Anomaly Detection using Diffusion Model without Diffusion

Abstract

Anomaly detection by generative models is achieved by comparing the reconstruction and the original image. However, existing generative models often lead to a blurred reconstruction and the loss of original image features (e.g., the orientation). They are practically problematic in industrial anomaly detection, such as the detail flows being overlooked and the need to align the orientation of target objects. Therefore, the generative models have only achieved inferior anomaly detection performance compared to batch-based models and the models for latent features. This paper proposes the reconstruction without diffusion by a diffusion model. This method reconstructs an image well while preserving original features and outperforms existing methods in the industrial dataset MVTeC AD.