Parallel/distributed implementation of cellular training for generative adversarial neural networks
This work addresses the computational bottleneck of training GANs for researchers and practitioners in machine learning, but it is incremental as it focuses on implementation improvements rather than new algorithmic insights.
The paper tackles the problem of training generative adversarial networks (GANs) more efficiently by proposing a parallel/distributed implementation of a cellular competitive coevolutionary method, reporting reduced training times and proper scaling with different grid sizes on the MNIST dataset.
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.