论文标题
缓解域概括的协变量和条件偏移
Mitigating Both Covariate and Conditional Shift for Domain Generalization
论文作者
论文摘要
域的概括（DG）旨在在几个源域上学习一个模型，希望该模型可以很好地推广到看不见的目标域。域之间的分布移位包含协变量和条件偏移，模型都必须能够处理以获得更好的概括性。在本文中，提出了一种新颖的DG方法来处理通过视觉对齐和不确定性指导信念集合（VAUE）的分布转移。具体而言，对于协变性，视觉对齐模块的设计旨在使图像样式的分布与常见的经验高斯分布相结合，以便可以在视觉空间中消除协变量移位。对于有条件的转变，我们基于主观逻辑和Dempster-Shafer理论采用不确定性引导的信念集成策略。给定测试样品的条件分布是通过源域的动态组合估算的。进行了全面的实验，以证明在四个广泛使用的数据集上，即办公室，VLCS，TerrainCognita和PACS上提出的方法的出色性能。
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.
