论文标题
Evolgan：进化生成对抗网络
EvolGAN: Evolutionary Generative Adversarial Networks
论文作者
论文摘要
我们建议使用质量估计器和进化方法来搜索在小型，困难数据集或两者兼而有之的生成对抗网络的潜在空间。新方法会导致产生明显更高的图像，同时保留原始发电机的多样性。人类评估者更喜欢新版本的图像，猫的频率为83.7％，时装犬74％，马匹为70.4％，艺术品的69.2％，以及已经出色的面部剂量的少量改进。这种方法适用于任何高质量的得分手和GAN发电机。
We propose to use a quality estimator and evolutionary methods to search the latent space of generative adversarial networks trained on small, difficult datasets, or both. The new method leads to the generation of significantly higher quality images while preserving the original generator's diversity. Human raters preferred an image from the new version with frequency 83.7pc for Cats, 74pc for FashionGen, 70.4pc for Horses, and 69.2pc for Artworks, and minor improvements for the already excellent GANs for faces. This approach applies to any quality scorer and GAN generator.
