Multi-projection of unequal dimension optimal transport theory for Generative Adversary Networks

As a major step forward in machine learning, generative adversarial networks (GANs) employ the Wasserstein distance as a metric between the generative distribution and target data distribution, and thus can be viewed as optimal transport (OT) problems to reflect the underlying geometry of the probability distribution. However, the unequal dimensions between the source random distribution and the target data, result in often instability in the …