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SESSION CLASS PROTOTYPE INCREMENTAL LEARNING(SCPIL) MITIGATING CATASTROPHICFORGETTING WITH DISTANCE-BASED PROTOTYPE LEARNING

Seongsu Kim, Sangwoo Yun, Illhwan Kim, Dongheon Lee, Joonki Paik Image Processing and Intelligent System Lab

This paper introduces a novel prototype-based incremental learning method designed to address the critical issue of catastrophic forgetting in incremental learning by leveraging prototypes. Incremental learning involves sessions containing multiple classes, each with numerous images. This study tackles catastrophic forgetting by utilizing class prototypes and session prototypes within a session. The proposed method defines class prototypes, representing the characteristic features of specific classes, and session prototypes, calculated as the average of all class prototypes within the session. Learning is performed based on the distances between these prototypes. The approach maximizes the distances between class prototypes within the same session while minimizing the distance between the session prototype and each class prototype. Additionally, when a new session is introduced, the method maximizes the distance between the session prototypes of the previous and new sessions to ensure clear differentiation between sessions. This strategy prevents the mixing of class prototypes, reduces interference between sessions, and effectively alleviates catastrophic forgetting. Experiments conducted on the ImageNet-100 and CIFAR-100 datasets validate the superior performance of the proposed method.