Hybrid Attention-Based Prototypical Networks for Noisy Few-Shot Relation Classification （2019 AAAI）
Hybrid Attention-Based Prototypical Networks for Noisy Few-Shot Relation Classification （2019 AAAI） Idea：爲了解決few-shot learning 中易受噪聲實例影響這一問題，該論文提出了一種基於原形網絡的混合attention網絡。該模型設計了實例級別和特徵級別的attention機制，分別
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相關文章
- 1. 論文筆記：Hybrid Attention-Based Prototypical Networks for Noisy Few-Shot Relation Classification
- 2. Reinforcement Learning for Relation Classification from Noisy Data
- 3. Appearance-and-Relation Networks for Video Classification
- 4. Prototypical Networks for Few-shot Learning
- 5. 論文筆記：AAAI 2019 Graph Convolutional Networks for Text Classification
- 6. Reinforcement Learning for Relation Classification from Noisy Data閱讀筆記
- 7. 論文閱讀 Prototypical Networks for Few-shot Learning
- 8. prototypical networks for few-shot learning論文
- 9. Relation Networks for Object Detection解讀
- 10. Relation Distillation Networks for Video Object Detection