I want to know whether a Deep Belief Network (or DBN) is a supervised learning algorithm or an unsupervised learning algorithm?
After lot of research into DBN working I am confused at this very question. Some of the papers clearly mention DBN as unsupervised and uses supervised learning at at one of its phases -> fine tune. Some other sites clearly specifies DBN as unsupervised and uses labeled MNIST Datasets for illustrating examples.
There are some papers stress about the performance improvement when the training is unsupervised and fine tune is supervised. To top it all in a DBN code, at fine tune stage labels are used to find difference for weight updating.
So what I understand is DBN is a mixture of supervised and unsupervised learning. Why is it is then everywhere mentioned as unsupervised?