Anomaly Detection Techniques and Applications

Latest papers

• Mitigating Convergence Collapse in Fixed-Target Anomaly Detectors via Kernel-Anchored Locality Regularization

  Jos\'e Lucas De Melo Costa, Fabrice Popineau, Arpad Rimmel, Bich-Li\^en Doan · 5 October 2026

  A family of tabular anomaly detectors trains a neural map toward a fixed target under squared-error loss and scores anomalies by the test-time residual; contraction matching, one-step rectified flow, and reconstruction autoencoders all fit this template. We characterize a convergence collapse: bette…

• Cog-VADU: A Training-Free Cognitive Reasoning Framework for Video Anomaly Detection and Understanding

  Mohd Ubaid Wani, Sara Atito, Josef Kittler, Muhammad Awais · 2 October 2026

  Video Anomaly Detection (VAD) aims to temporally localize abnormal events in videos. Most existing approaches rely on dataset-specific training and curated annotations, limiting generalization in open-set scenarios. Recent zero-shot methods based on Large Vision- Language Models (LVLMs) alleviate th…

• Have an LLM Write Your Anomaly Detector: Autonomous Discovery of Compact, Interpretable Detectors for Time Series

  David Berghaus · 2 October 2026

  Time-series anomaly detection trades off predictive accuracy, computational efficiency, and interpretability. We use a large language model not as the detector but as the author of one: an autonomous research loop in which the model repeatedly edits a single short NumPy program under a leakage-free …

• Detect, Explain, Interpret: An End-to-End Benchmark for Time Series Anomaly Detection, Explainability and Interpretability

  Roberto Stanzione, Jules Barbe, Magali Parrino, J\'er\'emie Fourmann, Paul Boniol · 2 October 2026

  Time Series Anomaly Detection has received increasing attention, driven by the growing availability of complex time series data. This surge has led to the development of numerous detection methods, as well as a variety of benchmarks aimed at thoroughly evaluating their performance. However, most exi…

• Generalist Representation, Specialist Detection: TS-Router for Time-Series Anomaly Detection

  Tian Lan, Yifei Gao, Yimeng Lu, Xuming An, Meng Wang, Yue Pan, Wenjun He, Chenghao Liu, Chen Zhang · 2 October 2026

  Time-series anomaly detection (TSAD) is difficult to generalize across datasets because heterogeneous temporal dynamics imply different notions of normality and favor different detection criteria. While time-series foundation models provide transferable representations, coupling them with a fixed an…

• Deep Learning for Anomaly Detection in Railway Systems: A Structured Survey

  Ammar Bouketta, Smail Niar, Hamza Ouarnoughi · 2 October 2026

  Ensuring safe and reliable operation of modern railway systems increasingly relies on data-driven monitoring and intelligent fault detection. Deep learning has emerged as an effective paradigm for railway anomaly detection, driven by the growing availability of heterogeneous sensor data from rolling…

• From Benchmarks to Production: Transferring Time Series Anomaly Detection Methods for Electricity Production Monitoring

  Nicolas Vautier, Paul Caron, Nardi Xhepi, F\'elicie Bizeul, Manel Boumghar, Christophe Degouy, Paul Boniol · 1 October 2026

  Accurate forecasting of electricity production is essential for maintaining the operational efficiency and strategic planning of energy utilities. In industrial settings, such forecasts are generated daily to ensure supply-demand balance and optimal management of production assets. However, the incr…

• What Streaming Anomaly Detection Finds (and Misses) in Industrial Time Series

  Magali Parrino, Antoine Ajenjo, Emmanuel Remy, Pierre Stephan, Paul Boniol · 1 October 2026

  EDF relies on continuous monitoring of its power plants to detect anomalies as soon as they occur. Given the absence of a universally optimal streaming method in unsupervised settings, we compare streaming methods with state-of-the-art TSAD models deployed online on a real nuclear power plant datase…

• In a Streaming World, Should You Stand Still? A Comprehensive Benchmark of Anomaly Detection in Streams

  Magali Parrino, Antoine Ajenjo, Emmanuel Remy, Pierre Stephan, Pierre Senellart, Paul Boniol · 1 October 2026

  Time series anomaly detection (TSAD) is increasingly deployed in streaming settings, where data arrive sequentially and may exhibit non-stationarity. As a result, several works from the recent literature propose streaming anomaly detection methods that rely on incremental updates to adapt over time.…

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

  Ammar Bouketta, Smail Niar, Hamza Ouarnoughi, Eva Mutuzo Brindle · 1 October 2026

  Passenger access doors are safety-critical subsystems in railway vehicles, yet detecting abnormal door behavior in real operation is challenging because faults are rare, diverse, and often unlabeled. This paper addresses railway door condition monitoring as a cycle-level unsupervised anomaly detecti…

• TED:Text-Axis Evidence Decomposition for Prompted Anomaly Localization

  JinYoung Kim, Geonho Kim, GiJeong Park, Geonu Lee, YoungJoon Yoo · 1 October 2026

  CLIP is a powerful vision-language model, but it was not designed for fine-grained defect localization; CLIP-based anomaly detectors therefore adapt it with prompts or lightweight modules to increase defect sensitivity. We show that stronger sensitivity does not necessarily make local evidence relia…

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

  Jiaheng Guo, Haochen Zhang, Yu-Chao Huang, Jinhao Duan, Nicholas Konz, Tianlong Chen · 30 September 2026

  Time series anomaly detection (TSAD) plays a crucial role in healthcare, finance, industrial monitoring, and other sectors. Within and between these settings, anomalies span vastly different temporal scales, from sub-second point spikes to multi-hour drift patterns. However, most existing TSAD metho…

