An autoencoder is a network with three or more layers, where the input layer and the output layer have the same number of neurons, and the intermediate (hidden) layers have a lower number of neurons. The network is trained to simply reproduce in output, for each input data, the same pattern of activity in the input. The remarkable aspect of the problem is that, due to the lower number of neurons in the hidden layer, if the network can learn from examples, and generalize to an acceptable extent, it performs data compression: the status of the hidden neurons provides, for each example, a compressed version of the input and output common states.
The remarkable aspect of the problem is that, due to the lower ...
