Autoencoder Neural Networks: A Performance Study Based On Image Recognition, Reconstruction And Compression - Tan, Chun Chet (2008)
Autoencoders are feedforwardneural networks which attempt to reconstruct the input data at the output layer. Since the hidden layer in the autoencoders is smaller than the input layer, the dimensionality of input data is reduced to a smaller dimensional code space at the hidden layer. The reduced codes from the hidden layer are then reconstructed back into the original data at the output layer. Like Principal Component Analysis (PCA), the autoencoders can give mappings in both directions between the data and the codes.