Semi-supervised Learning from Active Noisy Soft Labels for Anomaly Detection

Timo Martens; Lorenzo Perini; Jesse Davis

doi:10.1007/978-3-031-43412-9_13

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Semi-supervised Learning from Active Noisy Soft Labels for Anomaly Detection

Timo Martens Lorenzo Perini Jesse Davis

Year: 2023 Lecture notes in computer science Pages: 219-236 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-031-43412-9_13

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Keywords:

Computer science Anomaly detection Probabilistic logic Exploit Artificial intelligence Context (archaeology) Machine learning Anomaly (physics) Noise (video) Perspective (graphical) Process (computing) Supervised learning Pattern recognition (psychology) Data mining Image (mathematics) Artificial neural network

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Anomaly Detection Techniques and Applications

Physical Sciences → Computer Science → Artificial Intelligence

Data Stream Mining Techniques

Physical Sciences → Computer Science → Artificial Intelligence

Semi-supervised Learning from Active Noisy Soft Labels for Anomaly Detection

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Citation History

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