  Generative adversarial networks (GANs) have drawn enormous attention due to
the simple yet effective training mechanism and superior image generation
quality. With the ability to generate photo-realistic high-resolution (e.g.,
$1024\times1024$) images, recent GAN models have greatly narrowed the gaps
between the generated images and the real ones. Therefore, many recent works
show emerging interest to take advantage of pre-trained GAN models by
exploiting the well-disentangled latent space and the learned GAN priors. In
this paper, we briefly review recent progress on leveraging pre-trained
large-scale GAN models from three aspects, i.e., 1) the training of large-scale
generative adversarial networks, 2) exploring and understanding the pre-trained
GAN models, and 3) leveraging these models for subsequent tasks like image
restoration and editing. More information about relevant methods and
repositories can be found at https://github.com/csmliu/pretrained-GANs.
