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检索条件"主题词=Variational autoencoder"
1537 条 记 录,以下是401-410 订阅
排序:
Deep generative models in inversion: The impact of the generator's nonlinearity and development of a new approach based on a variational autoencoder
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COMPUTERS & GEOSCIENCES 2021年 152卷 104762-104762页
作者: Lopez-Alvis, Jorge Laloy, Eric Nguyen, Frederic Hermans, Thomas Univ Liege Urban & Environm Engn Appl Geophys Liege Belgium Univ Ghent Dept Geol Ghent Belgium Belgian Nucl Res Ctr Inst Environm Hlth & Safety Engn & Geosyst Anal Mol Belgium
When solving inverse problems in geophysical imaging, deep generative models (DGMs) may be used to enforce the solution to display highly structured spatial patterns which are supported by independent information (e.g... 详细信息
来源: 评论
An Improved Semi-supervised variational autoencoder with Gate Mechanism for Text Classification
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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2022年 第10期36卷 2253006-2253006页
作者: Ye, Haiming Zhang, Weiwen Nie, Mengna Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou 510006 Peoples R China
In recent years, semi-supervised learning has been investigated to take full advantages of increasing unlabeled data. Although pretrained deep learning models are successfully adopted on a massive amount of unlabeled ... 详细信息
来源: 评论
A dimensionality reduction algorithm for mapping tokamak operational regimes using a variational autoencoder (VAE) neural network
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NUCLEAR FUSION 2021年 第12期61卷 126063-126063页
作者: Wei, Y. Levesque, J. P. Hansen, C. J. Mauel, M. E. Navratil, G. A. Columbia Univ Dept Appl Phys & Appl Math New York NY 10027 USA Univ Washington Dept Aeronaut & Astronaut Seattle WA 98195 USA
A variational autoencoder (VAE) is a type of unsupervised neural network which is able to learn meaningful data representations in a reduced dimensional space. We present an application of VAE in identifying the opera... 详细信息
来源: 评论
Reconstructing a quantum state with a variational autoencoder
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INTERNATIONAL JOURNAL OF QUANTUM INFORMATION 2021年 第8期19卷 2140005-2140005页
作者: Chen, Chuangtao He, Zhimin Huang, Zhiming Situ, Haozhen Foshan Univ Sch Elect & Informat Engn 33 Guangyun Rd Foshan 528225 Guangdong Peoples R China Wuyi Univ Sch Econ & Management 22 Dongcheng Village Jiangmen 529020 Guangdong Peoples R China South China Agr Univ Coll Math & Informat 483 Wushan Rd Guangzhou 510642 Guangdong Peoples R China
Quantum state tomography (QST) is an important and challenging task in the field of quantum information, which has attracted a lot of attentions in recent years. Machine learning models can provide a classical represe... 详细信息
来源: 评论
A variational autoencoder (VAE)-based Deep Learning Anomaly Detection Model for Industrial Products with Dynamic Weights Assigned to Loss Function
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SENSORS AND MATERIALS 2023年 第7期35卷 2241-2264页
作者: Nakata, Shunta Kasahara, Takehiro Nambo, Hidetaka Kanazawa Univ Grad Sch Nat Sci & Technol Div Elect & Comp Sci Kanazawa Ishikawa 9201192 Japan Ind Res Inst Ishikawa Kanazawa Ishikawa 928203 Japan
In the industrial field, deep-learning-based image anomaly detections are attracting attention because of some of their advantages. The deep-learning-based models can overcome the shortcomings of traditional methods, ... 详细信息
来源: 评论
PuVAE: A variational autoencoder to Purify Adversarial Examples
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IEEE ACCESS 2019年 7卷 126582-126593页
作者: Hwang, Uiwon Park, Jaewoo Jang, Hyemi Yoon, Sungroh Cho, Nam Ik Seoul Natl Univ Elect & Comp Engn Seoul 08826 South Korea Seoul Natl Univ INMC Dept Elect & Comp Engn Seoul 08826 South Korea
Deep neural networks are widely used and exhibit excellent performance in many areas. However, they are vulnerable to adversarial attacks that compromise networks at inference time by applying elaborately designed per... 详细信息
来源: 评论
A mutual information-based variational autoencoder for robust JIT soft sensing with abnormal observations
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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2020年 204卷 104118-104118页
作者: Guo, Fan Huang, Biao Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 2G6 Canada
Considering industrial process with high-dimensional, intrinsic nonlinearities and possibly abnormal observations, a robust deep learning soft sensor model is developed under the just-in-time learning framework. As an... 详细信息
来源: 评论
Spatial-contextual variational autoencoder with attention correction for anomaly detection in retinal OCT images
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COMPUTERS IN BIOLOGY AND MEDICINE 2023年 152卷 106328-106328页
作者: Zhou, Xueying Niu, Sijie Li, Xiaohui Zhao, Hui Gao, Xizhan Liu, Tingting Dong, Jiwen Univ Jinan Sch Informat Sci & Engn Shandong Prov Key Lab Network Based Intelligent Co Jinan 250022 Shandong Peoples R China Shandong First Med Univ & Shandong Acad Med Sci Shandong Eye Hosp Shandong Eye Inst State Key Lab Cultivat BaseShandong Prov Key Lab Jinan 250022 Shandong Peoples R China
Anomaly detection refers to leveraging only normal data to train a model for identifying unseen abnormal cases, which is extensively studied in various fields. Most previous methods are based on reconstruction models,... 详细信息
来源: 评论
Randomly generating three-dimensional realistic schistous sand particles using deep learning: variational autoencoder implementation
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ENGINEERING GEOLOGY 2021年 291卷 106235-106235页
作者: Shi, Jia-jie Zhang, Wei Wang, Wei Sun, Yun-han Xu, Chuan-yi Zhu, Hong-hu Sun, Zheng-xing Nanjing Univ Sch Earth Sci & Engn Nanjing 210023 Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Peoples R China
Nanjing sand, a type of greenish-grey schistous sand, is rich in weathered mica fragments, making it anisotropic and significantly differ from the round-grained quartz sand in terms of composition, grading, and mechan... 详细信息
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Diverse Image Captioning via Conditional variational autoencoder and Dual Contrastive Learning
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ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 2024年 第1期20卷 29-29页
作者: Xu, Jing Liu, Bing Zhou, Yong Liu, Mingming Yao, Rui Shao, Zhiwen China Univ Min & Technol Sch Comp Sci & Technol Engn Res Ctr Mine Digitizat Minist Educ Xuzhou 221116 Jiangsu Peoples R China
Diverse image captioning has achieved substantial progress in recent years. However, the discriminability of generative models and the limitation of cross entropy loss are generally overlooked in the traditional diver... 详细信息
来源: 评论