Unsupervised Domain Adaptation

Unsupervised domain adaptation is a learning framework aimed at transferring knowledge learned from a large number of labeled training samples in the source domain to the target domain, which only has unlabeled data. This method improves the model's generalization ability in new environments by reducing the distribution discrepancy between the source and target domains, making it highly valuable for various applications.