Unsupervised domain adaptation (UDA) transfers knowledge from a label-rich source domain to a different but related fully-unlabeled target domain. To address the problem of domain shift, more and more UDA methods adopt pseudo labels of the target samples to improve the generalization ability on the target domain. However, inaccurate pseudo labels of the target samples may yield suboptimal performance with error accumulation during the optimization process. Moreover, once the pseudo labels are generated, how to remedy the generated pseudo labels is far from explored. In this paper, we propose a novel approach to improve the accuracy of the pseudo labels in the target domain. It first generates coarse pseudo labels by a conventional UDA method. Then, it iteratively exploits the intra-class similarity of the target samples for improving the generated coarse pseudo labels, and aligns the source and target domains with the improved pseudo labels. The accuracy improvement of the pseudo labels is made by first deleting dissimilar samples, and then using spanning trees to eliminate the samples with the wrong pseudo labels in the intra-class samples. We have applied the proposed approach to several conventional UDA methods as an additional term. Experimental results demonstrate that the proposed method can boost the accuracy of the pseudo labels and further lead to more discriminative and domain invariant features than the conventional baselines.

翻译：不受监督的域适应(UDA) 将知识从标签丰富源域向不同但又相关又完全没有标签的目标域转移。 为解决域转移问题,越来越多的UDA方法采用目标样品假标签以提高目标域的概括能力。 然而,目标样品的不准确假标签在优化过程中可能会产生不优化的性能,并累积出错误。 此外,一旦生成伪标签,如何纠正生成的假标签远非探索对象域。 在本文中,我们提出一种新的方法来提高目标域伪标签的准确性。 它首先通过传统的UDA方法生成粗化假标签。 然后,它反复利用目标样品的类内相似性来改进生成的粗化假标签的能力,并将源和目标域与改良的伪标签统一起来。 伪标签的准确性改进是通过首先删除不相近的样品,然后利用树涂层树来消除在内部类样品中错误的假标签的准确性。 我们将拟议的方法用于提高一些常规类类内标签的精确性,而不是实验性域的精确性方法,作为另一个术语。