Synthesizing Programs for Images using Reinforced Adversarial Learning

Understand

Advances in deep generative networks have led to impressive results in recent years.

• Nevertheless, such models can often waste their capacity on the minutiae of datasets, presumably due to weak inductive biases in their decoders.
• This is where graphics engines may come in handy since they abstract away low-level details and represent images as high-level programs.
• Current methods that combine deep learning and renderers are limited by hand-crafted likelihood or distance functions, a need for large amounts of supervision, or difficulties in scaling their inference algorithms to richer datasets.