Interpretable Anomaly Detection

Overview

Interpretable anomaly detection studies how to identify unusual observations or events while making clear why they were flagged. Approaches include intrinsically readable rules, prototypes and symbolic models, as well as feature attribution, counterfactuals, and language-based explanations layered onto deep detectors. These methods are used with images, time series, graphs, and tabular data in settings such as industrial monitoring, cybersecurity, healthcare, and scientific discovery, where explanations can guide investigation and response. A central challenge is ensuring that explanations are faithful and useful without reducing detection accuracy, especially as data and operating conditions shift.

Latest papers

CardsList

73 papers10 papers in the last four weeks, down 9% on the four weeks before. 0.1% of all new papers.

  1. Contrastive Learning for Interpretable Anomaly Detection at Collider Experiments

    Aug 13, 2026Haoyi Jia, Sagar Addepalli, Julia GonskiAnomaly DetectionInterpretable Anomaly Detection

  2. Dual Spatial-Temporal Attribution: Architecture-Aligned Post-Hoc Explainability for Recurrent Graph Anomaly Detection

    Aug 12, 2026Iyad Assaad Nekka, Hamida Seba, Khaled Walid Hidouci +1Gradient-Based AttributionInterpretable Anomaly Detection

  3. JAPE: Joint Anomaly Prediction and Intrinsic Explanation in Multivariate Time Series

    Aug 12, 2026Yian Wei, Yuanyuan Yao, Lu Chen +2Interpretable Anomaly DetectionTime-Series Anomaly Detection

  4. BREAD: Baseline-Referenced Explanations for Anomaly Diagnosis

    Aug 11, 2026Jiaqi Qiu, Rob Goedhart, Jannis Kurtz +1Feature AttributionExplainable Artificial Intelligence

  5. Beyond Hazard Resemblance: Contrastive Event Adjudication for Training-Free Video Anomaly Detection

    Aug 10, 2026Wenti Yin, Xiang Wang, Huaxin Zhang +4Interpretable Anomaly DetectionAnomaly Localization

  6. An Explainable LLM Agent Layer for Open-World Anomaly Detection in Oil Wells

    Aug 4, 2026Lucas Gouveia Omena Lopes, Thales Miranda de Almeida Vieira, Eduardo Toledo de Lima Junior +1Interpretable Anomaly DetectionLLM Agent Evaluation

  7. INCLAIR: Inception-Based Longitudinal Clinical Anomaly Detection with Informed Reasoning

    Jul 29, 2026Maxx Richard Rahman, Wolfgang MaassU-StatisticsInterpretable Anomaly Detection

  8. Financial Audit Assistance using Misinformation Detection and Explanation

    Jul 20, 2026Kshitij Madhav Jadhav, Sushodhan Vaishampayan, Manoj Apte +3Interpretable Anomaly DetectionFinancial Fraud Detection

  9. IBAD: Interpretable Behavioral Anomaly Detection on Human Mobility Data

    Jun 14, 2026Bita Azarijoo, John Krumm, Cyrus ShahabiInterpretable Anomaly DetectionAnomaly Detection

  10. ProtoX-AD: Self-Explainable Time Series Anomaly Detection and Characterization

    Jun 11, 2026Aitor Sánchez-Ferrera, Elisabeth Wetzer, Kristoffer Wickstrøm +2Prototype-Based LearningLLM-Based Time Series Anomaly Detection

  11. Tiny but Trusted: Efficient Vision-Language Reasoning for Time-Series Anomaly Detection

    May 28, 2026Xiaona Zhou, Muntasir Wahed, Tianjiao Yu +2LLM-Based Time Series Anomaly DetectionInterpretable Anomaly Detection

  12. CoReVAD: A Contextual Reasoning Framework for Training-Free Video Anomaly Detection

    May 22, 2026Hyeongmuk Lim, Youngbum HurInterpretable Anomaly DetectionVLM Reasoning

  13. LATERN: Test-Time Context-Aware Explainable Video Anomaly Detection

    May 14, 2026Mitchell Piehl, Muchao YeInterpretable Anomaly DetectionVLM Reasoning

  14. Detect, Localize, and Explain: Interactive Hierarchical Log Anomaly Analytics with LLM Augmentation

    May 9, 2026Lei Ma, Suhani Chaudhary, Ethan Shanbaum +5Online Anomaly DetectionInterpretable Anomaly Detection

  15. Detecting Time Series Anomalies Like an Expert: A Multi-Agent LLM Framework with Specialized Analyzers

    May 7, 2026Hyeongwon Kang, Jeongseob Kim, Jinwoo Park +1LLM-Based Time Series Anomaly DetectionInterpretable Anomaly Detection

  16. ZSG-IAD: A Multimodal Framework for Zero-Shot Grounded Industrial Anomaly Detection

    Apr 20, 2026Qiuhui Chen, Jiaxiang Song, Shuai Tan +1Multimodal GroundingInterpretable Anomaly Detection

  17. Conditional Attribution for Root Cause Analysis in Time-Series Anomaly Detection

    Apr 19, 2026Shashank Mishra, Karan Patil, Cedric Schockaert +2Feature AttributionInterpretable Anomaly Detection

  18. On-board Telemetry Monitoring in Autonomous Satellites: Challenges and Opportunities

    Apr 9, 2026Lorenzo Capelli, Leandro de Souza Rosa, Maurizio De Tommasi +8Interpretable Anomaly DetectionFault Detection

  19. Latent Anomaly Knowledge Excavation: Unveiling Sparse Sensitive Neurons in Vision-Language Models

    Apr 9, 2026Shaotian Li, Shangze Li, Chuancheng Shi +5Multimodal Anomaly DetectionZero-Shot Anomaly Detection

  20. SPG: Sparse-Projected Guides with Sparse Autoencoders for Zero-Shot Anomaly Detection

    Apr 3, 2026Tomoyasu Nanaumi, Yukino Tsuzuki, Junichi Okubo +2Zero-Shot Anomaly DetectionInterpretable Anomaly Detection

  21. Reasoning-Driven Anomaly Detection and Localization with Image-Level Supervision

    Mar 28, 2026Yizhou Jin, Yuezhu Feng, Jinjin Zhang +3Anomaly LocalizationMultimodal Anomaly Detection

  22. Motion Semantics Guided Normalizing Flow for Privacy-Preserving Video Anomaly Detection

    Mar 23, 2026Yang Liu, Boan Chen, Yuanyuan Meng +5Video Anomaly DetectionInterpretable Anomaly Detection

  23. Adoption and Effectiveness of AI-Based Anomaly Detection for Cross Provider Health Data Exchange

    Mar 19, 2026Cao Tram Anh HoangAnomaly DetectionHealthcare

  24. Balancing Performance and Fairness in Explainable AI for Anomaly Detection in Distributed Power Plants Monitoring

    Mar 19, 2026Corneille Niyonkuru, Marcellin Atemkeng, Gabin Maxime Nguegnang +1Semi-Supervised Anomaly DetectionAnomaly Detection