Domain Adaptation

Domain Adaptation is a transfer learning approach that addresses the challenge of adapting a model trained on a source domain to perform effectively on a target domain with a different data distribution, by identifying and compensating for domain-specific discrepancies while preserving task-relevant knowledge.

5K

Publications

393.7K

Citations

13.3K

Authors

2.1K

Institutions

Publications per year

2017–2026

4.3K

Authors

13.3K

  1. ML
    Mingsheng Long

    Tsinghua University, China

    40 publications · 15.3K citations

    32

    h-index

  2. KS
    Kate Saenko

    University of California, Berkeley, United States

    41 publications · 16.5K citations

    31

    h-index

  3. JW
    Jianmin Wang

    Tsinghua University, China

    37 publications · 14.9K citations

    30

    h-index

  4. ZD
    Zhengming Ding

    Northeastern University, United States

    33 publications · 2.9K citations

    27

    h-index

  5. TD
    Trevor Darrell

    University of California, Berkeley, United States

    31 publications · 16.1K citations

    25

    h-index

1–5 of 13.3K

Venues

  1. arXiv (Cornell University)

    Repository · 620 publications · 49.6K citations

    88

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 140 publications · 13.6K citations

    51

    h-index

  3. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 84 publications · 11.5K citations

    50

    h-index

  4. 45

    h-index

  5. IEEE Transactions on Geoscience and Remote Sensing

    116 publications · 6.5K citations

    45

    h-index

1–5