  Recent advances on unsupervised domain adaptation (UDA) rely on adversarial
learning to disentangle the explanatory and transferable features for domain
adaptation. However, there are two issues with the existing methods. First, the
discriminability of the latent space cannot be fully guaranteed without
considering the class-aware information in the target domain. Second, samples
from the source and target domains alone are not sufficient for
domain-invariant feature extracting in the latent space. In order to alleviate
the above issues, we propose a dual mixup regularized learning (DMRL) method
for UDA, which not only guides the classifier in enhancing consistent
predictions in-between samples, but also enriches the intrinsic structures of
the latent space. The DMRL jointly conducts category and domain mixup
regularizations on pixel level to improve the effectiveness of models. A series
of empirical studies on four domain adaptation benchmarks demonstrate that our
approach can achieve the state-of-the-art.
