Proxy-based Metric Learning
Proxy-Based Metric Learning is a metric learning approach that employs representative proxies as class surrogates in the embedding space to structure inter-class relationships and enhance discriminative performance through proxy-to-sample distance minimization.
15
Publications
1.5K
Citations
56
Authors
24
Institutions
Publications per year
2017–2026
14
Parents
15
No Fuss Distance Metric Learning Using Proxies
Yair Movshovitz-Attias, Alexander Toshev, Thomas Leung et al. · 2017 · 637 citations
Proxy Anchor Loss for Deep Metric Learning
Sung‐Yeon Kim, Dong-Won Kim, Minsu Cho et al. · 2020 · 392 citations
No Fuss Distance Metric Learning using Proxies
Yair Movshovitz-Attias, Alexander Toshev, Thomas Leung et al. · arXiv (Cornell University) · 2017 · 58 citations · Full text
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Jilin University
Changchun, China
2 h-index
6 publications
480 citations
Google (United States)
Mountain View, United States
2 h-index
10 publications
3.5K citations
Tsinghua University
Beijing, China
2 h-index
7 publications
149 citations

Monash Medical Centre
Clayton, Australia
1 h-index
1 publications
19 citations
Sungkyunkwan University
Seoul, South Korea
1 h-index
1 publications
13 citations
Amazon (United States)
Seattle, United States
1 h-index
3 publications
87 citations

Tampere University
Tampere, Finland
1 h-index
3 publications
42 citations
Naver (South Korea)
Seongnam-si, South Korea
1 h-index
3 publications
99 citations
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Repository · 3 publications · 102 citations
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1 publications · 14 citations
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TPAMI · 1 publications · 15 citations
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1 publications · 13 citations
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AMLE · 1 publications · 26 citations
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