FEW-SHOT LEARNING WITH GRAPH NEURAL NETWORKS 論文筆記
論文鏈接：FEW-SHOT LEARNING WITH GRAPH NEURAL NETWORKS Idea: Few-shot 學習不依靠於規則化擴展數據集來補償缺少有監督數據對模型帶來的影響，而是受啓發與人類學習去探索相似任務在空間上的分佈情況。 GNN網絡實際是CNN網絡在非歐幾里得空間上的擴展。 Problem Set-up 以下的定義可以應用於semi-supervised，few-
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相關文章
- 1. [2018][ICLR]FEW-SHOT LEARNING WITH GRAPH NEURAL NETWORKS 論文筆記
- 2. SuperGlue: Learning Feature Matching with Graph Neural Networks 2019 論文筆記
- 3. 論文閱讀筆記《Few-Shot Learning with Graph Neural Networks》
- 4. 【論文筆記】Learning Multi-view Camera Relocalization with Graph Neural Networks（CVPR2020）
- 5. GRNet: Learning Multi-view Camera Relocalization with Graph Neural Networks CVPR 2020 論文筆記
- 6. Few-Shot Learning with Graph Neural Networks筆記
- 7. 論文筆記——NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING
- 8. 論文筆記《Neural Architecture Search With Reinforcement Learning》
- 9. 【論文筆記】NEURAL COMBINATORIAL OPTIMIZATION WITH REINFORCEMENT LEARNING
- 10. FEW-SHOT LEARNING WITH GRAPH NEURAL NETWORK