Survey on leveraging pre-trained generative adversarial networks for image editing and restoration

Ming Liu, Yuxiang Wei, Xiaohe Wu, Wangmeng Zuo, Lei Zhang

Abstract: Generative adversarial networks (GANs) have drawn enormous attention due to their simple yet effective training mechanism and superior image generation quality. With the ability to generate photorealistic high-resolution (e.g., 1024 × 1024) images, recent GAN models have greatly narrowed the gaps between the generated images and the real ones. Therefore, many recent studies 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 study, 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.