Towards Unbiased Calibration using Meta-Regularization

• URL: http://arxiv.org/abs/2303.15057v3
• Date: Tue, 25 Jun 2024 11:00:05 GMT
• Title: Towards Unbiased Calibration using Meta-Regularization
• Authors: Cheng Wang, Jacek Golebiowski,
• Abstract summary: We propose to learn better-calibrated models via meta-regularization, which has two components. We evaluate the effectiveness of the proposed approach in regularizing neural networks towards improved and unbiased calibration on three computer vision datasets.
• Score: 6.440598446802981
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. We evaluate the effectiveness of the proposed approach in regularizing neural networks towards improved and unbiased calibration on three computer vision datasets. We empirically demonstrate that: (a) learning sample-wise gamma as continuous variables can effectively improve calibration; (b) SECE smoothly optimizes gamma-net towards unbiased and robust calibration with respect to the binning schemes; and (c) the combination of gamma-net and SECE achieves the best calibration performance across various calibration metrics while retaining very competitive predictive performance as compared to multiple recently proposed methods.

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