  Few-shot class-incremental learning (FSCIL) aims to continually fit new
classes with limited training data, while maintaining the performance of
previously learned classes. The main challenges are overfitting the rare new
training samples and forgetting old classes. While catastrophic forgetting has
been extensively studied, the overfitting problem has attracted less attention
in FSCIL. To tackle overfitting challenge, we design a new ensemble model
framework cooperated with data augmentation to boost generalization. In this
way, the enhanced model works as a library storing abundant features to
guarantee fast adaptation to downstream tasks. Specifically, the multi-input
multi-output ensemble structure is applied with a spatial-aware data
augmentation strategy, aiming at diversifying the feature extractor and
alleviating overfitting in incremental sessions. Moreover, self-supervised
learning is also integrated to further improve the model generalization.
Comprehensive experimental results show that the proposed method can indeed
mitigate the overfitting problem in FSCIL, and outperform the state-of-the-art
methods.
