D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 43 Citations 8,165 190 World Ranking 3948 National Ranking 2008

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Algorithm

His main research concerns Artificial intelligence, Image, Convolutional neural network, Computer vision and Algorithm. Zhangyang Wang combines subjects such as Machine learning and Pattern recognition with his study of Artificial intelligence. As a part of the same scientific family, Zhangyang Wang mostly works in the field of Image, focusing on Data mining and, on occasion, Residual and Streak.

His research in Convolutional neural network intersects with topics in Transfer of learning, Object detection and Orthogonality. His biological study spans a wide range of topics, including Visualization and Joint. His Algorithm research focuses on subjects like Benchmark, which are linked to Single image and Ground truth.

His most cited work include:

  • NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results (555 citations)
  • AOD-Net: All-in-One Dehazing Network (391 citations)
  • UnitBox: An Advanced Object Detection Network (276 citations)

What are the main themes of his work throughout his whole career to date?

His primary areas of investigation include Artificial intelligence, Machine learning, Computer vision, Deep learning and Robustness. His studies deal with areas such as Algorithm and Pattern recognition as well as Artificial intelligence. His work on Feature, Feature learning and Leverage as part of general Machine learning research is frequently linked to Context, thereby connecting diverse disciplines of science.

His Computer vision research incorporates themes from Code and Benchmark. His Deep learning research is multidisciplinary, incorporating perspectives in Transfer of learning, Visualization and Noise reduction. The concepts of his Robustness study are interwoven with issues in Adversarial system and Inference.

He most often published in these fields:

  • Artificial intelligence (73.08%)
  • Machine learning (33.22%)
  • Computer vision (17.83%)

What were the highlights of his more recent work (between 2020-2021)?

  • Artificial intelligence (73.08%)
  • Machine learning (33.22%)
  • Artificial neural network (10.84%)

In recent papers he was focusing on the following fields of study:

His primary areas of study are Artificial intelligence, Machine learning, Artificial neural network, Benchmark and Code. His research links Key with Artificial intelligence. Zhangyang Wang focuses mostly in the field of Key, narrowing it down to matters related to Computer vision and, in some cases, Usability and Glyph.

His Machine learning course of study focuses on Training set and Interpretability and Recurrent neural network. His study in Artificial neural network is interdisciplinary in nature, drawing from both Contextual image classification and Distributed computing. His Benchmark research is multidisciplinary, incorporating elements of Optimization problem, Mathematical optimization and Data mining.

Between 2020 and 2021, his most popular works were:

  • TransGAN: Two Transformers Can Make One Strong GAN (14 citations)
  • Robust Overfitting may be mitigated by properly learned smoothening (7 citations)
  • EnlightenGAN: Deep Light Enhancement Without Paired Supervision (7 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Algorithm

His primary scientific interests are in Artificial intelligence, Machine learning, Lottery, Benchmark and Pruning. His work on Artificial neural network as part of his general Artificial intelligence study is frequently connected to Architecture, thereby bridging the divide between different branches of science. Zhangyang Wang works mostly in the field of Machine learning, limiting it down to concerns involving Training set and, occasionally, Interpretability.

His Benchmark study combines topics in areas such as Contrast, Theoretical computer science and Convolutional neural network. His Overfitting study incorporates themes from Transfer of learning, Object detection and Robustness. His Deep learning research includes elements of Ground truth, Image restoration and Visualization.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

AOD-Net: All-in-One Dehazing Network

Boyi Li;Xiulian Peng;Zhangyang Wang;Jizheng Xu.
international conference on computer vision (2017)

538 Citations

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

Radu Timofte;Eirikur Agustsson;Luc Van Gool;Ming-Hsuan Yang.
computer vision and pattern recognition (2017)

491 Citations

Benchmarking Single-Image Dehazing and Beyond

Boyi Li;Wenqi Ren;Dengpan Fu;Dacheng Tao.
IEEE Transactions on Image Processing (2019)

460 Citations

UnitBox: An Advanced Object Detection Network

Jiahui Yu;Yuning Jiang;Zhangyang Wang;Zhimin Cao.
acm multimedia (2016)

384 Citations

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

Orest Kupyn;Tetiana Martyniuk;Junru Wu;Zhangyang Wang.
international conference on computer vision (2019)

218 Citations

Can we gain more from orthogonality regularizations in training deep CNNs

Nitin Bansal;Xiaohan Chen;Zhangyang Wang.
neural information processing systems (2018)

194 Citations

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

Zhangyang Wang;Ding Liu;Shiyu Chang;Qing Ling.
computer vision and pattern recognition (2016)

177 Citations

Robust Video Super-Resolution with Learned Temporal Dynamics

Ding Liu;Zhaowen Wang;Yuchen Fan;Xianming Liu.
international conference on computer vision (2017)

170 Citations

Studying Very Low Resolution Recognition Using Deep Networks

Zhangyang Wang;Shiyu Chang;Yingzhen Yang;Ding Liu.
computer vision and pattern recognition (2016)

169 Citations

ABD-Net: Attentive but Diverse Person Re-Identification

Tianlong Chen;Shaojin Ding;Jingyi Xie;Ye Yuan.
international conference on computer vision (2019)

169 Citations

Best Scientists Citing Zhangyang Wang

Radu Timofte

Radu Timofte

ETH Zurich

Publications: 71

Dacheng Tao

Dacheng Tao

University of Sydney

Publications: 42

Thomas S. Huang

Thomas S. Huang

University of Illinois at Urbana-Champaign

Publications: 31

Lei Zhang

Lei Zhang

Hong Kong Polytechnic University

Publications: 30

Chunhua Shen

Chunhua Shen

University of Adelaide

Publications: 27

Luc Van Gool

Luc Van Gool

ETH Zurich

Publications: 27

Xinbo Gao

Xinbo Gao

Chongqing University of Posts and Telecommunications

Publications: 26

Jiaying Liu

Jiaying Liu

Peking University

Publications: 25

Chang Xu

Chang Xu

University of Sydney

Publications: 24

Ming-Hsuan Yang

Ming-Hsuan Yang

University of California, Merced

Publications: 23

Wangmeng Zuo

Wangmeng Zuo

Harbin Institute of Technology

Publications: 21

Yun Fu

Yun Fu

Northeastern University

Publications: 20

Dong Liu

Dong Liu

University of Science and Technology of China

Publications: 20

Zhaowen Wang

Zhaowen Wang

Adobe Systems (United States)

Publications: 19

Qi Tian

Qi Tian

Huawei Technologies (China)

Publications: 19

Zheng-Jun Zha

Zheng-Jun Zha

University of Science and Technology of China

Publications: 19

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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