Suchergebnisse - "Supervised variational autoencoder"

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

“… Although significant progress has been made in multi‐view classification over the past few decades, handling multi‐view data with arbitrary view missing is …”
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Customization of latent space in semi-supervised Variational AutoEncoder von An, Seunghwan, Jeon, Jong-June

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Noise-Aware Self-Supervised Variational Autoencoder for Speech Enhancement von 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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Self-supervised Variational Autoencoder for Unsupervised Object Counting from Very High-Resolution Satellite Imagery: Applications in Dwelling Extraction in FDP Settlement Areas von Gella, Getachew Workineh, Gangloff, Hugo, Wendt, Lorenz, Tiede, Dirk, Lang, Stefan

“… In supervised learning, deep learning models demand a large corpus of annotated data for object detection and classification tasks. This constrains their …”
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Efficient few-shot medical image segmentation via self-supervised variational autoencoder von Zhou, Yanjie, Zhou, Feng, Xi, Fengjun, Liu, Yong, Peng, Yun, Carlson, David E., Tu, Liyun

“… Few-shot medical image segmentation typically uses a joint model for registration and segmentation. The registration model aligns a labeled atlas with …”
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Self-supervised variational autoencoder towards recommendation by nested contrastive learning von 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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Early Detection and Diagnosis of Wind Turbine Abnormal Conditions Using an Interpretable Supervised Variational Autoencoder Model von Oliveira-Filho, Adaiton, Zemouri, Ryad, Cambron, Philippe, Tahan, Antoine

“… The operation and maintenance of wind turbines benefit from reliable information on the wind turbine condition. Data-driven models use data from the …”
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A mechanical fault diagnosis model with semi-supervised variational autoencoder based on long short-term memory network von 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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Developing semi-supervised variational autoencoder-generative adversarial network models to enhance quality prediction performance von 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 von 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 …”
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A supervised variational autoencoder framework for dimensionality reduction and predictive modeling in high-dimensional socioeconomic data von Xue, Pei, Li, Tianshun

“… We introduce an estimation framework utilizing a Supervised Variational Autoencoder (SVAE …”
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Semi-Supervised Variational Autoencoder for Cell Feature Extraction In Multiplexed Immunofluorescence Images von Sandarenu, Piumi, Chen, Julia, Slapetova, Iveta, Browne, Lois, Graham, Peter H., Swarbrick, Alexander, Millar, Ewan K.A., Song, Yang, Meijering, Erik

“… Advancements in digital imaging technologies have sparked increased interest in using multiplexed immunofluorescence (mIF) images to visualise and identify the …”
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Spatial and temporal downscaling schemes to reconstruct high-resolution GRACE data: A case study in the Tarim River Basin, Northwest China von Xue, Dongping, Gui, Dongwei, Ci, Mengtao, Liu, Qi, Wei, Guanghui, Liu, Yunfei

“… of GRACE data effectively. In this study, we employ the semi-supervised variational autoencoder (SSVAER …”
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Adversarial Attack Type I: Cheat Classifiers by Significant Changes von Tang, Sanli, Huang, Xiaolin, Chen, Mingjian, Sun, Chengjin, Yang, Jie

“… Despite the great success of deep neural networks, the adversarial attack can cheat some well-trained classifiers by small permutations. In this paper, we …”
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Self-supervised Variational Autoencoder for Recommender Systems von Wang, Jing, Liu, Gangdu, Wu, Jun, Jia, Caiyan, Zhang, Zhifei

“… Variational autoencoder (VAE) is considered as an emerging model for ensuring competitive performance in recom-mender systems. However, its performance is …”
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SMAS: Structural MRI-based AD Score using Bayesian supervised VAE von Nemali, A., Bernal, J., Yakupov, R., Singh, D., Dyrba, M., Incesoy, E.I., Mukherjee, S., Peters, O., Ersözlü, E., Hellmann-Regen, J., Preis, L., Priller, J., Spruth, E., Altenstein, S., Lohse, A., Schneider, A., Fliessbach, K., Kimmich, O., Wiltfang, J., Hansen, N., Schott, B., Rostamzadeh, A., Glanz, W., Butryn, M., Buerger, K., Janowitz, D., Ewers, M., Perneczky, R., Rauchmann, B., Teipel, S., Kilimann, I., Goerss, D., Laske, C., Sodenkamp, S., Spottke, A., Coenjaerts, M., Brosseron, F., Lüsebrink, F., Dechent, P., Scheffler, K., Hetzer, S., Kleineidam, L., Stark, M., Jessen, F., Duzel, E., Ziegler, G.

“… )-related morphometric patterns using a deep learning Bayesian-supervised Variational Autoencoder (Bayesian-SVAE …”
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A Semi-Supervised Variational Autoencoder for Fault Detection of Low-Severity Inter-Turn Short-Circuit in PMSMs von Zhu, Mingda, Nguyen, Du, Han, Peihua, Huynh, Khang, Zhou, Jing

“… This study proposes a semi-supervised Variational Autoencoder (VAE) with Long Short-Term Memory Networks for fault detection …”
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Adversarial Training-Based Deep Layer-Wise Probabilistic Network for Enhancing Soft Sensor Modeling of Industrial Processes von Xie, Yongfang, Wang, Jie, Xie, Shiwen, Chen, Xiaofang

“… In this article, an adversarial training-based deep supervised variational autoencoder (Adv-DSVAE) is proposed to enhance the performance of industrial soft sensor models …”
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Semi-Supervised Variational Autoencoder for Survival Prediction von Pálsson, Sveinn, Cerri, Stefano, Dittadi, Andrea, Koen Van Leemput

“… In this paper we propose a semi-supervised variational autoencoder for classification of overall survival groups from tumor segmentation masks …”
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Semi-supervised Variational Autoencoder for Regression: Application on Soft Sensors von Zhuang, Yilin, Zhou, Zhuobin, Alakent, Burak, Mercangöz, Mehmet

“… We present the development of a semi-supervised regression method using variational autoencoders (VAE), which is customized for use in soft sensing …”
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