Shuffle Augmentation of Features from Unlabeled Data for Unsupervised Domain Adaptation
This addresses the challenge of improving classifier awareness in unsupervised domain adaptation for machine learning applications, though it appears incremental as it builds on existing adversarial UDA models.
The paper tackles the problem of classifiers in unsupervised domain adaptation being unaware of target classification boundaries due to domain discrepancy, and proposes Shuffle Augmentation of Features (SAF) to provide supervisory signals from target features, achieving performance improvements when integrated into existing adversarial UDA models.
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.