Out-of-Distribution Detection with a Single Unconditional Diffusion Model, Alvin Heng★, Alexandre H. Thiery, Harold Soh★, Neural Information Processing Systems (NeurIPS), 2024
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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.
Resources
You can find our paper here. Check out our repository here on github
Citation
Please consider citing our paper if you build upon our results and ideas.
Alvin Heng★, Alexandre H. Thiery, Harold Soh★, “Out-of-Distribution Detection with a Single Unconditional Diffusion Model”, Neural Information Processing Systems (NeurIPS), 2024
@article{heng2024out,
title={Out-of-Distribution Detection with a Single Unconditional Diffusion Model},
author={Heng, Alvin and Thiery, Alexandre H and Soh, Harold},
journal={arXiv preprint arXiv:2405.11881},
year={2024} }
Contact
If you have questions or comments, please contact Alvin or Harold.
Acknowledgements
This research/project is supported by the National Research Foundation Singapore and DSO National Laboratories under the AI Singapore Programme (AISG Award No: AISG2-RP-2020-017).
