We can take the weights from the DBN and use them to define an MLP: $h^{(1)} = \sigma (b^{(1)} + v^{\top}W^{(1)})$ $h^{(1)} = \sigma (b^{(1)} + h^{(l-1)\top}W^{(l)})\forall l \in 2, ..., m$ After initialization, we can train the MLP to perform a classification task, which is an example of discriminative fine-tuning.