An autoencoder is a type of neural network that learns to represent data. It takes input data, compresses it, and then rebuilds it. This has two parts: the encoder, which makes the data smaller, and the decoder, which reconstructs it. For example, Google's TensorFlow has tools to create autoencoders for image compression.
Autoencoders are helpful in many areas. They can remove noise, reduce dimensions, and find unusual patterns. They make data processing better. In image processing, autoencoders can clear up images by removing noise. In cybersecurity, they help spot strange patterns that may indicate problems.
- Types of autoencoders include:
- Denoising autoencoders: Remove noise from data.
- Sparse autoencoders: Focus on important features.
- Variational autoencoders: Create new data similar to the input.
Also Known As
autoencodersAE