元学习——从MAML到MAML++
作者：凯鲁嘎吉 - 博客园 http://www.cnblogs.com/kailugaji/
Few-shot learning领域最近有了实质性的进展。这些进步大多来自于将few-shot learning作为元学习问题。Model-Agnostic Meta-Learning (MAML)是目前利用元学习进行few-shot learning的最佳方法之一。MAML简单，优雅，功能强大，但是它有很多问题，比如对神经网络结构非常敏感，经常导致训练时不稳定，需要费力的超参数搜索来稳定训练和实现高泛化，并且在训练和推理时间上都非常昂贵的计算。在文"How to train your MAML"中，对MAML进行了各种改进，不仅稳定了系统，而且大幅度提高了MAML的泛化性能、收敛速度和计算开销。所提方法称之为MAML++。本博文首先介绍什么是元学习，经典的Model-Agnostic Meta-Learning的定义与执行过程，进而说明MAML面临的缺点与挑战，针对这些问题，进行相应改进，从而得到MAML++。
1. Meta Learning (Learn to Learn)
2. Black-Box Adaption vs Optimization-Based Approach
3. MAML
MAML Computation Graph
4. MAML Problems
5. MAML++
MAML with Multi-Step Loss Computation Graph
6. 参考文献
[1] Finn, C., Abbeel, P. & Levine, S. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. ICML 2017. Code: https://github.com/cbfinn/maml, https://github.com/dragen1860/MAML-Pytorch
Finn个人主页：https://ai.stanford.edu/~cbfinn/
[2] Antoniou, A., Edwards, H., & Storkey, A. How to train your MAML. ICLR 2019. Code: https://github.com/AntreasAntoniou/HowToTrainYourMAMLPytorch
[3] How to train your MAML: A step by step approach · BayesWatch https://www.bayeswatch.com/2018/11/30/HTYM/
[4] CS 330: Deep Multi-Task and Meta Learning http://web.stanford.edu/class/cs330/
[5] Meta-Learning: Learning to Learn Fast https://lilianweng.github.io/lil-log/2018/11/30/meta-learning.html
