Deep neural networks suffer from significant performance deterioration when there exists distribution shift between deployment and training. Domain Generalization (DG) aims to safely transfer a model to unseen target domains by only relying on a set of source domains. Although various DG approaches have been proposed, a recent study named DomainBed, reveals that most of them do not beat the simple Empirical Risk Minimization (ERM). To this end, we propose a general framework that is orthogonal to existing DG algorithms and could improve their performance consistently. Unlike previous DG works that stake on a static source model to be hopefully a universal one, our proposed AdaODM adaptively modifies the source model at test time for different target domains. Specifically, we create multiple domain-specific classifiers upon a shared domain-generic feature extractor. The feature extractor and classifiers are trained in an adversarial way, where the feature extractor embeds the input samples into a domain-invariant space, and the multiple classifiers capture the distinct decision boundaries that each of them relates to a specific source domain. During testing, distribution differences between target and source domains could be effectively measured by leveraging prediction disagreement among source classifiers. By fine-tuning source models to minimize the disagreement at test time, target domain features are well aligned to the invariant feature space. We verify AdaODM on two popular DG methods, namely ERM and CORAL, and four DG benchmarks, namely VLCS, PACS, OfficeHome, and TerraIncognita. The results show AdaODM stably improves the generalization capacity on unseen domains and achieves state-of-the-art performance.

翻译：深度神经网络在部署与训练之间存在分布偏移时，会遭受严重的性能下降。域泛化旨在仅依赖一组源域，将模型安全地迁移至未观测的目标域。尽管已有多种域泛化方法被提出，但名为DomainBed的近期研究揭示，大多数方法并未超越简单的经验风险最小化。为此，我们提出一个与现有域泛化算法正交的通用框架，能够持续提升其性能。与以往依赖静态源模型、期望其成为通用模型的域泛化工作不同，我们提出的AdaODM在测试时针对不同目标域自适应调整源模型。具体而言，我们在共享的域通用特征提取器上构建多个域专用分类器。特征提取器与分类器通过对抗方式训练，其中特征提取器将输入样本嵌入域不变空间，而多个分类器则捕获各自对应特定源域的独特决策边界。测试时，通过利用源分类器间的预测不一致性，可有效度量目标域与源域之间的分布差异。通过在测试时微调源模型以最小化不一致性，目标域特征得以与不变特征空间良好对齐。我们在两种主流域泛化方法（即ERM和CORAL）及四个域泛化基准（VLCS、PACS、OfficeHome和TerraIncognita）上验证了AdaODM。结果表明，AdaODM稳定提升了在未观测域上的泛化能力，并实现了当前最优性能。