Autoencoders are a type of unsupervised deep learning model used for dimensionality reduction, feature learning, and data compression. They consist of an encoder that maps the input data to a lower-dimensional representation and a decoder that reconstructs the original data from this lower-dimensional representation. Autoencoders learn to minimize the reconstruction error, forcing them to capture the most important features of the input data.