  Understanding unsupervised domain adaptation has been an important task that
has been well explored. However, the wide variety of methods have not analyzed
the role of a classifier's performance in detail. In this paper, we thoroughly
examine the role of a classifier in terms of matching source and target
distributions. We specifically investigate the classifier ability by matching
a) the distribution of features, b) probabilistic uncertainty for samples and
c) certainty activation mappings. Our analysis suggests that using these three
distributions does result in a consistently improved performance on all the
datasets. Our work thus extends present knowledge on the role of the various
distributions obtained from the classifier towards solving unsupervised domain
adaptation.
