Generative Adversarial Networks (GANs) have achieved a great success in unsupervised learning. Despite its remarkable empirical performance, there are limited theoretical studies on the statistical properties of GANs. This paper provides approximation and statistical guarantees of GANs for the estimation of data distributions that have densities in a H\"{o}lder space. Our main result shows that, if the generator and discriminator network architectures are properly chosen, GANs are consistent estimators of data distributions under strong discrepancy metrics, such as the Wasserstein-1 distance. Furthermore, when the data distribution exhibits low-dimensional structures, we show that GANs are capable of capturing the unknown low-dimensional st...
We show that every $d$-dimensional probability distribution of bounded support can be generated thro...
Generative adversarial networks (GANs), a class of distribution-learning methods based on a two-play...
Generative adversarial networks (GANs) have proven effective in modeling distributions of high-dimen...
This paper studies how well generative adversarial networks (GANs) learn probability distributions f...
We study how well generative adversarial networks (GAN) learn probability distributions from finite ...
In this paper, we study Wasserstein Generative Adversarial Networks (WGAN) using GroupSort neural ne...
We investigate the training and performance of generative adversarial networks using the Maximum Mea...
Generative adversarial networks (GANs) learn a target probability distribution by optimizing a gener...
Generative adversarial nets (GANs) are very successful at modeling distributions from given samples,...
We extend PAC-Bayesian theory to generative models and develop generalization bounds for models base...
Generative Adversarial Networks (GANs) have drawn great attention recently since they are the powerf...
Generative Adversarial Networks (GANs) have become one of the most successful and popular generative...
Within a broad class of generative adversarial networks, we show that discriminator optimization pro...
Generative adversarial networks (GANs) are a class of machine-learning models that use adversarial t...
Generative Adversarial Networks (GANs) were proposed in 2014 as a new method efficiently producing r...
We show that every $d$-dimensional probability distribution of bounded support can be generated thro...
Generative adversarial networks (GANs), a class of distribution-learning methods based on a two-play...
Generative adversarial networks (GANs) have proven effective in modeling distributions of high-dimen...
This paper studies how well generative adversarial networks (GANs) learn probability distributions f...
We study how well generative adversarial networks (GAN) learn probability distributions from finite ...
In this paper, we study Wasserstein Generative Adversarial Networks (WGAN) using GroupSort neural ne...
We investigate the training and performance of generative adversarial networks using the Maximum Mea...
Generative adversarial networks (GANs) learn a target probability distribution by optimizing a gener...
Generative adversarial nets (GANs) are very successful at modeling distributions from given samples,...
We extend PAC-Bayesian theory to generative models and develop generalization bounds for models base...
Generative Adversarial Networks (GANs) have drawn great attention recently since they are the powerf...
Generative Adversarial Networks (GANs) have become one of the most successful and popular generative...
Within a broad class of generative adversarial networks, we show that discriminator optimization pro...
Generative adversarial networks (GANs) are a class of machine-learning models that use adversarial t...
Generative Adversarial Networks (GANs) were proposed in 2014 as a new method efficiently producing r...
We show that every $d$-dimensional probability distribution of bounded support can be generated thro...
Generative adversarial networks (GANs), a class of distribution-learning methods based on a two-play...
Generative adversarial networks (GANs) have proven effective in modeling distributions of high-dimen...
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