Hugging Face's logo

Datasets:
J0nasW
/
paperswithcode

Tasks:
Text Classification
Feature Extraction
Languages:
English
Size Categories:
10K<n<100K
Tags:
License:
Dataset card Viewer Files Community
1
Dataset Viewer
Auto-converted to Parquet
Viewer
paperID
stringlengths
36
36
pwc_id
stringlengths
8
47
arxiv_id
stringlengths
6
16
⌀
nips_id
null
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
nulllengths
12
47
⌀
conference_url_abs
nulllengths
16
198
⌀
conference_url_pdf
nulllengths
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
91de2389-e4fa-47a8-b1d8-e711c5f1c68f
neural-concept-formation-in-knowledge-graphs
null
null
https://openreview.net/forum?id=V61-62OS4mZ
https://openreview.net/pdf?id=V61-62OS4mZ
Neural Concept Formation in Knowledge Graphs
In this work, we investigate how to learn novel concepts in Knowledge Graphs (KGs) in a principled way, and how to effectively exploit them to produce more accurate neural link prediction models. Specifically, we show how concept membership relationships learned via unsupervised clustering of entities can be reified and used to augment a KG. In a thorough set of experiments, we confirm that neural link predictors trained on these augmented KGs, or in a joint Expectation-Maximization iterative scheme, can generalize better and produce more accurate predictions for infrequent relationships. For instance, our method yields relative improvements of up to 8.6% MRR on WN18RR for rare predicates, and up to 82% in small-data regimes, where the model has access to just a small subset of the training triples. Furthermore, our proposed models are able to learn meaningful concepts.
['Pasquale Minervini', 'Antonio Vergari', 'Agnieszka Dobrowolska']
2021-06-22
null
null
null
akbc-2021-10
['novel-concepts']
['reasoning']
[ 1.54296324e-01 8.67769837e-01 -6.80537403e-01 -6.45391524e-01 -2.36760721e-01 -3.32420886e-01 3.21055084e-01 4.21708882e-01 -3.44073683e-01 1.20781672e+00 1.37622237e-01 -2.46026158e-01 -3.62160653e-01 -1.14378023e+00 -1.16703248e+00 -1.55001879e-01 -5.35619795e-01 7.03670561e-01 2.03727037e-01 -1.64004818e-01 -3.04270357e-01 3.12875748e-01 -1.26758432e+00 1.52926981e-01 1.31287313e+00 7.20617175e-01 -1.09426551e-01 2.76628315e-01 -7.17689320e-02 8.86690259e-01 -1.94362357e-01 -7.91421711e-01 -3.12800333e-02 -6.36593476e-02 -1.04167628e+00 -3.78318071e-01 4.07629371e-01 -1.51912794e-01 -4.75584954e-01 9.76112962e-01 9.73426402e-02 4.17182267e-01 6.79234982e-01 -9.45565283e-01 -1.05270863e+00 1.45925033e+00 -5.18380880e-01 3.15076470e-01 1.30442619e-01 -4.22742695e-01 1.61547422e+00 -9.14651453e-01 9.03729916e-01 1.07008874e+00 7.71530569e-01 4.86143947e-01 -1.49955952e+00 -8.76754344e-01 3.35886389e-01 4.21128839e-01 -1.37951922e+00 -3.04418683e-01 6.91665113e-01 -1.40967324e-01 1.10036898e+00 -1.47485942e-01 6.11174881e-01 8.83570611e-01 -2.07112491e-01 7.68143296e-01 6.13160074e-01 -4.40173090e-01 4.40081917e-02 2.02845171e-01 5.03733337e-01 8.19510043e-01 9.97105539e-01 1.28607079e-02 -8.50353420e-01 -7.48232082e-02 4.36459810e-01 -3.25672150e-01 -2.30837017e-01 -3.84809136e-01 -1.03440022e+00 7.87420809e-01 8.80579710e-01 3.34300369e-01 -3.43248546e-01 3.60578001e-01 5.27294353e-02 7.79926032e-02 6.21679604e-01 8.45471740e-01 -8.41132820e-01 1.47370607e-01 -7.24724770e-01 2.71667819e-02 7.90864646e-01 1.06612074e+00 9.77211475e-01 1.85516886e-02 6.27979338e-02 7.68414617e-01 1.94397017e-01 3.10206950e-01 2.56271183e-01 -6.02345586e-01 6.16449296e-01 4.77654159e-01 -1.91528946e-01 -1.02812564e+00 -5.25404394e-01 -7.59335101e-01 -5.87106884e-01 -6.03836834e-01 2.17561081e-01 -4.65853423e-01 -9.66696322e-01 2.04345345e+00 1.50942802e-01 5.17866075e-01 4.00926024e-01 3.01904559e-01 7.77806520e-01 3.28952134e-01 3.65907848e-01 -1.96941271e-01 9.30962086e-01 -5.95543325e-01 -5.41981816e-01 -2.33600602e-01 9.91550744e-01 -8.27136729e-03 6.31804049e-01 1.82377279e-01 -9.30660248e-01 -4.60798115e-01 -1.07936931e+00 3.40950377e-02 -5.14004827e-01 -3.65788490e-02 1.26865208e+00 4.62745547e-01 -1.10847092e+00 8.77874911e-01 -8.69197667e-01 -4.82717782e-01 8.62363040e-01 7.14474678e-01 -3.23161334e-01 -1.86642066e-01 -1.58791888e+00 8.14880192e-01 1.20535064e+00 -5.23759723e-02 -5.09980917e-01 -9.28665102e-01 -8.35100055e-01 3.43633145e-01 6.42029226e-01 -8.62521708e-01 6.27496004e-01 -5.37919700e-01 -1.04599440e+00 5.04348338e-01 -1.45569175e-01 -8.74333978e-01 -1.43613338e-01 -4.02217209e-01 -7.43405998e-01 1.36340320e-01 -7.74390176e-02 8.94989908e-01 3.86455685e-01 -1.30753803e+00 -7.88181961e-01 -2.49446020e-01 5.09227440e-02 1.61409289e-01 -8.04922163e-01 -4.68311995e-01 -2.91059136e-01 -5.53481460e-01 1.76352739e-01 -9.02530432e-01 -2.24313065e-02 -5.77306271e-01 -7.02638030e-01 -3.60121846e-01 4.68798280e-01 -5.36043942e-01 1.11379886e+00 -1.81866813e+00 1.95922747e-01 7.23736763e-01 5.10090351e-01 1.94408774e-01 -1.31532913e-02 1.62100479e-01 -1.97800353e-01 3.95630598e-01 -2.61885691e-02 2.04668287e-02 -7.90916830e-02 5.08852065e-01 -3.69455844e-01 -2.30955869e-01 3.25003028e-01 1.28797996e+00 -1.00783503e+00 -3.01557630e-01 -4.68648106e-01 1.23460189e-01 -6.54415190e-01 -2.86641836e-01 -4.41590607e-01 3.13007176e-01 -5.49597025e-01 6.68556511e-01 2.70766467e-01 -5.48367858e-01 7.51038730e-01 -3.14791858e-01 6.59715414e-01 4.54471290e-01 -8.24497521e-01 1.45373321e+00 -1.20974056e-01 6.72743678e-01 -6.43171966e-01 -1.38152456e+00 9.33336675e-01 4.73608309e-03 3.84570032e-01 -4.42687869e-01 -2.21639216e-01 2.02256456e-01 2.14500889e-01 -2.38965303e-01 5.94687819e-01 -9.98684540e-02 2.27492034e-01 2.21830308e-01 4.33053583e-01 4.94519830e-01 3.57395291e-01 5.06468236e-01 1.07102311e+00 -1.14398122e-01 3.67573023e-01 -1.16704486e-01 1.14721619e-01 4.19650562e-02 7.10421264e-01 8.32538605e-01 2.70909697e-01 -1.37723774e-01 4.99238759e-01 -1.91684350e-01 -7.08858490e-01 -1.20476747e+00 -2.49985337e-01 1.02314448e+00 5.10911569e-02 -4.05840158e-01 -1.13610208e-01 -9.99766946e-01 4.59419042e-01 1.05909431e+00 -5.72944820e-01 -6.10589683e-01 -5.02169073e-01 -1.01878309e+00 7.17326105e-01 9.68356133e-01 3.88936669e-01 -8.61568630e-01 2.62872219e-01 1.13766767e-01 1.02514826e-01 -1.25865257e+00 1.83710784e-01 4.58674014e-01 -1.26058197e+00 -9.43304598e-01 -5.89302368e-02 -8.01832438e-01 9.06691194e-01 -1.88905209e-01 1.24588275e+00 1.49115533e-01 1.42888993e-01 1.82237208e-01 -4.32926685e-01 -2.27361023e-01 -1.09749354e-01 3.93429935e-01 4.00368392e-01 -1.90480947e-01 6.45508707e-01 -9.80652571e-01 -9.14673582e-02 -5.65535687e-02 -5.86565554e-01 -9.32201296e-02 7.59984612e-01 9.54744756e-01 5.15304089e-01 4.26933765e-01 1.13403392e+00 -1.57007623e+00 5.62486291e-01 -6.95050597e-01 -3.30995291e-01 5.01288414e-01 -1.23084748e+00 3.75261724e-01 4.41573203e-01 -3.76560539e-01 -1.13326442e+00 -1.95027851e-02 1.94081962e-01 -1.87404394e-01 2.91815810e-02 1.17867815e+00 -1.09369077e-01 -1.19122401e-01 7.84758449e-01 -6.08246848e-02 -4.76921827e-01 -4.08787698e-01 9.31821227e-01 2.41673291e-01 6.30088329e-01 -9.80669677e-01 1.20522916e+00 2.30419621e-01 6.64009377e-02 -4.72127557e-01 -1.04533947e+00 -2.78203607e-01 -8.91273022e-01 1.51716396e-01 5.55257857e-01 -1.06172097e+00 -5.65568566e-01 -8.92939791e-02 -6.94038808e-01 -3.63962263e-01 -2.97772855e-01 7.93357790e-01 -1.80166990e-01 1.71667278e-01 -5.90228856e-01 -3.91574383e-01 -1.75156951e-01 -2.76097000e-01 2.53751934e-01 5.12196124e-01 -1.41901657e-01 -1.28443336e+00 -1.53903857e-01 4.18394715e-01 -2.59381570e-02 4.86373417e-02 1.47316492e+00 -1.27834928e+00 -6.74186289e-01 3.04106716e-02 -3.29404473e-01 1.25903085e-01 3.88705194e-01 -9.04032961e-02 -8.30075085e-01 -1.09632544e-01 -8.62295330e-01 -4.33192015e-01 1.29071057e+00 1.74776539e-01 1.23253644e+00 -3.04897338e-01 -9.52184498e-01 4.93061393e-01 1.15610719e+00 9.89194512e-02 4.49044019e-01 -5.40439300e-02 9.82153535e-01 4.69989717e-01 5.79279065e-01 -5.95825166e-02 6.24829471e-01 4.12507474e-01 7.48787671e-02 1.32651240e-01 -2.24249288e-02 -5.93027651e-01 4.14429158e-02 8.15588772e-01 -5.42255640e-01 -1.30858913e-01 -1.00525105e+00 7.40320206e-01 -1.97961426e+00 -8.60420823e-01 -8.69478285e-02 2.05713773e+00 1.40398097e+00 3.02619755e-01 -1.42530024e-01 -2.39827514e-01 6.78513348e-01 -1.99671566e-01 -7.08040118e-01 1.05778769e-01 -2.64913857e-01 4.85145956e-01 6.77855670e-01 3.18840325e-01 -9.44445312e-01 1.35345662e+00 7.08312511e+00 6.73294306e-01 -5.95649600e-01 -2.96677854e-02 3.53818387e-01 1.63599011e-02 -6.42089248e-01 1.66712403e-01 -9.88406420e-01 7.53943473e-02 9.96413112e-01 -2.68907189e-01 4.44938183e-01 7.85928130e-01 -3.94810557e-01 6.89831227e-02 -1.34594655e+00 5.64482152e-01 3.73541526e-02 -1.57672620e+00 3.19259524e-01 7.48553500e-02 1.21299374e+00 2.81192549e-02 2.08814722e-03 6.60464644e-01 8.88658285e-01 -1.05016327e+00 4.90959063e-02 6.20189309e-01 3.70458782e-01 -9.39867675e-01 7.14664042e-01 2.07789108e-01 -9.38364923e-01 -1.23287246e-01 -5.76673090e-01 1.54706359e-01 -7.37582967e-02 8.69483292e-01 -1.39433372e+00 9.37294960e-01 6.01992607e-01 1.08688664e+00 -7.39404619e-01 8.16554487e-01 -5.44497728e-01 9.57522929e-01 -3.42500776e-01 -4.31663431e-02 -8.05666596e-02 2.06994981e-01 3.58432025e-01 9.13303137e-01 1.63250998e-01 2.11941361e-01 5.70286922e-02 9.96942461e-01 -6.97220802e-01 4.33510020e-02 -5.58266819e-01 -5.24971604e-01 7.08471715e-01 1.07956135e+00 -5.83750069e-01 -3.82000744e-01 -4.40154821e-01 5.64417720e-01 1.06346142e+00 8.27485025e-01 -6.86698973e-01 -3.63645315e-01 4.93245721e-01 -9.92116407e-02 6.11651063e-01 -2.56308287e-01 -2.63826758e-01 -1.25542200e+00 -1.71062350e-01 -2.99697906e-01 6.45431399e-01 -7.45586395e-01 -1.47498977e+00 1.87445119e-01 2.14035988e-01 -4.94934112e-01 -3.54823381e-01 -5.97458482e-01 -5.02124786e-01 5.44774175e-01 -1.69775343e+00 -1.18025732e+00 4.02582213e-02 4.59502250e-01 -1.96929350e-01 -4.06777531e-01 7.57498205e-01 3.21255147e-01 -5.54865956e-01 9.62671816e-01 2.62274981e-01 4.49635327e-01 5.38924098e-01 -1.38547945e+00 2.54017264e-01 7.96475768e-01 8.35458219e-01 9.81095433e-01 4.75635022e-01 -1.09748077e+00 -9.66505110e-01 -1.34278750e+00 9.52201784e-01 -4.46191311e-01 8.06317091e-01 -1.16252832e-01 -1.20312989e+00 1.26185572e+00 -2.93314606e-01 1.30156785e-01 9.23527598e-01 1.10293770e+00 -6.50380850e-01 -2.22976774e-01 -9.18996096e-01 5.33542991e-01 1.44935024e+00 -4.90660012e-01 -9.06370819e-01 3.89639974e-01 9.86829460e-01 -2.10070089e-01 -1.32389522e+00 7.24486113e-01 2.85405427e-01 -4.26681221e-01 1.12417591e+00 -1.27767611e+00 3.83528501e-01 -2.18110293e-01 -1.94154844e-01 -1.49427414e+00 -3.59053284e-01 -2.92581737e-01 -8.06422353e-01 1.31825745e+00 1.07479000e+00 -7.94658184e-01 9.69575405e-01 4.68435764e-01 -1.14480570e-01 -7.61017263e-01 -5.83444417e-01 -8.00133348e-01 5.25152199e-02 -3.71922493e-01 5.36576450e-01 1.35025561e+00 3.49829584e-01 4.41470087e-01 -2.45611489e-01 4.71508175e-01 6.68380797e-01 1.46476433e-01 4.02039200e-01 -1.63718617e+00 -4.17120069e-01 -7.55878687e-02 -4.84834641e-01 -9.68418479e-01 6.44935191e-01 -1.37089276e+00 -2.21747056e-01 -1.60218894e+00 4.37171489e-01 -8.28386426e-01 -6.96125388e-01 8.90597343e-01 -5.53557575e-01 1.96182698e-01 -1.26641899e-01 8.43285322e-02 -6.74411595e-01 5.01221120e-01 1.00946665e+00 -2.80913591e-01 -2.73510277e-01 -6.45409599e-02 -1.08305252e+00 6.70156717e-01 7.44541049e-01 -5.66651940e-01 -8.06323826e-01 -2.67252326e-01 6.82683766e-01 -3.49197924e-01 3.48483920e-01 -8.29589069e-01 3.66674691e-01 -7.15344101e-02 7.11704433e-01 -4.60257471e-01 3.52368861e-01 -5.20030975e-01 -1.52743766e-02 1.69368714e-01 -5.84572971e-01 -6.24992132e-01 2.79017925e-01 1.14597070e+00 -8.97745416e-02 -1.92695335e-01 2.35125914e-01 1.28646165e-01 -9.48861659e-01 2.37733513e-01 2.47301668e-01 2.78479904e-01 8.73433948e-01 -6.89989179e-02 -5.99299669e-01 -3.44419897e-01 -1.12681186e+00 4.47020501e-01 1.59178879e-02 2.92811215e-01 4.95429695e-01 -1.54005945e+00 -4.87403393e-01 -1.52952388e-01 2.35162929e-01 1.23846568e-01 -2.33451594e-02 7.23745942e-01 2.81478814e-03 6.68254972e-01 -2.70230789e-02 -2.88260788e-01 -9.04641330e-01 4.81456101e-01 1.36109233e-01 -3.05560052e-01 -5.91322184e-01 1.09426725e+00 -2.30183899e-02 -3.71556967e-01 1.21164225e-01 -5.31019807e-01 -3.54247123e-01 -5.00646606e-03 1.14729084e-01 3.06716021e-02 -1.34680584e-01 -3.26807499e-01 -2.74597287e-01 2.62081534e-01 -5.15764534e-01 3.43700886e-01 1.46795702e+00 9.15250406e-02 2.66180728e-02 3.29140067e-01 8.28970134e-01 1.71726003e-01 -9.23688054e-01 -6.11876130e-01 4.13673520e-01 -1.16939045e-01 1.01354485e-02 -9.56775308e-01 -1.06665838e+00 2.95451552e-01 -1.16858445e-01 1.51363283e-01 8.96863818e-01 4.24597412e-01 6.72755897e-01 1.01220989e+00 3.42504978e-01 -1.00818384e+00 -9.78352949e-02 4.04584169e-01 2.96439052e-01 -1.13240910e+00 2.15809584e-01 -8.34400356e-01 -5.33152997e-01 9.03493345e-01 7.60529160e-01 6.26539737e-02 7.43508875e-01 -1.69315204e-01 -6.46621466e-01 -3.40695024e-01 -8.76147807e-01 -3.18750352e-01 6.22526228e-01 9.33897316e-01 2.59395123e-01 2.29645669e-01 -3.21698897e-02 8.84250641e-01 -3.22278649e-01 -1.15301743e-01 3.41280222e-01 4.52435374e-01 -5.20468473e-01 -1.04318523e+00 2.18967631e-01 9.36176836e-01 -1.98886320e-01 -3.69702935e-01 -4.25455540e-01 8.59742522e-01 9.27565545e-02 7.64972091e-01 2.55066180e-03 -6.81974113e-01 2.15373635e-02 4.73074853e-01 3.95681113e-01 -9.20711756e-01 2.20516864e-02 -5.97416639e-01 6.34780288e-01 -3.86187017e-01 -6.01859868e-01 -5.40972769e-01 -1.65364695e+00 -2.44568005e-01 -5.61599672e-01 1.68804213e-01 8.18842426e-02 1.25267923e+00 4.31552380e-01 6.51032031e-01 1.66343600e-01 3.28723341e-02 -3.05556953e-01 -9.42459345e-01 -8.01519036e-01 2.52806723e-01 -1.65566877e-01 -9.11929011e-01 -9.35374573e-02 5.56564108e-02]
[8.88937759399414, 7.979780673980713]
5657ebcc-0485-4be6-9c31-8ca3fb8c111a
a-large-scale-study-of-language-models-for
1804.01849
null
http://arxiv.org/abs/1804.01849v1
http://arxiv.org/pdf/1804.01849v1.pdf
A Large-Scale Study of Language Models for Chord Prediction
We conduct a large-scale study of language models for chord prediction. Specifically, we compare N-gram models to various flavours of recurrent neural networks on a comprehensive dataset comprising all publicly available datasets of annotated chords known to us. This large amount of data allows us to systematically explore hyper-parameter settings for the recurrent neural networks---a crucial step in achieving good results with this model class. Our results show not only a quantitative difference between the models, but also a qualitative one: in contrast to static N-gram models, certain RNN configurations adapt to the songs at test time. This finding constitutes a further step towards the development of chord recognition systems that are more aware of local musical context than what was previously possible.
['Filip Korzeniowski', 'David R. W. Sears', 'Gerhard Widmer']
2018-04-05
null
null
null
null
['chord-recognition']
['audio']
[ 1.31269753e-01 -1.49378553e-01 -1.12563297e-02 -5.72638437e-02 -6.83169484e-01 -1.00801635e+00 3.60530078e-01 -4.51352932e-02 -5.17974138e-01 4.59509373e-01 5.72915971e-01 -2.67934382e-01 -1.70638099e-01 -6.52473748e-01 -2.88768828e-01 -5.65615356e-01 -3.11165273e-01 6.61789775e-01 5.04404008e-01 -7.02310681e-01 4.10270900e-01 5.75588405e-01 -1.48328602e+00 4.05647784e-01 -7.73190930e-02 5.12066960e-01 1.17409259e-01 1.04943848e+00 2.16723934e-01 8.93398046e-01 -5.56805134e-01 -2.02781036e-01 2.07842022e-01 -7.14278162e-01 -9.40511882e-01 -4.52200830e-01 1.72453314e-01 2.57465690e-01 -3.03477645e-01 7.09769487e-01 7.43931890e-01 4.58440155e-01 5.56505919e-01 -4.78914171e-01 -3.54028970e-01 1.25539529e+00 -1.11388443e-02 3.21812689e-01 1.89961195e-01 2.43172765e-01 1.46057296e+00 -5.38937986e-01 7.32476175e-01 8.33171487e-01 1.01066327e+00 6.32754445e-01 -1.08668840e+00 -6.31997168e-01 -2.42333099e-01 1.52848348e-01 -1.28907251e+00 -4.97175694e-01 9.56564546e-01 -4.10863191e-01 1.11759186e+00 3.86183262e-01 9.78722215e-01 1.09604633e+00 -6.82775006e-02 5.41351736e-01 9.20548081e-01 -6.83233678e-01 -8.68554935e-02 -2.07361609e-01 1.25843525e-01 3.75627398e-01 -3.64612699e-01 2.37540185e-01 -7.75011003e-01 -1.15818709e-01 9.25716758e-01 -5.61442435e-01 -1.81221485e-01 -2.28258207e-01 -1.30872965e+00 6.02233946e-01 1.07921489e-01 7.85908222e-01 -2.01235622e-01 1.69225007e-01 8.56428504e-01 5.39795220e-01 6.70252219e-02 1.01381326e+00 -7.46257603e-01 -7.83511937e-01 -1.04718947e+00 3.43406677e-01 9.68120754e-01 2.94854850e-01 1.75931394e-01 4.91360992e-01 -4.52591032e-02 1.04295957e+00 -1.34237587e-01 -1.68231111e-02 9.45104241e-01 -9.13604319e-01 1.33674458e-01 3.14702779e-01 -5.50451159e-01 -7.91766524e-01 -4.97685701e-01 -8.62807214e-01 -5.97969592e-01 7.03033581e-02 6.68030500e-01 9.06473026e-04 -4.53650773e-01 1.85672808e+00 -1.96430430e-01 8.07841643e-05 -2.83565577e-02 6.05708420e-01 5.20456493e-01 3.73366296e-01 -2.39853665e-01 -1.61607891e-01 1.05505860e+00 -7.78208792e-01 -3.00843954e-01 1.48419246e-01 7.49439418e-01 -9.21212554e-01 1.33843100e+00 7.64374793e-01 -1.06492615e+00 -6.63830340e-01 -1.03292084e+00 1.19196475e-01 -2.18298241e-01 3.51417325e-02 6.90066636e-01 6.33679271e-01 -1.10804474e+00 9.35379028e-01 -4.85601872e-01 -4.15473491e-01 -2.86976337e-01 4.23222244e-01 -1.18279994e-01 6.62367046e-01 -1.28174007e+00 7.28535354e-01 6.60140872e-01 1.18579485e-01 -7.62506425e-01 -4.29677695e-01 -2.94580877e-01 1.44729227e-01 4.31812197e-01 -2.07738236e-01 1.69718277e+00 -9.34323132e-01 -1.63722897e+00 8.35450828e-01 1.76936179e-01 -5.94049215e-01 2.39139780e-01 -5.00658937e-02 -3.07195812e-01 -2.15690434e-01 -3.82817447e-01 3.91357422e-01 2.51582146e-01 -9.06759024e-01 -3.22900534e-01 -9.02993754e-02 -1.65730566e-01 8.51861835e-02 -2.94407040e-01 2.31358141e-01 -4.36261892e-01 -1.01730382e+00 2.81425156e-02 -1.23689067e+00 -2.92337954e-01 -7.01651454e-01 -5.30391216e-01 -2.60758042e-01 3.46663535e-01 -5.02110064e-01 1.62622869e+00 -1.90039551e+00 3.46180618e-01 3.86857510e-01 -2.19816282e-01 3.65125477e-01 -1.53313756e-01 7.70112514e-01 -2.67436892e-01 2.75151227e-02 -3.52043547e-02 -9.20425132e-02 -1.72214225e-01 1.05947345e-01 -6.40409052e-01 -4.75772917e-02 -2.46950328e-01 8.10916364e-01 -5.68893194e-01 -1.92889050e-01 -1.00450426e-01 3.93333852e-01 -6.09071732e-01 7.20130354e-02 -4.35132653e-01 3.96552771e-01 -7.13579953e-02 4.15550113e-01 -4.38571334e-01 -7.98689872e-02 4.80161756e-01 1.30411357e-01 -1.94330007e-01 7.39332974e-01 -9.54098642e-01 1.67928040e+00 -2.78918326e-01 7.40936100e-01 -5.71908057e-01 -8.07934761e-01 1.04196012e+00 5.75052917e-01 4.68866587e-01 -4.35469419e-01 1.51401252e-01 2.85347342e-01 6.72387838e-01 -1.21668115e-01 1.00752664e+00 -3.43690187e-01 -2.81916618e-01 5.77535987e-01 2.24555969e-01 6.07091449e-02 3.63688827e-01 -1.53261587e-01 1.03517532e+00 2.37000704e-01 3.40056062e-01 -1.37236759e-01 3.54095191e-01 -1.91935956e-01 6.24972939e-01 9.50802624e-01 1.11478731e-01 7.66633928e-01 5.56288481e-01 -5.71411431e-01 -1.04244328e+00 -6.75786614e-01 3.62609513e-02 1.45484245e+00 -6.73551619e-01 -8.64257991e-01 -6.41723931e-01 -2.46246696e-01 -3.98431659e-01 3.36100370e-01 -7.53008842e-01 -6.83619082e-02 -8.86928499e-01 -4.74218041e-01 1.28907764e+00 6.87972128e-01 -1.40085826e-02 -1.68874574e+00 -6.08267546e-01 3.48665357e-01 -1.20301964e-02 -8.26220810e-01 -2.77439386e-01 5.85840702e-01 -8.87646258e-01 -1.09604967e+00 -5.43635309e-01 -7.75441527e-01 -1.73591152e-01 -2.37055361e-01 1.29162216e+00 1.79257452e-01 -1.31619588e-01 4.23982106e-02 -4.41678762e-01 -3.89242917e-01 -6.05476499e-01 7.28075385e-01 1.28893465e-01 -4.91069645e-01 2.37437233e-01 -8.88184011e-01 -2.17262372e-01 1.70029446e-01 -6.84972346e-01 -1.17349967e-01 5.13470888e-01 6.23253286e-01 5.68349004e-01 -1.46878988e-01 6.37597442e-01 -9.32132661e-01 9.64036226e-01 -1.83061033e-01 -4.69675690e-01 2.97120005e-01 -4.46417749e-01 -3.71290706e-02 8.16959023e-01 -7.14485765e-01 -4.78425890e-01 1.20366784e-02 -4.29954588e-01 -1.29997984e-01 -2.70289667e-02 7.08586097e-01 9.93736014e-02 7.82076493e-02 8.23793948e-01 3.16028327e-01 -3.50252122e-01 -7.11565077e-01 2.12322235e-01 4.77960408e-01 8.43864858e-01 -8.30152750e-01 7.26119399e-01 -1.31604418e-01 4.79460731e-02 -1.04737043e+00 -5.30141652e-01 -3.49541247e-01 -9.61238086e-01 -3.39869678e-01 5.82751751e-01 -5.37012458e-01 -8.16820800e-01 3.72393847e-01 -7.17267096e-01 -6.28719032e-01 -4.37180579e-01 3.18538249e-01 -7.68511176e-01 3.46652836e-01 -1.02327251e+00 -9.52465951e-01 -3.21774423e-01 -9.87948298e-01 4.55711961e-01 7.10207149e-02 -8.12365949e-01 -1.01405632e+00 7.39211857e-01 1.31844863e-01 4.00052249e-01 -5.02625406e-02 1.07993937e+00 -1.00088215e+00 -3.87870342e-01 -1.27801597e-01 3.72530282e-01 2.38712966e-01 2.41400618e-02 3.54299575e-01 -1.05175054e+00 -1.25978604e-01 -2.64176071e-01 -5.76773703e-01 8.65834773e-01 2.47307256e-01 1.15911746e+00 4.92239743e-02 1.31307468e-01 5.02974987e-01 9.87766087e-01 3.10376912e-01 4.64264631e-01 6.18092537e-01 5.52635372e-01 3.70370358e-01 4.97695625e-01 2.58715421e-01 1.49425283e-01 9.69816685e-01 5.90875633e-02 2.45660678e-01 -6.71810806e-02 -4.67633575e-01 3.17761928e-01 1.43757463e+00 -4.91490573e-01 -6.48636594e-02 -1.10645211e+00 6.52202904e-01 -1.77166343e+00 -1.11391151e+00 2.10660651e-01 2.28169131e+00 9.28531468e-01 4.84306037e-01 8.08876038e-01 6.88983083e-01 1.98852181e-01 3.27433228e-01 -2.51252741e-01 -5.71826398e-01 -1.96424335e-01 5.24115860e-01 9.52888876e-02 2.99970359e-01 -8.80585134e-01 1.18838775e+00 7.55928850e+00 8.39303970e-01 -1.27428973e+00 -3.88047427e-01 1.69180840e-01 -3.80335689e-01 -2.53506154e-01 1.27154395e-01 -6.28138244e-01 1.87303409e-01 1.38823044e+00 -3.39375176e-02 7.20916152e-01 6.40985131e-01 1.03702746e-01 3.53453577e-01 -1.02559662e+00 4.82551485e-01 2.92912107e-02 -1.55775607e+00 9.13169608e-02 1.32509479e-02 3.93635333e-01 3.39759052e-01 -9.62274848e-04 4.83925819e-01 3.67505044e-01 -1.09979773e+00 7.89215207e-01 5.43741763e-01 5.57027459e-01 -9.38796282e-01 4.18402046e-01 3.42730343e-01 -1.18788123e+00 -1.59711331e-01 -2.39133105e-01 -3.07849795e-01 1.63378894e-01 7.58380666e-02 -9.83530283e-01 3.48070592e-01 6.71414375e-01 6.09257102e-01 -7.50527442e-01 9.89582717e-01 -1.27165481e-01 1.04835403e+00 -1.72469050e-01 -1.09156139e-01 7.97745734e-02 -9.20548365e-02 6.72003865e-01 1.43422687e+00 2.01341629e-01 -1.23447493e-01 -1.01718847e-02 4.02876645e-01 6.99502081e-02 3.57700139e-01 -5.80821395e-01 -4.07136738e-01 3.74957442e-01 1.00806022e+00 -8.68623853e-01 -3.92725244e-02 -5.55245718e-03 5.36551952e-01 6.12151980e-01 7.98527524e-02 -3.01592112e-01 -3.43313724e-01 5.09807825e-01 7.92261735e-02 3.27811599e-01 -5.92334926e-01 -1.70956448e-01 -9.09152508e-01 -2.00780973e-01 -1.27434647e+00 4.73877251e-01 -6.84005737e-01 -9.88519073e-01 8.59604418e-01 -1.90172806e-01 -1.06773221e+00 -8.17373037e-01 -6.09241247e-01 -8.39829028e-01 6.58229232e-01 -9.38885629e-01 -1.19272768e+00 4.21873510e-01 3.51590723e-01 5.33148408e-01 -5.53187788e-01 1.32440269e+00 1.67767763e-01 -3.93989682e-01 6.17184997e-01 1.79106399e-04 4.72655654e-01 7.28653729e-01 -1.23858893e+00 6.97629094e-01 5.98264337e-01 1.01370561e+00 9.56723034e-01 8.02963972e-01 -3.76265883e-01 -9.52061892e-01 -5.37969589e-01 9.60451066e-01 -4.78487790e-01 8.89047265e-01 -2.75875181e-01 -1.17277741e+00 8.22746575e-01 1.67076111e-01 -4.86440748e-01 1.13518798e+00 9.38667417e-01 -3.92283440e-01 1.04052924e-01 -1.92995802e-01 6.95086896e-01 9.71167505e-01 -1.01573765e+00 -7.84851909e-01 -8.43967572e-02 5.85991740e-01 -3.31358045e-01 -8.97217333e-01 5.97049057e-01 1.01893449e+00 -1.23872781e+00 8.72541904e-01 -9.03329253e-01 4.13562268e-01 -1.60063550e-01 -2.74443120e-01 -1.19440496e+00 -3.62161666e-01 -7.26280093e-01 4.81784418e-02 1.29682052e+00 6.42105997e-01 -1.16100617e-01 8.83654892e-01 6.74606338e-02 -2.82441616e-01 -8.22256267e-01 -8.00681651e-01 -6.99531853e-01 3.33316743e-01 -8.15773547e-01 3.44906628e-01 9.74268377e-01 3.18335950e-01 3.67750138e-01 -5.30321956e-01 -3.50940138e-01 1.07630098e-03 2.20398858e-01 8.63908589e-01 -1.30835080e+00 -9.06684756e-01 -7.54862368e-01 -4.61777747e-01 -7.83671856e-01 2.46254325e-01 -9.48615611e-01 -1.18338346e-01 -7.23545253e-01 5.96893616e-02 -4.11469728e-01 -7.62201667e-01 5.83679736e-01 1.13261119e-01 6.48772478e-01 3.41531724e-01 2.32894674e-01 -4.62056369e-01 2.45828047e-01 7.96937764e-01 2.24757209e-01 -4.61877704e-01 2.75065243e-01 -6.26478374e-01 9.54468191e-01 1.06193781e+00 -4.47629899e-01 -4.60998237e-01 -1.36031032e-01 6.89333975e-01 2.59438127e-01 2.50643380e-02 -1.25274551e+00 2.90019393e-01 -5.60038127e-02 1.89638123e-01 -6.72077656e-01 4.59571749e-01 -3.36191446e-01 1.91931814e-01 3.25402886e-01 -8.31559181e-01 2.88229078e-01 3.35138917e-01 3.11683714e-01 -2.60902852e-01 -3.29675347e-01 5.26366949e-01 -3.10753971e-01 -6.28391087e-01 -1.30236819e-01 -6.72412574e-01 5.86912669e-02 4.23199624e-01 -2.28094757e-01 9.60236862e-02 -5.09791374e-01 -9.74203885e-01 -3.28663975e-01 4.66290027e-01 5.98022461e-01 1.15824610e-01 -1.21648145e+00 -4.98653859e-01 2.27820054e-01 1.03488810e-01 -4.07822728e-01 2.09922642e-02 5.74408650e-01 -2.51937836e-01 5.01643360e-01 -2.90650725e-01 -3.24429780e-01 -1.62317848e+00 2.16879576e-01 3.57554018e-01 -5.06654739e-01 -5.72949350e-01 7.55040407e-01 -3.00138086e-01 -7.85525084e-01 2.51042128e-01 -2.59388566e-01 -4.26375389e-01 1.00682028e-01 3.42411101e-01 1.68746002e-02 -1.45643363e-02 -7.48153687e-01 -2.74724197e-02 5.26893318e-01 -4.53017242e-02 -3.36473346e-01 1.34708679e+00 3.15012008e-01 -1.29530460e-01 1.43848121e+00 6.52405858e-01 3.36430907e-01 -8.94677520e-01 -2.01316103e-01 3.60525936e-01 -4.65032570e-02 -3.82473290e-01 -8.56681943e-01 -7.72314906e-01 8.77527058e-01 1.84073180e-01 3.53127569e-01 9.47556376e-01 -1.20689698e-01 5.43528736e-01 7.21732199e-01 2.07541063e-01 -1.09010756e+00 3.67250778e-02 1.00420213e+00 7.63144374e-01 -7.04603553e-01 -1.33082688e-01 3.38183105e-01 -6.97906375e-01 1.24561524e+00 4.04177845e-01 -3.12953860e-01 4.16830540e-01 2.12925196e-01 3.86801958e-01 -1.22123308e-01 -1.17795289e+00 -1.12720490e-01 4.39107269e-01 2.82877505e-01 9.95818913e-01 -1.13908820e-01 -1.56650499e-01 6.82784200e-01 -9.07116890e-01 -7.10807294e-02 5.29217720e-01 5.20380735e-01 -3.43502790e-01 -1.51848376e+00 -1.31915957e-01 1.91757634e-01 -6.79947436e-01 -7.04689696e-02 -8.03751647e-01 9.47030008e-01 -1.82695404e-01 6.27866030e-01 -1.75879925e-01 -7.80264020e-01 2.45638371e-01 4.12133574e-01 4.40176100e-01 -5.75708926e-01 -1.30729449e+00 1.46586940e-01 2.46196881e-01 -2.57506311e-01 -3.02654803e-01 -7.00523913e-01 -1.12439835e+00 -5.71960509e-02 -2.42751345e-01 3.61497730e-01 4.89206761e-01 7.88432837e-01 -7.64882639e-02 7.07521200e-01 4.37237352e-01 -9.95050251e-01 -4.30039406e-01 -1.06719518e+00 -6.58716321e-01 1.90137908e-01 1.03192382e-01 -1.49010435e-01 -1.97172523e-01 -1.08706973e-01]
[15.893318176269531, 5.330941200256348]
6bb64aa4-f278-433d-8d44-b75d3ffadc49
consistent-and-symmetry-preserving-data
2104.11578
null
https://arxiv.org/abs/2104.11578v1
https://arxiv.org/pdf/2104.11578v1.pdf
Consistent and symmetry preserving data-driven interface reconstruction for the level-set method
Recently, machine learning has been used to substitute parts of conventional computational fluid dynamics, e.g. the cell-face reconstruction in finite-volume solvers or the curvature computation in the Volume-of-Fluid (VOF) method. The latter showed improvements in terms of accuracy for coarsely resolved interfaces, however at the expense of convergence and symmetry. In this work, a combined approach is proposed, adressing the aforementioned shortcomings. We focus on interface reconstruction (IR) in the level-set method, i.e. the computation of the volume fraction and apertures. The combined model consists of a classification neural network, that chooses between the conventional (linear) IR and the neural network IR depending on the local interface resolution. The proposed approach improves accuracy for coarsely resolved interfaces and recovers the conventional IR for high resolutions, yielding first order overall convergence. Symmetry is preserved by mirroring and rotating the input level-set grid and subsequently averaging the predictions. The combined model is implemented into a CFD solver and demonstrated for two-phase flows. Furthermore, we provide details of floating point symmetric implementation and computational efficiency.
