Summary of Eero: Early Exit with Reject Option For Efficient Classification with Limited Budget, by Florian Valade (lama) et al.
EERO: Early Exit with Reject Option for Efficient Classification with limited budget
by Florian Valade, Mohamed Hebiri, Paul Gay
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: 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 paper proposes EERO, a new methodology for adaptive computation in machine learning models. It tackles the challenge of managing computational resources by using an Early Exit strategy, which allows for processing paths to be shortened for simpler data instances. The approach translates the early exiting problem into a multiple classifier problem with a reject option, selecting the best exiting head for each instance. Experimental results demonstrate that EERO effectively manages budget allocation and enhances accuracy in overthinking scenarios on Cifar and ImageNet datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EERO is a new way to make machine learning models work better by using less computation when possible. It’s like having a special kind of traffic light that helps models decide when to take shortcuts for easier problems. This approach is important because it can help make AI more efficient and accurate. The authors tested EERO on some big datasets and found that it worked well, especially in situations where models were getting stuck. |
Keywords
* Artificial intelligence * Machine learning
