{"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-deep-and-inductive-anomaly-detection","title":"Robust, Deep and Inductive Anomaly Detection","arxiv_id":"1704.06743","date":"2017-04-22","proceeding":null,"authors":["Raghavendra Chalapathy","Aditya Krishna Menon","Sanjay Chawla"],"abstract":"PCA is a classical statistical technique whose simplicity and maturity has\nseen it find widespread use as an anomaly detection technique. However, it is\nlimited in this regard by being sensitive to gross perturbations of the input,\nand by seeking a linear subspace that captures normal behaviour. The first\nissue has been dealt with by robust PCA, a variant of PCA that explicitly\nallows for some data points to be arbitrarily corrupted, however, this does not\nresolve the second issue, and indeed introduces the new issue that one can no\nlonger inductively find anomalies on a test set. This paper addresses both\nissues in a single model, the robust autoencoder. This method learns a\nnonlinear subspace that captures the majority of data points, while allowing\nfor some data to have arbitrary corruption. The model is simple to train and\nleverages recent advances in the optimisation of deep neural networks.\nExperiments on a range of real-world datasets highlight the model's\neffectiveness.","url_abs":"http://arxiv.org/abs/1704.06743v3","url_pdf":"http://arxiv.org/pdf/1704.06743v3.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-deep-and-inductive-anomaly-detection","repo_url":"https://github.com/raghavchalapathy/rcae","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"robust-deep-and-inductive-anomaly-detection","repo_url":"https://github.com/ThinhNgVhust/ocsvm","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"robust-deep-and-inductive-anomaly-detection","repo_url":"https://github.com/raghavchalapathy/oc-nn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"robust-deep-and-inductive-anomaly-detection","repo_url":"https://github.com/raghavchalapathy/oc-nn_old","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"robust-deep-and-inductive-anomaly-detection","repo_url":"https://github.com/tmiranda101/OC-NN_remote","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"}],"methods":[{"method_slug":"pca","method_name":"PCA"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1704.06743","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}