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.

本文提出了一种可以同时学习源域标记数据和目标域未标记数据的适应分类器和可转移特征的深度网络领域适应新方法。改进了以往方法假设源分类器和目标分类器共享分类器的假设，并通过将多个层的特征与张量积融合并将它们嵌入可再生核希尔伯特空间来匹配特征适应的分布。实验结果表明新方法在标准领域适应基准上优于现有最先进的方法。