# Cross-Domain Generalization in Optical Networks via Joint Contrastive and Classification Learning

arXiv: https://arxiv.org/abs/2607.20666 · HTML: https://mlsanity.com/paper/2607.20666/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 22 Jul 2026.

## Builds on (most cited first, 5 of 5)

- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning](https://mlsanity.com/paper/1904.06627/) (2019)
- [PyTorch Metric Learning](https://mlsanity.com/paper/2008.09164/) (2020)
- [A Unified Siamese Learning Framework for Zero-Day Anomaly Detection and Classification in Optical Networks](https://mlsanity.com/paper/2606.10827/) (2026)

## Built on by (most cited first, 1 of 1)

- [Retrieval-Based Cross-Domain Generalization in Optical Networks via Global Features](https://mlsanity.com/paper/2608.00044/) (2026)

## Abstract (arXiv)

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.