https://github.com/sophiaas/gtc-invariance

Official PyTorch Implementation of "A General Framework for Robust G-Invariance in G-Equivariant Networks," NeurIPS 2023
https://github.com/sophiaas/gtc-invariance

bispectrum convolutional-neural-networks deep-learning equivariance geometric-deep-learning group-theory invariance

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

• Host: GitHub
• URL: https://github.com/sophiaas/gtc-invariance
• Owner: sophiaas
• License: mit
• Created: 2024-01-15T00:03:31.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-01-24T01:51:26.000Z (12 months ago)
• Last Synced: 2024-01-30T14:29:12.892Z (12 months ago)
• Topics: bispectrum, convolutional-neural-networks, deep-learning, equivariance, geometric-deep-learning, group-theory, invariance
• Language: Python
• Homepage: https://arxiv.org/abs/2310.18564
• Size: 30.3 KB
• Stars: 4
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# 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](https://drive.google.com/file/d/1zXDnPNlzo5uTfYo97RlKIDHstaWVQD3L/view?usp=sharing).

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](https://wandb.ai/). A user's wandb entity and project directories should be specified in `configs/logger`.

## License

This repository is licensed under the MIT License.