Suchergebnisse - semi-supervised conventional variational autoencoder~

SS-VPE: Semi-Supervised Variational Prototyping Encoder with Student's-t Mixture Model von 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 …”
Volltext

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 …”
Volltext

A Semi-supervised Gaussian Mixture Variational Autoencoder method for few-shot fine-grained fault diagnosis von 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 …”
Volltext

Semi-Supervised Adversarial Variational Autoencoder von Zemouri, Ryad

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

Developing semi-supervised latent dynamic variational autoencoders to enhance prediction performance of product quality von 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 …”
Volltext

S2VQ-VAE: Semi-Supervised Vector Quantised-Variational AutoEncoder for Automatic Evaluation of Trail Making Test von 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 …”
Volltext

Physics-informed variational autoencoders for improved robustness to environmental factors of variation von 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 …”
Volltext

Guided Variational Autoencoder for Speech Enhancement with a Supervised Classifier von 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 …”
Volltext

Anomaly Detection Based on Graph Convolutional Network–Variational Autoencoder Model Using Time-Series Vibration and Current Data von 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 …”
Volltext

Novel Semi-Supervised Seasonal-Trend VTN for Multimode Process IoT Soft Sensing von 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 …”
Volltext

Statistical Speech Enhancement Based on Probabilistic Integration of Variational Autoencoder and Non-Negative Matrix Factorization von 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 …”
Volltext

Semi-Supervised Source Localization With Residual Physical Learning von Bianco, Michael J., Gerstoft, Peter

“… This challenge has recently been addressed using semi-supervised learning (SSL) based on deep generative modeling with variational autoencoders …”
Volltext

Speech Enhancement with Variational Autoencoders and Alpha-stable Distributions von 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 …”
Volltext

Gated Mixture Variational Autoencoders for Value Added Tax audit case selection von 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 …”
Volltext

Entropy optimized semi-supervised decomposed vector-quantized variational autoencoder model based on transfer learning for multiclass text classification and generation von 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 …”
Volltext

Vehicular Trajectory Classification and Traffic Anomaly Detection in Videos Using a Hybrid CNN-VAE Architecture von 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 …”
Volltext

A Transformer–VAE Approach for Detecting Ship Trajectory Anomalies in Cross-Sea Bridge Areas von 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 …”
Volltext

Semi-Supervised Source Localization in Reverberant Environments With Deep Generative Modeling von 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 …”
Volltext

Anomaly based Resilient Network Intrusion Detection using Inferential Autoencoders von 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 …”
Volltext

Multi-Modal Sentiment Classification With Independent and Interactive Knowledge via Semi-Supervised Learning von 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 …”
Volltext