Weakly-supervised anomaly detection for multimodal data distributions
School of Marine Science and Technology（海洋科学与技术学院）；Northwestern Polytechnical University（西北工业大学）；School of Computing（计算机学院）；University of Eastern Finland（东芬兰大学）；University of Turku（图尔库大学）；Singapore Institute of Technology（新加坡理工学院）
Weakly-supervised anomaly detection can outperform existing unsupervised methods with the assistance of a very small number of labeled anomalies, which attracts increasing attention from researchers. However, existing weakly-supervised anomaly detection methods are limited as these methods do not factor in the multimodel nature of the real-world data distribution. To mitigate this, we propose the Weakly-supervised Variational-mixture-model-based Anomaly Detector (WVAD). WVAD excels in multimodal datasets. It consists of two components: a deep variational mixture model, and an anomaly score estimator. The deep variational mixture model captures various features of the data from different clusters, then these features are delivered to the anomaly score estimator to assess the anomaly levels. Experimental results on three real-world datasets demonstrate WVAD's superiority.
RobustA: Robust Anomaly Detection in Multimodal Data
Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；IMT School for Advanced Studies（IMT高级研究学院）；Johannes Kepler University Linz（林茨约瑟夫·冯·克莱门茨大学）；Human-centered AI Group, AI Lab, Linz Institute of Technology（以人为中心的人工智能小组、人工智能实验室、林茨技术研究所）
Honda Research Institute Europe GmbH（本田欧洲研究院）；Università Politecnica delle Marche（马尔凯理工大学）
The continuously growing amount of monitored data in the Industry 4.0 context requires strong and reliable anomaly detection techniques. The advancement of Digital Twin technologies allows for realistic simulations of complex machinery, therefore, it is ideally suited to generate synthetic datasets for the use in anomaly detection approaches when compared to actual measurement data. In this paper, we present novel weakly-supervised approaches to anomaly detection for industrial settings. The approaches make use of a Digital Twin to generate a training dataset which simulates the normal operati…
UTOPYA: A Multimodal Deep Learning Framework for Physics-Informed Anomaly Detection and Time-Series Prediction
Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU)（化学工程系，挪威科学与技术大学）；Department of Computing, Imperial College London（计算系，帝国理工学院伦敦分校）
University of Colorado at Boulder（科罗拉多大学博尔德分校）；University of Maryland, College Park（马里兰大学帕克分校）；IUPUI（印第安纳大学与普渡大学印第安纳波利斯联合分校）
Detecting anomalous activity in human mobility data has a number of applications including road hazard sensing, telematic based insurance, and fraud detection in taxi services and ride sharing. In this paper we address two challenges that arise in the study of anomalous human trajectories: 1) a lack of ground truth data on what defines an anomaly and 2) the dependence of existing methods on significant pre-processing and feature engineering. While generative adversarial networks seem like a natural fit for addressing these challenges, we find that existing GAN based anomaly detection algorithm…
Weakly Supervised Anomaly Detection via Knowledge-Data Alignment
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）；CreateLink Technology（创联科技）
Learning Feature Encoder with Synthetic Anomalies for Weakly Supervised Graph Anomaly Detection
Sichuan University（四川大学）；University of Connecticut（康涅狄格大学）；University of Electronic Science and Technology of China（电子科技大学）；University of Adelaide（阿德莱德大学）
Out of Context: Reliability in Multimodal Anomaly Detection Requires Contextual Inference
Aalborg University（奥尔堡大学）；Pioneer Centre for Artificial Intelligence（先锋人工智能中心）；Technical University of Denmark（丹麦技术大学）；Milestone Systems（Milestone系统）；University of Bucharest（布加勒斯特大学）；University of Surrey（萨里大学）
NTT Corporation（日本电报电话公司）；International Christian University（国际基督教大学）；Toyo University（东洋大学）
