Source-free domain adaptation (SFDA) aims to transfer knowledge learned from a source domain to an unlabeled target domain, where the source data is unavailable during adaptation. Existing approaches for SFDA focus on self-training usually including well-established entropy minimization and pseudo-labeling techniques. Recent work suggested a co-learning strategy to improve the quality of the generated target pseudo-labels using robust pretrained networks such as Swin-B. However, since the generated pseudo-labels depend on the source model, they may be noisy due to domain shift. In this paper, we view SFDA from the perspective of label noise learning and learn to de-confuse the pseudo-labels. More specifically, we learn a noise transition matrix of the pseudo-labels to capture the label corruption of each class and learn the underlying true label distribution. Estimating the noise transition matrix enables a better true class-posterior estimation results with better prediction accuracy. We demonstrate the effectiveness of our approach applied with several SFDA methods: SHOT, SHOT++, and AaD. We obtain state-of-the-art results on three domain adaptation datasets: VisDA, DomainNet, and OfficeHome.

该研究针对无源领域自适应（SFDA），提出了一种通过学习噪声转移矩阵和准确预测的方法，改进了基于伪标签的SFDA方法，获得了在VisDA、DomainNet和OfficeHome三个领域自适应数据集上的最新成果。

无源域自适应中的伪标签解析