Make Continual Learning Stronger via C-Flat
Ang Bian Wei Li Hangjie Yuan Chengrong Yu Zixiang Zhao Mang Wang Aojun Lu Tao Feng
Model generalization ability upon incrementally acquiring dynamically
updating knowledge from sequentially arriving tasks is crucial to tackle the
sensitivity-stability dilemma in Continual Learning (CL). Weight loss landscape
sharpness minimization seeking for flat minima lying in neighborhoods with
uniform low loss or smooth gradient is proven to be a strong training regime
improving model generalization compared with loss minimization based optimizer
like SGD. Yet only a few works have discussed this training regime for CL,
proving that dedicated designed zeroth-order sharpness optimizer can improve CL
performance. In this work, we propose a Continual Flatness (C-Flat) method
featuring a flatter loss landscape tailored for CL. C-Flat could be easily
called with only one line of code and is plug-and-play to any CL methods. A
general framework of C-Flat applied to all CL categories and a thorough
comparison with loss minima optimizer and flat minima based CL approaches is
presented in this paper, showing that our method can boost CL performance in
almost all cases. Code will be publicly available upon publication.