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.

翻译：我们考虑了两种概念上不同的方法来评估分类器个体预测的可靠性：鲁棒性量化（RQ）和不确定性量化（UQ）。我们在多个基准数据集上比较了这两种方法，结果表明两者之间没有明显的优劣之分，而是互补的，可以结合形成一种混合方法，其性能优于单独的RQ和UQ。作为我们方法的副产品，对于每个数据集，我们还获得了不确定性和鲁棒性作为不可靠性来源的相对重要性评估。