Summary of Revisiting Confidence Estimation: Towards Reliable Failure Prediction, by Fei Zhu et al.
Revisiting Confidence Estimation: Towards Reliable Failure Prediction
by Fei Zhu, Xu-Yao Zhang, Zhen Cheng, Cheng-Lin Liu
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 A recent study reveals that popular confidence estimation methods in deep neural networks are actually counterproductive, leading to worse separation between correctly classified and misclassified examples. This phenomenon hampers reliable confidence estimation, a crucial requirement in many risk-sensitive applications. The authors investigate this issue and propose an approach to enlarge the confidence gap by finding flat minima, achieving state-of-the-art performance in various classification scenarios. This paper not only provides a strong baseline for reliable confidence estimation but also bridges understanding of calibration, OOD detection, and failure prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep neural networks are often overconfident when they make mistakes. This makes it hard to know if we can trust their predictions. A new study shows that many methods used to improve this problem actually make things worse. Instead of helping us understand how confident our models are, these methods often confuse us even more. The researchers in this paper found a way to fix this by making the model’s confidence gap bigger. This helps us detect when our models are wrong and makes it easier to use them in real-world applications. |
Keywords
* Artificial intelligence * Classification
