We propose a simple post hoc calibration method to estimate the confidence/uncertainty that a model prediction is correct on data with covariate shift, as represented by the large-scale corrupted data benchmark [Ovadia et al, 2019]. We achieve this by synthesizing surrogate calibration sets by corrupting the calibration set with varying intensities of a known corruption. Our method demonstrates significant improvements on the benchmark on a wide range of covariate shifts.

翻译：我们提出一个简单的临时后校准方法,以估计一种模型预测对以大规模腐败数据基准[Ovadia等人,2019] 表示的具有共变式变化的数据正确性的信心/不确定性。我们通过以已知腐败程度不同的方式破坏校准系统,将替代校准组合成出来,从而实现这一点。我们的方法显示,在一系列广泛的共变式变化的基准上取得了显著改进。