Core Concepts
Leveraging domain relations for out-of-domain generalization.
Abstract
The paper introduces D3G, a method that focuses on addressing domain shifts by leveraging domain relations to improve model robustness. Unlike traditional approaches that aim for domain invariance, D3G learns domain-specific models based on domain metadata. The method incorporates consistency regularization and refines domain relations to enhance training and inference processes. Empirical evaluations on various datasets show that D3G consistently outperforms state-of-the-art methods, demonstrating its effectiveness in improving out-of-domain generalization.
Stats
Published as a conference paper at ICLR 2024
Average improvement of 10.6%
Number of examples nd ≳ n for all training domains d ∈ Dtr
Quotes
"Unlike learning a single domain-invariant model, we posit that models may perform better if they were specialized to a given domain."
"Our results show that D3G consistently outperforms state-of-the-art methods."
"D3G achieves superior out-of-domain generalization by leveraging domain relations to reweight training domain-specific functions."