# Autoencoders

arXiv: https://arxiv.org/abs/2003.05991 · HTML: https://mlsanity.com/paper/2003.05991/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2020.

## Built on by (most cited first, 3 of 3)

- [GMA-SAWGAN-GP: A Novel Data Generative Framework to Enhance IDS Detection Performance](https://mlsanity.com/paper/2603.28838/) (2026)
- [Model of models -- Part 1](https://mlsanity.com/paper/2308.04600/) (2023)
- [Artificial Neural Networks for Photonic Applications: From Algorithms to Implementation](https://mlsanity.com/paper/2408.02685/) (2024)

## Abstract (arXiv)

An autoencoder is a specific type of a neural network, which is mainly designed to encode the input into a compressed and meaningful representation, and then decode it back such that the reconstructed input is similar as possible to the original one. This chapter surveys the different types of autoencoders that are mainly used today. It also describes various applications and use-cases of autoencoders.