{"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/multimodal-image-synthesis-with-conditional","title":"Multimodal Image Synthesis with Conditional Implicit Maximum Likelihood Estimation","arxiv_id":"2004.03590","date":"2020-04-07","proceeding":null,"authors":["Ke Li","Shichong Peng","Tianhao Zhang","Jitendra Malik"],"abstract":"Many tasks in computer vision and graphics fall within the framework of conditional image synthesis. In recent years, generative adversarial nets (GANs) have delivered impressive advances in quality of synthesized images. However, it remains a challenge to generate both diverse and plausible images for the same input, due to the problem of mode collapse. In this paper, we develop a new generic multimodal conditional image synthesis method based on Implicit Maximum Likelihood Estimation (IMLE) and demonstrate improved multimodal image synthesis performance on two tasks, single image super-resolution and image synthesis from scene layouts. We make our implementation publicly available.","url_abs":"https://arxiv.org/abs/2004.03590v1","url_pdf":"https://arxiv.org/pdf/2004.03590v1.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":"multimodal-image-synthesis-with-conditional","repo_url":"https://github.com/zth667/Diverse-Image-Synthesis-from-Semantic-Layout","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"multimodal-image-synthesis-with-conditional","repo_url":"https://github.com/jsll/pytorch_6dof-graspnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"multimodal-image-synthesis-with-conditional","repo_url":"https://github.com/zvict/papr","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"image-super-resolution","task_name":"Image Super-Resolution"},{"task_slug":"super-resolution","task_name":"Super-Resolution"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2004.03590","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}