Contrastive Learning

Contrastive Learning is a self-supervised learning framework that learns meaningful representations by attracting similar (positive) pairs and repelling dissimilar (negative) pairs in the embedding space.

2.8K

Publications

240.2K

Citations

10.5K

Authors

1.4K

Institutions

Publications per year

2017–2026

2.7K

Authors

10.5K

  1. SP
    Shirui Pan

    Monash University, Australia

    16 publications · 1.4K citations

    15

    h-index

  2. CH
    Chao Huang

    University of Hong Kong, Hong Kong SAR China

    15 publications · 2.2K citations

    15

    h-index

  3. XL
    Xinwang Liu

    National University of Defense Technology, China

    14 publications · 1.1K citations

    12

    h-index

  4. CM
    Chunyan Miao

    Nanyang Technological University, Singapore

    13 publications · 756 citations

    12

    h-index

  5. LX
    Lianghao Xia

    University of Hong Kong, Hong Kong SAR China

    12 publications · 1.9K citations

    12

    h-index

1–5 of 10.5K

Venues

  1. arXiv (Cornell University)

    Repository · 502 publications · 49.9K citations

    79

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 124 publications · 8.8K citations

    52

    h-index

  3. 50

    h-index

  4. 42

    h-index

  5. 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

    Conference · 61 publications · 6.3K citations

    41

    h-index

1–5