Abstract
This article presents an approach using parallel/distributed generative adversarial networks for image data augmentation, applied to generate COVID-19 training samples for computational intelligence methods. This is a relevant problem nowadays, considering the recent COVID-19 pandemic. Computational intelligence and learning methods are useful tools to assist physicians in the process of diagnosing diseases and acquire valuable medical knowledge. A specific generative adversarial network approach trained using a co-evolutionary algorithm is implemented, including a three-level parallel approach combining distributed memory and fine-grained parallelization using CPU and GPU. The experimental evaluation of the proposed method was performed on the high performance computing infrastructure provided by National Supercomputing Center, Uruguay. The main experimental results indicate that the proposed model is able to generate accurate images and the \(3\times 3\) version of the distributed GAN has better robustness properties of its training process, allowing to generate better and more diverse images.
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Acknowledgment
The work of S. Nesmachnow is partly supported by ANII and PEDECIBA, Uruguay. J. Toutouh has been partially funded by EU Horizon 2020 research and innovation programme (Marie Skłodowska-Curie grant agreement No 799078), by the Spanish MINECO and FEDER projects TIN2017-88213-R and UMA18-FEDERJA-003, and the Systems that learn initiative at MIT CSAIL.
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Toutouh, J., Esteban, M., Nesmachnow, S. (2021). Parallel/Distributed Generative Adversarial Neural Networks for Data Augmentation of COVID-19 Training Images. In: Nesmachnow, S., Castro, H., Tchernykh, A. (eds) High Performance Computing. CARLA 2020. Communications in Computer and Information Science, vol 1327. Springer, Cham. https://doi.org/10.1007/978-3-030-68035-0_12
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