Výsledky vyhledávání - semi-supervised conventional variational autoencoder

SS-VPE: Semi-Supervised Variational Prototyping Encoder with Student's-t Mixture Model Autor Liu, Yukun, Shi, Daming

“… However, a latent prototype is mostly sensitive to outliers; therefore, a general method using a semi-supervised variational prototyping encoder (SS-VPE) is proposed…”
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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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A Semi-supervised Gaussian Mixture Variational Autoencoder method for few-shot fine-grained fault diagnosis Autor Zhao, Zhiqian, Xu, Yeyin, Zhang, Jiabin, Zhao, Runchao, Chen, Zhaobo, Jiao, Yinghou

“… To tackle those issue, we propose a novel semi-supervised Gaussian Mixed Variational Autoencoder method, SeGMVAE, aimed at acquiring unsupervised representations that can be transferred across fine…”
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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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Developing semi-supervised latent dynamic variational autoencoders to enhance prediction performance of product quality Autor Lee, Yi Shan, Chen, Junghui

“…•Dynamic features of process and quality data are learned for quality prediction.•Bi-directional RNN is trained by past and future data to prevent…”
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S2VQ-VAE: Semi-Supervised Vector Quantised-Variational AutoEncoder for Automatic Evaluation of Trail Making Test Autor Tang, Zeshen, Tang, Shiyu, Wang, Haoran, Li, Renren, Zhang, Xiaochen, Zhang, Wei, Yuan, Xiao, Zang, Yaning, Li, Yanping, Zhou, Tian, Li, Yunxia

“… We proposed a novel deep representation learning approach named Semi-Supervised Vector Quantised-Variational AutoEncoder (S2VQ-VAE…”
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Physics-informed variational autoencoders for improved robustness to environmental factors of variation Autor Thoreau, Romain, Risser, Laurent, Achard, Véronique, Berthelot, Béatrice, Briottet, Xavier

“… In this paper, we introduce p 3 VAE, a variational autoencoder that integrates prior physical knowledge modeling the generative latent factors of variation that are related to the data acquisition conditions…”
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Guided Variational Autoencoder for Speech Enhancement with a Supervised Classifier Autor Carbajal, Guillaume, Richter, Julius, Gerkmann, Timo

“…Recently, variational autoencoders have been successfully used to learn a probabilistic prior over speech signals, which is then used to perform speech enhancement…”
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Anomaly Detection Based on Graph Convolutional Network–Variational Autoencoder Model Using Time-Series Vibration and Current Data Autor Choi, Seung-Hwan, An, Dawn, Lee, Inho, Lee, Suwoong

“… To address this issue, we employ a semi-supervised learning approach that relies solely on normal data to effectively detect abnormal patterns, overcoming the limitations of conventional methods…”
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Novel Semi-Supervised Seasonal-Trend VTN for Multimode Process IoT Soft Sensing Autor He, Yan-Lin, Zhou, Yang-Xiao-Yu, Xu, Yuan, Zhu, Qun-Xiong, Li, Xingyuan

“… However, modern process industries involve highly dynamic systems with multimode and nonlinear data, posing challenges for conventional soft sensor models that assume uniform data distributions…”
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Statistical Speech Enhancement Based on Probabilistic Integration of Variational Autoencoder and Non-Negative Matrix Factorization Autor Bando, Yoshiaki, Mimura, Masato, Itoyama, Katsutoshi, Yoshii, Kazuyoshi, Kawahara, Tatsuya

“…This paper presents a statistical method of single-channel speech enhancement that uses a variational autoencoder (VAE…”
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Semi-Supervised Source Localization With Residual Physical Learning Autor Bianco, Michael J., Gerstoft, Peter

“… This challenge has recently been addressed using semi-supervised learning (SSL) based on deep generative modeling with variational autoencoders…”
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Speech Enhancement with Variational Autoencoders and Alpha-stable Distributions Autor Leglaive, Simon, Simsekli, Umut, Liutkus, Antoine, Girin, Laurent, Horaud, Radu

“…This paper focuses on single-channel semi-supervised speech enhancement. We learn a speaker-independent deep generative speech model using the framework of variational autoencoders…”
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Gated Mixture Variational Autoencoders for Value Added Tax audit case selection Autor Kleanthous, Christos, Chatzis, Sotirios

“… This gives rise to a semi-supervised learning framework that leverages the latest advances in deep learning and robust regularization using variational inference…”
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Entropy optimized semi-supervised decomposed vector-quantized variational autoencoder model based on transfer learning for multiclass text classification and generation Autor Malhotra, Shivani, Kumar, Vinay, Agarwal, Alpana

“…Semisupervised text classification has become a major focus of research over the past few years. Hitherto, most of the research has been based on supervised…”
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Vehicular Trajectory Classification and Traffic Anomaly Detection in Videos Using a Hybrid CNN-VAE Architecture Autor Kumaran Santhosh, Kelathodi, Dogra, Debi Prosad, Roy, Partha Pratim, Mitra, Adway

“…) and Variational Autoencoder (VAE) architecture. First, we introduce a high level features for varying length object trajectories using color gradient representation…”
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A Transformer–VAE Approach for Detecting Ship Trajectory Anomalies in Cross-Sea Bridge Areas Autor Hou, Jiawei, Zhou, Hongzhu, Grifoll, Manel, Zhou, Yusheng, Liu, Jiao, Ye, Yun, Zheng, Pengjun

“… Most existing anomaly detection methods heavily rely on labeled or semi-supervised data, thus limiting their applicability in scenarios involving completely unlabeled ship trajectory data…”
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Semi-Supervised Source Localization in Reverberant Environments With Deep Generative Modeling Autor Bianco, Michael J., Gannot, Sharon, Fernandez-Grande, Efren, Gerstoft, Peter

“… We propose to address this issue with a semi-supervised learning (SSL) approach, based on deep generative modeling…”
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Anomaly based Resilient Network Intrusion Detection using Inferential Autoencoders Autor Hannan, Abdul, Gruhl, Christian, Sick, Bernhard

“…This article focuses on the application of conditional variational autoencoders as anomaly detectors to identify emerging threats in computer networks…”
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Multi-Modal Sentiment Classification With Independent and Interactive Knowledge via Semi-Supervised Learning Autor Zhang, Dong, Li, Shoushan, Zhu, Qiaoming, Zhou, Guodong

“… The key idea is to leverage the semi-supervised variational autoencoders to mine more information from unlabeled data for multi-modal sentiment analysis…”
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