Abstract
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.