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

arXiv: https://arxiv.org/abs/2308.02233 · HTML: https://mlsanity.com/paper/2308.02233/

## Key facts

- Cited by 1 papers 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 (most cited first, 5 of 5)

- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Self-Normalizing Neural Networks](https://mlsanity.com/paper/1706.02515/) (2017)
- [Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models](https://mlsanity.com/paper/1702.03275/) (2017)
- [On the Provable Advantage of Unsupervised Pretraining](https://mlsanity.com/paper/2303.01566/) (2023)
- [Machine learning-based EDFA Gain Model Generalizable to Multiple Physical Devices](https://mlsanity.com/paper/2009.05326/) (2020)

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

- [Multi-Span Optical Power Spectrum Evolution Modeling using ML-based Multi-Decoder Attention Framework](https://mlsanity.com/paper/2503.17072/) (2025)

## 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.