e-01 4.88867283e-01 -3.15969437e-01 8.52600217e-01 2.80798703e-01 -5.85388303e-01 -7.91588664e-01 -1.10689986e+00 -5.98112643e-01 4.99177337e-01 1.75268993e-01 -4.17193264e-01 -7.67433792e-02 -5.52136183e-01 9.66871995e-03 9.02154297e-02 -4.50193763e-01 -5.56388557e-01 -5.04301727e-01 -1.49481130e+00 6.09189451e-01 7.63117433e-01 7.48156667e-01 -4.63605255e-01 -3.01027864e-01 2.92024910e-01 -2.42702737e-01 -1.07601392e+00 -7.64130503e-02 -3.08067024e-01 -8.25685918e-01 -1.54794133e+00 -9.01216924e-01 -5.83469212e-01 2.35948488e-01 3.65245491e-01 7.51045823e-01 1.12845853e-01 -6.41275287e-01 2.85799764e-02 -5.18444359e-01 -2.59296566e-01 -3.01412910e-01 -6.26406297e-02 2.41375268e-01 4.77501526e-02 4.58336413e-01 -5.00056148e-01 -1.08684504e+00 2.09167793e-01 -7.87990212e-01 -4.73168530e-02 1.05958283e+00 1.00510550e+00 6.69784248e-01 3.02869350e-01 9.00068402e-01 -6.56023443e-01 4.46838111e-01 -7.43543923e-01 8.53601396e-02 2.40120411e-01 -5.79421282e-01 -1.48353532e-01 6.25951827e-01 -3.38482887e-01 -8.56231809e-01 -2.18686417e-01 -2.59136438e-01 -1.56473562e-01 -1.72638863e-01 6.41351104e-01 -8.43645334e-02 1.08813122e-01 6.35201454e-01 1.95798069e-01 3.07898581e-01 -4.42769527e-01 1.53614189e-02 1.02429473e+00 3.39799702e-01 -5.46448469e-01 4.29644197e-01 2.99048066e-01 3.65262002e-01 -8.89373064e-01 -8.14224541e-01 -4.60069180e-01 -4.04520303e-01 4.52827178e-02 9.44583714e-01 -1.05381167e+00 -7.16236770e-01 6.11672461e-01 -6.82386696e-01 2.41119787e-01 1.74386024e-01 8.04080307e-01 -1.07434802e-02 6.19406521e-01 -7.63263166e-01 -5.84264696e-01 -5.97681642e-01 -1.17221773e+00 9.85169172e-01 3.84245753e-01 2.65753940e-02 -7.49029338e-01 -6.18661568e-02 6.62633240e-01 5.24846256e-01 5.65157056e-01 1.33452487e+00 -6.51345909e-01 -2.17838943e-01 -2.38624960e-01 -8.24614584e-01 4.29331303e-01 6.25131488e-01 -1.10022888e-01 -8.94390106e-01 -1.05508588e-01 -1.77161261e-01 -1.98782295e-01 8.85911644e-01 3.47752869e-01 1.36579442e+00 7.43880719e-02 -3.85684490e-01 5.34565866e-01 1.26130700e+00 3.92590761e-01 5.38315892e-01 1.77845448e-01 9.56557214e-01 2.73031205e-01 2.54177600e-01 4.77887571e-01 5.25206089e-01 5.24715126e-01 9.06166658e-02 -1.06267199e-01 -1.58882633e-01 4.24676806e-01 1.13007538e-01 8.21821570e-01 -2.93856889e-01 1.63027182e-01 -8.95673335e-01 1.38519287e-01 -1.51590919e+00 -9.69017982e-01 -2.32920200e-01 1.99392283e+00 8.08028638e-01 -7.24133924e-02 1.26194134e-01 2.02089563e-01 7.07626164e-01 1.55192226e-01 -8.27872038e-01 1.96033955e-01 -1.96552143e-01 3.83537441e-01 7.67243505e-02 -2.73938894e-01 -1.28488338e+00 1.54308274e-01 5.57308769e+00 6.63876712e-01 -1.46830511e+00 -1.00189140e-02 7.37902284e-01 -2.93052942e-02 -3.86281051e-02 -6.26148582e-01 -4.53960478e-01 6.23958945e-01 8.53419125e-01 2.08952457e-01 8.23937282e-02 3.87821436e-01 1.03376873e-01 2.82623529e-01 -7.26236463e-01 1.18565643e+00 3.01116752e-03 -1.54411852e+00 -4.86518256e-02 -2.79039200e-02 3.85397881e-01 -5.41721992e-02 9.39542726e-02 4.41133946e-01 -5.08885346e-02 -1.07499969e+00 8.28969851e-03 5.87947190e-01 1.12453270e+00 -6.24848485e-01 9.55968916e-01 -1.07731037e-01 -1.25133753e+00 -2.11179852e-01 -4.15399462e-01 2.09923610e-01 6.29760921e-02 1.11183453e+00 -4.22859430e-01 1.09981132e+00 6.54748976e-01 8.99875641e-01 -4.40799952e-01 9.26864445e-01 1.22886829e-01 4.18519706e-01 -1.38763096e-02 1.83759540e-01 -1.23610273e-01 -3.10920119e-01 1.85698494e-01 1.05132151e+00 5.96922696e-01 4.74057883e-01 3.49173158e-01 7.06108153e-01 -4.19322141e-02 1.65277719e-01 -1.80575997e-01 -2.17748150e-01 4.49290216e-01 1.33157039e+00 -4.14974868e-01 -3.06192249e-01 -6.02402747e-01 5.59921622e-01 -6.59917518e-02 1.54209107e-01 -8.70279551e-01 -4.23864424e-01 7.66346753e-01 9.11671016e-03 5.01606837e-02 -2.46580560e-02 -2.71626681e-01 -1.39132667e+00 -7.63546079e-02 -8.06028664e-01 6.70485437e-01 -4.83860523e-01 -1.68584883e+00 5.36647439e-01 -3.53131086e-01 -1.27580905e+00 3.59593511e-01 -7.96996713e-01 -3.95435363e-01 8.53952646e-01 -1.59610581e+00 -1.05948222e+00 -5.60951710e-01 3.28573972e-01 1.64551511e-01 -4.62446719e-01 9.72152352e-01 6.17981851e-01 -1.17610657e+00 5.80088973e-01 4.15765107e-01 3.36720407e-01 7.88191736e-01 -1.07052290e+00 -1.26932681e-01 4.04027909e-01 -3.13143104e-01 8.36635947e-01 1.27279133e-01 -4.96084124e-01 -1.52276754e+00 -1.41491199e+00 4.21369463e-01 -1.75345063e-01 5.36652803e-01 1.73046559e-01 -9.81624186e-01 1.05431654e-01 -5.51480055e-01 3.52894247e-01 1.38143158e+00 1.19000733e-01 -5.90796828e-01 -2.77881682e-01 -1.17704690e+00 4.02822912e-01 1.01364946e+00 -4.89593416e-01 -4.57457244e-01 4.07992572e-01 4.45045888e-01 -3.65939885e-01 -1.51035070e+00 8.03179979e-01 9.95047033e-01 -8.29636872e-01 1.33955896e+00 -6.69036984e-01 8.64573181e-01 -2.19568923e-01 -3.92756432e-01 -1.06776881e+00 -7.64206767e-01 1.03985868e-01 -1.76086396e-01 1.07074571e+00 1.40044451e-01 -7.48744071e-01 5.17766714e-01 4.56710666e-01 -2.20354006e-01 -1.14638627e+00 -8.23437214e-01 -5.71407378e-01 6.43144324e-02 -6.89452663e-02 8.59359622e-01 1.09824514e+00 -3.61125290e-01 3.31497133e-01 -1.61007538e-01 2.77052581e-01 6.07228398e-01 4.45550084e-01 3.78498763e-01 -1.69291711e+00 -1.36630893e-01 -5.05578876e-01 -5.41848302e-01 -4.70243812e-01 -1.39654100e-01 -1.08179855e+00 -4.89977688e-01 -1.52860188e+00 5.96195936e-01 -8.17283154e-01 -9.94372368e-01 2.85967588e-01 -5.19044220e-01 3.76860768e-01 -2.07899123e-01 2.72657096e-01 -3.40787768e-01 5.36904454e-01 1.32926226e+00 -3.58839631e-01 -1.17575474e-01 -1.57649502e-01 -1.18062496e+00 4.45021510e-01 9.40135002e-01 -1.49353087e-01 -2.71600753e-01 -1.48609474e-01 -1.07764252e-01 1.38664618e-01 4.82598126e-01 -9.41008091e-01 -2.40340576e-01 -3.32454205e-01 8.18015218e-01 -2.90431261e-01 2.73600101e-01 -3.27399641e-01 2.44249761e-01 5.38050771e-01 -1.41620412e-01 -2.55965024e-01 -2.15087935e-01 5.55931866e-01 -5.24468832e-02 2.16458231e-01 7.90881932e-01 2.64832173e-02 -7.23400652e-01 7.13001549e-01 1.71688721e-02 -1.92090869e-01 7.70892620e-01 2.91441735e-02 -5.27225196e-01 2.41250530e-01 -5.92467010e-01 -3.45631167e-02 1.19833477e-01 4.91609067e-01 6.63666248e-01 -1.62532866e+00 -8.39021564e-01 3.15330595e-01 3.20600063e-01 5.89622334e-02 7.74302125e-01 1.29474795e+00 -3.36071789e-01 2.69802064e-01 -2.77645230e-01 -7.54486382e-01 -1.11099148e+00 4.53831226e-01 1.40888095e-01 -9.11959112e-02 -9.17875051e-01 5.79893172e-01 1.76196724e-01 -3.53238583e-01 -2.47944668e-01 -1.40977710e-01 -4.49499011e-01 2.04787433e-01 8.30793738e-01 6.47312164e-01 2.56501764e-01 -5.94908297e-01 -5.11984587e-01 7.53750920e-01 -1.47966981e-01 5.61485052e-01 1.60694838e+00 5.46804704e-02 -1.69810772e-01 1.92466378e-01 1.24473810e+00 -1.04465723e-01 -7.63332546e-01 -4.07169551e-01 -3.46854627e-01 -2.59702951e-01 2.09951460e-01 -7.42039084e-01 -1.40637720e+00 7.72346079e-01 8.20102572e-01 1.92730665e-01 1.14443612e+00 -9.34289768e-02 1.16898012e+00 1.16238840e-01 3.91638801e-02 -7.25409985e-01 8.31780657e-02 9.98752639e-02 5.89815199e-01 -1.38125479e+00 2.58234173e-01 -4.34842944e-01 -5.24712503e-01 1.39374757e+00 3.78930360e-01 1.41349763e-01 7.41475046e-01 -1.14768855e-01 1.36242673e-01 -2.87440449e-01 -3.12672764e-01 -4.64634225e-03 3.42626452e-01 6.74385309e-01 6.28904343e-01 3.04010123e-01 -4.54944402e-01 1.09673643e+00 5.55828027e-02 1.59410983e-01 2.77223021e-01 7.62542367e-01 -3.30814660e-01 -1.00565481e+00 -2.37643898e-01 9.11130130e-01 -5.28763533e-01 1.54259175e-01 -1.62194874e-02 4.56908822e-01 2.19508827e-01 9.93682384e-01 -3.40057313e-01 -8.05288076e-01 2.02574372e-01 -9.72553156e-03 3.59781086e-01 -3.84655118e-01 -9.41246524e-02 -1.01236412e-02 -9.99309346e-02 -5.73167980e-01 -5.31684220e-01 -4.86558259e-01 -1.12848961e+00 -2.65988946e-01 -1.50316477e-01 -1.25586554e-01 2.97162920e-01 1.05456698e+00 6.97550118e-01 7.85333753e-01 9.09193873e-01 -5.70599735e-01 -5.38854122e-01 -6.85853302e-01 -7.59714544e-01 4.85031217e-01 3.78037840e-01 -1.00367570e+00 -1.03868749e-02 -2.62011021e-01]	[14.251232147216797, 3.095715045928955]
ac39e483-8674-49eb-8cb5-fd016b97e1e4	anonet-weakly-supervised-anomaly-detection-in	1911.10608	null	https://arxiv.org/abs/1911.10608v1	https://arxiv.org/pdf/1911.10608v1.pdf	AnoNet: Weakly Supervised Anomaly Detection in Textured Surfaces	Humans can easily detect a defect (anomaly) because it is different or salient when compared to the surface it resides on. Today, manual human visual inspection is still the norm because it is difficult to automate anomaly detection. Neural networks are a useful tool that can teach a machine to find defects. However, they require a lot of training examples to learn what a defect is and it is tedious and expensive to get these samples. We tackle the problem of teaching a network with a low number of training samples with a system we call AnoNet. AnoNet's architecture is similar to CompactCNN with the exceptions that (1) it is a fully convolutional network and does not use strided convolution; (2) it is shallow and compact which minimizes over-fitting by design; (3) the compact design constrains the size of intermediate features which allows training to be done without image downsizing; (4) the model footprint is low making it suitable for edge computation; and (5) the anomaly can be detected and localized despite the weak labelling. AnoNet learns to detect the underlying shape of the anomalies despite the weak annotation as well as preserves the spatial localization of the anomaly. Pre-seeding AnoNet with an engineered filter bank initialization technique reduces the total samples required for training and also achieves state-of-the-art performance. Compared to the CompactCNN, AnoNet achieved a massive 94% reduction of network parameters from 1.13 million to 64 thousand parameters. Experiments were conducted on four data-sets and results were compared against CompactCNN and DeepLabv3. AnoNet improved the performance on an average across all data-sets by 106% to an F1 score of 0.98 and by 13% to an AUROC value of 0.942. AnoNet can learn from a limited number of images. For one of the data-sets, AnoNet learnt to detect anomalies after a single pass through just 53 training images.	['John Zelek', 'Manpreet Singh Minhas']	2019-11-24	null	null	null	null	['supervised-anomaly-detection']	['computer-vision']	[ 1.41116142e-01 2.58159876e-01 3.18008453e-01 -2.16447979e-01 -3.05801570e-01 -1.59439161e-01 1.98749989e-01 4.45526093e-01 -5.44244885e-01 1.72938004e-01 -4.74765569e-01 -4.00585175e-01 8.56863856e-02 -7.36457109e-01 -8.89147937e-01 -5.81209302e-01 -2.33479410e-01 2.82722086e-01 7.17229724e-01 -1.43265784e-01 1.07556626e-01 6.29799128e-01 -1.83860326e+00 1.46180034e-01 8.80699754e-01 1.31012309e+00 1.20284170e-01 6.40585661e-01 -3.66029628e-02 6.08426034e-01 -6.66652322e-01 -1.16742030e-01 3.21788460e-01 -3.00592870e-01 -7.10702837e-01 1.86800614e-01 8.33508611e-01 -3.30898225e-01 -1.26301229e-01 1.03231192e+00 3.73055130e-01 -4.79339175e-02 4.96526837e-01 -1.15517437e+00 -4.03291970e-01 -1.19241782e-01 -6.90135360e-01 3.08925718e-01 -1.43684521e-01 2.82928944e-01 7.56751716e-01 -9.11354959e-01 4.11401600e-01 9.08005416e-01 1.17019665e+00 6.02320015e-01 -1.30412567e+00 -3.53399098e-01 2.53665689e-02 1.31262675e-01 -1.29993808e+00 -2.22870216e-01 3.76151413e-01 -3.20856094e-01 1.16345978e+00 3.42976123e-01 9.17483926e-01 7.47056127e-01 -6.10502772e-02 6.22387946e-01 8.40583086e-01 -5.87688208e-01 2.75969028e-01 4.99202386e-02 2.08449401e-02 1.13082695e+00 4.25574630e-01 -2.84006912e-03 -2.94026911e-01 7.22103268e-02 9.44531262e-01 1.76660642e-01 -2.29232863e-01 -3.26078653e-01 -7.94869363e-01 8.25222731e-01 6.30548000e-01 3.23052377e-01 -1.31167531e-01 1.17360905e-01 5.88753998e-01 5.84242225e-01 3.32423806e-01 5.13067782e-01 -5.28281987e-01 -7.79974312e-02 -9.10783708e-01 -1.14492141e-01 6.28655493e-01 6.22792780e-01 8.61636937e-01 3.44307393e-01 2.36193284e-01 9.14302647e-01 5.56470715e-02 3.62821519e-01 5.45871437e-01 -7.86616147e-01 2.51442716e-02 1.02186310e+00 -3.43719482e-01 -9.47793007e-01 -5.07928550e-01 -4.39444035e-01 -9.26392376e-01 7.73423314e-01 7.40366399e-01 -1.62087783e-01 -1.49664199e+00 1.28179371e+00 1.28832534e-01 1.02574773e-01 -2.55035549e-01 7.11271465e-01 7.05235481e-01 4.24520850e-01 -3.33167702e-01 3.27526331e-01 1.30196679e+00 -9.28551614e-01 -2.43838429e-01 -6.47819400e-01 9.47886527e-01 -6.46706820e-01 1.26460958e+00 5.35738230e-01 -8.02029848e-01 -4.12494957e-01 -1.21080792e+00 -6.63939631e-03 -4.80656713e-01 2.36578554e-01 7.21573770e-01 5.90528309e-01 -1.23790789e+00 8.05882275e-01 -1.02882326e+00 -5.18458247e-01 7.13637233e-01 4.89078373e-01 -6.94067836e-01 -2.09804595e-01 -6.13640368e-01 8.26151073e-01 4.55498695e-01 3.73847708e-02 -7.90033698e-01 -8.10045123e-01 -1.03597212e+00 1.71739817e-01 3.51076305e-01 -2.59092867e-01 1.07014060e+00 -1.17061615e+00 -1.10271287e+00 8.93542409e-01 1.00606695e-01 -5.16932964e-01 5.66264570e-01 -2.00174361e-01 -3.41970265e-01 3.01142573e-01 1.20893806e-01 8.24001968e-01 8.20691943e-01 -8.45539927e-01 -8.16870093e-01 -2.69641690e-02 -1.98908001e-01 -1.79912105e-01 -4.79712069e-01 -1.06434613e-01 -6.41487181e-01 -7.59252906e-01 4.09214348e-01 -8.58237624e-01 -1.26505777e-01 6.01380289e-01 -4.53435302e-01 -1.09337851e-01 1.19738746e+00 -5.56584716e-01 8.30276310e-01 -2.30527139e+00 -3.50250870e-01 4.37886208e-01 4.22733247e-01 6.25014126e-01 -2.26975977e-02 1.65168971e-01 -3.63829434e-01 1.22875825e-01 -5.26725054e-01 -3.27713341e-01 -3.83061349e-01 4.61975336e-01 9.83951837e-02 6.28047764e-01 3.67147177e-01 5.84569395e-01 -6.47253633e-01 -3.09831440e-01 2.87926495e-01 4.59729671e-01 -6.99933469e-01 2.38824308e-01 1.16176577e-03 4.99559082e-02 7.86369517e-02 7.28102922e-01 5.64230382e-01 -3.04255277e-01 -2.47776583e-01 2.81091481e-02 -6.68312609e-03 -1.65179029e-01 -1.28462112e+00 1.42529917e+00 -2.96448946e-01 9.68157887e-01 6.39431551e-02 -1.17331970e+00 8.94987881e-01 3.57003808e-01 1.48244038e-01 -6.54977679e-01 -4.55747172e-02 4.78426874e-01 1.51513889e-01 -5.50926149e-01 -4.75935675e-02 4.18260917e-02 2.25273356e-01 3.30082536e-01 3.06472272e-01 1.14795715e-01 2.63600588e-01 -1.45626608e-02 1.50457633e+00 -2.93543160e-01 1.33107364e-01 -3.42544436e-01 2.79742837e-01 -1.67359084e-01 6.72534943e-01 7.08034813e-01 7.02805668e-02 8.63197625e-01 6.26600504e-01 -1.04955554e+00 -1.01048899e+00 -7.95882463e-01 -2.31982157e-01 8.52317691e-01 -7.83879012e-02 -3.49849373e-01 -8.31349134e-01 -8.22273970e-01 1.49975866e-01 5.35951495e-01 -7.84195185e-01 -1.88494131e-01 -4.51922417e-01 -5.41128397e-01 5.96494913e-01 6.81165278e-01 8.19910884e-01 -1.24027765e+00 -9.66481209e-01 -2.16844022e-01 2.53177971e-01 -8.40408266e-01 -3.35852891e-01 5.10565042e-01 -1.08940613e+00 -1.25330997e+00 -5.52829564e-01 -1.08577061e+00 1.36886454e+00 3.20929214e-02 1.06577182e+00 7.68652499e-01 -7.72238433e-01 1.55051604e-01 -1.82238057e-01 -6.66135132e-01 -1.82529554e-01 -8.36756751e-02 1.66136511e-02 3.38584185e-02 4.18060154e-01 -6.33903682e-01 -5.74403465e-01 3.69390994e-01 -9.68082547e-01 -8.41706842e-02 8.85148406e-01 1.03451133e+00 6.06574953e-01 3.06037098e-01 3.54914308e-01 -9.30992365e-01 2.55068183e-01 -1.45152166e-01 -5.45694172e-01 -4.40650508e-02 -8.28545630e-01 6.83983788e-02 6.04703188e-01 -4.05797750e-01 -7.39649177e-01 1.65808558e-01 -1.81667954e-01 -5.33026516e-01 -5.39770305e-01 1.22451045e-01 2.17169344e-01 -4.26184803e-01 8.96131575e-01 2.15077605e-02 2.78675377e-01 -5.76889396e-01 -6.20107315e-02 2.79797018e-01 5.61035216e-01 -2.31288210e-01 8.84949923e-01 4.81269598e-01 -3.98575440e-02 -1.14198315e+00 -7.91673124e-01 -3.51281554e-01 -6.86144292e-01 -1.11612298e-01 6.50975704e-01 -5.59718847e-01 -6.20591700e-01 7.95442879e-01 -8.36448789e-01 -5.08365571e-01 -5.31873226e-01 2.08781645e-01 -3.77116837e-02 2.59996325e-01 -6.27136528e-01 -5.30135930e-01 -3.45750213e-01 -8.59218240e-01 6.50776863e-01 2.43414983e-01 -2.87506908e-01 -1.14882255e+00 -3.35598260e-01 -8.42286125e-02 6.15535319e-01 4.46787149e-01 9.32939231e-01 -7.10260391e-01 -3.91545892e-01 -5.14351130e-01 -3.54152054e-01 7.63209522e-01 1.68719634e-01 -1.45678669e-01 -1.04215860e+00 -4.97397721e-01 -2.91062854e-02 -3.63995790e-01 9.98351395e-01 2.85571188e-01 1.43847036e+00 -2.35337093e-01 -2.86632836e-01 7.35178113e-01 1.42931676e+00 9.76694934e-03 6.89386606e-01 5.57231545e-01 7.89563656e-01 5.25220692e-01 2.99867392e-01 -5.36137335e-02 -1.53932497e-01 3.98689866e-01 6.95175052e-01 -6.99674726e-01 -1.16387211e-01 -2.40479931e-02 1.36648178e-01 4.43987072e-01 -1.16636962e-01 2.38157824e-01 -1.06388450e+00 7.64726758e-01 -1.64871478e+00 -7.33463287e-01 -1.30712360e-01 2.08492589e+00 6.17023766e-01 4.98771161e-01 9.02922079e-03 5.05029798e-01 4.42346632e-01 -2.41682097e-01 -5.37856996e-01 -4.87625271e-01 1.17375217e-02 2.56825149e-01 4.78964120e-01 2.40835518e-01 -1.09068418e+00 6.88536763e-01 5.97459269e+00 6.65033698e-01 -1.28675616e+00 -2.09747389e-01 6.56418085e-01 -9.73567814e-02 2.89584249e-01 -2.54151642e-01 -4.99500036e-01 3.81248921e-01 7.86855340e-01 3.73553783e-01 2.37850145e-01 1.01952434e+00 -2.78649390e-01 -3.37636679e-01 -1.13817286e+00 9.70372498e-01 2.11214349e-01 -1.22929275e+00 -1.44608125e-01 2.03391369e-02 5.44979274e-01 2.10071951e-01 -7.88010657e-02 2.26263687e-01 -8.98783561e-03 -1.36370456e+00 6.20308638e-01 2.11564019e-01 8.31477284e-01 -9.97792780e-01 1.00836790e+00 3.86850536e-01 -9.87034023e-01 -2.47216761e-01 -4.18390423e-01 -1.79702744e-01 -2.84651995e-01 7.21351981e-01 -9.38824475e-01 1.76589657e-02 1.33599961e+00 5.43653727e-01 -9.80441928e-01 1.30297673e+00 -3.39160979e-01 7.75844097e-01 -4.65483487e-01 2.46110708e-01 3.11937720e-01 -8.20279568e-02 4.36503470e-01 1.26721525e+00 3.58837932e-01 -3.31170142e-01 1.06214613e-01 6.81909084e-01 1.77772192e-03 -1.67505652e-01 -6.44304872e-01 3.46667409e-01 1.98850736e-01 1.30100393e+00 -1.11055839e+00 -2.29426613e-03 -5.42020440e-01 1.10129595e+00 3.53135914e-01 1.40766889e-01 -3.37669969e-01 -9.05074418e-01 4.19571102e-01 3.98211062e-01 5.64994097e-01 2.74766594e-01 -2.20510259e-01 -7.16648638e-01 2.82561243e-01 -9.28737283e-01 3.35723639e-01 -4.97306615e-01 -1.14772379e+00 8.49344194e-01 -2.42293179e-01 -1.03853154e+00 -4.50658314e-02 -9.82585311e-01 -9.23522949e-01 8.06162000e-01 -1.28801334e+00 -9.31121588e-01 -6.34996951e-01 4.41757292e-01 3.13501120e-01 -2.51028448e-01 1.03778768e+00 3.73392940e-01 -7.38012254e-01 6.81451321e-01 -2.32471809e-01 6.21840596e-01 5.86555183e-01 -1.66370022e+00 6.26116812e-01 1.08317876e+00 1.22086391e-01 5.78777373e-01 6.33341253e-01 -5.05942881e-01 -6.78728342e-01 -1.15532625e+00 6.31628931e-01 -3.69905740e-01 4.04849470e-01 -3.58904958e-01 -1.39755714e+00 7.19030619e-01 7.81446025e-02 6.66926861e-01 3.77587736e-01 1.32037267e-01 -3.14219743e-01 -1.44307703e-01 -1.28917432e+00 4.46537882e-01 8.38312149e-01 -3.89841110e-01 -3.31333101e-01 2.21627876e-01 3.66623908e-01 -5.93644202e-01 -6.21907532e-01 2.56218314e-01 2.76501477e-01 -1.28853810e+00 7.15197623e-01 -2.89407939e-01 2.36598089e-01 -4.06330615e-01 2.20258281e-01 -1.39434469e+00 -1.99619323e-01 -4.31439549e-01 -2.20617145e-01 9.60921526e-01 4.83750492e-01 -8.24834704e-01 9.60498214e-01 2.98852950e-01 -4.03693378e-01 -1.19449317e+00 -8.58779728e-01 -8.11048627e-01 -2.16161355e-01 -4.06638086e-01 2.11672634e-01 8.21141243e-01 -4.60698187e-01 8.12355950e-02 -9.45582986e-02 2.23520085e-01 4.83582437e-01 -3.11248064e-01 5.33359230e-01 -1.70814466e+00 -7.09650218e-02 -3.02644134e-01 -8.36677432e-01 -7.42600858e-01 -2.78484166e-01 -7.12386072e-01 1.68671124e-02 -1.40092444e+00 -1.71676427e-01 -5.35255849e-01 -1.53723314e-01 1.05447185e+00 2.66263727e-02 4.73578483e-01 -2.95889854e-01 -6.79288208e-02 -3.26575756e-01 8.93881246e-02 8.92903030e-01 -3.12352106e-02 -9.75931212e-02 1.16038173e-01 -5.91403425e-01 1.08002198e+00 7.94965804e-01 -5.67689896e-01 -4.22170907e-01 -3.40747446e-01 2.09016010e-01 -5.61169744e-01 6.30228519e-01 -1.37019885e+00 1.65990904e-01 3.97068620e-01 7.55251884e-01 -3.38164896e-01 7.00214431e-02 -9.49114442e-01 -2.28347495e-01 6.72425926e-01 4.85828929e-02 2.15658456e-01 6.65972650e-01 5.28946459e-01 -1.92718953e-01 -4.92175967e-01 1.06298077e+00 -1.50857776e-01 -9.11268532e-01 1.32765308e-01 -2.96725035e-01 2.18856320e-01 1.03877306e+00 -4.94987518e-01 -2.40207970e-01 -2.26375848e-01 -4.69909906e-01 2.00923234e-01 6.49415553e-01 3.17704678e-01 7.18697011e-01 -1.12184644e+00 -3.85524303e-01 6.53380573e-01 1.85683981e-01 4.96119797e-01 1.93844453e-01 8.17956448e-01 -1.10192347e+00 2.05358550e-01 -3.55891377e-01 -9.19805229e-01 -1.52831876e+00 3.17499936e-01 7.25943625e-01 -7.59109557e-02 -1.27226281e+00 1.06730914e+00 -2.77679344e-03 -4.89713460e-01 4.43017513e-01 -4.28883076e-01 -1.73093900e-01 -1.07171789e-01 6.79895103e-01 4.37632322e-01 4.11133498e-01 -3.36985558e-01 -2.09417984e-01 5.07318497e-01 -2.59295404e-01 3.21511120e-01 1.60372603e+00 3.80825430e-01 -2.14290887e-01 2.27156281e-01 1.18231726e+00 -2.18703300e-01 -1.44615972e+00 -1.87119737e-01 1.65432483e-01 -4.42365229e-01 3.05607021e-01 -7.34014809e-01 -1.42219341e+00 9.20911014e-01 9.89474893e-01 4.23118830e-01 1.19425941e+00 -1.28455386e-01 6.49338841e-01 5.96770465e-01 3.25415656e-02 -1.22531259e+00 3.39862108e-01 4.19513613e-01 8.49308789e-01 -1.29786587e+00 -1.18750393e-01 -2.35301554e-01 -4.32189494e-01 1.24890304e+00 9.99568403e-01 -1.51728854e-01 6.29047811e-01 3.76117468e-01 2.87606508e-01 -6.03233218e-01 -4.16773587e-01 8.91045481e-02 4.66382802e-01 7.06242740e-01 1.52021617e-01 -3.05889755e-01 4.82223332e-01 1.47820950e-01 -1.14654325e-01 -3.83342475e-01 3.96458030e-01 1.01682401e+00 -5.95338941e-01 -7.71467030e-01 -2.30203867e-01 8.14871192e-01 -5.46091318e-01 6.93532974e-02 -1.99018881e-01 1.07192576e+00 3.52488726e-01 6.67893410e-01 3.55008245e-01 -2.13479728e-01 5.76784551e-01 7.45951161e-02 6.40604869e-02 -8.29411507e-01 -4.79851723e-01 -1.60457745e-01 -1.37931690e-01 -7.69231200e-01 -1.29010752e-01 -4.79285538e-01 -1.36161387e+00 -2.36940354e-01 -4.40148324e-01 5.84397689e-02 4.10776317e-01 9.70002651e-01 2.50818223e-01 8.63210201e-01 2.05311477e-01 -7.41298556e-01 -2.48964563e-01 -1.00928295e+00 -6.55563772e-01 3.58776718e-01 6.04654551e-01 -5.88562131e-01 -6.37550652e-01 -4.55264151e-02]	[7.744766712188721, 2.120084285736084]
254706d9-276f-4cef-a673-3191c30c4b59	masked-modeling-duo-learning-representations	2210.14648	null	https://arxiv.org/abs/2210.14648v3	https://arxiv.org/pdf/2210.14648v3.pdf	Masked Modeling Duo: Learning Representations by Encouraging Both Networks to Model the Input	Masked Autoencoders is a simple yet powerful self-supervised learning method. However, it learns representations indirectly by reconstructing masked input patches. Several methods learn representations directly by predicting representations of masked patches; however, we think using all patches to encode training signal representations is suboptimal. We propose a new method, Masked Modeling Duo (M2D), that learns representations directly while obtaining training signals using only masked patches. In the M2D, the online network encodes visible patches and predicts masked patch representations, and the target network, a momentum encoder, encodes masked patches. To better predict target representations, the online network should model the input well, while the target network should also model it well to agree with online predictions. Then the learned representations should better model the input. We validated the M2D by learning general-purpose audio representations, and M2D set new state-of-the-art performance on tasks such as UrbanSound8K, VoxCeleb1, AudioSet20K, GTZAN, and SpeechCommandsV2. We additionally validate the effectiveness of M2D for images using ImageNet-1K in the appendix.	['Kunio Kashino', 'Noboru Harada', 'Yasunori Ohishi', 'Daiki Takeuchi', 'Daisuke Niizumi']	2022-10-26	null	null	null	null	['audio-tagging', 'keyword-spotting', 'speaker-identification']	['audio', 'speech', 'speech']	[ 9.80698168e-02 6.46381915e-01 -3.75512034e-01 -1.90442681e-01 -9.04200315e-01 -1.53080672e-01 3.89007300e-01 -5.26090860e-01 1.60424829e-01 6.97398007e-01 6.67500496e-01 7.24153146e-02 4.02410239e-01 -7.05311656e-01 -1.32694399e+00 -6.38225675e-01 -2.89419174e-01 1.76983833e-01 2.89860815e-01 -1.97707236e-01 -2.93770760e-01 2.93227332e-03 -1.69526720e+00 1.21332717e+00 3.58560890e-01 1.26915336e+00 1.87922508e-01 6.31760240e-01 1.22103453e-01 1.34806776e+00 -5.44041216e-01 1.94308348e-02 3.42443854e-01 -5.75648069e-01 -7.59148598e-01 2.29404181e-01 4.30426657e-01 -4.05829430e-01 -8.70556831e-01 8.05811524e-01 4.65819746e-01 -3.88436057e-02 5.77278674e-01 -1.02135956e+00 -1.00619757e+00 1.14205503e+00 -4.24580842e-01 2.72907823e-01 5.19620553e-02 3.16840410e-01 1.03796732e+00 -1.11382771e+00 4.86350387e-01 1.24267912e+00 5.64237893e-01 6.67855978e-01 -1.51573062e+00 -1.01942146e+00 5.11011899e-01 3.67071390e-01 -1.45322049e+00 -7.71983802e-01 8.31909716e-01 -4.34816957e-01 1.15630436e+00 1.10145405e-01 6.96828485e-01 1.51008236e+00 -9.23996866e-02 1.26433671e+00 1.09591246e+00 -3.84738207e-01 1.58943340e-01 3.56838375e-01 -1.26705408e-01 6.27553582e-01 -4.39423472e-01 8.53619754e-01 -1.19154584e+00 -9.73486621e-03 7.14711487e-01 -6.47217706e-02 -4.38933700e-01 1.36044279e-01 -1.33536136e+00 8.38470936e-01 7.55192220e-01 1.44111440e-01 -5.40416002e-01 3.37989241e-01 2.94364899e-01 7.31890023e-01 7.65901804e-01 2.23607481e-01 -5.92496634e-01 1.41201839e-01 -1.10606623e+00 -9.41251814e-02 2.96888798e-01 8.62502277e-01 1.02740705e+00 7.50624955e-01 -2.17218190e-01 9.00799274e-01 2.46151939e-01 5.28828263e-01 9.49844420e-01 -8.20083737e-01 4.55957979e-01 2.55099028e-01 -7.96895400e-02 -7.63011098e-01 -9.81640220e-02 -4.09931958e-01 -1.00874460e+00 3.33478451e-01 -3.10824271e-02 -1.71312839e-01 -1.26617074e+00 1.66679966e+00 -9.08543468e-02 6.20021820e-01 4.12848145e-01 1.05911517e+00 1.11497307e+00 1.15878451e+00 -3.81189138e-01 1.47939967e-02 8.57217789e-01 -1.38873982e+00 -5.56662798e-01 -6.16130471e-01 1.45404041e-01 -6.17773116e-01 8.58840823e-01 6.40956044e-01 -1.14839041e+00 -1.09016132e+00 -1.11313963e+00 2.65433431e-01 8.06159303e-02 6.09814763e-01 5.47480822e-01 1.35589108e-01 -1.23464918e+00 9.67382908e-01 -7.15035379e-01 7.29528591e-02 4.06323910e-01 1.95422813e-01 -3.50466520e-01 1.74782947e-01 -1.36791444e+00 6.68035448e-01 3.38415474e-01 -1.65024009e-02 -1.65432346e+00 -6.34570658e-01 -8.23804319e-01 1.93837449e-01 2.19036229e-02 3.27830128e-02 1.23921537e+00 -1.60417140e+00 -1.74197924e+00 8.17247868e-01 -8.28849375e-02 -9.11572993e-01 1.98316127e-01 -1.76670507e-01 -8.05873692e-01 4.35784906e-01 1.88473649e-02 1.24907184e+00 1.56440663e+00 -1.10619628e+00 -1.69436917e-01 -2.21381569e-03 -3.08276117e-01 -6.05301112e-02 -1.31749853e-01 -3.31277639e-01 3.04377428e-03 -9.34680045e-01 1.60673663e-01 -5.71264446e-01 -1.41288012e-01 -3.45241884e-03 -5.31217217e-01 8.58901814e-02 4.53400105e-01 -8.18598866e-01 8.09879065e-01 -2.77614570e+00 2.19809473e-01 1.24654107e-01 1.59379825e-01 4.22540754e-02 -5.37826598e-01 3.03071171e-01 -5.31227231e-01 -2.66071856e-01 8.70136172e-02 -4.08052534e-01 -1.02980323e-01 2.24019364e-01 -1.07748914e+00 3.44937772e-01 4.96929109e-01 9.23128903e-01 -5.95898986e-01 -1.47068784e-01 -1.01410784e-01 4.45318162e-01 -7.20649004e-01 4.04713750e-01 -4.55266327e-01 3.27109814e-01 1.16461024e-01 5.81507325e-01 4.98648614e-01 -4.46147501e-01 3.13622132e-03 -1.14551470e-01 5.90616316e-02 5.35242617e-01 -1.12866926e+00 1.79216468e+00 -4.51494485e-01 8.09715450e-01 -3.15055437e-02 -1.12112796e+00 1.06891680e+00 7.13426530e-01 2.71497637e-01 -7.56262183e-01 -1.45438626e-01 8.47110078e-02 -2.71975040e-01 -3.45389664e-01 3.39757651e-01 -2.33600348e-01 3.06700617e-01 4.60705847e-01 5.81725836e-01 2.18022570e-01 -6.73157215e-01 2.67973959e-01 9.05072451e-01 1.04544111e-01 -1.50972260e-02 6.64775446e-02 -1.02289030e-02 5.49536347e-02 4.31703031e-01 6.40127420e-01 9.26149338e-02 8.78024638e-01 2.90244132e-01 -3.90168965e-01 -7.42572784e-01 -1.06299341e+00 2.18582436e-01 1.13942420e+00 -1.93364676e-02 -3.95425856e-01 -4.55327660e-01 -5.87251544e-01 1.06826425e-01 6.40519261e-01 -9.81168509e-01 -4.48659778e-01 -1.99830845e-01 -3.14612657e-01 7.46793389e-01 7.68658221e-01 3.62910151e-01 -1.37749755e+00 -2.12027177e-01 4.35089022e-01 -2.61215657e-01 -7.31378078e-01 -5.53859413e-01 7.10836768e-01 -9.66514170e-01 -9.18878615e-01 -8.45714092e-01 -9.39761043e-01 6.25320733e-01 4.06331271e-01 1.02814770e+00 -7.42238238e-02 -1.31886244e-01 -5.72306812e-02 -3.61114144e-01 -1.67506695e-01 -5.41252851e-01 -1.89341679e-01 1.23941205e-01 3.08276385e-01 9.64844525e-02 -8.05873692e-01 -3.89379233e-01 3.57205540e-01 -4.79067653e-01 2.40977362e-01 6.41067445e-01 1.14223123e+00 9.08804715e-01 -7.90043324e-02 6.53510392e-01 -7.68860638e-01 3.53958122e-02 -6.72573388e-01 -3.33570391e-01 -1.04973882e-01 -3.22949767e-01 1.61412179e-01 7.30538309e-01 -9.67624903e-01 -8.10204685e-01 2.21919060e-01 -2.65807778e-01 -1.15223193e+00 -1.22892916e-01 3.46173704e-01 -9.81861260e-03 2.61035621e-01 1.10962677e+00 6.17288888e-01 1.27425507e-01 -8.06145072e-01 4.32767004e-01 7.05973327e-01 5.42126119e-01 -3.23477536e-01 7.21242964e-01 4.66361582e-01 -7.44085789e-01 -7.59968221e-01 -9.44947422e-01 -1.38672620e-01 -2.28186950e-01 -3.80470492e-02 6.57784522e-01 -1.64895952e+00 -2.39925146e-01 3.01355064e-01 -1.10062540e+00 -7.08055139e-01 -7.42777228e-01 5.23817360e-01 -5.82015395e-01 -5.98067902e-02 -7.25389957e-01 -5.46886861e-01 -2.60351419e-01 -9.67147946e-01 1.00784421e+00 -2.74420053e-01 -1.93354905e-01 -4.98274624e-01 1.57896295e-01 1.30713686e-01 5.36706865e-01 -2.45268121e-01 6.37167633e-01 -5.07065713e-01 -6.39905751e-01 -3.62863578e-02 2.64585279e-02 7.56535828e-01 1.13634706e-01 -3.88361633e-01 -1.64804649e+00 -3.01865578e-01 -1.47692144e-01 -7.59371936e-01 1.47063994e+00 3.65923256e-01 1.39767826e+00 -7.79854655e-01 -5.45026772e-02 7.33871281e-01 1.11228812e+00 -1.24811374e-01 7.74672687e-01 -7.52467886e-02 3.62004280e-01 3.25167030e-01 1.17308155e-01 3.23089868e-01 9.25437063e-02 5.32572210e-01 4.39987063e-01 -1.23499699e-01 -6.41274035e-01 -8.75902653e-01 8.89512599e-01 1.27058077e+00 1.47882923e-01 1.51242733e-01 -3.36279571e-01 6.70991778e-01 -1.69976652e+00 -1.15341735e+00 3.51833850e-01 1.87718093e+00 1.11406136e+00 2.52927125e-01 1.69818312e-01 2.98417926e-01 4.63988423e-01 3.28100622e-01 -5.82893848e-01 -8.83105099e-02 -3.49267751e-01 5.07869482e-01 1.35698572e-01 6.26167536e-01 -8.33037794e-01 1.03547382e+00 7.02048397e+00 9.25870478e-01 -1.52880883e+00 4.87103105e-01 5.53332627e-01 -2.86197990e-01 -3.49201828e-01 -2.95488792e-03 -7.31001377e-01 5.65574586e-01 1.08536232e+00 1.49450928e-01 7.84301102e-01 1.09978747e+00 -6.68454915e-02 2.62435108e-01 -1.26563406e+00 1.03321791e+00 5.69093674e-02 -1.71845543e+00 1.37275860e-01 -1.74131110e-01 8.20900977e-01 3.17550331e-01 2.59083420e-01 6.77476466e-01 2.66166836e-01 -1.13476789e+00 1.06589389e+00 4.01721030e-01 9.90446866e-01 -4.54278797e-01 2.36073405e-01 4.74187821e-01 -1.01748610e+00 -6.60435343e-03 -6.89786911e-01 -2.84092903e-01 -2.28323922e-01 4.93474573e-01 -9.47182298e-01 1.32962719e-01 7.95397103e-01 9.93691027e-01 -2.81885564e-01 7.35479057e-01 -6.32902205e-01 1.19993556e+00 -1.20370969e-01 2.35997736e-01 1.54017672e-01 4.79877263e-01 3.08712065e-01 1.03379822e+00 7.03628585e-02 -3.32756042e-02 2.10792914e-01 1.00424695e+00 -2.24222094e-01 -3.90835963e-02 -5.28915942e-01 -1.16880797e-01 3.77488345e-01 6.17044747e-01 -1.53173521e-01 -5.99661529e-01 -2.39878759e-01 1.18084180e+00 4.36311156e-01 5.62193274e-01 -7.71754146e-01 -6.86111450e-02 7.03942835e-01 2.93708622e-01 7.44699538e-01 1.33592442e-01 -3.15400846e-02 -1.31536198e+00 -1.34167731e-01 -1.17782080e+00 2.64707506e-01 -1.05393386e+00 -1.34553123e+00 1.05314910e+00 -2.19298840e-01 -1.63841259e+00 -2.43301675e-01 -4.67929602e-01 -6.45164669e-01 7.20824838e-01 -1.67580140e+00 -9.21621323e-01 -2.54664898e-01 8.51454616e-01 7.54083574e-01 -5.63101172e-01 1.16524661e+00 2.11571947e-01 -2.43910223e-01 7.29802668e-01 7.31440857e-02 8.69626328e-02 6.62814856e-01 -8.86152923e-01 4.13776994e-01 5.33103466e-01 7.65028656e-01 5.68169914e-02 4.05874580e-01 -4.74430948e-01 -1.06531370e+00 -1.29905427e+00 5.03066361e-01 2.13979166e-02 6.32669508e-01 -4.47431505e-01 -1.01730812e+00 9.54090238e-01 4.22530234e-01 3.91891211e-01 5.60703814e-01 -1.01459935e-01 -6.95405543e-01 -2.08396256e-01 -7.53012717e-01 1.13957159e-01 8.79370689e-01 -9.35752392e-01 -7.60236442e-01 5.89211643e-01 1.20083117e+00 -5.36081970e-01 -5.05653441e-01 -4.06690575e-02 1.89520732e-01 -8.29831898e-01 8.71729493e-01 -7.98665345e-01 5.92361152e-01 -2.48780280e-01 -5.16646385e-01 -1.71435165e+00 -3.93231302e-01 -5.95183551e-01 -5.34257233e-01 6.98440015e-01 7.76533186e-01 -5.55963933e-01 1.02168357e+00 -3.17524016e-01 -2.66661793e-01 -7.20972121e-01 -1.03824341e+00 -9.51240182e-01 -6.27662579e-04 -4.80956763e-01 7.55567610e-01 8.29837918e-01 1.30041027e-02 2.47736961e-01 -1.04787707e+00 3.53721261e-01 4.67742741e-01 3.36460620e-01 6.69710815e-01 -8.66123140e-01 -1.02483475e+00 -2.75687873e-02 -3.13758433e-01 -1.60546100e+00 3.01462293e-01 -1.08297384e+00 2.04561815e-01 -1.16277742e+00 -9.54971611e-02 -2.68246680e-01 -5.10021925e-01 1.02175570e+00 9.23956186e-02 4.16036338e-01 2.01372355e-02 3.64104152e-01 -3.02729636e-01 8.45455289e-01 1.18131971e+00 -6.65815532e-01 -2.99973875e-01 6.21111989e-02 -7.57759631e-01 4.76144552e-01 7.18318999e-01 -5.76394141e-01 -4.05205458e-01 -6.04169011e-01 -1.33464932e-01 1.59715503e-01 4.94709760e-01 -9.94583368e-01 1.02464907e-01 9.08335969e-02 8.28798950e-01 -5.95663667e-01 8.14107358e-01 -6.02237105e-01 1.97033197e-01 3.99998605e-01 -5.10065317e-01 -4.69332099e-01 3.22362334e-01 5.91977537e-01 -6.93390012e-01 1.40364274e-01 6.36797249e-01 -3.03254962e-01 -8.89177859e-01 2.68577188e-01 -4.31062967e-01 1.54369110e-02 6.07869983e-01 -2.54046679e-01 -2.23640889e-01 -6.37030900e-01 -1.28976274e+00 9.20502469e-02 1.86862908e-02 4.64095533e-01 1.08107746e+00 -1.63599944e+00 -8.47751260e-01 7.38822460e-01 -1.98059436e-02 -1.64871410e-01 3.79387379e-01 5.74597716e-01 -1.11925565e-01 3.52012850e-02 -1.65837064e-01 -6.32422745e-01 -8.91474366e-01 7.29825795e-01 4.29980665e-01 1.24829866e-01 -1.00455797e+00 1.37393892e+00 2.96596617e-01 -3.57060224e-01 6.40130162e-01 -3.44173580e-01 1.61539242e-02 6.21158862e-04 8.50304723e-01 -8.86007026e-02 3.08115799e-02 -4.50877875e-01 -2.45947212e-01 1.16437420e-01 -1.09086752e-01 -9.87444818e-02 1.55934656e+00 2.61447877e-01 2.85405785e-01 6.06857479e-01 1.12864542e+00 -7.23121986e-02 -1.62737370e+00 -6.38650715e-01 -5.04624188e-01 -3.47553819e-01 1.61453933e-01 -7.47205496e-01 -1.52409971e+00 1.23457968e+00 5.71045399e-01 -1.06222918e-02 1.23419797e+00 1.65308729e-01 6.17443204e-01 3.00166249e-01 4.10240442e-01 -8.49700809e-01 7.00854838e-01 3.57995391e-01 1.24505723e+00 -1.11305666e+00 -2.38265514e-01 -2.18515933e-01 -1.12917316e+00 9.44672525e-01 8.04027677e-01 -4.04443085e-01 9.29929495e-01 2.03373939e-01 2.59860396e-01 -2.70807464e-02 -1.22124445e+00 -5.10651320e-02 3.61774892e-01 5.12090385e-01 7.61683807e-02 1.91089094e-01 7.89927542e-01 1.11241210e+00 -3.49731356e-01 -9.39655975e-02 1.51954636e-01 5.85617185e-01 -5.09457707e-01 -6.95925057e-01 -4.25135672e-01 3.75068337e-01 -1.00626811e-01 -2.87206978e-01 -4.17115360e-01 4.65656996e-01 2.16313258e-01 8.27616394e-01 9.88339111e-02 -8.84287715e-01 2.30640784e-01 1.17400356e-01 3.05323929e-01 -8.03935885e-01 -5.75195789e-01 2.17413232e-01 -6.04912080e-02 -6.75592780e-01 -2.38497734e-01 -1.17200039e-01 -1.13928771e+00 -7.52259120e-02 -4.36354339e-01 2.98705339e-01 3.01555574e-01 6.03983641e-01 5.43857813e-01 7.21241117e-01 9.54899907e-01 -1.12807882e+00 -8.24347615e-01 -1.23535120e+00 -7.77005494e-01 1.33273929e-01 6.76658809e-01 -5.55414915e-01 -5.49333632e-01 1.80925578e-01]	[9.414860725402832, 1.4334845542907715]
418e4e4c-621e-4030-b6f4-03992360e92e	scene-restoring-for-narrative-machine-reading	null	null	https://aclanthology.org/2020.emnlp-main.247	https://aclanthology.org/2020.emnlp-main.247.pdf	Scene Restoring for Narrative Machine Reading Comprehension	This paper focuses on machine reading comprehension for narrative passages. Narrative passages usually describe a chain of events. When reading this kind of passage, humans tend to restore a scene according to the text with their prior knowledge, which helps them understand the passage comprehensively. Inspired by this behavior of humans, we propose a method to let the machine imagine a scene during reading narrative for better comprehension. Specifically, we build a scene graph by utilizing Atomic as the external knowledge and propose a novel Graph Dimensional-Iteration Network (GDIN) to encode the graph. We conduct experiments on the ROCStories, a dataset of Story Cloze Test (SCT), and CosmosQA, a dataset of multiple choice. Our method achieves state-of-the-art.	['Zhicheng Sheng', 'Yantao Jia', 'Jun Zhao', 'Kang Liu', 'Yuanzhe Zhang', 'Zhixing Tian']	null	null	null	null	emnlp-2020-11	['cloze-test']	['natural-language-processing']	[ 1.89560756e-01 1.84832960e-01 -8.93845037e-02 -2.55789995e-01 -4.30024087e-01 -6.19397104e-01 6.25678360e-01 4.32000607e-01 -1.56983227e-01 5.08988917e-01 9.87578630e-01 -3.64071727e-01 -4.08624709e-02 -1.20435834e+00 -8.37132752e-01 -4.10777256e-02 2.75101513e-01 4.22753483e-01 2.28906170e-01 -4.53304559e-01 5.37328899e-01 -1.60994112e-01 -1.02100348e+00 6.87219143e-01 9.25053298e-01 2.86736101e-01 4.42089558e-01 7.97384918e-01 -2.33277664e-01 1.64022481e+00 -7.33537734e-01 -7.84562230e-01 -3.36683661e-01 -1.09115577e+00 -1.42896271e+00 1.70105875e-01 7.35045895e-02 -5.43122292e-01 -8.13873708e-01 9.43049729e-01 7.91229904e-02 6.62815094e-01 9.40630317e-01 -6.81620598e-01 -1.02104282e+00 1.16384673e+00 -3.64050627e-01 3.87093723e-01 1.21270299e+00 2.07705900e-01 1.13511050e+00 -6.12846613e-01 8.97739112e-01 1.33272541e+00 3.44259381e-01 4.00832206e-01 -8.54711413e-01 -4.98151220e-02 3.02665651e-01 9.31072533e-01 -1.19104767e+00 -1.56868249e-01 1.04567707e+00 -1.93932235e-01 1.01264107e+00 3.23728383e-01 9.64822412e-01 1.38933933e+00 1.39899209e-01 9.84053850e-01 7.72251785e-01 -3.12613428e-01 2.00601920e-01 -2.85301805e-01 4.09114808e-01 6.09437287e-01 -1.15124896e-01 -3.17706645e-01 -8.41521919e-01 3.85266334e-01 7.83603072e-01 -3.29377174e-01 -7.42806852e-01 7.03367740e-02 -1.31659818e+00 8.28389764e-01 4.21810627e-01 1.59534678e-01 -4.27784353e-01 -1.17317975e-01 3.29310536e-01 2.73598403e-01 1.84817821e-01 7.14243829e-01 2.75726229e-01 -4.25548464e-01 -5.98867416e-01 4.19149011e-01 1.14776230e+00 9.51667011e-01 1.38565645e-01 -2.87892818e-01 -4.81682271e-01 6.37152851e-01 6.82604760e-02 8.84291232e-02 3.51665646e-01 -7.35631287e-01 8.37462604e-01 6.71110988e-01 -7.15852901e-02 -1.43766689e+00 -4.66710567e-01 -4.86251235e-01 -1.08155239e+00 -5.15111089e-01 2.98571169e-01 3.14331591e-01 -4.39974815e-01 1.52164865e+00 4.74345759e-02 3.93744081e-01 3.70852143e-01 1.16171598e+00 1.24258196e+00 1.02402592e+00 -4.47271690e-02 -4.03028160e-01 1.36397135e+00 -1.21657467e+00 -8.82026136e-01 -2.72752821e-01 4.69411910e-01 -3.89815450e-01 1.69103026e+00 6.48985505e-01 -1.32528043e+00 -5.85423768e-01 -1.25209653e+00 -4.27224845e-01 -5.62459528e-02 -2.12698787e-01 3.20517689e-01 6.97070360e-02 -7.35408485e-01 5.82274914e-01 -4.75218534e-01 -4.35105920e-01 4.64653760e-01 -5.91196239e-01 -1.33104280e-01 -3.64060253e-01 -1.40232182e+00 1.00351393e+00 7.93299675e-01 -2.20189746e-02 -1.23083997e+00 -5.54179072e-01 -8.67485821e-01 3.07984024e-01 6.56099319e-01 -1.00064647e+00 1.29601908e+00 -5.43591797e-01 -1.53072703e+00 8.26807380e-01 -1.37617618e-01 -3.82854193e-01 6.74874902e-01 -5.18353701e-01 -3.65966320e-01 5.85280359e-01 1.28928781e-01 2.77158648e-01 5.37999153e-01 -1.26277494e+00 -1.65387690e-02 1.21006437e-01 6.89901352e-01 5.53386033e-01 -8.02246574e-03 -2.33200699e-01 -3.88629586e-01 -7.19221652e-01 2.98679620e-01 -2.99448013e-01 -8.42609778e-02 -5.18478215e-01 -7.93045044e-01 -3.60660046e-01 3.07981074e-01 -1.07254910e+00 1.58608615e+00 -1.88833344e+00 6.23007476e-01 -5.50443605e-02 5.43703318e-01 -2.34624371e-01 -2.71888047e-01 8.74768436e-01 3.34477946e-02 3.18398565e-01 -2.52666503e-01 -2.14555904e-01 -8.41332078e-02 1.37417316e-01 -5.07985890e-01 1.50139630e-01 -7.82070681e-02 1.13831639e+00 -1.23072135e+00 -5.74349284e-01 -9.00122523e-02 -3.64557356e-02 -4.67380911e-01 4.96684015e-01 -7.91186690e-01 4.72873062e-01 -5.13170123e-01 1.91191599e-01 5.01038492e-01 -6.83179796e-01 -9.52926278e-03 -6.05573580e-02 2.67391741e-01 4.11109656e-01 -8.16315830e-01 2.17667532e+00 -1.57906756e-01 7.21322656e-01 -9.08357382e-01 -7.93801308e-01 6.92490339e-01 2.02347249e-01 -4.24866915e-01 -7.90160656e-01 2.70767033e-01 -5.43772757e-01 -2.64152616e-01 -1.13978028e+00 6.73762858e-01 -7.84815773e-02 -1.62903622e-01 6.88175201e-01 -2.13077128e-01 -2.98391253e-01 3.54266852e-01 8.71677756e-01 1.06767666e+00 2.18029544e-01 5.76379538e-01 4.83737588e-02 4.51471657e-01 3.47982466e-01 -1.34253437e-02 1.04035676e+00 2.07635716e-01 6.31817281e-01 9.77173626e-01 -3.97326440e-01 -9.61467862e-01 -1.32783663e+00 4.90017354e-01 8.52593303e-01 6.51370287e-01 -7.49682248e-01 -9.63759482e-01 -4.28599685e-01 -7.97660589e-01 1.53036594e+00 -5.67514479e-01 -2.06906632e-01 -6.61742330e-01 -3.85994762e-01 5.67996681e-01 5.20742059e-01 1.12415886e+00 -1.16486645e+00 -6.93192720e-01 3.54422241e-01 -8.62234473e-01 -1.19842708e+00 -3.55318010e-01 -5.87603629e-01 -5.26762545e-01 -1.15814662e+00 -2.39670396e-01 -8.65028143e-01 3.95424724e-01 4.02919620e-01 1.46005869e+00 5.53339481e-01 2.66067296e-01 3.72174621e-01 -9.52492535e-01 -3.31549696e-03 -5.41671574e-01 9.33559611e-02 -4.35392976e-01 -2.99988776e-01 1.38115644e-01 -6.59936070e-01 -4.32144701e-01 7.66896158e-02 -1.01202047e+00 9.22716737e-01 9.15027484e-02 7.55885303e-01 3.96514118e-01 3.25253934e-01 4.15851980e-01 -7.95568764e-01 1.29647505e+00 -6.34041429e-01 -5.99493757e-02 6.43327057e-01 -2.14875564e-02 -1.47697672e-01 9.13414061e-01 -4.94085908e-01 -1.39269483e+00 -5.77480614e-01 -1.22522758e-02 -1.31001920e-01 -8.20497200e-02 1.01158178e+00 -2.39089116e-01 6.02774382e-01 8.07026446e-01 5.07901967e-01 -5.30138075e-01 -2.70883292e-01 6.85581267e-01 1.64336056e-01 9.93508518e-01 -6.67110145e-01 7.28898942e-01 1.91435575e-01 -2.12980151e-01 -6.23179376e-01 -1.43597567e+00 -2.79391855e-01 -7.65442371e-01 -6.46661878e-01 1.20106995e+00 -7.17920125e-01 -8.61716747e-01 3.73389333e-01 -1.55323803e+00 -3.77242655e-01 -1.83809847e-02 4.62725729e-01 -7.76714385e-01 3.72712433e-01 -7.02608049e-01 -4.49711829e-01 4.81292531e-02 -7.12682903e-01 4.83352661e-01 5.58347523e-01 -5.22866964e-01 -1.08416069e+00 1.60923898e-01 4.61387992e-01 -2.54660472e-02 6.05924666e-01 1.25202250e+00 -5.53780675e-01 -7.79011548e-01 2.33162776e-01 -2.55759716e-01 -1.71572566e-01 -2.13955551e-01 -3.49414289e-01 -6.88085198e-01 2.90322769e-02 2.49926597e-01 -5.83320856e-01 8.52195919e-01 3.98527551e-03 1.21159828e+00 -6.20603204e-01 -1.11646339e-01 5.18521369e-01 1.29733181e+00 6.18170202e-02 1.07644629e+00 3.90682429e-01 8.90034497e-01 4.39961165e-01 4.38233763e-01 5.41754007e-01 8.95844519e-01 7.60730579e-02 2.58538693e-01 2.63171673e-01 -1.75090685e-01 -9.73385870e-01 9.91320908e-02 1.19555771e+00 -2.10440367e-01 -9.47418392e-01 -9.45102096e-01 5.19189954e-01 -1.86494517e+00 -1.31513417e+00 -2.60194659e-01 1.38250995e+00 7.99754441e-01 3.50555509e-01 -2.11546108e-01 1.33693427e-01 5.40417612e-01 5.55743456e-01 -5.41913211e-01 -3.26045096e-01 -4.61534530e-01 -2.83357650e-02 -1.93959698e-01 6.23694539e-01 -7.21690774e-01 1.28483653e+00 6.34129143e+00 8.87966990e-01 -5.19501388e-01 -1.30369917e-01 6.87011778e-01 1.29668802e-01 -5.29443383e-01 1.41476452e-01 -7.98587948e-02 5.92565222e-04 5.81251264e-01 -6.06476605e-01 9.70453680e-01 4.27415341e-01 2.66058415e-01 -5.14593959e-01 -1.17242205e+00 9.21367168e-01 5.30829906e-01 -1.56300449e+00 5.58757186e-01 -7.12809563e-01 5.45392394e-01 -6.79333031e-01 -2.35905096e-01 3.65794033e-01 1.39821157e-01 -1.42562771e+00 8.11987638e-01 8.40675116e-01 5.95881939e-01 -7.65760958e-01 4.48644459e-01 8.30010116e-01 -1.05272734e+00 3.53085250e-01 -3.74550104e-01 -5.94399095e-01 5.32409489e-01 7.74406316e-03 -8.39487135e-01 8.00867498e-01 4.22397286e-01 9.12557542e-01 -8.49877238e-01 8.23641121e-01 -9.44675505e-01 9.16210115e-01 2.40126446e-01 -5.19433737e-01 2.32580498e-01 -1.60953149e-01 8.17021549e-01 1.01108181e+00 6.78194687e-02 1.01335359e+00 9.80587676e-02 1.04232788e+00 -3.25175405e-01 1.71060801e-01 -3.71123880e-01 -1.90605357e-01 3.22850257e-01 8.14211607e-01 -7.99166083e-01 -6.02266014e-01 -2.31000170e-01 1.24556541e+00 7.07592964e-01 3.99234712e-01 -9.73208547e-01 -2.14038372e-01 -9.11555663e-02 1.32180424e-02 -8.80611539e-02 -2.88496971e-01 -4.55240995e-01 -1.30390477e+00 1.53120607e-01 -9.77134943e-01 5.05037904e-01 -1.47451866e+00 -1.25699127e+00 6.85428619e-01 4.02753323e-01 -1.12844372e+00 -3.13783512e-02 -1.91578567e-01 -1.05617130e+00 4.77596998e-01 -1.08037591e+00 -8.42332006e-01 -7.41398633e-01 5.82931101e-01 9.68281090e-01 8.82797018e-02 6.56865358e-01 -4.27199215e-01 -2.62021422e-01 2.98000485e-01 -4.32949901e-01 2.64051795e-01 3.33705693e-01 -1.07727027e+00 7.01583564e-01 9.92308259e-01 3.15152198e-01 6.15823925e-01 9.72044528e-01 -9.79639173e-01 -1.21235991e+00 -6.98496521e-01 7.52963841e-01 -4.25927311e-01 7.38460660e-01 -1.05625629e-01 -1.32514131e+00 7.94503927e-01 7.88229227e-01 -9.94557500e-01 6.09121859e-01 1.64317247e-02 -4.16566700e-01 5.96613050e-01 -8.34170401e-01 1.01593852e+00 1.66890383e+00 -5.40802419e-01 -1.49688840e+00 6.45089149e-01 1.17440820e+00 -8.38081419e-01 -6.56659782e-01 2.85465573e-03 1.88343614e-01 -8.56002927e-01 7.59917736e-01 -8.88378084e-01 1.19041586e+00 -2.93678671e-01 -4.34260070e-02 -1.75501227e+00 -4.50502992e-01 -6.60687387e-01 5.32272011e-02 1.02785528e+00 2.72153467e-01 -1.38243929e-01 4.06216204e-01 2.81462342e-01 -2.00166360e-01 -4.14480269e-01 -6.79206908e-01 -5.01277804e-01 8.69125314e-03 -5.29453754e-01 8.41429651e-01 1.04006445e+00 5.06066680e-01 7.81643510e-01 -4.11469966e-01 1.59357354e-01 4.01592851e-01 -5.25206514e-03 7.40947723e-01 -8.47075224e-01 -3.97943795e-01 -4.28982496e-01 -4.90972772e-02 -1.53508639e+00 2.10948125e-01 -9.52632129e-01 -7.92158321e-02 -2.08679223e+00 5.71123838e-01 4.78323758e-01 1.57227084e-01 -5.37550636e-02 -3.80612433e-01 -3.08896989e-01 2.53312469e-01 1.22531988e-01 -1.08560574e+00 6.25183403e-01 1.92591763e+00 -3.08366060e-01 -2.20328942e-01 -4.87144530e-01 -7.44569242e-01 7.61026621e-01 7.73702264e-01 -2.10235491e-01 -8.15869212e-01 -6.37906313e-01 7.46038377e-01 6.39326632e-01 6.86924577e-01 -9.96087849e-01 5.78165472e-01 -3.67721587e-01 5.26675701e-01 -9.83291030e-01 1.32240191e-01 -2.62344331e-01 2.15024993e-01 1.12961061e-01 -7.53946543e-01 4.03019994e-01 2.71164346e-03 6.48208320e-01 -3.86458248e-01 -2.65777469e-01 3.31526488e-01 -3.30003440e-01 -8.60141158e-01 -7.69695714e-02 -4.64092374e-01 5.07996321e-01 7.92658567e-01 -2.70427704e-01 -8.51069033e-01 -1.00264347e+00 -7.94710159e-01 4.66387779e-01 3.81562591e-01 4.77299213e-01 1.15086496e+00 -1.41761589e+00 -1.10980022e+00 -2.71543622e-01 1.83402598e-01 2.06557542e-01 6.15364611e-01 3.12179804e-01 -9.47874784e-01 5.84937334e-02 -1.70658603e-01 -4.18238074e-01 -9.42516446e-01 6.89696789e-01 2.03345299e-01 -2.31181175e-01 -1.06793272e+00 8.59097660e-01 2.83921599e-01 2.59176582e-01 6.54101931e-03 -1.87461972e-01 -8.83709848e-01 -7.64294118e-02 8.75075281e-01 3.19400221e-01 -4.53259826e-01 -4.45477247e-01 7.22633153e-02 5.04490077e-01 -9.87434983e-02 -2.60221630e-01 1.12491000e+00 -3.17340165e-01 -1.67206392e-01 6.99833572e-01 9.29253697e-01 -2.00714648e-01 -1.00928378e+00 -1.78171247e-01 5.21515869e-02 -5.22970438e-01 -4.18363571e-01 -9.15260255e-01 -3.40790331e-01 6.99984610e-01 -3.99817437e-01 3.45846534e-01 1.21780920e+00 2.88135141e-01 8.58856916e-01 6.89142466e-01 2.70623296e-01 -9.23480392e-01 5.99583566e-01 8.34876955e-01 1.46013963e+00 -8.42042685e-01 1.18590973e-01 -4.81446415e-01 -1.15463758e+00 1.26433241e+00 6.78463399e-01 -1.59770176e-01 1.07156508e-01 -3.17767590e-01 -1.35655940e-01 -4.18630660e-01 -9.03334677e-01 9.67052430e-02 2.88180590e-01 4.35541689e-01 7.68428892e-02 2.26987794e-01 -2.93573409e-01 1.00278330e+00 -8.37064147e-01 -1.51228294e-01 1.00995672e+00 6.03543401e-01 -5.35007656e-01 -3.62799436e-01 -3.36717218e-01 2.30561227e-01 6.36910796e-02 -2.54746079e-01 -6.25203073e-01 8.18742812e-01 -3.35809350e-01 1.23818409e+00 6.56148186e-03 -4.70261604e-01 5.55193007e-01 -1.58802509e-01 7.38821864e-01 -4.28207695e-01 -4.51495856e-01 -3.28299224e-01 4.01859373e-01 -5.59085727e-01 -2.93677777e-01 -5.33528924e-01 -1.44722307e+00 -5.88990867e-01 6.12562522e-02 1.19334526e-01 3.22165526e-02 1.15094769e+00 -2.75254339e-01 8.43542337e-01 4.34899479e-01 -2.49026701e-01 2.85553336e-02 -1.02983022e+00 -3.33107382e-01 6.89851761e-01 8.55591968e-02 -4.07314897e-01 -2.73099422e-01 3.55718702e-01]	[11.2647123336792, 8.813675880432129]
65d20078-ae14-4e1b-ac13-860d91577e48	desnet-decomposed-scale-consistent-network	2211.10994	null	https://arxiv.org/abs/2211.10994v1	https://arxiv.org/pdf/2211.10994v1.pdf	DesNet: Decomposed Scale-Consistent Network for Unsupervised Depth Completion	Unsupervised depth completion aims to recover dense depth from the sparse one without using the ground-truth annotation. Although depth measurement obtained from LiDAR is usually sparse, it contains valid and real distance information, i.e., scale-consistent absolute depth values. Meanwhile, scale-agnostic counterparts seek to estimate relative depth and have achieved impressive performance. To leverage both the inherent characteristics, we thus suggest to model scale-consistent depth upon unsupervised scale-agnostic frameworks. Specifically, we propose the decomposed scale-consistent learning (DSCL) strategy, which disintegrates the absolute depth into relative depth prediction and global scale estimation, contributing to individual learning benefits. But unfortunately, most existing unsupervised scale-agnostic frameworks heavily suffer from depth holes due to the extremely sparse depth input and weak supervised signal. To tackle this issue, we introduce the global depth guidance (GDG) module, which attentively propagates dense depth reference into the sparse target via novel dense-to-sparse attention. Extensive experiments show the superiority of our method on outdoor KITTI benchmark, ranking 1st and outperforming the best KBNet more than 12% in RMSE. In addition, our approach achieves state-of-the-art performance on indoor NYUv2 dataset.	['Jian Yang', 'Jun Li', 'Zhenyu Zhang', 'Xiang Li', 'Kun Wang', 'Zhiqiang Yan']	2022-11-20	null	null	null	null	['depth-completion']	['computer-vision']	[ 3.07158440e-01 1.24775171e-01 -2.72354603e-01 -5.68586230e-01 -1.08091426e+00 -1.65371448e-01 3.12100947e-01 -9.55062807e-02 -3.00178081e-01 7.34583318e-01 4.70701903e-01 3.06285322e-01 -2.27931872e-01 -9.98030722e-01 -6.40671074e-01 -7.87140250e-01 2.19723314e-01 4.26514119e-01 2.02452749e-01 -1.67352576e-02 3.08751404e-01 4.42633092e-01 -1.46488857e+00 -1.43696442e-01 1.13930869e+00 1.32792354e+00 2.37400308e-01 3.34957629e-01 -2.38158479e-01 9.48934674e-01 -1.44836262e-01 -2.11084094e-02 4.38045353e-01 2.09031682e-02 -4.82613236e-01 4.61157300e-02 9.01014507e-01 -7.72322834e-01 -6.57387972e-01 1.06006885e+00 5.25543213e-01 2.05688160e-02 3.98691177e-01 -1.02765083e+00 -4.11191851e-01 4.01318640e-01 -8.92382145e-01 1.47312760e-01 3.18451762e-01 6.45130575e-02 1.02616358e+00 -1.04707253e+00 4.60321516e-01 1.00099766e+00 5.03174365e-01 4.38974887e-01 -1.05827367e+00 -6.15179479e-01 4.84228492e-01 3.82003747e-02 -1.47752297e+00 -4.88119334e-01 1.08852601e+00 -2.67557234e-01 7.75644779e-01 -3.51535790e-02 6.09880090e-01 9.36938047e-01 -1.65629357e-01 7.74663866e-01 1.09752083e+00 5.24008423e-02 4.43259001e-01 -4.50198770e-01 -1.61162153e-01 7.01483071e-01 3.55127245e-01 1.71934932e-01 -8.67014349e-01 3.15968484e-01 1.04427576e+00 1.74772307e-01 -5.56086302e-01 -5.23374259e-01 -1.25808012e+00 6.72509849e-01 9.82240736e-01 -1.18169948e-01 -4.37753886e-01 4.33514774e-01 1.58510447e-01 -2.28986349e-02 7.09270895e-01 1.83670625e-01 -5.24070799e-01 -7.06857592e-02 -9.21047151e-01 3.05337943e-02 2.74779052e-01 9.55216885e-01 1.35652864e+00 6.51312321e-02 1.24436513e-01 5.63306570e-01 4.75547820e-01 5.72233796e-01 4.09258187e-01 -1.13687909e+00 7.86058664e-01 7.46385038e-01 -9.06788334e-02 -9.00660038e-01 -5.11165023e-01 -5.51212311e-01 -1.03082597e+00 6.49069622e-02 3.72112125e-01 1.15727432e-01 -1.09562075e+00 1.64714110e+00 4.85917658e-01 4.09764856e-01 1.65431842e-01 1.12655747e+00 1.03205729e+00 4.50383037e-01 -1.52179524e-01 -8.90723094e-02 7.89917231e-01 -1.02597988e+00 -3.42861533e-01 -8.35359991e-01 4.60441887e-01 -3.77412558e-01 9.96055782e-01 4.57135171e-01 -8.59649122e-01 -4.29866731e-01 -1.17385364e+00 -4.67231095e-01 1.22443981e-01 -1.90470787e-03 9.73105371e-01 3.51722002e-01 -9.55877721e-01 5.66626489e-01 -1.05457723e+00 -1.75694138e-01 5.31866729e-01 1.25835508e-01 -4.12688494e-01 -6.05759442e-01 -6.61281049e-01 1.65224463e-01 1.42912596e-01 2.17183948e-01 -9.24560308e-01 -9.76151526e-01 -1.15645003e+00 -2.32132509e-01 5.28042436e-01 -8.90095532e-01 1.00433660e+00 -5.46188116e-01 -1.38988686e+00 5.76564789e-01 -3.11835110e-01 -3.05708885e-01 4.94681865e-01 -3.81967008e-01 1.52195126e-01 4.95613605e-01 4.97043222e-01 8.99096727e-01 7.38136053e-01 -1.33075857e+00 -7.81584859e-01 -7.82617986e-01 1.95201293e-01 2.98303574e-01 -3.26265842e-01 -9.47169006e-01 -6.04402125e-01 -5.20629168e-01 1.11797988e+00 -5.85121095e-01 -3.52273166e-01 3.40317696e-01 -3.35269332e-01 6.67971298e-02 5.46107590e-01 -4.13186193e-01 9.93470371e-01 -2.02475691e+00 3.15135300e-01 6.98135868e-02 5.57265043e-01 -5.39997220e-01 4.41209879e-03 7.76880905e-02 2.72110641e-01 -3.14208746e-01 -5.14749527e-01 -8.06352973e-01 -1.80542290e-01 4.54752237e-01 -1.77594870e-01 6.18455768e-01 1.51395231e-01 8.12678695e-01 -1.13198841e+00 -4.12121892e-01 4.13971990e-01 6.20026886e-01 -7.70401537e-01 1.82039589e-01 -1.55920267e-01 6.74104512e-01 -6.73355460e-01 1.10661125e+00 9.05757070e-01 -2.78791815e-01 -1.65695384e-01 -5.39516687e-01 -2.63545454e-01 3.04959655e-01 -1.07345533e+00 2.45077920e+00 -7.08695650e-01 3.84630919e-01 1.11168005e-01 -7.72224903e-01 9.13024843e-01 -1.98098153e-01 6.41115367e-01 -8.59907866e-01 -1.37395918e-01 4.10712570e-01 -4.27901208e-01 -2.34827921e-01 5.15839756e-01 -2.26196170e-01 2.25282535e-02 -9.65526700e-02 -2.12977707e-01 -5.35778403e-01 -3.43442380e-01 4.09551412e-01 1.26865125e+00 2.65875340e-01 2.46238455e-01 3.28092501e-02 5.07867157e-01 -2.93636441e-01 7.68393159e-01 4.29682612e-01 -2.33314395e-01 9.58905458e-01 1.96913034e-01 -2.17368484e-01 -6.58176780e-01 -1.20274878e+00 5.58487978e-03 6.16589606e-01 5.47990680e-01 -3.09846252e-01 -2.75170982e-01 -5.54240108e-01 3.76404114e-02 1.49124146e-01 -7.32254386e-01 2.35176310e-02 -4.66036439e-01 -4.11142200e-01 1.43015176e-01 8.55553091e-01 7.66311705e-01 -5.91060460e-01 -5.01068830e-01 1.92735478e-01 -2.85552919e-01 -1.40109801e+00 -3.36666346e-01 3.39847296e-01 -1.15260673e+00 -8.86894643e-01 -7.17042625e-01 -5.04283726e-01 5.66970170e-01 6.73073828e-01 1.06892121e+00 -1.43888265e-01 -5.20217866e-02 4.40644979e-01 -3.78880769e-01 -3.44190970e-02 4.55360740e-01 1.64944559e-01 -1.11638576e-01 -5.88780595e-03 -1.57089531e-02 -1.06131184e+00 -1.15038288e+00 9.06527787e-02 -7.87801743e-01 4.83276136e-02 7.95026362e-01 5.97061038e-01 1.00210309e+00 2.81145182e-02 4.98751730e-01 -7.39364088e-01 -1.84592187e-01 -4.11633581e-01 -5.49270093e-01 -3.19222003e-01 -6.52486801e-01 -1.34595688e-02 4.61686641e-01 1.02955326e-02 -9.65547621e-01 2.86516190e-01 -3.09950978e-01 -5.92146397e-01 -1.75110307e-02 4.15727109e-01 -5.41196525e-01 -3.31600577e-01 4.93161052e-01 3.66359413e-01 -3.41777563e-01 -4.32450384e-01 3.25131983e-01 1.92456499e-01 7.87329555e-01 -5.34925878e-01 1.02211118e+00 1.11195803e+00 2.20451325e-01 -6.90893471e-01 -1.27697170e+00 -7.87950635e-01 -5.28419614e-01 -8.98935795e-02 6.68755949e-01 -1.53411436e+00 -3.86716932e-01 5.92626512e-01 -8.78899872e-01 -5.09681106e-01 -3.76043618e-01 4.97120023e-01 -4.68743593e-01 6.04734778e-01 -5.86926937e-01 -7.23116457e-01 -2.81503528e-01 -9.82212186e-01 1.56732297e+00 1.74686596e-01 3.02609921e-01 -8.46875966e-01 1.67665328e-03 5.39154589e-01 2.85504401e-01 3.84925783e-01 4.03886139e-01 1.88342273e-01 -1.09473360e+00 -6.91301823e-02 -5.68506718e-01 3.01881284e-01 2.77702481e-01 -3.06964606e-01 -1.27435863e+00 -2.91013598e-01 -2.43283715e-02 -4.84591067e-01 1.21581841e+00 3.52064639e-01 1.20596004e+00 1.07789328e-02 -1.24377117e-01 1.12750518e+00 1.75889742e+00 -3.74295652e-01 5.84080756e-01 4.60842341e-01 1.36085725e+00 6.05446935e-01 8.32746625e-01 7.61162698e-01 8.43832552e-01 4.40255523e-01 9.41854477e-01 3.23962346e-02 -3.00172567e-01 -4.89504486e-01 8.22411105e-02 7.98321187e-01 -6.69063814e-03 -2.04437599e-02 -6.35418534e-01 4.92356837e-01 -1.71597493e+00 -6.75361991e-01 -9.18164253e-02 2.08517790e+00 6.44285142e-01 3.79457593e-01 -1.64768398e-01 2.76980340e-01 1.04697324e-01 5.22998631e-01 -9.52309072e-01 4.76900339e-01 -2.66515046e-01 2.16113105e-01 7.83136904e-01 6.65914416e-01 -8.81717563e-01 8.90672922e-01 4.69675255e+00 7.29128897e-01 -1.22221470e+00 1.28847882e-01 4.68064398e-01 -2.51654327e-01 -7.36224711e-01 -1.61838681e-02 -7.51739025e-01 3.19194824e-01 4.16799366e-01 5.21809496e-02 2.47071326e-01 1.01606774e+00 3.23845774e-01 -3.88464808e-01 -1.03786564e+00 1.27649665e+00 -2.91750785e-02 -1.16870677e+00 -8.17317665e-02 1.61602348e-01 9.87190962e-01 3.92115653e-01 -1.11438565e-01 2.39254653e-01 -4.78807241e-02 -7.28763938e-01 8.80537450e-01 4.84479964e-01 9.19335842e-01 -7.18929112e-01 5.53314447e-01 3.85278046e-01 -1.62694180e+00 -1.34737924e-01 -6.30118251e-01 -2.10511535e-01 1.63716212e-01 1.11877418e+00 -2.04778701e-01 7.53049791e-01 7.63616741e-01 1.32903385e+00 -4.97511595e-01 1.10441673e+00 -4.75935936e-01 2.65262514e-01 -4.95117188e-01 5.28236032e-01 3.34396720e-01 -2.32773155e-01 3.20622146e-01 6.60071433e-01 4.30788070e-01 1.90586284e-01 1.36081204e-01 6.06300235e-01 -1.23074412e-01 1.27943372e-02 -4.71795678e-01 3.69001776e-01 5.37494421e-01 1.25771809e+00 -7.25343585e-01 -2.23316178e-01 -4.69632000e-01 1.06554365e+00 4.40529466e-01 2.66911328e-01 -5.51973045e-01 -4.60011065e-02 8.37211311e-01 2.92528093e-01 3.56332809e-01 -3.96489888e-01 -5.39792955e-01 -1.26709616e+00 3.38011712e-01 -4.84535605e-01 2.20458582e-01 -8.53566587e-01 -1.29798853e+00 5.10115087e-01 -2.17449784e-01 -1.43763578e+00 4.49404642e-02 -3.27518374e-01 -4.54555392e-01 6.08419836e-01 -2.35013509e+00 -1.23885000e+00 -1.15746093e+00 5.80869734e-01 7.58752167e-01 3.52002859e-01 2.17191026e-01 5.35720587e-01 -5.87763667e-01 4.76757348e-01 -2.28735134e-01 -1.79125965e-01 7.57667661e-01 -1.21975136e+00 2.85669118e-01 8.07556689e-01 -1.85149051e-02 3.01109403e-01 3.44268292e-01 -6.95270836e-01 -1.63644183e+00 -1.29698706e+00 4.55416024e-01 -2.71436512e-01 6.82073832e-01 -1.04393616e-01 -8.52221787e-01 4.17618483e-01 -6.58077717e-01 4.91857827e-01 1.47856623e-01 -2.12930113e-01 -5.67942798e-01 -4.59558189e-01 -1.04733133e+00 2.90477425e-01 1.50083518e+00 -6.46244526e-01 -1.52618557e-01 2.74201632e-01 1.07736135e+00 -6.37419164e-01 -8.34546328e-01 7.50765800e-01 4.72658098e-01 -1.46322978e+00 1.17395043e+00 2.68012941e-01 8.35471392e-01 -4.52570856e-01 -6.19528532e-01 -8.64892304e-01 -1.51768327e-01 -2.02763468e-01 -3.77403468e-01 1.20414329e+00 8.08265284e-02 -6.24535143e-01 1.38577950e+00 3.78746837e-01 -5.61257064e-01 -9.33072269e-01 -1.09146738e+00 -6.95188224e-01 -9.44232941e-02 -7.81680286e-01 5.13008237e-01 7.74145067e-01 -4.97552365e-01 3.80870014e-01 -2.88783222e-01 5.62076509e-01 1.03526342e+00 1.75085172e-01 9.04356956e-01 -1.16459990e+00 -3.37012142e-01 -2.58492291e-01 -6.17211342e-01 -1.83590877e+00 3.95911634e-02 -5.91518521e-01 3.39650810e-01 -1.81109869e+00 -1.28087224e-02 -6.11588836e-01 -1.47173464e-01 3.33862185e-01 -1.98265076e-01 5.83638072e-01 -1.11450702e-01 4.10885423e-01 -6.10047936e-01 9.55430508e-01 1.33183122e+00 -1.94626257e-01 -1.29972875e-01 -1.48241192e-01 -7.83821523e-01 8.97577226e-01 5.49304903e-01 -3.89362574e-01 -7.33198762e-01 -7.31175900e-01 3.58859628e-01 1.61927655e-01 4.79368627e-01 -1.35085118e+00 2.80305684e-01 -1.88870162e-01 3.37796420e-01 -8.65314901e-01 6.75897062e-01 -7.42646396e-01 -2.64248520e-01 2.44557425e-01 3.61862965e-02 -3.36020172e-01 -2.02251911e-01 1.05954921e+00 -3.26968729e-01 1.14178710e-01 7.49990880e-01 -1.29079551e-01 -1.11810255e+00 8.11199427e-01 3.83681744e-01 7.86715150e-02 6.31032526e-01 -4.84507233e-01 -2.65040994e-01 -3.35230231e-01 -3.39968085e-01 2.23817781e-01 7.36181438e-01 3.88938040e-02 9.85319793e-01 -1.21297765e+00 -5.58012307e-01 1.98553741e-01 5.00225067e-01 9.91794705e-01 5.13555765e-01 9.60306287e-01 -5.70408225e-01 2.04281583e-01 1.54218391e-01 -8.47222984e-01 -6.53458118e-01 2.71746248e-01 1.54814899e-01 -2.80191135e-02 -9.60164905e-01 1.29082608e+00 6.11284614e-01 -2.68852115e-01 3.68896484e-01 -6.58679068e-01 4.22903113e-02 -2.41796616e-02 4.07259971e-01 3.23523343e-01 1.56199470e-01 -5.00784218e-01 -5.08639932e-01 1.05483699e+00 1.11379646e-01 4.40512970e-02 1.34485137e+00 -4.46383059e-01 4.57412004e-02 3.74082893e-01 1.32040763e+00 2.70678014e-01 -1.86123753e+00 -4.65002686e-01 -3.15991193e-01 -8.66012096e-01 4.60497051e-01 -3.07432413e-01 -1.42820501e+00 8.35764468e-01 4.90388632e-01 -3.04798633e-01 1.24891698e+00 1.42986691e-02 1.01757336e+00 4.16263968e-01 7.24192083e-01 -7.21095085e-01 4.24242139e-01 3.68330449e-01 7.20132470e-01 -1.75193155e+00 4.03732926e-01 -6.88289165e-01 -2.64373034e-01 8.38221073e-01 8.64556313e-01 -1.11828499e-01 5.99480093e-01 1.87944755e-01 3.12766917e-02 -2.58390814e-01 -4.35541362e-01 -3.03118289e-01 1.30649060e-01 6.78797245e-01 1.36060968e-01 -1.17520645e-01 8.15552399e-02 4.01712298e-01 -2.39325479e-01 -9.67770293e-02 3.45951676e-01 8.28213334e-01 -5.96841812e-01 -6.81248426e-01 -2.31388628e-01 3.53669494e-01 1.34511501e-01 -1.98372066e-01 -6.80117011e-02 6.96442664e-01 5.83401658e-02 7.60131061e-01 8.32397118e-02 -4.34510559e-01 3.97226006e-01 -5.58119416e-01 5.40464878e-01 -6.72645271e-01 9.72755477e-02 8.93181786e-02 -2.31734082e-01 -1.07001698e+00 -5.11618137e-01 -5.36410093e-01 -1.35410929e+00 -2.46093780e-01 -6.31850138e-02 -2.18627587e-01 6.13133669e-01 8.92564178e-01 8.80988091e-02 5.46874881e-01 8.09620798e-01 -1.23536944e+00 -2.18238339e-01 -8.45592141e-01 -6.33640289e-01 6.05573989e-02 5.69040179e-01 -7.03455806e-01 -8.10772002e-01 -4.33876127e-01]	[8.844820976257324, -2.64323091506958]
