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Explaining f-Divergence-Based Regularization via Local Curvature and Sharpness-Aware Minimization
Divergence-based regularization and Sharpness-Aware Minimization (SAM) are two prominent approaches for improving generalization in deep learning, both motivated by robustness to perturbations. However, their relationship has remained largely unexplored. Building on classical second-order expansions
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