Generative adversarial networks (GAN) have been effective for learning generative models for real-world data. However, existing GANs (GAN and its variants) tend to suffer from training problems such as instability and mode collapse. In this paper, we propose a novel GAN framework called evolutionary generative adversarial networks (E-GAN) for stable GAN training and improved generative performance. Unlike existing GANs, which employ a pre-defined adversarial objective function alternately training a generator and a discriminator, we utilize different adversarial training objectives as mutation operations and evolve a population of generators to adapt to the environment (i.e., the discriminator). We also utilize an evaluation mechanism to measure the quality and diversity of generated samples, such that only well-performing generator(s) are preserved and used for further training. In this way, E-GAN overcomes the limitations of an individual adversarial training objective and always preserves the best offspring, contributing to progress in and the success of GANs. Experiments on several datasets demonstrate that E-GAN achieves convincing generative performance and reduces the training problems inherent in existing GANs.

本文提出了一种新的 GAN 框架，名为进化生成对抗网络 (E-GAN)，该框架使用不同的对抗训练目标作为突变操作，通过进化一群生成器来适应环境(判别器)以实现稳定的 GAN 训练和改进的生成能力，并利用评估机制来衡量生成样本的质量和多样性，从而始终保留最佳基因用于进一步的训练，从而提高了GAN的整体训练效果和生成性能。

进化生成式对抗网络