Progressive Prototype Evolving for Dual-Forgetting Mitigation in Non-Exemplar Online Continual Learning

Online Continual Learning (OCL) aims at learning a model through a sequence of single-pass data, usually encountering the challenges of catastrophic forgetting both between different learning stages and within a stage. Currently, existing OCL methods address these issues by replaying part of previous data but inevitably raise data privacy concerns and stand in contrast to the setting of online learning where data can only be accessed once. Moreover, their performance will dramatically drop without any replay buffer. In this paper, we propose a Non-Exemplar Online Continual Learning method named Progressive Prototype Evolving (PPE). The core of our PPE is to progressively learn class-specific prototypes during the online learning phase without reusing any previously seen data. Meanwhile, the progressive prototypes of the current learning stage, serving as the accumulated knowledge of different classes, are fed back to the model to mitigate intra-stage forgetting. Additionally, to resist inter-stage forgetting, we introduce the Prototype Similarity Preserving and Prototype-Guided Gradient Constraint modules which distill and leverage the historical knowledge conveyed by prototypes to regularize the one-way model learning. Consequently, extensive experiments on three widely used datasets demonstrate the superiority of the proposed PPE against the state-of-the-art exemplar-based OCL approaches. Our code is available at https://github.com/zhoujiahuan1991/MM24-PPE.