University AI - Autoencoders (Auto-associative Neural Net)

Autoencoders (Auto-associative Neural Net)

An ANN where the training data is defined as $\tau =\{(x_i,x_i)\}$
Train a neural network such that it has at least 1-hidden layer, with dimensions of the last hidden layer smaller than the dimension of the input layer, also it’s data set is a supervised set that has same input and output $Y=\{(\underline{x_1},\underline{x_1}),\dots ,(\underline{x_n},\underline{x_n})\}$

If we separate the output layer what we end up with is an encoder and a decoder for our input data.

• We can use just the encoder and attach it to the beginning of a new NN and use it to reduce the dimension of the input data.
• We can use just the encoder to reduce all our input data and then use the new input with faster training time (smaller dimensions)
• We can use the whole Autoencoder as a noisy filter for our data, worsening the training data to obtain a more general model.

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