Results 11 to 20 of about 4,738,087 (364)
Multi-Perspective Anomaly Detection [PDF]
Sensors, 2021 Anomaly detection is a critical problem in the manufacturing industry. In many applications, images of objects to be analyzed are captured from multiple perspectives which can be exploited to improve the robustness of anomaly detection.
Peter Jakob +3 more
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A Survey of AI-Based Anomaly Detection in IoT and Sensor Networks
Sensors, 2023 Machine learning (ML) and deep learning (DL), in particular, are common tools for anomaly detection (AD). With the rapid increase in the number of Internet-connected devices, the growing desire for Internet of Things (IoT) devices in the home, on our ...
Kyle DeMedeiros +2 more
doaj +2 more sources
Enhancing Time Series Anomaly Detection: A Knowledge Distillation Approach with Image Transformation [PDF]
SensorsAnomaly detection is critical in safety-sensitive fields, but faces challenges from scarce abnormal data and costly expert labeling. Time series anomaly detection is relatively challenging due to its reliance on sequential data, which imposes high ...
Haiwoong Park, Hyeryung Jang
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CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting [PDF]
Scientific DataAnomaly detection in network traffic is crucial for maintaining the security of computer networks and identifying malicious activities. Most approaches to anomaly detection use methods based on forecasting.
Josef Koumar +3 more
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SimpleNet: A Simple Network for Image Anomaly Detection and Localization [PDF]
Computer Vision and Pattern Recognition, 2023 We propose a simple and application-friendly network (called SimpleNet) for detecting and localizing anoma-lies. SimpleNet consists of four components: (1) a pre-trained Feature Extractor that generates local features, (2) a shallow Feature Adapter that ...
Zhikang Liu +3 more
semanticscholar +1 more source
Towards Total Recall in Industrial Anomaly Detection [PDF]
Computer Vision and Pattern Recognition, 2021 Being able to spot defective parts is a critical component in large-scale industrial manufacturing. A particular challenge that we address in this work is the cold-start problem: fit a model using nominal (non-defective) example images only.
Karsten Roth +5 more
semanticscholar +1 more source
Anomaly Detection via Reverse Distillation from One-Class Embedding [PDF]
Computer Vision and Pattern Recognition, 2022 Knowledge distillation (KD) achieves promising results on the challenging problem of unsupervised anomaly detection (AD). The representation discrepancy of anomalies in the teacher-student (T-S) model provides essential evidence for AD.
Hanqiu Deng, Xingyu Li
semanticscholar +1 more source
SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation [PDF]
European Conference on Computer Vision, 2022 Visual anomaly detection is commonly used in industrial quality inspection. In this paper, we present a new dataset as well as a new self-supervised learning method for ImageNet pre-training to improve anomaly detection and segmentation in 1-class and 2 ...
Yang Zou +4 more
semanticscholar +1 more source
ADBench: Anomaly Detection Benchmark [PDF]
Neural Information Processing Systems, 2022 Given a long list of anomaly detection algorithms developed in the last few decades, how do they perform with regard to (i) varying levels of supervision, (ii) different types of anomalies, and (iii) noisy and corrupted data?
Songqiao Han +4 more
semanticscholar +1 more source
Research Progress of Video Anomaly Detection Technology [PDF]
Jisuanji kexue yu tansuo, 2022 Video anomaly detection refers to the detection and identification of events that deviate from normal behavior, which has a wide range of applications in surveillance video.
WU Kaijun, HUANG Tao, WANG Dicong, BAI Chenshuai, TAO Xiaomiao
doaj +1 more source