  Generative Adversarial Networks (GANs) have achieved state-of-the-art
performance for several image generation and manipulation tasks. Different
works have improved the limited understanding of the latent space of GANs by
embedding images into specific GAN architectures to reconstruct the original
images. We present a novel StyleGAN-based autoencoder architecture, which can
reconstruct images with very high quality across several data domains. We
demonstrate a previously unknown grade of generalizablility by training the
encoder and decoder independently and on different datasets. Furthermore, we
provide new insights about the significance and capabilities of noise inputs of
the well-known StyleGAN architecture. Our proposed architecture can handle up
to 40 images per second on a single GPU, which is approximately 28x faster than
previous approaches. Finally, our model also shows promising results, when
compared to the state-of-the-art on the image denoising task, although it was
not explicitly designed for this task.
