論文閱讀筆記《Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea》
核心思想 本文提出一種頗具野心的元學習算法用於解決小樣本學習任務，題目中涵蓋了三個關鍵詞Model-Agnostic——無模型限制的，即該算法可以應用於多種類型的網絡模型和任務類型；Meta-Learning——元學習，即該算法不是學習得到一種具體的模型，而是學習如何進行學習（learn to learn）；Fast Adaptation——快速適應，即該算法能夠在少量樣本，有限迭代次數條件下
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相關文章
- 1. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea 論文筆記
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