Fine-tuning

Fine-Tuning is a transfer learning technique that involves adapting a pre-trained model to a specific downstream task by selectively updating its parameters through continued training on task-specific data.

3.9K

Publications

282.5K

Citations

18.5K

Authors

3.1K

Institutions

Publications per year

2017–2026

2.4K

Authors

18.5K

  1. ZL
    Zhiyuan Liu

    Tsinghua University, China

    15 publications · 1.9K citations

    14

    h-index

  2. DT
    Dacheng Tao

    The University of Sydney, Australia

    12 publications · 1.2K citations

    12

    h-index

  3. IV
    Ivan Vulić

    University of Cambridge, United Kingdom

    11 publications · 659 citations

    11

    h-index

  4. JL
    Juanzi Li

    Tsinghua University, China

    11 publications · 1.8K citations

    11

    h-index

  5. JG
    Jianfeng Gao

    Microsoft (United States), United States

    9 publications · 2.6K citations

    9

    h-index

1–5 of 18.5K

Venues

  1. arXiv (Cornell University)

    Repository · 476 publications · 35K citations

    78

    h-index

  2. 34

    h-index

  3. Journal of High Energy Physics

    JHEP · 49 publications · 2.6K citations

    31

    h-index

  4. IEEE Access

    57 publications · 3.6K citations

    29

    h-index

  5. 26

    h-index

1–5