MICS: Midpoint Interpolation To Learn Compact and Separated Representations for Few-Shot Class-Incremental Learning

Abstract

Few-shot class-incremental learning (FSCIL) aims to learn a classification model for continually accepting novel classes with a few samples. The key of FSCIL is the joint success of the following two training stages: Base training stage to classify base classes and Incremental training stage with sequential learning of novel classes. However, recent efforts show a tendency to focus on one of the stages, or separately design strategies for each stage, so that less effort has been paid to devise a consistent strategy across the consecutive stages. In this paper, we first emphasize the particular aspects of the successful FSCIL algorithm that are worthwhile to consistently pursue during both stages, i.e., intra-class compactness and inter-class separability of the representation, which allows a model to reserve feature space in between current classes for preparing the acceptance of novel classes in the future. To achieve these aspects, we propose a mixup-based FSCIL method called MICS, which theoretically guarantees to enlarge the thickness of the margin space between different classes, leading to outstanding performance on the existing benchmarks. Code is available at https://github.com/solangii/MICS.