['Nikolaus Adams', 'Deniz A. Bezgin', 'Aaron B. Buhendwa']
2021-04-23
null
null
null
null
['face-reconstruction']
['computer-vision']
[ 1.12194330e-01 -1.17852084e-01 4.91536885e-01 2.18671620e-01 -6.04186475e-01 -1.84181243e-01 6.49611294e-01 3.24491858e-01 -3.71626735e-01 9.88221288e-01 -2.43738443e-01 -3.24923217e-01 -3.40482146e-01 -9.80118155e-01 -4.43558455e-01 -1.04268193e+00 1.76164676e-02 7.28078723e-01 1.59679070e-01 -2.62490511e-01 3.82438898e-01 8.94627273e-01 -1.89849782e+00 3.27333897e-01 1.10450327e+00 1.22653198e+00 -2.15366483e-01 6.90595329e-01 -3.64773512e-01 6.21974707e-01 -8.50257427e-02 1.42021403e-01 2.05133572e-01 -2.13170886e-01 -6.50380492e-01 -1.56142637e-01 4.37544197e-01 -1.95397720e-01 2.90798634e-01 5.66363811e-01 4.33526844e-01 3.89648944e-01 9.61053610e-01 -7.73768306e-01 1.29567146e-01 -1.65334642e-02 -3.96267146e-01 -9.27667171e-02 3.99685472e-01 -3.48245562e-03 5.72766006e-01 -1.27787817e+00 5.43397427e-01 8.96344304e-01 9.03654635e-01 2.14743987e-01 -1.34385800e+00 -1.31554723e-01 -3.64412695e-01 -3.08443040e-01 -1.40134358e+00 -1.99051753e-01 9.42138553e-01 -1.15583324e+00 1.02973413e+00 5.01426399e-01 7.79581904e-01 1.84860945e-01 4.99231398e-01 -1.88859403e-01 1.22431850e+00 -5.04071951e-01 5.42737901e-01 3.79016727e-01 -4.70780395e-02 4.85610038e-01 5.46492696e-01 3.26729476e-01 -1.08214796e-01 -4.79813367e-01 9.95769262e-01 -2.97847509e-01 -3.73061538e-01 -6.70880616e-01 -6.52650952e-01 8.95210862e-01 2.29594335e-01 6.68829322e-01 -3.32514018e-01 -3.45846862e-01 2.55787343e-01 -7.71080563e-03 9.35268700e-01 8.22741210e-01 -4.56334472e-01 2.04645768e-02 -1.30791402e+00 6.74820304e-01 1.08451462e+00 1.18653819e-01 9.31366265e-01 2.97377348e-01 3.73376422e-02 4.60776269e-01 2.35013634e-01 3.01102728e-01 2.78150946e-01 -1.10331893e+00 3.33109200e-02 8.19178164e-01 4.37752932e-01 -1.07415259e+00 -5.07425606e-01 -3.52176458e-01 -9.70098555e-01 9.04226899e-01 7.03760445e-01 -3.15561473e-01 -5.10869563e-01 9.92363572e-01 7.77220666e-01 2.34230205e-01 1.59028485e-01 9.85309422e-01 6.93901241e-01 6.29844546e-01 -3.03210132e-02 -4.25705492e-01 9.71804202e-01 -9.51465189e-01 -6.38393998e-01 3.59359294e-01 8.11798453e-01 -7.13849247e-01 5.00077069e-01 4.24119532e-01 -1.20716250e+00 -6.52891338e-01 -8.87475610e-01 2.71202996e-02 -5.46924472e-01 7.19068274e-02 2.50991881e-01 4.40158606e-01 -1.14178061e+00 1.37665772e+00 -6.76310718e-01 1.13667533e-01 -8.00511837e-02 2.65640944e-01 -3.17656726e-01 5.26178956e-01 -9.37568724e-01 7.74172068e-01 -9.16509479e-02 1.30205497e-01 -2.43508201e-02 -1.25612938e+00 -9.13988888e-01 5.58354631e-02 -2.03486323e-01 -7.91058958e-01 8.47517908e-01 -1.01203489e+00 -1.87394130e+00 6.88349903e-01 -3.43091160e-01 -3.24416369e-01 1.07059586e+00 -9.16980058e-02 4.90591154e-02 1.66835994e-01 -1.76498398e-01 1.33982733e-01 6.11335039e-01 -1.56495810e+00 -3.23858678e-01 -1.30515575e-01 -2.61280417e-01 1.40130475e-01 1.69072255e-01 -5.47362804e-01 3.67898285e-01 -3.36917102e-01 1.57858893e-01 -6.49264991e-01 -3.61759156e-01 4.75339182e-02 1.22209385e-01 -5.08567095e-02 7.75145590e-01 -8.39189768e-01 1.19119000e+00 -1.70974839e+00 2.24859998e-01 3.50104958e-01 2.68997788e-01 4.30273086e-01 5.06566763e-01 5.52715898e-01 -3.50543141e-01 -2.03106329e-01 -5.84249973e-01 -4.54592913e-01 -4.22007501e-01 -5.05739935e-02 -1.98942333e-01 7.19124734e-01 2.95676112e-01 4.87152040e-01 -4.86600131e-01 -3.17172021e-01 5.03639042e-01 8.65257680e-01 -7.36627042e-01 2.22098425e-01 5.97265139e-02 1.06279719e+00 -1.79102540e-01 1.84381008e-01 1.03979683e+00 -1.86947271e-01 7.82901794e-02 -2.45503724e-01 -9.60739791e-01 1.67131528e-01 -1.54734588e+00 1.13150799e+00 -6.91898942e-01 2.12811157e-01 6.38716161e-01 -1.14134967e+00 1.15341783e+00 5.09080470e-01 8.87130678e-01 -5.70134878e-01 1.47258595e-01 7.56048918e-01 -6.96991235e-02 -3.58806878e-01 5.47833145e-01 -4.12492096e-01 3.56232017e-01 4.11766171e-02 -2.44372889e-01 -3.04798573e-01 2.18763370e-02 -3.46086621e-01 5.11733413e-01 3.67215186e-01 5.16053855e-01 -7.58919716e-01 1.21955407e+00 2.28386834e-01 1.02629520e-01 2.45701551e-01 1.56602576e-01 7.11475074e-01 5.02999842e-01 -7.54022360e-01 -1.15166295e+00 -6.07390285e-01 -6.29577637e-01 4.64175910e-01 1.22591652e-01 -4.60621528e-02 -9.53781247e-01 1.65910631e-01 3.42684031e-01 4.69855398e-01 -4.61129785e-01 2.13651165e-01 -9.79862094e-01 -5.54182351e-01 2.32918616e-02 1.77698061e-01 3.07579368e-01 -9.51329768e-01 -8.02128196e-01 3.89127165e-01 2.59563297e-01 -8.77988815e-01 2.95240968e-01 5.49443252e-02 -1.20619023e+00 -1.17076540e+00 -6.79642677e-01 -4.54469323e-01 3.37697446e-01 -2.32508644e-01 1.08172274e+00 3.08061928e-01 -1.23417050e-01 2.42087647e-01 -9.62030515e-02 2.20233947e-01 -5.47651768e-01 -1.83144503e-03 5.89210875e-02 8.47118422e-02 -2.84474134e-01 -6.62954688e-01 -5.42983592e-01 7.28274658e-02 -5.95667720e-01 1.68128356e-01 9.12860632e-02 8.61060143e-01 4.83620048e-01 -1.96481854e-01 4.65341151e-01 -8.37299287e-01 3.35864842e-01 -2.88597763e-01 -8.81592453e-01 -2.63867676e-01 -6.98983610e-01 -3.28707770e-02 8.59770656e-01 -2.33295694e-01 -1.09794378e+00 -4.23294492e-02 -5.41340530e-01 -5.03769577e-01 -2.70428151e-01 1.66551456e-01 1.27486527e-01 -6.62740171e-01 4.40303952e-01 8.83566141e-02 1.31633386e-01 -7.41711497e-01 -2.29484528e-01 4.80307192e-01 1.07400388e-01 -8.29617798e-01 5.44320524e-01 4.97001678e-01 4.47654128e-01 -1.17002559e+00 -1.64283179e-02 -4.20632899e-01 -8.97548795e-01 -3.65801036e-01 6.77710176e-01 -5.00321388e-01 -8.81947875e-01 5.77338636e-01 -1.31250751e+00 -2.92710632e-01 -6.39462471e-01 4.98432487e-01 -5.00759721e-01 3.04313660e-01 -8.16922903e-01 -1.05679178e+00 -3.96097779e-01 -1.29219139e+00 1.13629031e+00 2.70543337e-01 -1.70054168e-01 -1.27007353e+00 3.24479610e-01 7.02790767e-02 6.80333555e-01 6.60041630e-01 8.39184940e-01 -3.37322176e-01 -2.97749251e-01 1.89847443e-02 -5.99917173e-02 4.64505656e-03 -9.41178650e-02 1.83734670e-01 -1.08590186e+00 -2.42498145e-01 3.53382438e-01 3.28316242e-01 7.27457225e-01 6.13495708e-01 7.31608868e-01 -3.37363780e-01 -4.67186660e-01 7.12076604e-01 1.84181523e+00 1.92569822e-01 3.59282136e-01 1.04664095e-01 5.73020339e-01 7.29007602e-01 3.13693911e-01 6.67870164e-01 -1.12716913e-01 8.22502136e-01 7.35613629e-02 -4.41821128e-01 -2.59115696e-02 1.72452942e-01 -1.03662260e-01 6.37648523e-01 -5.11417389e-01 1.87399954e-01 -1.04547334e+00 2.72228807e-01 -1.43303549e+00 -6.55588865e-01 -5.66073179e-01 2.31816769e+00 5.90651929e-01 1.50842369e-02 -9.43322554e-02 4.61712569e-01 5.36690533e-01 -1.03423998e-01 -1.96831107e-01 -9.64996457e-01 1.69985309e-01 4.59779054e-01 3.28610778e-01 1.23098481e+00 -1.09935427e+00 3.92361462e-01 5.46131992e+00 4.90444630e-01 -1.53164375e+00 1.45409882e-01 5.82243025e-01 1.78011909e-01 -1.16623163e-01 -1.74111605e-01 -7.99925268e-01 4.01002139e-01 9.02090549e-01 1.22184142e-01 4.76617396e-01 7.04280376e-01 4.13623989e-01 -3.17235023e-01 -6.49363279e-01 5.59578180e-01 -1.52069628e-01 -1.49597108e+00 7.46134948e-03 -3.31947557e-03 6.44937098e-01 -3.83251935e-01 -3.94848585e-01 1.23218365e-01 -5.77891946e-01 -1.02660811e+00 4.91897583e-01 7.46535301e-01 1.01386750e+00 -5.18141150e-01 9.40890491e-01 4.55588132e-01 -1.56870842e+00 1.56509534e-01 1.58290327e-01 -5.26232183e-01 2.83630669e-01 6.80291891e-01 -4.70221251e-01 8.24147999e-01 5.31601012e-01 4.03347462e-01 -1.04442790e-01 8.44738603e-01 6.48158908e-01 2.93361962e-01 -4.78617281e-01 1.57522857e-01 2.06377119e-01 -9.61468816e-01 7.09875286e-01 1.09786582e+00 6.12812757e-01 1.33527577e-01 2.44233832e-01 1.09231710e+00 5.87625265e-01 3.71902138e-01 -5.50471187e-01 4.53880578e-01 -1.72714721e-02 1.35677111e+00 -7.10014403e-01 -1.98679194e-01 -2.15401977e-01 3.19189698e-01 1.55200496e-01 3.45572680e-01 -5.44295251e-01 -1.59499004e-01 8.30243289e-01 8.71468902e-01 3.02242428e-01 -1.92760572e-01 -4.74809557e-01 -7.65425444e-01 -1.09394915e-01 -3.40819627e-01 6.76535070e-02 -4.18290973e-01 -1.13375556e+00 7.03493834e-01 1.12286121e-01 -1.07084966e+00 -2.74764866e-01 -9.73246753e-01 -5.31640708e-01 1.24298632e+00 -1.74723339e+00 -8.38295043e-01 -2.05485955e-01 1.68374628e-01 1.88627660e-01 2.02287331e-01 7.98647940e-01 4.97321069e-01 -2.03097805e-01 1.26976371e-01 1.77945077e-01 -9.86803770e-02 2.96676695e-01 -1.28542149e+00 -1.72281221e-01 3.59918714e-01 -8.67231727e-01 3.87428284e-01 9.00365114e-01 -7.89895535e-01 -1.28013134e+00 -9.27214861e-01 1.00286746e+00 2.02254519e-01 4.29718167e-01 -3.94165590e-02 -1.51426983e+00 3.85439619e-02 5.00513092e-02 3.05326939e-01 2.79071003e-01 -3.52427155e-01 1.65047497e-01 -1.01714976e-01 -1.35573816e+00 2.70764083e-01 3.65263551e-01 -1.99469343e-01 -4.30824310e-01 -1.19760484e-02 3.71041775e-01 -7.54552722e-01 -1.07970548e+00 7.27293551e-01 5.12830317e-01 -1.18713188e+00 9.28911448e-01 -2.35626221e-01 4.19851601e-01 -3.96170080e-01 1.61671326e-01 -9.38355684e-01 -2.01207936e-01 -4.33934987e-01 -8.21843371e-02 1.02226472e+00 1.92949578e-01 -1.09040034e+00 6.37931705e-01 5.45643449e-01 -1.33440599e-01 -1.23076415e+00 -1.50348830e+00 -1.85672075e-01 5.45462847e-01 -7.88531080e-02 1.64044738e-01 9.78516281e-01 -5.66606596e-02 -4.83253747e-02 -1.15459785e-01 2.29567513e-01 5.37786901e-01 3.24522883e-01 3.62445176e-01 -1.67141914e+00 -1.95723385e-01 -4.54733849e-01 -1.30179031e-02 -5.30850112e-01 1.69312015e-01 -7.15298951e-01 -7.92973787e-02 -1.22251797e+00 -5.58133125e-01 -7.49243259e-01 2.71045268e-01 -1.71806365e-01 1.03073306e-01 2.36479729e-01 -2.08523408e-01 8.43345374e-02 1.41407013e-01 3.87220562e-01 1.42852592e+00 3.67585570e-01 -6.17436349e-01 -9.67595801e-02 2.20067203e-02 7.76267707e-01 8.57943892e-01 -1.49339184e-01 3.08892727e-01 2.60464996e-01 1.13701485e-02 3.22793752e-01 3.93094957e-01 -1.39075053e+00 2.14461610e-01 1.41836241e-01 5.16812086e-01 -4.55879182e-01 3.74019653e-01 -9.14206624e-01 3.86210740e-01 6.48024201e-01 -1.53398767e-01 -8.01497772e-02 4.73752767e-01 1.25806570e-01 -1.93179250e-01 -2.06163123e-01 1.18537593e+00 -2.51288414e-01 -6.61949962e-02 -1.06922254e-01 -4.79566514e-01 -1.87860489e-01 9.27814066e-01 -4.48647052e-01 5.46538047e-02 1.61490008e-01 -5.93275487e-01 -2.85609126e-01 6.42991602e-01 -3.23932618e-01 3.09392065e-01 -1.07370591e+00 -6.42830372e-01 6.15677893e-01 -4.98257101e-01 2.23002389e-01 4.08576012e-01 1.19373178e+00 -1.05675745e+00 3.46334219e-01 -1.31565258e-01 -7.06727684e-01 -1.02405214e+00 4.20566052e-01 1.13603246e+00 -6.92758501e-01 -6.18522763e-01 4.14001405e-01 4.89958711e-02 -6.53132319e-01 -2.81174779e-01 -3.36685926e-01 -6.34095550e-01 2.42565379e-01 3.84692699e-01 7.99124122e-01 3.92030686e-01 -9.22247112e-01 -1.61986172e-01 1.25146675e+00 6.57809496e-01 9.93543789e-02 1.21604097e+00 5.17997630e-02 -4.08599436e-01 5.82356751e-01 1.15384555e+00 2.30988741e-01 -1.32943654e+00 2.30379462e-01 -2.81574905e-01 -2.21730530e-01 1.92570135e-01 -3.70081991e-01 -8.80840957e-01 1.06058788e+00 4.91271108e-01 2.70369679e-01 8.71696830e-01 -4.89155024e-01 6.96866155e-01 -1.04907311e-01 2.16218308e-01 -7.63318956e-01 -6.29301012e-01 5.80501795e-01 8.21216166e-01 -8.09940994e-01 2.22646490e-01 -8.03378880e-01 -1.11704646e-02 1.53565180e+00 4.68739748e-01 -4.76716042e-01 8.96569192e-01 8.19221854e-01 -7.05150738e-02 9.68679786e-02 -4.04017299e-01 1.65897265e-01 3.42485547e-01 6.96283579e-02 7.16399074e-01 -2.56545860e-02 -7.22663522e-01 3.58402848e-01 1.11811429e-01 1.14464357e-01 1.02217764e-01 7.53003418e-01 -3.93208206e-01 -8.76279175e-01 -7.26872683e-01 2.91914701e-01 -2.84662962e-01 1.32618798e-02 1.73104391e-01 8.12457085e-01 5.49264073e-01 6.78784370e-01 4.43998426e-01 -4.61646728e-02 2.90724903e-01 3.05567592e-01 3.61134052e-01 -1.99070707e-01 -9.45801198e-01 1.10453561e-01 3.16949119e-03 -4.29895639e-01 -3.59274805e-01 -6.26548052e-01 -1.37839413e+00 -2.07545698e-01 -1.56556159e-01 7.13263869e-01 4.77473378e-01 9.85494792e-01 2.15730384e-01 5.39482892e-01 4.43992347e-01 -1.71669555e+00 -8.85231197e-02 -9.18256104e-01 -7.53582895e-01 2.66955853e-01 5.34698009e-01 -9.92743850e-01 -8.68624926e-01 -1.45767584e-01]
[6.387056827545166, 3.3242926597595215]
3963eb53-5252-41fe-a220-3e7e72c7c72f
resources-and-evaluations-for-multi
2306.12601
null
https://arxiv.org/abs/2306.12601v1
https://arxiv.org/pdf/2306.12601v1.pdf
Resources and Evaluations for Multi-Distribution Dense Information Retrieval
We introduce and define the novel problem of multi-distribution information retrieval (IR) where given a query, systems need to retrieve passages from within multiple collections, each drawn from a different distribution. Some of these collections and distributions might not be available at training time. To evaluate methods for multi-distribution retrieval, we design three benchmarks for this task from existing single-distribution datasets, namely, a dataset based on question answering and two based on entity matching. We propose simple methods for this task which allocate the fixed retrieval budget (top-k passages) strategically across domains to prevent the known domains from consuming most of the budget. We show that our methods lead to an average of 3.8+ and up to 8.0 points improvements in Recall@100 across the datasets and that improvements are consistent when fine-tuning different base retrieval models. Our benchmarks are made publicly available.
