Anomaly detection in complex dynamical systems is essential for ensuring reliability, safety, and efficiency in industrial and cyber-physical infrastructures. Predictive maintenance helps prevent costly failures, while cybersecurity monitoring has become critical as digitized systems face growing threats. Many of these systems exhibit oscillatory behaviors and bounded motion, requiring anomaly detection methods that capture structured temporal dependencies while adhering to physical consistency principles. In this work, we propose a system-theoretic approach to anomaly detection, grounded in classical embedding theory and physics-inspired consistency principles. We build upon the Fractal Whitney Embedding Prevalence Theorem that extends traditional embedding techniques to complex system dynamics. Additionally, we introduce state-derivative pairs as an embedding strategy to capture system evolution. To enforce temporal coherence, we develop a Temporal Differential Consistency Autoencoder (TDC-AE), incorporating a TDC-Loss that aligns the approximated derivatives of latent variables with their dynamic representations. We evaluate our method on two subsets (FD001, FD003) of the C-MAPSS dataset, a benchmark for turbofan engine degradation. TDC-AE machtes LSTMs and outperforms Transformers while achieving a nearly 100x reduction in MAC operations, making it particularly suited for lightweight edge computing. Our findings support the hypothesis that anomalies disrupt stable system dynamics, providing a robust signal for anomaly detection.
CCE: Confidence-Consistency Evaluation for Time Series Anomaly Detection
Pengcheng Laboratory, Shenzhen, Guangdong, 518066, China（鹏城实验室，深圳，广东，518066，中国）；School of Future Technology, South China University of Technology, Guangzhou, Guangdong 510650, China（未来技术学院，华南理工大学，广州，广东 510650，中国）；School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong 510650, China（计算机科学与工程学院，华南理工大学，广州，广东 510650，中国）；Department of Computer Science, Hong Kong Baptist University, Hong Kong SAR, China（计算机科学系，香港 Baptist 大学，香港特别行政区，中国）
SMT-AD: a scalable quantum-inspired anomaly detection approach
Science, Mathematics and Technology Cluster, Singapore University of Technology and Design（新加坡科技设计大学科学、数学与技术集群）；Centre for Quantum Technologies, National University of Singapore（新加坡国立大学量子技术中心）；Artificial Intelligence and Data Analytics Strategic Technology Centre, ST Engineering（ST工程人工智能与数据分析战略技术中心）；Engineering Product Development Pillar, Singapore University of Technology and Design（新加坡科技设计大学工程产品开发支柱）
Quantum-Inspired Tensor Network Autoencoders for Anomaly Detection: A MERA-Based Approach
Department of Physics, Sinop University（锡诺普大学物理系）；Institute for Quantum Materials and Technologies, Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院量子材料与技术研究所）
A fundamental problem in the field of unsupervised machine learning is the detection of anomalies corresponding to rare and unusual observations of interest; reasons include for their rejection, accommodation or further investigation. Anomalies are intuitively understood to be something unusual or inconsistent, whose occurrence sparks immediate attention. More formally anomalies are those observations-under appropriate random variable modelling-whose expectation of occurrence with respect to a grouping of prior interest is less than one; such a definition and understanding has been used to dev…
Information Science and Technology Institute (ISTI) of CNR（意大利国家研究委员会信息科学与技术研究所（ISTI））
In this work, we propose CBiGAN -- a novel method for anomaly detection in images, where a consistency constraint is introduced as a regularization term in both the encoder and decoder of a BiGAN. Our model exhibits fairly good modeling power and reconstruction consistency capability. We evaluate the proposed method on MVTec AD -- a real-world benchmark for unsupervised anomaly detection on high-resolution images -- and compare against standard baselines and state-of-the-art approaches. Experiments show that the proposed method improves the performance of BiGAN formulations by a large margin a…
If machine failures can be detected preemptively, then maintenance and repairs can be performed more efficiently, reducing production costs. Many machine learning techniques for performing early failure detection using vibration data have been proposed; however, these methods are often power and data-hungry, susceptible to noise, and require large amounts of data preprocessing. Also, training is usually only performed once before inference, so they do not learn and adapt as the machine ages. Thus, we propose a method of performing online, real-time anomaly detection for predictive maintenance …
Fraunhofer Institute for Applied and Integrated Security（弗劳恩霍夫应用与集成安全研究所）
