This is the official implementation of EAGAN: Efficient Two-stage Evolutionary Architecture Search for GANs. We introduce a novel NAS framework, namely EAGAN, to alleviate the instability when searching GANs. Our EAGAN decouples the search into two stages, where stage-1 searches G with a fixed D and adopts the many-to-one training strategy, and stage-2 searches D with the optimal G found in stage-1 and adopts the one-to-one training strategy and the weight-resetting strategy to enhance the stability of GAN training.

The framework of the proposed method:

git clone https://github.com/marsggbo/EAGAN
cd EAGAN

We recommend using Anaconda to manage the python environment:

conda create -n EAGAN python=3.8
conda activate EAGAN
pip install -r requirements.txt

If you find this work useful for your research, please kindly cite our paper:

@inproceedings{ECCV2022EAGAN,
  title={EAGAN: Efficient Two-stage Evolutionary Architecture Search for GANs},
  author={Xin He, Guohao Ying, Bin Gao, Bo Han, and Xiaowen Chu.},
  booktitle={European Conference on Computer Vision (ECCV)},
  year={2022}
}

Thanks NVIDIA AI TECHNOLOGY CENTER (NVAITC) for providing GPU clusters to support our work.