Search Results - asymmetric denoising autoencoder*

Multi-Type Missing Imputation of Time-Series Power Equipment Monitoring Data Based on Moving Average Filter–Asymmetric Denoising Autoencoder

Authors: Ling Jiang Juping Gu Xinsong Zhang et al.

Source: Sensors, Vol 23, Iss 24, p 9697 (2023)

Subject Terms: power equipment monitoring data, multi-type missing, data imputation, asymmetric denoising autoencoder, moving average filter, Chemical technology, TP1-1185

File Description: electronic resource

Relation: https://www.mdpi.com/1424-8220/23/24/9697; https://doaj.org/toc/1424-8220

Access URL: https://doaj.org/article/c083a7495d9148b593133fcfc4f031df

A channel estimation method using denoising autoencoder for large-scale asymmetric backscatter systems

Authors: Chae Yoon Jung Jae-Mo Kang Dong In Kim

Source: ICT Express, Vol 10, Iss 2, Pp 400-405 (2024)

Subject Terms: Backscatter communication, Beamforming, Channel estimation, Deep learning, Denoising autoencoder, Information technology, T58.5-58.64

File Description: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2405959523001169; https://doaj.org/toc/2405-9595

Access URL: https://doaj.org/article/60e6f8807c1e43dc88d5cd7e99775171

Semi-Supervised Text Simplification with Back-Translation and Asymmetric Denoising Autoencoders

Authors: Lu Chen Zhi Chen Yanbin Zhao et al.

Source: Proceedings of the AAAI Conference on Artificial Intelligence. 34:9668-9675

Subject Terms: FOS: Computer and information sciences, Computer Science - Computation and Language, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, Computation and Language (cs.CL), 01 natural sciences, 0105 earth and related environmental sciences

Access URL: https://ojs.aaai.org/index.php/AAAI/article/download/6515/6371
http://arxiv.org/abs/2004.14693
https://dblp.uni-trier.de/db/conf/aaai/aaai2020.html#ZhaoCCY20
https://ojs.aaai.org/index.php/AAAI/article/view/6515
https://aaai.org/ojs/index.php/AAAI/article/view/6515
https://arxiv.org/abs/2004.14693

Manifold spatial clustering via asymmetric convolutional denoising autoencoder

Authors: Mengyin Yang Junfen Chen Wenjie Wang et al.

Source: Journal of Intelligent & Fuzzy Systems. 43:2933-2944

Subject Terms: 0209 industrial biotechnology, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Semi-Supervised Text Simplification with Back-Translation and Asymmetric Denoising Autoencoders

Authors: Zhao, Yanbin Chen, Lu Chen, Zhi et al.

Index Terms: Computer Science - Computation and Language, text

URL: http://arxiv.org/abs/2004.14693

Two-stage noise aware training using asymmetric deep denoising autoencoder

Authors: Nam Soo Kim Woo Hyun Kang Kang Hyun Lee et al.

Source: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :5765-5769

Subject Terms: 03 medical and health sciences, 0305 other medical science

Access URL: https://www.sigport.org/documents/two-stage-noise-aware-training-using-asymmetric-deep-denoising-autoencoder
https://dblp.uni-trier.de/db/conf/icassp/icassp2016.html#LeeKKK16
http://dblp.uni-trier.de/db/conf/icassp/icassp2016.html#LeeKKK16
http://ieeexplore.ieee.org/document/7472782/
https://sigport.org/sites/default/files/ICASSP2016_%ED%8F%AC%EC%8A%A4%ED%84%B0_%EC%9D%B4%EA%B0%95%ED%98%84_%EA%B7%B8%EB%9E%98%ED%94%842.pdf
https://ieeexplore.ieee.org/document/7472782/

Transfer learning and subword sampling for asymmetric-resource one-to-many neural translation

Authors: Virpioja, Sami Kurimo Mikko et al.

Contributors: Virpioja, Sami Kurimo Mikko et al.

Source: Machine Translation

Subject Terms: ta113, FOS: Computer and information sciences, Computer and information sciences, Computer Science - Computation and Language, Multilingual machine translation, 02 engineering and technology, Low-resource languages, 01 natural sciences, Transfer learning, Denoising sequence autoencoder, Multi-task learning, Subword segmentation, Languages, 0202 electrical engineering, electronic engineering, information engineering, Computation and Language (cs.CL), 0105 earth and related environmental sciences

File Description: application/pdf

Access URL: https://helda.helsinki.fi/bitstream/10138/330171/1/2004.04002.pdf
http://arxiv.org/abs/2004.04002
http://juuli.fi/Record/0374141320
https://doi.org/10.1007/s1059002009253x
https://helda.helsinki.fi/handle/10138/330171
https://dblp.uni-trier.de/db/journals/corr/corr2004.html#abs-2004-04002
https://link.springer.com/article/10.1007/s10590-020-09253-x
http://hdl.handle.net/10138/330171
https://aaltodoc.aalto.fi/handle/123456789/102739

Multi-Type Missing Imputation of Time-Series Power Equipment Monitoring Data Based on Moving Average Filter–Asymmetric Denoising Autoencoder.

Authors: Jiang, Ling Gu, Juping Zhang, Xinsong et al.

