# Information Theoretic-Learning Auto-Encoder

arXiv: https://arxiv.org/abs/1603.06653 · HTML: https://mlsanity.com/paper/1603.06653/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2016.

## Built on by (most cited first, 1 of 1)

- [On the Adversarial Robustness of Generative Autoencoders in the Latent Space](https://mlsanity.com/paper/2307.02202/) (2023)

## Abstract (arXiv)

We propose Information Theoretic-Learning (ITL) divergence measures for variational regularization of neural networks. We also explore ITL-regularized autoencoders as an alternative to variational autoencoding bayes, adversarial autoencoders and generative adversarial networks for randomly generating sample data without explicitly defining a partition function. This paper also formalizes, generative moment matching networks under the ITL framework.