Supervised Metric Learning
Supervised Metric Learning is a subfield of metric learning that, as a genus, belongs to the broader category of distance metric learning methods, and, by differentia, specifically leverages labeled training data to learn a distance function that enhances discriminative performance by minimizing distances between instances of the same class and maximizing distances between instances of different classes.
118
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6.4K
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380
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178
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2017–2026
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No Fuss Distance Metric Learning Using Proxies
Yair Movshovitz-Attias, Alexander Toshev, Thomas Leung et al. · 2017 · 637 citations
Online and batch learning of pseudo-metrics
Shai Shalev‐Shwartz, Yoram Singer, Andrew Y. Ng · 2004 · 302 citations
Deep Adversarial Metric Learning
Yueqi Duan, Wenzhao Zheng, Xudong Lin et al. · 2018 · 206 citations
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Tsinghua University
Beijing, China
6 h-index
12 publications
890 citations
Laboratoire Hubert Curien
Saint-Etienne, France
4 h-index
9 publications
341 citations
Google (United States)
Mountain View, United States
4 h-index
15 publications
3.8K citations

Chinese Academy of Sciences
Beijing, China
4 h-index
10 publications
500 citations

Ghent University
Ghent, Belgium
3 h-index
6 publications
350 citations
University of Chinese Academy of Sciences
Beijing, China
3 h-index
8 publications
290 citations
Georgia Institute of Technology
Atlanta, United States
3 h-index
5 publications
539 citations
IBM (United States)
Armonk, United States
3 h-index
6 publications
758 citations
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10 publications · 449 citations
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Repository · 10 publications · 252 citations
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5 publications · 343 citations
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3 publications · 194 citations
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3 publications · 179 citations
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