['Simran Arora', 'Omar Khattab', 'Soumya Chatterjee']
2023-06-21
null
null
null
null
['retrieval', 'question-answering', 'information-retrieval']
['methodology', 'natural-language-processing', 'natural-language-processing']
[-2.10464269e-01 -5.10327697e-01 -5.26404142e-01 -2.33647972e-01 -1.98825192e+00 -1.10904813e+00 6.78387940e-01 5.10692894e-01 -5.34735560e-01 9.06216025e-01 1.99299380e-01 -8.69033709e-02 -6.29670799e-01 -7.66481400e-01 -6.29947305e-01 -3.76926154e-01 1.20171323e-01 1.33626032e+00 6.35550082e-01 -3.41625720e-01 4.74821061e-01 5.02526760e-01 -1.44645298e+00 5.30076087e-01 6.28048897e-01 9.49928164e-01 3.17754522e-02 8.14154923e-01 -3.53760511e-01 6.21657252e-01 -1.09526861e+00 -2.37133235e-01 4.58187640e-01 -4.30445597e-02 -1.02265215e+00 -5.20315230e-01 6.89613879e-01 -5.95242500e-01 -6.05933547e-01 7.11037576e-01 6.96214318e-01 4.30305600e-01 1.08636510e+00 -1.09436989e+00 -9.92352009e-01 3.66921127e-01 -6.86262429e-01 5.66915810e-01 6.70047998e-01 -4.42831308e-01 1.31119549e+00 -5.66285372e-01 8.23876143e-01 1.25270343e+00 1.78994775e-01 4.36694443e-01 -1.18605244e+00 -5.20595551e-01 -9.81143042e-02 4.66380157e-02 -1.62287390e+00 -3.95600498e-01 1.75908074e-01 -1.73716128e-01 1.01544213e+00 3.96992385e-01 -1.54837728e-01 6.79993093e-01 -1.34873360e-01 8.76143873e-01 5.87220252e-01 -4.88195896e-01 1.24982327e-01 3.89184415e-01 7.27203131e-01 -1.89261898e-01 5.40669560e-01 -3.10237139e-01 -3.30013424e-01 -9.24172759e-01 1.81139827e-01 -3.00491564e-02 -3.08684349e-01 -2.65292227e-01 -7.45120823e-01 7.78861165e-01 4.02308702e-02 2.55580693e-01 -2.67910331e-01 8.63225535e-02 2.45122612e-01 6.90499485e-01 4.71157789e-01 7.28127539e-01 -7.56554067e-01 -1.11610340e-02 -8.25352371e-01 8.48132610e-01 1.19710636e+00 1.46993566e+00 9.72282887e-01 -9.63652194e-01 -5.62637329e-01 1.28200006e+00 3.71703543e-02 1.13023424e+00 4.22991037e-01 -8.90456617e-01 7.13556767e-01 4.73915488e-01 6.49003386e-01 -8.80567312e-01 -7.98443779e-02 1.56256035e-01 -2.55017608e-01 -7.31111526e-01 6.10755861e-01 -3.70432697e-02 -8.11471999e-01 1.60783911e+00 1.80142671e-01 -2.15675935e-01 2.52517276e-02 7.05952644e-01 6.94130003e-01 8.30986917e-01 -5.43106534e-02 -1.23115070e-02 1.27462888e+00 -8.16395819e-01 -3.22359085e-01 -5.00997752e-02 6.92619860e-01 -9.16212857e-01 1.06358111e+00 2.47476265e-01 -1.07964981e+00 -1.89479783e-01 -7.32777059e-01 -1.72013849e-01 -5.39161265e-01 -2.12495878e-01 1.34355605e-01 5.63230813e-01 -1.10259485e+00 2.37279087e-01 -2.90870547e-01 -3.65123242e-01 3.68452258e-02 2.21890762e-01 -1.57688946e-01 -5.67805290e-01 -1.27228701e+00 7.94507205e-01 2.17892140e-01 -7.26059496e-01 -7.72283494e-01 -1.02078259e+00 -2.89490163e-01 2.17587575e-01 1.43994629e-01 -7.90662885e-01 1.51543057e+00 -6.15731299e-01 -7.84862638e-01 8.33298862e-01 -2.20011562e-01 -2.17534110e-01 1.98448390e-01 -5.72351813e-01 -3.03217649e-01 4.94579911e-01 2.35580653e-01 4.50444192e-01 5.25297761e-01 -1.14611304e+00 -7.19731510e-01 -3.83134216e-01 1.95312843e-01 3.60809147e-01 -5.72792292e-01 3.69572282e-01 -9.76906121e-01 -4.31799591e-01 -4.12905931e-01 -8.39144588e-01 1.10862322e-01 -2.95685947e-01 -2.59679347e-01 -4.30873215e-01 4.81163770e-01 -6.13275111e-01 1.54915380e+00 -2.00362873e+00 -1.07715338e-01 3.93912405e-01 5.46918102e-02 4.46975343e-02 -4.89291340e-01 7.22837448e-01 3.31476092e-01 3.12292188e-01 2.27727562e-01 -1.56645954e-01 1.66450202e-01 1.21114530e-01 -6.48328722e-01 2.50883847e-01 -2.39768699e-01 6.69876039e-01 -8.79350662e-01 -5.05356610e-01 -4.12306011e-01 1.80232003e-01 -5.47534049e-01 3.25833291e-01 -6.54382408e-01 -1.45861313e-01 -6.69885516e-01 6.50182962e-01 8.10724199e-01 -5.26731312e-01 9.78258774e-02 3.31609100e-01 5.11828303e-01 4.61714506e-01 -1.01205218e+00 1.66771770e+00 -3.29862475e-01 3.75749588e-01 -2.16442287e-01 -5.42708218e-01 6.93843067e-01 1.56576201e-01 5.37919343e-01 -1.18913031e+00 -2.99894750e-01 3.90320897e-01 -5.01990199e-01 -2.01132998e-01 1.22098088e+00 1.72276840e-01 -4.41761225e-01 8.73075604e-01 -4.98644039e-02 2.73141600e-02 5.01437783e-01 4.96156156e-01 1.50923181e+00 -6.70056462e-01 -1.60148457e-01 -3.03573102e-01 1.14897817e-01 3.11317116e-01 1.34242609e-01 1.31092119e+00 2.18271703e-01 6.40087605e-01 3.61114532e-01 -5.90663105e-02 -1.24730325e+00 -1.18495035e+00 -2.35594437e-01 1.58614254e+00 4.39371198e-01 -3.41923416e-01 -2.67061472e-01 -5.59668660e-01 5.04759192e-01 5.71873069e-01 -2.25201219e-01 -1.14639260e-01 -4.49783742e-01 -6.62979364e-01 6.15357757e-01 4.00216669e-01 1.58077672e-01 -7.16556847e-01 -1.58619538e-01 2.89469715e-02 -3.01820755e-01 -7.97332227e-01 -7.10460484e-01 -2.11486384e-01 -5.95567703e-01 -1.04508209e+00 -1.33613718e+00 -6.71377003e-01 3.49379361e-01 5.70682228e-01 1.65184200e+00 1.03098683e-01 -3.76437515e-01 9.19138253e-01 -3.45575988e-01 -3.29300106e-01 -2.58610904e-01 6.17093801e-01 -1.78743377e-01 -7.10070133e-01 8.91451061e-01 -2.46904671e-01 -8.32356572e-01 4.99647588e-01 -1.24159253e+00 -8.68189692e-01 4.30039823e-01 7.10525095e-01 6.16129100e-01 -1.27709225e-01 8.49907637e-01 -1.09186912e+00 1.13930035e+00 -1.05774331e+00 -5.91771066e-01 8.69489074e-01 -6.96328282e-01 2.10195675e-01 3.70663732e-01 -5.91599464e-01 -8.59669745e-01 -5.60195923e-01 3.40228915e-01 -4.40146327e-01 -1.00519925e-01 3.72622728e-01 1.30816504e-01 1.71220422e-01 1.06099153e+00 1.63540408e-01 -2.52203643e-01 -6.83416486e-01 5.96187949e-01 9.75769758e-01 1.26480192e-01 -1.25879419e+00 4.81483757e-01 1.32423267e-01 -6.37352049e-01 -6.25263810e-01 -8.47820818e-01 -1.31638348e+00 -1.83153376e-01 3.52972895e-01 2.59312898e-01 -1.03515553e+00 -3.49853039e-01 2.60046989e-01 -1.05989587e+00 -3.69141221e-01 -2.36371443e-01 9.23504159e-02 -3.12284261e-01 4.72331345e-01 -7.00798512e-01 -6.13853216e-01 -6.26217425e-01 -6.13228261e-01 1.36344886e+00 3.76329571e-01 -1.43894628e-01 -9.46080983e-01 6.85973346e-01 1.19876757e-01 8.21595609e-01 -3.11569184e-01 1.19515693e+00 -1.32472217e+00 -6.93227053e-01 -2.52399385e-01 -4.23869073e-01 1.95812210e-01 1.48612529e-01 -2.80216753e-01 -7.55165517e-01 -5.90859473e-01 -4.41465676e-01 -7.20275640e-01 9.08749223e-01 4.03964147e-02 1.14735389e+00 -3.39935213e-01 -7.56632149e-01 8.49612057e-02 1.62635589e+00 2.96487033e-01 6.10982060e-01 5.87682903e-01 2.29911739e-03 3.68519813e-01 7.17562437e-01 5.72927594e-01 4.92474794e-01 7.58156836e-01 -3.28489482e-01 2.99410343e-01 -6.19733660e-03 -2.16906741e-01 -8.32367688e-02 5.62258124e-01 5.53382933e-01 -6.92254364e-01 -9.80512559e-01 1.09816837e+00 -1.78680277e+00 -8.52440536e-01 4.18366373e-01 2.61397576e+00 1.16588652e+00 -1.89971328e-01 3.36811900e-01 -4.79109585e-01 6.25747800e-01 -8.40068534e-02 -7.78314471e-01 -1.80782557e-01 -1.88129827e-01 5.17807543e-01 6.48828268e-01 4.22958970e-01 -9.28617001e-01 6.39470994e-01 7.45815659e+00 1.00716126e+00 -7.47942805e-01 -3.09231997e-01 5.63163042e-01 -3.72852534e-01 -7.15625286e-01 -1.34991571e-01 -1.36025262e+00 5.17840743e-01 1.33377528e+00 -9.05253768e-01 3.57172996e-01 8.82199287e-01 -6.96579218e-01 -2.13350981e-01 -1.14004374e+00 9.19005990e-01 1.12540744e-01 -1.07029486e+00 4.02917117e-01 -1.01331644e-01 8.33236516e-01 1.94268584e-01 1.13194317e-01 6.72653735e-01 6.89140618e-01 -7.37011433e-01 1.80854082e-01 6.36747599e-01 7.46642232e-01 -7.83563316e-01 4.31370378e-01 4.33459967e-01 -7.68857598e-01 1.59735475e-02 -7.68346608e-01 5.86655855e-01 -6.88094646e-02 5.13676405e-01 -7.27968097e-01 6.71717048e-01 7.71723509e-01 2.52238065e-01 -4.70663309e-01 1.39218676e+00 3.11839461e-01 3.69565815e-01 -7.40254998e-01 -2.34735519e-01 -3.19619626e-02 2.26440415e-01 3.03183556e-01 1.40467155e+00 3.51251543e-01 4.68341261e-02 1.42149091e-01 3.94968927e-01 -4.85379189e-01 1.58604428e-01 -7.29548216e-01 2.36439090e-02 1.11442792e+00 8.91547441e-01 -1.05967872e-01 -5.63913226e-01 -4.70826834e-01 8.59810591e-01 4.29756254e-01 6.20129704e-01 -7.05494940e-01 -8.73808861e-01 7.88394213e-01 1.34144187e-01 4.25040811e-01 5.75608760e-02 2.47855276e-01 -1.14282179e+00 3.18554968e-01 -9.43027496e-01 1.06969810e+00 -5.24027586e-01 -2.08099604e+00 3.98416549e-01 5.39144814e-01 -9.40682113e-01 -5.76773584e-01 -4.59302753e-01 1.80102199e-01 1.04980659e+00 -1.95056701e+00 -4.86625195e-01 -1.09596729e-01 6.91616714e-01 1.61090344e-01 -1.03391141e-01 9.76172864e-01 8.90287697e-01 -6.16891012e-02 1.00361192e+00 9.13102150e-01 7.76434243e-02 1.35099208e+00 -1.33137631e+00 1.44735903e-01 1.94080785e-01 1.20838188e-01 9.74953175e-01 2.26453915e-01 -2.62378633e-01 -1.38401556e+00 -8.81601036e-01 9.30084586e-01 -9.22664165e-01 6.54977739e-01 -1.34167030e-01 -1.11887455e+00 6.60623729e-01 2.72697240e-01 -1.92255035e-01 9.36290503e-01 3.23555052e-01 -8.33326519e-01 -2.69518852e-01 -1.35229003e+00 1.97215796e-01 6.96875513e-01 -6.58407629e-01 -8.23459029e-01 6.30715847e-01 7.63152897e-01 -4.82298255e-01 -1.09173286e+00 1.42958760e-01 5.60032368e-01 -4.74633902e-01 1.28145730e+00 -9.11900640e-01 2.13612795e-01 -3.15082744e-02 -4.97729540e-01 -1.24728084e+00 -1.11996368e-01 -3.77096653e-01 -2.76944995e-01 1.18144333e+00 6.40274107e-01 -7.21047342e-01 5.44979274e-01 1.11265171e+00 4.67389643e-01 -3.19828242e-01 -6.60283983e-01 -1.03662503e+00 7.00481415e-01 4.46830243e-02 7.62175798e-01 7.55078435e-01 -1.24424711e-01 4.64362025e-01 4.13364470e-02 1.18307106e-01 4.32666153e-01 5.12163579e-01 9.49667454e-01 -1.04481232e+00 -4.31912273e-01 -4.14868802e-01 4.46262695e-02 -1.84266901e+00 6.64005652e-02 -7.00559556e-01 -6.38923049e-02 -1.53416860e+00 6.89626932e-01 -9.07898784e-01 -5.39141297e-01 3.77361149e-01 -2.63738602e-01 -1.91985250e-01 -5.65703698e-02 6.04536057e-01 -1.21363103e+00 3.01340759e-01 7.36844182e-01 -3.17933381e-01 -1.03564031e-01 -1.10534444e-01 -1.05854344e+00 -5.75078167e-02 6.03125513e-01 -6.51232123e-01 -6.42566264e-01 -7.97090352e-01 2.42334634e-01 2.80797184e-01 -1.10724024e-01 -6.63465619e-01 6.32508159e-01 -1.19314596e-01 1.68033808e-01 -6.94619477e-01 2.96932012e-01 -5.39940119e-01 -1.42199755e-01 -2.67436564e-01 -6.50119245e-01 3.07811797e-01 5.47794044e-01 7.64462054e-01 -4.15502846e-01 -2.51828641e-01 4.33942795e-01 -1.08195119e-01 -6.36404634e-01 4.10918802e-01 -8.50819945e-02 8.26346636e-01 7.80222058e-01 3.26398164e-01 -9.64292705e-01 -5.02419651e-01 -3.19649011e-01 5.94592154e-01 5.07871389e-01 4.89295661e-01 3.84374470e-01 -1.27305436e+00 -7.67602861e-01 -3.27545255e-01 6.38330996e-01 7.48595968e-02 1.50706246e-01 2.38000378e-01 -4.55914855e-01 8.49137902e-01 1.31229550e-01 -4.75324929e-01 -1.15788412e+00 6.70414209e-01 1.99481159e-01 -7.40425766e-01 -1.81254670e-01 6.95053339e-01 9.79521349e-02 -5.92109561e-01 4.18832690e-01 1.34482132e-02 9.28795785e-02 -5.43609783e-02 9.46446836e-01 3.92488033e-01 1.61019608e-01 5.13414033e-02 -3.19842964e-01 3.81760299e-01 -7.82013893e-01 -3.27741235e-01 1.16475153e+00 -1.04393713e-01 -2.03342974e-01 2.48969004e-01 1.54656672e+00 2.84143742e-02 -5.45943022e-01 -7.51376510e-01 4.75865692e-01 -8.01338494e-01 -3.07374775e-01 -1.00976324e+00 -7.21379876e-01 4.78264898e-01 2.95737565e-01 4.23808843e-01 1.24462032e+00 1.85980976e-01 1.05436969e+00 9.49855208e-01 7.25983202e-01 -1.01303220e+00 1.59685001e-01 5.13992369e-01 6.78313613e-01 -9.39490795e-01 -5.32279462e-02 1.71374992e-01 -2.95503616e-01 8.01475585e-01 5.68838775e-01 -1.50028661e-01 8.58451962e-01 1.17560983e-01 -3.97195108e-02 -2.59681731e-01 -1.08548295e+00 -1.33495227e-01 3.39597285e-01 4.53502417e-01 3.08308482e-01 -1.59981176e-01 -2.91802198e-01 4.70817357e-01 1.98266521e-01 -1.64362207e-01 6.88529387e-02 1.10753727e+00 -6.31344795e-01 -1.45260918e+00 -2.56700367e-01 7.12334096e-01 -7.25854933e-01 -2.24797949e-01 -5.13515770e-01 7.93828666e-01 -7.76638627e-01 8.71950328e-01 1.86477616e-01 -5.68910642e-03 4.95715648e-01 5.58735244e-02 4.50885564e-01 -6.55357063e-01 -3.41234237e-01 -1.83576420e-01 2.58565277e-01 -3.93429786e-01 -2.03580543e-01 -5.34708560e-01 -8.90202582e-01 -4.83454376e-01 -2.80722678e-01 5.70730627e-01 2.74991125e-01 4.32385147e-01 8.58044744e-01 -1.44281015e-01 6.66110218e-01 -1.78667028e-02 -1.05060303e+00 -1.00882506e+00 -7.46104121e-01 6.41465127e-01 2.86599666e-01 -4.29311901e-01 -4.53520358e-01 -2.63552397e-01]
[11.454911231994629, 7.704309463500977]
b328b38b-0cbc-44e0-b008-7896e324eaa0
chili-pepper-disease-diagnosis-via-image
2306.12057
null
https://arxiv.org/abs/2306.12057v1
https://arxiv.org/pdf/2306.12057v1.pdf
Chili Pepper Disease Diagnosis via Image Reconstruction Using GrabCut and Generative Adversarial Serial Autoencoder
With the recent development of smart farms, researchers are very interested in such fields. In particular, the field of disease diagnosis is the most important factor. Disease diagnosis belongs to the field of anomaly detection and aims to distinguish whether plants or fruits are normal or abnormal. The problem can be solved by binary or multi-classification based on CNN, but it can also be solved by image reconstruction. However, due to the limitation of the performance of image generation, SOTA's methods propose a score calculation method using a latent vector error. In this paper, we propose a network that focuses on chili peppers and proceeds with background removal through Grabcut. It shows high performance through image-based score calculation method. Due to the difficulty of reconstructing the input image, the difference between the input and output images is large. However, the serial autoencoder proposed in this paper uses the difference between the two fake images except for the actual input as a score. We propose a method of generating meaningful images using the GAN structure and classifying three results simultaneously by one discriminator. The proposed method showed higher performance than previous researches, and image-based scores showed the best performanc
['Sungyoung Kim', 'Jongwook Si']
2023-06-21
null
null
null
null
['image-reconstruction', 'anomaly-detection']
['computer-vision', 'methodology']
[ 3.03958982e-01 -3.16793501e-01 1.62073508e-01 -1.66590855e-01 -6.73410371e-02 -2.61246473e-01 1.68067962e-01 -1.79791704e-01 2.76662922e-03 4.86118883e-01 -4.02705550e-01 6.00404851e-02 -1.01132356e-01 -1.41629064e+00 -4.16858345e-01 -1.01171851e+00 3.96085948e-01 1.16422191e-01 6.36012927e-02 -2.81823158e-01 1.10325798e-01 4.16846305e-01 -1.43413019e+00 3.09709191e-01 1.06688607e+00 1.09913659e+00 4.15244251e-01 4.76284802e-01 -4.45358843e-01 6.64755881e-01 -1.15306151e+00 -2.56817043e-01 3.49206924e-01 -1.20515418e+00 -3.62644255e-01 1.76843092e-01 -4.52688336e-02 -6.44359231e-01 3.48820090e-02 1.43946350e+00 4.71086919e-01 -3.73298347e-01 6.93641007e-01 -1.46339977e+00 -1.11075723e+00 4.51759696e-01 -6.32218838e-01 -9.54380035e-02 -1.17159851e-01 -7.71759450e-02 4.75447267e-01 -2.73902863e-01 3.14659208e-01 9.62934434e-01 4.75038946e-01 4.46407467e-01 -1.08917058e+00 -7.45732367e-01 -3.38602632e-01 6.53143406e-01 -1.17744231e+00 2.86941171e-01 9.82470751e-01 -4.15290922e-01 3.66623968e-01 2.18197942e-01 9.11033034e-01 9.47407961e-01 3.59739780e-01 6.81345344e-01 1.26322722e+00 -4.78309780e-01 1.64923728e-01 8.30114633e-02 -5.82066178e-02 6.47063255e-01 3.35011274e-01 1.73864271e-02 2.12761819e-01 2.15423062e-01 8.16055477e-01 3.93414557e-01 -3.87796849e-01 5.44072837e-02 -1.02337563e+00 1.05728352e+00 6.37933135e-01 9.73192155e-01 -4.32672679e-01 -3.01857740e-01 9.32451412e-02 3.06063056e-01 2.09022388e-01 3.04585725e-01 -3.40182483e-01 3.06725383e-01 -1.03775597e+00 -3.09688658e-01 6.86972678e-01 4.18878824e-01 3.59118432e-01 5.88346660e-01 -1.85894027e-01 6.83250010e-01 8.42114240e-02 6.49359822e-01 8.17216575e-01 -5.82523525e-01 -2.30881795e-02 7.74557769e-01 -2.52807379e-01 -1.48524618e+00 -6.80819228e-02 -4.15291190e-01 -1.33646441e+00 5.38109362e-01 2.92535663e-01 -2.31429413e-01 -1.09781098e+00 1.53341842e+00 1.74478292e-01 6.72251880e-02 2.63095617e-01 9.04898584e-01 9.20001447e-01 1.22931397e+00 -1.51584581e-01 -3.12319249e-01 1.07223785e+00 -1.02593637e+00 -1.18475950e+00 2.69368947e-01 3.02214533e-01 -7.15787470e-01 6.36161149e-01 8.56158257e-01 -6.16515458e-01 -7.69321978e-01 -1.27699971e+00 4.33041722e-01 -4.62456614e-01 7.00011075e-01 4.69476908e-01 5.55380404e-01 -7.77606905e-01 7.10776329e-01 -5.59031963e-01 -4.49081600e-01 2.37523764e-01 9.81822237e-03 -4.24101114e-01 1.66494116e-01 -9.54662144e-01 9.19442892e-01 6.40508056e-01 3.18686754e-01 -6.45097375e-01 -9.42354351e-02 -4.80227321e-01 3.92412394e-01 1.26003236e-01 -1.39812231e-01 7.09235549e-01 -1.64838958e+00 -1.69761896e+00 3.77516568e-01 3.77957016e-01 -2.50504076e-01 3.09902430e-01 -4.91620526e-02 -8.01382303e-01 2.99439996e-01 8.43487754e-02 4.14541930e-01 8.26946557e-01 -1.05899906e+00 -4.73472595e-01 -4.01220769e-01 -1.77741453e-01 -1.83657914e-01 -3.89186293e-01 -1.57592282e-01 1.43728644e-01 -7.06207514e-01 3.90035003e-01 -5.21961927e-01 8.41345340e-02 1.38664886e-01 -3.17879885e-01 1.60527807e-02 1.36640906e+00 -1.21358645e+00 8.82984042e-01 -2.26600361e+00 -1.05197234e-02 1.47529855e-01 3.52263055e-03 4.93451715e-01 -2.20700517e-01 6.67188913e-02 -7.81744570e-02 8.16676095e-02 -3.58116835e-01 5.43707132e-01 -3.69768620e-01 2.58911669e-01 -5.98544721e-03 1.04180105e-01 5.08820653e-01 7.17929721e-01 -5.60587049e-01 -5.28641880e-01 2.58418798e-01 5.93499899e-01 -1.14113785e-01 3.94500166e-01 -6.81650490e-02 2.36834511e-01 -4.47565764e-01 6.61260247e-01 1.03832293e+00 -1.77288130e-01 1.31809250e-01 -5.38080931e-01 -1.80850700e-02 -4.90443259e-01 -1.27161014e+00 1.25872135e+00 -2.87716925e-01 5.09561419e-01 1.08046360e-01 -1.41678929e+00 1.24465883e+00 3.68412852e-01 4.53698963e-01 -7.02986062e-01 3.77277523e-01 2.84991600e-02 1.53362438e-01 -7.57504940e-01 2.48993910e-03 7.45929107e-02 3.88173610e-01 1.35982081e-01 9.02555510e-02 -1.84379548e-01 1.52768388e-01 -9.45955738e-02 1.03395021e+00 2.83460736e-01 3.67615074e-01 7.41328821e-02 5.85763872e-01 1.91816598e-01 8.03063810e-01 2.42481470e-01 -1.33603029e-02 5.22858262e-01 5.80750942e-01 -4.15627003e-01 -9.36186612e-01 -8.69971395e-01 -1.27452567e-01 3.50668520e-01 2.28678614e-01 1.36754364e-01 -9.30163562e-01 -7.22151399e-01 -1.74271077e-01 6.31399810e-01 -5.18747687e-01 -2.73474842e-01 -3.88544738e-01 -9.42774773e-01 5.65615475e-01 4.13773835e-01 1.43153358e+00 -1.40916264e+00 -6.67997837e-01 3.64032924e-01 -2.32024118e-01 -6.36384130e-01 1.54140458e-01 2.69378126e-01 -9.28205490e-01 -9.86715794e-01 -9.07113016e-01 -9.90850508e-01 7.87073910e-01 1.89230502e-01 7.14298248e-01 2.60741413e-01 -3.69853139e-01 -2.78863937e-01 -7.06435621e-01 -3.86072606e-01 -6.12582326e-01 -1.18433371e-01 -4.48247135e-01 2.80496404e-02 2.98371226e-01 -3.00876170e-01 -4.65001553e-01 1.82094261e-01 -1.18816161e+00 1.05535187e-01 9.12716985e-01 1.10575640e+00 3.70639592e-01 6.09805346e-01 5.98185122e-01 -7.75990903e-01 3.56817901e-01 -3.00226182e-01 -7.40445912e-01 4.19820189e-01 -5.89179695e-01 5.80797978e-02 8.56703460e-01 -4.12101686e-01 -1.19884038e+00 -2.02574264e-02 -2.69711345e-01 -2.01302320e-01 -5.13592362e-01 3.42294246e-01 -4.51721072e-01 2.53306087e-02 4.07258630e-01 2.90143102e-01 2.57868290e-01 -2.87900269e-01 2.46800128e-02 7.99461663e-01 2.97863871e-01 2.50377089e-01 6.18116915e-01 1.78007230e-01 1.39012769e-01 -6.94970429e-01 -4.42136735e-01 1.08454436e-01 -3.46618921e-01 -3.37145448e-01 1.33564830e+00 -5.14538169e-01 -6.84253812e-01 8.09814572e-01 -1.25947046e+00 1.56838983e-01 -1.67397961e-01 5.92052519e-01 -5.64602986e-02 3.50376576e-01 -8.63836348e-01 -5.27630806e-01 -5.84941804e-01 -1.06024063e+00 6.13198698e-01 4.23320144e-01 3.39571029e-01 -6.02279723e-01 -1.61455780e-01 6.74563497e-02 5.27561724e-01 5.27272880e-01 1.12933993e+00 -4.26548481e-01 -4.87074733e-01 -3.33538324e-01 -2.12144002e-01 9.29563165e-01 3.99210721e-01 2.24840134e-01 -8.02023709e-01 -2.42829714e-02 5.77750564e-01 -1.84942544e-01 7.35792220e-01 4.27710205e-01 1.25276804e+00 -4.44648653e-01 -1.10747747e-01 5.07614493e-01 1.81298256e+00 7.81981766e-01 9.30466354e-01 1.90691665e-01 6.67594790e-01 4.13315117e-01 4.92707521e-01 1.32536620e-01 -2.38581195e-01 3.71923983e-01 6.06191754e-01 -4.21550304e-01 -1.73585013e-01 2.88419034e-02 2.83074498e-01 1.10081995e+00 -4.65968736e-02 -4.06761855e-01 -2.85116851e-01 2.98881114e-01 -1.49972570e+00 -1.41018617e+00 -3.89143556e-01 1.87211406e+00 5.05041540e-01 -1.07854761e-01 -3.21918160e-01 6.14752471e-01 1.00632644e+00 -1.34065002e-01 -5.70552588e-01 -4.56284404e-01 -4.18397188e-01 3.84589076e-01 3.43597084e-01 -6.21838234e-02 -8.87473464e-01 4.13723648e-01 5.77344465e+00 9.48017418e-01 -1.48915946e+00 1.47175267e-01 6.76606357e-01 4.03962910e-01 7.14841411e-02 -1.93533272e-01 -2.69206494e-01 7.59365916e-01 5.02174616e-01 3.39177251e-01 3.66061896e-01 9.24548745e-01 -1.82901219e-01 -2.95605689e-01 -5.50759375e-01 9.00606871e-01 4.08769876e-01 -7.63559997e-01 7.90821314e-02 -1.52924269e-01 7.63999522e-01 -5.19795239e-01 -1.58184305e-01 7.32145160e-02 3.26882228e-02 -8.14664125e-01 2.76727736e-01 4.50183272e-01 4.95765924e-01 -5.44589937e-01 1.04787683e+00 4.04297441e-01 -9.75602806e-01 -9.96776968e-02 -5.17122805e-01 1.17302358e-01 -2.57600129e-01 9.92419124e-01 -5.02106607e-01 7.20276892e-01 6.28242016e-01 5.79738081e-01 -4.70647782e-01 9.52828944e-01 -4.64675575e-01 7.53321767e-01 -3.10991764e-01 -1.66686952e-01 -9.31006819e-02 -6.52268291e-01 1.35598257e-01 6.98911667e-01 9.49169278e-01 8.85810778e-02 -5.27846254e-02 1.16113126e+00 1.86997458e-01 2.95090199e-01 -7.13639677e-01 -1.51098356e-01 7.19815260e-03 1.43468857e+00 -9.63601530e-01 -4.48798776e-01 -2.65440583e-01 1.39513230e+00 -2.26026177e-01 -2.07947684e-03 -8.93087268e-01 -8.96711707e-01 -2.63222098e-01 -2.77902007e-01 4.79567260e-01 1.25476420e-01 -4.41574231e-02 -1.09922063e+00 5.06058782e-02 -8.41606319e-01 1.89120099e-01 -1.07265580e+00 -1.06006777e+00 6.80537283e-01 -3.36075157e-01 -1.21209764e+00 -1.01816311e-01 -6.70850873e-01 -7.07626522e-01 7.78792500e-01 -1.13319886e+00 -1.07166016e+00 -8.00868571e-01 3.54475707e-01 5.02115190e-01 -2.47035220e-01 1.09557247e+00 4.06224728e-01 -6.18048310e-01 2.36406252e-01 4.48306173e-01 3.29499424e-01 4.44106519e-01 -1.10561478e+00 -2.01642692e-01 8.75242829e-01 1.17986791e-01 -2.07917884e-01 3.18047673e-01 -7.85872340e-01 -7.98411191e-01 -7.80946195e-01 6.36342704e-01 3.08299184e-01 -1.92361642e-02 2.18345195e-01 -8.86027157e-01 1.61853582e-01 5.20084620e-01 -2.08832592e-01 3.80287141e-01 -5.79654992e-01 5.93066253e-02 -4.39425886e-01 -1.67367530e+00 8.02929848e-02 4.02355224e-01 -2.79966332e-02 -4.56078649e-01 9.52068418e-02 4.76096362e-01 -5.29555455e-02 -6.93382502e-01 5.52305222e-01 4.17210877e-01 -9.77603316e-01 5.10391295e-01 4.46671508e-02 7.60608256e-01 -5.79438567e-01 -2.01056357e-02 -1.45127690e+00 -5.37475288e-01 3.07323277e-01 3.01914334e-01 1.42316341e+00 5.21426462e-02 -5.40503621e-01 6.37439787e-01 -2.60373533e-01 2.61910945e-01 -4.78312522e-01 -3.53992581e-01 -6.61059737e-01 -2.51100600e-01 2.27383465e-01 8.49085808e-01 1.11448646e+00 -4.84173685e-01 2.73367196e-01 -3.57934147e-01 1.49869755e-01 4.25808936e-01 3.00014913e-01 3.56332719e-01 -1.42173660e+00 -3.92834932e-01 -2.19328016e-01 -6.28482282e-01 -4.48409379e-01 -2.02290818e-01 -7.87854135e-01 2.86975615e-02 -1.76119316e+00 5.46369739e-02 -9.01202485e-02 -2.90273994e-01 4.97690201e-01 -4.29797508e-02 3.02803993e-01 3.57037663e-01 1.60952985e-01 9.03072283e-02 4.08572286e-01 1.44376218e+00 -4.94673520e-01 -1.43686056e-01 -6.58959448e-02 -3.68468851e-01 6.02971196e-01 1.32515025e+00 -4.69243824e-01 -3.46768856e-01 -5.38816988e-01 4.43265662e-02 2.88677722e-01 5.24126470e-01 -1.38559592e+00 -7.44238421e-02 -7.72674978e-02 9.45284963e-01 -7.03670621e-01 1.45317271e-01 -1.21787488e+00 4.71352458e-01 9.12581146e-01 -1.32041126e-01 2.35653281e-01 -1.29331931e-01 2.16538057e-01 -3.72613102e-01 -7.14441299e-01 7.50160098e-01 -3.34175438e-01 -6.20841861e-01 -7.87889138e-02 -3.35341245e-01 -5.38399875e-01 1.28464210e+00 -3.12201619e-01 -3.78573060e-01 -2.61734009e-01 -5.94901204e-01 -2.84150451e-01 1.88350119e-02 1.80879653e-01 6.95554852e-01 -1.46357393e+00 -5.35495996e-01 5.37998915e-01 -2.41478607e-01 -8.46219361e-02 2.58665502e-01 4.44652826e-01 -9.91656601e-01 8.42300057e-02 -9.07253742e-01 -5.58839977e-01 -1.14141107e+00 4.20077920e-01 2.56567955e-01 -2.09749639e-01 -4.54166979e-01 2.54584163e-01 -2.54811533e-02 -7.95510933e-02 -6.23307237e-03 -1.40843019e-01 -6.44503057e-01 6.02000207e-02 3.64252716e-01 5.04735768e-01 1.12503983e-01 -4.79916990e-01 1.25314787e-01 5.88177323e-01 1.82105899e-01 1.55589402e-01 1.30720329e+00 4.00386244e-01 -4.37227398e-01 1.52087480e-01 1.02488935e+00 -2.45742857e-01 -6.38971269e-01 3.47638428e-01 -4.83566344e-01 -4.16334063e-01 2.08627447e-01 -1.11329663e+00 -1.69151235e+00 9.07766104e-01 1.28635263e+00 6.27478004e-01 1.60212028e+00 -5.46081722e-01 8.65638375e-01 3.50304633e-01 2.98584789e-01 -1.01918817e+00 1.10810854e-01 1.71667431e-02 6.35292649e-01 -1.20463204e+00 -2.37035453e-01 -3.85125339e-01 -4.66877550e-01 1.27222669e+00 8.77031386e-01 -1.69434771e-01 5.31115294e-01 2.77392566e-01 2.88242191e-01 5.61249405e-02 -6.76667914e-02 -1.55401751e-01 -9.50584114e-02 7.82151878e-01 2.01748326e-01 1.34929299e-01 -5.85181475e-01 6.18780315e-01 -8.30535404e-03 2.16912717e-01 4.74409014e-01 8.26369226e-01 -4.97458667e-01 -1.12149227e+00 -7.26630270e-01 7.45873392e-01 -5.51381290e-01 7.61886388e-02 -5.96702397e-02 5.47514856e-01 6.35460079e-01 1.01353014e+00 5.82852103e-02 -5.17646492e-01 1.51230469e-01 -3.47045902e-03 3.76084477e-01 -1.47326276e-01 -4.90357935e-01 -1.01544872e-01 -5.28088868e-01 -2.49742001e-01 -6.21159017e-01 -1.70479953e-01 -1.07337296e+00 -2.59377629e-01 -8.44462395e-01 2.04366431e-01 9.55939233e-01 7.42762744e-01 5.02109453e-02 7.77924418e-01 8.47442389e-01 -3.02923411e-01 -5.61142623e-01 -1.01399004e+00 -7.72976577e-01 6.98014915e-01 -7.91724473e-02 -2.95957386e-01 -3.26246381e-01 1.62072062e-01]
[7.676959991455078, 1.907288908958435]
c13f7897-3384-4256-85c6-222f39ed7c89
channel-recurrent-attention-networks-for
2010.03108
null
https://arxiv.org/abs/2010.03108v1
https://arxiv.org/pdf/2010.03108v1.pdf
Channel Recurrent Attention Networks for Video Pedestrian Retrieval
Full attention, which generates an attention value per element of the input feature maps, has been successfully demonstrated to be beneficial in visual tasks. In this work, we propose a fully attentional network, termed {\it channel recurrent attention network}, for the task of video pedestrian retrieval. The main attention unit, \textit{channel recurrent attention}, identifies attention maps at the frame level by jointly leveraging spatial and channel patterns via a recurrent neural network. This channel recurrent attention is designed to build a global receptive field by recurrently receiving and learning the spatial vectors. Then, a \textit{set aggregation} cell is employed to generate a compact video representation. Empirical experimental results demonstrate the superior performance of the proposed deep network, outperforming current state-of-the-art results across standard video person retrieval benchmarks, and a thorough ablation study shows the effectiveness of the proposed units.
