  VAE, or variational auto-encoder, compresses data into latent attributes, and
generates new data of different varieties. VAE based on KL divergence has been
considered as an effective technique for data augmentation. In this paper, we
propose the use of Wasserstein distance as a measure of distributional
similarity for the latent attributes, and show its superior theoretical lower
bound (ELBO) compared with that of KL divergence under mild conditions. Using
multiple experiments, we demonstrate that the new loss function exhibits better
convergence property and generates artificial images that could better aid the
image classification tasks.
