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.
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