Edge-Labeling based Directed Gated Graph Network for Few-shot Learning

TL;DR

This paper introduces a novel directed gated graph network (DGGN) for few-shot learning that uses edge-labeling and gated recurrent units to improve similarity computation between nodes, outperforming traditional CNN-based methods.

Contribution

The paper proposes a new DGGN model with gated node aggregation and improved GRU-based edge update modules for enhanced few-shot learning performance.

Findings

DGGN achieves comparable performance to state-of-the-art methods on benchmark datasets.

The model effectively updates node similarities using gated recurrent units.

Edge-labeling enhances the learning of node relationships in few-shot tasks.

Abstract

Existing graph-network-based few-shot learning methods obtain similarity between nodes through a convolution neural network (CNN). However, the CNN is designed for image data with spatial information rather than vector form node feature. In this paper, we proposed an edge-labeling-based directed gated graph network (DGGN) for few-shot learning, which utilizes gated recurrent units to implicitly update the similarity between nodes. DGGN is composed of a gated node aggregation module and an improved gated recurrent unit (GRU) based edge update module. Specifically, the node update module adopts a gate mechanism using activation of edge feature, making a learnable node aggregation process. Besides, improved GRU cells are employed in the edge update procedure to compute the similarity between nodes. Further, this mechanism is beneficial to gradient backpropagation through the GRU sequence across layers. Experiment results conducted on two benchmark datasets show that our DGGN achieves a comparable performance to the-state-of-art methods.