Domain Adaptation

Domain adaptation refers to the task of adjusting models between different data distributions. Its core objective is to enable machine learning models to generalize to the target domain and effectively handle distribution differences between the source and target domains, thereby enhancing the model's performance and robustness in new environments. This technique has significant value in cross-domain data applications and can be widely used in image recognition, natural language processing, and other fields.