Results 1 to 10 of about 5,452,269 (307)
ACM Computing Surveys, 2009 The paper presents a revolutionary framework for the modeling, detection, characterization, identification, and machine-learning of anomalous behavior in observed phenomena arising from a large class of unknown and uncertain dynamical systems. An evolved behavior would in general be very difficult to correct unless the specific anomalous event that ...
Varun Chandola, Arindam Banerjee
exaly +4 more sources
Quantitative benchmarking of anomaly detection methods in digital pathology images [PDF]
Machine Learning: HealthAnomaly detection has been widely studied in the context of industrial defect inspection, with numerous methods developed to tackle a range of challenges.
Can Cui +10 more
doaj +2 more sources
Anomaly Detection Using Graph Anomaly Rules
Big Data Mining and AnalyticsAnomaly detection in attribute networks is utilized to discover patterns of individuals or groups that deviate from the majority, and is widely used in areas such as e-commerce and social media.
Bowen Dong +4 more
doaj +2 more sources
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
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
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
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
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
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies [PDF]
IEEE Workshop/Winter Conference on Applications of Computer Vision, 2023 Detecting anomalies in images is an important task, especially in real-time computer vision applications. In this work, we focus on computational efficiency and propose a lightweight feature extractor that processes an image in less than a millisecond on
Kilian Batzner +2 more
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