Autoencoder (Автоэнкодер)

A type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning). A common implementation is the variational autoencoder (VAE).

A system that learns to extract the most important information from the input. Autoencoders are a combination of an encoder and decoder. Autoencoders rely on the following two-step process: 1. The encoder maps the input to a (typically) lossy lower-dimensional (intermediate) format. 2. The decoder builds a lossy version of the original input by mapping the lower-dimensional format to the original higher-dimensional input format. Autoencoders are trained end-to-end by having the decoder attempt to reconstruct the original input from the encoder's intermediate format as closely as possible. Because the intermediate format is smaller (lower-dimensional) than the original format, the autoencoder is forced to learn what information in the input is essential, and the output won't be perfectly identical to the input. For example: - If the input data is a graphic, the non-exact copy would be similar to the original graphic, but somewhat modified. Perhaps the non-exact copy removes noise from the original graphic or fills in some missing pixels. - If the input data is text, an autoencoder would generate new text that mimics (but is not identical to) the original text. See also variational autoencoders.