[Few-shot learning][2.4] MAML: Model-Agnostic Meta-Learning
Channel: Max Patacchiola
Date: 2020-02-20

In this episode I am giving an overview of MAML (Model-Agnostic Meta-Learning) which has been introduced in 2017 at ICML. I provide a step-by-step explanation of the algorithm and an overview of the pytorch implementation. MAML is particularly interesting because it allows estimating a set of generic meta-parameters which can be rapidly adapted to solve specific tasks. This can be done in a fully differentiable way.

Paper: https://arxiv.org/pdf/1703.03400.pdf
GitHub (tensorflow): https://github.com/cbfinn/maml
GitHub (pytorch): https://github.com/tristandeleu/pytorch-meta/tree/master/examples/maml

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My Blog: https://mpatacchiola.github.io/blog/
GitHub: https://github.com/mpatacchiola
Linkedin: https://www.linkedin.com/in/massimiliano-patacchiola-94579b140/

#machinelearning #deeplearning #metalearning #MAML #fewshotlearning #neuralnetworks