Summary of Diffusion Based Semantic Outlier Generation Via Nuisance Awareness For Out-of-distribution Detection, by Suhee Yoon et al.
Diffusion based Semantic Outlier Generation via Nuisance Awareness for Out-of-Distribution Detection
by Suhee Yoon, Sanghyu Yoon, Hankook Lee, Ye Seul Sim, Sungik Choi, Kyungeun Lee, Hye-Seung Cho, Woohyung Lim
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 Medium Difficulty summary: The paper proposes a novel framework called Semantic Outlier generation via Nuisance Awareness (SONA) for out-of-distribution (OOD) detection. Traditional OOD detection methods often struggle to capture subtle distinctions between in-distribution (ID) and OOD samples, resulting in outliers that are too far removed from the ID. SONA generates challenging outliers by directly leveraging pixel-space ID samples through diffusion models, incorporating guidance on semantic and nuisance regions. This approach produces outliers with explicit semantic-discrepant information while maintaining various levels of nuisance resemblance to ID. The improved OOD detector training with SONA outliers enables learning focused on semantic distinctions, leading to better performance. Experimental results demonstrate the effectiveness of the framework, achieving an impressive AUROC of 88% on near-OOD datasets, surpassing baseline methods by approximately 6%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper is about improving how we detect things that are outside the normal range of what we expect to see. Right now, some of these detection methods don’t do a very good job because they’re too focused on big differences rather than small ones. The authors came up with a new way called SONA that creates fake “outliers” that look more like real data and are better at capturing the subtle differences between what’s normal and what’s not. This makes it easier to train a detection model that can tell the difference between normal and abnormal things. They tested their approach on some datasets and found that it worked really well, getting 88% of the “outliers” right. |
Keywords
» Artificial intelligence » Diffusion