6de82205-2639-4161-83d9-81cb4a629e41	a-fairness-aware-hybrid-recommender-system	1809.09030	null	http://arxiv.org/abs/1809.09030v1	http://arxiv.org/pdf/1809.09030v1.pdf	A Fairness-aware Hybrid Recommender System	Recommender systems are used in variety of domains affecting people's lives. This has raised concerns about possible biases and discrimination that such systems might exacerbate. There are two primary kinds of biases inherent in recommender systems: observation bias and bias stemming from imbalanced data. Observation bias exists due to a feedback loop which causes the model to learn to only predict recommendations similar to previous ones. Imbalance in data occurs when systematic societal, historical, or other ambient bias is present in the data. In this paper, we address both biases by proposing a hybrid fairness-aware recommender system. Our model provides efficient and accurate recommendations by incorporating multiple user-user and item-item similarity measures, content, and demographic information, while addressing recommendation biases. We implement our model using a powerful and expressive probabilistic programming language called probabilistic soft logic. We experimentally evaluate our approach on a popular movie recommendation dataset, showing that our proposed model can provide more accurate and fairer recommendations, compared to a state-of-the art fair recommender system.	['Getoor Lise', 'Srinivasan Sriram', 'Thompson Spencer K.', 'Kouki Pigi', 'Farnadi Golnoosh']	2018-09-13	null	null	null	null	['movie-recommendation']	['miscellaneous']	[-6.03057183e-02 3.23408514e-01 -6.70514524e-01 -1.09318614e+00 -4.57624495e-02 -3.57760221e-01 7.13527083e-01 3.55707914e-01 -4.39659208e-01 9.55514729e-01 7.51402915e-01 -1.31661505e-01 -4.01044041e-01 -1.01999569e+00 -4.52408135e-01 -1.45676211e-01 2.97850877e-01 5.38537979e-01 8.39978755e-02 -6.03306711e-01 8.77034783e-01 -3.51654477e-02 -1.78029263e+00 4.69220906e-01 1.15939665e+00 7.57860541e-01 -3.52223724e-01 2.28233635e-01 -3.96000482e-02 9.63065445e-01 -4.06796366e-01 -1.17195451e+00 3.12440068e-01 7.11923391e-02 -5.31490922e-01 -6.14801943e-01 6.00662649e-01 -5.09390473e-01 1.58518806e-01 1.11681330e+00 6.21062279e-01 3.21153551e-01 1.00458312e+00 -1.42447257e+00 -1.31808352e+00 1.24591482e+00 -2.99449682e-01 -4.61925846e-03 5.05585074e-01 -4.29849386e-01 1.21618211e+00 -5.37701666e-01 3.79961640e-01 1.54898143e+00 7.69558907e-01 6.23349786e-01 -1.17895544e+00 -8.32300484e-01 4.76159602e-01 -5.63137494e-02 -8.95690084e-01 -3.20890874e-01 5.64169705e-01 -6.80549979e-01 5.16531050e-01 8.16778898e-01 3.26705337e-01 1.15722680e+00 3.87769371e-01 3.90436143e-01 1.33315527e+00 -2.15474576e-01 4.74469125e-01 9.41522300e-01 5.10848165e-01 9.19602066e-02 7.54812121e-01 4.52612936e-01 -4.39999729e-01 -8.21683884e-01 2.47831549e-02 4.63829041e-01 4.83259559e-02 -1.43306017e-01 -5.51138520e-01 1.09210229e+00 2.87151188e-01 -8.96719918e-02 -5.16956449e-01 -1.16370663e-01 8.42862278e-02 2.91893661e-01 8.51161540e-01 5.27377725e-01 -3.48947436e-01 2.24253193e-01 -1.08968997e+00 8.71213734e-01 9.65590954e-01 6.47374809e-01 1.28003985e-01 -5.06311178e-01 -4.72872227e-01 9.85145509e-01 8.52177083e-01 7.06043422e-01 4.51208979e-01 -9.20707762e-01 1.11975178e-01 5.61178744e-01 4.89907891e-01 -1.56258357e+00 -2.61367738e-01 -3.82058173e-01 -6.70409977e-01 2.88455307e-01 3.86110514e-01 -1.45612419e-01 -2.59967655e-01 1.74970269e+00 2.96842813e-01 -2.85210758e-01 -2.24408865e-01 1.07267177e+00 8.52669716e-01 3.07219505e-01 2.87108272e-01 -1.47413522e-01 1.20336604e+00 -4.73389506e-01 -6.59479737e-01 -3.06128729e-02 2.58969039e-01 -7.51147807e-01 8.73603463e-01 6.41667306e-01 -9.62356925e-01 -2.16306746e-01 -8.05421114e-01 9.91238207e-02 -4.26628262e-01 -5.36884964e-02 7.50436306e-01 1.28811765e+00 -5.78400970e-01 9.85561430e-01 2.41065457e-01 -3.28748077e-01 4.37158287e-01 3.00795645e-01 2.21474111e-01 2.95732487e-02 -1.51803493e+00 1.00830674e+00 -2.27455512e-01 -4.11297321e-01 -1.34384781e-01 -9.85907197e-01 -2.13328212e-01 1.60155948e-02 1.15792677e-01 -7.65153170e-01 1.08018053e+00 -1.07125831e+00 -1.40821946e+00 6.33730471e-01 1.02323152e-01 -3.70687693e-01 7.07796216e-01 -3.85753155e-01 -8.52640212e-01 -7.82185853e-01 2.49834657e-02 2.28582293e-01 6.31602526e-01 -1.11679363e+00 -7.58192301e-01 -4.87226844e-01 2.07757086e-01 1.38589874e-01 -3.26015294e-01 4.76779461e-01 6.02363825e-01 -7.05091059e-01 -4.60740536e-01 -8.51893485e-01 -3.86077285e-01 -1.02616057e-01 -9.95923951e-02 -4.17533427e-01 4.16728705e-02 -4.34179932e-01 1.57816458e+00 -1.57117450e+00 -3.11336428e-01 4.87892836e-01 1.15817234e-01 1.29929766e-01 2.69161612e-01 2.93938845e-01 2.80371845e-01 3.12163621e-01 2.67881721e-01 -1.19419441e-01 3.63292783e-01 2.06313372e-01 -6.90588236e-01 3.43457878e-01 -4.55833822e-01 3.12792987e-01 -9.47593689e-01 -4.18972433e-01 -1.44917637e-01 2.90214866e-01 -1.13589585e+00 1.31696403e-01 -1.42581269e-01 -1.32050931e-01 -4.01534706e-01 5.04626989e-01 8.65998805e-01 2.02988535e-01 3.38601857e-01 -1.57531530e-01 -1.85586855e-01 6.96795762e-01 -1.33507884e+00 9.82587099e-01 -2.00216249e-01 -5.80844767e-02 -5.32703161e-01 -6.18006468e-01 1.05718732e+00 4.25543822e-02 5.93810529e-03 -5.19818485e-01 3.90139818e-01 9.82965529e-02 -3.08836028e-02 -4.01846617e-01 1.02020442e+00 -3.15964967e-01 -1.78484440e-01 8.31273735e-01 -2.99197465e-01 1.92713290e-01 -9.69319195e-02 3.57117593e-01 5.70586205e-01 4.76758517e-02 3.57288510e-01 -7.30836451e-01 4.58256185e-01 -2.83891052e-01 8.49051476e-01 1.11968303e+00 -2.01670408e-01 3.31624031e-01 3.82991910e-01 -4.97194231e-01 -7.43264973e-01 -7.67662585e-01 -3.05815130e-01 1.65537286e+00 1.34184957e-01 -3.27979803e-01 -2.64318109e-01 -9.56720233e-01 5.06171107e-01 1.24054813e+00 -9.73274469e-01 -1.97988868e-01 1.55945137e-01 -1.04787731e+00 1.88373074e-01 3.39703858e-01 -1.30554497e-01 -5.55529058e-01 -1.92890212e-01 6.96383268e-02 -5.85309193e-02 -2.56696016e-01 -5.44448137e-01 -4.70972508e-01 -8.67798030e-01 -8.51981282e-01 -3.98534417e-01 8.24735016e-02 6.10120475e-01 6.18059523e-02 1.46526468e+00 2.03145538e-02 3.22284102e-01 9.08493549e-02 -3.38356465e-01 -7.32055485e-01 -3.12953204e-01 -3.94172430e-01 5.06388128e-01 -4.91094813e-02 6.45931721e-01 -5.09717226e-01 -8.73538494e-01 6.31215751e-01 -5.49102187e-01 -3.19291741e-01 2.80850232e-01 6.15493119e-01 -4.49439976e-03 -1.89352185e-01 8.94541502e-01 -1.73833418e+00 1.19148505e+00 -1.10249877e+00 -4.41711575e-01 3.14196855e-01 -1.53033543e+00 3.08616795e-02 5.37061274e-01 -5.08742630e-01 -1.45760691e+00 -6.36296332e-01 -3.30369957e-02 5.21198452e-01 -2.14462839e-02 2.13008106e-01 -6.54359162e-02 7.93452412e-02 1.05110514e+00 -5.79750240e-01 -2.66251981e-01 -6.01289332e-01 5.67681611e-01 1.30571890e+00 8.05273876e-02 -7.74738550e-01 2.48200446e-01 4.39689904e-01 -3.80543143e-01 1.32873714e-01 -1.05314112e+00 -2.54649758e-01 -1.15581587e-01 -3.10462356e-01 1.42692342e-01 -5.16183734e-01 -7.77030110e-01 2.69598011e-02 -6.59589350e-01 9.44784656e-02 -2.35401452e-01 5.81350327e-01 -2.00551376e-02 4.61856835e-02 -3.69911730e-01 -1.20708144e+00 -6.98666871e-01 -7.33247519e-01 2.81990647e-01 2.79345185e-01 -6.98608160e-01 -8.35671604e-01 6.02892816e-01 5.79141617e-01 7.38804758e-01 -3.23997922e-02 6.92156017e-01 -1.00814199e+00 1.27260491e-01 -3.29364181e-01 -2.71758378e-01 2.00505435e-01 -1.56427041e-01 2.23664463e-01 -8.78302038e-01 -4.88104001e-02 -3.17274868e-01 -1.20251872e-01 5.12803078e-01 3.02660912e-01 9.06485558e-01 -7.66138434e-01 -2.67403871e-01 -9.58091840e-02 1.31959343e+00 -6.80668876e-02 4.32485968e-01 4.44102995e-02 4.04777288e-01 8.89015019e-01 8.29742074e-01 7.58385479e-01 8.86273384e-01 6.56537950e-01 3.05389702e-01 3.31169873e-01 2.32580185e-01 -3.82067889e-01 2.70631433e-01 3.66838098e-01 -4.79072660e-01 -2.03208134e-01 -4.01143432e-01 3.98812771e-01 -2.10198665e+00 -1.24996150e+00 -3.06872070e-01 2.59336162e+00 7.60785937e-01 1.08567551e-01 3.79047126e-01 -5.40996194e-02 7.38216519e-01 -1.43433094e-01 -4.14629728e-01 -1.05482805e+00 1.28083423e-01 -1.59418389e-01 6.98897719e-01 5.29653132e-01 -7.41053820e-01 3.84155601e-01 7.06956339e+00 4.16689843e-01 -8.37862909e-01 1.03106350e-01 7.21195102e-01 -5.10566711e-01 -1.14049268e+00 -1.99580267e-02 -6.56656265e-01 8.76878738e-01 8.35729778e-01 -3.83313388e-01 3.60313237e-01 9.41857815e-01 7.43707493e-02 -4.62256037e-02 -1.10053289e+00 5.32400489e-01 2.63224155e-01 -1.16061211e+00 5.70523366e-02 2.82292366e-01 1.01401174e+00 -1.49669051e-01 7.10131004e-02 4.25605237e-01 1.01096809e+00 -9.05061483e-01 8.59834790e-01 9.76811230e-01 2.26028025e-01 -7.92735815e-01 9.71822143e-01 3.81383300e-01 -1.58382297e-01 -2.31035590e-01 -7.44993150e-01 -6.81488574e-01 -3.23097184e-02 1.23331118e+00 -4.08464789e-01 4.16088134e-01 7.80768216e-01 6.95900798e-01 -2.75407672e-01 8.93176556e-01 -1.61913726e-02 6.26699924e-01 -1.00449599e-01 -4.30112243e-01 -4.62537855e-01 -2.03069344e-01 3.04575741e-01 1.08635139e+00 3.78229469e-01 2.95769393e-01 -1.73489407e-01 7.26960897e-01 -2.95211375e-01 5.27973652e-01 -4.03142601e-01 4.59628940e-01 7.01115847e-01 1.26422870e+00 -1.31077752e-01 -3.72072041e-01 -4.17950094e-01 3.38807136e-01 2.19413832e-01 -8.52746665e-02 -5.40243149e-01 1.05403155e-01 8.03434074e-01 3.28530967e-01 -2.75705576e-01 7.33804226e-01 -5.73929727e-01 -1.17058575e+00 -4.61288005e-01 -9.51452971e-01 6.05320156e-01 -5.55822551e-01 -1.86389160e+00 2.28025571e-01 -4.94105145e-02 -1.01635396e+00 4.09171497e-03 -3.20366740e-01 -6.22397065e-01 8.38244796e-01 -1.30347013e+00 -9.21308339e-01 1.97815802e-02 2.83954084e-01 -1.03584439e-01 -4.29504097e-01 8.49199414e-01 5.33161938e-01 -2.49900728e-01 8.35343480e-01 9.75340679e-02 -5.56673586e-01 1.42401576e+00 -1.42916179e+00 4.68509868e-02 5.77302158e-01 -3.11074257e-01 9.25580442e-01 1.10973513e+00 -8.08107734e-01 -8.09223831e-01 -8.52819026e-01 1.26965654e+00 -8.88062716e-01 3.32975328e-01 -1.13054723e-01 -5.62700093e-01 5.00101447e-01 -2.83854529e-02 -2.83599049e-01 1.53092611e+00 7.93835282e-01 -7.24512935e-01 -2.73358047e-01 -1.92327702e+00 4.84501630e-01 9.97382164e-01 -1.40603006e-01 -9.46172118e-01 1.46453425e-01 2.63493210e-01 -3.95246223e-02 -9.67640638e-01 1.60977051e-01 1.33906150e+00 -1.28426123e+00 9.42858875e-01 -8.96967351e-01 7.33719468e-01 -9.67674628e-02 -3.89797032e-01 -1.57308352e+00 -9.36557531e-01 -2.44263276e-01 -2.95861121e-02 1.51585209e+00 6.06533706e-01 -7.70863175e-01 4.98010129e-01 1.47327542e+00 3.90808195e-01 -5.80589890e-01 -1.88545033e-01 -1.43021241e-01 2.71380514e-01 -4.61334497e-01 1.26493549e+00 1.14482534e+00 3.94665033e-01 9.72310826e-02 -9.63055611e-01 8.95702243e-02 8.68246853e-01 4.39476550e-01 5.72506905e-01 -1.77764487e+00 -3.34999830e-01 -4.46376383e-01 -2.48522945e-02 -4.31536585e-01 -1.59539118e-01 -8.37694228e-01 -2.47368276e-01 -1.20587647e+00 5.75838387e-01 -9.62943137e-01 -7.37129807e-01 1.35141775e-01 -2.72595376e-01 3.66381228e-01 -9.61203314e-03 1.30468488e-01 -4.98786241e-01 1.60217479e-01 8.53543401e-01 1.54795349e-01 -1.24900416e-03 4.51467693e-01 -1.66667163e+00 9.27522719e-01 8.40116739e-01 -8.63522828e-01 -3.72280389e-01 -1.16565518e-01 1.10135484e+00 -4.09596354e-01 2.50227749e-02 -4.78986382e-01 1.63429841e-01 -6.19475901e-01 3.06150764e-01 -3.04536641e-01 -1.81199118e-01 -7.43302763e-01 2.78739065e-01 1.68786198e-01 -9.21268702e-01 -2.59531468e-01 -4.32987928e-01 5.96728206e-01 4.00918275e-01 -4.53799754e-01 4.42444831e-01 -1.20903105e-02 6.65190369e-02 9.51888338e-02 -2.16987282e-01 -4.98939641e-02 5.77604711e-01 2.41304949e-01 -7.04621136e-01 -4.07949477e-01 -4.58598226e-01 1.55425876e-01 4.83458817e-01 7.59715259e-01 2.40659013e-01 -1.40776217e+00 -9.24829900e-01 -1.68458536e-01 2.92026132e-01 -1.02262950e+00 2.33677983e-01 6.13175213e-01 8.55546147e-02 2.81495124e-01 -3.59667569e-01 2.85364777e-01 -1.32297981e+00 5.16924202e-01 1.22169599e-01 -1.21657111e-01 9.52979252e-02 7.89446473e-01 -1.34716541e-01 -9.77893412e-01 2.33739525e-01 -2.93999314e-01 -7.72104681e-01 1.79206073e-01 8.43382716e-01 1.02156973e+00 -2.80822158e-01 -4.91643846e-01 -4.43612367e-01 2.14626998e-01 -2.03189418e-01 4.51646559e-02 1.21379161e+00 -3.76478285e-01 -2.19761238e-01 3.86542678e-01 4.07899350e-01 5.98327696e-01 -6.11979485e-01 -3.35593045e-01 -2.18313202e-01 -9.71282899e-01 3.45379412e-01 -1.43594062e+00 -8.35682034e-01 3.11052233e-01 6.80402875e-01 4.76029694e-01 6.87509954e-01 -4.09126669e-01 4.89160061e-01 1.45158634e-01 4.71310377e-01 -1.37333798e+00 -7.46449232e-01 -9.61800739e-02 8.44773114e-01 -1.53389168e+00 5.75123489e-01 -2.61325598e-01 -7.79338062e-01 6.43945813e-01 4.18314338e-01 -3.69934201e-01 9.92458403e-01 -6.54262677e-02 3.54723819e-02 9.13660303e-02 -9.36577320e-01 2.77843952e-01 6.57570243e-01 3.94915819e-01 8.42273831e-01 5.42696595e-01 -1.15587378e+00 1.47958839e+00 -3.04139018e-01 5.34190118e-01 7.02506959e-01 4.11351830e-01 -3.67972672e-01 -1.48410487e+00 -3.93196195e-01 1.00806463e+00 -9.13750052e-01 -9.21010002e-02 -3.47757399e-01 -8.66755471e-02 5.38052917e-01 1.20265925e+00 -7.62276119e-04 -5.31996012e-01 3.66236508e-01 -2.00417668e-01 2.98351556e-01 -4.43646133e-01 -9.25974309e-01 -5.20401895e-01 5.20820022e-01 -6.74534738e-01 -4.72671390e-01 -8.29162896e-01 -6.33330703e-01 -7.54667222e-01 -4.24375325e-01 3.91197681e-01 7.04131007e-01 8.57725620e-01 4.14192647e-01 2.77420193e-01 7.41820514e-01 -4.16611403e-01 -8.15618098e-01 -7.91479707e-01 -7.41763413e-01 6.22481942e-01 -5.21394461e-02 -8.76822889e-01 -3.83629441e-01 -3.07922035e-01]	[9.657923698425293, 5.686207294464111]
24247d14-e779-45c4-942c-920419c3284e	automated-annotation-with-generative-ai	2306.00176	null	https://arxiv.org/abs/2306.00176v1	https://arxiv.org/pdf/2306.00176v1.pdf	Automated Annotation with Generative AI Requires Validation	Generative large language models (LLMs) can be a powerful tool for augmenting text annotation procedures, but their performance varies across annotation tasks due to prompt quality, text data idiosyncrasies, and conceptual difficulty. Because these challenges will persist even as LLM technology improves, we argue that any automated annotation process using an LLM must validate the LLM's performance against labels generated by humans. To this end, we outline a workflow to harness the annotation potential of LLMs in a principled, efficient way. Using GPT-4, we validate this approach by replicating 27 annotation tasks across 11 datasets from recent social science articles in high-impact journals. We find that LLM performance for text annotation is promising but highly contingent on both the dataset and the type of annotation task, which reinforces the necessity to validate on a task-by-task basis. We make available easy-to-use software designed to implement our workflow and streamline the deployment of LLMs for automated annotation.	['Neil Fasching', 'Samuel Wolken', 'Nicholas Pangakis']	2023-05-31	null	null	null	null	['text-annotation']	['natural-language-processing']	[ 3.35999370e-01 4.85811412e-01 -1.24140009e-01 -3.35859269e-01 -1.06713998e+00 -1.03899646e+00 8.66795003e-01 6.09650433e-01 -5.32046497e-01 4.68188673e-01 4.27496284e-01 -5.77106118e-01 -6.02636039e-02 -2.18684077e-01 -4.81491476e-01 -4.60385755e-02 5.16393900e-01 8.58364582e-01 9.55958888e-02 1.38994912e-02 4.25266147e-01 6.97352812e-02 -1.33057618e+00 4.64714438e-01 8.00105631e-01 2.56611198e-01 2.75749415e-01 4.58543330e-01 -5.97180784e-01 1.00300193e+00 -6.26120925e-01 -6.47330344e-01 3.03675532e-02 -4.26885784e-01 -1.25166309e+00 -1.62511408e-01 3.39961588e-01 2.14133918e-01 2.93561697e-01 7.62285590e-01 5.81805527e-01 -1.65101945e-01 6.33654952e-01 -1.28704226e+00 -5.88059306e-01 1.12717354e+00 -1.60327852e-01 5.98797984e-02 3.58438045e-01 1.82154670e-01 1.06435943e+00 -8.35502863e-01 9.94728029e-01 1.18058956e+00 1.17334437e+00 3.36452007e-01 -1.32498562e+00 -6.36448979e-01 -1.55037642e-03 -4.76380914e-01 -1.15858972e+00 -5.92743874e-01 3.06207478e-01 -9.65133131e-01 1.06383026e+00 2.75215447e-01 4.74380493e-01 1.20073748e+00 -6.37881309e-02 4.81769264e-01 1.26916099e+00 -6.94685936e-01 1.40585303e-01 2.88541049e-01 2.61730790e-01 6.06223524e-01 4.48671818e-01 -7.75045514e-01 -4.50776130e-01 -5.70839286e-01 3.58758688e-01 -4.94643420e-01 -1.92668382e-02 -3.37054464e-03 -1.25818503e+00 7.31095493e-01 -1.70240805e-01 6.19949341e-01 -2.32818976e-01 2.00697109e-01 5.86871207e-01 4.76662554e-02 8.23087871e-01 1.03929245e+00 -6.53064549e-01 -6.33225858e-01 -1.20021272e+00 2.53955096e-01 1.12394273e+00 9.76033270e-01 5.11588454e-01 -4.33025032e-01 -4.18266445e-01 9.57154810e-01 5.44853985e-01 6.44967854e-02 5.84382474e-01 -1.05394161e+00 4.34825420e-01 8.70347261e-01 2.54900426e-01 -7.73858249e-01 -7.04032838e-01 -3.83815587e-01 -3.51252705e-01 -1.06851935e-01 5.68259239e-01 -1.90921277e-01 -5.51238060e-01 1.54473841e+00 2.63508558e-01 -3.80899668e-01 -1.92494944e-01 4.15460408e-01 8.77521932e-01 2.21534848e-01 7.64982402e-01 -8.20099637e-02 1.43736255e+00 -5.88720024e-01 -7.97146261e-01 -4.07664955e-01 1.38687587e+00 -1.01082158e+00 1.49601912e+00 1.60122104e-02 -1.02795458e+00 -3.90756905e-01 -6.09218001e-01 -4.41181660e-01 -4.75038350e-01 1.60004780e-01 4.91650581e-01 6.93929970e-01 -9.92606640e-01 4.67353821e-01 -7.94687688e-01 -7.57793307e-01 5.54547489e-01 8.00416619e-02 -3.06026787e-01 2.30562940e-01 -9.50075746e-01 1.08504009e+00 6.18847191e-01 -8.56497511e-02 -3.80427301e-01 -8.51527750e-01 -6.67870879e-01 8.18437785e-02 3.58945966e-01 -6.03506684e-01 1.50730157e+00 -7.43325710e-01 -9.24883783e-01 1.23994505e+00 3.49282920e-02 -2.42173672e-01 6.74802601e-01 -1.81073591e-01 -2.10797712e-02 -2.39954233e-01 5.42519510e-01 5.67630351e-01 2.07849994e-01 -1.10408056e+00 -4.06854153e-01 -1.05080009e-01 -6.04478456e-02 7.20860437e-02 -4.59480524e-01 4.81237203e-01 -2.23080963e-01 -4.83770072e-01 -2.43263960e-01 -1.03359568e+00 -2.32541382e-01 -2.93017954e-01 -2.16938630e-01 -4.51442987e-01 5.90702534e-01 -6.05286121e-01 1.38770080e+00 -2.01459885e+00 -1.66045204e-01 -2.89958790e-02 4.04097825e-01 2.72431582e-01 -7.44346306e-02 6.51080132e-01 8.10263008e-02 8.53296876e-01 -2.46857747e-01 -7.98924983e-01 2.95740306e-01 2.77589411e-01 -5.54561242e-02 2.80911654e-01 1.43664166e-01 1.16226411e+00 -1.06989324e+00 -8.28645766e-01 -1.19958349e-01 3.27836663e-01 -3.75167102e-01 9.41850096e-02 -3.73336941e-01 5.12593150e-01 -3.47570598e-01 3.72840732e-01 1.28416136e-01 -6.90718710e-01 3.36456418e-01 5.28988428e-03 -4.67965901e-01 5.31908035e-01 -9.03461635e-01 1.76133227e+00 -3.88377726e-01 8.67631376e-01 9.94832814e-02 -5.39698839e-01 7.85630882e-01 5.33223689e-01 3.97554159e-01 -1.61211908e-01 -3.79114524e-02 4.44590777e-01 5.76446913e-02 -6.39577568e-01 5.76287270e-01 -7.10574165e-02 -5.08571081e-02 1.01678574e+00 3.56324106e-01 -3.61811131e-01 4.32459503e-01 2.98230082e-01 9.79119658e-01 3.83342981e-01 3.60649198e-01 -5.12121081e-01 -7.28046894e-03 3.43544751e-01 3.49554271e-01 1.05497718e+00 7.80358911e-02 4.02171999e-01 6.43244803e-01 -3.93843621e-01 -1.37701964e+00 -1.84082389e-01 -3.30936849e-01 1.48129678e+00 -7.61705160e-01 -8.25811863e-01 -7.54313529e-01 -8.00302923e-01 -2.15474725e-01 8.92147005e-01 -6.89816475e-01 2.27061674e-01 -2.93829530e-01 -9.57461834e-01 9.10779595e-01 3.23675185e-01 6.15180023e-02 -1.06332338e+00 -6.97011650e-01 3.89132500e-01 -3.82802278e-01 -1.31032443e+00 -1.54568046e-01 2.77413130e-01 -4.93071526e-01 -1.01225412e+00 -3.39214414e-01 -5.27012944e-01 5.75971544e-01 -8.94497707e-02 1.54860842e+00 2.49484777e-01 6.08001370e-03 5.60436606e-01 -4.87912834e-01 -8.44740927e-01 -9.98903632e-01 6.15791917e-01 -2.86958337e-01 -6.59341335e-01 5.44013083e-01 -3.42600495e-01 -1.12846635e-01 1.26640812e-01 -9.72858608e-01 3.33732694e-01 4.80147749e-01 5.61339080e-01 4.16707188e-01 -2.20252693e-01 5.79001784e-01 -1.45056391e+00 9.96602476e-01 -5.66119373e-01 -5.43012917e-01 2.77954340e-01 -9.53697562e-01 -4.78889532e-02 1.96940571e-01 -3.33465278e-01 -7.75832653e-01 2.74867611e-03 -3.63276124e-01 2.77028024e-01 -1.14730686e-01 9.46133852e-01 9.56332907e-02 7.11328611e-02 8.43323052e-01 -3.73744935e-01 -4.67144512e-02 -6.50749266e-01 5.73357701e-01 7.25832164e-01 2.58878022e-01 -7.26933002e-01 5.81853688e-01 1.18753925e-01 -2.16140270e-01 -4.48181272e-01 -1.21550500e+00 -3.84536058e-01 -8.10998321e-01 -9.94925946e-02 8.06892037e-01 -8.41974854e-01 -4.26165819e-01 -2.75663435e-02 -1.06361043e+00 -6.98706150e-01 -4.38605666e-01 9.08793360e-02 -2.95187861e-01 3.89705330e-01 -4.41158950e-01 -7.54750490e-01 -5.40143251e-01 -1.05925870e+00 1.22456205e+00 -7.20155751e-03 -1.05857921e+00 -1.25315619e+00 1.14926092e-01 6.16425753e-01 3.59630555e-01 2.79441863e-01 9.80059683e-01 -1.23099899e+00 -5.53687960e-02 -4.48383182e-01 -1.26431733e-01 -1.57207903e-02 -1.12439185e-01 2.47657850e-01 -9.60102022e-01 -8.66136700e-03 -1.79850698e-01 -5.25641501e-01 2.23515958e-01 1.84885919e-01 1.00644732e+00 -4.23826456e-01 -4.36741590e-01 1.19116783e-01 1.17352557e+00 -4.16836888e-01 2.96294689e-01 7.18702137e-01 9.13252711e-01 6.02572918e-01 3.01519126e-01 2.55114615e-01 4.88697916e-01 4.14397508e-01 -5.63303828e-02 -8.88728350e-02 -1.39314067e-02 -3.12017828e-01 -2.15845481e-02 7.43291199e-01 7.30720088e-02 -4.36692238e-01 -1.44143009e+00 4.35063869e-01 -1.99333060e+00 -8.15393150e-01 -6.72445774e-01 1.92934787e+00 1.14779162e+00 3.10198516e-01 5.17609417e-02 -2.84697395e-02 2.64852434e-01 -1.05914898e-01 4.66940971e-03 -3.16536844e-01 -1.63484707e-01 5.38875088e-02 3.22509170e-01 4.07664239e-01 -8.03770006e-01 8.87093544e-01 7.25634289e+00 7.05126166e-01 -6.30820870e-01 5.81501067e-01 5.64863801e-01 1.71159089e-01 -4.57070917e-01 3.12228918e-01 -8.64947915e-01 5.65048218e-01 1.21195161e+00 -8.83416086e-02 7.16341138e-02 7.82771349e-01 3.60566020e-01 -9.69912950e-03 -1.14964700e+00 4.58015114e-01 -1.30535632e-01 -1.35782266e+00 -1.84276819e-01 8.41306821e-02 6.26854777e-01 3.65410537e-01 -3.41036439e-01 4.72058207e-01 6.19986832e-01 -1.09383631e+00 7.18554437e-01 3.64033580e-01 7.01943636e-01 -7.15013444e-02 1.05765951e+00 5.40273011e-01 -7.30478883e-01 1.60401598e-01 3.87461074e-02 -2.53941476e-01 2.88309515e-01 5.30835032e-01 -1.15284657e+00 2.15503901e-01 5.28994024e-01 3.58191609e-01 -1.03248525e+00 8.11965168e-01 -2.15765551e-01 9.17070508e-01 -1.79774329e-01 2.56466083e-02 1.89645529e-01 5.09710610e-02 2.94897586e-01 1.83230245e+00 6.49546087e-02 -1.78986847e-01 4.13277656e-01 8.65317762e-01 -2.11146206e-01 3.92171800e-01 -5.28871357e-01 -5.73368669e-01 6.33739710e-01 1.70444024e+00 -1.11209750e+00 -4.67096090e-01 -5.34389853e-01 5.52784979e-01 3.87107551e-01 3.46747488e-02 -5.48931599e-01 -4.79740184e-03 4.97330613e-02 3.01273644e-01 -3.11896622e-01 -2.83644348e-01 -5.92329979e-01 -9.17089045e-01 -1.85406104e-01 -1.03909338e+00 4.67059433e-01 -1.10656810e+00 -1.32296634e+00 4.97418821e-01 1.07888192e-01 -7.02909112e-01 -2.29665890e-01 -3.61006320e-01 -7.58712441e-02 8.96454453e-01 -9.84106541e-01 -1.60545409e+00 -4.29830194e-01 -1.87908933e-01 4.50589776e-01 2.26227671e-01 1.00450253e+00 1.89686686e-01 -3.21661621e-01 3.03849488e-01 -1.13520578e-01 2.73896992e-01 8.56380403e-01 -1.37062478e+00 7.14191556e-01 6.30364954e-01 3.90482694e-01 7.92147934e-01 8.21881711e-01 -8.26073825e-01 -9.22667027e-01 -8.60842109e-01 1.38058174e+00 -1.33722496e+00 9.85405624e-01 -6.75923705e-01 -1.12462544e+00 1.06421244e+00 2.83439845e-01 -3.01122189e-01 1.22365963e+00 5.49280703e-01 -1.54012099e-01 7.15530038e-01 -8.81628752e-01 5.10348916e-01 1.02659965e+00 -6.65896297e-01 -8.08688164e-01 7.90604830e-01 6.59780800e-01 -6.02947116e-01 -1.16831565e+00 1.91162109e-01 5.10855198e-01 -3.56297016e-01 5.46446145e-01 -7.95055866e-01 3.90748650e-01 -2.16631159e-01 3.07104792e-02 -9.29674685e-01 -4.64346945e-01 -8.04189146e-01 1.78955719e-01 1.80650723e+00 7.46433675e-01 -5.67552924e-01 3.12095881e-01 1.23395193e+00 -3.24360937e-01 -4.41332221e-01 -6.07387364e-01 -3.93752158e-01 1.38223305e-01 -6.77332222e-01 4.34209615e-01 1.34839416e+00 1.64907798e-01 6.69084311e-01 -3.95223089e-02 -1.94987476e-01 1.09230734e-01 -3.16129893e-01 9.16723430e-01 -1.74403048e+00 -1.13463409e-01 -5.54635823e-01 -1.16693676e-02 -4.01050150e-01 2.98022002e-01 -1.14927256e+00 4.47921455e-02 -1.86444032e+00 5.14636874e-01 -7.19723523e-01 -8.09999998e-04 1.03398240e+00 -3.87203902e-01 4.04453933e-01 1.27270654e-01 6.78171992e-01 -8.50184679e-01 -8.88027549e-02 6.13330603e-01 2.54213363e-01 -2.35649228e-01 -4.83890146e-01 -1.08271241e+00 9.22658384e-01 5.96939504e-01 -8.02760959e-01 -2.45146230e-01 -5.35353720e-01 7.86850035e-01 -5.83378434e-01 2.45867208e-01 -8.21609437e-01 1.70789734e-01 -4.78540175e-02 2.67587423e-01 -1.84405208e-01 -8.19885060e-02 -5.95578551e-01 3.22017342e-01 1.02292590e-01 -7.80666113e-01 2.16019884e-01 4.60059881e-01 1.62960812e-01 2.55252510e-01 -6.68606281e-01 3.54507059e-01 -4.23813254e-01 -3.93200815e-01 -2.86320373e-02 -6.35887086e-01 4.66845006e-01 6.06492460e-01 5.12428954e-02 -6.29680097e-01 -1.28231421e-01 -4.49380815e-01 1.71079129e-01 7.35391736e-01 3.51084113e-01 -2.59226233e-01 -9.08239961e-01 -7.82093525e-01 -2.80556828e-01 3.65532637e-01 5.88753913e-03 -1.84766546e-01 8.79373550e-01 -3.13323110e-01 4.00793701e-01 6.73741326e-02 -4.87688392e-01 -1.13144767e+00 3.61649454e-01 5.69331236e-02 -4.45684731e-01 -6.29552245e-01 6.89862549e-01 -2.69966543e-01 -5.03166556e-01 -1.02564752e-01 -2.60907263e-01 -2.19033495e-01 3.30647081e-01 2.71476924e-01 1.11898310e-01 1.12341344e-01 -4.63484287e-01 -1.67319104e-01 4.70113009e-02 4.97387797e-02 -3.89177978e-01 1.47726476e+00 -2.67118901e-01 -3.03864777e-01 8.75498414e-01 7.44780123e-01 2.32435986e-01 -9.03466105e-01 -8.67970511e-02 5.33339262e-01 -1.50360689e-01 -4.09750789e-02 -9.66466188e-01 -2.02538148e-01 4.17750210e-01 1.45161644e-01 4.88976061e-01 5.55840552e-01 3.39079291e-01 2.93131649e-01 1.71465248e-01 1.31830379e-01 -1.24836922e+00 -1.48716524e-01 4.35404748e-01 7.64735162e-01 -1.30555797e+00 2.53682137e-01 -1.94525376e-01 -6.68097377e-01 9.68183160e-01 3.57738018e-01 6.05927348e-01 4.05948639e-01 4.42165136e-01 1.83060110e-01 -5.83069265e-01 -7.29656875e-01 -4.41863388e-02 3.21995676e-01 4.40172970e-01 1.24861419e+00 -1.91455543e-01 -5.62307179e-01 5.67453802e-01 -3.36346000e-01 2.24878073e-01 4.80373770e-01 1.05715787e+00 -1.91264302e-01 -1.30178952e+00 -2.45989472e-01 6.08071983e-01 -6.88602507e-01 -3.32944125e-01 -6.36164308e-01 8.62110376e-01 1.53141871e-01 8.99811447e-01 -1.18126139e-01 9.42795426e-02 5.82771236e-03 5.89083612e-01 1.53147951e-01 -1.14804339e+00 -8.81456733e-01 -2.08372083e-02 5.42071342e-01 -1.71595544e-01 -6.12503409e-01 -8.04816306e-01 -1.15701616e+00 -7.43240409e-04 -2.81279832e-01 3.02653670e-01 8.54743898e-01 1.18165278e+00 6.12908721e-01 2.63669223e-01 -3.23060095e-01 -5.02924621e-01 -2.88208723e-01 -1.42475390e+00 6.68929471e-03 5.53674281e-01 -2.09018856e-01 -4.96250153e-01 -7.89502338e-02 4.10964131e-01]	[9.64116096496582, 8.528844833374023]
