We have provided the pre-trained IGGAN models on CIFAR-10 and CIFAR-100 datasets for better obtaining the results we reported in the paper. The code of this module is built by ourselves based on the test codes of the DiffAug-GAN [link] and NDA-GAN [link].

PyTorch version==1.7.1.

TensorFlow 1.14 or 1.15 with GPU support (for IS and FID calculation).

Other python libs in DiffAug-GAN [link] and NDA-GAN [link].

Pre-trained IGGAN on Unconditional CIFAR-10 dataset [link]

Pre-trained IGGAN on Conditional CIFAR-10 dataset [link]

Pre-trained IGGAN on Conditional 10% CIFAR-10 dataset [link]

Pre-trained IGGAN on Unconditional CIFAR-100 dataset [link]

Pre-trained IGGAN on Conditional CIFAR-100 dataset [link]

Pre-trained IGGAN on Conditional 10% CIFAR-100 dataset [link]

To evaluate a pre-trained IGGAN model on Unconditional CIFAR-10, run the following command:

python eval.py --dataset=C10U --network='./pretrained/IGGAN_C10U.pth'

@inproceedings{zhang2024improving,
  title={Improving the Fairness of the Min-Max Game in GANs Training},
  author={Zhang, Zhaoyu and Hua, Yang and Wang, Hui and McLoone, Se{\'a}n},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={2910--2919},
  year={2024}
}