{"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/augmented-cyclegan-learning-many-to-many","title":"Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data","arxiv_id":"1802.10151","date":"2018-02-27","proceeding":"ICML 2018 7","authors":["Amjad Almahairi","Sai Rajeswar","Alessandro Sordoni","Philip Bachman","Aaron Courville"],"abstract":"Learning inter-domain mappings from unpaired data can improve performance in\nstructured prediction tasks, such as image segmentation, by reducing the need\nfor paired data. CycleGAN was recently proposed for this problem, but\ncritically assumes the underlying inter-domain mapping is approximately\ndeterministic and one-to-one. This assumption renders the model ineffective for\ntasks requiring flexible, many-to-many mappings. We propose a new model, called\nAugmented CycleGAN, which learns many-to-many mappings between domains. We\nexamine Augmented CycleGAN qualitatively and quantitatively on several image\ndatasets.","url_abs":"http://arxiv.org/abs/1802.10151v2","url_pdf":"http://arxiv.org/pdf/1802.10151v2.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":"augmented-cyclegan-learning-many-to-many","repo_url":"https://github.com/ErfanMN/Augmented_CycleGAN_Pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"augmented-cyclegan-learning-many-to-many","repo_url":"https://github.com/NathanDeMaria/AugmentedCycleGAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"augmented-cyclegan-learning-many-to-many","repo_url":"https://github.com/aalmah/augmented_cyclegan","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"structured-prediction","task_name":"Structured Prediction"}],"methods":[{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"cycle-consistency-loss","method_name":"Cycle Consistency Loss"},{"method_slug":"gan-least-squares-loss","method_name":"GAN Least Squares Loss"},{"method_slug":"instance-normalization","method_name":"Instance Normalization"},{"method_slug":"patchgan","method_name":"PatchGAN"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.10151","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.10151"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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