Dynamic Addition of Noise in a Diffusion Model for Anomaly Detection

Justin Tebbe, Jawad Tayyub; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 3940-3949

Diffusion models have found valuable applications in anomaly detection by capturing the nominal data distribution and identifying anomalies via reconstruction. Despite their merits they struggle to localize anomalies of varying scales especially larger anomalies like entire missing components. Addressing this we present a novel framework that enhances the capability of diffusion models by extending the previous introduced implicit conditioning approach [??] in three significant ways. First we incorporate a dynamic step size computation that allows for variable noising steps in the forward process guided by an initial anomaly prediction. Second we demonstrate that denoising an only scaled input without any added noise outperforms conventional denoising process. Third we project images in a latent space to abstract away from fine details that interfere with reconstruction of large missing components. Additionally we propose a fine-tuning mechanism that facilitates the model to effectively grasp the nuances of the target domain. Our method undergoes rigorous evaluation on prominent anomaly detection datasets VisA BTAD and MVTec yielding strong performance. Importantly our framework effectively localizes anomalies regardless of their scale marking a pivotal advancement in diffusion-based anomaly detection.