Adapting the Hypersphere Loss Function from Anomaly Detection to Anomaly Segmentation

• URL: http://arxiv.org/abs/2301.09602v2
• Date: Sat, 2 Dec 2023 14:10:36 GMT
• Title: Adapting the Hypersphere Loss Function from Anomaly Detection to Anomaly Segmentation
• Authors: Joao P. C. Bertoldo, Santiago Velasco-Forero, Jesus Angulo, Etienne Decenci\`ere
• Abstract summary: We propose an incremental improvement to Fully Convolutional Data Description (FCDD) FCDD is an adaptation of the one-class classification approach from anomaly detection to image anomaly segmentation (a.k.a. anomaly localization) We analyze its original loss function and propose a substitute that better resembles its predecessor, the Hypersphere (HSC)
• Score: 1.9458156037869137
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We propose an incremental improvement to Fully Convolutional Data Description (FCDD), an adaptation of the one-class classification approach from anomaly detection to image anomaly segmentation (a.k.a. anomaly localization). We analyze its original loss function and propose a substitute that better resembles its predecessor, the Hypersphere Classifier (HSC). Both are compared on the MVTec Anomaly Detection Dataset (MVTec-AD) -- training images are flawless objects/textures and the goal is to segment unseen defects -- showing that consistent improvement is achieved by better designing the pixel-wise supervision.

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