論文閱讀筆記《Edge-Labeling Graph Neural Network for Few-shot Learning》
核心思想 本文采用基於圖神經網絡的算法實現了小樣本學習任務，先前基於GNN的方法通常是基於節點標籤框架，隱式地建立類內相似性和類間差異性的模型。而本文提出的邊標籤圖卷積神經網絡（Edge-labeling Graph Neural Network，EGNN）學習預測邊標籤而不是節點標籤，這使其能夠顯式地表示類內相似性和類間差異性。這樣描述還是很抽象，難以理解的，下面就直接介紹本文提出的模型。圖
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相關文章
- 1. 論文閱讀筆記《DPGN: Distribution Propagation Graph Network for Few-shot Learning》
- 2. 粗讀CVPR2019論文 Edge-Labeling Graph Neural Network for Few-shot Learning
- 3. 論文《Learning both Weights and Connections for Efficient Neural Network》閱讀筆記
- 4. 論文閱讀筆記《Few-Shot Learning with Graph Neural Networks》
- 5. 論文筆記--Edge-Labeling Graph Neural Network for Few-shot Learning--CVPR2019
- 6. 論文筆記：Edge-Labeling Graph Neural Network for Few shot Learning
- 7. 論文閱讀總結(Text Level Graph Neural Network for Text Classification)
- 8. Graph Neural Networks for User Identity Linkage 2019 論文閱讀筆記
- 9. Machine Learning & Deep Learning 論文閱讀筆記
- 10. 論文閱讀筆記《Meta-Learning of Neural Architectures for Few-Shot Learning》