Autoencoders

TL;DR

Autoencoders are neural networks designed for data compression and reconstruction, with various types and applications explored in this survey.

Contribution

This paper provides a comprehensive survey of different autoencoder types and their practical applications in modern machine learning.

Findings

Multiple autoencoder architectures are used for diverse tasks.

Autoencoders effectively learn meaningful data representations.

Applications include data denoising, dimensionality reduction, and feature learning.

Abstract

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.