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 incorporating classifier guidance into the diffusion process 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，通过引导扩散模型重演面临遗忘风险的样本。现有的生成式策略通过从生成模型中随机采样重演样本来对抗灾难性遗忘，这种方法与基于缓冲区的策略形成矛盾——在后者中采样策略起着关键作用。我们提出通过将分类器引导机制融入扩散过程来弥补这一差距，从而生成专门针对持续训练模型已遗忘信息的重演样本。该方法能够从先前任务分布中生成样本，这些样本在近期遇到类别的上下文中更可能被误分类。实验结果表明，GUIDE显著降低了灾难性遗忘，其性能优于传统的随机采样方法，并在生成式回放的持续学习任务中超越了当前最先进的方法。