  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.
