What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
His primary areas of investigation include Artificial intelligence, Computer vision, Artificial neural network, Pattern recognition and Convolutional neural network.
As part of his studies on Artificial intelligence, Yue Huang often connects relevant subjects like Algorithm.
His research on Computer vision frequently links to adjacent areas such as Probabilistic logic.
The Pattern recognition study combines topics in areas such as Bayesian network, Iterative reconstruction, Compressed sensing and Bayesian inference.
Yue Huang has researched Convolutional neural network in several fields, including Feature extraction, Image segmentation, Kernel and Robustness.
His Deep learning study combines topics in areas such as Discriminative model, Feature learning and Categorization.
His most cited work include:
- Removing Rain from Single Images via a Deep Detail Network (355 citations)
- A Weighted Variational Model for Simultaneous Reflectance and Illumination Estimation (224 citations)
- A fusion-based enhancing method for weakly illuminated images (168 citations)
What are the main themes of his work throughout his whole career to date?
Yue Huang mostly deals with Artificial intelligence, Pattern recognition, Computer vision, Deep learning and Image.
Segmentation, Artificial neural network, Compressed sensing, Convolutional neural network and Feature are the subjects of his Artificial intelligence studies.
His study in Convolutional neural network is interdisciplinary in nature, drawing from both Pyramid, Training set, Data mining and Pyramid.
The concepts of his Pattern recognition study are interwoven with issues in Supervised learning and Noise reduction.
His biological study spans a wide range of topics, including Transfer of learning, Labeled data and Robustness.
In the field of Image, his study on Single image overlaps with subjects such as Naturalness and Underwater.
He most often published in these fields:
- Artificial intelligence (84.25%)
- Pattern recognition (47.24%)
- Computer vision (30.71%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (84.25%)
- Pattern recognition (47.24%)
- Deep learning (22.05%)
In recent papers he was focusing on the following fields of study:
Yue Huang focuses on Artificial intelligence, Pattern recognition, Deep learning, Discriminative model and Convolutional neural network.
As part of his studies on Artificial intelligence, Yue Huang often connects relevant areas like Machine learning.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Normalization, Feature, Interpolation, Supervised learning and Generative grammar.
Yue Huang interconnects Robustness and Computer vision in the investigation of issues within Deep learning.
Yue Huang combines subjects such as Classifier, Feature and Feature vector with his study of Discriminative model.
His Convolutional neural network research includes themes of Algorithm, Error detection and correction, Compressed sensing and Labeled data.
Between 2019 and 2021, his most popular works were:
- Lightweight Pyramid Networks for Image Deraining (59 citations)
- Harmonizing Transferability and Discriminability for Adapting Object Detectors (20 citations)
- Segmentation and Classification in Digital Pathology for Glioma Research: Challenges and Deep Learning Approaches. (17 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
Artificial intelligence, Pattern recognition, Feature extraction, Segmentation and Artificial neural network are his primary areas of study.
Yue Huang does research in Artificial intelligence, focusing on Deep learning specifically.
His work deals with themes such as Annotation, Entropy and Precision medicine, which intersect with Deep learning.
His Pattern recognition study combines topics from a wide range of disciplines, such as Adversarial system, Image synthesis, Image, Translation and Supervised learning.
His work on Image segmentation as part of general Segmentation study is frequently connected to Weighted network, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His biological study deals with issues like Pyramid, which deal with fields such as Convolutional neural network.
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