Image Processing Using Multi-Code GAN Prior
Jinjin Gu1  Yujun Shen2  Bolei Zhou2 
1The Chinese University of Hong Kong, Shenzhen
2The Chinese University of Hong Kong
Overview
We propose multi-code GAN prior (mGANprior) to incorporate the well-trained GANs as effective prior to a variety of image processing tasks. In particular, we employ multiple latent codes to invert a fixed GAN model, and then introduce adaptive channel importance to compose the features maps from these codes at some intermediate layer of the generator. The resulting high-fidelity image reconstruction enables the trained GAN models as prior to many real-world applications, such as image colorization, super-resolution, image inpainting, and semantic manipulation.
Results
Various image processing tasks with mGANprior.
BibTeX
@inproceedings{gu2020mganprior,
  title     = {Image Processing Using Multi-Code GAN Prior},
  author    = {Gu, Jinjin and Shen, Yujun and Zhou, Bolei},
  booktitle = {CVPR},
  year      = {2020}
}
Related Work
Y. Shen, J. Gu, X. Tang, B. Zhou. Interpreting the Latent Space of GANs for Semantic Face Editing. CVPR 2020.
Comment: Interprets the face semantics emerging in the latent space of GANs.
D. Ulyanov, A. Vedaldi, V. Lempitsky. Deep Image Prior. CVPR 2018.
Comment: Uses deep neural networks (discriminative model) as structured image prior.