Relevant bibliographies by topics / Semi-autoencoder

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Semi-autoencoder'

Author: Grafiati
Published: 5 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Semi-autoencoder"

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Zemouri, Ryad. "Semi-Supervised Adversarial Variational Autoencoder." Machine Learning and Knowledge Extraction 2, no. 3 (2020): 361–78. http://dx.doi.org/10.3390/make2030020.

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We present a method to improve the reconstruction and generation performance of a variational autoencoder (VAE) by injecting an adversarial learning. Instead of comparing the reconstructed with the original data to calculate the reconstruction loss, we use a consistency principle for deep features. The main contributions are threefold. Firstly, our approach perfectly combines the two models, i.e., GAN and VAE, and thus improves the generation and reconstruction performance of the VAE. Secondly, the VAE training is done in two steps, which allows to dissociate the constraints used for the construction of the latent space on the one hand, and those used for the training of the decoder. By using this two-step learning process, our method can be more widely used in applications other than image processing. While training the encoder, the label information is integrated to better structure the latent space in a supervised way. The third contribution is to use the trained encoder for the consistency principle for deep features extracted from the hidden layers. We present experimental results to show that our method gives better performance than the original VAE. The results demonstrate that the adversarial constraints allow the decoder to generate images that are more authentic and realistic than the conventional VAE.
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Lai, Jie, Xiaodan Wang, Qian Xiang, Wen Quan, and Yafei Song. "A Semi-Supervised Stacked Autoencoder Using the Pseudo Label for Classification Tasks." Entropy 25, no. 9 (2023): 1274. http://dx.doi.org/10.3390/e25091274.

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The efficiency and cognitive limitations of manual sample labeling result in a large number of unlabeled training samples in practical applications. Making full use of both labeled and unlabeled samples is the key to solving the semi-supervised problem. However, as a supervised algorithm, the stacked autoencoder (SAE) only considers labeled samples and is difficult to apply to semi-supervised problems. Thus, by introducing the pseudo-labeling method into the SAE, a novel pseudo label-based semi-supervised stacked autoencoder (PL-SSAE) is proposed to address the semi-supervised classification tasks. The PL-SSAE first utilizes the unsupervised pre-training on all samples by the autoencoder (AE) to initialize the network parameters. Then, by the iterative fine-tuning of the network parameters based on the labeled samples, the unlabeled samples are identified, and their pseudo labels are generated. Finally, the pseudo-labeled samples are used to construct the regularization term and fine-tune the network parameters to complete the training of the PL-SSAE. Different from the traditional SAE, the PL-SSAE requires all samples in pre-training and the unlabeled samples with pseudo labels in fine-tuning to fully exploit the feature and category information of the unlabeled samples. Empirical evaluations on various benchmark datasets show that the semi-supervised performance of the PL-SSAE is more competitive than that of the SAE, sparse stacked autoencoder (SSAE), semi-supervised stacked autoencoder (Semi-SAE) and semi-supervised stacked autoencoder (Semi-SSAE).
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Yao, Shihong, Chuli Hu, Tao Wang, and Xinyou Cui. "Autoencoder-like semi-NMF multiple clustering." Information Sciences 572 (September 2021): 331–42. http://dx.doi.org/10.1016/j.ins.2021.04.080.

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Ahed, Mleih Al-Sbou, and Hafhizah Abd Rahim Noor. "An improved hybrid semi-stacked autoencoder for itemfeatures of recommendation system (iHSARS)." An improved hybrid semi-stacked autoencoder for itemfeatures of recommendation system (iHSARS) 30, no. 1 (2023): 481–90. https://doi.org/10.11591/ijeecs.v30.i1.pp481-490.

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In recent years, information overload has become a phenomenon where it makes people difficult to filter relevant information. To address issues such as high-dimensional data, cold start, and data sparsity, semi-autoencoder is one of the unsupervised deep learning methods used in the recommendation systems. It is particularly useful for reducing data dimensions, capturing latent representations, and flexibly reconstructing various parts of input data. In this article, we propose an improved hybrid semi-stacked autoencoder for itemfeatures of recommendation system (iHSARS) framework. This method aims to show better performance of the hybrid collaborative recommendation via semi-autoencoder (HRSA) technique. Two novel elements for iHSARS’s architecture have been introduced. The first element is an increase sources of side information of the input layer, while the second element is the number of hidden layers has been expanded. To verify the improvement of the model, MovieLens-100K and MovieLens-1M datasets have been applied to the model. The comparison between the proposed model and different state-of-the-art methods has been carried using mean absolute error (MAE) and root mean square error (RMSE) metrics. The experiments demonstrate that our framework improved the efficiency of the recommendation system better than others.
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Fu, Hongliang, Peizhi Lei, Huawei Tao, Li Zhao, and Jing Yang. "Improved semi-supervised autoencoder for deception detection." PLOS ONE 14, no. 10 (2019): e0223361. http://dx.doi.org/10.1371/journal.pone.0223361.

