FEW-SHOT LEARNING WITH GRAPH NEURAL NETWORKS 论文笔记
论文链接：FEW-SHOT LEARNING WITH GRAPH NEURAL NETWORKS Idea: Few-shot 学习不依靠于规则化扩展数据集来补偿缺少有监督数据对模型带来的影响，而是受启发与人类学习去探索相似任务在空间上的分布情况。 GNN网络实际是CNN网络在非欧几里得空间上的扩展。 Problem Set-up 以下的定义可以应用于semi-supervised，few-
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相关文章
- 1. [2018][ICLR]FEW-SHOT LEARNING WITH GRAPH NEURAL NETWORKS 论文笔记
- 2. SuperGlue: Learning Feature Matching with Graph Neural Networks 2019 论文笔记
- 3. 论文阅读笔记《Few-Shot Learning with Graph Neural Networks》
- 4. 【论文笔记】Learning Multi-view Camera Relocalization with Graph Neural Networks（CVPR2020）
- 5. GRNet: Learning Multi-view Camera Relocalization with Graph Neural Networks CVPR 2020 论文笔记
- 6. Few-Shot Learning with Graph Neural Networks笔记
- 7. 论文笔记——NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING
- 8. 论文笔记《Neural Architecture Search With Reinforcement Learning》
- 9. 【论文笔记】NEURAL COMBINATORIAL OPTIMIZATION WITH REINFORCEMENT LEARNING
- 10. FEW-SHOT LEARNING WITH GRAPH NEURAL NETWORK