SC2GAN: Rethinking Entanglement by Self-correcting Correlated GAN Space

TL;DR

SC2GAN introduces a novel method to improve disentanglement in GAN latent spaces by re-projecting and correcting editing directions using low-density samples, effectively reducing attribute entanglement.

Contribution

The paper proposes SC2GAN, a framework that enhances disentanglement in GANs by leveraging low-density latent samples to correct attribute directions, addressing limitations of previous methods.

Findings

Effective disentanglement of correlated attributes.

Improved generation of infrequent attribute combinations.

Strong performance with minimal low-density samples.

Abstract

Generative Adversarial Networks (GANs) can synthesize realistic images, with the learned latent space shown to encode rich semantic information with various interpretable directions. However, due to the unstructured nature of the learned latent space, it inherits the bias from the training data where specific groups of visual attributes that are not causally related tend to appear together, a phenomenon also known as spurious correlations, e.g., age and eyeglasses or women and lipsticks. Consequently, the learned distribution often lacks the proper modelling of the missing examples. The interpolation following editing directions for one attribute could result in entangled changes with other attributes. To address this problem, previous works typically adjust the learned directions to minimize the changes in other attributes, yet they still fail on strongly correlated features. In this work, we study the entanglement issue in both the training data and the learned latent space for the StyleGAN2-FFHQ model. We propose a novel framework SC$^2$GAN that achieves disentanglement by re-projecting low-density latent code samples in the original latent space and correcting the editing directions based on both the high-density and low-density regions. By leveraging the original meaningful directions and semantic region-specific layers, our framework interpolates the original latent codes to generate images with attribute combination that appears infrequently, then inverts these samples back to the original latent space. We apply our framework to pre-existing methods that learn meaningful latent directions and showcase its strong capability to disentangle the attributes with small amounts of low-density region samples added.