  The recent research in semi-supervised learning (SSL) is mostly dominated by
consistency regularization based methods which achieve strong performance.
However, they heavily rely on domain-specific data augmentations, which are not
easy to generate for all data modalities. Pseudo-labeling (PL) is a general SSL
approach that does not have this constraint but performs relatively poorly in
its original formulation. We argue that PL underperforms due to the erroneous
high confidence predictions from poorly calibrated models; these predictions
generate many incorrect pseudo-labels, leading to noisy training. We propose an
uncertainty-aware pseudo-label selection (UPS) framework which improves pseudo
labeling accuracy by drastically reducing the amount of noise encountered in
the training process. Furthermore, UPS generalizes the pseudo-labeling process,
allowing for the creation of negative pseudo-labels; these negative
pseudo-labels can be used for multi-label classification as well as negative
learning to improve the single-label classification. We achieve strong
performance when compared to recent SSL methods on the CIFAR-10 and CIFAR-100
datasets. Also, we demonstrate the versatility of our method on the video
dataset UCF-101 and the multi-label dataset Pascal VOC.
