Entropy-regularized Optimal Transport Generative Models

Data-driven learning of a probability distribution by a generative model is an important problem in statistical signal processing and machine learning. Recently neural network based generative models are popular tools to study underlying probability distribution of datasets. A prominent example is generative adversarial network (GAN) [1], which learns implicit distribution models. In the GAN of [1], a generator produces synthetic samples and a discriminator endeavors to distinguish between real samples and synthetic samples. Generators and discriminators are realized using (deep) neural networks.Discriminator and generator play an adversary game against each other using a'min-max' optimization to learn parameters of neural networks.