  Consistency regularization-based methods are prevalent in semi-supervised
learning (SSL) algorithms due to their exceptional performance. However, they
mainly depend on domain-specific data augmentations, which are not usable in
domains where data augmentations are less practicable. On the other hand,
Pseudo-labeling (PL) is a general and domain-agnostic SSL approach that, unlike
consistency regularization-based methods, does not rely on the domain. PL
underperforms due to the erroneous high-confidence predictions from poorly
calibrated models. This paper proposes an uncertainty-aware pseudo-label
selection framework that employs uncertainty sets yielded by the conformal
regularization algorithm to fix the poor calibration neural networks, reducing
noisy training data. The codes of this work are available at:
https://github.com/matinmoezzi/ups conformal classification
