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Domain adaptation algorithms have been\nproposed to compensate for the degradation in performance due to domain shift.\nIn this paper, we address the case when the target domain is unlabeled,\nrequiring unsupervised adaptation. CORAL is a \"frustratingly easy\" unsupervised\ndomain adaptation method that aligns the second-order statistics of the source\nand target distributions with a linear transformation. Here, we extend CORAL to\nlearn a nonlinear transformation that aligns correlations of layer activations\nin deep neural networks (Deep CORAL). 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