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.

本文提出了一种名为 Adversarial Bi-Regressor Network (ABRNet) 的方法，旨在通过采用对抗训练机制和领域特定的数据增强模块，帮助解决领域自适应回归（DAR）问题，实现在室内（Wi-Fi）定位等特定任务中学习跨领域回归模型的目的。