Semantically consistent multi-view representation learning

In this work, we devote ourselves to the challenging task of Unsupervised Multi-view Representation Learning (UMRL), which requires learning a unified feature representation from multiple views in an unsupervised manner. Existing UMRL methods mainly focus on the learning process within the feature s...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Zhou, Yiyang [verfasserIn] Zheng, Qinghai [verfasserIn] Bai, Shunshun [verfasserIn] Zhu, Jihua [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Multi-view representation learning Contrastive learning Semantic consensus information

Übergeordnetes Werk:	Enthalten in: Knowledge-based systems - Amsterdam [u.a.] : Elsevier Science, 1987, 278
Übergeordnetes Werk:	volume:278

DOI / URN:	10.1016/j.knosys.2023.110899

Katalog-ID:	ELV063999528

Internformat


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100	1		\|a Zhou, Yiyang \|e verfasserin \|0 (orcid)0000-0002-1534-8005 \|4 aut
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520			\|a In this work, we devote ourselves to the challenging task of Unsupervised Multi-view Representation Learning (UMRL), which requires learning a unified feature representation from multiple views in an unsupervised manner. Existing UMRL methods mainly focus on the learning process within the feature space while ignoring the valuable semantic information hidden in different views. To address this issue, we propose a novel approach called Semantically Consistent Multi-view Representation Learning (SCMRL), which aims to excavate underlying multi-view semantic consensus information and utilize it to guide the unified feature representation learning process. Specifically, SCMRL consists of a within-view reconstruction module and a unified feature representation learning module. These modules are elegantly integrated using a contrastive learning strategy, which serves to align the semantic labels of both view-specific feature representations and the learned unified feature representation simultaneously. This integration allows SCMRL to effectively leverage consensus information in the semantic space, thereby constraining the learning process of the unified feature representation. Compared with several state-of-the-art algorithms, extensive experiments demonstrate its superiority. Our code is released on https://github.com/YiyangZhou/SCMRL.
650		4	\|a Multi-view representation learning
650		4	\|a Contrastive learning
650		4	\|a Semantic consensus information
700	1		\|a Zheng, Qinghai \|e verfasserin \|0 (orcid)0000-0002-8684-1577 \|4 aut
700	1		\|a Bai, Shunshun \|e verfasserin \|0 (orcid)0009-0001-0516-9687 \|4 aut
700	1		\|a Zhu, Jihua \|e verfasserin \|0 (orcid)0000-0002-3081-8781 \|4 aut
773	0	8	\|i Enthalten in \|t Knowledge-based systems \|d Amsterdam [u.a.] : Elsevier Science, 1987 \|g 278 \|h Online-Ressource \|w (DE-627)320580024 \|w (DE-600)2017495-0 \|w (DE-576)253018722 \|x 0950-7051 \|7 nnns
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912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2106
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912			\|a GBV_ILN_2111
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912			\|a GBV_ILN_2122
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912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
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936	b	k	\|a 54.72 \|j Künstliche Intelligenz \|q VZ
951			\|a AR
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Indexfelder

