Multi-view affinity-based projection alignment for unsupervised domain adaptation via locality preserving optimization

This is the authors' abstract. We don't add key points for this paper.

Unsupervised Domain Adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain with differing data distributions. However, it remains difficult due to noisy pseudo-labels in the target domain, inadequate modeling of local geometric structure, and reliance on a single input view that limits representational diversity in challenging tasks. We propose a framework named Multi-view Affinity-based Projection Alignment (MAPA) that uses a teacher-student network and multi-view augmentation to stabilize pseudo-labels and enhance feature diversity. MAPA transforms each sample into multiple augmented views, constructs a unified affinity matrix that combines semantic cues from pseudo-labels with feature-based distances, and then learns a locality-preserving projection to align source and target data in a shared low-dimensional space. An iterative strategy refines pseudo-labels by discarding low-confidence samples, thereby raising label quality and strengthening supervision for the target domain. MAPA also employs a consistency-weighted fusion mechanism to merge predictions from multiple views, improving stability under domain shift. Finally, MAPA leverages class-centric and cluster-level relationships in the projected space to further refine label assignments, enhancing the overall adaptation process. Experimental results on Office-Home, ImageCLEF, and VisDA-2017 show that MAPA surpasses recent state-of-the-art methods, and it maintains robust performance across backbones including ResNet-50, ResNet-101, and Vision Transformer (ViT).