{"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/fapm-fast-adaptive-patch-memory-for-real-time","title":"FAPM: Fast Adaptive Patch Memory for Real-time Industrial Anomaly Detection","arxiv_id":"2211.07381","date":"2022-11-14","proceeding":null,"authors":["Donghyeong Kim","Chaewon Park","Suhwan Cho","Sangyoun Lee"],"abstract":"Feature embedding-based methods have shown exceptional performance in detecting industrial anomalies by comparing features of target images with normal images. However, some methods do not meet the speed requirements of real-time inference, which is crucial for real-world applications. To address this issue, we propose a new method called Fast Adaptive Patch Memory (FAPM) for real-time industrial anomaly detection. FAPM utilizes patch-wise and layer-wise memory banks that store the embedding features of images at the patch and layer level, respectively, which eliminates unnecessary repetitive computations. We also propose patch-wise adaptive coreset sampling for faster and more accurate detection. FAPM performs well in both accuracy and speed compared to other state-of-the-art methods","url_abs":"https://arxiv.org/abs/2211.07381v2","url_pdf":"https://arxiv.org/pdf/2211.07381v2.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":"fapm-fast-adaptive-patch-memory-for-real-time","repo_url":"https://github.com/donghyung87/FAPM_official","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/anomaly-detection-on-mvtec-ad","task":"Anomaly Detection","dataset":"MVTec AD","model":"FAPM","rank_in_archive_order":47,"of":148,"metrics":{"Detection AUROC":"99","FPS":"44.1","Segmentation AUROC":"98"},"uses_additional_data":true}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2211.07381","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}