Conditional entropy minimization principle for learning domain invariant representation features
Thuan Doan Nguyen, Boyang Lyu, Prakash Ishwar, Matthias J. Scheutz, Shuchin Aeron · 2022 26th International Conference on Pattern Recognition (ICPR) · 2022
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 mixing of true invariant features and spurious invariant features1. 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.