Deep Generalized Max Pooling

arXiv:1908.05040v2 Announce Type: replace
Abstract: Global pooling layers are an essential part of Convolutional Neural Networks (CNN). They are used to aggregate activations of spatial locations to produce a fixed-size vector in several state-of-the-art CNNs. Global average pooling or global max pooling are commonly used for converting convolutional features of variable size images to a fix-sized embedding. However, both pooling layer types are computed spatially independent: each individual activation map is pooled and thus activations of different locations are pooled …

abstract art arxiv cnn cnns convolutional neural networks cs.cv features generalized global images locations max networks neural networks part pooling spatial state type vector