LEAD: Min-Max Optimization from a Physical Perspective

This is the code associated with the paper "LEAD: Min-Max Optimization from a Physical Perspective". If you find this code useful please cite us:

@article{
askari hemmat2023lead,
title={{LEAD}: Min-Max Optimization from a Physical Perspective},
author={Reyhane Askari Hemmat and Amartya Mitra and Guillaume Lajoie and Ioannis Mitliagkas},
journal={Transactions on Machine Learning Research},
issn={2835-8856},
year={2023},
url={https://openreview.net/forum?id=vXSsTYs6ZB},
note={Featured Certification}
}

Video describing the paper: https://www.youtube.com/watch?v=EfwIc0GXb8E

Blogpost: https://reyhaneaskari.github.io/LEAD.html

For any questions about the code please create an issue.

The code requires pytorch and tensorflow. But TF is only used for computing the inception score.

Acknowledgement

DCGAN code adpoted from https://github.com/Zeleni9/pytorch-wgan
ResNet code adopted from https://github.com/GongXinyuu/sngan.pytorch
SGA code implemented based on https://github.com/deepmind/symplectic-gradient-adjustment/blob/master/Symplectic_Gradient_Adjustment.ipynb
Extra-Adam optim source code from https://github.com/GauthierGidel/Variational-Inequality-GAN
CGD optim source code from https://github.com/devzhk/Implicit-Competitive-Regularization

ReyhaneAskari/Least_action_dynamics_minmax

LEAD: Min-Max Optimization from a Physical Perspective

Acknowledgement