  This project report compares some known GAN and VAE models proposed prior to
2017. There has been significant progress after we finished this report. We
upload this report as an introduction to generative models and provide some
personal interpretations supported by empirical evidence. Both generative
adversarial network models and variational autoencoders have been widely used
to approximate probability distributions of data sets. Although they both use
parametrized distributions to approximate the underlying data distribution,
whose exact inference is intractable, their behaviors are very different. We
summarize our experiment results that compare these two categories of models in
terms of fidelity and mode collapse. We provide a hypothesis to explain their
different behaviors and propose a new model based on this hypothesis. We
further tested our proposed model on MNIST dataset and CelebA dataset.
