Summary of Consistency Calibration: Improving Uncertainty Calibration Via Consistency Among Perturbed Neighbors, by Linwei Tao et al.
Consistency Calibration: Improving Uncertainty Calibration via Consistency among Perturbed Neighbors
by Linwei Tao, Haolan Guo, Minjing Dong, Chang Xu
First submitted to arxiv on: 16 Oct 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 The proposed paper introduces a novel approach to model calibration in deep learning applications, particularly crucial in fields like healthcare and autonomous driving. The traditional reliability-based view is challenged by the concept of consistency, inspired by uncertainty estimation literature in large language models. A post-hoc calibration method called Consistency Calibration (CC) is developed, adjusting confidence based on the model’s consistency across perturbed inputs. CC demonstrates state-of-the-art performance on standard datasets like CIFAR-10 and ImageNet, as well as long-tailed datasets like ImageNet-LT. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Calibration in deep learning is important for making accurate decisions. Right now, most models are not very good at predicting how confident they should be about their answers. The proposed method tries to solve this problem by looking at how consistent the model’s predictions are when given slightly different input data. This approach doesn’t need any extra training or labels, just small changes to the original input data. It works well on many datasets and can even help with more challenging datasets where some classes have much fewer examples. |
Keywords
* Artificial intelligence * Deep learning