['Mehrtash Harandi', 'Lars Petersson', 'Jieming Zhou', 'Pan Ji', 'Pengfei Fang']
2020-10-07
null
null
null
null
['person-retrieval']
['computer-vision']
[ 3.31758559e-01 -4.68979299e-01 -1.05405629e-01 -6.46921322e-02 -6.49204075e-01 -2.54998449e-02 6.67910695e-01 -3.15437496e-01 -3.57983410e-01 5.90136170e-01 6.06874585e-01 1.86678290e-01 1.51827484e-01 -5.35718501e-01 -8.61878991e-01 -7.90900230e-01 -8.10200050e-02 -2.36137047e-01 9.76259857e-02 5.31656183e-02 1.66045710e-01 1.85503915e-01 -1.78323710e+00 6.54671311e-01 5.30960917e-01 1.37397957e+00 4.62384909e-01 7.65930533e-01 1.72042683e-01 1.27195692e+00 -5.97364604e-01 -2.40849674e-01 1.72969699e-02 -2.99013674e-01 -5.15770674e-01 -1.24579743e-01 4.61369187e-01 -7.20947623e-01 -1.12005746e+00 7.20463574e-01 7.06781924e-01 4.39777046e-01 8.03683400e-01 -8.37787747e-01 -1.27053511e+00 4.55318987e-01 -5.71735203e-01 8.28846335e-01 5.04759789e-01 2.21892580e-01 1.13395238e+00 -1.27254629e+00 4.42217529e-01 1.35545611e+00 3.70649338e-01 5.31252980e-01 -6.36750579e-01 -7.04881012e-01 5.10400414e-01 5.34082174e-01 -1.57326961e+00 -3.38898182e-01 5.36602139e-01 -3.85908067e-01 1.32602966e+00 2.28667170e-01 8.96827400e-01 1.36251545e+00 3.68034840e-01 1.38195455e+00 4.65168446e-01 -1.50739595e-01 -1.79835960e-01 -4.03938860e-01 2.86220342e-01 5.69570601e-01 1.54663220e-01 -4.79262806e-02 -6.42467797e-01 1.30327344e-01 1.11391568e+00 6.11045361e-01 -3.79978359e-01 -5.65207191e-02 -1.05234671e+00 5.92715025e-01 1.02713966e+00 1.23757228e-01 -7.30051875e-01 8.11306596e-01 5.31709790e-01 -9.08276141e-02 3.47688079e-01 -1.05489463e-01 2.84898371e-01 4.62614782e-02 -6.47924662e-01 1.98573411e-01 -9.41105187e-02 1.37553179e+00 3.90717834e-01 1.13905333e-01 -1.10755980e+00 8.22113037e-01 3.25612783e-01 6.47158742e-01 2.85227060e-01 -7.69902647e-01 5.76689661e-01 4.62697327e-01 2.09996074e-01 -9.97038066e-01 -2.61932351e-02 -4.60300624e-01 -1.03910327e+00 -5.38742602e-01 -1.68063521e-01 -5.25146872e-02 -1.06746137e+00 1.41548896e+00 -2.85615474e-01 4.02631372e-01 -8.94120038e-02 1.26898086e+00 1.23716497e+00 7.98462331e-01 4.68934625e-01 2.03209165e-02 1.22194135e+00 -1.37442064e+00 -6.95151508e-01 -9.03852135e-02 2.67415158e-02 -4.44087148e-01 7.74941742e-01 -4.26742435e-02 -1.32496512e+00 -9.53461528e-01 -8.47656608e-01 -3.63437325e-01 -3.32657725e-01 4.16273564e-01 6.23970926e-01 2.11876661e-01 -1.07771444e+00 1.36147723e-01 -5.61324835e-01 -3.03885937e-01 7.88899064e-01 3.51004273e-01 -1.17599912e-01 -2.35436127e-01 -1.26324809e+00 2.72586882e-01 8.67248103e-02 6.79696620e-01 -1.13087749e+00 -4.58244234e-01 -9.73761380e-01 5.83157837e-01 1.80266239e-02 -1.00644100e+00 8.62911642e-01 -7.85013378e-01 -1.20361137e+00 5.98375559e-01 -3.60758662e-01 -7.38174379e-01 1.52159214e-01 -6.66613042e-01 -1.67543098e-01 5.31284451e-01 7.72281364e-02 1.09389853e+00 1.22835958e+00 -9.79743302e-01 -6.23268425e-01 -2.07092047e-01 1.05009172e-02 3.28697026e-01 -6.31854534e-01 1.89416349e-01 -1.26209843e+00 -1.12961328e+00 -4.49442178e-01 -7.08898783e-01 -2.57207900e-01 -3.39722455e-01 -3.56507748e-01 -4.62415904e-01 6.58003032e-01 -6.48955107e-01 1.72670758e+00 -2.05465817e+00 5.06599724e-01 2.11050838e-01 3.79946202e-01 4.13666695e-01 -4.49644476e-02 1.49759185e-02 -7.54321888e-02 9.00064781e-03 8.36687535e-02 -3.61666799e-01 -1.07648805e-01 -1.86326906e-01 -5.51490068e-01 3.16604316e-01 3.92385125e-01 1.48640215e+00 -7.97562003e-01 -4.31003600e-01 4.15103883e-01 9.16530073e-01 -6.78185582e-01 3.99139225e-01 -5.90936560e-03 1.22878104e-01 -7.70385504e-01 6.84439361e-01 2.61175632e-01 -5.56139827e-01 -2.26139575e-01 -2.01451778e-01 -1.05433213e-02 -2.23006099e-01 -5.01880050e-01 1.78631115e+00 -1.60972804e-01 9.79705989e-01 -4.51983631e-01 -7.99657285e-01 6.87444746e-01 1.57740355e-01 4.03488040e-01 -1.16002548e+00 3.44225585e-01 -2.78315544e-01 -3.38425070e-01 -4.95276511e-01 8.75390410e-01 9.60133374e-01 -2.28289679e-01 3.67770672e-01 1.64228976e-01 8.80720496e-01 -4.71945927e-02 3.53392333e-01 1.01563334e+00 2.31823027e-01 -3.20543982e-02 -1.98876336e-01 6.82313144e-01 -5.04500568e-01 1.91139981e-01 1.01063609e+00 -2.15589598e-01 9.41270888e-01 2.34763697e-01 -6.26080930e-01 -1.15620863e+00 -7.22391725e-01 1.08727612e-01 1.50722468e+00 3.67341757e-01 -6.13169134e-01 -8.42895687e-01 -3.81017685e-01 -1.08555607e-01 6.85253814e-02 -1.08809185e+00 -2.50407070e-01 -8.21140409e-01 -5.12175262e-01 5.06235957e-01 1.02286255e+00 9.21106577e-01 -1.50192213e+00 -1.02249634e+00 9.89328921e-02 -3.43466729e-01 -1.10080588e+00 -8.53333116e-01 -3.58952612e-01 -1.81251228e-01 -7.46518195e-01 -1.50455236e+00 -8.46463501e-01 6.52038634e-01 6.92918718e-01 1.00520909e+00 3.75182599e-01 -6.32318079e-01 7.50351727e-01 -4.37035918e-01 -2.55322546e-01 6.23989403e-01 1.51141524e-01 -3.58333826e-01 3.64811659e-01 4.45842892e-01 -3.01664397e-02 -1.26246881e+00 1.59494027e-01 -8.60402882e-01 -1.96675897e-01 7.30599999e-01 1.04123604e+00 7.61534452e-01 -4.03154254e-01 5.39897859e-01 -6.32511079e-01 5.77637374e-01 -5.24454355e-01 -1.84548229e-01 3.24704140e-01 2.42715120e-01 -1.05309799e-01 3.43714833e-01 -1.53498381e-01 -9.72141743e-01 -3.84473689e-02 1.65502027e-01 -8.65880251e-01 1.01661704e-01 2.46194914e-01 3.57844830e-02 -2.34993733e-03 2.52561778e-01 4.31281954e-01 -2.04403803e-01 -2.05984205e-01 2.55335301e-01 7.01008618e-01 5.02269626e-01 -4.28934455e-01 1.54917583e-01 4.30505246e-01 -3.61489534e-01 -8.35864842e-01 -6.98389053e-01 -6.19458914e-01 -4.58922148e-01 -5.07860124e-01 1.28290772e+00 -1.29481459e+00 -8.76434386e-01 4.69353318e-01 -1.17963994e+00 -3.09769928e-01 2.06827633e-02 2.59698570e-01 -5.13814092e-01 1.59175694e-01 -5.75371742e-01 -8.60857129e-01 -8.08076799e-01 -1.33008873e+00 1.42339611e+00 5.60746193e-01 8.38587955e-02 -6.43591762e-01 -5.31289577e-01 2.08214149e-01 5.07481694e-01 4.28666025e-02 4.59283501e-01 -2.10567906e-01 -1.09353626e+00 -9.25810486e-02 -7.27516234e-01 -5.55787012e-02 -1.99980408e-01 -1.82325229e-01 -1.08279431e+00 -4.70733374e-01 -7.33150363e-01 -3.34383547e-01 1.64706230e+00 8.20927978e-01 1.82209134e+00 -3.59523408e-02 -6.12609625e-01 6.21727526e-01 1.12650061e+00 2.48790737e-02 1.12366831e+00 2.83734381e-01 9.13727820e-01 1.06250465e-01 4.06604499e-01 5.79496264e-01 4.07261014e-01 7.06364512e-01 2.49879509e-01 -2.92225003e-01 -4.80645806e-01 -8.09509009e-02 2.46929660e-01 3.80532771e-01 -4.99972969e-01 -4.91096407e-01 -6.30429983e-01 6.47782505e-01 -2.14899182e+00 -1.40500247e+00 4.15681720e-01 2.03573704e+00 7.69078135e-02 5.22946902e-02 1.05358124e-01 -2.48030469e-01 7.78960347e-01 3.01208645e-01 -5.56712508e-01 -2.04665035e-01 -2.75898695e-01 2.23625481e-01 3.28348726e-01 1.58725455e-01 -1.44275033e+00 1.07687509e+00 6.57635117e+00 7.23224759e-01 -9.07915592e-01 -1.76186845e-01 8.84682417e-01 -2.07106695e-01 -1.44640401e-01 -5.92075109e-01 -9.69374061e-01 5.90022445e-01 5.06725311e-01 5.48643656e-02 3.61505955e-01 6.40498579e-01 6.32264465e-02 6.54165149e-02 -8.98351252e-01 1.33640337e+00 6.35904074e-01 -1.54244745e+00 6.83505416e-01 -2.35469230e-02 7.84331739e-01 -6.85077459e-02 5.44752002e-01 4.49306548e-01 8.52441508e-03 -1.38392293e+00 6.89370453e-01 1.13807130e+00 9.61455941e-01 -8.91221941e-01 7.01126337e-01 -2.74630666e-01 -1.74365604e+00 -5.82285345e-01 -5.12309611e-01 1.25861824e-01 5.69179319e-02 -1.88079283e-01 -2.02286795e-01 4.23689336e-01 1.29385889e+00 1.34930706e+00 -8.62128139e-01 1.33436882e+00 1.55707136e-01 4.09399867e-01 7.52733573e-02 -1.93803415e-01 4.58541542e-01 1.67370275e-01 5.15815496e-01 1.73505032e+00 1.20850854e-01 2.68600553e-01 1.07632384e-01 6.37858629e-01 -3.08062673e-01 -6.59214556e-02 -6.37579441e-01 1.45909503e-01 3.15088004e-01 1.02107096e+00 -3.93751085e-01 -5.59050918e-01 -4.72180247e-01 1.13417339e+00 4.54120606e-01 9.78910923e-01 -1.06019580e+00 -5.01785934e-01 5.89986086e-01 -1.73068255e-01 9.82222080e-01 -2.79215630e-02 1.08636625e-01 -1.03741944e+00 1.80675223e-01 -5.61445117e-01 6.25794113e-01 -1.10425448e+00 -1.08281529e+00 8.20262313e-01 -6.29499853e-02 -1.17880940e+00 -2.35495150e-01 -4.39745843e-01 -5.00537992e-01 9.54470694e-01 -1.66423619e+00 -1.38694060e+00 -7.47426212e-01 9.03217316e-01 9.30176020e-01 -5.75608432e-01 5.46609402e-01 4.37166899e-01 -8.45605612e-01 1.05114424e+00 -1.57326251e-01 4.06522661e-01 5.09808004e-01 -8.84015143e-01 3.25993598e-01 7.84930527e-01 -1.50636747e-01 9.52742398e-01 5.15421964e-02 -5.32029569e-01 -1.55284762e+00 -1.53689206e+00 3.47281337e-01 -2.58335203e-01 3.74112636e-01 -3.50391507e-01 -6.65450990e-01 7.81127751e-01 5.24241447e-01 3.57592016e-01 4.73981470e-01 -2.97011763e-01 -3.54026705e-01 -6.37827814e-02 -5.07461369e-01 5.41079700e-01 1.31155980e+00 -6.40697420e-01 -3.19792807e-01 1.12854332e-01 8.46568167e-01 -4.18904722e-01 -7.24616706e-01 2.75467664e-01 9.34356153e-01 -6.79265797e-01 1.47088754e+00 -7.01831281e-01 5.97075582e-01 -3.20396990e-01 -2.40601957e-01 -8.09167802e-01 -9.04914021e-01 -6.88043058e-01 -3.99675518e-01 1.00566161e+00 1.42063096e-01 -1.07955791e-01 5.56351364e-01 4.07585025e-01 -2.65179217e-01 -8.02576244e-01 -8.00228715e-01 -2.49613866e-01 -6.64203689e-02 -1.50862500e-01 4.22437698e-01 2.47099981e-01 -2.80382812e-01 4.01431680e-01 -6.78564548e-01 3.52993235e-02 6.63296402e-01 -7.44086877e-02 6.00877702e-01 -7.01311946e-01 -5.18631637e-02 -6.59045994e-01 -6.77394629e-01 -1.71774936e+00 2.01197669e-01 -6.11249328e-01 -2.79996656e-02 -1.67988646e+00 6.62375271e-01 -2.62789339e-01 -6.78028882e-01 2.02802539e-01 -5.91276348e-01 7.23249912e-01 4.04075235e-01 2.54898489e-01 -1.40356886e+00 7.09218144e-01 1.15069687e+00 -4.88381505e-01 -1.36153087e-01 -3.09692115e-01 -6.73621833e-01 3.26194882e-01 2.53818780e-01 2.88920999e-01 -2.84196019e-01 -8.97199929e-01 -9.61497147e-03 -1.44344181e-01 6.84359312e-01 -1.11517012e+00 5.12045741e-01 3.60571712e-01 1.06352365e+00 -9.42921221e-01 5.68444133e-01 -6.24335945e-01 -2.42475718e-01 1.39584005e-01 -6.84775889e-01 1.63789734e-01 1.73854724e-01 8.82949889e-01 -2.64582038e-01 3.48760635e-01 2.64807165e-01 -4.18964103e-02 -1.18497252e+00 7.27191508e-01 -3.25148612e-01 -8.46192911e-02 1.00692284e+00 -1.47854388e-01 -2.79219270e-01 -2.53620058e-01 -7.34961331e-01 5.20649672e-01 -1.43302545e-01 7.03916788e-01 1.10864222e+00 -1.72513378e+00 -7.32331872e-01 3.06029856e-01 3.21897328e-01 -1.16468966e-01 6.63386524e-01 4.68648344e-01 -2.20501304e-01 1.01027560e+00 -2.91028082e-01 -8.40894163e-01 -1.10457671e+00 7.48034775e-01 2.50949800e-01 -2.19733473e-02 -1.00679517e+00 1.03462267e+00 6.03493214e-01 6.82816148e-01 6.71912193e-01 -2.28408381e-01 -8.22257757e-01 -5.99515103e-02 1.12279975e+00 2.41319448e-01 -2.68967450e-01 -8.28319252e-01 -1.34563878e-01 7.40891933e-01 -3.87071580e-01 3.29975158e-01 1.15806925e+00 -3.70794117e-01 1.49831697e-01 -8.86603072e-02 1.16513705e+00 -4.80817705e-01 -1.52212238e+00 -1.86383530e-01 -5.69046438e-01 -4.97116894e-01 -1.17778862e-02 -2.94272244e-01 -1.08762193e+00 9.26757276e-01 5.53696632e-01 -7.95304105e-02 1.18145216e+00 -5.29500656e-02 9.62673187e-01 4.70929503e-01 1.48994759e-01 -9.65572655e-01 4.23335582e-01 5.18200159e-01 1.17205858e+00 -1.05269563e+00 -1.07310176e-01 -2.28873074e-01 -6.91443384e-01 9.04085457e-01 8.83259535e-01 -5.38289607e-01 5.45215130e-01 -1.22813292e-01 -5.22259951e-01 -9.65090618e-02 -7.85133898e-01 -4.92332935e-01 5.34055591e-01 5.89682221e-01 4.75510925e-01 -4.63496484e-02 1.15519859e-01 6.36983156e-01 2.47345626e-01 8.22252557e-02 6.77122734e-04 7.03432977e-01 -4.38431293e-01 -4.61615950e-01 -2.78446883e-01 5.64855754e-01 -4.01809603e-01 -3.68664533e-01 -2.76899248e-01 4.10453975e-01 -1.43120930e-01 6.63219154e-01 4.89549190e-01 -2.71483034e-01 2.26197019e-01 -3.93880248e-01 4.37555015e-01 -2.29218230e-01 -5.08149564e-01 2.62189239e-01 -2.27557838e-01 -8.27680945e-01 -5.65719604e-01 -5.54526031e-01 -1.01555288e+00 2.77957302e-02 7.09498972e-02 1.72321945e-02 -9.26582292e-02 8.09075832e-01 7.01692283e-01 1.08174038e+00 4.76707548e-01 -1.13575602e+00 1.00181259e-01 -9.06694949e-01 7.54621476e-02 4.60671633e-01 4.31188345e-01 -9.65963542e-01 2.87881851e-01 2.42348164e-01]
[9.441946983337402, 0.6835160255432129]
07343c04-d472-4117-93fd-aaedd6793ec2
can-neural-networks-do-arithmetic-a-survey-on
2303.07735
null
https://arxiv.org/abs/2303.07735v1
https://arxiv.org/pdf/2303.07735v1.pdf
Can neural networks do arithmetic? A survey on the elementary numerical skills of state-of-the-art deep learning models
Creating learning models that can exhibit sophisticated reasoning skills is one of the greatest challenges in deep learning research, and mathematics is rapidly becoming one of the target domains for assessing scientific progress in this direction. In the past few years there has been an explosion of neural network architectures, data sets, and benchmarks specifically designed to tackle mathematical problems, reporting notable success in disparate fields such as automated theorem proving, numerical integration, and discovery of new conjectures or matrix multiplication algorithms. However, despite these impressive achievements it is still unclear whether deep learning models possess an elementary understanding of quantities and symbolic numbers. In this survey we critically examine the recent literature, concluding that even state-of-the-art architectures often fall short when probed with relatively simple tasks designed to test basic numerical and arithmetic knowledge.
