Data Augmentation

Data Augmentation is a data transformation technique that expands the effective size and diversity of a dataset through the application of domain-specific, rule-based, or learned perturbations to existing data instances, thereby improving model generalization and mitigating overfitting in machine learning contexts.

6.9K

Publications

516.1K

Citations

25K

Authors

4.3K

Institutions

Publications per year

2017–2026

6.4K

Authors

25K

  1. YZ
    Yudong Zhang

    University of Leicester, United Kingdom

    30 publications · 3K citations

    26

    h-index

  2. ED
    Ekin D. Cubuk

    Google (United States), United States

    19 publications · 9.7K citations

    19

    h-index

  3. QV
    Quoc V. Le

    Google (United States), United States

    19 publications · 14.1K citations

    18

    h-index

  4. SW
    Shuihua Wang‎

    University of Leicester, United Kingdom

    18 publications · 1.9K citations

    17

    h-index

  5. MZ
    Mingyuan Zhou

    The University of Texas at Austin, United States

    15 publications · 701 citations

    13

    h-index

1–5 of 25K

Venues

  1. arXiv (Cornell University)

    Repository · 749 publications · 56.4K citations

    99

    h-index

  2. IEEE Access

    250 publications · 15.9K citations

    64

    h-index

  3. Sensors

    142 publications · 12.2K citations

    47

    h-index

  4. Applied Sciences

    124 publications · 5.5K citations

    43

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 87 publications · 7.9K citations

    41

    h-index

1–5