Generative adversarial networks (GANs) provide a way to learn deep representations without
extensively annotated training data. They achieve this by deriving backpropagation signals …

… To overcome those, generative adversarial networks have been introduced in this study.
The common datasets for generation of 3D objects using GAN are 2D-to-3D deformable …

Generative adversarial networks (GAN) has received great attention and made great progress
since its emergence in 2014. In this paper, we focus on the theoretical achievements of …

Generative Adversarial Networks (GANs) have received wide attention in the machine
learning field for their potential to learn high-dimensional, complex real data distribution. …

… development of generative adversarial networks, along with … and comparisons of these
networks for data oversampling. Even … As there are few overview papers available on the topic of …

… as Generative Adversarial Networks (GANs), generative models … We will try to provide an
overview of this said challenge, what … This section will give a brief overview of the most relevant …

The study of generative adversarial networks (GAN) provides a new approach and framework
for computer vision and makes an outstanding choice for cross-domain image translation …

… 2.1 Generative adversarial network A conventional generative adversarial network is
composed of two neural network modules, ie, a generator and a discriminator. The generator can …

… Abstract—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 …

… in order to perform such generative tasks. It does this by … This study gives a brief overview
of generative adversarial networks its … and the variations of generative adversarial network. …