論文閱讀筆記《Meta-Learning of Neural Architectures for Few-Shot Learning》
核心思想 本文提出一種基於參數優化的小樣本學習算法（MetaNAS）。本文最重要的改進就是將神經架構搜索（neural architecture search，NAS）引入到小樣本學習算法中，簡單地理解就是MAML和Reptile等元學習算法，是在確定網絡結構的基礎上，通過元訓練的方式獲得較好的初始化參數，而本文引入NAS後，不僅要對初始化參數進行學習，而且要對網絡結構參數進行學習。爲了實現這
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相關文章
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