Mar 2, 2017 · We show that training of generative adversarial network (GAN) may not have good generalization properties; eg, training may appear successful.
Abstract. It is shown that training of generative adversarial network (GAN) may not have good generalization properties; e.g., training may appear successful ...
This existence of equilibrium inspires MIX+GAN pro- tocol, which can be combined with any existing. GAN training, and empirically shown to improve some of them.
This paper makes progress on several open theoretical issues related to Generative Adversarial Networks. A definition is provided for what it means for the ...
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In this talk, we will first introduce the ba- sics of GANs and then discuss the fundamental statistical question about GANs — assuming the training can succeed ...
It is shown that training of generative adversarial network (GAN) may not have good generalization properties, and an approximate pure equilibrium exists in ...
This existence of equilibrium inspires MIX+GAN protocol, which can be combined with any existing GAN training, and empirically shown to improve some of them.
”a solution concept of a non-cooperative game involving two or more players in which each player is assumed to know the equilibrium.
Generalization is defined training of generative adversarial network (GAN), and it's shown that generalization is not guaranteed for the popular distances ...
Duration: 43:28
Posted: Mar 30, 2017
Posted: Mar 30, 2017