Integrated neural network framework for multi-object detection and recognition using UAV imagery by Mohammed Alshehri, Tingting Xue, Tingting Xue, Ghulam Mujtaba, Yahya AlQahtani, Nouf Abdullah Almujally, Ahmad Jalal, Ahmad Jalal, Hui Liu, Hui Liu, Hui Liu

“…Combining DenseNet and SuperPoint embeddings that were improved with an AutoEncoder is done during feature extraction. In the end, using an attention function, Vision Transformer-based models classify vehicles seen from above. …”
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The geometry of efficient codes: How rate-distortion trade-offs distort the latent representations of generative models. by Leo D'Amato, Gian Luca Lancia, Giovanni Pezzulo

“…To address this question, here we investigate how rate-distortion trade-offs shape the latent representations of images in generative models, specifically Beta Variational Autoencoders ([Formula: see text]-VAEs), under varying constraints of model capacity, data distributions, and task objectives. …”
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Structure-Based Deep Learning Framework for Modeling Human–Gut Bacterial Protein Interactions by Despoina P. Kiouri, Georgios C. Batsis, Christos T. Chasapis

“…The framework leverages graph-based protein representations and variational autoencoders (VAEs) to extract structural embeddings from protein graphs, which are then fused through a Bi-directional Cross-Attention module to predict interactions. …”
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The Evolution of Generative AI: Trends and Applications by Maria Trigka, Elias Dritsas

“…This survey explores the core methodologies, advancements, applications, and ongoing challenges of generative AI, covering key models such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Models, and Transformer-based architectures. …”
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Creating a Novel Attention-Enhanced Framework for Video-Based Action Quality Assessment by Wenhui Gong, Wei Li, Huosheng Hu, Zhijun Song, Zhiqiang Zeng, Jinhua Sun, Yuping Song

“…We further leverage Variational Autoencoders (VAEs) to capture complex latent representations and quantify uncertainty. …”
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Improving the Parameterization of Complex Subsurface Flow Properties With Style‐Based Generative Adversarial Network (StyleGAN) by Wei Ling, Behnam Jafarpour

“…Deep learning techniques, such as Variational Autoencoders (VAE) and Generative Adversarial Networks (GAN), have recently been proposed to address this difficulty by learning complex spatial patterns from prior training images and synthesizing similar realizations using low‐dimensional latent variables with Gaussian distributions. …”
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Generative AI for drug discovery and protein design: the next frontier in AI-driven molecular science by Uddalak Das

“…This review systematically delineates the theoretical underpinnings, algorithmic architectures, and translational applications of deep generative models—including variational autoencoders (VAEs), generative adversarial networks (GANs), autoregressive transformers, and score-based denoising diffusion probabilistic models (DDPMs)—in the rational design of bioactive small molecules and functional proteins. …”
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A neural network-based synthetic diagnostic of laser-accelerated proton energy spectra by Christopher J. G. McQueen, Robbie Wilson, Timothy P. Frazer, Martin King, Matthew Alderton, Ewan F. J. Bacon, Ewan J. Dolier, Thomas Dzelzainis, Jesel K. Patel, Maia P. Peat, Ben C. Torrance, Ross J. Gray, Paul McKenna

“…This model combines variational autoencoders for dimensionality reduction with feed-forward networks for predictions based on secondary diagnostics of the laser-plasma interactions. …”
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Suppression of Multiple Reflection Interference Signals in GPR Images Caused by Rebar Using VAE-GAN by Chuan Li, Qibing Ma, Yawei Wang, Xi Yang, Hao Liu, Lulu Wang

“…This study proposes an unsupervised generative network model based on Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). …”
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Generative AI models for different steps in architectural design: A literature review by Chengyuan Li, Tianyu Zhang, Xusheng Du, Ye Zhang, Haoran Xie

“…Recent advances in generative artificial intelligence (AI) technologies have been significantly driven by models such as generative adversarial networks (GANs), variational autoencoders (VAEs), and denoising diffusion probabilistic models (DDPMs). …”
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Generative artificial intelligence based models optimization towards molecule design enhancement by Tarek Khater, Sara Awni Alkhatib, Aamna AlShehhi, Charalampos Pitsalidis, Anna Maria Pappa, Son Tung Ngo, Vincent Chan, Vi Khanh Truong

