Deep neural networks are able to learn powerful representations from large
quantities of labeled input data, however they cannot always generalize well
across changes in input distributions. Domain adaptation algorithms have been
proposed to compensate for the degradation in performance due to domain shift.
In this paper, we address the case when the target domain is unlabeled,
requiring unsupervised adaptation. CORAL is a "frustratingly easy" unsupervised
domain adaptation method that aligns the second-order statistics of the source
and target distributions with a linear transformation. Here, we extend CORAL to
learn a nonlinear transformation that aligns correlations of layer activations
in deep neural networks (Deep CORAL). Experiments on standard benchmark
datasets show state-of-the-art performance.