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MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection
Computer Vision and Pattern Recognition, 2019The detection of anomalous structures in natural image data is of utmost importance for numerous tasks in the field of computer vision. The development of methods for unsupervised anomaly detection requires data on which to train and evaluate new ...
Paul Bergmann +3 more
semanticscholar +1 more source
PVEL-AD: A Large-Scale Open-World Dataset for Photovoltaic Cell Anomaly Detection
IEEE Transactions on Industrial Informatics, 2023The anomaly detection in photovoltaic (PV) cell electroluminescence (EL) image is of great significance for the vision-based fault diagnosis. Many researchers are committed to solving this problem, but a large-scale open-world dataset is required to ...
Binyi Su, Zhong Zhou, Haiyong Chen
semanticscholar +1 more source
Unsupervised Image Anomaly Detection and Segmentation Based on Pretrained Feature Mapping
IEEE Transactions on Industrial Informatics, 2023Image anomaly detection and segmentation are important for the development of automatic product quality inspection in intelligent manufacturing. Because the normal data can be collected easily and abnormal ones are rarely existent, unsupervised methods ...
Qian Wan, Liang Gao, Xinyu Li, Long Wen
semanticscholar +1 more source
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
Information Processing in Medical Imaging, 2017Obtaining models that capture imaging markers relevant for disease progression and treatment monitoring is challenging. Models are typically based on large amounts of data with annotated examples of known markers aiming at automating detection.
T. Schlegl +4 more
semanticscholar +1 more source
2015
Anomaly detection is the process of finding outliers in the data set. Outliers are the data objects that stand out amongst other objects in the data set and do not conform to the normal behavior in a data set. Anomaly detection is a data mining application that combines multiple data mining tasks like classification, regression, and clustering.
Vijay Kotu, Bala Deshpande
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Anomaly detection is the process of finding outliers in the data set. Outliers are the data objects that stand out amongst other objects in the data set and do not conform to the normal behavior in a data set. Anomaly detection is a data mining application that combines multiple data mining tasks like classification, regression, and clustering.
Vijay Kotu, Bala Deshpande
openaire +2 more sources
PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization
ICPR Workshops, 2020We present a new framework for Patch Distribution Modeling, PaDiM, to concurrently detect and localize anomalies in images in a one-class learning setting.
Thomas Defard +3 more
semanticscholar +1 more source
A Methodological Overview on Anomaly Detection
2013In this Chapter we give an overview of statistical methods for anomaly detection (AD), thereby targeting an audience of practitioners with general knowledge of statistics. We focus on the applicability of the methods by stating and comparing the conditions in which they can be applied and by discussing the parameters that need to be set.
CALLEGARI, CHRISTIAN +9 more
openaire +4 more sources
2006
Publisher Summary This chapter discusses how to detect anomalies in code coverage and anomalies in data accesses. It also demonstrates how to infer invariants from multiple test runs automatically, in order to flag later invariant violations. One of the simplest methods for detecting anomalies operates per the following logic: every failure is caused ...
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Publisher Summary This chapter discusses how to detect anomalies in code coverage and anomalies in data accesses. It also demonstrates how to infer invariants from multiple test runs automatically, in order to flag later invariant violations. One of the simplest methods for detecting anomalies operates per the following logic: every failure is caused ...
openaire +3 more sources
Sparse Coding with Anomaly Detection
Journal of Signal Processing Systems, 2013We consider the problem of simultaneous sparse coding and anomaly detection in a collection of data vectors. The majority of the data vectors are assumed to conform with a sparse representation model, whereas the anomaly is caused by an unknown subset of the data vectors - the outliers - which significantly deviate from this model.
Amir Adler +3 more
openaire +2 more sources
2019
Considerable attention has been given to the vulnerability of machine learning to adversarial samples. This is particularly critical in anomaly detection; uses such as detecting fraud, intrusion, and malware must assume a malicious adversary. We specifically address poisoning attacks, where the adversary injects carefully crafted benign samples into ...
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Considerable attention has been given to the vulnerability of machine learning to adversarial samples. This is particularly critical in anomaly detection; uses such as detecting fraud, intrusion, and malware must assume a malicious adversary. We specifically address poisoning attacks, where the adversary injects carefully crafted benign samples into ...
openaire +2 more sources