Deep Neural Networks (DNNs) are becoming an important tool in modern computing
applications. Accelerating their training is a major challenge and techniques range from …

It is shown that training of generative adversarial network (GAN) may not have good
generalization properties; eg, training may appear successful but the trained distribution …

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 study of generative models is a promising branch of deep learning techniques, which
has been successfully applied to different scenarios, such as Artificial Intelligence and the …

While Generative Adversarial Networks (GANs) have empirically produced impressive
results on learning complex real-world distributions, recent works have shown that they …

Abstract Generative Adversarial Networks (GANs) have seen great research interest in
recent years, due to both their ability to represent structure in data and generate novel …

Generative adversarial networks (GANs) have been extremely effective in approximating
complex distributions of high-dimensional, input data samples, and substantial progress has …

Generative adversary networks (GANs) suffer from training pathologies such as instability
and mode collapse. These pathologies mainly arise from a lack of diversity in their …

Generative Adversarial Networks (GANs) are a thriving unsupervised machine learning
technique that has led to significant advances in various fields such as computer vision …

Generative Adversarial Networks (GANs) have become one of the dominant methods for
deep generative modeling. Despite their demonstrated success on multiple vision tasks …