Reconstruction-Based Anomaly Detection

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Overview

These methods learn what normal images, time series, sounds, graphs, or sensor readings look like, often from normal examples alone, then use reconstruction errors or residual patterns to flag anomalies. Common approaches include autoencoders, masked-feature reconstruction, diffusion models, and projections onto normal subspaces; they are used for tasks such as industrial defect localization, medical imaging, and equipment monitoring. Current work combines reconstruction with forecasting, density estimation, pretrained features, or multiple sensor views to detect anomalies that a single error score may miss. A central tension is that models capable of reconstructing normal data well may also reproduce anomalies, so improving reconstruction fidelity does not necessarily improve detection; robust evaluation across domains and operating conditions remains important.

Latest papers

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138 papers19 papers in the last four weeks, up 90% on the four weeks before. 0.1% of all new papers.

  1. Reconstruction-Based Diagnosis through Gödel-Regularized Virtual Observability

    Nov 3, 2026Oliver Niggemann, Jonas Ehrhardt, Alexander DiedrichIndustrial Fault DiagnosisFault Localization

    Chair of Computer Science in Mechanical Engineering, Helmut Schmidt University, Hamburg, Germany

  2. Benchmarking Unsupervised Jamming Detection in 5G Open RAN

    Nov 3, 2026Sneihil Gopal, Fernando J. Cintron, Richard A. RouilUnsupervised Anomaly DetectionAnomaly Detection

    PREP Associate, National Institute of Standards and Technology (NIST), USA · Department of Physics, Georgetown University, USA · National Institute of Standards and Technology (NIST), USA.

  3. A Hybrid Anomaly Detection Method for Multivariate Time Series

    Nov 1, 2026Ali Babalhavaeji, Hui Song, Mahdi JaliliTime-Series Anomaly DetectionAnomaly Detection

    RMIT University, Melbourne, Australia

  4. Reproducing INP-Former: Reconstructing Intrinsic Normal Prototypes for Industrial Anomaly Detection

    Oct 31, 2026Yunfei Liang, Juan Pablo Osorio RiveraAnomaly DetectionIndustrial Anomaly Detection

    Faculty of Computer and Information Science University of Ljubljana

  5. VariaFlow: Uncertainty-Aware Flow Matching for Unsupervised Anomaly Detection

    Oct 30, 2026Arnela Hadzic, Martin Urschler, Cosmin I. BerceaMedical Image Anomaly DetectionUnsupervised Anomaly Detection

    Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria · Institute of Visual Computing, Graz University of Technology, Graz, Austria · Technical University of Munich, School of Medicine and Health, TUM University Hospital, Klinikum rechts der Isar, Department of Diagnostic and Interventional Radiology, Munich, Germany

  6. CLOSURE: Cross-Channel Closure for Multivariate Time Series Anomaly Detection

    Oct 26, 2026Fan Xu, Hao Wu, Zhi Song +5Time-Series Anomaly DetectionReconstruction-Based Anomaly Detection

    University of Science and Technology of China, Hefei, Anhui, China · Tsinghua University, Beijing, China · City University of Hong Kong, Hongkong, China +3

  7. Flownomaly: Single-Step Conditional-Mean Recovery for Few-Shot Anomaly Detection

    Oct 23, 2026Bernardo Perrone Ribeiro, Erkut Akdag, Egor BondarevFew-Shot Anomaly DetectionIndustrial Anomaly Detection

    AIMS Group, Department of Electrical Engineering, Eindhoven University of Technology

  8. Learning Pathology-Sensitive Representations for Unsupervised Retinal OCT Anomaly Detection

    Oct 23, 2026Tania Haghighi, Sina Gholami, Behafarin Emam +5Medical Image Anomaly DetectionUnsupervised Anomaly Detection

    University of North Carolina at Charlotte, Charlotte, North Carolina, USA · University of Illinois Chicago, Chicago, IL, USA · Byers Eye Institute at Stanford, Stanford University School of Medicine, Stanford, CA, USA +1

  9. Diff-IrisPAD: Modeling Bona Fide Iris Distributions for Unsupervised Presentation Attack Detection

    Oct 21, 2026Geetanjali Sharma, Harsh Yadav, Tanmay Deshpande +3Biometric IdentificationReconstruction-Based Anomaly Detection