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Liu, Xingye, Bin Li, Jingye Li, Xiaohong Chen, Qingchun Li, and Yangkang Chen. "Semi‐supervised deep autoencoder for seismic facies classification." Geophysical Prospecting 69, no. 6 (2021): 1295–315. http://dx.doi.org/10.1111/1365-2478.13106.

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Yin, Wutao, Longhai Li, and Fang-Xiang Wu. "A semi-supervised autoencoder for autism disease diagnosis." Neurocomputing 483 (April 2022): 140–47. http://dx.doi.org/10.1016/j.neucom.2022.02.017.

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Deng, Yang, Wang Zhou, Amin Ul Haq, Sultan Ahmad, and Alia Tabassum. "Differentially private recommender framework with Dual semi-Autoencoder." Expert Systems with Applications 260 (January 2025): 125447. http://dx.doi.org/10.1016/j.eswa.2024.125447.

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Wu, Chuhan, Fangzhao Wu, Sixing Wu, Zhigang Yuan, Junxin Liu, and Yongfeng Huang. "Semi-supervised dimensional sentiment analysis with variational autoencoder." Knowledge-Based Systems 165 (February 2019): 30–39. http://dx.doi.org/10.1016/j.knosys.2018.11.018.

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Li, Ivy, Aarón Higuera, Shixiao Liang, Juehang Qin, and Christopher Tunnell. "Energy Reconstruction with Semi-Supervised Autoencoders for Dual-Phase Time Projection Chambers." EPJ Web of Conferences 295 (2024): 09022. http://dx.doi.org/10.1051/epjconf/202429509022.

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This paper presents a proof-of-concept semi-supervised autoencoder for the energy reconstruction of scattering particle interactions inside dualphase time projection chambers (TPCs), such as XENONnT. This autoencoder model is trained on simulated XENONnT data and is able to simultaneously reconstruct photosensor array hit patterns and infer the number of electrons in the gas gap, which is proportional to the energy of ionization signals in the TPC. Development plans for this autoencoder model are discussed, including future work in developing a faster simulation technique for dual-phase TPCs.
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Dissertations / Theses on the topic "Semi-autoencoder"

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Schembri, Massimo. "Anomaly Prediction in Production Supercomputer with Convolution and Semi-supervised autoencoder." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22379/.

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Un sistema HPC (High Performance Computing) è un sistema con capacità computazionali molto elevate adatto a task molto esigenti in termini di risorse. Alcune delle proprietà fondamentali di un sistema del genere sono certamente la disponibilità e l'affidabilità che possono essere messe a rischio da problemi hardware e software. In quest'attività di tesi si è realizzato e analizzato le performance di un sistema di anomaly detection in termini di capacità di rilevazione e predizione di un'anomalia su vari nodi di un sistema HPC, in particolare utilizzando i dati relativi al sistema MARCONI del consorzio CINECA.
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Dabiri, Sina. "Semi-Supervised Deep Learning Approach for Transportation Mode Identification Using GPS Trajectory Data." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/86845.

