Transfer Learning

Transfer Learning is a machine learning paradigm in which knowledge acquired from a source domain or task is leveraged to improve learning efficiency and performance in a related target domain or task, thereby distinguishing it from traditional learning approaches that assume complete independence between training and deployment environments.

10.8K

Publications

826.1K

Citations

31.6K

Authors

4.6K

Institutions

Publications per year

2017–2026

9.1K

Authors

31.6K

  1. QY
    Qiang Yang

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

    72 publications · 33.3K citations

    49

    h-index

  2. ML
    Mingsheng Long

    Tsinghua University, China

    53 publications · 16.7K citations

    39

    h-index

  3. JW
    Jianmin Wang

    Tsinghua University, China

    53 publications · 16.7K citations

    38

    h-index

  4. DT
    Dacheng Tao

    The University of Sydney, Australia

    40 publications · 3.2K citations

    27

    h-index

  5. SJ
    Sinno Jialin Pan

    Nanyang Technological University, Singapore

    27 publications · 27.5K citations

    25

    h-index

1–5 of 31.6K

Venues

  1. arXiv (Cornell University)

    Repository · 1K publications · 106.6K citations

    119

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 241 publications · 17.9K citations

    70

    h-index

  3. IEEE Access

    282 publications · 17.1K citations

    66

    h-index

  4. 49

    h-index

  5. 48

    h-index

1–5