Semi Supervised Domain Generalization

Semi-supervised domain generalization (SSDG) aims to train machine learning models that generalize well to unseen domains using limited labeled data and abundant unlabeled data from multiple source domains. Current research focuses on improving pseudo-labeling techniques to leverage unlabeled data effectively, developing algorithms that learn domain-invariant features, and employing strategies like model averaging and uncertainty estimation to enhance robustness and reduce overfitting. SSDG addresses the critical need for data-efficient and generalizable models in scenarios with limited labeled data across diverse domains, impacting fields like healthcare and computer vision where data acquisition is expensive or challenging.