Autoencoder

An autoencoder is a learning architecture that compresses input data into a narrower representation and then attempts to reconstruct it. In the process, the network learns a latent representation that captures essential patterns in the data. It is used in tasks such as denoising, anomaly detection, dimensionality reduction, and representation learning. Its ability to learn structure without labels makes it important in unsupervised and self-supervised learning.