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.

本文介绍了一种新的方法 OTMatch，通过使用最优输运损失函数，利用类别之间的语义关系，提高半监督学习的性能。与现有最先进的方法 FreeMatch 相比，在 CIFAR-10、STL-10 和 ImageNet 数据集上分别实现了 3.18%、3.46% 和 1.28% 的错误率降低，证明了我们方法在半监督学习中利用语义关系提升学习性能的有效性和优越性。