Universal Domain Adaptation

Universal Domain Adaptation is a machine learning technique aimed at addressing the issue of data distribution differences between source and target domains. It enables models to maintain good generalization performance on unseen target domains through transfer learning methods. In the field of computer vision, this technology can effectively enhance the model's cross-domain recognition capabilities, increasing its robustness and adaptability in real-world application scenarios, making it highly valuable for practical applications.