The neural network in this paper, an autoencoder, doesn't require labelled data.
Autoencoders take high dimensional input, map it to a lower dimensional space and then try to recreate the original high dimensional input as closely as possible.
The idea is to learn a compressed representation for the data and hope that this compressed representation works as a high level featureset.
As the model is just trying to represent the original input, no labelled data is required for the initial part. Labelled data is later introduced when the high level features are used for classification.
What's most interesting about this paper is that one of the features learned by the model maps quite well to "image contains a face" without any prompting by the researchers.
Autoencoders take high dimensional input, map it to a lower dimensional space and then try to recreate the original high dimensional input as closely as possible. The idea is to learn a compressed representation for the data and hope that this compressed representation works as a high level featureset.
As the model is just trying to represent the original input, no labelled data is required for the initial part. Labelled data is later introduced when the high level features are used for classification. What's most interesting about this paper is that one of the features learned by the model maps quite well to "image contains a face" without any prompting by the researchers.
For more details, check out http://www.stanford.edu/class/cs294a/sparseAutoencoder.pdf