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.

翻译：本文提出了一种显著缓解持续学习（CL）中灾难性遗忘的新视角，核心在于提升模型保留已有知识并吸收新信息的能力。现有基于回放的方法对所有任务及数据样本一视同仁，因而无法充分挖掘回放缓冲区的潜力。为此，我们提出受人类认知复习过程启发的认知回放方法（CORE）。CORE包含两大关键策略：自适应数量分配与质量导向数据选择。前者根据各任务的遗忘率自适应调节其在回放缓冲区中的分配比例，后者则确保缓冲区中纳入最能表征各任务特征的典型数据。本方法在split-CIFAR10数据集上实现了37.95%的平均准确率，较最优基线方法提升6.52%；同时，在最差性能任务上的准确率较最优基线方法显著提升6.30%。