CLEAR: Channel-Filtered Learning With Ensemble Attention and Robustness for Cognitive Domain Generalization

In dynamic and cognitively demanding human–machine environments, achieving reliable system performance across diverse domains is critical for ensuring ergonomic interaction and cognitive efficiency. This article introducesChannel-filtered Learning with Ensemble Attention and Robustness(CLEAR), a domain generalization framework that simultaneously suppresses domain-specific feature interference and enhances domain-invariant representation learning. CLEAR integrates ensemble multiscale spatial attention with a progressive channel filtering mechanism to selectively eliminate features most indicative of domain identity. Furthermore, a momentum-based inference consistency loss is introduced to promote semantic stability by aligning class prototypes across domains over time. These components operate in concert to enable consistent task-relevant feature extraction and improved generalization under unseen distributional conditions. Extensive evaluations on multiple public benchmarks demonstrate that CLEAR achieves state-of-the-art performance in domain generalization while enhancing robustness and cognitive reliability in real-world human–machine systems. The framework contributes to the development of perceptually ergonomic AI models capable of maintaining accuracy across varying operational contexts.