  Unsupervised domain adaptation (UDA) aims to address the domain-shift problem
between a labeled source domain and an unlabeled target domain. Many efforts
have been made to address the mismatch between the distributions of training
and testing data, but unfortunately, they ignore the task-oriented information
across domains and are inflexible to perform well in complicated open-set
scenarios. Many efforts have been made to eliminate the mismatch between the
distributions of training and testing data by learning domain-invariant
representations. However, the learned representations are usually not
task-oriented, i.e., being class-discriminative and domain-transferable
simultaneously. This drawback limits the flexibility of UDA in complicated
open-set tasks where no labels are shared between domains. In this paper, we
break the concept of task-orientation into task-relevance and task-irrelevance,
and propose a dynamic task-oriented disentangling network (DTDN) to learn
disentangled representations in an end-to-end fashion for UDA. The dynamic
disentangling network effectively disentangles data representations into two
components: the task-relevant ones embedding critical information associated
with the task across domains, and the task-irrelevant ones with the remaining
non-transferable or disturbing information. These two components are
regularized by a group of task-specific objective functions across domains.
Such regularization explicitly encourages disentangling and avoids the use of
generative models or decoders. Experiments in complicated, open-set scenarios
(retrieval tasks) and empirical benchmarks (classification tasks) demonstrate
that the proposed method captures rich disentangled information and achieves
superior performance.
