Výsledky vyhledávání - Supervised variational autoencoder

A Supervised Variational Autoencoder for Incomplete Multi‐View Classification Autor Xu, Yi, Chen, Anchi

“… Specifically, we design a supervised multi‐view variational autoencoder for missing view completion, which involves a Product of Experts (PoE…”
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Self-supervised Variational Autoencoder for Unsupervised Object Counting from Very High-Resolution Satellite Imagery: Applications in Dwelling Extraction in FDP Settlement Areas Autor Gella, Getachew Workineh, Gangloff, Hugo, Wendt, Lorenz, Tiede, Dirk, Lang, Stefan

“… This constrains their utility in humanitarian emergency response. To overcome this problem, we have proposed an unsupervised dwelling counting from very high-resolution satellite imagery by combining a Variational Autoencoder(VAE…”
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Supervised Variational Autoencoders for Soft Sensor Modeling With Missing Data Autor Xie, Ruimin, Jan, Nabil Magbool, Hao, Kuangrong, Chen, Lei, Huang, Biao

“… Recently, assuming the latent variables to be random variables, a probabilistic variant of it called variational autoencoder (VAE…”
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Customization of latent space in semi-supervised Variational AutoEncoder Autor An, Seunghwan, Jeon, Jong-June

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Fault diagnosis of power equipment based on variational autoencoder and semi‐supervised learning Autor Ye, Bo, Li, Feng, Zhang, Linghao, Chang, Zhengwei, Wang, Bin, Zhang, Xiaoyu, Bodanbai, Sayina

“…) method based on variational autoencoder and semi‐supervised learning is proposed in this paper…”
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Exploring semi-supervised variational autoencoders for biomedical relation extraction Autor Zhang, Yijia, Lu, Zhiyong

“…•A semi-supervised method is proposed based on variational autoencoders (VAE) for biomedical relation extraction…”
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Noise-Aware Self-Supervised Variational Autoencoder for Speech Enhancement Autor Dixit, Amogh, Nataraj, K. S., Tiwari, Nitya

“… To address this, we propose a Noise-Aware Self-Supervised Variational Autoencoder (NASS-VAE) framework, designed to improve speech enhancement without the need for paired clean and noisy data…”
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Efficient few-shot medical image segmentation via self-supervised variational autoencoder Autor Zhou, Yanjie, Zhou, Feng, Xi, Fengjun, Liu, Yong, Peng, Yun, Carlson, David E., Tu, Liyun

“… To address this, we developed EFS-MedSeg, an end-to-end model using two labeled atlases and few unlabeled images, enhanced by data augmentation and self-supervised learning…”
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Early Detection and Diagnosis of Wind Turbine Abnormal Conditions Using an Interpretable Supervised Variational Autoencoder Model Autor Oliveira-Filho, Adaiton, Zemouri, Ryad, Cambron, Philippe, Tahan, Antoine

“… The proposed approach is based on a supervised implementation of the variational autoencoder model, which allows the projection of the wind turbine system onto a low-dimensional representation space…”
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Self-supervised variational autoencoder towards recommendation by nested contrastive learning Autor Wang, Jing, Wu, Jun, Jia, Caiyan, Zhang, Zhifei

“…,as user’s preference may be highly complex. In this paper, we propose a Nested Self-supervised Variational Autoencoder (NSVAE…”
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A mechanical fault diagnosis model with semi-supervised variational autoencoder based on long short-term memory network Autor Qu, Yuanyuan, Li, Tao, Fu, Shichen, Wang, Zhisheng, Chen, Jian, Zhang, Yupeng

“… A mechanical fault diagnosis model with Semi-Supervised Variational Autoencoder based on Long Short-Term Memory network (LSTM-SSVAE…”
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Supervised and semi-supervised probabilistic learning with deep neural networks for concurrent process-quality monitoring Autor Wang, Kai, Yuan, Xiaofeng, Chen, Junghui, Wang, Yalin

“…Concurrent process-quality monitoring helps discover quality-relevant process anomalies and quality-irrelevant process anomalies. It especially works well in…”
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Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders Autor Whiteway, Matthew R., Biderman, Dan, Friedman, Yoni, Dipoppa, Mario, Buchanan, E. Kelly, Wu, Anqi, Zhou, John, Bonacchi, Niccolò, Miska, Nathaniel J., Noel, Jean-Paul, Rodriguez, Erica, Schartner, Michael, Socha, Karolina, Urai, Anne E., Salzman, C. Daniel, Cunningham, John P., Paninski, Liam

“… Here we introduce a new video analysis tool that combines the output of supervised pose estimation algorithms (e.g. DeepLabCut…”
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Developing semi-supervised variational autoencoder-generative adversarial network models to enhance quality prediction performance Autor Ooi, Sai Kit, Tanny, Dave, Chen, Junghui, Wang, Kai

“… Such discrepancy exists because of the time lag for obtaining quality data. This paper proposes semi-supervised variational autoencoder-generative adversarial network (S2-VAE/GAN…”
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Semi-supervised Variational Autoencoder for WiFi Indoor Localization Autor Chidlovskii, Boris, Antsfeld, Leonid

“…We address the problem of indoor localization based on WiFi signal strengths. We develop a semi-supervised deep learning method able to train a prediction model…”
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Self-Supervised Variational Graph Autoencoder for System-Level Anomaly Detection Autor Zhang, Le, Cheng, Wei, Xing, Ji, Chen, Xuefeng, Nie, Zelin, Zhang, Shuo, Hong, Junying, Xu, Zhao

“… However, most industrial scenarios are without graphs. Hence, a self-supervised variational graph autoencoders (SS-VGAE) method is proposed…”
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Disease Progression Score Estimation From Multimodal Imaging and MicroRNA Data Using Supervised Variational Autoencoders Autor Kmetzsch, Virgilio, Becker, Emmanuelle, Saracino, Dario, Rinaldi, Daisy, Camuzat, Agnes, Le Ber, Isabelle, Colliot, Olivier

“…Frontotemporal dementia and amyotrophic lateral sclerosis are rare neurodegenerative diseases with no effective treatment. The development of biomarkers…”
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Semi-Supervised Variational Autoencoders for Out-of-Distribution Generation Autor Lavda, Frantzeska, Kalousis, Alexandros

“…) data in a semi-supervised manner. Unlike previous approaches that use new factors of variation during testing, our method uses only existing attributes from the training data but in ways that were not seen during training (e.g., small objects of a specific shape during training and large objects of the same shape during testing)…”
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Learning from small medical data—robust semi-supervised cancer prognosis classifier with Bayesian variational autoencoder Autor Hsu, Te-Cheng, Lin, Che

“… Results We propose a robust Semi-supervised Cancer prognosis classifier with bAyesian variational autoeNcoder (SCAN…”
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Semi-Supervised Adversarial Variational Autoencoder Autor Zemouri, Ryad

“…We present a method to improve the reconstruction and generation performance of a variational autoencoder (VAE…”
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