Natural Synthetic Anomalies for Self-supervised Anomaly Detection and Localization

Hannah M. Schlüter; Jeremy Tan; Benjamin Hou; Bernhard Kainz

doi:10.1007/978-3-031-19821-2_27

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Natural Synthetic Anomalies for Self-supervised Anomaly Detection and Localization

Hannah M. Schlüter Jeremy Tan Benjamin Hou Bernhard Kainz

Year: 2022 Lecture notes in computer science Pages: 474-489 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-031-19821-2_27

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Keywords:

Computer science Anomaly detection Artificial intelligence Synthetic data Anomaly (physics) A priori and a posteriori Code (set theory) Pattern recognition (psychology) Image (mathematics) Range (aeronautics) Task (project management) Natural (archaeology) Data mining Computer vision

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150

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54.30

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1.00

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Topics

Anomaly Detection Techniques and Applications

Physical Sciences → Computer Science → Artificial Intelligence

COVID-19 diagnosis using AI

Health Sciences → Medicine → Radiology, Nuclear Medicine and Imaging

Domain Adaptation and Few-Shot Learning

Physical Sciences → Computer Science → Artificial Intelligence

Natural Synthetic Anomalies for Self-supervised Anomaly Detection and Localization

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Citation History

Topics

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