Summary of Scalable Ensemble Diversification For Ood Generalization and Detection, by Alexander Rubinstein et al.
Scalable Ensemble Diversification for OOD Generalization and Detection
by Alexander Rubinstein, Luca Scimeca, Damien Teney, Seong Joon Oh
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 paper explores the concept of training a diverse ensemble of machine learning models for practical applications such as model selection with better out-of-distribution (OOD) generalization and detecting OOD samples via Bayesian principles. The existing approach encourages models to disagree on provided OOD samples, but it is computationally expensive and requires well-separated ID and OOD examples, limiting its scalability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about training a group of different machine learning models together. This can be useful for picking the best model for a job or detecting when new data is unusual. Right now, there’s a way to do this that makes sure the models disagree on weird data points. However, it takes a lot of computer power and needs very clear differences between normal and weird data. |
Keywords
» Artificial intelligence » Generalization » Machine learning
