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

Project README

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.

Open Source Agenda is not affiliated with "Maml" Project. README Source: cbfinn/maml
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