Improved Predictive Uncertainty using Corruption-based Calibration

• URL: http://arxiv.org/abs/2106.03762v1
• Date: Mon, 7 Jun 2021 16:27:18 GMT
• Title: Improved Predictive Uncertainty using Corruption-based Calibration
• Authors: Tiago Salvador, Vikram Voleti, Alexander Iannantuono, Adam Oberman
• Abstract summary: We propose a simple post hoc calibration method to estimate the confidence/uncertainty that a model prediction is correct on data. We achieve this by synthesizing surrogate calibration sets by corrupting the calibration set with varying intensities of a known corruption.
• Score: 64.49386167517582
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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