  Unsupervised domain adaptation (UDA) aims to solve the problem of knowledge
transfer from labeled source domain to unlabeled target domain. Recently, many
domain adaptation (DA) methods use centroid to align the local distribution of
different domains, that is, to align different classes. This improves the
effect of domain adaptation, but domain differences exist not only between
classes, but also between samples. This work rethinks what is the alignment
between different domains, and studies how to achieve the real alignment
between different domains. Previous DA methods only considered one distribution
feature of aligned samples, such as full distribution or local distribution. In
addition to aligning the global distribution, the real domain adaptation should
also align the meso distribution and the micro distribution. Therefore, this
study propose a double classifier method based on high confidence label (DCP).
By aligning the centroid and the distribution between centroid and sample of
different classifiers, the meso and micro distribution alignment of different
domains is realized. In addition, in order to reduce the chain error caused by
error marking, This study propose a high confidence marking method to reduce
the marking error. To verify its versatility, this study evaluates DCP on
digital recognition and target recognition data sets. The results show that our
method achieves state-of-the-art results on most of the current domain
adaptation benchmark datasets.
