AI RESEARCH
Variational Autoencoder Domain Adaptation for Cross-System Generalization in ML-Based SOP Monitoring
arXiv CS.LG
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ArXi:2604.18035v1 Announce Type: new Machine learning (ML) models trained to detect physical-layer threats on one optical fiber system often fail catastrophically when applied to a different system, due to variations in operating wavelength, fiber properties, and network architecture. To overcome this, we propose a Domain Adaptation (DA) framework based on a Variational Autoencoder (VAE) that learns a shared representation capturing event signatures common to both systems while suppressing system-specific differences.