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Quickly Transforming Discriminator in Pre-Trained GAN to Encoder
  • Cheng Yu ,
  • Wenmin Wang
Cheng Yu
Macau University of Science and Technology, Macau University of Science and Technology

Corresponding Author:[email protected]

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Wenmin Wang
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Abstract– Fine-designed deep Generative Adversarial Networks (GANs) can generate high-quality (HQ) images. However, the discriminator in GAN only plays a role to distinguish candidates produced by the generator from the true data distribution, and numerous generated samples are still not clear and true. From pre-trained GAN, we offer a self-supervised method to quickly transform the discriminator into an encoder and fine-tune the pre-trained GAN to an auto-encoder. The parameters of the pre-trained discriminator are reused and converted into an encoder for outputting reformed latent space. The transformation changes the previous GAN to a symmetrical architecture and the generator can reconstruct the HQ image by reforming latent space. By fixing the generator, the reformed latent space can perform better representation than the pre-trained GAN, and the performance of the pre-trained GAN can be improved by the transformed encoder.
Jan 2022Published in Pattern Recognition Letters volume 153 on pages 92-99. 10.1016/j.patrec.2021.11.026