Model Agnostic Meta Learning (MAML) implemented in Flax, the neural network library for JAX.

This repository implements a MAML example for sinusoid regression in Flax. The idea of MAML is to learn the initial weight values of a model that can quickly adapt to new tasks. For more information, check the paper.

This implementation uses only default Flax components like flax.nn.Model and flax.nn.Module, showing that this kind of optimization-based Meta Learning algorithms can easily be implemented in Flax/JAX.

It is based on the MAML implementation in JAX by Eric Jang and updated to use Flax components. I have only implemented the sinusoid example so far, but I intend to add the Omniglot example too.

There is also an implementation of a model that fits just to one sinusoid, without meta learning, useful to see the difference between the two approaches. This approach is implemented in main_wo_maml.py.

Just run python main.py to train MAML for fast adaptation to sinusoid regression tasks.

If you use this code in your work please cite the original paper:

@inproceedings{finn2017model,
  title={Model-agnostic meta-learning for fast adaptation of deep networks},
  author={Finn, Chelsea and Abbeel, Pieter and Levine, Sergey},
  booktitle={Proceedings of the 34th International Conference on Machine Learning-Volume 70},
  pages={1126--1135},
  year={2017},
  organization={JMLR. org}
}