Edge-Labeling Graph Neural Network for Few-Shot Learning

Jongmin Kim, Taesup Kim, Sungwoong Kim, Chang D. Yoo; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 11-20

Abstract


In this paper, we propose a novel edge-labeling graph neural network (EGNN), which adapts a deep neural network on the edge-labeling graph, for few-shot learning. The previous graph neural network (GNN) approaches in few-shot learning have been based on the node-labeling framework, which implicitly models the intra-cluster similarity and the inter-cluster dissimilarity. In contrast, the proposed EGNN learns to predict the edge-labels rather than the node-labels on the graph that enables the evolution of an explicit clustering by iteratively updating the edge-labels with direct exploitation of both intra-cluster similarity and the inter-cluster dissimilarity. It is also well suited for performing on various numbers of classes without retraining, and can be easily extended to perform a transductive inference. The parameters of the EGNN are learned by episodic training with an edge-labeling loss to obtain a well-generalizable model for unseen low-data problem. On both of the supervised and semi-supervised few-shot image classification tasks with two benchmark datasets, the proposed EGNN significantly improves the performances over the existing GNNs.

Related Material


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[bibtex]
@InProceedings{Kim_2019_CVPR,
author = {Kim, Jongmin and Kim, Taesup and Kim, Sungwoong and Yoo, Chang D.},
title = {Edge-Labeling Graph Neural Network for Few-Shot Learning},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}