概括
转移学习在训练光学矩阵乘法器的神经网络模型时显著减少错误,即使实验数据有限. 这种方法使用合成数据进行预训练,提高光子芯片应用的准确性.
科学领域:
- 光子学是指光子学的使用方法.
- 光学计算是指光学计算的应用.
- 机器学习 机器学习
背景情况:
- 为光学矩阵乘法器训练神经网络 (NN) 模型通常需要大量的实验数据.
- 数据稀缺性在开发精确的光子设备方面构成了重大挑战.
研究的目的:
- 评估转移学习用于训练NN模型的基于马赫-泽恩德干扰仪网状光学矩阵乘法器的转移学习.
- 为了解决NN模型培训中的实验数据稀缺性,用于光子应用.
主要方法:
- 从分析模型中使用合成数据进行NN模型预训练.
- 在有限的实验数据下微调预训练模型.
- 使用规范化技术和整体平均化.
主要成果:
- 与独立的分析模型或NN模型相比,转移学习显著减少了建模错误.
- 在3x3矩阵权重上实现了<1dB的平方中根误差.
- 成功训练模型,只使用25%的可用实验数据.
结论:
- 转移学习是克服数据稀缺的有效策略,用于训练光学矩阵乘法器的NN模型.
- 拟议的方法可以在减少实验力度的情况下准确地建模光子装置.
- 这种方法提高了开发复杂光子集成电路的实用性.
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