Unsupervised domain adaptation (UDA) enables knowledge transfer from the
labelled source domain to the unlabeled target domain by reducing the
cross-domain discrepancy. However, most of the studies were based on direct
adaptation from the source domain to the target domain and have suffered from
large domain discrepancies. To overcome this challenge, in this paper, we
propose the domain-augmented domain adaptation (DADA) to generate pseudo
domains that have smaller discrepancies with the target domain, to enhance the
knowledge transfer process by minimizing the discrepancy between the target
domain and pseudo domains. Furthermore, we design a pseudo-labeling method for
DADA by projecting representations from the target domain to multiple pseudo
domains and taking the averaged predictions on the classification from the
pseudo domains as the pseudo labels. We conduct extensive experiments with the
state-of-the-art domain adaptation methods on four benchmark datasets: Office
Home, Office-31, VisDA2017, and Digital datasets. The results demonstrate the
superiority of our model.

本文提出了一种基于伪领域生成的领域增强领域自适应方法（DADA），通过生成与目标域差异更小的伪领域，减小跨域偏差，进而增强知识转移过程并将标签方法应用于 DADA 模型中，结果表明该模型优秀。