  Generative adversarial nets (GANs) have been successfully applied in many
fields like image generation, inpainting, super-resolution and drug discovery,
etc., by now, the inner process of GANs is far from been understood. To get
deeper insight of the intrinsic mechanism of GANs, in this paper, a method for
interpreting the latent space of GANs by analyzing the correlation between
latent variables and the corresponding semantic contents in generated images is
proposed. Unlike previous methods that focus on dissecting models via feature
visualization, the emphasis of this work is put on the variables in latent
space, i.e. how the latent variables affect the quantitative analysis of
generated results. Given a pretrained GAN model with weights fixed, the latent
variables are intervened to analyze their effect on the semantic content in
generated images. A set of controlling latent variables can be derived for
specific content generation, and the controllable semantic content manipulation
be achieved. The proposed method is testified on the datasets Fashion-MNIST and
UT Zappos50K, experiment results show its effectiveness.
