Generative adversarial networks (GANs) have been advancing and gaining tremendous
interests from both academia and industry. With the development of wireless technologies, a …

A recent technical breakthrough in the domain of machine learning is the discovery and the
multiple applications of Generative Adversarial Networks (GANs). Those generative models …

The generative adversarial network (GAN) is usually built from the centralized, independent
identically distributed (iid) training data to generate realistic-like instances. In real-world …

Generative Adversarial Networks (GANs) are a type of deep learning techniques that have
shown remarkable success in generating realistic images, videos, and other types of data …

This work studies training generative adversarial networks under the federated learning
setting. Generative adversarial networks (GANs) have achieved advancement in various …

Generative Adversarial Networks (GANs) is a novel class of deep generative models that
has recently gained significant attention. GANs learn complex and high-dimensional …

Recently introduced generative adversarial networks (GANs) have been shown numerous
promising results to generate realistic samples. In the last couple of years, it has been …

Abstract Generative Adversarial Networks (GANs) are a powerful class of generative models
in the deep learning community. Current practice on large-scale GAN training utilizes large …

Generative adversarial network (GANs) is one of the most important research avenues in the
field of artificial intelligence, and its outstanding data generation capacity has received wide …

Generative adversarial networks (GANs) have been effective for learning generative models
for real-world data. However, accompanied with the generative tasks becoming more and …