Domain Generalization: A Survey

🤖 Plain-English Summary

Generalization to out-of-distribution (OOD) data is a capability natural to humans yet challenging for machines to reproduce. Then, we conduct a thorough review into existing methods and theories.

🔑 Key Findings

• This is because most learning algorithms strongly rely on the i.i.d.
• assumption on source/target data, which is often violated in practice due to domain shift.
• Domain generalization (DG) aims to achieve OOD generalization by using only source data for model learning.

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