Calibration for Non-Exemplar Based Class-Incremental Learning

Fei Zhu, Xu-Yao Zhang, Cheng-Lin Liu

Abstract: Catastrophic forgetting is the central challenge in incremental learning. Notable studies address the problem by using regularization or experience replay strategies. However, the performance is far from ideal without storing previous data, especially in the scenario of class-incremental learning (CIL). In CIL setting, an important factor causing catastrophic forgetting is the severe bias between the new and previously learned classes, in both classifier and feature extractor. In this paper, we propose calibrateCIL which contains two simple modifications to calibrate the bias in non-exemplar based CIL. Specifically, local softmax is proposed to calibrate the classifier, and cutout training is used to calibrate the feature extractor by learning richer, more generalizable and transferable features. Our method can give balance class scores without any post-processing technique. We show that our method outperforms state-of-the-art non-exemplar based methods on the challenging problem of CIL, and the ablation study demonstrates the effectiveness of the two modifications.