Discriminative distribution alignment: A unified framework for heterogeneous domain adaptation

Highlights：

• We design a discriminative embedding constraint for the heterogeneous domain adaptation problem, which enhances the discriminative power of the common subspace.

• To the best of our knowledge, we are the first to integrate the classifier adaptation, distribution alignment, and discriminative embedding constraints into a unified framework.

• Many loss (e.g., cross-entropy loss or squared loss) and projection (e.g., linear projection or non-linear projection) functions can be incorporated into the proposed Discriminative Distribution Alignment framework. Two approaches are developed by using the cross-entropy loss and the squared loss, respectively.

• Extensive experimental results are reported on the tasks of categorization across domains and modalities, which demonstrate the effectiveness of the proposed Discriminative Distribution Alignment framework.