Unified multi-level neighbor clustering for Source-Free Unsupervised Domain Adaptation.

Source-Free Unsupervised Domain Adaptation (SFUDA) transfers knowledge from the source domain to the target domain with using the source domain model instead of the source data, as the source data cannot be accessed in data privacy scenarios. Our method is based on a clustering assumption: although there is a domain shift, target data with similar semantic still form a cluster in the source feature space. We identify two levels of clustering. One is class-level neighbor clustering: data with the same label tend to form a large cluster. The other is instance-level neighbor clustering: data and its neighbors tend to share the same label. Previous methods only consider one level, and we consider that both are complementary. We propose a new SFUDA method called unified multi-level neighbor clustering to address class and instance consistency in a complementary way. Our proposed method achieves competitive results on three domain adaptation benchmark datasets. [Display omitted] • An unified and complementary multi-level neighbor clustering method to solve SFUDA problem. • A dot-product loss is used to filter unreliable samples with ambiguous features. • The consistency of the labels of neighbors is used to reduce unconfident samples. [ABSTRACT FROM AUTHOR]