Unsupervised domain adaptation methods aim to alleviate performance
degradation caused by domain-shift by learning domain-invariant
representations. Existing deep domain adaptation methods focus on holistic
feature alignment by matching source and target holistic feature distributions,
without considering local features and their multi-mode statistics. We show
that the learned local feature patterns are more generic and transferable and a
further local feature distribution matching enables fine-grained feature
alignment. In this paper, we present a method for learning domain-invariant
local feature patterns and jointly aligning holistic and local feature
statistics. Comparisons to the state-of-the-art unsupervised domain adaptation
methods on two popular benchmark datasets demonstrate the superiority of our
approach and its effectiveness on alleviating negative transfer.

本文介绍了一种方法，它通过学习领域不变的局部特征模式并联合对齐整体和局部特征统计量，从而进一步实现细粒度特征对齐，并在两个流行的基准数据集上将其与现有的无监督领域适应方法进行比较，证明了我们方法的优越性和对减轻负迁移的有效性。