{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T17:59:48Z","timestamp":1772906388378,"version":"3.50.1"},"reference-count":42,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"Anhui Provincial Key Natural Science Foundation Project: Research on Pavement Defect Detection Method Based on Deep Learning","award":["2023AH052699"],"award-info":[{"award-number":["2023AH052699"]}]},{"name":"Anhui Provincial Key Natural Science Foundation Project: High-Frequency Modeling and Application Research of Multiphase Peak Current Mode Buck Power Converter in Industrial Internet","award":["2022AH052364"],"award-info":[{"award-number":["2022AH052364"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/access.2025.3551594","type":"journal-article","created":{"date-parts":[[2025,3,14]],"date-time":"2025-03-14T17:55:22Z","timestamp":1741974922000},"page":"48643-48655","source":"Crossref","is-referenced-by-count":1,"title":["Enhancing Tabular Data Generation With Dual-Scale Noise Modeling"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-1125-0978","authenticated-orcid":false,"given":"Xiaorong","family":"Zhang","sequence":"first","affiliation":[{"name":"Anhui Technical College of Mechanical and Electrical Engineering, Wuhu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Li","sequence":"additional","affiliation":[{"name":"Anhui Technical College of Mechanical and Electrical Engineering, Wuhu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuting","family":"Hu","sequence":"additional","affiliation":[{"name":"Anhui Technical College of Mechanical and Electrical Engineering, Wuhu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"issue":"10","key":"ref1","first-page":"4959","article-title":"A comprehensive survey on deep generative models for tabular data","volume":"33","author":"Liu","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"3","key":"ref2","first-page":"149","article-title":"Generative models for tabular data: A survey","volume":"10","author":"Nguyen","year":"2023","journal-title":"Int. J. Data Sci. Anal."},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2022.04.053"},{"issue":"7","key":"ref4","first-page":"1783","article-title":"Diffusion models for high-dimensional data: Applications to image and tabular data","volume":"45","author":"Langlois","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref5","article-title":"Causal-TGAN: Modeling tabular data using causally-aware GAN","volume-title":"Proc. ICLR Workshop Deep Generative Models Highly Struct. Data","author":"Wen"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM51629.2021.00103"},{"key":"ref7","article-title":"CTAB-GAN: Effective table data synthesizing","volume-title":"Proc. Asian Conf. Mach. Learn.","author":"Zhao"},{"key":"ref8","article-title":"CTAB-GAN+: Enhancing tabular data synthesis","author":"Zhao","year":"2022","journal-title":"arXiv:2204.00401"},{"key":"ref9","article-title":"Modeling tabular data using conditional GAN","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Xu"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3236009"},{"key":"ref11","article-title":"Comparative analysis of generative models: Enhancing image synthesis with VAEs, GANs, and stable diffusion","author":"Vivekananthan","year":"2024","journal-title":"arXiv:2408.08751"},{"key":"ref12","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Ho"},{"key":"ref13","article-title":"Improved denoising diffusion probabilistic models","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Nichol"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3229161"},{"key":"ref15","article-title":"TABDDPM: Modeling tabular data with diffusion models","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Kotelnikov"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2024.3366991"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3315592"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-34341-2"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-73003-5_196"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9884.00117"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3422622"},{"key":"ref22","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/978-3-030-70679-1_5","article-title":"Variational autoencoder","volume-title":"Variational Methods for Machine Learning With Applications to Deep Networks","author":"Cinelli","year":"2021"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.14778\/3231751.3231757"},{"key":"ref24","article-title":"TVAE: Triplet-based variational autoencoder using metric learning","author":"Ishfaq","year":"2018","journal-title":"arXiv:1802.04403"},{"key":"ref25","article-title":"Single-cell data analysis using MMD variational autoencoder for a more informative latent representation","author":"Zhang","year":"2019","journal-title":"bioRxiv"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/SCW63240.2024.00018"},{"key":"ref27","article-title":"AutoDiff: Combining auto-encoder and diffusion model for tabular data synthesizing","author":"Suh","year":"2023","journal-title":"arXiv:2310.15479"},{"key":"ref28","first-page":"1235","article-title":"TabMT: Generating tabular data with masked transformers","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"36","author":"Gulati"},{"key":"ref29","first-page":"226","article-title":"Scaling up the accuracy of Naive-Bayes classifiers: A decision-tree hybrid","volume-title":"Proc. KDD","author":"Kohavi"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2017.2736643"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2016.35"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/0304-405X(93)90023-5"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.5555\/2188385.2188395"},{"key":"ref34","first-page":"18932","article-title":"Revisiting deep learning models for tabular data","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Gorishniy"},{"key":"ref35","volume-title":"Iris Dataset","author":"Fisher","year":"1936"},{"key":"ref36","article-title":"Heart disease","author":"Janosi","year":"1989","journal-title":"UCI Mach. Learn. Repository"},{"key":"ref37","article-title":"Wine quality","author":"Cortez","year":"2009","journal-title":"UCI Mach. Learn. Repository"},{"key":"ref38","article-title":"Bank marketing","author":"Moro","year":"2014","journal-title":"UCI Mach. Learn. Repository"},{"key":"ref39","article-title":"Twitter geospatial data","author":"Helwig","year":"2015","journal-title":"UCI Mach. Learn. Repository"},{"key":"ref40","article-title":"HARTH","author":"Logacjov","year":"2021","journal-title":"UCI Mach. Learn. Repository"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/tbdata.2024.3442534"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2025.104292"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10820123\/10926531.pdf?arnumber=10926531","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T18:46:53Z","timestamp":1742842013000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10926531\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":42,"URL":"https:\/\/doi.org\/10.1109\/access.2025.3551594","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}