Search Results - semi-supervised conventional variational autoencoder

S2VQ-VAE: Semi-Supervised Vector Quantised-Variational AutoEncoder for Automatic Evaluation of Trail Making Test

Authors: Zeshen Tang Shiyu Tang Haoran Wang et al.

Source: IEEE Journal of Biomedical and Health Informatics. 28:4456-4470

Subject Terms: Male, Aged, 80 and over, Trail Making Test, Deep Learning, Humans, Cognitive Dysfunction, Female, Signal Processing, Computer-Assisted, Supervised Machine Learning, Diagnosis, Computer-Assisted, Algorithms, Aged

Access URL: https://pubmed.ncbi.nlm.nih.gov/38819974

Enhancing Semi-Supervised Learning in Educational Data Mining Through Synthetic Data Generation Using Tabular Variational Autoencoder.

Authors: Kostopoulos, Georgios Fazakis, Nikos Kotsiantis, Sotiris et al.

Source: Algorithms; Oct2025, Vol. 18 Issue 10, p663, 35p

Subject Terms: DATA mining, DATA augmentation, METADATA, SUPERVISED learning, PROBABILISTIC generative models

Semi-supervised contrastive learning variational autoencoder Integrating single-cell multimodal mosaic datasets.

Authors: Wang, Zihao Wu, Zeyu Deng, Minghua

Source: BMC Bioinformatics; 8/4/2025, Vol. 26 Issue 1, p1-13, 13p

Subject Terms: DATA integration, AUTOENCODERS, MACHINE learning, STATISTICAL bias, SUPERVISED learning, CELL analysis

Semi-Supervised Adversarial Variational Autoencoder

Authors: Zemouri, Ryad

Contributors: Zemouri, Ryad

Source: Machine Learning and Knowledge Extraction, Vol 2, Iss 3, Pp 361-378 (2020)
Machine Learning and Knowledge Extraction
Volume 2
Issue 3
Pages 20-378

Subject Terms: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], TK7885-7895, Computer engineering. Computer hardware, 0202 electrical engineering, electronic engineering, information engineering, artificial_intelligence_robotics, variational autoencoder, data generation, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], 02 engineering and technology, deep feature consistent, adversarial learning

File Description: application/pdf

Access URL: https://www.mdpi.com/2504-4990/2/3/20/pdf
https://doaj.org/article/ee9bbbc5397b46549c2cc2bdd8ddde75
https://www.preprints.org/manuscript/202008.0051/v1
https://www.preprints.org/manuscript/202008.0051/v1/download
https://www.mdpi.com/2504-4990/2/3/20
https://www.mdpi.com/2504-4990/2/3/20/pdf
https://dblp.uni-trier.de/db/journals/make/make2.html#Zemouri20
https://hal-cnam.archives-ouvertes.fr/hal-02931571/document
https://hal-cnam.archives-ouvertes.fr/hal-02931571
https://cnam.hal.science/hal-02931571v1
https://doi.org/10.3390/make2030020
https://cnam.hal.science/hal-02931571v1/document

A Semi-supervised Gaussian Mixture Variational Autoencoder method for few-shot fine-grained fault diagnosis

Authors: Zhiqian Zhao Yeyin Xu Jiabin Zhang et al.

Source: Neural Networks. 178:106482

Subject Terms: Deep Learning, Normal Distribution, Neural Networks, Computer, Supervised Machine Learning, Algorithms

Access URL: https://pubmed.ncbi.nlm.nih.gov/38945116

Entropy optimized semi-supervised decomposed vector-quantized variational autoencoder model based on transfer learning for multiclass text classification and generation

Authors: Malhotra, Shivani Kumar, Vinay Agarwal, Alpana

Index Terms: Computer Science - Machine Learning, Computer Science - Information Theory, text

URL: http://arxiv.org/abs/2111.08453

Comparison of the Effectiveness of Semi-Supervised Learning Methods Based on Autoencoders for Skin Cancer Photo Classification Tasks ; ПОРІВНЯННЯ ЕФЕКТИВНОСТІ МЕТОДІВ НАПІВКЕРОВАНОГО НАВЧАННЯ НА ОСНОВІ АВТОЕНКОДЕРІВ ДЛЯ ЗАДАЧ КЛАСИФІКАЦІЇ ФОТОГРАФІЙ РАКОВИХ ЗАХВОРЮВАНЬ ШКІРИ

Authors: Зарицький, О. О. Данилов, В. Я.

Source: Visnyk of Vinnytsia Politechnical Institute; No. 2 (2024); 71-77 ; Вестник Винницкого политехнического института; № 2 (2024); 71-77 ; Вісник Вінницького політехнічного інституту; № 2 (2024); 71-77 ; 1997-9274 ; 1997-9266 ; 10.31649/1997-9266-2024-173-2

Subject Terms: skin cancer, semi-supervised learning, autoencoders, variational autoencoder, Ladder Network, HAM10000, ракові захворювання шкіри, напівкероване навчання, автоенкодер, варіаційний автоенкодер, сходова нейронна мережа

File Description: application/pdf

Relation: https://visnyk.vntu.edu.ua/index.php/visnyk/article/view/3012/2788; https://visnyk.vntu.edu.ua/index.php/visnyk/article/view/3012

Availability: https://visnyk.vntu.edu.ua/index.php/visnyk/article/view/3012
https://doi.org/10.31649/1997-9266-2024-173-2-71-77

Anomaly Detection Based on Graph Convolutional Network–Variational Autoencoder Model Using Time-Series Vibration and Current Data.

Authors: Choi, Seung-Hwan An, Dawn Lee, Inho et al.

