Domain generalization aims to learn a generalization model that can perform well on unseen test domains by only training on limited source domains. However, existing domain generalization approaches often bring in prediction-irrelevant noise or require the collection of domain labels. To address these challenges, we consider the domain generalization problem from a different perspective by categorizing underlying feature groups into domain-shared and domain-specific features. Nevertheless, the domain-specific features are difficult to be identified and distinguished from the input data. In this work, we propose DomaIn-SPEcific Liberating (DISPEL), a post-processing fine-grained masking approach that can filter out undefined and indistinguishable domain-specific features in the embedding space. Specifically, DISPEL utilizes a mask generator that produces a unique mask for each input data to filter domain-specific features. The DISPEL framework is highly flexible to be applied to any fine-tuned models. We derive a generalization error bound to guarantee the generalization performance by optimizing a designed objective loss. The experimental results on five benchmarks demonstrate DISPEL outperforms existing methods and can further generalize various algorithms.

翻译：域泛化旨在仅通过有限源域训练，学习一个能在未见测试域上表现良好的泛化模型。然而，现有域泛化方法常引入与预测无关的噪声，或需要收集域标签。为解决这些挑战，我们从不同角度考虑域泛化问题，将潜在特征组划分为域共享特征和域特定特征。然而，域特定特征难以从输入数据中识别和区分。在本工作中，我们提出域特定释放（DISPEL），这是一种后处理细粒度掩蔽方法，可在嵌入空间中过滤未定义且不可区分的域特定特征。具体而言，DISPEL利用一个掩码生成器，为每个输入数据生成唯一掩码以过滤域特定特征。DISPEL框架具有高度灵活性，可应用于任何微调模型。我们通过优化设计的目标损失函数，推导出泛化误差界以保证泛化性能。在五个基准数据集上的实验结果表明，DISPEL优于现有方法，并能进一步泛化多种算法。