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.

翻译：半监督学习通过有效利用少量标注数据并充分利用未标注数据中的丰富信息，已取得显著进展。然而，现有算法通常优先对齐通过自训练技术生成的特定类别的图像预测，从而忽略了这些类别内部存在的固有关系。本文提出一种名为OTMatch的新方法，该方法通过采用最优传输损失函数来利用类别间的语义关系。通过使用最优传输，我们提出的方法持续优于现有的最先进方法。值得注意的是，与当前最先进方法FreeMatch相比，我们在准确率上观察到了特定百分比的显著提升。在每类1个标注样本的CIFAR-10、每类4个标注样本的STL-10以及每类100个标注样本的ImageNet上，OTMatch分别实现了比FreeMatch低3.18%、3.46%和1.28%的错误率。这证明了我们的方法在半监督场景中通过利用语义关系来增强学习性能的有效性和优越性。