  Most semi-supervised learning methods over-sample labeled data when
constructing training mini-batches. This paper studies whether this common
practice improves learning and how. We compare it to an alternative setting
where each mini-batch is uniformly sampled from all the training data, labeled
or not, which greatly reduces direct supervision from true labels in typical
low-label regimes. However, this simpler setting can also be seen as more
general and even necessary in multi-task problems where over-sampling labeled
data would become intractable. Our experiments on semi-supervised CIFAR-10
image classification using FixMatch show a performance drop when using the
uniform sampling approach which diminishes when the amount of labeled data or
the training time increases. Further, we analyse the training dynamics to
understand how over-sampling of labeled data compares to uniform sampling. Our
main finding is that over-sampling is especially beneficial early in training
but gets less important in the later stages when more pseudo-labels become
correct. Nevertheless, we also find that keeping some true labels remains
important to avoid the accumulation of confirmation errors from incorrect
pseudo-labels.
