元学习——MAML、Reptile与ANIL
作者：凯鲁嘎吉 - 博客园 http://www.cnblogs.com/kailugaji/
之前介绍过元学习——从MAML到MAML++，这次在此基础上进一步探讨，深入了解MAML的本质，引出MAML高效学习的原因究竟是快速学习，学到一个很厉害的初始化参数，还是特征重用，初始化参数与最终结果很接近？因此得到ANIL(Almost No Inner Loop)，随后我们阅读了Reptile——On first-order meta-learning algorithms，另一种元学习方法，并比较了MAML、Reptile与模型预训练之间的区别。
1. Meta Learning vs Machine Learning
2. MAML vs Model Pre-Training
3. MAML——Feature Reuse
4. MAML vs ANIL
5. Reptile: On First-Order Meta-Learning Algorithms
6. MAML, Model Pre-Training, and Reptile
7. 参考文献
[1] GitHub - Fafa-DL/Lhy_Machine_Learning: 李宏毅2021春季机器学习课程课件及作业 https://github.com/Fafa-DL/Lhy_Machine_Learning
[2] Finn, C., Abbeel, P. & Levine, S. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. ICML 2017.
[3] Aniruddh Raghu, Maithra Raghu, Samy Bengio, Oriol Vinyals, Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML, ICLR, 2020.
[4] Nichol A, Achiam J, Schulman J. On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999, 2018.
[5] Nichol A, Schulman J. Reptile: a scalable metalearning algorithm. arXiv preprint arXiv:1803.02999, 2018, 2(3): 4.
[6] Reptile: A Scalable Meta-Learning Algorithm. OpenAI. https://openai.com/blog/reptile/
[7] 一文入门元学习（Meta-Learning）（附代码） - 知乎 https://zhuanlan.zhihu.com/p/136975128
文章来源: 博客园
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