Jun-Yan Zhu

Adobe Inc.
0000-0001-8504-3410
https://people.csail.mit.edu/junyanz/
Publications54
h-index35
Citations29,555
Highly Influential Citations4,762
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Image-to-Image Translation with Conditional Adversarial Networks
TLDR
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. Expand
“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告
Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks
TLDR
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). Expand
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
TLDR
A new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs) is presented, which significantly outperforms existing methods, advancing both the quality and the resolution of deep image synthesis and editing. Expand
Semantic Image Synthesis With Spatially-Adaptive Normalization
TLDR
S spatially-adaptive normalization is proposed, a simple but effective layer for synthesizing photorealistic images given an input semantic layout that allows users to easily control the style and content of image synthesis results as well as create multi-modal results. Expand
CyCADA: Cycle-Consistent Adversarial Domain Adaptation
TLDR
A novel discriminatively-trained Cycle-Consistent Adversarial Domain Adaptation model that adapts representations at both the pixel-level and feature-level, enforces cycle-consistency while leveraging a task loss, and does not require aligned pairs is proposed. Expand
Toward Multimodal Image-to-Image Translation
TLDR
This work aims to model a distribution of possible outputs in a conditional generative modeling setting that helps prevent a many-to-one mapping from the latent code to the output during training, also known as the problem of mode collapse. Expand
Video-to-Video Synthesis
TLDR
This paper proposes a novel video-to-video synthesis approach under the generative adversarial learning framework, capable of synthesizing 2K resolution videos of street scenes up to 30 seconds long, which significantly advances the state-of-the-art of video synthesis. Expand
Generative Visual Manipulation on the Natural Image Manifold
TLDR
This paper proposes to learn the natural image manifold directly from data using a generative adversarial neural network, and defines a class of image editing operations, and constrain their output to lie on that learned manifold at all times. Expand
Real-time user-guided image colorization with learned deep priors
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… Expand
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