论文标题
Ca-uda：最佳分配和伪标签改进的班级无监督域的适应
CA-UDA: Class-Aware Unsupervised Domain Adaptation with Optimal Assignment and Pseudo-Label Refinement
论文作者
论文摘要
关于无监督的域适应性（UDA）的最新著作集中于选择良好的伪标签作为目标数据中缺失标签的替代物。但是，由于源和目标域的共享网络通常用于伪标签选择，因此仍然存在恶化伪标记的源域偏差。次优的特征空间源对目标域的比对也可能导致性能不令人满意。在本文中，我们建议CA-UDA通过最佳分配，伪标签的修补策略和阶级感知的域名来提高伪标签和UDA结果的质量。我们使用辅助网络来减轻伪标签细化的源域偏置。我们的直觉是，可以完全利用目标域中的基本语义，以帮助优化从域移位下的源特征推断出的伪标记。此外，我们的最佳分配可以在源到目标域中最佳地对齐功能，并且我们的class感知域对齐方式可以同时缩小域间隙，同时保留分类决策边界。在几个基准数据集上进行的大量实验表明，我们的方法可以在图像分类任务中实现最新性能。
Recent works on unsupervised domain adaptation (UDA) focus on the selection of good pseudo-labels as surrogates for the missing labels in the target data. However, source domain bias that deteriorates the pseudo-labels can still exist since the shared network of the source and target domains are typically used for the pseudo-label selections. The suboptimal feature space source-to-target domain alignment can also result in unsatisfactory performance. In this paper, we propose CA-UDA to improve the quality of the pseudo-labels and UDA results with optimal assignment, a pseudo-label refinement strategy and class-aware domain alignment. We use an auxiliary network to mitigate the source domain bias for pseudo-label refinement. Our intuition is that the underlying semantics in the target domain can be fully exploited to help refine the pseudo-labels that are inferred from the source features under domain shift. Furthermore, our optimal assignment can optimally align features in the source-to-target domains and our class-aware domain alignment can simultaneously close the domain gap while preserving the classification decision boundaries. Extensive experiments on several benchmark datasets show that our method can achieve state-of-the-art performance in the image classification task.
