PROGRESSIVE POINT TO SET METRIC LEARNING FOR SEMI-SUPERVISED FEW-SHOT CLASSIFICATION

Few-shot learning aims to learn models that can generalize to unseen tasks from very few annotated samples of available tasks. The performance of few-shot learning is greatly affected by the number of samples per class. The massive unlabeled data can help to boost the performance of few shot learning models. In this paper, we propose a novel progressive point to set metric learning (PPSML) model for semisupervised few-shot classification. The distance metric is defined for an image of the query set to a class of the support set by point to set distance. A self-training strategy is designed to select the samples locally or globally with high confidence and use these samples to progressively update the point to set distance. Experiments on benchmark datasets show that our proposed PPSML significantly improves the accuracy of few shot classification and outperforms the state-of-the-art semisupervised few-shot learning methods.