Recent progress in deep generative models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) has enabled synthesizing photo-realistic images, such as faces and scenes. However, it remains much less explored on what has been learned inside the deep representations learned from synthesizing images. In this talk, I will present some of our recent progress in interpreting the semantics in the latent space of the GANs, as well as reversing real images back into the latent space. Identifying these semantics not only allows us to better understand the internal mechanism in generative models, but also facilitates versatile real image editing applications.