Unsupervised domain adaptation via discriminative feature learning and classifier adaptation from center-based distances

Abstract

Current feature adaptation methods align the joint distributions across domains. But they may be limited because the difference between distributions cannot be completely eliminated. Existing classifier adaptation methods find the shared classifier across domains based on the original features or Manifold features. However, the shared classifier may be ineffective due to the high granularity at the category level of the features. Inspired by these, we propose the unsupervised domain adaptation via Discriminative feature learning and Classifier adaptation from Center-based Distances (DCCD). First, we define the data-centers and class-centers. Second, Discriminative feature learning from Center-based Distances (DCD) is established by using the data-centers and class-centers to align the joint distributions across domains and maximize the intra-class compactness and inter-class separability of features at the category level. Specifically, the optimization objective of DCD is constructed by minimizing the maximum mean discrepancy (MMD) between two domains and Class-center-Sample distances (CS), and maximizing the Data-center-Class-center (DC) and Class-center-Nearest-Class-center distances (CNC). Next, we propose Classifier adaptation from Center-based Distances (CCD). In detail, CCD applies Structural Risk Minimization (SRM), dynamic distribution alignment, and the constructed Laplacian Regularization to solve the shared classifier, where the constructed Laplacian Regularization extra considers CS and CNC to measure the local structure of features. Benefited from CCD, the joint distributions can be further aligned at the classifier level. Besides, integrating with the learned features from DCD, the shared classifier can be effective on the two domains. Finally, extensive experiments on four benchmark datasets show that DCCD outperforms the state-of-the-art UDA methods.