Source: Mathematics (2227-7390); Dec2024, Vol. 12 Issue 23, p3750, 16p

Subject Terms: INDUSTRIAL robots, ANOMALY detection (Computer security), AUTOENCODERS, SYSTEM failures, FEATURE extraction, DEEP learning

Physics-informed variational autoencoders for improved robustness to environmental factors of variation

Authors: Romain Thoreau Laurent Risser Véronique Achard et al.

Source: Machine Learning. 114

Subject Terms: FOS: Computer and information sciences, I.2.6, I.2.10, Statistics - Machine Learning, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Machine Learning (stat.ML), 68T45

Access URL: http://arxiv.org/abs/2210.10418

Multi-Class Anomaly Detection in Network Intrusion Detection Using Variational Autoencoder.

Authors: Al-zubidi, Azhar F. Farhan, Alaa Kadhim

Source: International Journal of Safety & Security Engineering; Jun2025, Vol. 15 Issue 6, p1265-1278, 14p

Subject Terms: INTRUSION detection systems (Computer security), ANOMALY detection (Computer security), LATENT variables, CYBERTERRORISM, OPTIMIZATION algorithms, OUTLIER detection, SUPERVISED learning

Physics-informed variational autoencoders for improved robustness to environmental factors of variation.

Authors: Thoreau, Romain Risser, Laurent Achard, Véronique et al.

Source: Machine Learning; Sep2025, Vol. 114 Issue 9, p1-40, 40p

Self-Organizing Maps-Assisted Variational Autoencoder for Unsupervised Network Anomaly Detection.

Authors: Huang, Hailong Yang, Jiahong Zeng, Hang et al.

Source: Symmetry (20738994); Apr2025, Vol. 17 Issue 4, p520, 15p

Subject Terms: DETECTION algorithms, SELF-organizing maps, FEATURE selection, ANOMALY detection (Computer security), AUTOENCODERS

ПОРІВНЯННЯ ЕФЕКТИВНОСТІ МЕТОДІВ НАПІВКЕРОВАНОГО НАВЧАННЯ НА ОСНОВІ АВТОЕНКОДЕРІВ ДЛЯ ЗАДАЧ КЛАСИФІКАЦІЇ ФОТОГРАФІЙ РАКОВИХ ЗАХВОРЮВАНЬ ШКІРИ. (Ukrainian)

Alternate Title: Comparison of the Effectiveness of Semi-Supervised Learning Methods Based on Autoencoders for Skin Cancer Photo Classification Tasks. (English)

Authors: Зарицький, О. О. Данилов, В. Я.

Source: Visnyk of Vinnytsia Politechnical Institute; 2024, Issue 2, p71-77, 7p

Landslide Susceptibility Assessment via Imbalanced Data Augmentation with Tabular Variational Autoencoder and Quality–Diversity Post-Selection.

Authors: Xu, Zhengyang Wang, Shitai Yin, Min et al.

Source: Applied Sciences (2076-3417); Nov2025, Vol. 15 Issue 22, p11965, 25p

Subject Terms: LANDSLIDES, DATA augmentation, BOOSTING algorithms, NATURAL disasters, RISK assessment

Developing semi-supervised variational autoencoder-generative adversarial network models to enhance quality prediction performance

Authors: Kai Wang Sai Kit Ooi Junghui Chen et al.

Source: Chemometrics and Intelligent Laboratory Systems. 217:104385

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 0210 nano-technology

Access URL: https://www.sciencedirect.com/science/article/pii/S0169743921001532

A novel semi-supervised power transformer defect monitoring technique using unreliable pseudo-labels with highly imbalanced partial discharge signals.

Authors: Rajamayil, Manimala Basharan, Vigneshwaran

Source: Electrical Engineering; Apr2025, Vol. 107 Issue 4, p4939-4957, 19p

Subject Terms: LONG short-term memory, CONVOLUTIONAL neural networks, AUTOENCODERS, MARGINAL distributions, ELECTRIC transformers

Full-field expansion and damage detection from sparse measurements using physics-informed variational autoencoders.

Authors: Kulkarni, Nitin Nagesh Sabato, Alessandro

Source: Structural Health Monitoring; Jan2026, Vol. 25 Issue 1, p607-629, 23p

Graph-Variational Convolutional Autoencoder-Based Fault Detection and Diagnosis for Photovoltaic Arrays.

Authors: Arifeen, Murshedul Petrovski, Andrei Hasan, Md Junayed et al.

Source: Machines; Dec2024, Vol. 12 Issue 12, p894, 15p

Subject Terms: SOLAR cells, RENEWABLE energy sources, PHOTOVOLTAIC power systems, SOLAR energy, AUTOENCODERS

Fault diagnosis using variational autoencoder GAN and focal loss CNN under unbalanced data.

Authors: Li, Weihan Liu, Dunke Li, Yang et al.

Source: Structural Health Monitoring; May2025, Vol. 24 Issue 3, p1859-1872, 14p

Subject Terms: CONVOLUTIONAL neural networks, GENERATIVE adversarial networks, FAULT diagnosis, AUTOENCODERS, GRAYSCALE model

A Transformer–VAE Approach for Detecting Ship Trajectory Anomalies in Cross-Sea Bridge Areas.

Authors: Hou, Jiawei Zhou, Hongzhu Grifoll, Manel et al.

Source: Journal of Marine Science & Engineering; May2025, Vol. 13 Issue 5, p849, 25p

Subject Terms: MARITIME safety, SHIPBORNE automatic identification systems, AUTOENCODERS, NAVIGATION in shipping, TRAFFIC safety