{"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/who-said-what-modeling-individual-labelers","title":"Who Said What: Modeling Individual Labelers Improves Classification","arxiv_id":"1703.08774","date":"2017-03-26","proceeding":null,"authors":["Melody Y. Guan","Varun Gulshan","Andrew M. Dai","Geoffrey E. Hinton"],"abstract":"Data are often labeled by many different experts with each expert only\nlabeling a small fraction of the data and each data point being labeled by\nseveral experts. This reduces the workload on individual experts and also gives\na better estimate of the unobserved ground truth. When experts disagree, the\nstandard approaches are to treat the majority opinion as the correct label or\nto model the correct label as a distribution. These approaches, however, do not\nmake any use of potentially valuable information about which expert produced\nwhich label. To make use of this extra information, we propose modeling the\nexperts individually and then learning averaging weights for combining them,\npossibly in sample-specific ways. This allows us to give more weight to more\nreliable experts and take advantage of the unique strengths of individual\nexperts at classifying certain types of data. Here we show that our approach\nleads to improvements in computer-aided diagnosis of diabetic retinopathy. We\nalso show that our method performs better than competing algorithms by Welinder\nand Perona (2010), and by Mnih and Hinton (2012). Our work offers an innovative\napproach for dealing with the myriad real-world settings that use expert\nopinions to define labels for training.","url_abs":"http://arxiv.org/abs/1703.08774v2","url_pdf":"http://arxiv.org/pdf/1703.08774v2.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":"who-said-what-modeling-individual-labelers","repo_url":"https://github.com/seunghyukcho/doctornet-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1703.08774","atlas_url":"https://app.syntology.ai/?focus=1703.08774","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}