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The main idea behind\nour scheme is to train a multi-class model to discriminate between dozens of\ngeometric transformations applied on all the given images. The auxiliary\nexpertise learned by the model generates feature detectors that effectively\nidentify, at test time, anomalous images based on the softmax activation\nstatistics of the model when applied on transformed images. We present\nextensive experiments using the proposed detector, which indicate that our\nalgorithm improves state-of-the-art methods by a wide margin.","url_abs":"http://arxiv.org/abs/1805.10917v2","url_pdf":"http://arxiv.org/pdf/1805.10917v2.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":"deep-anomaly-detection-using-geometric","repo_url":"https://github.com/izikgo/AnomalyDetectionTransformations","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"deep-anomaly-detection-using-geometric","repo_url":"https://github.com/khw11044/MY_GOAD","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"deep-anomaly-detection-using-geometric","repo_url":"https://github.com/ninatu/anomaly_detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"}],"methods":[{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/anomaly-detection-on-one-class-cifar-10","task":"Anomaly Detection","dataset":"One-class CIFAR-10","model":"ADT","rank_in_archive_order":25,"of":36,"metrics":{"AUROC":"86"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-one-class-cifar-100","task":"Anomaly Detection","dataset":"One-class CIFAR-100","model":"Geom","rank_in_archive_order":13,"of":15,"metrics":{"AUROC":"78.7"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.10917","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.10917"}},"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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