Max Pooling

Max Pooling is a downsampling technique in convolutional neural networks that belongs to the class of aggregation features and is distinguished by selecting the maximum value within localized regions of a feature map to reduce spatial dimensionality while preserving dominant activation patterns.

666

Publications

85.6K

Citations

2.4K

Authors

790

Institutions

Publications per year

2017–2026

504

Variants

Max-pooling

Authors

2.4K

  1. JS
    Jürgen Schmidhuber

    Dalle Molle Institute for Artificial Intelligence Research, Switzerland

    7 publications · 2.1K citations

    7

    h-index

  2. DC
    Dan Cireşan

    Dalle Molle Institute for Artificial Intelligence Research, Switzerland

    6 publications · 2K citations

    6

    h-index

  3. YZ
    Yudong Zhang

    University of Leicester, United Kingdom

    6 publications · 522 citations

    6

    h-index

  4. YL
    Yann LeCun

    New York University, United States

    6 publications · 3.8K citations

    6

    h-index

  5. JM
    Jonathan Masci

    Università della Svizzera italiana, Switzerland

    6 publications · 804 citations

    6

    h-index

1–5 of 2.4K

Venues

  1. arXiv (Cornell University)

    Repository · 72 publications · 12.8K citations

    38

    h-index

  2. IEEE Access

    34 publications · 2K citations

    23

    h-index

  3. Applied Sciences

    15 publications · 1.4K citations

    13

    h-index

  4. Neurocomputing

    10 publications · 611 citations

    10

    h-index

  5. Multimedia Tools and Applications

    9 publications · 381 citations

    9

    h-index

1–5