{"url":"/method/label-quality-model","slug":"label-quality-model","name":"Label Quality Model","full_name":"Label Quality Model","full_name_withheld":false,"description_markdown":"**Label Quality Model** is an intermediate supervised task aimed at predicting the clean labels from noisy labels by leveraging rater features and a paired subset for supervision. The LQM technique assumes the existence of rater features and a subset of training data with both noisy and clean labels, which we call paired-subset. In real world scenarios, some level of label noise may be unavoidable. The LQM approach still works as long as the clean(er) label is less noisy than a label from a rater that is randomly selected from the pool, e.g., clean labels can be from either expert raters or aggregation of multiple raters. LQM is trained on the paired-subset using rater features and noisy label as input, and inferred on the entire training corpus. The output of LQM is used during model training as a more accurate alternative to the noisy labels.","description_state":"present","introduced_year":null,"introduced_by":{"title":"An Instance-Dependent Simulation Framework for Learning with Label Noise","paper":"/paper/a-realistic-simulation-framework-for-learning","first_author":"Keren Gu","n_authors":6,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/a-realistic-simulation-framework-for-learning"},"source":{"url":"https://arxiv.org/abs/2107.11413v4","title":"An Instance-Dependent Simulation Framework for Learning with Label Noise","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"General","area_id":"general","collection":"Label Correction","url":"/methods/category/label-correction","pwc_aliases":[]}],"n_papers_tagged":1,"archive_num_papers":1,"papers_newest_first":[{"paper":"/paper/a-realistic-simulation-framework-for-learning","title":"An Instance-Dependent Simulation Framework for Learning with Label Noise","date":"2021-07-23","arxiv_id":"2107.11413","n_code_links":1,"syntology":null}],"papers_shown":1,"tasks":[{"task":"/task/learning-with-noisy-labels","name":"Learning with noisy labels","papers":1}],"tasks_shown":1,"n_tasks":1,"usage_by_year":[{"year":"2021","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/label-quality-model"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}