author_variant	y z yz q z qz s b sb j z jz
matchkey_str	article:09507051:2023----::eatclyossetutverpee
hierarchy_sort_str	2023
bklnumber	54.72
publishDate	2023
allfields	10.1016/j.knosys.2023.110899 doi (DE-627)ELV063999528 (ELSEVIER)S0950-7051(23)00649-4 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Zhou, Yiyang verfasserin (orcid)0000-0002-1534-8005 aut Semantically consistent multi-view representation learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this work, we devote ourselves to the challenging task of Unsupervised Multi-view Representation Learning (UMRL), which requires learning a unified feature representation from multiple views in an unsupervised manner. Existing UMRL methods mainly focus on the learning process within the feature space while ignoring the valuable semantic information hidden in different views. To address this issue, we propose a novel approach called Semantically Consistent Multi-view Representation Learning (SCMRL), which aims to excavate underlying multi-view semantic consensus information and utilize it to guide the unified feature representation learning process. Specifically, SCMRL consists of a within-view reconstruction module and a unified feature representation learning module. These modules are elegantly integrated using a contrastive learning strategy, which serves to align the semantic labels of both view-specific feature representations and the learned unified feature representation simultaneously. This integration allows SCMRL to effectively leverage consensus information in the semantic space, thereby constraining the learning process of the unified feature representation. Compared with several state-of-the-art algorithms, extensive experiments demonstrate its superiority. Our code is released on https://github.com/YiyangZhou/SCMRL. Multi-view representation learning Contrastive learning Semantic consensus information Zheng, Qinghai verfasserin (orcid)0000-0002-8684-1577 aut Bai, Shunshun verfasserin (orcid)0009-0001-0516-9687 aut Zhu, Jihua verfasserin (orcid)0000-0002-3081-8781 aut Enthalten in Knowledge-based systems Amsterdam [u.a.] : Elsevier Science, 1987 278 Online-Ressource (DE-627)320580024 (DE-600)2017495-0 (DE-576)253018722 0950-7051 nnns volume:278 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 278
spelling	10.1016/j.knosys.2023.110899 doi (DE-627)ELV063999528 (ELSEVIER)S0950-7051(23)00649-4 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Zhou, Yiyang verfasserin (orcid)0000-0002-1534-8005 aut Semantically consistent multi-view representation learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this work, we devote ourselves to the challenging task of Unsupervised Multi-view Representation Learning (UMRL), which requires learning a unified feature representation from multiple views in an unsupervised manner. Existing UMRL methods mainly focus on the learning process within the feature space while ignoring the valuable semantic information hidden in different views. To address this issue, we propose a novel approach called Semantically Consistent Multi-view Representation Learning (SCMRL), which aims to excavate underlying multi-view semantic consensus information and utilize it to guide the unified feature representation learning process. Specifically, SCMRL consists of a within-view reconstruction module and a unified feature representation learning module. These modules are elegantly integrated using a contrastive learning strategy, which serves to align the semantic labels of both view-specific feature representations and the learned unified feature representation simultaneously. This integration allows SCMRL to effectively leverage consensus information in the semantic space, thereby constraining the learning process of the unified feature representation. Compared with several state-of-the-art algorithms, extensive experiments demonstrate its superiority. Our code is released on https://github.com/YiyangZhou/SCMRL. Multi-view representation learning Contrastive learning Semantic consensus information Zheng, Qinghai verfasserin (orcid)0000-0002-8684-1577 aut Bai, Shunshun verfasserin (orcid)0009-0001-0516-9687 aut Zhu, Jihua verfasserin (orcid)0000-0002-3081-8781 aut Enthalten in Knowledge-based systems Amsterdam [u.a.] : Elsevier Science, 1987 278 Online-Ressource (DE-627)320580024 (DE-600)2017495-0 (DE-576)253018722 0950-7051 nnns volume:278 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 278
allfields_unstemmed	10.1016/j.knosys.2023.110899 doi (DE-627)ELV063999528 (ELSEVIER)S0950-7051(23)00649-4 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Zhou, Yiyang verfasserin (orcid)0000-0002-1534-8005 aut Semantically consistent multi-view representation learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this work, we devote ourselves to the challenging task of Unsupervised Multi-view Representation Learning (UMRL), which requires learning a unified feature representation from multiple views in an unsupervised manner. Existing UMRL methods mainly focus on the learning process within the feature space while ignoring the valuable semantic information hidden in different views. To address this issue, we propose a novel approach called Semantically Consistent Multi-view Representation Learning (SCMRL), which aims to excavate underlying multi-view semantic consensus information and utilize it to guide the unified feature representation learning process. Specifically, SCMRL consists of a within-view reconstruction module and a unified feature representation learning module. These modules are elegantly integrated using a contrastive learning strategy, which serves to align the semantic labels of both view-specific feature representations and the