Summary of Calibrating Deep Neural Network Using Euclidean Distance, by Wenhao Liang et al.
Calibrating Deep Neural Network using Euclidean Distance
by Wenhao Liang, Chang Dong, Liangwei Zheng, Zhengyang Li, Wei Zhang, Weitong Chen
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
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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 a novel loss function, Focal Calibration Loss (FCL), which combines the benefits of Focal Loss with improved probability calibration. By minimizing the Euclidean norm through a strictly proper loss, FCL penalizes instance-wise calibration error and constrains bounds. The authors demonstrate that FCL achieves state-of-the-art performance in both calibration and accuracy metrics on various models and datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines learn better by making them more confident or accurate when they’re not sure what’s going to happen. Right now, some machine learning methods are great at guessing what people will do, but they don’t always know how likely it is that they’re right. The researchers introduce a new way to make these models more reliable and accurate. They test their method on different machines and data sets and show that it works really well. |
Keywords
» Artificial intelligence » Loss function » Machine learning » Probability
