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.

本研究探讨了生成对抗网络（GANs）中潜在空间的交错问题，并提出了一种新的框架SC$^2$GAN以实现解交错。通过重新投影原始潜在编码样本并根据高密度和低密度区域进行编辑方向的修正，利用原始的有意义方向和语义区域特定层插值原始潜在编码，生成罕见的属性组合的图像，然后将这些样本反转回原始潜在空间。我们将该框架应用于学习有意义潜在方向的现有方法，并展示了它在添加了少量低密度区域样本的情况下解交错属性的强大能力。

SC2GAN: 自校正相关 GAN 空间重新思考纠缠