{"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/semi-supervised-tuning-from-temporal","title":"Semi-supervised Tuning from Temporal Coherence","arxiv_id":"1511.03163","date":"2015-11-10","proceeding":null,"authors":["Davide Maltoni","Vincenzo Lomonaco"],"abstract":"Recent works demonstrated the usefulness of temporal coherence to regularize\nsupervised training or to learn invariant features with deep architectures. In\nparticular, enforcing smooth output changes while presenting temporally-closed\nframes from video sequences, proved to be an effective strategy. In this paper\nwe prove the efficacy of temporal coherence for semi-supervised incremental\ntuning. We show that a deep architecture, just mildly trained in a supervised\nmanner, can progressively improve its classification accuracy, if exposed to\nvideo sequences of unlabeled data. The extent to which, in some cases, a\nsemi-supervised tuning allows to improve classification accuracy (approaching\nthe supervised one) is somewhat surprising. A number of control experiments\npointed out the fundamental role of temporal coherence.","url_abs":"http://arxiv.org/abs/1511.03163v3","url_pdf":"http://arxiv.org/pdf/1511.03163v3.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":"semi-supervised-tuning-from-temporal","repo_url":"https://bitbucket.org/vincenzo_lomonaco/norbcreator","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}