Výsledky vyhľadávania - autoencoder network

Autoencoder in Autoencoder Networks Autor Zhang, Changqing, Geng, Yu, Han, Zongbo, Liu, Yeqing, Fu, Huazhu, Hu, Qinghua

“… To address this issue, we propose a novel unsupervised multiview representation learning (UMRL) algorithm, termed autoencoder in autoencoder networks…”
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AE2-Nets: Autoencoder in Autoencoder Networks Autor Zhang, Changqing, Liu, Yeqing, Fu, Huazhu

“… Differently, in this paper, we focus on unsupervised representation learning and propose a novel framework termed Autoencoder in Autoencoder Networks (AE^2-Nets…”
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Pilots' Fatigue Status Recognition Using Deep Contractive Autoencoder Network Autor Wu, Edmond Q., Peng, X. Y., Zhang, Caizhi Z., Lin, J. X., Sheng, Richard S. F.

“…The evaluation of pilots' fatigue status is of substantial significance in aviation safety, which faces two major issues. They are how to get the fatigue…”
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A masked autoencoder network for spatiotemporal predictive learning: A Masked Autoencoder Network Autor Sun, Fengzhen, Jin, Weidong

“… More importantly, we build a spatiotemporal autoencoder by the random clip mask strategy, which leads to better feature mining for temporal…”
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Recognition of multivariate geochemical anomalies using a geologically-constrained variational autoencoder network with spectrum separable module – A case study in Shangluo District, China Autor Zhao, Bo, Zhang, Dehui, Tang, Panpan, Luo, Xiaoyan, Wan, Haoming, An, Lin

“…This study has developed a novel variational autoencoder architecture by incorporating the spectrum separable module, termed SSM-VAE, so as to recognize the multi-mineral-species geochemical patterns…”
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Gated Autoencoder Network for Spectral–Spatial Hyperspectral Unmixing Autor Hua, Ziqiang, Li, Xiaorun, Jiang, Jianfeng, Zhao, Liaoying

“…Convolution-based autoencoder networks have yielded promising performances in exploiting spatial…”
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Orthogonal Nonnegative Matrix Factorization using a novel deep Autoencoder Network Autor Yang, Mingming, Xu, Songhua

“… to their simple network structures and DLS models rely on an iterative procedure to derive weights, leading to a less efficient solution process and cannot be reused to factorize new matrices…”
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Network traffic classification using deep convolutional recurrent autoencoder neural networks for spatial–temporal features extraction Autor D’Angelo, Gianni, Palmieri, Francesco

“… For this purpose, a novel autoencoder-based deep neural network architecture…”
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SSF-Net: A Spatial-Spectral Features Integrated Autoencoder Network for Hyperspectral Unmixing Autor Wang, Bin, Yao, Huizheng, Song, Dongmei, Zhang, Jie, Gao, Han

“…) due to its powerful learning capabilities. Particularly, the unsupervised unmixing method based on autoencoder (AE…”
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Research on Ship Radiated Noise Separation Method Based on Neural Network Autoencoder Combined With Time-Frequency Masking Autor Xia, Zhi, Zhang, Ziang, Shi, Jie, Zhao, Zihao

“… The proposed method integrates neural network autoencoders with time-frequency masking techniques…”
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SSANet: An Adaptive Spectral–Spatial Attention Autoencoder Network for Hyperspectral Unmixing Autor Wang, Jie, Xu, Jindong, Chong, Qianpeng, Liu, Zhaowei, Yan, Weiqing, Xing, Haihua, Xing, Qianguo, Ni, Mengying

“…Convolutional neural-network-based autoencoders, which can integrate the spatial correlation between pixels well, have been broadly used for hyperspectral unmixing and obtained excellent performance…”
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Adversarial Autoencoder Network for Hyperspectral Unmixing Autor Jin, Qiwen, Ma, Yong, Fan, Fan, Huang, Jun, Mei, Xiaoguang, Ma, Jiayi

“… Since the unmixing procedure can be explained as finding a set of low-dimensional representations that reconstruct the data with their corresponding bases, autoencoders (AEs…”
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Optimizing sEMG Gesture Recognition with Stacked Autoencoder Neural Network for Bionic Hand Autor Yadav, Mr. Amol Pandurang, Patil, Dr. Sandip.R.

“…This study presents a novel deep learning approach for surface electromyography (sEMG) gesture recognition using stacked autoencoder neural network…”
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Distributed autoencoder classifier network for small‐scale and scattered COVID ‐19 dataset classification Autor Yang, Yuan, Zhang, Lin, Ren, Lei, Wang, Xiaohan

“… We propose a distributed autoencoder (AE) classifier network for this purpose. It contains a central classifier and multiple distributed AEs…”
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Data space reduction, quality assessment and searching of seismograms: autoencoder networks for waveform data Autor Valentine, Andrew P., Trampert, Jeannot

“…SUMMARY What makes a seismogram look like a seismogram? Seismic data sets generally contain waveforms sharing some set of visual characteristics and…”
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DAEN: Deep Autoencoder Networks for Hyperspectral Unmixing Autor Su, Yuanchao, Li, Jun, Plaza, Antonio, Marinoni, Andrea, Gamba, Paolo, Chakravortty, Somdatta

“… In this paper, we develop a new technique for unsupervised unmixing which is based on a deep autoencoder network (DAEN…”
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Cyber–physical anomaly detection for inverter-based microgrid using autoencoder neural network Autor Tabassum, Tambiara, Toker, Onur, Khalghani, Mohammad Reza

“…Contemporary cyber–physical systems have evolved into highly autonomous and distributed entities, enabled by cutting-edge control frameworks and advanced communication networks…”
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EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral Unmixing Autor Ozkan, Savas, Kaya, Berk, Akar, Gozde Bozdagi

“… In this paper, we propose a novel endmember extraction and hyperspectral unmixing scheme, so-called EndNet , that is based on a two-staged autoencoder network…”
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CAD-Skin: A Hybrid Convolutional Neural Network–Autoencoder Framework for Precise Detection and Classification of Skin Lesions and Cancer Autor Khan, Abdullah, Sajid, Muhammad Zaheer, Khan, Nauman Ali, Youssef, Ayman, Abbas, Qaisar

“… neural networks and autoencoders to improve the classification efficiency of skin cancer…”
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Deep Spectral Clustering Using Dual Autoencoder Network Autor Yang, Xu, Deng, Cheng, Zheng, Feng, Yan, Junchi, Liu, Wei

“… We first devise a dual autoencoder network, which enforces the reconstruction constraint for the latent representations and their noisy versions, to embed the inputs into a latent space for clustering…”
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