Results 51 to 60 of about 4,738,087 (364)
Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection [PDF]
IEEE International Conference on Computer Vision, 2019 Deep autoencoder has been extensively used for anomaly detection. Training on the normal data, the autoencoder is expected to produce higher reconstruction error for the abnormal inputs than the normal ones, which is adopted as a criterion for ...
Dong Gong +6 more
semanticscholar +1 more source
Geometric anomaly detection in data
Proceedings of the National Academy of Sciences, 2020 Significance The problem of fitting low-dimensional manifolds to high-dimensional data has been extensively studied from both theoretical and computational perspectives. As datasets get more heterogeneous and complicated, so must the spaces that are used to approximate them.
Heather A. Harrington +6 more
openaire +4 more sources
Creep Characterization of Inconel 718 Lattice Metamaterials Manufactured by Laser Powder Bed Fusion
Advanced Engineering Materials, EarlyView., 2023 Herein, the creep characteristics of additively manufactured Inconel 718 metamaterials are investigated. The creep behavior of metamaterials and the effects of microstructural defects are assessed, and the microstructure defects are accurately captured using Kachanov's creep damage model.
Akash Singh Bhuwal +5 more
wiley +1 more source
Toward Practical Crowdsourcing-Based Road Anomaly Detection With Scale-Invariant Feature
IEEE Access, 2019 Road anomaly detection with crowdsourced sensor data has become an increasingly important field of research over the last few years. Traditional ways for road anomaly detection are either threshold-based detection techniques or feature-based detection ...
Yuanyi Chen +3 more
doaj +1 more source
A Survey on Explainable Anomaly Detection
ACM Transactions on Knowledge Discovery from Data, 2023 In the past two decades, most research on anomaly detection has focused on improving the accuracy of the detection, while largely ignoring the explainability of the corresponding methods and thus leaving the explanation of outcomes to practitioners. As anomaly detection algorithms are increasingly used in safety-critical domains, providing explanations
Zhong Li +2 more
openaire +3 more sources
Anomaly detection with inexact labels [PDF]
Machine Learning, 2020 We propose a supervised anomaly detection method for data with inexact anomaly labels, where each label, which is assigned to a set of instances, indicates that at least one instance in the set is anomalous. Although many anomaly detection methods have been proposed, they cannot handle inexact anomaly labels.
Tomoharu Iwata +3 more
openaire +3 more sources
Machine Learning for Anomaly Detection: A Systematic Review
IEEE Access, 2021 Anomaly detection has been used for decades to identify and extract anomalous components from data. Many techniques have been used to detect anomalies. One of the increasingly significant techniques is Machine Learning (ML), which plays an important role
Ali Bou Nassif +3 more
doaj +1 more source
PKU-GoodsAD: A Supermarket Goods Dataset for Unsupervised Anomaly Detection and Segmentation [PDF]
IEEE Robotics and Automation Letters, 2024, 2023 Visual anomaly detection is essential and commonly used for many tasks in the field of computer vision. Recent anomaly detection datasets mainly focus on industrial automated inspection, medical image analysis and video surveillance. In order to broaden the application and research of anomaly detection in unmanned supermarkets and smart manufacturing ...
arxiv +1 more source
AnoDDPM: Anomaly Detection with Denoising Diffusion Probabilistic Models using Simplex Noise
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2022 Generative models have been shown to provide a powerful mechanism for anomaly detection by learning to model healthy or normal reference data which can subsequently be used as a baseline for scoring anomalies. In this work we consider denoising diffusion
Julian Wyatt +3 more
semanticscholar +1 more source
Learning Memory-Guided Normality for Anomaly Detection [PDF]
Computer Vision and Pattern Recognition, 2020 We address the problem of anomaly detection, that is, detecting anomalous events in a video sequence. Anomaly detection methods based on convolutional neural networks (CNNs) typically leverage proxy tasks, such as reconstructing input video frames, to ...
Hyunjong Park, Jongyoun Noh, Bumsub Ham
semanticscholar +1 more source