  Domain adaptation (DA) aims to transfer the knowledge of a well-labeled
source domain to facilitate unlabeled target learning. When turning to specific
tasks such as indoor (Wi-Fi) localization, it is essential to learn a
cross-domain regressor to mitigate the domain shift. This paper proposes a
novel method Adversarial Bi-Regressor Network (ABRNet) to seek more effective
cross-domain regression model. Specifically, a discrepant bi-regressor
architecture is developed to maximize the difference of bi-regressor to
discover uncertain target instances far from the source distribution, and then
an adversarial training mechanism is adopted between feature extractor and dual
regressors to produce domain-invariant representations. To further bridge the
large domain gap, a domain-specific augmentation module is designed to
synthesize two source-similar and target-similar intermediate domains to
gradually eliminate the original domain mismatch. The empirical studies on two
cross-domain regressive benchmarks illustrate the power of our method on
solving the domain adaptive regression (DAR) problem.
