  This paper introduces a novel perspective to significantly mitigate
catastrophic forgetting in continuous learning (CL), which emphasizes models'
capacity to preserve existing knowledge and assimilate new information. Current
replay-based methods treat every task and data sample equally and thus can not
fully exploit the potential of the replay buffer. In response, we propose
COgnitive REplay (CORE), which draws inspiration from human cognitive review
processes. CORE includes two key strategies: Adaptive Quantity Allocation and
Quality-Focused Data Selection. The former adaptively modulates the replay
buffer allocation for each task based on its forgetting rate, while the latter
guarantees the inclusion of representative data that best encapsulates the
characteristics of each task within the buffer. Our approach achieves an
average accuracy of 37.95% on split-CIFAR10, surpassing the best baseline
method by 6.52%. Additionally, it significantly enhances the accuracy of the
poorest-performing task by 6.30% compared to the top baseline. Code is
available at https://github.com/sterzhang/CORE.
