Výsledky vyhledávání - class-specific deep autoencoder*

Sample reconstruction with deep autoencoder for one sample per person face recognition

Autoři: Yan Zhang Hua Peng

Zdroj: IET Computer Vision, Vol 11, Iss 6, Pp 471-478 (2017)

Témata: 4. Education, Computer applications to medicine. Medical informatics, R858-859.7, 0211 other engineering and technologies, OSPP face recognition, 02 engineering and technology, class-specific deep autoencoder, QA76.75-76.765, multisample subject to single-sample subject intraclass variations, CDA, one sample per person face recognition, 0202 electrical engineering, electronic engineering, information engineering, Computer software, sample reconstruction, 10. No inequality

Přístupová URL adresa: https://doaj.org/article/760c7173812b459682d146d586f85181
https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/iet-cvi.2016.0322
https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2016.0322
https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-cvi.2016.0322
https://dblp.uni-trier.de/db/journals/iet-cvi/iet-cvi11.html#ZhangP17
https://doi.org/10.1049/iet-cvi.2016.0322

Enhanced prediction of recombination hotspots using input features extracted by class specific autoencoders

Autoři: Subbiah Karthikeyan Abhigyan Nath

Zdroj: Journal of Theoretical Biology. 444:73-82

Témata: Recombination, Genetic, 0301 basic medicine, 0303 health sciences, 03 medical and health sciences, Deep Learning, Base Sequence, Neural Networks, Computer, Saccharomyces cerevisiae, Classification, Algorithms

Přístupová URL adresa: https://pubmed.ncbi.nlm.nih.gov/29462625
http://ui.adsabs.harvard.edu/abs/2018JThBi.444...73N/abstract
https://www.ncbi.nlm.nih.gov/pubmed/29462625
http://europepmc.org/abstract/MED/29462625
https://www.sciencedirect.com/science/article/pii/S0022519318300766

An Autoencoder-Based Machine- and Deep-Learning Approach for Predicting Bridge Deck Conditions in Texas.

Autoři: Bayat, Mahmoud Kharel, Subham Li, Jianling

Zdroj: Journal of Structural Design & Construction Practice; Nov2025, Vol. 30 Issue 4, p1-16, 16p

Témata: AUTOENCODERS, MACHINE learning, DEEP learning, TRANSPORTATION agencies, CONDITION-based maintenance, INFRASTRUCTURE (Economics), PREDICTION models

Geografický termín: TEXAS

Korporace: TEXAS. Dept. of Transportation

Two-Phase Multitask Autoencoder-Based Deep Learning Framework for Subject-Independent EEG Motor Imagery Classification

Autoři: Changgyun Jin Andrew H. Song Seong-Eun Kim

Zdroj: IEEE Access, Vol 12, Pp 77356-77367 (2024)

Témata: subject-independent, 0301 basic medicine, 0303 health sciences, 03 medical and health sciences, motor imagery, deep learning, EEG, Electrical engineering. Electronics. Nuclear engineering, multitask autoencoder, Classification, TK1-9971

Přístupová URL adresa: https://doaj.org/article/e224854d86d643978058e2430c63f522

FCDAE: Fully Connected Deep Autoencoders for snow and glacier features classification from hyperspectral data

Autoři: Naillah Gul Syed Zubair Ahmad Shah Mohd Anul Haqq a další

A deep learning framework with hybrid stacked sparse autoencoder for type 2 diabetes prediction.

Autoři: Abdussamad Daud, Hanita Sokkalingam, Rajalingam a další

Zdroj: Scientific Reports; 10/21/2025, Vol. 15 Issue 1, p1-22, 22p

Témata: TYPE 2 diabetes, FEATURE selection, FORECASTING, COMPUTER performance, ARTIFICIAL neural networks, MEDICAL care, DEEP learning

Data-driven deep learning approach for suggesting process parameters for the milling operations

Autoři: Asghar E. Ratti A. Tolio T. a další

Přispěvatelé: Asghar E. Ratti A. Tolio T. a další

Témata: 3D convolutional neural networks, class-specific autoencoders, Manufacturing knowledge reuse, removed volume geometry of tool passes

Relation: ispartofbook:Procedia CIRP; 57th CIRP Conference on Manufacturing Systems 2024, CMS 2024; volume:130; firstpage:307; lastpage:312; numberofpages:6; serie:PROCEDIA CIRP; https://hdl.handle.net/11311/1286315

Dostupnost: https://hdl.handle.net/11311/1286315
https://doi.org/10.1016/j.procir.2024.10.092

Two-Stream Deep Architecture for Hyperspectral Image Classification

Autoři: Siyuan Hao Wei Wang Yuanxin Ye a další

Zdroj: IEEE Transactions on Geoscience and Remote Sensing. 56:2349-2361

Témata: 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, Class-specific fusion, convolutional neural networks (CNNs), deep learning, Feature extraction, hyperspectral image (HSI) classification, Hyperspectral imaging, Machine learning, remote sensing, stacked denoising autoencoder (SdAE), Training, two-stream architecture, Electrical and Electronic Engineering, Earth and Planetary Sciences (all), 02 engineering and technology

Popis souboru: application/pdf

Přístupová URL adresa: https://dblp.uni-trier.de/db/journals/tgrs/tgrs56.html#HaoWYNB18
https://ui.adsabs.harvard.edu/abs/2018ITGRS..56.2349H/abstract
http://ieeexplore.ieee.org/document/8240968/
https://ieeexplore.ieee.org/document/8240968/

BSCGAN: structured minority class image generation under class-balanced pretraining.