Automated anomaly detection is essential for managing information and communications technology (ICT) systems to maintain reliable services with minimum burden on operators. For detecting varying and continually emerging anomalies as differences from normal states, learning normal relationships inherent among cross-domain data monitored from ICT systems is essential. Deep-learning-based anomaly detection using an autoencoder (AE) is therefore promising for such complicated learning; however, its interpretation is still problematic. Since the dimensions of the input data contributing to the det…
MCL-AD: Multimodal Collaboration Learning for Zero-Shot 3D Anomaly Detection
Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan)（计算能力网络与信息安全部教育部重点实验室，山东计算机科学中心（济南国家超级计算机中心））；Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东科学院））；Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing, Shandong Fundamental Research Center for Computer Science（山东省计算能力互联网与服务计算重点实验室，山东省计算机科学基础研究中心）
Sichuan University（四川大学）；University of Electronic Science and Technology of China（电子科技大学）；University of Adelaide（阿德莱德大学）
Weakly-supervised anomaly detection aims at learning an anomaly detector from a limited amount of labeled data and abundant unlabeled data. Recent works build deep neural networks for anomaly detection by discriminatively mapping the normal samples and abnormal samples to different regions in the feature space or fitting different distributions. However, due to the limited number of annotated anomaly samples, directly training networks with the discriminative loss may not be sufficient. To overcome this issue, this paper proposes a novel strategy to transform the input data into a more meaning…
NSF-MAP: Neurosymbolic Multimodal Fusion for Robust and Interpretable Anomaly Prediction in Assembly Pipelines
Artificial Intelligence Institute, University of South Carolina（南卡罗来纳大学人工智能研究所）；Clemson Composites Center, Clemson University（克莱姆森大学复合材料中心）；Intelligent Circuits, Architectures and Systems Lab, University of South Carolina（南卡罗来纳大学智能电路、架构和系统实验室）
RobustMAD: Evaluating Real-World Robustness of Multimodal Small Language Models for Deployable Anomaly Detection Assistants
Singapore University of Technology and Design（新加坡科技设计大学）；Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局）；Nanyang Technological University（南洋理工大学）；Chongqing University（重庆大学）
Shanghai University of Finance and Economics（上海财经大学）；University of Science and Technology of China（中国科学技术大学）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）；Carnegie Mellon University（卡内基梅隆大学）
Anomaly detection (AD) is a crucial task in machine learning with various applications, such as detecting emerging diseases, identifying financial frauds, and detecting fake news. However, obtaining complete, accurate, and precise labels for AD tasks can be expensive and challenging due to the cost and difficulties in data annotation. To address this issue, researchers have developed AD methods that can work with incomplete, inexact, and inaccurate supervision, collectively summarized as weakly supervised anomaly detection (WSAD) methods. In this study, we present the first comprehensive surve…
Multi-Normal Prototypes Learning for Weakly Supervised Anomaly Detection
School of Software and Microelectronics Peking University（北京大学软件与微电子学院）；School of Computer Science Peking University（北京大学计算机学院）；National Engineering Center of Software Engineering Peking University（北京大学软件工程国家工程研究中心）
Complex devices are connected daily and eagerly generate vast streams of multidimensional state measurements. These devices often operate in distinct modes based on external conditions (day/night, occupied/vacant, etc.), and to prevent complete or partial system outage, we would like to recognize as early as possible when these devices begin to operate outside the normal modes. Unfortunately, it is often impractical or impossible to predict failures using rules or supervised machine learning, because failure modes are too complex, devices are too new to adequately characterize in a specific en…
TSRNet: Simple Framework for Real-time ECG Anomaly Detection with Multimodal Time and Spectrogram Restoration Network
University of Arkansas（阿肯色大学）；University of Science, Vietnam National University（越南国家大学科学大学）；John von Neumann Institute, Vietnam National University（越南国家大学约翰·冯·诺依曼研究所）；West Virginia University（西弗吉尼亚大学）
Weakly-Supervised Anomaly Detection in the Milky Way
Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；University of California, Berkeley（加州大学伯克利分校）；Rutgers University（罗格斯大学）；SLAC National Accelerator Laboratory（SLAC国家加速器实验室）；Bosch Research North America（博世北美研究院）
TRANSIT your events into a new mass: Fast background interpolation for weakly-supervised anomaly searches
Département de Physique Nucléaire et Corpusculaire, University of Geneva（核物理与粒子物理系，日内瓦大学）；Department of Computer Science, University of Geneva（计算机科学系，日内瓦大学）
Guilin University of Electronic Technology（桂林电子科技大学）
Anomaly detection is widely used to distinguish system anomalies by analyzing the temporal and spatial features of wireless sensor network (WSN) data streams; it is one of critical technique that ensures the reliability of WSNs. Currently, graph neural networks (GNNs) have become popular state-of-the-art methods for conducting anomaly detection on WSN data streams. However, the existing anomaly detection methods based on GNNs do not consider the temporal and spatial features of WSN data streams simultaneously, such as multi-node, multi-modal and multi-time features, seriously impacting their e…
In recent years, we have witnessed a surge of interests in learning a suitable distance metric from weakly supervised data. Most existing methods aim to pull all the similar samples closer while push the dissimilar ones as far as possible. However, when some classes of the dataset exhibit multimodal distribution, these goals conflict and thus can hardly be concurrently satisfied. Additionally, to ensure a valid metric, many methods require a repeated eigenvalue decomposition process, which is expensive and numerically unstable. Therefore, how to learn an appropriate distance metric from weakly…
Shanghai Jiao Tong University（上海交通大学）；UNC Chapel Hill（北卡罗来纳大学教堂山分校）；University of California, Berkeley（加州大学伯克利分校）
Phrase localization is a task that studies the mapping from textual phrases to regions of an image. Given difficulties in annotating phrase-to-object datasets at scale, we develop a Multimodal Alignment Framework (MAF) to leverage more widely-available caption-image datasets, which can then be used as a form of weak supervision. We first present algorithms to model phrase-object relevance by leveraging fine-grained visual representations and visually-aware language representations. By adopting a contrastive objective, our method uses information in caption-image pairs to boost the performance …
Multiple modalities often co-occur when describing natural phenomena. Learning a joint representation of these modalities should yield deeper and more useful representations. Previous generative approaches to multi-modal input either do not learn a joint distribution or require additional computation to handle missing data. Here, we introduce a multimodal variational autoencoder (MVAE) that uses a product-of-experts inference network and a sub-sampled training paradigm to solve the multi-modal inference problem. Notably, our model shares parameters to efficiently learn under any combination of…
Ecole Normale Supérieure（巴黎高等师范学院）；Ecole des Hautes Etudes en Sciences Sociales（巴黎高等社会科学研究学院）；PSL Research University（PSL研究大学）；Centre National de la Recherche Scientifique（法国国家科学研究中心）；Facebook AI Research（脸书人工智能研究院）
University of Illinois at Urbana-Champaign (UIUC)（伊利诺伊大学厄巴纳-香槟分校）；Pohang University of Science and Technology (POSTECH)（浦项科技大学）
Most recent studies on detecting and localizing temporal anomalies have mainly employed deep neural networks to learn the normal patterns of temporal data in an unsupervised manner. Unlike them, the goal of our work is to fully utilize instance-level (or weak) anomaly labels, which only indicate whether any anomalous events occurred or not in each instance of temporal data. In this paper, we present WETAS, a novel framework that effectively identifies anomalous temporal segments (i.e., consecutive time points) in an input instance. WETAS learns discriminative features from the instance-level l…
Towards Scalable SOAP Note Generation: A Weakly Supervised Multimodal Framework
Department of Computer Science, University of Maryland, Baltimore County（计算机科学系，马里兰大学巴尔的摩县分校）；Department of Dermatology, Johns Hopkins University School of Medicine（皮肤科系，约翰霍普金斯大学医学院）