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Identification of travelers' transportation modes is a fundamental step for various problems that arise in the domain of transportation such as travel demand analysis, transport planning, and traffic management. This thesis aims to identify travelers' transportation modes purely based on their GPS trajectories. First, a segmentation process is developed to partition a user's trip into GPS segments with only one transportation mode. A majority of studies have proposed mode inference models based on hand-crafted features, which might be vulnerable to traffic and environmental conditions. Furthermore, the classification task in almost all models have been performed in a supervised fashion while a large amount of unlabeled GPS trajectories has remained unused. Accordingly, a deep SEmi-Supervised Convolutional Autoencoder (SECA) architecture is proposed to not only automatically extract relevant features from GPS segments but also exploit useful information in unlabeled data. The SECA integrates a convolutional-deconvolutional autoencoder and a convolutional neural network into a unified framework to concurrently perform supervised and unsupervised learning. The two components are simultaneously trained using both labeled and unlabeled GPS segments, which have already been converted into an efficient representation for the convolutional operation. An optimum schedule for varying the balancing parameters between reconstruction and classification errors are also implemented. The performance of the proposed SECA model, trip segmentation, the method for converting a raw trajectory into a new representation, the hyperparameter schedule, and the model configuration are evaluated by comparing to several baselines and alternatives for various amounts of labeled and unlabeled data. The experimental results demonstrate the superiority of the proposed model over the state-of-the-art semi-supervised and supervised methods with respect to metrics such as accuracy and F-measure.<br>Master of Science<br>Identifying users' transportation modes (e.g., bike, bus, train, and car) is a key step towards many transportation related problems including (but not limited to) transport planning, transit demand analysis, auto ownership, and transportation emissions analysis. Traditionally, the information for analyzing travelers' behavior for choosing transport mode(s) was obtained through travel surveys. High cost, low-response rate, time-consuming manual data collection, and misreporting are the main demerits of the survey-based approaches. With the rapid growth of ubiquitous GPS-enabled devices (e.g., smartphones), a constant stream of users' trajectory data can be recorded. A user's GPS trajectory is a sequence of GPS points, recorded by means of a GPS-enabled device, in which a GPS point contains the information of the device geographic location at a particular moment. In this research, users' GPS trajectories, rather than traditional resources, are harnessed to predict their transportation mode by means of statistical models. With respect to the statistical models, a wide range of studies have developed travel mode detection models using on hand-designed attributes and classical learning techniques. Nonetheless, hand-crafted features cause some main shortcomings including vulnerability to traffic uncertainties and biased engineering justification in generating effective features. A potential solution to address these issues is by leveraging deep learning frameworks that are capable of capturing abstract features from the raw input in an automated fashion. Thus, in this thesis, deep learning architectures are exploited in order to identify transport modes based on only raw GPS tracks. It is worth noting that a significant portion of trajectories in GPS data might not be annotated by a transport mode and the acquisition of labeled data is a more expensive and labor-intensive task in comparison with collecting unlabeled data. Thus, utilizing the unlabeled GPS trajectory (i.e., the GPS trajectories that have not been annotated by a transport mode) is a cost-effective approach for improving the prediction quality of the travel mode detection model. Therefore, the unlabeled GPS data are also leveraged by developing a novel deep-learning architecture that is capable of extracting information from both labeled and unlabeled data. The experimental results demonstrate the superiority of the proposed models over the state-of-the-art methods in literature with respect to several performance metrics.
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Golshan, Arman. "A contemporary machine learning approach to detect transportation mode - A case study of Borlänge, Sweden." Thesis, Högskolan Dalarna, Mikrodataanalys, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:du-35966.

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Understanding travel behavior and identifying the mode of transportation are essential for adequate urban devising and transportation planning. Global positioning systems (GPS) tracking data is mainly used to find human mobility patterns in cities. Some travel information, such as most visited location, temporal changes, and the trip speed, can be easily extracted from GPS raw tracking data. GPS trajectories can be used as a method to indicate the mobility modes of commuters. Most previous studies have applied traditional machine learning algorithms and manually computed data features, making the model error-prone. Thus, there is a demand for developing a new model to resolve these methods' weaknesses. The primary purpose of this study is to propose a semi-supervised model to identify transportation mode by using a contemporary machine learning algorithm and GPS tracking data. The model can accept GPS trajectory with adjustable length and extracts their latent information with LSTM Autoencoder. This study adopts a deep neural network architecture with three hidden layers to map the latent information to detect transportation mode. Moreover, different case studies are performed to evaluate the proposed model's efficiency. The model results in an accuracy of 93.6%, which significantly outperforms similar studies.
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Book chapters on the topic "Semi-autoencoder"

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Gogna, Anupriya, and Angshul Majumdar. "Semi Supervised Autoencoder." In Neural Information Processing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46672-9_10.

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Zhang, Shuai, Lina Yao, Xiwei Xu, Sen Wang, and Liming Zhu. "Hybrid Collaborative Recommendation via Semi-AutoEncoder." In Neural Information Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70087-8_20.

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Pálsson, Sveinn, Stefano Cerri, Andrea Dittadi, and Koen Van Leemput. "Semi-supervised Variational Autoencoder for Survival Prediction." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46643-5_12.

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Kang, Mingeun, Kiwon Lee, Yong H. Lee, and Changho Suh. "Autoencoder-Based Graph Construction for Semi-supervised Learning." In Computer Vision – ECCV 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58586-0_30.

