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.
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by Seungjae Shi... kl. arxiv.org 03-13-2024
https://arxiv.org/pdf/2403.07329.pdfDybere Forespørgsler