Design of Capsule-Shaped All-Terrain Robot: A Perpendicular Track Mobility Solution by Abhishek Sebastian, Vinay Murali, R. Pragna, A. Annis Fathima

“…The perpendicular dual-track system enables the robot to perform precise lateral movements, allowing navigation through confined spaces, while the PID controller ensures continuous real-time adjustments to maintain balance across uneven terrains. The variational autoencoder enhances the robot’s ability to detect and correct abnormal movements, improving overall system reliability. …”
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A Novel Generative Model for Face Privacy Protection in Video Surveillance with Utility Maintenance by Yuying Qiu, Zhiyi Niu, Biao Song, Tinghuai Ma, Abdullah Al-Dhelaan, Mohammed Al-Dhelaan

“…This paper proposes a novel generative framework called Quality Maintenance-Variational AutoEncoder (QM-VAE), which takes full advantage of existing privacy protection technologies. …”
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Deep Learning Methods for Omics Data Imputation by Lei Huang, Meng Song, Hui Shen, Huixiao Hong, Ping Gong, Hong-Wen Deng, Chaoyang Zhang

“…This review provides a comprehensive overview of the currently available deep learning-based methods for omics imputation from the perspective of deep generative model architectures such as autoencoder, variational autoencoder, generative adversarial networks, and Transformer, with an emphasis on multi-omics data imputation. …”
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VAE-Based Adversarial Multimodal Domain Transfer for Video-Level Sentiment Analysis by Yanan Wang, Jianming Wu, Kazuaki Furumai, Shinya Wada, Satoshi Kurihara

“…We first perform variational autoencoder (VAE) to make visual, linguistic and acoustic representations follow a common distribution, and then introduce adversarial training to transfer all unimodal representations to a joint embedding space. …”
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Automatic Fruit Morphology Phenome and Genetic Analysis: An Application in the Octoploid Strawberry by Laura M. Zingaretti, Amparo Monfort, Miguel Pérez-Enciso

“…In addition, we develop a variational autoencoder to automatically detect the most likely number of underlying shapes. …”
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A Lightweight Hybrid Deep Learning Privacy Preserving Model for FC-Based Industrial Internet of Medical Things by Mohammed Amin Almaiah, Aitizaz Ali, Fahima Hajjej, Muhammad Fermi Pasha, Manal Abdullah Alohali

“…Second, a deep-learning scheme with a Variational AutoEncoder (VAE) technique for privacy and Bidirectional Long Short-Term Memory (BiLSTM) for intrusion detection is designed. …”
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Detecting Anomalies in Time Series Using Kernel Density Approaches by Robin Frehner, Kesheng Wu, Alexander Sim, Jinoh Kim, Kurt Stockinger

“…In addition, we propose a data augmentation strategy involving variational autoencoder-generated events and a smoothing step for enhanced model robustness. …”
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Policy-Gradient and Actor-Critic Based State Representation Learning for Safe Driving of Autonomous Vehicles by Abhishek Gupta, Ahmed Shaharyar Khwaja, Alagan Anpalagan, Ling Guan, Bala Venkatesh

“…Through a combination of variational autoencoder (VAE), deep deterministic policy gradient (DDPG), and soft actor-critic (SAC), we focus on uninterrupted and reasonably safe autonomous driving without steering off the track for a considerable driving distance. …”
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Combining outlier analysis algorithms to identify new physics at the LHC by Melissa van Beekveld, Sascha Caron, Luc Hendriks, Paul Jackson, Adam Leinweber, Sydney Otten, Riley Patrick, Roberto Ruiz de Austri, Marco Santoni, Martin White

“…In this study, we compare the anomaly scores of a variety of anomaly detection techniques: an isolation forest, a Gaussian mixture model, a static autoencoder, and a β-variational autoencoder (VAE), where we define the reconstruction loss of the latter as a weighted combination of regression and classification terms. …”
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A Dual-Module System for Copyright-Free Image Recommendation and Infringement Detection in Educational Materials by Yeongha Kim, Soyeon Kim, Seonghyun Min, Youngung Han, Ohyoung Lee, Wongyum Kim

