Bidirectional Gated Edge-Labeling Graph Recurrent Neural Network for Few-Shot Learning

Abstract

Many existing graph-based methods for few-shot learning problem focused on either separately learning node features or edge features or simply utilizing graph convolution, failing to fully retain or exploit graph structure information. In this article, we proposed a bidirectional gated edge-labeling graph recurrent neural network (bi-GEGRN) which adopts both edge-labeling graph framework and graph convolution operation in the meta-learning scheme. We modified the gated graph neural network to adjacency matrix generator-based bidirectional formation which is able to process sequence graph data in two directions and then organically combined it with edge-labeled graph framework to cyclically upgrade features meanwhile aggregate graph structure information. In view of the excellent aggregating capability of graph convolution and good performance of the alternately cyclic update strategy, bi-GEGRN improves the information transferring between tasks in meta learning. To verify the validity and universality on both supervised and semi-supervised regimes, extensive experiments were conducted on three few-shot benchmark data sets and bi-GEGRN showed a good performance.