adea4965-f0f9-42f8-a6c4-82703d1bdbb6	assessing-four-neural-networks-on-handwritten	1811.08278	null	https://arxiv.org/abs/1811.08278v2	https://arxiv.org/pdf/1811.08278v2.pdf	Assessing four Neural Networks on Handwritten Digit Recognition Dataset (MNIST)	Although the image recognition has been a research topic for many years, many researchers still have a keen interest in it[1]. In some papers[2][3][4], however, there is a tendency to compare models only on one or two datasets, either because of time restraints or because the model is tailored to a specific task. Accordingly, it is hard to understand how well a certain model generalizes across image recognition field[6]. In this paper, we compare four neural networks on MNIST dataset[5] with different division. Among them, three are Convolutional Neural Networks (CNN)[7], Deep Residual Network (ResNet)[2] and Dense Convolutional Network (DenseNet)[3] respectively, and the other is our improvement on CNN baseline through introducing Capsule Network (CapsNet)[1] to image recognition area. We show that the previous models despite do a quite good job in this area, our retrofitting can be applied to get a better performance. The result obtained by CapsNet is an accuracy rate of 99.75\%, and it is the best result published so far. Another inspiring result is that CapsNet only needs a small amount of data to get excellent performance. Finally, we will apply CapsNet's ability to generalize in other image recognition field in the future.	['Nan Chen', 'Hanyang Mao', 'Feiyang Chen', 'Hanlin Hu']	2018-11-16	null	null	null	null	['handwritten-digit-recognition']	['computer-vision']	[ 2.23276809e-01 -6.22960255e-02 -2.14205310e-01 -4.90533918e-01 -7.07014427e-02 -3.10868919e-01 4.50516850e-01 -5.48265100e-01 -4.04508829e-01 6.07313871e-01 1.67790070e-01 -2.85399407e-01 -2.55772769e-01 -8.00614297e-01 -8.03373575e-01 -5.45262218e-01 -1.64284166e-02 -2.45187450e-02 3.78056616e-01 -3.46504152e-01 3.53056461e-01 6.50728643e-01 -1.31020081e+00 3.56106460e-01 5.70454597e-01 1.10755169e+00 2.59282947e-01 3.78492624e-01 -6.43486083e-02 1.08574355e+00 -5.41656613e-01 -4.07164156e-01 4.44401741e-01 -2.73363650e-01 -1.19455290e+00 -1.95773110e-01 4.62548643e-01 -1.81505848e-02 -6.71797574e-01 1.04951251e+00 4.33625221e-01 -7.06394836e-02 6.04614496e-01 -1.24173284e+00 -1.11429226e+00 6.12635612e-01 -4.23330158e-01 2.64736056e-01 -1.69895619e-01 1.46683976e-01 6.61780536e-01 -5.93241274e-01 4.58223432e-01 1.06787181e+00 8.35899293e-01 6.68114185e-01 -6.75174773e-01 -9.81986880e-01 1.34480461e-01 2.91411996e-01 -1.34791934e+00 -1.14669397e-01 6.48622632e-01 -3.21817875e-01 1.07031202e+00 3.25055301e-01 3.44042778e-01 1.35670865e+00 2.37946853e-01 7.92403042e-01 1.29296374e+00 -2.93309689e-01 -1.44675910e-01 1.98855639e-01 3.91483724e-01 4.21150237e-01 1.67377427e-01 2.63644964e-01 -7.65131786e-02 3.90838176e-01 8.35957170e-01 3.38719040e-01 -3.71466070e-01 -3.79231796e-02 -1.12912118e+00 7.22682595e-01 9.97137666e-01 7.74696767e-01 -1.35998577e-01 -3.81743684e-02 4.74725038e-01 4.07673717e-01 1.64943248e-01 5.50153553e-01 -5.49291730e-01 -1.25156259e-02 -8.04451704e-01 1.62277892e-01 7.52777457e-01 9.50180292e-01 5.39795697e-01 3.31102461e-01 -2.60785688e-03 1.09357214e+00 6.02098890e-02 2.07583576e-01 9.96528089e-01 -4.56614852e-01 3.46753091e-01 7.57307291e-01 -3.69820297e-01 -1.20053208e+00 -5.06451130e-01 -7.22495496e-01 -1.40913582e+00 2.44134486e-01 4.59087372e-01 -3.13629985e-01 -1.15433168e+00 1.66010535e+00 -6.10257030e-01 3.89594257e-01 2.05745757e-01 9.48335052e-01 1.22709990e+00 6.27935052e-01 6.64840192e-02 3.29893023e-01 1.28444636e+00 -1.40124619e+00 -3.59390438e-01 -3.29057127e-01 3.91084760e-01 -1.00567186e+00 7.80371726e-01 5.40795982e-01 -8.17058265e-01 -9.89596963e-01 -1.35332930e+00 1.71954960e-01 -8.29963148e-01 1.46428883e-01 8.21664751e-01 7.84375370e-01 -1.23043239e+00 6.40457869e-01 -6.02926850e-01 -8.80227804e-01 4.47482795e-01 6.40103161e-01 -4.53530461e-01 -4.03672755e-02 -1.18386972e+00 1.03847969e+00 5.24614513e-01 3.17441046e-01 -7.28885353e-01 -2.83291101e-01 -5.17512560e-01 -2.36897115e-02 2.09397361e-01 -3.86773050e-01 9.47839022e-01 -1.20126235e+00 -1.21585000e+00 6.59268022e-01 1.04061954e-01 -7.48531938e-01 3.69649500e-01 -1.11939549e-01 -7.35658526e-01 -2.60697722e-01 -2.60038197e-01 9.35896695e-01 4.11341012e-01 -1.12339449e+00 -4.49929446e-01 -1.59960672e-01 2.50282407e-01 -1.50758550e-01 -3.99914354e-01 6.82035908e-02 -3.26374501e-01 -8.59761000e-01 1.66262969e-01 -1.01958859e+00 -1.99355051e-01 -2.71069407e-01 -4.86058056e-01 -2.77671069e-01 1.02080905e+00 -3.81594509e-01 1.01799703e+00 -2.18563128e+00 -9.74650010e-02 -1.15836129e-01 -5.41862026e-02 7.61625171e-01 -3.92718375e-01 3.16509843e-01 -5.23987889e-01 5.00402391e-01 -2.53116369e-01 -4.17581797e-02 -2.32698083e-01 3.04546535e-01 -3.79619718e-01 3.71889293e-01 3.33251178e-01 1.00604630e+00 -4.06739622e-01 -1.22115009e-01 1.77963197e-01 4.99545068e-01 -4.26301926e-01 1.28955796e-01 1.19304977e-01 3.42473775e-01 -3.77684653e-01 6.29367530e-01 9.76904392e-01 -2.85642415e-01 -1.20204978e-01 -6.39449298e-01 -1.41609251e-01 -1.34260535e-01 -1.24642348e+00 1.49710500e+00 -1.67579949e-01 7.01834321e-01 -3.27907383e-01 -1.58774316e+00 1.08463967e+00 3.14019531e-01 3.65062892e-01 -8.00795615e-01 2.59524345e-01 4.02826443e-02 3.79429102e-01 -7.22186387e-01 4.10094291e-01 2.04433911e-02 1.46970034e-01 1.57652169e-01 1.59771442e-01 2.68123299e-01 4.09322269e-02 -1.42336115e-01 1.06175649e+00 -9.88451913e-02 1.36593804e-01 -3.77292573e-01 6.07525766e-01 1.18232965e-01 4.84416127e-01 8.35436702e-01 -3.54195088e-01 1.07558250e+00 1.35919705e-01 -8.22798491e-01 -1.10514688e+00 -8.44933510e-01 -3.42436254e-01 7.00007021e-01 1.47157118e-01 -2.57447083e-02 -6.18623018e-01 -6.82814538e-01 -2.79358864e-01 1.82600319e-01 -7.79266596e-01 -1.19332410e-01 -7.38598168e-01 -1.14000738e+00 8.99809599e-01 8.73353779e-01 1.38491714e+00 -1.47805095e+00 -2.05360591e-01 7.26491734e-02 1.04744203e-01 -1.04263508e+00 -1.90565720e-01 3.71687680e-01 -9.65809703e-01 -1.02319407e+00 -8.28810632e-01 -1.18835008e+00 6.35951161e-01 3.73166561e-01 9.42164421e-01 2.79532164e-01 -2.79623955e-01 4.95463191e-03 -5.57769537e-01 -3.81974310e-01 -1.85795158e-01 4.66181755e-01 -3.77054438e-02 -8.07269588e-02 4.69244242e-01 -4.03705090e-01 -6.39855206e-01 5.36941230e-01 -1.02593780e+00 -4.39173914e-03 8.85994732e-01 8.11116636e-01 7.88991004e-02 7.10044503e-02 6.74831688e-01 -1.03709149e+00 5.96697628e-01 -5.88894784e-01 -2.92024046e-01 1.39206693e-01 -6.98748171e-01 -1.43390849e-01 8.84505808e-01 -4.78486657e-01 -9.11666751e-01 -5.32029830e-02 -3.45004827e-01 -2.12700754e-01 -4.43632692e-01 4.45088595e-01 4.59986329e-02 -2.87481129e-01 7.73535073e-01 3.45514685e-01 1.94790959e-03 -6.49556637e-01 4.70475741e-02 8.74822319e-01 4.55776453e-01 -3.37265193e-01 7.36691356e-01 2.60894954e-01 -2.41720229e-01 -6.04097366e-01 -8.71267736e-01 -3.39116991e-01 -5.36817253e-01 9.41144302e-02 9.20871854e-01 -9.16554391e-01 -7.72907734e-01 6.24809384e-01 -9.56292868e-01 -1.68847099e-01 1.85225070e-01 5.77943146e-01 -8.02791715e-02 3.17015886e-01 -7.38161802e-01 -4.11372185e-01 -2.90066063e-01 -1.44938481e+00 4.90716308e-01 5.38881302e-01 5.59164118e-03 -1.05790901e+00 -1.92797616e-01 3.96174848e-01 9.30123806e-01 3.11077535e-01 5.47341764e-01 -7.69842625e-01 -7.20930099e-01 -2.27365538e-01 -7.35777557e-01 9.23504055e-01 1.55651480e-01 -7.40097687e-02 -1.22256362e+00 -4.08593178e-01 2.51515992e-02 -4.11652774e-01 1.09010088e+00 2.54138350e-01 1.47211158e+00 -2.82659858e-01 -3.87012571e-01 6.95720494e-01 1.66290963e+00 4.99103218e-01 1.15468299e+00 5.16408563e-01 5.50663471e-01 2.05748126e-01 1.22876488e-01 -1.77966908e-01 2.70438194e-01 5.96113682e-01 5.75294316e-01 -4.33390617e-01 -2.74827808e-01 5.41984029e-02 3.81101072e-01 9.20032978e-01 -3.48569036e-01 -3.23886871e-01 -8.50440741e-01 4.45703536e-01 -1.77723980e+00 -1.02000248e+00 -1.50042892e-01 1.82479858e+00 4.94495839e-01 2.39173666e-01 5.19414172e-02 1.76645726e-01 5.87188363e-01 2.35085800e-01 -3.90811026e-01 -4.77737635e-01 -3.02109659e-01 4.31641757e-01 6.48553729e-01 5.23545034e-02 -1.19911480e+00 8.56212676e-01 6.82843065e+00 8.37796211e-01 -1.62968111e+00 -6.35634437e-02 8.31575394e-01 4.87676442e-01 3.20127785e-01 -4.29038182e-02 -8.14693809e-01 5.33815384e-01 7.65219629e-01 1.18419193e-01 3.37703139e-01 9.74867642e-01 -2.87728250e-01 1.35891870e-01 -9.14331734e-01 1.02172327e+00 2.67278790e-01 -1.43691432e+00 1.76127940e-01 -5.63510656e-02 6.56498432e-01 3.59954208e-01 2.10612014e-01 7.01697171e-01 2.54516542e-01 -1.57210064e+00 2.68267393e-01 5.88535845e-01 4.25141245e-01 -5.78907609e-01 1.11477661e+00 3.55873883e-01 -1.10599220e+00 -1.53189540e-01 -7.27643192e-01 -2.39522412e-01 -3.02391231e-01 3.96329701e-01 -6.80762112e-01 6.31128073e-01 9.31030273e-01 8.27295959e-01 -9.30762589e-01 1.29572976e+00 1.42010614e-01 8.46105397e-01 -2.56842077e-02 -2.32448548e-01 6.40408576e-01 -2.31143162e-02 9.63900760e-02 1.41758680e+00 2.46727675e-01 7.37888240e-06 -7.13256828e-04 9.14158106e-01 -3.01914483e-01 3.62201221e-02 -7.83529282e-01 2.11429194e-01 6.83558509e-02 1.46572804e+00 -7.08020747e-01 -2.23968640e-01 -6.98356509e-01 8.85608852e-01 2.23347276e-01 3.93451095e-01 -7.80855179e-01 -4.69495237e-01 3.24318051e-01 -1.76997453e-01 2.14639738e-01 4.53940360e-03 -4.07979697e-01 -1.09220719e+00 3.04458663e-02 -8.76700222e-01 2.75664389e-01 -7.76025534e-01 -1.46290994e+00 1.06527150e+00 -1.74478829e-01 -1.26426351e+00 8.42379332e-02 -1.14788067e+00 -6.50056064e-01 7.76127458e-01 -1.60590732e+00 -9.72213387e-01 -5.40466368e-01 5.54528594e-01 6.03952408e-01 -5.36803722e-01 7.89991438e-01 6.30952656e-01 -7.19651103e-01 7.47143865e-01 -5.58171459e-02 7.18255579e-01 6.87473595e-01 -9.53771532e-01 4.11308974e-01 6.33418441e-01 6.25566021e-02 8.00564528e-01 3.92201126e-01 -2.15957254e-01 -1.24970078e+00 -1.22125328e+00 6.98491633e-01 -3.86538684e-01 3.58285785e-01 -2.12754905e-01 -1.13994765e+00 8.28799427e-01 7.17029929e-01 2.23720834e-01 3.98805439e-01 5.38016623e-03 -4.20999169e-01 -4.82240379e-01 -1.12379587e+00 5.92808187e-01 1.02827156e+00 -1.38625741e-01 -6.13165200e-01 1.62841067e-01 5.76806545e-01 -3.72592330e-01 -9.06336367e-01 7.52204895e-01 4.85770732e-01 -1.17692351e+00 1.05315232e+00 -5.74974716e-01 4.91460621e-01 -2.37100571e-01 -2.17576236e-01 -1.25066006e+00 -5.89313865e-01 -1.01390131e-01 5.60850561e-01 1.17316341e+00 5.94855368e-01 -7.82912314e-01 7.95374990e-01 2.98654109e-01 -1.76134214e-01 -8.47629905e-01 -5.69526553e-01 -1.06408489e+00 4.53531832e-01 -4.37659621e-01 6.78237200e-01 1.13008869e+00 -1.95100203e-01 3.17362815e-01 -5.32220125e-01 7.90713541e-03 1.21835187e-01 -7.80088753e-02 7.60148942e-01 -1.07398415e+00 4.07335572e-02 -5.94348192e-01 -6.10971808e-01 -1.00151575e+00 -1.23302639e-02 -1.05252612e+00 -2.48146221e-01 -1.38923025e+00 4.60609376e-01 -6.52010739e-01 -7.12246120e-01 7.63907671e-01 2.03364953e-01 6.15164816e-01 4.00178671e-01 1.93908170e-01 -3.60199928e-01 1.59915835e-01 1.42968524e+00 -3.15188676e-01 2.49072030e-01 -7.97540769e-02 -9.74561393e-01 7.30825841e-01 1.01720798e+00 -2.69203156e-01 -4.02034372e-01 -5.09735763e-01 -9.42885950e-02 -3.69623959e-01 4.61353540e-01 -1.53509438e+00 4.23338592e-01 1.20902710e-01 7.30482340e-01 -3.80140543e-01 7.48913288e-02 -8.30421925e-01 2.74812996e-01 5.26566565e-01 -2.37276688e-01 1.79794833e-01 3.97801012e-01 9.05895010e-02 -5.20723164e-01 -4.72394407e-01 9.07923937e-01 -4.35750037e-01 -9.65084910e-01 4.88430738e-01 -1.38627559e-01 2.50168368e-02 8.28137815e-01 -3.80682141e-01 -5.04821002e-01 -1.76659048e-01 -5.83603501e-01 1.53189832e-02 1.12457797e-01 8.21089387e-01 5.79728842e-01 -1.36784077e+00 -6.16477072e-01 3.02154332e-01 -8.08194503e-02 -7.06552267e-02 1.43058687e-01 7.32811749e-01 -5.86811960e-01 6.27205610e-01 -5.13043582e-01 -6.77741647e-01 -8.58949423e-01 5.77096760e-01 4.93549407e-01 -2.31527135e-01 -5.95872104e-01 7.47616231e-01 2.17963666e-01 -6.15089059e-01 4.61433232e-01 -4.39075977e-01 -4.36418772e-01 -2.12205216e-01 4.83738542e-01 2.10260108e-01 1.07726306e-01 -5.73167324e-01 -3.65784138e-01 8.53421450e-01 -4.50412184e-01 4.87347692e-01 1.52055919e+00 2.98927009e-01 2.87356339e-02 1.62075892e-01 1.50617158e+00 -5.44065833e-01 -1.04036856e+00 -1.58130646e-01 1.07115461e-02 -2.26881057e-01 -1.34194091e-01 -9.95392859e-01 -1.70509446e+00 8.01813006e-01 8.93656015e-01 3.57884705e-01 1.20199621e+00 -3.12499821e-01 6.96518302e-01 4.38698083e-01 2.02673540e-01 -9.67703342e-01 3.32218371e-02 7.12475300e-01 1.14952350e+00 -1.41579211e+00 -1.16594397e-01 -1.26986608e-01 -5.74802577e-01 1.31319356e+00 8.99439931e-01 -5.85832000e-01 8.93071294e-01 3.50743115e-01 2.00408876e-01 -6.65051341e-02 -5.34756064e-01 -1.53012872e-01 2.59906977e-01 5.46113074e-01 6.63148165e-01 -1.14857182e-01 -2.16196612e-01 5.90350270e-01 -3.15668821e-01 3.31018299e-01 4.87650335e-01 7.15311825e-01 -2.92287052e-01 -1.15989947e+00 -2.49100283e-01 4.43758935e-01 -7.04091489e-01 -1.89044606e-02 -3.73128593e-01 1.23339021e+00 4.36838329e-01 8.97616684e-01 -1.22479387e-01 -6.54575527e-01 5.58983088e-01 -1.62095398e-01 2.90193588e-01 -4.80680168e-01 -9.29257810e-01 -3.19139451e-01 -2.03084111e-01 -4.80654508e-01 -6.26972854e-01 -2.26250812e-01 -9.59513366e-01 -4.43415999e-01 -2.22000450e-01 -1.05655961e-01 5.65176785e-01 8.94599199e-01 2.27988377e-01 6.01562917e-01 4.74386573e-01 -4.73229021e-01 -3.54737073e-01 -1.33407772e+00 -5.05946457e-01 6.14126682e-01 6.84276447e-02 -5.47543526e-01 -3.00460070e-01 -1.38691235e-02]	[9.342048645019531, 2.183336019515991]
7622642d-ec31-42b9-a5c8-55c5dd9008dc	self-supervised-video-representation-learning-7	2106.10137	null	https://arxiv.org/abs/2106.10137v3	https://arxiv.org/pdf/2106.10137v3.pdf	Self-supervised Video Representation Learning with Cross-Stream Prototypical Contrasting	Instance-level contrastive learning techniques, which rely on data augmentation and a contrastive loss function, have found great success in the domain of visual representation learning. They are not suitable for exploiting the rich dynamical structure of video however, as operations are done on many augmented instances. In this paper we propose "Video Cross-Stream Prototypical Contrasting", a novel method which predicts consistent prototype assignments from both RGB and optical flow views, operating on sets of samples. Specifically, we alternate the optimization process; while optimizing one of the streams, all views are mapped to one set of stream prototype vectors. Each of the assignments is predicted with all views except the one matching the prediction, pushing representations closer to their assigned prototypes. As a result, more efficient video embeddings with ingrained motion information are learned, without the explicit need for optical flow computation during inference. We obtain state-of-the-art results on nearest-neighbour video retrieval and action recognition, outperforming previous best by +3.2% on UCF101 using the S3D backbone (90.5% Top-1 acc), and by +7.2% on UCF101 and +15.1% on HMDB51 using the R(2+1)D backbone.	['Vincent Tao Hu', 'Maarten Stol', 'Ioannis Gatopoulos', 'Martine Toering']	2021-06-18	null	null	null	null	['self-supervised-action-recognition']	['computer-vision']	[ 2.14647308e-01 -2.29683518e-01 -6.94866598e-01 -2.36333072e-01 -5.27091742e-01 -5.10279059e-01 7.82374620e-01 1.39150880e-02 -5.75502396e-01 4.28390235e-01 4.89118099e-01 1.42378032e-01 -6.93668723e-02 -4.14860368e-01 -8.68001819e-01 -7.23589838e-01 -3.76388907e-01 5.35908163e-01 2.41847321e-01 -7.14754760e-02 2.81929761e-01 6.17905855e-01 -1.79065204e+00 7.06732750e-01 1.91729262e-01 1.20108199e+00 -5.54875750e-03 9.20185387e-01 -5.42199314e-02 1.03937268e+00 -2.76648819e-01 -2.79571056e-01 4.08911735e-01 -2.48049602e-01 -7.23293304e-01 1.27878040e-01 1.11514318e+00 -5.19189179e-01 -1.01022172e+00 5.72470367e-01 3.06436270e-01 5.54565072e-01 6.89843416e-01 -1.40765774e+00 -7.95750320e-01 5.14403544e-02 -6.47047818e-01 4.95181322e-01 4.64887142e-01 1.98966682e-01 1.15098369e+00 -1.08408606e+00 9.25464213e-01 1.24451041e+00 3.13420922e-01 7.72772074e-01 -1.22187591e+00 -3.66029084e-01 3.76020730e-01 8.78926516e-01 -1.27108848e+00 -6.55002296e-01 6.57462418e-01 -5.13909578e-01 1.40700424e+00 2.14083537e-01 9.48535681e-01 1.11650109e+00 -1.16836011e-01 9.37917292e-01 6.53028905e-01 -2.13233501e-01 1.78271428e-01 3.00741307e-02 -1.22420922e-01 7.77475059e-01 -8.01588297e-02 1.65115669e-01 -1.09919858e+00 1.01397663e-01 6.09919786e-01 1.77109972e-01 -5.83332121e-01 -8.48458469e-01 -1.25779593e+00 7.25906610e-01 5.38350821e-01 -4.52487580e-02 -3.79640639e-01 4.76879001e-01 5.75750947e-01 3.33002388e-01 4.42439467e-01 3.46558988e-01 -4.55545902e-01 -4.00460333e-01 -1.09091890e+00 2.40128353e-01 4.21325207e-01 8.49166512e-01 6.97109580e-01 -1.47522595e-02 -2.64132529e-01 6.33285224e-01 3.10410142e-01 4.79797721e-01 5.83011985e-01 -1.28470016e+00 5.81098855e-01 3.95822972e-01 5.53762503e-02 -1.05416858e+00 -8.37121531e-02 -2.17309967e-01 -7.88506508e-01 1.99448109e-01 2.98008531e-01 4.56616580e-01 -9.34479892e-01 1.56997490e+00 2.18632698e-01 7.97664762e-01 2.27438569e-01 1.00885558e+00 5.46840072e-01 9.79104578e-01 -7.19000623e-02 -1.80631101e-01 9.67665553e-01 -1.31588531e+00 -4.27982360e-01 -9.73857418e-02 7.12104201e-01 -5.58503091e-01 9.75689590e-01 4.44901407e-01 -1.17091548e+00 -8.30034316e-01 -9.04977858e-01 -2.49525055e-01 -2.71852344e-01 -9.25636739e-02 4.73488778e-01 2.65709251e-01 -1.13028431e+00 7.32468307e-01 -9.15142894e-01 -9.79676321e-02 5.82246125e-01 1.76242635e-01 -8.05787444e-01 -6.04933202e-01 -8.16852152e-01 6.55222356e-01 2.47748401e-02 -1.84270907e-02 -8.69723201e-01 -9.45440412e-01 -9.44756985e-01 2.69233100e-02 2.62739450e-01 -4.94555831e-01 7.57595360e-01 -8.14639747e-01 -1.21197200e+00 8.50164056e-01 -3.03380907e-01 -5.31175435e-01 6.24714077e-01 -4.98036355e-01 -3.24425995e-01 5.13639569e-01 -1.22168384e-01 1.23712182e+00 9.66007352e-01 -1.00308990e+00 -6.20631993e-01 -4.00921583e-01 7.66095147e-02 3.14102262e-01 -4.38938141e-01 -3.22526485e-01 -7.09268987e-01 -6.45174086e-01 -1.14861019e-01 -9.68814790e-01 3.20321098e-02 7.53404796e-01 1.04265898e-01 -2.38770410e-01 9.91519392e-01 -5.87442219e-01 1.01992643e+00 -2.18777871e+00 6.77091658e-01 7.35791028e-02 2.00019300e-01 5.03857255e-01 -3.69744331e-01 1.46720886e-01 -6.36784285e-02 -1.37593940e-01 7.66773988e-03 -6.44409418e-01 -8.49216953e-02 4.02807355e-01 -3.28002125e-01 6.28224075e-01 4.60030109e-01 8.10679674e-01 -1.01139128e+00 -3.12670410e-01 5.20337880e-01 6.56791449e-01 -9.99916852e-01 3.20825130e-01 -6.89050332e-02 2.09303126e-01 3.48802991e-02 3.83467108e-01 3.83884341e-01 -2.67967224e-01 1.61529079e-01 -4.35392141e-01 5.20229898e-03 5.96220121e-02 -1.19768453e+00 2.10727310e+00 -3.28610241e-01 1.04440355e+00 -3.92906636e-01 -1.19535804e+00 7.33512878e-01 1.12040497e-01 7.01847196e-01 -7.82354951e-01 -4.03714597e-01 -7.21414387e-02 -2.09794521e-01 -7.54169524e-01 5.51940858e-01 1.93802759e-01 3.06039125e-01 2.41417050e-01 2.25190759e-01 2.73491949e-01 2.91794151e-01 2.55385458e-01 1.07464659e+00 5.54464996e-01 6.04706332e-02 1.12652801e-01 6.11201406e-01 -1.20100155e-01 4.85096365e-01 4.75123078e-01 -3.49921107e-01 7.17783868e-01 5.35933793e-01 -6.93929493e-01 -1.23562908e+00 -9.69538093e-01 8.19046795e-02 1.02875805e+00 1.14234217e-01 -5.92273951e-01 -2.42523253e-01 -6.96823299e-01 1.95657715e-01 3.86302918e-01 -7.79661238e-01 -3.43683213e-01 -8.22654366e-01 -2.77081430e-01 2.99216539e-01 7.96316862e-01 2.31433421e-01 -8.80100429e-01 -7.32350945e-01 1.23655967e-01 -2.10934147e-01 -1.22474587e+00 -5.49062669e-01 -7.67033920e-02 -8.23691666e-01 -1.22227299e+00 -8.18269670e-01 -5.93665600e-01 4.61336493e-01 4.96163189e-01 1.07031894e+00 1.14746764e-01 -4.13186222e-01 6.71324849e-01 -6.12499774e-01 3.97796810e-01 -1.11149050e-01 -2.46431723e-01 1.35210767e-01 3.17012817e-01 2.71626443e-01 -3.51555407e-01 -6.91154778e-01 1.77447766e-01 -7.66455054e-01 -5.19248471e-02 2.96029598e-01 8.60018075e-01 7.93739438e-01 -6.36757553e-01 4.71350923e-02 -3.87596965e-01 -9.16639939e-02 -4.55228388e-01 -3.33965302e-01 2.92706937e-01 -4.20137823e-01 2.15382546e-01 6.54926658e-01 -5.36659241e-01 -5.74787915e-01 6.12623915e-02 2.88169414e-01 -1.34325421e+00 -1.55557647e-01 1.35779321e-01 9.50207263e-02 1.30653903e-02 5.53011537e-01 2.77770638e-01 2.09744051e-01 -4.76181209e-01 5.50443530e-01 3.72085363e-01 6.35690212e-01 -2.99400181e-01 4.79224741e-01 6.14919066e-01 1.56687409e-01 -9.08948839e-01 -5.54729402e-01 -6.53580844e-01 -7.49299526e-01 -3.55688781e-01 9.34170604e-01 -8.82606268e-01 -6.04847729e-01 3.46600652e-01 -9.89560723e-01 -3.12387586e-01 -5.27113795e-01 7.53906488e-01 -9.03393805e-01 4.94473487e-01 -4.21818107e-01 -5.05921066e-01 -6.26308918e-02 -1.09532237e+00 1.13789594e+00 -6.03383444e-02 -2.32205018e-01 -7.37837255e-01 2.70031482e-01 4.87373650e-01 2.58392245e-01 2.99749132e-02 7.85301268e-01 -5.24297893e-01 -7.59681106e-01 -2.99223904e-02 -1.45445660e-01 5.32108188e-01 -5.00955582e-02 2.13185757e-01 -8.84513795e-01 -4.53917742e-01 -4.26141828e-01 -3.86440694e-01 1.10015965e+00 1.04791500e-01 1.32448041e+00 -2.70700395e-01 -3.33393097e-01 8.83868456e-01 1.34074616e+00 7.99802914e-02 7.40150332e-01 2.50011086e-01 8.04880619e-01 5.94589531e-01 5.35050690e-01 5.40951371e-01 2.17535377e-01 9.59556878e-01 6.25336766e-01 3.10099930e-01 -4.78376627e-01 -2.05345169e-01 5.36903739e-01 4.81807053e-01 -1.97209358e-01 -4.40887332e-01 -8.16371441e-01 5.27873039e-01 -1.97225761e+00 -1.24059629e+00 1.75954506e-01 2.28185797e+00 4.25869077e-01 4.68563400e-02 1.77590698e-01 2.58630186e-01 5.44424474e-01 5.66583157e-01 -5.20563841e-01 -1.70340985e-01 -8.30910280e-02 3.33632767e-01 1.79063156e-01 6.15024030e-01 -1.18038321e+00 8.21868539e-01 5.41910696e+00 4.71336216e-01 -1.16869044e+00 -8.56304169e-02 6.16838515e-01 -6.14496350e-01 -6.17563501e-02 -1.85356826e-01 -6.58821106e-01 5.49512565e-01 8.70960653e-01 2.77793020e-01 5.82398236e-01 5.43742716e-01 1.29242241e-01 8.94569829e-02 -1.46879876e+00 1.30352533e+00 4.41307157e-01 -1.78948998e+00 3.58300418e-01 5.34480140e-02 6.24062359e-01 -2.84283999e-02 4.85327765e-02 2.27551773e-01 -4.27113503e-01 -1.02145588e+00 7.10163057e-01 8.61136019e-01 8.18916738e-01 -5.77689171e-01 4.91147041e-01 -1.78876724e-02 -1.12861240e+00 -1.30467132e-01 -3.52719784e-01 -2.42156535e-02 2.07480103e-01 -3.63404900e-02 -3.99803013e-01 2.64692724e-01 9.59593713e-01 1.35351527e+00 -4.75030959e-01 1.12430871e+00 7.80974552e-02 3.16803902e-01 -2.90460289e-01 1.01574473e-01 4.05877739e-01 -1.08707458e-01 4.51638222e-01 1.17816019e+00 2.24856153e-01 -9.20361560e-03 1.70756325e-01 3.05508077e-01 -1.51030481e-01 1.70910135e-02 -5.70669949e-01 6.70013800e-02 2.73709714e-01 9.22325492e-01 -2.51491427e-01 -6.18871450e-01 -6.33232236e-01 1.20428002e+00 5.00936687e-01 3.43827784e-01 -8.98206532e-01 -8.49168599e-02 1.15567327e+00 1.95094258e-01 7.02332020e-01 -2.34101236e-01 4.44761097e-01 -1.26360917e+00 2.27624461e-01 -7.04410672e-01 4.11449820e-01 -8.50475430e-01 -1.13373089e+00 4.06696886e-01 3.86917144e-02 -1.36377060e+00 -4.77611154e-01 -8.86956453e-01 -2.58317292e-01 3.81786525e-01 -1.69243479e+00 -8.00927639e-01 -5.07363617e-01 7.75103211e-01 6.38547540e-01 -3.21209043e-01 9.04213846e-01 6.23995781e-01 -4.15043205e-01 6.51725173e-01 2.52670109e-01 6.25059530e-02 8.24198365e-01 -1.04323888e+00 2.47284323e-01 5.51508307e-01 6.21738732e-01 1.25035979e-02 4.37741876e-01 -1.57196611e-01 -1.57262433e+00 -1.10114789e+00 7.82154799e-01 -2.87643790e-01 6.80458724e-01 -2.46482775e-01 -1.01643717e+00 6.10054195e-01 2.63722297e-02 7.50217259e-01 6.07234836e-01 -2.81056792e-01 -6.97066903e-01 -3.28320801e-01 -9.31355298e-01 4.92365718e-01 1.35967755e+00 -6.40330255e-01 -4.64474767e-01 4.11148846e-01 5.21040022e-01 -3.62214744e-01 -1.00692749e+00 2.67890692e-01 6.36646926e-01 -1.04820311e+00 1.23687291e+00 -1.28048849e+00 6.15513682e-01 -2.86500692e-01 -5.66038013e-01 -1.10187292e+00 -4.07898664e-01 -4.44839448e-01 -7.60093093e-01 8.84018660e-01 1.63642675e-01 -2.00216770e-01 1.07742524e+00 3.32131743e-01 -1.47201419e-01 -1.01476979e+00 -1.03123665e+00 -7.83976018e-01 -1.30087480e-01 -4.04741168e-01 1.34367585e-01 8.84620130e-01 -2.20543310e-01 9.54465941e-02 -6.65721476e-01 -8.08259994e-02 5.31896174e-01 8.07087049e-02 7.34584510e-01 -7.84613788e-01 -5.76622367e-01 -4.86165971e-01 -9.86065388e-01 -1.45187414e+00 3.15496534e-01 -8.21265578e-01 -2.20656097e-01 -1.18003666e+00 6.16071001e-02 -3.10828000e-01 -4.89992142e-01 4.36859787e-01 -5.28680235e-02 5.42185187e-01 5.70112646e-01 2.69183040e-01 -7.21413076e-01 7.32821703e-01 1.11166227e+00 -3.43490213e-01 4.83390018e-02 -3.67750704e-01 -1.25547528e-01 4.32784438e-01 6.11283839e-01 -2.03313544e-01 -5.25556505e-01 -6.61756217e-01 -5.07297069e-02 9.95794982e-02 5.69124222e-01 -1.08722365e+00 1.89116135e-01 3.90868261e-03 5.79418838e-01 -4.67648298e-01 9.42342222e-01 -7.82098651e-01 -5.33005074e-02 5.08767724e-01 -5.87657511e-01 3.45712662e-01 4.74593602e-02 8.96648765e-01 -2.53051668e-01 -9.58711356e-02 7.16430068e-01 2.04446223e-02 -1.30160964e+00 5.86951375e-01 -1.31427512e-01 2.39617810e-01 1.19335651e+00 -4.27947402e-01 -3.86572301e-01 -2.64303386e-01 -9.32538331e-01 1.72463924e-01 5.04598558e-01 7.33184457e-01 8.73630762e-01 -1.49729168e+00 -6.24661028e-01 3.67138803e-01 2.30068192e-01 -1.42485648e-01 3.21405292e-01 7.13951051e-01 -5.53479850e-01 4.01909620e-01 -5.01188099e-01 -8.72156978e-01 -1.21018875e+00 7.46458888e-01 3.47192019e-01 -5.27949780e-02 -7.42721617e-01 1.00315380e+00 -5.58069050e-02 8.08274820e-02 4.84779328e-01 -1.41312346e-01 -1.47647187e-01 3.18010002e-01 6.86621428e-01 6.62080884e-01 -7.22721294e-02 -8.26041400e-01 -5.09744883e-01 8.02127242e-01 -1.73329636e-01 7.35812485e-02 1.41757298e+00 1.42495900e-01 1.66859806e-01 2.92431265e-01 1.76784730e+00 -4.03695464e-01 -1.66887879e+00 -2.74282843e-01 -2.35671014e-01 -1.01311636e+00 -1.47608211e-02 -3.52262616e-01 -1.37966597e+00 1.16039670e+00 7.60958076e-01 -1.21712461e-01 8.95159543e-01 1.15717269e-01 5.95940113e-01 3.96894217e-01 1.24974407e-01 -8.02098989e-01 5.14817297e-01 3.66641998e-01 8.83542776e-01 -1.20653903e+00 -1.02583162e-01 -4.20676805e-02 -6.05710030e-01 1.18274629e+00 6.77464128e-01 -3.96194220e-01 4.66964722e-01 -1.28039464e-01 -2.59503096e-01 2.77853590e-02 -1.11715662e+00 1.00749917e-01 4.27160531e-01 6.75589979e-01 2.70798981e-01 -1.59920931e-01 2.71527886e-01 -2.54057407e-01 2.74157107e-01 -2.03354955e-01 3.80930185e-01 9.07788813e-01 -3.34669769e-01 -1.00707531e+00 -7.17706755e-02 4.56849456e-01 -5.88116758e-02 -8.95102918e-02 -1.02656357e-01 8.26617539e-01 2.30042823e-02 5.15824139e-01 6.57991648e-01 -3.84832323e-01 3.92413408e-01 1.01196088e-01 7.44546354e-01 -4.10324037e-01 -1.20287821e-01 -2.01448902e-01 -1.45771103e-02 -1.08268952e+00 -6.62292242e-01 -8.97116959e-01 -1.09604561e+00 -4.05176073e-01 1.64721772e-01 -7.09388703e-02 3.07164043e-01 7.40349889e-01 5.87975621e-01 2.02701613e-01 5.65280795e-01 -1.27605724e+00 -5.02590895e-01 -5.34172952e-01 -3.12109590e-01 6.58933640e-01 5.33575416e-01 -8.43510389e-01 -3.43927115e-01 1.90589219e-01]	[8.686342239379883, 0.7110565304756165]
