Few-shot learning via weighted prototypes from graph structure

Few-shot learning is attracting extensive research because of its ability to classify only a few co-trainable samples. Current few-shot learning approaches focus on learning class prototypes representation to solve problems by a simple averaging approach, but this approach ignores intra-class differences. In this paper, we propose a new weighted prototype network for few-shot learning. Our model consists of two modules, feature extraction and prototype modification. We first construct graphs from the embeddings obtained from the feature extraction module. Then we fed these graphs into graph neural networks in order to explore the contribution of each sample to its class prototype from the graph structure. The experimental results on three benchmark datasets show that our proposed model is comparable to the state-of-the-art few-shot learning approach.

Automated summary

This paper proposes a new weighted prototype network for few-shot learning, which explores the contribution of each sample to its class prototype using graph neural networks. Experimental results show that the proposed model performs comparably to state-of-the-art approaches on three benchmark datasets.