Out-of-Distribution Detection with a Single Unconditional Diffusion Model
<|reference_start|>Out-of-Distribution Detection with a Single Unconditional Diffusion Model: 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.<|reference_end|>
@article{heng2024out-of-distribution,
    title={Out-of-Distribution Detection with a Single Unconditional Diffusion
  Model},
    author={Alvin Heng, Alexandre H. Thiery, Harold Soh},
    journal={arXiv preprint arXiv:2405.11881},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.11881},
    primaryClass={cs.LG cs.AI stat.ML}
}