论文阅读笔记《Meta-Learning of Neural Architectures for Few-Shot Learning》
核心思想 本文提出一种基于参数优化的小样本学习算法（MetaNAS）。本文最重要的改进就是将神经架构搜索（neural architecture search，NAS）引入到小样本学习算法中，简单地理解就是MAML和Reptile等元学习算法，是在确定网络结构的基础上，通过元训练的方式获得较好的初始化参数，而本文引入NAS后，不仅要对初始化参数进行学习，而且要对网络结构参数进行学习。为了实现这
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相关文章
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