Enabling Efficient Domain Adaptation via Noise-Enhanced Flow Matching

Abstract

: Domain adaptation remains a significant challenge in deploying data-driven models under distribution shifts, particularly when transferring from simulated to real-world environments. Existing approaches often rely on large labeled target datasets, suffer negative transfer, and provide limited interpretability. In this paper, we present NoiseFlow, a data-efficient domain adaptation framework that leverages noise-aware modeling and flow matching to enable robust cross-domain generalization. Our key insight is that feature dimensions exhibit heterogeneous sensitivity to noise, which can be amplified under domain shift. NoiseFlow introduces a feature-aware teacher student architecture that combines knowledge distillation, distribution alignment, and continuous flow matching to learn smooth transformations between source and target domains. Experimentation on wireless network configuration tasks demonstrates that NoiseFlow achieves good performance in low-data regimes, reaching 69.8% accuracy with a single target sample and improving zero-shot transfer performance by up to 40% over existing methods.