Transferability Metrics

Transferability Metrics is a quantitative evaluation framework that measures the effectiveness of knowledge transfer from a source task to a target task in transfer learning by assessing performance gains, feature reuse, or representation similarity across domains.

57

Publications

3.2K

Citations

192

Authors

77

Institutions

Publications per year

2017–2026

42

Authors

192

  1. AT
    Adam Trischler

    Microsoft (United States), United States

    2 publications · 116 citations

    2

    h-index

  2. TM
    Tsendsuren Munkhdalai

    Chungbuk National University, South Korea

    2 publications · 116 citations

    2

    h-index

  3. DT
    Dacheng Tao

    The University of Sydney, Australia

    2 publications · 31 citations

    2

    h-index

  4. TV
    Tu Vu

    University of Massachusetts Amherst, United States

    2 publications · 116 citations

    2

    h-index

  5. BD
    Bo Du

    Wuhan University, China

    2 publications · 31 citations

    2

    h-index

1–5 of 192

Venues

  1. arXiv (Cornell University)

    Repository · 9 publications · 451 citations

    9

    h-index

  2. 3

    h-index

  3. Knowledge-Based Systems

    1 publications · 18 citations

    2

    h-index

  4. 2

    h-index

  5. Neurocomputing

    1 publications · 22 citations

    1

    h-index

1–5