In this paper, we investigate whether we could use pruning as a reliable method to boost the generalization ability of the model. We found that existing pruning method like L2 can already offer small improvement on the target domain performance. We further propose a novel pruning scoring method, called DSS, designed not to maintain source accuracy as typical pruning work, but to directly enhance the robustness of the model. We conduct empirical experiments to validate our method and demonstrate that it can be even combined with state-of-the-art generalization work like MIRO(Cha et al., 2022) to further boost the performance. On MNIST to MNIST-M, we could improve the baseline performance by over 5 points by introducing 60% channel sparsity into the model. On DomainBed benchmark and state-of-the-art MIRO, we can further boost its performance by 1 point only by introducing 10% sparsity into the model. Code can be found at: https://github.com/AlexSunNik/Pruning-for-Better-Domain-Generalizability

本文研究：剪枝是否可靠地提升模型的泛化能力。我们发现现有剪枝方法像L2已经可以在目标域性能上提供小的改进。我们进一步提出了一种被称为DSS的新的剪枝评分方法，旨在直接提高模型的鲁棒性。通过实证实验，我们验证了我们的方法，并表明它可以与最先进的泛化工作（如MIRO）相结合，进一步提高性能。在MNIST到MNIST-M上，我们可以通过将60％通道稀疏引入模型来提高基线性能5个点以上。在DomainBed基准和最先进的MIRO上，我们只需将10％稀疏引入模型即可进一步提高其性能。

剪枝以实现更好的领域通用性