🤖 AI Summary
This work addresses the limitation of existing domain generalization methods that enforce global invariance, thereby overlooking transferable features shared only among subsets of source domains and constraining representational capacity. To overcome this, the authors propose a subset-shared invariance hypothesis and introduce a mixture-of-experts architecture to learn localized invariances: each expert aligns representations within a specific subset of domains, while a routing mechanism dynamically combines expert outputs. The approach jointly optimizes feature representations, selective alignment objectives, and a confidence-balanced routing strategy, further enhanced by a training scheme that encourages diverse expert specialization. Evaluated on the DomainBed benchmark, the method achieves substantial improvements in out-of-domain generalization and demonstrates heightened robustness under increased domain heterogeneity.
📝 Abstract
Domain generalization (DG) aims to learn a model from one or more source domains that generalizes to an unseen target domain without accessing target data during training. A common approach enforces invariance of representations across all source domains, assuming predictive structure is globally shared. However, we demonstrate that enforcing invariance across more domains gradually restricts the feasible representation space, discarding transferable predictive factors that are not universally shared. To address this limitation, we propose subset-shared invariance, where predictive structure is assumed stable only within domain subsets. We implement this principle with a mixture-of-experts architecture, where each expert aligns the specific domains it serves and a routing mechanism composes subset-invariant components for prediction. This creates a routing-conditioned invariance, jointly learned with the representation. To facilitate effective decomposition, we develop training objectives that encourage selective alignment, confident and balanced routing, and diverse expert specialization. Experiments on DomainBed benchmarks demonstrate improved out-of-domain generalization and greater robustness under increasing domain heterogeneity. Our results suggest that DG should move beyond enforcing a single global invariance and instead model invariance through partially shared structure across domain subsets.