Pseudo Informative Episode Construction for Few-Shot Class-Incremental Learning

Few-Shot Class-Incremental Learning (FSCIL) studies how to empower the machine learning system to learn novel classes with only a few annotated examples continually. To tackle the FSCIL task, recent state-of-the-art methods propose to employ the meta-learning mechanism, which constructs the pseudo incremental episodes/tasks in the training phase. However, these methods only select part of the base classes to construct the pseudo novel classes in the feature space of the base classes, which cannot mimic the real novel classes of the testing scenario. To deal with this problem, we propose a new Pseudo Informative Episode Construction (PIEC) framework. Specifically, we first perform distribution-level mixing to generate a set of pseudo novel classes in the feature space of the novel class. Then, we propose two diversity criteria to select the informative pseudo novel classes that have large discrepancies with each other and high information gain over the base classes to construct the pseudo incremental session. In this way, we can allow the model to learn rich new concepts beyond the base classes as in the real incremental session during the episodic training procedure, thus improving its generalization ability. Extensive experiments on three popular classification benchmarks (i.e., CUB200, miniImageNet, and CIFAR100) show that the proposed framework can outperform other state-of-the-art methods.