IEEE

Class Incremental Learning With Less Forgetting Direction and Equilibrium Point

Abstract: Catastrophic forgetting is the core problem of class incremental learning (CIL). Existing work mainly adopts memory replay, knowledge distillation, and dynamic architecture to alleviate this ...
IEEE

Class Relationship Embedded Learning for Source-Free Unsupervised Domain Adaptation

Abstract: This work focuses on a practical knowledge transfer task defined as Source-Free Unsupervised Domain Adaptation (SFUDA), where only a well-trained source model and unlabeled target data are ...