Learning priors for adversarial autoencoders
Hui-Po Wang, Wen-Hsiao Peng, Wei-Jan Ko · APSIPA Transactions on Signal and Information Processing · 2020
Learning priors for adversarial autoencodershui-po wang, wen-hsiao peng and wei-jan ko 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 crossdomain translation in a text-to-image synthesis task.