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：为了解决few-shot learning 中易受噪声实例影响这一问题，该论文提出了一种基于原形网络的混合attention网络。该模型设计了实例级别和特征级别的attention机制，分别
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相关文章
- 1. 论文笔记：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. 论文笔记：AAAI 2019 Graph Convolutional Networks for Text Classification
- 6. Reinforcement Learning for Relation Classification from Noisy Data阅读笔记
- 7. 论文阅读 Prototypical Networks for Few-shot Learning
- 8. prototypical networks for few-shot learning论文
- 9. Relation Networks for Object Detection解读
- 10. Relation Distillation Networks for Video Object Detection