Search Results - deep adversarial convolution autoencoder network

Enhanced Prediction of the Remaining Useful Life of Rolling Bearings Under Cross-Working Conditions via an Initial Degradation Detection-Enabled Joint Transfer Metric Network by Qi, Lingfeng, Pan, Jiafang, Huang, Tianping, Zhou, Zhenfeng, Huang, Faguo

“…), in which RAPP is obtained from a novel deep adversarial convolution autoencoder network (DACAEN) and compares discrepancies between the input and the reconstruction…”
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Video anomaly detection and localization via multivariate gaussian fully convolution adversarial autoencoder by Li, Nanjun, Chang, Faliang

“… Gaussian Fully Convolution Adversarial Autoencoder (MGFC-AAE), while the latent representations of anomalies…”
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Learning to Generate SAR Images With Adversarial Autoencoder by Song, Qian, Xu, Feng, Zhu, Xiao Xiang, Jin, Ya-Qiu

“… A novel adversarial autoencoder (AAE) is then proposed as an SAR representation and generation network…”
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L2G-ECG: Learning to Generate Missing Leads in ECG Signals using Adversarial Autoencoder by Srivastava, Apoorva, Sheet, Debdoot, Patra, Amit

“… A convolution neural network-based encoder-decoder-like network is trained to generate the missing lead signals, guided by a Visual Turing Test discriminator to enhance signal realism…”
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Pixel-Level and Global Similarity-Based Adversarial Autoencoder Network for Hyperspectral Unmixing by Tao, Wei, Zhang, Haiyang, Zeng, Shan, Wang, Long, Liu, Chaoxian, Li, Bing

“… To address these challenges, we propose a novel adversarial autoencoder unmixing network considering pixel-level and global…”
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Synthetic Time-Series Data Generation for Smart Grids Using 3D Autoencoder GAN by Zhang, Guihai, Sikdar, Biplab

“… In this paper, we introduce the 3D Autoencoder Generative Adversarial Network (3DAE GAN) as a solution to generate high-resolution and multivariate synthetic time-series…”
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DVAEGMM: Dual Variational Autoencoder With Gaussian Mixture Model for Anomaly Detection on Attributed Networks by Khan, Wasim, Haroon, Mohammad, Khan, Ahmad Neyaz, Hasan, Mohammad Kamrul, Khan, Asif, Mokhtar, Umi Asma, Islam, Shayla

“… Deep learning approaches like graph autoencoders are utilized to perform anomaly detection through obtaining node embeddings while dealing with the network nonlinearity and sparsity…”
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Reconstructing feedback representations in the ventral visual pathway with a generative adversarial autoencoder by Al-Tahan, Haider, Mohsenzadeh, Yalda

“… the computational role of feedback processes poorly understood. Here, we developed a generative autoencoder neural network model and adversarially trained it on a categorically diverse data set of images…”
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An unsupervised approach for thermal to visible image translation using autoencoder and generative adversarial network by Patel, Heena, Upla, Kishor P.

“… The current research on image-to-image translation for day-time has achieved remarkable performance using deep learning methods…”
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A Novel Fault Detection Method Based on One-Dimension Convolutional Adversarial Autoencoder (1DAAE) by Wang, Jian, Li, Yakun, Han, Zhiyan

“…Fault detection is an important and demanding problem in industry. Recently, many researchers have addressed the use of deep learning architectures for fault detection applications such as an autoencoder…”
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OD-SHIELD: Convolutional Autoencoder-Based Defense Against Adversarial Patch Attacks in Object Detection by Kim, Byeongchan, Kim, Heemin, Kang, Minjung, Nam, Hyunjee, Park, Sunghwan, Lee, Jaewoo, Kwak, Il-Youp

“…In the evolving landscape of deep neural network security, adversarial patch attacks present a serious challenge for object detection systems…”
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Monitoring the misalignment of machine tools with autoencoders after they are trained with transfer learning data by Demetgul, Mustafa, Zheng, Qi, Tansel, Ibrahim Nur, Fleischer, Jürgen

“…CNC machines have revolutionized manufacturing by enabling high-quality and high-productivity production. Monitoring the condition of these machines during…”
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Generating Synthetic Short-Range FMCW Range-Doppler Maps Using Generative Adversarial Networks and Deep Convolutional Autoencoders by de Oliveira, Marcio L. Lima, Bekooij, Marco J. G.

“…In this paper, we discuss the usage of Generative Adversarial Networks (GANs) and Deep Convolutional Autoen-coders (CAE…”
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Multi-Scale Autoencoder Suppression Strategy for Hyperspectral Image Anomaly Detection by Tu, Bing, Zhou, Tao, Liu, Bo, He, Yan, Li, Jun, Plaza, Antonio

“…Autoencoders (AEs) have received extensive attention in hyperspectral anomaly detection…”
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Drug-target interaction prediction based on graph convolutional autoencoder with dynamic weighting residual GCN by Zeng, Ming, Wang, Min, Xie, Fuqiang, Ji, Zhiwei

“… of network’s representation capabilities. Results In this paper, we propose a graph convolutional autoencoder model, named DDGAE, for DTIs prediction…”
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SDCA: a novel stack deep convolutional autoencoder – an application on retinal image denoising by Ghosh, Swarup Kr, Biswas, Biswajit, Ghosh, Anupam

“… This study represents a deep learning based approach to denoising images and restoring features using stack denoising convolutional autoencoder…”
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Spatial Super Resolution of Hyperspectral Images: Novel Approaches for Learning Deep Spatial-Spectral Prior by Kotapati, Hemanth, P.V, Arun

“… and deep learning models such as autoencoders(AE), convolution neural networks(CNN) and generative adversarial networks(GAN…”
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Deep Learning in Image Processing and Pattern Recognition

“… Recent years have seen the rapid development of image processing, especially with the application of deep learning, enabling it to become the most successfully applied intelligent technology…”
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A Systematic Review of Textile Anomaly Detection Systems by Doshi, Ananya, Dodiya, Vansh, Shah, Hetansh, Ghag, Kranti, Patil, Nilesh, Narvekar, Meera

“… The review encompasses a thorough examination of machine learning and deep learning models like Convolution Neural Networks, Generative Adversarial Networks, autoencoders, and ensemble models…”
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Blind Image Deconvolution Using Deep Generative Priors by Asim, Muhammad, Shamshad, Fahad, Ahmed, Ali

“…) using deep generative networks as priors. We employ two separate pretrained generative networks - given lower-dimensional Gaussian vectors as input, one of the generative models samples from the distribution of sharp images, while the other…”
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