Generalizing to Unseen Domains: A Survey on Domain Generalization

🤖 Plain-English Summary

Machine learning systems generally assume that the training and testing distributions are the same. Third, we introduce the commonly used datasets, applications, and our open-sourced codebase for fair evaluation.

🔑 Key Findings

• To this end, a key requirement is to develop models that can generalize to unseen distributions.
• Domain generalization (DG), i.e., out-of-distribution generalization, has attracted increasing interests in recent years.
• Domain generalization deals with a challenging setting where one or several different but related domain(s) are given, and the goal is to learn a model that can generalize to an unseen test domain.

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