  Domain adaptation is widely used in learning problems lacking labels. Recent
studies show that deep adversarial domain adaptation models can make markable
improvements in performance, which include symmetric and asymmetric
architectures. However, the former has poor generalization ability whereas the
latter is very hard to train. In this paper, we propose a novel adversarial
domain adaptation method named Adversarial Residual Transform Networks (ARTNs)
to improve the generalization ability, which directly transforms the source
features into the space of target features. In this model, residual connections
are used to share features and adversarial loss is reconstructed, thus making
the model more generalized and easier to train. Moreover, a special
regularization term is added to the loss function to alleviate a vanishing
gradient problem, which enables its training process stable. A series of
experiments based on Amazon review dataset, digits datasets and Office-31 image
datasets are conducted to show that the proposed ARTN can be comparable with
the methods of the state-of-the-art.