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Liu, Jingquan, Xiaoyong Du, Yuanzhe Li, and Weidong Hu. "Hypergraph Variational Autoencoder for Multimodal Semi-supervised Representation Learning." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-15937-4_33.

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Vengalil, Sunil Kumar, and Neelam Sinha. "Semi-supervised Learning Using Variational Autoencoder - A Cluster Based Approach." In Lecture Notes in Computer Science. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-12700-7_54.

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Xiao, Hui, Donghai Guan, Rui Zhao, Weiwei Yuan, Yaofeng Tu, and Asad Masood Khattak. "Semi-supervised Time Series Anomaly Detection Model Based on LSTM Autoencoder." In Communications in Computer and Information Science. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3150-4_4.

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Mendes, Andre, Julian Togelius, and Leandro dos Santos Coelho. "Adversarial Autoencoder and Multi-Task Semi-Supervised Learning for Multi-stage Process." In Advances in Knowledge Discovery and Data Mining. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47436-2_1.

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Torres-Calderon, Rosa, Bernardo Gonzalez-Torres, and Ricardo Menchaca-Mendez. "Generating Universum Instances from Variational Autoencoder Latent Space for Semi-supervised Learning." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-77293-1_8.

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Sedai, Suman, Dwarikanath Mahapatra, Sajini Hewavitharanage, Stefan Maetschke, and Rahil Garnavi. "Semi-supervised Segmentation of Optic Cup in Retinal Fundus Images Using Variational Autoencoder." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66185-8_9.

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Conference papers on the topic "Semi-autoencoder"

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Dana, Soven K., Jitender Kumar, and Rahul Modanwal. "Autoencoder Aided Semi-supervised Incremental Learning of Handwritten Characters." In 2024 IEEE Region 10 Symposium (TENSYMP). IEEE, 2024. http://dx.doi.org/10.1109/tensymp61132.2024.10752210.

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Sandarenu, Piumi, Julia Chen, Iveta Slapetova, et al. "Semi-Supervised Variational Autoencoder for Cell Feature Extraction In Multiplexed Immunofluorescence Images." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635107.

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Liu, Pengyu, Fangyi Wan, Yaohui Xie, and Yudong Qiang. "A Semi-Supervised Fault Diagnosis Method for Gearbox Based on Convolutional Autoencoder." In 2024 Global Reliability and Prognostics and Health Management Conference (PHM-Beijing). IEEE, 2024. https://doi.org/10.1109/phm-beijing63284.2024.10874561.

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Wang, Chundong, and Weijie Yang. "Semi-Supervised Blockchain Anomaly Transaction Detection Based on Deep AutoEncoder and Multi-Layer Perceptron." In 2024 4th International Conference on Digital Society and Intelligent Systems (DSInS). IEEE, 2024. https://doi.org/10.1109/dsins64146.2024.10992069.

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Zhu, Mingda, Du Nguyen, Peihua Han, Khang Huynh, and Jing Zhou. "A Semi-Supervised Variational Autoencoder for Fault Detection of Low-Severity Inter-Turn Short-Circuit in PMSMs." In 2025 IEEE Workshop on Electrical Machines Design, Control and Diagnosis (WEMDCD). IEEE, 2025. https://doi.org/10.1109/wemdcd61816.2025.11014178.

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Abbasnejad, M. Ehsan, Anthony Dick, and Anton van den Hengel. "Infinite Variational Autoencoder for Semi-Supervised Learning." In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017. http://dx.doi.org/10.1109/cvpr.2017.90.

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Chidlovskii, Boris, and Leonid Antsfeld. "Semi-supervised Variational Autoencoder for WiFi Indoor Localization." In 2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE, 2019. http://dx.doi.org/10.1109/ipin.2019.8911825.

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Zhang, Xiao, Yong Jiang, Hao Peng, Kewei Tu, and Dan Goldwasser. "Semi-supervised Structured Prediction with Neural CRF Autoencoder." In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2017. http://dx.doi.org/10.18653/v1/d17-1179.

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Kamimura, Ryotaro, and Haruhiko Takeuchi. "Supervised semi-autoencoder learning for multi-layered neural networks." In 2017 Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS). IEEE, 2017. http://dx.doi.org/10.1109/ifsa-scis.2017.8023324.

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Wang, Jindong, Fan Wang, and Dong Yin. "Feature Decoupled Autoencoder: Semi-Supervised Learning for Image Dehazing." In 2022 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2022. http://dx.doi.org/10.1109/icme52920.2022.9859652.

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