{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/synthesizing-tabular-data-using-generative","title":"Synthesizing Tabular Data using Generative Adversarial Networks","arxiv_id":"1811.11264","date":"2018-11-27","proceeding":null,"authors":["Lei Xu","Kalyan Veeramachaneni"],"abstract":"Generative adversarial networks (GANs) implicitly learn the probability\ndistribution of a dataset and can draw samples from the distribution. This\npaper presents, Tabular GAN (TGAN), a generative adversarial network which can\ngenerate tabular data like medical or educational records. Using the power of\ndeep neural networks, TGAN generates high-quality and fully synthetic tables\nwhile simultaneously generating discrete and continuous variables. When we\nevaluate our model on three datasets, we find that TGAN outperforms\nconventional statistical generative models in both capturing the correlation\nbetween columns and scaling up for large datasets.","url_abs":"http://arxiv.org/abs/1811.11264v1","url_pdf":"http://arxiv.org/pdf/1811.11264v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"synthesizing-tabular-data-using-generative","repo_url":"https://github.com/sdv-dev/TGAN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"synthesizing-tabular-data-using-generative","repo_url":"https://github.com/DAI-Lab/TGAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"synthesizing-tabular-data-using-generative","repo_url":"https://github.com/Pushkar-v/Generating-Synthetic-Data-using-GANs","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"synthesizing-tabular-data-using-generative","repo_url":"https://github.com/TimoKuenstle/TGAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"synthesizing-tabular-data-using-generative","repo_url":"https://github.com/glederrey/datgan","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"synthesizing-tabular-data-using-generative","repo_url":"https://github.com/kyriacosar/FairTGAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"synthesizing-tabular-data-using-generative","repo_url":"https://github.com/Diyago/GAN-for-tabular-data","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":null,"task_name":"Generative Adversarial Network"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1811.11264","atlas_url":"https://app.syntology.ai/?focus=1811.11264","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.11264"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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