Summary of Towards Robust Out-of-distribution Generalization: Data Augmentation and Neural Architecture Search Approaches, by Haoyue Bai
Towards Robust Out-of-Distribution Generalization: Data Augmentation and Neural Architecture Search Approaches
by Haoyue Bai
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 research explores strategies for deep learning models to perform well even when presented with out-of-distribution (OoD) data. While deep learning has shown impressive results, it often struggles when training and testing data come from different distributions. The goal is to develop methods that allow deep learning models to generalize robustly across distribution shifts in the test data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps us understand how to make AI models work better even when they see new kinds of data they weren’t trained on. Right now, these models often do poorly when faced with unfamiliar data. The goal is to find ways to train these models so they can handle this kind of situation and still perform well. |
Keywords
* Artificial intelligence * Deep learning
