  Despite the success of Generative Adversarial Networks (GANs) in image
synthesis, there lacks enough understanding on what generative models have
learned inside the deep generative representations and how photo-realistic
images are able to be composed of the layer-wise stochasticity introduced in
recent GANs. In this work, we show that highly-structured semantic hierarchy
emerges as variation factors from synthesizing scenes from the generative
representations in state-of-the-art GAN models, like StyleGAN and BigGAN. By
probing the layer-wise representations with a broad set of semantics at
different abstraction levels, we are able to quantify the causality between the
activations and semantics occurring in the output image. Such a quantification
identifies the human-understandable variation factors learned by GANs to
compose scenes. The qualitative and quantitative results further suggest that
the generative representations learned by the GANs with layer-wise latent codes
are specialized to synthesize different hierarchical semantics: the early
layers tend to determine the spatial layout and configuration, the middle
layers control the categorical objects, and the later layers finally render the
scene attributes as well as color scheme. Identifying such a set of
manipulatable latent variation factors facilitates semantic scene manipulation.

Computer Vision and Pattern Recognition;Graphics;Machine Learning