Efficient Learning of GAN

Abstract

GAN or Generative Adversarial Network is a combination of two deep Neural Networks in which one network acts as a generator where the other acts as a discriminator which differentiate between real and generated fake samples. There are different variants of GAN. For every variant of GAN we have to train two deep neural networks simultaneously and the hardest part about GAN is it’s training. During training many GAN models suffer various major problems like non-convergence, mode collapse, high sensitivity to the selection of hyper-parameters and vanishing gradient. In this project we tried to address the problem Mode-collapse. Where the generator generates only one or limited variants of samples irrespective of the inputs.