A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative AI. The concept...

The Wasserstein Generative Adversarial Network (WGAN) is a variant of generative adversarial network (GAN) proposed in 2017 that aims to "improve the stability...

variational autoencoder and generative adversarial network produced the first practical deep neural networks capable of learning generative models, as opposed...

potential samples of input variables. Generative adversarial networks are examples of this class of generative models, and are judged primarily by the...

2014, the adversarial network principle was used in a generative adversarial network (GAN) by Ian Goodfellow et al. Here the adversarial network (discriminator)...

contrast, many alternative generative modeling methods such as variational autoencoder (VAE) and generative adversarial network do not explicitly represent...

StyleGAN is a generative adversarial network (GAN) introduced by Nvidia researchers in December 2018, and made source available in February 2019. StyleGAN...

nor used gradient descent." In 2014, this adversarial principle was used in a generative adversarial network (GAN) by Ian Goodfellow et al. Here the environmental...

figure's voice being used against them. This method uses a generative adversarial network (GAN), a deep machine learning technique where two machine learning...

conditional generative model for existing information from text can be done by variational autoencoder and generative adversarial network (GAN). There...