A spectral filtering approach to represent exemplars for visual few-shot classification.
Journal:
Neural networks : the official journal of the International Neural Network Society
Published Date:
Apr 30, 2025
Abstract
Prototype is widely used to represent internal structure of category for few-shot learning, which was proposed as a simple inductive bias to address the issue of overfitting. However, for categories where prototypes do not exist or are difficult to represent, prototype representation may lead to underfitting, and these categories are better represented by exemplars. In this paper, we propose Shrinkage Exemplar Networks (SENet) for few-shot classification. In SENet, categories are represented by samples that shrink towards prototypes, appropriately describing both the presence and absence of prototypes. The shrinkage of samples is achieved through appropriate spectral filtering. Furthermore, a shrinkage exemplar loss is proposed to replace the widely used cross entropy loss for capturing the information of individual shrinkage samples. Several experiments were conducted on miniImageNet, tiered-ImageNet and CIFAR-FS datasets. The experimental results demonstrate the effectiveness of our proposed method. The source code is publicly available at: https://github.com/zhangtao2022/SENet.