“…It utilizes a Convolutional Variational Autoencoder (CVAE) model to extract significant features from copyrighted images and compares them against user-provided images. …”
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Efficient Classification and Rapid Processing of Big Data in Power Distribution Networks by Luan Ning, Cheng Li, Wang Dingji, Wang Shuaimei

“…Subsequently, variational autoencoder (VAE) is used to extract the hidden features of the data and the dimension reduction methods are applied to the hidden structure of data obtained from VAE. …”
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Deep Learning-Based Anomaly Detection in Occupational Accident Data Using Fractional Dimensions by Ömer Akgüller, Larissa M. Batrancea, Mehmet Ali Balcı, Gökhan Tuna, Anca Nichita

“…This study examines the effectiveness of Convolutional Autoencoder (CAE) and Variational Autoencoder (VAE) models in detecting anomalies within occupational accident data from the Mining of Coal and Lignite (NACE05), Manufacture of Other Transport Equipment (NACE30), and Manufacture of Basic Metals (NACE24) sectors. …”
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PRISM: Personalizing Reporting With Intelligent Summarization Through Multiple Frames by Kyohoon Jin, Sejin Paik, Jungmin Yun, Soojin Jang, Youngbin Kim

“…Utilizing a conditional variational autoencoder structure, we created and trained data that embeds multiple frames within a single document, enabling the summary to reflect frame-specific information. …”
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Low-dimensional learned feature spaces quantify individual and group differences in vocal repertoires by Jack Goffinet, Samuel Brudner, Richard Mooney, John Pearson

“…Here, by contrast, we apply the variational autoencoder (VAE), an unsupervised learning method, to learn features directly from data and quantify the vocal behavior of two model species: the laboratory mouse and the zebra finch. …”
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Discovering highly potent antimicrobial peptides with deep generative model HydrAMP by Paulina Szymczak, Marcin Możejko, Tomasz Grzegorzek, Radosław Jurczak, Marta Bauer, Damian Neubauer, Karol Sikora, Michał Michalski, Jacek Sroka, Piotr Setny, Wojciech Kamysz, Ewa Szczurek

“…Here, we propose HydrAMP, a conditional variational autoencoder that learns lower-dimensional, continuous representation of peptides and captures their antimicrobial properties. …”
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Robot Concept Acquisition Based on Interaction Between Probabilistic and Deep Generative Models by Ryo Kuniyasu, Tomoaki Nakamura, Tadahiro Taniguchi, Takayuki Nagai, Takayuki Nagai

“…In this method, unsupervised multimodal clustering and cross-modal inference, as well as unsupervised representation learning, can be performed by integrating the multimodal latent Dirichlet allocation (MLDA)-based concept formation and variational autoencoder (VAE)-based feature extraction. Multimodal clustering, representation learning, and cross-modal inference are critical for robots to form multimodal concepts from sensory data. …”
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Reference Based Face Super-Resolution by Zhi-Song Liu, Wan-Chi Siu, Yui-Lam Chan

“…We propose a novel Conditional Variational AutoEncoder model for this Reference based Face Super-Resolution (RefSR-VAE). …”
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Comprehensive data optimization and risk prediction framework: machine learning methods for inflammatory bowel disease prediction based on the human gut microbiome data by Yan Peng, Yue Liu, Yifei Liu, Jie Wang

“…It then utilized importance-weighted variational autoencoder (IWVAE) to reduce redundant information from the high-dimensional microbiome data. …”
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Mining Hard Negative Samples for SAR-Optical Image Matching Using Generative Adversarial Networks by Lloyd Haydn Hughes, Michael Schmitt, Xiao Xiang Zhu

“…Our approach makes use of a variational autoencoder (VAE) which is trained in an adversarial manner in order to learn a latent distribution of the training data, as well as to be able to generate realistic, high quality image patches. …”
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A data generation framework for extremely rare case signals by Thasorn Chalongvorachai, Kuntpong Woraratpanya

“…Previously, we initiated a framework, called Data Augmentation and Generation for Anomalous Time-series Signals (DAGAT), that was in cooperation with important components: Data Augmentation, Variational Autoencoder (VAE), Data Picker (DP), Signal Fragment Assembler (SFA), and Quality Classifier (QC). …”
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