We provided Theano implementations for Evolutionary Generative Adversarial Networks (E-GAN). Meanwhile, we are working on new Pytorch-based implementations.
- Clone this repo:
git clone git@github.com:WANG-Chaoyue/EvolutionaryGAN.git
cd EvolutionaryGAN- Install Theano 1.0.0+, lassagne 0.2+ and other dependencies (requirements.txt).
The proposed E-GAN was trained on two synthesis dataset and three real-world datasets. Among them, the two mixture Gaussians datasets are adopted from here. For three real-world datasets, you should download them first from Cifar-10, LSUN bedroom, and CelebA, and then moving them into datasets. Then,
cd dataset
python dataset.pyThe download CelebA or LSUN bedroom datasets can be converted to 'hdf5' files.
- Train a model (take cifar-10 as an example)
cd cifar10
python train_cifar10.pyNote that related hpyer-parameters can be configured within 'train_cifar10.py'
vim train_cifar10.pyIf you use this code for your research, please cite our paper.
@article{wang2018evolutionary,
title={Evolutionary Generative Adversarial Networks},
author={Wang, Chaoyue and Xu, Chang and Yao, Xin and Tao, Dacheng},
journal={arXiv preprint arXiv:1803.00657},
year={2018}
}
Evolving Generative Adversarial Networks | Two Minute Papers #242