Výsledky vyhledávání - "autoencoder network"

Towards Enhanced Interpretability: A Mechanism-Driven domain adaptation model for bearing fault diagnosis across operating conditions Autor Jiang, Fei, Kuang, Yicong, Li, Tao, Zhang, Shaohui, Wu, Zhaoqian, Feng, Ke, Li, Weihua

“…Deep learning has emerged as a formidable tool in bearing fault diagnosis, yet its effectiveness is often hampered by the opaqueness of feature interpretation…”
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Softmax regression based deep sparse autoencoder network for facial emotion recognition in human-robot interaction Autor Chen, Luefeng, Zhou, Mengtian, Su, Wanjuan, Wu, Min, She, Jinhua, Hirota, Kaoru

“…Deep neural network (DNN) has been used as a learning model for modeling the hierarchical architecture of human brain. However, DNN suffers from problems of…”
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DAEN: Deep Autoencoder Networks for Hyperspectral Unmixing Autor Su, Yuanchao, Li, Jun, Plaza, Antonio, Marinoni, Andrea, Gamba, Paolo, Chakravortty, Somdatta

“…Spectral unmixing is a technique for remotely sensed image interpretation that expresses each (possibly mixed) pixel as a combination of pure spectral…”
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An unsupervised feature learning based health indicator construction method for performance assessment of machines Autor Guo, Liang, Yu, Yaoxiang, Duan, Andongzhe, Gao, Hongli, Zhang, Jiangquan

“…•A multi-scale CAE network is used to extract features from three scale levels.•Only the data collected under health conditions are used for network…”
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Sequential three-way decision based on multi-granular autoencoder features Autor Zhang, Libo, Li, Huaxiong, Zhou, Xianzhong, Huang, Bing

“…Autoencoder network is an efficient representation learning method. In general, a finer feature set obtained from autoencoder leads to a lower error rate and…”
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Adversarial Autoencoder Network for Hyperspectral Unmixing Autor Jin, Qiwen, Ma, Yong, Fan, Fan, Huang, Jun, Mei, Xiaoguang, Ma, Jiayi

“…Spectral unmixing (SU), which refers to extracting basic features (i.e., endmembers) at the subpixel level and calculating the corresponding proportion (i.e.,…”
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Hyperspectral Unmixing for Additive Nonlinear Models With a 3-D-CNN Autoencoder Network Autor Zhao, Min, Wang, Mou, Chen, Jie, Rahardja, Susanto

“…Spectral unmixing is an important task in hyperspectral image processing for separating the mixed spectral data pertaining to various materials observed aiming…”
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A new efficient training strategy for deep neural networks by hybridization of artificial bee colony and limited–memory BFGS optimization algorithms Autor Badem, Hasan, Basturk, Alper, Caliskan, Abdullah, Yuksel, Mehmet Emin

“…Working up with deep learning techniques requires profound understanding of the mechanisms underlying the optimization of the internal parameters of complex…”
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Dual-Branch Subpixel-Guided Network for Hyperspectral Image Classification Autor Han, Zhu, Yang, Jin, Gao, Lianru, Zeng, Zhiqiang, Zhang, Bing, Chanussot, Jocelyn

“…Deep learning (DL) has been widely applied to hyperspectral image (HSI) classification, owing to its promising feature learning and representation…”
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Application of Robust Zero-Watermarking Scheme Based on Federated Learning for Securing the Healthcare Data Autor Han, Baoru, Jhaveri, Rutvij H., Wang, Han, Qiao, Dawei, Du, Jinglong

“…The privacy protection and data security problems existing in the healthcare framework based on the Internet of Medical Things (IoMT) have always attracted…”
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LSTM-DNN Based Autoencoder Network for Nonlinear Hyperspectral Image Unmixing Autor Zhao, Min, Yan, Longbin, Chen, Jie

“…Blind hyperspectral unmixing is an important technique in hyperspectral image analysis, aiming at estimating endmembers and their respective fractional…”
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Nonlinear Unmixing of Hyperspectral Data via Deep Autoencoder Networks Autor Wang, Mou, Zhao, Min, Chen, Jie, Rahardja, Susanto

“…Nonlinear spectral unmixing is an important and challenging problem in hyperspectral image processing. Classical nonlinear algorithms are usually derived based…”
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Waste classification using AutoEncoder network with integrated feature selection method in convolutional neural network models Autor Toğaçar, Mesut, Ergen, Burhan, Cömert, Zafer

“…[Display omitted] •Classification of organic and recyclable wastes with deep learning models.•We extracted and combined the features from the layer of…”
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A multistage graph-based autoencoder network with global-local features for hyperspectral unmixing Autor Dong, Hua, Zhang, Xiaohua, Meng, Hongyun, Jiao, Licheng

“…Hyperspectral unmixing using deep learning has received increasing attention as a technique for estimating endmember spectra and fractional abundances of land…”
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SAE-Net: A Deep Neural Network for SAR Autofocus Autor Pu, Wei

“…The sparsity-driven technique is a widely used tool to solve the synthetic aperture radar (SAR) imaging problem. However, it always encounters sensitivity to…”
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Multilinear hyperspectral unmixing based on autoencoder and recurrent neural network Autor Jin, Zehui, Yi, Xiaorui, Liu, Yue, Zhang, Hongjuan

“…Spectral unmixing techniques estimate the endmember spectra and corresponding abundance fractions that constitute the pixels of hyperspectral remote sensing…”
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Recognition of geochemical anomalies using a deep variational autoencoder network Autor Luo, Zijing, Xiong, Yihui, Zuo, Renguang

“…Deep learning (DL) algorithms have received increased attention in various fields. In the field of geoscience, DL has been shown to be a powerful tool for…”
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A Self-Adaptive Discriminative Autoencoder for Medical Applications Autor Ge, Xiaolong, Qu, Yanpeng, Shang, Changjing, Yang, Longzhi, Shen, Qiang

“…Computer aided diagnosis (CAD) systems play an essential role in the early detection and diagnosis of developing disease for medical applications. In order to…”
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Deep Embedding Clustering Based on Residual Autoencoder Autor Li, Mengli, Cao, Chao, Li, Chungui, Yang, Shuhong

“…Clustering algorithm is one of the most widely used and influential analysis techniques. With the advent of deep learning, deep embedding clustering algorithms…”
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Autoencoder-based image fusion network with enhanced channels and feature saliency Autor Wang, Hongmei, Lu, Xuanyu, Li, Ze

“…The existing deep learning based infrared and visible image fusion technologies have made significant progress, but there are still many problems need to be…”
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