Semi-supervised learning is a machine learning paradigm that utilizes a training dataset comprising both a limited quantity of labeled examples and a substantial quantity of unlabeled examples. This approach seeks to leverage the structural information present in the unlabeled data to improve the performance and generalization capability of models, addressing scenarios where obtaining sufficient labeled data is prohibitively expensive or difficult compared to acquiring unlabeled data.