Out-of-Distribution Detection with a Single Unconditional Diffusion Model

Alvin Heng, Alexandre H. Thiery, Harold Soh

Keywords: out-of-distribution detection, anomaly detection, diffusion model

TL;DR: We propose to perform unsupervised out-of-distribution detection using a single unconditional diffusion model by characterizing properties of the diffusion path.

Abstract: Out-of-distribution (OOD) detection is a critical task in machine learning that seeks to identify abnormal samples. Traditionally, unsupervised methods utilize a deep generative model for OOD detection. However, such approaches require a new model to be trained for each inlier dataset. This paper explores whether a single model can perform OOD detection across diverse tasks. To that end, we introduce Diffusion Paths (DiffPath), which uses a single diffusion model originally trained to perform unconditional generation for OOD detection. We introduce a novel technique of measuring the rate-of-change and curvature of the diffusion paths connecting samples to the standard normal. Extensive experiments show that with a single model, DiffPath is competitive with prior work using individual models on a variety of OOD tasks involving different distributions. Our code is publicly available at https://github.com/clear-nus/diffpath.

Primary Area: Diffusion based models

Submission Number: 3230