mlsanityData to 14 Oct 2026

Self-Normalizing Neural Network, Enabling One Shot Transfer Learning for Modeling EDFA Wavelength Dependent Gain

arXiv 2308.02233 ↗First posted 4 Aug 2023cs.NI cs.LGcited by 1

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Abstract (arXiv)

We present a novel ML framework for modeling the wavelength-dependent gain of multiple EDFAs, based on semi-supervised, self-normalizing neural networks, enabling one-shot transfer learning. Our experiments on 22 EDFAs in Open Ireland and COSMOS testbeds show high-accuracy transfer-learning even when operated across different amplifier types.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 5 of its 14 references that are in the map. First posted on arXiv 4 Aug 2023.

Builds on

5 of 14 references.

References · 5

  1. 2019A Comprehensive Survey on Transfer Learningcited by 1,160
  2. 2017Self-Normalizing Neural Networkscited by 598
  3. 2017Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Modelscited by 128
  4. 2023On the Provable Advantage of Unsupervised Pretrainingcited by 22
  5. 2020Machine learning-based EDFA Gain Model Generalizable to Multiple Physical Devicescited by 2

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Citing papers per year: 2025: 1.

1 paper.

  1. 2025Multi-Span Optical Power Spectrum Evolution Modeling using ML-based Multi-Decoder Attention Frameworkcited by 2