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Supervised approaches classify input data using a set of representative samples for each class, known as training samples. The collection of such samples are expensive and time-demanding. Hence, unsupervised feature learning, which has a quick access to arbitrary amount of unlabeled data, is conceptually of high interest. In this paper, we propose a novel network architecture, fully Conv-Deconv network...
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