Generative adversarial networks (GANs) have achieved impressive results
today, but not all generated images are perfect. A number of quantitative
criteria have recently emerged for generative model, but none of them are
designed for a single generated image. In this paper, we propose a new research
topic, Generated Image Quality Assessment (GIQA), which quantitatively
evaluates the quality of each generated image. We introduce three GIQA
algorithms from two perspectives: learning-based and data-based. We evaluate a
number of images generated by various recent GAN models on different datasets
and demonstrate that they are consistent with human assessments. Furthermore,
GIQA is available to many applications, like separately evaluating the realism
and diversity of generative models, and enabling online hard negative mining
(OHEM) in the training of GANs to improve the results.

本研究提出 Generated Image Quality Assessment (GIQA) 算法，从学习和数据两个角度，定量评估 GAN 模型生成的图像质量。实验证明 GIQA 算法结果与人工评估基本一致，可应用于 GAN 模型的真实性和多样性评估，并能在训练中支持在线 hard negative mining。