  Unsupervised domain adaptation~(UDA) aims at reducing the distribution
discrepancy when transferring knowledge from a labeled source domain to an
unlabeled target domain. Previous UDA methods assume that the source and target
domains share an identical label space, which is unrealistic in practice since
the label information of the target domain is agnostic. This paper focuses on a
more realistic UDA scenario, i.e. partial domain adaptation (PDA), where the
target label space is subsumed to the source label space. In the PDA scenario,
the source outliers that are absent in the target domain may be wrongly matched
to the target domain (technically named negative transfer), leading to
performance degradation of UDA methods. This paper proposes a novel Target
Domain Specific Classifier Learning-based Domain Adaptation (TSCDA) method.
TSCDA presents a soft-weighed maximum mean discrepancy criterion to partially
align feature distributions and alleviate negative transfer. Also, it learns a
target-specific classifier for the target domain with pseudo-labels and
multiple auxiliary classifiers, to further address classifier shift. A module
named Peers Assisted Learning is used to minimize the prediction difference
between multiple target-specific classifiers, which makes the classifiers more
discriminant for the target domain. Extensive experiments conducted on three
PDA benchmark datasets show that TSCDA outperforms other state-of-the-art
methods with a large margin, e.g. $4\%$ and $5.6\%$ averagely on Office-31 and
Office-Home, respectively.
