{"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/copy-the-old-or-paint-anew-an-adversarial","title":"Copy the Old or Paint Anew? An Adversarial Framework for (non-) Parametric Image Stylization","arxiv_id":"1811.09236","date":"2018-11-22","proceeding":null,"authors":["Nikolay Jetchev","Urs Bergmann","Gokhan Yildirim"],"abstract":"Parametric generative deep models are state-of-the-art for photo and\nnon-photo realistic image stylization. However, learning complicated image\nrepresentations requires compute-intense models parametrized by a huge number\nof weights, which in turn requires large datasets to make learning successful.\nNon-parametric exemplar-based generation is a technique that works well to\nreproduce style from small datasets, but is also compute-intensive. These\naspects are a drawback for the practice of digital AI artists: typically one\nwants to use a small set of stylization images, and needs a fast flexible model\nin order to experiment with it. With this motivation, our work has these\ncontributions: (i) a novel stylization method called Fully Adversarial Mosaics\n(FAMOS) that combines the strengths of both parametric and non-parametric\napproaches; (ii) multiple ablations and image examples that analyze the method\nand show its capabilities; (iii) source code that will empower artists and\nmachine learning researchers to use and modify FAMOS.","url_abs":"http://arxiv.org/abs/1811.09236v1","url_pdf":"http://arxiv.org/pdf/1811.09236v1.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":"copy-the-old-or-paint-anew-an-adversarial","repo_url":"https://github.com/zalandoresearch/famos","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"copy-the-old-or-paint-anew-an-adversarial","repo_url":"https://github.com/Archangel212/psgan-batik","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"copy-the-old-or-paint-anew-an-adversarial","repo_url":"https://github.com/MQSchleich/SatelliteFAMOS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"copy-the-old-or-paint-anew-an-adversarial","repo_url":"https://github.com/MQSchleich/SatelliteGAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"copy-the-old-or-paint-anew-an-adversarial","repo_url":"https://github.com/oist/psgan","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"image-stylization","task_name":"Image Stylization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1811.09236","atlas_url":"https://app.syntology.ai/?focus=1811.09236","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.09236"}},"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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