{"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/student-teacher-feature-pyramid-matching-for","title":"Student-Teacher Feature Pyramid Matching for Anomaly Detection","arxiv_id":"2103.04257","date":"2021-03-07","proceeding":null,"authors":["Guodong Wang","Shumin Han","Errui Ding","Di Huang"],"abstract":"Anomaly detection is a challenging task and usually formulated as an one-class learning problem for the unexpectedness of anomalies. This paper proposes a simple yet powerful approach to this issue, which is implemented in the student-teacher framework for its advantages but substantially extends it in terms of both accuracy and efficiency. Given a strong model pre-trained on image classification as the teacher, we distill the knowledge into a single student network with the identical architecture to learn the distribution of anomaly-free images and this one-step transfer preserves the crucial clues as much as possible. Moreover, we integrate the multi-scale feature matching strategy into the framework, and this hierarchical feature matching enables the student network to receive a mixture of multi-level knowledge from the feature pyramid under better supervision, thus allowing to detect anomalies of various sizes. The difference between feature pyramids generated by the two networks serves as a scoring function indicating the probability of anomaly occurring. Due to such operations, our approach achieves accurate and fast pixel-level anomaly detection. Very competitive results are delivered on the MVTec anomaly detection dataset, superior to the state of the art ones.","url_abs":"https://arxiv.org/abs/2103.04257v3","url_pdf":"https://arxiv.org/pdf/2103.04257v3.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":"student-teacher-feature-pyramid-matching-for","repo_url":"https://github.com/CuberrChen/STFPM-Paddle","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"paddle","reach":{"status":"unanswered"}},{"paper_slug":"student-teacher-feature-pyramid-matching-for","repo_url":"https://github.com/Rthete/STPM-mindspore","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"mindspore","reach":{"status":"unanswered"}},{"paper_slug":"student-teacher-feature-pyramid-matching-for","repo_url":"https://github.com/SimonThomine/DistillationAD","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"student-teacher-feature-pyramid-matching-for","repo_url":"https://github.com/SimonThomine/RememberingNormality","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"student-teacher-feature-pyramid-matching-for","repo_url":"https://github.com/gdwang08/STFPM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"student-teacher-feature-pyramid-matching-for","repo_url":"https://github.com/hcw-00/STPM_anomaly_detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"student-teacher-feature-pyramid-matching-for","repo_url":"https://github.com/open-edge-platform/geti","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"student-teacher-feature-pyramid-matching-for","repo_url":"https://github.com/xiahaifeng1995/STPM-Anomaly-Detection-Localization-master","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"student-teacher-feature-pyramid-matching-for","repo_url":"https://github.com/kingcong/models/tree/main/stpm","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"student-teacher-feature-pyramid-matching-for","repo_url":"https://github.com/openvinotoolkit/anomalib","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"unsupervised-anomaly-detection","task_name":"Unsupervised Anomaly Detection"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/anomaly-detection-on-mvtec-ad","task":"Anomaly Detection","dataset":"MVTec AD","model":"STPM","rank_in_archive_order":88,"of":148,"metrics":{"Detection AUROC":"95.5","Segmentation AUROC":"97.0"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-visa","task":"Anomaly Detection","dataset":"VisA","model":"STPM","rank_in_archive_order":37,"of":50,"metrics":{"Detection AUROC":"83.3","Segmentation AUPRO (until 30% FPR)":"62.0"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2103.04257","atlas_url":"https://app.syntology.ai/?focus=2103.04257","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}