Search Results - "supervised variational autoencoder"

A Supervised Variational Autoencoder for Incomplete Multi‐View Classification by 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 by An, Seunghwan, Jeon, Jong-June

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Noise-Aware Self-Supervised Variational Autoencoder for Speech Enhancement by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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 by 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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