# Adversarially Regularized Autoencoders

arXiv: https://arxiv.org/abs/1706.04223 · HTML: https://mlsanity.com/paper/1706.04223/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2017.

## Built on by (most cited first, 6 of 6)

- [Why is constrained neural language generation particularly challenging?](https://mlsanity.com/paper/2206.05395/) (2022)
- [Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks](https://mlsanity.com/paper/2410.02331/) (2024)
- [A Variational AutoEncoder for Transformers with Nonparametric Variational Information Bottleneck](https://mlsanity.com/paper/2207.13529/) (2022)
- [SparseGAN: Sparse Generative Adversarial Network for Text Generation](https://mlsanity.com/paper/2103.11578/) (2021)
- [OptAGAN: Entropy-based finetuning on text VAE-GAN](https://mlsanity.com/paper/2109.00239/) (2021)
- [Explainable Recommender with Geometric Information Bottleneck](https://mlsanity.com/paper/2305.05331/) (2023)

## Abstract (arXiv)

Deep latent variable models, trained using variational autoencoders or generative adversarial networks, are now a key technique for representation learning of continuous structures. However, applying similar methods to discrete structures, such as text sequences or discretized images, has proven to be more challenging. In this work, we propose a flexible method for training deep latent variable models of discrete structures. Our approach is based on the recently-proposed Wasserstein autoencoder (WAE) which formalizes the adversarial autoencoder (AAE) as an optimal transport problem. We first extend this framework to model discrete sequences, and then further explore different learned priors targeting a controllable representation. This adversarially regularized autoencoder (ARAE) allows us to generate natural textual outputs as well as perform manipulations in the latent space to induce change in the output space. Finally we show that the latent representation can be trained to perform unaligned textual style transfer, giving improvements both in automatic/human evaluation compared to existing methods.