  The recent success of deep neural networks relies on massive amounts of
labeled data. For a target task where labeled data is unavailable, domain
adaptation can transfer a learner from a different source domain. In this
paper, we propose a new approach to domain adaptation in deep networks that can
jointly learn adaptive classifiers and transferable features from labeled data
in the source domain and unlabeled data in the target domain. We relax a
shared-classifier assumption made by previous methods and assume that the
source classifier and target classifier differ by a residual function. We
enable classifier adaptation by plugging several layers into deep network to
explicitly learn the residual function with reference to the target classifier.
We fuse features of multiple layers with tensor product and embed them into
reproducing kernel Hilbert spaces to match distributions for feature
adaptation. The adaptation can be achieved in most feed-forward models by
extending them with new residual layers and loss functions, which can be
trained efficiently via back-propagation. Empirical evidence shows that the new
approach outperforms state of the art methods on standard domain adaptation
benchmarks.
