GradientBased Pooling for Convolutional Neural Networks Pooling layers are an important part of convolutional neural networks (CNNs). They reduce the dimensionality of feature maps and pass salient information to subsequent layers. In this paper, we introduce a novel gradientbased feature pooling method that can downsample feature maps while better preserving key information. This method considers the spatial gradient of the pixels within a pooling region as a key to select the most possible descriptive information in contrast to the current practice of existing methods that mostly rely on the pixel values. Extensive experiments on different benchmark image classification tasks and CNN architectures demonstrate that the proposed method achieves superior results over existing pooling approaches.

Deep Generalized Max Pooling Global pooling layers are an essential part of Convolutional Neural Networks (CNN). They are used to aggregate activations of spatial locations to produce a fixedsize vector in several stateoftheart CNNs. Global average pooling or global max pooling are commonly used for converting convolutional features of variable size images to a fixsized 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 reweighting 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 (CLAMMu002716, CLAMMu002717), as well as writer identification (HistoricalWIu002717).