{"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/robust-conditional-generative-adversarial","title":"Robust Conditional Generative Adversarial Networks","arxiv_id":"1805.08657","date":"2018-05-22","proceeding":"ICLR 2019 5","authors":["Grigorios G. Chrysos","Jean Kossaifi","Stefanos Zafeiriou"],"abstract":"Conditional generative adversarial networks (cGAN) have led to large\nimprovements in the task of conditional image generation, which lies at the\nheart of computer vision. The major focus so far has been on performance\nimprovement, while there has been little effort in making cGAN more robust to\nnoise. The regression (of the generator) might lead to arbitrarily large errors\nin the output, which makes cGAN unreliable for real-world applications. In this\nwork, we introduce a novel conditional GAN model, called RoCGAN, which\nleverages structure in the target space of the model to address the issue. Our\nmodel augments the generator with an unsupervised pathway, which promotes the\noutputs of the generator to span the target manifold even in the presence of\nintense noise. We prove that RoCGAN share similar theoretical properties as GAN\nand experimentally verify that our model outperforms existing state-of-the-art\ncGAN architectures by a large margin in a variety of domains including images\nfrom natural scenes and faces.","url_abs":"http://arxiv.org/abs/1805.08657v2","url_pdf":"http://arxiv.org/pdf/1805.08657v2.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":"robust-conditional-generative-adversarial","repo_url":"https://github.com/grigorisg9gr/rocgan","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"conditional-image-generation","task_name":"Conditional Image Generation"},{"task_slug":"image-generation","task_name":"Image Generation"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.08657","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}