eb28c8e2-f46f-4216-bc07-1b79d50722e4	propnet-propagating-2d-annotation-to-3d	2305.17871	null	https://arxiv.org/abs/2305.17871v1	https://arxiv.org/pdf/2305.17871v1.pdf	propnet: Propagating 2D Annotation to 3D Segmentation for Gastric Tumors on CT Scans	Background: Accurate 3D CT scan segmentation of gastric tumors is pivotal for diagnosis and treatment. The challenges lie in the irregular shapes, blurred boundaries of tumors, and the inefficiency of existing methods. Purpose: We conducted a study to introduce a model, utilizing human-guided knowledge and unique modules, to address the challenges of 3D tumor segmentation. Methods: We developed the PropNet framework, propagating radiologists' knowledge from 2D annotations to the entire 3D space. This model consists of a proposing stage for coarse segmentation and a refining stage for improved segmentation, using two-way branches for enhanced performance and an up-down strategy for efficiency. Results: With 98 patient scans for training and 30 for validation, our method achieves a significant agreement with manual annotation (Dice of 0.803) and improves efficiency. The performance is comparable in different scenarios and with various radiologists' annotations (Dice between 0.785 and 0.803). Moreover, the model shows improved prognostic prediction performance (C-index of 0.620 vs. 0.576) on an independent validation set of 42 patients with advanced gastric cancer. Conclusions: Our model generates accurate tumor segmentation efficiently and stably, improving prognostic performance and reducing high-throughput image reading workload. This model can accelerate the quantitative analysis of gastric tumors and enhance downstream task performance.	['Li Zhang', 'Lei Tang', 'Bin Dong', 'Hongfeng Li', 'Yiting Liu', 'Jie Zhao', 'Jiazheng Li', 'ZiFan Chen']	2023-05-29	null	null	null	null	['tumor-segmentation']	['computer-vision']	[ 2.81921439e-02 4.46013510e-01 -4.98504102e-01 -5.83636649e-02 -1.24108171e+00 -5.06053030e-01 -6.69677258e-02 5.38856030e-01 -4.86389637e-01 5.15549958e-01 2.70591259e-01 -6.73403800e-01 -2.07738802e-02 -6.73477054e-01 -2.22149208e-01 -1.00251687e+00 -1.74488902e-01 7.25225985e-01 5.27058661e-01 1.86378717e-01 -5.33419177e-02 5.58463514e-01 -6.58068001e-01 3.67935359e-01 1.06046784e+00 8.17798138e-01 5.66230893e-01 8.96708429e-01 1.56644434e-02 5.29368341e-01 -2.78231680e-01 -2.05428496e-01 1.95376873e-01 -2.34791487e-01 -9.59935308e-01 1.22170448e-01 -2.08704267e-03 -2.64076442e-01 -3.97371054e-02 9.47244942e-01 7.20861018e-01 -3.74026716e-01 6.34271502e-01 -5.03832996e-01 -3.63804311e-01 5.26504219e-01 -7.57399440e-01 1.87242687e-01 -2.08132386e-01 4.46784079e-01 6.01537406e-01 -5.86632490e-01 6.04525447e-01 3.55484217e-01 1.24781930e+00 6.51919663e-01 -1.18290806e+00 -4.51277375e-01 -3.73219579e-01 -1.89689726e-01 -1.33850646e+00 8.30226243e-02 -1.39321070e-02 -5.10658801e-01 7.54673958e-01 4.12035525e-01 1.15264261e+00 4.27200049e-01 5.41860819e-01 6.25265241e-01 9.08351541e-01 -2.41189092e-01 8.80755633e-02 1.31738931e-01 1.18280776e-01 1.19690871e+00 5.14946461e-01 -5.25726229e-02 5.68889342e-02 -7.54297674e-02 8.78557205e-01 2.38373224e-02 -4.84370917e-01 -1.84713364e-01 -1.44279408e+00 8.14647317e-01 6.09000742e-01 3.71969461e-01 -4.69416618e-01 -1.78762816e-03 6.02557063e-01 -2.59302109e-01 4.96186912e-01 1.44119650e-01 -2.85607390e-02 6.13571331e-02 -1.05809629e+00 -3.96669596e-01 7.38844573e-01 6.92944586e-01 7.48695359e-02 -4.26917493e-01 -2.23934770e-01 6.64790869e-01 3.15563858e-01 5.07143617e-01 9.25097942e-01 -7.22197175e-01 -1.22070886e-01 6.65609300e-01 -6.75089061e-02 -6.94080710e-01 -9.61401761e-01 -7.65486300e-01 -1.14828277e+00 8.86882618e-02 6.19359434e-01 -2.77056042e-02 -1.11434388e+00 1.29281998e+00 2.45028242e-01 7.21489340e-02 -3.00114378e-02 9.79743123e-01 9.50387299e-01 9.12878141e-02 4.89144534e-01 -3.05853337e-01 1.70889020e+00 -1.07678616e+00 -4.52906728e-01 3.02251071e-01 1.38746250e+00 -6.28377855e-01 9.38847363e-01 1.54594719e-01 -1.28462386e+00 -1.43654868e-01 -5.07479250e-01 -7.01458473e-03 -1.24002703e-01 5.63123643e-01 5.91608107e-01 8.38808656e-01 -1.60194016e+00 2.68668652e-01 -1.18518770e+00 -7.11863875e-01 6.72572315e-01 5.73817551e-01 -4.20766443e-01 -5.78950765e-03 -7.44292557e-01 1.01409006e+00 6.17895365e-01 -1.24158941e-01 -7.24808335e-01 -1.22012627e+00 -4.98505473e-01 -2.25105286e-01 2.42543831e-01 -1.17600596e+00 1.30484235e+00 -5.20741820e-01 -1.26833582e+00 1.21275127e+00 -1.05289698e-01 -5.03969610e-01 8.11305940e-01 2.48419046e-01 3.60005721e-02 4.69749868e-01 3.41148853e-01 7.92735636e-01 -3.61519642e-02 -1.11149144e+00 -1.02557588e+00 -4.08302873e-01 -6.28566086e-01 4.43962365e-01 5.32575976e-03 -4.23410535e-01 -8.04532170e-01 -6.18578732e-01 2.30206326e-01 -1.10020590e+00 -7.47731507e-01 4.10401553e-01 -2.48488516e-01 1.84177935e-01 5.55295408e-01 -1.08580148e+00 1.15819323e+00 -1.94767630e+00 -3.80971670e-01 6.56126142e-01 7.15798020e-01 3.54465425e-01 3.65049690e-01 -4.37831998e-01 -3.40868793e-02 4.73605961e-01 -1.91860616e-01 -6.70974609e-03 -3.93457949e-01 1.92997530e-01 5.97741127e-01 5.29464483e-01 -3.59359205e-01 1.53058338e+00 -1.11568439e+00 -1.02475893e+00 2.97378093e-01 3.06062013e-01 -4.52053547e-01 4.35685180e-02 2.15212882e-01 6.02174759e-01 -3.37973356e-01 8.30411851e-01 5.89447260e-01 -7.20442235e-01 3.80091161e-01 -4.52785432e-01 -1.81357965e-01 -3.38398397e-01 -6.15878522e-01 1.78090966e+00 -3.11027646e-01 1.74861386e-01 2.35678628e-01 -7.03872800e-01 6.87243998e-01 5.69536269e-01 1.02119362e+00 -4.74147379e-01 3.32031310e-01 4.74616170e-01 -8.45616963e-03 -7.28002429e-01 8.79056975e-02 -3.03181529e-01 2.31085584e-01 3.90597641e-01 -3.07687759e-01 -6.01742089e-01 1.58955872e-01 1.33204892e-01 1.20119655e+00 -3.66770804e-01 5.93066394e-01 -6.10008359e-01 4.79060173e-01 6.01463020e-01 3.71182859e-01 8.17859173e-01 -6.44521534e-01 4.54290837e-01 5.50822198e-01 -3.94510359e-01 -7.99804926e-01 -9.75980461e-01 -1.91546753e-01 5.13734162e-01 1.56856298e-01 -5.99039420e-02 -7.23633647e-01 -1.01322126e+00 -4.10098061e-02 5.12733340e-01 -1.01127207e+00 3.44795585e-02 -4.42540884e-01 -1.15880740e+00 6.38596296e-01 6.14791989e-01 4.74942237e-01 -4.49040860e-01 -8.65805209e-01 2.74621487e-01 -4.98794973e-01 -8.74380469e-01 -4.30228800e-01 2.37737969e-01 -1.35999715e+00 -1.13895786e+00 -1.13680565e+00 -9.56111252e-01 1.01332593e+00 4.62420434e-01 1.24567747e+00 2.58925229e-01 -5.44165432e-01 1.83769971e-01 -2.15835005e-01 -5.26009083e-01 -6.50560856e-01 5.00440709e-02 -5.07371902e-01 -6.66233659e-01 2.04089150e-01 -7.25825429e-02 -1.07734632e+00 6.78471625e-01 -7.66476393e-01 5.11669636e-01 1.08124328e+00 9.87577438e-01 1.15240514e+00 -3.86319980e-02 7.35119656e-02 -9.47769880e-01 4.42739159e-01 -3.21557790e-01 -3.87144595e-01 3.12631696e-01 -7.88837433e-01 -3.42325687e-01 2.64117926e-01 -6.56482875e-02 -9.40111995e-01 1.79021955e-01 -2.19300523e-01 4.26677689e-02 -2.07783118e-01 4.68934923e-01 5.29987693e-01 -2.28842884e-01 7.45282590e-01 -2.02646144e-02 7.15829909e-01 4.63273935e-03 -6.88941479e-02 2.93973416e-01 5.10018945e-01 -2.29205042e-01 3.46810788e-01 8.45549583e-01 3.97179842e-01 -5.71746111e-01 -7.79039860e-01 -8.12884748e-01 -7.86787450e-01 -2.58405775e-01 1.04384959e+00 -8.10163319e-01 -5.06417155e-01 2.65867859e-01 -5.51203191e-01 -6.97586238e-01 -5.86909175e-01 7.83211410e-01 -3.69505584e-01 4.60573494e-01 -9.01802182e-01 -3.00911255e-02 -9.31897938e-01 -1.57237673e+00 1.07301462e+00 3.44994426e-01 -2.54917741e-01 -1.28426266e+00 9.83650312e-02 3.23616415e-01 6.65033519e-01 5.23401976e-01 8.89041424e-01 -7.00577557e-01 -3.61719370e-01 -2.75983483e-01 -6.16962671e-01 -1.37115613e-01 3.39142650e-01 2.65431218e-02 -6.62152529e-01 -1.62192047e-01 -3.00856024e-01 1.39228269e-01 7.75137365e-01 9.00936902e-01 1.29045451e+00 1.15945712e-01 -8.66933167e-01 6.27796412e-01 1.34298158e+00 2.18831986e-01 4.14440513e-01 2.93458581e-01 4.95632052e-01 3.61854225e-01 5.47756255e-01 2.45963782e-01 3.83054912e-01 2.96910703e-01 5.51240802e-01 -6.21206284e-01 -2.95537621e-01 -2.90339738e-02 -3.20673794e-01 6.30571842e-01 -3.32344890e-01 -8.79351608e-03 -1.31532419e+00 6.84666634e-01 -1.57145226e+00 -5.63851535e-01 -5.60053647e-01 1.91563356e+00 7.95969546e-01 -8.58258642e-03 -2.59450749e-02 -8.22078437e-02 6.39876187e-01 -5.01283228e-01 -3.53304625e-01 -8.40770900e-02 1.26079157e-01 2.20831826e-01 7.21081555e-01 3.71909261e-01 -9.82745886e-01 6.07445478e-01 6.72816992e+00 8.84050369e-01 -1.29428053e+00 2.20504969e-01 9.48496163e-01 1.07656755e-01 6.46578521e-02 -4.19958264e-01 -4.52778757e-01 2.09788769e-01 8.70928228e-01 -1.25991479e-01 -4.87088025e-01 5.72575152e-01 5.98752141e-01 -5.95464706e-01 -6.75610662e-01 7.68323183e-01 -4.71795946e-02 -1.72973263e+00 -2.69615185e-02 3.80358607e-01 6.90286100e-01 1.26106441e-01 -4.32901038e-03 2.31874585e-02 5.05043030e-01 -1.07584167e+00 1.71620190e-01 5.53964257e-01 1.22280371e+00 -4.70285743e-01 1.41855085e+00 2.02591389e-01 -1.08417833e+00 3.37578773e-01 3.89048504e-03 8.06593776e-01 2.11206377e-01 8.51416051e-01 -1.70877457e+00 5.71196914e-01 5.74450970e-01 5.62725246e-01 -6.96356833e-01 1.20096612e+00 -2.90175043e-02 5.98820925e-01 -2.38898605e-01 -4.00014706e-02 3.21074754e-01 4.57515419e-02 2.16211230e-01 1.48022223e+00 5.04506469e-01 4.79574025e-01 1.71647500e-02 3.51446867e-01 2.06553623e-01 3.69346201e-01 -1.28385369e-02 3.18038315e-01 1.60024405e-01 1.61387086e+00 -1.18022239e+00 -5.04230320e-01 -3.28786194e-01 7.49549925e-01 -1.98372588e-01 -1.15975536e-01 -1.05773556e+00 5.92738129e-02 -6.41495585e-02 2.85713673e-01 -1.32023916e-01 1.23597614e-01 -6.79659903e-01 -6.25516236e-01 -6.06187940e-01 -4.97746259e-01 8.96090448e-01 -6.51211858e-01 -9.61545110e-01 3.02857012e-01 -2.48351365e-01 -1.17579603e+00 2.62573808e-01 -7.89245844e-01 -5.74332654e-01 6.99513674e-01 -1.52537119e+00 -1.27401900e+00 -7.87777781e-01 4.17915493e-01 2.50157237e-01 2.76762724e-01 9.78126943e-01 5.11571206e-02 -4.76341307e-01 6.86071396e-01 1.17879555e-01 1.47738099e-01 8.23495924e-01 -1.58872330e+00 -3.56496841e-01 2.76625246e-01 -7.29042292e-01 2.25472808e-01 1.35772914e-01 -6.36439264e-01 -9.73025203e-01 -1.34334612e+00 7.19368219e-01 -3.18502545e-01 7.05352306e-01 5.89583874e-01 -7.76494801e-01 4.55704272e-01 -1.42320111e-01 2.14337572e-01 1.41160679e+00 -5.31017721e-01 2.94697374e-01 1.84544832e-01 -1.54277217e+00 5.88318169e-01 6.45639122e-01 -1.19753776e-03 -2.58489907e-01 2.85784423e-01 3.84321451e-01 -8.60046268e-01 -1.44217205e+00 5.23954093e-01 7.42296219e-01 -9.77791667e-01 9.34124053e-01 1.01742864e-01 1.06243066e-01 -3.50872189e-01 2.60355234e-01 -1.06837547e+00 -5.08238614e-01 -2.10225150e-01 2.49372557e-01 2.57564008e-01 6.93917155e-01 -5.42363167e-01 1.10297418e+00 6.63408458e-01 -6.32711351e-01 -9.94199157e-01 -8.02411318e-01 -1.05087280e-01 2.04160824e-01 -8.31039995e-02 1.92671925e-01 7.04800904e-01 2.09431276e-01 -4.68488991e-01 4.68879908e-01 1.71052948e-01 6.22635841e-01 -1.59603804e-01 4.10145640e-01 -1.01947427e+00 -9.48897377e-02 -8.02420735e-01 -2.72339612e-01 -8.39070439e-01 -3.47532600e-01 -1.16601694e+00 -2.32476845e-01 -1.78283560e+00 7.19648063e-01 -6.32451296e-01 -2.48676166e-01 6.09438121e-01 -3.48724306e-01 5.26338160e-01 -2.79528528e-01 5.70766628e-01 -4.29254919e-01 -1.63917392e-01 1.78720367e+00 2.33614985e-02 -2.21354738e-01 -1.73320514e-04 -7.98995733e-01 7.77118087e-01 1.02860725e+00 -1.90871447e-01 -9.36420038e-02 -1.97235957e-01 -3.11771512e-01 2.63410717e-01 5.34823895e-01 -9.98585105e-01 1.91445217e-01 -9.84129608e-02 6.03589594e-01 -8.07086468e-01 -2.11386412e-01 -8.10041428e-01 2.71552086e-01 1.31373811e+00 -3.23502235e-02 -6.14589453e-02 3.30416799e-01 3.95986527e-01 -1.56531036e-01 -2.18898609e-01 1.10721385e+00 -5.00671625e-01 -6.65911436e-01 4.24201399e-01 -3.34765911e-01 -1.46281943e-01 1.48174036e+00 -5.77963710e-01 -2.39689842e-01 -1.31825767e-02 -1.16454017e+00 2.82703072e-01 4.88375872e-01 -3.67580742e-01 4.09735918e-01 -9.81751978e-01 -8.23129833e-01 -3.90110761e-02 3.09201865e-03 5.11381149e-01 3.39432597e-01 1.82832503e+00 -1.25342262e+00 5.46793103e-01 1.13284670e-01 -1.03743160e+00 -1.41455293e+00 1.67179376e-01 8.40091228e-01 -8.71673703e-01 -6.47235215e-01 8.83021235e-01 2.61426121e-01 -2.75540173e-01 -6.63351566e-02 -5.53769469e-01 -5.92744052e-02 -1.43335192e-02 3.19897830e-01 4.52489257e-01 1.99003220e-01 -5.55130303e-01 -2.13923499e-01 6.14244521e-01 -2.03355938e-01 1.75052062e-01 1.00507319e+00 -3.13715219e-01 -1.28110528e-01 -3.33692878e-02 9.03000593e-01 -2.11898349e-02 -9.01556551e-01 -1.07044250e-01 -6.47553876e-02 -1.44486442e-01 2.74811566e-01 -1.08879733e+00 -1.16250658e+00 4.89965141e-01 8.58521879e-01 3.99359204e-02 1.19794524e+00 1.49262026e-01 7.76489854e-01 -2.64322609e-01 1.59679756e-01 -8.68033111e-01 2.48120259e-02 3.02051544e-01 2.97002256e-01 -1.31069636e+00 3.84681672e-02 -6.46943271e-01 -7.29482174e-01 1.23881805e+00 3.41315478e-01 2.61617899e-01 6.88773811e-01 3.64033788e-01 3.90827954e-01 -3.11878562e-01 -3.95099491e-01 -9.51504186e-02 1.45758256e-01 5.29576302e-01 5.63402236e-01 4.17616278e-01 -5.06684363e-01 5.65653443e-01 -1.03026919e-01 2.94254512e-01 2.62713492e-01 8.66394699e-01 -6.98518276e-01 -7.66421199e-01 -4.02131617e-01 5.26522696e-01 -5.72269261e-01 1.37859387e-02 -4.96179163e-02 1.17629337e+00 1.90284401e-01 5.99994659e-01 -1.36469245e-01 -3.69995572e-02 2.52712876e-01 -1.20595463e-01 8.27567875e-02 -4.46471840e-01 -7.34807551e-01 4.55613166e-01 -5.89601435e-02 -4.88983035e-01 -4.75612700e-01 -4.90365237e-01 -1.62385261e+00 -2.08348542e-01 -4.77859288e-01 3.63954842e-01 6.16845667e-01 5.01273811e-01 2.93043464e-01 6.83319211e-01 4.52521086e-01 -5.29879510e-01 -3.30171943e-01 -6.33525312e-01 -5.42100728e-01 2.69741178e-01 -3.43118571e-02 -2.74862617e-01 -2.71743268e-01 3.31499487e-01]	[14.618683815002441, -2.701077461242676]
49dfbef3-e6f8-4571-bc05-691fb8abab58	diffskill-skill-abstraction-from-1	2203.17275	null	https://arxiv.org/abs/2203.17275v1	https://arxiv.org/pdf/2203.17275v1.pdf	DiffSkill: Skill Abstraction from Differentiable Physics for Deformable Object Manipulations with Tools	We consider the problem of sequential robotic manipulation of deformable objects using tools. Previous works have shown that differentiable physics simulators provide gradients to the environment state and help trajectory optimization to converge orders of magnitude faster than model-free reinforcement learning algorithms for deformable object manipulation. However, such gradient-based trajectory optimization typically requires access to the full simulator states and can only solve short-horizon, single-skill tasks due to local optima. In this work, we propose a novel framework, named DiffSkill, that uses a differentiable physics simulator for skill abstraction to solve long-horizon deformable object manipulation tasks from sensory observations. In particular, we first obtain short-horizon skills using individual tools from a gradient-based optimizer, using the full state information in a differentiable simulator; we then learn a neural skill abstractor from the demonstration trajectories which takes RGBD images as input. Finally, we plan over the skills by finding the intermediate goals and then solve long-horizon tasks. We show the advantages of our method in a new set of sequential deformable object manipulation tasks compared to previous reinforcement learning algorithms and compared to the trajectory optimizer.	['Chuang Gan', 'David Held', 'Joshua B. Tenenbaum', 'Yunzhu Li', 'Zhiao Huang', 'Xingyu Lin']	2022-03-31	diffskill-skill-abstraction-from	https://openreview.net/forum?id=Kef8cKdHWpP	https://openreview.net/pdf?id=Kef8cKdHWpP	iclr-2022-4	['deformable-object-manipulation']	['robots']	[-1.08824529e-01 1.11556441e-01 7.83842057e-02 -5.04368171e-02 -6.51837885e-01 -9.58224118e-01 3.21099281e-01 -9.32805911e-02 -5.77497721e-01 8.68936181e-01 -3.45951468e-01 -6.01875829e-03 -5.24434209e-01 -6.56892657e-01 -1.29480243e+00 -6.12783611e-01 -2.92151630e-01 8.21587205e-01 4.55462635e-01 -5.16946495e-01 2.37644613e-01 7.38946915e-01 -1.45666158e+00 -1.26501620e-01 1.01773024e+00 6.89917922e-01 7.63810337e-01 1.15151322e+00 1.86491981e-01 7.86635756e-01 -2.50235230e-01 3.04918587e-01 5.55975080e-01 -2.67469704e-01 -8.54684114e-01 -1.30647402e-02 2.67748713e-01 -6.94059908e-01 -6.92977250e-01 8.83460045e-01 2.68297583e-01 9.35097992e-01 3.71115685e-01 -1.34701741e+00 -3.95325959e-01 5.61484635e-01 2.37454146e-01 -1.41308382e-01 4.90637153e-01 9.16163862e-01 6.55948818e-01 -4.07223970e-01 9.28888261e-01 1.53778934e+00 3.43566269e-01 8.71475041e-01 -9.78979230e-01 -2.54635900e-01 4.27485704e-01 9.02555045e-03 -7.75013804e-01 2.25171983e-01 3.44444782e-01 -4.48673040e-01 1.17200649e+00 6.53579235e-02 1.21827185e+00 1.08157516e+00 4.36049819e-01 9.30387259e-01 1.12463820e+00 -3.08212303e-02 4.13447440e-01 -4.12950546e-01 -1.94329508e-02 1.31996727e+00 -1.65939420e-01 7.30156660e-01 -3.72977853e-01 -1.58933908e-01 1.10977769e+00 1.34856209e-01 -2.77381718e-01 -8.32732081e-01 -1.42025828e+00 5.10382712e-01 7.42437243e-01 -2.04474568e-01 -5.08706510e-01 7.80983090e-01 1.07654678e-02 5.96190751e-01 -1.80284694e-01 9.77023005e-01 -5.28508306e-01 -3.37830454e-01 -4.23456043e-01 1.07052493e+00 1.04633355e+00 1.17387879e+00 5.59905291e-01 2.27633312e-01 -3.79484743e-01 2.57968605e-02 1.74523264e-01 8.15981030e-01 7.72267357e-02 -1.43390548e+00 4.18721020e-01 3.67452621e-01 7.49129295e-01 -2.98510045e-01 -5.34820139e-01 2.29080513e-01 8.90116394e-03 1.06702244e+00 5.48394322e-01 -2.48471946e-01 -1.23857522e+00 1.48095727e+00 6.26258135e-01 1.98132351e-01 1.31709903e-01 1.15151477e+00 3.89247924e-01 7.14549065e-01 -6.13410734e-02 2.33755693e-01 6.43618226e-01 -1.13910604e+00 -5.02279818e-01 1.31177813e-01 3.65745813e-01 -1.28490821e-01 1.36836982e+00 4.56702560e-01 -1.40408027e+00 -5.15725791e-01 -9.86916363e-01 -3.30472626e-02 -2.05693603e-01 -7.30866008e-03 5.51842332e-01 -1.23451926e-01 -9.96147513e-01 1.63578737e+00 -1.61311364e+00 -2.41560727e-01 1.03070721e-01 6.71600163e-01 2.19027046e-02 3.34417820e-01 -6.69130147e-01 1.34250319e+00 6.07309282e-01 1.60575554e-01 -1.83131206e+00 -7.55566835e-01 -7.72976160e-01 -2.39818767e-01 6.94871902e-01 -7.72518396e-01 1.51458514e+00 -3.29265803e-01 -2.41399336e+00 3.82062197e-01 3.44497144e-01 -2.81385601e-01 6.93354070e-01 -6.04387999e-01 4.51657295e-01 2.09575251e-01 -3.55436414e-01 6.20852232e-01 8.72820258e-01 -1.27627361e+00 -5.38784623e-01 -1.59077406e-01 7.34344661e-01 4.80169684e-01 1.56899944e-01 -4.52728331e-01 -1.75028607e-01 -2.55553186e-01 -7.71017969e-02 -1.54552817e+00 -5.05836785e-01 5.07749319e-01 -1.87102854e-01 -2.27400929e-01 8.13470781e-01 -5.04768550e-01 3.80842179e-01 -1.63308847e+00 1.28422976e+00 1.20326988e-01 2.46246252e-02 1.51292026e-01 -2.89931208e-01 4.72704589e-01 5.37916780e-01 -5.40793419e-01 -2.26447970e-01 -9.19473022e-02 2.27132753e-01 5.14624894e-01 -4.72865045e-01 3.62571687e-01 -1.46279950e-02 1.37467301e+00 -1.50319731e+00 -1.65108323e-01 3.64528328e-01 1.21274374e-01 -6.27574801e-01 5.30753374e-01 -1.01353776e+00 1.03690279e+00 -1.09671521e+00 4.99802709e-01 2.05613017e-01 6.14849813e-02 -4.39373851e-02 1.65723428e-01 -5.04964352e-01 2.16348581e-02 -1.03458297e+00 2.31176281e+00 -6.36818826e-01 2.16099009e-01 3.91987354e-01 -8.40023994e-01 6.96432292e-01 1.12505652e-01 4.46195990e-01 -9.96945500e-02 1.59126788e-01 3.85445744e-01 -9.47318152e-02 -8.59421909e-01 5.21441758e-01 -1.87838208e-02 -1.06553026e-01 3.13068300e-01 2.29362786e-01 -1.39052129e+00 -3.92146781e-02 6.05981909e-02 1.31890416e+00 1.07662332e+00 -2.80263603e-01 -1.20244496e-01 2.22274333e-01 4.62088495e-01 2.44242549e-02 1.02386796e+00 -1.47439055e-02 2.32014447e-01 5.43101765e-02 -2.96346188e-01 -1.03478110e+00 -1.51728606e+00 4.38303441e-01 9.17224884e-01 4.94774342e-01 -3.19946855e-02 -7.26183474e-01 -5.39564788e-01 5.03989816e-01 6.69236243e-01 -4.22905266e-01 -2.75338829e-01 -1.05387843e+00 1.21001251e-01 1.53885022e-01 6.28013015e-01 2.89492607e-01 -1.58135152e+00 -1.25755978e+00 3.07463288e-01 2.05479696e-01 -8.44545960e-01 -2.78697520e-01 8.91390294e-02 -1.10903144e+00 -1.03026533e+00 -7.68391192e-01 -5.75582027e-01 5.29221535e-01 -1.19827718e-01 6.42698169e-01 1.17806651e-01 -5.84659874e-01 1.04765642e+00 -2.48568207e-01 -3.39021593e-01 -5.34661889e-01 -1.37734056e-01 1.95451349e-01 -7.32856572e-01 -6.78942144e-01 -4.77312058e-01 -5.10943532e-01 -4.82820533e-02 -5.47544599e-01 -1.05170012e-01 4.03610110e-01 7.11022675e-01 6.68367743e-01 -1.94961682e-01 -9.52942669e-02 -1.02792449e-01 6.62617326e-01 -1.23861901e-01 -1.22777057e+00 2.80211061e-01 -3.58149968e-02 5.15543818e-01 6.31455660e-01 -9.55408096e-01 -1.01944089e+00 3.50830615e-01 1.51325449e-01 -8.03220510e-01 1.47349298e-01 1.89976171e-01 3.21984440e-01 -5.19945085e-01 6.03639007e-01 2.36758843e-01 1.74698606e-01 -3.04971009e-01 5.88637412e-01 8.63429382e-02 4.06549752e-01 -1.30114901e+00 9.75563407e-01 5.65661967e-01 3.32064837e-01 -4.76744443e-01 -7.04165936e-01 -1.24900684e-01 -7.60991991e-01 -4.92251754e-01 9.94189322e-01 -3.70443195e-01 -1.55051422e+00 6.16120577e-01 -1.01806164e+00 -1.35678685e+00 -7.78721392e-01 8.05590510e-01 -1.51603639e+00 1.37998804e-01 -7.43869960e-01 -8.30542922e-01 -9.78647172e-02 -1.37522519e+00 1.23979115e+00 2.20904619e-01 -6.28047362e-02 -8.03054214e-01 1.88413039e-01 -2.50143796e-01 3.37536663e-01 6.81806386e-01 5.88815272e-01 2.19126213e-02 -1.16416609e+00 -9.32390243e-03 4.01433885e-01 -8.55463836e-03 -7.31522813e-02 -2.93707222e-01 -3.86333764e-01 -6.55639231e-01 -3.01731825e-02 -7.95711517e-01 6.23170674e-01 2.76080251e-01 1.37337375e+00 -1.45153329e-01 -5.88124096e-01 6.12917662e-01 1.28069723e+00 2.97148854e-01 1.23893224e-01 3.45629543e-01 8.10426831e-01 4.55598801e-01 1.09076869e+00 4.07940924e-01 1.96787834e-01 6.78325295e-01 6.61690593e-01 3.89210135e-01 -2.86427746e-03 -3.96230578e-01 5.40886760e-01 3.51579636e-01 -5.80776453e-01 8.03201273e-02 -7.06486344e-01 4.40036744e-01 -2.15560365e+00 -6.92308724e-01 6.38021007e-02 2.19119334e+00 8.10248911e-01 -2.53895987e-02 1.92638278e-01 -3.76829535e-01 9.38351303e-02 -2.28166744e-01 -1.25687456e+00 -3.03150952e-01 5.92550874e-01 4.83995318e-01 5.61131477e-01 8.50387633e-01 -8.18192780e-01 1.36424315e+00 6.08465672e+00 4.60370541e-01 -9.11709666e-01 -1.57403007e-01 -6.20532632e-01 -4.74317282e-01 -1.31319657e-01 1.71027139e-01 -6.14760399e-01 1.84073597e-01 5.71231484e-01 -3.11507374e-01 1.11126244e+00 9.12347853e-01 1.79810271e-01 -1.61037728e-01 -1.55532849e+00 5.54590285e-01 -4.38990831e-01 -1.04278636e+00 -1.68736294e-01 -1.91400290e-01 7.07119465e-01 6.62753955e-02 8.49485323e-02 5.36391556e-01 1.04435980e+00 -8.58192563e-01 1.07617378e+00 8.50472271e-01 3.25067490e-01 -4.56032455e-01 -1.00097947e-01 7.18365669e-01 -1.04160488e+00 -3.92115176e-01 -3.95596087e-01 -1.77065492e-01 5.04214406e-01 -1.40110239e-01 -5.34734249e-01 3.90189499e-01 6.00636423e-01 4.90301222e-01 3.38529497e-01 1.05606544e+00 -3.72911006e-01 1.05181023e-01 -5.44793069e-01 -6.55185997e-01 5.74182332e-01 -3.92703652e-01 1.00735843e+00 6.28533900e-01 3.44109207e-01 4.71750379e-01 7.30906785e-01 1.08384955e+00 3.20374608e-01 -4.89532799e-01 -6.89170599e-01 -8.91469419e-02 -1.31055084e-03 9.76419568e-01 -6.57346606e-01 -4.69738096e-01 1.60695642e-01 1.21686745e+00 6.16995513e-01 3.85192215e-01 -9.70997214e-01 -3.68209451e-01 7.81212628e-01 -2.58078784e-01 3.85799348e-01 -9.70245242e-01 4.33155388e-01 -1.12354052e+00 -5.85573837e-02 -7.22108424e-01 -9.20851454e-02 -9.57062542e-01 -6.03252411e-01 1.17430590e-01 4.07305062e-01 -1.04435182e+00 -2.20822215e-01 -8.24726760e-01 -2.52331644e-01 7.10565686e-01 -1.40800917e+00 -9.53622937e-01 -4.64775056e-01 6.75535619e-01 7.17416942e-01 1.38929904e-01 7.28042722e-01 -5.66420496e-01 1.08334951e-01 4.36808690e-02 1.53990686e-01 -2.69756854e-01 2.17914894e-01 -1.62621295e+00 3.47955197e-01 1.93885952e-01 -2.43380472e-01 4.75195110e-01 7.93592572e-01 -8.50003958e-01 -2.11285877e+00 -6.93708122e-01 -2.45634317e-01 -6.53538465e-01 1.00019395e+00 -2.42011935e-01 -8.43956053e-01 9.53320920e-01 -9.65158492e-02 4.09780219e-02 -4.53094572e-01 -3.77180606e-01 2.74396062e-01 3.32546830e-01 -1.23074520e+00 8.58207345e-01 1.50689030e+00 -2.97913939e-01 -7.24327564e-01 6.97306931e-01 9.34656262e-01 -1.38765645e+00 -8.64072323e-01 2.85098135e-01 6.70947552e-01 -8.02225173e-02 9.76553321e-01 -1.09120941e+00 3.01441073e-01 -3.46213490e-01 6.51104599e-02 -1.68780220e+00 -1.06343396e-01 -1.00407362e+00 -4.86704201e-01 2.39963785e-01 9.96768698e-02 -5.00080168e-01 7.08451450e-01 4.82783169e-01 -3.94113958e-01 -7.66502798e-01 -8.23940694e-01 -1.25482416e+00 3.66525948e-01 -1.27186418e-01 4.51350868e-01 3.14626962e-01 5.15057594e-02 -4.70493913e-01 -2.75460660e-01 4.27848041e-01 8.45752537e-01 5.61203122e-01 8.81480515e-01 -7.86337614e-01 -7.23983169e-01 -1.91777840e-01 -1.57485500e-01 -1.39001369e+00 6.01073503e-01 -9.66475368e-01 7.73316205e-01 -1.74983978e+00 -1.31181004e-02 -6.49980843e-01 4.86569591e-02 4.73049849e-01 -1.68173373e-01 -5.63107967e-01 5.78153312e-01 5.69268391e-02 -5.77648580e-01 7.26245880e-01 2.07134604e+00 -1.41759083e-01 -6.04350924e-01 2.05705345e-01 3.53114337e-01 6.29642546e-01 6.42194033e-01 -3.41237038e-01 -5.23743987e-01 -5.66016436e-01 4.10522241e-03 5.64567864e-01 7.23130941e-01 -1.10016084e+00 1.83158204e-01 -6.84943497e-01 3.99170183e-02 -2.95014352e-01 7.79497921e-01 -6.94421113e-01 -1.16033792e-01 1.12514758e+00 -5.87284565e-01 -6.09402210e-02 4.11373436e-01 8.34334731e-01 3.81606847e-01 -3.53053629e-01 5.53184986e-01 -5.60726762e-01 -7.94880271e-01 4.92038608e-01 -5.04459918e-01 -4.59710509e-02 1.21907759e+00 8.28021839e-02 2.37638503e-02 -1.19206958e-01 -1.36827099e+00 6.57612741e-01 5.71312129e-01 4.61980969e-01 7.44700074e-01 -1.01511514e+00 -3.44172478e-01 -2.94798464e-01 -3.06255668e-01 2.88363487e-01 8.30702782e-02 5.04381657e-01 -6.32808745e-01 1.84814017e-02 -4.37724471e-01 -5.82660496e-01 -1.08151782e+00 6.38691664e-01 5.78807831e-01 -1.95345089e-01 -1.02989113e+00 9.58604574e-01 -1.36574447e-01 -9.05153215e-01 2.60724455e-01 -1.02147806e+00 2.97687501e-01 -7.09228575e-01 3.44828144e-02 6.62114799e-01 -3.46739918e-01 6.82569668e-02 -4.95534129e-02 8.27176571e-01 2.74967223e-01 -5.43746769e-01 1.39388847e+00 3.88851106e-01 9.89936069e-02 5.63788056e-01 8.22296023e-01 -5.34629405e-01 -2.02274942e+00 7.70078599e-02 -2.41332397e-01 -4.58645254e-01 -2.18006894e-01 -7.32250214e-01 -5.84429264e-01 7.83399940e-01 4.90007520e-01 -1.98978409e-01 4.63558733e-01 6.23460412e-02 8.19662273e-01 1.24261665e+00 9.49510515e-01 -1.22902763e+00 6.53598845e-01 7.98285186e-01 1.31234014e+00 -9.42342460e-01 -1.02690667e-01 -1.93337291e-01 -4.51465994e-01 1.23208869e+00 7.25133479e-01 -7.02279687e-01 4.56328183e-01 4.50317591e-01 -2.19355553e-01 -2.26382345e-01 -4.67615247e-01 -3.27243775e-01 1.13052785e-01 6.71710968e-01 -3.99610758e-01 1.46368831e-01 9.19655412e-02 -1.46140501e-01 -3.16507638e-01 3.25674921e-01 3.39391351e-01 1.52619314e+00 -8.28720808e-01 -1.03598630e+00 -2.50683278e-01 6.77647218e-02 8.65253285e-02 4.82378721e-01 -1.46131530e-01 9.13747847e-01 -2.73711979e-01 4.73266393e-01 -1.32590339e-01 1.76894106e-02 7.16533422e-01 -8.75340328e-02 1.54368281e+00 -7.49855816e-01 -6.58209264e-01 -5.83327293e-01 -2.36422539e-01 -1.26539922e+00 -1.19373620e-01 -7.78013527e-01 -1.89321327e+00 -1.16534539e-01 -1.48519471e-01 -2.89879479e-02 8.65120828e-01 9.69522774e-01 1.30481780e-01 7.45655954e-01 2.98192441e-01 -1.62624919e+00 -1.29148841e+00 -6.37152731e-01 -3.26964736e-01 2.72438288e-01 6.78223908e-01 -1.04062259e+00 -1.68569103e-01 -1.39766410e-01]	[4.776680946350098, 0.5727523565292358]
f2d0845c-ba14-4ab1-abde-964a27fa4867	smile-semantically-guided-multi-attribute	2010.02315	null	https://arxiv.org/abs/2010.02315v1	https://arxiv.org/pdf/2010.02315v1.pdf	SMILE: Semantically-guided Multi-attribute Image and Layout Editing	Attribute image manipulation has been a very active topic since the introduction of Generative Adversarial Networks (GANs). Exploring the disentangled attribute space within a transformation is a very challenging task due to the multiple and mutually-inclusive nature of the facial images, where different labels (eyeglasses, hats, hair, identity, etc.) can co-exist at the same time. Several works address this issue either by exploiting the modality of each domain/attribute using a conditional random vector noise, or extracting the modality from an exemplary image. However, existing methods cannot handle both random and reference transformations for multiple attributes, which limits the generality of the solutions. In this paper, we successfully exploit a multimodal representation that handles all attributes, be it guided by random noise or exemplar images, while only using the underlying domain information of the target domain. We present extensive qualitative and quantitative results for facial datasets and several different attributes that show the superiority of our method. Additionally, our method is capable of adding, removing or changing either fine-grained or coarse attributes by using an image as a reference or by exploring the style distribution space, and it can be easily extended to head-swapping and face-reenactment applications without being trained on videos.	['Radu Timofte', 'Luc van Gool', 'Andrés Romero']	2020-10-05	null	null	null	null	['face-reenactment']	['computer-vision']	[ 5.42886078e-01 1.90013479e-02 -2.06808075e-02 -3.72117817e-01 -4.69437301e-01 -9.66138780e-01 8.75625134e-01 -5.20785272e-01 -2.10375041e-01 8.20143878e-01 1.77634552e-01 2.04291567e-01 -2.57507771e-01 -7.16347516e-01 -6.07712090e-01 -1.12124097e+00 4.07323658e-01 5.60514212e-01 -2.03445256e-01 -3.29765737e-01 -1.53534785e-01 6.59963667e-01 -1.71567833e+00 1.49498314e-01 6.90998971e-01 8.45095038e-01 -1.65584788e-01 2.31290504e-01 -7.16334581e-02 2.54059970e-01 -7.01893151e-01 -8.43192697e-01 3.85013640e-01 -4.80334163e-01 -3.55958760e-01 3.36385816e-01 7.83825397e-01 -8.64608884e-02 -2.93969452e-01 1.25703478e+00 5.74418426e-01 -6.20234832e-02 8.13457131e-01 -1.72941828e+00 -7.90532231e-01 2.56269932e-01 -5.38944423e-01 -5.17597139e-01 6.03092551e-01 1.25251830e-01 6.88398838e-01 -7.38062441e-01 8.46052408e-01 1.48484695e+00 2.74513900e-01 8.83890331e-01 -1.66738594e+00 -1.07418430e+00 1.27024040e-01 2.26684794e-01 -1.45295119e+00 -4.76324469e-01 1.16258633e+00 -5.46495378e-01 2.13334821e-02 4.31438297e-01 5.72690845e-01 1.84557736e+00 -1.01193406e-01 5.44284165e-01 1.58531451e+00 -4.03502762e-01 1.95225105e-01 2.92737573e-01 -4.23241347e-01 4.64511037e-01 4.46418673e-02 1.99530721e-01 -4.97464776e-01 -3.24154049e-01 6.54743075e-01 -6.98767900e-02 -2.75344312e-01 -8.05737615e-01 -1.35880518e+00 8.71141851e-01 1.49395213e-01 9.73760262e-02 -2.35585555e-01 -1.68576971e-01 1.98064968e-01 4.30157721e-01 1.98806182e-01 3.51823956e-01 -2.36072063e-01 6.76709041e-02 -7.49932647e-01 2.56939977e-01 7.38026202e-01 1.05426097e+00 8.50361049e-01 9.01299417e-02 -1.94011733e-01 7.54413545e-01 7.61298016e-02 6.85745418e-01 4.29713309e-01 -9.40300226e-01 1.52959242e-01 5.18138230e-01 -3.75995645e-03 -1.03950095e+00 -1.75853878e-01 -1.73158497e-01 -1.12009823e+00 5.83463669e-01 5.61869562e-01 -1.08785897e-01 -1.08244777e+00 2.38482547e+00 5.09548068e-01 9.21425223e-02 -9.36665088e-02 6.65994942e-01 7.42681384e-01 3.53533700e-02 1.77989662e-01 -1.32592037e-01 1.50001943e+00 -5.53147495e-01 -9.72795665e-01 -1.52297467e-01 4.23490116e-03 -7.56278098e-01 1.06358480e+00 4.12446797e-01 -7.46104062e-01 -4.15399641e-01 -9.11063075e-01 -1.51063697e-02 -6.13021851e-01 -1.45544158e-02 4.41444844e-01 8.41245949e-01 -9.57674682e-01 1.80594221e-01 -5.03202438e-01 -3.05149555e-01 4.96717185e-01 5.29451489e-01 -1.02495909e+00 -2.85529345e-01 -1.34888721e+00 7.65191972e-01 1.05468728e-01 9.35356074e-04 -6.57518923e-01 -4.86194044e-01 -9.11457360e-01 -1.30461812e-01 4.44577575e-01 -9.00495708e-01 5.23043931e-01 -1.32616949e+00 -1.57396317e+00 1.01504242e+00 1.62998550e-02 1.39792532e-01 6.85422063e-01 -5.94636649e-02 -4.67969239e-01 -4.24219742e-02 2.65945066e-02 7.65244544e-01 1.48255730e+00 -1.42965460e+00 -9.62868780e-02 -6.90566242e-01 2.57183999e-01 1.36989281e-01 -4.06351596e-01 -1.42647447e-02 -2.45213091e-01 -9.58238602e-01 1.18690334e-01 -1.28691626e+00 2.21113294e-01 2.14980274e-01 -6.92483723e-01 2.21612215e-01 1.06128550e+00 -5.85141361e-01 7.66780734e-01 -2.50028110e+00 7.34187305e-01 2.35726103e-01 2.49217674e-01 8.06284919e-02 -2.58981407e-01 2.59708732e-01 -2.21834093e-01 1.98157802e-01 -3.87721688e-01 -3.17180902e-01 2.65191764e-01 3.98110986e-01 -9.57756639e-02 3.60256314e-01 2.79312193e-01 7.62646139e-01 -7.06705689e-01 -5.43253243e-01 1.04537934e-01 9.08604562e-01 -3.66329372e-01 1.42590463e-01 5.46758622e-02 8.91169906e-01 -2.63229311e-01 6.36972785e-01 8.42707634e-01 2.77974695e-01 1.56892806e-01 -5.10934532e-01 4.61478710e-01 -3.93428892e-01 -1.42326307e+00 1.70478702e+00 -4.16429341e-01 5.07874608e-01 3.01190645e-01 -6.00316763e-01 9.77815151e-01 3.54599595e-01 3.73022825e-01 -4.46740240e-01 4.47522961e-02 8.87746140e-02 9.06723142e-02 -3.36580753e-01 1.71879038e-01 -4.09277618e-01 -1.54349923e-01 4.04142648e-01 2.77182102e-01 -1.33727536e-01 3.82251628e-02 -9.79300775e-03 8.10486317e-01 3.30218196e-01 3.14747274e-01 -8.55972841e-02 5.38030922e-01 -5.28499246e-01 5.66262424e-01 5.10494471e-01 -1.62095904e-01 8.00233364e-01 6.62849963e-01 -2.34940678e-01 -9.52500522e-01 -1.22474456e+00 -2.73561299e-01 1.09313607e+00 7.37603754e-02 -4.30568457e-02 -8.61494601e-01 -8.59143555e-01 1.33816913e-01 6.92937732e-01 -1.03119707e+00 -4.46005493e-01 -2.92225331e-01 -6.16733670e-01 6.11669481e-01 2.08880424e-01 5.17813146e-01 -1.09709239e+00 -2.47997746e-01 -1.02219842e-01 -2.65122354e-01 -1.19872129e+00 -3.67415130e-01 -8.90173540e-02 -4.64998454e-01 -9.00999248e-01 -5.59198141e-01 -4.46028978e-01 6.63678110e-01 -2.44416401e-01 1.02560341e+00 -2.65126526e-01 -1.43991902e-01 5.04591763e-01 -2.07841456e-01 -2.49148622e-01 -4.73523736e-01 -6.80372864e-02 1.88686699e-01 7.82117546e-01 3.04773778e-01 -8.44554007e-01 -4.40181524e-01 4.92295325e-01 -1.15053153e+00 8.19682553e-02 5.15497148e-01 1.02241552e+00 3.98283601e-01 -1.45867795e-01 5.12996435e-01 -1.23144865e+00 5.42253435e-01 -4.02567565e-01 -1.37599707e-01 3.20980340e-01 -2.02797338e-01 7.90746361e-02 3.64946991e-01 -9.21263337e-01 -1.09991729e+00 1.91711470e-01 -3.16037191e-03 -6.55445933e-01 -5.74603617e-01 2.58277562e-02 -1.01329315e+00 -1.29034713e-01 5.56621730e-01 1.24139696e-01 2.99551666e-01 -3.62860173e-01 6.84631228e-01 4.34247464e-01 4.73314673e-01 -8.06247115e-01 1.21393323e+00 6.98643923e-01 2.84674406e-01 -5.25717378e-01 -5.30805051e-01 1.78980678e-01 -9.87416863e-01 -1.41698420e-01 8.12777042e-01 -6.70850098e-01 -5.17057657e-01 5.66770196e-01 -8.25479686e-01 9.87485051e-02 -4.62314755e-01 2.80040801e-01 -6.35652781e-01 1.91198662e-01 -2.17661247e-01 -2.86988378e-01 1.01859778e-01 -1.44532132e+00 1.10945618e+00 9.20857936e-02 -3.26574534e-01 -9.26088989e-01 -2.21844479e-01 2.82837570e-01 5.30492187e-01 7.57735848e-01 1.08078945e+00 -7.81207263e-01 -4.51668113e-01 -3.77851129e-01 7.03165773e-03 2.79509634e-01 6.42637730e-01 5.22540286e-02 -1.09786904e+00 -3.40503812e-01 4.99628633e-02 -3.86609346e-01 5.40796340e-01 -7.09646940e-02 1.03179371e+00 -4.06943470e-01 -1.49687380e-01 7.32163370e-01 1.13530958e+00 5.38722379e-03 7.90500402e-01 2.68170357e-01 9.02249932e-01 7.63894975e-01 3.68259400e-01 2.76904583e-01 8.98737162e-02 9.46793020e-01 6.17176473e-01 -2.08693117e-01 -2.13926792e-01 -1.43866315e-01 2.32051596e-01 2.57473171e-01 -2.25680262e-01 -2.28520557e-01 -5.23963153e-01 2.16022626e-01 -1.44054961e+00 -9.69257057e-01 3.44515920e-01 2.28743744e+00 8.41144860e-01 -2.21363246e-01 3.06015220e-02 1.28383785e-01 9.26241398e-01 5.39553091e-02 -6.04631364e-01 -2.45483741e-01 -4.71486181e-01 2.80504674e-01 1.17603101e-01 2.61493981e-01 -1.09585726e+00 7.45780528e-01 5.63000631e+00 9.27940488e-01 -1.02845991e+00 4.91960347e-02 4.17391956e-01 -1.72046602e-01 -4.91003305e-01 -1.71883404e-01 -5.13080001e-01 5.63987076e-01 3.38952094e-01 -6.33788705e-02 6.84814453e-01 5.37279785e-01 -2.37473786e-01 2.42084250e-01 -1.24216425e+00 9.78775322e-01 2.40145549e-01 -6.67081952e-01 4.41830188e-01 2.56494164e-01 6.20888948e-01 -6.48531556e-01 5.78527927e-01 1.78663686e-01 2.42563009e-01 -1.21691155e+00 6.28248751e-01 6.75649583e-01 1.18111336e+00 -7.09355175e-01 5.87683916e-01 1.64660797e-01 -7.06603527e-01 1.10420495e-01 4.82996367e-02 2.91576564e-01 -1.07567132e-01 2.35812157e-01 -2.90371239e-01 5.40292442e-01 5.14820576e-01 3.66332531e-01 -7.90235758e-01 5.49514174e-01 -4.13118839e-01 2.02091500e-01 -3.67441118e-01 4.05312806e-01 -1.82930529e-01 -4.07768667e-01 7.43645430e-01 8.72662246e-01 3.56506735e-01 -1.07758030e-01 -2.95716561e-02 9.05473292e-01 -2.09017828e-01 -1.85335353e-02 -7.73309290e-01 1.13553353e-01 5.20022750e-01 1.40168893e+00 -3.89467806e-01 -4.06828262e-02 -4.47534472e-01 1.08685207e+00 1.31839916e-01 6.34337366e-01 -8.00692081e-01 -1.87879592e-01 9.12286937e-01 4.08011638e-02 1.66059554e-01 -2.58355979e-02 -1.00499123e-01 -1.16708887e+00 -5.09091141e-03 -1.26750815e+00 1.60149246e-01 -7.03514934e-01 -1.44585299e+00 7.62302101e-01 1.38437763e-01 -1.25344670e+00 -3.18677545e-01 -4.48022753e-01 -2.30848148e-01 9.32497680e-01 -1.09240425e+00 -1.61385119e+00 -5.17919779e-01 1.01507771e+00 9.72393379e-02 -4.54011619e-01 1.02280688e+00 2.83749759e-01 -4.36634421e-01 9.16038930e-01 1.06097028e-01 1.63692817e-01 1.07414234e+00 -1.17434776e+00 -9.98615772e-02 5.48023283e-01 2.75541008e-01 6.52977824e-01 8.07884574e-01 -3.18608761e-01 -1.34421134e+00 -8.87167096e-01 4.13066536e-01 -5.33221781e-01 5.76535642e-01 -7.35768795e-01 -1.00244796e+00 7.22144067e-01 3.40351462e-01 2.02586770e-01 8.69481266e-01 8.93009827e-02 -6.51321590e-01 -1.06138766e-01 -1.49209988e+00 7.03160465e-01 1.14441156e+00 -6.00201190e-01 -2.75965244e-01 1.15411028e-01 5.32037735e-01 -3.45308483e-01 -9.92305160e-01 6.22953653e-01 6.27704203e-01 -1.07026899e+00 9.84805882e-01 -6.55954361e-01 3.08213830e-01 -2.64483154e-01 -2.86148190e-01 -1.52340281e+00 -3.09404224e-01 -5.07964492e-01 6.80159964e-03 1.70038700e+00 1.49586245e-01 -6.82325542e-01 6.76778316e-01 8.04345012e-01 3.63107264e-01 -4.86972660e-01 -1.11339796e+00 -6.31802320e-01 1.08517982e-01 -8.64440799e-02 1.06235540e+00 1.16927731e+00 -5.48260152e-01 2.71713078e-01 -5.75497389e-01 6.87721968e-02 7.46283829e-01 1.18874930e-01 9.68072951e-01 -1.14164031e+00 -2.07662374e-01 -4.15598214e-01 -7.61118352e-01 -3.59127283e-01 5.31124294e-01 -7.53861725e-01 -3.29941213e-01 -9.40669179e-01 1.04261868e-01 -4.21123743e-01 -2.51045614e-01 6.38585687e-01 -9.49750990e-02 5.21688521e-01 3.58690381e-01 3.30443121e-02 1.82159280e-03 7.72836268e-01 1.44961441e+00 -3.56663495e-01 1.08982965e-01 -1.53549416e-02 -7.01195121e-01 7.29528189e-01 7.64475644e-01 -4.45473820e-01 -4.16472793e-01 -2.03774780e-01 1.00132175e-01 -2.33577546e-02 6.88416719e-01 -7.91328847e-01 -4.02507605e-03 -1.81314960e-01 4.03232127e-01 7.24623799e-02 6.56594098e-01 -1.17957807e+00 6.13652110e-01 -2.28341599e-03 -2.43255377e-01 -1.90474708e-02 2.73192704e-01 7.19065130e-01 -3.83351296e-01 -1.46393076e-01 8.50438714e-01 -2.74299141e-02 -5.62971711e-01 3.45235378e-01 1.11147377e-03 1.33028589e-02 1.09916294e+00 -3.77669215e-01 -2.26579219e-01 -4.50125098e-01 -1.15525234e+00 -2.91888058e-01 7.69972146e-01 5.89463472e-01 4.55608577e-01 -1.66079032e+00 -7.77274549e-01 7.19771087e-01 2.92998254e-01 -2.66572356e-01 2.65767276e-01 6.37640953e-01 1.48221143e-02 -2.84396857e-01 -7.43805289e-01 -6.47726357e-01 -1.33841336e+00 8.41213763e-01 2.87701905e-01 5.61876297e-02 -2.13741079e-01 5.35607696e-01 6.23309672e-01 -4.90705252e-01 -8.24607089e-02 4.03967857e-01 -3.21895212e-01 3.68562162e-01 1.17107399e-01 1.77460030e-01 -9.21660140e-02 -1.07666826e+00 -1.94485947e-01 8.26662123e-01 4.40601967e-02 -2.57503629e-01 9.60911512e-01 -1.53892398e-01 -2.07550794e-01 4.11172688e-01 1.12857604e+00 2.29258418e-01 -1.22442973e+00 -3.50701958e-01 -4.30159032e-01 -7.06263423e-01 -4.19495374e-01 -8.36380064e-01 -1.30355847e+00 7.97939837e-01 6.44958794e-01 5.71384802e-02 1.38247001e+00 -6.91261142e-02 2.07916468e-01 -5.43275513e-02 4.40719277e-01 -7.02865064e-01 -2.05450915e-02 -1.49232363e-02 1.17067409e+00 -1.19596076e+00 -1.18881308e-01 -5.47996223e-01 -8.13634872e-01 9.38337207e-01 5.66274464e-01 9.91321877e-02 6.35648012e-01 1.41091019e-01 1.39029786e-01 -5.36366478e-02 -3.23798984e-01 -8.17811117e-02 4.05877143e-01 8.52728963e-01 1.58785462e-01 1.60669416e-01 -8.42877999e-02 2.75088310e-01 -3.41921985e-01 -3.50562692e-01 4.60282981e-01 5.94138563e-01 2.99405992e-01 -1.51874077e+00 -5.72871208e-01 3.53275001e-01 -3.12366396e-01 1.03240646e-01 -5.73235750e-01 1.13414693e+00 4.08779919e-01 7.12728858e-01 -1.65270507e-01 -5.18969178e-01 4.00693297e-01 3.17631334e-01 6.76429510e-01 -3.47202241e-01 -3.12354177e-01 -1.27557833e-02 -1.98637217e-01 -4.43682492e-01 -5.88381112e-01 -8.74827147e-01 -6.27873659e-01 -3.14353615e-01 -1.51126385e-01 -1.92698434e-01 6.93020582e-01 7.76972651e-01 3.47180694e-01 2.73178101e-01 5.74990094e-01 -7.84443080e-01 -4.28792834e-01 -9.82953668e-01 -6.49718046e-01 1.08395922e+00 2.84678400e-01 -1.23470592e+00 -5.01651645e-01 2.27899253e-01]	[12.711227416992188, 0.017525680363178253]
60e94794-7da3-48b1-a018-863a49cb862b	seeds-emulation-of-weather-forecast-ensembles	2306.14066	null	https://arxiv.org/abs/2306.14066v1	https://arxiv.org/pdf/2306.14066v1.pdf	SEEDS: Emulation of Weather Forecast Ensembles with Diffusion Models	Probabilistic forecasting is crucial to decision-making under uncertainty about future weather. The dominant approach is to use an ensemble of forecasts to represent and quantify uncertainty in operational numerical weather prediction. However, generating ensembles is computationally costly. In this paper, we propose to generate ensemble forecasts at scale by leveraging recent advances in generative artificial intelligence. Our approach learns a data-driven probabilistic diffusion model from the 5-member ensemble GEFS reforecast dataset. The model can then be sampled efficiently to produce realistic weather forecasts, conditioned on a few members of the operational GEFS forecasting system. The generated ensembles have similar predictive skill as the full GEFS 31-member ensemble, evaluated against ERA5 reanalysis, and emulate well the statistics of large physics-based ensembles. We also apply the same methodology to developing a diffusion model for generative post-processing: the model directly learns to correct biases present in the emulated forecasting system by leveraging reanalysis data as labels during training. Ensembles from this generative post-processing model show greater reliability and accuracy, particularly in extreme event classification. In general, they are more reliable and forecast the probability of extreme weather more accurately than the GEFS operational ensemble. Our models achieve these results at less than 1/10th of the computational cost incurred by the operational GEFS system.	['John Anderson', 'Fei Sha', 'Ignacio Lopez-Gomez', 'Rob Carver', 'Lizao Li']	2023-06-24	null	null	null	null	['decision-making-under-uncertainty', 'decision-making-under-uncertainty', 'decision-making']	['medical', 'reasoning', 'reasoning']	[-2.50409454e-01 1.36900917e-01 5.90826571e-01 -5.60432911e-01 -7.96638250e-01 -8.13549757e-01 1.07478416e+00 -2.02612415e-01 1.34725019e-01 1.21465993e+00 4.66742903e-01 -6.31009519e-01 -4.96882461e-02 -1.36094964e+00 -4.46446925e-01 -9.84548509e-01 -1.31276920e-01 8.44225824e-01 -3.51744115e-01 -5.41455507e-01 -2.39770599e-02 7.09632337e-01 -1.66534102e+00 1.18184462e-01 1.33935809e+00 8.17732096e-01 1.57325566e-02 1.04356873e+00 -8.43951628e-02 7.54960895e-01 -7.46174693e-01 1.03672661e-01 7.36413822e-02 -5.95146298e-01 -2.73946077e-01 -2.47490644e-01 1.19929537e-01 -2.12956429e-01 7.49603510e-02 5.53439856e-01 5.83532631e-01 5.49297631e-01 1.20064616e+00 -9.36678052e-01 -3.22615057e-01 7.04216480e-01 -3.43221575e-02 3.26596797e-01 -1.36362717e-01 2.50414401e-01 6.88091695e-01 -8.92349184e-01 1.11805946e-01 1.19733918e+00 8.39923263e-01 9.22736228e-02 -1.47940207e+00 -6.87897921e-01 1.22135237e-01 -4.94147629e-01 -1.43741632e+00 -5.17040312e-01 3.42494607e-01 -9.64680076e-01 1.03491521e+00 4.33741510e-01 6.14436150e-01 8.13462138e-01 9.13628459e-01 5.99513203e-02 1.39120066e+00 -1.87968045e-01 7.07335293e-01 1.34371612e-02 -4.48380597e-02 3.23763013e-01 2.33873993e-01 8.11966836e-01 -2.02490345e-01 -5.28525770e-01 3.31210166e-01 1.10571049e-01 -3.36267561e-01 4.78281289e-01 -8.07554960e-01 1.06531096e+00 4.40449119e-01 1.58952668e-01 -9.47935224e-01 3.11159819e-01 -1.70370564e-01 1.17484897e-01 1.37261295e+00 5.42768300e-01 -4.49332386e-01 2.93305200e-02 -1.53713155e+00 6.51961446e-01 1.02572346e+00 2.81878382e-01 6.59498036e-01 7.54967749e-01 -1.20872527e-01 4.69737947e-01 6.78129017e-01 1.42475688e+00 2.02697724e-01 -8.93250644e-01 2.84556653e-02 1.89590417e-02 5.06199837e-01 -9.51150596e-01 -4.84302223e-01 -8.49461854e-01 -1.08036673e+00 6.23419225e-01 -1.31827280e-01 -7.41585255e-01 -1.21109629e+00 1.41749477e+00 1.49164200e-01 3.42151165e-01 4.48905826e-01 5.33252835e-01 4.30895418e-01 1.44941461e+00 2.51940101e-01 3.48909795e-02 9.22442555e-01 -4.52248007e-01 -5.64549744e-01 -2.57454127e-01 5.36236942e-01 -7.18551755e-01 2.49845326e-01 1.43773630e-01 -6.03794277e-01 -6.12874210e-01 -9.90570188e-01 7.90709198e-01 -5.71346164e-01 7.37918466e-02 5.21380365e-01 6.98850572e-01 -1.37228847e+00 7.68153906e-01 -7.93010890e-01 2.58657455e-01 -2.05565080e-01 -2.10162327e-01 2.57840633e-01 2.70070285e-01 -1.60120714e+00 1.22568536e+00 5.77163815e-01 4.24072534e-01 -1.07312882e+00 -9.03143823e-01 -1.00465715e+00 1.33985966e-01 -4.06747639e-01 -9.63317394e-01 1.17034662e+00 -5.91951311e-01 -1.45300198e+00 -1.60103161e-02 -3.19009364e-01 -5.40929198e-01 2.26928160e-01 -1.20220497e-01 -7.66008019e-01 -5.18160880e-01 -2.85313781e-02 3.59661907e-01 8.46482873e-01 -1.53891873e+00 -5.88495076e-01 -7.72647485e-02 -2.94176280e-01 1.41952828e-01 4.25032139e-01 -5.29949188e-01 5.98280489e-01 -8.83757174e-01 1.21352568e-01 -1.19390106e+00 -6.45645618e-01 -8.35225403e-01 -7.83152282e-02 4.48987409e-02 6.70260429e-01 -8.69934857e-01 1.18229163e+00 -1.86145687e+00 5.22189494e-03 6.00856304e-01 8.87078643e-02 5.45394123e-02 4.24164347e-03 7.24512756e-01 -4.54022810e-02 3.15308690e-01 -6.96255863e-01 -5.78798234e-01 -2.64788116e-03 6.32650316e-01 -1.39608073e+00 1.03623401e-02 3.12788934e-01 6.87090158e-01 -7.42402375e-01 4.62953337e-02 3.88080031e-01 5.63905895e-01 -2.49893501e-01 4.55858529e-01 -6.01817846e-01 7.78747201e-01 -2.33446181e-01 2.11598516e-01 8.26596200e-01 -1.45817667e-01 4.78917435e-02 3.40365916e-01 -2.41575986e-01 1.89986840e-01 -1.20243776e+00 9.40483928e-01 -9.16444004e-01 5.18423080e-01 -2.59046912e-01 -4.24356103e-01 1.22404385e+00 4.17878658e-01 -1.55053169e-01 -7.80254453e-02 -3.12778026e-01 3.55526984e-01 -1.38142213e-01 -9.03373063e-02 9.22573864e-01 -6.02500856e-01 -1.56681314e-01 9.00470257e-01 1.19261751e-02 -9.24717307e-01 -5.69036230e-02 2.37843424e-01 3.32186133e-01 2.47287050e-01 4.46782634e-02 -4.66680586e-01 1.02548413e-01 -1.38464971e-02 3.32167894e-01 1.04425466e+00 5.92924178e-01 6.63231850e-01 1.56031176e-01 -6.34660244e-01 -9.46300268e-01 -1.09152853e+00 -3.82370025e-01 9.07057047e-01 -5.94096005e-01 -2.35329866e-01 -3.96483183e-01 -3.27782482e-01 2.22927853e-01 1.58737028e+00 -8.12390745e-01 1.11799493e-01 -2.51243301e-02 -1.66650343e+00 5.40211439e-01 5.03048241e-01 1.60919905e-01 -7.48117328e-01 -2.80066967e-01 5.47654569e-01 -1.78112611e-01 -5.66059589e-01 2.59530336e-01 8.50517973e-02 -1.03113806e+00 -4.35556352e-01 -7.33253479e-01 3.22395384e-01 4.63399082e-01 -3.02269936e-01 1.47276878e+00 -1.74688756e-01 3.20943058e-01 2.40257576e-01 -2.12959781e-01 -8.03905249e-01 -8.86080325e-01 3.55461985e-02 1.30954623e-01 4.14514542e-03 -2.77414948e-01 -7.40452528e-01 -4.02333587e-01 9.30697992e-02 -8.63076270e-01 1.42689243e-01 2.78137147e-01 7.74134576e-01 3.58708113e-01 2.95439094e-01 5.29684842e-01 -7.65445292e-01 6.63176119e-01 -9.97139096e-01 -8.23915064e-01 1.35551542e-01 -9.33675587e-01 3.38334560e-01 4.25511837e-01 1.00073121e-01 -1.55681610e+00 -1.11848183e-01 -1.87576100e-01 -6.86795339e-02 -2.43693665e-01 1.04806626e+00 6.37337267e-01 1.74673051e-01 7.30219841e-01 4.33432102e-01 -2.58278698e-01 -3.55727911e-01 4.92279351e-01 6.48858309e-01 4.42173153e-01 -6.81896806e-01 7.48453081e-01 3.94369900e-01 1.15541657e-02 -7.40511000e-01 -9.04082775e-01 1.20928891e-01 -1.48622170e-01 -4.96664286e-01 4.97882724e-01 -1.25798869e+00 -7.87768327e-03 7.03973770e-01 -1.14440429e+00 -2.35730156e-01 -3.14534366e-01 6.86681807e-01 -2.33448312e-01 -2.03304738e-01 -2.26887643e-01 -1.52285028e+00 -5.18455327e-01 -8.67897987e-01 1.09836924e+00 3.05870116e-01 -3.32069248e-01 -1.38857722e+00 8.74889731e-01 -3.53494048e-01 9.69768643e-01 7.49306917e-01 7.06190944e-01 -3.41467649e-01 -3.14526409e-01 -1.87732205e-01 2.89329588e-02 4.30384189e-01 7.59855211e-02 4.74021703e-01 -1.32201529e+00 -5.14821373e-02 7.69754350e-02 1.36856884e-01 1.31070781e+00 5.94487369e-01 7.78691828e-01 -2.48772189e-01 -2.46918127e-01 5.83555043e-01 1.23479998e+00 1.13936841e-01 5.35045385e-01 -2.21850157e-01 3.47709775e-01 5.90399444e-01 1.04334436e-01 5.83525658e-01 4.14893717e-01 -9.57492068e-02 2.89243966e-01 -9.60271955e-02 2.17395961e-01 -1.57297760e-01 3.47182363e-01 9.13824499e-01 -6.63712144e-01 -5.88442147e-01 -1.43944478e+00 4.15030032e-01 -1.70086920e+00 -1.20586693e+00 -3.39575529e-01 2.09513927e+00 6.27845466e-01 6.20280684e-04 -5.61817408e-01 -2.05413789e-01 3.46413285e-01 5.23822069e-01 -2.39676476e-01 -4.42424715e-01 -3.05023819e-01 2.91803539e-01 4.80239391e-01 1.01009905e+00 -1.08567297e+00 6.84791744e-01 7.28973675e+00 4.89681125e-01 -1.16836178e+00 4.68687415e-02 9.67330575e-01 1.27544597e-01 -8.86671782e-01 1.07533172e-01 -1.03820860e+00 6.28243744e-01 1.78613853e+00 -3.33746314e-01 2.58109659e-01 7.08957732e-01 3.87759000e-01 -4.76269662e-01 -6.97063804e-01 4.11447287e-01 -1.62268013e-01 -1.79134250e+00 2.34573364e-01 1.28584117e-01 1.33828294e+00 3.67854029e-01 1.97156683e-01 3.88309956e-01 1.11133647e+00 -1.27223814e+00 6.51350677e-01 1.50090969e+00 4.86817211e-01 -9.24212515e-01 1.16332173e+00 5.74156284e-01 -9.95890081e-01 1.54773086e-01 -2.44071811e-01 -3.15134794e-01 4.82885957e-01 1.48116255e+00 -8.29374731e-01 8.16525459e-01 6.89232826e-01 5.42788029e-01 -2.23133698e-01 5.77051938e-01 -2.35504746e-01 1.15047503e+00 -7.13462114e-01 2.09430188e-01 4.85833496e-01 -4.33050632e-01 5.67566991e-01 1.18482649e+00 1.00898254e+00 3.77902061e-01 -6.11539697e-03 7.93541491e-01 2.46860966e-01 -3.04487020e-01 -8.39872777e-01 -1.77614298e-02 3.52794647e-01 9.89467263e-01 -2.48140395e-01 -8.25881600e-01 3.92810136e-01 4.27245110e-01 -4.40080240e-02 6.34036064e-01 -6.50379300e-01 -1.56369414e-02 6.34042084e-01 -1.73780784e-01 2.32053086e-01 -3.44916016e-01 -4.40021724e-01 -9.88496721e-01 -5.47185540e-01 -5.70855439e-01 1.70169592e-01 -1.22359991e+00 -1.40817833e+00 1.09236813e+00 2.06908762e-01 -1.06674516e+00 -1.04631615e+00 -3.61087233e-01 -1.07052636e+00 1.81138217e+00 -1.41437840e+00 -8.75642240e-01 -2.85966396e-01 -6.67779297e-02 4.11614217e-02 -1.28685802e-01 1.39080536e+00 -4.00787294e-01 -3.16143841e-01 -3.33188027e-01 9.76710439e-01 -4.07692671e-01 4.66513664e-01 -1.36241639e+00 9.08970594e-01 7.62507319e-01 -7.59339780e-02 4.38569099e-01 1.05901635e+00 -8.91718626e-01 -7.22202241e-01 -1.56371260e+00 1.05444729e+00 -6.15824997e-01 5.85034907e-01 5.50538674e-02 -1.13577557e+00 7.00778186e-01 3.26188087e-01 -1.76679283e-01 8.13065171e-01 2.78553128e-01 -1.58044621e-01 4.80817864e-03 -9.47705925e-01 2.70662755e-01 2.76309490e-01 -4.22430605e-01 -8.19359064e-01 4.41921741e-01 4.01849806e-01 -5.04726410e-01 -1.01222646e+00 4.74493682e-01 4.67598259e-01 -8.52959275e-01 5.82077563e-01 -5.43300450e-01 6.00631297e-01 -5.95869958e-01 -3.61919969e-01 -2.28909898e+00 -3.68766367e-01 -5.41371763e-01 -2.45459184e-01 1.04317284e+00 7.68593311e-01 -1.25314641e+00 2.05219854e-02 6.08044565e-01 -5.53834811e-02 -3.04575831e-01 -9.28135514e-01 -6.52220547e-01 4.65680718e-01 -5.75961113e-01 9.92749751e-01 8.65786016e-01 -8.02594483e-01 -9.08971354e-02 -4.00023103e-01 7.76018202e-01 7.98485756e-01 5.44012606e-01 5.58055758e-01 -1.53336954e+00 -2.50016600e-01 -2.21286297e-01 1.56780988e-01 -5.31384766e-01 1.00152053e-01 -7.44341850e-01 2.35813394e-01 -1.45787418e+00 -4.03051734e-01 -8.42697620e-01 -2.38911837e-01 2.54035771e-01 -3.02700669e-01 2.49552175e-01 -5.54111637e-02 3.58644605e-01 2.89174616e-01 9.60678935e-01 7.41824389e-01 6.60665110e-02 -9.52028036e-02 5.54169528e-02 -3.96284074e-01 7.31424093e-01 1.03184903e+00 -7.01843858e-01 -2.23092362e-01 -3.15655202e-01 5.76094508e-01 1.61513746e-01 4.16555345e-01 -1.13500535e+00 -5.75286970e-02 -3.43421489e-01 7.53677249e-01 -9.29389894e-01 2.16563836e-01 -2.59442031e-01 9.51987863e-01 2.99692750e-01 9.24532041e-02 2.18537360e-01 5.68476260e-01 5.55068970e-01 -3.01757932e-01 -1.50049925e-01 5.02949059e-01 -9.58016142e-02 -5.35392165e-01 8.11703876e-02 -1.08157182e+00 -1.79979146e-01 7.44945645e-01 4.40400779e-01 -2.33007669e-01 -6.91595972e-01 -1.01389313e+00 2.62347877e-01 2.32036129e-01 1.14817001e-01 2.18119770e-01 -9.75391030e-01 -1.40005744e+00 3.58216077e-01 -2.17910439e-01 -6.39732182e-02 5.36258399e-01 3.10880303e-01 -5.14110625e-01 4.20108080e-01 1.91553131e-01 -5.42165160e-01 -3.36587161e-01 -2.64737885e-02 7.93682933e-01 -3.95510614e-01 -3.15270931e-01 7.50303209e-01 1.56820819e-01 -7.68129170e-01 -7.07225680e-01 -5.15279830e-01 -1.70170814e-01 3.50347996e-01 7.52878368e-01 1.91099107e-01 1.84884116e-01 -5.87579012e-01 -3.73111293e-02 2.67635137e-01 4.29524958e-01 -6.48679554e-01 1.30845988e+00 -1.30633712e-01 -1.14814632e-01 9.23247337e-01 4.87545013e-01 2.21501514e-02 -1.31938004e+00 -3.89792286e-02 -4.11377519e-01 -1.17034681e-01 7.25655854e-01 -1.11028755e+00 -8.57301593e-01 9.03371036e-01 5.27739108e-01 4.17455047e-01 7.89020300e-01 -3.25580299e-01 3.62248242e-01 2.70625919e-01 1.84040278e-01 -8.25277746e-01 -9.03747261e-01 9.07044053e-01 1.40565276e+00 -1.12303233e+00 1.21272951e-01 2.15053365e-01 -7.08572090e-01 1.12208056e+00 -2.63092034e-02 -2.65459210e-01 1.17772841e+00 4.74906564e-01 2.96646208e-01 -1.44670665e-01 -8.84444356e-01 1.13998860e-01 5.57459116e-01 3.42245758e-01 3.81165624e-01 6.73908770e-01 1.74557894e-01 4.67873156e-01 -5.79375267e-01 -6.51225671e-02 4.66884524e-01 6.70695901e-01 -5.65744519e-01 -5.66231370e-01 -9.15747285e-01 6.53836131e-01 4.47639413e-02 -6.58682823e-01 1.97468624e-01 7.24816993e-02 -9.60874185e-02 9.62499559e-01 4.60838318e-01 -2.35805839e-01 -6.07030466e-02 5.22213221e-01 -7.63393715e-02 -4.19763356e-01 -5.27444065e-01 -2.19682261e-01 2.16265664e-01 -2.52307713e-01 -3.57349277e-01 -8.12146246e-01 -8.46688271e-01 -7.12629974e-01 -6.73006475e-02 6.69640541e-01 8.06577623e-01 1.06901181e+00 6.68142617e-01 7.26834357e-01 8.40467751e-01 -1.46023226e+00 -6.34247839e-01 -1.26350379e+00 -6.87414229e-01 -3.50054294e-01 3.70289892e-01 -6.11184657e-01 -8.40816677e-01 -4.17170897e-02]	[6.548797130584717, 3.0263218879699707]
c248d4cc-1b12-489c-b931-42bc68f04d18	simon-a-simple-framework-for-online-temporal	2211.04905	null	https://arxiv.org/abs/2211.04905v1	https://arxiv.org/pdf/2211.04905v1.pdf	SimOn: A Simple Framework for Online Temporal Action Localization	Online Temporal Action Localization (On-TAL) aims to immediately provide action instances from untrimmed streaming videos. The model is not allowed to utilize future frames and any processing techniques to modify past predictions, making On-TAL much more challenging. In this paper, we propose a simple yet effective framework, termed SimOn, that learns to predict action instances using the popular Transformer architecture in an end-to-end manner. Specifically, the model takes the current frame feature as a query and a set of past context information as keys and values of the Transformer. Different from the prior work that uses a set of outputs of the model as past contexts, we leverage the past visual context and the learnable context embedding for the current query. Experimental results on the THUMOS14 and ActivityNet1.3 datasets show that our model remarkably outperforms the previous methods, achieving a new state-of-the-art On-TAL performance. In addition, the evaluation for Online Detection of Action Start (ODAS) demonstrates the effectiveness and robustness of our method in the online setting. The code is available at https://github.com/TuanTNG/SimOn	['Kwanghoon Sohn', 'Kwonyoung Kim', 'Jungin Park', 'Tuan N. Tang']	2022-11-08	null	null	null	null	['action-localization']	['computer-vision']	[ 7.37317353e-02 -1.53044194e-01 -5.66196263e-01 -3.71402353e-01 -6.99035883e-01 -5.27478576e-01 6.34248257e-01 -2.49645129e-01 -5.24214447e-01 3.57063442e-01 5.68044126e-01 1.71560403e-02 2.28999764e-01 -3.54342610e-01 -7.52883136e-01 -4.91468489e-01 -4.13212627e-01 -9.91670638e-02 6.97322905e-01 1.11967335e-02 1.44515187e-01 1.90874115e-01 -1.46691155e+00 6.95453942e-01 1.76392794e-01 1.26061571e+00 2.34832689e-01 8.91185462e-01 3.14266503e-01 1.51141405e+00 -2.64620423e-01 -2.83548623e-01 3.95899683e-01 -4.09985095e-01 -7.59105265e-01 1.33977309e-01 4.71338868e-01 -9.41679955e-01 -9.76856112e-01 4.57986981e-01 4.29386616e-01 3.02968770e-01 -6.57760575e-02 -1.37151635e+00 -6.19121253e-01 5.45177698e-01 -1.85288966e-01 6.60636723e-01 6.01706147e-01 6.28747702e-01 1.09131980e+00 -1.05866051e+00 6.69852912e-01 1.16647923e+00 4.87232089e-01 5.43773293e-01 -8.63612533e-01 -4.58327472e-01 6.99938595e-01 9.14168715e-01 -1.20731688e+00 -7.45184243e-01 6.27812684e-01 -2.48386398e-01 1.11443853e+00 -3.79997231e-02 8.67327094e-01 1.30617952e+00 1.06707372e-01 1.38749135e+00 7.52170861e-01 -1.20994829e-01 3.70970607e-01 -5.07701457e-01 -2.63315737e-01 6.98875844e-01 -5.33110857e-01 2.14902908e-01 -1.07801628e+00 -1.99566083e-03 7.72745609e-01 4.26410437e-01 -3.12380075e-01 -2.46622324e-01 -1.57681763e+00 4.18611407e-01 3.73335183e-01 6.40280396e-02 -6.09392881e-01 7.14920759e-01 4.90174443e-01 3.20499837e-01 3.24836850e-01 1.47461286e-03 -5.14989078e-01 -7.93730080e-01 -7.07280457e-01 1.38790339e-01 4.42888856e-01 8.34080219e-01 6.13342524e-01 -8.78506228e-02 -5.84327996e-01 1.86994612e-01 -1.14648663e-01 3.22287470e-01 5.55541396e-01 -1.42943299e+00 5.72873890e-01 4.67955828e-01 4.28618819e-01 -7.04088688e-01 -7.04275593e-02 -9.39519927e-02 -1.43852890e-01 -1.19507715e-01 3.41228306e-01 7.61496369e-03 -8.64814937e-01 1.77685106e+00 5.64861953e-01 9.71170366e-01 -8.59880522e-02 9.67271030e-01 2.93230027e-01 5.89254558e-01 5.90193793e-02 -4.70796824e-02 9.87720132e-01 -1.28143597e+00 -7.42720306e-01 -4.98990953e-01 6.47979736e-01 -4.09618169e-01 1.02938890e+00 2.68206865e-01 -1.01975763e+00 -6.43100441e-01 -8.06233168e-01 -1.46078944e-01 -1.01614758e-01 3.35958213e-01 7.42093146e-01 -5.56758791e-02 -9.93972063e-01 8.50043833e-01 -1.56774175e+00 -3.68554920e-01 6.67080700e-01 7.24012926e-02 -3.63455057e-01 -1.56703696e-01 -1.03618824e+00 4.58526582e-01 3.24894547e-01 1.48994029e-01 -1.22805488e+00 -5.55310071e-01 -8.41801465e-01 7.17515964e-03 9.17166591e-01 -4.64148283e-01 1.87391913e+00 -1.16059518e+00 -1.71888936e+00 3.11583728e-01 -4.07331139e-01 -8.43509793e-01 7.26098239e-01 -6.78807974e-01 -3.20997447e-01 7.06045091e-01 1.65921181e-01 7.00222969e-01 8.20099771e-01 -3.89041662e-01 -9.49724197e-01 -7.05814436e-02 5.70317805e-01 2.19006777e-01 -3.14256847e-01 2.13787444e-02 -7.48975933e-01 -6.49302244e-01 -4.36470211e-02 -9.50207829e-01 -1.48944646e-01 5.66738844e-01 3.94361392e-02 -2.85490632e-01 8.72086048e-01 -5.68636000e-01 1.33949625e+00 -2.29697466e+00 -2.40904298e-02 -3.18739533e-01 -1.57110335e-03 1.26074091e-01 -2.00456858e-01 7.30573535e-01 -2.24688686e-02 -2.15756252e-01 5.29984348e-02 -4.91691589e-01 3.87382098e-02 3.03147942e-01 -5.49757540e-01 5.92473865e-01 2.68849760e-01 9.87327158e-01 -1.26462531e+00 -4.00231123e-01 4.81394589e-01 4.42761868e-01 -5.67483544e-01 3.84610951e-01 -3.86562079e-01 4.62476015e-01 -6.69145882e-01 7.67531455e-01 -3.66010368e-02 -4.17881936e-01 2.42647856e-01 -9.91023257e-02 -1.56119764e-01 4.89947379e-01 -9.75805998e-01 2.05605316e+00 -1.83875620e-01 5.82753778e-01 -4.49294001e-01 -6.72393858e-01 2.70418525e-01 4.97423798e-01 7.86857247e-01 -8.67057621e-01 -1.25057474e-01 -1.53961569e-01 -2.86118478e-01 -7.15368807e-01 2.97532290e-01 3.78372252e-01 6.26579449e-02 4.53236938e-01 1.59515068e-01 7.11130202e-01 3.34136575e-01 4.75403935e-01 1.60059118e+00 7.32029021e-01 3.09563130e-01 4.93248791e-01 2.74240077e-01 -2.02002898e-01 9.89907801e-01 7.23672509e-01 -6.73522294e-01 3.90411168e-01 5.41341066e-01 -6.64216518e-01 -6.36459172e-01 -1.08922863e+00 5.42185128e-01 1.32510293e+00 7.97238126e-02 -8.63751233e-01 -3.92222226e-01 -9.25779164e-01 -6.35432377e-02 5.23306549e-01 -7.46941090e-01 -1.87105805e-01 -8.33803236e-01 6.40338613e-03 2.74624467e-01 1.01083386e+00 7.28421748e-01 -1.16467774e+00 -1.03306496e+00 3.64543527e-01 -5.34858704e-01 -1.49780190e+00 -8.35741162e-01 -2.78763145e-01 -8.66123438e-01 -1.18147647e+00 -4.27853614e-01 -2.29539752e-01 2.36973867e-01 3.98924083e-01 8.87409747e-01 -7.78307170e-02 -1.61555097e-01 9.20266271e-01 -6.75119340e-01 -1.19448699e-01 -3.61816213e-03 -3.11326206e-01 -3.16514485e-02 4.45453703e-01 2.94690520e-01 -7.65174091e-01 -1.13157678e+00 3.15662414e-01 -8.12953889e-01 1.76148072e-01 5.37728608e-01 5.80461323e-01 8.33650351e-01 -3.48199725e-01 4.64036137e-01 -3.85935009e-01 -1.43860087e-01 -5.47811925e-01 -3.91170710e-01 1.04122445e-01 -4.57858026e-01 -1.69324279e-01 5.76392889e-01 -6.02831066e-01 -9.75025475e-01 4.22176600e-01 1.13473043e-01 -8.67183447e-01 -3.69778723e-02 3.64136547e-01 1.59079209e-02 3.75322044e-01 4.50268030e-01 4.32328761e-01 -3.16791058e-01 -5.87290585e-01 4.26432729e-01 2.03664318e-01 8.03455591e-01 -1.92863792e-01 5.42444825e-01 9.97751296e-01 -2.76030421e-01 -3.66971582e-01 -1.07105422e+00 -6.02188885e-01 -7.58057415e-01 -5.72899461e-01 7.88947284e-01 -1.12489951e+00 -7.25057364e-01 5.11738598e-01 -9.81232345e-01 -7.65891790e-01 -4.35337007e-01 5.37960649e-01 -9.22783196e-01 4.50427562e-01 -7.05977142e-01 -8.05659890e-01 -1.44471928e-01 -7.59032428e-01 1.05667377e+00 3.67098227e-02 -2.92074885e-02 -6.77385807e-01 1.05175264e-01 3.77487957e-01 8.85212570e-02 3.18238139e-01 5.36092035e-02 -3.61558765e-01 -1.05812752e+00 -2.51695126e-01 2.23674044e-01 3.35351437e-01 1.47638828e-01 -1.23612471e-01 -9.33256388e-01 -3.88124019e-01 -1.96032807e-01 -4.37389553e-01 1.06905031e+00 1.75237775e-01 1.33419108e+00 -4.80526417e-01 -2.46800587e-01 6.19567037e-01 1.16498423e+00 1.92854047e-01 6.92126334e-01 2.76169896e-01 5.23274481e-01 1.21210016e-01 1.11767817e+00 9.08102334e-01 5.54143488e-01 7.52362072e-01 6.49601817e-01 2.23320067e-01 -1.52961895e-01 -7.43003666e-01 8.88276577e-01 3.46082121e-01 -1.56878233e-01 -1.70006514e-01 -6.27216160e-01 7.43400753e-01 -2.37101841e+00 -1.46850383e+00 4.73990768e-01 2.05086517e+00 6.67157352e-01 1.11416005e-01 3.90403897e-01 -6.15023375e-02 4.96536285e-01 5.57639241e-01 -9.31595743e-01 1.63403139e-01 1.84145138e-01 5.12959659e-02 4.01750267e-01 3.75971705e-01 -1.36095154e+00 1.16448796e+00 5.64013243e+00 4.34567124e-01 -1.08046842e+00 3.99684340e-01 3.39952648e-01 -7.41475105e-01 3.87350082e-01 3.00528139e-01 -5.29054284e-01 6.58893168e-01 1.04181612e+00 -7.13567585e-02 5.10958970e-01 8.50470722e-01 6.23378277e-01 -2.69333810e-01 -1.42408955e+00 1.03161979e+00 9.41362325e-03 -1.42421317e+00 -1.68235913e-01 -2.35910520e-01 3.48758012e-01 3.38020682e-01 -7.69726187e-03 4.30535346e-01 1.69072613e-01 -5.43412447e-01 1.18669069e+00 7.65760601e-01 6.80308759e-01 -4.58913743e-01 1.77089885e-01 2.87832111e-01 -1.49290621e+00 -5.88675261e-01 -9.51596349e-02 -4.10801023e-01 4.12891120e-01 1.36104777e-01 -7.54764199e-01 2.50031710e-01 9.19988513e-01 1.44345951e+00 -6.45723283e-01 9.52956975e-01 -5.04243910e-01 8.76163423e-01 -2.46907681e-01 2.93039322e-01 3.68018150e-01 3.24691296e-01 4.76596504e-01 1.01366091e+00 2.69516587e-01 3.25757086e-01 4.20980781e-01 4.54614848e-01 -5.29253036e-02 -2.69799531e-01 -4.04155195e-01 -1.65114090e-01 5.12598574e-01 9.95361388e-01 -3.11987817e-01 -5.13391733e-01 -6.09762609e-01 1.31418610e+00 3.79425049e-01 5.14591873e-01 -1.16735244e+00 -2.03618780e-02 7.31540740e-01 1.52073741e-01 7.55866349e-01 -2.99085766e-01 4.37439680e-01 -1.36515069e+00 4.58275139e-01 -7.46752083e-01 7.57268906e-01 -9.33823228e-01 -8.90423715e-01 1.61931753e-01 8.92586708e-02 -1.54182398e+00 -3.52839828e-01 -3.43894958e-01 -6.28790796e-01 1.72002077e-01 -1.54565334e+00 -1.24251032e+00 -1.83561280e-01 9.01687801e-01 8.31624806e-01 1.52242377e-01 4.35625672e-01 2.23577335e-01 -5.73192596e-01 3.89071405e-01 -2.09990650e-01 3.03772539e-01 7.81998634e-01 -1.08741891e+00 5.42489946e-01 1.13554418e+00 3.31145912e-01 1.45673782e-01 5.55348992e-01 -5.57306051e-01 -1.57867956e+00 -1.18393862e+00 8.15584898e-01 -5.58350801e-01 1.03615022e+00 -3.18760902e-01 -6.73119724e-01 1.34536183e+00 1.07319988e-01 4.33617562e-01 5.24944484e-01 -2.84665495e-01 -3.43614161e-01 -3.01888853e-01 -5.99234700e-01 7.51469314e-01 1.50895345e+00 -5.65380335e-01 -4.98121798e-01 4.44298565e-01 7.98676133e-01 -5.02832770e-01 -6.97682083e-01 9.71398652e-02 7.87908912e-01 -1.15190601e+00 8.72922778e-01 -7.85996854e-01 3.84590179e-01 -3.66159707e-01 -2.29289070e-01 -8.44218791e-01 -3.39585125e-01 -1.03701317e+00 -7.47913957e-01 8.56766343e-01 2.73387805e-02 -5.87889075e-01 7.47429013e-01 6.72973871e-01 -2.20934153e-01 -9.28233385e-01 -1.24681711e+00 -8.51462126e-01 -5.69221973e-01 -6.93080485e-01 5.24715543e-01 5.80799460e-01 -1.83819551e-02 -9.83061548e-03 -6.49278224e-01 3.03528339e-01 3.68246794e-01 2.11000279e-01 8.63573849e-01 -5.16336799e-01 -6.24703765e-01 4.16613892e-02 -5.62283754e-01 -1.51642311e+00 1.00241743e-01 -5.54899216e-01 -1.00820012e-01 -1.34083593e+00 2.37107538e-02 7.18365833e-02 -7.26387620e-01 9.36866939e-01 -4.50167879e-02 -8.58212542e-03 4.78136927e-01 2.41662756e-01 -1.27025878e+00 8.71118307e-01 1.11357331e+00 3.21891755e-02 -9.73554254e-02 3.99035662e-02 -3.25035483e-01 8.07847023e-01 7.83956409e-01 -4.87855315e-01 -6.04292691e-01 -5.92186749e-01 6.46857917e-02 2.77388752e-01 8.43766987e-01 -1.24733460e+00 3.57566565e-01 -4.35720742e-01 2.69137383e-01 -6.38037682e-01 7.47194231e-01 -7.40814924e-01 -1.32649913e-01 4.00900930e-01 -4.55731452e-01 2.65199959e-01 -8.43459889e-02 1.06039226e+00 -3.41898799e-02 3.19435924e-01 4.36467499e-01 -8.16720277e-02 -1.28654850e+00 7.12990761e-01 -2.69747376e-01 3.14953662e-02 1.14012682e+00 -1.62035316e-01 -4.14115489e-01 -6.26864910e-01 -8.37173581e-01 5.50780714e-01 3.22522223e-01 5.47369897e-01 7.58228302e-01 -1.44350219e+00 -4.19836879e-01 -1.60444126e-01 2.39018917e-01 -3.62812907e-01 4.70406294e-01 1.10922205e+00 -6.48293644e-02 1.83060542e-01 -3.75222438e-03 -6.18195772e-01 -1.13953078e+00 6.89306200e-01 4.79532778e-01 -1.65305182e-01 -9.64954495e-01 6.12423062e-01 1.32888496e-01 2.13147864e-01 3.78103644e-01 -4.64333773e-01 3.63776349e-02 -2.53595710e-01 8.74625862e-01 4.92075324e-01 -3.28920543e-01 -4.26705867e-01 -3.82441849e-01 -5.21310158e-02 6.50403183e-03 -3.20811987e-01 1.40957224e+00 -1.99376985e-01 2.78713465e-01 7.06152976e-01 9.50141132e-01 -3.77368361e-01 -2.15021896e+00 -5.56597173e-01 -4.53118421e-02 -8.68519545e-01 -3.89257297e-02 -8.97043288e-01 -1.06640899e+00 6.56302154e-01 5.72864830e-01 -3.04459244e-01 1.29686856e+00 7.76821151e-02 1.02428126e+00 5.93708336e-01 3.98626149e-01 -1.29199588e+00 5.38239539e-01 3.83434743e-01 9.75749910e-01 -1.18663907e+00 -1.39358878e-01 -2.24144515e-02 -7.17808306e-01 9.13051784e-01 7.12101817e-01 -1.52274117e-01 5.72462857e-01 9.19606313e-02 1.96199100e-02 -2.45468710e-02 -1.38508475e+00 -3.18457931e-01 -3.00768726e-02 3.38247150e-01 -6.08438440e-02 -1.46855459e-01 2.28633638e-02 5.22240877e-01 2.60385334e-01 5.12000084e-01 3.81707042e-01 1.41340387e+00 -3.34378600e-01 -8.58671963e-01 -1.84549391e-02 2.12241352e-01 -5.65521002e-01 9.21017826e-02 -2.93558598e-01 6.49676621e-01 1.58446038e-03 1.01855791e+00 9.05528218e-02 -4.94213164e-01 4.29020405e-01 1.68541148e-01 4.02851611e-01 -4.99395311e-01 -3.15706164e-01 -6.05678856e-02 9.45468098e-02 -1.64136612e+00 -6.39151216e-01 -8.31071615e-01 -1.33407342e+00 -6.54497296e-02 1.64724782e-01 -3.15908790e-01 1.47796139e-01 9.40434873e-01 7.82852471e-01 3.12412411e-01 7.47997999e-01 -1.02816558e+00 -6.24925792e-01 -8.33604813e-01 -2.48511687e-01 4.95232999e-01 6.00632250e-01 -7.74836898e-01 -2.99231887e-01 3.16168398e-01]	[8.339548110961914, 0.48956796526908875]
