{"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/deep-incremental-boosting","title":"Deep Incremental Boosting","arxiv_id":"1708.03704","date":"2017-08-11","proceeding":null,"authors":["Alan Mosca","George D. Magoulas"],"abstract":"This paper introduces Deep Incremental Boosting, a new technique derived from\nAdaBoost, specifically adapted to work with Deep Learning methods, that reduces\nthe required training time and improves generalisation. We draw inspiration\nfrom Transfer of Learning approaches to reduce the start-up time to training\neach incremental Ensemble member. We show a set of experiments that outlines\nsome preliminary results on some common Deep Learning datasets and discuss the\npotential improvements Deep Incremental Boosting brings to traditional Ensemble\nmethods in Deep Learning.","url_abs":"http://arxiv.org/abs/1708.03704v1","url_pdf":"http://arxiv.org/pdf/1708.03704v1.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":"deep-incremental-boosting","repo_url":"https://github.com/nitbix/toupee","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1708.03704","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}