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Jul, 2023
用于数据稀缺光谱应用的生成对抗网络
Generative adversarial networks for data-scarce spectral applications
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Juan José García-Esteban, Juan Carlos Cuevas, Jorge Bravo-Abad
TL;DR
本文介绍了生成对抗网络在合成光谱数据方面的应用,提出了改进的 Wasserstein GANs 来避免模型崩溃,并证明了利用 CWGAN 数据增强可充分提高性能,同时显示 CWGAN 在低数据量条件下可作为简单 FFNN 的代理模型。
Abstract
generative adversarial networks
(GANs) are one of the most robust and versatile techniques in the field of generative artificial intelligence. In this work, we report on an application of GANs in the domain of synthetic
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