  Following the success of supervised learning, semi-supervised learning (SSL)
is now becoming increasingly popular. SSL is a family of methods, which in
addition to a labeled training set, also use a sizable collection of unlabeled
data for fitting a model. Most of the recent successful SSL methods are based
on pseudo-labeling approaches: letting confident model predictions act as
training labels. While these methods have shown impressive results on many
benchmark datasets, a drawback of this approach is that not all unlabeled data
are used during training. We propose a new SSL algorithm, DoubleMatch, which
combines the pseudo-labeling technique with a self-supervised loss, enabling
the model to utilize all unlabeled data in the training process. We show that
this method achieves state-of-the-art accuracies on multiple benchmark datasets
while also reducing training times compared to existing SSL methods. Code is
available at https://github.com/walline/doublematch.
