SOURCE-LINKED INTELLIGENCE

Joint Distribution Alignment for Universal Domain Adaptation

arXiv · AI, language, vision and robotics · article · Aug 25, 2026 · UTC

Unsupervised domain adaptation (UDA) has been widely concerned in the fields of machine learning, pattern recognition, and computer vision. Traditional UDA learning usually assumes that the label spaces of the source and target domains are exactly the same and only needs to solve the problem of sample distribution drift existing between two domains. However, in real world applications, the label spaces between two domains may be different. In this case, there are both sample distribution drift and class spatial difference between domains, namely Universal Domain Adaptation (UniDA) learning sce

Read original source ↗ Open in workspace

recordType

paper

region

Global