论文阅读笔记《Edge-Labeling Graph Neural Network for Few-shot Learning》
核心思想 本文采用基于图神经网络的算法实现了小样本学习任务，先前基于GNN的方法通常是基于节点标签框架，隐式地建立类内相似性和类间差异性的模型。而本文提出的边标签图卷积神经网络（Edge-labeling Graph Neural Network，EGNN）学习预测边标签而不是节点标签，这使其能够显式地表示类内相似性和类间差异性。这样描述还是很抽象，难以理解的，下面就直接介绍本文提出的模型。图
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相关文章
- 1. 论文阅读笔记《DPGN: Distribution Propagation Graph Network for Few-shot Learning》
- 2. 粗读CVPR2019论文 Edge-Labeling Graph Neural Network for Few-shot Learning
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