Skip to search formSkip to main contentSkip to account menu

Generative adversarial networks

Generative adversarial networks are a neural network framework where a generative model is estimated via an adversarial process. Initially developed… 
Wikipedia (opens in a new tab)

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2020
2020
We propose a loss function for generative adversarial networks (GANs) using Renyi information measures with parameter $\alpha… 
Review
2019
Review
2019
I review unsupervised or self-supervised neural networks playing minimax games in game-theoretic settings. (i) Adversarial… 
2019
2019
Generative adversarial networks (GANs) have shown impressive power in the field of machine learning. Traditional GANs have… 
Review
2018
Review
2018
Generative adversarial networks (GANs) and other adversarial methods are based on a game-theoretical perspective on joint… 
2018
2018
Conditional GANs are at the forefront of natural image synthesis. The main drawback of such models is the necessity for labelled… 
2018
2018
In this paper, we propose Generative Adversarial Network (GAN) architectures that use Capsule Networks for image-synthesis. Based… 
2018
2018
Traditional approaches for semantic image synthesis mainly focus on text descriptions while ignoring the related structures and… 
2018
2018
Generating complex discrete distributions remains as one of the challenging problems in machine learning. Existing techniques for… 
2017
2017
The deep generative adversarial networks (GAN) recently have been shown to be promising for different computer vision… 
2017
2017
Generative Adversarial Networks (GANs) have shown impressive performance in generating photo-realistic images. They fit…