提升光网络跨域泛化能力，实现小样本快速适应。
Cross-Domain Generalization in Optical Networks via Joint Contrastive and Classification Learning
- 联合对比学习与分类任务，同步优化表征与任务目标。
- 在光路传输质量估计任务中显著优于基线方法。
- 适合部署在拓扑多变、数据稀缺的光网络场景。
机器学习在异构光网络中的鲁棒性仍是开放挑战。基于特定拓扑或配置训练的模型在未见网络中性能常下降。本文提出一种表征学习方法，旨在捕捉跨域稳定的任务相关关系。该方法采用新颖的联合对比与分类学习框架，使表征学习与任务优化同时进行，共同塑造潜在空间。在典型应用场景——光路传输质量估计上的实验表明，相比基线方法，本方法表现更优，且具备快速适应能力，即使仅有少量微调也表现出色。
原文摘要 · Abstract (English)
The robustness of machine learning techniques across heterogeneous network domains remains an open challenge in optical networks. Models trained on data from a specific topology or operational configuration often exhibit degraded performance when deployed in unseen networks. In this work, we address this challenge by proposing a representation learning technique aimed at capturing task-relevant relationships that remain stable across domains. The proposed technique is based on a novel joint contrastive and classification learning approach in which representation learning and task optimization are performed simultaneously, allowing both objectives to shape the latent space. Experimental results on a representative use case, namely, lightpath quality of transmission estimation, demonstrate the effectiveness of our approach compared to baseline approaches, and highlight its capacity for rapid adaptation, providing excellent performance even with limited fine-tuning.
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