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和培训之间的分布转移存在时出现显著的性能恶化。 常规通用( DG) 的目的是将模型安全地转移到不为人知的目标域, 仅依靠一组源域。 尽管提出了各种DG方法。 最近一项名为 DomeBed 的研究显示, 大部分DG方法没有击败简单的“ 经验风险最小化 ” ( ERM ) 。 为此, 我们提议了一个与现有的DG 算法相对应的总体框架, 可以不断改进它们的业绩。 与以往的DG工作在静态源模型上的利害关系不同, 希望成为通用源域的通用源模型, 我们提议的AdaODDM 旨在安全地将模型转移到未知的目标域。 虽然提出了各种DG方法。 尽管已经提出了各种不同的DG方法。 最近的一项研究发现, 我们创建了多个特定域的分类方法, 将输入的输入样本嵌入到一个域域域-, 并且多个分类者捕捉到它们每一个特定的源域。 在测试、 目标和源域域间分配差异中, 我们的CLDGMM 能够有效地测量CF 的域域域域内, 。