Self-supervised Learning

Self-Supervised Learning is a machine learning paradigm in which a model generates its own supervisory signals from unlabeled data by designing pretext tasks that exploit inherent data structure, thereby enabling representation learning without explicit human annotations.

4.7K

Publications

351.1K

Citations

15.4K

Authors

2.2K

Institutions

Publications per year

2017–2026

4.3K

Authors

15.4K

  1. SW
    Shinji Watanabe

    Johns Hopkins University, United States

    23 publications · 1.4K citations

    19

    h-index

  2. AZ
    Andrew Zisserman

    University of Oxford, United Kingdom

    27 publications · 3.5K citations

    19

    h-index

  3. LJ
    Licheng Jiao

    Xidian University, China

    20 publications · 1.1K citations

    18

    h-index

  4. IS
    In So Kweon

    Korea Advanced Institute of Science and Technology, South Korea

    19 publications · 2.8K citations

    18

    h-index

  5. HL
    Hung-yi Lee

    National Taiwan University, Taiwan

    22 publications · 1.7K citations

    18

    h-index

1–5 of 15.4K

Venues

  1. arXiv (Cornell University)

    Repository · 891 publications · 60.8K citations

    101

    h-index

  2. 58

    h-index

  3. 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

    Conference · 102 publications · 15.1K citations

    54

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 141 publications · 12.3K citations

    49

    h-index

  5. 42

    h-index

1–5