تقرير
Adapting the Hypersphere Loss Function from Anomaly Detection to Anomaly Segmentation
| العنوان: | Adapting the Hypersphere Loss Function from Anomaly Detection to Anomaly Segmentation |
|---|---|
| المؤلفون: | Bertoldo, Joao P. C., Velasco-Forero, Santiago, Angulo, Jesus, Decencière, Etienne |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | 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. Comment: Submitted to the 2023 IEEE International Conference on Image Processing (ICIP 2023) |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2301.09602 |
| رقم الأكسشن: | edsarx.2301.09602 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |