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This project report compares some known GAN and VAE models proposed prior to2017. There has been significant progress after we finished this report. Weupload this report as an introduction to generative models and provide somepersonal interpretations supported by empirical evidence. Both generativeadversarial network models and variational autoencoders have been widely usedto approximate probability distributions of data sets. Although they both useparametrized distributions to approximate the underlying data distribution,whose exact inference is intractable, their behaviors are very different. Wesummarize our experiment results that compare these two categories of models interms of fidelity and mode collapse. We provide a hypothesis to explain theirdifferent behaviors and propose a new model based on this hypothesis. Wefurther tested our proposed model on MNIST dataset and CelebA dataset.

A Probe Towards Understanding GAN and VAE Models