  Generative Adversarial Networks are known for their high quality outputs and
versatility. However, they also suffer the mode collapse in their output data
distribution. There have been many efforts to revamp GANs model and reduce mode
collapse. This paper focuses on two of these models, PacGAN and VEEGAN. This
paper explains the mathematical theory behind aforementioned models, and
compare their degree of mode collapse with vanilla GAN using MNIST digits as
input data. The result indicates that PacGAN performs slightly better than
vanilla GAN in terms of mode collapse, and VEEGAN performs worse than both
PacGAN and vanilla GAN. VEEGAN's poor performance may be attributed to average
autoencoder loss in its objective function and small penalty for blurry
features.
