… 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 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. …

Aiming at the intensive calculations of convolution and the invalid calculations caused by “zero”
inserted of deconvolution in Generative Adversarial Network (GAN), which makes …

… 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…

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 …

… 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 …

… 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 …

… 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 …

… 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 …

… Generative adversarial networks harness both generative and discriminative deep models
in a game theoretical framework to generate close-to-real synthetic data. The generative …