Recent state-of-the-art source-free domain adaptation (SFDA) methods have focused on learning meaningful cluster structures in the feature space, which have succeeded in adapting the knowledge from source domain to unlabeled target domain without accessing the private source data. However, existing methods rely on the pseudo-labels generated by source models that can be noisy due to domain shift. In this paper, we study SFDA from the perspective of learning with label noise (LLN). Unlike the label noise in the conventional LLN scenario, we prove that the label noise in SFDA follows a different distribution assumption. We also prove that such a difference makes existing LLN methods that rely on their distribution assumptions unable to address the label noise in SFDA. Empirical evidence suggests that only marginal improvements are achieved when applying the existing LLN methods to solve the SFDA problem. On the other hand, although there exists a fundamental difference between the label noise in the two scenarios, we demonstrate theoretically that the early-time training phenomenon (ETP), which has been previously observed in conventional label noise settings, can also be observed in the SFDA problem. Extensive experiments demonstrate significant improvements to existing SFDA algorithms by leveraging ETP to address the label noise in SFDA.

翻译：近期最先进的源域无源域适应（SFDA）方法聚焦于在特征空间中学习有意义的簇结构，从而在不访问私有源域数据的情况下，成功将源域知识迁移至未标记的目标域。然而，现有方法依赖源模型生成的伪标签，而这类标签可能因域偏移而存在噪声。本文从带噪标签学习（LLN）的视角研究SFDA。与常规LLN场景中的标签噪声不同，我们证明SFDA中的标签噪声遵循不同的分布假设。同时，我们证明这种差异使得依赖其分布假设的现有LLN方法无法处理SFDA中的标签噪声。实验证据表明，将现有LLN方法应用于SFDA问题仅能带来边际改进。另一方面，尽管两种场景下的标签噪声存在根本性差异，但我们从理论上证明，先前在常规带噪标签设置中观察到的早期训练现象（ETP）同样可在SFDA问题中观察到。大量实验表明，利用ETP处理SFDA中的标签噪声可显著提升现有SFDA算法的性能。