Continual Anomaly Detection

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10 papers1 paper 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
Neural-Collapse-guided Task-Free Continual Anomaly Detection
Sep 3, 2026Xiaotong Kong, Chaoyang Song, Ziai Zhou +3Continual Anomaly Detection Continual Learning
Key Laboratory of Measurement and Control of CSE, Ministry of Education, School of Automation, Southeast University, Nanjing 210096, China
Memory-Bounded Continuation of Greedy Sampling for Continual Anomaly Detection
Aug 15, 2026Yoon Gyo Jung, Jaewoo Park, Kuan-Chuan Peng +2Continual Anomaly Detection Anomaly Detection
Electrical and Computer Engineering Department Northeastern University Boston, MA, USA · AIVEX Inc. Seoul, South Korea · Mitsubishi Electric Research Laboratories (MERL) Cambridge, MA, USA
CL-Anomaly: Layer-Adaptive Mixture-of-Experts with Multimodal Large Language Model for Continual Learning in Anomaly Detection
Jul 3, 2026Wen Dong, Zhao Wang, Shuangqing Zhang +5Continual Anomaly Detection Mixture of Experts
Nanjing University, Nanjing, China · China Mobile Zijin Innovation Institute, Nanjing, China · Nanjing University of Science and Technology, Nanjing, China
DeCoFlow: Structural Decomposition of Normalizing Flows for Continual Anomaly Detection
Jun 25, 2026Hun Im, Jungi Lee, Subeen Cha +1Normalizing Flows Continual Anomaly Detection
Seoul National University, Seoul, Korea
TaskFusion: Continual Anomaly Detection for Heterogeneous Tabular Data
Jun 10, 2026Dayananda Herurkar, Federico Raue, Joachim Folz +2Continual Anomaly Detection Tabular Anomaly Detection
German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany · RPTU Kaiserslautern-Landau, Germany · Hochschule Bonn-Rhein-Sieg (H-BRS), Germany
Normality-Preserving Continual Industrial Anomaly Detection via Orthogonal LoRA Banks
Jun 1, 2026Weibai Fang, Haijun Che, Feiyang Ren +1Industrial Anomaly Detection Anomaly Localization
Rethinking Continual Anomaly Detection on the Edge: Benchmarking Under Realistic Industrial Conditions
May 22, 2026Chad Weatherly, Sen LinContinual Anomaly Detection Edge Inference
University of Houston
Exploring Multimodal Prompts For Unsupervised Continuous Anomaly Detection
Mar 23, 2026Mingle Zhou, Jiahui Liu, Jin Wan +2Continual Anomaly Detection Unsupervised Anomaly Detection
Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China · Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing, Shandong Fundamental Research Center for Computer Science, Jinan, China · Faculty of Data Science, City University of Macau, Macau, China
Online Continual Learning for Anomaly Detection in IoT under Data Distribution Shifts
Mar 8, 2026Matea Marinova, Shashi Raj Pandey, Junya Shiraishi +3Continual Anomaly Detection Online Anomaly Detection
Faculty of Electrical Engineering and Information Technologies, Ss. Cyril and Methodius University in Skopje, 1000 Skopje, North Macedonia · Dept. of Electronic Systems, Aalborg University, Aalborg, Denmark

Oct 10, 2026J/K move · Enter open · S save

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.