Unsupervised domain adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain. In this paper, we introduce a novel approach called class-aware optimal transport (OT), which measures the OT distance between a distribution over the source class-conditional distributions and a mixture of source and target data distribution. Our class-aware OT leverages a cost function that determines the matching extent between a given data example and a source class-conditional distribution. By optimizing this cost function, we find the optimal matching between target examples and source class-conditional distributions, effectively addressing the data and label shifts that occur between the two domains. To handle the class-aware OT efficiently, we propose an amortization solution that employs deep neural networks to formulate the transportation probabilities and the cost function. Additionally, we propose minimizing class-aware Higher-order Moment Matching (HMM) to align the corresponding class regions on the source and target domains. The class-aware HMM component offers an economical computational approach for accurately evaluating the HMM distance between the two distributions. Extensive experiments on benchmark datasets demonstrate that our proposed method significantly outperforms existing state-of-the-art baselines.

翻译：无监督域适应（UDA）旨在将知识从有标签的源域迁移至无标签的目标域。本文提出一种名为类别感知最优传输（OT）的新方法，该方法计算源类别条件分布上的分布与源数据与目标数据混合分布之间的OT距离。我们的类别感知OT利用一个代价函数，该函数用于确定给定数据样本与源类别条件分布之间的匹配程度。通过优化该代价函数，我们可找到目标样本与源类别条件分布之间的最优匹配，从而有效解决两域间出现的数据偏移与标签偏移问题。为高效处理类别感知OT，我们提出一种摊销解决方案，利用深度神经网络构建传输概率与代价函数。此外，我们提出最小化类别感知高阶矩匹配（HMM），以对齐源域与目标域上对应的类别区域。该类别感知HMM组件提供了一种经济高效的计算方法，用于准确评估两个分布之间的HMM距离。在基准数据集上的大量实验表明，本文方法显著优于现有最先进的基线方法。