用检索方法提升光网络跨域传输质量预测，无需重训练
Retrieval-Based Cross-Domain Generalization in Optical Networks via Global Features
- 通过全局特征检索实现跨域传输质量预测
- 在多个数据集上优于传统机器学习和对比学习方法
- 适合需要快速适配新场景的光网络自动化系统
我们提出一种基于检索的跨域传输质量（QoT）估计框架，利用可迁移的特征表示，避免依赖源域特定的决策边界。该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，相比传统机器学习基线和近期对比学习方法，该方法显著提升了泛化性能，凸显了基于检索推理在鲁棒光网络自动化中的潜力。
原文摘要 · Abstract (English)
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.
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