Transferable and discriminative broad network for unsupervised domain adaptation

Abstract

Unsupervised domain adaptation uses labeled data from a source domain to train a robust classifier for an unlabeled target domain with a distinct distribution. The Broad Learning System (BLS), known for its efficiency and effectiveness, is widely applied in classification and regression problems. This paper introduces a novel method named TD-BLS for unsupervised domain adaptation. TD-BLS combines UDA-BLSAE and discriminative BLS into an iterative network. UDA-BLSAE performs domain adaptation and data reconstruction simultaneously, balancing the preservation of intrinsic structure with the reduction of distribution discrepancy. Additionally, the discriminative BLS used in TD-BLS employs pseudo-labeling and manifold learning in the classifier stage to leverage high-confidence predictions and data geometric information. Finally, experiments on multiple public domain adaptation datasets demonstrate that our approach achieves rapid domain adaptation with higher accuracy compared to existing methods.