Semi-Supervised Learning (SSL) has become a preferred paradigm in many deep learning tasks, which reduces the need for human labor. Previous studies primarily focus on effectively utilising the labelled and unlabeled data to improve performance. However, we observe that how to select samples for labelling also significantly impacts performance, particularly under extremely low-budget settings. The sample selection task in SSL has been under-explored for a long time. To fill in this gap, we propose a Representative and Diverse Sample Selection approach (RDSS). By adopting a modified Frank-Wolfe algorithm to minimise a novel criterion $\alpha$-Maximum Mean Discrepancy ($\alpha$-MMD), RDSS samples a representative and diverse subset for annotation from the unlabeled data. We demonstrate that minimizing $\alpha$-MMD enhances the generalization ability of low-budget learning. Experimental results show that RDSS consistently improves the performance of several popular SSL frameworks and outperforms the state-of-the-art sample selection approaches used in Active Learning (AL) and Semi-Supervised Active Learning (SSAL), even with constrained annotation budgets.

本研究针对半监督学习（SSL）中样本选择不足的问题，通过提出代表性和多样化样本选择方法（RDSS）填补这一空白。该方法采用改进的Frank-Wolfe算法，最小化新的标准$\alpha$-最大均值差异（$\alpha$-MMD），显著提升了低预算学习的泛化能力。实验结果表明，RDSS有效提高了多种流行SSL框架的性能，并在有限标注预算下优于现有的样本选择方法。

通过代表性和多样化样本选择增强半监督学习