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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Deep Learning with Python - Learn Best Practices of Deep Learning Models with PyTorch (2nd Edition) by Ketkar, Nikhil, Moolayil, Jojo

“…, convolution neural networks, recurrent neural networks, long short-term memory networks, autoencoders…”
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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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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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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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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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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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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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A Real-Time Label-Free Self-Supervised Deep Learning Intrusion Detection for Handling New Type and Few-Shot Attacks in IoT Networks by Tong, Jianheng, Zhang, Ying

“… In this article, we proposed a self-supervised deep learning method combining supervised learning which is improved residual temporal convolution neural network adversarial autoencoder with efficient channel attention (IResTAE2A…”
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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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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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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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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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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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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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Land use and land cover classification with hyperspectral data: A comprehensive review of methods, challenges and future directions by Moharram, Mohammed Abdulmajeed, Sundaram, Divya Meena

“…Recently, many efforts have been concentrated on land use land cover (LULC) classification due to rapid urbanization, environmental pollution, agriculture…”
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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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Revolutionizing hyperspectral image classification for limited labeled data: unifying autoencoder-enhanced GANs with convolutional neural networks and zero-shot learning by Ranjan, Pallavi, Kaushal, Anukriti, Girdhar, Ashish, Kumar, Rajeev

“… To overcome these limitations, this paper proposes a novel semi-supervised framework that synergizes autoencoders, generative adversarial networks and zero-shot learning…”
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