Summary of Is Out-of-distribution Detection Learnable?, by Zhen Fang et al.
Is Out-of-Distribution Detection Learnable?
by Zhen Fang, Yixuan Li, Jie Lu, Jiahua Dong, Bo Han, Feng Liu
First submitted to arxiv on: 26 Oct 2022
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 investigates the generalization ability of out-of-distribution (OOD) detection algorithms, which aim to detect unknown classes during training. The authors study the probably approximately correct (PAC) learning theory of OOD detection, proposing an open problem in the field. They find a necessary condition for learnability and prove several impossibility theorems under certain scenarios, showing that not all OOD detection tasks are learnable. However, they also identify conditions where learnability is possible, providing theoretical supports for representative OOD detection works. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well machine learning algorithms can handle new data that’s different from what they were trained on. The researchers explore a special type of learning called out-of-distribution (OOD) detection, which is important because it helps us detect things we didn’t know about before. They figure out some rules for when OOD detection works and when it doesn’t, which can help us create better algorithms that can handle new data. |
Keywords
* Artificial intelligence * Generalization * Machine learning
