FIXED: Frustratingly Easy Domain Generalization with Mixup. (arXiv:2211.05228v1 [cs.CV])

Domain generalization (DG) aims to learn a generalizable model from multiple
training domains such that it can perform well on unseen target domains. A
popular strategy is to augment training data to benefit generalization through
methods such as Mixup~\cite{zhang2018mixup}. While the vanilla Mixup can be
directly applied, theoretical and empirical investigations uncover several
shortcomings that limit its performance. Firstly, Mixup cannot effectively
identify the domain and class information that can be used for learning
invariant representations. Secondly, Mixup may introduce …

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