{"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/decoupling-when-to-update-from-how-to-update","title":"Decoupling \"when to update\" from \"how to update\"","arxiv_id":"1706.02613","date":"2017-06-08","proceeding":"NeurIPS 2017 12","authors":["Eran Malach","Shai Shalev-Shwartz"],"abstract":"Deep learning requires data. A useful approach to obtain data is to be\ncreative and mine data from various sources, that were created for different\npurposes. Unfortunately, this approach often leads to noisy labels. In this\npaper, we propose a meta algorithm for tackling the noisy labels problem. The\nkey idea is to decouple \"when to update\" from \"how to update\". We demonstrate\nthe effectiveness of our algorithm by mining data for gender classification by\ncombining the Labeled Faces in the Wild (LFW) face recognition dataset with a\ntextual genderizing service, which leads to a noisy dataset. While our approach\nis very simple to implement, it leads to state-of-the-art results. We analyze\nsome convergence properties of the proposed algorithm.","url_abs":"http://arxiv.org/abs/1706.02613v2","url_pdf":"http://arxiv.org/pdf/1706.02613v2.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":"decoupling-when-to-update-from-how-to-update","repo_url":"https://github.com/emalach/UpdateByDisagreement","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"face-recognition","task_name":"Face Recognition"},{"task_slug":"gender-classification","task_name":"Gender Classification"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1706.02613","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}