Anomaly Detection via Mean Shift Density Enhancement
School of Data Science, Indian Institute of Science Education and Research（数据科学学院，印度科学教育与研究学院）；Institute of Computer Science, University of Rostock（计算机科学研究所，罗斯托克大学）；Leibniz-Institute for Food Systems Biology, Technical University of Munich（食品系统生物学莱比锡研究所，慕尼黑技术大学）；Stellenbosch Institute of Advanced Studies (STIAS)（斯托尔波茨堡高级研究 institute (STIAS)）
Pritam Kar, Rahul Bordoloi, Olaf Wolkenhauer, Saptarshi Bej
Unsupervised anomaly detection stands as an important problem in machine learning. Existing unsupervised anomaly detection algorithms rarely perform well across different anomaly types, often excelling only under specific structural assumptions. This lack of robustness also becomes particularly evident under noisy settings. We propose Mean Shift Density Enhancement (MSDE), a fully unsupervised framework that detects anomalies through their geometric response to density-driven manifold evolution. MSDE is designed as a general purpose anomaly detection framework, based on the principle that normal samples, being well supported by local density, remain stable under iterative density enhancement, whereas anomalous samples undergo large cumulative displacements as they are attracted toward nearby density modes. To operationalize this idea, MSDE employs a weighted mean-shift procedure with adaptive, sample-specific density weights derived from a manifold learning-based fuzzy neighborhood graph. We evaluate MSDE on an anomaly detection benchmark comprising 46 real-world tabular datasets, four realistic anomaly generation mechanisms, and six noise levels. Compared to 13 established unsupervised baselines, MSDE achieves consistently strong, balanced and robust performance for several standard classification metrics, at several noise levels and on average over several types of anomalies. These results demonstrate that displacement-based scoring provides a robust alternative to the existing state-of-the-art for unsupervised anomaly detection.
Bayerische Motorenwerke AG（宝马股份公司）；Leiden Institute of Advanced Computer Science（莱顿高级计算机科学研究所）
Anomaly detection describes methods of finding abnormal states, instances or data points that differ from a normal value space. Industrial processes are a domain where predicitve models are needed for finding anomalous data instances for quality enhancement. A main challenge, however, is absence of labels in this environment. This paper contributes to a data-centric way of approaching artificial intelligence in industrial production. With a use case from additive manufacturing for automotive components we present a deep-learning-based image processing pipeline. Additionally, we integrate the c…
COGNOS: Universal Enhancement for Time Series Anomaly Detection via Constrained Gaussian-Noise Optimization and Smoothing
School of Information Science and Technology, Beijing University of Technology, China（信息科学与技术学院，北京理工大学，中国）；Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, China（北京计算智能与智能系统重点实验室，北京理工大学，中国）
Modeling Spectral Energy Shifts in Spatio-Temporal Graph Anomaly Detection
Department of Computation, University of Torontoland, Torontoland, Canada（计算系，托伦托兰大学，加拿大）；School of Computation, University of Edenborrow, Edenborrow, United Kingdom（计算学院，伊登伯恩大学，英国）；College of Connected Computing, Vanderbilt University, Nashville, USA（连接计算学院，范德比大学，美国）；Department of Civil and Environmental Engineering, Electrical and Computer Engineering, Vanderbilt University, Nashville, USA（土木与环境工程系，电气与计算机工程系，范德比大学，美国）
Johns Hopkins University（约翰斯·霍普金斯大学）；University of Maryland（马里兰大学）
Ear related concerns and symptoms represents the leading indication for seeking pediatric healthcare attention. Despite the high incidence of such encounters, the diagnostic process of commonly encountered disease of the middle and external presents significant challenge. Much of this challenge stems from the lack of cost effective diagnostic testing, which necessitating the presence or absence of ear pathology to be determined clinically. Research has however demonstrated considerable variation among clinicians in their ability to accurately diagnose and consequently manage ear pathology. Wit…
