比较鲁棒性与不确定性，发现二者互补，融合效果更优。
Robustness and uncertainty: two complementary aspects of the reliability of the predictions of a classifier
- 用鲁棒性与不确定性双视角评估分类器预测可靠性
- 融合方法在多个数据集上表现优于单一方法
- 揭示不同数据集下可靠性短板是不确定性还是鲁棒性
我们研究了评估分类器个体预测可靠性两种概念不同的方法：鲁棒性量化（RQ）和不确定性量化（UQ）。在多个基准数据集上对比两者，发现没有明显优劣，但二者具有互补性，可结合形成混合方法，性能超越单独使用RQ或UQ。作为副产物，我们还获得了各数据集中不确定性与鲁棒性作为不可靠性来源的相对重要性评估。
原文摘要 · Abstract (English)
We consider two conceptually different approaches for assessing the reliability of the individual predictions of a classifier: Robustness Quantification (RQ) and Uncertainty Quantification (UQ). We compare both approaches on a number of benchmark datasets and show that there is no clear winner between the two, but that they are complementary and can be combined to obtain a hybrid approach that outperforms both RQ and UQ. As a byproduct of our approach, for each dataset, we also obtain an assessment of the relative importance of uncertainty and robustness as sources of unreliability.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。