Search Results - multichannel autoencoder priors

Accelerated model‐based iterative reconstruction strategy for sparse‐view photoacoustic tomography aided by multi‐channel autoencoder priors by Song, Xianlin, Zhong, Wenhua, Li, Zilong, Peng, Shuchong, Zhang, Hongyu, Wang, Guijun, Dong, Jiaqing, Liu, Xuan, Xu, Xiaoling, Liu, Qiegen

“…‐channel autoencoder priors was proposed. A multi‐channel denoising autoencoder network was designed to learn prior information, which provides constraints for model…”
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Fusing multichannel autoencoders with dynamic global loss for self-supervised fault diagnosis by Li, Chuan, Xiong, Manjun, Shen, Hongmeng, Bai, Yun, Yang, Shuai, Pu, Zhiqiang

“… Considering the randomness and drift of fault features, this paper proposes fusing multichannel autoencoders with dynamic global loss (FMA-DGL…”
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Rejecting Unknown Gestures Based on Surface-Electromyography Using Variational Autoencoder by Dai, Qingfeng, Wong, Yongkang, Kankanhalli, Mohan, Li, Xiangdong, Geng, Weidong

“… In this work, we propose a novel variational autoencoder based approach for open-set gesture recognition based on sparse multichannel sEMG signals…”
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Semi-Supervised Multichannel Speech Enhancement With a Deep Speech Prior by Sekiguchi, Kouhei, Bando, Yoshiaki, Nugraha, Aditya Arie, Yoshii, Kazuyoshi, Kawahara, Tatsuya

“…This paper describes a semi-supervised multichannel speech enhancement method that uses clean speech data for prior training…”
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Mineralized-Anomaly Identification Based on Convolutional Sparse Autoencoder Network and Isolated Forest by Yang, Na, Zhang, Zhenkai, Yang, Jianhua, Hong, Zenglin

“…According to the characteristic that mineralized-anomaly samples have larger reconstruction errors, traditional autoencoder networks have been applied widely in mineralized-anomaly identification…”
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REDAEP: Robust and Enhanced Denoising Autoencoding Prior for Sparse-View CT Reconstruction by Zhang, Fengqin, Zhang, Minghui, Qin, Binjie, Zhang, Yi, Xu, Zichen, Liang, Dong, Liu, Qiegen

“… However, less projection views usually lead to low-resolution images. To address this issue, we propose a robust and enhanced mechanism on the basis of denoising autoencoding prior, or robust EDAEP (REDAEP…”
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SPAER: Sparse Deep Convolutional Autoencoder Model to Extract Low Dimensional Imaging Biomarkers for Early Detection of Breast Cancer Using Dynamic Thermography by Yousefi, Bardia, Akbari, Hamed, Hershman, Michelle, Kawakita, Satoru, Fernandes, Henrique C., Ibarra-Castanedo, Clemente, Ahadian, Samad, Maldague, Xavier P. V.

“…) prior to mammography. In this study, we propose a sparse deep convolutional autoencoder model named SPAER to extract low-dimensional deep thermomics to aid breast cancer diagnosis…”
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Cauchy Multichannel Speech Enhancement with a Deep Speech Prior by Fontaine, Mathieu, Nugraha, Aditya Arie, Badeau, Roland, Yoshii, Kazuyoshi, Liutkus, Antoine

“…We propose a semi-supervised multichannel speech enhancement system based on a probabilistic model which assumes that both speech and noise follow the heavy-tailed multi-variate complex Cauchy distribution…”
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A Flow-Based Deep Latent Variable Model for Speech Spectrogram Modeling and Enhancement by Nugraha, Aditya Arie, Sekiguchi, Kouhei, Yoshii, Kazuyoshi

“…This article describes a deep latent variable model of speech power spectrograms and its application to semi-supervised speech enhancement with a deep speech prior…”
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Highly undersampled magnetic resonance imaging reconstruction using autoencoding priors by Liu, Qiegen, Yang, Qingxin, Cheng, Huitao, Wang, Shanshan, Zhang, Minghui, Liang, Dong

“… Methods A denoising autoencoder (DAE) network is leveraged as an explicit prior to address the highly undersampling MR image reconstruction problem…”
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Unfolding Neural Networks for Compressive Multichannel Blind Deconvolution by Tolooshams, Bahareh, Mulleti, Satish, Ba, Demba, Eldar, Yonina C.

“…We propose a learned-structured unfolding neural network for the problem of compressive sparse multichannel blind-deconvolution…”
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Unfolding Neural Networks for Compressive Multichannel Blind Deconvolution by Tolooshams, Bahareh, Mulleti, Satish, Demba Ba, Eldar, Yonina C

“…We propose a learned-structured unfolding neural network for the problem of compressive sparse multichannel blind-deconvolution…”
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