[Re] GANSpace: Discovering Interpretable GAN Controls

This work undertakes a reproducibility study to validate the claims and reproduce the results presented in GANSpace: Discovering Interpretable GAN Controls, which was accepted at NeurIPS 2020. GANSpace is a technique that creates interpretable controls for image synthesis in an unsupervised fashion using pretrained GANs and Principal Component Analysis (PCA). The authors claim that layer-wise perturbations along the principal directions identified using PCA (applied on the latent or feature space) can be used to define a large number of interpretable controls that affect low and high level features of the image such as lighting attributes, facial attributes, and object pose and shape. In our study, we primarily focus on reproducing results on the StyleGAN and StyleGAN2 models. We also present additional results which were not in the original paper.