Inspired by recent advances in coverage-guided analysis of neural networks, we propose a novel anomaly detection method. We show that the hidden activation values contain information useful to distinguish between normal and anomalous samples. Our approach combines three neural networks in a purely data-driven end-to-end model. Based on the activation values in the target network, the alarm network decides if the given sample is normal. Thanks to the anomaly network, our method even works in strict semi-supervised settings. Strong anomaly detection results are achieved on common data sets surpa…
Anomaly detection has been considered under several extents of prior knowledge. Unsupervised methods do not require any labelled data, whereas semi-supervised methods leverage some known anomalies. Inspired by mixture-of-experts models and the analysis of the hidden activations of neural networks, we introduce a novel data-driven anomaly detection method called ARGUE. Our method is not only applicable to unsupervised and semi-supervised environments, but also profits from prior knowledge of self-supervised settings. We designed ARGUE as a combination of dedicated expert networks, which special…
FZI Research Center for Information Technology（FZI信息技术研究中心）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
Great progress has been achieved in the community of autonomous driving in the past few years. As a safety-critical problem, however, anomaly detection is a huge hurdle towards a large-scale deployment of autonomous vehicles in the real world. While many approaches, such as uncertainty estimation or segmentation-based image resynthesis, are extremely promising, there is more to be explored. Especially inspired by works on anomaly detection based on image resynthesis, we propose a novel approach for anomaly detection through style transfer. We leverage generative models to map an image from its…
University of Glasgow（格拉斯哥大学）；Physical Research Laboratory（物理研究实验室）；Indian Institute of Technology（印度理工学院）；Durham University（杜伦大学）
Anomaly detection through employing machine learning techniques has emerged as a novel powerful tool in the search for new physics beyond the Standard Model. Historically similar to the development of jet observables, theoretical consistency has not always assumed a central role in the fast development of algorithms and neural network architectures. In this work, we construct an infrared and collinear safe autoencoder based on graph neural networks by employing energy-weighted message passing. We demonstrate that whilst this approach has theoretically favourable properties, it also exhibits fo…
Corporate Research and Development Center, Toshiba Corporation（东芝公司企业研发中心）
Generative adversarial networks (GANs) have shown promise for various problems including anomaly detection. When anomaly detection is performed using GAN models that learn only the features of normal data samples, data that are not similar to normal data are detected as abnormal samples. The present approach is developed by employing a dual-encoder in a bidirectional GAN architecture that is trained simultaneously with a generator and a discriminator network. Through the learning mechanism, the proposed method aims to reduce the problem of bad cycle consistency, in which a bidirectional GAN mi…
Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification
Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高性能计算研究所）；Medical College, Shantou University（汕头大学医学院）；College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；College of Computer Science, Sichuan University（四川大学计算机学院）；School of Electronics and Information Engineering, Soochow University（苏州大学电子信息学院）；School of Biomedical Engineering, Anhui Medical University（安徽医科大学生物医学工程学院）；Longchuan People’s Hospital（龙川县人民医院）；Puning People’s Hospital（普宁市人民医院）；Haifeng PengPai Memory Hospital（海丰县彭湃纪念医院）；College of Intelligence and Computing, Tianjin University（天津大学智能与计算学部）；The Chinese University of Hong Kong（香港中文大学）；Soochow University（苏州大学）
Extreme time extrapolation capabilities and thermodynamic consistency of physics-inspired Neural Networks for the 3D microstructure evolution of materials via Cahn-Hilliard flow
University of Milano-Bicocca（米兰比可卡大学）；Institut Lumière Matière（光与物质研究所）
Columbia University（哥伦比亚大学）；University of Connecticut（康涅狄格大学）；NEC Laboratories America, Inc.（NEC美国实验室）
Detecting abnormal activities in real-world surveillance videos is an important yet challenging task as the prior knowledge about video anomalies is usually limited or unavailable. Despite that many approaches have been developed to resolve this problem, few of them can capture the normal spatio-temporal patterns effectively and efficiently. Moreover, existing works seldom explicitly consider the local consistency at frame level and global coherence of temporal dynamics in video sequences. To this end, we propose Convolutional Transformer based Dual Discriminator Generative Adversarial Network…
