Deep-learning (DL) performs well in computer-vision and medical-imaging automated
decision-making applications. A bottleneck of DL stems from the large amount of labelled …

In this paper, we introduce FairFaceGAN, a fairness-aware facial Image-to-Image translation
model, mitigating the problem of unwanted translation in protected attributes (eg, gender …

Recently, image-to-image translation research has witnessed remarkable progress.
Although current approaches successfully generate diverse outputs or perform scalable …

Unsupervised image-to-image translation aims to learn a domain mapping function that
preserves the semantics of an input image while adapting its style to target domains without …

Attribute image manipulation has been a very active topic since the introduction of
Generative Adversarial Networks (GANs). Exploring the disentangled attribute space within …

Unsupervised image-to-image translation aims to learn the mapping between two visual
domains with unpaired samples. Existing works focus on disentangling domain-invariant …

Generative models have become increasingly popular in various domains to solve
challenging tasks, including image generation, dialogue generation, and story generation …

Image generation is arguably one of the most attractive, compelling, and challenging tasks
in computer vision. Among the methods which perform image generation, generative …