{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-survey-on-leveraging-pre-trained-generative","title":"A Survey on Leveraging Pre-trained Generative Adversarial Networks for Image Editing and Restoration","arxiv_id":"2207.10309","date":"2022-07-21","proceeding":null,"authors":["Ming Liu","Yuxiang Wei","Xiaohe Wu","WangMeng Zuo","Lei Zhang"],"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., $1024\\times1024$) 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.","url_abs":"https://arxiv.org/abs/2207.10309v1","url_pdf":"https://arxiv.org/pdf/2207.10309v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-survey-on-leveraging-pre-trained-generative","repo_url":"https://github.com/csmliu/pretrained-gans","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"image-restoration","task_name":"Image Restoration"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2207.10309","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}