  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.
