论文标题
分析分布式协同进化GAN训练的组件
Analyzing the Components of Distributed Coevolutionary GAN Training
论文作者
论文摘要
分布式协同进化生成对抗网（GAN）培训在克服GAN训练病理方面的成功表明了成功。这主要是由于培训过程中发电机和歧视者种群的多样性维持。此处研究的方法是在整个摩尔社区重叠的空间网格的每个单元上进行的辅助子选集。我们研究了影响共同进化过程中多样性的两种算法组件的性能的影响：每个子人群内部的基于绩效的选择/替换以及通过在重叠社区中的解决方案（网络）迁移的通信。在MNIST数据集的实验中，我们发现这两个组件的组合提供了最佳的生成模型。此外，迁移解决方案而无需在子人群中应用选择的解决方案取得了竞争性的结果，而无需细胞之间的无通信的选择会降低性能。
Distributed coevolutionary Generative Adversarial Network (GAN) training has empirically shown success in overcoming GAN training pathologies. This is mainly due to diversity maintenance in the populations of generators and discriminators during the training process. The method studied here coevolves sub-populations on each cell of a spatial grid organized into overlapping Moore neighborhoods. We investigate the impact on the performance of two algorithm components that influence the diversity during coevolution: the performance-based selection/replacement inside each sub-population and the communication through migration of solutions (networks) among overlapping neighborhoods. In experiments on MNIST dataset, we find that the combination of these two components provides the best generative models. In addition, migrating solutions without applying selection in the sub-populations achieves competitive results, while selection without communication between cells reduces performance.
