PSDA用配对采样给UDA降方差，比同类方法稳定，效果很实在。
本文提出一种名为PSDA（Paired Sampling for Domain Adaptation）的方差减少技术，专门用于解决无监督域自适应（UDA）中相关对齐和最大均值差异（MMD）损失的高方差问题。PSDA通过配对域内和跨域观测形成四元组，并在训练中始终一起采样。该方法将配对问题转化为线性分配问题以最小化期望梯度方差。在三个域迁移数据集上的实验表明，PSDA提高了目标域准确率。
Correlation alignment and the maximum mean discrepancy are two widely used distribution-matching frameworks for unsupervised domain adaptation (UDA). However, high variance in these losses has been shown to undermine their effectiveness in minibatch optimisation settings. Furthermore, the losses lack finite-sum structure, which renders them incompatible with classical stochastic variance reduction (SVR) methods. This paper proposes Paired Sampling for Domain Adaptation (PSDA), a novel SVR technique tailored to such objectives. PSDA pairs observations both within and across domains, to form quadruplets that are always sampled together during training. The pairings are designed to minimise expected gradient variance, and reduce to solving a set of linear assignment problems. Our simulations demonstrate reduced variance compared to related methods, and experiments on three domain shift datasets show improved target domain accuracy.