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 …

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

Deep generative models allow for photorealistic image synthesis at high resolutions. But for
many applications, this is not enough: content creation also needs to be controllable. While …

In recent years, deep learning has garnered tremendous success in a variety of application
domains. This new field of machine learning has been growing rapidly and has been …

“Any AI smart enough to pass a Turing test is smart enough to know to fail it.”–*** Ian
McDonald Neural networks were developed to simulate the human nervous system for …

Unsupervised image-to-image translation is an important and challenging problem in
computer vision. Given an image in the source domain, the goal is to learn the conditional …

Deep learning has demonstrated tremendous success in variety of application domains in
the past few years. This new field of machine learning has been growing rapidly and applied …

We present a new method for synthesizing high-resolution photo-realistic images from
semantic label maps using conditional generative adversarial networks (conditional GANs) …

Although the performance of person Re-Identification (ReID) has been significantly boosted,
many challenging issues in real scenarios have not been fully investigated, eg, the complex …

Although Generative Adversarial Networks (GANs) have shown remarkable success in
various tasks, they still face challenges in generating high quality images. In this paper, we …