In recent years, the research on source-free domain adaptation has received increasing attention and has achieved considerable progress. It can overcome the dependence on source domain data and obtain a target domain model with robust performance on the target domain only by using the source domain model and unlabeled target domain data. However, existing studies typically handle the target domain distribution in a relatively coarse manner and are consistently susceptible to model noise interference. Therefore, we propose a progressive optimization strategy for the target domain distribution, including two parts: inter-category and intra-category. Regarding inter-category, we decide whether to separate category pairs based on the degree of discrepancy between them. Regarding intra-category, we consider whether to aggregate sample pairs based on whether their pairwise similarity—among samples assigned to the same predicted category—is sufficiently high. And as the training progresses, all data will be optimized. Additionally, for some difficult-to-distinguish categories, we propose a screening strategy that fuses source domain and target domain knowledge. We also further optimized the samples belonging to these categories. Our results on three image datasets demonstrate the effectiveness of our method.
Liang et al. (Sun,) studied this question.