{"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/weakly-supervised-anomaly-detection-a-survey","title":"Weakly Supervised Anomaly Detection: A Survey","arxiv_id":"2302.04549","date":"2023-02-09","proceeding":null,"authors":["Minqi Jiang","Chaochuan Hou","Ao Zheng","Xiyang Hu","Songqiao Han","Hailiang Huang","Xiangnan He","Philip S. Yu","Yue Zhao"],"abstract":"Anomaly detection (AD) is a crucial task in machine learning with various applications, such as detecting emerging diseases, identifying financial frauds, and detecting fake news. However, obtaining complete, accurate, and precise labels for AD tasks can be expensive and challenging due to the cost and difficulties in data annotation. To address this issue, researchers have developed AD methods that can work with incomplete, inexact, and inaccurate supervision, collectively summarized as weakly supervised anomaly detection (WSAD) methods. In this study, we present the first comprehensive survey of WSAD methods by categorizing them into the above three weak supervision settings across four data modalities (i.e., tabular, graph, time-series, and image/video data). For each setting, we provide formal definitions, key algorithms, and potential future directions. To support future research, we conduct experiments on a selected setting and release the source code, along with a collection of WSAD methods and data.","url_abs":"https://arxiv.org/abs/2302.04549v1","url_pdf":"https://arxiv.org/pdf/2302.04549v1.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":"weakly-supervised-anomaly-detection-a-survey","repo_url":"https://github.com/yzhao062/wsad","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"weakly-supervised-anomaly-detection-a-survey","repo_url":"https://github.com/yzhao062/yzhao062","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"supervised-anomaly-detection","task_name":"Supervised Anomaly Detection"},{"task_slug":"survey","task_name":"Survey"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"weakly-supervised-anomaly-detection","task_name":"Weakly-supervised Anomaly Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2302.04549","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.04549"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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