Adapting the Hypersphere Loss Function from Anomaly Detection to Anomaly Segmentation - Archive ouverte HAL Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2023

Adapting the Hypersphere Loss Function from Anomaly Detection to Anomaly Segmentation

Résumé

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.

Dates et versions

hal-04319313 , version 1 (02-12-2023)

Licence

Paternité - Partage selon les Conditions Initiales

Identifiants

Citer

Joao P C Bertoldo, Santiago Velasco-Forero, Jesus Angulo, Etienne Decencière. Adapting the Hypersphere Loss Function from Anomaly Detection to Anomaly Segmentation. 2023. ⟨hal-04319313⟩
15 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More