Survey on video image reconstruction method based on generative model by Yanwen WANG, Weimin LEI, Wei ZHANG, Huan MENG, Xinyi CHEN, Wenhui YE, Qingyang JING

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Deep generative modeling of annotated bacterial biofilm images by Angelina A. Holicheva, Konstantin S. Kozlov, Daniil A. Boiko, Maxim S. Kamanin, Daria V. Provotorova, Nikita I. Kolomoets, Valentine P. Ananikov

“…This study introduces a versatile approach for creating synthetic datasets of annotated biofilm images with employing deep generative modeling techniques, including VAEs, GANs, diffusion models, and CycleGAN. …”
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Compensating CBCT Motion Artifacts with Any 2D Generative Model by Yipeng Sun, Linda-Sophie Schneider, Mingxuan Gu, Siyuan Mei, Siming Bayer, Andreas K. Maier

“…Leveraging two-dimensional (2D) generative models while ensuring consistency between adjacent CBCT slices, our method addresses the limitations of traditional deep learning approaches that process each 2D slice of a three-dimensional (3D) volume independently. …”
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Investigating the intrinsic top-down dynamics of deep generative models by Lorenzo Tausani, Alberto Testolin, Marco Zorzi

“…Abstract Hierarchical generative models can produce data samples based on the statistical structure of their training distribution. …”
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Multiple Attractors and Nonlinear Dynamics in an Overlapping Generations Model with Environment by Ahmad Naimzada, Mauro Sodini

“…This paper develops a one-sector productive overlapping generations model with environment where a CES technology is assumed. …”
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Predicting cell morphological responses to perturbations using generative modeling by Alessandro Palma, Fabian J. Theis, Mohammad Lotfollahi

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On the design and evaluation of generative models in high energy density physics by Ankita Shukla, Yamen Mubarka, Rushil Anirudh, Eugene Kur, Derek Mariscal, Blagoje Djordjevic, Bogdan Kustowski, Kelly Swanson, Brian Spears, Peer-Timo Bremer, Tammy Ma, Pavan Turaga, Jayaraman J. Thiagarajan

“…This paper introduces HEDP-Gen, a framework for training and evaluating generative models tailored for HEDP. Central to HEDP-Gen is Geom-WAE-a generalized Wasserstein auto-encoder accommodating both Euclidean and non-Euclidean latent spaces. …”
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Research on geomagnetic indoor high-precision positioning algorithm based on generative model by Shuai MA, Ke PEI, Huayan QI, Hang LI, Wen CAO, Hongmei WANG, Hailiang XIONG, Shiyin LI

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A generative model for evaluating missing data methods in large epidemiological cohorts by Lav Radosavljević, Stephen M. Smith, Thomas E. Nichols

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An Effective Manifold Learning Approach to Parametrize Data for Generative Modeling of Biosignals by Lorenzo Manoni, Claudio Turchetti, Laura Falaschetti

Subjects: “…Biosignal generative modeling…”
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Discovery of new topological insulators and semimetals using deep generative models by Tao Hong, Taikang Chen, Dalong Jin, Yu Zhu, Heng Gao, Kun Zhao, Tongyi Zhang, Wei Ren, Guixin Cao

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Traditional Chinese Medicine Prescription Generation Model Based on Search Enhancement by ZHAO Zijuan, REN Xueting, SONG Kai, QIANG Yan, ZHAO Juanjuan, ZHANG Junlong

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Forecast generation model of municipal solid waste using multiple linear regression by J.A. Araiza-Aguilar, M.N. Rojas-Valencia, R.A. Aguilar-Vera

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Time series generation model based on multi-discriminator generative adversarial network by Yanhui LU, Han LIU, Hang LI, Guangxu ZHU

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Prompting for products: investigating design space exploration strategies for text-to-image generative models by Leah Chong, I-Ping Lo, Jude Rayan, Steven Dow, Faez Ahmed, Ioanna Lykourentzou

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Risks and countermeasures of artificial intelligence generated content technology in content security governance by Zhe QIAO

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Multimodal Image Translation Algorithm Based on Singular Squeeze-and-Excitation Network by Hangyao Tu, Zheng Wang, Yanwei Zhao

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Computationally secure steganography based on speech synthesis by Menghan LI, Kejiang CHEN, Weiming ZHANG, Nenghai YU

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Inverse design of promising electrocatalysts for CO2 reduction via generative models and bird swarm algorithm by Zhilong Song, Linfeng Fan, Shuaihua Lu, Chongyi Ling, Qionghua Zhou, Jinlan Wang

“…Abstract Directly generating material structures with optimal properties is a long-standing goal in material design. Traditional generative models often struggle to efficiently explore the global chemical space, limiting their utility to localized space. …”
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Semi-supervised emotion-driven music generation model based on category-dispersed Gaussian Mixture Variational Autoencoders by Zihao Ning, Xiao Han, Jie Pan

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