Edge-labeling Graph Neural Network for Few-shot Learning

CVPR 2019 Jongmin Kim Taesup Kim Sungwoong Kim Chang D. Yoo

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... (read more)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Few-Shot Image Classification Mini-Imagenet 5-way (5-shot) EGNN + Transduction Accuracy 76.37 # 33
Image Classification Tiered ImageNet 5-way (5-shot) EGNN+Transduction Accuracy 80.15 # 1

Methods used in the Paper


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