Jun-Yan Zhu

Adobe Inc.
0000-0001-8504-3410
https://people.csail.mit.edu/junyanz/
Publications56
h-index28
Citations17,222
Highly Influential Citations3,326
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  • Publications
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Image-to-Image Translation with Conditional Adversarial Networks
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… Expand
  • 5,915
  • 1264
“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告
  • 3,905
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Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks
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.… Expand
  • 2,060
  • 287
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
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… Expand
  • 1,039
  • 188
CyCADA: Cycle-Consistent Adversarial Domain Adaptation
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… Expand
  • 796
  • 119
Toward Multimodal Image-to-Image Translation
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… Expand
  • 502
  • 96
Semantic Image Synthesis With Spatially-Adaptive Normalization
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… Expand
  • 344
  • 84
Video-to-Video Synthesis
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… Expand
  • 282
  • 45
Generative Visual Manipulation on the Natural Image Manifold
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… Expand
  • 644
  • 37
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
  • 160
  • 25