Class Incremental Learning With Less Forgetting Direction and Equilibrium Point

Catastrophic forgetting is the core problem of class incremental learning (CIL). Existing work mainly adopts memory replay, knowledge distillation, and dynamic architecture to alleviate this problem, but seldom from the aspect of parameter regularization. However, existing parameter regularization methods struggle to achieve an appropriate balance between old and new tasks. To bring it back to CIL, we first propose constrained incremental learning with less forgetting direction (LFD) to leave more plasticity for the new task under a strong stability constraint for old tasks. Specifically, the new parameters are constrained to be close to the LFD of old tasks instead of a single group of old parameters. To validate the effectiveness of this regularization, we investigate the connectivity between the old parameters and the new parameters, and additionally find that a higher accuracy interval exists along the linear connection. Therefore, we further propose a post-processing procedure to find an equilibrium point in this interval for better balance between old and new tasks. Extensive classification experiments on CIFAR-100, ImageNet-100, and ImageNet-1K show our method can significantly improve performance compared with existing CIL methods and the object detection experiments on PASCAL-VOC show its broad generality on other tasks.