Open Peer Review. Open Publishing. Open Access. Open Discussion. Open Directory. Open Recommendations. Open API. Open Source.
Learning Priors for Adversarial Autoencoders
Hui-Po Wang, Wei-Jan Ko, Wen-Hsiao Peng
Feb 15, 2018 (modified: Feb 15, 2018)ICLR 2018 Conference Blind Submissionreaders: everyoneShow Bibtex
Abstract:Most deep latent factor models choose simple priors for simplicity, tractability
or not knowing what prior to use. Recent studies show that the choice of
the prior may have a profound effect on the expressiveness of the model,
especially when its generative network has limited capacity. In this paper, we propose to learn a proper 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. Experimental results show that the proposed model can generate better image quality and learn better disentangled representations than
AAEs in both supervised and unsupervised settings. Lastly, we present its
ability to do cross-domain translation in a text-to-image synthesis task.
TL;DR:Learning Priors for Adversarial Autoencoders