提出新损失函数STAR，缓解持续学习中的遗忘问题。
STAR: Stability-Inducing Weight Perturbation for Continual Learning
- 设计新损失函数，通过扰动参数最小化预测差异以增强模型稳定性。
- 在多种基线方法上提升性能最高达15%，显著减少遗忘。
- 可无缝集成现有重放方法，适合需要稳定训练的场景。
人类能自然地顺序学习新任务。持续学习是一类在序列中接收新数据（可能涉及新任务）时更新模型的学习算法。其关键挑战是：随着模型学习新任务，会因灾难性遗忘而丢失旧知识。缓解遗忘的常用方法是维护一个小型历史样本缓冲区并重放这些样本。但该方法受限于缓冲区大小，遗忘仍存在。本文提出一种新损失函数STAR，通过利用使模型预测与局部参数邻域间KL散度最小化的最坏情况参数扰动，促进模型稳定性并减轻遗忘。STAR可作为即插即用组件与几乎任何现有重放方法结合使用。实验表明，STAR在不同基线方法上性能提升最高达15%，达到或优于当前最先进的重放型持续学习方法。
原文摘要 · Abstract (English)
Humans can naturally learn new and varying tasks in a sequential manner. Continual learning is a class of learning algorithms that updates its learned model as it sees new data (on potentially new tasks) in a sequence. A key challenge in continual learning is that as the model is updated to learn new tasks, it becomes susceptible to catastrophic forgetting, where knowledge of previously learned tasks is lost. A popular approach to mitigate forgetting during continual learning is to maintain a small buffer of previously-seen samples and to replay them during training. However, this approach is limited by the small buffer size, and while forgetting is reduced, it is still present. In this paper, we propose a novel loss function, STAR, that exploits the worst-case parameter perturbation that reduces the KL-divergence of model predictions with that of its local parameter neighborhood to promote stability and alleviate forgetting. STAR can be combined with almost any existing rehearsal-based method as a plug-and-play component. We empirically show that STAR consistently improves the performance of existing methods by up to 15% across varying baselines and achieves superior or competitive accuracy to that of state-of-the-art methods aimed at improving rehearsal-based continual learning.
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