Generative Modeling using the Sliced Wasserstein Distance

Understand

Generative Adversarial Nets (GANs) are very successful at modeling distributions from given samples, even in the high-dimensional case.

• However, their formulation is also known to be hard to optimize and often not stable.
• While this is particularly true for early GAN formulations, there has been significant empirically motivated and theoretically founded progress to improve stability, for instance, by using the Wasserstein distance rather than the Jenson-Shannon divergence.
• Here, we consider an alternative formulation for generative modeling based on random projections which, in its simplest form, results in a single objective rather than a saddle-point formulation.