learned unified feature representation simultaneously. This integration allows SCMRL to effectively leverage consensus information in the semantic space, thereby constraining the learning process of the unified feature representation. Compared with several state-of-the-art algorithms, extensive experiments demonstrate its superiority. Our code is released on https://github.com/YiyangZhou/SCMRL. Multi-view representation learning Contrastive learning Semantic consensus information Zheng, Qinghai verfasserin (orcid)0000-0002-8684-1577 aut Bai, Shunshun verfasserin (orcid)0009-0001-0516-9687 aut Zhu, Jihua verfasserin (orcid)0000-0002-3081-8781 aut Enthalten in Knowledge-based systems Amsterdam [u.a.] : Elsevier Science, 1987 278 Online-Ressource (DE-627)320580024 (DE-600)2017495-0 (DE-576)253018722 0950-7051 nnns volume:278 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 278
allfieldsGer	10.1016/j.knosys.2023.110899 doi (DE-627)ELV063999528 (ELSEVIER)S0950-7051(23)00649-4 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Zhou, Yiyang verfasserin (orcid)0000-0002-1534-8005 aut Semantically consistent multi-view representation learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this work, we devote ourselves to the challenging task of Unsupervised Multi-view Representation Learning (UMRL), which requires learning a unified feature representation from multiple views in an unsupervised manner. Existing UMRL methods mainly focus on the learning process within the feature space while ignoring the valuable semantic information hidden in different views. To address this issue, we propose a novel approach called Semantically Consistent Multi-view Representation Learning (SCMRL), which aims to excavate underlying multi-view semantic consensus information and utilize it to guide the unified feature representation learning process. Specifically, SCMRL consists of a within-view reconstruction module and a unified feature representation learning module. These modules are elegantly integrated using a contrastive learning strategy, which serves to align the semantic labels of both view-specific feature representations and the learned unified feature representation simultaneously. This integration allows SCMRL to effectively leverage consensus information in the semantic space, thereby constraining the learning process of the unified feature representation. Compared with several state-of-the-art algorithms, extensive experiments demonstrate its superiority. Our code is released on https://github.com/YiyangZhou/SCMRL. Multi-view representation learning Contrastive learning Semantic consensus information Zheng, Qinghai verfasserin (orcid)0000-0002-8684-1577 aut Bai, Shunshun verfasserin (orcid)0009-0001-0516-9687 aut Zhu, Jihua verfasserin (orcid)0000-0002-3081-8781 aut Enthalten in Knowledge-based systems Amsterdam [u.a.] : Elsevier Science, 1987 278 Online-Ressource (DE-627)320580024 (DE-600)2017495-0 (DE-576)253018722 0950-7051 nnns volume:278 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 278
allfieldsSound	10.1016/j.knosys.2023.110899 doi (DE-627)ELV063999528 (ELSEVIER)S0950-7051(23)00649-4 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Zhou, Yiyang verfasserin (orcid)0000-0002-1534-8005 aut Semantically consistent multi-view representation learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this work, we devote ourselves to the challenging task of Unsupervised Multi-view Representation Learning (UMRL), which requires learning a unified feature representation from multiple views in an unsupervised manner. Existing UMRL methods mainly focus on the learning process within the feature space while ignoring the valuable semantic information hidden in different views. To address this issue, we propose a novel approach called Semantically Consistent Multi-view Representation Learning (SCMRL), which aims to excavate underlying multi-view semantic consensus information and utilize it to guide the unified feature representation learning process. Specifically, SCMRL consists of a within-view reconstruction module and a unified feature representation learning module. These modules are elegantly integrated using a contrastive learning strategy, which serves to align the semantic labels of both view-specific feature representations and the learned unified feature representation simultaneously. This integration allows SCMRL to effectively leverage consensus information in the semantic space, thereby constraining the learning process of the unified feature representation. Compared with several state-of-the-art algorithms, extensive experiments demonstrate its superiority. Our code is released on https://github.com/YiyangZhou/SCMRL. Multi-view representation learning Contrastive learning Semantic consensus information Zheng, Qinghai verfasserin (orcid)0000-0002-8684-1577 aut Bai, Shunshun verfasserin (orcid)0009-0001-0516-9687 aut Zhu, Jihua verfasserin (orcid)0000-0002-3081-8781 aut Enthalten in Knowledge-based systems Amsterdam [u.a.] : Elsevier Science, 1987 278 Online-Ressource (DE-627)320580024 (DE-600)2017495-0 (DE-576)253018722 0950-7051 nnns volume:278 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 278
language	English
source	Enthalten in Knowledge-based systems 278 volume:278
sourceStr	Enthalten in Knowledge-based systems 278 volume:278
format_phy_str_mv	Article
bklname	Künstliche Intelligenz
institution	findex.gbv.de
topic_facet	Multi-view representation learning Contrastive learning Semantic consensus information
dewey-raw	004
isfreeaccess_bool	false
container_title	Knowledge-based systems
authorswithroles_txt_mv	Zhou, Yiyang @@aut@@ Zheng, Qinghai @@aut@@ Bai, Shunshun @@aut@@ Zhu, Jihua @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	320580024
