Anomaly Detection with Conditioned Denoising Diffusion Models

Understand

Traditional reconstruction-based methods have struggled to achieve competitive performance in anomaly detection.

• In this paper, we introduce Denoising Diffusion Anomaly Detection (DDAD), a novel denoising process for image reconstruction conditioned on a target image.
• This ensures a coherent restoration that closely resembles the target image.
• Our anomaly detection framework employs the conditioning mechanism, where the target image is set as the input image to guide the denoising process, leading to a defectless reconstruction while maintaining nominal patterns.