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.