Dynamic Residual Classifier for Class Incremental Learning
Xiuwei Chen Xiaobin Chang
The rehearsal strategy is widely used to alleviate the catastrophic
forgetting problem in class incremental learning (CIL) by preserving limited
exemplars from previous tasks. With imbalanced sample numbers between old and
new classes, the classifier learning can be biased. Existing CIL methods
exploit the long-tailed (LT) recognition techniques, e.g., the adjusted losses
and the data re-sampling methods, to handle the data imbalance issue within
each increment task. In this work, the dynamic nature of data imbalance in CIL
is shown and a novel Dynamic Residual Classifier (DRC) is proposed to handle
this challenging scenario. Specifically, DRC is built upon a recent advance
residual classifier with the branch layer merging to handle the model-growing
problem. Moreover, DRC is compatible with different CIL pipelines and
substantially improves them. Combining DRC with the model adaptation and fusion
(MAF) pipeline, this method achieves state-of-the-art results on both the
conventional CIL and the LT-CIL benchmarks. Extensive experiments are also
conducted for a detailed analysis. The code is publicly available.