J
Jun-Yan Zhu
Researcher at Adobe Systems
Publications - 101
Citations - 59863
Jun-Yan Zhu is an academic researcher from Adobe Systems. The author has contributed to research in topics: Computer science & Generative model. The author has an hindex of 49, co-authored 96 publications receiving 42462 citations. Previous affiliations of Jun-Yan Zhu include University of California & University of California, Berkeley.
Papers
More filters
Proceedings ArticleDOI
Image-to-Image Translation with Conditional Adversarial Networks
TL;DR: Conditional adversarial networks are investigated as a general-purpose solution to image-to-image translation problems and it is demonstrated that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks.
Proceedings ArticleDOI
Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks
TL;DR: CycleGAN as discussed by the authors learns a mapping G : X → Y such that the distribution of images from G(X) is indistinguishable from the distribution Y using an adversarial loss.
Posted Content
Image-to-Image Translation with Conditional Adversarial Networks
TL;DR: Conditional Adversarial Network (CA) as discussed by the authors is a general-purpose solution to image-to-image translation problems, which can be used to synthesize photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks.
Posted Content
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
TL;DR: This work presents an approach for learning to translate an image from a source domain X to a target domain Y in the absence of paired examples, and introduces a cycle consistency loss to push F(G(X)) ≈ X (and vice versa).
Proceedings ArticleDOI
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
TL;DR: In this paper, a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs) is presented.