  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.
