Class-Discriminative Feature Embedding For Meta-Learning Based Few-Shot Classification

Although deep learning-based approaches have been very effective in solving problems with plenty of labeled data, they suffer in tackling problems for which labeled data are scarce. In few-shot classification, the objective is to train a classifier from only a handful of labeled examples in a support set. In this paper, we propose a few-shot learning framework based on structured margin loss which takes into account the global structure of the support set in order to generate a highly discriminative feature space where the features from distinct classes are well separated in clusters. Moreover, in our meta-learning-based framework, we propose a context-aware query embedding encoder for incorporating support set context into query embedding and generating more discriminative and task-dependent query embeddings. The task-dependent features help the meta-learner to learn a distribution over tasks more effectively. Extensive experiments based on few-shot, zero-shot and semi-supervised learning on three benchmarks show the advantages of the proposed model compared to state-of-the-art.