{"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/robust-subspace-recovery-layer-for","title":"Robust Subspace Recovery Layer for Unsupervised Anomaly Detection","arxiv_id":"1904.00152","date":"2019-03-30","proceeding":"ICLR 2020 1","authors":["Chieh-Hsin Lai","Dongmian Zou","Gilad Lerman"],"abstract":"We propose a neural network for unsupervised anomaly detection with a novel robust subspace recovery layer (RSR layer). This layer seeks to extract the underlying subspace from a latent representation of the given data and removes outliers that lie away from this subspace. It is used within an autoencoder. The encoder maps the data into a latent space, from which the RSR layer extracts the subspace. The decoder then smoothly maps back the underlying subspace to a \"manifold\" close to the original inliers. Inliers and outliers are distinguished according to the distances between the original and mapped positions (small for inliers and large for outliers). Extensive numerical experiments with both image and document datasets demonstrate state-of-the-art precision and recall.","url_abs":"https://arxiv.org/abs/1904.00152v2","url_pdf":"https://arxiv.org/pdf/1904.00152v2.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":"robust-subspace-recovery-layer-for","repo_url":"https://github.com/dmzou/RSRAE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"robust-subspace-recovery-layer-for","repo_url":"https://github.com/marrrcin/rsrlayer-pytorch","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":"decoder","task_name":"Decoder"},{"task_slug":"unsupervised-anomaly-detection","task_name":"Unsupervised Anomaly Detection"},{"task_slug":"unsupervised-anomaly-detection-with-specified-6","task_name":"Unsupervised Anomaly Detection with Specified Settings -- 0.1% anomaly"},{"task_slug":"unsupervised-anomaly-detection-with-specified-5","task_name":"Unsupervised Anomaly Detection with Specified Settings -- 1% anomaly"},{"task_slug":"unsupervised-anomaly-detection-with-specified-7","task_name":"Unsupervised Anomaly Detection with Specified Settings -- 10% anomaly"},{"task_slug":"unsupervised-anomaly-detection-with-specified-4","task_name":"Unsupervised Anomaly Detection with Specified Settings -- 20% anomaly"},{"task_slug":"unsupervised-anomaly-detection-with-specified","task_name":"Unsupervised Anomaly Detection with Specified Settings -- 30% anomaly"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/unsupervised-anomaly-detection-on-20news-1","task":"Unsupervised Anomaly Detection","dataset":"20NEWS","model":"RSRAE","rank_in_archive_order":1,"of":1,"metrics":{"AUC (outlier ratio = 0.5)":"0.831"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-anomaly-detection-on-caltech-101-1","task":"Unsupervised Anomaly Detection","dataset":"Caltech-101","model":"RSRAE","rank_in_archive_order":1,"of":1,"metrics":{"AUC (outlier ratio = 0.5)":"0.772"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-anomaly-detection-on-fashion-1","task":"Unsupervised Anomaly Detection","dataset":"Fashion-MNIST","model":"RSRAE","rank_in_archive_order":1,"of":1,"metrics":{"AUC (outlier ratio = 0.5)":"0.833"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-anomaly-detection-on-reuters-1","task":"Unsupervised Anomaly 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