  State-of-the-art semi-supervised learning (SSL) approaches rely on highly
confident predictions to serve as pseudo-labels that guide the training on
unlabeled samples. An inherent drawback of this strategy stems from the quality
of the uncertainty estimates, as pseudo-labels are filtered only based on their
degree of uncertainty, regardless of the correctness of their predictions.
Thus, assessing and enhancing the uncertainty of network predictions is of
paramount importance in the pseudo-labeling process. In this work, we
empirically demonstrate that SSL methods based on pseudo-labels are
significantly miscalibrated, and formally demonstrate the minimization of the
min-entropy, a lower bound of the Shannon entropy, as a potential cause for
miscalibration. To alleviate this issue, we integrate a simple penalty term,
which enforces the logit distances of the predictions on unlabeled samples to
remain low, preventing the network predictions to become overconfident.
Comprehensive experiments on a variety of SSL image classification benchmarks
demonstrate that the proposed solution systematically improves the calibration
performance of relevant SSL models, while also enhancing their discriminative
power, being an appealing addition to tackle SSL tasks.
