can be noise and the noise comes in three different forms so first of all you use data augmentation which we've already seen this is sort of like random cropping or mild rotations color jitter whatever they use a rand augment here which is a specific technique to apply these augmentations um they use dropout which is a fairly old technique where you in the student model that you train you randomly drop out connections which makes it more robust and more generalizing and then you also use stochastic depth now stochastic