Does Pooling Really Matter? An Evaluation on Gait Recognition
Autor(a) principal: | |
---|---|
Data de Publicação: | 2019 |
Outros Autores: | , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/978-3-030-33904-3_71 http://hdl.handle.net/11449/201356 |
Resumo: | Most Convolutional Neural Networks make use of subsampling layers to reduce dimensionality and keep only the most essential information, besides turning the model more robust to rotation and translation variations. One of the most common sampling methods is the one who keeps only the maximum value in a given region, known as max-pooling. In this study, we provide pieces of evidence that, by removing this subsampling layer and changing the stride of the convolution layer, one can obtain comparable results but much faster. Results on the gait recognition task show the robustness of the proposed approach, as well as its statistical similarity to other pooling methods. |
id |
UNSP_6ca858eb7b72cf4473ea7397ba06f120 |
---|---|
oai_identifier_str |
oai:repositorio.unesp.br:11449/201356 |
network_acronym_str |
UNSP |
network_name_str |
Repositório Institucional da UNESP |
repository_id_str |
2946 |
spelling |
Does Pooling Really Matter? An Evaluation on Gait RecognitionConvolutional Neural NetworksDeep learningGait recognitionMost Convolutional Neural Networks make use of subsampling layers to reduce dimensionality and keep only the most essential information, besides turning the model more robust to rotation and translation variations. One of the most common sampling methods is the one who keeps only the maximum value in a given region, known as max-pooling. In this study, we provide pieces of evidence that, by removing this subsampling layer and changing the stride of the convolution layer, one can obtain comparable results but much faster. Results on the gait recognition task show the robustness of the proposed approach, as well as its statistical similarity to other pooling methods.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Federal University of São Carlos - UFSCarState University of Sao Paulo - UNESPCenpes Petróleo Brasileiro S.A. – PetrobrasState University of Sao Paulo - UNESPFAPESP: 2013/07375-0FAPESP: 2014/12236-1FAPESP: 2016/06441-7FAPESP: 2017/25908-6CNPq: 307066/2017-7CNPq: 427968/2018-6CNPq: 429003/2018-8Universidade Federal de São Carlos (UFSCar)Universidade Estadual Paulista (Unesp)Petróleo Brasileiro S.A. – Petrobrasdos Santos, Claudio Filipi GoncalvesMoreira, Thierry Pinheiro [UNESP]Colombo, DaniloPapa, João Paulo [UNESP]2020-12-12T02:30:27Z2020-12-12T02:30:27Z2019-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject751-760http://dx.doi.org/10.1007/978-3-030-33904-3_71Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 11896 LNCS, p. 751-760.1611-33490302-9743http://hdl.handle.net/11449/20135610.1007/978-3-030-33904-3_712-s2.0-85075696640Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)info:eu-repo/semantics/openAccess2024-04-23T16:11:12Zoai:repositorio.unesp.br:11449/201356Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-04-23T16:11:12Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Does Pooling Really Matter? An Evaluation on Gait Recognition |
title |
Does Pooling Really Matter? An Evaluation on Gait Recognition |
spellingShingle |
Does Pooling Really Matter? An Evaluation on Gait Recognition dos Santos, Claudio Filipi Goncalves Convolutional Neural Networks Deep learning Gait recognition |
title_short |
Does Pooling Really Matter? An Evaluation on Gait Recognition |
title_full |
Does Pooling Really Matter? An Evaluation on Gait Recognition |
title_fullStr |
Does Pooling Really Matter? An Evaluation on Gait Recognition |
title_full_unstemmed |
Does Pooling Really Matter? An Evaluation on Gait Recognition |
title_sort |
Does Pooling Really Matter? An Evaluation on Gait Recognition |
author |
dos Santos, Claudio Filipi Goncalves |
author_facet |
dos Santos, Claudio Filipi Goncalves Moreira, Thierry Pinheiro [UNESP] Colombo, Danilo Papa, João Paulo [UNESP] |
author_role |
author |
author2 |
Moreira, Thierry Pinheiro [UNESP] Colombo, Danilo Papa, João Paulo [UNESP] |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Federal de São Carlos (UFSCar) Universidade Estadual Paulista (Unesp) Petróleo Brasileiro S.A. – Petrobras |
dc.contributor.author.fl_str_mv |
dos Santos, Claudio Filipi Goncalves Moreira, Thierry Pinheiro [UNESP] Colombo, Danilo Papa, João Paulo [UNESP] |
dc.subject.por.fl_str_mv |
Convolutional Neural Networks Deep learning Gait recognition |
topic |
Convolutional Neural Networks Deep learning Gait recognition |
description |
Most Convolutional Neural Networks make use of subsampling layers to reduce dimensionality and keep only the most essential information, besides turning the model more robust to rotation and translation variations. One of the most common sampling methods is the one who keeps only the maximum value in a given region, known as max-pooling. In this study, we provide pieces of evidence that, by removing this subsampling layer and changing the stride of the convolution layer, one can obtain comparable results but much faster. Results on the gait recognition task show the robustness of the proposed approach, as well as its statistical similarity to other pooling methods. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-01-01 2020-12-12T02:30:27Z 2020-12-12T02:30:27Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1007/978-3-030-33904-3_71 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 11896 LNCS, p. 751-760. 1611-3349 0302-9743 http://hdl.handle.net/11449/201356 10.1007/978-3-030-33904-3_71 2-s2.0-85075696640 |
url |
http://dx.doi.org/10.1007/978-3-030-33904-3_71 http://hdl.handle.net/11449/201356 |
identifier_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 11896 LNCS, p. 751-760. 1611-3349 0302-9743 10.1007/978-3-030-33904-3_71 2-s2.0-85075696640 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
751-760 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1799964411771748352 |