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 …

Deep learning (DL) is being successfully applied across multiple domains, yet these models
learn in a most artificial way: they require large quantities of labeled data to grasp even …

In image-to-image translation, each patch in the output should reflect the content of the
corresponding patch in the input, independent of domain. We propose a straightforward …

You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

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 …

Despite the success of Generative Adversarial Networks (GANs) in image synthesis,
applying trained GAN models to real image processing remains challenging. Previous …

Image restoration algorithms are typically evaluated by some distortion measure (eg PSNR,
SSIM, IFC, VIF) or by human opinion scores that quantify perceived perceptual quality. In this …

Image-based virtual try-on systems for fitting new in-shop clothes into a person image have
attracted increasing research attention, yet is still challenging. A desirable pipeline should …

Distinguishing manipulated from real images is becoming increasingly difficult as new
sophisticated image forgery approaches come out by the day. Naive classification …

State-of-the-art methods in the image-to-image translation are capable of learning a
mapping from a source domain to a target domain with unpaired image data. Though the …