Unsupervised Domain Adaptation Through Dynamically Aligning Both the Feature and Label Spaces

In unsupervised domain adaptation (UDA), a target-domain model is trained by the supervised knowledge from a source domain. Although UDA has recently received much attention, most existing UDA methods have ignored the alignment in label space while mostly concentrating on alignment of feature space. Even worse, they have payed less attention to the dynamic relationship between domain alignment and discrimination, leading to degenerated performance. In this work, we propose a new kind of UDA through aligning in both the feature and label spaces (DAFL), in which a dynamic weight is designed and deployed between domain alignment and discrimination enhancement according to their conditions. Specifically, the cross-domain distribution divergence is reduced by the weighted class-level feature space alignment as well as the compacted and discriminative label space alignment. Furthermore, the balancing weight between adaptation alignment and discrimination enhancement is dynamically adjusted to regularize the adversarial domain adaptation. Then, the generalization ability of the DAFL model is enhanced by adding discrepant classification with theoretical analysis. Finally, extensive experiments validate effectiveness and superiority of the proposed approach.