Prototype Rectification for Few-Shot Learning

Few-shot learning is a challenging problem that requires a model to recognize novel classes with few labeled data. In this paper, we aim to find the expected prototypes of the novel classes, which have the maximum cosine similarity with the samples of the same class. Firstly, we propose a cosine similarity based prototypical network to compute basic prototypes of the novel classes from the few samples. A bias diminishing module is further proposed for prototype rectification since the basic prototypes computed in the low-data regime are biased against the expected prototypes. In our method, the intra-class bias and the cross-class bias are diminished to modify the prototypes. Then we give a theoretical analysis of the impact of the bias diminishing module on the expected performance of our method. We conduct extensive experiments on four few-shot benchmarks and further analyze the advantage of the bias diminishing module. The bias diminishing module brings in significant improvement by a large margin of 3% to 9% in general. Notably, our approach achieves state-of-the-art performance on miniImageNet (70.31% in 1-shot and 81.89% in 5-shot) and tieredImageNet (78.74% in 1-shot and 86.92% in 5-shot), which demonstrates the superiority of the proposed method.