Self-supervised Representation Learning

Self-Supervised Representation Learning is a machine learning framework that belongs to the class of representation learning methods, distinguished by its ability to generate supervisory signals from raw data without human-labeled annotations, thereby enabling the acquisition of meaningful feature representations through pretext tasks designed on the intrinsic structure of the input data.

1.2K

Publications

104.4K

Citations

4.4K

Authors

912

Institutions

Publications per year

2017–2026

1.1K

Authors

4.4K

  1. HL
    Hung-yi Lee

    National Taiwan University, Taiwan

    13 publications · 1.4K citations

    13

    h-index

  2. IS
    In So Kweon

    Korea Advanced Institute of Science and Technology, South Korea

    12 publications · 2.1K citations

    12

    h-index

  3. AZ
    Andrew Zisserman

    University of Oxford, United Kingdom

    11 publications · 982 citations

    11

    h-index

  4. AB
    Alexei Baevski

    Meta (Israel), Israel

    10 publications · 1.6K citations

    10

    h-index

  5. YL
    Yann LeCun

    New York University, United States

    10 publications · 858 citations

    10

    h-index

1–5 of 4.4K

Venues

  1. arXiv (Cornell University)

    Repository · 265 publications · 28.1K citations

    61

    h-index

  2. 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

    Conference · 33 publications · 7.2K citations

    26

    h-index

  3. 23

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 34 publications · 2K citations

    22

    h-index

  5. 14

    h-index

1–5