One effective way to obtain domain-invariant features is adversarial training (Ganin et al., 2016;Li et al., 2017;Zheng et al., 2019).
a domain discriminator is learned by minimizing the classification error of distinguishing the source from the target domains, while a deep classification model learns transferable representations that are indistinguishable by the domain discriminator.