  Semi-supervised learning has made remarkable strides by effectively utilizing
a limited amount of labeled data while capitalizing on the abundant information
present in unlabeled data. However, current algorithms often prioritize
aligning image predictions with specific classes generated through
self-training techniques, thereby neglecting the inherent relationships that
exist within these classes. In this paper, we present a new approach called
OTMatch, which leverages semantic relationships among classes by employing an
optimal transport loss function to match distributions. We conduct experiments
on many standard vision and language datasets. The empirical results show
improvements in our method above baseline, this demonstrates the effectiveness
and superiority of our approach in harnessing semantic relationships to enhance
learning performance in a semi-supervised setting.
