The aim of the paper was to produce an unsupervised system that would generate high level features from noisy data.
These high level features could then be used in supervised systems where labelled data is added.
Thus, the paper is about using an unsupervised system to help a later supervised system. An advantage of this is that, as the unsupervised system isn't trained to recognise object X, it instead learns features that are discriminative. This same network could be used to recognise arbitrary objects (which is what they do later on in the paper with ImageNet).
In other words: imagine a baby. She sees 100k "images" of faces. Thanks to the statistical regularity of the world, she now has a "subsystem" that recognizes a face in the absence of her knowing what it's called. Then, when she is told "this is a face" she pins this thing pointed-to to the existing, unnamed representation.
Thus, the paper is about using an unsupervised system to help a later supervised system. An advantage of this is that, as the unsupervised system isn't trained to recognise object X, it instead learns features that are discriminative. This same network could be used to recognise arbitrary objects (which is what they do later on in the paper with ImageNet).