Generative adversarial networks (GANs) have recently become a hot research topic;
however, they have been studied since 2014, and a large number of algorithms have been …

The basic Generative Adversarial Networks (GAN) model is composed of the input vector,
generator, and discriminator. Among them, the generator and discriminator are implicit …

Abstract Generative Adversarial Networks (GANs) have been extremely successful in
various application domains such as computer vision, medicine, and natural language …

In 2018, clinics and hospitals were hit with numerous attacks leading to significant data
breaches and interruptions in medical services. An attacker with access to medical records …

With the advent of generative adversarial networks, synthesizing images from text
descriptions has recently become an active research area. It is a flexible and intuitive way for …

Retouching can significantly elevate the visual appeal of photos, but many casual
photographers lack the expertise to do this well. To address this problem, previous works …

Many image processing, computer graphics, and computer vision problems can be treated
as image-to-image translation tasks. Such translation entails learning to map one visual …

Abstract Recently, deep learning (DL)‐based methods for the generation of synthetic
computed tomography (sCT) have received significant research attention as an alternative to …

Despite the recency of their conception, Generative Adversarial Networks (GANs) constitute
an extensively-researched machine learning sub-field for the creation of synthetic data …

Abstract Domain knowledge of face shapes and structures plays an important role in face
inpainting. However, general inpainting methods focus mainly on the resolution of …