论文阅读笔记《Learning to propagate labels: Transductive propagation network for few-shot learning》
核心思想 本文提出一种采用直推式传播网络（Transductive Propagation Network，TPN）的小样本学习算法。在介绍本文之前，我们首先了解一下什么是直推式学习（Transductive Learning），我们常见的有监督学习方法其实属于归纳学习（Inductive Learning），也就是训练集是带有真实标签的，而测试集是不带有真实标签的，且二者之间不存在重合的部分
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相关文章
- 1. 论文阅读笔记《Learning to Compare: Relation Network for Few-Shot Learning》
- 2. 论文阅读笔记《DPGN: Distribution Propagation Graph Network for Few-shot Learning》
- 3. Machine Learning & Deep Learning 论文阅读笔记
- 4. 论文阅读笔记《Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning》
- 5. 论文阅读笔记《Edge-Labeling Graph Neural Network for Few-shot Learning》
- 6. 论文阅读笔记《Meta-SGD: Learning to Learn Quickly for Few-Shot Learning》
- 7. 【论文阅读】Learning Deconvolution Network for Semantic Segmentation
- 8. 论文阅读笔记《Learning Embedding Adaptation for Few-Shot Learning》
- 9. 论文《Learning both Weights and Connections for Efficient Neural Network》阅读笔记
- 10. Learning to Compare: Relation Network for Few-Shot Learning 论文笔记