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In unsupervised domain adaptation, rich domain-specific characteristics bringgreat challenge to learn domain-invariant representations. However, domaindiscrepancy is considered to be directly minimized in existing solutions, whichis difficult to achieve in practice. Some methods alleviate the difficulty byexplicitly modeling domain-invariant and domain-specific parts in therepresentations, but the adverse influence of the explicit construction lies inthe residual domain-specific characteristics in the constructeddomain-invariant representations. In this paper, we equip adversarial domainadaptation with Gradually Vanishing Bridge (GVB) mechanism on both generatorand discriminator. On the generator, GVB could not only reduce the overalltransfer difficulty, but also reduce the influence of the residualdomain-specific characteristics in domain-invariant representations. On thediscriminator, GVB contributes to enhance the discriminating ability, andbalance the adversarial training process. Experiments on three challengingdatasets show that our GVB methods outperform strong competitors, and cooperatewell with other adversarial methods. The code is available athttps://github.com/cuishuhao/GVB.

Gradually Vanishing Bridge for Adversarial Domain Adaptation