mlsanityData to 7 Oct 2026

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

arXiv 2207.10309 ↗First posted 21 Jul 2022cs.CV eess.IVcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 127 of its 175 references that are in the map. First posted on arXiv 21 Jul 2022.

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127 of 175 references.

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  1. 2023Text-guided Image-and-Shape Editing and Generation: A Short Surveycited by 3