Blog | Autoencoders: Compression, Reconstruction, and Beyond

Autoencoders: Compression, Reconstruction, and Beyond

NOTE: This post is part of my Machine Learning Series where I discuss how AI/ML works and how it has evolved over the last few decades.

Autoencoders are a type of neural network architecture used for tasks such as dimensionality reduction, feature extraction, and data denoising. With their ability to learn efficient representations of data, autoencoders have found applications in various fields, from image processing to anomaly detection. In this post, we'll explore the structure and functionality of autoencoders and delve into their use cases.

Understanding Autoencoders

An autoencoder consists of two primary components: an encoder and a decoder. The encoder compresses the input data into a lower-dimensional representation called the latent space, while the decoder reconstructs the original data from this latent representation.

Autoencoder: The Encoder-Decoder Architecture

Encoder: Data Compression

The encoder is a neural network that receives input data and reduces its dimensionality, creating a compressed representation in the latent space. This process captures the most important features of the data.

Decoder: Data Reconstruction

The decoder is another neural network that takes the compressed representation and reconstructs the original data. The goal is to produce a reconstruction that closely resembles the original input.

Training: Minimizing Reconstruction Error

Autoencoders are trained to minimize the reconstruction error between the original input and the reconstructed output. Common loss functions include mean squared error (MSE) and binary cross-entropy.

Variants of Autoencoders

Variational Autoencoders (VAEs)

Variational autoencoders (VAEs) are a probabilistic extension of autoencoders that learn the distribution of the latent space. VAEs are used for tasks such as image generation and unsupervised learning.

Variational Autoencoders Explained

Denoising Autoencoders

Denoising autoencoders are trained to reconstruct input data that has been intentionally corrupted with noise. They are effective for image denoising and removing artifacts.

Denoising Autoencoders for Image Restoration

Applications of Autoencoders

  • Dimensionality Reduction: Autoencoders can reduce the dimensionality of data while preserving essential features, similar to PCA.
  • Anomaly Detection: Autoencoders can detect anomalies by measuring high reconstruction error for atypical data points.
  • Image Generation: Variational autoencoders can generate new images by sampling from the learned latent space.


Autoencoders are neural networks that compress and reconstruct data, with an encoder for compression and a decoder for reconstruction. Variants like VAEs and denoising autoencoders extend their capabilities. Autoencoders are used for dimensionality reduction, anomaly detection, and image generation.

Further Reading

  1. Autoencoders: Deep Learning with TensorFlow's Eager Execution - Derrick Mwiti
  2. Building Autoencoders in Keras - François Chollet


  • Autoencoders
  • Neural Networks
  • Dimensionality Reduction
  • Feature Extraction
  • Data Denoising
  • Variational Autoencoders
  • VAEs
  • Denoising Autoencoders
  • Anomaly Detection
  • Image Generation
  • Machine Learning
  • Deep Learning
  • AI
  • Latent Space
  • Encoder
  • Decoder