Fuzzy Graph Neural Network for Few-Shot Learning

Code

• github.com/sadbb/few-shot-fgnn
  Officialpytorch★ 12

Abstract

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.

Tasks

Reproductions