['Alberto Testolin']
2023-03-14
null
null
null
null
['numerical-integration', 'automated-theorem-proving', 'automated-theorem-proving']
['miscellaneous', 'miscellaneous', 'reasoning']
[-1.94684893e-01 -1.03545956e-01 -2.23597452e-01 -2.23596275e-01 -2.01922163e-01 -6.41880989e-01 7.34340131e-01 5.51803887e-01 -4.35138553e-01 8.27689946e-01 -2.87558585e-01 -9.73234534e-01 -4.46500242e-01 -1.21110022e+00 -7.15499222e-01 -2.55844891e-01 -3.75246882e-01 5.26835799e-01 -2.19003975e-01 -5.23171842e-01 5.66715717e-01 7.35024750e-01 -1.34258640e+00 8.90057236e-02 8.95370960e-01 1.14068627e+00 -5.63145936e-01 7.82502770e-01 -3.47819000e-01 1.35976529e+00 -5.87597668e-01 -6.86285436e-01 2.14038014e-01 -3.53685528e-01 -9.71815825e-01 -8.58779728e-01 5.89076579e-01 -3.99743825e-01 -5.18593788e-01 1.15479875e+00 8.26887861e-02 6.68514818e-02 3.96888673e-01 -1.49970329e+00 -8.68238091e-01 7.50834882e-01 -2.18655810e-01 5.37041664e-01 1.54518858e-01 2.58585483e-01 1.23982167e+00 -4.63477015e-01 2.28397295e-01 1.06942832e+00 9.03282762e-01 3.30524653e-01 -1.27727914e+00 -8.88032794e-01 -2.00579226e-01 6.11452937e-01 -1.06507051e+00 -2.03544155e-01 7.14670539e-01 -3.81650984e-01 9.50039804e-01 8.03056732e-02 9.51819718e-01 5.53608716e-01 4.14165884e-01 6.02828860e-01 9.44259167e-01 -3.43557954e-01 3.22474509e-01 -1.89598933e-01 3.09556752e-01 7.87036777e-01 5.52626014e-01 9.90807340e-02 -3.06288928e-01 1.99838653e-01 1.05948532e+00 -3.90113771e-01 3.43147740e-02 -4.08415198e-01 -1.51690888e+00 1.07360387e+00 4.57908094e-01 5.25841653e-01 -5.06422594e-02 5.62164307e-01 6.11999512e-01 5.64512789e-01 -2.83016209e-02 1.20214176e+00 -5.99097908e-01 -3.97468626e-01 -9.62200642e-01 7.94652522e-01 9.79053020e-01 4.93842632e-01 5.12476623e-01 4.04175043e-01 2.67422050e-01 9.96632054e-02 -2.26545289e-01 1.49206653e-01 1.92603528e-01 -1.13211024e+00 4.55090642e-01 6.63539350e-01 -1.56755880e-01 -1.21343291e+00 -4.84327257e-01 -7.19065547e-01 -1.12661982e+00 4.26955193e-01 8.33364069e-01 -1.69891939e-01 -4.03449863e-01 1.65239465e+00 7.56399287e-03 2.54185170e-01 7.05354065e-02 5.43031156e-01 8.92309546e-01 6.06039107e-01 -1.24877598e-02 1.63785815e-01 8.28852117e-01 -4.89289403e-01 -3.15627605e-01 -7.42847621e-02 8.16166520e-01 -4.19056773e-01 6.43507957e-01 7.46353209e-01 -1.37068021e+00 -6.87323272e-01 -1.20905709e+00 -4.14563596e-01 -6.46072388e-01 -3.40602219e-01 1.44824946e+00 6.85209632e-01 -1.08585441e+00 9.25030410e-01 -6.42258465e-01 1.62943393e-01 7.33924031e-01 5.17916262e-01 -7.35655352e-02 -3.78729030e-02 -1.45513737e+00 1.19313443e+00 6.17761016e-01 -1.14886360e-02 -6.22793853e-01 -1.26095724e+00 -7.79859424e-01 4.80309844e-01 2.43689582e-01 -5.74853957e-01 1.31369090e+00 -6.45823777e-01 -1.25460947e+00 6.64979160e-01 2.64522403e-01 -9.08035278e-01 4.10101712e-01 6.74001547e-03 -3.58637065e-01 -7.60677010e-02 -2.73578137e-01 5.03144860e-01 2.21518829e-01 -6.65269315e-01 -4.86147761e-01 -2.58809090e-01 5.17132401e-01 -1.17720447e-01 -2.01087683e-01 -2.95953937e-02 3.56114507e-01 -4.54039603e-01 1.63962040e-02 -3.39329153e-01 -1.40229806e-01 1.20312333e-01 -2.00936440e-02 -5.68388522e-01 2.92102963e-01 -3.36859286e-01 1.03205872e+00 -1.71081030e+00 2.55172938e-01 8.45040753e-02 5.98612309e-01 3.56066823e-01 7.21440911e-02 1.75692409e-01 -3.00138623e-01 3.09637517e-01 2.96096746e-02 4.57129538e-01 1.72768742e-01 -1.18130453e-01 -4.92963880e-01 3.86430144e-01 3.81242007e-01 1.25136960e+00 -1.19382989e+00 -4.18769240e-01 3.13678175e-01 1.36495009e-01 -5.62900782e-01 -2.11017191e-01 -6.65329933e-01 2.63869345e-01 -2.42343187e-01 4.58578885e-01 3.95723343e-01 -4.80384082e-01 1.79612264e-01 1.42986029e-01 -7.77156577e-02 5.58995724e-01 -1.22188461e+00 1.34355140e+00 -2.65753895e-01 1.11161745e+00 -2.19980285e-01 -1.51534843e+00 6.12476766e-01 1.73467740e-01 4.05426800e-01 -8.99737060e-01 4.25274491e-01 3.05796653e-01 6.93282485e-01 -2.86233574e-01 4.12712783e-01 -2.56289184e-01 7.01156855e-02 4.24928993e-01 3.40987854e-02 -5.75670004e-01 5.73534667e-01 4.60780077e-02 1.01412773e+00 2.56169010e-02 3.67868602e-01 -8.65734220e-02 7.62889326e-01 3.78358305e-01 2.86481500e-01 6.68664634e-01 -1.45372331e-01 -1.02013409e-01 7.62779951e-01 -9.23740506e-01 -1.19298697e+00 -1.08121276e+00 -2.10849196e-01 9.97251868e-01 -2.97045439e-01 -2.41198316e-01 -4.80049133e-01 2.53944471e-03 2.07988858e-01 7.34520078e-01 -5.81469238e-01 -1.64211750e-01 -8.37070465e-01 -5.12681127e-01 9.34927106e-01 8.26902330e-01 8.06959987e-01 -1.11821771e+00 -6.69002116e-01 2.01958194e-01 1.74690306e-01 -1.01320744e+00 6.43107235e-01 3.21931869e-01 -1.19131219e+00 -1.34328914e+00 -4.35793251e-01 -9.72936571e-01 2.45537996e-01 -1.69607326e-01 1.50330198e+00 3.03013474e-01 -3.40873420e-01 -3.32727581e-02 1.23981573e-01 -4.71422821e-01 -5.99945664e-01 1.60297588e-01 6.11609668e-02 -7.28162706e-01 4.57310021e-01 -6.04092300e-01 -1.00781895e-01 -4.35295194e-01 -8.39391291e-01 1.34192640e-02 4.25046295e-01 8.35977912e-01 -2.71845870e-02 3.81105334e-01 6.73812449e-01 -4.23545241e-01 7.44171560e-01 -3.05085957e-01 -9.28272367e-01 1.96960583e-01 -4.56045777e-01 1.69965088e-01 1.11783755e+00 -2.57021338e-01 -4.77904141e-01 -6.90089881e-01 -5.61487749e-02 -2.04865970e-02 -1.51205435e-01 8.67239714e-01 2.52871096e-01 -3.28946084e-01 6.61610305e-01 1.89305112e-01 -3.24851833e-02 -1.78665540e-03 3.47858280e-01 -3.26816700e-02 6.99927449e-01 -1.03933895e+00 9.99807239e-01 5.52479289e-02 6.06932163e-01 -7.14029372e-01 -9.79007840e-01 1.16610564e-01 -5.42505980e-01 -7.10438984e-03 5.43038189e-01 -5.54384768e-01 -1.31238925e+00 4.71387923e-01 -1.12252355e+00 -4.70368594e-01 -1.79367244e-01 4.62012082e-01 -5.49689889e-01 9.72583964e-02 -7.70140350e-01 -6.06751978e-01 -1.62627757e-01 -1.05449569e+00 1.92716092e-01 2.31127441e-01 -4.01184529e-01 -1.35081089e+00 1.46460468e-02 1.66277498e-01 6.01907969e-01 3.98148358e-01 1.70807123e+00 -6.23244941e-01 -6.97004855e-01 -2.88480848e-01 -4.84492868e-01 4.08814937e-01 -2.03765929e-01 5.87094389e-02 -8.02718043e-01 1.06939167e-01 -1.08622439e-01 -7.15996265e-01 6.86586499e-01 1.91021815e-01 1.55477071e+00 -1.15781970e-01 1.04326651e-01 6.70225024e-01 1.24610507e+00 3.51512693e-02 7.12277949e-01 4.52391893e-01 2.78739363e-01 2.34821320e-01 7.38974437e-02 3.91655341e-02 3.62197727e-01 1.64853092e-02 4.27975774e-01 2.55493701e-01 1.61669046e-01 -3.54686193e-02 -1.20347023e-01 6.19508803e-01 -3.04896921e-01 2.04558223e-01 -1.37198973e+00 4.13953632e-01 -1.32585061e+00 -1.32079566e+00 -9.01678577e-03 1.99118316e+00 1.16604733e+00 6.03558660e-01 -4.39464413e-02 7.09702432e-01 2.03015506e-01 1.09502010e-01 -6.35857582e-01 -6.28984034e-01 8.84718895e-02 9.52758610e-01 6.73450604e-02 2.76727825e-01 -9.63133216e-01 9.03758526e-01 6.92445946e+00 5.45904994e-01 -1.08267128e+00 -6.08969867e-01 7.64953494e-01 2.57952094e-01 -2.30977282e-01 -3.32204342e-01 -5.62483847e-01 4.95057292e-02 1.04735565e+00 -3.10043365e-01 5.76506019e-01 8.18642020e-01 -1.89475507e-01 -8.65726694e-02 -1.53680444e+00 9.04714942e-01 -1.60714179e-01 -1.92928803e+00 3.78976017e-02 -1.00327283e-01 7.03449845e-01 -2.26953998e-01 3.81066829e-01 8.00736129e-01 4.97496575e-01 -1.81240606e+00 3.77182305e-01 3.25441599e-01 7.70641923e-01 -8.63484621e-01 6.83768570e-01 2.81192303e-01 -8.38603497e-01 -1.22167662e-01 -2.48222321e-01 -9.90570366e-01 -5.07298291e-01 4.07533556e-01 -5.58688164e-01 1.74374133e-01 4.87858802e-01 5.60671806e-01 -5.73145390e-01 1.13312769e+00 -1.73299208e-01 6.45325482e-01 -2.09351376e-01 -5.11257470e-01 4.13773865e-01 -2.25807145e-01 3.22770998e-02 8.17214787e-01 -1.20897539e-01 2.00182572e-01 -2.54020482e-01 1.23208857e+00 -3.25553894e-01 -1.54141113e-01 -4.11747307e-01 -4.99495000e-01 3.65372926e-01 9.66262221e-01 -8.15497041e-01 -4.39678103e-01 -4.70692664e-01 1.33868068e-01 5.31728327e-01 2.03114256e-01 -9.76392388e-01 -5.53903520e-01 8.01484883e-01 -2.02066526e-01 1.71103515e-02 -8.21953773e-01 -1.08802664e+00 -1.12400699e+00 -1.74698055e-01 -1.29126072e+00 1.24429993e-01 -5.94417155e-01 -9.92140651e-01 -1.16053067e-01 -1.32854562e-02 -5.73571801e-01 -2.73252696e-01 -1.14246452e+00 -6.46107614e-01 8.50034833e-01 -1.38819087e+00 -4.37212050e-01 -2.63707429e-01 2.77214795e-01 1.37478590e-01 -2.95340002e-01 1.01945794e+00 6.40655085e-02 -2.54800677e-01 5.10530889e-01 2.71717250e-01 6.83023453e-01 9.80647802e-02 -1.11866570e+00 5.41063070e-01 5.16699970e-01 2.50776291e-01 9.16175842e-01 6.76044166e-01 -2.42625549e-01 -1.63459325e+00 -5.10348856e-01 8.80337179e-01 -3.34705293e-01 1.13967443e+00 -2.77789116e-01 -9.43936467e-01 6.72505677e-01 -8.06912035e-02 -3.11963893e-02 4.96905297e-01 3.75072837e-01 -6.86149120e-01 -2.10089296e-01 -1.00371444e+00 7.04473972e-01 5.93519211e-01 -4.89746392e-01 -8.25139940e-01 2.13589981e-01 5.40738225e-01 -6.94543958e-01 -9.48806942e-01 4.35727775e-01 6.77770197e-01 -1.02266991e+00 1.17006493e+00 -1.08493376e+00 1.09881079e+00 9.95428488e-02 1.08992690e-02 -9.98165905e-01 -2.35185042e-01 -3.80336136e-01 -4.91539896e-01 7.05792427e-01 1.09111175e-01 -4.75240856e-01 1.01891589e+00 5.27334630e-01 4.43191230e-02 -1.04067814e+00 -5.60529053e-01 -6.88255966e-01 1.08476222e+00 -5.80303669e-01 6.12146139e-01 1.14168251e+00 2.35470384e-01 3.55974287e-01 4.00516421e-01 -3.11246544e-01 4.44130242e-01 3.39507163e-01 7.64159501e-01 -1.58522952e+00 -1.28237486e-01 -1.26797736e+00 -7.70754695e-01 -8.17487538e-01 4.18072343e-01 -1.08789504e+00 -5.61012387e-01 -1.62167382e+00 -1.70072224e-02 -4.37077641e-01 -2.18751684e-01 4.64178920e-01 8.87069851e-02 2.63508201e-01 2.81574354e-02 -4.53058004e-01 -4.35055941e-01 3.06151420e-01 1.28303552e+00 -3.08630377e-01 2.01296702e-01 -4.43177819e-02 -8.02285612e-01 8.99110258e-01 1.01969457e+00 1.03614755e-01 -1.31177366e-01 -4.96986300e-01 1.01318049e+00 7.59388581e-02 7.97202826e-01 -1.54440665e+00 2.73741663e-01 -4.72214341e-01 9.13076818e-01 -6.21513665e-01 3.92735861e-02 -5.18736064e-01 -4.87625062e-01 6.27665520e-01 -6.53140545e-01 3.14605474e-01 6.25449836e-01 4.52183709e-02 -1.30188629e-01 -2.00609237e-01 7.83858657e-01 -2.55468160e-01 -8.01632762e-01 1.53815687e-01 -1.43172279e-01 5.98695397e-01 8.14545333e-01 -1.64541677e-02 -3.78790438e-01 -3.65668565e-01 -3.11874449e-01 2.92119205e-01 1.41237855e-01 2.73261040e-01 4.95783687e-01 -1.07167172e+00 -6.73620701e-01 -1.57074764e-01 -3.76373768e-01 2.73492873e-01 -1.38577357e-01 5.53243577e-01 -1.01651692e+00 9.45496023e-01 -4.58454669e-01 -1.65571064e-01 -6.90270722e-01 6.27803147e-01 5.91557741e-01 -5.31632960e-01 -3.94146174e-01 8.14563870e-01 -2.50400484e-01 -4.86111909e-01 5.22270858e-01 -7.64574349e-01 1.50756100e-02 -2.86734670e-01 5.14240682e-01 7.31962323e-01 1.24831028e-01 -1.41576387e-03 -1.70925975e-01 2.33060256e-01 9.93102491e-02 3.12427968e-01 1.46521330e+00 7.20143676e-01 -3.69525224e-01 6.02409482e-01 9.70574617e-01 -4.26758021e-01 -6.02748871e-01 -9.15872604e-02 5.04212156e-02 2.05525368e-01 3.25027184e-04 -8.69976521e-01 -7.79016793e-01 1.38715136e+00 -7.00996071e-02 3.16917062e-01 8.52125704e-01 -4.80685204e-01 7.21144974e-01 1.02105236e+00 5.06632805e-01 -8.31432641e-01 1.43523097e-01 1.24899232e+00 5.90173960e-01 -1.28155434e+00 4.41378772e-01 3.97902206e-02 1.13970034e-01 1.62077022e+00 6.80786729e-01 -3.38118345e-01 6.45864248e-01 3.30220371e-01 -4.75933552e-01 -6.17659204e-02 -5.54962695e-01 1.16343275e-01 2.47254789e-01 3.97594452e-01 8.34535241e-01 -2.05193013e-01 4.39371765e-02 3.41036290e-01 -9.04473007e-01 2.96654195e-01 5.32280862e-01 7.22127914e-01 -4.95296717e-01 -7.78560638e-01 -3.12557369e-01 6.57892466e-01 -4.88904893e-01 -4.14681405e-01 -1.41164020e-01 1.15465844e+00 5.11206426e-02 5.75505614e-01 2.30568469e-01 -1.37388542e-01 -2.34990925e-01 3.45081151e-01 1.03291750e+00 -4.38613832e-01 -6.99865758e-01 -8.46018076e-01 -1.00178018e-01 -2.52172828e-01 -1.68465465e-01 -4.45633173e-01 -1.44674027e+00 -9.61880803e-01 5.66422865e-02 1.45393163e-01 4.88139033e-01 1.18881905e+00 -1.53308481e-01 7.50155509e-01 3.37892994e-02 -6.20039105e-01 -9.56502199e-01 -8.63433838e-01 -3.27866286e-01 -8.84958655e-02 4.92377639e-01 -5.73641658e-01 -1.87171668e-01 -1.81466550e-01]
[9.254510879516602, 7.161590099334717]
372e1a7f-7a3a-4cdf-af48-6fd0413ca8d8
pac-assisted-value-factorisation-with
2206.11420
null
https://arxiv.org/abs/2206.11420v3
https://arxiv.org/pdf/2206.11420v3.pdf
PAC: Assisted Value Factorisation with Counterfactual Predictions in Multi-Agent Reinforcement Learning
Multi-agent reinforcement learning (MARL) has witnessed significant progress with the development of value function factorization methods. It allows optimizing a joint action-value function through the maximization of factorized per-agent utilities due to monotonicity. In this paper, we show that in partially observable MARL problems, an agent's ordering over its own actions could impose concurrent constraints (across different states) on the representable function class, causing significant estimation error during training. We tackle this limitation and propose PAC, a new framework leveraging Assistive information generated from Counterfactual Predictions of optimal joint action selection, which enable explicit assistance to value function factorization through a novel counterfactual loss. A variational inference-based information encoding method is developed to collect and encode the counterfactual predictions from an estimated baseline. To enable decentralized execution, we also derive factorized per-agent policies inspired by a maximum-entropy MARL framework. We evaluate the proposed PAC on multi-agent predator-prey and a set of StarCraft II micromanagement tasks. Empirical results demonstrate improved results of PAC over state-of-the-art value-based and policy-based multi-agent reinforcement learning algorithms on all benchmarks.
['Vaneet Aggarwal', 'Tian Lan', 'Hanhan Zhou']
2022-06-22
null
null
null
null
['starcraft-ii']
['playing-games']
[ 9.33378178e-04 3.37637067e-01 -7.03318775e-01 -7.28595704e-02 -1.04379749e+00 -4.86216396e-01 7.58728504e-01 1.25225976e-01 -8.62132728e-01 1.56500614e+00 4.47000980e-01 -1.93858057e-01 -4.48424280e-01 -7.45422244e-01 -8.85571718e-01 -9.10247803e-01 -6.83379650e-01 7.03703523e-01 -2.49959201e-01 -3.14699680e-01 1.89516563e-02 -3.92066725e-02 -1.31687689e+00 3.90387863e-01 7.54361808e-01 9.52060044e-01 8.90948176e-02 6.61737859e-01 3.20014536e-01 1.33430743e+00 -6.44056678e-01 -4.94902045e-01 5.52560925e-01 -3.72807205e-01 -5.86950481e-01 -1.54029906e-01 -1.58371627e-01 -9.40099418e-01 -2.42287472e-01 9.04352486e-01 4.39851731e-01 4.73658800e-01 6.73738301e-01 -1.83149004e+00 -3.37780595e-01 1.17881715e+00 -5.31198561e-01 -1.75321057e-01 2.00210989e-01 6.36239350e-01 1.39208126e+00 4.43545468e-02 5.32073319e-01 1.66264749e+00 2.60085315e-01 8.45506847e-01 -1.45269680e+00 -3.12609583e-01 4.46296781e-01 2.69246399e-01 -5.45602739e-01 -4.82851192e-02 5.41304290e-01 -1.22144274e-01 1.28955257e+00 4.91448939e-02 9.68147695e-01 1.23477244e+00 6.27071977e-01 1.37311864e+00 1.28786016e+00 -9.97485146e-02 7.30385900e-01 -1.66452482e-01 -7.49645889e-01 6.63807034e-01 2.51128674e-01 6.90079510e-01 -5.55276155e-01 -7.26344049e-01 5.95860183e-01 2.43498031e-02 7.34889656e-02 -6.75348938e-01 -1.15248823e+00 1.09791851e+00 1.23441242e-01 -4.89854544e-01 -8.97097051e-01 7.07553566e-01 5.39891958e-01 5.77562153e-01 2.66708583e-01 6.78598702e-01 -8.23552370e-01 -4.41485137e-01 -4.32931870e-01 8.67719829e-01 9.18038368e-01 5.06323040e-01 6.20160639e-01 3.45473915e-01 -6.06903374e-01 4.67553824e-01 5.68201244e-01 7.01789260e-01 5.75752079e-01 -1.54333842e+00 5.68783462e-01 3.10773492e-01 6.15254462e-01 -2.76182264e-01 -3.51213902e-01 -2.17902213e-01 -3.51948768e-01 7.88061738e-01 2.88534850e-01 -7.79579878e-01 -5.63349962e-01 2.12380505e+00 5.04250586e-01 1.83737800e-01 5.09576201e-01 7.27732360e-01 -2.30757415e-01 5.75448513e-01 1.68056801e-01 -7.08830893e-01 1.03480399e+00 -9.52578902e-01 -7.07423449e-01 -4.43271995e-02 5.85742891e-01 4.26854119e-02 5.48841238e-01 4.44755971e-01 -1.10491216e+00 1.59898758e-01 -9.11768675e-01 6.50072575e-01 1.03171468e-01 -3.72921646e-01 9.94818926e-01 5.74614704e-01 -7.95354724e-01 8.47948074e-01 -1.11341822e+00 4.22253072e-01 6.88251197e-01 4.49884146e-01 -1.05356358e-01 3.31653357e-01 -1.16013098e+00 1.03891122e+00 5.14834166e-01 -3.71881634e-01 -1.74040949e+00 -8.97633195e-01 -8.24619889e-01 1.79158673e-01 1.14615273e+00 -7.83721626e-01 1.72279561e+00 -1.12395585e+00 -2.19870710e+00 -8.04744139e-02 4.97983068e-01 -1.29634833e+00 7.25045204e-01 -2.80800611e-01 1.82695035e-02 2.05180556e-01 1.78009570e-01 6.62814796e-01 1.08631921e+00 -1.09034920e+00 -9.64785993e-01 -1.39334694e-01 6.20362103e-01 5.05293429e-01 -1.37928993e-01 -5.02657712e-01 5.87661743e-01 -5.58714330e-01 -1.17985570e+00 -9.60332334e-01 -6.37093306e-01 -1.94511920e-01 1.30440397e-02 -3.08873087e-01 3.07234168e-01 -4.75784093e-01 1.06780303e+00 -1.65924716e+00 5.12117863e-01 -1.18018143e-01 6.60343915e-02 2.37165261e-02 -4.94971484e-01 5.81305385e-01 3.67470354e-01 -1.12707712e-01 -3.91453087e-01 -3.49867314e-01 6.45905197e-01 4.42358732e-01 -4.67689216e-01 6.11498773e-01 7.66036436e-02 8.77340138e-01 -1.15771735e+00 -2.67007440e-01 1.98932275e-01 4.94866341e-04 -1.10046911e+00 3.82579446e-01 -9.77307796e-01 1.45085305e-01 -5.58685422e-01 2.93477774e-01 4.39755172e-01 2.20940962e-01 7.20639467e-01 3.34149688e-01 -6.34455159e-02 7.19335824e-02 -1.14301550e+00 1.75555921e+00 -6.69028938e-01 -3.76383886e-02 5.19005992e-02 -9.80142236e-01 2.94235289e-01 3.57025892e-01 1.01809931e+00 -6.20204806e-01 2.01902896e-01 2.42220866e-03 1.41893119e-01 -2.14106500e-01 5.24940372e-01 -2.96175271e-01 -3.94665807e-01 7.93282688e-01 3.07090372e-01 -4.86099310e-02 3.95333380e-01 1.17781594e-01 1.05915248e+00 6.44609809e-01 8.15406919e-01 -2.69761592e-01 3.32937509e-01 -6.31527882e-03 9.72520828e-01 1.03548527e+00 -3.67097616e-01 -3.37529331e-01 8.88695836e-01 -4.11765218e-01 -9.17648077e-01 -1.03934050e+00 3.90757352e-01 1.21984708e+00 -1.50751933e-01 -3.33162278e-01 -6.05316162e-01 -1.01618981e+00 6.82485282e-01 1.07375193e+00 -8.68065298e-01 -1.05153620e-01 -4.47150111e-01 -9.74517345e-01 3.56750667e-01 3.48945111e-01 3.94410223e-01 -1.08815515e+00 -1.33206987e+00 5.69484591e-01 1.18385486e-01 -5.04330456e-01 -2.77196497e-01 1.84975907e-01 -6.00475907e-01 -1.05502963e+00 -5.66541076e-01 8.66818801e-02 2.19354436e-01 -2.95736015e-01 9.36616123e-01 -4.50458914e-01 7.02650100e-02 7.71080852e-01 -1.74661651e-01 -3.92156869e-01 -5.08075178e-01 -2.16846123e-01 4.77385223e-01 2.46933755e-02 -4.34817746e-02 -4.86553192e-01 -6.70408905e-01 -5.41488640e-02 -8.44975054e-01 1.88379940e-02 5.43002248e-01 1.30379689e+00 4.07310873e-01 -1.19676217e-02 8.97384405e-01 -4.95710254e-01 9.34372604e-01 -7.73312032e-01 -1.12557852e+00 5.10201991e-01 -6.28230512e-01 8.34768772e-01 9.04153228e-01 -5.13485849e-01 -1.38842607e+00 -2.61453595e-02 2.86631346e-01 -2.30351195e-01 2.95662999e-01 3.58871222e-01 -5.20854965e-02 4.52448130e-01 3.23657244e-01 2.53950864e-01 4.76955324e-01 -1.72330797e-01 6.72888279e-01 4.16205019e-01 7.43082389e-02 -1.17240787e+00 4.05213118e-01 4.42564696e-01 2.19400108e-01 -2.89888173e-01 -5.19881010e-01 9.15511325e-02 1.04020268e-01 -2.50611812e-01 6.00326896e-01 -1.08476985e+00 -1.71258080e+00 3.57876688e-01 -9.49642062e-01 -7.69085705e-01 -6.90497875e-01 6.64726079e-01 -1.36322951e+00 2.29903638e-01 -3.22702318e-01 -1.32389235e+00 -2.35469520e-01 -1.20583928e+00 8.04352939e-01 1.77967548e-01 2.54936099e-01 -8.95778418e-01 5.83407342e-01 7.18432814e-02 2.45159149e-01 2.84066230e-01 6.61835074e-01 -4.08959836e-01 -5.81555426e-01 3.98664117e-01 4.24907684e-01 2.69356757e-01 -5.76924160e-02 -3.27872664e-01 -4.96919483e-01 -7.52484858e-01 -1.36061966e-01 -7.45107591e-01 8.34690392e-01 4.82993692e-01 6.05009854e-01 -1.10718501e+00 -1.13598771e-01 3.23695391e-01 1.52646339e+00 3.84099662e-01 9.09742564e-02 4.78570849e-01 1.05006382e-01 2.42528707e-01 8.62053037e-01 1.39929581e+00 5.89135885e-01 6.97100699e-01 9.21071708e-01 9.42356050e-01 4.51550752e-01 -4.78309631e-01 1.08287609e+00 -8.30701888e-02 -1.56990007e-01 -2.75089860e-01 -3.28742981e-01 5.43333292e-01 -2.39215922e+00 -1.33843708e+00 6.99287295e-01 2.24071455e+00 1.05038488e+00 -1.65671274e-01 6.83962107e-01 -4.72255796e-01 1.99312150e-01 1.83704287e-01 -1.29198563e+00 -4.79869843e-01 9.67422500e-02 -1.97299849e-03 7.99122572e-01 8.06339622e-01 -1.02540302e+00 7.68131196e-01 5.60291910e+00 9.44094956e-01 -6.74134552e-01 2.24010482e-01 3.71483684e-01 -6.67146564e-01 -5.41683972e-01 -1.74038321e-01 -7.09798872e-01 4.98234659e-01 9.16520536e-01 -6.50132716e-01 1.08010411e+00 1.11298239e+00 2.87472993e-01 -3.55167419e-01 -1.08264208e+00 5.95241308e-01 -2.94228286e-01 -1.33786511e+00 -1.98860690e-02 1.47271276e-01 9.83794868e-01 2.03162163e-01 1.05373248e-01 6.97981000e-01 1.29086137e+00 -7.46483445e-01 9.34355497e-01 3.95284861e-01 4.75735188e-01 -1.16478431e+00 4.82214242e-01 4.78270799e-01 -9.67157543e-01 -8.38496685e-01 -3.60108614e-01 -5.07923484e-01 1.35277525e-01 -1.28475986e-02 -9.68096673e-01 5.98078549e-01 1.17733508e-01 5.62845886e-01 1.62450120e-01 6.40745640e-01 -3.83026749e-01 3.51536006e-01 -3.32445055e-01 -3.65023911e-01 5.65423369e-01 -1.93183348e-01 7.64570892e-01 6.40106976e-01 1.14213988e-01 -1.84291616e-01 4.80858773e-01 7.42711663e-01 -6.63542524e-02 -1.51923358e-01 -4.63505000e-01 -2.11606145e-01 3.32944036e-01 1.08457804e+00 -2.39867881e-01 -2.39888921e-01 -3.06333303e-01 7.11900711e-01 5.04762530e-01 2.55925059e-01 -1.04163110e+00 2.11158186e-01 1.40298128e+00 -5.74831128e-01 4.28957164e-01 -4.43589650e-02 2.31407240e-01 -1.44323552e+00 -1.95340782e-01 -1.10680342e+00 6.51096463e-01 -6.87343627e-02 -1.32454038e+00 1.46991923e-01 2.93420076e-01 -1.04093802e+00 -1.09745824e+00 -5.38385689e-01 -3.89247030e-01 4.13201779e-01 -1.56304073e+00 -8.93261492e-01 7.41001308e-01 4.57495332e-01 5.52422643e-01 -5.88329196e-01 8.31722677e-01 -2.18834579e-01 -4.45681393e-01 4.99103457e-01 6.02870107e-01 -3.42094362e-01 2.81258762e-01 -1.53269744e+00 1.67083386e-02 5.40048718e-01 -9.67810303e-02 2.95924582e-02 7.35498309e-01 -6.44888937e-01 -1.80979502e+00 -9.16302741e-01 -1.23394012e-01 -1.70172408e-01 8.97175491e-01 -2.62052827e-02 -1.06859230e-01 6.71842456e-01 4.97390807e-01 -2.14433610e-01 4.50182855e-01 -1.07136607e-01 -7.15269670e-02 -1.24035418e-01 -1.35587704e+00 7.46059358e-01 7.57285476e-01 -7.02171698e-02 -6.60639107e-01 1.12594873e-01 8.71695757e-01 -1.98518664e-01 -8.75532806e-01 2.08732672e-02 7.41121948e-01 -7.91386366e-01 8.70224416e-01 -1.23165011e+00 5.97655535e-01 -1.19542174e-01 -3.59385133e-01 -1.93340254e+00 -1.14691265e-01 -1.15275264e+00 -5.85125744e-01 6.96111262e-01 2.14470774e-01 -7.61599839e-01 7.20213592e-01 5.89311361e-01 8.78902748e-02 -8.36403072e-01 -1.36487031e+00 -1.01184762e+00 2.54169077e-01 -2.96592325e-01 7.68500686e-01 5.24155915e-01 3.64793748e-01 8.53901356e-02 -9.46587443e-01 8.56445637e-03 1.16261804e+00 1.79251462e-01 6.97226882e-01 -6.09041870e-01 -1.03938997e+00 -4.33294892e-01 1.36379488e-02 -8.15634847e-01 6.46081865e-01 -8.26641023e-01 6.43978419e-04 -1.26473927e+00 2.10695878e-01 -3.37241627e-02 -4.05289948e-01 6.98035955e-01 -9.23118591e-02 -5.13833404e-01 8.44970226e-01 -3.64275157e-01 -9.73299146e-01 1.18349040e+00 1.28106689e+00 -3.07166487e-01 -1.96088210e-01 -1.00426748e-02 -5.28979301e-01 5.61940432e-01 8.49689066e-01 -5.32867074e-01 -6.74673796e-01 -1.63926005e-01 4.17685002e-01 7.60071099e-01 2.28533104e-01 -6.45271480e-01 -1.43355474e-01 -8.88990343e-01 -8.03364590e-02 -9.63267386e-02 4.03724790e-01 -7.95816720e-01 -2.39696391e-02 9.80218291e-01 -6.14052892e-01 6.87014684e-02 1.42594233e-01 9.72351134e-01 1.04825027e-01 -2.25996315e-01 5.42287171e-01 -3.03989440e-01 -6.54312372e-01 3.08042318e-01 -5.99920630e-01 2.12201238e-01 1.34658110e+00 5.79137266e-01 -3.58167291e-01 -4.72638845e-01 -3.70442539e-01 7.84955263e-01 1.05784863e-01 1.74735740e-01 5.46952546e-01 -1.35905182e+00 -9.66397285e-01 -9.70876887e-02 -2.87527114e-01 -4.39103037e-01 3.83265972e-01 4.70035881e-01 1.05420671e-01 2.71450073e-01 -6.89276695e-01 8.49629045e-02 -8.29861701e-01 5.27653456e-01 4.31635678e-01 -1.00481570e+00 -2.36133039e-01 4.12345499e-01 -8.18188637e-02 -3.35972279e-01 1.49157736e-02 -2.85659909e-01 5.61473742e-02 2.57235974e-01 5.77340424e-01 6.59244359e-01 -6.36461139e-01 -6.11579493e-02 -1.69395670e-01 -3.33849639e-01 -1.66016638e-01 -7.68898904e-01 1.71007776e+00 6.38932362e-02 1.94887683e-01 1.17384732e-01 7.61449337e-01 -4.26690936e-01 -2.16701674e+00 -1.31656662e-01 -1.20615214e-01 -4.35063332e-01 3.55943665e-02 -1.14783597e+00 -7.77451038e-01 4.03203487e-01 3.14866513e-01 1.44622833e-01 7.97676504e-01 -4.89541501e-01 5.08008480e-01 6.79156840e-01 8.55292857e-01 -1.40780234e+00 8.52802768e-03 4.15512025e-01 8.91213059e-01 -1.26981246e+00 1.01627141e-01 5.49201667e-01 -1.20611846e+00 9.05010283e-01 4.30087179e-01 -2.71876574e-01 3.66365492e-01 3.83643597e-01 -4.21863019e-01 3.52783054e-01 -1.50564778e+00 -2.55090803e-01 -1.80136397e-01 7.45104730e-01 -1.05019420e-01 5.85931659e-01 -2.99183398e-01 6.92234159e-01 6.81456402e-02 1.02622606e-01 7.15881169e-01 9.69927549e-01 -3.49842429e-01 -1.37356448e+00 -2.25601256e-01 5.83191037e-01 -4.91879165e-01 6.43225983e-02 1.87986329e-01 5.86246669e-01 -2.21974298e-01 8.26819539e-01 9.67953820e-03 -3.77523638e-02 -1.08718477e-01 -8.41557235e-02 8.67223680e-01 -2.46206850e-01 -6.90250635e-01 -1.15186945e-01 1.81923479e-01 -8.27576160e-01 -5.16867220e-01 -8.86951983e-01 -1.19284105e+00 -2.93250859e-01 1.55884862e-01 3.88563484e-01 4.90961194e-01 9.81453240e-01 3.58391136e-01 6.44516766e-01 7.20746517e-01 -8.15241396e-01 -1.71348548e+00 -7.75450706e-01 -5.55872679e-01 2.06164271e-01 5.72068214e-01 -1.00535619e+00 -1.78484961e-01 -4.57110435e-01]
[3.7670390605926514, 2.0681231021881104]
86976613-7bf1-456b-82c5-d500533d2921
monocular-3d-object-detection-using-multi
2212.11804
null
https://arxiv.org/abs/2212.11804v1
https://arxiv.org/pdf/2212.11804v1.pdf
Monocular 3D Object Detection using Multi-Stage Approaches with Attention and Slicing aided hyper inference
3D object detection is vital as it would enable us to capture objects' sizes, orientation, and position in the world. As a result, we would be able to use this 3D detection in real-world applications such as Augmented Reality (AR), self-driving cars, and robotics which perceive the world the same way we do as humans. Monocular 3D Object Detection is the task to draw 3D bounding box around objects in a single 2D RGB image. It is localization task but without any extra information like depth or other sensors or multiple images. Monocular 3D object detection is an important yet challenging task. Beyond the significant progress in image-based 2D object detection, 3D understanding of real-world objects is an open challenge that has not been explored extensively thus far. In addition to the most closely related studies.
