Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation

This paper explores the Class-Incremental Source-Free Unsupervised Domain Adaptation (CI-SFUDA) problem, where the unlabeled target data come incrementally without access to labeled source instances. This problem poses two challenges, the disturbances of similar source-class knowledge to target-class representation learning and the new target knowledge to old ones. To address them, we propose the Multi-Granularity Class Prototype Topology Distillation (GROTO) algorithm, which effectively transfers the source knowledge to the unlabeled class-incremental target domain. Concretely, we design the multi-granularity class prototype self-organization module and prototype topology distillation module. Firstly, the positive classes are mined by modeling two accumulation distributions. Then, we generate reliable pseudo-labels by introducing multi-granularity class prototypes, and use them to promote the positive-class target feature self-organization. Secondly, the positive-class prototypes are leveraged to construct the topological structures of source and target feature spaces. Then, we perform the topology distillation to continually mitigate the interferences of new target knowledge to old ones. Extensive experiments demonstrate that our proposed method achieves state-of-the-art performances on three public datasets.