Diamond in the rough: Improving image realism by traversing the GAN latent space

TL;DR

This paper introduces an unsupervised method to enhance the realism of images generated by existing low-complexity GANs by traversing their latent space, improving image fidelity without altering the network architecture.

Contribution

The authors propose a novel unsupervised technique to find a latent space direction that improves image realism, applicable to various GAN architectures without retraining.

Findings

Improved Frechet Inception Distance (FID) scores along the latent trajectory.

Method generalizes across multiple datasets and GAN architectures.

Enhances image realism without modifying the original GAN models.

Abstract

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.