Future-Proofing Class Incremental Learning
Quentin Jodelet Xin Liu Yin Jun Phua Tsuyoshi Murata
Exemplar-Free Class Incremental Learning is a highly challenging setting
where replay memory is unavailable. Methods relying on frozen feature
extractors have drawn attention recently in this setting due to their
impressive performances and lower computational costs. However, those methods
are highly dependent on the data used to train the feature extractor and may
struggle when an insufficient amount of classes are available during the first
incremental step. To overcome this limitation, we propose to use a pre-trained
text-to-image diffusion model in order to generate synthetic images of future
classes and use them to train the feature extractor. Experiments on the
standard benchmarks CIFAR100 and ImageNet-Subset demonstrate that our proposed
method can be used to improve state-of-the-art methods for exemplar-free class
incremental learning, especially in the most difficult settings where the first
incremental step only contains few classes. Moreover, we show that using
synthetic samples of future classes achieves higher performance than using real
data from different classes, paving the way for better and less costly
pre-training methods for incremental learning.