{"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/data-augmentation-using-gans","title":"Data Augmentation Using GANs","arxiv_id":"1904.09135","date":"2019-04-19","proceeding":null,"authors":["Fabio Henrique Kiyoiti dos Santos Tanaka","Claus Aranha"],"abstract":"In this paper we propose the use of Generative Adversarial Networks (GAN) to\ngenerate artificial training data for machine learning tasks. The generation of\nartificial training data can be extremely useful in situations such as\nimbalanced data sets, performing a role similar to SMOTE or ADASYN. It is also\nuseful when the data contains sensitive information, and it is desirable to\navoid using the original data set as much as possible (example: medical data).\nWe test our proposal on benchmark data sets using different network\narchitectures, and show that a Decision Tree (DT) classifier trained using the\ntraining data generated by the GAN reached the same, (and surprisingly\nsometimes better), accuracy and recall than a DT trained on the original data\nset.","url_abs":"http://arxiv.org/abs/1904.09135v1","url_pdf":"http://arxiv.org/pdf/1904.09135v1.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":"data-augmentation-using-gans","repo_url":"https://github.com/fhtanaka/directed_research_CS_2018","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"smote","method_name":"SMOTE"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.09135","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}