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.
@article{arxiv.2102.10485,
title = {Scalable Balanced Training of Conditional Generative Adversarial Neural Networks on Image Data},
author = {Massimiliano Lupo Pasini and Vittorio Gabbi and Junqi Yin and Simona Perotto and Nouamane Laanait},
journal= {arXiv preprint arXiv:2102.10485},
year = {2021}
}