Out-of-Distribution Detection with a Single Unconditional Diffusion Model
- URL: http://arxiv.org/abs/2405.11881v1
- Date: Mon, 20 May 2024 08:54:03 GMT
- Title: Out-of-Distribution Detection with a Single Unconditional Diffusion Model
- Authors: Alvin Heng, Alexandre H. Thiery, Harold Soh,
- Abstract summary: 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.
This paper explores whether a single generalist model can also perform OOD detection across diverse tasks.
- Score: 54.15132801131365
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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