Add to ORKGThis paper studies how well generative adversarial networks (GANs) learn probability distributions from finite samples. Our main results establish the convergence rates of GANs under a collection of integral probability metrics defined through H\"older classes, including the Wasserstein distance as a special case. We also show that GANs are able to adaptively learn data distributions with low-dimensional structures or have H\"older densities, when the network architectures are chosen properly. In particular, for distributions concentrated around a low-dimensional set, we show that the learning rates of GANs do not depend on the high ambient dimension, but on the lower intrinsic dimension. Our analysis is based on a new oracle inequality dec...
A fundamental and still largely unsolved question in the context of Generative Adversarial Networks ...
International audienceGenerative Adversarial Networks (GANs) are a class of generative algorithms th...
Generative adversarial networks (GANs), a class of distribution-learning methods based on a two-play...
We study how well generative adversarial networks (GAN) learn probability distributions from finite ...
Generative Adversarial Networks (GANs) have achieved a great success in unsupervised learning. Despi...
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) have drawn great attention recently since they are the powerf...
Generative Adversarial Networks (GANs) have become one of the most successful and popular generative...
Generative Adversarial Networks (GANs) are recently invented generative models which can produce hig...
Generative adversarial networks (GANs) have proven effective in modeling distributions of high-dimen...
Any binary classifier (or score-function) can be used to define a dissimilarity between two distrib...
Generative Adversarial Networks (GANs) provide a new way of generating data. In this thesis, a stric...
We extend PAC-Bayesian theory to generative models and develop generalization bounds for models base...
Generative adversarial nets (GANs) are very successful at modeling distributions from given samples,...
A fundamental and still largely unsolved question in the context of Generative Adversarial Networks ...
International audienceGenerative Adversarial Networks (GANs) are a class of generative algorithms th...
Generative adversarial networks (GANs), a class of distribution-learning methods based on a two-play...
We study how well generative adversarial networks (GAN) learn probability distributions from finite ...
Generative Adversarial Networks (GANs) have achieved a great success in unsupervised learning. Despi...
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) have drawn great attention recently since they are the powerf...
Generative Adversarial Networks (GANs) have become one of the most successful and popular generative...
Generative Adversarial Networks (GANs) are recently invented generative models which can produce hig...
Generative adversarial networks (GANs) have proven effective in modeling distributions of high-dimen...
Any binary classifier (or score-function) can be used to define a dissimilarity between two distrib...
Generative Adversarial Networks (GANs) provide a new way of generating data. In this thesis, a stric...
We extend PAC-Bayesian theory to generative models and develop generalization bounds for models base...
Generative adversarial nets (GANs) are very successful at modeling distributions from given samples,...
A fundamental and still largely unsolved question in the context of Generative Adversarial Networks ...
International audienceGenerative Adversarial Networks (GANs) are a class of generative algorithms th...
Generative adversarial networks (GANs), a class of distribution-learning methods based on a two-play...