2000 character limit reached
Anomaly detection in image or latent space of patch-based auto-encoders for industrial image analysis (2307.02495v1)
Published 4 Jul 2023 in cs.CV, cs.AI, and eess.SP
Abstract: We study several methods for detecting anomalies in color images, constructed on patch-based auto-encoders. Wecompare the performance of three types of methods based, first, on the error between the original image and its reconstruction,second, on the support estimation of the normal image distribution in the latent space, and third, on the error between the originalimage and a restored version of the reconstructed image. These methods are evaluated on the industrial image database MVTecADand compared to two competitive state-of-the-art methods.
- Paul Bergmann et al. : The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection. International Journal of Computer Vision, 129(4):1038–1059, Apr 2021.
- Thomas Defard et al. : PaDiM: A Patch Distribution Modeling Framework for Anomaly Detection and Localization. In Pattern Recognition. ICPR, 2021.
- Nicolas Pinon et al. : One-Class SVM on siamese neural network latent space for Unsupervised Anomaly Detection on brain MRI White Matter Hyperintensities. In MIDL, volume in press, 2023.
- Akcay Samet et al. : Anomalib: A deep learning library for anomaly detection, 2022.
- Aaron van den Oord et al. : Neural Discrete Representation Learning. In Advances in Neural Information Processing Systems, volume 30, 2017.
- Jiawei Yu et al. : FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows. CoRR, 2021.
Sponsor
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.