An Introduction to Autoencoders
It serves as an educational resource for students and researchers, offering a comprehensive review of existing knowledge on autoencoders, but it is incremental as it does not introduce novel contributions.
This paper provides an introductory overview of autoencoders, covering their fundamental concepts, mathematics, and typical applications such as dimensionality reduction and anomaly detection, without presenting new experimental results or numerical findings.
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.