  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. By utilizing optimal transport, our proposed
method consistently outperforms established state-of-the-art methods. Notably,
we observed a substantial improvement of a certain percentage in accuracy
compared to the current state-of-the-art method, FreeMatch. OTMatch achieves
3.18%, 3.46%, and 1.28% error rate reduction over FreeMatch on CIFAR-10 with 1
label per class, STL-10 with 4 labels per class, and ImageNet with 100 labels
per class, respectively. This demonstrates the effectiveness and superiority of
our approach in harnessing semantic relationships to enhance learning
performance in a semi-supervised setting.

Computer Vision and Pattern Recognition