论文阅读笔记《Few-Shot Learning with Graph Neural Networks》
核心思想 本文提出一种采用图神经网络（Graph Neural Network，GNN）的小样本学习算法，将距离度量由欧式空间转移到非欧空间中，其核心思想还是将有标签支持样例的标签信息传递到无标签的查询样例上去，这种信息的传递可以看做一个图模型根据输入的图像和标签给出的后验推断。那么具体是如何实现的呢？首先，作者将输入数据集划分为三部分 其中 ( x 1 . . . x s ) (x_1...
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相关文章
- 1. 论文阅读笔记《Meta-Learning with Memory-Augmented Neural Networks》
- 2. Few-Shot Learning with Graph Neural Networks笔记
- 3. Few-Shot Learning with Graph Neural Networks
- 4. GRAPH ATTENTION NETWORKS--论文阅读笔记
- 5. [2018][ICLR]FEW-SHOT LEARNING WITH GRAPH NEURAL NETWORKS 论文笔记
- 6. SuperGlue: Learning Feature Matching with Graph Neural Networks 2019 论文笔记
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- 8. 【论文笔记】Learning Multi-view Camera Relocalization with Graph Neural Networks（CVPR2020）
- 9. GRNet: Learning Multi-view Camera Relocalization with Graph Neural Networks CVPR 2020 论文笔记
- 10. Machine Learning & Deep Learning 论文阅读笔记