Multi-Source Domain Adaptation (MSDA) deals with the transfer of task knowledge from multiple labeled source domains to an unlabeled target domain, under a domain-shift. Existing methods aim to minimize this domain-shift using auxiliary distribution alignment objectives. In this work, we present a different perspective to MSDA wherein deep models are observed to implicitly align the domains under label supervision. Thus, we aim to utilize implicit alignment without additional training objectives to perform adaptation. To this end, we use pseudo-labeled target samples and enforce a classifier agreement on the pseudo-labels, a process called Self-supervised Implicit Alignment (SImpAl). We find that SImpAl readily works even under category-shift among the source domains. Further, we propose classifier agreement as a cue to determine the training convergence, resulting in a simple training algorithm. We provide a thorough evaluation of our approach on five benchmarks, along with detailed insights into each component of our approach.

该论文提出了一种新的包括深度模型及自监督隐式对准在内的MSDA方法，并通过利用伪标记目标样本并在伪标记上强制执行分类器一致性来实现自适应，该方法被称为Self-supervised Implicit Alignment (SImpAl)。通过在五个基准测试上的全面评估，展现了该方法的有效性。

分类器秘密地满足多源域自适应