Java Deep Learning Projects

Autoencoders

An AE is a network with three or more layers, where the input layer and the output layer have the same number of neurons, and those intermediate (hidden layers) have a lower number of neurons. The network is trained to reproduce in the output, for each piece of input data, the same pattern of activity as in the input.

Useful applications of AEs are data denoising and dimensionality reduction for data visualization. The following diagram shows how an AE typically works. It reconstructs the received input through two phases: an encoding phase, which corresponds to a dimensional reduction for the original input, and a decoding phase, which is capable of reconstructing the original input from the encoded (compressed) representation: ...