Few-Shot Class-Incremental Learning Based on Feature Distribution Learning

This paper focuses on a very challenging problem：few-shot class-incremental learning for deep neural networks, where the deep neural network model can gradually learn new knowledge from a small number of samples without forgetting the learned knowledge. To balance the model’s memory of old knowledge and learning of new knowledge, it proposes a few-shot class-incremental learning method based on feature distribution learning. First, it learns the model on the base classes to obtain a well-performing feature extractor and take the feature distribution information as the learned knowledge. Then, it maps the learned knowledge together with the features of the novel classes into a low-dimensional subspace to review old knowledge and learn new knowledge uniformly. Finally, within the subspace, it also generates classification weight initializations for each novel class to improve the adaptability of the model to novel classes. Extensive experiments show that the method can effectively alleviate the model’s forgetting of learned knowledge and improve the model’s adaptability to new knowledge. © 2023 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.