  To address the distribution shifts between training and test data, domain
generalization (DG) leverages multiple source domains to learn a model that
generalizes well to unseen domains. However, existing DG methods generally
suffer from overfitting to the source domains, partly due to the limited
coverage of the expected region in feature space. Motivated by this, we propose
to perform mixup with data interpolation and extrapolation to cover the
potential unseen regions. To prevent the detrimental effects of unconstrained
extrapolation, we carefully design a policy to generate the instance weights,
named Flatness-aware Gradient-based Mixup (FGMix). The policy employs a
gradient-based similarity to assign greater weights to instances that carry
more invariant information, and learns the similarity function towards flatter
minima for better generalization. On the DomainBed benchmark, we validate the
efficacy of various designs of FGMix and demonstrate its superiority over other
DG algorithms.
