A VAE-based domain adaptation framework learns shared event representations from two distinct optical systems and achieves 95.3% and 73.5% cross-system accuracy for SOP monitoring, with large gains over single-system DNN baselines.
Detection and classification of eavesdropping and me- chanical vibrations in fiber optical networks by analyzing polarization signatures over a noisy environment
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Variational Autoencoder Domain Adaptation for Cross-System Generalization in ML-Based SOP Monitoring
A VAE-based domain adaptation framework learns shared event representations from two distinct optical systems and achieves 95.3% and 73.5% cross-system accuracy for SOP monitoring, with large gains over single-system DNN baselines.