Domain Feature

Domain features, representing characteristics specific to different data sources or domains, are central to addressing the challenge of model generalization across diverse datasets. Current research focuses on disentangling domain-invariant and domain-specific features using techniques like contrastive learning, hypergraph signal decoupling, and style-based augmentation, often within federated learning frameworks to maintain data privacy. These advancements aim to improve the robustness and reliability of machine learning models in various applications, including recommendation systems, image classification, and speech recognition, by mitigating the negative impact of domain shifts. The ultimate goal is to build models that generalize effectively to unseen data, enhancing the reliability and applicability of machine learning across diverse real-world scenarios.