Fuzzy Graph Neural Network for Few-Shot Learning

원문 초록

Recent works have shown that graph neural networks (GNNs) can substantially improve the performance of fewshot learning benefitting from their natural ability to learn interclass uniqueness and intra-class commonality. However, previous GNN methods have not achieved satisfactory performance due to the absence of a strong relational inductive bias which determines how entities interact and are isolated. In this paper, inspired by the fuzzy theory, we propose a novel meta-learning method called Fuzzy GNN (FGNN), which obtains superior relational inductive biases in each episode, for few-shot learning. Specifically, we employ an edge-focused GNN to perform the edge prediction by iteratively updating the edge-labels. According to the output of edge prediction, we design a fuzzy membership function to achieve more exact relationship representations for node classification. The parameters of the FGNN are learned by episodic training with mixed loss including node-label and edgelabel. Extensive experimental evaluation clearly demonstrates the effectiveness of FGNN. The results show that our method achieves state-of-the-art performance and a significant improvement over other GNN methods on two few-shot learning benchmarks.