EHM: Exploring dynamic alignment and hierarchical clustering in unsupervised domain adaptation via high-order moment-guided contrastive learning

Abstract

Unsupervised domain adaptation (UDA) aims to annotate unlabeled target domain samples using transferable knowledge learned from the labeled source domain. Optimal transport (OT) is a widely adopted probability metric in transfer learning for quantifying domain discrepancy. However, many existing OT-based UDA methods usually employ an entropic regularization term to solve the OT optimization problem, inevitably resulting in a biased estimation of domain discrepancy. Furthermore, to achieve precise alignment of class distributions, numerous UDA methods commonly employ deep features for guiding contrastive learning, overlooking the loss of discriminative information. Additionally, prior studies frequently use conditional entropy regularization term to cluster unlabeled target samples, which may guide the model toward optimizing in the wrong direction.

To address the aforementioned issues, this paper proposes a new UDA framework called EHM, which employs a Dynamic Domain Alignment (DDA) strategy, a Reliable High-order Contrastive Alignment (RHCA) strategy, and a Trustworthy Hierarchical Clustering (THC) strategy. Specially, DDA leverages a dynamically adjusted Sinkhorn divergence to measure domain discrepancy, effectively eliminating the biased estimation issue. Our RHCA skillfully conducts contrastive learning in a high-order moment space, significantly enhancing the representation power of transferable features and reducing the domain discrepancy at the class-level. Moreover, THC integrates multi-view information to guide unlabeled samples towards achieving robust clustering. Extensive experiments on various benchmarks demonstrate the effectiveness of our EHM.