Jun-Yan Zhu

E326792

Jun-Yan Zhu is a computer scientist and researcher known for his influential work in computer vision and generative models, particularly in image-to-image translation.

Try in SPARQL Jump to: Surface forms Statements Referenced by

All labels observed (1)

Label Occurrences
Jun-Yan Zhu canonical 3

Statements (49)

Predicate Object
instanceOf academic
computer scientist
researcher
awardReceived best paper awards at major vision or graphics conferences
citations highly cited in computer vision and generative modeling literature
coAuthorOf CycleGAN paper
contributedTo GAN-based image manipulation
controllable image generation
methods for unpaired image-to-image translation
degree PhD in computer science
educatedAt CMU
surface form: Carnegie Mellon University

Massachusetts Institute of Technology
fieldOfWork computer vision
generative models
image synthesis
image-to-image translation
machine learning
givesTalkAt major AI and vision conferences
hasAcademicAdvisor Antonio Torralba
hasAcademicPublicationType conference papers
journal articles
preprints
hasAffiliation Adobe Research
Meta AI
surface form: Facebook AI Research

Computer Science and Artificial Intelligence Laboratory (CSAIL)
surface form: MIT Computer Science and Artificial Intelligence Laboratory

OpenAI
Robotics Institute, Carnegie Mellon University
surface form: Robotics Institute at Carnegie Mellon University

School of Computer Science at Carnegie Mellon University
hasEmployer CMU
surface form: Carnegie Mellon University
hasRole university faculty member
knownFor CycleGAN
surface form: Cycle-consistent adversarial networks (CycleGAN)
nationality Chinese
notableFor generative adversarial networks applications
image-to-image translation research
occupation computer science researcher
professor
positionHeld assistant professor
associate professor
publishedIn IEEE Computer Society Conference on Computer Vision and Pattern Recognition
surface form: CVPR

European Conference on Computer Vision
surface form: ECCV

IEEE International Conference on Computer Vision
surface form: ICCV

ICLR
NeurIPS
ACM SIGGRAPH
surface form: SIGGRAPH
researchInterest 3D vision
creative AI
unsupervised learning for images
visual recognition
supervises PhD students in computer vision and machine learning

Referenced by (3)

Full triples — surface form annotated when it differs from this entity's canonical label.

Alexei Efros notableStudent Jun-Yan Zhu
CycleGAN introducedBy Jun-Yan Zhu
Phillip Isola coAuthor Jun-Yan Zhu