On Diffusion Modeling for Anomaly Detection

Understand

Known for their impressive performance in generative modeling, diffusion models are attractive candidates for density-based anomaly detection.

• This paper investigates different variations of diffusion modeling for unsupervised and semi-supervised anomaly detection.
• In particular, we find that Denoising Diffusion Probability Models (DDPM) are performant on anomaly detection benchmarks yet computationally expensive.
• By simplifying DDPM in application to anomaly detection, we are naturally led to an alternative approach called Diffusion Time Estimation (DTE).