Generative adversarial parallelization
… We observe that more parallelization leads to less of a spread between the train and … ,
we show individual learning curves of the parallelized GANs (see Figure 13 in Appendix A.3). …
we show individual learning curves of the parallelized GANs (see Figure 13 in Appendix A.3). …
Parallelizing training of deep generative models on massive scientific datasets
SA Jacobs, B Van Essen, D Hysom… - 2019 IEEE …, 2019 - ieeexplore.ieee.org
… We present a novel tournament method to train traditional as well as generative adversarial
networks built on LBANN, a scalable deep learning framework optimized for HPC systems. …
networks built on LBANN, a scalable deep learning framework optimized for HPC systems. …
A Reconfigurable Parallelization of Generative Adversarial Networks based on Array Processor
X Xie, M Chai, Z Du, K Yang… - 2021 Asia-Pacific Signal …, 2021 - ieeexplore.ieee.org
Aiming at the intensive calculations of convolution and the invalid calculations caused by “zero”
inserted of deconvolution in Generative Adversarial Network (GAN), which makes …
inserted of deconvolution in Generative Adversarial Network (GAN), which makes …
Parallel/distributed generative adversarial neural networks for data augmentation of COVID-19 training images
J Toutouh, M Esteban, S Nesmachnow - Latin American High Performance …, 2020 - Springer
… in practice, and proposed the Generative Adversarial Parallelization (GAP) framework for the
… This approach extends the two-player generative adversarial game into a multi-player game…
… This approach extends the two-player generative adversarial game into a multi-player game…
Parallel/distributed implementation of cellular training for generative adversarial neural networks
E Pérez, S Nesmachnow, J Toutouh… - 2020 IEEE …, 2020 - ieeexplore.ieee.org
Generative adversarial networks (GANs) are widely used to learn generative models. GANs
consist of two networks, a generator and a discriminator, that apply adversarial learning to …
consist of two networks, a generator and a discriminator, that apply adversarial learning to …
Data-parallel training of generative adversarial networks on hpc systems for hep simulations
S Vallecorsa, D Moise, F Carminati… - 2018 IEEE 25th …, 2018 - ieeexplore.ieee.org
… essential and the accent should therefore be on optimizing the computing resources
needed to train the networks, studying parallelization and cross-platform development. From a …
needed to train the networks, studying parallelization and cross-platform development. From a …
Stable parallel training of Wasserstein conditional generative adversarial neural networks
M Lupo Pasini, J Yin - The Journal of Supercomputing, 2023 - Springer
… Our method to parallelize the GANs training is better suited than previous distributed GANs
training methods to attain accurate results in situations of fixed and limited computational …
training methods to attain accurate results in situations of fixed and limited computational …
Distributed training of generative adversarial networks for fast detector simulation
… typically used in the field of image recognition: Generative Adversarial Networks (GANs) [6]. …
needed to train the networks, studying parallelization on clusters and cross-platform …
needed to train the networks, studying parallelization on clusters and cross-platform …
Computationally efficient neural rendering for generator adversarial networks using a multi-GPU cluster in a cloud environment
A Ravikumar, H Sriraman - IEEE Access, 2023 - ieeexplore.ieee.org
… In this study, the training procedure for the GAN is parallelized and carried out over many …
Due to their strong modeling capability, generative adversarial networks (GANs) can learn …
Due to their strong modeling capability, generative adversarial networks (GANs) can learn …
Ganax: A unified mimd-simd acceleration for generative adversarial networks
A Yazdanbakhsh, H Falahati, PJ Wolfe… - 2018 ACM/IEEE 45th …, 2018 - ieeexplore.ieee.org
… Generative adversarial networks harness both generative and discriminative deep models
in a game theoretical framework to generate close-to-real synthetic data. The generative …
in a game theoretical framework to generate close-to-real synthetic data. The generative …