Centrala begrepp
The author introduces Unknown Domain Inconsistency Minimization (UDIM) as a novel objective to enhance domain generalization by reducing loss landscape inconsistency between source and unknown domains. UDIM outperforms existing methods in various scenarios, showcasing its robustness and effectiveness.
Sammanfattning
The study introduces UDIM, a novel approach that significantly improves domain generalization by minimizing loss landscape inconsistencies between source and unknown domains. Through empirical validation, UDIM consistently outperforms existing methods across multiple benchmark datasets, highlighting its efficacy in enhancing model adaptability to unseen domains.
The research focuses on the development of UDIM, which leverages both parameter and data perturbed regions to optimize domain generalization. By aligning the loss landscapes of source and unknown domains, UDIM establishes an upper bound for the true objective of the task. Theoretical analysis and empirical results demonstrate the superiority of UDIM over existing methods in scenarios with limited domain information.
Key points from the content include:
- Introduction of Unknown Domain Inconsistency Minimization (UDIM) for enhancing domain generalization.
- Validation of UDIM's effectiveness through empirical experiments on benchmark datasets.
- Theoretical analysis supporting UDIM's approach in optimizing domain adaptation.
- Comparison of UDIM with existing methods showcasing superior performance in various scenarios.
Statistik
SAM variants have delivered significant improvements in DG tasks.
UDIM consistently outperforms SAM variants across multiple DG benchmark datasets.
Citat
"UDIM reduces the loss landscape inconsistency between source domain and unknown domains."
"Our experiments on various DG benchmark datasets illustrate that UDIM consistently improves the generalization ability."