Training Population Augmentation

Training Population Augmentation is a methodological contribution in the field of machine learning that expands the composition of the training population through the systematic incorporation of additional data instances, synthetic samples, or diverse representations to enhance model generalizability and robustness.

31

Publications

2.5K

Citations

115

Authors

51

Institutions

Publications per year

2017–2026

28

Authors

115

  1. PA
    Pieter Abbeel

    University of California, Berkeley, United States

    2 publications · 159 citations

    2

    h-index

  2. KY
    Kai Yu

    Soochow University, China

    1 publications · 60 citations

    1

    h-index

  3. GM
    Gianluca Maguolo

    University of Padua, Italy

    1 publications · 14 citations

    1

    h-index

  4. AS
    A. S. Sorin

    Joint Institute for Nuclear Research, Russia

    1 publications · 30 citations

    1

    h-index

  5. JM
    João Manuel R. S. Tavares

    Universidade do Porto, Portugal

    1 publications · 37 citations

    1

    h-index

1–5 of 115

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 205 citations

    4

    h-index

  2. Neurocomputing

    2 publications · 103 citations

    2

    h-index

  3. Cytometry Part A

    1 publications · 12 citations

    1

    h-index

  4. The Visual Computer

    1 publications · 29 citations

    1

    h-index

  5. Frontiers in Plant Science

    1 publications · 34 citations

    1

    h-index

1–5