用原型样本压缩记忆，少量数据也能有效防止遗忘。
Memory-efficient Continual Learning with Prototypical Exemplar Condensation
- 用原型样本替代真实数据存储，减少内存占用。
- 每类仅需少量样本（<20）即保持良好性能。
- 适合资源受限场景，兼顾隐私与高效学习。
基于重放的持续学习通过保留先前任务的部分样本来缓解灾难性遗忘。现有方法主要通过核心集选择策略优化内存存储，但通常需每类存储超过20个样本才能维持较好性能。本文提出通过合成原型样本进一步压缩内存占用，这些原型样本经特征提取器处理后能形成代表性表征。由于其代表性，模型可用极少量样本保留旧知识，同时保护隐私。此外，引入基于扰动的增强机制，在训练中生成过往数据的合成变体，提升持续学习性能。在多个主流基准数据集和设置上的广泛评估表明，该方法优于现有基线，尤其在大规模数据集和高任务数场景下表现更优。
原文摘要 · Abstract (English)
Rehearsal-based continual learning (CL) mitigates catastrophic forgetting by maintaining a subset of samples from previous tasks for replay. Existing studies primarily focus on optimizing memory storage through coreset selection strategies. While these methods are effective, they typically require storing a substantial number of samples per class (SPC), often exceeding 20, to maintain satisfactory performance. In this work, we propose to further compress the memory footprint by synthesizing and storing prototypical exemplars, which can form representative prototypes when passed through the feature extractor. Owing to their representative nature, these exemplars enable the model to retain previous knowledge using only a small number of samples while preserving privacy. Moreover, we introduce a perturbation-based augmentation mechanism that generates synthetic variants of previous data during training, thereby enhancing CL performance. Extensive evaluations on widely used benchmark datasets and settings demonstrate that the proposed algorithm achieves superior performance compared to existing baselines, particularly in scenarios involving large-scale datasets and a high number of tasks.
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