面向持续学习的显著性引导隐式联想回放方法
Saliency-Guided Hidden Associative Replay for Continual Learning
AI总结：
针对持续学习中回放方法内存开销大、现有局部数据检索机制不符合人类记忆规律的问题，提出结合联想记忆与回放策略的SHARC框架，通过稀疏编码存储显著片段、基于内容检索实现高效回忆，在多任务上验证了有效性。
摘要：
持续学习（Continual Learning，CL）是下一代人工智能领域的新兴方向，聚焦于像人类学习一样在一系列任务上训练神经网络。尽管持续学习相比传统监督学习具备优势，但其核心挑战仍是缓解灾难性遗忘，确保在后续学习过程中保留先前任务的知识。在各类应对策略中，基于回放的方法因契合生物记忆机制成为主流方案。然而这类方法内存开销大，通常需要保存完整数据样本，这与人类选择性保留显著经验的记忆模式不符。近年已有研究探索仅在情景记忆中存储数据的重要部分，但局部数据的特性要求创新的检索机制。现有解决方案如修复（inpainting）通过局部线索近似重建完整数据，该方式与真实的人类记忆过程存在差异。针对这些问题，本文提出面向持续学习的显著性引导隐式联想回放方法（Saliency Guided Hidden Associative Replay for Continual Learning，SHARC）。该新型框架将联想记忆与基于回放的策略相结合：SHARC 首先通过稀疏记忆编码存储显著数据片段；更关键的是，它借助联想记忆范式引入了基于内容的记忆检索机制，有望实现快速且近乎完美的回忆，让持续学习更贴近真实的人类记忆过程。大量实验结果表明，该方法在多种持续学习任务上均具备有效性。
英文摘要：
Continual Learning is a burgeoning domain in next-generation AI, focusing on training neural networks over a sequence of tasks akin to human learning. While CL provides an edge over traditional supervised learning, its central challenge remains to counteract catastrophic forgetting and ensure the retention of prior tasks during subsequent learning. Amongst various strategies to tackle this, replay based methods have emerged as preeminent, echoing biological memory mechanisms. However, these methods are memory intensive, often preserving entire data samples, an approach inconsistent with humans selective memory retention of salient experiences. While some recent works have explored the storage of only significant portions of data in episodic memory, the inherent nature of partial data necessitates innovative retrieval mechanisms. Current solutions, like inpainting, approximate full data reconstruction from partial cues, a method that diverges from genuine human memory processes. Addressing these nuances, this paper presents the Saliency Guided Hidden Associative Replay for Continual Learning. This novel framework synergizes associative memory with replay-based strategies. SHARC primarily archives salient data segments via sparse memory encoding. Importantly, by harnessing associative memory paradigms, it introduces a content focused memory retrieval mechanism, promising swift and near-perfect recall, bringing CL a step closer to authentic human memory processes. Extensive experimental results demonstrate the effectiveness of our proposed method for various continual learning tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
