Feature Pooling

Feature Pooling is a feature aggregation technique that reduces the dimensionality of feature representations by applying a fixed mathematical operation—such as maximum, average, or sum—over spatial or channel-wise regions to retain dominant or representative activation patterns.

237

Publications

34.6K

Citations

808

Authors

283

Institutions

Publications per year

2017–2026

151

Authors

808

  1. SY
    Shuicheng Yan

    National University of Singapore, Singapore

    4 publications · 311 citations

    4

    h-index

  2. JF
    Jiashi Feng

    National University of Singapore, Singapore

    4 publications · 6.2K citations

    4

    h-index

  3. YL
    Yann LeCun

    New York University, United States

    4 publications · 3.6K citations

    4

    h-index

  4. TD
    Trevor Darrell

    University of California, Berkeley, United States

    4 publications · 958 citations

    4

    h-index

  5. YB
    Y-Lan Boureau

    Meta (Israel), Israel

    4 publications · 3.6K citations

    4

    h-index

1–5 of 808

Venues

  1. arXiv (Cornell University)

    Repository · 22 publications · 2.4K citations

    16

    h-index

  2. IEEE Transactions on Multimedia

    9 publications · 194 citations

    9

    h-index

  3. Neurocomputing

    8 publications · 528 citations

    8

    h-index

  4. IEEE Access

    7 publications · 477 citations

    7

    h-index

  5. 6

    h-index

1–5