{"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/unsupervised-learning-of-disentangled","title":"Unsupervised Learning of Disentangled Representations from Video","arxiv_id":"1705.10915","date":"2017-05-31","proceeding":"NeurIPS 2017 12","authors":["Emily Denton","Vighnesh Birodkar"],"abstract":"We present a new model DrNET that learns disentangled image representations\nfrom video. Our approach leverages the temporal coherence of video and a novel\nadversarial loss to learn a representation that factorizes each frame into a\nstationary part and a temporally varying component. The disentangled\nrepresentation can be used for a range of tasks. For example, applying a\nstandard LSTM to the time-vary components enables prediction of future frames.\nWe evaluate our approach on a range of synthetic and real videos, demonstrating\nthe ability to coherently generate hundreds of steps into the future.","url_abs":"http://arxiv.org/abs/1705.10915v1","url_pdf":"http://arxiv.org/pdf/1705.10915v1.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":"unsupervised-learning-of-disentangled","repo_url":"https://github.com/edenton/drnet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"torch","reach":{"status":"unanswered"}},{"paper_slug":"unsupervised-learning-of-disentangled","repo_url":"https://github.com/code-implementation1/Code1/tree/main/DRNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1705.10915","atlas_url":"https://app.syntology.ai/?focus=1705.10915","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}