Skip to content
/ maml Public
forked from tianheyu927/maml

Code for "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks"

License

Notifications You must be signed in to change notification settings

ojasjoshi/maml

 
 

Repository files navigation

Model-Agnostic Meta-Learning

This repo contains code accompaning the paper, Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks (Finn et al., ICML 2017). It includes code for running the few-shot supervised learning domain experiments, including sinusoid regression, Omniglot classification, and MiniImagenet classification.

For the experiments in the RL domain, see this codebase.

Dependencies

This code requires the following:

  • python 2.* or python 3.*
  • TensorFlow v1.0+

Data

For the Omniglot and MiniImagenet data, see the usage instructions in data/omniglot_resized/resize_images.py and data/miniImagenet/proc_images.py respectively.

Usage

To run the code, see the usage instructions at the top of main.py.

Contact

To ask questions or report issues, please open an issue on the issues tracker.

About

Code for "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks"

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 99.1%
  • Shell 0.9%