論文閱讀筆記《Learning to propagate labels: Transductive propagation network for few-shot learning》
核心思想 本文提出一種採用直推式傳播網絡（Transductive Propagation Network，TPN）的小樣本學習算法。在介紹本文之前，我們首先了解一下什麼是直推式學習（Transductive Learning），我們常見的有監督學習方法其實屬於歸納學習（Inductive Learning），也就是訓練集是帶有真實標籤的，而測試集是不帶有真實標籤的，且二者之間不存在重合的部分
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相關文章
- 1. 論文閱讀筆記《Learning to Compare: Relation Network for Few-Shot Learning》
- 2. 論文閱讀筆記《DPGN: Distribution Propagation Graph Network for Few-shot Learning》
- 3. Machine Learning & Deep Learning 論文閱讀筆記
- 4. 論文閱讀筆記《Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning》
- 5. 論文閱讀筆記《Edge-Labeling Graph Neural Network for Few-shot Learning》
- 6. 論文閱讀筆記《Meta-SGD: Learning to Learn Quickly for Few-Shot Learning》
- 7. 【論文閱讀】Learning Deconvolution Network for Semantic Segmentation
- 8. 論文閱讀筆記《Learning Embedding Adaptation for Few-Shot Learning》
- 9. 論文《Learning both Weights and Connections for Efficient Neural Network》閱讀筆記
- 10. Learning to Compare: Relation Network for Few-Shot Learning 論文筆記