Test-time Fourier Style Calibration for Domain Generalization
This addresses the problem of model overfitting to source domains in domain generalization, offering a test-time calibration approach that is incremental but effective for improving generalizability.
The paper tackles domain generalization by reducing style gaps between source and target domains, introducing Test-time Fourier Style Calibration (TF-Cal) and Augment Amplitude Features (AAF), which outperform state-of-the-art methods on multiple benchmarks.
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.