Clustering-based Self-supervision

Clustering-Based Self-Supervision is a self-supervised learning paradigm that belongs to the category of pretext task frameworks, distinguished by its use of unsupervised clustering to generate supervisory signals by assigning pseudo-labels to data instances based on learned feature space groupings.

11

Publications

453

Citations

41

Authors

20

Institutions

Publications per year

2017–2026

10

Authors

41

  1. CR
    Christian Rupprecht

    University of Oxford, United Kingdom

    2 publications · 87 citations

    2

    h-index

  2. YM
    Yuki M. Asano

    University of Oxford, United Kingdom

    2 publications · 87 citations

    2

    h-index

  3. AV
    Andrea Vedaldi

    University of Oxford, United Kingdom

    2 publications · 87 citations

    2

    h-index

  4. MP
    Mandela Patrick

    University of Oxford, United Kingdom

    2 publications · 87 citations

    2

    h-index

  5. ML
    Ming Li

    Duke Kunshan University, China

    1 publications · 19 citations

    1

    h-index

1–5 of 41

Venues

  1. arXiv (Cornell University)

    Repository · 2 publications · 82 citations

    2

    h-index

  2. 1

    h-index

  3. Oxford University Research Archive (ORA) (University of Oxford)

    Repository · 1 publications · 16 citations

    1

    h-index

  4. Knowledge-Based Systems

    1 publications · 42 citations

    1

    h-index

  5. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 1 publications · 15 citations

    1

    h-index

1–5