Search Results - "convolutional adversarial autoencoder"

Multi-scale convolutional adversarial autoencoder-based anomaly detector for lesion edge localization on diffusion MRI

Authors: Shuxin Cao Tenglong Wang Qingwei You et al.

Source: ISMRM Annual Meeting.

Parameter Space Compression and Random Structure Automatic Generation for the Inverse Design of Photonic Crystal Fibers Based on Convolutional Adversarial Autoencoder

Authors: Haomiao Yu Wei Huang Fangxin Xiao et al.

Source: Journal of Lightwave Technology. 42:7871-7881

Landslide Area Detection from Synthetic Aperture Radar Images Using Convolutional Adversarial Autoencoder and One-class SVM

Authors: Takashi Kuremoto Soichiro Hirata Shingo Mabu

Source: Proceedings of International Conference on Artificial Life and Robotics. 26:575-580

Access URL: https://alife-robotics.co.jp/members2021/icarob/data/html/data/GS/GS4/GS4-1.pdf

Unsupervised anomaly detection of nuclear power plants under noise background based on convolutional adversarial autoencoder combining self-attention mechanism

Authors: Xiang Sun Shunsheng Guo Shiqiao Liu et al.

Source: Nuclear Engineering and Design. 428:113493

Deep feature representation with online convolutional adversarial autoencoder for nonlinear process monitoring

Authors: Xu Yang Jieshi Xiao Jian Huang et al.

Source: Journal of the Taiwan Institute of Chemical Engineers. 155:105236

A Novel Fault Detection Method Based on One-Dimension Convolutional Adversarial Autoencoder (1DAAE)

Authors: Jian Wang Yakun Li Zhiyan Han

Source: Processes
Volume 11
Issue 2
Pages: 384

Subject Terms: autoencoder, convolutional layer, 02 engineering and technology, unsupervised learning, 0210 nano-technology, 01 natural sciences, fault detection, Tennessee Eastman process, 0105 earth and related environmental sciences

File Description: application/pdf

Reducing variations in multi-center Alzheimer’s disease classification with convolutional adversarial autoencoder

Authors: Cobbinah, BM Sorg, C Yang, Q et al.

Source: Medical Image Analysis. 82:102585

Subject Terms: 03 medical and health sciences, 0302 clinical medicine, Alzheimer Disease, Humans, Brain, Cognitive Dysfunction, Neuroimaging, Magnetic Resonance Imaging, 3. Good health

Access URL: https://pubmed.ncbi.nlm.nih.gov/36057187
https://ora.ox.ac.uk/objects/uuid:5808697c-6e7a-4a78-a743-2f08e71267de
https://doi.org/10.1016/j.media.2022.102585

Anomaly Detection Using Convolutional Adversarial Autoencoder and One-class SVM for Landslide Area Detection from Synthetic Aperture Radar Images

Authors: Shingo Mabu Soichiro Hirata Takashi Kuremoto

Source: Journal of Robotics, Networking and Artificial Life. 8:139

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

Access URL: https://www.atlantis-press.com/article/125959134.pdf
https://download.atlantis-press.com/journals/jrnal/125959134/view

Semisupervised hyperspectral imagery classification based on a three-dimensional convolutional adversarial autoencoder model with low sample requirements

Authors: Xiaorun Li Liaoying Zhao Zeyu Cao

Source: Journal of Applied Remote Sensing. 14:1

Subject Terms: 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Access URL: https://www.spiedigitallibrary.org/journals/journal-of-applied-remote-sensing/volume-14/issue-02/024522/Semisupervised-hyperspectral-imagery-classification-based-on-a-three-dimensional-convolutional/10.1117/1.JRS.14.024522.full
http://ui.adsabs.harvard.edu/abs/2020JARS...14b4522C/abstract

Deep feature representation with online convolutional adversarial autoencoder for nonlinear process monitoring.

Authors: Yang, Xu Xiao, Jieshi Huang, Jian et al.

Source: Journal of the Taiwan Institute of Chemical Engineers. Feb2024, Vol. 155, pN.PAG-N.PAG. 1p.

Subject Terms: Deep learning, Generative adversarial networks, Manufacturing processes, Gaussian distribution, Feature extraction, Fault currents

A Novel Fault Detection Method Based on One-Dimension Convolutional Adversarial Autoencoder (1DAAE).

