  The generalization power of deep-learning models is dependent on
rich-labelled data. This supervision using large-scaled annotated information
is restrictive in most real-world scenarios where data collection and their
annotation involve huge cost. Various domain adaptation techniques exist in
literature that bridge this distribution discrepancy. However, a majority of
these models require the label sets of both the domains to be identical. To
tackle a more practical and challenging scenario, we formulate the problem
statement from a partial domain adaptation perspective, where the source label
set is a super set of the target label set. Driven by the motivation that image
styles are private to each domain, in this work, we develop a method that
identifies outlier classes exclusively from image content information and train
a label classifier exclusively on class-content from source images.
Additionally, elimination of negative transfer of samples from classes private
to the source domain is achieved by transforming the soft class-level weights
into two clusters, 0 (outlier source classes) and 1 (shared classes) by
maximizing the between-cluster variance between them.
