Training neural networks on domain randomized simulations for ultrasonic inspection [version 2; peer review: 2 approved]
To overcome the data scarcity problem of machine learning for nondestructive testing, data augmentation is a commonly used strategy. We propose a method to enable training of neural networks exclusively on simulated data. Simulations not only provide a scalable way to generate and access training da...
Main Authors: | Klaus Schlachter, Sebastian Zambal, Kastor Felsner |
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Format: | Article |
Language: | English |
Published: |
F1000 Research Ltd
2022-05-01
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Series: | Open Research Europe |
Subjects: | |
Online Access: | https://open-research-europe.ec.europa.eu/articles/2-43/v2 |
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