Cross-Domain Meta-Learning Frameworks for Real- Time Data Adaptation

Abstract

Meta-learning—often described as “learning to learn”—has rapidly advanced the frontier of machine intelligence by enabling models to leverage prior experience for swift adaptation to novel tasks. Traditional meta-learning frameworks predominantly assume that training and evaluation tasks originate from a single, homogeneous domain, yet real-world applications frequently involve significant domain shifts and dynamic data streams. This manuscript addresses this gap by developing and thoroughly evaluating cross-domain meta-learning frameworks explicitly designed for real-time data adaptation. We introduce a unified approach that synergistically combines domain-aware parameter initialization, task-conditioned inner-loop learning rates, and continuous feature-space alignment to facilitate efficient specialization in previously unseen domains. Our methodology begins by disentangling domain-generic and domain-specific components through per-domain perturbations of a shared base initialization, thereby providing a robust starting point for rapid fine-tuning. Furthermore, we employ a lightweight neural controller to predict adaptive inner-loop learning rates based on support-set characteristics, ensuring update magnitudes are calibrated to the degree of domain similarity. To counteract distribution drift inherent in streaming data, we incorporate an online feature-alignment module that continually aligns emerging target features to the meta-learned source distribution through incremental whitening and recoloring transforms. We validate our framework on three challenging cross-domain benchmarks—visual recognition (miniImageNet→CUB-200), time-series forecasting under varying noise profiles, and reinforcement learning with altered dynamics—demonstrating an average improvement of 8–10% in adaptation accuracy or reward over state-of-the-art baselines, with convergence accelerated by 20–30%. Computational overhead remains modest, enabling practical deployment in resource-constrained environments. Collectively, our contributions establish a scalable and generalizable foundation for deploying adaptive AI systems that maintain performance amidst evolving operational contexts,

highlighting the practical viability of cross-domain meta-learning in real-time scenarios.