An autoencoder is an artificial neural network used for efficient encoding in unsupervised learning, often for dimensionality reduction.

It is also a data compression algorithm in which the compression and decompression functions of the data are lossy and automatically learned from samples, but in most cases where autoencoders are mentioned, the compression and decompression functions are implemented through neural networks.

Autoencoders are data-specific or data-dependent, which means that they can only compress data that is similar to the training data.

Autoencoders are lossy, meaning that the decompressed output is degraded compared to the original input, as are compression algorithms like MP3, JPEG, etc.

Autoencoders are automatically learned from data samples, which means it is easy to train a specific encoder for a given class of input without doing any new work.