  Significant progress has been made by the advances in Generative Adversarial
Networks (GANs) for image generation. However, there lacks enough understanding
of how a realistic image is generated by the deep representations of GANs from
a random vector. This chapter gives a summary of recent works on interpreting
deep generative models. The methods are categorized into the supervised, the
unsupervised, and the embedding-guided approaches. We will see how the
human-understandable concepts that emerge in the learned representation can be
identified and used for interactive image generation and editing.
