@inproceedings{
heng2024outofdistribution,
title={Out-of-Distribution Detection with a Single Unconditional Diffusion Model},
author={Alvin Heng and Alexandre H. Thiery and Harold Soh},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=tTnFH7D1h4}
}
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.
Out-of-Distribution Detection with a Single Unconditional Diffusion Model
Alvin Heng Alexandre H. Thiery Harold Soh