Search Results - semi-supervised convolutional variational autoencoder~

Fault Classification in High-Dimensional Complex Processes Using Semi-Supervised Deep Convolutional Generative Models by Ko, Taeyoung, Kim, Heeyoung

“… To make effective use of a large amount of unlabeled data for fault classification, we propose in this article a new approach using semi-supervised deep generative models, allowing the complex…”
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Supervised and semi-supervised deep probabilistic models for indoor positioning problems by Qian, Weizhu, Lauri, Fabrice, Gechter, Franck

“… In this work, we propose two novel deep learning-based models, the convolutional mixture density recurrent neural network and the variational autoencoder-based semi-supervised learning model…”
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A Semi-Supervised Approach For Identifying Abnormal Heart Sounds Using Variational Autoencoder by Banerjee, Rohan, Ghose, Avik

“… In this paper, we propose a semi-supervised approach to solve the problem. A convolutional Variational Autoencoder (VAE…”
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A semi-supervised deep learning approach for vessel trajectory classification based on AIS data by Duan, Hongda, Ma, Fei, Miao, Lixin, Zhang, Canrong

“…Automatic identification system (AIS) refers to a new type of navigation aid system equipped in maritime vehicles to monitor ship performance. It provides…”
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A Semi-Supervised and Incremental Modeling Framework for Wafer Map Classification by Kong, Yuting, Ni, Dong

“… The Ladder network and the semi-supervised variational autoencoder are adopted to classify wafer bin maps in comparison with a standard convolutional neural network (CNN…”
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Semi-supervised diagnosis method of refrigeration compressor hidden defect based on convolutional transformer autoencoder model by Li, Kang, Sun, Zhe, Jin, Huaqiang, Xu, Yingjie, Gu, Jiangping, Huang, Yuejin, Shi, Ling, Yao, Qiwei, Shen, Xi

“…•A semi-supervised diagnosis method based on convolution transformer autoencoder has been proposed for diagnosing hidden defects in refrigeration compressors…”
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Anomaly Detection Based on Graph Convolutional Network–Variational Autoencoder Model Using Time-Series Vibration and Current Data by 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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Exploring semi-supervised variational autoencoders for biomedical relation extraction by Zhang, Yijia, Lu, Zhiyong

“…•A semi-supervised method is proposed based on variational autoencoders (VAE) for biomedical relation extraction…”
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Abnormal Event Detection From Videos Using a Two-Stream Recurrent Variational Autoencoder by Yan, Shiyang, Smith, Jeremy S., Lu, Wenjin, Zhang, Bailing

“… To make use of a large number of video surveillance videos of regular scenes, we propose a semi-supervised learning scheme, which only uses the data that contains the ordinary scenes…”
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Electricity Theft Detection in Incremental Scenario: A Novel Semi-supervised Approach based on Hybrid Replay Strategy by Yao, Ruizhe, Wang, Ning, Ke, Weipeng, Liu, Zhili, Yan, Zhenhong, Sheng, Xianjun

“… From the detection method perspective, this paper designs a semi-supervised ETD architecture that uses a temporal convolutional attention network (TCAN…”
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Semi-supervised method for tunnel blasting quality prediction using measurement while drilling data by Jin, Hengxiang, Fang, Qian, Wang, Jun, Chen, Jiayao, Wang, Gan, Zheng, Guoli

“… In this study, a new semi-supervised learning method using convolutional variational autoencoder (CVAE…”
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A semi-supervised autoencoder framework for joint generation and classification of breathing by Pastor-Serrano, Oscar, Lathouwers, Danny, Perkó, Zoltán

“…•A novel semi-supervised algorithm based on Adversarial Autoencoders that allows joint classification and generation of breathing…”
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Enhanced heart sound anomaly detection via WCOS: a semi-supervised framework integrating wavelet, autoencoder and SVM by Zeng, Peipei, Kang, Shuimiao, Fan, Fan, Liu, Jiyuan

“…) based on semi-supervised clustering, which combines wavelet reconstruction, convolutional autoencoder…”
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Prognosis prediction of patients with malignant pleural mesothelioma using conditional variational autoencoder on 3D PET images and clinical data by Matsuo, Hidetoshi, Kitajima, Kazuhiro, Kono, Atsushi K., Kuribayashi, Kozo, Kijima, Takashi, Hashimoto, Masaki, Hasegawa, Seiki, Yamakado, Koichiro, Murakami, Takamichi

“… Methods A 3D convolutional conditional variational autoencoder (3D‐CCVAE), which adds a 3D‐convolutional layer and conditional VAE to process 3D images, was used for dimensionality reduction of PET images…”
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Semi-Supervised EEG Signals Classification System for Epileptic Seizure Detection by Abdelhameed, Ahmed M., Bayoumi, Magdy

“… The system employs a mixing of unsupervised and supervised deep learning utilizing a one-dimensional convolutional variational autoencoder…”
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Self-Selecting Semi-Supervised Transformer-Attention Convolutional Network for Four Class EEG-Based Motor Imagery Decoding by Ng, Han Wei, Guan, Cuntai

“… In this study, we propose a variational autoencoder and transformer-attention based convolutional neural network (SSTACNet…”
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Future Frame Prediction Using Convolutional VRNN for Anomaly Detection by Lu, Yiwei, Kumar, K Mahesh, Nabavi, Seyed shahabeddin, Wang, Yang

“… Inspired by the practicability of generative models for semi-supervised learning, we propose a novel sequential generative model based on variational autoencoder (VAE…”
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CSCAD: Correlation Structure-Based Collective Anomaly Detection in Complex System by Qin, Huiling, Zhan, Xianyuan, Zheng, Yu

“…) model for high-dimensional anomaly detection problem in large systems, which is also generalizable to semi-supervised or supervised settings…”
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Deep Convolutional Variational Autoencoder for Anomalous Sound Detection by Nguyen, Minh-Hieu, Nguyen, Duy-Quang, Nguyen, Dinh-Quoc, Pham, Cong-Nguyen, Bui, Dai, Han, Huy-Dung

“… In this paper, we propose applying the convolutional variational autoencoder (CVAE) to ASD task. Through experiments using machine sound data, the CVAE is proven to be effective in detecting abnormal sound and outperform existing methods…”
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Solving two-stage stochastic integer programs via representation learning by Wu, Yaoxin, Cao, Zhiguang, Song, Wen, Zhang, Yingqian

“… To solve two-stage SIPs efficiently, we propose a conditional variational autoencoder (CVAE) for scenario representation learning…”
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