{"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/learning-unsupervised-cross-domain-image-to","title":"Learning Unsupervised Cross-domain Image-to-Image Translation Using a Shared Discriminator","arxiv_id":"2102.04699","date":"2021-02-09","proceeding":null,"authors":["Rajiv Kumar","Rishabh Dabral","G. Sivakumar"],"abstract":"Unsupervised image-to-image translation is used to transform images from a source domain to generate images in a target domain without using source-target image pairs. Promising results have been obtained for this problem in an adversarial setting using two independent GANs and attention mechanisms. We propose a new method that uses a single shared discriminator between the two GANs, which improves the overall efficacy. We assess the qualitative and quantitative results on image transfiguration, a cross-domain translation task, in a setting where the target domain shares similar semantics to the source domain. Our results indicate that even without adding attention mechanisms, our method performs at par with attention-based methods and generates images of comparable quality.","url_abs":"https://arxiv.org/abs/2102.04699v1","url_pdf":"https://arxiv.org/pdf/2102.04699v1.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":"learning-unsupervised-cross-domain-image-to","repo_url":"https://github.com/rajiv1990/Unsupervised-Cross-domain-I2I-using-a-Shared-Discriminator","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-to-image-translation","task_name":"Image-to-Image Translation"},{"task_slug":"translation","task_name":"Translation"},{"task_slug":"unsupervised-image-to-image-translation","task_name":"Unsupervised Image-To-Image Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-to-image-translation-on-apples-and","task":"Image-to-Image Translation","dataset":"Apples and Oranges","model":"Shared discriminator GAN","rank_in_archive_order":1,"of":1,"metrics":{"Kernel Inception Distance":"4.4"},"uses_additional_data":false},{"leaderboard":"/sota/image-to-image-translation-on-zebra-and","task":"Image-to-Image Translation","dataset":"Zebra and Horses","model":"Shared discriminator GAN","rank_in_archive_order":1,"of":1,"metrics":{"Kernel Inception Distance":"5.8"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}