Collaborative Dual-Evidence Representation Learning for Domain Generalization

Domain Generalization (DG) seeks to induce a model from several source domains that generalizes to unseen target domains. Existing alignment-based DG methods strive to promote statistical invariance by reducing representation discrepancies across source domains. However, such alignment alone may not be sufficient for reliable generalization, since different representations may achieve similar levels of source-domain alignment while preserving different discriminative structures, and consequently behave differently on unseen domains. This motivates us to complement the statistical alignment with an additional inductive bias that provides a distinct discriminative signal for more reliable prediction. To this end, we propose **Co**llaborative **D**ual-**E**vidence **R**epresentation Learning (CoDER), which implements the two inductive biases through collaborative pathways and integrates dual predictive evidence for final prediction. Specifically, one pathway derives evidence from a CORAL-regularized representation, while the other utilizes a shared latent dictionary to generate context-aware modulation signals for this representation, yielding adaptive discriminative evidence. Each pathway employs a different classifier to provide Dirichlet evidence, which is fused at inference. Extensive experiments on five benchmarks demonstrate that CoDER consistently outperforms strong competitors, with evidence fusion *effectively enhancing* predictive performance and reliability.