Domain alignment (DA) has been widely used in unsupervised domain adaptation. Many existing DA methods assume that a low source risk, together with the alignment of distributions of source and target, means a low target risk. In this paper, we show that this does not always hold. We thus propose a novel metric-learning-assisted domain adaptation (MLA-DA) method, which employs a novel triplet loss for helping better feature alignment. We explore the relationship between the second largest probability of a target sample’s prediction and its distance to the decision boundary. Based on the relationship, we propose a novel mechanism to adaptively adjust the margin in the triplet loss according to target predictions. Experimental results show that the use of proposed triplet loss can achieve clearly better results. We also demonstrate the performance improvement of MLA-DA on all four standard benchmarks compared with the state-of-the-art unsupervised domain adaptation methods. Furthermore, MLA-DA shows stable performance in robust experiments.

域对齐（DA）已广泛用于无监督域适应。许多现有的 DA 方法假设低源风险，以及源和目标分布的对齐，意味着低目标风险。在本文中，我们表明这并不总是成立。因此，我们提出了一种新颖的度量学习辅助域适应 (MLA-DA) 方法，该方法采用新颖的三元组损失来帮助更好地对齐特征。我们探索了目标样本预测的第二大概率与其到决策边界的距离之间的关系。基于这种关系，我们提出了一种新机制，可以根据目标预测自适应地调整三元组损失中的余量。实验结果表明，使用建议的triplet loss可以取得明显更好的结果。与最先进的无监督域适应方法相比，我们还展示了 MLA-DA 在所有四个标准基准上的性能改进。此外，MLA-DA 在稳健的实验中表现出稳定的性能。