10 citations · 20 across the 4 of their papers we have counts for
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cs.LG2018★ 10 cited
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★ 8 cited
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…