  The accuracy of deep learning (e.g., convolutional neural networks) for an
image classification task critically relies on the amount of labeled training
data. Aiming to solve an image classification task on a new domain that lacks
labeled data but gains access to cheaply available unlabeled data, unsupervised
domain adaptation is a promising technique to boost the performance without
incurring extra labeling cost, by assuming images from different domains share
some invariant characteristics. In this paper, we propose a new unsupervised
domain adaptation method named Domain-Adversarial Residual-Transfer (DART)
learning of Deep Neural Networks to tackle cross-domain image classification
tasks. In contrast to the existing unsupervised domain adaption approaches, the
proposed DART not only learns domain-invariant features via adversarial
training, but also achieves robust domain-adaptive classification via a
residual-transfer strategy, all in an end-to-end training framework. We
evaluate the performance of the proposed method for cross-domain image
classification tasks on several well-known benchmark data sets, in which our
method clearly outperforms the state-of-the-art approaches.
