  Semi-Supervised Learning (SSL) approaches have been an influential framework
for the usage of unlabeled data when there is not a sufficient amount of
labeled data available over the course of training. SSL methods based on
Convolutional Neural Networks (CNNs) have recently provided successful results
on standard benchmark tasks such as image classification. In this work, we
consider the general setting of SSL problem where the labeled and unlabeled
data come from the same underlying probability distribution. We propose a new
approach that adopts an Optimal Transport (OT) technique serving as a metric of
similarity between discrete empirical probability measures to provide
pseudo-labels for the unlabeled data, which can then be used in conjunction
with the initial labeled data to train the CNN model in an SSL manner. We have
evaluated and compared our proposed method with state-of-the-art SSL algorithms
on standard datasets to demonstrate the superiority and effectiveness of our
SSL algorithm.
