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.

翻译：类别增量学习旨在以增量方式学习新类别，同时避免遗忘先前学过的类别。已有研究表明，增量模型可利用额外数据帮助缓解灾难性遗忘问题。本研究基于近期文本到图像生成模型及其广泛应用的突破性进展，提出使用预训练的稳定扩散模型作为类别增量学习的额外数据来源。相较于依赖外部真实图像数据集（通常无标注）的竞争性方法，我们的方法能够生成与先前图像同属相同类别的合成样本。这使得这些额外数据样本不仅能用于蒸馏损失，还可用于分类损失中的重放机制。在具有挑战性的CIFAR100、ImageNet-Subset和ImageNet基准测试上的实验表明，这种新方法可用于进一步提升大规模数据集上最先进的类别增量学习方法的性能。