{"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/reversible-gans-for-memory-efficient-image-to","title":"Reversible GANs for Memory-efficient Image-to-Image Translation","arxiv_id":"1902.02729","date":"2019-02-07","proceeding":"CVPR 2019 6","authors":["Tycho F. A. van der Ouderaa","Daniel E. Worrall"],"abstract":"The Pix2pix and CycleGAN losses have vastly improved the qualitative and\nquantitative visual quality of results in image-to-image translation tasks. We\nextend this framework by exploring approximately invertible architectures which\nare well suited to these losses. These architectures are approximately\ninvertible by design and thus partially satisfy cycle-consistency before\ntraining even begins. Furthermore, since invertible architectures have constant\nmemory complexity in depth, these models can be built arbitrarily deep. We are\nable to demonstrate superior quantitative output on the Cityscapes and Maps\ndatasets at near constant memory budget.","url_abs":"http://arxiv.org/abs/1902.02729v1","url_pdf":"http://arxiv.org/pdf/1902.02729v1.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":"reversible-gans-for-memory-efficient-image-to","repo_url":"https://github.com/tychovdo/RevGAN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"reversible-gans-for-memory-efficient-image-to","repo_url":"https://github.com/ganslate-team/ganslate","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"reversible-gans-for-memory-efficient-image-to","repo_url":"https://github.com/silvandeleemput/memcnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"image-to-image-translation","task_name":"Image-to-Image Translation"},{"task_slug":"translation","task_name":"Translation"}],"methods":[{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"cycle-consistency-loss","method_name":"Cycle Consistency Loss"},{"method_slug":"dropout","method_name":"Dropout"},{"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":"pix2pix","method_name":"Pix2Pix"},{"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=1902.02729","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}