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.

翻译：未受监督的域适应(UDA)通过减少跨域差异,使知识从标记源域向未标记的目标域转移,但是,大多数研究都是基于源域对目标域的直接调整,并存在巨大的域差异。为了克服这一挑战,我们在本文件中提议,为生成与目标域差异较小的伪域而采用域强化的域适应(DADA),以生成与目标域差异较小的伪域,通过尽量减少目标域与伪域之间的差异来增强知识转移过程。此外,我们设计了一种DADA的假标签方法,从目标域向多个伪域投放代表,并将伪域分类的平均数预测作为假标签。我们用最先进的域适应方法对四个基准数据集进行了广泛的实验:办公室、办公室31、VisDA2017和数字数据集。结果显示了我们模型的优越性。