Richard Zhang

Research Scientist
Adobe Research
San Francisco, CA

rizhang at
[GitHub] [Google Scholar] [Resume/CV]

My research interests are in computer vision, machine learning, deep learning, graphics, and image processing. I obtained a PhD at UC Berkeley, advised by Prof. Alexei (Alyosha) Efros. I obtained BS and MEng degrees from Cornell University in ECE. I often collaborate with academic researchers, either through internships or university collaboration.


[Dec 2019] See our new work on detecting CNN-generated images below.
[Nov 2019] I presented our "Detecting Photoshop" ICCV19 work at Adobe MAX (5 min), on stage with John Mulaney (aka Peter Porker/Spider-Ham)!
[Oct 2019] Thank you Oxford and UCL for hosting me.
[Oct 2019] This video shows interactive colorization in Photoshop Elements 2020, based on our SIGGRAPH 2017 work.
[Sept 2019] See our new work on interactive sketch to image synthesis below.
[Jun 2019] See our new work on detecting Photoshopped images below.
[May 2019] Our work on anti-aliasing convolutional networks has been accepted to ICML 2019. Try anti-aliasing your convnet here!


A Differentiable Perceptual Audio Metric Learned from Just Noticeable Differences
Pranay Manocha, Adam Finkelstein, Zeyu Jin, Nicholas J. Bryan, Richard Zhang, Gautham J. Mysore
In ArXiv, 2020.
[Paper] [Webpage] [GitHub] [Bibtex]
Making Convolutional Networks Shift-Invariant Again
Richard Zhang
In ICML, 2019.
[Paper] [Webpage] [GitHub] [Talk] [Slides (127mb)] [Poster] [Bibtex]
CNN-generated images are surprisingly easy to spot...for now
Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, Alexei A. Efros
In ArXiv, 2019.
[Paper] [Webpage] [GitHub] [Bibtex]
Detecting Photoshopped Faces by Scripting Photoshop
Sheng-Yu Wang, Oliver Wang, Andrew Owens, Richard Zhang, Alexei A. Efros
In ICCV, 2019.
[Paper] [Webpage] [GitHub] [Video] [Poster] [Adobe Max] [Adobe Blog] [Bibtex]
Interactive Sketch & Fill: Multiclass Sketch-to-Image Translation
Arnab Ghosh, Richard Zhang, Puneet Dokania, Oliver Wang, Alexei A. Efros, Philip H.S. Torr, Eli Shechtman In ICCV, 2019.
[Paper] [Webpage] [GitHub] [Video] [Bibtex]
Deep Parametric Shape Predictions using Distance Fields
Dmitriy Smirnov, Matthew Fisher, Vladimir G. Kim, Richard Zhang, Justin Solomon
In ArXiv, 2019.
[Paper] [Webpage] [Bibtex]
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, Oliver Wang
In CVPR, 2018.
[Paper] [Webpage] [GitHub] [Poster] [Adobe Blog] [Two Min Papers] [Bibtex]
Stochastic Adversarial Video Prediction
Alex X. Lee, Richard Zhang, Frederik Ebert, Pieter Abbeel, Chelsea Finn, Sergey Levine
In ArXiv, 2018.
[Paper] [Webpage] [GitHub] [Bibtex]
Toward Multimodal Image-to-Image Translation
Jun-Yan Zhu, Richard Zhang, Deepak Pathak, Trevor Darrell, Alexei A. Efros, Oliver Wang, Eli Shechtman
In NIPS, 2017.
[Paper] [Webpage] [GitHub] [Video (YouTube)(mp4)] [Poster] [Two Min Papers] [Bibtex]
Real-Time User-Guided Image Colorization with Learned Deep Priors
Richard Zhang*, Jun-Yan Zhu*, Phillip Isola, Xinyang Geng, Angela S. Lin, Tianhe Yu, Alexei A. Efros
(*indicates equal contribution)
In SIGGRAPH, 2017.
[Paper] [Webpage] [Fastforward] [Talk] [Video (YouTube)(mp4)] [PSE 2020] [GitHub] [Slides (141mb)] [Bibtex]
Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction
Richard Zhang, Phillip Isola, Alexei A. Efros
In CVPR, 2017.
[Paper] [Webpage] [GitHub] [Poster] [Seminar Talk] [Bibtex]
Colorful Image Colorization
Richard Zhang, Phillip Isola, Alexei A. Efros
In ECCV, 2016 (oral presentation).
[Paper] [Webpage] [GitHub] [Talk] [Slides (138mb)] [Poster] [Bibtex]
Sensor Fusion for Semantic Segmentation of Urban Scenes
Richard Zhang, Stefan Candra, Kai Vetter, Avideh Zakhor
In ICRA, 2015.
[Paper (pdf)(official)] [Slides] [Poster] [Talk] [Annotations (tar)(zip) ] [Bibtex]
Automatic Identification of Window Regions on Indoor Point Clouds Using LiDAR and Cameras
Richard Zhang, Avideh Zakhor
In WACV, 2014.
[Paper (pdf)(official)] [Bibtex]


Image Synthesis for Self-Supervised Visual Representation Learning
Richard Zhang
Spring 2018.
[Thesis] [Dissertation Talk] [Fast Forward] [Slides (396 MB)] [Bibtex]


Reviewer recognitions, CVPR 2018, NeurIPS 2019
Thesis Fast Forward, Best Presentation, SIGGRAPH 2018
Adobe Research Fellowship 2017

Student collaborators/interns

I have had the opportunity to work with some wonderful collaborators.

PhD [interns]
Taesung Park, UC Berkeley (Fellowship winner, 2020)
Arnab Ghosh, Oxford
Minyoung (Jacob) Huh, MIT
Tamar Rott Shaham, Technion (Fellowship winner, 2020)
Peiye Zhuang, UIUC
Dima Smirnov, MIT
Noa Fish, Tel Aviv

Masters [interns]
Seungjoo Yoo, Korea Univ (WIT scholarship winner, 2019)

PhD [university collaborators]
Pranay Manocha, Princeton
Rawan Alghofaili, George Mason
Alvin Wan, UC Berkeley

Undergrad [university collaborators]
Sheng-Yu Wang, UC Berkeley
Xin Qin, now @ USC
Hemang Jangle
Angela S. Lin, now @ UT Austin
Xinyang Geng, now @ UC Berkeley
Tianhe Yu, now @ Stanford
Stefan A. Candra


Introduction to Artificial Intelligence (CS 188), UC Berkeley
Graduate Student Instructor (GSI) with Prof. Anca Dragan
Spring 2017

Computer Vision (CS 280), UC Berkeley
Graduate Student Instructor (GSI) with Prof. Alexei A. Efros, Prof. Trevor Darrell
Spring 2016

Introduction to Circuits (ECE 2100), Cornell University
Teaching Assistant (TA) with Prof. Alyosha Molnar
Spring 2010

My Name

Confused by the contents of this page? Well, you may have been looking for Professor Richard Zhang or Professor Richard Zhang. My Chinese name is 章睿嘉.