{"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/artgan-artwork-synthesis-with-conditional","title":"ArtGAN: Artwork Synthesis with Conditional Categorical GANs","arxiv_id":"1702.03410","date":"2017-02-11","proceeding":null,"authors":["Wei Ren Tan","Chee Seng Chan","Hernan Aguirre","Kiyoshi Tanaka"],"abstract":"This paper proposes an extension to the Generative Adversarial Networks\n(GANs), namely as ARTGAN to synthetically generate more challenging and complex\nimages such as artwork that have abstract characteristics. This is in contrast\nto most of the current solutions that focused on generating natural images such\nas room interiors, birds, flowers and faces. The key innovation of our work is\nto allow back-propagation of the loss function w.r.t. the labels (randomly\nassigned to each generated images) to the generator from the discriminator.\nWith the feedback from the label information, the generator is able to learn\nfaster and achieve better generated image quality. Empirically, we show that\nthe proposed ARTGAN is capable to create realistic artwork, as well as generate\ncompelling real world images that globally look natural with clear shape on\nCIFAR-10.","url_abs":"http://arxiv.org/abs/1702.03410v2","url_pdf":"http://arxiv.org/pdf/1702.03410v2.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":"artgan-artwork-synthesis-with-conditional","repo_url":"https://github.com/BiancaMMoreno/Artgan-Implementation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"artgan-artwork-synthesis-with-conditional","repo_url":"https://github.com/aliciafmachado/artgan-implementation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"artgan-artwork-synthesis-with-conditional","repo_url":"https://github.com/cs-chan/ArtGAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"BSD-3-Clause"}},{"paper_slug":"artgan-artwork-synthesis-with-conditional","repo_url":"https://github.com/cs-chan/Artwork-Synthesis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[{"task_slug":"art-analysis","task_name":"Art Analysis"},{"task_slug":"conditional-image-generation","task_name":"Conditional Image Generation"},{"task_slug":"image-generation","task_name":"Image Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1702.03410","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}