Unsupervised domain adaptation (UDA) deals with the problem of classifying unlabeled target domain data while labeled data is only available for a different source domain. Unfortunately, commonly used classification methods cannot fulfill this task adequately due to the domain gap between the source and target data. In this paper, we propose a novel uncertainty-aware domain adaptation setup that models uncertainty as a multivariate Gaussian distribution in feature space. We show that our proposed uncertainty measure correlates with other common uncertainty quantifications and relates to smoothing the classifier's decision boundary, therefore improving the generalization capabilities. We evaluate our proposed pipeline on challenging UDA datasets and achieve state-of-the-art results. Code for our method is available at https://gitlab.com/tringwald/cvp.

翻译：未受监督的域适应(UDA)处理无标签目标域数据分类问题,而标签数据只提供给不同的源域。不幸的是,由于源和目标数据之间的领域差距,常用的分类方法无法充分完成这项任务。在本文件中,我们提议建立一个新的不确定性域适应机制,将不确定性模型作为地物空间多变量的Gaussian分布模式。我们表明,我们提议的不确定性措施与其他常见的不确定性量化方法相关联,并且与调和分类者的决定界限有关,从而改进一般化能力。我们评估了我们提议的关于挑战UDA数据集的管道,并取得了最新结果。我们的方法代码可在https://gitlab.com/tringwald/cvp查阅。