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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Published in | 2022 IEEE International Conference on Image Processing (ICIP) pp. 3041 - 3045 |
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Main Authors | , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
16.10.2022
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Subjects | |
Online Access | Get full text |
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Summary: | 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-dimensional subspace truly spanned by said features. We show that the feature reconstruction error (FRE), which is the ℓ 2 -norm of the difference between the original feature in the high-dimensional space and the pre-image of its low-dimensional reduced embedding, is highly effective for OOD and anomaly detection. To generalize to intermediate features produced at any given layer, we extend the methodology by applying nonlinear kernel-based methods. Experiments using standard image datasets and DNN architectures demonstrate that our method meets or exceeds best-in-class quality performance, but at a fraction of the computational and memory cost required by the state of the art. It can be trained and run very efficiently, even on a traditional CPU. |
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ISSN: | 2381-8549 |
DOI: | 10.1109/ICIP46576.2022.9897694 |