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

Authors

56

  1. AT
    Alexander Toshev

    Google (United States), United States

    2 publications · 695 citations

    2

    h-index

  2. TL
    Thomas Leung

    Hong Kong Polytechnic University, Hong Kong SAR China

    2 publications · 695 citations

    2

    h-index

  3. SI
    Sergey Ioffe

    Google (United States), United States

    2 publications · 695 citations

    2

    h-index

  4. YM
    Yair Movshovitz-Attias

    Google (United States), United States

    2 publications · 695 citations

    2

    h-index

  5. YX
    Yunyan Xing

    National University of Defense Technology, China

    1 publications · 19 citations

    1

    h-index

1–5 of 56

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 102 citations

    3

    h-index

  2. Neural Networks

    1 publications · 14 citations

    1

    h-index

  3. 1

    h-index

  4. Pattern Recognition Letters

    1 publications · 13 citations

    1

    h-index

  5. Academy of Management Learning and Education

    AMLE · 1 publications · 26 citations

    1

    h-index

1–5