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ACM Manag. Data"],"published-print":{"date-parts":[[2025,2,10]]},"abstract":"<jats:p>\n                    Due to the scarcity of reliable anomaly labels, recent anomaly detection methods leveraging noisy auto-generated labels either select clean samples or refurbish noisy labels. However, both approaches struggle due to the unique properties of anomalies.\n                    <jats:italic toggle=\"yes\">Sample selection<\/jats:italic>\n                    often fails to separate sufficiently many clean anomaly samples from noisy ones, while\n                    <jats:italic toggle=\"yes\">label refurbishment<\/jats:italic>\n                    erroneously refurbishes\n                    <jats:italic toggle=\"yes\">marginal<\/jats:italic>\n                    clean samples. To overcome these limitations, we design Unity, the\n                    <jats:italic toggle=\"yes\">first<\/jats:italic>\n                    learning from noisy labels (LNL) approach for anomaly detection that elegantly leverages the merits of both sample selection and label refurbishment to iteratively prepare a diverse clean sample set for network training. Unity uses a pair of deep anomaly networks to collaboratively select samples with clean labels based on prediction agreement, followed by a disagreement resolution mechanism to capture marginal samples with clean labels. Thereafter, Unity utilizes unique properties of anomalies to design an anomaly-centric contrastive learning strategy that accurately refurbishes the remaining noisy labels. The resulting set, composed of\n                    <jats:italic toggle=\"yes\">selected and refurbished<\/jats:italic>\n                    clean samples, will be used to train the anomaly networks in the next training round. Our experimental study on 10 real-world benchmark datasets demonstrates that Unity consistently outperforms state-of-the-art LNL techniques by up to 0.31 in F-1 Score (0.52 \\rightarrow 0.83).\n                  <\/jats:p>","DOI":"10.1145\/3709657","type":"journal-article","created":{"date-parts":[[2025,2,11]],"date-time":"2025-02-11T15:45:06Z","timestamp":1739288706000},"page":"1-24","source":"Crossref","is-referenced-by-count":3,"title":["Agree to Disagree: Robust Anomaly Detection with Noisy Labels"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8102-3081","authenticated-orcid":false,"given":"Dennis M.","family":"Hofmann","sequence":"first","affiliation":[{"name":"Worcester Polytechnic Institute, Worcester, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0285-6019","authenticated-orcid":false,"given":"Peter M.","family":"VanNostrand","sequence":"additional","affiliation":[{"name":"Worcester Polytechnic Institute, Worcester, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9252-2492","authenticated-orcid":false,"given":"Lei","family":"Ma","sequence":"additional","affiliation":[{"name":"Worcester Polytechnic Institute, Worcester, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8547-2483","authenticated-orcid":false,"given":"Huayi","family":"Zhang","sequence":"additional","affiliation":[{"name":"ByteDance, San Jose, USA, &amp; Worcester Polytechnic Institute, Worcester, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6763-3490","authenticated-orcid":false,"given":"Joshua C.","family":"DeOliveira","sequence":"additional","affiliation":[{"name":"Worcester Polytechnic Institute, Worcester, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9909-8607","authenticated-orcid":false,"given":"Lei","family":"Cao","sequence":"additional","affiliation":[{"name":"University of Arizona, Tucson, USA, &amp; Massachusetts Institute of Technology, Cambridge, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5375-9254","authenticated-orcid":false,"given":"Elke A.","family":"Rundensteiner","sequence":"additional","affiliation":[{"name":"Worcester Polytechnic Institute, Worcester, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,2,11]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3276463"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1964.tb00553.x"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3588700"},{"key":"e_1_2_1_4_1","unstructured":"Raghavendra Chalapathy and Sanjay Chawla. 2019. 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