“…We explore key generative architectures, including variational autoencoders, generative adversarial networks, and transformer-based models, highlighting their unique contributions to drug discovery. …”
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Toward Generative AI-Based Intrusion Detection Systems for the Internet of Vehicles (IoV) by Isra Mahmoudi, Djallel Eddine Boubiche, Samir Athmani, Homero Toral-Cruz, Freddy I. Chan-Puc

“…In response to these challenges, Generative AI-based IDS—leveraging models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformers—have emerged as promising solutions that offer enhanced adaptability, synthetic data generation for training, and improved detection capabilities for evolving threats. …”
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The potential of generative AI with prostate-specific membrane antigen (PSMA) PET/CT: challenges and future directions by Islam Md Zobaer, Spiro Ergi, Yap Pew-Thian, Gorin Michael A., Rowe Steven P.

“…., generative adversarial networks, variational autoencoders, diffusion models, and large language models have played crucial roles in overcoming many such challenges across various imaging modalities, including PET, computed tomography, magnetic resonance imaging, ultrasound, etc. …”
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The Hybrid Modern Network Model: A Multi-Technique Framework for Comprehensive Network Analysis by Theodoros Kyriazos, Mary Poga

“…The HMNM integrates foundational models like the Stochastic Block Model (SBM) and Preferential Attachment with advanced machine learning techniques, including Graph Neural Networks (GNNs), Reinforcement Learning (RL), Hierarchical Random Graphs (HRGs), Generative Adversarial Networks (GANs), and Variational Autoencoders (VAEs). The methods employed involve constructing initial network structures using SBM, simulating network growth through preferential Attachment, learning node embeddings with GNNs, dynamically optimizing network properties using RL, capturing hierarchical community structures with HRGs, controlling degree distributions using GANs, and uncovering latent patterns with VAEs. …”
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SGM-EMA: Speech Enhancement Method Score-Based Diffusion Model and EMA Mechanism by Yuezhou Wu, Zhiri Li, Hua Huang

“…The score-based diffusion model has made significant progress in the field of computer vision, surpassing the performance of generative models, such as variational autoencoders, and has been extended to applications such as speech enhancement and recognition. …”
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Interpretable artificial intelligence (AI) for cervical cancer risk analysis leveraging stacking ensemble and expert knowledge by Priyanka Roy, Mahmudul Hasan, Md Rashedul Islam, Md Palash Uddin

“…Generative artificial intelligence methods, such as variational autoencoders and generative teaching networks, were evaluated but showed suboptimal performance. …”
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Challenges in AI-driven multi-omics data analysis for Oncology: Addressing dimensionality, sparsity, transparency and ethical considerations by Maryem Ouhmouk, Shakuntala Baichoo, Mounia Abik

“…On the other hand, generative methods such as variational autoencoders (VAEs), generative adversarial networks (GANs), and generative pretrained transformers (GPTs) focus on creating adaptable representations that can be shared across multiple modalities. …”
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One-shot generative distribution matching for augmented RF-based UAV identification by Amir Kazemi, Salar Basiri, Volodymyr Kindratenko, Srinivasa Salapaka

“…This approach, when utilizing a distributional distance metric, demonstrates significant promise in low-data regimes, outperforming deep generative methods such as conditional generative adversarial networks (GANs) and variational autoencoders (VAEs). The paper provides a theoretical guarantee for the effectiveness of one-shot generative models in augmenting limited data, setting a precedent for their application in limited RF environments. …”
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Simulation of Spring Discharge Using Deep Learning, Considering the Spatiotemporal Variability of Precipitation by Chunmei Ma, Haoyu Jiao, Yonghong Hao, Tian‐Chyi Jim Yeh, Junfeng Zhu, Huiqing Hao, Jiahui Lu, Jiankang Dong

“…This study addresses this issue by employing a coupled deep learning model that integrates a variation autoencoder (VAE) for augmenting precipitation and a long short‐term memory (LSTM) network for karst spring discharge prediction. …”
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A Novel Deeply-Learned Image Quality Analysis Algorithm for Clustering by Zhongzhe Chen, Xing Gao

“…Addressing the limitations of existing deep clustering methods, which struggle with variations in image size and quality and are vulnerable to data noise and model deviations, we propose a deeply-learned clustering paradigm in an unsupervised context. …”
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