Summary of On the Learnability Of Out-of-distribution Detection, by Zhen Fang et al.
On the Learnability of Out-of-distribution Detection
by Zhen Fang, Yixuan Li, Feng Liu, Bo Han, Jie Lu
First submitted to arxiv on: 7 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); 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 Supervised learning typically assumes that training and test data come from the same distribution. However, this assumption is often unrealistic, especially when dealing with out-of-distribution (OOD) data. The latter refers to test data coming from classes unknown during training. To improve OOD detection algorithms, researchers need models that generalize well to unseen data. This paper explores the probably approximately correct (PAC) learning theory for OOD detection, focusing on evaluation metrics commonly used in the literature. The authors derive a necessary condition for learnability and prove several impossibility theorems for certain scenarios. Although these findings may seem discouraging, they also highlight conditions under which learnability is possible. The paper provides theoretical support for representative OOD detection works. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research studies how to make sure a computer program can recognize new and unusual data it hasn’t seen before. When training a program, we usually assume that the new data will look similar to what we used to train it. But in real life, this might not be true. The authors of this paper want to figure out why some programs are better than others at recognizing new data that’s very different from what they were trained on. They found some rules that help us understand when a program can learn to recognize these unusual data and when it can’t. This knowledge can help us create better computer programs that work well even with unexpected data. |
Keywords
» Artificial intelligence » Supervised
