[2402.11789] Statistical Test for Diffusion-Based Anomaly Localization via Selective Inference Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Statistics > Machine Learning arXiv:2402.11789 (stat) [Submitted on 19 Feb 2024 ( v1 ), last revised 26 Apr 2026 (this version, v5)] Title: Statistical Test for Diffusion-Based Anomaly Localization via Selective Inference Authors: Teruyuki Katsuoka , Tomohiro Shiraishi , Daiki Miwa , Vo Nguyen Le Duy , Ichiro Takeuchi View a PDF of the paper titled Statistical Test for Diffusion-Based Anomaly Localization via Selective Inference, by Teruyuki Katsuoka and 4 other authors View PDF HTML (experimental) Abstract: Anomaly localization in images -- identifying regions that deviate from normal patterns -- is vital in applications such as medical diagnosis and industrial inspection. A recent trend is the use of image generation models in anomaly localization, where these models generate normal-looking counterparts of anomalous images, thereby allowing flexible and adaptive anomaly localization. However, these methods inherit the uncertainty and bias implicitly embedded in the employed generative model, raising concerns about the reliability. To address this, we propose a statistical framework based on selective inference to quantify the significance of detected anomalous regions. Our method provides $p$-values to assess the false positive detection rates, providing a principled measure of reliability. As a proof of concept, we consider anomaly localization using a diffusion model and its applications to medical diagnoses and industrial inspections. The results indicate that the proposed method effectively controls the risk of false positive detection, supporting its use in high-stakes decision-making tasks. Comments: 35 pages, 6 figures Subjects: Machine Learning (stat.ML) ; Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) Cite as: arXiv:2402.11789 [stat.ML] (or arXiv:2402.11789v5 [stat.ML] for this version) https://doi.org/10.48550/arXiv.2402.11789 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Teruyuki Katsuoka [ view email ] [v1] Mon, 19 Feb 2024 02:32:45 UTC (977 KB) [v2] Mon, 29 Jul 2024 09:51:35 UTC (2,143 KB) [v3] Thu, 3 Oct 2024 22:59:49 UTC (1,482 KB) [v4] Thu, 22 May 2025 18:00:03 UTC (3,173 KB) [v5] Sun, 26 Apr 2026 06:13:21 UTC (2,255 KB) Full-text links: Access Paper: View a PDF of the paper titled Statistical Test for Diffusion-Based Anomaly Localization via Selective Inference, by Teruyuki Katsuoka and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: stat.ML < prev | next > new | recent | 2024-02 Change to browse by: cs cs.CV cs.LG stat References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from