World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
41
Citations
9034
World Ranking
8699
National Ranking
1127

Overview

Kaizhu Huang is affiliated with Duke Kunshan University in China and specializes in the field of Computer Science. Their research contributions span 467 publications, prominently in subfields such as Computer Vision and Pattern Recognition, Artificial Intelligence, Computer Networks and Communications, Biomedical Engineering, and Computational Mechanics.

Their work addresses multiple core topics including Domain Adaptation and Few-Shot Learning, Generative Adversarial Networks and Image Synthesis, Handwritten Text Recognition Techniques, Advanced Neural Network Applications, Adversarial Robustness in Machine Learning, Topic Modeling, and Multimodal Machine Learning Applications.

Kaizhu Huang has published extensively in well-known venues. Frequent publication outlets include:

  • arXiv (Cornell University)
  • Cognitive Computation
  • SSRN Electronic Journal
  • Neural Networks
  • Pattern Recognition

Several recent papers illustrate the breadth of Huang's research interests. These include:

  • Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence, 2023, Cognitive Computation
  • Deep learning for brain age estimation: A systematic review, 2023, Information Fusion
  • EPtask: Deep Reinforcement Learning Based Energy-Efficient and Priority-Aware Task Scheduling for Dynamic Vehicular Edge Computing, 2023, IEEE Transactions on Intelligent Vehicles
  • Rethinking Data Augmentation for Single-Source Domain Generalization in Medical Image Segmentation, 2023, Proceedings of the AAAI Conference on Artificial Intelligence
  • Gradient Distribution Alignment Certificates Better Adversarial Domain Adaptation, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Collaboration has been an integral part of their scientific output. Frequent co-authors include:

  • Qiufeng Wang
  • Amir Hussain
  • Xi Yang
  • Rui Zhang
  • Yuyao Yan

Kaizhu Huang's research undertakings demonstrate a multidisciplinary approach within computer science, focusing on artificial intelligence methods, deep learning, and their applications in domains such as medical imaging and vehicular edge computing. Their scholarly contributions are distributed across a variety of collaborative partnerships and well-established academic forums.

Best Publications

  • Robust Text Detection in Natural Scene Images

    Xu-Cheng Yin;Xuwang Yin;Kaizhu Huang;Hong-Wei Hao

  • Customer churn prediction in the telecommunication sector using a rough set approach

    Adnan Amin;Sajid Anwar;Awais Adnan;Muhammad Nawaz

  • Cross-modality interactive attention network for multispectral pedestrian detection

    Lu Zhang;Zhiyong Liu;Shifeng Zhang;Xu Yang

  • Hybrid metaheuristic algorithms: past, present, and future

    T. O. Ting;Xin-She Yang;Shi Cheng;Kaizhu Huang

  • A Unified Gradient Regularization Family for Adversarial Examples

    Chunchuan Lyu;Kaizhu Huang;Hai-Ning Liang

  • Localized support vector regression for time series prediction

    Haiqin Yang;Kaizhu Huang;Irwin King;Michael R. Lyu

  • A scalable deep neural network architecture for multi-building and multi-floor indoor localization based on Wi-Fi fingerprinting

    Kyeong Soo Kim;Sanghyuk Lee;Kaizhu Huang

  • IAN: The Individual Aggregation Network for Person Search

    Jimin Xiao;Yanchun Xie;Tammam Tillo;Kaizhu Huang

  • Reliability Does Matter: An End-to-End Weakly Supervised Semantic Segmentation Approach

    Bingfeng Zhang;Jimin Xiao;Yunchao Wei;Mingjie Sun

  • Deep Learning for Brain Age Estimation: A Systematic Review

    Unknown

  • Learning classifiers from imbalanced data based on biased minimax probability machine

    Kaizhu Huang;Haiqin Yang;I. King;M.R. Lyu

  • Sparse Metric Learning via Smooth Optimization

    Yiming Ying;Kaizhu Huang;Colin Campbell

  • The Minimum Error Minimax Probability Machine

    Kaizhu Huang;Haiqin Yang;Irwin King;Michael R. Lyu

  • Fast k NN graph construction with locality sensitive hashing

    Yan-Ming Zhang;Kaizhu Huang;Guanggang Geng;Cheng-Lin Liu

  • Biased support vector machine for relevance feedback in image retrieval

    Chu-Hong Hoi;Chi-Hang Chan;Kaizhu Huang;M.R. Lyu

  • Maxi–Min Margin Machine: Learning Large Margin Classifiers Locally and Globally

    Kaizhu Huang;Haiqin Yang;I. King;M.R. Lyu

  • Sparse learning for support vector classification

    Kaizhu Huang;Danian Zheng;Jun Sun;Yoshinobu Hotta

  • Imbalanced learning with a biased minimax probability machine

    Kaizhu Huang;Haiqin Yang;Irwin King;M.R. Lyu

  • EPtask: Deep Reinforcement Learning Based Energy-Efficient and Priority-Aware Task Scheduling for Dynamic Vehicular Edge Computing

    Unknown

  • Rethinking Data Augmentation for Single-Source Domain Generalization in Medical Image Segmentation

    Unknown

  • Zero-Shot Learning via Attribute Regression and Class Prototype Rectification

    Changzhi Luo;Zhetao Li;Kaizhu Huang;Jiashi Feng

  • Learning large margin classifiers locally and globally

    Kaizhu Huang;Haiqin Yang;Irwin King;Michael R. Lyu

  • Machine Learning: Modeling Data Locally and Globally

    Kai-Zhu Huang;Hai-Qin Yang;Irwin King;Michael Lyu

Frequent Co-Authors

Amir Hussain
Amir Hussain Edinburgh Napier University
Irwin King
Irwin King Chinese University of Hong Kong
Michael R. Lyu
Michael R. Lyu Chinese University of Hong Kong
Cheng-Lin Liu
Cheng-Lin Liu Chinese Academy of Sciences
Zenglin Xu
Zenglin Xu Harbin Institute of Technology
Frans Coenen
Frans Coenen University of Liverpool
Zhiyong Liu
Zhiyong Liu University of Science and Technology Beijing
Jianke Zhu
Jianke Zhu Zhejiang University
Hong Qiao
Hong Qiao Chinese Academy of Sciences
Bo Xu
Bo Xu Chinese Academy of Sciences

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