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.

本文介绍了一种新颖的观点，以显著减轻连续学习中的灾难性遗忘，强调模型保持现有知识和吸收新信息的能力。我们提出了COgnitive REplay (CORE)的方法，包括自适应数量分配和质量导向数据选择这两个关键策略。该方法在split-CIFAR10上的平均准确率为37.95%，超过最佳基准方法6.52%。此外，它还将最差表现任务的准确率相比顶级基准提高了6.30%。

CORE：通过认知重播减轻连续学习中的灾难性遗忘