What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Computer vision
Xiaoyan Sun mainly investigates Artificial intelligence, Computer vision, Pattern recognition, Mathematical optimization and Algorithm.
Xiaoyan Sun undertakes multidisciplinary studies into Artificial intelligence and Set in his work.
In the subject of general Pattern recognition, his work in Convolutional neural network is often linked to Action recognition, thereby combining diverse domains of study.
His work focuses on many connections between Mathematical optimization and other disciplines, such as Crossover, that overlap with his field of interest in Estimation of distribution algorithm and Artificial bee colony algorithm.
His Algorithm study combines topics from a wide range of disciplines, such as Theoretical computer science and Bit allocation.
His studies deal with areas such as Image resolution and Image processing, Image texture as well as Image quality.
His most cited work include:
- Image Compression With Edge-Based Inpainting (169 citations)
- Drifting reduction and macroblock-based control in progressive fine granularity scalable video coding (145 citations)
- Structure-Revealing Low-Light Image Enhancement Via Robust Retinex Model (125 citations)
What are the main themes of his work throughout his whole career to date?
Xiaoyan Sun focuses on Artificial intelligence, Computer vision, Algorithm, Pattern recognition and Mathematical optimization.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Genetic algorithm and Machine learning.
His research in the fields of Data compression, Image compression, Inpainting and Motion compensation overlaps with other disciplines such as Encoder.
His studies in Image compression integrate themes in fields like Image quality and JPEG.
His Pattern recognition research incorporates themes from Image, Image retrieval, Noise reduction and Feature detection.
Xiaoyan Sun interconnects Macroblock, Bitstream and Coding tree unit in the investigation of issues within Scalable Video Coding.
He most often published in these fields:
- Artificial intelligence (51.76%)
- Computer vision (30.20%)
- Algorithm (16.86%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (51.76%)
- Machine learning (9.80%)
- Evolutionary algorithm (8.63%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Machine learning, Evolutionary algorithm, Algorithm and Feature extraction.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Frame, Computer vision and Pattern recognition.
His research integrates issues of Gradient descent and Normalization in his study of Computer vision.
His Machine learning research is multidisciplinary, relying on both Structure, Probabilistic logic, Estimation of distribution algorithm and State.
Xiaoyan Sun combines subjects such as Network planning and design, Network architecture, Leverage and Posterior probability, Bayesian probability with his study of Algorithm.
His work carried out in the field of Feature extraction brings together such families of science as Data reduction, Reduction and Hausdorff distance.
Between 2018 and 2021, his most popular works were:
- Communication-Efficient Federated Deep Learning With Layerwise Asynchronous Model Update and Temporally Weighted Aggregation (90 citations)
- Binary differential evolution with self-learning for multi-objective feature selection (73 citations)
- Variable-Size Cooperative Coevolutionary Particle Swarm Optimization for Feature Selection on High-Dimensional Data (41 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
The scientist’s investigation covers issues in Artificial intelligence, Convolutional neural network, Machine learning, Feature extraction and Deep learning.
His work in the fields of Object detection overlaps with other areas such as Upload.
His Convolutional neural network research also works with subjects such as
- Inference which connect with Segmentation and Net,
- Kernel, which have a strong connection to Contextual image classification and Pattern recognition.
His Machine learning research incorporates elements of Transform coding, Structure and Probabilistic logic.
As part of the same scientific family, Xiaoyan Sun usually focuses on Feature extraction, concentrating on Crossover and intersecting with Feature selection, Cluster analysis and Particle swarm optimization.
His Frame research is multidisciplinary, incorporating elements of Normalization and Computer vision.
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.
