  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 together. In
contrast, we propose Deep Generalized Max Pooling that balances the
contribution of all activations of a spatially coherent region by re-weighting
all descriptors so that the impact of frequent and rare ones is equalized. We
show that this layer is superior to both average and max pooling on the
classification of Latin medieval manuscripts (CLAMM'16, CLAMM'17), as well as
writer identification (Historical-WI'17).
