Learning Priors for Adversarial Autoencoders

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

Feb 11, 2018 (modified: Feb 12, 2018) ICLR 2018 Workshop Submission readers: everyone
  • 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