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.

无监督领域自适应方法中引入了一种新的方法 - 称为类感知最优输运（OT），用于测量来自标记源领域的分布与源和目标数据分布的混合之间的 OT 距离。我们的类感知 OT 利用了一个成本函数，该函数确定了给定数据示例与源类条件分布之间的匹配程度，通过优化这个成本函数，我们可以找到目标示例与源类条件分布之间的最佳匹配，从而有效地解决了两个领域之间发生的数据和标签偏移问题。为了高效处理类感知 OT 问题，我们提出了一种使用深度神经网络来制定输运概率和成本函数的摊薄解决方案。此外，我们还提出了最小化类感知高阶矩匹配（HMM）来对齐源领域和目标领域上相应的类区域的经济计算方法。在基准数据集上进行的大量实验证明了我们提出的方法明显优于现有的最先进基准线。