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Margin-Based Few-Shot Class-Incremental Learning with Class-Level Overfitting Mitigation
Yixiong Zou · Shanghang Zhang · Yuhua Li · Ruixuan Li


Few-shot class-incremental learning (FSCIL) is designed to incrementally recognize novel classes with only few training samples after the (pre-)training on base classes with sufficient samples, which focuses on both base-class performance and novel-class generalization. A well known modification to the base-class training is to apply a margin to the base-class classification. However, a dilemma exists that we can hardly achieve both good base-class performance and novel-class generalization simultaneously by applying the margin during the base-class training, which is still under explored. In this paper, we study the cause of such dilemma for FSCIL. We first interpret this dilemma as a class-level overfitting (CO) problem from the aspect of pattern learning, and then find its cause lies in the easily-satisfied constraint of learning margin-based patterns. Based on the analysis, we propose a novel margin-based FSCIL method to mitigate the CO problem by providing the pattern learning process with extra constraint from the margin-based patterns themselves. Extensive experiments on CIFAR100, Caltech-USCD Birds-200-2011 (CUB200), and miniImageNet demonstrate that the proposed method effectively mitigates the CO problem and achieves state-of-the-art performance.

Author Information

Yixiong Zou (Huazhong University of Science and Technology)
Shanghang Zhang (UC Berkeley)
Yuhua Li (Huazhong University of Science and Technology)
Ruixuan Li (Huazhong University of Science and Technology)

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