['Ashish Patel', 'Abonia Sojasingarayar']
2022-12-22
null
null
null
null
['monocular-3d-object-detection']
['computer-vision']
[ 9.89828184e-02 -1.63990825e-01 2.02761710e-01 -2.35028028e-01 9.24237967e-02 -7.84826398e-01 4.96305585e-01 -2.50436049e-02 -4.58533257e-01 2.91924417e-01 -5.47745168e-01 -4.77302819e-01 4.83384699e-01 -6.86647594e-01 -6.26309335e-01 -3.28630507e-01 1.65992066e-01 5.87148786e-01 9.19364214e-01 -2.90166825e-01 3.80129874e-01 1.23715281e+00 -1.79464543e+00 -2.30604485e-01 1.47022709e-01 1.24738967e+00 5.61934471e-01 9.15202320e-01 -6.19707219e-02 2.75320232e-01 -3.07790279e-01 -1.01208396e-01 4.97306108e-01 2.72824485e-02 -1.29995018e-01 3.38278443e-01 4.68838543e-01 -6.34278953e-01 -3.99475634e-01 1.03968060e+00 1.94326997e-01 -9.45656840e-03 4.10432816e-01 -1.28548002e+00 -3.42275441e-01 -4.74266976e-01 -7.74313807e-01 6.98203743e-02 8.45731854e-01 4.99429330e-02 2.40629956e-01 -9.33380306e-01 6.13104343e-01 1.38215435e+00 2.00562581e-01 4.14714187e-01 -8.07822227e-01 -4.03183430e-01 -1.78769249e-02 1.62667349e-01 -1.29212844e+00 -1.05244279e-01 9.63974774e-01 -2.69819468e-01 9.09391880e-01 4.31095511e-01 6.65184081e-01 6.18712485e-01 1.09042093e-01 6.82853997e-01 1.26285887e+00 -6.06583893e-01 1.62831724e-01 5.30719161e-01 4.47686203e-02 6.05355263e-01 7.50368178e-01 2.10646912e-01 -3.18810910e-01 2.91132092e-01 1.10427904e+00 4.20270830e-01 -7.69366547e-02 -1.08907783e+00 -1.39966261e+00 3.96104604e-01 7.56373584e-01 1.03753708e-01 -2.38640457e-01 2.16805175e-01 -1.86139435e-01 -1.50657333e-02 1.75696582e-01 3.95479232e-01 -4.30872351e-01 -1.91562325e-01 -1.92124948e-01 1.63145319e-01 3.96898657e-01 1.29211652e+00 8.73116970e-01 -1.13254212e-01 7.16016352e-01 1.59712479e-01 4.79325891e-01 9.66049194e-01 2.09081113e-01 -1.00240147e+00 3.97469759e-01 1.13636875e+00 5.77457964e-01 -9.85809863e-01 -4.93972301e-01 -1.17130503e-01 -4.87836301e-01 9.53754663e-01 4.28888440e-01 1.35976702e-01 -8.81526887e-01 9.13458169e-01 7.66047001e-01 -1.67783067e-01 -1.62603706e-01 1.28967762e+00 8.92805696e-01 3.32516670e-01 -6.85304999e-01 2.09516674e-01 1.61491501e+00 -4.06413585e-01 -4.63425219e-01 -6.66247547e-01 3.65786135e-01 -8.92942548e-01 7.70284772e-01 4.61037844e-01 -1.03524244e+00 -4.99213189e-01 -1.19219363e+00 -3.93225014e-01 -7.66642034e-01 3.80063779e-03 9.03358698e-01 8.53989720e-01 -6.59385741e-01 -9.79079530e-02 -8.84943604e-01 -4.99639481e-01 1.81026757e-01 4.11366135e-01 -7.77124524e-01 -2.33778179e-01 -7.19502270e-01 1.42608249e+00 4.49418277e-01 2.02739894e-01 -3.18250686e-01 -2.75683522e-01 -1.12588418e+00 -3.98238868e-01 7.11759627e-01 -5.71850061e-01 1.18368423e+00 -2.05944642e-01 -1.44152057e+00 1.32878006e+00 -3.43890578e-01 -3.34545434e-01 5.30427158e-01 -3.63103956e-01 -6.10496886e-02 8.13797340e-02 -1.85880035e-01 7.50687003e-01 5.98856390e-01 -1.50728786e+00 -6.53452277e-01 -1.10093844e+00 1.58819228e-01 5.42697310e-01 3.57189357e-01 -2.75520217e-02 -2.79547215e-01 1.09912559e-01 9.88515258e-01 -1.00491285e+00 -3.18995416e-01 6.60965145e-01 -2.00093165e-01 -3.73961893e-03 1.15534246e+00 -6.04411662e-02 3.41785789e-01 -2.02185869e+00 -2.28717208e-01 -1.71328261e-01 2.52493232e-01 3.28465521e-01 3.66584867e-01 1.62618250e-01 3.13592166e-01 -1.48376271e-01 2.30744585e-01 -2.44626716e-01 -3.39177460e-03 6.02739118e-02 -2.35341653e-01 7.30477870e-01 1.01302154e-01 1.00002658e+00 -8.85741115e-01 -3.11430037e-01 9.33082402e-01 5.80792069e-01 -8.45682099e-02 1.15578659e-01 6.39617397e-03 3.00319374e-01 -4.64936137e-01 7.62275517e-01 9.48179185e-01 -1.80245023e-02 -2.43844122e-01 -3.08895335e-02 -3.04390073e-01 1.42285243e-01 -1.50673711e+00 1.49509251e+00 -3.17391276e-01 9.63978231e-01 -2.22204756e-02 -7.04239905e-01 1.29268682e+00 1.62679777e-01 1.39895082e-01 -1.00465500e+00 1.20640412e-01 2.61402100e-01 -1.71884939e-01 -3.32965195e-01 8.42371762e-01 -1.37531847e-01 -1.47266760e-01 2.35974416e-01 -5.97886801e-01 -8.30265641e-01 -2.52632618e-01 5.01463152e-02 9.65476274e-01 1.04367211e-01 6.92148149e-01 4.48307395e-01 2.43340909e-01 2.42693275e-01 8.72032940e-02 7.26805627e-01 -2.68062413e-01 6.47645533e-01 -1.54039174e-01 -5.24936080e-01 -1.07064617e+00 -1.24941528e+00 -2.94731498e-01 2.56452560e-01 8.75368595e-01 2.24268466e-01 2.99054906e-02 -2.87675112e-01 3.62782180e-01 4.09077048e-01 -4.56954241e-01 1.49804354e-01 -4.86489236e-01 -3.11380863e-01 -1.93650246e-01 6.14880383e-01 3.53073329e-01 -8.18812430e-01 -1.72625721e+00 -6.41069561e-03 3.28729361e-01 -1.53023648e+00 4.84520160e-02 3.59416574e-01 -9.43788052e-01 -1.02504373e+00 -6.65745676e-01 -4.67075974e-01 5.99708557e-01 1.14589286e+00 1.00635242e+00 -3.04744005e-01 -6.65737689e-01 4.85947311e-01 -4.52914178e-01 -9.95991349e-01 7.89618418e-02 -5.59195936e-01 2.16916233e-01 -2.42530093e-01 6.45901322e-01 -2.23949119e-01 -6.93622768e-01 6.14799440e-01 -4.14617896e-01 1.56515896e-01 5.95206618e-01 1.44596338e-01 5.20182312e-01 -6.83974102e-02 3.59715410e-02 -3.77084523e-01 -5.87611087e-02 -6.76469058e-02 -1.04344738e+00 -1.43416628e-01 8.99133179e-03 -5.34032166e-01 9.55686998e-03 -4.15223777e-01 -7.84067035e-01 5.42647362e-01 4.03569192e-01 -3.85881811e-01 -8.45284104e-01 -1.79088935e-01 -1.71989158e-01 -1.82729572e-01 7.06326842e-01 1.59286648e-01 -1.24373771e-02 -4.85587358e-01 2.52189100e-01 9.54394341e-01 6.59380257e-01 6.48297742e-02 8.31591785e-01 1.03555179e+00 3.78087997e-01 -1.15195656e+00 -7.05837786e-01 -9.04904664e-01 -1.17487836e+00 -2.43676096e-01 7.79697657e-01 -8.94972861e-01 -1.03942358e+00 1.79413378e-01 -1.53107953e+00 2.99681835e-02 -2.97622949e-01 7.92540252e-01 -5.94185770e-01 2.70329803e-01 -6.31916299e-02 -1.40587020e+00 2.72888929e-01 -1.00091219e+00 1.39689386e+00 4.49531436e-01 -4.20951098e-02 -5.40753841e-01 -3.42902422e-01 4.75547165e-01 6.12745844e-02 4.49055344e-01 3.13199133e-01 -1.00771859e-01 -1.08374560e+00 -6.67996585e-01 -4.19983119e-01 -1.89339131e-01 1.86518684e-01 -1.78223923e-01 -9.68736351e-01 1.06194519e-01 2.13683948e-01 -2.86640506e-02 4.60984826e-01 2.60296077e-01 6.08674765e-01 5.00213861e-01 -4.97490913e-01 3.65653723e-01 1.25445199e+00 7.09848583e-01 4.30527180e-01 3.87700945e-01 5.42865872e-01 5.08483589e-01 1.03635764e+00 1.82294190e-01 3.31150264e-01 1.06409836e+00 9.48722482e-01 -2.32376590e-01 -2.11442277e-01 -9.77462754e-02 -7.28338137e-02 3.02512497e-01 -5.23554683e-02 3.04354671e-02 -1.06601143e+00 2.28256986e-01 -1.56670988e+00 -7.03655422e-01 -7.17432976e-01 2.38818789e+00 2.43855104e-01 3.36478442e-01 -3.83762196e-02 4.55943763e-01 4.14828718e-01 -4.80065197e-01 -8.63987327e-01 -2.78267235e-01 -1.70353785e-01 -1.70149758e-01 5.87779045e-01 2.93675661e-01 -9.46630418e-01 8.13804984e-01 6.10014391e+00 4.29335283e-03 -1.20270538e+00 -1.97102696e-01 8.25014040e-02 1.10757716e-01 1.03534997e-01 -4.34279405e-02 -1.05265510e+00 1.40969634e-01 2.20849499e-01 1.83512419e-01 1.62638471e-01 8.97400677e-01 6.18965849e-02 -9.44080770e-01 -1.14255452e+00 1.39729881e+00 1.41041175e-01 -8.45635355e-01 -4.63727027e-01 3.47424299e-01 5.27832985e-01 -2.31635440e-02 8.70263390e-03 -1.81230739e-01 8.10011402e-02 -6.72165513e-01 7.75125921e-01 3.89089465e-01 6.28998399e-01 -3.55420381e-01 7.37081349e-01 8.94498944e-01 -1.12008095e+00 -5.92407472e-02 -7.13972747e-01 -4.95583653e-01 3.09276372e-01 6.86252892e-01 -1.30925739e+00 1.84777781e-01 6.43408895e-01 4.20477748e-01 -5.90819359e-01 1.39805484e+00 -2.53432691e-01 -1.33569628e-01 -5.76143384e-01 -3.19073826e-01 -9.69595611e-02 -8.38915706e-02 5.59829473e-01 7.46799707e-01 3.78651828e-01 3.71605903e-01 -6.74181210e-04 8.06064844e-01 2.75960267e-01 -3.16066474e-01 -1.13784707e+00 3.85543734e-01 3.28750223e-01 1.03783917e+00 -1.00151432e+00 -1.13712490e-01 -5.39315403e-01 9.32545125e-01 -1.38112694e-01 1.67646989e-01 -4.58802611e-01 -4.25381154e-01 4.56540048e-01 3.89777869e-01 3.38879883e-01 -9.51399624e-01 -3.30475777e-01 -1.12381136e+00 1.99327141e-01 -1.80086970e-01 -2.90396065e-01 -1.43457913e+00 -8.36209118e-01 4.72210646e-01 1.11458078e-01 -1.44224024e+00 -9.25438926e-02 -1.16368520e+00 -5.05599491e-02 8.07836652e-01 -1.59489906e+00 -9.73667324e-01 -6.48715138e-01 4.33394670e-01 3.92487407e-01 2.74009317e-01 6.24429822e-01 -5.65153286e-02 -2.96007264e-02 -1.33796409e-01 -2.22989142e-01 -1.05183981e-01 4.14280921e-01 -1.15883732e+00 5.24285078e-01 7.07527757e-01 3.57940555e-01 4.21788335e-01 6.95248008e-01 -5.34787118e-01 -1.92218554e+00 -4.91386771e-01 5.10407925e-01 -1.08456540e+00 3.27806532e-01 -7.84875214e-01 -6.34742677e-01 5.84169984e-01 -3.58136475e-01 4.96110082e-01 1.75232217e-01 -1.65170074e-01 -2.87071615e-01 -1.28785416e-01 -1.18400574e+00 3.25483292e-01 1.18151772e+00 -3.89306456e-01 -5.95474482e-01 9.53933820e-02 6.86073363e-01 -1.00435305e+00 -4.55754668e-01 4.35425878e-01 7.65202701e-01 -1.08465648e+00 1.39145041e+00 -2.61628002e-01 -2.60184377e-01 -7.16015697e-01 -4.18656677e-01 -8.10469270e-01 1.90480456e-01 -2.80519605e-01 -3.98797959e-01 6.56937242e-01 9.16160122e-02 -7.92577147e-01 1.02292442e+00 7.72642195e-01 4.14555669e-02 -3.60031843e-01 -9.93365467e-01 -9.06415522e-01 -5.41740477e-01 -5.42499602e-01 4.93574977e-01 4.10534620e-01 -2.50017613e-01 2.35797748e-01 -6.59031197e-02 4.55745041e-01 7.37053454e-01 6.76480114e-01 1.26661170e+00 -1.61564422e+00 1.67168602e-01 -2.47588173e-01 -1.13792133e+00 -1.44910717e+00 -3.77540559e-01 -4.12192225e-01 1.81822218e-02 -1.46936262e+00 3.20329219e-02 -6.06613219e-01 2.14678958e-01 -6.36852980e-02 4.57182266e-02 6.30554378e-01 4.38567221e-01 4.53412794e-02 -6.05870187e-01 3.38215679e-01 1.45355701e+00 2.69490302e-01 -1.08231999e-01 2.58189321e-01 -3.20690423e-01 8.61795187e-01 7.86392212e-01 -3.51804644e-01 -2.60458112e-01 -3.08924794e-01 2.04024419e-01 2.55209982e-01 7.10166156e-01 -1.03461027e+00 3.31732243e-01 -2.89445907e-01 7.52551794e-01 -1.34480608e+00 1.12276793e+00 -1.19221318e+00 -1.13379955e-01 2.88537234e-01 5.18634617e-01 1.48871750e-01 1.46203622e-01 4.19791341e-01 3.41650397e-02 -2.46501416e-01 5.38404465e-01 -6.40824437e-01 -1.05105114e+00 7.66621903e-02 -2.52846599e-01 -3.48956078e-01 1.38028252e+00 -9.66118574e-01 -2.41010904e-01 -3.39342326e-01 -6.69874489e-01 4.97728260e-03 8.15359235e-01 4.91302371e-01 1.02350688e+00 -1.12143159e+00 -2.96116412e-01 4.35013086e-01 2.28739515e-01 4.51944053e-01 -1.20571163e-02 5.20373166e-01 -6.26091063e-01 8.71086299e-01 -1.71185642e-01 -1.12639070e+00 -1.32879245e+00 8.87144923e-01 2.10309446e-01 4.09764528e-01 -6.25804961e-01 4.86222297e-01 3.24628860e-01 -5.01915932e-01 2.07419544e-01 -4.67172593e-01 1.33524016e-01 -4.23066288e-01 5.58509886e-01 3.30351770e-01 1.35284096e-01 -6.71326816e-01 -4.07785773e-01 1.00146413e+00 1.77799851e-01 -1.12608664e-01 1.10873854e+00 -4.95685399e-01 2.55378813e-01 8.17756116e-01 8.93166840e-01 -2.39807442e-01 -1.26472318e+00 -2.27829620e-01 -8.76457617e-02 -1.05746961e+00 7.18456088e-03 -5.10875463e-01 -4.85686600e-01 1.43428457e+00 7.10105121e-01 3.53394449e-01 8.45767438e-01 2.93024719e-01 3.66160363e-01 5.70524991e-01 9.78606820e-01 -6.77535117e-01 3.10902923e-01 4.71035719e-01 7.88828671e-01 -1.64383256e+00 1.88488513e-01 -6.55140221e-01 -3.18559289e-01 1.04258013e+00 8.28895748e-01 9.17029157e-02 4.76778209e-01 3.57434958e-01 6.00680076e-02 -3.24176759e-01 -3.42558116e-01 -5.85570455e-01 2.90996909e-01 8.85687888e-01 1.34736761e-01 2.29286104e-01 2.64532000e-01 -1.97666317e-01 -9.53592509e-02 -2.21555397e-01 6.18064582e-01 1.15122998e+00 -7.24608064e-01 -8.12770307e-01 -8.07936370e-01 2.91343704e-02 1.52582437e-01 5.78174591e-01 -4.15729254e-01 9.84591484e-01 4.81373689e-04 9.15134847e-01 2.88666040e-01 1.82830449e-02 7.50860333e-01 -2.18009546e-01 8.74217033e-01 -7.98695743e-01 1.22716501e-01 -1.44985646e-01 -4.48649287e-01 -5.27108252e-01 -2.66394496e-01 -6.59921587e-01 -1.33121765e+00 -1.77611895e-02 -6.27578318e-01 -4.78181452e-01 1.49152422e+00 5.57521045e-01 1.48811594e-01 -2.83949450e-02 5.64953804e-01 -1.44993734e+00 -1.09531589e-01 -4.71559227e-01 -6.07055128e-01 1.67976663e-01 6.16993368e-01 -8.80258501e-01 -1.41506493e-01 -2.88056433e-01]
[7.7036943435668945, -2.5074880123138428]
252dfae8-3c79-4859-8731-65362d70fa17
towards-a-better-understanding-of
2305.18491
null
https://arxiv.org/abs/2305.18491v1
https://arxiv.org/pdf/2305.18491v1.pdf
Towards a Better Understanding of Representation Dynamics under TD-learning
TD-learning is a foundation reinforcement learning (RL) algorithm for value prediction. Critical to the accuracy of value predictions is the quality of state representations. In this work, we consider the question: how does end-to-end TD-learning impact the representation over time? Complementary to prior work, we provide a set of analysis that sheds further light on the representation dynamics under TD-learning. We first show that when the environments are reversible, end-to-end TD-learning strictly decreases the value approximation error over time. Under further assumptions on the environments, we can connect the representation dynamics with spectral decomposition over the transition matrix. This latter finding establishes fitting multiple value functions from randomly generated rewards as a useful auxiliary task for representation learning, as we empirically validate on both tabular and Atari game suites.
['Rémi Munos', 'Yunhao Tang']
2023-05-29
null
null
null
null
['value-prediction']
['computer-code']
[ 3.80673148e-02 2.57456988e-01 -7.59773910e-01 -1.18984714e-01 -8.37758243e-01 -7.46941507e-01 5.37485898e-01 3.18429887e-01 -4.77282286e-01 1.03467464e+00 3.78732532e-01 -5.25980055e-01 -5.67838490e-01 -5.89721203e-01 -7.18707085e-01 -5.01070082e-01 -6.04621530e-01 4.44654077e-01 -6.87963970e-04 -5.53710878e-01 3.91249061e-01 2.79594153e-01 -1.45029783e+00 1.58292890e-01 6.62259221e-01 8.60214949e-01 -1.50202572e-01 7.26712406e-01 1.90846041e-01 1.40294504e+00 -5.05558014e-01 -4.22862284e-02 4.02510583e-01 -4.99859959e-01 -8.95516455e-01 -1.01493932e-02 -2.99152702e-01 -6.48896754e-01 -5.93281746e-01 7.77311206e-01 4.19166237e-01 6.81637347e-01 7.72844434e-01 -1.27633119e+00 -3.55051637e-01 8.64426851e-01 -2.72892684e-01 2.79180557e-01 4.01785046e-01 5.51923931e-01 1.16538286e+00 -1.65450647e-01 6.82727873e-01 1.19862902e+00 6.75402522e-01 6.14339888e-01 -1.57255006e+00 -3.23363513e-01 3.31809640e-01 2.89703518e-01 -8.68991375e-01 -4.97960389e-01 6.08908355e-01 -4.53580379e-01 1.04406381e+00 -1.48476705e-01 8.88034225e-01 1.05621600e+00 2.12434098e-01 9.97765303e-01 1.22816348e+00 -4.65785921e-01 6.42826557e-01 -1.84881464e-01 2.04167038e-01 3.56345087e-01 2.64547288e-01 8.08484614e-01 -4.82897103e-01 -1.97181106e-01 8.03001285e-01 -2.17989951e-01 1.43145576e-01 -8.78617525e-01 -7.93631613e-01 9.19541895e-01 6.69075623e-02 -1.36664718e-01 -3.82018983e-01 8.03466618e-01 6.20818555e-01 7.77819276e-01 2.72094965e-01 7.06955135e-01 -4.66924012e-01 -1.05813575e+00 -4.82259989e-01 6.68047786e-01 7.11752534e-01 6.96010172e-01 5.73175490e-01 4.52575445e-01 -4.26660478e-01 5.04254282e-01 1.65916264e-01 4.99914050e-01 4.30846602e-01 -1.38789475e+00 3.33618701e-01 1.86104655e-01 5.33237278e-01 -3.58315945e-01 -3.82359058e-01 -4.97905672e-01 5.76081797e-02 3.20303112e-01 8.65058064e-01 -5.78202128e-01 -6.83918595e-01 2.03439450e+00 5.28369024e-02 1.00339547e-01 2.65309304e-01 5.07428288e-01 -1.18515864e-01 4.18843180e-01 -6.25875778e-03 -4.28561509e-01 5.15016913e-01 -4.72355574e-01 -7.56641090e-01 -1.79564893e-01 1.15978694e+00 -1.37993664e-01 1.08134973e+00 5.07227480e-01 -1.23126924e+00 -2.61617899e-01 -7.92036235e-01 4.03068125e-01 1.19681373e-01 -3.34471405e-01 8.02063882e-01 5.59153616e-01 -1.00081229e+00 9.50474381e-01 -1.01103044e+00 -2.78428376e-01 3.37630272e-01 3.95080239e-01 3.11637431e-01 -3.80227366e-03 -1.27275133e+00 1.26452816e+00 3.81973326e-01 -4.05846655e-01 -1.44415152e+00 -4.05913204e-01 -5.66489041e-01 6.24105558e-02 6.18825912e-01 -2.93697357e-01 1.96638048e+00 -1.07732010e+00 -1.70285749e+00 2.83633947e-01 7.30588958e-02 -8.93574655e-01 4.10222888e-01 1.68444943e-02 -1.33980930e-01 -9.81900468e-02 -2.39392459e-01 2.83432454e-01 7.17412174e-01 -1.24512136e+00 -5.00271857e-01 -2.28593990e-01 3.94405216e-01 2.48121232e-01 -1.78601667e-02 -4.09722567e-01 2.61138767e-01 -4.09318119e-01 -3.20135593e-01 -1.07403791e+00 -3.69664907e-01 -3.12406749e-01 1.24730663e-02 -1.72545552e-01 1.53849825e-01 -2.75912881e-01 1.25856936e+00 -1.91025555e+00 1.11368291e-01 4.58445728e-01 -3.48398276e-02 -4.60051298e-02 -3.22523981e-01 7.48011827e-01 -2.87165463e-01 -1.15438335e-01 1.31639361e-01 -4.81262952e-02 3.58432770e-01 2.67021805e-01 -6.30686820e-01 5.02956927e-01 -8.28297138e-02 1.00286484e+00 -1.02423882e+00 -2.84779929e-02 9.29817781e-02 -6.73002452e-02 -7.40045726e-01 2.90781677e-01 -6.31797493e-01 2.47905046e-01 -4.79068995e-01 1.86406255e-01 1.06670462e-01 2.10273638e-02 5.51409364e-01 5.11704385e-01 -8.28981679e-03 6.46549463e-01 -1.11726332e+00 1.63333130e+00 -1.60742536e-01 3.74061614e-01 -3.95507157e-01 -1.14933085e+00 8.14868212e-01 -5.34603335e-02 6.91739261e-01 -1.10451043e+00 1.69406250e-01 -2.12287493e-02 4.11519021e-01 -2.80992270e-01 6.17374897e-01 -2.98184991e-01 -3.35978121e-01 8.95126343e-01 -9.51808915e-02 -4.87682968e-02 2.63206631e-01 1.60371661e-01 1.11806738e+00 5.08038163e-01 3.19736034e-01 -1.38012484e-01 -2.71406710e-01 4.52027142e-01 5.76725900e-01 9.60283935e-01 -2.33196557e-01 1.71383899e-02 1.13674879e+00 -3.10696334e-01 -1.05356145e+00 -1.14435112e+00 3.68037187e-02 1.47605669e+00 -6.73940480e-02 -4.76975322e-01 -5.75310349e-01 -4.62228328e-01 2.64823318e-01 1.00195992e+00 -8.43419015e-01 -7.65979886e-01 -2.48746663e-01 -5.09787798e-01 6.09720230e-01 8.23104024e-01 -7.67151862e-02 -9.36309457e-01 -7.93115079e-01 5.05811512e-01 1.72382638e-01 -5.16914606e-01 -2.84195006e-01 6.98377788e-01 -1.12022722e+00 -9.52619791e-01 -3.46545130e-01 -3.01898003e-01 1.52048290e-01 8.02575275e-02 1.01119423e+00 -1.81485444e-01 2.75873601e-01 9.23847079e-01 -3.01222980e-01 -3.68764222e-01 -6.07464969e-01 3.39713879e-02 2.34698474e-01 -5.99944890e-01 1.56309634e-01 -6.44102812e-01 -3.89819145e-01 3.93871740e-02 -5.69597006e-01 -1.59907565e-01 3.82523745e-01 9.32343721e-01 5.03156066e-01 -1.08258403e-03 7.04106688e-01 -7.60377109e-01 1.19971633e+00 -6.19518042e-01 -7.29956448e-01 1.95618913e-01 -9.50589120e-01 6.80695891e-01 5.45852005e-01 -5.97446144e-01 -8.54445279e-01 -1.02400124e-01 2.25548729e-01 -3.87195587e-01 1.92698345e-01 8.14325154e-01 3.36826324e-01 3.11482340e-01 8.92521977e-01 4.73539978e-01 4.14383203e-01 -1.39465034e-01 5.14117956e-01 1.94824263e-01 2.05637529e-01 -1.20855117e+00 6.32791340e-01 -3.36061306e-02 3.80886793e-02 -3.82014543e-01 -6.92203820e-01 9.12660733e-03 -2.71497935e-01 -2.96473503e-01 3.74936521e-01 -9.48299348e-01 -1.16705930e+00 1.68507516e-01 -6.06996536e-01 -1.36225760e+00 -7.73235798e-01 2.97861189e-01 -1.33145082e+00 1.12976238e-01 -4.91048008e-01 -1.31704414e+00 1.50081605e-01 -1.04002917e+00 3.62745345e-01 -1.58686694e-02 -1.84767365e-01 -1.13730967e+00 4.77648258e-01 -6.73738793e-02 3.47964406e-01 1.50665537e-01 1.14834368e+00 -6.10889375e-01 -3.69903475e-01 1.55836269e-01 2.26979077e-01 1.32663444e-01 -1.45665482e-01 -2.62504518e-01 -8.49658370e-01 -5.12900710e-01 -2.21065432e-01 -7.78185189e-01 7.55752027e-01 5.57053328e-01 1.10208356e+00 -2.98062861e-01 1.05986401e-01 2.79852837e-01 1.33807600e+00 5.73733985e-01 6.33902490e-01 7.30590105e-01 2.68817961e-01 5.40176034e-01 1.00097132e+00 1.03438652e+00 4.60125715e-01 5.82551420e-01 3.97444934e-01 4.68019247e-01 2.23489091e-01 -6.29403591e-01 8.17709863e-01 2.07137287e-01 -8.40084180e-02 -1.20599531e-02 -9.37941968e-01 4.97027576e-01 -2.21183658e+00 -1.35601461e+00 4.07669723e-01 2.62960815e+00 9.91784632e-01 4.83443618e-01 9.89752352e-01 5.59352979e-04 3.72695774e-01 2.10566238e-01 -1.23361993e+00 -6.47024989e-01 3.26539904e-01 3.58918518e-01 6.91065609e-01 4.02918249e-01 -5.18597364e-01 8.58765006e-01 7.70582581e+00 7.07486928e-01 -9.00993466e-01 -9.73780975e-02 5.12142122e-01 -4.14491534e-01 -4.82538164e-01 1.86696276e-02 -4.50060010e-01 3.54605675e-01 1.41561317e+00 -6.09408557e-01 1.05262995e+00 1.02859724e+00 5.71226180e-01 -1.31113052e-01 -1.31582332e+00 6.50725842e-01 -5.80967903e-01 -1.18162119e+00 -4.63388830e-01 2.77983934e-01 7.55112708e-01 4.05166335e-02 4.32469279e-01 9.01920557e-01 1.19352055e+00 -1.17044199e+00 9.21512306e-01 3.19858015e-01 8.21125805e-01 -1.24843311e+00 3.52123916e-01 4.20303702e-01 -9.40162301e-01 -5.97599924e-01 -3.92415762e-01 -4.91093189e-01 -2.34110638e-01 -6.89121708e-02 -9.53989327e-01 8.88069645e-02 -6.80796057e-02 8.58601570e-01 -4.11052287e-01 8.08722436e-01 -2.15743735e-01 8.56845796e-01 -9.32886750e-02 -1.93914901e-02 4.02216703e-01 -1.07068047e-01 2.33146578e-01 8.92810524e-01 7.76174292e-02 -5.60536608e-02 2.30775759e-01 9.30684090e-01 2.13147730e-01 -3.83818626e-01 -7.56162643e-01 -3.69757265e-01 5.94552934e-01 4.94490921e-01 -3.87611330e-01 -7.98225477e-02 5.46662845e-02 3.35950196e-01 5.03190935e-01 5.09904146e-01 -7.93764770e-01 -7.53320083e-02 9.89908516e-01 1.69935197e-01 3.29676926e-01 -4.88106877e-01 -3.43910545e-01 -9.85949755e-01 -4.37065691e-01 -1.19505346e+00 3.30458283e-01 -5.12194335e-01 -1.00649595e+00 -7.78167322e-02 2.17502668e-01 -1.35844696e+00 -1.10202444e+00 -3.70314211e-01 -4.93130922e-01 4.20666784e-01 -1.37325656e+00 -4.68296289e-01 2.95633554e-01 6.49408519e-01 3.73155296e-01 -1.53354213e-01 6.86924458e-01 -4.08066064e-01 -6.03422880e-01 7.31273353e-01 7.77297974e-01 -2.10758001e-02 4.62258577e-01 -1.40020049e+00 3.15335423e-01 5.43325245e-01 -6.43248409e-02 5.74585319e-01 1.06220984e+00 -4.40440893e-01 -1.83065224e+00 -8.47335279e-01 -1.57714680e-01 -4.22580063e-01 8.88270557e-01 -9.45084095e-02 -6.81755781e-01 8.35725009e-01 -5.20956889e-02 -1.99025825e-01 5.17285228e-01 5.06753087e-01 -2.75608242e-01 7.44828582e-02 -1.00775003e+00 5.84417224e-01 1.08448374e+00 -6.78418159e-01 -6.73501074e-01 -9.24868062e-02 7.27786899e-01 -6.08762383e-01 -8.19095135e-01 -2.53319442e-01 6.72558725e-01 -8.63700151e-01 8.64296079e-01 -1.16387725e+00 5.56963861e-01 5.68190441e-02 -2.87401855e-01 -1.73089635e+00 -3.23855996e-01 -8.13764811e-01 -5.11491537e-01 7.78375447e-01 3.00287485e-01 -5.58786690e-01 8.21383953e-01 9.29816008e-01 -4.81993444e-02 -8.34408700e-01 -7.54252255e-01 -1.26408327e+00 5.69105983e-01 -8.46326232e-01 4.61712748e-01 7.32019067e-01 4.98235583e-01 1.38890475e-01 -4.46049660e-01 -3.81874591e-01 6.92769051e-01 4.80496176e-02 7.71642685e-01 -1.12371993e+00 -7.30851889e-01 -3.44263434e-01 -1.37953684e-01 -1.17595851e+00 4.03427392e-01 -8.10782731e-01 3.47020999e-02 -1.29187989e+00 8.44349638e-02 -5.40590048e-01 -5.56117594e-01 6.42683148e-01 2.55516291e-01 -4.34819967e-01 4.54890162e-01 6.61990121e-02 -1.01284814e+00 7.88333118e-01 1.24837267e+00 1.18322372e-01 -5.19787550e-01 5.00845052e-02 -8.74045312e-01 3.06305498e-01 9.74037826e-01 -6.21332824e-01 -9.02608573e-01 -2.00964525e-01 4.86750692e-01 5.85162640e-01 -9.95542016e-03 -6.90735042e-01 -1.60506010e-01 -8.79692078e-01 9.25717950e-02 -2.07158163e-01 3.71817201e-01 -5.36436677e-01 -1.47371829e-01 6.50701344e-01 -9.80336368e-01 1.71954647e-01 5.19844770e-01 8.16603124e-01 3.32997710e-01 -3.10454398e-01 6.23435438e-01 -4.14511785e-02 -7.92642176e-01 1.32120818e-01 -9.18783903e-01 6.55769646e-01 9.22467232e-01 -3.31185430e-01 -3.07904154e-01 -8.76446545e-01 -7.95619249e-01 3.22302490e-01 5.12052238e-01 7.55068362e-02 5.29169381e-01 -1.42411745e+00 -3.60615373e-01 -3.37047167e-02 1.13790572e-01 -3.57799292e-01 -1.22404788e-02 7.04271913e-01 1.07310295e-01 2.29559287e-01 -4.19297218e-01 -2.06663415e-01 -8.06231499e-01 4.57832515e-01 5.13140857e-01 -4.62740809e-01 -2.75640666e-01 2.77796537e-01 -2.97468305e-01 -8.85646492e-02 3.13972533e-01 -4.66256529e-01 -3.19452360e-02 1.64408758e-01 3.13111991e-01 4.85717952e-01 -3.85054201e-01 -3.37194875e-02 2.51396243e-02 1.72331948e-02 -2.71826148e-01 -5.33661902e-01 1.60997653e+00 -1.73184335e-01 6.13304436e-01 7.92468727e-01 7.97209799e-01 -5.85394323e-01 -1.79901528e+00 -1.72655523e-01 1.68151304e-01 -4.95789170e-01 1.67012140e-01 -8.15206528e-01 -6.95562780e-01 7.25479245e-01 6.65408492e-01 2.71870703e-01 7.46411324e-01 -3.44781160e-01 4.35063004e-01 8.40210676e-01 6.96539700e-01 -1.38509464e+00 5.58174312e-01 8.63507390e-01 5.28671682e-01 -9.76045549e-01 -9.22730714e-02 4.52357739e-01 -1.17077434e+00 9.75949764e-01 5.15287936e-01 -2.85977334e-01 1.89580694e-01 1.45606741e-01 -2.87873656e-01 4.87864837e-02 -1.39403951e+00 -5.15839934e-01 -2.12826058e-01 1.03934538e+00 3.48126471e-01 4.54576202e-02 7.17670694e-02 4.98514980e-01 -1.27227411e-01 2.46800959e-01 9.25217688e-01 1.07790589e+00 -6.88775837e-01 -1.23555255e+00 -2.20588043e-01 5.24956524e-01 -2.20515519e-01 1.04295015e-01 -2.22902641e-01 8.16892803e-01 -5.50581098e-01 8.86534512e-01 6.72857091e-02 -5.62233984e-01 3.37137163e-01 1.69338658e-01 8.73762012e-01 -6.37038171e-01 -6.19434655e-01 -9.56145376e-02 2.91111380e-01 -6.46364450e-01 6.98618740e-02 -9.11398292e-01 -1.62692094e+00 -6.42023981e-01 -4.21863794e-02 7.79739320e-02 3.02705497e-01 9.80395555e-01 3.21824908e-01 5.20903409e-01 8.07703614e-01 -5.29839039e-01 -1.32104397e+00 -6.70369208e-01 -8.75632405e-01 2.81870365e-01 4.55187082e-01 -9.11629379e-01 -3.86647701e-01 -2.90610313e-01]
[4.067844867706299, 1.9328854084014893]
a4d27304-421c-46e7-8f4e-b6cd0beaa69a
multi-modal-page-stream-segmentation-with
null
null
https://link.springer.com/article/10.1007/s10579-019-09476-2
https://www.inf.uni-hamburg.de/en/inst/ab/lt/publications/2019-wiedemann-lre-pss.pdf
Multi-modal Page Stream Segmentation with Convolutional Neural Networks
In recent years, (retro-)digitizing paper-based files became a major undertaking for private and public archives as well as an important task in electronic mailroom applications. As first steps, the workflow usually involves batch scanning and optical character recognition (OCR) of documents. In the case of multi-page documents, the preservation of document contexts is a major requirement. To facilitate workflows involving very large amounts of paper scans, page stream segmentation (PSS) is the task to automatically separate a stream of scanned images into coherent multi-page documents. In a digitization project together with a German federal archive, we developed a novel approach for PSS based on convolutional neural networks (CNN). As a first project, we combine visual information from scanned images with semantic information from OCR-ed texts for this task. The multi-modal combination of features in a single classification architecture allows for major improvements towards optimal document separation. Further to multimodality, our PSS approach profits from transfer-learning and sequential page modeling. We achieve accuracy up to 95% on multi-page documents on our in-house dataset and up to 93% on a publicly available dataset.
