On the Calibration of Image Semi-Supervised Learning Models
Mehrab Mustafy Rahman, Jayanth Mohan, Tiberiu Sosea, Cornelia Caragea
Abstract
Semi-supervised learning (SSL) has demonstrated high performance in image classification tasks by effectively utilizing both labeled and unlabeled data. However, existing SSL methods often suffer from poor calibration, with models yielding overconfident predictions that misrepresent actual prediction likelihoods. Recently, neural networks trained with mixup that linearly interpolates random examples from the training set have shown better calibration in supervised settings. However, calibration of neural models remains under-explored in SSL settings. Although effective in supervised model calibration, random mixup of pseudolabels in SSL presents challenges due to the overconfidence and unreliability of pseudolabels. In this work, we introduce CalibrateMix, a targeted mixup-based approach that aims to improve the calibration of SSL models while maintaining or even improving their classification accuracy. Our method leverages training dynamics of labeled and unlabeled samples to identify
BibTeX
@inproceedings{aaai2026_onthecalibration,
title = {On the Calibration of Image Semi-Supervised Learning Models},
author = {Mehrab Mustafy Rahman and Jayanth Mohan and Tiberiu Sosea and Cornelia Caragea},
booktitle = {AAAI 2026},
year = {2026}
}