Articles | Volume 15, issue 19
https://doi.org/10.5194/amt-15-5793-2022
https://doi.org/10.5194/amt-15-5793-2022
Research article
 | 
14 Oct 2022
Research article |  | 14 Oct 2022

Neural network processing of holographic images

John S. Schreck, Gabrielle Gantos, Matthew Hayman, Aaron Bansemer, and David John Gagne

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Agrawal, S., Barrington, L., Bromberg, C., Burge, J., Gazen, C., and Hickey, J.: Machine learning for precipitation nowcasting from radar images, arXiv [preprint], https://doi.org/10.48550/arXiv.1912.12132, 11 December 2019. a
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Short summary
We show promising results for a new machine-learning based paradigm for processing field-acquired cloud droplet holograms. The approach is fast, scalable, and leverages GPUs and other heterogeneous computing platforms. It combines applications of transfer and active learning by using synthetic data for training and a small set of hand-labeled data for refinement and validation. Artificial noise applied during synthetic training enables optimized models for real-world situations.