['Gerhard Heyer', 'Gregor Wiedemann']
2019-09-27
null
null
null
lang-resources-evaluation-2019-9
['page-stream-segmentation']
['natural-language-processing']
[ 6.70916975e-01 -1.95391372e-01 1.54130861e-01 -2.95114279e-01 -1.16281581e+00 -8.68752360e-01 5.69551170e-01 4.09916759e-01 -4.00897682e-01 3.82297307e-01 -6.19797818e-02 -3.81673992e-01 -2.39493340e-01 -6.03458226e-01 -6.65960968e-01 -2.46021464e-01 3.54760557e-01 8.67896736e-01 3.68163407e-01 1.07595190e-01 5.19239783e-01 1.10770488e+00 -1.40636790e+00 9.17419136e-01 7.19632030e-01 8.65691960e-01 6.16234779e-01 1.15006709e+00 -8.91650319e-01 3.25680375e-01 -6.82069123e-01 -4.61209953e-01 1.03707209e-01 -2.37383887e-01 -1.15249753e+00 6.73698127e-01 7.37189949e-01 -2.41813689e-01 -1.31252892e-02 7.99045146e-01 2.35415891e-01 -1.32422656e-01 6.04295015e-01 -8.07358027e-01 -5.24065316e-01 3.69088590e-01 -4.41428304e-01 -2.79318020e-02 1.53390259e-01 -1.13259152e-01 1.09169221e+00 -6.29075646e-01 7.20682383e-01 1.03781760e+00 6.70941293e-01 8.17949604e-03 -1.35752118e+00 -2.74146553e-02 -1.44049138e-01 6.92183152e-02 -8.87268960e-01 -3.51540208e-01 6.34411991e-01 -5.13467550e-01 1.01510859e+00 4.35195684e-01 6.23851120e-01 6.29724383e-01 5.40107302e-02 1.14081228e+00 9.53085899e-01 -8.61701846e-01 7.33097494e-02 4.96015579e-01 3.17144871e-01 4.07974243e-01 1.97954476e-01 -8.34538519e-01 -2.25555703e-01 1.06867194e-01 7.17786610e-01 -9.21562612e-02 -1.13130897e-01 -4.00089234e-01 -9.56688106e-01 5.10910988e-01 -1.22303039e-01 6.05722666e-01 -2.71742374e-01 -2.27916032e-01 2.75362641e-01 1.82035625e-01 2.90955693e-01 5.64881265e-01 -2.91130483e-01 -3.84309500e-01 -1.45212817e+00 1.59764186e-01 9.29325998e-01 8.35402846e-01 5.70990324e-01 -3.44941914e-01 -3.47559117e-02 1.10932243e+00 2.51331151e-01 1.82685435e-01 3.95503610e-01 -7.45866835e-01 7.52825797e-01 7.54539013e-01 1.07457824e-01 -7.53686845e-01 -3.22545260e-01 -3.49657685e-01 -4.10773158e-01 1.19744979e-01 5.96499443e-01 3.34955305e-01 -1.11342263e+00 8.00785303e-01 -1.82737634e-02 -7.06249833e-01 -2.47663453e-01 6.22739613e-01 3.94169748e-01 6.73546374e-01 -1.00201145e-01 -5.62673211e-02 1.55788195e+00 -8.68057907e-01 -6.66785479e-01 -1.50857404e-01 6.06973231e-01 -1.19263053e+00 1.01372838e+00 9.01822329e-01 -1.16054833e+00 -3.74094844e-01 -1.19741714e+00 -1.53807908e-01 -5.84590793e-01 4.44113165e-01 3.62002522e-01 8.05436790e-01 -1.02638900e+00 7.41416931e-01 -5.87711573e-01 -8.25241089e-01 5.74507535e-01 4.42115903e-01 -5.42751789e-01 -2.48372525e-01 -5.34904242e-01 6.58326328e-01 2.98489243e-01 1.44586831e-01 -6.79685697e-02 -3.73215884e-01 -3.38343173e-01 2.49522343e-01 3.37089062e-01 -4.29339446e-02 1.14926136e+00 -1.04535353e+00 -1.38186407e+00 9.34449315e-01 1.08033970e-01 -1.65848896e-01 8.57684314e-01 -4.60890681e-01 -4.84345287e-01 5.77925384e-01 -5.93964159e-02 4.80268538e-01 8.47389698e-01 -1.37100184e+00 -6.04701400e-01 -4.35533822e-01 -4.42316890e-01 1.14708923e-01 -6.30324423e-01 2.55608857e-01 -9.88522530e-01 -4.33355212e-01 8.60331021e-03 -7.94692695e-01 4.37274963e-01 -2.39716679e-01 -4.00131077e-01 -4.78064455e-02 1.03522730e+00 -1.19057620e+00 1.03143263e+00 -1.99600172e+00 2.68967953e-02 4.03146356e-01 -7.85055533e-02 5.89080393e-01 -1.91624478e-01 5.39868832e-01 -6.74891993e-02 7.93180764e-02 -2.31177241e-01 -4.19148386e-01 8.95067230e-02 -2.01682940e-01 -1.74493656e-01 2.21154302e-01 3.45019281e-01 7.86719799e-01 -4.56158251e-01 -7.79698670e-01 1.42164811e-01 2.90314138e-01 -1.56806931e-01 4.69220169e-02 -2.39933565e-01 -1.09862559e-01 -1.23128511e-01 7.59342551e-01 6.18955553e-01 -3.06538403e-01 4.74496692e-01 -7.90725425e-02 -1.55426681e-01 -5.33126518e-02 -1.30480576e+00 1.59101975e+00 -3.52390975e-01 1.01858687e+00 3.06989014e-01 -8.33661556e-01 8.36208761e-01 1.31783128e-01 3.89033467e-01 -7.93265879e-01 7.93746114e-02 3.21894318e-01 -3.42281491e-01 -4.82705265e-01 1.03269207e+00 1.94878638e-01 1.78134531e-01 6.47276878e-01 4.57680635e-02 -8.96822587e-02 5.47775805e-01 2.36974120e-01 9.96162295e-01 2.30030626e-01 -2.00890794e-01 -3.92756425e-02 4.14940953e-01 3.85733634e-01 1.64749194e-02 5.96253395e-01 4.34187055e-02 1.02214491e+00 6.43324494e-01 -2.03588963e-01 -1.27046418e+00 -6.78864181e-01 -8.69567022e-02 7.83824027e-01 -4.39961195e-01 -2.52517372e-01 -1.08244419e+00 -5.05608499e-01 2.03884356e-02 5.70708156e-01 -1.84860125e-01 4.74148512e-01 -7.01974988e-01 -6.51698887e-01 5.16523957e-01 5.19919515e-01 1.99421093e-01 -1.04272151e+00 -3.56738210e-01 2.54999638e-01 1.10467598e-01 -1.14321506e+00 -3.12209755e-01 5.48920095e-01 -7.45615125e-01 -1.12866330e+00 -1.04155421e+00 -8.72454107e-01 5.96800327e-01 2.60829210e-01 7.29201198e-01 -1.84826493e-01 -6.87909305e-01 5.79346895e-01 -3.34002435e-01 -2.24067703e-01 -5.39272964e-01 3.26795489e-01 -3.58543664e-01 2.11254969e-01 2.50097603e-01 -1.20222278e-01 -2.36558542e-01 4.34825150e-03 -1.39526975e+00 -6.78346157e-02 8.59980345e-01 4.36035872e-01 4.27045971e-01 1.71031937e-01 8.72686952e-02 -9.46930528e-01 8.55437994e-01 -2.31460091e-02 -7.15905011e-01 5.65348268e-01 -6.62184119e-01 -8.85397289e-03 4.69148725e-01 -2.01777026e-01 -1.18079925e+00 1.38037562e-01 1.41534984e-01 -2.52581865e-01 -4.38591957e-01 2.63429850e-01 -3.69370252e-01 1.20435111e-01 2.15435207e-01 2.76402801e-01 4.31870110e-02 -8.51947367e-01 2.48830557e-01 1.28980613e+00 5.26907742e-01 -3.55670452e-01 5.27165890e-01 3.54153007e-01 -1.63876325e-01 -1.40571141e+00 -2.74820507e-01 -6.59721851e-01 -1.12884665e+00 -2.53640383e-01 1.00092959e+00 -5.46557546e-01 -3.38743836e-01 7.28236914e-01 -1.13495135e+00 -2.78387874e-01 1.08709179e-01 2.24562973e-01 -3.30061018e-01 8.71328890e-01 -5.49860775e-01 -6.75732613e-01 -1.38253868e-01 -1.04622173e+00 1.09339380e+00 2.16874212e-01 -4.73002315e-01 -6.97868764e-01 2.35071220e-02 9.33900237e-01 1.40772268e-01 -2.09782869e-01 1.18076575e+00 -9.55102682e-01 -7.79929399e-01 -6.57731533e-01 -4.90664482e-01 4.42914128e-01 1.40334532e-01 3.28871548e-01 -1.04308283e+00 -5.14089130e-02 -3.30614150e-01 -1.22254968e-01 8.99728417e-01 1.12369530e-01 1.08676314e+00 2.20206305e-01 -2.15606526e-01 3.08491766e-01 1.29195225e+00 4.02078807e-01 8.01118135e-01 8.87158692e-01 8.23902965e-01 8.74153316e-01 5.04190147e-01 1.66312099e-01 -4.70212288e-03 5.58923066e-01 -4.39723060e-02 1.60391867e-01 -2.38741636e-01 1.03252120e-01 1.10380627e-01 5.97126782e-01 2.21853495e-01 -4.84666944e-01 -1.11005473e+00 5.09854496e-01 -1.51591790e+00 -8.60367715e-01 -4.56539840e-01 2.10817599e+00 6.18775308e-01 2.94144064e-01 9.01580378e-02 2.72268355e-01 8.74960661e-01 -7.30334967e-02 -1.67250708e-01 -6.65550411e-01 -8.10132325e-02 -1.08706225e-02 4.79830623e-01 2.05462754e-01 -1.19423234e+00 6.91355348e-01 5.96249342e+00 8.51129413e-01 -9.92911041e-01 -2.99364269e-01 4.97645408e-01 -2.29095250e-01 -1.66856632e-01 -2.45026529e-01 -9.80981469e-01 5.57590187e-01 1.05079055e+00 4.37640578e-01 3.59408021e-01 7.05700874e-01 3.86093222e-02 -3.36583704e-01 -9.13674176e-01 8.96142006e-01 2.18879566e-01 -1.37866867e+00 1.20742910e-01 3.33040476e-01 4.02063489e-01 -1.27740592e-01 -1.39780328e-01 -1.64387316e-01 -9.36845466e-02 -9.44279611e-01 9.30941701e-01 4.80934739e-01 6.46913469e-01 -8.19782913e-01 6.42282069e-01 6.01780973e-02 -8.71344388e-01 2.21775174e-02 3.38425823e-02 5.64239085e-01 9.76865888e-02 6.00750804e-01 -7.96811998e-01 3.98815155e-01 6.91724718e-01 3.29772949e-01 -7.32987046e-01 1.06987011e+00 2.38588706e-01 2.91161656e-01 -1.65448800e-01 -8.68159086e-02 3.12148124e-01 -3.91580701e-01 2.55670696e-01 1.71918738e+00 1.46171808e-01 -2.44818822e-01 -1.98173195e-01 6.90009356e-01 -1.69634923e-01 1.71870783e-01 -1.16481021e-01 -7.94568896e-01 1.01740636e-01 1.59839416e+00 -1.48223639e+00 -2.30806738e-01 -3.13169897e-01 1.14870882e+00 1.63251758e-01 1.59400553e-01 -3.85978997e-01 -9.72243369e-01 1.53495833e-01 1.23870626e-01 7.21588135e-01 -4.94053245e-01 -5.56081772e-01 -8.23012531e-01 3.87971014e-01 -9.50793922e-01 1.74133442e-02 -6.78474844e-01 -7.83906162e-01 6.27910256e-01 -3.40630740e-01 -1.13593030e+00 6.77976012e-02 -8.70262206e-01 -4.26874220e-01 8.80470932e-01 -1.37822962e+00 -1.17476583e+00 -3.02825481e-01 1.70516744e-01 7.42294312e-01 -2.81682670e-01 5.58804214e-01 3.07771027e-01 -7.45509803e-01 1.44566581e-01 6.49997294e-01 2.51826763e-01 8.30434561e-01 -1.48895323e+00 3.79810065e-01 7.89242446e-01 3.73873025e-01 4.17232096e-01 3.36680353e-01 -8.27167034e-01 -1.54644835e+00 -8.82806122e-01 9.49800670e-01 -3.84810954e-01 5.89825273e-01 -4.53841776e-01 -1.07729006e+00 4.33234006e-01 4.23336476e-01 -7.09935367e-01 8.97005737e-01 -1.35194764e-01 -8.94399956e-02 2.17727050e-02 -8.74768615e-01 3.90629947e-01 3.57793629e-01 -6.16517663e-01 -7.17057407e-01 3.25263143e-01 1.56333104e-01 -2.65290707e-01 -7.34374225e-01 -3.97931784e-01 6.01096213e-01 -8.95177901e-01 6.69505298e-01 -3.45700920e-01 6.20121300e-01 -1.80757374e-01 7.70188286e-04 -8.85206997e-01 -1.48877591e-01 -5.42529464e-01 1.31755531e-01 1.58041334e+00 3.30708623e-01 -2.85532832e-01 8.61209273e-01 8.30604672e-01 -6.72521293e-02 -1.90808341e-01 -3.77129674e-01 -7.32849121e-01 -2.20782533e-01 -5.69716513e-01 3.52588326e-01 5.75586915e-01 -1.79297298e-01 3.00240442e-02 1.60994343e-02 -4.91634309e-02 6.05307162e-01 1.90904051e-01 6.28132284e-01 -1.41545582e+00 -3.47660154e-01 -6.96310997e-01 -1.16355464e-01 -6.54743135e-01 -8.29354301e-03 -9.06358242e-01 1.23625677e-02 -1.93166637e+00 2.40667298e-01 -8.54513571e-02 -1.52369514e-01 3.06777120e-01 2.36339882e-01 2.25686312e-01 4.67042744e-01 4.87528145e-01 -5.47590971e-01 -5.45827076e-02 7.49837220e-01 -3.11436981e-01 -3.29712957e-01 -1.60997838e-01 -4.63457197e-01 4.55722898e-01 7.12609172e-01 -3.33173573e-01 -8.64222087e-03 -4.90489304e-01 2.42836058e-01 -8.30321386e-02 1.72547787e-01 -9.64573324e-01 2.49775052e-01 1.00740351e-01 8.70051980e-01 -1.10789669e+00 2.82924920e-01 -7.53748059e-01 -1.87275801e-02 1.64670378e-01 -3.67856801e-01 -1.83727816e-01 3.82157415e-01 4.73772496e-01 -2.29599789e-01 -5.56669593e-01 5.96998274e-01 -3.29168737e-01 -6.04474723e-01 -3.11801553e-01 -6.70191288e-01 -4.21387851e-01 8.49620223e-01 -6.10669255e-01 -2.41383851e-01 2.35719769e-03 -5.59109271e-01 6.90711662e-02 6.37605965e-01 4.79598731e-01 4.40600604e-01 -7.85479486e-01 -3.15135241e-01 2.40561157e-01 -8.99953097e-02 5.54922670e-02 1.16642289e-01 7.16254711e-01 -1.14134026e+00 1.05759466e+00 -3.98027480e-01 -5.25138199e-01 -1.74912691e+00 1.27760079e-02 -1.08472563e-01 -1.73986971e-01 -5.95012546e-01 5.87025523e-01 -3.93872321e-01 -9.45211947e-02 2.94492632e-01 -1.83649600e-01 -2.88583159e-01 6.79727852e-01 6.75634205e-01 5.89709818e-01 6.88094199e-01 -2.86793232e-01 -1.29247695e-01 3.60960275e-01 -4.97338980e-01 -4.05405819e-01 1.77650321e+00 -5.43680601e-02 -2.30982795e-01 3.40667307e-01 1.38627100e+00 -4.42330055e-02 -1.14386737e+00 1.07295923e-02 4.47559357e-01 -3.61631334e-01 2.30020016e-01 -7.50842452e-01 -8.00446153e-01 9.80005205e-01 4.21254545e-01 4.61160123e-01 9.44502711e-01 -3.71198468e-02 6.98690712e-01 5.45175195e-01 -6.47687390e-02 -1.70968604e+00 1.18943147e-01 3.98085475e-01 7.24540234e-01 -1.11087644e+00 1.20587796e-01 -2.24253699e-01 -6.02500737e-01 1.74277067e+00 1.37336537e-01 2.82531679e-01 7.20450729e-02 2.28968471e-01 -3.65070738e-02 -2.05597162e-01 -2.41757125e-01 -8.91535208e-02 4.36940640e-01 4.19465184e-01 4.69789565e-01 -1.75036743e-01 -8.14484153e-03 5.46996832e-01 9.91025940e-02 8.25853944e-02 7.03808010e-01 1.40721583e+00 -6.14148378e-01 -1.29327261e+00 -8.06521058e-01 6.47769690e-01 -4.90462333e-01 -6.63712174e-02 -8.10941577e-01 8.09377789e-01 -3.50019664e-01 7.18095899e-01 3.62293005e-01 -1.84140597e-02 1.54448599e-01 5.53334415e-01 5.53797781e-01 -5.55854082e-01 -6.65913224e-01 4.33096468e-01 1.64215162e-01 -3.53386641e-01 -2.19032973e-01 -1.05430722e+00 -1.18095911e+00 -3.34157348e-02 -9.64412838e-02 -8.05741251e-02 1.24719715e+00 1.02661693e+00 3.55575114e-01 6.19611442e-01 2.44239941e-01 -9.37988043e-01 -3.87596071e-01 -6.95858300e-01 -8.53534162e-01 3.03733498e-01 1.35111846e-02 -9.33669433e-02 -1.35943651e-01 4.04616207e-01]
[11.750812530517578, 2.6896584033966064]
31bf57ad-19aa-4903-96d6-71fe643559c7
video-face-clustering-with-unknown-number-of
1908.03381
null
https://arxiv.org/abs/1908.03381v2
https://arxiv.org/pdf/1908.03381v2.pdf
Video Face Clustering with Unknown Number of Clusters
Understanding videos such as TV series and movies requires analyzing who the characters are and what they are doing. We address the challenging problem of clustering face tracks based on their identity. Different from previous work in this area, we choose to operate in a realistic and difficult setting where: (i) the number of characters is not known a priori; and (ii) face tracks belonging to minor or background characters are not discarded. To this end, we propose Ball Cluster Learning (BCL), a supervised approach to carve the embedding space into balls of equal size, one for each cluster. The learned ball radius is easily translated to a stopping criterion for iterative merging algorithms. This gives BCL the ability to estimate the number of clusters as well as their assignment, achieving promising results on commonly used datasets. We also present a thorough discussion of how existing metric learning literature can be adapted for this task.
