Tabular GANs for uneven distribution

[2010.00638] Tabular GANs for uneven distribution Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2010.00638 (cs) [Submitted on 1 Oct 2020 ( v1 ), last revised 8 Apr 2026 (this version, v2)] Title: Tabular GANs for uneven distribution Authors: Insaf Ashrapov View a PDF of the paper titled Tabular GANs for uneven distribution, by Insaf Ashrapov View PDF HTML (experimental) Abstract: Generative models for tabular data have evolved rapidly beyond Generative Adversarial Networks (GANs). While GANs pioneered synthetic tabular data generation, recent advances in diffusion models and large language models (LLMs) have opened new paradigms with complementary strengths in sample quality, privacy, and controllability. In this paper, we survey the landscape of tabular data generation across three major paradigms - GANs, diffusion models, and LLMs - and introduce a unified, modular framework that supports all three. The framework encompasses data preprocessing, a model-agnostic interface layer, standardized training and inference pipelines, and a comprehensive evaluation module. We validate the framework through experiments on seven benchmark datasets, demonstrating that GAN-based augmentation can improve downstream performance under distribution shift. The framework and its reference implementation are publicly available at this https URL , facilitating reproducibility and extensibility for future research. Comments: 11 pages Subjects: Machine Learning (cs.LG) ; Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2010.00638 [cs.LG] (or arXiv:2010.00638v2 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2010.00638 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Insaf Ashrapov [ view email ] [v1] Thu, 1 Oct 2020 18:39:32 UTC (4,763 KB) [v2] Wed, 8 Apr 2026 20:54:38 UTC (1,240 KB) Full-text links: Access Paper: View a PDF of the paper titled Tabular GANs for uneven distribution, by Insaf Ashrapov View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2020-10 Change to browse by: cs cs.CV References & Citations NASA ADS Google Scholar Semantic Scholar DBLP - CS Bibliography listing | bibtex Insaf Ashrapov export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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