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Hidden Representation

Hidden Representations are part of feature learning and represent the machine-readable data representations learned from a neural network’s hidden layers. The output of an activated hidden node, or neuron, is used for classification or regression at the output layer, but the representation of the input data, regardless of later analysis, is called hidden representation.

Another the way to look at it is that the output from hidden layers in a neural network are just raw data. What the machine learns to do with this data, how they represent these features, is hidden representation.

Source : DeepAI.org