{"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/pixcolor-pixel-recursive-colorization","title":"PixColor: Pixel Recursive Colorization","arxiv_id":"1705.07208","date":"2017-05-19","proceeding":null,"authors":["Sergio Guadarrama","Ryan Dahl","David Bieber","Mohammad Norouzi","Jonathon Shlens","Kevin Murphy"],"abstract":"We propose a novel approach to automatically produce multiple colorized\nversions of a grayscale image. Our method results from the observation that the\ntask of automated colorization is relatively easy given a low-resolution\nversion of the color image. We first train a conditional PixelCNN to generate a\nlow resolution color for a given grayscale image. Then, given the generated\nlow-resolution color image and the original grayscale image as inputs, we train\na second CNN to generate a high-resolution colorization of an image. We\ndemonstrate that our approach produces more diverse and plausible colorizations\nthan existing methods, as judged by human raters in a \"Visual Turing Test\".","url_abs":"http://arxiv.org/abs/1705.07208v2","url_pdf":"http://arxiv.org/pdf/1705.07208v2.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":[],"tasks":[{"task_slug":"colorization","task_name":"Colorization"}],"methods":[{"method_slug":"colorization","method_name":"Colorization"},{"method_slug":"pixelcnn","method_name":"PixelCNN"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/colorization-on-imagenet-val","task":"Colorization","dataset":"ImageNet val","model":"PixColor","rank_in_archive_order":3,"of":4,"metrics":{"FID-5K":"24.32"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1705.07208","atlas_url":"https://app.syntology.ai/?focus=1705.07208","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}