Calibrated Semi-Supervised Models for Disaster Response based on Training Dynamics

Abstract

Despite advancements in semi-supervised learning (SSL) techniques that can be used when labeled data is limited, many SSL approaches still face challenges related to miscalibration. Calibration is crucial for ensuring the accuracy, reliability, and robustness of uncertainty estimates. In this work, we analyze the calibration performance of various SSL methods in the disaster response domain. Our results show that traditional self-training (ST) and mixup-based SSL methods often suffer from high Expected Calibration Error (ECE) despite achieving competitive F1 scores. In contrast, a newly introduced approach in the disaster domain, AUM-ST-Mixup, significantly improves calibration, achieving the lowest ECE across all settings. This improvement suggests that incorporating uncertainty-aware
selection via Area Under the Margin (AUM) alongside mixup regularization enhances both predictive performance and model confidence alignment. Our findings highlight the importance of calibration-aware SSL methods, paving the way for more trustworthy model predictions in low-resource settings.