Enhancing Cross-Domain Generalization through Unified Representation Learning in Multi-Task Artificial Intelligence Frameworks

Abstract

Cross-domain generalization remains a critical challenge in modern Artificial Intelligence (AI), especially within multi-task learning (MTL) frameworks. This paper investigates how unified representation learning can improve generalization across heterogeneous domains. By analyzing previous research in representation learning, domain-invariant feature extraction, and task-shared knowledge transfer, we present a consolidated framework that fosters cross-domain robustness. Using empirical data from previous benchmarks, we demonstrate that learning shared representations across tasks not only improves performance on known tasks but also enables better adaptation to unseen domains.