{"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/unsupervised-diverse-colorization-via","title":"Unsupervised Diverse Colorization via Generative Adversarial Networks","arxiv_id":"1702.06674","date":"2017-02-22","proceeding":null,"authors":["Yun Cao","Zhiming Zhou","Wei-Nan Zhang","Yong Yu"],"abstract":"Colorization of grayscale images has been a hot topic in computer vision.\nPrevious research mainly focuses on producing a colored image to match the\noriginal one. However, since many colors share the same gray value, an input\ngrayscale image could be diversely colored while maintaining its reality. In\nthis paper, we design a novel solution for unsupervised diverse colorization.\nSpecifically, we leverage conditional generative adversarial networks to model\nthe distribution of real-world item colors, in which we develop a fully\nconvolutional generator with multi-layer noise to enhance diversity, with\nmulti-layer condition concatenation to maintain reality, and with stride 1 to\nkeep spatial information. With such a novel network architecture, the model\nyields highly competitive performance on the open LSUN bedroom dataset. The\nTuring test of 80 humans further indicates our generated color schemes are\nhighly convincible.","url_abs":"http://arxiv.org/abs/1702.06674v2","url_pdf":"http://arxiv.org/pdf/1702.06674v2.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":"unsupervised-diverse-colorization-via","repo_url":"https://github.com/ccyyatnet/COLORGAN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"colorization","task_name":"Colorization"},{"task_slug":"diversity","task_name":"Diversity"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1702.06674","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}