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Related Experiment Video

Updated: Sep 17, 2025

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

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Published on: January 18, 2020

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

Weibin Luo¹, Mingye Chen¹, Jian Gao¹

¹School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, 213000, China.

Scientific Reports

|July 2, 2025

Summary

This summary is machine-generated.

Multi-view Affinity-based Projection Alignment (MAPA) improves Unsupervised Domain Adaptation by stabilizing pseudo-labels and enhancing feature diversity. This method achieves state-of-the-art results across various datasets and backbones.

Keywords:

Feature alignment Locality preserving projection Multi-view learning Pseudo-labeling Unsupervised domain adaptation Vision transformer

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Unsupervised Domain Adaptation (UDA) addresses challenges in transferring knowledge between domains with different data distributions.
Existing UDA methods struggle with noisy pseudo-labels, limited local structure modeling, and lack of representational diversity from single input views.

Purpose of the Study:

To propose a novel framework, Multi-view Affinity-based Projection Alignment (MAPA), for robust Unsupervised Domain Adaptation.
To enhance pseudo-label stability and feature diversity using a teacher-student network and multi-view augmentation.

Main Methods:

MAPA employs multi-view augmentation to create diverse sample representations.
It constructs a unified affinity matrix combining semantic pseudo-labels and feature distances.
A locality-preserving projection aligns source and target data, refined iteratively by discarding low-confidence pseudo-labels.

Main Results:

MAPA demonstrates superior performance compared to state-of-the-art methods on Office-Home, ImageCLEF, and VisDA-2017 datasets.
The framework shows robust adaptation capabilities across different backbones, including ResNet and Vision Transformer (ViT).

Conclusions:

MAPA effectively addresses key challenges in UDA, including pseudo-label noise and feature representation.
The proposed multi-view approach and iterative refinement significantly improve domain adaptation performance and stability.