A deep encoder-decoder network for anomaly detection in driving trajectory behavior under spatio-temporal context by Wenhao Yu, Qinghong Huang

“…This method is able to detect the deviation of movement from the normal traffic state on the spatial–temporal units. Finally, a variational autoencoder is utilized to quantify the abnormity degree of each driver according to the reconstruction probability of driving feature vector. …”
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Augmentation Embedded Deep Convolutional Neural Network for Predominant Instrument Recognition by Jian Zhang, Na Bai

“…AEDCN adds two fully connected layers into the backbone neural network and integrates data augmentation directly into the recognition process by introducing a proposed Adversarial Embedded Conditional Variational AutoEncoder (ACEVAE) between the added fully connected layers of the backbone network. …”
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Voice Conversion Based Augmentation and a Hybrid CNN-LSTM Model for Improving Speaker-Independent Keyword Recognition on Limited Datasets by Yeshanew Ale Wubet, Kuang-Yow Lian

“…To overcome this, we generated new raw voices from the original voices using an auxiliary classifier conditional variational autoencoder (ACVAE) method. In this study, the main intention of voice conversion is to obtain numerous and various human-like keywords’ voices that are not identical to the source and target speakers’ pronunciation. …”
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A graph-embedded topic model enables characterization of diverse pain phenotypes among UK biobank individuals by Yuening Wang, Rodrigo Benavides, Luda Diatchenko, Audrey V. Grant, Yue Li

“…We integrate existing biomedical knowledge graph information in the form of pre-trained graph embedding into the embedded topic model. Via a variational autoencoder framework, we infer patient phenotypic mixture by modeling multi-modal discrete patient medical records. …”
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Efficient large invisible color watermark embedding using conditional deep autoencoder model for medical applications by Konka Kishan, B. Vijay Kumar

“…The proposed model uses a Conditional Variational Autoencoder (CVAE) to encode the watermark into the cover image. …”
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Wi-Fi-Based Indoor Localization and Navigation: A Robot-Aided Hybrid Deep Learning Approach by Xuxin Lin, Jianwen Gan, Chaohao Jiang, Shuai Xue, Yanyan Liang

“…Furthermore, we design two deep learning models based on a variational autoencoder for the localization and navigation tasks, respectively. …”
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Unsupervised Learning of Total Variability Embedding for Speaker Verification with Random Digit Strings by Woo Hyun Kang, Nam Soo Kim

“…In this paper, we propose a novel technique for extracting an i-vector-like feature based on the variational autoencoder (VAE), which is trained in an unsupervised manner to obtain a latent variable representing the variability within a Gaussian mixture model (GMM) distribution. …”
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Harnessing structural stochasticity in the computational discovery and design of microstructures by Leidong Xu, Nathaniel Hoffman, Zihan Wang, Hongyi Xu

“…Autoencoder (AE), Variational Autoencoder (VAE), and Adversarial Autoencoder (AAE) are compared to understand their relative merits in the property-aware learning of the unified feature space. …”
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Tackling multimodal device distributions in inverse photonic design using invertible neural networks by Michel Frising, Jorge Bravo-Abad, Ferry Prins

“…We compare a commonly used conditional variational autoencoder (cVAE) and a conditional invertible neural network (cINN) on a proof-of-principle nanophotonic problem, consisting in tailoring the transmission spectrum trough a metallic film milled by subwavelength indentations. …”
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Understanding and mitigating the impact of race with adversarial autoencoders by Kathryn Sarullo, S. Joshua Swamidass

“…This study aims to mitigate the impact of race on data-derived models, using an adversarial variational autoencoder (AVAE). In this study, race is a self-reported feature. …”
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Unsupervised Balanced Hash Codes Learning With Multichannel Feature Fusion by Yaxiong Chen, Dongjie Zhao, Xiongbo Lu, Shengwu Xiong, Huangting Wang

“…To tackle these issues, we develop an unsupervised hashing algorithm, namely, <italic>variational autoencoder balanced hashing</italic> (VABH), to leverage multichannel feature fusion and multiscale context information to perform RSI retrieval task. …”
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Open-Vocabulary Predictive World Models from Sensor Observations by Robin Karlsson, Ruslan Asfandiyarov, Alexander Carballo, Keisuke Fujii, Kento Ohtani, Kazuya Takeda

“…The model is implemented through a hierarchical variational autoencoder (HVAE) capable of predicting diverse and accurate fully observed environments from accumulated partial observations. …”
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Autoencoding Labeled Interpolator, Inferring Parameters from Image and Image from Parameters by Ali SaraerToosi, Avery E. Broderick

“…This study presents an image generation tool in the form of a generative machine-learning model, which extends the capabilities of a variational autoencoder. This tool can rapidly and continuously interpolate between a training set of images and can retrieve the defining parameters of those images. …”
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Unsupervised abnormal track detection method based on GRU-VAE by LI Lei, ZHANG Jing, OUYANG Qicheng, ZHOU Mingkang

“…The Gate Recurrent unit-variational Autoencoder model is trained by historical track data without abnormal information labels. …”
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Artificial Intelligence-Powered Computational Strategies in Selecting and Augmenting Data for Early Design of Tall Buildings with Outer Diagrids by Pooyan Kazemi, Aldo Ghisi, Alireza Entezami

“…By augmenting an initial dataset, which was notably limited, through four distinct techniques—namely Gaussian copula, conditional generative adversarial networks, Gaussian copula generative adversarial network, and variational autoencoder—this study demonstrates a methodical approach to data enhancement in architectural design. …”
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Device Light Fingerprints Identification Using MCU-Based Deep Learning Approach by Chung-Wen Hung, Jun-Rong Wu, Ching-Hung Lee

“…We utilize the convolutional neural network, the improved multi-class greedy autoencoder and variational autoencoder with domain adaptation techniques to develop the identification algorithm. …”
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Design of target specific peptide inhibitors using generative deep learning and molecular dynamics simulations by Sijie Chen, Tong Lin, Ruchira Basu, Jeremy Ritchey, Shen Wang, Yichuan Luo, Xingcan Li, Dehua Pei, Levent Burak Kara, Xiaolin Cheng

“…Our method integrates a Gated Recurrent Unit-based Variational Autoencoder with Rosetta FlexPepDock for peptide sequence generation and binding affinity assessment. …”
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Explainable semi-supervised deep learning shows that dementia is associated with small, avocado-shaped clocks with irregularly placed hands by Sabyasachi Bandyopadhyay, Jack Wittmayer, David J. Libon, Patrick Tighe, Catherine Price, Parisa Rashidi

“…In this study, we used the relevance factor variational autoencoder (RF-VAE), a deep generative neural network, to represent digitized clock drawings from multiple institutions using an optimal number of disentangled latent factors. …”
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Traffic Control Recognition with Speed-Profiles: A Deep Learning Approach by Hao Cheng, Stefania Zourlidou, Monika Sester

“…The results showed that the deep-learning classifier Conditional Variational Autoencoder can predict regulators with 90% accuracy, outperforming a random forest classifier (88% accuracy) that uses the summarized statistics of movement as features. …”
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A Customizable Face Generation Method Based on Stable Diffusion Model by Wenlong Xiang, Shuzhen Xu, Cuicui Lv, Shuo Wang

“…In addition, we modify the structure of Variational Autoencoder to enhance generation efficiency. …”
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