论文标题
无监督的域适应性，随着渐进域的增强
Unsupervised Domain Adaptation with Progressive Domain Augmentation
论文作者
论文摘要
域的适应性旨在利用在不同标签范围目标域中学习分类器的富标签源域。当两个域之间存在显着差异时，这尤其具有挑战性。在本文中，我们提出了一种基于渐进域增强的新型无监督域适应方法。所提出的方法通过域插值生成虚拟的中间域，逐步增加源域，并通过在格拉曼（Grassmann）歧管上进行多个子空间对准来桥接源目标域的差异。我们对多个领域适应任务进行实验，结果表明所提出的方法实现了最先进的性能。
Domain adaptation aims to exploit a label-rich source domain for learning classifiers in a different label-scarce target domain. It is particularly challenging when there are significant divergences between the two domains. In the paper, we propose a novel unsupervised domain adaptation method based on progressive domain augmentation. The proposed method generates virtual intermediate domains via domain interpolation, progressively augments the source domain and bridges the source-target domain divergence by conducting multiple subspace alignment on the Grassmann manifold. We conduct experiments on multiple domain adaptation tasks and the results shows the proposed method achieves the state-of-the-art performance.
