  The problem of fully supervised classification is that it requires a
tremendous amount of annotated data, however, in many datasets a large portion
of data is unlabeled. To alleviate this problem semi-supervised learning (SSL)
leverages the knowledge of the classifier on the labeled domain and
extrapolates it to the unlabeled domain which has a supposedly similar
distribution as annotated data. Recent success on SSL methods crucially hinges
on thresholded pseudo labeling and thereby consistency regularization for the
unlabeled domain. However, the existing methods do not incorporate the
uncertainty of the pseudo labels or unlabeled samples in the training process
which are due to the noisy labels or out of distribution samples owing to
strong augmentations. Inspired by the recent developments in SSL, our goal in
this paper is to propose a novel unsupervised uncertainty-aware objective that
relies on aleatoric and epistemic uncertainty quantification. Complementing the
recent techniques in SSL with the proposed uncertainty-aware loss function our
approach outperforms or is on par with the state-of-the-art over standard SSL
benchmarks while being computationally lightweight. Our results outperform the
state-of-the-art results on complex datasets such as CIFAR-100 and
Mini-ImageNet.
