What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Baochang Zhang spends much of his time researching Artificial intelligence, Pattern recognition, Deep learning, Computer vision and Feature extraction.
Baochang Zhang combines Artificial intelligence and Process in his studies.
Baochang Zhang combines subjects such as Object, Filter, Kernel and Robustness with his study of Pattern recognition.
His research integrates issues of Regularization and Convolutional neural network in his study of Deep learning.
His Facial recognition system, Histogram and Eye tracking study in the realm of Computer vision connects with subjects such as Gabor wavelet.
His Feature extraction research includes themes of Classifier and Linear discriminant analysis.
His most cited work include:
- Local Derivative Pattern Versus Local Binary Pattern: Face Recognition With High-Order Local Pattern Descriptor (780 citations)
- Histogram of Gabor Phase Patterns (HGPP): A Novel Object Representation Approach for Face Recognition (530 citations)
- Gabor Convolutional Networks (146 citations)
What are the main themes of his work throughout his whole career to date?
Baochang Zhang focuses on Artificial intelligence, Pattern recognition, Convolutional neural network, Computer vision and Algorithm.
As a part of the same scientific family, he mostly works in the field of Artificial intelligence, focusing on Machine learning and, on occasion, Reduction.
His study explores the link between Pattern recognition and topics such as Feature that cross with problems in Contextual image classification.
Baochang Zhang interconnects Network architecture, Backpropagation, Normalization and Filter in the investigation of issues within Convolutional neural network.
His work in Algorithm tackles topics such as Pruning which are related to areas like Optimization problem.
His study in Facial recognition system is interdisciplinary in nature, drawing from both Cognitive neuroscience of visual object recognition, Linear discriminant analysis and Pattern recognition.
He most often published in these fields:
- Artificial intelligence (79.64%)
- Pattern recognition (48.36%)
- Convolutional neural network (26.91%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (79.64%)
- Convolutional neural network (26.91%)
- Pattern recognition (48.36%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Convolutional neural network, Pattern recognition, Algorithm and Machine learning.
His Artificial intelligence study frequently links to adjacent areas such as Reduction.
His Convolutional neural network research integrates issues from Backpropagation, Normalization and Overhead.
His Pattern recognition study combines topics in areas such as Object detection and Convolution.
The concepts of his Algorithm study are interwoven with issues in Gradient descent and Pruning.
His research investigates the connection between Machine learning and topics such as Benchmark that intersect with issues in Data science and Focus.
Between 2019 and 2021, his most popular works were:
- HRank: Filter Pruning Using High-Rank Feature Map (72 citations)
- Channel Pruning via Automatic Structure Search (18 citations)
- Fine-Grained Spatial Alignment Model for Person Re-Identification With Focal Triplet Loss (15 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His primary areas of study are Artificial intelligence, Algorithm, Artificial neural network, Pattern recognition and Convolutional neural network.
His Artificial intelligence study frequently intersects with other fields, such as Spatial analysis.
In general Algorithm study, his work on Regularization and Quantization often relates to the realm of Source code and Focus, thereby connecting several areas of interest.
The study incorporates disciplines such as Convolution and Reduction in addition to Artificial neural network.
His Pattern recognition study frequently draws connections to other fields, such as Prior probability.
As a member of one scientific family, he mostly works in the field of Machine learning, focusing on Channel and, on occasion, Edge computing, Facial recognition system, Cognitive neuroscience of visual object recognition and Network architecture.
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