# Comparison of Methods Generalizing Max- and Average-Pooling

Source: [Comparison of Methods Generalizing Max- and Average-Pooling](<https://www.sota2.com/research/paper/68fa69741e209131cea22327>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:00:47.140Z

Authors: Florentin Bieder, Robin Sandk\\"uhler, Philippe C. Cattin

Published: 2021-03-02T14:26:51.000Z

## Abstract

Max- and average-pooling are the most popular pooling methods for downsampling in convolutional neural networks. In this paper, we compare different pooling methods that generalize both max- and average-pooling. Furthermore, we propose another method based on a smooth approximation of the maximum function and put it into context with related methods. For the comparison, we use a VGG16 image classification network and train it on a large dataset of natural high-resolution images (Google Open Images v5). The results show that none of the more sophisticated methods perform significantly better in this classification task than standard max- or average-pooling.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2103.01746>)

[PDF](<https://arxiv.org/pdf/2103.01746.pdf>)