What is domain generalization evaluation and how is it different from domain adaptation?

Domain generalization evaluates performance on unseen domains without access to target domain data.

Domain generalization assumes no access to target domain during training or tuning, while domain adaptation uses target domain data for adaptation. Evaluation focuses on robustness across unseen distributions. Metrics often include worst-domain accuracy, average performance across source domains, and generalization gap.