Výsledky vyhľadávania - "autoencoder network"

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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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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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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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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Recognition of geochemical anomalies using a deep autoencoder network Autor Xiong, Yihui, Zuo, Renguang

“…In this paper, we train an autoencoder network to encode and reconstruct a geochemical sample population with unknown complex multivariate probability distributions…”
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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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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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Automatic Fabric Defect Detection with a Multi-Scale Convolutional Denoising Autoencoder Network Model Autor Mei, Shuang, Wang, Yudan, Wen, Guojun

“… This approach is used to reconstruct image patches with a convolutional denoising autoencoder network at multiple Gaussian pyramid levels and to synthesize detection results from the corresponding resolution channels…”
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LSTM-DNN Based Autoencoder Network for Nonlinear Hyperspectral Image Unmixing Autor Zhao, Min, Yan, Longbin, Chen, Jie

“… This paper proposes a nonsymmetric autoencoder network to overcome this issue. The proposed scheme benefits from the universal modeling ability of deep neural networks and enables to learn the nonlinear relation from the data…”
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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

“… However, DNN suffers from problems of learning efficiency and computational complexity. To address these problems, deep sparse autoencoder network (DSAN…”
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TANet: An Unsupervised Two-Stream Autoencoder Network for Hyperspectral Unmixing Autor Jin, Qiwen, Ma, Yong, Mei, Xiaoguang, Ma, Jiayi

“…Spectral unmixing is a major technique for the further development of hyperspectral analysis. It aims to determine the corresponding proportion (fractional…”
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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…”
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Orthogonal Nonnegative Matrix Factorization using a novel deep Autoencoder Network Autor Yang, Mingming, Xu, Songhua

“…Orthogonal Nonnegative Matrix Factorization (ONMF) offers an important analytical vehicle for addressing many problems. Encouraged by record-breaking successes…”
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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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Multi-stage convolutional autoencoder network for hyperspectral unmixing Autor Yu, Yang, Ma, Yong, Mei, Xiaoguang, Fan, Fan, Huang, Jun, Li, Hao

“…Hyperspectral unmixing (HU) is a fundamental and critical task in various hyperspectral image (HSI) applications. Over the past few years, the linear mixing…”
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A masked autoencoder network for spatiotemporal predictive learning: A Masked Autoencoder Network Autor Sun, Fengzhen, Jin, Weidong

“…This paper is about predictive learning, which is generating future frames given previous images. Suffering from the vanishing gradient problem, existing…”
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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

“…In recent years, deep learning (DL) has received tremendous attention in the field of hyperspectral unmixing (HU) due to its powerful learning capabilities…”
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SAEN-BGS: Energy-efficient spiking autoencoder network for background subtraction Autor Zhang, Zhixuan, Li, Xiao Peng, Liu, Qi

“…, and disturbances like air turbulence or swaying trees. To address this problem, we design a spiking autoencoder network, termed SAEN-BGS, based on noise resilience and time-sequence sensitivity of spiking neural networks (SNNs…”
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Dual Autoencoder Network for Retinex-Based Low-Light Image Enhancement Autor Park, Seonhee, Yu, Soohwan, Kim, Minseo, Park, Kwanwoo, Paik, Joonki

“…This paper presents a dual autoencoder network model based on the retinex theory to perform the low-light enhancement and noise reduction by combining the stacked and convolutional autoencoders…”
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LSTM-Autoencoder Network for the Detection of Seismic Electric Signals Autor Xue, Jiyan, Huang, Qinghua, Wu, Sihong, Nagao, Toshiyasu

“…Seismic electric signals (SESs) are essential short-term precursors of earthquakes. Accurate and efficient detection of SESs is significant to short-term…”
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