🤖 AI Summary
This paper investigates the conditions under which domain context—i.e., permutation-invariant representations of in-domain samples—enhances model generalization to unseen domains, specifically examining the applicability boundary of marginal transfer learning in domain generalization (DG).
Method: We derive two verifiable necessary conditions characterizing when context-assisted generalization is effective; analyze its robustness mechanisms against specific distribution shifts; and design an out-of-distribution (OOD) extrapolation detector coupled with a dual-objective model selection strategy to dynamically balance predictive accuracy and robustness.
Contribution/Results: Theoretical analysis and empirical evaluation demonstrate that the proposed criteria accurately identify favorable/unfavorable generalization scenarios, reliably detect invalid extrapolation, and support adaptive switching across domains. Our work establishes an interpretable, theoretically grounded, and practically deployable framework for context utilization in DG—bridging theory and practice through formal guarantees and actionable diagnostics.
📝 Abstract
In this work, we analyze the conditions under which information about the context of an input $X$ can improve the predictions of deep learning models in new domains. Following work in marginal transfer learning in Domain Generalization (DG), we formalize the notion of context as a permutation-invariant representation of a set of data points that originate from the same domain as the input itself. We offer a theoretical analysis of the conditions under which this approach can, in principle, yield benefits, and formulate two necessary criteria that can be easily verified in practice. Additionally, we contribute insights into the kind of distribution shifts for which the marginal transfer learning approach promises robustness. Empirical analysis shows that our criteria are effective in discerning both favorable and unfavorable scenarios. Finally, we demonstrate that we can reliably detect scenarios where a model is tasked with unwarranted extrapolation in out-of-distribution (OOD) domains, identifying potential failure cases. Consequently, we showcase a method to select between the most predictive and the most robust model, circumventing the well-known trade-off between predictive performance and robustness.
