arXiv 2507.211092025-09-15cs.LGcs.AI

基于间隔回忆的任务聚焦巩固：让神经网络像大学生一样学习

Task-Focused Consolidation with Spaced Recall: Making Neural Networks Learn like College Students

Prital Bamnodkar

备注：: Improved Grammar, consistency and flow. Some sections like the Discussion Section have been rewritten for improvement. Figures and Tables have improved formatting, while the algorithm pseudocode is now consistent with the experiments and less ambiguous

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AI总结：

本文提出受人类学习策略启发的持续学习方法TFC-SR，通过引入主动回忆探针机制增强标准经验回放框架，有效稳定过去知识表示并显著缓解灾难性遗忘，在记忆受限环境中表现优异。

摘要：

深度神经网络经常面临一个被称为灾难性遗忘的关键局限性，即在学习新任务后，过去任务上的性能会下降。本文引入了一种受人类学习策略（如主动回忆、刻意练习和间隔重复）启发的新型持续学习方法，命名为基于间隔回忆的任务聚焦巩固（TFC-SR）。TFC-SR通过一种我们称之为主动回忆探针的机制增强了标准经验回放框架。这是一种周期性的、任务感知的模型记忆评估，能够稳定过去知识的表示。我们在Split MNIST和Split CIFAR-100基准上，将TFC-SR与领先的基于正则化和基于回放的基线方法进行了对比测试。结果表明，TFC-SR的表现显著优于这些方法。例如，在Split CIFAR-100上，它达到了13.17%的最终准确率，而标准经验回放仅为7.40%。我们证明这一优势源于探针本身的稳定作用，而非回放量的差异。此外，我们分析了记忆大小与性能之间的权衡，表明虽然TFC-SR在记忆受限的环境中表现更好，但当可用记忆充足时，更高的回放量仍然更有效。我们得出结论，TFC-SR是一种鲁棒且高效的方法，突出了将主动记忆检索机制整合到持续学习系统中的重要性。

英文摘要：

Deep neural networks often suffer from a critical limitation known as catastrophic forgetting, where performance on past tasks degrades after learning new ones. This paper introduces a novel continual learning approach inspired by human learning strategies like Active Recall, Deliberate Practice, and Spaced Repetition, named Task-Focused Consolidation with Spaced Recall (TFC-SR). TFC-SR enhances the standard experience replay framework with a mechanism we term the Active Recall Probe. It is a periodic, task-aware evaluation of the model's memory that stabilizes the representations of past knowledge. We test TFC-SR on the Split MNIST and the Split CIFAR-100 benchmarks against leading regularization-based and replay-based baselines. Our results show that TFC-SR performs significantly better than these methods. For instance, on the Split CIFAR-100, it achieves a final accuracy of 13.17% compared to Standard Experience Replay's 7.40%. We demonstrate that this advantage comes from the stabilizing effect of the probe itself, and not from the difference in replay volume. Additionally, we analyze the trade-off between memory size and performance and show that while TFC-SR performs better in memory-constrained environments, higher replay volume is still more effective when available memory is abundant. We conclude that TFC-SR is a robust and efficient approach, highlighting the importance of integrating active memory retrieval mechanisms into continual learning systems.

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