利用多频率补充输入的深度学习模型加强频谱预测

Xiaohua Xing, Yuqi Ren, Die Zou, Qiankun Zhang, Bingxuan Mao, Jianquan Yao, Deyi Xiong, Liang Wu
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引用次数: 0

摘要

近来,深度学习技术的飞速发展为各个领域带来了前所未有的变革和创新。虽然基于神经网络的方法可以有效地编码数据并检测潜在的特征模式,但不同领域的数据格式和组成各不相同,这给有效利用这些数据带来了挑战,尤其是对某些处于深度学习集成早期阶段的研究领域而言。因此,找到更有效的方法来利用现有数据集至关重要。在此,我们证明了在目标频谱预测过程中,只需在现有数据集上添加补充多频输入,就能显著提高网络的预测精度。这种设计方法为深度学习与其他领域(如光子学、复合材料设计和生物医学)的跨学科研究和应用铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhanced spectrum prediction using deep learning models with multi-frequency supplementary inputs
Recently, the rapid progress of deep learning techniques has brought unprecedented transformations and innovations across various fields. While neural network-based approaches can effectively encode data and detect underlying patterns of features, the diverse formats and compositions of data in different fields pose challenges in effectively utilizing these data, especially for certain research fields in the early stages of integrating deep learning. Therefore, it is crucial to find more efficient ways to utilize existing datasets. Here, we demonstrate that the predictive accuracy of the network can be improved dramatically by simply adding supplementary multi-frequency inputs to the existing dataset in the target spectrum predicting process. This design methodology paves the way for interdisciplinary research and applications at the interface of deep learning and other fields, such as photonics, composite material design, and biological medicine.
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