Subspace Modeling for Fast Out-Of-Distribution and Anomaly Detection
This paper presents a fast, principled approach for detecting anomalous and out-of-distribution (OOD) samples in deep neural networks (DNN). We propose the application of linear statistical dimensionality reduction techniques on the semantic features produced by a DNN, in order to capture the low-di...
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Main Authors | , , |
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Format | Journal Article |
Language | English |
Published |
19.03.2022
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Subjects | |
Online Access | Get full text |
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