Online Domain Adaptation

Online Domain Adaptation refers to the process of continuously learning and adjusting model parameters during actual deployment to achieve performance optimization in new environments, targeting unpredictable and undefined domain shifts. This method aims to enhance the generalization and real-time adaptability of models, ensuring their efficiency and accuracy in dynamically changing application scenarios. Online Domain Adaptation is of great significance for improving the robustness and practicality of machine learning systems and is widely applied in natural language processing, computer vision, and other fields.