{"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/text-to-image-to-text-translation-using-cycle","title":"Text-to-Image-to-Text Translation using Cycle Consistent Adversarial Networks","arxiv_id":"1808.04538","date":"2018-08-14","proceeding":null,"authors":["Satya Krishna Gorti","Jeremy Ma"],"abstract":"Text-to-Image translation has been an active area of research in the recent\npast. The ability for a network to learn the meaning of a sentence and generate\nan accurate image that depicts the sentence shows ability of the model to think\nmore like humans. Popular methods on text to image translation make use of\nGenerative Adversarial Networks (GANs) to generate high quality images based on\ntext input, but the generated images don't always reflect the meaning of the\nsentence given to the model as input. We address this issue by using a\ncaptioning network to caption on generated images and exploit the distance\nbetween ground truth captions and generated captions to improve the network\nfurther. We show extensive comparisons between our method and existing methods.","url_abs":"http://arxiv.org/abs/1808.04538v1","url_pdf":"http://arxiv.org/pdf/1808.04538v1.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":"text-to-image-to-text-translation-using-cycle","repo_url":"https://github.com/CSC2548/text2image2textGAN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"text-to-image-to-text-translation-using-cycle","repo_url":"https://github.com/Russzheng/CS280","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-to-text","task_name":"Image to text"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1808.04538","atlas_url":"https://app.syntology.ai/?focus=1808.04538","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}