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In this work we present a new approach to learn compressible representations in deep architec-tures with an end-to-end training strategy. Our method is based on a soft (continuous) relaxation of quantization and entropy, which we anneal to their discrete counterparts throughout training. We showcase this method for two challenging applications: Image(More)
We propose a highly structured neural network architecture for semantic segmentation of images that combines i) a Haar wavelet-based tree-like convolutional neural network (CNN), ii) a random layer realizing a radial basis function kernel approximation, and iii) a linear clas-sifier. While stages i) and ii) are completely pre-specified, only the linear(More)
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