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.

我们提出了一种新的无样本增量学习方法，通过采用多分布匹配扩散模型来统一训练数据的质量，弥合不同域之间的差距，并通过选择性合成图像增强来扩展训练数据的分布，从而增强模型的可塑性和最终组件的性能，在增量训练期间提升模型的稳定性，实验证明这种方法优于之前的无样本增量学习方法并达到了最先进的性能。