Natural Synthetic Anomalies for Self-supervised Anomaly Detection and Localization

Schlüter HM, Tan J, Hou B, Kainz B (2022)

Publication Type: Conference contribution

Publication year: 2022

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 13691 LNCS

Pages Range: 474-489

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Tel Aviv IL

ISBN: 9783031198205

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

Abstract

We introduce a simple and intuitive self-supervision task, Natural Synthetic Anomalies (NSA), for training an end-to-end model for anomaly detection and localization using only normal training data. NSA integrates Poisson image editing to seamlessly blend scaled patches of various sizes from separate images. This creates a wide range of synthetic anomalies which are more similar to natural sub-image irregularities than previous data-augmentation strategies for self-supervised anomaly detection. We evaluate the proposed method using natural and medical images. Our experiments with the MVTec AD dataset show that a model trained to localize NSA anomalies generalizes well to detecting real-world a priori unknown types of manufacturing defects. Our method achieves an overall detection AUROC of 97.2 outperforming all previous methods that learn without the use of additional datasets. Code available at https://github.com/hmsch/natural-synthetic-anomalies.

Authors with CRIS profile

Bernhard Kainz Professur für Image Data Exploration and Analysis

Involved external institutions

Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB)

How to cite

APA:

Schlüter, H.M., Tan, J., Hou, B., & Kainz, B. (2022). Natural Synthetic Anomalies for Self-supervised Anomaly Detection and Localization. In Shai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, Tal Hassner (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 474-489). Tel Aviv, IL: Springer Science and Business Media Deutschland GmbH.

MLA:

Schlüter, Hannah M., et al. "Natural Synthetic Anomalies for Self-supervised Anomaly Detection and Localization." Proceedings of the 17th European Conference on Computer Vision, ECCV 2022, Tel Aviv Ed. Shai Avidan, Gabriel Brostow, Moustapha Cissé, Giovanni Maria Farinella, Tal Hassner, Springer Science and Business Media Deutschland GmbH, 2022. 474-489.

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