論文閱讀筆記《Few-Shot Learning with Graph Neural Networks》
核心思想 本文提出一種採用圖神經網絡（Graph Neural Network，GNN）的小樣本學習算法，將距離度量由歐式空間轉移到非歐空間中，其核心思想還是將有標籤支持樣例的標籤信息傳遞到無標籤的查詢樣例上去，這種信息的傳遞可以看做一個圖模型根據輸入的圖像和標籤給出的後驗推斷。那麼具體是如何實現的呢？首先，作者將輸入數據集劃分爲三部分 其中 ( x 1 . . . x s ) (x_1...
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相關文章
- 1. 論文閱讀筆記《Meta-Learning with Memory-Augmented Neural Networks》
- 2. Few-Shot Learning with Graph Neural Networks筆記
- 3. Few-Shot Learning with Graph Neural Networks
- 4. GRAPH ATTENTION NETWORKS--論文閱讀筆記
- 5. [2018][ICLR]FEW-SHOT LEARNING WITH GRAPH NEURAL NETWORKS 論文筆記
- 6. SuperGlue: Learning Feature Matching with Graph Neural Networks 2019 論文筆記
- 7. FEW-SHOT LEARNING WITH GRAPH NEURAL NETWORKS 論文筆記
- 8. 【論文筆記】Learning Multi-view Camera Relocalization with Graph Neural Networks（CVPR2020）
- 9. GRNet: Learning Multi-view Camera Relocalization with Graph Neural Networks CVPR 2020 論文筆記
- 10. Machine Learning & Deep Learning 論文閱讀筆記