Learning Informative Invariant Representations via Hierarchical Latent Decomposition

Domain generalization aims to learn models that generalize to unseen target domains using labeled data from multiple source domains. Many existing approaches focus on learning domain-invariant representations, but enforcing invariance alone may fail to preserve sufficient task-relevant information under domain shift. We propose BLENDER, a reconstruction-aware framework for domain generalization that employs a hierarchical latent decomposition to separate domain-invariant and domain-specific factors. This structure prioritizes informativeness in the domain-invariant representation and captures residual domain-specific variation through conditional modeling. We further provide a theoretical analysis establishing a bound on the reconstruction risk for unseen target domains, revealing how domain invariance and latent disentanglement contribute to generalization beyond the observed domains. Experiments on standard domain generalization datasets demonstrate strong performance under substantial domain shifts, and qualitative analyses show that BLENDER promotes a structured allocation of information between domain-invariant and domain-specific latent representations.