{"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/image-colorization-with-generative","title":"Image Colorization with Generative Adversarial Networks","arxiv_id":"1803.05400","date":"2018-03-14","proceeding":null,"authors":["Kamyar Nazeri","Eric Ng","Mehran Ebrahimi"],"abstract":"Over the last decade, the process of automatic image colorization has been of\nsignificant interest for several application areas including restoration of\naged or degraded images. This problem is highly ill-posed due to the large\ndegrees of freedom during the assignment of color information. Many of the\nrecent developments in automatic colorization involve images that contain a\ncommon theme or require highly processed data such as semantic maps as input.\nIn our approach, we attempt to fully generalize the colorization procedure\nusing a conditional Deep Convolutional Generative Adversarial Network (DCGAN),\nextend current methods to high-resolution images and suggest training\nstrategies that speed up the process and greatly stabilize it. The network is\ntrained over datasets that are publicly available such as CIFAR-10 and\nPlaces365. The results of the generative model and traditional deep neural\nnetworks are compared.","url_abs":"http://arxiv.org/abs/1803.05400v5","url_pdf":"http://arxiv.org/pdf/1803.05400v5.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":"image-colorization-with-generative","repo_url":"https://github.com/ImagingLab/Colorizing-with-GANs","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"image-colorization-with-generative","repo_url":"https://github.com/Abhijeet8901/Image-Colorization-using-GANs","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"image-colorization-with-generative","repo_url":"https://github.com/elad205/PhotoLife","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"image-colorization-with-generative","repo_url":"https://github.com/hbenbel/Colorization","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"image-colorization-with-generative","repo_url":"https://github.com/srihari-humbarwadi/image_colorization_gan_tf2.0","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"image-colorization-with-generative","repo_url":"https://github.com/vishalj0501/Image-Colorization-cGAN-","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"image-colorization-with-generative","repo_url":"https://github.com/rimijoker/Colorization_using_GANs_and_UNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"colorization","task_name":"Colorization"},{"task_slug":null,"task_name":"Generative Adversarial Network"},{"task_slug":"image-colorization","task_name":"Image Colorization"}],"methods":[{"method_slug":"colorization","method_name":"Colorization"},{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.05400","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}