An Introduction to Autoencoders

TL;DR

This paper provides a comprehensive introduction to autoencoders, covering their fundamental concepts, mathematical foundations, typical applications, and key components like activation functions and loss functions.

Contribution

It offers an in-depth, educational overview of autoencoders, including their limitations, use cases, and practical examples, suitable for a PhD-level audience.

Findings

Autoencoders are effective for dimensionality reduction and denoising.

The role of activation and loss functions is crucial in autoencoder performance.

Autoencoders can be used for anomaly detection and classification.

Abstract

In this article, we will look at autoencoders. This article covers the mathematics and the fundamental concepts of autoencoders. We will discuss what they are, what the limitations are, the typical use cases, and we will look at some examples. We will start with a general introduction to autoencoders, and we will discuss the role of the activation function in the output layer and the loss function. We will then discuss what the reconstruction error is. Finally, we will look at typical applications as dimensionality reduction, classification, denoising, and anomaly detection. This paper contains the notes of a PhD-level lecture on autoencoders given in 2021.