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Global Reconstructed and Contrastive Prototypical Network for Few-shot Learning

Few-shot learning aims to learn classification with only a few labeled samples. Prototypical network has become the focus of few-shot learning, but the prototypical network has some problems such as the lack of representativeness of the extracted feature space and the deviation of the prototype representation due to too few samples. In response to these problems, we propose Global Reconstructed and Contrastive Prototypical Network (GR-CPN), a method based on the losses of global reconstruction and Contrastive learning. The contributions of our work can be summarized as follows: 1) Inspired by the prototypical network, our approach combine image reconstruction and contrastive learning to learn the embedding space, in which embedding feature strengthens the representation of the object itself rather than just the distinguishability between samples of different classes; 2) We label query samples with pseudo-label to expand the support set so that intra-class bias caused by too few support samples for each class is reduced in the embedding space. The results of experiments on miniImagenet and FC100 datasets show that our GR-CPN is a simple, high-efficiency approach that can extract the representative features of the object and outperforms the state-of-the-art.