  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
necessitate a different model when evaluating abnormality against a new
distribution. With the emergence of foundational generative models, this paper
explores whether a single generalist model can also perform OOD detection
across diverse tasks. To that end, we introduce our method, Diffusion Paths,
(DiffPath) in this work. DiffPath proposes to utilize a single diffusion model
originally trained to perform unconditional generation for OOD detection.
Specifically, 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 outperforms prior
work on a variety of OOD tasks involving different distributions. Our code is
publicly available at https://github.com/clear-nus/diffpath.
