Domain Generalization

Domain generalization (DG) refers to learning from one or multiple training domains to extract a domain-agnostic model that is applicable to unseen domains. Its core objective is to improve the model's generalization ability in new environments without access to target domain data, thereby enhancing the robustness and adaptability of the model. DG holds significant value in multi-domain application scenarios, such as cross-dataset image recognition and natural language processing, effectively reducing the need for labeling new data and improving system practicality and efficiency.