Anomaly detection with diffusion modelsFučka, Matic (Avtor) Skočaj, Danijel (Mentor) Zavrtanik, Vitjan (Komentor) computer visionanomaly detectiondiffusion modelsSurface defect detection is a critical challenge in ensuring product quality, as defects can pose safety risks and diminish product lifespan. Unsupervised anomaly detection is a closely related problem that tries to detect anomalies without any explicit information about them during the training phase. While deep learning has introduced numerous methods, the problem has barely seen any attempts with diffusion models. To address this, we propose Transfusion, a TRANSparent difFUSION model. Our approach focuses on iterative erasure of anomalies. To achieve this we redesigned the base diffusion process and the base architecture for diffusion models. Notably, Transfusion achieves exceptional performance in anomaly detection, surpassing state-of-the-art results on VisA dataset with an impressive 98.5% AUROC and achieving competitive results on MVTec AD with an 99.2% AUROC. This advancement offers promising prospects for reliable and efficient surface defect detection in manufacturing processes.20232023-09-04 07:35:07Magistrsko delo/naloga149117VisID: 35310COBISS_ID: 164268803sl