Res-SVDNet: A Metric Learning Method for Few-shot Image Classification

Abstract

Metric learning based few-shot image classification has recently received much attention for simplicity and efficiency. Its performance depends highly on the feature extractor and classifier. In this paper, we propose an alternative metric learning method for few-shot image classification––Res-SVDNet. Taking the advantages of good representation and anti-overfitting, the proposed Res-SVDNet utilizes ResNet-18 architecture as the backbone to extract image features. As for the classifier, Euclidean distance is employed to meet the few-shot classification preference for simple inductive bias. Considering the requirement of Euclidean distance for orthogonality, singular value decomposition (SVD) is further involved between feature extraction and classification to improve the classification performance of Res-SVDNet. Experimental results on Mini-ImageNet and Tired-ImageNet demonstrate that the proposed Res-SVDNet outperforms the state-of-the-art methods such as prototypical networks by 2%~6%.