  In just a few years, the photo-realism of images synthesized by Generative
Adversarial Networks (GANs) has gone from somewhat reasonable to almost perfect
largely by increasing the complexity of the networks, e.g., adding layers,
intermediate latent spaces, style-transfer parameters, etc. This trajectory has
led many of the state-of-the-art GANs to be inaccessibly large, disengaging
many without large computational resources. Recognizing this, we explore a
method for squeezing additional performance from existing, low-complexity GANs.
Formally, we present an unsupervised method to find a direction in the latent
space that aligns with improved photo-realism. Our approach leaves the network
unchanged while enhancing the fidelity of the generated image. We use a simple
generator inversion to find the direction in the latent space that results in
the smallest change in the image space. Leveraging the learned structure of the
latent space, we find moving in this direction corrects many image artifacts
and brings the image into greater realism. We verify our findings qualitatively
and quantitatively, showing an improvement in Frechet Inception Distance (FID)
exists along our trajectory which surpasses the original GAN and other
approaches including a supervised method. We expand further and provide an
optimization method to automatically select latent vectors along the path that
balance the variation and realism of samples. We apply our method to several
diverse datasets and three architectures of varying complexity to illustrate
the generalizability of our approach. By expanding the utility of
low-complexity and existing networks, we hope to encourage the democratization
of GANs.
