We propose a stable, parallel approach to train Wasserstein Conditional
Generative Adversarial Neural Networks (W-CGANs) under the constraint of a
fixed computational budget. Differently from previous distributed GANs training
techniques, our approach avoids inter-process communications, reduces the risk
of mode collapse and enhances scalability by using multiple generators, each
one of them concurrently trained on a single data label. The use of the
Wasserstein metric also reduces the risk of cycling by stabilizing the training
of each generator. We illustrate the approach on the CIFAR10, CIFAR100, and
ImageNet1k datasets, three standard benchmark image datasets, maintaining the
original resolution of the images for each dataset. Performance is assessed in
terms of scalability and final accuracy within a limited fixed computational
time and computational resources. To measure accuracy, we use the inception
score, the Frechet inception distance, and image quality. An improvement in
inception score and Frechet inception distance is shown in comparison to
previous results obtained by performing the parallel approach on deep
convolutional conditional generative adversarial neural networks (DC-CGANs) as
well as an improvement of image quality of the new images created by the GANs
approach. Weak scaling is attained on both datasets using up to 2,000 NVIDIA
V100 GPUs on the OLCF supercomputer Summit.

提出了稳定且并行的方法，用固定的计算资源训练 Wasserstein 条件 GAN，通过使用多个生成器并避免进程间通信来减少模式塌陷的风险和提高可扩展性。在 CIFAR10，CIFAR100 和 ImageNet1k 数据集上验证了该方法的性能，并以固定计算时间内的可扩展性和最终准确性来评估性能。使用达到 2000 个 NVIDIA V100 GPU 的 OLCF 超算机 Summit 进行弱扩展.