  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.
