Representation Transfer

Representation Transfer is a machine learning paradigm in which knowledge in the form of feature representations, acquired from a source task or domain, is adapted and applied to improve performance in a target task or domain, thereby enabling more efficient and effective learning when data or annotations are limited.

268

Publications

31.2K

Citations

876

Authors

294

Institutions

Publications per year

2017–2026

203

Authors

876

  1. ML
    Mingsheng Long

    Tsinghua University, China

    7 publications · 1.2K citations

    7

    h-index

  2. JW
    Jianmin Wang

    Tsinghua University, China

    7 publications · 1.2K citations

    7

    h-index

  3. MI
    Michael I. Jordan

    University of California, Berkeley, United States

    5 publications · 728 citations

    5

    h-index

  4. CZ
    Chiyuan Zhang

    Columbia University, United States

    5 publications · 1.1K citations

    5

    h-index

  5. QY
    Qiang Yang

    Hong Kong University of Science and Technology, Hong Kong SAR China

    5 publications · 1.1K citations

    5

    h-index

1–5 of 876

Venues

  1. arXiv (Cornell University)

    Repository · 64 publications · 8.5K citations

    29

    h-index

  2. Neural Information Processing Systems

    Conference · 6 publications · 547 citations

    6

    h-index

  3. 5

    h-index

  4. 5

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 5 publications · 560 citations

    5

    h-index

1–5