Domain-incremental Learning

Domain-Incremental Learning is a continual learning paradigm where the model incrementally adapts to new data distributions from distinct domains while preserving knowledge from previous domains, with the assumption that the task identity is known during inference.

119

Publications

7.6K

Citations

474

Authors

182

Institutions

Publications per year

2017–2026

95

Variants

Domain-incremental Continual Learning

Authors

474

  1. BL
    Bing Liu

    China University of Mining and Technology, China

    4 publications · 183 citations

    4

    h-index

  2. CS
    Chowdhury Sadman Jahan

    Rochester Institute of Technology, United States

    3 publications · 48 citations

    3

    h-index

  3. AM
    Abu Md Niamul Taufique

    Rochester Institute of Technology, United States

    3 publications · 48 citations

    3

    h-index

  4. MS
    Michael S. Lew

    Leiden University, Netherlands

    3 publications · 182 citations

    3

    h-index

  5. IL
    Issam Laradji

    University of British Columbia, Canada

    3 publications · 101 citations

    3

    h-index

1–5 of 474

Venues

  1. arXiv (Cornell University)

    Repository · 17 publications · 425 citations

    13

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 6 publications · 229 citations

    6

    h-index

  3. 4

    h-index

  4. 3

    h-index

  5. Neural Information Processing Systems

    Conference · 3 publications · 99 citations

    3

    h-index

1–5