ee70da49-8ce0-4622-89ff-891e255cbcc0	imagen-editor-and-editbench-advancing-and	2212.06909	null	https://arxiv.org/abs/2212.06909v2	https://arxiv.org/pdf/2212.06909v2.pdf	Imagen Editor and EditBench: Advancing and Evaluating Text-Guided Image Inpainting	Text-guided image editing can have a transformative impact in supporting creative applications. A key challenge is to generate edits that are faithful to input text prompts, while consistent with input images. We present Imagen Editor, a cascaded diffusion model built, by fine-tuning Imagen on text-guided image inpainting. Imagen Editor's edits are faithful to the text prompts, which is accomplished by using object detectors to propose inpainting masks during training. In addition, Imagen Editor captures fine details in the input image by conditioning the cascaded pipeline on the original high resolution image. To improve qualitative and quantitative evaluation, we introduce EditBench, a systematic benchmark for text-guided image inpainting. EditBench evaluates inpainting edits on natural and generated images exploring objects, attributes, and scenes. Through extensive human evaluation on EditBench, we find that object-masking during training leads to across-the-board improvements in text-image alignment -- such that Imagen Editor is preferred over DALL-E 2 and Stable Diffusion -- and, as a cohort, these models are better at object-rendering than text-rendering, and handle material/color/size attributes better than count/shape attributes.	['William Chan', 'Peter Anderson', 'Mohammad Norouzi', 'Jason Baldridge', 'Radu Soricut', 'David J. Fleet', 'Sarah Laszlo', 'Yasumasa Onoe', 'Stefano Pellegrini', 'Shai Noy', 'Jordi Pont-Tuset', 'Ceslee Montgomery', 'Chitwan Saharia', 'Su Wang']	2022-12-13	null	http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Imagen_Editor_and_EditBench_Advancing_and_Evaluating_Text-Guided_Image_Inpainting_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_Imagen_Editor_and_EditBench_Advancing_and_Evaluating_Text-Guided_Image_Inpainting_CVPR_2023_paper.pdf	cvpr-2023-1	['text-guided-image-editing', 'image-inpainting']	['computer-vision', 'computer-vision']	[ 6.82676673e-01 -1.37447044e-02 3.70180346e-02 -4.01061416e-01 -7.14394093e-01 -6.11851275e-01 7.46030331e-01 -1.50256678e-01 -2.90532321e-01 4.23602164e-01 3.05486709e-01 3.32244523e-02 1.38467997e-01 -5.02319574e-01 -1.09497654e+00 -1.73884571e-01 5.72572470e-01 6.44375384e-01 1.95272982e-01 -2.74150968e-01 4.94840562e-01 5.05763590e-01 -1.41100276e+00 9.91025805e-01 8.07421386e-01 8.19911122e-01 6.30695283e-01 1.12397671e+00 -2.90527523e-01 1.12053227e+00 -7.74302900e-01 -4.95199591e-01 4.05243337e-01 -6.33525550e-01 -7.36666441e-01 2.91959524e-01 1.25471842e+00 -6.73132777e-01 -2.92174816e-01 8.26811790e-01 4.56994683e-01 5.42767458e-02 7.99270809e-01 -1.11019707e+00 -1.22720778e+00 4.93139118e-01 -5.92647612e-01 7.02587515e-02 3.19010854e-01 8.15197527e-01 8.65067899e-01 -1.22802091e+00 1.12156725e+00 1.50168049e+00 7.47081101e-01 6.76026881e-01 -1.73285365e+00 -4.97472018e-01 1.43652335e-01 -3.15407902e-01 -9.41197515e-01 -6.56499386e-01 6.44678116e-01 -5.87661803e-01 1.11753285e+00 4.52601939e-01 5.44668019e-01 1.31362879e+00 3.63378972e-01 5.94222605e-01 1.13245177e+00 -4.11525875e-01 -2.80841757e-02 1.68713480e-01 -7.03506768e-01 6.94368660e-01 -3.88521910e-01 2.59639233e-01 -9.68322754e-01 2.96033204e-01 1.35527492e+00 -8.60888734e-02 -2.59351522e-01 -7.61277005e-02 -1.39554369e+00 4.74137664e-01 3.13265353e-01 -1.39758170e-01 -3.66296202e-01 4.96107578e-01 2.15237647e-01 4.32657301e-01 7.28540659e-01 1.04109180e+00 -2.45034680e-01 -1.57385945e-01 -1.37912452e+00 5.14658332e-01 3.24263066e-01 1.41322696e+00 6.41428888e-01 2.19752580e-01 -7.10078239e-01 7.93610454e-01 -2.46246040e-01 5.13296187e-01 2.68966258e-01 -1.55756187e+00 5.78346908e-01 4.87487912e-01 3.22018415e-02 -8.37647736e-01 2.49493774e-02 -1.47268012e-01 -8.53114307e-01 8.77170444e-01 3.47592860e-01 1.15302570e-01 -1.09825420e+00 1.33484709e+00 -1.11361548e-01 -1.87731937e-01 -4.26126152e-01 8.85232627e-01 7.68402696e-01 6.31998777e-01 8.62578005e-02 1.85141414e-01 1.16274703e+00 -1.26720011e+00 -6.68252707e-01 -4.78329599e-01 2.39879549e-01 -1.22900796e+00 1.73519599e+00 4.99455065e-01 -1.65173078e+00 -9.67310548e-01 -8.12377334e-01 -7.34918237e-01 -1.41295031e-01 1.94148585e-01 2.47899130e-01 1.87046647e-01 -1.23369586e+00 1.03022385e+00 -5.29582381e-01 -2.31921956e-01 6.64418995e-01 -1.81251373e-02 -2.77659982e-01 7.07037449e-02 -4.67260391e-01 8.06181729e-01 1.18500955e-01 -3.34701329e-01 -1.08171606e+00 -1.27165496e+00 -6.08255088e-01 -3.97599675e-02 1.63891479e-01 -1.00043428e+00 1.42288148e+00 -1.19879961e+00 -1.55552423e+00 1.13598907e+00 -9.88782421e-02 -4.11577642e-01 1.05904639e+00 -3.57957721e-01 -1.71859801e-01 2.11171195e-01 1.76901549e-01 1.41128194e+00 1.61991799e+00 -1.40272307e+00 -5.92137814e-01 8.89506564e-02 -8.85820240e-02 2.12991357e-01 -1.38802513e-01 4.13423851e-02 -5.87817490e-01 -1.18512213e+00 -3.97747308e-01 -5.25557756e-01 -1.19838051e-01 8.26094687e-01 -5.73918819e-01 2.43438885e-01 1.09246492e+00 -8.20184886e-01 9.30027127e-01 -2.27848458e+00 3.12076628e-01 -1.82684571e-01 4.91060376e-01 4.07744199e-02 -4.63689774e-01 2.54357487e-01 -2.11653545e-01 1.38567194e-01 -3.14322531e-01 -9.88668025e-01 -5.90827502e-02 8.90357196e-02 -6.80000186e-01 -5.59573062e-03 5.60498059e-01 1.19273663e+00 -6.79748535e-01 -6.64762020e-01 4.80729192e-01 4.40344274e-01 -8.04888248e-01 4.59991395e-01 -8.11704755e-01 4.46415812e-01 6.10304549e-02 5.34959793e-01 6.42963886e-01 -1.38968259e-01 -4.65818554e-01 -4.99419719e-01 -1.65827513e-01 -4.29831222e-02 -7.61719823e-01 2.09134889e+00 -5.16733050e-01 1.22099710e+00 1.95600063e-01 -2.79146284e-01 8.47021818e-01 -9.61075276e-02 1.93675041e-01 -1.05448425e+00 -7.77084678e-02 -2.72347610e-02 -3.74389321e-01 -5.00503242e-01 1.00059032e+00 9.30583850e-02 3.30660224e-01 6.90571725e-01 7.31232315e-02 -9.24343228e-01 1.49703890e-01 5.80373168e-01 7.83596754e-01 5.38465738e-01 -2.44599193e-01 -2.13047892e-01 -1.44494429e-01 9.62054133e-02 -1.60989314e-01 9.85183060e-01 3.14720541e-01 1.33221829e+00 4.12819654e-01 -4.69004840e-01 -1.60520840e+00 -1.08967865e+00 1.29946664e-01 1.25063980e+00 -5.94239868e-02 -4.85801846e-01 -1.01032782e+00 -5.48397303e-01 6.41573370e-02 1.06622481e+00 -8.30268383e-01 -1.17478602e-01 -4.95942533e-01 -1.14203431e-01 5.13455749e-01 6.58115745e-01 4.22018558e-01 -1.37653327e+00 -6.96663976e-01 1.15009300e-01 4.79296409e-02 -1.13296854e+00 -1.18206596e+00 1.15442537e-01 -8.11984599e-01 -8.40158224e-01 -8.25958014e-01 -6.67537212e-01 8.40377867e-01 1.88510612e-01 1.67003226e+00 1.97607726e-01 -6.77410662e-01 4.89558429e-01 -1.98177040e-01 -3.14417809e-01 -8.83681893e-01 6.23032302e-02 -2.84834713e-01 -2.13272288e-01 -4.93248969e-01 -5.29073179e-01 -8.35828722e-01 4.62139755e-01 -1.07874131e+00 7.18430102e-01 6.42971933e-01 6.97543144e-01 7.19859779e-01 -3.50054920e-01 -3.60694597e-04 -1.01982236e+00 7.98093319e-01 2.46456727e-01 -4.03688163e-01 1.77707180e-01 -5.50925910e-01 1.20327666e-01 7.43153751e-01 -7.02283025e-01 -1.27506125e+00 -7.20057935e-02 4.08856617e-03 -7.97302544e-01 -1.27354758e-02 -1.93563685e-01 3.28937657e-02 -1.46248862e-01 1.06907403e+00 3.52784663e-01 6.84911907e-02 -4.63367671e-01 7.20241010e-01 3.91991585e-01 9.70464349e-01 -1.00840485e+00 7.28133440e-01 4.75466281e-01 -2.32282266e-01 -7.13497758e-01 -5.94744742e-01 2.38437548e-01 -5.33158481e-01 -1.35715619e-01 9.20778215e-01 -7.95201838e-01 -4.12147969e-01 5.79972506e-01 -1.38349187e+00 -1.08629000e+00 -8.57607782e-01 -1.42812267e-01 -8.47066820e-01 -1.63457636e-02 -7.40722835e-01 -3.32897812e-01 -2.91219264e-01 -1.28430700e+00 1.41686666e+00 -8.87604617e-03 -6.00670397e-01 -8.37380707e-01 -3.06684852e-01 2.28488669e-01 7.53071368e-01 1.57189265e-01 9.15578067e-01 -6.26391247e-02 -7.37661779e-01 3.08562696e-01 -6.65332675e-01 5.12671292e-01 1.62142396e-01 3.78206849e-01 -1.01470888e+00 -1.22955732e-01 -1.82526678e-01 -4.35560644e-01 7.46951580e-01 3.74722898e-01 1.57703602e+00 -2.82869518e-01 -4.12819199e-02 1.02803290e+00 1.15925336e+00 -1.54124752e-01 8.72798681e-01 2.28733525e-01 9.25337911e-01 4.61818516e-01 5.63849092e-01 4.90831137e-01 -1.80805270e-02 7.30309367e-01 3.61513972e-01 -5.40896356e-01 -9.11958516e-01 -5.93232989e-01 2.24372655e-01 1.71610713e-01 -8.42831507e-02 -2.46355206e-01 -4.57940787e-01 2.12476596e-01 -1.50981379e+00 -9.81256962e-01 -2.49620169e-01 1.85615230e+00 1.33373952e+00 3.07087302e-01 7.94062912e-02 -4.29334402e-01 4.58163261e-01 1.30896762e-01 -7.49267101e-01 -4.91180539e-01 -3.48805755e-01 4.24643964e-01 3.70532453e-01 6.96012259e-01 -6.44533277e-01 1.21295440e+00 6.63806486e+00 9.75604475e-01 -1.21954191e+00 1.33394852e-01 1.07748091e+00 -4.16481286e-01 -3.86149019e-01 -9.19502378e-02 -6.31663144e-01 5.42460978e-01 3.34971339e-01 3.96651551e-02 8.31962109e-01 7.08139360e-01 4.12577480e-01 -1.29705757e-01 -1.63326275e+00 1.07702589e+00 1.20572858e-01 -1.93293571e+00 5.45938730e-01 -1.45644307e-01 1.13554013e+00 -3.56298000e-01 4.87840116e-01 4.29452211e-02 3.45853269e-01 -1.28283715e+00 1.27227163e+00 8.17528009e-01 1.55837333e+00 -4.75802183e-01 -5.31410016e-02 7.24237710e-02 -7.65856266e-01 2.66906530e-01 -3.86178851e-01 9.46170315e-02 9.35522467e-02 5.34514725e-01 -6.82616472e-01 -3.69462296e-02 7.38067627e-01 6.43363416e-01 -7.43608415e-01 6.39962554e-01 -2.25434512e-01 3.16339523e-01 2.21420657e-02 4.40070063e-01 -8.38392153e-02 1.22040957e-02 3.36207181e-01 1.60888040e+00 2.28098199e-01 -6.55118749e-02 -5.36397919e-02 1.57216656e+00 -4.49995250e-01 -1.64150000e-01 -3.98870021e-01 -2.01707423e-01 3.41577083e-01 1.16930997e+00 -5.38101256e-01 -5.22654712e-01 -3.23172915e-03 1.81490934e+00 2.72748351e-01 4.61125880e-01 -8.99326742e-01 -3.26931208e-01 4.88651216e-01 5.39354861e-01 2.41480961e-01 -2.67625511e-01 -5.96294522e-01 -7.32166469e-01 -6.38738871e-02 -1.22429037e+00 -2.26209566e-01 -1.50564289e+00 -1.13759589e+00 6.63565576e-01 -1.59162298e-01 -1.05598354e+00 -2.07471065e-02 -7.50012994e-01 -6.72374129e-01 1.07564819e+00 -1.02730954e+00 -1.38302231e+00 -6.18081570e-01 6.10351920e-01 1.08108783e+00 -5.45584075e-02 6.69175208e-01 1.24346226e-01 -2.17134967e-01 6.65145814e-01 -1.09058112e-01 -1.61232740e-01 1.20299172e+00 -1.33903348e+00 9.09128487e-01 7.25130320e-01 2.07251400e-01 4.99870002e-01 9.09686506e-01 -8.73191953e-01 -1.27303731e+00 -1.30645800e+00 4.00376499e-01 -8.16802859e-01 3.25360924e-01 -4.80715394e-01 -8.21354032e-01 7.18869269e-01 7.21158266e-01 2.19215006e-02 -1.41935676e-01 -3.94613802e-01 -5.53024828e-01 8.98346212e-03 -1.15888286e+00 7.96904922e-01 1.33280313e+00 -8.11572015e-01 -3.19120526e-01 5.12878001e-01 7.97495067e-01 -8.94406378e-01 -8.00241411e-01 -1.83562174e-01 5.05095005e-01 -1.24044049e+00 1.07373106e+00 -4.98423964e-01 1.23276913e+00 -2.25037664e-01 6.82776794e-02 -1.53971171e+00 -3.81739527e-01 -1.04065704e+00 7.41945580e-02 1.31225848e+00 2.47869983e-01 4.13187640e-03 7.25280344e-01 6.38356030e-01 -2.44595006e-01 -5.22117317e-01 -3.76572579e-01 -5.64361453e-01 -6.27481788e-02 -4.13730770e-01 4.79989141e-01 7.87464380e-01 -5.67391634e-01 1.38213128e-01 -4.68154877e-01 -4.54682082e-01 5.08692563e-01 -1.68444738e-01 1.09644377e+00 -7.40370512e-01 -6.04266226e-01 -8.27102542e-01 2.04814494e-01 -1.22423720e+00 -1.77377418e-01 -5.70952713e-01 8.39124173e-02 -1.48474169e+00 2.16970652e-01 -3.24953794e-01 3.84657383e-01 4.46183443e-01 -2.22297370e-01 5.74739099e-01 5.00463605e-01 3.20344687e-01 -4.31860685e-01 4.37756479e-01 1.89553750e+00 -4.15593952e-01 -2.03332484e-01 -3.97981316e-01 -7.35898972e-01 6.31546974e-01 4.84893113e-01 -3.76890600e-01 -4.32104349e-01 -9.28551733e-01 3.34977806e-01 2.24531498e-02 6.68593109e-01 -1.01926315e+00 1.49528474e-01 -2.65054613e-01 7.89841354e-01 -4.72065389e-01 4.76895750e-01 -5.39741576e-01 1.89968064e-01 1.03640206e-01 -8.05196106e-01 3.62896979e-01 4.42574292e-01 1.61331236e-01 1.00210972e-01 -6.61407486e-02 9.62280989e-01 -2.41060928e-01 -6.54795527e-01 2.97247648e-01 -7.49981450e-03 3.33917171e-01 6.08015954e-01 -4.96333987e-01 -4.69802469e-01 -5.71012139e-01 -6.14885509e-01 -1.04284696e-01 9.50113535e-01 4.60428953e-01 9.22499359e-01 -1.15467513e+00 -9.35484171e-01 3.95474792e-01 4.62358594e-02 2.11358190e-01 3.13221693e-01 5.50442696e-01 -7.36557186e-01 -1.57147154e-01 -4.49701816e-01 -6.36301219e-01 -1.16915345e+00 4.11974281e-01 4.21296507e-01 3.78028937e-02 -6.96355879e-01 1.22871649e+00 6.01500630e-01 -1.88005596e-01 3.46918672e-01 -6.66849792e-01 7.39781618e-01 -2.76211679e-01 5.38976252e-01 1.65863574e-01 -1.11998104e-01 -1.49759099e-01 1.49066254e-01 5.38006961e-01 -3.85419250e-01 -3.75411153e-01 1.20316374e+00 5.75075038e-02 -3.55428271e-02 1.07318006e-01 8.48154604e-01 2.85624176e-01 -1.98011208e+00 -7.16515258e-02 -5.32350361e-01 -6.72425747e-01 -7.41610005e-02 -1.09388661e+00 -1.11185682e+00 9.99538064e-01 4.76426363e-01 -1.40525103e-01 9.77798045e-01 -2.30934694e-01 6.67715907e-01 1.49828926e-01 -3.37870866e-02 -1.18568778e+00 7.74420977e-01 2.19618827e-01 1.60194421e+00 -1.07657003e+00 9.89919752e-02 -3.33984703e-01 -7.85510004e-01 1.11564350e+00 1.03675306e+00 -2.12800160e-01 7.40231052e-02 6.05778396e-01 2.97257281e-03 -9.00516436e-02 -6.56982660e-01 3.85810405e-01 3.17547411e-01 6.62258625e-01 4.45906460e-01 -1.53618991e-01 4.54972297e-01 1.79994747e-01 -3.89206231e-01 -1.69468559e-02 3.49001676e-01 5.56727111e-01 -1.98467225e-01 -9.93833661e-01 -5.12600064e-01 5.94138920e-01 6.57526264e-03 -4.59127635e-01 -7.08956599e-01 6.61672652e-01 2.90959090e-01 5.99532127e-01 2.58734316e-01 -3.30613434e-01 4.73855972e-01 -3.26088876e-01 8.16271484e-01 -6.27702653e-01 -7.91456938e-01 -1.33389393e-02 -3.68955880e-02 -7.73528397e-01 1.29580110e-01 -3.84322345e-01 -9.98093367e-01 -3.90959978e-01 -3.25072408e-02 -3.21446955e-01 7.24890530e-01 6.10428154e-01 6.97836161e-01 9.49491978e-01 2.77970582e-01 -1.33365929e+00 -3.90311271e-01 -9.18121457e-01 -3.22251618e-01 7.70342529e-01 2.30669916e-01 -1.71086147e-01 -1.29747644e-01 4.93400484e-01]	[11.39923095703125, -0.3002430498600006]
55e9fdcd-f12a-4385-8a80-5599e57927dc	temporal-sequence-object-based-cnn-ts-ocnn	null	null	https://doi.org/10.1016/j.cj.2022.07.005	https://www.sciencedirect.com/science/article/pii/S2214514122001751?via%3Dihub	Temporal Sequence Object-based CNN (TS-OCNN) for crop classification from fine resolution remote sensing image time-series	Accurate crop distribution mapping is required for crop yield prediction and field management. Due to rapid progress in remote sensing technology, fine spatial resolution (FSR) remotely sensed imagery now offers great opportunities for mapping crop types in great detail. However, within-class variance can hamper attempts to discriminate crop classes at fine resolutions. Multi-temporal FSR remotely sensed imagery provides a means of increasing crop classification from FSR imagery, although current methods do not exploit the available information fully. In this research, a novel Temporal Sequence Object-based Convolutional Neural Network (TS-OCNN) was proposed to classify agricultural crop type from FSR image time-series. An object-based CNN (OCNN) model was adopted in the TS-OCNN to classify images at the object level (i.e., segmented objects or crop parcels), thus, maintaining the precise boundary information of crop parcels. The combination of image time-series was first utilized as the input to the OCNN model to produce an ‘original’ or baseline classification. Then the single-date images were fed automatically into the deep learning model scene-by-scene in order of image acquisition date to increase successively the crop classification accuracy. By doing so, the joint information in the FSR multi-temporal observations and the unique individual information from the single-date images were exploited comprehensively for crop classification. The effectiveness of the proposed approach was investigated using multitemporal SAR and optical imagery, respectively, over two heterogeneous agricultural areas. The experimental results demonstrated that the newly proposed TS-OCNN approach consistently increased crop classification accuracy, and achieved the greatest accuracies (82.68% and 87.40%) in comparison with state-of-the-art benchmark methods, including the object-based CNN (OCNN) (81.63% and 85.88%), object-based image analysis (OBIA) (78.21% and 84.83%), and standard pixel-wise CNN (79.18% and 82.90%). The proposed approach is the first known attempt to explore simultaneously the joint information from image time-series with the unique information from single-date images for crop classification using a deep learning framework. The TS-OCNN, therefore, represents a new approach for agricultural landscape classification from multi-temporal FSR imagery. Besides, it is readily generalizable to other landscapes (e.g., forest landscapes), with a wide application prospect.	['Huapeng Li']	2022-07-05	null	null	null	the-crop-journal-2022-7	['crop-yield-prediction', 'crop-classification', 'crop-yield-prediction']	['computer-vision', 'miscellaneous', 'miscellaneous']	[ 5.03998458e-01 -6.91568077e-01 -3.07769954e-01 -1.19976819e-01 -5.81047058e-01 -7.21416891e-01 2.68013656e-01 3.04344416e-01 -3.44557762e-01 9.03075695e-01 -6.36143804e-01 -4.39799666e-01 -4.22490507e-01 -1.40793431e+00 -6.85955465e-01 -1.04423738e+00 -5.17505944e-01 -2.38982081e-01 -1.63078561e-01 -4.58549857e-01 -1.26391217e-01 9.00724947e-01 -2.02755618e+00 1.66295424e-01 1.20175958e+00 1.25077617e+00 9.50377524e-01 5.45274973e-01 -1.02939829e-01 2.91186899e-01 -3.08344573e-01 4.65476334e-01 3.21392179e-01 -1.66634709e-01 -3.20894033e-01 3.32403511e-01 2.64763802e-01 -5.39016008e-01 1.88481435e-01 1.24365520e+00 9.13182497e-02 7.84884691e-02 5.52332163e-01 -8.10498059e-01 -9.28803027e-01 5.37978888e-01 -1.13146758e+00 1.55507773e-02 -3.76521468e-01 6.51768073e-02 6.27772510e-01 -5.53045571e-01 3.30998808e-01 8.69402826e-01 6.30270660e-01 -1.61611527e-01 -1.18516326e+00 -7.98607409e-01 3.22623760e-01 1.06307268e-01 -1.46489727e+00 4.47083227e-02 5.77641964e-01 -5.24100959e-01 7.31300592e-01 2.81096995e-01 9.77491379e-01 4.40022230e-01 3.64892870e-01 6.36044085e-01 1.42079771e+00 -2.92362481e-01 -5.11743687e-02 -3.51571053e-01 3.44764084e-01 2.54420131e-01 4.25792187e-01 6.35433972e-01 -1.34121060e-01 1.31147310e-01 1.02896333e+00 3.53483528e-01 -3.29097629e-01 -8.76124799e-02 -1.06854928e+00 8.13906074e-01 1.17311132e+00 7.70678103e-01 -1.00070655e+00 -1.97381482e-01 2.24767983e-01 1.97095945e-01 6.26925707e-01 3.22286904e-01 -7.97868490e-01 5.07059872e-01 -1.21783054e+00 3.00172061e-01 1.14584178e-01 8.41903806e-01 1.07011580e+00 4.53304499e-01 9.26115885e-02 9.46638107e-01 9.59776267e-02 1.00639105e+00 3.67029518e-01 -4.33123827e-01 8.95526782e-02 5.15918016e-01 2.02679902e-01 -1.24191332e+00 -4.41802949e-01 -5.58583081e-01 -1.34640586e+00 3.43416512e-01 3.01451772e-01 -5.24529815e-02 -1.34249353e+00 1.45055103e+00 -1.24300450e-01 -1.08611435e-01 3.48240584e-01 1.07696855e+00 5.97070754e-01 9.18506682e-01 4.03159410e-01 -3.19691688e-01 1.43486118e+00 -2.56978303e-01 -7.59117842e-01 -1.10362329e-01 5.01431644e-01 -6.94300234e-01 5.15374005e-01 1.58864543e-01 -3.42310965e-01 -8.44873369e-01 -1.20307577e+00 5.70721447e-01 -9.05501068e-01 8.89803112e-01 8.98062646e-01 2.80323029e-01 -9.70347285e-01 5.53479433e-01 -7.48715401e-01 -5.21555483e-01 6.23178840e-01 4.09198143e-02 -5.05455315e-01 -1.93915084e-01 -1.18405807e+00 6.92312300e-01 9.36769485e-01 9.21140254e-01 -9.86529410e-01 -6.84080243e-01 -8.11832190e-01 1.72364175e-01 3.09181482e-01 1.35424122e-01 6.72668099e-01 -1.40096605e+00 -1.13738704e+00 7.63334692e-01 3.01268566e-02 -4.93183881e-01 6.92520142e-02 -1.73999950e-01 -3.95024419e-01 1.67583302e-01 2.44188398e-01 5.99121571e-01 7.26443470e-01 -1.39399588e+00 -9.98149216e-01 -6.90448403e-01 5.91505691e-02 9.93629172e-02 -2.05003545e-01 -1.45349160e-01 5.43075204e-01 -7.88373470e-01 5.30633569e-01 -9.24844086e-01 -1.40625849e-01 -8.71303603e-02 5.61714768e-02 2.75610477e-01 9.68234062e-01 -8.66144478e-01 8.34735215e-01 -2.10765791e+00 -1.41701460e-01 -8.06590170e-03 -1.69518083e-01 6.20173812e-01 -3.16348702e-01 2.17924938e-01 -3.86967212e-01 1.34787127e-01 -7.76363790e-01 6.33017063e-01 -5.95277727e-01 1.12877198e-01 -1.99982271e-01 4.77689385e-01 5.92149317e-01 8.69491518e-01 -7.21866071e-01 -1.61462098e-01 5.89368045e-01 4.22393113e-01 3.02603036e-01 1.06720529e-01 -2.70414054e-01 2.62404203e-01 -5.79575121e-01 1.10626483e+00 1.32497203e+00 1.57091126e-01 1.55322850e-01 -4.05176163e-01 -8.34526122e-01 -7.00738370e-01 -9.07369196e-01 1.37705994e+00 -5.55333555e-01 6.59209549e-01 9.64593664e-02 -1.36740887e+00 1.49046218e+00 3.58919084e-01 3.81368876e-01 -7.62994528e-01 -7.27891028e-02 5.46591103e-01 1.19910412e-01 -4.81545925e-01 6.28470063e-01 5.40839657e-02 1.76327378e-01 -1.83621570e-01 3.20959054e-02 1.29570335e-01 1.23105004e-01 -5.64833045e-01 1.85435385e-01 4.80619133e-01 4.87836182e-01 -7.12054491e-01 5.03599405e-01 6.04007125e-01 2.98475713e-01 7.54409313e-01 -1.94892064e-01 4.48157996e-01 -5.72162680e-02 -7.73784876e-01 -9.36580777e-01 -7.02781200e-01 -5.46238601e-01 8.83105934e-01 1.00866344e-03 6.57694280e-01 -4.23485875e-01 -2.54947335e-01 1.81864172e-01 5.40045857e-01 -6.86483324e-01 2.16280416e-01 -4.78572696e-01 -1.46192825e+00 4.20714706e-01 6.06679082e-01 1.15386736e+00 -1.33665299e+00 -9.40916538e-01 5.61842680e-01 -1.23387031e-01 -8.37142169e-01 3.75156939e-01 5.13306141e-01 -9.98811722e-01 -1.01476002e+00 -1.10266960e+00 -6.35320604e-01 3.17988604e-01 8.24400425e-01 7.66605198e-01 -9.65641588e-02 -4.90490288e-01 -2.65286297e-01 -8.19834054e-01 -4.34352010e-01 -1.84803024e-01 2.47532934e-01 -5.40835023e-01 1.97762594e-01 5.04902542e-01 -4.75099057e-01 -3.61661315e-01 9.50401351e-02 -1.07487738e+00 3.17020388e-03 9.92094398e-01 1.08059049e+00 6.58985317e-01 3.82888645e-01 8.50065112e-01 -4.76434320e-01 -8.50338861e-03 -5.71426451e-01 -1.02906775e+00 4.20020998e-01 -3.13687623e-01 -3.97450626e-01 6.29622757e-01 -3.58127385e-01 -1.22189784e+00 2.27901608e-01 1.34345919e-01 -1.09682284e-01 -7.78029561e-01 1.22587407e+00 -8.71663913e-02 7.79373897e-03 5.07587790e-01 6.21972322e-01 -1.14858657e-01 -3.00964981e-01 5.02898656e-02 8.66921484e-01 5.71825683e-01 -2.47223273e-01 6.36238813e-01 3.65596533e-01 -4.69871890e-03 -1.23987699e+00 -6.31054401e-01 -5.64222932e-01 -9.40130174e-01 -3.03462327e-01 9.81519580e-01 -1.22815955e+00 -4.30528939e-01 1.12542534e+00 -1.04216146e+00 -3.63887995e-01 2.16937959e-01 7.02104032e-01 -2.63379484e-01 -4.81816428e-03 -1.51883855e-01 -1.03255677e+00 -4.10245717e-01 -9.86118138e-01 1.03584254e+00 4.72565681e-01 4.87631172e-01 -7.10742176e-01 -4.82846975e-01 -1.73010573e-01 5.66834867e-01 7.67119884e-01 8.43994260e-01 -1.75787404e-01 -6.53900385e-01 -3.17466319e-01 -8.91855717e-01 3.41654807e-01 6.29103243e-01 3.48953605e-01 -1.02403057e+00 -2.73388565e-01 -2.96579599e-01 -1.51433825e-01 1.06140780e+00 9.86217439e-01 1.03863442e+00 2.59596527e-01 -2.83926427e-01 7.14725137e-01 2.12064457e+00 7.71533906e-01 6.21958017e-01 6.09767616e-01 5.89434445e-01 6.29855275e-01 1.17449820e+00 4.12701696e-01 -1.54554650e-01 3.32692742e-01 7.60272622e-01 -4.32609200e-01 2.09897235e-01 1.50614113e-01 3.03010866e-02 1.54721975e-01 -4.58626181e-01 -2.01511353e-01 -9.09981012e-01 9.11119401e-01 -1.82874084e+00 -1.15140986e+00 -1.95114195e-01 2.01491904e+00 3.97067279e-01 -3.13180804e-01 -2.88752764e-01 1.92468688e-01 9.72449899e-01 5.66081405e-01 -6.30876720e-01 -5.58394305e-02 -7.27786005e-01 3.05883437e-01 1.14981139e+00 7.42881820e-02 -1.60922277e+00 1.07161224e+00 4.46424770e+00 8.97471845e-01 -1.39979529e+00 -1.86003014e-01 4.77873117e-01 5.70300162e-01 3.18296760e-01 -1.25773102e-01 -7.88274527e-01 5.59530705e-02 5.91184735e-01 5.45721017e-02 4.72899824e-01 6.85913563e-01 4.37517077e-01 -3.73587012e-01 -2.25498363e-01 5.85715890e-01 -3.92176867e-01 -1.13092983e+00 3.93201336e-02 2.15676147e-02 8.31099331e-01 -1.08247977e-02 -3.27428542e-02 1.08500391e-01 2.65221536e-01 -9.39822435e-01 5.34416974e-01 5.34066081e-01 9.99388397e-01 -8.46998096e-01 1.11726189e+00 3.40390325e-01 -1.77585387e+00 -3.77244920e-01 -6.29860103e-01 -6.93939924e-02 -2.80727535e-01 6.47657752e-01 -2.54846841e-01 1.30469203e+00 9.78093624e-01 1.23143876e+00 -4.32593435e-01 6.99094892e-01 2.15147436e-01 5.26271641e-01 -2.58560687e-01 1.41269237e-01 7.19796658e-01 -3.77408862e-01 2.99853504e-01 1.05718696e+00 7.14961767e-01 2.93853968e-01 2.06577897e-01 8.97044957e-01 3.26784700e-01 7.90391490e-02 -7.30950177e-01 -2.49882668e-01 4.01426673e-01 1.50113714e+00 -8.82006824e-01 -1.64995208e-01 -1.71657383e-01 4.54941988e-01 -4.75435853e-02 4.72621709e-01 -5.32910585e-01 -4.96501267e-01 6.06806874e-01 -3.10377806e-01 4.87043679e-01 -3.76440316e-01 -1.45023972e-01 -9.44802165e-01 -2.16095112e-02 -7.96593070e-01 1.23833723e-01 -9.68333781e-01 -1.05226231e+00 7.36745000e-01 1.95462912e-01 -1.34475398e+00 9.85170901e-02 -8.98521185e-01 -3.21265638e-01 1.35385346e+00 -2.19944572e+00 -1.55006182e+00 -9.69183266e-01 4.65711467e-02 5.47807634e-01 -2.51242965e-01 1.19168353e+00 -4.98450585e-02 -5.35429239e-01 2.09852252e-02 5.54015875e-01 1.32504672e-01 2.85703301e-01 -1.10587668e+00 4.41138856e-02 1.01769912e+00 -4.78351533e-01 7.33647421e-02 2.23987684e-01 -6.86877072e-01 -1.10904837e+00 -1.65491724e+00 4.65681314e-01 5.74611604e-01 5.80233335e-01 2.47347578e-01 -1.05570996e+00 5.36220014e-01 -7.81894922e-02 2.66871043e-02 2.73059994e-01 -1.32478431e-01 -1.20447516e-01 -6.11078858e-01 -1.13341606e+00 1.97691217e-01 5.80949783e-01 -3.48889023e-01 -2.06959769e-02 1.20625511e-01 6.90914154e-01 -1.03382617e-01 -1.05674458e+00 8.47021580e-01 8.42867613e-01 -7.23981142e-01 8.80177855e-01 -1.07457921e-01 6.79699719e-01 -4.31221336e-01 -5.99404216e-01 -1.47396457e+00 -6.09973013e-01 1.87374100e-01 5.67485094e-01 1.08742476e+00 1.35825202e-01 -7.04264462e-01 2.62738079e-01 -2.33281597e-01 -2.48041108e-01 -3.23350817e-01 -4.82926816e-01 -8.91344428e-01 2.72211552e-01 -1.57521337e-01 9.06579375e-01 9.71522808e-01 -8.59828055e-01 -5.39029717e-01 -8.37059692e-02 8.37261319e-01 6.17239833e-01 4.84166294e-01 3.40805143e-01 -1.37370467e+00 2.84506202e-01 -4.39111352e-01 -2.98553050e-01 -4.00711954e-01 1.29312858e-01 -6.88891947e-01 5.37207760e-02 -1.32626903e+00 -1.65962335e-02 -6.78785324e-01 -3.21639448e-01 7.44131744e-01 -4.59818631e-01 2.06512079e-01 2.26294234e-01 2.98229545e-01 5.62214494e-01 4.24448162e-01 1.18434238e+00 -2.88239568e-01 -3.74335110e-01 7.16983378e-02 -3.50547820e-01 3.05443347e-01 1.13066435e+00 -2.33780280e-01 1.12968348e-02 -4.73282933e-01 -2.74144232e-01 3.16633105e-01 8.36693585e-01 -1.04633343e+00 -2.76457161e-01 -3.82024556e-01 5.80333889e-01 -1.13290858e+00 -1.62874788e-01 -9.43913698e-01 4.15263027e-01 4.01356220e-01 -2.26115629e-01 -2.35666066e-01 7.44147420e-01 1.82062373e-01 -4.55725253e-01 -2.00842813e-01 9.50853229e-01 -2.81802356e-01 -1.21006560e+00 4.79348093e-01 -4.98690188e-01 -7.66117930e-01 9.84881163e-01 -1.62943125e-01 -3.85127187e-01 3.23464841e-01 -5.97241163e-01 8.99254382e-02 7.22595975e-02 5.53591311e-01 3.93637806e-01 -1.18218076e+00 -1.05510163e+00 4.16366965e-01 4.20006275e-01 9.34925526e-02 6.61206543e-01 5.11172414e-01 -8.37019980e-01 6.19388163e-01 -8.20928574e-01 -9.00741935e-01 -1.08015645e+00 5.31174183e-01 5.48073173e-01 -2.30551645e-01 -2.45432451e-01 3.09157312e-01 1.33193552e-01 -4.35127348e-01 -4.18234050e-01 -4.61289287e-01 -6.35954559e-01 4.96700197e-01 4.22582626e-01 -6.20374270e-02 1.18272789e-01 -7.87631512e-01 -1.49672285e-01 7.94463098e-01 9.91038457e-02 2.81322956e-01 1.62707198e+00 -3.83888669e-02 -2.60481685e-01 5.13424098e-01 1.07201183e+00 -7.83136070e-01 -1.24906158e+00 -2.49498770e-01 -1.36937112e-01 -5.40806592e-01 4.61867899e-01 -1.02184582e+00 -1.28519905e+00 8.77558589e-01 1.07209301e+00 5.35468459e-01 1.64101100e+00 -6.52458668e-01 2.77066499e-01 3.20255846e-01 5.37526608e-01 -6.48420513e-01 -7.45118678e-01 2.84594715e-01 9.33052480e-01 -1.59778380e+00 -5.20702340e-02 -3.12072247e-01 -2.47470766e-01 1.50528181e+00 5.15168011e-01 -2.28226051e-01 7.93509483e-01 9.68237296e-02 2.30501726e-01 4.90571372e-02 -1.94096312e-01 -7.02849329e-01 5.02722524e-03 8.65140676e-01 5.30061662e-01 6.82877719e-01 -1.52494028e-01 2.82730669e-01 1.46153480e-01 4.93208528e-01 2.95785189e-01 9.46987092e-01 -5.95499337e-01 -5.38463414e-01 -6.41443431e-01 5.63572526e-01 -1.38994217e-01 -1.37845054e-01 2.04244405e-01 1.08853543e+00 2.65883386e-01 9.07333732e-01 1.76572084e-01 -1.89983323e-01 1.92720771e-01 -2.46533841e-01 2.44451612e-01 -2.74217576e-01 -4.62252259e-01 2.00568840e-01 -3.69327396e-01 -1.86133161e-01 -9.67335165e-01 -7.47859001e-01 -6.74879313e-01 -2.86053836e-01 -7.40573525e-01 -1.28256455e-01 8.76382768e-01 8.08157265e-01 1.02369979e-01 6.62492096e-01 8.98480892e-01 -1.22795832e+00 -3.08036476e-01 -1.19433153e+00 -1.12869120e+00 -2.18204930e-01 5.31056881e-01 -6.84209824e-01 -1.88000605e-01 1.06103964e-01]	[9.382157325744629, -1.579405665397644]
