# Learning Priors for Adversarial Autoencoders

arXiv: https://arxiv.org/abs/1909.04443 · HTML: https://mlsanity.com/paper/1909.04443/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2019.

## Built on by (most cited first, 2 of 2)

- [Learning Regularization Parameters of Inverse Problems via Deep Neural Networks](https://mlsanity.com/paper/2104.06594/) (2021)
- [LatentGAN Autoencoder: Learning Disentangled Latent Distribution](https://mlsanity.com/paper/2204.02010/) (2022)

## Abstract (arXiv)

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.