领域泛化中的协变量和条件转移
Mitigating Both Covariate and Conditional Shift for Domain Generalization
摘要：
领域泛化（DG）的目的是在多个源领域上学习一个模型，希望该模型能够很好地泛化到未知的目标领域。域之间的分布偏移包含协变量偏移和条件偏移，模型必须能够处理这两种偏移以获得更好的泛化能力。提出了一种新的基于视觉对齐和不确定性引导的信念增强（VAUE）的分布式预测方法来处理分布偏移。针对协变量偏移，设计了视觉对齐模块，将图像风格的分布对齐到共同的经验高斯分布，从而消除视觉空间中的协变量偏移。对于条件转移，我们采用了基于主观逻辑和Dempster-Shafer理论的不确定性引导的信念集成策略。给定一个测试样本的条件分布是由源域的条件分布的动态组合来估计的。在四个广泛使用的数据集上进行了全面的实验，以证明所提出的方法的优越性能，即，家庭、VLCS、TerraIncognita和PACS。
英文摘要：
Domain generalization (DG) aims to learn a model on several source domains, hoping that the model can generalize well to unseen target domains. The distribution shift between domains contains the covariate shift and conditional shift, both of which the model must be able to handle for better generalizability. In this paper, a novel DG method is proposed to deal with the distribution shift via Visual Alignment and Uncertainty-guided belief Ensemble (VAUE). Specifically, for the covariate shift, a visual alignment module is designed to align the distribution of image style to a common empirical Gaussian distribution so that the covariate shift can be eliminated in the visual space. For the conditional shift, we adopt an uncertainty-guided belief ensemble strategy based on the subjective logic and Dempster-Shafer theory. The conditional distribution given a test sample is estimated by the dynamic combination of that of source domains. Comprehensive experiments are conducted to demonstrate the superior performance of the proposed method on four widely used datasets, i.e., Office-Home, VLCS, TerraIncognita, and PACS.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
