Summary of Recent Advances in Ood Detection: Problems and Approaches, by Shuo Lu et al.
Recent Advances in OOD Detection: Problems and Approaches
by Shuo Lu, Yingsheng Wang, Lijun Sheng, Aihua Zheng, Lingxiao He, Jian Liang
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This survey paper reviews recent advances in out-of-distribution (OOD) detection, a crucial component in building reliable machine learning systems. The authors uniquely categorize OOD detection methods based on whether the training process is controlled or not, with separate discussions on large pre-trained model-based approaches and evaluation scenarios. Applications and future research directions are also explored. The paper proposes a new taxonomy for OOD detection, which can benefit the development of new methods and practical scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Out-of-distribution detection is important because it helps ensure machine learning systems are reliable. This survey looks at recent advancements in this area and groups them into categories based on how they were trained. It also discusses how to evaluate these approaches and where they might be useful. Overall, the goal is to help develop new methods and use OOD detection in more practical scenarios. |
Keywords
* Artificial intelligence * Machine learning
