发表机构
University of Applied Sciences and Arts of Southern Switzerland; University of Southern Switzerland; Chalmers University of Technology（瑞士南部应用科学与艺术大学; 南瑞士大学; 查尔姆斯理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对光网络中机器学习技术跨域鲁棒性挑战，提出联合对比和分类学习方法，同时进行表征学习与任务优化以捕捉跨域稳定关系，实验证明该方法在光通路传输质量估计上相比基线方法更有效且适应快。
AI 中文摘要
机器学习技术在异构网络域中的鲁棒性在光网络中仍是一个开放挑战。在特定拓扑或操作配置数据上训练的模型，部署到未见网络时性能常下降。本文提出一种表征学习技术应对此挑战，旨在捕捉跨域稳定的任务相关关系。该技术基于联合对比和分类学习方法，同时进行表征学习和任务优化，使两个目标塑造潜在空间。在光通路传输质量估计的代表性用例上的实验结果表明，与基线方法相比，该方法有效，且具有快速适应能力，即使微调有限也能提供出色性能。
英文摘要
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.
Comments6 pages, 2 figures. Accepted and presented at the 30th International Conference on Optical Network Design and Modelling (ONDM 2026), Munich, Germany, 12-15 May 2026
