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Joint Use of Skip Connections and Synthetic Corruption for Anomaly Detection with Autoencoders

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Control Charts and Machine Learning for Anomaly Detection in Manufacturing

Part of the book series: Springer Series in Reliability Engineering ((RELIABILITY))

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Abstract

In industrial vision, the anomaly detection problem can be addressed with an autoencoder trained to map an arbitrary image, i.e. with or without any defect, to a clean image, i.e. without any defect. In this approach, anomaly detection relies conventionally on the reconstruction residual or, alternatively, on the reconstruction uncertainty. To improve the sharpness of the reconstruction, we consider an autoencoder architecture with skip connections. In the common scenario where only clean images are available for training, we propose to corrupt them with a synthetic noise model to prevent the convergence of the network towards the identity mapping, and introduce an original Stain noise model for that purpose. We show that this model favors the reconstruction of clean images from arbitrary real-world images, regardless of the actual defects appearance. In addition to demonstrating the relevance of our approach, our validation provides the first consistent assessment of reconstruction-based methods, by comparing their performance over the MVTec AD dataset [1], both for pixel- and image-wise anomaly detection. Our implementation is available at https://github.com/anncollin/AnomalyDetection-Keras.

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Acknowledgments

Part of this work has been funded by the “Pôle Mecatech ADRIC” Walloon Region project, and by the Belgian National Fund for Scientific Research (FRS-FNRS).

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Authors and Affiliations

  1. UCLouvain, Louvain-La-Neuve, Belgium

    Anne-Sophie Collin & Christophe De Vleeschouwer

Authors
  1. Anne-Sophie Collin
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  2. Christophe De Vleeschouwer
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Correspondence to Anne-Sophie Collin .

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  1. ENSAIT& GEMTEX, University of Lille, Lille, France

    Kim Phuc Tran

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Collin, AS., De Vleeschouwer, C. (2022). Joint Use of Skip Connections and Synthetic Corruption for Anomaly Detection with Autoencoders. In: Tran, K.P. (eds) Control Charts and Machine Learning for Anomaly Detection in Manufacturing. Springer Series in Reliability Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-83819-5_8

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  • DOI: https://doi.org/10.1007/978-3-030-83819-5_8

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-83819-5

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