Title: Synthesizing Programs for Images using Reinforced Adversarial Learning,
Abstract: 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. To mitigate these
issues, we present SPIRAL, an adversarially trained agent that generates a
program which is executed by a graphics engine to interpret and sample images.
The goal of this agent is to fool a discriminator network that distinguishes
between real and rendered data, trained with a distributed reinforcement
learning setup without any supervision. A surprising finding is that using the
discriminator's output as a reward signal is the key to allow the agent to make
meaningful progress at matching the desired output rendering. To the best of
our knowledge, this is the first demonstration of an end-to-end, unsupervised
and adversarial inverse graphics agent on challenging real world (MNIST,
Omniglot, CelebA) and synthetic 3D datasets.