Semi-Supervised Domain Generalization

Semi-Supervised Domain Generalization is a machine learning approach aimed at leveraging a small amount of labeled data and a large amount of unlabeled data to enhance the model's generalization ability in unseen domains. This method optimizes the model's adaptability to different domain data by combining supervised and unsupervised learning strategies, thereby achieving more robust performance in tasks such as computer vision, and improving reliability and effectiveness in practical application scenarios.