  Deep semi-supervised learning has been widely implemented in the real-world
due to the rapid development of deep learning. Recently, attention has shifted
to the approaches such as Mean-Teacher to penalize the inconsistency between
two perturbed input sets. Although these methods may achieve positive results,
they ignore the relationship information between data instances. To solve this
problem, we propose a novel method named Metric Learning by Similarity Network
(MLSN), which aims to learn a distance metric adaptively on different domains.
By co-training with the classification network, similarity network can learn
more information about pairwise relationships and performs better on some
empirical tasks than state-of-art methods.
