  Recent works on domain adaptation reveal the effectiveness of adversarial
learning on filling the discrepancy between source and target domains. However,
two common limitations exist in current adversarial-learning-based methods.
First, samples from two domains alone are not sufficient to ensure
domain-invariance at most part of latent space. Second, the domain
discriminator involved in these methods can only judge real or fake with the
guidance of hard label, while it is more reasonable to use soft scores to
evaluate the generated images or features, i.e., to fully utilize the
inter-domain information. In this paper, we present adversarial domain
adaptation with domain mixup (DM-ADA), which guarantees domain-invariance in a
more continuous latent space and guides the domain discriminator in judging
samples' difference relative to source and target domains. Domain mixup is
jointly conducted on pixel and feature level to improve the robustness of
models. Extensive experiments prove that the proposed approach can achieve
superior performance on tasks with various degrees of domain shift and data
complexity.
