  Unsupervised domain adaptation (UDA) is to make predictions for unlabeled
data on a target domain, given labeled data on a source domain whose
distribution shifts from the target one. Mainstream UDA methods learn aligned
features between the two domains, such that a classifier trained on the source
features can be readily applied to the target ones. However, such a
transferring strategy has a potential risk of damaging the intrinsic
discrimination of target data. To alleviate this risk, we are motivated by the
assumption of structural domain similarity, and propose to directly uncover the
intrinsic target discrimination via discriminative clustering of target data.
We constrain the clustering solutions using structural source regularization
that hinges on our assumed structural domain similarity. Technically, we use a
flexible framework of deep network based discriminative clustering that
minimizes the KL divergence between predictive label distribution of the
network and an introduced auxiliary one; replacing the auxiliary distribution
with that formed by ground-truth labels of source data implements the
structural source regularization via a simple strategy of joint network
training. We term our proposed method as Structurally Regularized Deep
Clustering (SRDC), where we also enhance target discrimination with clustering
of intermediate network features, and enhance structural regularization with
soft selection of less divergent source examples. Careful ablation studies show
the efficacy of our proposed SRDC. Notably, with no explicit domain alignment,
SRDC outperforms all existing methods on three UDA benchmarks.
