  Online continual learning suffers from an underfitted solution due to
insufficient training for prompt model update (e.g., single-epoch training). To
address the challenge, we propose an efficient online continual learning method
using the neural collapse phenomenon. In particular, we induce neural collapse
to form a simplex equiangular tight frame (ETF) structure in the representation
space so that the continuously learned model with a single epoch can better fit
to the streamed data by proposing preparatory data training and residual
correction in the representation space. With an extensive set of empirical
validations using CIFAR-10/100, TinyImageNet, ImageNet-200, and ImageNet-1K, we
show that our proposed method outperforms state-of-the-art methods by a
noticeable margin in various online continual learning scenarios such as
disjoint and Gaussian scheduled continuous (i.e., boundary-free) data setups.
