Reconstruction-Free Anomaly Detection with Diffusion Models via Direct Latent Likelihood Evaluation

Abstract

Diffusion models, with their robust distribution approximation capabilities, have demonstrated excellent performance in anomaly detection. However, conventional reconstruction-based approaches rely on computing the reconstruction error between the original and denoised images, which requires careful noise-strength tuning and over ten network evaluations per input-leading to significantly slower detection speeds. To address these limitations, we propose a novel diffusion-based anomaly detection method that circumvents the need for resource-intensive reconstruction. Instead of reconstructing the input image, we directly infer its corresponding latent variables and measure their density under the Gaussian prior distribution. Remarkably, the prior density proves effective as an anomaly score even when using a short partial diffusion process of only 2-5 steps. We evaluate our method on the MVTecAD dataset, achieving an AUC of 0.991 at 15 FPS, thereby setting a new state-of-the-art speed-AUC anomaly detection trade-off.