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

Recognized as a realistic image generator, Generative Adversarial Network (GAN) occupies
a progressive section in deep learning. Using generative modeling, the underlying …

Mountain roads are a source of important basic geographic data used in various fields. The
automatic extraction of road images through high-resolution remote sensing imagery using …

Generative Adversarial Networks (GANs) have been widely applied in different scenarios
thanks to the development of deep neural networks. The original GAN was proposed based …

Abstract Generative Adversarial Networks (GANs) have recently attracted considerable
attention in the AI community due to their ability to generate high-quality data of significant …

Zero-shot Learning (ZSL) classification categorizes or predicts classes (labels) that are not
included in the training set (unseen classes). Recent works proposed different semantic …

Abstract Generative Adversarial Networks (GAN) have shown great potential in not only
producing acceptable realizations of geologically complex models but also successfully …

Since their introduction in 2014 Generative Adversarial Networks (GANs) have been
employed successfully in many areas such as image processing, computer vision, medical …

Conventional predictive Artificial Neural Networks (ANNs) commonly employ deterministic
weight matrices; therefore, their prediction is a point estimate. Such a deterministic nature in …

We propose a generative adversarial network (GAN) that introduces an evaluator module
using pretrained networks. The proposed model, called a score-guided GAN (ScoreGAN), is …