World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
72
Citations
26327
World Ranking
1650
National Ranking
849

Overview

Zhangyang Wang is affiliated with The University of Texas at Austin in the United States and has an extensive publication record primarily within the field of Computer Science. Their research spans multiple subfields, with a significant focus on Computer Vision and Pattern Recognition as well as Artificial Intelligence. Additional areas of study include Radiology, Nuclear Medicine and Imaging, Electrical and Electronic Engineering, and Computer Graphics and Computer-Aided Design.

Their recent published papers include:

  • EnlightenGAN: Deep Light Enhancement Without Paired Supervision, 2021, IEEE Transactions on Image Processing
  • Graph Contrastive Learning with Augmentations, 2020, arXiv (Cornell University)
  • Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study, 2020, IEEE Transactions on Image Processing
  • TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up, 2021, arXiv (Cornell University)
  • Connecting Image Denoising and High-Level Vision Tasks via Deep Learning, 2020, IEEE Transactions on Image Processing

Wang's frequent coauthors include Tianlong Chen, Gregory Holste, Zhiwen Fan, Dejia Xu, and Ajay Jaiswal, indicating ongoing collaborative efforts within their research network.

They have contributed regularly to a set of publication venues, notably:

  • arXiv (Cornell University)
  • IEEE Transactions on Image Processing
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Their work covers several main topics including:

  • Advanced Neural Network Applications
  • Adversarial Robustness in Machine Learning
  • Domain Adaptation and Few-Shot Learning
  • Generative Adversarial Networks and Image Synthesis
  • Advanced Vision and Imaging
  • Advanced Image Processing Techniques
  • Anomaly Detection Techniques and Applications

Zhangyang Wang's research contributions also span multiple publications in domains related to enhanced image processing and the application of machine learning techniques to vision tasks. Their work involves exploring neural network architectures and addressing challenges in environments with poor visibility, as well as advancing image denoising and synthesis.

Best Publications

  • EnlightenGAN: Deep Light Enhancement Without Paired Supervision

    Yifan Jiang;Xinyu Gong;Ding Liu;Yu Cheng

  • AOD-Net: All-in-One Dehazing Network

    Boyi Li;Xiulian Peng;Zhangyang Wang;Jizheng Xu

  • Benchmarking Single-Image Dehazing and Beyond

    Boyi Li;Wenqi Ren;Dengpan Fu;Dacheng Tao

  • UnitBox: An Advanced Object Detection Network

    Jiahui Yu;Yuning Jiang;Zhangyang Wang;Zhimin Cao

  • NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results

    Radu Timofte;Eirikur Agustsson;Luc Van Gool;Ming-Hsuan Yang

  • DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better

    Orest Kupyn;Tetiana Martyniuk;Junru Wu;Zhangyang Wang

  • Graph Contrastive Learning with Augmentations

    Yuning You;Tianlong Chen;Yongduo Sui;Ting Chen

  • ABD-Net: Attentive but Diverse Person Re-Identification

    Tianlong Chen;Shaojin Ding;Jingyi Xie;Ye Yuan

  • DeepAffinity: interpretable deep learning of compound-protein affinity through unified recurrent and convolutional neural networks.

    Mostafa Karimi;Di Wu;Zhangyang Wang;Yang Shen

  • Single Image Deraining: A Comprehensive Benchmark Analysis

    Siyuan Li;Xiaochun Cao;Iago Breno Araujo;Wenqi Ren

  • AutoGAN: Neural Architecture Search for Generative Adversarial Networks

    Xinyu Gong;Shiyu Chang;Yifan Jiang;Zhangyang Wang

  • Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study

    Wenhan Yang;Ye Yuan;Wenqi Ren;Jiaying Liu

  • Studying Very Low Resolution Recognition Using Deep Networks

    Zhangyang Wang;Shiyu Chang;Yingzhen Yang;Ding Liu

  • TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up

    Yifan Jiang;Shiyu Chang;Zhangyang Wang

  • Robust Video Super-Resolution with Learned Temporal Dynamics

    Ding Liu;Zhaowen Wang;Yuchen Fan;Xianming Liu

  • Can we gain more from orthogonality regularizations in training deep CNNs

    Nitin Bansal;Xiaohan Chen;Zhangyang Wang

  • When image denoising meets high-level vision tasks: a deep learning approach

    Ding Liu;Bihan Wen;Xianming Liu;Zhangyang Wang

  • D3: Deep Dual-Domain Based Fast Restoration of JPEG-Compressed Images

    Zhangyang Wang;Ding Liu;Shiyu Chang;Qing Ling

  • Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning

    Tianlong Chen;Sijia Liu;Shiyu Chang;Yu Cheng

  • The Lottery Ticket Hypothesis for Pre-trained BERT Networks

    Tianlong Chen;Jonathan Frankle;Shiyu Chang;Sijia Liu

  • Plug-and-Play Methods Provably Converge with Properly Trained Denoisers

    Ernest K. Ryu;Jialin Liu;Sicheng Wang;Xiaohan Chen

  • Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds

    Xiaohan Chen;Jialin Liu;Zhangyang Wang;Wotao Yin

  • Drawing Early-Bird Tickets: Toward More Efficient Training of Deep Networks

    Haoran You;Chaojian Li;Pengfei Xu;Yonggan Fu

Frequent Co-Authors

Thomas S. Huang
Thomas S. Huang University of Illinois at Urbana-Champaign
Shiyu Chang
Shiyu Chang University of California, Santa Barbara
Jianchao Yang
Jianchao Yang ByteDance
Zhaowen Wang
Zhaowen Wang Adobe Systems (United States)
Qing Ling
Qing Ling Sun Yat-sen University
Hailin Jin
Hailin Jin Adobe Systems (United States)
Yu Cheng
Yu Cheng Microsoft (United States)
Ji Liu
Ji Liu Facebook (United States)
Jiayu Zhou
Jiayu Zhou Michigan State University
Wotao Yin
Wotao Yin Alibaba Group (China)

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