  Neural networks have been successfully used as classification models yielding
state-of-the-art results when trained on a large number of labeled samples.
These models, however, are more difficult to train successfully for
semi-supervised problems where small amounts of labeled instances are available
along with a large number of unlabeled instances. This work explores a new
training method for semi-supervised learning that is based on similarity
function learning using a Siamese network to obtain a suitable embedding. The
learned representations are discriminative in Euclidean space, and hence can be
used for labeling unlabeled instances using a nearest-neighbor classifier.
Confident predictions of unlabeled instances are used as true labels for
retraining the Siamese network on the expanded training set. This process is
applied iteratively. We perform an empirical study of this iterative
self-training algorithm. For improving unlabeled predictions, local learning
with global consistency [22] is also evaluated.
