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231226s2023 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2022.3159732
|2 doi
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|a pubmed25n1127.xml
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|a DE-627
|b ger
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|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Liu, Weiyang
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a SphereFace Revived
|b Unifying Hyperspherical Face Recognition
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| 264 |
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1 |
|c 2023
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| 336 |
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|a Text
|b txt
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|a ƒaComputermedien
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|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Completed 06.04.2023
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| 500 |
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|a Date Revised 06.04.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a This paper addresses the deep face recognition problem under an open-set protocol, where ideal face features are expected to have smaller maximal intra-class distance than minimal inter-class distance under a suitably chosen metric space. To this end, hyperspherical face recognition, as a promising line of research, has attracted increasing attention and gradually become a major focus in face recognition research. As one of the earliest works in hyperspherical face recognition, SphereFace explicitly proposed to learn face embeddings with large inter-class angular margin. However, SphereFace still suffers from severe training instability which limits its application in practice. In order to address this problem, we introduce a unified framework to understand large angular margin in hyperspherical face recognition. Under this framework, we extend the study of SphereFace and propose an improved variant with substantially better training stability - SphereFace-R. Specifically, we propose two novel ways to implement the multiplicative margin, and study SphereFace-R under three different feature normalization schemes (no feature normalization, hard feature normalization and soft feature normalization). We also propose an implementation strategy - "characteristic gradient detachment" - to stabilize training. Extensive experiments on SphereFace-R show that it is consistently better than or competitive with state-of-the-art methods
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| 650 |
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4 |
|a Journal Article
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| 700 |
1 |
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|a Wen, Yandong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Raj, Bhiksha
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Singh, Rita
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Weller, Adrian
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 45(2023), 2 vom: 31. Feb., Seite 2458-2474
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnas
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| 773 |
1 |
8 |
|g volume:45
|g year:2023
|g number:2
|g day:31
|g month:02
|g pages:2458-2474
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|u http://dx.doi.org/10.1109/TPAMI.2022.3159732
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