Autoři: Wan, Qian Zhou, Bin Wang, Yanjiang

Zdroj: Visual Computer; Apr2025, Vol. 41 Issue 6, p3853-3865, 13p

Témata: GENERATIVE adversarial networks, DATA structures, DATA distribution, AUTOENCODERS, DEEP learning

Attention-enhanced residual autoencoder for NIR spectral feature extraction and classification of grain varieties.

Autoři: Kabiso, Abel Chernet Ko, Cheng-Hao

Zdroj: Scientific Reports; 9/24/2025, Vol. 15 Issue 1, p1-18, 18p

Témata: NEAR infrared spectroscopy, DEEP learning, MACHINE learning, CROP yields, AUTOENCODERS, BIOLOGICAL classification, AGRICULTURE

A performance analysis of convolutional autoencoder modified WaveGAN architectures for realistic 12 lead electrocardiogram synthesis.

Autoři: Liu, Jiaqi Chui, Kwok Tai Lee, Lap-Kei a další

Zdroj: Scientific Reports; 10/17/2025, Vol. 15 Issue 1, p1-22, 22p

Témata: GENERATIVE adversarial networks, ELECTROCARDIOGRAPHY, ROUTINE diagnostic tests, FEATURE extraction, PROBABILISTIC generative models, DEEP learning, RESEARCH evaluation, DATA augmentation

Data-driven deep learning approach for suggesting process parameters for the milling operations.

Autoři: Asghar, Eram Ratti, Andrea Tolio, Tullio

Zdroj: Procedia CIRP. 2024, Vol. 130, p307-312. 6p.

Leveraging Sentinel-1/2 time series and deep learning for accurate forest tree species mapping.

Autoři: Tan, Jun Li, Jing Ma, Tianyue a další

Zdroj: Frontiers in Forests & Global Change; 2025, p1-19, 19p

Témata: DEEP learning, VEGETATION classification, CARBON sequestration, AUTOENCODERS, BIODIVERSITY, REMOTE sensing, FOREST management

Fine-Tuned Visual Transformer Masked Autoencoder Applied for Anomaly Detection in Satellite Images.

Autoři: Gajda, Jakub Kwiecień, Joanna

Zdroj: Applied Sciences (2076-3417); Jun2025, Vol. 15 Issue 11, p6286, 15p

Témata: TRANSFORMER models, REMOTE-sensing images, DEEP learning, AUTOENCODERS, IMAGE databases

Interpretability of neural network using layer conductance analysis for the concept detection.

Autoři: Baazzouz, Amine Dabounou, Jaouad

Zdroj: Journal of Engineering & Applied Science; 9/16/2025, Vol. 72 Issue 1, p1-18, 18p

Témata: DEEP learning, AUTOENCODERS, ARTIFICIAL neural networks, COGNITION, PATTERN perception

FAIR-Net: A Fuzzy Autoencoder and Interpretable Rule-Based Network for Ancient Chinese Character Recognition.

Autoři: Ge, Yanling Zhang, Yunmeng Roh, Seok-Beom

Zdroj: Sensors (14248220); Sep2025, Vol. 25 Issue 18, p5928, 18p

Témata: OPTICAL character recognition, FUZZY neural networks, PATTERN recognition systems, DEEP learning, CULTURAL maintenance, FEATURE extraction

Intelligent Intrusion Detection System Against Various Attacks Based on a Hybrid Deep Learning Algorithm.

Autoři: Susilo, Bambang Muis, Abdul Sari, Riri Fitri

Zdroj: Sensors (14248220); Jan2025, Vol. 25 Issue 2, p580, 26p

Témata: CONVOLUTIONAL neural networks, LONG short-term memory, MACHINE learning, CYBERTERRORISM, AUTOENCODERS, INTRUSION detection systems (Computer security), DEEP learning

Deep Learning Approach for Automatic Heartbeat Classification.

Autoři: Guerra, Roger de T. Yamaguchi, Cristina K. Stefenon, Stefano F. a další

Zdroj: Sensors (14248220); Mar2025, Vol. 25 Issue 5, p1400, 25p

Témata: LONG short-term memory, DEEP learning, AUTOENCODERS, AUTOMATIC classification, PHYSICIANS, ARRHYTHMIA

LatentResNet: An Optimized Underwater Fish Classification Model with a Low Computational Cost.

Autoři: Hariri, Muhab Avsar, Ercan Aydın, Ahmet

Zdroj: Journal of Marine Science & Engineering; Jun2025, Vol. 13 Issue 6, p1019, 26p

Témata: IMAGE recognition (Computer vision), IMAGE reconstruction, FEATURE extraction, AUTOENCODERS, CLASSIFICATION of fish, DEEP learning

Histopathological image recognition with discriminant-oriented extreme learning machine autoencoder based deep feature dimension reduction.

Autoři: Cheng, Rong Zhao, Yu Bai, Yanping a další

Zdroj: Signal, Image & Video Processing; May2025, Vol. 19 Issue 5, p1-15, 15p