Resolving Task Confusion in Dynamic Expansion Architectures for Class Incremental Learning

Abstract

The dynamic expansion architecture is becoming popular in class incremental learning, mainly due to its advantages in alleviating catastrophic forgetting. However, task confu- sion is not well assessed within this framework, e.g., the discrepancy between classes of different tasks is not well learned (i.e., inter-task confusion, ITC), and certain prior- ity is still given to the latest class batch (i.e., old-new con- fusion, ONC). We empirically validate the side effects of the two types of confusion. Meanwhile, a novel solution called Task Correlated Incremental Learning (TCIL) is pro- posed to encourage discriminative and fair feature utilization across tasks. TCIL performs a multi-level knowledge distil- lation to propagate knowledge learned from old tasks to the new one. It establishes information flow paths at both fea- ture and logit levels, enabling the learning to be aware of old classes. Besides, attention mechanism and classifier re- scoring are applied to generate more fair classification scores. We conduct extensive experiments on CIFAR100 and Ima- geNet100 datasets. The results demonstrate that TCIL con- sistently achieves state-of-the-art accuracy. It mitigates both ITC and ONC, while showing advantages in battle with catas- trophic forgetting even no rehearsal memory is reserved. Source code: https://github.com/YellowPancake/TCIL.