Summary of Introducing ‘inside’ Out Of Distribution, by Teddy Lazebnik
Introducing ‘Inside’ Out of Distribution
by Teddy Lazebnik
First submitted to arxiv on: 5 Jul 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 A novel perspective on out-of-distribution (OOD) samples is proposed, dividing them into inside and outside cases. The study examines the inside-outside OOD profiles of datasets and their impact on machine learning (ML) model performance. Results show that different OOD profiles lead to nuanced declines in ML model performance, emphasizing the importance of distinguishing between inside and outside OOD for developing effective counter-OOD methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models need to understand when they’re working outside their usual range. This study looks at how well models perform on new data that’s either very different or only slightly different from what they’ve seen before. By understanding these differences, we can create better ways to handle unexpected data and make sure our models are reliable. |
Keywords
» Artificial intelligence » Machine learning
