First, we introduce a class similarity distillation (CSD) loss based on the similarity between new and old class prototypes, ensuring the model's plasticity to learn new classes and stability to detect old classes. 
CSD
Second, to better extract class similarity features, we propose a global similarity distillation (GSD) loss that maximizes the mutual information between the new class feature and old class features. 
GSD
Additionally, we present a region proposal network (RPN)-based method that assigns positive and negative labels to prevent mislearning issues. 
RPN