The topic of generalizing machine learning models learned on a collection of source domains to unknown target domains is challenging. While many domain generalization (DG) methods have achieved promising results, they primarily rely on the source domains at train-time without manipulating the target domains at test-time. Thus, it is still possible that those methods can overfit to source domains and perform poorly on target domains. Driven by the observation that domains are strongly related to styles, we argue that reducing the gap between source and target styles can boost models' generalizability. To solve the dilemma of having no access to the target domain during training, we introduce Test-time Fourier Style Calibration (TF-Cal) for calibrating the target domain style on the fly during testing. To access styles, we utilize Fourier transformation to decompose features into amplitude (style) features and phase (semantic) features. Furthermore, we present an effective technique to Augment Amplitude Features (AAF) to complement TF-Cal. Extensive experiments on several popular DG benchmarks and a segmentation dataset for medical images demonstrate that our method outperforms state-of-the-art methods.
翻译:将收集源域到未知目标域的机械学习模型推广到未知目标域的问题具有挑战性。虽然许多域通用(DG)方法已经取得了令人乐观的成果,但它们主要依赖在培训时的源域,而没有在测试时操纵目标域。因此,这些方法仍然有可能超越源域,在目标域上表现不佳。由于观察到域与风格密切相关,我们提出缩小源与目标样式之间的差距可以促进模型的通用性。为了解决在培训期间无法进入目标域的两难困境,我们在测试时采用Tyer Styld Styer校准(TF-Cal)来校准飞行的目标域样式。为了获取样式,我们使用Fourier转换方法将特性分解成振动(风格)特性和阶段(管理)特性。此外,我们提出了一种有效的增强调度特性技术,以补充TF-Cal。我们广泛试验了几个流行的通用的DG基准和医疗图像分解数据集,以显示我们的方法超越了状态方法。