[1] K.Roth et al, “Towards Total Recall in Industrial Anomaly Detection,” CVPR 2022

[2] Jeeho Hyun et al, “ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly Detection,” WACV 2024

[3] Jaehyeok Bae et al, “Pni: industrial anomaly detection using position and neighborhood information.” pages 6373?6383, 2023.

[4] https://www.mvtec.com/company/research/datasets/mvtec-ad

[5] https://paperswithcode.com/sota/anomaly-detection-on-mvtec-ad

[6] Samet Akcay et al, “GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training,” ACCV 2018

[7] Hui Zhang et al, “DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection,” arxiv 2023.

[8] Arian Mousakhan et al, “Anomaly Detection with Conditioned Denoising Diffusion Models,” arxiv 2023.

[9] Marco Rudolph et al, “Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows,” WACV 2021

[10] Denis Gudovskiy et al, “CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows,” WACV 2022

[11] Thomas Defard et al, “PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization,” ICLR 2020