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Scalable Balanced Training of Conditional Generative Adversarial Neural Networks on Image Data

21 February 2021
Massimiliano Lupo Pasini
Vittorio Gabbi
Junqi Yin
S. Perotto
N. Laanait
    GAN
    AI4CE
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Abstract

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 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 1,000 processes and 2,000 NVIDIA V100 GPUs on the OLCF supercomputer Summit.

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