  Unsupervised Domain Adaptation (UDA), a branch of transfer learning where
labels for target samples are unavailable, has been widely researched and
developed in recent years with the help of adversarially trained models.
Although existing UDA algorithms are able to guide neural networks to extract
transferable and discriminative features, classifiers are merely trained under
the supervision of labeled source data. Given the inevitable discrepancy
between source and target domains, the classifiers can hardly be aware of the
target classification boundaries. In this paper, Shuffle Augmentation of
Features (SAF), a novel UDA framework, is proposed to address the problem by
providing the classifier with supervisory signals from target feature
representations. SAF learns from the target samples, adaptively distills
class-aware target features, and implicitly guides the classifier to find
comprehensive class borders. Demonstrated by extensive experiments, the SAF
module can be integrated into any existing adversarial UDA models to achieve
performance improvements.
