A Beginner's Guide to Autoencoders: Architecture, Functionality and Use Cases

Introduction

Autoencoders, a cornerstone of unsupervised learning, have emerged as a powerful tool in the domain of artificial intelligence. By compressing and then reconstructing input data, these neural networks uncover meaningful representations, paving the way for applications ranging from image denoising to anomaly detection. In this tutorial we want to take a closer look at the Autencoder architecure and find out how it works.

What is an Autoencoder?

At its core, an autoencoder is a type of artificial neural network designed for unsupervised learning. Unlike supervised learning, where the model learns from labeled data, autoencoders work with unlabeled data, extracting meaningful features without explicit guidance. The fundamental principle driving autoencoders is reconstruction: they aim to reconstruct the input data, typically by compressing it into a lower-dimensional representation and then reconstructing it back to its original form.

Architecture

An autoencoder comprises two primary components: an encoder and a decoder. The encoder takes the input data and compresses it into a latent space representation, also known as a code or bottleneck layer: