Abstract
Finely tuned deep generative adversarial networks (GANs) can generate
high-quality (HQ) images. However, the discriminator in GAN is only able
to distinguish true or fake images. Moreover, numerous synthesized
images from GANs are imperfect, and we can not reconstruct those images
via GANs. In this paper, we revisit pre-trained GANs and offer a
self-supervised method to quickly transform GAN’s discriminators into
encoders. We reuse parameters of the GAN’s discriminator and replace its
output layer, so that it can be transformed into an encoder and output
reformed latent vectors. The transformation makes the pre-trained GAN a
symmetrical architecture and allows for better performance. Based on the
method, GANs can be made to reconstruct synthesized images via encoders.
Compared to synthesized images, these reconstructions can maintain or
even attain higher quality.