  Unsupervised domain adaptation (UDA) involves a supervised loss in a labeled
source domain and an unsupervised loss in an unlabeled target domain, which
often faces more severe overfitting (than classical supervised learning) as the
supervised source loss has clear domain gap and the unsupervised target loss is
often noisy due to the lack of annotations. This paper presents RDA, a robust
domain adaptation technique that introduces adversarial attacking to mitigate
overfitting in UDA. We achieve robust domain adaptation by a novel Fourier
adversarial attacking (FAA) method that allows large magnitude of perturbation
noises but has minimal modification of image semantics, the former is critical
to the effectiveness of its generated adversarial samples due to the existence
of 'domain gaps'. Specifically, FAA decomposes images into multiple frequency
components (FCs) and generates adversarial samples by just perturbating certain
FCs that capture little semantic information. With FAA-generated samples, the
training can continue the 'random walk' and drift into an area with a flat loss
landscape, leading to more robust domain adaptation. Extensive experiments over
multiple domain adaptation tasks show that RDA can work with different computer
vision tasks with superior performance.
