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Soft Calibration Objectives for Neural Networks

Archit Karandikar
Nick Cain
Dustin Tran
Jon Shlens
Michael C. Mozer
Becca Roelofs
Advances in Neural Information Processing Systems (NeurIPS) (2021)

Abstract

Optimal decision making requires that classifiers produce uncertainty estimates consistent with their empirical accuracy. However, deep neural networks are often under- or over-confident in their predictions. Consequently, methods have been developed to improve the calibration of their predictive uncertainty, both during training and post-hoc. In this work, we propose differentiable losses to improve calibration based on a soft (continuous) version of the binning operation underlying popular calibration-error estimators. When incorporated into training, these soft calibration losses achieve state-of-the-art single-model ECE across multiple datasets with less than 1% decrease in accuracy. For instance, we observe an 82% reduction in ECE (70% relative to the post-hoc rescaled ECE) in exchange for a 0.7% relative decrease in accuracy relative to the cross-entropy baseline on CIFAR-100. When incorporated post-training, the soft-binning-based calibration error objective improves upon temperature scaling, a popular recalibration method. Overall, experiments across losses and datasets demonstrate that using calibration- sensitive procedures yield better uncertainty estimates under dataset shift than the standard practice of using a cross-entropy loss and post-hoc recalibration methods.

Research Areas