PolyGAN: High-Order Polynomial Generators

Understand

Generative Adversarial Networks (GANs) have become the gold standard when it comes to learning generative models for high-dimensional distributions.

• Since their advent, numerous variations of GANs have been introduced in the literature, primarily focusing on utilization of novel loss functions, optimization/regularization strategies and network architectures.
• In this paper, we turn our attention to the generator and investigate the use of high-order polynomials as an alternative class of universal function approximators.
• Concretely, we propose PolyGAN, where we model the data generator by means of a high-order polynomial whose unknown parameters are naturally represented by high-order tensors.