论文标题
用临时歧视者学习分布式gan
Learn distributed GAN with Temporary Discriminators
论文作者
论文摘要
在这项工作中，我们提出了一种使用顺序临时歧视因子训练分布式GAN的方法。我们提出的方法以联邦学习方式应对培训gan的挑战：如何通过临时歧视者流动更新发电机？我们采用建议的方法来学习来自多个数据中心的一系列本地歧视者的自适应发生器。我们展示了我们的损失功能设计的设计确实可以通过可证明的保证来学习正确的分布。经验实验表明，我们的方法能够生成合成数据，这对于诸如训练分割模型之类的现实世界应用是实用的。
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.
