We introduce GUIDE, a novel continual learning approach that directs diffusion models to rehearse samples at risk of being forgotten. Existing generative strategies combat catastrophic forgetting by randomly sampling rehearsal examples from a generative model. Such an approach contradicts buffer-based approaches where sampling strategy plays an important role. We propose to bridge this gap by integrating diffusion models with classifier guidance techniques to produce rehearsal examples specifically targeting information forgotten by a continuously trained model. This approach enables the generation of samples from preceding task distributions, which are more likely to be misclassified in the context of recently encountered classes. Our experimental results show that GUIDE significantly reduces catastrophic forgetting, outperforming conventional random sampling approaches and surpassing recent state-of-the-art methods in continual learning with generative replay.

翻译：我们提出GUIDE，一种新颖的持续学习方法，该方法引导扩散模型重放存在被遗忘风险的样本。现有生成式策略通过从生成模型中随机采样重放样本来对抗灾难性遗忘，但此类方法与采样策略发挥重要作用的基于缓冲区的策略相矛盾。我们通过将扩散模型与分类器引导技术相结合来弥合这一差距，专门针对持续训练模型已遗忘的信息生成重放样本。该方法能够生成来自先前任务分布的样本，这些样本在新近学习的类别背景下更可能被错误分类。实验结果表明，GUIDE显著降低灾难性遗忘，其性能优于传统随机采样方法，并超越了当前基于生成重放的持续学习最先进方法。