发表机构
SUPSI（瑞士南部应用科学大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出一种基于检索的跨域QoT估计框架，利用可迁移特征表示实现零样本与少样本适应，无需重训模型，在跨域QoT数据集上的泛化性能优于传统基线及对比学习方法，为光网络自动化提供了新途径。
AI 中文摘要
我们提出一种用于跨域传输质量（QoT）估计的基于检索的框架，该框架利用可迁移特征表示，同时避免依赖源域特定的决策边界。所提方法支持零样本和少样本适应，无需模型重训练。在跨域QoT数据集上的实验结果表明，与传统机器学习基线及近期对比学习方法相比，其泛化性能更优，凸显了基于检索的推理在鲁棒光网络自动化中的潜力。
英文摘要
We propose a retrieval-based framework for crossdomain quality-of-transmission (QoT) estimation that leverages transferable feature representations while avoiding reliance on source-domain-specific decision boundaries. The proposed approach supports both zero-shot and few-shot adaptation without requiring model retraining. Experimental results on cross-domain QoT datasets demonstrate improved generalization performance compared with conventional machine learning baselines and recent contrastive learning approaches, highlighting the potential of retrieval-based inference for robust optical network automation.
Comments5 Pages, 2 Figures. Accepted and presented at the 26th International Conference on Transparent Optical Networks (ICTON 2026), Prague, Czech Republic, 12-16 July 2026
