Autoencoders are a special form of deep neural networks primarily used for feature extraction or dimension reduction. As they can work with unlabeled data, they belong to the field of unsupervised learning. The architecture consists of two main components: the encoder, which compresses the input data into a low-dimensional representation, and the decoder, trained to reconstruct the original data from this representation.

This article provides a detailed overview of the structure of autoencoders and explains the individual components of the architecture. We also look at the challenges that can arise during training and the applications that build on this model. Finally, we take a closer look at the advantages and disadvantages of the method and compare it with other dimension reduction algorithms.

What is an Autoencoder?

An autoencoder is a special form of artificial neural network trained to represent the input data in a compressed form and then reconstruct the original data from this compressed form. What initially sounds like an unnecessary transformation is an integral part of dimensionality reduction, as it…