  This paper describes a simple technique to analyze Generative Adversarial
Networks (GANs) and create interpretable controls for image synthesis, such as
change of viewpoint, aging, lighting, and time of day. We identify important
latent directions based on Principal Components Analysis (PCA) applied either
in latent space or feature space. Then, we show that a large number of
interpretable controls can be defined by layer-wise perturbation along the
principal directions. Moreover, we show that BigGAN can be controlled with
layer-wise inputs in a StyleGAN-like manner. We show results on different GANs
trained on various datasets, and demonstrate good qualitative matches to edit
directions found through earlier supervised approaches.
