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Deep Convolutional Neural Networks (DCNNs) commonly use generic `max-pooling'(MP) layers to extract deformation-invariant features, but we argue in favor ofa more refined treatment. First, we introduce epitomic convolution as abuilding block alternative to the common convolution-MP cascade of DCNNs; whilehaving identical complexity to MP, Epitomic Convolution allows for parametersharing across different filters, resulting in faster convergence and bettergeneralization. Second, we introduce a Multiple Instance Learning approach toexplicitly accommodate global translation and scaling when training a DCNNexclusively with class labels. For this we rely on a `patchwork' data structurethat efficiently lays out all image scales and positions as candidates to aDCNN. Factoring global and local deformations allows a DCNN to `focus itsresources' on the treatment of non-rigid deformations and yields a substantialclassification accuracy improvement. Third, further pursuing this idea, wedevelop an efficient DCNN sliding window object detector that employs explicitsearch over position, scale, and aspect ratio. We provide competitive imageclassification and localization results on the ImageNet dataset and objectdetection results on the Pascal VOC 2007 benchmark.

Untangling Local and Global Deformations in Deep Convolutional Networks for Image Classification and Sliding Window Detection