Suchergebnisse - sparse convolutional autoencoder ((same OR sage))*

Feature extraction of fields of fluid dynamics data using sparse convolutional autoencoder von Obayashi, Wataru, Aono, Hikaru, Tatsukawa, Tomoaki, Fujii, Kozo

“… The technique here is based on the convolutional and sparse autoencoder learning algorithms and is called sparse convolutional autoencoder …”
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Intelligent Fault Diagnosis Method for Blade Damage of Quad-Rotor UAV Based on Stacked Pruning Sparse Denoising Autoencoder and Convolutional Neural Network von Yang, Pu, Wen, Chenwan, Geng, Huilin, Liu, Peng

“… This paper introduces a new intelligent fault diagnosis method based on stack pruning sparse denoising autoencoder and convolutional neural network (sPSDAE-CNN …”
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Temperature scaling unmixing framework based on convolutional autoencoder von Xu, Jin, Xu, Mingming, Liu, Shanwei, Sheng, Hui, Yang, Zhiru

“… •The framework is a new spatial level constraint method and can be transferred to other convolutional autoencoder-based methods …”
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Deep learning for pixel-level image fusion: Recent advances and future prospects von Liu, Yu, Chen, Xun, Wang, Zengfu, Wang, Z. Jane, Ward, Rabab K., Wang, Xuesong

“… By integrating the information contained in multiple images of the same scene into one composite image, pixel-level image fusion is recognized as having high significance in a variety of fields …”
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3D Segmentation of Perivascular Spaces on T1-Weighted 3 Tesla MR Images With a Convolutional Autoencoder and a U-Shaped Neural Network von Boutinaud, Philippe, Tsuchida, Ami, Laurent, Alexandre, Adonias, Filipa, Hanifehlou, Zahra, Nozais, Victor, Verrecchia, Violaine, Lampe, Leonie, Zhang, Junyi, Zhu, Yi-Cheng, Tzourio, Christophe, Mazoyer, Bernard, Joliot, Marc

“… ) in deep white matter (DWM) and basal ganglia (BG). This algorithm is based on an autoencoder and a U-shaped network (U-net …”
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K-Means Clustering Optimizing Deep Stacked Sparse Autoencoder von Bi, Yandong, Wang, Peng, Guo, Xuchao, Wang, Zhijun, Cheng, Shuhan

“… How to speed up training is a problem deserving of study. In order to accelerate training, K-means clustering optimizing deep stacked sparse autoencoder (K-means sparse SAE …”
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Image Compression: Sparse Coding vs. Bottleneck Autoencoders von Watkins, Yijing, Iaroshenko, Oleksandr, Sayeh, Mohammad, Kenyon, Garrett

“… In this work, we explore the ability of sparse coding to improve reconstructed image quality for the same degree of compression …”
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Multi-frequency and multi-domain human activity recognition based on SFCW radar using deep learning von Jia, Yong, Guo, Yong, Wang, Gang, Song, Ruiyuan, Cui, Guolong, Zhong, Xiaoling

“… On the other hand, multi-frequency spectrograms furnish same type of features while with different …”
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A Robust System for Noisy Image Classification Combining Denoising Autoencoder and Convolutional Neural Network von Singha, Sudipta, Imran, Sk, A., M., Murase, Kazuyuki

“… To solve this issue, several researches have been conducted utilizing denoising autoencoder (DAE …”
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Non-revisiting genetic cost-sensitive sparse autoencoder for imbalanced fault diagnosis von Peng, Peng, Zhang, Wenjia, Zhang, Yi, Wang, Hongwei, Zhang, Heming

“… ). In this research, we propose a non-revisiting genetic cost-sensitive sparse autoencoder(NrGCS-SAE) solution, which not only incorporates cost-sensitive learning with sparse autoencoder but also solves the problem of class weights assignment …”
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Multiday EMG-Based Classification of Hand Motions with Deep Learning Techniques von Zia ur Rehman, Muhammad, Waris, Asim, Gilani, Syed Omer, Jochumsen, Mads, Niazi, Imran Khan, Jamil, Mohsin, Farina, Dario, Kamavuako, Ernest Nlandu

“… (MYB, a wearable EMG sensor). The classification was performed by a convolutional neural network (CNN …”
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NOISY IMAGE CLASSIFICATION USING HYBRID DEEP LEARNING METHODS von Roy, Sudipta Singha, Ahmed, Mahtab, Akhand, Muhammad Aminul Haque

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Advancing Handwritten Digit Recognition in Defense Systems: Comparative Analysis of Autoencoder-Based Transfer Learning von Jain, Shruti, Kapur, Shivani, Vandana

“… These practical challenges led to the need for the development of models that can obtain knowledge from one domain and use it in another similar (but not same …”
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Variational Autoencoders for Localized Mesh Deformation Component Analysis von Tan, Qingyang, Zhang, Ling-Xiao, Yang, Jie, Lai, Yu-Kun, Gao, Lin

“… In this paper we propose a mesh-based variational autoencoder architecture that is able to cope with meshes with irregular connectivity and nonlinear deformations, assuming that the analyzed dataset …”
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Seismic noise attenuation by signal reconstruction: an unsupervised machine learning approach von Gao, Yang, Zhao, Pingqi, Li, Guofa, Li, Hao

“… ABSTRACT Random noise attenuation is an essential step in seismic data processing for improving seismic data quality and signal‐to‐noise ratio. We adopt an …”
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A Cylindrical Near-Field Acoustical Holography Method Based on Cylindrical Translation Window Expansion and an Autoencoder Stacked with 3D-CNN Layers von Wang, Jiaxuan, Zhang, Weihan, Zhang, Zhifu, Huang, Yizhe

“… The performance of near-field acoustic holography (NAH) with a sparse sampling rate will be affected by spatial aliasing or inverse ill-posed equations …”
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A radar emitter structural identification method for complex conditions based on compressed sensing and an autoencoder von Song, Yu, Zhang, Wei, Yang, Yaozu, Zhang, Xin, Jiang, Yilin

“… A stacked convolutional autoencoder (SCAE) network with strong representation learning capability, along with a loss function that integrates time-domain and sparse-domain constraints (TCS …”
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A radar emitter structural identification method for complex conditions based on compressed sensing and an autoencoder: A radar emitter structural identification method for complex conditions based von Song, Yu, Zhang, Wei, Yang, Yaozu, Zhang, Xin, Jiang, Yilin

“… A stacked convolutional autoencoder (SCAE) network with strong representation learning capability, along with a loss function that integrates time-domain and sparse-domain constraints (TCS …”
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Deep Convolutional Autoencoder Architecture for Predictive Maintenance Applications von Catak, Yigit, Sahin, Kerem, Guney, Osman Berke, Ozkan, Huseyin

“… In this study, we develop an auto-encoder extension of previously proposed deep convolutional network that is trained successfully on the modelling of electroencephalogram (EEG …”
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Compression of Vehicle Trajectories with a Variational Autoencoder von Rákos, Olivér, Aradi, Szilárd, Bécsi, Tamás, Szalay, Zsolt

“… ). This paper presents a Variational Autoencoder (VAE) solution to solve the compression problem, and as an added benefit, it also provides classification information …”
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