  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.
