Active Unsupervised Domain Adaptation with Deep Learning

Unsupervised domain adaptation aims to transfer predictive knowledge from a labeled source dataset to an unlabeled target dataset whose feature distributions differ. While recent deep learning approaches have shown success in aligning latent representations across domains, a fundamental challenge remains: determining when the source information is truly transferable to the target problem. In this work, we propose a deep active unsupervised domain adaptation framework that integrates active learning principles into the domain adaptation process. Our method strategically selects a small subset of target samples for labeling based on model uncertainty and representativeness in the learned latent space, thereby maximizing the informational value of limited labeling effort. These selectively labeled data will enable formal assessment of transferability between the source and target domains. This study highlights the importance of adaptive sample selection in bridging domain gaps and guiding data-efficient model adaptation in high-dimensional settings.