Adversarial Bi-Regressor Network for Domain Adaptive Regression
Adversarial Bi-Regressor Network for Domain Adaptive Regression
Haifeng Xia, Pu Wang, Toshiaki Koike-Akino, Ye Wang, Philip Orlik, Zhengming Ding
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
Main Track. Pages 3608-3614.
https://doi.org/10.24963/ijcai.2022/501
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 inter- mediate 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.
Keywords:
Machine Learning: Representation learning
Machine Learning: Regression
Machine Learning: Unsupervised Learning
Robotics: Localization, Mapping, State Estimatino