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Learning sufficient representation for spatio-temporal deep network using information filter

This article introduced an improved spatio - temporal deep network based on information filter method for learning sufficient representation. The proposed method aims to improve feature learning capability while modeling spatial and temporal dependencies. Experiments on pattern recognition are condu...

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Main Authors: Hu Y., Neoh D.T.H., Sahari K.S.M., Loo C.K.
Other Authors: 56096604000
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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id my.uniten.dspace-21973
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spelling my.uniten.dspace-219732023-05-16T10:46:23Z Learning sufficient representation for spatio-temporal deep network using information filter Hu Y. Neoh D.T.H. Sahari K.S.M. Loo C.K. 56096604000 56942483000 57218170038 55663408900 This article introduced an improved spatio - temporal deep network based on information filter method for learning sufficient representation. The proposed method aims to improve feature learning capability while modeling spatial and temporal dependencies. Experiments on pattern recognition are conducted to validate the effectiveness of the proposed method. © 2014 IEEE. Final 2023-05-16T02:46:23Z 2023-05-16T02:46:23Z 2014 Conference Paper 10.1109/SII.2014.7028116 2-s2.0-84946193982 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946193982&doi=10.1109%2fSII.2014.7028116&partnerID=40&md5=b2fdd414903d11217d4ef6780ca3cf2b https://irepository.uniten.edu.my/handle/123456789/21973 7028116 655 658 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description This article introduced an improved spatio - temporal deep network based on information filter method for learning sufficient representation. The proposed method aims to improve feature learning capability while modeling spatial and temporal dependencies. Experiments on pattern recognition are conducted to validate the effectiveness of the proposed method. © 2014 IEEE.
author2 56096604000
author_facet 56096604000
Hu Y.
Neoh D.T.H.
Sahari K.S.M.
Loo C.K.
format Conference Paper
author Hu Y.
Neoh D.T.H.
Sahari K.S.M.
Loo C.K.
spellingShingle Hu Y.
Neoh D.T.H.
Sahari K.S.M.
Loo C.K.
Learning sufficient representation for spatio-temporal deep network using information filter
author_sort Hu Y.
title Learning sufficient representation for spatio-temporal deep network using information filter
title_short Learning sufficient representation for spatio-temporal deep network using information filter
title_full Learning sufficient representation for spatio-temporal deep network using information filter
title_fullStr Learning sufficient representation for spatio-temporal deep network using information filter
title_full_unstemmed Learning sufficient representation for spatio-temporal deep network using information filter
title_sort learning sufficient representation for spatio-temporal deep network using information filter
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806427342700019712
score 13.214268

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