With its tremendous success in many machine learning and pattern recognition tasks, deep learning, as one type of data-driven models, has also led to many breakthroughs in other disciplines including physics, chemistry and material science. Nevertheless, the supremacy of deep learning over conventional optimization approaches heavily depends on the huge amount of data collected in advance to train the model, which is a common bottleneck of such a data-driven technique. In this work, we present a comprehensive deep learning model for the design and characterization of nanophotonic structures, where a self-supervised learning mechanism is introduced to alleviate the burden of data acquisition. Taking reflective metasurfaces as an example, we demonstrate that the self-supervised deep learning model can effectively utilize randomly generated unlabeled data during training, with the total test loss and prediction accuracy improved by about 15% compared with the fully supervised counterpart. The proposed self-supervised learning scheme provides an efficient solution for deep learning models in some physics-related tasks where labeled data are limited or expensive to collect.

中文翻译：

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用于纳米光子结构设计和表征的数据有效自我监督深度学习模型

凭借在许多机器学习和模式识别任务中的巨大成功，深度学习作为一种数据驱动的模型，在物理，化学和材料科学等其他领域也取得了许多突破。尽管如此，深度学习优于常规优化方法的优势在很大程度上取决于预先收集的大量数据来训练模型，这是这种数据驱动技术的常见瓶颈。在这项工作中，我们为纳米光子结构的设计和表征提供了一个全面的深度学习模型，其中引入了一种自我监督的学习机制来减轻数据采集的负担。以反射超表面为例，我们证明了自我监督的深度学习模型可以在训练期间有效利用随机生成的未标记数据，与完全监督的对应模型相比，总测试损失和预测准确性提高了约15％。所提出的自我监督学习方案为某些与物理相关的任务中的深度学习模型提供了有效的解决方案，其中标注的数据有限或收集起来昂贵。