OTGANs
OTGANs, or Optimal Transport GANs, are a class of generative models that incorporate optimal transport theory into the framework of generative adversarial networks. The central idea is to measure the discrepancy between the real data distribution and the model’s generated distribution using an optimal transport distance, rather than traditional divergences like Jensen-Shannon. This distance reflects the minimum cost of morphing one distribution into the other under a specified transport cost.
In OTGAN formulations, the transport distance between real samples x and generated samples G(z) is defined with
Computationally, OTGANs frequently employ regularization strategies to make the transport problem tractable. Entropic regularization leads to
OTGANs are related to, and in some respects complement, Wasserstein GANs and other OT-based methods. They aim