Deep Structured Generative Models

Understand

Deep generative models have shown promising results in generating realistic images, but it is still non-trivial to generate images with complicated structures.

• The main reason is that most of the current generative models fail to explore the structures in the images including spatial layout and semantic relations between objects.
• To address this issue, we propose a novel deep structured generative model which boosts generative adversarial networks (GANs) with the aid of structure information.
• In particular, the layout or structure of the scene is encoded by a stochastic and-or graph (sAOG), in which the terminal nodes represent single objects and edges represent relations between objects.