论文标题
学习基于梯度的混音，以使最小值用于领域概括
Learning Gradient-based Mixup towards Flatter Minima for Domain Generalization
论文作者
论文摘要
为了解决训练和测试数据之间的分布变化，域的概括（DG）利用多个源域来学习一个概括地看不见域的模型。但是，现有的DG方法通常遭受过度适合源域的影响，部分原因是特征空间中预期区域的覆盖范围有限。在此激励的情况下，我们建议与数据插值和外推进行混合，以涵盖潜在的看不见区域。为了防止不受限制的外推的有害影响，我们仔细设计了一种策略来生成实例权重，名为Flatness-Awarnents-Awarnents-Awarnement-Angient-Awarnges-Aware Antents-Altents-Awments-Altentment-Actient-Actient-Actient-Awments-Aware Mixup（FGMIX）。该政策采用基于梯度的相似性，将更大的权重分配给携带更多不变信息的实例，并了解相似性的功能，以提高最小值以更好地概括。在域基准测试中，我们验证了FGMIX各种设计的功效，并证明了其优于其他DG算法。
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.
