# Addressing Data Scarcity in Optical Matrix Multiplier Modeling Using Transfer Learning

arXiv: https://arxiv.org/abs/2308.11630 · HTML: https://mlsanity.com/paper/2308.11630/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 5 references that are in the map.
- First posted on arXiv 10 Aug 2023.

## Builds on (most cited first, 1 of 1)

- [Data-efficient Modeling of Optical Matrix Multipliers Using Transfer Learning](https://mlsanity.com/paper/2211.16038/) (2022)

## Abstract (arXiv)

We present and experimentally evaluate using transfer learning to address experimental data scarcity when training neural network (NN) models for Mach-Zehnder interferometer mesh-based optical matrix multipliers. Our approach involves pre-training the model using synthetic data generated from a less accurate analytical model and fine-tuning with experimental data. Our investigation demonstrates that this method yields significant reductions in modeling errors compared to using an analytical model, or a standalone NN model when training data is limited. Utilizing regularization techniques and ensemble averaging, we achieve < 1 dB root-mean-square error on the matrix weights implemented by a 3x3 photonic chip while using only 25% of the available data.