A Bayesian Non-parametric Approach to Generative Models: Integrating
  Variational Autoencoder and Generative Adversarial Networks using Wasserstein
  and Maximum Mean Discrepancy
Forough Fazeli-Asl Michael Minyi Zhang
Generative models have emerged as a promising technique for producing
high-quality images that are indistinguishable from real images. Generative
adversarial networks (GANs) and variational autoencoders (VAEs) are two of the
most prominent and widely studied generative models. GANs have demonstrated
excellent performance in generating sharp realistic images and VAEs have shown
strong abilities to generate diverse images. However, GANs suffer from ignoring
a large portion of the possible output space which does not represent the full
diversity of the target distribution, and VAEs tend to produce blurry images.
To fully capitalize on the strengths of both models while mitigating their
weaknesses, we employ a Bayesian non-parametric (BNP) approach to merge GANs
and VAEs. Our procedure incorporates both Wasserstein and maximum mean
discrepancy (MMD) measures in the loss function to enable effective learning of
the latent space and generate diverse and high-quality samples. By fusing the
discriminative power of GANs with the reconstruction capabilities of VAEs, our
novel model achieves superior performance in various generative tasks, such as
anomaly detection and data augmentation. Furthermore, we enhance the model's
capability by employing an extra generator in the code space, which enables us
to explore areas of the code space that the VAE might have overlooked. With a
BNP perspective, we can model the data distribution using an
infinite-dimensional space, which provides greater flexibility in the model and
reduces the risk of overfitting. By utilizing this framework, we can enhance
the performance of both GANs and VAEs to create a more robust generative model
suitable for various applications.