What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Computer vision, Feature extraction and Machine learning.
Artificial intelligence is a component of his Feature, Discriminative model, Dimensionality reduction, Contextual image classification and Robustness studies.
His work is dedicated to discovering how Pattern recognition, Subspace topology are connected with Theoretical computer science and other disciplines.
Dacheng Tao has included themes like Artificial neural network, Representation, Feature vector, Facial recognition system and Neural coding in his Feature extraction study.
Within one scientific family, he focuses on topics pertaining to Image retrieval under Machine learning, and may sometimes address concerns connected to Graph.
In his study, Algorithm is strongly linked to Convolutional neural network, which falls under the umbrella field of Deep learning.
His most cited work include:
- General Tensor Discriminant Analysis and Gabor Features for Gait Recognition (1012 citations)
- DehazeNet: An End-to-End System for Single Image Haze Removal (888 citations)
- Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval (783 citations)
What are the main themes of his work throughout his whole career to date?
Dacheng Tao focuses on Artificial intelligence, Pattern recognition, Machine learning, Computer vision and Feature extraction.
His study in Discriminative model, Image, Feature, Robustness and Subspace topology is carried out as part of his studies in Artificial intelligence.
His Pattern recognition research incorporates themes from Contextual image classification, Facial recognition system and Cluster analysis.
His Machine learning research integrates issues from Training set, Image retrieval, Metric and Benchmark.
His is doing research in Image processing, Image quality and Pixel, both of which are found in Computer vision.
His work in Feature extraction is not limited to one particular discipline; it also encompasses Visualization.
He most often published in these fields:
- Artificial intelligence (74.47%)
- Pattern recognition (37.82%)
- Machine learning (22.65%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (74.47%)
- Machine learning (22.65%)
- Pattern recognition (37.82%)
In recent papers he was focusing on the following fields of study:
Dacheng Tao mostly deals with Artificial intelligence, Machine learning, Pattern recognition, Deep learning and Artificial neural network.
Dacheng Tao focuses mostly in the field of Artificial intelligence, narrowing it down to topics relating to Computer vision and, in certain cases, Frame.
His research on Machine learning often connects related areas such as Inference.
His work in Pattern recognition addresses subjects such as Code, which are connected to disciplines such as Object.
He works mostly in the field of Artificial neural network, limiting it down to topics relating to Algorithm and, in certain cases, Kernel.
The study incorporates disciplines such as Multi-task learning, Inpainting, Data mining and Benchmark in addition to Feature.
Between 2019 and 2021, his most popular works were:
- Generalized Latent Multi-View Subspace Clustering (137 citations)
- Spatial Pyramid-Enhanced NetVLAD With Weighted Triplet Loss for Place Recognition (126 citations)
- An Underwater Image Enhancement Benchmark Dataset and Beyond (103 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His primary scientific interests are in Artificial intelligence, Artificial neural network, Pattern recognition, Deep learning and Machine learning.
His research ties Computer vision and Artificial intelligence together.
His work deals with themes such as Algorithm, Quantum neural network and Topology, which intersect with Artificial neural network.
His Pattern recognition research includes themes of Subspace topology, Data point, Texture and Cluster analysis.
His Deep learning research is multidisciplinary, relying on both Variety, Field, Scalability and Data science.
His Machine learning research integrates issues from Data modeling and Upper and lower bounds.
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