  This paper addresses unsupervised domain adaptation, the setting where
labeled training data is available on a source domain, but the goal is to have
good performance on a target domain with only unlabeled data. Like much of
previous work, we seek to align the learned representations of the source and
target domains while preserving discriminability. The way we accomplish
alignment is by learning to perform auxiliary self-supervised task(s) on both
domains simultaneously. Each self-supervised task brings the two domains closer
together along the direction relevant to that task. Training this jointly with
the main task classifier on the source domain is shown to successfully
generalize to the unlabeled target domain. The presented objective is
straightforward to implement and easy to optimize. We achieve state-of-the-art
results on four out of seven standard benchmarks, and competitive results on
segmentation adaptation. We also demonstrate that our method composes well with
another popular pixel-level adaptation method.
