BGaitR-Net: an effective neural model for occlusion reconstruction in gait sequences by exploiting the key pose information by Kumar, Somnath Sendhil, Singh, Binit, Chattopadhyay, Pratik, Halder, Agrya, Wang, Lipo

“…This vector is next fused with the corresponding spatial information using a Conditional Variational Autoencoder (CVAE) to obtain an effective embedding. …”
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LN3Diff: scalable latent neural fields diffusion for speedy 3D generation by Lan, Yushi, Hong, Fangzhou, Yang, Shuai, Zhou, Shangchen, Meng, Xuyi, Dai, Bo, Pan, Xingang, Loy, Chen Change

“…Our approach harnesses a 3D-aware architecture and variational autoencoder (VAE) to encode the input image into a structured, compact, and 3D latent space. …”
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WyCryst: Wyckoff inorganic crystal generator framework by Zhu, Ruiming, Nong, Wei, Yamazaki, Shuya, Hippalgaonkar, Kedar

“…To address this, we introduce a generative design framework (WyCryst) composed of three components: (1) a Wyckoff position-based inorganic crystal representation, (2) a property-directed variational autoencoder (VAE) model, and (3) an automated density functional theory (DFT) workflow for structure refinement. …”
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MetaCancer: A deep learning-based pan-cancer metastasis prediction model developed using multi-omics data by Somayah Albaradei, Francesco Napolitano, Maha A. Thafar, Takashi Gojobori, Magbubah Essack, Xin Gao

“…We quantitatively assess the proposed convolutional variational autoencoder (CVAE) and alternative feature extraction methods. …”
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scGMM-VGAE: a Gaussian mixture model-based variational graph autoencoder algorithm for clustering single-cell RNA-seq data by Eric Lin, Boyuan Liu, Leann Lac, Daryl L X Fung, Carson K Leung, Pingzhao Hu

“…This model feeds a cell-cell graph adjacency matrix and a gene feature matrix into a graph variational autoencoder (VGAE) to generate latent data. These data are then used for cell clustering by the Gaussian mixture model (GMM) module. …”
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Comparison of autoencoder architectures for fault detection in industrial processes by Deris Eduardo Spina, Luiz Felipe de O. Campos, Wallthynay F. de Arruda, Afrânio Melo, Marcelo F. de S. Alves, Gildeir Lima Rabello, Thiago K. Anzai, José Carlos Pinto

“…Performances obtained for shallow and deep autoencoders were compared with those of the denoising and variational autoencoders for undercomplete and sparse structures. …”
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Modeling of Botnet Detection Using Chaotic Binary Pelican Optimization Algorithm With Deep Learning on Internet of Things Environment by Fadwa Alrowais, Majdy M. Eltahir, Sumayh S. Aljameel, Radwa Marzouk, Gouse Pasha Mohammed, Ahmed S. Salama

“…The convolutional variational autoencoder (CVAE) method is applied for botnet detection. …”
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VAEEG: Variational auto-encoder for extracting EEG representation by Tong Zhao, Yi Cui, Taoyun Ji, Jiejian Luo, Wenling Li, Jun Jiang, Zaifen Gao, Wenguang Hu, Yuxiang Yan, Yuwu Jiang, Bo Hong

“…In order to obtain more intuitive, concise, and useful representations of brain activity, we constructed a reconstruction-based self-supervised learning model for EEG based on Variational Autoencoder (VAE) with separate frequency bands, termed variational auto-encoder for EEG (VAEEG). …”
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Powder X‐Ray Diffraction Pattern Is All You Need for Machine‐Learning‐Based Symmetry Identification and Property Prediction by Byung Do Lee, Jin-Woong Lee, Woon Bae Park, Joonseo Park, Min-Young Cho, Satendra Pal Singh, Myoungho Pyo, Kee-Sun Sohn

“…For this purpose, a fully convolutional neural network (FCN), transformer encoder (T‐encoder), and variational autoencoder (VAE) are used. The results are compared to those obtained from a well‐established crystal graph convolutional neural network (CGCNN). …”
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Evaluation of Synthetic Categorical Data Generation Techniques for Predicting Cardiovascular Diseases and Post-Hoc Interpretability of the Risk Factors by Clara García-Vicente, David Chushig-Muzo, Inmaculada Mora-Jiménez, Himar Fabelo, Inger Torhild Gram, Maja-Lisa Løchen, Conceição Granja, Cristina Soguero-Ruiz

“…We performed a comparative study of several categorical synthetic data generation methods, including Synthetic Minority Oversampling Technique Nominal (SMOTEN), Tabular Variational Autoencoder (TVAE) and Conditional Tabular Generative Adversarial Networks (CTGANs). …”
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COVID-19 Genome Sequence Analysis for New Variant Prediction and Generation by Amin Ullah, Khalid Mahmood Malik, Abdul Khader Jilani Saudagar, Muhammad Badruddin Khan, Mozaherul Hoque Abul Hasanat, Abdullah AlTameem, Mohammed AlKhathami, Muhammad Sajjad

