A Regularized Conditional GAN for Posterior Sampling in Inverse Problems [arXiv]

Setup

See docs/setup.md for basic environment setup instructions.

Reproducing our Results

MRI

See docs/mri.md for instructions on how to setup and reproduce our MRI results.

Extending the Code

See docs/new_applications.md for basic instructions on how to extend the code to your application.

Questions and Concerns

If you have any questions, or run into any issues, don't hesitate to reach out at bendel.8@osu.edu.

TODO

• Migrate to PyTorch Lightning
• Reimplement MRI rcGAN
• Update MRI experiment to R=8
• Reimplement inpainting rcGAN
• Extend to super resolution

References

This repository contains code from the following works, which should be cited:

@article{zbontar2018fastmri,
  title={fastMRI: An open dataset and benchmarks for accelerated MRI},
  author={Zbontar, Jure and Knoll, Florian and Sriram, Anuroop and Murrell, Tullie and Huang, Zhengnan and Muckley, Matthew J and Defazio, Aaron and Stern, Ruben and Johnson, Patricia and Bruno, Mary and others},
  journal={arXiv preprint arXiv:1811.08839},
  year={2018}
}

@article{devries2019evaluation,
  title={On the evaluation of conditional GANs},
  author={DeVries, Terrance and Romero, Adriana and Pineda, Luis and Taylor, Graham W and Drozdzal, Michal},
  journal={arXiv preprint arXiv:1907.08175},
  year={2019}
}

@inproceedings{Karras2020ada,
  title={Training Generative Adversarial Networks with Limited Data},
  author={Tero Karras and Miika Aittala and Janne Hellsten and Samuli Laine and Jaakko Lehtinen and Timo Aila},
  booktitle={Proc. NeurIPS},
  year={2020}
}

@inproceedings{zhao2021comodgan,
  title={Large Scale Image Completion via Co-Modulated Generative Adversarial Networks},
  author={Zhao, Shengyu and Cui, Jonathan and Sheng, Yilun and Dong, Yue and Liang, Xiao and Chang, Eric I and Xu, Yan},
  booktitle={International Conference on Learning Representations (ICLR)},
  year={2021}
}

@misc{zeng2022github,
    howpublished = {Downloaded from \url{https://github.com/zengxianyu/co-mod-gan-pytorch}},
    month = sep,
    author={Yu Zeng},
    title = {co-mod-gan-pytorch},
    year = 2022
}

Citation

If you find this code helpful, please cite our paper:

@journal{bendel2022arxiv,
  author = {Bendel, Matthew and Ahmad, Rizwan and Schniter, Philip},
  title = {A Regularized Conditional {GAN} for Posterior Sampling in Inverse Problems},
  year = {2022},
  journal={arXiv:2210.13389}
}