In the name of god. Autoencoders Mostafa Heidarpour. Autoencoders. An auto-encoder is an artificial neural network used for learning efficient codings The aim of an auto-encoder is to learn a compressed representation (encoding) for a set of data
Where h is feature vector or representation or code computed from x
maps from feature space back into input space, producing a reconstruction
attempting to incur the lowest possible reconstruction error
Good generalization means low reconstruction error at test examples, while having high reconstruction error for most other x configurations
To capture the structure of the data-generating distribution, it is therefore important that something in the training criterion or the parameterization prevents the autoencoder from learning the identity function, which has zero reconstruction error everywhere. This is achieved through various means in the different forms of autoencoders, we call these regularized autoencoders.
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