Feature Selection for Unsupervised Anomaly Detection and Localization Using Synthetic Defects

Lars Heckler; Lars Heckler; Rebecca König

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Paper

Feature Selection for Unsupervised Anomaly Detection and Localization Using Synthetic Defects

Topics: Deep Learning for Visual Understanding ; Features Extraction; Object Detection and Localization; Segmentation and Grouping

In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3 VISAPP: VISAPP, 154-165, 2024 , Rome, Italy

Authors: Lars Heckler ^{1

;

2} and Rebecca König ¹

Affiliations: ¹ MVTec Software GmbH, Germany ; ² Technical University of Munich, Germany

Keyword(s): Anomaly Detection, Feature Selection, Visual Inspection, Synthetic Defects.

Abstract: Expressive features are crucial for unsupervised visual Anomaly Detection and Localization. State-of-the-art methods like PatchCore or SimpleNet heavily exploit such features from pretrained extractor networks and model their distribution or utilize them for training further parts of the model. However, the layers commonly used for feature extraction might not represent the optimal choice for reaching maximum performance. Thus, we present the first application-specific feature selection strategy for the task of unsupervised Anomaly Detection and Localization that identifies the most suitable layer of a pretrained feature extractor based on the performance on a synthetic validation set. The proposed selection strategy is applicable to any feature extraction-based AD method and may serve as a competitive baseline for future work by not only outperforming single-layer baselines but also features ensembled from multiple layer outputs.

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Paper citation in several formats:

Heckler, L. and König, R. (2024). Feature Selection for Unsupervised Anomaly Detection and Localization Using Synthetic Defects. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-679-8; ISSN 2184-4321, SciTePress, pages 154-165. DOI: 10.5220/0012385500003660

@conference{visapp24,
author={Lars Heckler and Rebecca König},
title={Feature Selection for Unsupervised Anomaly Detection and Localization Using Synthetic Defects},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2024},
pages={154-165},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012385500003660},
isbn={978-989-758-679-8},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Feature Selection for Unsupervised Anomaly Detection and Localization Using Synthetic Defects
SN - 978-989-758-679-8
IS - 2184-4321
AU - Heckler, L.
AU - König, R.
PY - 2024
SP - 154
EP - 165
DO - 10.5220/0012385500003660
PB - SciTePress