Instance-Aware Test-Time Adaptation for Domain Generalization

Domain generalization (DG) aims to enhance the generalization capability of models to unseen target distributions by leveraging multiple source distributions. This paper focuses on robust test-time adaptation (TTA) based DG that is mostly needed when the model experiences new unseen domains while testing. We consider two practical challenges under domain shifts: (1) existing DG methods mostly rely on domain-specific information and do not explicitly utilize class-specific information. Therefore, these approaches ignore mixed features, which are both class and domain-specific, thus resulting in the loss of useful information; (2) while existing TTA methods explicitly require a memory bank of test time samples, which is computationally complex and impractical in many applications. To overcome these limitations, we propose a new framework called IADG that utilizes class-specific information along with domain-specific information to ensure robust generalization. Our method exploits disentangled features by pulling class-relevant features to increase diversified negative pairs, facilitating flawless integration of class and domain-specific features. To leverage high-confidence samples during testing, we introduce a novel confidence-guided low-risk instance TTA approach that only considers one unlabeled sample during inference and therefore does not require a dedicated memory bank. Extensive evaluations on five public benchmarks consistently demonstrate the superior performance of our approach over the state-of-the-art. Project code can be found here: https://github.com/takihasan/IADG .