Semi-Supervised Anomaly Detection

Latest papers

12 papers3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

InCLAD: A Continual Learning Benchmark for Industrial Visual Anomaly Detection
Oct 10, 2026Łukasz Marcjan, Dorota Wilk-Kołodziejczyk, Roberto CorizzoTask-Incremental Learning Continual Anomaly Detection
AGH University of Krakow, Krakow, Poland · American University, Washington, DC, USA
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 Detection Attention Mechanisms
UNITES Lab, University of North Carolina at Chapel Hill
Post-Anomaly Detection Inference for Deep SVDD
Sep 29, 2026Cao Le Cong Thanh, Dang Quang Vinh, Vo Nguyen Le DuySemi-Supervised Anomaly Detection Anomaly Detection
University of Information Technology, Ho Chi Minh City, Vietnam · Vietnam National University, Ho Chi Minh City, Vietnam
Interpretable Multi-Hypersphere Deep Anomaly Detection for Open-set Supervised Anomaly Detection
Sep 19, 2026Zhiji Yang, Fangyong Wang, Yue Li +2Semi-Supervised Anomaly Detection Anomaly Detection
School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming, 650221, China · Beijing National Center for Applied Mathematics, Capital Normal University, Beijing, 100048, China
Extreme Volatility Warning under Label Scarcity via Multi-Source Anomaly Fusion
Jul 26, 2026Jin Qian, Zhangzhi Xiong, Mingrui Li +1Multimodal Anomaly Detection Semi-Supervised Anomaly Detection
ShanghaiTech University, Shanghai, China
Extended Hybrid Timed Petri Nets with Semi-Supervised Anomaly Detection for Switched Systems, Modelling and Fault Detection
Apr 5, 2026Fatiha Hamdi, Abdelhafid Zeroual, Fouzi HarrouSemi-Supervised Anomaly Detection Fault Detection
Department of Electronics, Faculty of technology, LAAAS Laboratory, Batna 2 University, Batna, Algeria. · Department of Electrical Engineering, Faculty of Science and Applied Sciences, Larbi Ben M’hidi University, Oum El Bouaghi, Algeria. · Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
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 Detection Anomaly Detection
African Institute for Mathematical Sciences (AIMS), Kigali, Rwanda. · Department of Mathematics, Rhodes University, PO Box 94, Makhanda, 6140, South Africa. · National Institute for Theoretical and Computational Sciences (NITheCS), Stellenbosch, 7600, South Africa. +2
Interpretable Maximum Margin Deep Anomaly Detection
Mar 7, 2026Zhiji Yang, Mei Huang, Xinyu Li +3Semi-Supervised Anomaly Detection Anomaly Detection
the School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming 650221, China · the Academy for Multidisciplinary Studies, Capital Normal University, Beijing 100048, China
Learning Discriminative and Generalizable Anomaly Detector for Dynamic Graph with Limited Supervision
Feb 23, 2026Yuxing Tian, Yiyan Qi, Fengran Mo +3Semi-Supervised Anomaly Detection Graph-Based Anomaly Detection
Universit´e de Montréal · International Digital Economy Academy (IDEA) · McGill University +1
Sparse, self-organizing ensembles of local kernels detect rare statistical anomalies
Nov 5, 2025Gaia Grosso, Sai Sumedh R. Hindupur, Thomas Fel +3Kernel Methods Semi-Supervised Anomaly Detection
NSF AI Institute for Artificial Intelligence and Fundamental Interactions, Cambridge, 02139, MA · Laboratory for Nuclear Science, Massachusetts Institute of Technology, Cambridge, 02139, MA · School of Engineering and Applied Sciences, Harvard University, Allston, 02134, MA +1
Monotonic anomaly detection
Oct 30, 2024Oliver Urs Lenz, Matthijs van LeeuwenIsolation Forest Semi-Supervised Anomaly Detection
Department of Statistics, Data Science and Modelling, National Institute for Public Health and the Environment, The Netherlands · Leiden Institute of Advanced Computer Science, Leiden University · Department of Applied Mathematics, Computer Science and Statistics, Ghent University
Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data
May 29, 2024Hiroshi Takahashi, Tomoharu Iwata, Atsutoshi Kumagai +1Weakly Supervised Learning Semi-Supervised Anomaly Detection
NTT

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InCLAD: A Continual Learning Benchmark for Industrial Visual Anomaly Detection

Łukasz Marcjan, Dorota Wilk-Kołodziejczyk, Roberto Corizzo

AGH University of Krakow, Krakow, Poland · American University, Washington, DC, USA

Task-Incremental Learning Continual Anomaly Detection Semi-Supervised Anomaly Detection Industrial Anomaly Detection Anomaly Detection Anomaly Localization

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Industrial anomaly detection benchmarks have advanced rapidly, yet they remain largely static and therefore underrepresent deployment settings in which inspection systems must be extended from one industrial component to another over time. In our setting, continual industrial anomaly detection is framed as a task-incremental problem: each component defines a task, while the learning objective remains semi-supervised. The goal extends beyond conventional anomaly detection, since models should preserve their performance on all components as they are sequentially exposed to new components. This framing is especially relevant because many existing industrial pipelines either assume fully supervised defect labels or study only isolated datasets, leaving open the question of how semi-supervised anomaly detectors behave across a sequence of heterogeneous components. We address this gap by organizing the benchmark as a sequence of related component-level tasks and by defining multiple scenarios, including random, easy-to-hard, and hard-to-easy. In addition to single-dataset continual scenarios, we propose challenging scenarios spanning across multiple datasets. We further evaluate continual performance from two complementary perspectives: image-level anomaly detection and pixel-level anomaly localization. By recasting industrial anomaly detection as a continual benchmark rather than a static train/test problem, our work establishes a concrete path toward studying knowledge retention, adaptation, and generalization in evolving industrial environments.