Suchergebnisse - sparse convolutional autoencoder ((sage OR space))

A novel multichannel sparse convolutional autoencoder for electrocardiogram signal compression von Bekiryazıcı, Tahir, Damkacı, Mehmet, Aydemir, Gürkan, Gürkan, Hakan

“… Deep neural networks, particularly autoencoders, offer significant potential for compressing ECG signals by mapping them to lower-dimensional spaces …”
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DOA estimation method for sparse arrays based on deep convolutional autoencoder and deep convolutional neural network von Guo, Shuhan, Zhang, Qin, Fu, Xiaolong, Zheng, Guimei, Zhou, Hao

“… This paper proposes a Direction-of-Arrival (DOA) estimation method based on Deep Convolutional Autoencoder (DCAE …”
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TumorAwareNet: Deep representation learning with attention based sparse convolutional denoising autoencoder for brain tumor recognition von Bodapati, Jyostna Devi, Balaji, Bharadwaj Bagepalli

“… ) images suitable for effective tumor recognition. The proposed model employs a Sparse Convolutional Denoising Autoencoder (SCDA …”
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XAI-DSCSA: explainable-AI-based deep semi-supervised convolutional sparse autoencoder for facial expression recognition von Mohana, M., Subashini, P., Ghinea, George

“… as the Deep Semi-supervised Convolutional Sparse Autoencoder to address the aforementioned issues and enhance FER performance and prediction …”
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Online Public Opinion Situation Assessment by Deep Sparse Autoencoder and Time-Series Modeling Convolutional Neural Network von Sun, Wei, Xiao, Ran, Li, Xuanmo

“… This model employs Deep Sparse Autoencoder (DSAE) and Time-Series Modeling Convolutional Neural Networks (TBSMA-CNN …”
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CWGAN-Based Channel Modeling of Convolutional Autoencoder-Aided SCMA for Satellite-Terrestrial Communication von Li, Dongbo, Liu, Xiangyu, Yin, Zhisheng, Cheng, Nan, Liu, Jie

“… In this article, a convolutional autoencoder-aided SCMA paradigm based on the stochastic channel modeling and autoencoder structure is developed …”
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Prior knowledge embedding convolutional autoencoder: A single-source domain generalized fault diagnosis framework under small samples von Lu, Feiyu, Tong, Qingbin, Jiang, Xuedong, Du, Xin, Xu, Jianjun, Huo, Jingyi

“… generalization fault diagnosis (SDGFD) framework, the prior knowledge embedded convolutional autoencoder (PKECA), is proposed …”
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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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Early Parkinson's Disease Prediction Using rS-fMRI Functional Connectivity and Autoencoder Graph Convolutional Network von Limas, Lesbia Lopez, Manian, Vidya

“… We propose a deep learning framework that combines resting-state functional MRI (rs-fMRI) data and a Graph Convolutional Network (GCN …”
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Deep Learning Augmented Data Assimilation: Reconstructing Missing Information with Convolutional Autoencoders von Wang, Yueya, Shi, Xiaoming, Lei, Lili, Fung, Jimmy Chi-Hung

“… By training a convolutional autoencoder (CAE) with a long simulation at a coarse “forecast” resolution (T63), we obtained a deep learning approximation …”
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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

“… We implemented a deep learning (DL) algorithm for the 3-dimensional segmentation of perivascular spaces (PVSs …”
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Deep hyperspectral clustering using attention-enhanced 3D-2D convolutional autoencoder for mineral mapping von Peyghambari, Sima, Zhang, Yun

“… However, the most commonly used 3D-convolutional autoencoder (3D-CAE) models have several disadvantages, including intensive computational costs and the potential to lose spatial information …”
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Deep Graph Convolutional Autoencoder With Conditional Normalizing Flow for Power Distribution Systems Fault Classification and Location von Saffari, Mohsen, Khodayar, Mahdi, Khodayar, Mohammad E., Fazlhashemi, Seyed Saeed

“… To overcome these limitations, a novel deep space-time generative graph convolutional autoencoder (SGGCA) is proposed …”
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Smart manufacturing under limited and heterogeneous data: a sim-to-real transfer learning with convolutional variational autoencoder in thermoforming von Ramezankhani, Milad, Harandi, Mehrtash, Seethaler, Rudolf, Milani, Abbas S.

“… Data in advanced manufacturing are often sparse and collected from various sensory devices in a heterogeneous and multi-modal fashion …”
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SDC-GAE: Structural Difference Compensation Graph Autoencoder for Unsupervised Multimodal Change Detection von Han, Te, Tang, Yuqi, Chen, Yuzeng, Yang, Xin, Guo, Yuqiang, Jiang, Shujing

“… To address the reliance on labeled data and enhance the robustness of structural features in the existing methods, we propose a structure difference compensation graph autoencoder (SDC-GAE …”
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Leveraging variant of CAE with sparse convolutional embedding and two-stage application-driven data augmentation for image clustering von Liu, Yanming, Liu, Jinglei

“… To achieve this, we propose a variant of the convolutional autoencoder (CAE) called SCDAC, which incorporates sparse convolutional embedding and a two-stage application-driven data augmentation approach …”
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A novel stacked sparse denoising autoencoder for mammography restoration to visual interpretation of breast lesion von Ghosh, Swarup Kr, Biswas, Biswajit, Ghosh, Anupam

“… A deep stacked convolutional autoencoder is designed by combining the autoencoder and the deconvolution network which conjointly reduces noisy artifacts and improves image details in mammogram …”
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Applying Convolutional Neural Networks to data on unstructured meshes with space-filling curves von Heaney, Claire E., Li, Yuling, Matar, Omar K., Pain, Christopher C.

“… This paper presents the first classical Convolutional Neural Network (CNN) that can be applied directly to data from unstructured finite element meshes or control volume grids …”
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Feature learning and change feature classification based on deep learning for ternary change detection in SAR images von Gong, Maoguo, Yang, Hailun, Zhang, Puzhao

“… In this study, sparse autoencoder, convolutional neural networks (CNN) and unsupervised clustering are combined to solve ternary change detection problem without any supervison …”
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Cost-Benefit Analysis of Metavariable Variation in Convolutional Autoencoders Applied to Acoustic Backscattering Data from Small Underwater Targets von Linhardt, Timothy, Gupta, Ananya Sen

“… ) of the data to a low-dimensional vector space with convolutional autoencoders and sparse convolutional autoencoders …”
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