Metallic

Metallic is a meta-learning library based on PyTorch.

Different from other meta-learning libraries, Metallic tries to:

• Provide a clean, lightweight and modularized toolbox for setting up meta-learning experiments quickly with the least amount of code.

• For gradient-based meta-learning methods (like MAML), support more optimizers instead of SGD only using higher.

The library is work in progress.

Features

Algorithms

The supported interface algorithms currently include:

Gradient-based

• Model-Agnostic Meta-Learning (MAML), including first-order version (FOMAML)

• Reptile

• Minibatch Proximal Update

• Almost No Inner Loop (ANIL)

Metric-based

• Matching Networks

• Prototypical Networks

Datasets

The supported datasets currently include:

• Omniglot

• Mini-ImageNet

Installation

git clone https://github.com/Renovamen/metallic.git
cd metallic
python setup.py install

or

pip install git+https://github.com/Renovamen/metallic.git --upgrade

Github

https://github.com/Renovamen/metallic

API Documentation

• metallic.data
• metallic.data.datasets
• metallic.data.benchmarks
• metallic.data.transforms
• metallic.models
• metallic.metalearners
• metallic.trainer
• metallic.functional
• metallic.utils

Acknowledgements

Metallic is highly inspired by the following awesome libraries:

• learn2learn

• Torchmeta

• higher

License

Metallic is MIT licensed, see the LICENSE file for more details.