Semi-supervised learning has achieved notable success by leveraging very few labeled data and exploiting the wealth of information derived from unlabeled data. However, existing algorithms usually focus on aligning predictions on paired data points augmented from an identical source, and overlook the inter-point relationships within each batch. This paper introduces a novel method, RelationMatch, which exploits in-batch relationships with a matrix cross-entropy (MCE) loss function. Through the application of MCE, our proposed method consistently surpasses the performance of established state-of-the-art methods, such as FixMatch and FlexMatch, across a variety of vision datasets. Notably, we observed a substantial enhancement of 15.21% in accuracy over FlexMatch on the STL-10 dataset using only 40 labels. Moreover, we apply MCE to supervised learning scenarios, and observe consistent improvements as well.

翻译：半监督学习通过利用极少量标注数据并充分挖掘未标注数据中的丰富信息，已取得了显著成功。然而，现有算法通常专注于对齐源自同一数据增强所产生的配对数据点预测，却忽视了每个批次内数据点间的相互关系。本文提出了一种名为RelationMatch的新方法，该方法通过矩阵交叉熵（MCE）损失函数来利用批次内关系。通过应用MCE，我们提出的方法在多个视觉数据集上持续超越FixMatch、FlexMatch等现有最优方法。值得注意的是，在仅使用40个标签的STL-10数据集上，我们观察到相较于FlexMatch，准确率提升了15.21%。此外，我们将MCE应用于监督学习场景，同样观察到了持续的性能提升。