Towards Unbiased Calibration using Meta-Regularization

Understand

Model miscalibration has been frequently identified in modern deep neural networks.

• Recent work aims to improve model calibration directly through a differentiable calibration proxy.
• However, the calibration produced is often biased due to the binning mechanism.
• In this work, we propose to learn better-calibrated models via meta-regularization, which has two components: (1) gamma network (gamma-net), a meta learner that outputs sample-wise gamma values (continuous variable) for Focal loss for regularizing the backbone network; (2) smooth expected calibration error (SECE), a Gaussian-kernel based, unbiased, and differentiable surrogate to ECE that enables the smooth optimization of gamma-Net.