Ad²Mix: Adversarial and Adaptive Mixup for Unsupervised Domain Adaptation

Transformer has recently gained tremendous popularity in unsupervised domain adaptation tasks due to its superior generalization ability. State-of-the-art methods leverage mixup to build an intermediate domain to reduce domain gap. However, such strategy becomes less effective when the domain gap becomes large, as the domain gap between intermediate domain and source domain is not minimized and the constructed intermediate domain is non informative. How to address the adaptation problem when domain gap becomes large is an important research problem in domain adaptation. In this paper, we propose an adversarial and adaptive mixup (Ad<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>mix) framework which gradually aligns the intermediate domain towards source domain to fully unleash the potential of both the transformer architecture and mixup to address the large domain gap problem. Specifically, we formulate a general framework for intermediate domain learning with mixup. We propose adversarial mixup with a specially designed mixup alike adversarial adaptation operation to reduce the domain gap between the intermediate domain and source domain. To construct an informative intermediate domain, unlike existing methods which utilize a Beta distribution to generate mixup coefficients to interpolate source and target data, we adaptively assign mixup coefficient for each target data instance based on their transferability and discriminativity information. Our framework creates a natural curriculum of intermediate domains from near source domain to near target domain for gradual adaptation. Extensive experimental studies and evaluations on three public domain adaptation benchmark datasets and one medical domain adaptation task demonstrate the superiority of our framework.