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检索条件"机构=Key Laboratory of Computer Vision and Machine Learning"
337 条 记 录,以下是181-190 订阅
排序:
A Deep Model Towards Accurate Boundary Location and Strong Generalization for Medical Image Segmentation
SSRN
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SSRN 2023年
作者: Wang, Bing Geng, Peipei Li, Tianxu Yang, Ying Tian, Xuedong Zhang, Guochun Zhang, Xin College of Mathematics and Information Science Hebei University Hebei Baoding071000 China Hebei Key Laboratory of Machine Learning and Computational Intelligence Hebei University Hebei Baoding071000 China Hebei University Affiliated Hospital Hebei Baoding071000 China College of Cyber Security and Computer Hebei University Hebei Baoding071000 China College of Electronic Information Engineering Hebei University Hebei Baoding071000 China
Accurate medical image segmentation plays a crucial role in computer-assisted diagnosis and monitoring. However, due to the complexity of medical images and the limitations of image acquisition, most of the current se... 详细信息
来源: 评论
Is L2 Physics-Informed Loss Always Suitable for Training Physics-Informed Neural Network?
arXiv
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arXiv 2022年
作者: Wang, Chuwei Li, Shanda He, Di Wang, Liwei School of Mathematical Sciences Peking University China Machine Learning Department School of Computer Science Carnegie Mellon University United States National Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University China Center for Data Science Peking University China Zhejiang Lab China
The Physics-Informed Neural Network (PINN) approach is a new and promising way to solve partial differential equations using deep learning. The L2 Physics-Informed Loss is the de-facto standard in training Physics-Inf... 详细信息
来源: 评论
Dual-consistency guidance semi-supervised medical image segmentation with low-level detail feature augmentation
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computers in Biology and Medicine 2024年 181卷 109046-109046页
作者: Wang, Bing Ju, Mengyi Zhang, Xin Yang, Ying Tian, Xuedong College of Mathematics and Information Science Hebei University Wusi Road 180 Hebei Baoding071000 China Hebei Key Laboratory of Machine Learning and Computational Intelligence Hebei University Wusi Road 180 Hebei Baoding071000 China College of Electronic Information Engineering Hebei University Qiyi Road 2666 Hebei Baoding071000 China Hebei University Affiliated Hospital Hebei University Wusi Road 180 Hebei Baoding071000 China College of Cyber Security and Computer Hebei University Wusi Road 180 Hebei Baoding071000 China
In deep-learning-based medical image segmentation tasks, semi-supervised learning can greatly reduce the dependence of the model on labeled data. However, existing semi-supervised medical image segmentation methods fa... 详细信息
来源: 评论
Multiscale Convolutional Transformer with Diverse-aware Feature learning for Motor Imagery EEG Decoding
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IEEE Transactions on Cognitive and Developmental Systems 2025年
作者: Hang, Wenlong Wang, Junliang Liang, Shuang Lei, Baiying Wang, Qiong Li, Guanglin Chen, Badong Qin, Jing Nanjing Tech University College of Computer Nanjing211816 China Nanjing Tech University Information Engineering Nanjing211816 China Nanjing University of Posts and Telecommunications School of Internet of Things Nanjing210023 China Shenzhen University School of Biomedical Engineering Shenzhen518060 China Guangdong Provincial Key Laboratory of Computer Vision Shenzhen518055 China Virtual Reality Technology Shenzhen518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China Shenzhen Institute of Advanced Technology Shenzhen518055 China Chinese Academy of Sciences Shenzhen518055 China Xi'an Jiaotong University Institute of Artificial Intelligence and Robotics Xi'an710049 China Hong Kong Polytechnic University School of Nursing Hung Hom Hong Kong
Electroencephalogram (EEG)-based motor imagery (MI) brain-computer interfaces (BCIs) have significant potential in improving motor function for neurorehabilitation. Despite recent advancements, learning diversified EE... 详细信息
来源: 评论
Line Drawing Guided Progressive Inpainting of Mural Damage
arXiv
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arXiv 2022年
