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Official PyTorch Implementation of "A General Framework for Robust G-Invariance in G-Equivariant Networks," NeurIPS 2023

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The $G$-Triple Correlation Layer for Robust $G$-Invariance in $G$-Equivariant Networks

This repository is the official accompaniment to A General Framework for Robust G-Invariance in G-Equivariant Networks (2023) by Sophia Sanborn and Nina Miolane, published in the Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS).

Installation

To install the requirements and package, run:

pip install -r requirements.txt
python install -e .

Datasets

To download the datasets:

  1. Download the zip file here.
  2. Place the file in the top node of this directory, i.e. in gtc-invariance/.
  3. Run:
    unzip datasets.zip
    rm -r datasets.zip
    

Training

The full set of hyperparameters and training configurations are specified in the config files in the configs/ folder. To train a model on a particular experiment, you will call the following:

scripts/run_data_agent.py --config [name of config]
scripts/run_train_agent.py --config [name of config]

The first call will generate the transformed dataset, and the second will train the model on that dataset. The config argument should be followed by the name of a particular config file from configs/experiments, e.g. o2mnist_d16_maxpool. The .py extension of the config should be excluded. Each of the configs in the configs/experiments folder combines various model, trainer, etc configs also specified in the configs folder. The scripts are set up to log the model with Weights & Biases. A user's wandb entity and project directories should be specified in configs/logger.

License

This repository is licensed under the MIT License.

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Official PyTorch Implementation of "A General Framework for Robust G-Invariance in G-Equivariant Networks," NeurIPS 2023

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