GUIDE: Guidance-based Incremental Learning with Diffusion Models
Bartosz Cywiński Kamil Deja Tomasz Trzciński Bartłomiej Twardowski Łukasz Kuciński
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.