# Retrieval-Based Cross-Domain Generalization in Optical Networks via Global Features

arXiv: https://arxiv.org/abs/2608.00044 · HTML: https://mlsanity.com/paper/2608.00044/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 24 Jul 2026.

## Builds on (most cited first, 3 of 3)

- [Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning](https://mlsanity.com/paper/1904.06627/) (2019)
- [A Unified Siamese Learning Framework for Zero-Day Anomaly Detection and Classification in Optical Networks](https://mlsanity.com/paper/2606.10827/) (2026)
- [Cross-Domain Generalization in Optical Networks via Joint Contrastive and Classification Learning](https://mlsanity.com/paper/2607.20666/) (2026)

## Abstract (arXiv)

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.