Source-free domain adaptation (SFDA) aims to adapt a well-trained source model to an unlabelled target domain without accessing the source dataset, making it applicable in a variety of real-world scenarios. Existing SFDA methods ONLY assess their adapted models on the target training set, neglecting the data from unseen but identically distributed testing sets. This oversight leads to overfitting issues and constrains the model's generalization ability. In this paper, we propose a consistency regularization framework to develop a more generalizable SFDA method, which simultaneously boosts model performance on both target training and testing datasets. Our method leverages soft pseudo-labels generated from weakly augmented images to supervise strongly augmented images, facilitating the model training process and enhancing the generalization ability of the adapted model. To leverage more potentially useful supervision, we present a sampling-based pseudo-label selection strategy, taking samples with severer domain shift into consideration. Moreover, global-oriented calibration methods are introduced to exploit global class distribution and feature cluster information, further improving the adaptation process. Extensive experiments demonstrate our method achieves state-of-the-art performance on several SFDA benchmarks, and exhibits robustness on unseen testing datasets.

翻译：源无关领域自适应（SFDA）旨在无需访问源数据集的情况下，将预训练的源模型适应至无标签的目标领域，从而适用于多种现实场景。现有SFDA方法仅评估其在目标训练集上的适应模型，忽略了来自未见但同分布的测试集数据。这种局限性导致过拟合问题，并限制了模型的泛化能力。本文提出一种一致性正则化框架，旨在开发更具泛化能力的SFDA方法，同时提升模型在目标训练集和测试集上的性能。该方法利用弱增强图像生成的软伪标签监督强增强图像，促进模型训练过程并增强适应模型的泛化能力。为利用更多潜在的有效监督，我们提出一种基于采样的伪标签选择策略，将具有更严重领域偏移的样本纳入考量。此外，引入全局导向的校准方法以利用全局类别分布与特征聚类信息，进一步优化适应过程。大量实验表明，我们的方法在多个SFDA基准测试中达到最先进性能，并在未见测试数据集上展现出鲁棒性。