  The distribution shifts between training and test data typically undermine
the performance of models. In recent years, lots of work pays attention to
domain generalization (DG) where distribution shifts exist, and target data are
unseen. Despite the progress in algorithm design, two foundational factors have
long been ignored: 1) the optimization for regularization-based objectives, and
2) the model selection for DG since no knowledge about the target domain can be
utilized. In this paper, we propose Mixup guided optimization and selection
techniques for DG. For optimization, we utilize an adapted Mixup to generate an
out-of-distribution dataset that can guide the preference direction and
optimize with Pareto optimization. For model selection, we generate a
validation dataset with a closer distance to the target distribution, and
thereby it can better represent the target data. We also present some
theoretical insights behind our proposals. Comprehensive experiments
demonstrate that our model optimization and selection techniques can largely
improve the performance of existing domain generalization algorithms and even
achieve new state-of-the-art results.
