Learning Priors for Adversarial Autoencoders

Hui-PoWang, Wei-Jan Ko, Wen-Hsiao Peng

Abstract:

Recent studies show that the choice of the prior has a profound effect on the expressiveness
of deep latent factor models. In this paper, we propose to learn the
prior from data for adversarial autoencoders (AAEs). We introduce the notion of
code generators to transform manually selected simple priors into ones that can
better characterize the data distribution.

TL;DR: Learning a better prior from data for adversarial autoencoders

Keywords: adversarial autoencoder, generative adversarial networks, prior, disentangled representations