  Expanding visual categorization into a novel domain without the need of extra
annotation has been a long-term interest for multimedia intelligence.
Previously, this challenge has been approached by unsupervised domain
adaptation (UDA). Given labeled data from a source domain and unlabeled data
from a target domain, UDA seeks for a deep representation that is both
discriminative and domain-invariant. While UDA focuses on the target domain, we
argue that the performance on both source and target domains matters, as in
practice which domain a test example comes from is unknown. In this paper we
extend UDA by proposing a new task called unsupervised domain expansion (UDE),
which aims to adapt a deep model for the target domain with its unlabeled data,
meanwhile maintaining the model's performance on the source domain. We propose
Knowledge Distillation Domain Expansion (KDDE) as a general method for the UDE
task. Its domain-adaptation module can be instantiated with any existing model.
We develop a knowledge distillation based learning mechanism, enabling KDDE to
optimize a single objective wherein the source and target domains are equally
treated. Extensive experiments on two major benchmarks, i.e., Office-Home and
DomainNet, show that KDDE compares favorably against four competitive
baselines, i.e., DDC, DANN, DAAN, and CDAN, for both UDA and UDE tasks. Our
study also reveals that the current UDA models improve their performance on the
target domain at the cost of noticeable performance loss on the source domain.
