发表机构
University of Southampton（南安普顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对无监督域适应中分布匹配框架损失方差高且缺乏有限和结构的问题，提出PSDA技术，通过在域内和域间配对观测值形成四元组，最小化预期梯度方差，经实验验证该方法能降低方差并提高目标域准确性。
AI 中文摘要
相关对齐和最大均值差异是无监督域适应（UDA）中广泛使用的两种分布匹配框架。然而，这些损失中的高方差已被证明会削弱它们在小批量优化设置中的有效性。此外，这些损失缺乏有限和结构，这使得它们与经典随机方差减少（SVR）方法不兼容。本文提出了用于域适应的配对采样（PSDA），这是一种针对此类目标量身定制的新型SVR技术。PSDA在域内和域间对观测值进行配对，形成四元组，在训练期间总是一起采样。配对设计用于最小化预期梯度方差，并简化为解决一组线性分配问题。我们的模拟表明与相关方法相比方差降低，并且在三个域转移数据集上的实验显示目标域准确性提高。
英文摘要
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.