Source: Sensors (14248220); Dec2023, Vol. 23 Issue 24, p9697, 16p

Subject Terms: MOVING average process, MULTIPLE imputation (Statistics), MISSING data (Statistics), SUPERVISORY control systems, ACQUISITION of data, PRIOR learning

Virtual restoration of ancient mold-damaged paintings based on spectral-guided asymmetric autoencoder for hyperspectral images.

Authors: Wang, Sa Cen, Yi Qu, Liang et al.

Source: npj Heritage Science; 10/23/2025, Vol. 13 Issue 1, p1-20, 20p

Subject Terms: SILK, HYPERSPECTRAL imaging systems, FUNGAL growth, IMAGE reconstruction, AUTOENCODERS, ANCIENT art, SPECTRUM analysis

Attention Aware Dual-Path Autoencoder with Asymmetric Loss for Recognition in Complex Scenes.

Authors: Rosli, Hashim Ali, Rozniza Hitam, Muhamad Suzuri et al.

Source: International Journal of Advanced Computer Science & Applications; 2025, Vol. 16 Issue 7, p502-511, 10p

Manifold spatial clustering via asymmetric convolutional denoising autoencoder.

Authors: Yang, Mengyin Chen, Junfen Wang, Wenjie et al.

Source: Journal of Intelligent & Fuzzy Systems; 2022, Vol. 43 Issue 3, p2933-2944, 12p

Subject Terms: GAUSSIAN mixture models, DEEP learning, COMPUTER vision, MACHINE learning

Fog-empowered anomaly detection in IoT networks using one-class asymmetric stacked autoencoder.

Authors: Ayad, Aya G. Sakr, Nehal A. Hikal, Noha A.

Source: Cluster Computing; 2025, Vol. 28 Issue 8, p1-28, 28p

Subject Terms: ANOMALY detection (Computer security), INTERNET of things, REAL-time computing, DEEP learning, FEATURE extraction, HOME security measures, EDGE computing

A Novel Deep Learning Approach for Speckle Denoising Using Hyperparameter Tuning.

Authors: Patil, Nilima Deshpande, M. M. Pawar, V. N.

Source: Journal of Image & Graphics (United Kingdom); 2025, Vol. 13 Issue 3, p267-274, 8p

Subject Terms: CONVOLUTIONAL neural networks, DEEP learning, SPECKLE interference, AUTOENCODERS, ULTRASONIC imaging

Reinforced Residual Encoder–Decoder Network for Image Denoising via Deeper Encoding and Balanced Skip Connections.

Authors: Boucherit, Ismail Kheddar, Hamza

Source: Big Data & Cognitive Computing; Apr2025, Vol. 9 Issue 4, p82, 22p

Subject Terms: IMAGE denoising, IMAGE reconstruction, SIGNAL-to-noise ratio, FEATURE extraction, AUTOENCODERS

An Enhanced Intrusion Detection System Using Attention-Based Stacked Sparse Autoencoder Feature Extraction.

Authors: Varanasi, Venkata Ramani Razia, Shaik

Source: Engineering, Technology & Applied Science Research; Aug2025, Vol. 15 Issue 4, p24436-24441, 6p

Subject Terms: INTRUSION detection systems (Computer security), FEATURE extraction, SIGNAL denoising, LONG short-term memory, ARTIFICIAL neural networks, BIG data, MACHINE learning

Asymmetric Training and Symmetric Fusion for Image Denoising in Edge Computing.

Authors: Zhang, Yupeng Liao, Xiaofeng

Source: Symmetry (20738994); Mar2025, Vol. 17 Issue 3, p424, 27p

Subject Terms: IMAGE reconstruction, REMOTE sensing, EDGE computing, IMAGE fusion, SUPERVISED learning, IMAGE denoising

Denoising-autoencoder-facilitated MEMS computational spectrometer with enhanced resolution on a silicon photonic chip.

Authors: Zhou, Jing Zhang, Hui Qiao, Qifeng et al.

Source: Nature Communications; 11/26/2024, Vol. 15 Issue 1, p1-12, 12p

Subject Terms: SIGNAL-to-noise ratio, SPECTROMETERS, PHOTONICS, SILICON, INTERFEROMETERS, IMAGE denoising

A hybrid deep learning framework for denoising acoustic partial discharge signals.

Authors: Kumar, Chandan Ganguly, Biswarup Dey, Debangshu et al.

Source: Signal, Image & Video Processing; Dec2025, Vol. 19 Issue 12, p1-17, 17p

Enhancing SNR in CEST imaging: A deep learning approach with a denoising convolutional autoencoder.

Authors: Kurmi, Yashwant Viswanathan, Malvika Zu, Zhongliang

Source: Magnetic Resonance in Medicine; Dec2024, Vol. 92 Issue 6, p2404-2419, 16p

Subject Terms: OVERHAUSER effect (Nuclear physics), MAGNETIZATION transfer, DEEP learning, IMAGE denoising, PROTONS

DEEP NON-NEGATIVE MATRIX FACTORIZATION MODEL FOR CLUSTERING-BASED IMAGE DENOISING.

Authors: Malik, Shaily Jatana, Nishtha Jatain, Divya et al.

Source: Proceedings on Engineering Sciences; 2024, Vol. 6 Issue 4, p1889-1896, 7p

Subject Terms: IMAGE denoising, PATTERN recognition systems, COMPUTER vision, MATRIX decomposition, FACTORIZATION, NONNEGATIVE matrices, ALGORITHMS, AUTOENCODERS, NONLINEAR analysis