Autoencoders
What are autoencoders? An autoencoder is a neural network that reconstructs a high dimensional input through a compressed lower dimension bottleneck. The idea for autoencoders is to take a high dimensional input, compress it to a lower dimension that represents the image’s features, and then reconstruct the image from the bottleneck. The autoencoder is essentially like a dimensionality reduction method like PCA (Principal Component Analysis) The idea was originally from the 1980s and was later promoted by Hinton & Salakhutdinov, 2006[1] ...