DiffClass: Diffusion-Based Class Incremental Learning
Zichong Meng Jie Zhang Changdi Yang Zheng Zhan Pu Zhao Yanzhi WAng
Class Incremental Learning (CIL) is challenging due to catastrophic
forgetting. On top of that, Exemplar-free Class Incremental Learning is even
more challenging due to forbidden access to previous task data. Recent
exemplar-free CIL methods attempt to mitigate catastrophic forgetting by
synthesizing previous task data. However, they fail to overcome the
catastrophic forgetting due to the inability to deal with the significant
domain gap between real and synthetic data. To overcome these issues, we
propose a novel exemplar-free CIL method. Our method adopts multi-distribution
matching (MDM) diffusion models to unify quality and bridge domain gaps among
all domains of training data. Moreover, our approach integrates selective
synthetic image augmentation (SSIA) to expand the distribution of the training
data, thereby improving the model's plasticity and reinforcing the performance
of our method's ultimate component, multi-domain adaptation (MDA). With the
proposed integrations, our method then reformulates exemplar-free CIL into a
multi-domain adaptation problem to implicitly address the domain gap problem to
enhance model stability during incremental training. Extensive experiments on
benchmark class incremental datasets and settings demonstrate that our method
excels previous exemplar-free CIL methods and achieves state-of-the-art
performance.