Abstract: Unsupervised Anomaly Localization Using Variational Auto-Encoders

David Zimmerer; Fabian Isensee; Jens Petersen; Simon Köhl; Klaus Maier‐Hein

doi:10.1007/978-3-658-29267-6_43

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Abstract: Unsupervised Anomaly Localization Using Variational Auto-Encoders

David Zimmerer Fabian Isensee Jens Petersen Simon Köhl Klaus Maier‐Hein

Year: 2020 Informatik aktuell Pages: 199-199 Publisher: Springer Nature

DOI: 10.1007/978-3-658-29267-6_43

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Keywords:

Encoder Artificial intelligence Autoencoder Computer science Unsupervised learning Image (mathematics) Anomaly detection Anomaly (physics) Pattern recognition (psychology) Deep learning Computer vision Algorithm Physics

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Anomaly Detection Techniques and Applications

Physical Sciences → Computer Science → Artificial Intelligence

COVID-19 diagnosis using AI

Health Sciences → Medicine → Radiology, Nuclear Medicine and Imaging

AI in cancer detection

Physical Sciences → Computer Science → Artificial Intelligence

Abstract: Unsupervised Anomaly Localization Using Variational Auto-Encoders

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