Comparison of Methods Generalizing Max- and Average-Pooling

• URL: http://arxiv.org/abs/2103.01746v1
• Date: Tue, 2 Mar 2021 14:26:51 GMT
• Title: Comparison of Methods Generalizing Max- and Average-Pooling
• Authors: Florentin Bieder, Robin Sandk\"uhler, Philippe C. Cattin
• Abstract summary: Max- and average-pooling are the most popular methods for downsampling in convolutional neural networks. In this paper, we compare different pooling methods that generalize both max- and average-pooling. The results show that none of the more sophisticated methods perform significantly better in this classification task than standard max- or average-pooling.
• Score: 1.693200946453174
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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