Summary of Out-of-distribution Detection with a Single Unconditional Diffusion Model, by Alvin Heng et al.
Out-of-Distribution Detection with a Single Unconditional Diffusion Model
by Alvin Heng, Alexandre H. Thiery, Harold Soh
First submitted to arxiv on: 20 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach to out-of-distribution (OOD) detection using a single diffusion model, dubbed Diffusion Paths (DiffPath). This method leverages a deep generative model originally trained for unconditional generation and applies it to OOD tasks. The authors introduce a new technique to measure the rate-of-change and curvature of the diffusion paths connecting samples to the standard normal. Experimental results demonstrate that DiffPath is competitive with prior work using individual models on various OOD tasks involving different distributions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores ways to detect abnormal samples, also known as out-of-distribution (OOD) detection. Traditionally, this task requires a new model for each dataset. The authors introduce a single model called Diffusion Paths (DiffPath) that can perform OOD detection across many tasks. They use a technique called measuring the rate-of-change and curvature of the diffusion paths connecting samples to the standard normal. This approach is shown to be as good as other methods that require separate models for each task. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Generative model
