论文阅读笔记《Principal characteristic networks for few-shot learning》
小样本学习&元学习经典论文整理||持续更新 核心思想 本文提出一种基于度量学习的小样本分类算法（PC-Net），其中最重要的改进点是作者提出了本质特征（Principal Characteristic ）的概念。作者首先指出PN网络中使用取平均值的方法来计算原型（类别表征）的方式是不合适的，因为这样无法区分各个支持集样本的特征向量对于原型的贡献区别，这种做法其实是认为每个样本的贡献是均等的。作
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相关文章
- 1. Machine Learning & Deep Learning 论文阅读笔记
- 2. 论文阅读 Prototypical Networks for Few-shot Learning
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