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：近期最先进的无源域适应方法专注于在特征空间中学习有意义的聚类结构，这些方法成功地将源域知识迁移到无标签目标域，且无需访问私有源数据。然而，现有方法依赖源模型生成的伪标签，而由于域偏移，这些伪标签可能包含噪声。本文从标签噪声学习的视角研究无源域适应问题。与常规标签噪声场景不同，我们证明无源域适应中的标签噪声遵循不同的分布假设。进一步证明，这种差异使得依赖特定分布假设的现有标签噪声学习方法无法解决无源域适应中的标签噪声问题。实验证据表明，将现有标签噪声学习方法应用于无源域适应仅能带来微小的性能提升。另一方面，尽管两种场景下的标签噪声存在本质差异，我们理论上证明，先前在常规标签噪声设置中观测到的早期训练现象在无源域适应问题中同样存在。大量实验表明，利用早期训练现象处理无源域适应中的标签噪声，能够显著提升现有无源域适应算法的性能。