Authors: Wang, Jian Li, Yakun Han, Zhiyan

Source: Processes; Feb2023, Vol. 11 Issue 2, p384, 18p

Subject Terms: DEEP learning, LATENT variables

Intelligent Unsupervised Network Traffic Classification Method Using Adversarial Training and Deep Clustering for Secure Internet of Things

Authors: Weijie Zhang Lanping Zhang Xixi Zhang et al.

Source: Future Internet, Vol 15, Iss 298, p 298 (2023)

Subject Terms: network traffic classification, convolutional adversarial autoencoder, Internet of things, unsupervised learning, deep clustering, Information technology, T58.5-58.64

Relation: https://www.mdpi.com/1999-5903/15/9/298; https://doaj.org/toc/1999-5903; https://doaj.org/article/d1abdee04cdb459b8a75478c7dff7a18

Availability: https://doi.org/10.3390/fi15090298
https://doaj.org/article/d1abdee04cdb459b8a75478c7dff7a18

Intelligent Unsupervised Network Traffic Classification Method Using Adversarial Training and Deep Clustering for Secure Internet of Things †.

Authors: Zhang, Weijie Zhang, Lanping Zhang, Xixi et al.

Source: Future Internet. Sep2023, Vol. 15 Issue 9, p298. 20p.

Subject Terms: *Internet of things, *Classification, Computer network traffic, Intelligent networks, Supervised learning, Computational complexity

Semisupervised hyperspectral imagery classification based on a three-dimensional convolutional adversarial autoencoder model with low sample requirements.

Authors: Cao, Zeyu Li, Xiaorun Zhao, Liaoying

Source: Journal of Applied Remote Sensing; Apr-Jun2020, Vol. 14 Issue 2, p24522-24522, 1p

Deep Learning for Simultaneous Inference of Hydraulic and Transport Properties.

Authors: Zhou, Zitong Zabaras, Nicholas Tartakovsky, Daniel M.

Source: Water Resources Research; Oct2022, Vol. 58 Issue 10, p1-20, 20p

Subject Terms: DEEP learning, INVERSE problems, CEPHALOMETRY, PREDICTION models

A data-driven methodology for bridge indirect health monitoring using unsupervised computer vision.

Authors: Hurtado, A. Calderon Alamdari, M. Makki Atroshchenko, E. et al.

Source: Mechanical Systems & Signal Processing. Mar2024, Vol. 210, pN.PAG-N.PAG. 1p.

Subject Terms: *BRIDGES, *STRUCTURAL health monitoring, *FEATURE extraction, *BRIDGE inspection, *WAVELET transforms, *THRESHOLDING algorithms

An adversarial model for electromechanical actuator fault diagnosis under nonideal data conditions.

Authors: Wang, Chao Tao, Laifa Ding, Yu et al.

Source: Neural Computing & Applications; 4/15/2022, Vol. 34 Issue 8, p5883-5904, 22p

Subject Terms: ACTUATORS, CONVOLUTIONAL neural networks, FAULT diagnosis, FEATURE extraction

Integration of Adversarial Autoencoders With Residual Dense Convolutional Networks for Estimation of Non-Gaussian Hydraulic Conductivities.

Authors: Shaoxing Mo Zabaras, Nicholas Xiaoqing Shi et al.

Source: Water Resources Research; Feb2020, Vol. 56 Issue 2, p1-24, 24p

Subject Terms: HYDRAULIC conductivity, FACIES, PHYSICS, PARAMETERIZATION

Semi-supervised Learning for Real-world Object Recognition using Adversarial Autoencoders

Authors: Mittal, Sudhanshu

Index Terms: Semi-supervised Learning, Object Recognition, Adversarial Training, Autoencoders, Computer Sciences, Datavetenskap (datalogi), Student thesis, info:eu-repo/semantics/bachelorThesis, text

URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-220794

Enhancing inverse modeling in groundwater systems through machine learning: a comprehensive comparative study.

Authors: Chen, Junjun Dai, Zhenxue Yin, Shangxian et al.

Source: Hydrology & Earth System Sciences; 2025, Vol. 29 Issue 17, p4251-4279, 29p

Subject Terms: OPTIMIZATION algorithms, MACHINE learning, RANDOM fields, RANDOM noise theory, STANDARD deviations, METAHEURISTIC algorithms