Learn distributed GAN with Temporary Discriminators

• URL: http://arxiv.org/abs/2007.09221v1
• Date: Fri, 17 Jul 2020 20:45:57 GMT
• Title: Learn distributed GAN with Temporary Discriminators
• Authors: Hui Qu, Yikai Zhang, Qi Chang, Zhennan Yan, Chao Chen, Dimitris Metaxas
• Abstract summary: We propose a method for training distributed GAN with sequential temporary discriminators. We show our design of loss function indeed learns the correct distribution with provable guarantees.
• Score: 16.33621293935067
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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