f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization

Sebastian Nowozin, Botond Cseke, Ryota Tomioka

Neural Information Processing Systems 

We discuss the benefits of various choices of divergence functions on training complexity and the quality of the obtained generative models.