Exemplar-free class-incremental learning (EFCIL) aims to mitigate
catastrophic forgetting in class-incremental learning without available
historical data. Compared with its counterpart (replay-based CIL) that stores
historical samples, the EFCIL suffers more from forgetting issues under the
exemplar-free constraint. In this paper, inspired by the recently developed
analytic learning (AL) based CIL, we propose a representation enhanced analytic
learning (REAL) for EFCIL. The REAL constructs a dual-stream base pretraining
(DS-BPT) and a representation enhancing distillation (RED) process to enhance
the representation of the extractor. The DS-BPT pretrains model in streams of
both supervised learning and self-supervised contrastive learning (SSCL) for
base knowledge extraction. The RED process distills the supervised knowledge to
the SSCL pretrained backbone and facilitates a subsequent AL-basd CIL that
converts the CIL to a recursive least-square problem. Our method addresses the
issue of insufficient discriminability in representations of unseen data caused
by a frozen backbone in the existing AL-based CIL. Empirical results on various
datasets including CIFAR-100, ImageNet-100 and ImageNet-1k, demonstrate that
our REAL outperforms the state-of-the-arts in EFCIL, and achieves comparable or
even more superior performance compared with the replay-based methods.

提出了一种表示增强的分析学习方法，通过双流基础预训练和表示增强蒸馏过程来改善无示例约束下的渐进学习过程，解决现有基于分析学习的渐进学习中骨干网络冻结导致的表征不足问题。在不同数据集上的实验结果表明，该方法在无示例约束的渐进学习中优于现有方法，并且与基于回放的方法相比具有可比甚至更优越的性能。