论文阅读笔记《Variational Prototyping-Encoder: One-Shot Learning with Prototypical Images》
核心思想 本文提出一种利用变分自动编码器（VAE）生成原型图像（Prototypical Images），并利用最近邻算法解决小样本的图标或标志分类问题的算法。整个算法思想很简单，首先作者指出实际中我们拍摄采集到的图片通常都会有背景模糊，形状或光照干扰等因素，用这些图片做小样本分类的话自然效果很差。于是作者提出了原型图像的概念（Prototypical Images与原型网络 Prototyp
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相关文章
- 1. Variational Prototyping-Encoder: One-Shot Learning with Prototypical Images 论文笔记
- 2. 论文阅读 Prototypical Networks for Few-shot Learning
- 3. 论文：Variational Graph Auto-Encoders阅读笔记
- 4. 论文阅读笔记 Prototypical Networks for Few-shot Learning
- 5. 论文阅读笔记《Few-Shot Learning with Localization in Realistic Settings》
- 6. Machine Learning & Deep Learning 论文阅读笔记
- 7. 论文阅读笔记《Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders》
- 8. 论文阅读笔记《Few-Shot Learning with Graph Neural Networks》
- 9. 论文阅读笔记《Baby Steps Towards Few-Shot Learning with Multiple Semantics》
- 10. 论文笔记：Prototypical Networks for Few-shot Learning