Adapting the Hypersphere Loss Function from Anomaly Detection to Anomaly Segmentation

01/23/2023
by   João P C Bertoldo, et al.
0

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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset