# A Showcase of the Use of Autoencoders in Feature Learning Applications

arXiv: https://arxiv.org/abs/2005.04321 · HTML: https://mlsanity.com/paper/2005.04321/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2020.

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

- [EvoAAA: An evolutionary methodology for automated neural autoencoder architecture search](https://mlsanity.com/paper/2301.06047/) (2023)

## Abstract (arXiv)

Autoencoders are techniques for data representation learning based on artificial neural networks. Differently to other feature learning methods which may be focused on finding specific transformations of the feature space, they can be adapted to fulfill many purposes, such as data visualization, denoising, anomaly detection and semantic hashing. This work presents these applications and provides details on how autoencoders can perform them, including code samples making use of an R package with an easy-to-use interface for autoencoder design and training, textttruta. Along the way, the explanations on how each learning task has been achieved are provided with the aim to help the reader design their own autoencoders for these or other objectives.