Purpose In radiotherapy, MRI is used for target volume and organs-at-risk delineation for its
superior soft-tissue contrast as compared to CT imaging. However, MRI does not provide the …

Deep Learning is one of the newest trends in Machine Learning and Artificial Intelligence
research. It is also one of the most popular scientific research trends now-a-days. Deep …

Among the major remaining challenges for generative adversarial networks (GANs) is the
capacity to synthesize globally and locally coherent images with object shapes and textures …

We consider the single image super-resolution problem in a more general case that the low-
/high-resolution pairs and the down-sampling process are unavailable. Different from …

We propose a general framework for unsupervised domain adaptation, which allows deep
neural networks trained on a source domain to be tested on a different target domain without …

We propose a method for semi-supervised semantic segmentation using an adversarial
network. While most existing discriminators are trained to classify input images as real or …

Most existing zero-shot learning methods consider the problem as a visual semantic
embedding one. Given the demonstrated capability of Generative Adversarial Networks …

With the help of convolutional neural networks (CNN), the single image super-resolution
problem has been widely studied. Most of these CNN based methods focus on learning a …

The creation or manipulation of facial appearance through deep generative approaches,
known as DeepFake, have achieved significant progress and promoted a wide range of …

Abstract While Generative Adversarial Networks (GANs) have seen huge successes in
image synthesis tasks, they are notoriously difficult to adapt to different datasets, in part due …