dewey-sort	14
id	ELV063999528
language_de	englisch
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author	Zhou, Yiyang
spellingShingle	Zhou, Yiyang ddc 004 bkl 54.72 misc Multi-view representation learning misc Contrastive learning misc Semantic consensus information Semantically consistent multi-view representation learning
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topic_title	004 VZ 54.72 bkl Semantically consistent multi-view representation learning Multi-view representation learning Contrastive learning Semantic consensus information
topic	ddc 004 bkl 54.72 misc Multi-view representation learning misc Contrastive learning misc Semantic consensus information
topic_unstemmed	ddc 004 bkl 54.72 misc Multi-view representation learning misc Contrastive learning misc Semantic consensus information
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title	Semantically consistent multi-view representation learning
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title_full	Semantically consistent multi-view representation learning
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title_sort	semantically consistent multi-view representation learning
title_auth	Semantically consistent multi-view representation learning
abstract	In this work, we devote ourselves to the challenging task of Unsupervised Multi-view Representation Learning (UMRL), which requires learning a unified feature representation from multiple views in an unsupervised manner. Existing UMRL methods mainly focus on the learning process within the feature space while ignoring the valuable semantic information hidden in different views. To address this issue, we propose a novel approach called Semantically Consistent Multi-view Representation Learning (SCMRL), which aims to excavate underlying multi-view semantic consensus information and utilize it to guide the unified feature representation learning process. Specifically, SCMRL consists of a within-view reconstruction module and a unified feature representation learning module. These modules are elegantly integrated using a contrastive learning strategy, which serves to align the semantic labels of both view-specific feature representations and the learned unified feature representation simultaneously. This integration allows SCMRL to effectively leverage consensus information in the semantic space, thereby constraining the learning process of the unified feature representation. Compared with several state-of-the-art algorithms, extensive experiments demonstrate its superiority. Our code is released on https://github.com/YiyangZhou/SCMRL.
abstractGer	In this work, we devote ourselves to the challenging task of Unsupervised Multi-view Representation Learning (UMRL), which requires learning a unified feature representation from multiple views in an unsupervised manner. Existing UMRL methods mainly focus on the learning process within the feature space while ignoring the valuable semantic information hidden in different views. To address this issue, we propose a novel approach called Semantically Consistent Multi-view Representation Learning (SCMRL), which aims to excavate underlying multi-view semantic consensus information and utilize it to guide the unified feature representation learning process. Specifically, SCMRL consists of a within-view reconstruction module and a unified feature representation learning module. These modules are elegantly integrated using a contrastive learning strategy, which serves to align the semantic labels of both view-specific feature representations and the learned unified feature representation simultaneously. This integration allows SCMRL to effectively leverage consensus information in the semantic space, thereby constraining the learning process of the unified feature representation. Compared with several state-of-the-art algorithms, extensive experiments demonstrate its superiority. Our code is released on https://github.com/YiyangZhou/SCMRL.
abstract_unstemmed	In this work, we devote ourselves to the challenging task of Unsupervised Multi-view Representation Learning (UMRL), which requires learning a unified feature representation from multiple views in an unsupervised manner. Existing UMRL methods mainly focus on the learning process within the feature space while ignoring the valuable semantic information hidden in different views. To address this issue, we propose a novel approach called Semantically Consistent Multi-view Representation Learning (SCMRL), which aims to excavate underlying multi-view semantic consensus information and utilize it to guide the unified feature representation learning process. Specifically, SCMRL consists of a within-view reconstruction module and a unified feature representation learning module. These modules are elegantly integrated using a contrastive learning strategy, which serves to align the semantic labels of both view-specific feature representations and the learned unified feature representation simultaneously. This integration allows SCMRL to effectively leverage consensus information in the semantic space, thereby constraining the learning process of the unified feature representation. Compared with several state-of-the-art algorithms, extensive experiments demonstrate its superiority. Our code is released on https://github.com/YiyangZhou/SCMRL.
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title_short	Semantically consistent multi-view representation learning
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author2	Zheng, Qinghai Bai, Shunshun Zhu, Jihua
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up_date	2024-07-06T20:07:06.892Z
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