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Porting state of the art deep learning algorithms to resource constrained compute platforms (e.g. VR, AR, wearables) is extremely challenging. We propose a fast, compact, and accurate model for convolutional neural networks that enables efficient learning and inference. We introduce LCNN, a lookup-based convolutional neural network that encodes convolutions by few lookups to a dictionary that is trained to cover the space of weights in CNNs. Training LCNN involves jointly learning a dictionarydoi:10.1109/cvpr.2017.98 dblp:conf/cvpr/BagherinezhadRF17 fatcat:3rbk5fwiu5aurpjwvvy5ntckzq