“…Finally, can synthetic approaches such as variational autoencoder-decoder networks be employed to generate a synthetic new variant from random noise? …”
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Application of Conditional Generative Adversarial Networks to Efficiently Generate Photon Phase Space in Medical Linear Accelerators of Different Primary Beam Parameters by Mateusz Baran, Zbisław Tabor, Krzysztof Rzecki, Przemysław Ziaja, Tomasz Szumlak, Kamila Kalecińska, Jakub Michczyński, Bartłomiej Rachwał, Michael P. R. Waligórski, David Sarrut

“…We also present the second-best type of deep learning architecture that we studied: a variational autoencoder. Differences between dose distributions obtained in a water phantom, in a test phantom, and in real patients using generative-adversarial-network-based and Monte-Carlo-based phase spaces are very close to each other, as indicated by the values of standard quality assurance tools of radiotherapy. …”
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Enhanced Dwarf Mongoose optimization algorithm with deep learning-based attack detection for drones by Yazan A. Alsariera, Waleed Fayez Awwad, Abeer D. Algarni, Hela Elmannai, Margarita Gamarra, José Escorcia-Gutierrez

“…To detect attacks, the EDMOA-DLAD technique uses a deep variational autoencoder (DVAE) classifier. Finally, the EDMOA-DLAD technique applies the beetle antenna search (BAS) technique for the optimum hyperparameter part of DVAE model. …”
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A novel diagnostic framework for breast cancer: Combining deep learning with mammogram-DBT feature fusion by Nishu Gupta, Jan Kubicek, Marek Penhaker, Mohammad Derawi

“…Feature extraction was performed using Disentangled Variational Autoencoder (D-VAE), capturing critical texture features. …”
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A two-tier optimization strategy for feature selection in robust adversarial attack mitigation on internet of things network security by Kashi Sai Prasad, P Udayakumar, E. Laxmi Lydia, Mohammed Altaf Ahmed, Mohamad Khairi Ishak, Faten Khalid Karim, Samih M. Mostafa

“…Moreover, the TTOS-RAAM technique employs the conditional variational autoencoder (CVAE) technique to detect adversarial attacks. …”
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Leveraging transfer learning-driven convolutional neural network-based semantic segmentation model for medical image analysis using MRI images by Amal Alshardan, Nuha Alruwais, Hamed Alqahtani, Asma Alshuhail, Wafa Sulaiman Almukadi, Ahmed Sayed

“…Lastly, the crayfish optimization (CFO) technique with diffusion variational autoencoder (D-VAE) architecture is used as a classification mechanism, and the CFO technique effectively tunes the D-VAE hyperparameter. …”
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Dear-DIAXMBD: Deep Autoencoder Enables Deconvolution of Data-Independent Acquisition Proteomics by Qingzu He, Chuan-Qi Zhong, Xiang Li, Huan Guo, Yiming Li, Mingxuan Gao, Rongshan Yu, Xianming Liu, Fangfei Zhang, Donghui Guo, Fangfu Ye, Tiannan Guo, Jianwei Shuai, Jiahuai Han

“…Dear-DIAXMBD first integrates the deep variational autoencoder and triplet loss to learn the representations of the extracted fragment ion chromatograms, then uses the k-means clustering algorithm to aggregate fragments with similar representations into the same classes, and finally establishes the inverted index tables to determine the precursors of fragment clusters between precursors and peptides and between fragments and peptides. …”
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Data Augmentation for Motor Imagery Signal Classification Based on a Hybrid Neural Network by Kai Zhang, Guanghua Xu, Zezhen Han, Kaiquan Ma, Xiaowei Zheng, Longting Chen, Nan Duan, Sicong Zhang

“…The results showed that the deep convolutional generative adversarial network (DCGAN) provided better augmentation performance than traditional DA methods: geometric transformation (GT), autoencoder (AE), and variational autoencoder (VAE) (<i>p</i> < 0.01). Public datasets of the BCI competition IV (datasets 1 and 2b) were used to verify the classification performance. …”
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Multimodal few-shot classification without attribute embedding by Chang, Jun Qing, Rajan, Deepu, Vun, Nicholas

“…The model consists of a variational autoencoder to learn the visual latent representation, which is combined with a semantic latent representation that is learnt from a normal autoencoder, which calculates a semantic loss between the latent representation and a binary attribute vector. …”
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Deep unsupervised representation learning for feature-informed EEG domain extraction by Ng, Han Wei, Guan, Cuntai

“…This study proposes a novel inference model, the Joint Embedding Variational Autoencoder, that offers conditionally tighter approximation of the estimated spatiotemporal feature distribution through the use of jointly optimised variational autoencoders to achieve optimizable data dependent inputs as an additional variable for improved overall model optimisation and scaling without sacrificing model tightness. …”
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