论文标题
域名域的适应性
Domain-Augmented Domain Adaptation
论文作者
论文摘要
无监督的域适应性（UDA）通过减少跨域差异，可以将知识从标记的源域转移到未标记的目标域。但是，大多数研究都是基于从源域对目标域的直接适应，并且遭受了较大的域差异。为了克服这一挑战，在本文中，我们提出了域增强的域适应性（DADA），以生成伪域与目标域差异较小，以通过最大程度地降低目标域和伪域之间的差异来增强知识传递过程。此外，我们通过从目标域将表示形式投射到多个伪域，并将对伪域分类的平均预测作为伪标记来设计出DADA的伪标记方法。我们对四个基准数据集的最新域适应方法进行了广泛的实验：Office Home，Office-31，Visda2017和Digital DataSet。结果证明了我们模型的优势。
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.
