Abstract: The problem of catastrophic forgetting is prevalent in incremental learning scenarios,and forgetting of old knowledge can severely affect the average performance of the model over the entire task sequence.Therefore,a class incremental learning approach based on prototype replay and dynamic update is proposed to address the problem of old knowledge forgetting caused by prototype offset in the incremental learning process.This method further updates the prototypes of the old classes in real time using a dynamic update strategy after retaining the prototypes of the new classes in the prototype update phase.Specifically,after learning the new task,the strategy achieves an approximate estimation of the unknown bias present in the old-class prototypes based on the known bias of the currently accessible data,and finally completes the update of the old-class prototypes,thus being able to alleviate the mismatch between the original old-class prototypes and the current feature mapping.Experimental results on CIFAR-100 and Tiny-ImageNet datasets show that the proposed class incremental learning approach based on prototype replay and dynamic update is effective in reducing catastrophic forgetting of old knowledge,thus improving the classification performance of the model in class incremental learning scenarios.

Key words: Class incremental learning, Prototype update, Knowledge distillation, Prototype replay, Catastrophic forgetting