    Indian Institute of Technology Mandi, Himachal Pradesh, India · SAFE Center, Norwegian University of Science and Technology (NTNU), Gjøvik, Norway

  10. Benchmarking Deep Learning Models for Anomaly Detection in Real-World Cloud Telemetry

    Oct 11, 2026Sumit Dhyani, Sougata MukherjeaTime-Series Anomaly DetectionAnomaly Detection

    Electrical Engineering Department, Indian Institute of Technology, Delhi, India

  11. Correlation-Aware Reconstruction for Multivariate Time Series Anomaly Detection

    Oct 11, 2026Md. Johir Raihan, Zahra Zamanzadeh Darban, Yueyang Liu +3Time-Series Anomaly DetectionAnomaly Detection

    Monash University, Melbourne, Victoria, Australia · University of New South Wales, Sydney, New South Wales, Australia

  12. Local Diagnostics of Learned Event Representations for Anomaly Detection at the HL-LHC

    Oct 9, 2026Donatella Genovese, Philip Ploner, Simone Scardapane +2Anomaly DetectionInterpretable Anomaly Detection

    Department of Computer, Control and Management Engineering, Sapienza University of Rome · Department of Physics, ETH Zurich · Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome +1

  13. NV-Tesseract-AD: Curriculum-Enhanced Conditional Diffusion for Multivariate Time-Series Anomaly Detection

    Oct 8, 2026Aditi Gautam, Manikandan Ravikiran, Saira Qureshi +4Time-Series Anomaly DetectionReconstruction-Based Anomaly Detection

    NVIDIA

  14. Unsupervised Maneuver-Aware Acoustic Fault Detection for Autonomous Drones

    Oct 7, 2026Ali M Ali, Ziyi Tang, Nurdaulet Nazarbay +1Fault DetectionUnsupervised Anomaly Detection

    Department of Mechanical and Aerospace Engineering, Carleton University, Ottawa, ON, K1S-5B6, Canada · Department of Electrical and Computer Engineering at the University of Alberta, Edmonton, AB, Canada · The Hong Kong Polytechnic University, Hong Kong

  15. Efficient Patch-Based Anomaly Detection Fused with Diffusion Driven Generative Modeling for Semiconductor Wafer Bin Map Open Set Anomaly Detection

    Oct 7, 2026Limon Bin Hossain, Md Sadib Rahman AnantaAnomaly DetectionIndustrial Anomaly Detection

    Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka, 1000, Bangladesh

  16. A Multi-Source Ultrasound Benchmark Revealing the Limits of Contemporary Self-Supervised Anomaly Detection Methods

    Oct 7, 2026Marco Riedenauer, Daniel Kienzle, Pratik Mayekar +1Medical Image Anomaly DetectionUnsupervised Anomaly Detection

    University of Augsburg

  17. Quasi-Binarized Autoencoders: An Architecture-Independent Information Bottleneck for Medical Image Anomaly Detection

    Oct 7, 2026Shouhei Hanaoka, Takahiro Nakao, Atsushi Takamatsu +2Medical Image Anomaly DetectionUnsupervised Anomaly Detection

    Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan · Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan · Department of Radiology, Kanazawa University Hospital, 13-1 Takara-machi, Kanazawa, Ishikawa, 920-8641, Japan

  18. MORA: Modeling Observed Changes for Drift-Robust Time-Series Anomaly Detection

    Oct 7, 2026Xudong Mou, Tiejun Wang, Rui Wang +5Time-Series Anomaly DetectionNon-Stationary Time Series

    Beihang University, Beijing, China · Beihang Hangzhou Innovation Institute, Hangzhou, China · China University of Geosciences (Beijing), Beijing, China

  19. GRC-Net: Global Representation Consistency Network for Unsupervised Multimodal Anomaly Detection

    Oct 7, 2026Seyoung Jeong, Jong Pil Yun, Sang Jun LeeAnomaly DetectionIndustrial Anomaly Detection

    Jeonbuk National University, Jeonju, Republic of Korea · Korea Institute of Industrial Technology (KITECH), Incheon, Republic of Korea · Chung-Ang University, Seoul, Republic of Korea

  20. GCTAuto-encoder: A Cross modal Framework for Security Flaw Detection in IoT Networks

    Oct 5, 2026Najmieh Sadat SafarabadiCross-Modal LearningIoT Intrusion Detection

    Department of Computational Linguistics, University of Stuttgart, Stuttgart, Germany.

