A Survey on Leveraging Pre-trained Generative Adversarial Networks for Image Editing and Restoration

2022/07/21 by Ming Liu, Liu, Ming, Yuxiang Wei +7
Computer Science · #Generative Adversarial Networks and Image Synthesis #Advanced Image Processing Techniques #Digital Media Forensic Detection

paper · pdf · doi:10.48550/arxiv.2207.10309

Abstract

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., 1024×1024) 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.

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