Meta-Learning of Neural Architectures for Few-Shot Learning

This is the implmentation for Meta-Learning of Neural Architectures for Few-Shot Learning.

Requirements and Setup

Install requiered packages.

Run

conda env create -f environment.yml

to create a new conda environment named metanas with all requiered packages and activate it.

Download the data

Download the data sets you want to use (Omniglot or miniImagenet). You can also set download=True for the data loaders in torchmeta_loader.py to use the data download provided by Torchmeta.

How to Use

Please refer to the scripts folder for examples how to use this code. E.g., for experiments on miniImagenet:

• Running meta training for MetaNAS: run_in_meta_train.sh
• Running meta testing for a checkpoint from the above meta training experiment: run_in_meta_testing.sh
• Scaling up an optimized architecture from above meta training experiment and retraining it: run_in_upscaled.sh

Purpose of this Project

This software is a research prototype, solely developed for the publication cited above. It will neither be maintained nor monitored in any way.

License

'Meta-Learning of Neural Architectures for Few-Shot Learning' is open-sourced under the AGPL-3.0 license. See the LICENSE file for details.

For a list of other open source components included in 'Meta-Learning of Neural Architectures for Few-Shot Learning', see the file 3rd-party-licenses.txt.