GAN Prior

GAN priors, pre-trained generative adversarial networks, are increasingly used to improve various image processing tasks by leveraging their learned understanding of realistic image structures. Current research focuses on effectively integrating GAN priors into diverse applications, such as model inversion attacks, dataset distillation, image retouching, and super-resolution, often by exploring intermediate features within the GAN architecture or developing novel parameterization methods for better control and generalization. This approach enhances the quality and realism of generated or manipulated images, leading to advancements in areas like few-shot learning and improving the efficiency of existing algorithms. The impact spans both improved security analysis of deep learning models and the development of more powerful and efficient image editing and generation tools.