Metric learning: a new approach for defining similarity measures for nanophotonics

Le résumé fourni par la source

In this work, we present a new approach based on metric learning for defining new similarity measures that are well-matched for design tasks in nanophotonics. Majority of the existing approaches use mean squared error (MSE) or mean absolute error (MAE) as the similarity measure to compare the desired and optimal spectra while it is clear that point-wise distance cannot capture the important features of the responses. Here, our goal is to use deep metric learning to provide a systematic approach for defining new metrics in nanophotonics.

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