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. To address the challenge%2C we propose an efficient online continual learning method using the neural collapse phenomenon. In particular%2C 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%2F100%2C TinyImageNet%2C ImageNet-200%2C and ImageNet-1K%2C 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.%2C boundary-free) data setups.?quality=80&w=800)
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