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 this https URL

本文介绍了一种名为 Diffusion Paths（DiffPath）的方法，通过利用一个经过训练的单一扩散模型对无条件生成进行 ODD 检测，并引入了一种测量扩散路径变化率和曲率的新技术。实验表明，DiffPath 在涉及不同分布的各种 ODD 任务中，单一模型的性能优于之前的方法。