Jun-Yan Zhu | Semantic Scholar

Image-to-Image Translation with Conditional Adversarial Networks

Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros

Computer Science

2016 (First Publication: 21 November 2016)

We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but… Continue Reading

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Jun-Yan Zhu, 拓海杉山

Computer Science

2017 (First Publication: 12 September 2017)

Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks

Jun-Yan Zhu, Taesung Park, Phillip Isola, Alexei A. Efros

Computer Science

2017 (First Publication: 30 March 2017)

Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs.… Continue Reading

High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Andrew Tao, Jan Kautz, Bryan Catanzaro

Computer Science

2018 (First Publication: 30 November 2017)

We present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs). Conditional GANs have… Continue Reading

CyCADA: Cycle-Consistent Adversarial Domain Adaptation

Judy Hoffman, Eric Tzeng, +5 authors Trevor Darrell

Computer Science

ICML

2018 (First Publication: 8 November 2017)

Domain adaptation is critical for success in new, unseen environments. Adversarial adaptation models applied in feature spaces discover domain invariant representations, but are difficult to… Continue Reading

Toward Multimodal Image-to-Image Translation

Jun-Yan Zhu, Richard Zhang, +4 authors Eli Shechtman

Computer Science, Mathematics

NIPS

2017 (First Publication: 1 November 2017)

Many image-to-image translation problems are ambiguous, as a single input image may correspond to multiple possible outputs. In this work, we aim to model a \emph{distribution} of possible outputs in… Continue Reading

Semantic Image Synthesis With Spatially-Adaptive Normalization

Taesung Park, Ming-Yu Liu, Ting-Chun Wang, Jun-Yan Zhu

Computer Science

2019 (First Publication: 18 March 2019)

We propose spatially-adaptive normalization, a simple but effective layer for synthesizing photorealistic images given an input semantic layout. Previous methods directly feed the semantic layout as… Continue Reading

Video-to-Video Synthesis

Ting-Chun Wang, Ming-Yu Liu, +4 authors Bryan Catanzaro

Computer Science

NeurIPS

2018 (First Publication: 20 August 2018)

We study the problem of video-to-video synthesis, whose goal is to learn a mapping function from an input source video (e.g., a sequence of semantic segmentation masks) to an output photorealistic… Continue Reading

Generative Visual Manipulation on the Natural Image Manifold

Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman, Alexei A. Efros

Computer Science

ECCV

2016 (First Publication: 12 September 2016)

Realistic image manipulation is challenging because it requires modifying the image appearance in a user-controlled way, while preserving the realism of the result. Unless the user has considerable… Continue Reading

Real-time user-guided image colorization with learned deep priors

Richard Zhang, Jun-Yan Zhu, +4 authors Alexei A. Efros

Computer Science

TOGS

2017 (First Publication: 8 May 2017)

We propose a deep learning approach for user-guided image colorization. The system directly maps a grayscale image, along with sparse, local user "hints" to an output colorization with a… Continue Reading