Search Results - "Convolutional Autoencoder"

Convolutional Autoencoder for Spectral-Spatial Hyperspectral Unmixing by Palsson, Burkni, Ulfarsson, Magnus O., Sveinsson, Johannes R.

“…Blind hyperspectral unmixing is the process of expressing the measured spectrum of a pixel as a combination of a set of spectral signatures called endmembers…”
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Audio based depression detection using Convolutional Autoencoder by Sardari, Sara, Nakisa, Bahareh, Rastgoo, Mohammed Naim, Eklund, Peter

“…•A novel audio-based depression detection system using Convolutional Autoencoder…”
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Unsupervised Spatial-Spectral Feature Learning by 3D Convolutional Autoencoder for Hyperspectral Classification by Mei, Shaohui, Ji, Jingyu, Geng, Yunhao, Zhang, Zhi, Li, Xu, Du, Qian

“…) convolutional autoencoder (3D-CAE). The proposed 3D-CAE consists of 3D or elementwise operations only, such as 3D convolution, 3D pooling, and 3D batch normalization, to maximally explore spatial-spectral structure information for feature extraction…”
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MR‐DCAE: Manifold regularization‐based deep convolutional autoencoder for unauthorized broadcasting identification by Zheng, Qinghe, Zhao, Penghui, Zhang, Deliang, Wang, Hongjun

“…‐based deep convolutional autoencoder (MR‐DCAE) model for unauthorized broadcasting identification…”
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Temporal convolutional autoencoder for unsupervised anomaly detection in time series by Thill, Markus, Konen, Wolfgang, Wang, Hao, Bäck, Thomas

“…Learning temporal patterns in time series remains a challenging task up until today. Particularly for anomaly detection in time series, it is essential to…”
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Convolutional Autoencoder Model for Finger-Vein Verification by Hou, Borui, Yan, Ruqiang

“…This paper presents a novel deep learning-based method that integrates a Convolutional Auto-Encoder (CAE) with support vector machine (SVM) for finger-vein…”
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Segmentation of digital rock images using deep convolutional autoencoder networks by Karimpouli, Sadegh, Tahmasebi, Pejman

“…). Among them, convolutional autoencoder networks have produced accurate results in different applications when various images are available for the training…”
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Auto-AD: Autonomous Hyperspectral Anomaly Detection Network Based on Fully Convolutional Autoencoder by Wang, Shaoyu, Wang, Xinyu, Zhang, Liangpei, Zhong, Yanfei

“…Hyperspectral anomaly detection is aimed at detecting observations that differ from their surroundings, and is an active area of research in hyperspectral…”
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Identifying strong lenses with unsupervised machine learning using convolutional autoencoder by Cheng, Ting-Yun, Li, Nan, Conselice, Christopher J, Aragón-Salamanca, Alfonso, Dye, Simon, Metcalf, Robert B

“…ABSTRACT In this paper, we develop a new unsupervised machine learning technique comprised of a feature extractor, a convolutional autoencoder, and a clustering algorithm consisting of a Bayesian Gaussian mixture model…”
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Modeling Task fMRI Data Via Deep Convolutional Autoencoder by Huang, Heng, Hu, Xintao, Zhao, Yu, Makkie, Milad, Dong, Qinglin, Zhao, Shijie, Guo, Lei, Liu, Tianming

“…Task-based functional magnetic resonance imaging (tfMRI) has been widely used to study functional brain networks under task performance. Modeling tfMRI data is…”
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CNN and Convolutional Autoencoder (CAE) based real-time sensor fault detection, localization, and correction by Jana, Debasish, Patil, Jayant, Herkal, Sudheendra, Nagarajaiah, Satish, Duenas-Osorio, Leonardo

“…Increasing advances in sensing technologies and analytics have led to the proliferation of sensors to monitor structural and infrastructural systems. Accurate…”
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Convolutional Autoencoder-Based Multispectral Image Fusion by Azarang, Arian, Manoochehri, Hafez E., Kehtarnavaz, Nasser

“… This method can be categorized as a component substitution method in which a convolutional autoencoder network is trained to generate original panchromatic images from their spatially degraded versions…”
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Hybrid robust convolutional autoencoder for unsupervised anomaly detection of machine tools under noises by Yan, Shen, Shao, Haidong, Xiao, Yiming, Liu, Bin, Wan, Jiafu

“…•A new FDD loss function to suppress the noises is designed.•Construct the PCDF module to enhance the robustness of the network.•The unsupervised anomaly…”
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Hyperspectral Data Compression Using Fully Convolutional Autoencoder by La Grassa, Riccardo, Re, Cristina, Cremonese, Gabriele, Gallo, Ignazio

“… In this paper, we propose a spectral signals compressor network based on deep convolutional autoencoder (SSCNet…”
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Deep convolutional autoencoder for radar-based classification of similar aided and unaided human activities by Seyfioglu, Mehmet Saygin, Ozbayoglu, Ahmet Murat, Gurbuz, Sevgi Zubeyde

“… Moreover, a three-layer, deep convolutional autoencoder (CAE) is proposed, which utilizes unsupervised pretraining to initialize the weights in the subsequent convolutional layers…”
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A graph convolutional autoencoder approach to model order reduction for parametrized PDEs by Pichi, Federico, Moya, Beatriz, Hesthaven, Jan S.

“…The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM…”
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Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning by Park, Jiwoong, Lee, Minsik, Chang, Hyung Jin, Lee, Kyuewang, Choi, Jin Young

“…We propose a symmetric graph convolutional autoencoder which produces a low-dimensional latent representation from a graph…”
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Anomaly detection of defects on concrete structures with the convolutional autoencoder by Chow, J.K., Su, Z., Wu, J., Tan, P.S., Mao, X., Wang, Y.H.

“… A convolutional autoencoder was trained as a reconstruction-based model, with the defect-free images, to rapidly and reliably detect defects from the large…”
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Multi-level convolutional autoencoder networks for parametric prediction of spatio-temporal dynamics by Xu, Jiayang, Duraisamy, Karthik

“… A convolutional autoencoder is used as the top level to encode the high dimensional input data along spatial dimensions into a sequence of latent variables…”
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One-Dimensional Residual Convolutional Autoencoder Based Feature Learning for Gearbox Fault Diagnosis by Yu, Jianbo, Zhou, Xingkang

“… In this article, a new DNN, one-dimensional residual convolutional autoencoder (1-DRCAE), is proposed for learning features from vibration signals directly in an unsupervised-learning way…”
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