  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). DTE estimates the distribution over diffusion
time for a given input and uses the mode or mean of this distribution as the
anomaly score. We derive an analytical form for this density and leverage a
deep neural network to improve inference efficiency. Through empirical
evaluations on the ADBench benchmark, we demonstrate that all diffusion-based
anomaly detection methods perform competitively for both semi-supervised and
unsupervised settings. Notably, DTE achieves orders of magnitude faster
inference time than DDPM, while outperforming it on this benchmark. These
results establish diffusion-based anomaly detection as a scalable alternative
to traditional methods and recent deep-learning techniques for standard
unsupervised and semi-supervised anomaly detection settings.
