Unsupervised domain adaptation through adversarial enhancement and gradient discrepancy minimization. (January 2023) - Tian, Qing; Yang, Hong; Lu, Zhanghu; Liu, Mengyu
Abstract: Although the research involving Unsupervised Domain Adaptation (UDA) has been greatly emerging, many proposed UDA algorithms assume that the domain discrepancy is directly minimized. However, this is impractical in practice. Some methods reduce domain discrepancy by obtaining domain invariant and domain private features, but the disadvantage of this approach is that the domain invariant features obtained contain domain private attributes. So we propose a novel UDA algorithm that introduces an adversarial enhancement and gradient discrepancy minimization (AEGDM) to promote domain alignment. Specifically, the AEGDM method, on the one hand, restrains domain private attributes through enhanced adversation losses to reduce the difficulty of overall transfer and improve the degree of domain alignment, on the other hand, it uses gradient feedback signals to update model classifiers. Furthermore, this paper considers that using a classifier trained in the source domain to assign pseudo-labels to target samples may not be accurate enough, so, we adopt a self-supervised clustering algorithm to obtain pseudo-labels of target samples. The average classification accuracy of our method has achieved 89.5% on Office-31, 89.9% on ImageCLEF-DA and 68.8% on Office-Home, which have 3.3%, 0.2% and 1.5% improved to the ETD, respectively.