作者: Li, Luxi Zou, Qin Zhang, Fan Yu, Hongkai Chen, Long Song, Chengfang Huang, Xianfeng Wang, Xiaoguang Li, Qingquan Department of Computer Science Technology United International College of Beijing Normal University HongKong Baptist University Zhuhai China Machine Vision and Robotics Laboratory School of Computer Science Wuhan University Wuhan China State Key Laboratory of Surveying Mapping and Remote Sensing Information Engineering Wuhan University Wuhan China Department of Electrical Engineering and Computer Science Cleveland State University OH United States Institute of Automation Chinese Academy of Sciences Beijing China Cultural Heritage Intelligent Computing Laboratory Wuhan University Wuhan China Guangming Laboratory Shenzhen University Shenzhen China
Mural image inpainting is far less explored compared to its natural image counterpart and remains largely unsolved. Most existing image-inpainting methods tend to take the target image as the only input and directly r... 详细信息
来源: 评论
Ct-Gan: A Conditional Generative Adversarial Network of Transformer Architecture for Text-to-Image
SSRN
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SSRN 2022年
作者: Wang, Bing Zhang, Xin Jiao, Wentao Tian, Xuedong College of Electronic Information Engineering Hebei University Hebei Baoding071000 China Hebei Key Laboratory of Machine Learning and Computational Intelligence Hebei University Hebei Baoding071000 China College of Mathematics and Information Science Hebei University Hebei Baoding071000 China College of Cyber Security and Computer Hebei University Hebei Baoding071000 China
How to generate an image from a text description is an imaginative and challenging task. This study proposes a conditional generative adversarial network (GAN) of transformer architecture for text-to-image tasks calle... 详细信息
来源: 评论
An improved group theory-based optimization algorithm for discounted 0-1 knapsack problem
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Advances in Computational Intelligence 2021年 第5期1卷 1-11页
作者: Wang, Ran Zhang, Zichao Ng, Wing W. Y. Wu, Wenhui College of Mathematics and Statistics Shenzhen University Shenzhen China Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China School of Computer Science and Engineering South China University of Technology Guangzhou China College of Electronics and Information Engineering Shenzhen University Shenzhen China
Discounted 0-1 knapsack problem (D0-1KP) has been proved to be NP-hard, thus a lot of researches focus on designing non-deterministic algorithms to solve it. Group theory-based optimization algorithm (GTOA), as a rece...
来源: 评论
Computationally Efficient Approximations for Matrix-based Rényi's Entropy
arXiv
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arXiv 2021年
作者: Gong, Tieliang Dong, Yuxin Yu, Shujian Dong, Bo The School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an710049 China The Machine Learning Group UiT - The Arctic University of Norway Department of Computer Science Vrije University Amsterdam Amsterdam Netherlands
The recently developed matrix-based Rényi's αorder entropy enables measurement of information in data simply using the eigenspectrum of symmetric positive semi-definite (PSD) matrices in reproducing kernel H... 详细信息
来源: 评论
A hybrid monotone decision tree model for interval-valued attributes
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Advances in Computational Intelligence 2021年 第1期2卷 1-11页
作者: Chen, Jiankai Li, Zhongyan Wang, Xin Zhai, Junhai School of Control and Computer Engineering North China Electric Power University Beijing China Hebei Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Information Science Hebei University Baoding China School of Mathematics and Physics North China Electric Power University Beijing China
The existing monotonic decision tree algorithms are based on a linearly ordered constraint that certain attributes are monotonously consistent with the decision, which could be called monotonic attributes, whereas oth...
来源: 评论
Towards defending against adversarial examples via attack-invariant features
arXiv
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arXiv 2021年
作者: Zhou, Dawei Liu, Tongliang Han, Bo Wang, Nannan Peng, Chunlei Gao, Xinbo State Key Laboratory of Integrated Services Networks School of Telecommunications Engineering Xidian University China Trustworthy Machine Learning Lab School of Computer Science The University of Sydney Australia Department of Computer Science Hong Kong Baptist University Hong Kong State Key Laboratory of Integrated Services Networks School of Cyber Engineering Xidian University China Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications China
Deep neural networks (DNNs) are vulnerable to adversarial noise. Their adversarial robustness can be improved by exploiting adversarial examples. However, given the continuously evolving attacks, models trained on see... 详细信息
来源: 评论