  Class-incremental learning aims to learn new classes in an incremental
fashion without forgetting the previously learned ones. Several research works
have shown how additional data can be used by incremental models to help
mitigate catastrophic forgetting. In this work, following the recent
breakthrough in text-to-image generative models and their wide distribution, we
propose the use of a pretrained Stable Diffusion model as a source of
additional data for class-incremental learning. Compared to competitive methods
that rely on external, often unlabeled, datasets of real images, our approach
can generate synthetic samples belonging to the same classes as the previously
encountered images. This allows us to use those additional data samples not
only in the distillation loss but also for replay in the classification loss.
Experiments on the competitive benchmarks CIFAR100, ImageNet-Subset, and
ImageNet demonstrate how this new approach can be used to further improve the
performance of state-of-the-art methods for class-incremental learning on large
scale datasets.
