电子能量损失光谱的机器学习数据增强策略:生成对抗网络
Daniel Del-Pozo-Bueno1,2, Demie Kepaptsoglou3,4, Quentin M Ramasse3,5
1LENS-MIND, Departament d'Enginyeria Electrònica i Biomèdica, Universitat de Barcelona, 1-11 Martí i Franquès, 08028 Barcelona, Spain.
概括
本研究引入了数据增强生成对抗网络 (DAG),以从有限的样本中创建现实的电子能量损失光谱 (EELS) 数据. 生成的数据有效地训练人工神经网络 (ANN) 和支持矢量机器 (SVM) 用于光谱分类.
科学领域:
- 材料科学 材料科学 材料科学
- 数据科学数据科学数据科学
- 频谱学是一种光谱学.
背景情况:
- 监督机器学习 (ML) 需要大量高质量的数据来进行有效的算法训练.
- 电子能量损失光谱 (EELS) 数据通常是有限的,这对ML模型开发构成了挑战.
研究的目的:
- 开发一种新的数据增强 (DA) 策略,用于使用生成对抗网络 (GAN) 的EELS数据.
- 为了使ML分类器能够训练具有有限EELS光谱数据的ML分类器.
主要方法:
- 实施数据增强生成对抗网络 (DAG) 方法.
- 探索最佳GAN配置以生成现实的EELS频谱.
- 利用生成的光谱来训练人工神经网络 (ANN) 和支持矢量机器 (SVM).
主要成果:
- DAG成功地从一个小数据集 (大约100个光谱) 中生成了现实的EELS光谱.
- 在DAG生成的数据上训练的分类器在分类实验EEL光谱方面取得了成功.
结论:
- 开发的DAG战略有效地解决了EELS的数据稀缺问题.
- 生成的EELS光谱可用于为现实应用培训强大的ML分类器.
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