Subdomain adaptation

Subdomain adaptation is a machine learning technique aimed at improving model performance on specific subdomains by leveraging data from a source domain and a small amount of data from the target domain. Its goal is to reduce distribution differences between different subdomains, thereby enhancing the model's generalization and adaptability. In practical applications, subdomain adaptation can effectively address cross-domain data shift issues, increasing the robustness and practicality of models in new environments. It is widely used in fields such as natural language processing and computer vision.