对比两种评估分类器预测可靠性的方法，发现鲁棒性量化更优且可与不确定性量化互补。
Robustness Quantification and Uncertainty Quantification: Comparing Two Methods for Assessing the Reliability of Classifier Predictions
- 用鲁棒性量化和不确定性量化比较分类器预测可靠性
- 在多个数据集上，鲁棒性量化表现优于不确定性量化
- 两者结合可进一步提升可靠性评估效果，适合模型可信度研究者
本文探讨了评估分类器个体预测可靠性两种方法：鲁棒性量化（RQ）与不确定性量化（UQ）。通过分析二者概念差异，在多个基准数据集上进行对比实验，结果表明RQ在标准设置及分布偏移场景下均优于UQ。此外，本文还揭示了两者的互补性：联合使用RQ与UQ能获得更可靠的预测评估结果。该研究为模型可靠性评估提供了新视角。
原文摘要 · Abstract (English)
We consider two approaches for assessing the reliability of the individual predictions of a classifier: Robustness Quantification (RQ) and Uncertainty Quantification (UQ). We explain the conceptual differences between the two approaches, compare both approaches on a number of benchmark datasets and show that RQ is capable of outperforming UQ, both in a standard setting and in the presence of distribution shift. Beside showing that RQ can be competitive with UQ, we also demonstrate the complementarity of RQ and UQ by showing that a combination of both approaches can lead to even better reliability assessments.
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