Cross-Domain Generalization in Optical Networks via Joint Contrastive and Classification Learning
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 re...
| Authors | Ali Al Housseini, Carlos Natalino, Paolo Monti et al. |
|---|---|
| Primary category | cs.NI |
| Published | 22 July 2026 |
| Last updated at source | 22 July 2026 |
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