DisRot: boosting the generalization capability of few-shot learning via knowledge distillation and self-supervised learning

Abstract

Few-shot learning (FSL) aims to adapt quickly to new categories with limited samples. Despite significant progress in utilizing meta-learning for solving FSL tasks, challenges such as overfitting and poor generalization still exist. Building upon the demonstrated significance of powerful feature representation, this work proposes disRot, a novel two-strategy training mechanism, which combines knowledge distillation and rotation prediction task for the pre-training phase of transfer learning. Knowledge distillation enables shallow networks to learn relational knowledge contained in deep networks, while the self-supervised rotation prediction task provides class-irrelevant and transferable knowledge for the supervised task. Simultaneous optimization for these two tasks allows the model learn generalizable and transferable feature embedding. Extensive experiments on the miniImageNet and FC100 datasets demonstrate that disRot can effectively improve the generalization ability of the model and is comparable to the leading FSL methods.