# Generalized few-shot transfer learning architecture for modeling the EDFA gain spectrum

arXiv: https://arxiv.org/abs/2507.21728 · HTML: https://mlsanity.com/paper/2507.21728/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 43 references that are in the map.
- First posted on arXiv 29 Jul 2025.

## Builds on (most cited first, 14 of 14)

- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [Deep CORAL: Correlation Alignment for Deep Domain Adaptation](https://mlsanity.com/paper/1607.01719/) (2016)
- [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)
- [Domain adaptation in small-scale and heterogeneous biological datasets](https://mlsanity.com/paper/2405.19221/) (2024)
- [SNR optimization of multi-span fiber optic communication systems employing EDFAs with non-flat gain and noise figure](https://mlsanity.com/paper/2106.03639/) (2021)
- [Toward 6G Optical Fronthaul: A Survey on Enabling Technologies and Research Perspectives](https://mlsanity.com/paper/2406.00308/) (2024)
- [Machine learning-based EDFA Gain Model Generalizable to Multiple Physical Devices](https://mlsanity.com/paper/2009.05326/) (2020)
- [Multi-Span Optical Power Spectrum Evolution Modeling using ML-based Multi-Decoder Attention Framework](https://mlsanity.com/paper/2503.17072/) (2025)
- [Interference Identification in Multi-User Optical Spectrum as a Service using Convolutional Neural Networks](https://mlsanity.com/paper/2503.17079/) (2025)
- [Transfer Learning for EDFA Gain Modeling: A Semi-Supervised Approach Using Internal Amplifier Features](https://mlsanity.com/paper/2503.17094/) (2025)
- [Interference Detection in Spectrum-Blind Multi-User Optical Spectrum as a Service](https://mlsanity.com/paper/2505.21018/) (2025)

## Abstract (arXiv)

Accurate modeling of the gain spectrum in Erbium-Doped Fiber Amplifiers (EDFAs) is essential for optimizing optical network performance, particularly as networks evolve toward multi-vendor solutions. In this work, we propose a generalized few-shot transfer learning architecture based on a Semi-Supervised Self-Normalizing Neural Network (SS-NN) that leverages internal EDFA features - such as VOA input or output power and attenuation, to improve gain spectrum prediction. Our SS-NN model employs a two-phase training strategy comprising unsupervised pre-training with noise-augmented measurements and supervised fine-tuning with a custom weighted MSE loss. Furthermore, we extend the framework with transfer learning (TL) techniques that enable both homogeneous (same-feature space) and heterogeneous (different-feature sets) model adaptation across booster, preamplifier, and ILA EDFAs. To address feature mismatches in heterogeneous TL, we incorporate a covariance matching loss to align second-order feature statistics between source and target domains. Extensive experiments conducted across 26 EDFAs in the COSMOS and Open Ireland testbeds demonstrate that the proposed approach significantly reduces the number of measurements requirements on the system while achieving lower mean absolute errors and improved error distributions compared to benchmark methods.