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.
H. Qu, Y. Zhang and Q. Chang—Equal contribution.
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Acknowledgement
We thank anonymous reviewers for helpful comments. The research of Chao Chen is partially supported by NSF IIS-1855759, CCF-1855760 and IIS-1909038. The research of Dimitris Metaxas is partially supported by NSF CCF-1733843, IIS-1763523, CNS-1747778, and IIS-1703883.
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Qu, H., Zhang, Y., Chang, Q., Yan, Z., Chen, C., Metaxas, D. (2020). Learn Distributed GAN with Temporary Discriminators. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12372. Springer, Cham. https://doi.org/10.1007/978-3-030-58583-9_11
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