  Invariance-principle-based methods such as Invariant Risk Minimization (IRM),
have recently emerged as promising approaches for Domain Generalization (DG).
Despite promising theory, such approaches fail in common classification tasks
due to the mixing of true invariant features and spurious invariant features.
To address this, we propose a framework based on the conditional entropy
minimization (CEM) principle to filter-out the spurious invariant features
leading to a new algorithm with a better generalization capability. We show
that our proposed approach is closely related to the well-known Information
Bottleneck (IB) framework and prove that under certain assumptions, entropy
minimization can exactly recover the true invariant features. Our approach
provides competitive classification accuracy compared to recent
theoretically-principled state-of-the-art alternatives across several DG
datasets.
