{"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/gan-you-do-the-gan-gan","title":"GAN You Do the GAN GAN?","arxiv_id":"1904.00724","date":"2019-04-01","proceeding":null,"authors":["Joseph Suarez"],"abstract":"Generative Adversarial Networks (GANs) have become a dominant class of\ngenerative models. In recent years, GAN variants have yielded especially\nimpressive results in the synthesis of a variety of forms of data. Examples\ninclude compelling natural and artistic images, textures, musical sequences,\nand 3D object files. However, one obvious synthesis candidate is missing. In\nthis work, we answer one of deep learning's most pressing questions: GAN you do\nthe GAN GAN? That is, is it possible to train a GAN to model a distribution of\nGANs? We release the full source code for this project under the MIT license.","url_abs":"http://arxiv.org/abs/1904.00724v1","url_pdf":"http://arxiv.org/pdf/1904.00724v1.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":"gan-you-do-the-gan-gan","repo_url":"https://github.com/jsuarez5341/gan-you-do-the-gan-gan","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-generation","task_name":"Image Generation"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}