Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibration

• URL: http://arxiv.org/abs/2102.12182v1
• Date: Wed, 24 Feb 2021 10:18:30 GMT
• Title: Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibration
• Authors: Christian Tomani, Daniel Cremers, Florian Buettner
• Abstract summary: We introduce a novel calibration method, Parametrized Temperature Scaling (PTS) We demonstrate that the performance of accuracy-preserving state-of-the-art post-hoc calibrators is limited by their intrinsic expressive power. We show with extensive experiments that our novel accuracy-preserving approach consistently outperforms existing algorithms across a large number of model architectures, datasets and metrics.
• Score: 57.568461777747515
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We address the problem of uncertainty calibration and introduce a novel calibration method, Parametrized Temperature Scaling (PTS). Standard deep neural networks typically yield uncalibrated predictions, which can be transformed into calibrated confidence scores using post-hoc calibration methods. In this contribution, we demonstrate that the performance of accuracy-preserving state-of-the-art post-hoc calibrators is limited by their intrinsic expressive power. We generalize temperature scaling by computing prediction-specific temperatures, parameterized by a neural network. We show with extensive experiments that our novel accuracy-preserving approach consistently outperforms existing algorithms across a large number of model architectures, datasets and metrics.

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