{"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/r2-ad2-detecting-anomalies-by-analysing-the","title":"R2-AD2: Detecting Anomalies by Analysing the Raw Gradient","arxiv_id":"2206.10259","date":"2022-06-21","proceeding":null,"authors":["Jan-Philipp Schulze","Philip Sperl","Ana Răduţoiu","Carla Sagebiel","Konstantin Böttinger"],"abstract":"Neural networks follow a gradient-based learning scheme, adapting their mapping parameters by back-propagating the output loss. Samples unlike the ones seen during training cause a different gradient distribution. Based on this intuition, we design a novel semi-supervised anomaly detection method called R2-AD2. By analysing the temporal distribution of the gradient over multiple training steps, we reliably detect point anomalies in strict semi-supervised settings. Instead of domain dependent features, we input the raw gradient caused by the sample under test to an end-to-end recurrent neural network architecture. R2-AD2 works in a purely data-driven way, thus is readily applicable in a variety of important use cases of anomaly detection.","url_abs":"https://arxiv.org/abs/2206.10259v1","url_pdf":"https://arxiv.org/pdf/2206.10259v1.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":"r2-ad2-detecting-anomalies-by-analysing-the","repo_url":"https://github.com/Fraunhofer-AISEC/R2-AD2","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"semi-supervised-anomaly-detection","task_name":"Semi-supervised Anomaly Detection"},{"task_slug":"supervised-anomaly-detection","task_name":"Supervised Anomaly Detection"}],"methods":[{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}