论文标题
通过不确定的特征对齐方式适应无监督的域
Unsupervised Domain Adaptation by Uncertain Feature Alignment
论文作者
论文摘要
无监督的域适应性（UDA）介绍了从给定源域中使用标记数据到未标记的目标域的模型的适应。在本文中，我们利用模型的固有预测不确定性来完成域的适应任务。不确定性是通过蒙特卡洛辍学术来衡量的，用于我们提出的基于不确定性的过滤和特征对准（UFAL），该滤波和特征对齐（UFAL）结合了不确定的特征损失（UFL）功能（UFL）功能（UFL）功能（UBF）方法，用于欧几里得空间中特征的特征。我们的方法超过了最近提出的架构，并在多个具有挑战性的数据集上实现了最先进的结果。代码可在项目网站上找到。
Unsupervised domain adaptation (UDA) deals with the adaptation of models from a given source domain with labeled data to an unlabeled target domain. In this paper, we utilize the inherent prediction uncertainty of a model to accomplish the domain adaptation task. The uncertainty is measured by Monte-Carlo dropout and used for our proposed Uncertainty-based Filtering and Feature Alignment (UFAL) that combines an Uncertain Feature Loss (UFL) function and an Uncertainty-Based Filtering (UBF) approach for alignment of features in Euclidean space. Our method surpasses recently proposed architectures and achieves state-of-the-art results on multiple challenging datasets. Code is available on the project website.
