Learn Distributed GAN with Temporary Discriminators
Abstract
In this work, we propose a method for training distributed GAN with sequential temporary discriminators. Our proposed method tackles the challenge of training GAN in the federated learning manner: How to update the generator with a flow of temporary discriminators? We apply our proposed method to learn a self-adaptive generator with a series of local discriminators from multiple data centers. We show our design of loss function indeed learns the correct distribution with provable guarantees. The empirical experiments show that our approach is capable of generating synthetic data which is practical for real-world applications such as training a segmentation model. Our TDGAN Code is available at: https://github.com/huiqu18/TDGAN-PyTorch.
Cite
Text
Qu et al. "Learn Distributed GAN with Temporary Discriminators." Proceedings of the European Conference on Computer Vision (ECCV), 2020. doi:10.1007/978-3-030-58583-9_11Markdown
[Qu et al. "Learn Distributed GAN with Temporary Discriminators." Proceedings of the European Conference on Computer Vision (ECCV), 2020.](https://mlanthology.org/eccv/2020/qu2020eccv-learn/) doi:10.1007/978-3-030-58583-9_11BibTeX
@inproceedings{qu2020eccv-learn,
title = {{Learn Distributed GAN with Temporary Discriminators}},
author = {Qu, Hui and Zhang, Yikai and Chang, Qi and Yan, Zhennan and Chen, Chao and Metaxas, Dimitris},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2020},
doi = {10.1007/978-3-030-58583-9_11},
url = {https://mlanthology.org/eccv/2020/qu2020eccv-learn/}
}