Online Prototype Learning for Online Continual Learning

TL;DR

This paper introduces the OnPro framework for online continual learning, addressing shortcut learning by using online prototypes to improve feature representation and class separation, leading to better generalization and performance.

Contribution

The paper proposes a novel online prototype learning framework with equilibrium and adaptive feedback mechanisms to mitigate shortcut learning in online continual learning.

Findings

OnPro outperforms state-of-the-art methods on benchmark datasets.

The equilibrium mechanism improves class separation and feature discrimination.

Adaptive prototypical feedback enhances boundary learning for easily misclassified classes.

Abstract

Online continual learning (CL) studies the problem of learning continuously from a single-pass data stream while adapting to new data and mitigating catastrophic forgetting. Recently, by storing a small subset of old data, replay-based methods have shown promising performance. Unlike previous methods that focus on sample storage or knowledge distillation against catastrophic forgetting, this paper aims to understand why the online learning models fail to generalize well from a new perspective of shortcut learning. We identify shortcut learning as the key limiting factor for online CL, where the learned features may be biased, not generalizable to new tasks, and may have an adverse impact on knowledge distillation. To tackle this issue, we present the online prototype learning (OnPro) framework for online CL. First, we propose online prototype equilibrium to learn representative features against shortcut learning and discriminative features to avoid class confusion, ultimately achieving an equilibrium status that separates all seen classes well while learning new classes. Second, with the feedback of online prototypes, we devise a novel adaptive prototypical feedback mechanism to sense the classes that are easily misclassified and then enhance their boundaries. Extensive experimental results on widely-used benchmark datasets demonstrate the superior performance of OnPro over the state-of-the-art baseline methods. Source code is available at https://github.com/weilllllls/OnPro.