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.
@article{arxiv.2301.09602,
title = {Adapting the Hypersphere Loss Function from Anomaly Detection to Anomaly Segmentation},
author = {Joao P. C. Bertoldo and Santiago Velasco-Forero and Jesus Angulo and Etienne Decencière},
journal= {arXiv preprint arXiv:2301.09602},
year = {2023}
}
Comments
Submitted to the 2023 IEEE International Conference on Image Processing (ICIP 2023)