Comparison of Methods Generalizing Max- and Average-Pooling

Florentin Bieder; Robin Sandkühler; Philippe C. Cattin

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arXiv · 2103.01746

Comparison of Methods Generalizing Max- and Average-Pooling

Florentin Bieder·Robin Sandkühler·Philippe C. Cattin

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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.

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Florentin Bieder, Robin Sandkühler, Philippe C. Cattin. 2021-03-02. Comparison of Methods Generalizing Max- and Average-Pooling. https://arxiv.org/abs/2103.01746

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