Domain Generalization: A Survey

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.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

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

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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