Summary of Rethinking Confidence Calibration For Failure Prediction, by Fei Zhu et al.
Rethinking Confidence Calibration for Failure Prediction
by Fei Zhu, Zhen Cheng, Xu-Yao Zhang, Cheng-Lin Liu
First submitted to arxiv on: 6 Mar 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 investigates the relationship between confidence calibration methods and their effectiveness in detecting misclassification errors, also known as failure prediction. The authors find that many popular confidence calibration methods are actually harmful for this task, worsening the separation between correct and incorrect samples. Instead, they propose a simple hypothesis that flat minima is beneficial for failure prediction. Through extensive experiments, they verify this hypothesis and further improve performance by combining two flat minima techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers study how to make predictions more reliable. They looked at many ways to fix overconfident AI models and found that most of these methods actually make things worse when trying to identify mistakes. Instead, they suggest that a different approach called “flat minima” is better for spotting errors. They tested their idea and showed it can improve results. |
