Auxiliary Task Learning

Auxiliary Task Learning is a multi-task learning strategy in which an auxiliary task, distinct from the primary task, is introduced to improve model performance by providing additional inductive bias through shared representations.

348

Publications

55.8K

Citations

1.3K

Authors

383

Institutions

Publications per year

2017–2026

333

Authors

1.3K

  1. TD
    Trevor Darrell

    University of California, Berkeley, United States

    6 publications · 35.5K citations

    6

    h-index

  2. AG
    Alexander G. Hauptmann

    Carnegie Mellon University, United States

    4 publications · 553 citations

    4

    h-index

  3. RG
    Ross Girshick

    Meta (Israel), Israel

    4 publications · 35.3K citations

    4

    h-index

  4. HZ
    Hang Zhao

    National University of Defense Technology, China

    4 publications · 1.1K citations

    4

    h-index

  5. JM
    Jitendra Malik

    University of California, Berkeley, United States

    4 publications · 35.3K citations

    4

    h-index

1–5 of 1.3K

Venues

  1. arXiv (Cornell University)

    Repository · 49 publications · 5K citations

    24

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 20 publications · 1.8K citations

    15

    h-index

  3. IEEE Access

    12 publications · 410 citations

    12

    h-index

  4. IEEE Transactions on Multimedia

    6 publications · 283 citations

    6

    h-index

  5. 5

    h-index

1–5