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.

翻译：我们提出并实验验证了利用迁移学习解决在训练马赫-曾德尔干涉仪网状光学矩阵乘法器的神经网络模型时面临的实验数据稀缺问题。该方法先使用由精度较低的分析模型生成的合成数据对模型进行预训练，再通过实验数据进行微调。研究表明，在训练数据有限的情况下，与直接使用分析模型或独立的神经网络模型相比，该方法能显著降低建模误差。结合正则化技术与集成平均方法，我们仅利用可用数据的25%，便使3×3光子芯片实现的矩阵权重的均方根误差低于1 dB。