The proliferation of web platforms has created incentives for online abuse. Many graph-based anomaly detection techniques are proposed to identify the suspicious accounts and behaviors. However, most of them detect the anomalies once the users have performed many such behaviors. Their performance is substantially hindered when the users' observed data is limited at an early stage, which needs to be improved to minimize financial loss. In this work, we propose Eland, a novel framework that uses action sequence augmentation for early anomaly detection. Eland utilizes a sequence predictor to pred…
Bias in Unsupervised Anomaly Detection in Brain MRI
Technical University of Munich（慕尼黑工业大学）；Helmholtz AI（亥姆霍兹人工智能研究所）；Helmholtz Center Munich（亥姆霍兹慕尼黑中心）；King’s College London（伦敦国王学院）；Klinikum Rechts der Isar（伊萨尔河畔临床教学中心）；Imperial College London（帝国理工学院）
We introduce an algorithmic method for population anomaly detection based on gaussianization through an adversarial autoencoder. This method is applicable to detection of `soft' anomalies in arbitrarily distributed highly-dimensional data. A soft, or population, anomaly is characterized by a shift in the distribution of the data set, where certain elements appear with higher probability than anticipated. Such anomalies must be detected by considering a sufficiently large sample set rather than a single sample. Applications include, but not limited to, payment fraud trends, data exfiltration, d…
Enhancing Network Anomaly Detection with Quantum GANs and Successive Data Injection for Multivariate Time Series
Department of Computer Engineering（计算机工程系）；Polytechnique Montreal（蒙特利尔理工学院）；Department of Compute Engineering（计算机工程系）；Department of Computer Science（计算机科学系）；Université de Sherbrooke（谢布鲁克大学）
Quantum anomaly detection in the latent space of proton collision events at the LHC
Institute for Particle Physics and Astrophysics, ETH Zurich（苏黎世联邦理工学院粒子物理与天体物理研究所）；European Organization for Nuclear Research (CERN)（欧洲核子研究组织（CERN））；Faculty of Computer Science, University of Vienna（维也纳大学计算机学院）；Departamento de Física, Universitat Autònoma de Barcelona（巴塞罗那自治大学物理系）；IBM Quantum, IBM Research – Zurich（IBM量子（IBM苏黎世研究实验室））；Institute for Quantum Electronics, ETH Zurich（苏黎世联邦理工学院量子电子学研究所）
Functional Localization Enforced Deep Anomaly Detection Using Fundus Images
Heinrich Heine University Düsseldorf（杜塞尔多夫海因里希·海涅大学）；Philipps University of Marburg（马尔堡菲利普斯大学）；University Hospital Knappschaftskrankenhaus Bochum（波鸿克纳普schaft医院大学医院）；University of Münster（明斯特大学）
Deep anomaly detection methods learn representations that separate between normal and anomalous images. Although self-supervised representation learning is commonly used, small dataset sizes limit its effectiveness. It was previously shown that utilizing external, generic datasets (e.g. ImageNet classification) can significantly improve anomaly detection performance. One approach is outlier exposure, which fails when the external datasets do not resemble the anomalies. We take the approach of transferring representations pre-trained on external datasets for anomaly detection. Anomaly detection…
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Hong Kong University of Science and Technology（香港科技大学）；Stanford University（斯坦福大学）
Graph Neural Networks (GNNs) are widely applied for graph anomaly detection. As one of the key components for GNN design is to select a tailored spectral filter, we take the first step towards analyzing anomalies via the lens of the graph spectrum. Our crucial observation is the existence of anomalies will lead to the `right-shift' phenomenon, that is, the spectral energy distribution concentrates less on low frequencies and more on high frequencies. This fact motivates us to propose the Beta Wavelet Graph Neural Network (BWGNN). Indeed, BWGNN has spectral and spatial localized band-pass filte…
Carnegie Mellon University（卡内基梅隆大学）；Bosch Research & Technology Center（博世研究与技术中心）
Anomaly detection has many important applications, such as monitoring industrial equipment. Despite recent advances in anomaly detection with deep-learning methods, it is unclear how existing solutions would perform under out-of-distribution scenarios, e.g., due to shifts in machine load or environmental noise. Grounded in the application of machine health monitoring, we propose a framework that adapts to new conditions with few-shot samples. Building upon prior work, we adopt a classification-based approach for anomaly detection and show its equivalence to mixture density estimation of the no…
