Results 21 to 30 of about 4,738,087 (364)
Graph Neural Network-Based Anomaly Detection in Multivariate Time Series [PDF]
AAAI Conference on Artificial Intelligence, 2021 Given high-dimensional time series data (e.g., sensor data), how can we detect anomalous events, such as system faults and attacks? More challengingly, how can we do this in a way that captures complex inter-sensor relationships, and detects and explains
Ailin Deng, Bryan Hooi
semanticscholar +1 more source
CutPaste: Self-Supervised Learning for Anomaly Detection and Localization [PDF]
Computer Vision and Pattern Recognition, 2021 We aim at constructing a high performance model for defect detection that detects unknown anomalous patterns of an image without anomalous data. To this end, we propose a two-stage framework for building anomaly detectors using normal training data only.
Chun-Liang Li +3 more
semanticscholar +1 more source
DRÆM – A discriminatively trained reconstruction embedding for surface anomaly detection [PDF]
IEEE International Conference on Computer Vision, 2021 Visual surface anomaly detection aims to detect local image regions that significantly deviate from normal appearance. Recent surface anomaly detection methods rely on generative models to accurately reconstruct the normal areas and to fail on anomalies.
Vitjan Zavrtanik, M. Kristan, D. Skočaj
semanticscholar +1 more source
Deep Industrial Image Anomaly Detection: A Survey [PDF]
Machine Intelligence Research, 2023 The recent rapid development of deep learning has laid a milestone in industrial image anomaly detection (IAD). In this paper, we provide a comprehensive review of deep learning-based image anomaly detection techniques, from the perspectives of neural ...
Jiaqi Liu +6 more
semanticscholar +1 more source
A Unified Model for Multi-class Anomaly Detection [PDF]
Neural Information Processing Systems, 2022 Despite the rapid advance of unsupervised anomaly detection, existing methods require to train separate models for different objects. In this work, we present UniAD that accomplishes anomaly detection for multiple classes with a unified framework.
Zhiyuan You +6 more
semanticscholar +1 more source
Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning [PDF]
IEEE International Conference on Computer Vision, 2021 Anomaly detection with weakly supervised video-level labels is typically formulated as a multiple instance learning (MIL) problem, in which we aim to identify snippets containing abnormal events, with each video represented as a bag of video snippets ...
Yu Tian +5 more
semanticscholar +1 more source
SA-PatchCore: Anomaly Detection in Dataset With Co-Occurrence Relationships Using Self-Attention
IEEE Access, 2023 Various unsupervised anomaly detection methods using deep learning have recently been proposed, and the accuracy of the anomaly detection technique for local anomalies has been improved.
Kengo Ishida +5 more
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CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows [PDF]
IEEE Workshop/Winter Conference on Applications of Computer Vision, 2021 Unsupervised anomaly detection with localization has many practical applications when labeling is infeasible and, moreover, when anomaly examples are completely missing in the train data.
Denis A. Gudovskiy +2 more
semanticscholar +1 more source
Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2021 Anomaly detection on attributed networks attracts considerable research interests due to wide applications of attributed networks in modeling a wide range of complex systems.
Yixin Liu +5 more
semanticscholar +1 more source
G2D: Generate to Detect Anomaly [PDF]
2021 IEEE Winter Conference on Applications of Computer Vision (WACV), 2021 In this paper, we propose a novel method for irregularity detection. Previous researches solve this problem as a One-Class Classification (OCC) task where they train a reference model on all of the available samples. Then, they consider a test sample as an anomaly if it has a diversion from the reference model.
Pourreza, Masoud +5 more
openaire +6 more sources