{"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/deep-unsupervised-anomaly-detection","title":"Deep unsupervised anomaly detection","arxiv_id":null,"date":"2021-01-05","proceeding":"Winter Conference on Applications of Computer Vision 2021 1","authors":["Tangqing Li","Zheng Wang","Siying Liu","Wen-Yan Lin"],"abstract":"This paper proposes a novel method to detect anomalies in large datasets under a fully unsupervised setting. The key idea behind our algorithm is to learn the representation underlying normal data. To this end, we leverage the latest clustering technique suitable for handling high dimensional data. This hypothesis provides a reliable starting point for normal data selection. We train an autoencoder from the normal data subset, and iterate between hypothesizing nor-mal candidate subset based on clustering and representation learning. The reconstruction error from the learned autoencoder serves as a scoring function to assess the normality of the data. Experimental results on several public benchmark datasets show that the proposed method outperforms state-of-the-art unsupervised techniques and is comparable to semi-supervised techniques in most cases","url_abs":"https://openaccess.thecvf.com/content/WACV2021/papers/Li_Deep_Unsupervised_Anomaly_Detection_WACV_2021_paper.pdf","url_pdf":"https://openaccess.thecvf.com/content/WACV2021/papers/Li_Deep_Unsupervised_Anomaly_Detection_WACV_2021_paper.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":[],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"unsupervised-anomaly-detection","task_name":"Unsupervised Anomaly 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