Continual Few-shot Learning with Transformer Adaptation and Knowledge Regularization

Abstract

Continual few-shot learning, as a paradigm that simultaneously solves continual learning and few-shot learning, has become a challenging problem in machine learning. An eligible continual few-shot learning model is expected to distinguish all seen classes upon new categories arriving, where each category only includes very few labeled data. However, existing continual few-shot learning methods only consider the visual modality, where the distributions of new categories often indistinguishably overlap with old categories, thus resulting in the severe catastrophic forgetting problem. To tackle this problem, in this paper we study continual few-shot learning with the assistance of semantic knowledge by simultaneously taking both visual modality and semantic concepts of categories into account. We propose a Continual few-shot learning algorithm with Semantic knowledge Regularization (CoSR) for adapting to the distribution changes of visual prototypes through a Transformer-based prototype adaptation mechanism. Specifically, the original visual prototypes from the backbone are fed into the well-designed Transformer with corresponding semantic concepts, where the semantic concepts are extracted from all categories. The semantic-level regularization forces the categories with similar semantics to be closely distributed, while the opposite ones are constrained to be far away from each other. The semantic regularization improves the model’s ability to distinguish between new and old categories, thus significantly mitigating the catastrophic forgetting problem in continual few-shot learning. Extensive experiments on CIFAR100, miniImageNet, CUB200 and an industrial dataset with long-tail distribution demonstrate the advantages of our CoSR model compared with state-of-the-art methods.