['Sanja Fidler', 'Marc T. Law', 'Makarand Tapaswi']
2019-08-09
null
null
null
iccv-2019-10
['face-clustering']
['computer-vision']
[ 6.90754205e-02 -6.53285980e-02 -2.68651247e-01 -3.28140646e-01 -5.03036916e-01 -8.36068809e-01 5.81524193e-01 2.08750412e-01 -4.23652768e-01 3.41593415e-01 -1.22059703e-01 -7.69765377e-02 -1.24244347e-01 -5.36969364e-01 -5.45166552e-01 -7.11296618e-01 -1.84486344e-01 8.21935058e-01 1.97814584e-01 1.63019478e-01 2.17853025e-01 6.68225884e-01 -1.63628864e+00 -9.54759249e-04 4.91583139e-01 8.11958075e-01 -1.15332134e-01 6.89902425e-01 7.82606527e-02 6.90635562e-01 -3.14777851e-01 -7.44517744e-01 3.62934977e-01 -6.88437700e-01 -8.27109456e-01 4.73708838e-01 5.31078994e-01 -1.20429300e-01 -9.25364345e-02 1.11080432e+00 2.00998172e-01 1.17579386e-01 8.84958744e-01 -1.49367893e+00 -1.59282044e-01 4.51136440e-01 -8.74767005e-01 2.53641754e-01 2.90704668e-01 -5.05403697e-01 1.16798413e+00 -9.55850124e-01 6.36828244e-01 1.10986066e+00 6.19185269e-01 6.15397274e-01 -1.46934223e+00 -6.01677179e-01 2.19574660e-01 5.03594637e-01 -1.78971708e+00 -7.07196474e-01 7.12023973e-01 -7.61112332e-01 1.03899278e-01 2.74592638e-01 5.75975239e-01 6.68046236e-01 -4.64951366e-01 6.09658718e-01 6.42889321e-01 -6.42260432e-01 3.68374527e-01 4.57699388e-01 7.12639689e-02 5.77532291e-01 2.44399771e-01 -4.24619526e-01 -4.78956282e-01 -3.00434232e-01 5.16524017e-01 -2.17072800e-01 -1.08598322e-01 -9.16120648e-01 -9.97844934e-01 1.09055829e+00 -6.95002452e-02 3.50024223e-01 -3.38157751e-02 8.64976048e-02 2.17327222e-01 3.36598545e-01 4.02656972e-01 2.10945383e-01 -1.19771548e-01 -7.53222406e-02 -1.29203022e+00 2.91420817e-01 9.26029325e-01 9.62017953e-01 8.39658558e-01 -3.57979596e-01 1.27025247e-01 6.91594899e-01 2.98497051e-01 6.71521202e-02 -4.73592710e-03 -1.05590463e+00 1.54397637e-01 3.87496084e-01 3.55128080e-01 -1.25710428e+00 -1.52431816e-01 2.49160212e-02 -3.83274734e-01 1.42840564e-01 8.66358399e-01 -7.36787319e-02 -5.40710509e-01 1.82675910e+00 6.67282701e-01 6.44644082e-01 -4.17229205e-01 7.08047509e-01 3.69207799e-01 2.98426419e-01 -3.85605723e-01 -3.15565944e-01 1.44016802e+00 -6.02453887e-01 -6.15547597e-01 1.82628334e-02 5.89569867e-01 -8.85193706e-01 6.32532418e-01 5.16884983e-01 -8.81092191e-01 -3.50582272e-01 -9.21288550e-01 2.33677387e-01 -1.93634555e-01 4.03953820e-01 5.56460321e-01 1.13937378e+00 -1.21807516e+00 6.64164543e-01 -9.20955896e-01 -5.62746048e-01 5.46640813e-01 5.59054255e-01 -4.31937695e-01 -1.12894915e-01 -5.26452065e-01 6.64814830e-01 2.15961456e-01 3.89886498e-02 -6.30907238e-01 -5.62079430e-01 -8.45458925e-01 -1.85057223e-01 4.32956666e-01 -2.64436692e-01 1.10827041e+00 -1.17464697e+00 -1.27307439e+00 1.16845834e+00 -3.88371319e-01 -2.37861961e-01 5.76361299e-01 4.83911932e-02 -2.47379646e-01 3.71990293e-01 1.08305939e-01 5.11048794e-01 1.01059544e+00 -1.25080514e+00 -7.41155982e-01 -5.78630745e-01 8.04230049e-02 1.13851957e-01 -7.76659310e-01 4.13742572e-01 -8.69487822e-01 -5.62877357e-01 1.31980717e-01 -1.06713796e+00 4.99205738e-02 2.86051154e-01 -2.49062285e-01 -4.07333553e-01 8.47159922e-01 -3.39532763e-01 1.35519338e+00 -2.12920856e+00 2.81935841e-01 3.42222720e-01 4.32436317e-01 -3.06958482e-02 1.10227115e-01 3.93919617e-01 -9.67795104e-02 -9.57694836e-03 -5.33359759e-02 -6.98131323e-01 4.70746011e-02 7.99786672e-02 -1.09228425e-01 9.91501868e-01 1.12235293e-01 2.00821310e-01 -9.02657986e-01 -7.10459352e-01 1.32505387e-01 2.75306880e-01 -6.45262361e-01 6.92409426e-02 2.31862351e-01 4.20896649e-01 -3.04230660e-01 5.48481584e-01 7.39438713e-01 -1.92039967e-01 3.10157061e-01 -2.21149344e-03 1.33828908e-01 -4.73162457e-02 -1.79929149e+00 1.36024642e+00 -1.26665672e-02 7.74866104e-01 3.71375352e-01 -1.07454503e+00 6.05093896e-01 2.85868943e-01 9.73941267e-01 7.32990652e-02 2.65287876e-01 1.28348973e-02 -4.20928523e-02 -3.06514353e-01 1.89357325e-01 -3.20817590e-01 1.98833555e-01 7.25677669e-01 -6.66446611e-03 1.46272361e-01 4.63771731e-01 2.40161762e-01 9.76925015e-01 -2.05130175e-01 1.48573592e-01 -3.20120811e-01 5.52265286e-01 -1.91098630e-01 6.97669625e-01 4.16134357e-01 -3.56561661e-01 7.63916373e-01 6.07359529e-01 -2.00862497e-01 -1.03498220e+00 -1.09875333e+00 -4.08200651e-01 1.16060877e+00 2.34026819e-01 -6.61571503e-01 -1.08065522e+00 -7.28665650e-01 -7.53217796e-03 2.19610959e-01 -9.17703331e-01 -6.29418641e-02 -5.69938600e-01 -6.35406733e-01 2.53923267e-01 4.84029114e-01 -3.06118757e-01 -7.48684168e-01 -2.68957198e-01 -3.35199498e-02 -1.91924706e-01 -1.15206981e+00 -7.84227550e-01 -9.52127129e-02 -5.44895947e-01 -1.36866212e+00 -5.08458614e-01 -8.96500647e-01 9.28203642e-01 2.93829829e-01 1.07119012e+00 5.76582588e-02 -3.44311029e-01 6.26761556e-01 -4.64919955e-01 -3.72593492e-01 -2.66648710e-01 -3.63524407e-02 2.50561833e-01 6.10943675e-01 7.11842299e-01 -3.66165668e-01 -4.69338059e-01 5.55129945e-01 -6.96872413e-01 -4.51215506e-01 3.99515852e-02 4.06257719e-01 3.61068249e-01 2.58228779e-01 2.70325691e-01 -8.41580927e-01 2.42218018e-01 -6.55742586e-01 -5.69423974e-01 1.32329509e-01 -3.60555589e-01 -3.05068910e-01 4.51989174e-01 -6.29355907e-01 -3.74413311e-01 4.05024707e-01 2.35893801e-01 -5.66565633e-01 -8.57593641e-02 -5.15422300e-02 -3.02064657e-01 -2.19595402e-01 3.73367876e-01 -2.66750474e-02 8.13993290e-02 -3.58569384e-01 3.27695489e-01 6.80904746e-01 4.18915659e-01 -5.29026270e-01 8.95445049e-01 8.44465971e-01 -1.35820299e-01 -8.12344432e-01 -5.63647389e-01 -8.58020723e-01 -1.14819384e+00 -4.05127972e-01 8.81698549e-01 -7.80061007e-01 -9.52759385e-01 4.74661887e-02 -8.73278916e-01 -1.37572750e-01 -1.48455605e-01 6.20579898e-01 -7.17082739e-01 6.63855493e-01 -4.34532434e-01 -9.19141710e-01 2.41024420e-01 -1.05559003e+00 9.59532738e-01 7.16180056e-02 -4.59051073e-01 -1.07051253e+00 1.07476473e-01 3.35132897e-01 -9.90058780e-02 2.42083073e-01 6.14768147e-01 -6.07868075e-01 -2.84832627e-01 -4.18771923e-01 2.28798017e-02 1.69140145e-01 2.98751831e-01 2.07127109e-01 -7.90865242e-01 -5.16629100e-01 5.52556962e-02 -2.91385740e-01 5.34060776e-01 3.61984611e-01 1.08421719e+00 -1.21653035e-01 -6.27998114e-01 5.14383376e-01 1.14530575e+00 4.66984734e-02 2.87978023e-01 1.44914925e-01 7.44710922e-01 8.87802482e-01 5.67516387e-01 6.67203426e-01 3.55534405e-01 1.02653980e+00 3.06216151e-01 2.78196037e-02 1.34189829e-01 -7.23710358e-02 3.42146963e-01 4.86097485e-01 1.23971567e-01 -1.13792807e-01 -8.57358456e-01 6.88077629e-01 -1.85472250e+00 -1.06709218e+00 -6.08066209e-02 2.50110412e+00 8.91334891e-01 6.06080294e-02 6.59898043e-01 3.68356556e-01 1.03422916e+00 -1.42035306e-01 -4.12124276e-01 -6.91424087e-02 9.73071083e-02 -7.44141564e-02 3.87740403e-01 3.77876580e-01 -1.29233325e+00 8.56165767e-01 6.52478933e+00 8.46960664e-01 -8.59491229e-01 -2.87699699e-03 6.35529816e-01 -3.55398029e-01 4.88993078e-02 1.65582582e-01 -1.02327013e+00 5.99594593e-01 7.39185274e-01 3.19210738e-02 4.99868512e-01 7.12740123e-01 1.74316198e-01 -4.52265479e-02 -1.54169023e+00 1.14973438e+00 4.24430341e-01 -9.47486281e-01 -1.29231170e-01 2.68908560e-01 6.11856282e-01 -3.44868422e-01 1.00223541e-01 -8.01625401e-02 3.80868971e-01 -1.08618498e+00 8.38349998e-01 2.94443727e-01 6.70452178e-01 -9.33163583e-01 3.11179608e-01 2.70819962e-01 -1.28670025e+00 -1.67836323e-01 -4.46105182e-01 8.24641287e-02 4.40989994e-02 4.57681805e-01 -7.27881074e-01 2.44756892e-01 7.14915395e-01 8.83118272e-01 -5.78463674e-01 1.15260983e+00 1.04200624e-01 7.92964816e-01 -5.60685337e-01 2.12278903e-01 8.67192298e-02 -6.48191690e-01 3.69922817e-01 9.81432438e-01 2.65550107e-01 -1.82916950e-02 2.71159559e-01 6.03731275e-01 -1.04072832e-01 3.13644409e-01 -5.44775486e-01 5.23918755e-02 6.70386910e-01 1.42940104e+00 -1.24441051e+00 -7.68957585e-02 -4.60363060e-01 9.75767434e-01 3.58683288e-01 1.14490882e-01 -8.07534218e-01 -4.00212824e-01 8.96920681e-01 4.04029548e-01 7.30551302e-01 -2.05532938e-01 2.57506792e-04 -1.06248152e+00 1.13534324e-01 -7.48027444e-01 6.14199162e-01 -2.65589118e-01 -1.23521841e+00 4.68596995e-01 -5.33645712e-02 -1.27802753e+00 -1.13180675e-01 -6.26488447e-01 -5.31119466e-01 2.74330109e-01 -1.21207631e+00 -9.86583948e-01 -6.31656125e-02 6.77616954e-01 2.63972759e-01 -1.26682892e-01 5.51954627e-01 4.97918725e-01 -8.41093242e-01 7.62981951e-01 3.28239381e-01 4.62117046e-01 8.22282255e-01 -1.28113115e+00 -8.08985531e-02 7.46175706e-01 5.90831697e-01 4.93944585e-01 8.35833430e-01 -2.68308252e-01 -1.21211362e+00 -9.53038096e-01 8.40692937e-01 -9.23906326e-01 7.56695330e-01 -9.43737388e-01 -6.09298885e-01 7.07447410e-01 -1.13449544e-01 2.38116056e-01 1.06173861e+00 2.91397363e-01 -3.47710222e-01 -2.16913208e-01 -1.08615768e+00 4.89189178e-01 9.73443210e-01 -3.65254909e-01 -1.76111042e-01 5.26696384e-01 2.80814152e-02 -3.77322175e-02 -8.22490811e-01 2.90943645e-02 4.98100579e-01 -9.41672146e-01 9.21371639e-01 -6.55667543e-01 2.73227133e-02 -5.03745735e-01 -1.11419089e-01 -8.75386119e-01 -2.64031261e-01 -7.40333796e-01 -2.72128582e-01 1.46435595e+00 9.87778157e-02 -1.36391804e-01 1.12186229e+00 5.06556928e-01 2.86346346e-01 -7.26899028e-01 -9.96467829e-01 -7.71796584e-01 1.61204580e-02 -3.11783344e-01 4.44884688e-01 1.18061078e+00 9.89439785e-02 2.27800086e-01 -4.61240262e-01 1.60611585e-01 7.47753620e-01 1.05567761e-01 8.42634737e-01 -1.56983244e+00 -1.52959615e-01 -5.64176977e-01 -6.97018862e-01 -8.59206140e-01 3.73894811e-01 -7.98556387e-01 -1.39240660e-02 -9.77938473e-01 4.26242828e-01 -5.00538409e-01 -9.43160504e-02 2.27582738e-01 -1.93531767e-01 6.14444852e-01 5.45005277e-02 3.30740839e-01 -8.06684315e-01 3.51510882e-01 7.01576948e-01 9.62237418e-02 2.05099285e-02 3.86613905e-01 -7.08172798e-01 8.22124422e-01 5.84545195e-01 -6.50002241e-01 -4.27266270e-01 -1.94640249e-01 1.48771331e-01 -2.30953738e-01 1.63825601e-01 -9.16623771e-01 3.01636219e-01 -1.24733098e-01 2.63205916e-01 -4.24124092e-01 4.37495708e-01 -9.78371620e-01 -3.54902223e-02 1.80632129e-01 -2.34588981e-01 7.19122812e-02 -4.77168150e-02 7.28331387e-01 -8.45256969e-02 -5.62289238e-01 9.97717798e-01 1.24334708e-01 -3.28128934e-01 5.07955909e-01 -4.08920109e-01 8.75587538e-02 1.48713732e+00 -6.41137719e-01 3.68274897e-01 -5.20567834e-01 -8.84784162e-01 2.69913435e-01 7.51421630e-01 3.87558937e-01 2.94454724e-01 -1.48428929e+00 -7.63984263e-01 1.21759072e-01 3.28104824e-01 -3.71328712e-01 -1.59514487e-01 8.89744818e-01 -4.21388805e-01 6.52272031e-02 1.37570098e-01 -6.42909467e-01 -1.66989481e+00 7.45220721e-01 2.68951923e-01 1.79696694e-01 -3.55146050e-01 8.43401432e-01 2.98323072e-02 -2.13596150e-01 4.12565202e-01 2.69455522e-01 -4.89814162e-01 5.49576700e-01 7.21504450e-01 4.38556463e-01 1.54655986e-02 -1.02258396e+00 -4.99590278e-01 7.30505943e-01 -2.25364342e-01 -7.18957260e-02 1.37904811e+00 -2.86869556e-01 -1.59423247e-01 4.93108183e-01 1.43936348e+00 2.06654623e-01 -1.22341800e+00 -2.78860509e-01 3.17540020e-01 -5.99758506e-01 -2.31854871e-01 1.17692024e-01 -1.10720563e+00 8.13728094e-01 5.74805856e-01 2.57455260e-01 1.02305186e+00 2.84649014e-01 5.19661129e-01 6.04989007e-02 2.07227856e-01 -1.22033978e+00 2.31850505e-01 1.70506269e-01 2.97823519e-01 -1.20683610e+00 4.75172177e-02 -4.45211083e-01 -4.50358003e-01 1.07750762e+00 3.42046708e-01 -1.44913301e-01 8.65985632e-01 2.75259852e-01 -7.69458264e-02 -2.32418448e-01 -4.59311068e-01 -2.36537993e-01 2.34651372e-01 6.63086176e-01 3.31714451e-01 -1.73766464e-01 -9.81074274e-02 2.95269728e-01 -3.02193105e-01 -2.30128765e-01 6.22003555e-01 8.79330397e-01 -4.69882488e-01 -1.15862870e+00 -5.39242148e-01 4.02577311e-01 -5.09743094e-01 2.44994491e-01 -6.69851124e-01 5.79325020e-01 3.24512511e-01 1.15183985e+00 3.03958327e-01 -2.47247368e-01 7.67323002e-02 -7.00880662e-02 6.36108339e-01 -8.74286711e-01 -1.27377391e-01 9.58589613e-02 -2.49200463e-01 -3.17905545e-01 -5.36267579e-01 -1.16216218e+00 -9.40047026e-01 -4.49236274e-01 -5.69339335e-01 4.08736825e-01 4.48640645e-01 7.72847295e-01 7.33170658e-02 -1.38238087e-01 9.11105275e-01 -7.64273882e-01 -3.96022141e-01 -7.59966731e-01 -8.52391779e-01 6.73628211e-01 2.64586926e-01 -8.76734614e-01 -3.77935946e-01 4.41927791e-01]
[13.462641716003418, 1.0532432794570923]
1fd2aa26-8c5c-4b6d-bea3-fb247190d80c
multi-view-subspace-clustering-via-partition
1912.01201
null
https://arxiv.org/abs/1912.01201v1
https://arxiv.org/pdf/1912.01201v1.pdf
Multi-view Subspace Clustering via Partition Fusion
Multi-view clustering is an important approach to analyze multi-view data in an unsupervised way. Among various methods, the multi-view subspace clustering approach has gained increasing attention due to its encouraging performance. Basically, it integrates multi-view information into graphs, which are then fed into spectral clustering algorithm for final result. However, its performance may degrade due to noises existing in each individual view or inconsistency between heterogeneous features. Orthogonal to current work, we propose to fuse multi-view information in a partition space, which enhances the robustness of Multi-view clustering. Specifically, we generate multiple partitions and integrate them to find the shared partition. The proposed model unifies graph learning, generation of basic partitions, and view weight learning. These three components co-evolve towards better quality outputs. We have conducted comprehensive experiments on benchmark datasets and our empirical results verify the effectiveness and robustness of our approach.
['Zenglin Xu', 'Boyu Wang', 'Zhao Kang', 'Juncheng Lv', 'Luping Ji']
2019-12-03
null
null
null
null
['multi-view-subspace-clustering']
['computer-vision']
[-1.57365635e-01 -5.27896643e-01 -1.45767763e-01 -5.70858158e-02 -5.12539804e-01 -7.55040526e-01 3.75528932e-01 7.99397826e-02 1.44503817e-01 2.03171283e-01 4.52410221e-01 1.74532682e-01 -4.09128547e-01 -7.21879840e-01 -1.36038601e-01 -9.43844259e-01 2.30492398e-01 2.18531981e-01 3.64398628e-01 1.33844435e-01 2.96466410e-01 1.69014886e-01 -1.37958312e+00 3.36331189e-01 1.04537046e+00 4.93329942e-01 1.54781461e-01 3.51318508e-01 8.63310508e-03 5.63896537e-01 -1.23103879e-01 -2.02345952e-01 1.27893493e-01 -4.28036213e-01 -5.78272879e-01 6.33885801e-01 -3.17890197e-01 2.20500290e-01 -7.89122954e-02 1.17281795e+00 6.05324626e-01 5.61744012e-02 6.88735545e-01 -1.39036250e+00 -3.46076429e-01 4.51104224e-01 -1.06953609e+00 5.72663322e-02 2.96456158e-01 -1.44384146e-01 1.06956291e+00 -8.87486994e-01 5.64091027e-01 1.24959540e+00 3.35865855e-01 2.84164050e-03 -1.38661599e+00 -5.75590849e-01 3.37497473e-01 3.38861912e-01 -1.48359179e+00 -3.30207795e-01 1.19367588e+00 -5.59316635e-01 4.02794272e-01 3.98462676e-02 6.83031023e-01 7.37039387e-01 1.89542606e-01 7.98249245e-01 1.17570436e+00 -1.77048266e-01 1.53805539e-01 3.67375873e-02 8.53815377e-02 5.13228476e-01 3.70609403e-01 -3.04209948e-01 -2.50430793e-01 -3.48327249e-01 2.63598382e-01 3.29493105e-01 -4.16278005e-01 -1.07926536e+00 -1.18059897e+00 7.36031294e-01 2.53316373e-01 3.13999414e-01 -3.57315540e-01 -5.60258627e-01 4.37545180e-01 6.95011988e-02 4.24130321e-01 -5.64786270e-02 -5.15758358e-02 1.62256300e-01 -7.43795276e-01 -1.52542576e-01 4.96543497e-01 8.42681110e-01 7.79340208e-01 -1.70961350e-01 2.00150222e-01 1.03382528e+00 4.25131291e-01 2.38387212e-01 5.25881827e-01 -8.04613173e-01 5.82596362e-01 1.20108914e+00 -2.41078123e-01 -1.46173918e+00 -5.21835327e-01 -4.94738787e-01 -1.23844838e+00 -1.77887261e-01 -5.99499904e-02 -1.94931373e-01 -6.57689035e-01 1.55478704e+00 4.61707652e-01 2.22859725e-01 2.93066740e-01 8.10544431e-01 7.95246303e-01 4.88848805e-01 -2.08518684e-01 -4.78099674e-01 1.12609315e+00 -1.00219357e+00 -5.91502428e-01 1.87740177e-01 2.43080840e-01 -8.18838537e-01 5.10580719e-01 6.32409692e-01 -7.95694172e-01 -5.98601937e-01 -1.12790263e+00 5.95567822e-01 -1.61943957e-01 1.30846590e-01 4.09249932e-01 5.87549984e-01 -8.82807910e-01 2.47712329e-01 -7.54734159e-01 -3.56402278e-01 2.30493471e-01 3.03490430e-01 -5.25403142e-01 -2.96777427e-01 -8.53218555e-01 2.02683181e-01 6.85879171e-01 -7.21031204e-02 -4.25624132e-01 -2.01286450e-01 -6.20680332e-01 4.30839099e-02 6.67718530e-01 -8.56714845e-01 3.80804658e-01 -5.73754668e-01 -1.05075622e+00 4.60536331e-01 -2.22568363e-01 -2.64728833e-02 5.90388589e-02 2.46816024e-01 -7.38394737e-01 3.53772819e-01 2.18449175e-01 2.66710848e-01 7.58609891e-01 -1.78760684e+00 -6.19644225e-01 -7.55236924e-01 -2.43447006e-01 4.97128695e-01 -4.47408319e-01 -1.57272488e-01 -9.19402480e-01 -4.47107315e-01 8.26219082e-01 -1.04420805e+00 -3.75432879e-01 -9.61951077e-01 -5.30669510e-01 -4.97336164e-02 1.03906655e+00 -3.75675231e-01 1.60997069e+00 -2.22948527e+00 6.90533280e-01 4.93118554e-01 5.45772195e-01 -6.04960062e-02 3.02793413e-01 7.27390766e-01 -3.11723799e-01 1.41806722e-01 -1.10664390e-01 1.23729790e-02 -2.86408454e-01 -7.29900971e-02 1.61086157e-01 4.63620842e-01 -2.40394041e-01 5.62646151e-01 -8.01358700e-01 -6.98255599e-01 2.86278576e-01 1.83604196e-01 -5.29030979e-01 1.56434804e-01 2.79424578e-01 4.88729477e-01 -5.19258440e-01 5.37623167e-01 8.22416663e-01 -6.21797442e-01 5.84412873e-01 -4.99693036e-01 8.02293345e-02 -4.43731904e-01 -1.73938572e+00 1.59459305e+00 -7.54524618e-02 -1.54466569e-01 1.91935614e-01 -1.09573615e+00 8.22699666e-01 4.31884587e-01 9.39497709e-01 -1.16285749e-01 7.14005083e-02 -4.30030972e-02 1.45551458e-01 -4.17308092e-01 3.22487444e-01 -1.46041140e-01 -2.64319833e-02 5.72544992e-01 -2.24102922e-02 2.68224657e-01 2.80578136e-01 4.84371811e-01 7.07605004e-01 -5.31071499e-02 5.37503362e-01 -1.49276793e-01 1.00592446e+00 -1.79991975e-01 7.09661245e-01 2.24539582e-02 -1.23603523e-01 7.41924942e-01 5.70178092e-01 -1.65355220e-01 -7.07324386e-01 -1.01352310e+00 3.25794250e-01 6.84075236e-01 3.95377487e-01 -6.90252721e-01 -6.36751592e-01 -8.20929646e-01 -2.48271704e-01 1.58015117e-01 -4.17476535e-01 -2.32115328e-01 -1.52300581e-01 -9.40799475e-01 -4.29267660e-02 4.66441244e-01 4.26094621e-01 -5.72973609e-01 -1.09305248e-01 1.62808448e-01 -4.41307873e-01 -1.00761223e+00 -4.30897474e-01 1.02825444e-02 -1.01853180e+00 -1.41022110e+00 -5.58501601e-01 -6.74407721e-01 7.07301974e-01 1.01858902e+00 8.68601799e-01 3.50115485e-02 1.27269089e-01 5.85651994e-01 -4.69108760e-01 1.94648132e-02 -2.13554591e-01 1.60615101e-01 1.90293506e-01 5.25907040e-01 3.08661044e-01 -9.07585084e-01 -6.42560840e-01 5.36349595e-01 -1.01542854e+00 -7.82186259e-03 4.71141070e-01 7.62965560e-01 7.78281271e-01 6.39592350e-01 5.47266245e-01 -1.04848015e+00 6.98673785e-01 -6.93905294e-01 -4.09983933e-01 3.14293772e-01 -7.86548793e-01 -1.05315097e-01 7.84086525e-01 3.37629020e-02 -1.00014675e+00 2.83810914e-01 3.39463502e-01 -8.39361250e-01 -2.05712095e-01 6.16254270e-01 -6.55167997e-01 1.94265097e-01 1.20721273e-01 4.57839340e-01 7.94490054e-02 -3.20273459e-01 4.53766584e-01 6.09319746e-01 1.52309880e-01 -3.99861038e-01 8.54087353e-01 6.37089729e-01 -4.09764349e-02 -6.52262151e-01 -5.63728631e-01 -8.46713603e-01 -9.42021906e-01 -3.83471519e-01 9.65216458e-01 -1.12304342e+00 -7.11752653e-01 3.71210873e-01 -5.93799114e-01 6.68460786e-01 4.04588848e-01 5.29790938e-01 -3.56352806e-01 8.61162663e-01 -2.36013502e-01 -7.39962637e-01 -1.79756328e-01 -1.33158946e+00 7.40020156e-01 2.77526319e-01 1.31317481e-01 -1.01891601e+00 4.76302020e-02 5.09792149e-01 -8.35200846e-02 2.94259816e-01 9.40637946e-01 -7.27460325e-01 -3.06496978e-01 -2.43115555e-02 -1.16406918e-01 8.43406469e-02 4.43495512e-01 1.19074561e-01 -8.26669216e-01 -5.43665767e-01 1.13296606e-01 -3.05655096e-02 8.24413300e-01 3.64974856e-01 1.07525361e+00 1.28029495e-01 -6.53495312e-01 5.02009451e-01 1.64529741e+00 4.15035963e-01 1.82501867e-01 2.08245844e-01 1.06002080e+00 6.40770495e-01 5.38376510e-01 4.75408763e-01 6.80714726e-01 5.03054142e-01 4.27079111e-01 -5.73184006e-02 3.82017940e-01 -1.31915793e-01 1.26143560e-01 1.48110533e+00 -1.69194147e-01 -3.29209834e-01 -9.39140618e-01 5.91603816e-01 -2.02683139e+00 -1.22279382e+00 -3.56133372e-01 2.03862023e+00 9.84540433e-02 1.55222937e-01 5.56912243e-01 3.10929507e-01 1.01604307e+00 3.85731220e-01 -4.11283016e-01 9.52263698e-02 -1.40237123e-01 -4.02440429e-01 1.36649996e-01 1.11654930e-01 -1.21735346e+00 6.69361591e-01 5.40288162e+00 5.56466699e-01 -8.40840757e-01 -3.96177173e-02 6.38749897e-01 7.49179572e-02 -4.52457190e-01 1.02736741e-01 -3.65266949e-01 4.35194343e-01 4.86589134e-01 -2.54837126e-01 4.73167837e-01 6.70919359e-01 3.10939819e-01 -2.19277255e-02 -6.58095479e-01 1.04987299e+00 3.32591146e-01 -9.10303831e-01 1.88739046e-01 2.14244142e-01 9.17916417e-01 -2.10190922e-01 -7.79666454e-02 4.85454947e-02 4.36504394e-01 -5.61619580e-01 1.74428135e-01 3.84795755e-01 1.93500713e-01 -1.32609105e+00 7.11925924e-01 5.60126364e-01 -1.59490359e+00 -2.12341815e-01 -2.96831131e-01 2.69569010e-01 7.77024776e-02 5.16090631e-01 -5.33462167e-01 1.50814390e+00 5.93260586e-01 1.00482631e+00 -7.78980613e-01 9.43681061e-01 1.04693927e-01 5.16035378e-01 1.97880752e-02 4.24495518e-01 2.17259899e-01 -7.87225366e-01 5.34348130e-01 6.55565739e-01 2.08094507e-01 2.03167155e-01 6.59956694e-01 4.80933070e-01 2.18136773e-01 4.10962075e-01 -8.10404539e-01 1.76717103e-01 3.69909286e-01 1.54698479e+00 -1.24163020e+00 -3.33118677e-01 -6.85703933e-01 7.55242944e-01 2.77426988e-01 3.70188951e-01 -6.97403491e-01 -1.03311695e-01 2.79715359e-01 -1.39368743e-01 5.25142670e-01 -1.56000361e-01 -3.06653440e-01 -1.39754093e+00 -2.12464891e-02 -1.00563788e+00 8.14075828e-01 -5.21253765e-01 -1.31624675e+00 4.98371303e-01 -4.15711962e-02 -1.79284108e+00 -1.05166003e-01 -6.89014122e-02 -5.07921636e-01 5.20562351e-01 -1.03847265e+00 -1.07358038e+00 -2.84500152e-01 7.92241335e-01 4.82274950e-01 -3.18032533e-01 5.81764162e-01 2.32301205e-01 -7.61058629e-01 2.62259513e-01 3.01853210e-01 7.06613138e-02 6.52638316e-01 -1.25552726e+00 -7.36888871e-02 1.06490505e+00 1.90635815e-01 7.25650966e-01 2.17760026e-01 -6.78950071e-01 -1.52559793e+00 -1.05485153e+00 2.17587456e-01 -2.26224959e-01 4.85503286e-01 -1.78801045e-01 -8.93269122e-01 4.38388407e-01 4.12108004e-01 -2.14066535e-01 1.16580379e+00 1.19308926e-01 -2.31679693e-01 -1.99898571e-01 -8.27117860e-01 5.55582404e-01 7.67908514e-01 -2.86465555e-01 -5.10803699e-01 -5.68649657e-02 4.30667937e-01 -1.19578689e-01 -1.15974438e+00 4.07040656e-01 4.21986252e-01 -1.42241204e+00 9.53588903e-01 -4.09186721e-01 4.23289835e-01 -7.43772566e-01 -2.72401750e-01 -1.57613778e+00 -7.30026484e-01 -2.62150377e-01 9.26444754e-02 1.61517608e+00 2.01336548e-01 -6.01796627e-01 6.03318810e-01 3.55142392e-02 7.02696294e-02 -6.15673542e-01 -4.56422687e-01 -5.06919801e-01 -3.68653327e-01 -1.16275482e-01 6.54433131e-01 1.15686822e+00 4.86409329e-02 8.09766829e-01 -4.10849899e-01 5.33602059e-01 8.38593900e-01 8.41335535e-01 8.14613461e-01 -1.42230654e+00 -3.34654093e-01 -4.40654904e-01 -3.26713771e-01 -4.49555337e-01 -2.45924685e-02 -9.38547254e-01 -2.39437699e-01 -1.69758630e+00 7.48279393e-01 -1.55356362e-01 -5.12847066e-01 1.78265700e-03 -7.33534515e-01 1.01803914e-01 2.84906119e-01 4.72884655e-01 -9.30189431e-01 5.17048597e-01 1.24468136e+00 -5.11413552e-02 -4.68993545e-01 1.21560127e-01 -9.29937422e-01 7.98656285e-01 6.95127189e-01 -3.50008011e-01 -7.67181814e-01 -3.08905020e-02 -1.19933538e-01 3.81337821e-01 -8.96486267e-02 -1.17987883e+00 3.23922247e-01 -1.10476315e-01 5.61159790e-01 -8.54709029e-01 1.09896705e-01 -1.18034232e+00 4.24681157e-01 2.61462390e-01 2.55330056e-01 3.07406366e-01 -1.21809430e-01 1.07055199e+00 -5.66329777e-01 1.97843581e-01 7.28584230e-01 -3.69851828e-01 -6.17385864e-01 2.53974199e-01 -1.58054709e-01 1.26883417e-01 1.12267959e+00 -3.83192748e-01 -2.91824881e-02 -4.24318224e-01 -8.61835003e-01 5.65082371e-01 7.19905555e-01 5.07241130e-01 4.52569723e-01 -1.56053221e+00 -4.35541064e-01 1.84840873e-01 3.14139158e-01 -3.86283472e-02 4.67321783e-01 9.94124770e-01 -4.05596048e-02 2.76563913e-01 -1.12302810e-01 -9.18012321e-01 -1.49740958e+00 1.01540649e+00 -1.62129834e-01 -3.13177526e-01 -3.82263988e-01 1.81789890e-01 2.64927447e-01 -3.64726841e-01 -7.51300603e-02 2.39807770e-01 -7.40960658e-01 4.98513132e-01 1.53284088e-01 4.68273431e-01 -8.33402276e-02 -8.75509322e-01 -3.62708956e-01 8.72587919e-01 -1.05117103e-02 5.06693721e-02 1.46721649e+00 -4.98421639e-01 -2.99606293e-01 6.32164061e-01 1.08442807e+00 2.27704227e-01 -7.44970500e-01 -1.19091034e-01 -6.82812696e-03 -3.21020722e-01 -1.41374364e-01 -2.65492201e-01 -1.25524437e+00 7.53513277e-01 3.45737606e-01 4.94454414e-01 1.45013976e+00 -1.29571185e-01 5.76620460e-01 -4.26058937e-03 2.35777482e-01 -1.01152170e+00 1.89607382e-01 1.54675022e-01 3.19742173e-01 -1.16910553e+00 2.58348823e-01 -6.97366357e-01 -8.70401144e-01 9.49732065e-01 7.24086404e-01 8.26929957e-02 7.44312763e-01 -3.93443070e-02 -6.66191801e-02 -4.31442976e-01 -7.33775198e-01 -2.14479178e-01 2.25818619e-01 4.27403718e-01 2.68308043e-01 4.15098257e-02 -3.01961750e-01 6.25954330e-01 7.57181942e-02 -3.30795616e-01 3.46735716e-01 7.03755915e-01 -3.65455627e-01 -1.16290295e+00 -4.93080348e-01 4.67912138e-01 -3.50357443e-01 1.84360459e-01 -5.55042088e-01 5.72183073e-01 1.71765909e-02 1.20502925e+00 -3.45780939e-01 -7.96166599e-01 3.40439349e-01 2.33724833e-01 8.10261816e-02 -5.65111220e-01 -4.84947413e-01 7.19362080e-01 -2.34273627e-01 -4.24821317e-01 -7.97772944e-01 -7.71734834e-01 -1.01336205e+00 -7.02513158e-02 -4.54532892e-01 4.31686074e-01 1.01860672e-01 7.07522333e-01 5.92139304e-01 5.62162161e-01 1.25190151e+00 -4.44912970e-01 -1.92742720e-01 -6.73242807e-01 -6.75438166e-01 5.00893414e-01 -1.76179245e-01 -7.43530810e-01 -2.74381757e-01 1.24570698e-01]
[8.217903137207031, 4.655970096588135]
End of preview. Expand in Dataset Viewer.

A cleaned dataset from paperswithcode.com

Last dataset update: July 2023

This is a cleaned up dataset optained from paperswithcode.com through their API service. It represents a set of around 56K carefully categorized papers into 3K tasks and 16 areas. The papers contain arXiv and NIPS IDs as well as title, abstract and other meta information. It can be used for training text classifiers that concentrate on the use of specific AI and ML methods and frameworks.

Contents

It contains the following tables:

  • papers.csv (around 56K)
  • papers_train.csv (80% from 56K)
  • papers_test.csv (20% from 56K)
  • tasks.csv
  • areas.csv

Specials

UUIDs were added to the dataset since the PapersWithCode IDs (pwc_ids) are not distinct enough. These UUIDs may change in the future with new versions of the dataset. Also, embeddings were calculated for all of the 56K papers using the brilliant model SciNCL as well as dimensionality-redused 2D coordinates using UMAP.

There is also a simple Python Notebook which was used to optain and refactor the dataset.

Downloads last month
0
Edit dataset card
Size of downloaded dataset files:
788 MB
Size of the auto-converted Parquet files:
500 MB
Number of rows:
55,760