Title: REPRESENTATIVE PROTOTYPE WITH CONSTRASTIVE LEARNING FOR SEMI-SUPENVISED FEW-SHOT CLASSIFICATION. Abstract: Few-shot learning aims to learn novel classes in the dataset with few samples per class, which is a very challenging task. To mitigate this issue, the prior work obtain representative prototypes with semantic embeddin based on prototypical networks. While the above methods do not meet the requirement of few-shot learning, which requires abundant labeled samples. Therefore, We propose a new model framework to get representative prototypes with semi-supervised learning. Specifically, we introduces the dataset containing unlabeled samples to assist training the model. More importantly, to fully utilize these unlabeled samples, we adopt conditional variational autoencoder to construct more representative prototypes. Simultaneously, we develop novel contrastive loss to improve the model generalization ability. We evaluate our method on miniImageNet and tieredImageNet benchmarks for both 1-shot and 5-shot settings and achieve better performance over the state-of-the-art semisupervised few-shot method.