[sitkack]

This was recently posted to HN, http://tjake.github.io/blog/2013/02/18/resurgence-in-artific...

Which mentions running the NN in reverse, quote

    By far the most interesting thing I’ve learned about Deep Belief 
    Networks is their generative properties. Meaning you can look 
    inside the ‘mind’ of a DBN and see what it’s imagining. Since a 
    deep belief networks are two-way like restricted boltzmann 
    machines you can make hidden inputs generate valid visual 
    inputs. Continuing with our handwritten digit example you can 
    start with the label input say a ‘3’ label and activate it then 
    go reverse through the DBN and out the other end will pop out a 
    picture of a ‘3’ based on the features of the inner layers. This 
    is equivalent to our ability to visualize things using words, go 
    ahead imagine a ‘3’, now rotate it.

[cafebeen]

It's worth pointing out that a naive bayes or k-nearest-neighbor classifier can similarly generate examples of valid inputs.