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This analysis aims to understand why empirical risk minimization (ERM) excels in open set domain adaptation despite challenges.

Why Empirical Risk Minimization Performs Well for Open Set Domain Adaptation: A Theoretical Analysis From Causal View

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Theoretical analysis reveals how empirical risk minimization effectively handles unknown classes in OSDA, implying insights for model training.

This analysis aims to understand why empirical risk minimization (ERM) excels in open set domain adaptation despite challenges.
Developed a causal theoretical framework for OSDA
Introduced fully informative causal invariance model (FICIM) and partially informative causal invariance model (PICIM)
Conducted experiments on various datasets to validate theoretical findings
ERM performs well under the FICIM source domain
ERM performs poorly under the PICIM source domain
The performance discrepancy is linked to the available information during risk estimation

Du et al. (2026) studied this question.

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View Full Paper

Ask AI

Bookmark

Authors

Theoretical analysis reveals how empirical risk minimization effectively handles unknown classes in OSDA, implying insights for model training.

This analysis aims to understand why empirical risk minimization (ERM) excels in open set domain adaptation despite challenges.
Developed a causal theoretical framework for OSDA
Introduced fully informative causal invariance model (FICIM) and partially informative causal invariance model (PICIM)
Conducted experiments on various datasets to validate theoretical findings
ERM performs well under the FICIM source domain
ERM performs poorly under the PICIM source domain
The performance discrepancy is linked to the available information during risk estimation

Du et al. (2026) studied this question.

View Full Paper

Ask AI

Bookmark