Self-supervised Contrastive Feature Refinement for Few-Shot Class-Incremental Learning

Few-Shot Class-Incremental Learning (FSCIL) is to learn novel classes with few data points incrementally, without forgetting old classes. It is very hard to capture the underlying patterns and traits of the few-shot classes. To meet the challenges, we propose a Self-supervised Contrastive Feature Refinement (SCFR) framework which tackles the FSCIL issue from three aspects. Firstly, we employ a self-supervised learning framework to make the network to learn richer representations and promote feature refinement. Meanwhile, we design virtual classes to improve the models robustness and generalization during training process. To prevent catastrophic forgetting, we attach Gaussian Noise to encountered prototypes to recall the distribution of known classes and maintain stability in the embedding space. SCFR offers a systematic solution which can effectively mitigate the issues of catastrophic forgetting and over-fitting. Experiments on widely recognized datasets, including CUB200, miniImageNet and CIFAR100, show remarkable performance than other mainstream works.