  Source-free domain adaptation (SFDA) aims to adapt a source-trained model to
an unlabeled target domain without access to the source data. SFDA has
attracted growing attention in recent years, where existing approaches focus on
self-training that usually includes pseudo-labeling techniques. In this paper,
we introduce a novel noise-learning approach tailored to address noise
distribution in domain adaptation settings 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, resulting in better
prediction accuracy. We demonstrate the effectiveness of our approach when
combined with several SFDA methods: SHOT, SHOT++, and AaD. We obtain
state-of-the-art results on three domain adaptation datasets: VisDA, DomainNet,
and OfficeHome.