266903c8-fc08-4bfb-955c-0012141d59a9	prosfda-prompt-learning-based-source-free	2211.11514	null	https://arxiv.org/abs/2211.11514v1	https://arxiv.org/pdf/2211.11514v1.pdf	ProSFDA: Prompt Learning based Source-free Domain Adaptation for Medical Image Segmentation	The domain discrepancy existed between medical images acquired in different situations renders a major hurdle in deploying pre-trained medical image segmentation models for clinical use. Since it is less possible to distribute training data with the pre-trained model due to the huge data size and privacy concern, source-free unsupervised domain adaptation (SFDA) has recently been increasingly studied based on either pseudo labels or prior knowledge. However, the image features and probability maps used by pseudo label-based SFDA and the consistent prior assumption and the prior prediction network used by prior-guided SFDA may become less reliable when the domain discrepancy is large. In this paper, we propose a \textbf{Pro}mpt learning based \textbf{SFDA} (\textbf{ProSFDA}) method for medical image segmentation, which aims to improve the quality of domain adaption by minimizing explicitly the domain discrepancy. Specifically, in the prompt learning stage, we estimate source-domain images via adding a domain-aware prompt to target-domain images, then optimize the prompt via minimizing the statistic alignment loss, and thereby prompt the source model to generate reliable predictions on (altered) target-domain images. In the feature alignment stage, we also align the features of target-domain images and their styles-augmented counterparts to optimize the source model, and hence push the model to extract compact features. We evaluate our ProSFDA on two multi-domain medical image segmentation benchmarks. Our results indicate that the proposed ProSFDA outperforms substantially other SFDA methods and is even comparable to UDA methods. Code will be available at \url{https://github.com/ShishuaiHu/ProSFDA}.	['Yong Xia', 'Zehui Liao', 'Shishuai Hu']	2022-11-21	null	null	null	null	['source-free-domain-adaptation']	['computer-vision']	[ 6.02975726e-01 3.53420645e-01 -5.19802630e-01 -6.86566412e-01 -1.21453881e+00 -5.32990873e-01 2.30327144e-01 2.27842450e-01 -5.11047542e-01 7.80736148e-01 7.99530745e-02 -3.23801965e-01 -1.55493617e-01 -4.04102653e-01 -6.44838691e-01 -8.47212255e-01 3.90138745e-01 8.69722426e-01 2.01187640e-01 2.34049425e-01 -5.95955700e-02 2.88961083e-01 -9.01215911e-01 2.02295825e-01 1.18177748e+00 1.04244745e+00 4.16807801e-01 4.29598153e-01 -2.81792022e-02 3.38803649e-01 -5.58511794e-01 -2.91990459e-01 4.75225627e-01 -6.07013762e-01 -9.49902475e-01 5.00280559e-01 2.11241841e-01 -4.96272981e-01 -1.69445679e-01 1.14469564e+00 6.84778810e-01 6.13163859e-02 8.48991096e-01 -1.09414804e+00 -3.92830819e-01 2.89549440e-01 -7.43414760e-01 2.52824694e-01 1.03340372e-01 1.24053866e-01 5.50487101e-01 -5.74302912e-01 9.41104174e-01 7.68897891e-01 4.41343635e-01 6.99549615e-01 -1.35355151e+00 -8.23556364e-01 4.67326827e-02 -4.78971787e-02 -1.23038495e+00 -3.36408198e-01 7.27072895e-01 -5.20663917e-01 3.40812385e-01 1.82595909e-01 2.08629787e-01 1.10206831e+00 1.20527044e-01 9.14067507e-01 1.22478211e+00 -3.73898625e-01 2.59946227e-01 5.12662530e-01 1.19999029e-01 6.29356980e-01 1.21935353e-01 -2.57304851e-02 -1.89145565e-01 -4.95438516e-01 8.70650291e-01 3.52452025e-02 -2.71573693e-01 -6.70330584e-01 -1.27351284e+00 7.17793882e-01 2.35054106e-01 7.11639225e-02 -3.94439429e-01 -5.15207887e-01 4.45572168e-01 1.33382156e-01 5.42323291e-01 4.17030126e-01 -5.94687343e-01 1.90755814e-01 -1.01701021e+00 1.24636196e-01 5.32541335e-01 1.17762232e+00 6.94860756e-01 -4.66444820e-01 -3.39280486e-01 1.01997507e+00 7.99176991e-02 4.14593369e-01 6.51123524e-01 -1.03663540e+00 3.79021615e-01 4.44852620e-01 -4.24306504e-02 -6.40835524e-01 -4.31180656e-01 -3.95236015e-01 -9.45088446e-01 -7.51165822e-02 7.03050494e-01 -1.58319607e-01 -1.23621607e+00 1.59392357e+00 6.22589946e-01 1.24297343e-01 4.42231745e-02 9.59625185e-01 6.68250561e-01 3.81256193e-01 1.45759955e-01 -2.88770020e-01 1.34458983e+00 -8.06592107e-01 -5.68969071e-01 -2.00391889e-01 7.12116838e-01 -7.26972580e-01 1.18586636e+00 2.66853958e-01 -8.30916643e-01 -4.20104682e-01 -8.18999171e-01 4.47928496e-02 9.51745063e-02 3.22899848e-01 2.87509859e-01 5.53654730e-01 -7.93185890e-01 3.97112995e-01 -1.00374413e+00 -3.48549992e-01 1.02601194e+00 5.84124088e-01 -3.96470666e-01 -4.07809734e-01 -9.82123554e-01 6.04010582e-01 4.57441211e-01 -3.14660698e-01 -8.15423608e-01 -9.21075165e-01 -7.76412189e-01 -4.68482077e-01 5.03950179e-01 -6.79110289e-01 1.18543303e+00 -1.03111947e+00 -1.42370009e+00 1.21294463e+00 -1.89820305e-01 -4.42451298e-01 6.80482090e-01 1.76231280e-01 -2.76539534e-01 2.37370312e-01 4.22138721e-01 8.96994114e-01 9.50651944e-01 -1.24021387e+00 -7.30738699e-01 -3.59717041e-01 -3.04895490e-01 1.92380667e-01 -1.69045329e-01 -1.85111120e-01 -6.28414452e-01 -7.56375015e-01 6.98029026e-02 -1.05518782e+00 -5.90149164e-01 8.84239078e-02 -5.92975140e-01 -5.07876649e-03 6.00817144e-01 -7.54044712e-01 1.14283872e+00 -2.38354039e+00 -1.31043270e-01 4.80796784e-01 3.32190156e-01 3.46508265e-01 -5.98359741e-02 -2.35223383e-01 -8.79165437e-03 -7.81535208e-02 -7.59651244e-01 -1.89397693e-01 -3.18505108e-01 3.23064387e-01 -7.83960670e-02 4.83756632e-01 3.07936557e-02 5.72709322e-01 -8.26573551e-01 -8.31302524e-01 1.35980666e-01 1.74766630e-01 -7.06139266e-01 2.17257038e-01 -1.95025802e-01 1.04732680e+00 -7.96683669e-01 6.88135505e-01 9.02479112e-01 -6.56594992e-01 2.86275834e-01 -2.50805497e-01 3.63624305e-01 1.00932345e-01 -9.24497485e-01 1.88725746e+00 -2.39433557e-01 2.26154834e-01 -8.13135430e-02 -1.15908730e+00 8.43378782e-01 2.78727442e-01 9.35100198e-01 -6.65910244e-01 2.05483094e-01 3.83321673e-01 -3.54160070e-02 -4.77723300e-01 1.23898312e-01 -9.51343924e-02 -7.01566190e-02 3.03194791e-01 1.23082869e-01 -6.62167072e-02 -6.36895373e-02 1.68604642e-01 1.00194979e+00 1.10629179e-01 4.54152673e-01 -2.75368392e-01 5.13995826e-01 3.21583927e-01 8.70878696e-01 6.43930972e-01 -5.92804074e-01 9.54391539e-01 3.93752247e-01 -2.51078337e-01 -1.01146758e+00 -1.13565493e+00 -5.68089843e-01 8.36314142e-01 2.31266975e-01 2.63450108e-02 -9.18499649e-01 -1.32262647e+00 -9.82342958e-02 6.38613105e-01 -5.14603972e-01 -6.65498525e-02 -4.09331471e-01 -8.60994399e-01 4.52964574e-01 3.97387028e-01 4.89259750e-01 -7.13379800e-01 -4.40938443e-01 3.47557127e-01 -2.95191407e-01 -1.02485049e+00 -8.78915608e-01 1.80189058e-01 -9.72424984e-01 -9.17688012e-01 -1.17352664e+00 -8.02593350e-01 1.05561161e+00 -1.49795637e-01 8.85911167e-01 -2.06905499e-01 -2.64934421e-01 2.85078138e-01 -2.74919689e-01 -4.97366220e-01 -5.50898492e-01 2.19701514e-01 -1.16390958e-01 4.50721309e-02 3.97326887e-01 -3.94374967e-01 -8.28874648e-01 5.25964677e-01 -9.13685203e-01 7.02556521e-02 7.45739341e-01 1.17176151e+00 1.18462479e+00 -1.87144831e-01 6.65494680e-01 -1.45038760e+00 2.95853376e-01 -5.32019794e-01 -6.27054751e-01 2.40481421e-01 -8.28963459e-01 -7.92048350e-02 4.15359527e-01 -4.94759977e-01 -1.18556225e+00 5.46323419e-01 -2.46228650e-01 -5.51296949e-01 -4.73641157e-01 3.39415699e-01 -2.55265445e-01 1.37271181e-01 9.07350779e-01 2.41665795e-01 2.53835529e-01 -4.65638697e-01 4.44140919e-02 8.25929224e-01 5.53701520e-01 -5.31300902e-01 4.76365209e-01 5.89572132e-01 -2.32940301e-01 -5.21680534e-01 -8.48033726e-01 -6.32552266e-01 -6.77316666e-01 2.22683787e-01 8.75035703e-01 -8.58393788e-01 -5.81969656e-02 4.19872224e-01 -9.10208285e-01 -3.79058838e-01 -4.75442082e-01 6.33819938e-01 -6.46699786e-01 4.82214153e-01 -3.74245882e-01 -2.56755203e-01 -2.36831173e-01 -1.50839710e+00 9.55191731e-01 2.38520667e-01 -3.73381555e-01 -1.02339149e+00 -3.03557906e-02 5.80854535e-01 1.31261334e-01 2.95708001e-01 9.78424847e-01 -1.12356305e+00 -4.24047142e-01 -8.52209926e-02 -3.01031530e-01 4.67075020e-01 5.05115390e-01 -5.71582437e-01 -9.32658315e-01 -3.19274545e-01 1.66394785e-01 -1.56524926e-01 5.88566363e-01 9.31767106e-01 1.37259614e+00 -3.30331832e-01 -5.49848735e-01 6.98065758e-01 1.12754667e+00 3.50473732e-01 4.52895671e-01 2.21500382e-01 5.85166514e-01 5.54552734e-01 1.03531289e+00 5.34656942e-01 4.04265463e-01 6.33334279e-01 -1.40433833e-02 -2.93149352e-01 -1.71003029e-01 -2.00144976e-01 -3.08635961e-02 5.16954124e-01 2.49490470e-01 -1.09228477e-01 -9.87299025e-01 7.62119949e-01 -1.65713012e+00 -3.62870425e-01 1.64392859e-01 2.20484257e+00 1.31707811e+00 5.72001049e-03 2.10911527e-01 -4.09507215e-01 7.82909572e-01 -1.78053260e-01 -9.42213655e-01 -1.05387881e-01 7.54479393e-02 2.07472041e-01 8.13971639e-01 2.46139169e-01 -1.30146182e+00 6.89053297e-01 5.56936121e+00 1.14386177e+00 -1.11754310e+00 3.65412414e-01 1.12726796e+00 5.60068414e-02 -2.46558085e-01 -2.19648466e-01 -7.09033549e-01 5.01299560e-01 7.87516356e-01 -2.53278792e-01 1.41441435e-01 1.03598583e+00 9.81544331e-02 -1.92445040e-01 -1.16481864e+00 1.00688303e+00 -1.24271676e-01 -1.32481372e+00 -1.22755900e-01 2.89358974e-01 8.82266641e-01 -4.59434791e-03 2.52548784e-01 4.85689305e-02 2.11518258e-01 -7.98769712e-01 3.16569209e-01 2.15024799e-01 1.14153826e+00 -4.79059547e-01 7.69578516e-01 3.25473517e-01 -6.83521867e-01 2.03424260e-01 -3.83933544e-01 6.19230747e-01 3.27227637e-02 8.20744693e-01 -1.27939093e+00 6.49015844e-01 6.71646476e-01 7.10975528e-01 -3.58442813e-01 9.79868352e-01 1.98680282e-01 7.28689432e-01 -2.72864550e-01 4.61610556e-01 5.07823192e-02 -1.23679355e-01 5.86608529e-01 1.07775152e+00 2.66122311e-01 1.84526712e-01 3.17822993e-01 8.24483514e-01 -1.33763969e-01 2.38739625e-01 -4.29718196e-01 3.76641527e-02 4.71636415e-01 1.06905055e+00 -8.53894293e-01 -3.87811422e-01 -2.99020171e-01 1.04613733e+00 -1.13358825e-01 3.11557978e-01 -7.85509586e-01 -7.31585696e-02 5.36023080e-01 2.31626526e-01 1.50346905e-01 2.90508658e-01 -6.16516709e-01 -9.74700153e-01 -6.43460304e-02 -1.09330130e+00 8.73188615e-01 -3.78464282e-01 -1.42855024e+00 6.69308841e-01 1.57752454e-01 -1.54586101e+00 -2.76755750e-01 -3.45272154e-01 -1.23170502e-01 9.49387193e-01 -1.50286269e+00 -9.68998373e-01 -6.65898323e-02 8.30046117e-01 5.61302006e-01 -1.93172961e-01 8.65920424e-01 4.69712615e-01 -4.42934871e-01 1.00628889e+00 3.04215610e-01 1.67785868e-01 1.11693203e+00 -1.08048999e+00 -5.45616262e-02 5.75710952e-01 -1.45313412e-01 3.93130779e-01 5.89460969e-01 -6.73749208e-01 -7.45201886e-01 -1.27208436e+00 5.33325195e-01 -4.66791481e-01 1.84190363e-01 -7.52437711e-02 -1.11695659e+00 7.76541114e-01 -7.24381357e-02 3.02860886e-01 8.25814366e-01 -3.47101212e-01 -6.16779178e-02 -1.90036401e-01 -1.65410197e+00 2.92678505e-01 8.47404420e-01 -2.73510665e-01 -3.80660564e-01 3.58185917e-01 6.23891592e-01 -7.76690006e-01 -1.09226966e+00 4.27775860e-01 2.81694084e-01 -5.98373175e-01 8.34747493e-01 -3.98224771e-01 2.73485273e-01 -2.62545943e-01 1.05068851e-02 -1.12156796e+00 -1.79117262e-01 -3.90617669e-01 2.52341926e-01 1.19908226e+00 6.28942609e-01 -7.49936938e-01 1.00263631e+00 8.68322313e-01 -7.72830620e-02 -6.48983717e-01 -1.03426671e+00 -6.29055500e-01 1.65794909e-01 -2.37173021e-01 5.49809635e-01 1.06894147e+00 -1.97448507e-01 -2.39561573e-01 -2.50501037e-01 3.72788101e-01 7.27621734e-01 2.52781250e-02 6.37643933e-01 -9.33446467e-01 -4.64712858e-01 -6.69906810e-02 -1.56270117e-01 -1.07590270e+00 -1.97303481e-02 -1.04871118e+00 1.63395733e-01 -1.35334349e+00 2.59616554e-01 -8.05388987e-01 -5.85346043e-01 5.86440444e-01 -2.76116520e-01 1.75987139e-01 -1.40312850e-01 4.16399777e-01 -4.47448552e-01 1.92017004e-01 1.54981351e+00 -1.34176403e-01 -3.42714816e-01 3.09251577e-01 -7.66269147e-01 6.68404400e-01 7.58257568e-01 -8.49920630e-01 -5.92502177e-01 -2.37125680e-01 -4.20977503e-01 1.52991042e-01 2.74744749e-01 -7.85361946e-01 1.98591247e-01 -2.57621199e-01 4.58454430e-01 -4.85288143e-01 -1.06199095e-02 -8.71015072e-01 6.21425994e-02 4.02413338e-01 -4.56771493e-01 -4.79344815e-01 1.83504835e-01 5.73310137e-01 -2.32760042e-01 -1.14461623e-01 1.04763710e+00 -1.19937755e-01 -6.76477075e-01 5.56672871e-01 -2.01621518e-01 2.69664556e-01 1.01699102e+00 -2.98724174e-01 8.14517029e-03 -2.19383880e-01 -9.19699609e-01 2.96471119e-01 4.67920512e-01 9.02045071e-02 5.48823237e-01 -1.08748722e+00 -6.92105770e-01 3.30888897e-01 2.78050989e-01 5.09665191e-01 5.86854815e-01 1.12103486e+00 -3.01960468e-01 2.46616691e-01 -2.01248124e-01 -8.85598660e-01 -1.13408971e+00 5.44435859e-01 3.57199818e-01 -5.79166353e-01 -6.20939910e-01 8.96190107e-01 7.18625307e-01 -7.03556716e-01 7.24927187e-02 -2.41720960e-01 1.39921412e-01 -2.11438969e-01 2.82452703e-01 1.69819519e-01 3.92418690e-02 -4.93740469e-01 -4.20575440e-01 3.50035936e-01 -5.85680902e-01 3.33045386e-02 1.14712000e+00 -3.07324290e-01 2.61702597e-01 -1.06509455e-01 1.22058320e+00 -9.27744508e-02 -1.62201965e+00 -6.33329451e-01 1.05794389e-02 -5.11552691e-01 -4.77303453e-02 -1.10481215e+00 -1.19261670e+00 6.77246571e-01 9.18697834e-01 -4.25487012e-01 1.48471999e+00 1.66377753e-01 9.62119579e-01 1.47364419e-02 2.92957544e-01 -1.17457700e+00 -9.51463208e-02 1.93393286e-02 5.55021763e-01 -1.51973927e+00 3.45443785e-02 -5.55743158e-01 -1.12308109e+00 7.54511654e-01 4.49257821e-01 7.30788037e-02 7.34216034e-01 1.42311752e-01 3.58777136e-01 1.32913098e-01 -2.18934193e-01 5.65358847e-02 4.18698519e-01 8.78623843e-01 5.28911762e-02 7.22289011e-02 -3.15612525e-01 9.46968377e-01 1.08269729e-01 3.67313862e-01 2.53244668e-01 8.63202572e-01 -8.25999212e-03 -1.52101862e+00 -4.07064348e-01 7.11348295e-01 -6.17703021e-01 5.54008484e-02 -3.76642719e-02 6.44075453e-01 2.62319952e-01 6.20635211e-01 -1.47543773e-01 -2.39671811e-01 3.23572367e-01 -6.08836450e-02 3.19558084e-01 -7.31784463e-01 -2.53959745e-01 3.52975160e-01 -1.27339050e-01 -4.77424413e-01 -3.84051412e-01 -9.33676481e-01 -1.51937461e+00 1.04115307e-01 -1.59427404e-01 -4.26337635e-03 3.80296528e-01 8.81297231e-01 6.52288735e-01 3.88350040e-01 6.68132126e-01 -3.61636639e-01 -5.61263382e-01 -6.46928728e-01 -6.14510596e-01 5.35055995e-01 3.10515702e-01 -5.47774851e-01 -4.79030721e-02 2.70955950e-01]	[14.60484790802002, -1.9961415529251099]
101fd148-63f0-40b9-9747-8b8f7d749014	unsupervised-light-field-depth-estimation-via	2301.08433	null	https://arxiv.org/abs/2301.08433v1	https://arxiv.org/pdf/2301.08433v1.pdf	Unsupervised Light Field Depth Estimation via Multi-view Feature Matching with Occlusion Prediction	Depth estimation from light field (LF) images is a fundamental step for some applications. Recently, learning-based methods have achieved higher accuracy and efficiency than the traditional methods. However, it is costly to obtain sufficient depth labels for supervised training. In this paper, we propose an unsupervised framework to estimate depth from LF images. First, we design a disparity estimation network (DispNet) with a coarse-to-fine structure to predict disparity maps from different view combinations by performing multi-view feature matching to learn the correspondences more effectively. As occlusions may cause the violation of photo-consistency, we design an occlusion prediction network (OccNet) to predict the occlusion maps, which are used as the element-wise weights of photometric loss to solve the occlusion issue and assist the disparity learning. With the disparity maps estimated by multiple input combinations, we propose a disparity fusion strategy based on the estimated errors with effective occlusion handling to obtain the final disparity map. Experimental results demonstrate that our method achieves superior performance on both the dense and sparse LF images, and also has better generalization ability to the real-world LF images.	['Edmund Y. Lam', 'Nan Meng', 'Shansi Zhang']	2023-01-20	null	null	null	null	['disparity-estimation', 'occlusion-handling']	['computer-vision', 'computer-vision']	[ 1.74924716e-01 -4.92562532e-01 -7.70496130e-02 -8.72244596e-01 -3.53392899e-01 1.43245384e-01 9.75148156e-02 -4.29143876e-01 -2.80092120e-01 7.13200510e-01 2.79154360e-01 1.91828847e-01 5.26355430e-02 -1.07277668e+00 -4.60402966e-01 -8.34512472e-01 7.14774847e-01 -1.99164152e-02 4.77977157e-01 2.08207548e-01 5.60544431e-01 3.30217570e-01 -1.84413207e+00 1.65461689e-01 1.15738034e+00 1.29731679e+00 5.12672961e-01 7.35744163e-02 -4.14501786e-01 8.23781431e-01 -2.41078600e-01 -2.07584500e-02 4.06289339e-01 -1.60204664e-01 -3.34570587e-01 2.97163606e-01 6.87499344e-01 -9.39680278e-01 -3.82163465e-01 1.25430942e+00 7.50476360e-01 -4.11056466e-02 4.16655868e-01 -1.14237130e+00 -2.28546456e-01 -6.25983030e-02 -8.63846302e-01 4.19677459e-02 3.64296675e-01 1.72166169e-01 4.66339409e-01 -9.94595170e-01 3.95542920e-01 1.35625374e+00 3.53549987e-01 2.86375195e-01 -8.92218649e-01 -9.52342093e-01 1.94885910e-01 4.76003557e-01 -1.35163188e+00 -5.99860072e-01 9.97309387e-01 -4.27573651e-01 5.51214159e-01 -3.28238249e-01 7.19002008e-01 4.59619850e-01 1.94274455e-01 6.68617666e-01 1.23765981e+00 -2.70041853e-01 -6.93584755e-02 -2.61476077e-02 -1.54404104e-01 8.26085389e-01 2.66055197e-01 3.05366814e-01 -5.43179035e-01 2.43714437e-01 1.11069334e+00 1.62750214e-01 -6.56869769e-01 -4.10914481e-01 -1.11493099e+00 7.21799314e-01 6.44652247e-01 -7.10815266e-02 -2.02480271e-01 1.30622098e-02 1.97892204e-01 7.56387226e-03 5.60436308e-01 -1.75363317e-01 -4.24441963e-01 1.64983571e-01 -5.42046666e-01 -5.64298593e-02 3.03045720e-01 8.74894619e-01 1.54197252e+00 -1.29124463e-01 4.55523692e-02 1.07878697e+00 5.57012081e-01 5.21908462e-01 3.87044340e-01 -1.14849746e+00 7.49294341e-01 8.08219492e-01 -6.93783239e-02 -1.22723114e+00 -3.42403740e-01 -2.43987799e-01 -9.93204176e-01 4.93936032e-01 1.76842108e-01 -6.33063093e-02 -8.32881749e-01 1.55401230e+00 5.22588849e-01 3.12494457e-01 -4.42122556e-02 1.15360665e+00 1.04088056e+00 7.33160198e-01 -1.64317355e-01 -4.47144419e-01 8.70867968e-01 -7.51645327e-01 -6.94440484e-01 -2.95301318e-01 2.14569196e-01 -9.28426981e-01 8.28953326e-01 5.17068624e-01 -1.02871144e+00 -8.16455364e-01 -1.11220574e+00 -4.30274218e-01 1.17828183e-01 2.29737237e-01 7.64166117e-01 2.33536780e-01 -7.50048876e-01 3.63555759e-01 -6.05204999e-01 4.32176422e-03 4.52708393e-01 5.31971455e-01 -2.01484025e-01 -5.13458192e-01 -1.10329914e+00 6.42910302e-01 5.29524684e-01 4.01571512e-01 -5.28338671e-01 -4.59877670e-01 -1.07138717e+00 -2.41267160e-01 1.14269212e-01 -8.18942130e-01 7.89112449e-01 -7.93468952e-01 -1.40880513e+00 7.63800025e-01 -4.23119009e-01 2.10473821e-01 3.04947436e-01 -3.42605785e-02 -2.71827877e-01 1.77508950e-01 2.56421268e-01 8.80771995e-01 6.23016775e-01 -1.22184312e+00 -1.10420012e+00 -4.73707497e-01 1.05507128e-01 5.81121206e-01 -1.04175821e-01 -4.44749981e-01 -4.88405883e-01 -8.21196064e-02 8.68533134e-01 -3.64060909e-01 -1.09973803e-01 3.37442994e-01 -2.33769268e-01 -1.32649496e-01 6.67906046e-01 -4.09805238e-01 1.02786779e+00 -2.06757545e+00 4.74468619e-02 1.81388974e-01 2.64481187e-01 8.10833722e-02 1.11063965e-01 -1.32772923e-01 1.66026220e-01 -5.47663093e-01 -1.69919074e-01 -2.83259749e-01 -5.36216497e-01 3.53506535e-01 3.13670328e-03 4.96590972e-01 -1.00999840e-01 4.09719467e-01 -8.68617833e-01 -8.00684333e-01 7.61490345e-01 3.82643670e-01 -6.36728942e-01 3.89779806e-01 -1.36901736e-01 6.95070088e-01 -6.07556880e-01 5.82948685e-01 1.16658461e+00 -1.76419809e-01 -3.64474833e-01 -7.02452123e-01 -2.97534078e-01 7.70644024e-02 -1.39219952e+00 2.04956222e+00 -5.95008612e-01 4.77518737e-01 -1.12528935e-01 -6.06889248e-01 1.20220780e+00 -1.29495353e-01 4.70502168e-01 -8.43024790e-01 2.63264507e-01 4.21623260e-01 -1.93385050e-01 -7.46484399e-01 1.49116829e-01 -1.52758643e-01 4.55177218e-01 1.97395429e-01 -1.47233948e-01 -2.38460302e-01 -1.22622810e-01 -2.05012023e-01 4.77196604e-01 2.81340480e-01 1.44364536e-01 -7.18952566e-02 1.17594361e+00 -4.22668129e-01 1.20075047e+00 6.28035367e-02 -9.99208316e-02 7.29744613e-01 1.35636432e-02 -6.95127010e-01 -7.56544769e-01 -9.88130093e-01 -4.57079649e-01 2.44183823e-01 8.30896199e-01 5.08884415e-02 -4.57102269e-01 -3.72200400e-01 -2.58810576e-02 1.51782762e-02 -2.00243041e-01 -1.72157660e-01 -5.53643167e-01 -5.78392565e-01 -1.38099015e-01 4.59676415e-01 1.33561659e+00 -9.37703252e-01 -2.19602913e-01 2.38627702e-01 -4.90228623e-01 -1.02766109e+00 -4.48512733e-01 -3.17738891e-01 -9.47173536e-01 -1.09952986e+00 -5.82424343e-01 -1.03151524e+00 8.85595739e-01 5.30330718e-01 7.85956919e-01 2.16892496e-01 -1.01396553e-01 -6.76821694e-02 1.22885495e-01 -1.88580990e-01 2.55837172e-01 -1.60514116e-01 -1.24570848e-02 2.24716291e-01 5.76535642e-01 -8.77086580e-01 -1.14116943e+00 5.39420307e-01 -8.53885710e-01 4.34850097e-01 5.48853815e-01 8.55559230e-01 8.49265039e-01 1.24756575e-01 4.04270887e-01 -5.87803066e-01 1.73754916e-01 -3.64795551e-02 -9.61414397e-01 6.36573927e-03 -7.20730960e-01 -1.29028752e-01 4.84948874e-01 -1.39743760e-01 -1.35456920e+00 2.13339373e-01 -2.85908133e-01 -4.75243837e-01 -9.88664180e-02 1.95894629e-01 -5.87373614e-01 -5.35219014e-01 3.13321173e-01 4.00185019e-01 2.05773525e-02 -3.89571011e-01 -3.46585698e-02 7.73933172e-01 5.23204148e-01 -4.35534924e-01 6.15353584e-01 6.98793530e-01 2.11166695e-01 -3.72542113e-01 -1.21420383e+00 -4.82208729e-01 -5.60452938e-01 -2.93628722e-01 9.98578966e-01 -1.36811960e+00 -8.62259269e-01 8.85405242e-01 -1.33310473e+00 6.25768825e-02 2.16738716e-01 8.21626246e-01 -5.73538959e-01 5.27860641e-01 -6.93249762e-01 -4.63289678e-01 -1.55365840e-01 -1.25233245e+00 1.09986019e+00 6.49903834e-01 6.10468209e-01 -9.88369644e-01 6.41031656e-03 2.77712137e-01 8.97114873e-02 6.11155592e-02 7.85954475e-01 5.32288194e-01 -1.25808704e+00 3.27133805e-01 -7.19972372e-01 5.01108885e-01 3.81099820e-01 -8.34092647e-02 -1.16951501e+00 -1.24203242e-01 3.48390728e-01 -1.93313852e-01 8.76277924e-01 6.77972138e-01 1.43960869e+00 1.69788748e-01 -2.34756678e-01 1.28589761e+00 1.91354871e+00 3.35941672e-01 9.27260995e-01 4.10323948e-01 9.81087446e-01 7.44541585e-01 8.27824712e-01 3.80794317e-01 6.89049006e-01 4.05354172e-01 4.17107612e-01 -1.31073713e-01 -1.90855414e-01 -5.52728176e-01 -5.01412936e-02 1.06946874e+00 9.59351435e-02 5.73135167e-02 -5.48984051e-01 1.65940210e-01 -1.78231311e+00 -8.08404446e-01 -2.08441883e-01 2.24996138e+00 7.72621334e-01 2.06399187e-01 -4.19312835e-01 1.14600519e-02 8.60332191e-01 1.96464524e-01 -7.59632945e-01 1.51212096e-01 -1.42178997e-01 -1.57777697e-01 3.68045628e-01 6.39991164e-01 -8.42739642e-01 7.68281400e-01 5.79852581e+00 8.14934611e-01 -1.17867136e+00 -1.26070052e-01 7.19832301e-01 1.28414452e-01 -5.25753021e-01 1.73451051e-01 -1.03084242e+00 7.25770235e-01 -1.16628550e-01 1.04926638e-01 3.69270116e-01 6.05708361e-01 3.10373336e-01 -5.87930381e-01 -9.34286356e-01 1.60433042e+00 1.93086237e-01 -1.16504371e+00 6.49337023e-02 3.98700237e-02 9.67018008e-01 2.41531711e-02 -1.85609326e-01 -1.66445091e-01 -6.60439804e-02 -6.91020548e-01 1.72198474e-01 6.86188281e-01 9.51516747e-01 -7.79216707e-01 8.20125163e-01 5.23530602e-01 -1.35079491e+00 -6.64653406e-02 -1.06696761e+00 -2.34260142e-01 3.05198580e-01 1.01485431e+00 -1.97241101e-02 6.02385879e-01 5.23401499e-01 1.34480429e+00 -3.56030256e-01 1.26059508e+00 -4.48182315e-01 -2.34667614e-01 -3.00599784e-01 2.74977475e-01 -1.42665878e-01 -6.03102267e-01 3.97650488e-02 3.58805954e-01 2.93916434e-01 2.35705897e-01 3.37307930e-01 7.37605333e-01 1.07404135e-01 2.00177997e-01 -5.63827634e-01 7.91609704e-01 5.68240345e-01 1.15737832e+00 -2.35524103e-01 -2.79606283e-01 -5.95389068e-01 7.71021843e-01 4.54308808e-01 3.45671713e-01 -5.08720219e-01 -4.81134534e-01 5.82247674e-01 1.01155313e-02 -9.21488553e-02 -1.70291383e-02 -3.14123005e-01 -1.47241664e+00 2.03435346e-01 -4.60946321e-01 2.39947572e-01 -1.21037066e+00 -1.39512992e+00 4.23934609e-01 -1.91687420e-01 -1.69331896e+00 -2.18570214e-02 -4.37837362e-01 -7.19351292e-01 1.16010451e+00 -2.18085718e+00 -7.70440698e-01 -1.04035640e+00 7.87123621e-01 3.43966097e-01 8.64587445e-03 2.87248611e-01 6.58425510e-01 -5.16195774e-01 1.90127447e-01 -1.22158967e-01 -1.16805872e-02 8.66200686e-01 -7.28155732e-01 -1.62116870e-01 6.37640178e-01 -3.87106925e-01 2.89646447e-01 1.17043473e-01 -5.84583521e-01 -9.63652730e-01 -9.42060113e-01 6.95767164e-01 1.94894373e-02 -7.59803131e-03 -2.21329015e-02 -8.02784026e-01 4.60569501e-01 -1.48863807e-01 1.33141533e-01 2.83638179e-01 -1.77232489e-01 -2.32344158e-02 -7.15153039e-01 -1.22687066e+00 3.41531783e-01 1.30094326e+00 -5.73538899e-01 -4.20065016e-01 2.69556850e-01 7.39119053e-01 -6.33412480e-01 -7.53387868e-01 7.26320386e-01 5.77031314e-01 -1.57825923e+00 9.69160259e-01 2.97883421e-01 6.67955339e-01 -7.01676071e-01 -7.69523829e-02 -9.95970726e-01 -2.65704125e-01 -2.49036867e-02 2.66870260e-01 1.30193460e+00 -1.35047466e-01 -1.03644431e+00 9.80360210e-01 5.19439220e-01 -1.61950558e-01 -8.27861547e-01 -8.20025384e-01 -3.66673797e-01 -3.35064590e-01 -1.70376703e-01 7.88239300e-01 7.60969579e-01 -4.89181310e-01 4.08545583e-01 -2.84930199e-01 2.27025822e-01 9.51871991e-01 4.47651714e-01 1.00073564e+00 -1.37115645e+00 -1.24817543e-01 -1.84784546e-01 -6.34819388e-01 -1.69089794e+00 2.04477862e-01 -5.79145670e-01 2.28528097e-01 -1.77654076e+00 1.93647325e-01 -7.67658055e-01 -1.83375016e-01 1.84734073e-02 -2.35331833e-01 1.16648696e-01 -3.30848843e-01 5.58281660e-01 -3.25960726e-01 7.92003691e-01 1.79861462e+00 8.03290959e-03 -1.28941983e-01 -1.08027317e-01 -2.81391323e-01 1.02307379e+00 5.91710210e-01 -1.77676082e-01 -6.62776172e-01 -9.16021705e-01 7.41141811e-02 2.70005107e-01 1.53804705e-01 -1.20822859e+00 4.15321589e-01 -3.09488237e-01 7.77111828e-01 -1.03869593e+00 4.75284547e-01 -8.64941359e-01 3.14262584e-02 3.12847883e-01 1.88428294e-02 -2.73888946e-01 -2.25392193e-01 4.67739701e-01 -4.75873172e-01 -1.81400791e-01 8.22556853e-01 -2.22997367e-01 -1.02547169e+00 9.66027498e-01 3.58240664e-01 -9.69537571e-02 8.61695707e-01 -4.92917597e-01 -4.73072499e-01 -3.48488718e-01 -2.42832452e-01 5.63476443e-01 7.63699293e-01 1.18477896e-01 1.02463675e+00 -1.53582501e+00 -4.90630388e-01 7.88495958e-01 3.03361624e-01 5.66496968e-01 5.25994837e-01 5.65423310e-01 -9.16207254e-01 1.13981269e-01 -4.38987106e-01 -9.22284603e-01 -1.00962174e+00 3.02179575e-01 5.11563838e-01 7.93966278e-02 -5.34780681e-01 8.27875137e-01 6.56995893e-01 -2.96416968e-01 2.53861070e-01 -2.15443939e-01 -2.95856357e-01 -3.89051348e-01 5.23383737e-01 2.70104110e-01 -1.93238616e-01 -4.57791954e-01 -1.42041668e-01 1.35933232e+00 3.72250974e-02 6.50096908e-02 1.12407470e+00 -5.93741596e-01 -3.92482877e-01 2.71603495e-01 1.48969746e+00 -8.00643563e-02 -1.55494392e+00 -4.94288862e-01 -5.91233850e-01 -1.19792092e+00 2.62164414e-01 -3.28034282e-01 -1.40337121e+00 1.17558146e+00 7.96284199e-01 -3.26925337e-01 1.38668942e+00 -4.40104008e-01 1.07978272e+00 2.29436815e-01 7.48737872e-01 -1.02294743e+00 5.29585918e-03 2.96618223e-01 4.63353962e-01 -1.59120405e+00 2.26794183e-01 -8.41800034e-01 -1.03361785e-01 1.24932575e+00 1.18074274e+00 -1.70222670e-01 7.98940778e-01 -1.01266727e-01 2.35307470e-01 -9.30565298e-02 -4.58125800e-01 -2.52773196e-01 4.97229211e-02 6.58178627e-01 1.02391370e-01 -4.57118481e-01 -3.40226442e-01 -1.49336040e-01 1.10596508e-01 2.11193204e-01 3.41845006e-01 5.70638180e-01 -8.27828169e-01 -1.21689129e+00 -1.93244562e-01 5.46156943e-01 -1.68164410e-02 -5.51789142e-02 3.43585163e-01 4.31585342e-01 6.07575834e-01 9.50684011e-01 2.56874084e-01 -4.77082878e-01 2.60647416e-01 -4.46779251e-01 6.43349946e-01 -6.96107209e-01 7.13441074e-02 4.64968868e-02 -2.20963612e-01 -6.64797306e-01 -8.19347799e-01 -3.54295462e-01 -1.20497382e+00 -3.27464968e-01 -4.60555375e-01 -2.24107847e-01 4.85538691e-01 9.09879863e-01 1.45433500e-01 2.06301764e-01 1.15381670e+00 -7.96762764e-01 -7.76679516e-02 -6.26157641e-01 -7.18469262e-01 4.37946200e-01 2.85659283e-01 -8.49895597e-01 -6.19185925e-01 -2.16464981e-01]	[9.178482055664062, -2.4517691135406494]

Subsets and Splits

No community queries yet

The top public SQL queries from the community will appear here once available.