Autoencoder
Neural network that learns efficient data encoding in an unsupervised manner / From Wikipedia, the free encyclopedia
Dear Wikiwand AI, let's keep it short by simply answering these key questions:
Can you list the top facts and stats about Autoencoder?
Summarize this article for a 10 year old
An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning).[1][2] An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation. The autoencoder learns an efficient representation (encoding) for a set of data, typically for dimensionality reduction.
Variants exist, aiming to force the learned representations to assume useful properties.[3] Examples are regularized autoencoders (Sparse, Denoising and Contractive), which are effective in learning representations for subsequent classification tasks,[4] and Variational autoencoders, with applications as generative models.[5] Autoencoders are applied to many problems, including facial recognition,[6] feature detection,[7] anomaly detection and acquiring the meaning of words.[8][9] Autoencoders are also generative models which can randomly generate new data that is similar to the input data (training data).[7]