idea basically they say these computations here they if you have a very deep network each layer only has to basically um do very a little bit of computation that that can be bypassed uh fairly efficiently for a lot of data points so it's not that hurtful to bypass a layer and in this case they actually use it to just bypass some of these small computations and inject some more robustness into the student model so with these three strategies to bring noise into the training process one is on the data and two is on the