论文标题
用于生成对抗神经网络的细胞训练的平行/分布式实施
Parallel/distributed implementation of cellular training for generative adversarial neural networks
论文作者
论文摘要
生成的对抗网络（GAN）广泛用于学习生成模型。 GAN由两个网络组成，即一个生成器和一个歧视器，它们应用对抗性学习以优化其参数。本文介绍了一种平行/分布式的实施，对培训两个甘人种群的蜂窝竞争协调方法进行了平行/分布式实施。提出了分布式内存并行实现，以在高性能/超级计算中心执行。报告了有效的结果，以解决手写数字的生成（MNIST数据集样本）。此外，拟议的实施能够在考虑不同的网格尺寸进行培训时减少训练时间并正确扩展。
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 optimize their parameters. This article presents a parallel/distributed implementation of a cellular competitive coevolutionary method to train two populations of GANs. A distributed memory parallel implementation is proposed for execution in high performance/supercomputing centers. Efficient results are reported on addressing the generation of handwritten digits (MNIST dataset samples). Moreover, the proposed implementation is able to reduce the training times and scale properly when considering different grid sizes for training.
