Scalable balanced training of conditional generative adversarial neural networks on image data

Lupo Pasini, Massimiliano; Gabbi, Vittorio; Yin, Junqi; Perotto, Simona; Laanait, Nouamane

doi:10.1007/s11227-021-03808-2

Scalable balanced training of conditional generative adversarial neural networks on image data

Journal Article · Mon Apr 26 00:00:00 EDT 2021 · Journal of Supercomputing

DOI:https://doi.org/10.1007/s11227-021-03808-2· OSTI ID:1783019

^[1]; Gabbi, Vittorio ^[2]; ^[1]; ^[2]; Laanait, Nouamane ^[3]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Politecnico di Milano (Italy)
Anthem, Inc., Atlanta, GA (United States)

Here, we propose a distributed approach to train deep convolutional generative adversarial neural network (DC-CGANs) models. Our method reduces the imbalance between generator and discriminator by partitioning the training data according to data labels, and enhances scalability by performing a parallel training where multiple generators are concurrently trained, each one of them focusing on a single data label. Performance is assessed in terms of inception score, Fréchet inception distance, and image quality on MNIST, CIFAR10, CIFAR100, and ImageNet1k datasets, showing a significant improvement in comparison to state-of-the-art techniques to training DC-CGANs. Weak scaling is attained on all the four datasets using up to 1000 processes and 2000 NVIDIA V100 GPUs on the OLCF supercomputer Summit.

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1783019

Journal Information:: Journal of Supercomputing, Journal Name: Journal of Supercomputing Vol. 77; ISSN 0920-8542

Publisher:: SpringerCopyright Statement

Country of Publication:: United States

Language:: English

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Scalable balanced training of conditional generative adversarial neural networks on image data

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