  Semi-Supervised Learning (SSL) aims to learn a model using a tiny labeled set
and massive amounts of unlabeled data. To better exploit the unlabeled data the
latest SSL methods use pseudo-labels predicted from a single discriminative
classifier. However, the generated pseudo-labels are inevitably linked to
inherent confirmation bias and noise which greatly affects the model
performance. In this work we introduce a new framework for SSL named NorMatch.
Firstly, we introduce a new uncertainty estimation scheme based on normalizing
flows, as an auxiliary classifier, to enforce highly certain pseudo-labels
yielding a boost of the discriminative classifiers. Secondly, we introduce a
threshold-free sample weighting strategy to exploit better both high and low
confidence pseudo-labels. Furthermore, we utilize normalizing flows to model,
in an unsupervised fashion, the distribution of unlabeled data. This modelling
assumption can further improve the performance of generative classifiers via
unlabeled data, and thus, implicitly contributing to training a better
discriminative classifier. We demonstrate, through numerical and visual
results, that NorMatch achieves state-of-the-art performance on several
datasets.
