Abstract Generative Adversarial Networks (GANs) have been extremely successful in
various application domains such as computer vision, medicine, and natural language …

Many image processing, computer graphics, and computer vision problems can be treated
as image-to-image translation tasks. Such translation entails learning to map one visual …

We present the full-resolution correspondence learning for cross-domain images, which aids
image translation. We adopt a hierarchical strategy that uses the correspondence from …

We present a general framework for exemplar-based image translation, which synthesizes a
photo-realistic image from the input in a distinct domain (eg, semantic segmentation mask …

Deep learning has produced state-of-the-art results for a variety of tasks. While such
approaches for supervised learning have performed well, they assume that training and …

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 …

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 …

Learning inter-domain map**s from unpaired data can improve performance in structured
prediction tasks, such as image segmentation, by reducing the need for paired data …

Scarcity of labeled data has motivated the development of semi-supervised learning
methods, which learn from large portions of unlabeled data alongside a few labeled …

Unsupervised image-to-image translation is a central task in computer vision. Current
translation frameworks will abandon the discriminator once the training process is …