  21. Normality Constraint Learning: Adapting Foundation Models for Time Series Anomaly Detection

    Oct 5, 2026Xiaohui Zhou, Yijie Wang, Hongzuo Xu +2Reconstruction-Based Anomaly DetectionTime-Series Anomaly Detection

    National Key Laboratory of Parallel and Distributed Computing · College of Computer Science and Technology, National University of Defense Technology · Intelligent Game and Decision Lab (IGDL) +1

  22. Cycle-Aware Autoencoder with Cross-SignalConsistency for Railway Door Anomaly Detection

    Sep 30, 2026Ammar Bouketta, Smail Niar, Hamza Ouarnoughi +1Industrial Anomaly DetectionFault Detection

    Universit´e Polytechnique Hauts-de-France, LAMIH CNRS UMR 8201, Valenciennes, France · Alstom, Crespin, France · Universit´e Polytechnique Hauts-de-France, LAMIH CNRS UMR 8201, INSA Hauts-de-France, Valenciennes, France +1

  23. LEARN-TS: LLM-Enhanced Alignment and Reconstruction with Normality Guidance for Multivariate Time-Series Anomaly Detection

    Sep 30, 2026Jahyeob Koo, Kio Yun, Byoungmo Koo +1LLM-Based Time Series Anomaly DetectionAnomaly Localization

    Department of Industrial and Management Engineering, Korea University Seoul, Republic of Korea

  24. No Scale Left Behind: Multi-Scale Autoencoder with Bi-directional Attention for Time Series Anomaly Detection

    Sep 29, 2026Jiaheng Guo, Haochen Zhang, Yu-Chao Huang +3Reconstruction-Based Anomaly DetectionAttention Mechanisms

    UNITES Lab, University of North Carolina at Chapel Hill

  25. Bayesian Tensor Autoencoder with Physics-informed Predictive Prior for Multi-dimensional Time Series Anomaly Detection

    Sep 25, 2026Jianan Liu, Chunguang LiTime-Series Anomaly DetectionAutoencoders

    Zhejiang University, Hangzhou, China

  26. When Does Unsupervised Learning Succeed or Fail? A PoS Perspective on Reconstruction-Based Anomaly Detection

    Sep 23, 2026Mehmet Yamaç, Yagmur Mustu, Muhammad Numan Yousaf +2Unsupervised Representation LearningReconstruction-Based Anomaly Detection

    Tampere University, Tampere, Finland · Radboud University, Nijmegen, The Netherlands

  27. Beyond Isolated Entities: Relation-Aware Multi-Entity Modeling for Unsupervised Video Anomaly Detection

    Sep 23, 2026Zhongpeng Pan, Xina Cheng, Kailun YangReference-Based Video Anomaly DetectionVideo Anomaly Detection

    School of Artificial Intelligence, Xidian University, China · School of Artificial Intelligence and Robotics, Hunan University, China · National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University, China

  28. Unsupervised Brain Anomaly Detection as a Bayesian Inverse Problem with Diffusion Prior

    Sep 21, 2026Hugues Roy, Reuben Dorent, Ninon BurgosMedical Image Anomaly DetectionAnomaly Localization

    Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, F-75013, Paris, France

  29. FoundAna: A GNN-assisted Foundation Model for Graph Anomaly Detection

    Sep 16, 2026Suprim Nakarmi, Chahana Dahal, Yue Zhao +2Graph Neural NetworksReconstruction-Based Anomaly Detection

    Department of Computer Science, University of Nevada Las Vegas, Las Vegas, USA · Department of Computer Science, University of Southern California, Los Angeles, USA

  30. Not All Relations Are Equal: Relation-Balanced and Calibrated Graph Learning for Provenance-Based Intrusion Detection

    Sep 15, 2026Lijie Zheng, Ji He, Zhiwei Zhang +3Reconstruction-Based Anomaly DetectionUnsupervised Anomaly Detection

    School of Computer Science and Technology, Xidian University, Xi’an, China