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cs.LG2018
Fictitious GAN: Training GANs with Historical Models
Hao Ge, Yin Xia, Xu Chen +2
Generative adversarial networks (GANs) are powerful tools for learning generative models. In practice, the training may suffer from lack of convergence. GANs are commonly viewed as…
cs.LG2018
Training Generative Adversarial Networks via Primal-Dual Subgradient Methods: A Lagrangian Perspective on GAN
Xu Chen, Jiang Wang, Hao Ge
We relate the minimax game of generative adversarial networks (GANs) to finding the saddle points of the Lagrangian function for a convex optimization problem, where the discrimina…