Few-Shot Class-Incremental Learning via Asymmetric Supervised Contrastive Learning

Few-Shot Class-Incremental Learning (FSCIL) is to continuously learn novel classes from a few samples without forgetting previous knowledge. Adapting directly to limited novel data typically results in significant forgetting of base class knowledge. Consequently, prevailing FSCIL methods are devoted to training a strong initial model that can be frozen in incremental sessions. However, these works face a dilemma in poor generalization: they benefit mainly from base class performance, yet underperform in novel classes. To alleviate this issue, we design a two-stage training framework to simultaneously enhance generalization for novel classes and maintain base class discrimination. In the first stage, an asymmetric supervised contrastive learning (AsyCon) algorithm is proposed. AsyCon introduces a predicted feature to achieve an asymmetric alignment of positive pairs. It alleviates over-similarity within positive features, allowing the model to better transfer to new classes in incremental sessions. In the second stage, the model is finetuned for promoting its performance on base classes. To maintain the generalization obtained in the first stage, we employ an L2 normalized regularization (LR) to keep the feature consistent with the model in the first stage. The finetuned model, termed AsyCLR, effectively balances generalization and discrimination, significantly outperforming existing FSCIL works especially in novel class accuracy. Experiments on CUB200, CIFAR100, and mini-ImageNet verify the effectiveness of our method. Additionally, our method also performs well in the standard few-shot recognition scenario due to its strong generalization ability. Our codes are available at https://github.com/APORduo/AsyCLR.