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.