GAN training improved using optimal transport techniques

AI Impact Summary

This capability introduces optimal transport-based losses into GAN training to improve convergence stability and sample diversity. OT approaches, such as Wasserstein or Sinkhorn distances, can reduce mode collapse but add computational overhead and require changes to loss functions and training loops. Business impact includes higher quality generated content and more robust models, offset by increased compute cost and the need for validation to compare OT-enhanced GANs against baselines.