• Augmenting Visual Anomaly Detection with Automated Interpretability

  Antonio De Santis, Arsenio Leo, Marco Brambilla · 29 September 2026

  Visual anomaly detectors identify deviations from known-normal data, but their anomaly signals may mix evidence of actual anomalies with benign visual variation. We investigate whether automated interpretability can augment visual anomaly detectors by identifying and intervening on different compone…

• When Less Compute Is More: Adaptive Early Exit Improves Pretrained Outlier Detection

  Tianyang Zhou, Leman Akoglu · 29 September 2026

  Pretrained tabular foundation models process every dataset at a fixed depth, with inference costs growing with dataset size. To address this, we present the first study of depth-adaptive early-exit for pretrained outlier detection models. While early-exit is typically motivated by efficiency, we unc…

• VD-DeepStack: Bridging Visual Comparison and Language Reasoning for Few-Shot Anomaly Detection

  Mengyang Zhao, Zhuolin He, Haiyang Yu, Yuxuan Liang, Yifang Xu, Yuchuan Wu, Xiaolei Chen, Zhengtao Yao, Fan Shi, Yang Liu, Bin Li, Xiangyang Xue · 29 September 2026

  Few-shot visual anomaly detection is fundamentally a visual comparison task, requiring fine-grained inspection of a query against normal references. Many recent methods based on large vision-language models (LVLMs) emphasize comparative reasoning through language chain-of-thought. Yet discrete, abst…

• Supervision Recovery for Time Series Anomaly Detection via Context-Anchored Pairing

  Yifei Gao, Tian Lan, Yimeng Lu, Xuming An, Meng Wang, Wenjun He, Yijie Li, Chen Zhang · 29 September 2026

  Time series anomaly detection (TSAD) remains challenging not only because anomaly labels are scarce, but also because temporal anomalies are highly context-dependent. Existing methods often rely on unsupervised objectives or surrogate abnormal patterns, providing limited supervision for context-depe…

• Beyond Empirical Support: Structured Outlier Generation via Sinkhorn Optimal Transport

  Haixiang Sun, Andrew L. Liu · 28 September 2026

  Outliers are essential for evaluating and improving the robustness of machine learning systems, especially when future distributions may differ significantly from historical training data. In high-stakes applications, robustness often depends on rare cases that finite datasets fail to capture, makin…

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

  Jianan Liu, Chunguang Li · 28 September 2026

  Multi-dimensional time series, inherently tensorial, are common in practice. Despite great progress in time series anomaly detection, most existing methods are confined to uni-/multi-variate time series. When handling multi-dimensional time series using these methods, reshaping operations are requir…

• Adapting Visualization Techniques for Time-Series Anomaly Detection: From Convolutional Neural Networks to Convolutional-Recurrent Neural Networks

  Fabien Poirier, Myriam Lamolle · 28 September 2026

  Deep neural networks achieve strong performance on complex tasks but are often regarded as "black boxes," which limits their adoption in domains where transparency is essential. This lack of interpretability raises ethical and legal concerns, particularly in sensitive applications such as security, …

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

  Hyeongwon Kang, Jeongseob Kim, Jinwoo Park, Pilsung Kang · 28 September 2026

  Time-series anomaly detection often returns scores or intervals, while analysts need to understand the abnormal behavior and the evidence supporting it. We introduce SAGE (Specialized Analyzer Group for Expert-like Detection), a multi-agent framework for evidence-grounded diagnosis of univariate tim…

• Continuous Online Fault Detection for Mobile Robots via Adaptive Edge Models

  Jordan Levy, Nicolas Verstaevel, Vincent Talon, Benoit Gaudou · 25 September 2026

  Mobile robots require robust, real-time fault detection capable of continuous adaptation on constrained edge hardware. While deep time-series models excel at unsupervised anomaly detection, their computational cost prohibits high-frequency onboard execution. This paper bridges this gap via a Teacher…

• AIR: Analytic Imbalance Rectifier for Continual Learning

  Di Fang, Yinan Zhu, Zhiping Lin, Cen Chen, Ziqian Zeng, Huiping Zhuang · 25 September 2026

  Continual learning (CL) agents incrementally learn from sequentially arriving data and adapt to the dynamic, ever-changing nature of real-world environments. However, many existing CL methods suffer performance degradation in evolving, imbalanced data streams due to limited adaptation to changing cl…

• Industrial Anomaly Detection via Defect-Grounded Reasoning in Visual Latent Space

  Jaron Yeh, Yen-Wei Chang, Jiang Liu, Shao-Yuan Lo · 25 September 2026

  Industrial anomaly detection (IAD) is evolving beyond conventional detection and localization toward multimodal inspection systems that can describe, explain, and reason about fine-grained defects. Although recent multimodal large language model (MLLM)-based methods improve anomaly understanding thr…

• Predicting Emerging Topics from Outliers: A Prospective Study of Weak Signals in Embedding Space

  Evangelia Zve, Gauvain Bourgne, Jean-Gabriel Ganascia · 25 September 2026

  Some documents that embedding-based topic models initially classify as noise later become founding members of emerging topics. At publication time, however, they appear as scattered points in embedding space and are difficult to distinguish from ordinary noise without the benefit of hindsight. We st…

• Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data

  Hiroshi Takahashi, Tomoharu Iwata, Atsutoshi Kumagai, Yuuki Yamanaka · 25 September 2026

  Semi-supervised anomaly detection, which aims to improve the anomaly detection performance by using a small amount of labeled anomaly data in addition to unlabeled data, has attracted attention. Existing semi-supervised approaches assume that most unlabeled data are normal, and train anomaly detecto…