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Image Processing Using Multi-Code GAN Prior
[article]
2020
arXiv
pre-print
Despite the success of Generative Adversarial Networks (GANs) in image synthesis, applying trained GAN models to real image processing remains challenging. Previous methods typically invert a target image back to the latent space either by back-propagation or by learning an additional encoder. However, the reconstructions from both of the methods are far from ideal. In this work, we propose a novel approach, called mGANprior, to incorporate the well-trained GANs as effective prior to a variety
arXiv:1912.07116v2
fatcat:cxb6ezakbre6hhktd6hmctpk2u