{"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/explainable-deep-one-class-classification","title":"Explainable Deep One-Class Classification","arxiv_id":"2007.01760","date":"2020-07-03","proceeding":"ICLR 2021 1","authors":["Philipp Liznerski","Lukas Ruff","Robert A. Vandermeulen","Billy Joe Franks","Marius Kloft","Klaus-Robert Müller"],"abstract":"Deep one-class classification variants for anomaly detection learn a mapping that concentrates nominal samples in feature space causing anomalies to be mapped away. Because this transformation is highly non-linear, finding interpretations poses a significant challenge. In this paper we present an explainable deep one-class classification method, Fully Convolutional Data Description (FCDD), where the mapped samples are themselves also an explanation heatmap. FCDD yields competitive detection performance and provides reasonable explanations on common anomaly detection benchmarks with CIFAR-10 and ImageNet. On MVTec-AD, a recent manufacturing dataset offering ground-truth anomaly maps, FCDD sets a new state of the art in the unsupervised setting. Our method can incorporate ground-truth anomaly maps during training and using even a few of these (~5) improves performance significantly. Finally, using FCDD's explanations we demonstrate the vulnerability of deep one-class classification models to spurious image features such as image watermarks.","url_abs":"https://arxiv.org/abs/2007.01760v3","url_pdf":"https://arxiv.org/pdf/2007.01760v3.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":"explainable-deep-one-class-classification","repo_url":"https://github.com/liznerski/fcdd","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"one-class-classification","task_name":"One-Class Classification"},{"task_slug":"outlier-detection","task_name":"Outlier Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/anomaly-detection-on-mvtec-ad","task":"Anomaly Detection","dataset":"MVTec AD","model":"FCDD (semi-supervised)","rank_in_archive_order":139,"of":148,"metrics":{"Segmentation AUROC":"94"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-mvtec-ad","task":"Anomaly Detection","dataset":"MVTec AD","model":"FCDD (unsupervised)","rank_in_archive_order":144,"of":148,"metrics":{"Segmentation AUROC":"88"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-one-class-cifar-10","task":"Anomaly Detection","dataset":"One-class CIFAR-10","model":"FCDD","rank_in_archive_order":16,"of":36,"metrics":{"AUROC":"92"},"uses_additional_data":true},{"leaderboard":"/sota/anomaly-detection-on-one-class-imagenet-30","task":"Anomaly Detection","dataset":"One-class ImageNet-30","model":"FCDD","rank_in_archive_order":5,"of":11,"metrics":{"AUROC":"91"},"uses_additional_data":true}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2007.01760","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.01760"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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